An iPython Notebook exploring the creation of an automatic Character Network Detection Tool, as well as character networks within the Sherlock Holmes corpus.

Tristan Dahn - Character Networks and Holmes

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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "It's Elementary my Dear Reader: Character Networks in Sherlock Holmes"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Tristan Dahn"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      ">*\"You see, my dear Watson\"\u2014he propped his test-tube in the rack and began to lecture with the air of a professor addressing his class\u2014\"it is not really difficult to construct a series of inferences, each dependent upon its predecessor and each simple in itself. If, after doing so, one simply knocks out all the central inferences and presents one's audience with the starting-point and the conclusion, one may produce a startling, though possibly a meretricious, effect.\"*  -The Adventure of the Dancing Men"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Introduction"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This project is an off-shoot of work I've been doing for Prof. Piper at txtLAB. The focus of the work is to determine in what ways networks based on algorithmically generated character dictionaries differ from those that are created by hand. For this purpose I have received from Prof. Piper the entire corpus of the Adventures of Sherlock Holmes as well as hand coded character dictionaries for each text within that corpus. These are included, along with the algorithmically ones, in the zip file that this notebook has been handed in with for the purposes of testing the code within this notebook."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "When discussing possible projects to help with at txtLAB, I was immediately attracted to working with this corpus when it was presented to me. Detective fiction, in particular the works of Per Wahloo and Maj Sjowall, whose Martin Beck series I am intersted in adapting this technique for, has long been a favorite genre of mine. In the initial time void created by finishing my undergraduate degree, I started reading fiction varaciously, and was then given a copy of the complete Sherlock Holmes stories by my mother for my birthday. I have read and enjoyed quite a few of them, and it has been rewarding to revisit these texts under a new light."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Social Network Analysis"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The concept of social network analysis, initially rooted in classical sociology and more recently in the social scientific, mathematic, and computer science realms, dates at least as far back as John Scott's 1988 Trend report in the journal of sociology [7]. Classically it can be though of as the invisible bonds that connect one person to another, and through degrees of separation, how tightly woven lattices of interactions can imagined in order to examine social structure [7]. In more recent years, and with the creation of real entities known as \"social networks,\" such as Facebook, this area of analysis has grown, both in terms of public interest in it, as well as in terms of the raw data and the technical tools available to interpret that data [2]. Social persuasion no longer exists exclusively in the realm of the state, institutions, or corporations, but is now relegated more to the realm of the networks that structure our societies, the actors within them and the relationships which structure them [8]."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![Social Network Model](http://www.sumankundu.info/suman/documents/articles/How_To_Code_ICM/grpah_representing_sn.png)\n",
      "Image from: http://www.sumankundu.info/articles/detail/How-To-Code-Independent-Cascade-Model-of-Information-Diffusion"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For those readers less familiar with these concepts, I will now explain some of the main terms and concepts."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Network maps consist of vertices (nodes) and edges, the lines drawn between vertices [5]. *Degree centrality* then is concerned with what are \"the most important, or central vertices in a network\" [5] p.2. A simple interpretation of this question is simply how many edges are connected to a vertex, or how many other vertices are directly connected to a vertex [5]. A more complex version of this is known as *eigenvector centrality*, which looks at not just how many other vertices are connected to a single vertex, but how important these vertices are based on their connectedness to other vertices [5]. This concept is one that may be familiar through such concepts as \"Page Rank\" used by many online search algorithms to determine the authority of a web page based on the other web pages that link to it, as well as their centrality [5]."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In looking at the example social network map above, we immediately can start to see groupings of vertices. But how can we classify and interpret these groupings? This is where the concepts of *cliques, plexes* and *cores* comes from."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A *clique* is a group of vertices in which the connections between them are maximized, that is, vertices in a *clique* are connected to one another via an edge. An example of a *clique* with four vertices within a network taken from [5] p. 31 is below:"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![An example of a clique.](http://s1.postimg.org/mqfs474zj/Screen_Shot_2015_04_16_at_7_57_44_PM.png)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "However, the requirement that a vertex be connected to all other vertices in a group in order to be significant may be too severe for interpreting groups with organically formed networks [5]. This is where the idea of a *plex* or *k-plex* comes in. A *k-plex* is \"a maximal subset of n vertices within a network such that each vertex is connected to at least n minus k of the others\" [5] p. 32. A value of k=1 then is equivalent to a *clique*, where as k=2 would be a grouping in which vertices are connected to all, or all but one of the other vertices in the group, and so on [5]."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A *k-core* then is its inverse, in which all vertices in a group must be connected to k other vertices within that group in order to be a part of it [5]. It follows then, that \"a k-core of n vertices is also an (n \u2212 k)-plex\" [5] p.33. However, the set of *k-cores* in a group for a given value of k is not inherent the same as the set of *k-plexes* for the same value k, as the group size n is variable [5]. Additionally, *k-cores* are exclusive entities as \"by their definition two k-cores that shared one or more vertices would just form a single larger k-core\" [5] p.33."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Additionally, some network analysis looks at the concept of *betweeness* [5]. *Betweeness* looks at the extent to which a particular vertex lies on the path between other vertices [5]. The importance here is that some vertices may act as *brokers* between other vertices, and though they may not themselves have a high degree of centrality, their degree of influence over a network may be significant, as they act as the sole or primary path between clusters in a network [5]."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "An example of a \"low degree vertex with high betweeness\" from [5] p.24"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![Betweeness](http://s23.postimg.org/dx5k6defv/Screen_Shot_2015_04_16_at_7_37_46_PM.png)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Character Networks"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The analysis of character networks them, is an extension of social network analysis into the realm of literary analysis."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As with many modes of inquiry familiar to us within the humanities, social network analysis in the literary realm \"begins with the primacy of character as its object of study.\" [6] p.2 As such, if a network is composed of vertices and edges, and a story composed of a plot, it's characters and their actions, then a literary character network is composed of the characters as vertices and their interactions as edges [3]. Here is an example taken from an article by Franco Moretti on network analysis from the New Left Review for a character network in Shakespeare's Hamlet [3]:"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![The Hamlet Network](http://www.newleftreview.org/assets/images/3020501large.gif)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For Moretti, this is \"time turned into space\" [3]. All concepts of fabula and sujet are disregarded and reduced to a flat representation. It is a \"character-*system* arising out of many character-*spaces*\", in which the \"past is just as visible as the present\" [3]. What we are looking at then, when we examine a character network is no longer the text per se, but rather a model of the text [3]. An interpretation then says little about the author\u2019s actual words, but rather it looks at the underlying structures of the text [3]."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\"It\u2019s like an X-ray: suddenly, you see the region of death of Figure 5, which is otherwise hidden by the very richness of the play.\" [3]"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![Hamlet: the region of death](http://www.newleftreview.org/assets/images/3020503large.gif)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Additionally, Moretti reminds us, character networks are not simply observable, they are alterable [3]. By creating a model, we are creating a system with which we can \"experiment\" [3]. These experiments in turn can shed new light on the underlying structure of texts. An example offered by Moretti is the removal of central figures from a text [3]. So what does Hamlet without Hamlet look like?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![Hamlet without Hamlet](http://s7.postimg.org/5a5dh7upn/Screen_Shot_2015_04_17_at_12_53_18_PM.png)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "From this, Moretti asserts, we can see that what is so important about a protagonist is not simply their essence, but also their centrality, for they are what holds the broader network, and as such the under lying structure of our narratives together [3]. In looking closely, we can see that in this play, with the removal of just three characters, Hamlet, Horatio and Claudius, the entire structure of the narrative would collapse. In character network analysis then, the place of any character in a narrative is defined through their relationships with the other characters in the narrative, and is not indicative of the character based solely on traits [6]."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Development of a Character Network Tool"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In this section I will explain step by step the development of a tool in iPython Notebook for the mapping of character networks."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Importing Libraries"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First the necessary libraries need to be imported."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "OS is used in this script to iterate over files within folders to extract the characters from the texts to create character dictionaries, csv files which list the characters as well as assigning them a unique identifier, and again to put them back out from the dictionaries from the dictionaries to match the character names against the texts, which are iterated over concurrently."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "String is used in this script for the express purpose of assigning a list of punctuation marks to a variable for the purposes of stripping the punctuation from the character names and individual token entities of the text in order to maximize matches."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import string"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "CSV is important, as it has a function that allows me to read csv files (character dictionaries) into a [dictionary object](https://docs.python.org/2/tutorial/datastructures.html#dictionaries) (different from \"character dictionary,\" this is a data structure in python), using column headings as the key. Since the hand coded CSV character dictionaries have information in them unimportant to my purposes, this is essential in isolating the two pieces of information I am interested in using, namely the character names and their identifiers."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import csv"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "NLTK (Natural Language Took Kit) provides me with the tools I will need for extracting character names from texts for the creation of the algorithmically generated character dictionaries (explained in detail below)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import nltk"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Pandas is used in this context because of its ability to create dataframes that can be written to csv files. Since the character dictionaries provided to me are in csv form, in order to make the network building scripts work for both the computer generated dictionaries and the hand coded, they must be in the same format."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Networkx and pyplot are needed for creating the visualizations of the character network maps."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import networkx as nx\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 61
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Finding Names"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First, let's looks at the process of extracting character names from text using named entity recognition (NER). These characters will act as the vertices of the network maps created by the algorithm. NER is a form of information extraction that, through the use of hand built as well as computer trained algorithms, seeks to identify specific units of text, such as people, dates, geographic locations [4]. Early versions were specifically built to fine specific entities, such as company names in Lisa Rau's 1991 paper, which used exclusively a process of heuristics and hand built rules [4]. More recent versions rely on supervised machine learning, in which positive and negative instances of entity recognition are evaluated and fed back into the algorithm in order to refine its thinking [4]. Unfortunately, a relatively small amount of genre or domain specific research exists, resulting in dramatically different results in precision and recall across types of text [4]. The NER used in this project was selected for the sake of convenience, as it exists as part of the Natural Language Took Kit available for use with python. The relative inaccuracy of the NER was at first problematic, but certain steps were taken to mitigate this, and will be described below."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First we need a directory to same these new character dictionaries in:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "directory = \"Sherlock_Masterfolder_v2/NERDictionaries\"\n",
