{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d898885e",
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use(\"../styles/hda.mplstyle\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f25ae71d",
   "metadata": {},
   "source": [
    "(chp-getting-data)=\n",
    "# Parsing and Manipulating Structured Data\n",
    "\n",
    "(sec-getting-data-intro)=\n",
    "## Introduction\n",
    "\n",
    "In this chapter, we describe how to identify and visualize the \"<span class=\"index\">social\n",
    "network</span>\" of characters in a well-known play, *Hamlet*, using Python (see {numref}`fig:hamlet-network`). Our focus in this introductory chapter is not on the analysis of the properties of the network but rather on the necessary processing and parsing of machine-readable versions of texts. It is these texts, after all, which record the evidence from which a character network is constructed. If our goal is to identify and visualize a character network in a manner which can be reproduced by anyone, then this processing of texts is essential. We also review, in the context of a discussion of parsing various data formats, useful features of the Python language, such as tuple unpacking.\n",
    "```{margin}\n",
    "For the idea of visualizing the character network of *Hamlet* we are indebted to {cite:t}`moretti:2011`. Note, however, that Moretti's *Hamlet* network is not reproducible and depends on ad hoc determinations of whether or not characters interacted.\n",
    "```\n",
    "\n",
    "```{figure} img/hamlet-minimum-10-interactions.png\n",
    "---\n",
    "name: fig:hamlet-network\n",
    "---\n",
    "Network of *Hamlet* characters. Characters must interact at least ten times to be included.\n",
    "```\n",
    "\n",
    "To lend some thematic unity to the chapter, we draw all our examples from Shakespeariana,\n",
    "making use of the tremendously rich and high-quality data provided by the <span class=\"index\">Folger Digital Texts repository</span>, an important digital resource dedicated to the preservation and sharing of <span class=\"index\">William Shakespeare</span>'s plays, sonnets, and poems. We begin with processing the simplest form of data, plain text, to explain the important concept of \"character encoding\" (section {ref}`sec-getting-data-txt`). From there, we move on to various popular forms of more complex, structural data markup. The extensible markup language (XML) is a topic that cannot be avoided here (section {ref}`sec-getting-data-xml`), because it is the dominant standard in the scholarly community, used, for example, by the influential Text Encoding Initiative (TEI) (section {ref}`sec-getting-data-tei`). Additionally, we survey Python's support for other types of structured data such as CSV (section {ref}`sec-getting-data-csv`), HTML (Section {ref}`sec-getting-data-html`), PDF (section {ref}`sec-getting-data-pdf`), and JSON (section {ref}`sec-getting-data-json`). In the final section, where we eventually (aim to) replicate the *Hamlet* character network, we hope to show how various file and data formats can be used with Python to exchange data in an efficient and platform-independent manner.\n",
    "\n",
    "(sec-getting-data-txt)=\n",
    "## Plain Text\n",
    "\n",
    "Enormous amounts of data are now available in a machine-readable format. Much of this data is of interest to researchers in the humanities and interpretive social sciences. Major resources include [Project Gutenberg](https://www.gutenberg.org/), [Internet Archive](https://archive.org), and [Europeana](http://www.europeana.eu/portal/en). Such resources present data in a bewildering array of file formats, ranging from plain, unstructured text files to complex, intricate databases. Additionally, repositories differ in the way they organize the access to their collections: organizations such as Wikipedia provide [nightly dumps](https://dumps.wikimedia.org/backup-index.html) of their databases, downloadable by users to their own machines. Other institutions, such as [Europeana](http://www.europeana.eu/), provide access to their data through an <span class=\"index\">Application Programming Interface</span> (API), which allows interested parties to search collections using specific queries. Accessing and dealing with pre-existing data, instead of creating it yourself, is an important skill for doing data analyses in the humanities and allied social sciences.\n",
    "\n",
    "Digital data are stored in file formats reflecting conventions which enable us\n",
    "to exchange data. One of the most common file formats is the \"<span class=\"index\">plain text</span>\" format, where\n",
    "data take the form of a series of human-readable characters. In Python, we can read such\n",
    "plain text files into objects of type `str`. The chapter's data are stored as a compressed\n",
    "tar archive, which can be decompressed using Python's standard library <span class=\"index\">`tarfile`</span>:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d51764eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tarfile\n",
    "tf = tarfile.open('data/folger.tar.gz', 'r')\n",
    "tf.extractall('data')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53648bec",
   "metadata": {},
   "source": [
    "Subsequently, we read a single plain text file into memory, using:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fbebb3cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Henry IV, Part I\n",
      "by William Shakespeare\n",
      "Edited by Barbara A. Mowat and Paul Werstine\n",
      "  with Michael Poston and Rebecca Niles\n",
      "Folger Shakespeare Library\n",
      "http://www.folgerdigitaltexts.org/?chapter=5&play=1H4\n",
      "Created on Jul 31, 2015, from FDT version 0.9.2\n",
      "\n",
      "Characters in the Play\n",
      "======================\n"
     ]
    }
   ],
   "source": [
    "file_path = 'data/folger/txt/1H4.txt'\n",
    "stream = open(file_path)\n",
    "contents = stream.read()\n",
    "stream.close()\n",
    "\n",
    "print(contents[:300])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0ce8e7b",
   "metadata": {},
   "source": [
    "Here, we open a file object (a so-called *stream*) to access the contents of a plain text version of one of Shakespeare's plays (*Henry IV, Part 1*), which we assign to `stream`. The location of this file is specified using a path as the single argument to the function <span class=\"index\">`open()`</span>. Note that the path is a so-called \"<span class=\"index\">relative path</span>\", which indicates where to find the desired file *relative* to Python's current position in the computer's file system. By convention, plain text files take the `.txt` extension to indicate that they contain plain text. This, however, is not obligatory. After opening a file object, the actual contents of the file is read as a string object by calling the method <span class=\"index\">`read()`</span>. Printing the first 300 characters shows that we have indeed obtained a human-readable series of characters. Crucially, file connections should be closed as soon as they are no longer needed: calling <span class=\"index\">`close()`</span> ensures that the data stream to the original file is cut off. A common and safer shortcut for opening, reading, and closing a file is the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9e45cad2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Henry IV, Part I\n",
      "by William Shakespeare\n",
      "Edited by Barbara A. Mowat and Paul Werstine\n",
      "  with Michael Poston and Rebecca Niles\n",
      "Folger Shakespeare Library\n",
      "http://www.folgerdigitaltexts.org/?chapter=5&play=1H4\n",
      "Created on Jul 31, 2015, from FDT version 0.9.2\n",
      "\n",
      "Characters in the Play\n",
      "======================\n"
     ]
    }
   ],
   "source": [
    "with open(file_path) as stream:\n",
    "    contents = stream.read()\n",
    "\n",
    "print(contents[:300])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e39dea39",
   "metadata": {},
   "source": [
    "The use of the <span class=\"index\">`with`</span> statement in this code block ensures that `stream` will be automatically closed after the indented block has been executed. The rationale of such a `with` block is that it will execute all code under its scope; however, once done, it will close the file, *no matter what has happened*, i.e. even if an error might have been raised when reading the file. At a more abstract level, this use of `with` is an example of a so-called \"<span class=\"index\">context manager</span>\" in Python that allows us to allocate and release resources exactly when and how we want to. The code example above is therefore both a very safe and the preferred method to open and read files: without it, running into a reading error will abort the execution of our code before the file has been closed, and no strict guarantees can be given as to whether the file object will be closed. Without further specification, `stream.read` loads the contents of a file object in its entirety, or, in other words, it reads all characters until it hits the <span class=\"index\">end of file marker</span> (`EOF`).\n",
    "\n",
    "It is important to realize that even the seemingly simple plain text format requires a good deal of conventions: it is a well-known fact that internally computers can only store binary information, i.e., arrays of zeros and ones. To store characters, then, we need some sort of \"map\" specifying how characters in plain text files are to be encoded using numbers. Such a map is called a \"<span class=\"index\">character encoding standard</span>\". The oldest character encoding standard is the <span class=\"index\">ASCII</span> standard (short for \"American Standard Code for Information Interchange\"). This standard has been dominant in the world of computing and specifies an influential mapping for a restrictive set of 128 basic characters drawn from the English-language alphabet, including some numbers, whitespace characters, and punctuation. (128 ($2^7$) distinct characters is the maximum number of symbols which can be encoded using seven bits per character.) ASCII has proven very important in the early days of computing, but in recent decades it has been gradually replaced by more inclusive encoding standards that also cover the characters used in other, non-Western languages.\n",
    "\n",
    "Nowadays, the world of computing increasingly relies on the so-called <span class=\"index\">Unicode</span> standard, which covers over 128,000 characters. The Unicode standard is implemented in a variety of actual encoding standards, such as <span class=\"index\">UTF-8</span> and UTF-16. Fortunately for everyone---dealing with different encodings is very frustrating---UTF-8 has emerged as the standard for text encoding. As UTF-8 is a cleverly constructed superset of ASCII, all valid ASCII text files are valid UTF-8 files. Python nowadays assumes that any files opened for reading or writing in text mode use the default encoding on a computer's system; on macOS and Linux distributions, this is typically UTF-8, but this is not necessarily the case on Windows. In the latter case, you might want to supply an extra `encoding` argument to `open()` and make sure that you load a file using the proper encoding (e.g., `open(..., encoding='utf8')`). Additionally, files which do not use UTF-8 encoding can also be opened through specifying another `encoding` parameter. This is demonstrated in the following code block, in which we read the opening line---[KOI8-R](https://en.wikipedia.org/wiki/KOI8-R) encoded---of *Anna Karenina*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bdd7fb6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Все счастливые семьи похожи друг на друга, каждая несчастливая семья несчастлива по-своему.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open('data/anna-karenina.txt', encoding='koi8-r') as stream:\n",
    "    # Use stream.readline() to retrieve the next line from a file,\n",
    "    # in this case the 1st one:\n",
    "    line = stream.readline()\n",
    "\n",
    "print(line)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00aee69e",
   "metadata": {},
   "source": [
    "Having discussed the very basics of plain text files and file encodings, we now move on to other, more structured forms of digital data.\n",
    "\n",
    "(sec-getting-data-csv)=\n",
    "## CSV\n",
    "\n",
    "The plain text format is a human-readable, non-binary format. However, this does not necessarily imply that the content of such files is always just \"raw data\", i.e., unstructured text. In fact, there exist many simple data formats used to help structure the data contained in plain text files. The CSV-format we briefly touched upon in chapter {ref}`chp-introduction-cook-books`, for instance, is a very common choice to store data in files that often take the `.csv` extension. <span class=\"index\">CSV</span> stands for \"Comma-Separated Values\". It is used to store tabular information in a spreadsheet-like manner. In its simplest form, each line in a CSV file represents an individual data entry, where attributes of that entry are listed in a series of fields separated using a delimiter (e.g., a comma):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "59757f77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['fname,author,title,editor,publisher,pubplace,date\\n', '1H4,William Shakespeare,\"Henry IV, Part I\",Barbara A. Mowat,Washington Square Press,New York,1994\\n', '1H6,William Shakespeare,\"Henry VI, Part 1\",Barbara A. Mowat,Washington Square Press,New York,2008\\n']\n"
     ]
    }
   ],
   "source": [
    "csv_file = 'data/folger_shakespeare_collection.csv'\n",
    "with open(csv_file) as stream:\n",
    "    # call stream.readlines() to read all lines in the CSV file as a list.\n",
    "    lines = stream.readlines()\n",
    "\n",
    "print(lines[:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "738aae8a",
   "metadata": {},
   "source": [
    "This example file contains bibliographic information about the Folger Shakespeare collection, in which each line represents a particular work. Each of these lines records a series of fields, holding the work's filename, author, title, editor, publisher, publication place, and date of publication. As one can see, the first line in this file contains a so-called \"<span class=\"index\">header</span>\", which lists the names of the respective fields in each line. All fields in this file, header and records alike, are separated by a delimiter, in this case a comma. The comma delimiter is just a convention, and in principle any character can be used as a delimiter. The tab-separated format (extension `.tsv`), for instance, is another widely used file format in this respect, where the delimiter between adjacent fields on a line is the tab character (`\\t`). Loading and parsing data from CSV or <span class=\"index\">TSV</span> files would typically entail parsing the contents of the file into a list of lists:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c07b3c11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['fname', 'author', 'title', 'editor', 'publisher', 'pubplace', 'date']\n",
      "['1H4', 'William Shakespeare', '\"Henry IV', ' Part I\"', 'Barbara A. Mowat', 'Washington Square Press', 'New York', '1994']\n",
      "['1H6', 'William Shakespeare', '\"Henry VI', ' Part 1\"', 'Barbara A. Mowat', 'Washington Square Press', 'New York', '2008']\n"
     ]
    }
   ],
   "source": [
    "entries = []\n",
    "for line in open(csv_file):\n",
    "    entries.append(line.strip().split(','))\n",
    "\n",
    "for entry in entries[:3]:\n",
    "    print(entry)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fdc22c2",
   "metadata": {},
   "source": [
    "In this code block, we iterate over all lines in the CSV file. After removing any trailing whitespace characters (with <span class=\"index\">`strip()`</span>), each line is transformed into a list of strings by calling `split(',')`, and subsequently added to the `entries` list. Note that such an ad hoc approach to parsing structured files, while attractively simple, is both naive and dangerous: for instance, we do not protect ourselves against empty or corrupt lines lacking entries. String variables stored in the file, such as a text's title, might also contain commas, causing parsing errors. Additionally, the header is not automatically detected nor properly handled. Therefore, it is recommended to employ packages specifically suited to the task of reading and parsing CSV files, which offer well-tested, flexible, and more robust parsing procedures. Python's standard library, for example, ships with the <span class=\"index\">`csv`</span> module, which can help us parse such files in a much safer way. Have a look at the following code block. Note that we explicitly set the `delimiter` parameter to a comma (`','`) for demonstration purposes, although this in fact is already the parameter's default value in the `reader` function's signature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3e36ef1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('fname', 'title')\n",
