crypto_arbitrage.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crypto Arbitrage\n",
    "\n",
    "In this Challenge, you'll take on the role of an analyst at a high-tech investment firm. The vice president (VP) of your department is considering arbitrage opportunities in Bitcoin and other cryptocurrencies. As Bitcoin trades on markets across the globe, can you capitalize on simultaneous price dislocations in those markets by using the powers of Pandas?\n",
    "\n",
    "For this assignment, you’ll sort through historical trade data for Bitcoin on two exchanges: Bitstamp and Coinbase. Your task is to apply the three phases of financial analysis to determine if any arbitrage opportunities exist for Bitcoin.\n",
    "\n",
    "This aspect of the Challenge will consist of 3 phases.\n",
    "\n",
    "1. Collect the data.\n",
    "\n",
    "2. Prepare the data.\n",
    "\n",
    "3. Analyze the data. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Import the required libraries and dependencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collect the Data\n",
    "\n",
    "To collect the data that you’ll need, complete the following steps:\n",
    "\n",
    "Instructions. \n",
    "\n",
    "1. Using the Pandas `read_csv` function and the `Path` module, import the data from `bitstamp.csv` file, and create a DataFrame called `bitstamp`. Set the DatetimeIndex as the Timestamp column, and be sure to parse and format the dates.\n",
    "\n",
    "2. Use the `head` (and/or the `tail`) function to confirm that Pandas properly imported the data.\n",
    "\n",
    "3. Repeat Steps 1 and 2 for `coinbase.csv` file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Using the Pandas `read_csv` function and the `Path` module, import the data from `bitstamp.csv` file, and create a DataFrame called `bitstamp`. Set the DatetimeIndex as the Timestamp column, and be sure to parse and format the dates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the CSV file called \"bitstamp.csv\" using the Path module. \n",
    "# The CSV file is located in the Resources folder.\n",
    "# Set the index to the column \"Date\"\n",
    "# Set the parse_dates and infer_datetime_format parameters\n",
    "bitstamp = pd.read_csv(Path('./Resources/bitstamp.csv'), index_col=\"Timestamp\", parse_dates=True, infer_datetime_format=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Use the `head` (and/or the `tail`) function to confirm that Pandas properly imported the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <th>2018-01-01 00:00:00</th>\n",
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       "      <th>2018-01-01 00:03:00</th>\n",
       "      <td>13610.27</td>\n",
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       "                         Open      High       Low      Close  BTC Volume  \\\n",
       "Timestamp                                                                  \n",
       "2018-01-01 00:00:00  13681.04  13681.04  13637.93  $13646.48    3.334553   \n",
       "2018-01-01 00:01:00  13646.48  13658.75  13610.18  $13658.75    2.663188   \n",
       "2018-01-01 00:02:00  13616.93  13616.93  13610.06  $13610.22    0.084653   \n",
       "2018-01-01 00:03:00  13610.27  13639.09  13610.27  $13639.09    7.182986   \n",
       "2018-01-01 00:04:00  13635.35  13636.35  13620.00   $13620.0    1.069665   \n",
       "\n",
       "                       USD Volume  Weighted Price  \n",
       "Timestamp                                          \n",
       "2018-01-01 00:00:00  45482.128785    13639.647479  \n",
       "2018-01-01 00:01:00  36361.390888    13653.332816  \n",
       "2018-01-01 00:02:00   1152.144036    13610.136247  \n",
       "2018-01-01 00:03:00  97856.416478    13623.361128  \n",
       "2018-01-01 00:04:00  14582.660932    13632.923329  "
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the head (and/or tail) function to confirm that the data was imported properly.\n",
    "bitstamp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <th>2018-03-31 23:55:00</th>\n",
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       "      <td>6929.98</td>\n",
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       "                        Open     High      Low     Close  BTC Volume  \\\n",
       "Timestamp                                                              \n",
       "2018-03-31 23:55:00  6935.01  6939.07  6922.56  $6922.56    1.044354   \n",
       "2018-03-31 23:56:00  6922.02  6922.02  6918.00  $6920.32    3.069539   \n",
       "2018-03-31 23:57:00  6920.33  6936.42  6920.33  $6934.72   28.239049   \n",
       "2018-03-31 23:58:00  6927.65  6929.42  6927.65  $6927.65    0.839507   \n",
       "2018-03-31 23:59:00  6929.98  6929.98  6928.00  $6928.01    0.209363   \n",
       "\n",
       "                        USD Volume  Weighted Price  \n",
       "Timestamp                                           \n",
       "2018-03-31 23:55:00    7240.034602     6932.550078  \n",
       "2018-03-31 23:56:00   21245.076275     6921.260233  \n",
       "2018-03-31 23:57:00  195789.408220     6933.286106  \n",
       "2018-03-31 23:58:00    5817.007705     6929.080007  \n",
       "2018-03-31 23:59:00    1450.735763     6929.289993  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bitstamp.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Repeat Steps 1 and 2 for `coinbase.csv` file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the CSV file called \"coinbase.csv\" using the Path module. \n",
    "# The CSV file is located in the Resources folder.\n",
    "# Set the index to the column \"Timestamp\"\n",
    "# Set the parse_dates and infer_datetime_format parameters\n",
    "coinbase = pd.read_csv(Path('./Resources/coinbase.csv'), index_col=\"Timestamp\", parse_dates=True, infer_datetime_format=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <th>2018-01-01 00:00:00</th>\n",
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       "                         Open      High       Low      Close  BTC Volume  \\\n",
       "Timestamp                                                                  \n",
       "2018-01-01 00:00:00  13620.00  13620.00  13608.49  $13608.49   20.812754   \n",
       "2018-01-01 00:01:00  13607.14  13607.14  13601.66  $13601.66   13.474359   \n",
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       "2018-01-01 00:04:00  13550.34  13585.95  13550.34  $13583.44    9.955364   \n",
       "\n",
       "                       USD Volume  Weighted Price  \n",
       "Timestamp                                          \n",
       "2018-01-01 00:00:00  283451.08537    13619.105106  \n",
       "2018-01-01 00:01:00  183283.97801    13602.426919  \n",
       "2018-01-01 00:02:00  156789.19686    13590.872506  \n",
       "2018-01-01 00:03:00  221413.64182    13560.332806  \n",
       "2018-01-01 00:04:00  135141.26944    13574.719401  "
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the head (and/or tail) function to confirm that the data was imported properly.\n",
    "coinbase.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-31 23:59:00</th>\n",
       "      <td>6937.30</td>\n",
       "      <td>6937.30</td>\n",
       "      <td>6931.09</td>\n",
       "      <td>$6934.0</td>\n",
       "      <td>1.012720</td>\n",
       "      <td>7022.275088</td>\n",
       "      <td>6934.070316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Open     High      Low     Close  BTC Volume  \\\n",
       "Timestamp                                                              \n",
       "2018-03-31 23:55:00  6945.20  6948.06  6930.00   $6930.0    5.802288   \n",
       "2018-03-31 23:56:00  6930.00  6930.01  6930.00  $6930.01    0.625053   \n",
       "2018-03-31 23:57:00  6930.01  6933.91  6930.01  $6933.91    0.776431   \n",
       "2018-03-31 23:58:00  6933.91  6938.00  6933.90  $6937.31    0.133413   \n",
       "2018-03-31 23:59:00  6937.30  6937.30  6931.09   $6934.0    1.012720   \n",
       "\n",
       "                       USD Volume  Weighted Price  \n",
       "Timestamp                                          \n",
       "2018-03-31 23:55:00  40243.918480     6935.869979  \n",
       "2018-03-31 23:56:00   4331.620701     6930.005567  \n",
       "2018-03-31 23:57:00   5382.532162     6932.405747  \n",
       "2018-03-31 23:58:00    925.356547     6936.048538  \n",
       "2018-03-31 23:59:00   7022.275088     6934.070316  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coinbase.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare the Data\n",
    "\n",
    "To prepare and clean your data for analysis, complete the following steps:\n",
    "\n",
    "1. For the bitstamp DataFrame, replace or drop all `NaN`, or missing, values in the DataFrame.\n",
    "\n",
    "2. Use the `str.replace` function to remove the dollar signs ($) from the values in the Close column.\n",
    "\n",
    "3. Convert the data type of the Close column to a `float`.\n",
    "\n",
    "4. Review the data for duplicated values, and drop them if necessary.\n",
    "\n",
    "5. Repeat Steps 1–4 for the coinbase DataFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: For the bitstamp DataFrame, replace or drop all `NaN`, or missing, values in the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Open              473\n",
       "High              473\n",
       "Low               473\n",
       "Close               0\n",
       "BTC Volume        473\n",
       "USD Volume        473\n",
       "Weighted Price    473\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For the bitstamp DataFrame, replace or drop all NaNs or missing values in the DataFrame\n",
    "bitstamp.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "bitstamp = bitstamp.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Open              0\n",
       "High              0\n",
       "Low               0\n",
       "Close             0\n",
       "BTC Volume        0\n",
       "USD Volume        0\n",
       "Weighted Price    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bitstamp.isnull().sum() #Confirmed no missing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Use the `str.replace` function to remove the dollar signs ($) from the values in the Close column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp\n",
       "2018-01-01 00:00:00    $13646.48\n",
       "2018-01-01 00:01:00    $13658.75\n",
       "2018-01-01 00:02:00    $13610.22\n",
       "2018-01-01 00:03:00    $13639.09\n",
       "2018-01-01 00:04:00     $13620.0\n",
       "                         ...    \n",
       "2018-03-31 23:55:00     $6922.56\n",
       "2018-03-31 23:56:00     $6920.32\n",
       "2018-03-31 23:57:00     $6934.72\n",
       "2018-03-31 23:58:00     $6927.65\n",
       "2018-03-31 23:59:00     $6928.01\n",
       "Name: Close, Length: 129067, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bitstamp.loc[:,\"Close\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Projects\\Softwares\\anaconda3\\envs\\dev\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# Use the str.replace function to remove the dollar sign, $\n",
    "bitstamp.loc[:,\"Close\"] = bitstamp.loc[:, \"Close\"].str.replace(\"$\", \"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp\n",
       "2018-01-01 00:00:00    13646.48\n",
       "2018-01-01 00:01:00    13658.75\n",
       "2018-01-01 00:02:00    13610.22\n",
       "2018-01-01 00:03:00    13639.09\n",
       "2018-01-01 00:04:00     13620.0\n",
       "                         ...   \n",
       "2018-03-31 23:55:00     6922.56\n",
       "2018-03-31 23:56:00     6920.32\n",
       "2018-03-31 23:57:00     6934.72\n",
       "2018-03-31 23:58:00     6927.65\n",
       "2018-03-31 23:59:00     6928.01\n",
       "Name: Close, Length: 129067, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bitstamp.loc[:,\"Close\"] #Confirmed no dollar sign in Close column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Convert the data type of the Close column to a `float`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the Close data type\n",
    "bitstamp.loc[:,\"Close\"].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the Close data type to a float\n",
    "bitstamp.loc[:,\"Close\"] = bitstamp.loc[:, \"Close\"].astype(\"float\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bitstamp.loc[:,\"Close\"].dtypes #Confirmed type changed from object to float"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: Review the data for duplicated values, and drop them if necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review the data for duplicate values, and drop them if necessary\n",
    "bitstamp.duplicated().sum() # no duplicate data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bitstamp = bitstamp.drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5: Repeat Steps 1–4 for the coinbase DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Repeat Steps 1–4 for the coinbase DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Open              218\n",
       "High              218\n",
       "Low               218\n",
       "Close               0\n",
       "BTC Volume        218\n",
       "USD Volume        218\n",
       "Weighted Price    218\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For the coinbase DataFrame, replace or drop all NaNs or missing values in the DataFrame\n",
    "coinbase.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "coinbase = coinbase.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Open              0\n",
       "High              0\n",
       "Low               0\n",
       "Close             0\n",
       "BTC Volume        0\n",
       "USD Volume        0\n",
       "Weighted Price    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coinbase.isnull().sum() #Confirmed no missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp\n",
       "2018-01-01 00:00:00    $13608.49\n",
       "2018-01-01 00:01:00    $13601.66\n",
       "2018-01-01 00:02:00     $13580.0\n",
       "2018-01-01 00:03:00    $13550.34\n",
       "2018-01-01 00:04:00    $13583.44\n",
       "                         ...    \n",
       "2018-03-31 23:55:00      $6930.0\n",
       "2018-03-31 23:56:00     $6930.01\n",
       "2018-03-31 23:57:00     $6933.91\n",
       "2018-03-31 23:58:00     $6937.31\n",
       "2018-03-31 23:59:00      $6934.0\n",
       "Name: Close, Length: 129322, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the str.replace function to remove the dollar sign, $\n",
    "coinbase.loc[:,\"Close\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Projects\\Softwares\\anaconda3\\envs\\dev\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "coinbase.loc[:,\"Close\"] = coinbase.loc[:, \"Close\"].str.replace(\"$\", \"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp\n",
       "2018-01-01 00:00:00    13608.49\n",
       "2018-01-01 00:01:00    13601.66\n",
       "2018-01-01 00:02:00     13580.0\n",
       "2018-01-01 00:03:00    13550.34\n",
       "2018-01-01 00:04:00    13583.44\n",
       "                         ...   \n",
       "2018-03-31 23:55:00      6930.0\n",
       "2018-03-31 23:56:00     6930.01\n",
       "2018-03-31 23:57:00     6933.91\n",
       "2018-03-31 23:58:00     6937.31\n",
       "2018-03-31 23:59:00      6934.0\n",
       "Name: Close, Length: 129322, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coinbase.loc[:,\"Close\"] #Confirmed no dollar sign in Close column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the Close data type\n",
    "coinbase.loc[:,\"Close\"].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the Close data type to a float\n",
    "coinbase.loc[:,\"Close\"] = coinbase.loc[:, \"Close\"].astype(\"float\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coinbase.loc[:,\"Close\"].dtypes #Confirmed type changed from object to float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review the data for duplicate values, and drop them if necessary\n",
    "coinbase.duplicated().sum() # no duplicate data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# coinbase = bitstamp.drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze the Data\n",
    "\n",
    "Your analysis consists of the following tasks: \n",
    "\n",
    "1. Choose the columns of data on which to focus your analysis.\n",
    "\n",
    "2. Get the summary statistics and plot the data.\n",
    "\n",
    "3. Focus your analysis on specific dates.\n",
    "\n",
    "4. Calculate the arbitrage profits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Choose columns of data on which to focus your analysis.\n",
    "\n",
    "Select the data you want to analyze. Use `loc` or `iloc` to select the following columns of data for both the bitstamp and coinbase DataFrames:\n",
    "\n",
    "* Timestamp (index)\n",
    "\n",
    "* Close\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timestamp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:00:00</th>\n",
       "      <td>13646.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:01:00</th>\n",
       "      <td>13658.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:02:00</th>\n",
       "      <td>13610.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:03:00</th>\n",
       "      <td>13639.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:04:00</th>\n",
       "      <td>13620.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Close\n",
       "Timestamp                    \n",
       "2018-01-01 00:00:00  13646.48\n",
       "2018-01-01 00:01:00  13658.75\n",
       "2018-01-01 00:02:00  13610.22\n",
       "2018-01-01 00:03:00  13639.09\n",
       "2018-01-01 00:04:00  13620.00"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use loc or iloc to select `Timestamp (the index)` and `Close` from bitstamp DataFrame\n",
    "bitstamp_sliced = bitstamp.loc[:,[\"Close\"]]\n",
    "\n",
    "\n",
    "# Review the first five rows of the DataFrame\n",
    "bitstamp_sliced.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timestamp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:00:00</th>\n",
       "      <td>13608.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:01:00</th>\n",
       "      <td>13601.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:02:00</th>\n",
       "      <td>13580.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:03:00</th>\n",
       "      <td>13550.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:04:00</th>\n",
       "      <td>13583.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Close\n",
       "Timestamp                    \n",
       "2018-01-01 00:00:00  13608.49\n",
       "2018-01-01 00:01:00  13601.66\n",
       "2018-01-01 00:02:00  13580.00\n",
       "2018-01-01 00:03:00  13550.34\n",
       "2018-01-01 00:04:00  13583.44"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use loc or iloc to select `Timestamp (the index)` and `Close` from coinbase DataFrame\n",
    "coinbase_sliced = coinbase.loc[:,[\"Close\"]]\n",
    "\n",
    "\n",
    "# Review the first five rows of the DataFrame\n",
    "coinbase_sliced.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Get summary statistics and plot the data.\n",
    "\n",
    "Sort through the time series data associated with the bitstamp and coinbase DataFrames to identify potential arbitrage opportunities. To do so, complete the following steps:\n",
    "\n",
    "1. Generate the summary statistics for each DataFrame by using the `describe` function.\n",
    "\n",
    "2. For each DataFrame, create a line plot for the full period of time in the dataset. Be sure to tailor the figure size, title, and color to each visualization.\n",
    "\n",
    "3. In one plot, overlay the visualizations that you created in Step 2 for bitstamp and coinbase. Be sure to adjust the legend and title for this new visualization.\n",
    "\n",
    "4. Using the `loc` and `plot` functions, plot the price action of the assets on each exchange for different dates and times. Your goal is to evaluate how the spread between the two exchanges changed across the time period that the datasets define. Did the degree of spread change as time progressed?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>129067.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10459.842453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2315.976088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5944.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8613.370000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10145.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11444.810000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17234.980000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Close\n",
       "count  129067.000000\n",
       "mean    10459.842453\n",
       "std      2315.976088\n",
       "min      5944.000000\n",
       "25%      8613.370000\n",
       "50%     10145.950000\n",
       "75%     11444.810000\n",
