Project_5_NLP_on_Financial_Statements.ipynb

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    "# Project 5: NLP on Financial Statements\n",
    "## Instructions\n",
    "Each problem consists of a function to implement and instructions on how to implement the function.  The parts of the function that need to be implemented are marked with a `# TODO` comment. After implementing the function, run the cell to test it against the unit tests we've provided. For each problem, we provide one or more unit tests from our `project_tests` package. These unit tests won't tell you if your answer is correct, but will warn you of any major errors. Your code will be checked for the correct solution when you submit it to Udacity.\n",
    "\n",
    "## Packages\n",
    "When you implement the functions, you'll only need to you use the packages you've used in the classroom, like [Pandas](https://pandas.pydata.org/) and [Numpy](http://www.numpy.org/). These packages will be imported for you. We recommend you don't add any import statements, otherwise the grader might not be able to run your code.\n",
    "\n",
    "The other packages that we're importing are `project_helper` and `project_tests`. These are custom packages built to help you solve the problems.  The `project_helper` module contains utility functions and graph functions. The `project_tests` contains the unit tests for all the problems.\n",
    "\n",
    "### Install Packages"
   ]
  },
  {
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   "execution_count": 1,
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    }
   ],
   "source": [
    "import sys\n",
    "!{sys.executable} -m pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import pprint\n",
    "import csv\n",
    "import ast\n",
    "\n",
    "import project_helper\n",
    "import project_tests\n",
    "\n",
    "from tqdm import tqdm\n",
    "from bs4 import BeautifulSoup\n",
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download NLP Corpora\n",
    "You'll need two corpora to run this project: the stopwords corpus for removing stopwords and wordnet for lemmatizing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
      "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
      "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get 10ks\n",
    "We'll be running NLP analysis on 10-k documents. To do that, we first need to download the documents. For this project, we'll download 10-ks for a few companies. To lookup documents for these companies, we'll use their CIK. If you would like to run this against other stocks, we've provided the dict `additional_cik` for more stocks. However, the more stocks you try, the long it will take to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cik_lookup = {\n",
    "    'AMZN': '0001018724',\n",
    "    'BMY': '0000014272',   \n",
    "    'CNP': '0001130310',\n",
    "    'CVX': '0000093410',\n",
    "    'FL': '0000850209',\n",
    "    'FRT': '0000034903',\n",
    "    'HON': '0000773840'}\n",
    "\n",
    "additional_cik = {\n",
    "    'AEP': '0000004904',\n",
    "    'AXP': '0000004962',\n",
    "    'BA': '0000012927', \n",
    "    'BK': '0001390777',\n",
    "    'CAT': '0000018230',\n",
    "    'DE': '0000315189',\n",
    "    'DIS': '0001001039',\n",
    "    'DTE': '0000936340',\n",
    "    'ED': '0001047862',\n",
    "    'EMR': '0000032604',\n",
    "    'ETN': '0001551182',\n",
    "    'GE': '0000040545',\n",
    "    'IBM': '0000051143',\n",
    "    'IP': '0000051434',\n",
    "    'JNJ': '0000200406',\n",
    "    'KO': '0000021344',\n",
    "    'LLY': '0000059478',\n",
    "    'MCD': '0000063908',\n",
    "    'MO': '0000764180',\n",
    "    'MRK': '0000310158',\n",
    "    'MRO': '0000101778',\n",
    "    'PCG': '0001004980',\n",
    "    'PEP': '0000077476',\n",
    "    'PFE': '0000078003',\n",
    "    'PG': '0000080424',\n",
    "    'PNR': '0000077360',\n",
    "    'SYY': '0000096021',\n",
    "    'TXN': '0000097476',\n",
    "    'UTX': '0000101829',\n",
    "    'WFC': '0000072971',\n",
    "    'WMT': '0000104169',\n",
    "    'WY': '0000106535',\n",
    "    'XOM': '0000034088'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get list of 10-ks\n",
    "The SEC has a limit on the number of calls you can make to the website per second. In order to avoid hiding that limit, we've created the `SecAPI` class. This will cache data from the SEC and prevent you from going over the limit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sec_api = project_helper.SecAPI()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the class constructed, let's pull a list of filled 10-ks from the SEC for each company."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Upload `Stock RSS URL` from `csv` file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "sec_data = {}\n",
    "\n",
    "with open('sec_data.csv', 'r') as f:\n",
    "    reader = csv.reader(f)\n",
    "    for row in reader:\n",
    "        ticker, rss_url = row\n",
    "        sec_data[ticker] = ast.literal_eval(rss_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('https://www.sec.gov/Archives/edgar/data/1018724/000101872417000011/0001018724-17-000011-index.htm',\n",
      "  '10-K',\n",
      "  '2017-02-10'),\n",
      " ('https://www.sec.gov/Archives/edgar/data/1018724/000101872416000172/0001018724-16-000172-index.htm',\n",
      "  '10-K',\n",
      "  '2016-01-29'),\n",
      " ('https://www.sec.gov/Archives/edgar/data/1018724/000101872415000006/0001018724-15-000006-index.htm',\n",
      "  '10-K',\n",
      "  '2015-01-30'),\n",
      " ('https://www.sec.gov/Archives/edgar/data/1018724/000101872414000006/0001018724-14-000006-index.htm',\n",
      "  '10-K',\n",
      "  '2014-01-31'),\n",
      " ('https://www.sec.gov/Archives/edgar/data/1018724/000119312513028520/0001193125-13-028520-index.htm',\n",
      "  '10-K',\n",
      "  '2013-01-30')]\n"
     ]
    }
   ],
   "source": [
    "example_ticker = 'AMZN'\n",
    "pprint.pprint(sec_data[example_ticker][:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download 10-ks\n",
    "As you see, this is a list of urls. These urls point to a file that contains metadata related to each filling. Since we don't care about the metadata, we'll pull the filling by replacing the url with the filling url."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get `10-k` data from a `csv` file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_fillings_by_ticker = {}\n",
    "colnames=['ticker', 'file_date', '10-k'] \n",
    "\n",
    "pd_fillings_by_ticker = pd.read_csv('raw_fillings_by_ticker.csv', \n",
    "                                    header=None, \n",
    "                                    names=colnames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_fillings_by_ticker = defaultdict(dict)\n",
    "\n",
    "for i, row in pd_fillings_by_ticker.iterrows():\n",
    "    raw_fillings_by_ticker[row.ticker][row.file_date] = row['10-k']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example Document:\n",
      "\n",
      "<SEC-DOCUMENT>0001018724-17-000011.txt : 20170210\n",
      "<SEC-HEADER>0001018724-17-000011.hdr.sgml : 20170210\n",
      "<ACCEPTANCE-DATETIME>20170209175636\n",
      "ACCESSION NUMBER:\t\t0001018724-17-000011\n",
      "CONFORMED SUBMISSION TYPE:\t10-K\n",
      "PUBLIC DOCUMENT COUNT:\t\t92\n",
      "CONFORMED PERIOD OF REPORT:\t20161231\n",
      "FILED AS OF DATE:\t\t20170210\n",
      "DATE AS OF CHANGE:\t\t20170209\n",
      "\n",
      "FILER:\n",
      "\n",
      "\tCOMPANY DATA:\t\n",
      "\t\tCOMPANY CONFORMED NAME:\t\t\tAMAZON COM INC\n",
      "\t\tCENTRAL INDEX KEY:\t\t\t0001018724\n",
      "\t\tSTANDARD INDUSTRIAL CLASSIFICATION:\tRETAIL-CATALOG & MAIL-ORDER HOUSES [5961]\n",
      "\t\tIRS NUMBER:\t\t\t\t911646860\n",
      "\t\tSTATE OF INCORPORATION:\t\t\tDE\n",
      "\t\tFISCAL YEAR END:\t\t\t1231\n",
      "\n",
      "\tFILING VALUES:\n",
      "\t\tFORM TYPE:\t\t10-K\n",
      "\t\tSEC ACT:\t\t1934 Act\n",
      "\t\tSEC FILE NUMBER:\t000-22513\n",
      "\t\tFILM NUMBER:\t\t17588807\n",
      "\n",
      "\tBUSINESS ADDRESS:\t\n",
      "\t\tSTREET 1:\t\t410 TERRY AVENUE NORTH\n",
      "\t\tCITY:\t\t\tSEATTLE\n",
      "\t\tSTATE:\t\t\tWA\n",
      "\t\tZIP:\t\t\t98109\n",
      "\t\tBUSINESS PHONE:\t\t2062661000\n",
      "\n",
      "\tMAIL ADDRESS:\t\n",
      "\t\tSTREET 1:\t\t410 TERRY AVENUE NORTH\n",
      "\t\tCITY:\t\t\tSEATTLE\n",
      "\t\tSTATE:\t\t\tWA\n",
      "\t\tZIP:\t\t\t98109\n",
      "</SEC-HEADER>\n",
      "<DOCUMENT>\n",
      "<TYPE>10-K\n",
      "<SEQUENCE>1\n",
      "<FILENAME...\n"
     ]
    }
   ],
   "source": [
    "print('Example Document:\\n\\n{}...'.format(next(iter(raw_fillings_by_ticker[example_ticker].values()))[:1000]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Documents\n",
    "With theses fillings downloaded, we want to break them into their associated documents. These documents are sectioned off in the fillings with the tags `<DOCUMENT>` for the start of each document and `</DOCUMENT>` for the end of each document. There's no overlap with these documents, so each `</DOCUMENT>` tag should come after the `<DOCUMENT>` with no `<DOCUMENT>` tag in between.\n",
    "\n",
    "Implement `get_documents` to return a list of these documents from a filling. Make sure not to include the tag in the returned document text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "\n",
    "def get_documents(text):\n",
    "    \"\"\"\n",
    "    Extract the documents from the text\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    text : str\n",
    "        The text with the document strings inside\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    extracted_docs : list of str\n",
    "        The document strings found in `text`\n",
    "    \"\"\"\n",
    "    \n",
    "    # TODO: Implement\n",
    "    doc_start_pattern = re.compile(r'<DOCUMENT>')\n",
    "    doc_end_pattern = re.compile(r'</DOCUMENT>')\n",
    "    \n",
    "    doc_start_is = doc_start_pattern.finditer(text)\n",
    "    doc_end_is = doc_end_pattern.finditer(text)\n",
    "    \n",
    "    extracted_docs = []\n",
    "    for doc_start, doc_end in zip(doc_start_is, doc_end_is):\n",
    "        extracted_docs.append(text[doc_start.end():doc_end.start()])\n",
    "    \n",
    "    return extracted_docs\n",
    "\n",
    "\n",
    "project_tests.test_get_documents(get_documents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the `get_documents` function implemented, let's extract all the documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Getting Documents from AMZN Fillings: 100%|██████████| 17/17 [00:00<00:00, 67.50filling/s]\n",
      "Getting Documents from BMY Fillings: 100%|██████████| 23/23 [00:00<00:00, 36.68filling/s]\n",
      "Getting Documents from CNP Fillings: 100%|██████████| 15/15 [00:00<00:00, 27.89filling/s]\n",
      "Getting Documents from CVX Fillings: 100%|██████████| 21/21 [00:00<00:00, 26.81filling/s]\n",
      "Getting Documents from FL Fillings: 100%|██████████| 16/16 [00:00<00:00, 41.51filling/s]\n",
      "Getting Documents from FRT Fillings: 100%|██████████| 19/19 [00:00<00:00, 36.92filling/s]\n",
      "Getting Documents from HON Fillings: 100%|██████████| 20/20 [00:00<00:00, 46.93filling/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Document 0 Filed on 2017-02-10:\n",
      "\n",
      "<TYPE>10-K\n",
      "<SEQUENCE>1\n",
      "<FILENAME>amzn-20161231x10k.htm\n",
      "<DESCRIPTION>FORM 10-K\n",
      "<TEXT>\n",
      "<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.01 Transitional//EN\" \"http://www.w3.org/TR/html4/loose.dtd\">\n",
      "<html>\n",
      "\t<he...\n",
      "\n",
      "Document 1 Filed on 2017-02-10:\n",
      "\n",
      "<TYPE>EX-12.1\n",
      "<SEQUENCE>2\n",
      "<FILENAME>amzn-20161231xex121.htm\n",
      "<DESCRIPTION>COMPUTATION OF RATIO OF EARNINGS TO FIXED CHARGES\n",
      "<TEXT>\n",
      "<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.01 Transitional//EN\" \"http:...\n",
      "\n",
      "Document 2 Filed on 2017-02-10:\n",
      "\n",
      "<TYPE>EX-21.1\n",
      "<SEQUENCE>3\n",
      "<FILENAME>amzn-20161231xex211.htm\n",
      "<DESCRIPTION>LIST OF SIGNIFICANT SUBSIDIARIES\n",
      "<TEXT>\n",
      "<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.01 Transitional//EN\" \"http://www.w3.org/TR/h...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "filling_documents_by_ticker = {}\n",
    "\n",
    "for ticker, raw_fillings in raw_fillings_by_ticker.items():\n",
    "    filling_documents_by_ticker[ticker] = {}\n",
    "    for file_date, filling in tqdm(raw_fillings.items(), desc='Getting Documents from {} Fillings'.format(ticker), unit='filling'):\n",
    "        filling_documents_by_ticker[ticker][file_date] = get_documents(filling)\n",
    "\n",
    "\n",
    "print('\\n\\n'.join([\n",
    "    'Document {} Filed on {}:\\n{}...'.format(doc_i, file_date, doc[:200])\n",
    "    for file_date, docs in filling_documents_by_ticker[example_ticker].items()\n",
    "    for doc_i, doc in enumerate(docs)][:3]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Document Types\n",
    "Now that we have all the documents, we want to find the 10-k form in this 10-k filing. Implement the `get_document_type` function to return the type of document given. The document type is located on a line with the `<TYPE>` tag. For example, a form of type \"TEST\" would have the line `<TYPE>TEST`. Make sure to return the type as lowercase, so this example would be returned as \"test\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def get_document_type(doc):\n",
    "    \"\"\"\n",
    "    Return the document type lowercased\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    doc : str\n",
    "        The document string\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    doc_type : str\n",
    "        The document type lowercased\n",
    "    \"\"\"\n",
    "    \n",
    "    # TODO: Implement\n",
    "    regex = re.compile(r'<TYPE>(.*)')\n",
    "    matches = regex.finditer(doc)\n",
    "    \n",
    "    for x in matches:\n",
    "        doc_type = x.group(1).lower() \n",
    "    \n",
    "    return doc_type\n",
    "\n",
    "project_tests.test_get_document_type(get_document_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the `get_document_type` function, we'll filter out all non 10-k documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "  {\n",
      "    cik: '0001018724'\n",
      "    file: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>amzn-2016123...\n",
      "    file_date: '2017-02-10'},\n",
      "  {\n",
      "    cik: '0001018724'\n",
      "    file: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>amzn-2015123...\n",
      "    file_date: '2016-01-29'},\n",
      "  {\n",
      "    cik: '0001018724'\n",
      "    file: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>amzn-2014123...\n",
      "    file_date: '2015-01-30'},\n",
      "  {\n",
      "    cik: '0001018724'\n",
      "    file: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>amzn-2013123...\n",
      "    file_date: '2014-01-31'},\n",
      "  {\n",
      "    cik: '0001018724'\n",
      "    file: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>d445434d10k....\n",
      "    file_date: '2013-01-30'},\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "ten_ks_by_ticker = {}\n",
    "\n",
    "for ticker, filling_documents in filling_documents_by_ticker.items():\n",
    "    ten_ks_by_ticker[ticker] = []\n",
    "    for file_date, documents in filling_documents.items():\n",
    "        for document in documents:\n",
    "            if get_document_type(document) == '10-k':\n",
    "                ten_ks_by_ticker[ticker].append({\n",
    "                    'cik': cik_lookup[ticker],\n",
    "                    'file': document,\n",
    "                    'file_date': file_date})\n",
    "\n",
    "\n",
    "project_helper.print_ten_k_data(ten_ks_by_ticker[example_ticker][:5], ['cik', 'file', 'file_date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess the Data\n",
    "### Clean Up\n",
    "As you can see, the text for the documents are very messy. To clean this up, we'll remove the html and lowercase all the text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_html_tags(text):\n",
    "    text = BeautifulSoup(text, 'html.parser').get_text()\n",
    "    \n",
    "    return text\n",
    "\n",
    "\n",
    "def clean_text(text):\n",
    "    text = text.lower()\n",
    "    text = remove_html_tags(text)\n",
    "    \n",
    "    return text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `clean_text` function, we'll clean up all the documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Cleaning AMZN 10-Ks: 100%|██████████| 17/17 [00:33<00:00,  1.99s/10-K]\n",
      "Cleaning BMY 10-Ks: 100%|██████████| 23/23 [01:11<00:00,  3.10s/10-K]\n",
      "Cleaning CNP 10-Ks: 100%|██████████| 15/15 [00:51<00:00,  3.46s/10-K]\n",
      "Cleaning CVX 10-Ks: 100%|██████████| 21/21 [01:47<00:00,  5.13s/10-K]\n",
      "Cleaning FL 10-Ks: 100%|██████████| 16/16 [00:26<00:00,  1.63s/10-K]\n",
      "Cleaning FRT 10-Ks: 100%|██████████| 19/19 [00:52<00:00,  2.76s/10-K]\n",
      "Cleaning HON 10-Ks: 100%|██████████| 19/19 [00:53<00:00,  2.80s/10-K]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "  {\n",
      "    file_clean: '\\n10-k\\n1\\namzn-20161231x10k.htm\\nform 10-k\\n\\n\\n...},\n",
      "  {\n",
      "    file_clean: '\\n10-k\\n1\\namzn-20151231x10k.htm\\nform 10-k\\n\\n\\n...},\n",
      "  {\n",
      "    file_clean: '\\n10-k\\n1\\namzn-20141231x10k.htm\\nform 10-k\\n\\n\\n...},\n",
      "  {\n",
      "    file_clean: '\\n10-k\\n1\\namzn-20131231x10k.htm\\nform 10-k\\n\\n\\n...},\n",
      "  {\n",
      "    file_clean: '\\n10-k\\n1\\nd445434d10k.htm\\nform 10-k\\n\\n\\nform 1...},\n",
      "]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for ticker, ten_ks in ten_ks_by_ticker.items():\n",
    "    for ten_k in tqdm(ten_ks, desc='Cleaning {} 10-Ks'.format(ticker), unit='10-K'):\n",
    "        ten_k['file_clean'] = clean_text(ten_k['file'])\n",
    "\n",
    "\n",
    "project_helper.print_ten_k_data(ten_ks_by_ticker[example_ticker][:5], ['file_clean'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lemmatize\n",
    "With the text cleaned up, it's time to distill the verbs down. Implement the `lemmatize_words` function to lemmatize verbs in the list of words provided."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.corpus import wordnet\n",
    "\n",
    "\n",
    "def lemmatize_words(words):\n",
    "    \"\"\"\n",
    "    Lemmatize words \n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    words : list of str\n",
    "        List of words\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    lemmatized_words : list of str\n",
    "        List of lemmatized words\n",
    "    \"\"\"\n",
    "    \n",
    "    # TODO: Implement\n",
    "    \n",
    "    return [WordNetLemmatizer().lemmatize(w, pos='v') for w in words]\n",
    "\n",
    "\n",
    "project_tests.test_lemmatize_words(lemmatize_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the `lemmatize_words` function implemented, let's lemmatize all the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Lemmatize AMZN 10-Ks: 100%|██████████| 17/17 [00:04<00:00,  3.5510-K/s]\n",
      "Lemmatize BMY 10-Ks: 100%|██████████| 23/23 [00:10<00:00,  2.1810-K/s]\n",
      "Lemmatize CNP 10-Ks: 100%|██████████| 15/15 [00:08<00:00,  1.7810-K/s]\n",
      "Lemmatize CVX 10-Ks: 100%|██████████| 21/21 [00:10<00:00,  2.0710-K/s]\n",
      "Lemmatize FL 10-Ks: 100%|██████████| 16/16 [00:03<00:00,  4.0610-K/s]\n",
      "Lemmatize FRT 10-Ks: 100%|██████████| 19/19 [00:06<00:00,  3.0510-K/s]\n",
      "Lemmatize HON 10-Ks: 100%|██████████| 19/19 [00:05<00:00,  3.5310-K/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "  {\n",
      "    file_lemma: '['10', 'k', '1', 'amzn', '20161231x10k', 'htm', '...},\n",
      "  {\n",
      "    file_lemma: '['10', 'k', '1', 'amzn', '20151231x10k', 'htm', '...},\n",
      "  {\n",
      "    file_lemma: '['10', 'k', '1', 'amzn', '20141231x10k', 'htm', '...},\n",
      "  {\n",
      "    file_lemma: '['10', 'k', '1', 'amzn', '20131231x10k', 'htm', '...},\n",
      "  {\n",
      "    file_lemma: '['10', 'k', '1', 'd445434d10k', 'htm', 'form', '1...},\n",
      "]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "word_pattern = re.compile('\\w+')\n",
    "\n",
    "for ticker, ten_ks in ten_ks_by_ticker.items():\n",
    "    for ten_k in tqdm(ten_ks, desc='Lemmatize {} 10-Ks'.format(ticker), unit='10-K'):\n",
    "        ten_k['file_lemma'] = lemmatize_words(word_pattern.findall(ten_k['file_clean']))\n",
    "\n",
    "\n",
    "project_helper.print_ten_k_data(ten_ks_by_ticker[example_ticker][:5], ['file_lemma'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove Stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Remove Stop Words for AMZN 10-Ks: 100%|██████████| 17/17 [00:01<00:00,  8.5810-K/s]\n",
      "Remove Stop Words for BMY 10-Ks: 100%|██████████| 23/23 [00:04<00:00,  5.5110-K/s]\n",
      "Remove Stop Words for CNP 10-Ks: 100%|██████████| 15/15 [00:03<00:00,  4.3010-K/s]\n",