      "('1H4', 'Henry IV, Part I')\n",
      "('1H6', 'Henry VI, Part 1')\n",
      "('2H4', 'Henry IV, Part 2')\n",
      "('2H6', 'Henry VI, Part 2')\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "\n",
    "entries = []\n",
    "with open(csv_file) as stream:\n",
    "    reader = csv.reader(stream, delimiter=',')\n",
    "    for fname, author, title, editor, publisher, pubplace, date in reader:\n",
    "        entries.append((fname, title))\n",
    "\n",
    "for entry in entries[:5]:\n",
    "    print(entry)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cd5edd0",
   "metadata": {},
   "source": [
    "The code is very similar to our ad hoc approach, the crucial difference being that we leave the error-prone parsing of commas to the <span class=\"index\">`csv.reader`</span>. Note that each line returned by the `reader` immediately gets \"unpacked\"\" into a long list of seven variables, corresponding to the fields in the file's header. However, most of these variables are not actually used in the subsequent code. To shorten such lines and improve their readability, one could also rewrite the unpacking statement as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "98d32216",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('fname', 'title')\n",
      "('1H4', 'Henry IV, Part I')\n",
      "('1H6', 'Henry VI, Part 1')\n",
      "('2H4', 'Henry IV, Part 2')\n",
      "('2H6', 'Henry VI, Part 2')\n"
     ]
    }
   ],
   "source": [
    "entries = []\n",
    "with open(csv_file) as stream:\n",
    "    reader = csv.reader(stream, delimiter=',')\n",
    "    for fname, _, title, *_ in reader:\n",
    "        entries.append((fname, title))\n",
    "\n",
    "for entry in entries[:5]:\n",
    "    print(entry)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bad2557",
   "metadata": {},
   "source": [
    "The `for`-statement in this code block adds a bit of syntactic sugar to conveniently extract the variables of interest (and can be useful to process other sorts of sequences too). First, it combines regular variable names with underscores to unpack a list of variables. These underscores allow us to ignore the variables we do not need. Below, we exemplify this convention by showing how to indicate interest only in the first and third element of a collection:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "77c0571c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 2\n"
     ]
    }
   ],
   "source": [
    "a, _, c, _, _ = range(5)\n",
    "print(a, c)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1735bff3",
   "metadata": {},
   "source": [
    "Next, what does this `*_` mean? The use of these asterisks is exemplified by the following lines of code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "96880c23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 [1, 2, 3, 4]\n"
     ]
    }
   ],
   "source": [
    "a, *l = range(5)\n",
    "print(a, l)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b804e887",
   "metadata": {},
   "source": [
    "```{margin}\n",
    "Readers familiar with programming languages like Lisp or Scheme will feel right at home here, as these \"first, rest\" pairs are reminiscent of Lisp's `car` and `cdr` operations.\n",
    "```\n",
    "Using this method of \"<span class=\"index\">tuple unpacking</span>\", we unpack an iterable through splitting it into a \"first, rest\" tuple, which is roughly equivalent to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ff6c3467",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 range(1, 5)\n"
     ]
    }
   ],
   "source": [
    "seq = range(5)\n",
    "a, l = seq[0], seq[1:]\n",
    "print(a, l)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c0449d1",
   "metadata": {},
   "source": [
    "To further demonstrate the usefulness of such \"starred\" variables, consider the following example in which an iterable is segmented in a \"first, middle, last\" triplet:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "40d47d58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 [1, 2, 3] 4\n"
     ]
    }
   ],
   "source": [
    "a, *l, b = range(5)\n",
    "print(a, l, b)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21d594c2",
   "metadata": {},
   "source": [
    "It will be clear that this syntax offers interesting functionality to quickly unpack iterables, such as the lines in a CSV file.\n",
    "\n",
    "In addition to the CSV reader employed above (i.e., `csv.reader`), the `csv` module provides another reader object, <span class=\"index\">`csv.DictReader`</span>, which transforms each row of a CSV file into a dictionary. In these dictionaries, keys represent the column names of the CSV file, and values point to the corresponding cells:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d8ab2110",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1H4 Henry IV, Part I\n",
      "1H6 Henry VI, Part 1\n",
      "2H4 Henry IV, Part 2\n",
      "2H6 Henry VI, Part 2\n",
      "3H6 Henry VI, Part 3\n"
     ]
    }
   ],
   "source": [
    "entries = []\n",
    "\n",
    "with open(csv_file) as stream:\n",
    "    reader = csv.DictReader(stream, delimiter=',')\n",
    "    for row in reader:\n",
    "        entries.append(row)\n",
    "\n",
    "for entry in entries[:5]:\n",
    "    print(entry['fname'], entry['title'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e156ab3",
   "metadata": {},
   "source": [
    "The CSV format is often quite useful for simple data collections (we will give another example in chapter \\ref{chp:working-with-data}), but for more complex data, scholars in the humanities and allied social sciences commonly resort to more expressive formats. Later on in this chapter, we will discuss XML, a widely used digital text format for exchanging data in a more structured fashion than plain text or CSV-like formats would allow. Before we get to that, however, we will first discuss two other common file formats, PDF and JSON, and demonstrate how to extract data from those.\n",
    "\n",
    "(sec-getting-data-pdf)=\n",
    "## PDF\n",
    "\n",
    "The \"Portable Document Format\" (<span class=\"index\">PDF</span>) is a file format commonly used to exchange digital documents in a way that preserves the formatting of text or inline images. Being a free yet proprietary format of Adobe since the early nineties, it was released as an open standard in 2008 and published by the International Organization for Standardization (ISO) under ISO 32000-1:2008. PDF documents encapsulate a complete description of their layout, fonts, graphics, and so on and so forth, with which they can be displayed on screen consistently and reliably, independent of software, hardware, or operating system. Being a fixed-layout document exchange format, the Portable Document Format has been and still is one of the most popular document sharing formats. It should be emphasized that PDF is predominantly a display format. As a source for (scientific) data storage and exchange, PDFs are hardly appropriate, and other file formats are to be preferred. Nevertheless, exactly because of the ubiquity of PDF, researchers often need to extract information from PDF files (such as OCR'ed books), which, as it turns out, can be a hassle. In this section we will therefore demonstrate how one could parse and extract text from PDF files using Python.\n",
    "\n",
    "```{margin}\n",
    "Note that the PyPDF2 library is already installed when the installation instructions of this book have been followed (see chapter {ref}`chp-introduction-cook-books`).\n",
    " ```\n",
    "Unlike the CSV format, Python's standard library does not provide a module for parsing PDF files. Fortunately, a plethora of third-party packages fills this gap, such as [pyPDF2](https://pythonhosted.org/PyPDF2/), [pdfrw](https://github.com/pmaupin/pdfrw), and [pdfminer](http://www.unixuser.org/~euske/python/pdfminer/index.html). Here, we will use <span class=\"index\">pyPDF2</span>, which is a pure-Python library for parsing, splitting, or merging PDF files. The library is available from the <span class=\"index\">Python Package Index</span> ([PyPI](https://pypi.python.org/pypi)), and can be installed by running `pip install pypdf2` on the command-line. After installing, we import the package as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a530b817",
   "metadata": {},
   "outputs": [],
   "source": [
    "import PyPDF2 as PDF"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea2bed8b",
   "metadata": {},
   "source": [
    "Reading and parsing PDF files can be accomplished with the library's <span class=\"index\">`PdfFileReader`</span> object, as illustrated by the following lines of code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0a7c810a",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = 'data/folger/pdf/1H4.pdf'\n",
    "pdf = PDF.PdfFileReader(file_path, overwriteWarnings=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "687044d3",
   "metadata": {},
   "source": [
    "A `PdfFileReader` instance provides various methods to access information about a PDF file. For example, to retrieve the number of pages of a PDF file, we call the method `PdfFileReader.getNumPages()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c6fa60ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PDF has 113 pages.\n"
     ]
    }
   ],
   "source": [
    "n_pages = pdf.getNumPages()\n",
    "print(f'PDF has {n_pages} pages.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59e74b72",
   "metadata": {},
   "source": [
    "Similarly, calling `PdfFileReader.getPage(i)` retrieves a single page from a PDF file, which can then be used for further processing. In the code block below, we first retrieve the PDF's first page and, subsequently, call `extractText()` upon the returned `PageObject` to extract the actual textual content of that page:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "36ae5cc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Front\n",
      "MatterFrom the Director of the Folger Shakespeare\n",
      "Library\n",
      "Textual Introduction\n",
      "Synopsis\n",
      "Characters in the Play\n",
      "ACT 1\n",
      "Scene 1\n",
      "Scene 2\n",
      "Scene 3\n",
      "ACT\n"
     ]
    }
   ],
   "source": [
    "page = pdf.getPage(1)\n",
    "content = page.extractText()\n",
    "print(content[:150])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eab5bcb",
   "metadata": {},
   "source": [
    "Note that the data parsing is far from perfect; line endings in particular are often particularly hard to extract correctly. Moreover, it can be challenging to correctly extract the data from consecutive text regions in a PDF, because being a display format, PDFs do not necessarily keep track of the original *logical* reading order of these text blocks, but merely store the blocks' \"coordinates\" on the page. For instance, when extracting text from a PDF page containing a header, three text columns, and a footer, we have no guarantees that the extracted data aligns with the order as presented on the page, while this would intuitively seem the most logical order in which a human would read these blocks. While there exist excellent parsers that aim to alleviate such interpretation artifacts, this is an important limitation of PDF to keep in mind: PDF is great for human reading, but not so much for machine reading or long-term <span class=\"index\">data storage</span>.\n",
    "\n",
    "To conclude this brief section on reading and parsing PDF files in Python, we demonstrate how to implement a simple, yet useful utility function to convert (parts of) PDF files into plain text. The function <span class=\"index\">`pdf2txt()`</span> below implements a procedure to extract textual content from PDF files. It takes three arguments: (i) the file path to the PDF file (`fname`), (ii) the page numbers (`page_numbers`) for which to extract text (if `None`, all pages will be extracted), and (iii) whether to concatenate all pages into a single string or return a list of strings each representing a single page of text (`concatenate`).\n",
    "\n",
    "The procedure is relatively straightforward, but some additional explanation to refresh your Python knowledge won't hurt. The lines following the function definition provide some documentation. Most functions in this book have been carefully documented, and we advise the reader to do the same. The body of the function consists of seven lines. First, we create an instance of the `PdfFileReader` object. We then check whether an argument was given to the parameter `page_numbers`. If no argument is given, we assume the user wants to transform all pages to strings. Otherwise, we only transform the pages corresponding to the given page numbers. If `page_numbers` is a single page (i.e., a single integer), we transform it into a list before proceeding. In the second-to-last line, we do the actual text extraction by calling `extractText()`. Note that the extraction of the pages happens inside a so-called \"list comprehension\", which is Python's syntactic construct for creating lists based on existing lists, and is related to the mathematical set-builder notation. Finally, we merge all texts into a single string using `'\\n'.join(texts)` if `concatenate` is set to `True`. If `False` (the default), we simply return the list of texts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "256a5384",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pdf2txt(fname, page_numbers=None, concatenate=False):\n",
    "    \"\"\"Convert text from a PDF file into a string or list of strings.\n",
    "\n",
    "    Arguments:\n",
    "        fname: a string pointing to the filename of the PDF file\n",
    "        page_numbers: an integer or sequence of integers pointing to the\n",
    "            pages to extract. If None (default), all pages are extracted.\n",
    "        concatenate: a boolean indicating whether to concatenate the\n",
    "            extracted pages into a single string. When False, a list of\n",
    "            strings is returned.\n",
    "\n",
    "    Returns:\n",
    "        A string or list of strings representing the text extracted\n",
    "        from the supplied PDF file.\n",
    "\n",
    "    \"\"\"\n",
    "    pdf = PDF.PdfFileReader(fname, overwriteWarnings=False)\n",
    "    if page_numbers is None:\n",
    "        page_numbers = range(pdf.getNumPages())\n",
    "    elif isinstance(page_numbers, int):\n",
    "        page_numbers = [page_numbers]\n",
    "    texts = [pdf.getPage(n).extractText() for n in page_numbers]\n",
    "    return '\\n'.join(texts) if concatenate else texts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70a76bac",
   "metadata": {},
   "source": [
    "The function is invoked as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "03a3cc10",
   "metadata": {},
   "outputs": [],
   "source": [
    "text = pdf2txt(file_path, concatenate=True)\n",
    "sample = pdf2txt(file_path, page_numbers=[1, 4, 9])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d4fdd66",
   "metadata": {},
   "source": [
    "(sec-getting-data-json)=\n",
    "## JSON\n",
    "\n",
    "<span class=\"index\">JSON</span>, JavaScript Object Notation, is a lightweight data\n",
    "format for storing and exchanging data, and is the dominant data-interchange format on the\n",
    "web. JSON's popularity is due in part to its concise syntax, which draws on conventions\n",
    "found in <span class=\"index\">JavaScript</span> and other popular programming languages.\n",
    "JSON stores information using four basic data types---\"string\" (in double quotes),\n",
    "\"number\" (similar to Python's `float`---JSON has no integer type and is therefore unable to represent very large integer values.), \"boolean\" (`true` or `false`) and \"null\" (similar to Python's `None`)---and two data structures for collections of data---`object`, which is a collection of name/value pairs similar to Python's `dict`, and `array`, which is an ordered list of values, much like Python's `list`.\n",
    "\n",
    "Let us first consider <span class=\"index\">JSON</span> objects. Objects are enclosed with curly brackets (`{}`), and consist of name/value pairs separated by commas. JSON's name/value pairs closely resemble Python's dictionary syntax, as they take the form `name: value`. As shown in the following JSON fragment, names are represented as strings in double quotes:\n",
    "\n",
    "```json\n",
    "{\n",
    "  \"line_id\": 14,\n",
    "  \"play_name\": \"Henry IV\",\n",
    "  \"speech_number\": 1,\n",
    "  \"line_number\": \"1.1.11\",\n",
    "  \"speaker\": \"KING HENRY IV\",\n",
    "  \"text_entry\": \"All of one nature, of one substance bred,\"\n",
    "}\n",
    "```\n",
    "\n",
    "Just as Python's dictionaries differ from lists, <span class=\"index\">JSON</span> objects are different from arrays. JSON arrays use the same syntax as Python: square brackets with elements separated by commas. Here is an example:\n",