       "max     17234.980000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate the summary statistics for the bitstamp DataFrame\n",
    "bitstamp_sliced.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>129322.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10449.140958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2317.197419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5882.310000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8609.230000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10137.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11397.237500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17177.990000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Close\n",
       "count  129322.000000\n",
       "mean    10449.140958\n",
       "std      2317.197419\n",
       "min      5882.310000\n",
       "25%      8609.230000\n",
       "50%     10137.440000\n",
       "75%     11397.237500\n",
       "max     17177.990000"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate the summary statistics for the coinbase DataFrame\n",
    "coinbase_sliced.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Coinbase Prices'}, xlabel='Timestamp'>"
      ]
     },
     "execution_count": 35,
     "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": [
    "# Create a line plot for the bitstamp DataFrame for the full length of time in the dataset \n",
    "# Be sure that the figure size, title, and color are tailored to each visualization\n",
    "coinbase_sliced.plot(title= \"Coinbase Prices\",  color=\"orange\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Bitstamp Prices'}, xlabel='Timestamp'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FFvDLX4aX/fLL9Lwwb6yGYRj5ElkpqOorwPKQQ9cC/wcE3bqNAB5Q1bWqOh+YCwwVkW2A1qo6WZ0nvvHA0YE6ftSCicDBfi+ikvTvD48+mjxHkAt+C954I/x40AfSxo1uOCh1UtkninsNwzCMQihoTkFEjgL+p6r/TTnUFfgosL/Iy+vqpVPzk+qo6gbgC6BDIfLlwssvF/d8qepsSKg/wmQPqY05yYviXsMwDKMQ8lYKIrIlcDHwm7DDIXmaJT9bndTrjhGRaSIybenSpVHFTeOvf4W//929lD/7DA44wOXvvnvep0yRMz2vdev0vBUrEumnnko//sQTifQXX4RfK1MvxDAMI1cK6Sn0AnoA/xWRBUA34D8i0gXXAwgYc9IN+NjL7xaST7COiDQD2hAyXKWqt6nqEFUd0qlTp7yFHzMGvvtdF/QmeJpivWB9pTB4cCLviCOy11m8OHl/5EhX55prstezFc+GYRSLvJWCqs5Q1c6q2l1Vu+Ne6rup6ifA48BIz6KoB25C+XVVXQysEpE9vfmCk4HHvFM+Dozy0scBL2gZIgD96lelOW+TJvDaa/DMM4m834T1qQKMTZlaf+ABt03tYRx/fPJ+AR0mwzCMJHIxSb0fmAzsLCKLRGR0prKqOhN4EHgXeAr4qapu9A6fDtyOm3z+AHjSy78D6CAic4GfAxfk2Ja8WLWqdOfee29o1y6xv/PO2ecu5s0Lzw8qhTPOgIkTk48Hr2EYhlEIWRw8J6Oq32vkePeU/SuAK0LKTQP6h+SvAY5Pza81Vq6MXva++9y2VatEXpgFUiYTVsMwjFypyxXNc+ZkPnbCCaW99vz50csuXOi2zZtnL9e0afbjhmEYUalLpZDNdbU/jl8qXn01etm99nLbxtZIBJ3vGYZhFIIphTKz007Ry+67r9tmiuPgDy9ZvGfDMIpFXSqFXCKoFZuDD3bb/fZrvKwfvyGTvPvv77bWUzAMo1jUpVIQgQMPrMy1d97ZbYOxn1P58Y+TVzqH8fvfJ3o8fqQ4wzCMQqlLpQCJL/Yg2V7UxWLbbZ2Po9RobEGOOabx81x4YWWHwQzDqE3qVimE+Rm68MLyXLtJk8xeVPfdF4YPz17/rbfctlnAoHh5mKtCwzCMHKlbpbDVVul5ffuWX45Urrqq8TK77OK2wZ5Ch7K5DjQMo5apW6Ww7bbpeZV31A2ff954Gd9E1YaPDMMoNnWrFI47rtIShJPpiz8sxkKTIjy9adPSHfEZhlG/1K1SqKZVwMFJ50xDWGee6SySiukicPVq5yo8rNdkGEZ9UrdKoZpo2TKR3mKL8l33xRcT6cmTy3ddwzCqF1MKwEsvwdSplbt+0Mtps8guCpPJZ61CcA7lgw/yu65hGLVF3SuFnXZyK4OHDi3/tf2oauecA7vu6tL5TnYHextRmD072fT1xz/O77qGYdQWeX6X1gbr1lXW4uiIIxJzBC+8AB99lL18NnK1REpdk5GrUjEMozapa6VQTSadbdu6X660aAFr1oQvxsvGI48k79viN8MwwIaPYs/q1U4xlD5wqWEY9YAphRpgzRq4+urShhY1DKM+MKVQQ+QSwMcwDCMMUwo1hPUUDMMoFFMKNcTIkZWWwDCMuGNKoU4JLpjz+fLL8sthGEZ1YUqhTuneHTp2hFmzEnmtWsHcuRUTyTCMKiCyUhCRO0XkUxF5J5B3tYi8JyJvi8gjItI2cOxCEZkrIrNFZFggf7CIzPCOXS/ilo+JSHMRmeDlTxWR7sVpohE217BxI+yzD/Tpk5z/85+XRybDMKqTXHoK44DDUvKeBfqr6kBgDnAhgIj0BUYC/bw6N4uI75f0FmAM0Nv7+eccDXyuqjsC1wJjc22Mkc6MGdC6NVx3XXL+hg3hnmJ91xuGYdQnkZWCqr4CLE/Je0ZVN3i7U4BuXnoE8ICqrlXV+cBcYKiIbAO0VtXJqqrAeODoQJ27vPRE4GC/F2Hkz+9/77bnnguLFjm3HpMnu56C73zv3HMrJ59hGNVFMecUfgQ86aW7AkFPPou8vK5eOjU/qY6naL4ALMhkgTzwQCL9i1+47d57J/cUTjopUaZ58+LLMHZsdUS1MwyjcYqiFETkYmADcK+fFVJMs+Rnq5N6rTEiMk1Epi1dujQfceuWBx9MpNetS/QUghHc/vxnmDIlOfBPoVxwgdu+8ELxzmkYRmkoWCmIyCjgCOD73pAQuB7AdoFi3YCPvfxuIflJdUSkGdCGlOEqAFW9TVWHqOqQTp06FSp+zbFsWSI9b17mch99BOPHu3RqWM+99oLbb4c5c4or28EHm3WTYVQ7BSkFETkMOB84SlW/Dhx6HBjpWRT1wE0ov66qi4FVIrKnN19wMvBYoM4oL30c8EJAyRgReeihRLpXr2h1NmxIpNesSaRXrCiKSEn07u2UjhGdN9+Em26qtBRGvSBR37sicj9wANARWAL8Fmdt1Bzwv0+nqOppXvmLcfMMG4BzVPVJL38IzpJpC9wcxJmqqiLSArgb2BXXQxipqlm+dWHIkCE6bdq0qG2tWVLH6/1HGmUc34/77PcW2rZNKIPtt4eFC8PrffGFK/vQQ3DssbnJF5TRyM7atc4LLsTnnn36KXTqZPNI1YyITFfVIaHH4vwxbkrBUahSyFY205/HuecmzFxVYelS9/Jq1Sq53OLFsO220c9rJHj3XejXL7Efh3u2YkVitXwc5K1XsikFW9Fcg2zalDwMFIXgXITPlVdmLv/228n7nTu79RBBNmwIVwhGNIIKAZwZcbWyYoX7sNhmm0SeKYV4YkqhBvnkEzj++MbLBc1V27dPP77jjuH1Fi+OZkmULbJdrpHi6o0wpZ6roi8l8+fDV18l9n0FFpRxwYKyimQUCVMKNUjXrvCPfzRe7rvfzX78739Pz5s9O/vXf9SX/YsvRitXr2yxRXre+vXllyOMgw6Cnj1hq61gyRI3h/Dxx43XM+KBKYU6prE5h6CV0GefuZdSmLKZMCGRvvtut00d+oDk4YSLL44up+FYt67SEsD11ycr9C5dYODA8LJffx2eb1Q3phSMjPjuL1SdNcnmm8Ozz6aXC8ZxePllt3333eQyqUpi6lRnWWNE5/PPK3v9devg7LPT85csSd5/+GG37d+/9DIZxceUgtEowWGLp5/OXrZly+R1DwBHHAFhRmK+qWW9MmmS661ddllyftCdeZDgHFAlCBtOBLf2BJw1mmryhLiZpcYPUwo1QNj4czFJfcln48Yb0yeYn3gioQB69kw+Vs8vjd/+1m1/85vk/L59w8tfckm4lVi5WLw4PP/9993W70U09uFgVDemFGqA1O57vqS6u/Ap5gTnU08V71xxZ/LkxssccEDyfseOJRElI3vv7RT3aac5IwNw6xA+/TRznV//OpE+7bTSymcUH1MKNUCrVo2vKk7lrrvS8/71L7dNHSYoplLwhxqC+D6Y6o3VqxsvE2alle2FnC9r16bP8SxbllBcf/mL84cF8Nxzbo4pE9tv73oPTZuGx+wwqhtTCjXCn/4UrdzRR7tt2MKiYcOcW4vjjkvkffEFrFwZfq6wF3wUUnsko0ZVfhK1knzrW4l02HqRVEoRS7tFi/Q5ngEDwsu2bOm22ZTTjju6xYw33VS8nqxRHkwp1AiZhn58fB9Ht90Go0fDCSeEl9t+++T9tm0zO9Z75pmcxQScTbtvuuqzww75nasWeO65xFd6UDn6Vl2jRyeXX7UK3nmneCafmXosmeYQdt7ZbTt1cophw4bwjwy/LV26FC6jUT5MKdQIjSkFn06d3DBAMSx/unfPr97WW8MPfpCcFxZHulZZudK91IO0aAHbbZecd68XnaRDSqipRYvcV/ywYRSFRx/Nv26nTtGGiPbYI/9rGOXFlEKNUO6x23Hj3Hb+fLd9443cz5FqiVQvtGkTPjSzaFHyvq/oTz89Of9vf3PbSZMKk+Ojj9wk8oknph8r9kK511831yZxwZRCjZCtp1AKk9VRXuSL7t3d0EGfPsnHd9jBuUHINlcQdR6kHgm+QFN7ZMGYGYWQOlQYvG4wLOuhhybSufhfSl3pfMst0esalaNZpQUwikO2nsKPflT662++efL+v//duIfUo4+Gl15yZpff+U6JBIsplVq/cffd6YYFTz8NV13lFHwuMbzfeCO5/M9+5iafgzHBjerD4inUCMuXp489A/To4dYG7LRTbufL9lIaPhz++c/kvGCgHn8/Kj16wH77hZvJ1iK5xLnwefpp1ztLteQp5N83quIp9jVi/MqpGbLFU7CeQo2QafgoW5zmfOnWLT0v+M9//fW5nW/BAverB6WQr6uKYcMqY9qZyRzZqF1sTsHImcasjnr0KIsYseT880t7/jPOcAq6WC/z1Eh6ufKHPxRHDqN8mFKoEcrZJW9sjuKII/I7bzUFkSkVH36YSOdqMRbFWsufzM3mf+i//402N/D730eTKxvnnWdWR3HDlEKNsNVWpb/GD3/ohjC23ro05x8zpjTnrUbGjcu8Ivjaa8PzH3ww8/n+9KfkIbxPPgkvt24dDBqUbHK6YkX4ArbBgzNfLxfq2elhHDGlUCNkC31ZLLp0cbGYS8Xdd8OTT4b77I8zqaErwa0NaN8efve79PJnnRV+nrCXtB/x7Je/TM7fZZfwc5xxRnpemzZu8dxuuyXnF9OUecWK4p3LKC2mFGqYLbfMv+5//pOe17Zt9joHHphwB50vw4fnPlFdzXz0USJ0ZRBfifuxtFu1cvdv0qToq9PB1Qkj09f5HXdkPtf06cn7++wTXY7GaNOmeOcySkvkPz8RuVNEPhWRdwJ57UXkWRF539u2Cxy7UETmishsERkWyB8sIjO8Y9eLuD9fEWkuIhO8/Kki0r1IbaxbCvGNs+uu6Xn+CywTL7zgfP4Xg/Hj4Yor4j0evXZt8gKx4HyCj//y7tLF3b9vfjP7OV97LXl/zpzwcn6Mg8ZIdWroW4BNmGDDPvVKLj2FccBhKXkXAM+ram/geW8fEekLjAT6eXVuFhF/Wu0WYAzQ2/v55xwNfK6qOwLXAmNzbYxRWsrplmLUKPjVr+LtejlVKYc5/fNdUH/3u9HOuffe6UYFp56aXi7VNUYm/BgJPief7EKpRpUnH5Ytg8svr46Y00Y6kZWCqr4CLE/JHgH41uV3AUcH8h9Q1bWqOh+YCwwVkW2A1qo6Wd2qufEpdfxzTQQO9nsRRjS++AJ++tNKS5E7QVfdtUSUcfT27d1K4bC5haj4cQ6i4JvEnn++s1AK+w/LNB9RLDp2dIF4DjqotNcx8qPQOYWtVXUxgLf1pyG7Ah8Fyi3y8rp66dT8pDqqugH4AghZo2tkonVruOii4p1vm23KMxb85pulv0YlyGYtFKRt29zmESA9hGdU/MA3V12V7NOoEqQOhRnVQalWNId94WuW/Gx1kk8sMgY3/MT2YR696pximov6li2l7q/l4k8nLjz8MFxwQfixKVMKP3++cy3r15fHUi0qGzfGe4iwFim0p7DEGxLC2/qW14uAoHf4bsDHXn63kPykOiLSDGhD+nAVqnqbqg5R1SGdssUErFNK8Q/29NOlDcae6mG1FsjWSyhGbIFsQzy7756wSrr++kSYVag+pTBjRvSyp57qPlDMd1JpKVQpPA54TpQZBTwWyB/pWRT1wE0ov+4NMa0SkT29+YKTU+r45zoOeEHj7K2vhjj00NIONXzjG6U7d6VYnvY54yjW/Ml++6Xn3Xefi7XwxhsutvPQoW7Nx7e/7Y7feKNbMJfq0baSvPtu9LL+3Il5WS0xqhrpB9wPLAbW477qR+PG/J8H3ve27QPlLwY+AGYDhwfyhwDveMduJOGptQXwd9yk9OtAz8ZkGjx4sBrpJIJvVlqSaNx0U7LMoDpwYLzakEqwLWeckUivXl2c869enX7Pwq7t/2bMqPzfRZhcU6Y0Xu/zz1VPOqny8tcSwDTN8F4119k1iD8H8OabzqVBtfPFF8kL4+67zwWz91dPx+1PdPJkZzrqExyyKWZbfvELuOaaxL5/7nxcc5eDMLmmTWvcnYa53y4+2Vxn24rmGiYu3krbtHH28T7f+56z3x8+vDRR40rNs88m7zdrBnPnOmVRTK6+urjnqwQbN1ZaAiMVi6dQw+Rq5lhJUoPWg3MNsXq1s7SJU1v23DM9r1cv9ysmTZo4n0Vr1uQ2YVtNbNhQaQmMVGL0r2bkSpxM/fx4vkE3Gb4FT2qUN5+NG4v/9V0Mhg1LpFu3Lu21vvrKuc/o3z+Rd999pb1mMfCHNVNXfZ9wAvzgB8kWU2EsXJi+GtsoDjanUIP4Y7CrV7svyTigCvfc414KvnWM347DD09/SQTDf06fnu7hs5IEx8AXLkz2f1QOUkOjZipTbn74Q+db6csvYepUOPhgl79hg/uAWbLE+YBKlTHbHEmMX18VxeYU6pQ4DbmIOFPDMHPJJ59Mz3vllUT6uutg1aqSiZYTQeV1773lVwjg7uWxx7r0t76Vfnz48PLK4/O3v7mhwJYtk9dKfOc7bhtUCOAMECC7z631650jwWp5/rVAjF4bRq7EafgoV4KK4u67YdttKyeLz6ZNiTUBUFqnco1xyy3uZTtxYnL+OedEd79RbEQSX/1LlybyH3ssvPzq1c4yad48Z4kWtj5h881dj6PUw3T1hCmFGiZOPYUw3nsvkZ43L/lY6pDCl1+WXp7G8N1O+zSroBlH585OIQR9V33+uYvq1rJl5eTy+eyzxsusXZuI6/Hpp86dulF6Yv7aMMIY5a0Lj7uP2WDQ+FTLnWr0sHnnnZWWIJxNm9yvsSBJleTRR9Pzco0HksnXVDH47DPX86oHE1pTCjXIHXfAypWVlqJwwtYoLFjgJhczxTcuBhs3OjPPXPHH8cGNdVcLwWGbaiF1uOeYY9LL9O2b2znHligCi4hbN/Pww3DppYm5jlrFlEIN0rRp8ld2XEm1nPrFL9yCvEsucWaLpeLEE/NbNLfTTm47dWplh47iQDYniEE/l0cc4baZ4laXmmeeSd6/7DLX41q9uiLilAVTCkbVkvpi9l06ZIszXCjTpkWbiH3uOTekFTSJ9BdimUJonEGDXPzqMILrPNq1cx85f/pTWcRKYtWqZFmCpMY/37ABnngC3nnH9SyiRr6rRkwpGLEjU0SzqVMLP/fuuyfSS5ZkLnfIIW7ye9KkRN5ll7ltLQzdlYNu3dLzhg2Dv/zFpU8/3b1se/RIKNoLL0yv8/e/w4ABcPTRxZXvk0+ilx07Fo46yskBcOut8TWTNaVgxI6vvkqkg6apYe4lCiEshvDGjXDDDYn9n/zEbVescIvoALp2TatmROQ3v0l8hd9yi1OwQdPqoMmvz3HHuUBNa9cWV5Yf/Sh62blzo+XFAevoGrHmsMNKd+7tt09fMZs6NDRrltsGfTcV++VUT7z0UrKH2VQXJ/6E+Z57unH9/fd3+2vXug+ENWuKt4o/2AscMSJ5PcVhh7lejIhTWmERD997D3bdtTiylBNTCkZVs/nm4V/spWDcuPzq/fOfyesk2rcvijh1SYdGorL7vYiWLZP9XvkOAbfYojDXF3Pnwo47Juf9+9+www7JSuGpp5LdoYd524mrbyYbPjKqmuBXY7HZtCnZFv6UU8LLNIZvIQMubGk1rK6OK6NHZz/+jW84l+H33FP8a0+YAL17w/33w2uvJZ7rXnu5Z3rffeHzTKefDs8/n55/113OfLVFC+cDKy6YQzyjqnnooewhLFXhm990X3P+fjZ+8xv3NXnhhYmhiHXr3FdfmC3/hAnJ7ioas/fftKn61gRUM6n3KpMTvMaea7B8atn1612MjnPOgX32yXyO3XZzgalSST1ftuf7yivhoVLDzlNJzCGeEVvCxmpTCQ77NLbi9LLL4KKLkvP8bn7YBPEJJ8CBB7p0FDfdphDyJ9NL01+hn43LL3fbfv3Sj11zjfu42Hff7OcIUwhhnHlm5mP77ut8ccUZUwpGVZPNqZ+/XiH4Iv71r3O/hh/BLJMp6UsvOTv5Ug5l1Sv+RHEqviv0nXeONtdz8cVuBXTYorig+4uOHXMWMQ3f9DgVf14j08LK1I+RasWUglHVbLNN5mO+L5+gUrj11mjnDZoLjh/vehjZ7Mp/+cvk/dWr4fvfT8479dRo1zYSZFL6hx/utrnMHSxcCG+95dJ//rPrHaTSrl14vVx6eG3auEnnE09Mzg8GOgrjyiujzVFVGlMKRlXTvTt88EH4sfnz3XaHHRJ5n3/u/COFERye6N07+VjwazLK2G+LFvCHPyTXue22xusZyWSalL/0UpgyBYaEjnqH89VX7m9l0yY3f3Dcce5lH/yy79kTzjgjuV6YgUHwnGEcdVRyTIhUMv0NTZmSuU61YBPNRiwI+5KbMgX22CP8eNif9ZQpzpKkMVSzfzleeWVCiZx4onupHHJI4+c10lm1yln7jB5dePyPXL7258xJfBjkG9ntww+TP0hSy27Y4AIA9eyZ/BFSDa9cm2g2ahLfpUBUcom58MgjmY8FexX33WcKoRBatYIxY8ofEKoxFxSzZzf+VR9c3Bjmyr1ZMzj0ULfu4ZxzEvkiiUWP1UhRlIKInCsiM0XkHRG5X0RaiEh7EXlWRN73tu0C5S8UkbkiMltEhgXyB4vIDO/Y9SJmy2FkJtUpWTZefTV9dWw2iu1Hx6gusimhV191Hm/9XmhjqIavUwiSOjndty/MnFmdcwwFKwUR6QqcBQxR1f5AU2AkcAHwvKr2Bp739hGRvt7xfsBhwM0i4j+iW4AxQG/vV0InBkacCK4SvuOO9Inf1Bd+MJ7BunXOdvy66xq/zqJFifSHH+YsplFBXnghetmVKzPHvMi2liFfwlyx9+/vlNPcuW4YqlpicBRr+KgZsIWINAO2BD4GRgB+gMK7gKO99AjgAVVdq6rzgbnAUBHZBmitqpPVTXSMD9Qx6pwnnkikf/SjhBmpzwEHJO8H/d3nEi0ruFYh6M/IqH5yCTO6337OhUqQ99+PFiY0H7L1THr3dh8gqfJUioKVgqr+D/gj8CGwGPhCVZ8BtlbVxV6ZxUBnr0pXIOhJfZGX19VLp+YbRqMTxKlfYsHYxNkm9oJjvY1x5JFmdlrNZItjoep+nTunHzvhBLfdccfGfS+VGpHKT0QXY/ioHe7rvwewLdBSRLLFxQqbJ9As+anXGyMi00Rk2tKlS/MR2Yghjc0uZTuebdzWXyQVhccfN7PTaiZTz+6ooxLpYI/TZ/PNXcyGUvPGG9HK5fI3WQqKMXz0LWC+qi5V1fXAw8DewBJvSAhv60fVXQQEH1833HDTIi+dmp+Eqt6mqkNUdUinYNw+w8hA6vBR0HVGY3GAzQ12fEh9HWzYAP/3f3D77Ym8oUOTy7z7LixfXh7rp6g9AH8BXqUohlL4ENhTRLb0rIUOBmYBjwO+15JRgO949nFgpIg0F5EeuAnl170hplUisqd3npMDdQwjb+bMSd4PerocPDh73WwLlIzqIxhLoWlTFxEt27djv37OSKEcSiGXhXiVpBhzClOBicB/gBneOW8DrgIOEZH3gUO8fVR1JvAg8C7wFPBTVfW/5U4HbsdNPn8ABOJqGUZ2VMNj6r72WvZ6f/yj2553XvoxEffltnx5weIZZWDs2MbL9OqVnleOuNr+fMHxx7v9oNv2ZcsqP5fgYyuajdjgzxtk+5OdMQMGDkwud/nlmR3lxfjP3whBFZo0SaTD2LQpvWcwcCD897+llc3nq69cb7VnTzfHsXFjYl1MlL/xYpBtRbNFXjNqirBVzrmsZDbijQi8/np2c+ImIeMj5YruB850tmdPlz7yyPAyS5Y4S6lKLN81NxdGbPDNCnMpD4kAPD7+V9nw4UURy6gydt8dunTJXmaXXZL333uvdPLkQ5cucPPNlbm2KQWjZrnLWzo5b156/sUXw6OPll0ko0qYOTM5jGo18rOfVea6Nqdg1BypoRlzDe1o1A/ZwnhWgnnzkifCSyWTeUk1DMOIAf5cg0855zp8TCkYNc2nnzZexqhf/HCgb79dWTmCTJiQSO+2G0ydWt7rm1Iwappg7ANIX9Fq1DcvveSGaHKNzVFKvvvdRHrmTNhzz8T+3ntHW4tRCKYUjJrjrLMS6aDLgJNOssllI75s2gSTJ7sPnWBvotiYUjBqjksvTaSDpofnnQfbbFN+eQyjUO69N3nB3ciRpbuWKQWj5mjbNpG+775Eeqedyi6KYRSFH4T4nb7wwtJcy5SCUTeYczujlrjqqtKc15SCUTeEuTcwjGpk9OjKXdv+TQzDMKqM669vvMzll5fm2uYQzzAMo8rYcsvMx555xjl5POaY0lzblIJRFzRvXmkJDKNwXn4Z9tuvtNcwpWDUBRZW04g7K1dCq1alv47NKRh1QXCVqGHEgT59kvfLoRDAegpGjTJ7Nuy8c2K/ffvKyWIY+fDss/DII3DggS5aW7kwpWDUJKkL1dasqYwchpEv3brBmWeW/7o2fGTUBV27VloCw4gHphSMuuCUUyotgWHEA1MKRl0QdCZmGEZmiqIURKStiEwUkfdEZJaI7CUi7UXkWRF539u2C5S/UETmishsERkWyB8sIjO8Y9eLpAZSNIz8WL260hIYRjwoVk/hz8BTqtoH+AYwC7gAeF5VewPPe/uISF9gJNAPOAy4WUT877hbgDFAb+93WJHkM+qcVPM+wzDCKVgpiEhrYD/gDgBVXaeqK4ARwF1esbuAo730COABVV2rqvOBucBQEdkGaK2qk1VVgfGBOoaRMx07JtLW5zSMaBSjp9ATWAr8TUTeFJHbRaQlsLWqLgbwtp298l2BjwL1F3l5Xb10ar5h5MWhh1ZaAsOIH8VQCs2A3YBbVHVX4Cu8oaIMhH2zaZb85MoiY0RkmohMW7p0aT7yGnXCjTdWWgLDiB/FUAqLgEWqOtXbn4hTEku8ISG87aeB8tsF6ncDPvbyu4XkJ6Gqt6nqEFUd0qlTpyKIb9Qq7drBkUfC3XdXWhLDiA8FKwVV/QT4SER8pwIHA+8CjwOjvLxRwGNe+nFgpIg0F5EeuAnl170hplUisqdndXRyoI5h5MXjj4eHMjQMI5xiubk4E7hXRDYH5gGn4BTOgyIyGvgQOB5AVWeKyIM4xbEB+KmqbvTOczowDtgCeNL7GYZhGGVCnKFPPBkyZIhOmzat0mIYhmHEChGZrqpDwo7ZimbDMAyjAVMKhmEYRgOmFAzDMIwGTCkYhmEYDZhSMAzDMBqItfWRiCwFFhZwio7AZ0USp9zEWfaoxL2NcZY/zrJD/OWPQiFt3EFVQ1f/xlopFIqITMtkllXtxFn2qMS9jXGWP86yQ/zlj0Kp2mjDR4ZhGEYDphQMwzCMBupdKdxWaQEKIM6yRyXubYyz/HGWHeIvfxRK0sa6nlMwDMMwkqn3noJhGIYRwJRCleO5ETeqGHtGlcPuffGpaaUgIk29baz+cMRxroh00xof37NnVBniet8h/vc+KpV6RjWpFETkhyLyJnB2pWXJFRE5GXgR2BVYGcd/2ijYM6oMcb7vEO97H5VKP6Oam2gWkT7AeOBpYADwc1WdJyJNVHVTZaXLjoh8E3gVGKqq01KOSa18Fdkzqgxxvu8Q73sflWp4RjXRUxCRVn5aVd/DhfK8Fhfd7WdeflX+0ftdRABVfQ2YCuziHbtARI4Uka3i/gdvz6gyxPm+Q7zvfVSq7RnFXimIyAXAmyIyVkR+6GXPVtXlwCNALxHZzytbVe0Vkd8BvxGRoA+S04C7ROQtoC0u1OnV3hdELLFnVBnifN8h3vc+KlX5jFQ1tj/gIOAVoAdwILAYGBg4vhVwDnBvIK9pFcjdHLgQ58zvEeDQlONnAIO9dCfgUWBYpeW2ZxSfZxTX+14L9z7uz6jqvg5yZDPgTVWdr6ovAn8Grgwc/wqYCHwpIpeJyB+A7uUXM431wD+AvsAU4EAR6eEfVNWbVXW6l14KLAfaV0LQImDPqDLE9b5D/O99VKryGcVdKWwJdBCRFgCqehWwjYgc7+0rsAY3YXM6sFRVP6iUsD7qxgfnqOpXwASgGzBURJpDwgRNRNqLyJ+AgcAblZK3QOwZVYZY3neoiXsflap8RrFQCpnMzlT1EaAXcEQg+w/AzwP7VwIzge1V9eqSCZmBLLKv9bYLgEnA/kAfL0+9L6MJuK+J/VV1blkEzpPgZFlw7DMmzyiT7FX/jESkfSAdq/sOWeWv+nsfFRHZJSy/ap9ROcfQcv0BI4C7gEEp+QI099IjceNy3b397YGbgFbefosqlL2JBsYHgdbADcCJwEnAkV5+h0o/gwjtPBxnNz4euDiQ3zQGzyib7FX9jIDDvHs6HvhTIL9Jtd/3CPJX9b3PsZ03APP9ZxCHZ1TxmxZyE/21EwcCbwPTcV2ndsHjXrqn95L9HXA78FPgKeDWmMjeNrB/FvA5MBcYXunn0Fg7vRfnabhu+3BgD9w48I9SylbdM8pR9qp5RgHZx+DG2kd4L5GXgMOr+b7nKX/V3Ptc2piyfy/wH+DHviKo5mekWmVKIeWl2R3YBjdDPw7XRfSPNQEuAJYC+wJtgG/ivszPi4nsi3FfqoLrFs8DLqr0M8ixncOB3oH9c3CLbfD++av5GUWRvWqeUYrsfYFmXroz8KD3cvW/rC+upvuep/xVc+/zbKPfljOBU3E90v6B4+fjQmlWzTPyf82oEkTkZ8DBIvIKcL+6sUSAxSIyDNhfROaq6v+ALsAXwE6q+rlX7jURmaKqG2Mie19fdhFZAAxQN7FWtQTa+SowXlX/JSJNRaSZqm7ALSp6zyvemep8RrnIXhXPKOXv6wFVfdfL3xU31NAM95JZDfySKrrvnpz5yF8V9z4qgTa+DDyoqh+LyOa4YbJRuI/EkSLyOs7UdiXuo6QqnlESldZKntY8BteVPxD4G3Aj8I3A8W8A9wDHhNRtSkqXLUayN6v0vS+wnYOC7cD1ivaOyTOKKntFn1G2vy/c8MP2Xnor3Mt012q570WQPxb/HxnauJt37FJv+z2cIphFYC6kGp5R6q9arI/2AG5RZ6t7CW5i5mz/oKr+F3fTB4jIQd4qQN/fyUb17m6FKET2DRWQN1/C2nkWgKpu8MwFtwOmi0g3ETkVqvoZRZW90s8oTPZzAFR1nqp+6KW/xFnjtIeque9QmPyVvvdRCWvjGd6xb3u90/OBx3BzKV9BVT2jJCqqFALmmvNwlgWo6kLgn0BLETkqUPx+3GTNBKBDOeUMI86y50KEdo7wjvfBte1s4HGqYDFRDcu+ZcrfFyLyK6AfznyRSr9o4i5/FBppY1sR2Qu3IO3fqjpIVU/CDR/v4pWtyjaWVSmISJtAOujVcCLwdeCfdDHOIqGvOLbC3dwZuGXg50F5b2qcZc+FPNq5i/fP0RP3x94D+LaqjoWqf0Zxlr2vV/ZwEZkE7AQcp6qflEvmIHGXPwo5tvEFYD+ci4rzA6c5RlXfLIvAeVIWpSAie4jIY8BfReRHItJcVVUSHhA/x/k4Od272V/gxhhbeDd+DXC2qn5bVReXQ+ZakD0XCmjnFl475wL7qOrpMXpGsZbdOz4LOE1VT67E31fc5Y9Cnm1siXsHbBJn0NAEQFXXVKYV0Sm5UhCRgTgLg4ne7yBgRwBNzLRvgfMfvhi4TUS2xQXRWO+V26Cqn5Za1lTiLHsuFKmdM1S17K4G6lj2dV65Bar6TplFB+IvfxQKbOMGv5xWsXvyVMrRUxgMzFXVu4FngRbAh/54nIhchtOyWwO/AJYA9wErgKvKIF824ix7LhTSzivDTlhG6lX2avj7irv8UaiHNiajxTfP2h/YI7DfGVgLXAEsws2+34mzR26Bu4E7ppxjy2LLVeuy10s7TfbK/X3FXX5rY8R7UMSb2Qp4GOfG9k481w7esT7AWODkwI3/F54tr5fXpIJ/CLGVvV7aabLb3421sTy/Yg4frcPNuP8A+Bg43j+gLsRcH5ymBecT6GPcEnak8jFi4yx7LsS5nSZ75Yi7/FGohzZGoiClICIni8j+ItJWnavb24HngDnAEBHZKVD8GeC33ljcSKA/zvcHlbihcZY9F+LcTpO9cn9fcZc/CvXQxnzwvXpGr+BuShfcWNom4AOc+dXZqvqZV6Y3zt/HWlW9zMvbArgNN0bXFDhLPR8o5SLOsudCnNtpslfu7yvu8kehHtpYMDmOu/me/3YC7vHSzXA+wx9KKXsMcDPQG2/ixSvbpRLjZHGWvV7aabJX7u8r7vJbG4v3i+QlVUSa4Xx+NxWRf+GCXmyEBt8xZwEfi8j+qvqyl/+IuIhDTwJbiciBqjoLKOuKxTjLngtxbqfJXrm/r7jLH4V6aGMxaXROQUT2x02stMOt/LwMt+jnQBEZCg3uAH6Hcwbl1zse5xf9RZx7h1nFFr4x4ix7LsS5nSZ75f6+4i5/FOqhjUUnQpdrX+CkwP7NuGhiPwSme3lNcON0DwI9AvX2rWQ3KM6y10s7TXaT39pYXb8o1kfTgQcl4efjNZwP9HG47tiZ6mbfuwEbVXU+gKq+qqqvRjh/KYmz7LkQ53aa7JUj7vJHoR7aWFQaVQqq+rWqrtWEn49DcKH+AE7BeZr8B8499H9KI2Z+xFn2XIhzO032yhF3+aNQD20sNpHDcXqaVnE+Ph73slcBF+FsduerCzdZdcRZ9lyIcztN9soRd/mjUA9tLBa5LF7bBGyGW7Ax0NOuvwY2qeqkKr+hcZY9F+LcTpO9csRd/ijUQxuLQy4TEMCeuJs7CRhd6QmRepG9Xtppspv81sbK/3Ja0Swi3YCTgGvULQuPDXGWPRfi3E6TvXLEXf4o1EMbi0HObi4MwzCM2qWsMZoNwzCM6saUgmEYhtGAKQXDMAyjAVMKhmEYRgOmFAzDMIwGTCkYdYOIdBCRt7zfJyLyPy/9pYjcXMLrHiAie5fq/IZRTCK7uTCMuKOqy4BBACJyCfClqv6xDJc+APgS+HcZrmUYBWE9BaPu8b7k/+GlLxGRu0TkGRFZICLHisgfRGSGiDwlIpt55QaLyMsiMl1EnhaRbbz8s0TkXRF5W0QeEJHuwGnAuV6vZF8ROVJEporImyLynIhsneO1F4jIWBF53fvtWJEbZ9QkphQMI51ewLeBEcA9wIuqOgBYDXzbeznfABynqoOBO4ErvLoXALuq6kDgNFVdANwKXKuqg9S5Y54E7KmquwIPAP8X9dqBcitVdShwI3Bdkdtv1DE2fGQY6TypqutFZAYuSPtTXv4MoDuwM86z5rMigldmsVfmbeBeEXkUeDTD+bsBE7zexebA/Byu7XN/YHttzi00jAxYT8Ew0lkLoC74ynpN+ILZhPuQEmCm9+U/SFUHqOqhXplvAzcBg4Hp4uIDp3IDcKPXA/gJ0CKHa/tohrRhFIQpBcPIndlAJxHZC0BENhORfiLSBNhOVV/EDQm1BbbC+e1vFajfBvBdNY/KU4YTAtvJeZ7DMNKw4SPDyBFVXScixwHXi0gb3P/RdcAc4B4vT3DzCCtE5AlgooiMAM7EBYj/u4j8D5gC9MhDjOYiMhX3Yfe9QttkGD7mJdUwYoaILACGqOpnlZbFqD1s+MgwDMNowHoKhmEYRgPWUzAMwzAaMKVgGIZhNGBKwTAMw2jAlIJhGIbRgCkFwzAMowFTCoZhGEYD/w9SFnwqy3iWXQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a line plot for the coinbase DataFrame for the full length of time in the dataset \n",
    "# Be sure that the figure size, title, and color are tailored to each visualization\n",
    "bitstamp_sliced.plot(title= \"Bitstamp Prices\",  color=\"blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Bitstamp  v. Coinbase - Full Length of Dataset'}, xlabel='Timestamp'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Overlay the visualizations for the bitstamp and coinbase DataFrames in one plot\n",
    "# The plot should visualize the prices over the full lenth of the dataset\n",
    "# Be sure to include the parameters: legend, figure size, title, and color and label\n",
    "bitstamp_sliced[\"Close\"].plot(legend=True, figsize=(15, 10), title=\"Bitstamp  v. Coinbase - Full Length of Dataset\", color=\"blue\", label=\"Bitstamp\")\n",
    "coinbase_sliced[\"Close\"].plot(legend=True, figsize=(15, 10), color=\"orange\", label=\"Coinbase\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Bitstamp  v. Coinbase - January 2018'}, xlabel='Timestamp'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using the loc and plot functions, create an overlay plot that visualizes \n",
    "# the price action of both DataFrames for a one month period early in the dataset\n",
    "# Be sure to include the parameters: legend, figure size, title, and color and label\n",
    "bitstamp_sliced[\"Close\"].loc['2018-1-1' : '2018-1-31'].plot(legend=True, figsize=(15, 10), title=\"Bitstamp  v. Coinbase - January 2018\", color=\"blue\", label=\"Bitstamp\")\n",