      "Remove Stop Words for CVX 10-Ks: 100%|██████████| 21/21 [00:04<00:00,  5.0210-K/s]\n",
      "Remove Stop Words for FL 10-Ks: 100%|██████████| 16/16 [00:01<00:00, 10.0310-K/s]\n",
      "Remove Stop Words for FRT 10-Ks: 100%|██████████| 19/19 [00:02<00:00,  7.2610-K/s]\n",
      "Remove Stop Words for HON 10-Ks: 100%|██████████| 19/19 [00:02<00:00,  8.3810-K/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stop Words Removed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from nltk.corpus import stopwords\n",
    "\n",
    "\n",
    "lemma_english_stopwords = lemmatize_words(stopwords.words('english'))\n",
    "\n",
    "for ticker, ten_ks in ten_ks_by_ticker.items():\n",
    "    for ten_k in tqdm(ten_ks, desc='Remove Stop Words for {} 10-Ks'.format(ticker), unit='10-K'):\n",
    "        ten_k['file_lemma'] = [word for word in ten_k['file_lemma'] if word not in lemma_english_stopwords]\n",
    "\n",
    "\n",
    "print('Stop Words Removed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis on 10ks\n",
    "### Loughran McDonald Sentiment Word Lists\n",
    "We'll be using the Loughran and McDonald sentiment word lists. These word lists cover the following sentiment:\n",
    "- Negative \n",
    "- Positive\n",
    "- Uncertainty\n",
    "- Litigious\n",
    "- Constraining\n",
    "- Superfluous\n",
    "- Modal\n",
    "\n",
    "This will allow us to do the sentiment analysis on the 10-ks. Let's first load these word lists. We'll be looking into a few of these sentiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>negative</th>\n",
       "      <th>positive</th>\n",
       "      <th>uncertainty</th>\n",
       "      <th>litigious</th>\n",
       "      <th>constraining</th>\n",
       "      <th>interesting</th>\n",
       "      <th>word</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>abandon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>abandonment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>abandonments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>abdicate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>abdication</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    negative  positive  uncertainty  litigious  constraining  interesting  \\\n",
       "9       True     False        False      False         False        False   \n",
       "12      True     False        False      False         False        False   \n",
       "13      True     False        False      False         False        False   \n",
       "51      True     False        False      False         False        False   \n",
       "54      True     False        False      False         False        False   \n",
       "\n",
       "            word  \n",
       "9        abandon  \n",
       "12   abandonment  \n",
       "13  abandonments  \n",
       "51      abdicate  \n",
       "54    abdication  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "\n",
    "sentiments = ['negative', 'positive', 'uncertainty', 'litigious', 'constraining', 'interesting']\n",
    "\n",
    "sentiment_df = pd.read_csv(os.path.join('..', '..', 'data', 'project_5_loughran_mcdonald', 'loughran_mcdonald_master_dic_2016.csv'))\n",
    "sentiment_df.columns = [column.lower() for column in sentiment_df.columns] # Lowercase the columns for ease of use\n",
    "\n",
    "# Remove unused information\n",
    "sentiment_df = sentiment_df[sentiments + ['word']]\n",
    "sentiment_df[sentiments] = sentiment_df[sentiments].astype(bool)\n",
    "sentiment_df = sentiment_df[(sentiment_df[sentiments]).any(1)]\n",
    "\n",
    "# Apply the same preprocessing to these words as the 10-k words\n",
    "sentiment_df['word'] = lemmatize_words(sentiment_df['word'].str.lower())\n",
    "sentiment_df = sentiment_df.drop_duplicates('word')\n",
    "\n",
    "\n",
    "sentiment_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bag of Words\n",
    "using the sentiment word lists, let's generate sentiment bag of words from the 10-k documents. Implement `get_bag_of_words` to generate a bag of words that counts the number of sentiment words in each doc. You can ignore words that are not in `sentiment_words`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "from collections import defaultdict, Counter\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "\n",
    "def get_bag_of_words(sentiment_words, docs):\n",
    "    \"\"\"\n",
    "    Generate a bag of words from documents for a certain sentiment\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    sentiment_words: Pandas Series\n",
    "        Words that signify a certain sentiment\n",
    "    docs : list of str\n",
    "        List of documents used to generate bag of words\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    bag_of_words : 2-d Numpy Ndarray of int\n",
    "        Bag of words sentiment for each document\n",
    "        The first dimension is the document.\n",
    "        The second dimension is the word.\n",
    "    \"\"\"\n",
    "    \n",
    "    # TODO: Implement\n",
    "    cnt_vec = CountVectorizer(min_df=2, vocabulary=sentiment_words)\n",
    "    bag_of_words = cnt_vec.fit_transform(docs).toarray()\n",
    "    \n",
    "    return bag_of_words\n",
    "\n",
    "\n",
    "project_tests.test_get_bag_of_words(get_bag_of_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `get_bag_of_words` function, we'll generate a bag of words for all the documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "  {\n",
      "    negative: '[[0 0 0 ..., 0 0 0]\\n [0 0 0 ..., 0 0 0]\\n [0 0 0...\n",
      "    positive: '[[16  0  0 ...,  0  0  0]\\n [16  0  0 ...,  0  0 ...\n",
      "    uncertainty: '[[0 0 0 ..., 1 1 3]\\n [0 0 0 ..., 1 1 3]\\n [0 0 0...\n",
      "    litigious: '[[0 0 0 ..., 0 0 0]\\n [0 0 0 ..., 0 0 0]\\n [0 0 0...\n",
      "    constraining: '[[0 0 0 ..., 0 0 2]\\n [0 0 0 ..., 0 0 2]\\n [0 0 0...\n",
      "    interesting: '[[2 0 0 ..., 0 0 0]\\n [2 0 0 ..., 0 0 0]\\n [2 0 0...},\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "sentiment_bow_ten_ks = {}\n",
    "\n",
    "for ticker, ten_ks in ten_ks_by_ticker.items():\n",
    "    lemma_docs = [' '.join(ten_k['file_lemma']) for ten_k in ten_ks]\n",
    "    \n",
    "    sentiment_bow_ten_ks[ticker] = {\n",
    "        sentiment: get_bag_of_words(sentiment_df[sentiment_df[sentiment]]['word'], lemma_docs)\n",
    "        for sentiment in sentiments}\n",
    "\n",
    "\n",
    "project_helper.print_ten_k_data([sentiment_bow_ten_ks[example_ticker]], sentiments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Jaccard Similarity\n",
    "Using the bag of words, let's calculate the jaccard similarity on the bag of words and plot it over time. Implement `get_jaccard_similarity` to return the jaccard similarities between each tick in time. Since the input, `bag_of_words_matrix`, is a bag of words for each time period in order, you just need to compute the jaccard similarities for each neighboring bag of words. Make sure to turn the bag of words into a boolean array when calculating the jaccard similarity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import jaccard_similarity_score\n",
    "\n",
    "\n",
    "def get_jaccard_similarity(bag_of_words_matrix):\n",
    "    \"\"\"\n",
    "    Get jaccard similarities for neighboring documents\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    bag_of_words : 2-d Numpy Ndarray of int\n",
    "        Bag of words sentiment for each document\n",
    "        The first dimension is the document.\n",
    "        The second dimension is the word.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    jaccard_similarities : list of float\n",
    "        Jaccard similarities for neighboring documents\n",
    "    \"\"\"\n",
    "    \n",
    "    # TODO: Implement\n",
    "    bag_of_words = np.array(bag_of_words_matrix, dtype=bool)\n",
    "    jaccard_similarities = [ jaccard_similarity_score(u,v) for u,v in zip(bag_of_words, bag_of_words[1:]) ]\n",
    "    \n",
    "    return jaccard_similarities\n",
    "\n",
    "\n",
    "project_tests.test_get_jaccard_similarity(get_jaccard_similarity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `get_jaccard_similarity` function, let's plot the similarities over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe53f1d8978>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get dates for the universe\n",
    "file_dates = {\n",
    "    ticker: [ten_k['file_date'] for ten_k in ten_ks]\n",
    "    for ticker, ten_ks in ten_ks_by_ticker.items()}  \n",
    "\n",
    "jaccard_similarities = {\n",
    "    ticker: {\n",
    "        sentiment_name: get_jaccard_similarity(sentiment_values)\n",
    "        for sentiment_name, sentiment_values in ten_k_sentiments.items()}\n",
    "    for ticker, ten_k_sentiments in sentiment_bow_ten_ks.items()}\n",
    "\n",
    "\n",
    "project_helper.plot_similarities(\n",
    "    [jaccard_similarities[example_ticker][sentiment] for sentiment in sentiments],\n",
    "    file_dates[example_ticker][1:],\n",
    "    'Jaccard Similarities for {} Sentiment'.format(example_ticker),\n",
    "    sentiments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TFIDF\n",
    "using the sentiment word lists, let's generate sentiment TFIDF from the 10-k documents. Implement `get_tfidf` to generate TFIDF from each document, using sentiment words as the terms. You can ignore words that are not in `sentiment_words`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "\n",
    "def get_tfidf(sentiment_words, docs):\n",
    "    \"\"\"\n",
    "    Generate TFIDF values from documents for a certain sentiment\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    sentiment_words: Pandas Series\n",
    "        Words that signify a certain sentiment\n",
    "    docs : list of str\n",
    "        List of documents used to generate bag of words\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    tfidf : 2-d Numpy Ndarray of float\n",
    "        TFIDF sentiment for each document\n",
    "        The first dimension is the document.\n",
    "        The second dimension is the word.\n",
    "    \"\"\"\n",
    "    \n",
    "    # TODO: Implement\n",
    "    tv = TfidfVectorizer(vocabulary = sentiment_words)\n",
    "    tfidf = tv.fit_transform(docs).toarray()\n",
    "    \n",
    "    return tfidf\n",
    "\n",
    "\n",
    "project_tests.test_get_tfidf(get_tfidf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `get_tfidf` function, let's generate the TFIDF values for all the documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "  {\n",
      "    negative: '[[ 0.          0.          0.         ...,  0.   ...\n",
      "    positive: '[[ 0.22288432  0.          0.         ...,  0.   ...\n",
      "    uncertainty: '[[ 0.          0.          0.         ...,  0.005...\n",
      "    litigious: '[[ 0.  0.  0. ...,  0.  0.  0.]\\n [ 0.  0.  0. .....\n",
      "    constraining: '[[ 0.          0.          0.         ...,  0.   ...\n",
      "    interesting: '[[ 0.01673784  0.          0.         ...,  0.   ...},\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "sentiment_tfidf_ten_ks = {}\n",
    "\n",
    "for ticker, ten_ks in ten_ks_by_ticker.items():\n",
    "    lemma_docs = [' '.join(ten_k['file_lemma']) for ten_k in ten_ks]\n",
    "    \n",
    "    sentiment_tfidf_ten_ks[ticker] = {\n",
    "        sentiment: get_tfidf(sentiment_df[sentiment_df[sentiment]]['word'], lemma_docs)\n",
    "        for sentiment in sentiments}\n",
    "\n",
    "    \n",
    "project_helper.print_ten_k_data([sentiment_tfidf_ten_ks[example_ticker]], sentiments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cosine Similarity\n",
    "Using the TFIDF values, we'll calculate the cosine similarity and plot it over time. Implement `get_cosine_similarity` to return the cosine similarities between each tick in time. Since the input, `tfidf_matrix`, is a TFIDF vector for each time period in order, you just need to computer the cosine similarities for each neighboring vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "\n",
    "def get_cosine_similarity(tfidf_matrix):\n",
    "    \"\"\"\n",
    "    Get cosine similarities for each neighboring TFIDF vector/document\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    tfidf : 2-d Numpy Ndarray of float\n",
    "        TFIDF sentiment for each document\n",
    "        The first dimension is the document.\n",
    "        The second dimension is the word.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    cosine_similarities : list of float\n",
    "        Cosine similarities for neighboring documents\n",
    "    \"\"\"\n",
    "    \n",
    "    # TODO: Implement\n",
    "    cos_sim = cosine_similarity(tfidf_matrix, tfidf_matrix[1:])\n",
    "    cosine_similarities = cos_sim[0].tolist()\n",
    "    \n",
    "    return cosine_similarities\n",
    "\n",
    "\n",
    "project_tests.test_get_cosine_similarity(get_cosine_similarity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the cosine similarities over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe53f1643c8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cosine_similarities = {\n",
    "    ticker: {\n",
    "        sentiment_name: get_cosine_similarity(sentiment_values)\n",
    "        for sentiment_name, sentiment_values in ten_k_sentiments.items()}\n",
    "    for ticker, ten_k_sentiments in sentiment_tfidf_ten_ks.items()}\n",
    "\n",
    "\n",
    "project_helper.plot_similarities(\n",
    "    [cosine_similarities[example_ticker][sentiment] for sentiment in sentiments],\n",
    "    file_dates[example_ticker][1:],\n",
    "    'Cosine Similarities for {} Sentiment'.format(example_ticker),\n",
    "    sentiments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate Alpha Factors\n",
    "Just like we did in project 4, let's evaluate the alpha factors. For this section, we'll just be looking at the cosine similarities, but it can be applied to the jaccard similarities as well.\n",
    "### Price Data\n",
    "Let's get yearly pricing to run the factor against, since 10-Ks are produced annually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ticker</th>\n",
       "      <th>A</th>\n",
       "      <th>AA</th>\n",
       "      <th>AAAP</th>\n",
       "      <th>AABA</th>\n",
       "      <th>AAC</th>\n",
       "      <th>AADR</th>\n",
       "      <th>AAIT</th>\n",
       "      <th>AAL</th>\n",
       "      <th>AAMC</th>\n",
       "      <th>AAME</th>\n",
       "      <th>...</th>\n",
       "      <th>ZUMZ</th>\n",
       "      <th>ZUO</th>\n",
       "      <th>ZVZZC</th>\n",
       "      <th>ZVZZCNX</th>\n",
       "      <th>ZX</th>\n",
       "      <th>ZXYZ_A</th>\n",
       "      <th>ZYME</th>\n",
       "      <th>ZYNE</th>\n",
       "      <th>ZZK</th>\n",
       "      <th>ZZZ</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1962-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>6.40835399</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>10.61223421</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>9.04493392</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>3.80659044</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>3.14982608</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.32975302</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.39785181</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.69892591</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.51433946</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.63082711</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.09677772</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.15548749</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.70833333</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.85161770</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>4.32812500</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>4.71821581</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>29.61250000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>4.54301839</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-01</th>\n",
       "      <td>52.37855258</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>108.17500000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.15548749</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>37.09385272</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>15.03000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.86380242</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>19.31590395</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>8.87000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.05391026</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01</th>\n",
       "      <td>12.16813872</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>8.17500000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.51899897</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-01</th>\n",
       "      <td>19.81048865</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>22.51500000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.79570362</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-01-01</th>\n",
       "      <td>16.32807033</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>37.68000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.88889374</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-01</th>\n",
       "      <td>22.55441748</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>39.18000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>35.76072005</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.51613326</td>\n",
       "      <td>...</td>\n",
       "      <td>21.61000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>23.61133822</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>25.54000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>51.85015549</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.75842757</td>\n",
       "      <td>...</td>\n",
       "      <td>29.54000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>24.89183834</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>23.26000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>14.16371007</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.30466169</td>\n",
       "      <td>...</td>\n",
       "      <td>24.36000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>10.58953275</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>12.20000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>7.44292854</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.69147070</td>\n",
       "      <td>...</td>\n",
       "      <td>7.45000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01</th>\n",
       "      <td>21.05033797</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>16.78000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>4.66025539</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.19283355</td>\n",
       "      <td>...</td>\n",
       "      <td>12.72000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>28.06937567</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>16.63000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>28.82785959</td>\n",
       "      <td>nan</td>\n",
       "      <td>9.63825546</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.89175945</td>\n",
       "      <td>...</td>\n",
       "      <td>26.87000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>23.66553928</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>16.13000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>27.36247977</td>\n",
       "      <td>nan</td>\n",
       "      <td>4.88171380</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.85276561</td>\n",
       "      <td>...</td>\n",
       "      <td>27.76000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.99225131</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>28.01179940</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>19.90000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>30.02536396</td>\n",
       "      <td>27.10695167</td>\n",
       "      <td>12.99864622</td>\n",
       "      <td>82.00000000</td>\n",
       "      <td>2.97706591</td>\n",
       "      <td>...</td>\n",
       "      <td>19.41000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.99000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>39.53485221</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>40.44000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>36.74348283</td>\n",
       "      <td>31.27057728</td>\n",
       "      <td>24.31228275</td>\n",
       "      <td>930.00000000</td>\n",
       "      <td>3.96461868</td>\n",
       "      <td>...</td>\n",
       "      <td>26.00000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.60000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>39.43238724</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>50.51000000</td>\n",
       "      <td>30.92000000</td>\n",
       "      <td>36.88899069</td>\n",
       "      <td>32.90504993</td>\n",
       "      <td>51.89970750</td>\n",
       "      <td>310.12000000</td>\n",
       "      <td>3.94778326</td>\n",
       "      <td>...</td>\n",
