tiingo-python/examples/basic-usage-with-pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tiingo-Python\n",
    "\n",
    "\n",
    "This notebook shows basic usage of the `tiingo-python` library. If you're running this on `mybinder.org`, you can run this code without installing anything on your computer. You can find more information about what available at the [Tiingo website](https://api.tiingo.com/docs/general/overview), but this notebook will let you play around with real code samples in your browser.\n",
    "\n",
    "If you've never used `jupyter` before, I recommend this [tutorial](https://www.datacamp.com/community/tutorials/tutorial-jupyter-notebook) from Datacamp."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Setup\n",
    "\n",
    "First, you'll need to provide your API key as a string in the cell below. If you forget to do this, the notebook cannot run. You can find your API key by visiting [this link](https://www.tiingo.com/account/api/token) and logging in to your Tiingo account."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "TIINGO_API_KEY = 'REPLACE-THIS-TEXT-WITH-A-REAL-API-KEY'\n",
    "\n",
    "# This is here to remind you to change your API key.\n",
    "if not TIINGO_API_KEY or (TIINGO_API_KEY == 'REPLACE-THIS-TEXT-WITH-A-REAL-API-KEY'):\n",
    "    raise Exception(\"Please provide a valid Tiingo API key!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tiingo import TiingoClient\n",
    "\n",
    "config = {\n",
    "    'api_key': TIINGO_API_KEY,\n",
    "    'session': True # Reuse HTTP sessions across API calls for better performance\n",
    "}\n",
    "\n",
    "# Throughout the rest of this notebook, you'll use the \"client\" to interact with the Tiingo backend services.\n",
    "client = TiingoClient(config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Minimal Data Fetching Examples\n",
    "\n",
    "Below are the code samples from the `README.rst` along with sample outputs, but this is just the tip of the iceberg of this library's capabilities. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'exchangeCode': 'NASDAQ', 'endDate': '2019-01-30', 'startDate': '2004-08-19', 'ticker': 'GOOGL', 'description': \"Google Inc. (Google) is a global technology company. The Company's business is primarily focused around key areas, such as search, advertising, operating systems and platforms, enterprise and hardware products. The Company generates revenue primarily by delivering online advertising. The Company also generates revenues from Motorola by selling hardware products. The Company provides its products and services in more than 100 languages and in more than 50 countries, regions, and territories. Effective May 16, 2014, Google Inc acquired Quest Visual Inc. Effective May 20, 2014, Google Inc acquired Enterproid Inc, doing business as Divide. In June 2014, Google Inc acquired mDialog Corp. Effective June 25, 2014, Google Inc acquired Appurify Inc, a San Francisco-based developer of mobile bugging application software.\", 'name': 'Alphabet Inc.(\"Google\") - Class A'}\n"
     ]
    }
   ],
   "source": [
    "# Get Ticker Metadata for the stock \"GOOGL\"\n",
    "ticker_metadata = client.get_ticker_metadata(\"GOOGL\")\n",
    "print(ticker_metadata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'adjClose': 1097.99, 'adjHigh': 1099.52, 'adjLow': 1076.64, 'adjOpen': 1077.36, 'adjVolume': 1450619, 'close': 1097.99, 'date': '2019-01-30T00:00:00+00:00', 'divCash': 0.0, 'high': 1099.52, 'low': 1076.64, 'open': 1077.36, 'splitFactor': 1.0, 'volume': 1450619}]\n"
     ]
    }
   ],
   "source": [
    "# Get latest prices, based on 3+ sources as JSON, sampled weekly\n",
    "ticker_price = client.get_ticker_price(\"GOOGL\", frequency=\"weekly\")\n",
    "print(ticker_price)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For values of `frequency`:\n",
    "\n",
    "You can specify any of the end of day frequencies (daily, weekly, monthly, and annually) or any intraday frequency for both the ``get_ticker_price`` and ``get_dataframe`` methods.  Weekly frequencies resample to the end of day on Friday, monthly frequencies resample to the last day of the month, and annually frequencies resample to the end of day on 12-31 of each year.  The intraday frequencies are specified using an integer followed by `Min` or `Hour`, for example `30Min` or `1Hour`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'date': '2017-08-01T00:00:00.000Z', 'close': 946.56, 'high': 954.49, 'low': 944.96, 'open': 947.81, 'volume': 1205799, 'adjClose': 946.56, 'adjHigh': 954.49, 'adjLow': 944.96, 'adjOpen': 947.81, 'adjVolume': 1205799, 'divCash': 0.0, 'splitFactor': 1.0}, {'date': '2017-08-02T00:00:00.000Z', 'close': 947.64, 'high': 949.1, 'low': 932.521, 'open': 948.37, 'volume': 2019979, 'adjClose': 947.64, 'adjHigh': 949.1, 'adjLow': 932.521, 'adjOpen': 948.37, 'adjVolume': 2019979, 'divCash': 0.0, 'splitFactor': 1.0}]\n"
     ]
    }
   ],
   "source": [
    "# Get historical GOOGL prices from August 2017 as JSON, sampled daily\n",
    "historical_prices = client.get_ticker_price(\"GOOGL\",\n",