    "coinbase_sliced[\"Close\"].loc['2018-1-1' : '2018-1-31'].plot(legend=True, figsize=(15, 10), color=\"orange\", label=\"Coinbase\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Bitstamp  v. Coinbase - March 2018'}, xlabel='Timestamp'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7ZVmDw9XX0E0REREREZE6RkM3RUREREREEuD555+PW9vq0RMREREREaljlOiJ+Hz6KRgDN9+c6EhERERERKpGiZ6IT3GCd9dddsInIiIiIlJbKdET8TnllERHICIiIiISG0r0RHy6d7M4asAMXM4iAGrpziMiIiIiEsbmzZuZMmUKnTt3pmfPnkyYMIGlS5eGrbtx48aotjxIT0+PdZgxo1U3RXxa5f2XGX8/z3/u8VqU2kdTRERERGohy7I47rjjOPPMM3nttdcAmDt3Llu2bKFbt24h9Vu1asVbb71V3WHGlHr0RHzcnuCsLicHFixIUDAiIiIiEjPffPMNSUlJXHDBBf6y/v37c/DBB3P11VfTu3dv+vTpw+uvvw7A6tWr6d27N2BvgTB58mTGjx9P165dueaaa4Lavuqqqxg4cCCHHXYY27ZtA+A///kPQ4YMoV+/fhx//PHk5uYC8Oabb9K7d2/69evHIYccAoDH4+Hqq69myJAh9O3bl6effjomz1k9eiI+uZ7MoPNTpnr46GMneXmQmpqgoERERETqkt8vh11zY9tm4/4w6JEyqyxYsIBBgwaFlL/zzjvMnTuXefPmsX37doYMGeJPwEqaO3cuc+bMISUlhe7du3PppZfStm1bcnJyGDhwIP/85z+54447uP3223niiSeYPHky5557LgA33XQTzz77LJdeeil33HEHn332Ga1bt2b37t0APPvsszRs2JBZs2ZRUFDAiBEjGDt2LB07dqzSy6IePalTtmyB0UPWsGxpxSfYeYrcQeeffmq38eSTMQlNRERERGqYH374galTp+J0OmnevDmjRo1i1qxZIfUOO+wwGjZsSGpqKj179mTNmjUAOBwOTj75ZABOO+00fvjhB8BOLEeOHEmfPn2YPn06CxcuBGDEiBFMmzaN//znP3g8HgA+//xzXnzxRfr378+wYcPYsWMHy5Ytq/JzU4+e1Ck/vf8j315xMP99/jm63nNW2Dr79kG4ebMetweSYPbKQQzu9DtNG2zjrEP+xw3XXsEVV6TFOXIRERGR/UA5PW/x0qtXr7Bz7qwoV99LSUnxHzudTtxud9h6xrdH17Rp03jvvffo168fzz//PN9++y0ATz31FL/++isfffQR/fv3Z+7cuViWxeOPP864ceMq+KzKph49qVO6FtwBwN96nw3Ar7/C+vWB699/DxkZ8MUXofe6fT16gzv9DsCmf7XinpNv5NhB72sFThEREZFa7NBDD6WgoID//Oc//rJZs2bRuHFjXn/9dTweD9u2beO7775j6NChUbfr9Xr9CeQrr7zCwQcfDEB2djYtW7akqKiI6dOn++uvWLGCYcOGcccdd9CkSRPWrVvHuHHjePLJJykqsld+X7p0KTk5OVV+zurRkzqld9bnQefDh0NKCuTn2+fz59s/x44NbJ+wYQPs2OHr0QvjtUunUlh4Eskp+l5EREREpDYyxvDuu+9y+eWXc99995GamkqHDh145JFH2LdvH/369cMYwwMPPECLFi1YvXp1VO3Wr1+fhQsXMmjQIBo2bOhfzOXOO+9k2LBhtG/fnj59+pCdnQ3A1VdfzbJly7Asi8MOO4x+/frRt29fVq9ezcCBA7Esi6ZNm/Lee+9V/TlH211Z0wwePNiaPXt2osOQmuYVu7v891UDeTfvd+6+2y4u/jV/6SU444zgMl8PO+8/9BzHND8nbLM/ttzBiDGZYa+JiIiISGSLFy+mR48eiQ6j1gv3Ohpjfrcsa3C4+uqikDqpbeY6nn9yPX/e15t/n3WhvzwlBRzGgzFejIGLLgrck8ZGAKyxv4a0d/ap2+Ies4iIiIhIrCjRkzqpWcNtrH+8Lb3bLuTCw5/CN+SZjAzwvOzC+7K9Z96TT0L3lks4a9RzfPalvYeCyejib2fZtl4AnDjszep9AiIiIiIiVaBET+qMzz+3h2yGc8+tOwDYvXWnv+y4we8A8NTZF/Dceecw8oDv7QtJGf46qzt/C0CLNvVjH7CIiIjIfqK2TherKSrz+inRk1pt1iw7wQMYNw4cxhu2XsN1dwJw3y3r/GWDOtqrazZruBWAYwd9gMfrAGOvUVSU0plBg5MA6NdHb04iIiIilZGamsqOHTuU7FWSZVns2LGD1NTUCt2nVTelyv5aYvHC/3K54+765ORAw4bV99jFq99aFpw8/DUGdJgbtt6JIz5k3bpH6NP2T3/ZjZPuYcve5vRsvdhf5nR47dVZjt9BkjMV5257ywWv3phEREREKqVNmzasX7+ebdu05kFlpaam0qZNmwrdo0RPqmz+85dzT7/H6NlhMYs3HMCqVdChQ/wf1+2GgR3sjc1hPK9dOhWAOav7hyR8q/YexDUnw0+XnB5U/tgZ/xe+8RR7hU2H014K1wrfUSgiIiIi5UhKSqJjx46JDmO/o6GbUmEvvGDRrJmFpyAXVvyPE/s+BsBtk28BYOXK+D7+p5/CHXfAvn3w+92D+fTaI9m4MXC9oH6JeXoT5rN2e1vWrPHy888Vf6zirRdAPXoiIiIiUnso0ZMKOzPJwdZHHCx44f/g17P95ScNfxNruuGIwwMbj//1V+wf/8gj4dZboaigyF/WunXgemar5oGTRn1o12QdEwd+yLGD3ovYpnXELxRkjiNn3OagclOc6WnopoiIiIjUIkr0pNK6Jk0PW/6fv50LwE9vvM9L193FG2/E7jE9HnA63KQm5bFxXZ6//OThr/mPk9OzQu5rkJbNe1ceFyiYvI3svHT/qWk6jJTxn1I/q3nQfcWJnqUePRERERGpRZToSaXVS8kLW35AqyUAHOSexF0n3sxDN/4Ss8d88EH45sYx5D1fD3dRIPkqnp8H0G7kFMjoBn1uj9xQahO+XnQoAL82jDymUz16IiIiIlIbKdGTCnn99cjXDr79e35eNhyn0wSVz/j70TF7/FnfbWLkAT8AsGnF2pDruaPn40hvDRP/gj63hG9ksr2dwpG3PUv+kLcYNmFYxMczDvu5GKNET0RERERqDyV6UiFTpkS+9sNfB5NcL5169YKToiYZO2L2+Ic2v8t//Mh9W0Ku12vVp/xGUpsCkJzRhNSux5dccSUM9eiJiIiISO2jRE9iysIBWGzdGp/2OzYNLOn55Q1HRHdTp7P8h7NWDqnQ4wXm6ImIiIiI1B5K9KRCGtffGVK2tvsP7BtXvAGmwWG8vPgiFLkD2zRmP5vB1zPWV/nx+3dcHPHaP+b/Gv7C4H/BsWtwT85l0LXfV+jxijv7MgpiN89QRERERCTelOhJWMaEH9G485ngFS1Peux12g0aQXpWEwAsDGDRujUs3tjDXy8jbR8v/uPLKsU0cSI0SN4WUt7h/1aRdEYhF908NPyNrjSo3w5XahqOpJQKPWbxHL3BLT6ocLwiIiIiIomiRE+CTJwITZsGzneUM73usRf7lSoxGCw6t8+lb7s/g64MGZhfpdiaZT9L/dTckPJHnmrBuvVJ1K9fpebDMmXO3xMRERERqZmU6EmQbUt+oW/zr7CmG64++gEOOQROPRX27StV79A8OGoxLbp1Dyq3MGAskgtXhLS9b/OqKsX27Hl/8x9f9/Ma//GkySm0aFGlpiNSoiciIiIitZESPQnyy+0H8tUNhwPwwNRr+WuJm1degYwMeyjn7pyGFHlTaNoiFRoeEKYFQ16uxSMPuQFYubWj/8q1Ex+IWZz3PtKyxEPGMxkr0bZW3hQRERGRWkKJngBwzuhnOe3gl0PKx/f7NOh8W3ZTFmVPLqMle+jmooVFADz16z946YfTYhmq/SgOV/mVYvI4gUTPu/D+oGvZ2bB6dbWEISIiIiJSIdXzaVlqvJLDIkuaeuCrJDmLaN14A6cd/DJdWyxn9s6DI7ZjWYaBHedww7H3AHDQqAxcbV6CvXYS6XaDqxK/dbm5UM93PGf9CAb4evHyClNJq3hzUSs5dHPZ12/Tvfd1vPnKPt584ive/PkYwOD1xrlTUURERESkgtSjJ2U6dcQrvHvFZJ6YdinDu9jbFzg8eyLW37zFzngmDX4fgPYdkjn6aHh77tkATJtSuQ32/vNUgf94wP/ZbS/KfIt9hyyoVHvRKtmj173JbAB2zLyVNy6ZxNc3Hsq3N43iwQfjGoKIiIiISIWpR08qbEPBCAZGuHbsoOBtCNp3yQDAZe0FYFq3U4CKb7NgbZgBxQuupNhbPPQcf3yF26mKQncSycDgtvZefGN6fgvARdcsZPv2Xtx/f+R7RURERESqk3r0pMK+/ib6Xxtn/WYAHDvgLQBGdq/YhuXFGjRKrtR9sZTsKmL3D3eQmhS8xcPCB3rzwAP28M1duxIUnIiIiIhICUr0pMKuu6le+ZV8nPWaBJ2nJBVW6jH3FjYH4MpXnqjU/bHSYPVt9G67MKT8j7sHYE03dG67MwFRiYiIiIgEU6InFddsVFTVFuwYR3pDe6mU71afWKWHHDzIA8CRJ3WuUjuVsa7hzf5jhyP8FgsDOswF4P6p11ZHSCIiIiIiZVKiJ3i9pQqajoADroxY39W4e8RrJXWb+qj/+NklrwEwc/EhIfXy8mDsWNi8OXJbLqed6LVuU/3TStsedUfUdc8d8984RiIiIiIiEh0leoLbXargiB9g4D/5asGhgbLJ29hbkMV7f5xMZlbkvQTenTUJgNmFd5LcJJAQ/uvfDr5acCgOUzqrhPHjIWnbRxzQvShiu5bHDtJZmb0ZRERERET2M0r0hJtuCl++LbspADfOeB5Sm9DgrO1MevC1MveMy2hmz6Vr0KpjUHl6OngsJ0mu0GQuf8OvfHT10dx2zNUATJ0Ko0cH1/H4slGH01n+E6omX6y/yn9893s3ADB73ZhEhSMiIiIi4qdET3jwwdBeNoCCtD4ATD27bdRtHXbhpeTXH0y3kYeGXBvb5wt7L76CHeAOrFx53BGrAZg85B0Avv98PTtXzg+61+uxh246k2pGj95vK4ZwxDWBDfQufvou5m4ajYPS3aMiIiIiItWvZnxqloSxLHA6PIGCUTP8h6fffRV71xxC784jom7PNOpF6rGzyq70dhOKktuTdMJqAK44yN5MPSt9B5MmwfrH7cRy6VKLbt18cXrygZozdPOGN//BlzcDB78J6z+gUWODlxSSnNpfQUREREQSTz16+7kTToB2WWsB+DHnXmh9lP+aIymVBl1Ggon9r0lS4RpOHTGd/HxIcdq9e/VTc/n043x/ne4l1nxxeewEyukM3/tY3Vp3aGQftDsBDnoRALeVQpKzIHFBiYiIiIj4KNHbz+XmwoqHuwAwbFh857+9seCaoPPpF5/Gxx8H15l15xD/8bUT7/MfF3nsDdMdKY3iFl9ZPnEu4qv0lf7zf70UuvKox0pWoiciIiIiNYISvf1cQYm8xNnp5Lg+lmfv2pCyEbuCE6Y+bRf4j++bcn2Jm+1AnUkp8QmuHEee3IPDjgksMJPeMDWkjttKIclRuQ3hRURERERiSYleDbdxI/TsCWtDc6SYOGpCYH6eSWsWnwfxKV5spaTmaUvLvslTAHsWg9dO9FwpiUn0it3420oeWrUh7DW3N4Vk9eiJiIiISA1QM1a2kIieew4WL4ZnnoG77gq+tmsX5OdDy5aVb39g6v2BE2doL1UspSRVvLer6N2uJBWuo5XzBACcScmxDqtC7n6kY8RrHiuFJJcSPRERERFJPPXo1XCZzkVY0w31cn8JKl+4EDIzoVWrqrX/yWd2rr/NMbpqDVXRV4uPDFueVLgOgG6pbwHgTE5sj15ZPJZ69ERERESkZlCiV8Ol7rRXK7lh8IFs2QKFvk6x3r1j0/62XLuHakeHx2PTYJQe/fSyoPOf990edL5me7uw97lS0uIWU1W5rRSS1aMnIiIiIjWAEr0abuOa3f7jaeM+4bK/rY9p+xMn2FsbdO9VP6btlue0e2/wH+/Ny2Dc8Z3859e+eh8/LT0o7H1OV3xXBq2KjZuTSXEV2psTioiIiIgkkBK9Gu6EoW/5jz+5ZgK3DBuK2w3DuvzCv8+6kHNG/5dbb614uz/+CG+8AQ5vDgDGVS9WIUdkYfzHJddUefH7M+jQLdN/fvG/ruXFObeE3N/m0nUkJ3aKXplWr/M9KW9RYgMRERERkf2eFmOp4Q5o9VfQeavGm3CluHG/dKC/rOPlh3H77ZEXCQnn4IPtn1dNyGXSqYAr/j165pB34btJAKRnBVb4bFRvd1AS2K4djDuxh/98294mtLl0PYXumjs/D6CgyI7P8hRgnDU4IxURERGROk89ejWIZcGqVeXXu+HYe4LOVz3SKULNMh8Nh/FQP9Xu0cNZDXPf2hwLI9+FQz4AY5i16RgATjt4Ok2bBle9/PLA8dNfn4/bm8KgQfEPsSoGdJgDgLVvdWIDEREREZH9nhK9GuSBB6BTJ3tFTYA//wxf744TKjFW08cY+7+1j7XD87KLjk1XUeR2gaOa5r61nQRtJtqxePOD4rrk7U+54r0Z/rKDbvuRB2ZczbQHb8XjgdmzqyfEyhrRfzUA3h3zEhuIiIiIiOz3lOjVIF99sotrjr6f33/3AtC3b/weq22WvajLtENeoMiTFL8HKsOGAnv46fPfnQnAE2+P4+E3jvJfT21zEPd/9gBt2iUmvoq67KlrAfjg264JjkRERERE9ndK9GqQ03texv1Tr+PHtz7zl32zaHS5983fNjbqxzi89xf0bz8nqGzDrtZR3x9L7i5X8czX55Ld7bGw17/+GnbsqOagqqB4jt7yZd4ERyIiIiIi+zstxlKDnHrQdABO6v0gcCRjen7NmJ7fln+jFUgs+veHKVPguutCq23fDl9cH5oU1m/ZI7RyNZh8cgbfNn+G0aMT8vAx57Xs7006tFeiJyIiIiKJpR69GqhTs5UAfH3jYVHVz3It8h8f3OwJvn7l87D13ngj/P1pzbtXLMAYMQbGjLF/1gXHn2D/OR3QXYmeiIiIiCRWuYmeMeY5Y8xWY8yCEmUnGmMWGmO8xpjBpepfb4xZboz5yxgzrkT5IGPMn75rjxljf7w3xqQYY173lf9qjOkQw+dXq0z/42oAXvj+zArd1zpzo//4iWmX8vl148LWO6D10rDljXscUaHHk/AGDbL/nByOQKKXlwdnnQWbNiUqKhERERHZH0XTo/c8ML5U2QJgMvBdyUJjTE9gCtDLd8+/jTHFyzk+CZwHdPX9V9zmOcAuy7K6AA8D91f4WdQRK9emRr544Ivc8tbt/tPt2VmYU60Ktf/mq/vCX2gUx1Vf9iPGYf85eT2BRO+l5/bw+Mh0bjvjf4kKS0RERET2Q+UmepZlfQfsLFW22LKsv8JUPxZ4zbKsAsuyVgHLgaHGmJZAA8uyfrYsywJeBCaVuOcF3/FbwGHFvX37m+I5ejdNuit0OGPH08kvCiSCDuNlzZrgKp+HH7Hp98svEf53p7WoYKQSjsNpv76WN5DojfBOJj01h6fPOpsNGxIVmYiIiIjsb2I9R681sK7E+XpfWWvfcenyoHssy3IDe4CscI0bY84zxsw2xszetm1bjENPvC4tVgDgcnrCXu/VeqH/eOu+NrRrBz8vG+4vWzYrsPHeRx+F3n/GyBdCCyVmjLH/nFzuLf6yXllf+4/bt6/2kERERERkPxXrRC9cT5xVRnlZ94QWWtYzlmUNtixrcNOmTSsZYu0wuNOskLIzD3nRf3xACzup+3NdHwDcbpjY8mL/9S+eeCTk/mmjX/Yf/+h4M1ahik9xj16vPaeGvd668Zqw5SIiIiIisRbrRG890LbEeRtgo6+8TZjyoHuMMS6gIaWGiu6PZt051H+8pteckOt3zLeThvMO/Q8AS+ZupYFZ5r/+yOlXhNyz0poGwO6Rf2EcyQAs2jI4pJ5UjnE4Aye75mN53EHXP756QjVHJCIiIiL7q1gneh8AU3wraXbEXnTlN8uyNgHZxpjhvvl3ZwDvl7ineJnJE4CvffP49huWBY8/Hvl6+379AVi7p6e/7Po72wXV+e2nvfzzg4v857NXDgppZ6/bzrUdaU0xLntz710FHSoZtZTmcJT4c/qkH4X5hUHXe7VZhIiIiIhIdYhme4VXgZ+B7saY9caYc4wxxxlj1gMHAh8ZYz4DsCxrIfAGsAj4FLjYsqziCWcXAv/FXqBlBfCJr/xZIMsYsxy4Egiz1Xfd9txz8OIjoUM1S0sdN8N/nJRk//zXF3Zy988HCrjzxFv813fnNgq6d/x4+G5mEQCuJBd5DY/g9ndu4ZXlT1YxeinmNMGJXWF+QYIiEREREZH9nau8CpZlTY1w6d0I9e8G7g5TPhvoHaY8HzixvDjqsrce/5xZd4bf+66kjGYtQ8pmLh7FxUf8m4UPBF7arxeOoUXT4CTjpHZnM/WgVwFwJScx5lAHixbfzhVnVDF48SvKyw46/8f/vc8dpUZrer3giHU/uoiIiIhIKeUmehJ/l4x9LKp6zuJuvBIK3ckhZWnpKbgcgT3z/vhxA2ePDuzj5kpyYQxcckklgpWI8tf/CGmB8zsmnBVSp7DAIjVtv9w9RERERESqkfoWaoDP5o+Nql5yipODbvuR7n9f4i9r0WhzSD2vlYzLBHr0Bq5pE3Td4XSWvkViYNmeQ8OW/7LvJv9xQV5h2DoATz4Jxx4b87BEREREZD+kRK8GeOyM/4t47becG4POV2UfxBGTu/vPM+uHLlC6YXMKRQV2QvHcc2HWtdk/96OPu0EHNw9bnpKewY+5/wDw/38pLT8nj/tvWc3CX5fHLT4RERER2X9o6GYN17NPatD5pk3B1/OLgq8//fWFDOrwGz1a271+7ZYfEWZmpMSDiTD5zlG4nW+/b8uIcfDnvELGhE61ZNN/erL60dUAeL2W5vGJiIiISJXo42SCFZSzMGN6xxFlXm/WIniO3jHHOhjc6XcAfvgBDu/9VZXik+i5Xc3Clq9d72JI648BmPVV+C0WOjZb7T/O2bo6bB0RERERkWgp0Uuwv01ZEXSenxwYlsmk9dB8TJn3O9ofF3Re0OQY/vvteQAs/N/5IfVf/+WkSkYq5WnRtjHPfWsvwPLNysDr7Ox+PmN7fwrAmAZX8vDDZbfz4f9+jluMIiIiIrJ/UKKXYC+d2CXofG2zfwdOkhuXe//l17cOOu8w/AjSGjQA4PzDngmpf8RN/61ElBKNrCzIHH0n3ywaTW7ri/3l4ya35/PlZwIwpPNsHrt3VZntnNL+lLjGKSIiIiJ1nxK9GqbAlBj+5wjdTqG05GR45utzAwXG4MhfH1Kv4d92wykWmc0yYhClRDLplNYMuuYbUlK8/jKnEz5de43/fNUjnRIRmoiIiIjsR5To1SRjf6agsMQqmSa6tXLWpdq9R2/8bid84/p+FnR95daOrFrfMDYxSrkaNABXvaygsvT00D81y7L/27czdOVUEREREZGqUKJXzWbMgMcfj3CxyXBSkjyB8yi3QWjdux+O0zw0m2gP1Vy7o13Q9Y/mHEVmZmWilcpq17dP0HlGRuifmsNh/7dqWU7ItZwcuGzKd/zxW27cYhQRERGRukuJXjWbOBEuuwwyMsDjge923gxA4chvAOjTp+J73J1/Pvz5p4PRo+3zBml7g643bbCtSjFLxXXqBGu2t+Ppr+yFcTIahP6pNaq3i4kDP+D6C+eFXJvz41oeO2YUW2f8Le6xioiIiEjdo330EiA9NZu+reaTnT0Cr2UnAEmtR9kXG/WtcHvGQK9egfOMNr2gMLDgx+Hj06sUr1TO0LvXcLFvTZb0MD16391yCH3aLgh7r8n+C4BuTUOTQBERERGR8ijRq2bf3TySkQf8AMDWfbtYv64IdyMnLoevJ88YGPAPyBxS6cdoevi98PEM/3mT/seVUVviZcuWwHHJHr29+Zk0gJAkb96avvRrPx8Ax44fIR1ycyvewysiIiIiokSvmhUneQD335PL2Z0/weX0BFfq8feqPUij3niPWY9JSse4UsGZUrX2pMoyGjjAN6J2xc4+uGdB6VT+gNZ/+Y8PTL8dgE7NVlZThCIiIiJSl2iOXgL98+DW9Go5Jy5tO9JbY1IaKsmrIRo1CGy34DKF/PzN1pA6liMtpOzbxaPjGZaIiIiI1FFK9KrBokVwzjmJjkISqX//wLHDFPHju9+E1Cns91RIWX5RahyjEhEREZG6SoleNejVC557DtauTXQkkiiOBh1g9KfM2TwWlymkYdqekDqmxWj++fGVQWWTh7xbTRGKiIiISF2iRK8atMlcx7RD/kf79uD2OBMdjiRKq3HkexrSvfl8nvnb+SGXk1KSGHOYhtqKiIiISNUp0asGH18zgf+dfzb1UnL4a1N39uQ1THRIkiBekxzxWmpaMr//XolVNvO3gye/ClGJiIiISF2jRC/ONm0KLKM/otuPuBxu3J7gxU6/3Pt0IkKTRDBJQadXTX8wcOJwkZQc+JN8//djWLm9e/ltvtOU3e8cHqsIRURERKQOUKIXZyefHDj+/LpxdG+1lKz0Hf6yz9bfwKhzzktAZJIIXoJ79CZNaR44caTQtkcn/2lW83QcptTWGxE0KvqR7OyYhCgiIiIidYASvThr4P0zpGznvsb+43GX/R9JSSFVpI7auDk40TNJ6SVODPV7T+P+D6/hf7vW0r7eT3TIWo7HbUXV9oknxjJSEREREanNlOjF2aWT3g4py0zfFThJbVaN0UiiDWz5aXBBUkbQ6fADDWc8dD9nXdyWtpmrAXj05h9CG8pdD5bF1i2BJLBpzkuxDldEREREaiklenG0YgUkZ3ZJdBhSg3RtsTzofP6C0H3yWrYMPt+6ZmNwwc458F5b8v98gtm/BMZr3nPSDTGLU0RERERqNyV6cfLee9ClCzz/fOgcq5Xbu8IRP8DJWilxf/fDT65y64wZEbzn3uJZdrKYuuAymu55xl/eNmt9bIMTERERkVpLiV6czJsHd590A4f2/BqAb6yP/dd+c02HpiPAqT3T9ndd2u2KeG21OQ2Ahi1bB5VbO+f6j4e4ro5LXCIiIiJSu5XfnSCV8uuPuXw87V7/+bDx/eEz+/i4qS0SE5TUOEeOd0NB+GtF7c6DNS/z7vspdO63hOR69WjYsh09rXvC1nd7nPqDFhERERFAPXpxMzj9waDzpJTA0popGU2qOxypIbY2ODfo3JTRq2tc9u/MGb3+j/TvB7B5+hFltu1yRrcVg4iIiIjUfUr04uSOE24NOnclJUGDHr6TtAREJDVB2oC/B51bOAHYvK9DSF3jsPvnerVZRFpyPt1bLS2z7d05DWMTpIiIiIjUekr04sTjDX5pjSsVxv0Kk7cmKCKpCTIaBg+u7NzT3l4jt/ExIXW93tD7c3LCt7ttbxOW7R5R5fhEREREpG5Qohcn97xfaql7Z4q9Z1pq08QEJDWDCSR6T391Hs269YbDv6PT8Q+GVHUXhW6UvufP1/3H78w6zn+cU1Afh3HHOFgRERERqa20dkOcOEyY7hiRJHt45fPfnUlenyftsmYjw1bNyAhN9FqtnOI/7tBktf84O78BDs3RExEREREfJXpxYFngdJT40N12cuKCkZoluSFMWse0Kc3BUXaHepuOjWBh5OsDO87xHxeZRjhQoiciIiIiNg3djIO//gKX001Ofj0Y/TEM/1+iQ5KapF4bcCSVX69BN35cPaHcaktyJmEcToxRoiciIiIiNiV6cbBjB6Qm5eNyuqHVkZDUINEhSS3VrvGKsOW/bxrPrrzmAOywhmLhxKhHT0RERER8lOjFQXq9Qi4Z+y9SkgoTHYrUcg7yw5a3qfcHaxvdBkCDdn3wWk4c6tETERERER8lenFQz1qb6BCkjkhNKgpb3rzhVvpNPp9dQ36iz/ijsXBqjp6IiIiI+CnRi4MlSxIdgdQVmxpeHvmiMTTueiBgb7yuOXoiIiIiUkyJXhysWa0P3BIbvY+7OKp6Fi6c2kdPRERERHyU6MXBsCH23Lz5Ga8lOBKp9ZypUVWz0Bw9EREREQlQohcHniI70XOlpCU4Eqn1TPg/0ckPvx10Prz123Rpuqg6IhIRERGRWkAbpsdYTg4U5NmJniMpJcHRSF007rlc3vxKXyKIiIiISGTq0YuhK66A9HS49eYCAByu5ARHJHXR62+m0qDU1oxv/XYSBUX6fRMRERERm3r0Yui15zcxsMNGMuvvBMCZpA/eUnU9r1lI84Zb2J7dhOFdfuH+8SakTrM2Ddmdl0nzBMQnIiIiIjWPEr0YmnHLBQxq/kGgwKmhm1J1737Vk507e5KZCW+80YfGjUPrWDhwGG/1ByciIiIiNZISvRhKKlgVdF5QENrzIlJR3bsHjm++OVItrbopIiIiIgGaoxdDfdv9GXTevHV6giKR/Y2FE6dDiZ6IiIiI2JToxdDGXS2DzrM6do9QUyS27H30NHRTRERERGxK9GIo290q0SHI/so4cKhHT0RERER8lOjFUMsjbkp0CLKfsnDi1Bw9EREREfFRohdDDXpO4r/rZwPwx6oBCY5G9icauikiIiIiJSnRi7Ejj7IXMm3VUr0rUo2MQ4uxiIiIiIifEr0Ya93aTvSat0hwILJ/MU5cTiV6IiIiImJTohdrGV2h1QTM0KcTHYnsRyycvgMN3xQRERERbZgee85kGP1RoqOQ/Y0pkegZfX8jIiIisr/TJ0KRuqA4ubM0fFNERERElOiJ1AnFQzctrxI9EREREVGiJ1I3mOJET3P0RERERESJnkjd4Bu66fWoR09ERERElOiJ1AkWSQB4PUUJjkREREREagIleiJ1gTEAeL1WggMRERERkZpAiZ5IXeAfuqk5eiIiIiKiRE+kTjC+Hj1LPXoiIiIighI9kTrBaOimiIiIiJSgRE+kDihO9DweJXoiIiIiokRPpE4wDvtPWUM3RURERASU6InUDf6hm1qMRURERESU6InUCZqjJyIiIiIlKdETqQMirrq58nl4xYA7t/qDEhEREZGEUaInUhf45uiF9Oj9cpb9M3dDuU3s3g3HHgtbtsQ4NhERERGpdkr0ROoA/9DNSBumW+5y23j2WfjgA3jggVhGJiIiIiKJoERPpA4ob8N0y+uJui1L0/xEREREaj0leiJ1gH8fvQiJ3rLffi+3jcJda8l5rh5fvjU/prGJiIiISPVToidSFxj7T3nvzn1hLzdcd0e5TSRt+5h6KXncccItMQ1NRERERKqfEj2ROsBTkAdA2uyTwl7PzTXltnFA7wwAJg1+P3aBiYiIiEhCKNETqQOKCosA6NxsWdjrTkf5E+8aNAgs5LKh/EU6RURERKQGU6InUgd8O7PsHrt2mSvLbaN+vUCiN7TPBn75pcphiYiIiEiCKNETqQMOaD6vzOs5BfVZ8eZVeL49IWIdy5HmP97wRBtW/zYzZvGJiIiISPUqN9EzxjxnjNlqjFlQoizTGPOFMWaZ72fjEteuN8YsN8b8ZYwZV6J8kDHmT9+1x4xvmUBjTIox5nVf+a/GmA4xfo4idd6wjt+Veb1+Sg6dix7CufFtsCLstectDDqd/dmsWIUnIiIiItUsmh6954HxpcquA76yLKsr8JXvHGNMT2AK0Mt3z7+NMU7fPU8C5wFdff8Vt3kOsMuyrC7Aw8D9lX0yIvurvbn1KlB5SfjyUoneg6deXYWIRERERCSRyk30LMv6DthZqvhY4AXf8QvApBLlr1mWVWBZ1ipgOTDUGNMSaGBZ1s+WZVnAi6XuKW7rLeCw4t4+EYlOkdsVdV335h+CzgsK4IYbYO+eotC67iqHJiIiIiIJUNk5es0ty9oE4PvZzFfeGlhXot56X1lr33Hp8qB7LMtyA3uArHAPaow5zxgz2xgze9u2bZUMXaTusaww3414PXi9oeXbPQODzr+esZZ7eht2L/4spG5OTsxCFBEREZFqFOvFWML1xFlllJd1T2ihZT1jWdZgy7IGN23atJIhitQ9Vrg/ozlX4wizrcLqdcHDPNP22vP7Jg95N6RuUe7e2AQoIiIiItWqsoneFt9wTHw/t/rK1wNtS9RrA2z0lbcJUx50jzHGBTQkdKioiJTB6w3zp/zXw2HrtmgRvBhLdnaExVmAH2fmVikuEREREUmMyiZ6HwBn+o7PBN4vUT7Ft5JmR+xFV37zDe/MNsYM982/O6PUPcVtnQB87ZvHJyJRCtujF6muJzixa9s28r2blsy3f24Cj6dysYmIiIhI9Ytme4VXgZ+B7saY9caYc4D7gCOMMcuAI3znWJa1EHgDWAR8ClxsWVbxx8MLgf9iL9CyAvjEV/4skGWMWQ5ciW8FTxGJXk5B/cDJ6lfLrOv1Bid6v/0WOYNrlTKbLVugbRs3Rx2l719EREREaotyl+qzLGtqhEuHRah/N3B3mPLZQO8w5fnAieXFISKR7aUH9ncrwE+nQIdIf7bgLdU1d3rva4Ovj5uL47P+AGyxDuH6q3bifimLrxeOAb6OYdQiIiIiEi+xXoxFRBKgWbPoh26WTvTSzNagc0dWPxZlPAvArC8XMaKevZ/eob2+qWKUIiIiIlJdot98S0RqrjB53mfzxzKu7+ch5ZY3ONHbZ7Ul3awLKstzdQbgmb+dH3yvx41x6m1DREREpKZTj55InRCa6a3Z3p6Nu1qGlJfu0ZtfcEVoa47wbw1FhaGbqouIiIhIzaNET6QO8FgpIWU9D3CDcYaUe0utuhl2Nc0w9wHk7lOiV13mzd7Llzcfxdol68qvLCIiIlKKEj2ROuCrzdeHFloePN7QYZb/vndB0HkXx/P+47vfuwGA9evDvzUs/e3PygcpFfLY39/k8B4fs2bGrYkORURERGohJXoidYDHpAcXbP+VFOcevIT2zP37rIsD1T48g5Zp8/3ngzr+DkDHTuF79D74rxZkqS5ey357tryRN7QXERERiUSJnkgdcMMNpQo+H86QFu8H9egt2tAj5L4m2S8FnR/YawkAqWnh3xqmjXy+SnFK9P53/tkAWF7tXygiIiIVp0RPpA5o1Ch8eacmf/mP85JCE73SGh7zEQCupPA9el1arKhwbFI1meYPnr/yJiV8IiIiUiFK9ETquJVbOwKQ0bxt8IW8Tf7DXTmN4RQLGvUCIClJbw01RZ+2C5g2+G4KcnMTHYqIiIjUIvo0J1LH7R0xj3fNZv6wHgTg+yUHA7Bnd6G/jscb3IPnSg7fo7c9OytOUUp58rP3JjoEERERqUWU6InUYbkFafQfksFxU5vjcLlYuL4nm/e0ACA/1+2v5zDBC34kJYe+NWzZ04wluw6Nb8AS0ZAB++hR/uhbEREREUCJnkid9q8vAitsHn+8vZJjq5Z2Utf81y7+a5npO4PuS/WsD2lrZ04TXE6tAJkoyx7qxuAmL5Gfn+hIREREpDZQoidShzVrHlh10+mE5BQHxnixylnXw5VkQstcDkALgiTSSxeewcaNiY5CREREagMleiJ1WJNG+4LO3W4H27d6uOeesu9zJSeHlHlxAOrRS7Q3Hvk40SGIiIhILaBET6QOO6rrE0HnhUVOjhn0IXM+/6HM+5xZfUPKLMuBUaJXaUVFsGtX1ds5sO37VW9ERERE6jwleiJ12NNfnRd03iRjGwBvnT+y7BuTG/kPR9z+A+ZUCwslelVx4VmbOHnU5xW+r9ttFjPmTvKfp3Y+OoZRiYiISF2lRE+kDjMNugedpyZFv5LHos39AXjw4TR27LB79DR0s/JuGnIgn183Lur62/Y25adtF7B0KTQbfKK/3PJ64hGeiIiI1DFK9ETqsAN6BC+e4nIWRX1vijMPgBat65GZiXr0KqiwEAoKAucdmq4BwOuJckEbY/eiAjRkob9YiZ6IiIhEQ4meSB029JgjggvKW26zhNQkO9FzJqcB9tYMSvSi1++AnbRpui2k3OOOLtl2GK8/0WtU+J2/XImeiIiIREOJnkgdddMii9QWwYuqNK6/O2zddTvahJS99fsZADjSmgDq0auoxXdlse2pZqxYQdDed+7CQiwruLcvHHsTe/stemnKrYELSvREREQkCkr0ROqosWOjq/dT2lfMa/VHSPn9n9xB6rQ8nCn1Ad+qm0aJXkW9fO2tXHPhCv+5u6CA//s/SE2FzZsj31cy0Rs44XBGPrAUUI+eiIiIREeJnkgddcgh5ddZ2GkpBx13KEcf3zTk2rRphoKiVBo2tM/Vo1c5t06+g0t6Hxko2PQJX7y9mHevmMSFFwQnbasWrKEgrxDwJXrG3ri+fn2Y/ooTAMtSoiciIiLlU6Insh9zJadGvHbXXZCbC/Xq2efaR6/yMlKz/cf7lrzH4n/0ZNLg99m7/Ft/+Z7te+k4vwOvXnkRAKbEYiwAxmEnehq6KSIiItFQoieyP3MmR7zkcEBaWuDcwuEbTijlKb3mTZIrsADLtq2B8gdP+bv/OC87B4BpI58FgoduAjhdvh49JXoiIiISBSV6IvuxNetSoq7r1dDNqJVO9Jpk7PAf7ylo7j8e0GGu/9j4hmkWs4duBt6iHU4N3RQREZHoKdETqYOm/zwtqnrde0Tu0QuhxViiZnkjb2Ox3YzwH/+Vc6z/2F0qf3M4vEFDN4sTPZToiYiISBSU6InUMWe9v4E2k5+Jqm7jJurRiwevN/LrdFzzU/3HCzcN5umn7eO9fwT+n+3e5fEN3Qz08vkTPQ3dFBERkSgo0ROpY/73eitGjUkKey2nID3ovEFDZ9TtWqhHL1pl9eiV1D31VRoumMKu9WvpURTYK++bO07AGCto6GbxHD316ImIiEg0lOiJ7EcsZ+RVNstncKhHLypl9eiV1KvNIqYc+DqrVhYFlR835D2cjuDFWBxK9ERERKQClOiJ7EfqN2lT6XstHGCi66na33k9FUuIN28oDH+h5GIsDiV6IiIiEj0leiL7ETPmYxjxWqXu1T560bNKL7tZjpefmBO+nTDbKwyud2/lAxMREZH9hhI9kf1JWktof3KlbtU+etGraI/eKxefGrbc6w0sxlKc6NVz7al8YCIiIrLfUKInIlGxtOpm1CwrNq/TqtVhtlcQERERiYISPRGJklbdjJbXE5u5jOlFc/3HTpeJXFFERESkFCV6IhIVr6Whm9GyfKtuztz3cIXuu/bV+4LOmzXY6j92+N6tP197eZViExERkf2DK9EBiEgCHPgSNDiggjepRy9axYmeMRXrhesxtFvQ+cEH/BR0nluYRlKy3rZFRESkfOrRE9kfdTwNsgZX6BZ7MZYauLT/ogfglZr1VuYt3jDdVCyu/oPrM2POUf7zP/edHnTdsgygLS5ERESkfDXr05GI1Fj2Yiw1MMmYey1gsXFdfqIj8QtsmB54iy2iYbn3JdXLoHVz+3n8uOE0ep/1ZNB1yzJQwa0bREREZP+kRE9EomRq9By9ua/9M2z5nu174RVD9ssdoWhf9QRjhQ7d3D5kdpm3fPjH0aS3H05ach4AmcMuICklObhZqrYgS04OFBVVqQkRERGpJZToiUhU7M27a25vUrf6H4Yt37RmOwAZjtVYv11QLbG43fbrZJUYuulwhCZp+/Lr+49N1/No38HQ8rBrADhgWO+Q+hmp+0h27K10XKeOeo+LTlta6ftFRESk9lCiJyJRCu7R27oVnvz7f8lZ83MCYwro0uhXFn/7dUj5rNkp/uOZn2+vllhOPcV+nZYudVCY3AmAZk0D1/8qmkaH/1vFKc997y9r1dpeZKVhr2PhFAuTEn6o54jm/610XO9deRz/ObZ7pe8XERGR2kOJnkhd0fwwyBwUv/ZN8By9u27P48KB51L/x4Pi95hRWLenq/+4x8bDQq7P+fQr//GuvWnVEtOeLZsBmDXLkNz5eABMSmP/9e1mJHOXdeChJxoEbnIkVUtsIiIisn/QOt0idcVhX8a1ebfbgWV5ee01aNAAJjQ5L66PF63CouB5bDNnwqhRgfOLD7ndf5xTUJ/q8MalJwHQNmsN9LsXelwNKZn+690HtadRI8hOCQzndLri973bhlkf0XrZ0XFrX0RERGoe9eiJSFRWrDQYvEydCkcdBV0az0p0SADsKcgKOr/n0s+CzjPTd/qPN+1uSc+e8Y+pS4sVAJxy0GvgcEJq06DrTXrZPY8lF2vxFGTHLZ5131R+uKeIiIjUTkr0RCQqXsuBMRZv/d/x/GvaRfy28kD/ta1rNjJoEKxfH+cY3B5+efkZ3IWBpSMLnW2D6nx23fig82WbA0M7rz76QRbdZFi+JH5JVUmZzUr1II7+BIb/L3BeItGzrPi9HXssDd4QERHZ3yjRE5GoeC0HDuPl+KHvcNERT2KaDvNfS/p2BL9fZdjy9olxjeHH6S8y3HE+i168yF9mWeG3fPjPkzm882Y+z3wdOsS0aNviuMUI8OdOe5hkgyNfCb7Qajx0muY/dTgCb8F7d1R+Nc3ytKv3a3BB3qa4PZaIiIjUDEr0RCQqllVqH70tM/2HjZNWAzCo6Vvg9cQthvr5vwHQt16JoYgREr1zG6YzcOMBeLzOkGtJ1ra4xFdsfYG9QI2jQZcy65Ucuumsl1VGzapp1XhDcMG7reL2WCIiIlIzKNETkagUD90sNvWg1yJUzI9bDEv2jANg9srBgcIIiR5Ah6Zrwm7y3nL1tFiHFsRYvmTXlP0WW69+INFr3a1T3OIJl+yKiIhI3aZET0Si4vU6SHIWlVvPcufFLYZT2hwHwOBOswOPF6YH8YsvAscOR2ii996vY2MfXAkG32OashOshk0aAbBqz0A6D+gWt3g+mXdk3NoWERGRmkmJnohExcKQkbav3HpLFuRUQzS2HTvgoLbvhJSfM3Wt/7hecm7I9Vxn2UMqq644+TRl1iIpHU4uoOOFv1eoda/HKr9SCanNeoSUrVoFN90EVsWa8tu5E/bsqdy9IiIiEn9K9EQkKiU3Sy9Lx2UHll+pgoqK4KAS+7Kv39MNLAsz6/yw9dc+1t5/fNOkuwB4d9Ykf1n3bvGbRwh2j57H6whaVTMiZ3L5dUrxeCIPVw1nXNv7Q8quvuAvhhcczaI/K9cDm5UFjRpV6lYRERGpBkr0RCQqlx/5aFT1Uq3Yr+i4ZGEB/xwXSCDbNFyKe8svZO58ptx7mzbYDsC1r93Pw59cDpSYQxcjOZuWsHPlPLZutc8NHrxx2C7hiz8PB2DHxl1VbuuyAy/l6AEf4V77SaXuf+3Sk3n+/DOrHIeIiIjEhxI9EanxPLsWc2DXX4LKXF8fFFzpqEVltuGql8mVLz9MXmEqxDjRq/9NDzJ/6U/z5r4CyxuXBVDW7rbn8S3/36RKt/HbiiF8t3w8efl2b+OMGRXrHSx28vA3OPOQFykshF27ILt6tiYUERGRKCnRE5Ea75E7/irz+u3v3AINQ+ehlfT55w4sy7cCZYwTvdKMiU+PXsd2BQAc3P3HSreRmuYkvb6HXTmNAdi0oWqvRUoKZGZCixZVakZERERiTImeiNR4z587pczrbXuVneQBJKfYb3cerxNDfBK9qyY8SFERzJsbnx69HTur1qb7+AK8lhODl/QMu63urZeF1MvNtacXPvJI+W02bbDVf4+IiIjUHEr0RKTWa9PzgJAyrzd4IZSs5hkAeKz49eg9eOrVDBoETuPB643922uHBn/4j+fPr/iKma6UZCwcOIyHns1+BuDSQ24OqbfLNwXwgQfKb3Prk80Z3fMbBnT4o/zKIiIiUm2U6IlIpd3y1u3+41vfvi1hcXi9dsazbldHf9md7wUSmFd+moozyemr68Tgjlssf/5p790Xjx69AR3m+I+PO2IF//1vxdso7tHr1GxVxDoOB7TNWkthfvn7JgJ8c+Oh/HH3oIoHIyIiInGjRE9EKuW2t29l0k23+M/bj5iUsFiaZtkJyZ68TH9ZkSeJmYsPAaBL58CCI26vK6ZDN0snW91bLeWycY+TmV71lTFLczkDca94uAtm2b+ium/t9rZ8tWoagL9Hr7T8fPj0U9/jWHtY+1h7Xr9gfFCdPXtg2DD4q+wpkyIiIlIDKNETkUrpNaIPAwcGzoce0Zf1ezqzZOuQao+luIdu3e7O/rIj+33Cc/PsLSHW5QVW6PTGeDGWc88NPl/yj+4xa7s8PetND1u+ezeccAJs22afOxxeVq329Wj6evR+WT4s6J6//x2OPBJmzwbjsSfcHdb766A6H38Mv/0Gt94Ke/IaxfS5iIiISGwp0RORSllecHzQucNp2JLdgSJvxTcAL5O3/GGWBrvH7vZPA91rSY07M35qfzpevpL19S/1l3us2C7GcvrBL8asrYpKb5wBBTtDymc/cgJvTTa8/rDdRecwXv8qoBYODF4yGwW/Bu7t8/nx1oPYsyMHL0lhH88Y6Nx8OV6vxYadrWL8bERERCSWlOiJSKVM8S2EOTN9IZd98Rc9egSSiFhaOfMN//HJj7/G5qHLeGfWcUF1jNNOLgcOzfCXtTj0OqZMgcee7cillwYWZmmbuZa+mR/ELL4XL6y+TcP/2tov6Lxv08/h7ayQeod3exuAS3ofCQQneoWFDtxuDzvy2wOwbKu9Yum5g6/moG4/07DgB7wR/hdmmdksf6grZ3c8kp6ty963UERERBJLiZ6IROWq6Q8GnXf0rXsy6piePPa/bhgTn0Rv84YC//FK98m06NKFA3sGkowPNz9J39H2cNFHHgG3xx6imFSvAcbAxIn24iIl1U+K/fy5RNqxo+TJrKBrRXn7cDoCq4D2b/U9fVvPZue2PAByPC0BSvT4ebFKZHpr1tg/t2+Het4VAIzv91lIDAVFMe7JFRERkSpRoiciURkyLDCcL9KKkpEW+qiKRsnr/Mc/+vYJb1k/sBrIxCsvwOG0e+ySk+3eKwBncmpM46gJNruOC1u+bN76wMlnQ4OuuYs8OExgFdCMtH0AHDXgY1+N4qTOfg1vvcVi/rxAotehA+zbB02bwvPPR/4nw+nw8PLLMG1a1E9HRERE4kiJnohEpU0rO4ErcrtY2nd72Dpey4kxse3RczcKLKSSHEWnkcNhb7WQlFr3Er2RF97MzHVnh5SnFS6OeI/H7cHhCAzdLK1/y2/B8mL5rjsdHp56qkSi13QVGb4RsTt3hWnjoFcBe0XQ00+HF16Az0I7/ERERKSaKdETkehY9of/77deSo++jcJXicPQTUdy/Urdl1xOoldYENs4Sys4dl/M23Q4Hcz6s0lIeZIjN+I9XrfHHroZIdED2L56BZbvn4P+7efi9QR6ZVc90ol1j7ehXkpO+DY6TOG7XbcB+JP88eNh8+ZonpGIiIjEixI9EYmK5T8yZdRyxLxHL9LCIABv/zY5pOzoBz9ixpyjSElzldmuOz/2iVhJKfUrl6CW5+8THggp275sTpiatuXLvUFDN8NZuhT2ZNtDc+888RZO7PNw0PU2mRvIeS6dk4a9EVReYDW2D4zddtcWyzj/sKcAuPRSRGq9vDy4cNKn/PRd5C9TRERqKiV6IhIVy5fpmTLyvMrM0ftjdiHP3vUphTl7Ijxu5EyvsPM1IWW3Pj2BH10zcEXI835dbs9hcxcWVSjOmuyQxrcDMP+7+SHXBq5uEbTqZjg//exg5+7AHMxezX8NW2/qQa8Fnacc85t94Ev0fr9rEE+dfSHWdMNbb1mlbxepdVbMW8GTJx1J7ldnJDoUEZEKU6InIlGxovjcXrwZd0Xs++Bwzul0JMuePbbMB96dNMJf1OzCLUx88AOefntYSPUhQ+DeeyM/3vSfpwGQs8/NvHkVCrVMXpPYVSf/+ORb+q7vF/ZayVU3X/1pSsj1xo2hWYOtgfpE2duZ0cX+6bATvfTUHP+l0w5+Obo2RGqwlGT7i6sxXd9NcCQiIhWnRE9EomKKB2+W26NXsUTvkAO+B6BXk5nh2/SN3Vydcbu/bNveZsyYM5HzzqvQQwFw/Al2V991F8xn9r/OYfu2qq0S+v7vxwDgmBhYCTS/MKVKbVbUV+svJ3fbmojXU5IK/UM3h3WdHXJ9QH8Ph/b6xn+ekbK3Qo+/Lyd0WOhjZ1zGF19UqBmR6lG0D345Bwp3l1s1Kal4kaL4zukVEYkHJXoiEpVAj17kTC+JvXTM+ovCfHdMHrNo51Lq7/vW97CBx120CIYOtffIqyjjtIcovnDmWM4Z/RyLf428YmU0MlKzmb+2D6R34MVNPwCUOUyyqrbubRZSdlibRyjI3lnmfcUxdWq6PPRiqeGxBe6KJaqffxGa6DWuv5uxY6PrCRapVsuehJXPYS0M3/U/fDhcdZV9nOSMzXuZiEgiKNETkZgZ3u4TAH57682YtJf0aXe65t8KgHEEkokePeDXX/Ev+18R69YUBJ1/OKNqb4OH9vqGvu3+tE889oIN9VLyqtRmWRzO8Iuq1K9f1iI5gURv/rYjQq95gns1k5zlz19c3TMw7jU1KT9CLYtHHim3KZFq9ddf9hcbLz69mX2lRikXFkLB5jnMeNXuoT/pRCV6IlJ7KdETkQopO52wuUzsV6hzOqN55PJ1Svsy6LxJg90xaRegiWthzNqKxDLhE70mua+XeV9xopddENieYVGD//kaDU70OjdfWWZbuYevoEP/vv7z+6ZcHz7W6Q4+mxF+kR2RRNm29A8AzjzkRR69LnjTxzmzcplzz0D+evAAAHJylOiJSO2lRE9EolORMXje2K9omen5PibteAjeX69e9rcxaRegXlbzmLUVyaaCIWHLd+2OvH0C4J+jl9IwkOh5HA3sgzJWNg2nXrNOEa/lNTw86HxE76oNjRWJtV2pE/zHJ3e6OOia2f170PncewZUS0wiIvGgRE9EotKipf2zeYvye9aGOy+scPu/bg5dDbKkTa4TKtxmWI6koNN+TT+JTbtA5zEnx6ytSApzQ4eFfrP8eD7865Iy7ytedXPgWfcFCh32wjTphb+zbneXmMSX1vagoPPDh6+ISbsisdK0RWAOapcWK+AV+z3tk1d+Z+ieQ/zX5s0stV1JlF92DRsGD4RudykiUu2U6IlIVLoP6QVAjwP7x7TdHzecCsCWXZls2BC5nuVIjXyxIhzBG+yNPOCH2LQLtG0X/7fUJRvs3rT7PriW5hduZtWOriSnJXPYocG9cpt2twg6Lx666