       "      <td>38.63000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.43000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>100.00000000</td>\n",
       "      <td>0.50000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>40.79862571</td>\n",
       "      <td>nan</td>\n",
       "      <td>31.27000000</td>\n",
       "      <td>33.26000000</td>\n",
       "      <td>19.06000000</td>\n",
       "      <td>38.06921608</td>\n",
       "      <td>30.53000000</td>\n",
       "      <td>41.33893271</td>\n",
       "      <td>17.16000000</td>\n",
       "      <td>4.91270038</td>\n",
       "      <td>...</td>\n",
       "      <td>15.12000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.79000000</td>\n",
       "      <td>14.10000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>10.07000000</td>\n",
       "      <td>100.00000000</td>\n",
       "      <td>1.00000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>44.93909238</td>\n",
       "      <td>28.08000000</td>\n",
       "      <td>26.76000000</td>\n",
       "      <td>38.67000000</td>\n",
       "      <td>7.24000000</td>\n",
       "      <td>39.81959334</td>\n",
       "      <td>nan</td>\n",
       "      <td>46.08991196</td>\n",
       "      <td>53.50000000</td>\n",
       "      <td>4.05320152</td>\n",
       "      <td>...</td>\n",
       "      <td>21.85000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>100.01000000</td>\n",
       "      <td>14.26000000</td>\n",
       "      <td>1.19890000</td>\n",
       "      <td>8.00000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>15.59000000</td>\n",
       "      <td>100.00000000</td>\n",
       "      <td>25.00000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>66.65391782</td>\n",
       "      <td>53.87000000</td>\n",
       "      <td>81.62000000</td>\n",
       "      <td>69.85000000</td>\n",
       "      <td>9.00000000</td>\n",
       "      <td>58.83570736</td>\n",
       "      <td>nan</td>\n",
       "      <td>51.80358470</td>\n",
       "      <td>81.60000000</td>\n",
       "      <td>3.37888199</td>\n",
       "      <td>...</td>\n",
       "      <td>20.82500000</td>\n",
       "      <td>nan</td>\n",
       "      <td>100.10000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.21650000</td>\n",
       "      <td>nan</td>\n",
       "      <td>7.59310000</td>\n",
       "      <td>12.52000000</td>\n",
       "      <td>25.00000000</td>\n",
       "      <td>0.01000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>61.80000000</td>\n",
       "      <td>46.88000000</td>\n",
       "      <td>81.63000000</td>\n",
       "      <td>73.35000000</td>\n",
       "      <td>9.81000000</td>\n",
       "      <td>52.88000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>37.99000000</td>\n",
       "      <td>67.90000000</td>\n",
       "      <td>2.57500000</td>\n",
       "      <td>...</td>\n",
       "      <td>24.30000000</td>\n",
       "      <td>26.74000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.38000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>15.34000000</td>\n",
       "      <td>9.17000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>57 rows × 11941 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "ticker               A          AA        AAAP         AABA         AAC  \\\n",
       "date                                                                      \n",
       "1962-01-01         nan         nan         nan          nan         nan   \n",
       "1963-01-01         nan         nan         nan          nan         nan   \n",
       "1964-01-01         nan         nan         nan          nan         nan   \n",
       "1965-01-01         nan         nan         nan          nan         nan   \n",
       "1966-01-01         nan         nan         nan          nan         nan   \n",
       "1967-01-01         nan         nan         nan          nan         nan   \n",
       "1968-01-01         nan         nan         nan          nan         nan   \n",
       "1969-01-01         nan         nan         nan          nan         nan   \n",
       "1970-01-01         nan         nan         nan          nan         nan   \n",
       "1971-01-01         nan         nan         nan          nan         nan   \n",
       "1972-01-01         nan         nan         nan          nan         nan   \n",
       "1973-01-01         nan         nan         nan          nan         nan   \n",
       "1974-01-01         nan         nan         nan          nan         nan   \n",
       "1975-01-01         nan         nan         nan          nan         nan   \n",
       "1976-01-01         nan         nan         nan          nan         nan   \n",
       "1977-01-01         nan         nan         nan          nan         nan   \n",
       "1978-01-01         nan         nan         nan          nan         nan   \n",
       "1979-01-01         nan         nan         nan          nan         nan   \n",
       "1980-01-01         nan         nan         nan          nan         nan   \n",
       "1981-01-01         nan         nan         nan          nan         nan   \n",
       "1982-01-01         nan         nan         nan          nan         nan   \n",
       "1983-01-01         nan         nan         nan          nan         nan   \n",
       "1984-01-01         nan         nan         nan          nan         nan   \n",
       "1985-01-01         nan         nan         nan          nan         nan   \n",
       "1986-01-01         nan         nan         nan          nan         nan   \n",
       "1987-01-01         nan         nan         nan          nan         nan   \n",
       "1988-01-01         nan         nan         nan          nan         nan   \n",
       "1989-01-01         nan         nan         nan          nan         nan   \n",
       "1990-01-01         nan         nan         nan          nan         nan   \n",
       "1991-01-01         nan         nan         nan          nan         nan   \n",
       "1992-01-01         nan         nan         nan          nan         nan   \n",
       "1993-01-01         nan         nan         nan          nan         nan   \n",
       "1994-01-01         nan         nan         nan          nan         nan   \n",
       "1995-01-01         nan         nan         nan          nan         nan   \n",
       "1996-01-01         nan         nan         nan   0.70833333         nan   \n",
       "1997-01-01         nan         nan         nan   4.32812500         nan   \n",
       "1998-01-01         nan         nan         nan  29.61250000         nan   \n",
       "1999-01-01 52.37855258         nan         nan 108.17500000         nan   \n",
       "2000-01-01 37.09385272         nan         nan  15.03000000         nan   \n",
       "2001-01-01 19.31590395         nan         nan   8.87000000         nan   \n",
       "2002-01-01 12.16813872         nan         nan   8.17500000         nan   \n",
       "2003-01-01 19.81048865         nan         nan  22.51500000         nan   \n",
       "2004-01-01 16.32807033         nan         nan  37.68000000         nan   \n",
       "2005-01-01 22.55441748         nan         nan  39.18000000         nan   \n",
       "2006-01-01 23.61133822         nan         nan  25.54000000         nan   \n",
       "2007-01-01 24.89183834         nan         nan  23.26000000         nan   \n",
       "2008-01-01 10.58953275         nan         nan  12.20000000         nan   \n",
       "2009-01-01 21.05033797         nan         nan  16.78000000         nan   \n",
       "2010-01-01 28.06937567         nan         nan  16.63000000         nan   \n",
       "2011-01-01 23.66553928         nan         nan  16.13000000         nan   \n",
       "2012-01-01 28.01179940         nan         nan  19.90000000         nan   \n",
       "2013-01-01 39.53485221         nan         nan  40.44000000         nan   \n",
       "2014-01-01 39.43238724         nan         nan  50.51000000 30.92000000   \n",
       "2015-01-01 40.79862571         nan 31.27000000  33.26000000 19.06000000   \n",
       "2016-01-01 44.93909238 28.08000000 26.76000000  38.67000000  7.24000000   \n",
       "2017-01-01 66.65391782 53.87000000 81.62000000  69.85000000  9.00000000   \n",
       "2018-01-01 61.80000000 46.88000000 81.63000000  73.35000000  9.81000000   \n",
       "\n",
       "ticker            AADR        AAIT         AAL         AAMC        AAME  \\\n",
       "date                                                                      \n",
       "1962-01-01         nan         nan         nan          nan         nan   \n",
       "1963-01-01         nan         nan         nan          nan         nan   \n",
       "1964-01-01         nan         nan         nan          nan         nan   \n",
       "1965-01-01         nan         nan         nan          nan         nan   \n",
       "1966-01-01         nan         nan         nan          nan         nan   \n",
       "1967-01-01         nan         nan         nan          nan         nan   \n",
       "1968-01-01         nan         nan         nan          nan         nan   \n",
       "1969-01-01         nan         nan         nan          nan         nan   \n",
       "1970-01-01         nan         nan         nan          nan         nan   \n",
       "1971-01-01         nan         nan         nan          nan         nan   \n",
       "1972-01-01         nan         nan         nan          nan         nan   \n",
       "1973-01-01         nan         nan         nan          nan         nan   \n",
       "1974-01-01         nan         nan         nan          nan         nan   \n",
       "1975-01-01         nan         nan         nan          nan         nan   \n",
       "1976-01-01         nan         nan         nan          nan         nan   \n",
       "1977-01-01         nan         nan         nan          nan         nan   \n",
       "1978-01-01         nan         nan         nan          nan         nan   \n",
       "1979-01-01         nan         nan         nan          nan         nan   \n",
       "1980-01-01         nan         nan         nan          nan         nan   \n",
       "1981-01-01         nan         nan         nan          nan         nan   \n",
       "1982-01-01         nan         nan         nan          nan         nan   \n",
       "1983-01-01         nan         nan         nan          nan         nan   \n",
       "1984-01-01         nan         nan         nan          nan  6.40835399   \n",
       "1985-01-01         nan         nan         nan          nan 10.61223421   \n",
       "1986-01-01         nan         nan         nan          nan  9.04493392   \n",
       "1987-01-01         nan         nan         nan          nan  3.80659044   \n",
       "1988-01-01         nan         nan         nan          nan  3.14982608   \n",
       "1989-01-01         nan         nan         nan          nan  2.32975302   \n",
       "1990-01-01         nan         nan         nan          nan  1.39785181   \n",
       "1991-01-01         nan         nan         nan          nan  0.69892591   \n",
       "1992-01-01         nan         nan         nan          nan  1.51433946   \n",
       "1993-01-01         nan         nan         nan          nan  1.63082711   \n",
       "1994-01-01         nan         nan         nan          nan  2.09677772   \n",
       "1995-01-01         nan         nan         nan          nan  2.15548749   \n",
       "1996-01-01         nan         nan         nan          nan  2.85161770   \n",
       "1997-01-01         nan         nan         nan          nan  4.71821581   \n",
       "1998-01-01         nan         nan         nan          nan  4.54301839   \n",
       "1999-01-01         nan         nan         nan          nan  2.15548749   \n",
       "2000-01-01         nan         nan         nan          nan  1.86380242   \n",
       "2001-01-01         nan         nan         nan          nan  2.05391026   \n",
       "2002-01-01         nan         nan         nan          nan  1.51899897   \n",
       "2003-01-01         nan         nan         nan          nan  2.79570362   \n",
       "2004-01-01         nan         nan         nan          nan  2.88889374   \n",
       "2005-01-01         nan         nan 35.76072005          nan  2.51613326   \n",
       "2006-01-01         nan         nan 51.85015549          nan  2.75842757   \n",
       "2007-01-01         nan         nan 14.16371007          nan  1.30466169   \n",
       "2008-01-01         nan         nan  7.44292854          nan  0.69147070   \n",
       "2009-01-01         nan         nan  4.66025539          nan  1.19283355   \n",
       "2010-01-01 28.82785959         nan  9.63825546          nan  1.89175945   \n",
       "2011-01-01 27.36247977         nan  4.88171380          nan  1.85276561   \n",
       "2012-01-01 30.02536396 27.10695167 12.99864622  82.00000000  2.97706591   \n",
       "2013-01-01 36.74348283 31.27057728 24.31228275 930.00000000  3.96461868   \n",
       "2014-01-01 36.88899069 32.90504993 51.89970750 310.12000000  3.94778326   \n",
       "2015-01-01 38.06921608 30.53000000 41.33893271  17.16000000  4.91270038   \n",
       "2016-01-01 39.81959334         nan 46.08991196  53.50000000  4.05320152   \n",
       "2017-01-01 58.83570736         nan 51.80358470  81.60000000  3.37888199   \n",
       "2018-01-01 52.88000000         nan 37.99000000  67.90000000  2.57500000   \n",
       "\n",
       "ticker         ...            ZUMZ         ZUO        ZVZZC     ZVZZCNX  \\\n",
       "date           ...                                                        \n",
       "1962-01-01     ...             nan         nan          nan         nan   \n",
       "1963-01-01     ...             nan         nan          nan         nan   \n",
       "1964-01-01     ...             nan         nan          nan         nan   \n",
       "1965-01-01     ...             nan         nan          nan         nan   \n",
       "1966-01-01     ...             nan         nan          nan         nan   \n",
       "1967-01-01     ...             nan         nan          nan         nan   \n",
       "1968-01-01     ...             nan         nan          nan         nan   \n",
       "1969-01-01     ...             nan         nan          nan         nan   \n",
       "1970-01-01     ...             nan         nan          nan         nan   \n",
       "1971-01-01     ...             nan         nan          nan         nan   \n",
       "1972-01-01     ...             nan         nan          nan         nan   \n",
       "1973-01-01     ...             nan         nan          nan         nan   \n",
       "1974-01-01     ...             nan         nan          nan         nan   \n",
       "1975-01-01     ...             nan         nan          nan         nan   \n",
       "1976-01-01     ...             nan         nan          nan         nan   \n",
       "1977-01-01     ...             nan         nan          nan         nan   \n",
       "1978-01-01     ...             nan         nan          nan         nan   \n",
       "1979-01-01     ...             nan         nan          nan         nan   \n",
       "1980-01-01     ...             nan         nan          nan         nan   \n",
       "1981-01-01     ...             nan         nan          nan         nan   \n",
       "1982-01-01     ...             nan         nan          nan         nan   \n",
       "1983-01-01     ...             nan         nan          nan         nan   \n",
       "1984-01-01     ...             nan         nan          nan         nan   \n",
       "1985-01-01     ...             nan         nan          nan         nan   \n",
       "1986-01-01     ...             nan         nan          nan         nan   \n",
       "1987-01-01     ...             nan         nan          nan         nan   \n",
       "1988-01-01     ...             nan         nan          nan         nan   \n",
       "1989-01-01     ...             nan         nan          nan         nan   \n",
       "1990-01-01     ...             nan         nan          nan         nan   \n",
       "1991-01-01     ...             nan         nan          nan         nan   \n",
       "1992-01-01     ...             nan         nan          nan         nan   \n",
       "1993-01-01     ...             nan         nan          nan         nan   \n",
       "1994-01-01     ...             nan         nan          nan         nan   \n",
       "1995-01-01     ...             nan         nan          nan         nan   \n",
       "1996-01-01     ...             nan         nan          nan         nan   \n",
       "1997-01-01     ...             nan         nan          nan         nan   \n",
       "1998-01-01     ...             nan         nan          nan         nan   \n",
       "1999-01-01     ...             nan         nan          nan         nan   \n",
       "2000-01-01     ...             nan         nan          nan         nan   \n",
       "2001-01-01     ...             nan         nan          nan         nan   \n",
       "2002-01-01     ...             nan         nan          nan         nan   \n",
       "2003-01-01     ...             nan         nan          nan         nan   \n",
       "2004-01-01     ...             nan         nan          nan         nan   \n",
       "2005-01-01     ...     21.61000000         nan          nan         nan   \n",
       "2006-01-01     ...     29.54000000         nan          nan         nan   \n",
       "2007-01-01     ...     24.36000000         nan          nan         nan   \n",
       "2008-01-01     ...      7.45000000         nan          nan         nan   \n",
       "2009-01-01     ...     12.72000000         nan          nan         nan   \n",
       "2010-01-01     ...     26.87000000         nan          nan         nan   \n",
       "2011-01-01     ...     27.76000000         nan          nan         nan   \n",
       "2012-01-01     ...     19.41000000         nan          nan         nan   \n",
       "2013-01-01     ...     26.00000000         nan          nan         nan   \n",
       "2014-01-01     ...     38.63000000         nan          nan         nan   \n",
       "2015-01-01     ...     15.12000000         nan          nan         nan   \n",
       "2016-01-01     ...     21.85000000         nan 100.01000000 14.26000000   \n",
       "2017-01-01     ...     20.82500000         nan 100.10000000         nan   \n",
       "2018-01-01     ...     24.30000000 26.74000000          nan         nan   \n",
       "\n",
       "ticker             ZX      ZXYZ_A        ZYME        ZYNE          ZZK  \\\n",
       "date                                                                     \n",
       "1962-01-01        nan         nan         nan         nan          nan   \n",
       "1963-01-01        nan         nan         nan         nan          nan   \n",
       "1964-01-01        nan         nan         nan         nan          nan   \n",
       "1965-01-01        nan         nan         nan         nan          nan   \n",
       "1966-01-01        nan         nan         nan         nan          nan   \n",
       "1967-01-01        nan         nan         nan         nan          nan   \n",
       "1968-01-01        nan         nan         nan         nan          nan   \n",
       "1969-01-01        nan         nan         nan         nan          nan   \n",
       "1970-01-01        nan         nan         nan         nan          nan   \n",
       "1971-01-01        nan         nan         nan         nan          nan   \n",
       "1972-01-01        nan         nan         nan         nan          nan   \n",
       "1973-01-01        nan         nan         nan         nan          nan   \n",
       "1974-01-01        nan         nan         nan         nan          nan   \n",
       "1975-01-01        nan         nan         nan         nan          nan   \n",
       "1976-01-01        nan         nan         nan         nan          nan   \n",
       "1977-01-01        nan         nan         nan         nan          nan   \n",
       "1978-01-01        nan         nan         nan         nan          nan   \n",
       "1979-01-01        nan         nan         nan         nan          nan   \n",
       "1980-01-01        nan         nan         nan         nan          nan   \n",
       "1981-01-01        nan         nan         nan         nan          nan   \n",
       "1982-01-01        nan         nan         nan         nan          nan   \n",
       "1983-01-01        nan         nan         nan         nan          nan   \n",
       "1984-01-01        nan         nan         nan         nan          nan   \n",
       "1985-01-01        nan         nan         nan         nan          nan   \n",
       "1986-01-01        nan         nan         nan         nan          nan   \n",
       "1987-01-01        nan         nan         nan         nan          nan   \n",
       "1988-01-01        nan         nan         nan         nan          nan   \n",
       "1989-01-01        nan         nan         nan         nan          nan   \n",
       "1990-01-01        nan         nan         nan         nan          nan   \n",