    "\n",
    "```json\n",
    "[\n",
    "  {\n",
    "    \"line_id\": 12664,\n",
    "    \"play_name\": \"Alls well that ends well\",\n",
    "    \"speech_number\": 1,\n",
    "    \"line_number\": \"1.1.1\",\n",
    "    \"speaker\": \"COUNTESS\",\n",
    "    \"text_entry\": \"In delivering my son from me, I bury a second husband.\"\n",
    "  },\n",
    "  {\n",
    "    \"line_id\": 12665,\n",
    "    \"play_name\": \"Alls well that ends well\",\n",
    "    \"speech_number\": 2,\n",
    "    \"line_number\": \"1.1.2\",\n",
    "    \"speaker\": \"BERTRAM\",\n",
    "    \"text_entry\": \"And I in going, madam, weep o'er my father's death\"\n",
    "  }\n",
    "]\n",
    "```\n",
    "\n",
    "It is important to note that values can in turn again be objects or arrays, thus enabling the construction of nested structures. As can be seen in the example above, developers can freely mix and nest arrays and objects containing name-value pairs in JSON.\n",
    "\n",
    "Python's [<span class=\"index\">`json`</span>](https://docs.python.org/3.7/library/json.html) module provides a number of functions for convenient encoding and decoding of JSON objects. Encoding Python objects or object hierarchies as JSON strings can be accomplished with <span class=\"index\">`json.dumps()`</span>---`dumps` stands for \"dump s(tring)\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "960dccc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"line_id\": 12664, \"play_name\": \"Alls well that ends well\", \"speech_number\": 1, \"line_number\": \"1.1.1\", \"speaker\": \"COUNTESS\", \"text_entry\": \"In delivering my son from me, I bury a second husband.\"}\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "line = {\n",
    "    'line_id': 12664,\n",
    "    'play_name': 'Alls well that ends well',\n",
    "    'speech_number': 1,\n",
    "    'line_number': '1.1.1',\n",
    "    'speaker': 'COUNTESS',\n",
    "    'text_entry': 'In delivering my son from me, I bury a second husband.'\n",
    "}\n",
    "\n",
    "print(json.dumps(line))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba85c933",
   "metadata": {},
   "source": [
    "Similarly, to serialize Python objects to a file, we employ the function <span class=\"index\">`json.dump()`</span>:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7441a01d",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('shakespeare.json', 'w') as f:\n",
    "    json.dump(line, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3d9a9ef",
   "metadata": {},
   "source": [
    "The function <span class=\"index\">`json.load()`</span> is for decoding (or <span class=\"index\">deserializing</span>) JSON files into a Python object, and <span class=\"index\">`json.loads()`</span> decodes JSON formatted strings into Python objects. The following code block gives an illustration, in which we load a JSON snippet containing bibliographic records of 173 editions of Shakespeare's *Macbeth* as provided by [OCLC WorldCat](http://www.worldcat.org/). We print a small slice of that snippet below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2ae3a156",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'url': ['http://www.worldcat.org/oclc/71720750?referer=xid'], 'publisher': '1st World Library', 'form': ['BA'], 'lang': 'eng', 'city': 'Fairfield, IA', 'author': 'William Shakespeare.', 'year': '2005', 'isbn': ['1421813572'], 'title': 'The tragedy of Macbeth', 'oclcnum': ['71720750']}, {'url': ['http://www.worldcat.org/oclc/318064400?referer=xid'], 'publisher': 'Echo Library', 'form': ['BA'], 'lang': 'eng', 'city': 'Teddington, Middlesex', 'author': 'by William Shakespeare.', 'year': '2006', 'isbn': ['1406820997'], 'title': 'The tragedy of Macbeth', 'oclcnum': ['318064400']}]\n"
     ]
    }
   ],
   "source": [
    "with open('data/macbeth.json') as f:\n",
    "    data = json.load(f)\n",
    "\n",
    "print(data[3:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b01fbe3",
   "metadata": {},
   "source": [
    "After deserializing a JSON document with `json.load()` (i.e., converting it into a Python object), it can be accessed as normal Python `list` or `dict` objects. For example, to construct a frequency distribution of the languages in which these 173 editions are written, we can write the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "da55f684",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('eng', 164), ('ger', 3), ('spa', 3), ('fre', 2), ('cat', 1)]\n"
     ]
    }
   ],
   "source": [
    "import collections\n",
    "\n",
    "languages = collections.Counter()\n",
    "for entry in data:\n",
    "    languages[entry['lang']] += 1\n",
    "\n",
    "print(languages.most_common())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eae64c53",
   "metadata": {},
   "source": [
    "For those unfamiliar with the [<span class=\"index\">`collections`</span>](https://docs.python.org/3/library/collections.html) module and its <span class=\"index\">`Counter`</span> object, this is how it works: A `Counter` object is a `dict` subclass for counting immutable objects. Elements are stored as dictionary keys (accessible through `Counter.keys()`) and their counts are stored as dictionary values (accessible through `Counter.values()`). Being a subclass of `dict`, `Counter` inherits all methods available for regular dictionaries (e.g., `dict.items()`, `dict.update()`). By default, a key's value is set to zero. This allows us to increment a key's count for each occurrence of that key (cf. lines 4 and 5). The method <span class=\"index\">`Counter.most_common()`</span> is used to construct a list of the *n* most common elements. The method returns the keys and their counts in the form of `(key, value)` tuples.\n",
    "\n",
    "(sec-getting-data-xml)=\n",
    "## XML\n",
    "\n",
    "In digital applications across the humanities, <span class=\"index\">XML</span> or the [eXtensible Markup Language](https://www.w3.org/XML/) is the dominant format for modeling texts, especially in the field of Digital Scholarly Editing, where scholars are concerned with the electronic editions of texts {cite:p}`pierazzo:2015`. XML is a powerful and very common format for enriching (textual) data. XML is a so-called \"<span class=\"index\">markup language</span>\": it specifies a syntax allowing for \"semantic\" data annotations, which provide means to add layers of meaningful, descriptive metadata on top of the original, raw data in a plain text file. XML, for instance, allows making explicit the function or meaning of the words in documents. Reading the text of a play as a plain text, to give but one example, does not provide any formal cues as to which scene or act a particular utterance belongs, or by which character the utterance was made. XML allows us to keep track of such information by making it explicit.\n",
    "\n",
    "The syntax of <span class=\"index\">XML</span> is best explained through an example, since it is very intuitive. Let us consider the following short, yet illustrative example using the well-known \"Sonnet 18\" by Shakespeare:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "152405d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<?xml version=\"1.0\"?>\n",
      "<sonnet author=\"William Shakepeare\" year=\"1609\">\n",
      "  <line n=\"1\">Shall I compare thee to a summer's <rhyme>day</rhyme>?</line>\n",
      "  <line n=\"2\">Thou art more lovely and more <rhyme>temperate</rhyme>:</line>\n",
      "  <line n=\"3\">Rough winds do shake the darling buds of <rhyme>May</rhyme>,</line>\n",
      "  <line n=\"4\">And summer's lease hath all too short a <rhyme>date</rhyme>:</line>\n",
      "  <line n=\"5\">Sometime too hot the eye of heaven <rhyme>shines</rhyme>,</line>\n",
      "  <line n=\"6\">And often is his gold complexion <rhyme>dimm'd</rhyme>;</line>\n",
      "  <line n=\"7\">And every fair from fair sometime <rhyme>declines</rhyme>,</line>\n",
      "  <line n=\"8\">By chance, or nature's changing course, <rhyme>untrimm'd</rhyme>;</line>\n",
      "  <volta/>\n",
      "  <line n=\"9\">But thy eternal summer shall not <rhyme>fade</rhyme></line>\n",
      "  <line n=\"10\">Nor lose possession of that fair thou <rhyme>ow'st</rhyme>;</line>\n",
      "  <line n=\"11\">Nor shall Death brag thou wander'st in his <rhyme>shade</rhyme>,</line>\n",
      "  <line n=\"12\">When in eternal lines to time thou <rhyme>grow'st</rhyme>;</line>\n",
      "  <line n=\"13\">So long as men can breathe or eyes can <rhyme>see</rhyme>,</line>\n",
      "  <line n=\"14\">So long lives this, and this gives life to <rhyme>thee</rhyme>.</line>\n",
      "</sonnet>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open('data/sonnets/18.xml') as stream:\n",
    "    xml = stream.read()\n",
    "\n",
    "print(xml)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3054db22",
   "metadata": {},
   "source": [
    "The first line (`<?xml version=\"1.0\"?>`) is a sort of \"prolog\" declaring the exact version of XML we are using---in our case, that is simply version 1.0. Including a prolog is optional according to the XML syntax but it is a good place to specify additional information about a file, such as its encoding (`<?xml version=\"1.0\" encoding=\"utf-8\"?>`). When provided, the prolog should always be on the first line of an XML document. It is only after the prolog that the actual content comes into play. As can be seen at a glance, <span class=\"index\">XML</span> encodes pieces of text in a similar way as <span class=\"index\">HTML</span> (see section {ref}`sec-getting-data-html`), using start tags (e.g., `<line>`, `<rhyme>`) and corresponding end tags (`</line>`, `</rhyme>`) which are enclosed by angle brackets. Each start tag must normally correspond to exactly one end tag, or you will run into parsing errors when processing the file. Nevertheless, XML does allow for \"solo\" elements, such the `<volta/>` tag after line 8 in this example, which specifies the classical \"turning point\" in sonnets. Such tags are \"self-closing\", so to speak, and they are also called \"empty\" tags. Importantly, <span class=\"index\">XML tags</span> are not allowed to overlap. The following line would therefore not constitute valid XML:\n",
    "\n",
    "```xml\n",
    "<line n=\"11\">\n",
    "  Nor shall Death brag thou wander'st in his <rhyme>shade,\n",
    "</line></rhyme>\n",
    "```\n",
    "\n",
    "The problem here is that the `<rhyme>` element should have been closed by the corresponding end tag (`</rhyme>`), before we can close the parent element using `</line>`. This limitation results from the fact that XML is a *hierarchical* markup language: it assumes that we can and should model a text document as a tree of branching nodes. In this tree, elements cannot have more than one direct parent element, because otherwise the hierarchy would be ambiguous. The one exception is the so-called root element, which is the highest node in a tree. Hence, it does not have a parent element itself, and thus cannot have siblings. All non-root elements can have as many siblings and children as needed. All the `<line>` elements in our sonnet, for example, are siblings, in the sense that they have a direct parent element in common, i.e., the `<sonnet>` tag. The fact that elements cannot overlap in XML is a constant source of frustration and people often come up with creative workarounds for the limitation imposed by this hierarchical format.\n",
    "\n",
    "XML does not come with predefined tags; it only defines a syntax to define those tags. Users can therefore invent and use their own tag set and markup conventions, as long as the documents formally adhere to the XML standard syntax. We say that documents are \"well-formed\" when they conform completely to the XML standard, which is something that can be checked using validation applications (see, e.g., the [W3Schools validator](http://www.w3schools.com/xml/xml_validator.asp)). For even more descriptive precision, XML tags can take so-called \"attributes\", which consist of a name and a value. The `sonnet` element, for instance, has two attributes: the attribute names `author` and `year` are mapped to the values `\"William Shakespeare\"` and `\"1609\"` respectively. Names do not take surrounding double quotes but values do; they are linked by an equal sign (`=`). The name and element pairs inside a single tag are separated by a space character. Only start tags and standalone tags can take attributes (e.g., `<volta n=\"1\"/>`); closing tags cannot. According to the <span class=\"index\">XML</span> standard, the order in which attributes are listed is insignificant.\n",
    "\n",
    "Researchers in the humanities nowadays put a lot of time and effort in creating digital data sets for their research, such as <span class=\"index\">scholarly editions</span> with a rich markup encoded in XML. Nevertheless, once data have been annotated, it can be challenging to subsequently extract the textual contents, and to fully exploit the information painstakingly encoded. It is therefore crucial to be able to parse XML in an efficient manner. Luckily, Python provides the necessary functionality for this. In this section, we will make use of some of the functionality included in the <span class=\"index\">`lxml`</span> library, which is commonly used for XML parsing in the Python ecosystem, although there exist a number of alternative packages. It should be noted that there exist languages such as <span class=\"index\">XSLT</span> ([Extensible Stylesheet Language Transformations](https://www.w3.org/Style/XSL/)) which are particularly well equipped to manipulate XML documents. Depending on the sort of task you wish to achieve, these languages might make it easier than Python to achieve certain transformations and manipulations of XML documents. Languages such as XSLT, on the other hand, are less general programming languages and might miss support for more generic functionality.\n",
    "\n",
    "(sec-getting-data-parsing-xml)=\n",
    "### Parsing XML\n",
    "\n",
    "We first import the lxml's central module `etree`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "97e55897",
   "metadata": {},
   "outputs": [],
   "source": [
    "import lxml.etree"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3fb7de1",
   "metadata": {},
   "source": [
    "After importing `etree`, we can start parsing the XML data that represents our sonnet:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "37b755fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<lxml.etree._ElementTree object at 0x10f779380>\n"
     ]
    }
   ],
   "source": [
    "tree = lxml.etree.parse('data/sonnets/18.xml')\n",
    "print(tree)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcd02ade",
   "metadata": {},
   "source": [
    "We have now read and parsed our sonnet via the <span class=\"index\">`lxml.etree.parse()`</span> function, which accepts the path to a file as a parameter. We have also assigned the XML tree structure returned by the `parse` function to the `tree` variable, thus enabling subsequent processing. If we print the variable `tree` as such, we do not get to see the raw text from our file, but rather an indication of `tree`'s object type, i.e., the <span class=\"index\">`lxml.etree._ElementTree`</span> type. To have a closer look at the original <span class=\"index\">XML</span> as printable text, we transform the tree into a string object using `lxml.etree.tostring(tree)` before printing it (note that the initial line from our file, containing the XML metadata, is not included anymore):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0453d72d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<sonnet author=\"William Shakepeare\" year=\"1609\">\n",
      "  <line n=\"1\">Shall I compare thee to a summer's <rhyme>day</rhyme>?</line>\n",
      "  <line n=\"2\">Thou art more lovely and more <rhyme>temperate</rhyme>:</line>\n",
      "  <line n=\"3\">Rough winds do shake the darling buds of <rhyme>May</rhyme>,</line>\n",
      "  <line n=\"4\">And summer's lease hath all too short a <rhyme>date</rhyme>:</line>\n",
      "  <line n=\"5\">Sometime too hot the eye of heaven <rhyme>shines</rhyme>,</line>\n",
      "  <line n=\"6\">And often is his gold complexion <rhyme>dimm'd</rhyme>;</line>\n",
      "  <line n=\"7\">And every fair from fair sometime <rhyme>declines</rhyme>,</line>\n",
      "  <line n=\"8\">By chance, or nature's changing course, <rhyme>untrimm'd</rhyme>;</line>\n",
      "  <volta/>\n",
      "  <line n=\"9\">But thy eternal summer shall not <rhyme>fade</rhyme></line>\n",
      "  <line n=\"10\">Nor lose possession of that fair thou <rhyme>ow'st</rhyme>;</line>\n",
      "  <line n=\"11\">Nor shall Death brag thou wander'st in his <rhyme>shade</rhyme>,</line>\n",
      "  <line n=\"12\">When in eternal lines to time thou <rhyme>grow'st</rhyme>;</line>\n",
      "  <line n=\"13\">So long as men can breathe or eyes can <rhyme>see</rhyme>,</line>\n",
      "  <line n=\"14\">So long lives this, and this gives life to <rhyme>thee</rhyme>.</line>\n",
      "</sonnet>\n"