Uiu5y8zvmGgB+z5G20bhVmkJVoTSnwg7vF3PN7A6+Dxlr1xvUh18xL+d/JIBged99sQvF2J5Sl7pMLSpTB8aCH5m+fxj7u2VS1IEZEYUKInItFpPQGOWgwdT41Jc2538RfkdoJyTI9/M/PeUyLW79Q1NvvU7XG3i0k7EbU4HIY8Gbfm27axf67b2ZYD+jfHshwYyxsyXHbdnu50uyqw5UPx0M0gpuL/BBQe+EFoYaM+cIpl/5eUEbQU/aqVmuMkNYsJN8+1qPy9I73uwjKvP3rD1/xyeQrz7u3PtqdCV8cVEaluSvREJHoND4i66trVBRGv7d0LSUlw0EEEzQ875aBXI97jdCVFvFYRf+w6O+j8m9VnVLotb7ipbYd+AV0vqHSb5TG+fQpPOskwc6a9dyFY4A3+EPr56kuZu6Kb/3x4l1/8x/sOmcvuA56FCAu7FPtp1Th25LUKKkvuWLE9Lb76MvbzNUWqInfeU2EK15Z738oVkRO97L0W/zjq6KCyrVth1SrIj7QorYhInCnRE5G4aPdT5KGWO3bAoI6z+flSQ3NX6Abe4TiTYtOjd8ih9YLOXcmVb9fjtufsLNg3tUoxVYhvnpDx7StoWQaHKWJUWmD3+Ic/uZzvVx9PvRJPtWSil96mH40Gnl1uj966vb3Z2zty8h3R8BcoctlDRyNvvSASH7+9MZ1Zb0b+vT2s1+chZXu+/bv/+Mtm4efilTV0c/ptT4Rsq3L2+BkcOWIJBx9cXsQiIvFRpUTPGPN/xpgFxpiFxpjLfWWZxpgvjDHLfD8bl6h/vTFmuTHmL2PMuBLlg4wxf/quPWaMic066iJSI7m8u5h9l716ZJcWkRfr2J4b6E2KVaI3YFBwL5ZVhR29PW57W4Kd7h5ViqliiuP1JXo4GN763aAa97x/A926Bd/VOnNjaFOlEr2P5kwIOl+xOpmiwoqtyAlApzPIGz0XgH+eclXF7xepgqHu0xhSFHkYeDgNc+xFmS55/nEOPzx8nfR6kXv0Dmk3PaRsxt8nsuTBHvRMe5FFiyoUjohITFQ60TPG9AbOBYYC/YCjjTFdgeuAryzL6gp85TvHGNMTmAL0AsYD/zaBgfJPAucBXX3/ja9sXCJS87k826Oqt3lf58A9ybEZuulwBCd6hkokMj4ej+/ecoZAxpYv0fN/Hxb6vdgrbzflwQfLbyl/x5qg87ZZ64LODxyRxM7CzlRGg6b2HKX1+UMrdb9IdZqzuj8Ah48NfZ+Zt+ukcu/flXFixGsvXngmr9x4F4+ffweP3Tqr0jGKiFRUVXr0egC/WJaVa1mWG5gJHAccC7zgq/MCMMl3fCzwmmVZBZZlrQKWA0ONMS2BBpZl/WzZX62/WOIeEanFliwJX+6I8p2nd7PA3nlOV4ySqRK9WFuzW0AVEj13kcfXZPWNgs9zN7Ifm3QALCv0sY84AlJSgsteX3xbSD2Xd1fQed92fwadHzCwPV37twVgW3ZTOD66BB0AY9i0pxVb82KzbYNIRW3dCoUROuH25dfnzb/u9J//tMzeFqRxZmiit7f+WAAsb+Tef09Si4jXAO468WYuHXUrl3Wve198eDxwyYX5fPN15UdHiEh8VOXTyQLgEGNMljGmHjABaAs0tyxrE4DvZ/HSU62Bkl8Xr/eVtfYdly4XkVrmuW/PCjpfty58vcrkRbHKpZKS7aXVN+V0p8CdWuHNwksqHrpZnT16P2bfzhUvPcS2NHvfQavU2/jrv4TvfRh0xi0hZcnO3DIfq+WIaWRlAZO30fRvayElq0KxerwunGgxFkmMf55zP/dd9Lr/fM1qL799t5VlW3uxYMd4jrnhJuYdYM8hbZRhz68zpRZ9ev67M0v0npeR6IVb1XY/8df8bTwxMo2PH3qQOXMSHY2IlFTpj06WZS0G7ge+AD4F5gFlraMdbt6dVUZ5aAPGnGeMmW2Mmb1tm/aoEalpXv7xtKDzhkU/ha2XyFm4KWkudvf7gCZTZvp6wyr/LbTXN3TTUY09ejfcnEb3Y6/gxJPtD5ZWqbfQkde9Hu42unQNfdGd3tBEb9aOMwMnxb2fqU3AWfF9DD1eF6bMfxZE4uf+qddxy6FTcBd5+eYbeO6qOxm6vjn1XLtYs85JSgr062//HR12wMcA7NgZnOgdcfPz+OfDlvFW4bXsduZmfkFu48Bc177XzYvdE6qh0p2bALhk7BOaiyhSw1Tp04llWc9aljXQsqxDgJ3AMmCLbzgmvp9bfdXXY/f4FWsDbPSVtwlTHu7xnrEsa7BlWYObNm1aldBFJA4aNwo+T/OGX7J8147E9vI06jWRpAx7D7oq9egV2ltIWCY2C8VEIzUVLrgg0MNZukevVaswN0VgvKF7h/U762FW1b8ZplQ9QSvyJOFQoicJtnzxXk6dvJHbj78NsBcm2pdvD30u7o1v0WiLfZ63GYB/znyCR2b+k9atIZndAHjz7C+YLcv+b9YsWO8bj/Tea/Z9mW1aU2/8DNb1/JkFDV/l58V9+XnZ8Pg/yRgY0H09p564s8L3Fc+fbt9kLQUldtXJz4e77448fFZE4q+qq2428/1sB0wGXgU+AIq/Ej4TeN93/AEwxRiTYozpiL3oym++4Z3ZxpjhvtU2zyhxj4jUIpdcGnzu8brC1vvsk/ITvbB71MWYF0fVFmNx+5IYZ2wWiqmc8rtHl27qGrZ8JWeFlCWnN6bjsXeAo+pD0TyWC2OU6En1sTyekLI1i9ey8V/BM0Jad/F9I1JqeIHxZANw1dMXc/nTV9p1c+199zZ+Za9wdMygD7nmopUMHQptfV9fP37mZb7mnGAMbfsPp/dRU6hfH3q3WeBvf8nG7lV8hvEz59a2/Peois+csazAa7h0SSDTGzfwR3Jn3c1DD8UkPBGphKqON3rbGLMI+BC42LKsXcB9wBHGmGXAEb5zLMtaCLwBLMIe6nmxZVnF78gXAv/FXqBlBfBJFeMSkYQI/tCUbzUOW+vzz8J/+N+b38h/7C6qhkwPU8U5er7nYcIntNWhMC/yxvQATS8tYMyDC8NeO/zEAfEIyc/jdalHT6rVH28/H1L27SsfhZQNPCz8736L0aHbgWSk2D15WcnLAPjw78fwj5GdObj79wzuFLyKptcbmmhmpNk953vyGpCSXB3va5WXlhzY93LPzgJ4xTD7pX/idhPUW1eSt8T+gpPSR/Hjl5vAW8TMmw7m7pNuYt/id+IdtohEUNWhmyMty+ppWVY/y7K+8pXtsCzrMMuyuvp+7ixR/27LsjpbltXdsqxPSpTPtiyrt+/aJVZVNrYSkYTZvCn4TzctPfy8rqaN9oQt37CnI/v2FDD9oW8oyC/xgajluLD1q8oe9liFOXq+RM84Epfo9Ws/33/8z4+vDLm+aUsya9aF73F0xTlsy1PEvp27Wbkyvo8jUmz31l0hZfeefENIWVa/48LeP/zgeiFlMxbYg5S6Zv3B118Hyr+/5RBm3Tk0aAGSBpkNIsa2L78RDhOaCNYEhdtCJ9d994K9gPpg59+5e8qtpLxt2Lc7O6SetyiQ6A3v8isjtrbi23/d6y+7a9zxcYhYRKJRfSsIiEid16lT8Lkzwoea64++M2y5Aw+zn72eU1scypdv2N+Uf7/3bhjzaUzjLGYvxlL5b9i9Hl+il9ChmwG3vvfPkDKXK/4JXSS92ixidM+ZvDa97F5HkVjZVDgk4rXNu5tz1ifZFBxXiNMVZv/J1S+Fve+tOYEhzt4vQ3dTP2fyH/59+Bq3jjz00el043DUzERvzszQRC8pPbAWwq2T7wDgzgtCe0c9RaFD8Vf+uTp2wYlIpSnRE5GYcSUFf3jyhpkvA0CD4L3Vfl45ml35LXA63Lhy7L3cjkw7EgATg7likXhxYGr50M2SVqxIdAThrfrmxUSHIPuJevUjf6xp0WgL/3spnZS08F/MHHX6oLDlA0qM8jy891ch1/+4exADOszl64Vjwt7/xE/2FzCbc7phqjCCIJ5Kvg1mb99BURH89Glo8nf/MVNZ/eFt4AmssOJxhyZ6Z4/+XzzCFJEKUqInIjFTvK6B2+Nb+t/rwe2Gl14KXlxlPccG3be8+ass3jEKh/FwcOcvAUh12MM745noFeQ72L49Fj16NSPRa9480RGEN6jj74kOQfYTXbtU/u+5YdseYcunTInu/h6tFoctv+DhK1gzeAfZViccpmbO0Su5TUvG502ol1bEHcffFLZuh+zbWfKNPftm82a45CLtlSlSUynRE5GY2ZM8kme+Ppczn7aHQHk9Hh5/HM44A557rkTFUktqTjo+FQsnzjArNMYz0evdZh7HDqr8Ir81YY5ebdC7645EhyD7idKjCO794Dr/8Q+7wycuO7IzeeyzS8Neq4iWjTeHLXclGdp3y8TCgamhiV52dvBojKIXy94y5oCtkwA4cfT3fHbNoQAsc14esb5VHcsoi0gIJXoiEjO9+7o4/9lnGDLGnqxneT1s9e2kWfzTdyHoPldSEhbOsAsVmGrYjNyaOQneblLh+2paj15lPPrpZVw1/cG4PsY268C4ti+CO4+tH/6NvF3ByVZap8Dm5b37hV8c6rLvdnD1a49FbjrScpMV5qixPXrN2zSs1H3f33KI/3h3w6kh1/fl1wfKGMYvInGlRE9EYqZpU3sj4cMO9w3d9HhwOKBH60UYz94SNUsnei4sXFhhliaPZ4+e/zE2vA8FFe91snyJnqMGJHrz1vSt1H3OoY8y6pSJACza1D+GEcH4++3hXc1bhf+ALRIriz97lWbZzzLcc6q/LM/dgCnnBvaQbDjs6rD3Tp8eeesAAHdBfsRr27IDC5ZsSj29nChNjU30nCn1K1T/rdmhSd3uvcFzH3/ecw13vWf3orqLtM2KSCIo0RORmHP4euE8Hi9Oh4dFD/RivHOU/3rpYTxOlwvLOHE53SxY1yvoWnUken6FoUuzA7BlJuRvDSkODN2sxhhLuf6tRwFISS1/4/RwLrkEDh7flVveup0vC9+LYWTw+yrf4hbaMUfiyLJgy6YwCdTAhyly2+9FBe5kjKvs4YgR2/dGTlIWFV3kP245uexFh6q6nUs8eT3hE9BPN97uPx591zf+47ZtQ9/zGjQMvL4zt9/IwLPuY9x4+0swjxI9kYRQoiciMbdxk/0h4PXXPAxsbH/4GdBhLu/7p8MFf6hwOB1YuGjVeBN78+x9qDbvtlcW2ba9+nrLrI/7hym04KvR8E5z+GIk5G3yXyoejuRwJi7RO/vagwGwrMolegCZWYbrp9/Cpde1j1VYAHgt3z8xVVjZVKQ8J54I33+2Jqjsr6yXSet1Fmkp9kIhFpXfAqV4iHax9xdfxO3v3MLxj7yF08qJuh2rBg/dtHx/ox+sus1f5h32IrmNjwZgT24DZi4ejTnVYvnmzliWVXqqNUNHtcCasBDP6K8Yddld9pdPvhWJ3W4N3RRJBCV6IhJzW7fbic/aNR7/6pkAX/77cfvAKvWPvjFg7HsO6vYzYC+FDrBiVfwSvZ+WBs8dM7lrwV3qg5unxLCtbT/Amjf8p16v/e28w5m4t9JAZ1nlEz2AtLTAqqmx4vXar4ulRE/i6O23Lc4c+UJQWU76GDCGJm1bsaXJ9Zij5lW6fatUopfVdQiXPnM7b/92PO1dM6JvyNTcRK/4S6sGTTIBWL+7A47OpzNiVAYA7yy5mvnz4fff7S9wLMvLrh3Bq22a5EaYRj1xtjo0UOhbqKp49IOIVK/ETywRkTrHwk7anA4PJYcqPX7mZSxfdqG/h2drn29p1qGN757wb0dr18avt2xP2uHAz8Fluz00LLEuy/w5OQTNfvMGJvMUD3dyOGKcIVVIzRwKBoEePSV6Ek9/3D2Qdk3WBZUNOKiVfWAMzcfeU6X2S88d9ph6ZNr5EHne6BdxqtE9er7uuXppdvKW72kEQPMuXdliVnD6iR1x+TpFV3zvgCIv3qISX4L1vRPCDWH3JXqaoyeSGOrRE5GYGzbc/gf/0DEeTKlhmqvnL+O55+wyb1obyOgMgGXCJ3RHD6zAN+YV5HSFvgWWnkuybPG+oPMts17zHxf36IVrp7rU5OlvGrop1WFAh7khZbHsnS49R69ksra13ikAvPpT+Zvt5efb2yv85z+xiy1Wir+MadzM7sFrdUB3/7XmnTvhSgq8oBYOsLz+RWp+LHgceoffusLhS/7UoyeSGEr0RCTmMrPsf9w7dvCEDNN854W//B+UjCnxFhQh0UtPj18m4yDMRr95G4JOXQQP5WyeNAdeMfy1xMJTA3r0iufmGVPzMr7TTguT6FlWzc5OpdbbsLNVTNvr0SP493VfUj//8YrCyazc2pElSbeV204DayEN0rKZ8Ur4jdUTyfK9l+Wn9IKR71BvdFnZqIPt2718+L49usFylLGqrq9Hz6NETyQhlOiJSMwVL05iCE30vp/bOZDoldgjz+MNP3SzKLVr2PJY+GJN6CbJuQv+F3TeJWtu2HsvOP5btqzdCUDOvgR+iPElyzVxSNi994Umet7X61P04YAERSR13dQnXqHVxRvKr1gB9bseTX6bc/3nRxzfw3+8z92czlesZHth93C3BhnR6XMA3j+3Z0zjiwXLKl5YygFtj4OkjIh1uzRdyMR+b9Nxy1l2gTMlYl3jn6OnxVhEEkGJnojEnH+7AcvLuBY3BF/DItlV6KsXeAtaND94iGTghvhNJT7s6Bac88x/g8pWrwpOmHrtOQ2Ap746P6j8mxsPxYv9Aadeg8gfiuKtMMleKfP1ORcnLIZIirfZWLtid6DMm0fSvsovjCFS2tJNgS+Dnnh3YswXFcKRREHvR/2nrhJvSaedBuPHw403xvgxq1lxj15Ftoo5os+XAGz8a3nEOsZZ3KMXZvSEiMSdEj0RibniVSj7pj4eci3ZVcjT51wABA/dvGxcaF2APG+zOERoGzsWLr8yeNl1hwn/zfM3a/8WUtYk005Y6zVIi31wUfI4G2NOtXh73nkJiyGS4t+DU/rdR1FehERepIry3fX8x1nN0+PyGC5X+OyxYUP45BNoVcHRopYFW0O35gzrw7d3cfVFqyv2ABVUvOqmqxLzjTs1+DXyRYf9/qpETyQxlOiJSMwZ33y7ZkmhPTdDO//mP/Z4y//2uOWoC2IXWBip+cExurNGQf52+PlMKAokJ3fcE+YDpG8FTldy5TZijoXi3ouaOO2t5LYTr/x7PkUlPuvN/3VjAiKSauMtgvUfVstDWU470duQNzBujxHrrTJnPXkJzb40bFi5vdy6Ewsy+cfBHbG88fsj97jtL60q816W1PP8iNeKe/W9nhr4BiWyH1CiJyIxZ8r4VHT0gMAqmvmF5X966tkrvrvA5OwLHqqZmZUM7zSFVS9iLXrAX+5MCU708gtTwGN/OHK6EpfotW1r/zzzzISFEJGjxNDcM1uO4PwjnvOfL3v1UvZl68NfnbXgTvjuGNj4adwfKr2ePUe2VfuGcXuM1HrJrC08gk3dKr8K8P0fXuM/HtroXwB8+d+Xo75/X3b8esX27LRX0HSllLGwSgSNMyO//xUvVOVx17w5xCL7AyV6IhJzjjISvV05jf3H7dok/h//fc5SCyOUWDzGLLzTf9ywRWtueOM+/3lqcgGtHJ8B4EyOvBhBvGVlQVERXHFFwkKIyFlquNtz553jPz5+6Du88+jb1R2SVJd9K+2fBeX3WFXV+uxeAJgD4vhHYBy0m/Y5LQcfVekm6qfkhJR1cL4b9f2fT/+5/EqVNKGRPRfZVYn3skbdR0W8llporzBauO7byoQlIlWkRE9EYq6sRO/UEa/4j01a0+oIp0zL3afx/HdncujdX9kFVugKmn+sGkDTZoYxhwV/c9056S0gsUM3wV4cIuYLUMSAo5x/Yc7odGL1BCLVz7/Savx/MfcVNianIB3aTIz7Y1XFI59eHlI2svv3UQ+7Tln1DxbOz6tyHJ98Anv2hL9mXOX36G3v/REbrPH+8/RGkedFmq1fA+Be9WbFghSRmFCiJyIxF/XKbabst6Blm7vEIJqyeUjjrKefJymjpV1ghS7Gkp5hf1j988/wC7UkskevJisv0ZO6a9t2O3vZsSv+vwQGL16r5v+yuVND388cDouZn20p875flg8D4OgBH7H+1ROqFMPOnTBhAhx3XPjrDZo2KbeNJn0n0PrEwBd2Zf2du9LsJLB/65+YUflRryJSSTX/nVFEap2yevQqYkbR7Ji0U5bMTPtnVlM75lTPmpA63Zr8AcCO7KywbbiSKz6vZX9QE3sZpXqsWG736C1cGP9fAoMHrxXj1VLi4OcIIy/Tdn/mP/Z4CFq06I03ghetGtfn4yrF4PWCNd1weLNbAMjJgWnTAtfrp0f5sTC5cfl1gGYHBlYrnvfR+9GGKSIxokRPRGLOEUWP3ta0k4LOlxacHFLnimvjt7hCsUmT4KWX4NoLlgDQZd91EevO2n4GZz71PO/OmhRUnpSsjCaSRRt6lF9J6pzhLd8AoKN5Ke6PZYwHTy1I9Fq2DF+e7Mr3H7tckJwMEyfaK+l+9MQLjOj2U8xicPtGpt9wjD3/eMYM+OXzJXi8Dh786KqYPU6x1BKDHQ7MeqJGrg4sUpcp0RORmCu5rH4kG+pdFHTe8mh7H729edW7+bgx9qbH2YXhe+tKuv0OJy9+fyb1UnKDyl3xXRi0VvtnHD48Su3RyCyJ+2M48OCNYquWmsrUC2zCZ003WNMN6/+cQ04OvHDBtCq373bDNdfA9u3gLgqeg5yVto4lD/bA6fDSrGXs9wNNato3EIfH8NZbsWt7wgR4W+s5iZRJiZ6IxFw0c/Qc3uAV6Oo1agTA+rSL2ZB6NtutofEILaI/1h1Ubp0DD7S/ZW9Ub3dQueaiReZwJH5lVUkcYxVW7Iat38O+1RV7DGpHjx7AqDu/5dWfpgSVeT1ePv0UPvwg0N01556B5O9cH5PH/OwzWPjFR7RuWcAtN+YHXbv3hqX+46T85RVruPHAcudZJzdu5z8e2+cLfv7oj4o9Rhk+Ps0w9+U7y68osh/TxxMRib0oJmdZVnAC4ExKgiluek69h9aTn6XJqb/GK7qwvN7oh182yijwH5fcG0tCde8amuhlj1mWgEgkEXKyK5jofXkIfNCxQrcY48WqBYuxAJx93Sj++dX9QWVej4e7L/+ef98SvOdgQU7gy7CZTbYBkJ0XeYXLSBo5lvDR1UdT8EIqq3//LejaGQe/6D9OclVwn77xs2FK2feUnq7daN97FXuMCD771H5fufPEW3C74dGr3mDRH2UvaiOyP6od74wiUueYcMuuO5wJW8HDW0bH08QHPwg635wX2HvvutfuL11dShgyNHRSTkbLLszeOJ4Fm6q311aqX/OGWzEGsrPj9xiOWrIYC8CZZ8Jrrwe/xy347CO+v+UQPrlmQlB5Qb6bNXv7sXlPK0aNbcLP645hY3bnCj9manIgGfv6xsP8x926wZmHBBK9a96p4HxKY8rt0SttYMfY9Og9dXNgYZc1f23m/wadzNq3LyrjDpH9kxI9EakW7tHfBp3XtB0Jykr0ZswJ3p9rb5tAL97mzfGKqG44oE+jCFccGDSsc39w1IAZLIvQibtvH1Vedt+Y2pPoAZikegB8t2QkANNGPhu23vZf/kv7BvNo0XAjAF53Id2bzYPCXRV6vKSU8K/N0tuCE86Ff8V+jl5pRw/4qFL3XXklXHCBfbxpo5d3r5jsvzbrqcsBGN/rnaqGJ1LnKNETkWphmh0SdN65c836YDZlSuRr114bfD7xjP7+4+bN4xNPXdF8yEnsSjk0pNzCgTFK9PYHM/4+kefv/ybstXPPtVeYXLKk8r1+jlo0Rw+gbecsJj23gvUtnwx7fcG6XvbB9h+Dykd09A3tfCuzQo/3xOPR/Z2lxT/Pq7S9c/5Lo433AfC/B38JujblwNcTEZJIraBET0SqhXEEf3ucnJqUoEjCa9068rV77w0tO+eZ/3LgrbFb9rzOMg4aHvcV1/+6rlS5qdU9ei8+X0jHZqvIy0t0JLXDeYMvD1u+aoWbTs1WkJ0Nk48NLBSyalX0bTtqWY9ecjK892Unho8Iv/9mZvpOAIZ2nhW5kQrsU7B95dJy6/za6Ldy68TKX3/B3r0Vu+e/557LfVOuByA5Qg8lwOpV2r9BpCQleiJSLUKm3pmatSdBpKmBizf1CnvtuZnnkJ9+YHyDqiMcDrj30TalS2t1j55zzqWseqQT2zfYH8r37vHy4ZtbExxVzfXHyn5hyy8++AZWPNyFpMK1XDbwRH/5glkbIrbl8cBNN8GuXbB8OWzZUnsWYykpYsyu8FvMeLwl6nui/4bhnSuOL/O6Z/Iehk0YEnV7VbF1b1NGD9/E0eMr/w2Jx4o87j97/Z+VblekLqp974wiUiuFJEuZgxISR0V16BL+Q9f27fDjj2EvSQTu3vfgGWlPyLJwYKi9374f3OULADat3c2HH8InD97NxKLm/DU3coKyPztjZPiFPga2+RYAZ9FmJg4MTNbr7g7Tje7z/vsw7+MP+fuVRVx+2o+cMPRtPLVxHz13btjize7gL5CWdfgegOveesJfZnk9MQvDmdogZm2V5Zd1E9mwszWb/tWKy4ae7i9v1sz+9yE3F157DdasCb5v9crAKsfXXLGXl6ZH/pKwXr1a+HsgEkdK9EQkPgY+xB5P+8jXkyq+THgimANfCFuelQX16lVzMLWcq+/1ONse5Tur3YuxFLntD5SnnermmGOgaz17kYn3H3o+gVHVPhb261g6cXFvCD+nb+ZMmPPp13z492OY2P5mPrjEXtAkP7/2fWng9YRP1lyO4N6udj3svehOOCkw3D0/v5J/O1MquN1FDPyy+VQAPFYyAzrMBeCEofZO5/m5RWx9xN4k/qbrcljz/rWcfHjwHLzvH/yb/3jXvNdxOuzXbeHOcf7yj9beCoDXU8EtIkTqOCV6IhIfB1zBIs8ViY6iypKyuiU6hDqpti/G4vbYCcqwLr9yxZEP4Xbbz+Wa8TclMqxaJzfPfh0NwUlPz9aLwtYfPRoWzbVXnfTsXobDYSd4TTO2xS/IOGnRNfx+gRv39Qw6T65njypYVnSKv8xdVIm/nRP3gCOJrxeOsc8PfhNGfVjxdipo+BUvwVQPOXnB87JffRUGd//Lf35Z515cO/EBfrk9uEfz1IOm+4//87fzmHdvfwC8XQLbKewqtL9U9HrcsQ5fpFZToicicWMStCdeVZ39TGC589Ib/kpsmNq8GMuif9Amcz0AL114Bg+ddlXZC2dIWEuXwtCO9vhnlzO0d+uPP2Dx4uCy4V1+5v4p9jK4xw8NLKffolHt2yw7o3EG7+z52n/unrSTgnEryet4LZc8/7i/3KQ0BmDM4fW49IXHALDK2g+mlHlbDmV3XiYk2UM0M47+kMfWrIJ2J0Dro2PxVMrm229vy7bgIZdTLcOC+/v4zzs0DYzZLNi73X9cnMyHNOsIvDm369LIPvCqR0+kJCV6IhI3mzYFJ3r5RTVs87xSnvv2LAAuvqj2DQOrbSxTO+foFezeCHOvIT01J2KdX75cWY0R1V5TJyzwH+fvC309Bw2CnsGdW/x8+0F0abEi3qFVm2YNd/iPXfUak5LVkeNOSOX2Vy8Jqdu6NUyebH9s87jLnqPn8dhz3gDyi+qxOTswjH7IgfW57PoOVQ++glo22hR13ZQZTVm+3E72d+c0DFvH4Qwkjq5U31xqJXoiQZToiUjcZOa+HXSemlQQoWbNYkztS0Bqn9o5dPP888vvpR6+tTOFfz4S/2Bque9vHOo/HrDnqJDr7ZqsoUWjTWyrfaMyo9am+Z6w5VlZkDNmIfsOeCL4gq8XyxumR6+oCP4+9SO+/WQjF10E9evbuzAYPFg1YPuJw3t/VaH6771dwKBBkFNQP+z1oC01HMn2T0uJnkhJSvREJG5GHTAz0SFUyO7cRgC4aQCHfw/9709sQHWaA0ctTPRmfBzd/o/Jf14B6z+IczQ1305vX2ZtPo71u+wFRfC62fLWceSs+516KWUvsb/m0Q5s+lcr3ngjii9ehj1bfp0aqEOf7hGv1W/Zk/SBFweVGWN/bPN6Qv927jvlZh6ceDSjd7Xmy/dX8e1No8jP82Lw4CXxiV5FDWn8FDccezetMzeGve52e+HkAjhpHzh8f5dajEUkiBI9ERGfzzbdxeUvPcxG1wnQ7GDoeU2iQ6q7TO3s0Tv30P9EX/m7YyGGy+DXJmt3duKH9aeSedo88r2NcRr7ddi0bDnNC99j10enRrx3/c7WQeed1x4JwL8fLWOX7c5nVz3oRGh2cIWqG0f4RG/VKrj5uLv85yse6sSoHt8x96XbAC81+eNepCH9T7/QlLtPiry4kbcwF5zJ4KoPDnsYp+XVYiwiJdXcv3wRqfW83tq1GEtaRj0e/fRyLL01xl3r+vNpn7U60WFU2L0n31Ch+rs2RT8vqW6xSvwdOXH4lsTPL7DLigojJ/lPfB48P218v8/Iz4eLmoafq7Wqzdthy+ukCD16SxaH/0Jh7h9FOGpYj94pb20OOv88N/z/vzuOv6XMduoXzPYfG1+PXsvVp/Pd0/+sYoQidYc+zYhI/DiC32Ly3akAFLraJCIaqUHa1J9rH9ShHq93UsLMmyqsO8+vIuxhufYXPZZx+nv0HL73hLKG7YZbtOOqKyO/jvmuDpUPtCZIbQH97o6qaqBHL/j1SFtxZ9j63Yb0oEPjRaS6yh4mWx2Ke2pfeauJv2xF+y+YeMFRnPffp/1l3+55ECDiojs//DUCgK0Z5wUKfYlew7TdHJLx90C55YWcdRT8eCnuLb/F5HmI1CZK9EQkbhyl9sZataOH/bPli4kIJ2q1dFeIWqr2LnyzO6chf2R9D8Cfmw9i8vGBX5wzn3oegMKC/TPRMybQo2eV6NHLzbPLMlJ2hr3vi73P8X/jHwsp75ZzddB5/xvm+I/bdUiLScwJM3kT9Iqyp9jYPXO//uLFGPjzT7u4SfPwr0FGvVyy6m+ha9bvsYi0SqZN/4HTn3zRv6AMQOcDR2MMXHb6XH+Z1bh/yL3Xzwpsn3HACbfystfi4HHtAhUcoXNnrXdawatOeL8dKWuewPXVsJg8D5HaRImeiMRNjvMAALbntAwqt5zhh2AlWmGh/TMpuvU2JBas2jdPr9ivGyfTok06AI6sgQAc9ejPTHjgI06eYn+YtepQj2VF2Hsk+nr0sHv0srNh0rH2YhmZ6bvC3pdmrQlb/n/jHg46/31Zb/9xala70tXrrOIe0aU//4I13fDobfNo3x62e/qFrf/eW7nVGV6ZXp/RgYseOD240De3rlnjbH9R937B/14A3PNQM35I/53P115Jkz5HcNpppb6QM8Fv2pY7H5Mf2jP8+Sf5lX8CIrWQEj0RiZvlDR4CYN0ee2W54n3TamofTh/f3r0dOyY2jv3Krj8SHUGlOZLSaNWrP4UjPqfXafa8oLe+Hc49z08gPaOOJHqbvoBV0yt8W8kePYzdo/fKK/DwaVeUeZ/LUcTOnMYh5XPWDgk6dya5KErtbB8n1/IevQooHrp5dBf7vfXK4VOZd1Mj9u7YZ5+/HDw/7Z4TrqreAMuQlQUHHug7GfszDA0M19zgmuI/btm9R8i9xsDBxwxk7HXh59/lFQQneuaN8L8T7m8mVzBqkdpNiZ6IxI3XpAad78prCoBlaubG6XfdBb//HrpJs8RP0edjEx1CpQ0aan+4TG5/hL36H5CWBv374x9iV+vnIH4zFn4+rcK3Bc3Rw4nT4aFF0i9M6P9JSN2fMhf5j3Obnkxm/dDevm0FgQ//3ol2r1/SxLkwYYF/gZL9wabV2wFokWG/Bj1bL6ZR/T18NWMDAIVu+/fw64VjEhNgtJoMhy6BOXb73Fn+Y+MwrO30UYWa++gTV/mVgLTkPPLVqSf7kf3n3VFEEqa4J++GGa9wwXNPUpDaK8ERhZeUBAMHJjqK/UsS2eVXqoCDD4bTKp6XVErmyDJWBfQlepZVyxO9SrMoTvQwDgwW77+4KGzNg8YHkrgxx/ZibXYf//mfyXbP1bLV9hDZBfl/w5HhG6qZlA6NauZ7Sby0LnoFgCYZO4LK22SuB2DscR0AaNgpuAe0pktPC97/Lr3z6Ard7/GWPd7+kU/+D4AxPb/lhzsO9w/TF6nrlOiJSLXZndeEp7+6INFhSB32448wveIjDaO2bHtf++DkfEjJjFzR1JGhm1HYt/Z3CrODEw+H8ZbYXsGBw3jJL0oNvdnHU68LYA9N3NPudn95QVJXAC4+4t8AtJt4bwwjr31yC+qFLb/6aHulyrGnDCP7oF8YOC26VTxrivq+RG/1Dvv3IDm5YttBbNseuf79H17DYdc84j8/vPdXXHf6hxUPUqQWUqInInHnctj/iGuREwkrhpscXzvxPs4e9WzM2istr6g+f2w8HJxlDz+2qCNDN332bI+8WXn6D4PZ+vLwoDJjLP9qGVbeRlKSChnT85uQe5dttj/YO8d9D6M/AWP8CeHsDeNwe5ODb3Akl25iv9Kgcdlvoin10snoMAzjdPHLxpP85V/ueCDeoVVJtyF2z2yL8XYin5xasX8sBgwM/3F24oMfcN1r9/vnXxebdMAjFY5RpDZSoicicVPP/RcAvVv8BMAbb8DVV0O/8AvEyf7Kil2id9+U63n2vL/FrL3STNBG4GXUc9StoZsL33+6zOttGi4POi/5Oh3awd5O5dwx/w25r3HxfLy0FtBqPADuInsl1lx3Y36fE5xQZ2Q2qHjwdYjTWXZPl3EFek2tEitRths8Km4xxURaczjFIrXrCQAkpwT+xvYO+arc2y+6LIMZc45ixubA1hx/rBrAR3OPYunS0PqHdP+66jGL1AJK9EQkbvY4+gMwe8MRAHToAA88ELKPuuznLE/sEr3yfPwxtG5d+fsNXizK32ixuEevrgzd7FgYurddWUyJxVjK5KofUtS073jueu9Gtnf4F86U4ERvf9/jsn5GOUMaS7xAJRO91LTaO5yiQddDy61Tr77h6H/MoN+EY/xljab+gdfroKs9+pf7lqzjnGfsLxuWbOwel1hFahp93BKRuOkxcjgXvPIe1sj3Ex2K1DCn/utl//GKecvLqFk57gi5440XzeHsoXcye3bl2jXGIqp/OotX3awjPXpv/TIp6Pycc+DUU8EqtVfKV1/BZ5+V3/P5v1+vAyCv9bkh17p0dXL9q3cxeWomE4+tmSv0Jkrnk/8V8dqctiuCzk2JjclT69W+RM/d50GKmk+s0D3pjewvDn7bdjqdOgVfu+6ONvzn63NYua0LafX37yHAsv9QoicicZORAU/NOJYhw/effa4kOr+vGuQ/7rJsAN7pTshZG7P2P/88fPlvdwzlzhNv4dZbKpeAGbxYVvndSsX7neGNbkP4wkL4JHTngRojtzB4EZB538xm2S+/sXdPcKZX8NkEUn8YhcNRdo/exmZ3M+jG2ZheN4a9XjxC0ZQzF3J/42rQlq822nvjFXiDh7EOGBmc2RzYLDBXNbVBw/gHF2OuPleRdNgHFbqncYsmbB40n4EXPBP2usMBnZoup33DP2MRokiNp0RPRESq3clTgv/5cRgvvN8+Zu3//Ox9YcuTXHZX3y/f7+buu2H9+oq2bEU3fjDKVTevO/Vdnn98KbfcAhMmwHffVTSe6pFeP5CwbtpQwOy7hvDbncNo2yp4e4wJ/T9hVI/vwLLYlxP5I8b11zv4bNYg2rQt+7X0WvqYUtqwA+3k19v9Guh1E0UeF3M2lj28Ma1hVpnX65IW3fvgSom8wqvI/iS6HSZFRERi6MzTPbA4fu23ywwexvbYY3DQQTDYd37b5Nt49YWpfPHJAL79IfoeZ4OFFVXyYSd67qKyE737jpoMwMk/2D1jGzdGHUq16tkj8DzmPXkqLXvax1/fED7BaFhvL8uXR07iHA5o0iSKB7ai6xHdn6QPvR5Sc0nrdyW40kjqdycDyrknKVWjKkK8Yth+WB5NmisplLpLX5WJiEi1czis8Bc2fhqT9kuv8PjH288z7bgF/vNLxz3BT7eN4IoRFVuhM9pFRj78yE707r9tXVTt2iM8La66qkLhxM2CBZBdorPO5QxMehzf823/8eBOv0ds49wx/wHgpeUvBZVv9oyIOo6GJbYTWL5raNT31WlJ6TDoYXBVIHnb31exieCHl15IdAgicaVET0REql2kz53W6vjsdv78+Wex4P4+7MsPXuXxqN6vh60/YwaMHRu62Ei02yvM+rUAgOfOOye0kTCyct7Emu4g3fqr3LrVoU8fGD8+cD6y2eMVbiMjbR8A27yDgsobjX8++jaatcEyLgq63kyHsyJMvJSwitwatBXO2u1t/ce//rF/b9chdZ8SPRERqXZeb/jk59vvkrDytuHduSDs9ZIsC3btqtjjpqfmBJ27nOGHVk6cCF98AR99FFwebY+eyxHoAVv439PKrX/HsZcB8MSZl5RbN94sC6zphgmtb2TT7sBeFF1aLOf118IPpdyV04h9+8K3l+QK/n+d0rB59MG40jBTi0gZcgeuerVvQZFEKp6PKsGWbDrAf3xU18irmIrUBUr0RESk2lkREj0rezXm3WY4Pu1TbhtPPAGZmbBiBfz5J2zfHrv4RnT7gafOPp+JE4PjtHv0yk/0Tjg+kBD1qv9KufVdxu4BzGqUV8FIY6+4A/LGSfcElS9/qCubvrw77D3rd7bh1InBky5X7LWHaCYlBV6L+ZsPwSRnxDBaicRdz94rbqP3sARHUrOM7fOF//jg7j8mMBKR+FOiJyIi1c4bYduBQ3t94z8uLCy7jffes38uXAh9+9r/7c5tVKE4Fm3oEVL22w+7+eHWkZx/2DOM6jEz6Fq0++h169eiQnFkpttdky5X+cM84y1opKkJjqdPs29D6q/c2pFkZyHvn9szqNwx1N5kPSvTbuPPdb3pe+XMkPslPlzjvoFRM2h12peJDqVGefnHMxIdgki1UaInIiLVrrxtBwCuuWBpmddbpi/lg6smcvIJefRpOx9PzhZyCuzeosXbDy75YBHb2GUGhZQNXdvYf/zqxVODrhm8UfXojTq2b7l1wvFaiZ9XVTIHL+55dXvsxWWMsVi5tWNQ/S17mtO9Vej/q2TfSo8tW9gN1q+vBUGqVVpLaH1UoqOocTJadPAfP/bZpYkLRKQaKNETEZFql+fqWm6dRw7vXub1Sw+8lIkDZ3DlhIeYf18/tjzZAsuf1AWylZx9wUnlVwsCWwI4TODaIzfM5O3Ljw+q27LxZn8P14MPQlFRdIuxOEpUWb+zTcR6he6koPN8b+L3O/N6QnsVHcZ+PQ/t9Q3fLxnpL/d0v5oDu/4Sth3LkQxAcrJ9r/bEk5pgefLVXPfavezc15gkZ1HU93mKPHz78DVsXbM5jtGJxJbedUVEpNq5SWf4rT9XqY1h7exVGO8+6SZ/WZvMDb6jQLJy+f8FJ3qH9f7af2xK9PZd3ns0xw99J+RxPvnE/nn11cUJT/k9U8bAv7+4EACny8HatcHbFeTnw759YFml20r8vnElF8pp1XgTELwdxpmHvMiu3MbQ6iicB0RePMbjsT9iNG1qP0dtYi01weV/T+ewS68jvygVp6P8kQXF1n90DaOb/4M1b5wTx+hEYkuJnoiIVLtOnSDFVRC39g1e1q6xOPOQF9g6/7My6pX/Qa9Z6l+43XBYry9p3nBL1HuSTb7333y48CywLB4+/2EO7T/Lf23K2N857sAvQj5oOkz0PQzxEmlF1JI272oOo2dA/XYh1/I89tDXdu3s16njgH5sbHw9rU56I7aBilSC0wlHHAFujytoddyyeNZ/RvvchwBwmcQvmCQSrcRPBhARkf1Oo0Zwy0P92bmuMde+/zL/OTW2c4kMXv768m1euGBa+Ounevnzvj7gLL8HrUnjfPLzLL684QgArN3RfUfaogVkNHAAXh4+/UoABnVdzDMvt+O9Cwb76/2x91w8GX0xa6bjSEr8kvjhhm6WVuRJingt9fiFsP4dTEYHu8AYWh15T8T6Iong9rpwOaP7e5v9wxaG+Y4HtPoGfjwVRsRnz0+RWFKPnoiIJETfwQ3JOn8nE8+fwOdNLDze6P9JWvBn2cmIwUsam8JeW7qpK5ZlcCU7o+rRm3JqCkdPCPQ+btsa/aIiDqcDU2I45u+392DQiuBN23O8rRhy6iUkpbhw1pIeve4tAxu7b88OzCtclX0gpl5L6HZxXGITiZVOzVZx9IAZUdUtcpf6m1/zCuSuj0NUIrGlRE9ERBKiaVN7Kf9jjoGxY+1v2Etbtiz8vQVljPpcsrkXBi/d3XeFvZ5rtQLAazmDkrBIfrmpByl7AtsC5OZGP6/HwuFfyCSSts6PffEk1ZChm+XXSUkK7H2RlBT4ELw6Z0Q8QhKJi+JtTcqTnx/63lT4XuVW1hWpTkr0RESkRnCHGQ543KELwtZt4IiQAQI78jtjYdE0Y2vY6/1b2UmbZdnDKqPx2XXj/cenHPRqVPcAGOPAlJPofTf3AAA8VhJOk/ihm5ZVsb38kscHNqDOL3DGOhyRhEtODf29Tia6JFEkkZToiYhIjRCuR2/B/X3C9jClpkb+58syjrA9dbe9fSsAm7I72PVw4Ihi6GZVWDhomrGtzDoX/u9JwN5Dr7YM3SwprVV//3Fy9qzIFUVqkLd+O778Sj4NG+kLDKmdlOiJiEiN0DBtT9jyvH2h4zQtE3kxELATvXUFI4NKx50ziRlzjmJe5qeAPXSzZI/er8uHAvDvFb9iTvWyIPmfYVsvvWF4WYw7u8zrd7xzM5u22nP2vCTVjEQvwmIsL35/euCk01lh65TcukKkJlu9rQM5+fUAePttyMmJXLdd3mPVFJVIbCnRExGRGqfkwiy5e/eFXI80vPDzDTcCBiwvK/ceGHStbZdmHP2PGYw/0d6I3Z4/F+jRMwZ+XDmOi24eimUZcly9g+7PK0zlxjfu4kfvC1E/j0Pavhjx2q6cRtz69h00aGCfe60knI4akOhF6NEbPalP4GT4c9UUjUh8DBrsJCWpgK1PNuP4AsPpo0P30CzWwD076Dy3II3FmzRHT2o+JXoiIlLjOB2BnrYNX9wXcj1SolfUeAQYB0WFXn79NbhOk3atgs6LF2PxemH7dkhN8ZKeXuKfRRM8lDQtOZ+737uR068M7imsrMXtZvPLLyXic82jc5PFFBVWbOhkrFmlEr285B4AtG5TVi+qbWHnVXGJSSTWHPlrcTk9NGtoD61+54oyhnKW+pNcuPvoCn0ps2CB/UXSs8/Cn39WJlqRylGiJyIiNVr/lAdDC30T975bFxhOOOL2H6DlkVg4yErfQbMGwYuxpKaWbsQe4vnCYwv5+o6T6NtmNsYR+Gdx57rVMXoGwU567HXu/eA6DhrXmWHDAuXdWtoLzOzcuDkujxst557g3ou0JvZQVWdmL7sgpWnIPfPM/aza3Y9ewzrEOzyRmBjV/rWQMhNx55RAz/8rP03FSxJJzsJIlUN88e4K3C85OSfNsOy5yfTsCQsXVjBgkUpQoiciIrVOcY9eycVU7v3PCI46yi5t1nAb0w4pe4ilhQNjPDTbcA0nDX8TgL5NPvJfz1nxWZXjzC9MCSk74pyTyOlyb8R73EXxXSCmPKZwh//4vTX/gANfhGHPQcsj4ISdMHlLyD39pl5Dx4vmVmOUIrF3ULcfmRVmPSGPsefRrky/gwOvegUvSbgq0KPXt+Gb/lEKk4e8S/f673L3ndEniiKVpURPRERqncA8ssCYqkMO8R2Y6P5pK3I7yUxexVEDPg57fdbygUHnWxhdwSghNTl0IZlzz4W7wmzx9+Xux4Gy9wisDpYV6NbwtjwaUrKgs2/xleTGZXV7iNRqP956MJPGbWDduuDyJcn2H2xu05Pp2BG8JFeoR89yBA97fveKyYzKur1KsZ5yCvztb1VqQvYDSvRERKRGWVE4Oeh87qbRIXWKE73ixVRmbZoYuBgmEdnpOSCkbFj7L2mTuSFiHGPGNQo6X5N0fsS6keS50wG49tX7eC9pL5wSef7d7Dn22NKn/p3YTM8i8Pp17JycwEhEqt+GJ9pw/ZmfBJUVeu0ePYfTnrdrkUSSM/oePeMInd/aNHlxFaKE9XO+Y9PvH3HnuW+E3YJGBCB00yIREZEEWld4KJ2TAyvg7XH0D6lTnOgZp52IFJgWJa6GfofZuNOgqB57/raxFK+lN27qYCgxerP/uEOjaqOkpKN/I3fD99w75Twc5Xy1OrbL8wC0z3sI+FeFHytWvFYg0Jatyl+ARaQ2evLLC7jw8KfCXnv5bxMoOVrA8mVSDqf9JYjXJFdo6KbDEfoFz+Qh71Yg2lDf3TzKf7x75wQaNUmvUntSN6lHT0REahSDm125jUuch85Z83rsMle7sczLv4yBZ9ziv7Zqdal/2gY9jhkSXeLUcMStgcfNGgTHbYSpXjjFIrlBs4o8DTu+zB7U61N+kgfQtck8AC4+4t98/DF4ypiqd+KJcNJJFQ4nKiUTvZRUDdOUuunICdF/iVG8Eq3TYf89WCaJZFf0Qzf/v737jm+rOv84/nkk24mz9w5ZJIQsRsIMe88CZW8otGxoKaUU2kKBtoxSRtlQRlvKKD92gbJXmGGFhJBFErL3Tjwknd8f91qyLNlybNm6lr7v18svX5177tWxHttXj8655/TqujalbObiIfU+PpNNG3K/LIsEkxI9EREJFBeLxu8Ti8ZChIikqeOPVQqXss1PbqdNt37xfX061bjBZqsLoaRjvZ7bWndNLijt3Wz3pLUvTawXePoJy7jtttQ6zkGPjkt55cX1vPxC6vqC2RCJJYZrdu5cR0WRFmzgj66rd92YqxpB4L1tdpRs1tDN1YxOKVsQ2TNNzfqJ1PiX+KuT/5u+ohQ8JXoiIhIIU9cc4m24KC98dTwA6za1h7Q9el6iZ2kmXtlv1JsNb0QodZbM5vLlosTQ0GX39qBkeeokMRNe+Y6l9/Rk/UPtWftghyZpx49PrrbeYOtetVcUacnq+PCnrDL5/0DVB0vVe/SKwtHEB05prF4Z4bE7v8A57wOamvbs91ADGu0pX7866fG/zj+VcYMncs01DT6l5CkleiIiEghLykd5Gy7Klc/cQddzlrO+vB0hV3uPnmUYEzln2YBa9