Lectures/L-NLP/01-Twitter-nlp.ipynb

{
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     "end_time": "2020-11-02T16:20:36.095077Z",
     "start_time": "2020-11-02T16:20:36.085887Z"
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   "outputs": [],
   "source": [
    "from nbindex import floating\n",
    "floating.tableofcontent()\n",
    "\n",
    "dirtodataset='/v/courses/dataesp2023.public/Datasets/D-NLP/'\n",
    "dirtodataset='../../Datasets/D-NLP/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For previous topics\n",
    "\n",
    "Datavisualization manuals:\n",
    "* https://python-graph-gallery.com/\n",
    "* https://www.data-to-viz.com/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regular expressions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fastest way to find patterns in strings is to use a library that can match so-called regular expressions. In Python, it is the `re` library that does the trick, but regular expressions are also common in other languages, and the syntax remains almost identical in every case. \n",
    "\n",
    "You can even use regexp search and replace in most text editors, for example while editing LaTeX files, or code on your computer. `grep` and `sed` also understand regular expressions in the terminal. Thus, once mastered, regexps are going to appear in almost each and every one of your works or data cleaning. Below, some common everyday examples are shown, alongside with the Python usage.\n",
    "\n",
    "As to learning the syntax, check out the first two links:\n",
    "\n",
    "* This is a quite good introduction:\n",
    "http://www.zytrax.com/tech/web/regex.htm\n",
    "\n",
    "* This is a really good tutorial that shows how regular expressions match or do not match:\n",
    "https://regexone.com/\n",
    "\n",
    "* And this link covers Pythons regex library as a starter.\n",
    "https://developers.google.com/edu/python/regular-expressions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A cheat sheet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Some Definitions\n",
    "\n",
    "* **literal** \tA literal is any character we use in a search or matching expression, for example, to find ind in windows the ind is a literal string - each character plays a part in the search, it is literally the string we want to find.\n",
    "* **metacharacter** \tA metacharacter is one or more special characters that have a unique meaning and are NOT used as literals in the search expression, for example, the character ^ (circumflex or caret) is a metacharacter.\n",
    "* **target string** \tThis term describes the string that we will be searching, that is, the string in which we want to find our match or search pattern.\n",
    "* **search expression** \tMost commonly called the regular expression. This term describes the search expression that we will be using to search our target string, that is, the pattern we use to find what we want.\n",
    "* **escape sequence** \tAn escape sequence is a way of indicating that we want to use one of our metacharacters as a literal. In a regular expression an escape sequence involves placing the metacharacter \\ (backslash) in front of the metacharacter that we want to use as a literal, for example, if we want to find (s) in the target string window(s) then we use the search expression \\(s\\) and if we want to find \\\\file in the target string c:\\\\file then we would need to use the search expression \\\\\\\\file (each \\ we want to search for as a literal (there are 2) is preceded by an escape sequence \\)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **\\[ \\]**  \n",
    "Match anything inside the square brackets for ONE character position, once and only once. For example, [12] means match the target to 1 and if that does not match then match the target to 2 while [0123456789] means match to any character in the range 0 to 9.\n",
    "* **-**  \n",
    "The - (dash) inside square brackets is the 'range separator' and allows us to define a range, in our example above of [0123456789] we could rewrite it as [0-9].\n",
    "\n",
    "* **^**  \n",
    "The ^ (circumflex or caret) inside square brackets negates the expression (we will see an alternate use for the circumflex/caret outside square brackets later), for example, [^Ff] means anything except upper or lower case F and [^a-z] means everything except lower case a to z."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **?**  \n",
    "The ? (question mark) matches when the preceding character occurs 0 or 1 times only, for example, colou?r will find both color (u is found 0 times) and colour (u is found 1 time).\n",
    "* **\\***  \n",
    "The * (asterisk or star) matches when the preceding character occurs 0 or more times, for example, tre* will find tree (e is found 2 times) and tread (e is found 1 time) and trough (e is found 0 times and thus returns a match only on the tr).\n",
    "* **+**  \n",
    "The + (plus) matches when the preceding character occurs 1 or more times, for example, tre+ will find tree (e is found 2 times) and tread (e is found 1 time) but NOT trough (0 times)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Backslash Sequences\n",
    "* \\d \tMatch any character in the range 0 - 9 (equivalent of POSIX [:digit:])\n",
    "* \\D \tMatch any character NOT in the range 0 - 9 (equivalent of POSIX [^[:digit:]])\n",
    "* \\s \tMatch any whitespace characters (space, tab etc.). (equivalent of POSIX [:space:] EXCEPT VT is not recognized)\n",
    "* \\S \tMatch any character NOT whitespace (space, tab). (equivalent of POSIX [^[:space:]]).\n",
    "* \\w \tMatch any character in the range 0 - 9, A - Z, a - z and underscore (_) [0-9A-Za-z_]. Equivalent of POSIX [:word:])\n",
    "* \\W \tMatch any character NOT the range 0 - 9, A - Z, a - z and punctuation (equivalent of POSIX [^[:graph:]])\n",
    "#### Positional Abbreviations\n",
    "* \\b \tWord boundary. Match any character(s) at the beginning (\\bxx) and/or end (xx\\b) of a word, thus \\bton\\b will find ton but not tons, but \\bton will find tons.\n",
    "* \\B \tNot word boundary. Match any character(s) NOT at the beginning(\\Bxx) and/or end (xx\\B) of a word, thus \\Bton\\B will find wantons but not tons, but ton\\B will find both wantons and tons."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The `re` library in Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:28.930491Z",
     "start_time": "2020-11-02T15:29:28.924916Z"
    }
   },
   "outputs": [],
   "source": [
    "# importing the library\n",
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a hello world example for the `re` library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:28.946231Z",
     "start_time": "2020-11-02T15:29:28.934052Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['apple', 'pear', 'plum', 'cherries']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re.findall('\\w+','apple pear plum cherries')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T22:18:29.178607Z",
     "start_time": "2020-11-02T22:18:29.173240Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['apple', 'pear', 'plum', 'cherries', 'are', 'tasty']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re.findall('\\w+','apple pear plum cherries are tasty!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The file `robinson.txt` contains Defoe's famous novel downloaded from the website of the [Project Gutenberg](http://www.gutenberg.org). Let's count how many times the pronoun `the` appears in the novel!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:28.964626Z",
     "start_time": "2020-11-02T15:29:28.949237Z"
    }
   },
   "outputs": [],
   "source": [
    "robinson = open(dirtodataset+'robinson.txt').read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:28.977145Z",
     "start_time": "2020-11-02T15:29:28.966966Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20623"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(re.findall('the',robinson))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's extract possible names beginning with a capital `F` from the novel.We exclude words that are supposed to be at the beginning of a sentence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:28.994734Z",
     "start_time": "2020-11-02T15:29:28.979442Z"
    }
   },
   "outputs": [],
   "source": [
    "# this is a find and replace command for strings\n",
    "# arguments are old_pattern, new_pattern, string\n",
    "# returns substituted string\n",
    "robinson_no_newlines = re.sub('\\n',' ',robinson)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because of the above excluding conditions, we check the preceding two characters of the names. But we would only like to return the names themselves without the preceding characters. Therefore, we use group capturing by putting brackets around the desired parts `()`, which makes sure that `re.findall` only returns the matched groups, not the whole pattern. If we capture more than one group, the returned list would contain tuples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:29.042579Z",
     "start_time": "2020-11-02T15:29:28.997173Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Father',\n",
       " 'February',\n",
       " 'Fernand',\n",
       " 'Finland',\n",
       " 'Firma',\n",
       " 'First',\n",
       " 'Flanders',\n",
       " 'Fontarabia',\n",
       " 'Formosa',\n",
       " 'Fowling',\n",
       " 'France',\n",
       " 'French',\n",
       " 'Frenchman',\n",
       " 'Friday',\n",
       " 'Frozen',\n",
       " 'Frye',\n",
       " 'Fyal'}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(re.findall('[^\\.!\\?\" ] (F[a-z]+)', robinson_no_newlines))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:29.090360Z",
     "start_time": "2020-11-02T15:29:29.046916Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'  Friday, ',\n",
       " ' \"Friday, ',\n",
       " ' \"Friday; ',\n",
       " ' \"Friday? ',\n",
       " ', Friday!\"',\n",
       " ', Friday, ',\n",
       " ', Friday,\"',\n",
       " ', Friday?\"',\n",
       " '; Friday, '}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(re.findall('[^\\w][^\\w]Friday[^\\w][^\\w]',robinson_no_newlines))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some command line examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T22:23:35.509562Z",
     "start_time": "2020-11-02T22:23:35.342112Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    1 10000 50000 /v/courses/dataesp2023.public/Datasets/D-NLP/random.txt\r\n"
     ]
    }
   ],
   "source": [
    "!wc /v/courses/dataesp2023.public/Datasets/D-NLP/random.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reformatting the output of `uniq -c`!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:29.235514Z",
     "start_time": "2020-11-02T15:29:29.093848Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     45 0.00\r\n",
      "    101 0.01\r\n",
      "    103 0.02\r\n",
      "    104 0.03\r\n",
      "    109 0.04\r\n",
      "     85 0.05\r\n",
      "    101 0.06\r\n",
      "     97 0.07\r\n",
      "     99 0.08\r\n",
      "    107 0.09\r\n"
     ]
    }
   ],
   "source": [
    "!cat /v/courses/dataesp2023.public/Datasets/D-NLP/random.txt | sed 's/ /\\n/g' | sort -n | uniq -c |head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:29.365389Z",
     "start_time": "2020-11-02T15:29:29.239139Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "45,0.00\r\n",
      "101,0.01\r\n",
      "103,0.02\r\n",
      "104,0.03\r\n",
      "109,0.04\r\n",
      "85,0.05\r\n",
      "101,0.06\r\n",
      "97,0.07\r\n",
      "99,0.08\r\n",
      "107,0.09\r\n"
     ]
    }
   ],
   "source": [
    "!cat /v/courses/dataesp2023.public/Datasets/D-NLP/random.txt | sed 's/ /\\n/g' | sort -n | uniq -c | sed -e 's/^\\s*//g' | sed -e 's/ /,/g' | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding `0.` to floats smaller than `1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:29.486805Z",
     "start_time": "2020-11-02T15:29:29.368787Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# This is a sample file for illustration purposes.\r\n",
      "# This is a comment line.\r\n",
      "But this is not, it just happens to contain a '#'.\r\n",
      "0.3\r\n",
      "0.4\r\n",
      "0.64\r\n",
      "1.45\r\n",
      "2.5\r\n"
     ]
    }
   ],
   "source": [
    "!cat /v/courses/dataesp2023.public/Datasets/D-NLP/sample.txt | sed -e 's/^\\./0\\./g'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Excluding comment lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:29.606998Z",
     "start_time": "2020-11-02T15:29:29.490179Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "But this is not, it just happens to contain a '#'.\r\n",
      ".3\r\n",
      ".4\r\n",
      ".64\r\n",
      "1.45\r\n",
      "2.5\r\n"
     ]
    }
   ],
   "source": [
    "!cat /v/courses/dataesp2023.public/Datasets/D-NLP/sample.txt | grep -ve '^#'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to feed a text to the computer?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First of all, we want to decide what elements we want as building blocks. A common approach is to break the text down to words. But even this task is not that simple as it first seems. The next string contains an example message of Donald Trump from the Twitter social network.\n",
    "\n",
    "Possible problems:\n",
    "* lower and uppercase\n",
    "* punctuation\n",
    "* emojis\n",
    "* character encoding\n",
    "* named entity recognition..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:29.615082Z",
     "start_time": "2020-11-02T15:29:29.610533Z"
    }
   },
   "outputs": [],
   "source": [
    "trump_tweet = 'Having great meetings and discussions with my friend, \\\n",
    "    President @EmmanuelMacron of France. We are in the midst of meetings \\\n",
    "    on Iran, Syria and Trade. We will be holding a joint press conference shortly, \\\n",
    "    here at the @WhiteHouse. 🇺🇸🇫🇷'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Does this work?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:29.626028Z",
     "start_time": "2020-11-02T15:29:29.617865Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "having great meetings and discussions with my friend president emmanuelmacron of france we are in the midst of meetings on iran syria and trade we will be holding a joint press conference shortly here at the whitehouse\n"
     ]
    }
   ],
   "source": [
    "print(' '.join(re.findall('\\w+',trump_tweet.lower())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Natural Language Processing\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## nltk\n",
    "Natural Language Toolkit - https://www.nltk.org/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora* and lexical resources such as [WordNet](https://wordnet.princeton.edu/), along with a suite of text processing libraries for classification[[1](https://monkeylearn.com/what-is-text-classification/)][[2](https://towardsdatascience.com/machine-learning-nlp-text-classification-using-scikit-learn-python-and-nltk-c52b92a7c73a)], tokenization[[1](https://www.geeksforgeeks.org/nlp-how-tokenizing-text-sentence-words-works/)][[2](https://text-processing.com/demo/tokenize/)], stemming[[1](https://www.geeksforgeeks.org/introduction-to-stemming/)][[2](https://www.datacamp.com/community/tutorials/stemming-lemmatization-python)], tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries, and an active discussion forum.\"\n",
    "\n",
    "\\*all the writings or works of a particular kind or on a particular subject, especially the complete works of an author"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:30.708736Z",
     "start_time": "2020-11-02T15:29:29.628581Z"
    }
   },
   "outputs": [],
   "source": [
    "import nltk\n",
    "#nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at our former task and find the pieces that carry any information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:30.730605Z",
     "start_time": "2020-11-02T15:29:30.712305Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "having great meetings and discussions with my friend , president @ emmanuelmacron of france . we are in the midst of meetings on iran , syria and trade . we will be holding a joint press conference shortly , here at the @ whitehouse . 🇺🇸🇫🇷\n"
     ]
    }
   ],
   "source": [
    "print(' '.join(nltk.word_tokenize(trump_tweet.lower())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look for a good tweet tokenizer that has Twitter-specific regular expressions built-in either within `nltk` or outside!\n",
    "http://www.nltk.org/api/nltk.tokenize.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tokenizers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:30.740451Z",
     "start_time": "2020-11-02T15:29:30.733179Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Let', \"'\", 's', 'see', 'how', 'it', \"'\", 's', 'working', '.']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.tokenize import WordPunctTokenizer\n",
    "\n",
    "#  WordPunctTokenizer – It seperates the punctuation from the words.\n",
    "tokenizer = WordPunctTokenizer() \n",
    "tokenizer.tokenize(\"Let's see how it's working.\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:30.752273Z",
     "start_time": "2020-11-02T15:29:30.743066Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Hello | everyone. | Welcome | to | this | lecture | ! | We | have | a | pleasant | sunny | weather | today | , | do | n't | we | ?\""
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.tokenize import TreebankWordTokenizer \n",
    "\n",
    "# These tokenizers work by separating the words using punctuation and spaces,\n",
    "# and it does not discard the punctuation, allowing a user to decide \n",
    "# what to do with the punctuations at the time of pre-processing.\n",
    "\n",
    "tokenizer = TreebankWordTokenizer() \n",
    "text = \"Hello everyone. Welcome to this lecture! We have a pleasant sunny weather today, don't we?\"\n",
    "\" | \".join(tokenizer.tokenize(text))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:30.766171Z",
     "start_time": "2020-11-02T15:29:30.755900Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"Let's\", 'see', 'how', \"it's\", 'working']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.tokenize import RegexpTokenizer \n",
    "\n",
    "# tokenizes according to the given regular expression\n",
    "\n",
    "tokenizer = RegexpTokenizer(\"[\\w']+\") \n",
    "text = \"Let's see how it's working.\"\n",
    "tokenizer.tokenize(text) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:30.780618Z",
     "start_time": "2020-11-02T15:29:30.769075Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['L', 't', 's s', ' how it', 's working.']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer = RegexpTokenizer(\"[^e']+\") \n",
    "tokenizer.tokenize(text) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:30.796258Z",
     "start_time": "2020-11-02T15:29:30.783645Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['@remy',\n",
       " ':',\n",
       " 'This',\n",
       " 'is',\n",
       " 'waaayyy',\n",
       " 'too',\n",
       " 'much',\n",
       " 'for',\n",
       " 'you',\n",
       " '!',\n",
       " '!',\n",
       " '!']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.tokenize import  TweetTokenizer\n",
    "\n",
    "tokenizer = TweetTokenizer(strip_handles=False, reduce_len=True)\n",
    "s1 = '@remy: This is waaaaayyyy too much for you!!!!!!'\n",
    "tokenizer.tokenize(s1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:30.809593Z",
     "start_time": "2020-11-02T15:29:30.799114Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['@remy',\n",
       " ':',\n",
       " 'This',\n",
       " 'is',\n",
       " 'waaaaayyyy',\n",
       " 'too',\n",
       " 'much',\n",
       " 'for',\n",
       " 'you',\n",
       " '!',\n",
       " '!',\n",
       " '!']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer = TweetTokenizer(strip_handles=False)\n",
    "s1 = '@remy: This is waaaaayyyy too much for you!!!!!!'\n",
    "tokenizer.tokenize(s1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Text normalization\n",
    "Stemming and Lemmatization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we are interested in the core meaning, often we can throw away third person forms or plurals etc.\n",
    "* **Stemming** allows the computer to group together words according to their various inflections that are tagged with a particular stem. For instance: “walk,” “walked” and “walking.”\n",
    "* **Lemmatization** is a bit more complex in that the computer can group together words that do not have the same stem, but still have the same inflected meaning. Grouping the word “good” with words like “better” and “best” is an example of lemmatization\n",
    "\n",
    "Further Reading:\n",
    "* https://www.datacamp.com/community/tutorials/stemming-lemmatization-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:30.833354Z",
     "start_time": "2020-11-02T15:29:30.816502Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "having -> having\n",
      "great -> great\n",
      "meetings -> meet\n",
      "and -> and\n",
      "discussions -> discuss\n",
      "with -> with\n",
      "my -> my\n",
      "friend -> friend\n",
      ", -> ,\n",
      "president -> presid\n"
     ]
    }
   ],
   "source": [
    "from nltk.stem import SnowballStemmer\n",
    "\n",
    "snow = SnowballStemmer('english',ignore_stopwords=True)\n",
    "for word in nltk.word_tokenize(trump_tweet.lower())[0:10]:\n",
    "    print(word,'->',snow.stem(word))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examples of Stemmer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:30.840694Z",
     "start_time": "2020-11-02T15:29:30.836612Z"
    }
   },
   "outputs": [],
   "source": [
    "from nltk.stem import PorterStemmer as porter,\\\n",
    "                      LancasterStemmer as lancaster,\\\n",
    "                      SnowballStemmer as snow\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:30.859244Z",
     "start_time": "2020-11-02T15:29:30.843611Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word                Porter Stemmer      lancaster Stemmer   Snowball Stemmer    \n",
      "friend              friend              friend              friend              \n",
      "friendship          friendship          friend              friendship          \n",
      "friends             friend              friend              friend              \n",
      "friendships         friendship          friend              friendship          \n",
      "stabil              stabil              stabl               stabil              \n",
      "destabilize         destabil            dest                destabil            \n",
      "misunderstanding    misunderstand       misunderstand       misunderstand       \n",
      "railroad            railroad            railroad            railroad            \n",
      "moonlight           moonlight           moonlight           moonlight           \n",
      "football            footbal             footbal             footbal             \n",
      "troubling           troubl              troubl              troubl              \n",
      "connecting          connect             connect             connect             \n"
     ]
    }
   ],
   "source": [
    "#A list of words to be stemmed\n",
    "word_list = [\"friend\", \"friendship\", \"friends\", \"friendships\",\"stabil\",\"destabilize\",\"misunderstanding\",\n",
    "             \"railroad\",\"moonlight\",\"football\", \"troubling\", \"connecting\"]\n",
    "print(\"{0:20}{1:20}{2:20}{3:20}\".format(\"Word\",\"Porter Stemmer\",\"lancaster Stemmer\",\"Snowball Stemmer\"))\n",
    "for word in word_list:\n",
    "    print(\"{0:20}{1:20}{2:20}{3:20}\".format(word, porter().stem(word), lancaster().stem(word), snow('english').stem(word)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stop words\n",
    "Stop Words are words which do not contain important significance to be used in Search Queries. Usually, these words are filtered out from search queries because they return a vast amount of unnecessary information. Each language will give its own list of stop words to use. Mostly they are words that are commonly used in the English language such as 'as, the, be, are' etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### When to remove stop words?\n",
    "\n",
    "If we have a task of text classification or sentiment analysis then we should remove stop words as they do not provide any information to our model, i.e keeping out unwanted words out of our corpus, but if we have the task of language translation then stopwords are useful, as they have to be translated along with other words.\n",
    "\n",
    "There is no hard and fast rule on when to remove stop words. But I would suggest removing stop words if our task to be performed is one of Language Classification, Spam Filtering, Caption Generation, Auto-Tag Generation, Sentiment analysis, or something that is related to text classification.\n",
    "\n",
    "On the other hand, if our task is one of Machine Translation, Question-Answering problems, Text Summarization, Language Modeling, it’s better not to remove the stop words as they are a crucial part of these applications"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Further reading: \n",
    "* https://www.analyticsvidhya.com/blog/2019/08/how-to-remove-stopwords-text-normalization-nltk-spacy-gensim-python/\n",
    "* https://medium.com/@saitejaponugoti/stop-words-in-nlp-5b248dadad47"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:31.006878Z",
     "start_time": "2020-11-02T15:29:30.861686Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /v/wfct0p/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to /v/wfct0p/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "# importing NLTK libarary stopwords \n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "nltk.download('stopwords')\n",
    "nltk.download('punkt')\n",
    "from nltk.tokenize import word_tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:31.014087Z",
     "start_time": "2020-11-02T15:29:31.009513Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\", \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn', \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn', \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", 'won', \"won't\", 'wouldn', \"wouldn't\"]\n"
     ]
    }
   ],
   "source": [
    "print(stopwords.words('english'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:31.036593Z",
     "start_time": "2020-11-02T15:29:31.016733Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Oh', 'man', ',', 'this', 'is', 'pretty', 'cool', '.', 'We', 'will', 'do', 'more', 'such', 'things', '.']\n",
      "['Oh', 'man', ',', 'pretty', 'cool', '.', 'We', 'things', '.']\n"
     ]
    }
   ],
   "source": [
    "# random sentecnce with lot of stop words\n",
    "sample_text = \"Oh man, this is pretty cool. We will do more such things.\"\n",
    "text_tokens = word_tokenize(sample_text)\n",
    "\n",
    "tokens_without_sw = [word for word in text_tokens if not word in stopwords.words('english')]\n",
    "\n",
    "print(text_tokens)\n",
    "print(tokens_without_sw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore a document\n",
    "\n",
    "1.    Take a document as the input.\n",
    "2.    Read the document line by line\n",
    "3.    Tokenize the line -> list of words\n",
    "4.    Stem the words\n",
    "5.    Output the stemmed words (print on screen or write to a file)\n",
    "6.    Repeat step 2 to step 5 until it is to the end of the document.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Term-document matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A useful algebraic concept for representing a collection of texts is having a matrix. On this matrix, it is possible to try many concepts and methods from linear algebra, machine learning etc.\n",
    "\n",
    "https://en.wikipedia.org/wiki/Document-term_matrix\n",
    "\n",
    "<img src=\"https://qph.fs.quoracdn.net/main-qimg-27639a9e2f88baab88a2c575a1de2005\">\n",
    "\n",
    "Let us have a look at such a matrix on some Trump tweets again http://www.trumptwitterarchive.com/archive:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:32.919680Z",
     "start_time": "2020-11-02T15:29:31.039047Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import cufflinks as cf\n",
    "from IPython.display import display,HTML\n",
    "from ipywidgets import interact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:32.985351Z",
     "start_time": "2020-11-02T15:29:32.922983Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.18.2.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We set the all charts as public\n",
    "cf.set_config_file(sharing='public',theme='pearl',offline=False)\n",
    "cf.go_offline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:33.010311Z",
     "start_time": "2020-11-02T15:29:32.988021Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   hello  omg  pony  she  there  went  why\n",
      "0      2    0     0    0      1     0    1\n",
      "1      1    1     1    0      0     0    0\n",
      "2      0    1     0    1      1     1    0\n"
     ]
    }
   ],
   "source": [
    "vec = CountVectorizer()\n",
    "docs = ['why hello hello there', 'omg hello pony', 'she went there? omg']\n",
    "X = vec.fit_transform(docs)\n",
    "simple_df = pd.DataFrame(X.toarray(), columns=vec.get_feature_names())\n",
    "print(simple_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The file `corpus.txt` contains a small collection of wikipedia articles fromt he disciplines of chemical physics (21 articles), experimental physics (24 articles), and computational biology (18 articles).??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:33.033358Z",
     "start_time": "2020-11-02T15:29:33.013186Z"
    }
   },
   "outputs": [],
   "source": [
    "articles = pd.read_csv(dirtodataset+'/corpus.txt',header=None)\n",
    "# articles = pd.read_csv('data/got_tweets.csv')\n",
    "# articles = pd.read_csv('data/obamacare.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:33.041171Z",
     "start_time": "2020-11-02T15:29:33.035884Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([0], dtype='int64')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "articles.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:33.058519Z",
     "start_time": "2020-11-02T15:29:33.043679Z"
    }
   },
   "outputs": [
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       "                                                   0\n",
       "0   the nuclear overhauser effect noe is the tran...\n",
       "1   a quantum solvent is essentially a superfluid...\n",
       "2   coupling is a coupled rotational and vibratio...\n",
       "3   in physics an effective field theory is as an...\n",
       "4   chemical physics is a of chemistry and physic..."