      "if not os.path.exists(directory):\n",
      "    os.makedirs(directory)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 112
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we need to make a dictionary object which will hold our file names, which we will iterate over in order to make a unique character dictionary for each story in the Holmes corpus. We will use this dictionary again when we look for interactions in our text. The dictionary object is important here, since we can both use a key to look up the results from specific stories, as well as pass that key to new dictionaries when we move the data around in order to ensure that the correct dictionaries are matched with the correct stories when mapping interactions."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "textDir = \"Sherlock_Masterfolder_v2/Texts\"\n",
      "textDirDict = {}\n",
      "textDictCount = 0\n",
      "for filename in os.listdir(textDir):\n",
      "    textDirDict[textDictCount] = (textDir + \"/\" + filename)\n",
      "    textDictCount = textDictCount + 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 182
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Next, since the NLTK NER algorithm (used below in the nerTagger function) provides us with its output in the [nltk.tree](http://www.nltk.org/_modules/nltk/tree.html) structure with branches labeled as \"PEOPLE\", \"LOC\" or \"ORG\", we must define a function that extracts the branches which are labeled as \"PEOPLE\", as these are our potential characters within the text."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The following function is taken from a [notebook by Stefan Sinclair](http://nbviewer.ipython.org/gist/sgsinclair/bf112f84f130c8bac96c), which provides us with a \"quick and dirty\" corpus level look look at character networks in Holmes [9]."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def findPeople(tree, people):\n",
      "    if type(tree) is nltk.tree.Tree and tree.label() == \"PERSON\":\n",
      "        people.append(\" \".join([word for word, pos in tree]))\n",
      "    elif (type(tree) is nltk.tree.Tree) or (type(tree) is list):\n",
      "        [findPeople(branch, people) for branch in tree]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 52
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we will definine the function that does the NER tagging. Since this uses the built in funtions [pos_tag and ne_chunk (NER)](http://www.nltk.org/book/ch05.html) from the NLTK library, it is a surprisingly short script, one of the benefits of using the appropriate libraries, such as NLTK, within python."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def nerTagger(textString, characters):\n",
      "    #tokenize textFile\n",
      "    textTokens = nltk.word_tokenize(textString)\n",
      "    #tag for parts of speech\n",
      "    textPos = nltk.pos_tag(textTokens)\n",
      "    #tag for PERSON, ORG, or LOC, NER tagger\n",
      "    textNE = nltk.ne_chunk(textPos)\n",
      "    \n",
      "    #call findPeople from above to extract the PERSON entities from the chunked text\n",
      "    findPeople(textNE, characters)\n",
      "    \n",
      "    #eliminate duplicates\n",
      "    uniqueCharacters = set(characters)\n",
      "    \n",
      "    characters = uniqueCharacters\n",
      "    \n",
      "    #remove punction from Characters, this will be done with the text as well\n",
      "    characterList = []\n",
      "    punct = set(string.punctuation)\n",
      "\n",
      "    for character in uniqueCharacters:\n",
      "        character = \"\".join(ch for ch in character if ch not in punct)\n",
      "        characterList.append(character)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 58
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's check out what we get from this for one story:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#read the first story into a string\n",
      "f = open(textDirDict[25], \"r\")\n",
      "textString = f.read()\n",
      "f.close()\n",
      "\n",
      "characterList = []\n",
      "\n",
      "nerTagger(textString, characterList)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 116
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(characterList)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['Watson', 'Accustomed', 'Holmes', 'Watson', 'Watson', 'Thurston', 'Thurston', 'Watson', 'Mr. Hilton Cubitt', 'Watson', 'Baker Street', 'Mr. Holmes', 'Holmes', 'Mr. Holmes', 'Holmes', 'Mr. Hilton Cubitt', 'Watson', 'Patrick', 'Mr. Holmes', 'Elsie', 'Hilton', 'Mr. Holmes', 'Anyhow', 'None', 'Mr. Holmes', 'Holmes', 'Mr. Cubitt', 'Hilton Cubitt', 'Mr. Holmes', 'Elsie', 'Mr. Hilton Cubitt', 'Watson', 'Hilton Cubitt', 'Hilton Cubitt', 'Liverpool Street', 'Mr. Holmes', 'Mr. Holmes', 'Mr. Holmes', 'Holmes', 'Hilton', 'Mr. Hilton Cubitt', 'Mr. Holmes', 'Mr. Holmes', 'Holmes', 'Hilton Cubitt', 'Watson', 'Holmes', 'Holmes', 'Hilton Cubitt', 'Holmes', 'Hudson', 'Hilton Cubitt', 'Ridling Thorpe', 'Holmes', 'Because Inspector Martin', 'Holmes', 'Mr. Hilton Cubitt', 'Holmes', 'Inspector Martin', 'Mr. Holmes', 'Holmes', 'Mr. Holmes', 'Martin', 'Hilton Cubitt', 'Mr. Hilton Cubitt', 'Holmes', 'Holmes', 'Inspector Martin', 'Inspector Martin', 'Holmes', 'Holmes', 'Mr. Cubitt', 'Holmes', 'George', 'Sherlock Holmes', 'Inspector Martin', 'Holmes', 'Holmes', 'Inspector Martin', 'Large', 'Holmes', 'Inspector Martin', 'Holmes', 'Mr. Holmes', 'Holmes', 'Elrige', 'Holmes', 'Mr. Abe Slaney', 'Elrige', 'Farm', 'East Ruston', 'Holmes', 'Watson', 'Hilton Cubitt', 'Holmes', 'Watson', 'Mr. Hilton Cubitt', 'E', 'Mr. Hilton', 'Martin', 'Slaney', 'Abe', 'Wilson Hargreave', 'Abe Slaney', 'Hilton Cubitt', 'Watson', 'Mr. Holmes', 'Sherlock Holmes', 'Holmes', 'Holmes', 'Martin', 'Hilton Cubitt', 'Hilton Cubitt', 'God', 'Holmes', 'Mr. Abe Slaney', 'Holmes', 'Mr. Slaney', 'Hilton Cubitt', 'Slaney', 'Elsie', 'Patrick', 'Elsie', 'Elsie', 'Elsie', 'God', 'Inspector Martin', 'Mr. Sherlock Holmes', 'Holmes', 'Watson', 'Holmes', 'Watson', 'Abe Slaney', 'Hilton Cubitt', 'Hilton Cubitt']\n"
       ]
      }
     ],
     "prompt_number": 117
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As we can see, there are a number of duplicates that need to be removed:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "uniqueCharacters = set(characterList)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 118
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(uniqueCharacters)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{'Because Inspector Martin', 'Hilton', 'Patrick', 'George', 'Mr. Hilton Cubitt', 'Large', 'East Ruston', 'Holmes', 'Watson', 'God', 'Wilson Hargreave', 'E', 'Elsie', 'Elrige', 'Hilton Cubitt', 'Slaney', 'Abe Slaney', 'Anyhow', 'Abe', 'Liverpool Street', 'Mr. Holmes', 'Thurston', 'Inspector Martin', 'None', 'Mr. Sherlock Holmes', 'Martin', 'Hudson', 'Baker Street', 'Mr. Cubitt', 'Ridling Thorpe', 'Sherlock Holmes', 'Farm', 'Mr. Slaney', 'Mr. Hilton', 'Accustomed', 'Mr. Abe Slaney'}\n"
       ]
      }
     ],
     "prompt_number": 119
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Great! However, if we look at the results of this process above, we see that there are still entities in our list that are clearly not characters. This likely has to do with what was discussed above in terms of non-specific corpus training, and additionally with ever-pervasive errors that exist in POS tagging [1]."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In order to clean up this list, I have chosen a two-pronged approach. My initial process consisted of using a list of words in English, and checking the characters in my list against them. If the character name contains in it an English word, that name is excluded from the character list. However, I found that too many things were excluded, including essential names such as \"Holmes,\" which is in the list of English words I was using, as well as many common or biblical names such as \"James\" and titles such as \"Lord\" or \"Colonel.\" As such, an inclusionList needed to be created for which the characters in the character list are also checked against.\n",
      "\n",
      "This inclusion list is created by importing the male and female names lists from the NLTK corpora (a body of various texts included in the NLTK library), and then adding any extra words, such as titles or abbreviations into an optional list that is appended to the names list."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First let's import our [english word list](http://www-01.sil.org/linguistics/wordlists/english/wordlist/wordsEn.txt). I am also appending the alphabet to this list as certain individual characters (i.e. \"E\") have been tagged as character names, which in some cases may be a legitimate tagging, this creates issues when looking for character name occurance within the text, since almost all individual letters exist in all paragraphs."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import urllib.request\n",
      "enDictURL = \"http://www-01.sil.org/linguistics/wordlists/english/wordlist/wordsEn.txt\"\n",
      "enDictString = urllib.request.urlopen(enDictURL).read().decode()\n",
      "enDictTokens = nltk.word_tokenize(enDictString)\n",
      "\n",
      "alphabet = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']\n",
      "for ch in alphabet:\n",
      "    enDictTokens.append(ch)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 49
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now let's create our inclusion list. I made the choices for what to include in the inclusionListAdditions by first running this process with no filter and then viewing the raw NER character lists and looking for what names, titles and abbreviations occur within the character names returned that would be eliminated by their presence in the English word list."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#get the location of the names list in the NLTK corpera\n",
      "maleNamesFile = nltk.data.find(\"corpora/names/male.txt\")\n",
      "femaleNamesFile = nltk.data.find(\"corpora/names/female.txt\")\n",
      "\n",
      "#create a list of these file locations for iterating over\n",
      "namesFileList = [maleNamesFile, femaleNamesFile]\n",
      "\n",
      "#create an empty inclusion list that we will add our names to\n",
      "inclusionList = []\n",
      "\n",
      "#iterate over the lists, open the files and add their words as tokens to our inclusion list one at a time\n",
      "for filename in namesFileList:\n",
      "    f = open(filename, \"r\")\n",
      "    nameString = f.read()\n",
      "    f.close()\n",
      "    nameTokens = nltk.word_tokenize(nameString)\n",
      "    for token in nameTokens:\n",
      "        inclusionList.append(token.lower())\n",
      "\n",
      "#use the line below to add any other words you think may occur in character names\n",
      "inclusionListAdditions = ['miss', 'mr', 'mrs', 'dr', 'doctor', 'holmes', 'watson', 'general', 'colonel', 'mister', 'inspector', \n",
      "                          'lady', 'sir', 'duke', 'duchess', 'm', 'count', 'de', 'captain', 'st', 'lord', 'madam', 'madame']\n",
      "\n",
      "#iterate over this list and add them to our inclusion list one at a time\n",
      "for word in inclusionListAdditions:\n",
      "    inclusionList.append(word)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we will check our character list against the english word list and the inclusion list we've just created."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def nerCleanUp(cleanCharacterList, characterList, inclusionList):\n",
      "        \n",
      "        #iterate over character list\n",
      "        for name in characterList:\n",
      "            \n",
      "            #create a boolean check, if a word in the name is found to be a common word\n",
      "            #this will be switched, and the word will not be included in our final characterList\n",
      "            wordCheck = False\n",
      "            \n",
      "            #splits the name in the case that it is more than one word\n",
      "            nameList = name.split()\n",
      "            \n",
      "            #check each word in the nameList against the word list, and the inclusion list\n",
      "            #any name containing a word in the english word list and not in the inclusion list\n",
      "            for token in nameList:\n",
      "                for word in enDictTokens:\n",
      "                    if token.lower() == word and token.lower() not in inclusionList:\n",
      "                        wordCheck = True\n",
      "            if wordCheck == False:\n",
      "                cleanCharacterList.append(name)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "uniqueCharacters = set(characterList)\n",
      "\n",
      "cleanCharacterList = []\n",
      "\n",
      "nerCleanUp(cleanCharacterList, uniqueCharacters, inclusionList)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 123
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(cleanCharacterList)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['Hilton', 'Patrick', 'George', 'Mr. Hilton Cubitt', 'Holmes', 'Watson', 'Wilson Hargreave', 'Elsie', 'Elrige', 'Hilton Cubitt', 'Slaney', 'Abe Slaney', 'Abe', 'Mr. Holmes', 'Thurston', 'Inspector Martin', 'Mr. Sherlock Holmes', 'Martin', 'Hudson', 'Mr. Cubitt', 'Ridling Thorpe', 'Sherlock Holmes', 'Mr. Slaney', 'Mr. Hilton', 'Mr. Abe Slaney']\n"
       ]
      }
     ],
     "prompt_number": 124
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And we can see, even though there may still be some dubious entries for some stories, the list is much better."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now let's put it all together and write our character dictionaries as csv files. In order to mimic that format of the hand built dictionaries, we will require 5 columns, many of which will be extraneous for these purposes, but may have other uses. These are \"character\", which is the numeric identifier for a character, \"value\", which is the character's name, \"rule\" is undefined in the README file included with the original dictionaries, but in all cases is set to 1, \"identification\" is TRUE in the case of a proper name and FALSE if the entry is an alias (such as \"my companion\" for Watson), since the NER process cannot indicate aliases, this will be written as TRUE for all cases (aliasing can be done by hand after), and lastly \"type of perpetrator\" which is a corpus specific piece of data that indicates the characters role in the crime.  The scripts will be need to be adapted slightly, leaving out the \"rule\" and \"type of perpetrator\" column, for use with other texts and corpora, but for now they will be left in in order to make the script that reads the csv files and finds interactions work for both the hardcoded and algorithmically generated dictionaries."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here we go! (This script takes some time to run, as there are many processes running, and POS tagging in particular is processor heavy.)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#this will be used for interating over our dictionary containing the text file locations\n",