       "1991-01-01        nan         nan         nan         nan          nan   \n",
       "1992-01-01        nan         nan         nan         nan          nan   \n",
       "1993-01-01        nan         nan         nan         nan          nan   \n",
       "1994-01-01        nan         nan         nan         nan          nan   \n",
       "1995-01-01        nan         nan         nan         nan          nan   \n",
       "1996-01-01        nan         nan         nan         nan          nan   \n",
       "1997-01-01        nan         nan         nan         nan          nan   \n",
       "1998-01-01        nan         nan         nan         nan          nan   \n",
       "1999-01-01        nan         nan         nan         nan          nan   \n",
       "2000-01-01        nan         nan         nan         nan          nan   \n",
       "2001-01-01        nan         nan         nan         nan          nan   \n",
       "2002-01-01        nan         nan         nan         nan          nan   \n",
       "2003-01-01        nan         nan         nan         nan          nan   \n",
       "2004-01-01        nan         nan         nan         nan          nan   \n",
       "2005-01-01        nan         nan         nan         nan          nan   \n",
       "2006-01-01        nan         nan         nan         nan          nan   \n",
       "2007-01-01        nan         nan         nan         nan          nan   \n",
       "2008-01-01        nan         nan         nan         nan          nan   \n",
       "2009-01-01        nan         nan         nan         nan          nan   \n",
       "2010-01-01        nan         nan         nan         nan          nan   \n",
       "2011-01-01 2.99225131         nan         nan         nan          nan   \n",
       "2012-01-01 2.99000000         nan         nan         nan          nan   \n",
       "2013-01-01 2.60000000         nan         nan         nan          nan   \n",
       "2014-01-01 1.43000000         nan         nan         nan 100.00000000   \n",
       "2015-01-01 0.79000000 14.10000000         nan 10.07000000 100.00000000   \n",
       "2016-01-01 1.19890000  8.00000000         nan 15.59000000 100.00000000   \n",
       "2017-01-01 1.21650000         nan  7.59310000 12.52000000  25.00000000   \n",
       "2018-01-01 1.38000000         nan 15.34000000  9.17000000          nan   \n",
       "\n",
       "ticker             ZZZ  \n",
       "date                    \n",
       "1962-01-01         nan  \n",
       "1963-01-01         nan  \n",
       "1964-01-01         nan  \n",
       "1965-01-01         nan  \n",
       "1966-01-01         nan  \n",
       "1967-01-01         nan  \n",
       "1968-01-01         nan  \n",
       "1969-01-01         nan  \n",
       "1970-01-01         nan  \n",
       "1971-01-01         nan  \n",
       "1972-01-01         nan  \n",
       "1973-01-01         nan  \n",
       "1974-01-01         nan  \n",
       "1975-01-01         nan  \n",
       "1976-01-01         nan  \n",
       "1977-01-01         nan  \n",
       "1978-01-01         nan  \n",
       "1979-01-01         nan  \n",
       "1980-01-01         nan  \n",
       "1981-01-01         nan  \n",
       "1982-01-01         nan  \n",
       "1983-01-01         nan  \n",
       "1984-01-01         nan  \n",
       "1985-01-01         nan  \n",
       "1986-01-01         nan  \n",
       "1987-01-01         nan  \n",
       "1988-01-01         nan  \n",
       "1989-01-01         nan  \n",
       "1990-01-01         nan  \n",
       "1991-01-01         nan  \n",
       "1992-01-01         nan  \n",
       "1993-01-01         nan  \n",
       "1994-01-01         nan  \n",
       "1995-01-01         nan  \n",
       "1996-01-01         nan  \n",
       "1997-01-01         nan  \n",
       "1998-01-01         nan  \n",
       "1999-01-01         nan  \n",
       "2000-01-01         nan  \n",
       "2001-01-01         nan  \n",
       "2002-01-01         nan  \n",
       "2003-01-01         nan  \n",
       "2004-01-01         nan  \n",
       "2005-01-01         nan  \n",
       "2006-01-01         nan  \n",
       "2007-01-01         nan  \n",
       "2008-01-01         nan  \n",
       "2009-01-01         nan  \n",
       "2010-01-01         nan  \n",
       "2011-01-01         nan  \n",
       "2012-01-01         nan  \n",
       "2013-01-01         nan  \n",
       "2014-01-01  0.50000000  \n",
       "2015-01-01  1.00000000  \n",
       "2016-01-01 25.00000000  \n",
       "2017-01-01  0.01000000  \n",
       "2018-01-01         nan  \n",
       "\n",
       "[57 rows x 11941 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pricing = pd.read_csv('../../data/project_5_yr/yr-quotemedia.csv', parse_dates=['date'])\n",
    "pricing = pricing.pivot(index='date', columns='ticker', values='adj_close')\n",
    "\n",
    "\n",
    "pricing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dict to DataFrame\n",
    "The alphalens library uses dataframes, so we we'll need to turn our dictionary into a dataframe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>ticker</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.98065125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.95001565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.93176312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.89701433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.85838968</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date ticker sentiment      value\n",
       "0 2016-01-01   AMZN  negative 0.98065125\n",
       "1 2015-01-01   AMZN  negative 0.95001565\n",
       "2 2014-01-01   AMZN  negative 0.93176312\n",
       "3 2013-01-01   AMZN  negative 0.89701433\n",
       "4 2012-01-01   AMZN  negative 0.85838968"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosine_similarities_df_dict = {'date': [], 'ticker': [], 'sentiment': [], 'value': []}\n",
    "\n",
    "\n",
    "for ticker, ten_k_sentiments in cosine_similarities.items():\n",
    "    for sentiment_name, sentiment_values in ten_k_sentiments.items():\n",
    "        for sentiment_values, sentiment_value in enumerate(sentiment_values):\n",
    "            cosine_similarities_df_dict['ticker'].append(ticker)\n",
    "            cosine_similarities_df_dict['sentiment'].append(sentiment_name)\n",
    "            cosine_similarities_df_dict['value'].append(sentiment_value)\n",
    "            cosine_similarities_df_dict['date'].append(file_dates[ticker][1:][sentiment_values])\n",
    "\n",
    "cosine_similarities_df = pd.DataFrame(cosine_similarities_df_dict)\n",
    "cosine_similarities_df['date'] = pd.DatetimeIndex(cosine_similarities_df['date']).year\n",
    "cosine_similarities_df['date'] = pd.to_datetime(cosine_similarities_df['date'], format='%Y')\n",
    "\n",
    "\n",
    "cosine_similarities_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alphalens Format\n",
    "In order to use a lot of the alphalens functions, we need to aligned the indices and convert the time to unix timestamp. In this next cell, we'll do just that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>1D</th>\n",
       "      <th>factor</th>\n",
       "      <th>factor_quantile</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th>asset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">1994-01-01</th>\n",
       "      <th>BMY</th>\n",
       "      <td>0.53264104</td>\n",
       "      <td>0.44665176</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CVX</th>\n",
       "      <td>0.22211880</td>\n",
       "      <td>0.64730294</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FRT</th>\n",
       "      <td>0.17159556</td>\n",
       "      <td>0.46696523</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1995-01-01</th>\n",
       "      <th>BMY</th>\n",
       "      <td>0.32152919</td>\n",
       "      <td>0.43571094</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CVX</th>\n",
       "      <td>0.28478156</td>\n",
       "      <td>0.62523346</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         1D     factor  factor_quantile\n",
       "date       asset                                       \n",
       "1994-01-01 BMY   0.53264104 0.44665176                1\n",
       "           CVX   0.22211880 0.64730294                5\n",
       "           FRT   0.17159556 0.46696523                3\n",
       "1995-01-01 BMY   0.32152919 0.43571094                3\n",
       "           CVX   0.28478156 0.62523346                5"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import alphalens as al\n",
    "\n",
    "\n",
    "factor_data = {}\n",
    "skipped_sentiments = []\n",
    "\n",
    "for sentiment in sentiments:\n",
    "    cs_df = cosine_similarities_df[(cosine_similarities_df['sentiment'] == sentiment)]\n",
    "    cs_df = cs_df.pivot(index='date', columns='ticker', values='value')\n",
    "\n",
    "    try:\n",
    "        data = al.utils.get_clean_factor_and_forward_returns(cs_df.stack(), pricing, quantiles=5, bins=None, periods=[1])\n",
    "        factor_data[sentiment] = data\n",
    "    except:\n",
    "        skipped_sentiments.append(sentiment)\n",
    "\n",
    "if skipped_sentiments:\n",
    "    print('\\nSkipped the following sentiments:\\n{}'.format('\\n'.join(skipped_sentiments)))\n",
    "factor_data[sentiments[0]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alphalens Format with Unix Time\n",
    "Alphalen's `factor_rank_autocorrelation` and `mean_return_by_quantile` functions require unix timestamps to work, so we'll also create factor dataframes with unix time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "unixt_factor_data = {\n",
    "    factor: data.set_index(pd.MultiIndex.from_tuples(\n",
    "        [(x.timestamp(), y) for x, y in data.index.values],\n",
    "        names=['date', 'asset']))\n",
    "    for factor, data in factor_data.items()}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Factor Returns\n",
    "Let's view the factor returns over time. We should be seeing it generally move up and to the right."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe53f103160>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cOlcgx1tNBIUCuZ3dI1fuS84uISw1TKPzOjd2Zk6Hh26neCSFhYWcPXuWvXv38uGHH3Lw4EG+++47oqKiuHDhAtra2qSmplJQUMDrr7/O7t27sbCwUMv5rl+/HoC0tDT+/vtvACIiIhg0aBDDhw8HwMTEhMmTJwPw7rvvsm7dOrXOe0W2LFy4kKCgIFasWAFAWFgYmzdvZubMmRw8eBAPDw/Mzc0f69klni4k596AeLBSpnS8vQS7Nq40MjYh/OTRGnfuULyZqT5uZHoYL7zwAgDe3t5E3/tQOnjwIFOmTEH7Xh6jcePGXL58mcuXL9OnTx+geHVvY2OjHqdEJKw8Ll++zLvvvktaWhqZmZn069ev0rY8yMSJExkyZAgzZ85k/fr1vPTSS1V6XomnF8m5NyByw8LRtrZGy8QEKI63a2lrY6F0VPeRybRw6uxDSOCf5GVno1MFffLHQcfBgexzQYiiWK7M7uOusKuDtrY2KpVK/XtuKXEzHR0dALS0tCgsLAQo13ZRFHFxcVErSz6Ivr7+Q+efMGECv/76Kx4eHmzcuJEjR46U2688Wx6kadOmWFlZcejQIc6cOcPmzZsfOq+ERGmkmHsDIi8sDF2n++Ptlo4typQ9OnXpTlFBAZFBp2vcJoVSiZiTQ2FiYo3PVVmsrKxISkri9u3b5OXl8fvvvz+yf9++fVm9erXawaampuLk5ERycrLauRcUFHDlypVy739QEjgjIwMbGxsKCgqq7IzLkxeeNGkSL774Iv7+/mhpaVVpPImnF8m5NxBU+fnkRUWpK2WKCgtJjLyGbUunMn1tWzphaG5RKxua6mPFjFwuZ8GCBXTs2JFBgwbhXIHu/aRJk2jWrBnu7u54eHjw888/o1Ao2LFjB3PmzMHDwwNPT09OnjxZ7v0jR47k888/x8vLi8jISD766CM6duxInz59Kpz7QXr16kVoaKg6oQowePBgMjMzpZCMRNWojHRkTfxIkr9VIyc0VAx1chbT9+4VRVEUE65dFZf6DxTDTh4rt/+RH9eJy0YNFrMz7taoXfnx8WKok7OYumWLuk2SmtUs586dE7t161bl+6S/w5MJdST5K1FDqCtlnEqSqcXJVZtyVu5QXDWjKioi4kz5q01NoW1lhaCrS35UdI3O87SyePFihg0bJh3ZJ1FlJOdeQ2SdOkVeRITGxssLC0PQ1UVhXyy1G381DIPGZhiZl6+uaenQHFMbW8JP1uyGJkEmQ2FvX6/CMk8Sc+fOJSYmhm7dar7ySeLJQnLuNUBRZhax014jft58jY2ZGx6OTsuWCPcSagnXwh+6aofiA6KdunQn9solstLuaMyO8lAoJXVICYn6huTca4CMP/ch5uSQe+kSuZXYll4RoigWV8rcq2/PTk8jPfEWti0fnaxz7tIdUVRx9fTxatvwKBQOSvLj4hALCmp0HgkJicojOfcaIG3nLuTNmiHo6HAnIKDa4xUmJVOUloaOU7Ezjy9n81J5mNk1w7ypPWEna7ZqRqFUQlER+XFxNTqPhIRE5ZGcu4bJu36dnAsXMB0xAqNnn+Xub7+jysqq3pglO1OdWgGQcPVfZFraWDo2r/Bepy7diQ8P5W5KUrVseBQ6JeWQUlJVQqLeIDl3DZO+axdoaWE8ZDAm/v6osrK4u696Z4aXrZQJx1LpgFyhU+G9Tl18AAg/VXOhmfpY625gYABAfHy8WvPlQcXFPXv2sHjx4keOU/p+CYmGhOTcNYhYUEDar7sx6NkTbXNz9Lw80WnZgjvbqheayQsLQ25ri5aREaqiIm5FRmDTqnKbY0ytbbFybEl4DYZmtExM0DIxqVfOvQRbW1t27NgBlHXugwcPZu7cuZW+X0KiISE5dw2Seew4RSkpmAwrFoQSBAETP/9qJ1Zzw8PVO1NTYmMoyMutMN5eGucuPiRej+DOrfjHtqEi6mvFTIn8b3lyuhs3bmT69OkAREZG0qlTJ9q3b8+CBQvUK//S8sG5ubm89NJLuLm54eXlxeHDhwHuGwdg0KBBHDlyhKKiIiZMmICrqytubm58+eWXtfz0Ek8zknCYBknbtRMtc3MMfHzUbcZDBpP0xRfc2b4dm/ffr/KYqrw88qOiMOrXF/hv81J5sgMPo1VnH/7+aT3hJ4/R6YWHqxlWB4VSSVY52/NvLVpE3r+alfzVae2M9bx5VbpHoVCUkdPduHGj+vqMGTOYMWMGo0aNYvXq1eWO8e233wJw6dIlwsLC6Nu3L1evPvxM+ODgYG7evMnly5eBYplgCYnaQlq5a4jClBQyj/yN8ZDBCKWEvLSMjTHq35+7e35Dde/QhqqQF3ENVKr/KmWuhtHI2AQjC6tKj2FkbkET5zY1uqFJoVRSmJRU7eRxXXHq1Cn8/PwAGD16dLl9jh8/ztixYwFwdnbG3t7+kc7d0dGR69ev8/rrr/Pnn39iZGSkecMlJB6CtHLXEOl7foPCQkzuaXSXxmSEP+m7d3N3715MqpicK1MpExGOTUvncuV1H4VTl+4cWr+alBvRmDdTVuneylByYHd+TAyUsq2qK+z6TLGsR1keJjFsamrKxYsX2b9/P99++y0BAQHqwz4kJGoaaeWuAURRJG3XTvQ8PdFpXrY8Uc/LC0WL5twJ2F7lsXPDwhEaNULerBk5GXe5k3AT20omU0vTqmNXBEFWY0qR9bFi5kHKk9MtoVOnTuzcuROArVu3ltune/fuagnfq1evcuPGDZycnFAqlQQHB6NSqYiNjeXs2bMApKSkoFKpGDZsGB999BH//FM7B5dLSIDk3DVCbkgI+dciMR5WdtUOxYlVU39/ckNCyP333yqNnRcWhm7LlggyGQnXSjYvVT7eXoK+iSlNXd0JO3n0oSvQ6qCwbwZAXlSUxsfWFOXJ6ZawfPlyli1bRocOHUhISMDY2LjM/dOmTaOoqAg3NzdGjBjBxo0b0dHRoWvXrjg4OODm5sasWbNo27YtADdv3qRnz554enoyYcIESfxLolaRwjIaIG3HTgQ9PYyeffahfYwHDybpi2Wkbd+O9YIFlRpXFEVyr17FqH9/ABKuhiHIZFg7tnwsO527dOfAmq9JiorEyrHFY43xMGS6umjb2tSbI/cyMzMBUCqV6oRm48aNOXfu3H39JkyYAECTJk04ffo0giCwdetW2rVrV+Z+XV3d+5KwJQiC8NBDOaTVukRdIa3cq4kqO5u7e/di1L8/WvfK5wBu34y9T7BLy8QEo/79SK9CYrXw1i1U6elqTZn4iHAsmjkg19V9LFtbdOiMTEubsBpKrOrU03LIynD+/Hk8PT1xd3dn5cqVfPHFF3VtkoREtZCcezW5e+AAqqwsdW07QEF+Hlvfn8MfX39+X18Tf39UmZmV3rGaG1acTNVxckKlKuLWtfBKb14qDz0DQ5QeXoSfOoZYKgGoKeprrXtl8PHx4eLFi4SEhHD06FFatNDsNxsJidpGcu7VJH3nLuT2zdDz9la3XT11nNyMu8ReCSE55r8YtF7btiiaN6+0mFhe+D3ZgVZOpMbFkp+TU6X69vJw6tKdjJRktfiYJlEolagyMhCLijQ+toSERNWo0LkLgrBeEIQkQRAuP+S6IAjC14IgXBMEIUQQhLaaN7N+kh8TQ/a5c5i8MOy+0sTgA39gYmWDtkKHC3/+pm4vTqz6kXsxRL0qfxS5YeHImzZFy0Cf+JKTl6qxcgdo7t0RbbmiRmreS8ohJecuIVE+oigS/848Mg4erPG5KrNy3wj0f8T1Z4GW935eAVZV36yGQdovv4BMhvHQIeq2xOvXuHXtKl7PDqaNTy/+PXaEnIy76uvGQ4YgKBSkVWL1nhcero63J0SEo2dohImVTbVs1mnUCIe27Qg/dQyVSrNOuKQcksJCjY4rIfGkkBkYSPovv1CQcKvG56rQuYuieBRIfUSXIcCme2e3ngZMBEGongdqAIhFRaT/8iv6Pt2QW/23WzT4wF60dXRw6fEMXv0HUViQz6VDB9TXtUxMMKxEYlWVk0N+TMx9O1NtWjpVefNSeTh36U52ehpxoeV+GXts5La2IJcjSs5dQqIMqrw8EhcvQadlC0xHjazx+TQRc28CxJb6Pe5e2xNN1smTFCYmYvLCMHVbblYmYSf+pnW3nug00se8mZJmru4E7/8DValQhak6sfrnQ8fPi4gAlQpdZydyszJJvRlbJbGwR+Hg1Q65rp7Gq2YELS0UzZo90c590aJFj3XfpEmTCK1APG716tVs2rTpscaXqP+kbthIQVwcVu+8g6Bd81XomnDu5S0ly90lIwjCK4IgBAmCEJScnKyBqeuOtJ270DI1xbBXT3Vb6N+BFObn4dFngLrNq/9gMm4ncy3otLpNz9v7XmL1/o00pSldKXPrWrF+yeNsXioPuY4uLdp1JOLMSYoKNXs0nkKpfCqduyiK90kQPMj3339PmzZtHjn2lClTGDduXLXsk6ifFCQmkvLddxj09kW/S5damVMTzj0OaFrqdzugXG1ZURS/E0WxnSiK7SwsLDQwdd1QeOcOGYGBGA9+DkGhAIr/5w7+ax82LZ2wcvhPgsDRuz1GFlZc2Fe1xGpeWDgyfX3kTZoQfzUMBAGbFq009gxOXbqTm5lBzKVgjY0JoFDaQ2FhjeyCrQqbNm3C3d0dDw8Pxo4dS0xMDL6+vri7u+Pr68uNGzeA4k1Mb7zxBl26dMHR0VGt3Z6QkED37t3x9PTE1dWVY8eOMXfuXHJycvD09GTMmDFER0fTunVrpk2bRtu2bYmNjWXq1Km0a9cOFxcX3i+lAtqzZ0+CgoKA4oNE5s+fj4eHB506dSIxMRGADz74gKVLl6r7z5kzhw4dOtCqVSuOHSuWjcjOzsbf3x93d3dGjBhBx44d1eNK1F+SvvgCCguxmjOn1ubUxHeDPcB0QRC2Ah2BdFEUEzQwbr3l7m+/Q0EBxqVCMrFXQrgTH0f/aW/e11cm08Kr30D+/mk9SdHXsVQ6AmBUsmM1YDvWC94rM0dueDg6Tk5q2QHzpvYo9Bpp7BmUHl7o6OsTfuIojl7tNTZuSVJVLChAUCg4FnCVlNhMjY0PYN7UAB//h3/QXblyhU8++YQTJ05gbm5Oamoq48ePZ9y4cYwfP57169fzxhtv8OuvvwLFjvz48eOEhYUxePBghg8fzs8//0y/fv2YP38+RUVFZGdn4+Pjw4oVKwgOLv5AjI6OJjw8nA0bNrBy5UoAPvnkExo3bkxRURG+vr6EhITg7u5+n31ZWVl06tSJTz75hP/973+sXbuWd999t8xzFBYWcvbsWfbu3cuHH37IwYMHWblyJaampoSEhHD58mU8PT019bJK1BDZFy5wd89vmL36KoqmTSu+QUNUphRyC3AKcBIEIU4QhJcFQZgiCMKUe132AteBa8BaYFqNWVsPEEWRtJ070XV1VSs1Alw8sBddA0OcOvuUuce1V1+0de4vi9Q2NcWwXz/S9+xBlZNTZo6SShlRpSIhIgxbDcXbS9DSltOyQ1euBZ3W6PmqOiXlkHl5Ghuzqhw6dIjhw4djbm4OFMsOnDp1Si3lO3bsWI4f/+/YwaFDhyKTyWjTpo16Fd2+fXs2bNjABx98wKVLlzA0NCx3Lnt7ezp16qT+PSAggLZt2+Ll5cWVK1fKjbMrFAoGDRoEgLe3N9EP2fj1wj2F0dJ9jh8/zsiRxck4V1fXMh8cEvULUaUi8ZNFaFtaYv7K5Fqdu8KVuyiKoyq4LgKvacyiek5uaCh54eFYv/+fPkxm6m0izp3Ce+BQtO+FaUqja2CAS/dnuHzkID6jJ9DIqFiUytTfj7u//cbdvfvu3+F6Mx5VZiY6Ts6kJtwkLytLY/H20nj07k/Y8SNsmDmFtgOH0GGIHzqNqvftQKFUwsWLiPn5AI9cYdcUoihWWFVU+rqOzn9n0ZaEk7p3787Ro0f5448/GDt2LLNnzy43Hq6vr6/+d1RUFEuXLuXcuXOYmpoyYcIEtfxvaeRyuXp+LS0tCh+Soyixq3Sfug53SVSN9F9+IffyZWw//wxZqfdKbdAwd6jW4Rs8fecuBB0djAYOVLeFBO5HVKnw6P1w4TCv/s9RVFDApcD96ja9du1QODqWqXlXa7g7O5FwVTObl8rDukWWms6kAAAgAElEQVQrXlq+mladunL21+2smzGZ4P1/UFSNhKiWmRkIQp2u3H19fQkICOD27dsApKam0qVLF7WU7+bNm+nWrdsjx4iJicHS0pLJkyfz8ssvqwXA5HI5BQXlJ6Hv3r2Lvr4+xsbGJCYmsq+aB6OXR7du3Qi4934JDQ3l0qVLGp9DQjMUZWSQtOxL9Dw9Mbr3Ta02aXjOPTEUvvct/m8to8rLI/333zHs0wete6fqFBUWcinwT5QebTGxfnh5v5ldM5q5eRL811618xQEARN/P3IuXiQ3/D85gNyw4gSqTsuWJESEo6OvT2ObmqkuNTK35Nnpb/Pip8sxs2tK4PpVbJo9ncjzZx5rlSgIAoK2Nqp7K/e6wMXFhfnz59OjRw88PDx46623+Prrr9mwYQPu7u78+OOPfPXVV48c48iRI3h6euLl5cXOnTuZMWMGAK+88gru7u6MGTOmzD0eHh54eXnh4uLCxIkT6dq1q8afbdq0aSQnJ+Pu7s6SJUtwd3cvV55You5JWbmKotRUrObP18j+lCojimKd/Hh7e4uPxfWjovhZc1H8yFIUT68RRZXq8cZ5DNJ+/10MdXIWM0+dUrddPX1CXOo/UIw4d7rC+68FnRaX+g8Uw04eU7cVpKaK/7q6iQkLP1K3xU5/XbzWt58oiqK4cdZr4o5FCzT4FA9HpVKJ14JOi+tmviou9R8obvvwHfFWZESVxwk5cULMCQurAQslCgsLxZycHFEURfHatWuivb29mJeXV27f0NDQ2jRNohS5kdfFUBdX8ea8eRofGwgSK+FjG97K3cEHpp4EpQ/smw1bRkJm7dTMp+/chbxJExp16KBuC/5rL4bmFji2bVfh/Q5e7TC2subCn3vUbeUlVnOvhqPj7Ex+TjYpsTHYtNB8vL08BEGguXdHxn++At+JU0m5Ec1P78xk34ovuJtS+ddY0NZGLCioEeXJp53s7Gy6deuGh4cHzz//PKtWrUJRTp5Hom5JXLIYma4ulm++WXHnGqLhOXcAA0sYsx2e/QwiD8OqLnCtZoV4Cm7eJOvUKYxfeB5BVvyypcbf5MalYNx9+yOTaVU4RnFZ5CBuhoWSeP2aut3E3w9VRgZ39/2JKiuLghuxxfH2a1dBFB/rWL3qoKWtjWe/gbz89Vo6DBlO+OnjbJj5Kse3biKvElr0JbvvxDoMzTypGBoaEhQUpJYnfvYRB8RI1A2Zf/9N1t9HMZ82De17FVt1QcN07lB8CHPHV+GVw9DIDH4aBn++A4U1k8hLu1cTbTJ0qLot5OBeZFrauD3Tt9LjuPTsjVxHlwt//q5ua9S+PQoHB9ICAsi9WuzQdZycSbgny2utwc1LVUGnkT4+oycwcfkaWnbqyplfAoqTrgf23ien8CBq516HSVUJibpAzM8n8dPFKJRKGr9YNi9TmzRc516ClUuxg+/wCpxeCWt9IaliOd2qIKpUpO/6Bf3OnZE3KU5sFuTlcvnIQVp26Iy+iWmlx9LVN6BND1/CThwhOz0NKEms+pMTHFy8QQrQdWpFQkQYjZs0RVff4FFD1jhG5pYMmP42YxZ9iVmTpgSuW0nAwnkPv0Gr+FtMXSZVJSTqgtQffyI/Ohqrd+aqd6/XFQ3OuYsqFVlnzt7fKNeDAZ/DqG2QEQ/f9YBz32usZDL7zBkKbt687wDs8JPHyMvKwrPvwEfcWT5e/QZRVFhISKmySOOhQxDkcu5s3YrMyAgtGxviI8JrPSTzKKybt8T//U/p+PwIboZdITP1drn9BJmsOO6eJzl3iaeHwuRkUlauRL9Hdwx69Khrcxqec7+1NYAb48dza9mXZRN2Tv1h6imw7wp/vA1bR0NW+Q6oKqTt3IXMyAjD3r3VbcEH9mJm14wmrV2qPJ6ZXVPs3b24eOC/mnJtU1MM+/YtVoJs1Yr0pFvkZtytkc1L1UEQBBw8i0+dSoy69vB+Ch3EfCksI/H0kPTlclT5+VjNnVvXpgAN0LmfatGRffYdufPdd8S8MaPM1n0MrWDMDuj3aXGSdVWX4qTrY1KUnk7GgQMYDxqE7N6OwVvXrpJ4PQKPvgMeu3617bODybyTSsTZk+o2kxH+AOg4O6s3L2ladkATWCodEQTZfUnhBxF0FHUac+9SCeW95cuXk13Jw8ofl+DgYPbu3av+fc+ePSxevLhG55SofXIuXSJ91y4ajx2rluCoaxqcc3+hgwN2Hy/ke7fBZB0M5NqoMRTc0wNRI5NB52kw+RDoGsOPQ2H//MdKtt7duxcxP/++kEzwX3uR6+jSxueZx34OB09vTKxs7lOLbNS+PebTpmLiN5z4iHAUeno0tqs9oaHKItfVpXETu0c6d5lCB7GoqM7kf0+ePFlhn8dx7kVVPELwQec+ePBg5taTlV19pSAhgcRPP6UgSXOaRzWJKIokfrIILXNzzKdNrWtz1DQ45w7g374ZQxfNYonPJDKvXSfiBT9yLl8p29HaDV45Au1ehlMrYF0fSL1eqTlElYo7W7Zwa+kytJyc0b2nxZ2bmUn4iaO09ulZLR0WQSbDq/8g4q/+y63IiOI2QcDijTfQdSqWHbBu4VSpEsu6wMqheYUrd6i7ckgDg+Ik9JEjR+jZsyfDhw/H2dmZMWPGIIoiX3/9NfHx8fTq1YtevXoBcODAATp37kzbtm3x8/MjM7NYzVKpVLJw4UK6devG9u3biYyMpH///nh7e+Pj40PYPdnm7du34+rqioeHB927dyc/P58FCxawbds2PD092bZtGxs3bmT69OnAw+WGVSoV06ZNw8XFhUGDBjFgwAD1taeBxEWLSP1hE9EjRpIbfrWuzamQu7/9Rk5wMJZvvomWQd0WP5Sm5o8DqSF8WlpgvnAS7y0z543A1TB6DHaff4ZRvwfKEhWNYNAyaNEbfp0Ka3rC0JXQ+uFaD7lhYcQveJ+8kBCCLVuxt91YSs7HufL3QQoL8u87kONxcenZm+PbfuLCn7/x7GtvqdsLcnNJvhFFx6F+1Z6jprBybEHoscNkpt7GoLFZmevCvRDW4R/XkXLrpkbntrR3pNeEVyrd/8KFC1y5cgVbW1u6du3KiRMneOONN1i2bBmHDx/G3NyclJQUPv74Yw4ePIi+vj5Llixh2bJlLFhQLBCnq6urVpL09fVl9erVtGzZkjNnzjBt2jQOHTrEwoUL2b9/P02aNCEtLQ2FQsHChQsJCgpixYoVAGzcuPE+28qTG961axfR0dFcunSJpKQkWrduzcSJEzXz4tVzss+fJ+OvgxgPHUrWyZPEjB5Nk6++wqCb5qUcNIEqK4ukpV+g6+qK8fNDK+xfqCrkZPxJrPWtaWVasyXODXLlXkJrGyO+edePr4fPI9zAmpszZpCyek35mijOA+DVo2DmCNvGFIdpiu4XgFJlZ5P42edcf2E4KVej+Mx7NN8PnsmxLB0u37yLqFJx8a+92LZqrdZlrw46jfRx6eFL+MmjZKXdUbffuh6BqFLViFiYprBybAk8PKkqyOWAAFUMY9QEHTp0wM7ODplMhqenZ7kSu6dPnyY0NJSuXbvi6enJDz/8QExMjPr6iBEjAMjMzOTkyZP4+fnh6enJq6++SkJC8fEFXbt2ZcKECaxdu7bS4Zvy5IaPHz+On58fMpkMa2tr9TeLJx1RFElc8hnalpZYv78AZcA25HZ2xL76KncqcaB8XZCy5jsKk5Kwmj9PvbmxPGIzYvn6n6/pt6MfrwW+RkB4zT9Pg125l2BjrMeGt/rxupkxCdtX0Wv5cvKuX8fm44+QPVhnamoPE/fD/nnFYZq4IPDbAEa2ZBw+TMJHH1MUH89+ZSf2dBjKHP+OdG5uRodPDhIQFIuRYyF3EuLpNOyRKshVwqv/IIL3/05I4J90vjduyeal2pIdeBxKJ1Wbe3csc12QyRAUcnyeG1arBxSUR2lJ34dJ7IqiSJ8+fdiyZUu5Y5RI+6pUKkxMTNQHdpRm9erVnDlzhj/++ANPT89y+zzKtpJFSbmLk6eAjH37yA0JweaTT5Dp6SHT08N+82ZuvvUmtxa8T8GNG1i89dYjnWhtkn/jBqkbNmA0+DkaeXmVuZ5XlMfBmIP8EvELZ26dQSbI6GrblXkt59G9afcat69+vErVxFBXztpJXYl+9X/80Lo/d/fsIWbceApvl1MGqa0DA7+AYevg1iUKvvAh7uXRxE2dRmy2yCyf10ic/Ca/vjOA/q7WGOvJGeBmw6/BN/nnwB/oGRrRqqPmviI2trVD6enNxb/2qc8zjb8ahqlNE/QMjTQ2j6apTFJV0NGp17tUDQ0NycjIAKBTp06cOHGCa9eKnyc7O5urV8vGe42MjHBwcGD79u1AsSO+ePEiAJGRkXTs2JGFCxdibm5ObGzsfXNUlm7durFz505UKhWJiYkcOXKkGk/ZMFDl55O07Et0nJwwHjpE3a5loE/TlSsxGTWS29+v4+bMN1GVo5Ff26hyckhY8D7I5Vi+Peu+a+Gp4Sw6s4hnAp5h7rG5xGXGMd1zOvuH7Wdl75X42vsil8lr3MYnwrkDyLVkLBnuQZPp0/ik/VgyLl/hup9/8Xb+chDbPE9q41lc36Ug8/Q/hLs0Y9GQOcyfO5LFw9wx1vvvxfdrZ4cqM43rQWdw7dWn3AM5qkPb/s+RdSeVq2dOIooiCRFh9a6+vTysHJqTGBX50OsyhQJVfn69XYm+8sorPPvss/Tq1QsLCws2btzIqFGjcHd3p1OnTupE6YNs3ryZdevW4eHhgYuLC7t37wZg9uzZuLm54erqSvfu3fHw8KBXr16EhoaqE6qVYdiwYdjZ2eHq6sqrr75Kx44dn3hZ3zubf6YgLg7L2bMRtO4vIhC0tbFesADLOXPI+OsvYsY/ZOFWSxTeucONCS+RfeYM1vPnIbeyJCM/g4DwAEb8PoLhvw1n59WddG3SlbV917L3hb286vEq1vrWtWtoZaQja+LnsSV/K8HO87Fi/ymrxFNeHcVQL28x48iR+67nXLkiXh82XAx1cha39xws/jazryi+byQW/jBUFDNTyoxXVKQSJ834WPzcf6B451aCxu1VFRWJ62ZMFjfPf0tMS0wQl/oPFIMP/KHxeTTN+T9+FZf6DxQzbt//mpVIzRakpIjZly6JRfn5dWFegyYjI0MURVFMSUkRHR0dxYSEqr/vGorkb+GdO2JY+w5izMuTKuybfuCA+K+HpxjxjK+YG1F1OerqkhcTI17r20/8191DTP9zv3gu4Zw479g8sd2P7UTXja7iC7tfEH8K/UlMy02rMRt4YiV/K8ELbe147+3hzOn9JtG6ptyYOo3UH35AlZVF4qeLiRrux+2oWBa3G8P6IW+jfHsbPPcVWjEnYI0PxN4vbyCqinBOv0KMXjPuams+VCLIZHj2e46EiHD+uVf3blMPNy89iKVjC+ARSdV78eT6HJqprwwaNAhPT098fHx47733sLau5VVfLZKyeg2qzEwsZ8+usK9Rnz7Y/7gJVV4e0aNGk3XqVC1YWEzOpUtEjxxFUVoaVmtXMUPcwkv7X+LQjUM81/w5tg7cyo7ndjCm9RiMder+m9YT6dwBurQwZ+3bA1jS/03O2riQ+OliInr2IvWHH/jLoROTfWfT4eWR7H69G652JuA9AV4+AFpy2PAsnFqp1qaJPH8Gsu9y2ciF7edja8Relx6+KPT0+GffHuQ6upg3ta+ReTSJpdIRBOGhcfcS4SRJ+rfqHDlyhODgYEJDQ5kwYUJdm1Nj5MfGkrp5M8bPD73vwPlHoefmhsO2rcitrbgx+RXSdu6qYSsh4/BhYsaNR9aoEXY//8h7GZv5J+kf5naYS6BfIAs6L8DF3KVuTlx6CE+scwdoZWXItpnPsGPwa2xx7kOkgRVv+Uzn+OBJ7Jzdj9d6tUCuVeolsPWEV/6GVv1h/zsQMA5y07l44A+MLCyx9/Rmx/k4ilSajyHrNGqES8/eIIpYN2+JTKt+bl4qjUJXj8a25SdVRVEsLoeUyaSVex0g1tM8x4Mkf/klgrY2Fm+8UaX75E2aYP/zz+h36EDC/PkkLV9eY4fD3Nm6jbjXpqPj6Ij9lp9ZkvgTf8f9zfyO8xnTegyN5NU7VL6maJDOvSpvXCsjXbZN6Urc0LH8r/t0Xpw0iK2TO+Fo8ZCdZHomMOIn6PsxhP3B7eW9uXE5BHff/vi3tychPZdjETVz8pNXv0EgCNg6tamR8WsCa8cWZZKqurq66sOpZQqFtHKvZURR5Pbt2+jq6ta1KY8kJziYu3v3YfbSS8itrKp8v5ahIU3XrMbEbzi3V68hftZsVBpcSIiiSNLy5dz64AP0fbphv+kHvrsZwK6IXbzi/gr+Tv4am6smaHB17rdvZnJwYwi9J7hj1qRyW331dbRZN74d+UUqdLQrsSIWBOjyOqKtN+c+n4NMEHHr1QdtA2Ma6yvYHhRHTyfLaj5JWUxtmjD6o6U0bmKn8bFrCvVO1TupGJg2BsDOzo64uDiSk5MpTE1FLChAXsMCXRL3o6uri51d/X0fiaJI4mefo2VujtnLj7/7VpDLsV64EIW9PUlLvyA/OprGL03AsHdvZHp6j29ffj4J7y0gffduTPz8sH5/Adsjd7H64mqeb/E80z2nP/bYtUWDc+7RF4O5efkbtnzQF5+RfXHvZYcgqzjOJQhC5Rz7PfKys/hzx2GupRjjZXqTRncugUl3hno24cfT0aRm5dNYX/Ni/A2hBLI06qTq9WsYeBefLSuXy3G4p4yXtHw5t9d+j3PwhXu7ViUkIOPgQXL++QfrDz9Edm+D2OMiCAJmkyYhb9qMpM8/J372/5Dp62P4bH9Mhg5Fz9u7SrHwoowMbs6YQdbJU1jMeAOzKVM4FHuIT858Qne77izovKBexdYfRoMLyyjdm2Fqa07e3V84/MNqdn8VRFa6ZmO6SdHX+emdmUSeP0OP0ePoZZ8K59YBMKJ9UwqKRH65oFm9lIZKRUlVhVIJRUXkx8bVrmES9RaxoIDkpV+gaN4ck1Jqq9XFqF9fmh/Yj/2PmzDs14+MvfuIeXEskX37kbziW/LjKn4PFiQmEvPiWLLOnsNm0SLMp04lODmYOUfn4GrmyufdP0db1jDWxA3Oud/USWNXp2jsenamKO8iUedWsPnd37kerJk4+OUjB9ny7iwK8/Lwf/9T2g3xR2j7IoT9Dhm3cLI2xMPOmO1BsQ0maVWTqJOqDymH1FEqAcgvR89F4unkzrYA8mNisJw9S33erqYQZDIatW+P7aJPaHn8GLZLFiO3a0LKt98S2bsPMWPHkbZzF0WZWWXuzb16legRIymIjaXp6tWYvPA8kWmRTA+cjo2+DSt8V9Tb5Gl5NDjnLhNkFMiKWKQfgHxEOxSNCslI3sRvy3/g0KZQ8nMfTz+8ID+P/au/Zv+q5dg6OfPi4q9o3NyBby58w07LpqAqhH9+BMC/fVPCbmUQEpeuyUdrsFg5tnj0yh3JuUsUU5SRQcqKFTTq1KnGj6KTNWqE8ZAh2G/YQIvAg1jMnEFhUhIJ8+cT4eND/Jw5ZJ06pT66M2bMi1BUhP3mnzDo1pVbWbeYcnAKCi0Fq3qvwlS38mcl1wcq5dwFQegvCEK4IAjXBEEoc9KAIAjNBEE4LAjCBUEQQgRBqL4e7kNwNXdlx3M7GOgwkLUZOzk3QMTGtQ2FOUe4eGAlWz48TGLU3SqNmXYrgS3vzeby4QN0fH4Ew+Z/xD+Zl3l+9/N8F/IdS0LXk+HQHc5vBFURz3nYoiuXsS2oZmreGxpWDi3IupNK5p3UMte0TEzQMjWVnLsEALe/+46itLTiVXstxq3ltraYT5mC45/7sP/5Z4yfe46MQ4e58dJErvn2JnbSJLQtLVFu3YJu69bczb/L1INTycjPYFXvVdgZ1t/k9MOo0LkLgqAFfAs8C7QBRgmC8GCt3rtAgCiKXsBIYKWmDS2NgcKART6LWOKzhPDcKL52OIzl4G4I4k1uR68l4JPtBO2NQlWJevRr507z0zszyUhO4vk579N6yADmHn+HaYHT0NHWYX7H+eQU5rC7qQvcjYOr+zHSlTPA1YbfguPJya97Sdu6xsqxOcAjV++Sc5coiI8n9YdNGA8ZjJ5L1c8e1gSCINCorRc2Cz+k5bGj2H6xFJ1WLTHo7Yvy583ImzQhryiPNw69QfTdaJb3Wo5z4/q/W7w8KrNy7wBcE0XxuiiK+cBWYMgDfUSgZF++MRCvORMfzgDHAewYvINWjVvxWeFmkoY3w8TWjPyMXzi+9Xt2fX6Guyk55d6rKiri6OYN7F76MSbWtoz5dDkhRjcZsnsIf8X8xVSPqex4bgcjnUfiYeHB1rRLqAxtIKg4serfvikZeYXsu5xQG49ar7F0aF5hUlVy7hJJy5eDIGAxY0ZdmwKATFcX44EDabZmDXZffomWsTFFqiLeOfYO5xPPs6jbIjrZdKprMx+byjj3JkDp+EPcvbbSfAC8KAhCHLAXeF0j1lWCJgZNWN9vPdM8p7E34292dIyiaa8uFOVdJDZkNZvf/53w0wn3JT8z76Sy/aP5nNuzE48+z9J11nRmXXyX9068h6OxIzue28E0z2kotIpLHUc7jyYm4wanWveBa4GQGkVHh8bYmzUiQArNVJhUVSiVFCYlocoqm8SSeDrIuXKFu3t+o/G4cchtbevanHIRRZEl55bwV8xfzG43m2cdnq1rk6pFZZx7eYGxB+Mdo4CNoijaAQOAHwVBKDO2IAivCIIQJAhCUHKy5nZ5asu0meoxlY39N6LSgk8abUMxoj06egXk3N7Mn6s3s//7y+RmFRAbeokf57zBresR9J02g+iOOvjtG8GVlCu81+k9NvbfSHOT5veN38e+D2a6ZvwsywZBBuc3IAgC/u2acvp6KjG3Jadl5diCpIqSqqVONpJ4ehBFkaQln6FlaorZK5Pr2pyHsu7yOraEbWF8m/GMcxlX1+ZUm8o49zig9FE6dpQNu7wMBACIongK0AXMHxxIFMXvRFFsJ4piOwsLi8ez+BF4Wnqy47kdDHAYwHcZOzg7AGzcWlOYc4R//17Dxtlfsn3hfHT0Degwayofpq/hq3++oluTbvw65Ff8nfyRlf1MQq4lx8/Jj2OJ54ht9Qxc+AkK8xjW1g6ZgLR6pzipmnkn9b7jAksoce55UVG1bJVEfSDzyBGyz57FfPpraBka1rU55bL72m6++ucrBjgM4K12b1V8QwOgMs79HNBSEAQHQRAUFCdM9zzQ5wbgCyAIQmuKnXvNCLBUgIHCgE99PmWxz2LCc6/ztfIIVoN9EMSbZN0+irGNK8nDmjIl+G3u5N5hec/lLO+1HCv9R2tb+LXyQ0vQYpuFLWTfhtDdWBvr0qOVRY2JiTUkHpVUVdg3A6RyyKcRsbCQpKVfoFAqMfWvn1osx+KO8f7J9+lk04mPu35c7gKvIVLhU4iiWAhMB/YD/1JcFXNFEISFgiAMvtftbWCyIAgXgS3ABLGOd/gMdBzI9ue208K0BUsKfyJlhBITj4HkZPsSeOEcfq38+HXor/ja+1ZqPMtGlvS2782u5CCyGzvct2M18W4eR6/WyWdZvaEkqXorMqLMNZmuLtq2NuRHP15YJi8ykpxLlyjKzKyumRK1TNqOneRHRmI56+16KT9xKv4Ubx55k1amrfiy55fIteqfjY9LpbaHiaK4l+JEaem2BaX+HQpo7mBRDWFnaMeG/htYG7KW1SGrkVkfZXTyfEYkzGRcWx/kiqrJ6o5yHsWf0X+yt6UPw89shsQrPOPcGjN9BQFBsfRy1ryYWENBoatHY5smj9ip6lDllXvOpcukrFxJ5uHD6jZtCwsUDg4oHBzQcXRQ/1tua1vmeDaJuiXr9GmSv/kGPW9vDHwrt4iqTc4knOH1Q69jb2TPd32+w0BROSHChkLDEEmoBtoybaZ6TqVLky6E3g6lU6du7P36Mmd/i6LrsBZVGsvL0gsnUye25MUzTEsH4dw6FIOW8bxXE344Fc3tzDzMDHQqHOdJxcqxBbFXQsq9plAqSf/tt2Kd9wo2r+SEhJD87bdk/X0UmbExFjPeQKdVK/Kjosi7HkV+VBQZf/5JWvp/O4QFhQKFvT0KR0cUDkp0HBxQODqi26aN5PTvIapUpO/ahSovD+MhQ9EyqJ5g18PIuXiRpOXLyT51Gm0rK6wXvFfvhLbO3TrH9MDpNDVsytq+azHRNalrkzTOE+/cS/Cw8MDDwgOANt1suXjwBi28LbFSVv7YPEEQGOU8ig9OfcA/zr54h2yDPh/i374p3x+P4pcLN5nk41hTj1BvKFIVkVWYhZHi/tfOyrEl/x4/QlbaHfRN7t+qrVAqUWVkUJSairaZWbnjZl+4QMrKVWQdO4aWsTEWM2di+uIYtAzKX1EV3rlD/vXr9zn9vPBwMg4ehKLizWWN2rXDdtkXyC2f3m9VAIUpKcTPmUvWiRMAJH+5HJNhwzAd+yIKDUkD54aHk7z8KzIPH0arcWOs3pmLyciRyHTq14LnfOJ5Xgt8DVsDW9b2XUtj3cZ1bVKN8NQ499J0GdaCmMu3ObTpX/zntUdLu/IJlAGOA1h2fhk/G+jinZ8JIQG0av8ynk1N2HYulpe7OdS7VYomUYkqZv09iwtJFwj0C0RL9t+quHRS1bFt+/vuUzgogeKk6oPOPfuff0hZ8S1ZJ0+iZWqKxdtvYTpqdIUrS21TU7S9vWnk7X1fu5ifT35cHFmnT5P02edEvTCMJsu+QL9Dh8d86oZN1qlT3Pzf/1DdzcD6gw/QbdOa1E0/krp5M6k//oihry+NJ4xHr23bx3rv5kVFkfLNCu7u24fMwACLmTNoPHZstaV8a4ILSReYdnAaVo2sWNdvHeZ6ZYr6nhiejLRwFdHR06bnGCdS47M4vy+6SvfqaevxQssXCEy+QKK1CwStB1FkRPumRCRlEhyb9th2ZeRn8Opfr3Iw5uBjjx1l8yAAACAASURBVFHTrAlZw8EbB7mde5uwO2H3XXvUTlV1rXupcsjsc+eImfASMaPHkBsejuXs2bQ4+BfmkydXK2QgKBToODrSePRolAHb0DIw4MZLE7n9/fdPlZKnWFhI0ldfcWPiy2gZGqEMCMB05Aj03N1psvRzWhz8C7NJk8g+WyyaFT3cj/Q9eyp9clZBfDzx777L9UHPkXH4MGaTJxf//aZMqZeO/WLyRaYenIpFI4sn3rHDU+rcAZRu5rTqYMX5fTGkxFWtCsPfyR+VqGJ7szaQeBlizzLI3QY9uVa1at4Xn13MyfiTfHT6IzLyMx57nJri0I1DrAxeSQ+7YjW/oFtB911/VFJVbmsLcjl5UVFknT5DzLjxxIwdR961a1jOmVPsaF6eqHGnoNuqFcod2zHs3ZukpV8QN/11iu5WTViuIVJw6xYxEyZwe9VqjJ9/Hocd28scQC23tsbyrTdpceQw1h98gConh/j/zeGab29SVq+m8E7ZPQtQHOK59ckiIvv15+7uPZiOHk2Lvw5g+dabaBkb18bjVZlLyZeY8tcUGus2Zl3fdVg2evLDdE+tcwfo5t8SHX1tDm36F1VR5Q/XbWrYlO523dl+9yr5CkMIWoehrpwBbjb8djGB7Pyqyw4HxgSyJ3IPfe37cif3DmsurqnyGDVJZFok7xx7BxczF5b2WEozw2YEJQaV6Wfl2ILEcsohBS0tFM2acefHn7gxYQL5UVFYzXuH/7N33tFRVG0cfmZ303vvCakkhIQAofcO0hXpVrAgKIiAoqigIlUEP0FEQDqi9E6ooZcAgRAgvffe2+7O98fSS3oomuecnN3szty5k+z+5s5bXQ77YfLO2zVqiVYRUl1dbBb/jMX0L8j39ydqyOsU37pVZ8d73uQdP07UoMEU37yF9fx5WP84G4n20+uQS7S0MBo+DKe9e7D7YwUabm6kLV5CeOcuJH39DSVhqv+nIieH1EU/E96jJ1mbNmEwaCDOhw5i+dWXyExf3FVwcEYwHxz+AEMNQ1b3Wl1hTsu/hf+0uGvpqtNhmBtpsXkEHqnainuk+0gyS7Lwa9gRgndAQQbDWtiRXyJnf1BylcZKL0pn1rlZeBh7MLfjXAa7DmbjrY1E50RXaZy6Irc0l4nHJ6Ip02Rxl8VoyjTxtfTlSsoVlOLDF0ULp6dnquq0aqlytH31Fc6H/TB+8806FfUHEQQB47fewmHdWsTiYqKHjyB727ZncuxnhVhaSsrcecSP+wiZlRWO27ZiMGBAxTveQZBI0O3QAftVK3HasxuDAQPI2b2byP4DiBn9BuHde5CxYgV6XbvivG8vVt9//8LWibnLrYxbvO/3Pvoa+qzutRpLHcvnPaVnxn9a3AFcmpvj5GPGxb1RZKdUvolza+vWNNBvwGZZMShKIXADLRoY4WiqUyXTjCiKzDw7k4KyAuZ0mIOaRI2Pm36MhkyDhQELq3NKtYpCqeDzk5+TkJfAz51/vvfl8LXwJbc0l7Csh1fpFo73e6o+iuU33+B64jjGb4xGoqlZ95N/AtrNmuG4YztazZqS9NUMEr/6CmVx8XOZS21SGhdH9KjRZK5Zg9HIkTT4azMad/rYVgcNV1esvv8OlxPHMZs0kbK0VLRbtMBx105sflp4z4fyIhOSGcJ7h99DR02HVb1WYaVr9byn9Ez5z4u7IAh0HOGGTE3CsfW3ECtZRkAiSBjuPpzr2WEE2/tCwJ8IosjrvrZcjMokKr1yxcS2h23HP96fSc0n3StYZqplygfeH+Af78/phNPVPrfa4H9X/8fphNNMbzWdZhbN7r3ua+EL8Jhpxtyx/J6qLwIyExPsV67E5MMPyNm2negRIymNjX3e06o2uQcOEDX4VUpjYrD5ZQmW33xda+GHMiMjTD/8EJdDh7BbthTNhi9HA/fQrFDG+o1FU6rJql6rsNF9tJDtv5//vLgD6Bho0G6IK0nhOQT5V77x9QDnAWjJtNhkZgVZURB5rErFxOLy4ph/aT6tLFsxymPUQ++N8hiFvZ498y/Np0xZVuVzqg0ORh9k1Y1VDHEbwtCGD9cFsdK1wkbX5nGnqpZ2uZmqLwqCVIr5pEnYLv+NssREol4bQt7Ro897WlVCWVxM0rczSfh0MhrOzjhu345+z57Pe1rPnfCscN7zew91iTqre63GTs+u4p3+hdSL+x3c21hi38iYczsjntrg41H01PUY4DyAg9m3yNQxhUursdDXpEtDc7ZdjkdejpNWoVTw1emvkAgSvm/3/WPFitSl6kxtMZWonCi23N5So3OrDiGZIXxz5ht8zHz4suWXT9ymuUVzLqdcfiy8sLyeqi8aep0747htG+r29sSPn0DqwoWI8ur14X2WlERGET10GNlbtmAydgwOG9ajbvvfW50+SmR2JGP8xiAVpKzqtQp7ffvnPaXnRr2430EQBDqNaogAnNh4u9Lx0CPcR1CqLGW7S0sIPQA58QxtYUdqXgn+5RQTWxO8hqupV/my1ZdPtQV2su1EW+u2LAtcRmbx4/1J64qs4iwmHp+InpoeP3d5ejElXwtfskqyiMiOeOh1CycX8jMznuhUfRFRt7XBYdNGDIcNI2PlKmLfeZeiwMAXNia+LDGRmLfeRJ6Wht0fKzCfMuWFLMr1LBFFkQtJFxjjNwYBgZW9VtLAoMHzntZzpV7cH0DfRIs2g52Ju5XF7XOVa5/nbOhMK8tW/F2WglwU4fJaurqbY6qrwe/+kU/s4xqSGcKvgb/Sw6EH/Zz6PXVsQRCY1mIahfJCll5dWu3zqgpypZyp/lNJK0xjcZfF5SZ6+Fo+2e5+z6n6gptmHkSioYHVrJlYz5tLUXAw0cNHEN6tGynzF1AUFPTCCL0iP5+4D8chFhXjsG4tuh06PO8pPVcyizNZc2MNA3YOYKzfWAQEVvVahZPBv78MSEXUi/sjNO5og5WLAWe2hlOQXVKpfUa4jyCpKA1/59ZwZR1qKJjS042L0Zn8c/lh23upopTpp6djoG7A160rLqjkbOjMcPfhbA3bSkhmSLXPq7L8FPATF5Iv8HWbr/Ey8yp3W1tdWyy0LZ7uVI14ecT9LgYDB+LqfwLreXPRdHUjc/16ol8fSkSPnqT+9BPFN28+N6EX5XISPp1MSUQENksWo+Hq+lzm8bxRikouJF1gqv9Uuv3TjZ8u/4SRphGz289m36v7Huuk9l+lXtwfQZAIdH3DA3mZEv/NIZX6Iney64SVjhWbdbUgPxlu72Oorx0tHY35cf9t0vPvXyR+vforYVlhfNfuO4w0jcoZ9T7jmoxDX12feZfm1amw7I7YzYZbGxjlMYpBLoMq3F4QBHwtfQlIDnhoXupa2hi9BE7VpyHV08Ng4EDsfl+O2+lTWM2ejbqjIxl/riHq1deI6N2b1MWLKQ6p3OejNhBFkeTZsyk4dQrLb79Bt90LV2H7MZSiksMxh/GP8ycpP6nGf6uMogxW31hN/x39Ges3ljOJZxjecDg7BuxgXZ919wIc6lHxnywcVhGGFtq07O/Iue0RhF9OxdW3/Iw2mUTG0IZDWXJlCRFG9jgHrELiOYgfB3vxypJTfL/3JkuGNyUgOYA1wWsY4jaEjrYdKz0fAw0DxvuMZ/aF2RyNPUp3h+41PcXHuJF+g1lnZ9HSsiWf+X5W6f18LXzZF7mP6NxoHA3ux1VbODoTf+tGrc/zWSM1MMDwtVcxfO1V5FlZ5B0+TN7Bg2Ss+IOM5b+j7uSEfu/e6PfpXacr6cy1a8ne/BfGY959YTsaPYgoisy5MIe/Qv6695qemh6uRq64GrniZuSGm5Ebrkau6Kg9veTE3VX61tCtHIs7hlwpp5l5Mz5s8iE9HHqgKXs++RIvA/Xi/hR8utkRcTmVU1tCsXU3QktXvdztX3N9jd8Cf2OznSMzrh+G9DBczF0Z19mZJUfD6ONtxOJbM7DVs2Wq79Qqz2eI2xC2hGxhYcBCOth2QENae2VU04vSmXh8IqZapizstBA1SeWdcw/Guz8k7k4u3D7j/8Tyvy8rMiMjjIYOxWjoUOQZGeQdPkzu/gOk//Yb6cuWodGwIVY/fI+WV/nmrKqSd/QoqfPmo9ezJ+afVf7C+zz5NfBX/gr5izcbvUkPhx6EZoXe+9kbuZeCsvt5IDa6Ng+JvZuRGzpqOuyJ2MO2sG3E5cWhr67P8IbDGeI2pN7sUknqxf0pSKQSur7pwd8/XuLM1nC6v92o3O2NNI3o49iH3dGHmChVRy9gNfSew0ddnNlzPZGvTv6AXDuJtb3Xoq329DofT0MmkfF5y895z+891gWv4z3v2ukiX6YoY/KJyeSW5LL+lfWVNhXdxUHfAVMtUwKSA3jd7fV7r1s6qVaxKVHhODVt8bTdX1pkJiYYDR+O0fDhyNPSyD3kR8aqVcSMHIXFV19hOGxorZR+LroRTMKUqWh6eWE9by6CpGaW1JDMELTVtOs09ntt8FpWXF/Ba66vMcV3CoIg4GPuc+99URRJKkh6SPBDs0Lxj/d/rJxFc4vmfOTzET0cetTqgua/QL24l4OJjS5Nutlx9XAsTXvYY2JTfhuuER4j2BWxi93OLRgVuBG6fo2GujZDO+aw9OYFGmkNeuhDXlVaW7Wmm303/gj6gwHOA2qlANLci3O5mnqVBR0X4G7sXuX9BUHA18KXgJSAh7osPZip+m8U9weRmZlhPHoU+n1fIXHqNJJnzqTo6lUsZ35bo9o5ZUlJxI8bh9TIELulv9ZorMKyQv539X9svLURbTVtFnZaSHub9tUe72nsCNvBwoCF9HTo+dSAAUEQsNa1xlrXms52ne+9XiwvJjInktCsUNKL0ulq37U+6qUG1DtUK6BZLwfUNaRc3BtV4baeJp54m3nzl6wEZXEOBG8noyiDzZGL0Jc4EHC1BTcTa1Zu9jPfz5Ar5Sy5sqRG46QUpDDt5DT+Dv2bdxu/S2/H3tUey9fCl9TCVOLz4u+9ds+pGhlRzp7/LmRGRtj9vhzTCRPI2b2b6GHDq9w39i6K/ALiPhyHsqgIu+XLkZmZVXteF5Iu8OruV9lwawND3IZgq2vL+KPj2Xx7c7XHfBJ+0X7MPDeTdtbtmNth7kONXCqDpkyTRiaNGOQyiLFeY+uFvYbUi3sFaOqo0aS7PZFX00iNqViYR7iPILoolXNW7ojH5zDrzNfkl+bzv+7zMdLWYvqOIBSVrF/zJOz07HjL8y32RO7hWtq1Ku9fqihlZdBK+u/sz9GYo3zY5EM+afpJtecD5cW7O7+0ETPVRZBKMZswHrsVK5CnpBA15HVy/fyqNIYol5Mw+VNKwsOxWbIYTTe3ind6Anmlecw8O5OxfmORClL+7PUn37T5hnV91tHBpgM/XviRORfmIFfWPCP3bMJZPj/1OU3MmrCo86KnJr7V8+yoF/dK0KSbHRo6Mi7srnj13suhFyaaJmy2dWWnmM3xhFN80uwTmlk14ut+jbgWl82G8zE1ms9Yr7GYaZkx7+K8x2yU5eEf58+gXYNYcmUJbazasGvQLsb7jK/yCutRnAycMNY0flzcnVzIz0h/aTJVaxPdDu1x3L4NdUdHEj6ZSMr8BZUqayCKIik//kjByVNYflP9kMe7/+sd4Tt4x/Mdtg3Ydu8irK2mzZIuS3ij0Rtsur2JT4598pCDs6oEpgYy6cQknA2c+bXbr9XyKdVT+9SLeyXQ0JLRrKcDscEZJIWX30ZPTarGELchnEy/zhwzM1oUFfOGmqq8wIAm1nRwNWX+wdsk5VSufs2T0FHTYVLzSQSlB7E3cm+F20fnRPPRkY+YcGwCMomM37v/zpKuS7DVq53GyIIg0Nyi+WNFxCycXr5M1dpEzcYGh40bMBo5gszVq4l9+x3KUlPL3Sdr3TqyNm1WhTwOq3rIY1ZxFl+c+oIJxyagr67Pxlc2Mtl38mMhg1KJlGktpvF16685m3iWNw68QVJ+5bKyHyQkM4SPjn6EubY5y3ssf6xpej3Pj3pxryRenW3R0lPjwp7ICrd93e11JIIEqUyTH5RGSPZMhKJsBEFg9iAvFKLIt7uCazSffk798DL1YvHlxRSWPbkOfUFZAT9f/pnBuwdzJfUKU3ynsK3/NtratK3RsZ9Ec4vmJBYkkpifeO818wb3G2b/V5Goq2P5zTdYL5hPUXAwUa++RsHFi0/cNu/YMVLmzkOvR48qhzyKosjB6IMM2jWIQ1GH+LDJh2zpt4XGpo3L3W9ow6Es676M5PxkRuwbQVBaUKWPGZMbwweHP0Bbps2KHiv+9T1JXzbqxb2SqGlIad67AQkh2cTfLr+Il4WOBd+3+55fuv4P64HLIT8FDn0FgL2JNpO6u+F3M4VDwVXr2PQgEkHC5y0/J60ojZVBKx96TxRF9kbupf+O/qy+sZq+jn3ZO3gvb3m+VXu20EeyDZ9U311D+7/nVH0aBv3702DLX/ebda9a9VDGZlFwMAmfTUGzcWOs58+rUshjWmEak45PYqr/VCx1LPmr31+M9xmPurT83Iy7tLVuy/pX1qMp0+SdQ+9wKPpQhfskFyTzvt/7KEUlK3quwFr3xe7I9F+kXtyrgGdHa3SNNLiwO7LCVOr+zv1pYdkCbJpB+08hcAOEqr40Y9o74m6px7e7gskrrn6t9iZmTejn1I+1wWuJy1PVsLmVcYu3Dr7F9FPTsdC2YOMrG/mh/Q+1t6pSKmDXBFjeXvX8Dq5GrhhoGDzRNPNfNcs8ykPNuhcsJOGTT1Dk5alCHj+8E/K4bGmlQx5FUWRn+E4G7hrI6YTTfNr8Uza+spGGxlVvqOFs6MymvptoZNKIKf5T+OP6H0/9jGcVZ/HB4Q/IKc3htx6/1Ue1vKDUi3sVkKlJ8X2lAcmRucTcyKj8jp2mgbkn7P4EirJQk0qY+5o3KXnF/OQXWqM5TWo2CalEypwLc/ju3HcM2zuMmNwYvmv7HRv7bsTbzLtG4z+EUgm7P4ar6yHlBiRcvveWRJDQzLzZU52qhTnl+yr+KzzYrDvv+Amihgwh7v0PqhzymFyQzLgj4/j6zNe4GLqwdcBW3m38LjJJ9VNXjDWN+aPnH/R16ssvV39hxpkZlCpKH9omvzSfD498SEJ+Ar92/RVPE89qH6+euqVe3KuIe1sr9E01Vav3yoY0yjRg0DIoSIMDXwDgY2fIW20asPZcNFdjqx9NYqFjwVivsZxKOMX2sO2M8hjFnsF7GOw6+LEGIDVCqYS9EyFwI7T9GAQphB58aBNfC1/i8uJIKUi5Pz+np/dU/a9yr1n32jWIhUWUREZis7jyIY8FZQWMOTSGK6lXmN5yOmt6r3mo9ENN0JBqMKf9HD7y+YjdEbt5z+89sopVn89ieTEfH/uY0MxQFnVedC/6pp6qEZNRQHGZouINa0ilvv2CIPQWBCFEEIRwQRC+eMo2QwVBuCkIQrAgCJtqd5ovDlKphBb9HEmPyycy8OnNOB7D2gc6ToHrf8Ht/QB81tMNCz1Npm8Poqycrk0V8ZbnW0xqNoltA7bxecvPaz9iQRRh/xS4sg46ToWeP4B9Gwh9OH77SfHud52qyZEPN9KuB7SbN8dx9y4ct29Dt33lQh5FUeS7c98Rnx/Pb91/Y6THyNq9iKO6+IxrMo75HedzI/0Go/aPIiwrjCn+U7iccpnZ7WdXqfBdPfc5HpJKv/+dZu6B23V+rAo/FYIgSIGlQB+gETBCEIRGj2zjCkwH2omi6AlMqoO5vjC4tbTEyFKbC3uintiM46l0mAIWXrB3EhRmoqepxqyBntxOzmPV6Ypj6J+GhlSDMV5j6qagkijCgc8hYJXKd9BF5RjGrSekBEHO/azUhkYN0VPTq3eqVgGZkVGVmk7vDN/J/qj9jGsyjuYWzetwZtDHsQ+req2ioKyAIXuG4B/vz4zWM3jF6ZU6Pe6/EVEU+e1EBO+uuYSdkTZj2tfOnVZ5VOaS3xIIF0UxUhTFUuAvYOAj27wHLBVFMQtAFMXyg3lfciQSgRb9HMlKKiDsUkrFO9xFpq4yzxRmwIFpAPTytKRnIwsWHwklNuPJIY3PDVFURflc/B3aTIBu38LdWiFud8oVhN6PrJBKpDS1aFrvVK0jIrIj+PHCj7SybMV7XrVTOK4ifMx92NR3E80tmjPVd+pjjdLrqZiiUgWf/BXIvIO36etlxbZxbbEzrvtEr8qIuw3wYDuh+DuvPYgb4CYIwhlBEM4LgvDEQiWCILwvCEKAIAgBaWlVMGm8gLg0M8fEVpdLe6NQVMWkYuUNHadB0D9waw8AswZ6IpNI+Grni9PODVGEI9/C+aXQapzKFPNgEShTNzBq8JC4g8ruHp0bTXpR+r3XLByd652qNaRYXswU/yloq2kzp8OcGmcVVwUbXRtW91rNm55vPrNj/luIzypkyPKz7L2eyLTeDfnfiKZoqT+b/11lxP1JdUsfVSAZ4Ap0BkYAKwVBMHxsJ1FcIYqiryiKvmY1KIT0IiBIBFoNcCInrYiQc1WMV+8wGSy9Ye+nUJCBlYEWU3s15FRYOruvJVa8f10jinDsBzizBFqMhd5zHhZ2UP3u1hui/KH0/h3Hk+Ld652qNWfepXmEZ4czu/1szLTNUMjllJVWrg1kPc+H85EZDPj1DLGZhax+qwUfdXZRVcnMiICy6meoV5bKiHs88GDxZ1vgUQWKB3aJolgmimIUEIJK7P/VNPAywbyBPpf2RaEoq8LqXaoGg5dDUbbKUQmMbu2Aj50h3+25SXZhaQUD1DH+8+DUQmj2FvRZ8Liw38WtF8iLIfrUvZc8TDzQlmk/ZJoxd6wX95pwMPogW0O38k7jd2hv0x6FXM6WWV+wcfqn9QL/AiKKIuvORTN65QUMtdXYOb4dXdzNVW+GHoIVneHwt3U+j8qI+yXAVRAER0EQ1IHhwO5HttkJdAEQBMEUlZmm4jz9lxxBEGg9wIn8rBKCT1dxxW3hCZ0/h+DtELwTqURgzqteZBeV8duJ5+h8PLkATswBn9HQbzGUlynp0A7UdB4KiZRJZDQ1b8rllPsx8Pecqv9yu7tCWfvhbXF5ccw6OwtvM28+bvoxABd2bCEp9DYZ8bGc21q7ZXvrqRklcgVfbAvim13BdHIzY+f4djib6apCif0XwKZhYOyoCieuYyoUd1EU5cAE4BBwC/hbFMVgQRC+EwRhwJ3NDgEZgiDcBI4DU0VRrEKWz8uLrYcR1q6GXD4QTVlpFb/c7T4FKx/YNxny0/Cw0qe3pyV/XYqjqKpj1QanF6vMMd7DYcAv5Qs7qOL3nbuoViMP+Ap8LX0Jzw6/Fx8Nd5yq/+KImQNRB2izuQ0bbm6oNb9JmaKMaf7TEASB+R3noyZRIykshPPbt9CoQxe8uvYkYM/2+juiF4TU3GJGrDjPloA4JnRx4Y83fdHXVIOSPPjnTTj+A3gPhXcPgWHddcK6S6UCZEVR3C+Kopsois6iKM6+89o3oijuvvNcFEVxsiiKjURR9BJF8a/yR/z3IAgq23thbik3TiRUbWepTGWeKclTCbwo8mYbB3KKytjzrG3v55aqHKiNX1NF9FTWYefWG3ITVBmrd7hrd39w9W7h6ExeRtq/0qmaXpTO7AuzERCYd2ken/l/Rn5pfo3HXXJlCTcybjCr7SxsdG0oKy7mwNKf0DU2oeu7H9Jx9LvoGBhy6LfFKCpRTrieuiMwLpv+v57mVlIeS0c2Y0qvhkgkd+zrK3uoclt6/QiDfwe16nfUqgr1Gaq1gLWrIfaNjLlyKIbS4ip+ycw9oPN0uLUbgrfT0tEYd0s91pyNfnaRMxd+h0NfQqNBMHhF5YUdwLWn6vGBqBlPE080pZr/Gafqjxd+pKisiM19NzO5+WSOxR5j+L7hhGZVv7TEyfiTrL25lmENh9HDoQcA/htWk5WcRJ+PPkVDWwdNHV26jR1PWmw0l3Ztra3TeWkpKZITGZhG7M0MkiNzyEjMJy+zmJLCsqrlo1SRbZfjGfr7OdSkEraNa0tfb1WJb8KOwB9dID8Z3tgObcY/3X9VB9T3UK0lWg5wYuvcAK4djaNF3yomKLT9BG7vhX1TEBp04M02DfhyRxCXY7LwbWBcJ/O9x6WVqph7937w2krV3URV0LMA66Yqce+ocg6rSdVoYt7kEafq/fK/jk3/PWnrR2KOcDjmMBObTcTJ0AknQye8TL2YenIqo/aNYkbrGQx0eTQtpHxSClL46vRXuBm5MbXFVACirgZw7fB+mvcbjJ3n/XpBLr6taNimA+e3/4Vrq7aY2NrX6vm9LBTklLB7SSCZiU9vOiJTl6CuKUNNU4q6pgx1TSlqmjLUtaSYO+jj3dkWQVJ58ZUrlPy4/zarz0TRxsmEpaOaYayjrjJRnlkMR2aBRWMYvkEVNvyMqV+51xIWDfRxbGJK4OFYiguqWOlRKoNBv0FpAez9lEE+Vuhrylh7rmYdmyrk+j+w7zNw6wND/lRF8VQHt94QfwkK7se2+1r4EpoVSk5JDgAa2joYWVkTev401w7vJ+Z6IDmpKSjrwAn5rMgpyeGH8z/gYezBW55v3Xvd19KXf/r/g7eZNzPOzGDm2ZkUy4srNaZCqeCLU19QoihhQacFaEg1KMzN4dDyJZjaOdB+2BuP7dP1nQ9Q09Lm0PIlL/Xf8y5JOUUsPR5OTmHlvkd5mcXsWHiF3Ixier/fmFenNKPfx03oOdaTLm+4026ICy37O9K4ow0NvE0xt9dDW18dpVIkL7OYpLAcTv8dxp5fr1GUV7lItUvRmQz9/Ryrz0TxdtsGrBvTUiXspQWw9R04MhM8B8OYQ89F2KF+5V6rtOzvxJZrFwk8HEvrQVUsBWDWELp+BYe/QTt4M0N9fVlzNprUvh6Y62tWvH9Vib0Auz4Ch/YwdK0qe7a6uPVSRdiEHQafEYBK3EVErqRcoYt9FwDc23Xm0q6tHFm57N6uUpkMA3NLDC2tMLS0xtDSCiMLctoFcAAAIABJREFU1XN9M3Mk0meXrFNV5l+aT05JDst7LEdN8vCF0VTLlN97/M6ywGX8EfQHwRnBLOq0CDv98h1pK66vICAlgB/a/YCTgROiKHLkj6UU5eXx6vRZyNQf/z9pGxjS9a332P/rT1w9sJfmfat2p/AiEZNRwMg/LpCQXcSWS3H8NroZntYGT90+J62QnT9fpbRIwcCJPlg6PX3bpyGKIjdPJ3JqSxhbZl+i11hPrFweS9MBICg+h4V+IfiHpmGmp8HPw5owuOmdjmZZ0fDXKEi9Cd1nQbuJz9QM8yjC88qI9PX1FQMCAire8CXj0MobRAdl8Mb3bdDWr6JgKhWw4VWIOkVK3zW03iZlYjdXJnWvXoPkp5IVA390BU19GHsUtGto+lEqYZEHOLSB19cAUKIooe2mtgx3H37PtAAgKpXkZ2WSnZxIVnIS2cmJZN95zEpJQl5yP25bIpViYG6BocUDwm9pjaGVNQZmFs9V+E8nnGbckXG85/UenzQrv8H4yfiTTD81HVEU+b7993Sz7/bE7S4lX2Ks31j6Ovblxw4/AhDsf5SDy36mw8i3aTlwyFOPIYoiO+d/R2zwdd5asBRDC8vqn9xzIiwlj1ErL1CqUDK9jzuLDoeSXVjG7MFeDGn+eEvIzMQCdi25ilIuMmCiD2b2ejU6flpcHgdX3CAvo5g2g53x6W6nSjoCQlPyWOQXysHgZAy11RjXyZk32zS4n20acVy1YheVMGQ1uHSv0VzKQxCEy6IoVmjbrBf3WiYruYDNsy7g3c2O9kOqkcdVnAtr+0FaKN+bzmV3hi1nPu+KuqyWLGjFubC6lyrCZcwRMKulC8fujyF4J0yLvGfeeefgOxTKC9nSb0ulhhBFkYLsLLKTEslKuSv6SWTduQCUFd/P6pNIpeibmd8Te0MLa4ysrDF6Biv+grICBu8ajJZMi3/6/1OpjkcJ+Ql8duIzgjOCedvzbT5p9slDq/3M4kxe3/062mrabOm3BW01bXJSU1g3bQJmDk4M/fZHJBU4uvMy0lnz2Tgsnd0YMuOHe8L0MhCcmMMbqy4iEQQ2jm1FQ0s90vNL+HjTVc5FZjCipT0zBzRCQ6b6G6TF5bF7SSASicCAST6YWOvWyjxKiuQcW3eLyKtpODYxxa2/A0vPRLEzMAEddRlj2jsypoOjKsQRVPb1c0vh8Ndg5g7DN4Jx3TYvqay415tlahkjSx0atrbkhn8CPt3s0TXSqNoAmvowaius6skXmd9yIv8rDgY3YkCTWmhjplTAtjGQFgKjt9WesAO49lKVBI49B46qcrC+lr6suL6CvNI89NQrXlUJgoCukTG6RsbYNnq496coihTmZKuEPinxnuBnJScSfyuYspL7Nu27K34jKxua9u5PgybNau88gZ8v/0xyQTLr+qyrdCs7G10b1vVZx/xL81kTvIbraddZ0GkB5trmKEUlM07PILskm6Xdl6Ktpo1SqeDgsp8B6DN+coXCDqBnYkrHUe9yZOVSbhw/jFfXnjU6z2fFldgs3l59EV0NGRvfa42jqQ4AproarB/TkoV+oSz3j+BmYg7LRjdHmlXK3v9dQ01DysBJTTG0qL0iXBpaMnq/35jT+6O4tjeawKBUAvTlvN/RiQ86Oavs6ncpLYQ9EyHob/AYoPKbadTORaY2qBf3OqBFX0dCL6RwdlsYPcZ4Vn0FpWsOb2xHtqoXmzTn8fUpCwY0qQU7qt8MCPODvotUyUe1iVNnkKqrombuiruFL8vF5VxNvVrj+t+CIKBjaISOoRG27g93/7kn/EkJ90Q/OymRpIhQts35lravj6T14GFV6kv6NAKSA9gSsoXRHqPxMfep0r7qUnVmtJ5BU/OmzDo3i9f3vM78jvO5nXmbUwmn+LLVl7gbuwNwed8u4m/doNe4SRiYW1T6GN7dehFy9iT+61fh6NMcXWOTKs3xWXMuIoOxay9hqqfBxrGtsDV6WKhlUglf9HGnqb0hU/6+xtgFpxiQq46uoQYDJ/mgb1K7MeMZ+SUsOxHB+vMxWOjB66VajMyT0kFHDyPtB/wqyTdgxweQEgxdv4YOnz1X+/qTqDfL1BGX9kVxcU8UrQc50bx3g+oNknSN0pV9iC4zRPHWATycHWowoVWqRKlW46DP3OqPUx7rX4XsWPhY9X8tkhfRdnNb3mj0BpObT66bY5ZDWUkxh/9Yyq1Tx3Fq1oI+4z9DU7f6K6tieTFD9gxBoVSwbcA2tNWqv2KMyI5g8onJROdGI0FCJ7tO/Nz5ZwRBIC0mio1ffopj0xYM+OzLKi8OspITWTf1Yxy8fRg4ZcZzM8/ISxUE+Sdgbq+HtZvhY/M4EZLKB+svY2eszcaxrbCoIHDg4rkEzq0LIUdQYviKLRNeuZMoVAvkFJXxx8lIVp+JorhMwavNbJnYzRUzdTUO/3mT2OAMXFtY0HmEC+pXlsHxH0HTUJXw59qjVuZQWerNMs8Z31cakJVcyPmdkRiaa+PczLzqg1g1ofT19TTYPJSEbaNgkh+oV0NQIo7D/qmqhKNes6u+f2Vx6w0Hpqqy8kyc0ZJp4WXqxeXkyxXvWweoaWjSZ/xkrFwbcmLtSjZMn0j/yV9i4Vi9pibLApcRkxvDyp4rayTsoGpIvbnvZmZfmE1IZgiz2s5CEATkZWXs//UnNHR06fH+hGoJs5GlNe2GjsJ/w2pCzp3Cve2z75pUlFfK/t+ukxyZC4CBuRaN2lnj3sYKbX11Dt5I5uPNV3A112P9mJaY6JZvvowMTOPyxlDMrXWIdZCx+nQEgel5/DzUBwPt6oXwFpcpuBSdyYmQNP4JiCO3WE5fbys+7e6Gi/n9RUC/8d5cPhTDxd2RpAXdpLfOaky8e6tqL+m8uHdG9Sv3OkRepmDXz1dJj8tn8JRmmDtUr/3dpj+XMDz6W+QuPVEfualqiUZpobCyOxjYqGpaaNZyC74HyYqGJU2g1xxo8xEAv1z5hdU3VnN2xNkaC2JNSAy9zZ6f51Ccl0e3sR/RuHPVohnutpsb7DKYmW1n1s0kUWWhBuzZzuDPv8WpWYtqj6NUKNj89RRy0lJ5+6dlaOtXPUSwumSnFrL3f9fIzy6h6xvuiEqR4NOJJIXnIJEIqDfQYWNaJgYNdFnzbisMtMoX59BLyRz58xbmDnr0m9AEDW0Z68/H8P3em1gaaPLbqOY0tqn4/ERRJDK9gJOhafiHpnE+MoPiMiXqUgmdG5oxsbvrk8MuRRECVhO/exN+GRMok+jTaaQH7m1rwQ9WDeqjZV4QCnNL2To3AIVCyetf+KJrVPWY9dvJuWz43zf8oPYnNB0NA36tnH2vMFMV8liSB+8dA6MamHUqy9JWoGsBb6kKh55NOMsHRz7g9+6/09ambd0fvxwKc7LZu2Q+ccHX8e7emy5vf4BMreJVX5mijGH7hpFTksPOgTsr5RyuDnE3g/j7uy/x7taLHu9NqPF46bHRrP9iEg3btOeVj6fUwgwrJikih/3LroMAfT/yfijuPDOpgB1bQ8i8mYW2KKBrrIFne2vc21g/NfDg5plEjm+4jbWLIX3He6OueX9hcyU2i/Ebr5BZUMr3gxoz1PfxHIK84jLORmTcE/T4LFXElZOpDh3dzOjkZkYrJ2O01Z+yYMpNVEWChR8Bpy4UdF3C4b8zSAjNxqOdFR2HuSF7Rs037lJvlnlB0NZXp+94b7YtuMy+ZdcZ/Fmzhz6glcHdUp9wh2H8mZrPO1c3qMSz2zfl7yQvhS1vqD6cb+15NsIOqoSmc8tUIZea+viY+yAVpASkBDx3cdc2MGTIV99zest6Lu3aSmpUBP0nT0fftHyT2cqglYRlhfFr11/rTNhLCgs4sHQRhhaWdHpjTK2MaWrfgFaDh3Ju6ybc23Wq0Z1AZQi/nMqRP2+ia6RBv4+bYGj+8J3ajohUvk9IoouvKVM87Qk7l8yF3SrflIOXKZ7trbH3NEYiVTm+rx+P59SWUOwaGdPnQy/UHhHRZnaG7B9tzabtOyjY+SdxR2Ox0Swh07oTp2Rt+CvFhsuxuciVIjrqUtq6mPJhJ2c6uZlVrs1d0FZVBre8BF5ZCC3GoiMIDJhox8W9UVw+EENBdin9Jni/kGGn9Sv3Z0R0UDr7l12ngbcpfT7wqlINC4ADQUmM23iZ0412YRv5N/SeB60/fPLGogi7J8DVDfDqH6oyo8+KmLPwZx8Yug4aqSJ8Ru0bhVQiZV2fdc9uHhUQdvEsB5f9jESmRr9PpuHg/eTIl9CsUIbtHUZPh57M6zivzuZzcNnP3Dx5nOHfzcfazb3WxlXIy9jwxSSKC/J5+6dlaGjr1NrYdxFFkWtH4zizLRxLRwNe+cgLLd2HQ0R/PRbGQr9QentasmSEz7149ezUQm6dSeLWuSSKckvRMdTAo60VggCX9kXj2MSUXmMbI1WTqO5EEy5DfAAkBKieF6nKSpdKtLgib0ARmrQRbqAplJEtGBBt1hkNr8E4t+yDukYl75oLM1XBB8E7wLaFqpKjyeN+mmtH4zj9Txjd32lEw