      ]
     },
     "execution_count": 45,
     "metadata": {},
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   "source": [
    "articles.head()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make an array counting the occurrence of words in each article!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:33.072829Z",
     "start_time": "2020-11-02T15:29:33.061341Z"
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    "scrolled": true
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[0, 0, 0, ..., 0, 1, 0],\n",
       "        [0, 0, 0, ..., 1, 0, 0],\n",
       "        [1, 1, 1, ..., 0, 0, 1]])"
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     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "c = CountVectorizer()\n",
    "c.fit_transform(articles[0].head(3)).todense()"
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   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:33.084185Z",
     "start_time": "2020-11-02T15:29:33.075181Z"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'above absorption abundance accomplished according achieved act actual addition aggregate albert all allowing already also although american an analytical and anderson angular another any appear applicable application are around as atomic atoms available awarded ball based basis be because been being below between biological bonds both branch branches but by can carver case centre certain change characterizing chemical chemistry close common commonly compensate complete component components compounds conditions conduction confirm conformation conjunction connected consequently consists constant controlled corresponds could coupled coupling cross currently decreases demonstrated demonstrating derived described determine did different differs dimensional dipole discovery distances do due each early effect effectively electron electronic electrons energy engineering enhanced entire equations essentially etc examined excitation exhibit experiment experimental experimentally explains explanation extension far field fine first following for formula freedom frequency from gap gas general generally give given gravity ground had has have helium help here higher highly however impossible impractical in increases infrared intensities inter into investigation involves irradiation is isotopes it its kinetic kinetics known light like linear liquid macromolecules magnetic make many mass matter mechanical medium metals methods microwave model molecular molecule molecules momenta most much nanotechnology natural nature necessary need nmr no nobel noe non not nuclear number object observation observe observed observing obvious occur occurs of often one only or order organic other otherwise overhauser particles peak peaks perhaps phenomenon photons physicist planck polarization population positions possible practice precise predicted prediction presence prize proceeds properties proposed published quantum radiation raman rather ratio referred region relaxation requires resonance results rigid rotational rotor rule see selection seminal shell should show shown similarly simultaneously slichter slightly small so solomon solution solvation solvent solvents some space spacing species spectra spectroscopic spectroscopy spectrum speed spin spins state states strongest structure structures studied subsequent successfully such superfluid suspended techniques than that the their then theoretical theoretically there these this three through thus to together tools totally transfer transferred transition transitions two under unpaired upon use used useful using variation vector verified very via viable vibration vibrational was well when where whereas which who will with without work would'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "' '.join(c.get_feature_names())"
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  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:33.136691Z",
     "start_time": "2020-11-02T15:29:33.086711Z"
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   },
   "outputs": [],
   "source": [
    "vec = CountVectorizer()\n",
    "X = vec.fit_transform(articles[0].head(100))\n",
    "df = pd.DataFrame(X.toarray(), columns=vec.get_feature_names())"
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  },
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     "start_time": "2020-11-02T15:29:33.139043Z"
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   "source": [
    "df[['physics', 'molecule','chemistry', 'experiment', 'effect']].iplot(kind='heatmap',colorscale='spectral',title='Term document matrix as a heatmap')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's go back to the Trump tweets!\n",
    "\n",
    "First, we have to create our own tokenizer, because the built-in one leaves us with punctuation marks and numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:33.626401Z",
     "start_time": "2020-11-02T15:29:33.612123Z"
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   },
   "outputs": [],
   "source": [
    "trump_tweets = pd.read_csv('/v/courses/dataesp2023.public/Datasets/D-NLP/trumptweets.csv')\n",
    "trump_tweets.dropna(subset=[\"text\"],inplace=True)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:33.636602Z",
     "start_time": "2020-11-02T15:29:33.629164Z"
    }
   },
   "outputs": [],
   "source": [
    "tt = nltk.tokenize.TweetTokenizer(preserve_case=True,strip_handles=True,reduce_len=True)\n",
    "ss = nltk.stem.SnowballStemmer('english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:33.653266Z",
     "start_time": "2020-11-02T15:29:33.639317Z"
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   "source": [
    "def custom_tokenizer(text):\n",
    "    \"\"\"\n",
    "    The input is the string to tokenize.\n",
    "    \n",
    "    The output is the list of desired words.\n",
    "    \"\"\"\n",
    "    words = tt.tokenize(text)\n",
    "    return [ss.stem(w) for w in words if re.match('^[a-z]+$',w)!=None]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The TF-IDF matrix\n",
    "term frequency - inverse document frequency is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus.\n",
    "\n",
    "It is often used as a *weighting factor* in searches of information retrieval, *text mining*, and *user modeling*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:33.676688Z",
     "start_time": "2020-11-02T15:29:33.656102Z"
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   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.decomposition import LatentDirichletAllocation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:33.745335Z",
     "start_time": "2020-11-02T15:29:33.679313Z"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 28)\n"
     ]
    }
   ],
   "source": [
    "c = TfidfVectorizer(tokenizer=custom_tokenizer,stop_words='english',min_df=5,norm='l2')\n",
    "# articles.text.str.contains('Clinton')\n",
    "#X = c.fit_transform(articles[0])\n",
    "X = c.fit_transform(trump_tweets[trump_tweets.text.str.contains('Clinton')].text.iloc[:100])\n",
    "print(X.shape)\n",
    "words = c.get_feature_names()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting an LDA model to it.\n",
    "\n",
    "is a generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. For example, if observations are words collected into documents, it posits that each document is a mixture of a small number of topics and that each word's presence is attributable to one of the document's topics. LDA is an example of a topic model and belongs to the machine learning toolbox and in wider sense to the artificial intelligence toolbox. \n",
    "\n",
    "Further reading for the Latent Dirichlet Allocation model:\n",
    "* https://programmerbackpack.com/latent-dirichlet-allocation-for-topic-modelling-explained-algorithm-and-python-scikit-learn-implementation/\n",
    "* https://towardsdatascience.com/topic-modeling-and-latent-dirichlet-allocation-in-python-9bf156893c24\n",
    "* https://www.youtube.com/watch?v=Y79sCtzddyA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Topic Modelling** is an unsupervised Machine Learning task where we try to discover \"abstract topics\" that can describe a collection of documents. This means we have a collection of texts and we try to find patterns of words and phrases that can help us cluster the documents and group them by \"topics\".\n",
    "\n",
    "**Latent Dirichlet Allocation** is a type of Unsupervised Machine Learning. We don't know the topics of documents before we begin, we can only specify how many topics we want to find. At the end of the parsing we can look into the results and figure out if they are helpful or not"
   ]
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  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:51:10.606681Z",
     "start_time": "2020-11-02T15:51:10.299125Z"
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   },
   "outputs": [],
   "source": [
    "l = LatentDirichletAllocation(n_components=3,n_jobs=-1)\n",
    "T = l.fit_transform(X)\n",
    "W = l.components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:51:12.967639Z",
     "start_time": "2020-11-02T15:51:12.958217Z"
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   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame()\n",
    "df['Weight in topic 1'] = W[0]\n",
    "df['Weight in topic 2'] = W[1]\n",
    "df['Weight in topic 3'] = W[2]\n",
    "df['Words'] = words"
   ]
  },
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   "cell_type": "code",
   "execution_count": 62,
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    "ExecuteTime": {
     "end_time": "2020-11-02T15:51:13.683875Z",
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    "df.set_index('Words').iplot()"
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     "start_time": "2020-11-02T15:50:47.673441Z"
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   "source": [
    "l = LatentDirichletAllocation(n_components=2,n_jobs=-1)\n",
    "T = l.fit_transform(X)\n",
    "W = l.components_"
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   "cell_type": "code",
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     "start_time": "2020-11-02T15:50:54.889716Z"
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    "df = pd.DataFrame()\n",
    "df['Weight in topic 1'] = W[0]\n",
    "df['Weight in topic 2'] = W[1]\n",
    "#df['Weight in topic 3'] = W[2]\n",
    "df['Words'] = words"
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.set_index('Words').iplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T14:55:08.707441Z",
     "start_time": "2020-11-02T14:55:08.659437Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic 0:  thing like great make vs veri bush america donald hillaryclinton realli clinton poll onli vote android know video need new\n",
      "Topic 1:  onli run android know video fals new presid twitter beat poll need debat vote realli america trump vs hillaryclinton like\n"
     ]
    }
   ],
   "source": [
    "topics = pd.DataFrame()\n",
    "for i,line in enumerate(W):\n",
    "    topics[i] = [ words[ind]  for ind in np.argsort(line)[0:20] ]\n",