      "textCount = 0\n",
      "\n",
      "for filename in textDirDict:\n",
      "    f = open(textDirDict[textCount], \"r\")\n",
      "    textString = f.read()\n",
      "    f.close()\n",
      "    \n",
      "    characterList = []\n",
      "    \n",
      "    #call the NE tagger\n",
      "    nerTagger(textString, characterList)\n",
      "    \n",
      "    #remove duplicates\n",
      "    uniqueCharacterList = set(characterList)\n",
      "    \n",
      "    cleanCharacterList = []\n",
      "    \n",
      "    #create a variable outof the inclusionList generated above\n",
      "    localInclusionList = inclusionList\n",
      "\n",
      "    #call the NE list clean up method from above\n",
      "    nerCleanUp(cleanCharacterList, uniqueCharacterList, localInclusionList)\n",
      "    \n",
      "    #create the header for the csv files\n",
      "    csvCharacterList = []\n",
      "    csvCharacterList.append(['id', 'name', 'rule', 'identification', 'type of perpetrator'])\n",
      "    characterCount = 1\n",
      "\n",
      "    #add characters from the cleanCharacterList into the list to be turned into a csv file\n",
      "    for character in cleanCharacterList:\n",
      "        characterEntry = [str(characterCount), character, '1', 'TRUE', '']\n",
      "        csvCharacterList.append(characterEntry)\n",
      "        characterCount += 1\n",
      "    \n",
      "    #turn the csvCharacterList into a matrix in order to write it to file\n",
      "    charcterMatrix = pd.DataFrame(csvCharacterList)\n",
      "    csvFile = charcterMatrix.to_csv(index=False, header=False)\n",
      "    \n",
      "    #from the textDirDict extract the filename and clean the beginning and end off for writing the csv filenames\n",
      "    removeDir = len('Sherlock_Masterfolder_v2/Texts/')\n",
      "    removeTxt = textDirDict[textCount].find('_Text')\n",
      "    filename = textDirDict[textCount][removeDir:removeTxt]\n",
      "    \n",
      "    #create a new csv file and write the matrix to it\n",
      "    f = open(\"Sherlock_Masterfolder_v2/NERDictionaries/\" + filename + \"_NERDictionaries.csv\", \"w\")\n",
      "    f.write(csvFile)\n",
      "    f.close()\n",
      "    \n",
      "    textCount += 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 125
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And lo! The files are now written into the directory we created at the beginning."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![CSV Files in their right place](http://s15.postimg.org/j7k7w7c0b/Screen_Shot_2015_04_16_at_4_46_49_PM.png)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Building Character Networks"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, I will explain the process of network construction using these dictionaries and the hand-coded dictionaries for comparison. The first step in this process is to write a script that can parse the csv files in order to extract the name entities and their unique identifiers. For the NER Dictionaries, the ids will serve as an instance of a name until alias creation is done by hand, but for the already hand coded dictionaries, matching the numeric ids with the names is an important step, as the potential strength of the hand coded dictionaries is that character names and their aliases can be represented as one entity in order to retrieve the maximum number of interactions, which in theory can be used to construct a more robust network representation. For the purposes of these networks, co-occurrence of characters in a paragraph will be assumed to be an interaction, and as such will create an edge or increment the weight of an edge in the network model."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The next block of code is where the bulk of the work is being done. The tricky part of this process is that the iterating over the files (both character dictionaries and stories) must occur at once in order for it to be a fully automated process. Since it all occurs in a single for loop, exposition/explanation will occur as comments in the script for the next section."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The result of this action is to iterate over the csv character dictionaries and check them against the text files in the txtDirDict created above. In the end a dictionary object of {key : [text, para, name and id]} (key = text) is created."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*Selecting which directory we load the csv files from by running one of the two cells below allows us to output either the results of the hand coded or the name entity generated character dictionaties.*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "csvDir = \"Sherlock_Masterfolder_v2/NERDictionaries\" #NER Dictionaries"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 186
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "csvDir = \"Sherlock_Masterfolder_v2/Dictionaries\" #hand coded dictionaries"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 180
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here we go again!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "textCount = 0\n",
      "hitDict = {}\n",
      "hitDictCount = 0\n",
      "\n",
      "for filename in os.listdir(csvDir):\n",
      "    \n",
      "    #if the file is not a csv file move onto the next iteration in the loop\n",
      "    #this is necessary as system generated hidden files (DSStore in OSX for example) will cause an error\n",
      "    #(thanks Stefan Sinclair for this snippet) \n",
      "    if filename.find(\".csv\") == -1:\n",
      "        continue\n",
      "        \n",
      "    #create temporary dictionary under the headings in the character dictionary for each csv\n",
      "    d = {}\n",
      "    d['character'] = []\n",
      "    d['value'] = []\n",
      "    d['rule'] = []\n",
      "    d['identification'] = []\n",
      "    d['role'] = []\n",
      "    d['type of perpetrator'] = []\n",
      "    \n",
      "    #read csv into dictionary, field names match the names of the column headers\n",
      "    csvDict = csv.DictReader(open((csvDir + \"/\" + filename), 'rt', encoding=\"utf-8\"), \n",
      "        fieldnames = ['character', 'value', 'rule', 'identification', 'role', 'type of perpetrator'], \n",
      "        delimiter = ',', quotechar = '\"')\n",
      "    \n",
      "    #skip over header column (fieldnames)\n",
      "    next(csvDict)\n",
      "    \n",
      "    #these two variables are needed for removing the punctuation in the next step\n",
      "    emptyString = \"\"\n",
      "    exclude = set(string.punctuation)\n",
      "    \n",
      "    #create character dictionary with id and name, strip punctuation and make lowercase\n",
      "    characterDict = {}\n",
      "    for row in csvDict:\n",
      "        charNameString = row['value']\n",
      "        charNameStringClean = emptyString.join(ch for ch in charNameString if ch not in exclude)\n",
      "        characterDict[charNameStringClean.lower()] = row['character']\n",
      "        \n",
      "    #open text from textDirDict and read to a string\n",
      "    f = open(textDirDict[textCount], \"r\")\n",
      "    textString = f.read()\n",
      "    f.close()\n",
      "    \n",
      "    #strip punctuation and make lowercase\n",
      "    cleanTextString = emptyString.join(ch for ch in textString if ch not in exclude).lower()\n",
      "    \n",
      "    #split text string on paragraphs\n",
      "    textParagraphs = cleanTextString.split(\"\\n\")\n",
      "    textParagraphs\n",
      "    \n",
      "    #these counters are used for keeping track of what paragraph an instance is found in\n",
      "    #this is essential for validating the results\n",
      "    paraCount = 0\n",
      "    hitCount = 0\n",
      "    paraHits = {}\n",
      "    \n",
      "    #iterate over paragraphs, iterate over characterDict, match name in para and add instance to paraHits\n",
      "    #this creates a temporary paragraph level dictionary, from which a list of all hits for the current paragraph is created\n",
      "    for para in textParagraphs:\n",
      "        for name, id in characterDict.items():\n",
      "            if name in para:\n",
      "                paraHits[hitCount] = [textCount, paraCount, name, id]\n",
      "                hitCount = hitCount + 1\n",
      "        paraCount = paraCount + 1\n",
      "\n",
      "    #add paraHits into text level list called hitList\n",
      "    hitList = []\n",
      "\n",
      "    for hitCount, instance in paraHits.items():\n",
      "        hitList.append(instance)\n",
      "        \n",
      "    #add hitList to hitDict indexed by number as string\n",
      "    #index must be a string if you want to iterate over this dictionary, otherwise you receive the error\n",
      "        #\"cannot iterate over integer\"\n",
      "    hitDict[str(hitDictCount)] = hitList\n",
      "    \n",
      "    #increment counters\n",
      "    textCount = textCount + 1\n",
      "    hitDictCount = hitDictCount + 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 187
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can now check that this hit dictionary is populated correctly. The keys are simply numbers corresponding to the texts in the txtDir. This is the first value in the lists you see below. The next value is the paragraph number, the one after the character name, and the last that characters unique identifier."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hitDict['25'][:20] #first twenty for legibility"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 226,
       "text": [
        "[[25, 1, 'holmes', '5'],\n",
        " [25, 2, 'watson', '6'],\n",
        " [25, 3, 'holmes', '5'],\n",
        " [25, 6, 'watson', '6'],\n",
        " [25, 12, 'watson', '6'],\n",
        " [25, 14, 'thurston', '15'],\n",
        " [25, 16, 'watson', '6'],\n",
        " [25, 18, 'holmes', '5'],\n",
        " [25, 21, 'hilton', '1'],\n",
        " [25, 21, 'mr hilton cubitt', '4'],\n",
        " [25, 21, 'mr hilton', '24'],\n",
        " [25, 21, 'watson', '6'],\n",
        " [25, 21, 'hilton cubitt', '10'],\n",
        " [25, 23, 'mr holmes', '14'],\n",
        " [25, 23, 'holmes', '5'],\n",
        " [25, 24, 'holmes', '5'],\n",
        " [25, 25, 'mr holmes', '14'],\n",
        " [25, 25, 'holmes', '5'],\n",
        " [25, 26, 'holmes', '5'],\n",
        " [25, 28, 'holmes', '5']]"
       ]
      }
     ],
     "prompt_number": 226
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now that we have a dictionary of all the \"hits\", or occurrences of a character name, for all of the paragraphs in all of the texts we need to combine those hits into paragraph entries in order to map specific interactions, as we have defined them as co-occurrence in a paragraph. This is done by creating a dictionary, checking if the paragraph exists as a key, and if it doesn't, creating it and assigning to it a list containing the id of the character in the hit we are looking at, and if it does exist, appending that list and adding the current character. A second if statement is used to check if an id has already been found in a paragraph in order to avoid passing duplicates into the id list for the paragraphs."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "interactionDict = {}\n",
      "\n",
      "for key in hitDict:\n",
      "    paraHitDict = {}\n",
      "    for text, para, name, id in hitDict[key]:\n",
      "        #check if paragraph exists as ket in paraHitDict\n",
      "        if para in paraHitDict:\n",
      "            #check if id already exists in the id list, if so pass, if not append\n",
      "            if id in paraHitDict[para]:\n",
      "                pass\n",
      "            else:\n",
      "                paraHitDict[para].append(id)\n",
      "        else:\n",
      "            #if para does not exists as a key, as it and assign the id as a list\n",
      "            paraHitDict[para] = [id]\n",
      "    interactionDict[key] = paraHitDict"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 227
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Test the results."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(interactionDict['25'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{1: ['5'], 2: ['6'], 3: ['5'], 6: ['6'], 12: ['6'], 14: ['15'], 16: ['6'], 18: ['5'], 21: ['1', '4', '24', '6', '10'], 23: ['14', '5'], 24: ['5'], 25: ['14', '5'], 26: ['5'], 28: ['5'], 29: ['1', '4', '24', '6', '10'], 30: ['21', '14', '2', '8', '5'], 31: ['1', '8'], 32: ['14', '5'], 33: ['14', '8', '5'], 34: ['5'], 35: ['20'], 36: ['1', '10'], 37: ['14', '8', '5'], 42: ['1', '4', '24', '10'], 43: ['22', '5'], 44: ['6'], 46: ['1', '10'], 48: ['14', '5'], 50: ['14', '5'], 52: ['14', '5'], 56: ['5'], 59: ['5'], 62: ['1'], 70: ['5'], 72: ['1', '4', '24', '10'], 73: ['14', '5'], 74: ['5'], 77: ['22', '1', '6', '5', '10'], 78: ['5'], 79: ['1', '5', '10'], 81: ['5'], 84: ['19', '1', '5', '10'], 85: ['21'], 87: ['5'], 89: ['18', '16'], 90: ['5'], 91: ['21'], 92: ['1', '4', '24', '10'], 93: ['21', '5'], 94: ['18', '16'], 95: ['14', '5'], 98: ['5'], 99: ['14', '5'], 101: ['18', '16', '1', '4', '24', '10'], 102: ['5'], 117: ['18', '16', '5'], 119: ['18', '16', '5'], 121: ['20', '5'], 123: ['5'], 125: ['3'], 128: ['22', '18', '16', '5'], 137: ['5'], 138: ['5'], 142: ['18', '16'], 143: ['5'], 144: ['18', '16'], 145: ['5'], 148: ['14', '5'], 149: ['9'], 155: ['5'], 156: ['9'], 157: ['12', '11', '25', '9', '13', '5'], 158: ['6', '5'], 159: ['22', '1', '5', '10'], 160: ['1', '4', '24', '6', '5', '10'], 161: ['13'], 162: ['1', '4', '24', '10'], 163: ['8'], 168: ['11', '13'], 172: ['18', '16'], 174: ['12', '11', '7', '1', '13', '10'], 175: ['8'], 176: ['6'], 177: ['12', '11', '9', '13'], 185: ['14', '5'], 186: ['22', '5'], 188: ['5'], 189: ['18', '5'], 190: ['1', '10'], 191: ['1', '10'], 193: ['8'], 197: ['12', '11', '25', '13', '5'], 198: ['8'], 201: ['11', '23', '1', '5', '10'], 204: ['11'], 205: ['2', '8'], 206: ['8'], 207: ['8'], 208: ['18', '16'], 211: ['22', '5', '17'], 212: ['5'], 213: ['6'], 216: ['6', '5'], 217: ['12', '11', '1', '13', '10'], 219: ['22', '5']}\n"
       ]
      }
     ],
     "prompt_number": 228
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Great! We have exactly what we want, a dictionary object containing the paragraph numbers with a list of the id numbers for the characters that are found within that paragraph. This is great, but it's not quite in the form of interactions yet. Interactions occur between two characters, so let's pretend we have a paragraph with three characters, 1, 2, 3 and 4. That means in that paragraph the possible interactions to be mapped are:\n",