1bPLmms3izzAnLtWByn/w6jaU972r7qUqV95QolHeYfx81Mi7W6S+H2PlWhL68nZC2e+UWVVNFxmqqkQS2TkltMam4JXrZPsE8q5LDAGdz7qiIJgEWXF7H+5nrOjjiLluzZlDutDJmJCez+aTaZCfG0GzaalgOHPBQuKVfKGb1/NEkFSewcuBMjTaM6mUfohTPsWTSH1q8Np93Q0bU+flJ4CJtnTMW9c3di3PtSVCqnvasZTe0NUZPWLDxUqRQ5/XcYQSficW5mRve3Gz1kphBFkQWHQlh2IoLBTW1YMMQb2ROOqVAoibmeQfDpRGJvZoAIro1kdGtxC2niHTHPvNv/R1BVU7VpDra+YOMLZu4cDc3gyK0UWtlo0EV6DYPog6rQ3NJ80DCAhr1V8ejOXZ8emBDqp1oYFWZCl+nQduJTfVxKpciOhZfJTili5MxWaOnVoIRHFag3y7yAeHexJTulkKt+sRhaaNOoXeUdMjKphFGt7FnoF0rkxMU4FWbCjg9B2+ThmPXb++DwN6ryvZ2n1/o57LkUx97Nt1GTw7ezO2Cm98hqSCpTpV6H+anKEkgk+Fr48ueNP7medp1WVq1qfU7VxdjahpGzf+Lwil85/dc6EsNu02f8ZDR1VJES62+uJzgjmAWdFjxR2MtKislNSyM3PZW89DQKsrMQBAGJTIZEIlE9SmVIpBIkUhlSqRRBKkUqld15lCIicviPpVg4udD61eF1cp5GDi6oNenMreN+bL+lSbKOLb8cC0dHXUobZxM6uJrRwdUUR1OdKpkXykoU+K0KJvp6Oj497Gk72PneHWlxmYKQ5Dy2BMSx6UIsI1raM3tQ4yeHLhZkIE0Jwqk4GCfbG+SKsaQmS3DKOI3kkBL0rFRC3vQNlZhbNwWNx81j3Tws6OZxNyegIfgOhbJiiDyhakYfsg+ubwE1bdVntNFAVQSZpr7KL3XoK7iyFswbqRIJrbwfO8aDSCQCnUe78/fsS5z+J4we73qWu/2zpn7l/oxRKpTsXXqdhNtZDJjog03Dyq8G0/NLaDvnGCNb2TOzhw38+Qpkx8Dbe1Uf+KTrqtICZu7w9r7qhU0+hbyCUpYsv4JWeAGaouoLKutuyQdDGj2+8fV/YPtYGHsMbJuTX5pPu7/a8b73+4z3GV9rc6otRFHk6sE9+K9fhb6pOQM++5J8Axi1fShtdZrzfoM3yUu/L+K56ankpqVSlJdbK8eXqWsweu5iTGxqtzuPXKFk+9UEFh8OJTUrj3dTt2Gorc7wOUu4FJ/PqbA0ToWlE5OhanBuY6hFRzdT2ruY0c7FBEPtp69EC3JK2L/sOmmxeXgPcKTMUYebSbncuvMTlV7A3RLqY9o7MqOvB4JSARnhqsYuyUGqx5RgyEu6P7CuhapMrmVjlaDb+KoivWoDRRnEnFEJ/a29qjrrUnVVAl5aiCpHo90n0OUrVZexSnJxTySX9kXTb0ITHBrXfWhkvVnmBaaksIxt8y9TmFvKkM99q5Q+PXlLIH43Uzj/ZTd0S9JgVU8oK4Rh62HbWNVG7x0DvdqxAYqiyPHD0QTsjkJHDnJzDYa86ck/i66QpCswd17nx1d7hZkq00yHKffMQsP2DkNbps2fvf+slXnVBQm3b7Jn8VyK83IpRY70kb4rMg0N9E3N0Tc1Uz2aqZ7rmZmjb2qOrpExoiiiVCpQyhUoFXKUCsWdn/vPFXI5olKJQi5HqZBjYG6JnolprZ2HKIocCk5hoV8I4an5NLEz5PNeDbErSeTv777EqXlLOo58+17t95iMAk6FpXMqLI2z4RnklcgRBPC2NaSjqykd7phwlKJIWEo+QTfTSdsfDyUKjhoquKa8XybX1kgLd0t9Glnp4WmuQYucgxhnBUFKEKTeBsWdwnASNdUixMJTJeQWd350zWrt71AuSqWqTPWt3Sqxl2lC/yWq4ndVRFGmZMvsi5SVKhjxTasqB0xUlXpxf8HJSSti69wANHXVeG1aczR1KpeIERiXzaClZ/h+oCdvtGkA6WEqgS/KVDWrfvdghbeTlSUhLItda4IRM0rJVBNpMdCJ3t1VjUhW/hxAfkgObT5tQquGTxCm1X1Uts4PTwGw4NIC/rr9F2dHnkVDWsV6O1WkWF5MRnEGmUWZKFGiJlFDJpE98fHuj0wiQxAECrKzWL/qBy6lXqK9Rw/ae3RD38wcPVMztPT0X8ioiAc5G5HOvIMhXIvLxtlMh6m93OnlaYEgCIgx5zm5ZBaBSVLkSnD2bUXLgUOwdvO4t79coeRafM69VX1gXDYKpYiWmpQyhRLLUoHBBeoogetO6tg4GeBhpY+HlT7uVnr3C2qFH4F9UyArCnTM7q/G74q4qVvNyky/YCRF5LB94WW8u9jSYWgtV3F9hHpxfwlIDM9m1+KrWDkb0P8TH6SVdG4N/PU0BaUKDn/aUSU28ZdVtdm7z4SGfWo8r+zUQvz/CSM+KIN8QSTTSYtpHzbHRO++KEeFZrJ/USCZ7jp8PekJdvTTP6saFky+BfrWHI89zifHP+HPXn/ia1n1TkxypZzM4kwyijJIL0onvSidjOL7z9OL0skoyiCjKIO8srxqnbdMkCGTyChVltLaqjXLuy9/4cX8LkHxOcw/dJtTYelYG2gysbsrPR1MyYjLJzUqi9RrN0lNV6NM1EZUFiEvuYqy5CqiWIK2YQPsvXpi37gp+mba6JtoomeiiUxNSk5RGeciMjgfmYF+agkaV3LQMdZg4Cc+GJk/4Y4zNxEOToebO8HERRVCWNt1jF5QTm4OIehkAq9Na46lY90ljdU7VF8CrF0M6TLanaNrbnFyUwidR7tXSkzebNOAz/65xtmIDNq5mIJtcxh/ocbzKc4vI2B/NNdPxFMqKrmsraDHYFemtW/w2LwauBpRoitFHp5HQYkcHY1HPkpuvVXiHnoIfN+hmUUzBAQCUgLwMfchpySH7JLs+z/FqseckhyySrIeey27JBuRxxciOmo6mGqZYqJpgpuRG6bWpqrftUww0TRBIkiQK+WUKcsq/SiTyBjlMeqlEPaItHwW+YVy7FoSLjJ1vrCzwlopIf3vGDblqfrVSgQ5prJU3O0kmLfvhlRdndyrGWSH5hOfV0JmXhK3T60g5KwpMo0WSNQbIggStA3U7wi9Fh1kArcDsrF2NaTPh16P32kq5HDxd1XNFaUcusxQ2a+rYLt+2Wk9yJnIa+mc2HCb16e3QFpbZbqrSf3K/QXg/K4ILh+Iod0QF3y6V9wDs7hMQdu5x/B1MGLFmzXvR6ooU3L9RDwB+6MpKZJzXV1Osp0GC95oRkPLpyft7NkaQuyRBIwG2zOy1yOhnaIIS7xVt+AjNgMwZPcQwrPDUYhPbwWnLlHHUNMQQw1DjDSMMNAwwFDDEGMtY0w17wv33ccXKbSyqoiiSEpULsmROaoLiaCqfikIIEjuPD74uuT++7klcg5diiclOg8ruQR95Z0LkQDGVjqY2+thzjUsIn/BRCcD6YCfwKP/wxPISYDD36AI2sbt0oZcyHYlKzMbLX1TbNw7o2PclPxsBXkZxeRnl+Dqa0GXUe6qmPMHib2gigtPuQGuPVH0nEN2qQZZiQnkZ2Xi1qot2gZP7mz0byPqWhr7fwui1QAnfF9pUCfHqF+5v0S06u9EdkohZ7aFY2CujaN3+c41TTUpI1ra8duJCOKzCh8rk1pZRFEk/HIq53dGkJteTKqOwD69Yvq2t+eXVzzQVCs/rbp7b0f+OJpA0MkEeFTcBUG1er+6AcqKQE2Lyc0n4x/vj6GG4T0BN9AwwEjD6N5zLZnWS7FirgmlxXLCLqVw42QC6XH51R7HENDTUqOBuxG2LoZYNNDHzF4P9ZIUlZku8gQ07AUDdqoamT+KgQ0MWYW0xVg8D0yjkfoeIh3acjFbm/CLW9HS86Npn/70eq8fmjq6j/1fClNiyNz9PVnBJ8nEnEy9UWRdUpCz7zOUivsX8Is7/2HQtK8xb1C3TSxeBBybmOHS3JxL+6NwbmaGkWXtJ41VlvqV+wtCWamCnT9dITO5kJ5jPGngZVKuyCVmF9F+3jHe7+jMF32qltEoiiLR19O5uDeK9Lh8pEbqbFcWkKYjsGBIE7o3qnz98GVzzlMUW0C/GS1oaPNIUbKwI7DxNVXM8DMui/oikpGYT7B/AiEXkiktVmBio0vjTjY4NzVDIhUQRdX/RlSqHrn7uwiiUrz3/pozUaw9G8O8EU3o2eyRMMGgrapVtKIMev0Izd+uXB0ipQKuroej30FhJvF2Q7mUbEbk9WuoaWji1a0XOoZGZCbGk5UYT2ZsJMXF96NkpDIZhpbWGNvYYmxti5GVDcY2tijkcvb9soDi/DxeGf8Zrq2ebwmKZ0FhbimbZp7H2FqHwZObVTkbvSLqHaovIQU5Jez46Qo5qUUYW+vQrKc9Li0snupo/XD9ZS5EZXBuercKV9mgEoaYGxlc2htFakwe2sYahJpK+Cslk7auJiwa6lNhTe1HuXE1Bf/fgylsZsjU9x/peFRWDPMdwWck9P2pSuP+W1CUKYkITOWGf4KqKqJMwKW5OY072mLpVPXom7CUPF755RR9Glvxy4im998oylJFp9zYWm7KfIUUZYP/PLjwO2joktZ4PJeiJNw+ewpRqURHXw9jaQ5GYgrGFuYYdR6LsWc79M3MntotKj8rk90LZ5MUHkK7oaNp9eqwf/3d2a2ziRxbd5tOIxvSuGMtxenfoV7cX1IUCiXhl1K44hdLZmIBukYa+HS3x6Od1WPxs2cj0hn5xwUWDPHm9SdUxLuLKIrE3czk4t4oUqJyEXRkXDcUOZSfj1Qq8FnPhnzQ0alajQ9EpcjPn54gEyXfLuryeDr75pGqhJVJ11+4TjV1SW56EcGnE7l1JpGivDL0TTXx7GiDR1urx+quVBalUmTI8rNEphdwZHInTO/WQI84Djs/goJU6PQFtP+0amWhn0TqbTj4BUQeBzN3itp+gSTuPBpX/wAtQ+jxPTQZAZXsbiUvLcVvxf+4deo4Ddt2pNe4iaipv3zOVoVcTlJ4CDHXA4kLvo62vgE+vfph5+n10AVLFEV2LQ4kLSaXEd+2rnq7zXKoF/eXnLur7Kt+sSSGZaOhLcOrsy1enW3R1le/t02vxSdRl0nYM6H9Y6shURSJD8ni3K5I0qJyKVEX8JeVEKSmoJGtPoN8bBjQxBrzKq7WH2XL2hukn0vF4U0X+rV9xCF8eS3s+QTGnQOLJ2Sz/otQKkVigzO4cTKBmBsZCICDlylenWyw8zCu8e35unPRfLMrmJ9eb8JrzW1Vvowjs+DCb2DiCq+uAJta7BcrihCyHw59qarVDyozT7dvQdu4GsOJXNq9jVOb12Lh6MLAqV+hZ1x7yVt1gSiKZMTHEnM9kNgbgcTdvEFZcRGCIMHc0ZnctBSK8nIxtXOgaZ/+eLTvjNqdAmU5aYVs/u4i9o2MeWVc7eSeQC2LuyAIvYElgBRYKYriE/u0CYIwBPgHaCGKYrnKXS/ulSc5KoerfrFEBqYhlUnwaGOFTw87DMy02XA+hhk7b7D9o7Y0s79fyiDmViZH/wmlKLGQfInIOY0ysizVGdDUhoE+Ng91mqkpORlFrPvqLKnWGnz/TfuH38xNgkXuKkHo8Oxb7T0rUmNyObjiBnkZxWjrq9OovTWN2lujZ1yzC+ddErOL6LHIn2YORqx7tyVC0jXY/j6kh0DLD1Q5DrVYbuIhyooheDuYNlSF3daQiMsX2PfLQtS1tBg45SusXBrWwiRrj7zMdGKDrhETFEhsUCAF2arKk0ZW1tg39sHB2we7Rt5o6upSVlpCyJmTXDmwm7SYKDR19fDq1gufnq+gb2rOFb8Yzm2PoPcHjXFuWo1ubE+g1sRdEAQpEAr0AOKBS8AIURRvPrKdHrAPUAcm1It77ZOVXEDgkThun09CVIg4NTXHo6sN/dZfpKuHOYuH+XDidBxXD8SgkVlGviByXV+JU0tLBvna0MzeqM5snUu+PUNxajFv/tgWK6NHwhN/7wgyLRhzqE6O/bzJyyzmn7kByGQS2r7mgqOPaaUT0iqDKIqMXRvA2YgM/Ca2xe7m73BiLmibwqClqiJYLxnpsdHsmP89hdlZ9Bw3EY92nZ7bXEoKC4m/FUTM9UBiggLJTIgDQEvfAPvGTXDw9sGhsQ/6Zk8XZ1EUSbgVzJWDuwm/eB4Al5at8enVn7M7CinKLWPkzFZoVLMl4IPUpri3AWaKotjrzu/T75zMnEe2WwwcAaYAU+rFve4oyCnh+rF4bvjHU1qsoNREnePF+Xgr1LEqhgJBJN9Jm459GtDJw6LGZV0rw7mTcVzZFAYdzBg/yuvhN4//CCcXwNSIat3Ov8iUFsvZvuAKeRlFvDqtOSbWtXdHdJc91xL5ePNV5nfRYWjcbIhEYv3XAAAVsklEQVS/CJ6vqpzUL/HfszA3hz2L5hB/6watBg+j3dBRD5VcritEpZKUqAiir10h+toVksJuo1QokKlrYOvhib2XDw5ePpjZN6jWfHLTUwn020/Q0UMU5+dhZO1Afo47np060+0tr4oHqIDaFPchQG9RFMfe+f0NoJUoihMe2KYpMEMUxdcEQTjBU8RdEIT3gfcB7O3tm8fExFThlOp5lNIiOcGnErl8OIaSvDLKZGDsa8qA1xpiqPdsnVUKhZJfJp4gTR1m/9Tl4TuEhMuqdn9VbRxSkqcqHXx7P4hKVZEpi8aqRwPb5+6gVSpFDiwPIiYonX4TmmDvWfsVAbMKSun+0wnGaPkzrnQ1glQN+i56ch3/lxCFvIyjq34j6JgfLi1a02fCZ6hr1n5iWl5mOjHXrhJ9/SoxQYEU36noae7oTAPvpjh4N8O6oUel2i5WlrKSYm6d9ufqgd2kx8WAoEWjDj1oP+LVGvkaalPcXwd6PSLuLUVR/PjO7xLgGPC2KIrR5Yn7g9Sv3GsPRZmS5MgczBz06rwiXXmsWXaVvOuZ+HzkSQfvB6pSKpXwU0Nw7ABDVpc/SGEmhB6Em7sh4piqiqCOGahpqUqy3kXD4I7Ye94XfXMP0Kj9lfPTOLMtnMDDsXQc7oZXZ9s6OcasTUfpePs7ukgCVaVpBy6rvRK4Lwii+P/27j06qupe4Pj3N5kkkJAQAiEJkIQA8gjP8Ao+oFSoQBShapfW2mrr0r6utQ9rre31Wr1eW+5ql7XVurxLbWtL1VqkCPhA5aFFKK8kPEIgPAIJIQkESMhrMpN9/zgHGEMCk2GSyUx+n7VmnTP7POacvSa/nNlnn9827HhnBev+/BID0tJZ/PBjl2wC8UWzq4mywt0czt9OScEOK7gCsQn9yJiQbQf07C55ctYYw6H8PFY9+yquun04IiK4+rY7mXHL7X7tL5BPqJYC3v3shgDHvN7HAeOAdfbVWgqwQkRuvlyAV4EREenoUF74zjJ/4Qj+UbCFte8e+mxwdzisQRH2vm3lIGndTa+2AvautFKvHv7Yyk3SNw2m3Ws9Mp+WA44IaDwDlYUX8oBX7IH818DllSis39ALV/fJYyH9mk5JI7vnk2PkrTlyvgcTZ0qtY0rLsboKBkDhh3/hgaKfEBfRDPOXwLT7fO56GEpEhMm5i0gcNISVv13CXx79AQu//xP6p2XQ4nbbaZKbz6dLbnG78Xg8tLibranHbZd7qD1ZRUnBDkr37MLd7CLC6WTw6LHMmnU9GROyScrI7PI+9iLCsEnZLH44g+W/Xs+AQYcYmNn5T+v6Ety3AFeJSCZQBtwB3HluoTHmDHD+N4avV+4q/KSkxdHULxJHSR1nGlz07e3Vn3vkPMj7CxzdDEOvhdNH7UETVsCRTYCBxOFwzQPWUGiDsi9udunVF9JnWK9zjLGu6Ct2W69Ke1pkN+Ug1ug9I+dZ6RCSx11xc05p0SnWLy0ifXgk1yX9A15YDccLrIWOSGsYt6xFMDoXevvxT7fxDO5VDzNm52sURQwn9r6/QuqYy28X4oZOmsKdT/2a5Uue4I0nHvV7P4mDhjBh7nwyJmaTNmY8kb0C02PpSqWNTiRr5miKNiUQnxS4cXLb42tXyFzgGayukC8bY54SkSeArcaYFa3WXYc2y/RY7686wP63S4idP4h7Fnt9gZtq4VeZ1tOT7gY4tsMqTx5nBfMxC61mlUBdVTU3WEH+wEdWM0/ZNqs8fsiFQJ8502ru8ZW7idPbP+HNV5uJkZPcmvAQ0Y4GSJsOo3IhZbz1eXv+CWeOgsNpNaVkLbbGlPXl5uehj2H5t2k5c4zfuRcx456nyRnRdYMvdweNZ8+y91/raWlpIcIZgcPpJCLC2Wpqlbcu69Unjj6JnT8akr8a65pZ+vgmRuakcN1tV/m1D32ISQWFq9HN8z/cQGW8g//55ezPLlx6uxVoB0+5END9eUTeH7UV1s3Zfe9aT3Q211ndM4fNtoP9PIhvY0zb+mpr4Im9q2gs2sSbx/+TJtOHL01dTXz2LLhq3sXNPsZA2XYrp/me5dYvC4cTMmfZV/QLIbZVAGpuhI+ehE9/T1N8Jnec+Dqjp17P07dcee8K1b3UVjfSp1+0381DGtxV0Lzw6y007q/h+p9kMyHT62q1qRZcdQEbAtBv7iY4/ImVa37fOxdu1KZOtK7oh860UiYUrYaSjWA8eGIH8fbpxyk/PYDF3xtH6igfk6sZA+V5sNsO9KcOg0RYvxrOBfraY7Dsm1BViGfqvdxavIBjdcKaH36Ovr0D13tDhQcN7ipoivee5L1n8qkdG8cjD0wL9uFcmjFQtde6ot/3nnVPwLRYywZmwagFmJELWLs2lsKNx5l7zxhGzUj1/7OOF1wI9NUHsZK0O6wHkhY9x/NlQ1nybhEv3DWF+eN6VnOM8o3mc1dBM3xUIo2xDhr31dDk9hDtvHzGyqARsdr6B46xEm7VV1s3eAeOhkSrR0PemiMUbixmyoKMdgN7ZU0jr2w8zMwRA8gZ1p+ItvLIiFi/DlInwpzHrF4/u5db9wdmPcSh+mie+dMG5o9N0cCurpgGdxVwIkLm9GTK15azan0Jt8wJoUEaYhKtXi62QwUn2LismOGTk8hZ2PZ5tLQYHnwtj08PnuQP6w6QHB/NokmDWTxpMGNS49puWxWxbsCmjD+/j0de3US008EvFo3tlFNTPUv4dZpV3cK83OF4MOxYXxrsQ/HbidJa3n9pNwPT45hzT1a7WR1f/tchPj14kl/cPJbf35nN+MEJvPzJIXKf/Zh5z2zg+XXFlJ1uuORnvbH1KJsPVfNo7pgO59RXqi165a46RWxcFKTFkFBaT0nlWTICmIWyK9SdaWLVcwX0inGS+50JREa13bRUdLyWJe8VMXdMMl+7OgMR4aYJg6iuc7FqZzn/3FHGkneLWPJuEdMzE/li9mByx6XS1yuBVGVNI0+tLmTGsETumNZ+Xn6lOkKv3FWnue6GofQywvKVxcE+lA5xuzysfr6Axno3ud+ZQGzftvP0NLk9fP/1POJ7OfnlrZ8drCExNoqvzsjgzW9fw4Yff54ffWEkJ8428dNlO5n21Ad889WtvLurnMZmD/+1YjdN7haevmVC2I9QpLqOXrmrTpM9JYW1r+7ldEE1LS3Gr5Geulp9jYv1fyui8kgtC745nqS0uHbX/c2afRSW1/DS3VMvjIrUhvT+MTww5yr+4/oR7Cqr4a0dZazIP8Z7uyvoE+3kbJObh+ePInNA8AZTVuFHg7vqNOIQkif1p9e/T/DRtjLmTuuc5FpXqrnJw6H8Koo2V3C0sBrTYrjm1hEMm9R+TppNB0/y4oaDfHl6OnPG+NbnXUQYP6Qv44f05dHc0Ww8cJLlO8podHu4b2YI3XRWIUGDu+pUCxaOYOm/q/jX+yXdKri3eFooLTrFvs0VHMirwt3koU9iNNk3pDNqegqJg9q/iq5pbOZHb+STkRjDz2/0L+eLM8LBrJFJzBoZ+KRmSoEGd9XJ+ifF0JwUTa/SRk7WNNE/PniDIhtjOHH0LEWbj7N/SwX1NS6iejsZOS2ZUTnJpA5P8Gmc08dX7OZ4TSN//9bVxEbrn5DqnvSbqTrdlM+nseuNAyxbXcx9d3R9H+6akw3s31JB0eYKTpXX4YgQho4fwMicZDLG9ccZ6ftDVqt3lrNsexnfm3PVZ8asVaq70eCuOt11n0tj27IDHN9aibk9K+A9QtzNHprq3TTVu3E1uGmsa8bV4Ka+xsWh/BMc238agNQRfZn9lVEMnzyQXrEdz9lSUdPIo2/tZOKQvjxw/YiAnoNSgabBXXW6iAgHcaMTiNx1im17TzB1TPvtzM1NHuprmqg/46K+1mVNa1w01jV7BfAL800NbjzNLe3uLyE5hpybhzFyejLxA/wfvs0Yw4/fLKCx2cNvbp/UJePSKnUlNLirLjFv4XDe3rWNFUsLKZ1UTZLTibveTUONFbzr7Km7yXPxxgLRMU6iYyKJ7u0kOsZJbEL0hfexTrs8kqgYa/m5973jIgPyS+HVTSVs2FfFk4vHMTwptB7IUj2TBnfVJdIz+uIZEEVylYuyNWWUAS4HSO8IYuKjGJAaw9Dx/YntG01MfBQx8VH0PjftE4kjiFfKxZVneWpVIbNHJXFXTnrQjkOpjtDgrrrM/T/N4VhZLQdrG9h14izbS0+Td+Q0tQ1n4Ug1iSejmJyewOTofkzuHcXE5Bh6t/PYf1dxuVv4wet5xERFsORWfYJUhQ4N7qrL9IqNZNjIRIYBc+0yT4uhuPIs20pOsf3IKbaXnOKDwkoAnA4ha1A8k9P7nU+mZbDGH2hrGIJzYxOcWyYCE9MSuHpYf5x+Xvn/7qP97Cw7wwt3TWagJvRSIUSDuwqqCIcwKiWOUSlx3Gk3eVTXudhx5NT5gP/6lqM0NLfRFu+jxNgo5o1N5sbxg5gxLNHnQL+tpJrn1hZz25QhzB/n5wAdSgWJjsSkuj1Pi6HZc3GPmHMtJIK0UQYuTwsf7z/BqoJyPiisoN7lsQN9CjdNSCUns/1AX9fkZsFvP6bFGN55cCZxvXS4O9U96EhMKmxEOIQIR8fb3p0RDuaNTWHe2BQamz2sK6qy0vDmlfG3fx+hf2wU88alcNP4VKa3CvRPrtzD0VP1vH7/1RrYVUjS4K56hF6REcwfZw1f1+DysH5fJSsLynlrexlLNx9hQB/riv7GCanUNLh5bctRvj17ONO9B/hWKoRos4zq0RpcHtYWVbJqZzkfFVaeb9vPSo1n+XevJcqpDyup7kWbZZTyQe+oCHLHp5I7PpV6l5u1e6v4pPgE983M1MCuQpoGd6VsMVFObpyQyo0TtGeMCn16aaKUUmFIg7tSSoUhn4K7iMwXkSIRKRaRR9pY/kMR2SMiBSLyoYhkBP5QlVJK+eqywV1EIoDngAVAFvBlEclqtdoOYKoxZgLwJrAk0AeqlFLKd75cuU8Hio0xB40xLuA1YJH3CsaYtcaYevvtJqD7DJaplFI9kC/BfTBw1Ot9qV3WnnuBd9paICL3i8hWEdlaVVXl+1EqpZTqEF+Ce1s5Ttt88klE7gKmAv/b1nJjzIvGmKnGmKlJSTrqu1JKdRZf+rmXAmle74cAx1qvJCJzgZ8BnzPGNAXm8JRSSvnjsukHRMQJ7APmAGXAFuBOY8xur3WysW6kzjfG7Pfpg0VqgSKgL3Cmg8ftzzYA6cARP7bz9/NCYTutk7b5Uy+hcm6h8PejddL+dinGmLjLrmmMuewLyMUK8AeAn9llTwA32/MfABVAnv1a4cM+t9rTF305hlbbdngbe7sqP7fz9/O6/XZaJ4GrlxA6t27/96N10v5252Ln5V4+pR8wxqwGVrcqe8xrfu5FG/nu7S7aBuC0n9v5+3mhsJ3WSdv8qZdQObdQ+PvROml/u8m+rBi0rJAistX4kNks1D+zu9M6aZvWy8W0Ti7WneNYMNMPvNhDPrO70zppm9bLxbROLtZt41jQrtyVUkp1Hk0cppRSYSjkg7uIvCwilSKyy6tsooh8KiI7ReRtEYm3y6NE5BW7PF9EZntts85OjpZnvwYG4XQCQkTSRGStiBSKyG4RedAuTxSRNSKy3572s8tFRJ61E8MViMhkr33dba+/X0TuDtY5XakA14nH63uyIljndKX8qJPR9t9Vk4g81Gpfl0wuGCoCXCeH7ViTJyJdP+ycP91xutMLmIV193iXV9kWrIepAL4BPGnPfxd4xZ4fCGwDHPb7dVjJz4J+TgGok1Rgsj0fh9WNNQsrodsjdvkjwK/s+VyslBECzAA22+WJwEF72s+e7xfs8wtmndjLzgb7fIJUJwOBacBTwENe+4nA6iY9DIgC8oGsYJ9fMOvEXnYYGBCscwn5K3djzAagulXxKGCDPb8GuNWezwI+tLerxOrGFHZ3/40x5caY7fZ8LVCIlQ9oEfAne7U/AYvt+UXAn41lE5AgIqnAPGCNMabaGHMKqy7nd+GpBEwA6yRsdLROjDGVxpgtQHOrXV02uWCoCGCdBF3IB/d27AJutue/xIX0CfnAIhFxikgmMIXPplZ4xf4J9Z8i0lZOnZAjIkOBbGAzkGyMKQfrS4x11QHtJ4fraNK4kHCFdQLQy06At0lEFhMGfKyT9vTk78mlGOB9EdkmIvd31nG2J1zHUP0G8KyIPAasAFx2+cvAGGArUAJsBNz2sq8YY8pEJA74B/BV4M9detQBJiJ9sM7l+8aYmkv8v2ovOZzPSeNCRQDqBCDdGHNMRIYBH4nITmPMgU443C7RgTppdxdtlPWU78mlXGt/TwYCa0Rkr93S0CXC8srdGLPXGHODMWYK8Des9kCMMW5jzA+MMZOMMYuABGC/vazMntYCS7F+aoYsEYnE+nL+1RizzC6uONe0YE8r7fL2ksP5lDQuVASoTjDGnJsexLpXk93pB99JOlgn7enJ35N2eX1PKoG36OKYEpbB/VxPFxFxAD8HXrDfx4hIrD3/BcBtjNljN9MMsMsjgZuwmnZCkt2k9BJQaIz5jdeiFcC5Hi93A//0Kv+a3UNkBnDG/un5HnCDiPSzewfcYJeFnEDViV0X0fY+BwDXAnu65CQCzI86ac8W4CoRyRSRKOAOex8hJ1B1IiKxdisAdsy5ga6OKcG6kxuoF9aVeTnWDY1SrMFCHsS6y70P+CUXHtYaipWJshAr2VmGXR6L1XOmANgN/BaICPa5XUGdXIf1s7iAC8nccoH+WDeU99vTRHt9wRpK8QCwE69eQ1hNXMX26+vBPrdg1wlwjf0+357eG+xz68I6SbH/xmqwOiOUAvH2souSC4biK1B1gtVzKN9+7Q5GnegTqkopFYbCsllGKaV6Og3uSikVhjS4K6VUGNLgrpRSYUiDu1JKhSEN7qrHEpHHW2fya7V8sYhkdeUxKRUoGtyVat9irGRzSoUc7eeuehQR+RnwNaxEV1VYD6+dAe7HSldbjJVXaBKw0l52hguZRZ8DkoB64D5jzN6uPH6lfKXBXfUYIjIF+COQg5U0bztWaopXjDEn7XX+G6gwxvxORP4IrDTGvGkv+xD4ljFmv4jkAE8bY67v+jNR6vLCNSukUm2ZCbxljKkH8BpFaZwd1BOAPrSRP8fOEngN8HevDIHRnX7ESvlJg7vqadr6qfpHYLExJl9E7gFmt7GOAzhtjJnUeYemVODoDVXVk2wAvigive2MfQvt8jig3M4I+hWv9WvtZRhjaoBDIvIlOD/G6sSuO3SlOkbb3FWP4nVDtQQrg98eoA542C7bCcQZY+4RkWuB/wOagNuAFuAPWONsRgKvGWOe6PKTUMoHGtyVUioMabOMUkqFIQ3uSikVhjS4K6VUGNLgrpRSYUiDu1JKhSEN7kopFYY0uCulVBjS4K6UUmHo/wF+A5EvTEwVYwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe53f1f02b0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ls_factor_returns = pd.DataFrame()\n",
    "\n",
    "for factor_name, data in factor_data.items():\n",
    "    ls_factor_returns[factor_name] = al.performance.factor_returns(data).iloc[:, 0]\n",
    "\n",
    "(1 + ls_factor_returns).cumprod().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basis Points Per Day per Quantile\n",
    "It is not enough to look just at the factor weighted return. A good alpha is also monotonic in quantiles. Let's looks the basis points for the factor returns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fe53f2dd710>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fe53f3a06a0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fe53f2ed390>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fe53f28e518>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fe53f29dda0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fe53f29deb8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fe53f28a550>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fe53f247208>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fe53f307320>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fe53f260d30>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fe53f278588>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fe53f2ce320>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fe53f318358>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fe53f6c3ba8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fe53f6d13c8>]], dtype=object)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe53f3d7278>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "qr_factor_returns = pd.DataFrame()\n",
    "\n",
    "for factor_name, data in unixt_factor_data.items():\n",
    "    qr_factor_returns[factor_name] = al.performance.mean_return_by_quantile(data)[0].iloc[:, 0]\n",
    "\n",
    "(10000*qr_factor_returns).plot.bar(\n",
    "    subplots=True,\n",
    "    sharey=True,\n",
    "    layout=(5,3),\n",
    "    figsize=(14, 14),\n",
    "    legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Turnover Analysis\n",
    "Without doing a full and formal backtest, we can analyze how stable the alphas are over time. Stability in this sense means that from period to period, the alpha ranks do not change much. Since trading is costly, we always prefer, all other things being equal, that the ranks do not change significantly per period. We can measure this with the **Factor Rank Autocorrelation (FRA)**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe53f6adef0>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7OnFozTl0WokBky5vb74cTY1aDq5K4syhPJx91Uy8T/9S2fH9abYvPUVWghujZgWiNOn+F8mVcDjnMC8dfIm5q0uwq5Hhs+xXznGM949/wY/me3gv4j36OfXr1jqzzpSy88czNDZpuG5OIKW5NZw+kEO9ToUFUNPQQGxuCnEFKSSVZpBZmUlxfS5V2gJGxGvxaTJjg9dhqvL2c6/MjoI6iVf2vcQk/8kMdx2OUq5ss97i7CryzlUwYmYAMpmg7zhPEo/ms39VEu7BtsSXneSVg6+QU53D/N7zebz/45gqLpqZEvOqqG/SAXDsnI6Xhr7Eywdf5uczP3NP2D3d2kcAxzatJfHQPkY5ZVHaYMLh1b/i4OVN4JARHZar2rkTXU0N1tNv7naZ2uMfeez21UYul1NlKlBUV19rUXqcriqG/PwTFMoF4Xa9e1CqthEyGXZyOalCwTsjBFNWN3B6gJL+Js4cWZ+CVqNj8BTfK35+cXY1O747RVlBLQMmeTNkmu+FVS8znh1A1KY0YndkkJdSwaT7Q7F3t2z3WbXV1axas5XgwUMY2MsLE0X3HjrQpG1i8fHF/HD6B27OdmbUKS0Ojz2GeZ9w7iOcwS6DeX7/89y97W4e7fco94Xd95dHxVqtjqhNqcRuz8TW1YKbn+xHvbqa7/N24K8N4dnPFnPIYzM6eRlCXFwvIumUKHWOhJRa4J+nwcyznA8rc/Es1RBjKiel3orsg/t5LH0LVkorxnqNZZLPpFZKIn5PNgqljJAR+kUHcoWMMXOCWP/Rcb76fhXfmy/CzdKNZZOWMchlUCv5j2fpLQBOVirWxmaz5uGb2JW5i8XHFzPKfRSBtoF/qX+akxITyYEVP9Fr2AhM6j4hSCNRah7MH198jM1CVxy92/+cVmzYgNLDA/NBrdvQUxgVQyepNlOgqq271mL0OHIbG7QVnTclxSVvAaCv99ieEqlD7B2cOFddjlP+MWYOGM/3h9LZ9uRoZHJB1OY0dFqJIVN9u7TaRJIkTu3L4dCac6jMFUx7oh+eIS1t43K5jOEz/PHoZcufP55h9aJoRt0WSOhot1Z15ack89sH79JUVsS+bctZYhOKFDaGgcE+DPG1o7+XDabKK39JZ1Vl8cL+F4gvjmeu801MX7IXk9BQHB5+6EKecMdwVk9dzcIjC1l8fDGReZH8e/S/cTK/MlNYZXEdO74/TUFaJb1HuxF4o5olZz9j9dm1aGlC2N1PYGEoaV6pOFg542vtRW97HwbLqvHLOYiU8Ds/xzmhVcqYH2qPMuweDtZ4MqT4PlLyPLinaQz3jZvEjvQd7M7azaaUTS2URH/1IJKiCgga6oKpxUVlUWaXS67bGZzjgpg9bR5Pj38Uc2Xb5pfYjDKcrFTcO8qXRX8kklZcw2vDX2PGxhm8fPBllt+4vN3ZSlcozsrg988/xNnXn6bRDsw/5YxfUxM/+whWHDZnwwcLufPdTzBXW7cq25SXR82Rozg8+ihCdvVOMDKeldRJas2UmNfWX2sxehy5tXWXZgxx+dGYSBK9/G/sQanax9k/BICC09G8MLkXJgoZ72xNYNy8EHqPdCV6azpH1qXo9590gvqaJv5YEs/+lUm497Jl9qtDWimF5nj2tuP2V4fgHmjDvuVn2fbtKeprmgD9xq3oLetZseA5Ghoa2e06CYewQfQpjyfs4BfErvqeB77eSfgbO7htyWE+2J7IvqQiqhs07dZ3KX+k/cGszbNIr0jno4gPmbO5CqmmFrf3FiGULV9qViZWvBfxHm+NeIv44nhmbprJvqx9na7rPMnRBax6O4qy/FoGzXVhn+9Kpm6ZwuqkNWirBtCXRbzw4D2otCoWuT/HL/0iWFgfxx27HiJg/VxkMcuIqQ6ktNGcsY++hPKejTD4Pvz7j+Zr8xlYNTSQnRRPhEcEb496m32z9vHluC+53ut69mTt4bFdj/HkktfRNOloDCmgSdtEk66Jr098zdzf5xLrvx0TlYLQhOsxU7S/QCA2s5z+Xjbc0t8dmYB1sTnYmdrx2vDXSCxNZEncki73zaXUVVWy4YOFKFUqFLf05/XTX+Cq0ZJiYsKJskhufvYVasrL2Pzxv9FqmlqVr9i4CSTpqpqRwDhj6DR15ioci6uutRg9TldNSXE1WfTGBKWqazuluwuH3qHIYw+Rl5pDmJUpT44L5J2tCexNKmLMncHIFDKO/5mJVqtj1G2BHc4ccpPL+HPZGWorGxl5awB9r/dEyC4/0zBXm3DT4305sTOLoxtSWJURRcQsD47/sYy0EzEEDB7GUmkwdnY23H/fUMrz84jauBrFvl30rU5A4z+QmOoBLNlXzpd7UpDLBGFuaob62TPEx47BPnZYm7d8ydc21bIoahHrz62nn2M/3ot4D/MdkeTt2oXT88+jCmh7IYAQghmBM+jr1Jfn9z3P47sfZ27IXJ4e+DQmcpMO29nUoOXAb0kkHMrDxkvF2QG7+TJ1LTIhY2bgTHRlY1h2poJXHhuCc90R3GzLObmxhD4ODyE3UUHgBAiZRqXdQI689Cz+g4biP2Lchee7Wpuxx2oaz3CSjCYlmtJsFHYeKOVKIjwiiPCIoEnbxOGcw5z4vJpC63SWnPoEq7NW2JvZk16Zzk1+N/HikBfJ9qpm769nSYoqoNdQl1ZtKa5uILO0ljuHeuGkNiUiyJF1sdk8MyGIcV7jmOY/je/jv2eMxxj6OPa57GegLbQaDVs+XUR1STG2d1/PO2c+ZKRNMB+d+JPpvgH8qKpnmVpi0kNPsPWLj9jz47eMv/+iK1aSJCrWr8d80CBMPHtupVRbGBVDJ2mwMMcyu2tO2X8ichsbdJWVSBoNQtHxx6OpoYYzUgOzLf2vknStMfX3R13fQGGlgMo85o/wYcWxTN7acoaRT0UQcXsQcrmMk7uz0Gokrrs9qNXLXqfVEb01neit6agdzJj5/ECcvDteTXMpQiboP9ELt0AbtizezLpFnyNEA2PveRjf0RN46u2dPDNAP/OwcXFl4kNPMGzm7URtXMup3dsZqotm7vDrMBk4gfhqFSeSSji0O5O0xkxStWU4qxKpsT5LrOhNlNKWMsf1oChCVjmeU5mTmL//AIt+X0SGkz9zs9yQFv4JgJ2FCd/eNRA/x5b+Dz9rP36d8isfR3/MLwm/EFMQw/sR7+Nj7dNm+5r7W8p7p7DU6kuUxUruCL6De8LuQSHZMPq9XbzrFU3IL49AQwX9FaP5XfsM5/r9Qq8pY8BEb9LZ+/G7IMHY+Q+2qqe/jz3p8f5I2kIyvn0S/xfXtkhXypV4l4dyqjqOO+69gfFugWxP305yeTKfjPmE8d76Za7qkWoSDudxaE0y3mH2LcxNAMczy1GgYdI3d5P6to6HwsawuMSKo8e9GDEwkBeGvEBkXiQvH3yZ1VNXt3Bag36Z7NGNKQQNcabPWE9UZq2/K/v+8z2Zp+KwunkIH+YuZYznGD6yHYrJ8R3M9ZnMh2kbOH1mNaFjFlCUlcGxjWtw8PKl30T97LvuxAkaMzKwf7B1P/U0RlNSJ9FYWGJZJ6HVaq+1KD3KhU1unViBlZS6jQaZILwNx97VwsTHB3VdI8Vac6TMo5goZLx2U2/SimtYdigNIQQjbwtgwCQvTu/PYc8vieh0F81KVaX1bPjkOMd+TydoqAuzXhncZaVwHq1GQ9LR9ZTnrkRlbo7S4g4yE9yIPF2EJMEQ35YmKbWDE+Pve4T7F39P6JjJnIs8xKnPX8F09fcMjcvmxmolg7QCSxrIro1gZGMOAyyWUOOyBHtZIc/W+vOqlSd3BCp5OX4NSgFJdz/NjX3dmdLHlSl9XCmoqOeNzWfaNKWp5CpeGvoSi69fTF5NHrO2zGLDuQ0t8kqSRPzebFYviqKovJTNIV+yyf475ofPZ9vMbbww5AWczJ34cfcJ3pc+YU7hx+DWF+asxnvBGmxdLTh+ygFJqTfppJ+IITnyMENnzMLaqfURFAO9bNhhOQChk0g7lQ6nN7TKE783GwtrEwIHujDaYzRvj3qbVTetuqAUQK+or5vTi/rqJiI3prZ6xvHMMt4QP1N/oghteQW2e7ayIOonbO+cRspNN1Hz7scsqp9CRU4an8V+1qJs8rECti09RUOdhshNafz88mEiN6VSX33RFBS3axvHt21GNTSAxU2rmeA9gY+v+xiTer3/bma/h7GU4Ies7QCMuv0u/AYMZs+P35B5Kg6AivUbEGZmWE2a1Er+nsY4Y+gkktoahQ4KC/Nwdf3vPZfngmIoL29zM1JzTmbsBqBvwE09Lld7yK2ssJEpyUBO2ekD2IXNYEwvJ8aHOLN4VzIz+rvjrDZl2HR/ZAoZ0b+no9NKXD8vmPS4Enb/JwGdVmL8Pb3bNDl0lorCAn7//H3yks8SNnYiY+c/QMrxMvavTKIxrYJeKgV9PfV9K0kSFYV15CaXk5tcTk5yGdWlvVBaeIL2OE21x9Fpz+Dp7cVosQmHkXfxy2E/1mif4if7Nxhl4c7bDabYFURD4W5KkywoSLHG5ZZevNg/H7z9wNYHhMDHwYKFW86wM6GQCb3bPgtojOcY1kxdw4sHXmTBoQUcyT3CgmELUDSp2PBdJCUJjWTanCEqeCMz+97M3JC52JjaXChfevYAs6PvxkVeBuPfgBFPgkyGAPpP8GT3z4lkJZTiFmDFrh+WYOvqzqCpt7QpywBvW96y8WJWXiNZMnvY+iz4RoC5/rNYll9D5plShkz1vey5SI6eVoSP9eTkniyCh7vi7HtR4dsl/MLNxQfI1tnjNlaJ+duRfPrVJooOHeUBdQWVm7dgUVPDN0C+zY/EDUvB67obyVEFsW9zPq4BNkx5LJyKojpiDLPNk7uyCLvOHSevanZ9vwS5nyPf2u5iss9k3h39LgqZAmpLQMixtHLjNpUHPzVkkVWRiae1Fzf+6zmWv/p/bP50EXe89m8q//gD9cQJyC2vvpnWqBg6idxGvzk7Jzv9v1wx6FdGdGaTW1zJGZy0Es7O1/ZkSkcnZ9BUUZAUz3lVtuCmECZ8vJ/3/kjk49n9EEIwdKofcrkgclMaRVlVlObW4OhlxcT7Q7FxuvKNQ2ePHOTPbxcjSRJTnnye4BERAISMMMPFz5pvFkUxrUrJoeVn0TTqyE0up7ayEQAzKyVugTb0n2CLW6AN9m5TqK+pInblEo7v3s1yXT8czBuIMf+R4JxbecLsde69dQYyIQOdloZjOyhc9zwWAebYqE/Axj16odTu4D2C+d6j2ODowsItZxgd6NDu6idnC2e+m/gdS+OX8vXJr8lMLmbIqenI61XE+e9k8MQAXg1Zh5WJ1cVCOh0c+gSbXe9Qgx0Ft27ALSyixXODBrtwdGMqJ/7MJCchkfL8PGa+shCFsu3VPiGuakxUSswUFuRKTdRWVWG+/WWYoXcEn9qXg0wuOn30yZBpvpyLKWDfirPc+uIgZDKBNmU/88u/IqXEn2oLDVUmdViYmHDdLeO4Jd+MfjPDua2/K/WJZ6mIPMyp35dgte8QMSc0JPaag11tBgPyzlG3PQuHqVO54aE+lORWE/NHBrHbTtFY+SuYmrPO4xxTA6fx1oi3Li4Nri0Bc3sQgjsDbuE/pz/jl9jPeGnsR6jMzZn+/AKWv/wMG95ZwOCaaqynT7/QlpLqBrbG5zFjgAeWqp59dRsVQydR2emX9RXlZV0m5z+b5jOGyxHXUEy40uaqLqNrCwffAGRJsRTmlRDSVAdKM7ztLXggwpcv96Rw5zAvBnrrVcagG32RKWQcXZ9Cv/GeDJvuj/wK9xQ0NdSz96fviNu1DZeAIKY88Tw2zi1nHSa2Kn4wq+MRN3sSj+RjaavCI1ivBNwCbbBxNm/lEDfTVTGyfCkDB1lywv0J9v6+Ft9GaOArNJud2V1dgk94OG4BQRR8/CPCzALX7zchHB2gKBEyDumvtP0o4lezTmXLp9UT+HmPDQ9O7N9ue+QyOQ/3fZj+FoM5/Gk+jYo67G+t5POIN1ov+azKh3UPQto+tumGcTR0AW+FjWr9TKWMvtd7cmhNLKlRvxE0bBQ+4e3LoJTL6OthQ2GqB2jSyXCZRcjJnyFsJo2eY0k8kkfAQCcsrDu3g9vEVMGoWUFsX3qKU/uyCe+RtLpYAAAgAElEQVSrQfrtLjIkZxqKLYn2N2d/ssD77VcZNvN2/BwsWBObzazBnpiFhWIWFkrQtCEsWraEUWm34WJbzyCzMzQcjSJ3ywakpiZsbr0VezdLxt7pT87pr2io1qBU3sbs03aE2XpQE9KI2t6wOuq8YgCce03hxsh3WZ+1m0fqy7ExtcHWxY2bnn6RtW+/SlyQF6GDB19oy2/R2by3LZGhfvYEOVu11dxuw6gYOonaQT8NryjMu8aS9CydVQylpefIksNttr2uhlgdYurnh9XJI+TXmENOLPiMBODRMQGsjcnhjU1n2PDYSOQGp/OAid6Ej/FA8Rd2KxdnprPls/cpyc5k8LSZjJx9F/I2nPXHM8tpkCT6T/djhI8dCqWs4z0VjbWwcg401WF6/2aC1E48XPsJDzvcQUChM/F7oojbuZWTOzYBYFXXgOekMZCahIdKiYVzb3DuDUMe0B/fkRWF8sCHPJf8G5WHtlClfQir6564YJq5FEmSKPxDjpmwYO7zo3B0a722nuSdsP4haKxhvecLPJ/al90TBrTOZyB0tBv7f/0YSRKMmXd/h/0KMNDblgMqX0Y3pJBaZEmIYzBsfoqk8I001mu7fBaW/wBHvHrbEbkxFf+TC1HodLxYdh9P1fxKrbAgyKqIrPQUVr3xIje5+rNO6k1GcTjeDgYTTpwdo9JuI932FCEP9sHb9yMkSSJj7l0UfvwJVhMnIrO05I8vP6YkN4OdAwuI6JfB8II+JBzKI+FgHr2GuzBgkjc2NRcVA2o37hY2bJI0rDq7iof66veduDm5EZJbwhk3ew6vWcGo2+9Cp5NYHpXBUF+7HlcKYFQMncbWRf9hrCspvMaS9CydVQzxSfoXU7hnRIf5rgYmvj6o6xooaLBEyjyKMCgGC5WCl24M5smVJ1gdncXtQy6en3SlSkGSJOJ2bmPvT0sxMTdn5stv4dO3/ZdiVFoJMqF/2V322AxJgk2PQ95JuGMlOAVzPGMXWrnEsGGT6efUDxOLkcTvy2Dk2HqylrxPhacbyennOPPpIgBs3TzwCAnFIyQMj5Aw1F5D4c7V5CceJW75q0yM/ASOL4XB98Hwf4Fly4Avpw/kknm6lNGzg1orBU0j7HoTjnwBTqHkTfiS53/MY/ZgTzzt2jfFZZ2JRduQgsI8AkT7u8PPM9Dblh9sfZieW09WSjLSPYvh+4nEbTuNo5dbC19BZxBCMHpWACvfPMyh9AjOhc8hIDGNXFsrlEo5N7glwf3fEBefTuTGtcyo2MyaN+O55d67KS104Oj6VHz62bPH5QBvRa2jv2s/bE1tcXnlZdJm3krxl19yLsCL5KjDRAeXMva6W3l+8LMIIRg8xZfYHZmcOZhL4uE8AtXjGBicccHkGeg9hlE5f7A8cTl3h92NSq6icvMmvIvK4eapRK5fhYOXN3n2wWSV1vH8pOAO29pdGFcldRJXd/25O01lJddYkp5FZmkJcvllFUNcXiRySaJ34NSrJFn7qHx9sa5tpFFSUHk2skXatL5uDPax5f3tZ6moa72BqKvs//UHdn73Je4hocx7f3GHSgEgKr2UUDfrztmED34Mp9bC+Nehlz5SbnRBNCq5ilB7/RlNQ6b5oTI3JWFbGUEawe2ffcNjy1Yy5+2PiLjzHmxdXEk6cpA/vviIpY/dw7KnHqQg9RwuwcM4E/EVExveo9htLBxeDJ/2gT9ehMpcACqKajm09hwewbb0ue6SeFklKbBsol4pDL4fHtjFJyf1s5/Hx7Z/dERTQz17fvwGW1cPFKYDOLnr8qbY/l621ClNsVSYU9NQT7mJBzkBL1NWbUV4n7oriplgE/ceAyxWk1w/in3ZvowsSybPzorAsGCUMh1KbSUDp0znwS++Jyt4MrVlJaxb9Ab7flqIk1cRk+4L5Z2It6lorGDh0YVIkoRp797YzJrF6U3rObpmBefcqxk8dSbPD37+goyWtqZEzA7irreH03ecJ6mVoayInE7MtnS9YL4R3F1WSml9KZtTNiNJEuUbNmDevz8Tn3oO9+DebP/6M9ZuP4KDpYpJoVe+QKIrGBVDJ3F3N4w2K/+79zIIIfS7ny9zLEZcZRpBkgKzdkwSVxOlhwfqJv0y4oKUiyeggr49b0wLpby2kU93Jv2leiSdjlN7/iRg8HBmvvQmFjYdR+Zr0Gg5nlneaplqmyRuhV0Loc9tMPKpC7djCmLo69j3wtEMphZKQi3SKFV50vjAWyhsbZErFLgG9mLwtJnMeOF1Hv1+OXe99zlj5z+AVqNh9cJXyE1K5OHr/KmxDmJu+YNoHomEsFsg6lv4rC+6zU+z67sTyGSC6+eFtNzrEfcbfBMBpakw+xeY8hFpFTrWxuZw51AvXKzbD6oTuX41lUWFTHjgUYIGu3LmYC4NtR0raDsLE/wcLKhQ679zGSdiiK+agKm8moCUZ6Gpi0fTnFgOhz9nQIQNlg6m9MnTYFqWhUYIQoYZznGtKQZAYWLCdTffzFm7u1CYT0Bp0kTmyf+w/OWn4Gwhj4Y/yp8Zf7I1bSsAulumcdLDARNNPb1vn84zA59pU3FZWKsYeYs/85wewsetjGNb0qkuqwef0QypbyDExJ6fTv9EbXwcjedSsJ4xHblCybRnXsbE0gqHo8uZHaru9jO22sOoGDqJmZk51SqQVxl3P2s1jcTragk3+3tETBMKBfaOzgigsBIoOdciPdTNmjuGePHzkQySCq78/1eUmU59dRWBQ4Z3yuEen11Bg0Z3ecVQmADrHgC3fjBtMRheLFWNVZwtO8tA54uBpWpjj2O99gOs5VXEJpjQ1Nh6X41MJsfJx48BN97M7DcWYWalZs07Cyg+l8CCm0JIzK9ieYoJTP8KnoiFfndy4kA5eRn1RAQcwUrSB76hoRrWP6KXzTkMHj4EIfoZ4mc7kzCRy3hkTPubG0tzc4jevJaQUWPwDA2n/0Qvmhq0FwLrdMQAb1siTb0xbWwief9B0uLL6D3ICkX5Wdjz7mXLXyArCjY/Cb4RKKa8i+0oZ2wlOWlOHpiZmePVf6ihY/WWAEmSsE+tY3ijKXU+g3hk6TImP/YMGo2GLZ++h/zHGMZUBvPukXdIzU3k188X0qTQMepsPvMrQjuezdSXYyYqGD2iDEmSiP4jAywcEE6h3NMoJ70ynYRfvkaoVKgnTwbA3NqG6tF3YaprwDFqJZqmvz7r7QxGxdAFqs3kmNTWXmsxepzLKYa0jL3UyAThTu2vLrnamPn6YqXVUVBvCVmRrdL/b2IvLFUK3tx8utPnJl1K1mn9xiMzv84tlYxM04cWGezTgWKoLYUVt4OJBdy+HJQXz/Y5UXgCnaRjgLPeXKWrrSX3xRcxcXFmzENDqC5rIHZ7RocyqB2cmP3GIizt7Fn779cJkQoYGWDPRzuSKK1pBFsfSga8TWTNPPxcCwgq/Ri+GARr7oVvr4OTKyDiebj7d7DRH8uQXFDFxpO5zBvhjZNV27MFSZLY/cMS5EoTrrvrPgAcPKzwDLElbnc2WsNx1+0x0NuWKCtPHKrqyElLRpJ0hE4bBgPm6c1ZOTEdlgegPEvvyFe7w20/gVxJktDQUHOOKpM6/AaNRnbex1JTjCRJHFpzjvidWVS5m/KzpooGHfSOuJ67P/qSKU8+j1wux+dgHRN2qln2xlNItQ3YzB+Hta8fhYveQ1ffwXlqBuWjdrKm90g3Eg7lUllcB74RTMg6jafKBdnOg1iNH4/cSu9gbtToWJWqI7/fDEpSk9i59Msr/vx2BaNi6AI1ZgpMa/5HTljtQDHEpe0AINzv6u/IbA+Vrw/qqloKGtRIGUdapdtZmPB/E4M4dK6E7afzr6iOU7EHqLLQ8srJtzqVPyqtlCBnS+ws2jmDSNsEv82Dyjy9UlC3VDixhbEohIJwB31EssIPP6QpKwvXRf/GI9yVwMHOHN+eqX+5dIClnT2zX/83Nk4ubHjvLR4J0FDdoOHDHWfRanTs/PEMKnMFY56ZiXg6HoY/Dme3QWMNzN8M178C8os+kk93JmOulPNQRPuzhaSjh8iIO87I2XNbmNz6T/CmtrKRs1Ed/w8GettSbGaDtcwUra4RVz/Dks+Jb4OlM2z8l94Z3h6NNbDyDtA0wJxVF1ZhxWaW4Va4GdBRVemHJJODmS1STTEHViZxclcW4WM9uH5uL2qbtGw7pZdTJpMTPCKCee8vZtqzr2Bn7YRthQK76SN48IbncH71VZpycihZ1kFoGYNiwNyegZN9EEIQvTUdfEej0NTxaIEPZrVaSsZejEC3/XQ+xdWN3Dx9MsNvvYPT+3aSl3y2w77rDrpFMQghbhBCnBVCnBNCvNhG+idCiBOGK0kIUd4sTdssbVN3yNNT1JqpMK/r4MP4X8JlFUNRHGqdhLfX6KsoVceY+PigrqmjTqOgOqXt0eScIV4Eu1ixcEsC9U1dO9rkWG4UeYkJ5NrXEV8cT2l9x4EGtTqJmIyyjmcL216C9AMw9TPwaH2sSExBDL0demOuNKf60CHKlq/Abt48LIYMAWDELf4IGRxae65V2UuxsLHlttfexdbdg+ilH3KvZy0rojLZujKR4qxqxtwZjJmVCVg6wcSF8H+J8Hg0+Lb8H5/OreD3+DzuG+XbrsJrrK9j789LcfTxo9/EKS3SPEJssfew5MSfmUi69ke+AY6WWJkqKHEyrDCzNqwGNLWGmz6BwtNw8JO2C+t0sP5hKDgNty4DR/2Saq1OIicxjTpFLaYqNQUZpqTEFiGZObI3NpD4fTn0n+DFqFmBDPaxw9venLWx2S0eLWQyAgcP51+f/MScxV/xwOxX9fINHYLV5Bso+XYpTTntmMqaKQZLWxVhEe4kHs2n3HwgCBlhB1IpsxL8ZH78QpFfjmbgYWtGRJAjw2fewe1vfYBbUM+vTPrLikEIIQe+BCYDvYE7hBAtorZIkvS0JEn9JEnqBywG1jVLrjufJknS1Q0a3EXqzU2xrO/8kcj/VC4Xk+FkfQHhMstrvrGtOSa+vqhr9Uq7MLdAb6K5BIVcxhvTQskpr+Obfa3Pz2mPyLxIFqx7CqVGxsQIfbD4o7lHOyyTkFdJdYOmff9C9DI4thRG/Av6tQ6AWK+pJ744noHOA9FWVpL38iuY+Pvj+PRFx7SlrSkDJ/uQeryIrITLR8Q1V1tz24J3cPDyxezAfxhdl0nGwXyCh7vg16/lslVM1aBqvbT0kz+TUZsquG+0X7v1HFmzgurSEsbf9wgyecslukII+k/woiy/lozT7a/wk8kEAzxtKLLoj1zYUpZ+8mJir8kQdivs/wAKzrQuvG8RJGyCCQv1J7oaSCqook/2CUotzQgfORJHLysO/pbEzoJ5nMkNYtCNPgy/xR8hBEIIZg7w4HBKCdllrc3HQghcnVqGj3V+7jkACj74sO1GNVMMAANu8EauEBzbWYJGHUbd6VzKx/Tjz6xdZFZmklxQRWRaKXOGeiGXCYRMhnuvkHb7rDvpjm/2EOCcJEmpkiQ1AiuBjg4PvwNY0Q31XnUaLSywrOvYNvrfgNzaGqm+vk17aXVVHilCS7j1tTtRtS1MfH1R1zcA6P0M2cfazDfMz56bwl35au858isuH1/jUM4hHtv1GIGV+tClk0bNRm2i5nDu4Q7LnfcvnFcMTQUFpM+dS9rMW0mbegNpT7xL2j4/0r5J0N+75Dp3ywwWfl/P+De3kzbjFjTFxbgtWoTMtKVNv994T9QOphz4LRmt9vKfTTNLK25b8DbOvgH0zdtKdWMC5b06dxbPyaxydiYU8MBoP6zNWh9poWls5Pj2LcRu3UjY2Am4BbX9EgsY5ISlrYrjOzI7rG+ApQVKYYldTSN56Sk0Nf88Tn5Pr7w2PQ66ZrO/U+tg33vQby4MbxlNODazjOBq/cq0sGkzGDMnmJrKRpJKQhni9CdDp/m1cB7P6K9fsrs+9vLOctCHmrV/8AGqtm2j5mhrP9elisFcbUL4WA+SjhWQmdsbJOh/56MoZAp+PvMzv0ZmopQLZg26ukduQ/coBneg+eLkbMO9VgghvAFfYHez26ZCiGghxFEhxPS2yhnKPmjIF11UVNQNYncdnZUV5o1Q/V8e4rOjTW6nkjYhCUFf947j1F5t5La2mFipUZuoKKy3atMBfZ5nJ/aiQaNj44mOv/B7s/byr93/wtfalxGaEBy8fLCytWeY6zCO5B7p0AkYlVaCl505rtZ6Z3L1nr3URccgt1ShqE9FYWWKImAACgdHFA4Ora5ySyi3EFi6eKIKCMDt3Xcw6xPWqh6FUs6o2wIpy6vh1N7OvcBU5ha4hcxHKNxQ1Gxj1eqNnQoO9NGfSdiaK7lnVMswlE0N9cT8voHvnrif3cuW4BrYi9Fz7m73OXK5jL7jPMlNLqcgvf1TfB3yG2lAwqMiH51OR3bCqYuJFg4w+X29E/roV/p7ucdhw6PgOQxu+vjC6q7znEgrRqGoxd7UAltX/Ua5iNlBjOlzgsFWq1vV72lnzjA/O9bGZnfa4Wt/770o3d0peOcdJM0lfVpTDAqzC8ePg97nolTJOZ4fjqldI65WTUz1n8qGcxtYezyRyWGuOFh27viP7qQ7FENb67Pa68XbgTWSJDU38HpJkjQImAN8KoRocygqSdK3kiQNkiRpkKOjY1tZehxhrXeiZWV33gzxT6QjxRCXcxCAsKC/l9VPCIHKxwcbjURBkw1ktq8YfBws6Otpw+a43Hbz/JnxJ0/veZpetr34ZuzXFCYl4xWqdwqOcBtBYV0hKeUpbZaVJIlj6WUtzEh1cXHIbW3wHJqG57h6PH/dgOfS7/H8Zkmb1093e7Dhod74ffsdnt8swfrm9ifhPuEOePW2I2pL2oXD+ToiK7GUMweLGTDlX9gF9mZI9g6+XPJrh2Wi00vZn1TEw9f5X9is11hXS9TGNSx9/D72/vwddm4e3LbgXWa/8V6bYSqb03uUGyZminZnDbWVjVQmVXBapUU4eCCTICP+RMtMYTMhaDLsfgcyjsCKOXqFMfsXULR+mdYd3kuNqZJeAy6eP9RnjAehwTV606Ou9Yzr1oGepJfUEpNR1mF7ziMzNcXpxRdoSE6mbOWqSxpVqpevGaaWSkLDTcm3CEEX4Axp+5nfez4N2gYaLA4yd5h3p+rtbrpDMWQDzec6HkB737jbucSMJElSruFnKrAX+PusgbwEpa1+CliQ0/EU+J9Oh4qh/Bx+WoHa+upPby+Hia8vVuWVVDfIqM04qV/10w5Tw105lVNJWnFNq7StqVt5bt9zhDmE8e3Eb6nNzEfT1IinQTEMd9NvimrPnJRSVE1pTSNDmjme606cwMxRQhQlwK0/gEPbEdYAmnRNnCw62WL/QkcIIRg1KxBNg5bIjW0rq/M01Dax+6cEbJzNGXVrCPNeW0iDSxCKw6vZuWZtu+U+2pGEg6WKecN9qK+p5siaFSx97F4OLP8RZ19/bn/zfWa99i5eYeGd2plsYqogLMKN1OOFVBS1XlV1+kAOOq1EhbspCfZ+2FbXkn7ikkUFQuhnBnIl/DAZ6ivgjhWtjvkAKK9txC07BiFJhN12iU/HwgEkLdS3/rxPDnPB3ETeygndEVbjx2MxYjhFn3+OpqyZQqktafOMKs/cvSg0tSQ6z4O0/fha+2LW1Acz+6OEebS/ebAn6Q7FcAwIFEL4CiFM0L/8W60uEkL0AmyBI83u2QohVIbfHYCRQBvepL8H5vb6g/TKCjo3Zf+nclExtHRASzodcdoqwk2vLIB8T2Pi64tlod6OW1gth/z4dvNOCddvzttysuUYZuO5jbx08CX6OfVjyYQlWJlYkXn6JELI8OitN+W4Wbrho/bhcF7biuFS/4K2qorGtFRMTbJg4jsQMK7NcudJKEmgTlPXacUAYOtiQfg4T84czqMwo33zzIHfkqmpaGT83b1RmMhRmJhw3+tvkWnpy8nVPxC9ZX2rMofPFXMktYRHhjkTs245Sx+7l8Orf8U9JJQ573zEzJffwj24dxu1dUz4WH3o1JM7Ww60tFodp/fn4NXbjuAgW3YrXXGoqqMkJ4ua8ktG7mo3uOHfeuVwyzfg0nYYztiMUixEBfYosHK7xNJtbhjFG3Y/N8dCpWBymCtbTuZ1eiWbEALnl19GV1ND0afNgvw0O1n1PFJjI/W/byRAlUF6VRAF6VXEp2ZTkjMcnayazSmbO1Vnd/OXFYMkSRrgcWA7kAD8JknSaSHEW0KI5vaGO4CVUktjXQgQLYQ4CewBFkmS9LdVDNZO+pdJdVHBNZakZ7kYk6HlCCo75whlMkG4w5XFwO1p9Ifp6U0p7W10O4+rtRlDfOxamJPWJK1hwaEFDHEZwtfjv8ZCqXfKZp6Kw8nXH1OLi6t0RriNICY/hgZtQ6tnR6WV4mSlwtteb0uu3/wNSGA2ZDQMe+Sy7Ygp0I+Mz29s6yyDb/TBzMqE/SuT2lwKmnq8iLNH8xl4g3eLg+hc7CwJu+sJki382fef74lc/9uFNEmS+Pz3WCbWRFH985tEbvgNn/D+3PXe50x/bgGuAVd+uq6FjYqgoS4kHM6jrvqiCSztRDE1FY30GePBQG9b4izccDD8XzMvNScB9J8LL2Rc2JXdFqf27kOjEAT2Dm+daGF4Wde2VgwAMwe6U9Wg6dL+F1VAAHZz76T8t9+oP2N4pbWhGKoPHEBbVkb/6b0xNYOoqlkc2/c7Km0Ave3C+PnMz2h1Vz9qZLesN5QkaaskSUGSJPlLkvSO4d5rkiRtapbnDUmSXryk3GFJkvpIktTX8PP77pCnp3By0y9Payhr+wP030J7pqS4FH0YwnCfjke81wqVry9KnQ61pZpCrVOHigFgal9XkgqqOZtfxYrEFbx55E1Guo/ki3FfYKbQO42b6uvJSz6LV1jLF8oItxHUa+s5Xni8xX1JkohKK2WIr53epJIXR92mLwAwu39xK4doW8QUxOCj9sHBzOGyeZtjYqZgxAx/CtIqW20gq61sZO/yRBy9rBh0o0+rsneP9icldDrZdiEcXPkzh1f/SlVpMb98+hn9j3xJr6JYAgcP4+4Pv2TqMy/h5NP+ctWu0G+8J5omHaf2XZyFx+/NRu1gileYPQO8bGmUK5G5eGGCaO1nuND4jgMtNUTtRK7V0Wf6zNaJHcwYAIb52uNuY8aamM6bkwAcHnsMua0t+W+/o3det6EYytevR+7ggO3Y0fSf6E1m4wB0SWe5uZ8H9/W5h8yqTPZk7elSvd3B32ch+j8ATy/9agxdReccUf9UZCoVwsystWIojMVMJ+HvO76dktcWpbc3yGTYqswoaLDSn5PTAZP7uCITsOjwN7wb+S5jPcfy2djPUMkvOi5zzp5Bp9VccDyfZ7DLYBQyRSs/Q3ZZHXkV9Qz1tYPqIlg5h7pSM0x8vJDbXX7RhE7SEVsY2yUzUnN6DXXB2VfNkXUpNNbpV8VIksTeXxNprNMy7u6QNgMTKeUyXpsWxgZ1BPJeQy74EPIjd5NjF8xdH37Njf96FnsPr1Zl/wr2bpZ4h9kTvzcbTaOW4uxqcpPLCbvOA5lM4GVnjoOlCanOAdhXVJMRd7zLR0I0NDQiq87DobYRy/C2ZgwGxdDOjEEmE8wc4M6hc8WdWuJ8HrlajdMzT1MXG0vlpo3QUHlRCQGasjKq9+3HeupUhEJBn3E+qOTVmNUEM3eYF+O8xuFh6cEPp3+4KsdgNMeoGLqArY09jXIQlZcPe/lPp63dz3E1OYQJUxTKa+MQuxwyExOU7u5YN2mpqNFRX1agPy+nHRwsVfgHRhFd9RMTvCfw0ZiPMJG33M2beToOmVyOe3Boi/vmSnP6OfbjSG7L4zeizp+P5GUJq+YiVRdTV6nGrF/nzELJZclUNVZdsWIQMsHo2UHUVjbqj1sAzh7NJ+1kMUNv9sPerf14CKMDHZkQ6so3usGETp6B7cDr+Nn9Dkbd+zhOHj0Xzrb/BC/qqppIPJpP/N5sFEoZISP0ZlshBAO8bDls5o59ZQ3VZaWU5nRt5H5k9z6QgZ2zT9ubMs+P4mva33A3c6AHOgnWHe9a3da33IJpWBiFH36Erkm0cD5XbvkdmpouhO9UKGXo1MlUNAViXVSOXCZnXug84oriOFHUzkyphzAqhi4gl8upNhMoa/679zFA693P9XVlJIomwq18rp1QncDExwfLEr1CK6y3aNecJEkSX534inz5Opoq+jHP7xWUstabtrJOx+ES0AulaWtlOMJtBImliRTXXRxpRqWVYm2qoFf0G5B1lKYRC9GWV2LWt42Rahuc9y9cqWIAcPZREzLClZO7s8g68//snXd8m/W1/99fbVmyLe/YiVf2dkImIxA2tIEUCKPQS9KWUtrCpe1Nf9CWcimlvbm9XNrelkIpm1ISCiUESkvLHklInMR2nEWGHcex4yF5W7I1vr8/HsnxXpIsyXner5df1vg+j448nqPzPed8joOPN31O1jQbBRcPXUn2k1Wz8QJvGhaySbeE1MxMrl3Yb1tSyMiabiM9N549/zzO55+dYvrSDEyW07+LRblJfOhPQEM/ZatDcPDtNzG4PUy55Ir+F+iMYEwYMGIAyE2xsCQviVd3Db+nARQJjYwf/whPXT31+609tpKaXnsN0+zZmGZMB2DrUTv/khqsmno+2/w5Ukq+NPVL2Iw2nil9ZtivGQpUxzBCWk06jGeCkF5iYo+I4eCRt/AIwfzMpRG0amgM+XnEVSif6mrdSf1uJ0kp+c3u3/BY8WNcmXcV3pobeau072S+jvY2ao4e6ZNfCHBOltLkt736tDzGjnIH9yR/hNjzAqxYj9OpCOOZCwqGZf+uml1kWjLJsg5PwXUgln9pCjq9hjd+W4SUcPHaWWg0Q+c3spPjuOP8ybxZUs3BUy1895Jp6LThvUwIIVhwaQ7N9S48bh/zLuwZnSzKTaLJaEWfnoVVo+X43j0DnKkvrrZW2irSUQAAACAASURBVKqPkdnYxpQrB8mNxaUMmGMIsGbRJI7WtVF0YmQzWeIWLiTx4uU4DlnpbFQSya5Dn+Pav78rWgBFF6nMPJ1FiVs4Va2lYp8Ds87MTTNv4oMTH1DWVDai1w0G1TGMkLY4PWZn30qU8UbvraTiig8BmB8FE9sGw5ifj6G1DastmRqy4URfTaOiuiKeKn2K66Zdx4bzH+L8aRm8WVyFr1clT+WBUqT09ckvBJiZPBOb0da1nVTb4iLLsZ2bGh6DGV+EC3+Ms6QYYTJhnD59SNullOyq2TXiaqT+iEswsPSqyUgJ566ZSkKqeeiD/Hxr5VQm2szMyIhn1fzgHNRwmbIwjcQ0MxOn20id1HOm8dyJiei1gqpJ00lpbKVy/168vbuKB+DwZ1sBiVGbiC55kMFKllRoG1xR4QvzMjHpNSPqaQiQdtMFCI2k5kmlHLhp82bQ60m4ahUANc0u/rm/hmuXTGbWTBfxegc73jiGlJKbZtyEQWvg+f3Pj/h1R4vqGEaI02zCEoIRkdGO1tYzYihpOMBEL6SmjY2I12gx5CsFAqnJqdQ6TXCqVBk4043KFuUfe92cdWiEhqsKsqhqcrG7omdRQUVpCTq9gcxp/atZajVazs48m61VW5FSsm9vEY/q/4+OxClKTb1Gg6u4BNOcOQjd0KM9K1oqsLvsQW0jdWf+RZO4+YFlzFkxsq0gs0HLG3edx6ZvLkc7jCgjFGi0Gq79wSKuvKNvKbRJr2VOViK7E3JIcTTR6XRy6sjwpvGVvPtP4jo6EXP7qtf2IC71tJbRAMSb9FwxZwJbiqpGrM6rN7pJndNC67ZdtLz3Pk1vvIH1gvPRJSnOauOOE3h9kpuX5qCdch5LzC9Se7yFsuJ6UswpXD3larYc2dJj2zKcqI5hhHRY4rA6x76ueKwJ5BikXyagpLOB+YbIj/EcioBjSDIYcTS56PTKPkNd7E7lApBiVvZ7L5mdgVGn4c2S6h7rTpQWkzVjFjrDAPMUULqg6531HK4pYs5H3wQEuq9sAmM8vs5OXAcOYO6vEqYfQpFf6I4QgqQJwxPI602yxYAtbuD3HQ7iEgwY4/rmeUDZTnpbm0FKqxOBGNZ2UnN9HaeOHGRiQyupF184+GLL0FtJoCShm10e3j3Qd+txUNodJE9vw5CXS9X69Xjr67Fdcw0AHq+Pl3ZUsGJaKnmpFsg/nxnmD0hM9CpRg09y6+xbcfvcvHRwbPRHVccwQrxWK1aXxOsd385Ba7OBz4evpYWamhJOaQXzU6I7WgDQpaejiYsjweUGCXUua588g91lx6AxYNUrFTpWo46LZqbzZkk1Xv92UntzE3UV5eTMHTw3EJDH2Pav9aS4Kvh9+v3oU5Ua/46DB5GdnSPKLySbkslPyB968RnGotwkjhuT0SXYSDaYOL63eMhjDn6qbH8mtHiYfeGywRcHIoYhEsvnTEklM9HEK7sGrnbrl3Y7wpKodES3t6NNSsK6Qpl18e7BWk41u07rImUWoDHFszRvD/aTbRzZXUteYh4XZl/IpkObaHeHf4qk6hhGiExIROeD2trqoRfHMNpEf5NbUxN7D/8NgPk5Q3zqigKEEBjy8oivU8pGa3RT+lQm2Z12UswpPTR9rirIor61g8+OKdFE5X5FTiN7zuBd3hMsE5iii2dr63EecK8lfuZFXc85i/2jQEdQkbQoY9GwtIbONBblJoEQOPJnktzYQvXhg3QMMWb3wCcfEO9yczJrJgmWIUqsLangcyt6S4Og1QiuWTiRjw7XU9s8/J6GQHOb9fzzSV57K6l33YnwR6J/2n6czEQTF8/0S81otJB3LlM7XiIp08LON8vw+SRfnftVkoxJnGwNvySP6hhGiDZR2U45WVkeWUPCTHdZjJJTO9BLycypX4iwVcPDkJ+P5ngFcYk2auUEqNzRQznT7rKTbOq5LXbhjHQsBm2XREZFaQl6k5mMydMGf7GSv3C2/SSFcRZe8F3QR1FVl5aGbsKEIW0+1XaKk60nOSs9+MTzeCQjwcREm5n9qZNJrqpB+nyc2D+wFlZdRTn1FeVk1zfSvmDJgOu6CDSeDZFnAGU7yeuTbB5Ctr0H7fVdpaoZP/whyTffDEB5fRsfH67npiU5Pau/8s9H03iMpRfF03CqncM7TrEgfQGvf+l1piUN8TcZAlTHMEJMKUr3al31CEPJGKO7LEZxawWzpAGDMX6Io6IDQ34+nqoq0nPzqW3RKZ8C60/PyXW4HF35hQBmg5ZLZmfw99JTuL0+KvaVMGnWHLSDJY1P7oItd3JO/GQ68WG0VlCQbet62llcjHlBwbAigFDnF8Yji3KTeF+fia3dhU6n6183yc+BTz5AIMhsaiPlgmGMoLUMLovRnSlpVhbm2Hh118nh9zS023t0PQf4844KtBrBTUt79ZjkKTZPsRaRmm1lx9/K8Xp9aMTYXLJVxzBC4tOUT39N430rye8YOutr2S87mG8NX+drqDHk54GUpNhSqLc34/GJHttJdqedFFNKn+Oump9FY7ub93YfpqGqcsAyVQBaTsHGW8CSzqJrnwepY0LGcUx6ZZSlp6EBd0UFphEknq16K9OThi5rPVM5K8fGDm0qWqOJNLOV4yX9J6Clz8fBTz4kyauh0pLB/IKBJc67iBtcSK831y/K5lBNC2uf2Ulh+dBjVWl39NFJcrm9/KXwBJfNziAjoddWV/psiEtBlH/E0qsm01zn5ND24Yv4BYvqGEZIygSl9M9pH2FVQowRcAynjn6GSyMoiKFPssZAZZJWj/T5qBcTuwb3+KSv34gBYMX0VBJMOj76UOlLyB4o8ex2wcabwdUMX34JacrE256LNJ8uoXSV+PML84efeF6YvhCtRjv04jOURbnJeDVa2qfMJLWxBUdVJS32vhfykwf302KvY9LJU+zNmsXk1GFUZo0gYgC4cUk291wxk9KTTax5fBtffmI7W4/W9x9BdAno9dy+fGtvNQ3t7v6H8Wg0StRQ9jF5c5NJz0tg59/K8LrHZrSw6hhGyISJyi/R3TD0XmQso01IACGorVQkg+dNuTLCFg0fQ67yO0r0NyLWGGd1RQxNHU14pbffiMGo03LF3AnUf74Po8VKem4/1UFSwht3K9tI1zwOE+ayp6IRT9s0GjzHqWtXmqScxcWg0WCeO6fvOXrhcDk41nRM3UYagpmZ8Zj1WsomTCXx2HGgf3mM/Z+8j06nZ0JTC20FS4fV8X06xzA8x6DVCL61cgqf3HMh931xFkfqWrn5j59x/ePb+OBQbU8H4W4Hj6tPxPCn7ceZnGrhnCl9/xYByF8BzZWIhjKWXZVPq6OD/Z8OPHUwlKiOYYRMmpiHD6B5ZG3xsYbQatEkJNDoOEWKV5KVOUSDUBShsVjQZWSgrz6FyWKl1pMCjqPQVt+nh6E3q+ZnkdFWiWHStP4F17b+Fko2wsofwWxl3MiOcge+NiUhGJDHcBaXYJw2DY1l6E+re2qULRHVMQyOXquhIDuR7XETiW/vwBxn6bOd5HG7+Xz7J2SZrXRqDKSfPYzEMyiy3fq4QYX0+iPOoOO2FZP5+P9dyIOr51DV6GTdMzv50qOf8q/9NYqDCEQh3RzDvqomdlc0cvOynIFzUPkXKN/LPiJ7djKZUxPZ9fdyPJ3hL5VXHcMIMRqNtJtA29ISaVPCjtaWiLPFyXxdYv8XySjGkJ9PZ/lx0vOnUNPkD79PfIbdpfzj965KCjAn3k2ip4UyQz9SEJ//E/51P8xeDRf8v66Hd5TZmZkyk2RTstIF7fPh3Lt32I1thTWFGLVG5qQMHV2c6SzKTeJt0hEaDRMsCVSUFvf4dF62ZycdbW2kllexJ20aC/JHMNMiLnXYEUNvTHott56dxwc/uJAN186jod3NN54v5MrffMwnJf7Ch27znv+0vQKjTsOaRYPk7lKmQnwmlH+MEIJlVyn9MY21MdLHIIS4QghxSAhxRAhxbz/PrxNC1Akhivxft3V7bq0Q4rD/a20o7Ak3LSYthiFqqMcFVjPCBfPHoDwu1Bjy8+gsKyM9fwr1p+rwCgOc+AyHS0kU9reVBFB1oBSAD5oTae/spsdTdwhe/TpMmAtfeqxr4E6Hx8ueikaW5aeyPHM526q20VFehq95ZIqqBWkF6LX9d/2qnGZRbhItWiPevCmkNLXS3tRIfUV51/MHPvkAs8VKemUVuzJmsiDHNvDJejPM7ufBMOg03LQ0h/f+4wL+9/oCOj0+/vh2IQAfVvrweH20uNy8XnSSqwqyBu8uF8KfZ/gIpGTijCT+7aFz+mhJhYOgHYMQQgs8ClwJzAa+LITobwDsJinlAv/Xk/5jk4H/BJYBS4H/FEIMonQVHbSbdZjOAIXVNp2TeJekYNIwyv2iDGN+Pr6WFlLTMvB6PNgTFkDFZ0NuJVWUFqO3JlItEngnIHvQ7oCXblLkmW96CQynt4f2VjbR4fGxND+Zc7LOwe6yU771n8DwFFVbOls41HBI3UYaJguzlctDdfZ0Eg4dAU7nGVxtrRzbvZO81AloAMfshSSYRuBsg4gYeqPTarhu0ST+9f0LuPtsJTr9z3equeSRD/nRa6W0d3r7Tzr3Jv98Rdyv7iAAWn3slKsuBY5IKY9JKTuBjcDqYR57OfAvKaVDStkA/AsYQDQ9emg3G4lzdg69MMaxa1qwOmFOlCuq9keXZpJQqnxqdVOhag/29lq0QkuiMbHPMVJKTuwrYfL8AjISTbxRXAVeD7zyVWXgz41/AlvPevPPAoN58pK75DGqd36ExmLBMHno8ZdFtUX4pE91DMMkyWJgcpqFIlsupuZWbClpXY7h8Gdb8brdZNY6qEzIIG/2MMpUu2NJHXGOYSi0GsFZqcpW5n3Xr8Bi1PFGcRVzJyZQMKnv32Af8s9Xvpd9HFK7hiIUjmEi0L3bq9L/WG+uE0KUCCFeEUIE/ruGeyxCiNuFEIVCiMK6usHlccONK86E1TU82d9Y5pS2lQQnxFnTI23KiAk4BlNjM3qTmZqOBPB2YHccJtmU3G+jkONkJW2NDeTMLeCL87L48FAdHW/9EI59AKt+BTnL+xyzs9zB9AwryRYD6XHpTLVNRew7jGnePIR26NLT3bW70Qkd89OGt+2kAotyknhbkwFAZnwilftL8bjdHPjkA2wZEzDuLmZH2gzOyh3BNhIoyeH2+iH1kkZMez0ILZcsmMabd53Hn7+xjEdvPmt40idJuWDLgbIPQ2vTEITCMfT37nr/ZN8A8qSU84F3gOdGcKzyoJRPSCkXSykXp6UNPTs3nHRaLFidY1NPHCl8Xg/lJg+mTpCdsRcd6TMzEQYD7vLjpOdNptah6NrYmyoG3kbapwiz5cyZz1UFmVzDuxh3PQHLvw1n/Vuf9V6fpLC8gSV5pxPZ56UsJfVkG/p5/e2m9mVXzS5mp87GrBv+vIQznUW5SRyRFsiYQEpTG57ODj7f/gkn9u9lSs5kRGcnuzJmsDBnhLvSllSlrLSzLbQG+3WS0GgQQnDOlFRyU0agept/PpR/0kPWJdyEwjFUAt3j60lAj2JbKaVdShmYbvNHYNFwj41GfPHxxHVCa+v4HfFZXvEh9jjFb3cf8RkrCK0WQ24unWVlZORPobayEl9iLnZn3YAVSSf2lRCfmkZixgQW+PbzkP4ZSkyL4dKf9bv+QHUzrR2eHvpI57VmopVwImfoC73L42Jv/V51G2mELMpVLviNU2YTv/8QQqPhwxeeUhK0Te149AbKJ05natrA8637ZYS9DMMm4BhGS/4F4GqEmoG1oUJNKBzDTmCaECJfCGEAbgK2dF8ghMjsdvdq4ID/9tvAZUKIJH/S+TL/Y1GNSFT+ME9UHouwJeGj+NjbtPivbd0H9sQShvz8rsokT0cHDbZFODztpPTjGKTPx4l9e8mZU4BoOoF4+VaaTVnc2nwHDlf/n9QC+YXujiG3UomutiUNvVe9t34vHp+HxRmx0yMSDUxJs5Jg0nEwdTKito4JOfm0NzWSOXUGmh2FfJ45nTl5acNrbOtOV/dziJtX+5HDGBF+3STKPgqNPcMgaMcgpfQAd6Jc0A8AL0sp9wkhHhRCXO1f9u9CiH1CiGLg34F1/mMdwM9QnMtO4EH/Y1GNPkn5JdecrIiwJeGjpK4Yr1++JRYjBgBDXh6dlZWkZyvVH6dENnYBKaJvpUpdRTmu1hZyZk6Hl74MXjeOq5+j0RfH30v718XaUWYnJzmOzMTT0YGn9ABNyUY+bBt6YH1hTSECwYL0BaN8h2cmGo1gYU4SH/p7TSZYEwCYNmc+nceP85Ft6si3kSDMEUMQQ64SMiFl2pgmoENS+ySlfEtKOV1KOUVK+XP/Y/dLKbf4b/9QSjlHSlkgpbxQSnmw27FPSymn+r+eCYU94SYuRUl8NdSEXxc9UpQ4T5EVp1zwYjliwOPB6gWd3sDJNj2dGkGKs7nP2opSJb+QffwFqN0P1z/N1FkLmZxmUaqTeiGlZGd5Q49oARQpDPfMfI40HqGmrWZQ+3bX7GZ60nQSDAlBvMszk0W5SXzksSKs8UxqczN50VImdSqR3c70GZw1kv6FABb/p/ogexn60FYfXMQAijzG8U/BOzZjhWOrnTVKSExXdsZa6wb/x49V2ltrOSK85KUpn7Rj1TEY8/MA8JyoIC03n+pT/h6G5r4qlSf2lZCUaCL++Ftw2UMw9RKEEFw1P4vPyhzU9BrKcrSuFUdbJ0u7JZ7dtbV4qqtJW3wOANuqtw1om9vnpriuWM0vjJJFuUn40OCaMRttaSnX/L/78Xy2k/bUCVRZUrv6HUZEOCIGnw+cjh5dz6Mi/3zobIWqoSPRUKA6hlGQnpUDQEfD2AzmHmv2HX4DnxBMn6pc4GLVMQRKVgN5hoaKEyAhpb6sxzqf10tlaRHZogwW3KJUIfm5qiATKRUlzO70l18IKKpmL7+IFFMKW6u2DmjbAfsBnB6n6hhGSUG2DY2A45nT6DxyFHdtLW2ffcahnDlMSbeSOMDs6EExxoPWENqIwdUI0hd8xBDIM5SPTZ5BdQyjIDtHueD4mhoibEl4KK5U9jLnzr0GodfHrGPQJiaiTU6ms7yc9PwpeF0dxLfrSLEfUySz/dTs+DudnW5yctKUfoVu9eVT0+OZlZnQZztpR5mD9HgjuSlxXY85i4tBr8c8ew5nZ53N9qrt+GT/ievAYJ6zMtSJbaPBatQxc0ICO/xzQhxPPYVsb+dd62TOGk1+AZTfe2D2c6ho96dMg3UMllRInzNmCWjVMYyCJFsKnVoQzbGZlB2KkobD5HkhKXkyWpsNT4w6BlCihg5/ySpASrOBZK8HTir6NbTWUvHKLwDI/urvFNmLXqyan8nuikZOOBR9LCklO8ocLM1P7tGk5CwuwTRjBhqTiXOyzqGho4GDjoN9zgeKY8hLyCPVHOQWwxnMWbk2/u5OBp2Ohpc2gl7Ppwl5nJUbhKpOCPSSehBwMsEknwPknw8V28HTMfTaIFEdwyjQarW0mgX6tvHXxyB9Pko8Tcw3KU2EWlsivhitSoKAmF45Kdm5oBGkNBlI8kplcI+nAzbeQkWjjtTMDOKy+pdQuGq+Uv3yN/92UmWDk+omF8u6bSNJrxdXaWmXouryTKVLur/tJJ/0sbt2t7qNFCSLcpNweDUwfSays5PW6XNx6YwsHE3iOUAI9ZKA0+cKNmIAxTFIqQg6hhnVMYySVpMO4zgU0quqLsSuFcxPmQuANtGGtyF2IwZjfj5eux3hdOJNMZPRakGXMQdObIc3v4enYidVriRyFiwb8Bw5KXEUZNu6tpN2BPSRujmGjiNH8bW3Y16gCOelxaUxPWk626r6JqAPNxympbNFdQxBsihH+fmfypkBwOe5c7AadUxLD0J91JIWpoghBJHh1Evg3uOQGX75FNUxjJK2OD1mZ/hDurGm5MhbAMzPuxgAbZINb1PsOobuCWhnipakJh1y0lI49iEUvUj1jG/i8XgHHuPp56r5meyrauZYXSs7yhwkmvVM73YBchYr1SLdZzCck3UOu2t30+7uKdEeyC+ojiE4spPNpFqNFE6YhdDreTtxBgXZiWhH2tjWHUuocwwBxxCCiEFnAP3YSKeojmGUOM0mLM6xqSkeS0pqdmHySaZNvhwg9nMMeYpj6CgroyHRg74DWhLnARJmXUWFbh5CaJg0a/AhOV+cr5Qov1lSzY5yB0vyknt01jpLStAmJqLPPS2lfHbW2Xh8HgprCnuca1fNLjItmWRZ+xkGpDJshBAsyrXxN20Wkz7dyicu8+gTzwHiUpSyULdr6LXDod0OOrMyIS6GUB3DKOmwxGF1hn/E3lhT0lbJHGFCp1fanrU2G97Gpv6HnMcAhuxJoNXSWVZOtVXJCdXoJsMVG+BLj3Ni/17S86dgsgyuq5OZaGZpXjIv7aigrL6tR34BwFVcgqlgfo9k9FnpZ2HUGntsJ0kp2VWzS40WQsSi3CTK7e18UN6MTxK8Y7CEuJchWDmMCKE6hlHitVqxuiRe7/hxDp0dLRwQbubH53Q9pk1MBLcbGaMT64Rej2HSJDrLyqgw2pECaisrYfm3cEsd1YcPkTN3eHu2VxVkUt2kfJLs3r/gbW2l48gRzPN7bkeZdCYWZSzqkYCuaKnA7rKrZaohIuAInvxY6U1ZkB1E4hlO5wJClWdoqw9NRdIYozqGUSITEtH54NQ4ksU4cPhvuIWgYMLSrse0NuUfLVZ7GUDJM7iOHaUVJ9rUBGrLjgJw8tB+fF4POXOG5xiunJeJRkCcQcucrNMyFq7SUpCy31Ge52Sdw7GmY5xqU7qt1fxCaJk7MRG9VlB0opHJqRaSLIOMyhwOIY8Y7MF3PUcA1TGMEm2i8ing5InjEbYkdKQmTebbifMpmPGlrscCjiGm8wz5+bgrKhA+SdzEdGr8jqFiXwkarZaJMwfPLwRItRq5Yu4ELp6VgU57+l/HWax0PJvnzetzTGCqW2A7aVfNLpJNyeQn5Af1nlQUTHotcycqk9BGJZzXm7gQK6wGK7kdIXSRNiBWMaUodf71p04MsTJ2mDhxKd+a+GKPx8ZHxJAHnW5Sm7Uk5eZwougIrQ0OTpQWkzltBnqTadjn6m/ylrO4GENeXtfPqjvTbNNINaeytWor10y7piu/MKzpXSrDYlFOEnsqGoPrXwgQENJTcwwqoyE+bQIAzbVRP1coKMaDYzD6S1azHJLMydMBOLF/LzXHjpI9zG2kAL0v6FJKnCUl/W4jBdafk3UO26q3UdVaxcnWk+o2Uog5b1oqWo1g+eQQXIBNNtDoQpNj8Lqho0l1DGcSKROU0dROe2TnT4ebLscQ093Pfsdgh7ypSuPe7r9tRkrfsPMLA+GpqsJbX49p/sDnOTvrbJo6mvjTgT8BSrWSSuhYOSOdHT+6mKnpI5zY1h9CnJ79HCyhlMMYY0LiGIQQVwghDgkhjggh7u3n+e8LIfYLIUqEEO8KIXK7PecVQhT5v7b0PjZamTBReQvuxhBPe4oytAlKkjWWIwZtSgqeOANZDkl6UiZJmRM5dfQwOr2BzGkzgzq306+oai4YeNjO2ZlKnmHTwU1Y9VamJ00P6jVV+pJi7atxNWriUkOTYwhl1/MYE7RjEEJogUeBK4HZwJeFEL0noe8BFksp5wOvAL/s9pxTSrnA/3U1McKkiXn4AGL4k/RwEAYDGoslph2DEILmCfFkN2gxao2k+wX1smbMQmcIrorFWVSMMBoxzRj4Yp9iTmFW8iw6fZ0sTF+IVqMN6jVVwowl1BHDmbmVtBQ4IqU8JqXsBDYCq7svkFK+L6UMFMJvByaF4HUjitFopN0E2taWSJsSdpQmt9h1DAD2NBNZfgXkgNJqzhAyGMPBWVKCafZshH5w/f9AdZKaX4gB4lJDk2M4wx3DRKB7aU6l/7GB+Drw9273TUKIQiHEdiHElwY6SAhxu39dYV1ddOzrt5i0GNraIm1G2BkPjqE6RWBr8uBrbyd79jw0Wh2Tz1oS1DllZyeu/fsxFwztYC7OuRidRsd5E88L6jVVxgBLiBRWY9gxhKJctb+6u371E4QQXwEWAxd0ezhHSlklhJgMvCeE2CulPNrnhFI+ATwBsHjx4qjQZ2gz6zG3h0hTJYrR2mwxnXwGOG7zANBZXs6E2bO569mXg95Gch36HNnRMWBFUnfmp81n25e3YdINvzRWJULEpYKrSakq0o5iElyAtjM7+VwJZHe7PwnoU8MphLgE+DFwtZSyS5ZUSlnl/34M+ABYGAKbxoT2OANxzs5ImxF2tImJMR8xHElQIruOMkU6IVinAOAsKQZ6KqoOhuoUYoSuXoYgE9DtdjAlBudcIkQoHMNOYJoQIl8IYQBuAnpUFwkhFgJ/QHEKtd0eTxJCGP23U4Fzgf0hsGlMcMWZsTg9kTYj7ASE9GKVTm8nR6ztSAGdZeUhO6+rpARtaiq6LFUldVwRKr2kGO16hhBsJUkpPUKIO4G3AS3wtJRynxDiQaBQSrkF+B/ACvzF3yBU4a9AmgX8QQjhQ3FSG6SUMeMY3BYL8a6aSJsRdrQ2G77mZqTXi9DGXkWNw+XArRe402x0lpeH7LzO4hLMBQVqF/N4I1R6SWeyYwCQUr4FvNXrsfu73b5kgOO2An0FZmIEX3w85k5obW3Fag1Bc02UorXZQEq8zc3okkKgRzPG2F3KloAvewKd/q2kYPE2NtJZXk7iNdeE5HwqUUQoI4aE2Iwm1c7nIBCJykXyREWfXPm4QpsU27IYdqfiGHR5uXSWlYVktoRz716AYSWeVWKMroghBDmGGI0YVMcQBIYk5Zd+qqoiwpaEF22iol4Z644hbvI0fO3teGqDL3d2FpeAEJjmxmzAqzIQ5iRABBcxSKk6hjMVS0oGAA3jaCZDf8S6kF5gK8k2TWnID8V2krO4GOPUqWitlqDPpRJlaLRKiWkwOQZ34gbd6QAAIABJREFUO3hcqmM4E0nMUPYP2+rHdwL6tGOIzcoku9OOWWcmwa+L1FkenGOQUuIqUUZ5qoxTgu1+juHmNlAdQ1CkZSkjMDscIdJuj1JiPWJwuBwkm5LRZWQgzOagIwb38eN4m5qG3b+gEoNYUoPLMaiO4cwlZ5Ii5+xrckTYkvCiiY8HrTZmHYPdZSfFnILQaDDk5XU1uY2W4SiqqsQ4cSnBRQxtqmM4Y0lOSaVTB6KlOdKmhBUhBNqEBLxNMeoYnHZSTMo/qDE/L+gmN2dRMZq4OIxTpwRvnEp0EqxeUiBiiMF5z6A6hqBpMWnQt54pQnqxmWNwuBykmBXHYMjLw33yJL7O0UuZOEtKMM2dG5PNfirDJC5VGcvp847u+Bge0gOqYwiaVrMWY7sz0maEnVhVWPX4PDS4GroiBkN+Pvh8uI8fH9X5fC4XroMHh6WoqhLDWFIBCc6G0R3fbgehBWNiSM0aK1THECTtZgNx7R1DL4xxYtUxNHY0IpEkm5RPboY8JS/UMUppDNf+A+DxqI1t451AbmC0eYZ2uxItaGLzEhubVkcRzjgjFqc70maEnVh1DIHmtq6tpPw8YPRiegFF1cFmPKuMA4LVS2qvj9nEM4RIK+lMpsMSh9U1ynAzhtAmJsbkTIZAc1tgK0lrtaJLSxtRyaqUEvfx47Tt3EnTX19Dl5WJPj09LPaqRAnB6iW1O2Jy1nMA1TEEiddqxeqUeL1etOM4Gam12ZBOJ76ODjTGEA5eDzO9IwZQ8gyDOQbp89Fx+AjthTtpLyykvbAQb51ygdAmJ5PyjW+E12iVyBN0xGCH1IHngEc7qmMIEhlvQyuhqrqS7Em5kTYnbHRvctNkZETYmuHjcCk9Jr0dQ8s//tF1X3o8uA4c7HICzsLCruhIN2ECluVnE7d4MXFLFmPIz1dlts8EunIMo2xyi2GdJFAdQ9BobUpSs7qy7IxxDPoYcgx2px29Rk+8Pr7rMUN+Ht6mJur+77c49+7FuXs3Pv/sbn1uDtZLLiZu8RLilixGP3Gi6gjORLR6ZfraaCIGn8+/laQ6hjMWc3IaAHWnKiNsSXjpcgwNsZWA7up67nZxN82YAUD973+PcdpUEq6+SokIFi9Bn6HmDlT8jFYvydUI0qs6BiHEFcBvUCa4PSml3NDreSPwPLAIsAM3SinL/c/9EPg64AX+XUr5dihsGivi0ycA0FxbHWFLwkvXTIYYS0DbXfauUtUAccuXk/vSnzHk5cXk4CGVMWK03c/tjtPHxyhBl6sKIbTAo8CVwGzgy0KI2b2WfR1okFJOBX4F/Lf/2NkoM6LnAFcAv/efL2ZIzpgEgMsevMZ/NBOrMxkcTkdXRVIAIQRxCxeqTkFlcOJSR5djiPGuZwhNH8NS4IiU8piUshPYCKzutWY18Jz/9ivAxUKJ7VcDG6WUHVLKMuCI/3wxQ5Y/r+BuDHLaU5QTqwqrdqe9R+JZRWXYWFJGGTHEtoAehMYxTAROdLtf6X+s3zVSSg/QBKQM81gAhBC3CyEKhRCFdXXR8+k8KysHH0BzbG2xjBSNyYQwmWLKMfikT9FJMsXuP6hKBInzS2+PdBSs6hgA6K9ko/dPcqA1wzlWeVDKJ6SUi6WUi9PS0kZoYvgwGo20mQTa1tZImxJ2Yq37ubmjGY/0qBGDyuiwpILPoySTR0IgyjjDHUMlkN3t/iSgaqA1QggdkAg4hnls1NNq1mBoO0MUVmMo+dy761lFZUR0dT+PcJu43Q46Mxhid+xrKBzDTmCaECJfCGFASSZv6bVmC7DWf3sN8J6UUvofv0kIYRRC5APTgB0hsGlMaTXrMbe7Im1G2NEmJsZUxBBobks2x24SUCWCWPwfKEaaZ4jxHgYIQbmqlNIjhLgTeBulXPVpKeU+IcSDQKGUcgvwFPCCEOIISqRwk//YfUKIl4H9gAf4jpRylALokcNpNhDfegY4BpuNjsOHI23GsOmSw1AjBpXRMFq9pICyagwTkj4GKeVbwFu9Hru/220XcP0Ax/4c+Hko7IgUrjgzE+rUHEO00bWVpOYYVEbDaPWSYlwOA1TZ7ZDgtliId/kibUbYCeQY5EirNCKE3WlHK7TYjLZIm6ISi4w2YmiLbcltUB1DSPDFx2PuhNZxXpmktdnA68UXI+/T7rKTZEpCI9Q/c5VRoDeBwXq6/HS4tDtiuusZVMcQEkSi0kF7ouJohC0JL7HW/Wx32tX8gkpwxKWMLGLwuqGjSY0YVMCQpPwRnKqqiLAl4SXWup8dLkcfnSQVlRExUr2kgE5SjCefVccQAiwpigx1Q83JCFsSXmLNMahyGCpBM1KF1XHQ9Qyq7HZISMzIAqCtvibCloSXWHIMUkpFclvdShpz3G43lZWVuFzjoIR75r+DpwMOHBjeek8HXP4y+NKHf0wYMJlMTJo0Cb1eP6rjVccQAtKycgDocIxyDGCM0CW9HQMzGdrcbXR4O9SIIQJUVlYSHx9PXl5e7A85ajoJbXWQOROG816cDdAApM0EvTns5vWHlBK73U5lZSX5+fmjOoe6lRQCciYpP3xfkyPCloQXbbwyBS0WZDHUHobI4XK5SElJiX2nAKDVARLkMMvRfR7luyZyn7mFEKSkpAQVsamOIQQkp6TSqQPR0hxpU8KK0OnQJCTExFaS2vUcWcaFU4DTF/jABX8ofH7hBk1kx8oE+/NXHUOIaDFp0LeeIUJ6MeAYunSS1KoklWAYsWPwgNBCjPfOxLb1UUSrWYux3RlpM8JOrDiGrohB3UpSCYZ+HENjYyO///3vu+5XVVWxZs0a5Y7XE/FoIRSojiFEtJsNxLV3RNqMsKO1xYbCaiDHkGRSx3eqBMEwHENWVhavvPLK6XURzC+Eith/B1GCM85IRu34zjGAEjF0HiuLtBlDYnfasRlt6DWjK9dTCQ0/fWMf+6tC+38xOyuB/7xqzoDPl5eXc+WVV3LeeeexdetWJk6cyOuvv05VVRXf+c53qKurIy4ujj/+8Y/MnDmTo0ePcsstt+D1ernyyit55JFHaG1tpbW1ldWrr6ahthq3Dx76xQZWr17Nvffey9GjR1mwYAGXXnop3/nOd1i1ahWlpaUsu+w6nv7tBuakzQBg5cqV/O///i8zZ87krrvuYu/evXg8Hh544AFWr+49ATl6UCOGENFhicPqijnF8BGjTYyRrSS1h+GM5vDhw3znO99h37592Gw2Xn31VW6//XZ++9vfsmvXLh5++GG+/e1vA3D33Xdz9913s3PnTrKysrrOYTKZeO2vr7H77Zd4/42X+Y//+A+klGzYsIEpU6ZQVFTE//zP//R43ZtWX87Lr/8DgOrqaqqqqli0aBE///nPueiii9i5cyfvv/8+P/jBD2iL4uFeasQQIrxWK1anxOv1otXG/h7jQGhtifhaW5FuN2KUzTNjgdr1HB0M9sk+nOTn57NgwQIAFi1aRHl5OVu3buX660+r/3d0KFu/27ZtY/PmzQDcfPPNrF+/HlD6AX704x/z0Xv/RKPVcfLkSWpqBm9ivWHVJVx6y5389Je/4eWXX+56vX/+859s2bKFhx9+GFBKeisqKpg1a1Zo33iIUB1DiJDxNrQSqqoryZ6UG2lzwkZX93NTE7rU6FWQdLgczE6ZHWkzVCKE0Wjsuq3VaqmpqcFms1FUVDTsc7z44ovU1dWx651X0RvjyFt08eC9AT4vEzPTSElOpqSkhE2bNvGHP/wBUJzMq6++yowZM0b9nsaSoLaShBDJQoh/CSEO+7/3yfQJIRYIIbYJIfYJIUqEEDd2e+5ZIUSZEKLI/7UgGHsiidamlEVWV0b//nswxIosht2lRgwqp0lISCA/P5+//OUvgHKhLi4uBmD58uW8+uqrAGzcuLHrmKamJtLT09Ebzbz/0SccP34cgPj4eFpaWvq+iD9BfdOaa/nlL39JU1MT8+bNA+Dyyy/nt7/9bdcskz179oTnjYaIYHMM9wLvSimnAe/67/emHbhVSjkHuAL4tRCi++SUH0gpF/i/hu/OowxzchoAdacqI2xJeOkeMUQrLo+LNnebmmNQ6cGLL77IU089RUFBAXPmzOH1118H4Ne//jWPPPIIS5cupbq6mkS/vPwtt9xCYWEhiy+5lhdf2cLMmTMBSElJ4dxzz2Xu3Ln84Ac/OP0C/ua2Nddew8aNG7nhhhu6nvrJT36C2+1m/vz5zJ07l5/85Cdj9K5HR7BbSauBlf7bzwEfAPd0XyCl/Lzb7SohRC2QBkT3R84REp8+AYDm2uoIWxJetInRHzGochhnNnl5eZSWlnbdD+QMAP7xj3/0WT9x4kS2b9+OEIKNGzeyePFiAFJTU9m2bRs0VSqqqZkFXcf8+c9/7nGO0tJScCnVVxmZmXg8PRvizGZz17ZSLBCsY8iQUlYDSCmrhRDpgy0WQiwFDED3iTY/F0Lcjz/ikFL22wwghLgduB0gJycnSLNDT3LGJABc9roIWxJeYmErSZXDUBkJu3bt4s4770RKic1m4+mnn+65QKNTtJJ8PtAMsskSBTpJoWLIdyCEeAeY0M9TPx7JCwkhMoEXgLVSdilS/RA4heIsnkCJNh7s73gp5RP+NSxevDjqhg5nTcrFBbgbRzgGMMaIKcegRgwqw2DFihVd+YZ+6d7kpjEMvO5McgxSyksGek4IUSOEyPRHC5lA7QDrEoC/AfdJKbd3O3dg36VDCPEMsL6/42OBrKwcjgA0R+/eeyjQWOJAr49ux+DfSlJ1klRCQo/u5+E4htgvVw82+bwFWOu/vRZ4vfcCIYQBeA14Xkr5l17PZfq/C+BLQGnv42MFo9FIm0mg7a9aYRwhhPDLYkSvAwwI6KkRg0pIGK6QXkAOYxwoywbrGDYAlwohDgOX+u8jhFgshHjSv+YG4HxgXT9lqS8KIfYCe4FU4KEg7YkorWYNhrb2SJsRdrSJ0a2XZHfaseqtGLXGoRerqAyFdoSOYRwQ1LuQUtqBi/t5vBC4zX/7T8CfBjj+omBeP9poNesxt4+DcYZDEO0Kq2oPg0pIGXbE4B03jkHVSgohTrOBOGdnpM0IO1HvGJyqTpJKcDz++OM8//zzADz73AtUnarrcgy33XYb+/fv73uQb3xIboPqGEKKK86M1TXMgR4xTNQ7BjViUAmSO+64g1tvvRWAZ597jqraBvC5AXjyySeZPbsfuRV1K0mlP9xWK/HOwUW2xgM6v2OQUkblCEe7087SCUsjbYYKwN/vhVN7Q3vOCfPgyg0DPl1eXs4VV1zBsmXL2LNnD9OnT+f5559n27ZtrF+/Ho/Hw5IlS3jssccwGo3ce++9bNmyBZ1Ox2WXXcbDDz/MAw88gNVqJS8vj8LCQm65817MZjPbduziyiuv5OGHH2bnzp2UlZXxy1/+EqTk2Zf+yq6Dx/ntH57mT3/6E//3f/9HZ2cny5Yt4/e//31MiWuqEUMI8cXHY3JDc0v0VuyEAk1iItLtRjqjb2Kd2+umubNZ3Uo6wzl06BC33347JSUlJCQk8Mgjj7Bu3To2bdrUNRPhsccew+Fw8Nprr7Fv3z5KSkq47777epxnzZo1LF68mBcf/1+K3nsNs9nc47m//vWvyh3pZdMb/+TG667hwIEDbNq0iU8//ZSioiK0Wi0vvvjiWL79oFEjhhAiEhUNwRMVZcyZE7N6gEPSvclNExcXYWt6opaqRhmDfLIPJ9nZ2Zx77rkAfOUrX+FnP/sZ+fn5TJ8+HYC1a9fy6KOPcuedd2Iymbjtttv44he/yKpVq/o/odD1ST6npaUxefJktm/fzrT8bA4dPc65557Do8+8xK5du1iyZAkATqeT9PRBRSGiDtUxhBBDkiJDXVN1/IxxDPpug02igS6dJDViOKMZ7hanTqdjx44dvPvuu2zcuJHf/e53vPfee30XarVdInndufHGG3n55ZeZOSWPa664EKHVI6Vk7dq1/Nd//VewbyNiqFtJIcSSonwqaDhVFWFLwosuimUxVDkMFYCKigpFAA946aWXuOSSSygvL+fIkSMAvPDCC1xwwQW0trbS1NTEF77wBX7961/3O68hPj6eljYXSK+imdSNa6+9ls2bN/PSy3/hxqsvA42Oiy++mFdeeYXaWkUIwuFwdEl2xwpqxBBCbBnKp+f2+vGdgI5mvSQ1YlABmDVrFs899xzf/OY3mTZtGr/5zW9Yvnw5119/fVfy+Y477sDhcLB69WpcLhdSSn71q1/1Ode6deu443v3YNZr2fbZjh7PJSUlMXv2bPbvK2Xpwrmg0TJ79mweeughLrvsMnw+H3q9nkcffZTc3NgZ4KU6hhCSnpUNQEdjfYQtCS8av159NM5kUCMGFQCNRsPjjz/e47GLL764z4CczMxMduzoebEHeOCBB7puX3fddVz3hYuhoQwMej744IMea998801oqYGWqq5y1RtvvJEbb7yRWEXdSgoh2dmTAfA2NkTYkvASzVtJDpcDk9aEWWceerGKynAZqvvZ5wEEiPFxSR0f7yJKSEpKoUMHmpbmSJsSVoTBgCYuLiodQ6C5LRr7K1TGht6DekLCcBzDOBHQA9UxhJwWswZ9a2ukzQg70dr9rMphqISF4TqGcYLqGEJMm1mLqT36Gr9CjdZmwxONjsFlJ9mszmFQCTEBDSTVMaiMhjazAbOz3+mk4wqtzYYvCmcyqBGDSlgQQrnwD+gYxo+yKqiOIeQ4zSYszjNBSC/6ZjJ4fV4aOxrViiSV8DCoY/CcntswDgjKMQghkoUQ/xJCHPZ/TxpgnbfbkJ4t3R7PF0J85j9+k3/aW0zTaYkj3tm3Q3K8EY05hsaORnzSp470VAkPAziGza/9lf2HDg8pud1dynsgioqKeOutt4IyMxQEGzHcC7wrpZwGvOu/3x9OKeUC/9fV3R7/b+BX/uMbgK8HaU/E8Vjjsbokbrc70qaEFa3Nhre5GemNHifY1dymRgwq4UCjA29Px+DxeNi8eTP7Pz825FZSdynvgYgWxxBs7LMaWOm//RzwAXDPcA70z3m+CLi52/EPAI8FaVNkSUhAI6G6upKcnPxIWxM2tDYbSIm3uRldUr+B4pjT1dym5hiihv/e8d8cdBwM6TlnJs/knqUDX2bKy8tZtWpVV8nqww8/TGtrKx988AHLli3j/fffp7GxkaeeeooVK1bg9Xq55557ePvttxFC8I1vfIO77rqLXbt28f3vf5/W1lZSU1N59jc/JzPRwMqVKznnnHP49NNPueyyy9jyxpt8+P67PPS753n1tc289957PPHEE3R2djJ16lReeOEF4uLiuqS8169fz8qVK/vYsmzZMu6//36cTieffPIJP/zhD7nvvvvYunUraWlp+Hw+pk+fzvbt20lNTQ3pz7Q3wTqGDCllNYCUsloIMZCEoEkIUQh4gA1Sys1ACtAopQy44Epg4kAvJIS4HbgdICcnJ0izw4fOplyUqirLx79jAHxNTRAtjkGNGFSGwOPxsGPHDt566y1++tOf8s477/DEE09QVlbGnj170Ol0OBwO3G43d911F6+//jppaWls2rSJH//8YZ7+b8UhNTY28uGHHwJw+OB+Vq1YwJq1d4AxHpvNxje+8Q0A7rvvPp566inuuuuuYdny4IMPUlhYyO9+9zsADh48yIsvvsh3v/td3nnnHQoKCsLuFGAYjkEI8Q4woZ+nfjyC18mRUlYJISYD7wkh9gL9dYHJgU4gpXwCeAJg8eLFA66LNKaUNADspyojbEl40QZkMaIoz6BGDNHHYJ/sI8G1114LwKJFiygvLwfgnXfe4Y477kCnUy6HycnJlJaWUlpayqWXXgqA1+slM93/dyVlL7kL/+XIv5VUWlrKfffdR2NjI62trVx++eXDtqU3X/va11i9ejXf/e53efrpp/nqV786ync+MoZ0DFLKSwZ6TghRI4TI9EcLmUDtAOeo8n8/JoT4AFgIvArYhBA6f9QwCYh5WdL4NMWHNtdVR9iS8BKIGKKpl8HusqPT6EgwJETaFJUIotPp8PlOq6C6XK6u20ajEQCtVovHo2xW9DeJUErJnDlzuhRaAXA2QEM5ILFYLN0W+1/L7xjWrVvH5s2bKSgo4Nlnn+2jrTSYLb3Jzs4mIyOD9957j88++2zMBv4Em3zeAqz1314LvN57gRAiSQhh9N9OBc4F9kspJfA+sGaw42ON1MxJALjsdRG2JLxEo8Kqw+kg2ZSsymGc4WRkZFBbW4vdbqejo0MRuRuEyy67jMcff7zr4uxwOJgxYwZ1dXVdjsHtdrPv4GHlANlzwyLeYqGlra2rKqmlpYXMzEzcbveIL+Tx8fG0tLT0eOy2227jK1/5CjfccMOYjQcN1jFsAC4VQhwGLvXfRwixWAjxpH/NLKBQCFGM4gg2SCn3+5+7B/i+EOIISs7hqSDtiThZE/MAcDfaI2tImIlGx2B3qc1tKqDX67n//vtZtmwZq1atYubMmYOuv+2228jJyWH+/PkUFBTw5z//GYPBwCuvvMI999xDQUEBCxYsYOtnO/1H9HQMN127iv957AUWnrWIo0eP8rOf/Yxly5Zx6aWXDvnavbnwwgvZv38/CxYsYNOmTQBcffXVtLa2jtk2EoCQMmq36wdk8eLFsrCwMNJm9Ivb7ebz+fP59ILZ3P74q5E2J2xIn4+Dc+eR8s3bSb/77kibA8ANb9xAijmFxy6J7cK2WOfAgQPMmjUr0maEHq8bakohcRJY0k4/3lAOnW2QMScsL1tYWMj3vvc9Pv744xEd19/vQQixS0q5eKhj1c7nEKPX62k1CbStLUMvjmGERoM2IUGNGFTOHAINbL16GcKpk7Rhwwauu+66MR8TqjqGMNBq0mBsa4+0GWEnmrqfpZQ4XA61VFUlfAgNCG3f7mdv+BzDvffey/HjxznvvPPCcv6BUB1DGGgz6zG3u4ZeGONEk2No7mzG4/OoEYNKeOlPFmOcKauC6hjCQrvZSJxzfEtiQMAxRIfCaqC5TZXcVgkr/TqG8aWsCqpjCAsdFjPWM0JhNXoiBrW5TWVM0PZyDD4v4FMdg8rQdFosWF2+oRfGONrERLxN0RUxqDkGlbDSO2Lw+UUkh1BWjTVUxxAGZEICJjc0NjVE2pSwok2yIdvb8XV2RtoUNWJQ6YHVagWgqqqKNWuUHtreyqVbtmxhw4YNg56n+/HAaccQKPMPOAk1YlAZCpGgiMpVVpRF2JLw0tXk1hD57SS7045GaLAZbZE2RSWKyMrK4pVXXgH6Ooarr76ae+8daFJA3+OBbrOf/ZHCOHUM4+vdRAmGJOVTa+2pEzDvrAhbMzI62tswxlmGXkjP7md9xkDCumODw+UgyZiEdpyF9LHOqV/8go4DoZXdNs6ayYQf/WhYawMS3Lt37+4jae10OruUTI8ePcott9yC1+vlyiuv5JFHHqG1tbWHhLfL5eJb3/ouhYU70RktPPKrX3HhsgKe3bSFwsPV/O73jwOwatUq1q9fz4oVK/j6179OYWEhQgi+9rWv8b3vfS+kP4twoUYMYcCSolwkG07Flibg8ZIiHr/936g5dmRY66NJFsPusqsVSSoDYjAYePDBB7nxxhspKirqpY4Kd999N3fffTc7d+4kKyur33M8+uijIDTsffdlXnr+KdauXYurvVV5UvS9lBYVFXHy5ElKS0vZu3fvmEpaBIsaMYQBW4byh9VeXxNhS0bGhKnTEBoNe/7xJld8+7tDru9yDE2RdwwOp0PNL0Qhw/1kH2m2bdvG5s2bAbj55ptZv359nzWffPIJd92hzFmYOW0Kubm5fP755/5n+wo3Tp48mWPHjnHXXXfxxS9+kcsuuyxs9ocaNWIIA+lZ2QB0NNZH2JKRYYyzMPv8izi49UPam4euNoqmmQx2l12tSFIJK1LK09VHgdyCz4tOb8DXTXMuIPOdlJREcXExK1eu5NFHH+W2224ba5NHjeoYwkB29mQAvI2xV5W08IpVeN1u9r779pBrT28lRbZkVUqJ3anqJKkMTn+S1gGWL1/Oq68qopcbN27sd83555/Piy8piqefHzpERUUFM6bkkJebTVFRET6fjxMnTrBjxw4A6uvr8fl8XHfddfzsZz9j9+7dYXhX4UF1DGEgKSmFDh1oWvobUhfdpEzKIWduAcX/+js+r3fQtRqzGWE0RjxiaPe04/K61IhBZVD6k7QO8Otf/5pHHnmEpUuXUl1dTaI/Gu7Ot7/9bbw+H/MuvoEb132TZ599FqNex7nLl5Kfn8+8efNYv349Z52lFJycPHmSlStXsmDBAtatWzfmQnjBoOYYwkSLWYO+tTXSZoyKhVdcxesPP8SRwu1MX3buoGujoftZ7WFQ6U2r/38vLy+P0tJSQBnZuXPnzh7r1q1bB8DEiRPZvn07Qgg2btzI4sWL+xxvMpl49tlnoWYf6C2QnAe1BxA644ADeWIpSuiO6hjCRJtZi6ndGWkzRsXkRUtISMtgzz/eGJ5jiHD3s8PlANSuZ5XRs2vXLu68806klNhsNp5++umBF3fvfvZ5QDO88u5YIijHIIRIBjYBeUA5cIOUsqHXmguBX3V7aCZwk5RysxDiWeACIHBlWSelLArGpmihzWzA7OyItBmjQqPRsuCyL/DRi89Qd7yMtNz8AddqExOjJmJINqnlqiqjY8WKFRQXFw9vsUanDO2Rclwqq0LwOYZ7gXellNOAd/33eyClfF9KuUBKuQC4CGgH/tltyQ8Cz48XpwDgNJuwxLCQ3tyLLkNnMLLn7cHn5UbFVpJL3UpSGUO6ZDG8p++PM4J1DKuB5/y3nwO+NMT6NcDfpZTjfopNpyWOeOfgydtoxmyNZ9Z5F3Dg4w9wDjKNLiocg1OV3FYZQwIKq97xKYcBwTuGDCllNYD/+1C6CDcBL/V67OdCiBIhxK+EEMaBDhRC3C6EKBRCFNbV1QVn9RjgscZjdUnc7tidy7DwiqsSzQ78AAAUrElEQVTwdHZQ+v6/BlwTyDFEcna43WUn0ZiIXqOPmA0qZxAaHSDB29nt/vhiSMcghHhHCFHaz9fqkbyQECITmAd0L5D/IUrOYQmQDNwz0PFSyieklIullIvT0tIGWhY9JCSgkVBdXRlpS0ZNWm4+k2bNpejtv+Hz9R/9aG028HjwtbWNsXWnUXsYVMaUgCPw+Kc0jkN9riEdg5TyEinl3H6+Xgdq/Bf8wIW/dpBT3QC8JqXs+ggtpayWCh3AM8DS4N5O9KCzKReqqsryyBoSJAsuX0VzXQ3Hdhf2+3w0dD+rs55Vxopf/OIX/TiGoSOG2267jf379w+65vHHH+f5558P1sSQEOxW0hZgrf/2WuD1QdZ+mV7bSN2cikDJT5QGaU/UYEpRohr7qdiNGACmLlmONTmFPf94o9/ntUmRl962u+xqRZLKmNDDMbhPOwYpJT7fwMO5nnzySWbPnj3oue+44w5uvfXWUJkaFMFujm0AXhZCfB2oAK4HEEIsBu6QUt7mv58HZAMf9jr+RSFEGooCVRFwR5D2RA3xaRMAaK6rjrAlwaHV6Si49At8uukF7JUnSJmU3fP5KFBYVbeSopePX/6c+hOhbfRMzbay4obpg655/vnnefjhhxFCMH/+fB566CG+9rWvUVdXR1paGs888ww5OTmsW7eOhIQECgsLOXXqFL/85S9Zs2YN1dXV3HjjjTQ3N+PxeHjsscf429/+htPpZMGSc5gzZSI/v/currzlO1x4yeVs276dzZs3s2HDBnbu3InT6WTNmjX89Kc/BWDlypU8/PDDLF68GKvVyt13382bb76J2Wzm9ddfJyMjgwceeACr1cr69etZuXIly5Yt4/3336exsZGnnnqKFStW0N7ezrp16zh48CCzZs2ivLycRx99tKshL1QEFTFIKe1SyoullNP83x3+xwsDTsF/v1xKOVFK6et1/EVSynn+ran/396dB0dZ53kcf3/TdAgkIaABOTJARFaOmEMEImTRiJagiCjBoxiODSWumMHZHc+iRGSlBgvHRWSVIh7sjniAMCulOCByLacEcwARESQoA4IGEQIhB/ntH8+TJg2dpEnS/STk+6pK0XmeJ3k+3aT7+5zf3++NMU3zVmEfojvFAHCusPGfKK9N/NA7cbVoQc7qSy9ddbowlJwvoaisSA8lKY89e/Ywa9Ys1q5dS25uLq+99hoZGRmMHz+evLw8xo4dy9SpUz3LHz16lE2bNvHpp596Bu55//33ufPOO8nJySE3N5fExERmz55Nq1atyMn+msXzZ4Gp4NsDBYyfMIHs7Gy6devGrFmzyMrKIi8vjw0bNpCXl3dJvjNnzpCcnExubi5DhgwhMzPT5/MoLy/nq6++Yu7cuZ4C88Ybb9CuXTvy8vJ4/vnn2blzZwBeQb3zOWA6d+nOWaDsZKHTUeqtdVRbrh80hD0b1pLy0ARatm7tmXeh9bYzdz9rO4zGrbYt+0BYu3YtaWlpREdHA1YrjK1bt7J8+XIAxo0bx9NPP+1ZftSoUYSEhNCnTx+OHbNa5ffv35/09HTKysoYNWoUiYmJF1YQ4qJym7pbTGeSk5M9s5YsWcLChQspLy/n6NGj5OfnEx8f75UvNDSUESNGANCvXz+++ML3VX/333+/Z5mCggLAav39xBNPABAXF3fJ724o2kQvQDp37kqFAH60r24KkobdQ9m5YvZsWOM13dWmDeDcHoOnMOgeg7IZY7BOW1av6vyWLS9cJV952fWQIUPYuHEjXbp0Ydy4cZeeFHZZ29Th4Rc2kg4ePMgrr7zCl19+SV5eHnfffbenBXdVbrfbs36Xy0V5ue8bYStzVV0mWJeFa2EIELfbTVGY4Krh5rCmpGOPnnTqeT05qz7FVDnJJi1aEBIZ6Vhh8PRJ0j0GZRs6dChLliyhsNDaaDhx4gSDBg3ytNNevHgxKSkpNf6OQ4cO0aFDBx555BEmTZrkaYbndrute5M8l6heKDCnTp0iPDycqKgojh07xueff97gzy0lJYUlS5YAkJ+fz65duxp8HaCHkgKqKCyElmeunJu8k4bdw8rXX6EgL5vYxH6e6U7e/VzZDkPvelaV+vbty7Rp07jllltwuVwkJSUxb9480tPTmTNnjufkc03Wr1/PnDlzcLvdREREePYYJk+eTHx8PDf27cmspx71GrgtISGBpKQk+vbty7XXXsvgwTU3oKyLKVOmMGHCBOLj40lKSiI+Pt5ni/D6EifvWK2rm266yWRl+b6uvjFZcVsSZW4Xo1c1/qz+OF9eRubj6XSI7cH9z87wTD845gFcUVF0fcv3SbRAyszLZF72PHaM3UFYi7Cgr19d6ptvvqF3795OxwisXw9B8QmI7AiRnYK22vPnz1NWVkZYWBgHDhxg6NCh7Nu3j9DQ0EuW9fX/ICI7jTG1XsKkewwBdLZVS6JON83W2764WriJv30YW5d9yK8/HaFdR2tsa1fbtpz/1ZnR6grPFRLuDteioIKr8l6GILfDOHv2LKmpqZSVlWGM4c033/RZFOpLzzEEUEl4KyKacIdVX+JvH05ISAg5qz7zTHNFRTl6VZKeX1BBV3mOIciFITIykqysLHJzc8nLy2P48OEBWY8WhgAqDQ8n4lz1d0M2RRHtruKfklPYve4LSs9Ze0NOn2PQK5JU0Dm0xxAsWhgCyLRpQ1gZnPzNmcMsgZI0bASlxWfJ37gOsApDxenTmGouuwukE8UndI9BBZ+7NUgItKi2IXSTpoUhgKRNOwAO/3DQ4SQNq1PPXnSI7WFdumqMoze5aZ8k5YjQ1tApAVwNf3y/MdDCEEChV1lbsseO/OBwkoYlIiQNu4fCwz/ww+5cx9pilFWUcbLkpB5KUqqBaWEIoIirrwHg5LF/OJyk4fUaNIRWkW3I/vunju0x/HrOOkSnh5LUxQYNGlTrMnPnzuXs2cDeZ5STk8PKlSs9369YsYLZs2cHdJ0NQQtDALW9pgsARYXHHE7S8FqEhnLD0Dv5fudXnLHHvg32HoO2w1DV2bJlS63L1KUwnD9/ecP1XlwYRo4c6WnU15hdmafUG4lrOncFoPRE02+k50vCHXexY8Uy8ndn05Hgj8lQedezFobGa92ihRw/9H2D/s4O3a4ldeLkGpeJiIigqKiI9evXM2PGDKKjo9m9ezf9+vXjvffe4/XXX+fIkSOkpqYSHR3NunXrWL16NS+88AIlJSX06NGDd999l4iICLp37056ejqrV68mIyOD/v378/jjj/Pzzz/TunVrMjMz6dWrF0uXLuXFF1/E5XIRFRXFmjVrmD59OsXFxWzatInnnnuO4uJisrKymD9/frUtvysqKsjIyGDDhg3ExsZSUVFBeno6aWlpDfo61kQLQwDFdI3lKGBOXVlXJVVqE92e6/ons+erLbQXcW6PQQ8lqRpkZ2ezZ88eOnfuzODBg9m8eTNTp07l1VdfZd26dURHR/PLL7/w0ksvsWbNGsLDw3n55Zd59dVXmT59OgBhYWFs2rQJsHoxLViwgJ49e7J9+3amTJnC2rVrmTlzJqtWraJLly6cPHmS0NBQZs6c6SkEAIsWLfLKVtnye+/evYwcOZK0tDSWL19OQUEBu3bt4vjx4/Tu3Zv09PSgvmZaGAKobVQ7DrpBTp1yOkrAJA27h++2b+HI1VF0CHJhqGygp1clNV61bdkHw4ABA4iJscZHSUxMpKCg4JImetu2bSM/P9/T36i0tJSbb77ZM//BBx8EoKioiC1btjBmzBjPvJKSEgAGDx7MxIkTeeCBBzwts2vjq+X3pk2bGDNmDCEhIXTs2JHU1NQ6PvO6q1dhEJExwAygNzDAGOOzKZCIDANeA1zAW8aY2fb0WOBD4Crga2CcMaa0Ppkam6KwEELPnHE6RsDE9I4jumt3DpWdJ86BPYaWrpaEu8ODul7VtFRtq11dm2tjDHfccQcffPDBJfMAwsOtv7GKigratm1LTk7OJcssWLCA7du389lnn5GYmOhzmZqyVfatawz96+p78nk3cD+wsboFRMQF/BcwHOgDPCwilYOfvgz8pzGmJ/ArMKmeeRqdolYtaHnmyumXdDHr0tURnHK7+Onnn4K67sJzVjuM2nrvK+VLZGQkp09bbfGTk5PZvHkz+/fvB6yeRPv27bvkZ9q0aUNsbCxLly4FrA/x3NxcAA4cOMDAgQOZOXMm0dHR/Pjjj17r8FdKSgrLli2joqKCY8eOsX79+no8y7qp79Ce3xhjvq1lsQHAfmPM9/bewIfAvWK9m28DPraX+29gVH3yNEZnW7lpXVzidIyA6p1yK26E7377JajrLSzWdhiq7iZPnszw4cNJTU2lffv2LFq0iIcffpj4+HiSk5PZu3evz59bvHgxb7/9NgkJCfTt25dPPvkEgKeeeoobbriBuLg4hgwZQkJCAqmpqeTn55OYmMhHH33kV67Ro0cTExNDXFwcjz76KAMHDgxIa+2aNEjbbRFZDzzp61CSiKQBwyrHgBaRccBArENQ24wx19nTfwd8boyJq2Ydk4HJAF27du136NCheucOhoV/HIsUFfHIW584HSWg1j77J8pLS7njL/OCtgU/P3s+LnHxWOJjQVmf8k+zaLsdYEVFRURERFBYWMiAAQPYvHkzHTt2vKzfEdC22yKyBvCVaJoxxp9PO1+fEqaG6T4ZYxYCC8Eaj8GP9TYKk+cudjpCUNw2+y9BX2dGUkbQ16lUMIwYMYKTJ09SWlrK888/f9lFob5qLQzGmNvruY7DwO+qfB8DHAF+AdqKSAtjTHmV6Uop1aw5cV6hqmDc+bwD6CkisSISCjwErDDWMax1QOVdGxOAK/t4i1LNRGO4sqY5q+/rX6/CICL3ichh4GbgMxFZZU/vLCIr7YDlQAawCvgGWGKM2WP/imeAfxeR/cDVwNv1yaOUcl5YWBiFhYVaHBxijKGwsJCwsLqPaqhjPiulGlRZWRmHDx/m3LlzTkdptsLCwoiJicHtdntN1zGflVKOcLvdxMbGOh1D1YN2V1VKKeVFC4NSSikvWhiUUkp5aZInn0XkZ6Ahb32OxrqvoqnR3MGluYNLcze8bsaY9rUt1CQLQ0MTkSx/ztQ3Npo7uDR3cGlu5+ihJKWUUl60MCillPKihcGy0OkAdaS5g0tzB5fmdoieY1BKKeVF9xiUUkp50cKglFLKS7MqDCIyTES+FZH9IvKsj/ldRWSdiGSLSJ6I3OVEzov5kbubiHxpZ14vIjFO5Lwo0zsiclxEdlczX0Rknv2c8kTkxmBn9MWP3L1EZKuIlIjIk8HOVx0/co+1X+c8EdkiIgnBzuiLH7nvtTPniEiWiKQEO6MvteWuslx/ETlvj2TZdBhjmsUX4AIOANcCoUAu0OeiZRYCj9mP+wAFTST3UmCC/fg24K+NIPcQ4EZgdzXz7wI+xxrJLxnY7nRmP3N3APoDs7CGs3U8s5+5BwHt7MfDm9DrHcGFc6HxwF6nM/uT217GBawFVgJpTme+nK/mtMcwANhvjPneGFMKfAjce9EyBmhjP46icYwo50/uPsCX9uN1PuYHnTFmI3CihkXuBf7HWLZhjebXKTjpqldbbmPMcWPMDqAseKlq50fuLcaYX+1vt2GNmOg4P3IXGftTFginhuF/g8mPv2+APwDLgOOBT9SwmlNh6AL8WOX7w/a0qmYAv7cHH1qJ9R/rNH9y5wKj7cf3AZEicnUQstWHP89LBcYkrL21JsEeEGwv8BmQ7nQef4hIF6z34gKns9RFcyoM4mPaxVsfDwOLjDExWIc6/ioiTr9G/uR+ErhFRLKBW4B/AOWBDlZP/jwv1cBEJBWrMDzjdBZ/GWP+ZozpBYwC/sPpPH6aCzxjjDnvdJC6aE4D9RwGflfl+xguPVQ0CRgGYIzZKiJhWA2xnNwVrDW3MeYIcD+AiEQAo40xvwUtYd348/+hGpCIxANvAcONMYVO57lcxpiNItJDRKKNMY21SV2lm4APRQSsz5C7RKTcGPO/zsbyj9Nbw8G0A+gpIrEiEgo8BKy4aJkfgKEAItIbCAN+DmrKS9WaW0Siq+zZPAe8E+SMdbECGG9fnZQM/GaMOep0qCuViHQFlgPjjDH7nM7jLxG5TuxPV/vKtVCg0Rc1Y0ysMaa7MaY78DEwpakUBWhGewzGmHIRyQBWYV0t8I4xZo+IzASyjDErgD8BmSLyb1iHNSZWOfHlCD9z3wr8WUQMsBF43LHANhH5ACtXtH3O5gXADWCMWYB1DucuYD9wFvgXZ5J6qy23iHQEsrAuUqgQkT9iXSV2yqHIgF+v93TgauAN+3O23DSCDqB+5B6NtQFRBhQDDzr9ngS/cjdp2hJDKaWUl+Z0KEkppZQftDAopZTyooVBKaWUFy0MSimlvGhhUEqpRs7fpn32svVuqqmFQanLJCIzauqsKiKjRKRPMDOpK94i7Jtv/fAKVh+yeGAm8OfLXZkWBqUa3iisxoZKNQhfTfvsu8D/LiI7ReT/RKSXPaveTTW1MCjlBxGZZo+JsQa43p72iIjsEJFcEVkmIq1FZBAwEphjjyHQo4Y3sFL1sRD4gzGmH1a/tDfs6fVuqtls7nxWqq5EpB9WK5IkrPfM18BOYLkxJtNe5iVgkjHmdRFZAXxqjPnYnvcl8K/GmO9EZCDWG/g2B56KukLYPdEGAUvtO9kBWtr/PgnMF5GJWJ0QLrupphYGpWr3z8DfjDFnAewPfoA4uyC0xRpQZtXFP1jLG1ipugoBThpjEi+e0RBNNfVQklL+8dU7ZhGQYYy5AXgRq+nixTxv4CpfvQOYUzUDdm+ugyIyBjxD5SbYj+vdVFMLg1K12wjcJyKtRCQSuMeeHgkcFRE3MLbK8qfteTW+gZXyl920bytwvYgcFpFJWH9zk0QkF9jDhZPMtwLfisg+4BqsYWgvb33aRE+p2onINGA8cAhrLIl84AzwtD1tFxBpjJkoIoOBTKAESAMqgDeBTlgdOD80xswM+pNQyk9aGJRSSnnRQ0lKKaW8aGFQSinlRQuDUkopL1oYlFJKedHCoJRSyosWBqWUUl60MCillPLy//A+sNBGNSBTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe53f6a8f28>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ls_FRA = pd.DataFrame()\n",
    "\n",
    "for factor, data in unixt_factor_data.items():\n",
    "    ls_FRA[factor] = al.performance.factor_rank_autocorrelation(data)\n",
    "\n",
    "ls_FRA.plot(title=\"Factor Rank Autocorrelation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sharpe Ratio of the Alphas\n",
    "The last analysis we'll do on the factors will be sharpe ratio. Let's see what the sharpe ratio for the factors are. Generally, a Sharpe Ratio of near 1.0 or higher is an acceptable single alpha for this universe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "negative       -0.24000000\n",
       "positive       -1.33000000\n",
       "uncertainty     0.83000000\n",
       "litigious       1.34000000\n",
       "constraining   -2.99000000\n",
       "interesting    -2.48000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_annualization_factor = np.sqrt(252)\n",
    "\n",
    "(daily_annualization_factor * ls_factor_returns.mean() / ls_factor_returns.std()).round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's it! You've successfully done sentiment analysis on 10-ks!\n",
    "## Submission\n",
    "Now that you're done with the project, it's time to submit it. Click the submit button in the bottom right. One of our reviewers will give you feedback on your project with a pass or not passed grade. You can continue to the next section while you wait for feedback."
   ]
  }
 ],
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