     ]
    }
   ],
   "source": [
    "# decoding is needed to transform the bytes object into an actual string\n",
    "print(lxml.etree.tostring(tree).decode())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41ef02a5",
   "metadata": {},
   "source": [
    "In what follows, we will demonstrate how to navigate the XML tree. Often we will be interested in specific elements in the tree only, such as the rhyme words inside the `<rhyme>` tags, instead of the entirety of the tree's complex structure. The high-level method `interfind()` allows us to easily loop over all the element in our tree and search it for specific elements. To query the tree for all rhyme elements, we pass the string `\"//rhyme\"` as an argument to this function: this string can be formatted using [<span class=\"index\">XPath</span>](http://www.w3schools.com/xml/xml_xpath.asp) query syntax, a search language used to query XML files. We cannot fully cover that query syntax here, but in our present case, the double back slash simply indicates that we are interested in `<rhyme>` elements, no matter where in the tree they occur. Again, printing the rhyme elements themselves is not exactly insightful, since we only print rather prosaic information about the Python objects representing our rhyme words. We can use the `tag` attribute of such elements to print the tag's name and the `text` attribute to extract the text contained in the elements, i.e., the actual rhyme words:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d8eaf6ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "element: rhyme -> day\n",
      "element: rhyme -> temperate\n",
      "element: rhyme -> May\n",
      "element: rhyme -> date\n",
      "element: rhyme -> shines\n",
      "element: rhyme -> dimm'd\n",
      "element: rhyme -> declines\n",
      "element: rhyme -> untrimm'd\n",
      "element: rhyme -> fade\n",
      "element: rhyme -> ow'st\n",
      "element: rhyme -> shade\n",
      "element: rhyme -> grow'st\n",
      "element: rhyme -> see\n",
      "element: rhyme -> thee\n"
     ]
    }
   ],
   "source": [
    "for rhyme in tree.iterfind('//rhyme'):\n",
    "    print(f'element: {rhyme.tag} -> {rhyme.text}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e98d5e8",
   "metadata": {},
   "source": [
    "Until now, we have been iterating over the `<rhyme>` elements in their simple order of appearance: we haven't really been exploiting the hierarchy of the XML tree yet. Let us see now how to actually navigate and traverse the XML tree. First, we select the root node or top node, which forms the beginning of the entire tree:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3390e496",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sonnet\n"
     ]
    }
   ],
   "source": [
    "root = tree.getroot()\n",
    "print(root.tag)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dff76efd",
   "metadata": {},
   "source": [
    "As explained above, the `<sonnet>` root element in our <span class=\"index\">XML</span> file\n",
    "has two additional attributes. The values of the attributes of an element can be accessed via the attribute `attrib`, which allows us to access the attribute information of an element in a dictionary-like fashion, thus via key-based indexing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "858369ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1609\n"
     ]
    }
   ],
   "source": [
    "print(root.attrib['year'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2575e71",
   "metadata": {},
   "source": [
    "Now that we have selected the root element, we can start drilling down the tree's structure. Let us first find out how many child nodes the root element has. The number of children of an element can be retrieved by employing the function <span class=\"index\">`len()`</span>:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1bba8e13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n"
     ]
    }
   ],
   "source": [
    "print(len(root))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6653ea9e",
   "metadata": {},
   "source": [
    "The root element has fifteen children, that is: fourteen `<line>` elements and one `<volta>` element. Elements with children function like iterable collections, and thus their children can be iterated as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5522d106",
   "metadata": {},
   "outputs": [],
   "source": [
    "children = [child.tag for child in root]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3257a6d5",
   "metadata": {},
   "source": [
    "How could we now extract the actual text in our poem while iterating over the tree? Could we simply call the `text` property on each element?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "568f9950",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shall I compare thee to a summer's \n",
      "Thou art more lovely and more \n",
      "Rough winds do shake the darling buds of \n",
      "And summer's lease hath all too short a \n",
      "Sometime too hot the eye of heaven \n",
      "And often is his gold complexion \n",
      "And every fair from fair sometime \n",
      "By chance, or nature's changing course, \n",
      "\n",
      "But thy eternal summer shall not \n",
      "Nor lose possession of that fair thou \n",
      "Nor shall Death brag thou wander'st in his \n",
      "When in eternal lines to time thou \n",
      "So long as men can breathe or eyes can \n",
      "So long lives this, and this gives life to \n"
     ]
    }
   ],
   "source": [
    "print('\\n'.join(child.text or '' for child in root))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c959c72",
   "metadata": {},
   "source": [
    "The answer is *no*, since the text included in the `<rhyme>` element would not be included: the `text` property will only yield the first piece of pure text contained under a specific element, and not the text contained in an element's child elements or subsequent pieces of text thereafter, such as the verse-final punctuation. Here the `itertext()` method comes in useful. This function constructs an iterator over the entire textual content of the subtree of the `<line>` element. For the very first verse line, this gives us the following textual \"offspring\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "49545461",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shall I compare thee to a summer's day?\n"
     ]
    }
   ],
   "source": [
    "print(''.join(root[0].itertext()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0520e023",
   "metadata": {},
   "source": [
    "To extract the actual text in our lines, then, we could use something like the following lines of code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9dd8210e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "line  1: Shall I compare thee to a summer's day?\n",
      "line  2: Thou art more lovely and more temperate:\n",
      "line  3: Rough winds do shake the darling buds of May,\n",
      "line  4: And summer's lease hath all too short a date:\n",
      "line  5: Sometime too hot the eye of heaven shines,\n",
      "line  6: And often is his gold complexion dimm'd;\n",
      "line  7: And every fair from fair sometime declines,\n",
      "line  8: By chance, or nature's changing course, untrimm'd;\n",
      "line  9: But thy eternal summer shall not fade\n",
      "line 10: Nor lose possession of that fair thou ow'st;\n",
      "line 11: Nor shall Death brag thou wander'st in his shade,\n",
      "line 12: When in eternal lines to time thou grow'st;\n",
      "line 13: So long as men can breathe or eyes can see,\n",
      "line 14: So long lives this, and this gives life to thee.\n"
     ]
    }
   ],
   "source": [
    "for node in root:\n",
    "    if node.tag == 'line':\n",
    "        print(f\"line {node.attrib['n']: >2}: {''.join(node.itertext())}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d534bee4",
   "metadata": {},
   "source": [
    "(sec-getting-data-creating-xml)=\n",
    "### Creating XML\n",
    "\n",
    "Having explained the basics of *parsing* XML, we now turn to creating XML, which is equally relevant when it comes to exchanging data. <span class=\"index\">XML</span> is a great format for the long-term storage of data, and once a data set has been analyzed and enriched, XML is a powerful output format for exporting and sharing data. In the following code block, we read a plain text version of another sonnet by Shakespeare (\"Sonnet 116\"):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "283f4ab7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let me not to the marriage of true minds\n",
      "Admit impediments. Love is not love\n",
      "Which alters when it alteration finds,\n",
      "Or bends with the remover to remove:\n",
      "O no; it is an ever-fixed mark, \n",
      "That looks on tempests, and is never shaken;\n",
      "It is the star to every wandering bark,\n",
      "Whose worth's unknown, although his height be taken.\n",
      "Love's not Time's fool, though rosy lips and cheeks \n",
      "Within his bending sickle's compass come; \n",
      "Love alters not with his brief hours and weeks, \n",
      "But bears it out even to the edge of doom.\n",
      "If this be error and upon me proved,\n",
      "I never writ, nor no man ever loved.\n"
     ]
    }
   ],
   "source": [
    "with open('data/sonnets/116.txt') as stream:\n",
    "    text = stream.read()\n",
    "\n",
    "print(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff321e9f",
   "metadata": {},
   "source": [
    "In what follows, we will attempt to enrich this raw text with the same markup as \"Sonnet 18\". We start by creating a root element and add two attributes to it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3246bf43",
   "metadata": {},
   "outputs": [],
   "source": [
    "root = lxml.etree.Element('sonnet')\n",
    "root.attrib['author'] = 'William Shakespeare'\n",
    "root.attrib['year'] = '1609'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72d56deb",
   "metadata": {},
   "source": [
    "The root element is initiated through the <span class=\"index\">`Element()`</span> function. After initializing the element, we can add attributes to it, just as if we would do when populating an ordinary Python dictionary. After transforming the root node into an instance of <span class=\"index\">`lxml.etree._ElementTree`</span>, we print it to screen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e966729a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'<sonnet author=\"William Shakespeare\" year=\"1609\"/>'\n"
     ]
    }
   ],
   "source": [
    "tree = lxml.etree.ElementTree(root)\n",
    "stringified = lxml.etree.tostring(tree)\n",
    "print(stringified)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d75566ee",
   "metadata": {},
   "source": [
    "Note the `b` which is printed in front of the actual string. This prefix indicates that we are dealing with a string of <span class=\"index\">bytes</span>, instead of <span class=\"index\">Unicode</span> characters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d1734c02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'bytes'>\n"
     ]
    }
   ],
   "source": [
    "print(type(stringified))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ae8073e",
   "metadata": {},
   "source": [
    "For some applications, it is necessary to decode such objects into a proper string:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8a9cbd45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<sonnet author=\"William Shakespeare\" year=\"1609\"/>\n"
     ]
    }
   ],
   "source": [
    "print(stringified.decode('utf-8'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f2d7c6b",
   "metadata": {},
   "source": [
    "Adding children to the root element is accomplished through initiating new elements, and, subsequently, appending them to the root element:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "463bb864",
   "metadata": {},
   "outputs": [],
   "source": [
    "for nb, line in enumerate(open('data/sonnets/116.txt')):\n",
    "    node = lxml.etree.Element('line')\n",
    "    node.attrib['n'] = str(nb + 1)\n",
    "    node.text = line.strip()\n",
    "    root.append(node)\n",
    "    # voltas typically, but not always occur between the octave and sextet\n",
    "    if nb == 8:\n",
    "        node = lxml.etree.Element('volta')\n",
    "        root.append(node)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "385300b5",
   "metadata": {},
   "source": [
    "We print the newly filled tree structure using the `pretty_print` argument of the function <span class=\"index\">`lxml.etree.to_string()`</span> to obtain a human-readable, indented tree:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5e3b3c14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<sonnet author=\"William Shakespeare\" year=\"1609\">\n",
      "  <line n=\"1\">Let me not to the marriage of true minds</line>\n",
      "  <line n=\"2\">Admit impediments. Love is not love</line>\n",
      "  <line n=\"3\">Which alters when it alteration finds,</line>\n",
      "  <line n=\"4\">Or bends with the remover to remove:</line>\n",
      "  <line n=\"5\">O no; it is an ever-fixed mark,</line>\n",
      "  <line n=\"6\">That looks on tempests, and is never shaken;</line>\n",
      "  <line n=\"7\">It is the star to every wandering bark,</line>\n",
      "  <line n=\"8\">Whose worth's unknown, although his height be taken.</line>\n",
      "  <line n=\"9\">Love's not Time's fool, though rosy lips and cheeks</line>\n",
      "  <volta/>\n",
      "  <line n=\"10\">Within his bending sickle's compass come;</line>\n",
      "  <line n=\"11\">Love alters not with his brief hours and weeks,</line>\n",
      "  <line n=\"12\">But bears it out even to the edge of doom.</line>\n",
      "  <line n=\"13\">If this be error and upon me proved,</line>\n",
      "  <line n=\"14\">I never writ, nor no man ever loved.</line>\n",
      "</sonnet>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(lxml.etree.tostring(tree, pretty_print=True).decode())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c4193da",
   "metadata": {},
   "source": [
    "The observant reader may have noticed that one difficult challenge remains: the existing tree must be manipulated in such a way that the rhyme words get enclosed by the proper tag. This is not trivial, because, at the same time, we want to make sure that the verse-final punctuation is not included in that element because, strictly speaking, it is not part of the rhyme. The following longer piece code takes care of this. We have added detailed comments to each line. Take some time to read it through."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "00c7a30d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<sonnet author=\"William Shakespeare\" year=\"1609\">\n",
      "  <line n=\"1\">Let me not to the marriage of true <rhyme>minds</rhyme></line>\n",
      "  <line n=\"2\">Admit impediments. Love is not <rhyme>love</rhyme></line>\n",
      "  <line n=\"3\">Which alters when it alteration <rhyme>finds</rhyme>,</line>\n",
      "  <line n=\"4\">Or bends with the remover to <rhyme>remove</rhyme>:</line>\n",
      "  <line n=\"5\">O no; it is an ever-fixed <rhyme>mark</rhyme>,</line>\n",
      "  <line n=\"6\">That looks on tempests, and is never <rhyme>shaken</rhyme>;</line>\n",
      "  <line n=\"7\">It is the star to every wandering <rhyme>bark</rhyme>,</line>\n",
      "  <line n=\"8\">Whose worth's unknown, although his height be <rhyme>taken</rhyme>.</line>\n",
      "  <line n=\"9\">Love's not Time's fool, though rosy lips and <rhyme>cheeks</rhyme></line>\n",
      "  <volta/>\n",
      "  <line n=\"10\">Within his bending sickle's compass <rhyme>come</rhyme>;</line>\n",
      "  <line n=\"11\">Love alters not with his brief hours and <rhyme>weeks</rhyme>,</line>\n",
      "  <line n=\"12\">But bears it out even to the edge of <rhyme>doom</rhyme>.</line>\n",
      "  <line n=\"13\">If this be error and upon me <rhyme>proved</rhyme>,</line>\n",
      "  <line n=\"14\">I never writ, nor no man ever <rhyme>loved</rhyme>.</line>\n",
      "</sonnet>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Loop over all nodes in the tree\n",
    "for node in root:\n",
    "    # Leave the volta node alone. A continue statement instructs\n",
    "    # Python to move on to the next item in the loop.\n",
    "    if node.tag == 'volta':\n",
    "        continue\n",
    "    # We chop off and store verse-final punctuation:\n",
    "    punctuation = ''\n",
    "    if node.text[-1] in ',:;.':\n",
    "        punctuation = node.text[-1]\n",
    "        node.text = node.text[:-1]\n",
    "    # Make a list of words using the split method\n",
    "    words = node.text.split()\n",
    "    # We split rhyme words and other words:\n",
    "    other_words, rhyme = words[:-1], words[-1]\n",
    "    # Replace the node's text with all text except the rhyme word\n",
    "    node.text = ' '.join(other_words) + ' '\n",