      "\n",
      "* 1-2\n",
      "* 1-3\n",
      "* 1-4\n",
      "* 2-3\n",
      "* 2-4\n",
      "* 3-4"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The following script, again taken from [Stefan Sinclair's notebook](http://nbviewer.ipython.org/gist/sgsinclair/bf112f84f130c8bac96c), on networks in Holmes will iterate through the occurrence lists above and count the number of times each character occurs with another in a paragraph [9]. This count is very important, as it determines the weight of our edges in our network map. The more times a character interacts with another particular character, the stronger their relationship is, and the thicker the edges of the network graph will be."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from collections import defaultdict\n",
      "\n",
      "textEdgesDict = {}\n",
      "\n",
      "for key in interactionDict:\n",
      "    edgesDictionary = defaultdict(int)\n",
      "    for para, people in interactionDict[key].items():\n",
      "        for personA in people:\n",
      "            for personB in people:\n",
      "                if personA < personB:\n",
      "                    edgesDictionary[personA + \" -- \" + personB] += 1\n",
      "    textEdgesDict[key] = edgesDictionary"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 229
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "textEdgesDict['25']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 230,
       "text": [
        "defaultdict(<class 'int'>, {'5 -- 6': 4, '13 -- 5': 2, '11 -- 9': 2, '13 -- 9': 2, '1 -- 10': 19, '10 -- 22': 2, '2 -- 5': 1, '18 -- 24': 1, '5 -- 8': 3, '25 -- 9': 1, '1 -- 6': 4, '14 -- 8': 3, '1 -- 12': 2, '13 -- 25': 2, '10 -- 5': 6, '25 -- 5': 2, '12 -- 25': 2, '1 -- 19': 1, '11 -- 13': 6, '1 -- 24': 8, '16 -- 22': 1, '1 -- 4': 8, '10 -- 11': 3, '1 -- 5': 6, '1 -- 16': 1, '11 -- 7': 1, '10 -- 13': 2, '18 -- 5': 4, '21 -- 5': 2, '10 -- 19': 1, '23 -- 5': 1, '1 -- 11': 3, '16 -- 18': 10, '1 -- 22': 2, '18 -- 22': 1, '2 -- 8': 2, '10 -- 23': 1, '2 -- 21': 1, '12 -- 13': 5, '24 -- 6': 3, '10 -- 7': 1, '5 -- 9': 1, '10 -- 18': 1, '21 -- 8': 1, '1 -- 18': 1, '11 -- 12': 5, '10 -- 24': 8, '18 -- 4': 1, '12 -- 7': 1, '1 -- 23': 1, '14 -- 5': 14, '22 -- 5': 7, '13 -- 7': 1, '10 -- 6': 4, '1 -- 8': 1, '14 -- 21': 1, '11 -- 25': 2, '1 -- 13': 2, '4 -- 6': 3, '11 -- 5': 3, '11 -- 23': 1, '10 -- 4': 8, '17 -- 22': 1, '20 -- 5': 1, '14 -- 2': 1, '4 -- 5': 1, '22 -- 6': 1, '17 -- 5': 1, '16 -- 5': 3, '1 -- 7': 1, '24 -- 5': 1, '16 -- 4': 1, '12 -- 5': 2, '16 -- 24': 1, '24 -- 4': 8, '12 -- 9': 2, '19 -- 5': 1, '10 -- 16': 1, '10 -- 12': 2})"
       ]
      }
     ],
     "prompt_number": 230
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, in order to plot this, we need to turn it into a frequency distribution."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "edgesFreqsDict = {}\n",
      "\n",
      "for key in textEdgesDict:\n",
      "    edgesFreqs = nltk.FreqDist(textEdgesDict[key])\n",
      "    edgesFreqsDict[key] = edgesFreqs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 231
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "edgesFreqsDict['25']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 232,
       "text": [
        "FreqDist({'1 -- 10': 19, '14 -- 5': 14, '16 -- 18': 10, '1 -- 24': 8, '1 -- 4': 8, '10 -- 4': 8, '10 -- 24': 8, '24 -- 4': 8, '22 -- 5': 7, '11 -- 13': 6, ...})"
       ]
      }
     ],
     "prompt_number": 232
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*Change the key of the dictionary below before plotting to view the network model for each story.*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "edgesFreqs = edgesFreqsDict['25']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 233
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And now to plot the network graph, again taken from [Stefan Sinclair's notebook](http://nbviewer.ipython.org/gist/sgsinclair/bf112f84f130c8bac96c) [9]."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "G = nx.Graph()\n",
      "plt.figure(figsize=(10,10))\n",
      "\n",
      "# create graph edges (node pairs) and keep track of edges for each count \n",
      "edges = defaultdict(list)\n",
      "for names, count in edgesFreqs.most_common():\n",
      "    if count > 1:\n",
      "        parts = names.split(\" -- \")\n",
      "        G.add_edge(parts[0], parts[1], width=count)\n",
      "        edges[count].append((parts[0], parts[1]))\n",
      "    else:\n",
      "        break\n",
      "\n",
      "# draw labels (nx.draw(G) doesn't really work)\n",
      "pos = nx.spring_layout(G)\n",
      "nx.draw_networkx_labels(G, pos)\n",
      "\n",
      "# draw edges with different widths\n",
      "for count, edgelist in edges.items():\n",
      "    g = G.subgraph(pos)\n",
      "    nx.draw_networkx_edges(g, pos, edgelist=edgelist, width=count, alpha=0.1)\n",
      "\n",
      "plt.axis('off')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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CSkLXR1GSRbtqu8TI6MARYjLMKtIF/Qvqd2ZlH1GYmx7gQmg9/nVGWEGE51Yrn1nRKgg6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\nABL4/yHrOP5kaD6QAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c32f400>"
       ]
      }
     ],
     "prompt_number": 234
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now let's compare this a screenshot of a network created for the same story using the hand coded dictionaries."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![Hand coded network graph for the same story](http://s30.postimg.org/pydqzzy35/Screen_Shot_2015_04_17_at_1_38_16_PM.png)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As we can see, these are distinctly different graphs, however we do see some similar formations, both including overlapping cliques. There are also significantly more \"characters\" in the NER version, which complicates things. My guess is that this is largely for the reason that no aliases have been assigned in the NER network map. I will now take the time to quickly assign the same id number to \"characters\" that are actually the same individual in the NER character dictionary so that we can compare the results with the hand coded dictionaries. (NOTE: One addition besides aliasing via ids has been included in the network below. That is the inclusion of the word \"NARRATOR\" aliased to Watson. This is because the txt files of the stories have been altered to substitute the narratorial \"I\" as spoken by Watson with the word \"NARRATOR.\" It is my guess that a quick inclusion of this term in the dictionary adds a large degree of accuracy.)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![NER Network with Aliases](http://s10.postimg.org/maopfnssp/Screen_Shot_2015_04_17_at_2_44_27_PM.png)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can see then, that this graph is a simpler, likely less robust version than the hand-coded process. However, some of the main attributes, such as the clique that exists between Holmes, Watson, Hilton (the victim) and Slaney (the perpetrator) is maintained, as represented by [1,2,4,18] in the hand coded and [1,5,6,10] in the aliased NER networks."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The question then, in choosing a process for the creation of character networks seems to be rooted in trade offs. Yes, we see that careful hand coding, though not free from error entirely (during a more detailed validation I did previously on these dictionaries I encountered inaccurate and missed aliasing, which I took the time to correct before showing the results in this notebook), creates a far more robust network map for us. However hand coding, here done by crowd sourcing using [Amazon Mechanical Turk](https://www.mturk.com/mturk/welcome), I believe, can be either a time consuming or costly endeavor. Whereas, once the writing of a algorithm for network creation is complete, running the process on a large corpus is relatively quick, with the aliasing being another relatively brief step. Both processes suffer from missed interactions via pronoun use and dialog, which can only be minimized by coding the text, the way in which these texts have included the word \"NARRATOR\" as discussed above."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Precision and Recall"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In order to validate some of the assumptions made above in terms of the relative accuracy of these models, I would like to take the time now to look at recall and precision in terms of the interactions between characters for the story modeled above. Precision and recall are concepts that come from the field of information retrieval. Let's first imagine the four cases for which retrieved information relates to its relevancy, in terms of character interactions. That is true positives (TP), a case in which an interaction is captured by the algorithm, false positives (FP), a case in which an interaction is registered when there actually was none, false negatives (FN), the case in which an interaction occurred that was missed by the algorithm, and true negatives, the case in which no interaction occurred and no interaction was registered.\n",
      "\n",
      "Precision is a measure of the accuracy or information returned by a particular information seeking behavior. It is calculated by dividing the number of relevant retrieved items by the total retrieved items. In our case of character interactions, it is calculated as TP/TP+FP. Recall is calculated by dividing the number of retrieved relevant items by the total possible number of relevant items. In our case of character interactions, it is TP/TP+FN. For a visual representation of these concepts, the image below from the [Wikipedia page on precision and recall](https://en.wikipedia.org/wiki/Precision_and_recall) is provided:"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![Precision and Recall](https://upload.wikimedia.org/wikipedia/commons/thumb/2/26/Precisionrecall.svg/350px-Precisionrecall.svg.png)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now how to calculate these for our story above? The answer is, unfortunately for me, that this must be done by hand. Remember the structure of the interaction dictionary from before? In it the paragraphs are numbered, and the characters in each paragraph listed. For the purposes of comparing these lists to each paragraph I have written a quick script that spits out each paragraph as its own numbered .txt file."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "textDir = \"Sherlock_Masterfolder_v2/Texts\"\n",
      "textDirDict = {}\n",
      "textDictCount = 0\n",
      "\n",
      "for filename in os.listdir(textDir):\n",
      "    textDirDict[textDictCount] = (textDir + \"/\" + filename)\n",
      "    textDictCount = textDictCount + 1\n",
      "    \n",
      "    directory = filename\n",
      "    if not os.path.exists(directory):\n",
      "        os.makedirs(directory)\n",
      "\n",
      "    f = open(\"Sherlock_Masterfolder_v2/Texts/\" + filename, \"r\")\n",
      "    textString = f.read()\n",
      "    f.close()\n",
      "    \n",
      "    textParagraphs = textString.split(\"\\n\")\n",
      "    textParagraphs\n",
      "\n",
      "    paraCount = 0\n",
      "    for para in textParagraphs:\n",
      "        f = open(filename + \"/\" + str(paraCount) + \".txt\", \"w\")\n",
      "        f.write(para)\n",
      "        f.close()\n",
      "        paraCount = paraCount + 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 200
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The following image will give you an idea of what this validation process looks like, in terms of counting these interaction types:"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![Validation by Hand](http://s11.postimg.org/x6bs0air7/Screen_Shot_2015_04_17_at_5_26_03_PM.png)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And the results (on a scale of 0 to 1, 1 being perfect results):"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**NER Recall:** 0.30573248407643\n",
      "\n",
      "**NER Precision:** 0.7741935483871\n",
      "\n",
      "**Hand Coded Recall:** 0.8150289017341\n",
      "\n",
      "**Hand Coded Precision:** 0.88679245283019"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So we see that, in this instance in terms of precision, the number of identified interactions that were true interactions, the results of the NER and hand coded character dictionaries are very close, with the hand coded dictionaries ourperforming the NER created dictionaries by just a little bit. However, in terms of recall we see a very significant drop. This means that the NER version is not capturing nearly the same number of interactions that the hand coded version does. One of the reason for this that became very evident for me as I performed this validation, is that there are many characters in this story that are not given proper names, but do have significant roles, such as \"the country doctor\", \"the station master\", and \"the stable-boy.\" Character entities such as this will not be detected by NER, and as such are excluded from its results entirely."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "It seems to me then, that if one is concerned more with the action of the principle characters of a narrative, the NER technique may still be valid, and may still represent the basic underlying structures of the text. As discussed above, these networks are not the text, but are instead models of the text for us to experiment with and make interpretations and insights from [3]. The centrality of the main characters, Holmes, Watson, the victim, and the perpetrator, are certainly not lost using the NER process, and as such much of the fundamental structure as revealed by the network model may still be in tact."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Interpretation"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So far I have laid out an understanding of character networks and showed processes for developing these network models using python, but the real questions that are of interest pertain more to what we can learn from these models. In an attempt to investigate this, I would like to create five network models pertaining to the five story anthologies that make up the Sherlock Holmes corpus. These are The Adventures of Sherlock Holmes (1891\u20131892), The Memoirs of Sherlock Holmes (1892\u20131893), The Return of Sherlock Holmes (1903\u20131904), His Last Bow (1908\u20131917) and The Case-Book of Sherlock Holmes (1921\u20131927). Since these are compiled chronologically, it gives us an opportunity to examine Sir Arthur Conan Doyle's works diachronically."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First, I have made folders for each anthology and create a list of those folder titles."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "anthDirList = ['Sherlock_Masterfolder_v2/01Adventures', \n",
      "               'Sherlock_Masterfolder_v2/02Memoirs',\n",