    "                                            fmt='json',\n",
    "                                            startDate='2017-08-01',\n",
    "                                            endDate='2017-08-31',\n",
    "                                            frequency='daily')\n",
    "\n",
    "# Print the first 2 days of data, but you will find more days of data in the overall historical_prices variable.\n",
    "print(historical_prices[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OrderedDict([('ticker', '000001'), ('exchange', 'SHE'), ('assetType', 'Stock'), ('priceCurrency', 'CNY'), ('startDate', '2007-08-30'), ('endDate', '2019-01-29')]), OrderedDict([('ticker', '000002'), ('exchange', 'SHE'), ('assetType', 'Stock'), ('priceCurrency', 'CNY'), ('startDate', '2000-01-04'), ('endDate', '2019-01-29')])]\n"
     ]
    }
   ],
   "source": [
    "# See what tickers are available\n",
    "# Check what tickers are available, as well as metadata about each ticker\n",
    "# including supported currency, exchange, and available start/end dates.\n",
    "tickers = client.list_stock_tickers()\n",
    "\n",
    "print(tickers[:2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each ticker, you may access\n",
    "\n",
    "- `ticker`: The ticker's abbreviation\n",
    "- `exchange`: Which exchange it's traded on\n",
    "- `priceCurrency`: Currency for the prices listed for this ticker\n",
    "- `startDate`/ `endDate`: Start / End Date for Tiingo's data about this ticker\n",
    "\n",
    "Note that Tiingo is constantly adding new data sources, so the values returned from this call will probably change every day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Search news articles about particular tickers\n",
    "# This method will not work error if you do not have a paid Tiingo account associated with your API key.\n",
    "articles = client.get_news(tickers=['GOOGL', 'AAPL'],\n",
    "                           tags=['Laptops'],\n",
    "                           sources=['washingtonpost.com'],\n",
    "                           startDate='2017-01-01',\n",
    "                           endDate='2017-08-31')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'publishedDate': '2018-10-30T19:33:13Z',\n",
       " 'crawlDate': '2018-10-30T19:49:31.438654Z',\n",
       " 'title': 'It’s not clear which MacBook laptop to buy anymore',\n",
       " 'url': 'https://qz.com/1444562/which-macbook-should-you-get-any-of-them/',\n",
       " 'description': 'Which new Apple MacBook should you get? Maybe an iPad.',\n",
       " 'id': 13583735,\n",
       " 'tags': ['Apple',\n",
       "  'Apple October Event',\n",
       "  'Edited By Matt Quinn',\n",
       "  'Gadgets',\n",
       "  'Information Technology',\n",
       "  'Laptops',\n",
       "  'Macbook',\n",
       "  'Macbook Air',\n",
       "  'Macbook Pro',\n",
       "  'Stock',\n",
       "  'Technology'],\n",
       " 'tickers': ['aapl'],\n",
       " 'source': 'qz.com'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display a sample article\n",
    "articles[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Pandas Dataframe Examples\n",
    "\n",
    "Pandas is a popular python library for data analysis an manipulation. We provide out-of-the-box support for returning responses from Tiingo as Python Dataframes. If you are unfamiliar with `pandas`, I recommend the `Mode Notebooks` [python data analysis tutorial](https://mode.com/python-tutorial/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Boilerplate to make pandas charts render inline in jupyter\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scan some historical Google data\n",
    "ticker_history_df = client.get_dataframe(\"GOOGL\",\n",
    "                                         startDate='2018-05-15',\n",
    "                                         endDate='2018-05-31',\n",
    "                                         frequency='daily')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['adjClose', 'adjHigh', 'adjLow', 'adjOpen', 'adjVolume', 'close',\n",
       "       'divCash', 'high', 'low', 'open', 'splitFactor', 'volume'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check which columns you'd like to work with\n",
    "ticker_history_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>adjClose</th>\n",
       "      <th>adjHigh</th>\n",
       "      <th>adjLow</th>\n",
       "      <th>adjOpen</th>\n",
       "      <th>adjVolume</th>\n",
       "      <th>close</th>\n",
       "      <th>divCash</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>splitFactor</th>\n",
       "      <th>volume</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-05-15 00:00:00+00:00</th>\n",
       "      <td>1084.87</td>\n",
       "      <td>1099.12</td>\n",
       "      <td>1078.78</td>\n",
       "      <td>1096.90</td>\n",
       "      <td>1786926</td>\n",
       "      <td>1084.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1099.12</td>\n",
       "      <td>1078.78</td>\n",
       "      <td>1096.90</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1786926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-16 00:00:00+00:00</th>\n",