    "    # We create the rhyme element, with punctuation (if any) in its tail\n",
    "    elt = lxml.etree.Element('rhyme')\n",
    "    elt.text = rhyme\n",
    "    elt.tail = punctuation\n",
    "    # We add the rhyme to the line:\n",
    "    node.append(elt)\n",
    "\n",
    "tree = lxml.etree.ElementTree(root)\n",
    "print(lxml.etree.tostring(tree, pretty_print=True).decode())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8afbd8c9",
   "metadata": {},
   "source": [
    "This code does not contain any new functionality, except for the manipulation of the\n",
    "`tail` attribute for some elements. By assigning text to an element's `tail` attribute, we can specify text which should immediately follow an element, before any subsequent element will start. Having obtained the envisaged rich XML structure, we will now save the tree to an XML file. To add an XML declaration and to specify the correct file encoding, we supply a number of additional parameters to the <span class=\"index\">`lxml.etree.tostring()`</span> function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "93c15e70",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data/sonnets/116.xml', 'w') as f:\n",
    "    f.write(\n",
    "        lxml.etree.tostring(\n",
    "            root, xml_declaration=True, pretty_print=True, encoding='utf-8').decode())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa3e449a",
   "metadata": {},
   "source": [
    "The current encoding of both our sonnets is an excellent example of an <span class=\"index\">XML</span> document in which elements can contain both sub elements, as well as \"free\" text. Such documents are in fact really common in the humanities (e.g. many text editions will be of this type) and are called *mixed-content* XML, meaning that nodes containing only plain text can be direct siblings to other elements. <span class=\"index\">Mixed-content XML</span> can be relatively more challenging to parse than XML that does not allow such mixing of elements. In the following, longer example, we create an alternative version of the sonnet, where all text nodes have been enclosed with `w`-elements (for the purely alphabetic strings in words) and `c`-elements (for punctuation and spaces). This XML content can no longer be called \"mixed\", because the plain text is never a direct sibling to a non-textual element in the tree's hierarchy. While such a file is extremely verbose, and thus much harder to read for a human, it can in some cases be simpler to parse for a machine. As always, your approach will be dictated by the specifics of the problem you are working on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "de05ca10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<sonnet author=\"William Shakespeare\" year=\"1609\">\n",
      "  <line n=\"1\">\n",
      "    <w>Let</w>\n",
      "    <c> </c>\n",
      "    <w>me</w>\n",
      "    <c> </c>\n",
      "    <w>not</w>\n",
      "    <c> </c>\n",
      "    <w>to</w>\n",
      "    <c> </c>\n",
      "    <w>the</w>\n",
      "    <c> </c>\n",
      "    <w>marriage</w>\n",
      "    <c> </c>\n",
      "    <w>of</w>\n",
      "    <c> </c>\n",
      "    <rhyme>\n",
      "      <w>minds</w>\n",
      "    </rhyme>\n",
      "    <c> </c>\n",
      "  </line>\n",
      "  ...\n"
     ]
    }
   ],
   "source": [
    "root = lxml.etree.Element('sonnet')\n",
    "# Add an author attribute to the root node\n",
    "root.attrib['author'] = 'William Shakespeare'\n",
    "# Add a year attribute to the root node\n",
    "root.attrib['year'] = '1609'\n",
    "\n",
    "for nb, line in enumerate(open('data/sonnets/116.txt')):\n",
    "    line_node = lxml.etree.Element('line')\n",
    "    # Add a line number attribute to each line node\n",
    "    line_node.attrib['n'] = str(nb + 1)\n",
    "\n",
    "    # Make different nodes for words and non-words\n",
    "    word = ''\n",
    "    for char in line.strip():\n",
    "        if char.isalpha():\n",
    "            word += char\n",
    "        else:\n",
    "            word_node = lxml.etree.Element('w')\n",
    "            word_node.text = word\n",
    "            line_node.append(word_node)\n",
    "            word = ''\n",
    "\n",
    "            char_node = lxml.etree.Element('c')\n",
    "            char_node.text = char\n",
    "            line_node.append(char_node)\n",
    "\n",
    "    # don't forget last word:\n",
    "    if word:\n",
    "        word_node = lxml.etree.Element('w')\n",
    "        word_node.text = word\n",
    "        line_node.append(word_node)\n",
    "\n",
    "    rhyme_node = lxml.etree.Element('rhyme')\n",
    "    # We use xpath to find the final w-element in the line\n",
    "    # and wrap it in a line element\n",
    "    rhyme_node.append(line_node.xpath('//w')[-1])\n",
    "    line_node.replace(line_node.xpath('//w')[-1], rhyme_node)\n",
    "\n",
    "    root.append(line_node)\n",
    "\n",
    "    # Add the volta node\n",
    "    if nb == 8:\n",
    "        node = lxml.etree.Element('volta')\n",
    "        root.append(node)\n",
    "\n",
    "tree = lxml.etree.ElementTree(root)\n",
    "xml_string = lxml.etree.tostring(tree, pretty_print=True).decode()\n",
    "# Print a snippet of the tree:\n",
    "print(xml_string[:xml_string.find(\"</line>\") + 8] + '  ...')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca8a50fd",
   "metadata": {},
   "source": [
    "(sec-getting-data-tei)=\n",
    "### TEI\n",
    "\n",
    "A name frequently mentioned in connection to XML and computational work in the humanities\n",
    "is the <span class=\"index\">Text Encoding Initiative</span>\n",
    "([TEI](http://www.tei-c.org/index.xml)). This is an international scholarly consortium,\n",
    "which maintains a set of guidelines that specify a \"best practice\" as to how one can best\n",
    "mark up texts in humanities scholarship. The <span class=\"index\">TEI</span> is currently used in a variety of digital projects across the humanities, but also in the so-called GLAM sector (Galleries, Libraries, Archives, and Museums). The TEI provides a large online collection of tag descriptions, which can be used to annotate and enrich texts. For example, if someone is editing a handwritten codex in which a scribe has crossed out a word and added a correction on top of the line, the TEI guidelines suggest the use of a `<del>` element to transcribe the deleted word and the `<add>` element to mark up the superscript addition. The TEI provides over 500 tags in their current version of the Guidelines (this version is called \"P5\").\n",
    "\n",
    "The <span class=\"index\">TEI</span> offers *guidelines* and it is not a standard, meaning that it leaves users and projects free to adapt these guidelines to their own specific needs. Although there are many projects that use TEI, there are not that many projects that are fully compliant with the P5 specification, because small changes to the TEI guidelines are often made to make them usable for specific projects. This can be a source of frustration for developers, because even though a document claims to \"use the TEI\" or \"to be TEI-compliant\", one never really knows what that exactly means.\n",
    "\n",
    "For digital text analysis, there are a number of great datasets encoded using \"TEI-inspired\" XML. The <span class=\"index\">Folger Digital Texts</span> is such a dataset. All XML encoded texts are located under the `data/folger/xml` directory. This resource provides a very rich and detailed markup: apart from extensive metadata about the play or detailed descriptions of the actors involved, the actual lines have been encoded in such a manner that we perfectly know which character uttered a particular line, or to which scene or act a line belongs. This allows us to perform much richer textual analyses than would be the case with raw text versions of the plays.\n",
    "\n",
    "As previously mentioned, XML does not specify any predefined tags, and thus allows developers to flexibly define their own set of element names. The potential danger of this practice, however, is that name conflicts arise when XML documents from different XML applications are mixed. To avoid such name conflicts, XML allows the specification of so-called <span class=\"index\">XML namespaces</span>. For example, the root elements of the Folger XML files specify the following namespace:\n",
    "\n",
    "```xml\n",
    "<TEI xmlns=\"http://www.tei-c.org/ns/1.0\">\n",
    "```\n",
    "\n",
    "By specifying a namespace at the root level, the tag names of all children are internally prefixed with the supplied namespace. This means that a tag name like `author` is converted to `{http://www.tei-c.org/ns/1.0}author`, which, crucially, needs to be accounted for when navigating the document. For example, while titles are enclosed with the `title` tag name, extracting a document's title requires prefixing `title` with the specified namespace:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "33434106",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Othello\n"
     ]
    }
   ],
   "source": [
    "tree = lxml.etree.parse('data/folger/xml/Oth.xml')\n",
    "print(tree.getroot().find('.//{http://www.tei-c.org/ns/1.0}title').text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94990b4c",
   "metadata": {},
   "source": [
    "Note that, because of the introduction of a namespace, we can no longer find the original element *without* this namespace prefix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "29232d8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "print(tree.getroot().find('title'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de74ccb4",
   "metadata": {},
   "source": [
    "To reduce search query clutter, <span class=\"index\">lxml</span> allows specifying namespaces as an argument to its search functions, such as `find()` and `xpath()`. By providing a namespace map (of type `dict`), consisting of self-defined prefixes (keys) and corresponding namespaces (values), search queries can be simplified to `prefix:tag`, where \"prefix\" refers to a namespace's key and \"tag\" to a particular tag name. The following example illustrates the use of these namespace maps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ceb1294e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Othello"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "NSMAP = {'tei': 'http://www.tei-c.org/ns/1.0'}\n",
    "print(tree.getroot().find('.//tei:title', namespaces=NSMAP).text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66cdf0b2",
   "metadata": {},
   "source": [
    "(sec-getting-data-html)=\n",
    "## HTML\n",
    "\n",
    "In this section we will briefly discuss how to parse and process <span class=\"index\">HTML</span>, which is another common source of data for digital text analysis. <span class=\"index\">HTML</span>, which is an abbreviation for \"HyperText Markup Language\", is the standard markup language for the web. HTML is often considered a \"cousin\" of XML, because both markup languages have developed from a common ancestor (SGML), which has largely grown out of fashion nowadays. Historically, an attractive and innovative feature of HTML was that it could support hypertext: documents and files that are linked by so-called directly referenced links, now more commonly known as hyperlinks. HTML documents may contain valuable data for digital text analysis, yet due to a lack of very strict formatting standards and poorly designed websites, these data are often hard to reach.\n",
    "\n",
    "To unlock these data, this section will introduce another third-party library called \"[BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/)\", which is one of the most popular Python packages for parsing, manipulating, navigating, searching, and, most importantly, pulling data out of HTML files. This chapter's introduction of <span class=\"index\">BeautifulSoup</span> will necessarily be short and will not be able to cover all of its functionality. For a comprehensive introduction, we refer the reader to the excellent documentation available from the library's website. Additionally, we should stress that while BeautifulSoup is intuitive and easy to work with, data from the web are typically extremely noisy and therefore challenging to process.\n",
    "\n",
    "The following code block displays a simplified fragment of HTML from Shakespeare's \"<span class=\"index\">Henry IV</span>\":\n",
    "\n",
    "```html\n",
    "<html>\n",
    "  <head>\n",
    "    <title>Henry IV, Part I</title>\n",
    "  </head>\n",
    "  <body>\n",
    "    <div>\n",
    "      <p class=\"speaker\">KING</p>\n",
    "      <p id=\"line-1.1.1\">\n",
    "        <a id=\"ftln-0001\">FTLN 0001</a>\n",
    "        So shaken as we are, so wan with care,\n",
    "      </p>\n",
    "      <p id=\"line-1.1.2\">\n",
    "        <a id=\"ftln-0002\">FTLN 0002</a>\n",
    "        Find we a time for frighted peace to pant\n",
    "      </p>\n",
    "      <p id=\"line-1.1.3\">\n",
    "        <a id=\"ftln-0003\">FTLN 0003</a>\n",
    "        And breathe short-winded accents of new broils\n",
    "      </p>\n",
    "      <p id=\"line-1.1.4\">\n",
    "        <a id=\"ftln-0004\">FTLN 0004</a>\n",
    "        To be commenced in strands afar remote.\n",
    "      </p>\n",
    "    </div>\n",
    "  </body>\n",
    "</html>\n",
    "```\n",
    "\n",
    "Essentially, and just like XML, HTML consists of tags and content. Tags are element names surrounded by angle brackets (e.g., `<html>`), and normally come in pairs, i.e., `<html>` and `</html>` where the first tag functions as the start tag and the second as the end tag. Note that the end tag adds a forward slash before the tag name. The `<html>` element forms the root of the HTML document and surrounds all children elements. `<head>` elements provide meta information about a particular document. In this fragment, the `<head>` provides information about the document's title, which is enclosed with `<title>Henry IV, Part I</title>`. The body of an HTML document provides all content visible on a rendered webpage. Here, the body consists of a single `div` element, which is commonly used to indicate a division or section in HTML files. The `div` element has five children, all `<p>` elements defining paragraphs. The `<p>` elements provide additional meta information through their use of attributes: the first paragraph is of the class \"speaker\"; the remaining paragraphs have explicit IDs assigned to them (e.g., `id=\"line-1.1.4\"`). The `<a>` tags inside the paragraph elements define hyperlinks, which can be used to navigate from one page to another or to different positions on a page.\n",
    "\n",
    "To parse this fragment into a Python object, we employ BeautifulSoup's main workhorse class, `BeautifulSoup`, with which HTML documents can be represented as nested data structures in Python:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5f73bc32",
   "metadata": {},
   "outputs": [],
   "source": [
    "import bs4 as bs\n",
    "\n",
    "html_doc = \"\"\"\n",
    "<html>\n",
    "  <head>\n",
    "    <title>Henry IV, Part I</title>\n",
    "  </head>\n",
    "  <body>\n",
    "    <div>\n",
    "      <p class=\"speaker\">KING</p>\n",
    "      <p id=\"line-1.1.1\">\n",
    "        <a id=\"ftln-0001\">FTLN 0001</a>\n",
    "        So shaken as we are, so wan with care,\n",
    "      </p>\n",
    "      <p id=\"line-1.1.2\">\n",
    "        <a id=\"ftln-0002\">FTLN 0002</a>\n",
    "        Find we a time for frighted peace to pant\n",
    "      </p>\n",
    "      <p id=\"line-1.1.3\">\n",
    "        <a id=\"ftln-0003\">FTLN 0003</a>\n",
    "        And breathe short-winded accents of new broils\n",
    "      </p>\n",
    "      <p id=\"line-1.1.4\">\n",
    "        <a id=\"ftln-0004\">FTLN 0004</a>\n",
    "        To be commenced in strands afar remote.\n",
    "      </p>\n",
    "    </div>\n",
    "  </body>\n",
    "</html>\n",
    "\"\"\"\n",
    "\n",
    "html = bs.BeautifulSoup(html_doc, 'html.parser')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59d96b4f",
   "metadata": {},
   "source": [
    "After parsing the document, the `BeautifulSoup` object provides various ways to navigate or search the data structure. Some common navigation and searching operations are illustrated in the following code blocks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6887d2b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<title>Henry IV, Part I</title>\n"
     ]
    }
   ],
   "source": [
    "# print the document's <title> (from head)\n",
    "print(html.title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4cd37848",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<p class=\"speaker\">KING</p>\n"
     ]
    }
   ],
   "source": [
    "# print the first <p> element and its content\n",
    "print(html.p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6d57381c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Henry IV, Part I\n"
     ]
    }
   ],
   "source": [
    "# print the text of a particular element, e.g. the <title>\n",