y1d3zelrLht+81ocXatq+iW9HiXtr9PqVO8JrHCerr7frKhvNw770fhpyCkW/kl/0XMS/q+XLA77y37FSFLTuCqhm6Givz/N/7kKtHKRK/e7EePZPZT53P11d4ggLfuuJaTu4zl8ze+xMWqfVj1o1mNbm/F6kUpZROv34E/NG4iT8lDjUr0zOwXZjbFzCab2eNm1trMupjZ62Y2w//euVr935jZTDObZmYHVisfa2bf+PvuMNPczSIihcZVzQ8Wi1IZKWLl+q5EokXMmFFHj16GRK/S1T5Bwfz+/0gp6z2ga5qazePhr25Nejxu8OcpdSoqa/y86boKGumd3+7lbehSLAWi4vClPP75BcR2eZwYJYRDyf9znEvu0Sur9IY3RyoS9+kNKn6eQZF7uO66GK2Ky+hT+gUAobJ5lM69DYDrv5gM7QYnnXvhQmjbegNm8M039WvvuJ3Sz4b75pWbP2GU5LcGJ3pm1he4GBjnnBsFhIETgCuAN51zQ4E3/ceY2Qh//0jgIOBuMwv7p7sH+Bkw1P86qKHtEhGRFsr8RM9F4vlLNBYmbLX36IUyJHp15SoRlzw8q9NPV9W/rU1gXWXPpMeT541MqRONJfewRSvKst6Obu29BdP1mavku68GzOH18Ge0aVfCibfcydjd+oKF44ubV4n36Pmzbr76P69Hb+GC1Pv0Hjj7p5Q9UoqfG+Jixvb9PwDglFNT/199N3EWGx5qh3vMePSWT+rV7l8ecgsAP33w/qTyfUa+Xa/jpXA0duhmEVBqZkVAG2AhcATwqL//UeBIf/sI4AnnXLlzbjYwE9jRzHoDHZxzHznnHPCPaseIiEiB+GGe99nfD3OjXHGFVxaJFlEUTk30Yv5QqEw9enX1dw0blJjMpHLAOUye1mmz2pttV/8hzOvf7Bd/PKr/lJQ6nTsl/0QVZdlP9Koo0ZN8t+34Aex//LjkQv++36rkDhLLuYT9oZsDBnk9aiVFqYneWXt5995t3Og9jsYSf0fO79+YuOhwYn5519gH8f1njvgJa1Mn6Exx/v73APCTo7/j6a/OSt7par9vcM5rd7J69heZn0DyRoMTPefcAuAvwA/AImCNc+41oKdzbpFfZxFQNR91X6D6VGjz/bK+/nbN8hRm9jMzm2hmE5ctW9bQpouISAAN9ROvfj1Wctll3qjESKyI/l3nsWpW8jDGxD164ZTzVGd1pHqdhh8Q3y7qujX9+tVatVmMHg2Vo/+aXOgc8997hMjGNQC07Zw8AcvM6U23uLqFlOhJAfITvarh4ZAYulnVozduR69HL0ztSywM7up9UNNt7sXxsqpzrokOZE1ZJ++crTrF94/s9y1nHlv/e/i22Wcsux93SHJhrPbZQAcuv4hOH9VvTVHJD40ZutkZr5duENAHaGtmp9R1SJoyV0d5aqFz9zvnxjnnxnXv3n1zmywiIgG2cycvydmnX2LNu2gszPhhH9L5k+RP3eu6R286F8W3l7Wt47JUrcfKhl3QoDZnW9/+yWt7zZv0Bf3mn8nHNx1ONArlod5J+1/9r3r0RLLL+58SiSTu04uvo+cvr4B5PXqxaO1J1aAecwAY3GN2vKxtadXohBBh884fCSevz3n/j3fM2MLl67x7iUu2PJGeY4+EHe7hzWXechGuljZdft6M+PaGDRmfQvJEY4Zu7gfMds4tc85VAs8AuwJL/OGY+N+X+vXnA/2rHd8Pb6jnfH+7ZrmIiBSQGKkLGEeiiXvSLjg/Fh/WFP+EPU2it6H9vgB8u3J/djnrivo9eVBml6wx7Gr9Wq/Hbrdh7/Pwtc+kTOceq2y6RE8delKIQuu8GVHevPdeIhFvZEF8eQW/R69q1s1YpP6LpgP0GrKFf3yYkD/hS6QyOTHr2n5lxvO8990efDNvFEXF5vVADj2XiHkzBkcjifOVlXlfAKcNPCpe3r/nSp55Biq1znrea0yi9wOws5m18WfJ3BeYCrwAnO7XOR143t9+ATjBzFqZ2SC8SVc+9Yd3rjOznf3znFbtGBERKRDfVp4PwJRVh8bLorHE0MytN17MSSfB9L8fTdtZ3tID6Xr0tt3LW5x48H5ntriZI0NuU9Ljbz6YGt8+e6ujUxK9XXZsukRvydKW9dqJZEPXVt8DcFD3nzP7jqH85cYynEuejKWswvtgaN3a1BmB61TsDb2OxUKE8P6Wo5uZLAK0Ko7iSB627khNPktLvS9Ivuf3uUuP5Mdlxp2//r/Nfm5pWRpzj94nwNPAF8A3/rnuB24A9jezGcD+/mOcc1OAp4BvgVeBC5xzVX8h5wEP4k3QMgt4paHtEhGRlmnEtt5qPFsNSnyiHS5O9LRdeMBdlG78lGGlzzCy2ztA+h49az8YTozSeviJTdvgJtC+9yAAZi4eAsBxA85O2h+rkegVWdMlemOLrmyyc4sElUW9e4VDIcfQXjMZtux4IpHkoZv/fdn7v/TIQ95QzMmPXbVZz9Gp/B3atNrEf1+sJFpR/0QvumEJFUu+okOHKG3a1Ej0zF/bz+/RW7cO3GOGe8x48oFv+WjVpfG6ewx/H4BfjDtms9otLU+jZt10zl3tnBvunBvlnDvVn1FzhXNuX+fcUP/7ymr1/+icG+Kc28o590q18on+OYY45y50rgkWBhIRkUArauvde13UbUyijOSbSe46/sjkg2qbjMUaO6l0bgwc1oXvtnfMip2csu/z2dsnL7wMXHlF0yV6bYpWNNm5RYJqRN+pSY+PGPsCS2Z6E6SEi7wevYqIl+i9+YaX6I2yP2U87z9mPR7f3mHIRAAevektNq5dX9shKdY8sS0lb25HiCixGj16m8q9+waP3mcSEyfCk5cmPiQ6vu1IhoT+We/nkfzRMq+EIiKSfwafDsMuhm1vjBeN2SJ5BeFIjXXkMq2j1xINHw6hdd+mlC8qHxsfuvnemj8CcMDo15qsHRPXX9Zk5xZpSU7d6U4gcY/escd635+86Ph6n2On8W1Syp467yCKZ9+dUh6NpO/v6FK6GACz1KGbzz3vtenlyw/lV6e/zdl7/z1pf4+Omq2+EOXfFVJERFqmcGsYdzuUdKy1Sp+O85Ieh8KNu4yt7nIGG0rGZK7YzPbf6umUssOGPxBP9KJ4bxqvPOLPTdaGtp3aNtm5RVqicHFrABZM+hSALXvNSlpvry5b7ZlYzuWJb6+Nb+814l0A7vhfYrbgmhO01BSr2EhFZfKHXoN6L4lvv33VPvVqE8DBB2sQXT5ToiciIi1WpgXTM+l00MO0PebrLLWm6S2Y770BjFISL4ukriefFTvsmDoLqki+e/GLw2rdZ0X+sgrWKlH2ROb/Qe8v/Kn3QZavXe8tU+qM/Uli+Gdleep9e2eclBhKvdtWExjd64Ok/T858OWM7QBYv/8i3J6Juq+cGmLp0joOkBZNiZ6IiLRYje3RaykisSJWbuzBFst/BUBsY+Kd2cImWpDIeu7dNCcWCbLS3hmr9O3fKmOd6mZ3ujHp8V6nncCqDZ2SynYdX8KTM28HSDtByyOHdavzOUqK6ne/bqhVB6zvwcnPPWZmk31gJLlVGFdIERHJS43t0QuqFRuSF1H+etHeLFw7mG0GTAKgfE0i0Xv1vsdpEu2HNM15RQKsQ7vMs2DuvmdJnfs/XvBjODIxzPzUszon7W/X3uhYuiapzMLF9OjtnbfST/TWPNqP20+7hGmT16Y8xyc9pic97rLvX1LqvDl5H8b/4QOufy4xK2gonDqB1f9dcjQzZ2gIZz7KzyukiIgUhOkzapl1s4UrOmIKr3x9EOe/swGOLyPqighZ4iP3Dl1K49tl8z/KRRNF8tLQrbzeutsn/pstr0qsazn6iknx7VYDD0w57q0piR7wTZGO0KYf74Ze5o3y/6RdznPNphr3IpthIS/Ri1Z6iV7H4gVcctAdbDUp9b7lnfYbmvS4Vb/xcFJysjbi/BeZMG08F95/fbwsXOL9fNNHLYqXbTNgEk/d93lqI6XFU6InIiItRvU3UwBGfi7q3bFHNw6+8RXuvr8NhFuBhQkRZcJKby2ski0SEzuMGbmpttM0yJcL9uDrRXtl9ZwiLUWfQ26EUb/nkr8cx8zZrXn7270496F7uPyPoxOVOmyVclyHjkW8V3YfAEXF3gdQe55wMPudmX6tujWbusa3P6jwjiPsJXrLl1awZk26o+DO1y/m6W8uzvhzfNvrdXr38yZt6tQpUV7s33o7bEwvvl59XLx81Nabv3C7BJ8SPRERaTH2Gfl20uP9di+MKcOjFRX07TCDDz/0LttF4cR6env1ezCrz2XEcHp7IIWqpBOM+UN8jc57Z7zN+4vO5eTUpS2TOEI482bCjFq7jE8T2e2l+HYMb6KWqdO8RO/an3/OCXu+kva4Cx++nWP+fHvG84/YZ7+UsgUr+yQ9jvY+Or7ds0e0ZnXJA/pPLiIiLZb13CvXTWgWuwx6jfal6/nVYd59ONvvPnCzz7FwIRy85zxevO1BXB2345jFcHnaUyqyuZ58EqZMgUy3A8dcmJ1POIU3Fl/J9mdeW3dlYMvthzNl2AKe+fw4hu7n9fr973Uv0XvyohN45fJDUo751/p5KWX1Nf4ux1OVC5LKRu20RbUfILsjAyQYijJXERERya35q7agX+cfUneE8vMevUzCXUawseNBtFnzar2Peene53nlnCMBmP7JdgzbeWzaetv0+TAbTRTJa3O3msCAaePjjx1hWpWWsN+lf6z3OUaO68PIcU/GHx96eN2TvJzys36ZT7rXq9BucErxhAmpVUv67MyCPvfQd+F5WFSJXj5Sj56IiATWkuHPA96n5ZJs1YjHMtZZ9PkrTH/0VAD6Fb0VL6/ctLHJ2iVSCAaM3RVOcnyw6EyArAx3HtIvC0PR+xwIHYZmrucra78LAC4Wa/xzS+Ao0RMRkcCqaD0CAOeShxIu7nol/Oj7XDQpMNp16hDf/s+f7ktbp/e0QxhW/C/KNpTT2U2Ml0ejmkpdJBt6lHwLQO92Mxt9rhefXplSNuH7A5k/+guW7TKr0edPJ+yvRRpTopeXlOiJiEhgxWJVCUlyore244+h3aDmb1AAfDZrHAAdOiXuvjh24Ll1HlM54RwqQ4m1vBKva3qLN6QO/RKRVMO6fgLAwM5TGn2uTZWlKWWVvY6h3+jt6D6oaf4mQ1WJXlSJXj5SoiciIoFV1fMUq3G5sgK7N2/5+u7x7dYjzwZIuzZXFRcpY8bXiYkblk55n8pQ4hzRSO1v6pat78nk5akz9olI0/rNrXulFrpIalkWVfXoOadELx8p0RMRkcDq0tl789GhvTFnaGI2gcrKwrp8lVW2jm8PP/ycjPVf/d1xDJ2yRVLZytCu8e1QqPYePcNhdWWRIpLiuDuezFwpgwGjh8e356z1eu679WjaeROrevR0j15+KqwrpYiItCidungJTpctBjCw6/R4eSxSlqsm5cQ7oTeYMn8E1z372/iCxzVFKrx1sJyDg0e/mLRvSM/v6dU/sbZX63a1r/PlyN+F6EWy7fXlNwPwxIfHZai5eQac8jRLuv2WUYedltXz1qREL79peQUREQmudgNh/BNYr/1h4/x4caSyaYczBc0p5w1j3rwpHH1Y7XXmzo3wu6vDXHYZbJ9m/5IfVsFAbztSx8tnOEwfA4vUy/4XXwZclrWek61/M4+24cVMPGkAPQ+4LktnrV1YiV5e079yEREJtgHHQ6su0HlMvKh3r8J7U9K/P4wYUfv+7bYpZ4fwLzj1qPSz8x0z8IL49sMP1r5mlvl9eiLS/N75tB+PvDCu2Z6vqkfvmf8rvP+phUCJnoiItDg9R+yS6yYEzk/3foBfHHwbj593VMa6xwy9qo69ukdPJFd69oRRo5rv+cJFXipw16lnNN+TSrNRoiciIi1Pgc26WZuz/u9rNlV49zHecvJlAIzZ4puMx43qN6nuCkr0RApCVaIn+UnRFRERaaHueXwMb6y6bbOPW72xU637zBwhvTsQKQhV9+hJftJkLCIi0mL8b9IBhMIh9s91QwKipAQ6d61lGs46vP/d7pxcyz4tryBSOCykv/V8pjReRERajF2u/B+7XvlKrpsRKKtW1v1G7eWvDk4pO3n8v9PWjUYhZFHWrtPQWJFCUFKcWFNz4YLa19eUlkmJnoiItBgdOkDbtrluRbCsWbKgzv2d97mRr5fsXa9zTZwIHdusZeOKus8pIvkh1K5vfPu+i6/NYUukKSjRExERacH6tJmStvzjDh/yYZ8V7HLQaCDRQ7doVS+e+Sz9zJztIt4kLcfu9HTW2ykiwfaHo6/JdRMky3SPnoiISAu2cm2blLLyyhJ2PqzaEhSWSPRaFZez94i3qayE4hq39/0wYyUjS5qqpSISdNEohDVyO2+oR09ERKQFWx4ZnVLWqrgi6XE4FAWgLNKeLu1W0bntav7+99RzRcrWNUkbRaRlCD9pzPhuU66bIVmiRE9ERKQFO/HqizLWGdXtDQA+WnFuvOznF5el1BuwVS8AopRmqXUi0tJ8+PgTuW6CZIkSPRERkRasY6f6j7P66OPEHRtL7+mRsr+i0ntbMLXzPxvfMBFpkTq7z3PdBMkSJXoiIiIF4vTdH41vdyhNDNOMRiESgWhlJQBWnHrfn4jkp+2u/CLp8Y+G34XTSgt5QYmeiIhInpnZ7oa05X27LExbXlTkTcwSjXiJXlHNWVpEJG+9O2m7lLL7zj6Xx+6b0/yNkaxSoiciIpInZq8YBkCo565p95db6nBNgJtPuoznLj2C5Uu9RC+sRE+kYHTokFp27r73cXL7QWnrv/MOvP9+07ZJskOJnoiISJ5YstZb/LjIkmfdfHzTAibPG0nlwd/Hy2Zs+lF8+7JDb+GIsS9w19+U6IkUonveOLfWfR98AJuqTcR57QVvcd9v/tUMrZLG0jp6IiIiLd2uj8OKT1lnxzF32fF0O2Rc0u4Tz+oDTE4qW1SxA0NrnGb34d7H9Bq6KVJYXv3+Inaa/QnbD/oyqXzunBif3/8Lni29iFvu2xKAt67a1997SjO3UjaXEj0REZGWbuAJMPAE9h8LMLfOqi7cBotuxFw0Zd8Vh3v39hWVKNETKSQHnzCCsed9gXvMkspf+vPVXHLQHcAdfPvlCkZs1yW+b+OGGG3aanBgkCk6IiIiBaTy0HkAvPdOBatXJ+8rCnvJnxI9kcJyzjnw0Uep5RfseX18e8TUrkQiiX0r5sxshpZJYyjRExERKSDWyvtE/qoj/8Qz937AK6+k1gmFleiJFBIz2HnnzPU2bUjc/3vowWVN2CLJBiV6IiIiBSRU7cq/cuobzJ2+IrWS1X8RdhHJH+9/t1ud+8vWb4xvT7phG5Yta+oWSWMo0RMRESkg1RO9WOVGum14LKVOl86p9++JSP7b47r32fWaCbXuHzkieUbfjz+sbOomSSMo0RMRESkgVm2uhcsPu5k1i1M/krcONefjFJFC8LvfwUczdmXIL9Lff7f0np5Jjyc//ReOOKI5WiYNoURPRESkgEU2LE0tNEstE5G8d+21sHgxzFw8pF71X/5sN175b0XmipITWl5BRESkgC1Y1TfXTRCRAOnZM3OdKu//fg8qIsWAkr0gUo+eiIhIAevarsZkLO22zE1DRCRQxlzxdXx79c6fAvDsZ0cC8PGys+L7SooqWZpmYIDknhI9ERGRAuYthlzNni/kpiEiEih3/msM6yM9AOg0eAeisRBH7fAcAO3auqS6U74pb+7mST1o6KaIiIgkdNw61y0QkQDYYw9glxkQ9YZlhkOx+L65KwYwqk2i7hZdfwA0iVPQqEdPRERERERSFXeA1t1SilfN/4EF3a6LP45VavH0IFKiJyIiIiIi9bZFzxX03f8qPi15GoA1qzQZSxAp0RMRERERkXrb8cSzwYypM73xmxdeoIXTg0j36ImIiAgcvQJCJbluhYgE3LrS3Wg/5FAAOscmAnDPmecBX+awVZKOEj0RERGBVl1y3QIRaQHWD7+H9v72sK7esgvbDfwqZ+2R2mnopoiIiIiI1Etpp8SHQm32/QcAc5YNyFVzpA5K9EREREREpF7adOwU3+7Rpy0AA7vP5eqrwQxisVoOlGanRE9ERKTARWmd6yaISMB9Pnt7AEpKS+Nlxa28+3qnLxrKXbeuYtdhE4hGc9I8SUP36ImIiBSaQ6fC2qne5CtrvyPc78hct0hEAm70JW+wZtksOprFy8JhmLZkFMsrh/LSZQez85afUBmrRClGMCgKIiIihabjcO8LoO+huW2LiLQIJe06U9JuXEr5Vj0nsxWTiUTDAMRirrmbJrXQ0E0REREREWmUorA3ZtM5y1BTmosSPRERERERyQrn6u7Re/GFKH++vryZWlPYlOiJiIiIiEhWZJp1s/TjA/nNYE0A1RyU6ImIiIiISFbccsPaOvfvN+rNZmqJKNETEREREZGscNPuyHUTxKdET0REREREGuT/lj2X9HiHwZ/V78AM9/JJ4ynRExERERGRBtliSLukxyVFFfU6LtOkLdkUjcIjj1Bwi7kr0RMRERERkQZZOf3jpMdtW22o13GTPpjSFM1J66ab4Mwz4d57m+0pA0GJnoiIiIiINEgkWpz0eJehH9dSM9n7D93XFM1J66HbZ+AeM758e1KzPWcQKNETEREREZEGWdbulAYdd+EBd2W5JbW7/+LrAHjwx9s023MGgRI9ERERERFpkMOPaFWvelOef4Cnb326iVuTXseSRUmPo1Ewg9tvz0lzmo0SPRERERERaZCi4nC96o3c8DOO6XlsE7cmvbBVJj1+8G8LcY8Zc//315y0p7ko0RMRERERkQYJFxellG1aMT8HLUnlHCycs4q1mzomlZ/Toy8Afz3ll7loVrNRoiciIiIiIg2Srkfv3399s17Hzpg4ueFP7BzE6l4v4dP/PEmfD7uweFkJAMvXdU2t9Fz/hrch4JToiYiIiIhIg4SLUhO9s0aeUa9jy1YvafgTT78LniiCsqW1VtkpcgIAx+7k3RvYrf2K1Eobg9H72BSU6ImIiIiISIOEw/W7Ry+dnmsaMfPm7Ee87xt+qPchb03Zu+HP1wIp0RMRERERkQYJhRueTvQof7bBx1ZWesM23frZ9T6mKBzBudTyTRtjDW5HkCnRExERERGRhjHLydMWr/vKe/oJx9X7mD2Gv8+tN65MKf/m4++z1axAUaInIiIiIiI5EV3XvPfIDVn+s5Sy6W8936xtaC5K9EREREREpNlMH/RJfHvS/Wc0+nyRtfVPFmPR1Jk6d+jyMBUb1ja6HUGjRE9ERERERJpMzfvi1m8qjW/3aDW10ef/9uPprE2Tp734xWEpZUft8BwAs5cOjJdt1WsKJc93TKnb0inRExERERGRrHKxRHYXqzHXyYoVifv6Zi8b1OjnGrHkAJY8ODSlvHvP4vj2vn96I2nf4jW96HJ+Re0njVU2ul25pkRPRERERESyKhaNxLcjkeR9u+ye6NHbousPLFoEL7+8GSefcW/Sw6JwlKG9ZlKxYU1SueEN07z+uaug89ikfU9+fDzzFhST1rxn4IkSyh5tzYR//H0zGhYsDU70zGwrM/uq2tdaM/u5mXUxs9fNbIb/vXO1Y35jZjPNbJqZHVitfKyZfePvu8MsR9P3iIiIiIhIgyxb3zu+vWbJ4vh2dNPqpHql3RK9eFt0m8e/f/VLHrn2qbRLH6T12Xlpi5fPTsyeuWIF7NT3BaYuGM6aAdfz8hudkuoecsmFlJaS1vfvPgNA6+JyxhedXc9GBU+DEz3n3DTn3LbOuW2BscBG4FngCuBN59xQ4E3/MWY2AjgBGAkcBNxtZlUrLN4D/AwY6n8d1NB2iYiIiIhILiQytUrXNlG65rukWuGi5BTkl4f8lacuPp5o2bp6PEXt2WDRpJ/HtxdOmwXA1n2/4+aboVWr5LoHHFREqFozNpYnsr7BoceSK8dqdEm2ENkaurkvMMs5Nxc4AnjUL38UONLfPgJ4wjlX7pybDcwEdjSz3kAH59xHzjkH/KPaMSIiIiIiEmBThs4DwHBMKr/E27ZEQhatLE85pvNPVxKNJaci/312ecbnWrN0Wa37evBefNvFar//7jcv/jOlzNWRFq1b8G3GdgVRthK9E4DH/e2ezrlFAP73Hn55X2BetWPm+2V9/e2a5SnM7GdmNtHMJi5bVnuQRURERESkeVT10DmMdXiTokQqEjOwxCo3pRwzf2ln5q4YnFTWdU1qAlZTWXn97vB65/Xaewd/euXu8e3xf/iA7xZuRVG49slXFs9PbX9L0OhEz8xKgB8B/8lUNU2Zq6M8tdC5+51z45xz47p37755DRURERERkawLh/238w7MHw8ZiSQSvcqiPinHtG0Lm8pLksoWzZqXUq8mc4lhlD8s719rvb1735pS9vC7ZwDQvmt8ChEefG48H847luJQJZXlESbcek7KcZ++uzBju4IoGz16BwNfOOeW+I+X+MMx8b8v9cvnA9Wj0Q9Y6Jf3S1MuIiIiIiJBV9VtY45w2EsvotUSvUg0nOYgGNkveUjkQVs/kfGpopWJnrelm7bky0UHpK0XC7VPKTvx9oeZMiZG9z4d4mVbbw39B5YQCjk+eX0q43veH99XtdbeGPt9xnYFUTYSvRNJDNsEeAE43d8+HXi+WvkJZtbKzAbhTbryqT+8c52Z7ezPtnlatWNERERERCTAwlUZhQNLk+jFotF6nee95b/KWCcaSSR6qyNDWDzoYSZ+Pzal3jbtHgBg6oLh8bLWrWHkqNTBhKEib5mFIdG/xcte+2Z/npp9FwCj+09m6rvvpRwXdI1K9MysDbA/8Ey14huA/c1shr/vBgDn3BTgKeBb4FXgAudcVdTPAx7Em6BlFvBKY9olIiIiIiLNo2roppkjVDV0szKR6EUj3lv+yx+/kUNu+m+t53n7i1G17nvh+t+z+oFORCoSiV558SAO/nEfBpwzkTcm7wvAt9/CyqXr43WeXfxoyrlSmJfo9d7wQLyoY6dizr5sm/jjrRfsmfk8AVPUmIOdcxuBrjXKVuDNwpmu/h+BP6YpnwjUHlkREREREQkkCyW69ELhEERh7doYuBh8/wixyq0BGLf31hyx0yG1nqd38QRwR0GNJbVXr4YfDb4OgBX+0M3/fHIM/Y6/HIDu3WG/UW8CcPaRH3LXGRfQZaB37KV/aFiKsVP/l1kZblSqlHPZmnVTREREREQKUFWPXshihELe9o6LBsLjYfjkLNZ+fCMAS5aFGT8+9fiKaGvAW09vxit3p+z/9XH/iG/bprkA9B1/MrvsmpqIfXjNeLYb+FX8cet2bTK2v3z1/LTlFkp/b2FLoURPREREREQarEtXL7krLXVYKPUeuHDFIgAWLkrfQ7Zh72nx7WkTPknZf98Zp8e3K9d58zxW3VeXDYcMvi2l7F8fnJzUsbjT7z/O2vM1FyV6IiIiIiLSYKXt2nrfh59CyFJXSSvf5A23jLn0PWRFJYmkbWCHL+p8rsmTvXOEilolldc2s2dD9d3xR4RKu8Uff/x12mW+A02JnoiIiIiINFxRKRy7Brb/a83b6wBoXVIGgCN9MmZFifX0RvWfUudTHd33DO8pS5LX4Pti4X7x7U0VrevT6rjTH/8svv3oe6cBsNMxR9KhA3wxdBNle36Gte1X2+GBpURPREREREQap7gDhMJYmh69EX2nAjBwcHKiNzN8MQsqdiFc1zDM13dP/3Stknv0Sks2xrcro5s3rHMl4+LbA457kF9OWExpWy+R3H6H1rTuO662QwNNiZ6IiIiIiGRFuqGbVQ48MDnR2/L42+l7xoep99v92/jo7su87WUfpD1XUavkHr21kS3i221bbWDOsgGM++1nNQ9L65FHEtu771nMLXf1TNsz2dIo0RMRERERkazoVvFi7TtrmcUyXFSSUrZLp1vqfJ7iGone1qfexaT5Xs9bOBTj7akH8OAz9euJ61ptsbhwy55oM4kSPRERERERyYo2kWm177T0WVSoaPOzq42VHZIed+nVkdW9fh5/vFWvyWy7bf3Pt+WlMxh/87zNbkeQKdETEREREZGsKC8aVOu+2talM4ObXvpVxnPPW5GYEGXQyP4p+197PZHa7Drso4znq27ynC15+5OWN+FKXZToiYiIiIhIVizqeGmt+6yWHj0z6LbfTXw6d6+k8mnfxeLbZRWt6N81sbB5+/ap59m97yPx7QmLTk+tUIfWraEkdQRpi6ZET0REREREsqJ1XSsb1NKjB/CTn8Cs1TsSiyVmQXn+zzckzltSnvG5312TqL++7T4Z6+c7JXoiIiIiIpIVQ3bZq9Z9tQ3drDJ9VmtCocSsnaN7vb9Zz/2rawbGt/f/2WmbdWw+UqInIiIiIiLZUUcylynRK6tM7g7sULo2bb33pqXvrevcvT1RWjGr2wOElOVQlOsGiIiIiIhI/suY6FUkJ3rjh32Ytl679rVkcaEiwieVMaRBrcs/ynVFRERERKTJFRVvXo9edUc8uiy+PWnNiVlrUz5ToiciIiIiIln3xeztePLj4+KPzepOPcojrdKWbyhrw/P/60ZlxBuMeOyvz8haG/OZEj0REREREcm6m//7K6z94Pjj0jZWR20Yt2P6Hj0zb4KW4uMWwj5v0LadUpj60KskIiIiIiJZd9Kvj6F798Rwzbbd616QfOKnZWnLd7/Wn32zdXfotW/W2pfvlOiJiIiIiEjWRV0x+MM1v1ywJ8XFddffa+t30pb/46WxWW5ZYVCiJyIiIiIiWdejB3SwaQBs1/fdjPXXl7VLWz5yZFabVTC0vIKIiIiIiGTdrrsCc56qd/0v527XdI0pQOrRExERERGRnLvkhoNy3YS8okRPRERERESyZ+vL4pvPfHZUvQ/r2LHuWTll8yjRExERERGR7NnuZjjJWxKhtGRTvQ/r1z91tpaPV1+atWYVGiV6IiIiIiLSJCoiJQBc8cSfM9a1Nj1h54eTynY+/5YmaVchUKInIiIiIiJNYt2m9gAU96jnRCuDz2i6xhQYzbopIiIiIiJN4p9T/8bk+aPY+cz9N/vYDWVtaNsEbSoU6tETEREREZEmESvqzI0vXkGbtpufdjz6/ulN0KLCoURPRERERESaRCzmfQ81IOt47gfdn9cYGropIiIiIiJNok8f73v79vU/5psNZ9DZfc5rb5Y2TaMKhBI9ERERERFpEnffDQceCDvtVP9jRv/04cyVJCMN3RQRERERkSbRvj2cckquW1GYlOiJiIiIiIjkGSV6IiIiIiIieUaJnoiIiIiISJ5RoiciIiIiIpJnlOiJiIiIiIjkGSV6IiIiIiIieUaJnoiIiIiISJ5RoiciIiIiIpJnlOiJiIiIiIjkGSV6IiIiIiIieUaJnoiIiIiISJ5RoiciIiIiIpJnlOiJiIiIiIjkGSV6IiIiIiIieUaJnoiIiIiISJ5RoiciIiIiIpJnlOiJiIiIiIjkGSV6IiIiIiIieUaJnoiIiIiISJ5RoiciIiIiIpJnzDmX6zY0iJktA+bmuh1pdAOW57oRojgEhOIQHIpFMCgOwaA4BIPiEAyKQ/0E9XUa4Jzrnm5Hi030gsrMJjrnxuW6HYVOcQgGxSE4FItgUByCQXEIBsUhGBSH+mmJr5OGboqIiIiIiOQZJXoiIiIiIiJ5Role9t2f6wYIoDgEheIQHIpFMCgOwaA4BIPiEAyKQ/20uNdJ9+iJiIiIiIjkGfXoiYiIiIiI5BkleiIiIiIiInlGiZ60eGZmuW6DiEgVMyvOdRtEgkbXapHmp0RvM5lZ2P+uf1g5ZJ5fmFk/pxtNc8rMhppZ61y3o9CZ2Rgza5frdhQy///SNcDPqx7ntEEFTNfqYNC1Ojh0rc4sH/9vKNGrJzM7w8y+BC7JdVsKnZmdBrwNbAeszac/yJbEzI4ws1nAtcCDZtYl120qRGZ2splNAv4APGlmJbluUyEys1Pw/i+dBpwCoDe2zU/X6uDQtToYdK3OLJ//byjRqwczGw6cD7wE7GFmg51zzsz0+jUzMxsPPAJc5pw7zTm3turNlC4izce/UJwNnOScOxFYClxlZsNy27LCYmYHA+cA5znnjgKGAIf7+/T30AzMLGxmZwE/BS53zg0GFpjZyBw3reDoWh0culYHg67VmeX7/428+CGagpm1r9p2zn2H9yntrcC3wIV+eSw3rSssNWIxAfgM2Nrfd4WZHW5m7fTpedOqHoeqIqDqb+AJ4GjgEPUoNa2qoSW+d5xzezjnJphZR+B7v47p76FpVcXBORcFnnfO7emc+9TMtgbW4f19SBOrPlxZ1+rcqhELXatzJM0Qfl2rayik9/hK9NIwsyuAL83sRjM7wy+e5pxbCTwLDDGzPfy6eg2bUI1YnOUXnw88amZfAZ2Ai4Cb/U9lpAlUi8NNZnaS/7fwDXC6mXUGxgETgV5A3xw2Na+Z2bXA782su19U7pf3BF4GVuNdxPX30ISqxaEHgHNuuV9uzrmpwEBgW79M14gmYmaXA+/4/5dOA+9Nm67Vza9GLM70i3WtbmbV4nCzmZ0ArELX6iSF9h6/xf8A2WZm+wCHAPsDrwJ/NrMx1T6Bmoo35vwc8DL+Gp+wS5akicX1Zra9c+5zvAvG2c65K4CTgd7AgJw1No/ViMMrwC3+sI97gQrgX8B44GpgZ0Cf1maZmbUys98ApwNj8O55iX/i6JxbAhzinDsJ79PIYcDgHDU3b6WJw7Y1qlRdUx8DdoP8+VQ4SMysq5k9gvem9WzgU+ACM6v+xlXX6mZQSyzONbMB1a7VZ+la3bRqicMvgC7AA+haDRTme3wleqmKgS+dc7Odc28DtwN/rrZ/A/A0sN7MrjOzm/A+vZXsSxeLPwE45+5yzk30t5cBK/H+oUn21YzD34BbnHNznXOX4l3ET3bOTQHmozg0hUq8+wdGAB8De5vZoOoVnHNr/O8r8e7D6NzcjSwAdcbBH8YJXk/rGvPoOpt9G4DXnHPHOee+At7E67XoV6OOrtVNr7ZY9IX4tfpzf1vX6qZTMw5vAJOBwc6573Wtjiu49/i6AKVqA3Q1fwpa59wNQG8zO9Z/7IAyYDRwHrDMOTcrV43Nc+li0aMqFuDdaGxmt+B9uv5ZbpqZ92rG4U9AXzM73n+82Mz6m9ldeBf3ablran7ye4WmO+c2AE/ivaHd0cxaQWJyA//v4S94PU36e8iyesSh6pPf74AznUc9elnmnCsDXqxWFMH7nV8ESfeo6lrdxOqIxfzq9XStblq1xGEbYEm1OrpWF+B7/IJN9Kza9LLVP3F1zj2LN3PdYdWq3wRcWu3xn4EpwBbOuZubuKl5r6Gx8D9JfxzvE5o9nXMzm6XBeaoBcfh5tcd3AmHgUP9NsDRQHXEo97/PAT4A9gSGV6s7BniKxN/D9GZqcl5qSByq9eh9CPzJzIqqknBpmDrisK5ata7AUufcD/6+qmFYN6JrddY0JBZ+3cF4k4DoWp0FDY2D7y4K4Fpt3qRYKQrxPX5RrhvQ3MzsIOBKYI6ZLXPO/dIfgxsCiv2L+O3AxWY20b+Yfwjsbmbt/T+ki/xPT6QRGhmLErxPb0/0h6pJAzUyDu2cc+vx4rAxZz9EHsgQh/i9An4y8TgwChjpJ3irnHMvmdmJ/vAoaaBGxGE0sME596xzrhK4LVc/Qz7YzDj0w5t0AjM70NvtXgMudM5tytXPkC8aGYty59w7/v+mFTn7IfJAlv4mTsznBA/AzP4GHGZme/vvV6jn+5m8fI9fED16/n0SYTP7GXANcAvwW2CseetQ4ZyLOefK/U+engTeAX5rZhcA9wPhqk9L8ukXoLllMRYVzrkyJXkNk8U4rPfrKslrgM2IQ8yPQ3u/bC0wA+/T2avxp85WktcwWYrDNfizoErDNDQOwB5AiZndA/wOb+gVSvIaLkux+D0Q9esqyWuALMZhk18375K8NKMmuuAlufuZP6y+kN/j532iZ+aN1fc/4fgA2M059zzehWApMMX8+yrM7CrgE7zZ0m4BHgZ2BN50zp2bkx8gjygWwaA4BEMD4jAB2MW/8A/HGzp7s3NuS+fcy7n5KVo+xSEYGhiH8f7hI4GdgO+cc7s5595r/p8gf2QxFuOdc+83/0+QHxSHzKpeI3+76h7pj4F78GZ5HVqt7q/xZiMtqPcz5vJ43UozuxDYF3gPeMI5V3Wj9nZ4n8AW4d0UvMk5d5lf/zHn3Kpq56jqBpdGUCyCQXEIhsbGwbwbycP5+Olsc1IcgiELcTgM+MA5tzoX7c8nikUwKA6ZVXuN3gWecs4tNO+2nmfxlsA5HyjBS+7mArvivZYF9X4mbxM9MzsKbyzz5Xgr3m8AHnDOfe133Uaccz+YWTtgAbCXc+5L/9gwEHP5+uI0M8UiGBSHYGhkHIqcc5FctT2fKA7B0Mg4FDvvfkjJAsUiGBSHzGp5jR5yzn1hZn9wzl1tZicC9+G9Rrs5f/hwob2fyeehmzsB9zhvnYxrgNn4swQ6b02Rqtm51uON1+0C8W7gaKH8AjQTxSIYFIdgaEwclFxkj+IQDI2JQ96/oW1mikUwKA6ZpXuNzvf3HWpm7wO/Bp7HG8q5AQrz/UzeJXpm8ZsyvwdOAnDOzQX+C7Qxsx/VqP9bvLHMU/y6BRP8pqZYBIPiEAyKQzAoDsGgOASHYhEMikNmGV6jTma2C96smh8657Z1zp0K9AK29uvm/WtUU4tP9MysY7VtqxbEp4GNZnaE/3gR3iw7I/y6B5vZB8Aw4Bjn3OLma3V+UiyCQXEIBsUhGBSHYFAcgkOxCAbFIbPNfI3ewptt9DHn3K+rneaoqqGthajFJnpmtpOZPQ88YGY/MbNWzjlniVl3VuHdkHme/8uxBmgHlPr7pwLnOudOc/5NrtIwikUwKA7BoDgEg+IQDIpDcCgWwaA4ZNbA16gt0Nr56wlaYn3BvFouYXO1yETPvAWC78LL6J8G9gG2BHCJ2XNKgf/hZfn3m1kfYDugwq83xzk3uZmbnncUi2BQHIJBcQgGxSEYFIfgUCyCQXHIrJGvUaSqnnMu1sxND6QWmegBY4GZzrl/Aq8DrYEfzLyxu2Z2HV6m3xP4JbAE+DewGrghFw3OY4pFMCgOwaA4BIPiEAyKQ3AoFsGgOGSm1yiLWsTyCma2J1DmnPvEf9wDmAf8BW+tjPnAt/7XncBDwO+dczOrnaONc25jc7c93ygWwaA4BIPiEAyKQzAoDsGhWASD4pCZXqOmFegePTNrb2bP4GXu55hZZwDn3FJgG7wFI690zu0MPIrXvTvCOXeSc25m1fhc/xj9AjSCYhEMikMwKA7BoDgEg+IQHIpFMCgOmek1ah6BTvTwxiO/BZwCLASOrdrhnPsOGI6X6QN87tep6toNaXxuVikWwaA4BIPiEAyKQzAoDsGhWASD4pCZXqNmELhEz8xOM7M9zayTc64ceBB4A5gOjDOzYdWqvwZc7Y/bPQEYBSwH0C9A4ykWwaA4BIPiEAyKQzAoDsGhWASD4pCZXqPmF4h79Pwg9sK7mTIGzMKbJvUS59xyv85QvLG65c656/yyUuB+oAcQBi52zn3b/D9B/lAsgkFxCAbFIRgUh2BQHIJDsQgGxSEzvUa5lfMePTMLOy/bbA8scM7tC5wPrATuq6rnnJuB13Xb28yGmnfj5SbgTOB059x++gVoHMUiGBSHYFAcgkFxCAbFITgUi2BQHDLTa5R7Rbl6YjMrAq4Fwmb2MtABiAI45yJmdjGw0Mz2dM6965c/a2ZbA68A7cxsb+fcVGBxbn6K/KBYBIPiEAyKQzAoDsGgOASHYhEMikNmeo2CIyc9euZNpfo50BmYCVwHVAJ7m9mOAP4nANcC11Q77ljgKuBtYIz/CyCNoFgEg+IQDIpDMCgOwaA4BIdiEQyKQ2Z6jYIlJ/fomdnuwEDnLYaImd0NfANsAi5yzo01b9rUHsAdwK+dc7P943DOvd/sjc5TikUwKA7BoDgEg+IQDIpDcCgWwaA4ZKbXKFhydY/e58BTZhb2H08AtnDOPYLXzXuR82bU6QdEnXOzwQu+fgGyTrEIBsUhGBSHYFAcgkFxCA7FIhgUh8z0GgVIThI959xG51y5cy7qF+0PLPO3zwS2NrOXgMeBL3LRxkKhWASD4hAMikMwKA7BoDgEh2IRDIpDZnqNgiVnk7GANxsP4ICewAt+8TrgSrz1MmY75xbkqHkFRbEIBsUhGBSHYFAcgkFxCA7FIhgUh8z0GgVDrpdXiAHFeAsgjvEz/N8BMefcB/oFaFaKRTAoDsGgOASD4hAMikNwKBbBoDhkptcoAHK+YLqZ7Qx86H897Jz7e04bVMAUi2BQHIJBcQgGxSEYFIfgUCyCQXHITK9R7gUh0esHnAr81TlXntPGFDjFIhgUh2BQHIJBcQgGxSE4FItgUBwy02uUezlP9ERERERERCS7cn2PnoiIiIiIiGSZEj0REREREZE8o0RPREREREQkzyjRExERERERyTNK9ERERERERPKMEj0REckbZtbVzL7yvxab2QJ/e72Z3d2Ez7uXme3aVOcXERHZXEW5boCIiEi2OOdWANsCmNk1wHrn3F+a4an3AtbjLQwsIiKSc+rRExGRvOf3uL3kb19jZo+a2WtmNsfMfmxmN5nZN2b2qpkV+/XGmtm7Zva5mf3PzHr75Reb2bdmNsnMnjCzgcC5wC/83sPdzexwM/vEzL40szfMrOdmPvccM7vRzD71v7bMyQsnIiItlhI9EREpREOAQ4EjgH8BbzvnRgObgEP9hOtvwDHOubHAQ8Af/WOvALZzzo0BznXOzQHuBW51zm3rnHsf+ADY2Tm3HfAEcHl9n7tavbXOuR2BO4Hbsvzzi4hIntPQTRERKUSvOOcqzewbIAy86pd/AwwEtgJGAa+bGX6dRX6dScBjZvYc8Fwt5+8HPOn3ApYAszfjuas8Xu37rZv9E4qISEFTj56IiBSicgDnXAyodM45vzyG9yGoAVP8HrptnXOjnXMH+HUOBe4CxgKfm1m6D03/Btzp99SdA7TejOeu4mrZFhERyUiJnoiISKppQHcz2wXAzIrNbKSZhYD+zrm38YZjdgLaAeuA9tWO7wgs8LdPb2Abjq/2/aMGnkNERAqUhm6KiIjU4JyrMLNjgDvMrCPe9fI2YDrwL7/M8O7LW21mLwJPm9kRwEXANcB/zGwB8DEwqAHNaGVmn+B9KHtiY38mEREpLJYYMSIiIiJBYGZzgHHOueW5bouIiLRMGropIiIiIiKSZ9SjJyIiIiIikmfUoyciIiIiIpJnlOiJiIiIiIjkGSV6IiIiIiIieUaJnoiIiIiISJ5RoiciIiIiIpJn/h9ot5fgN2hCwgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using the loc and plot functions, create an overlay plot that visualizes \n",
    "# the price action of both DataFrames for a one month period later in the dataset\n",
    "# Be sure to include the parameters: legend, figure size, title, and color and label \n",
    "bitstamp_sliced[\"Close\"].loc['2018-3-1' : '2018-3-31'].plot(legend=True, figsize=(15, 10), title=\"Bitstamp  v. Coinbase - March 2018\", color=\"blue\", label=\"Bitstamp\")\n",
    "coinbase_sliced[\"Close\"].loc['2018-3-1' : '2018-3-31'].plot(legend=True, figsize=(15, 10), color=\"orange\", label=\"Coinbase\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question** Based on the visualizations of the different time periods, has the degree of spread change as time progressed?\n",
    "\n",
    "**Answer** According to the visualizations, the degree of spread has reduced over the time frame of the entire dataset, and this is also true for the month of March 2018, however during January,2018, the degree of spread has increased towards the end of the month. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Focus Your Analysis on Specific Dates\n",
    "\n",
    "Focus your analysis on specific dates by completing the following steps:\n",
    "\n",
    "1. Select three dates to evaluate for arbitrage profitability. Choose one date that’s early in the dataset, one from the middle of the dataset, and one from the later part of the time period.\n",
    "\n",