    "for T in topics.columns:\n",
    "    print(\"Topic %d: \"%T,' '.join(topics[T]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T14:54:14.358401Z",
     "start_time": "2020-11-02T14:54:14.354700Z"
    }
   },
   "outputs": [],
   "source": [
    "#!pip install --user wordcloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:26:25.130562Z",
     "start_time": "2020-11-02T15:26:24.815659Z"
    }
   },
   "outputs": [],
   "source": [
    "import wordcloud\n",
    "from wordcloud import WordCloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2020-11-02T15:26:13.181Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460x322 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wordcloud = WordCloud(width = 800, height = 800, \n",
    "                      background_color ='white',\n",
    "                      #stopwords = stopwords, \n",
    "                      min_font_size = 10).generate(' '.join(topics[1].values)) \n",
    "\n",
    "plt.clf();\n",
    "plt.figure(figsize = (10, 7), dpi=46, facecolor = None) \n",
    "plt.imshow(wordcloud) \n",
    "plt.axis(\"off\") \n",
    "plt.tight_layout(pad = 0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:04:33.654505Z",
     "start_time": "2020-11-02T15:04:33.092741Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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uXfpOKiEERqOB6dPzMzKixYsreO/7NiYVlOced9WqmWzcOJ+nntzX/7vfH+HYsWZmziwcdnuryZjwdA+xb6OqsmHBDDYsmDFomdlo4K51i7lr3eL+39fMnta/PNl2Z/PxGy4e8rj5Thtfve+6Ibcdb4QQXDGrip9t3UFXcGDMZXcwxGunapmZl77HX0rJ0bYOjiYJGjcoCjfOnzumkBxFCO5ZtpgnjxyjtsszaHlU03i5uoZtdQ2sKC/jziULuXjmdPJttgsiwHw0ZNQoMWtWMatWzUzrCySEYOWqmSxeXDHg97q6Dk5Wt2ZyWBNCn7d5pCErQogxf7GNRpW33LsWl8ua1r4UVbBhwzyMxoFmgrPNIsORasx9y8/+G25Z3/KhlqX6S7XtRDEtx83qaRWDfu9LfwzHR+YEfOF4NcEkkQLlbhdrK6eN+dzKXE7+7fJLybEMrZiEYnG21Nbz2Sef4/7f/pFvvrSJ3aebCEZjExaeNVnIqLC86KKZWK3G1Cv2YjYbuPTSeQN+i8U09uzNbBGCTCOEIDfPjsl0RjGPxeK8+MKhpOFS4830ygKWLEn/5RFCUFlZgMUy8F6dbcfMMnJURXDzwuQa3+GWNo63p/cxAvCEw7xcXZN02cZZMyiwjz2dUwjBVXOq+PINV1Hmcg67blzXqe328Kvtu3j3H/7Mu//wOL/avouarm7i2sQ/8+eDjAlLg0FhztySEX3thBDMX1A2SMBWV7cSjw8OmJ1MzJhRSFGRq//fUsLf/76bB3/9Gu3tviHjLceDhQvLcDhG5sSwWIzY7QO3CYWig+xtWdJHCMHqaRVMyxncD6nP/phOIV0pJXsbm5OWNbMYDNwwytjKZKiKwrXz5vDze27junmzMaeRSRWKxdjT2Mw3X36d+377Rz775HNsqa0nFLuwtc2MCUtVVSgrG3kWRkGBE7d74FeypblnUDrjZMPttnH99UsHpAxGInEefXQb//ovv+O3v3mdurqOUU3NR8qcOSP7SEEirfNc+6r2JtEQxpOElzq5Q+mlE6fwhiNJl51NX3rjuWE9APOLCllYPLwTbqQoQjC3sIDv3HID37v1RtZMr8CUhudeSklnMMjfDh3hQ3/6Gx95/O+8VH3qghWaGROWVqtpxNoNJMJtznXmeDzBAXGYkxFFEdx8ywrWrRvYPkBKaGzs5re/fZ1PffJhvva1v/Paa8fo7gqg6zLjD5EQguJiV+oVh9g2S2YRQnDdvDmJUmbnUNftYW9jc8pnoM3nZ0ttfZJ9J8qdWTNY9u3MvhNFSq6ZO4uf3X0b37/tRi6fNRO7KT2zWjgeZ3NNPZ/861P8y9+e5kBL64TOriaCjF11i8U44kIWkBA6TudAYRmLaQSD0Ukfb+lwmPnXT9/Iww9v5tln9hMMnkm5lBJ6ekK89upRXt90jJKSHNavn80VVyxg1uziEaUXDofRqGId5zjCLOkjhGB+USGLSorY2dA4YFlU03j66HEumzVjyPQDKSWba+tp9wcGLcu1WofUWjOFEAKH2cQ1c2excdYMjra288SRY7x04hSNXl/KosrheJwXTpxkd2MTH16/hvtWLMVsyMyzfr7JYCGN0VfQMZ0TCC2lJDbK9MGJRAhBTo6ND33oSi6+eA6PPbadvXvqBpkQdF3S1NTN44/v4Jln9rNiRSW33b6KJUum9RcgGS2KkijQkWXyYDUauH7eHHY1NA6Kkdxa10Crz0/pEA6VuK7z7LETaEm0zzXTKqjIcU+I4OkrhLGsvJTFpcV8YN1qttU18NSRY+xsaMIbDg+bpNoVDPGdV16nze/nE5ddnLSi/FQjc8V/x3D/RJLv7EQp8JmYFRuNKitWVLJoUQWHDzfy7DP72bmzBo8nMGj/gUCE118/zs6dNVx66Tze/vZLKK8YeTmsPoTIFl6ebAghuHzWTB7Yup2OQHDAslafn6219dyxZGHSe17blZiqn4sqBDctnIvxPKQgqopCkcPOLQvnccP8OdR2eXj66HGeOnKc2q7upIIdEpr0Qzv3UuJ08o6Llk/5+MyMCcu4po/aRhGNDdTEEuXEJuahyJRTQwiB2Wxg+fLpLFlSQXOzh61bq9n02lGqT7YRPUfbDIdjPP/8QY4da+ZfP30jixZN/R4l40Fc60bXPRgNQ9cMkFIS0+oBickwY0LHNxRlbifrKqfxxOFjA37XpeTZY9XcvHD+oBqeUko21dTRk8QJ1BfDeT6fkb6kgzmF+cwqWMf9K5fy/PGT/H73fo63dyT19Mc0jV+8sZMNMyupyh+9UjAZyJhEivTWTRwpUkr8/oG5r6qqYrONf9UTKRPhMpkkUbdSpaIij3vuWcO3vn0f3/jGvdx407KkNtj6+k6++52naGzsviA9iGPFG/wLLZ7/RMrhug5Kuny/otP3ywkbVyoMisKNC5Jrgnsam6n3eAbd73A8zitDtI64YnYVuTbruIx1NChCkG+z8dblS3jovjv5pw3rcJmT285bfX7+cnDqF0zJmLAMBqMDHBwj2e7sHGUAp9OCKU0v3FiIxeJJaz9mgr7sEavVxLJl0/nEJ67jO9+9j9tuWzkorrS+vpO//GVnNsYxCQ7rNRS4/hkhUjmxJBNnvEmNEIJVFeVMT5K/7QmFeP2cYr6yt23s4da2Qesnev3MnnTmlr5nPM9m48MXr+FL114xpPf89Zo6AhmqW3q+yJiwjMc1mps9I96uq8tPT89Au05xsQuTaWjPejJNfjQmAI8nOG7C8lwMBpXKygI++rFr+OQnrx8UEP7GtpN4PMEhtr6wkDKGN/gUkdjJs36TBCM7CYQ3IaVE03vo9v8ef+hFNN3DuYJQ18P4w6/R7X+EUHTvgOWJ7X34Qi8l9hF+HV0PnbVcJxKrxhN4DE/gMSKxGqTMfIxprtXCVXOqBv0ugeePnyRyTqXyLbX1SeMwF5UUMb94+Jz9841BUbh54TxuW7Qg6fLTnp5BOfNTjQwKS52T1a0jmkpKKampaScQGPiAzJhROCCV8FzO9bxLKQkEIiOexjY2duH1TtwN7GstcfkVCwa1wOjo8NHVNXRL0gsLhUDkdbr9DyJlQmBIIrT3fJtIvC/FTyCEQiCyiW7/Q0jO2HyljNPh/V/aPF8lEjtMl+8XBCJb+5freg/NXZ+h2/8gkdgxOrzfo63nG+gy0lsK7gUaOz9GMLKNYGQrjZ0f7RfSmUQIwTVzZyfVto60tlF3VgGLuK7zysmaQbpxX09wW5K4zcmGKgTXzZ+TNKA9FIvjj6QOyJ/MZNSLsmt37YgqhmuazpYt1QOmn4oiWLps+J4lToflnFqR0NDQOaKHXdd1tm09eV6yVgwGlaXLpg/4TdN0wuGpPU1JFyFUXNZbCES2oOmJEmGR2DFiWisOyxUAqIoTt+1e7ObLONffH43X0RP6GyW5/0Nxzv9Qmvd1FJGw50kp8YaeIK53Up7/Y4pz/ovS3O/gC79AOHoAXXpp9/4veY73Upr7TUpzv0Wu4+20e7+LLgfHNo6V+UWFLE7SDtcfifJ6zZkaCA2eHg61DJ6C59ttXD47veI05xshBLlWa9K+OxKJPonMJKMho8Ly2NFmjhxpSkto9VW52bVzYLGAoiIX8+eXDrttQaFzkAPowMHT+P3pfbmklDQ1edi8+Xha62eaRBHkgWNVVWVQYYsLGatpKYpwEYhsRUodX+hZbKaVGNXSXsHQWzEoiYyIxusQmDAbE9XHFeHEbFzYvzwY2Ymmd9Dm+Rot3Z+j0/dTNK2baLyeuNZBXGvDal6NECpCqNjMF/X+nvlqV2aDyvXzB/fNlsCmmloiWiIddkdDY9Iiv2umV1DuHl2G1kQjpcQXiRDVBitMRkXFbpzarSoyKixDoSgPPbiJ7u5ASoEZCER46MFNg+yVGy6dR17e8P1KXE4rlZUFA35rqO/kxRcOpWW7DAQiPPjr12hr86ZcNxm6LqmtbSc4iql/n8lg02sDQ0ry8hyTPmMpkwhhw2W9Dm/wSTS9C3/4FVy2W4F0ssAGT1aFUAYsN6rlWM0XYTWvxmZeR0nul7GZVzJ0VOr4aD1CCDbOmpm0StCR1naavT5ius6rSabgRlXlxgVzUTOsVSZsunrGzQ4S2FxTRyRJEZxCh33MLYHPNxkPZjxwoIFvf/spamra0c+5IVImcqPb2rz8309eYNu2gb2WCwud3HTTspQB7kaTyvqLB+Zk67rktw9v5qWXDhGNxvuPdfZxdV2ntraD733vaV599eioz1HXdX7ykxf493//A394ZBvHj7f020zPPu6Z8z7TVqK52cNP/+9F9u9vGLDOsmXTyM0Ze9mtqYIQAof1GqLxk/hCzyAwYDWvHNAGY/B1TPzbZJiOJEokdiJxXaWfSPTMx8dqvghdBnFYrsRtuxO37Q5c1hswqtMwqAUY1RJC0Z1IqSOlTjCyG4Oaj0HNbIGKPkpdiZjLc/GEwuxvaqEjEGBfU8ug5dNz3Kyelvn422Asxvde3cyrJ2vpCYeTXuuR0Lf9nsZmHtt/KOk6C4oLybFO5oLeqclYUHplZQE9PUE8niDb3zjJ8ePNXHTRTJYtm05BgQujQcHnD3PsWDObXjtGU1P3AFulwaBy3/3rqajIT/lwCCG47LL5/OPve6ivP1MWv8cT5LvfeZoXXzjE2rWzKS3LwWRSiUTitLd52X+ggT276+juTtim3G4rs+eUDDIFpEMkHOPw4SYOH27i4Yc3U1qaw8yqQqqqiiguduN0WDAaVfTevkPtbV4OH2lk/74GOjoG1o3My3Nw+x0XId5kLUhNhumYjfPo9P0Mt+1uFHEmBTAc3Uc4doBA+HWi8Tq6/b/BqJbisF6DyTADl/VGWrq/hM28Dk3v6nUAJabubtsthCLbOd35AcyGOegyhC79lOZ+C1XJo8D1Sdp7vkMwsqv3WHspdP8bihhdB8ZUGBSFmxbM46kjxwdUEtKlZHv9aUyqmtRTfOWcKnKsmY+t1HTJM8dO8Ovtu5mZn8tlVTO4tGoG84sKcZpNGEbQTlhKiT8a5aUTp/jBa1toS5LTblQTnvJsBk8vd9+zGoNB5Uc/eo5gIIqnO8gLzx/ixRcOYehNpNc0PalDRVUFN920fFDJs+HIz3fw7ndfyne/+/QAb3o0Gmf79lNs336qt5e5QNMl8XOqGJktRt717ktxuazs3VM3JkdPOByjpqadmpp2XnrxMIoiUFUFRRFIyZDnDWC3m3n/By5nzpziKWHEzyRCGClwfoxgdCdOy9UDlukyjJQx7OZ12M3ren8Lkug3ZKTQ9a/YzZcQ0xoxGxeiCDOaTHyEFOGmNPcbBKO7icZrUIQFi3ERqpLoc+6wXIHJUEkougsQ5Ds/gMkw+uZf6bCyopQZeTlUd3QN+P1QSxu6lIPKsfXFVo6bgJEQ03WOt3dyvL2Th3fto8hhZ1FJEQuKi1hQVECZy4XDbMJiNPYK0MQMLqJpBCJRmrxedp9u4rVTtRxubSeqJa8Utnb6NC6ZMbbOoZOBjAhLu93MvHllzJhRgKoq/OLnL9PeW3W7r93CUFitJm6/YxX3379+2HChcxFCsOHSeYTCMf7fL1+hq2vwF22o47pzbLz7XZdy403LqKvrwGQyjDiTx2YzIwa3XQYSD5SuD19iTlESfYje+76NrFs3e0r1S88kFtMiLKZFg363W9Zht6wbcjtFseKwXpF0WSJY2o7DcilwaZLlCmbjbMzG2aMe90hxWSxcPXcWJzu6BtgmT3V1cbqnZ9D6i0uKmVdYOGHjC8fj1Ht6qPf08PTREyhCYFJVXBYzVqMRo6qgCEFcl0RicbyRCMFodMi88D7K3S4+ffmGtEu9TWZGLSzP/kaUlLgpK8tBVRWuuGIhM2cW8tiftvPGGyfp6QkmFShWq4mFC8u4867VXHTRzFGVd1NVhWuvXcLMGYU8/vgOdu+uweNJfjwAl8vKipWV3H33GubPL0VRFPLznbhzrP3CcigP7LnH/cQnr+PVV4+wdUs1p08n4jXTcS7ZbCamTctn4+XzueqqRb0tcEdeuPfMOWawksYU//JPZhQhuHbubB7euQ9/9MyHORSLEzqnNoIiBNfPn5NW1fLxQpeScDxO2D/66l/lLhdfueFqFk7ygPp0EUMYdod966WUnDrV1p/94nJZB3QFPNuZcexYM6dOtdHdHUDXJE6nhWnT85k/v5TKygJMprG32JRSomk6ra09VFe3UVvTTkenn0gkhtGokptrZ8aMAubOLe0X6n3H1DSd6urWfmFpMhmYO7ckLeEtpSQW02hv99Ha0kNTUzftHT683hDhULS3l7eC1WoiL89OeUUelZX5lJXl9ocJjfTcW1o8tLSc0UQURWH27CJstpF5GuNxjRMnWolEzsR2Op1WqqoujAd7MhKKxfjwY39PWtj3bIocdh55+1uYNk7l2HyRCPc89AdOdnalXnkUGBSF1dPK+cyVl7KouGgqP08DBj4qYZklS5aRI6XkD3sP8J/PvDjsC3brovl846ZrMabR2mE06LpkW30Df9hzgO31p+kKBjPywhsVhTmF+bx1+VJuXDgX9zBdI6cIWWGZJcv5otnr4383bU3a4hYSlpC3rVw2LiFDZ5OItZQ09njZ09TM9vrTVHd00tTjxRuJouk6Wl/I3TkyQhECRQhURWAzGpmem8PS0hI2zprBivIynGbTVNYmzyYrLLNkyTIQKSXBWIxANEpXMERHIEh3KEQgEiUUj6NpOrqUGFUFi9FIjsVCqCfC8SMtLJxRzPUbFmIah95A55kBwvKCO7ssWbKMHCEEdpMJu8lEkSN1vGk0Fuc/vvcEm3ZU84LFyOyKQhbOGT5Nearz5oxXyZIly5iQEiK9poR4XCeWJMXxQiOrWWbJkmXEmIwq77prPQW5DmZVFjKvanBlpQuNrM0yS5YsWZIzwGaZnYZnyZIlSxpkp+FZspyFlJJQOEYgFCEeT+Rrm4wqDpt5RAkU4UgMjzeEqirkum0Y1IReous6Hm+IaCyO3WbGYTOn3KeUkmA4RiAQIa7pGAwKDpsZq8U46hAdKSXBUBR/MIKmSYwGBfsY9znaccTiGv5AhEg0jpSJzq4WkwG7zYyqDq/P9SXABIIRQpFYfyKIxWzAYbf0X/dMkBWWoyASivLgt56kpaFz2PVWXTafG99+8Zs273skSCnxdgXY8/pxDu+qwdPhw2QyUD6riFWXzadqUfmAzKtMHzsQirL/aCObd57k2KlW2jp9hMIxhACHzUxZkZuLV83ihssXkeOyDhiHlJK/v7CfrbtPcc9NqygtdPPDh15m3+HTmEwGLl83h/ffewkmo4E/PrmLvz23D18wQnlxDu+4Yw0b185NWkBGSklLu5cnXjrIG3traG7rIRKNYzEbKS1ys+GiWdx4xWIKcu1DXpdoNM4vH91MfVMXd1y3gjXLKunxhfn7C/t47Y1qmtt6iMY0rBYjxQVONq6by703r8KYpE3v317Yz7bdg7tP2m1mPvK2yyhIUYf23P11dgd4aesxNu86RWOLB58/jK5LzGYDOU4rFaW5rF5WyY2XL8JqGVg4WJeS9g4f2/fXsm1PDXWNXXT3BIlE4xgNKm6XlappBVx32ULWr5iJ0aiO+dnJCstRoGk6+7acoOZI07Dr5RY4s9bfNJBScmJ/Az/+wp84efD0oBz7x3/2Mre8awNv/adrMY9DNXldl/zwwZd57rXDxHq1SYOqJErs6ZLWDh+tHT72HjnNG3tr+NI/30hB7kDBcLKunU07TlJWnENrh5dtu2sQArz+MH9+ei85Lhs5Tiu/+uMWFEUQi2kcO9XKd3/5ItNKc5k9Y2AtTSkluw7U8/3/9yK1jYm0RJNRxaAmSh129wQ5cqKZV984wb9/+FrmzkyeVqjpkn1HGjl4vIm5VcXMqMjjGz99jh37a5EyEWAukQTDUTo9AaoqC4esdHS6qZvt++qIxzW0s+5RjsvKu+9aP6LrvfNAHT966BVqGjr66xwIQCiCYDhKd0+QmtOdtLR7ue7ShYP20dHp59Nf//OA7U1GFYNBJRSO4fWHaWjqZuvuU9x/22rec/f6UdWfOJussMxy3vF0+PnJF/7EiXMKIvcR8IZ47GcvUVyRx3VvXZdx7VJRBEvnl7NpezVLZxaxekklc2YW4XRY0DSdEzVt/Omp3TQ0d7PrYD1/eXYv77v3kqRC5blNRygrcvOVf70FVVX4v9++xqmGDv72/D5UReG6jQu5YeMiqmvb+enDr9HdE+S17dXMqhxYW+FEbTtf/+mztLR7Kch1cMtVS1ixqAKrxUSPL8SmHdU8+9oRjp1q5RsPPMs3PnMHRSmKstSd7uKHD77C/qOnWb+iivUrZ1Jc4ELTdWpPd7L7YAOXrKoaskziPTet5NI1swmGonj9Yf763D72H20c0bWWUrLvyGm++uOn6egOoCqCebOKufiiWcysyMdkMuD1hTh+qo19R09z5fp52G2D21Hk5tioLM/D5w+zbEEFq5ZMp6IkB6vFiD8YZdueU/zjhQMEQlH++ORu1iybwdL5FWOqFZMVllnOK1JKdrx0iOpDw7908ajG07/fymU3r8DmzGzOsRCCK9fPY8GsEqaV5fZPQYUQSClZMq+M+bNL+Pdv/IXuniCv7zjJ229fM2hqCOALhHn7HddwyUWzAOjsDvCNnz5Le6efeVXFfPRtl+F0WFgwu4SXtx5jz+HTVNe1E9f0/uNGonH+36ObaWn3kpdj478/dTPLFpQPGNPqZTMoK3Lz80de50RNG395di8fvG/DsMJg045qzCYD//zuK7hh4yKMxjOa1qWrZ/PWWy5C6e0FnuwaFRe4KC5I9APSdJ3dB+tHLCz9wQgP/G4THd0BDAaF+265iLfdvgbHOYVgrrtsYa8ZJPkJGVSFj7/zcqSUFBc4+9fruz4rF08jP8fBTx9+jWAoypZdp1gyvxwxhhJdWWNalvOKlJKD20+hp1F8+fSpNtpH0Zs+HWxWE7MqCzEZDb31MM+8fEII5s4oYun8hMDq9gbpGaLfvNNuYV5Vcf92s2cU9leYWr6wAofdkmiJrKpMK8sDoKsn2F8cWkrJ0ZOt7DqYqEx00xVLWDq/fNCYVEVw05VLKC/JQUp45Y3jeP3Jx9RHJBrnussWctMVi/udVWf/mYyGMU9VU7H7YANHTiZaaFy8sop33bWu38l17p/NahrS4SSEoKTQRWmRG6W3svvA66Nwxfq55LgTrVpOt3jSesaGIysss5xXpC5pPZ1eqbB4NE5X6+BCuROBwaBQXJBoexGPa0Rjyes85risWM+yq9qspl4BDOXFOWc0P0H/9DISjQ2w0+7YX0coHMNiNrB+5cyk2qIQAqfd3G/rbO/00djqGfYcLGYD129cmNLDPF5IKdmy+xSapmNQFW69ZhkW8/h533PdNuzWxDUOhqKDCoKMlOw0PMt5JxZJr8CslJJomuuOBiklkoQw9PrC+IOJcJZ4XCOu6XT29m5KNKBLvg+L2YjhrNJqqpJoLyKEwGE/EyYkepcByLMUHiklx2sSLXkVRaGjy8/uQ8ltuVJKotHE9QhH4njO6ZR6Lvk5DorOmrJONOFInPpeZ1Wu20bVtIKM1LLVe6+DxxsiGIoSjWtocZ1QOEq0t1tCJjpZZoVllvOLENhc6dkgFUXJuL2yD03Tqa5r56Utx9hzuIH2Tj+hSIxYTEPTdXRNH+ABHgpDb++loZalQtdlv1AOhqL8x/efSPscItHhPyRWi/G8VgaKxuL0+BKN2VxOa1LHTbpIKQlHYuw8UM+rb5zg2MlWenwhwtEY8bjee88Gl5cbC+ftykVCUWqPNQ+yIxRPyye38Px9/bJMLEIIps8uYdcrqVsT291WSqblZ3wMkWicR/6xgz8+sZseXwhFEeS57ZQXJbp09tnOjp1qpSZFbO3w