       "      <td>1084.09</td>\n",
       "      <td>1094.38</td>\n",
       "      <td>1081.63</td>\n",
       "      <td>1085.09</td>\n",
       "      <td>1281406</td>\n",
       "      <td>1084.09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1094.38</td>\n",
       "      <td>1081.63</td>\n",
       "      <td>1085.09</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1281406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-17 00:00:00+00:00</th>\n",
       "      <td>1081.26</td>\n",
       "      <td>1091.00</td>\n",
       "      <td>1076.42</td>\n",
       "      <td>1081.46</td>\n",
       "      <td>1286907</td>\n",
       "      <td>1081.26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1091.00</td>\n",
       "      <td>1076.42</td>\n",
       "      <td>1081.46</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1286907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-18 00:00:00+00:00</th>\n",
       "      <td>1069.64</td>\n",
       "      <td>1073.73</td>\n",
       "      <td>1064.68</td>\n",
       "      <td>1066.00</td>\n",
       "      <td>1774149</td>\n",
       "      <td>1069.64</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1073.73</td>\n",
       "      <td>1064.68</td>\n",
       "      <td>1066.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1774149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-21 00:00:00+00:00</th>\n",
       "      <td>1084.01</td>\n",
       "      <td>1093.30</td>\n",
       "      <td>1078.00</td>\n",
       "      <td>1079.00</td>\n",
       "      <td>1258999</td>\n",
       "      <td>1084.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1093.30</td>\n",
       "      <td>1078.00</td>\n",
       "      <td>1079.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1258999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           adjClose  adjHigh   adjLow  adjOpen  adjVolume  \\\n",
       "date                                                                        \n",
       "2018-05-15 00:00:00+00:00   1084.87  1099.12  1078.78  1096.90    1786926   \n",
       "2018-05-16 00:00:00+00:00   1084.09  1094.38  1081.63  1085.09    1281406   \n",
       "2018-05-17 00:00:00+00:00   1081.26  1091.00  1076.42  1081.46    1286907   \n",
       "2018-05-18 00:00:00+00:00   1069.64  1073.73  1064.68  1066.00    1774149   \n",
       "2018-05-21 00:00:00+00:00   1084.01  1093.30  1078.00  1079.00    1258999   \n",
       "\n",
       "                             close  divCash     high      low     open  \\\n",
       "date                                                                     \n",
       "2018-05-15 00:00:00+00:00  1084.87      0.0  1099.12  1078.78  1096.90   \n",
       "2018-05-16 00:00:00+00:00  1084.09      0.0  1094.38  1081.63  1085.09   \n",
       "2018-05-17 00:00:00+00:00  1081.26      0.0  1091.00  1076.42  1081.46   \n",
       "2018-05-18 00:00:00+00:00  1069.64      0.0  1073.73  1064.68  1066.00   \n",
       "2018-05-21 00:00:00+00:00  1084.01      0.0  1093.30  1078.00  1079.00   \n",
       "\n",
       "                           splitFactor   volume  \n",
       "date                                             \n",
       "2018-05-15 00:00:00+00:00          1.0  1786926  \n",
       "2018-05-16 00:00:00+00:00          1.0  1281406  \n",
       "2018-05-17 00:00:00+00:00          1.0  1286907  \n",
       "2018-05-18 00:00:00+00:00          1.0  1774149  \n",
       "2018-05-21 00:00:00+00:00          1.0  1258999  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Browse the first few entries of the raw data\n",
    "ticker_history_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x126e7e240>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x128ee9160>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# View your columns of data on separate plots\n",
    "columns_to_plot = ['adjClose', 'adjOpen']\n",
    "ticker_history_df[columns_to_plot].plot.line(subplots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x129002940>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P7YsFZ69cFgBTLjr2BLBOStkTWGf+GeAvwB4pZX/gTuB1ACGEEXgbuAboC8wWQvS12wx7XAme/nprTKPRtGiSsgrwcjfQtb31DcJq4lRxkVJuAE5fdPgGYKH5+UJguvl5X+Bn83VJQIQQIhgYBhyRUh6TUpYDn5nHsA9untD7OkhaBZXldhtWo9FoXInkrAJ6BfthNNhm+2LB2SuX2giWUmaan2cBwebnCcAMACHEMKArEA6EAek1rs8wH7Mf0TdC6Tk4+rNdh9W0ALS5qaaFkJRVQJQd6lssuKK4VCOllICla9cLQFshxB7gUWA30ChvFiHEfUKIeCFEfE5OTsMv7DYRvNrqrTHNhVSWwxuxsPkNZ89Eo7GJ3MIycgvL6B1in0wxcE1xOSWECAEwP2YDSCnzpZTzpJSxqJhLEHAMOAF0rnF9uPnYJUgp35dSDpFSDgkKCmr4jNw8VAvkpO+gotSa96RpiRxeA2dT9YpW0+xJNjcIs1cwH1xTXFYCc83P5wIrAIQQbYUQHubj9wAbpJT5wA6gpxAi0vz6LPMY9iX6RigvgKPr7D60ppmSsFQ9ntyt22JrmjUHM+3nKWbB2anIS4E4IEoIkSGEuBu1/XWVEOIwcKX5Z4A+wH4hRDIqM+y3AFLKSuARYDVwEPhCSplo98lGjoc27WC/9hrTAMWn4dBq8A2G0rNqBaPRNFOSswro4OtJB1/bGoTVxHYDGRuQUs6u46VJtZwbB/SqY5zvge/tOLVLMbpDn6mwbxmUF4OHt0Nvp3Fx9i0DUwVM+juseFitXgIjnD0rjcYqkk8V2HVLDFxzW8x1iZkBFUVwZK2zZ6JxNglLoFM/6DcTDO5KXDSaZkhVdYMwLS7Oo+sY8AnSW2OtnewkJSYD5qg6qOBoOLnH2bPSaKwiNa+IskqTXeMtoMWlcRjdoM80tddeXuTs2WicRcJSEEa1agEIHajERQf1Nc2QpOpMMfulIYMWl8YTMwMqS+DQj86eicYZmKpg7+fKMdvXnM4eGgtl5+D0MefOTaOxgqSsAgwCegb72nVcLS6NpctI8O2kt8ZaK8d+hYJMGFAjFyV0oHrM1FtjmuZHclY+ER188HI32nVcLS6NxWCEvjfA4bVQVuDs2WiamoTPwCtAtWKwENQHjJ46qK9pliRlFdDHzltioMXFOmJmQFUZJP/g7JlompLSfDi4CmJuUoF8C24eOqivaZYUlVWSdrrY7sF80OJiHeHDwC9Ub421Ng6sUPG2AXMufS10IGQmaCNLTbPi0KkCpLRvZb4FLS7WYDAoO5ij66DkrLNno2kqEj6D9j0gfMilr4UOhLJ8HdTXNCssnmJ6W8yViJkBVeWQ7FhjAI2LcCYFUjepQL6opd9FaKx61HEXTTMiKasAbw8j4YFt7D62U+1fmjVhgyGgi7Lhj61lm0TTskj4HBDQ/9baXw/qDW5eKmOs/8wmndplKS+CBddDZRn4dADfjqoYuOY/X8vzjuDu5ewZa5qIpKx8ojr5YbBTg7CaaHGxFiEgejpsfUeZGHq3c/aMNI5CSlU4GTkW2nau/Ryju7KDccWVS1ocnNwFXUdDRQlk7ICiXCgvrP18D78aYlNTgDoqcbKIkF+wypzTNEukVLYvU2I6OWR8LS62EH0jbHkDkr6FQXc6ezYaR5G+Dc4ch/GP139eSKwSIZNJxeVchdQ45Sgw5wvwrFEoV14MRTlKaIqyzc9zoND8WJStYkjp29Q51OJAMPguuOo58LL/nr3GsWQXlHGmuMKu3SdrosXFFkIHKifcxOVaXFoye5aAu49yxa6P0IGw43+QdwSCajXwdg5pWyGk/4XCAsrZ26MrBHa9/BimKrVCrxahXCU6Oz6Awz/BtNehx5WOmb/GIVTbvtix+2RNXOjrVTNECIieAcfWm7/ZaVocFSXqy0PfaZd+OF+MpVLflbbGKsvgRDx0GWXbOAaj2ioLjoZuE6DfzXDtf+DuteDhA5/eBN88rLMnmxFJ5gZh9nZDtqBXLrYSfSNsekUV1w2Z5+zZaOxN0ncqxXhAXa2HatChF7i1MTsm1xH4vwwl5VVMfWsTJeVVhAe2oXM7bzoHep9/3q4NHf28MDY0AJuZAJWl0GWEVfO5LOFD4P4NsP5F2Py6Ss+//jWImuKY+2nsRnJWAZ38vWjr7XH5k61Ai4utdOqnah8Sv9bi0hJJ+Az8wyFi7OXPNbqp7ScbPMbWHMjiSHYhV/bpyNniCjYezuFUftkF57gbBWFtldiEB7YhPNDbLELqeQdfD4QlXTp1i3rsMtLqOV0Wdy+48mm1bbjiYVh6q8qqm/KCTnRxYZKyChxSPGnBqeIihPgIuB7IllLGmI+1Az4HIoAU4BYp5RkhRADwKdAFNe+XpZQfm6+pAvaZh02TUk5rwjehtsY2vgyF2SqjRtMyKMhS38TH/L7hAfrQgbBrkYpRGBpvBLhsZwbhgW14/44h1emhZZVVnDhTQvqZEjLOFJN+uoT0M8VknClhTeIp8orKLxijjbvRLDptePzMGjp5R7A1pZLhkeUE+jjmWyoAYYPgvvXqb2Hjf+HoL3D9K5ePVWmanIoqE0eyCxnbs4PD7uHslcsC4C1gUY1jTwDrpJQvCCGeMP/8OPAwcEBKOVUIEQQkCyEWSynLgRIpZWwTz/080TfChpeUPciwe502DY2d2fsFSFPDtsQshMRCxXzIPQwdezfqdifPlrDpSC6PXdHzgroDTzcj3YJ86RZUe8ynqKySE2dLSD9dTPppJTrpZ4rJyCsiJH8PP1QO5YlPd+FmEIzt2YGpA0K5qm8wfl7ujZpfg3DzgIl/gd7Xw4qH4PPb1Zeva/+j0pg1LkFKbhHlVSZ6h7TQlYuUcoMQIuKiwzcAE8zPFwK/osRFAn5Crfd9gdNAZVPM87IE91VFdPu/1uLSUrDUtoQPhQ49G35dzaB+I8Vl+e4TSAk3DQpv1HU+nm70Cvaj18UppdkH4Z0ipk27mV6dRrE6MYtvEzL5vy8S8HAzMDEqiKkDQpnUO5g2Hva1WyekP9z7C2x6TcVjjm9QAhN9Y+0OB5om5aA5Uywq2HEp5K6YLRYspcw0P88Cgs3P3wL6ACdRW2C/lVJaXAK9hBDxQoitQojpDblJUVklhWV21KYBsyBtC6x5SnckbAlk7YXsA+q/a2Po0FOlLTcyY0xKybKdGQyPbEeX9t6Nu2ddmOMt3j1GM6hLIE9e04eNf57IVw+OYs6wLuxKO8sjS3Yz+Pm1PLZ0N2sPnKKssso+9wZVWDr+Tyrg37YzLJsHX9yhto81TiU5Kx83g6B7Rx+H3cPZ22L1IqWUQgjLJ/XVwB7gCqA7sFYIsVFKmQ90lVKeEEJ0A34WQuyTUh69eDwhxH3AfQAenXoQ8/RqunXwITosgJhQf2LCAogO9bcue2LUY3A2XRVVFuXCtDfUH5emebJnKRg91JZOYzAYrQrq70o7w/HcIh6a0L1x96uPtK2qsV1g5PnpGQSDuwYyuGsgT13fl+3HT7Nq70l+2JfJyoST+Hm5cXV0J6YOCGVU9/a4G+3w/TO4L9z9E8S9Cb/8G1KGwTUvqTbRehXjFJKzCugW5IOnm51XrDVwRXE5JYQIkVJmCiFCAMvXnHnAC1JKCRwRQhwHegPbpZQnAKSUx4QQvwIDgUvERUr5PvA+QFRMrHx0ci/2n8hnd9oZViWcrD4vrG0bYsL8iQkNUIIT5k9Hv8v4LRmMcN1/wTcYfv0XFOfBzAWqUE3TvKiqgH1fqoZg1mQ7hQ6E+I+hqlJlkDWAZTsz8PYwcm2/kMbfry7S4lQKch0f4EaDYGT39ozs3p5np0Wz+UguqxIyWb0/i2U7M2jn48GUmE5M7R/KsMh2DU9/rvVmbioxIuo6FYv5+l61jXz9q+Bvx/esaRAHMwsY3DXQofdwRXFZCcwFXjA/rjAfTwMmARuFEMFAFHBMCBEIFEspy4QQHYDRwEuXu4mflxuPXHF+L/1MUTkHMvPZf+Ic+0/mk3jiHKsTT1W/3tHPkxjzCic6TIlOaIDX+ZRPUH/EEx5XxWbf/QEW3QBzPtfpmM2NIz9BcW7tfVsaQuhAqHwHcpNV0eFlKCmv4tuETK6JCcHH005/kmfT4Vw6jHq0Qae7Gw1MiOrIhKiOlFbEsOFQDqv2ZrJ81wmWbEujo58n1/UPYeqAUAZ2bnvh//eNIagX/GY1bH0Xfn4O3hkOV/9bmb/qVUyTkF9awYmzJcwZ3sWh93F2KvJSVPC+gxAiA3gaJSpfCCHuBlKBW8ynPwcsEELsAwTwuJQyVwgxCnhPCGFCxZBekFIeaOxcAn08GN2jA6N7nM9oKSit4GBmgVlwzpF4Ip9fk7MxmTfqAr3dzVtpAcSE+TO4ayAhAW1gyG/AuwN8dTd8NAXu+BoCGhek1TiRPUvUf78ek6y7PsRiv7+nQeKy5kAWBWWV3DzYjv+PpG1Vj1YUT3q5G5kc3YnJ0Z0oLq9k3cFsvt17ksXb0vh4cwphbdtw/YAQpg0IJTrUCuNKgxFGPaJWhiseUSuZxOUw9TX9d9IEHLLYvjiwxgWcny1WV47nJX/VUsqTwORajm8B+tl5agD4ebkzLLIdwyLPrzxKyqtIysqvXt3sP3mODzcdo6JK4m4U/GFyFPeN7Yah7zRo8zV8Ngc+nAx3LIegKEdMU9MATCbJi6uTGNg5sH4X2OLTcOhHGHqP9TGz9j3Aw1cF9QfedtnTLbUtwyPtuMJN26LcjYNjbBrG28ONqQNCmToglPzSCtYmnmLV3pN8uPE4760/xmOTevJ/V1npo9a+O9z1nfJj++kZeHsEXP08DJqrVzEOxNGeYhZccVvMpWnjYWRgl0AGdjm/X1leaeLQqQLe+vkIL/yQxK/J2bxySyyhkWPVH8+nN8FHV8OcL6HzUCfOvvXy0upk3lt/jCA/TyZEBeHlXkcgc/9XqglcY2pbLsZgUKuXBmSM1VXbYjNpW6HzMKsKOevC38udmwaHc9PgcM4UlfPP7w/yxrrDCOD31gqMwQDD74eek2Hlo7Dqt+ZVzBsNM9TUNJrkrAL8vNwIDXBs3x5XTEVudni4GYgJC+Dd2wfx0k392ZtxjimvbeDbvSdV5tDda8CrLSycCofWOHu6rY6vdmYwf/1RhkW0I6egjG92n6j75ITPoGO0svWxhdBYOLVfJQfUg7W1LfVSfFqlUXd1nOVLoI8HL93Un5mDw3l93WFeXXvItgHbRcKdK+G6VyAjHt4ZCdv/p9oXaOxKUlY+vTv5WR83ayBaXOyIEIJbhnbm+8fGEhnkyyNLdvOHLxIo8A5XAhPUC5bOUmmumiYhPuU0T369j1Hd27P43uFEh/rz/sZjmEy11CLlHlYOwrF1tDJuDKEDlWFkTlKdpziktgUgfbt6dKSfGCqt+UV7CozBAEPvhoe2Qpfh8P0f1Rey08fsM2ENUkqHe4pZ0OLiACI6+LDsgZE8dkUPlu/O4No3NrIzzw3mfgsRY+CbB2DzG86eZosn/XQx93+yk9C2Xrxz2yDcjQbuH9+dYzlF/HTw1KUX7Fmimmr1u+XS1xpLA+z3LbUtdg3kg4q3GNxVK24Hc7HAvPaTjQIDquDy9q9h2luqmPWdURD3jvJr09jEyXOlFJRWEtXJ8c3dtLg4CHejgf+bHMUX949ESpg5P45XN2ZROetzZYGx9ilY8ze97HcQhWWV3LsonvIqEx/MHVpdGHttTCdlDLnhom/DJhPs/VxliPkF1zJiIwmMBE9/lTFWB1/GO6C2BVS8JWwQuLex77h1UFNgXvvJTgIjBAy6Q61iIsfC6ifh42vU6lJjNclZqodLH71yaf4MiWjH978dy/TYMF5fd5iZH+4ideKbMOw+2PKmSsO8zL68pnFUmSS/+2w3h7MLeee2QfToeN7w0c1o4J4xkcSnniE+5fT5i1I2QP6Jxtu91IXBACED6ly5lJRX8e1eO9e2gGpudmKX4/q31IFFYG62p8AABISp9szT56stxvljVN8YvYqxioOZKlOslxaXloG/lzuv3BrLm7MHciS7kGvf2MyXQY8iJ/5VmSN+NgfKi5w9zRbDS6uT+OlgNn+/vi