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   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use(\"../styles/hda.mplstyle\")"
   ]
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  {
   "cell_type": "markdown",
   "id": "0cb47ba6",
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   "source": [
    "(chp-vector-space-model)=\n",
    "# Exploring Texts using the Vector Space Model\n",
    "\n",
    "(sec-vector-space-model-introduction)=\n",
    "## Introduction\n",
    "\n",
    "Reasoning about space is easy and straightforward in the real world: we all have intuitive\n",
    "estimates as to how far two places are apart in everyday life and we are used to planning\n",
    "our traveling routes accordingly. We know how to look up the location of places on digital\n",
    "maps online or even in an old-fashioned atlas. We understand how a place's coordinates, in\n",
    "terms of longitude and latitude, can capture that spot's exact location on earth. We grasp\n",
    "how such data can readily be used to retrieve nearby locations, i.e., the concept of a\n",
    "\"neighborhood\". Even for entirely fictional universes, such as Middle Earth, research has\n",
    "demonstrated that readers build up similar ideas about the spatial structures that are\n",
    "encoded in literary narratives (cf. {cite:t}`louwerseEA2012,louwerse_sterling:2012`). In this chapter, we explore to which extent this sort of spatial reasoning can be applied to textual data: is it possible to think of texts as mere points in a space, that have exact coordinates that allow us to calculate how far two texts are separated from one another? Is it feasible to plot entire corpora onto abstract maps and inspect their internal structure?\n",
    "\n",
    "In order to study texts in a spatial fashion, we need a representation that is able to model the individual characteristics of texts using a \"coordinate system\"---just like places in the real world also have a unique set of coordinates that describe their properties across different dimensions (e.g., longitude, latitude, but also altitude). To this end, we will introduce the <span class=\"index\">*vector space model*</span>, a simple, yet powerful representation strategy that helps us to encode textual documents into the concrete numbers (or \"coordinates\") that are necessary for further processing. The text-as-maps metaphor will help the reader understand why <span class=\"index\">geometry</span>, the mathematical study of (points in) space, is so central to computational text analysis nowadays (as it is in this chapter). Just as with the locations on real maps, geometry offers us established methodologies to explore relations, similarities, and differences between texts. Interestingly, these methods typically require relatively few assumptions about what textual properties might precisely be salient in a given document.\n",
    "\n",
    "Why would such a spatial perspective on (textual) data in fact be desirable? To illustrate the potential of this approach, this chapter will work with a simple, but real-world example throughout, namely a corpus of French-language drama of the Classical Age and the Enlightenment Age (seventeenth--eighteenth century), that has been quantitatively explored by {cite:t}`schoech:2017` in a paper that will function as our baseline in this chapter. We will focus on plays drawn from three well-studied subgenres: \"Tragédie\", \"Comédie\", and \"Tragi-comédie\". Readers, even those not familiar at all with this specific literature, will have very clear expectations as to which texts will be represented in these genres, because of these generic genre labels. The same readers, however, may lack grounds to explain this similarity formally and express their natural intuition in more concrete terms. We will explore how the vector space model can help us understand the differences between these subgenres from a quantitative, geometric point of view. What sort of lexical differences, if any, become apparent when we plot these different genres as spatial data? Does the subgenre of \"Tragi-comédies\" display a lexical mix of both \"Comédies\" and \"Tragédies\" markers or is it relatively closer to one of these constituent genres?\n",
    "\n",
    "The first section of this chapter (section {ref}`sec-vector-space-model-vector-space-model`) elaborates on several low-level preprocessing steps that can be taken when preparing a real-life corpus for numerical processing. It also discusses the ins-and-outs of converting a corpus into a bag-of-words representation, while critically reflecting on the numerous design choices that we have in implementing such a model in actual code. In section {ref}`sec-vector-space-model-mapping-genre`, then, these new insights are combined, as well as those from chapter {ref}`chp-getting-data`, in a focused case study about the dramatic texts introduced above. To efficiently represent and work with texts represented as vectors, this chapter uses Python's main numerical library \"<span class=\"index\">NumPy</span>\" (section {ref}`sec-vector-space-model-numpy-intro`), which is at the heart of many of Python's data analysis libraries {cite:p}`walt2011numpy`. For readers not yet thoroughly familiar with this package, we offer an introductory overview at the end of this chapter, which can safely be skipped by more proficient coders. Finally, this chapter introduces the notion of a \"nearest neighbor\" (section {ref}`sec-vector-space-model-nearest-neighbors`) and explains how this is useful for studying the collection of French drama texts.\n",
    "\n",
    "(sec-vector-space-model-vector-space-model)=\n",
    "## From Texts to Vectors\n",
    "\n",
    "As the name suggests, a <span class=\"index\">bag-of-words</span> representation models a\n",
    "text in terms of the individual words it contains, or, put differently, at the lexical\n",
    "level. This representation discards any information available in the sequence in which\n",
    "words appear. In the bag-of-words model, word sequences such as \"the boy ate the fish\",\n",
    "\"the fish ate the boy\", and \"the ate fish boy the\" are all equivalent. While linguists\n",
    "agree that syntax is a vital part of human languages, representing texts as bags of words\n",
    "heedlessly ignores this information: it models texts by simply counting how often each\n",
    "unique word occurs in them, regardless of their order or position in the original document\n",
    "(see chp. 6 in {cite:t}`jurafskyinpressspeech`, and {cite:t}`sebastiani:2002`). While disgarding this information seems, at first glance, limiting in the extreme, representing texts in terms of their word frequencies has proven its usefulness over decades of use in information retrieval and quantitative text analysis.\n",
    "\n",
    "When using the <span class=\"index\">vector space model</span>, a <span\n",
    "class=\"index\">corpus</span>---a collection of documents, each represented as a bag of\n",
    "words---is typically represented as a matrix, in which each row represents a document from\n",
    "the collection, each column represents a word from the collection's <span\n",
    "class=\"index\">vocabulary</span>, and each cell represents the frequency with which a\n",
    "particular word occurs in a document. In this tabular setting, each row is interpretable\n",
    "as a <span class=\"index\">*vector*</span> in a vector space. A matrix arranged in this way\n",
    "is often called a *document-term matrix*---or *term-document matrix* when rows are\n",
    "associated with words and documents are associated with columns. An example of a <span class=\"index\">document-term matrix</span> is shown in {numref}`tbl-document-term-matrix`.\n",
    "\n",
    "```{table} Example of a vector space representation with four documents (rows) and a vocabulary of four words (columns). For each document the table lists how often each vocabulary item occurs.\n",
    ":name: tbl-document-term-matrix\n",
    "\n",
    "|       | _roi_ | _ange_ | _sang_ | _perdu_ |\n",
    "|-------|-------|--------|--------|---------|\n",
    "| $d_1$ |     1 |      2 |     16 |      21 |\n",
    "| $d_2$ |     2 |      2 |     18 |      19 |\n",
    "| $d_3$ |    35 |     41 |      0 |       2 |\n",
    "| $d_4$ |    39 |     55 |      1 |       0 |\n",
    "```\n",
    "\n",
    "In this table, each document $d_i$ is represented as a vector, which, essentially, is a list of numbers---word frequencies in our present case. A <span class=\"index\">vector space</span> is nothing more than a collection of numerical vectors, which may, for instance, be added together and multiplied by a number. Documents represented in this manner may be compared in terms of their *coordinates* (or *components*). For example, by comparing the four documents on the basis of the second coordinate, we observe that the first two documents ($d_1$ and $d_2$) have similar counts, which might be an indication that these two documents are somehow more similar. To obtain a more accurate and complete picture of document similarity, we would like to be able to compare documents more holistically, using *all* their components. In our example, each document represents a point in a four-dimensional vector space. We might hypothesize that similar documents use similar words, and hence reside close to each other in this space. To illustrate this, we demonstrate how to visualize the documents in space using the first and third components."
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      "text/plain": [
       "<Figure size 1600x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/vector-space-model/notebook_2_4.png"
      },
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "doc_in_space"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/vector-space-model/notebook_2_5.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "document_term_matrix = np.array([[1, 16], [2, 18], [35, 0], [39, 1]])\n",
    "labels = '$d_1$', '$d_2$', '$d_3$', '$d_4$'\n",
    "fig, ax = plt.subplots()\n",
    "plt.quiver([0, 0, 0, 0], [0, 0, 0, 0],\n",
    "           document_term_matrix[:, 0], document_term_matrix[:, 1],\n",
    "           color=[\"C0\", \"C1\", \"C2\", \"C3\"], angles='xy', scale_units='xy', scale=1)\n",
    "for i, label in enumerate(labels):\n",
    "    plt.annotate(label, xy=document_term_matrix[i], fontsize=15)\n",
    "plt.ylim(-1, 20); plt.xlim(-1, 44)\n",
    "plt.xlabel(\"Dimension 1\")\n",
    "plt.ylabel(\"Dimension 3\");\n",
    "\n",
    "from myst_nb import glue\n",
    "glue(\"doc_in_space\", fig, display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa1eb7c3",
   "metadata": {},
   "source": [
    "```{glue:figure} doc_in_space\n",
    "---\n",
    "name: fig-vector-space-model-documents-in-space\n",
    "---\n",
    "\n",
    "Demonstration of four documents (represented as vectors) residing in a two-dimensional space.\n",
    "```\n",
    "\n",
    "The plot in {numref}`fig-vector-space-model-documents-in-space` makes visually clear that documents $d_1$ and $d_2$ occupy neighboring positions in space, both far away from the other two documents. As the number of dimensions increases (collections of real-world documents typically have vocabularies with tens of thousands of unique words), it will become unfeasible to visually analyze the similarity between documents. To quantify the distance (or similarity) between two documents in high-dimensional space, we can employ <span class=\"index\">distance function</span>s or metrics, which express the distance between two vectors as a non-negative number. The implementation and application of such <span class=\"index\">distance metric</span>s will be discussed in section {ref}`sec-vector-space-model-distance-metrics`.\n",
    "\n",
    "```{figure} images/bow.png\n",
    "---\n",
    "name: fig-vector-space-steps\n",
    "---\n",
    "\n",
    "Extracting a document-term matrix from a collection of texts.\n",
    "```\n",
    "\n",
    "Having a basic theoretical understanding of the vector space model, we move on to the practical part of implementing a procedure to construct a <span class=\"index\">document-term matrix</span> from plain text. In essence, this involves three consecutive steps. In the first step, we determine the <span class=\"index\">vocabulary</span> of the collection, optionally filtering the vocabulary using information about how often each unique word (type) occurs in the corpus. The second step is to count how often each element of the vocabulary occurs in each individual document. The third and final step takes the bags of words from the second step and builds a document-term matrix. The right-most table in {numref}`fig-vector-space-steps` above represents the document-term matrix resulting from this procedure. The next section will illustrate how this works in practice.\n",
    "\n",
    "(sec-vector-space-model-text-processing)=\n",
    "### Text preprocessing\n",
    "\n",
    "A common way to represent text documents is to use strings (associated with Python's `str` type). Consider the following code block, which represents the ten mini-documents from the figure above as a list of strings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c1356ee7",
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus = [\"D'où me vient ce désordre, Aufide, et que veut dire\",\n",
    "          \"Madame, il était temps qu'il vous vînt du secours:\",\n",
    "          \"Ah! Monsieur, c'est donc vous?\",\n",
    "          \"Ami, j'ai beau rêver, toute ma rêverie\",\n",
    "          \"Ne me parle plus tant de joie et d'hyménée;\",\n",
    "          \"Il est vrai, Cléobule, et je veux l'avouer,\",\n",
    "          \"Laisse-moi mon chagrin, tout injuste qu'il est;\",\n",
    "          \"Ton frère, je l'avoue, a beaucoup de mérite;\",\n",
    "          \"J'en demeure d'accord, chacun a sa méthode;\",\n",
    "          'Pour prix de votre amour que vous peignez extrême,']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "539c2de9",
   "metadata": {},
   "source": [
    "In order to construct a <span class=\"index\">bag-of-words</span> representation of each \"text\" in this corpus, we must first process the strings into distinct words. This process is called \"<span class=\"index\">tokenization</span>\" or \"<span class=\"index\">word segmentation</span>\". A naive tokenizer might split documents along (contiguous) whitespace. In Python, such a tokenizer can be implemented straightforwardly by using the string method `split()`. As demonstrated in the following code block, this method employs a tokenization strategy in which tokens are separated by one or more instances of whitespace (e.g., spaces, tabs, newlines):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "498f0d10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Ah!', 'Monsieur,', \"c'est\", 'donc', 'vous?']\n"
     ]
    }
   ],
   "source": [
    "document = corpus[2]\n",
    "print(document.split())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52f0cdf5",
   "metadata": {},
   "source": [
    "The tokenization strategy used often has far-reaching consequences for the composition of the final document-term matrix. If, for example, we decide not to lowercase the words, _Il_ ('he') is considered to be different from *il*, whereas we would normally consider them to be instances of the same word *type*. An equally important question is whether we should incorporate or ignore punctuation marks. And what about contracted word forms? Should _qu'il_ be restored to _que_ and *il*?\n",
    "\n",
    "Such choices may appear simple, but they may have a strong influence on the final text representation, and, subsequently, on the analysis based on this representation. Unfortunately, it is difficult to provide a recommendation here apart from advising that <span class=\"index\">tokenization</span> procedures be carefully documented. To illustrate the complexity, consider the problem of modeling thematic differences between texts. For this problem, certain linguistic markers such as punctuation might not be relevant. However, the same linguistic markers might be of a crucial importance to another problem. In authorship attribution, for example, it has been demonstrated that punctuation is one of the strongest predictors of authorial identity {cite:p}`grieve2007`. We already spoke about lowercasing texts, which is another common <span class=\"index\">preprocessing</span> step. Here as well, we should be aware that it has certain consequences for the final text representation. For instance, it complicates identifying proper nouns or the beginnings of sentences at a later stage in an analysis. Sometimes reducing the information recorded in a text representation is motivated by necessity: researchers may only have a fixed budget of computational resources available to analyze a corpus.\n",
    "\n",
    "The best recommendation here is to follow established strategies and exhaustively document the preprocessing steps taken. Distributing the code used in preprocessing is an excellent idea. Many applications employ off-the-shelf tokenizers to preprocess texts. In the example below, we apply a tokenizer optimized for French as provided by the *Natural Language ToolKit* (<span class=\"index\">NLTK</span>) {cite:p}`birdEA2009`, and segment each document in `corpus` into a list of word tokens:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b6a4a781",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Ami', ',', \"j'ai\", 'beau', 'rêver', ',', 'toute', 'ma', 'rêverie']\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "import nltk.tokenize\n",
    "\n",
    "# download the most recent punkt package\n",
    "nltk.download('punkt', quiet=True)\n",
    "\n",
    "document = corpus[3]\n",
    "print(nltk.tokenize.word_tokenize(document, language='french'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10794577",
   "metadata": {},
   "source": [
    "It can be observed that this tokenizer correctly splits off sentence-final punctuation such as full stops, but retains contracted forms, such as *j'ai*. Be aware that the clitic form *j* is not restored to *je*. Such an example illustrates how tokenizers may come with a certain set of assumptions, which should be made explicit through, for instance, properly referring to the exact tokenizer applied in the analysis.\n",
    "\n",
    "Given the current word segmentation, removing (repetitions of) isolated punctuation marks can be accomplished by filtering non-punctuation tokens. To this end, we implement a simple utility function called <span class=\"index\">`is_punct()`</span>, which checks whether a given input string is either a single punctuation marker or a sequence thereof:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "83612168",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "\n",
    "PUNCT_RE = re.compile(r'[^\\w\\s]+$')\n",
    "\n",
    "\n",
    "def is_punct(string):\n",
    "    \"\"\"Check if STRING is a punctuation marker or a sequence of\n",
    "       punctuation markers.\n",
    "\n",
    "    Arguments:\n",
    "        string (str): a string to check for punctuation markers.\n",
    "\n",
    "    Returns:\n",
    "        bool: True is string is a (sequence of) punctuation marker(s),\n",
    "            False otherwise.\n",
    "\n",
    "    Examples:\n",
    "        >>> is_punct(\"!\")\n",
    "        True\n",
    "        >>> is_punct(\"Bonjour!\")\n",
    "        False\n",
    "        >>> is_punct(\"¿Te gusta el verano?\")\n",
    "        False\n",
    "        >>> is_punct(\"...\")\n",
    "        True\n",
    "        >>> is_punct(\"«»...\")\n",
    "        True\n",
    "\n",
    "    \"\"\"\n",
    "    return PUNCT_RE.match(string) is not None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a57a5cb2",
   "metadata": {},
   "source": [
    "The function makes use of the <span class=\"index\">regular expression</span> `[^\\w\\s]+$`. For those with a rusty memory of regular expressions, allow us to briefly explain its components. `\\w` matches Unicode word characters (including digit characters), and `\\s` matches Unicode whitespace characters. By using the set notation `[]` and the negation sign `^`, i.e., `[^\\w\\s]`, the regular expression matches any character that is *not* matched by `\\w` or `\\s`, i.e., is not a word or whitespace character, and thus a punctuation character. The `+` indicates that the expression should match one or more punctuation characters, and the `$` matches the end of the string, which ensures that a string is only matched if it solely consists of punctuation characters.\n",
    "\n",
    "Using the function <span class=\"index\">`is_punct()`</span>, filtering all non-punctuation tokens can be accomplished using a `for` loop or a <span class=\"index\">list comprehension</span>. The following code block demonstrates the use of both looping mechanisms, which are essentially equivalent:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dba5a016",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Ah', 'Monsieur', \"c'est\", 'donc', 'vous']\n",
      "['Ah', 'Monsieur', \"c'est\", 'donc', 'vous']\n"
     ]
    }
   ],
   "source": [
    "tokens = nltk.tokenize.word_tokenize(corpus[2], language='french')\n",
    "\n",
    "# Loop with a standard for-loop\n",
    "tokenized = []\n",
    "for token in tokens:\n",
    "    if not is_punct(token):\n",
    "        tokenized.append(token)\n",
    "print(tokenized)\n",
    "\n",
    "# Loop with a list comprehension\n",
    "tokenized = [token for token in tokens if not is_punct(token)]\n",
    "print(tokenized)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e1881a3",
   "metadata": {},
   "source": [
    "After tokenizing and removing the punctuation, we are left with a sequence of alphanumeric strings (\"words\" or \"word tokens\"). Ideally, we would wrap these preprocessing steps in a single function, such as <span class=\"index\">`preprocess_text()`</span>, which returns a list of word tokens and removes all isolated punctuation markers. Consider the following implementation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "44e46894",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_text(text, language, lowercase=True):\n",
    "    \"\"\"Preprocess a text.\n",
    "\n",
    "    Perform a text preprocessing procedure, which transforms a string\n",
    "    object into a list of word tokens without punctuation markers.\n",
    "\n",
    "    Arguments:\n",
    "        text (str): a string representing a text.\n",
    "        language (str): a string specifying the language of text.\n",
    "        lowercase (bool, optional): Set to True to lowercase all\n",
    "            word tokens. Defaults to True.\n",
    "\n",
    "    Returns:\n",
    "        list: a list of word tokens extracted from text, excluding\n",
    "            punctuation.\n",
    "\n",
    "    Examples:\n",
    "        >>> preprocess_text(\"Ah! Monsieur, c'est donc vous?\", 'french')\n",
    "        [\"ah\", \"monsieur\", \"c'est\", \"donc\", \"vous\"]\n",
    "\n",
    "    \"\"\"\n",
    "    if lowercase:\n",
    "        text = text.lower()\n",
    "    tokens = nltk.tokenize.word_tokenize(text, language=language)\n",
    "    tokens = [token for token in tokens if not is_punct(token)]\n",
    "    return tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a675480",
   "metadata": {},
   "source": [
    "The `lowercase` parameter can be used to transform all word tokens into their lowercased form. To test this new function, we apply it to some of the toy documents in `corpus`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1b35ef4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original: Ah! Monsieur, c'est donc vous?\n",
      "Tokenized: ['ah', 'monsieur', \"c'est\", 'donc', 'vous']\n",
      "Original: Ami, j'ai beau rêver, toute ma rêverie\n",
      "Tokenized: ['ami', \"j'ai\", 'beau', 'rêver', 'toute', 'ma', 'rêverie']\n"
     ]
    }
   ],
   "source": [
    "for document in corpus[2:4]:\n",
    "    print('Original:', document)\n",
    "    print('Tokenized:', preprocess_text(document, 'french'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a55157a",
   "metadata": {},
   "source": [
    "Having tackled the problem of preprocessing a corpus of document strings, we can move on to the remaining steps required to create a <span class=\"index\">document-term matrix</span>. By default, the vocabulary of a corpus would comprise the complete set of words in all documents (i.e., all unique <span class=\"index\">word types</span>). However, nothing prevents us from establishing a <span class=\"index\">vocabulary</span> following a different strategy. Here, too, a useful rule of thumb is that we should try to restrict the number of words in the vocabulary as much as possible to arrive at a compact model, while at the same time, not throwing out potentially useful information. Therefore, it is common to apply a threshold or frequency cutoff, with which less informative lexical items can be ignored. We could, for instance, decide to ignore words that only occur once throughout a corpus (so-called \"<span class=\"index\">hapax legomena</span>\", or \"hapaxes\" for short). To establish such a vocabulary, one would typically scan the entire corpus, and count how often each unique word occurs. Subsequently, we remove all words from the vocabulary that occur only once. Given a sequence of items (e.g., a `list` or a `tuple`), counting items is straightforward in Python, especially when using the dedicated <span class=\"index\">`Counter`</span> object, which was discussed in chapter {ref}`chp-getting-data`. In the example below, we compute the frequency for all tokens in `corpus`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "461941cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "\n",
    "vocabulary = collections.Counter()\n",
    "for document in corpus:\n",
    "    vocabulary.update(preprocess_text(document, 'french'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5846a276",
   "metadata": {},
   "source": [
    "`Counter` implements a number of methods specialized for convenient and rapid tallies. For instance, the method <span class=\"index\">`Counter.most_common`</span> returns the *n* most frequent items:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8ed408a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('et', 3), ('vous', 3), ('de', 3), ('me', 2), ('que', 2)]\n"
     ]
    }
   ],
   "source": [
    "print(vocabulary.most_common(n=5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c490f217",
   "metadata": {},
   "source": [
    "As can be observed, the most common words in the vocabulary are function words (or \"<span class=\"index\">stop words</span>\" as they are commonly called), such as *me* (personal pronoun), *et* (conjunction), and *de* (preposition). Words residing in lower ranks of the frequency list are typically <span class=\"index\">content words</span> that have a more specific meaning than function words. This fundamental distinction between word types will re-appear at various places in the book (see, e.g., chapter {ref}`chp-stylometry`). Using the `Counter` object constructed above, it is easy to compose a vocabulary which ignores these hapaxes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a91256c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original vocabulary size: 66\n",
      "{'et', 'est', 'il', 'me', 'je', 'que', \"qu'il\", 'vous', 'de', 'a'}\n",
      "Pruned vocabulary size: 10\n"
     ]
    }
   ],
   "source": [
    "print('Original vocabulary size:', len(vocabulary))\n",
    "pruned_vocabulary = {token for token, count in vocabulary.items() if count > 1}\n",
    "print(pruned_vocabulary)\n",
    "print('Pruned vocabulary size:', len(pruned_vocabulary))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c913204",
   "metadata": {},
   "source": [
    "To refresh your memory, a Python <span class=\"index\">`set`</span> is a data structure which is well-suited for representing a vocabulary. A Python `set`, like its namesake in mathematics, is an unordered sequence of distinct elements. Because a set only records distinct elements, we are guaranteed that all words appearing in it are unique. Similarly, we could construct a vocabulary which excludes the *n* most frequent tokens:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fd0b55c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original vocabulary size: 66\n",
      "Pruned vocabulary size: 61\n"
     ]
    }
   ],
   "source": [
    "n = 5\n",
    "print('Original vocabulary size:', len(vocabulary))\n",
    "pruned_vocabulary = {token for token, _ in vocabulary.most_common()[n:]}\n",
    "print('Pruned vocabulary size:', len(pruned_vocabulary))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5cf3d59",
   "metadata": {},
   "source": [
    "Note how the size of the pruned vocabulary can indeed be aggressively reduced using such simple frequency thresholds. Abstracting over these two concrete routines, we can now implement a function <span class=\"index\">`extract_vocabulary()`</span>, which extracts a vocabulary from a tokenized corpus given a minimum and a maximum frequency count:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9bcd38e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_vocabulary(tokenized_corpus, min_count=1, max_count=float('inf')):\n",
    "    \"\"\"Extract a vocabulary from a tokenized corpus.\n",
    "\n",
    "    Arguments:\n",
    "        tokenized_corpus (list): a tokenized corpus represented, list\n",
    "            of lists of strings.\n",
    "        min_count (int, optional): the minimum occurrence count of a\n",
    "            vocabulary item in the corpus.\n",
    "        max_count (int, optional): the maximum occurrence count of a\n",
    "            vocabulary item in the corpus. Defaults to inf.\n",
    "\n",
    "    Returns:\n",
    "        list: An alphabetically ordered list of unique words in the\n",
    "            corpus, of which the frequencies adhere to the specified\n",
    "            minimum and maximum count.\n",
    "\n",
    "    Examples:\n",
    "        >>> corpus = [['the', 'man', 'love', 'man', 'the'],\n",
    "                      ['the', 'love', 'book', 'wise', 'drama'],\n",
    "                      ['a', 'story', 'book', 'drama']]\n",
    "        >>> extract_vocabulary(corpus, min_count=2)\n",
    "        ['book', 'drama', 'love', 'man', 'the']\n",
    "\n",
    "    \"\"\"\n",
    "    vocabulary = collections.Counter()\n",
    "    for document in tokenized_corpus:\n",
    "        vocabulary.update(document)\n",
    "    vocabulary = {word for word, count in vocabulary.items()\n",
    "                  if count >= min_count and count <= max_count}\n",
    "    return sorted(vocabulary)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91cb19cb",
   "metadata": {},
   "source": [
    "Note that the default maximum count is set to infinity (`max_count=float('inf')`). This ensures that none of the high-frequency words are filtered without further specification. The function can be called as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "49a9227f",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenized_corpus = [preprocess_text(document, 'french') for document in corpus]\n",
    "vocabulary = extract_vocabulary(tokenized_corpus)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5396659b",
   "metadata": {},
   "source": [
    "Once the desired <span class=\"index\">vocabulary</span> has been established, we are ready to proceed to the second step of creating a <span class=\"index\">document-term matrix</span>. Recall that this second step consists of determining for each word in the vocabulary how often it occurs in each document. There are multiple ways to implement this. We will demonstrate two of them. First, we will represent the <span class=\"index\">vector space model</span> as a list of `Counter` objects, one for each document. Using a <span class=\"index\">list comprehension</span>, this can be easily implemented as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "66f4476d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'ah': 1, 'monsieur': 1, \"c'est\": 1, 'donc': 1, 'vous': 1})\n"
     ]
    }
   ],
   "source": [
    "bags_of_words = []\n",
    "for document in tokenized_corpus:\n",
    "    tokens = [word for word in document if word in vocabulary]\n",
    "    bags_of_words.append(collections.Counter(tokens))\n",
    "\n",
    "print(bags_of_words[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3b19ff4",
   "metadata": {},
   "source": [
    "When we print the second document in the dummy corpus above, we can see that `Counter` objects do not store words with zero counts in a document, which is why some counters in our corpus consist of very few elements. This is an efficient data type in terms of memory impact, because no memory has to be reserved for items that do not occur in a text. By contrast, a traditional tabular representation of the <span class=\"index\">document-term matrix</span> uses more memory since it allocates memory to store counts of zeros. Such a more verbose representation is constructed in the next code block, using the function <span class=\"index\">`corpus2dtm()`</span>. So-called \"<span class=\"index\">sparse matrices</span>\" (e.g., from the <span class=\"index\">``scipy.sparse``</span> library) overcome the problem of <span class=\"index\">sparsity</span> in such frequency tables and will figure in some of the next chapters in the book (see, e.g., chapter {ref}`chp-stylometry`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "877d3254",
   "metadata": {},
   "outputs": [],
   "source": [
    "def corpus2dtm(tokenized_corpus, vocabulary):\n",
    "    \"\"\"Transform a tokenized corpus into a document-term matrix.\n",
    "\n",
    "    Arguments:\n",
    "        tokenized_corpus (list): a tokenized corpus as a list of\n",
    "        lists of strings. vocabulary (list): A list of unique words.\n",
    "\n",
    "    Returns:\n",
    "        list: A list of lists representing the frequency of each term\n",