/oPUz7SmS+K974SqCPJzHWk38TObh3+dhRiLe2Ps6PLMuRkNSr9mPVKklLR1+vnBr19i2+5TRGMaJqNKYb6T0mI3TrsZq8WEyajy2vZqgiPsrzUU501YHt1Tx3++5xfEz7H9fPi/7uSmd1xynkaVZaIRAlZtnM9Tv9tCJMVDfdHlC8gtdA67zkiRUvLsa4d58E9bicV1ZlTk8c471rFsYQU5Livms5roff9XL6UWlmNEVUS/V9ztsvHdL9yFy5GeNu1Mc73zhSoEpt5zi0Y14poGjDxuNhqN8+OHXuG1NxKN1S5dPZt7b17FjIp8nPYzWT/hSIwDRxuntrCUUnJgW3XSlyOdpl9ZLhyEECxeU8WGG5fx0p93DmlbmjG/jHs+chUGY2a9tcFwlH+8eIBYXMdpt/DZj1zH4rllg2Y2ui6HdOpkEkVJOJKOnmwhGo2jqmJEmTGTGbPJkPBOn+6kuyeIzx/BaR+5gK+u7+CNvTUALJlfzuc+eh1u5+D+6onpeObkyXlxi0UjMQ7tGJw2leXNicli5IP/cTu3v28jecUuDEYVIUBRFZw5Nq64YxVf/Nl7KJ+Z+dawwVCMxpZuAKaX5zJ/VklSE1A0Fqd5AjzxQsDieWUIIBSOcvB4c0acE5MBk8nA7MrEPezxhzh4vGnE5yalpLGlm0CvorV+xcwhNe/2bn//epngvGiWnnYftceaz8ehs0xChBA4c2y87/O3cOu7L6XueAv+niAmi5Fps4oon1mEwTT23N6kxz7r/6uKkjQrJ5FR08axU60ZP36yEa1bPpPf/XU7Hm+If7ywnw0XzcLlsAx5/n0CZ7Lb+YUQXHLRLP72/D6iMY0/P7OHi5ZOJ9dlG9G5id7QcgmohuT6nq5LNm2vxh8YPvZ0JEy4Ziml5Pj+Bnye0EQfOsskRgiBalApmZ7P2qsXcdVdq7n0puXMmF+G0Tz2dslDYbEYKSlMZKU0NHVTXdc+QNvRdZ1T9R388Ncv4wtExmUMZyMEVJbncfOVSxBCcPB4E9/9xQvUN3URi2v9Y9P1hDe4vdPHK2+cYN+R0+M+tj6klEhdngmfkomMHtnb4Gw4lswrY92KmQAcPN7MNx94jlMNHURj8f7t+86ttcPL7kMN/QH70BuMXuTqD/Tfsa8Of/DMfekLp3pp6zEefWLnkCFeo2HCNUspJYd2nEJ7E5ShzzL5sVtNXLl+Lidq2+j2Bvnyj57i+o2LqCjJIRiKcri6hc07T+IPRrhkVRVbklTdyTSqqnD/baupbexk885TvLTlGHuPnGbh7FKKCpyoisAfiNDa6aO+sQuPN8SH7t/A8oXTxmU8fcHkJ+s6CAQjib9QlAPHEoVkAqEoP/nNq+Tl2LBZzdhtJpx2M5evm9ufHtmH2WTgQ/dfSlNrD9V17by+4ySHjjczv6qYkiI3JqOKLxCmpd1L3eku8nLs/PQrbx2QWTRreiGL5pSy80A9uw7U8T8/fIpLV8/GZbfQ4fGz60A9O/bXUZDroKw4hyPVLRm5DhMuLKPhOIe2Z+2VWSYHQghuv3Y5tY1dvLj5KDUNnfz04dcSWTO9Wkl+rp1PvOcKFs4uZd8Ic6FHi8th4XMfuZ7f/XU7T758kM7uAJt2VA9aT1EEbqd13J1A/3jxAJu2Dz4+QCyuDfqIGA0qMysKBglLIQSV5Xl8+V9v4eePvM62PTV09wTZuqdm0H4VISgtdg+aVVgtRv7pXZfz1Z88Q3VtO1t2nWLrrlMgEskCiiKYP6uEf33/VRw41sTRk5kxn0y4sGyp76S5fnzDL7JkGQl2m4l/ff/VXLp6Npt3nuR0SzfxuE6Oy8qC2SVctnYOMysKiMU1Pv2Bq4lGNfJybAP2cdUl85k5rYD8XPsALSjHZeWjb7+MaExj3qyBfWouWzObkkIXbpcV0zlefiEEOS4rH3rbpVx32QLe2FvL4epmOroDSClx2MyUF+cwb1YxS+eXU1rkTnpuRkNCS+3yBBLHMY3ulb/9mmWsXTYj7fUVRVBZkZd0mRCCaaW5fOnjN3DkZCs79tVSXddOd28Gkt1morTIzYLZJaxcNH1A+Fbf9rMqC/nWZ+/glW0n2H2ogU6PH7U3kmDloulsWD2LXLeNXLcNk1GlINcx5jTPCe/B8+yj2/jBv/1hyOUf+Z+7uOVdGya1sTroD/Ppu36Ysp7lDfev5+NfvQflPOXiZhkZCZsZyN7HX3DGsXA+n8e+d/TcsdEbZD6Z35VUjPXcBt8zgRAZuybnr2+4runs35pclc+S5XyTqFwD6WTaTCRnBDZMtrGNlbGe20TeswlVefw9IY7vq5/IQ2bJkiVLRpgwYSml5HRNG+1N3RN1yCxZsmTJGBOqWR7eUUM0HJvIQ2bJkiVLRpgwm6UW1znwRnVGg0QvJPoM3VpcJ+gPEw5GiEbiaL0NtFQ1EbRtshix2sxYbCaEcv6dD1kyw4juv733/k8C59No6Hfq6JJwMEo4GCESjqHFdTRN7091NZkMWOxmbA5Lf6bO+TzXcROWfZH48WicgC9MzdEmThxInWUQDccI+sNk2mArALPViDrOZfNHgpSSeEyjvambY3vrObKrhtpjLXS3ewl4Q4SDUeK9xUtVg4LBaMDmMGN3WSkoyaFqYRlzl09nzpJp5Ba6UA3j0yo2XXRdEg5Gxu2DaDIbMI4y9CWTSJl4yYcq+qKoAovVlJYnNx7TaGvs5vi+eg7vrKHu+DD332TAZp+89384pJTEonE6mnuoPtDAiQMN1B5tprO1h4A3RCgQIRZNfByEANWoYrGasLus5Ba6mDm/lEVrqpi3vBKH20qm5MNInqkxhw6dcf1Lgr4w3u4AzfWd1B1rpv54Kw0nW2lt6CLgS9z8VNhdVqx2c8r1RorRpPIv372fxWtmjXlf6YYO3fKuDXz4v+8c1DdcSkkoEGHP68d5+S87ObTjFN6uwKgqLqkGhZwCJ0vWzuKKOy5iydpZA7SOiaSjxcN/vvvneLuHr9g9GgTw1n++lhvfdnHG9z1SQoEIX/nQr6g/kTzYubyqkP/61QewWJMXt+2//5uO8dJfdnF4Zybu/2yuvGMVi9fNSktQTxRSSjwdPna8fIRNT+7lxP4GfJ7gqPrhqKqCI8eWsQ+mEIL7//larr9/yDa+Ywsd6vtCBH1h2hq7aartoPZYE7VHm2lp6KKzxUMoEEHX9FFpGAFviIA383njRpM6ri0JkmG1Dy5+EIvG2b3pGI898BJHd9f2aw6jRYvrdLb08MrfdrP56f0sWl3FPR+5kqXr52S8nFkqdE3S2eKlp8s/LvsPpWjINVFIXdLd7qNjiOZpsWicrjYvZZUFSZftfu0Yjz3wIkf31GXw/u9i89P7WLSmins+fH7u/9lIKfF5grz4+A6e+t0Wmmrb0bWxTTk0TaenM3PPlhAQHEGhjRELy3Awyrc/8TDVBxrw+0KEA9ELpoRUprHaz2gWUkq8XQEe+eFzPPvotrS07JESi8bZu/k4x/fVc9M7LuGej1yFw22dNFrGm4WAN0Tb6a4BwlJKSU/v/X9uPO//68c5vreem9+5gbs/ciUO18Te/75iGIe2n+Khbz/JkV21F0yN2hELSy2ucXxfPZ3nqcveVEEI0a9ZSilpqe/kh5/7I/u2nECO88MT9Id5/Gcv0VjTzke/fBd5Ra6swJxA4jGN+hOtLLt4Tv/9b67r5Eef+yP7tk7M/X/sgRc5faqNj335LnIn6P5LKdHiGs//aQcPffvJjGqBk4Hzby2/QBECrA4zUkpaG7r4zqd+x+Gdg4sFjBe6Ltn67H50TedT37kPZ87QNQOzZJ5ThxMFN87c/4c5sqt2wo7fd/+lrvOpb9+HYwLuv67p/OOh1/nNd54aF835fJMVluNEQrM04/ME+ckXH5tQQdmHlPDGi4f43fef4f1fvG1iPMlZeQzA6ZNtRCMxIqEYP/7CnyZUUPYhJbzxwiEe/sEzvP8L43v/dV3n5b/u4jfffTptQSmEwGQxkFfkJq/Ihc1pwWBU0TWdUCBCT6ef9mYP4WBkzPbOs1FUBVVVMFuN2EbQtygrLMcJoQjMFiN/+umL7HotdedCBNidFuwuKzn5TuxOC0azAS2uE/CG8HT48HqCibCqETw3Upc8++gbLL14Dhdft2RctQuH28r9n7iWzpYefJ7ggL+gP0wsEicWPfM3VufGZKbtdBc9nQH+/uAmdm86lnqDYe6/3xuip+/++0bm5NJ1yXOPvsHyi+ey7trF43L/pZScPHiaB7/1JOE0CiQrqsLM+aVcdvMKlm+YS1F5Lla7+UyRZ5kwZYRDETydfg7vqGHTU3s5uO0kkXSTWgSYLSasdjN2l4XC0hyKKvIomZZPUXkuxdPyyC92j6gB3oiFpcls5I4PXN4bC5k+Pk+Q5x59I60OfnOXTx/psFKiqgql0zPfRnUodF3y2j/2sPW5g0PbqAS48xwsWTeLNVcuomphOYVlOYnQDyVRIEDKxMMYCcVoPd3Fsb11vP7UPo7sqiESSu/BiYSi/PEnL7Bk7axxnY7bHBZuedelQOL8E9W0e+NtY3FCgQghf4SgP0IoEO4PNWs93cWff/HKBZXd1d3h49GfPM8rf92V8v4vXTeL1VcuYtaicgpKc/pDv4a8/0/23v80r1c4GOXRnzzP4rWzxsXhFwnFePj7z9LZktqP4c538JaPXs3Vd68e+lkUYDQbMJoNOHPsVFQVcfltK9mz6Ri/+e7TKUP2EHDlHRdx09svIa/IhSvPjtFkQOnt6T7a8x+xsDSaDdz5gctHfKDmug5e+8eelMJy9ZULueVdG0a8/8mGrum8+OedQy53uKxcdfdqbrj/YiqqClHU4QOKTWYjzhwbsxaVc/Vdq9m3tZrfff8ZTuyvTytE6+Sh02x77iDXvGXNaE4nbfrOQVUFnBW5YrYYsZ/Tga8viqK7zcsTv9l8QQnLeEzjqYe3DLnc4bZy9d1ruP6+9SO+/1fdtZr9W07w8PefofpAQ1r3v/rgabY9f5Cr7149mtMZEiklO14+zJ40tOei8lw+8a23svySOYNij4dDCIHZamLtNYuZMb+MH3zmD+zbcmKYQcGh7ad46z9dQ0kGFaQR54Ynvngj/5uo42R6HONB1cJyPv/Ae/jAF29j+pxiVEP6zbj6HpzVVyzgS794LxtuXN6f9jgcWlznxT/vIBqZPAJpMt2TiaRqUTlfeOA9vP8Lt47q/lusJlZfuZD/+MX7uOTGZWltq8V1Xnx8R8ZjjaPhGM88spVYdPj92hxmPvxfd7LikrkjEpRnI4SgeFoe//T1tzB9Tsmw67ae7uLPP3+ZeAbb12Sr0k4wi9fM4gsPvJvll8wZ0UtyLkII8ovdfOyrd7Py0nlpbXNifwP1xyeiQ2GWoViydhZf+Ol7WHZxBu5/iZuPf+UeVlw6N61tju+rp+FEZvrRQEKrrDnazNHdtSnXvequNay5amFaH/bhEEJQNqOAt33yOgym4YPuNz+9n/rjmTvfrLCcQGYuKOMT37qX0sqCjGhUQghcuXbe+7lbyC9O3lbgbMLBCPu3VWeTCM4TMxeU8YlvvpXSyvzM3f88O+/93K3kFblSrh8ORtm/7WRG7/+eTccI+od36rjy7Nz49osz1jFACMHqKxcyd+nwvg1fT5BNT+7LWFB8VlhOEHaXlQ986XbKZxZmdOophGDG/FKuecsaUu1W9tpyLmQv9GTF4bLywS/dTtnMzHwo+xBCMHN+Kdfck879T3RWzdTUVNN09m9L3flg8eqqjD/3FpuJDTctGz5UTcLOlw+n9JOkS1ZYThDXvGUNS9fPHhcbnaIoXH7bKhxuW8p160+0XFCOlKnCNW9Zy5Lxuv+qwhW3p3f/6443Z+z+e7sCNNd1DLuOEIKlF49Pnvqii6pSFt1paeikpSEzDRKzwnICKCjN4eZ3bEAZo71mOMpmFDB7Seq+0d7uAJ1t3nEbR5bBFJTmcPM7Lxnf+z+zkNmLK1Ku5+0K0NWamfvv7Q7Q3e4bdh3VqDB7cUXGPxJCCEqm5+PKsQ+7XsAXprm2IyOmh6ywnADWX7uYkumZsVMNhcGosmj1zJTrRUJRurPCckJZf90SiqeN//1fuLoq5XqRcCylgEuXrjYvWgqTjsVqSsuePhrMFiP5JcPvW+oyY623s8JynDFZjGy8deW4ahV9zJxfltLbGItq+DzBrJNngjBbjGy8ZcXE3P8Fadz/SByvJ5CR+9/T6U+5H7vTitlqHPOxkqGoCq684TVLIGPKQVZYjjPlMwupnFs67vGEQgjyil2YLcM/mIk6g4FxHUuWM5TNLKRy3gTd/6L07r/fk5nizJFQNGXmbV/mzHgghMBsTi2IQ8HUKZjpkBWW48yCVTOwu9JP1h8LdpcVUxoPT9bBM3EsXDUTu3Ni7r/DZcVoTp2Ul3Z+dQriMS11nQIBYhyrq0zk/CgrLMeZ+StmTNixTGZjWl/xia4Y/2Zm/srKCTuWyWxAVVN7nWMZuv8GkyFllal4NI42ihYS6ZDImU8dFjRUe4+RkhWW44jFZmLa7KIJS+lTewsFpOJCqVw92bHYTEybVTxh918xqGllyOh6ZoSXO9ee8tz8vjCR8PjUttQ0HU9H6gLDrjxHRo6XFZbjiNVuHjdPYJbJj9VhIa84dWZNphBMbDnRwvJc1BQzmXAwMmSvorES8ofpbEm975JpeRk5XlZYjiMOtw1HTupA4SwXJk63FecFfP/zilzkpkiz1GI6x/bWZzz6QkpJ/YlWvCmcVVa7OWPpxVlhOY648+wpv7xZLlxceY5x8wRPBtx5dirnDl/9R0rJ7lePjoudfPdrR1M6K/OKXZTOGNxlczRcuHdyEuDIsU1IfF2WyYkzxzbqcmRTAUVVWH3lwpRa25HdtRzbW5dR7bKztYfXn9qXcr1l6+eMqHXEcFy4d3ISYLWZ35T1GrMkSFQ8P9+jGF9WXTaP3KLhWzOEAhH+9H8vEvJHMiIwtbjGE7/ZTHPd8Jk5JrORi69fmjGFJSssxxGDUeWCf1uyDInRaLig778QguKKPC65YVnKdXdvOsYjP3qOaDg2JoGp6zqvPbGXfzy0KeV+Fq2eyaLVMzOmsGSF5TiiGjJfaSXL1EE1KBd8s0vVoPb3uhkOXdP5269e5Zdf/TueDh9SyhEJzb6Yyqd/v5Wf/ufjKRu3Wexm7vjAFZgzFGMJ2e6O44oQ2c6wb2ouYK3ybKbNLub2923kwW89iT5MAHosqvHkb1/n0I5T3PzODay5ciHufAcGY/KK8VImmt4F/WGO7qnjHw+9zu5NR4lHhy/eIRTBtfesYcWGORk1g2WFZZYsWcaEEHDD/es5+MZJtr90eNh1pYSaI0385IuP8YdiN3OXTmPmwjJKpuXjzndgNBnQNZ2AL0x7Uzf1J1o5tqeOxtr2tNN0l66bzX3/fC0GY2bFW1ZYZsmSZUwIIbC7rHzov++ku93HiQMNKbfRNZ32pm7am7rZ/Mz+3tbPZ2mBvdP0kZo356+s5J++/hbc+ZnJ2jmbrM0yS5YsY0YIQen0fP7le/czb/nwvXGSIXWJruln/vSRCUohBCsvncenv/92ymZktnVHH1lhmSVLlowghKBybgmf/+m7ufy2lRhNEzNxtTst3P7+jfz7j985boISstPwLFmyZBAhBEXleXziW29l1eUL+MsvXqH2WPOwjp/RYjIbWby2irs/fBVL1s4alz4/Z5MVllmyZMk4FquJK++4iOlzivnup35PfYb6lasGlZwCB8vWz+HKOy9i0ZoqzBbjhCR/ZIVllixZMo4W19nx0iF+9Y0naKhuTbqOalBwuG1EwzGikTi6piFlIvRHVRUMRhWz1YTNbqZ4Wj5VC8tYeNFM5iydTn6xa8LjmLPCMkuWLBklFonzj4c28bv/fXbI4PGiilze+vFrWXbxbALeMOFghFhMQ+oSRRUYDAYsNiN2lxVnjh2r3YSiqonY5fMUv5oVllmyZMkY8ZjGn3/5Cr//wTNDVhpatLqKj375rkSDtSkUuJ8VllmyZMkIUkpee2IPf/jRc8MKyn/737dTVJ47pQQlZEOHsmTJkiGa6zv57XefJhxM3kYiv9jFR/77zikpKGFChWV6FyfbzzpLlqmHruk88/uttDQMUTZNwNX3rJ1yU++zmTBhqRqUNDsPZtu0Zsky1fB5gmx5Zv+QvWnNZiOXXL9kSleOn7CRG4xqWi0WetLo1pYlS5bJg5SShpOtdAzTPMyVZyevaGo375swYWlzWDBbjCnXO32qDZlt1Zoly5Sio7mHSGiYWeEFUK9wwoSl0aSSUzB8+XmA+hMteLsDEzCiLFmyZIpU5rOeDh8N1a1T2icxYcJSURSmzS5OuV5bYzeHd9ZM6YuaJcubDZvDMqzmGI3E+f0PnqWtsXvKvtsTJiyFIqhaWIZI0TxIi+s88dvXCfrDU/aiZsnyZkIIQUGJG0uKFg4Htp/kf97/S154bAftTd3EYxq6pve3mJjs7/uEBaULIVh40UwsNhMhf2TYdfdvrebvD27ing9fNe6VRLJkyTJ2yquKKKrIo/74MAUzJJw63MQP/u0RcotclM8spHxmITmFTqw2M4qaXs8ipTdv3GIz4cyxk1voJL/Ejc1h6e2oeQGUaCurLGTm/DIO76wZdj0trvPHn7yAoijc8q4NWO3ZlrJZskxm7C4LG29ZwcPfeyalhqjrks6WHjpbeti/tXpUxxMiMVtVVAWDqmJ1Wiidns/sJdNYeek85q2YjivXkbE2uDDBwtJiN3H5bSs5sqs25QUNB6P89rtPsff149z8rg0svGgmTrct0TFvqOZGMtFTOB7TiEXjRCMxejoDdLX1EApEuPi6pVlNdQKRUqLrEj2uo2k6mpaYdmnxxF84GCEUiNDW2I0WH74JFUBzXSdHd9ditZux2M2YzAYUVUFVlf7/9sXzCiGyH9gJRAjB9fet543nD3J8f+q2EmNFSpCaRNc04miEQ1G627wc3lnDk799nZJp+Vx2ywquvms1JZUFGRGaEyoshRBccsMynvjN69SfSF626Wy0uM7ezcc5tOMkBaU5VM4rHdDYSOqSaDRGOBAl6A8T8IYIeEP4ekL4ugP4PEGi0ThaTCO/xM1Fly/ICssMEo9p1BxtwtvlJ+ANE/CF++9D0Jf4dzgYIRyM9v5F+v8bCcfQ4jq6nmgjMFQu8dk884etvPD4DhRFoKgCo9GA2WbCYjNjsZmwWE2J/9pMWO1mbA4LNqcFu9OKzWnB5rBgd1rILXRSOa80K0wziBCC3EInH/3K3XznU7/j9Mm28zYWLa7TWNPOH370HC/9eSe3v38j1927bsxT9AkvpJFb6OTuj1zFjz/3x7ReEEi00Gyu66S5bohUqjTQ9cxXan6z4+8J8s2P/4bGmvYJOV5CIz0n73ikj4SAxWtm8fVHPooh29c9owghmLtsOl/82Xv51df/zp5Nx4ilaFs7nkgJrae7+OVX/sbhHTV84Eu3UVCaM2qBOeG5R0IILrt5OVfccVH2y34BoE+1BAIJMvvhHBf6+nzbXRYuu2UFReV553tIQOIju+mpvXzvXx+hs6Vn1F7381KizWQ28p7P3oy3y8+2Fw5lM3ayZJnCSCmJhmMc2V3LS3/eyd7NJ+hs8UyuD6mEvZuP88B//ZlPfec+bA7LiJW18yIshRC4cu3809fvxeH+By/9ZVdaBv4sWbJMHqSU6JrkyO5aHnvgRfZtOTFkebbJwrbnD/LsH7Zxx/suH3H65Xkr/iuEIKfAwce+cjfzV87gsQdepKW+a9IHpmbJkiUhKAO+MI//7CX+8dDrBLyhlNuoBgWz1YTVZsbhtmI0G1HV9CSW3ttXPBaN9zsMY7E4sUiceCx9RUuL6/z1/73K+uuWUDq9IO3t4DxXShdCYLaauP6+9ay8bB7P/2k7m57YS1NtO1o8M3YlIQR2l5WKqqKMlocSSurQlAm3yYp0x5XBQ6ZxvPOHJFnNsMRwx/BR7u0DM5HXOV3SC5ka28CklHg6/Dzwn4/z+tP7h21zK4SguCKXlZfNZ+n62UyfU0JuoROD0YCSpqA8c9yEvVnXE9N+T4ePtqZu6o61cGBbNcf3N6QltDuaPbz6t93c+7FrUmYUDjiXITS5CVfv+sbh6w5ydE8tezcf5/i+BlrqOwn6w8RjGpqmI3WdviEnAlMVFEWgGhQMBhWrw0JBiZviijymzSlm1qIKyqsKKSzNxWzNTMtMTdOpP95CODT8lCOnwEHJtPwJEyaxaJzaY80pv7SFZbnkF7vGPK54TKPueHPaUQ0TjdT99HR/Ek0fmFVic1hZsPwLWO23j2q/6d9/JyXT8ibd/S8qyyVvDPff3xPiB595hC3PHBh2JphX7OK2927kqjsuIrfINS7NxvqO3xfG9uiPX2Db8wdT9imfvaSCb/7h49icluFWGzDYSSMsBw1ASiKhKEF/hM7WHno6/Xi7A0QjMeK94QiqUcVsMWJzWHC4beQUOHC4bZgtRkxmI4o6mbWeLOONrvfQ2XYtmnZukLTA6f5P7I4PnpdxTWW0uMbvfvAsj/7keXRtaDFROa+Ef/7GvcxfUYmiTEzQjZSSgDfEDz/7KJue3DfsujaHhW/84WPMXlIxnIwYsGDSNiwTQvQGG5vJK3JldN9SSuIyhpQ6RiUzqZRSSmIyiipUVDH0ZZVSEtFDGBXTsOtlyTLZkFJybG89T/zm9WEFZWFZDv/ynfuZs3TahCorfSa3t33qeg68cRLPMIXEQ4Ewtceamb2kIu39T90a72Nke+ezPN38UMb2p0uNvzf+jL3drw27XlSP8Lvab1AfOJaxY2fJMhHoms4Tv3kdnyc45DqKqnDXh65k9pKJFZR9CCEon1nE4rWzhl1PSkacTPGmFZYRPURYz1yRYSEE02xzKTCXpVhTEtC8xGW211CWqUVLQxe7Xxv+I18xs5CNt6zIaAGLkaIaFOYvr0zpYOto9oxov1NmHtg3dfbFuonJKGbFgsuYhyJUpJT0xDqwqg5iMkow7sWs2nAaclGE0r99IN5DUPNjMzgH7dsTa8ducKPJGP6YB4NiwmXMQxWG3mNH8ca60NFxGfIwKWeCWgNxL/54N5W2BbiMeYP2rUuNnlgHOjpW1TExFyxLlgwipeTwzhq8nuEVjOWXzsOdf36fcSEEheW5KIqCNoyjJxQYvlTkuUwZYRmTEZ5o/H+0R06jCJVg3MfSnEu5rOgOpNR5sulXuIx5dESaicsogbiXDQW3sCrvagCO+nbyUuujmBQLZsWKQGBSE54wic7fG39OlWMxNf5DhDQ/Eslbpn+KXGMRXdFWnml+kEDcixAKRmHi2tJ3UGqZgRCC5lANO7teoC5whCuK72FN/nUDxv1c88PUBg5jM7hwG/OJ6SO7SVmynG8S9sq64bPtBCxaPXNSOFUtNnPK2K2RJsJMGWFpFGY2FN6K3eDCIExU+/fxdNODrMq7CpvqQJMap4PVvGX6J7Eb3Ozufpk3up5lUc56pISXWh9lRe7lrMy9kkDcyyN136ZQLe/ff1QPc9S7k1vLP4jLmEdMj+IwuNFknBdb/4DTmMftFR9FQWF717M80/wQb6v8d8yqlVmOpVTaF/C7um8OCqU44dtLtX8/91V+GrexgCPeHRzueWOCr16WLGMjHtNoqR++aonZYqSoPHeCRjQ8sUicVEE9pjQaKJ7NlLJZuoz5dEfbqQ0cxh/3EJcxtLNsf3NdK8kzlWBRbVTa5hPRQsT0KN3RVkJagPnO1ZhVK3mmIqociwfsWyJZ4FpDkXkaVtXRP8UPawHqg8dYlnMZdoMLq8HBYvcldEVb6I4mylAJIej7v3OpDRxmmm0OheZyLKqN2Y5l2A1TuyVoljcfWlxP2UjQaDJitQ8btzghSCnpbvemzE135dpHtN8po1l2RVv4e+PPsah2Cs3lidAfdORZXw+b6uyfAihC6V0m+50pRsWMQCABs2LFR3f/tgIS9sRz5N3ZIUZ9GBQjCmpa0+moHu6f9ifGYMyGDGWZckhdpuzgqCjivDp2+pC6pOZoU8oCPSPVgqeMZnmwZysSyZ0VH+Oq4rey2H0x6aZt2Q0uBIKeWHuijBQ6XdHBxUkFDNIOLaodu8FFR6Sxv6mSJ9qOROI0pi5BlWssojPaQlwmslz88R4ieuqUrCxZJhWClPU/Y9E4kRRZTeONlBJ/T4gD204Ou56qKlRUFY1o31NGxXEac/HGOjnu240iVA54tiSd9iYjx1hIpW0+L7T8gYvyrsIT66A5dIpia2XKbc2KlbX51/N6+9+J6mEMwsTOrudZlnMpLmMuUup0x9oJxHsIaQG6Y220hGqxG3JwGnNY4F7DHs+rvNr2Z0qtlZzw7cuGDWWZciiqgtUx/BQ7HIzS2tjN7CXTJmhUyXnjxYM01Q4fQ2l3WZk+p2REzqgpIywXu9cT0YIc8e7AYXBzScFN5JtLMClmhFCYZptLnulMX/I++6BBJDJlri97Jzs7X+Cwdztl1iquKXkbXdG+nGFBpX0BbtPgKiRCCJbnbsRucHPEuwNdxlmVdxWL3OtRhEpcj7Kt4yl6Yp04DTl0RVp4pe1xqhyLWZ13LUXmCu6s+Ch7u1/luK+bRe51uIx5OLJ2yyxTCKNRpbA0Z9h1pJTsfvUo669ZnNGiNekipaS1oYs//fSllIV4KueWUFiWM6L9T9rc8CxZxko2NzxzSCl59Ccv8NC3nhx2vfxiN1/7/UeZNrtoQkOIpJT0dPr5wb8/yhvPHxx2XSEE7//irdzx/stTjXHAwiljs8ySJcv5QwjB4jWzMJqGn4x2tvbw8PeeJugPT9DIEg6dxlPtfOdffsf2Fw6lXD+/xM3F1y+dGpXSs6QmofHH0XUPyDCgIBQHQjgRIvU3LuGM8iF1HxINIUy929rG/YsvpY6UQaQMg4wgSQT/ChQQKgIzQrECIy/tn+XMsyFlAClDIONIJAIVhKn3HlvTek5Gwsz5pVTOLaH64Olh19v8zD5sTgvv/sxNuPMd43aPpZT4PEE2PbmXxx94ieYUcaCQEPrX3rt2VP2BssJygonFjuH3fgvos6kI7I4PYTKvBfpehBjRyOsEAw8Tjx1KCExhRFWKMZpWY7Pfh8G4GCEGeyellGjxGkLB3xOJvIamNYGMIoQdVa3AZLkUq/UuVMPMjLxMZ4R6B/HYUWLRvcRih9C1JnS9G136QEZJWHZUhLAgFCeKko+qTsNoWobRtAqDYQ5C2KeU8JQyRijweyKRV5IuF8KG3fHh3ns11oK7OlL6iEX3EY1uIRbdj641o+vdiY8SGggTinCgKIWohipM5rWYTOtQDdMBw5jHYHNauPKu1Zw63DhsDKOuSZ774xvUHW/m3o9dw7KL52CxmYCx17OUUhKLxmlt6GL7S4d55a+7qDnalHax8NlLKrjp7ZeMqjBz1mY5wUQj2+jquAc4k2rlcP07dsc/I4RA14MEfN8jGHgQKZNXdxFKHg7nJ7HZ34UQZ7IQpNSIhJ/B1/NlNK1+yDEoajlO1+exWG9BjDLmU0oNXWslEtlEJPwcsehedL0dGHkhYCHsGAxzsdruSYxJyUzB3PG0WUoZJej/NT7ft0AODgUTwoHT9Xms9rcNuEcjP45E11sJB/9CKPgY8fhJIN3wHIGi5GEyX4rN/k6MplVjGguAp9PPf7zrZ5zYf+41TY7JbGD2kmmsu2YxS9bOonhaHjanBZN5+ELcifNOxHZGglF8niANJ9uoPtjAoe2nqD3anDJI/lxy8h187v/exZJ1s9N9vqZGPcs3E/HYSUBLFPvw/5iA/+cMJ3Sk3oXP+3WEsGK13YcQSqJOZvhZejyfQerdQ24LoGuNeD2fRwgLZsv1aQumPi0yHjtEKPhHwuEX0LVmzhb8o0HKALHYHmI9+wkF/4TT/SWMpjUZn0ZmCiljBP2/xj+koHTidP8HVtu9Y/gYSSBKOPQsAd/3iMerOTMbSXsv6Hon4dBfiYRfwmq7G7vzn1CU0Ttf3Hl23vVvN/GNjz+Evyd1vHA0EufwzhqO7KrFaDaQW+CksDyX3EInOfkO7E4rRnNC69XiGvGYRigQIeAL4fME8XUH8XT48HT6E/12RtnY0O6y8v4v3sbitWkLykFkheUkQNNOIWWEWHQbQf8vSUs7kyH8vu9jMq3GYJyDFq/G1/PfKQVl/+ayB5/3GxhNK1HV4tQb9BKNbMXT/ZG0jzMyNGKxPXi6Powr5xuYLddNumn5GY3ym7225IEIkYMr57+wWO9KaiZJ/zg+/L7vEww8lPQ4I9+fl2Dg18Ri+3C5v47BuGhU11YIwYoNc3n7p67n1998Mu0g9L52ua2nu2g93TXi446FnAInH/jSbWy8deWYMowm56f7TYYWb0TXW/H7foKUfdWdBaD2/iVH15oJBn+PlBH8vh+fM91Mvb0WP0k49ETaHTWFEBiMC1CUdLviCRKPmEriu6ySTtaVrrfh7fkS8fiRSdXtc6BGmURQKrm4cr46JkGZmH568Hq+lPhwphSUfde372+46yuJRXfh6f4Y8djBUV9boQhufPslvPPTN2BzmFNvcJ4QQjB7cQWf/fE7ufy2lahjjP3MapaTACl9BP0PEYvuAkA1zMZivQ2jcTGgEY1sIhT8M1L6zt2SSOg5TKa1RMLP9P4mMBjmYbHeisG4ANCJRrYQCv4JKb3nbK8TDj2FzXY/CGtaY1WUfKy2e/B7v85g07aCUNyoaiVG43xUw0xUtRQh3AhhRMoQmtZCPHaEaPQNtHgNQ2nRutZIwPsD3Hk/BCZDcYYoQf+DQ2qUilKAK+drmC03jtF8EMbX8zXCocdJPu0WKEoBRtMKjMZlqIZKhOJGoCB1H5p2mlhsP9HoDnStNek+tPgJejz/Rk7ez1HVkVc0T3w0VW57z0YKS3P59TefoLmuY1RnO1648uxcc89a7nj/RvKKxt6cD7LCclIgZZBg4FeAhsl0Ma7c7/Y+xAlbpNlyDQbjUryeLwADX1RNO4235z97BanAbLkBV86XUZQzqVxmyzUYjYvp8Xxu0Pbx+DE0rRmDUpXWWIVQsFhvIxh4EF1rAhQUpQCTaQ1my9UYTStR1DKEMAPKoIc0oc3oSN1DOPw0Ad+PkjhgEkQirxCL7sdoWn1ep+NnNMpvDyEoC3HlfBOz5doxCUopNYKB3xEKPkoyISeUPGy2t2G134uqVgCDnST90QlaC6HgnwgEfoXUB09747H9+L3fwp37HUbzMRJCoBoEG25cRtXCMh79vxfY/PR+gr6Ji68cPCZw5zlYd+1ibnr7JcxcUIaaIp99RPvPpDfcH6sjEGvAbpxGZ2g3APnWldgN0xFCJJwQWhcd4R3EtB5cpjnkWhajS43mwEuU2DdiEFZO+5/FZiwlz7ycQLyeQKyBIuvFk9bgPxKSecP7UJQicgt+h8GwMMlLEMXT9VEi4aeG3LfBMI/c/N+hqKVJtg/R3fkeopFzewQZyMn7Re+Lnq6jR8fv/SbR6BYs1jsxW67sfXkHC8dh9oKUEI8dxNP9MbR4ddK1bI4P43R9cVT3PhPe8FQ2SkUtxZ3zLUzmK8YoKCXx2AG6O9/RG1UwENUwC3fOtzCa1gLpdS2VUica3Yy3+9NDfJBMuHP/F4v11jF9jBLhajonDjTw9O+3sPvVo3S1+1JW/ckEQoDNaaVyXgnrr1nMumuXUFpZgJKZfvbj5w3viRznaPePsRsrcRhnEIg1cMr7e9YU/wCboYxg/DR72/8Hi6EQs1pAvf8flNquZIbrbmp9j+Eyzcas5nPC80tcprnkFS2jJfAaEb2LIuslmRzqpMRsuRqDYd4QN9mI1XYnkfDzQLJCHAKr/R1JBWUCC2bzFUmEZbx3OjwSBHbnx7HzcYQYbdCxQAgwGBfjdH2Rnu6PJg2VikW2kQiVmfipeEqNUi3DnfM9TOYNGfiQR/H7fpRUUCaO8/3e0J/0r7UQCibTBpzuL9PT/fGz7OFnjhn0/wKz5XKEGH2tgr5p+fwVlcxZMo32Zg/7Nh9n12tHOXmokc6WHmLR+JiFpxACoQjsTgsFpTnMmFfKojVVLFg5g7IZBZitpnGdgWR8Gh7VvKws+hA5poXEdB9bWz5CV3gPNkcpNd4/YjOWs6zgCwgMdIX3sLv9Pyh3XIPdUIE/VkdcBrEbK4nq3cR0H75YNUXWSyadVzTzKJgtVzGUQ0YIgdG0HEXJRdcHl5dTlHzMliuGvE5CCAymxSRu+UA74XAxmUPtSwhn6hXT3JfZchkm8yW9H4KBaFozutaBaki/ZWl6DC/cUmmUqlqJO/e7GE3rM/JsRqO7iEZeTrLEgMP5KYymlaP2XpstV2Cx3k4o+PCg5bHYQaKRzZgtN4z5PPqEZun0fEqmrePae9fi7Q7Q0dxD3bFmGmvbaTvdjafDh98bIugNEYtqxOMauqYjFIFqUDCaDFisJqx2M3a3DXeunbwiF0XT8iirLKCwLJecAgdWu3nAscebjAtLm7EUR++026DYMCk5xPUAEo3u8AEkOvvavwJAXIaJaF1EtG5cprn4otWEtTbyLEvoDh/EH6snFG/DaZqZ6WFOOoRw9GaxDH3ThchBVSuSCkvVUIWqDt9ZUlEKEMKOlD0Dfte0kbUEzTxmzJZrkwpLXfag6x5UMiksBULYhlyaSqNU1Rm4c3+QMVuqlHFCwUeTatZG49LeafJYNFcDVvt9hEN/QcpzA7mjhEP/wGy5nnTrw6ZD4oMqyMl3kpPvZPbiiv5Ac61XOGpaoj4sMmGSQfTWlFUEihAIVUHt/ROZmVaPiYwLSwXTsGETLtMcCq0JuwtAmf1q7MZpaDJCnfdPiLiRGa67iWgeuiP70WUUywjiAKcqipKPogyfryqEEUUtTToLNxoXkOp2KsKBEJZBwjLx796n9TwghMBoXASYgXOqz8tIkuljJo6ZvANhSo3SMBd37vcxGpdn7OXVtGai4U1JlihYbHcNOdZ0EUJgMMzFYJhPLLZr0PJYdDe63oGqjqwY7mjGoapizCE854sJ84YLVPIsywnFmym2bUAVFhItH0IYhA2boYyI1o0iDNgNFeSaF1Hn+wsWQxFGZWS9MqYiipKDUFJNbVUUJZltSaCq00k1tRTCkjTdLZFbfH7jGYXiRih2ZJJWHTIDQdnnHA1FGSyAUmmUBsM83Lk/zEiu95ljSmLRnej64NAbIVyYzRsyciwhbBhMi5IKS01rQYvXjbuwnOpMoIgXVLnfSlyGeKP1k+zt+B92tH2GAx3fRJcxLGoBuowBCiY1F6dpNt7oCZzGmRM7zPOEUPIYLoC8dy1E0nhIBUUtTv1Sib7A8HOQGiNPpcssAiOCIfKW5cjzzVMd7Vyba0Kj/BU+3zeTTocNxiW4836SUUHZe2Sika0kizc1GGYkZhIZIKFdzhliaZx47EhGjnMhk1HNMse8gNk570IRvRVGUKhy34fVkPDQWtRiVhV+la7IAcLxVgyKgxzzgv715+V+EEWYEajYDGUszv80btOCTA5x0qIoOaSaBife0WQfDhVFSaf5kpL0GH2N3c4vgokzAwjEWbOVvvjGITVK41Jycn+ImsKmPDpixGPJi9WqhqohPo6jY+i0VommnUZKed7tgpOZjApLu7ECu/GMIV4IhWLbhrP+LTCqToptFyfdvuis3w3CSoXjhkwOb1IjkkwLk6xFcoEiEGJymioScbyJQPREbKkE2deVU+/9k+iyh4nSboUwITD3jk8nHHoCv/cbyR0sptW4c77XK7gyL0h0vRNNa026TFFykXpPxj5jUg59fRNmAJ3Us5s3L9kMnkmCEGOJIxS9GTPnjz6hKGUAXWtF0xrR4jVoWgu63oGU/t6CwBGkjPY6bqJIokgZARlG11MXb80IwgzCmCjuENmMr+e/kqSSgtG0BnfuD1DVynHTuHStO0kaaoJQ8BHCob9l7FhSDl30QsoQWWE5PFlhOUkQmMayMefjIU8IyERdy2h0K5Hwa8RjB9C05l4P9mSY3g+mz9EVjx3A6/kMup5Ms1N6M5NGnjs9EqTs6RVUyZYFh6xpmvlx9BVozjIUWWE5WRhzBsjENocCnXj8GKHA7wiHn0PXWhhrXcuJQmBG05rwer6AptUNsZZOwP9zjMalmMwbx0+z1L2cb+dalvTICstJw9Tw+Cd6+/QQ8P+cYOC3yImaOmcQKYN4PV8kHts//Hp6F17P58nJ+wUG4+B8/cyMZXCoVJbJSVZYZkkbKSW61oS353NEwi+RjkYkhC0RQykcKIobIVwoioMzTbVsCGFDygDBwK+HnJJmEl1vHWLqPRhNq8Xb83ly8n6GoqQRnjViMh0WlWW8yArLLGkj9S56PP9CNJIs26QPMwbjLEymdRjNazCoVShqPopwgzBxxlxwttARaPFTBIO/T9qmYfwxYjKvR9daicePDVoai+7A1/NfuHK+1Rufmcm0wKEdczb7ezCaLsrYsYYjkSqbFQfDkb06WdJCyjh+/4+JRjYPsYaC0bSmv1NlouDvCITKeQrvU9UK7M6PYbHeRTx+HE/XB3vrdA4kHHoKVa3A4foMQozBGXcOQ+eoCwzGxVhtd2TsWFnGxtQwlGU5ryRqLR4kFPwjyafeBmz295Gb/yvMlmsTqZsjna5OuCPWgsV6J7n5j2C1vQNFcWA0rsDl/p8hypXFCfh/1VvwInOOLKHkAsm0SzlxoVRZ0mLKCkspJZ2Rbk4Hm2kMtRDVB1aXiOsaTaFWTgebaQ93TqpeLlORUOivQzYps1hvxOH6zOiEZD8aDBM0nWlsjnfhzv1Ob7B54jVIlDO7FofrXyBpKFcYn/ebRCOvZux5UpScpHnqQH9WTZbJwZQVlgBvdO3mt3WP8d+HvkdzaKDBPqSFeKLpeX5S/SA/Pfkb4hnUBt5sSBkYcvotRA52x8eHLXmW3jEiwwZNZxbR2xvIMki4C2HAZn8nNvs7SRa7mvCQf4F47HBGBJmiFA7ZAE6Ln+LcNiBZzh9TVlgKIbih5EreX3U/FtXcmz53BqfRwQeq3sY1JZeh96bUZRkdut6BlsSOB2A0LcNgHHvOtKa3Mqg823lCCBMO179gtlyXdLmm1eHt+Ty63jJmgSmEFYNxXtJl8dhxdG1i28ZmGZrzJiyllJzy19ER6aIh2MTu7gPUBU6jnaUB6lKnLdzBnu6DHOw5ii8WGPBwCiFQhjmFVMuzpIfUvUh9cDog0Puij83hkShTtpfJ80ETCOHG5f5vjMaVSdeIRXcOmSY5MhRM5ktI9irqehfR6PbsVHyScN684RLJk80vEtbCqEJFFSr1wUauL7mCq4svBWBb5y7+0fQCxZYCInqUqB7jPTPupdxaMkCTydZJGV8S9SST2xMVJW/MWqWUwWG87OcHIQSKWoYr5+t4uj6YJNNH9nrIp43JQy6EwGRah1ByknRhjBMO/gWL9UaSO4GyTCTnXe0KaxE+POsdfHz2e7ir4iaeaXkZT8yLJ+blz6ef5rby6/j47PfyqTkfoNJWzp8bn+qdVmeZKBIFg4cQiDJZ87T06St+G48dGNN+xoNET5nFuHK+2uu1PheNQOBXhAKPjMlDrhoqMZuTN+SLRDcTjbye1S4nAeddWM52zsSqWlEVlfmu2QS1EN1RD63hdiJ6lAWuORgUFaNiZFnOIuoCpwnGBwYuZx+j8UUI+5BVkeLx+mFLf6VCSh8B/08mrGDESBFCYDJvxOn69+S1JWUYn++bRCKvjFqgCWHCarsPkl1jGcLn/Ta61pgVmOeZ8y4sxVkaS599USLRpY4iBMpZy1WhJJadIx6z0/DxRSi5KEp+0mWx2L4hQ4qGI5FjHibg+1FvpfDJixAqVttbsdk/QHIPuQef54vEY4dGLdBM5vWYzVclXRaPHeh1KLVlTGAmrn8UXfdkhXCanHdhWR9sJC41pJScDjVjUoy4jE4KzfkoKDSGEh5HieSkv44CUx52NXPVo7OkRlFyMRiTtyTQ4qcIh/4+Yu1SSh9+73cI+H/JVKhWJIQJu/PjWKy3kuzznPCQfw5dbx7lEUw4nJ9ESdqhUxIJv4in60PEojuRo26zIZEyhhZvJBz6M56uDxHwPTDKfb35OO/pjm3hDn5T+yfyzbls79zDmrwV5JlyEcCVxRt4uO5xVuYuIaiFOOA5wtsq70IVKiEtzDFvNW2RTkJamL2ew3RFPcy0TyPH6KY10s7pYDMn/Kfoifp4o3MPuSY3c51VGJUher1kGQIVs+U6IuFXGCzY4vh930MoBVis1wHGIR0+CQ0mSiy6C7/vR0Qjr5+zP8FkNqoIYcfp/hK61kw0um3Q8lh0Fz7Pf+PK/TZCOEfk+ErYRxfgcH4ab88XkuTIS2LR7XR3vguL9UYsttsxGhf3VshXGSzA+8Ll4uhaF5rWQCy2n2hkK7Hovt52yhqKPTM9ft4MnHdhuSZvOTPs06gPNnFr+XUsz1mE2ptRcUPJFVTZKznmO0mu0c3H57yXCmuin09Yi3DcfwpN6lxRdAkhLcQJXw25Jjc5Rjdt4U6q/TU4DHbW5C+nIdREZ7SbmfbpWWE5QhKZLddgMPySePz4oOW63onX8yki4WuwWG/FYFyI0v8S60giaFozsegeIuEXe7Wjgf2rTaZLEEoOkfCTE3NSo0AIgaIU48r5Bt1d70eLVw9aJxx+CsVXjtP174zUgy2EgtV2F7rWjN/3Q5LFnUrpIRT8PaHg46hqKQbjbFR1OkLJTTjipIaUoUS/9d6K9brWga57ku4vS/qcd2GpKiqr85azJn/FoGUGxcAi91wWuecOWpZrcvOWabcOud+lOQtYmvPmaHY2EShKEXbnx+nxfCZpUy8pg4RDfyMcegohbIkGbMIEMoaUPnQ9AERJFoJkNK7AlftNYtH9RMLPMpnLlgkhUA1zcLm/Rk/3x9D19nPW0Aj6H8SgTsdqfwdCjKyCvRAG7M6PgFAI+H44TMm6CJpWi6bVjuY0soyC826zzDI1EEJgsd6K3f5eGKplLQAxpOxB0+rQ4ifQtNreghDJYjVFb5+bH6KqMzEal6bZuO38kvCQX4zT/cUh0jzD+LzfIhJ+eRTOk0Q/Jbvjo7hyvoGilmdiyFkywHkVlhXWUgrNyb2sWSYjRhzOf8Hh/GeEGKtQM2Ox3k1O3v/1d05U1GIMhlkZGel4I4SCxXo7dsdHSTZBk9KDr+dLxGMHRykwjVisd5Gb/xss1tvGnHs/GBVVrcRkXJ7h/V64nLdpuEBwe/n15+vwWUaBEAKJFbvzExhNKwn4/o9obPcIC/aaMZoWY7O/D4v1esDc7wgRwobRuIJYdNe4jD/TCGHE5vgwmtZIKPgo52rOmlaH1/NZcvJ+jqKWjTjTSQgFg2E+7twfEIvuIhh4hGjk9d62taOJIDCjqAUYTSuxWK7HZL4YRSkaxX7enIghvnqT1yU5xdG0jl4v8MAXy2Ccj8GwIOULFYvuJz7IsWDAbLksYSccBiljvVND/4DfFSUXk/myEdnXEnF6QWLR3UQiLxKL7kbTWhJ9rmUEkAhhAGFFEW4UtQSTaTkm86UYTSuH9BbHYyeJxfZz5hEUmMzrUdWStMd2ZozR3vMNnLNEYDQuwWCcPeJ9JjkIuu4hEnkdSJbNpGA0LUNVZ4wpLTTxnsZ7HWU7iEZ3EI8dTjhvZE9va+EYCa3U0Nuyw4mi5KAapmMwLuo95wUoSh5gGNeulanQdJ1f79vNia6BNTsrXG4+umoNqjIpLIQDLlBWWGYZE2een74A50DvS6sjUEGYUYQDobjom8icz5d0yiPPrq8VReo+dBkAGUX2apsCQ+91t/bagM945SfLte8MBrnzT7+n3tsz4PelRcU8dvd9GNVJ0b98wMU6797wLFObMy+fGVUtPq9jeVMgzs55MyNUMwrJ62FOZk50d9IS8KdecRIxKXTdLFmyvHmQUrK98TRRbfJnbp1NVlhmyZJlQolqGjubGs/3MEZMVlhmyZJlQukIBTnSeW4w/+QnKyyzZMkyYUgpOdTeRk946vUWygrLLFmyTBgS2N54mpg+9Qp4Z4VllixZJoyIFmdn89SzV0JWWGbJkmUCOd3TQ61n5MWiJwNZYZklS5YJ40B7K97I1CwVlxWWWbJkmRB0KdnS0DBl0wOzwjJLliwTgjcS5kBby/kexqjJCsssWbJMCLUeDw3n5IJPJSY8N7yv8EIgFqUzFKLF76PF76c9GMAXjRKJx9Gkjkk1YDMYKbTbKHO6qHC6KLTZsRkThWcnS0GA8abveoXjcTqCQZr8Phq8PXQEA/giEaK6hkBgNhhwm80U2OyUOZwU2x3kWa3YTSYEk+969Z1XKB6nIxigxe/ntM9LRzCANxIhosVBgklVcZjNFFhtlLtclDqcFNps2I0mYPKd13D0nXNc1+mJhOkOh2nx+2gPBOgIBfFHo0Q0DU1PdDY1GwzYjUbcZgv5NhulDge5Fiu5FuuUeg/6zntXcxPh+OStgp+KCROWcV2nLeBnR1MjmxvqOdjeSovfjy8aQZeyt4PjYJTewgFOs5lpLjdryytYWFCEmqGHxG2xcMm0SgwZKAkVjMV4ta6G2BA5r2VOFytLy1DSGLuUkmA8xqG2Nl6qPcX2ptPU93joiQx9vfqEoiIEDpOJEruDOXn5rC6rYHlJKVU5uViNxrSOP15oUqc7FGJ3SzOb6mvZ19pCo9eLd5jnoO+8BOAwmShzulhcWMxl0yu5qKycQpt9spT0GoSUkoim0eL3cbCtjX1tzRxoa6XZ56MzFCQcjyN710v2/PfdKSEEqhC4LRaKe+/rmrIKVpaWUenOwayqk0Jw9p1HVNMIxqI0+nzU9Xg43tnBk9XHUtorPeEwT5w4lrH3+2wqXG5WlJSO+jqNa4k2KSWalBxub+PRwwd4pbaG1oAfbRL1KV5UWMSjd96L3WQa875Oe3u45dGH8QyRnXDp9Ep+cdPtmA1Df6P6hOTLtTU8fGAvB9paCcaS1UkcGQ6TiaqcXC6fUcX9i5dSbJ+49g1SSnQpOdndxZ+PHubZU9U0envGHJisCkGJw8nVM2dx94JFzC8oRBXivAqNvvcposU51d3NK3U1vF5fx7GuDjzhMHoGn30BuMwWlhYVc+u8BVw1o4oci2VCzv9suRGMxegOh2j0+Tje2cGJrg6qu7v6P+7BWCyj5z1a7pi3kO9ec/1Irs/ElGiTUtLg7eFnu3fwxIljUzZcIJO0BQLDfih0KTnU3sr3tm1hc0NdRrMc/NEo+9taqfF4uGbmrAkTllJKTnu9PLh/N389doSu0Eiqqg+PJiWNPi8P7d/DX48d4Za583jf8lVMd+dMuPYsAV3XaQ34ebm2hn+cOMqh9jYC0ei4eX8l0BMJs6mhji2n65mXX8D7V1zE9bPmYDWOTwdT2XvND7a3caKzgyMd7dR7e2j2+/BGImi6PmW93anIuLDs0yJerD3FNze/xqkpGoA6HvSEw4TisX57Ux9SSuK6zl+OHeF72zbTOo51/irdbmbk5I7b/vuQUhLTdZ6uPs7/bt9Krad7XF+inkiYhw/s47X6Wj655mJumjMPo6KMu5bVN3s63tnBY0cO8eypE7T4/ROuSWlScrijnc++9Byb6mv5zMWXUWy3j8v5P3xgH7/cs3NSzRAngowLy7iu8/CBffzgjS14o1lt8mzC8TjdoRD51jPNp/psWr/YvYOf7tpOaJwN4OvKpw0S1plGSklPJMwPt2/jkUP7J9SoX9/Tw+dffp5D7W3805p1uM2WcT2eBH5/cB/f37YFT+T8F4eIahp/PXaEZr+fb119HRVOV8YFZlSLv+kEJWQ4dEjTdX5/cD/f2fp6VlAmIaLF6Q4PnIbGdI2f7NjGj3e+Me6C0qAobJheOa5TVCkl7cEAn33xOR7av+e8eD/D8Ti/3reb/3jlRbpCwVF0V0wfAczKzSOsTR4vrwS2NTbwhZeepzscHtfzfzORMc1SSskrdTV8b9tmgvH0HBIWg4GqnFwWFRYzNz+fUoezPyQkGI/R6vdzoquTQ+1tVHd3jsnRIUgIC4Oi4OoNsSmy2VlZWoZRnRhPakTT6AqFkFIihCCuazy4bw8/37NzQqpGlzocLCgYv25+UkraggH+/cVnea2udlTTbkFfBIRAkvCsjmZKq0vJEyeOoUvJly+/GrfZPC5TUiEEq0rLWF1azqaGujHtqy/yQwjR71Uey3R+8+l6frJzG5+9+LLJ0tNmSpMRYdln9P365tfS0igtBgNXz5zF/YuXsqiwCKdp6AdZSkkgFuN4ZwePHj7AU9XH8UejaR1jdm4+09wuKpxuKlwuyp2Jv744NavROKHeU11K2gKJToNSSp49Wc2PdmxLS1DajEZcJjNOs5kciwWLakARIjG1D4foiUTwRSPDflCWFpVQYLNm7HzOxRuN8F+vvjQiQakKQaHdzuzcfOblF1DqcJJjsWA2GJBSEorHaA8GaejxcLijnTqPJ+1Ziy4lT1UfJ9di5QsbNg4bhTAWzKqBty5eyrbGhrSdcjajkXyrlXKnm5k5uZQ4HORbbdiMRkyqiiZ1/NEY7UE/p7q7OdLRToO3Z0Saui4lfzp8iGurZrOmrCJjz7nVYByVeSMYi6a8Pmpv2Ns5juiMMFbzU0aeHl1KfrV3Nye7u1KuW+Jw8PlLNnJt1WxMacSGid6Lt6KklCVFxdwwey7/89rL1KRwHLnNFr525TUsKChAEcqkCcxu9vsAONLRztc2vzqk4FeEoMTuYGVpGZdNn9GveTtNZlRFkOi8nphyaVLHG4nQ4vdR3dXFjqZG9rY2U9fj6X+5BHBZ5UzEODyEkLCV/Xj7Np47VZ2WoLQZjVxcMZ3b5y1gVWkZeVYbBiX5fepzGkY0jVpPN8+fquavx45Q1+NJeSxdSh49fIB5+QXct3jpuJgghBBsmFbJosJi9rY2J13HqCiUOV0sKSrmkmnTWVBQyHRXDnaTCbU3NnZgM7IEfQ6kUCzGofY2/nb8CE9Xn6AnTfuoLxrhV3t3s6KkDFOGtMt3L1vJ7fMWjGgbTUq+tXUTL9fWDLverNw8fnDtjRmJez4Xp9mceqVhyIiwPNXdxd+OH0m5XpHNzneuvp6LK6aPouG8wKiqbJw+g/+99kb++dknqe3xDLl+a8DPA7u2871rbsBgOP9Cso++EItvbHmNJp9v0HIBzM7L595FS7i+ag4lDkdiejbs9VKxGowU2x0sLSrhzvkL8UYiHO/q4IWaU7xUe5KecISVYwjIHQ4pJU9XH+d3B/elnDYKYGVpGf+0ej3rKiowKel9MFUhsCkKCwuLWFBQyD0LF/PLPbt49NCBlGafqKbxox3bWFFSyoKCwnG5Bk6TibcsWsyBtpZ+54cqBGVOF5dOr+SqGVUsKS4hz2JN436eQQiBQQicZjNryyu4qKycO+cv5JtbNrG7uSmtD9PW0/Uc6+xgSdHYu2+K3plAod0+ou3iuo7TlFpYWQwGZuflT0qzwZiFpS4lfzt+lO4U8XNGReGTay9m/SgE5dkIIVhcVMznNmzkk88+RWiYF+Wl2lNsa2zgsulja3CfSRq8Pfx63262NNQPWuYwmnjbkmW8e9nKUYd99G3jtli4qLScVaXlfGjlaqq7Oql054x1+IPoi6f9wfYtKR1UBkXhngWL+Nd1G8izWkefSSEEpQ4nn73kMhYUFPLV11+hJ0Ucb2vAz492bOP7196IZRym40IIrpk5m1/v3U2Tz8uq0nJun7eAi6dNp8BqG5GAHO4YBiG4qLScH19/C5976TleqRteU4NEjO1LtadYXFg0ad6DiSAe11I6twQC1ZBeiNmYnxp/NMqLNSdTfuFWl1Vw27z5GZkGCSG4vHImV82s4okTx4ZcLxyP88ihA6yvmJ6xKchY2dfawoG21kGhF8V2B/952RVcUzUbtXc6Olb6UgTzrFZWl5VnYI+Dies6P9+9k1qPZ9j1VCF4x5LlfHr9BqwGQ0YEh0lVuXP+QnQp+e/XXkoprF+urWFzQx1XzqgaF6GRb7Xy+Q0bcZpMLCosxtz7zGX6WEIIiu12vnz5VXzwyb9xpGP45l8S2NxQx4dWrh6XD8Vk5Tf/9xKH9zUMu05+kYtPfulWrLbUGXxjMgzI3mDcup7hK4moQvCWhYuxGjIX32dUFN6ycDEWdfibv6PpNA3enkkTPhHX9UEOnRKHg+9cfT3XzZrTb7fLNGIcHFlSSg60tfKPE0dTrnvljCo+tfZibEZjRsehKgp3zF/IXQsWpbxuES3Ob/fvHddwpo3TZ7CqtBxL7wdhvDQ5IQTlThf/vGZ9v1AejlpPN53B4LiMZbKyct0srr9jJdffsZJFK6YTDsdYuW4W1966gnUb5wEwb3E5JlN6H5AxW1EPtbcNOxUGKLLbWVs+LaMPjhCCZcUlTHO7h12vOxRid3NTxo6baRwmE/9x6RVcPG36eS1wMRo0KfnNgb0pU1nLHE7+7eJLe72cmceoKHxw5WoqXMM/CwA7mhs50NY6Lh/P8RSOQx1v4/QZLC8pTbmuNxKh3uuZNErDRLBs9UyuumkZl1+/hPYWL+/756u57/2Xcc2ty7nrHRfz4U9fz+G99cTj6YXtjVlY7k+jmOe8/ELyrJnPpLAZTSmN1pJEaajJiILg/kVLuaZq9pQTlAAnujp5JYV3UxGCty1ZxqzcvHHVsiqcLm5Lw0MbjMV4qvr4uIzjfGAxGLhx9tw0tGotqUPxQqbv46VrktYmD3antf93AIvNTHtLz8QIy75CBqmYnZeHQcm8zVAVgtm5+SkflFOeLmL6+Ad9j5TZefm8b8WqcSlHNd7oUvLkiWN4U4SwlDmc3D5vwTgFLA3khllzcKURHrK5oW5SpCZmgkRQfDmONDzNfWFrbzZUg0LlrEIe+eWr7NtRQ31NO4f21PP7X7xKcXlu2tPwMVl7g7HYkOXI+hBAqcM5btkTJQ5Hf8bDUHSHQgRjMUwp7JsTSULjWkqhbXyKHYw3vmgkLcfelTOqKBmn+382Qghm5uQyJy8/5Uyi3tvDye4uLiodH6fXRFPhcpFnteJLEayfKmLlQkVRBPe9fyO//elL/OhrTxAOxzCZDcyZX8q7P34VBmN6ityYpEdU01Iay4UQuM2WcdMsXGZzyn0HYjEiE5BOOBLKnE6urZozJQWllJIj7e3DxrlCosr51VWzJkSrBDAbDKwoKUspLGOaxq7mJlaVlE3J638uZtVAqcNJXYr7Md61ByYrQggKipx84ku34ukKEAlHMZmM5OTZ0w4bgjFPw/Uhq4L3DxTGNWwnnSyguK6j65PLsL1hWiVFIwzsnUxsa2xI+aEstNnHLQg8GUII5uXnp7T/SuBwe9sFUzlHFYJcS2qfwETUH5jMBAMRWhq76Wz3k5NvR5eSWDSettNrbPNSmV5JdU1mrojtucR0nVTnqipiUjlQVKFw6fQZE6ZxZZq4rrOjqTHlevPzC8a9RNrZCKDSnYsqRMpMolPdXUQ1bVzS6iYaIURa8ZOToVr5+UBKSd3JNn789SdprO8kJ8/Ot3/5Xg7trWfv9lO8/xPXoBpSK3RjelIMqpIyxkuX4IukLnwxWryRMMm7l5zBrBom1UvhNptZVDB1sym6QiFq0yjqPCe/YMKve77VmtYx24MBgrHxey4nEgGTtgfRZEDXJI/9ZgtLVlXyXz+4H7MlEe9dWOziyP7TRCLpmSfGdIWtBmNK76NE0uL3jUt8l5SSZl/qfbvNlnErsz8aih0OCmy21CtOUpr9PjpSBDgrQjBjHNIrU2E2GNK61+F4PKMtLrJMXjRNp6Wxm8uuWUxu3hmHqtlsTKREpmmiG6OwNFDmdKVcr7q7i3gG+8n0oUnJia7OlKaAEodjUqV5TXflTJr0y9FQ6+kmnsK0oghBkT1hF9J0fcL++uqWpiKm6ym9x1kuDIQisDsttDR295vspJTU17TjdFknxhsOsKy4hGdOnhh2nUPtbXSHQxRluEmWLxphX2vqoPhlxSWTyj5YaLdN2WmTlJK6np6U9i9N1/nKplewGccna2co4rqWVohMXNcJxSaHd7ivDFtM04jrOprU+/8d1TQivf+NavHe/579m0Y4HuNUGuUR36wYDArX3rqc3z7wMnMXldPZ7uN3P3+FvdtruO/9l2EyT0CcJcCq0nKsBuOwKY+tfj9bTzdw69z5GbPTSSnZ1dTI6RRB8WY1EU4ymcizWCeV8B4JEmgJpA5uljCpm9UlGqpNnHe4z1QkAX80QlsgwGmvlxpPN/VeD22BAN2hED2RMP5olGA8RlzX+2t5Sgk6sr+3ui4TJq6heq1nOYMQgnUb52O1mXn56f2UVuTS0x3kPR+/iosuTj98b0zCUgjB/IICZuflcaCtdcj14jLRxOyKGTNxZcg7GorH+e2BfSnDIapyc1k0iUpTCcCS4WISE0lc1y+I4OaxtmxI+zhSEozFqPF0s7O5kR1NjRzr7KAt4CcwSfppvxlQVYUVa6tYvqYKqesIRSFRbzn993DMmqXDaOLmOfM42NY2rFd6T0sTDx/YxwdXrh5zKwdN13n8yCG2nh5cE/JsBHDTnHnjVsBhtExle6UuZbYHfAr6tMF6bw/PnjzB86dOcryrY1x7iGcZHk3TObi7jq2vHsNiMfLW919KT3eQaDhGxYyCtOTRmA1nQghumTuf6Smq/2hS8tOd23n08IExOXvius5T1cf53hubU/bzmOZ2c8e8BZMqxhISBTSmKrqUaTeke7Mhe51ZRzra+dIrL3LPY3/gW1s2sbulCX9WUJ43pJRseuEwP/7Gk7Q0drFj8wniMZ2Gmg5++b/PE4tOUNUhgBK7g3ctXZGyIIQ/FuVrm17lm1teo9HnTdha0piG9K3XEQzwk51v8MWXX0iZk25QFD6w4iJKHc4RnUuW4ZFSvukzQZIhpaQ7HOaH27fyrr89ziOH9tMZCmYF5CQgHtd58Ym9vPtjV/HhT9/Q79ApKnXT1eEjlqajL2PxNHcvWMSrdTW8Wl877HrBeIxf7d3Ns6equXHWXDZWzmRufj42owmDovRrgVJK4rpOWItT093N6w11/OP4UU55utPq83LdrDncMW/hlLUNTmaydraBSCk52N7Glze9zM6mppRJEqOlv01wb4MzBdFvd4vE4xdM+mamkbokHIpRNi0PRREDfp9QmyWc6cD42Usuo7bHkzKhXwKnvV5+vmcnvzmwlxyLhTKHiwJbohWoAMKaRlcoSJPPR2coOKKe4StKSvncJZeNufVlluSkY9ZQeguoTDYTSB8GRcmI7VhKydbGBj734nPUe4fvGJBqPG6zBZfZTJ7VSoHVRr7NhtNkxmEyYTMasRgMWAxGrAZD7/9O/KlC4cubXmFnc+oU1DcjqkGhbHoeW14+wvrL5yfaa/vDbHrhMBWVBZjN6cmJjGmWiSIGBXztimv4txeeoSnN2nnheJwWv58Wv3/sYyAhKL9z9fWUTUBZsDcjQiQqk6ciz2LlgZtupcA6SYuFiEShj7EgpWR3SzOfeeHZtOq6no1FNTDN7WZJUTErSkqZm1dAscPR39P+7JbAfU/xUM9zRItjzyoGQ6IogrvfcTE//OoTvPrcQdpbvfzXJ3+PxWriE1+6FdWQnjUyo2ktQgjWVUzju9fcwJdeeYHqCQyUVYXg2qrZfH7DRsqdrqygHCcUIdJKJ9SkTonDSXkaGV5TESklrYEA//XqiyMSlEU2O9fOms0Ns+aysLAQp8mckc6PWYZGCEHFjAK++J17ObKvgc52H+5cG4uWTycnL/16shnPAVSEYG15Bb+4+Xa+/8YWnjtVPa4NogRQ4nDy/hWreMvCJdincAzjVEARSlqVhMJxjcgFXD8xruv8bPd2DrW3pbW+1WDgjvkLed/yVVS6c7IC8jzgcJhZsbZqgFM5GoljMqfXbXRcEqaFEFTm5PLNq67jljnz+fmeHexsasyo2VuQaB9745x53L9oKTNzcyetfexCQlUEeVZryvU0XaczFKIqdwIGNcFIKTnS0c5fjx1J65nOtVj5/IbLuHXugikdYztVkVLS1NDFH3/9OvU17WjamZBDd66dz339bmz21G05xq26RF/R3xKHIyMPiACMqkquxcLCwiKumjGLyypnUOZwZr/SE4iCoCyNcCxN6rRcoD1fJPDYkUMpw9cA7EYj/3nZFdw8Z9741QOQ2QiF4dA1nYcfeIWAP8zN96zuL9EGYDIbJqYHz3DENI2/Hz/Kd7a9PqTzxm40sWF6JVJKOkNBAtEocV1HCIFJVbEZjeRbbRTb7VS6E/1VZuXlkW+1YVTSLwefJXP0zRqUFAV2NSk51d2NlCMLz5gKdIWCvJYiRK6PexYu4abxFJQkhHdYu3BNHmNF0yStzR4+/G/XM2fB6FuJjIuwjGhxfrVnFz/a8caQBTbm5uXzuQ0bubhi+lkvnuwvoSSE6PcIKmf97yznn9m5eZgUNeULeryrg7iuY7zApp7HOzvTcuoU9yVrjHOFKV1KfNkU1CFRFEFBkYuAb2zXKOPCUtMTRTP+d/s2IkO8TOvKp/G1K69hhjsnKwCnIMUOByUOR8qGZYfa2/BHo+SmYeOcKiQC0FvR0kjZ3TCtkgqXa9yTW9PpsvpmRlEV1m2cy+9+/grNp7soLHH3B6cbTQYWLpuGIY22EhkVllJKtpyu54fbtw4pKBcVFvGtq65LPERZQTklyTFbmJtfkFJYtgb8HOvsYG15xQVzryVQ6/GkdOz0RYWMd094KSVNPi/+6IXRImM80OIaW145ht8X5h9/3I5ylqbvzrXzxW+/ZeKFpe//t/fecXJd9d3/+9x7p7fd2dletbvSqvdiyZZky5J7CbYhGAg4BEJ40n4J5EmBPAl5SH4QAiSB5BcIcWIbTMc24C7JsmUVq/eVdrW99zZ95t7z+2NWK622zUq70tqe9+u1CM/cOXPuzJ3vPedbPt9olH85dGBCVRq7ycTnb7ktZSjf5ShCcFthMa/XXpzUaITjcXbW1bAhv+CGzW22iRsGPaHJW2pAoiKnLN17Q67zCz3dBN4j/YRmA82k8if/56FxO7wKIbDZk1MlmzFnipSSfU0NnJpEuXx1Th4bCt47q4z3MxvyC0hLov3q7vpauoKBGzCjG4OUklASpbeKEKRbZ9/9YEjJmw31c16wQ0BSq2xjFiL7QggcTisut23Mn9NlvTF9w69El5KddTWTyqatzy/Aqs6dXjgprg0hBMWetKQU6BsG+nnpYtWsNKy7WSTz21KFQFVmf1HQPDTI4dbmWX+fmcCcRB+sqB6fUnrxZjFjxjKm65ybpJpBEYJsR/KlRSnmNmZV5eGKRVM2BzOk5OlTJ2geluR7tyOEwJLEDV835KxWrkHis33hQuW7YuUuhMCZRD+moWgU/xxtJDdjxjIcjzMwSfqClJK+UPg98YNJkbj4txSVUJHhm/LYuv4+/v3IO+8JHUxVCDxJuB90adA2i0n5Ukpq+nr50dnTc34LDolteGYS7Z97QsEZEdWZDWbMWArBpCkSEthZd5H+SMpgvlfwWCz81rKVSbWe/cX5czx75tRIE653K4oQSQlKxw2DM50ds3auwViMbxzcN6sGeSYRQpDrcqOJya+VqK5zsKVpTl4jM2YsLaqGyzJ5feWx9jb+fu8e2vxDc/LDSDE9hBDcW76Atbn5Ux4b1XW+8c4+fjhsMG80hpR0BQLXvTW+JEU4lQ6BBPY01E9LhzVZwvEY3z58kNdrL8742LNJebo3qdLnly5WMRCZe3mjM2YsTao65ZbMkJJfnD/HR5//KV/Zv5e3Guup7esdEfeNXtELeaq/2PBf3DDQh1crKQN843GZzfzx+o14prhRAvijUb6y7y2++c5++kKhWf++Ep0Voxxra+Uf3n6Tjzz3ExqnyA1NhooMH44k/G+nO9t5q7F+xs5TSkkgGuVf3jnAkyePveuU0XOdLvJcU6/KK7u7+HnluTlX7z5joWlVCO4omcdLF6smXTlcSur97rHD/NfxI7gtFhwmM2ZVTdytkwgAXRLpsKgaaVYrPrudIreHeeleFmb4yHY4EwKqqWDSrCOEYF1ePr+zci3/cmj/lD/gYDzGd44d5mBLE59ds56NBUUzKqunGwaDkQjVvT0caG7kjYY6qnp6CMVjmBRlRn6ABW4Pi3yZHJoiCh3Rdb5+cB/l3gzKrzPn8pKP8pvv7OfVmup3naEE8FitrM7Nm1LnNm4Y/NuRdyj2pLFt3jyUKbbuN4oZVUrfWjyPJZlZnJwk1/JK9OEmT30zVKqlKQoOk4my9Ay2FpdwV2k55d6M6269m2JyFCF4YsUqzvd08fLFqikDDoaUHG9v4w9feZHl2dk8OH8hG/ILKPR4MCvqyBZ3vO/s0irtUt/vmK7THQrS7h/iTGcnJzraONvVScvQ4KxFoy2qyr3l8znS1jKl8a3p6+VPX3uJv9myjVU5udNSyJIy0c2nMxDgV1Xnefr0cZoHx69JFzDnAz2KENxTNp/nzlcSMyYP9vWFQ/z57lf57Jr1PLZo6cjOZTq/48vXisSQife/HhnHGU16TLNY+d8bN/PHr75IdxJVDjNN3DAYiEQ41t7KsfZW/vvkMe4sKeN3Vq2mIiMzpXc5S1zqwfTXm2+nJxTknZbk8v4iepzDrS0cbm3BY7FQ4PZQkeGj2JNGlsOJ02we8XHFDYNIPI4/GqU/HKYz6KdlaJC2oSF6QiH6w6Ebmp93d+l8njp5fMqST4AzXZ387ovP89CCRTxcsZCydC8Ok3lcwymlJKzHGYpEOd/TxZsNdeysraFpcHDCRmhui4Unlq/iqVMn5qSv70rW5uWzNCuL4+1tUx7bGwrxlX1v8fyFSu4tm8/6/AIK3R6smmkkwT1hCCW6IYkZOkPRKP3hEH2hEN2hEF0BP21+P61Dg3x2bWInc63MeFuJDfkFfPmO7XzprTdueqSuPxzm5+fPsrexnt9bs54PL1mGVUtOFTnF9BBCkO1w8tVtd/P5nS9zpK11Wq8fiEQY6OocUR6/tAq49E1JEobEkLPVOzF5hBBkO508sWIV/3fvnqS2xH3hME+dOs5PK89Q7EmjLN1LnstFmsWGSVWI6jr+aJTOQIBW/yD1/f30hoJEpki3sqgaf7JhE48tWsr+5sZpf+43GqfJzCdXrOZzXa8klUqmS8nZrk7OdXVi0TQcJjPpVisWTUMMPx+JxwnF4wRjsUQMQybiGHHDGHWtfGTp8uua+4yX06iKwl2l5RS4Pfzj/r3sbbz5pVidwQD/sO9Nqnt7+ItbN+NOoi1CiukjhKDI4+Gf77qfv96zkzcb66/ZR2gMG8a5iiIEjy5awttNjeysq0n6dcFYjMruLiq7u657DhZV4w/XbeAjS1dgUhRW5eTNeWMphODO0jLuLVvAC1WVSb9OksjlDsfjSdXmzwYz6jmVUjIYCbO3sYEfnz1N6zQ73s0mccPgJ+dO85V9ewnGYqnI+SwhhCDP7eafdtzDx5atwJpEidu7FYfJzF/dtpUlmVk3/L1tmsYfr9/Ip1atHXFVrMvLR50jwZDJsGkmPr/xVpZn5dzsqUyLGflkpZR0Bvw8efIYj//iJ3zq18/xzOkTXOzrvemryivRpeSnlWd45tSJOTWv9xqCRN+ZL9x2O1+9866E+s7NnhSJXc9M+q2FEJR40vj6jntZnpU9Y+NORabdwZe23smnVq3BMnwzEkKwyJeJ1/bu2DXlu9x8bfvd7yqDeV3GUg77C35VfZ6Pv/Bz/uHtNznX3TUtR7tI8m+miBsG3ztxhPPdXanV5SxyqTXIg/MX8j8PPcLvrFpLxk0SAbZqGuvz8vmbLXdQ4PbM6NhCCBZ4M/j2PQ9yb9n8pHqqXyuKEKzJzePf732QRxctGaNAn2l3UJaeMWvvP5MIIZjvzeDb9z7APbP8uc0U17xHksNpP18/+DY/rzw7pSNaUxTSrTbK0r0UuT1kOhy4zBZManK9dORwxCtq6ARjMQbDYXrDIToCfpoHBxmIhJNOFekOBnnq1HG+fPv291zLg7mGEIJ8l5u/2LSZDy1ews8qz/JKTTWtQ0OzWsljN5ko9qSxpaiEHaXlLPZlzppLQAhBgdvN13fcyy+rzvPkiaPU9PXOWC6kIgSFbg8fXbqCxxYvIc0yvqyYSVVZk5vHwZamGXnf2UYIQYEr8bm9WH2BJ08e42JPD3E5N1WHrunqkVLSGwrxhT2v83ptzaSOeIuqckt+IR9YuJh1efmkW21YNO26t0NSSnRpDEcRY1T3drOnoY4Xq6uSisLvrq+leWhwRltbmFWVJZlZk/ZDEULgS0JQ4L2EEAJVCOZ7ffzvTZv55Io1HGxpYlddDac6O2jzDxHT9Wt2jQjAqpnIcjgo92awLjePdXkFlHu9OM2WG5IyJoTAZjLxocVL2VZSymt1F/l11QXOdXcyFIlM+9wuuTIqfD7uK1/AtpIycpzOSc9FEYJbC4vY39w4btuLIs/MrqpnAiEEdpOJxxYtYVtJKXsa6vh19QVOd3bQFw5dc5BPABZNw2ezU+jxsCI7h8XX6VsWE2xFJ51hMBbjr/fs5Lnz5yY9sMSTxuc33sad88qwqOqspuxcSuCt7evlb97czf7mxkmPF8CX79jO40uWz9i8ZJIR3FTr3sufVX84TMNAP+d7ukYagXUHAwxGIgRjMWJGwohqQsGkKthNZlxmMx6rlSy7k3yXm3KvlxJPGtlOJx6L9YZ/vrphEArH0A0Dk6ZisyRarUZ0nabBAU52tHGms5P6gT66g0EGwmEiuo5uGChKQvLNYTKRZrWR5XBQkpbOkswsFmb4yHO5p9XJdLJr8MomgHMVKSVxI6HYVNndxZmuTmr7eukKBugLhQgMXxOGIUc+O7vJhMtsId1qxWd3UOj2MC8tneI0D1kOJ+lW20he5jTPfdTB0zaWhpT8+Oxp/ubNXZP6JsvTvXzjrvtYmpl1w7+c2r5efvuXv6BxcGDS4x5esJBv3HVfKll9jiClJGYkdgtxw8CQBolOABJBwgCqikATCpqiYFLVm/rdSSkZCkZ45uXDvHWilqFQhKx0Jw9vXsYDty7GdEVfFynliK5B4tzkcJJ5Ip9UUwSaomJW1SsSrhPMdQM301xa+FxCH74mYoaOLi91gB17TZhUddotsq+sCLv0qiteP2qgaW3DpZR0+P189/jhSQ2l22Lhb7duuymGEqAkLZ0PLl7KNw7um3Tle7Gvl1A8lpQoQiyus/+diwSDUex2M5vWl2MyjfZ3SilpaOohGtWZX3Zzzv3dzKWgUDLKNHOFp18+zDMvHxlZzXX1+alp7saX5uC25fNGrgEhBBZNG4leT8WB7nP8oH43AL9bfh/L00pn5wTmIM3BLv75wnOEjbF9he7LXc99eetn7LcVNeJ8u/oFav1t5Nt8/OnCR7Gq49uDaYegXqmtpnFg8hXbg/MrbmpHP0UIthaXTHlh9oVCDEWSbPQkIRyOcfJMM997ai+RyPjSW6/sPMPzLx4ftznSpMNLycnTTQQCc1MlOsVY/KEo+07Vjdn2hqNx3jx2ffJpfVE/ZwfqOTtQz1Ds5iRh3yxiUqcl1E3NUBuVA42cHWgY+euM9M/oexnSoM7fztmBBi76Wyd1o01rZRnVdV65WD3pgHbNxKOLlt705Ngch4sMm52WSRLjg7EY4XhyeoMmk8pd25aQ4XVyvmriutYP/sZa9GF/ynQIR+I8/aMDfO4P7sLhmFruLMXNJxyJMRQc/+bWPRAYtbVLkTyF9ky+vuozBPUIYT3K2YF6/qvmVWJydtt0TMW0jGV7wE91b8+kx5Skpd2wFqCTYdFU3BYLLZMExmOGPip9RdcNqmo6OHaiAX8ggs/rZMutC8j0JTT4JjunlrY+fvXySeJxg8UL87j9tooRtbm6hm4qL7RSNi+Lg4drMQyD9WtKWVSRixBw6Fg9h47UUnWxg6d/dACnw0KWz8Vjv7EGIQSxuM7xk42cPteCxayxcX0ZpSWZKIqg6mI79Y09FOSl887ROoSAjevKWFCefdO/g/c6Jk3FYh7/J+SyW1OG8hoxKRr59tHauKpQiN3ktOhpLf+aBgYYnELVZF5aOk5zcn14ZxNBMnJMl6OmhiF5bfdZ/v6fXqSrewiH3UJtQxcDg6Gk3s/jtrNu9Tw6Ogc5emK04Gtrez///YN9PP2jA1itJvoHQ/zfr/2K2vpEfbDFpJKT5UbTFOaXZrG4Ipd5JT5AYBiSZ3/6Dk/9cD9Oh4VoLM5XvvkSR44n3qOxuZfvPb2XH/7sEE67ha5uP3/31V/R3NJ3LR9bimngclhYMm9sBYpJU9m8Yt5NmFGK2WRaK8vmoYEpU2PyXO45EV2OG8aUSeqW4egZgN8f5ifPHeYjj23gnu1LUBQFw5BJtT0FcDosrF5RxNET9eMa2FhM54mPbKK8NItwOEZdfTdnz7dSXprFyuVFeNMdPPficTauLyMvN23kda3tA7y2+yx/+af3sXRRPlKCy2nlRz8/xKrlCbkpXTf41Cc2U1TgJRCM8rkv/Jjz1e0UFniTm3yKa0IRgk8+sIHO3iHO1rUTjeukOW18YOsytqwqS63s32NMy1gOhKcOPsyFVSXAUDRCd3Byx7jTbMZmSuTE9fYFCIZiLFucjzJsQKfrd5wMX4aLnGwPQghMZg2X00I4PLW/tKtrCCQU5l9ybUgWlGXz61dOMuRPrPKzM91kZjgRQmC1aDjslgkDUClmDiEExTnpfP2PfoPGjj5CkRiZaU7yfO4prx0pJf54iPpAB33RIRShkG1Np9CeiUUxJb2Fl1ISlzpdkQFagt0E4mFMika2NY18uw+rYp7QaCe0M6MYSCyKCVUoGEh6o0M0Bjrxx0NoQiXD4qbQnoldtUx6A5DD6VAhPUpHuI+uyACheARFKLhNdvJsXrxmN6qYXnrP9RI3dDoj/TQFugjpERyalWJHNhkW97ScytMylro0pqxE0OdAqZKUknNdXQRik0e6vTYbLnMimKKqSuLC02dn/qp62S0gRv5najSTimHIUdH1WFxP5JepCaOuagriih/nu0E1+72CEAKHzcyikuSENC5Vnu3pPMmPGvbQHOomoscQgF2zsNBdxBPzdmCZIH3lSgwpqRpq5scNezjRX4M/FiImdRShYFfNlDrz+GDhZm7xLcakjE3HCutR/vLkf9EW7uP3yh9gjXc+P258k53tx+iLDhEz4gihYFVMFDmy+M2irWzOXIY2zliGNLg41MqujuMc6a2iI9xPWI8SlzoCgVnRcGk2Vnvn89GSbRTZZz+1TkpJd2SQ79fvYm/XaQZjQeJSxyRU0i0udmSv5jcKbsWimJIab1rG0qaZpvwhdvgDSClv6hYkbhi8UjN5LyCAcm8GluGcvgyvk+xMN7v2nOPxxzZgMqnE4jqKomCzmi4nrw6fvCEZdZ5XPi/lZbHaZD8HbTiBubc/QFZmIqCkqgr5uWm4XVaOHK/n9s0V6HGDA4dqKC/NxpmKmr/rMJD8ovltnqx5lbARRRUKGRYXdtXKYCzI8b6L1Afa2Z6zevJxpOTNzlN8u+p5eqJDqELBpdlxm+yE9Sj9MT+n+mupGmziw8V38JGSOzBfZRQMJD3RITrCfRzrrWZv12n2dJxEU1TSzS4siomBWIChWJDzg038Y+VPiBs623NWj7mu44bO92pf4VDPeQSJII3H5MCpWYkacXqjQ3RHB3mt/Sg1/jb+7/JPkGud3UBwd2SQL5/9Aaf6a5GATTWTbU5HIumLDvHDhjdoCHYmvcCblrHMsNsQQkyq1lPV200oHsOeRKL3bCCl5ERHG2801E16nEKi0dYlbDYTn35iC//+vTc4fqoJh8NMIBjlkx+7jdUriqi62MHut87T1NxLd4+f7/73HjK8Th66byXedAc791RyobqdE6cbiUZ1/v0/36CowMsD9ySnzuzLcLJ0UT7//O87KchLoyDfyyc/dhset43f/q3bePqH+9nz9gWi0ThSSv7oM9sn3uqlXGVzEikllQONPFO3k7ARJcPs4rdL72FDRgVm1UQoHuFATyVP173Oz5venjD1SErJhcEmvlX1PL3RITItHj5WcicbMhZi0yzEDZ0afyvfr9/Nqf5anm3Yjdfi4oG8WyaMJ7zWfpS41FntLefx4m3Mc+SgKgr+WIhX2o7w48Y9hPQoP2jYzdqMCtLNzlGvNykad+WsZjAWYJNvMavSy8m1eTErGro0aAh08mTtK5zqr6PG38qLrYf4VOk9M/8hDxM3dH5Qv4uT/bUI4JaMRXx83nbybBlIoCPcy08a3+KtzlMk+4OZlrEs9qSjCjFpkKeqp5vK7m5W5+Te8NWllJKuYIB/3L+XwUnELADSbdZR/a6FECxbnM9Xv/QYjc2JKhyP20bRcJAkPd3B6pVFrF5ZxMMPrAQS+ohWa+JuXVyYgdttZd2akpExnXYLiiJYXJHHH33mzpFjFUXwicdvxeW6rD1oMqn8P5/dQW1DF5FIDF+Gi0SzS8Eta0uZX5pFU0sfmqYwr8iHw5HwH61aXkR+bhpmkzY8tsKnP7EFb7rjuj/PFDOLLg2eb97H0LAv8DPlD7A9Z9VI90KPycHD+ZtwaFb+qfKnE44TNeL8oH4XvdEh7KqFzy18jA0ZC0f93rxmFyWOHL546n+oGmrm2frdrPUumHA1FzFiLPEU81eLP0K62TlyjMfk4OPzttMe7uX19mO0hnqoHmpmfcbCUa8XQrA1azmbfIuxjePbTDM5+ZOKR/nc8e/QGx3ieN9FokYci5rcFni6NAY72d1xAoByZz6fX/QYGWb3yLzSTA7+dOGjBOJh3uk5n9SY0zKWeU4XOU4XTZPUXAdiMZ48cZTF2+8ZCZ7cCKSUdAYD/M2buziahLT+LfmFFLg9o75UIQQet41liwvGHJ/lc5Hlm7jn8YLyiX1W6Wl20tMuKw0JIcYcL4TAbjezdFH+1S9PKBVluPBljH3/DK+TDO/lu7yiCBYuyJ1wLiluHgkjkWhBUerM4dbMJWPavCpCsMm3mHmOXC4MjS+11hDs4ER/Ypz1GRWs8c4fY5yEEGRaPDxaeBv/WPkTOsJ9vN11hg8Wbhl3TE2oPFq4eZShvPK5W31L2N1xgqgRpznYzTrvWBeTSdEwKeObFCEEBXYfZc48ensvMBgNENIjs2IspZQc7rnAUDwR4L0vb/0oQ3lpPg7VygN5t3C0t5q4nLof0LSMpddmY3lW9qTGEuD12os8efIYn1q1BrMy+2pDupSc7GjjK/v2crStZcrghk3T+PCS5e8KwdHZ5JJGaCgSo7vfT99QiHA0hmFITJqKy27B53Hgcdkwa7P7PU5nzlJKwtE43f0BegYDhKNxdMPArGk4bWa8HgdpTuvInOfCvCGx2hkcLl1c6inBro7vc7arVircBeMay0tbeX88nHAlZVSgifFr6YUQrEgrxW2y0xf1c6S3ikcLbkMd53iXycZST8m4n5UQgnSzC5NQ0aVBIH5tHSQ1oeI2JQSgY1KfNd1KXRqcH2xCAg7VyhJP8bjHCSFY4M7HoVkZiAWmHHdaxlIRgocqFvF6Xc2kndlihsG3Dh2gPxzid1etw2e3z/gFe0nKqa6/jx+ePcXzFyrpT7L/+PbSctbl51/TnAb8YX659zSh6Ogczgy3nQduXTJhRce1YEjJW8drqGoa3dxKEYKtq8qYX5h5TeNKKekbCnK4som9J2o539AxYijjcQMpJYqiYDGrOKxmCrLSWL+4iM0ryyjLz0CbQaGLmpZudh+pHqWws3llKQuLR6+8pZTE4jpn69rZdbiaoxea6O4PEIxEiesG0pCoqoLZpGK3mMnyOllcksPaRYWsKM8nwzP9a3AoEOaFvWcITjMNS1UE996yiLzM0fqR7aE+9OEVTKF9Ym1FRQhybRPnyDYEEh0wNUWbMqrsMtnJtHjoi/rpCPUR0CO4lbF6qhlmN05t4pYUqlC4lHRsMLGRk1ISNqL0RoZoC/fSGxkipEeIGjGiRpy6QMeVR084zvWgS4P2cKIow65Z8Fk8E35GdtVCutk588ZSCMHG/EJW5eRO2Rs6ous8eeIY+5oa+ejSFdxRMo8sh+Oac6wu6fTFhrXuTrS38VrtRd5paaY/HEr6Yy/yePijdbdgHif9IRnMJpW3T9ZxrGr0+TtsZpaU5lBRlJ10IvtUDAXCfOf5/Vxs7h71eJrTxp1r5097PCkl/lCUl/af4+dvnKSxo39ckVhIyGIFwwbBcIyu/gAnqlr44evH2LKynI/ds4Z5eRkzUnxQ09LDd184MOoxs0kbZSyllNS19vKfvzzA/tN1BCfIT43rBnE9MefugQDn6jp47q3T3LdxEV/87bumHfcaCkZ45pUj9A5OT8hCUxVWlOeNMZaBeIhLyXdu0+QC0E5t4hYcl37YihCTHgdgEip2NWEEg3qEqB6DcXa+NtUyxiUwHaSUDMVD7Gw/xu6OE9QHOkZyOLkixjF18uH1I0nksAJoioptkjQsVSjYJljhX820l0FOs5nfX7uBc12dDEUnz2M0pKSyu4v/8+Yuso44WJmdy6qcXBZ4feS73bjMZkyKiqoIBAJJQqtOl4mevxFdpy+UaB3RODhAbV8vld1dNA0O0BdK3kBewmOx8MXbbr+u2nWrWePOdfM5Xt185TVAMBRlz7EaFhRlIWYgHC2l5HRNG40dY8sWV1cUUJCVNu3xalp6+OaP9nD0fBP6dFWRgMFAhF/vO8uhcw38r0dv4671FaM0G2eKzr6hkbQrw5DsP13HPz37Bi1dk7t/xkPXDRaVZM+JBIErP/Gprr/JbkSX1B6T0rkUYuTmLZm45/r1CN9IKemM9PO1yp9yrLcaA4nX7GKRu4giRyZpJic21YJVNfGrlncm9MXOJJcydhQmd8MkVxadYNrGUgjBxoIiPrVqLd86fDCpPiqGlLT7/bzir+bVmmo0JSHe6jCbsZtMWNREm4lLvaKjuk4oHiMUixMfFvzUjeu7J7ktFr5w2+1sKym9LpeAEIJNy+bhdR+iZ+Dy0l0Cb52o4aN3r8Flv/78Rylh95FqorHR7g5VEexYvwBNTf7illJyvKqZv/+f12ns6L/uuXX2+fnqM7vo6Q/w+F2rZ9xg9gwEhlXEFd4+VcuX//s1+oaSq9G/Gpfdwsr5Y4NmyTKTpbtW1TyyKAjpk2drhPWJFyKu4VWpISXBKfyHcUMnNDyWVTGPm5x+vRhInqnbxZHeKgSCHTmr+fi8HeRY01GFOmLUpZQc6rkw68ZSIEZWizFDJ2rEJ9So1DGI6smpGV2Tg01TFH5n1Rp6QkF+cPrUtKp2JAmfZswwCCXZYOx6yXO6+OLm27mrtBx1BoI6uT436xYV8srB0SkH9W29nKltY+PSkut+j85+P4fOjW2NkZ/pYXVF4bTaDJyta+dL//Uqrd1j5eoUIcjw2CkvyKQwOw23I9GWwR+K0NI1QFVjF519Q2NWoqFIjO++cAC71cwjty8bKRGdCXoGg8TiBtVNHXz1mV1jDKXVrOFx2sjw2HHZE/MNhKN09/sZCIQJhqIjN9biHC8FWdfWZynNZeOzj9xKV7+fwUCYwUBk+N8wg8HE+wwEwoSS9Gn6LIlSv7jUaQv1TnjcpcqTiSga9nfGpU5rqIdF7qIJzy+oh+kZHstrcWOfxC95rfRGBjnYUwlAsSOLz5Y/OG5UXZcGYWP2y3AVIfBZPVT7WwgbUfqiQxO6PWJGPCl/JVxHd0e7ycz/3rgZu8nEUyeP3zDDNx00RWFTQRF/tvE2FmdmzdgqQRGCHesr2HWkmlj88sovFtfZdbiKDYuLrst4SCk5cq6Rzn7/mOduW1FKmjO5lrJSSjr7/Hzt+7vHNZSF2Wl8ePsqNq8sI8NtR1PVUf7WuG4w4A+x71Qd33/1KPVto3/gkVic77ywn7KCDFbOv7aA2Xj0DgTp6B3iGz/cQ2df4jMQQF6mhx3rKti0vITiHC92q2nkO5UyMZ/W7gHO1XWw/1Qdp2paWbeo8JqDbnarmYc2Lx3Z0hlGYudz6d9YXOc7z+/nJ7tOJDVesSMbm2pmKB7i3EADMaljFmPnFjPi1PgnTn9b5C7CrloI6hFO9NVwR/ZK1HEcDVJKqoda6IsmPsOlnmJME0TOr4dAPDxicIod2eMaSoCheJC20OQSjzOBJlTKnLkc6D6HPxaixt82biBMSklLsIfB2TaWAnCYzfzJhltZmJHJvxw6QF3/3JAFU4SgPN3LEytW8+CChThMphmNxgshWFGeR1FOOjVXBV/eOddAd3+ALO/EOZlTEY3r7DxSNaZSym4xcefaBUkHkOK6wf+8dIjK+o5Rjwtg47J5fP4jd1CQ5Rk5p6sxaSq+NCcPbV7K6oWF/L9P7eRw5ejVbv9QiO88v5+v/+HDOGwzU37ZMxjg2z/by7m6dgAsJo2HNi/l4/euJds7sbaoxazhdlipKMrioc1LaO0axGzWrttfeem9VFVwpamxmjXs1uTzBLMsHirchRzpreL8YBPnBhpYkTbaLSSlpMafUAifaC6lzhyWeEo43HuBfd1neWhoI/NdY29WQT3C8837iUsdp2ZjS9ay5E96GmiKikmoxIgT0WPo0hiTzmRIgz0dp2i9AcYSYJ23gp82vkXEiPFK22E2ZizCpo2+PnVpsLvjRNKr3eveO5lVlYcWLOSphx7ld1etJcvhuGnOdKumsTonj7/beiff/8AH+fCSZTjNE6uuXA8uu5Wtq8rGPN7V5+fw+aZJS0InQ0pJU0c/p2vGqrEvLMmmvMCX9PmcqW3j5f2VY3y9S8ty+cITOyjI8iSVhyiEoCDTw198/E4Ks9PGPH+yupW3T9Vd8zlfTSAU5a3jNUgShvKzj2zij39zC9leV9Lz1VSVopx0coZfMxcwKyYeyNuASWgE9Qj/VvVLzg02EDPiibbORpwLQ818u/qFSXMZLYqJj5TcgUuz0Rf18/XzP+NUf+2IoYobOm2hHv6j+tcjfsS7ctZQ7py51f+VeExO8mwZAFQONnKqvxbd0IdjEAb+eIiXWg/xP3WvTXmNXBIaiRs6cUMnoscIxSMjoamIHiOkR4kZw/EMQ8eQxqhxhRBUuAtYlV4OwLHeap6qe53eyBD68GuGYkF+1XKQV9oOJx2QnZGkwEtN5v/81i18eOlyXq2p5pWL1dT09eKfQvnnelCEwGU2U5KWzsaCQraVlLLYl4V9hleS4yEEbFs9nx/vPE4gdPkcdUOy63AVO9ZVYDZd25Zn78lahgKjfywCuHPtgpE2q1MRi+v8dPdJAuHRn7/TZub3H7uNzDTHtD4jIQSFWWk8vmM1//TsG6NUkOK6wa/fPsvtq8pnLM9UDr/no3es4EN3rpqVqPuNRgjBRt9i7s5dw0uth6j2t/DnJ/6LMmcuHpOD3ugQDYEODCl5tHAzzzXvG8nLvHqcFWmlfLr8Pr5z8UUuDDXzlyefpNiRjc/iJhiP0BDspDsygECw0Zeoi56tVi9Ozcr9eRv4t+pfMhAL8KUz32dVehmZljT88RBVQy00BDoosPvYkbOanzXtnXCsukA7/1P7Gv54mKAeJqRHCcbDRIdXfy+2vsO+7rNYFBN2zYpDs7AirYwPFW0dZfIsionfKbuHhkAHbeFeftL4Jnu7zlDkyEIAzcHuEX+vXbMkVfI4YxnUl3oSz0tL5zOr1/GxZSup7evlSFsLx9raqOnrpTPoZygSGW5nebkF5YRjXjGuqii4zBa8Nhs5ThcLM3wszcxioS+TfJcbm8l0Q0WHhRDMy/OytDSXd842jHruVE0rTZ19lOZlTNtohyIx3jxWPeZz8XrsbFo2foXFeDR19HPoqnkB3LK0hGVledd0MxHDyfBPv3yY9p7R/TrO1LbT3NVPWb5vgldPn9I8Lx+7Z820Iv9zHbOi8Zny+7FrFl5uPYw/HuJkfy2QSHPJsXl5Yt5dLEubx96u03RHBsdd+ShC4b7c9XjNLp6u20mtv43Kwctbd4HAa3Zxd+5afrNoKx7T2JujIJEypAol0Wt9knkLEr3bVaEgxtmQ3pu3joFYgOea9zEYC7Kn89TI60yKxqr0Mn5v/gNIKXmt7ejwOGPfsS/qZ3/3uTFJTpdyQIN6hGBodCaBSWiMbmabuFbLnXl8Ycnj/MfFFzk/2ERLqJuWUMJtZlY0NmQs5H/Nf5AD3ZUc6a0antPEzFy5yRUIIXCazSzLymZZVjZPrIBgLEpfOExfKESbf4jeUIj+cJhgLErUSCy7IfHlmVUVm8mE02TGY7Xgtdnx2ey4LRZcFgtOk/myNuRN3GKZNJUd6ys4fK5xlLjIgD/M2ydrKc3LmPaY5xs6udg81q+zblERuRnupMaQMpGbOBgcvTpVFZHIjbwO45PusrNkXs4YYxkMRzlV3XpNN4jxEMDDm5fh80xvBTzXEcOJ5J8pf4B7ctdxsq+W9nAfmlAocWazIq2MTIsHA8k/rvw0MRknxzp+NY+mJGq2V6SVUjnYRNVgM4PxABbFTJEjkyWeEnKs6SP9ta/Gqpr5u2WfIGbEsakWTJPoOpa58vjWmt/HkJJ0y2h/vBACi2Lit+Zt5/as5Zzsr6M93IshDbxmFxXuQipcBVhVM7o0+Nba3wcSIh1Xs8hdxHfW//G0inscmnVcwyuEYImnhK+s+B3ODNRzYaiZQDyEx+RkiaeYhe5CLIqJu3PXsiq9DLNqmrRWfVaM5ZWThcSF7zRbcJotFLo9LM8e27fk3YgQgg1LishMd9LRO9p47Dl2kce2rcRhTV6qzjAkbxytJhIbnVlg0lS2r6tIWrk9rhscOd/E1e6hNJc9kaB9HcZHUxXK8n3sOlI9eu5ScqGpc8a0TN1OKxunsZJ+NxCM++mPdZNjLUQVKqXOXEqdY0VPInqY/lg3Bfa8UVU1gfggnZFElDzNlEGaKeG/dpnsrPMuoMThJKAPIRDkWouxqJOnCSlCocgxcdnllVhVM/PGmeslhBCoCEqcOZQ4R/++B2K9tEcSkokZ5pxxI9O6jNMVacNnzqHMmZfUnJJBCIHTZOMW3yJu8S0a9xiXyYbLNHWGyXtnf3OTyPQ42