      "               'Sherlock_Masterfolder_v2/03Return',\n",
      "               'Sherlock_Masterfolder_v2/04LastBow',\n",
      "               'Sherlock_Masterfolder_v2/05Casebook']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Next, I iterate over those folders and the files within them, reading the text into an anthology level string."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "anthStringDict = {}\n",
      "anthCount = 0\n",
      "\n",
      "for folder in anthDirList:\n",
      "    anthString = ''\n",
      "    for filename in os.listdir(folder):\n",
      "        if filename.find(\".txt\") == -1:\n",
      "            continue\n",
      "        f = open(folder + \"/\" + filename, 'r')\n",
      "        textString = f.read()\n",
      "        f.close()\n",
      "        anthString += textString\n",
      "    anthStringDict[str(anthCount)] = anthString\n",
      "    anthCount += 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Check the results."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "anthStringDict['1'][:1000]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "'Adventure I. Silver Blaze\\n\\n\\n\"I am afraid, Watson, that I shall have to go,\" said Holmes, as we sat\\ndown together to our breakfast one morning.\\n\\n\"Go! Where to?\"\\n\\n\"To Dartmoor; to King\\'s Pyland.\"\\n\\nNAR was not surprised. Indeed, my only wonder was that he had not already\\nbeen mixed up in this extraordinary case, which was the one topic of\\nconversation through the length and breadth of England. For a whole day\\nmy companion had rambled about the room with his chin upon his chest and\\nhis brows knitted, charging and recharging his pipe with the strongest\\nblack tobacco, and absolutely deaf to any of my questions or remarks.\\nFresh editions of every paper had been sent up by our news agent, only\\nto be glanced over and tossed down into a corner. Yet, silent as he was,\\nNARRATOR knew perfectly well what it was over which he was brooding. There was\\nbut one problem before the public which could challenge his powers of\\nanalysis, and that was the singular disappearance of the favorite for\\nthe Wessex Cu'"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Create a folder to put our anthology level character dictionaries in."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "directory = \"Sherlock_Masterfolder_v2/AnthDictionaries\"\n",
      "if not os.path.exists(directory):\n",
      "    os.makedirs(directory)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 135
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And now to create the character dictionaries, using a slightly modified version of the code from above. (This takes a long time to run.)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#this will be used for interating over our dictionary containing the text file locations\n",
      "anthCount = 0\n",
      "\n",
      "for filename in anthDirList:\n",
      "    anthString = anthStringDict[str(anthCount)]\n",
      "    \n",
      "    characterList = []\n",
      "    \n",
      "    #call the NE tagger\n",
      "    nerTagger(anthString, characterList)\n",
      "    \n",
      "    #remove duplicates\n",
      "    uniqueCharacterList = set(characterList)\n",
      "    \n",
      "    cleanCharacterList = []\n",
      "    \n",
      "    #create a variable outof the inclusionList generated above\n",
      "    localInclusionList = inclusionList\n",
      "\n",
      "    #call the NE list clean up method from above\n",
      "    nerCleanUp(cleanCharacterList, uniqueCharacterList, localInclusionList)\n",
      "    \n",
      "    #create the header for the csv files\n",
      "    csvCharacterList = []\n",
      "    csvCharacterList.append(['id', 'name', 'rule', 'identification', 'type of perpetrator'])\n",
      "    characterCount = 1\n",
      "\n",
      "    #add characters from the cleanCharacterList into the list to be turned into a csv file\n",
      "    for character in cleanCharacterList:\n",
      "        characterEntry = [str(characterCount), character, '1', 'TRUE', '']\n",
      "        csvCharacterList.append(characterEntry)\n",
      "        characterCount += 1\n",
      "    \n",
      "    #turn the csvCharacterList into a matrix in order to write it to file\n",
      "    charcterMatrix = pd.DataFrame(csvCharacterList)\n",
      "    csvFile = charcterMatrix.to_csv(index=False, header=False)\n",
      "    \n",
      "    #from the anthDirList extract the filename and clean the beginning and end off for writing the csv filenames\n",
      "    removeDir = len('Sherlock_Masterfolder_v2/')\n",
      "    filenameString = filename[removeDir:]\n",
      "    \n",
      "    #create a new csv file and write the matrix to it\n",
      "    f = open(\"Sherlock_Masterfolder_v2/AnthDictionaries/\" + filenameString + \"_Dictionary.csv\", \"w\")\n",
      "    f.write(csvFile)\n",
      "    f.close()\n",
      "    \n",
      "    anthCount += 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 136
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "After the csv character dictionaries are created, there is actually a fairly large step here that involves cleaning them up and mapping aliases. To do this, I opened each of the character dictionaries up in a text editor, and one by move through the names using the find function to see what other entries share a first of last name. Entries that are clearly the same character were given the same identifier, while ambiguous ones, such as \"James\" when there exists three separate characters with that name are removed. The few obvious mis-taggings, such as \"Charybdis\", a reference not a character, or \"Strasburg\" a place, as well as first name only entries that are also English words, such as \"Mark\", \"Will\" or \"Bill\" are also removed. In general I tried to move names with a higher assigned id to be aliases with their lower number counterpart, but the numbering in the end is no longer sequential."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This process was somewhat lengthy, taking a few hours, but if you compare the results created from the un-cleaned character dictionaries (image below) to the cleaned up dictionaries (further below), it is clearly worth it.\n",
      "\n",
      "![Anthology 3 Uncleaned Results](http://s7.postimg.org/ucm3d86zv/Screen_Shot_2015_04_19_at_2_56_14_PM.png)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Next I move on to creating my hitDictionary, which stores the occurrences of characters in paragraphs using a slightly modified version of the script from above. Run either of the two cells first to create network models out of the NER and cleaned up NER character dictionaries. This will give you an idea of how different the aliased results are from the automatically outputted results."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "csvDir = \"Sherlock_Masterfolder_v2/AnthDictionaries\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 165
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "csvDir = \"Sherlock_Masterfolder_v2/CleanAnthDictionaries\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 172
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "anthCount = 0\n",
      "hitDict = {}\n",
      "hitDictCount = 0\n",
      "\n",
      "for filename in os.listdir(csvDir):\n",
      "    \n",
      "    #if the file is not a csv file move onto the next iteration in the loop\n",
      "    #this is necessary as system generated hidden files (DSStore in OSX for example) will cause an error\n",
      "    #(thanks Stefan Sinclair for this snippet) \n",
      "    if filename.find(\".csv\") == -1:\n",
      "        continue\n",
      "        \n",
      "    #create temporary dictionary under the headings in the character dictionary for each csv\n",
      "    d = {}\n",
      "    d['character'] = []\n",
      "    d['value'] = []\n",
      "    d['rule'] = []\n",
      "    d['identification'] = []\n",
      "    d['role'] = []\n",
      "    d['type of perpetrator'] = []\n",
      "    \n",
      "    #read csv into dictionary, field names match the names of the column headers\n",
      "    csvDict = csv.DictReader(open((csvDir + \"/\" + filename), 'rt', encoding=\"utf-8\"), \n",
      "        fieldnames = ['character', 'value', 'rule', 'identification', 'role', 'type of perpetrator'], \n",
      "        delimiter = ',', quotechar = '\"')\n",
      "    \n",
      "    #skip over header column (fieldnames)\n",
      "    next(csvDict)\n",
      "    \n",
      "    #these two variables are needed for removing the punctuation in the next step\n",
      "    emptyString = \"\"\n",
      "    exclude = set(string.punctuation)\n",
      "    \n",
      "    #create character dictionary with id and name, strip punctuation and make lowercase\n",
      "    characterDict = {}\n",
      "    for row in csvDict:\n",
      "        charNameString = row['value']\n",
      "        charNameStringClean = emptyString.join(ch for ch in charNameString if ch not in exclude)\n",
      "        characterDict[charNameStringClean.lower()] = row['character']\n",
      "        \n",
      "    #grab the text from anthStringDict\n",
      "    anthString = anthStringDict[str(anthCount)]\n",
      "    \n",
      "    #strip punctuation and make lowercase\n",
      "    cleanTextString = emptyString.join(ch for ch in anthString if ch not in exclude).lower()\n",
      "    \n",
      "    #split text string on paragraphs\n",
      "    textParagraphs = cleanTextString.split(\"\\n\")\n",
      "    textParagraphs\n",
      "    \n",
      "    #these counters are used for keeping track of what paragraph an instance is found in\n",
      "    #this is essential for validating the results\n",
      "    paraCount = 0\n",
      "    hitCount = 0\n",
      "    paraHits = {}\n",
      "    \n",
      "    #iterate over paragraphs, iterate over characterDict, match name in para and add instance to paraHits\n",
      "    #this creates a temporary paragraph level dictionary, from which a list of all hits for the current paragraph is created\n",
      "    for para in textParagraphs:\n",
      "        for name, id in characterDict.items():\n",
      "            if name in para:\n",
      "                paraHits[hitCount] = [anthCount, paraCount, name, id]\n",
      "                hitCount = hitCount + 1\n",
      "        paraCount = paraCount + 1\n",
      "\n",
      "    #add paraHits into text level list called hitList\n",
      "    hitList = []\n",
      "\n",
      "    for hitCount, instance in paraHits.items():\n",
      "        hitList.append(instance)\n",
      "        \n",
      "    #add hitList to hitDict indexed by number as string\n",
      "    #index must be a string if you want to iterate over this dictionary, otherwise you receive the error\n",
      "        #\"cannot iterate over integer\"\n",
      "    hitDict[str(hitDictCount)] = hitList\n",
      "    \n",
      "    #increment counters\n",
      "    anthCount = anthCount + 1\n",
      "    hitDictCount = hitDictCount + 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 173
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Test the results."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hitDict['4'][:20]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 174,
       "text": [
        "[[4, 0, 'mazarin', '57'],\n",
        " [4, 2, 'watson', '2'],\n",
        " [4, 2, 'billy', '106'],\n",
        " [4, 4, 'billy', '106'],\n",
        " [4, 6, 'billy', '106'],\n",
        " [4, 10, 'watson', '2'],\n",
        " [4, 14, 'holmes', '1'],\n",
        " [4, 14, 'mr holmes', '1'],\n",
        " [4, 14, 'hudson', '195'],\n",
        " [4, 16, 'billy', '106'],\n",
        " [4, 18, 'billy', '106'],\n",
        " [4, 20, 'billy', '106'],\n",
        " [4, 22, 'billy', '106'],\n",
        " [4, 26, 'holmes', '1'],\n",
        " [4, 26, 'mr holmes', '1'],\n",
        " [4, 26, 'lord cantlemere', '34'],\n",
        " [4, 30, 'holmes', '1'],\n",
        " [4, 30, 'mr holmes', '1'],\n",
        " [4, 32, 'holmes', '1'],\n",
        " [4, 32, 'mr holmes', '1']]"
       ]
      }
     ],
     "prompt_number": 174
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And now create the interactionDict."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "interactionDict = {}\n",
      "\n",
      "for key in hitDict:\n",
      "    paraHitDict = {}\n",
      "    for text, para, name, id in hitDict[key]:\n",
      "        #check if paragraph exists as ket in paraHitDict\n",
      "        if para in paraHitDict:\n",
      "            #check if id already exists in the id list, if so pass, if not append\n",
      "            if id in paraHitDict[para]:\n",
      "                pass\n",
      "            else:\n",
      "                paraHitDict[para].append(id)\n",
      "        else:\n",
      "            #if para does not exists as a key, as it and assign the id as a list\n",
      "            paraHitDict[para] = [id]\n",
      "    interactionDict[key] = paraHitDict"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 175
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Creat the edges dictionary."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from collections import defaultdict\n",
      "\n",
      "textEdgesDict = {}\n",
      "\n",
      "for key in interactionDict:\n",
      "    edgesDictionary = defaultdict(int)\n",
      "    for para, people in interactionDict[key].items():\n",
      "        for personA in people:\n",
      "            for personB in people:\n",
      "                if personA < personB:\n",
      "                    edgesDictionary[personA + \" -- \" + personB] += 1\n",
      "    textEdgesDict[key] = edgesDictionary"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 176
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Turn it into a frequency distribution."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "edgesFreqsDict = {}\n",
      "\n",
      "for key in textEdgesDict:\n",
      "    edgesFreqs = nltk.FreqDist(textEdgesDict[key])\n",
      "    edgesFreqsDict[key] = edgesFreqs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 177
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And now to iterate over the textEdgesDict in order to draw our five network maps."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for key in textEdgesDict:\n",
      "    print(\"Anthology \"+str(int(key)+1))\n",
      "    edgesFreqs = edgesFreqsDict[key]\n",
      "\n",
      "    G = nx.Graph()\n",
      "    plt.figure(figsize=(10,10))\n",
      "\n",
      "    # create graph edges (node pairs) and keep track of edges for each count \n",
      "    edges = defaultdict(list)\n",
      "    for names, count in edgesFreqs.most_common():\n",
      "        if count > 1:\n",
      "            parts = names.split(\" -- \")\n",
      "            G.add_edge(parts[0], parts[1], width=count)\n",
      "            edges[count].append((parts[0], parts[1]))\n",
      "        else:\n",
      "            break\n",
      "\n",