    "print(html.title.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "be6cd561",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<div>\n",
      "<p class=\"speaker\">KING</p>\n",
      "<p id=\"line-1.1.1\">\n",
      "<a id=\"ftln-0001\">FTLN 0001</a>\n",
      "        So shaken as we are, so wan with care,\n",
      "      </p>\n",
      "<p id=\"line-1.1.2\">\n",
      "<a id=\"ftln-0002\">FTLN 0002</a>\n",
      "        Find we a time for frighted peace to pant\n",
      "      </p>\n",
      "<p id=\"line-1.1.3\">\n",
      "<a id=\"ftln-0003\">FTLN 0003</a>\n",
      "        And breathe short-winded accents of new broils\n",
      "      </p>\n",
      "<p id=\"line-1.1.4\">\n",
      "<a id=\"ftln-0004\">FTLN 0004</a>\n",
      "        To be commenced in strands afar remote.\n",
      "      </p>\n",
      "</div>\n"
     ]
    }
   ],
   "source": [
    "# print the parent tag (and its content) of the first <p> element\n",
    "print(html.p.parent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "9b6dadf1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "div\n"
     ]
    }
   ],
   "source": [
    "# print the parent tag name of the first <p> element\n",
    "print(html.p.parent.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f1ce9e27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<a id=\"ftln-0001\">FTLN 0001</a>, <a id=\"ftln-0002\">FTLN 0002</a>, <a id=\"ftln-0003\">FTLN 0003</a>, <a id=\"ftln-0004\">FTLN 0004</a>]\n"
     ]
    }
   ],
   "source": [
    "# find all occurrences of the <a> element\n",
    "print(html.find_all('a'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "9dbc4d7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<p id=\"line-1.1.3\">\n",
      "<a id=\"ftln-0003\">FTLN 0003</a>\n",
      "        And breathe short-winded accents of new broils\n",
      "      </p>\n"
     ]
    }
   ],
   "source": [
    "# find a <p> element with a specific ID\n",
    "print(html.find('p', id='line-1.1.3'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b820653",
   "metadata": {},
   "source": [
    "The examples above demonstrate the ease with which HTML documents can be navigated and manipulated with the help of BeautifulSoup. A common task in digital text analysis is extracting all displayed text from a webpage. In what follows, we will implement a simple utility function to convert HTML documents from the Folger Digital Texts into plain text. The core of this function lies in the method <span class=\"index\">`BeautifulSoup.get_text()`</span>, which retrieves all textual content from an HTML document. Consider the following code block, which implements a function to convert HTML documents into a string:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "400e9ff3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def html2txt(fpath):\n",
    "    \"\"\"Convert text from a HTML file into a string.\n",
    "\n",
    "    Arguments:\n",
    "        fpath: a string pointing to the filename of the HTML file\n",
    "\n",
    "    Returns:\n",
    "        A string representing the text extracted from the supplied\n",
    "        HTML file.\n",
    "\n",
    "    \"\"\"\n",
    "    with open(fpath) as f:\n",
    "        html = bs.BeautifulSoup(f, 'html.parser')\n",
    "    return html.get_text()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "dbe04e7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Henry V, Romeo and Juliet, and others. Editors choose which version to use as their base text, and then amend that text with words, lines or speech prefixes from the other versions that, in their judgment, make for a better or more accurate text.\n",
      "Other editorial decisions involve choices about whether an unfamiliar word could be understood in light of other writings of the period or whether it should be changed; decisions about words that made it into Shakespeare’s text by accident through four \n"
     ]
    }
   ],
   "source": [
    "fp = 'data/folger/html/1H4.html'\n",
    "text = html2txt(fp)\n",
    "start = text.find('Henry V')\n",
    "print(text[start:start + 500])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "946f036e",
   "metadata": {},
   "source": [
    "While convenient, this function merely acts like a wrapper around existing functionality of BeautifulSoup. The function would be more interesting if it could be used to extract specific components from the documents, such as acts or scenes. Moreover, accessing such structured information will prove crucial in section {ref}`sec-getting-data-character-interaction-networks`, where we will attempt to extract character interactions from Shakespeare's plays.\n",
    "\n",
    "In what follows, we will enhance the function by exploiting the hypertext markup of the Folger Digital Texts, which enables us to locate and extract specific data components. Each text in the collection contains a table of contents with hyperlinks to the acts and scenes. These tables of contents are formatted as HTML tables in which each section is represented by a row (`<tr>`), acts by table data elements (`<td>`) with the class attribute `act`, and scenes by list elements (`<li>`) with `class=\"scene\"`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "81cf6175",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(fp) as f:\n",
    "    html = bs.BeautifulSoup(f, 'html.parser')\n",
    "toc = html.find('table', attrs={'class': 'contents'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef29751c",
   "metadata": {},
   "source": [
    "Extracting the hypertext references (`href`) from these tables enables us to locate their corresponding elements in the HTML documents. The function <span class=\"index\">`toc_hrefs()`</span> below implements a procedure to retrieve such a list of hypertext references. To do so, it first iterates the table rows (`tr`), then the table data (`td`) elements, and, finally, the `a` tags containing the hypertext references:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "9903b41e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def toc_hrefs(html):\n",
    "    \"\"\"Return a list of hrefs from a document's table of contents.\"\"\"\n",
    "    toc = html.find('table', attrs={'class': 'contents'})\n",
    "    hrefs = []\n",
    "    for tr in toc.find_all('tr'):\n",
    "        for td in tr.find_all('td'):\n",
    "            for a in td.find_all('a'):\n",
    "                hrefs.append(a.get('href'))\n",
    "    return hrefs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2051f3b1",
   "metadata": {},
   "source": [
    "Testing this function on one of the documents in the Folger Digital Texts shows that the function behaves as expected:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "38890d77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['#FromTheDirector', '#TextualIntroduction', '#synopsis', '#characters', '#line-1.1.0']\n"
     ]
    }
   ],
   "source": [
    "items = toc_hrefs(html)\n",
    "print(items[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a032cfbb",
   "metadata": {},
   "source": [
    "The next step consists of finding the elements in the HTML document corresponding to the list of extracted hrefs. The hrefs can refer to either `div` or `a` elements. The function <span class=\"index\">`get_href_div()`</span> aims to locate the `div` element corresponding to a particular href by searching for either the `div` element of which the id is equal to the href or the `a` element of which the name is equal to the href. In the latter case, we still need to locate the relevant `div` element. This is accomplished by finding the next `div` element relative to the extracted `a` element:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "060b7ccb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_href_div(html, href):\n",
    "    \"\"\"Retrieve the <div> element corresponding to the given href.\"\"\"\n",
    "    href = href.lstrip('#')\n",
    "    div = html.find('div', attrs={'id': href})\n",
    "    if div is None:\n",
    "        div = html.find('a', attrs={'name': href}).findNext('div')\n",
    "    return div"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82520f5d",
   "metadata": {},
   "source": [
    "All that remains is enhancing our previous implementation of <span class=\"index\">`html2txt()`</span> with functionality to retrieve the actual texts corresponding to the list of extracted hrefs. Here, we employ a \"<span class=\"index\">list comprehension</span>\" which (i) iterates over all hrefs extracted with `toc_hrefs()`, (ii) retrieves the `div` element corresponding to a particular href, and (iii) retrieves the `div`'s actual text by calling the method `get_text()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c6755e15",
   "metadata": {},
   "outputs": [],
   "source": [
    "def html2txt(fname, concatenate=False):\n",
    "    \"\"\"Convert text from a HTML file into a string or sequence of strings.\n",
    "\n",
    "    Arguments:\n",
    "        fpath: a string pointing to the filename of the HTML file.\n",
    "        concatenate: a boolean indicating whether to concatenate the\n",
    "            extracted texts into a single string. If False, a list of\n",
    "            strings representing the individual sections is returned.\n",
    "\n",
    "    Returns:\n",
    "        A string or list of strings representing the text extracted\n",
    "        from the supplied HTML file.\n",
    "\n",
    "    \"\"\"\n",
    "    with open(fname) as f:\n",
    "        html = bs.BeautifulSoup(f, 'html.parser')\n",
    "    # Use a concise list comprehension to create the list of texts.\n",
    "    # The same list could be constructed using an ordinary for-loop:\n",
    "    #    texts = []\n",
    "    #    for href in toc_hrefs(html):\n",
    "    #        text = get_href_div(html, href).get_text()\n",
    "    #        texts.append(text)\n",
    "    texts = [get_href_div(html, href).get_text() for href in toc_hrefs(html)]\n",
    "    return '\\n'.join(texts) if concatenate else texts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12c662d0",
   "metadata": {},
   "source": [
    "To conclude this brief introduction about parsing HTML with Python and BeautifulSoup, we demonstrate how to call the `html2txt()` function on one of Shakespeare's plays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "6c2fce78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Scene 3\n",
      "\n",
      " Enter the King, Northumberland, Worcester, Hotspur,\n",
      "and Sir Walter Blunt, with others.\n",
      "\n",
      "KING , to Northumberland, Worcester, and Hotspur \n",
      " FTLN 0332 My blood hath been too cold and tempera\n"
     ]
    }
   ],
   "source": [
    "texts = html2txt(fp)\n",
    "print(texts[6][:200])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "addf21a9",
   "metadata": {},
   "source": [
    "(sec-getting-data-scraping-html)=\n",
    "### Retrieving HTML from the web\n",
    "\n",
    "```{margin}\n",
    "For a detailed account of web scraping with Python, see {cite:t}`mitchell:2015`.\n",
    "```\n",
    "So far, we have been working with data stored on our local machines, but HTML-encoded data is of course typically harvested from the web, through downloading it from remote servers. Although <span class=\"index\">web scraping</span> is not a major focus of this book, it is useful to know that Python is very suitable for querying the web. Downloading HTML content from webpages is straightforward, for instance, using a dedicated function from Python's standard library <span class=\"index\">`urllib`</span>:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "91dcac0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import urllib.request\n",
    "\n",
    "page = urllib.request.urlopen('https://en.wikipedia.org/wiki/William_Shakespeare')\n",
    "html = page.read()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa9fc073",
   "metadata": {},
   "source": [
    "We first establish a connection (`page`) to the webpage using the <span class=\"index\">`request.urlopen()`</span> function, to which we pass the address of Wikipedia's English-language page on William Shakespeare. We can then extract the page content as a string by calling the method `read()`. To extract the text from this page, we can apply <span class=\"index\">BeautifulSoup</span> again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "0a3ad057",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "William Shakespeare - Wikipedia\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "William Shakespeare\n",
      "\n",
      "From Wikipedia, the free encyclopedia\n",
      "\n",
      "\n",
      "\n",
      "Jump to navigation\n",
      "Jump to search\n",
      "English poet, playwright, and actor (1564–1616)\n",
      "\"Shakespeare\" redirects here. For other uses, see Shakespeare (disambiguation) a\n"
     ]
    }
   ],
   "source": [
    "import bs4\n",
    "\n",
    "soup = bs4.BeautifulSoup(html, 'html.parser')\n",
    "print(soup.get_text().strip()[:300])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddb6077b",
   "metadata": {},
   "source": [
    "Unfortunately, we see that not only text, but also some JavaScript leaks through in the extracted text, which is not interesting for us here. To explicitly remove such JavaScript or style-related code from our result too, we could first throw out the `script` (and also inline `style`) elements altogether, and extract the text again, followed by a few cosmetic operations to remove multiple linebreaks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "0f833d60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "William Shakespeare - Wikipedia\n",
      "William Shakespeare\n",
      "From Wikipedia, the free encyclopedia\n",
      "Jump to navigation\n",
      "Jump to search\n",
      "English poet, playwright, and actor (1564–1616)\n",
      "\"Shakespeare\" redirects here. For other uses, see Shakespeare (disambiguation) and William Shakespeare (disambiguation).\n",
      "Willia\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "for script in soup(['script', 'style']):\n",
    "    script.extract()\n",
    "text = soup.get_text()\n",
    "text = re.sub('\\s*\\n+\\s*', '\\n', text)  # remove multiple linebreaks:\n",
    "print(text[:300])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54b6d65e",
   "metadata": {},
   "source": [
    "Following a similar strategy as before, we extract all hyperlinks from the retrieved webpage:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "7c5f8fd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<a href=\"/wiki/Chandos_portrait\" title=\"Chandos portrait\">\n",
      " Chandos portrait\n",
      "</a>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "links = soup.find_all('a')\n",
    "print(links[9].prettify())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6ec1469",
   "metadata": {},
   "source": [
    "The extracted links contain both links to external pages, as well as links pointing to other sections on the same page (which lack an `href` attribute). Such links between webpages are crucial on the world wide web, which should be viewed as an intricate network of linked pages. Networks offer a fascinating way to model information in an innovative fashion and lie at the heart of the next section of this chapter.\n",
    "\n",
    "(sec-getting-data-character-interaction-networks)=\n",
    "## Extracting Character Interaction Networks\n",
    "\n",
    "The previous sections in this chapter have consisted of a somewhat tedious listing of various common file formats that can be useful in the context of storing and exchanging data for quantitative analyses in the humanities. Now it is time to move beyond the kind of simple tasks presented above and make clear how we can use such data formats in an actual application. As announced in the introduction we will work below with the case study of a famous character network analysis of *Hamlet*.\n",
    "\n",
    "The relationship between fictional characters in literary works can be conceptualized as <span class=\"index\">social network</span>s. In recent years, the computational analysis of such fictional social networks has steadily gained popularity. Network analysis can contribute to the study of literary fiction by formally mapping character relations in individual works. More interestingly, however, is when network analysis is applied to larger collections of works, revealing the abstract and general patterns and structure of character networks.\n",
    "\n",
    "Studying relations between speakers is of central concern in much research about dramatic works (see, e.g., {cite:t}`Ubersfeld:1999`). One example which is well-known in literary studies and which inspires this chapter is the analysis of *Hamlet* in {cite:t}`moretti:2011`. In the field of computational linguistics, advances have been made in recent years, with research focusing on, for instance, social network analyses of nineteenth-century fiction {cite:p}`elson:2010`, *Alice in Wonderland* {cite:p}`agarwal:2013`, Marvel graphic novels {cite:p}`alberich2002marvel`, or love relationships in French classical drama {cite:p}`karsdorp:2015`.\n",