    "2. For each of the three dates, generate the summary statistics and then create a box plot. This big-picture view is meant to help you gain a better understanding of the data before you perform your arbitrage calculations. As you compare the data, what conclusions can you draw?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Bitstamp  v. Coinbase - Jan 16, 2018'}, xlabel='Timestamp'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bQgghhBBCCJED99xzz5CdWyp6QgghhBBCCLGbkaAnhBBCCCGEELsZCXpCCCGEEEIIsZuRoLe7inTAjqdz3QohhBBCCCFEDkjQ210t/xks+SzUv5frlgghhBBCCJFzO3fu5Nxzz2X69OnMmTOHU045hXXr1nW5b3V1dZ+WPCgsLMx2M7NGgt5uqqGmHoCffftjpk+H446DSCTHjRJCCCGEECIHDMPgc5/7HEcddRQbN25k1apV/PrXv2bXrl1d7j9u3Dgee+yxYW5ldknQ201V1ZQBcMkxt3D7BRdyWPG1vPuOJD0hhBBCCLHnee2117Db7VxyySWJbQsXLmTx4sVcdtllzJ07l3nz5vHwww8DUFVVxdy5cwG9BMLnP/95TjrpJGbOnMlPfvKTtHP/6Ec/YtGiRRx77LHU1dUBcNddd3HAAQewYMECzjzzTHw+HwCPPvooc+fOZcGCBRxxxBEARKNRLrvsMg444ADmz5/PnXfemZX3LOvo7aaMQCMUwqSxrUwY/RonzLiX55c64Iif5rppQgghhBBiT7XsUmhant1zli6E/W7ucZdPPvmE/fbbL2P7448/zvLly1mxYgX19fUccMABiQCWavny5Xz00Uc4nU5mzZrF9773PSZOnEhHRweLFi3ij3/8I9dddx3XXnstt956K5///Oe56KKLALjqqqu4++67+d73vsd1113HCy+8wPjx42lubgbg7rvvpri4mA8++IBgMMhhhx3GCSecwNSpUwf1ZZGK3m7KHq1jxfaD4PQqLJ/bwqc1+zLW8nqumyWEEEIIIUTeeOutt/jiF7+I1Wpl9OjRHHnkkXzwwQcZ+x177LEUFxfjcrmYM2cOW7ZsAcBisfCFL3wBgC9/+cu89dZbgA6Whx9+OPPmzeP+++/n008/BeCwww7jwgsv5K677iIajQLw4osvct9997Fw4UIOOuggGhoaWL9+/aDfm1T0dlMuVU9jbHzi8daWuew34bUctkgIIYQQQuzxeqm8DZV99tmnyzF3hmH06Xin05m4b7VaiXQz+YVSCoALL7yQJ554ggULFnDPPffw+uuvA3DHHXfw3nvv8cwzz7Bw4UKWL1+OYRjccsstnHjiif18Vz2Tit4IdtddcMghsGlT5nNFjjoi1orE47rgHEYVbodw6zC2UAghhBBCiNw75phjCAaD3HXXXYltH3zwAaWlpTz88MNEo1Hq6up44403OPDAA/t83lgslgiQDzzwAIsXLwagra2NsWPHEg6Huf/++xP7b9y4kYMOOojrrruOiooKtm3bxoknnsjtt99OOBwGYN26dXR0dAz6PUtFbyTadA/Gpn8ye3uQP5xosP2RKUz7vz+Ds5xjj4X2hkbe/OEuNkRGJw5pRg8mpfEjGH1kjhouhBBCCCHE8FNK8Z///IdLL72U3/72t7hcLqZMmcLNN99Me3s7CxYsQCnF73//e8aMGUNVVVWfzltQUMCnn37KfvvtR3FxcWIyl1/+8pccdNBBTJ48mXnz5tHW1gbAZZddxvr16zEMg2OPPZYFCxYwf/58qqqqWLRoEYZhUFlZyRNPPDH499zXcmW+2X///Y2lS5fmuhkQDcKOp6BgKpTvP/SvF2qCJyYTsZXxxrJpVBTVM3/SSjjkXzD1SxR7Wtj650kUe1p52fExx501D4BrftbMz+eUY5n/c9T8a4a+nUIIIYQQQgCrV69m9uzZuW7GiNfV11EptcwwjC5DiHTdHKx3L4C3zoE3Pzc8r7fzVYi0sa78fo799at8/ZH3icUU0fpl7HjiO1x75tUUe1q5+bkfsNeB8xKHFZaVsGzzfsS2PTM87RRCCCGEEELkjAS9wap7U9/6tkMsPPSv174RgPV1uivm4iNdbGuciHX9TYz33calJ/8JgHvf+yETJyYPq6iAf739ZawtS6HxQ70xJuvqCSGEEEIIsTuSoDcYER/4q8FjJqrArqF/zfaNdEQqOOPsYgCOPRbW75yZtksoYmfs9AmYk/4AMH8+/OutL+sH1c9Bw1J4yA67liT28fuHvPVCCCGEEEKIYSBBbzDazekuR5mLKvp2DP6cvuoeK4Phpo2srJoOgNUKRx8NL39yHG+vO5Rzb3kQgA27ZrDf/unf2kWL4JQzytjaOAU+vgrW3wZAYM2/AFi7FjweeOSRwb8FIYQQQgghUo3UeUHyxUC+fhL0UsWifd+39k1Y8hl9Px70/NWDe/1oAJ4Yr8f9dSXSgdHwIWuq9wbg5JOhoAB+998rWHzt2+x7+tkEDvov1y15ms9/PvPwyZPhiQ9O0w82/QOAF5/SVchPltWz7PpFsOJng3sPQgghhBBCpHC5XDQ0NEjYGyDDMGhoaMDlcvXrOFleIa59Mzw1DQ5/HCb2YWKVjy6Dji0w9XwYe7LeNtiKXtNyfbvlQTj0X6A65fANf8NBE3999WKuvx6+/W29+cEHYelSuPwKK/AZHnq669OPHw+X/fHXfPOMV3H6PwFg73FrwDCwNb7FoqkfsYiPYOdxMOaYwb0XIYQQQgghgAkTJrB9+3bq6upy3ZQRy+VyMWHChH4dI0Evru4tffvJL3WAm/UD0ga5pWqvgob3YN8/wuwfghEDRyk0rxhcGxpSloto2wjelLF3RgzW/JEtvsP534ZDeeNysJnfvXPP1f96M348+IIF/Pb+z3P153XQ22vsevxVr2FtWw5lEAi7cG66FyVBTwghhBBCZIHdbmfq1Km5bsYeR7pumvw1y/Wdpo/gw/+Djs3d79y8Ut9WHMy3vw1FXgvPLzuMtk1v9u9Fgw3QsTX5OB42AVpXp+/bvhl821iy7StUViZDXn+MH69vn/no1MQ2X9BNeN19LCq9j9U79ubxDz6HqroPat/o/wsIIYQQQggh8oIEPYD6/+GuujFtU+P2Hrphtq7St8VzeP55Pfbt7XWLKTLWQqAfJeln58GTk/X9WARqXoCJ5uC6zkGv+WMAllctYOzYvr9Eqvnz4eKLYf7RB1DdNJa7XvsG764/BG/DvYzzbub12sv551vm+MBllwJwyinwt78N7PWEEEIIIYQQuSFBDxIVuuse/zkbO3SXxZ2btkPErytb4Ta9n2HAB9+GT38N7vFErSVs2waf/SxUhxfrfere7vvr+mv0bbABWj6BcDNvVJ1FS3gcRu3b8Mpx8PQceOEgWPNHQPH+2n0GHPQcDrjzTvjb3xSfvXs79625i39/cCYAj753FiWLLmRr+ETuWnYjNH1EpO5DzpzwdZY+dAe88TndpVUIIYQQQgiR9yToAfh3AvCrJ68kcsgTALTVboOPfw4vHwlPTIJn58Nz+8L626H8QFjwK2pqIBLRFb1Q0f4Ew05Y+h34+OoeX27lSvjmBTuTG2rf1GPygB/8fG/ufukLqOr/wq5XwFmmq33NK4mNPZUVnxYwbdrg3/KyDy288ALc/ca3WHTlMn76xN847jiYNQt+dteX6YiUYXtpP75+1N+542vfgu1PwM5XBv/CQgghhBBCiCEnQQ8wArvoCJdSXOpkxuwiWnxeKoLPwoY7wDUKDB20aN+gJ2k56lmMqRewbp0+fvJkmDbDyVWP/pLqei/+j27gl9eYq4/7qnVlMOLXE6oAv/1NhAMcVyUb8ME3oUV3B91UO42/L/la8rl9b4STl8HZLbzv/i/t7XrtvGzweOCJJxRzD1/E/5YVU1kJ++8P9W2VHHnNC+yquJKV2+YmD4h0ZOeFhRBCCCGEEENKgh7wxgs72VI7hsmT9SLkNa1TmV60BJQdjnoWTvwArG5Y/G/Y72aw2PnlL+HYY/XxU6bA174Gf3jmMr56y8247X7ee+oVoh/+HJ4Yj/FYKcYTE+HpvaFpBd9ceBHfOPpu3lx3NB+6H4BALWz6B+3hclr9xayr2SvZuMIpibtvm71Cjzwye+/95JPhvvugokI//uEP9baPt+/Pssj1HPnLJfzo/j/oJ4MN2XthIYQQQgghxJCRoAfYozsJMJqbb9aP71p+C5saZsPRz0PZflC8N3zBB+NOTBzzpz8lj997b13V++pX4fXVR9HQVsaFR9yDWv9nsJegYkFUqAHa1sNzCzliwj3c+8b5nPK7JznsC2eA1QMdm9nWPJPiYghHHcmTOysJBOAHP4Af/1gHssrKoftauFxw3nkQDsMrr0BTRxk3Pvsj2oKlEKwfuhcWQgghhBBCZM0eH/R8Pqgs2oWrZAyHHaa3tToP55DrV0HFQV0eU18PjY36/osvJpfbKyyEUMTJK1UXctaB/8YSbeV9bueYX73CnJ98yn8+PDtxjj88fyXtgSICYTe+A5/l+Ya/8IU/3sl+++nnG0N6rZH/Pq1YsgT+/Ge9Pb5EwlA69lhwu+FGcyLSWbNgZ1MFEZ9U9IQQQgghhBgJ9vigV19nML50B1HHuMS2iROhthaCwa6POVNPVMndd8Pxxye3H364vu2Y/iv+8tK3qW4ay2e+cQyvrToG16g5nP+Xu1lafRJ/ffUirv/TXomun2uajuTk73+bldvmM3u2rqqdefdKSi5q4rrrYGnKOurxgDmUxo6Fa6/V9ydPhltvhfq2cpp3SUVPCCGEEEKIkWAAy27vXpp2NTDJ6QfH5MS2iRP17fXXwxVXQEFBcv8f/xjeeAO+8hW44IL0c519NlRVwejRLtwX/YXv3nMroDjySHj4YRgzpogDLnuOBQtg+d9g+nSYNy89LBqG7pr5+lv6RZcuhW3b9La6OjjuuKH5OnT24x/rdfcWLdIVy3efr2B+6H3dwHgJUwghhBBCCJGX9vig56vXa8PZiiclts2YoW+vvx62mEvH/etfOuMA7LMP/PGPeuKWziabefHii+Gvf1Ucdhjcfz+MHq27Xe7YkQxre+0Fo0bp6mHqa8+ercNdnNcLl10GRx0FEyZk4U33gVJwojkk0TCg0TeGAkstbLgTZl4yPI0QQgghhBBCDMgeH/TCzVsBcFckg97ixfDqq/DQQ/DXv+pt55wD06ZBNAq/+hXY7T2f98479b9U55+vZ7g82xyq53DApk16bB/AL38J3/++fo0XX0wed9NNcOqpg3mXg6MU/Gvl9Vyw+G/QtiF3DRFCCCGEEEL0yR4f9GivgkLwjkkGPaX0WnVHHKGrax0duqLmcHR/mr749a/1v1QFBbrid/318M1v6irhuefqGS+ff17vs+++g3vdbCgdO5rtTZOZEKjtfWchhBBCCCFETu3xQW8ML1JVN5mJleUZz1mtcOmlQ9+G887T/+ImTIDnntPr5j36qJ4cJdcWLICaxkpGd9TRSzFTCCGEEEIIkWN79qyb4Tame17i2U++gNWWfxOMHHYY3Hxzfsx9cvDBUNs6Cl+jVPSEEEIIIYTId3t20GtajtUSZX3rkbluSd474ABo7Kgk0FyXmJRGCCGEEEIIkZ92z6C38e/w+Fho/rTr5wN1EA1B44cAtFrzYBBcnvN6YfrcSoqdtdTXSdITQgghhBAin/Ua9JRSf1dK1SqlPknZ9kul1MdKqeVKqReVUuNSnvupUmqDUmqtUurElO37KaVWms/9WSndIVEp5VRKPWxuf08pNaUvDW9oAJ+vmye3PwGBnbD+tsznYhF4fBQ87IQPL2Vnyxg85XkwCG4EsHvH43IE8Vcvz3VThBBCCCGEED3oS0XvHuCkTttuMAxjvmEYC4GngV8AKKXmAOcC+5jH3KaUiq82dztwMTDT/Bc/59eBJsMwZgA3Ab/rS8O3bY3yj39082R8ZsjGD2HJaeDbkXyudU3irmFx8qW//Itx4xB90OA9n7rWCoq2/DLXTRFCCCGEEEL0oNegZxjGG0Bjp22tKQ8LgHhfvtOBhwzDCBqGsRnYAByolBoLeA3DeNcwDAO4Dzgj5Zh7zfuPAcfGq309WTh5OcX1d6Vta2qCDz+EcNNGvaHhf7Djv7D+9uROO55K3L1v28u8+umxnNQ5xoouFZSW8dj7Z+HteA7CbblujhBCCCGEEKIbAx6jp5T6lVJqG/AlzIoeMB7YlrLbdnPbePN+5+1pxxiGEQFagMy1DjoJRZycNvFy6NgKLx4KTR/z3fOWsv3+07DH6tN3do2G9y+BFw6GFVcC8O1//IULf3IY++yTH+vUjQQlJfDY+2dhJQD/+1qumyOEEEIIIYToxoCDnmEYVxqGMRG4H/iuubmrSpzRw/aejsmglLpYKbVUKbW00V+O19UE718M9e/S+tRJ3HbWsRw1920abEdS7UoGkdrqDthwJzS8R7XlNBb+7CNuf/nbgGL69L6+Y1FSAq9+eiw7osdC00e5bo4QQgghhBCiG9mYdfMB4Ezz/nZgYspzE4Bqc/uELranHaOUsgHFdOoqGmcYxl8Nw9jfMIz9Ha4ivbHmBQC89hpaOoq5r/ZDys95nXFH/4CAexEA7dV69s3L/30H47/4JOtqF/LNb+rDJ00a6Nve85SU6Nud/n0gWJfTtgghhBBCCCG6N6Cgp5SamfLwNCA+w8lTwLnmTJpT0ZOuvG8YRg3QppQ62Bx/dz7wZMoxF5j3zwJeNcfx9dxwhytx/xt33UXpRY1M+79NuMon642l84mduIzalkq84WUALFury3fz5sEEM3a6XIg+KiwEiwWafBUQbtVLVAghhBBCCCHyjq23HZRSDwJHARVKqe3A1cApSqlZQAzYAlwCYBjGp0qpR4BVQAT4jmEYUfNU30LP4OkGnjP/AdwN/FMptQFdyTu3Lw13OG1s3DWN6aM38eLKE2j2lQJQWZncx+OBmmAJ04tXA7CxVge94mL9D6CgoC+vJgCU0lW9+rYKGAWEGsAtS1MIIYQQQgiRb3oNeoZhfLGLzXf3sP+vgF91sX0pMLeL7QHg7N7a0ZnDAftc/il2a5j2QFFi+6hR6fv5ojoA+kMuApaJHHss/OlPMGUK7NgBP/pRf195z1ZaCjWNFTAdWHMj7HtDrpskhBBCCCGE6CQbY/RywmYDu9OVFvIgM+hFKAHg463zeeVVGy+/DLNng9sNv/0tFKUfLnoxcSJs3G6WTVf/Afw1uW2QEEIIIYQQIsOIDXoAa9fCDTfAAw/A1VfrbWM79SS0uEsAWLVzEXPmDG/7dkeTJ8O6qpLkhh3P5KwtQgghhBBCiK6N6KA3bhz8+MfwxS/qoBcM6nF5qYJjzmPppv14avlZuWnkbmbyZHjr41nERh2jNzQtz2l7hBBCCCGEEJlGdNBLpZQet9fZjKNO54CfL2XSAccOf6N2Q5MmgT/kZuuMV8A7S5ZZEEIIIYQQIg/1OhnLSFdWBlu2ZHbpFAMzY4a+/fRTmOKshEBtbhskhBBCCCGEyLDbVPR6MmkS2O25bsXu4YAD9NdyyRIwnKMgKEFPCCGEEEKIfLNHBD2RPR6PDns33AB33DNKKnpCCCGEEELkIQl6ot+m63XnqW0dhRFsgFgktw0SQgghhBBCpJGgJ/pt9Gh9W9s6CoUBwYbcNkgIIYQQQgiRRoKe6Lf4ovT1bRX6TkiCnhBCCCGEEPlEgp7ot3hFr7G9TN8JNuauMUIIIYQQQogMEvREv8UreomgF5KgJ4QQQgghRD6RoCf6zeXSt40dEvSEEEIIIYTIRxL0RL8tWgSTJ0vXTSGEEEIIIfKVBD3Rb14vVFXBdy71EolaCXVI0BNCCCGEECKfSNATAzZnjqKpoxRfkwQ9IYQQQggh8okEPTFgBQXQ1FGKEWzKdVOEEEIIIYQQKSToiQErKIBNtdMobX0IXj4KGpbmuklCCCGEEEIIJOiJQSgogPvf+ZJ+ULsEdr6Y2wYJIYQQQgghAAl6YhAKCuCBt8/jPftDekO4NbcNEkIIIYQQQgAS9MQgFBRAzLCyLvAFcFZI0BNCCCGEECJPSNATA1ZQoG87OgC7F8JtOW2PEEIIIYQQQpOgJwYsM+hJRU8IIYQQQoh8IEFPDFha0LMVSdATQgghhBAiT0jQEwNms4HVCjfeCFGLVPSEEEIIIYTIFxL0xKBEo9DSAv99wUs0KGP0hBBCCCGEyAcS9ERW7GoowtcqFT0hhBBCCCHygQQ9kRWtfi9OJUFPCCGEEEKIfCBBT2SFxenFYfVDLJLrpgghhBBCCLHHk6AnBqW6GmprQTmK9IaIjNMTQgghhBAi12y5boAY2caO1bcWh1ffCbeCozR3DRJCCCGEEEJIRU9kh80TD3pS0RNCCCGEECLXJOiJrHAW6K6bRkgmZBFCCCGEECLXJOiJrHAW6opesEOCnhBCCCGEELkmQU9khc2tg16oXYKeEEIIIYQQuSZBT2SFzWUGPZ+M0RNCCCGEECLXJOiJrLB79Bi9iF8qekIIIYQQQuSaBD2RFc5CCXpCCCGEEELkCwl6IisKCq20BwqIBaXrphBCCCGEELkmQU9kRUEBtPq9GOGWXDdFCCGEEEKIPZ4EPZEVBQVQ11qJNVyb66YIIYQQQgixx5OgJ7KioAB2tozBGavRG145Bjb+I7eNEkIIIYQQYg8lQU9kRUEB1DSPxWXshFgEdr0G730t180SQgghhBBijyRBT2RFPOh5LDsh0p7r5gghhBBCCLFHk6AnssLphF0tY7CqMHRsST4RkslZhBBCCCGEGG4S9ERWKAXNgbH6QduG5BOhhtw0SAghhBBCiD2YBD2RNf5Ymb7j257YZoQ7ctQaIYQQQggh9lwS9ETWBGNefce/I7Ft3WpfjlojhBBCCCHEnkuCnsiaZNCrTmyLhaSiJ4QQQgghxHCToCeyJmRkBj1LTCp6QgghhBBCDDcJeiJrugp6UtETQgghhBBi+EnQE1kTsxQQMxT4kkEvEpSKnhBCCCGEEMNNgp7IGodD0RHyQqQtsS0alIqeEEIIIYQQw02CnsgahwMd9FJEQ1LRE0IIIYQQYrhJ0BNZ43RCe1AHvZZAqd4o6+gJIYQQQggx7Gy5boDYfTgc0OYvBqDJNwq7CmBEpKInhBBCCCHEcJOKnsgahwNa/CUAtAQq6AgWQEQqekIIIYQQQgw3CXoiaxwOqGkeB0CTrxxfyIOSdfSEEEIIIYQYdhL0RNY4nbCjaTwALb5iOoIFWA2p6IksWPZ/8MJBuW6FEEIIIcSIIWP0RNY4HLB9hw56GGGqm8YxvWhLbhsldg9rb9a3hgFK5bQpQgghhBAjgVT0RNY4HNDQVgKATQX4eOt8xhV8ArFobhsmdh+BnblugRBCCCHEiCBBT2SN0wk1jRUA1DZXsHLbPJxWP7RvynHLxO5i44oNuW6CEEIIIcSIIEFPZI3DAa98chSx/W7j0n/+kU+376OfaF2d24aJ3cZf/7A+100QQgghhBgRZIyeyBqHA0DRMe5btPpgc91U/USHjNMTgxPDjoUwTmpz3RQhhBBCiBFBKnoia5xOfdvSom/r2yrwhdzQUZWzNondQCyKhTAAhY7GHDdGCCGEEGJkkKAnskZX9KC1Vd+OGaOoqptCtLUqZ20SI9yHP4LqZxMPSwua6JAVO4QQQggheiVBT2RN56A3aRJm0Nucu0aJkSvYCGtuhDdOS2wqK2jk9ddz1yQhhBBCiJGi16CnlPq7UqpWKfVJyrYblFJrlFIfK6X+o5QqMbdPUUr5lVLLzX93pByzn1JqpVJqg1Lqz0rpxbCUUk6l1MPm9veUUlOy/zbFcIh33YwHvYkTYeW2edjaP4GIlGFEP7VvzNhUXtTEq6/moC1CCCGEECNMXyp69wAnddr2EjDXMIz5wDrgpynPbTQMY6H575KU7bcDFwMzzX/xc34daDIMYwZwE/C7fr8LkRfiFb34GL2JE+HVT4/R46tq38xdw8TI1Ja+lEJLoJyK4iYaZZieEEIIIUSveg16hmG8ATR22vaiYRgR8+H/gAk9nUMpNRbwGobxrmEYBnAfcIb59OnAveb9x4Bj49U+MbJ07ro5bhy8tW4xQUph1W9z1zAxMqUEverm8axuOZlSTyN+fw7bJIQQQggxQmRjjN7XgOdSHk9VSn2klFqilDrc3DYe2J6yz3ZzW/y5bQBmeGwByrt6IaXUxUqppUqppXV1dVlousimggJ9W1+vb8eNA1+wgE8C34LaJRAL565xYuSpfR28e9O2//Oc8JvnsReUU+xukqAnhBBCCNEHg1pHTyl1JRAB7jc31QCTDMNoUErtBzyhlNoH6KpCZ8RP08Nz6RsN46/AXwH233//LvcRuTNqlL7dulXfjh2rb1t8XnChg57FnpO2iREmUA+1r7PR9TNmzDoRAE9pJYXhNgi3AUW5bZ8QQgghRJ4bcEVPKXUB8BngS2Z3TAzDCBqG0WDeXwZsBPZCV/BSu3dOAKrN+9uBieY5bUAxnbqKipFh9Gh9Gw96xcXgdkObz+zTGQvlpmEi/4WaYfnP9C1A00dgxLjoqqMTu0xcuD8AU73vD3/7hBBCCCFGmAEFPaXUScDlwGmGYfhStlcqpazm/WnoSVc2GYZRA7QppQ42x9+dDzxpHvYUcIF5/yzg1XhwFCNLvKK3ZYu+dbmgrAza2s0qnnTdFN3539dg1W9gy4P6ccsqAD7dvg8A118PhZMPImYoZpW/QzSaq4YKIYQQQowMfVle4UHgXWCWUmq7UurrwK3ovlMvdVpG4QjgY6XUCvTEKpcYhhGvzn0L+BuwAV3pi4/ruxsoV0ptAH4IXJGdtyaGm8MBpaXpQa+0FNo6JOiJXjS8p2/r3tW3ratoC5ZR2zqKVavgyisBRwlNgbG4Y1uw2eCRR3LWWiGEEEKIvNfrGD3DML7Yxea7u9n338C/u3luKTC3i+0B4Oze2iFGhtGjYc0afT9e0Wtpk66bogfhdvCbPbkbzW6ZLZ+yqXEfjjhCMXt2ctdQrACnVa/JeOutcM45w9xWIYQQQogRIhuzbgqREB+nB8mKXkurVPRED9rWARC1lmGEW8EwoGUVa6rnUFmZvms4VkCBUwe9nTuHu6FCCCGEECOHBD2RVXNTarYuF4wfDzvrzKBnDEHQi/hg6feTk3iIkadpOQCvrdiXkM8HgV0QamL55n0ygl6EAuZP+hjjfsX+o/8z/G0VQgghhBghJOiJrDr00OR9pxOmT4fm1iHsurnlQVh3C3z8C/jwR7Dt8ey/hhg6O56B977OpvpZLNu8Hyrmg5ZPAfhgfWZFL0IBUyr1INDT931ouFsrcmndbfDktFy3QgghhBgxJOiJrFq8OHnfZoMZMyAcHcKum7ZCfdvwHqy5Ed48M/uvIYbO9icA+OFDd9AWKMJhDUPtGxgolm1alBH0YsqTvB+zIPPz7kGWfgc6NkMsAkYM3vky1L+X61YJIYQQeUuCnsiqSZPSH8+YAeHIEAY9i1PfNpiTeHhnd7+vyCvt7fDWS9W02fflmQ+OwhfUIa517fOs2DKfZl9pZtCzFCTuGyj8/uFsscgL4Rbw10DV/fDGablujRBCCJG3ep11U4j++uQTWLFC3x87FkLRIey6GQ2kP3aPy/5riCGxfDl4VA1L3h9HJALYdNArDH3Ah5svBOgx6MViFlpawONB7EnCLRAxE77Vndu2CCGEEHlMKnoi6/bZB847T993Ooe4ohftVNKJtGX/NcSQ2LIFxpVWU9M8FoACr05sFmXQFigCMoMetpSgZ+igJ9039xRK34SaIViv78cr+kIIIYTIIEFPDCmHY4jH6HUOemEJeiPF5k0RRnlrqW7SVdjCkmRpzh/SlZqegp5SBgcfDEcfPeRNFfnAYv4dSQ16VlfOmiOEEELkOwl6YkhZrRAe0q6bUtEbqRp37MRiMWgO6opeSVky6AXC+gN8RUX6MaPGJoNeobOdlhZYsgQ+/njo2ytyLB70wi0S9IQQQog+kKAnhpRSYKghXEdPKnr5b/M/4bn9MjaHmrcB0BrWM/iUjsqs6Nnt6ceMGpcMekXu5Pd6x46stVbkK5Va0avT9yXoCSGEEN2SyVjEkFOWoey62Wkylki7HrSlVPZfSwzMu+fr21ALOIoTm50RvR5e2cTJABR3UdHLYEvuU+RKBr329mw1VuQt8+/I+tUtBOrrmecAokPQS0AIIYTYTUhFTww5wzJMXTc9E8CIZoY/kVPhiL6eFGjalra9yKKD3jV/mMwjj8Cc+ekVvS6zuj0ZFFMrem1SyN3txbACsO6Nl7DVvaC3hSXhCyGEEN2RoCeG3NBW9PzgrIDzDJhzhd4m4/TySnwGzZaaZNALBGBUwRZ80TIKigs5+2woLE4Gvakz3bS2dnGysSfBopsxiufhdSd3kIrebiLS0e00qtGQvoBz6sJnmT1+DY3tpYR88o0XQgghuiNBTww5ZR3ioBdfS8tWqG9lnF5eaQ/o78vPf7gtMZZu506YUlmFT01O7Jc662ah10VhYRcnsxfC3j9AjTkmreumVPRGtnAY7rqlDh4phNW/73IfS6yDO175JktWH8FD736Bh949FyMk33ghhBCiOxL0xNAbyq6bkZSgZy8yt8lV/nwSn1hlYvk2Hn9cb6upgZlj1hNyzkzsZ3cVpNzvZSFsWxGFrnZAV3+kojey3X473H6jrvhGNz6QuUMsjFWF2d44gaOuf51v3/8g7cEibMg3XggxjNo3Q7Ah160Qos8k6IkhN6RdN2OBZNAbykApBiw+lm7aqE28+abeVl8bYmrlZiialdzRlizhOT29BD17EVZLDI/TB0hFb6Tz+8GiYgBs3dbFDhH9ffYFPYBi/nxFVBVitwSH5u+KEEJ05alp8NSMXLdCiD6ToCeGnLIN4fIKKRW9qq0S9PJRiacZgL3GrGPnTr0t2rIJmzWKtXSv5I4ps6+4C3uZNt+s3sa7bw5JRc8w4J3zYeerQ3BykcrlwqzQQjDYaRaeJadh/LscSI73nDgROmJj9PO+7cPWTiGEINyc6xYI0WcS9MSQs8TH6A3FVOhRP9jcRKPw1W8MYeVQDEg0FMTj1DOj7jV2HR0duqulxbcJAGf59C6Pcxf23nUThjjo+Wug6p/wxulDcHKRyu0mMbmOYaQEvWgAqp/D5zmcq/99DS+u+hwAo0ZBXXC23qdl9XA3VwghhBgRJOiJIWd3WIjGrNmv6O1aAnVvgrKyciWEIvGKngS9fNFS3wTArsBsij2tFFl19SXq19sLysq6PM5T1MeKntktdEi6brZ8qm/d44bg5CKV05kS9FKfaFkFRoQq57e57vGrsXoqAB30WjGDXuuq4W2sEEIIMUJI0BNDzuGASMye/QDWtFzfTr+It96CcCRe0ZOum/lizUc1ADSXnk/MUJw5/3YAjGALAHZ3cZfHFXh7G6PnBZIVvRdfhMmTu52Zf2BazADhmZjFk4quxGJdVPSMGLx2AgDb2xYCUGDO11NZCfbCcurbK6F17XA3VwixJ4pFc90CIfpNgp4YcnY7hKOO7AeweD/5iWeyZQuEo9J1M998/F41AJMPPJot7QezcMK7ABhhHfRSF0BPVVjSx66b7jacTr1p61bo6Bh8mxPa1ulbay9tEYMWCnUR9PzVena74n2oatSTH8SDXmEhlJVBbcsoCDX2fPLmldC0At758tB0HxdC7Bmi/ly3QIh+k6AnhpzDYYawzkFv239g7S39P6FhwPrb9TTHtiKwWKmtTe26GdIf7t7+ooS+HPM36IXzXKXjCRqleOy6AmeNtuifCWt6F82Y+SG/uLRvXTe97lamTUturq3NUsMBAubJOjbL2oxDLDXoWS0RvbFdj+Nk0Y00NOifi3jQc7t10Gvu8BIzq8Ndal0Pz86H5xZC1f3Q9NEQvQMhxG5Pgp4YgSToiSHncMDG+n1gx3/15Apxb34eln1fd9Hqj+rn4INvw+Z7wVEC6A/48YpeMBDWIW/LQ8nudyInPGqHDm+u0UQtXgqdbYRCYI210B4qTptpE+CT1nMB8Jb2fTKWmcml+LIb9ILmyVo+hadnJzaH3/sx4aU/y+ILidSgF78YQPtmfVswlfp6XcW75BK9adEiKCmBFn8x0UBr9ycOdvqBaPkkuw0XQuw5JOiJEUiCnhhyDgfc8vov9DTo73wpsSZWQtvG/p2w6cOUk5ewZQs8/3yyotfWEgYjXhXYmBzLJ4ZXLMyZ82+nNTQaLHZi1iK87lY6OsBBC/5wZrfN5tn38K3nqyguc/Z8bvswBL1AXfK+X1cmQ0GD1uX/wL7uN7DyOl2RXnuLnhioY0sWX3zP0jnoGQa01mzCQPH60sm88w5UVMDZZ+uC/vjxUFwMrX4vRqiHil6wU7fORqnoCSEGSIKeGIEk6Ikh53DAG2uPgbIDYNvjsP3J9B0al/bvhKnTqdtL2H9/fdft0RU9X3so2WVz6Xfh7XMH2HIxGEbNK1QU1iXWOzNsRRS52+joAKelhUAsM+gdcZSD2++bjKW3v0w23YevyN3G9JQVGoakopfi8u9soLzIDA8rr9YV6WXfh1eOgienSFfhAQqFkustFrnaWL8ennloA1vrJ3L0sQ7eew/22Sf9mOJiaPEVo8I9VPTM8Xvf+e9Snlx2GsbGv/f/wpIQQoAEPTEiSdATQ87hgFBIwTEv6A3+HenTI/pr+nfCwM6Uk5dQX6/vNjTroBcJh5MfuP01sqByjgSbtwHwTMvjACi7l0JXBx3tUdy2FkJ0PRFLnygL/pCb8xffx8ljL8ft0FXirAW9WASjUzUoFvLTsuHdxON6Y3+2HVgPk1MuJLStz1ID9iyhEFQUNQDgcgT5dEUHe49dRW1wDjfdBNXV8GSn60Pxip4l2kNFzwx69z81ne/c8xeIBWHTP4bqbQghdmcRM+hZ7LlthxD9IEFPDDm7XX+Qw16iZzD074RIyuQWkX5OdBFJmVrRXoJXz7TPnLm662Y0FEqvrEQ6IDwUK2qLnvgbdIB3lo4HQDl0d8sDF7VT4GghogYR9AB/2MPUUVVM8f2e7a/8havP+lVWgp6x5s9E/jMDlb6iG007trH/1P8lHi/9uJx9Dy4HZ2Vyp/jae6JfQiEoN4MeQP32Xew9bg1TF8zh0kth7FiwWtOPiVf0rPi7r6QGGzCUlRZfMTsaJ9DiPEqPFRZCiP6KV/QsvUwWJkQekaAnhpyu6KEn3nCN0RW5QMon8sAuqHqw74ugRVJCm6MEpxOOPhp+eb2+yhaNpIzRS7zGTsTwCrdWU9daQXmlDuBWp07kXncrXlcLMevggl4wkpywpWzrT7jmc1fR1hzo4Yi+UR/+AFswc7ydb+MLfPv425OPQx4aGnSX1ITmlYN+/T2RrujV42MCACcXfg63I4C9Yk63x8QregB0130z1EhYlQJ60p/W6CQINWWz6UKIPUXUnF/A2ssYciHyiAQ9MeQSQQ/APUZX9FKD3vrb4Z3zoPb1vp0wJejF7KXU18PixVBUogNFLBzKXMrBL0FvuBn+Gmqax1JRoR+HSa59V+xp6XYNvb7ylnkytsV8uwZ1zrRZYYHDr3uDpz86FYCKuhsAuOXdW3lx5fFc/uDvAPhwpTd5QO0bg3v9PVQkHKXU00i7VQe7CQUfA+DZ64xuj4nPuglAuIvum5v/Re3HL7F5e1liky9UkH6hSAgh+ipe0bNKRU+MHBL0xJBzOCAc71nlGqMXou7qw1awvm8nTDm2I1yBYcCoUeBy2QCIRcKZ654F+jkOUAyaNVRNddO4RNDbZ6EORCWeZopcbSjn4IJeQVHmEgzW8CADfSA9KJbMPJyv/VWP6XIb26iqm8w6vsOJv32RDbtmUlAAD/07JejVv5PetVj0id1owmIxCDqTy1j87tmrsReWd3uM16u7bgJsvfcE2PE0PLdIz+rbsBTe/Qqj3BuobR2FTf9poD1QKN8fIcTAJMboSUVPjBwS9MSQczggGoVIBCiYAr5tsPbPmTt2XnahOynj7Zr9OkWMGgVOlyIcsaGibdBpfJVU9AZh033w2sn9/ho6Y+kVPatTV/TGlVZjsRhYXYMLeihrxiZHdJDfZ78Oeve+cT7n/eV+5s+HSTPKiMb0n8pdLaPTZvm86y6oqU/puhkLQ+vawbVhD+RAj8+LFuyd2Fbrn9nd7oAe+/t+1VH8660vMal0Iyz5rF4QvXWtXmMTWFc7l2dqbqC6Wh/T5i/U1X6ZHVUI0V+Jip4EPTFySNATQ67cvCjf0ADMv1Y/aNZds3CmXLHvyzi6WFjPnGdqaNfH64qeXkuvy1n4ZIzewP3vAqh5Hhre6/sxsSgey052toyjOJ7nCqcBipMXPAeAwzPIoEcsY4tbDbLrplnRu/Wl7/LgO+cxeTKc9yUrDW3652zG3DGUlCR332+/lHFi8Va1bxtcG/ZALqWr+apoSmJbc2yvXo8z7KVceOc9iSAOsHyFouqDN/m49lhm/d9KGjiYykq94HqLTy/LIVU9IUS/RaWiJ0YeCXpiyI0apW9ra9ELXXsmJoLX5uqUGQv7UjHq1OVzV3OyoudyQThqxxbLDHrNOyXoDVrnxad73Lceq4rSFhmLUuY2zzgYcyxfP+rvADgKBxn0jMygV2Tb2ec5fboSH+O3q2U0AEceCV/9KtS16Z/TsvGjKSxM7j92LLQFdEVva/1EAGrXfAjNMvtmf7jNoOfwVvLtf/yFa/59NUbJol6Pq6iAaMxGbcuoxLZtb97PlOIVPLrkSIDEhYb2dnjmBfObJ+P0hBD9Fe8JYHHkth1C9IMEPTHk0oIegKM08Qdze11Fcse+VN06LZOwoyE96IUiDmxG5gx8qz+UMXoDEkuZvbQ/sxX6dV85P2PTt485PnHX7R1sRS8z0VUU1dLWz9U6UrWYP6S1raOYNw9mzYLSUpg+W0/ooVyjKTJ7apaU6CpRTOlJYZr8owhF7Iypvw6enTvwRuyBPFZzDT1vObe//G2uffwaDjwos2tuZ3uZRb+a5uTP2Wdn/IE11bO45cXvAcmgd+ih0BGUip4QYoDis3kr+egsRg75aRVDrsugZ4pXSgA9gUIs2vPJOl2J37arHIsFysrA6dQVPTtmRS9lDFdloVT0BiSYXNssvvh0n/h1sA7bxqVvLz8gcbegdLAVvcygV+Jp1l2EB6itoQlf0M0zz7lYsSK53VU2Sd8pnIbT7LUzbZpeMaSsTJcsCwpVoosn0PvPci689w142hwHV/sG7Hw1t+0xeWy6oucpTV74Oeig3o+76SZ9gSc16AG8UnUxU/cqAZJB75VX4MBDpKInhBig+IXPLsaHC5GvJOiJIddT0GtsN6c+d42B9g29L7HQ6QPajl0eKivBYtH/wlEHDmUGvZSFrMsLJOgNSLAueb+vQc8wMN6/WN93darolSa743kGXdEzu246k+GgpKCZN9+EX/4Sqqr6f0Z/SzPNvhKmTiXZ5RTgwDvhhPdg6lfwmKs6nHKKvu2w6LLSu82X88LKE5PHpH7t8sXGu5OTxbx8JLx6bG7bYyq01xOMOHG4CxLb5s/v/biJE+HNN9HLdaTY6juASvPXPx70XC6we8yg17isf12RhRAisT6v6nE3IfKJBD0x5EpLwWrtOujd++YFtI/9Fix+VG/w7ej5ZPGgt/gROPF9amtVIkgCRGJ2nBaz66YZADoCHoqdu/q+ILtISgt6fey62VGF8u9ge+N45h8yMf05R3HX9wci/v087CE4aSm+wiMpdrdwwQXwi1/Avff2/5Rhnw56Ezs1G3shVBwIFhsHHghLlsA11+inPCUlqC8ZbAidxWPb7+HzN/1bn6tNLi70VaG9gbZgOcqiP0BZLPpvRl9MmAC/fvJnPLfipMS2qLU8UXktSpkU1eExg+T734RXjs5G04UQe4pE0JPPEmLkkKAnhpzFomferI8vk5cS9N5Zdygby24D7yy9IZw5vi5NfIxewVQoP4DaWhJX7kEHPZfVvLrvqjRfP4ZFxWRK9YEImEHP6ulzBaR580cAfP6mx/nc57v4tL7g1/rW0f0aaX1j/mfrGgVl++H0llFS0JxsR3OXB/V8xmAz/kgJdnvP+x1xRDKI3HQTLFoEJ5wATz8N51+sq5itu2RcaF95HfW0hvSFmbffhm39mLh09Gh4bsUpnPPnRxLbDEdZIuhFUoaZOjwpM+nEZ/4VQoi+iHfd7GIiMCHylQQ9MSw8HvCbMxOnBr2YYdVjquzmFPWRXoJevKJn0x/YGho6BT3DgdNqrsc3+YsAPLvc7GMXCw3iHeyh4hU9717Jit62x2HrY90esnXFR0SiVn7/13lp35uEfX4KX4yBdbAzl5lBzxwvYXWVUOJppsTTxNTKTQMKerZYMxFLSb+OmToVli2Dww7Tj91lY3Trtj/V/wYMl2jK70IeVLq9rnrazaB36KEwblwvB6SId7H1BT2JbRZXGXPN+XBKk39ucBYku4bKFOlCiH6Rip4YgSToiWHhcnUd9MBcX8/q1B+8Ql2sgZcqHvTsOug1NuqJWOJiRrIU88S7RzH2RwZL1hxpPilBr9+CZhm2aFby/oqrYNXvuj3EX7eRrY1TOOJod/fnVVkY41BuztZhN7uA2kso9zaz4jcL2HTz9AEFPaelGcNeMqhmFY3WKaWi6Q6I+AZ1riGTOtY1vjZUH6xbByefDB1ZnrTS62ygI1rR+47dOPNMfdEozl3g4Kqr4L//heOOS+7nKEypIrv7kSaFEEIqemIEkqAnhoXbDYGA+cCevsD0Sy+lbO+t62ZKRS8Wywx6kViySvTdS93s3KmXXAAk6A1EoI6YvZRnXp+I4a+GcDtG65r02Tg7cRs7aA6OxzLUf10OuANOfB884/VjRwkeezuTKnS/v/4GPZ8PipzN2Nwlg2rWqDFOfvmfq/SDfJ3GP23sZXOfD/vRj+D55+Hll3vZMdQEO1/p0zkNA4pd9URtA+/K++CDcOWVyceFhWCzwWc+k35NwVNclBhDiVUqekKIfpCKnhiBJOiJYeF2p1T0OgW9v/0NolF0ZSbcS0UvPkbPVkBbG8RinSp6JCt68a5cEvQGIVhHs7+Sl98eh4oFee/p11EYRP3dBz2vvRqfMQzVEps7bbkGHCVpT7e3RuiPmmqDEk8zjsKSXvftyahRsKV+sn4QDfS883BKXe7BXOcQ0DNQ9pHbLNL6eyoCrroBHiuDV4/LnMCneWVGV9H2tihlBY1YXAOv6NntcFJyLpa0Re1TFRfDf5Z+nm3Ob/T+t0YIIVIZ5t9QqeiJEUSCnhgWaV03U4LevHn6c19bG32v6FmcYLHTaM4Nkhr0wrFkd0F/SN8v9ErQG7BgHQGjkh1NumrW/Ml/AbDGWruc3MaIGYwq3EHEkYNucZ26XEYCrYTD8Oc/Q13nlQ423g21b6Ztqq3pwG6L4Coa3GygRUUQjrn0g3wKepGUleR9KUHvjdNg+5M9HmoYcPfdJH7nfD31SF3+k+T99s3J+9seh2fnw7b08Z27tjVhsRjYCwce9AAWL07e93i63sdr/unpCJX0q5IphBCJip4EPTGCSNATwyKt66YtOd/5WHOZtT/+EQx7cd+Cnjk+r8ksFqQGvUC0JHk/rD9s77W3DnpGVIJevwXqaAlWUt2kg9vCyv8mn+tiuYXGXS14nH4sBeOHq4VJ3r3THsaCrfzoR/CDH8D993fa971vwMtHpG3y7doAgKNs6qCaoRTYnGbQi+VR0Ev93Uqt6AHUvdXjoTU1cO3lWylu1d0eW/paDGvfmPIa7+jbtg2JTb//PZx6vK4Ou0oGF/QAHtp8F2fe/Bi7dnX9fDzotQdL9NjEaHDQrymE2EPEpOumGHkk6Ilh0V3XzXjQu/562NXo7b07VaQ9ERS7quhFLLoa4w+5AMVBB8GoMTroRcIDDHo7X05+SM1XwUao7fnD+sDOW0dda7KiN9pbQyymBz3Fuui+WbNJBwh36diM54Zc6QJSF7K1RFv4SK/0wFNP6QBWX09618FXkjN1GE2fAFA4fu6gm+JwxSt6eRQkwikVvc5BL/W5Lvh8sPXPk/n3pWcBRmaFtPaNrt9r44fQskZ3G01cGFCJLtiXXw4VRXqSn6LywS63AXud/A0e/+BMju5mibx40GsJlOg7vn6s4yCE2LNJRU+MQBL0xLCId92sraXLoAfw7lIvsWBv6+i1JZZWiAe91OnTDZsOeh3BAq66Ct56Cyw2HfTCgQEGvVePh5cOG9ixw+W1k+Dlw9PHYQ2WYUCwgZ1N5dS2Jlel31in1zys3a6D3sqVegFxgJot+sN85YQyhp3VCQVTEg+LXC2Jn5HXXtO3S5fCqpUp/Q53mROGfHwNh7p+QDDsoHTSzEE3xeHOw66bqRVYv7nG37GvQ+G0XoNepCMZ6t0Of3rQ2/U6vHxk1zOxrvotPDMbNt8L4Wa9bcVP4fHKxIyk8aBXPHrwFb1Fi/SP7YEHdv18fOxec4f5R+OdLw36NYUQewip6IkRSIKeGBZuN1RV6cWNv/vDZNAbPTq5z/ZdxQTa+9B10wx6W7boTWPGpDxvTsjR6vdSWqpn3rPazaAXzKPqSrY1fqBv+zFVfq+iATAi7Kgtxh/yEInq6eubozMAcK3+MQDz58NRR+lD6qr192/MpMGNcxuwmd9K3PW6W1m1Kv3pxvoQcz7pNFOHYcAn1+KxNrKtaQrW3lZL7wNXgTmjYz513Wxbl7jra9J9G3/9p/FsrS4i0NFz0Av7kpX2Qld7etDbZs5i2bEluT7fmBPgqOeT+3RshUBKf8poAKP6Of75rS/z9SPvBsBbMfig1xurVY+hvPCqz+oN8SVDhBCiN1LREyOQBD0xLNwpS6r95Y7kg9Sg1+r34rK09LyAc6SdiCqktRU+/BAmTYLUHl82tw4YgbCL+NrIyaC3B4zRy2bQMyfv2Li1iMsuU8QM/eei0aqrm97wsrTvlWFAU60OBK7CHAW92T+CBb8BoMKb2Q1428bM7qZt9ckP+/5IdtrtLsjDil5LMvX6GnRF75bbC9hSXUT1lvbujgIgknKRpMDZkb50Retafdu2DgI79f2JZ8C4E+Fks+9suBn8O9POGf3oF3x58f2ctt9/eXf9wVgLJwzgTfWf1wvtgSLufeN8+cAmhOg7WV5BjEAS9MSwiA9Z0pLjqFKDXou/GIuK9hxWIu288EohxcWwbBnst1/603aPGfRCrkQ3rUTQG0jXzZ5CZz