      "    # draw labels (nx.draw(G) doesn't really work)\n",
      "    pos = nx.spring_layout(G)\n",
      "    nx.draw_networkx_labels(G, pos)\n",
      "\n",
      "    # draw edges with different widths\n",
      "    for count, edgelist in edges.items():\n",
      "        g = G.subgraph(pos)\n",
      "        nx.draw_networkx_edges(g, pos, edgelist=edgelist, width=count, alpha=0.1)\n",
      "\n",
      "    plt.axis('off')\n",
      "    plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Anthology 2\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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49LbJZ7qGffzPRmbTn1kmq1j2MUmn6L7bTkapMQVd0rWeS01RpUVeQvRBejGx\nTDhP9D2C6AT+BGLW6DJwhMgi7AVeBlwC/GNuuPBrSj72NlGPrq2ZI75Hk+k8fIG1LSNG0RJjY2kM\n0BBL3Y8vJwKpI4WP7QVmunzaxVEVIk3WZQzQirsfTVmxQ8RNx2l3n+v6D8i25MaMnSYC20PAnLuf\nrvTAasjMDhB1XfPufioteeezWrNE+cc4cCrNcJQGUKZruPXatQiq55L2myy8fwEu1tWcSY/NpB1k\nsnvKdG3deSIwnUpL4cVz9B7WL0NKAyjoGm4b1XMp6JK26xp0AaTs1lki23XQzIrPle1T0LVFaXPB\nPKlZqrsXlxhHiO/dKjCRliGlARR0Dam0tDhOLC1eKHxslC5b54k7LxXRS+Ol3XPFC9Wa3wN3P09k\nHAw4pAvbrino2p6sfcR0er12y3ZlS9/KdjWEgq7htZNdi2qKKm0xydqNIkvdWhe4+1niwjYCXJpu\nVmRnFHRtQ9qwdIH4vk2zPuiaojCzsdwjlJ3QD2l4aWlRhlnPpcWiVOi9QCfw6pYFls1p2PX2XWyW\nmvoo5r9fI8QN8gXUPqIxFHQNoXTRmCCWFhV0yTDactCVnErPGSUCL507tyE/7Do9NEJn8LX0kEo/\nloj+cntYf5OsgvqG0YljOGVZrl4Xml7Liwq6pPHSEmF+mXDTAe4pODhJXADHiMCr11ghWU9LizuX\nD6q6NUpdJHrMZU2ApcYUdA2nnq0iUu+ibheTFXfXSVLaYF2WaysZl/ScE8QFbpxonCpbo6Br5+aJ\n71W2AlFcYsxqu0DZrtpT0DVk0rLIBHF33y3TpdE/0nbbXVq8KBXbnyAufJNmdqifB9Zi3YIuzVzc\nghTsZ7sU99G9oD7fxV6bPWpMQVdLmNl1ZnabmS2Y2c25x7/XzN5vZifM7Bgx7uQw0VnezewFZvYl\nMztrZkeB19L9bklLi9IWxZrFLQddACnje4K4yO3JD8iWnpTp2p1sl+IU68/F2ZJiPjCTmlLQ1R7f\nBG4C3lB4/CDw34Gr0ts88Lt0CjJvBR7u7geAbwXuBfxSl39fQZc0Xlo+z5/3VnYySzR9zgniQjid\nxrZIbwq6diFlWLOgapJY4s4Ya5cY1T6ixpSGbAl3fzeAmT0cuGfu8fdmf0+/iG8E3kkKutz9y7l/\nZoy4iBwt/vNoeVHaYcdLi0XuvmRmJ4narn1mtpoaqsp6+aArK2FQ0LU9s8QqxHT6ez6jtcfd581s\ngQjApum553GzAAAgAElEQVS0m5AaUTTcPhvtqJoCvhf4bL5w2MyeYWZniGDrBPD6wuctaWu3tETf\ngi64uKU/G+B8wMzUK6k7Zbp2KWVXF4hzfP48/7PAB1PA9bvpseliaYmZvc3Mriz5sKVAQVf7bBQc\nPQz4v4EXr/kE9z9y9xngO4D7AT9f+DxluaTxUqZ3w9E/O+Hu88ScRogB2dq2v56Crv7IsldTdM7L\ndxLB1huJ7+8ysWpxmLWlJeeAm5FKKehqn66ZLjO7H/BWIuj6UI/P/QbwauCphcdVzyVtsKXRPzvh\n7rPERS2b09irwfCwynejN9QYdUdSV/ol1s7GfS/wF3QC/6y26/9z93e6+/l0Y/Bq4AdKO1jpSkFX\n+6w7kZnZVcD7gV939zd2O9nlBgCPsX5LsoIuaYPi0mK3aQw75u7niAueAZdoQPYa2bUmC3qV5dq5\nLNtVvH5nNdq92kc8Cvj0gI9NNqGgqyXMLOtGPEZ0Jp5Mj90D+ADw++7+2i6f9xwzO0wEXFcD1wF/\nnnuKmqJKW/S1nqsbdz9D3LSMEIGXNisFBV19krJWK6zNdkHccI+lm+rsxnkKwMy+HXgJ8KKyjlO6\nU9DVHi8h7nBeDDyL+KX7NeD/Au4D3GBm59Lb2dznfT/wKToF9O8genVllOWSxkvBT/4itZqWavrO\n3U8RWbRRIvDSebZzrcn+VNC1O9kSYv41bbn3zwN3ufv5VFry58AvuvtHSjxG6cK0rC4AqbP2ni4f\nOqtt8NJ0ZrYXmMk9tODuJwf49Qy4lGjEugSc6Ff9WNOk78XdiCWvOaLVgc4ru5C+p1cQKxtGBLEv\nIr7Pz8xWJ1JpyYeA3+y20iHl0x2YZHoV/irTJW0w8KXFvNycxiXSnMYhHpCtnYt9lhsNtEq8tidJ\npSVE65JNS0ukGsp0CWY2Stw1FTlwRLuMpOnM7G6s3bl4bCed6HfwdUeAy4gL4kCza3WVNhQcpnMD\nNwEcH9Ty7rBI5+3LgV8Fnl/48I3E+fsGOkuREPGapidUSEGXYGZ7gG6Dexfd/XjZxyPST2Y2SSz1\nZVbcvTh1YZBff5QIvEaB+VTzNTRy3/8FIus3ChzVBp3dS2Uh00RpyFzuQ/r+1pSWFwW0tCjtNtBW\nEZtJF7+TdAZkz2zyKW2TX14cIbItCgj64zyR0Sq2J+lWnys1oKBLoHfQpU700gal1nN14+5LRODl\nwF4z21/2MVQof50xIviSPkivq0XiNZ0PtBR01ZSCriGXint79RJSpksarcvoH6eCoAsudhM/lY5h\nf9pROQzUo2uwzrM+6BpXj7h6UtAl43QfHaSmqNIGxSxXpcPb3X2BGJANMadxGDISCroGKL2mlujs\nZMwMw2urcRR0ieq5pM0qX1osSh3Fz6R3D6ZC8zZT0DV4s0St4nTuMQVdNaSgSxR0SZvVLuiCdQOy\nL2n5gGwFXYM3l96y+bkAY5r/WT8KukRBl7RSuuCUMvpnJ7oMyG5rDY6CrgFLS+azxE1FvlZQ2a6a\nUdA1xFL/oG6vAU+7YkSarJZZrrzCgOxL0+9k2yjoKscs8VqapPM9V9BVMwq6hptaRUib1T7ogosD\nsi8QWblLWzggW8OuS5Bme54mdsdmtV2jLV+6bpy2/XLL9mhpUVoptUIpvr5rGXQlJ4nfuzEi8GrF\nnMb0/8h6c2WNUdWna3DOE68j9eyqKQVdw01Bl7TVBGtboSzXuQVKqsk5CSzTrgHZWlosUZoneob4\nvmfB1lR1RyRFCrqGlJqiSss1YmkxL2WAThCBySTd56E2jYKu8p0gMotZQb2WGGtEQdfwKmYCMitK\n/0sLNC7ogotzGrOL5pSZHaz4kHZLQVfJ3P0Cscw4SifLpSXGmlDQNbx69W9RlksaLe0ArMXon51I\nS0RZ4DVtZgcqPqTdUNBVjRPpzz2FP6ViCrqGl+q5pK1qNfpnJ1LLlmxO4z4z21fxIe2Ugq4KuPtp\nYhf6BHEDMjIEkw8aQUHX8FLQJW1VvLgsVHIUu5SWibLA64CZTW/yKXWkoKs6p9KfWW2Xsl01oKBr\nCKXO12qKKm3VyHqubtIw42xO44yZNW0nmoKu6hwnAvZJUn1XS3bENpqCruGkpqjSSmn0T/68ttr0\nGwl3nwPOEoHLoYYtEynoqkiuoB6iWeoI629IpGQKuoaTiuilrVqT5cpz9/PEBTQLvJoyyHiEOGYn\nAuBG1da1QL6g3tASY+UUdA0n1XNJW7Uy6AJw97PAHJ05jU0YkD1CLG2toizXwJjZdWZ2m5ktmNnN\nuQ9lgfp/Bj4J/IuZ/XWXz58ws8+a2ddLOuSh1YRfWukjNUWVtmrg6J9tc/fTaTbjFBF4Ha9zp306\nQdciCroG6ZvATcDjyGWz3H3JzK4nlhefBHwJuFeXz38RcIxO0b0MiDJdw6dXU9RlNUWVhpukQaN/\nduEUEcQ0YUD2SHpzFHQNjLu/291vpbOcCICZfSvwWOClRNZrCri98Jz7AM8EfpPu1wbpozr/sspg\nqIhe2qoVrSI2k+qiThC/s2PUdE5jbti1iujLU3wdfDfwVeC5wAeA9wBPKrxeXgX8J1r6+1I3CrqG\nj4ropa1aW89VlAu8lokbqUuqPaKuitcXBV2DV9yocE/gwUT7iMcArwBeCTwIwMyeDFjKkkkJFHQN\nHxXRS+uk0T/5WkWn5a/p4oBsM6vbgOyRwp8KugavmOmaJzKiNxGbMG4DPgw8xsz2EkHYL5V6hENO\nhfRDRE1RpcWKWa7FYWhP4O4rZnYCuAzYY2ar7n5ms88riTJd5Su+5j+Z/pwHZomC+qyFx/2Aq4AP\np9XGCaIB753A97j710o54iGjTNdwUZZL2mpolhaL0oDsk8SFdK+Z7a/4kDJrGqO2dFNDLZjZaJpW\nMAaMmtlkyv7+NfA1omZrDLgG+AHgfcCnieXHa9Lbc4Cj6e/fKP0/MSQUdA0XFdFLWw1t0AXg7ot0\nAq/9aemoavnGqAq4BuslxPLhi4FnEZmt61NA/hPAjwGfAX4NeJ673+7uK+5+LHsjdsVmj2kn+4DY\nEGTgJTGzw3QvpD+ZZryJNI6ZTRDLa5kVdz9a1fFUycz2AFlt1yl3n6/wWPanY5kATrv78aqOZdil\n0VGHgRng+LD+ftSBMl1DIm0R1s5FaaOhznLlpSArq+k6WPGcRnWjr48x4uewjK77ldI3f3j0WlpU\nU1RpOgVdOe4+C5wjlvYuSZnAKqgxan1kN9yLxD147fq6DQsFXcNDRfTSOj0yuEMddAG4+zlit1oW\neFWxU12ZrvrIfv7Z+X60qgMZdgq6hoeCLmmj4uifJWVuQ2odMU9nQHbZF1oFXfWR3ZhkNyS69ldE\n3/jh0aueSzsXpcm0tLgBdz9FfE+qmNOYLS8q6KpQynIaUc+V/Rx07a+IvvFDYIOmqKtqiioNp6Br\ncyeJjPYYEXiVVc+TZbpU01WtbGlxmQiAQcuLlVHQ1SBm9m1m9gEzO21mXzCzJ+U+dq2Zfc7MZtNz\n7pX7VPXnktZJNxNDNfpnJ1Jn/pPE7/s4JQRehWHXriXfSmWrHEt0gi5d+yuib3xDpAvMrcCfEL1v\nfgF4s5nd38wuA94FXJ8+dhvw1tynq55L2mhdlmsYRv/sRAp6ThIZpwk6vbwGRUuL9ZHPdGl5sWJq\njtoQZvZg4O/cfX/usfcB/0CMbPhpd39EenyamCr/UHe/3cwup/uczRPuruUYaSQzuwSYyj10JrVL\nkB7SzdulxPLSnLufHtDXGQfuDuwDjrj7iUF8Hdlc7vx/LP15CTCf6v2kZIp2m20EeDDwQDqDTXH3\nOeCLwINT4Wyv7eJaXpQmK2ZwdQOxidycxlVg2sxmBvSlVM9VA2mZd4xY4lVNVw0o6GqOzwPHzOxF\nZjZuZj8CPIqYGn+AThfqzFniLrPXrkU1RZXGSg0/8+evlXRRkU2kzTP5Adn7BvBltLxYD/mlRdDy\nYuX0jW+IdKJ8EvCvgTuBXwbeRqSMV1hfozFDdKVWPZe0kXYt7kIakH2KCLwOpJKEflKPrnrIF9GD\nCukrp298g7j7p9z9B939Mnd/PHBfoqbr88A12fPMbG/62GdQ0CXtpKBrl9KQ+yxDPpOGZa+xi4aq\nCrrqYU3QlTaaODCiUUDVUNDVIGb2EDObMrNpM3shcAXweuC9wAPN7ClmNgX8F+Cf3P12NORaWibV\nKWr0Tx+k+s+zRGuHiwOyLewHLk83cdul5cV6KC4vgrJdldI3vVl+CrgDOAr8EPDD6W71LuDngZcT\ntRoPB56edhD1aoqq+hdpKo3+6SN3Pw+cJ76nh1KQdRjYnx7bv4NO9vlCev1sqlNcXgTVdVVKLSNa\nwMwOEgX1a7bMpzqNg10+5YK2cEtT5V7vmXNpwLPsQmrBcSVxoc56emW21V7CzC4F7gUcd/ev9/VA\nZUtSoHwlcZN9JPd41mpFLYMqoEi3HbJfnGKdi+q5WiQ1wl0wszel959pZudyb7NmtmpmD6v6WAdM\n9Vx9lnYwZt/XEWJjTv76MJ12jG5VlmFRRr063bJcoLYRlVLQ1Q4KuobDq4GPEks2uPst7r4/ewOe\nB3zJ3T9e5UEOUmrumb9YrKadeLIDZjaZmmceIJYSTxPnh1Ei8Mov426np1d2wdfPpjq9gi4tL1ZI\n3/QWSPUsS0T96wRcTC33aoqqE2HDmNnTiS3+f8XaC2HezwBvLOuYKlK8sdBreQfMbNTMDhHd6Yvn\niVPE+WSMta1oxrdRVJ9d8FVEX51uRfSgQvpK6ZveHsVsV68s17Lm0zWLmR0AbgReQI+Ay8yuAh7J\n8AVdWlrcprSUeDmwrkVEziniYj3O2sBr06L61IpA7SKqp+XFGlLQ1R7FoEutItrjJuB17n4HaWkx\nZSryncR/Gvgbd/9qFQdYIgVdO9RlKXEjTgRe2YDsbGlxhM2XGdWjqx7GiJ+jMl010mv5SZpnkfgF\nG093mqrnagEzeyhwLZAVx2cXy0uIn/Vyahvy08DLKjjE0qQeUvlgQaN/tiA1OD3AxpmtblaJwCvb\n7eZET689Zja7QS2dgq6KpdpHo/vKhmq6KqSgqyXc3c1skcgETKKgqy0eDdwb+FpqIL2PuKA9CHgC\nMTvvO4G7Ae+o6BjLUsxyLVRyFA2SsqH72PkFdoUIvA4RQdsq0dNrhugP2E3WGHUFBV1V6bW0CMp0\nVUpBV7tcIC5Me+m+fKCmqM3zWuAt6e8GvJAIwp5LnFgniQL6d+R7tLWUlha3KG2oOUh/zvHLxLig\ng8S5ZRWYM7N9qbFqUZbpWkJBV1V6FdHj7qtm5ijoqoSCrnbJLkL76Z7RUparYdx9HpjP3jez88C8\nu58wsxli6eepwFMqOsRSdBn94yjoWid9n2bY/lLiZhaJpcUDxPllFVg1s3l3LwZWo/Re2pJybJTp\ngvj5jZrZiKY5lEtBV4u4+5KZrRL1F8usH7/R6xdQGsLdb8y9O0tkHr6NGA3VZsUs15Iu6Guldg77\nGVwGY4EIpg6kN09/nio8T41Rq7eloIvOfEwpidKL7XOB+IXrVtOlTFeLpKXiC8Tv8fQmT286LS32\nYGYTZnaYyHAN+pw+T2dO4wxwIBuSnbPZBV8GKNeyw7tkITNqG1ERBV3ts0hkMIsnwuxj0i5ZHddW\nm1Y2lYKuAjMbSXMoL6N3i5hBmAXmiMDrINFgNU9BV7XGgZ8F3pPGht2c/6CZPQn4W+DzwMfN7Cdy\nH/shM/ugmZ02s6+UetRDQkFXA5nZ083ss2Z23sy+aGaPMLNvM7PbgK8DfwP8EfBduU/TckwLpXYR\nK8BYl4xDK5jZOBr9s0ZaSryc6jKc54is1whwWaovzGRlKwq6qjEG3Am8AnhD/gOpT9stwH8GHgBc\nD/yRmV2WnnIeeB3wotKOdsgo6GoYM/th4LeAZ7v7PqIL+ZeBO4CnETvbvh/4C+B/5D51qC9SLZdl\nu9q6xKgsV1LyUuJmzhI/i1HgW8xsj5m9HvgI8GHgQ2b2+CoPcEiNA+8FbgVOFD52PyKwen96/6+I\n88d9Adz9H939FkBZrgFRIX3z3Ajc6O4fBXD3O3MfO5PuWFaINft8Hx3ddbbXHFFAPWVmoxvUcTTV\n0AddaVfiAeoXWJ8menhNEBfurxONek8B9wLeZmYPGYJJCXWSX94ttg76BLHB4XHAPwA/TmyQ+GRp\nRzfkFHQ1SOos/Z3ArWb2BWKX4v8CXpSWmQC+RJyY7wJ+LvfpynS1VOq7M0/83KeJpZ9W6DFdYaiC\nrhJ2Je5W1rV+nOgrlw3R/jMiY/IdgIKu8uR7dK0pKXH3WTP798Bbid+rReCpqTWNlKCuv8TS3RXE\nie3fAI8AHkqMh/k1ADPbTywtfjfwPuB30+epKWr7tXWJcYK1d+vLLczkdVWzpcTNZAOyLyUyXytE\ngf/VwGcqPK6hkm7MR4gRWasUMl1m9h1EYPxIdx8nJl683syuKf1gh1Tdf5Flrexu5FXuftTdTxCB\n1RPM7AqisHaCuLv5HeAq4NtRlqv13H2JWE4YNbN+N8as0tAtLVa4K3E3sgHZE0TW697AHwN/6O63\nV3hcw6a4c7S4eepa4O/d/WMA7n4bscz42HIOTxR0NYi7nwK+AbGjK52YZ4hC1lE6yzDZkowRv3wK\nuoZDG9tHDFXQZWbTVLsrcTdWiYv8ZcCvEuek/2pmh81sf9qFKoOVLS2umtlUen/UzCbTEOxPAI/M\nMltm9jBiM9Yn0vuWPm88vTuZRkpJn6imq3luBp4PfJQImv89sVPxkcQJ70vECJD/SNRRHEFF9MNi\nnii2njCz8ZT9aqy0VDIUo39SQHKQ5mS2upkiguRnEEHjzxM3AOPETsf9ZrZCFG4vuHsrf5YVy14/\nLyTaQmSeBdzg7r9uZq8A3pXaRxwDXu7uf5me92jgA+nvTpxTPgQ8ZtAHPixMrZuaJd2t/L/EiW0R\n+BPg5cCPEHeXVxC72f4P8Grga8CnNF9rOJjZAWAfMOfup6s+nt1IWZ+DuYcW3f14VcczCGlX4n6a\nn50cIeq5biTaEjyf2Ml4ngjExonzVb62dJUUgAEX1Edw91IN4DhwV9NvutpKQVeDpTqurGnkJGsv\nUBAZr7PAF3O7G6XFUnbo8vTu0SYH22Z2iLWDm8+6+/mqjqffUlB5gHaUeRwiakj/jMhGZq+7VeAF\nwJ+n95fSx7NALJNlMbMsWGNft1VJO32vhHWthKRGtLzYbFl/Jui9LOHECU5B1xBw9xUzu0As9UwT\nmYamamU9V1pKnKH7fNQmmib+L98g2kMYnWHsd7n7N3IBZjZd4AyR+ZpKbxO5v7uZLRHnrPlh2a3a\nB2N06nilptpwhzXMZunsTukWdC2lx1s5HkZ6anz7iBSY5M9Pq01fLkm7EmeIQvO2BFyjxHI2RH84\nI7Jbc+mxS1PD3jmid+AC8XM9RARh82kX9lGi0Wp2cziRPn6FCvG3LN+fS2pKma4GKzTF7HZCWiZO\nguMt7VQuXbj7BTNbJs1jbGjBcquyXC1bSsybIc4xc3RuAJeJwGuRCJ6uBL6Zzj8nc9+LPcCkmZ1O\n5Q9zwFxaJpukk/kaT28qxN+YBo03gIKu5pslTmDFcQ/QOfFNEyexuS7PkXaaJS6Ie2lmwNKKoKuF\nS4l5+4gL/TKR5crq77JMyzzx/z5oZieyulJ3n0tL4AeJn/Ml6ebxjLuvpoL6rMCe1LJgKv37o8Rr\neq+ZqRB/rSzoUqarxhR0NZy7L8WNYVdZ0ep+FHQNmzkiGG/cPMY2jP5J/4dsVmLPX9AGGyf+b07U\nZ8H65a0F4twzARwysyNZYJRejycKWa8JMztT3PTj7ovEzePZFMTmM2DZ6CtPgdwwF+Jn339lumpM\nQVc79Gp+mp2sRoi7ylOlHZFUyt3dzOZIWQFiF2tTTNLg0T8pkNhPZ2dxG2XZ9fN0gqxuNUULRFB0\nID13zVzQzbJexS+am7xwLu3UVSE+F1uPjBK1j63//zaZgq52cGLWWfEkv0QsMS4T6fjGN8yUbcmC\nrmkzO9eg5Zfi0mIjdt62fCkxbz9x7Viis2kDugddc0TQNQXsM7O5YlCwnaxXl8+bBWZT0JEFXZPE\nz2ACOJALwBZafP5TEX1DtK2os5XM7Dozu83MFszs5tzj32tm7wc+C3wQ+H+IBoUQgdjNwO3A3xHz\ntc6b2SfLPXqpSrrAZJnOJs1jbFQ9Vxqd0rZdib1MsH5ZESLrNUJnFFBmhbWvwZle/3Buh+MF4gby\nEjM7lAKqDaVasDl3P0lM4ThJBHyrpCJ84LCZXWFmM2bWth3dKqJvCAVdzfBN4CbgDYXHDwKvB747\nvc0CN6SPLROjH64GHgI8AvgY8LbBH67USKPmMaYlo3wG3qnx7NA0XPxy4vvbxtqtvKxODWKZMJ+x\n2qiIez79uYeoMZzq9QXcfSW1kDhNBEx7iGCp5+d0+Tfc3Rfc/bS7HwGOE78H2WrAXqKVxZVmdtDM\npmyDwtiGUNDVEFpebAB3fzeAmT0cuGfu8ffmxr4AvBF4U/p7/pfvAnA34LuAfzfwA5bacPf59BoZ\nN7OJVJRcZ8UMxGIdl0XTOK4ZhqsH3gEiaFmkE0hlstKGbkHXQvrcCeJG/4CZbbjbcLu1XhvJFeKf\naXEhvpYXG0JBV7N0uxvLL2dcQwy8hvV3PD9KZLqa3KFcdiabXLCXGmeNklovLaaMSPa9bHp2ZDuy\nQGWVtcuKmc0u+gtE1mpP+jf2USiqL8rVeu0lvudbqvXa5N/cTiH+PBGANaEwXZmuhtDyYrN0uzOc\nTW/3B34R+O30ePHi+kTgT4meOcN0sZDO5IKprdTHVKy2QVduKXEfwxVwZUO5IQKlblmgzYKu/BIj\nRFH9lm763X2W9bVeB3f7Wk5LmbMbdMSfoQEd8dP30YCVOmaFZS1luppl3Yk+LR/dA7gFeD4RWF3O\n2hPjdxEF9n9Jp8mgenYNiTS5IMs07GWTDENVUhPM/IV0pQ67zYZ0KTFvhvi5XGxY2sVmQddS+tgY\nEdAsEkuOJ7dyAIWsV9b/bHI3Wa/Cv5+NLmpiR3z152oQBV3Nsu4uxsyuAt4P/Lq735Iem2Ltz/Yn\ngf9N3G1mO3kUdA2XWSLomqamQRc1y3IN8VJi3h4iSFqhd6+3XjsXi7KGvdOkYddmNrWdoMndZ9MN\nRL7Wa46o9epLlqdLR/x8AFbHjvhaWmwQBV0NkGoPxomf12g6CSwTM80+APy+u7829ynZCBiIE8UT\ngJ8jTnSTxAmjqTP5ZAfcfTHVqYxv90JXotoEXWkpMSscH1ajrF1W7BVUbHX8TL5DvaV/b2azovqi\nQWe9uny9C8Rrsa6F+CqibxAFXc3wEuClufefBdxInLTuA9xgZjekjzkRcO0n7j4fRxS+/h1pwCxx\n0mvqTD7ZuVkiQ7CXmjUcTVmlYs1M6a9PLSWukXWdn2fjn8VGOxfzPP07+RKHUbZQVN/1Hysh69Xl\na3YrxN9DvHarKsRXpqtBTHV37ZSaNRZ7M40SDRxXiN41x9xdd0dDIgU2VxDBeK1+9mlJ/JLcQ0vu\nfleJX19LiWvtJYKhZeDEJs/dT2R7zrK+lUTRBHCo8O86cNduXo+5rJcR57fTZWbyu3TEz7+GBtYR\nP71u70asit7Zz39bBkOZrvaaZX3QtUKnQWBWm9Bt+7e0UJrHOE9nHmOdfvaVLS2mgG+G4V5KzBsj\nXh/O1mZ2bmd5a5E4B40RGZolIkDJSiZ2JJf1OkQEdpcOOutV+PpVFeJrabFhFHS1lLsvp5NQsZPz\nEnFxGQf2mNlZbTMeKlkwXreffelBl5YSe5ohAqFZtrZktd0L/zyRRduTvsaZfmSA0jLe8S61XqVm\nvUouxNfSYsMo6Gq3WdYHXVlNxSTxyz7N2qG10mIpGL9A/Pxr8bMve/RPykTsY/j6bW1Ffpj1Vhop\nb3XnYt48nR2MJ/od+KesV9bNvvSsV5fjGWQh/lY3MUhNKOhqMXe/kO1Yyz2cXcyyTvZ7qcGFV0o1\nS9rFSj1+9utuDAZ1cdRS4oZ6DbPeyE4u+ueJ4GKCAfUMTPVhlWe9uhxXvwvx1aOrYRR0tV+2Yy2z\nSqdJ4RjEhaimLQRkANx9IdWWjNWkdcjAlxbTBW6G9QGedGTDrM+zdpj1Rra6cxEiMDjt7ku5jRN7\nGWDPwLplvQrHtkKaKNKlEH8ivc2kAKxXIb6WFxum7iNBZPfmWT+2o1u2S4ZLluGqw89+ovB+34Iu\nC/uJKQ0KuHrLsn+LbC8I2ko91yoR5NyVBQ3pJm+V6Bs30PE67r7s7seJ7J0TWa/LU61VLbj7qrvP\nuftJ4AjRqX+O9D0iln0Pm9kVZjZjZhMpUBsBVtPUievM7DYzWzCzm7N/28wemB4/aWanzewjZvaI\nCv6bgoKu1kt3c8WTaBZ0ZSedya3OQZPWmCMuQJMpC1SJHqN/+lKfkrIph4kLlmq3ettsmPVGNgu6\n5oj2JN2WsbPz0vQ2v+aO5GY4LhIB5qVphmOtXhseFtz9tLsfIVprzNLZeb6XaP1zTyI7OZL+D98E\nbgLeUPgnvwk8jRgFdwj4Y+AdZfxfZD1daIdDtmMtO7lcIC64+TvMurUQkAFKd8ZZQfNettYaYBD6\nvrSopcRtyfqTQe9h1hvpFXQtEdmtjTZEzJF2MZa1kzZX67WPTn+xymu9NrJBIf40UQ/m6e2viWXI\nhxMBWfb5Z0jn9nRzvQqop1dFFHQNAXdfyQ08zizRqRtYBKbN7FzJ4yukWrOk3VPpZ19FjUvfgi7t\nStyRbJh1tnNuO7rtXFwFzvXIbK2RdtIuEuegKTZvrNo37n4+180+q/WaBerURmWdQiF+dq7Ofmey\nYGw/EUjuJVeIb2aniRusO4DHlHrgcpGWF4dH8SRYrOsySkrzSz2kE/gicR4oPSuUalL6Mvon1edo\nKXF7sqLtnSwrwvqdixstJfZS6hJjXq7W6ywRNO6lZrVemxghvn9HgKPAaSJw9vSxGeAKM5sGcPeD\n6R47VlAAACAASURBVLE/Bt5et2XVYaGga0ikNH9+h0sx6IJ6FFVLuaosqF83LmW7mVYzGzWzS4h6\nFWXut26Ezm7FLOjYrqwWcB44nmqQtpspzzb6VFZb6O7nWV/rNdOAoOTi0m6hEH+WzoaIVXI979x9\nDvhV4GrgISUfr6Cga9jk70CX6OyMyU4uo6n4WIZHtotsYtC7yLooZhS2tbyV6nK0K3Fnsq7zmw2z\n3sgIEbAd3aR2q6dc93ao8KavR9brcF2zXqk2y4iAqxgwO7Eh5bS7H+myMWWUTpZMSqaga4iku5x8\n/50s85U/sewr74ikaoXdrWVf9HZUz2Vmk2Z2OZ0Bx7I900SGe4Wdb6CYJ5az5tl9j6jsZnDPhs8q\nQSHrNUZ9s17r+nOlrO8Ucdyj6fdkzMwea2YPTR8/APwO8Hl3/2IFxz30FHQNn/zdTXaRyy8xVpHx\nkGrNEnfHe1Kd1cClO/X8ctLqZtmSdNE4hJYSd2OMzo3VTuq4loilxFNsrzFqT7ni8Fpk2jfIehX7\nyVWp267RlxDn9xcDzyIC4v9MbBZ4CxEkf56ofXxiaUcqa1iNN2rIAKSL6hVEhmCU6PeyAhzPPW3O\n3U9XcHhSkVQXNUXs3trKzL3dfr29xBJXZiHVo/R6frbFv24Zh6a5hMiSzLK12YqZVeB89tpI55Er\niWD5yG4PKvd62PB1ULZ0c5DtcIT4vlW+wzH3+3pS00SaRZmuIZMKXbOt2St0Gu7lsw6lZTykNrIl\nnrJ2kW1paVFLiX21jwi4tjrMOjMP3FUIxvs9fmaeTrPe2px7apz10qDrhqrNi1tKlS+o77aL0dBO\nxqGSGjAuE/MYy1ji2TDo0lJi340Tv9PO1uu4loET7n6qy9DlrYz/2bJ0M7hATVvX5Gq9loj/+2VV\n1Xqlrzkah9Wf6Q1SHgVdQyjXnwnWjwTK1O7EJwNXSvuItCMsf7FaM/onLSUepgaF1S2SLeWeZ/NA\nyYkltGMbdGnva9CVVNazaytS1usuqs96ach1gynoGl7ZBTY7qRaL50fNTBe94ZL19Rn0LM6urSLS\nEN/DpHlyA/z6w+YAWx9mPU80ON1s+bHvF/5CtrXq5bueapD1GkTAKyXRiW1Iufs8Uc/lxMmjW3dw\ntY8YIqk4OKv3G2S2oRh0LaalxMtY/xqU3ZmkM59vo2XFjZYSuxnUhT97/dW6vCGX9TpH+VkvZboa\nTEHXcMuyXd3qugDG63zHKQNxsaB+EHfuXUb/TBFLX8qq9p/R6Tp/jrU9+jJbWUpc+4/Gz3CE2LnY\n71mtc+mYpupUUN+Lu59jfdbrwICzXgq6Gqz2L2oZqOwE1yvogprfcUp/pdqqC8S5YRCBUJblGifa\nF+xB56FByQ+z7jZMeoGtLSUWDeyin7JsF4iAsRGBeJes1z4Gm/XS8mKD6WQ3xHLtIxaJk0W3pZ2p\nqmaiVcHM7m9mC2b2pi4fe6mZrZrZY6o4thINsqB+isi+ZP2idjQ+Rja10TDrbCnx5BaXEosGfdGv\ndUF9LynrdZxO1uvSfme90rl4hNh80u8so5RAQZdkd7lLxN1lsd5m2NpHvBr4KIUBwGZ2X+CpwB1V\nHFSZUrPFFfq8vJwaYN6DtRkMBV39N0I0koVO9oX05zmi59ZO5y3CgIOuwuuvUTV+7r6Uy3pB/7Ne\n6s/VcAq6hlxuOWmjJcaB1PfUjZk9HTgF/BXrG3H+PjFeY1jqKPo2jzG3KzHLbmVWGZ7vZ5myZcUF\nOsOks6XEc33opl5GTVEZGzoGZoBZryzg1e9NQynoEojlpI2CrkHV99RGGgR7I/ACCgGXmT2NGE/y\nniqOrSLZPMYdFzSb2YiZHaSzK3HdrsXdHaJ0kR9mfYbIiJzcxVJiN2XUFGVB/56m3vANKOulIvqG\nU6dnwd0XzGyeyDyMkXYmFZ62l817/DTZTcDr3P0OM7uYCUhBw28A11Z2ZBVw91UzWyCC7b10Lhxb\nkpYS97P2xm5Lo39kx0ZZO8z6HDEvsW9zAge8c/Eid182s0UigJyi+