    "              in `vocabulary` for each document in the corpus.\n",
    "\n",
    "    Examples:\n",
    "        >>> tokenized_corpus = [['the', 'man', 'man', 'smart'],\n",
    "                                ['a', 'the', 'man' 'love'],\n",
    "                                ['love', 'book', 'journey']]\n",
    "        >>> vocab = ['book', 'journey', 'man', 'love']\n",
    "        >>> corpus2dtm(tokenized_corpus, vocabulary)\n",
    "        [[0, 0, 2, 0], [0, 0, 1, 1], [1, 1, 0, 1]]\n",
    "\n",
    "    \"\"\"\n",
    "    document_term_matrix = []\n",
    "    for document in tokenized_corpus:\n",
    "        document_counts = collections.Counter(document)\n",
    "        row = [document_counts[word] for word in vocabulary]\n",
    "        document_term_matrix.append(row)\n",
    "    return document_term_matrix\n",
    "\n",
    "\n",
    "document_term_matrix = corpus2dtm(tokenized_corpus, vocabulary)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d52b8199",
   "metadata": {},
   "source": [
    "The variable ``document_term_matrix`` now holds a tabular representation of the corpus.\n",
    "Each row is associated with a document and each column is associated with an element of\n",
    "the vocabulary. The table below shows the first few rows and several columns of this table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1c543099",
   "metadata": {
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
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       "      <th>amour</th>\n",
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       "      <th>beau</th>\n",
       "      <th>beaucoup</th>\n",
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       "      <th>chacun</th>\n",
       "      <th>chagrin</th>\n",
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       "|    |   a |   ah |   ami |   amour |   aufide |   beau |   beaucoup |   c'est |   ce |   chacun |   chagrin |   cléobule |   d'accord |   d'hyménée |   d'où |\n",
       "|----+-----+------+-------+---------+----------+--------+------------+---------+------+----------+-----------+------------+------------+-------------+--------|\n",
       "|  0 |   0 |    0 |     0 |       0 |        1 |      0 |          0 |       0 |    1 |        0 |         0 |          0 |          0 |           0 |      1 |\n",
       "|  1 |   0 |    0 |     0 |       0 |        0 |      0 |          0 |       0 |    0 |        0 |         0 |          0 |          0 |           0 |      0 |\n",
       "|  2 |   0 |    1 |     0 |       0 |        0 |      0 |          0 |       1 |    0 |        0 |         0 |          0 |          0 |           0 |      0 |\n",
       "|  3 |   0 |    0 |     1 |       0 |        0 |      1 |          0 |       0 |    0 |        0 |         0 |          0 |          0 |           0 |      0 |\n",
       "|  4 |   0 |    0 |     0 |       0 |        0 |      0 |          0 |       0 |    0 |        0 |         0 |          0 |          0 |           1 |      0 |"
      ],
      "text/plain": [
       "   a  ah  ami  amour  aufide  beau  beaucoup  c'est  ce  chacun  chagrin  \\\n",
       "0  0   0    0      0       1     0         0      0   1       0        0   \n",
       "1  0   0    0      0       0     0         0      0   0       0        0   \n",
       "2  0   1    0      0       0     0         0      1   0       0        0   \n",
       "3  0   0    1      0       0     1         0      0   0       0        0   \n",
       "4  0   0    0      0       0     0         0      0   0       0        0   \n",
       "\n",
       "   cléobule  d'accord  d'hyménée  d'où  \n",
       "0         0         0          0     1  \n",
       "1         0         0          0     0  \n",
       "2         0         0          0     0  \n",
       "3         0         0          0     0  \n",
       "4         0         0          1     0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.DataFrame(document_term_matrix, columns=vocabulary).iloc[0:5, 0:15]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4a6244c",
   "metadata": {},
   "source": [
    "While Python's `list` is a convenient data type for *constructing* a document-term matrix, it is less useful when one is interested in accessing and manipulating the matrix. In what follows, we will use Python's canonical package for scientific computing, <span class=\"index\">NumPy</span>, which enables us to store and analyze document-term matrices using less computational resources and with much less effort on our part. In order not to disrupt the narrative flow of the chapter, we shall not introduce this package in detail here: less experienced readers are referred to the introductory overview at the end of this chapter, which discusses the main features of the package at significant length (section {ref}`sec-vector-space-model-numpy-intro`).\n",
    "\n",
    "(sec-vector-space-model-mapping-genre)=\n",
    "## Mapping Genres\n",
    "\n",
    "#### Loading the corpus\n",
    "\n",
    "In what preceded, we have demonstrated how to construct a document-term matrix and which text preprocessing steps are typically involved in creating this representation of text (e.g., text cleansing and string segmentation). This document-term matrix is now ready to be casted into a two-dimensional <span class=\"index\">NumPy</span> array, allowing it to be more efficiently stored and manipulated. The resulting object's `shape` attribute can be printed to verify whether the table's dimensions still correctly correspond to our original vector space model (i.e., 10 rows, for each documents, and 66 columns, one for each term in the vocabulary)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1a85acb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 66)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "document_term_matrix = np.array(document_term_matrix)\n",
    "print(document_term_matrix.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e2508e4",
   "metadata": {},
   "source": [
    "Vector space models have proven to be invaluable for numerous computational approaches to textual data, such as text classification, information retrieval and stylometry (see chapter {ref}`chp-stylometry`). In the remainder of this chapter, we will use a vector space representation of a real-world corpus. The object of study will be a collection of French plays from the Classical and Enlightenment period (seventeenth--eighteenth century), which includes works by well-known figures in the history of French theatre (e.g., Molière and Pierre Corneille). Using a <span class=\"index\">vector space model</span>, we aim to illustrate how a bag-of-words model is useful in studying the lexical differences between three subgenres in the corpus. Before diving into the details of the genre information in the corpus, let us first load the collection of French plays into memory and, subsequently, transform them into a document-term matrix.\n",
    "\n",
    "The collection of dramatic texts under scrutiny is part of the larger <span class=\"index\">*Théâtre Classique*</span> corpus, which is curated by Paul Fièvre at [http://www.theatre-classique.fr/](http://www.theatre-classique.fr/). A distinct feature of this data collection, apart from its scope and quality, is the fact that all texts are available in a meticulously encoded <span class=\"index\">XML</span> format (see section {ref}`sec-getting-data-xml` in the previous chapter). An excerpt, slightly edited for space, of one of these XML files (`504.xml`) is shown below:\n",
    "\n",
    "```xml\n",
    "<div1 type='acte' n='1'>\n",
    "  <head>ACTE I</head>\n",
    "  <stage>Le Théâtre représente un salon où il y a plusieurs issues.</stage>\n",
    "  <div2 type='scene' n='1'>\n",
    "    <head>SCÈNE PREMIÈRE.</head>\n",
    "    <sp who='FABRICE'>\n",
    "      <speaker>FABRICE, seul.</speaker>\n",
    "      <stage>ARIETTE.</stage>\n",
    "      <l id=\"1\">J'aime l'éclat des Françaises,</l>\n",
    "      <l id=\"2\">L'air fripon des Milanaises,</l>\n",
    "      <l id=\"3\">La fraîcheur des Hollandaises,</l>\n",
    "      <l id=\"4\">Le port noble des Anglaises ; </l>\n",
    "      <l id=\"5\">Allemandes, Piémontaises,</l>\n",
    "      <l id=\"6\">Toutes m'enivrent d'amour,</l>\n",
    "      <l id=\"7\">Et m'enflamment tour tour !...</l>\n",
    "      <l id=\"8\">Mais mon aimable Jeanette</l>\n",
    "      <l id=\"9\">Est si belle, si bien faite,</l>\n",
    "      <l id=\"10\">Qu'elle fait tourner la tête ;</l>\n",
    "      <l id=\"11\">Elle enchante tous les yeux,</l>\n",
    "      <l id=\"12\">Elle est l'objet de mes vœux.</l>\n",
    "      <l id=\"13\">J'aime l'éclat, etc.</l>\n",
    "      <stage>Il sort.</stage>\n",
    "    </sp>\n",
    "  </div2>\n",
    "</div1>\n",
    "```\n",
    "\n",
    "The collection contains plays of different dramatic (sub)genres, three of which will be studied in the present chapter: \"<span class=\"index\">Comédie</span>\", \"<span class=\"index\">Tragédie</span>\" and \"<span class=\"index\">Tragi-comédie</span>\". The genre of each play is encoded in the `<genre>` tag, and, as can be observed from the excerpt above, all spoken text in these plays (i.e. direct speech) is enclosed with the `<l>` tag. The remaining texts reside inside `<p>` tags. Both elements can be retrieved using a simple <span class=\"index\">XPath</span> expression (cf. section {ref}`sec-getting-data-xml` in the previous chapter), as shown in the following code block:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a840191a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import lxml.etree\n",
    "import tarfile\n",
    "\n",
    "tf = tarfile.open('data/theatre-classique.tar.gz', 'r')\n",
    "tf.extractall('data')\n",
    "\n",
    "subgenres = ('Comédie', 'Tragédie', 'Tragi-comédie')\n",
    "\n",
    "plays, titles, genres = [], [], []\n",
    "for fn in os.scandir('data/theatre-classique'):\n",
    "    # Only include XML files\n",
    "    if not fn.name.endswith('.xml'):\n",
    "        continue\n",
    "    tree = lxml.etree.parse(fn.path)\n",
    "    genre = tree.find('//genre')\n",
    "    title = tree.find('//title')\n",
    "    if genre is not None and genre.text in subgenres:\n",
    "        lines = []\n",
    "        for line in tree.xpath('//l|//p'):\n",
    "            lines.append(' '.join(line.itertext()))\n",
    "        text = '\\n'.join(lines)\n",
    "        plays.append(text)\n",
    "        genres.append(genre.text)\n",
    "        titles.append(title.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ed2c8a5",
   "metadata": {},
   "source": [
    "Let us inspect the distribution of the dramatic subgenres (henceforth simply \"genres\") in\n",
    "this corpus:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d2e228b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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UJUuWrPV8fX19Lrroolx00UUtfub06dNz0kknZdq0aetcc/jhh+dnP/tZunfv3uJ+rdGnT580Njamd+/emTNnTk333pKc/Mt5bT0CW4kJw7q19QgAAABAodq39QAlmTFjRjXo9u7dO0cffXQGDhyYbt26pbm5OZMmTcr48ePT1NSUhx56KEcddVQmTZqUbt3WHm/Gjx+fs88+O0nSrl27nHLKKRk8eHDat2+fRx55JNdff32WLl2a0aNHp2PHjrngggvWOdvcuXMzZMiQzJo1K0lywAEH5PTTT0+vXr0yc+bMXHvttZk5c2YefvjhDBs2LA8++GC6dOlS498QAAAAALC5uVO3Fb7whS9k5syZ+epXv5rBgwenXbs1n17xwgsvZMiQIXn66aeTJGeccUZ+9KMfrbHu5Zdfzu6775433ngj7dq1yx133JHjjz9+tTWTJk3K4MGDs3jx4rRv3z5PPvlk9tprr7XO9pnPfCY333xzkuSUU07JDTfckPbt/9rsm5qactxxx+XBBx9Mklx44YW59NJLN+4XsRbu1IXWcacuAAAAsLFE3VZ47bXXssMOO7S47n/+539y4IEHJkk6d+6cl19+OZ07d15tzQUXXJB///d/T5Kcd955+f73v7/WvcaMGZOvfOUrSd4KtzfddNMaa6ZNm5b99tsvlUolPXv2zIwZM9LQ0LDGusbGxuy+++5pbm5O586d09jYmO23377Fn2dDiLrQOqIuAAAAsLG8KK0VNiToJsmHPvSh6h21ixcvzrPPPrvGmgkTJlSPv/zlL69zr5EjR1Yfk3DXXXet9dm7EyZMqL6Y7ayzzlpr0E3eemTE8OHDq3PdeeedG/TzAAAAAABbDlF3M9l2222rx+8MsdOmTcsLL7yQJNlnn32y6667rnOfrl275ogjjkiSLFq0qPr4hFXdc8891eOhQ4eud65Vz696HQAAAABQBlF3M3jzzTczY8aM6tc777zzauefeOKJ6vHBBx/c4n6rrln12iSpVCp56qmnkiT19fU56KCDNnovAAAAAGDLJ+puBjfddFNef/31JMmAAQPSo0eP1c6//RK1JOu9S3dta1a9Nklmz56dxYsXJ3nrubYdOnRY7159+/ZNfX19kuSZZ56JRyoDAAAAQFnat/UAW5uXX345F1xwQfXrCy+8cI01CxYsqB7vtNNOLe654447rvXajdmrQ4cO2XbbbTN//vwsW7YsixYtWuczeDfGsmXLMmXKlLWe69mzZ3r27FmzzwIAAACA9yJRt4befPPNnHTSSZk3b16S5IQTTsiJJ564xrqmpqbqcadOnVrcd5tttqkeL1y4cJP2enu/+fPnV/erZdSdN29eBg4cuNZzo0ePzsUXX1yzzwIAAACA9yJRt0ZWrlyZM888Mw899FCSZLfddsuPfvSjNp7q3detW7d1voDNXboAAAAAsOlE3RqoVCr5p3/6p9x4441Jkn79+uU3v/lN3v/+9691/ap3xjY3N7e4/5IlS6rHXbt23aS9WtpvU3Xo0CEDBgyo6Z4AAAAAwF95UdomqlQqOeecc3LNNdckeetlZffff3922WWXdV6z/fbbV49feeWVFj/j1VdfXeu1G7PX8uXL88YbbyR5K8B26dKlxWsAAAAAgC2HqLsJKpVKzj333Fx55ZVJkt69e2fixInZbbfd1nvdXnvtVT1+7rnnWvycVdesem2S9O3bN507d06SzJkzJ8uWLVvvXrNmzcqKFSuSJHvssUfq6upa/HwAAAAAYMsh6m6kt4Pu2LFjkyS9evXKxIkTs/vuu7d47f777189njx5covrV12z3377rXaurq4u/fv3T5KsWLEiU6dO3ei9AAAAAIAtn6i7Ed4ZdHv27JmJEydmjz322KDr99133/Tr1y9J8uc//znPP//8Otc2NTVVX77WuXPnHHnkkWusOfbYY6vH63pJ2dvuvvvu6vHQoUM3aF4AAAAAYMsh6m6EL37xi9Wg26NHj0ycODF77rlnq/Y4+eSTq8djxoxZ57qrr746ixYtSpIcf/zx1UctrGuvq666qrr+nRobG3PLLbckSbbZZpt86lOfatXMAAAAAEDbE3Vb6bzzzssPf/jDJG8F3QceeGCN59xuiK9+9avp2rVrkuSKK67IXXfdtcaaRx99NP/6r/+aJGnfvn1Gjx691r369++f4cOHJ0nmzp2bkSNHZvny5autaWpqyqmnnprm5uYkyahRo9Z46RoAAAAAsOWrq1QqlbYeohQXXnhhLrvssiRvPcv2W9/6Vvbee+8WrxswYED1cQuruv766zNixIgkSbt27XLKKafkmGOOSX19fR555JFcf/311Qh72WWX5Rvf+MY6P6OxsTGHHHJI5syZkyQ54IADMmLEiPTq1SszZ87MuHHjMnPmzCTJgQcemIceeigNDQ2t+vnXp0+fPmlsbEzv3r2rM2yNTv7lvLYega3EhGHd2noEAAAAoFCibiscddRRefDBB1t93Y9//ONqvH2nsWPHZtSoUdV4+0719fX55je/mUsuuaTFz5k2bVpOOumkTJ8+fZ1rDjvssNx2223p0aPHBs2+oURdaB1RFwAAANhY7dt6gPe6L3zhCzn66KNz5ZVX5t57783s2bOzcuXK9OrVK4MHD85ZZ52Vgw46aIP22nfffTN16tRce+21ufXWWzN9+vTMnz8/O+20Uw444IB89rOfzamnnpp27Tx1AwAAAABK5U5dasKdutA67tQFAAAANpZbNgEAAAAACiLqAgAAAAAURNQFAAAAACiIqAsAAAAAUBBRFwAAAACgIKIuAAAAAEBBRF0AAAAAgIKIugAAAAAABRF1AQAAAAAKIuoCAAAAABRE1AUAAAAAKIioCwAAAABQEFEXAAAAAKAgoi4AAAAAQEFEXQAAAACAgoi6AAAAAAAFEXUBAAAAAAoi6gIAAAAAFETUBQAAAAAoiKgLAAAAAFAQURcAAAAAoCCiLgAAAABAQURdAAAAAICCiLoAAAAAAAURdQEAAAAACiLqAgAAAAAURNQFAAAAACiIqAsAAAAAUBBRFwAAAACgIKIuAAAAAEBBRF0AAAAAgIKIugAAAAAABRF1AQAAAAAKIuoCAAAAABRE1AUAAAAAKIioCwAAAABQEFEXAAAAAKAgoi4AAAAAQEFEXQAAAACAgoi6AAAAAAAFEXUBAAAAAAoi6gIAAAAAFETUBQAAAAAoiKgLAAAAAFAQURcAAAAAoCCiLgAAAABAQURdAAAAAICCiLoAAAAAAAURdQEAAAAACiLqAgAAAAAURNQFAAAAACiIqAsAAAAAUBBRFwAAAACgIO1rsclvf/vbJMl+++2XHXbYYYOvW7BgQR5//PEkycc//vFajAIAAAAAsFWrSdQ96qijUldXlzvuuCPHH3/8Bl/36KOP5pOf/GTatWuX5cuX12IUAAAAAICt2hbx+IVKpdLWIwAAAAAAFKFNo+7bMbeurq4txwAAAAAAKEabRt0FCxYkSTp37tyWYwAAAAAAFKNNo+7tt9+eJOnXr19bjgEAAAAAUIxWvyjtzjvvzJ133rnWc9///vfzX//1X+u9vlKpZNGiRXnsscfyl7/8JXV1dfn4xz/e2jEAAAAAAN6TWh11H3vssVx33XVrPAe3Uqlk4sSJrdqrUqmkS5cuGTVqVGvHAAAAAAB4T9roxy9UKpXqv7V9r6V/2267bf7u7/4uv/vd77L77rvX5IcBAAAAANjatfpO3S996UsZMWJE9etKpZIPfvCDqaury1VXXZVjjjlmvde3a9cuDQ0Nef/739/qYQEAAAAA3utaHXW32267bLfddmt8v1KppFu3btl5551rMhgAAAAAAGtqddRdm+eeey5J0q1bt1psBwAAAADAOtQk6ro7FwAAAADg3bHRL0oDAAAAAODdV5M7dd9p7ty5efLJJzN//vw0Nzdv0DWnnXba5hgFAAAAAGCrUtOoe/PNN+fyyy/PE0880arr6urqRF0AAAAAgA1Qs6h7/vnn54orrkiSVCqVWm0LAAAAAMAqahJ177rrrvznf/5n9euPfvSjOeaYY9KnT5907NixFh8BAAAAAEBqFHWvuuqqJEl9fX2uu+66nHrqqbXYFgAAAACAd2hXi03++Mc/pq6uLp/73OcEXQAAAACAzagmUff1119PkgwePLgW2wEAAAAAsA41ibrdu3dPknTo0KEW2wEAAAAAsA41ibof/ehHkyR//vOfa7EdAAAAAADrUJOo+4UvfCGVSiXjx4/PsmXLarElAAAAAABrUZOo+4lPfCJf/OIXM3PmzIwYMULYBQAAAADYTNrXYpNZs2blK1/5Sl577bXcdNNNmTJlSs4555wceuih2WmnndKuXcvtuF+/frUYBQAAAABgq1aTqLvLLrukrq4uSVJXV5cZM2bkS1/60gZfX1dXl+XLl9diFAAAAACArVpNom6SVCqVWm0FAAAAAMA61CTqnn766bXYBgAAAACAFtQk6v74xz+uxTYAAAAAALSg5TeYAQAAAACwxRB1AQAAAAAKIuoCAAAAABRE1AUAAAAAKEhNXpT2wQ9+cJOur6ury1/+8pdajAIAAAAAsFWrSdR9/vnnU1dXl0qlst51dXV1SbLGure/DwAAAADA+tUk6vbr16/FMLtixYq89tprWbx4cZK3Qm6vXr3Svn1NRgAAAAAAeE+o2Z26G+qxxx7L9773vdx4443ZY489cvvtt2f77bevxRgAAAAAAFu9d/1FaQceeGBuuOGGjBkzJg888ED+7u/+rsXHNgAAAAAA8JZ3Peq+7Utf+lIOOeSQPPjgg7n++uvbagwAAAAAgKK0WdRNkk9/+tOpVCqiLgAAAADABmrTqNu3b98kybRp09pyDAAAAACAYrRp1J0/f36S5I033mjLMQAAAAAAitGmUffWW29NknTv3r0txwAAAAAAKEabRN2mpqacd955+c1vfpO6uroceeSRbTEGAAAAAEBx2tdikzPPPHOD1r355ptpbGzMH/7whzQ3NydJ6uvr89WvfrUWYwAAAAAAbPVqEnWvu+661NXVbfD6SqWSJOnUqVOuueaa7L///rUYAwAAAABgq1eTqJv8NdRuiA9+8IP55Cc/mX/+53/O7rvvXqsRAAAAAAC2ejWJus8999wGrevYsWO23377dOrUqRYfCwAAAADwnlOTqLvzzjvXYhsAAAAAAFrQrq0HAAAAAABgw4m6AAAAAAAFqdmL0la1fPnyPPLII5k0aVLmzp2bhQsXpmvXrunVq1c++tGP5mMf+1jat98sHw0AAAAAsFWraVmtVCr53ve+lzFjxuSll15a57oePXrkK1/5Sr785S+nrq6uliMAAAAAAGzVavb4hSVLluToo4/OBRdckJdeeimVSmWd/+bOnZuvfe1rOeaYY9Lc3FyrEQAAAAAAtno1u1P385//fCZOnJgkqaury6BBgzJkyJDsueeeaWhoSFNTU2bMmJFf/epXmThxYiqVSiZOnJjPf/7zufXWW2s1BgAAAADAVq0mUff+++/P7bffnrq6uvTr1y8333xzPvrRj6517de+9rX84Q9/yGc+85k899xzuf322zNx4sR84hOfqMUoAAAAAABbtZo8fuEnP/lJkqShoSEPPPDAOoPu2z7ykY/kvvvuS9euXZMk119/fS3GAAAAAADY6tUk6j7yyCOpq6vLGWeckZ133nmDrtlll11yxhlnpFKp5OGHH67FGAAAAAAAW72aRN25c+cmeesO3NZ4e/1LL71UizEAAAAAALZ6NYm6dXV1SZKVK1e26rpKpVKLjwcAAAAAeM+oSdTt0aNHkmTy5Mmtuu4Pf/jDatcDAAAAALB+NYm6hx9+eCqVSq677rrMmjVrg6554YUX8uMf/zh1dXU5/PDDazEGAAAAAMBWryZR97TTTkuSNDU15ROf+ET++Mc/rnf9H//4xwwePDhNTU1JktNPP70WYwAAAAAAbPXa12KTT3ziEznxxBNzxx135Pnnn88hhxySo446Kn/zN3+TPffcM126dMmiRYvyzDPP5Ne//nUmTpyYSqWSurq6nHjiiTnqqKNqMQYAAAAAwFavJlE3ScaPH59PfvKT+e1vf5tKpZKJEydm4sSJa1379gvSjjzyyNxwww21GgEAAAAAYKtXk8cvJMk222yT+++/P9/5znfSo0ePVCqVdf7r2bNn/uM//iP33Xdfttlmm1qN8K5YsWJFnnzyyVx33XU577zzcuihh6Zz586pq6tLXV1dRowYsUH7XHfdddVrNuTfxRdfvEH7Ll26NGPHjs2gQYPSs2fPdOzYMX369MmwYcMyfvz4rFy5cuN/eAAAAACgzdXsTt0kadeuXb72ta/ly1/+cn73u9/l0Ucfzdy5c7Nw4cJ07do1PXv2zEc/+tEcdthhad++ph/9rhk+fHhuv/32th5jraZPn56TTjop06ZNW+37jY2NaWxszN13352rrroqP/vZz9K9e/c2mhIAAAAA2BSbpay2b98+H//4x/Pxj398c2zfplasWLHa1zvssEN23HHHPPPMMxu953nnnZdBgwatd83ee++93vNz587NkCFDMmvWrCTJAQcckNNPPz29evXKzJkzc+2112bmzJl5+OGHM2zYsDz44IPp0qXLRs8MAAAAALSNMm+XbUMf+chHss8++2TgwIEZOHBgdt1111x33XU544wzNnrPAQMG5IQTTtikuUaNGlUNuqecckpuuOGG1e6GPv/883PcccflwQcfzJ/+9KdcfvnlufTSSzfpMwEAAACAd19Nnqk7d+7cauS85557Nuiae++9NwMGDMjBBx+cV199tRZjvCu+8Y1v5Nvf/nb+/u//Prvuumtbj5MkmTZtWiZMmJAk6dmzZ6655po1Hm/R0NCQG2+8MZ06dUqSjBkzJgsWLHi3RwUAAAAANlFNou5NN92UqVOn5rnnnsvgwYM36JpBgwZl1qxZmTJlSm666aZajPGeNWHChFQqlSTJWWedlYaGhrWu6927d4YPH54kWbx4ce688853bUYAAAAAoDZqEnXvv//+1NXVZdiwYXnf+963Qde8733vy3HHHZdKpZL//u//rsUY71mr3h09dOjQ9a5d9fyG3lUNAAAAAGw5ahJ1n3jiiSRvPW+2NT784Q+vdv171Q9/+MPss88+aWhoSOfOndOvX78cf/zxGTt2bBYvXrzeayuVSp566qkkSX19fQ466KD1rj/44IOrx+/13zsAAAAAlKgmUXfevHlJ3nqea2t07949SfLSSy/VYoxiTZ48OdOnT8+iRYuyZMmSzJ49Oz//+c9zzjnnZJdddskvfvGLdV47e/bsavjt06dPOnTosN7P6tu3b+rr65MkzzzzTPWxDQAAAABAGdq3vGQDNmnfPsuWLcvSpUtbdd2bb76ZJO/ZsFhfX59DDz00RxxxRPbcc880NDRkwYIF+dOf/pRbbrklr732Wl5++eUcf/zxufHGG/OZz3xmjT1WfdnZTjvt1OJndujQIdtuu23mz5+fZcuWZdGiRet8Bu/GWLZsWaZMmbLWcz179mx1+AcAAAAAVleTqPuBD3wgs2bNytNPP92q66ZPn55kw2Lk1ubwww/P888/nz59+qxx7h//8R/z7//+7xk5cmT1JWhnnnlmPvaxj6Vfv36rrW1qaqoed+rUaYM+e5tttsn8+fOTJAsXLqxp1J03b14GDhy41nOjR4/OxRdfXLPPAgBY1cm/nNfWI7CVmDCsW1uPAACwXjWJugMGDMgLL7yQW2+9NZdccknq6upavGblypW59dZbU1dXlwMOOKAWYxRl9913X+/5rl275sYbb8xLL72UBx54IM3NzfnOd76TK6644l2acON069ZtnS9gc5cuAAAAAGy6mjxTd+jQoUmSGTNm5Fvf+tYGXfOtb30rM2bMSJL87d/+bS3G2OrU19fn3/7t36pfr+3ZuqveZdvc3LxB+y5ZsqR63LVr102YcE0dOnTIgAED1vpP1AUAAACATVeTqPv5z38+vXr1SpJcdNFFOf/88/Pqq6+ude2rr76a8847L6NHj05dXV169OiRM844oxZjbJUOPfTQ6mMVZs2aVX0p2tu233776vErr7zS4n7Lly/PG2+8keStANulS5faDQsAAAAAbHY1efzC+973vtxwww0ZMmRIVqxYkSuuuCLjxo3LYYcdln333TcNDQ1pamrKtGnT8rvf/S5Lly5NpVJJhw4d8pOf/CQdO3asxRhbpXbt2mWHHXbIiy++mOStF6N17ty5er5v377p3LlzFi9enDlz5mTZsmXp0KHDOvebNWtWVqxYkSTZY489NuhRGQAAAADAlqMmUTdJPvGJT+SWW27J6aefnoULF6a5uTkTJ07MxIkTV1tXqVSSJNtuu22uv/76DB48uFYjbJVWrlxZfalZsvqduUlSV1eX/v37Z/LkyVmxYkWmTp2aj3zkI+vcb/LkydXj/fbbr+bzAgAAAACbV00ev/C2E044IU888UTOPvvsbLvttqlUKmv822677XLOOefkiSeeyKc+9alafvxWadKkSdVn4Pbp02e1u3Tfduyxx1aP1/WSsrfdfffd1eO3n4UMAAAAAJSjplE3Sfr165exY8fmtddey5QpU3LXXXdl/PjxueuuuzJlypS8+uqr+c///M/07du31h+91Vm5cmUuuuii6tfHHXfcWtedfPLJ1eOrrroqixYtWuu6xsbG3HLLLUmSbbbZRlQHAAAAgALV7PEL79SuXbsceOCBOfDAAzfXRxTr97//fZ544omcdtpp1ZegvdOiRYty9tln57777kuSdOzYMRdccMFa1/bv3z/Dhw/PLbfckrlz52bkyJH5yU9+kvbt//qft6mpKaeeemqam5uTJKNGjVrjUQ4AAAAAwJZvs0XdrdVzzz2Xa6+9drXvPf7449XjqVOn5sILL1zt/KBBgzJo0KDq1y+99FLOPvvsfOUrX8kxxxyTgQMHpm/fvunSpUtef/31TJkyJTfffHNeffXVJG89N3fcuHHZZZdd1jnXmDFj8rvf/S5z5szJT3/60zz11FMZMWJEevXqlZkzZ2bcuHGZOXNmkuTAAw/M17/+9U39VQAAAAAAbUDUbaUXXnghl1122TrPP/7446tF3iRp3779alH3bU1NTbnjjjtyxx13rHO/Hj16ZNy4cRk2bNh65+rdu3d+9atf5aSTTsr06dPz+OOPZ9SoUWusO+yww3LbbbeloaFhvfsBAAAAAFsmUbcNHH300bnzzjvz6KOP5g9/+ENmz56dV199NQsWLEjnzp3TrVu3DBgwIMOGDcvw4cPX+YiGd9p3330zderUXHvttbn11lszffr0zJ8/PzvttFMOOOCAfPazn82pp56adu1q/ihlAAAAAOBdUlepVCptPQTl69OnTxobG9O7d+/MmTOnrcfZbE7+5by2HoGtxIRh3dp6BICtjr/T1Iq/0wDAls4tmwAAAAAABRF1AQAAAAAKIuoCAAAAABRE1AUAAAAAKIioCwAAAABQEFEXAAAAAKAgoi4AAAAAQEFEXQAAAACAgoi6AAAAAAAFEXUBAAAAAAoi6gIAAAAAFETUBQAAAAAoiKgLAAAAAFAQURcAAAAAoCCiLgAAAABAQURdAAAAAICCiLoAAAAAAAURdQEAAAAACiLqAgAAAAAURNQFAAAAACiIqAsAAAAAUBBRFwAAAACgIKIuAAAAAEBBRF0AAAAAgIKIugAAAAAABRF1AQAAAAAKIuoCAAAAABRE1AUAAAAAKIioCwAAAABQEFEXAAAAAKAgoi4AAAAAQEFEXQAAAACAgoi6AAAAAAAFEXUBAAAAAAoi6gIAAAAAFETUBQAAAAAoiKgLAAAAAFAQURcAAAAAoCCiLgAAAABAQURdAAAAAICCiLoAAAAAAAURdQEAAAAACiLqAgAAAAAURNQFAAAAACiIqAsAAAAAUBBRFwAAAACgIKIuAAAAAEBBRF0AAAAAgIKIugAAAAAABRF1AQAAAAAKIuoCAAAAABRE1AUAAAAAKIioCwAAAABQEFEXAAAAAKAgoi4AAAAAQEFEXQAAAACAgoi6AAAAAAAFEXUBAAAAAAoi6gIAAAAAFETUBQAAAAAoiKgLAAAAAFAQURcAAAAAoCCiLgAAAABAQURdAAAAAICCiLoAAAAAAAURdQEAAAAACiLqAgAAAAAURNQFAAAAACiIqAsAAAAAUBBRFwAAAACgIKIuAAAAAEBBRF0AAAAAgIKIugAAAAAABRF1AQAAAAAKIuoCAAAAABRE1AUAAAAAKIioCwAAAABQEFEXAAAAAKAgoi4AAAAAQEFEXQAAAACAgoi6AAAAAAAFEXUBAAAAAAoi6gIAAAAAFETUBQAAAAAoiKgLAAAAAFAQURcAAAAAoCCiLgAAAABAQURdAAAAAICCiLoAAAAAAAURdQEAAAAACiLqAgAAAAAURNQFAAAAACiIqAsAAAAAUBBRFwAAAACgIKIuAAAAAEBBRF0AAAAAgIKIugAAAAAABRF1AQAAAAAKIuoCAAAAABRE1AUAAAAAKIioCwAAAABQEFEXAAAAAKAgoi4AAAAAQEFEXQAAAACAgoi6AAAAAAAFEXUBAAAAAAoi6gIAAAAAFETUBQAAAAAoiKgLAAAAAFAQURcAAAAAoCCiLgAAAABAQURdAAAAAICCiLoAAAAAAAURdQEAAAAACiLqAgAAAAAURNQFAAAAACiIqAsAAAAAUBBRFwAAAACgIKIuAAAAAEBBRN1WWrFiRZ588slcd911Oe+883LooYemc+fOqaurS11dXUaMGNHqPZ999tl87Wtfy3777ZftttsuDQ0N2WuvvXLuuefmsccea9VeS5cuzdixYzNo0KD07NkzHTt2TJ8+fTJs2LCMHz8+K1eubPV8AAAAAMCWo31bD1Ca4cOH5/bbb6/ZfldffXW+9KUvZcmSJat9f8aMGZkxY0auuuqqXHTRRbnooota3Gv69Ok56aSTMm3atNW+39jYmMbGxtx999256qqr8rOf/Szdu3ev2c8AAAAAALx7RN1WWrFixWpf77DDDtlxxx3zzDPPtHqv8ePH5+yzz06StGvXLqecckoGDx6c9u3b55FHHsn111+fpUuXZvTo0enYsWMuuOCCde41d+7cDBkyJLNmzUqSHHDAATn99NPTq1evzJw5M9dee21mzpyZhx9+OMOGDcuDDz6YLl26tHpmAAAAAKBtibqt9JGPfCT77LNPBg4cmIEDB2bXXXfNddddlzPOOKNV+7z88ss599xzk7wVdO+4444cf/zx1fOnnXZazjjjjAwePDiLFy/OhRdemBNOOCF77bXXWvcbNWpUNeiecsopueGGG9K+/V//855//vk57rjj8uCDD+ZPf/pTLr/88lx66aWt/fEBAAAAgDbmmbqt9I1vfCPf/va38/d///fZddddN3qf7373u3njjTeSJOeee+5qQfdthxxySDW8Ll++PJdccsla95o2bVomTJiQJOnZs2euueaa1YJukjQ0NOTGG29Mp06dkiRjxozJggULNnp+AAAAAKBtiLpt5O0ImyRf/vKX17lu5MiR1cck3HXXXWs8e/ftvSqVSpLkrLPOSkNDw1r36t27d4YPH54kWbx4ce68886Nnh8AAAAAaBuibhuYNm1aXnjhhSTJPvvss947frt27ZojjjgiSbJo0aI8+OCDa6y55557qsdDhw5d72even7V6wAAAACAMoi6beCJJ56oHh988MEtrl91zarXJkmlUslTTz2VJKmvr89BBx200XsBAAAAAFs+UbcNPP3009XjDXku76prVr02SWbPnp3FixcnSfr06ZMOHTqsd6++ffumvr4+SfLMM89UH9sAAAAAAJShfctLqLVVX1C20047tbh+xx13XOu1G7NXhw4dsu2222b+/PlZtmxZFi1atM5n8G6MZcuWZcqUKWs917Nnz/Ts2bNmnwUAAAAA70WibhtoamqqHnfq1KnF9dtss031eOHChZu019v7zZ8/v7pfLaPuvHnzMnDgwLWeGz16dC6++OKafRYAAAAAvBeJutRUt27d1vkCNnfpAgAAAMCmE3XbwKp3xjY3N7e4fsmSJdXjrl27btJeLe23qTp06JABAwbUdE8AAAAA4K+8KK0NbL/99tXjV155pcX1r7766lqv3Zi9li9fnjfeeCPJWwG2S5cuLV4DAAAAAGw5RN02sNdee1WPn3vuuRbXr7pm1WuTpG/fvuncuXOSZM6cOVm2bNl695o1a1ZWrFiRJNljjz1SV1e3wXMDAAAAAG1P1G0D+++/f/V48uTJLa5fdc1+++232rm6urr0798/SbJixYpMnTp1o/cCAAAAALZ8om4b2HfffdOvX78kyZ///Oc8//zz61zb1NSUhx56KEnSuXPnHHnkkWusOfbYY6vH63pJ2dvuvvvu6vHQoUNbMzYAAAAAsAUQddvIySefXD0eM2bMOtddffXVWbRoUZLk+OOPrz5qYV17XXXVVdX179TY2JhbbrklSbLNNtvkU5/61EbNDgAAAAC0HVG3jXz1q19N165dkyRXXHFF7rrrrjXWPProo/nXf/3XJEn79u0zevTote7Vv3//DB8+PEkyd+7cjBw5MsuXL19tTVNTU0499dQ0NzcnSUaNGrXGS9cAAAAAgC1f+7YeoDTPPfdcrr322tW+9/jjj1ePp06dmgsvvHC184MGDcqgQYNW+163bt3ygx/8ICNGjMjKlStz4okn5pRTTskxxxyT+vr6PPLII7n++uurEfaSSy7J3nvvvc65xowZk9/97neZM2dOfvrTn+app57KiBEj0qtXr8ycOTPjxo3LzJkzkyQHHnhgvv71r2/S7wEAAAAAaBuibiu98MILueyyy9Z5/vHHH18t8iZv3WX7zqibJKeffnoWL16cUaNGpbm5OTfddFNuuumm1dbU19fnm9/8Zr7xjW+sd67evXvnV7/6VU466aRMnz49jz/+eEaNGrXGusMOOyy33XZbGhoa1rsfAAAAALBlEnXb2Be+8IUcffTRufLKK3Pvvfdm9uzZWblyZXr16pXBgwfnrLPOykEHHbRBe+27776ZOnVqrr322tx6662ZPn165s+fn5122ikHHHBAPvvZz+bUU09Nu3aeugEAAAAApaqrVCqVth6C8vXp0yeNjY3p3bt35syZ09bjbDYn/3JeW4/AVmLCsG5tPQLAVsffaWrF32kAYEvnlk0AAAAAgIKIugAAAAAABRF1AQAAAAAKIuoCAAAAABRE1AUAAAAAKIioCwAAAABQEFEXAAAAAKAgoi4AAAAAQEFEXQAAAACAgoi6AAAAAAAFEXUBAAAAAAoi6gIAAAAAFETUBQAAAAAoiKgLAAAAAFAQURcAAAAAoCCiLgAAAABAQURdAAAAAICCiLoAAAAAAAURdQEAAAAACiLqAgAAAAAURNQFAAAAACiIqAsAAAAAUBBRFwAAAACgIKIuAAAAAEBBRF0AAAAAgIKIugAAAAAABRF1AQAAAAAKIuoCAAAAABRE1AUAAAAAKIioCwAAAABQEFEXAAAAAKAgoi4AAAAAQEFEXQAAAACAgoi6AAAAAAAFEXUBAAAAAAoi6gIAAAAAFETUBQAAAAAoiKgLAAAAAFAQURcAAAAAoCCiLgAAAABAQURdAAAAAICCiLoAAAAAAAURdQEAAAAACiLqAgAAAAAURNQFAAAAACiIqAsAAAAAUBBRFwAAAACgIKIuAAAAAEBBRF0AAAAAgIKIugAAAAAABRF1AQAAAAAKIuoCAAAAABRE1AUAAAAAKIioCwAAAABQEFEXAAAAAKAgoi4AAAAAQEFEXQAAAACAgoi6AAAAAAAFEXUBAAAAAAoi6gIAAAAAFETUBQAAAAAoiKgLAAAAAFAQURcAAAAAoCCiLgAAAABAQURdAAAAAICCiLoAAAAAAAURdQEAAAAACiLqAgAAAAAURNQFAAAAACiIqAsAAAAAUBBRFwAAAACgIKIuAAAAAEBBRF0AAAAAgIKIugAAAAAABRF1AQAAAAAKIuoCAAAAABRE1AUAAAAAKIioCwAAAABQEFEXAAAAAKAgoi4AAAAAQEFEXQAAAACAgoi6AAAAAAAFEXUBAAAAAAoi6gIAAAAAFETUBQAAAAAoiKgLAAAAAFAQURcAAAAAoCCiLgAAAABAQURdAAAAAICCiLoAAAAAAAURdQEAAAAACiLqAgAAAAAURNQFAAAAACiIqAsAAAAAUBBRFwAAAACgIKIuAAAAAEBBRF0AAAAAgIKIugAAAAAABRF1AQAAAAAKIuoCAAAAABRE1AUAAAAAKIioCwAAAABQEFEXAAAAAKAgoi4AAAAAQEFEXQAAAACAgoi6AAAAAAAFEXUBAAAAAAoi6gIAAAAAFETUBQAAAAAoiKgLAAAAAFAQURcAAAAAoCCiLgAAAABAQURdAAAAAICCiLoAAAAAAAURdQEAAAAACiLqAgAAAAAURNQFAAAAACiIqAsAAAAAUBBRFwAAAACgIKJuGzrqqKNSV1e3wf+ef/75Fvd89tln87WvfS377bdftttuuzQ0NGSvvfbKueeem8cee2yz/0wAAAAAwOYl6m5Frr766hxwwAH57ne/m6eeeipvvPFGFi1alBkzZuSHP/xhPvzhD+f//t//29ZjAgAAAACboH1bD8Bb7rjjjhbXdOvWbZ3nxo8fn7PPPjtJ0q5du5xyyikZPHhw2rdvn0ceeSTXX399li5dmtGjR6djx4654IILajY7AAAAAPDuEXW3ECeccMJGX/vyyy/n3HPPTfJW0L3jjjty/PHHV8+fdtppOeOMMzJ48OAsXrw4F154YU444YTstddemzo2AAAAAPAu8/iFrcB3v/vdvPHGG0mSc889d7Wg+7ZDDjkkl156aZJk+fLlueSSS97VGQEAAACA2hB1twITJkyoHn/5y19e57qRI0emS5cuSZK77rorS5Ys2eyzAQAAAAC1JeoWbtq0aXnhhReSJPvss0923XXXda7t2rVrjjjiiCTJokWL8uCDD74rMwIAAAAAtSPqbiGOO+649O7dO+973/vy/ve/P/3798/IkSMzceLE9V73xBNPVI8PPvjgFj9n1TWrXgsAAAAAlEHU3UL88pe/zIsvvphly5ZlwYIFmTZtWsaNG5dBgwZl8ODBmTt37lqve/rpp6vH67tLd21rVr0WAAAAAChD+7Ye4L3u/e9/f4455ph8+MMfTu/evVNfX5/Gxsbcd999ueeee1KpVHL//ffn0EMPzaRJk9KjR4/Vrl+wYEH1eKeddmrx83bccce1Xlsry5Yty5QpU9Z6rmfPnunZs2fNPxMAAAAA3ktE3Tb07W9/OwMHDsz73ve+Nc6NGjUqf/zjH3PSSSdl1qxZeeGFF3LmmWfm7rvvXm1dU1NT9bhTp04tfuY222xTPV64cOEmTL928+bNy8CBA9d6bvTo0bn44otr/pkAAAAA8F4i6rahQw89dL3nP/zhD+fee+/NQQcdlKVLl+aee+7J5MmTN+jZuW2lW7duueeee9Z6zl26AAAAALDpRN0t3D777JPPf/7zGTduXJLkF7/4xWpRt6GhoXrc3Nzc4n5LliypHnft2rWGk76lQ4cOGTBgQM33BQAAAADe4kVpBfjEJz5RPf7zn/+82rntt9++evzKK6+0uNerr7661msBAAAAgDKIugX4wAc+UD1+58vN9tprr+rxc8891+Jeq65Z9VoAAAAAoAyibgFWvQP3nXfX7r///tXjyZMnt7jXqmv222+/TR8OAAAAAHhXiboFmDhxYvX4nXfX7rvvvunXr1+Stx7N8Pzzz69zn6ampjz00ENJks6dO+fII4+s/bAAAAAAwGYl6m7hZsyYkRtuuKH69XHHHbfGmpNPPrl6PGbMmHXudfXVV2fRokVJkuOPPz6dO3eu4aQAAAAAwLtB1G0j3//+9/O73/1uvWumTp2aIUOGpLm5OUnyN3/zN/noRz+6xrqvfvWr6dq1a5LkiiuuyF133bXGmkcffTT/+q//miRp3759Ro8evak/AgAAAADQBtq39QDvVffff3/++Z//ObvttluOPvro7Lffftlxxx1TX1+fF198Mffdd1/uvvvurFy5Mkmy884758c//vFa9+rWrVt+8IMfZMSIEVm5cmVOPPHEnHLKKTnmmGNSX1+fRx55JNdff301Dl9yySXZe++937WfFQAAAACoHVG3jf3lL3/JX/7yl/WuGTJkSH70ox+lV69e61xz+umnZ/HixRk1alSam5tz00035aabblptTX19fb75zW/mG9/4Rk1mBwAAAADefaJuG/ne976Xv/3bv82jjz6a//mf/8m8efPyyiuvZOnSpdluu+2yyy675NBDD82pp5661kcurM0XvvCFHH300bnyyitz7733Zvbs2Vm5cmV69eqVwYMH56yzzspBBx20mX8yAAAAAGBzqqtUKpW2HoLy9enTJ42Njendu3fmzJnT1uNsNif/cl5bj8BWYsKwbm09AsBWx99pasXfaQBgS+dFaQAAAAAABRF1AQAAAAAKIuoCAAAAABRE1AUAAAAAKIioCwAAAABQEFEXAAAAAKAgoi4AAAAAQEFEXQAAAACAgoi6AAAAAAAFEXUBAAAAAAoi6gIAAAAAFETUBQAAAAAoiKgLAAAAAFAQURcAAAAAoCCiLgAAAABAQURdAAAAAICCiLoAAAAAAAURdQEAAAAACiLqAgAAAAAURNQFAAAAACiIqAsAAAAAUJD2bT0AAAAAAFu3k385r61HYCsxYVi3th5hi+BOXQAAAACAgoi6AAAAAAAFEXUBAAAAAAoi6gIAAAAAFETUBQAAAAAoiKgLAAAAAFAQURcAAAAAoCCiLgAAAABAQURdAAAAAICCiLoAAAAAAAURdQEAAAAACiLqAgAAAAAURNQFAAAAACiIqAsAAAAAUBBRFwAAAACgIKIuAAAAAEBBRF0AAAAAgIKIugAAAAAABRF1AQAAAAAKIuoCAAAAABRE1AUAAAAAKIioCwAAAABQEFEXAAAAAKAgoi4AAAAAQEFEXQAAAACAgoi6AAAAAAAFEXUBAAAAAAoi6gIAAAAAFETUBQAAAAAoiKgLAAAAAFAQURcAAAAAoCCiLgAAAABAQURdAAAAAICCiLoAAAAAAAURdQEAAAAACiLqAgAAAAAURNQFAAAAACiIqAsAAAAAUBBRFwAAAACgIKIuAAAAAEBBRF0AAAAAgIKIugAAAAAABRF1AQAAAAAKIuoCAAAAABRE1AUAAAAAKIioCwAAAABQEFEXAAAAAKAgoi4AAAAAQEFEXQAAAACAgoi6AAAAAAAFEXUBAAAAAAoi6gIAAAAAFETUBQAAAAAoiKgLAAAAAFAQURcAAAAAoCCiLgAAAABAQURdAAAAAICCiLoAAAAAAAURdQEAAAAACiLqAgAAAAAURNQFAAAAACiIqAsAAAAAUBBRFwAAAACgIKIuAAAAAEBBRF0AAAAAgIKIugAAAAAABRF1AQAAAAAKIuoCAAAAABRE1AUAAAAAKIioCwAAAABQEFEXAAAAAKAgoi4AAAAAQEFEXQAAAACAgoi6AAAAAAAFEXUBAAAAAAoi6gIAAAAAFETUBQAAAAAoiKgLAAAAAFAQURcAAAAAoCCiLgAAAABAQURdAAAAAICCiLoAAAAAAAURdQEAAAAACiLqAgAAAAAURNQFAAAAACiIqAsAAAAAUBBRFwAAAACgIKIuAAAAAEBBRF0AAAAAgIKIugAAAAAABRF1AQAAAAAKIuoCAAAAABRE1AUAAAAAKIioCwAAAABQEFEXAAAAAKAgoi4AAAAAQEFEXQAAAACAgoi6W6G77rorn/70p7PLLrukU6dO6datWw477LD8x3/8R9544422Hg8AAAAA2ATt23oAaqepqSmnnnpq7rrrrtW+//LLL+fll1/O73//+/zgBz/ILbfckkMOOaSNpgQAAAAANoU7dbcSK1asyKc//elq0O3evXsuvPDC3HTTTfnP//zPfOxjH0uSzJ49O0OHDs2f//znthyXrdSS117Kkzf+R5a89lJbjwIAvIO/0wCw5fJ3mtYSdbcS48aNy7333psk2XffffM///M/ufTSS/OZz3wm5557bh5++OF85StfSZLMnz8/Z599dluOy1aq+bWXMu2n302zP0IAsMXxdxoAtlz+TtNaou5WYMWKFbnkkkuqX99www3p3r37Guu+853v5MADD0ySPPTQQ/n1r3/9bo0IAAAAANSIqLsV+O1vf5u5c+cmSY488sgMGDBgrevq6+tz/vnnV7/+6U9/+q7MBwAAAADUjqi7Fbjnnnuqx0OHDl3v2k9+8pNrvQ4AAAAAKIOouxV44oknqscHH3zwetf26NEjffv2TZK89NJLefnllzfrbAAAAABAbYm6W4Gnn366erzrrru2uH7VNateCwAAAABs+dq39QBsugULFlSPd9pppxbX77jjjmu9dlPMmzcvSTJ37ty1vqQteeuZvu3alf3/I7zWvLKtR9iirVy+LEny29GfSbv2Hdp4mi1bn05l/28BYEvk7/T6+Tu94fydBqg9f6fXz9/pDfde+Dvdo0eP/PGPf1zvGlF3K9DU1FQ97tSpU4vrt9lmm+rxwoULazLDihUrkiQrV66sBl7eu5a+/kpbj7DFa2zrAQB4z/J3umX+TgPQVvydbpm/028RdamJTp06pbm5Oe3atcsOO+yw1jVbw526AAAAALA59ejRo8U1ou5WoKGhIfPnz0+SNDc3p6GhYb3rlyxZUj3u2rVrTWZYtGhRTfYBAAAAANbPbZNbge233756/MorLd+m/+qrr671WgAAAABgyyfqbgX22muv6vFzzz3X4vpV16x6LQAAAACw5RN1twL7779/9Xjy5MnrXfvSSy9l9uzZSZJu3brlAx/4wGadDQAAAACoLVF3K3DsscdWj++55571rr377rurx0OHDt1sMwEAAAAAm4eouxU48sgjq2/Fe+CBBzJlypS1rluxYkW+//3vV78+5ZRT3pX5AAAAAIDaEXW3AvX19bnooouqX5922mmZN2/eGuu+/vWv57HHHkuSfOxjH8uQIUPerREBAAAAgBqpq1QqlbYegk23fPnyDB06NP/93/+dJOnRo0dGjhyZfffdN6+99lp++tOf5uGHH06SbL/99nn44YfTv3//thwZAAAAANgIou5WZOHChfnsZz+bX/ziF+tc06dPn0yYMCGHHXbYuzgZAAAAAFArou5W6M4778xPfvKTTJ48OfPmzUvXrl2z22675e/+7u9y9tlnZ7vttmvrEQEAAACAjSTqAgAAAAAUxIvSAAAAAAAKIuoCAAAAABSkfVsPAPD/27v3uBzv/w/gr7tS6SASoSinoZwtOYeyMsxQS2bKYsN3ITan2fD9bjb7xvDNmeSQJcvP+TwlRs4xsjl0VihJvunc5/dHj67vne5D6Z7cvJ6PR4/H1X29r/fnc9/NPvf1vj7X5yoqKsLSpUuRm5sLQ0NDzJgxA/r6+jXdLSIiIiIiIiKi1xKLukRU4+bMmYOlS5dCR0cHoaGhLOgSERG9BgIDA5GRkQEdHR1MnToVdevWrekuERERkZY5c+YMTpw4AQAYMGAAnJycarhHbw4uv0BENWrPnj1YunQpAGDZsmXw8PDQWG6ZTAaZTIb+/fsr3O/j4yPFJCQkaKxdIiIibRcYGAg/Pz8sWrQIxsbGLOgSERFRlSUlJWH48OFYtGgRjhw5AgcHh5ru0huFRV2iVyAnJwfbt2/H+PHj0b59e1haWkJfXx9mZmZo2bIlRo0ahZ9//hmpqak13dVXKi4uDj4+PgCAr776CtOmTavZDhEREREuXLiAmTNnAgCmT58ubWuCra0tZDIZbG1tFe5fuHChdME1MjJSY+0SEREBHGdepYKCAnh4eCAzMxOtW7fGgQMHYGRkpJHc6v6OCQkJ0v6ymsObiEVdor9RcXExAgICYGtri08++QTBwcG4efMmHj16hMLCQmRnZyMuLg67d+/GjBkz0LRpU4wePRr37t2r6a7/7fLy8uDu7o6nT5/i448/xpIlS2q6S0RE9IaT/4Jf3Z839QQhMzMTH330EQoKCuDp6Ylly5bVdJeIiKiaOP5RTZg5cyYuXLgAS0tLHDlyBBYWFjXdpTcO19Ql+ptkZWVh9OjROHr0qPRaq1at4Obmhnbt2sHCwgI5OTlITU1FZGQkoqKiUFBQgJ07dyIvLw979uypuc6/AlOnTsXVq1fh7OyMzZs3QyaTvfI+BAcHIzg4+JW3S0RE9DoSQuCTTz5BYmIi+vfvj61bt9bI+ExERETabefOnQgMDISJiQkOHTqEFi1a1HSX3kgyIYSo6U4QvWmKioowcOBAnD59GgBgaWmJwMBAjBo1SunJUUZGBgICAvCf//wHgwYNeuOLuq9C2Wft5OTEW2uIiAjPnz/HsWPHlO6/ceMGvvnmGwCAvb09vvvuO6WxzZo1Q9euXTXex5q0ePFifP311+jQoQNOnz4NMzMzjbdha2uLxMRE2NjYcD17IqJXhOMfvUp//fUXHBwckJubiwMHDsDV1VXjbSxcuBCLFi0CAERERCh9js6bjjN1if4G8+bNkwq6zZo1w6lTp5SuHVfGwsICP/74Iz755BPs2LHjFfSSiIjo7WJkZIQPP/xQ6X75h4FZWFiojH0TzZs3D/PmzavpbhARkYZx/KNXqU2bNsjOzq7pbrwVuKYukYalpqZi5cqVAEpnioaEhKgt6Mqzt7fH999/r3Bfbm4uAgMDMWjQIDRu3Bj6+vqoX78+HBwcMH/+fLUPWgsODpbWQipbdiAmJga+vr5o0aIFateuDWtra4wYMQIXLlyocPzhw4cxfPhw2NjYwMDAANbW1hg/fjzi4+Mr/f5iYmIwbdo0dOrUCebm5jAwMECTJk0wZMgQBAUFoaioqFJ5bt26hc8++wzNmzeHoaEhLC0t4eTkhA0bNqC4uLhSOXx8fKTPQ91soby8PKxbtw5Dhw5F06ZNYWhoCDMzM7Rv3x5Tp07F7du3K9UmEREREREREVF1sahLpGGrV69Gfn4+AMDNzQ19+vTRSN6LFy+iTZs28PPzw4kTJ/DgwQMUFhYiMzMTly5dwvfff4/WrVsjKCioSn3t3r07goKCEB8fj7y8PNy/fx979uxBz549sW3bNgBAYWEhJkyYgPfffx/79u1DUlISCgoKcP/+fQQHB6Nz5864ePGiyrby8/Ph6+uLrl27YuXKlbh+/TqePHmCgoICpKWl4dChQ/D19UXnzp0RFxenMtfGjRvRuXNnbNiwAQkJCcjPz8ejR48QFRWFzz77DAMHDsTTp08r/Tmoc+rUKbRq1QqTJk3CwYMHkZKSgvz8fGRnZ+PmzZv4z3/+Azs7O/zwww8aa5OIiF4/kZGR0sXAhQsXAgDu3LmDmTNnwt7eHnXr1i23r0xKSgpWr16N0aNHw87ODqampqhVqxYsLCzg6OiIuXPnIjk5udL9SExMxNSpU/HOO++gdu3asLCwQI8ePbBixQrk5eUBKF3mQCaTVerC8r179zBnzhw4ODigQYMG0NfXh6WlJQYOHIgVK1bg+fPnlepXSkoK/P390aZNGxgZGUnvb+nSpcjNza1Ujqo8lby4uBghISHw8PCAra0tjI2NYWJigjZt2mDixIm4dOlSpdokIiLV3qTxryrjTFWUlJRg586dGDNmDFq2bAlTU1Po6+ujcePGcHZ2xr/+9S/cvXtXZY7Lly9j8uTJaNeuHczMzFC7dm3Y2Njgo48+wu7du9X24cX3XlBQgMDAQPTq1QsNGjSAiYkJOnXqhJ9++gk5OTnljn348CEWLlyITp06wczMDKampujRowc2btyIyq7aqqmJUMXFxVi3bh369u0Lc3NzGBkZoXXr1pgyZQpu3bpVqRzyDweszMP9NDX57JUTRKRR3bp1EwAEABEaGqqRnNeuXRPGxsZSXjs7O/HDDz+I0NBQsXr1avHee+9J+wCIjRs3KsyzefNmKcbd3V3IZDJhYWEhZs+eLUJCQkRwcLD46KOPpBg9PT1x584d4efnJwAIe3v7cu06OjpKsa1atRL5+fkK2y0sLBT9+/eXYps0aSJmzJghgoODRVhYmFi2bJno06ePtN/Kyko8evRIYa7w8HAhk8mk2IEDB4pVq1aJ0NBQsXjxYtGmTRsBQIwcOVKKcXJyUpjL29tbiomPj1cYc+jQIVGrVi0BQOjo6Ij3339fLFu2TOzcuVNs2bJF+Pn5CTMzMynP4sWL1f49iYjo9RQREaFy7JDfv2DBArFt2zZRu3btcmNw2T75Y+THLWU/+vr6Ssdvebt27Sr3neDFn06dOomUlBRhY2MjAAgbGxuluYqLi8XcuXOFnp6eyr5ZW1uLS5cuqezXgQMHhKmpqdIc7du3F0lJSWr7tWDBAumYiIgIpe398ccfom3btmo/1y+++EIUFRWp/VyJiN5mb9P4V9lxpiquXbtWqTGpbt26Co8vKioSU6ZMUft59e3bV+l5shCi3HtPS0srV5t48cfBwUE8efJECCHEuXPnhKWlpdJYLy8vUVJSovIziIyMFFZWVir7r6urq/Z8OSMjQzg4OCjNYWhoKEJCQtT+HePj46X93t7eStvLy8sTn376qdrP3t7eXty7d09l32sC19Ql0qCcnBxcvXpV+r13797VzllSUoKPP/5YupI2YcIErFmzBnp6//vnO3nyZGzatAkTJ06EEAJTp06Fs7Ozytk5v/76K7p27YqjR4/CwsJCet3b2xv29vZYsGABioqK4OnpiatXr2LixIlYs2YNdHV1pdiJEyfC1dUVJ0+exN27d7F37154eHhUaOubb76RroJOnDgRK1euhKGhYbkYf39/rFy5EtOmTcP9+/fh7++P7du3l4t5+vQpJk2aJF0p/PnnnzF9+vRyMTNnzsTYsWOxa9cu5R9qJaWlpWHs2LEoLCxEw4YNsXfvXvTo0aNczLhx4zB79my4ublJDxgYMWIE2rZtW+32iYjo9XX27Fl8//33kMlk8Pb2Rt++fWFsbIy7d++iWbNmUlxeXh6EEGjTpg0GDBgAOzs7WFhYQE9PDw8ePEBUVBT27NmDgoICTJw4EZaWlhg6dKjCNiMjI+Hl5SXNFunevTu8vLzQpEkTpKWlITQ0FNHR0fD09KzUjBJvb29prDU3N4enpye6deuGOnXq4NGjRzh48CAOHz6MlJQUDBgwAJcuXcI777xTIU90dDRGjhyJgoICAMC7776LMWPGoEmTJkhNTcWOHTtw6dIleHh4oLCwsMqf9YuuXr0KJycnPHv2DADQt29fDBkyBDY2NigpKcH169cRHByMhw8fIjAwEAUFBVi3bl212yUiojdj/NOk8+fPw9nZWTpft7KygqenJzp06ABjY2Okp6fj8uXLOHDggHRH74t8fHyk8bhWrVoYO3Ys+vXrB319fVy/fh1BQUFIT0/H6dOn0a9fP1y8eBEmJiZK+1RYWIhRo0bh8uXLGDRoED788ENYWFggLi4Oq1atQkpKCi5evIjp06dj4cKFcHV1RW5uLnx8fNCvXz/Url0bFy9exJo1a5Cbm4tffvkFLi4u+PTTTxW2V7ZMY2FhIXR0dODm5gYXFxdYWVkhLy8Ply5dwtatW/H06VNp7f65c+cq7Lebm5t0p425ubl0N29+fj4iIyMREhKC8ePHY9CgQZX/IylRVFQENzc3qVbRpEkTjB49Gh07doSRkRFSUlKwe/dunDlzBjdv3kS/fv1w9epVNGjQoNpta0zN1pSJ3ix3796VruQYGBhoJOe+ffuknB07dlQ522TSpElS7PTp0yvsl5+pq6+vL+Li4hTmyc3NLTfbpn379qKwsFBh7OnTp6W4Tz/9tML+hw8fCkNDQwFAuLi4qH2/Y8aMka7ipaSklNu3YsUKqS13d3elOXJyckSzZs1UXm0WQv1MXX9/f2l/VFSUyn7funVL6OrqCgBi0qRJat8nERG9fqoyUwmAaNiwobh27ZrKnAkJCSImJkZlzNWrV0XDhg0FANG6dWuFs2EKCwtFy5YtpbZnz55dIa6kpETMnz+/XB+VzVRau3atFDNs2DBpts6LwsPDpZm8vXv3rrC/qKhItGvXTsrl7+8viouLy8UUFxeXG1NV9UvdzJucnBzRokULAUAYGRmJffv2KcyTlZUlBgwYIOU6fvy4wjgiInq7xj9NztTNzs4uNzv1888/F7m5uQpji4qKxJ49eyq8HhYWJh1vbm4uLl++XCEmPT1ddOnSRYqbPHmywjbKZuoCEDKZTGzatKlCzIMHD0SjRo2kc+7OnTuL+vXriytXrlSI/e2338rNVFUkNTVVmJubS/9dnDt3TmFcSkqKaN++vdTurVu3KsQsXrxYaq9t27YiNTW1Qszp06crzNh+2Zm6c+bMkWImTpyo9G8nX4f4+OOPFcbUFBZ1iTTo/Pnz0j92S0tLjeT08fGRcm7ZskVlbEJCgnTbQLNmzSrsly/qjhw5UmUu+eUSAgMDlcYVFhZKyxP06NGjwv6VK1dKeY4ePaqyTSGEOHr0qNL36+TkJO1TNliUWbJkSbWKuiUlJaJ+/foCgOjZs6fafgshRM+ePQUA0bx580rFExHR66WqJ7X/93//p7G2N27cKOU9c+ZMhf3h4eHS/l69eqm8DVJ+SSNFJ7V5eXmicePGAoBo166d0uWTysybN0/KFx0dXW7f3r17pX3du3dX2q+SkhLRvXv3ap9sy59Ybdu2TWW/MzIyRJ06dQQA4ebmpjKWiOht9raMf0Jotqj7448/SrmGDBnyUjm6du0q5QgLC1MaFx8fLy15YWBgIB4+fFghRr6oO3HiRKW5vvvuu3J/T1XLRjo7O0txSUlJFfZraiJUQUGBtASErq6uyosGgYGB1S7qanLyWU3ig9KINEhUcgHxqjh//ry0/d5776mMtbGxkW77T0pKQlpamtLYF5cReFGjRo2k7e7duyuN09PTQ/369QEAT548qbA/KipK2n748CH27Nmj8ufOnTtSfGxsrLQthJBuwzAxMVHZJwBwdnZWuV+d2NhYPH78GABQr149tf3es2ePtDRF2UPniIjozWVjY4Phw4drLJ/8g1Wjo6Mr7N+zZ4+0PX36dMhkMqW5/P39VbZ17Ngx6TvC9OnToa+vrzLe29tb2j569Gi5ffIPbvH391faL5lMhpkzZ6pspzK2bNkCoPT21jFjxqiMrV+/PoYMGQKg9NZdZbe9EhFR5Wnz+KdpZQ8WB/BSD81OTEzElStXAAAtWrSAu7u70lhbW1t4eXkBKH0I+cGDB1Xm9vPzU7pP/jO3tLRUuIRimb59+0rb8ufnQOk5+tatWwEAPXv2LBerSNu2baXz+Be/T/z+++94+PAhAMDFxQUdO3ZUmmfChAmoW7euyrbU2blzp3TO/tVXX6mNL/suVFxcjN9++61abWsS19Ql0qCy4iYAZGVlaSRn2UmXqalpuUKrMu+88470RMi0tDQ0btxYYZx8XxUxMDCocqyiQmZCQoK0PW7cOJV5XpSZmSltP336VFqnqEWLFtDRUX1NqlWrVlVq60Xy/T506BAOHTpUpeMzMzPRpEmTavWBiIheX71791Z5YvmimJgYbN++HefOncOdO3eQnZ2ttMiYkpJS4bWLFy9K2wMGDFDZlrr98hdcnz17Vu6EWRH5dXBfPKG7cOGCtK3ugmp1L7hmZ2cjJiYGANC4cWPs27dP7TFln3FeXh7i4+O55j0RUTVp8/inSZmZmbh58yYAoHnz5ujQoUOVc8hP4Bo0aJDaz9XV1RVBQUEASgvg48ePVxhnbGyM9u3bK80jX1fo1q2bynNr+dgXJ3EpmgilzosTocqetVOV7xMGBgbo06cPDhw4oLY9ZRRNPlPl/v370vaL34VqEou6RBrUqFEj6OjooKSkBPn5+UhJSYG1tXW1cpY9BMTY2LhS8fILppcdq4i6oujLxr6oOsXtsgeuAMB///tfadvIyEjtsZX9vJSpblFevu9ERPTmqez4XlRUhH/84x/YsGFDpe/oyc7OrvBaamoqAKBOnTrlHnCqSL169VC3bl2lY5n8hcsvv/yyUn0qI3/B9cV+qXtwSP369VX2S53k5GSUlJQAAC5duoQRI0ZU6fgX+05ERFWnzeOfOklJSdLMWUWaNWuGrl27Aihf5LOzs3up9uTvrFX0INIXyceouivX3NxcZYH4ZSZwARUncWlyIlTZ3xmo3AQtTU7iqs7ks5rGoi6RBpmYmKBLly64fPkygNJbCDw9PauV09TUFFlZWdIsVXXki5+mpqbValsT5IvM2dnZL90n+TzPnz9XG1/Zz6sy7c2YMQNLly6tVj4iInqz1K5du1Jx06ZNw/r16wGUPtHazc0N3bt3h7W1NYyNjVGrVi0AwKNHj/D5558DKL2170Vl41plLmwCpRc3lZ3UauqCK/C/7x2a6Jc6vOBKRFTztHn8U+fkyZNKZ78CpbfgBwcHAyhfgJY/d6wK+UlYlZmU9CZN4AK0dxLX6/R9gkVdIg1zdXWVirpbtmypdlG3cePGyMrKwrNnz/Dw4UNYWlqqjL99+7a0/Trc/m9tbS3dKpmcnPzSVzHNzMxgbGyMnJwcxMXFQQih8urj3bt3X6qdMvJXoJOTk6uVi4iI3k7JyclYu3YtgNI1YCMiItC6dWuFsWW3cCpjbGyM7OzsSl3YBFRf3JQ/Kbx+/fpL3TIqn+vp06ca6Vdl2iozcuRIhIeHv3QuIiL6+7yu458m1alTR9qWL0hWhfyEp8r0+3WewFXdiVA1OYmrOpPPahoflEakYVOmTJFuUThy5Ah+//33auVzdHSUto8dO6YyNikpCX/++SeA0ltDKrMG79/NyclJ2j58+PBL55HJZHBwcABQOpjJr7mjSHUXL+/cuTPMzMwAABEREXy4ChERVdmJEyek5QLmzJmj9IQWKF1bTpWyC7XZ2dnIyMhQGfvkyROVM1A0eeHSyspK6ld6errK2MePH1drZkxZWwAvuBIRvc5e1/FPHR8fHwghlP6UzdIFSseksklGL7vGqvzzb+QfGK7M6ziBq4ymvk8AlZugxUlcpVjUJdIwKysrfPHFFwBKnwY5ZswYJCYmVvr4W7du4euvv5Z+HzVqlLS9dOlShbeklFmyZIm0XpH8cTVp9OjRUpF72bJlagdiVeTXzlu2bJnSuNzcXKxZs+al2wFKF3D/+OOPAQAZGRkq2yMiIlLkwYMH0ra6td/UXfgsu7AJlF5sVEXdfk1dcAUgPcUaKL1tVZXqXnC1sLCAvb09AODKlSvSU7KJiOj18rqOf5pkbm4ujUnx8fH4448/qpxDfgLX8ePH1cYfPXpU4bE1RZMToaryfSI/Px9nzpx56bYAzX4Xqkks6hL9DX744Qf07t0bQOns2R49eiA8PFzlAvGZmZmYP38+HBwcyt2C8v7770u3RV67dg2TJ09GUVFRheODg4OlW1yMjIwwbdo0Tb6ll2ZtbY2pU6cCKF383NXVFXFxcSqPuXbtmrSukjxvb2/pISxhYWEIDAysEFNQUIBPP/203MLnL2vevHmoW7cuAGD+/PlYvny5dMVZkZycHGzcuBG//PJLtdsmIiLtJ7/em6oZJXFxcdiyZYvKXMOHD5e2ly9frvI7xfLly1XmGjx4sDSeBgUFVWu2i/wFV1X9EkLg559/ful2ynh7ewMoXXfx22+/rXY+IiLSvNd1/NM0+QdszZ07t8rH29jYoFu3bgCAe/fu4ddff1Uam5iYiNDQUAClDy8bMmRIldvTNE1OhOrVq5e01OTx48dx48YNpbFBQUHVXs9Xk5PPahKLukR/g1q1amHfvn1wcXEBUHql0t3dHW3atMHUqVOxdu1a7Nq1C8HBwVi8eDEGDx4MKysrfP/99xXWhtHR0cH27dulgXHDhg3o0qULfvrpJ4SFhWHt2rUYPHgwxo8fLxUcV65cCRsbm1f7plVYvHgx3nvvPQClM2vatm0Ld3d3rFixAmFhYQgNDUVgYCAmTZqENm3aoHPnzti0aVOFPGZmZlizZo10m4ufnx9cXFywZs0ahIWFYcmSJejUqRNCQ0MxcuTIavfbysoKYWFhMDAwQElJCfz9/dG2bVvMnj0bW7ZsQXh4OIKDg7FgwQIMGTIEFhYWmDhxIu7du1fttomISPvJzy4KCAjA48ePK8QkJSVh2LBhateGGz58OFq2bAkAOHv2LObOnVvhxFYIgW+++QanT59WmcvY2BgLFy4EULpunaurK65evarymLt372LGjBl49OhRudeHDBmCdu3aAQCio6Mxa9Yshf2aNWsWoqOjVbZRGf/4xz9ga2sLAFi/fj1mz56NwsJCpfEFBQUICwvDqlWrqt02ERFVzus6/mnapEmTpNv4Dx48iEmTJiEvL09hbElJCfbv31/h9Tlz5kjbn3/+ucLx+PHjx3B3d5fWmvX19UXDhg018RaqTVMToWrVqiVNTCsuLoanp6fCO3LOnj2L2bNnV7vfmpx8VpP4oDSiv4m5uTmOHDmCgIAA/Pvf/8bjx49x584dlWvl6OrqwsvLC//617/Kvd6xY0dERERg5MiRSElJwY0bNxT+j8zIyAgrV66Er6+vxt9Pdejp6eHAgQOYO3cuVqxYgcLCQoSHh6t8wIn8GjfyRo0ahXXr1uGLL75AQUEBfvvttwq3czo5OSEoKAi7d++udt8HDRqEM2fOYOzYsfjrr79w584d/PTTT0rjdXV1X4u1jImIqOb17NkTjo6OOH/+PBITE9G2bVt89tlnaNeuHYqLixEdHY1t27YhJycHPj4+5dbqe5Genh42btyIQYMGoaioCEuWLEFkZCS8vLzQuHFjpKWlYefOnTh37hx69eqFxMRE3L9/X+lTradMmYLLly8jKCgIcXFx6NatG1xdXeHs7Axra2vIZDJkZmbi1q1bOH36tPTQ0xkzZpTLo6uri6CgIDg5OaGgoAABAQE4deoUxowZgyZNmiA1NRU7duzAxYsX4ejoiOTkZKSmpr70Z2pkZIR9+/ahX79+yMrKwk8//YTt27fD3d0dnTp1Qp06dfD8+XMkJyfjypUrOHHiBLKzs1+770ZERG+y13n80yRTU1P8+uuvcHZ2Rk5ODtatW4cDBw5g9OjR6NChA4yMjJCRkYGYmBgcOHAAOTk5FWaYuru7Y+zYsdi+fTsyMzPRo0cPjB07Fv369YO+vj7++OMPbNq0Sbqo2rZtW5Xno69a2USoYcOGIT8/H/7+/li9ejVGjBgBOzs7mJiY4NmzZ4iPj8elS5dw8uRJ5OXlVah5AMCXX36J8PBwXL58GbGxsbC3t4evry86d+6M/Px8REZGIiQkBDo6OhgyZAgOHjxYrb4vXrwY165dw7Fjx6TJZx988AH69u2Lxo0bo6SkBBkZGbhx4wYiIiJw+/Zt6OrqYt26ddVqV6MEEf3tnj17JrZs2SLGjRsn7OzshIWFhdDT0xN16tQRLVu2FCNHjhTLly8XaWlpKvM8f/5crFy5Ujg7OwtLS0tRq1YtUa9ePdGtWzcxb948cf/+fZXHb968WQAQAMTmzZtVxnp7e0ux8fHxKmNtbGwEAGFjY6MyTgghEhISxLfffiv69u0rGjVqJPT19YWhoaGwsrISAwYMEHPmzBERERGiuLhYZZ7Y2FgxYcIEYWNjIwwMDESDBg1E3759xbp160RRUZEQQkj9d3JyqvZ7LC4uFrt27RJjx44VrVu3FnXq1BG6urrCzMxMtG/fXnh5eYl169ap/RsSEdHrKyIiQuXYIb9/wYIFlcoZHx8vmjdvLh2n6MfPz0/ExcVJv3t7eyvNFxYWJoyMjJTm6tSpk0hJSRFWVlYCgOjYsaPSXCUlJWLJkiUq88n/WFhYiPT0dIW59u/fL0xNTZUe26FDB5GUlKT2O8OCBQukYyIiIpT2/e7du8LR0bFS/ZbJZOLbb79VmouI6G33No1/lR1nquLKlSuiVatWasejevXqKTy+sLBQTJ48WchkMpXH9+nTRzx69EhpPyp7Xh4fH1+pz1yIytcQLl68KNq0aVOpcVlXV1ds2LBBYZ709HTh4OCg9FhDQ0OxY8cOtX/Hyr7HgoICMXPmTKGnp1epvlem5vEqcaYu0StgYmKCcePGlVtz52XUrl0bfn5+8PPze6njfXx84OPjU6nY4OBglVdM5VVl/VobGxssWrQIixYtqvQxirRr1w4bNmxQGSNUrLcEVO096ujowN3dHe7u7pXtIhEREWxtbXH16lUsX74cu3fvltYWbNSoEXr16gVfX1/079+/0mOph4cHHBwcsHTpUhw5cgQpKSkwMjJC69atMXr0aEyaNAkGBgbIzMwEUHrnkDIymQyzZs3C+PHjERQUhBMnTiA2Nla6TbZu3bpo1aoV3n33XQwaNAjvvfceatWqpTDX0KFDERsbi4CAABw6dAjJyckwMjJCy5Yt4enpicmTJ8PIyKgKn5xqLVu2RHR0NI4dO4Zdu3bh7NmzSE1NxbNnz2BkZAQrKyvY2dnByckJw4YNQ/PmzTXWNhERqfc6j3+a1qVLF9y6dQs7duzAnj17cOnSJWRkZKC4uBgWFhaws7PDwIEDMWbMGIXH6+npYfXq1fD19cWGDRsQGRmJ+/fvo7CwEA0bNoSjoyO8vLw0ssTg3+Xdd99FbGwsdu/ejb179+L8+fN4+PAhcnJyYGJigqZNm6JDhw7o378/PvjgA6V3t1pYWODcuXPYsGEDtm/fjps3byI/Px9WVlZwcXGBn58f7OzspGWkqqtWrVoICAiAn58fgoKCEBERgTt37iAzMxM6OjqoX78+3nnnHTg6OsLV1RX9+vXTSLuaIhPqqh5ERERERKQ1/vjjD3Ts2BEAMG3atFf+4BgiIqKawPGP3jZ8UBoRERER0RskMDBQ2h4wYEAN9oSIiOjV4fhHbxsWdYmIiIiItMSpU6dU7l+1ahXWr18PAGjatCmGDBnyKrpFRET0t+L4R1QRl18gIiIiItISJiYmaNCgAQYPHoyOHTvCwsIC+fn5uHv3Lvbs2YOYmBgApevlHjp0CG5ubjXbYSIiIg3g+EdUEYu6RERERERawsTEBDk5OSpjjI2NsWnTJnh6er6iXhEREf29OP4RVcSiLhERERGRloiIiMDevXtx4cIFpKWl4fHjx8jLy0O9evXQtm1buLi4YNKkSWjQoEFNd5WIiEhjOP4RVcSiLhEREREREREREZEW4YPSiIiIiIiIiIiIiLQIi7pEREREREREREREWoRFXSIiIiIiIiIiIiItwqIuERERERERERERkRZhUZeIiIiIiIiIiIhIi7CoS0RERERERERERKRFWNQlIiIiIiIiIiIi0iIs6hIRERERERERERFpERZ1iYiIiIiIiIiIiLQIi7pEREREREREREREWoRFXSIiIiIiIiIiIiItwqIuERERERERERERkRZhUZeIiIiIiIiIiIhIi7CoS0RERERERERERKRFWNQlIiIiIiIiIiIi0iIs6hIRERERERERERFpERZ1iYiIiIiIiIiIiLQIi7pEREREREREREREWoRFXSIiIiIiIiIiIiItwqIuERERERERERERkRZhUZeIiIiIiIiIiIhIi7CoS0RERERERERERKRFWNQlIiIiIiIiIiIi0iIs6hIRERERERERERFpERZ1iYiIiIiIiIiIiLQIi7pEREREREREREREWoRFXSIiIiIiIiIiIiItwqIuERERERERERERkRZhUZeIiIiIiIiIiIhIi7CoS0RERERERERERKRFWNQlIiIiIiIiIiIi0iIs6hIRERERERERERFpERZ1iYiIiIiIiIiIiLQIi7pEREREREREREREWoRFXSIiIiIiIiIiIiItwqIuERERERERERERkRZhUZeIiIiIiIiIiIhIi7CoS0RERERERERERKRFWNQlIiIiIiIiIiIi0iIs6hIRERERadj+/fvxwQcfoHHjxjA0NESzZs3g7u6O3377DQAQGRkJmUwGmUyGhQsXqs01btw4tGrVCqampjAyMkLz5s0xduxYnDhxQuWxitpJTU3FvHnzYG9vDxMTE9SpUwddunTBP//5Tzx79kxlPltbW8hkMtja2gIA8vPzsWrVKvTv3x+NGzeGrq6utO9FUVFR+Oyzz9CuXTvUrVsXhoaGaNq0KUaNGoXw8HAIIVS2TURERET/o1fTHSAiIiIielMUFRXBx8cHISEh5V5PTk5GcnIywsPDMXPmTAwdOlRtruTkZHh6euLcuXMV9iUkJCAhIQEhISEYNWoUtm7dCiMjI7U5jx07Bi8vL2RmZpZ7PSYmBjExMQgJCcHJkydhZWWlNldCQgKGDRuGGzduqIzLysrCJ598ggMHDlTYl5KSgpSUFOzevRv9+vVDeHg4LCws1LZNRERE9LZjUZeIiIiISEMmTZokFXT19PQwduxYODk5wcDAANevX8emTZuwdOlSJCcnq8yTnJwMR0dHpKWlAQC6dOmCDz/8EK1atYKOjg7++usvbN26FXFxcQgPD0dOTg4OHToEmUymNGdMTAwCAgJQWFgIHx8f9OnTB6ampvjrr7+wevVqPHjwALdv38b48eNx7Ngxlf3Lz8/HyJEjcePGDfTo0QPu7u6wtrZGZmYmbt68KcVlZ2ejd+/eiI2NBQC0bt0aHh4eaNeuHfT19REXF4dffvkF169fR1RUFFxcXBAdHQ1DQ8NKfd5EREREbyuZ4H1ORERERETVFhERgYEDBwIA6tSpg2PHjsHR0bFcTEZGBlxcXHDt2jXptQULFpRbgkEIgd69e+PcuXPQ1dXFmjVrMHHixArt5efnw8fHB6GhoQCADRs2YMKECeViIiMjMWDAAOn3Jk2a4NixY7C3ty8X9+DBAzg4OCAlJQUAcPnyZXTt2rVCm7a2tkhMTJR+//HHHzF79myln4mXl5fUv4ULF2L+/PnQ1dUtF1NSUoJZs2Zh6dKlAICvv/4a3333ndKcRERERMQ1dYmIiIiINOLnn3+Wtv/9739XKOgCgIWFBUJDQ6Gnp/yGuf3790tLLixcuFBhQRcADAwMsGXLFmkN27KiqCrbt2+vUNAFgEaNGuHrr7+Wfj98+LDaXMOHD1dZ0L1+/bpU0PX19cWCBQsqFHQBQEdHBwEBAejduzcAIDAwEPn5+WrbJyIiInqbsahLRERERFRNeXl5OHLkCACgbt268PHxURrbtm1bDB48WOn+LVu2ACgt2k6dOlVlu/r6+vDy8gIA/Pnnn0hKSlIa27lz53Kzdl80aNAgaVvdOrkA1Pat7H0AwKxZs9TmGzduHADg6dOnOH/+vNp4IiIiorcZ19QlIiIiIqqma9euobCwEADQu3dv6Ovrq4wfMGAA9u/fr3BfVFQUAMDS0hInT55U2/aTJ0+k7djYWDRr1kxhXM+ePVXmsba2VphTEV1dXfTq1UtlTNn7MDQ0RGxsrLSurjL379+XtmNjY9GvXz+V8URERERvMxZ1iYiIiIiqKTU1Vdpu2bKl2vgWLVoofD0nJwcZGRkAgKSkJIwYMaJK/cjMzFS6z8LCQuWxBgYG0nZeXp7K2Pr166t9mFlCQoKUS5Pvg4iIiIi4/AIRERERUbXl5ORI20ZGRmrjjY2NFb6elZVVrX4UFBQo3aejo7mv/rVr11YbU533oup9EBERERFn6hIRERERVZt8kfb58+dq4+WLwPJMTEyk7a5du+Ly5cvV71wNMTExQVZWFszNzfH48eOa7g4RERHRG4UzdYmIiIiIqqlJkybS9r1799TGx8XFKXzdzMxMKuympKRopnM1pGyN3qysLPz3v/+t4d4QERERvVlY1CUiIiIiqqZOnTqhVq1aAICzZ8+qXT4gIiJC6T4nJycAwKNHj7R6pm7Z+ygpKcHRo0druDdEREREbxYWdYmIiIiIqsnQ0BCurq4AgCdPniA4OFhp7J9//onDhw8r3e/t7S1tz58/H0IIjfXzVRo3bpy0/c9//lPtw9eIiIiIqPJY1CUiIiIi0gB/f39p+6uvvsL58+crxGRkZGD06NEoKipSmsfd3R2Ojo4AgCNHjmDcuHEqly8oLi7GkSNH8N1331Wj95rXvXt3eHh4AACuX7+O4cOHIz09XWm8EAK///47vvzyy1fVRSIiIiKtxQelERERERFpwMCBA+Hr64tNmzYhOzsbffv2xdixY9GvXz8YGBjg+vXr2LRpE9LT0/HRRx8hLCwMAKCjU36ehUwmQ3h4OHr27Ink5GRs374dBw8ehIeHB7p16wZzc3Pk5eUhNTUV165dw/Hjx5Geng5nZ2fMnz+/Jt66Ups2bcLt27dx7do1HDt2DLa2thg1ahR69OiBBg0aoLCwEA8fPsT169dx4sQJpKSkoGXLlggICKjprhMRERG91ljUJSIiIiLSkLVr1yI3Nxc7duxAYWEhNm/ejM2bN5eLmTlzJtzc3KSirqmpaYU8VlZWuHTpEnx8fHD48GE8efIE69evV9l22YPJXiempqY4c+YMpkyZgu3bt+P58+fYtm0btm3bpvSY1/F9EBEREb1uWNQlIiIiItIQPT09hISEYPTo0Vi/fj0uXLiArKwsNGzYEI6Ojpg8eTKcnZ2xc+dO6Rhzc3OFuRo2bIhDhw4hOjoaISEhOHPmDJKTk5GVlQVDQ0M0atQI7dq1Q58+fTB06FDY29u/qrdZJSYmJti6dSvmzJmD4OBgnDp1CvHx8Xjy5An09fXRoEEDtGnTBr169cLgwYPRvXv3mu4yERER0WtPJrT1yQtERERERFpq5syZWLZsGQDg6tWr6Ny5c812iIiIiIi0Cou6RERERESv0NOnT9G6dWukp6ejQYMGSE1NhZ4eb6AjIiIiosrTUR9CRERERESVER8fj8TERKX7s7Ky4OHhgfT0dADAhAkTWNAlIiIioirjTF0iIiIiIg359ddfMXr0aPTu3RtOTk5o1aoVjI2N8eTJE1y+fBmhoaHIysoCALRq1QoxMTEwNjau2U4TERERkdbhtAAiIiIiIg0qLi5GVFQUoqKilMZ06dIFe/fuZUGXiIiIiF4KZ+oSEREREWnIs2fPsGvXLhw/fhw3b95ERkYGMjMzoauri4YNG8LBwQGjRo2Ch4cHdHS4EhoRERERvRwWdYmIiIiIiIiIiIi0CKcHEBEREREREREREWkRFnWJiIiIiIiIiIiItAiLukRERERERERERERahEVdIiIiIiIiIiIiIi3Coi4RERERERERERGRFmFRl4iIiIiIiIiIiEiLsKhLREREREREREREpEVY1CUiIiIiIiIiIiLSIv8PetSetnVA6FAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1600x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/vector-space-model/notebook_40_0.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "counts = collections.Counter(genres)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(counts.keys(), counts.values(), width=0.3)\n",
    "ax.set(xlabel=\"genre\", ylabel=\"count\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47d6858f",
   "metadata": {},
   "source": [
    "<!-- Figure: Distribution of dramatic subgenres in *Théâtre Classique*.\\label{fig:vector-space:subgenre-distribution} -->\n",
    "\n",
    "We clearly have a relatively skewed distribution: the most common genre of Comédies outnumbers the runner-up genre of Tragédies almost by two to one. The curious genre of Tragi-Comédies---the oxymoron in its name suggests it to be a curious mix of both Comédies and Tragédies---is much less common as a genre label in the dataset.\n",
    "\n",
    "The apparent straightforwardness with which we have discussed <span class=\"index\">literary genres</span> so far\n",
    "is not entirely justified from the point of view of literary theory (see, e.g., {cite:t}`devitt:1993,stephens2013retelling`), and even cultural theory at large {cite:p}`chandler1997`. Although \"genre\" seems a (misleadingly) intuitive concept when talking about literature, it is also a highly vexed and controversial notion: genres are mere conventional tags that people use to refer to certain \"text varieties\" or \"textual modes\" that are very hard to delineate using explicit, let alone objective criteria. They are certainly not mutually exclusive---a \"detective\" can be a \"romance\" too---and they can overlap in complex hierarchies---a \"detective\" can be considered a hyponym of \"thriller\". Genre properties can moreover be extracted at various levels from texts, including style, themes, settings, and successful authors often like to blend genres (e.g., a \"historical thriller\"). Genre classifications therefore rarely go uncontested and their application can be a highly subjective matter, where personal taste or the paradigm a scholar works in will play a significant role. Because of the (inter)subjectivity that is involved in genre studies, quantitative approaches can offer a valuable second opinion on genetic classifications, like the one offered by Paul Fièvre. Are there any lexical differences between the texts in this corpus that would seem to correlate, or perhaps contradict, the classification proposed? Can the textual properties in a bag-of-words model shed new light on the special status of the Tragi-Comédies? And so on.\n",
    "\n",
    "#### Exploring the corpus\n",
    "\n",
    "After loading the plays into memory, we can transform the collection into a <span class=\"index\">document-term matrix</span>. In the following code block, we first preprocess each play using the `preprocess_text()` function defined earlier, which returns a list of lowercase word tokens for each play. Subsequently, we construct the <span class=\"index\">vocabulary</span> with `extract_vocabulary()`, and <span class=\"index\">prune</span> all words that occur less than two times in the collection. The final step, then, is to assemble the document-term matrix by computing the token counts for all remaining words in the vocabulary for each document in the collection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2b859b36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "document-term matrix with |D| = 498 documents and |V| = 48048 words.\n"
     ]
    }
   ],
   "source": [
    "plays_tok = [preprocess_text(play, 'french') for play in plays]\n",
    "vocabulary = extract_vocabulary(plays_tok, min_count=2)\n",
    "document_term_matrix = np.array(corpus2dtm(plays_tok, vocabulary))\n",
    "\n",
    "print(f\"document-term matrix with \"\n",
    "      f\"|D| = {document_term_matrix.shape[0]} documents and \"\n",
    "      f\"|V| = {document_term_matrix.shape[1]} words.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82a1c0f6",
   "metadata": {},
   "source": [
    "We are now ready to start our analysis: we have an efficient bag-of-words representation of a corpus in the form of a NumPy matrix (a two-dimensional array) and list of labels that unambiguously encodes the genre for each document vector in that table.\n",
    "\n",
    "Let us start by naively plotting the available documents, as if the frequency counts for two specific words in our bag-of-words model were simple two-dimensional coordinates on a map. In previous work by {cite:t}`schoech:2017`, two words that had considerable discriminative power for these genres were \"monsieur\" (*sir*) and \"sang\" (*blood*), so we will use these as a starting point. We can select the corresponding columns from our <span class=\"index\">document-term matrix</span>, by first retrieving their index in the vocabulary. The index of the words in our vocabulary is aligned with the indices of the corresponding columns in the bag-of-words table: we will therefore always use the index of an item in the vocabulary to retrieve the correct frequency column from the bag-of-words model. (The <span class=\"index\">Pandas</span> library, which is discussed at length in chapter {ref}`chp-working-with-data`, simplifies this process considerably when working with so-called `DataFrame` objects.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f908ccb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "monsieur_idx = vocabulary.index('monsieur')\n",
    "sang_idx = vocabulary.index('sang')\n",
    "\n",
    "monsieur_counts = document_term_matrix[:, monsieur_idx]\n",
    "sang_counts = document_term_matrix[:, sang_idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32ca4360",
   "metadata": {},
   "source": [
    "While NumPy is optimized for dealing with numeric data, lists of strings can also be\n",
    "casted into <span class=\"index\">array</span>s. This is exactly what we will do to our list\n",
    "of genre labels too, in order to ease the process of retrieving the locations of specific genre labels in the list later on:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f7fc3916",
   "metadata": {},
   "outputs": [],
   "source": [
    "genres = np.array(genres)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd92cc3c",
   "metadata": {},
   "source": [
    "The column vectors, `monsieur_counts` and `sang_counts`, both have the same length and\n",
    "include the frequency counts for each of our two words in each document. Using the labels\n",
    "in the corresponding list of genre tags, we can now plot each document as a point in the\n",
    "two-dimensional space defined by the two count vectors. Pay close attention to the first\n",
    "two arguments passed to the `scatter()` function inside the `for` loop in which we iterate\n",
    "over the three genres: using the mechanism of \"<span class=\"index\">boolean\n",
    "indexing</span>\", we select the frequency counts for the relevant documents and we plot\n",
    "those as a group in each iteration. The figure below is generated using the following code block:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b2d8b57c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/vector-space-model/notebook_48_0.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "for genre in ('Comédie', 'Tragédie', 'Tragi-comédie'):\n",
    "    ax.scatter(monsieur_counts[genres == genre],\n",
    "               sang_counts[genres == genre],\n",
    "               label=genre, alpha=0.7)\n",
    "\n",
    "ax.set(xlabel='monsieur', ylabel='sang')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "388dc356",
   "metadata": {},
   "source": [
    "<!-- Figure: Absolute frequency of \"monsieur\" and \"sang\" in individual plays.\\label{fig:vector-space-models:monsieur-and-sang} -->\n",
    "\n",
    "What does this initial \"textual map\" tell us? As we can glean from this plot, the usage of these two words appears to be remarkably distinctive. Many Tragédies seem to use the term \"sang\" profusely, whereas the term is almost absent from the Comédies. Conversely, the term \"monsieur\" is clearly favored by the authors of Comédies, where it is perhaps predominantly used as a vocative, because conversations are often said to be more typical of this particular subgenre {cite:p}`schoech:2017`. Interestingly, the Tragi-comédies seem to hold the middle between the other two genres, as these seem to invite much less extreme frequencies for those terms.\n",
    "\n",
    "#### Genre vectors\n",
    "\n",
    "Do we have any more objective methods to verify these impressions? A first option would be\n",
    "to take a more aggregate view and look at the average usage of these term in the three\n",
    "genres. In the code block below, we calculate the <span class=\"index\">arithmetic\n",
    "mean</span> or \"<span class=\"index\">centroid</span>s\" for each genetic subcluster. This is\n",
    "easy to achieve in <span class=\"index\">NumPy</span>, which has a dedicated function for\n",
    "this, <span class=\"index\">`numpy.mean()`</span>, that we can apply to our entire bag-of-words model at once. Through setting the `axis` parameter to zero, we indicate that we are\n",
    "interested in the column-wise mean (as opposed to, e.g., the row-wise mean for which we\n",
    "could need to specify `axis=1`). \n",
    "\n",
    "```{warning} \n",
    "If this is all new to you, please study the materials in section {ref}`sec-vector-space-model-numpy-intro`.\n",
    "```\n",
    "\n",
    "Note how we again make use of the <span class=\"index\">boolean indexing</span> mechanism to retrieve only the vectors accociated with the specific genre in each line below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "380d771e",
   "metadata": {},
   "outputs": [],
   "source": [
    "tr_means = document_term_matrix[genres == 'Tragédie'].mean(axis=0)\n",
    "co_means = document_term_matrix[genres == 'Comédie'].mean(axis=0)\n",
    "tc_means = document_term_matrix[genres == 'Tragi-comédie'].mean(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7953a38",
   "metadata": {},
   "source": [
    "The resulting mean vectors will hold a one-dimensional list or vector for each term in our vocabulary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cfd3916d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(48048,)\n"
     ]
    }
   ],
   "source": [
    "print(tr_means.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32783903",
   "metadata": {},
   "source": [
    "We still can use the precomputed indices to retrieve the mean frequency of individual words from these summary vectors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b626d42a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute frequency of \"monsieur\"\n",
      "   in comédies: 45.46\n",
      "   in tragédies: 1.20\n",
      "   in tragi-comédies: 8.13\n"
     ]
    }
   ],
   "source": [
    "print('Mean absolute frequency of \"monsieur\"')\n",
    "print(f'   in comédies: {co_means[monsieur_idx]:.2f}')\n",
    "print(f'   in tragédies: {tr_means[monsieur_idx]:.2f}')\n",
    "print(f'   in tragi-comédies: {tc_means[monsieur_idx]:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a848604e",
   "metadata": {},
   "source": [
    "The mean frequencies for these words are again revealing telling differences across our\n",
    "three genres. This also becomes evident by plotting the mean values in a scatter plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c8d18424",
   "metadata": {},
   "outputs": [
    {
     "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/vector-space-model/notebook_56_0.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.scatter(\n",
    "    co_means[monsieur_idx], co_means[sang_idx], label='Comédies')\n",
    "ax.scatter(\n",
    "    tr_means[monsieur_idx], tr_means[sang_idx], label='Tragédie')\n",
    "ax.scatter(\n",
    "    tc_means[monsieur_idx], tc_means[sang_idx], label='Tragi-comédies')\n",
    "\n",
    "ax.set(xlabel='monsieur', ylabel='sang')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22ed2189",
   "metadata": {},
   "source": [
    "<!-- Figure: Mean frequencies for *monsieur* and *sang* in Comédies, Tragédies, and Tragi-comédies.\\label{fig:vector-space-model:mean-frequencies} -->\n",
    "\n",
    "(sec-vector-space-model-distance-metrics)=\n",
    "### Computing distances between documents\n",
    "\n",
    "Let us pause for a minute and have a closer look at the simplified representation of our corpus in the form of the three centroids. The chapter set out to explore how we could apply spatial reasoning to texts using a bag-of-words model. In the space defined by this model, we should understand by now why documents with similar vector representations are closer to each other. The wager in the rest of this chapter will be that the <span class=\"index\">geometric distance</span> between vectors can indeed serve as a proxy for human judgments of the dissimilarity of two documents. To put this into practice, a precise definition of distance in a vector space needs to be chosen. Let us review and illustrate a number of established methods to calculate the distance between document vectors and illustrate them on the basis of our three genre vectors. For the sake of simplicity, we define three vectors, one for each genre. We will use the points in this \"mini\" vector space to introduce a number of established distance metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c7daa768",
   "metadata": {},
   "outputs": [],
   "source": [
    "tragedy = np.array([tr_means[monsieur_idx], tr_means[sang_idx]])\n",
    "comedy = np.array([co_means[monsieur_idx], co_means[sang_idx]])\n",
    "tragedy_comedy = np.array([tc_means[monsieur_idx], tc_means[sang_idx]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73367c02",
   "metadata": {},
   "source": [
    "(sec-vector-space-model-euclidean-distance)=\n",
    "#### Euclidean distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "875d9e27",
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/vector-space-model/notebook_60_0.png"
      },
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "euclidean_fig"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/vector-space-model/notebook_60_1.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot([tr_means[monsieur_idx], tc_means[monsieur_idx]],\n",
    "        [tr_means[sang_idx], tc_means[sang_idx]],\n",
    "        'darkgrey', lw=2, ls='--')\n",
    "ax.plot([tr_means[monsieur_idx], co_means[monsieur_idx]],\n",
    "        [tr_means[sang_idx], co_means[sang_idx]],\n",
    "        'darkgrey', lw=2, ls='--')\n",
    "ax.plot([tc_means[monsieur_idx], co_means[monsieur_idx]],\n",
    "        [tc_means[sang_idx], co_means[sang_idx]],\n",
    "        'darkgrey', lw=2, ls='--')\n",
    "\n",
    "ax.scatter(co_means[monsieur_idx], co_means[sang_idx],\n",
    "           label='Comédies', zorder=3)\n",
    "ax.scatter(tr_means[monsieur_idx], tr_means[sang_idx],\n",
    "           label='Tragédie', zorder=3)\n",
    "ax.scatter(tc_means[monsieur_idx], tc_means[sang_idx],\n",
    "           label='Tragi-comédies', zorder=3)\n",
    "\n",
    "ax.set(xlabel='monsieur', ylabel='sang')\n",
    "plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.1), ncol=3);\n",
    "from myst_nb import glue\n",
    "glue(\"euclidean_fig\", fig, display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "715caab5",
   "metadata": {},
   "source": [
    "```{glue:figure} euclidean_fig\n",
    ":name: fig-vector-space-model-euclidean\n",
    "\n",
    "Illustration for the Euclidean distance metric for the genre vectors.\n",
    "```\n",
    "\n",
    "Let us start with perhaps the most straightforward distance that is imaginable between two\n",
    "points in space, namely, that of \"as the crow flies\". The <span class=\"index\">Euclidean\n",
    "distance</span> intuitively measures the length of the straight line which connects two\n",
    "points. These straight lines are shown in grey in Figure\n",
    "{numref}`fig-vector-space-model-euclidean`. Calculating the exact length of these lines\n",
    "happens through the application of the Euclidean distance. Using mathematical notation, we\n",
    "represent a vector as $\\vec{x}$. Thus, given two vectors $\\vec{a}$ and $\\vec{b}$ with $n$ coordinates, the length of the line connecting two points is computed as follows:\n",
    "\n",
    "\\begin{equation}\\label{eq:euclidean-distance}\n",
    "d_2(\\vec{a}, \\vec{b}) = \\sqrt{\\sum^n_{i=1} (a_i - b_i)^2}\n",
    "\\end{equation}\n",
    "\n",
    "Not everyone is familiar with these mathematical notations, so let us briefly explain how\n",
    "to read the formula. First, $d_2$ is a function which takes two vectors, $\\vec{a}$ and $\\vec{b}$. Second, $\\sum^n_{i=1}$ is a summation or sigma notation, which is a convenient notation for expressing the sum of the values of a variable. Here, the values are the squared differences between the $i$th value in vector $\\vec{a}$ and the $i$th value in vector $\\vec{b}$, i.e., $(a_i - b_i)^2$. We compute these differences for all $n$ coordinates (hence the little $n$ on top of the sigma sign; the expression $i=1$ underneath expresses that we start from the very first element in the series). Finally, we take the square root ($\\sqrt{}$) of this sum. Sometimes, the details of formulas become clearer in the form of code. The formula for the Euclidean distance is relatively easy to translate to the following function in Python (which basically boils down to a single line, thanks to NumPy's conciseness):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "95647fa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def euclidean_distance(a, b):\n",
    "    \"\"\"Compute the Euclidean distance between two vectors.\n",
    "\n",
    "    Note: ``numpy.linalg.norm(a - b)`` performs the\n",
    "    same calculation using a slightly faster method.\n",
    "\n",
    "    Arguments:\n",
    "        a (numpy.ndarray): a vector of floats or ints.\n",
    "        b (numpy.ndarray): a vector of floats or ints.\n",
    "\n",
    "    Returns:\n",
    "        float: The euclidean distance between vector a and b.\n",
    "\n",
    "    Examples:\n",
    "        >>> import numpy as np\n",
    "        >>> a = np.array([1, 4, 2, 8])\n",
    "        >>> b = np.array([2, 1, 4, 7])\n",
    "        >>> round(euclidean_distance(a, b), 2)\n",
    "        3.87\n",
    "\n",
    "    \"\"\"\n",
    "    return np.sqrt(np.sum((a - b) ** 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a50f159",
   "metadata": {},
   "source": [
    "In the code block below, we apply this <span class=\"index\">distance metric</span> to the three pairwise combinations of our three vectors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7af71b6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tragédies - comédies:       50.84\n",
      "tragédies - tragi-comédies: 12.98\n",
      " comédies - tragi-comédies: 39.89\n"
     ]
    }
   ],
   "source": [
    "tc = euclidean_distance(tragedy, comedy)\n",
    "print(f'tragédies - comédies:       {tc:.2f}')\n",
    "\n",
    "ttc = euclidean_distance(tragedy, tragedy_comedy)\n",
    "print(f'tragédies - tragi-comédies: {ttc:.2f}')\n",
    "\n",
    "ctc = euclidean_distance(comedy, tragedy_comedy)\n",
    "print(f' comédies - tragi-comédies: {ctc:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebf6d9f1",
   "metadata": {},
   "source": [
    "The resulting distances clearly confirm our visual impression from the plot: the Tragi-comédies are relatively more similar to Tragédies (than Comédies), because the distance between the corresponding vectors is smaller in our example.\n",
    "\n",
    "(sec-vector-space-model-cosine-distance)=\n",
    "#### Cosine distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "06fb9cd2",
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
    {