6KZnGBbnPyjhaflyOPTG52Fo/h8gd/i0VF0l6vpQXam8xw1WkJjWGjLDDtQgCmTsisDm/bkDmBzMZlHyfuB6NFGc8PhLswP4Pe+rr5tAcKqHDocNYRLKAtUISK9tJ1M5R8H69deTSxQEqIjv8M1L0FT+plJT5aVaknSypdCEV76TGBqWsvWuxYO9YmHv7skV+DJbng+VCy2fRts6+kywmFhBCiS7JguhiBJOiJYZFa0UuVGvSCMTMc9DQhS6iZxjad4DZsgLmd5s1wmmugBcLJoGdzmJOxhAYQ9GIjrLtnJIsVPXPcVpu/iH33BcPQAb2wrJSGdrObXcqH9+pqkmMs7Tmq6EEiZFZ4mzOe2rWtMWNbaOcHyftkJ+gVeHXQi4Ty6OfHt5XNddNYWzMrsakjUEB7oBA7vQW95PuYUrmFk2f9LeVJM+gVTofpX8c/9nyO//JRnHee+bx7HHRsS47RAyhdhCI5nvSDjSnrIQ6xsLkqSLOvRF/MkA9tQoi+kIqeGIEk6Ilh0Tno1bVW8N8PP8Oo5BwflI2KB71uxul1bAXfNj6q2hfQxbaJE9N3KSzW46tSg57VMYhZN3tb7iHfZLPrpvneA5GitEDuLCrBH4sHvWS3x+pqPdNlzLCArSB77egvmwfsJYwv3Z7xVKA1s4JTFn07cT+SpaDnLdVBr70ljyp64TbqW4tZU62XofCHXMQMK22BIhyWnrtuRkPp72PemHdTnvTBpHO4bfsGVrj+xpqSe2lor+DTT83n3WOh5dO04/Emw+aP77+BjmA3K5wPgfj1nuaOEsAYeb/jQojckIqeGIEk6Ilhkd51E0Z9q47T/vjfRBgDcBSYVaDuPnjtfAmA5z8+KbFpQqdhPfHF0TfVTkuc224GvehAKnoj7UNgNrtummP0XF4vlpS/FM6iUsIWc2BksJ45E1ZzyMx3WLsWPLYWQoZXr2WQS4VTGF9SlXjocXZw9kGPUFaYrOj5FtxHe6CAGe6nE9tiluwEvfJR+ge+rTl/gp4RbqW+xcv6XXsBEI7aufJKqBhThNvWc0Uv2qkyecj015IfeiJ+DKuH73wHFi6E7Wa+Nn/tdNCLdDq/W6/L2B4o4I/P/ngwb6vf4hW9Jp8582bVA/CQC1b8fFjbIYQYYWQyFjECSdATwyK1opeaAVwu+MMf4M03wVHQS9fNQC2gQ1xc56BXPOMILvrbX7n0nzcnu246zaA32IpeqK+rROfQEFT0ikrTw4+7uISozazorb+DT383h3euOYyVKw2KPS1EVA67bcYVTGFMURVWS4Q/n/89Hv7eF3jk+1/g1IXPALDF9T08c77MlsbpaYcpm6urs/Vb5Wg962ZHa54EPcOASBtt/iJshXqaWpc9wE9/Co6CIjyO9h7Ho8bC6e+jvLCRaN37+kHURyia/AXfZi5Nl5i81FmZPPDQB+C0zTr8pSgexh+Z9IoesP1J3UU7PntoXO2b3X+g822HphVD1kYhRB6SrptiBJKgJ4ZFatCLz5LpcunQ96MfweLFgKOXip5ZQYhEbYlN48en7+ItVvzttYvoCBZmv6L3WEn/jx9ukWxOxqKrMMXlOujFx+gVlJaA0wx62/+T2L22agdedyuGLUcTsaQqmEKlZwsHzXiP7514K5/ZVwe8BZNWEDMUkz93MyiFw6bHib226igAHM7sVCIrx+jA6GvLk6AX9aOMKK1+L95K/b1z2MIUFIDdXYjVEiMS7P4iQSycOdYwWL9J34n4CIQ9ie3xoBeLZyRXMujVh2dB4RQomArAhl0zuPxyeDvZe3bIpY3RA2INS/WG1tXwurnswo6n4eUjYOV1KWtnJRlPzYTnFtLQoNcHxV8z8iZuEkL0j3TdFCOQrfddhBi81KBXUQH19Znj9iwOHRBiwZaur0CYV9OiseTsfKmLpUN6tTAR9Fxm0Ivspl03Uz9gDrSi1/iRXnC8eE7ytOE2FFBqjp285u3XmRS5j4vOKsLmzvyP7oaTjmb6qA20Ow8fWBuyyTMRl62DsoL0yVemjdpER6iEIqV/wv7w5l8ZFXuFYMTJ0XNex+nMzsuPHmMnFlP4O/Ik6Jk/x22BIsZMSl+vzurWv0TNu3ZRMXlql4fHIplBL9RWh8cwIOrDF8oMeom19lIqeosOqeCw4yEUPJXK8NO8+dEUlm3I7No9lD7zGXjiCTjpNP2+LeFGojEbVksEqp+GUDPUm2MQP7kWgrVwwG1p51Dm+oiVlTEuOW8Dt50yCxbdDHv/YPjeiBBieElFT4xAUtETw8KbUuSJV/Q6Bz2rS+9kef9rEAtnniQWxlA2QPH1r8OqVT0PBYsHPYcZ9GIDCXojYfr1SEfy/kCD3vOL4Jl90jaFOnQ4KK7QE6tcc8vBnHDlbdgdiuJSKz/9961p+08ftQEA24KfDawN2eQoAWCUtzZts90WIRBLXh2oDh3K9U/8PFGtzFZRxlOgsFgMDiu+nlnj1vLEE9k574CZ1VlngZejT65Me6rddSgA0epXuz08FkkPrJGolXBHfWL5iPZAMujFx+g1NYHPR7L6C1Q3jeOhh+Dx/1i48+lTWbtzn6yF67564AHYuBGsJTOJxvR/gbe9dAns9ye9w2snw6e/Th6w/vZuz1Ve2EDVJ/rnnupnhqrJQoh8IBU9MQJJ0BPDIjXoVZif+zoHPZsnZaBO67rMkxgRDLMIfdxxMHt2z68ZnwzC4XKah/cQ9CK+rpcmaFre84vkg9SJLgbbdTPiA/8uAAJtrbT6i6io0H8mXC6YMUPvVloKr39yUMbh1z37Z1xTT8rYPuzMJRYmVWzNeCqskkGvc0U4OAQFuAOmfcCNN+b4CnBEh/aJ04qoGJ9e0YsW7kN101jsDa/1cHx6Ra++rQLDV5eY/Kfdn17R85gPP/wQfvenZLCMxtI7kRQWDv+8PW43TJsGpRUerBb9ge2DTQfQVPEtvUPD/4hZ+jYL6GM/OItnf3Kq+SjHExAJIYaWEV8SRip6YuSQoCeGRepkCyUl+rZz0PMUWDn0GnOwTttaMsSSQa+nyRvmmL0P4x8gHS67eXgPQe+RAvjP2IzNsdpOg4fW3wm7evhAnAuDreillrEe9cJTUyDSQaSjgYa28kQFNlVpKexqKsvY7iod1//XHwpm0JtYti3jqagt2e74e1uxdQEAY+ZmhtfB+te3v8Ln9rm19x2HklnRs7i8KGf6963Iq9hSP1l3UeyO2VXR2O9Wlo3+mLq2SgjVJy4stPrSK3qnn67v//KX8JubKjNOl3jt7ExyOiCjRsHrq44E4N8fnMn8hXYMi74otOiKN9N39u/s8hxHzn4jcT8mn/2E2L3JrJtiBJKgJ4ZFakUv3qWyc9ArKICV2+bpB61dBD0jQgx7xvk6e+cdvZh6nNNlIRyxYURTgl6wIT0gQdezfTZ/wvbGlBlfPrgEXjmm+xfPhcEGvdSFrI2o7o7XsopYoIGG9vJEBTZVaSk0dZRmbJ+4V54FvfLMoGfzJNs9zmzu6qYT4LSNTDvqnCFpznEzHhyS8/aVEdIVPYenCCzpVTWvV09MYg/v6LarshHVFT014yKs5fOob6vAEk5W9J59IRn0QiE49FA9UdKLL0KLT1+Vefh/5/CLX+iJV844Q+9b2LfC2ZAoKYHP3fQfjrlpPb+9oYDt2+Ft+/Pc8dp3WbFlAfe9+ZXkzlsfSTu2vYt1/zZuHOIGCyFyKyZj9MTII0FPDIvUClyBuZZ2V0GvPVBEyDoeWlZlniQWJmroD6k9fUAsLobpKbPmO50QijgSQS8WicC/K2h75oyeGx0LYzH8XQYaAvWZ23IltbvmQLpuBrqo5DSvRIXqqW+r6Dbotfgzy6rHnJpfQW9SeWbXzTGTkhWtiRP1bUcHUDgtY9/BWDU+WRWyWQYwPjSLAu3JMXoAHPEUnKoXMS8q0ksNFMVWwWOZVVoAFQsSiymw2CkqgrrWSuzRusSFhXWb0n+ZJ02Cu+5KHM23X6nny7f9i8JCHQJnztTP5DLo7b8/nPjZUu56aAZfMTPdu5uO4rv/uAVQXHDHfagvxVi7c2+ofj7t2KYO/XVa2p6cfMXfkdvvsRBiiElFT4xAEvTEsIiHO0iZJMWRvk98XE+LbRE0fpB5EiNCzAx6qefrjdMJoagDYvqD2NuP6gWyizpe7vlAs7tbOGLPfK7l0743YKhFkxW9aLgfFb1YBD78Mbz9xczn3vs6FeoDGtq777ppGJl/PkZPzuz+mhM9VPSUIxlQ04JelrkrkzNY5jro+Vt1Rc/tNftKTvhsYobVoqLkUgPdUbEAwYgTlKKoCBo7yrAbTYkLC75gsqJ37bVw0kn6X9zaqnIiUXvi9zZ+8aDzGMnh5PHAQw/pi0IlJbpNn3wC0WjqXoplVQdD07K0Ls6KGMsaz2eV6+bENq+zYbiaLoTIBZl1U4xAEvTEsEidcCH+YS/W6aJYfHs9h+ium8H0qfExIkQNHbo8HvrM6YSOYAFWw+y+1qqnTv+o+uieDzQnOQlHuwh6/pq+N2CopXTd9Df2o10N78GaP0LTR2mbN/uSX5eG9orEmMpUnT+gf6j+BMe+DlZH5s65YAY9j7OL4JtyNTYe9AayxGJvSsqTP6S5DnpBs6LnKc7s89ynoGcECUWdif3bA4V4LLWw4koAfCEPCxbor+MvfqEv4igFt9yij6+q0rfx3/Ems4fogQcO6m1l1cyZ8MIL+v4VVyS3v71mfwjU0lytpxMNhcBt92HYCtPW8fQ686jKL4TIPpl1U4xAEvTEsFu8GObPh3PPTd8e/xBYG16k77R8kr5DLJyYta8/FT2rFWqax1GgqgEoVnr8n91qfvhOnYwk9b5Z0fv7kq9lnjSQf0Hvo6qF2Jre6ftxdXqiGePw//DCxyfw3Xtu4baXv8+p1yQnDvG4Y1i6+CsRD3pVdZN1ExwTYPSRA2v/ULD10Ccwlgxd8TF6vc3gOhDe0mR3xsTPWo6Efa1EYxaKSjKvkBQW6q6bCdHMtlqMAKGoXuzO5YKOkFkZrH0d0MHvjjvA3umayHe/qydm2bw5+VoAX/86nHAC/N//DeZdZdepp8IuPeEsCxZAdTVcfz18ul0vO/LxO3om4NZWcDv8WOxuZs6EH/7rj8RiimJnbZeLqwshdhOJip4EPTFySNATw+7AA2HFCrjoovTtiav9/jH6TqAufYdYJBH0+lPRA9jZMp4i2w4Aymxm0LOY1Z5oypz6qZOZmItMV9VN4by/3J9+wryq6Onucy98fCKu2DboyOyu2KWG96FwBjvUGZz0uxf4y0vf5Tv/+BOrd8zhA/u/AJhUWd3lofEq388f+yUAMWeedNmMUxawxad07DTtvS051aPNBi+/DK+8kv0mWO3JBeJsKrdBLxJoo81fRGlZ5hIAFgsYKrlieSyYOSmRMoKEzYqeUhA20qfLPO/Ccg4+uOvXHjUqef0k/js+Y4aunnXVLThXfvKT5P2SEhg7Fi67DGqazZ9tc+bN1hYDj9OP1elhwgS46bkfctHf7sJqiYKvj797QoiRJ1HRk66bYuSQoCfyRvxD4IOPmwN4gp26QhkRwjE7dntm5aA3te3jKLbvACNGmUNPj+ewmqEuddbKcHJNulhI349ZiwiGO63qnFdBT7f/k+1z9eO+ti3URMg6mvffz3zqoqtPYUP9XB765KouD40HvX+99RXGfWcHqvKQfjZ6GJjdNxlzHBx6P5y6Chb+Hvb5adpuxx6rP9RnXUp/ZZs12MOO2XHnnfoCSldiwVZa/d4uu+ECFBUmg2hzbXP6kxv/zjFT7qXUk/x9DJMe9BxF3Se2MWOS93M5+UpvUv+mxCvWDgc8aC67Em7Vv1etzfp7aXO6E9XuzXXmeMz2zcPSViFEDshkLGIEkqAnhs2bb8Ibb3T/fDzoPfF8N0EvFiYStfW7mgfQFhmPx9YMoWZsljAAjkRFLzXotSbu+lvNcU3eIj0RhamhrYyYL4+Cntn+6iazH2JXy0R0IdDh47U3C7j44vTtXi+sWF3KzB+spIl9uzzWaoWPzKF9Nc3jWLhwIA0fYvGgZyuEKedB8WyYcxnYBvADNEgVhfWsWzF0PzOGAZdcQrffh4i/lbZAUVroSuVxJ7sc1u/s9PPzyXUABKLJSWxilvSgV9hFl9C4+DhI6F+X61xKDcSLDizCF3Inums31um/G+5C3TX3V7/SVX8AOiToCbHbkslYxAjUa9BTSv1dKVWrlPokZdsNSqk1SqmPlVL/UUqVpDz3U6XUBqXUWqXUiSnb91NKrTSf+7NS+nK3UsqplHrY3P6eUmpKdt+iyBeLF8Phh3f/vNutP6iGIk7aAkUQ7NR104gQidoG9GEx6jBnTeioSmxz2MygF25P7hhJVvR8ZtBzFKRX9FZXz6apOo+CXqSDSNSqF7GGPge9sN+HL+ihoSG90vLyy3D22fp+T13rUkOF09ntbrmTCHr5kS6qX71pyM7d3t7z80aoDV/Y2+1FkqfXXMK2hglAZkXPCLfxwsqTuXvTC8lt1vTSXElJZpfQuAkTkvdHStBLm2xIKeo7xmKP6K6bdTv1342iEh30fvYz+MwXJhKJWjHaZDE9IXZbMhmLGIH6UtG7Bzip07aXgLmGYcwH1gE/BVBKzQHOBfYxj7lNKWU1j7kduBiYaf6Ln/PrQJNhGDOAm4DfDfTNiJHvrbf0OnitwYouKnoRwgOs6Dm9ZmIxx9lEolZcNnPNuUjXFb1Amw56Vmd6RW919WxUXk3G4qMjVJBYmJpwCzR+lLkgfCexsA9fSH8x589Pbh81CuaavUB7+1qvXg3b8nVYUmpFL8daApVUb24gHB6a8zc29vy8irYS6dTdMo2jlFNueBaAtobm5HbDgHArSzfuiyqdl9jc4ks/V0/LJKRW9LrrOppvijstEdkaGosK1FBVBf9+WAe94rLkZDt7z7aztmYW/ppOE0gJIXYPhiEVPTEi9Rr0DMN4A2jstO1Fw0j8xP8PiF+zPR14yDCMoGEYm4ENwIFKqbGA1zCMdw3DMID7gDNSjrnXvP8YcGy82if2PAUFesxUY0cXQc+IEIrYB1QVKK7Qn9yCrXpx8GZfCS57z103wz593+oqIhBKTlZRVTeFssKm9ElccigW6sAX8OCP6PcYaa+B5xfBS4t7PjCSDHrTUtYKr6xMVmEaelkabO+90ys2eSWPKnox52hc1ibWrRua83cOeq+/Dscdl9xup42YNXNphbiiIhIXChKTsRgGRDpQRoRWvzdtHOOyj9OD3rx5dCv152PUqN7eSW499JD+unWuUJdN2Zv5E5ax914BdmzSF4sc7mTQO/hgWLF1AUZTN4MkhRAjW2oVTyp6YgTJxhi9rwHPmffHA6nX97eb28ab9ztvTzvGDI8tQB7NxSaGm9cLda1dVfTChCMDq+iVjtYfYltqdXfQ5o4S7NaI7oqRWvkKNSfuhv2tBEJO9p7jSKvoGa70WfhyLeRroyNYwJQZ+sN3YIe52HzT8h6relZ8iYWuUxe39niSyw1MnjwULR4m8YCXBxU9i6uEEk8zO3YMzflTg94998Ajj+iZRK+7Dh54AByqFYuz+4pe6lp61miz3vjuBfCoPqbV701bM658dPq5Kiu7b1txMRx1lJ4sJt994Qvw0kuZ28cdfA7FnlYC97h5+2rzAoo9WfabNw8+2bGAAram/Q0RQuwmjNTuGFLREyOHbTAHK6WuBCJAfO75ripxRg/bezqmq9e7GN39k0mTJvWrrWLk8Hqh3e/OrJgZEUJh54CCXnFlCTRAsEVX9Fr85oe0h+ww+tjkjqHkJ2ZrcAfVzeO44AKw+5NBL2KLB71qKJzS/8ZkU6QDe/0LfLTlWObsY6HVX4SXp5LPh9u6rWg5rD6cHg+vvgpHHw333QcvvqifO+QQPVZvcS9FwbymzD9veVDRs3tKKPHs4KMh6uaaGvS++tXk/b/e7uNE51lMXLAdm7fnil57oJBozMIU57Pw4E/Srlp7vF4OSZlY9Z8PFsK75oN9ruyxbUrBa6/1593kodHH0hadSJFVfwO/9te7+fu5RyeettuhLWaWxX3bwFGSg0YKIYZMyOzpYCuE2BD1wRdiCAy4oqeUugD4DPAlszsm6EpdyogMJgDV5vYJXWxPO0YpZQOK6dRVNM4wjL8ahrG/YRj7V/Z0CVmMaF4v+AJ2jM5/TGMRQuGBdd0sHaWDXdRndt1MXSB6V3IRNSPUlLjvim5la/0kSkvhyKNTKnrueNDLg3F69e9hjTbxjyVfZfZs8Lrb0p/v7j+kWASHNYSzwMPR5ufVZ5+FUMpyb8cem6eTrPSVMv+85TLoHfQ32PcGnEWllBY0sX1774cMRGMjKKWDmceZrOIeNOM9Tl6gO1yMndv9EhhOJxiGhVa/l2meVzO6Jh12tBeHI/l4zASzole6Lyy4PkvvIo9ZrGwpSi41ctltXwVL+hovlgLz74Kv67UnhRAjWLyHkWsUUtETI8mAgp5S6iTgcuA0wzB8KU89BZxrzqQ5FT3pyvuGYdQAbUqpg83xd+cDT6Ycc4F5/yzg1ZTgKPZAXi+EIg6MaKdFpo0wwQF23awYU0QsplDmTJ6Jil6KaMyCvyUZ9ArUNrY3TaSwEOyu5Bg9V1GZvhNu7n9Dss18P1vqJye6W6Yxugl6Uf1rG1PJL6bV2v/1CfNaYh6oHJr+dZj9Y6yuEkoLm4cw6MWI/cvKi1ccT8ffC/nMvv/lN1+4gmvPvFrvcOQzMOXcbo+3mcXPtAsgKfY7qFM10F4Iix+Do57NQutHhvLxyQGGs+dkdkRxlpjLm+TTRE1CiOyIBz3nKIiFYMsjuW2PEH3Ul+UVHkR30pmllNqulPo6cCtQBLyklFqulLoDwDCMT4FHgFXA88B3DMOImqf6FvA39AQtG0mO67sbKFdKbQB+CFyRrTcnRqbiYghH7RiRLip6IRspcyD0WUWlhbZAEbZocoxeZ1vrJ+FrMovJsShe+3Zq2yehFDhcOgG1+osoKNZjvoyal+DpvSFQl3GuYRFsTFQV69oqmT495blDH9C3ncNyXEQHvajKfbfGIROv6OXDwHlHCV53CzXVQ9MWu28VAMfPexmAq864nitO+x1H7P0mjYGJMP6UHo+3mpm4qwsgAJNndNHtc9KZ4O5mYb7dUOWEnmeSKRqlK3rRdgl6Qux2EhU9szfZ21/IXVuE6Idex+gZhvHFLjbf3cP+vwJ+1cX2pcDcLrYHgLN7a4fYc3i90BRxYMQ6V/QiBMP2AQU9mw3aAsW4PclZNztraC/H5TMreoFdWFWUlrDucWxze/GHXHzv3lvY5xQd9NTWh/W+62+DeVf3v1GDYRjw7+ScRY3tZZSXw/E3fsTixXD14g3mfj1X9Azr8C8ePmziFb3EtaYcspdgUQbRQCtQkvXTj1FvpD0+aMb7APzgvps54vgKzuzl+HjQ666ih6PrALgnsRX2HPSmznDT3FGMf8t2xs7vcVchxEiT1nVTiJEjG7NuCpFVXq+u6GWML4uFCYZtpPSi7JeOcAmFNl19aw2UZDzf2FGGNdII7VXwwoEARCx6gTCXx4Hnq37ue/MCnG4HoYgdI/7rU/tGxrmGXDTZY9ofLSUas1FaCg2xhby/fmFy/NC6W2H1HzKPNyt67M5Br2CqvnWNzm07ABz650gNUXffEuuajG2hiJ0/v/ADqvhSr8cngl4XF0AAsHezfU/Sywe800+HXa1jGdt2G7SsHqZGCSGGRaLrpswPIUYWCXoi75SV6TF6GUHPiAwq6AViJThteu289kjmh7amjlKs0SZYfgX49Tz4Vqfu2pj6mm63nqFQYXbDC7d1PtXQS1nvrz1cicul21hZCfX1sHmLGfQ2/BU+uizz+HjQs+3GQW/WD+Dwf8PkPOhiY87CqCLNiU1tbdDSkp3Tu1Vm9+E2v54wxdaHuZUTXTd96ZW7V6ovg2NeBmfZoNs44tnMCWhKui7XFRXB240/BqB9u6ynJ8RuJVin/wZYUz4MxPKgt4gQvZCgJ/JOZaWu6CkjveumYc66OdCg5zeSKz63RcZlPN/UUYpTNUHH5sQ2m0t304zPOOh2638dwZSxbZ3X+xsOoWRCaAlWUKoLRpSWwq5dcPElvcyqYlYElX03DnoWK0z8vJ7fP9fMoGc3kpP9jB0LJSXZOb3Hmvkz2B7UP7t96er85S/rQOiPpjcoZB0LY47t+qA9jVJw8nI4tvu1IiYc/DkA/vIHGacnxG4l2Ghe8Er5/+ShQa1QJsSwkKAn8k5lJYQjdqwqrMeimYxYhEhs4BW9kD258kdAjc14vtXvxaHaCDZWJbY5PDrQFRfDxInwj38kK3oJwdqBNWgwUip623ZVMMFcvKS0FLZsMbu+9iAa1Mcre+4XE98jmF03Hao5samj+7Xs+83rzKzoxSyFXHopnH9+78fPnAnhMLi8JWnbLU4Zm5emdEGP1c3jTy0lGHFSYKnhL3+BN3LQq1sIMQTCzfrvuJKPzWJkkcsRIu8UFUEUs4RmRECZoSUWJhIdeNDDkwx6hr0i4+lWvxeH1Q+GP7HNaS7aZ7fD1q1629NPdwp6kQ6I+ME2gFliBiqSDHofb5rC976n78cre70FvUh7HVYgYpOB5cPCHONWYG8mFCJtTbpsKHVnBr2oKuSmm/p3nmAsPdh1GOMH06w9jrIofLExlLpr+PJ39TZZLEiI3UCo2fw73qmHiBGT8Cfymvx0iryjFDhdyXCXEIsMKui5ypJBz12YuRJ4MJo5hby7KLPi5XYnu8UlDx7CJRb8OyHYoO+vvRV2LUnrurlh1wz231/fTwS9SKeg12mZhUi7rkIaDhlYPizMrpslnmbefReCweRT0UEO8/D7DMoLM7tuhoyifp8raJQk7j/07hdoLThhME3bI/mMsYz2StdNIXYr4Wb9d7xzqOs8O7gQeUaCnshLDpdZ8kj9I2pECEcHPkavcHQy6Hk6Bb011bNwezODnsebuc5cxhg9gMAQdt/8z1h4YpIe+L3se/DKUWldNzfXTWWs2RO124peNL2fYMxXS6u/CMdA1qoQ/Wf3YhiK0oImjjpKj4mLa2rq9qg+qdnWhtMeImKk/0yHjf53yw2bSz8s27yIj4se4itfyYPxjSNMyDqWsSUS9ITYrYSazQt2nf4mStATeU6CnshLLk8XFT0jPKgxeu6y5Li8Qm+y79zcy1dy6DXvUFSeGfQKirsOevGum/6Q2ZiUCVyGRNQHvq3Jx+FkRa/ZP4Zis8ddt0Ev3J720AjUUtsyakBrEooBUBbCqpiSgmYAHnss+VT9IOfyeetVnRQtlvQPIFHV/6AXUfoHKRB2ce65ydk4Rd8ZjlFUFNVzwrwXmDF6Pa2tvR8jhMhzoWawF2dW9KLBLncXIl9I0BN5yVOYWdFTxiC7bhYnx6MVeh2srd4LgE+3z6WpowybKzPoFZdmvpjDkQx67288kGDYQXjn+wNrVG8iKZW4+Npcdm+iovfwml9TE9ovMbFkt0Evkl7RU8Faalsl6A2niKWEEk9zxvbBBr333gkAYDECadujlgFU9FQJAP6Qm6lTB9euPZVyV1Je2MALV5zE+hv3YtWqXLdICDEosShE2vQYPem6KUYYCXoiL3lLdFCJRcyKnhFDYQyq62YiPAJFJQ7mXbES94XJhceVIzPolZZldl3bay9oC+jxT82+Ej6sWkRk1/8G1qjedGxL3m9bq2/d43XQs3q4482fMnZsso1Os/fejJmdg156Rc8SlqA33AxbCaUFmf00GxoGd976XeYV5X2uBO9sQqM+qx9b+x/0YtYSACKGm6L+D/ETgL2wAps1OfDyhRdkNhYhRrT45Gdddt2Uip7IbxL0RF7yluhQ1tRgXi2LRQCIRG0DDieelCXjiosV4aiDQDh5Mouzi6BXmnkeux32PWwSAG6Hn/U7Z6J82wfWqN74UoJefByg1Q3hFqIWLx98ALNmJXc59FC4+274ze96rujZwzuoaR4rQW8YWRwFeBy+jO2NjYM7b7jDnAio4lD4zCock08BYP6UNf0+V8ymu25aHQOd2lY4vekz+m76aHWOWiKEyIpQs77tajKWqFT0RH6ToCfyUnGpDiq1u+IVPX07mDF69pTs09VC1V113exuQWufbSYA40qq6QgWoKJZXBQtlX9H8n78P5toB4RbaeooxueDq65K7qIUfO1rUFLaQ0Uv3IbDaGBz3VQJesPI5vLgcWYGvUsvhffeG9g51772LI9/5zj9wGr+Ykw6BwDnzLP6fT67Rwe9YER+MAbKW5ke9I6ffGuOWrIbiQbgf1+Hji25bonYE8X/77UXk6joKXN1MqnoiTwnQU/kpdJyXdGrrzWvlpkDnoNh58DX0UvROcB9+ctgc+ugt65mZmJ7cTfrRYedMwAYX7aDjmABFiPzA3xWRJNr+hE0B3NFdNDb1ejlwAPpciyVw91DRa+jCoCquinSPW8Y2ZyeLit6ra1w8MEDO+dzf38x+SAe9Jxl8MUYTLuw3+e78Gt22oMFjJ8kQW+gOlf0ZlUulbX0Bmvnq7Dp77D0+7luidgTRc2/27aCZEXPav6NlDF6Is9J0BN5qbxSB5VdNWZFz7xqFowMLuj95qkrWFczMy3oPf003HsvOAsLueCOezjm168y74qPOegX/+t21sGoexoA63fOxBf0YMM3NCsjp87oFe8eGunACLWws8HLUUd1fZgrI+ilVPTaqwAd9Ar7P4xLDJCyuSkvasC4X/HDU/6YlXNaLSmL8FlTfjHUwJZFGD8ePPtfwb6nnz3Ilu3BnMmg54tV4nW1DHoc5h4vZH4BwzKFqciBSErQi1f04n9vJeiJPCdBT+SlMrOit2Vzdit6P3v4N8z68bq0SlZJCVgsUFAA9715ATsaJ/DJtnm8v/Ggbs/jLnBw+HVv8Jk/PE0gUoBSRnr1LVtS/xOJL68Q6SAabKW5o5hx47o+zOVxpG9IWY6B9k0AVNVLRW9YWT1MKNNdcf/v5Juyc8rugt4gWOZdBWOOy8q59kiuMYm7AesUij0t7NjRw/6id20b9a10kxO5EK/oWT2ZFT1ZXkHkOQl6Ii8pq65Iba1Kr+gFwq6sdN1MPUd8kpb+VLc8Hnhr7eHUtY5C2cwTRIag+2bqfyL+an0bC0Ggnla/l/Lyrg9zulIqesoCwZQZP5o+pD0ymrrWSqnoDSdbcjYgwxj8QuTR6NAEPTFI1uTC9YZ7MsWeFrYP0VxNe4pY2wYAoq1VuW2I2DMlKnoeEhU9m3TdFCODBD2Rnyy6IrVjW7yip9cIy9YYvdRJSAoK0m8BXn4ZNmzo/vjUGTx1dw70JCnZ1s1/IrbILlp8xd0GPZXS59QfLSXUuhPePBNW/BwaPmCb7wCcTpU2QY0YYtbBB71gEJ56St/3+ToFPYsEvXzjGTUFtyPAig/lqv9gdNTqpGz4a7n5ZkPGPIrhFR/j3lVFT6rMIs9J0BP5yaITSMAfJhQiUdmKKWdaIBuo3ip6e+0F06d3f3xq0LM6h7Ci1+k/kcb25HoPrX4vFRWdD8i0bVcZ1q33wbbH4dProXUNG5sPkGrecEup6NkdAwt6V14Jp58Ob70F7e1gs0aST0pFL3/Muw4Ad7lehmXpuy097S16EQvq9Sdt1ig//2k7K1fmuEFizxLtoqInk7GIEUKCnshPZkXPYQ3pdcbMwFPodQ50nok0qZWseGhLDZBpFbsupD5vc5kHRoaoomexJ64i7mgan3iqp66bqRo7yrCq1P+MDNbWSdAbdikVvdGjFXfe2f9TrF2rb2trYfJksFlSg56z64PE8Jv3czjPAIe+MLNtkwS9wbCEk13Pp4/eSFtbDhsj9jyRlDF6mOXk+IU1GaMn8pwEPZGfzIqe3RbWM9bFg15x9j/MdlXR625Zhc7HADjc5oPoEI3Rs5fA3GvYGVrEXa9dlHiqxd99181Uje1lGdtWVh8gE7EMt5SKnsWiuPji9KdjsZ4Pf/ll6DCvJWzaBOEwFDhTLi5YJOjlHbv+Q2IzJOgNhoMmquomA7D81/tSvOMGqHmxl6OEyJLEZCwuEkHPIrNuipFBgp7IT/GKns2s6JlXzbwl2f8w6zRPmRr0bLaej0kNes6Cflb01t2WnEWuN7EQoaiDtY6fc83/lvHOukMTT7X4y/oU1jKCnnscNY0VUtEbbtbUtel0WfqSS5JbWnuYOX7zZjj+eHjtNf34/ff1bZE7pbRh6eWHVgw/hw56HnsLkUgv+4quRUM4rR1s3JXsSz838hN47cQcNkrsUSI+c3yegpg5LtomY/TEyCBBT+Qns6LnsIbSKnolZYMLemvW6HXzUsW7ghYVwde+Bm+/3ft5UidzcRfp1BcJ9iHoRQOw9Dvw8pHp2wO18ORUaO40+CQWZNsOJ3vvDVu2pHfdbAxO6VM31qaO0vQNzgqam5GK3nBL6boZ/8bdfjv87W96U0sPRZ/Oz733nr4tckkftrxmLwGgtKAJ3xAU/PcIIT0+b3vLtBw3ROyxor6UHhlm14vE8gpS0RP5TYKeyE/m2JbSgiYaGyESNINe+eCC3qxZcOqpXT+nFNx9Nxx6aNfPp4pX/K6+GjxeXdHztfbhk1y8P3+wPn17zUvQUQXPzodN9yW3x0KEIuaagltgr3mjEk+1RKb0/npkBr26Zi/vvw9VVX06XGSLLXXgZzKhxwN3T+OOGhvTH281l1RMq+iJ/FM4BYBZY9dK0Bsg45VjALB4M4Pe0/+V6TfFMIhX9AAMs6Ink7GIEUKCnshPtiIMq5sxJTtpaID21qHrujlQhgHXXAMFxfo/AH9bHyt6QEYpzp5SXlt9Q8r+QYIR/Z5Xr4ZF+yWXTTAcmWPvutLq96Y93tmou5OdfHKfDhfZ0kvQa2/v/tCGhq63e9099PcUuWf30mrMYN8pH0nQGyDVugqA8kmZQe9/b/uHuzliT2MY6RU9o1NFT7puijwnQU/kJ6XANYZxpTsJtuyiZM1XASgszr8p5ItKdZAKtPXhQ3cs0PX21PF93lkp+ycrepC+5IPX23O/zVer/49v/f022gLpfTTrOiZSUQE33dR7c0UWddF1E5JjQ3uq6HUX9Eo8zYNvlxhSLdZ92XfyR4mJdMTAjJmcOfNUobOFxx6DRx/NQYPE7qtjG7xwELSshucWwtZHQZndeOJBz+IAlFT0RN6ToCfylnKPYVLlTiar5P/i3tLsVfTeeAOeeGLw5yku8xAMO4j4GnvfOdpN0AunhMSCycn7sSDBcPI9H388/OzlFznl98/0OjPokrYbueOVb6VX9OZdx61v/Y6pU8Eiv/3Dy5n6QbV/XTfjQW/RlGW8ffWhFDjbmTYlTKFL0kO+i9gnUOmtk4reQJgfqmOGYp+jj4TPrIE5lyeenmL/Lz/61hbOOSdXDRS7pdrXoeF9eGYONH+st7V8Yj5pBj1l1WEvGoRdryUDoBB5RqZpE/nLPZZxZWtZsT6a2FScxaB3+OHZOU9pmaKhvRzD1k3ZJVU86BmdxpakBr2U52KRECGz6+aMGXqM4U6O57kVcP6Cnl/qsssgEIBJtpSK3ryfs3ErTJnSe1NFlrlGJ++rgQW9J664gIlFnzJ/0sdMmJ1S+S0/OIsNFdlkcXjwOHx0tBukBnzRu1jIhwV4YdfvONltB/cssCWnCz532jc56dfFlF7cnLM2it2QSg6RoGQBtHwKY47Xj+Nj9JRFr1265SFY/Xs44HaYeUnmuYTIMbmmL/KXawyVRTV0tCW7Rgx2MpahUFoKDe3lqHA/gl5n4Vb9H4ejLK0rSCyix+j94Q96xlCAMWP0bW+zZhYWwu9+Bx5v+o41NTB2bO9NFVlmTynBplz9jXfd7G6MXiQCzz0HkybBxKJPE9tHlzbrO4fcBye+m+XGimyxOjzYrFH8vnCumzLitDTqirWroOvxrQAlBbJGociysHnV7YT/wckfwjkdcOR/9TajU0XPZ86M5ds+/O0Uog8k6In85RpFkaMRl7Ezsam0LP+K0AUFeq06e2yQXTdtXn2FMCXoGRHddbOoCKzmRcZ40Ovrulw2V7LrZjgMdXXJc4hhlDoBTzQ5gL+3it477+iQf8pB7ye2FbraqSxp1g/MKfxFfrI5dUgJdkjfzTiXC668svf9Gmv118xdVJDcOPncjP3Gl8mHbJFFEfOPcfEcs3LnAIv5H3BqRc+SHD+fdiFPiDwiQU/kL2clADNGrU5s6m1cWi64XLqi51J9qOh1M0NXa2MrW2q8+AIOjJR1eYyoXjA9dYF2r5nb+jrmx+ZOVvR27dK3UtHLsZRJedxuPV6yu6DX0gLTR2/g9jMOSmwrcHbgdTfrB448/KUQCTaX/uUN+SXoAbS2QjAIv/517/s21+uKnseb8gewaDqcviVtv28fdxvRKEJkR3woha0g87lEbwwLWFJ6GNkLM/cVIg9I0BP5y1kBwNwJnyQ2Wa3d7Zw7Fgs0+8twWQbedbOptpVWv5ft1Q4++TgZBo2orugVpPx/Ew99fQ16jsJkRW+nWRyVoJdjKRU9pXT3ze66bvr9MG3UprRtRa42vrOPuSCkVPTymsP8hX34fh8vvpjjxuTQd74D//iHXg+0r1oa9R+5otJOH7jdY1lWcyqn3/w8L648nlMXPkOrrDQisiXcBrYiXbXrLNF106z0xcWka7bITxL0RP4yg96kim05bkjvWv3leKyNmZOsdNZN0LOjg14o4qB2Z8p0zebyCqkVvf3317dnndW3tjkLkhW9mhp9K0EvRxb8Rt92quwWFXVf0fP5YGrl5rRtM8esx24xz+EoyXIjRTY53PqXd+M6HyeemOPG5MjDD8Ntt8HXvgbnn9+3YxoaoK1JV/SKyzzpT1rs3PrJ0zz1wYms3DaPmWPW09Issx6KLIm0pa9tm+LjlSlBL7WiF5U1HUV+kqAn8pcZ9EYCX6QYmyXU6+KpsbAOepGoGQjbq2Drv1GRZlr9XoIRJyXeZNBTMT0ZS2pFb9o0Pdbu3MyhKl0qKEquPRgPejJGL0f2uQLm/kIH/pSLAj0FPb9fB7tUCycvTz4YQb8neyKHWy+s7HHumV03w2H4ql4GlfJyWL48+Vx318XefRcqKuDVl/TXzFuW2YUuPrZ1bfUsPE4//ob8vyAoRohw90Hvmac7TcYSF/H3fqFXiByQoCfyV8oH2AffOZcfvpe/H5SiuM073Uy2YgoH9fPRCLoLyIuHwFtn4WIXda2VhCIOCtwpQc/QFb2CTp9zbP2Yk6awSE8C8mnwm4mgN3p0DweIoWU1g/fqP8Cq3wN65tbGbuby8flgckV6f7cFk1boOyd9CDZPF0eJfGGx6++Px5G/f7+GUmurvlgByWVCbvvqtzh136f5zW+S+51xBsybp++/8Ya+rdmmK3o2Z2bQi49VXlujlxmJNK3NdtPFnircqrtudmIYYLV0Wl4h7pNr4UELRGUBdZFf8m8KQyHiUhaYbvV7mTTPncPG9CymzA/vUT9Q0u1+kWCAxH8Nzx8AAT1ortSxjfq2CiZXbMFC8j8KixEgGHamdd3sr8JCUF/SVxoXL9ZXyh2OXg4SQyd+AWP5T/TthM8xcexY3nm/EKXg3nvTu7f5/XqWzVSTKrYRU3YsJfOGqdFiwMwgvidW9I44AnbsSN9WXljPt467g28ddwezrjX42c/09iefTO6zyRySmviadXEx47zzYOtWGFc0DoBI265sN1/sqbrputnaChbVTUUvLhZMH7snRI5JRU/kL6szsTiuP+xh0aIct6cHfa3oRcMpXTubPkx7zl1aibI6sBjmPtEQVhWmLVCUWGttIFLX23vrLZg/f+DnElng6lROfXovrjn6dLabM8TfeWf60z4feBwp4z+s+kOvxT0GLHKtLu9Z+1jRq3sbqp8bhgYNnzffTIa2cvO63aKp+u9eKOpk3Tr47GfTj7nzTvjrX/X9Aqeu6HU1++GcOXDfffDN7+g/cMGObmYzEqK/4pOxdFJfnxL0Os+6GSeTsog8I0FP5De77p9z8SVujjgix23pgWGJB72eB2RHQzoIKpXZl9/qqSSKI1nRi+gPLu2BQsrKBt62ziHxkEMGfi6RBa7MAZKzy15N3C8uhhtugKuv1t05fT4odJshofwgcOllR2TdphHCrEY98v0vUODsJowYMXhpMbx+ym47zmf8eHDYgtx3iS5Xt4b0jFBPP63HDu83dSlH7L2ESy7R+0+ZAoXxr5e1+y4No8brD+T+1m4GuQrRX5GOxEXmVHV10OLXf3ej1uJuKnoS9ER+kcvBIr9ZzYkMvPk9DsmwxLtu9lzRi0/GYrNkrnZePq6SWKODaSXLoOZF8OqxJ4FIEe5B9FqVoJdn3D0PkHzuOf0P9KQ5fj8UOH0w8Uw4/DF4xuyuafd2fxKRP1JCyn5TlwFHZu7T8H7yftsG8M4c+nYNswkTwLdrK2NKdBdLpy15Uey112Dp9QcAYPtKmAPnbuf5n5xAzFfLzraJjOlqPTNTgddDLKYI+SToiSyJ+rrsLlxfD7996gqaOkq58pgLGb3r+cxjDQl6Ir9IRU/kN2UunNfDFd28YO1bRS8e9CyWzKv246dVEDHMriCvnQhhfTXb6OLKYn90nrjl4IMHdToxWJ27bgKGobrcdfVqXdFzO/zJn7H4z0M3s8KJPJPygTFtttQURnvKZDt1bwxxg3Jj/HgoLWjSD0r3pcBWh0XpiS1eeSW534nzX+DRGx/Hq9ZTUtDCc5+cpReb7I5S+MKFRAIDCHrL/g+eXdj/48TuLeLr8jNHfT2EIk5ueeH77NxlBYs981ip6Ik8I0FP5Ld40LPl70QsAMqWOhlL92KR7it+E2aOwVAp/3G8cYY+dxY+0P/wh/p25szkWBmRI1ZXxqYY1i53Xb1aV/Q8zpQrzI5SfSsVvZHB2nvQ27CqIfmg9s3k/V2vw4uHwWsnQah5SJo3XE6ZfS+n7feUfuCdjUXFuOK03wLw6quwtWEKAFd//lrGRJ8F4DfP38D44y/v9dz+SBHWSAM0fdy/Rq29GZpX9O8YsfvrpqLXkPJr+vnPw2uvdbF2owQ9kWck6In8Fp9sIs8resrWt8lYiHQfBEsnTMNuiyY3tG8AwOocfND74x/hH//Q475EHiiem/bQYrVx8MHwox+l77ZqlVnRs6dcYY6P0etisgCRhyxWLnv8Hhrayhhd3PXMkOH2egCeXHYabL4XHlDgq4al39PdOmtegJZPh7PVWTWudAdnjL6Qq874ld4wWS8C+pl9nwagqgocVv238cDpH2CtfwUW/paf3vdjTjit97VgwkYRJ836Fzy3gJaarezqbQLOUAtsuCv5OBbtfl+R/1rXZW9sayyiw5o18+Jyuzlk9J57YPp0qK/r4ufGyByWIUQuSdAT+U2ZQS/f1wqz9a3rptFN0Jv6w2o8BYoCR0cXpx5c1824Cy+EL30pK6cSg3XC2zD964mHymLj3XfhpJPSd6uuhp07wWXzJT94xJdnkIreiPHEigtYvmUhXncrsS6KAPZoPc0dxSzfsjC5ce3N0PIJscnmL20k82/DyGDw0HfPTd9Uth/rI1/hkJn/481fHI5SMYo9LWzuODy5z94/7vMrREj+jfzccRsYkznfUbqPfgTvX5x8HGqCZZfCwx49+6kYOWpegqdnQdUD2Tlf/P/wLj5ztLeDxwMXXAAvvghul1T0RP6ToCfyW2KMXn533bQ6+jYZi4pmTrG+vvkwwraxKAXuLqZgd3ikcrPbsXthzhXJx+bP+axZmbuu/DiMzRpJfvCIry8pSyuMGErp2fqKPS0Eg5nPW6MNNLSX0+pPzqRqbPonAH97/nS9ITKylg+IF1gKXe0cvvdb6U86Sigeo9e/WzzrLT5/wOO4HQG2BY/Sz485ASxdd2fuSlQl/0ZOrtjSw54m/870x6EG2PZv/SG/8aM+v67IA7VL9K3ZA2bQIub/wV30ImpvT5/cLK0HTpwEPZFnJOiJ/KZGRtdNi71vFT0V87G1fmLatkjESIybc9szr9q7BrOInshfBZOT983QNmFC5m7u+Bp68d+BxMQ/oSFsnMgmpaDFV8y4kmqcz46D6hfSnrfH6qlvq6BsdDLoqeBOdjaP5rb7zVlWR1hFL2L2YPO6W9OfsDjA6mbUpLGJTd89/lYAQpZKOL0KjniiX68VtSaD3j+++TXe/MXi7nvyNXwA1c+kbws2JL++wfp+vbbIMX+NvnWP7Xm/vopfjO2mopce9OIVvZTJgiToiTwjQU/kt/hV3TyvXtgc+sO30cNkK6CDXlX9lLRt0UiMCrM3XldBz+2Vit5uKXXGNvOChlK6S9BDD8H//geTJ8Pk8fEPHmbAiy/SG+uiNCTyUjzolRc1YgnWwIqfpT3vMBqob6ugcnz62ohbGybR3G4uLTDCgl7A/FNY7GnRd+IX7Rwl+gsSn1QIOGqOrsoYtmJ9AaSfk28Z1vS/kYtnvU1bczcX3d74XOY2/w7dfRN0dU+MHP5qfWvJnORqQOIXa7sZo9dVRW8JTxBZrCcQkuUVRL6RoCfyW/zDgZHfg+WtTv2fTDTUc0XPYvho6kh+wMHu5aaXr01U9Jy2zA9zRaX53W1VZEHKAP7jj4cvfAEOOgg2bYKPP+xU0Ysv0huTit5IEe+6GbezPr1a4FL1NLSX4y1PD3q7WkaP2KAX76Ja7DaDnme8vk3MGqvDWaxoduIYw57+/vtKOTIvhnmf82B8+vvMnVPW5Pv6X/8GQLguZeZNqeiNLPGgl62/h526bp59Ntx2m97U3g4FKUs6xoPeb39v5/6HzSfyvaLn2w517+a6FWIYSdAT+a1wqr7N+66bPQS9iB869LgRq+HDF/RQ31ZOoPIM6o9u4akPTkgEvVAsc4KN0tIe1pASI9sxL+nbbj7EWywp4zrjXYkmnwuTzob51w1DA0U2TJmiK3px26rTL964LbrrZnFFZtBr84/MoJdR0XPHg575x27C6XDQ37Ac/XTiGOUYWNAznOO63B7a9krmxnil5shn+PcHZwIQqV2efF6C3sgSr8QOJugFG5Lj61P+3hoGPPYYfOc7esxpdxW9WMzClm1mD418D3pP7w0vHZrrVohhJEFP5Lf9boFD/gkVB+W6JT1yOCwEw47Eguhp3v0yPDkFYhEd9EIeKi+p57Qb/sPxx+tFWEebM