0aARnD3c+l36SARNF1qZrPA\nTrKOKqJvOAVdkpkl7p4miRNdcYjquJlN7rIWpJbM7KFEUPWw7KHch28C3k30kprp8vE2myWCrmm2\nGHSlGpzs4lJUfEyZrv6aIV6bJ4Fv9DGzlVdmpmWWOBftpcFBF1ycAnKXme0nAuN9RBb5VPrYVo0R\nGWgFXQ2loEsyc8SJrVfQBXHya13QBTwauDfwtbSSsY/oyP9AIntwT+DZ6bmXAm83s/8K/FabdxC5\n+6KZLREB957UULerlAE5QO8anAnWBqvLrM+mys7tI76/x4mAq2/ZrYIyMy0LxGtkwszG2jBnsEvW\n67KtZr3SDY0BywP8+cqAmX52kjGzS4CriHqQ412e4kQx7iDuoCuTxh1lu70MeCERhD2X+D+PEcHX\nNPDXwA3AB4lAdQGYa2MGEMDMpokLxKK7d3tNZM/ZbHTPPtYW5c+xzSVL6WmMCGpngePbzJxsS8r2\n7gXOuPvsZs/v49c77+5bHdTdCLmslxFB7IZZr3SeOgTMu/upco5S+k2ZLsk7SwRco+mtGFxl7SNa\ndfJLGZyLWRwzO0+c2E4Un5syP18ndjlmI1b2mNkKKVvYhjvynHkioJows/H8RSHdec/QffNFUfE5\nWlrsjwt0lpvODzLgSsou5J4jzjl7aN95p2vWa4PgUkX0LaBMl6xhZvcm7qbO0r2OYhU4qvT2xSW1\n6fSWv4HJBgvPt+H7lHZ27gNm3f1M+n/vZ+vtJEaAw7n3HTjW36McOtnuxEni55DtlBsoM7uS+Hke\nKWtpPbUbGSd2YXYre2i8QtZrCThdDKDTSsQULf4+DAO1jJCiLG1d3GmWyQKNoefuq+5+3t2PEfPX\nZkk1KMTd6xVmdrAF8yvz8xingcvZXv+u4v9fd+o750RD4yxo3ZseG/hyU1k7F7toZIf67Sj09cqy\nXgcKT1OmqwW0vChF54ilio0Chb10LsTCxd1JZ8zsLHE3Ok0ErtNEsLJMXDzmmlZ87+4r6fgPE/+f\n7e4kU6uI/rhA1FItpwAo2017tqQl7aou+nPEEvekmY22raY0k9vheIA4x+4zs0ngNJ2yj9W2/v+H\nhYIuWSP1ZzpLp3t4txPsWFvbR+xWWk6cB+bTnLT88uMBYL+ZXSCCr9ovEeSWErOmmyNsP+hSPdfu\nrBCBVf77PkNchC+UUdCeVNIjyt091zNuy+1Lmsrdz6a+iYdIWS86NyptqhcdSlpelG5O0Vkm62WY\n5jHuiLuvpLErR4ETdIKVKeASM7sijQap881PVru1SJzws51yW5U1282sogvHVl1cSswHXGkX2x7i\ne3m6xOOpsjFnFli2ejJGJmW9fhL4M+ArwGuJG+HiTNiXmtmqmT2m8Ph3mNnfmNk5MztiZr9Y1rHL\nxhR0STcXiABho4vrVM2DhVpx9wtpm/dRogB6iU4H8cvN7DIzq+OMy3N0+mntZB6eslw7c4EYTH02\nvxkjZU+zZcUzJS81VVZT5O4Xg/605DYMvkmMJvuf6f1xYCYV3WNm9wWeCtyR/yQzuwx4D/AHRKB2\nX+Avyjlk2YyCLulmkbiz3CyoUrZrm1Lx/Wzaadat+P7KOhXfp/qzbDlnnrjTzpYZt0L1XNuzQvRr\nOtGjTusgaYl3o2a1A1L1CJrWF9Tnufu73f1W4jyRnZOXiRKFw8CrgRezPgj+ZeC97v6WNP9x1t0/\nV+axS28KumSddGe9QGcsUC91zMw0RjohniGyX6eIgMSIi8plZna5me1P2Y0qjzMbEZW9LrJj3AqN\n/untZ4mMxJeB3yG3lGhm15rZ58xs1sw+YGb3SvMss9/HXzezO83shJn9iZndfZAHWuHOxbw5djmE\nvaGM+N6fB44QgdeTiZu1v+3y/O8BTpnZR8zsaHp9fEtpRysbGqYXrmzPBTqzz3rJmqXKLnjImrEe\npbODdIyoqbrczC41sz0VBrln0p9ZtmErtTWTaPTPRu4Efhe4BVjIlhLT8tA7geuJYurbgLcSGzEA\n/i3wSODbgbsTAfurBnyslbcrSMFedmMyFLVdBStpmXUe+BXgpcT5oXhO+BZibNkvAvciasLeUuJx\nygYUdEkvF4gT7GavEQVdfZQrvj9G9O3JgpxJ4gJ8hZnNpG7wZR5X1vB1mchWjRAbAjZSDNi1tLjW\n/yaCqTtYWyD9FODT7v7O9H2/AbiGqM2ZBb4VeJ+735V2EL8NeNCAj7XqpcXMUC0xJlmmOwt4bwD+\nEPgknYkE+cBrDniXu/+f9Pq4Efj+rBZMqqWgS/7/9u49SvK8rO/4++n73GdnZi+oAeUmi+iCGi9c\noogR74sQjiiwC1FjDmziwaggZMMGFkMiCSfAIaAHFzloRMCVcA6iEQGNNy4Ku2xW1gWVuOzMTk/P\npae7p2e6+8kfz/fb9evqunZV/epXVZ/XOXV2e6q66tfV1fV76vk+3+fZxcxuAf6cWPZ4DbXXyWOI\n5ZB7gHuB3wW+zcwWzGzKzG4zsytpx8yymV1IHe5lD9z9srufI5YUzlELdg4AV5vZ1WZ2oMSllgtE\npqrTgnoV0TdWvyuxPlPxdcBnYXtZbw74e+DRxO/gD4DvM7OHpWa1zwc+NOBjrkTQldqsbBJD2Ev9\n4DFE+e87B13fRWSx/gH4HJHZ+m0z+/l0/V3lHp50Q7vPpJEHgNcCP0yc4GeJT1QngX9FzB6EqEn5\nVeB6Is29D/gtd39h2Qc8zlKN3SqwmnaM5t5fefbh4dTHaKCDt1MPt2XiJHAwPf4MjU/EU+x8f3EU\ndEE8B+fqiuQdIO3KmyGK5ZcKI3fmiCVnS8uPv0P8bT5ABCB3AS8d8HEPfXmxYI3aAPUyW2aUKtVy\nzhK//2lgKv39P4Pa35YBnwReRnwgBrgDeL+ZvQn4v8CtwJ+krvcyZMp0yS6FXTN51Eg+QSxTC7im\niZPFKeBaIuCaIYpcJ+UTaOncfSPV/pwElojCdojn/3jq/TWw4vtCUX27bJdG/+y0SdRenQWmU4by\naKrfOkj6/RFB9GVquxTz7tH91AKMXyY+5BwjAo87qZ1wB6USma4k9+xaGPONPLcSH7Z+BngO8dp5\npbsvuftD6XKK2o7XVQB3/yjwSmL5+hTwSODHh/EDyG7KdEkrueh5k511A/cSJ4FTwE8QtT25tuB7\ngFNm9iDwZnd/W6lHPEHSUsulBoO3D1HofE8Uafdz8HZx0PICERTU3/+kt4rImb5pIuC8TARUrT7o\nrhNBzWeIk+TJlF08QNRz3ZNu973AL6alZ8zsLcRuxmPuvtTvH6QiOxe3pbFU68RrbB+1Oq+x4u63\nmdl/BK5LXz/Y5HZf0+Df3gbovbeClOmSVraonUyLn3CvB74W+AjwJiIYO0xsX76R2Fl1O3Cbmb3Y\nzA6mxp8LZjZnZjMTtuV7oOoGb+fi+y12F9/3pfdXKu6+SOudZJNSzzVNbcbmYSL7dA0xp/IAEXDl\nWaZTxAeYvDP4AhGwXiKen4vp8h6irutZZrYAvBr4jLvflx7zLuDmNM1gFngJ8MAgAq6kSkuL2aQU\n1M8Qf2NVyDBKHyjTJa0YcZKAeMPNb74bxNLWG4E/Iz6F/y1R2Alxgvkb4uTxPOD3G955rAxsdXOp\nwiftKksB0eW07JJn1c0RAcCBPg7evkAEGXm4dzHbUD/6Z5PRP2lMU8tczRa+brS8lcfzXCB+7u1L\n8Tk3s9uIbf/ZC4Db3P01ZvYc4C3Au4G/IP6OspcRLSK+kI7hbqJv06BUaWkxu0RqKmxmMyUN/B6G\nKga80gMFXdKKU1tinKaW3dgkhrBuESedFXZvW6bwfVcR9QiN5KWLjihQ60yD4vscgPVl8HZa9jpD\nLJnNENmevIRYv7Q4Slmu+qCqVXAFEYjkoHKDWHo928lrzt1vI7b/N7ruI0RGudF1p4i5fGWpXNCV\nNhSsER8m9hMB7jiq3HMvvVHQJbsUds3k10deGnkSMbj5QeLE/TPA/cBfp9t9D7GTZjnd9keJot/L\nxEk/FwJPFS57KYTtNlBzagFk/WWzcN0mtUCtnzVQQ5WyAMvActoht5/IUC0QxcibRGH8ajcZA3df\nMbMLxJLafmpB1ygsLc6wM6jK/9/o9ejUgqsr7AyyssvELMRxzEhUNduyyvgHXVV97mWPFHRJI7ey\ne9njvwKfBt5KdMFeIXp5vbhwuxuJcSZzRGD2ZnZ2Qp4FltISGABpGWyqy0u3gZpRG6XR2TdEoNZt\nRq3ygVpqKbGeaupy9muW2EF30MxyE9S1Dn+eB4mddnlb+ya7R/8Ms4h+pu4yTfvgKl+KQVYzW8CF\nvHNsTFUy2+LuV8zsCtGza2EvGdsRoKBrzNgInCdkyMzsMHFSNnbvUuuWE32K9jysty5Qm6azQK0M\nIxmopWLs/UQQlp8rp5b9apmpMrOHE+0OVqm1O8iuEPV/g1YfXOUAq5Fi5qo+yOrGCrA8zsvXKTi/\njnitnhz28dRLzWGPAutpjNbYqPpzL3ujTJd0Yp0IujaIE1YvrxsDrkrFr3tq1pcClU1qmYj2D1rb\n9t7NpVtGLZPS2TdUIKOWlsTOp6XCXBiflyH3F4rv19y9UWDyIFG312gsUD+XFo2dS4K59qqT4Kq4\nLNhtcFXvCvHBYRKyD1XPtKwRdYXzZjbd5PU5qqr+3MseKOiSTlwmTmBTRMPUq2g9CLsTh1KB97ky\nsj0pG9FVRmJEArVN2gdpHf3c6fewBqylur6c/aovvl8rZirTMs8PAT9GjKr5MLUC8b0EXcXgapad\ny4IND53dQdUV+j9cexKWEutVcmkxKxTU5z5149R1vdLPveyNgi5pK72xXSayH7PuvmhmR+m9R84+\nojv3UhWXaAYYqOUl0a7qzIoPQy0Iadv9f487PjfZWXy/L11y8f0RasuPV4D7gLcTc+FyvVS70T/G\nzp2C+dLsOdmiceaqjNfOKhFwVe51OmCjcOJfZTyDLmW6xpCCLulU7gA9T3Q4P5eWnQ73eL9zxPDm\nM+PQa6fbQK2kjQTQZSauSaC2Rvz+F4hAaR9wNGW/PgQ8DPhWaq+JHHAVu7MXg6x2wVWx1qqs4Kre\nFWJXYhV3YJah8id+d7+c3otmzGx+kPNHS1b55166p6BLOrWrB5O7X0ztBo6yt0AgmwZOmNnZMXrD\n7EhdfVpHKhCo5aW7nP3K9/9w4rUwT4wiysPSuwmurtD7Zo1+2CKK5Ffa3nK8jUKmCyLbdZidrUtG\n3ag899IFBV3SkVS3s0V8mtwuWHX3tfQp8xhd1CU1MAUcM7MLOtG1NqRALS9nThX+P2eujhAnvMNE\noLUPeAJwOt3+PLXAqrgsWIXgqpFJXUrcoWozF9tYJQL9BTObGoHjbSnVuxoxxaCqfyeyBwq6pBvr\nxAl1nsLYlxSQLRKBV9saoxYMOJJ2Np7v6Uhlh1aBWjq5Tje45B2CeSkwB2LFFhPT1ArZ88nBiekF\nl6gFarAzs1VFk76UWG9klrfShIRL1LKvo/7BrThyTcaIgi7pRsOgC8DdN1Pg1ax1QDcOpN1zZ/Up\nrzcpw9UooCpe2i095oBqnjgZ5N5beffkPDEWahX4R2KJ8f8RgVl+/Ll0OUht6PNlqrEUpKXExkZt\neWuVeH86wAgFXWb2buAZxHEvAu8A3pSufrKZvQH4J8BfAi9y9y8N5UClLxR0STeazdYDtrMpS2ln\n24EeH2uBqPNaGrPeO32VgtNWl06K5zdbXOaIE9kcEWzVZz0WiMDqy8TyzuX0feeptRVZTZe8ESO3\no9hPbYfjpfTfspeFtJTY3EgFXe6+nmpMZ8xsboQylv8J+El3v2RmXwt8HPg8cC/wG8C/BD4I3A68\nB/j2YR2o9E4d6aUrZnYN8WZ8ulVzSDM7QNT49FJgD3ECPztCb6B902LZrxhQtXt+87JiLlSvv+xq\ntmpmC9TaQzS6/7yTcY7IfDnwUuCVdbf7VeD9RKD2t+wcnp4DsNm6x7hCBPfrDPZkr6XENszsBPE7\nXhyV58nMDhHB/6q7n2t3+6pJQdcfAi8CvhG40d2fmq7bT2TCnuju9w3tIKUnynRJt9aJ1808LWo9\n0jDkDWK5sZcxPNPAcTPraXRQFfUhS1Ws02p46TSD02QUUP1jrROZoXXi9zpLBFJn3f3fm9nrgUcR\nAdnFdD+PJk7cX0ksOZKOLWe/jFoAloO4PAtyk9oSZL+WIbWU2LmRynQluaB+n5mdH5XyBDN7K3Az\n8XdwC5Hleh7w2Xwbd181s/uJTSoKukaUgi7p1jqxdDhPnFibSun+XGA/1NFBZesgS9XJTs9cM9Us\noOpp2bVB1/lGLpO61KdiZSN+n/Pp+M4UMp5T6bb5uLaIGq9HEFnPI8SyY5ETS4t5WHGu/ZpPx5QL\no/MyZA7A9rIcuEZkt7SU2EZh52LHgXsVpNrS3FNwH3W1p1Xl7i8xs5cC3wG8D7ifyDQv1t30AvGB\nREaUgi7pVh4JNGdm1u6TpLtvFAKvvowOcvezPd5PT/pUnN5Jlqrvn9LTyTTPV2z2+9igFmht1H3v\ncSITtUkEXMUsyDRxkiu+r6wSJ46ridfABq2LnC+ny0V2L0Pm/4faMuRl2u+u01Ji90Z591yuH9zP\niARdsF0T+zEzey/wA8AZdjefPsJ4dd2fOAq6pCsp23GFWkai7bJP+p4zxK62fT0ewr6UoRnY6KBC\nlqo486/b4vScpWpYS1V29qCLOq21RsFJes6PE8/JFeL5r8+0Taf7WWFn65CzxGvlEPEpPT9WO50s\nQ+ZjX2f3MuQWcNHdW2ZkpaFRXFoEtnsHHiE+GM6M4KSLWSLg+gdiyRHYrpN9FHDPkI5L+kBBl+zF\nOrUloI5qbdKnuLOpzutQj48/R21nY1dvqKOcpeqWmeWdh23rtNz9UoPr8/3MUmt+e5nmAW9eMj1P\ntJDINohlkZy5OkQtUOpU/TJkMfNVvwx5BTjHzqVP6U4+N4zq87dKBPgH2L2kXRlmdjXRLuKDxGv7\nu4Hnpv/+HfDLZvZsYszWq4HPqIh+tCnokr1YJ06cDVtHtOLuyynw6nV00AwNRgeV0EKh0jUue6nT\nanN/c0TANUWcFFr1Tpsi4uvLhbqabJna3EYjlknOsveTem5fkZch5wr3vUIEX1en19olYl6olhc7\nN8rLi1ALuvalKRdD/xDUhAP/GvgfxGv3PuCF7v5JADN7DvAW4N3AXxDF9TLC1DJCOmJmtxDbmJ8A\n/E+iPYAR9Tq/CXwTUTD9dHf/eAf3VzyZd3wY7B5HM0WcZC/TWQuFgRanD0MvdVpt7neB2KVo6fua\n1tKlY7iOeA5Pmdk8sRxZtEAEW5vUliKX6GKcUQtOBHYr6XFygFd8feXsWg7C9ObXhJldRzx3J6v8\nIaOVQsuLs+O281lGlzJd0qkHgNcCzySyKOvUTm5/DLwReC8dztNL2ZDT7BwdNEVt5Ex9dqrVsl8e\n+7HM7kBqR03VOJ1oe63TanPf+4kAyYCVDsYy5aXFPJNzPdX+FWu78pgWiCxVDurO0NscxjWiwelm\n4eu19HPkDNgCdcuQZpabsq6PYN3PwIzqzsUGVomgaz+d1RCKDJyCLumIu98JYGbfDHwVtaBr2t3f\nlK5rmrFo0ULBiR06nRTY5yxVMVtV/P91xnx0UL/qtNo8xkFqu6aWO2zTkYOu4kn6IhFUFV0gMmBn\nieOfS7dZ2sOhbhC7EpvWhqVg8zJwIS295gAs1yTOA2gZcodRX1rM1ojX8byZTY9iFlvGj4Iu6VbO\nqGyPBCoUp+evD9FdcfoF4mSdez/t6Jhe+P92xnJ0UBd1WqtE0LDn7ISZHSZqYZwIaDrdcr8j0wXb\nu8gOF67L168SP885apnOq4hArBNOBINd7UpMr4kVYCW9ZvMSZM6CHQQOmllxGXJ9xLM9ezGyOxeL\n3N3TEOw8ckqtFmToFHRJtxy2+29tUuvbtEVkLg7SeHdi2+L0Po0OmqUWeI3qzqtu67RW+xFkmtlR\navMQz3aZKdsVdCUXiWXK+n87TrxezlLr4XaYCMBbuUQEgz39vCkbur0bMmUQcxA2y85lyCvUsmAj\nHYh0KGe6RvbvpyAH+Aq6pBIUdEm3igHREpFhWaB2sl0n3tw2iEBsgwbz/RpJo4M2iZ2NvY4Oyjsb\n97TENiypTms/EQD0tU6rxWMakWlaoDbWp9uxO/n3VR8M5bEs9b/PFWonwnPp8felx2+UwWq7lNiL\nwjLkcmEZstgXbA44nJYh14kAbCDHUgFjkemC7drRDWII9sKovR/I+FHQJd3aDp7c/UqaiegAaVnm\nYi+jetz9UmqkmvtC7ZUBx9J28Uo3x+ywTusSEWj19aSRAq7jRFBRP9anGw0zXWmJJ2/fL7pEbQPA\nBtFL6SjRV2mTWuGzE0HYxbJq9VosQ+aeYDPAgTFehhyboCtZIbKt+6n1eRMZCgVd0pH06X+W1KU9\ntQTYSLPOilmZ+V4/UaZgLu9s7HV00OHUlfpcj/fTV2XWabU4hnZjfbrRqJA+u0gEU/WZu2Vq8/Eu\nE0uLR6g1Tz1PH5YSe9FgGXKWWh3Y2C1DptflOOxcLCoW1E+N0c8lI0h9uqQjZnYb8B/q/vk2d3+N\nmf098HAiK2Hpv1/j7l/q8TGN/owOgtrOxqG94Q6jTqvFsXQy1qeb+3sY8bt/sFFGqlAvVq9+Pl4O\nRJeBL1d5J2F6DotZsGJQuUktWLs8KjtqC/3V1t39zLCPp1/MLC9fVz7zLeNNQZdUXmFHXa82iOCi\n1CzEMOq02hxPp2N9Or2/3Bh1y91PNrnNDHBNk7vI46TyUuIUkRnbAhZHIWtUWIbMQVhxaXyLeJ5z\nFqyymZa0meUInfVmGxmFYHLD3R8a9vHI5NLyolSeu19IxbC5Wede5dFBS4MOboZZp9XBcXU61qdT\nzYrot6XdrrmOq17OCm0vJaZAbh9w3MxOVzlQgV3LkOcLy5C5ED8vSZKasuZi/KrtEBynnYvbxBF+\nMwAAGeFJREFUUrPeTaKgfq7KGVQZbwq6ZCS4+2oKvLodHVRvijiRd9ODqiNVqNNqpZuxPl1q1i6i\nXh7RU2+D3cup54jf1Tzx+1oclSU6iLpEInBZLiwrF4OwOeBQCgQuEUHYegV+xnEroi/KO2n3E3+H\nIqVT0CUjozA6KNci7ZUBR1OBfbu+UK3vqJaR2ceQ67Ra2cNYn260KqLf1mQ0UHaQws6ytOtxCTiR\nbn+MGBc0clJwvQqspmXIYuZrmlhKPUAU4xd3Qw7jtTLuQVcegn2+AgGuTCDVdMnISYHOVaQRLj3a\n0xJbF3Vaq8NeQqob69P3QuI0geAQHYwMMrN97B4NlC3WL/uk7OEJIjjpZ3auEtIyZK4Dqw/ai7sh\nB/4aSs/1taSh5YN+vGEws2PEc33e3VeGfTwyeRR0ychqsSOuW1eIdgktMzVVrdNqpYexPt08Rv49\nnOvk/s3sWhr3YLvk7rtmMKYi/BPEc36x1+xkVRWWIXNBfvE1lmeL5ixY39+4x3XnYlH6sHQMuOLu\np4d9PDJ5eqmNERmq1Hur2Qn4xcDvAV8E/luT27wM+EfgO4GrzWzWzF5mZl8wswtmdsrM3mlmDzOz\na4gT/wF2/91cJmqQTrl7Zbrgp2AoB1xnBxFwJW0L6es0y7QtpABrh7R7cYn4OQ6mHXZjx9233H01\nvYZOEsupK8RSX64XPAZcZ2bHzOxAyk71yzgvLQLRfJnIQs+mLKNIqRR0yUhLS2X5hFz0IPBG4Lea\nfOsjgB8AcouDKeLE9gHgnwIPA54MPAp4FbtryDaIXlKn3H0xnSwrscPOwjHiJL1FtIQYZCDYaSF9\ntkrz+q+GAVVadswNbo+kZcqx5u7r7n4+tTh4iPiAkZdfF4gavWvN7GozO9SHIGIsdy42kD989CNL\nLtIVBV0y8lJAscjOk/6HgT8gBio38rp0yZ/qc8bsHLG0c4Q4sTmQ61u2iMzDaXd/yN2Xh9ktvZHC\nWJ88R/GMD35GYEeF9FlaGmtWT7M/LbM1+r41oks9xEaIXqcVjAx333D3i+6+SLwezxI1g1tEsHSI\nyNZea2ZHzWwhvRYaMrOPmdmamS2ny73EB4vHAH9iZktmds7M/tTMnjr4n7BUOeja1+o5EhkEBV0y\nFlKh8SK7P6U3elP9QaI+5qPp6ynipJWLbH8E+Bvg7nSfbyGyRSdT5qGSmYAUrJwgCrI3icL0gR5r\nOmlNEbFUN5m+FXZnJyF+X02XD1Px80VqszUnbgd2WoZcq1uGvEjjZcjjTZYhHXipux9Kl+uJoOsk\n8FwicL+KyBS/r5yfrBxpufoy8bpt1MJEZGAUdMnYSFmnRXYOta0/sR8AXg7cSq0+a4udfwu/CzwS\n+Abga4AXVaVOq5nCLr9ZIvA8XVIn926XFoHtNgprTa4+0CoDkQrp16j1XOtnXdPIScuQF+qWIXN2\nM2dt8zLk4UKGcPs5LsxcPOfuX0zZyGnib+PBsn6WEmmJUYZCQZeMFQ9L1Iq160/e/w54P/BlItuy\nVbhNfZ3W54DXAzcN/MB7kGp5ThCZist0sBOzj7otoi9qVlA/RZuTYWodsU4EBse0TBQKy5BnaLwM\neZBaNvT1ZnbazP4P8PR0FxsAZnYufd8vAP+i3J+iFPk5mZ/EbKkMj4IuGUspG3KO3ZmupwA/Afw1\n8GngK4C3AS9sUqc1y86BzJWSshbHieDjEuUGXLDHTBdsL/M0yyB2skNxicjq5eapUtBgGXKR2jLk\n64BvA74JeA9wJ/DodB3ufpTIkP0W8N5xC2pTJi9nWpXtktIo6JKxlJZLtoiT8hTxyX4a+FHgO4Bv\nBJ5IZLx+Cnhz+r6fNLOr0/8/HngFkRmrnNRz6Djx8626+9IQumx3VUTfQLNs10z6+ZpKP+sSEfDN\nm1mzpqtC7AAtLEP+PlG/tULUbH2K+ECyXrj9KvH6fyzw9eUf8cBtF9QP9ShkoijoknF1K/Gm+nPE\n8sgXgX9LZL++4O4PpK7bm+zsYfVk4G4zWyY+/b+LaD1RKWmsT56juJJ6lg3DnjNdsN0KotkcvIMd\nfP8mUUi+RexGO9zmW4R43tx9JS1DniR+Bxcb1C7mWq/KZnv3ymvzMafbBfgi/aKO9DIR0s6+Y0S3\n7Zajaqpu0GN9ujyWvONzz73Auh0N1OQ+8jKroREvLZnZEWJp8ePEcuKPAm8nMr9fTQSxdxFLvLcD\nT3P3Jw3lYAcsNdo9QpNpCCL9pkyXTIRU37I4BgHXYSLgcmKn2dACrqSXQnpgu/9Ws52WbbNd6T4u\nU+vJNhHNU3swC7yW2Ol4GngpcKO73w8cBX6TyAh/Hrga+OEhHWcZ1oi/pflm/eFE+kmZLpERUZhx\nmMf6DL2NRWGO4sleCvgLGYdGHuq0/UXhfpzIvg26MayMuFQLuI8hZ41lMiiyF6m4IYz16cY03TdG\nbaTVaKCOsl2wq3nqVWoHIB1Qzy4pjYIukQqrG+uzSTljfTpSaErac4uKNqOB9nWz9KPmqdKN9Pe0\nQeyYnR/28ch4U9AlUlF1Y302iICrSiOIetq52MCeRgM1Utc89bjqdaQNZbukFHojEqmgBmN9Fksa\n69ONnovoi9ISZbPWBC1HAzWRm6fOoK710louqF9QgC6DpBeXSMUMeaxPN/qd6YLmS4xtRwPVS0uW\nZ4jjmyN25skEM7NbzOxTZnbJzO7I/576vX0r8CfAspn9kZk9vO57/7OZLabL60s+dBkTCrpEKqQC\nY3260bearqwPo4Hq72+Lnc1Tm+2QlMnwANEu49eK/2hmJ4B3ErNWH0N06H9P4fqfBm4EviFdfij9\nm0hXFHSJVERFxvp0YxCZLmg9Gqjr/lspkFsilo8OpOayMoHc/U53/wARiBc9G/gc8MH09e3ADWb2\n2PT1zcAb3P3L7v5l4A3Ai0o4ZBkzCrpEKqBurM/FIY716cZAgq5eRwO1uM+zROB1WM1TJ159fd/X\nAZ+lNgTbgPvTvwM8Pl2f3VW4TqRjCrpEhixlXo4Sb/QXUsuDUdDXQvo6zbJds2kJtmupt1l+bo+q\nPcBEq88gHyBeG3kjx3z6+lD6+iBwvnD7C+zxA4BMNgVdIkNUwbE+3ZiG7SLkvkoBUk+jgZrcb33z\n1Nm93peMtPpM10XgcFqKXnT3h4jJBsvF6wu3P0LzDwYiTSnoEhmSNNbnILWxPs3aJVRO2lZv9LGI\nvoFmOxkXeuk0nzKJq8T73zE1T51I9Zmue4AbIJai0zipR6V/z9c/sXD7G4gaMJGuKOgSKVmDsT5n\nKjTWp1ODKqIv6stooEZSzZyap04YM5tOG1ZmgGkzm09B953AE8zs2en6VwOfcff70re+C/hZM/sK\nM/tK4GeJ3Y4iXdEbjUiJmoz1aVY0XmUDD7r6ORqoCTVPnTy3EsH8y4EXEIXzr3L3ReA5wOuI18U3\nA8/L3+Tubyd2Nt5NFNF/0N1/pdxDl3Fg1d6RLjI+UpBwnOgyn8f6DDJTNDBpt+VRYMXdz7e7fQ+P\nMwVcy+4aHIhdnj1tOiiMWpoBLrn7Ui/3JyLSijJdIiVINUj1Y31GMuBKylhebDcaaH+v2al0/0vE\nMuaCmqeKyCAp6BIZsLRD7ji1sT6LFe4y36m+d6NvoW+jgRpR81QRKYuCLpEBajLWZxzW9EvJdEHb\n0UB9CZDUPFVEyqCgS2RARnCsTzdKC7qSZj2RpvsVIKUdpLk+Tc1TRaTvFHSJDMCIjvXpxiC70e8y\niNFATR5nlWiIqeapItJ3CrpE+myEx/p0JBWvTwFbJWfuWo0G6ltWyt2XUfNUERkABV0ifTTiY306\nVWYR/bY2o4EO9PmxzhF1ZGqeKiJ9ozcSkT4Z5bE+XSq7nquoWRDb02igJs6i5qki0kcKukR6NCZj\nfboxzKBrrcXj9rXVQ1o6PUNk1+aIGj0RkT1T0CXSgzEa69ONUovoi1Ig1CyDuK/f9Vdqnioi/aSg\nS2SPCiNk5ohsyKK7XxnuUZVimJkuiGapjQr4jT7XdsF2n7Az1JqnHur3Y4jIZFDQJbIHYzjWpxtD\nKaTPBj0aqMljXqHWPPVQagkiItIVBV0iXRrTsT7dGHamC6KgvlG2a4oBZLtgV/PUI2qeKiLdUtAl\n0oV0oh3HsT7dGHrQlbKKzTYrDCToSo9bbJ56TM1TRaQbCrpEOpTG+hxjPMf6dKTQGNUrkN0b+Gig\nRgrNU3PgpeapItIRBV0iHZiAsT6dGtrOxXqpzmrgo4GaPLaap4pI1/RGIdLGuI/16dJQi+gbKGU0\nUBNniaBPzVNFpCMKumQimNktZvYpM7tkZncU/v35ZrZcuKyY2ZaZPSld/7+Bk8B9wOeBM2Z213B+\nikoYej1XUZvRQIPOdjnRw0vNU0WkIwq6ZFI8ALwW+LXiP7r7b7j7oXwBXgJ8wd3/Oo31uRl4DHAi\nXf9nwG+XfOxVUqmgK2mW7ZofdKF7qms7QzwfC+k1IyLSkIIumQjufqe7f4A4QbbyIuBdjcb6mNlX\nA08D3jXAQ626ytR0FbQaDTSwnYxZ2km5RLSw2K/mqSLSjIIumTRN627M7BFEUPVBGo/1uQn4Y3f/\n0sCPsroql+lKy3wrTa7u+2igJsdwhVrgpeapItKQgi6ZNK1aPNwE/DnwEI3H+twEvHNwhzYSKhd0\nJauUOBqoEXdfZ2fz1IUyHldERoeCLpk0rXaY3QTcQa3L/HZgYWZPBa4F3jfYw6u8qu1eBIYzGqjJ\ncRSbp16l5qkiUqSgSyZNw0yXmT0FeBjwHndvNNbnZuD96aQ6yXJj1KplumAIo4EaSc1TV4jA63ia\n0ykioqBLJoOZTaflnhmiY/l8Xa3PzcD73H1XbVDqbv5cJnxpMTUANSqW5cqGNRqoybGcT8cyRfTw\n0nutiCjokolxK7H89HLgBcSOt1fB9nif5wK/3uR7nwWcdfePDf4wK62q9VxFrUYDlV3cXmyeelzN\nU0XEJmx0nIjsUWH25CV3Xxr28TRjZseBRt3or7j76ZKPZQo4QQRelX7eRGTwlOkSkbbM7Hrgw8C9\nwCfN7FlDPqRWhjkaaAc1TxWRIgVdItJSKgT/APB7wPXALcC7zewxQz2wJlLrhitNrh7oaKBGCs1T\nt1DzVJGJpqBLRNp5HLGz8+3p648Bfwq8cFgH1IFmzVIHPhqokdTv7Sxqnioy0RR0iUinioX0U8AT\nhngsLaXWHs0K/kvPdoGap4qIgi4Rae/zRJf+f0MUhH8X8M+AfcM8qA40y3YtlDEaqJEUDF6g1jx1\nbhjHISLDod2LItKWmX098Fbg8cAngEViN95PDfXAWkgtGq6l8YfLi+5+oeRD2mZmR4jeYVvE9ION\nYR2LiJRHmS4Racvd73b3p7n7cXf/PuBRRPBVWWkQdqvRQEN7/1PzVJHJpD90EWnLzL7ezBbMbL+Z\n/RyRQXrnkA+rEys0Hw007GJ2NU8VmTAKukSkEy8EvgycAp4O/PO0I6/SqjQaqF7KxC0BG8As0XhW\nRMaYgi6RCjGzW8zsU2Z2yczuqLtuv5m91cxOm9k5M/t44brfM7PlwmXdzO7q13G5+y+4+zF3P+Tu\nP+DuX+zXfZegSqOBdqhrnjqv5qki421m2AcgIjs8ALwWeCa7dwf+CvFB6XFEhuSJ+YpUZ7XNzD4K\nfGSgRzoi3P2Kma3TeDTQQZrXfZXC3TfNbAk4TtSabQ2zyF9EBke7F0UqyMxeC3yVu784ff044C+B\nr3T3Zpmb/L1fDdwPPNLdvzTgQx0JafzP8SZXL7l7syXI0qRjPEa0kzjv7s1aXojIiNLyokg11RdV\nfwvwD8Br0vLiXWb23CbfexPwxwq4atqMBhpqbVeWjvFc+vKwmqeKjB8FXSLVVJ+C/iqiA/w5YiTP\nzwN3mNlTzOxo2lmYA7WbGI2dhWWr1GigRtx9DTVPFRlbCrpEqqk+07VGZGpuT400P03MP3w60frg\nGHCdmX0/