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Sc+fO1aRJk5yLLlutVu3bt0+rV69WXl6ehysEAAAAAABoGkEC0A4ECQDao279hEsvvVRDhgyRwWCQJJWVlWn9+vXatGmTysvLPVwlAAAAAABAQwQJQDswtRGAjvDx8dGkSZM0b948RUZGOrfn5OTo22+/1cGDB5s8zwAAAAAAAHgCQQLQDoxIAOAOYWFhmj17tqZOnSpfX19JZ88vBw8e1Lfffqvs7Gw5HA4PVwkAAAAAAHo7ggSgHQgSALiLwWBQfHy8Lr30Ug0fPtw53VFFRYU2b96s9evXq6yszMNVAgAAAACA3owgAWiHpqYcIUgA0BFeXl4aP368FixYoL59+zq3nzp1yjndUVMhJgAAAAAAQGcjSADayG63NznVCGskAHCH4OBgzZgxQ9OnT5e/v7+ks+edgwcPavXq1SooKPBwhQAAAAAAoLchSADayNUTwYxIAOAuBoNBsbGxWrhwoUaMGOGc7qisrEzJycnavn27ampqPFwlAAAAAADoLQgSgDZqalojiSABgPuZzWaNGzdO8+fPV3h4uHN7WlqaVq1apYyMDBZjBgAAAAAAnY4gAWgjVyMSmNoIQGcJDQ3VnDlzNGHCBOe5pqamRlu3btX69etVXl7u4QoBAAAAAEBPRpAAtBFTGwHwBKPRqGHDhmnhwoWKjY11bq9bjPnw4cOy2+0erBAAAAAAAPRUBAlAG7ma2ogRCQDOB39/f02fPl0XX3yxczFmm82m/fv367vvvlNhYaGHKwQAAAAAAD0NQQLQRoxIANAV9O/fXwsWLNDw4cOdizGfOXNGa9eu1c6dO1VbW+vhCgEAAAAAQE9BkAC0g7+/v3x8fBqEBwQJAM43Ly8vjR8/XvPmzVNYWJhz+4kTJ7Rq1Srl5OR4sDoAAAAAANBTGBwOh8PTRcAzYmNjlZOTo5iYGGVnZ3u6nG7L4XDIZrPJaDTKaCSbA+AZdrtdx48f14EDBxpMwRYXF6cJEybIx8fHg9UBAAAAAIDujLueQAcZDAaZzWZCBAAeZTQaNXz4cC1cuFD9+/d3bs/MzNSqVauUlZXlweoAAAAAAEB3xp1PAAB6EH9/f1100UWaOnWqvL29JUk1NTXasmWLNm/erOrqag9XCAAAAAAAuhuCBAAAehiDwaD4+HgtWLBAMTExzu3Z2dn69ttvlZWVJWY2BAAAAAAArUWQAABAD+Xn56fp06dr2rRpjE4AAAAAAADtZvZ0AQAAoPMYDAbFxcUpKipKu3btUnZ2tiQpJydHBQUFmjhxogYMGCCDweDhSgEAAAAAQFfFiAQAAHoBX19fTZ8+XRdeeKF8fHwkSbW1tUpJSdGmTZtUVVXl4QoBAAAAAEBXxYgEAAB6kQEDBqhPnz7avXu3srKyJEm5ubkqLCzU+PHjFR8fz+gEAAAAAADQgMHBaou9VmxsrHJychQTE+Oc6gItO3PmjCwWi8xms0wmk0wmk8xms3P+cQDoLrKzs7Vz507V1NQ4t/Xv318XXHCBfH19PVgZAAAAAADoSggSejGChPbZtGmTcnJyGmzz9/fXZZdd5qGKAKD9ampqtHv3bmVmZjq3+fj46IILLlBMTIwHKwMAAAAAAF0FayQAbWSz2RptM5lMHqgEADrOx8dH06ZN00UXXeRcO6GmpkabNm3S9u3bZbFYPFwhAAAAAADwNIIEoI0IEgD0RDExMVqwYIH69+/v3JaWlqbvvvtOhYWFHqwMAAAAAAB4GkEC0EZWq7XRNrOZdcsBdH++vr666KKLdMEFFzjPa+Xl5UpOTtaBAwdkt9s9XCEAAAAAAPAEggSgjRiRAKAnMxgMGjx4sObPn6+IiAhJksPh0KFDh7R27VqVlZV5uEIAAAAAAHC+ESQAbUSQAKA3CAoK0uzZs5WYmCiDwSBJKi4u1urVq3X8+HE5HA4PVwgAAAAAAM4XggSgjZqa2oggAUBPZDQalZiYqLlz5yooKEjS2TB1165d2rhxo6qqqjxcIQAAAAAAOB8IEoA2ampEAmskAOjJwsPDNX/+fA0ZMsS57eTJk1q9erVycnI8WBkAAAAAADgfCBKANnA4HExtBKBXMpvNmjRpkpKSkuTr6ytJqqmp0aZNm7R9+3ZZLBYPVwgAAAAAADoLQQLQBna7vcl5wQkSAPQW0dHRuuSSS9S/f3/ntrS0NH333Xc6ffq0BysDAAAAAACdhSABaIOmRiNITG0EoHfx9fXVRRddpAsuuMB5/isvL9eaNWt07NgxFmIGAAAAAKCHIUgA2sBVkMCIBAC9jcFg0ODBgzV//nyFhYVJOjtqa/fu3dq8ebNqamo8XCEAAAAAAHAXggSgDaxWa5PbCRIA9FZBQUGaM2eOhg0b5tyWk5Oj7777ToWFhR6sDAAAAAAAuAtBAtAGjEgAgMZMJpMmTJigiy++WN7e3pKkyspKJScn6/Dhw0x1BAAAAABAN0eQALQBayQAgGv9+/fXJZdcosjISEmSw+HQ/v37tX79elVXV3u4OgAAAAAA0F4ECUAbMLURADTP399fs2bN0qhRo5zbTp06pdWrVysvL8+DlQEAAAAAgPYiSADagBEJANAyo9Go0aNHa+bMmfL19ZUkVVdXa/369dq3b5/sdruHKwQAAAAAAG1BkAC0AWskAEDr9e3bV5dccon69evn3Pbjjz8qOTlZFRUVHqwMAAAAAAC0BUEC0AZMbQQAbePr66ukpCSNHTtWBoNBklRUVKTVq1crJyfHw9UBAAAAAIDWIEgA2oCpjQCg7QwGgxISEjRnzhwFBARIkiwWizZt2sRURwAAAAAAdAMGh8Ph8HQR8IzY2Fjl5OQoJiZG2dnZni6nW6ioqFBJSYlsNpusVqtsNptsNpsGDx4sb29vT5cHAF1ebW2tduzY0eDfnaioKE2bNs25ngIAAAAAAOhaCBJ6MYIEAIAnOBwOHTt2THv37lXdZYifn58uvPBCRUZGerg6AAAAAABwLqY2AgAA55XBYNDw4cM1a9Ys5yiEqqoqJScn6+jRo+IZBwAAAAAAuhaCBAAA4BF9+vTR/Pnz1adPH0lnRyrs2bNHKSkpslgsHq4OAAAAAADUIUgAAAAe4+fnp5kzZ2rEiBHObVlZWVqzZo1KS0s9WBkAAAAAAKhDkAAAADzKaDRq3Lhxmj59usxmsySptLRU33//vbKysjxcHQAAAAAAIEgAAABdQmxsrObPn6+QkBBJktVq1ZYtW7Rnzx7Z7XYPVwcAAAAAQO9FkAAAALqMoKAgzZ07V3Fxcc5tR48e1bp161RVVeXBygAAAAAA6L0IEgAAQJdiNps1depUTZgwQUbj2UuVwsJCfffddyooKPBwdQAAAAAA9D5mTxcAdCebN29WRUWFTCaTzGazTCaTQkNDlZiY6OnSAKBHMRgMGjZsmMLDw7V582ZVVVWpurpa69at04QJEzR06FBPlwgAAAAAQK9BkAC0wZkzZ1RWVtZgm9Vq9VA1ANDzRUREaP78+UpJSVF+fr4cDod27dqlkpISTZgwQSaTydMlAgAAAADQ4zG1EdAGNput0TazmTwOADqTr6+vZsyYoeHDhzu3paam6ocfflB1dbUHKwMAAAAAoHcgSADaoKkggadhAaDzGY1GjR8/XlOmTGmwbsL333+v06dPe7g6AAAAAAB6NoIEoA0IEgDAswYOHKjZs2fL19dXklRZWam1a9cqKyvLw5UBAAAAANBzESQAreRwOJpcD4EgAQDOr7p1E8LDwyWdDXm3bNmi/fv3y+FweLg6AAAAAAB6HoIEoJXsdnuT21kjAQDOPz8/P82ePVvx8fHObYcPH9amTZtksVg8WBkAAAAAAD0PQQLQSk2NRpAYkQAAnmIymTRlyhSNGzdOBoNBkpSbm6s1a9aovLzcw9UBAAAAANBzECQArdTU+ggSQQIAeJLBYNCIESOUlJQkLy8vSVJpaam+//57nTp1ysPVAQAAAADQMxAkAK3kKkhgaiMA8Lx+/fpp3rx5CgoKkiTV1tZq/fr1Onr0KOsmAAAAAADQQQQJQCsxtREAdG1BQUGaO3euoqOjJUkOh0N79uzRjh07XK5zAwAAAAAAWkaQALQSIxIAoOvz9vbWRRddpISEBOe2tLQ0rV+/XrW1tR6sDAAAAACA7osgAWgl1kgAgO7BaDRq7NixmjZtmozGs5c6+fn5Wrt2LYswAwAAAADQDgQJbVRWVqZPPvlE9957r6ZPn64+ffrIy8tLwcHBSkhI0C9/+UutWrWqTfMxHz9+XP/5n/+p0aNHKyQkRIGBgRoxYoTuuece7dmzp/M+DNqEIAEAupe4uDjNmjVLPj4+ks4uwrxmzRoVFRV5uDIAAAAAALoXg4MVCFtt2bJleuyxx1RdXd1i26SkJK1YsUJxcXHNtnvttdf0wAMPqKqqqsn9JpNJf/zjH/XHP/6xXTU3JzY2Vjk5OYqJiVF2drbb++9pMjMzlZKS0mj73LlzFRER4YGKAACtUV5erg0bNqisrEzS2X9bp0yZogEDBni4MgAAAAAAugcmd2+Do0ePOkOEmJgYzZs3T5MmTVJUVJSqq6uVkpKiFStWOG9YzJo1SykpKYqKimqyvxUrVuiOO+6QdHYahhtuuEFz586V2WzWpk2b9M4776impkZPPPGEfHx89PDDD5+3z4rGGJEAAN1TYGCg5s6dq82bNys/P182m01btmxReXm5EhISZDAYPF0iAAAAAABdGiMS2uCuu+5Samqqfvvb32ru3LnOeZfry8jI0IIFC3TkyBFJ0q233qrly5c3aldQUKChQ4eqtLRURqNR//73v3XFFVc0aJOSkqK5c+eqsrJSZrNZBw4c0IgRI9z2eRiR0DbHjh3T7t27G22/9NJLFRQU5IGKAABtYbPZtHPnTqWnpzu3DRo0SJMmTWry33QAAAAAAHAWvzW3wTPPPKNvv/1W8+fPd3nDIT4+Xh9++KHz6w8//FCVlZWN2j333HMqLS2VJN1zzz2NQgRJmjZtmp5++mlJktVq1VNPPeWOj4F2cjUiwWxmYA8AdAcmk0mTJ0/W6NGjndvS0tK0fv161dbWerAyAAAAAAC6NoKENggPD29Vu3HjxjlHDlRWVur48eON2tQPGx588EGXfd1+++0KCAiQJH3++ecu11JA52NqIwDo/gwGg0aNGqVp06Y5HwrIz8/X2rVrVV5e7uHqAAAAAADomggSOklwcLDz9bk3/w8dOqSMjAxJ0siRIzVo0CCX/QQFBSkpKUmSVFFRoR9++KETqkVrWK3WJrcTJABA9xMXF6dZs2bJx8dHklRaWqo1a9aoqKjIw5UBAAAAAND1ECR0gtraWh09etT5dXx8fIP9+/fvd76ePHlyi/3Vb1P/vTi/AgMDFRUVpYiICIWGhiowMFD+/v7Mqw0A3VRkZKTmzp3rXOempqZG69atU1ZWlocrAwAAAACga2Fy907w/vvv68yZM5KkiRMnql+/fg321y3ELKnZ0QhNtan/XpxfQ4YM0ZAhQzxdBgDAjQIDAzV37lxt3rxZ+fn5stls2rJliyoqKpSQkODp8gAAAAAA6BIIEtysoKBADz/8sPPrxx9/vFGbkpIS5+vIyMgW+4yIiGjyve5isVi0a9euJvdFR0crOjra7ccEAKCr8Pb2VlJSknbu3Kn09HRJ0r59+1RdXa1x48bJYDB4tkAAAAAAADyMIMGNamtrdc011yg/P1+SdNVVV+nqq69u1K7+Yo6+vr4t9uvn5+d8XVZW5oZKG8rPz9ekSZOa3PfEE0/oySefdPsxAQDoSkwmkyZPnqyAgAAdPHhQknT06FFVV1dr8uTJrIcDAAAAAOjVCBLcxG6367bbbtOGDRsknZ0GZ/ny5R6uqnWioqL0zTffNLmP0QgAgN7CYDAoMTFRfn5+2rlzpxwOhzIzM1VTU6Pp06fLy8vL0yUCAAAAAOARBAlu4HA4dOedd+q9996TJMXFxen7779XWFhYk+0DAwOdr6urq1vsv6qqyvm6bkFId/Ly8tLEiRPd3i8AAN3R4MGD5evrqy1btshms+nUqVNat26dkpKSWjWSEAAAAACAnsbo6QK6O4fDobvvvluvv/66JCk2NlZr167VwIEDXb4nNDTU+bqwsLDFYxQVFTX5XgAA0Dn69++vmTNnytvbW5J0+vRprVmzplOmGAQAAAAAoKsjSOgAh8Ohe+65R6+88ookKSYmRsnJyRoyZEiz7xsxYoTzdVpaWovHqd+m/nsBAEDniYyM1Jw5c+Tv7y9Jqqio0Nq1a1VcXOzhygAAAAAAOL8IEtqpLkR4+eWXJZ19cjE5OVlDhw5t8b1jxoxxvt6+fXuL7eu3GT16dDuqBQAA7REcHKw5c+YoJCREklRTU6N169YpLy/Pw5UBAAAAAHD+GBwOh8PTRXQ354YI0dHRWrdunYYPH97qPuLj45WZmSnp7IgDV1MhlZeXq1+/fqqoqJC/v78KCgqcT0Z2VGxsrHJychQTE6Ps7Gy39NmTpaWlyWAwyGQyyWQyyWw2y8/Pr1PWrQAAdC21tbXauHGjc0pCg8GgKVOmKD4+3sOVAQAAAADQ+RiR0A733nuvM0To16+fkpOT2xQiSNL111/vfL1s2TKX7V577TVVVFRIkq644gq3hQhou127dmnbtm3asmWLNm7cqHXr1unIkSOeLgsAcB54e3trxowZiomJkXT2oYKtW7fy7wAAAAAAoFcgSGij++67Ty+99JKksyHCunXr2rVuwW9/+1vnk+wvvviiPv/880Zttm7dqj/84Q+SJLPZrCeeeKIDlaMjHA6HbDZbo+0mk8kD1QAAPMFsNuvCCy9ssBbS3r17tWfPHjHAEwAAAADQk5k9XUB38vjjj+uFF16QdHZKg/vvv1+HDx/W4cOHm33fxIkTFRcX12BbVFSU/vnPf2rx4sWy2+26+uqrdcMNN2j+/PkymUzatGmT3nnnHVVXV0uSnnrqKSUkJHTOB0OLmgoRJIIEAOhtjEajJk6cKF9fXx08eFCSdPToUVVXV2vKlCkyGnlGAwAAAADQ8xAktMHGjRudrx0Ohx599NFWve+tt97S4sWLG22/5ZZbVFlZqYceekjV1dV6//339f777zdoYzKZ9Nhjj+n3v/99h2pHx7gKEsxmfoQAoLcxGAxKTEyUn5+fdu7cKYfDoczMTFmtVl144YWEzAAAAACAHofH5jzsrrvu0r59+/TQQw9p1KhRCgoKUkBAgIYNG6Y777xT27dv11NPPeXpMns9RiQAAM41ePBgTZ8+3TkKITc3Vxs2bJDFYvFwZQAAAAAAuJfBwaS+vVZsbKxycnIUExOj7OxsT5fTpZWWlmrVqlWNtk+cOFFDhw71QEUAgK4iPz9fGzdulNVqlSRFREQoKSlJ3t7eHq4MAAAAAAD3YEQC0ApMbQQAcCUqKkozZ86Ul5eXJKmoqEjr1q1zrnMEAAAAAEB3R5AAtAJTGwEAmhMREaHZs2fLx8dHklRSUqLk5GRVVlZ6uDIAAAAAADqOIAFohbrpKs7FiAQAQJ3Q0FDNmTNH/v7+kqSysjKtXbtWZWVlHq4MAAAAAICOIUgAWoERCQCA1ggKCtLs2bMVGBgoSaqsrFRycrLOnDnj4coAAAAAAGg/ggSgFQgSAACtFRAQoNmzZyskJESSVF1dreTkZBUXF3u4MgAAAAAA2ocgAWgFpjYCALSFn5+fZs2apfDwcElSbW2t1q1bp4KCAg9XBgAAAABA2xEkAK3AiAQAQFv5+Pho5syZ6tOnj6SzofT69et18uRJD1cGAAAAAEDbECQAreBqRAJBAgCgOV5eXkpKSlJ0dLSks8H0pk2blJWV5eHKAAAAAABoPYIEoBVcjUhgaiMAQEvMZrOmT5+u2NhYSZLdbldKSooyMjI8XBkAAAAAAK1DkAC0AlMbAQA6wmQyadq0aRo0aJAkyeFwaOvWrUpPT/dsYQAAAAAAtAJBAtAKTU1tZDAYZDTyIwQAaB2j0agLLrhAQ4YMcW7btm0bYQIAAAAAoMvjLijQCk2NSGBaIwBAWxkMBk2cOFFDhw51btu2bZtSU1M9WBUAAAAAAM0jSABaoakggWmNAADtYTAYNGHCBA0bNsy5bceOHYQJAAAAAIAuiyABaAWCBACAOxkMBo0fP75RmHDixAkPVgUAAAAAQNOYmwVohUmTJmnMmDGy2WzOPwaDwdNlAQC6sbowwWAw6OjRo5KknTt3yuFwNJj6CAAAAAAATyNIAFrBz89Pfn5+ni4DANDDGAwGjRs3TgaDQUeOHJEk7dq1S5IIEwAAAAAAXQZTGwEAAHiQwWDQ2LFjlZCQ4Ny2a9cuHTt2zINVAQAAAADwfwgSAAAAPMxgMGjMmDENwoTdu3c7pzwCAAAAAMCTCBIAAAC6gLowYeTIkc5te/bscU55BAAAAACApxAkAAAAdBEGg0GjR4/WqFGjnNv27t1LmAAAAAAA8CiCBAAAgC6kLkxITEx0btu7dy/THAEAAAAAPIYgAQAAoAtKTExsECbs2bNHJ06c8GBFAAAAAIDeiiABAACgi0pMTGwwzdHOnTuVnp7uuYIAAAAAAL2S2dMFAF1ddXW1vvjiC5nNZplMJplMJpnNZg0fPlyDBg3ydHkAgB4uMTFRNpvNuU7C9u3bZTKZNGDAAA9XBgAAAADoLQgSgBbYbDY5HA5ZLBZZLBbn9vqvAQDoLAaDQWPHjpXNZtPx48flcDiUkpIio9GomJgYT5cHAAAAAOgFmNoIaIHNZmtyu8lkOs+VAAB6K4PBoAkTJjhHwjkcDm3ZskV5eXkergwAAAAA0BsQJAAtsFqtTW43mxnQAwA4fwwGgyZNmqS4uDhJkt1u16ZNm5Sfn+/hygAAAAAAPR1BAtACRiQAALoKo9GoKVOmKDY2VtLZf6M2btyowsJCD1cGAAAAAOjJCBKAFhAkAAC6EqPRqKlTpyo6OlrS2ZFzGzZs0OnTpz1cGQAAAACgpyJIAFrA1EYAgK7GZDJp+vTpioqKkiRZLBb98MMPOnPmjIcrAwAAAAD0RAQJQAsYkQAA6IpMJpMuvvhiRUZGSpJqa2v1ww8/qLS01MOVAQAAAAB6GoIEoAWuRiQQJAAAPM1sNispKUnh4eGSpOrqav3www8qLy/3cGUAAAAAgJ6EIAFogasRCUxtBADoCry8vDRjxgyFhoZKkqqqqvTDDz+osrLSs4UBAAAAAHoMggSgBUxtBADo6ry9vTVjxgwFBwdLkioqKrR+/XrV1NR4uDIAAAAAQE9AkAC0gKmNAADdga+vr2bOnKmAgABJUmlpqTZu3Ojy3zEAAAAAAFqLIAFoASMSAADdhZ+fn2bOnClfX19JUlFRkTZv3iy73e7hygAAAAAA3RlBAtCCpoIEo9Eoo5EfHwBA1xMYGKikpCR5eXlJkvLy8rRt2zY5HA4PVwYAAAAA6K64Ewq0oKkpIRiNAADoysLCwnTRRRc5Q+/MzEzt2bOHMAEAAAAA0C4ECUALmhqRYDabPVAJAACtFxUVpQsvvFAGg0GSdOzYMR0+fNjDVQEAAAAAuiOCBKAFTQUJjEgAAHQHMTExuuCCC5xfHzhwQCdOnPBgRQAAAACA7oggAWgBQQIAoDsbNGiQxo4d6/x6586dysrK8mBFAAAAAIDuhiABaEFTayQwtREAoDtJSEjQiBEjnF9v3bpVp06d8mBFAAAAAIDuhCABaAEjEgAAPcHYsWM1cOBASZLdbtemTZtUXFzs2aIAAAAAAN0Cj1UDLYiLi1NlZaWsVqtsNptsNptCQkI8XRYAAG1iMBh0wQUXqLa2Vrm5ubJardqwYYNmz56t4OBgT5cHAAAAAOjCDA6Hw+HpIuAZsbGxysnJUUxMjLKzsz1dDgAAOA/qAoSCggJJkr+/v+bMmSN/f38PVwYAAAAA6KqY2ggAAKAXMZvNuuiiixQaGipJqqys1Pr161VbW+vZwgAAAAAAXRZBAgAAQC/j7e2tpKQkBQYGSpJKS0u1efPmJtcFAgAAAACAIAEAAKAX8vPzU1JSknx8fCRJ+fn52rFjh5j1EgAAAABwLoIEAACAXiooKEgXXXSRTCaTJCkjI0MHDhzwcFUAAAAAgK6GIAEAAKAXi4yM1NSpU51fHz58WKmpqR6sCAAAAADQ1RAkAAAA9HKxsbEaP3688+udO3fq5MmTnisIAAAAANClECQAAABAw4cP1/DhwyVJDodDW7Zs0enTpz1cFQAA/x979xkl53mf9/96pmzvDYvt2DKz6ABBoogqBEFSpEgz9LEky1YsM7ZkHVtx7MQth/FfVGI7iXNsvYjjyLJkm4pKRDuyZJsSTZoUSbGhk0Sf7diO7b1Me/4vVhhhubMgyuzcU76fc3A0c8/s7AWBwO4+19y/GwAAJAKX6QBAIpufn1d7e7ucTqecTqdcLpecTqc2bdqkvLw80/EAAIip3bt3a2FhQf39/QoGg3r11Vd15MgR5ebmmo4GAAAAADCIIgG4jvn5ebW1ta1Zv/vuuykSAAApx7Is7d+/X4uLixofH9fS0pJeffVV3XvvvcrIyDAdDwAAAABgCKONgOsIhUJR151OZ5yTAAAQHy6XS+9///sjhfnMzIzeeOONdb8mAgAAAABSH0UCcB0UCQCAdJSZmakPfOADyszMlCSNjIzo5MmTsm3bcDIAAAAAgAkUCcB1BIPBqOsuF1PBAACpLT8/X+9///sj5fnly5d17tw5w6kAAAAAACZQJADXwY4EAEA6Ky0t1YEDByL3L168qK6uLoOJAAAAAAAmUCQA17HejgSKBABAuqipqdGePXsi90+dOqXh4WFzgQAAAAAAcUeRAFwHOxIAAJA8Ho88Ho8kybZtvfnmm5qZmTGcCgAAAAAQLxQJwHWsVyRwRgIAIN3s2rVLVVVVkqRAIKDXXntNy8vLhlMBAAAAAOKBIgG4DnYkAACwwuFw6MCBAyoqKpIkzc3N6fXXX1/3ayUAAAAAIHVQJADXEe2MBKfTKcuyDKQBAMAst9ut97///crKypIkjY2N6dSpU7Jt23AyAAAAAMBGokgAriPauyzZjQAASGc5OTm6++67I18Pe3p65PP5DKcCAAAAAGwkigTgOigSAABYq7S0VPv374/cP3PmjAYGBgwmAgAAAABsJIoE4DqijTbioGUAAKTa2lpt3749cv/YsWOampoyFwgAAAAAsGEoEoDrYEcCAADr27Ztm+rq6iStlO+vvfaalpaWDKcCAAAAAMQaRQJwHRQJAACsz7Is3XnnnSopKZEkLSws6I033oj69RMAAAAAkLwoEoDroEgAAOD6XC6X7r77bmVnZ0uSxsbGdOrUKdm2bTgZAAAAACBWKBKA6+CMBAAA3lt2drbuvvvuSNne09OjtrY2w6kAAAAAALFCkQBcBzsSAAC4MSUlJdq/f3/k/pkzZzQ0NGQwEQAAAAAgVigSgOugSAAA4MbV1tZq27ZtkiTbtvXmm29qZmbGcCoAAAAAwO2iSACuIxwOr1ljtBEAAOvbvn27ampqJK2MCHz99dfl9/sNpwIAAAAA3A7L5iS8tFVTU6OBgQFVV1erv7/fdJyEFQ6HFQwGFQqFFAqF5HQ6IwdKAgCAtYLBoF588UVNT09LkqqqqnT33XfLsizDyQAAAAAAt4IdCcB7cDgcysjIUHZ2tvLy8igRAAB4Dy6XS3fffbcyMjIkSYODg7pw4YLhVAAAAACAW0WRAAAAgJjLy8vTwYMHI/fPnz+vwcFBg4kAAAAAALeKIgEAAAAborKyUjt37ozcP3bsmGZnZw0mAgAAAADcCooEAAAAbJjW1tbI4cuBQECvv/66AoGA4VQAAAAAgJtBkQAAAIANY1mW7rrrLhUUFEiSZmZmdPz4cdm2bTgZAAAAAOBGUSQAAABgQ7ndbt19991yu92SpIGBAV26dMlwKgAAAADAjaJIAAAAwIbLz8/XgQMHIvfPnj2roaEhg4kAAAAAADeKIgFYRyAQ0Pz8vJaWlhQIBBQOh01HAgAgqVVVVWn79u2R+0ePHtXc3JzBRAAAAACAG+EyHQBIVAMDAzp+/PiqNYfDofvuu09FRUVmQgEAkOS2bdumyclJDQ4ORg5fPnLkiFwuvi0FAAAAgETFjgRgHcFgcM1aOByWw8FfGwAAbpVlWTpw4IDy8/MlSdPT0zpx4gSHLwMAAABAAuOKKLCOUCgUdZ13TAIAcHuuHr589WtqX1+ffD6f4VQAAAAAgPVQJADrWK9IcDqdcU4CAEDqKSgoWHP48sjIiMFEAAAAAID1UCQA64g22kiiSAAAIFaqq6u1bds2SZJt2zp69KgWFxcNpwIAAAAAvBtFArAOdiQAALDxtm3bpk2bNkmSlpaWdPToUYXDYcOpAAAAAADXokgA1hGtSHA6nbIsy0AaAABSk8Ph0IEDB5SdnS1JGh0d1fnz5w2nAgAAAABciyIBWEe00UbsRgAAIPaysrJ08ODBSFl/8eJFDQ0NGU4FAAAAALiKIgFYR7QdCS6Xy0ASAABSX3l5uXbu3Bm5f+zYMS0sLBhMBAAAAAC4iiIBWMd6o40AAMDG8Hq9qqqqkiT5/X69+eabnJcAAAAAAAmAIgFYB0UCAADxZVmW9u/fr9zcXEnS+Pi4zpw5YzgVAAAAAIAiAVhHtDMSGG0EAMDGysjI0KFDh+RwrHyb2tbWpv7+fsOpAAAAACC9USQA62BHAgAAZpSUlGj37t2R+ydOnNDc3JzBRAAAAACQ3igSgHVQJAAAYE5zc7Nqa2slSYFAQG+++WbUr80AAAAAgI1HkQCsg9FGAACYY1mW7rzzTuXl5UmSJicn9fbbb5sNBQAAAABpiiIBWAc7EgAAMMvtdut973tf5OtvZ2enLl++bDgVAAAAAKQfigQgCtu2KRIAAEgARUVF2rt3b+T+qVOnNDMzYzARAAAAAKQfigQginA4LNu216wz2ggAgPhrbGxUQ0ODpJXRg5yXAAAAAADxRZEARLHexQl2JAAAYMYdd9yhgoICSdL09LTeeecdw4kAAAAAIH1QJABRRDtoWaJIAADAFJfLpUOHDkW+Fnd0dGhwcNBwKgAAAABID8xpAaJwu9264447FAqFFAwGFQqFFAqFVFJSYjoaAABpq7CwULt379bp06clScePH9cDDzygnJwcw8kAAAAAILVRJABRuN1uNTc3m44BAADepampSVeuXNHAwID8fr+OHz+uD37wg3I42GgLAAAAABuFn7gAAACQNCzL0p133qns7GxJ0sjIiHw+n+FUAAAAAJDaKBIAAACQVDIzM3XgwIHI/XPnzml8fNxgIgAAAABIbRQJAAAASDoVFRXaunWrJMm2bR09elR+v99wKgAAAABITRQJAAAASErbt29XaWmpJGl+fl6nT5+WbduGUwEAAABA6qFIAAAAQFJyOBw6ePCg3G63JKm3t1eXL182nAoAAAAAUg9FAgAAAJJWbm6u9u3bF7l/+vRpzc7OGkwEAAAAAKmHIgEAAABJra6uTg0NDZKkYDCoo0ePKhQKmQ0FAAAAACnEZToAkIh6enrU19cnl8slp9MZ+d8dO3bI6XSajgcAAN5l7969Gh8f1+zsrCYnJ3Xu3Dnt3r3bdCwAAAAASAkUCUAUMzMzGhoaWrO+Y8cOA2kAAMB7cbvdOnjwoF588UWFw2H5fD5t2rRJlZWVpqMBAAAAQNJjtBEQRTAYjLrucPBXBgCARFVcXKydO3dG7h8/flzLy8sGEwEAAABAauCqKBBFtLnKLpdLlmUZSAMAAG6Ux+OJ7EJYWlrSqVOnZNu24VQAAAAAkNxiMtrov/yX/3JLH+dwOJSfn6+SkhLt3r1bO3bs4B3fSAjRigTORgAAIPFZlqW77rpLzz33nPx+v/r7+9Xb26v6+nrT0QAAAAAgacWkSPjCF74Qk3dql5eX6zOf+YyeeOIJZWdnxyAZcGuijTaiSAAAIDlkZ2dr3759evPNNyVJp0+fVnl5uXJycgwnAwAAAIDkFLO3/9u2Hfn17vvv/rXe4yMjI/qv//W/au/evRoYGIhVNOCmrTfaCAAAJIfa2lrV1dVJkgKBgI4fP86IIwAAAAC4RTG5MvrSSy9Jkr70pS/pb//2b+V0OvXQQw/p8OHDamxsVG5urubn59XV1aWXXnpJzz77rEKhkD7+8Y/r05/+tMbHx3X8+HF9/etf1+joqNra2vTYY4/pxIkTsYgH3DRGGwEAkPzuuOMOjY6OanFxUSMjI2pvb5fH4zEdCwAAAACSjmXH6K1Zv/M7v6MvfvGL2r17t/7v//2/8nq96z730qVL+sQnPqGzZ8/qt3/7t/XHf/zHkqSZmRl99KMf1QsvvCDLsvT000/rox/9aCziIYqamhoNDAyourpa/f39puMklH/5l3/R5OTkqrWysjLde++9hhIBAIBbceXKFb3yyiuSVt4UcP/996ugoMBwKgAAAABILjEZbfTDH/5Qf/qnf6ry8nK98MIL1y0RJKm1tVUvvPCCSktL9Sd/8id6+eWXJUkFBQX6u7/7OxUVFUmS/t//+3+xiAfctGhnJDDaCACA5LNp0ya1tLRIWtlxeOzYMYXDYcOpAAAAACC5xKRI+NKXviTLsvTLv/zLKikpuaGPKSsr06c//WnZtq0vfelLkfXCwkJ94hOfkG3bjDaCMYw2AgAgdezcuVP5+fmSpMnJSV24cMFwIgAAAABILjEpEo4fPy5J2rVr10193NXnHz16dNX6vn37JEkjIyMxSAfcPIoEAABSh8vl0oEDB2RZliTp4sWLGh8fN5wKAAAAAJJHTGa1XL3gv7y8fFMfd/X57y4MiouLJUW/mAvEA6ONgNVCoZAuX76svr4+jY2NaWpqSqFQSE6nU0VFRSorK1Ntba3q6+sp3QAkpJKSEm3btk3nz5+Xbds6fvy47r//fr6+AwAAAMANiMlPTsXFxbpy5Yp+9KMf6VOf+tQNf9yPfvQjSYqciXDV/Py8JN3wmCQglmzbZkcC8GPhcFhnzpzR2bNntbi4uObxUCik8fFxjY+Py+fzKTs7Wzt37tSuXbvkcMRk0xsAxMzWrVs1NDSkiYkJzc7O6syZM7rjjjtMxwIAAACAhBeTqzx33XWXbNvW17/+dZ08efKGPub48eP6xje+IcuytH///lWPdXR0SFo5RwGIt/V2wlAkIN1MTEzoe9/7no4fPx61RIhmcXFRx48f1/e+9z1NTExscEIAuDkOh0P79++PfE3v6OjQ8PCw4VQAAAAAkPhiUiR8+tOflrQyDub+++/XX/3VX0UdDXP1OV/96lf14Q9/WIFAQJL0K7/yK6ue88orr8iyLG3fvj0W8YCbsl6RwOgDpJPh4WH9wz/8g8bGxm7p48fGxvQP//APXKADkHAKCgpWnet14sQJ+f1+g4kAAAAAIPHF5MroT/3UT+nnf/7n9a1vfUszMzP6lV/5Ff3u7/6u7r77bjU1NSknJ0cLCwvq7OzUG2+8ocnJSdm2LUn6+Z//eT388MOR1+rt7dXrr78uSbr//vtjEQ+4KexIQLqbmJjQs88+Gyl7b1UgENCzzz6rxx57LHL2DQAkgubmZg0ODurKlStaXFzU6dOndfDgQdOxAAAAACBhxewt1l/72teUlZWlv/7rv5YkTU5O6vvf/37U514tEX75l39Zf/EXf7HqsXA4rG9/+9uSpA9/+MOxigfcsPV201AkIB2Ew2G9/PLLt10iXBUIBPTSSy/pscce48wEAAnDsizdddddeu655xQIBNTb26va2lpVV1ebjgYAAAAACSlmV3WcTqe++tWv6sUXX9Qjjzwit9st27bX/HK5XHrkkUf04osv6itf+cqai7MNDQ36mZ/5Gf3Mz/yM8vLyYhUPuGGMNkI6O3PmzC2PM1rP2NiYzpw5E9PXBIDblZOTo71790bunzp1ihFHAAAAALCOmF8ZPXz4sA4fPqzl5WW98847Ghwc1Pz8vHJzc1VVVaXdu3crMzMz1p8WiBlGGyFdhUIhnT17dkNe++zZs9q1axe7EgAklPr6evX19WloaEhLS0t6++23tX//ftOxAAAAACDhbNhbrDMzM/lBDEnJsiwVFBQoGAwqFAopFAopGAxSJCDlXb58WYuLixvy2ouLi+rp6VFjY+OGvD4A3ArLsrRv377IiKOenh7V1tZq8+bNpqMBAAAAQEJhVgvwLqWlpXrwwQdXrV091wNIZX19fRv++hQJABJNTk6Odu/erZMnT0qSTp48qQcffFBut9twMgAAAABIHMyYAG6AZVmyLMt0DGBDxfpshHi/PgDcqi1btmjTpk2SVnZQvfPOO4YTAQAAAEBi2ZAdCUNDQzp37pwmJye1tLR0Qx/zqU99aiOiAABu0NTUVFK/PgDcKsuydOedd+q5555TMBhUV1eXamtrI+UCAAAAAKS7mBYJ3/72t/Xf//t/v+nDOi3LokgAAMPWO2g8WV4fAG5Hbm6udu7cqbfeekvSyoijBx54gBFHAAAAAKAYjjb6d//u3+mTn/ykzp49K9u2b/pXsgiFQjp37pyeeuop/fqv/7oOHTqknJycyOibxx9//IZe56mnnop8zI38+sIXvrChvy8A2OgDxTmwHECia25uVnl5uSRpfn7+pt8cAwAAAACpKiY7Ev7xH/9R/+t//a/I/QMHDuj+++9XTU2NMjMzY/EpEsbHP/5x/f3f/73pGAAQc0VFRRofH9/Q1weARHZ1xNHzzz+vUCikjo4O1dbWRsoFAAAAAEhXMSkSvvzlL0taebfpU089pU9+8pOxeNmE9O7RHCUlJSotLVV7e/stv+av//qv6957773uc1pbW2/59QHgRpSVlW1okVBWVrZhrw0AsZKfn68dO3ZEDlw+ceKEHnjgAblcG3K0GAAAAAAkhZj8RHTy5ElZlqV//a//dUqXCJK0f/9+bd26Vfv27dO+ffu0ZcsWPfXUU/o3/+bf3PJr3nHHHXrsscdiFxIAbkFtba18Pt+Gvj4AJIOWlhb19/drfHxcc3NzOnfunPbs2WM6FgAAAAAYE5MiYXp6WpJ05MiRWLxcQnviiSdMR8AGm5yclN/vl9PpjPxyuVzKyckxHQ3YUPX19crOztbi4mLMXzs7O1sNDQ0xf10A2AgOh0N33XWXnn/+eYXDYbW3t6u2tlalpaWmowEAAACAETEpEjZt2qT+/n653e5YvBxg1KVLl9TX17dqLSsrS48++qihREB8OJ1O7dy5U8ePH4/5a+/cuVMOhyPmrwsAG6WgoEDbt2/X2bNnZdu2Tpw4ofvvv5+D4wEAAACkpZhc1Tlw4IAk6eLFi7F4OcCod5+DIYmLBkgbu3btivlZBmVlZdq1a1dMXxMA4sHr9aq4uFiSNDMzowsXLhhOBAAAAABmxKRI+NVf/VXZtq1vfOMbCgQCsXjJtPK///f/1tatW5WXl6ecnBzV1dXp0Ucf1Ze+9CUtLCyYjpd2KBKQzhwOh+65556Y7TDLyMjQ4cOH2Y0AICldHXFkWZYkyefzRUZ6AgAAAEA6icmVncOHD+vf/tt/q66uLj3++OOUCTfpxIkTunTpkubn57W4uKi+vj790z/9k37t135NDQ0NeuaZZ0xHTCvBYHDNmssVkylgQFIoKSnRQw89dNsFWkZGhh588MHIu3kBIBkVFRWptbVVkhQOh3Xq1CnZtm04FQAAAADEV0yujvb29uq3fuu3NDExoW9961s6ffq0fu3Xfk2HDh1SWVnZDb0Tta6uLhZRkorT6dShQ4f0gQ98QB6PR3l5eZqamtKpU6f0t3/7t5qYmNDo6KgeffRRffOb39TP/dzPbUiOQCCg06dPR31s8+bN2rx584Z83kTFjgRAqqioUGlpqcbHx6P+nXgvZWVlOnz4MCUCgJSwdetW9fX1aW5uTmNjY+ru7lZjY6PpWAAAAAAQNzEpEhoaGiJbvi3LUltbm37zN3/zhj/esqyo7wJPZe9///vV09OjmpqaNY99+tOf1v/4H/9Dn/nMZ/T000/Ltm390i/9ku6+++4NKVxGRka0b9++qI89+eST+sIXvhDzz5nIKBIAaWBgQH6/X3l5eVpeXtby8vINvQM3OztbO3fu1K5duxhnBCBluFwu7du3T6+88ook6Z133lFVVZWysrIMJwMAAACA+IjZvBa2eN+c5ubm6z6en5+vb37zm7py5YpefvllLS0t6Y//+I/153/+5zHPUlFRoWeffTbqY+m2G0FitBEgSW1tbZJWit6srCxlZmYqEAiopKREi4uLmpqaUigUktPpVFFRkcrKylRbW6uGhgYKBAApadOmTaqvr9fly5cVCAT09ttv6+DBg6ZjAQAAAEBcxOTq6C/+4i/G4mXwLk6nU3/4h3+o97///ZKkZ555ZkOKBLfbrTvuuCPmr5us2JGAdDc2Nqbx8fFVa5ZlqaSkRA8++CBFAYC0tXv3bg0NDcnv96u3t1cNDQ2qrKw0HQsAAAAANlxMioS/+Zu/icXLIIpDhw4pKytLS0tL6u3t1cLCgnJyckzHSmkUCUh3Pp8v6rrH46FEAJDWsrKytGvXLp08eVKSdOrUKX34wx9m5yIAAACAlMcVoQTncDhUUlISuT81NWUuTBqwbZsiAWltdnZWAwMDa9YzMjLU0NAQ/0AAkGC2bNmi8vJySdL8/LwuXLhgOBEAAAAAbDyKhAQXDoc1OTkZuV9UVGQuTBqIViJInJGA9NHe3h51vampib8HAKCVUW/79u2L7NDy+Xy80QMAAABAyqNISHBHjx7V4uKiJKmmpoaxRhss2kHLEjsSkD4yMjLWFAYOh+M9D4gHgHRSUFCg1tZWSSu7GU+dOiXbtg2nAgAAAICNQ5GQwMLhsD7/+c9H7j/yyCMG06SH9XYkUCQgXezYsUOPPPKIdu/erezsbElSXV1d5DYAYMXWrVuVn58vSRofH1dnZ6fhRAAAAACwcWI+p6Kvr0/f+MY3dPToUfX392tmZmbdi7NXWZaVVj98vfnmmzp79qw+9alPKSsrK+pz5ufn9dnPflYvvviiJCkzM1O/93u/F8+YaYnRRsDKrgSv16uWlhb19fWpuLjYdCQASDhOp1P79u3Tyy+/LEk6e/asqqurKV4BAAAApKSYXR0Nh8N64okn9MUvfjFyMfbdW7wty7ruejLo7u7WX/3VX61aO3PmTOT2W2+9pd///d9f9fi9996re++9N3L/ypUr+uxnP6vf+q3f0v333699+/aptrZWubm5mp6e1unTp/Xtb39b4+Pjklb+//nqV7/KQadxwGgj4CccDofq6+tNxwCAhFVRUaGGhgb19PQoEAjo7bff1qFDh0zHAgAAAICYi1mR8LnPfU5/+Zd/GSkJKisrNTw8LMuyVFZWJtu2NTExoXA4LGnl4nh1dXXSXaC9fPmy/uiP/mjdx8+cObOqWJBW3s1+bZFw1dzcnL773e/qu9/97rqvV1lZqa9+9at6+OGHbz00bhg7EgAAwM3YvXu3BgcH5ff71dfXp4aGBm3evNl0LAAAAACIqZickXDixAl9+ctfliQdOnRIHR0dGhwcjDz+la98RSMjI5qcnNTTTz+tXbt2ybZttba26vTp0+ru7o5FjKRx33336R/+4R/0xBNP6L777pPX61VZWZlcLpcKCgrU3Nysj3/84/ra176m7u5uSoQ44owEAABwMzIzM7Vnz57I/dOnT6+7wxEAAAAAklVM3mb9la98RZJUXFysZ555Zt152vn5+frYxz6mxx57TJ/85Cf1ne98Rx/96Ecj5wAkg3vuuWfNaKablZeXp0cffVSPPvpojFIhVigSAADAzaqvr1dPT49GRkY0Pz+vS5cuaceOHaZjAQAAAEDMxGRHwuuvvy7LsvTxj3/8hg7ldLvd+j//5/+ourpaL7/8sr75zW/GIgZw2zgjAQAA3CzLsrR3797IuV+XLl3S3Nyc4VQAAAAAEDsxKRKujjG68847oz6+vLy8Zi0rK0uPP/64bNvWt771rVjEAG4bZyQg3fT392tpacl0DABIeoWFhWppaZEkhcNhvf3222YDAQAAAEAMxeTq6Pz8vCSt2Y2Qk5OjxcVFTU9PR/24bdu2SZLOnj0bixjAbdu8ebM+9KEPKRgMKhQKKRQKKRgMKjs723Q0IOYWFxd19OhRSStjObxerwoKCgynAoDktX37dvX29mppaUmDg4MaGhri4GUAAAAAKSEmRUJ+fr6mpqa0uLi4ar24uFiLi4vrHqZ8tYAYHR2NRQzgtmVnZ1MaIG20t7crHA5Lkrq7u9Xd3a3Nmzdr9+7dFAoAcAvcbrd2796tY8eOSZLeeustVVRUMCIRAAAAQNKLyWijpqYmST8ZcXTVtm3bZNu2Xnnllagfd/z4cUniwi0AxFkgEFBnZ+ea9eHhYTkcMfnSAABpqa6uTuXl5ZKkubk5+Xw+w4kAAAAA4PbF5GrRHXfcIdu29c4776xaP3LkiCTpzTff1A9+8INVjx09elRPPfWULMvS7t27YxEDAHCDuru7FQgE1qxXV1crLy/PQCIASA3vPnj54sWLkV24AAAAAJCsYlIkHD58WJL0wx/+cNX6L/zCLygnJ0eS9Nhjj+njH/+4nnjiCX384x/XPffcE7mI9Yu/+IuxiAEAuAHhcFjt7e1RH/N6vXFOAwCpp6ioSM3NzZKkUCjEwcsAAAAAkl5MioSHH35YmZmZGhoa0nPPPRdZ37x5s/70T/9Utm0rGAzqO9/5jv74j/9Y3/nOd+T3+yVJDz74oB5//PFYxAAA3ICBgYGo744tKytTaWmpgUQAkHq2b9+uzMxMSSv/7g4PDxtOBAAAAAC3LiZFQl5enmZmZrS4uKj7779/1WOf/exn9fTTT6u5uVm2bUd+5eXl6Xd/93f1ve99LxYRAAA3wLbtded1ezyeOKcBgNSVkZGxanznW2+9pVAoZDARAAAAANw6V6xeyO12r/vYxz72MX3sYx9TT0+PhoeHlZubq9bW1ut+DAAg9sbHxzUxMbFmPS8vT1VVVQYSAUDqqq+vV2dnp8bHxzU7O6u2tjZt3brVdCwAAAAAuGkx2ZFwoxoaGnTw4EHt3LmTEgEADLjebgSHI65fEgAg5VmWpTvuuGPVwcsLCwuGUwEAAADAzYvZjoQbMTIyoqNHjyoYDGr37t1qamqK56cH3tPrr7+umZkZuVwuOZ1OOZ1OFRUVrRpNACSr2dlZDQwMrFnPyMhQQ0ND/AMBQBooLi5WY2OjOjs7FQwG9c477+jQoUOmYwEAAADATYlJkTAxMaGnnnpK0srBy16vd81z/uAP/kB/9Ed/pEAgEFn72Z/9Wf31X/+1srKyYhEDuG1zc3OanZ1dtRYOhw2lAWKrvb096npTU5Ncrrj2ygCQVnbs2KH+/n4tLy+rr69PTU1NqqioMB0LAAAAAG5YTOZYPP300/rt3/5tPfHEEyorK1vz+De/+U09+eSTCgQCqw5cfvrpp/X444/HIgIQE9EOQeQCK1LB8vKyuru716w7HA41NzcbSAQA6SMzM1M7d+6M3D99+jRvVAAAAACQVGJSJLz00kuSpA984AMqLS1d8/jnP/95SZJt2/pX/+pf6Td/8zdVW1sr27b1d3/3d3r11VdjEQO4bdGKBKfTaSAJEFudnZ1R//uuq6tTdna2gUQAkF62bNmikpISSdLMzIy6uroMJwIAAACAGxeTIqGtrU2WZUWd9/rGG2+ou7tblmXpD//wD/Xd735XX/ziF3XixAkVFxdLkr7+9a/HIgZw24LB4Jo1igQku1AopI6OjqiPRRtFBwCIPcuytGfPnsj9c+fOye/3mwsEAAAAADchJkXC2NiYJKmlpWXNYy+88IKklS3dv/EbvxFZr6io0M/93M/Jtm0dPXo0FjGA28ZoI6Si3t5eLS0trVmvrKxUYWGhgUQAkJ7KyspUW1srSfL7/bpw4YLhRAAAAABwY2JSJIyPj0uScnNz1zz2+uuvS1oZe/Tux3ft2iVp5SIXYFo4HI46r5gdCUhmtm3L5/NFfYzdCAAQf7t27Yp8b9HR0aHZ2VnDiQAAAADgvcWkSLAsS5I0OTm5aj0cDuvYsWOyLEsf+MAH1nzc1fMUFhYWYhEDuC3RdiNIFAlIbsPDw5qZmVmzXlhYqIqKCgOJACC95ebmyuPxSFr5XvnMmTOGEwEAAADAe4tJkXD1YlR7e/uq9aNHj0YuYB08eHDNx83NzUkSB30iIaxXJDDaCMmsra0t6rrX642UwACA+GptbVVWVpYkaWBgQCMjI4YTAQAAAMD1xaRI2Lt3r2zb1re//e1Vh8Z95StfkSRlZGTo7rvvXvNxXV1dkqSqqqpYxABuCzsSkGps29bmzZuVk5Ozaj07OzsyoxsAEH9ut1s7d+6M3H/77bejjlcEAAAAgEQRkyLhYx/7mCSpr69PR44c0V/8xV/oM5/5jL72ta/Jsiw9+uijUXcdHD16VJZlaevWrbGIAdyWYDAYdZ0iAcnKsix5PB595CMf0cGDB1VSUiJJam5u5r9rADCsoaFBRUVFkqSpqSn19PQYzQMAAAAA1xOTmS0/93M/pz/7sz/TsWPH9MYbb+iNN96IPJaZmaknn3xyzcdMTU3p5ZdfliQdOHAgFjGA28JoI6Qqh8Ohuro61dbWamxsTIWFhaYjAUDasyxLe/bsiXw/fO7cOdXW1srtdpsNBgAAAABRxOyw5e9///t67LHHZFmWbNuWbduqrq7Wd77zHW3btm3Nxzz11FMKBAKSpPvuuy8WMYDbwmgjpDrLslReXq6MjAzTUQAAWjlnrLq6WpK0tLSkS5cuGU4EAAAAANHF7K3WJSUl+vu//3uNjo6qq6tLubm52rZtmxyO6F3Ftm3b9Dd/8zeyLEv79u2LVQzgljHaCAAAxNvu3bs1NDSkcDgsn8+nxsZG5ebmmo4FAAAAAKvEfGZLeXm5ysvL3/N5DzzwQKw/NXBbGG0EAADiLS8vTy0tLfL5fAqHwzpz5owOHTpkOhYAAAAArBKT0UZAKmC0EQAAMGHr1q3KzMyUJPX19WlsbMxwIgAAAABYjSIB+DFGGwEAABMyMjK0ffv2yP23335btm0bTAQAAAAAq1EkAD/GaCMkO9u2df78eU1PT5uOAgC4SY2NjSooKJAkTUxMqLe313AiAAAAAPgJigTgxxhthGQ3Pj6u8+fP67nnntOPfvQjXblyhXe0AkCScDgc2rNnT+T+2bNn1/3eBAAAAADijSIB+DFGGyHZ+Xy+yO3h4WG98sorev755zUzM2MwFQDgRlVWVqqyslKStLCwoM7OTsOJAAAAAGAFM1uAH8vPz1dVVZVCoZCCwaBCoZBCoRBFApLC7OysBgYG1qwvLi4qJyfHQCIAwK3YuXOnhoeHJUkXLlxQQ0ODMjIyDKcCAAAAkO4oEoAfa2hoUENDg+kYwC1pa2uLut7U1MQ5HwCQRIqLi1VXV6fe3l75/X75fD7t3LnTdCwAAAAAaY7RRgCQ5JaXl9XT07Nm3eFwqLm5Of6BAAC3ZceOHXI4Vr5Nb2tr0+LiouFEAAAAANIdRQIAJLnOzs6oB3LW19crOzvbQCIAwO3Iy8tTU1OTJCkUCun8+fOGEwEAAABIdxQJAJDEQqGQOjo6oj7m8XjinAYAECtbt26NjKbr7u7WzMyM4UQAAAAA0hlFAgAkscuXL2tpaWnNemVlpQoLCw0kAgDEQlZWllpbWyVJtm3r3LlzhhMBAAAASGcUCQCQpGzbXveQZa/XG+c0AIBY83g8ysrKkiT19/drfHzccCIAAAAA6YoiAQCS1PDwcNRRF0VFRaqoqDCQCAAQSy6XS9u2bYvcP3PmjGzbNpgIAAAAQLqiSACAJOXz+aKuezweWZYV5zQAgI3Q2NiovLw8SdLo6KiGh4cNJwIAAACQjlymAwCJ4uqBtU6nUy6XS06nU7m5ucyZR0KanJzUyMjImvXs7GzV1tYaSAQA2AgOh0M7d+7Um2++KWllV8KmTZvkcPB+IAAAAADxQ5EA/NjZs2cVCARWrTU0NGj//v2GEgHrW+9shObmZjmdzjinAQBspJqaGpWUlGhiYkLT09Pq7e1VQ0OD6VgAAAAA0ghvZQJ+LBQKrVnjgiwS0cLCgnp7e9esu1wuNTU1GUgEANhIlmVp165dkfvnzp2L+n0LAAAAAGwUigRAUjgcVjgcXrPucrFpB4mnvb096mGbW7ZsUUZGhoFEAICNVlFRocrKSkkrhfLVkYwAAAAAEA8UCYCi70aQ2JGAxBMIBNTV1bVm3bIstbS0GEgEAIiXa3clXLx4cc1IRgAAAADYKBQJgKRgMBh1nSIBiaa7uzvqhaPq6mrl5eUZSAQAiJeioiLV19dLkvx+/7rn5QAAAABArFEkAFp/RwKjjZBIwuGw2tvboz7m9XrjnAYAYMK2bdtkWZYkqa2tTX6/33AiAAAAAOmAIgEQo42QPHbv3q3S0tJVa2VlZWvWAACpKT8/Xw0NDZJWxt35fD6zgQAAAACkBYoEQIw2QnJwOByqqanRkSNHdO+996q6uloSuxEAIN1s27ZNDsfKt/Ht7e1aXl42nAgAAABAqmNuCyBGGyH5lJWVqaysTHNzc8rJyTEdBwAQR7m5udqyZYs6OzsVDAZ16dIl7d6923QsAAAAACmMHQmAGG2E5JWXlxd5VyoAIH1s3bo18u9/R0eHFhcXDScCAAAAkMq4+gSIIgEAACSXnJwcNTU1SVr5PubSpUuGEwEAAABIZRQJgNY/I4HRRgAAIFG1trZG3vTQ2dmphYUFw4kAAAAApCqKBEDsSAAAAMknOztbzc3NkqRwOKyLFy8aTgQAAAAgVVEkAFp/RwJFAgAASGStra2RHZTd3d2an583nAgAAABAKqJIALT+jgRGG8GkUCikN954Q0NDQ7Jt23QcAEACyszMVEtLi6SVXQkXLlwwnAgAAABAKqJIAMRoIySmy5cvq7+/X6+++qqee+45dXd3r/vfKgAgfXm9XrndbklST0+PZmdnDScCAAAAkGooEgAx2giJx7ZttbW1Re7PzMzoxIkT+v73v89hmgCAVTIyMuTxeCStfP1gVwIAAACAWKNIABR9R4LT6ZRlWQbSANLw8LBmZmbWrGdlZSk7O9tAIgBAIvN4PMrIyJAk9fb2Rv0aAgAAAAC3iiIB0PpFAmCKz+eLuu71eim4AABruN1ueb1eSSu7Es6fP284EQAAAIBUQpEAKPpoIw5ahimTk5MaGRlZs56dna3a2loDiQAAyaC5uVmZmZmSpL6+Pk1PTxtOBAAAACBVcKUUkHTXXXdpz549CoVCCoVC656ZAMTDtWcjXKulpUUOB/0vACA6t9ut1tZWvfPOO5Kkixcv6uDBg4ZTAQAAAEgFXJECtHJIYW5urgoKClRcXKzy8nKVl5ebjoU0tLCwoN7e3jXrLpdLjY2NBhIBAJJJU1NT5KyEvr4+zc7OGk4EAAAAIBVQJABAAmlvb5dt22vWt2zZErkwBADAelwulzwej6SVsxIuXbpkOBEAAACAVECRAFwjHA5rYGBAIyMjmpqa0vz8vAKBQNQLu0CsBQIBdXV1rVm3LCtyUQgAgPfS3Nwst9stSerp6dH8/LzhRAAAAACSHWckANcIBAJ6/fXX16xbliW3262MjAy53W5lZmYqJycn8is3N1c5OTnKzs5mhj1uWXd3twKBwJr1mpoa5ebmGkgEAEhGGRkZamlp0YULFyK7Evbt22c6FgAAAIAkRpEAXMPv90ddt21bfr9/3cevsixLWVlZkWLh3UVDTk5O5B2CwLXC4fC6hyyzGwEAcLNaWlrU1tamYDCo7u5ubdu2TdnZ2aZjAQAAAEhSFAnANdxut3bu3BkpDQKBwJr/vd6oI9u2tbi4qMXFxet+jmuLhYKCAhUVFamwsJCSIY0NDAxoYWFhzXpZWZlKS0sNJAIAJLPMzEw1NTXJ5/MpHA7L5/Npz549pmMBAAAASFIUCcA1srKytHXr1us+x7ZtLS8va2FhQQsLC5qfn4/cvvpreXl53Y8PBAKamprS1NTUmsfy8vJUVFS06ld2drYsy7rd3xoSmG3b8vl8UR/zer1xTgMASBVer1cdHR0KhULq7OxUa2ursrKyTMcCAAAAkIQoEpD2bNtWOByWw+G4oQv2V8cXZWVlqaSkJOpzgsHgdYuGhYWFqLsa5ubmNDc3p/7+/shaRkbGmnIhPz9fTqfz1n/TSChjY2OamJhYs56Xl6eqqioDiQAAqSArK0uNjY1qb29XKBRSW1ubdu3aZToWAAAAgCREkYC0FwgE9L3vfU+WZcnpdMrlcsnpdMrj8ailpeWWXtPlcqmgoEAFBQVRHw+Hw1paWtL8/Hxkd8L09LSmp6cVCoVWPdfv92tkZEQjIyORNYfDofz8/FXlQnFxsTIyMm4pL8xabzeCx+NhNwoA4LZ4vV51dnYqHA6ro6NDra2tfL8AAAAA4KZRJCDtXb1wb9u2gsGggsHgqvWN4HA4ImcklJeXR9bD4bDm5uYi5cLVX0tLS6s+PhwOR4qHy5cvR9aLi4tVXl6uiooKlZWVcaEgCczOzmpwcHDNekZGhhoaGuIfCACQUnJyctTQ0KCuri4Fg0G1t7dr+/btpmMBAAAASDIUCUh7V4uDdzMxOsjhcER2MtTV1UXWl5aW1pQLs7Oza8YjTU5OanJyUm1tbbIsS0VFRaqoqIgUCxzmnHja2tqirjc3N8vl4p9oAMDta21tVXd3t2zbVnt7uzweD98TAAAAALgpXKVC2ltv50EiXcTNyspSZWWlKisrI2uhUEjT09ORYmFsbGzVAc62bUeKBZ/PJ8uyVFxcHCkWSktLuYhgmN/vV09Pz5p1h8Oh5ubm+AcCAKSkvLw81dfXq6enR36/Xx0dHdq6davpWAAAAACSSOJcKQUMWa9ISPTDjJ1Op0pKSlYd+Ly8vKzR0VGNjIxodHRU09PTkcds29bExIQmJiZ06dIlWZalkpKSVcVCIpUn6SAjI0P33HOPfD6fBgYGIjtM6uvrlZWVZTgdACCVtLa2RsrrtrY2tbS08HUfAAAAwA3jpwekvUQabXS7MjMzVVNTo5qaGkkrI5FGR0cj5cLMzEzkubZta3x8XOPj47p48aIcDkekWKiurlZRUREH/cZBaWmp3ve+92lubk7t7e3q7u6Wx+MxHQsAkGIKCgpUW1urvr4+LS8vq6uri683AAAAAG4YRQLSXjKMNrpVWVlZqq2tVW1traSfFAsjIyMaGRnR7Oxs5LnhcFhjY2MaGxvThQsXlJOTo6qqKlVXV6u8vFwOh8PUbyMt5OXlae/evdq5c2dK/LcHAEg8W7duVV9fnyTJ5/OpqakpKd84AQAAACD+uFqFtJeso41uxbuLhcXFxVXFwtzcXOS5CwsL6ujoUEdHh9xutzZv3qzq6mpVVlZytsIGokQAAGyUoqIiVVVVaXBwUIuLi+rr61NDQ4PpWAAAAACSAFeskPbSqUh4t+zsbNXV1amurk7SSnkwNDSkgYEBjYyMKBwOS5ICgYB6e3vV29srh8OhiooK1dTUqLq6WpmZmSZ/CwAA4CZ4vV4NDg5KWtmVUF9fzyhDAAAAAO+JIgFpb70zEtLxneE5OTlqampSU1OTAoGAhoeHNTAwoKGhIQUCAUkrI5CGh4c1PDysU6dOadOmTZQKAAAkibKyMpWUlGhiYkLT09O6cuWKKisrTccCAAAAkODS70op8C7pvCPhetxud2QMUjgc1ujoqAYGBjQ4OKiFhQVJKwc2UyoAAJA8LMuS1+vVm2++KWllVwJFAgAAAID3QpGAtLfejoR0LxKu5XA4tGnTJm3atEl79+7VxMSE+vr61N/fv26pUFlZqS1btqiqqoqDmgEASCDV1dXKzc3V/Py8rly5osnJSRUXF5uOBQAAACCBUSQg7a23IyEdRxvdCMuyVFpaqtLSUu3evXvdUmFoaEhDQ0PKzMxUQ0ODGhsblZ+fbzi9WQsLC3rttdfU3Nys+vp6yioAgBEOh0Mej0dvvfWWpJVdCQcPHjScCgAAAEAi40op0t56RQLvon9vN1IqLC8vy+fzyefzqby8XI2Njaqurk7Loqa9vV1TU1M6efKkzp49q+bmZjU3NzMGCgAQd1u2bNH58+fl9/vV19enXbt2KScnx3QsAAAAAAkq/a7kAe8SbbSRy+WSZVkG0iSvd5cKV65cUVdXlwYHBxUOhyVJo6OjGh0dldvtVn19vRobG1VUVGQ2eJwEAgF1dXVF7i8vL+v8+fPy+Xx65JFHlJGRYTAdACDduFwuNTc368KFC7JtW21tbdqzZ4/pWAAAAAASFEUC0l60HQmMnLk9lmWpsrJSlZWVWlpa0uXLl9XV1aXZ2VlJKxfVOzo61NHRoZKSEm3ZskV1dXVyu92Gk2+c7u5uBQKBNeubN2+mRAAAGNHc3KxLly4pHA6rq6tL27Zt42sSAAAAgKgoEpD2KBI2VlZWlrxerzwej8bGxtTV1aX+/v7I/+8TExOamJjQO++8o9raWjU2NqqkpCSldoSEw2G1tbVFfczr9cY5DQAAK7KystTQ0KCuri4Fg0F1dXWptbXVdCwAAAAACYgiAWlvvdFGiC3LslReXq7y8nLt3btXvb296urq0tTUlKSVP4fu7m51d3ersLBQW7ZsUX19fUqcH3DtmRHXKisrU0lJiYFEAACs8Hg8kdF77e3tamlp4Q0VAAAAANbgainSHjsS4i8jIyNy0PDExIS6u7t1+fLlSKkzPT2tt99+W2fOnFFdXZ1aW1tVUFBgOPWtuTp3Ohp2IwAATCsoKFBVVZUGBwe1uLiovr4+NTQ0mI4FAAAAIMFQJCDtUSSYVVJSopKSEu3evVt9fX3q6urS+Pi4pJWRQD09Perp6VF1dbVaW1tVWlpqOPHNGRsb08TExJr1vLw8VVVVGUgEAMBqra2tGhwclCT5fD7V19en1IhBAAAAALePIgFpr6GhQeXl5QqFQgqFQgoGg0n77vdk5nK5tGXLFm3ZskXT09ORMUdXDygeGBjQwMCAysvL1draqsrKyqS4yOHz+aKuezyepMgPAEh9paWlKi0t1fj4uKanp3XlyhVVVlaajgUAAAAggVAkIO21tLSYjoB3KSws1J49e7R9+3Z1dXWpra1Ni4uLkqTR0VGNjo6qsLBQra2tqq2tlcPhMJw4utnZ2cg7PK+VkZHB2AgAQMKwLEter1dvvPGGJOnSpUsUCQAAAABWScyrbwAgye12y+v16iMf+YjuvPNO5efnRx6bnp7WsWPH9Oyzz6q9vT3qodmmrXc2QnNzMwd6AwASSlVVlfLy8iRJIyMjmpycNJwIAAAAQCKhSACQ8JxOpxobG/Xggw/qfe97n0pKSiKPzc/P66233tL3v/99nT9/XsvLywaT/sTy8rJ6enrWrDscDjU3N8c/EAAA1+FwOOTxeCL31xvNBwAAACA9USQASBqWZammpkZHjhzRPffcs2rswvLyss6fP6/vf//7euutt7SwsGAwqdTR0RH1IO/6+nplZWUZSAQAwPU1NDQoMzNTktTX16f5+XnDiQAAAAAkCooEAEnHsixVVFTogx/8oB544AHV1dVFDi4OBoNqb2/X97//fR0/flzT09NxzxcKhdTR0RH1Ma/XG+c0AADcGJfLpaamJkmSbdtqb283nAgAAABAoqBIAJDUioqKdPDgQT300ENqbm6W0+mUtHIBpKenR88995xef/11zczMxC3T5cuXo45Y2rx5swoKCuKWAwCAm3Xt19Kuri75/X7DiQAAAAAkAooEACkhLy9Pd9xxhx5++GFt3bpVGRkZkccGBgb03HPP6eTJk1pcXNzQHLZtr3vIMrsRAACJLisrSw0NDZJWdvl1dnaaDQQAAAAgIVAkAEgpWVlZ2rlzpx5++GHt3r07ch6Bbdvq6urSD37wA509e1aBQGBDPv/w8HDU3Q9FRUUqLy/fkM8JAEAsXXvocnt7e9QzfwAAAACkF5fpAIBJ8/PzunjxopxOp1wul5xOp5xOJyNoUoDb7ZbX61VTU5Pa2tp06dIlBYNBhUIhXbx4UV1dXdq2bZsaGxsjIxxiwefzRV33er2RcxwAAEhk+fn5qq6u1sDAgJaWltTb26stW7aYjgUAAADAIIoEpLXFxUV1dXWtWc/JyaFISBEulytSGFy8eFGdnZ0Kh8NaXl7WW2+9pfb2du3YsUO1tbW3faF/fn5eo6Oja9azs7NVW1t7W68NAEA8eb1eDQwMSJLa2trU0NBAIQ4AAACkMUYbIa2tt1U/lu9QR2LIysrS3r179eCDD666qD83N6ejR4/qhRde0MjIyG19jtzcXH3kIx+Rx+ORy/WTnralpUUOB//cAgCSR1lZmUpLSyVJ09PTGhsbM5wIAAAAgElc2UJao0hIP3l5eTp06JDuu+8+VVRURNYnJyf18ssv60c/+pGmpqZu+fVzc3O1Z88ePfLII9q1a5cKCgrU2NgYg+QAAMRXc3Nz5DaHLgMAAADpjSIBaS0YDEZdv/bd5EhNJSUl+tCHPqQPfOADKiwsjKwPDw/r+eef1/Hjx7WwsHDLr5+RkaHW1lZ9+MMfVkZGRiwiAwAQVzU1NcrMzJQk9ff3a2lpyXAiAAAAAKZQJCCtsSMhvVmWpc2bN+v+++/X/v37lZOTE3msp6dHP/jBD/TOO+/I7/ff1ucAACAZOZ1ONTQ0SJLC4bC6u7vNBgIAAABgDEUC0tp6OxIoEtKLw+FQQ0ODHnroIe3atUtut1vSykUTn8+nH/zgB2pra1M4HDacFACA+Gpqaorc7urq4mshAAAAkKYoEpDW1tuRwGij9OR0OtXa2qqHH35YXq83ckCy3+/X22+/rRdeeIHDJgEAaSUvL0+VlZWSpPn5eV25csVwIgAAAAAmUCQgrTHaCNFkZGRo9+7deuihhyIjHSRpampKP/zhD3XixAktLy+bCwgAQBxduyuBQ5cBAACA9ESRgLTGaCNcT25urvbv368jR46oqKgost7d3a1nn31WnZ2d6/43BABAqti8ebOys7MlSUNDQ5qfnzecCAAAAEC8USQgrUXbkWBZVmSkDSBJpaWluu+++7Rnz57I2Cu/369Tp07pu9/9rk6ePKmlpSXDKQEA2BgOh0ONjY2SJNu21dXVZTgRAAAAgHjjainSWrQiwel0yrIsA2mQyBwOhzwejx566CHV1tZG1q9eUPmnf/onnTx5UjMzMwZTAgCwMRobGyPfH3V3d687HhIAAABAaqJIQFpbr0gA1pOdna1Dhw7prrvuWrV+tVB46aWXFA6HDaUDAGBjZGdnq7q6WpK0tLSkwcFBw4kAAAAAxBNFAtJatPn2V0fXANczMTERdb2pqYnRWACAlMShywAAAED64moX0ho7EnArlpaW1NPTs2bd4XCoubk5/oEAAIiDiooK5efnS5JGRkYY5wcAAACkEYoEpDWKBNyKzs7OqP/tNDQ0KCsry0AiAAA2nmVZkUOXJXYlAAAAAOmEIgFpjdFGuFmhUEgdHR1RH/N4PHFOAwBAfDU0NETedNHT0xP1eykAAAAAqYciAWmNHQm4WZcvX9by8vKa9c2bN6ugoMBAIgAA4iczM1O1tbWSpEAgoN7eXsOJAAAAAMQDRQLSGkUCboZt2/L5fFEf83q9cU4DAIAZHLoMAAAApB+KBKS1aNvxKRKwnqGhIc3Ozq5ZLyoqUnl5uYFEAADEX0lJiYqKiiRJk5OTmpiYMBsIAAAAwIajSEBaczqdcjhW/zXgjASsp62tLeq61+uVZVlxTgMAgBmWZbErAQAAAEgzXDFFWvupn/opSVI4HFYoFFIoFOKCMKKanJzUyMjImvXs7OzIrGgAANJFXV2d3nnnHQWDQfX29mr37t3KyMgwHQsAAADABmFHAiDJ4XDI7XYrKytLmZmZpuMgAa13NkJLS8uaXS0AAKQ6t9uthoYGSStnTvX09BjNAwAAAGBjcfULAN7DwsKC+vr61qy7XC41NjYaSAQAgHnXjjfq6uqSbdsG0wAAAADYSBQJAPAe2tvbo14caWxsZIwDACBtFRYWqqysTJI0MzOj0dFRw4kAAAAAbBSKBAC4jkAgoK6urjXrlmWppaXFQCIAABJHc3Nz5DaHLgMAAACpiyIBAK6jq6tLgUBgzXpNTY1yc3MNJAIAIHFUV1dHzpcaGBjQ0tKS4UQAAAAANgJFAgCsIxwOq729PepjXq83zmkAAEg8TqdTW7ZskbTydTPaLj4AAAAAyY8iAQDWMTMzE3U3Qnl5uUpKSgwkAgAg8TQ2NkZud3V1KRwOG0wDAAAAYCNQJCBt+f1+TU9Pa25uTouLiwoEAvzgi1WKior0yCOPaPfu3crJyYmssxsBAICfyMvLU2VlpSRpYWFBw8PDhhMBAAAAiDWX6QCAKcPDwzp69Oia9SNHjqi0tNRAIiQit9str9erlpYW9ff3a2BgQJs3bzYdCwCAhNLc3BwpEDo7O1VVVWU4EQAAAIBYokhA2goGg1HXnU5nnJMgGTgcDtXV1amurs50FAAAEk5lZaVycnK0sLCgoaEhzc/PKzc313QsAAAAADHCaCOkrVAoFHXd5aJfAwAAuBkOh2PVWQmdnZ0G0wAAAACINYoEpK31igR2JAAAANy8LVu2yLIsSVJ3d/e632sBAAAASD4UCUhbjDYCAACInezsbFVXV0uSlpeXNTAwYDgRAAAAgFihSEDaYrQRAABAbDU3N0du9/T0mAsCAAAAIKYoEm5SKBTSuXPn9NRTT+nXf/3XdejQIeXk5MiyLFmWpccff/ymX7Ojo0O/8zu/ox07dqiwsFB5eXnyer363Oc+p7fffjvmvwesiFYkWJYlh4O/FukqFAppcXHRdAwAAJJWeXm5cnJyJElXrlzR0tKS4UQAAAAAYoG3Xt+kj3/84/r7v//7mL3eX/7lX+o3f/M311y8bGtrU1tbm7785S/r85//vD7/+c/H7HNiRbTRRuxGSG89PT166623VF9fL4/Ho8LCQtORAABIKpZlqb6+XhcvXpRt2+rt7ZXH4zEdCwAAAMBt4qrpTXr3u9hLSkpUWlqq9vb2m36tb3zjG/rsZz8rSXI4HPrEJz6hI0eOyOVy6fXXX9fXvvY1LS8v68knn1RmZqZ+7/d+Lya/B6yItiOB8xHSl23bamtrUzgcVnd3t7q7u1VZWSmv16uKiorI4ZEAAOD6rhYJknT58mWKBAAAACAFMMPlJu3fv1//8T/+R/3d3/2durq6ND4+rieeeOKmX2d0dFSf+9znJK2UCN/97nf1zW9+U7/0S7+kT33qU/ryl7+sl19+ObI1/Pd///fl8/li+ntJdxQJuNbQ0JBmZ2dXrQ0PD+vMmTOGEgEAkJwKCgpUXFwsSZqcnNT09LThRAAAAABuF0XCTXriiSf03/7bf9NHP/pRbdmy5ZZf50/+5E80MzMjSfrc5z6nRx99dM1zDh48qD/4gz+QtDKG5z//5/98y58PazHaCNdar6jzeDzsRgAA4CbV19dHbl++fNlgEgAAAACxQJFgyNNPPx25/e///b9f93mf+cxnlJubK0n6x3/8Rw6CjSF2JOCqyclJjY6OrlnPzs5WbW2tgUQAACS3urq6SBHf29sr27YNJwIAAABwOygSDLhw4ULknVlbt2697s6G/Px8feADH5Akzc/P65VXXolLxnRAkYCrrrcbweHgn0kAAG5WVlaWNm3aJElaWFiIWtgDAAAASB5cITPg7Nmzkdt33XXXez7/2udc+7G4PdFGG1EkpJ/5+Xn19fWtWXe5XLc1vgwAgHTX0NAQuc14IwAAACC5USQYcO27n2/kQuW1z+HA5diJtiOBMxLST3t7e9RxC42NjcrIyDCQCACA1FBVVRX53qq/vz/qmzgAAAAAJAeumhowNTUVuV1WVvaezy8tLY36sbESCAR0+vTpqI9t3rxZmzdvjvnnTASMNkIgEFB3d/eadcuy1NLSYiARAACpw+VyqaamRj09PQoEAhoaGuLsIQAAACBJUSQYMDc3F7mdlZX1ns/Pzs6O3J6dnY15npGREe3bty/qY08++aS+8IUvxPxzJgKKBHR1dSkQCKxZr62tjRxyDgAAbl19fb16enokrYw3okgAAAAAkhNFAlRRUaFnn3026mOpuhvBtm1GG6W5cDis9vb2qI95PJ44pwEAIDWVl5crOztbi4uLGhoa0vLysjIzM03HAgAAAHCTuGpqQF5eXuT20tLSez5/cXExcjs/Pz/medxut+64446Yv24ii1YiSOxISCf9/f1aWFhYs15eXq6SkhIDiQAASD0Oh0N1dXXy+XyybVu9vb2MDwQAAACSEIctG1BUVBS5PTY29p7PHx8fj/qxuHXrHfZHkZAebNte9+Byr9cb5zQAAKS2+vr6yO3Lly8bTAIAAADgVrEjwYBrL1RGO+j13a59Dhc5Y8Plcmn//v0KBoMKhUKRXzdy+DWS3+joqCYnJ9es5+fnp+w4LwAATCkqKlJhYaGmp6c1MTGh2dnZDdllCwAAAGDjUCQYsHPnzsjtEydOvOfzr33Ojh07NiRTunG5XGpoaDAdA4a0tbVFXfd4PLIsK85pAABIffX19Tpz5oyklV0JfE8LAAAAJBdGGxmwbds21dXVSZIuXryonp6edZ87NzenV199VZKUk5OjD33oQ/GICKSsmZkZDQ4OrlnPzMxcNXoBAADEztXvfaWVIsG2bYNpAAAAANwsigRDfvZnfzZy+4tf/OK6z/vLv/xLzc/PS5IeffRR5eTkbHg2IJWttxuhqalJLhebtAAA2Ag5OTnatGmTJGl+fn7VGWAAAAAAEh9FgiG//du/HZkN++d//uf6x3/8xzXPOXbsmP6//+//k7QyiufJJ5+Ma0Yg1SwtLUU95NHhcKi5udlAIgAA0geHLgMAAADJi7ff3qTu7m791V/91aq1q/NeJemtt97S7//+7696/N5779W99967aq2iokJ/9md/pscff1zhcFg//dM/rU984hO6//775XQ69frrr+trX/ualpaWJEn/+T//Z7W2tm7Q7wpID/Pz88rKyors8rmqoaFBWVlZhlIBAJAeqqur5XQ6FQqF1NfXpz179sjpdJqOBQAAAOAGUCTcpMuXL+uP/uiP1n38zJkzq4oFaWU3wbuLBEn6xV/8RS0sLOg//If/oKWlJX3rW9/St771rVXPcTqd+k//6T/piSeeiM1vAEhjpaWleuihhzQwMCCfz6eJiQlJK4csAwCAjeV2u1VdXa3e3l75/X4NDw+rurradCwAAAAAN4AiwbBf/dVf1X333ae/+Iu/0D//8z+rr69P4XBYVVVVOnLkiH7lV35Fe/fuNR0TSBkOh0O1tbWqqanR2NiYxsbGVFBQYDoWAABpob6+Xr29vZKknp4eigQAAAAgSVi2bdumQ8CMmpoaDQwMqLq6Wv39/abjAAAAIMWFw2E988wzWlpaksPh0KOPPqqMjAzTsQAAAAC8B3YkIC319PSop6dHTqdTTqdTLpdLTqdTu3btktvtNh0PAAAgJV3dGdje3q5wOKy+vj41NTWZjgUAAADgPVAkIC3Nzc1pZGRkzfquXbsMpAEAAEgf9fX1am9vl7Ry/hhFAgAAAJD4HKYDACYEg8Go606nM85JAAAA0ktxcXHkfKKxsTHNzc0ZTgQAAADgvVAkIC2FQqE1aw6HQw4HfyUAAAA2kmVZqquri9y/evgyAAAAgMTFVVOkpWhFArsRUs/i4qKmpqZMxwAAAO9SX18fuX358mXZtm0wDQAAAID3QpGAtBRttJHLxZEhqcbn8+n555/XK6+8ouHhYS5SAACQIHJzc1VeXi5Jmp2d1eTkpOFEAAAAAK6HK6dIS+xISH1+v19dXV2SpCtXrujKlSsqLCxUa2vrqndBAgAAM+rr6zU6OipJ6unpUUlJieFEAAAAANbDjgSkJYqE1Nfd3b1m58n09LSGhoYMJQIAANeqqamJnE/V19encDhsOBEAAACA9VAkIC0x2ii1hcNhtbe3R33M4/HEOQ0AAIgmIyNDVVVVkqTl5WUNDw8bTgQAAABgPRQJSEvsSEht/f39WlhYWLNeXl7O2AQAABLIuw9dBgAAAJCYKBKQligSUpdt2/L5fFEf83q9cU4DAACup7KyUhkZGZKkoaGhqN+jAQAAADCPIgFpKdpoI4qE1DA6OqrJyck16/n5+dq8ebOBRAAAYD1OpzMy3igYDOrKlSuGEwEAAACIhiIBaSnau904IyE1rLcbwePxyLKsOKcBAADvpbq6OnJ7YGDAYBIAAAAA66FIQFpitFFqmpmZ0dDQ0Jr1zMzMVTOYAQBA4ti0aVPk+7DBwUGFw2HDiQAAAAC8G0UC0k44HI76AypFQvJra2uLut7c3MyOEwAAEpTL5VJlZaUkaXl5WePj44YTAQAAAHg3igSknfUO8eNCc3JbWlrS5cuX16w7nU41NTUZSAQAAG4U440AAACAxEaRgLSzXpHAjoTk1tnZGfXPtr6+XllZWQYSAQCAG1VVVRU5y2hgYEC2bRtOBAAAAOBaFAlIO8FgMOo6RULyCgaD6ujoiPqYx+OJcxoAAHCzMjIyVFFRIUman5/X1NSU2UAAAAAAVqFIQNphtFHquXz5spaXl9esV1VVqaCgwEAiAABwsxhvBAAAACQuigSkHUYbpRbbttc9ZJndCAAAJI+qqqrIbYoEAAAAILFQJCDtWJal4uJiFRQUKDc3V5mZmXK5XOxISFJDQ0OanZ1ds15cXKzy8nIDiQAAwK3IyclRSUmJJGl6elpzc3OGEwEAAAC4iiunSDvFxcW6//77TcdAjPh8vqjrXq83cmgjAABIDtXV1ZqYmJAk9ff3q7W11XAiAAAAABI7EgAksYmJCY2Ojq5Zz8nJUU1NjYFEAADgdlz79ZvxRgAAAEDioEgAkLRs21ZZWdma9ZaWFjkc/PMGAECyyc/PV0FBgSRpfHxci4uLhhMBAAAAkCgSACSx0tJS3XvvvTpy5IhqampkWZbcbrcaGxtNRwMAALeouro6cntwcNBgEgAAAABXcUYCgKRXWlqq973vfZqbm9P09LTcbrfpSAAA4BZVV1fr4sWLklbGGzU1NRlOBAAAAIAdCQBSRl5e3qp3MQIAgORTXFysnJwcSdLIyIj8fr/hRAAAAAAoEgAAAAAkDMuyVFVVJUkKh8MaHh42nAgAAAAARQIAAACAhFJTUxO53d/fbzAJAAAAAIkzEpCGJiYmtLi4KKfTKafTKZfLJZfLpfz8fNPRAAAAIKmsrEwZGRny+/0aHh5WKBSS0+k0HQsAAABIWxQJSDvt7e26fPnyqrWMjAw99thjZgIBAABgFYfDoaqqKvX09CgYDOrKlSuRcUcAAAAA4o/RRkg7oVBozRrvcEsOk5OTGhoakm3bpqMAAIANVl1dHbk9MDBgMAkAAAAAdiQg7QSDwTVrLhd/FZLBuXPnNDQ0pIKCAnk8HtXX11MCAQCQojZt2iSXy6VgMKjBwUGFw2E5HLwPCgAAADCB78SRdtiRkJxmZmY0NDQUuX3y5Ek988wzam9vN5wMAABsBJfLpcrKSknS8vKyxsfHDScCAAAA0hdFAtIORUJyamtrW7O2vLwsv99vIA0AAIiHa8cb9ff3G0wCAAAApDeKBKSdaKONKBIS29LS0poDsqWVP7empiYDiQAAQDxs3rxZlmVJWjkngXOSAAAAADMoEpB2ou1I4IyExNbR0RH1z62+vl5ZWVkGEgEAgHjIyMhQRUWFJGlhYUFTU1NmAwEAAABpiiIBaYfRRsklGAyqs7Mz6mMejyfOaQAAQLxdO95oYGDAYBIAAAAgfVEkIO1QJCSXy5cva3l5ec16VVWVCgoKDCQCAADxRJEAAAAAmEeRgLQT7YwERhslJtu2ox6yLLEbAQCAdJGdna3S0lJJ0vT0tGZnZw0nAgAAANIPRQLSSjgcjnpIHzsSEtPQ0FDUiwXFxcUqLy83kAgAAJjArgQAAADALIoEpJVouxEkioRE5fP5oq57vV5ZlhXnNAAAwBSKBAAAAMAsigSklWjnI0gUCYloYmJCo6Oja9ZzcnJUU1NjIBEAADAlPz8/cjbS+Pi4FhcXDScCAAAA0gtFAtLKekUCZyQknvXORmhpaZHDwT9dAACkm2t3JQwODhpMAgAAAKQfrsYhrTDaKDnMz8+rr69vzbrb7VZjY6OBRAAAwLRrdyRSJAAAAADxRZGAtMKOhOTQ3t4e9VDsLVu2yO12G0gEAABMKyoqUlZWliRpdHR03e/rAAAAAMQeRQLSCmckJD6/36+urq4165ZlyePxGEgEAAASgWVZ2rRpk6SVXabj4+OGEwEAAADpgyIBaYXRRomvu7s76p9TbW2tcnJyDCQCAACJ4mqRIElXrlwxmAQAAABILxQJSCuMNkps4XBY7e3tUR/zer1xTgMAABINRQIAAABgBkUC0gqjjRJbX1+fFhYW1qyXl5eruLjYQCIAAJBIsrOzVVhYKEmamJjQ8vKy4UQAAABAeuBt2Egrmzdv1pEjRxQMBhUKhRQKhRQMBpWbm2s6GiQVFhaqtrZW/f39qw5bZjcCAAC4atOmTZqenpYkjYyMqLa21nAiAAAAIPVRJCCtZGZmKjMz03QMrKOoqEiHDh3S/Py82tra1N3drezsbG3evNl0NAAAkCAqKyvV1tYmSRoeHqZIAAAAAOKAIgFAwsnNzdXevXu1fft2LSwsyLIs05EAAECCKCsrk8PhUDgc1pUrV2TbNt8rAAAAABuMMxIAJKyMjAwVFRWZjgEAABKIy+VSeXm5JGlhYUFzc3OGEwEAAACpjyIBAAAAQFLZtGlT5Pbw8LDBJAAAAEB6oEgAAAAAkFSuLRKuXLliMAkAAACQHigSAAAAACSVoqIiZWZmSpJGRkYUDocNJwIAAABSG0UCAAAAgKRiWVZkV0IwGNT4+LjhRAAAAEBqo0gAYEx/f786OzsVDAZNRwEAAEmmsrIycpvxRgAAAMDGcpkOAMTTa6+9pqmpKTmdzsivoqIi7du3z3S0tGPbts6ePavZ2VmdO3dOzc3NampqUlZWluloAAAgCVRUVERuDw8Pa8eOHQbTAAAAAKmNIgFpZXFxUQsLC6vWLMsylCa9DQ0NaXZ2VpK0vLys8+fP69KlS9q+fbtaW1sNpwMAAIkuJydHBQUFmpmZ0eTkpPx+vzIyMkzHAgAAAFISo42QVkKh0Jo1p9NpIAl8Pt+atVAopJycHANpAABAMro63si2bY2MjBhOAwAAAKQuigSkFYqExDAxMaHR0dE16zk5OaqpqTGQCAAAJKOrBy5LK+ONAAAAAGwMigSklWiH+lIkxF+03QiS1NLSIoeDf5YAAMCNKS8vj3zvcOXKFdm2bTgRAAAAkJq4Yoe0Em1HgsvFUSHxND8/r/7+/jXrbrdbjY2NBhIBAIBk5XK5VFZWJmnle4y5uTnDiQAAAIDURJGAtGHbNqONEkB7e3vUdws2NjbK7XYbSAQAAJLZteONrly5YjAJAAAAkLooEpA2wuFw1AvYFAnx4/f71dXVtWbdsiy1tLQYSAQAAJLd1QOXJYoEAAAAYKNQJCBtRNuNIDHaKJ66urqinlNRW1urnJwcA4kAAECyKyoqUmZmpiRpZGRE4XDYcCIAAAAg9VAkIG1Eu4AtsSMhXsLhsNrb26M+5vV645wGAACkCsuyVFFRIUkKBAKamJgwnAgAAABIPRQJSBvr7UigSIiPvr4+LS4urlmvqKhQcXGxgUQAACBVXHtOwvDwsMEkAAAAQGqiSEDaYLSRObZtq62tLepjHo8nzmkAAECq4ZwEAAAAYGNRJCBtsCPBnNHRUU1OTq5Zz8/P1+bNmw0kAgAAqSQnJ0f5+fmSpImJCfn9fsOJAAAAgNRCkYC0sd4ZCexI2Hg+ny/qusfjkWVZcU4DAABS0dVdCbZta3R01HAaAAAAILVQJCBtsCPBjJmZGQ0NDa1Zz8zMVENDQ/wDAQCAlMQ5CQAAAMDGoUhA2qBIMGO9sxGam5v5/x4AAMRMeXm5HI6VH284JwEAAACILYoEpA1GG8Xf0tKSenp61qw7nU41NTXFPxAAAEhZbrdbpaWlkqS5uTnNzc0ZTgQAAACkDooEpA12JMRfR0eHwuHwmvX6+nplZWUZSAQAAFLZteON2JUAAAAAxA5FAtLGejsSKBI2zpYtW+TxeNbs+vB4PIYSAQCAVHb1wGWJIgEAAACIJWa6IG2wIyH+cnNztWfPHm3fvl1dXV1qb29XUVGRCgoKTEcDAAApqKioSBkZGfL7/bpy5YrC4XDk3AQAAAAAt44iAWmjsLBQdXV1CgaDCoVCkV+ckbDx3G63vF6vWlpa5Pf7TccBAAApyuFwqKKiQv39/QoEApqamlJJSYnpWAAAAEDS4woq0kZNTY1qampMx0hrDoeDsxEAAMCGKi8vV39/vyRpbGyMIgEAAACIAfb5AgAAAEgZZWVlkdtjY2MGkwAAAACpgyIBAAAAQMooLCyMjK4cHx+XbduGEwEAAADJjyIBAAAAQMpwOBwqLS2VJC0uLmphYcFwIgAAACD5USQAAAAASClXiwSJ8UYAAABALFAkAIiZ8+fP6+zZs1paWjIdBQAApLFrz0kYHx83mAQAAABIDS7TAQCkBr/fL5/Pp2AwKJ/Pp/r6enk8HhUWFpqOBgAA0gw7EgAAAIDYYkcCgJjo6upSMBiUJIXDYXV3d+u5557TpUuXDCcDAADpxu12R97MMD09rUAgYDgRAAAAkNzYkYC00dbWpnA4LKfTKZfLJafTqby8PJWUlJiOlvTC4bDa29ujPrZp06Y4pwEAAFgZbzQ9PS3btjUxMcH3JAAAAMBtoEhA2rh48aKWl5dXrdXV1engwYOGEqWOvr4+LS4urlmvqKhQcXGxgUQAACDdlZWVqbOzU9LKeCOKBAAAAODWMdoIaSMUCq1ZczqdBpKkFtu25fP5oj7m9XrjnAYAAGAF5yQAAAAAsUORgLRg23Zkfv+1XC425dyu0dFRTU1NrVkvKChQZWVl/AMBAABIys3NVVZWliRpfHxc4XDYcCIAAAAgeVEkIC2s94MjOxJu33q7ETwejyzLinMaAACAFZZlqaysTJIUDAY1MzNjOBEAAACQvCgSkBai7UaQKBJu18zMjIaGhtasZ2Zmqr6+3kAiAACAn2C8EQAAABAbFAlIC9HOR5AYbXS71tuN0NzcTEkDAACMu7ojQaJIAAAAAG4HRQLSwnpFAhe7b93S0pIuX768Zt3pdKq5udlAIgAAgNWKiooi3++Nj48bTgMAAAAkL4oEpIX1RhuxI+HWdXR0RD17oqGhQZmZmQYSAQAArOZ0OlVSUiJJmp+f1+LiouFEAAAAQHKiSEBaYEdCbAWDQXV0dER9zOPxxDkNAADA+q49J4FdCQAAAMCtoUhAWqBIiK2enh75/f4161VVVcrPzzeQCAAAIDrOSQAAAABuH0UC0gKjjWLHtm21tbVFfczr9cY5DQAAwPVduyOBIgEAAAC4NRQJSAvsSIidwcFBzc3NrVkvKSlZ9Y4/AACARJCZmRnZMTk1NbXuG0wAAAAArI8iAWlhvR8YKRJuns/ni7ru8XhkWVac0wAAALy3q292CIfDmpycNJwGAAAASD4UCUgL7EiIjYmJiagjAXJyclRTU2MgEQAAwHtjvBEAAABweygSkBbWKxI4I+HmdHR0RF33eDxyOPjnBAAAJCYOXAYAAABuD1f+kBYYbRQbe/fu1Z49e5SbmxtZc7vd2rJli8FUAAAA15efn6+MjAxJ0vj4uGzbNpwIAAAASC68HRtpgdFGseF2u+XxeNTc3KyBgQH5fD6Vl5fL7XabjgYAALAuy7JUVlamwcFB+f1+zc7OqqCgwHQsAAAAIGlQJCAtRCsSLMtiHM8tcjgcqq2tVU1NjcLhsOk4AAAA76m0tFSDg4OSVsYbUSQAAAAAN46rqEgL0YoEp9Mpy7IMpEkdlmWxqwMAACSFa89JGB8fN5gEAAAASD4UCUgL0c5I4KBlAACA9FFcXBzZjcqBywAAAMDN4Uoq0sL+/ft1xx13KBQKKRgMKhQKccgeAABAGnG5XCoqKtLExIRmZ2e1vLyszMxM07EAAACApECRgLTgdDoZwQMAAJDmysrKNDExIWllvFFVVZXhRAAAAEByYLQRgHWxawMAAKSSa89JYLwRAAAAcOPYkQBgXW+88Ybcbrc8Ho+KiopMxwEAALgtpaWlkdsUCQAAAMCNo0gAENX09LQGBgYkST09Pdq0aZO8Xq82bdoky7IMpwMAALh52dnZys3N1fz8vCYnJxUKhRh/CQAAANwARhsBiKqtrW3V/StXruhHP/qROjo6DCUCAAC4fVfHG4VCIU1NTZkNAwAAACQJigQAaywtLeny5ctr1p1Op+rq6gwkAgAAiI1rxxtdPXgZAAAAwPVRJABYo6OjQ+FweM16Q0ODMjMzDSQCAACIjeLi4shtdiQAAAAAN4YiASnPtm35/X6FQiHZtm06TsILBoPrji/yeDxxTgMAABBbhYWFkdsUCQAAAMCN4bBlpLxQKKTvfe97kfsul0tOp1Ner1etra3mgiWonp4e+f3+NetVVVXKz883kAgAACB2XC6X8vPzNTs7q+npaYXDYTkcvL8KAAAAuB6+Y0bKC4VCq+4Hg0EtLy9HHd2T7mzbXnPI8lVerzfOaQAAADZGUVGRJCkcDmt2dtZsGAAAACAJUCQg5QWDwajrTqczzkkS3+DgoObm5tasl5SUqKyszEAiAACA2LtaJEiMNwIAAABuBEUCUt67dyRc5XIx2evdfD5f1HWv1yvLsuKcBgAAYGNQJAAAAAA3hyIBKW+9IoEdCauNj49rbGxszXpOTo6qq6sNJAIAANgYFAkAAADAzaFIQMpbb7QROxJWW+9sBI/HwwGEAAAgpWRlZSkzM1PSSpFg27bhRAAAAEBi4+ogUh47Et7b/Py8+vv716y73W5t2bLFQCIAAICNY1lWZFfC8vKylpaWzAYCAAAAEhxFAlIeRcJ7a2tri/pOvMbGRrndbgOJAAAANhbjjQAAAIAbR5GAlLfeaCOKhBV+v1/d3d1r1i3LUktLi4FEAAAAG48iAQAAALhxFAlIeevtSOCMhBVdXV1Ry5a6ujrl5OQYSAQAALDxKBIAAACAG0eRgJTHjoT1hUIhtbe3R33M4/HEOQ0AAED85Ofny+FY+XGIIgEAAAC4PooEpDzOSFhff3+/FhcX16xXVFSouLjYQCIAAID4cDgcKiwslCTNzs4qEAgYTgQAAAAkLooEpDxGG62vv78/6rrX641zEgAAgPi7drzR9PS0uSAAAABAgqNIQMpjR8L6Dh06pPe9730qLS2NrBUUFKiystJgKgAAgPjgnAQAAADgxvCWbKS8aGckWJYVmYmbzhwOh2pqalRTU6OxsTG1tbVp8+bNsizLdDQAAIANd+0oR4oEAAAAYH0UCUh50XYkMNZorbKyMpWVlZmOAQAAEDdXz0iQKBIAAACA6+Et2Uh50YoExhr9RHd3tzo6OmTbtukoAAAAceV2u5WXlydp5YyEcDhsOBEAAACQmCgSDLrnnntkWdYN/+rp6TEdOSlFG21EkbBiYWFBb731lk6fPq2XXnpJMzMzpiMBAADE1dVzEkKhkObm5syGAQAAABIURQJSHqON1jc4OBgpWsbGxvT888/r/Pnz6x5QDQAAkGo4cBkAAAB4b1xNTRDf/e533/M5FRUVcUiSehhttL7m5mYVFBTo5MmTmpubUzgc1vnz59Xf36+DBw+umhsMAACQit5dJNTV1ZkLAwAAACQoioQE8dhjj5mOkLIaGxu1adMmhUIhhUIhBYNB5ebmmo5lRCAQkNvtXrVWUVGhBx54QBcuXJDP55Nt25qentYLL7ygPXv2qLGxUZZlGUoMAACwsdiRAAAAALw3igSkvC1btpiOkBDC4bD+5V/+RTk5OfJ6vaqsrIwUBC6XS7t27VJtba2OHz+u6elphUIhnTp1SleuXNGdd96pjIwMw78DAACA2MvOzlZGRob8fj9FAgAAALAOzkgA0sTQ0JDm5uY0MjKiV199Vc8995y6urpWjX4qLi7WkSNH1NTUFFnr7+/X888/r7GxMROxAQAANpRlWZFdCUtLS1pcXDQbCAAAAEhAFAlAmvD5fKvuz8zM6OTJkxoaGlq17nK5tG/fPt19992RXQgLCwt66aWXdP78eYXD4bhlBgAAiIdrxxtNT0+bCwIAAAAkKIqEBPHII4+ourpaGRkZKi4u1vbt2/WZz3xGL730kuloSAHj4+NRdxTk5uaqqqoq6sdUV1frgQceUFlZmSTJtm2dP39er7zyihYWFjY0LwAAQDxdWyRMTk6aCwIAAAAkKIqEBPH9739fg4ODCgQCmpqa0oULF/TVr35V9957r44cObLmXePAzWhra4u63tLSIodj/X8GcnJydM8992j79u2R8xRGR0f1/PPPa2BgYEOyAgAAxBs7EgAAAIDr47Blw4qLi3X//ffrzjvvVHV1tZxOpwYGBvTiiy/q2WeflW3b+uEPf6hDhw7p6NGjqqysjHmGQCCg06dPR31s8+bN2rx5c8w/J+Jnbm5O/f39a9bdbvcNHUTtcDi0fft2VVRU6NixY1pYWJDf79frr7+uHTt2aOvWrZGSAQAAIBkVFBTI4XAoHA5z4DIAAAAQhWXbtm06RLp68803tW/fvsgc+nc7efKkfuZnfka9vb2SpIceekg/+MEPYvb5a2pq3vNd5U8++aS+8IUvxOxzIv7eeusttbe3r1lvbW3Vrl27buq1lpeXdfLkyVX/3dTV1emuu+6S0+m87awAAACmPP/885qampJlWfrpn/5puVy85woAAAC4iiIhwV28eFF79+7V8vKyJOn48eO66667YvLaV4uEiooKPfvss1Gfw46E5Ob3+/XMM88oGAyuWrcsSw8//LBycnJu+jVt29alS5d09uzZyFppaanuvvtuZWVl3XZmAAAAE44fP66enh5J0pEjR1RaWmo2EAAAAJBAeJtNgtu6dat+4Rd+QV/96lclSc8880zMioSr3G637rjjjpi+ZqJYWFjQmTNn5HK55HQ65XQ65XK5VFVVtWoWbqrq6upaUyJIK7sIbqVEkFZKiK1btyo/P1/Hjh1TKBTS+Pi4XnjhBb3//e9Pi/9fAQBA6rn2e5ipqSmKBAAAAOAaHLacBA4fPhy5ffHiRYNJks/S0pJ6e3vV1dWl9vZ2Xbp0SefOnUuLQ/RCoVDUkUaS5PV6b/v1a2pqdO+99yo7O1vSSmnzwx/+UIODg7f92gAAAPFWXFwcuc05CQAAAMBqFAlJoLy8PHKbH2puTigUirqeDvP8+/r6tLi4uGZ906ZNMds1UFxcrPvuuy/yg3cwGNRrr70mn88npqYBAIBkUlhYGLnN99wAAADAahQJSWBsbCxym7ExNyfaWB9JKX94nm3bamtri/qYx+OJ6efKzs7W4cOHVVtbG1l75513dPLkyXWLHAAAgESTkZGh3NxcSdL09DRvigAAAACuQZGQBF566aXI7ViMpEkn6bojYWRkJOo76QoKClRZWRnzz+dyuXTw4EFt27Ytstbd3a3XXntt3TIHAAAg0VzdlRAMBjU/P284DQAAAJA4KBISXFtbm77+9a9H7j/yyCMG0ySfdC0SfD5f1HWPxyPLsjbkc1qWpR07dujAgQNyOFb+ably5YpeffVVBQKBDfmcAAAAsZSfnx+5PTc3ZzAJAAAAkFgoEgz5n//zf+qNN9647nPeeustffjDH9bS0pIk6YEHHtCBAwfiES9lrPdu+FQuEqanpzU8PLxmPSsrS/X19Rv++evr6/WhD31IbrdbkjQ6Oqof/ehH8vv9G/65AQAAbkdeXl7k9uzsrMEkAAAAQGJJ7UHxCeyHP/yhfuM3fkNNTU267777tGPHDpWWlsrpdGpwcFAvvviifvCDHygcDktauTj7N3/zN4ZTJ5/1diSk8hkJ652N0NzcHLcCpby8XB/60IciBcL4+LhefvllfehDH1JmZmZcMgAAANwsdiQAAAAA0aXu1dQk0dnZqc7Ozus+58Mf/rD++q//WlVVVXFKlTrSbbTR4uKiLl++vGbd6XSqqakprllKSkp0zz336JVXXtHy8rKmpqb08ssv64Mf/KCys7PjmgUAAOBGUCQAAAAA0VEkGPKnf/qn+qmf+ikdO3ZM77zzjkZGRjQ2Nqbl5WUVFhaqoaFBhw4d0ic/+UnGGd2GdBtt1NHREdnFcq2GhgYjOwGKiop0+PBhvfzyy1paWtL09HRkZ0JOTk7c8wAAAFxPVlaWXC6XgsEgo40AAACAa1AkGNLU1KSmpib98i//sukoKS3ddiTMzMxEXfd4PHFO8hMFBQU6fPiwXnnlFS0sLGh2dlYvvfSS7rnnHuXm5hrLBQAA8G6WZSkvL09TU1Oan59XOByWw8GxcgAAAADfFSOlRSsSHA5Hyv5AePfdd+vIkSOqra2VZVmSpOrq6lXb9E3Iz8/X4cOHIwcYzs/P64c//CHv9AMAAAnn6vcrtm1rfn7ecBoAAAAgMaTm1VTgx6KNNkrlg5YlqbS0VIcOHdJHPvIRtbS0yOv1mo4kScrNzdXhw4cjpcbi4qJeeuklfkAHAAAJhXMSAAAAgLUoEpDSou1ISNWxRu+Wm5urvXv3qqyszHSUiOzsbB0+fFiFhYWSpKWlJf3oRz/S0tKS4WQAAAArru5IkMTuSQAAAODHKBKQ0tK5SEhUWVlZuueeeyLv9pudndWrr76qQCBgOBkAAMDqHQkUCQAAAMAKigSkNIqExJSZmakPfvCDys7OliRNTk7qjTfeWPdwbAAAgHi5dkcCo40AAACAFRQJSGnpeEZCssjNzdUHP/hBud1uSdKVK1d04sQJ2bZtOBkAAEhnmZmZke9P2JEAAAAArKBIQEpjR0JiKyws1Ac+8IHIn0lvb6/efvttygQAAGCMZVmR8UYLCwvsmAQAAABEkYAUl+pFwvT0dNJfdC8rK9OhQ4dkWZYkqb29XRcvXjScCgAApDPGGwEAAACrUSQgpaXyaCO/368XX3xR//zP/6zOzs6kfrdcVVWV7rzzzsj9c+fOqaury2AiAACQzigSAAAAgNUoEpDSMjIylJGRIYfjJ/+pp8qOhM7OTgWDQc3OzurUqVN65plndP78eS0vL5uOdku2bNminTt3Ru6fOnVKAwMDBhMBAIB0dXW0kcQ5CQAAAIAkpcZbs4F1PPjgg5Hb4XA4qd+1f61QKKSOjo5Va8vLyzp//ryqq6uVmZlpKNntaW1t1fLystra2mTbto4dO6YjR46osLDQdDQAAJBGri0S2JEAAAAAsCMBacThcMjtdsvtdpuOctv6+vq0uLi4Zn3Tpk0qKiqKf6AYsSxLu3fvVm1traSV0VSvv/66/H6/4WQAACCdXDvaiB0JAAAAAEUCkHRs25bP54v6mMfjiXOa2LMsS3fddVekEJmbm9OxY8eS/lBpAACQPDIyMiI7PNmRAAAAAFAkAElnZGRE09PTa9YLCgpUWVlpIFHsuVwuve9971NGRoYkaWhoSOfPnzecCgAApJOruxIWFxcVDAYNpwEAAADMokgAksx6uxG8Xq8sy4pzmo2Tl5enQ4cORX5PFy5c4PBlAAAQN5yTAAAAAPwERQKQRKanpzU8PLxmPSsrS3V1dQYSbaxNmzZp586dkfvHjh3TzMyMwUQAACBdcE4CAAAA8BMUCUASWW83QnNzs5xOZ5zTxIfX6+XwZQAAEHfsSAAAAAB+giIBSBKLi4vq7e1ds+50OtXU1GQgUXxcPXy5sLBQ0so7Ao8fP87hywAAYEOxIwEAAAD4CYoEpKzl5WWNj49rampKc3NzWlxclN/vVzgcNh3tlnR0dETNvmXLFmVmZhpIFD8ul0t333135PDlwcFBXbhwwXAqAACQyq4tEtiRAAAAgHTnMh0A2Cijo6N644031qwfPnxY5eXlBhLdumAwqM7OzqiPtbS0xDmNGXl5eTp48KBeffVV2bat8+fPq6KiIun+LAEAQHJwu93KysrS0tISOxIAAACQ9tiRgJQVDAajrrtcydefdXd3Rz0XoLq6etX83lRXWVmp7du3R+4fO3ZMgUDAYCIAAJDKrn6ftby8zBlNAAAASGsUCUhZoVAo6nqyHUocDofV3t4e9TGv1xvnNOa1traqrKxMkrSwsKC33nrLcCIAAJCqGG8EAAAArKBIQMpab0dCshUJg4ODUX9wLS0tVWlpqYFEZjkcDh04cCCys6Snp0f9/f2GUwEAgFR07c5PigQAAACkM4oEpKxU2ZHQ1tYWdd3j8ciyrDinSQy5ubm64447IvdPnjypxcVFg4kAAEAqunZHAuckAAAAIJ1RJCBlrVckJNMZCePj4xobG1uznpubq+rqagOJEkd9fb1qamokSX6/XydOnJBt24ZTAQCAVMKOBAAAAGAFRQJSVirsSPD5fFHXW1pa5HCk919fy7K0b98+ZWVlSZKGh4fV0dFhOBUAAEgl7EgAAAAAVqT3lUiktGhnJDidzqQZB2TbdtS8brdbW7ZsMZQqsWRmZmr//v2R+2fOnNHMzIzBRAAAIJU4nU7l5ORIYkcCAAAA0htFAlJWtB0JybQbwbIsHThwQB/5yEfk8XgiI5mamprkdrsNp0sclZWVam5ulrTyZ37s2LF1d6MAAADcrKu7Evx+v5aXlw2nAQAAAMygSEDKSvYi4arc3Fzt2bNHjzzyiHbv3h25aI6f2LVrV2SG8eTkpC5evGg4EQAASBWckwAAAABQJCCFRRttlEwHLb9bRkaGvF5vZHs9fsLlcunAgQORMVCXLl1ixBEAAIgJzkkAAAAAKBKQwlJlRwJuTElJiVpbWyVJ4XBYp0+flm3bhlMBAIBkl5ubG7m9uLhoMAkAAABgDkUCUhZFQvrZunVr5If9kZER9fX1GU4EAACSXXZ2duQ2RQIAAADSFUUCUlaqjTbCe3O5XNq7d2/k/ttvvy2/328wEQAASHYUCQAAAABFAlIYOxLSU1VVlaqqqiRJS0tLOn/+vOFEAAAgmWVlZUVuUyQAAAAgXVEkIGUlY5EwODgYdScFbs7evXsjf9YdHR2anJw0nAgAACQrh8MRKRMoEgAAAJCuKBKQspJttNH09LRee+01PfPMMzp37pyWlpZMR0paubm52rZtmyTJtm2dOnWKg5cBAMAtuzreaGlpSeFw2HAaAAAAIP4oEpCSbNtOuh0JPp9PkuT3+3XhwgU988wzOnHiBO98u0Uej0f5+fmSpImJCXV1dRlOBAAAktXVIsG2bS0vLxtOAwAAAMQfRQJSUjgcjvoO9EQtEhYXF9Xb27tqLRwOq7e3Vw4Hf01vhdPp1B133BG5f/bsWXZ5AACAW8KBywAAAEh3XKFESoq2G0FK3CKho6Mj6jb5LVu2KDMz00Ci1LBp0ybV1dVJWtnpcfbsWcOJAABAMqJIAAAAQLpL3IHxwG1wOp163/vep1AopGAwGPnf8vJy09HWCAaD6uzsjPpYS0tLnNOknt27d2toaEiBQEDd3d1qbm5WcXGx6VgAACCJXFskLCwsGEwCAAAAmEGRgJTkdDpVU1NjOsYN6e7ult/vX7NeXV0dmfGPW5edna1t27bpnXfekbQy4uiDH/yg4VQAACCZsCMBAAAA6Y7RRoBB4XBY7e3tUR/zer1xTpO6mpublZOTI0kaHh7WlStXDCcCAADJhCIBAAAA6Y4iATBocHBQc3Nza9ZLS0tVVlZmIFFqcjqd2rFjR+T+mTNnoh7GDQAAEM21RcLS0pLBJAAAAIAZFAmAQT6fL+q6x+OJc5LUV19fr6KiIknS5OSk+vr6zAYCAABJw+12y+l0SmJHAgAAANITRQJgyPj4uMbHx9es5+bmqrq62kCi1GZZlnbu3Bm5f+7cOYXDYYOJAABAsrAsK7IrgSIBAAAA6YgiATBkvd0ILS0tcjj4q7kRKisrVV5eLkmam5tTT0+P2UAAACBpXC0SAoGAAoGA4TQAAABAfHG1EjBgbm5OAwMDa9bdbre2bNliIFF6ePeuhAsXLigUChlMBAAAkgUHLgMAACCdUSQABrS3t0c97LepqUlut9tAovRRVlamyspKSdLCwoK6u7sNJwIAAMmAIgEAAADpzGU6ALARenp61NHRIafTKZfLJafTKafTqb179yojI8NoNr/fH/XitcPhUEtLi4FE6WfHjh0aHh6WtLIroaGhQS4X/xwCAID1USQAAAAgnbEjASlpfn5eExMTGh0d1dDQkPr7+3X58uWouwDirbOzU8FgcM16bW3tqh9QsXFKSkoiB1ovLS2ps7PTcCIAAJDoKBIAAACQzigSkJLWm3vvdDrjnGS1UCikjo6OqI95vd44p0lv27dvj9xua2vjrAQAAHBdFAkAAABIZxQJSEmJWiT09fVF/cFz06ZNKioqin+gNFZUVKSqqipJKxcD+vv7DScCAACJjCIBAAAA6YwiASkpWpHgdDplWZaBNCts25bP54v6GLsRzLj2/3efz5cQo68AAEBiysrKitymSAAAAEC6oUhASop2BoHpw3TD4bAqKyvldrtXrRcWFmrTpk2GUqW3srIylZSUSJKmpqY0MjJiOBEAAEhUTqdTmZmZkigSAAAAkH4oEpCS1tuRYJLT6dTu3bv1yCOPaM+ePcrJyZEkeTweozsl0pllWWt2JQAAAKzn6nijpaUlhcNhw2kAAACA+DH7Fm1ggyRikXCV2+2Wx+NRc3OzBgYGInP6YUZ1dbVyc3M1Pz+v4eFhTU9Pq7Cw0HQsAACQgLKzs/X/t3fn8VGW9/7/3zOTfSE7SdhjDEEQUASrUAGVRVCgaqke2yriQbDa9nxtbWuPrdKj1Z5z7KKt+1ax0tZTFa2AoGBBFEUBiwKBQIDIEkI2spBlZu7fH/xmOiGzJrMkM6/n45GH98x93fd13RIuZq7PdX2u+vp6GYahtra2TvsmAAAAANGMFQmISr0xtdGZzGazBg8e3GsCHLHKbDarpKTE+ZpVCQAAwBM2XAYAAECsIpCAqNSbVySg9ykqKnLuXXHo0CEGBgAAgFsEEgAAABCrCCQgKhFIQCDi4+NVXFws6fSm2Hv37o1wiwAAQG9EIAEAAACxikAColJfSG2E3qWkpERm8+kucd++fero6IhwiwAAQG9DIAEAAACxikACohIrEhCo5ORkDRkyRJLU0dGhioqKCLcIAAD0NgQSAAAAEKsIJCAq9YZAgt1u165du/iS2YeUlpY6j/fv3y/DMCLYGgAA0NsQSAAAAECsIpCAqGMYRq9IbXTkyBHt2LFDb731lj7++GM1NDSEtX4ELiMjQ7m5uZKkkydPqra2NsItAgAAvUlCQoJzcgqBBAAAAMQSAgmIOna73e374V6RUFZWJul0ew4cOKC3335b//jHP9Ta2hrWdiAwRUVFzmPSGwEAAFcmk0lJSUmSCCQAAAAgthBIQNRxtxpBCm8goaamRjU1NV3eb2pqUkJCQtjagcANGjTIuXrl0KFDHn+fAABAbHKkN+ro6OBzAgAAAGIGgQREHXf7I0jhDSQ4ViOcafjw4TKb+WvXm8XHx2vw4MGSTgelvvzyywi3CAAA9CbskwAAAIBYxIgmoo6nQEK49khoamrS4cOHu7wfHx+vYcOGhaUN6BnSGwEAAE8IJAAAACAWEUhA1In0ioQ9e/bIMIwu7xcXFys+Pj4sbUDP5OTkKD09XZJUXV2txsbGCLcIAAD0FomJic7jjo6OCLYEAAAACB8CCYg6ZrNZeXl5ys7OVr9+/ZSamqqkpKSwDOK3t7frwIEDbttUUlIS8voRHCaTqdOqBHd/pgAAIDa5fqZsb2+PYEsAAACA8AlPrhcgjPr166dLL700InXv27fP7aZ7Q4YM6bQMHr3f0KFDtWPHDhmGoQMHDmjUqFHsbwEAAJSQkOA8JpAAAACAWMGoGBAkNptNe/fudXtu+PDhYW4Neio5OVmFhYWSTuc/rqqqinCLAABAb+C6IoHURgAAAIgVBBKAIKmsrFRra2uX9/Pz85WZmRn+BqHH2HQZAACciRUJAAAAiEUEEoAgMAxDZWVlbs+VlpaGuTUIlsLCQueGikeOHGGwAAAAsCIBAAAAMYlAAhAEVVVVamho6PJ+RkaG8vPzI9AiBIPZbNbgwYMlSXa7XceOHYtwiwAAQKSxIgEAAACxiEACEAR79uxx+/7w4cNlMpnC3BoE06BBg5zHX375ZQRbAgAAegNWJAAAACAWEUgAeqi+vt7tTPWkpCQNGTIkAi1CMOXm5jpnHh47dkw2my3CLQIAAJFksVhksVgksSIBAAAAsYNAAtBDnlYjnH322c4vmei7zGazBgwYIEmyWq2qqqqKcIsAAECkOSYZsCIBAAAAsSIu0g0Agu3EiRNqbm52zhaLi4uTxWJRdnZ20Os6deqUDh061OV9i8Wi4uLioNeHyBg0aJAOHDgg6XR6I0dgAQAAxKb4+HidOnWKFQkAAACIGQQSEHUqKipUUVHR6b24uDhdc801Qa+rvLxcdru9y/tFRUVKTEwMen2IjPz8fMXFxclqterIkSOy2+0ym1nQBQBArHKsSLDZbLLZbKxCBQAAQNRjJAxRx10O+1B8ubNardq3b5/bc8OHDw96fYgci8WigoICSadzIZ84cSLCLQIAAJHEhssAAACINQQSEHWsVmuX9+LiQrP4ZtSoUUpNTe303sCBA5WWlhaS+hA5gwYNch5/+eWXEWwJAACINMeKBIkNlwEAABAbCCQg6oRrRUJcXJxKSko0a9YsTZw4UTk5OZKk0tLSoNeFyCsoKHCmMzpy5IgMw4hwiwAAQKSwIgEAAACxhj0SEHXCFUhwMJvNGjRokAYNGqS6ujplZWWFrC5ETkJCgvr3769jx46ppaVFdXV1IdnAGwAA9H6sSAAAAECsYUUCok44UxudiSBCdBs4cKDzmPRGAADELlYkAAAAINYQSEDUCfeKBMQO10DCsWPHItgSAAAQSaxIAAAAQKwhkICoQyABoZKUlKTMzExJUn19vdra2iLbIAAAEBGugQRWJAAAACAWEEhA1CGQgFDq37+/87i6ujqCLQEAAJHimtqIFQkAAACIBQQSEHUiuUcCop9rIOH48eMRbAkAAIgUViQAAAAg1hBIQFQxDCNkKxLa2tq0ZcsW1dfX9/he6Lvy8vJkMpkkEUgAACBWsSIBAAAAsYZp2ogq7oIIUnACCfv371dFRYUqKiqUn5+v4cOHq6CgwDmojNgQHx+vrKws1dbW6uTJk2ptbVVSUlKkmwUAAMKIFQkAAACINaxIQFTxFEjoaWojm82mvXv3Ol9XVVVp48aNWrNmDV8eYxDpjQAAiG1xcXHOySSsSAAAAEAsIJCAqBKqFQmVlZVqbW3t8n5SUlKnpe2IDQQSAACIbSaTyfkZkEklAAAAiAWkNkJUcbfRstSzQIJhGCorK3N7rrS0tNv3Rd+Vm5srs9ksu91OIAEAgBiVkJCg9vZ2ViQAAICIsdoNbalq02fH27X/pFVHmqyy2qU4szQgLU5n9YvT2P4JmpCfqDgzqbnRMwQSEFVCkdqoqqpKDQ0NXd7PyMhQfn5+t++LvisuLk7Z2dk6ceKEmpqa1NLSopSUlEg3CwAAhJHrigTDMNg3CwAAhI3Vbuitiha9VXFKDW32Luc77NLBk1YdPGnV+i9blZlo1uyiZF1ZlEJAAd1GaiNElVCkNvK0GmH48OF8YYxhpDcCACC2OTZcNgzD46pYAACAYKtstOqeTXV6eXez2yCCO/Vtdr28u1n3bKpTZSOfW9A9BBIQVYIdSKivr1dVVVWX95OSkjRkyJBu3RPRgUACAACxzXWfLNIbAQCAcCir7dDPPqhTxcnuBQMqTlr1sw/qVFbLHk8IHIEERBVPs8G6m9poz549bt8vKSnp8QbO6NtycnJkNp/uQmtqaiLcGgAAEG6OFQkSGy4DAIDQq2y06sEt9TplNXp0n1NWQw9uqdeXrExAgAgkIKoEc0XCqVOndOjQIbf3OuusswK+H6KLxWJRZmamJKmxsZEBBAAAYgwrEgAAQLhY7Yb+sP1kj4MIDqeshn6//aSs9uDcD7GBzZYRVQoKCjRjxgzZbDbZbDZZrVbZbDalpaUFfK+9e/fKbu+aa66oqEiJiYnBaC76uMzMTNXW1ko6nQYrLy8vwi0CAADhwooEAAAQLm9VtHQ7nZEnFSetequiRfOKU4N6X0QvAgmIKgkJCZ2+1HWX1WrV/v37u7xvMpk0fPjwHt8f0SErK8t5XFdXRyABAIAYwooEAAAQDla7oZUVp0Jy75UVp3RlUYrizKaQ3B/RhdRGgBsVFRVuvxAOHDiwW6sbEJ3ODCQAAIDY4Tp5hUACAAAIlS1Vbapv65oxIxjq2+zaUtUWknsj+rAiATiD3W73uMkyqxHgKiMjQyaTSYZhqL6+PtLNAQAAYeS6IoHURgAAoDvq6+vV3t7uTNHt7md9dZqk5JC14bPqdl1cmBSy+yN6EEgAznDkyBE1Nzd3eT8nJ0e5ubkRaBF6K4vFooyMDNXX1+vkyZOyWq2Ki6NbBQAgFrAiAQCA6NHe3q6GhgavA/p2uz2g8xMmTFB+fr7Xej/66CM1NDR4LXMgabxkCebTdlbRENy9FxC9GPECzlBWVub2/dLS0jC3BH1BVlaW6uvrZRiGGhoalJOTE+kmAQCAMGBFAgAAodHQ0KDW1lavg/aBDO4nJiZq2rRpXuusr6/Xe++9F9Tn8OfzgcXiO0LQaA7dagRJOtJEIAH+IZAAuDhx4oRqamq6vJ+WlqYBAwZEoEXo7TIzM53HdXV1BBIAAIgRrqsQbTZbBFsCAEBo2Gw21dTUBG1A32azaeTIkSouLvZa7+eff67Dhw8H9Tl88WdAP1L12kO8xW1HaLZfQBQikAC48LQ3QklJicxm9iZHV2y4DABAbHL94k8gAQAQak1NTWpqagragL7NZtOcOXO8jnW0t7cHfZa+P+kAgz2o39cDCWbZZQ9hbqN4hrvgJwIJwP+vqalJX375ZZf3ExISVFRUFIEWoS/IzMxkw2UAAGKQ68CL3c5UPgCIJVVVVbJarT4H7f0d3B86dKjOO+88r3Xu379fu3fvDupz2O12r4GE3jy4Hgh//p3uzc+abj+lBktaMJrk1oA0hofhH35TEFU2btyompoaWSwWWSwWxcXFqV+/frrooot8XpuSkqJJkyaprKxMJ06ccL5fXFzMBrrwKC4uTunp6Tp58qRzY6ZQfAABAAC9CysSACDyWlpaVF9fH7QBfbvdrssvv1zp6ele6920aZOs1uDllQ9WLv1A2Ww2r+MdvXlwPdA6DcOQyWQKSp0mk8k57uT4MZvNXd5LTU31ea/S0lINGzbM4z0sFotO7Dqlfxz2vZKju4oyGPOCf/hNQVRpb2/vskzO35REZrNZAwcO1MCBA1VTU6OysjIdPXpUZ599diiaiiiSmZmpkydPym63q7GxsdO+CQAAIDqZzWbnqkQCCQAgVVdXq729XTabzedsfX8G9HNycvTVr37Va53Hjh3TJ598EtTn8CdAEOzUx7019U4oUjwH+1n9GdC3WCw+AwmJiYmaOHGiX/cL5v+X3Nxcn2XOzzdCGkgYm5cQsnsjuhBIQFRx9w9Sd/6xzcnJ0cSJE9Xe3q6EBDpUeOc6W6WpqYlAAgAAMcJisTgHywCgt2hra9OJEyd6lDv/zJ+LLrpIAwYM8Frvtm3bgpruNRK59KXIpMHprYEEx8x7f9rnaQD/zMF4f74vDxs2THl5eX4N6HsLDgTCYrFo0KBBQblXsE3IT1Rmoln1bcH/vJGZaNaE/MSg3xfRiUACooq7mQM9+ceWIAL8kZb2r1yFTU1NEWwJAAAIJ8eMRFYkAPCktrZWzc3NAQ3aezuXlJSkWbNmea3z5MmT2rRpU1CfI1LpaMJdZ6Tq9Xeg3h/u0u54G4z3ZeLEiV3u6e4+wRrQl6SMjAxlZGQE7X59XZzZpNlFyXp5d3PQ7z27KFlx5uD92SG6EUhAVHH3jy/7GyDUCCQAABCbHAMwBBKA3s9qterYsWNBG9C32WwaPXq0hg8f7rXe3bt368svvwzac/gz8BtLg+u9/Vk9Deh3Z5Z+YWGhLrnkEp/3CnY6osLCwqDeD91zZVGKPjzSpoqTwdub46yMOF1VlBK0+yH6McKKqBKs1EZAIFwDCc3NwZ8hAAAAeifH50xSGwH/4isPuSQ1NDSooaGh27nz3b1/7bXXeh1A7ejo0AcffBDUZ/Unlz6D690XzBRDvvLnu55LSkryeb9x48bJbrf7HNAP5iz9lJQUpaQw6Bur4swm3X5eP/3sgzqdsho9vl9KnEm3j+0nC6sREAACCYgqwU5tBPgjISFB8fHx6ujoYEUCAAAxhNRG6M0M4/RAk6+BzC+//FJWqzXgQX1P54qKinTBBRd4rfPQoUPatWtX0J5VOv330FsgIVoG13tzIMGfmfCOgXZvA/qO88nJyT7vl5mZqcmTJ/u8VzAH9CUpLy8vqPcD/DE4PU53T8jUg1vqexRMSIkz6ScTMjUonWFhBIbfGEQNwzDczlggtRFCzWQyKS0tTXV1dWppaZHNZiOABQBADGBFAvxlt9t9DrI2NTWppqYmqLP0r7jiCvXr189rvR9//LFfM+v9FcmB7vj4+LDWGYlNee12u8/fJ38G9B057/0Z0LdYLD5/jyRp1KhRKikp8XqvYKfdSUhIUEFBQVDvCfRmpdnxun9iln6//WS30hwV9YvTHef1I4iAbuG3BlHD0wdWTx/c6uvrtX37dg0fPlyFhYVBn6GA2OIIJBiGoZaWFqWnp0e6SQAAIMQcA2L+DOyhdzAMQ4Zh+PyzOnr0qFpbW7s1eO/uXF5enqZOneq1zurqam3ZsiWIT+v/oH64Awmh+Lviq95I1Cn5v6eBr41sA9kcNzExUVOmTPE5oB/s78D+5PkH0HOD0uN0/6QsvVXRopUVp1Tf5juomZlo1uyiZF1ZlMLmyug2AgmIGp4+/Hr6oLVnzx4dP35cx48fV79+/VRSUqJhw4Yxkxzdkpqa6jxuamoikAAAQAxw/dxIICEwjtXEvj57t7a2ut0gt7uz9e12uyZNmqSBAwd6rXfHjh2qr68P2vP25nQ0sZJ6xzED31u5MwfwfQ3o5+Tk+GzXWWedpQEDBni8VygG9C0Wi/Lz84N6TwC9S5zZpHnFqbqyKEVbqtr0WXW7KhqsOtJkVYddijdLA9LiVJQRp7F5CZqQn0gAAT1GIAFRI5AVCadOndKhQ4ecr0+ePKlPP/1Uu3fv1qxZs/gSiIC5brjMPgkAAMQG18+ZNputT6bUdAzom0wmn5+Bjx8/rqampqAM6NtsNqWkpOiqq67yWmdTU5M+/vjjYD5yn980NhB9NZDgGPT3d0DfbDZ7TWvkMGXKFOds/HCk3ZFOf09w/a4AAMEUZzbp4sIkXVzoe5NwoKf63iddwANPH1jdfaHbu3ev2w/zhYWFBBHQLQQSAACIPa6fG4OxT4IjRZLFYvE6Q9lqterQoUM9yp3vet4wDI0ZM0YjRozw2r59+/apsrKyx8/paU0FaQAALnJJREFU0Ftnroei3r78rL5y55/5408u/QEDBujyyy/3Oks/FHJzc0NyXwAAYgGBBEQNf1MbdXR0aN++fV3KmUwmDR8+PCRtQ/RzDSQ0NzdHsCUAAKCnDMNwDsB6W2Xg+jmzvLxccXFxPRrQdwQjrr32Wq+Du1arVZ988kmQnva0SMyY78uD65Go09ugu6fB/pSUFJ/1XnjhhTIMw+39QpF2R5KSkpKUlMTsWQAA+hICCYga/q5IOHDggDo6OrqUGzhwIEtO0W3JycnOnKusSAAAIDgcaXccaYN8zVKuqKiQ1WoNSj596XRu8/Hjx3usz3Xgd9euXcF5aJ3+XOttUDmWBtd74wa5Z6bd8WdAPzEx0WedmZmZmjZtmsf7hWJA31EvAACALwQSEDX82SPBbrdrz549bsuVlpaGpF2IDSaTSUlJSWpublZra2ukmwMAQNC5DrD7ykV+8uRJVVVVdTt3vuuPw8yZM5WRkeG13u3bt7udMNJdvgacQ5V+xdfqgEgFEoL9vI5Akbf7djftjrfB/aysLJ9tGzNmjEaOHOn2HqH6c4+Pj1d2dnZI7g0AANBTBBIQNfwJJBw5csRt2pmcnBzl5OSErG2IDYmJiWpublZ7e7vPL8UAAHSXI+2OY5a+r4HWyspKtbS09HhA3zAMSVL//v01depUr3XW1tZq27ZtwXpkSf7PIg9nIGHIkCFqbW3V4cOHg1anP/U60s04/kzCUafkfVDfn81w3b3vS2JioqZPn+5xY9xQzdJnpTIAAEBnBBIQNTztkeCa2qisrMxtGVYjIBhcl6y3t7eT9xUAopxr2h273e6z3z916pQqKyv9nqXvqYzrbPVJkyZp4MCBXuvdvXu36urqgvLMEnntXeXl5amhoSHsgQTp9LO6+/zrGGAPdEDfn0k1JSUlGjp0qNv7hWpA32w2+7WCAAAAAKFFIAFRw9eKhBMnTqimpqbL+bS0NA0YMCCkbUNscA0ktLW1EUgAgDByHXC3WCw+U+8cPXpUDQ0N3cqd7y7tTnJysubMmeO1zubmZm3fvj0Yj+vUGwfXQ1FnpOrtyQbErnn0A52tn5CQ4LPeyy+/vMumuKFMuyOd/j1PTk4O2f0BAADQexFIQNTwtCLB8eXO02qEkpISUtAgKM4MJABALLLb7Z0G3JOTk73OVLZardq3b1+PB/RdU7yMGTNGI0aM8NrOgwcP6tChQ0F77lgaXI/Us/ozqO/pM52/G+KeeS49Pd1nnQMHDlROTo7be4WSr/0iAAAAgGAikICo4elLbVxcnJqamtwuOU9ISFBRUVGom4YY4Tp7kEACgEhzTbtjs9lkNps7BTzdqa6u1okTJwIa1D+zzJmDvddee63XQWWbzabPPvssKM/sek9fetPM9Z7orYEET4Po7mbQ+zuo70/O+okTJ0rqGjgIVdod6fS///6sIAAAAAD6MgIJiBqZmZkqKirqNLBhtVplsVi0c+dOt9cUFxd32kMB6AlWJABwxzCMTpvj2mw2JSUl+RzcLSsrk9VqDWgz3DPPuzrrrLM0fvx4r3VWVVV5/DezuxyphjyJ1OB6sGeLO1ZFeBuw7k2BBNe0O4HO1vcVkJKkrKwszZo1y+3muKHEBrkAAABAaDCCiqhRWFiowsLCLu+3tbWpoqKiy/tms1lnn312OJqGGOG6JwKBBKB3cjdzPiUlxes19fX1OnLkiN8pdtydc027I0kzZsxQZmam13p37typjo6OHj2vq96aeicUA8uReFbHCpDuBE0CHdB3Pe/PBrnnnXeexowZE9a0O3FxcX6lBQIAAADQNxBIQNRz5F0+05AhQ9gsDkFFaiPAf64z9B0D7omJiT43yN2/f79aWloCyp3vKY++JOXl5enSSy/1WmdDQ4M+//zzHj+zK38HumMlkGA2m/1KDRSsOiX3z+pu8D6Q9Du+JCQkaPbs2WFNuyOJzzsAAAAAeoxAAqKazWZTeXm523OlpaVhbg2iHamN0Be55tF3/NcwDJ/pQVpaWlRRURHwhrie8uhLp3ObDxo0yGu9+/fvV21tbY+e2VVvHVwPRb29/Vldfyc8pd3xd3A/KyvLZ52lpaUqLi7udF2oB/TNZjOpdwAAAAD0SQQSENUOHTqk1tbWLu8XFBQoIyMjAi1CNHMNJLS3t0ewJeir3A28JyYm+sxHXllZqfr6+m4P6J85Sz85OVlz5szxWuepU6f0xRdf9PiZXcXS4HokNubt7rP2ZEDfnw2eJWnmzJldNuENNTbIBQAAAAD/EUhA1DIMQ3v27HF7bvjw4WFuDWJBfHy8Mz0HKxL6tjM3xnUMuPfr18/rjOWOjg7t3r074A1xPaXdkaTRo0frnHPO8drew4cP69ChQz1+bofePnM9mPwZ0O8tOfx9Ddr7GthPTU31WeegQYOUn58f1rQ7ku99KgAAAAAAkUU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      "text/plain": [
       "<Figure size 1600x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/vector-space-model/notebook_66_0.png"
      },
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "cosine_fig"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/vector-space-model/notebook_66_1.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# following two blocks with much appreciated help from:\n",
    "# https://stackoverflow.com/questions/25227100/best-way-to-plot-an-angle-between-two-lines-in-matplotlib\n",
    "from matplotlib.lines import Line2D\n",
    "from matplotlib.patches import Arc\n",
    "import math\n",
    "\n",
    "def get_angle_plot(line1, line2, offset = 1, color = None, origin = [0,0], len_x_axis = 1, len_y_axis = 1):\n",
    "\n",
    "    l1xy = line1.get_xydata()\n",
    "\n",
    "    # Angle between line1 and x-axis\n",
    "    slope1 = (l1xy[1][1] - l1xy[0][1]) / float(l1xy[1][0] - l1xy[0][0])\n",
    "    angle1 = abs(math.degrees(math.atan(slope1))) # Taking only the positive angle\n",
    "\n",
    "    l2xy = line2.get_xydata()\n",
    "\n",
    "    # Angle between line2 and x-axis\n",
    "    slope2 = (l2xy[1][1] - l2xy[0][1]) / float(l2xy[1][0] - l2xy[0][0])\n",
    "    angle2 = abs(math.degrees(math.atan(slope2)))\n",
    "\n",
    "    theta1 = min(angle1, angle2)\n",
    "    theta2 = max(angle1, angle2)\n",
    "\n",
    "    angle = theta2 - theta1\n",
    "\n",
    "    if color is None:\n",
    "        color = line1.get_color() # Uses the color of line 1 if color parameter is not passed.\n",
    "\n",
    "    return Arc(origin, len_x_axis*offset, len_y_axis*offset, 0, theta1, theta2, color=color)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.scatter(co_means[monsieur_idx], co_means[sang_idx],\n",
    "           label='Comédies', zorder=3)\n",
    "ax.scatter(tr_means[monsieur_idx], tr_means[sang_idx],\n",
    "           label='Tragédie', zorder=3)\n",
    "ax.scatter(tc_means[monsieur_idx], tc_means[sang_idx],\n",
    "           label='Tragi-comédies', zorder=3)\n",
    "\n",
    "# plot vectors\n",
    "line_1 = Line2D([co_means[monsieur_idx], 0], [co_means[sang_idx], 0], 2, lw=2, ls='--', c='darkgrey')\n",
    "line_2 = Line2D([tr_means[monsieur_idx], 0], [tr_means[sang_idx], 0], 1, lw=2, ls='--', c='darkgrey')\n",
    "line_3 = Line2D([tc_means[monsieur_idx], 0], [tc_means[sang_idx], 0], 1, lw=2, ls='--', c='darkgrey')\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.add_line(line_1)\n",
    "ax.add_line(line_2)\n",
    "ax.add_line(line_3)\n",
    "\n",
    "angle_plot = get_angle_plot(line_1, line_2, 50)\n",
    "ax.add_patch(angle_plot) # To display the angle arc\n",
    "\n",
    "angle_plot = get_angle_plot(line_1, line_3, 12)\n",
    "ax.add_patch(angle_plot) # To display the angle arc\n",
    "\n",
    "angle_plot = get_angle_plot(line_2, line_3, 25)\n",
    "ax.add_patch(angle_plot) # To display the angle arc\n",
    "\n",
    "plt.xlabel('monsieur')\n",
    "plt.ylabel('sang')\n",
    "plt.ylim(0, 30)\n",
    "plt.xlim(-2, 50)\n",
    "plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.1), ncol=3)\n",
    "plt.tight_layout()\n",
    "\n",
    "from myst_nb import glue\n",
    "glue(\"cosine_fig\", fig, display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d253728",
   "metadata": {},
   "source": [
    "```{glue:figure} cosine_fig\n",
    ":name: fig-vector-space-model-cosine\n",
    "\n",
    "Illustration for the cosine distance metric for the genre vectors.\n",
    "```\n",
    "\n",
    "An interesting alternative to the Euclidean distance, is the well-known <span\n",
    "class=\"index\">cosine distance</span> from geometry, which is perhaps the most widely\n",
    "employed metric for computing dissimilarities between document vectors. When calculating\n",
    "the distance between two documents in a space, the Euclidean distance plainly looks at the\n",
    "exact coordinates of the two documents: it connects them with a straight line, so to\n",
    "speak, and returns the length of that line. The cosine distance, however, takes a quite\n",
    "different perspective on things: it is not primarily interested in those two *points* as\n",
    "such, but it will interpret them as arrows or *vectors* that find their offset in the space's origin---these vectors are shown as dashed grey lines in {numref}`fig-vector-space-model-cosine`.\n",
    "\n",
    "To estimate the similarity between two documents, the metric will measure the size of the angle between the two vectors that are defined by them. The similarity between two vectors is measured by the cosine of the angle between the two vectors, as the cosine of an angle increases as the angle decreases. Vectors pointing in the same direction (i.e., having a small angle between them) will, by this measure, be rated close to each other, even if the magnitude of the vectors is radically different and the length of the line connecting them is large.\n",
    "\n",
    "The mathematical formula for calculating the cosine distance between two vectors $\\vec{a}$ and $\\vec{b}$ is slightly more involved:\n",
    "\n",
    "\\begin{equation}\\label{eq:cosine-distance}\n",
    "d_{\\cos}(\\vec a, \\vec b) = 1 - \\frac{\\vec{a} \\cdot \\vec{b}}{|\\vec{a}||\\vec{b}|}\n",
    "\\end{equation}\n",
    "\n",
    "Let us unpack this formula a little. The numerator in the fraction on the right involves a <span class=\"index\">*dot product*</span>. This is the sum of multiplying each item in $\\vec{a}$ with its corresponding item in $\\vec{b}$, i.e.:\n",
    "\n",
    "\\begin{equation}\\label{eq:dot-product}\n",
    "\\vec{a} \\cdot \\vec{b} = \\sum^n_{i=1} a_i b_i = a_1 b_1 + a_2 b_2 + \\ldots + a_n b_n\n",
    "\\end{equation}\n",
    "\n",
    "With NumPy, the dot product can be calculated using <span class=\"index\">`numpy.dot()`</span> (see below). In the denominator of the fraction, we see how the <span class=\"index\">vector norm</span> (also called its length or its magnitude) is calculated for both $\\vec{a}$ and $\\vec{b}$, i.e., $|\\vec{a}|$ and $|\\vec{b}|$, and these numbers are then multiplied. The norm for a vector can be calculated using the following function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "eebb8cdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def vector_len(v):\n",
    "    \"\"\"Compute the length (or norm) of a vector.\"\"\"\n",
    "    return np.sqrt(np.sum(v ** 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8e81aa5",
   "metadata": {},
   "source": [
    "One aspect remains to be explained: the fraction in the formula gets subtracted from 1. Why is that? The fraction in the formula in fact corresponds to the cosine *similarity* (which will always lie between 0 and 1 for positive vectors). To turn this number into a distance, we take its complement, through subtracting it from 1.\n",
    "\n",
    "With these insights, we are now equipped to implement a function which calculates the cosine distance between vectors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3c489346",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cosine_distance(a, b):\n",
    "    \"\"\"Compute the cosine distance between two vectors.\n",
    "\n",
    "    Arguments:\n",
    "        a (numpy.ndarray): a vector of floats or ints.\n",
    "        b (numpy.ndarray): a vector of floats or ints.\n",
    "\n",
    "    Returns:\n",
    "        float: cosine distance between vector a and b.\n",
    "\n",
    "    Note:\n",
    "        See also scipy.spatial.distance.cdist\n",
    "\n",
    "    Examples:\n",
    "        >>> import numpy as np\n",
    "        >>> a = np.array([1, 4, 2, 8])\n",
    "        >>> b = np.array([2, 1, 4, 7])\n",
    "        >>> round(cosine_distance(a, b), 2)\n",
    "        0.09\n",
    "\n",
    "    \"\"\"\n",
    "    return 1 - np.dot(a, b) / (vector_len(a) * vector_len(b))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b23ad2c8",
   "metadata": {},
   "source": [
    "We can again compute the distances between our vectors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b01873cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tragédies - comédies:       0.93\n",
      "tragédies - tragi-comédies: 0.10\n",
      " comédies - tragi-comédies: 0.51\n"
     ]
    }
   ],
   "source": [
    "tc = cosine_distance(tragedy, comedy)\n",
    "print(f'tragédies - comédies:       {tc:.2f}')\n",
    "\n",
    "ttc = cosine_distance(tragedy, tragedy_comedy)\n",
    "print(f'tragédies - tragi-comédies: {ttc:.2f}')\n",
    "\n",
    "ctc = cosine_distance(comedy, tragedy_comedy)\n",
    "print(f' comédies - tragi-comédies: {ctc:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c704dbc7",
   "metadata": {},
   "source": [
    "The cosine distances agree with their Euclidean counterparts: the resulting angle is relatively smaller between the Tragédies and the Tragi-comédies.\n",
    "\n",
    "(sec-vector-space-model-cityblock-distance)=\n",
    "#### City block distance\n",
    "\n",
    "The <span class=\"index\">city block distance</span> is a metric which computes the distance between two points in space as the sum of the absolute differences of their coordinates in space. (City block distance is also referred to as <span class=\"index\">Manhattan distance</span> and $L_1$ distance.) To obtain an intuition of what this actually means in practice, consider the region of Manhattan shown in {numref}`fig-manhattan`.\n",
    "\n",
    "```{figure} images/manhattan-turned.png\n",
    ":name: fig-manhattan\n",
    "\n",
    "Route between two locations on a map of Manhattan, NY.\n",
    "```\n",
    "\n",
    "Imagine standing at the intersection of 10th Avenue and 39th Street (location A) and you want to go for lunch somewhere at the intersection of 3rd Avenue and 47th Street (location B). How far is that? Ignoring the possibility of flying for the moment, you should follow the grid-like structure of Manhattan's streets. If you follow the route indicated on the map, you go 4 blocks east, 4 blocks north, another 5 blocks east, and, finally, yet another 5 blocks north. This sums to a total of 18 blocks, and, essentially, the city block distance is just that: the sum of the horizontal and vertical distance between two points. Let us describe this more formally. Given two points in space $a$ and $b$ with coordinates $a_1, a_2$, and $b_1, b_2$, respectively, the city block distance $d$ can be computed using the following equation:\n",
    "\n",
    "\\begin{equation}\\label{eq:manhattan-example}\n",
    "d_1(a, b) = |a_1 - b_1| + |a_2 - b_2|\n",
    "\\end{equation}\n",
    "\n",
    "Plugging in the numbers from the Manhattan example, we obtain: $d_1(A, B) = |0 - 9| + |9 - 0| = 18$. Note that we need to compute the <span class=\"index\">absolute difference</span> between two values (i.e. $|x - y|$), as the distance between two points can never be below zero---this is part of the definition of a <span class=\"index\">*distance function*</span>. Just as with geographical landmarks, we can compute the <span class=\"index\">city block distance</span> between two documents when they are represented as points in space. However, because document vectors usually consist of numerous dimensions, a more general formulation of the city block distance is required. Using the sigma notation ($\\sum$), we can write the following:\n",