bSsZMzj1c3dXGjonNZYfUNB9p0eq8NZnJPOyvnJb50HA+ExJZIAeT436a7Jy+ymQghBTsb4QZOmjv5pVwZNRGFWGnm+5FbS7xbqg1X8tOm7RI3JE9LrAxd4tvFbBPXRN+CBWB/H+t7mR43/zju9u8e8riFYzf7u13iy7mt0RSZu13yj6Y60c6T3TZ6p/2fODh4Z95jeaBdP13+TzkjLtMcP6QG6I+0zFmCciJSxvE4URXDn2gVjgjmRaJw3jlZPWHt9NYkqmxbaxsmH3LKyDLcj+WTijt6hMQEigKx0J5qqoBvGdf3ZLeZxfTvd/YFRgZ/rYV5uxoh6/HuGJH/MBfZS7s/9GDZ1tMBurrWID+R/kmLHgjHbVCEEa9O3cn/uR7Grc0vLtNSxiA/kf5Js61jpw0t4TF4ezvsEGebp7ToTuaSnOdL75vVOc0pmdRv+fkAIwZLSHMryfWPyGfedquO3H1iP1z31xWsYkp2Hq8b0AHLYzGxbM1avcCKklHT1+8etKjlyvoknvvxsUuNMxlAwMq4AdCQWJ67rWGbgssrOcM2Jeu7ZoC/aTVu4ESkNih0LyDAnXCNxI8bpgUOE9ABW1Y4hjVFyakIIkEyY6pJIqxp//RM3YrSHm2kPN2JIgyxrHgW2MrRh/cmOcAvB+BAZlmzqAueJGlGK7fPJsGRzYegE6aZMcm2JbqJSSlpCdfjjg1S4VgAQk1GagjV0R9rRFI0caxG51iKUYeGcieYspaRy6Bj90R40oaHLyy4oXepcGDxBrq2Y7kg7vdFO0s0+5jkWYlLMBON+6gLnOdT7BrrU2d/zGgLBQvcqvOZMpJQE9cQxgfgQmdY8imxlaIqJuBHn/NBxCu1ltIeb6I/2kGHOosRRgTaB7zZlLGcAh9XMHavnjzGWrT2DHK9qScrYtfUMcrxqrJLTknk5zMubXkvb7oHguP7xAX+YAf+1aREmg2HMXMM3p21udAmdaYbiA/y67Qf4zNn0xbp5o+uXPFHyeXyWxIpKCIXWUAN1gfMsdK2cNOgyHboj7bzU9ixecxYSg12dz3N71oPc4r0TIQT1gQsc79+HU3OjCRNRI4IqVDItObQE6zkWeZsPF/4+mqJhoPN6x88pts+nwrWCsBHkuZb/piPcTJ61iKgRpTlYxwN5H0tqbgoqA7FejvS9SZFjAXYtsaLWZZw3u36NSTFjVe3YVAd7un7JmvQtbMv6DUJ6gK5IG4H4EJqiEdT9iOHXQcJX+tPm76AJE+nmTA737qHUuYi7sh8jLmPs7ngeh+bCpjowK1be6HyBTb672ey7d9zfa8pYzhBbV5Xxg9eOjDJGum7w+qEqtq4qR1MnUZGRkv2n6+m7SgpsJHo9ze3o4Dhb8HcbM6mZOZeIGmG2ZT1MqWMRYSPI92q/wkX/WXyWHDTFxArPLThUJ43B6qkHmwZZ1jw+UfI5zIoFiWR35/OcGTjMLd47R47pCDdz37w/o8BWOvxIIh1nRdotPFX/dbojbWTbCuiKtNEZaeG+3McBONX/Du3hJp4o+RzppkwkEkPqKEl4+YQQVLhW4LPkcGrg4JjnJRK76uSDhZ9BExpH+t5iX/er3Oq7B685i62ZD9AdacOq2rkz6wMjK1gpJQd7d2ESZh4v+gPMioWeaDtP1n2Nha6V5NmKMTBIM/t4OO8TqEJlf89rnOg/wC0Z2zCLsW6vlM9yBhBCUJCdNq66zfGqZtp6JhZFgGH/5ji5lZnpTjYsKZ52RDgam3t1+ikSeExecqwJIRSzYsWleQjrl2+SsxH9l8OVNM2hGvZ0/ZIX256lxn+OuBEdlc/oNWeSbytBEcrwX0Jp3mfJocBeyqmBdxJtpgePkWMtJMOcjUTSFLxIiX0B6aZMhBAoQkFTki8MmWybLhCUOhdhUswIoZBhziZmRDGkfsX4YuTYSxVeBgZNwRpKHBWYFcuw2nsm6WYfzaFEEFVBodSxaHiuCl5zNhEjPGGPsdTKcoYwqQo71lfw9sm6UUGdvqEQ+0/V8aE7V4578UgpqWntobKuY8xzG5eWkJnmHPP4teK0mbFPI5VpurzXciJnA02YrmrrMPufl0Syp+tXVA4eY713G4X2clSh0hCoGnWcKjQUxq7oBQpr0rfwctuPWOvdwtmBI2zJvA9VaIlVJHLWqoMAzMrVmgPJBcoEE/l35cgBV449VdljyljOEEII1iwsJNfnprmzf+RxKRO5kw9vXop1glLFt47XjClLTORWLkC9hgRyi2n8r/XRO1bw0bvXTHu8ZFGEwDkDWp7vVyRyjB2QUoJI/JAvp8bIUc9fukElnr98zIiAstSpHDzOqvTbWO+9AwOdcwNHJ5ECHo0QgiL7fMyKlYM9u4gZUUodi0cCTgW2eRzqfYOBWC8eU8K/rss4qtBGdCyvPMsr533pnOVV5zSde4iqaISNEBIDZOL3oqBQZC+nPniBjXI7Jiz0R3voi3aRZytJfvArSBnLGSTDbee25fP40c7jox4/39DJxZZulszLGbPy8gcjvHm8ZsxY5QU+lsy7ti6NE6UZ6bpBmtOWWv3NUXoi7dT4K2kO1eKPD3KwZyces5fF7jVYFTvnh07QG+2kI9zCYKyfAz2vk2nJpdy5hLAe4uzgEfpi3YT0AMf799ESqqfMuRivOYtS5yKO9L5JID6EPz7AQKx33MZlE2FRrKxK38RLbT9ibfoWnNpl9acVaRu56D/D0w3fINdaTFzGsCg2Hsz7GAoq5waO0B/rpS/aTfXQGQByrcUU2+cT0IeoHDxGT7SDYDzA0b63yLTkMt+5DIeWnHJXuWMxv277Ac+3PIVZMbPOewfZ1nw2ZNzJT5u+ww8b/w2vOYvG4EUWulZRbJ8/KuqeLCljOcPcuXYBz+89Q/iK1J1AOMqbx2tYMm9sDlllQ+eY1hECuGN1+bTkv64kIZIxNq2vo2+sPmaKG0eOtZCtmQ+gieH+8QjWZ9yBW0sDIC7jRI0IWZY8srIeAhLVPIY0AEnUCGNInTXpm4HE6u1SgruBQcQIY1VsbM9+BICYESEuYwgh2J71AYrt8xmM9VHqWEiWNZ/mYO3I1rPYMR/bJPmZQgiK7QtQhcqytPVcufSzq04+VPhZGoJV9EQ6MClmCmzz0IQJXeqEjTAgudV3V+I8jRixS/OWOhEjjEN1sT37AwBEjcjIyvRW3z3kWAtH3ivDks227A+MbJ+FECz2rMGuuekIN2FWLCNG1q2l83jRH1DjP0cgPsgi92qK7fMT/k9DsCXz/lFjZ1nzuSPzwZHv52pSxnIGEUJQUZxFRWEmJy+OFm99+2Qtv3XP2lGrPsOQvHWiZpSAMIDHaWPLOPJvyc4hK92F3WoepYYE0No1QCQWx2q+cf3cU1zGa8nCa7lcXiiEwmL36pH/zrEWjvrxXs2KtI0TPufQXNzqu3vC5y2qjaWedaMe83gup6RN9d5SSmoDlWRbC8izji5DFUJgVW2JnMurFoOa0Fjn3TrhuG5TOrf57pnw+eVpG0bP2eQduVlcQhUaZc5FlDlHlzMKIXBorjFjAGiKiZVpm0Y95jVn4jVP3DE1FQ2fYaxmjW1rx+ZVNrT3UVnfMcp/MxQMc3ic1hEr5udReI2tEAByMlykj6Om3to9MKpvUIr3Ni2NPfznN1/jO19/hbd3nhu3HDAWjfPyL47S0jC+KK8hdS76z3Kg53UO9LzOpoy7xgm4vD9IGcsZRgjB5hWlpLtGG6tYXGfPsYsjaQlSSs43dNLc1T/qOOUacyuvxGGzsKR0rL9zwB/mRHXrrNfQppgbeNLsrNlYRnNDDycO1477vUejcd587QzNjeMbS4mkLdRIc6iOHdmPssSz5n3r804Zy1kg1+dhzcKxW5pD5xpHEsalhP2n68Z0b8wbfu31XJCqIti4rAT1KpUiQ0pePXh+jKpRirmLHNZ+vfJvsueufN7ptrH6ljLyC8evAJNSYndY+KuvfpA1t4zv9lGFxubMe/lQ4WdYkbYRdZyeQe8X3r9nPotoqsJd6yt442j1qPK/tp5BztV1sGlZCYFwZIxoMMBty+eR7r5+ZaANi4vI83louiKNCeBYVTMHTtdz++ry9+0K4d2ClJKu9gFefu4YjbWdeNIdbH9wJQuXFiAEvPyLo2iait8f5tyJRmwOC/d+YDWLlid3s93565Ps232OWFTnY5+5nUXLJ/ZZpkitLGeNlQvyKcxKG/VYLK5z8Ew9ABebu8cYMpvFlPB3zsD7Z3gc3Ldp8ZjmZpFonP94bj9NHf0zth2/ekWTYmYYHAjxjS+9wNBAkHs/sIaCEh/f+vtfUXU2IWPW3NDDM//xBkMDIe7+jdVYrSa+9Q8vMtAXnGLkBLdsreAjn95KV8dg0q95P5MylrOE225l6+ryMY8fq2omEI5y4Ez9mC14RVEWFUVjhVGvBSEED29eSmne2DYPta09/O1/vUJNS/d1GTnDkAz4Qxw614g/NLlGY4rpIaXk1OE6BvuDPPEHd7L21vk8/OENLFtTwivPHRv53nILvHz4k5tZu6mcxz5+K+FQlK72gaTew+W2kV+UgeUaU9Teb6SM5SyhKII7VpePUUpv6uinpqWHo5VNox4XwLa185NuSJYMvjQHn31k07gljqdr2vjcv77Az944Sc9AYNLV4ZX+sFhcp6vPz4Ez9fzrT97i01/5MX/35KuzqmY0F0nmJnO9K+6Otn7SM5zYHYnos6oqFBT76GjrJxZN3GjziryYzIkabovVhKII9BlSfkoxmpTPchYpzc9gSWkOh65ID4pE47x57CK1rb2jjk1327lteemM+hGFENy6vJTfvn89333hwJh8ztbuQb7+7Bv86PXjrFlYwPLyPHIz3DisZtRhkeBINI4/FKGj1099Wy81Ld00tvfRPRAY8cdeHfl/t6MbBvF4or1qTNcJR+OEI7HEv9E44Wji/wfDUaqaxirSG1Ky/0w9/f4wNosJq1kb/jNhGf7XatbQ1ITWo6YpqMrYdYvTZSUUjKLHDRSzgpSSocEQNocZVRsu65tEzSrFzJIylrOI1Wxi+7oFHK5sHKmmMaTkl2+fGVMLvmZhIXmZM99GQVMVHt+xmnA0zvdfOTImEq4bksaOPho7+njuzdOYNRWTpiKURE1vXDeIxfUZU0B/N/DWiVqeevEQgXCUSDQhaBzXjav+9AmFzw1D8szLifYJihBoqoKmKqjD/2qqiqYqWMwaTpuFTz10C7cunzdqDCEES1YV89yzBzm8r5rla0ro6Rri0N4q7ntkTVKaAVJKDEOiG8P/6hIhRteS68M3BV03MAwjqb7s71dSxnKWuWVYOajzilLDq7esiS6RC2atla/ZpPHJB9bj89j5z18epHdwYmd+NK4TjY/tVf1+orvPz9m69hkZy5By0s9UCDFhoUB+UQZP/MGd/Pzp/fzs6f3ousHqW8rYevdSADSTiha/UkkdzBYNoQgMQ/LTp96m8lQzjbWdGBK+/Gc/pqwil8c/tYVwKMr3vvka3V2DdLUP8OP/3suuF09yxz3L2LxjyYyc+3uNlLGcZbLSEpqUv3r77ITHFGalsWp+wazd0YVIGMxHbl9BRXE2T/76IIfONY4JMF0LdouJpWW5ON6jyuY3E0UR3LKlgqWrigkMhTFbNNLSHShqYkv+6G9tQhqXV4pOt40v/fNHSPM6EQK2P7CSzXcuHjWm2WJCUxXsDguPf2oLxpX+TZEYI8X4pIzlLKMogh3DOZfjrS4Egq2ry6fVkOx65rK0NId/+L0HOHq+iV+9fZYT1S0M+ENJd2VUFYHVYqIkx8vaRYVsWVnGgqLMCWXhphxPCCwmbZREV8wwboTM48RzUsfOabZQhJi0xbEQApfbhusqIyaEwJM2Oh9XVRWyctNG/jsjc2LVHhVBTn76tU16jiOlpMPvx+dwoI3jC75WxATRuvePg+oGEIvrtPcMTRgZ9XrsOKyWMTmRs4mUEt0w6Oj1c66uncr6Dpo6++nq8xMIR4nFdRSRMIxuh5XMNAf5mR5K833ML/CR7XWNRO6vZ0UcCEfp6b+8DTWQ/P3RXawrKOTTyzfcFP/ZUDBM32Dohr2f12PHaXt/1lvPBoFolL985XX+dvsdeO3XVeAx6uJLrSxvACZNpTA77WZPYxRCCDRVJT/TQ57PzZ3rFqDrBrpxOU0oIfefWJEqioKqzLzz32E148i5vIU3pMTqMaFrN+9+7bJbcdlnf6WfYiytg4P0BEMszsocyRAIRqOc7ehkSXY2NpNG3DCo7e2jNxjE53AwLz0dTVUwpKR5YJBjLS2cae/gQGMTbouFdJuNJdlZI0LEnYEA9b39mFSF+b4MnGZzUtd1ylimuGwUNZW5lp4spcQfixKMR8m0OQjH40QNHZtmoj8SSvRWsdgwKerI8XFp0BcJYUiJx2zFqmroUtIbCZJhtaMg6AoHsGkmnJqZkB4josdJM6eEkW82Tf2D/O3O3Tz1oUfIcjqRUrK3voH/OHiYpz70CKE4fOOtfZxsayfL6aBjyM+m4iL+16YNqEKw62INh5ta6AuF2VVdi0VTWZDpY0l2FlJK3qqr59v73yHDbicUj6EKhS/t2EaBxz3ld58ylinmNG3BQf7P4ddYk1nApxat5/Xmal5qPE+G1c7FgR4GY2E2ZhfzZytvx6Zq9EaCfOPUXir7OhAIMqx2/nT5FnxWB3/09vN8Y9ND2DSNJ3b/mDsLyvnT5Vv4Re0ZWoOD/NmKiXUXU9wYluZk4bZYONjYzIOLKjCk5JUL1dxeOg+XxcLLF6o53NzCtx6+n1yXi5bBQf7whRdZkZfDtrJSPr56Jctzc7jY08Nf3LGFDLttROB4IBzmX/cd5ONrVnLvggVEdZ0vvraTp44e5wvbpv7uUxU8KeYkihA0Bwb44qFXWeXL55ML16EJhZihc6CjgdW+fP6/LY/w5XV3s6u5mpPdrRhS8v+dPUhvOMi/3vow/7HlUZZn5PJ3R3cCYFE1WoODNA714zRZqOzrJGYYnOvroNydcZPPOAWA3WRix/wyXrlQjW5IWgaHqOzsYsf8MiRwqKmZ5bk5FHo8mFSV4rQ0FmdlcqipBQmoioIy3C1SFSLx38Puo4a+AZoHBrjY3ctTx47zw5OnCESjnG7vQE+i0iplLFPMSQajEf7qnZdZk1nApxdtwKJqI9ukImcaOwoXkGG1szQjlxy7i7bgEEE9xltttTxWtowCZxqZNgePzFtGS2CA1uAgZe4Mqvu7OdfXwdrMAnRp0BocoCUwQEVaZmoLPgcQQrCtrJTa3l4a+vt5u76BwrQ0SjMSMnNRXcd8VU95s6YRiU8tOxgzdIzhwGY4Ficci7M8J4dHly1JKvkitQ1PMecQwEuNlZgUFX9srECHx2zFPOyjVEj0qZZIonqcqK7jNl0OzlhVDYuqMRgNs8Sbw9nedgLxGPcXLaQlOMD5vi4iuk6OPbnmWClmn8I0DxWZmbxefZEjza3cv2gBlmEDuSgrk9erawhEYzgtZgLRGBe7e3ho8cIRg6cKkSgGMEan6uW6XGTY7WwpLWFDYQGQSPvRDSOpgpCUsUwx55DA/UWLeLBkMX9+8EUKa0/xm+UrLvemnuC6tmtm0i02mvz9rM1M/Bh6I0FC8RjZdhcus4UXGyoRAso9PhanZ/NGaw3pFhtp5lQy9lxBUxTuX7iAf9l3AAFsKioaWfXfWzGfXRdr+NLO3azIzeFYaxsWTWPH/Mv6rLkuFxZN49v7DrIkJ5tMu53t88vIcTn50PKlfOWNvWwtLcFhNtHQP8CWecXcNX+sQtiYec3mSadIca04TGYWeHx8cc12/vrQK2TbnGzLn/yCtqoaH5m/iqerjmJVTbjMFp6tPs6mnGKKnWkMxSJ0hwP4rA58VjtLvTk8U3WUR+Ytm7VS0xTTRwjB+sICtpeXke9xk+m83HUyw27nn+6/l9erL9LY38/6gnx2zC/Ha798s/M57Hzt/rt5vfoizf0DZDkSr1eE4OOrV7EsJ5v9DU30hkIszc5iVV5yLadTxjLFnKPU5R3ZFq/25fNHy27jlaYLrPLl47M6WODJHDFuioCKtEwyrPaEhmfJEmyaxkuN54kYcdb48vnN8hWYFBWP2cqKjDwKnWmYFJVSt5eFaVkjq9AUc4c0m5U/2bxpzONCCHwOO4+vXD7ha4UQLMrKZFHW2E6NmipYV1jAusLpf+epCp4Uc4qrr8dLicSSsbvvS89d+d9XjnHla64+duT9ho9JBXdSjMOoiyJlLFOkSJFifEYZy1TqUIoUKVIkwUQ+y9SeJEWKFCmuILWyTJEiRYokSBnLFClSpEiClLFMkSJFiiRIGcsUKVKkSIL/H23o6x5uRfV/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 460x322 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wordcloud = WordCloud(width = 800, height = 800, \n",
    "                      background_color ='white',\n",
    "                      #stopwords = stopwords, \n",
    "                      min_font_size = 10).generate(' '.join(topics[0].values))\n",
    "plt.clf();\n",
    "plt.figure(figsize = (10, 7), dpi=46, facecolor = None) \n",
    "plt.imshow(wordcloud) \n",
    "plt.axis(\"off\") \n",
    "plt.tight_layout(pad = 0) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Venn- diagram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:04:33.654505Z",
     "start_time": "2020-11-02T15:04:33.092741Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460x322 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf();\n",
    "plt.figure(figsize = (10, 7), dpi=46, facecolor = None) \n",
    "plt.imshow(wordcloud) \n",
    "plt.axis(\"off\") \n",
    "plt.tight_layout(pad = 0) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top 10 applications of NLP\n",
    "https://www.analyticsvidhya.com/blog/2020/07/top-10-applications-of-natural-language-processing-nlp/\n",
    "\n",
    "* autocomplete or autocorrect (search engines)\n",
    "* machine translation (google translate)\n",
    "* social media monitoring\n",
    "* chatbots for basic customer queries\n",
    "* survey analysis\n",
    "* targeted advertising\n",
    "* hiring and recruitment\n",
    "* voice assistants\n",
    "* grammar checker\n",
    "* email filtering (e.g. spam filtering)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [Crowdbreaks](https://www.crowdbreaks.org/): Crowdbreaks is a health trend tracking system. Crowdbreaks collects tweets with keywords that might be related to specific health topics. By using natural language processing and machine learning techniques the system tries to filter relevant from non-relevant content. By providing more meta information (labelling) of the tweets, these algorithms will continuously improve to detect tweets which are truly relevant and understand their content.issues. \n",
    "\n",
    "* Sentiment analysis using social media: How people feel about topics, events or products [1](https://sproutsocial.com/insights/social-media-sentiment-analysis/) [2](https://blog.hootsuite.com/social-media-sentiment-analysis-tools/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other questions\n",
    "\n",
    "What to do when the texts do not have the same length? How to exclude not relevant words? How to make a weighting scheme for our purposes?\n",
    "\n",
    "How to deal with the 'timeseries' nature of texts? What inputs do neural networks expect?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "finalized": {
   "timestamp": 1649759327695,
   "trusted": true
  },
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}