    "\n",
    "```{margin}\n",
    "See {cite:t}`newman:2010` for an excellent and comprehensive introduction to network theory.\n",
    "```\n",
    "Before describing in more detail what kind of networks we will create from Shakespeare's plays, we will introduce the general concept of networks in a slightly more formal way. In <span class=\"index\">network theory</span>, networks consist of <span class=\"index\">*nodes*</span> (sometimes called <span class=\"index\">*vertices*</span>) and <span class=\"index\">*edges*</span> connecting pairs of nodes. Consider the following sets of nodes ($V$) and edges ($E$): $V = \\{1, 2, 3, 4, 5\\}$, $E = \\{1 \\leftrightarrow 2, 1 \\leftrightarrow 4, 2 \\leftrightarrow 5, 3 \\leftrightarrow 4, 4 \\leftrightarrow 5\\}$. The notation $1 \\leftrightarrow 2$ means that node 1 and 2 are connected through an edge. A network $G$, then, is defined as the combination of nodes $V$ and edges $E$, i.e., $G = (V, E)$. In Python, we can define these sets of vertices and edges as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "1699f7cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "V = {1, 2, 3, 4, 5}\n",
    "E = {(1, 2), (1, 4), (2, 5), (3, 4), (4, 5)}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aea867e8",
   "metadata": {},
   "source": [
    "In this case, one would speak of an \"undirected\" <span class=\"index\">network</span>, because the edges lack directionality, and the nodes in such a pair reciprocally point to each other. By contrast, a directed network consists of edges pointing in a single direction, as is often the case with links on webpages.\n",
    "\n",
    "To construct an actual network from these sets, we will employ the third-party package [<span class=\"index\">NetworkX</span>](https://networkx.github.io/), which is an intuitive Python library for creating, manipulating, visualizing, and studying the structure of networks. Consider the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1e27ea5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "\n",
    "G = nx.Graph()\n",
    "G.add_nodes_from(V)\n",
    "G.add_edges_from(E)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30f827cd",
   "metadata": {},
   "source": [
    "After construction, the network $G$ can be visualized with Matplotlib using <span\n",
    "class=\"index\">`networkx.draw_networkx()`</span>:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "765cc106",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: \"Roboto Condensed Regular\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/getting-data/notebook_135_2.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "nx.draw_networkx(G, font_color=\"white\")\n",
    "plt.axis('off');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6521baa3",
   "metadata": {},
   "source": [
    "<!-- Figure: Visualization of a toy network consisting of five nodes and five edges.\\label{fig:getting-data:example-network} -->\n",
    "\n",
    "Having a rudimentary understanding of networks, let us now define <span class=\"index\">social network</span>s in the context of literary texts. In the networks we will extract from Shakespeare's plays, nodes are represented by speakers. What determines a connection (i.e., an edge) between two speakers is less straightforward and strongly dependent on the sort of relationship one wishes to capture. Here, we construct edges between two speakers if they are \"in interaction with each other\". Two speakers $A$ and $B$ interact, we claim, if an utterance of $A$ is preceded or followed by an utterance of $B$.\n",
    "\n",
    "```{note}\n",
    "Our approach here diverges from {cite:t}`moretti:2011`'s own approach in which he manually extracted these interactions, whereas we follow a fully automated approach. For Moretti, \"two characters are linked if some words have passed between them: an interaction, is a speech act\" {cite:p}`moretti:2011`.\n",
    "```\n",
    "\n",
    "Furthermore, in order to track the frequency of character interactions, each of the edges in our approach will hold a count representing the number of times two speakers have interacted. This number thus becomes a so-called attribute or property of the edge that has to be explicitly stored. The final result can then be described as a <span class=\"index\">network</span> in which speakers are represented as nodes, and interactions between speakers are represented as weighted edges. Having defined the type of social network we aim to construct, the real challenge we face is to extract such networks from Shakespeare's plays in a data format that can be easily exchanged.\n",
    "\n",
    "Fortunately, the Folger Digital Texts of Shakespeare provides annotations for\n",
    "speaker turns, which give a rich information source that can be useful in the construction\n",
    "of the character network. Now that we are able to parse XML, we can extract speaker turns\n",
    "from the data files: the speaker turns and the entailing text uttered by a speaker are enclosed within `sp` tags. The ID of its corresponding speaker is stored in the `who` attribute. Consider the following fragment:\n",
    "\n",
    "```xml\n",
    "<sp xml:id=\"sp-0200\" who=\"#Rosalind_AYL\">\n",
    "  <speaker xml:id=\"spk-0200\">\n",
    "    <w xml:id=\"w0035750\">ROSALIND</w>\n",
    "  </speaker>\n",
    "  <ab xml:id=\"ab-0200\">\n",
    "    <w xml:id=\"w0035760\" n=\"1.2.30\">What</w>\n",
    "    <c xml:id=\"c0035770\" n=\"1.2.30\"> </c>\n",
    "    <w xml:id=\"w0035780\" n=\"1.2.30\">shall</w>\n",
    "    <c xml:id=\"c0035790\" n=\"1.2.30\"> </c>\n",
    "    <w xml:id=\"w0035800\" n=\"1.2.30\">be</w>\n",
    "    <c xml:id=\"c0035810\" n=\"1.2.30\"> </c>\n",
    "    <w xml:id=\"w0035820\" n=\"1.2.30\">our</w>\n",
    "    <c xml:id=\"c0035830\" n=\"1.2.30\"> </c>\n",
    "    <w xml:id=\"w0035840\" n=\"1.2.30\">sport</w>\n",
    "    <pc xml:id=\"p0035850\" n=\"1.2.30\">,</pc>\n",
    "    <c xml:id=\"c0035860\" n=\"1.2.30\"> </c>\n",
    "    <w xml:id=\"w0035870\" n=\"1.2.30\">then</w>\n",
    "    <pc xml:id=\"p0035880\" n=\"1.2.30\">?</pc>\n",
    "  </ab>\n",
    "</sp>\n",
    "```\n",
    "\n",
    "With this information about speaker turns, implementing a function to extract character interaction networks becomes trivial. Consider the function `character_network()` below, which takes as argument a `lxml.ElementTree` object and returns a character network represented as a <span class=\"index\">`networkx.Graph`</span> object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "d950eab5",
   "metadata": {},
   "outputs": [],
   "source": [
    "NSMAP = {'tei': 'http://www.tei-c.org/ns/1.0'}\n",
    "\n",
    "\n",
    "def character_network(tree):\n",
    "    \"\"\"Construct a character interaction network.\n",
    "\n",
    "    Construct a character interaction network for Shakespeare texts in\n",
    "    the Folger Digital Texts collection. Character interaction networks\n",
    "    are constructed on the basis of successive speaker turns in the texts,\n",
    "    and edges between speakers are created when their utterances follow\n",
    "    one another.\n",
    "\n",
    "    Arguments:\n",
    "        tree: An lxml.ElementTree instance representing one of the XML\n",
    "            files in the Folger Shakespeare collection.\n",
    "\n",
    "    Returns:\n",
    "        A character interaction network represented as a weighted,\n",
    "        undirected NetworkX Graph.\n",
    "\n",
    "    \"\"\"\n",
    "    G = nx.Graph()\n",
    "    # extract a list of speaker turns for each scene in a play\n",
    "    for scene in tree.iterfind('.//tei:div2[@type=\"scene\"]', NSMAP):\n",
    "        speakers = scene.findall('.//tei:sp', NSMAP)\n",
    "        # iterate over the sequence of speaker turns...\n",
    "        for i in range(len(speakers) - 1):\n",
    "            # ... and extract pairs of adjacent speakers\n",
    "            try:\n",
    "                speaker_i = speakers[i].attrib['who'].split('_')[0].replace('#', '')\n",
    "                speaker_j = speakers[i + 1].attrib['who'].split('_')[0].replace('#', '')\n",
    "                # if the interaction between two speakers has already\n",
    "                # been attested, update their interaction count\n",
    "                if G.has_edge(speaker_i, speaker_j):\n",
    "                    G[speaker_i][speaker_j]['weight'] += 1\n",
    "                # else add an edge between speaker i and j to the graph\n",
    "                else:\n",
    "                    G.add_edge(speaker_i, speaker_j, weight=1)\n",
    "            except KeyError:\n",
    "                continue\n",
    "    return G"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c375da40",
   "metadata": {},
   "source": [
    "Note that this code employs search expressions in the <span class=\"index\">XPath</span> syntax. The expression we pass to `tree.iterfind()`, for instance, uses a so-called predicate (`[@type=\"scene\"]`) to select all `div2` elements that have a `\"type\"` attribute with a value of `\"scene\"`. In the returned part of the XML tree, we then only select the speaker elements (`sp`) and parse their `who` attribute, to help us reconstruct, or at least approximate, the conversations which are going on in this part of the play.\n",
    "\n",
    "Let's test the function on one of Shakespeare's plays, <span class=\"index\">*Hamlet*</span>:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "ad727b84",
   "metadata": {},
   "outputs": [],
   "source": [
    "tree = lxml.etree.parse('data/folger/xml/Ham.xml')\n",
    "G = character_network(tree.getroot())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c81c732d",
   "metadata": {},
   "source": [
    "The extracted social network consists of 38 nodes (i.e. unique speakers) and 73 edges (i.e., unique <span class=\"index\">speaker interactions</span>):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "d5008b2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N nodes = 38, N edges = 73\n"
     ]
    }
   ],
   "source": [
    "print(f\"N nodes = {G.number_of_nodes()}, N edges = {G.number_of_edges()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f022a7a",
   "metadata": {},
   "source": [
    "An attractive feature of network analysis is to visualize the extracted network. The visualization will be a graph in which speakers are represented by nodes and interactions between speakers by edges. To make our network graph more insightful, we will have the size of the nodes reflect the count of the interactions. We begin with extracting and computing the node sizes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "d0d8a8bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "\n",
    "interactions = collections.Counter()\n",
    "\n",
    "for speaker_i, speaker_j, data in G.edges(data=True):\n",
    "    interaction_count = data['weight']\n",
    "    interactions[speaker_i] += interaction_count\n",
    "    interactions[speaker_j] += interaction_count\n",
    "\n",
    "nodesizes = [interactions[speaker] * 5 for speaker in G]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dc34b04",
   "metadata": {},
   "source": [
    "In the code block above, we again make use of a <span class=\"index\">`Counter`</span>,\n",
    "which, as explained before, is a dictionary in which the values represent the counts of\n",
    "the keys. Next, we employ NetworkX's plotting functionality to create the visualization of\n",
    "the character network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "b67d636e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: \"Roboto Condensed Regular\"\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 3000x3000 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/getting-data/notebook_145_2.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create an empty figure of size 15x15\n",
    "fig = plt.figure(figsize=(15, 15))\n",
    "# Compute the positions of the nodes using the spring layout algorithm\n",
    "pos = nx.spring_layout(G, k=0.5, iterations=200)\n",
    "# Then, add the edges to the visualization\n",
    "nx.draw_networkx_edges(G, pos, alpha=0.4)\n",
    "# Subsequently, add the weighted nodes to the visualization\n",
    "nx.draw_networkx_nodes(G, pos, node_size=nodesizes, alpha=0.4)\n",
    "# Finally, add the labels (i.e. the speaker IDs) to the visualization\n",
    "nx.draw_networkx_labels(G, pos, font_size=14)\n",
    "plt.axis('off');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c85ddf01",
   "metadata": {},
   "source": [
    "<!-- Figure: Visualization of the character interaction network in *Hamlet*.\\label{fig:getting-data:character-network} -->\n",
    "\n",
    "As becomes clear in the resulting plot, <span class=\"index\">NetworkX</span> is able to come up with an attractive visualization through the use of a so-called <span class=\"index\">layout algorithm</span> (here, we fairly randomly opt for the `spring_layout`, but there exist many alternative layout strategies). The resulting plot understandably assigns Hamlet a central position in the plot, because of his obvious centrality in the social story-world evoked in the play. Less central characters are likewise pushed towards the boundaries of the graph. If we want to de-emphasize the frequency of interaction and focus instead on the fact of interaction, we can remove the edge weights altogether from our links, because these did not play an explicit role in Moretti's graph. Below we make a copy (`G0`) of the original graph and set all of its weights to 1, before replotting the network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "b1dcec70",
   "metadata": {},
   "outputs": [
    {
     "data": {
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OnfJ5Y6QzGTBggBYsWGCMqJOaRmEsWrSo03WAgv/prp2Umncs88cipdzcXOO/Z48ePTRp0iRzAwEAuqXy8nItXrxYxcXFkiSbzabMzExdf/31JicDAKBrokgJAAAAAACgA4WEhMhms0m6cpGSzWZTRkaGsd60aZNqamraNd/VCgsL0y233KLJkycbY+rOnTunRYsWac+ePZ2uCxT8R1hYmDEKrayszOQ0bcdbpGSxWJSYmGhymo5VUFCgAwcOSJLsdrtmzZpl/F4FAKCtnDx5UosXL5bD4ZDU9Dh+7ty5GjhwoMnJAADouihSAgAAAAAA6EAWi0Xh4eGSJKfTecXjU1JSNHjwYElSfX29NmzY0K75roXFYtHo0aN15513KjY2VpLU2Nio9evXa/ny5aqurjY5IfyVt5tSZWWlXC6XuWHaQE1NjdFFKD4+3ijC8gcVFRXKzs421hkZGUbHOQAA2srevXu1fPly1dfXS2rq2jd//nwlJCSYnAwAgK6NIiUAAAAAAIAO5h351tDQoNra2iseP2nSJAUFBUlq6iBy8uTJds13rWJjYzV//nyNHj3a2Hfy5Em98847Onr0qInJ4K+aj3yrqKgwL0gb8Y6ckfxr1FtjY6NWrlyphoYGSdKQIUOMIk4AANqC2+1WTk6OcnNzjU6g/fr10+2332580AAAAFw9ipQAAAAAAAA6WPM3OFrSTSkkJERpaWnGOicnR42Nje2Sra3YbDZNnjxZt956q0JDQyVJdXV1+uSTT7RmzRrjU+lAR2hepOTtQNSVeUe9Sf5VpLRx40adPXtWUtN/0/T0dJMTAQC6k/r6en300Ufat2+fsW/s2LG68cYb/aprIQAA7YkiJQAAAAAAgA7m7aQkNY2faomhQ4cqKSlJkuRwOLR9+/Z2ydbWevbsqbvvvlsDBgww9h08eFDvvvuuSkpKTEwGfxITE2Nsd6ciJYvFosTERJPTdIxjx45p7969kpqKIGfNmiW73W5yKgBAd+FwOLR48WKjY6nVatX06dOVlpYmi8VicjoAALoPipQAAAAAAAA62NUUKVksFmVkZMhqbXo5Z8eOHV2m2CIoKEizZs3S9OnTjU+hV1ZWaunSpcrLy5Pb7TY5Ibq77tRJqaamxrgP8fHxftHZwel0as2aNcZ6ypQpio2NNS8QAKBbKSoq0qJFi4y/r8HBwbrttts0ZMgQc4MBANANUaQEAAAAAADQwa6mSElq6gYzZswYSZLb7VZ2dnabZ2tPQ4YM0YIFC4yOUB6PR9u2bdOSJUtUUVFhcjp0Z5GRkUaBX1lZmclprk1xcbGx7Q+j3txut1auXGmMiBwwYICGDx9ucioAQHeRn5+vDz/8UHV1dZKaHm/PmzfPeLwKAADaFkVKAAAAAAAAHexqi5QkKTU11bh9cXGxDh482KbZ2ltERIRuu+02TZgwwSgaOXPmjN59913t37/f5HTorqxWqyIjIyVJFRUV8ng8Jie6et5Rb5J/FCnl5eXp9OnTkpp+f9xwww0mJwIAdAcej0cbN27UunXrjK6evXv31h133GE8ZgAAAG2PIiUAAAAAAIAOFhISYhToOJ3OVt3WbrcrPT3dWG/cuFG1tbVtmq+9Wa1WXXfddbrjjjsUFRUlSXK5XMrOztaKFStUU1NjckJ0R96Rb42Nja0uDuxMvEVKFotFiYmJJqdpXydPntSOHTskNf3emDVrlgIDA80NBQDo8hoaGrRixQrt2rXL2Ddq1CjddNNN/J0BAKCdUaQEAAAAAADQwSwWi8LDwyW1vpOS1PQp7wEDBkiSamtrtWnTpjbN11Hi4+N11113acSIEca+wsJCLVy4UMePHzcxGbojb5GSJJWXl5uW41rU1NQY2ePj4xUQEGBuoHZUXV2tVatWGeuJEycqPj7exEQAgO7A6XRqyZIlxmNNi8WijIwMTZkyxfgQAQAAaD/8tQUAAAAAADCBd2RbQ0OD6urqWn37KVOmGJ/0PnDggEpKSto0X0fxdoaaM2eOgoODJTUVYnz00UfKycmRy+UyOSG6i+5QpFRcXGxsd+dRbx6PR6tWrTK6xPXp00djxowxORUAoKs7deqUFi1apHPnzkmSAgMDdcstt2j48OEmJwMAwH9QpAQAAAAAAGACb5GSdHXdlEJDQzVhwgRjvW7dOrnd7jbJZoY+ffro7rvvVp8+fYx9+/bt03vvvafS0lITk6G7iImJMba7apGSd9SbJCUnJ5uYpH1t377duK9hYWGaPn26uYEAAF3eoUOHtHTpUmOscFRUlObNm6eePXuanAwAAP9CkRIAAPj/7N13eFP32T/+t6YlWd57ycZgMCNgwGCW2TtATKBJ0zbpbrrH86R9OtI03btN8/TbX9s0bdI0T9KGYSAQIGybZabZniDZFt5Te/7+MD6xjAHvI5n367p8XfocH51zy8hG9nnrvomIiIhIBIMNKQHApEmThPFHra2tKC4uHpLaxKJWq7F69Wrk5uZCLpcD6Hxc+fn5uHjxIrxer8gVUiALCwsTbgdqSKmrk5JEIkF8fLzI1QyP27dv49y5cwA6H+eyZcuELmtERET95fV6cebMGRw+fFgI9CcmJiIvL8+nyyIRERGNDIaUiIiIiIiIiIhEMBQhJYlEgtzcXEgkEgDA+fPn0d7ePiT1iWnixIl4/PHHhQCWx+NBUVERdu3aNeCvFZFSqURwcDAAoKWlReRq+s9qtQp1x8TEQKFQiFzR0LPZbDh48KAQSJw5c+aoDWMREdHwc7lcOHDgAC5cuCBsmzhxItauXYugoCARKyMiInp4MaRERERERERERCQCrVYr3DaZTAM+TnR0NKZMmQIAcLvdKCwsHHRt/iA8PByPPfYYZsyYIYSwamtrsWXLFpSVlYlcHQWqro4JdrsdNptN3GL6qauLEtDZAWK08Xq9OHLkCCwWC4DOxzh9+nSRqyIiokBlNpuxc+dO3Lx5E0BnuH/evHnIzc2FVMrLo0RERGLh/8JERERERERERCIYik5KXbKzs4UOMdXV1aioqBjU8fyFVCpFdnY21q9fL3y9nE4nDh8+jAMHDsBut4tcIQWa7mNdAq2bktFoFG4nJCSIWMnwuHz5MgwGAwBApVJh6dKlQkCRiIioPxoaGrB9+3Y0NjYCABQKBVavXi0E+4mIiEg8DCkREREREREREYlAo9EI7+IebEhJoVBg/vz5wvrkyZNwOByDOqY/iY+Px6ZNmzB+/HhhW2VlJbZs2YKamhoRK6NAExERIdxubW0Vr5AB6OqkJJFIRt0ItPr6ehQVFQnrpUuXQqPRiFgREREFqsrKSuzatUvozBcSEoK8vDykpKSIXBkREREBDCkREREREREREYlCIpEII98GM+6tS1paGlJTUwEAFovF54L/aKBUKrF48WIsX74cQUFBADrHeOzevRunTp2C2+0WuUIKBN07KQVSSMlqtQqdn2JiYqBQKESuaOg4HA4cPHgQHo8HAJCVlYXk5GSRqyIiokB0/vx5HDhwAC6XC0Bn0H3jxo0+IWUiIiISF0NKREREREREREQi6Rph5nA4hmR02fz58yGXywEA165dQ319/aCP6W/S09OxefNmJCUlCdsuXbqE7du3o7m5WcTKKBAE6ri3ri5KAJCYmChiJUPv2LFjQje5uLg4ZGdni1wREREFGrfbjUOHDuHs2bPCtvHjx+PRRx+FSqUSsTIiIiLqiSElIiIiIiIiIiKRdIWUgMGPfAMArVbrc4G/oKBA6E4ymgQHB2Pt2rWYO3cuZDIZAKC5uRnbtm3D5cuX4fV6Ra6Q/JVGo4FSqQQQWJ2UjEajcDshIUHESobWtWvXUFlZCaCzW9qyZcuEMZhERER9YbFYsGvXLpSXlwvbZs+ejcWLFwuvE4mIiMh/8Dc+IiIiIiIiIiKRdI17A4Zm5BsATJkyBVFRUQCApqYmXLlyZUiO628kEgkeeeQRbNy4EZGRkQAAj8eDkydPYs+ePTCbzSJXSP6qq5uSyWQSxsH4u65OShKJBPHx8SJXMzSam5tx8uRJYb148WKfn4lEREQP0tzcjPz8fKF7qFwux8qVK5GVlSVuYURERHRPDCkREREREREREYlkqDspAYBUKkVubq6wPnv27JAFoPxRZGQkNm7ciKlTpwrbampqsGXLFqFDC1F33Ue+BUI3JavVKoymi4mJgUKhELmiwXM6nThw4ADcbjeAznBlWlqauEUREVFA0ev1yM/PF17nBgcHY8OGDfz/hIiIyM8xpEREREREREREJJLhCCkBQGxsLCZNmgQAcLlcOHHixJAd2x/JZDLMmTMHjz76KIKDgwEAdrsdBw4cwJEjR+BwOESukPxJoIWUurooAaNn1Nvx48eFr310dDRycnLELYiIiAJKcXEx9u3bJ3REjI2NxcaNGxEdHS1yZURERPQgDCkREREREREREYlkOMa9dZk9ezY0Gg0A4NatW7h169aQHt8fJSUlYfPmzUhPTxe2lZaWYuvWraitrRWxMvIngRxSSkxMFLGSoVFWVobS0lIAgEKhwLJlyyCTyUSuioiIAoHH48HRo0dx+vRpYdvYsWOxbt064XUvERER+TeGlIiIiIiIiIiIRBIcHAyptPPPM0PZSQkAlEol5s6dK6xPnDgBp9M5pOfwR0FBQVi+fDmWLFkCpVIJoPNru2vXLpw5cwYej0fkCklsERERwu1ACCkZjUYAgEQiQVxcnMjVDE5raysKCgqEdW5uLsLCwkSsiIiIAoXNZsPu3btRUlIibJs5cyaWLVsGuVwuYmVERETUHwwpERERERERERGJRCKRCN2UhjqkBHS+szw5ORlAZ6emc+fODfk5/FVGRgY2bdqE+Ph4AIDX68WFCxewY8eOgAim0PAJCQkRwoH+/lywWq1oaWkBAMTExAjBu0Dkdrtx4MABYTTPhAkTMG7cOJGrIiKiQNDS0oL8/Hyhu6BMJsOyZcswc+ZMkSsjIiKi/mJIiYiIiIiIiIhIRF0hJYfDAYfDMeTHX7BggTBK6fLly2hqahryc/irkJAQrF+/HrNnzxZCKQ0NDdi2bRuuXbsmcnUkFqlUKnTvaW1t9evuWt1HvSUkJIhYyeCdPHkSzc3NADq7Wc2fP1/kioiIKBBUV1djx44daG9vBwBoNBqsX78eY8eOFbkyIiIiGgiGlIiIiIiIiIiIRBQSEiLcHo5uSqGhoZgxYwaAzm5CBQUF8Hq9Q34efyWRSJCVlYW8vDyEh4cDAFwuFwoLC7F3715YrVZxCyRRdD0XPB7PsHzfDZXuIaXExEQRKxmcmzdvCsHAru4XHM1DREQPcvXqVbz33ntCkD8qKgp5eXmIjY0VuTIiIiIaKIaUiIiIiIiIiIhENNwhJQCYNm2aEMqor6/H9evXh+U8/iw6OhqPP/44Jk2aJGwzGAx45513oNfrRayMxND1/QD498g3o9EIoDNsFxcXJ3I1A9PR0YGjR48K6/nz5yMyMlLEioiIyN95PB4UFhbi+PHjQrg+LS0NGzZsELqQEhERUWBiSImIiIiIiIiISEQjEVKSSqXIzc0V1kVFRbBYLMNyLn8ml8uxYMECrF69Gmq1GgBgs9mwb98+FBQUwOVyiVwhjZSIiAjhtr+GlKxWK1paWgAAMTExUCqVIlfUfx6PBwcPHhQ6YIwdOxaZmZkiV0VERP7Mbrfjvffe8xnNm5WVhRUrVkChUIhYGREREQ0FhpSIiIiIiIiIiETU/d3gJpNp2M6TkJCACRMmAAAcDgdOnjw5bOfydzqdDps3b0Zqaqqw7fr169i6dSsaGhpErIxGSiB0Uuo+6i0hIUHESgbuzJkzqK+vB9A5erJ7WJKIiKintrY27NixAzU1NQA6g/aLFy/G7NmzIZFIRK6OiIiIhgJDSkREREREREREIhqJTkpdcnJyoFKpAAAVFRWorq4e1vP5M7VajVWrViE3NxdyuRzABxfGLly4IIwWodEpLCxMuN3VrcjfdA8pJSYmiljJwFRVVaG4uBhA50XmZcuWBWQ3KCIiGhlGoxH5+flCeFilUmHdunUYP368uIURERHRkGJIiYiIiIiIiIhIRBqNBlJp559ohjukpFKpMGfOHGFdWFj40I84mzhxIjZt2oSYmBgAneOpzpw5g127dg37vweJR6FQCF3M/LWTktFoBABIJBLExcWJXE3/mM1mHD58WFjn5OQI32NEREQ93bhxA3v27IHdbgfQOZZ148aNiI+PF7kyIiIiGmoMKRERERERERERiUgqlSI4OBjA8IeUAGD8+PHC6Kj29nZcuHBh2M/p78LCwvDYY49hxowZwiiR2tpabNmyBaWlpSJXR8Ola+Sbw+GAxWIRt5gerFar0OEpJiYmoDoQeb1eHDp0CDabDQCQmpqKRx55ROSqiIjIH3m9Xpw8eRLHjh2Dx+MBAKSkpOCxxx7z6TZKREREowdDSkREREREREREIuu6CONwOOBwOIb9fLm5uUL3puLiYr8ddzWSpFIpsrOzsWHDBuHfw+l04siRIzhw4IAQuKDRoyukBPhfN6Xuo966QoWB4vz580L9wcHBWLRokcgVERGRP3I4HNi3bx8uX74sbHvkkUewatWqgArnEhERUf8wpEREREREREREJLLu7xQfiW5K4eHhyMrKAtA53qygoABer3fYzxsI4uLisHnzZkyYMEHYVllZiS1btqC6ulrEymioMaQ09IxGI86fPw+gc0zdsmXLoFKpRK6KiIj8TUdHB3bu3AmDwQCgMyyem5uLuXPnCkF6IiIiGp34Pz0RERERERERkchGOqQEAFlZWQgNDQXQOdqMY80+oFAosGjRIqxYsQJBQUEAAIvFgj179uDkyZNwu90iV0hDISIiQrjtryEliUSC+Ph4kavpG5vNhkOHDgmBx+zs7ICpnYiIRk5tbS22b9+O5uZmAEBQUBDWrl2LiRMnilwZERERjQSGlIiIiIiIiIiIRKbVaoXbJpNpRM4pl8uxYMECYX3q1CmONOthzJgx2Lx5M5KTk4Vtly9f9rmwRoHLXzsp2Ww24fkVHR0dECNvvF4vDh8+DIvFAgBISkoSurURERF1KSsrw7vvviu85gwLC0NeXh4SExNFroyIiIhGCkNKREREREREREQiE6OTEgAkJydj3LhxAAC73Y5Tp06N2LkDRXBwMNasWYN58+ZBJpMBAJqbm7Ft2zZcunSJY/ICmFqtFjpltbS0iFzNB7qPeguUi7aXLl1CVVUVgM6v65IlSyCRSESuioiI/IXX60VRUREOHz4Mj8cDoPP/uLy8PISFhYlcHREREY0khpSIiIiIiIiIiEQmVkgJAObMmSN0aiktLYXRaBzR8wcCiUSCKVOmYOPGjYiMjAQAeDwenDp1Crt374bZbBa5Qhqorm5KZrMZTqdT3GLu6P49mJCQIGIlfVNfX48zZ84I66VLl0Kj0YhYERER+ROXy4UDBw7g4sWLwraJEydi7dq1QliYiIiIHh4MKRERERERERERiUyj0QhdR0Zq3Fv3c8+ePVtYFxYWwu12j2gNgSIyMhIbN27E1KlThW1GoxHvvPMOKisr4f3OTAABAABJREFURayMBsofR751dVKSSCSIj48XuZr7s9vtOHDggNAVY/r06UhKShK5KiIi8hdmsxk7d+7EzZs3AXT+3zZv3jzk5uZCKuUlSiIioocRXwEQEREREREREYlMKpVCq9UCGPlOSkDnu9ljY2MBdAY1iouLR7yGQCGTyTBnzhysW7cOwcHBAACHw4EDBw7g8OHDcDgcIldI/eFvISWbzYbm5mYAQHR0tNDlzF8dO3ZMCFbGx8dj5syZIldERET+oqGhAdu3b0djYyMAQKFQYPXq1ZgyZYrIlREREZGYGFIiIiIiIiIiIvIDXSPf7Hb7iAddJBIJFi5cKHRzunDhAtra2ka0hkCTmJiIzZs3Y+zYscK2srIybNmyBbW1tSJWRv0REREh3PaHkFJXFyWg8znmz65duyZ0xggKCsLSpUvZFYOIiAAAlZWV2LlzJywWC4DO17l5eXlISUkRuTIiIiISG39rJCIiIiIiIiLyA12dlICRH/kGdI4ye+SRRwAAbrcbhYWFI15DoAkKCsKyZcuwdOlSoeONyWTCrl27cObMGWEEFvkvf+ukZDQahdsJCQkiVnJ/TU1NOHnypLBevHixz88wIiJ6eJ0/fx4HDhwQxgfHx8dj48aNPsFgIiIiengxpERERERERERE5Ae6OikB4ox8A4Ds7GwhaFBTU4Py8nJR6gg048aNw+bNm4VQidfrxYULF5Cfn+8XwRe6t5CQEKH7T0tLi8jVfNBJSSKRID4+XuRqeud0On0uPk+ZMgWpqakiV0VERGJzuVw4dOgQzp49K2wbP348Hn30UahUKhErIyIiIn/CkBIRERERERERkR/wh5CSXC7H/PnzhfXJkydht9tFqSXQaLVarFu3DrNnzxZCL42Njdi2bRuuXbsmcnV0LxKJROim1N7eLmr3K5vNhubmZgBAdHS00J3L3xQWFgrjIKOjo5GTkyNyRUREJDaLxYJ3333XJ+Cek5ODxYsXQyaTiVgZERER+RuGlIiIiIiIiIiI/IDY4966pKamIi0tDQBgtVpRVFQkWi2BRiKRICsrC3l5eULwxeVyobCwEHv37oXVahW3QOpV17+Vx+NBe3u7aHV0dVEC/HfUW2lpKcrKygAACoUCy5cv58VnIqKHXFNTE/Lz81FfXw+gM/S+cuVKTJs2TeTKiIiIyB8xpERERERERERE5Af8oZNSl/nz50OhUAAArl+/jrq6OlHrCTTR0dF4/PHHMXnyZGGbwWDAO++8A71eL2Jl1JuukBIAUcfzdQ8pJSYmilbHvbS2tqKwsFBYL1y4EKGhoSJWREREYtPr9dixY4cQsNdqtXjssceEwDsRERFRTwwpERERERERERH5geDgYEgkEgDih5SCg4ORnZ0trAsKCkQdgxWIukbnrV69Gmq1GkDnOK99+/ahoKAALpdL5Aqpi7+ElIxGI4DOjlzx8fGi1dEbl8uFAwcOCM/bzMxMjB07VuSqiIhITMXFxdi3b5/wf0NsbCzy8vIQFRUlcmVERETkzxhSIiIiIiIiIiLyA1KpFMHBwQDEDykBwOTJkxEdHQ0AaG5uxuXLl0WuKDDpdDp86EMf8ukocP36dWzdulUYi0LiioiIEG6LFVKy2Wxobm4G0NmJS6lUilLHvZw6dUqoLyIiAvPmzRO5IiIiEovb7caRI0dw+vRpYdvYsWOxbt06aDQaESsjIiKiQMCQEhERERERERGRn+ga+Wa32+F0OkWtRSqVIjc3V1ifO3fOL8JTgUilUmHlypVYuHAh5HI5AKCtrQ07duzA+fPn2aVKZGFhYcJtsUJK3Ue9JSQkiFLDvVRWVuLatWsAOjuELV++XHgeExHRw8Vms2H37t0oLS0VtmVnZ2PZsmX8v4GIiIj6hCElIiIiIiIiIiI/0RVSAvyjm1JMTAwmT54MoHPc0/Hjx0WuKLBlZmZi06ZNiI2NBQB4vV6cPXsWu3btQnt7u8jVPbzkcrnwvdfS0iJKDd1DSomJiaLU0Jv29nYcO3ZMWM+bN8+n8xQRET08WlpasH37dtTW1gIAZDIZli9fjhkzZohcGREREQUShpSIiIiIiIiIiPyEVqsVbptMJhEr+cCsWbOE0R0GgwE3b94UuaLAFhYWhg0bNmDmzJmQSCQAgLq6OmzduhUlJSUiV/fwCg8PBwA4nU5YLJYRP7/RaAQASCQSxMfHj/j5e+PxeHDw4EE4HA4AwLhx45CZmSlyVUREJIaqqirs2LFDCNFrNBps2LAB6enpIldGREREgYYhJSIiIiIiIiIiP+FvnZQAQKlUYt68ecL6+PHjQmiBBkYqlWLmzJnYsGEDQkNDAXSGY44ePYr3338fNptN5AofPl0hJWDkuynZbDY0NzcDAKKjo6FUKkf0/PdSVFSEhoYGAEBoaKjP+EciInp4XLlyBXv37hVe/0VHR2Pjxo2IiYkRuTIiIiIKRAwpERERERERERH5CX8MKQFAeno6dDodAMBiseDs2bMiVzQ6xMXFYdOmTT7daW7evIktW7agurpaxMoePt1DSq2trSN67u6j3hISEkb03PdiMBhw6dIlAJ2huuXLl0OhUIhcFRERjSSPx4PCwkKcOHECXq8XAJCWloYNGzYgODhY5OqIiIgoUDGkRERERERERETkJ/w1pAQA8+fPh1wuBwBcvXoVjY2NIlc0OigUCixcuBArV66ESqUC0BkE27NnD06cOAGXyyVyhQ+HiIgI4baYIaXExMQRPXdvzGYzjhw5IqznzJmD6Oho8QoiIqIRZ7fb8d577+HatWvCtqysLKxYsUJ4PUhEREQ0EAwpERERERERERH5ieDgYEgkEgD+F1IKCQnBjBkzAABerxfHjh0T3lVPg5eWlobNmzcjOTlZ2HblyhVs374dTU1NIlb2cBCzk5LRaAQASCQSxMfHj+i5e/J4PDh48KAwcjAtLQ1TpkwRtSYiIhpZbW1tyM/PR01NDYDOjnpLlizB7NmzhdepRERERAPFkBIRERERERERkZ+QSqXC+AyTySRyNXebOnUqIiMjAQCNjY24evWqyBWNLhqNBmvWrMG8efMgk8kAAC0tLdi+fTsuXbrEUNgwUqlUQierlpaWETuvzWZDc3MzACA6OhpKpXLEzt2b8+fPo7a2FgCg1WqxaNEiUeshIqKRZTQakZ+fj7a2NgCd/z+uW7cOGRkZIldGREREowVDSkREREREREREfqRr5JvNZoPT6RS5Gl9SqRS5ubnC+syZMzCbzSJWNPpIJBJMmTIFjz/+OKKiogB0drc5deoUdu/e7ZfhtdGiq5uSxWKBw+EYkXN2H/WWkJAwIue8F6PRiPPnzwPofB4uW7YMQUFBotZEREQj5/r169izZw/sdjuAzlGoGzduFL3LHxEREY0uDCkREREREREREfkRrVYr3PbHQEpcXBwyMzMBAE6nEydOnBC5otEpIiICeXl5mDZtmrDNaDRiy5YtqKioELGy0UuMkW/dQ0qJiYkjcs7eWK1WHDp0SFjPmjULcXFxotVDREQjx+v14uTJkygoKIDH4wEA6HQ6PPbYY0J4noiIiGioMKRERERERERERORHul8M6ujoELGSe8vJyRFGY928eRMGg0HkikYnmUyGnJwcrFu3TgivORwOHDx4EIcOHRqxbj8PCzFCSkajEUBn5yKxOlV4vV4cPnwYFosFAJCcnOwTjiMiotHL4XBg3759uHz5srDtkUcewapVq0QfQUpERESjE0NKRERERERERER+JBBCSkFBQZg7d66wLiwshMvlErGi0S0xMRGbN2/GuHHjhG3l5eXYsmWLTyceGpyRDinZbDY0NzcDAKKiokS7GFxcXIzq6moAgEajwZIlSyCRSESphYiIRk5HRwd27NghhM2lUikWLlyIuXPn8v8BIiIiGjYMKRERERERERER+ZFACCkBQEZGBpKSkgB0jqU7d+6cyBWNbkqlEkuXLsXSpUuFMIvJZMKuXbtQVFQkjGehgYuIiBBuj0RIqba2Vrgt1qi3uro6nDlzRlgvWbIEarValFqIiGjk1NbWYvv27WhpaQHQGUBfu3atMNKXiIiIaLgwpERERERERERE5Ee6xnoBnSEUf7ZgwQJIpZ1/Xrp8+bLQFYaGz7hx47B582YkJCQI2y5evIj8/PwRG1E2Wmm1WshkMgAjE1LqGvUGiBNSstvtOHjwILxeLwBgxowZQvCQiIhGr9LSUrz77ruw2WwAgLCwMOTl5YkWmCUiIqKHC0NKRERERERERER+RKvVCiM2/LmTEtB5UWv69OkAAI/Hg4KCAiHwQMNHq9Vi3bp1yMnJEUJijY2N2Lp1K65evSpydYFLIpEII9/a2tqGvTtVV0hJIpEgPj5+WM/Vm6NHjwpByPj4eMyYMWPEayAiopHj9XpRVFSEI0eOCP/HJSUlIS8vD2FhYSJXR0RERA8LhpSIiIiIiIiIiPyIVCpFcHAwAP8PKQFAVlaWcGGrrq4ON27cELmih4NEIsG0adOQl5cnBGvcbjeOHz+O9957DxaLRdwCA1TX19Lr9aK9vX3YzmOz2YTOY1FRUcIIv5Fy9epV3Lp1CwCgUqmwbNkyIfBGRESjj8vlwvvvv4+LFy8K2yZNmoQ1a9YgKChIvMKIiIjoocPfPImIiIiIiIiI/EzXyDebzQaXyyVyNfcnk8mQm5srrE+fPg2r1SpiRQ+X6OhoPP7445gyZYqwraqqClu2bBFCKNR3XSElAGhpaRm289TW1gq3R3q8TmNjI06ePCmsFy9eLAQjiYho9DGbzdixY4fwukAikWD+/Pk+Y3uJiIiIRgpffRARERERERER+ZmQkBDhdiB0U0pMTERGRgYAwOFw+AQgaPjJ5XLMmzcPa9asgUajAdAZcNu/fz+OHTsGp9MpcoWBo3tIqbW1ddjO0zXqDRjZkJLT6cSBAweEMT+PPPIIdDrdiJ2fiIhGVn19PbZv346mpiYAgFKpxJo1azB58mSRKyMiIqKHlVzsAoiIiIiIiIiIyFfPkFJERISI1fTNnDlzYDAYYLfbUV5ejgkTJiApKUnssh4qKSkp2Lx5M44dOyZ0S7hx4waMRiOWLl2K2NhYcQsMAN2/10YipCSRSBAfHz9s5+mpoKBAGGMXExODnJycETs3ERGNrIqKChw5cgRutxsAEBoailWrVgXE60rqXUVFBYqLi2EwGGAymaBQKBAVFYWMjAzMmjVLCKs/rBwOB86fP4+SkhLU19fDbrdDo9EgKSkJU6ZMwaRJkyCRSMQuk/rJbDbj7NmzKC8vR2trK6xWK4KDgxEZGYkxY8Zg/PjxI/p6mmioeDwe3LhxAxcvXkRTUxPa29uhVCoREhKClJQUjB07FhkZGZDJZGKXOuQYUiIiIiIiIiIi8jNd494AwGQyiVhJ36nVauTk5ODYsWMAgMLCQmzevHlU/kHNn6lUKqxcuRI3btzAiRMn4HK50N7ejh07dmDmzJnIysriaJf7CAsLE24P17g3m82G5uZmAEBUVBSUSuWwnKenkpISlJeXA+jspLFs2TI+F4iIRqlz587h3Llzwjo+Ph4rV66ESqUSsarR49atWxgzZsygjnHhwgVkZWU9cL+Ghgb87//+L9544437jvJVKBRYsWIFvvjFL+LRRx/tVy09H8+iRYtw5MiRfh2jN6+99ho++clPCusf/OAHePHFFwd93J5OnDiBl19+Gbt27YLFYrnnfnFxcXjyySfxjW98A2lpaQ88rtvtRmRkpE/Au76+vs91/eEPf8DXv/51n223bt1Campqn+5/+vRpzJkzR1hv2rQJW7ZsuWu/oQhehYWF3TOg3/PfsTdyuRxhYWGIjIzE1KlTMXfuXHz0ox8dVHho7969+N///V/s27dPCFveS1JSEubMmYOVK1di/fr1SEhIGPB5exqp53FPeXl52LFjh7BWqVSora31+X2lr4biZxYATJs2DRcvXuz1cy+++CJ++MMf3vf+CoUCYWFhiImJQVZWFnJzc/HUU0/5dLPt0pfn3WB8/OMfx2uvvSasP/GJT+D1118f9HG3b9+OvLy8++6j1+vx8ssv4/XXXxc6Hd6LRqPBjBkzsHTpUqxZs8bnZ0Ig42+hRERERERERER+JtDGvXWZMGGC8IfotrY2XLhwQeSKHl6ZmZnYvHmz0D3J6/Xi7Nmz2Llzp3Chhe4mk8kQGhoKYPg6KdXW1gq3R2rUW0tLC44fPy6sFy5cKDxOIiIaPVwuFw4ePOgTUJowYQIeffRRBpQCjNfrxcsvv4z09HT8+Mc/vm9ACegc6bpnzx6sW7cOixYtEoLJo1ldXR02b96M+fPn49///vd9A0pd+7/88ssYP348vve97z1wJLJMJsP8+fOFdUNDA65fv97n+o4ePXrXtq43dAzk/osWLerzfUeay+VCU1MTysrKsHXrVjz33HNISUnBl7/8ZZjN5n4dq62tDU888QTWrFmDPXv2PDCgBAA1NTXYunUrnn32Wfz2t78d6MPwG42NjdizZ4/PNpvNhn//+98iVTQ0nE4nGhsbcf36dbz11lv44he/iMTERPzwhz+Ey+USu7wR8corr2DKlCn43e9+98CAEgBYLBYUFhbiRz/6EdatWzcCFY4MdlIiIiIiIiIiIvIzgRpSkkgkyM3NxdatW+HxeHDx4kWMGzeu13dG0vALDQ3Fhg0bcOHCBZw/fx5erxf19fXYsmUL5s+fjwkTJohdol8KDw9He3s7XC4XTCaTT2ezodA16g3AkL7L+166Llh3/eF/4sSJSE9PH/bzEhHRyLJYLNi/f79Pp5ecnBxMmzZNxKoeHv3tHnq/7jd2ux0f+9jHeu2aM3v2bEyZMgUJCQkwmUyoqanB4cOHfS52Hzt2DLNmzcLWrVuxdOnSftUVKIqLi7Fu3TpUV1f7bA8ODsbSpUuh0+kQERGB+vp6lJaWoqCgQAi7OJ1O/OxnP0NBQQF27tx5399VFi5ciPfee09YHzt2DBMnTuxTjYWFhXdtO3bsGJ5++uk+3b9noKmvIaWBdLKVy/seGZBIJHd14/R4PPB6vT7bXC4X/t//+384ceIEjh496vM77r2YTCasXr0ap06d8tkeExODefPmYdy4cdBqtTCZTKivr8f58+dx/fp1eDyePtcfCN58881eQ3Svv/46Pve5zw3JOYb7eSKVSu/6Odfb88RqteLFF1/E6dOnsXPnTuEcUqm0TzV6vV6ff//enp+9edCxe6u/L+537t/+9rd47rnnfLbJ5XLMmTMHjzzyCCIjIwEATU1NuH79Os6fPx9Qfw/qD4aUiIiIiIiIiIj8TPdQRKD9USoiIgJTp07FxYsX4fF4UFhYOKre8RdopFIpZs6ciZSUFBw6dEgI3xw9ehR6vR4LFy5kZ4UewsPDYTAYAHR2UxrqkNLt27eF2yMRUjp58qQwXi4yMhJz584d9nMSEdHIampqwt69e4WOJXK5HMuWLevzWCkanKEajwZ0XnB/8sknfcY8AcCnPvUpvPDCC73+m7pcLuzatQvf+MY3oNfrAXS+hlm3bh3ef/99n25Ao0FpaSmWL1+OxsZGYVtkZCR++ctf4iMf+Qg0Gs1d92loaMBLL72EX/3qV0Jwu6CgAKtWrcLRo0fv+Xq4ZzDo2LFjePbZZx9Y47Vr19DQ0ACgMzTRFczoayclj8fj0wUzMjISjzzyyAPvl5qa+sCuW4P1wgsv3DXuzOPxoLm5GUVFRfjTn/6E3bt3C5+7cOECPv/5z+PNN9984LH/53/+xyegFBcXh9///vf40Ic+dM+ATEtLC/Lz8/HWW2/h/fffH9iD8jP3Gjt24sQJlJWVISMjY1DHH8qfWffy6quv4hOf+ITPNrfbjfr6ehw/fhwvvfSSz3P8vffeww9+8AP89Kc/BQA888wzeOaZZx54np5j4Xp7fg7EwYMHsXjx4kEfp0tRURG++c1v+mz76le/iueffx4xMTG93sftduPYsWN45513+vT9E0g47o2IiIiIiIiIyM9IpVIEBwcD6Hw3aaCZMWOG8E5Zo9GI0tJSkSui2NhYbNq0CZmZmcK2W7duYcuWLXe9A/1h1/3d9EM98s1utwudDqKjo6FUKof0+D1VVFQIY0nkcjmWL1/er3dAExGR/7t16xZ27NghBJS0Wi0ee+wxBpQC1O9+9zufgJJSqcTOnTvx6quv3vPfVC6XY+PGjbhy5QrWrl0rbLdarfjIRz4ybCNsxeB0OvHUU0/5BJSysrJw7do1fOYzn+k1oAR0duL56U9/ipMnTwrdSoDO4MC3vvWte54vOzvb55gFBQV9qrN7GGn58uVC6L20tBR1dXUPvP+lS5d8/t1yc3MH1NVlpEilUkRHR2Pt2rV499138fOf/9zn82+99RauXLly32Po9Xr85S9/Edbh4eE4fvw4nnrqqfu+fo2IiMAnP/lJ7N+/H1evXsXKlSsH92BEdvnyZZ+x6QqFwufz9wowBQKZTIaEhARs3rwZx44dwxe+8AWfz7/00ks+39ujyXe/+12fLlK//e1v8Yc//OGeASWg8+u1ZMkS/OlPf0J1dTV++ctfjkSpI4IhJSIiIiIiIiIiP9QV8rFarcK7fQOFXC7HggULhPWpU6dgs9lErIiAzj9wL1y4ECtXrhTeLW6xWLBnzx6cOHEi4J5nw2U4Q0rduyglJiYO6bF7am9v97lAtmDBAo5eJCIaZS5evIj9+/cL/4fHxsYiLy8PUVFRIldGA3H58mV897vfFdYSiQRvv/021q9f36f7a7VabNmyxed1uMFgwJe+9KUhr1UsP/jBD3D+/HlhnZGRgf379yMuLq5P98/OzsZ7770HtVotbPvjH/+I/fv397q/QqHw6UJZVVWFmzdvPvA83V+DLVmyxOcYfemmNNBRb/7i29/+NhYuXCisvV4v8vPz73uf/Px8YSQfAHzrW9/C2LFj+3XeSZMmBXxI6bXXXvNZv/jiiz4hrTfeeOOukWmBSCqV4uWXX/YZQ22xWLB3714RqxoeTU1NPp2rJkyYgG984xv9OkZISAg+/elPD3Fl4mFIiYiIiIiIiIjID3UfMRWI3ZRSUlKEPzjabDYUFRWJXBF1SUtLw+bNm5GSkiJsu3LlCrZt2zZq37naH8MZUjIajcLt4Rz15na7ceDAATidTgCdF/DGjx8/bOcjIqKR5Xa7ceTIEZ/XV+PGjcP69evv2UmG/N8Pf/hDOBwOYf2pT30KGzdu7Ncx1Go1XnvtNZ/xZf/3f/+Hq1evDlmdYmlsbMTvf/97YS2RSPDqq6/etxNJb2bPno3vfe97wtrr9eL555+/5/69jXx7kO4dlxYuXIjc3Nx+3T/QQ0oA7hr11X20V2/OnTvns16xYsVQl+T3XC6Xz1gvlUqFL3/5yz7BK4PBgEOHDolR3pCTy+X42Mc+5rPtQc+TQFRcXOwTwFu+fLlfd0YbCQwpERERERERERH5oa5OSgDQ0dEhYiUDN2/ePGGc1Y0bN1BbWytyRdRFo9FgzZo1mD9/PmQyGYDOQE5+fj6Ki4tHxbtzB0qlUgkX9oazk9JwhpSKioqEwFlYWJhPRwUiIgpsNpsNu3fv9hmnm52djaVLlwr/p1Pg0ev1Pp1mgoODBzzaZ+zYsXd16XjppZcGUZ1/+POf/+zTnfXJJ5/0Cf/0xze/+U2f8Xlnzpy5Zziie0cgADh69Oh9j11ZWSmMU1apVJg1a5bPa7EH3R/wDSmFhYUhKyvrgffxN9OmTfNZP2jMXX19vc86NDR0yGvyd3v37vX5Oj322GMIDQ3FM88847NfII9866m/z5NAxOf23RhSIiIiIiIiIiLyQ6MhpKTRaDBr1ixhXVBQAI/HI2JF1NPkyZOxadMmREdHAwA8Hg9Onz6N3bt3B2QHr6ESEREBoHPkQPeOBoNht9vR1NQEAIiOjhYCfENNr9fj8uXLAACZTIbly5dDoVAMy7mIiGhktbS0YPv27ULwWy6XY/ny5ZgxY4bIldFg/eMf//DptLF58+ZBje377Gc/69Op480334TVah1UjWL729/+5rN+9tlnB3wspVJ5V6efV155pdd9c3JyEBQUJKwf1Amp++dnz54NpVKJnJwc4fXYlStX0NLScs/7X79+HQ0NDcJ6wYIFkEoD75J+zyDGg36n7fl6tbKycshr8nc9R709/fTTADrDSmFhYcL2bdu2jZrf1fr7PAlEfG7fLfB+ohERERERERERPQRGQ0gJACZNmiSMYGhpacGlS5dEroh6Cg8PR15ens87tI1GI7Zs2YLy8nLxChNR95Fv97uI1B/duyglJiYOyTF7MplMOHLkiLCeM2fOoC5wEhGR/6iqqkJ+fr7wulCj0WD9+vXCeF0KbD2763z0ox8d1PHGjBmDuXPnCmur1YozZ84M6phi0uv10Ov1wjopKWnQI9B6fo3vFT5SqVTIyckR1hUVFT4jfHvqfpyuLkwajQYzZ84E0Dlervs4uPvdv/sxAk3PjqTdQza9SU5O9ln/8Y9/HOqS/FpzczN27dolrGNiYrBq1SoAnc/BD33oQ8LnzGYz3nnnnRGvcTj093kSiHo+t3ft2uXz8+xhxJASEREREREREZEfGi0hJYlEgtzcXOGd3OfOnUN7e7vIVVFPUqkUs2fPxvr166HVagEADocDhw4dwqFDh4asm1Cg6B5SGqqRb90vZg3HqDePx4NDhw7BbrcDANLS0jB58uQhPw8REY28K1euYO/evXA6nQA6O/Jt3LhRCIJTYHO5XCgqKhLWEonEJxQzUHPmzPFZnzhxYtDHFEvPUWxz5szx6RQ1EBkZGT5h7ps3b95zPHXPoND9uil1/1z3cXTdb/f1/gAGHcYSy4ULF3zWSenjcbGqFVeMbbjdZr1rvPTixYt91rt378ZTTz2Fqqqq4S7VL7z99ts+v3M99dRTkMvlwnq0jnzr+TwZjb+/zJgxw+fvOxaLBUuXLsX+/ftFrEpcDCkREREREREREfmh4OBg4Xagt3KPjo7GlClTAABut/uuiwzkPxISErB582aMGzdO2FZeXo4tW7bc9x3jo03XuDdg6EJK3TspDUdI6dy5c8KFNa1WG7AXtIiI6AMejwcFBQU4ceKEcEF/zJgx2LBhg89rRQpsZWVlsFgswnrcuHF3jUAaiJ5jAC9evDjoY4qluLjYZz1UIw6nT5/us77X16jn66p7hYyMRiMqKioAdI7dnTdvnvC5voaUundZ0mq1QgemQPP3v//dZ62btgAXDC04p2/B4ZIG7Lp0Gzdq2+HxdP5sy8vLQ1JSks993n77baSnp2PFihX4zW9+gxMnTsBms43YYxhJ9xr11mXBggUYM2aMsD527Bhu3bo1ApUNH4fDgX/9618+29avXy9SNcNHoVDgc5/7nM+2yspKrFq1CuPHj8dzzz2H/Px81NXViVThyGNIiYiIiIiIiIjID8lkMmg0GgCB3UmpS3Z2tnAxraqqCpWVlSJXRPeiVCqxdOlSLF26FEqlEkBnUO7dd9/F6dOn4Xa7Ra5w+A11JyW73Y6mpiYAnaG9rq/rUKmpqRHehSyVSrF8+XIEBQUN6TmIiGhk2e127NmzB9evXxe2TZ8+HcuXL/fprkHiO3r0KORyeZ8+oqOj77p/Y2OjzzolJWVI6uo5YqjrtUggEvtrNG/ePCgUCmF9r5BR9+3Tp08XOpQCwPz584XuTxcuXOj1jSg3b9706Rw0b968fn2/6/X6Pj8Xu3/s3Lmzz+foix/+8Ic4deqUsI5OTod0zGwUV7fhgqEVV41tuNVkxrlbLThW1gC3x4ugoCC8+uqrkMlkPsdyuVw4cOAAvvnNb2L+/PkIDQ3FrFmz8I1vfAM7duyA1Wod0trFcP36dZ9xjBMnTkR2drbPPhKJxCe45PV6B9xNqT8/s7p/9AwLDobb7cYXv/hFGAwGYdvy5cvvetxiWbZsWb+/Pps2bbrn8V544QVMmjTpru1lZWX47W9/i40bNyI+Ph5paWn4yEc+gr/85S8BH0K7H4aUiIiIiIiIiIj8VFdLcKvVCpfLJXI1g6NQKDB//nxhfeLEiYduhFigGTduHDZv3ozExERhW3FxMfLz89HS0iJiZcMvODhYuCA0FI+1exel7l/PoWC1WnHo0CFhPWvWLMTGxg7pOYiIaGS1tbUhPz9f6GIolUqxZMkSzJo1a9Ajrmh4uN3uPn309pq+52uNsLCwIampe+gaAJqbm4fkuGIQ+2uk0Wh8Ohpdu3btruAUcO9RbwAQGRkpjLJyuVy9dpc9evSoz3ognTH7+lzs/uHxePp9np7nrKurw86dO7F69Wq8+OKLwueUGi0e+69fIS02FBPiQ5AeEwy5VILKBjNK6ztQ3WLBqcrOcNiqVauQn59/179Ld06nE2fPnsVLL72EvLw8xMfH40tf+pLP6+1A86AuSl16jnz75z//edfYvL4ayPNkoOfq4nQ6UV1djbfffhvz58/Hq6++KnwuOTn5rq+DmDwez4C+RvcSGhqKgwcP3vVzoSe9Xo+33noLn//855Geno5ly5bh4MGDQ/3wRMeQEhERERERERGRn+oKKQGBP/INANLS0pCamgoAsFgsKCoqErkiehCtVotHH30Uc+bMgVTa+afEpqYmbNu2DVeuXBG5uuEjkUiEiyMdHR2D7h7VfVTeUI5683q9OHz4sPAO8uTkZEydOnXIjk9ERCPPaDQiPz8fbW1tAACVSoX169cjIyND5MpouPTsmjpUo/x6Hqe9vX1IjisGf/gadQ8Meb1en7FsXe4XUgI6R3b1tu+9tvnj+N4f/vCHkEgkPh9yuRzx8fF47LHHsG/fPmHf+IxH8F//+x8snT8LcaEqhKkViNYGYUJ8Z2Cpw+bCzUY