    "\n",
    "\\begin{equation}\\label{eq:manhattan-distance}\n",
    "d_1(\\vec{a}, \\vec{b}) = \\sum^n_{i=1} |a_i - b_i|\n",
    "\\end{equation}\n",
    "\n",
    "The formula can be implemented in Python as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "94bc2718",
   "metadata": {},
   "outputs": [],
   "source": [
    "def city_block_distance(a, b):\n",
    "    \"\"\"Compute the city block distance between two vectors.\n",
    "\n",
    "    Arguments:\n",
    "        a (numpy.ndarray): a vector of floats or ints.\n",
    "        b (numpy.ndarray): a vector of floats or ints.\n",
    "\n",
    "    Returns:\n",
    "        {int, float}: The city block distance between vector a and b.\n",
    "\n",
    "    Examples:\n",
    "        >>> import numpy as np\n",
    "        >>> a = np.array([1, 4, 2, 8])\n",
    "        >>> b = np.array([2, 1, 4, 7])\n",
    "        >>> city_block_distance(a, b)\n",
    "        7\n",
    "\n",
    "    \"\"\"\n",
    "    return np.abs(a - b).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "025fc83e",
   "metadata": {},
   "source": [
    "How does this intuitively relate to our example with the vectors? Like in the case of the Manhattan street plan, we basically also project a grid onto our space, along both axes, and apply the same Manhattan-like reasoning. This is visualized in {numref}`fig-vector-space-model-cityblock`, where we plotted the individual paths between the data points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e42514ce",
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1600x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/vector-space-model/notebook_76_0.png"
      },
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "cityblock_fig"
      }
     },
     "output_type": "display_data"
    },
    {
     "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/vector-space-model/notebook_76_1.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "monsieur_trag = tr_means[monsieur_idx]\n",
    "sang_trag = tr_means[sang_idx]\n",
    "monsieur_com = co_means[monsieur_idx]\n",
    "sang_com = co_means[sang_idx]\n",
    "monsieur_tc = tc_means[monsieur_idx]\n",
    "sang_tc = tc_means[sang_idx]\n",
    "\n",
    "\n",
    "# trag-tc\n",
    "ax.plot([monsieur_trag, monsieur_tc], [sang_tc, sang_tc],\n",
    "        'C2', lw=2, ls='--')\n",
    "ax.plot([monsieur_trag, monsieur_trag], [sang_tc, sang_trag],\n",
    "        'C2', lw=2, ls='--')\n",
    "\n",
    "# com-tc\n",
    "ax.plot([monsieur_tc, monsieur_tc], [sang_tc, sang_com],\n",
    "        'C0', lw=2, ls='--')\n",
    "ax.plot([monsieur_tc, monsieur_com], [sang_com, sang_com],\n",
    "        'C0', lw=2, ls='--')\n",
    "\n",
    "# trag-com\n",
    "ax.plot([monsieur_trag, monsieur_com], [sang_trag, sang_trag],\n",
    "        'C1', lw=2, ls='--')\n",
    "ax.plot([monsieur_com, monsieur_com], [sang_trag, sang_com],\n",
    "        'C1', lw=2, ls='--')\n",
    "\n",
    "ax.scatter(co_means[monsieur_idx], co_means[sang_idx],\n",
    "           label='Comédies', zorder=3)\n",
    "ax.scatter(tr_means[monsieur_idx], tr_means[sang_idx],\n",
    "           label='Tragédie', zorder=3)\n",
    "ax.scatter(tc_means[monsieur_idx], tc_means[sang_idx],\n",
    "           label='Tragi-comédies', zorder=3)\n",
    "\n",
    "ax.set(xlabel='monsieur', ylabel='sang')\n",
    "plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.1), ncol=3);\n",
    "\n",
    "from myst_nb import glue\n",
    "glue(\"cityblock_fig\", fig, display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2d7ee7c",
   "metadata": {},
   "source": [
    "```{glue:figure} cityblock_fig\n",
    ":name: fig-vector-space-model-cityblock\n",
    "\n",
    "Illustration for the cityblock distance metric for the genre vectors.\n",
    "```\n",
    "\n",
    "Because the <span class=\"index\">city block distance</span> provides an entirely different view on the notion of distance, it is interesting to compare the intra-centroid distances yielded by this metric to the ones we obtained before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cfdb16db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tragédies - comédies:       69.28\n",
      "tragédies - tragi-comédies: 17.90\n",
      " comédies - tragi-comédies: 51.38\n"
     ]
    }
   ],
   "source": [
    "tc = city_block_distance(tragedy, comedy)\n",
    "print(f'tragédies - comédies:       {tc:.2f}')\n",
    "\n",
    "ttc = city_block_distance(tragedy, tragedy_comedy)\n",
    "print(f'tragédies - tragi-comédies: {ttc:.2f}')\n",
    "\n",
    "ctc = city_block_distance(comedy, tragedy_comedy)\n",
    "print(f' comédies - tragi-comédies: {ctc:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d56bcd5",
   "metadata": {},
   "source": [
    "The city block distance is a well-known distance function whose \"inner workings\" are not\n",
    "too hard to understand. While it tends not to be used that frequently anymore in text\n",
    "analysis, functions with a family resemblance to the city block distance do appear from\n",
    "time to time. In the chapter on stylometry (Chapter {ref}`chp:stylometry`), for instance,\n",
    "we will see that Burrows's popular Delta method is in fact a small variation on the city\n",
    "block distance.\n",
    "\n",
    "#### Comparing metrics\n",
    "\n",
    "For the sake of simplicity, so far we have worked with the very limited example of our three genre vectors. The main issue with this dummy case is that we only considered a bidimensional vector space that consisted of two cherry-picked word variables that we already knew to be an important characteristic for some of the genres considered. For all other words in our vocabulary (that contains tens of thousands of terms), we simply do not know whether they show equally remarkable patterns. Would we see any different patterns if we applied these metrics on the entire vocabulary, also including words that might display less distinct usage across the three genres? Let us find out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c932296e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cosine\n",
      "   tragédie - comédie: 0.04\n",
      "   tragédie - tragi-comédie: 0.01\n",
      "   comédie - tragi-comédie: 0.03\n",
      "manhattan\n",
      "   tragédie - comédie: 7147.79\n",
      "   tragédie - tragi-comédie: 5169.42\n",
      "   comédie - tragi-comédie: 8153.40\n",
      "euclidean\n",
      "   tragédie - comédie: 356.95\n",
      "   tragédie - tragi-comédie: 250.69\n",
      "   comédie - tragi-comédie: 505.08\n"
     ]
    }
   ],
   "source": [
    "import scipy.spatial.distance as dist\n",
    "\n",
    "genre_vectors = {'tragédie': tr_means, 'comédie': co_means, 'tragi-comédie': tc_means}\n",
    "metrics = {'cosine': dist.cosine, 'manhattan': dist.cityblock, 'euclidean': dist.euclidean}\n",
    "\n",
    "import itertools\n",
    "\n",
    "for metric_name, metric_fn in metrics.items():\n",
    "    print(metric_name)\n",
    "    for v1, v2 in itertools.combinations(genre_vectors, 2):\n",
    "        distance = metric_fn(genre_vectors[v1], genre_vectors[v2])\n",
    "        print(f'   {v1} - {v2}: {distance:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5b04bb4",
   "metadata": {},
   "source": [
    "This code block requires some additional explanation. We first import the <span class=\"index\">SciPy</span> versions of the distance metrics which we hand-coded above. SciPy (\"Scientific Python\") is another influential package in the Python ecosystem that is predominantly relevant for scientific uses of the language. This is merely for illustration purposes, since these implementations should run perfectly parallel to our own. Next, we store both our genre vectors and our freshly imported functions in separate dictionaries for easy looping. Finally, we import the <span class=\"index\">`itertools`</span> module from the standard library, which offers the function <span class=\"index\">`itertools.combinations()`</span> for extracting all unique combinations between the elements of an iterable. The main goal of the block is to calculate the distances between all genre pairs in our data. Our earlier observation seems to be confirmed in this comparison: all metrics agree that the distance between the Tragédies and Tragi-comédies is relatively smaller than that between Comédies and Tragi-comédies, if we consider the complete vocabulary. However, there is also some interesting disagreement: for the cosine distance, the distance between Comédies and Tragi-comédies is smaller than the distance between Comédies and Tragédies, which is something that we do not see with the other metrics.\n",
    "\n",
    "As we inspect the actual numbers returned by the distance metrics, we see that the city\n",
    "block and Euclidean distances are huge in comparison to the cosine distance, which is\n",
    "nicely clamped between 0 and 1. This is due to the fact that the cosine calculation\n",
    "automatically normalizes the distance measure, using the magnitude-based denominator in\n",
    "the fraction discussed above. Because the city block and Euclidean distance do not perform such a <span class=\"index\">normalization</span>, they are much more sensitive to document length. Also, this explains why the cosine distance is nowadays commonly preferred in text analysis, since texts typically vary in length.\n",
    "\n",
    "(sec-vector-space-model-nearest-neighbors)=\n",
    "### Nearest neighbors\n",
    "\n",
    "It seems logical that any text in a vector space will be surrounded by highly similar data points. Such clusters of similar data points might inform us about the behavior or characteristics of texts. Such an assumption is often referred to as a \"local\" form of reasoning, since we hypothesize that we can in fact characterize data points through inspecting (only) their immediate neighborhood, rather than the entire space at once. This leads us to an important concept in data analysis, namely that of the \"<span class=\"index\">nearest neighbor</span>\". In the case of the French drama genres, for instance, one might expect that a Tragédie will always have another Tragédie as nearest neighbor -- and whenever this is not the case for a particular text, this might be an indication that this document deserves a closer look, since its behavior is unusual. As such, approaching our corpus with a nearest neighbor method might be an interesting way of performing \"<span class=\"index\">outlier detection</span>\".\n",
    "\n",
    "Below we define a function, `nearest_neighbors()`, that takes a document-term matrix as input. The function makes use of <span class=\"index\">SciPy</span>'s <span class=\"index\">`pdist`</span> function to compute the distances between all vectors. We use this function, because a more naive implementation, in which we iterate over the matrix's document vectors and returns for each item the index of its nearest neighbor, would be terribly slow. `pdist` returns a so-called \"condensed <span class=\"index\">distance matrix</span>\", which we transform into a regular square-form distance matrix using the function <span class=\"index\">`squareform`</span>. In such a square-form distance matrix, cells hold the distance between points $i$ and $j$. All distances at the diagonal (i.e., where $i = j$) of the matrix are zero, since these represent the distance between documents and themselves. To conveniently extract the nearest neighbor of each document, while ignoring the zeros at the diagonal, we first set all diagonal values to infinity. Subsequently, we can use NumPy's convenient <span class=\"index\">`numpy.argmin()`</span> function, which returns the index of the minimal value in an array, i.e., the nearest neighbor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d4dffdaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def nearest_neighbors(X, metric='cosine'):\n",
    "    \"\"\"Retrieve the nearest neighbor for each row in a 2D array.\n",
    "\n",
    "    Arguments:\n",
    "        X (numpy.ndarray): a 2D array.\n",
    "        metric (str): the distance metric to be used,\n",
    "            one of: 'cosine', 'manhattan', 'euclidean'\n",
    "\n",
    "    Returns:\n",
    "        neighbors (list): A list of integers, corresponding to\n",
    "            the index of each row's nearest neighbor.\n",
    "\n",
    "    Examples:\n",
    "        >>> X = np.array([[1, 4, 2], [5, 5, 1], [1, 2, 1]])\n",
    "        >>> nearest_neighbors(X, metric='manhattan')\n",
    "        [1, 0, 0]\n",
    "\n",
    "    \"\"\"\n",
    "    distances = dist.pdist(X, metric=metric)\n",
    "    distances = dist.squareform(distances)\n",
    "    np.fill_diagonal(distances, np.inf)\n",
    "    return distances.argmin(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4885c688",
   "metadata": {},
   "outputs": [],
   "source": [
    "neighbor_indices = nearest_neighbors(document_term_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7a4f568",
   "metadata": {},
   "source": [
    "From the array `genres`, we can retrieve the original genre labels assigned to each\n",
    "document in the dataset. Then, using the indices which were returned by\n",
    "`nearest_neighbors()`, we can look up the genres of each of their <span class=\"index\">nearest neighbor</span>s:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6a9f9e72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Comédie' 'Tragédie' 'Comédie' 'Comédie' 'Comédie']\n"
     ]
    }
   ],
   "source": [
    "nn_genres = genres[neighbor_indices]\n",
    "print(nn_genres[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30e6d5a3",
   "metadata": {},
   "source": [
    "What is the correspondence between the genres that were actually assigned to the items, and the genres of the nearest neighbors which were retrieved? We can quantify this correspondence through summing the number of overlapping label pairs in both arrays and dividing it by the total number of pairs. With NumPy, such operations are a walk through the park:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f96996d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matching pairs (normalized): 0.90\n"
     ]
    }
   ],
   "source": [
    "overlap = np.sum(genres == nn_genres)\n",
    "print(f'Matching pairs (normalized): {overlap / len(genres):.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be6990b0",
   "metadata": {},
   "source": [
    "In approximately 90% of the cases, we see that the nearest neighbor of a text is indeed of\n",
    "the same genre. This nearest neighbor approach allows us to reconsider our data set in a\n",
    "variety of ways. With a <span class=\"index\">`Counter`</span>, we can for instance inspect the distribution of genres in the list of nearest neighbors in each genre:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1664126a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('Tragédie', 130), ('Tragi-comédie', 16), ('Comédie', 4)]\n",
      "[('Comédie', 298), ('Tragédie', 7), ('Tragi-comédie', 5)]\n",
      "[('Tragi-comédie', 20), ('Tragédie', 10), ('Comédie', 8)]\n"
     ]
    }
   ],
   "source": [
    "print(collections.Counter(nn_genres[genres == 'Tragédie']).most_common())\n",
    "print(collections.Counter(nn_genres[genres == 'Comédie']).most_common())\n",
    "print(collections.Counter(nn_genres[genres == 'Tragi-comédie']).most_common())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6af6b611",
   "metadata": {},
   "source": [
    "The resulting distributions show which type of nearest neighbor is most commonly associated with each genre. Likewise, we could iterate over the Tragi-comédies, and calculate each text's distance to the mean of the other genre."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6119f728",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean distance to comédie vector: 0.060\n",
      "Mean distance to tragédie vector: 0.042\n"
     ]
    }
   ],
   "source": [
    "t_dists, c_dists = [], []\n",
    "for tc in document_term_matrix[genres == 'Tragi-comédie']:\n",
    "    t_dists.append(cosine_distance(tc, tr_means))\n",
    "    c_dists.append(cosine_distance(tc, co_means))\n",
    "\n",
    "print(f'Mean distance to comédie vector: {np.mean(c_dists):.3f}')\n",
    "print(f'Mean distance to tragédie vector: {np.mean(t_dists):.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf700ecc",
   "metadata": {},
   "source": [
    "Another option is to plot the resulting distances in a so-called \"<span class=\"index\">box plot</span>\", that shows for each list a number of useful statistics, such as the median:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "80e2e7b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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ee+2VT33qU1m0aFFTlF8vNtlkk/rxm2++udr5M2bMaHBtc9X85z//mZ/+9KdJkp49e+bf//3f3/c5e+65Z44//vgkyVtvvZWrr756DboGAAAAAJpb6eB31KhR2WefffLaa6+lUqmkpqYmHTt2fN/5X/va15Ikc+bMyV133VW2/HrTs2fP+vGrr7662vnvnvPutc1Vc9SoUVmyZEmSd17eVhTFKp91wAEH1I8fffTRRvULAAAAAGwYSgW/s2fPzjHHHJMlS5akXbt2ueKKKzJ79uxV7hDt1q1b9thjjyTJPffcU6b8erXjjjvWjx977LFVzp06dWreeOONJO+cZbu2xyQ0Zc1//vOf9eNVBfPLvXvH8LtfMgcAAAAAbPhKBb+XXnppZs2aldra2tx999356le/mjZt2qx2Xf/+/VOpVDJu3Lgy5derd7/g7P1eSrbcu3cyH3LIIRtEzXef+bs8IF6Vf/zjH/XjzTbbbLXzAQAAAIANR6ng96677kpRFPn85z+fT37yk41et/wYgldeeaVM+fVq4MCB6dq1a5Lkvvvue9/Quq6uLr/61a/qvw8ePHiDqPnu3cN33HFH5syZs8rav//97+vHu++++xr1DQAAAAA0r1LB7wsvvJAk2Xfffddo3fJjBN56660y5dermpqanHPOOfXfjz/++EybNm2led/73vcyfvz4JO+8JO3AAw9s8HnXXHNNiqJIURTZe++913nNPffcM1tvvXWSZNasWTnmmGMyf/78leZVKpV8//vfz3333Zck2XjjjXP00Uc32B8AAAAAsGGqLbN4+a7RTp06rdG65S8Zq60tVX69GzJkSIYNG5bRo0dnwoQJ6du3b4YMGZJevXpl5syZueGGG/Lggw8meSfcvvzyyzeYmhtttFEuueSSDBo0KMuWLctdd92V7bffPscdd1x23HHHbLTRRnnllVdy00031YfISfKTn/wkH/nIR0r/DgAAAABg/SmVvHbq1CnTpk3LjBkz1mjda6+9liTZfPPNy5Rf72pra3Prrbfm2GOPzR133JEpU6bk/PPPX2le9+7dc9NNN6V3794bVM3DDjssN954Y04++eTMmjUr//znP3PhhRc2OLdVq1b56U9/mtNPP730bwAAAAAA1q9SRz1su+22SZKHH354jdbdfffdKYoiffv2LVO+WbRv3z5//vOf86c//SlHHHFEttpqq7Rq1Sqbb755PvGJT+TCCy/MM888k/79+2+QNY866qi89NJL+eUvf5mDDjooH/nIR9K6detstNFG2WKLLTJgwIAMHTo0L774otAXAAAAAD6gikqlUlnbxeeff36GDh2adu3a5YUXXqh/Edntt9+eQYMGpSiK1NXVrbDmnnvuyf7775+iKHLJJZfkG9/4RrlfwAdK9+7dM2nSpGy55ZaZOHFic7cDAABAlfPnUODDqtSO36997Wtp06ZN5s+fnyOPPHK1L2t7+OGHc8wxxyRJNt1005xwwgllygMAAAAA0IBSZ/x26dIlP/nJT3L66afn4YcfTs+ePfPVr351hV2+d911V15//fWMGDEid955Z5YtW5aiKPLLX/4ybdu2Lf0DAAAAAABYUangN0lOO+20TJs2LRdccEH9X5OkKIokyWc/+9n6uctPlTj33HPzpS99qWxpAAAAAAAaUOqoh+V+/OMf584778zOO++cSqXyvp8+ffrkjjvuyA9+8IOmKAsAAAAAQANK7/hd7qCDDspBBx2UZ555JmPGjMlrr72W2bNnp127dunevXsGDhyYXXbZpanKAQAAAADwPpos+F2uT58+6dOnT1M/FgAAAACARmqSox4AAAAAANhwCH4BAAAAAKpMkx/1sHjx4syePTuLFi1q1Pytt966qVsAAAAAAPhQa5Lg94UXXsgll1ySu+++O6+++moqlUqj1hVFkaVLlzZFCwAAAAAA/J/Swe8VV1yR0047LUuWLEmSRoe+AAAAAACsG6WC3/vvvz9f//rXUxRFKpVK2rVrl1133TVdu3ZNq1atmqpHAAAAAADWQKng92c/+1n9+Jxzzsn3vve9tG7dunRTAAAAAACsvVLB7yOPPJKiKHL00UfnRz/6URO1BAAAAABAGS3KLF64cGGS5NBDD22SZgAAAAAAKK9U8LvVVlslSWprS78jDgAAAACAJlIq+N1rr72SJE899VSTNAMAAAAAQHmlgt/TTz89NTU1+d///d+89dZbTdUTAAAAAAAllAp+e/funV//+teZNm1aDjrooEycOLGp+gIAAAAAYC2VPpz3a1/7WjbffPOcfPLJ2X777fPZz342u+++ezbbbLO0aLH6XPn4448v2wIAAAAAAO/SJG9lmzdvXtq3b58ZM2bklltuyS233NKodUVRCH4BAAAAAJpY6eD361//eq688sr675VKpewjAeADYc6cOZk4cWLmzZuXdu3apXv37unQoUNztwUAAADlgt8//vGPueKKK5K8s3t3v/32y6c+9al07do1rVq1apIGAWBDUqlUct999+XSSy/Nn/70p9TV1dXfq6mpyaBBg/KNb3wje++9d4qiaMZOAQAA+DArFfxecsklSZI2bdrkzjvvzMCBA5ukKQDYEI0bNy7HH398JkyY0OD9urq6+iOPevfuneuuuy79+vVbz10CAABAyeD373//e4qiyCmnnCL0BaCqjR49OoMGDcr8+fPrr3Xp0iUHHHBAOnbsmLfeeiujRo3K1KlTkyQTJkzIgAEDMmzYsOy///7N1TYAAAAfUqWC38WLFydJdttttyZpBgA2ROPGjVsh9N15553zve99L4cffnhatmxZP2/x4sUZNmxYLrzwwjz55JOZP39+Bg0alDFjxtj5CwAAwHrVoszirbfeOkmyaNGiJmkGADY0lUolxx9/fH3oe/jhh+ehhx7K0UcfvULomyQtW7bMF77whTz00EP53Oc+lySZP39+TjjhBC8/BQAAYL0qFfwedthhqVQqGTNmTFP1AwAblPvuu6/+TN+dd945N9xwQ1q3br3KNa1bt86NN96YnXfeOUnyzDPP5P7771/nvQIAAMBypYLf0047LVtssUV+97vfZfz48U3UEgBsOP7nf/6nfnzWWWetNvRdrnXr1vnud7/b4HMAAABgXSsV/Hbp0iXDhg1Lhw4dsv/+++emm27yn7ICUDXmzJmTYcOGJXnn33mDBg1ao/VHHHFEOnfunCS57bbbMmfOnCbvEQAAABpS6uVuJ510UpJkxx13zF//+tcce+yxOf3007Prrrtms802S4sWq86Vi6LIb3/72zItAMA6M3HixNTV1SVJDjjggJXO9F2dli1b5sADD8z111+furq6TJo0KR06dFgXrQIAAMAKSgW/11xzTYqiSJL6v06bNi133XVXo58h+AVgQzVv3rz6cceOHdfqGe8OeufOnVu6JwAAAGiMUsFvklJHOywPiwFgQ9SuXbv68VtvvbVWz3j38Q7t27cv3RMAAAA0Rqng99VXX22qPgBgg9O9e/fU1NSkrq4uo0aNyuLFi9fouIfFixdn5MiRSZLa2tpsueWW66pVAAAAWEGp4PejH/1oU/UBABucDh06ZNCgQbnlllsyderUDBs2LF/4whcavf62227LtGnTkiSDBg1yvi8AAADrzarfvgYAH3Lf+MY36scXXnhhFi1a1Kh1CxcuzEUXXdTgcwAAAGBdE/wCwCrsvffe6d27d5LkySefzODBg1cb/i5atCjHHHNMnnzyySRJnz59MnDgwHXeKwAAACwn+AWAVSiKItddd13atm2bJLn99tvTv3//3HTTTVm8ePEKcxcvXpwbb7wx/fv3z+23354kadu2ba699lovNAUAAGC9KnXGLwB8GPTr1y/Dhg3LoEGDMn/+/Pqdv126dMkBBxyQDh06ZM6cORk5cmT9mb7JO6HvsGHD0q9fv2bsHgAAgA8jwS8ANML++++fMWPG5Pjjj8+ECROSJFOnTs3111/f4Pw+ffrk2muvFfoCAADQLBz1AACN1K9fvzz99NP561//miOPPDI1NTUr3K+trc1RRx2Vv/71r3nqqaeEvgAAADQbO34BYA0URZG99947e++9d+bMmZNJkyZl7ty5ad++fbbccst06NChuVsEAAAAwS8ArK0OHToIegEAANggCX4BAACAVdp1110zZcqU5m5jrUyePLn+r927d2/mbtZc165d8/jjjzd3G8AHkOAXAAAAWKUpU6Zk0qRJzd1GKcuWLfvA/waANSH4BQAAABqlaNEirTft0txtrJFFs99MpVKXoqhJ6002b+52Gm3RrKmpLFvW3G0AH2CCXwAAAKBRWm/aJZ+9dnxzt/Gh8OcTdsrCGZObuw3gA6xJg9/HHnssI0eOzLPPPpuZM2dmyZIlueeee1aY8+abb2bx4sVp3bp1OnXq1JTlAQAAAABIEwW/L730Uk466aSMHTu2/lqlUklRFCvNveCCC/LLX/4yW2yxRSZNmpSampqmaAEAAAAAgP/TouwDxo0bl1133TVjx45NpVKp/7yfU045JZVKJdOnT8+oUaPKlgcAAAAA4D1KBb8LFy7M4Ycfnjlz5qSmpiZnn312nn/++dx8883vu2bbbbfNTjvtlCQZPXp0mfIAAAAAADSgVPB75ZVXZuLEiSmKIjfddFN+/OMfZ7vttstGG220ynV77bVXKpVKHn/88TLlAQAAAABoQKng9/bbb09RFDn44IMzaNCgRq/bYYcdkrxzNjAAAAAAAE2rVPA7YcKEJMmhhx66Rus6deqUJJk9e3aZ8gAAAAAANKBU8Dtr1qwkSefOnddo3ape/gYAAAAAQDm1ZRZ37NgxM2bMyJw5c9Zo3cSJE5Mkm222WZnyANCs5syZk4kTJ2bevHlp165dunfvng4dOjR3WwAAAFBux2+PHj2SJE888cQarbvnnnuSJL169SpTHgDWu0qlkr/+9a858sgj06lTp/Tu3Tuf+MQn0rt373Tq1ClHHXVU/vrXv/qvWwAAAGhWpYLffffdN5VKJTfddFOjd/2OHz8+I0eOTFEU2W+//cqUB4D1aty4cdlxxx2zzz775NZbb01dXd0K9+vq6nLLLbdkn332yY477phx48Y1U6cAAAB82JUKfocMGZLa2trMnDkzJ5xwQpYuXbrK+a+88kqOPPLIVCqVtGnTJieddFKZ8gCw3owePToDBgyof7FpknTp0iXHHXdcvvnNb+a4445Lly5d6u9NmDAhAwYMyOjRo5ujXQAAAD7kSgW/H/vYx3LmmWemUqlk+PDh2WmnnXLVVVfllVdeqZ/z7LPP5u677863vvWt9O3bN6+88kqKosjQoUOd8QvAB8K4ceMyaNCgzJ8/P0my884756abbsrrr7+e6667Lpdcckmuu+66vP7667nxxhuz8847J0nmz5+fQYMG2fkLAADAeldUSh5CWKlUcvzxx+f3v/99iqJY7dwk+cpXvpIrr7yyTFk+oLp3755JkyZlyy23rH/JH8CGrFKpZMcdd6zf6Xv44YfnhhtuSOvWrd93zaJFizJ48ODcfvvtSZI+ffrkqaeeWu2/JwEANlTL/yy38Wbd8tlrxzd3Ox8Kfz5hpyycMdmfn4G1VmrHb5IURZHrr78+v/nNb9K1a9dUKpX3/WyxxRa59NJLhb4AfGDcd9999aHvzjvvvNrQN0lat269ws7fZ555Jvfff/867xUAAACWq22qB5188sk58cQTM2rUqIwZMyavvfZaZs+enXbt2qV79+4ZOHBgDj744LRp06apSgLAOvc///M/9eOzzjprtaHvcq1bt853v/vdHHPMMfXP2XvvvddFiwAAALCSUkc9vP7660mSVq1arfBCG3g/jnoAPkjmzJmTTp06pa6uLl26dMnrr7+eli1bNnr94sWLs9VWW2XatGmpqanJzJkz06FDh3XYMQDAuuGoh/XPUQ9AWaWOeujRo0e22WabnHvuuU3VDwBsMCZOnJi6urokyQEHHLBGoW+StGzZMgceeGCSpK6uLpMmTWryHgEAAKAhpYLf5X8A3n333ZukGQDYkMybN69+3LFjx7V6xrt3+M6dO7d0TwAAANAYpYLfrl27JkmjzzsEgA+Sdu3a1Y/feuuttXrGnDlz6sft27cv3RMAAAA0Rqngd5dddkmSPPfcc03SDABsSLp3756ampokyahRo7J48eI1Wr948eKMHDkySVJbW5stt9yyyXsEAACAhpQKfo877rhUKpVcf/31a/yHYQDY0HXo0CGDBg1KkkydOjXDhg1bo/W33XZbpk2bliQZNGiQF7sBAACw3pQKfg8//PAcccQRefXVV3PsscdmwYIFTdUXAGwQvvGNb9SPL7zwwixatKhR6xYuXJiLLrqowecAAADAulZbZvHrr7+eCy64IAsXLsxtt92W//f//l9OOumk7LXXXunevXs23njj1T5j6623LtMCAKxTe++9d3r37p0JEybkySefzODBg3PjjTeu8nz7RYsW5ZhjjsmTTz6ZJOnTp08GDhy4vloGAACAcsFvjx49UhRFkqQoikycODHnn39+o9cXRZGlS5eWaQEA1qmiKHLddddlwIABmT9/fm6//fb0798/Z511VgYNGpSWLVvWz128eHFuu+22XHTRRfWhb9u2bXPttdfW//sSAAAA1odSwW+SVCqVVX4HgA+6fv36ZdiwYRk0aFDmz59fv/O3S5cuOeCAA9KhQ4fMmTMnI0eOrD/TN3kn9B02bFj69evXjN0DAADwYVQq+D3hhBOaqg8A2KDtv//+GTNmTI4//vhMmDAhyTsvfLv++usbnN+nT59ce+21Ql8AAACaRang9+qrr26qPgBgg9evX788/fTTuf/++3PppZdm2LBhqaurq79fW1ubQYMG5Rvf+EYGDhzoeAcAAACaTemjHgDgw6Qoiuy9997Ze++9M2fOnEyaNClz585N+/bts+WWW6ZDhw7N3SIAAAAIfgFgbXXo0EHQCwAAwAapRXM3AAAAAABA0xL8AgAAAABUmSY56uGtt97K1VdfnREjRuSZZ57JrFmz8vbbb692XVEUWbp0aVO0AAAAAADA/ykd/I4ZMyaDBw/O1KlTkySVSqV0UwAAAAAArL1Swe9rr72WQw89NAsWLKgPfLt3757u3bunVatWTdIgAAAAAABrplTwe9FFF2X+/PkpiiIHHXRQfvGLX6Rnz55N1RsAAAAAAGuhVPA7evToFEWRfv365Y477kiLFt4VBwAAAADQ3EoltZMmTUqSnHDCCUJfAAAAAIANRKm0tk2bNkmSbt26NUkzAAAAAACUVyr43X777ZMkU6dObZJmAAAAAAAor1TwO3jw4FQqldxxxx1N1Q8AAAAAACWVCn5PPvnk9OnTJyNHjsywYcOaqicAAAAAAEooFfy2atUqw4cPz7/+679m8ODBOe+88zJ79uwmag0AAAAAgLVRW2bxPvvskyRp3bp1lixZknPPPTfnn39+evbsmc033zwtWqw6Vy6KIvfcc0+ZFgAAAAAAeI9Swe99992XoiiSpP6vdXV1+fvf/77atZVKpX4NAAAAAABNp1Twm7wT4DbmGgAAAAAA60ep4HfZsmVN1QcAAAAAAE2k1MvdAAAAAADY8Ah+AQAAAACqjOAXAAAAAKDKlH6527tNnjw599xzT5599tnMnDkzS5YsyW9/+9umLAEAAAAAwGo0SfA7Y8aMfPvb386NN96Yurq6JEmlUklRFCsFv1//+tfzv//7v9lqq63y8ssvN0V5AAAAAADepfRRD6+++mp23nnn/P73v8/SpUtTqVRSqVTed/4pp5ySpUuX5rXXXsuYMWPKlgcAAAAA4D1KBb91dXU57LDDMnHixFQqlRx77LEZOXJkfv3rX7/vmr59+2b77bdPkowcObJMeQAAAAAAGlDqqIfrrrsuEyZMSFEU+eUvf5l/+7d/S5IsWLBgles+/elP54UXXsijjz5apjwAAAAAAA0oteP31ltvTZJ86lOfqg99G6NPnz5JkhdeeKFMeQAAAAAAGlAq+H3yySdTFEWOOOKINVq3+eabJ3nnpXAAAAAAADStUsHv8uC2e/fua1a0xTtlly1bVqY8AAAAAAANKBX8tm3bNsnqz/R9r8mTJydJOnXqVKY8AAAAAAANKBX8brXVVkmSp556ao3WPfDAA0mS7bbbrkx5AAAAAAAaUCr43XvvvVOpVHLjjTdm8eLFjVrz6quvZvjw4SmKIp/+9KfLlAcAAAAAoAGlgt8TTzwxRVHkn//8Z771rW+tdv7MmTNz1FFHZcmSJamtrc1JJ51UpjwAAAAAAA0oFfz27ds3X/7yl1OpVHLFFVfkoIMOyl/+8pfMnTu3fs6CBQvy3HPP5Re/+EX69u2bJ598MkVR5Fvf+lb9UREAAAAAADSd2rIP+M1vfpNXXnkl999/f0aPHp3Ro0cnSYqiSJK0b9++fm6lUkmSHHjggbngggvKlgYAAAAAoAGldvwmScuWLfOXv/wlZ511Vlq1apVKpVL/SbLC91atWuU73/lO7rjjjtTU1JRuHgAAAACAlZXe8ZskNTU1ueCCC3LGGWfkpptuypgxY/Laa69l9uzZadeuXbp3756BAwdm8ODB2XLLLZuiJAAAAAAA76NJgt/ltthii3zzm9/MN7/5zaZ8LAAAAAAAa6D0UQ8AAAAAAGxYBL8AAAAAAFVG8AsAAAAAUGVKnfG7zz77rNW6Fi1apH379unUqVP69u2bT3/609lxxx3LtAIAAAAAwP8pFfzed999KYqiSRrp379/LrvssvTu3btJngcAAAAA8GFV+qiHSqWSSqWy0veGPquaM3bs2Oy222554IEHyrYEAAAAAPChVir4XbZsWd56660cffTRqVQq+djHPpaLL74448aNy+zZs7NkyZLMnj0748aNy8UXX5yPfexjqVQq+cIXvpCZM2dmypQpGT58eA444IAkyaJFi3LUUUdl/vz5TfLjAAAAAAA+jEoFv5VKJUcccUT++Mc/5stf/nImTJiQb3/729lpp53SoUOH1NTUpEOHDtlpp53y7W9/OxMmTMjxxx+fm2++OUcffXS22GKLfOYzn8ndd9+d7373u0mS6dOn57e//W2T/DgAAAAAgA+jUsHvNddck3vuuSe9e/fOVVddlZYtW65yfsuWLfPb3/42O+ywQ+65555cd9119fcuuOCCbLvttkmSu+66q0xbAAAAAAAfaqWC3+uuuy5FUeT4449PixaNe1RNTU1OOOGEVCqVXHvttfXXi6LIUUcdlUqlkmeeeaZMWwAAAAAAH2qlgt/nnnsuSdKjR481Wrd8/t///vcVru+www5JkpkzZ5ZpCwAAAADgQ61U8Dt79uwkyYwZM9Zo3fL5b7311grXN9544yRJbW1tmbYAAAAAAD7USgW/3bp1S5IMGzZsjdYtn9+1a9cVri8PkjfbbLMybQEAAAAAfKiVCn733XffVCqVjB49Opdffnmj1lx++eUZPXp0iqLIPvvss8K9CRMmJEk6d+5cpi0AAAAAgA+1UsHvt771rfpjGb7xjW/kuOOOyxNPPNHg3CeeeCJf+tKX8o1vfCPJO8c5nH766SvMGTVqVIqiyC677FKmLQAAAACAD7VSh+n26dMn//Vf/5XTTz89RVHkD3/4Q/7whz+kffv22WabbdKmTZssWLAgr776aubOnZskqVQqSZKLL744ffr0qX/W//t//y/PPvtskuTQQw8t0xYAAAAAwIda6beonXbaaenUqVO+9a1vZdasWUmSOXPm5KmnnqqfszzsTZJNN900v/rVr/LFL35xhed8/OMfz/Tp05MknTp1KtsWAAAAAMCHVungN0m+9KUv5dBDD83VV1+d22+/PU888UQWLFhQf79NmzbZZZdd8rnPfS4nnnhiNt1005We0bp167Ru3bop2gEAAAAA+FBrkuA3eWcn7xlnnJEzzjgjlUolb731VubPn5+2bdumY8eOKYqiqUoBAAAAALAKTRb8vltRFNlkk02yySabrIvHAwAAAACwCi2auwEAAAAAAJqW4BcAAAAAoMoIfgEAAAAAqozgFwAAAACgygh+AQAAAACqjOAXAAAAAKDKCH4BAAAAAKqM4BcAAAAAoMoIfgEAAAAAqozgFwAAAACgygh+AQAAAACqTG1TPuzpp5/ObbfdlkceeSSTJ0/O3Llz0759+3zkIx/JJz7xiXz+859Pnz59mrIkAAAAAADv0STB75QpU/LVr341I0aMaPD+008/nZEjR+a8887LoYcemiuuuCJdu3ZtitIAAAAAALxH6aMeXnnllfTr1y8jRoxIpVJZ7efOO+/MLrvsktdee60J2gcAAAAA4L1KBb/Lli3L5z73uUyZMiWVSiVdu3bNeeedl0cffTSzZs3KkiVLMmvWrDz66KM599xz061bt1QqlUyePDmHHXZYli1b1lS/AwAAAACA/1Mq+L3++uszYcKEFEWRgw8+OM8991x+8IMfZLfddkvHjh1TU1OTjh07ZrfddssPf/jDPPfccznkkEOSJBMmTMjvf//7JvkRAAAAAAD8/0oFv7feemuS5KMf/WhuueWWdOjQYZXz27dvnz/+8Y/p0aNHkuSPf/xjmfIAAAAAADSgVPD75JNPpiiKnHjiidl4440btWbjjTfOSSedlEqlkieffLJMeQAAAAAAGlAq+H3zzTeTJD179lyjddtvv/0K6wEAAAAAaDqlgt/lu3znzp27RuuWz2/sLmEAAAAAABqvVPC79dZbJ0lGjx69RutGjRq1wnoAAAAAAJpOqeB33333TaVSyS233NLo8HfUqFG55ZZbUhRF9ttvvzLlAQAAAABoQKng95RTTkltbW0qlUoOP/zw/PznP8/ChQsbnLto0aL8/Oc/z6BBg1KpVFJbW5uvf/3rZcoDAAAAANCA2jKLt91225xzzjk555xzsmjRonznO9/Jueeemz333DPbb7992rZtm/nz5+fFF1/Mgw8+mHnz5qVSqaQoipxzzjnZdtttm+p3AAAAAADwf0oFv0nygx/8IG+//XYuuOCCLFu2LHPnzs3IkSMzcuTIFeZVKpUkSYsWLXL22Wfn+9//ftnSAAAAAAA0oNRRD8udf/75GTt2bAYNGpRWrVqlUqms9GnVqlU+//nP56GHHsp5553XFGUBAAAAAGhA6R2/y33iE5/IrbfemsWLF+dvf/tbJk+enLlz56Z9+/bp1q1bdtppp2y00UZNVQ4AAAAAgPfRZMHvci1btsxuu+3W1I8FAAAAAKCRSgW/Y8aMSZL06dMnnTp1avS62bNn56mnnkqSDBgwoEwLAAAAAAC8R6ngd++9905RFBk2bFgOO+ywRq979NFHc/DBB6dFixZZunRpmRYAAAAAAHiPJnm529qqVCrNWR4AAAAAoCo1S/C7PPAtiqI5ygMAAAAAVLVmCX5nz56dJGnTpk1zlAcAAAAAqGrNEvzedtttSZKtt966OcoDAAAAAFS1Rr/c7fbbb8/tt9/e4L1f/epX+dOf/rTK9ZVKJfPnz8/48ePz8ssvpyiKDBgwYI2aBQAAAABg9Rod/I4fPz7XXHPNSufyViqV/PWvf12jopVKJW3bts0ZZ5yxRusAAAAAAFi9NT7qoVKp1H8aura6T4cOHXLEEUfkoYceyrbbbtukPwYAAAAAgDXY8Xv66afny1/+cv33SqWSj33sYymKIpdffnn233//Va5v0aJF2rVrl0033XStmwWAtbHrrrtmypQp6+TZ06ZNS11dXWpqatK5c+d1UqNr1655/PHH18mzAQAAqE6NDn47duyYjh07rnS9Uqmkc+fO+ehHP9qkjQFAU5kyZUomTZq0TmssW7ZsndcAAACAxmp08NuQV199NUnW2Q4nAGgKXbt2XWfPnjx5cpYtW5YWLVqkW7du66TGuuwfAACA6lQq+LXLF4APgnV5TEL37t0zadKkdOvWLRMnTlxndQAAAGBNrPHL3QAAAAAA2LAJfgEAAAAAqozgdy0NHz48Rx11VHr06JHWrVunc+fO6d+/f372s59lzpw5H4ias2bNyiWXXJL9998/W2+9dVq3bp1OnTplhx12yOc///n8+te/9qIiAAAAAPgAKnXG74fRvHnz8sUvfjHDhw9f4fr06dMzffr0PPzww7nkkkty8803Z4899thga15zzTU588wzM2PGjBWuv/3225k1a1aee+653HbbbVm6dGlOP/30JvkdAAAAAMD6IfhdA3V1dTnqqKNy9913J0m6dOmSIUOGpFevXpk5c2ZuuOGGjB07Nm+88UYOOeSQjB07NjvssMMGV/O8887L0KFDkyQbbbRRPvvZz2bAgAHp2rVrli1bljfeeCOPPvpoRo0aVap3AAAAAKB5CH7XwFVXXVUfwPbq1Sv33ntvunTpUn//1FNPzZlnnpmLL744s2bNysknn5wxY8ZsUDVvuOGG+tC3b9++ueWWW7Lttts2OPftt9/OW2+9Vap/AAAAAGD9c8ZvI9XV1eXcc8+t/3799devEMAud+GFF2annXZKkjzwwAOlds02dc0ZM2bkm9/8ZpJkyy23zL333vu+oW+StGrVKp07d17r/gEAAACA5iH4baQxY8Zk8uTJSZKBAwemX79+Dc6rqanJaaedVv/9hhtu2GBqXnnllZk5c2aS5Pzzz0+nTp3WujcAAAAAYMMl+G2kESNG1I8POeSQVc49+OCDG1zX3DV/+9vfJklatmyZL3zhC2vdFwAAAACwYRP8NtLTTz9dP95tt91WObdr167ZaqutkiRTp07N9OnTm73m5MmT89JLLyVJ+vTpkzZt2uTFF1/MN7/5zWy77bbZeOON06lTp+yyyy75j//4j/zzn/9cq54BAAAAgOa3zoPfhQsX5he/+EWOOOKIHHbYYTnnnHPqjy/4IHn++efrx9tss81q5797zrvXNlfNxx57rH689dZb5/rrr0/fvn1z6aWX5uWXX86iRYsya9asjBs3Lj/96U+z7bbb5vrrr1+rvgEAAACA5lVbZvGTTz6ZE044IUVR5LLLLssnP/nJFe7PmTMne+21V5555pn6a3feeWd+85vfZNSoUdl5553LlF+vZs+eXT/efPPNVzt/s802a3Btc9V8d9j+9NNP589//nPq6uqy55575uijj07Xrl0zadKk3HDDDXnssceycOHCHH/88Wnbtm2OOOKIteofAAAAAGgepYLfW265Jc8880y6dOmSPfbYY6X73//+91c4rmC5GTNm5POf/3z+/ve/p1WrVmVaWG/mzZtXP27duvVq52+88cb147lz5zZ7zVmzZtWPX3755STJ0KFD86Mf/WiFeaeffnq++93v5r/+67+SJF/72tdy4IEHpm3btmvc/6osWbIk48aNa/Bet27d0q1btyatBwAAAAAfJqWOenj00UdTFEX233//FEWxwr25c+fmt7/9bYqiyNZbb51hw4Zl/Pjx+drXvpYk+cc//pHf/e53ZcqzBpYtW7bC9wEDBqwU+iZJURS58MILs8suuyR5J6RfF/+cpk2bll122aXBz+WXX97k9QAAAADgw6TUjt9JkyYlSYNHNowYMSKLFi1KURT57W9/m3333TdJctlll+WRRx7J008/nT/96U/5yle+UqaF9aZdu3b1u2YXLVqUdu3arXL+woUL68ft27dv9prv/X7yySe/73NatGiRIUOG5IknnkiS3HvvvaucvzY6d+6cESNGNHjPbl8AAAAAKKdU8Pvmm28maTiou//+++vvLQ99lzvqqKPy1FNP5amnnipTfr3aZJNN6kPYN998c7Uh7IwZM1ZY29w1N9100xW+L9/R+3523XXX+vHyoyGa0kYbbZR+/fo1+XMBAAAAgJJHPbz11lvvPKTFyo95+OGHUxTFSqFvkmy99dZJkunTp5cpv1717Nmzfvzqq6+udv6757x7bXPV/Nd//dcVvnfs2HGVz3r3/Tlz5qy2NgAAAACw4SgV/LZp0ybJygHuW2+9Vb+bt3///iutW/6isrq6ujLl16sdd9yxfvzYY4+tcu7UqVPzxhtvJHnnSIMtttii2Wv27t07tbX//wbv5aH9+3n3/dWFxAAAAADAhqVU8NujR48kyYMPPrjC9TvuuKP+ZWJ77rnnSuuWH0nwQQoUDzrooPrx+51Nu9xdd91VPz7kkEM2iJobb7xx9t577/rvy8/vfT+PP/54/XhtdywDAAAAAM2jVPC71157pVKpZPjw4fnb3/6W5J1jAS666KIkyUc+8pH06dNnpXXPPPNMkmSbbbYpU369GjhwYLp27Zokue+++zJu3LgG59XV1eVXv/pV/ffBgwdvMDW/9KUv1Y8vv/zy9627bNmyXHnllfXfDz744DXqGwAAAABoXqWC3yFDhqRFixZZtGhRdt999+yxxx75l3/5lzzzzDMpiiJDhgxpcN29996boijy8Y9/vEz59aqmpibnnHNO/ffjjz8+06ZNW2ne9773vYwfPz7JO7udDzzwwAafd80116QoihRFscJO3HVZ80tf+lJ69eqVJBkzZkzOPffcleZUKpWcddZZ9TuCe/TokaOPPrrB5wEAAAAAG6ba1U95fx//+MczdOjQDB06NEuWLMljjz2WSqVSf+873/nOSmuefvrpPPfccymKIp/61KfKlF/vhgwZkmHDhmX06NGZMGFC+vbtmyFDhqRXr16ZOXNmbrjhhvpjLzbZZJNV7qptjpo1NTW59tpr8+lPfzrz5s3Lj370o4wePTpf+MIX0rVr10yaNCl/+MMf6s8TbtmyZX7/+99no402Kv07AAAAAID1p1TwmyQ//OEP07dv31x55ZV56aWX0rZt2xxwwAH53ve+l4033nil+ZdcckmSd3aWvt/O1A1VbW1tbr311hx77LG54447MmXKlJx//vkrzevevXtuuumm9O7de4Orueuuu+bOO+/MF7/4xUycODFjx47N2LFjV5rXuXPn3HTTTQ2+nA8AAAAA2LCVDn6T5LDDDsthhx3WqLlXXHFFrrjiiqYo2yzat2+fP//5z7n99ttz3XXX5bHHHsu0adPSvn37/Mu//EuOOOKInHzyyU364rqmrjlgwIBMmDAhV111VYYNG5YXX3wxM2fOTMeOHdOrV68cdthhOfnkk9OuXbsm+w0AAAAAwPpTVJafzQDrQffu3TNp0qRsueWWmThxYnO3A1Ca/78GAHwYLP/fPBtv1i2fvXZ8c7fzofDnE3bKwhmT/e9MYK2VerkbAAAAAAAbniY56mG5xx57LCNHjsyzzz6bmTNnZsmSJbnnnntWmPPmm29m8eLFad26dTp16tSU5QEAAAAASBMFvy+99FJOOumkFV4SVqlUUhTFSnMvuOCC/PKXv8wWW2yRSZMmpaampilaAAAAAADg/5Q+6mHcuHHZddddM3bs2FQqlfrP+znllFNSqVQyffr0jBo1qmx5AAAAAADeo1Twu3Dhwhx++OGZM2dOampqcvbZZ+f555/PzTff/L5rtt122+y0005JktGjR5cpDwAAAABAA0oFv1deeWUmTpyYoihy00035cc//nG22267bLTRRqtct9dee6VSqeTxxx8vUx4AAAAAgAaUOuP39ttvT1EUOfjggzNo0KBGr9thhx2SvHM2MAAAALBhmzZtWpJk4ayp+fMJOzVvMx8SC2dNTfL//70HWFOlgt8JEyYkSQ499NA1WtepU6ckyezZs8uUBwAAANaDurq6dwbLlmXhjMnN28yHTP3fe4A1VCr4nTVrVpKkc+fOa7RuVS9/AwAAADYsNTU1WbZsWdKiRTbetEtzt/OhsHDW1GTZstTU1DR3K8AHVKngt2PHjpkxY0bmzJmzRusmTpyYJNlss83KlAcAAADWg86dO2fSpEnZeNMu+ey145u7nQ+FP5+wUxbOmLzGm+0Aliv1crcePXokSZ544ok1WnfPPfckSXr16lWmPAAAAAAADSgV/O67776pVCq56aabGr3rd/z48Rk5cmSKosh+++1XpjwAAAAAAA0oFfwOGTIktbW1mTlzZk444YQsXbp0lfNfeeWVHHnkkalUKmnTpk1OOumkMuUBAAAAAGhAqeD3Yx/7WM4888xUKpUMHz48O+20U6666qq88sor9XOeffbZ3H333fnWt76Vvn375pVXXklRFBk6dKgzfgEAAAAA1oFSL3dLkv/8z//MG2+8kd///vf5+9//npNPPjlJUhRFkmTHHXesn1upVJIkJ510Us4888yypQEAAAAAaECpHb/JOwHv9ddfn9/85jfp2rVrKpXK+3622GKLXHrppbnyyiuboncAAAAAABpQesfvcieffHJOPPHEjBo1KmPGjMlrr72W2bNnp127dunevXsGDhyYgw8+OG3atGmqkgAAAAAANKDJgt8kadmyZT7zmc/kM5/5TFM+FgAAAACANVD6qAcAAAAAADYsgl8AAAAAgCpTKvidNWtWPv/5z+eII47Ivffe26g19957b4444ogcddRRmTdvXpnyAAAAAAA0oNQZvzfddFOGDRuWtm3b5rrrrmvUmt133z1/+ctfMn/+/Bx00EH5yle+UqYFAAAAAADeo9SO31GjRiVJDjzwwLRr165Ra9q1a5eDDz44lUold999d5nyAAAAAAA0oFTw+7e//S1FUaR///5rtG6PPfaoXw8AAAAAQNMqFfxOnjw5SdK9e/c1WveRj3wkSfLPf/6zTHkAAAAAABpQKvitVCpJkmXLlq3RuuXzly5dWqY8AAAAAAANKBX8br755kmSl19+eY3WvfLKK0mSTp06lSkPAAAAAEADSgW/O+64YyqVSoYNG7ZG64YNG5aiKLLDDjuUKQ8AAAAAQANKBb8HHnhgkuTJJ5/M//7v/zZqzVVXXZVx48YlSQ4++OAy5QEAAAAAaECp4PcrX/lK/XENp5xySn7+85+nrq6uwbl1dXW5+OKLc+qppyZJOnbsmCFDhpQpDwAAAABAA2rLLG7Xrl1+/etf59hjj83SpUvzne98JxdffHEOPvjg9OrVK+3atcu8efPy7LPPZsSIEZkyZUoqlUqKosivf/3rdOzYsal+BwAAAAAA/6dU8JskgwcPzptvvpkzzjgjS5cuzZQpU3L11Vc3OLdSqaS2tja/+MUvcuyxx5YtDQAAAABAA0od9bDcN7/5zTz44IP1Z/5WKpWVPklyyCGH5KGHHqo/7gEAAAAAgKZXesfvcrvvvntGjBiRN998Mw8++GAmTpyYOXPmpEOHDunevXv22muvbLbZZk1VDgAAAACA99Fkwe9ym2++eQ4//PCmfiwAAAAAAI3UJEc9AAAAAACw4RD8AgAAAABUmSY96mHZsmV5+eWXM2vWrCxatKhRawYMGNCULQAAAAAAfOg1SfD70EMP5aKLLsro0aMbHfgmSVEUWbp0aVO0AAAAAADA/ykd/F588cU566yzUqlUUqlUmqInAAAAAABKKBX8PvDAA/nOd76ToihSqVSy5ZZb5tOf/nS6d++eVq1aNVWPAAAAAACsgVLB73//93/Xj88///z8x3/8R1q08L44AAAAAIDmVCr4ffjhh1MURQ4//PB8//vfb6qeAAAAAAAoodT23BkzZiRJPvvZzzZJMwAAAAAAlFcq+N18882TJG3btm2SZgAAAAAAKK9U8LvTTjslSV5++eWm6AUAAAAAgCZQKvj9yle+kkqlkhtvvLGp+gEAAAAAoKRSwe+gQYPy+c9/Pk899VS++93vNlVPAAAAAACUUFv2Ab/73e/SqlWrXHzxxXniiSdy+umn55Of/GT9+b8AAAAAAKxfpYLfmpqa+nGlUsl9992X++67r9Hri6LI0qVLy7QAAAAAAMB7lAp+K5XKKr8DAAAAALD+lQp+BwwYkKIomqoXAAAAAACaQKngd02OdQAAAAAAYP1o0dwNAAAAAADQtAS/AAAAAABVRvALAAAAAFBlBL8AAAAAAFWm1Mvd3m3BggW5/fbb88gjj2TixImZM2dO6urqVrmmKIrcc889TdUCAAAAAABpouD3sssuy9lnn5233nqr0WsqlUqKomiK8gAAAAAAvEvp4PfHP/5xhg4dmkqlstq5y4PexswFAAAAAGDtlDrj97nnnsvQoUOTJNtvv33uueeeLFy4MMk7Ie+f/vSnzJs3L08//XQuvPDCdOvWLUly4oknZtGiRas9CgIAAAAAgDVXasfvZZddlkqlkjZt2mTUqFHZeuutV5rTpk2b9O7dO717986QIUPyuc99Ltdcc03mz5+fG2+8sUx5AAAAAAAaUGrH7/3335+iKHLUUUc1GPq+1yabbJI//elP6dSpU/74xz9m+PDhZcoDAAAAANCAUsHv66+/niTZY489Gry/ePHila5tuummOeGEE1KpVHL99deXKQ8AAAAAQANKBb9z585NkmyxxRYrXN94441XuP9eO++8c5Lk8ccfL1MeAAAAAIAGlAp+27Ztm2Tlnb0dO3ZM8v/vCH6vpUuXJkmmTp1apjwAAAAAAA0oFfz26NEjycoBbs+ePVOpVDJ27NgG1/3tb39LkrRs2bJMeQAAAAAAGlAq+O3bt28qlUqefvrpFa4PGDAgSfLXv/41TzzxxAr3XnnllVx11VUpiiI77LBDmfIAAAAAADSgVPC79957J0nuvffeFa4ff/zxqa2tzbJly7LPPvvku9/9bq644op897vfza677pp58+YlSQYPHlymPAAAAAAADagts/izn/1sampq8o9//CMPPfRQ+vfvnyT5l3/5l5x99tk577zzMm/evFx88cUrre3Xr19OOeWUMuUBAAAAAGhAqeB3s802ywsvvJDFixenc+fOK9z70Y9+lLZt2+b888+v3+GbJEVR5Oijj85ll13mjF8AAAAAgHWgVPCbJNtss8373vvOd76T0047LQ8//HCmTJmStm3bZtddd023bt3KlgUAAAAA4H2UDn5Xp1WrVvVnAQMAAAAAsO6VerkbAAAAAAAbnlLBb4sWLVJbW5vhw4ev0bqRI0empqYmtbXrfMMxAAAAAMCHTunktVKprNd1AAAAAACsmqMeAAAAAACqTLMEvwsWLEiStG7dujnKAwAAAABUtWYJfh955JEkSefOnZujPAAAAABAVWv0Gb9PPfVUxo8f3+C9e++9N7Nnz17l+kqlkvnz52fcuHH53e9+l6Iosttuu61JrwAAAAAANEKjg99hw4blvPPOW+l6pVLJJZdcskZFK5VKiqLI17/+9TVaBwAAAADA6q3RUQ+VSmWFz/tdX92nS5cuufLKK7PPPvs0+Q8CAAAAAPiwa/SO38MPPzw9evRY4dqJJ56YoijyzW9+M/369Vvl+hYtWqRdu3bZZpttsuOOO6ampmatGgYAAAAAYNUaHfz27ds3ffv2XeHaiSeemCTZd999c9hhhzVtZwAAAAAArJVGB78Nufrqq5Nktbt9AQAAAABYf0oFvyeccEJT9QEAAAAAQBMpFfw2Vl1dXSZMmJClS5emZ8+eadu27fooCwAAAADwodSizOL58+fntttuy2233ZbXX3+9wTnXXXddunbtmp133jm77bZbOnfunLPPPjuVSqVMaQAAAAAA3kepHb+33HJLTjzxxNTU1OSVV15Z6f7dd9+dL3/5yymKoj7oXbhwYS688MLMnz8///3f/12mPAAAAAAADSi14/cvf/lLkmT33XfPVltttdL9s846K0lSqVTSt2/fHH744Wnfvn0qlUouvfTS/O1vfytTHgAAAACABpQKfp999tkURZEBAwasdG/8+PF5+umnUxRF/u3f/i1PPvlkbrvttjz22GNp27ZtKpVK/vd//7dMeQAAAAAAGlAq+H3zzTeTJD179lzp3qhRo5IktbW1Oeecc+qvb7/99jnyyCNTqVQyduzYMuUBAAAAAGhAkwS/HTp0WOnegw8+mCTZY489stlmm61wb/fdd0+SBs8FBgAAAACgnFLB77Jly5Ikb7311kr3Hn744RRFkb322mule1tssUWSZN68eWXKAwAAAADQgFLB7/KdvP/4xz9WuD5+/PjMmDEjSfLJT35ypXULFy5MkrRs2bJMeQAAAAAAGlAq+P34xz+eSqWSP/7xjytcv/baa995eIsW+dSnPrXSutdffz1J0q1btzLlAQAAAABoQKng93Of+1yS5LnnnssxxxyTu+++O//5n/+ZSy+9NEVRZL/99kvHjh1XWvfYY48lafilcAAAAAAAlFNbZvGJJ56Yn//853nxxRdz88035+abb06SVCqV1NTU5Ic//OFKaxYsWJC//OUvKYqi/iVvAAAAAAA0nVI7flu2bJmRI0dm5513TqVSqf+0adMml112Wfr377/SmhtvvDELFixIkuyzzz5lygMAAAAA0IBSO36TpEePHnniiSfyxBNP5KWXXkrbtm2z5557ZtNNN21wfuvWrTN06NAURdFgMAwAAAAAQDmlg9/ldtlll+yyyy6rnXfsscc2VUkAAAAAABpQ6qgHAAAAAAA2PIJfAAAAAIAqI/gFAAAAAKgyjTrjt6amJklSFEWWLl260vW19d7nAQAAAABQXqOC30qlskbXAQAAAABoPo0KfgcMGJCiKBp9HQAAAACA5tOo4Pe+++5bo+sAAAAAADQfL3cDAAAAAKgygl8AAAAAgCoj+AUAAAAAqDKNOuN3Verq6vLYY4/lsccey/Tp0zNjxowURZHNNtssW2yxRT7xiU9kl112SYsWMmYAAAAAgPVhrYPfSZMm5Sc/+Umuv/76zJ8/f5Vz27VrlxNPPDFnnXVWunXrtrYlAQAAAABohLXahnvzzTdn++23z2WXXZZ58+alUqms8jN37txccskl2W677XLbbbc19W8AAAAAAOBd1njH79VXX50hQ4bUh7pJsskmm+QTn/hEPvrRj2bTTTdNpVLJrFmz8tprr+XRRx/NnDlzUqlUsmDBghx99NG55ppr8qUvfanJfwwAAAAAAGsY/L766qs57bTTsmzZsiTJbrvtlh/+8If5zGc+875rKpVKhg8fnv/8z//M448/nmXLluXUU0/NXnvtlY9+9KPlugegKuy6666ZMmVKc7exViZPnlz/1+7duzdzN2uua9euefzxx5u7DQAAAJrYGgW/P/jBDzJ//vwURZEhQ4bk17/+dWprV/2Ioijyuc99LoceemhOPfXUXHnllZk3b15+8IMf5Prrry/VPADVYcqUKZk0aVJzt1HKsmXLPvC/AQAAgOrR6OB31qxZue2221IURQYOHJjf/OY3KYqi8YVqa/Ob3/wmzz//fMaMGZPbbrstv/71r9OxY8e1ahyA6lO0aJHWm3Zp7jbWyKLZb6ZSqUtR1KT1Jps3dzuNtmjW1FT+77/gAQAAoPo0Ovj985//nLfffjtFUeSnP/3pGoW+y7Vo0SIXXXRR9thjjyxatCjDhw/Pcccdt8bPAaA6td60Sz577fjmbuND4c8n7JSFMyY3dxsAAACsIy0aO/GJJ55IkvTu3Tu77777Whfcfffd06dPnyRxpiAAAAAAwDrQ6OD3ySefTFEU6d+/f+mie+65ZyqVSp588snSzwIAAAAAYEWNDn6Xv7Bm+W7dMnr37p0kmThxYulnAQAAAACwokYHv3PmzEmSbLLJJqWLLn/G8mcCAAAAANB01jj4bd++femiy58xd+7c0s8CAAAAAGBFjQ5+lyxZkiQpiqLJii9durTJngUAAAAAwDsaHfwCAAAAAPDBsMbBb1Pu+AUAAAAAoOnVrumCww8/fB20AQAAAABAU1nj4DdJKpVKqaJ2DQMAAAAArDtrFPyWDXyb+jkAAAAAAKys0cHvsmXL1mUfAAAAAAA0kTV+uRsAAAAAABs2wS8AAAAAQJUR/AIAAAAAVBnBLwAAAABAlRH8AgAAAABUGcEvAAAAAECVEfwCAAAAAFQZwS8AAAAAQJWpbe4GAAAAgA+GRbOm5s8n7NTcbayRRbPfTKVSl6KoSetNNm/udhpt0aypzd0C8AEn+AUAAAAapbJsWRbOmNzcbayVSj64vQOsDcEvAAAAsEpdu3Zt7hbW2uTJk7Ns2bK0aNEi3bp1a+521tgH+e890LwEvwAAAMAqPf74483dwlrr3r17Jk2alG7dumXixInN3Q7AeuPlbgAAAAAAVUbwCwAAAABQZQS/AAAAAABVRvALAAAAAFBlBL8AAAAAAFVG8AsAAAAAUGUEvwAAAAAAVUbwCwAAAABQZQS/AAAAAABVRvALAAAAAFBlBL8AAAAAAFVG8AsAAAAAUGUEvwAAAAAAVUbwCwAAAABQZQS/AAAAAABVRvALAAAAAFBlBL8AAAAAAFVG8AsAAAAAUGUEvwAAAAAAVUbwCwAAAABQZQS/AAAAAABVRvALAAAAAFBlBL8AAAAAAFVG8AsAAAAAUGUEvwAAAAAAVUbwCwAAAABQZQS/AAAAAABVRvALAAAAAFBlBL8AAAAAAFVG8AsAAAAAUGUEvwAAAAAAVUbwCwAAAABQZQS/AAAAAABVRvALAAAAAFBlBL8AAAAAAFVG8LuWhg8fnqOOOio9evRI69at07lz5/Tv3z8/+9nPMmfOnA9czb/+9a9p0aJFiqJIURTp0aNH0zQNAAAAAKx3tc3dwAfNvHnz8sUvfjHDhw9f4fr06dMzffr0PPzww7nkkkty8803Z4899vhA1FywYEG++tWvplKpNEm/AAAAAEDzEvyugbq6uhx11FG5++67kyRdunTJkCFD0qtXr8ycOTM33HBDxo4dmzfeeCOHHHJIxo4dmx122GGDr/kf//EfeeWVV9K2bdvMnz+/VL8AAAAAQPNz1MMauOqqq+oD2F69euVvf/tbzj///BxzzDE59dRT8+CDD+bf//3fkySzZs3KySefvMHXfOihh/LrX/86SfLjH/+4dL8AAAAAQPMT/DZSXV1dzj333Prv119/fbp06bLSvAsvvDA77bRTkuSBBx7IqFGjNtiaixYtykknnZRly5bl85//fA4//PC17hUAAAAA2HAIfhtpzJgxmTx5cpJk4MCB6devX4Pzampqctppp9V/v+GGGzbYmkOHDs3zzz+fTTbZpH7XLwAAAADwwSf4baQRI0bUjw855JBVzj344IMbXLch1Xz88cdz8cUXJ0kuuuiidO3adS27BAAAAAA2NILfRnr66afrx7vtttsq53bt2jVbbbVVkmTq1KmZPn36BlVzyZIlOemkk1JXV5e99947X/3qV9eqPwAAAABgwyT4baTnn3++frzNNtusdv6757x77YZQ88c//nGefvrptG7dOldccUWKolir/gAAAACADVNtczfwQTF79uz68eabb77a+ZtttlmDa5u75t/+9rdccMEFSZJzzjkn22233Vr1VtaSJUsybty4Bu9169Yt3bp1W88dAQAAAED1EPw20rx58+rHrVu3Xu38jTfeuH48d+7cDaLm0qVLc9JJJ2XJkiXp27dvvvOd76xVX01h2rRp2WWXXRq8N3To0PzoRz9avw0BAAAAQBUR/H6IXHTRRRk3blxqampy1VVXpba2+f7xd+7c+X1fQme3LwAAAACUI/htpHbt2mXWrFlJkkWLFqVdu3arnL9w4cL6cfv27Zu95t///vecd955SZLTTjstu+6661r11FQ22mij9OvXr1l7AAAAAIBq5eVujbTJJpvUj998883Vzp8xY0aDa5uj5rJly3LSSSfl7bffTo8ePXL++eevVT8AAAAAwAeDHb+N1LNnz7z66qtJkldffTU9evRY5fzlc5evbc6aTz/9dB555JEkSe/e/1979x6lVVn2D/wahpMDCDocZRDUNA6CZSgKKqAZqKklGIqaB8IwPJtKZh6WvpYJiKYWSoC+JkHJixSIR/CUomhYKIUKKGcERHBgOAz79weL5zfInDjOsPl81nK1Z/a97+t6ntbS/XxnP/fdJu6///5iry+6IdyXX34Zd999d+bnG2+8MWrUqLG9LwEAAAAAqACC33Jq27ZtTJo0KSIi3nnnnejatWuJY5csWRLz5s2LiM1r2TZo0KBCayZJkjmeMGFCTJgwoczaK1eujF/96leZn6+88krBLwAAAADsJSz1UE7du3fPHJe0KdkWEydOzByffvrpe1VNAAAAAGDvl5UUfRyUEhUWFkZeXl4sXrw4IiLefffdYjcnKywsjPbt28f06dMjImLSpEnRrVu3vaLm3Llz45BDDomIiObNm8fcuXN3qO/S5OXlxYIFC6Jp06Yxf/78XT4/sHeqXr16bNiwIaJKldjvgEYV3c4+Ye0XSyI2bYpq1arF+vXrK7odAIDdxudQYF9lqYdyys7Ojttuuy1+9rOfRUTEj3/843j55ZejYcOGW40bMGBAJoDt1KlTiQHsyJEj49JLL42IiM6dO8eUKVN2e02AyqqwsHDzwaZNsXb5ooptZh+Tee8BAABIFcHvdujbt2/83//9X7zwwgvxwQcfxFFHHRV9+/aN1q1bx4oVK2LUqFHx+uuvR0REvXr1YujQoXtlTYA9LTs7OzZt2uSJ3z1oyxO/2dnZFd0KAAAAu4HgdztUrVo1nn766ejdu3f8/e9/j8WLF8ddd921zbi8vLwYPXp0tGnTZq+sCbCnNWzYMBYsWBD7HdAoznx8ekW3s0/428XfirXLF23zLRIAAADSweZu26lOnTrxt7/9LcaNGxfnnHNONGvWLGrUqBH169ePDh06xL333hszZsyIjh077tU1AQAAAIC9l83d2KMsqg8UZ8u/G/bLbeKJ3z1kyxO//n0MAKSdz6HAvsoTvwAAAAAAKSP4BQAAAABIGcEvAAAAAEDKCH4BAAAAAFJG8AsAAAAAkDKCXwAAAACAlBH8AgAAAACkjOAXAAAAACBlBL8AAAAAACkj+AUAAAAASBnBLwAAAABAygh+AQAAAABSRvALAAAAAJAygl8AAAAAgJQR/AIAAAAApIzgFwAAAAAgZQS/AAAAAAApI/gFAAAAAEgZwS8AAAAAQMoIfgEAAAAAUkbwCwAAAACQMoJfAAAAAICUEfwCAAAAAKSM4BcAAAAAIGUEvwAAAAAAKSP4BQAAAABIGcEvAAAAAEDKCH4BAAAAAFJG8AsAAAAAkDKCXwAAAACAlBH8AgAAAACkjOAXAAAAACBlBL8AAAAAACkj+AUAAAAASBnBLwAAAABAygh+AQAAAABSRvALAAAAAJAygl8AAAAAgJQR/AIAAAAApIzgFwAAAAAgZQS/AAAAAAApI/gFAAAAAEgZwS8AAAAAQMoIfgEAAAAAUkbwCwAAAACQMoJfAAAAAICUEfwCAAAAAKRM1YpuAAC2KPhiSfzt4m9VdBvbpWDlskiSwsjKyo6a9epXdDvlVvDFkopuAQAAgN1I8AtApZFs2hRrly+q6DZ2SBJ7b+8AAACkj+AXgArXuHHjim5hhy1atCg2bdoUVapUiSZNmlR0O9ttb37vAQAAKJngF4AKN23atIpuYYfl5eXFggULokmTJjF//vyKbgcAAAAiwuZuAAAAAACpI/gFAAAAAEgZwS8AAAAAQMoIfgEAAAAAUkbwCwAAAACQMoJfAAAAAICUEfwCAAAAAKSM4BcAAAAAIGUEvwAAAAAAKSP4BQAAAABIGcEvAAAAAEDKCH4BAAAAAFJG8AsAAAAAkDKCXwAAAACAlBH8AgAAAACkjOAXAAAAACBlBL8AAAAAACkj+AUAAAAASBnBLwAAAABAygh+AQAAAABSRvALAAAAAJAygl8AAAAAgJQR/AIAAAAApIzgFwAAAAAgZQS/AAAAAAApI/gFAAAAAEgZwS8AAAAAQMoIfgEAAAAAUkbwCwAAAACQMoJfAAAAAICUEfwCAAAAAKSM4BcAAAAAIGUEvwAAAAAAKSP4BQAAAABIGcEvAAAAAEDKCH4BAAAAAFJG8AsAAAAAkDKCXwAAAACAlBH8AgAAAACkjOAXAAAAACBlBL8AAAAAACkj+AUAAAAASBnBLwAAAABAygh+AQAAAABSRvALAAAAAJAygl8AAAAAgJQR/AIAAAAApIzgFwAAAAAgZQS/AAAAAAApI/gFAAAAAEgZwS8AAAAAQMoIfgEAAAAAUkbwCwAAAACQMoJfAAAAAICUEfwCAAAAAKSM4BcAAAAAIGUEvwAAAAAAKSP4BQAAAABIGcEvAAAAAEDKCH4BAAAAAFJG8AsAAAAAkDKCXwAAAACAlBH8AgAAAACkjOAXAAAAACBlBL8AAAAAACkj+AUAAAAASBnBLwAAAABAygh+AQAAAABSRvALAAAAAJAygl8AAAAAgJQR/AIAAAAApIzgFwAAAAAgZQS/AAAAAAApI/gFAAAAAEgZwS8AAAAAQMoIfgEAAAAAUkbwCwAAAACQMoJfAAAAAICUEfwCAAAAAKSM4BcAAAAAIGUEvwAAAAAAKSP4BQAAAABIGcEvAAAAAEDKCH4BAAAAAFJG8AsAAAAAkDKCXwAAAACAlBH8AgAAAACkjOAXAAAAACBlBL8AAAAAACkj+AUAAAAASBnBLwAAAABAygh+AQAAAABSpmpFNwAAu1v79u1j8eLFu2XuRYsWZf43Ly9vt9Ro3LhxTJs2bbfMDQAAQDoJfgFIvcWLF8eCBQt2a41Nmzbt9hoAAABQXoJfAFKvcePGu23upUuXRmFhYWRnZ0fDhg13S43d2T8AAADpJPgFIPUskwAAAMC+xuZuAAAAAAApI/gFAAAAAEgZwS8AAAAAQMpY4xcAAACoUO3bt4/FixfvlrkXLVqU+d+8vLzdUqNx48b2lQAqHcEvAAAAUKEWL14cCxYs2K01Nm3atNtrAFQmgl8AAACgQjVu3Hi3zb106dIoLCyM7OzsaNiw4W6psTv7B9hRgl8AAACgQlkmAWDXs7kbAAAAAEDKCH4BAAAAAFJG8AsAAAAAkDKCXwAAAACAlBH8AgAAAACkjOAXAAAAACBlBL8AAAAAACkj+AUAAAAASBnBLwAAAABAygh+AQAAAABSRvALAAAAAJAygl8AAAAAgJQR/AIAAAAApIzgFwAAAAAgZQS/AAAAAAApI/gFAAAAAEgZwS8AAAAAQMoIfgEAAAAAUkbwCwAAAACQMoJfAAAAAICUEfwCAAAAAKSM4BcAAAAAIGUEvwAAAAAAKSP4BQAAAABIGcHvDho/fnyce+650aJFi6hZs2Y0bNgwOnbsGPfdd1+sWrWqUtZMkiTeeuutuPvuu+OMM86IFi1axH777Rc1a9aMgw46KLp37x4PPPBArFy5crf0DwAAAADsGVlJkiQV3cTe5KuvvooLLrggxo8fX+KYZs2axZgxY+K4446rNDVnzZoVp5xySsyfP7/Merm5uTF06NDo0aPHDvdckry8vFiwYEE0bdq0XL0AAAAAANtP8LsdCgsL4/vf/35MmjQpIiIaNWoUffv2jdatW8eKFSti1KhR8cYbb0RExAEHHBBvvPFGtGrVqlLUfOutt+L444+PiIgaNWpE165do1OnTnHwwQdHjRo14uOPP44//elPMXPmzIiIqFKlSowaNSp+9KMf7VT/Xyf4BQAAAIDdT/C7HYYOHRr9+vWLiIjWrVvHyy+/HI0aNdpqzM9//vMYNGhQRESceOKJ8eqrr1aKmm+99Vb86Ec/ihtvvDEuvPDCOOCAA7YZs3Hjxrj22mvj4YcfjoiIAw88MD755JOoV6/eTr2GogS/AAAAALD7CX7LqbCwMJo1axaLFi2KiIh33303jj766GLHtW/fPqZPnx4REc8991x873vfq/Ca+fn5Ua1atahevXqpNZMkifbt28d7770XEREjRoyISy65ZIf6L47gFwAAAAB2P5u7ldOrr76aCWA7d+5cbAAbEZGdnR1XX3115udRo0ZVipq1atUqM/SNiMjKyopzzz038/O//vWv7W0bAAAAAKhggt9yevbZZzPHp59+eqljTzvttGKv2xtqRkTsv//+meO1a9fu1FwAAAAAwJ4n+C2nf//735njY445ptSxjRs3jmbNmkVExJIlS+Lzzz/fa2p+vW7z5s13eB6AfcGiRYvijjvuyHxDAwCAysX9GrCvEvyW03//+9/M8SGHHFLm+KJjil5b2Wt+8cUXMXr06MzPZ5xxxg7NA7CvWLRoUdx5550+SAAAVFLu14B9VdWKbmBvsXLlysxx/fr1yxyfm5tb7LWVveYNN9wQX3zxRUREnHXWWdG2bdsdmqcsGzZsyGwg93VNmjSJJk2a7Ja6AAAAALAvEPyW01dffZU5rlmzZpnj99tvv8zx6tWr94qaf/jDH2LEiBEREVGvXr144IEHtnuO8lq6dGl85zvfKfbc7bffHnfcccduqw0AAAAAaSf4JSIiJkyYEFdddVVERFSpUiVGjBgRLVq02G31GjZsWOImdJ72BQAAAICdI/gtp9q1a2eWQCgoKIjatWuXOn7t2rWZ4zp16lTqmi+++GL07NkzNm7cGFlZWfHoo4/GD37wgx3qubyqVasWRx999G6tAQAAAAD7Kpu7lVO9evUyx8uWLStz/PLly4u9trLVfPnll+Oss86KgoKCyMrKit///vfRp0+f7W0VAAAAAKhEPPFbTt/85jdjzpw5ERExZ86cMpdB2DJ2y7WVsebLL78cZ555ZuZJ4Ycffjh++tOf7lCv5bV06dKI2Lyral5e3m6tBbAnbNiwISIiTjvttKhWrVoFdwMAwNe5XwPSqnHjxjFt2rQSzwt+y6lt27YxadKkiIh45513omvXriWOXbJkScybNy8iNq9l26BBg0pXc0vou2bNmoiI+N3vfhdXXHHFDvW5PQoLCyMiYtOmTbFgwYLdXg9gT9nyhy0AACon92vAvkbwW07du3eP++67LyIinn322bjppptKHDtx4sTM8emnn17pan499H3ggQfiyiuv3OE+t0fNmjWjoKAgsrOzo2HDhnukJgAAAACkTePGjUs9n5UkSbKHetmrFRYWRl5eXixevDgiIt59991iNycrLCyM9u3bx/Tp0yMiYtKkSdGtW7dKU3PKlClxxhlnZELfIUOGxDXXXLND/QEAAAAAlZPN3copOzs7brvttszPP/7xj4v9msiAAQMyAWynTp1KDGBHjhwZWVlZkZWVFV26dNkjNV955RWhLwAAAADsAzzxux02btwYp59+erzwwgsRsflx6r59+0br1q1jxYoVMWrUqHj99dcjIqJevXrx+uuvR5s2bYqda+TIkXHppZdGRETnzp1jypQpu7Xm9OnT44QTToj8/PyIiOjWrVv069evzNdcv379OOGEE8ocBwAAAABUHoLf7bR69ero3bt3/P3vfy9xTF5eXowePTo6duxY4pjyBr+7qmbRetujrN4AAAAAgMrHUg/bqU6dOvG3v/0txo0bF+ecc040a9YsatSoEfXr148OHTrEvffeGzNmzCg19N0bagIAAAAAey9P/AIAAAAApIwnfgEAAAAAUkbwCwAAAACQMoJfAAAAAICUEfwCAAAAAKSM4BcAAAAAIGUEvwAAAAAAKSP4BQAAAABImaoV3QAAAABAZbJx48YYNGhQrF27NmrWrBnXX399VK9evaLbAtgugl8A2IMeeuihWLZsWVSpUiWuvvrqqFevXkW3BADA1wwYMCAGDRoUVapUiT//+c9CX2CvZKkHANhDHnroobjqqqvizjvvjFq1agl9AQAqoXHjxsWgQYMiImLw4MFx7rnn7rK5s7KyIisrK7p06VLs+UsuuSQzZu7cubusLrBvEvwCwB7w9ttvxw033BAREddee23meFdo0aJFZGVlRYsWLYo9f8cdd2Q+QEyZMmWX1QUA9l75+fnx5JNPxqWXXhpHHnlkNGrUKKpXrx5169aNww47LHr06BH3339/LFy4sKJb3aNmz54dl1xySURE3HjjjXHNNddUbEMAO0HwC8BOmTt3biZU3Nl/ttxkp82KFSviRz/6Uaxfvz569eoVgwcPruiWAIB9VGFhYQwcODBatGgRF110UYwcOTI++OCDWLp0aWzYsCFWrVoVs2fPjrFjx8b1118fzZo1i/POOy8++eSTim59tysoKIiePXvGl19+GRdccEHce++9Fd0SwE6xxi8A7EZJksRFF10Un376aXTp0iWeeOKJyMrKqui2AIB90MqVK+O8886L5557LvO7b3zjG9G9e/do1apV1K9fP/Lz82PhwoUxZcqUePXVV2P9+vUxevToKCgoiHHjxlVc83vA1VdfHf/85z/jlFNOiREjRlTIPdvIkSNj5MiRe7wukE5ZSZIkFd0EAHuvNWvWxPPPP1/i+RkzZsSvfvWriIho06ZN3H333SWOPfjgg+Poo4/e5T1WpHvuuSd++ctfRtu2beO1116LunXr7vIaLVq0iE8//TSaN29uLTgAoFgbN26Mk08+OV577bWIiGjUqFE89NBD0aNHjxIDzmXLlsXAgQPjd7/7XZx66qmpD373hC3vdefOnS3BBex2nvgFYKfk5OTED37wgxLPF93ArH79+qWOTaNbbrklbrnllopuAwDYx91yyy2Z0Pfggw+OV155pcT9AbaoX79+/OY3v4mLLroonnrqqT3QJQC7kjV+AQAAIMUWLlwYDz74YERsfuL0T3/6U5mhb1Ft2rSJ//mf/yn23Nq1a+Ohhx6KU089NZo0aRLVq1eP3NzcOOaYY+LWW28tc3O4kSNHZvZ72LLEwfTp06NPnz5x6KGHxn777Rd5eXnxwx/+MN5+++1trn/22Wfj7LPPjubNm0eNGjUiLy8vLr300pgzZ065X9/06dPjmmuuiaOOOioOPPDAqFGjRhx00EFxxhlnxPDhw2Pjxo3lmmfmzJlx+eWXxyGHHBI1a9aMRo0aRefOneOxxx6LwsLCcs1xySWXZN6Psr7JVVBQEEOHDo3vf//70axZs6hZs2bUrVs3jjzyyLj66qtj1qxZ5aoJpFgCALvR5MmTk4hIIiLp3Llzqedvv/32JEmSZNasWcn111+ftG7dOqlbt+5W57aYN29e8vDDDye9evVKWrVqldSuXTupWrVqkpubmxx77LHJgAEDks8++6zcfc6dOze56qqrksMPPzypWbNmkpubm3To0CEZMmRIsnbt2iRJkqR58+ZJRCTNmzcvc76PP/44ufnmm5P27dsn9evXT6pVq5Y0bNgw6dq1azJkyJAkPz+/XH3Nmzcvufbaa5Mjjjgi2W+//TKvb+DAgcmaNWvK1dftt9+eeY8nT55car2NGzcmTz75ZNKzZ8+kefPmSU5OTlKrVq3kiCOOSH7yk58k77zzTrn6BgAqj1/+8peZe4HTTjttl8379ttvJ82aNcvMXdw/OTk5yR//+McS5xgxYkRm7IgRI5KHH344qVatWrFzValSJXniiSeSJEmS9evXJ3369Cmx7v7775+8/fbbpfZfUFCQXHbZZUlWVlapr6FNmzbJJ598Uupcjz32WFK9evUS5zjppJOSlStXlnpfnCRJcvHFF2fGzJkzp8R6U6ZMSZo2bVpq39nZ2ck999xTat9AulnqAYBK5cknn4zLL7881q5dW+KYKVOmxMknnxxJMcvUL1++PJYvXx5vv/12DB48OB555JHo06dPqTX/+te/xiWXXBL5+fmZ3xUUFMTy5ctj6tSpMWLEiJgwYUK5+t+0aVPceuutcd99923zdMjSpUtj6dKlMXny5Bg4cGCMGzcuvvOd75Q414QJE+L888+P1atXZ363du3azOsbOXJkTJw4sVx9lceMGTPi3HPPjf/85z/bnJs1a1bMmjUrhg0bFldeeWUMGTIksrOzd1ltAGD3mTRpUub44osv3iVz/utf/4quXbtm7p9at24dF110URxyyCGxYsWKGDduXDz//POxZs2a6NOnTyRJUuY92YQJE+Lpp5+O3Nzc6NOnT7Rr1y42bNgQEydOjDFjxsSmTZvisssui+OPPz4efPDB+OMf/xht2rSJCy+8MFP38ccfj6lTp8aqVauid+/e8cEHH0T16tW3qbVx48bo3r17Zp3dgw46KM4777xo165d5OTkxPz582Ps2LHx+uuvxwcffBAnnXRS/POf/4wGDRpsM9fYsWPj8ssvz9ybnnzyydGjR4/Izc2N2bNnx+OPPx6vvvpqXHbZZTv5rm+25SnnDRs2RJUqVaJ79+7x3e9+N5o2bRoFBQUxbdq0eOKJJ+LLL7/MLDn2i1/8YpfUBvYyFRw8A5By2/PE76mnnppUrVo1qVatWnLxxRcnw4YNS0aNGpXcddddyeOPP5655tlnn00iIvnmN7+Z9OvXL3nwwQeTp556KhkzZkzy4IMPJj179kyqVq2aRESSlZWV/O1vfyu1vy1jIyI59thjk/vvvz8ZPXp0MmTIkOS4445LIiLp1KlT5qmK0p74vfDCCzNzHXjggckVV1yRDBs2LBkzZkzy0EMPJaeddlrmfJ06dZL//ve/xc7z5ptvbvXUSPv27ZPBgwcnf/7zn5PBgwcn7du3TyIi6dChQ3LQQQft9BO/7733XlKnTp3MuBNPPDH5zW9+k4waNSr505/+lNx8881Jo0aNMucvv/zyEt8DAKDy+Oqrr5IqVapk/hs+b968nZ6zsLAwOfLIIzNz/uQnP0k2bNiwzbhhw4ZlnqbNyckp9gnWok/8RkRy9NFHJ59//vk24+68886txmRlZSV9+/ZNNm7cuNW4DRs2JCeffHJm7JgxY4p9DQMGDMiM6du3b+YbXl/3wAMPZMZdcMEF25xfuXJl0qBBg8yY+++/f5sx69atS84999ytXueOPvG7cOHC5MADD0wiImnYsGHy5ptvFjvP/PnzM/8fZWdnJzNnzix2HJBugl8AdqvtCX633MC+//77pc45d+7cZPr06aWO+ec//5k0bNgwiYjk8MMPTzZt2rTNmA0bNiSHHXZYpvbNN9+8zbhNmzYlt95661Y9lhSw/uEPf8iMOfPMM5Mvvvii2HFPP/10Jmzu1KnTNuc3btyYtGrVKjPXddddlxQWFm41prCwMLnuuuvK1VdZwW9+fn5y6KGHZj6UjR8/vth5Vq5cmXTt2jUz1wsvvFDsOACg8vj4448z/+2uUaPGLplz/PjxmTnbtWu3TfhaVL9+/TJjr7322m3OFw1+q1evnsyePbvYedauXbvVH6mPPPLIYsPmJEmS1157LTPusssu2+b8kiVLkpo1ayYRkXz3u98t8/X27t07E6DOnz9/q3NFg+GePXuWOEd+fn5y8MEH73TwW/T+79VXXy2175kzZybZ2dlJRCT9+vUr83UC6WNzNwAqlaFDh0a7du1KHdO8efM46qijSh3zrW99K+65556IiPjoo4/iH//4xzZjxo8fH5988klERHTs2DF+/etfR1ZW1lZjsrKy4q677ooTTjih1Hrr1q2LO++8MyIiWrVqFX/961+jXr16xY4955xz4qabboqIiDfeeCOmTp261fkJEybEzJkzIyLi2GOPjUGDBkWVKlv/J7tKlSoxaNCgOPbYY0vtqzyGDRsWs2fPjojN7/+ZZ55Z7Li6devGX/7yl9h///0jImLQoEE7XRsA2L2WL1+eOS7p3mR7jR07NnN8ww03lLr804ABAzL3V0WvK873v//9OOSQQ4o9V7Nmza2WyOrXr19UrVr86pXHHXdcVKtWLSIiPvzww23Ojx49OgoKCiIi4sYbbyy1p4j/vzxGYWFhvPTSS1ud+/p7UZKcnJzo379/mbVKkyRJPPHEExERcfzxx8eJJ55Y6viWLVtm7hWfe+65naoN7J2s8QtApdG8efM4++yzd9l8RcPat956Kzp16rTV+XHjxmWOr7322m1C36Kuu+66eP3110s8//zzz8eiRYsycxW3llxRF198cSaYfu6556JDhw6Zc0U/QFx33XUl9pWVlRU33HBD9OrVq9RaZXn88ccjIqJp06bRu3fvUsfm5ubGGWecEaNGjYopU6bEunXrokaNGjtVHwDYfZJi9kTYWUX/aP29732v1LHNmzePli1bxsyZM+Ozzz6LRYsWRZMmTYode9xxx5U6V+PGjTPHpf3xu2rVqpGbmxuLFy+OL774Ypvzr776auZ4yZIlW90TFmfBggWZ46JBcpIkMW3atIiIqF27dpl/kD/llFNKPV+WDz/8MBPkH3DAAWX2HRGZUH7OnDlRUFAQNWvW3KkegL2L4BeASqNTp06lhq9fN3369HjyySfjzTffjI8++ihWrVoV69atK3bs/Pnzt/ndO++8kznu2rVrqbXKOl/0A8Tq1avLvBHfsGFD5vjrT6K8/fbbmeOyPiDs7AeIVatWxfTp0yMiokmTJjF+/Pgyr9nyHhcUFMScOXOiZcuWO9UDALD75ObmZo5Xrly5S+bc8sfuOnXqbBXGluSII47IfJuptOC3aK/FKfrH5vKO3fJkb1Fz587NHP/4xz8udZ6vW7FiReb4yy+/zGxud+ihh27zDa2v+8Y3vrFdtb6uaN8TJ07c7k1+V6xYEQcddNBO9QDsXQS/AFQaeXl55Rq3cePG6N+/fzz22GPlfopl1apV2/xu4cKFERGx//77R/369Uu9/oADDoh69eqV+IGp6I34z3/+83L1tEXRDxBf76u4naOLys3NLbWvssybNy82bdoUERHTpk2LH/7wh9t1/dd7BwAql8aNG0eVKlVi06ZNsW7dupg/f36577lKsnr16oiIqFWrVrnG165de5tri1NWcLqjY79uZwLw9evXZ46/+uqrzHFOTk6Z15b3/SrJzgb3RXsH9g2CXwAqjf32269c46655pp49NFHIyKiWrVq0b179zj22GMjLy8vatWqlVnTbenSpfHTn/40IjavyfZ1W57QKM+NesTmm/WSbrh31QeIiP//IWJX9FUWHyAAIN1q164d3/72t+Pdd9+NiM37C+zsMlF16tSJlStXZu6lylI0IK1Tp85O1d4VigbRq1at2uGeis6zZs2aMseX9/0qT73rr7/efgtAmQS/AOxV5s2bF3/4wx8iYvOatJMnT47DDz+82LEffPBBqXPVqlUrVq1aVa4b9YjSb9aL3oj/61//irZt25ZrzpLm+vLLL3dJX+WptcU555wTTz/99A7PBQBUTt26dcsEv48//vhOB79NmjSJlStXxurVq2PJkiXRqFGjUsfPmjUrc1wZlhrIy8vLLHU1b968aN269Q7NU7du3ahVq1bk5+fH7NmzI0mSUpct+/jjj3eozhZFn9SeN2/eTs0F7Bt2/LsRAFABXnzxxczSBAMGDCgx9I3YvIlFabZ88Fi1alUsW7as1LFffPFFqU/H7sob8aZNm2b6+vzzz0sdu3z58p16andLrQgfIAAgrX72s59l1rydNGlSvPHGGzs1X9FNaZ9//vlSx3722Wfxn//8JyIiDj744HKtCby7de7cOXP87LPP7vA8WVlZccwxx0TE5qeai+7TUJyXXnpph2tFRHzrW9+KunXrRkTE5MmTS9zbAmALwS8Ae5XFixdnjsvaIKOsG/ktN+oRm2+eS1PW+V31ASJi612qX3755VLH7uwHiPr160ebNm0iIuK9996LJUuW7NR8AEDl07Rp07jyyisjIiJJkujdu3d8+umn5b5+5syZ8ctf/jLzc48ePTLHgwYNKnZJrS3uvffezJ4MRa+rSOedd14mCB88eHCZDwCUpuj+CIMHDy5x3Nq1a+P3v//9DteJiMjOzo4LLrggIiKWLVtWaj2ACMEvAHuZoptilPZ1udmzZ8fjjz9e6lxnn3125njIkCGlbhQ3ZMiQUuc67bTTMhuxDR8+fKe+ylf0A0RpfSVJEvfff/8O19ni4osvjojN6yDfdtttOz0fAFD5/PrXv45OnTpFxOancI877rh4+umnS73/WbFiRdx6661xzDHHbLWE1umnn55Z1ur999+PK664IjZu3LjN9SNHjsws0ZWTkxPXXHPNrnxJOywvLy+uvvrqiNi8qW63bt1i9uzZpV7z/vvvZ/aOKOriiy/O3AOOGTMmHnrooW3GrF+/Pi677LKtNgPeUbfcckvUq1cvIiJuvfXWGDJkSObbcMXJz8+PYcOGxahRo3a6NrD3scYvAHuVok/pDhw4MM4///zIzc3dasxnn30WZ555Zplr35599tlx2GGHxSeffBL/+Mc/4he/+EX8+te/3mpttiRJ4rbbbovXXnut1Llq1aoVd9xxR/Tv3z/WrFkT3bp1i7/+9a/x7W9/u8RrPv7443jkkUdiwIAB0bBhw8zvzzjjjGjVqlXMnDkz3nrrrbjpppvit7/97TZ93XTTTfHWW2+V2ld59O/fPx555JGYO3duPProo1GvXr24++67M5vkfd369etj3Lhx8fnnn0f//v13uj4AsPtVq1Ytxo8fH7169YoXX3wxFi9eHD179ozDDz88unfvHq1bt47c3NzIz8+PhQsXxmuvvRZTpkyJgoKCbeaqUqVKPPnkk9GxY8fIz8+Pxx57LN5888246KKLokWLFrFixYp45plnYtKkSZlrHnzwwWjevPmefMmluueee+L999+P559/Pt57771o2bJlnHXWWXHiiSdGkyZNYtOmTbFs2bKYMWNGTJ48OWbNmhXZ2dkxdOjQreapW7du/P73v49zzz03kiSJq666KsaNGxc9evSI3NzcmDNnTowcOTL+85//xDnnnBNjx47dqb6bNm0aY8aMiTPPPDPWrVsX1113XTzyyCPxwx/+MFq3bh21a9eO1atXx5w5c2LatGnx8ssvR0FBQdx11107VRfYOwl+AdirHH/88dGhQ4eYOnVqfPrpp9GyZcu4/PLLo1WrVlFYWBhvvfVW/O///m/k5+fHJZdcEiNHjixxrqpVq8awYcPi1FNPjY0bN8a9994bU6ZMifPPPz+aNGkSixYtitGjR8ebb74ZHTt2jE8//TQWLFgQVaoU/4WZn/3sZ/Huu+/G8OHDY/bs2fGd73wnunXrFqecckrk5eVFVlZWrFixImbOnBmvvfZaZlOR66+/fqt5srOzY/jw4dG5c+dYv359DBw4MF555ZXo3bt3HHTQQbFw4cJ46qmn4p133okOHTrEvHnzYuHChTv8nubk5MT48ePjpJNOipUrV8Zvf/vbePLJJ6Nnz55x1FFHxf777x9r1qyJefPmxXvvvRcvvvhirFq1Kvr06bPDNQGAPe/AAw+MSZMmxcCBA+O+++6L5cuXx0cffRQfffRRiddkZ2fH+eefv01w2K5du5g8eXKcc845MX/+/JgxY0bcfPPN21yfk5MTDz74YKW7b6hatWr8/e9/j1/84hfxwAMPxIYNG+Lpp58udaPbons6FNWjR48YOnRoXHnllbF+/fp46aWXtlmOq3PnzjF8+PCdDn4jIk499dR4/fXX48ILL4z//ve/8dFHH8Vvf/vbEsdnZ2dXirWVgT1P8AvAXufPf/5znHzyyTFnzpxYtmxZ3HPPPduMueqqq+K6664rNfiNiOjSpUs89dRTcckll8SaNWti6tSpMXXq1K3GHHXUUTFmzJjMRiZ16tQpcb5hw4bFN7/5zbjzzjtjzZo1MWnSpK2edvm6+vXrR82aNbf5/ZavX/bu3TtWr14d77zzTrzzzjtbjWnbtm385S9/iRNPPLHU11gebdu2jWnTpsUFF1wQU6dOjYULF8aDDz5Y4visrKytNoYDAPYO2dnZcfPNN0f//v1j7Nix8dJLL8W0adNi6dKlsXLlysjJyYkGDRrEUUcdFSeddFL06tWrxNDwmGOOiVmzZsWwYcPimWeeiRkzZsSKFSuidu3aceihh0a3bt2if//+mQ11K5tq1arFwIED46qrrorhw4fH5MmT46OPPooVK1ZElSpVIjc3N4444ojo0KFDdOvWLU466aQS5+rbt2+ccMIJMXjw4HjhhRdi8eLFsf/++0fLli3jwgsvjD59+kR2dvYu6719+/bx4YcfxtixY+OZZ56JqVOnxpIlSyI/Pz9q164dzZo1i7Zt20aXLl3irLPOEvzCPiorKW1BHwDYSVOmTImuXbtGxOYnHaZMmVLi+dtvvz3uuOOOcs375ZdfxpAhQ2Ls2LGZ9XQbN24cHTt2jD59+kSXLl1i7ty5ccghh0TE5vXXSguB586dG4MGDYpJkybF/PnzIycnJw4//PA477zzol+/flGjRo2oVatWrF27Nrp06VLmZm+ff/55DB8+PF588cX48MMPY/ny5RERUa9evfjGN74R7du3j1NPPTW+973vlbikQkTE/PnzY+DAgTFx4sSYN29e5OTkxGGHHRa9evWKK664InJycqJFixbx6aefRvPmzYtdO+6OO+6IO++8MyI2b1LXpUuXUnt//vnn4y9/+Uv84x//iIULF8bq1asjJycnmjZtGq1bt47OnTvHmWeemXlvAQAAqHwEvwBQDv/+97+jXbt2ERFxzTXXlLnZGwAAAFSk4hcpBAC2UnSH5i1PKAMAAEBlJfgFYJ/3yiuvlHr+4YcfjkcffTQiIpo1axZnnHHGnmgLAAAAdpilHgDY59WuXTsaNGgQp512WrRr1y7q168f69ati48//jjGjRsX06dPj4jNG5pNnDgxunfvXrENAwAAQBkEvwDs82rXrh35+fmljqlVq1b88Y9/jF69eu2hrgAAAGDHCX4B2OdNnjw5nnnmmXj77bdj0aJFsXz58igoKIgDDjggWrZsGd/97nejX79+0aBBg4puFQAAAMpF8AsAAAAAkDI2dwMAAAAASBnBLwAAAABAygh+AQAAAABSRvALAAAAAJAygl8AAAAAgJQR/AIAAAAApIzgFwAAAAAgZQS/AAAAAAApI/gFAAAAAEiZ/wexhIFgcBI6/QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1600x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/vector-space-model/notebook_93_0.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.boxplot([t_dists, c_dists])\n",
    "ax.set(xticklabels=('Tragédie', 'Comédie'), ylabel='Distances to genre means');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8d17c56",
   "metadata": {},
   "source": [
    "<!-- Figure: Box plot of distances to genre means.\\label{fig:vector-space-model:distance-to-centroids} -->\n",
    "\n",
    "The Tragi-comédies' mean distance in the plot above are again relatively smaller to the\n",
    "tragédies' genre vector in terms of their median distance. Any \"<span\n",
    "class=\"index\">outliers</span>\" in this plot are shown as individual data points that are\n",
    "outside the \"whiskers\" (using empty circles by default). Two Tragi-comédies seem to show\n",
    "an unexpectedly large distance to the tragedy centroid, with distance scores larger than\n",
    "the 0.7. These unexpected outliers therefore invite a closer analysis, using more\n",
    "conventional hermeneutic approaches. Retrieving the original titles of these outliers can\n",
    "be done by first identifying the index of the two most extreme distances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5b823bef",
   "metadata": {},
   "outputs": [],
   "source": [
    "t_dists = np.array(t_dists)\n",
    "outliers = t_dists.argsort()[::-1][:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9ba6543",
   "metadata": {},
   "source": [
    "Using a <span class=\"index\">negative step index</span> (`[::-1]`), we invert the result of <span class=\"index\">`numpy.argsort()`</span>, which defaults to an ascending order, whereas we are interested in the largest distances. Subsequently, we select the first two indices: our two outliers in the bar plot. Finally, we retrieve the original titles using these indices from the appropriate list of titles, which was extracted at the beginning of this chapter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4af1e0d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "STRATONICE, TRAGI-COMÉDIE\n",
      "BÉRÉNICE, TRAGI-COMÉDIE EN PROSE.\n"
     ]
    }
   ],
   "source": [
    "tc_titles = np.array(titles)[genres == 'Tragi-comédie']\n",
    "print('\\n'.join(tc_titles[outliers]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31526188",
   "metadata": {},
   "source": [
    "For the second outlier, *Bérénice*, the plain fact that the text is in prose might explain the pronounced distance from the Tragédie centroid: Tragèdies in the corpus are mostly composed in verse -- although prose tragedies do occur, e.g., Voltaire's *Socrates* from 1759. The lack of (stereotypical) rhyme words, amongst other factors, is likely to cause lexical shifts in the vocabulary. For the *Stratonice* (1660) by Philippe Quinault, the first outlier, other explanatory grounds are called for, because this is a clear verse text. In this case, thematic divergence seems to have caused the lexical distance from the average tragedy. The classical material by Plutarch from which Quinault heavily borrowed in this play already possessed little \"dramatic power\" in the eyes of contemporaries {cite:p}`brooks:2009`. In fact, the only significantly dramatic scene which occurred in that material was deleted altogether in the *Stratonice*, which helps explain why it behaves as such an \"un-tragedic\" play in terms of word choice.\n",
    "\n",
    "The fact that the *Stratonice* still received the (contemporary) label of Tragi-comédie illustrates that genre matters were as controversial a notion in seventeenth-century France as they are today. Some works in this period even attracted different genre labels across different editions of the very same text {cite:p}`hammond:2007`. Computational methods can help us model this genetic fluidity and make nuanced generalizations that would otherwise remain out of scope in humanistic research. Abstracting over the outliers discussed in the previous paragraph, all measurements above add (additional) quantitative evidence, for instance, for the existing view that the texts in the hybrid subgenre of Tragi-comédies are generally more similar to the typical tragedy than to the average comedy. As such, our results are congruent with what we know about the subgenre: Tragi-comédies are not comédies with some superficial tragic aspects thrown into the mix; rather at their core, they are tragedies to which some humorous twists were added to soften the dramatic aspects. A quantitative approach, however, does not only provide the means of confirmation regarding established views but also offers new methods to identify outliers which can help challenge and fine-tune existing perspectives in literary history.\n",
    "\n",
    "(sec-vector-space-model-further-reading)=\n",
    "## Further Reading\n",
    "\n",
    "This chapter introduced the vector space model of texts and how representing texts as vectors can be used to quantify similarities between texts. We introduced a number of important text <span class=\"index\">preprocessing</span> techniques and demonstrated the use of NumPy in this context, a library which provides data structures useful for storing and manipulating multidimensional numeric data. As a case study, we investigated a corpus of French plays from the Classical and Enlightenment period in France. Using the concept of distance metrics, we illustrated how the (dis)similarities between documents can be traced in a vector space. Likewise, the concept of a nearest neighbor proved a fruitful strategy to explore the morphology of our corpus -- and even detect outliers in it.\n",
    "\n",
    "This chapter has laid much groundwork for some of the more advanced data analyses that will feature later on in the book. Preprocessing texts is required by almost all quantitative text analysis and subsequent chapters will often contain preprocessing blocks that are reminiscent of what we treated in this chapter. Likewise, the flexible manipulation of (vocabularies represented as) numeric data tables is foundational in data science. Nearest neighbor reasoning, finally, lies at the basis of a number of highly influential machine learning algorithms that can be used to automatically classify documents. In the chapter on stylometry, we will see how Burrows's Delta is in fact a simple variation on the nearest neighbors algorithm.\n",
    "\n",
    "As will be clear by now, we often discuss implementations of certain basic algorithms in significant detail. This might seem superfluous: why recode a distance function from the ground up, if we can readily import a tried-and-tested implementation from a reference package like SciPy or NumPy? We insist on such low-level discussions, mainly because we believe that the black box is the biggest enemy of interpretative research. If we start to use (and accept) distance metrics as proxies for human judgment, it is important to have an understanding of -- and at least an intuition about -- how these distance metrics work internally, and which quantitative biases they come with.\n",
    "\n",
    "A more detailed description of text preprocessing techniques is offered by {cite:t}`birdEA2009`, an updated version of which is available [online](http://www.nltk.org/book/). Thorough coverage of text normalization, including lemmatization and word stemming, can be found in chapter 2 of {cite:t}`jurafskyinpressspeech`. {cite:t}`vanderplas:2016` covers the ins and outs of working with NumPy. chapter 6 of {cite:t}`jurafskyinpressspeech` covers the vector space model and distance metrics. chapter 16 of {cite:t}`manning1999foundations` covers nearest neighbors classification. Those interested in the conceptual underpinnings of the vector space model may wish to consult an introduction to linear algebra such as {cite:t}`axler2004linear`.\n",
    "\n",
    "## Exercises\n",
    "\n",
    "In this chapter's exercises, we will employ the vector space model to explore a rich and\n",
    "unique collection of '<span class=\"index\">chain letters</span>', which were collected,\n",
    "transcribed, and digitised by {cite:t}`vanarsdale:2019`. Here, we focus on one of the\n",
    "largest chain letter categories: \"luck chain letters\". The recipients of these letters are\n",
    "warned against sin, and the letters often contain prayers and emphasize good behavior according to Christian beliefs. The most characteristic and equally intriguing aspect of these chain letters is their explicit demand to be copied and redistributed to a number of successive recipients. If the recipient does not obey the letter's demands, and thus breaks the chain, he or she will be punished and bad fortune will be inevitable.\n",
    "\n",
    "The following code block loads the corpus into memory. Two lists are created, one for the contents of the letters and one for their dating. The letters are loaded in chronological order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "1a33a2b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "letters, years = [], []\n",
    "with open(\"data/chain-letters.csv\") as f:\n",
    "    reader = csv.DictReader(f)\n",
    "    for row in reader:\n",
    "        letters.append(row[\"letter\"])\n",
    "        years.append(int(row[\"year\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a6e747f",
   "metadata": {},
   "source": [
    "### Easy\n",
    "1. Use the preprocessing functions from section\n",
    "   {ref}`sec-vector-space-model-text-processing` to create (i) a tokenized version of the\n",
    "   corpus, and (ii) a list representing the vocabulary of the corpus. How many unique\n",
    "   words (i.e., word types) are there?\n",
    "2. Transform the tokenized letters into a document-term matrix, and convert the matrix\n",
    "   into a two-dimensional NumPy array. How many word tokens are there in the corpus?\n",
    "3. What is the average number of words per letter? (Hint: use NumPy's `sum()` and `mean()` to\n",
    "   help you with the necessary arithmetic.)\n",
    "\n",
    "### Moderate\n",
    "1. The length of the chain letters has changed considerably over the years. Compute the\n",
    "   average length of letters from before 1950, and compare that to the average length of\n",
    "   letters after 1950. (Hint: convert the list of years into a NumPy array, and use\n",
    "   boolean indexing to slice the document-term matrix.)\n",
    "2. Make a scatter plot to visualize the change in letter length over time. Add a label to\n",
    "   the X and Y axis, and adjust the opacity of the data points for better\n",
    "   visibility. Around what year do the letters suddenly become much longer?\n",
    "3. Not only the length of the letters has changed, but also the contents of the letters.\n",
    "   Early letters in the corpus still have strong religious undertones, while newer\n",
    "   examples put greater emphasis on superstitious beliefs. (The Luck chain letter is\n",
    "   generally believed to stem from the 'Himmelsbrief' (Letter from Heaven), which might\n",
    "   explain these religious undertones.) {cite:t}`vanarsdale:2019` points to an interesting\n",
    "   development of the postscript \"It works!\". The first attestation of this phrase is in\n",
    "   1979, but in a few years time, all succeeding letters end with this statement. Extract\n",
    "   and print the summed frequency of the words *Jesus* and *works* in letters written\n",
    "   before and written after 1950.\n",
    "\n",
    "### Challenging\n",
    "1. Compute the cosine distance between the oldest and the youngest letter in the\n",
    "   corpus. Subsequently, compute the distance between two of the oldest letters (any two\n",
    "   letters from 1906 will do). Finally, compute the distance between the youngest two\n",
    "   letters. Describe your results.\n",
    "2. Use SciPy's `pdist()` function to compute the cosine distances between all letters in the\n",
    "   corpus. Subsequently, transform the resulting condensed distance matrix into a regular\n",
    "   square-form distance matrix. Compute the average distance between letters. Do the same\n",
    "   for letters written before 1950, and compare their mean distance to letters written\n",
    "   after 1950. Describe your results.\n",
    "3. The function `pyplot.matshow()` in Matplotlib takes a matrix or an array as argument and\n",
    "   plots it as an image. Use this function to plot a square-form distance matrix for the entire letter collection. To enhance your visualization, add a color bar using the function\n",
    "   `pyplot.colorbar()`, which provides a mapping between the colors and the cosine\n",