0c7hzhKPdSRUcTRQI2n5eIV4DRxLA8dFZAwo6BKppvpMV96JaKmR5myD2xnwfOBDxFDl\na8zsiJnNqwfUtsqNBmokNU9dI96jj6t5qsh40B+ySIWY2XSq5ZkhWhrMp2Dg48CXgF9M190APDn9\ne7YA/CDw2+nrGaJe6TiRBTuW2k5UJrgYghVi9E49o0LZruQc0Tx1GjVPFRkLCrpEquVWooXBy4EX\nENmOV6UlxRuB7yfaRbwhXf8Jog/VBtFm4jzw5w3u14ig7ChwrZldbWaHJ61mqMqjgeoVmqe+lwi4\nL6QebPfm25jZM8zsb8xsxcz+yMwePqzjFZH21DJCZEykDNYC0Y9qnt11Yc1sAetE5/b11LBzbKXn\n6RoaPz8X2rXkKFvqufYBIoO55u5n07+fIFqD/ATwQeB24Gnu/u3DOlYRaU0FmiJjIo28WQFW0lLU\nHBGELRBLVM1MEY1Y9wGY2WVSEDYKo366lZqRrtF49uIBM1vx6n0azcui+8xsMzVPfTbwOXd/P4CZ\n3QYsmtlj3f2+4R2qiDRTmVS6iPSPh3V3P+/up4CHiFYE6zQeAF00BxwCrjaza1NLin1jVlPUrKB+\nmt2TAKrgdcDdxK7U7zazA8DXAZ/NN0i7M+8nWouISAUp0yUyAVJN2EXgYgqe5qktRbbKgk0TGaH9\ngNdlwTYGe9SDU/XRQHVeDtxDFNXfBPw68FRik8TputteoHobAkQkUdAlMmHS0tmldCE1Bs0B2CzN\na8FysDZPdEzfTPexTtSCVW1Jrp2LNA66ZsxsoQqjgQDc/ROFL9+RJhE8jTj+w3U3PwIsl3VsItId\nBV0iEy7VbV0BltPuvWIWrFUJwjSRbTlAZMHWqWXBmjUhrQx3XzezK9R6nhUdJAWlFZSD23uAm/M/\npiXHR6V/F5EKUk2XiGxz9y13X3P3s+5+ElgkMiftCupzS4ojREuKV5jZX5nZJTO7Y8cNzZ5lZveY\n2YX03xsL173MzL6QrjtlZneY2aE+/5hFzXYqzlVhNFBqbvvMNOZpxsyeT2S5PkzUdz3BzJ6deru9\nGviMiuhFqkstI0SkI6nVQs6CzdH6Q9v3ErvtvjPd/iVE5ugw8AXg2e7++2ls0XuBR7j7opk9Ejjr\n7mfN7CrgfcAn3f0VA/qxMLNr2VnX9maiZmofEXS+w91fN6jHb3NsJ4gJA48jRhjdC9zq7h9J1z8D\neAvwCOAvgBdp0LlIdWl5UUQ6kpYMV9MFM8v1XbmDftGH039vIAZ059YV30zsHPwzM5tz9w+Z2Qqx\nLLbo7l8s3McUEbg9OJifaNsKO2uj3gz8HBEkHgU+amafdvcPN/rmQXL3ReBbWlz/EeD68o5IRHqh\n5UUR2ZPUkuKCuz8EnCK64de3pKgvyr+HyNj8CHBNKgq/RG22JGb242Z2ntiZd9rd//sAfwzYPRro\nPuLnMGLX5gbRckNEpCcKukSkZ+6+6e4r7n4GOEmMr2k053CNaIHwNuDvgHcCP+3ua4X7+k13PwI8\nFrjezF424GNvNBrol4ieV38J3O7ufzXIYxCRyaCgS0T6KjVmveTu54lg5hLRP+oy0bjzvwDPAq4D\nvoNog3BDg/u5H3g90Ztq0FbYmaH7ReAriHmWt5tZ0yU+EZFOKegSkUFyIg67mOqTbiAKvj9BzBH8\nFJFN+u4m3z9LCY1KU73aGpGZWwZOufu5VDP1XuDHBn0MIjL+FHSJSN+Z2XRqYzADTJvZvJnNEGNr\nnkLsVlw1sycRLRA+m77vJ83s6vT/jwdeAby/pMO+QARby3VDv2dpPjZIRKRjCrpEZBBuJTJULwde\nQGSRXunuf0AsL/6OmS0TLSFe5+5/mL7vycDd6bo7gXcBbyzjgFOgdcLMnmdmB1Lg+EzgucAHyjgG\nERlv6tMlIpKkvljvI5ZBjdjJeLu7/6+hHpiIjAUFXSIiIiIl0PKiiIiISAkUdImIiIiUQEGXiIiI\nSAkUdImIiIiUQEGXiIiISAkUdImIiIiUQEGXiIiISAkUdImIiIiUQEGXiIiISAkUdImIiIiUQEGX\niIiISAkUdImIiIiUQEGXiIiISAkUdImIiIiUQEGXiIiISAkUdImIiIiUQEGXiIiISAkUdImIiIiU\nQEGXiIiISAkUdImIiIiUQEGXiIiISAkUdImIiIiUQEGXiIiISAkUdImIiIiUQEGXiIiISAkUdImI\niIiUQEGXiIiISAkUdImIiIiUQEGXiIiISAkUdImIiIiUQEGXiIiISAkUdImIiIiUQEGXiIiISAkU\ndImIiIiUQEGXiIiISAkUdImIiIiUQEGXiIiISAkUdImIiIiUQEGXiIiISAkUdImIiIiUQEGXiIiI\nSAkUdImIiIiUQEGXiIiISAkUdImIiIiUQEGXiIiISAkUdImIiIiUQEGXiIiISAkUdImIiIiUQEGX\niIiISAkUdImIiIiUQEGXiIiISAkUdImIiIiUQEGXiIiISAkUdImIiIiUQEGXiIiISAkUdImIiIiU\nQEGXiIiISAkUdImIiIiUQEGXiIiISAkUdImIiIiUQEGXiIiISAkUdImIiIiUQEGXiIiISAkUdImI\niIiUQEGXiIiISAkUdImIiIiUQEGXiIiISAkUdImIiIiUQEGXiIiISAkUdImIiIiUQEGXiIiISAkU\ndImIiIiUQEGXiIiISAkUdImIiIiUQEGXiIiISAkUdImIiIiUQEGXiIiISAkUdImIiIiUQEGXiIiI\nSAkUdImIiIiUQEGXiIiISAkUdImIiIiUQEGXiIiISAkUdImIiIiUQEGXiIiISAkUdImIiIiUQEGX\niIiISAkUdImIiIiUQEGXiIiISAkUdImIiIiUQEGXiIiISAkUdImIiIiUQEGXiIiISAkUdImIiIiU\nQEGXiIiISAkUdImIiIiU4P8DHAa+8FSvD80AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c0b6be0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Anthology 1\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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pIgZHsf3PUEZuKuq1fMbtKlocf71bM4izcoWEGi8fa4/V4HgI0RX0m0vAa9Ge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ZAAAg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\ndEmSJDVg6JIkSWrgnwGFZkQOt/nACQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10bcf6828>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Anthology 5\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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cvVT6t4ol/15azyhdEF6eFTcKbDaoaq7Y+TxMcY9NDZY71RVht1i5RtnqNu7U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\nIYQQogIkuoQQQgghKkCiSwghhBCiAv5/m+1dhSpHM0UAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c9780f0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Anthology 4\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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B8sbT3tfQ/+6iqcz7opPiku3jbuDxwMtQQbYKvB+4rRjrHjAkIkNHCI7jRro8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\nGZhGnVYQ1CfSi0FgWOquXruhRgygwqJMPTb8ZW/1QO5yXvU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nw5a5epM71teZcYZJbNUg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\nv5DoGh+qUo4n8KT6KQrBdTEb6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7WvoqsUmKoICbFXngh8CS8JfivwOPx//br49c8BnwTu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\nRJcQQgghRA38f5gNzefJWBMRAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10f96db38>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Anthology 3\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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8U1U/gj3PB2MeWxvAT6jqpqq+A3gnZvZbMluI67KWq+dGsZ6qv4S9/6JY/kCC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       "text": [
        "<matplotlib.figure.Figure at 0x10f96dc50>"
       ]
      }
     ],
     "prompt_number": 178
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So what can we tell from looking at these network maps? First, and most visually obviously, to no surprise Holmes and Watson are the two most central figures in all the anthologies. In all of the maps we see them (1 is Holmes, 2 is Watson) at the center with all action taking place around them. Additionally, the edge width between them is significantly wider, implying that they interact more than any other character.\n",
      "\n",
      "More interestingly it appears to me that there are often clustering of characters as cliques or k-plexes of a low value which represent individual stories within the collections. Often as well, we see that one of two characters within this clique, those either acting as brokers via their singular betweeness or having the thickest edges width between Holmes and Watson, seem to occupy the central role(s) in the story, and are what connect the broader group of actors in each story to our detectives. In looking at these it seems like most often this character is playing the role of the victim or perpetrator in the story (see character 4, 18, 32 and 97 in Anthology 4 or 43, 111, and 12 in Anthology 1 for examples).\n",
      "\n",
      "However, it is more complicated than that because of characters that exist in more than one story, either because they are part of the action on more than one case, or because Holmes and Watson are recalling a previous case during their exposition. As such we don't always see these as cleanly as the examples offered in the previous category, however it does seem to me that the structure of these Anthologies is our detectives at the center, with a second ring of important characters, often involved in the crime, and then the bit players existing on the outer edge of the network map connected only to one or two characters within the rest of the network.\n",
      "\n",
      "Also, in looking at the various anthologies, it seems as if the earlier sets of stories have less of this character overlap than the later ones. There are more isolated sections of the maps, and in general the maps seem to have less complexity. I know from my reading of the stories that there are indeed reoccurring characters within those stories, but that these characters carry on to the later anthologies as well. Perhaps, it is that as the series progresses there are actually just significantly more characters within the entire body of work.\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Story Level Structure"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I want to look at one more thing quickly before wrapping up. Because I built into my code a way to keep track of the paragraphs for each text, we can also looks at the progression of character appearance and the complexity of interaction in the ark of each story as it progresses."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The following script produces a chart that plots character occurance via chracter id on the y-axis and paragraph on the x-axis."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "textDir = \"Sherlock_Masterfolder_v2/Texts\"\n",
      "textNameDict = {}\n",
      "textDictCount = 0\n",
      "for filename in os.listdir(textDir):\n",
      "    textNameDict[textDictCount] = filename\n",
      "    textDictCount = textDictCount + 1\n",
      "\n",
      "paraList = []\n",
      "idList = []\n",
      "\n",
      "#type in the same # for titleNumber and hitDict[key], and run to view graph\n",
      "titleNumber = 22\n",
      "for text, para, name, id in hitDict['25']:\n",
      "    paraList.append(para)\n",
      "    idList.append(int(id))\n",
      "    title = textNameDict[titleNumber]\n",
      "    \n",
      "fig, axes = plt.subplots(figsize=(50,6))\n",
      "axes.grid(True)\n",
      "\n",
      "axes.plot(paraList, idList, 'ro')\n",
      "axes.set_xlabel('Paragraph Number')\n",
      "axes.set_xticks(idList)\n",
      "axes.set_ylabel('Character ID')\n",
      "axes.set_xticks(paraList)\n",
      "axes.set_title(title);\n",
      "\n",
      "#fileName = title + \".pdf\"\n",
      "\n",
      "#fig.savefig(fileName, dpi=200)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 185,
       "text": [
        "<matplotlib.text.Text at 0x10bbc3c50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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DqHNG1MTUEHK/M9ThXa5iJNq2KbAwMDlfPt9bhQN29LvpkWve6/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\nvZVlrER2Aq0nL2af3wY6h3qs6lJ2eZ1vhd+0EuqdPQFxU4EuB6M4LpxE3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       "text": [
        "<matplotlib.figure.Figure at 0x10b11db38>"
       ]
      }
     ],
     "prompt_number": 185
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I have run this script on all of the stories in the Holmes corpus using the hand coded dictionaries, and have found that the stories themselves seem to share a similar structure in terms of when characters appear throughout the text. (See image below for three side by side.)\n",
      "\n",
      "![Character occurance charts for three stories.](http://s10.postimg.org/6is9j967d/Screen_Shot_2015_04_19_at_3_43_06_PM.png)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What we see in all of these, is that the stories start typically with Holmes and Watson alone. Having read a number of the stories, this seems to be true to me. Next, we see another main character appear, likely the character discussed above as a broker, the perpetrator of the victim. Following this the broader network emerges rather quickly, but typically, as you can see above, one or two characters tend to always appear in the last third of the story."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Perhaps this consistency in structure, what we have come to think of as a formula, in conjunction with its serial fashion, helped make the Holmes stories so successful with popular audiences. Many of our favorite television, such as Law & Order with its crime, police procedural, law procedural, verdict formula, tend to also follow consistent structures. These structures give readers/viewers a familiarity that is both comforting and allows for deviations from and nuances to this formula to carry a particular significance. This is important to the detective/mystery genres, as the twists and reveals, the moments when this formula is temporarily disrupted, are often what we find so appealing about these genres."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Further Work"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In his paper about character networks in detective fiction, Andrew Piper outlines what might be the fundamental problem with applying this type of analysis to detective fiction, \"over-population\" [6]. \"We found that the strongest indicators of a detective story are the increased number of characters (nodes) and interactions (edges). The average number of characters in detective fiction was 9.76, while in short fiction it was 5.55. Similarly, the average number of edges or relationships was 13.52 versus 5.45. People, not clues, are detective fiction\u2019s primary formal edifice.\"[6] pg.8.\n",
      "\n",
      "![Short fiction vs. detective fiction](http://s17.postimg.org/6jgiwigmn/Screen_Shot_2015_04_19_at_3_25_46_PM.png)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Given the relatively poor results in terms of recall, it may seem as if hand coding may be the way to go in terms of creating character network maps. However, given that this process was looked at via one particular genre of writing, detective fiction, with its rapid fire narrative full of bit players and twists in plot, further validation is neceassary before the process can be discounted as a whole. It is my speculation that this technique, with its emphasis on proper names, may be more forgiving if applied to a text such as a novel, in which interactions and networks often develop more slowly. Additionally, most novels have significantly fewer characters than the Holmes corpus does as a whole. Some of the illegibility of the network graphs for each collection may be reduced by this fact."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I next plan to test these processes on other genres/forms. Determining what specific types of texts are best revealed by these processes can allow others approaching this type of work with insights into how to best approach the particular corpus they are looking at."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "References"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[1] Brill, Eric. \"Transformation-based error-driven learning and natural language processing: A case study in part-of-speech tagging.\" Computational linguistics 21, no. 4 (1995): 543-565."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[2] Carrington, Peter J., John Scott, and Stanley Wasserman, eds. Models and methods in social network analysis. Vol. 28. Cambridge university press, 2005."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[3] Franco Moretti, \u201cNetwork Theory, Plot Analysis,\u201d New Left Review 68 (2011)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[4] Nadeau, David, and Satoshi Sekine. \"A survey of named entity recognition and classification.\" Lingvisticae Investigationes 30, no. 1 (2007): 3-26."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[5] Newman, Mark. Networks: an introduction. Oxford University Press, 2010."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[6] Piper, A., Ahmed, S., Al-Zamal, F., and Ruths, D. \u201cCommunities of Detection: Social Network Analysis and Detective Fiction.\u201d (2014)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[7] Scott, John. \"Social network analysis.\" Sociology 22, no. 1 (1988): 109-127."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[8] Serrat, Olivier. \"Social network analysis.\" (2009)."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[9] Sinclair, Stefan. \"Notebook on Nbviewer.\" 2015. Accessed April 16, 2015. http://nbviewer.ipython.org/gist/sgsinclair/bf112f84f130c8bac96c."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}