9jewZd8votQzvT1tud92quXvYsBc8AVUHLDq4IAAAgAElEQVRuL0IHqqB+ZfklL61OzKKwrJKZQ+y8JXZiF5gqbO88aQUXC8zrP9lplSGEioM9vB26T4K1f4cPr4LsuuNZmtpJziogrG0b/B3hiH0RWlyakKkDQvnxd+OICQvgT1/t4+GMSRRf/YqqCF90g8ry0djEl/HpvLf+GLeP6MKdI2tPZfX2cOPOEV356eApjmQXQlkBHFwJMTeqxlf2IjRWpTXnHLzkpWU7M+jcrg3DIuzs3pBmbg7WeZh9x20gRrPA3DQonFd/OmQ/gQHw6wSzFsNNH8Lp4/DeWNjwsrLZ0TSI5KwChxdPWtDi0sSEtW3DkntH8PiU3qxJPMUVv0SQNPYtyNyramHOpjt7is2WHSmn+cvyfYzu0Z6np0bXm2p556gIPIwGPth4TLWorii2rbalNuoI6p84W8Lmo7ncNCjcvrUtoOItHfs61XLIaBC8dPN5gXljnR0FRgjod7NaxURdqyxkPrgCsvbb7x4tlPJKE0dzCpskUwy0uDgFo0Hw4ITuLH9oNN6eRqasacuinq8iC7KUwOjlfqOxZIaFB3rzzpzBl3Xz7eDryc2Dw/l61wnKd34K7bpB5+H2nVRgJHgFXCIuy3dl2L+2BVQcIn17k8dbaqOmwLyy1s4CA8q/75aFcMsiyD8J70+AX1+odQtSoziaU0ilSdpWmZ/T8FiaFhcn0i88gG8fHcNtw7vw9z1tecjjn1RWViiBsdQqaC5LYVkl9yyMp7LKxAdzhxDg3bD95HvHdqOj6RQe6ZvVqsXeRWVCmCv1z2eMWWpbRnRrR+d2dnbMPrUfyvKdEm+pDYvAzBgU5hiBAeh7Azy0DaKnw6//hv9NrDdDrzWTZM4Us2lbbO1TDT5Vi4uT8fZw45839uODO4ewrTiEq/P/Rr7BH7lwGhxa7ezpuTxVJslvl+7mSE4h79w2mO51tAKujYgOPvw5RH0QFfW+2TETDB0IpxKhsgyAnalnSMkr5ubBne1/LxvMKh2F0SD4z80DqgXmTUcIjE97uOkDmLUEinLgf1fAuueqf+caRVJWAR5GA5EdrGwQdipR+e41EC0uLsKVfYP58XdjCe/Wh4mnnyTFEI5cOlvZkWjq5MUfk1iXlM0zU/sypmcje7RLyeTKX9lq6sNSO2XOXkLoQJW9dSoRON+35Zr6zDOtJXULBHRWRYguRLXADAzjv44SGIDe18HD26D/rbDxZXhvPGTsdMy9miHJWQV07+hrfQO4za+rLqsNRIuLC9HRz4sF84by2LRRzCj+C7tkFFUrf6e/gdXBF/HpvL/hGHeM6ModIyMaP0DGDrzyj7Mr8Bo+2nSciioHFLSGmu33M/dU17Zc28/OtS2gWmunbXWpVUtNjAbBf2aeF5i3fnaQwLQJhBvfVSaxpefgwytV6nJFqWPu14xIyrQhU+xMKuxbBkPmNfgSLS4uhhCCuaMi+PzRq1jlfSPGqhJKU7Y5e1oux/bjp/nr8n2M6dGBv0/ta90ge5aAWxuir7ydk+dKldGovWnbVX3gndxdXdtid7sXgDPHoTDL4X5itlBTYF5e40CBAeg1GR7eCgNvV9+454+BtNb7d3S2uJys/FLrxSXuLRAGGPFQgy/R4uKi9Ar247ppM6mSgtT4hu9ztgbSTxfzwKc76RzozdtzBlm3zK8ohcSvoc9UxkZ3o1ewL++tP4bqom1HhFBbYyd3O662BWrEW1xXXKCJBcYrAKa9qXopVZaqRJkf/wLlxY67p4ti6eFiVRpyYQ7sWqTcKwLCGnyZFhcXZlCvCJIN3TCkrHf2VFyGgtIK7l64o9GZYZdw6Ae1bRI7G4NBcO/YbiRlFbDhcK59JwwQEovMPsiOoycdU9sCKt7i1RaCett/bDtjEZgbzQLz9i9HHHvD7lfAQ3GqQ+zWt2H+aEjZ7Nh7uhjJZnHpY00a8rb5amt+9G8bdZlTxUUI8ZEQIlsIsb/GsXZCiLVCiMPmx0Dz8QAhxCohRIIQIlEIMa/GNXPN5x8WQsx1xntxBEaD4EzHUUSUHOTcubPOno7TqTJJHlu6m6M5Rbx7+2C6NSIz7BL2LAW/UIgcD8ANsWEE+3vy3vqjdpptDUIHIkyV9CbN/rUtFizxFkPz+L5oNAheNgvMf1YnO15gPP3g+ldg7ipVD7TgWvj+T1BW6Nj7ughJWQW09Xano59n4y4szVedQvtMhQ49G3Wps/9PXABMuejYE8A6KWVPYJ35Z4CHgQNSygHABOC/QggPIUQ74GlgODAMeNoiSC2B4NircBdVJGzWW2Mv/HCQX5JzeGZaNKN7NDIzrCaF2cpyZ8Ct1Z0aPdwM/GZ0JFuO5rEv45ydZqyQ5qD+tI6n7F/bAmrbIu+wy2+JXYxFYKbHhjaNwABEjlOrmOEPqGZk746EY786/r5OxuoGYTsXqBX+mN81+p5OFRcp5QbgYkOtG4CF5ucLgemW0wE/oX47vubrKoGrgbVSytNSyjPAWi4VrGZL90GTqMCN/IPrnD0Vp/L5jjT+t/E4c0d25Y4RNra/3fclyKpL7F5mD++Cn6cb722w7+pl5xkf8qQfV/hn2nXcatLN8ZaurlE82RiMBsF/b4ltWoHx8IFrXoR5P6i+N4tuUO2VS/Mdf28nYDJJDmUV0LuxPVwqyyDubbW6t6I3kLNXLrURLKW0/BVmAZbmGm8BfYCTwD7gt1JKExAG1DTkyjAfaxEIT1+y/GLoci6evMLWmZK87Vgef/tmP2N7duCp663MDKvJnqUQOgiCoi447O/lzpzhXfh+XyZpefYL+i7bdYIDdKdLWbLdxryA1Dhw81JuAM0Qi8Dc0JQCA6oN9AObYOQjKmD9zki1om1hZJwpoai8qvHB/ITPVAbimN9bdV9XFJdqpErdsaTvXA3sAUKBWOAtIUSjpFgIcZ8QIl4IEZ+Tk2PfyToQr54TiRbHWbfbUZV+rktanjkzrJ03b80ZhJu1BWAWsvbBqX0QO6fWl+eNjsRoEHy4yT6tdYvLK/l2byalQf0x5iSpfiv2Ji0OwoaAm4f9x24ijAbBKzUE5p1fm0hgPLzh6n/Cb9ao55/eBN88DCUtJ8Zple2LqUqlcIfEQrcJVt3XFcXllBAiBMD8mG0+Pg/4WiqOAMeB3sAJoGZJcrj52CVIKd+XUg6RUg4JCrq0z4er0qH/lRiFJHXXGmdPpUmxZIaZJHw4dygBbezQg2LPUrUVEnNTrS93CvBiemwYn8enc7rIdhNES21Ll5hRaivO3u69ZYWQmeCyxZONoabAvPRjEwoMQOehcP9G9S09YSm8MwKSW0ac05KG3Cu4EeJycBWcPqp+H1Z67rmiuKwELBlfc4EV5udpwCQAIUQwEAUcA1YDk4UQgeZA/mTzsRaDCB9GhcGToJxtZJ1rHZXGVSbJo0t3czy3iHdvG2S9H9IFg1bCvi+g19X1WtLfN64bpRUmPolLtfmWltqWnrFj1YE6OlNaTcYOJVpdm1cwvy6MBsF/Zw6oFph3f3VA9l5duHvBlc/APT+pwtelt8LX9zX7PkvJWQV0be/dcFcIKWHTq9Cuu8oSsxJnpyIvBeKAKCFEhhDibuAF4CohxGHgSvPPAM8Bo4QQ+1BZZI9LKXOllKfNr+0w//uH+VjLwc2DirARjDQk8t0+BwWFXYx/fX+QX5NzePaGaEbZkhlWk6PrlLFhHVtiFnoG+zGpd0cWxqVQUm59O92MM8VsOZqnalsCwsCno/3FJW2rqpwOd05zMEfgZjTw35kDmDYglBd/TGpagQEIGwT3/Qrj/gz7v4K3h6tv8s2UpKx8ohqzajn2K2TuUXUt5mxKa3B2tthsKWWIlNJdShkupfxQSpknpZwkpewppbzSIhRSypNSyslSyn5Syhgp5ac1xvlIStnD/O9j570jx+EdNZEoQwYbdic6eyoO57PtaXy46Th3jYrgtuE2ZobVZPen4N0eelx12VPvG9eN00XlLNuVYfXtlu86cb5vixDKZyzTznbwaVsgOAa8bOjR4YK4GQ28cst5gZnviPqjeifgCVf8Fe79BfyC4fPb4ct5UOSAIlsHUlpRxfHcosbFWza9Cr6dVEW+DbjitpimNiLHAeCfGUf66ZZrX7G1RmbY367rY7+Bd3yoWhkPurNBge9hke2I7dyW/204RpWp8ZYwUkqW7cpgZLf252tbQgdCThKUFzV6vFqpqoCM+GZX39JQagrMCz84QWAAQvorgZn4V7V6eXs47P9abR01A45kF2KSNLxB2ImdcHw9jHxYCawNaHFpLoTEYvLwZ6QhkVWOMFh0AVLzinjw0510bW+nzDAL+7+G7/4AvaaoD4kGIITg/nHdSDtdzI/7sxp9y/jUM6TmFV9oUhk6EKRJZazZg8y9qj1zC4m31IZFYKY6U2CM7jD+z3D/etXOYNk8+OIOVYzr4hzMVJliDU5D3vSa8mRrhPtxXWhxaS4YjBgix3KFx0FWJbS8uEt+aQV3L4xHYsfMMIAj61RQtstImLlAfVA0kMnRnYho7837G4422tByWXwGPh5GrulXo2+LpQ7FXp0S0+LUYwtduVhwMxp4tYbAOMSipyEER8PdP6mg/6E18PYw2PuFS69ikrMK8HQzENG+AQkxuYfV6mzYfcoux0a0uDQnIsfRyZRFQdZRjmQXOHs2dqOyysSjS3aTklvEO7cNIsIemWEA6TvUXnlQb5i9FNzbNOpyo0Fwz9huJGScY+uxhueIFJdX8t0+1bfF26NGho5/iNrLtldQPy0OAiPBzwGNx1yMmgLzb2cKjNFNpec+sBHa94Cv74WlsyHfNb/wJZ8qoFewH8aGmKVufl0V4w5/wC731uLSnDDHXUYaElvU6uVf3yex/lAO/7ghhlHd7ZQZln0QlswE32C4/Sto09aqYW4eHE57Hw/eb4QlTL19W8z2+zYjpRKXFr5qqYlFYK7vH8K/f0hq1H8TuxMUBb9ZDZP/Ccd+gXeGw+7FLreKOZhZ0LAtsfyTqiJ/0B3gY5+/QS0uzYmOfcC7A9P8j7Jq70n79x5xAku3p/HR5uPMGx3BnOFd7DPomVT45EYwesKd36hsHyvxcjdy16gIfknOqbYtvxzLdmbQpZ03Q2vr2xIaC7mHbHfjzT0MxXktOt5SG25GA6/dGsv1/UP41/dOFhiDEUY9Ag9sho59YcVDsHgmnLM+w9Ce5BaWkVtY1rBMsbi3VTxw5CN2u78Wl+aEEBA5jsGmfRzLKeRAZvM22os7msdT3+xnfK8g/nqtnTLDCrPhk+kq0H3HcgiMsHnI20d0pY27kfc3XN4S5oLaltq2IkIHAhKy9to2qep4S/Mzq7QVlxIYgA494K7v4ZqXIHUzvD1CuQk7+cuf5cvQZQ0ri09D/MfQ72YItF/qvxaX5kbkOLzLsulpyGrWW2MpuUU8uHgnER18eHPOQPtkhpWeU95QBVlw2zIItoPJJRDo48GtQzuzYs8JMs/V7w1mqW2ZMagO79TqoL6NW2NpceATBO272zZOM8UiMNeZBeZ/DRB+h2IwwPD74cEtanW66rfqS84Z210erMVi+9I75DIrlx0fQEVRo5uBXQ4tLs2Nbqq51e2dUlmV0Dy3xvLNnmEAH84dgr+XHTLDKkpUYDX7ANzyCXS2b8X63WMikcBHm47XeU6ttS0X4xesmpTZmjGWukX5iVnp+9QScDMaeN0sMP/8/qDzBQagXSTcuRKue0XVIL0zUvWNMZmafCo/J50irG0bOvjWU69SXgRb31Vp+sHRdr2/FpfmRmAkBHRmkudBTpwtYXd683Jvrawy8ciS3aTmFfPubYPp2pAUyctRVQnLfqM+cG98D3peafuYF9G5nTfX9Qth6fZ0zpVU1HpOrbUttWFrUD//JJxNbVXB/LqoFph+SmA+2OgCAmMwwNC7VVOyzsPg+z/CwqlwuunmdvhUAZuP5F0+jrn7Uyg5bbWtfn1ocWlumOMuoWfi8XSDVQnNq6Dy+e8OsuFQDs9Pj2Fk9/a2D2gywcpHIfl7uPY/at/YQdw3rhuFZZUs2ZZW6+u11rbURuhA1TnS2uZUraS+paG4GQ28PksJzPPfuYjAALTtouJ+095UMbZ3RkHcO8rO3sEsikvFw83ArKGd6z6pqgK2vKn+P3KAq7YWl+ZI5DgMpWe4PaKA7/ZmWmVP4gwWb0tlwZYUfjM6klnD7JAZJiWsfQoSlsCEv8Cwe20fsx5iwgIY06MDH28+TlnlhR8Qdda21Ia57bHVQf20reDuA536W3d9C8RlBUYIZTn00FaIHAurn4SPr1HZfg4iv7SCr3ZlMLV/KO3r2xLb/xWcS3fIqgW0uDRPIpR9+4zAY2QXlLH9uOubQG85msvTKxKZEBXEX67tbZ9BN70KcW/BsPuVPUcTcP/4bmQXlLFi94UrxnprWy7G1qB+apzqP2JsoIV6K8HNaOA1VxQYgIAwmPMFTJ+v/OXmj1FFiw5YxSyLz6C4vIq7RkXUfZLJpKxeOvaFnpPtPgfQ4tI8CQiD9j3oXbIbbw+jy3uNHc8t4sFPdxHZwYc3ZtspM2znAlj3LPSbCVNeaLLA9pgeHegb4s/7G49hqrFi/DK+ntqWi/ENgoDO1olL6Tk4tV9vidWBu1lgru3XyfUERgiInQ0Pb4fuk2Dt3+HDqyA7yW63MJkki+JSGNSlLf3CA+o+8fBqyDloUzOwy6HFpbkSOR5j2hYm927PD/syqahq+myUhnCuRGWGGYTyDLNLZljiN/Dt75V1/vR3VQC1iRBCcP/4bhzJLuTnJGVceNnaltoIGWBdxlj6dkBqcakHd6OB12cNdE2BAWXXM2sx3PQhnD4O742FDS+rxBQbWX84h5S8YubWt2qREja+omJC0TNsvmddaHFprkSOg/JC5oTncaa4gs1HXK/PhMoM20X66WLm3z6YLu3rSM9tDEd/UX5O4cPglkWNMqK0F9f2CyGsbRveMxfvfb1LddWus7alNkIHqjayje3VnhYHBjcIH9K461oZFwvMh/WkkDsFIVTyycPbIOoa+Pk5+OAKm1PUF25JIcjPk2tiQuo+KS0OMrbDqMccurWqxaW5Yo67DDLtxc/LzSULKp//7iAbD+fy/PQYhnezQ2ZYxk747DZo3xPmfAYedhArK3A3Grh7TCQ7Us6wM/U0y3ZepralNkIHqsfMhMbdPDVOrXo87GTu2YKxCMw1MZ147tsDricwAL4d1ZekmQuV+eX/JsLqv1rV8yclt4hfk3OYM6wLHm71fLRvehW8O8DA222Y+OXR4tJc8WkPwf1wS93IlOhOrEnMorTC8SmODeXTrSoz7J4xkdw61A6ZYTnJsPhmZap3x9eqx7kTuXVoZwLauPPHL/eSdroBtS0XUy0ujfimWlmmmjnpLbEG42408MZsFxcYgOjp8Mh2lVkW95aykDm0plFDLIpLxc0guK2+2pas/XB4DYx4sNEu4Y3FqeIihPhICJEthNhf41g7IcRaIcRh82Og+fifhBB7zP/2CyGqhBDtzK+lCCH2mV+Ld9b7aXIix0HaNqbFtKegrJJfk3OcPSMAthzJ5emViUyMCuJJe3iGnU1XRpQGN7MRpfMt5n083bhjRFeO5xY1rLblYrzbqT3vxgT1T+6GqjItLo3kYoGpz2XBqbQJhKmvw7wf1Qf/kpmqtXLBqcteWlRWyZfx6VzbL4SO/l51n7j5NfDwg6H32HHitePslcsCYMpFx54A1kkpewLrzD8jpfyPlDJWShkLPAmsl1LWzMGdaH699WxGdxsPVWWM8jhKex8Pl8gaO55bxIOLd9E9SGWGNaiPRH0U5SqPprJCtWJp180+E7UDc0dF4OVu4Pr+oZevbamNxlbqVxdP2r/graVjEZgp0Z34hysLDCin6wc2qq6pSd/C20NVdmQ9FjJf7z5BQVll/YH808dVbcuQeVa3oGgMThUXKeUG4OIijRuAhebnC4HptVw6G1jqwKk1D7qMBGHEmLqRa/uFsO7gKYrKbM84sZZzxRXcvWAHRoPggzuH4mdrZlhpvjKiPHcC5nwOnfrZZ6J2IsjPk+8eG8tTU600yAyJhTMpUHKmYeenxkGHXnbrt9HacDcaeHPOeYH5eLMLC4ybp6rdenALBPdTRpgLrlXbwxchpWTRlhT6hQUwqEs9ohH3llr9j3jIgRM/j7NXLrURLKW0RKezgAuacQghvFGrna9qHJbAGiHETiHEfXUNLIS4TwgRL4SIz8lxjS0km/Dyh7BBcGw9UweEUlph4qeDl19CO4LKKhMPL9lF+hk7ZYZVlMJnc1RNxy2LXLZvSfcgX3w9rcy4scRdGpIhZDJB+la9JWYjNQXm2VUuLjAAHXrCXd/CtLdUA7x3R8Mv/1J/H2a2HM3jcHYhd47siqirZqUwW/mIDZitOqI2Aa4oLtVIZfl7sbfJVGDzRVtiY6SUg4BrgIeFEOPqGO99KeUQKeWQoKAgx0y6qYkcByd2MqSTG538vZyWNfbctwfYdCSXf07vx7DIBhQS1kdVJXx1N6RsVHUsvRxTQex0QhtRqZ9zUBVQanGxGYvAXB0dzLOrDrDA1QVGCNUh8pF4iL4R1r+oKvxTNgEq/bidjwdTB4TWPcbWd1VCiJ1t9evDFcXllBAiBMD8mH3R67O4aEtMSnnC/JgNLAfs67fuykSOA1mFIX0r1/cPYf2hbM4V1+7a6yg+iUthYVwq946N5Jb6jPIagpTw7W/VXvOUF6H/LXaZo0vSJlA1M2tIxljqFvXooiu45oa70cBbcwZxdXQwzzQHgQHl7HDT/1Tb7qpyWHAdRV8+yI6DR5k1tDNe7sbarys9p3q29L2hSfv/uKK4rATmmp/PBVZYXhBCBADjLzrmI4TwszwHJgPV2Wctns7DVTvf42prrKJKsvpAVpPdftPhXJ5ZdYArenfkiWvskBn209Nq+T7+cRjxgO3juToNDeqnbQW/EGhrv06BrR13o4E3ZzczgQHocaUywhz9W9okfs5ajz9yb9tddXe+jP8YyvJhzO+adJrOTkVeCsQBUUKIDCHE3cALwFVCiMPAleafLdwIrJFS1qwwCgY2CSESgO3Ad1LKH5vmHbgA7m1Uz4jjG+gfHkCXdt5NZsN/LKeQhxbvpEeQL6/PirU9M2zTa8rMb+g9MOFJ+0zS1QkdCGfTVKvZupBSZYp1Gdmqm4M5Ag83JTCT+yqBWbglxdlTahge3pROeJpZ4gUKvEIJ/PFBlfxyJuXC8ypKYes70G3i+RhfE+HsbLHZUsoQKaW7lDJcSvmhlDJPSjlJStlTSnllzdiKlHKBlHLWRWMck1IOMP+LllL+s+nfiZOJHA9Z+xAlZ5g6IIQtR/PILSxz6C3PFVdwz8J43IwGPpg7xPbMsF2L1Kol5ia45j+t50O0IQ7J59Ih/4SOtzgIDze1RTa5bzBPr0xsNgKzYs8JtpeEcWrmKrjmJUjfpoovN79+3qcsYSkUnnKYrX59uOK2mKaxRI4DJKRsZOqAUKpMkh/2O25rrKLKxENLdpJ+ppj37hjcONuT2ji4SqVadp+kLMmb0IjS6YQMUI/1iUuqub5Fx1scxsUCsyguxdlTqhcpJQu2pBIV7Mfw7kEw/H7lU9b9CuW2/L8Jqs3y5tchdJD5M6JpaUV/xS2YsEGqedTxDUQF+9Gzo69Dt8b+seoAm4/k8a8b+zXMYr4+jm9QLYrDBsOtn4Cbh30m2Vxo01YVhtYnLmlx4Omvem9oHIZFYK7qG8zfV7i2wMSnnuFgZj5zR0WcTz8OCIfZS+DWT1Xx8QeT4Mxxh9rq14cWl5aA0R26joLjGxBCMHVAKDtSTpN5rsTut1oUl8InW1O5f1w3Zg6xMTPsxC5YOhvadVeNlFqrGWPowPoNLNPiVOKGoY5sII3d8HAz8HYzEJgFW1Lw93Jj+sBa0o/7TFWrmGH3Q+/r1T8noMWlpRA5DnIPQX4m1/cPQUr4bq99a142Hs7h2VUHuLJPR/48xcZukrmHlRGldztl6+Jt4wqoORM6UMVVCmsp7C0+rToXasuXJsPVBSbrXCk/7s/i1qGd67Yd8gqAa19SfWOctM2sxaWl0G28ekzZSLcgX2LC/FllR3E5mlPIQ4t30bOjL6/NstEz7FwGLJoOwgB3fAP+9RR/tQYsQf3a6l3StqrHrqOabj6aaoG5so8SmE/iUpw9pWoWb0vFJCV3jIhw9lTqRYtLSyG4H3i1hePrAZjaP5SE9LOk5RXbPPTZ4nLuWRiPh9HA/+4cYr3dCUBRnnI4LstXxWBNWNTlslQH9WsTly1g9FBBWU2T4uFm4J3blMA85SICU1ZZxdLtaUzq3dE+zfcciBaXloLBAJFj4dgGkJLr+iv/IFudkiuqTDy0eBcnzpTYnhlWVqC2ws6mwezPzn+otna8/FUDtNqC+mlblbC412OjrnEYriYw3+3NJLewnDtHRjh1Hg1Bi0tLInI8nEuDMymEB3ozuGugTVljUkqeWZnIlqN5/GtGP4bYkhlWWaa6SGYmwM0fQ8Ro68dqiYTGXiou5cXqmE5BdirnBaajEpitqU6by8K4VLoF+TCmh+s7Y2txaUlYctmPbwBgav8QkrIKOHyqwKrhFsWlsnhbGveP79b4Tos1MVXBV/eoLbsb3obe11o/VksldCAUnLywMdSJnWCq1MWTLoASmMFKYL7Z7xSB2ZN+loT0s8wdGYHBVjeMJkCLS0uiQy/w7VQtLtf2D8EgsCqwv+FQDv/49gBX9gnmz1fbkBkmJXz7ezi4Eq7+N8TOtn6slkxtbY/T4gCh7H00TsfDzcDblhXMN/v5tIkFZuGWFHw93bjJli96TYgWl5aEEGr1clzFXTr6eTGiW3u+TTiJrMvUrhaOZBfy8BJLZpiNnmHr/gG7FsLYP8LIpmlS1Czp1B8QF26NpcWpwsk2gU6bluZCPN2M1QLztyYUmJyCMr7de5KbB4fbllDThN71NIgAABaOSURBVGhxaWlEjoOi7OqOdVMHhHIst4jEk/kNulxlhu3A0015htn0P/KWN2HTKzB4HlzxN+vHaQ14+qqVpyVjrKoS0rfreIsLYhGYSb2bTmCWbk+jokpyx8jm44qtxaWlUR13USnJU6I74WYQDcoaq6gy8eCnuzh5tpT37hhMeKANmWG7F8Oav0Hf6XDdf1uPEaUt1LTfP7Ufygt1vMVF8XQz8s7t5wVm8TbHCUxFlYnF21IZ27MD3YN8HXYfe6PFpaUR2FX1/DDHXQJ9PBjbswPfJmTWuzUmpeTplYnEHcvjhZv6MbirDZlhSd/BykeVzfeM97VtSUMJjYXCLMjPNMdb0OLiwlgE5oreHfnrcscJzOrELE7ll3HXqAiHjO8otLi0RCLHqRbBpipAbY2dOFvCrrSzdV6ycEsKS7al8eCE7swYZEPAMGUTfDlPfVDe+im4eVo/VmujZlA/LQ4CukBAmHPnpKkXTzcj79YQmCXb0ux+j4VbUujSzpsJUR3tPrYj0eLSEuk2QbU2zdoLwFV9g/F0M9RZ8/Jrcjb/+PYAV/UN5k+To6y/b2YCLJmlWvfetkzFETQNp1M/ZYlzYpey2dfxlmZBTYH5y/J9dhWYxJPn2JFyhjtHdrW9GV8To8WlJRIxVj2at8b8vNy5ondHvtuXSZXpwq2xI9kFPLpkN1Gd/Hnt1ljr8+dzj8AnM5SF/B3LW7cRpbV4+ECHKEhcrpIytFlls8FRArNwSwpt3I22O5A7AWe3Of5ICJEthNhf41g7IcRaIcRh82Og+fifhBB7zP/2CyGqhBDtzK9NEUIkCyGOCCGecNb7cRn8giGod7W4gNoayykoY9vxvOpjZ4rKuXthPJ7uKjPMx9rMsPyTyi8MlBGl3sqxntCBkHdYPe+izSqbExaBmRgVxF+W72PpdtsE5kxROSv2nOTGQWEEtLGx06sTcPbKZQEw5aJjTwDrpJQ9gXXmn5FS/kdKGSuljAWeBNZLKU8LIYzA28A1QF9gthBCd1WKHAepW6CyHICJUR3x8TCyKkEVVJZXmnhw8U4yz5by3h1DCGvbxrr7FJ9WwlJyBm5fBh162OsdtE4scZc27SDIhi1KjVPwdDMy/47BTIwK4smvbROYz+PTKas0cWczSj+uiVPFRUq5ATh90eEbgIXm5wuB6bVcOhtYan4+DDgipTwmpSwHPjOP0bqJHAcVxcpCBGjjYeSqvsH8sD+T8koTT6/cz9Zjp3nx5n4M7mplkV5ZISyeCaePw+yl5z8YNdYTarbf7zJCp283U9QKxjaBqTJJPolLZUS3dvTu5O+AWToeZ69caiNYSmnxK8kCgmu+KITwRq12vjIfCgPSa5ySYT52CUKI+4QQ8UKI+JycWhoztSS6jgbEJVtjZ4sreGTJLpZuT+ehCd25caCVmWGVZfD57XByF9z8kXJk1thOp37g3QF6Xe3smWhswMv9QoH5rJEC89PBU5w4W9Ls0o9r4oriUo1UhRkXF2dMBTZLKS9e8TRkvPellEOklEOCgoLsMkeXxbudsrSvIS5jewbh7+XGmgOnmNw3mD9amxlmqoLl98OxX2Dam9DHOW1UWyTubeAPSTBorrNnorERi8BMiAriiUYKzMItKYQGeHFln+DLn+yiuKK4nBJChACYH7Mven0W57fEAE4ANVMpws3HNJHjIGO7sm5HGe/NGx3JqO7tedXazDAp4bs/qIymq56DgbfbedIajO56S6yF4OVuZH4Ngfl8x+UF5tCpArYczeP2kV1xM7riR3TDcMWZrwQsX9v+v70zj5aiuvP45/veYwcREEExCKgwAipHUNwAFzDmGJXEaDQq4Dg6iYlbjrvjicYlRs9JzGaiRoWjQkIUlxAdTZyIGyrgFo0bjhhlUFQUUUTF95s/7m0png/eVl1V/fr3OadP19r96Vt9+9d169bvTgXuKK2Q1BOYkFwGLAC2kzRYUkdC8LkzI9diM3gCfP4pvP7YF4tOmzSUmcfv1vqeYX+/BBbdAHueCnuenJKo47RfSgFmwtC+nHVr0wFmxiNL6FhXwxG7DMzIsDzk3RV5FjAfGCbpDUnHAZcBkyS9DEyM8yW+AdxrZh+VFpjZWuAHwD3A88BsM3suq89QaAbuBjV1X+QZazPzr4IHroCdp8DEC9J5TcepAjp3qOXqY5oOMCs//ow5Tyzl4J22pHe3jhlbpkuuuZvNbEODe+y3ge2nE7ovN1x+F3BXamLthU7dYcCY9a67tJqn/wD3nAPbHwRfv9KbbRynhZQCzH/euIiz5/wDIQ7fZf2bI29Z9AYff/Z5RV/IL1HEZjEnTYZMCJl216xs/Wu8eDfcfmJoZjv0Ok9E6TitpBRgxm/Xl7PmPMPsBes6utbXGzfOX8LorXsxckDP/CRTwoNLe2fweLD6cENla3jtEfjTNNhiRzjiZk9E6ThtpBRgxjUIMPNeepsl765majs4awEPLu2frXaBus6taxpb9gzM/Db0/AocdSt06pG+n+NUIZ071HJNgwAz/ZElbN6jE18b2T9vvVTw4NLeqesULuy3NLi8+wrcdCh02gSm3A7d+pTHz3GqlIYBZt5Lb3PU2K3pUMHdj5O0j0/hbJzB48PIhh82MyvBB8vgxslQvzZkOO7ZhvFdHMfZIMkA07VjLUeOrbzsxxsi195iTkYMnhCelzwII7+58W1Xr4Cbvhmep94JfYeW389xqpjOHWq5YdouvL/6U/p0bz/XNP3MpRrYYlRo3mqqaezTj8I1lncXh4v3A0Zn4+c4VU5tjdpVYAE/c6kOautCIsuNBZe1n8LsKbB0IRw2I4xm6TiO00r8zKVaGDweVrwCK9/48rr6erj9u7D4b+EGyeEHZ+/nOE67woNLtTB4fHh+9cH1l5vB3WfCs7eGlC6jPRuv4zhtx4NLtbD5cOja58tNY/dfBguuhT1OCskoHcdxUsCDS7VQUwODxoUklhaHyHnsaph3GYw6OqTP93xhjuOkhAeXamLIBPhgKaz4X3hmdmgOG3YgHPQLDyyO46SK9xarJkr3u9z3Y3hhbjiT+db1oTeZ4zhOiviZSzXRewhsMgD+eTv0GwFHzIQOnfO2chynHeLBpZqQYIfDoP+OIRFl503yNnIcp53i7SHVxsQLYNKFeVs4jtPOyXuY4+slLZf0bGJZb0l/lfRyfO6VWLe3pKckPSdpXmL5Ekn/iOsWZv05Kgq/cO84Tgbk3Sw2HTigwbKzgfvMbDvgvjiPpE2Bq4CDzWwEcFiD/fYxs1FmNqa8yo7jOE5T5BpczOwBYEWDxYcAM+L0DGBynP4OMMfM/hX3XZ6JpOM4jtNi8j5zaYx+ZrYsTr8J9IvTQ4Feku6XtEjSlMQ+Btwbl5+QpazjOI7zZQp9Qd/MTFK8nZw6YDSwH9AFmC/pUTN7CdjLzJZK2hz4q6QX4lnResTAcwLAwIEDs/kQjuM4VUgRz1zekrQFQHwuNX+9AdxjZh+Z2TvAA8BOAGa2ND4vB24Ddm3shc3sGjMbY2Zj+vbtW+aP4TiOU70UMbjcCZRS804F7ojTdwB7SaqT1BUYCzwvqZukHgCSugH7A8/iOI7j5IaslMQwjzeXZgF7A5sBbwE/Am4HZgMDgdeAw81sRdz+DOBYoB74vZldKWkI4WwFQtPZTDO7pBnvvQp4MdUPVB42A97JW6IZuGe6uGd6VIIjVI7nMDPr0dRGuQaXPJG0sBK6LbtnurhnulSCZyU4QvvzLGKzmOM4jlPheHBxHMdxUqeag8s1eQs0E/dMF/dMl0rwrARHaGeeVXvNxXEcxykf1Xzm4jiO45QJDy4FQip2yuKi+zlO0b+jRfdLk3YdXCTtG2+sLDSSLpG0vRW/jbJDaaKaKkk5kNQ9MV3YsvQ6lDpVU4faZXCRdJSkRcA+wGd5+2wISd+R9ABwInB03j4bQtKRsTwvkXQKhLxvOWuth6TjJV0laZu8XTZG/G4uBK6Q9GMoXlmC16G0qYQ6BCH/oqSLJHVp62sVOnFlS5FUB5wKnAd8zcwezVmpUSRtAlwBDALOAbYHesZ1KtKXTtIY4CTg+8Bi4D5Jq8zs+iK4SqoFvgWcCSwDxkpaamZr8vRKEv+hdgZOB/YFfgi8C0yXNNvMCpGuKHrWAadQ4DokqQboAVyO16E0/ErH/T+As4A1wL3Ag2153XZ15mJma4GXgZuA1yR1lHSopC1zVlsPM/sAuNbMvmpmDxOGDDg8ritMpYiMAP5mZo/GhKE3A5dK6pmnq6QOAGb2OfAkIVnpb4HxhB+aQiCpgwU+Bm4zs31ixu6OhO/q0nwNAwnPz4CXCMe5cHUoetab2UoKXIdK38/IcMIAiIWqQwCSOiWO+yJC3bkaOFZSn7a8dsUHF0nnShqbWDSfkJPsbuAJ4BvADEnnxe1z+cxJT0m1ZpYcjvlWYK2kHfNwS9JIeb4JfFXS8DhfD3wAnBa3z7w8JZ0DXCdpmqTeZvaSmb0H3AIIGKfE8Nh50cCzT+kMRdJ+hD9AmwM/k3R6XJ7Xd7PkeaxCEti/A/+ieHUo6bmZmS1MXLcoUh1KetYBS4ADilSH4vueDcyOTcpfMbPH45+g3wJbARPb5GZmFfkAtiB8od4HXm6wbnfgUmCrOD8SeA/oUyTPxDaDCT82owpanpcDvyf8s7kcGAMsBLpl7PhvwCOxrA4C5hKaGjomttmfMILpfg32VQE8O8X1w4DeiekPgc1yOOaNef4grtsd+ElB6lBzjnsR6lBDz78kyvOnRahD0WUE4U/4TcBE4C7geMLJRm3c5khCJvohrX6fvA5ECgXUFTgiTi8AfphYV1eqyIllswjZPAvhCdQ12O5BYFqcrimKZ6I8NwF2ivODgF8Ser5k+aM9ADg1Mf/vwE0Nywy4kHDt7UDg+zmU5QY9N7D9LGB4QTxnxumOBapDGzvuSizPuw415jkrUZ6516H43j2B/RPzVwBTG9nuRkIW+l2Ao1r8PlkfgJQLqUd83otwgbRjwy9WPHi/IvyL6Fw0T9b9UzgZuLmg5VmX2KYjcB3w85wcuyWmRwB/Zt0ZQU183p0wyNz/AacVzTOxvC7+wPwF6Fogz84NtilCHdrYcS9SHWpOeeZahxo5rsuj53HA1on1k4CPgdeB41r6+hVxzUXSgMaWm9mq2NviIWAe8Lu4vD7udwjh9O9z4DArcw+i1nhauCAN8AlwWxZ931vhuTbutzOhPR5CD508HD9KzO4LvG5mn8R19ZL6Epod/gxsa2Y/L5pn3O9o4HHWfTdXF8jzi3oS69Aj5F+HNnbcS3VoDfnXoabKM7M6FN+vUU8ACxfxHzWzzYHzCWNoHR332xa4iNB0NszMrmvxm+cZOZsRWScS2igvbrBcrMuLVhef+wErCAPujIgFtRUwqMCe2wM7lP5FFNhzOLAt0AnoVyDHK1nXlLczMCBOl/36RRs8RwObAkOAwQX37AVsXcA61PC4D4rTRatDDcuzH9Cl3HWouZ6N7HMhcGac7g30b5NDuT9kKwpFhNPGq4CngMkN1tcmpvuSOOUkXDCrJ1wzKGsbdkqejwMjKsBzQTk9W+HYLU5fS7h/4AZCk01Zf6xT8ryLMv9YV5lnJR33onl2Ssz3iWU5LTWfcn7YNhbUDOCCOF1DvBCWWP9rwpDIw+P6Ywhd/s5wz8r0bIHjdoR/gR8Dz5K4iOqe7umezfYcSTg7/TUhGJ2ZpkdhUu5LOhnYEnjSzP6okMbjGsINchMJF5WWATMJP3oXEg7ae3H/McBiM3vfPSvDMwXH04AZZraiXI7u6Z5V4Hk0cFfqnllG1A1EWRFuJnqYkMbjeWLPBMIp5VzCvQA9CGkprgY2Texf556V5ZmCY225Hd3TPd2zjX5ZFEIzCulOYJ84fQDwC+DwON89sd14QvTtFgs2077s7lldju7pnu7Z+kemXZEbdhFMpBZYCIwDMLP/Bl4ARksaZmYfJnaZBKwG1lig3j2L7VkJju7pnu6ZPlnf57JeGufEB1wM9JC0Q5yfR7ibtQeApCMkPUvoFnmurevX7p7F96wER/d0T/dMmUyCi6TdJN0K/EbS/gpp0ksp8iF0yV0L7C+pzsz+SUilMCaufw34nplNMbPl7ll8z0pwdE/3dM/yUfbgImlvQr/rOcCLhDtAe0mqsXjnt5ktJpzebQOcHXf9hFAwmNl8M2vT2ALumZ1nJTi6p3u6Z3nJ4sxlR2CBmd1MSCXQAfjQ1qVouVjSdYS7SX8J7KowYtsK4J4M/NyzOh3d0z3ds5xY+r0XdgOGJuZHET7sj4C3gPuB64FvA3sQejBsm9i+O4nucuV6uGd1Obqne7pnto80C2ZTQvqAVcB/sX43uF1joRwa548jpEbYKbFNJl343LO6HN3TPd0zn0eazWLdCKdiJ8XpcaUVZvY4IZfNa3HR/8QCLd0hWmMZdI1zz6p0dE/3dM8caFNwkTRF0gRJm5jZUkLKgdmE1NdjFcfdltSJkLb7xLjrfoSsm2tgvW51ZcE9q8vRPd3TPfOnxbnFJAnoT2jvqwdeIUTbU8zsnbjNnsDhwEIzuzEuG0FoO+wPfEYY/vP5lD6He5bRsxIc3dM93bNgtLBtsDTi21DWDTNaSxjNbE6DbU8DLiacwnWJy7rQhjGZ3TN7z0pwdE/3dM/iPZpdMMClwE+BCcBBhGyfpfU1wJvAhMSy7oTBchYQejoMyOIAumf1OLqne7pncR9NXnORNIHQj7oXIdXARYRTs30k7QpftPtdEB8lDiS0Ez5FGG1xaVPv1Rbcs7oc3dM93bPgNCPyjgOOScxfBXwPmAYsSkTf/oQLUoPiskOA8VlFSfesLkf3dE/3LPajOQXUlTB2eqnN8CjgJ3H6KeCkOD0GmJXbB3HPqnJ0T/d0z2I/mmwWM7PVZvaJrcumOQl4O04fC2wvaS4wC3gCvpweOgvcs7oc3dM93bPY1DW9SUAhG6cRxoa+My5eBZxLGIv5VYttgxbDcR64Z3U5gnumjXumS6V4pk1LbqKsJyROewfYMUbc84F6M3vIinPRyT3ToxIcwT3Txj3TpVI806UlbWiExGr1wEPEsZqL+HDP6nJ0T/d0z+I9WnSHvqStgGOAn5nZJy0JYlninulRCY7gnmnjnulSKZ5p0uL0L47jOI7TFJkMc+w4juNUFx5cHMdxnNTx4OI4juOkjgcXx3EcJ3U8uDiO4zip48HFcXJC0gWSTt/I+smShmfp5Dhp4cHFcYrLZMCDi1OR+H0ujpMhks4DpgLLgdcJ432sBE4AOhLG/TgGGAXMjetWAofGl/gN0BdYDRxvZi9k6e84zcWDi+NkhKTRwHRgLCFp7BPA74AbzOzduM3FwFtm9itJ04G5ZnZLXHcf8F0ze1nSWEL69n2z/ySO0zTNzorsOE6bGQfcZmarASSVMuSOjEFlU8IQt/c03FFSd2AP4E+JrOydym7sOK3Eg4vj5M90YLKZPS1pGrB3I9vUAO+b2agMvRyn1fgFfcfJjgeAyZK6SOoBHBSX9wCWSepAGK2wxKq4DjP7AHhV0mEQBpWStFN26o7TMjy4OE5GmNkTwB+Bp4G7gQVx1fnAY8DDQPIC/R+AMyQ9KWkbQuA5TtLTwHOEsdYdp5D4BX3HcRwndfzMxXEcx0kdDy6O4zhO6nhwcRzHcVLHg4vjOI6TOh5cHMdxnNTx4OI4juOkjgcXx3EcJ3U8uDiO4zip8/8T9WYvx7MuPQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot multiple columns of data in the same chart\n",
    "ticker_history_df[columns_to_plot].plot.line(subplots=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1290cda20>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a histogram to see what typical trading volumes are\n",
    "ticker_history_df.volume.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You may also fetch data for multiple tickers at once, as long as you are only interested in 1 metric\n",
    "# at a time. If you need to compare multiple metrics, you must fetch the data 1 \n",
    "# Here we compare Google with Apple's trading volume.\n",
    "multiple_ticker_history_df = client.get_dataframe(['GOOGL', 'AAPL'],\n",
    "                                                  frequency='weekly',\n",
    "                                                  metric_name='volume',\n",
    "                                                  startDate='2018-01-01',\n",
    "                                                  endDate='2018-07-31')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/cameron/.pyenv/versions/3.7.1/envs/jupyter3/lib/python3.7/site-packages/pandas/core/arrays/datetimes.py:1172: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
      "  \"will drop timezone information.\", UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1291cf9e8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare the companies: AAPL's volume seems to be much more volatile in the first half of 2018.\n",
    "multiple_ticker_history_df.plot.line()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You now have all the tools you need to access the data! We look forward to seeing what you will make with `Tiingo`.a"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}