    "   distances. Describe the resulting plot. How many clusters do you observe?\n",
    "\n",
    "---\n",
    "\n",
    "(sec-vector-space-model-numpy-intro)=\n",
    "## Appendix: Vectorizing Texts with NumPy\n",
    "\n",
    "```{attention} \n",
    "Readers familiar with NumPy may safely skip this section.\n",
    "```\n",
    "\n",
    "<span class=\"index\">NumPy</span> (short for Numerical Python) is the de facto standard library for scientific computing and data analysis in Python. Anyone interested in large-scale data analyses with Python is strongly encouraged to (at least) master the essentials of the library. This section introduces the essentials of constructing arrays (section {ref}`sec-vector-space-model-numpy-constructing-arrays`), manipulating arrays (section {ref}`sec-vector-space-model-indexing-and-slicing`), and computing with arrays (section {ref}`sec-vector-space-model-numpy-aggregating-functions`). A complete account of NumPy's functionalities is available in NumPy's online documentation.\n",
    "\n",
    "(sec-vector-space-model-numpy-constructing-arrays)=\n",
    "### Constructing arrays\n",
    "\n",
    "NumPy's main workhorse is the N-dimensional array object <span class=\"index\">`ndarray`</span>, which has much in common with Python's `list` type, but allows arrays of numerical data to be stored and manipulated much more efficiently. NumPy is conventionally imported using the alias <span class=\"index\">`np`</span>:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e0fa5f42",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8024db6",
   "metadata": {},
   "source": [
    "NumPy arrays can be constructed either by converting a `list` object into an array or by\n",
    "employing routines provided by NumPy. For example, to initialize an array of floating points on the basis of a `list`, we write:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d29736a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([1.0, 0.5, 0.33, 0.25, 0.2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e1371d2",
   "metadata": {},
   "source": [
    "Similarly, an array of integers can be created with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "01c2c77c",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([1, 3, 6, 10, 15])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ac04224",
   "metadata": {},
   "source": [
    "A crucial difference between NumPy arrays and Python's built-in `list` is that all items of a NumPy array have a specific and fixed type, whereas Python's `list` allows for mixed types that can be freely changed (e.g., a mixture of `str` and `int` types). While Python's dynamically typed `list` provides programmers with great flexibility, NumPy's fixed-type arrays are much more efficient in terms of both storage and manipulation. The data type of an array can be explicitly controlled for by setting the <span class=\"index\">`dtype`</span> argument during initialization. For example, to explicitly set the data type for array elements to be 32-bit integers (sufficient for counting words in virtually all human-produced texts), we write the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "3c1787df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int32\n"
     ]
    }
   ],
   "source": [
    "a = np.array([0, 1, 1, 2, 3, 5], dtype='int32')\n",
    "print(a.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd6c8b8c",
   "metadata": {},
   "source": [
    "The trailing number 32 in `int32` specifies the number of bits available for storing the numbers in an array. An array with type `int8`, for example, is only capable of expressing integers within the range of -128 to 127. `int64` allows integers to fall within the range -9,223,372,036,854,775,807 to 9,223,372,036,854,775,807. (Python's native `int` has no fixed bounds.) The advantage of specifying data type is that doing so saves memory. The memory needed to store an integer of type `int8` amounts to a single byte, whereas those of type `int64` need 8 bytes. Such a difference might seem negligible, but once we start working with arrays which record millions or billions of term frequencies, the difference will be significant. As with integers, we can specify a type for floating numbers, such as `float32` and `float64`. Besides having a smaller memory footprint, numbers of type `float32` have a smaller precision than `float64` numbers. To change the data type of an existing array, we use the method <span class=\"index\">`ndarray.astype()`</span>:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ae930587",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float32\n"
     ]
    }
   ],
   "source": [
    "a = a.astype('float32')\n",
    "print(a.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e50f1d7",
   "metadata": {},
   "source": [
    "NumPy arrays are explicit about their dimensions, which is another important difference between NumPy's `array` and Python's `list` object. The number of dimensions of an array is accessed through the attribute <span class=\"index\">`ndarray.ndim`</span>:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "1291765e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "a = np.array([0, 1, 1, 2, 3, 5])\n",
    "print(a.ndim)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e8a92eb",
   "metadata": {},
   "source": [
    "To construct a two-dimensional array, we pass a sequence of ordered sequences (i.e., a `list` or a `tuple`) to `np.array`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "1e157c0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[0, 1, 2], [1, 0, 2], [2, 1, 0]])\n",
    "print(a.ndim)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09272b1a",
   "metadata": {},
   "source": [
    "Likewise, a sequence of sequences of sequences produces a three-dimensional array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "24c3857a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[[1, 3, 3], [2, 5, 2]], [[2, 3, 7], [4, 5, 9]]])\n",
    "print(a.ndim)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c6fbada",
   "metadata": {},
   "source": [
    "In addition to an array's number of dimensions, we can retrieve the size of an array in each dimension using the attribute <span class=\"index\">`ndarray.shape`</span>:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e3a4941e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 4)\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[0, 1, 2, 3], [1, 0, 2, 6], [2, 1, 0, 5]])\n",
    "print(a.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cdf5481",
   "metadata": {},
   "source": [
    "As can be observed, for an array with 3 rows and 4 columns, the shape will be `(3, 4)`. Note that the length of the `shape` tuple corresponds to the number of dimensions, `ndim`, of an array. The `shape` of an array can be used to compute the total number of items in an array, by multiplying the elements returned by `shape` (i.e., 3 rows times 4 columns yields 12 items).\n",
    "\n",
    "Having demonstrated how to create NumPy arrays on the basis of Python's `list` objects, let us now illustrate a number of ways in which arrays can be constructed from scratch using procedures provided by NumPy. These procedures are particularly useful when the shape (and type) of an array is already known, but its actual contents are yet unknown. In contrast with Python's `list`, NumPy arrays are not intended to be resized, because growing and shrinking arrays is an expensive operation. Fortunately, NumPy provides a number of functions to construct arrays of a predetermined size with initial placeholder content. First, we will have a look at the function <span class=\"index\">`numpy.zeros()`</span>, which creates arrays filled with zeros (of type `float64` by default):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "7b1bf58b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "print(np.zeros((3, 5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3e0fb10",
   "metadata": {},
   "source": [
    "The `shape` parameter of `numpy.zeros()` determines the shape of the constructed array. When `shape` is a single integer, a one-dimensional array is constructed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "fe49dc5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "print(np.zeros(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c9e88c5",
   "metadata": {},
   "source": [
    "The function <span class=\"index\">`numpy.ones()`</span> and <span class=\"index\">`numpy.empty()`</span> behave in a similar manner, with `numpy.ones()` creating arrays full of ones and `numpy.empty()` creating arrays as quickly as possible with no guarantee about their content."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "5e54f3ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 1 1 1]\n",
      " [1 1 1 1]\n",
      " [1 1 1 1]]\n"
     ]
    }
   ],
   "source": [
    "print(np.ones((3, 4), dtype='int64'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "3a72264f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.0e+000 4.9e-324]\n",
      " [4.9e-324 9.9e-324]\n",
      " [1.5e-323 2.5e-323]]\n"
     ]
    }
   ],
   "source": [
    "print(np.empty((3, 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a6a805f",
   "metadata": {},
   "source": [
    "Should an array filled with randomly generated values be desired, NumPy's submodule <span\n",
    "class=\"index\">`numpy.random()`</span> implements a rich variety of functions for producing\n",
    "random contents. Here, we demonstrate a function to sample random floating point numbers\n",
    "in the interval 0 to 1. The function works the same as before, and produces either\n",
    "one-dimensional or multidimensional arrays depending on the size parameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "b642acc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.04289059 0.11546802 0.40834152 0.33923439 0.2435908 ]\n"
     ]
    }
   ],
   "source": [
    "print(np.random.random_sample(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "ef2cb93d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.2681808  0.25061548 0.45567564]\n",
      " [0.99833773 0.28882588 0.14021018]]\n"
     ]
    }
   ],
   "source": [
    "print(np.random.random_sample((2, 3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aebeecb5",
   "metadata": {},
   "source": [
    "NumPy's counterpart of Python's `range` function is <span class=\"index\">`numpy.arange()`</span>, which produces sequences of numbers as array objects. An interesting difference between `range` and `numpy.arange()` is that the latter accepts floats as arguments, which enables us to easily create floating-point sequences like the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "6d8222a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.   0.25 0.5  0.75 1.   1.25 1.5  1.75]\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(0, 2, 0.25)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6b326b1",
   "metadata": {},
   "source": [
    "(sec-vector-space-model-indexing-and-slicing)=\n",
    "### Indexing and slicing arrays\n",
    "\n",
    "Indexing and slicing NumPy arrays behaves similarly to accessing elements in Python's `list`. Accessing a single element from a one-dimensional array can be done by specifying its corresponding index within square brackets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "80148ab4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(10)\n",
    "print(a[5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6f0d696",
   "metadata": {},
   "source": [
    "Similarly, an array can be sliced to retrieve a sub-array, just as with Python's `list`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a220dea9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 4 5 6 7]\n"
     ]
    }
   ],
   "source": [
    "print(a[3:8])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f209e921",
   "metadata": {},
   "source": [
    "The strength of NumPy arrays becomes more evident in the context of multidimensional arrays. While Python's `list` and NumPy's 1-dimensional arrays allow for only a single index (or slice), multidimensional arrays allow for a (slice) index per dimension (sometimes called axis), separated by commas. This syntax provides a powerful mechanism to index and manipulate arrays. Let us start with a simple example. In the following code block, we retrieve the frequency of the word *monsieur* (\"sir\") from the third document. This is done by providing two indexes separated by a comma, of which the first corresponds to the row index of the third document, and the second points to the column of the word *monsieur*:\n",
    "\n",
    "```{warning}\n",
    "Here, we assume that you have executed all code in the chapter above up until (and\n",
    "including) the code blocks in section {ref}`(sec-vector-space-model-mapping-genre)=`, so that you have\n",
    "the object `document_term_matrix` available. \n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "903fc45a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17\n"
     ]
    }
   ],
   "source": [
    "word_index = vocabulary.index('monsieur')\n",
    "document_term_matrix = np.array(document_term_matrix)\n",
    "print(document_term_matrix[2, word_index])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a29bc47b",
   "metadata": {},
   "source": [
    "Note that the order of these indexes corresponds to the shape of the `document_term_matrix`, in which the value at the first index indicates the number of documents, and the value in the second position counts the size of the vocabulary.\n",
    "\n",
    "To retrieve the frequency of a given word for a sequence of documents, we use the Python slice convention in the first position. The following line retrieves an array consisting of the frequencies of *monsieur* in the first five documents of the document-term matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b904c6df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 9  0 17  9 11]\n"
     ]
    }
   ],
   "source": [
    "print(document_term_matrix[:5, word_index])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "340971f0",
   "metadata": {},
   "source": [
    "Here, the left-hand side of the comma specifies a slice (i.e., the first five rows), and the index to the right of the comma indicates the column index (corresponding to *monsieur*). Similarly, to construct an array with frequencies for a number of specific columns, we can also use a slice index. Consider the following indexing operation, which constructs an array with counts corresponding to the words in columns 10 to 40 for the sixth document:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "e358ed41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(document_term_matrix[5, 10:40])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7de3a64b",
   "metadata": {},
   "source": [
    "To access all rows of a particular column (or collection of columns), we write the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "86119bde",
   "metadata": {},
   "outputs": [],
   "source": [
    "column_values = document_term_matrix[:, word_index]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02da9bfc",
   "metadata": {},
   "source": [
    "The same mechanism can be used to access all columns of a particular row (or collection of rows), as shown by the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "84db86f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 ... 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(document_term_matrix[5, :])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94f600ee",
   "metadata": {},
   "source": [
    "When an array is indexed with less indexes than the array has dimensions, NumPy assumes the missing indexes to be complete. This is why the following less verbose (and common) notation is equivalent to the previous example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "2615fca3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 ... 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(document_term_matrix[5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d6d6b7a",
   "metadata": {},
   "source": [
    "In addition to indexing by integers and slices, NumPy offers a number of \"fancy\" indexing\n",
    "techniques (\"fancy\" is, indeed, the common term for this form of indexing). We will\n",
    "demonstrate two of them: (i) sequence indexing, and (ii) boolean indexing. Sequence\n",
    "indexing is particularly useful when accessing discontinuous elements from an array. For\n",
    "example, to construct an array with word counts for a few discontinuous documents, a\n",
    "sequence of integers is given as a row index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "e98eb8e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "print(document_term_matrix[(1, 8, 3), :])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5de2100c",
   "metadata": {},
   "source": [
    "In a similar vein as the previous example, we can create a reduced array consisting of only a few columns. The following example shows how to construct a reduced array with word counts for the words *monsieur*, *madame*, and *amour*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "136dd7e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 9  3  1]\n",
      " [ 0  0  3]\n",
      " [17  4  0]\n",
      " ...\n",
      " [ 4 35  7]\n",
      " [ 0  1 11]\n",
      " [ 0 31 15]]\n"
     ]
    }
   ],
   "source": [
    "words = 'monsieur', 'madame', 'amour'\n",
    "word_indexes = [vocabulary.index(word) for word in words]\n",
    "print(document_term_matrix[:, word_indexes])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d928f5e2",
   "metadata": {},
   "source": [
    "We conclude this section with one final fancy indexing technique, <span class=\"index\">*boolean indexing*</span>. Say we are interested in all plays in which the word *de* occurs. Using pure Python, we could solve this problem by iterating over all rows in `document_term_matrix` (see above) using a `for` loop, and check for each row if the column corresponding to *de* has a frequency higher than zero. Unfortunately, this strategy is rather inefficient and slow, especially for large lists of numbers. NumPy provides a much more efficient solution through its use of so-called \"<span class=\"index\">vectorized operation</span>s\". But before we explain this solution, we first need to discuss the concept of vectorized operations. Consider the following list of numbers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "41aeb3b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "numbers = [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99069882",
   "metadata": {},
   "source": [
    "Imagine we want to update this list by multiplying each number by 10. In pure Python, a simple way to accomplish this is by means of a list comprehension, as shown in the following code block:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "e046e5c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 10, 10, 20, 30, 50, 80, 130, 210, 340, 550]\n"
     ]
    }
   ],
   "source": [
    "print([number * 10 for number in numbers])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c114ce0",
   "metadata": {},
   "source": [
    "Using NumPy's optimized vectorization mechanism, this can be rewritten to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "20f681ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0  10  10  20  30  50  80 130 210 340 550]\n"
     ]
    }
   ],
   "source": [
    "numbers = np.array(numbers)\n",
    "print(numbers * 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7118a5cb",
   "metadata": {},
   "source": [
    "With this notation, Python's `for`-loop is replaced with an optimized operation written\n",
    "using a lower-level programming language such as C. The performance difference between\n",
    "pure Python and NumPy for this specific example may be barely noticeable. However, the\n",
    "performance difference becomes increasingly important for larger lists of numbers.\n",
    "IPython's \"magic command\" <span class=\"index\">`%timeit`</span> enables us to conveniently time the speed of execution of a particular piece of code. Let us time the execution of multiplying a list of a million numbers by 10:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "3006d5d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "62.5 ms ± 720 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "numbers = list(range(1000000))\n",
    "%timeit [number * 10 for number in numbers]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f029f71",
   "metadata": {},
   "source": [
    "The exact execution times may fluctuate from machine to machine, but execution times of the above example typically fall in the range of milliseconds. The timing for the same computation with NumPy's <span class=\"index\">vectorized operation</span>s returns a much smaller number best described using *micro*seconds:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "1a39e73b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.37 ms ± 18 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "numbers = np.arange(1000000)\n",
    "%timeit numbers * 10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e123dd83",
   "metadata": {},
   "source": [
    "Number comparisons (e.g., `5 < 10`) can also be vectorized. Say we have a list of numbers, and we want to filter all numbers smaller than 10. In Python, a solution to this problem could be implemented as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "6e6682ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 1, 2, 3, 5, 8]\n"
     ]
    }
   ],
   "source": [
    "numbers = [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55]\n",
    "print([number for number in numbers if number < 10])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7fe052c",
   "metadata": {},
   "source": [
    "Employing NumPy's vectorized number comparison operation, we can rewrite this to the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "70c16a82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 1 2 3 5 8]\n"
     ]
    }
   ],
   "source": [
    "numbers = np.array(numbers)\n",
    "print(numbers[numbers < 10])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f74f40a5",
   "metadata": {},
   "source": [
    "How does this work? The part within square brackets (`numbers < 10`) performs a vectorized comparison operation, which returns a new array with boolean values representing the outcome (i.e., `True` or `False`) of the number comparison:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "d198fb4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True  True  True  True  True  True False False False False]\n"
     ]
    }
   ],
   "source": [
    "print(numbers < 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98ed7c41",
   "metadata": {},
   "source": [
    "We can use such a boolean array (a <span class=\"index\">*mask*</span>) to select from the original array of numbers all elements associated with a `True` value. In other words, using a boolean array, we filter all numbers that pass the conditional expression. Let us now return to the problem of filtering the document-term matrix to include only texts in which the word *de* occurs at least once. The boolean indexing mechanism can be employed to retrieve these texts as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "7881877e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "print(document_term_matrix[document_term_matrix[:, vocabulary.index('de')] > 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "286407be",
   "metadata": {},
   "source": [
    "(sec-vector-space-model-numpy-aggregating-functions)=\n",
    "### Aggregating functions\n",
    "\n",
    "We now proceed with a brief overview of some of the most important functions in NumPy used to\n",
    "aggregate data, including functions for summing over values and finding the maxumum value in an array. Many of these are also provided as built-in functions in Python. However, just as with the vectorized operations discussed above, their NumPy counterparts are highly optimized and executed in compiled code, which allows for fast aggregating computations. To illustrate the performance gain of utilizing NumPy's optimized aggregation functions, let us start by computing the sum of all numbers in an array. This can be achieved in Python by means of the built-in function `sum()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "368122cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50193.84079530211\n"
     ]
    }
   ],
   "source": [
    "numbers = np.random.random_sample(100000)\n",
    "print(sum(numbers))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09f11132",
   "metadata": {},
   "source": [
    "Summing over the values in `numbers` using NumPy is done using the function <span class=\"index\">`numpy.sum()`</span> or the method `ndarray.sum()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "85db4c4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50193.840795301425\n"
     ]
    }
   ],
   "source": [
    "print(numbers.sum())  # equivalent to np.sum(numbers)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9bd8898",
   "metadata": {},
   "source": [
    "While syntactically similar, NumPy's summing function is orders of magnitude faster than Python's built-in function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "fc1c87b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.1 ms ± 234 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23 µs ± 602 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit sum(numbers)\n",
    "%timeit numbers.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba1b561b",
   "metadata": {},
   "source": [
    "In addition to being faster, <span class=\"index\">`numpy.sum()`</span> is designed to work with multidimensional arrays, and, as such, provides a convenient and flexible mechanism to compute sums along a given axis. First, we need to explain the concept of \"<span class=\"index\">axis</span>\". A two-dimensional array, such as the document-term matrix, has two axes: the first axis (`axis=0`) runs vertically down the rows, and the second axis (`axis=1`) runs horizontally across the columns of an array. This is illustrated by {numref}`fig-vector-space-model-numpy-axis`.\n",
    "\n",
    "```{figure} images/axis-hq.png\n",
    "---\n",
    "name: fig-vector-space-model-numpy-axis\n",
    "width: 50%\n",
    "---\n",
    "\n",
    "Visualization of the axis ordering in two-dimensional NumPy arrays.\n",
    "```\n",
    "\n",
    "Under this definition, computing the sum of each row happens along the second axis: for each row we take the sum across its columns. Likewise, computing the sum of each column happens along the first axis, which involves running down its rows. Let us illustrate this with an example. To compute the sum of each row in the document-term matrix, or, in others words, the document lengths, we sum along the column axis (`axis=1`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "d9acaa0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "sums = document_term_matrix.sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d03d28df",
   "metadata": {},
   "source": [
    "Similarly, computing the corpus-wide frequency of each word (i.e., the sum of each column) is done by setting the parameter `axis` to 0:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "2e3cffc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 2 2 ... 4 3 2]\n"
     ]
    }
   ],
   "source": [
    "print(document_term_matrix.sum(axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b906890",
   "metadata": {},
   "source": [
    "Finally, if no value to `axis` is specified, `numpy.sum()` will sum over all elements in an array. Thus, to compute to total word count in the document-term matrix, we write:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "9ddd9b3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5356125\n"
     ]
    }
   ],
   "source": [
    "print(document_term_matrix.sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "088b5b99",
   "metadata": {},
   "source": [
    "NumPy provides many other aggregating functions, such as <span class=\"index\">`numpy.min()`</span> and <span class=\"index\">`numpy.max()`</span> to compute the minimum/maximum of an array or along an axis, or <span class=\"index\">`numpy.mean()`</span> to compute the arithmetic mean (cf. chapter {ref}`chp-statistics-essentials`). However, it is beyond the scope of this brief introduction into NumPy to discuss any of these functions in more detail, and for information we refer the reader to NumPy's excellent online documentation.\n",
    "\n",
    "(sec-vector-space-model-array-broadcasting)=\n",
    "### Array Broadcasting\n",
    "\n",
    "In what preceded, we have briefly touched upon the concept of array arithmetic. We conclude this introduction into NumPy with a slightly more detailed account of this concept, and introduce the more advanced concept of \"<span class=\"index\">array broadcasting</span>\", which refers to the way NumPy handles arrays with different shapes during arithmetic operations. Without broadcasting, array arithmetic would only be allowed when two arrays, for example $a$ and $b$, have exactly the same shape. This is required, because arithmetic operators are evaluated \"element-wise\", meaning that the operation is performed on each item in array $a$ and its corresponding item (i.e., with the same positional index) in array $b$. An example is given in the following code block, in which we multiply the numbers in array `a` with the numbers in array `b`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "0695bb25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2  8 18]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([1, 2, 3])\n",
    "b = np.array([2, 4, 6])\n",
    "print(a * b)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce1efbf7",
   "metadata": {},
   "source": [
    "Essentially, array broadcasting provides the means to perform array arithmetic on arrays with different shapes by \"stretching\" the smaller array to match the shape of the larger array, thus making their shapes compatible. The observant reader might have noticed that we already encountered an example of array broadcasting when the concept of vectorized arithmetic operations was explained. A similar example is given by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "051910f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 4 6]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([1, 2, 3])\n",
    "print(a * 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "377ddf8f",
   "metadata": {},
   "source": [
    "In this example, the numbers in array `a` are multiplied by the scalar `2`, which, strictly speaking, breaks the \"rule\" of two arrays having exactly the same shape. Yet, the computation proceeds correctly, working as if we had multiplied ``a`` by ``np.array([2, 2, 2])``. NumPy's <span class=\"index\">broadcasting</span> mechanism stretches the number `2` into an array with the same shape as array `a`. This stretching process is illustrated by {numref}`fig-vector-space-model-numpy-broadcasting`:\n",
    "\n",
    "```{figure} images/broadcasting-simple-hq.png\n",
    "---\n",
    "name: fig-vector-space-model-numpy-broadcasting\n",
    "width: 50%\n",
    "---\n",
    "\n",
    "Visualization of NumPy's broadcasting mechanism. Here the scalar 2 is stretched into an array with size 3.\n",
    "```\n",
    "\n",
    "Broadcasting operations are parsimonious, and avoid allocating intermediate arrays (e.g., `np.array([2, 2, 2])`) to perform the computation. However, conceptualizing array broadcasting as a stretching operation helps to better understand when broadcasting is applied, and when it cannot be applied. To determine whether or not array arithmetic can be applied to two arrays, NumPy assesses the compatibility of the dimensions of two arrays. Two dimensions are compatible if and only if (i) they have the same size, or (ii) one dimension equals 1. NumPy compares the shapes of two arrays element-wise, starting with the innermost dimensions, and then working outwards. Consider {numref}`fig-vector-space-model-broadcasting-matrix`, in which the upper half visualizes the multiplication of a $4 \\times 3$ array by a one-dimensional array with 3 items.\n",
    "\n",
    "```{figure} images/broadcasting-matrix-hq.png\n",
    "---\n",
    "name: fig-vector-space-model-broadcasting-matrix\n",
    "width: 50%\n",
    "---\n",
    "\n",
    "Visualization of NumPy's broadcasting mechanism in the context of multiplying a two-dimensional array with a one-dimensional array. Here the one-dimensional array `[1, 2, 3]` is stretched vertically to fit the dimensions of the other array.\n",
    "```\n",
    "\n",
    "Because the number of items of the one-dimensional array matches the size of the innermost\n",
    "dimension of the larger array (i.e., 3 and 3), the smaller 1 x 3 array can be broadcast\n",
    "across the larger $4 \\times 3$ array so that their shapes match (cf. the lower half of the\n",
    "figure). Another example would be to multiply a $4 \\times 3$ array by a $1 \\times 4$\n",
    "array. However, as visualized by {numref}`fig-vector-space-model-broadcasting-error`, <span class=\"index\">array broadcasting</span> cannot be applied for this combination of arrays, because the innermost dimension of the left array (i.e., 3) is incompatible with the number of items of the one-dimensional array (i.e., 4). As a rule of thumb, one should remember that in order to multiply a two-dimensional array with a one-dimensional array, the number of items in the latter should match the outermost dimension of the former.\n",
    "\n",
    "```{figure} images/broadcasting-error-hq.png\n",
    "---\n",
    "name: fig-vector-space-model-broadcasting-error\n",
    "width: 50%\n",
    "---\n",
    "\n",
    "Visualization of the inapplicability of NumPy's broadcasting mechanism in the context of multiplying a one-dimensional array whose size mismatches the outermost dimension of a two-dimensional array.\n",
    "```"
   ]
  }
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