4j/a9sTLFl9ROLQgq+1U1qaeUqxmxhzHMy9Wn/IMLqYwQ2SHzziH1DthbD4EfB0MaBP5KX774dZc5MXcQyr/hD5yCMQ8gdwWDpo8lVQXJF+oScYcRIzrHrtxRE2GUuioufpVNFzjdK3yqJnni2clhi3HHTNG9iLFUzqcrNq6WKNPP8OmHEJP7rxFNr8uhJodGw12zhJf+gXI4g5GHMwQe/fFfDq8fp+JNmDwpcyN9rWrZlBz2bVQS8as9LhGyFBL37BSJYU2WNI0BP5zV4IU7+c61b0yukEf8hNLNxFRW+HecU6UItN6YoewLvvwvLl+qn4dOA1LZO4/omrEof6Q24Jeru7eFey7iby6WHMiBgZysrg4COS1Sqn0+ClZ1soW34c//jjUgDaguUZFT2L0uE/QsGIregtmG1OxuI2L0x0sSzIKTf/j5KLmrB6ygf0WrZiHfQ2107hzJsfS2x3RHeBP2VRvWhAV+w8E7jxRogZVjoCHqxhc5+CSfr5aCD/P7ALzchC0AOoM2eGjSbHRDc3J59uasoMelF0T55Q1EFbPOiNlDF64eZct0AMEwl6QmSBwxEPel1U9Gzm/wz+amzoih4kF18FGGtOGLZrF2xrSM7KaRgWysqGqtUiL8S7ZHb3QT7eNckhiX8kGz8l+f1zW1uINq7iuLmvMCb2HACtofRZN4HEeN4duwpY8uxmandlaVHoYRAPeqefYlb04suC2DJnEfaOGUeLrwQ1wF7qzlId9EoKmnn8g8/z80ev49J/3qSfDNamNEoHOp+RXGivLVCE0zD38c7WFb+H3fD6KQNrjMiNgQa9aKdJrVLG6KUGvfZ26OhID3p/X3U3v37yp7y1djFtHfGK3ghZMF0q13sMCXpCZIHDAYGwq+sF0eMVG9927JZAIuil+v/27jtMsqrO//j7VK6uzpOZGWYGGDKSowIiCpgjijmgKOua9qcuq+ua1jWuuoYVMawRTGvAVYxkSZLzwMAAk1PnyuH8/ji3Yld1rO6u7vm8nqefunXr3lunp7p77ud+zz2nGPQ+9zk30l4lVfQWOP84U3OkdrvH4kTpMi8tPvTp3J7+OIWCIewbIIQL8D2RrQCMZBezaGm5anvd0OdL1f01i5/izPVXseH3l81+w6eo2HUz4h8EDKUUVyfonX329N6rbbGrFt668WTAED7+I+yIe5NSVl5A8f4+b9nh/ib/53+6oFdyyLvLXah3/AWKXTqldRUHaptq0MtWTP9hLdz8erccGB30ait6Gd8KPvyz/8BaH8Mj86DrZuXvgoLePkNBT6QJwmFIZqP1p1contiMPAa4yl+tYtfN/feHd7yzOggq6C1wfm9Y8HyDqTmKg0Mo6M1vgSgnvPnf+N2DryPiGyRsBgDojWwBIJFbRChs+PSVl3D2f/yF/7nl/SRrLgp152+f7VZPWbGiF/Htdd01iyeZdYLexz4Gv/wlnHvu1N6rozvCsR+6k0/f8DMOPRTe9CaItNcZxMa7mLJlu/ude+ELIZ2vCHod6+G4L5Wf/6ZirktpTcVpZpoR9HIjFO/5G8l084xnlF8aGoJksnrUzWjFf+V33zsPum4mtpaXM31z1w6ZVa09OZnIPFHsukm9il6xYuMFveI9egCHHQYPPVQejAXK1T2ARYtciJQFbLyKXnq3G3W2zgS+Mv+kC11Eg4OEGABgdcd9ACQLrmvjC//103zoKDitzgCUe5KrR69sUek0rOjexvLE92DFWXDIe2DwATj4H0Zt6/fDS+tMbzdRfj/8+rpjWbYMIt51k1hnsUt0xYga3u/Y3gH3O7diBTwZ9IKn8XHv/SHuvOu9vOn4Ibjvo259fDPE5s+/+z6n2PUy34SgV+wmf9THeejR6gsS97lfU1ZX/ChEKqbuy+bnQUUvM1BeVkVvn6GgJ9IEpa6b9U7W894V5ZHHAUjlyifsX/oS3HVXdZhbsQK+d/0b6YwO8ZGPzGSrpSWMV9FL7YbIktlrj8yoLF3EgoOlrptd4V1c++CZbOt3Zf3iieSjj7rHDRuAPa+DJ35Uf7CnFpVKwblP+yN+m4SjP+1G2zzj1zP2fmtqim+xrsYVvT39UcJhV50Jtnkjb/qiHH2M61762geWECzus+dmBb1W1syKXjHodR3B9k3Vm/3tb+7x6KPL64oVvXAYXvgidzqdzWTLPzutJjdcXs4o6O0rFPREmqA46qYp1DlZL/5H4nWbCEZc0IvF4JxzRndXWr0anvnN7wEuBMoCN25Fb4+6bS4gBV8Mv69AOF8eDfLKO1/Eli0uZHR2ur8Nu71bMw86CDj4h+y9/yr8+aE6R2xNIyPwrCOuJhdYQqB7itMmTENnrwt6NhenNMaLdzFld1+UJUvcbYO+sAt62UL5AtxTe1ZxYPHJ314FkWWw7MzZabhMXCHf3Hv0Kga+eqrm9sy//c1Vjo84oryuGPQCATj1NBfv4sNZuqfWkpmXrQh6I4/Dnf8PnvZJ9RZZ4HSPnkgTFCt6plDnZL0U9Nz/HNbfQUeHm0S53ihzlf+RrFvX/LZKixn3Hr3dEFZFb8HwTqrCdltpVSLTVhqF1xhYubK8uc9X3KYTv60Jeqk91V0TW0AmA5/9LNx4I5yw7nbMktPq/6GbYd2LXdBLx72K3uDDcN0LANi5J8IS71fKF3JBb9uu8snuN658ATz7ejevHsC1581Oo2VyKsPdlIPeYHm5IugVu2o++9nuxzeTgVWrqrtrFoOeMdDd64JeYqSFu25WVvQe+Ro8/EV45Ktz1x6ZFQp6Ik1QvEdvVEWvkC93HfICX4Yeurtd0KtncUXxpqvOfTqywNRW9BLb4OY3lYNfpg9CmmNjofAFXaCI2vLACPF0rGqbV71q9H7JXAcBO1y98pdL4E+nNr2NU3XTTa53wyWXwHe/k+Wg5Rvx9xw+J23pWer+TUcGvL+/j3699Nof/xot/Z0NRF3QS2airFsH3d3wn/9p+NZvTqcveIbbyBearWbLZBQqpkZoRkXPm37jwcd6uewyF+r+/Gdo864BdHRU71oZ9HoWe0Ev3spBz11N2rjjwPK67HCDjWWhUNATaYJi0PPZmopebvQf0ayvl498BN797llqnLS22orene+DTd+HLVe655kBzaG3gPhC7qyxzVRU9NLVXac+8YnR+6XynYR9dbpuDtzb1PZNx003lZcPWbGBoD8HXYfNSVu6e8LkC75yRa/YxQ/3t/rJJ91yKObO3lPZCHfdBffc40ZBvugi2P/l34Dlz3YDfhTytW8hcy3fhKCX3l1e9qbTeGyz+3v78Y+71cUpFWLV12NK99MOD8MiL+ilEi0c9LxQ99C2it/JvttbewAZmTYFPZEmCIfdicKooFfsChIo/w+R8/Xwtre5+/Ma2bkTtmyZgYZK6ykGvVwCbn8PDN7vnhufm9cpOwghlXYXCn/IlQHa/eWgd9yJbVx/ffV2mzfD7RWzKaQLnYT9rX31vbsb2iPD2B8b7v+sd19e1xFj7jNTenoN8XSMbHJ00EtlI6XBWyKdrloeDSXp6nJT3Jx0knstnm5nsPsCVzlKPDmbzZeJmG5Fb9tV8OBnys8H7gHjZ++QC//nn+9WFwNee83MICeeWF5evMRPoWBc0Hv00upKYavIDpMv+Hhkx8Hlddv/CHdfMndtkhmnoCfSBOWKXk3XzWLQaz+otMoGxq/OLF1afZ+OLGDFrpsD98AjX4HBB70XjOv2a/MQ7J6r1kmTBcKueuc3udK6c54X4/TTq7dbtQqOP778PEsn0UALnjxWMAZW9pS7pCbbjoeeY+ekLT09EE/FyKW9exgrgt7K/aNcfrlbji5x3dgq2105hP6r3uz9IU7umNH2yhRM5x69zb+E618MnYfDaT9267b9HiJLGRoqD4wE5YBXG/QWLSov9/a6KRa68nfC3y+Gp34xufbMhtwwI6l2ntqzf/X6HX+em/bIrNComyJNUByMxU+Dil7Hge5EHgi36X4PqVCs6FXOcQSuolccKEAVvQUjGB09wl04Nv6od3nTQSxUEfRsoZnNaoqREeiODZSeR0/4yJwMxAJe0Mu0UciMrug9/4WR0j164cXrAeiIjpReX768fJy+Ee/+2OLfcmkdU+26+cTlcPMbYNFJ8MzfQ6gbYmvdSJRdhzHo3c5ZDHrFe/Nqgx64qnskAsEgjOSD9Pgfdi+kdo7eeK5lXdDb0reqer21c9MemRUKeiJNUJxeIWDS7o9m8eSmVNE7oLRtbT9/2ccVK3q1Qe+Gl8FpXtlBFb0FIxhtg5qMFplA0Mv5u+mK9ruAZ3zYfIa5iVCjDQ3B9u3w3vfCc4/uK7/QccictamnB3alY4SzXtCrmE9v/zUVnZlio4c2DlScGfXFi0Gvb9R2Msem0nVz47fhtotg2TPhjCsh6KW3Jae5L9zPczTqwhuUK3f1/u+urLrnbZDFUTdfLqndozeea7kRhlMdo4NeYgtkR8r/FrKgqOumSBMUu24C1cPkF08Olj+ntEpBT6qYgKve1ZvA9qbXuMegKnoLRbhtdKiLdoz/R2HEHkAkmKYQ3w5AJjXFwSea7Ikn4Hkn3shpJ+zln1/4GX7/weeXX6y4wDXbwmFIZNoxeVep699ZrshVjXjsD2H9MXJr3lZadZDX0/6II6A/7nW1TyvotZxSRc9Afuzfhw0bYOSOr8Btb4MV58GZv2sYbAYHq0e8Lk7FUa+iV9UcWzFVeroFg152mOFkB5v3VvRNPvBC13PkoS/MXbtkRqmiJ9IExa6bABRSQLFK451cLHl6aVsFPalijKvqpXY13ibUPWvNkZkVbY/CSPW6ts7xK3oJv0sf6b0bibavJDmSJjwTDZykwU23c+NHTx/9wvMfAP/cdlOPZ3tYjutClxzqp8f7BysOxFJkXjVSdTL08pfD1VfD6adDe6zbrVTXzdZTrOIF2set6P3PBz7DZy74F1j1Unj6FeBv/NszOFjutgnu/jsY///uQosHvUJmiKFkJzsHl1HAj2/ZM+Hkb8OOv8LIY3PdPJkhquiJNEFVRS9XcZ9epp+CCfH5L7URf9YjHPi+jQp6Mpo/MnbQU0VvwYi0jw51sc7x/yikQy7oZfs2ApBMpMfafNbkk4P1X5jDbptF8dwion5XKW8PloNabdCrZQycdZbrwnnAgX5GMt3qutmKil03gx2Ng561FO7+Nz5zwb/w47+9Bp7xszFDHrium5UVvTpF+LriuYr5Tluw62YhtZe9I4soWD8pswrCXnvDS1oymEpzKOiJNIHfD+mcF/Se+FH5hUw/uwd7+OAHDdtH1vP4rgMn/J+G7EN8kbpzLpZoMJYFI9pRZzCWtsi4+9noajK5IIVBF/TS8eYEvWQS7r576vsnEg1e8PmnftAmSRYW0R50QS9EOahN5mLbMcdA30iPgl4ryo8T9KyFu96P78FP8q1r3sobvvEDzjxr/I5stRW94sToyWT97Yv6MhVdldNjXLibIya9hz3DbhSi7z38Pe4p/JsbhyWyxF1ofPz71QPcyIKgoCfSJJmCdwJ39z+XVw4/yu5B94f1fm96NFX0ZJTigCz1HPFhiK6YvbbIjIp1jP6sjW/8/4rbYn4e33UAJt7cit6b3wzHHgv9U+yZmE227tx+iVwvsdAw5FOEjas8FgqTG8LmmGNg10Avmbi6bracYkUv0FE9MAu4QYv+/g/w8BfZu/hdvP0736Rg/Vx/PaRSow9VaWioftAbbz/r927iC7S7il4rjWZZyOPL9bF3xI0s885PPJNjzjySG27AVfT674Jb3gQPfX5OmynNN+7/LsaY7xpjdhlj7q9Yd74x5gFjTMEYc0LF+rXGmKQx5m7v69KK1443xtxnjNlojPmKMW5YQmNM2BjzU2/9rcaYtU3+HkVmxXWPPNctdB/tHgcfgp3XcLk3oMa997rVCnoySmCMoLf+H2avHTLj2jundmt8Wxts3HkQgaRX0UtUVDCmcUJ59dXuMT3F3JhNjoxeeeb/Tbk9zRTPecMl/v1ijLF88IrPkn7pOGfrNQ49FDbtXodv782QrjNgksydUkWvq3oQtEIebnkLbLwUDv9n7g38F9aWT3dvvnnsw46MlKdUAFjhXWfrGWcKXL83TGchstIFz1yd3425kh3AUChV9IoeeABX0Stt19pzdcrkTaSi9z3gvJp19wMvA66vs/1j1tpjvK93VKz/BnARsN77Kh7zQqDfWnsQ8CXgsxNvvkjrKPjauGvPSymNnT60AYCr7nEB8Lrr3OpVq+rsLPu2QEfj18KLG78m807lhZ4P/PlvcOKljTeu2W/jjoMIZzeCtWRS5WRmp9HdKpt1j5kpDuKZT9c5mV35/NHr5kCy4AW9x78HQCK/lGhscgPE9PbC5//vAwQK/W5CbWkdXnfNBx7txhbvjS9k4abXwqbvw1GfgKM/za7d1VXcp54a+7AjI9W/py9/OXz72/DhD4+93935T/DbO1/AyIqL3YpWuu/Nu0hRG/T+4R/gD9dUBL2AplhYaMYNetba64G+mnUPWWs3TPRNjDErgE5r7c3WWgv8AHiJ9/KLge97y78Azi5W+0Tmk95eSKTC5auMXh/93cPuj2jxyvmBB85F66SljTWq5hyPXCjN5ffD2Z+5kf3euZVtmdNg/dsntF8sBo/tOpCAHcFu/zM9I78qvZaKj3Pz0BhsIcuaxU+M2y2t4f6Z1u26mSoGPU+GcUoydfT0wMPbD/UO2IKTYO/LvO6at9zZRSGbdP/33vAKeOqncOwX4KiPgDHsqrldLh6vc6ya1yuDns8HF17opuwYS6Brf170n79lyK53K3aPUzqcTek9AOwdWcS551ZXNS//1fLyE40uu+DMxD1664wxdxljrjPGFMdcXglsqdhmi7eu+NpmAGttDhgEqv86e4wxFxljbjfG3L57dwtdKRHBzbUznAiV7xXwRlHcPVS+WtbeXp6TR6Sk0aiaZ/1pdtshs+LB3U9n+8B+VfcBjScWo3R/jbn2XNZny51fUiONRkQZ3xcvuJgn/msdmfgUA1u2hbqn1UjXnEpkzeSDXnc3DCc7yNnw2CPjyuzzLqoOJLrx+wpwz4dg65VwwtfhsP9X2qz2/tOxgl4+7+7Fm8otFou9Ylnp//ybX+cmI28FFUHvssvglFPgE59wF6h/fcdL2BV8obeduicvNM0OetuB/a21xwL/BFxujOkE6lXoijcVjPVa9UprL7PWnmCtPWGJzpalxSxdCkMj4fLoX6ndJPOd4Atz1FFu1QEHuKG7Rao0CnornjO77ZBZUZyXa7JBL56uf/aZHJl6Re+CUy4HIJscp8zRQHFC8pJzbp1yW5otbauDXt4/tYoeGOL5pQp6rca7qDqY8P5+PvxFWPtaOLj6vuba0TLHCnrFUWSnEvT23989Prmz4vx05PHJH2gmePfeDcS7S393PvIR2LQJhpOdLHvlleQ6j4OMgt5C09SgZ61NW2v3est3AI8BB+MqeJV3Jq0CtnnLW4DVAMaYANBFTVdRkflg6VIYHK7uurlrcCmnnQb77edWjTd/k+yjNH3CPqV4Etk1iY89FoORVP37Z6bTdbMt7PbNpqZ2DH+hXAl8YvdaWHzSlNvSbBlT0zloCvNRRqNuntThjIJey8nXBD2Ata8btVntFCDxuBt86MtfHh36is+nE/Te+8+VQW/T5A80E/LuHyGRaaO94s9IZyecfLJbTuQXlyp/snA0NegZY5YYY/ze8gG4QVcet9ZuB4aNMad499+9AfiNt9uVwBu95VcAV3v38YnMK0uXwlA8hPWuMiYHdrN1zxJe+MLyFfyVK8c4gOy7NCH6PsUbnK9pFb3E3h3TblM2NbXun35brugF/Nlpt6OZTLB6zsJccNnkj2Fc983+5NKWnBttn1bIYK1hOFUezGpL5oxRmxUreuef74J7PA6//jW8733wzndWbzudoFfc58mtMbbHvfs6mxn0Mv2w7Q9T2zfnvjHrayNQM/DvZ71e4In8IgW9BWgi0ytcAdwMHGKM2WKMudAY81JjzBbgVOB3xpg/epufAdxrjLkHN7DKO6y1xercxcC3gY24St9V3vrvAIuMMRtx3T0vadL3JjKrli6FdLZc0cvFd7N7eAknnEDpClqxsidSRSOd7VOKJ1qTCXqRSOOg99XPPM7/TXNGg3x6akEvQEXQ8+Wm14gmC4XK/eSP/+RTxDrGGU2jgZ4e2DOyTIOxtJpCmjxhkpny9DT3PdQ2arNk0t028bOfuf+nf/QjuOAC91pxkLSi6QS9Sq+5/CFoWwXxJga9x74D1z2/FNomJed+v32h0d9YcdqIvuwhEH9icgOyJLZBSuGwlU1k1M1XW2tXWGuD1tpV1trvWGt/5S2HrbXLrLXnetv+r7X2CGvt0dba46y1v604zu3W2iOttQdaa/+xWLWz1qastedbaw+y1p5krW2RDs0ik7NkCaRzYYzNgS3gzw8znOwgEikPXb5s8heUZV9g83PdAplFUwl6xsDpz6x/9rluySauvHIKDakYSCWfSbJlwxPc+svJHSjkK3fdDLZYRa9ylMTNOzrHnQetkZ4eeGLvgW5gjZ3XNadxMn35NDkbJpWNlFbdd9/ozRKJ8qTnsRgMV4w7NFJzi+l0g96XvuQe77kHNxF5uol3IqV2uYngxwt6w4+5yeJ/fzQUp53IJ8gX/ERjwVGbd3e7x09/9yx3/Ie+MPFK5K9XQuWondJyZmLUTZF9UmcnZHLeUPiFDD6bJJmNEg5TGrpck6VLXYUpTmIm81Kx6+Z4w7XXuuRfR1d+9w73sm7pJp57+M/gcgOZgYkf8IF/Ly0WMgki153IyakXYwsTv3si7BthIO26KoQCrRX0QhUzk+wZ7Cid0E7W0qVw48OnuSd/fSb03TndpkkzFNLkCtUVvf/7P6i9+SeZLAe9SKT6tf5+yFUUoqcb9N77Xvj4x91xCybU3L/txUpbbpzq+3UvhEe/AQP3QsKbNDAXJ5WL0dExejS44gWQn13t3az3wH/AlQdMvF26UNnSFPREmqSjw+u6CZBP4yNJIt1GOFzusqmKntS16MTR67qOnP12yKwoVvRyk+zpGOkYffZ57+ancdzaOzmk4G60Gd726ISPlxveXlouZBMsbnddsBIjE5tUL5eDaHCEuHV/4GJtrRX0KoO0tb4pV/TWroXf3VIeZCb74Dem1zBpjkKGXKG6onfDDXDvvdWbVQa9wUH3+M1vwle+4pYrp18oBr32afSmL96TnyvMUNDLjzNwUmUX4+KFn1yCZLat7gBQxe81kwuzd7h32s2U1qKgJ9IkHR2u6yYAhQx+mySZiRIKwac+5e4LOPvsuW2jtKhlZ8HLdkH7Qe75c26C59fpgyQLwutf7x6PnGSWj3VER61bfdrLWb98I6u7HgFgy1MTHz1z5/ZyMPv+d8tVgi2bhsbc7447XNVkYAA6osOkzQoAfLTaPXrwv7e9rPR8OkFv5952TvzIndz71FGk+59oSvtkmvJpsoVQVUUP3JQBlZJJaPNu3evzelKuXl2e925vxYwCzbhHrxj0svnwDAW9MSp61oKt+D285c3w12dhc3GGE20ccsjoXSqnfKoMzey6cez22EJ5efOvNdl6i1LQE2mSjo6Krpv5BH6TKXXdjEbhta/VHHoyhsgSyHtnGaEpnpHKvPCqV7lq2Lp1k9sv2lb+L/tL132J9IsGOeiMFwDQEXU3G2WGG98TlEpVDzW/a2eWwYS7UTAaKgfEF5w7xCOP1D/Gb34DJ5wAP/yhq4S0h0fIBb0uC7a1gl44DK/4r19gXutOSKfadXPtWvd4++PH8uSeNbQNXwe7bmhKG2UaCmkyuTAd3dVBb+vW6s0q79ErVu/23x8WebNv7KkYS6SZQS+Tm6mum2NczElud3PmHfIe93zoIdh5Del4guFkbHIXl/5y+uh+sJVyFTc43vBSuPGCSRxcZouCnkiTtLdXdN3MuP4hxa6bIhNSvPdCQW/B8/snv4+v4n/s5avaCbd3Qmz/qopGarDxCHhHHll9AlvIZRnJuL5c33rrRaX1ndGhUaMRFj3wgHu87z7o77O0R0YwkUXuxPI5f5v8NzWD3D16xvuaXkWvaO/IInxk4S9nuIpHYmvD/WSG5dNk8mFMwFWhLIZAYHTQq+y6WbR6dTno/f735fVbt7oLslP9WYFy0Etn6wS9kU1jh6exTKSiV5ygfVH1fJYjgwkSmbZxg54xNW0bemiM9gxWPx/eUF7OZ2DwwbHfTGaFgp5Ik1R13fT+ICczUQU9mbieo92jgp6Mwx/xbqwxPp7qX19anxluHPQee6z6uY8MI+nRN+10tg1x8cVw++2jj1F5jjrYnyLgzxNsa4fjvwxLTpvMtzDjav/2TvXk/Ygjyst7RyomYf/L6XDbRaN3kNlRSJPOhjEBl+KML8SKFdVB75e/hI0by0HvDG+avc7OctfNT38a8t54IjfcAEcf3ZyKXipTE/QGH3KDnDz0+akdeLx79Hbd6H4mAXqOoXiBA2Bx9k8k0m0N5/K96abRU00AsPumxu3JVge9ocoe33d/EH53BMSfary/zAoFPZEmCYWggNd1MzsAULpHT2RCzvi1q4r49UMj9aWz7mcjGC2fiW4ZOri0bJMTn9PKR5ZktmPU+s6oO2P70IdG71MczCKdhpF+13UrMp2RK2ZQ7d/ejtHf6oSPc7I3IOHe4UXVLxZaq7vqPqWQIZ0L4w9695X5Quy3X3XQe/nL3WMx6P3+97Bli1tetaq8XTzuulPfcgucfvr0mlUMesnaoFcMRk/8ePIHLeTL+zcadXNbxWSasXUQ6q56eXHHnlGVzaJTT4WzzgKL62qwa3CJeyG5rXGbaoLe8FDFVaBiQExsbry/zAoFPZEm8gdrum5m2hT0ZOJCPS1XFZHW8o8//DbfuuatjESfXlq3I14eYcFk6we94WG47K1v44+XnFNa5yNL3o6eV6srOshb3wr33z/6ONu887677oKhPhf0olNNUDOs8m/vJz8Jy6cx3ddVV8GDD0LB1EzIbabQB1eaI58mnQ3hD3tBb8kzWLYMdu8evWnlFEfFqpbf70bfBPf78eij7n6+E06YXrO6ulw3676BEOTTFa94Fbb4E5M/aGWoalTRi+5XXg5EIVQ9gua6JZsaBr2ii350Jd+65q2seOd2CC9y9/w1UjOVizEVg7MEvYs/6T3Vg7bIrFPQE2mictBzXSxyhagGYBGRpvnjI6/nom9/q+okbne6XNEL2MF6u/Hkk/C2s77NOUf9udRNzWeyFBgd9N585v+wqCtO1OyoWj8wAD/2ihE33gg/+h8383RbZ2tW9Cq7bv7rv05vMKyeHjjsMOjtrpl6wuu9IXOgkCaVCWOCHXDOLfCMn9HVVa46V9rWoDBVvEbxzW/Cnd70iEcdNb1m+f3whjfAffeHyOcqKnoFL/Rlxx7Vtq6KES1to8FYsuWZ4H/7W0YFvY7oyLhB794tx3LRt79FwfohsgKevLzxfajZ+n9rAAh4fxOufwn89ayp35co06agJ9JEwXB1180c4/xVFRGZhO99zz0ee2x53YZt5aBnbP257CpPdEe8wfJ8ZCnUqeiddfi1fOa4dv7ygVMpVFyM/8UvqreLD7kDBaOtGfRmojfFpsGacs9YJ7syswppkpmwmzph8ckQbG8Y9GoHaCkq9jr+5Cfhda9zlbjDDpt+0173OkjnQhQqg15ldW+yXX4rgt6WJxt03fR+FpddvIMXvQhsuLqb8Rd+9/9GTRhfq+p3JhBzofSGl9XdtpCunk6haiAXX8VVll3Xuy+ZEwp6Ik0UKF5C9ro05KyCnog0z7OeBYVC9Rx819xRDno+W38496HB8klYMej5ixW9067gP67811H7rFv6RNV0DP39cNjKB8n+8iAOWPoY7RHvQMHW7LpZ/HPczJ6lD/U/my/d8PXyisEH4amfN+8NZOLyaZLpcGmOPHBTaAwNuQJS5c/uv/1b/UPU/mwccADjhqGJOPjgOtMrFMpBLz/QYP6SRiqC3iMPNaroDbJzcCm7hpYBkDPlit625NH86/9+oWrk3noqL+bY4qiZ6fpTtiT2VnfrrAp6lVMvAPT9few3lhmjoCfSRG0d3pmFd2UtR9sYW4uITF5tF8TLvtfLzf3/RK4QwGfqB730cHlW6HLQy5AnBGsvILjmBXX3Gxksny0PD8MXXvN+AsnHePWZfygN2lLqptViivdlTXZi+rG0t8NjOw+sXnnjK5v3BjJxhQyJVHXQ6+pyF0JGRsr36n3nO+VBWWrVjiP0H//RnKatXAnWhPBRGfTKy8NP3jm5A1YEvaG+OhW97X+mMLKZwUR5FN2RnBf0/FEue/hX43bbBNdT4Ac/cMuPFd7oFrrqlzjT/dvZUzE4kaEi6BUr3af/0lX3tvxaU5HMEQU9kSaKFWfkHXHjmBeMKnoiMrPOOANOfed/8sCes/BTP+jZeLnv5rB3K4/fl8V69+i9/9MnUTjxMnjuXVX7ZXY/CHtugyd+QnwkyzFr7wGgtzPB4g5v4JfwkiZ/R81x6KHu8eMfb94xOzpg10Bn8w4oU1dIk0iHRgU9cPeTFrsrL13a+BCVFb1kEs4/vzlN8/mgvTOM3+TdiJlQ1XUzuWtDgz0bqLgXNJ+pqOhlh+Cvz4ZrzsG346qqoDeU9ELYsV9g68C6CQU9gMMPd48v+eSXofuohoO/5OM7eHLPmtLzZZ3b4c/PcD2asoOw+uWw+qUQWQK7/wZ/PHFiDZCmUtATaaJw7/48tWd1qT+6gp6IzBZrQvhMzT16G77mTr5SO0urRobdjXcBk8UaF/SMz+Bb/zboWF+1+/4Pngh/OhluejWHRH/O4nZXJtl/yRaWdu5yG4UXz9B3ND2rVrkufM95TvOO2dEBu/pbs6vqvsbm08SToyt64O7T+7vXW/Dooxsfo7Ki14wum5UiMe+Gt+J9sxVdN5PDI7D377DtDxM7mFfRG0x0YrMVFb3Hvwc7/1p6OpjsKs37uHfEq+gFYnUnjW/k+OPh1a+GHTv9EF3pRhHPZ9y8eFt+W9rOl9nOjoGaoWx3/w3673H7BIuh0+uCMNYInjJjFPREmmjFCsO1Dz2z9FxBT0RmjS+Iv7br5h3vgt1/o5AeKK1KjLgTTr+vHPRKAo1nil4aupdQwJ207tftgt5ItmefmvexvR127dapU0vIp0nnRt+jBy7o3XADrFkDq1c3PsRMzgzS1u79XhS7bFZ03cwk4vDHk+Da507sYJl+svkgW/pWEQtUzB9RM73HYKKLSy91y1/8+tSCHrgBafbuhby/C4Yfhd+scfej/v3tpW2idjvbB1aM3vmBf4fkVu65L+AG28zFJ/7G0nT6ayXSRCtWwNb+laXnGbrG2FpEpIl8IYK++l03H73j4dJyYtjdvBaoF/TGsCRwb2n5tNW/5FWn/JSUHaNf3ALU0QEbth3CN/96EWd+8loAbEAVvjlRyJDONq7oPfQQHHPM2IeINb6uMW2xDhf08lnvd9Lrutkf7yY5PMnwk+lnKNXD47sOYGXXptIUKfiqL7IMJLo5+mg3HcjeEdd1813vm3zQ28+bki+R7XLdRlPeVCvF0TStJerbza6hpfzpvueQyQU58J8ed6/t+AsAX7/ieG69FQW9OaagJ9JEK1ZQ1Ucevyp6IjJLfCECvkzVlFVDGdet8sjV5dnPU3F3z03Al4VJBL21Xe7+PA7/ZwCWdO7BF923gl57OxSsn3d895tc//CZfP7/3k8hP8mh8vclhZzX5e83TT+uoTCqold5j15/PyxaVHfvEv8Mznff3ulCWN8eF/SsF/T6RnrZsbliVMqqSdUbyPQzkOhh0+51rFu6ib173C/5tm3Vk5FvGVxPR4cLuaHlJ/K7u57HT/9yPFddNbWgN5SquVjt94JePonP5BlIdHPxz/7I+o9k2LRrHamcu+hx4+ZX8a1rLuLhh4FIxd+IQv3pX2SCrj7HdcefBAU9kSZautRdUSsKhzVbuojMDuMPEvRnyVQU9fpT7oztyFXloDc84FX0/Fnw1Ql6L93OlpN3j1q9sncbmXwYjv4Pnux3UzpEe/etoFfZ1e8FL4B4OobfJsEWGu+0L8sMuC5/N7+hucf17nfL5EINu24ODJSfj+fNb25m45yOLhf0du9wbc2m3GM820vIVxH0fhoZf0LxTD/98R52jqyjMzpM3w435cGe7cNVm+3OuNnely2Do09azAu+8Dt2D7nf0Uz9Yn9dxaB33d9qgl4xqHmTvlt/B489ZnjySTd3YCLl/p483u8m+rzzTuBZf4VebyAWzac3PTv+7LrjT4KCnkgTLV5cXdGbiQl7RUTq8flDhAKZqvnDhtPuPp3D9nuotG6o3wW9kD+DNXX+SEWXE+utP8DKrsSBYHwsXePOBKO9Y9wAtQAV5yE76SQ351jOeimjwciE4vGCQdN4VbBGXTf37HHz6E0k6FkL3/1uc5sH5aBXrOilku4xbbtZvWhz9cb51NgHy/SzZ6iHdHAdACM7nwDAX6j+d02Ejigt13ZL3T6JsVAOPhj23x8GtrspEZIhbyjOxFb3D+Z9nhlbHoH2Fa+Aq+45j3zBx6d/+CIAHnsM6FwPp/+v6+H0QJPmr9gXTbEaqqAn0kRtbV6fdk9xwl4RkZlm6gQ9H+7kwOcrVwyGB91JZbBRRY/GJ8h70wcBEMU7a1z5wuk1ep4pzkF4yinu73tnr3c2naszt5mAnaFurV5Fr7brZiQCwSA8+aR73tMzM28/EaGoC3qZlAt4mWSadDZE3tfBQcs2Vm9cO8F4DZvuZ+9wD209rjrX1vdruO+T5NPlit6Xr3oPkUXl6Q4W11yrKU43MRHRKNx6K9hVrwDgGv7AwIH/5f7d03sg5943Z8pBb/16eP03fkj4jWke3ubm3ttZHOw3thpWvRTiT0y8EVItMzil3RT0RJrIGCBUDnqV/wGJiMwkX9AFvWRFcSlgRlcKhgdS5LN5fD6LaRD0aidlLxoseJOFn/A1d+K29IzpNnteWbbMPRaHsO/o8f7Ia8CJ+mYs6HnVsZqKnjGuqvfEE+75RLtuzoRQxAW9tFfJK+TcKKHWH6u68AKMH/S8rptdS116O7zw73Dfv2Ez5Yre+370ZVavLv/ivulN8OxnV7zFJD+K5cvhle9+Fua1luefv5q3/OMq90JiS7nrZsVARGvXgrU+8oVAad3OnXD55dDXB7StLFcEZfKyCnoiLSEQKQe9ubyaKCL7Fl/A3aNXWdEL+Ud3KXzm0s/ztY/f5J74Jz4YC8BBR3nDqS9/NpzxS/AFxt5hgXnNa+Cqq+Btb3PPO3tdyihkVdGrqzBDQS9fv6IHLtwVK3pzGfTCUdelp1jRK2TdKKHW3z5647GCni1gsgP0x3voWVE9ukyk4Mp0d+xwlfX99y+/5vfDe95Tfj6VyeAXLy7PL7ilr17QK1f0olEXDitt2QKvfS188pO4OfkKaUjvnXxDxI1+OgUKeiJN5o92l5bn8j8ZEdm3+IMhwsEMyUT5innQN7qi99ITf817DneVuEYVPffi6BC339rWnBx9thgD551Xrnh2L3JdN/t2KejVNcNdN2sHYwFX0WuNoFfddbOQS5PJhTDBOnM6ZMcIetkhDJb+eA/Rzm7yhfKpe6f/Se566gS+/eiVwOg5A4sXm5cuhSuumPz3YEw5vG3ucwfPj5SDHhVBD8qDuNT68pdh4zYvKA7cDY//YPKN2dep66ZIa4h0lit6CnoiMlsC3uhPiXj55DroH3uQBzNWRa9etS6yZEptW6gWLXMpY+c2dd2sa6YretnwqEFHurog641bMZe9aiJt7vcxl05BIYf1Jnj3h+sEvdzw6HVANp1jw7V/AKA/3kNbzM9gsrf0ek/4SdL5jtIgNLVBr7g+Fpv6VBJnn+291/KlZHMBvvjvW9i707XXF64Oehdc4B6f/Wz42Meqj3Phu7w5hq9+DtzyRteNUybmiSvgzn8qPf1//2/iuyroiTRZrMt1y/jGX96hoCcisybS5kLbQF95HPVQZdALdtXugvGPMTRwnYoe4X27oldrsRf0+neroldXZUVvIvPFTVTxHr1ceFSYC1T82DaqMM2GQMQFunN6/wF+EoRcknQ2TCA88a6bt37zAxyy59WAF/TaYDhT7r4ZCSTIFDo47DBXeVu5snr/YrfLJdO4PvPf/w0//jFcc62fOKt4zhG/Y/ixGwDwRzqqtv1//8+NRvub38CJ3owKz3yme3x8R03jCk38eVjobnoNDNxTevqlL018VwU9kSZbtMjQ9uY4//i9r+kePRGZNW3tLrT17y0Pwx3yp0hkvZOxOlWDMSt6R30UgEKw4r6gsCp6lWJd7t82m5hat6oFrzLoFSYxkdt4vJCQK4y+R+/ee93jz342txU9E3BBb1HocQB8hSEyuRCB2OgLLo26bh4c/VVpuRj04rnqiy2JwiLe8AZ3P1ztlE4HHgif/rQLX1MVCrl7U5ctg879j+KYNfew1vyEbC5AKBqp2tbng5e/3A1E99znunn0rrnGTe2wc2g51laM8jRWd1VpKJf3c+SRE99eQU+kyRYvhmSmjYL1q6InIrMm1uHO8uyQN3S7tYQDKe7fe573fPSk3sMjYwS9w94Pr7H4zt9TXqeKXpXoYtdXLpDZPM6W+6jKrpvNvF/Pqw6GIuFRI8QWR0R9wQua93ZTEqjuohkoDJLOhTGdh4zaNDlSP/QsbnuqtNwf7yEWg3ihukxZCPRiTP2umcbAJZeM7tI5Vb5YebSXoWQnPT0Nhuf13vtYN286y5fDUU8LsjdZMVrLOCONSn35QoD4JHqKK+iJNNmiiovfCnoiMlsCQRfaLlzl9ZmyOfy+AtsST3PPe08Ytc+evsmNukmwc/xt9iEdvZ3sHe4lkt80uR0T22DoUYgv8IBYVdGb2oTPdXkVvXDb6K7HP/+5q+pFo817uympCXoRtpPMRAkuPmLUppd9vX7o8ZnywErFit5A4dCqbYKx3trdZk7vcaXF4VTHpLqErlgBO4cqum9m69+XKGMLB9MkEhOfokJBT6TJFPREZE74ak568+7+vIIvCi/bBWdfPWqXc86aYJfD594FJ17aeIK9fZTfD0/1raWdJya34/Uvhv87GP5y5oy0q2UUZijoeRW9SFt41Es9PXDUUc17qymrCXodvs1c/eCz6Fwyuio+uHf86lYx6GVDa6rWRzpnMegd8GY2p92IvUPJzlGTso+lsxO29VcEPVX0piyfmfj9jQp6Ik1W+YdvOjdAi4hMSkXQy2QoBT1rIm60zGDHqF2WrV01sWP3HAPr396ERi482wbX0hV4YnI79d3uHuOTrATONzZfXq4X9Db9GP5y1uQn0fbu94vGRge9luELkM5Vt+/KO15Edze87Ev/yzu++43S+q62OhdcCvmqp/F0jLY2GIqcUbW+fXH13HozyhhSHacDrqI32aD32M4DyisajDQqE5Cf+OBPCnoiTVZZ0etULycRmS0VQe/738uWg54vUnfzDenXwMq5vpFp/utLrqQzuH1yO3kjoOby/smHnPlkvK6bN78Odl07+YFavK6b0fYWDnpAOldd1euP9xCJwK9ufxk3bnhGaf3JB906eudC7dQohlgMgr3r6XrrALuHXMrqWTqLFT2gd5kbNTSbC04q6HV1wc0bTiyv0GAsU3bOkb8vTSEyHgU9kSZbNIsX10RESiq6VfbtGMJ6QQ9//aCX9B+orphNkLI9tAUH6w52U1chD9lBEukoAX+ekcEFPDXDWIOx5CvC3WSDntd1s63Vg16+OuhlbQyfd+ZdsOVT8JMOuA2b2lu9cy5Z9dTncyNgxmIwlOxiJOOGFF19YHfT2z2W3qWuZ4DFTKrXUmcn3PDQyeUVd/3TqO9RJuZH//D6CQ/IoqAn0mTFCUo/9KG5bYeI7GOyQ6VFkxt0EzVDddA74sPlbULqctAMOdPtBs2o+PcfU9Z103tiz1oAHn1wAU/NMFZFb/CBitemWtEbYx7IFpApVAe9POXnD209jI/+78d43w+/iM9nie/ZVr1zTUUvGqVqdM2/b38RAP7o7Fb0jNcF3FozqekrOjth0+51JNZ5J0eFLGy8dAZauHBtWf41AEZSMRITvD6koCfSZMa4njif+tRct0RE9imZgdJiLjlEJuVOFH2BiqB39L9z9aY3AFAIKOg1Q87nne3+ogdyEzj7yvQD8MTutQA8/vDAzDSsFVQGPVsT9PrvKi9PMujls/Ojopc31UGPyt9FDJ/45Ud5ZMfBAPTtqvnZqah2DSfbSxWc006DU06B9ed/Bp53L7Svm4GWjyHoum6GI76qyenH425lMexYVnFyNLyxqU1bkLyq+L9f+QlWnPlONtnXsnNw2YSD3iQ+IhEREWlZy88uLRbSg+TS3rXcQHXXTb/PnXAXTM1M0zIlNthdfhJ/EroOG3sHL+jtiq8FYKR/AVf0xhp1cxpBL5PKEKX179GrHXkzHK6urzzxBGy43s0DER+q6cboVfTe/j/f4fIbz+ekk9zqWAxuvhncKfwcDC/qd9/T058+uW7fxd5OQ5WF70f/23V5PvG/1Y28kbz7uWjriOL3gw100BEZZocqeiIiIvuQ7qPgPG80x8wguWJFL1gd9Hx4J99G13qbIlTRfy21c/ztswMA5MJrAcgnF3DQq+q6WXOPXnJ7xUuTC3rZVJpsLkB7e2ufxhqv+lUU8X4V//Y3ePBBWLMGlu3ngl5ypCboeRW9xauWcf1NHdxww4w3d5ImF8yKg9P19cHgsovLL2y8tLobr1Tzgh5+93Nigu20R0Z0j56IiMg+x+uOaXJD5DJ1um4CSxa7ysqiJZOcLF3q8ke6y0+SExh906vo5aNrAdh8983c+JcJBMT5qNC462YmVZ4LLJueXNArjGxl9/ASOkbPGNJSApGaSdO9X8XTToPDvMJvW4c7gU/F61f0TCDCsce6gVhaQ3GU2KkFvbPPhu5n/zfxI75VfvGJHzWnaQuR1x3cBF0PDF+onbZwkkQ8P9ZeJQp6IiIiC4VXXWrz7SDvBT1/qDroHXKwO1Fbu05Brxn8bRUVveS2xht6snEX9AJdawH46Ms+wWGPHT4TTZt7YwzGkhwpDzZSGfomIpB4kAe2HNHyQS8Y6656Hq7T07S9ywW9dKJ+Rc8XiM5E06Zu6Zmw4lw4/suT2u3oo+G448rPtw2vLz958HOQ3NGc9i00XkXPF3Q/B76w+6HPJCY2PYWCnoiIyEIRWczWkUM5cfUfS6Nu1gY9c8JX4YC3uJM1mbZQ5cn8BILe0B4X9KJLypNHL+roI5drtMc8NkbQSyenUNFLbIPdfyOSmR9BL9JZPd9Sd/fobdq73Ql8Jlm/olfb9XrOBaJw1h/Gvxe1RjAIH/lI+fnDe06GVS+Goz4GWFcNf+Tr8NuDNe1CJW9y9EDYVfTCMdcdOD/wSPUUJQ0o6ImIiCwgDw08l5PWXEc26W7iiMZqThRjq+GU74C/xQeymCeiHR1cdvXb3JMJBL14fz+ZXJAlK6tDwEMPzUTr5tgY8+hlUykyOVdVzk006F1zLvz5GQRsnPs2H9XyQa+tp3rqg/e8Z/Q2Ma/rZjZVE268eTB9oRar6E3D6aeXl5/YHIEzfg1LvJXpPXD7P8Lwo7DnpjlpX0vKDgPg94Je71IX9F4QOAn7q5Uktt7VcFdQ0BMREVlQsv6lhAJZct4gH8V7gGRmdHYZ3v6dyxiOnAqp3eNunx7upz/ew6rV1fc4De4Znqkmzp0xKnr5bJqhpLtxK5eZYNBL7wJg2K7jiptf3fJBzxepDvPPf36dbbwuebl00oW7/nsAsF5VKxRtsYreNCxaBLd740U99ZS3MuQNx5l4qrzhzqtntV2trNB3NwBxv+veHYiUf+hNZg9t1x1Xb7cSBT0REZEFJBB2J4aZuBf0OhfOiWIrKg4y8dTOJSQHxw962bgLemvXQr5QPg3LDTw+Qy2cQ2NMr+C3qckHvVwCDv5Hrhi8n2SmreWDHqEJTGbujaaYSyV56rJj4apjINNPKu4qel29C+tCzfHHu/v1fvELePxxSgNIkaiohg8/Nidta0X5nbfy1J7VmNh+bkXNSK7jUdATERFZQIoVgMzIAADtneqiOZOKQe/mO5fQv338oGfT/cSzPUSjkM2XB8Sx8S0z1cS5M0ZFL+BLMZx0SS0/kaBnLeTiEOymf9h1Y2v9oNcz/jY+P9l8kOGdW9m/62EARnZuYnjAVfR6lyy8CzXf/Cbs3AkXXEC5opfcWt4gO1R3v33SwL3c+cRx5Z/1wOR+6BX0REREFpBImzsxzCUHyOSCdHT657hFC1sx6O0eXsLijj0ukIzBn+8ngwsAH/zZ10vrTWpro13mL9v4Hr2Aqei6mZ1A0MsnAQuBGMPD4PNBtNWLXRXVl9zxlzXcLJOPsn7Zg6XnfZuf4v9+4yp6ixZg0Dv5ZHe/4p13Qirv/QJV3t+aU9ArMumdbB9YUfo7Q6h7Uvsr6ImIiCwg0XZXwTPZAVLZSOtXPea5UtAbWkIokIXsGBOgb/oxhy69ozRE+ibfhQRenyVf8BHILMCgV9l1c9MP4LHvlJ4G/SkSOfePN6GKXs6bITrQztatsHw5mMlN5Tb7vK6bhWXPIXDI2xpuZn1RjlhVnjT8sXufpDDkui8uWbYwK/LHHw/5PNz3YAR8IUh4P//hJaroxZ+Cyw089Qt8uT52DS0t/x2P7jepQynoiYiILCBt7a4CELQu6AU1Xd6MqqzoAW5Ali1Xwsjoe+7s/Z8EYE2366J3xRVw620Bdg4uI1xYgEGvsoq36zq49a2lpyF/mkzB67o5kYpeKejF2LQJ1q1rZkNnSPs6OPUH+J7+4zE3C4Sj9MQGSs/P6nwvbz3rO9yx6TiWLmv1NDs1J5zgHk86CZK5Tui/062I7qegt/dW9/jQ5/EZy+6hJaV/LwJtkzqUgp6IiMgCEvMGX4n4B8jkF163r1YTDMIDD8ARx3lBL70Hrn8x/Hb9qG0zsWMBuCn1eQDa211lY+fQSmJmAd6jV2gwOaC1RIIpsriUvDz5A3jsf8Y+Vs6bIDrYPn+CHsC610NkyZibRGKuD2ohtIy/3v8sAO7cdCxnfeoaloy967y1di1cdJFbjvr2lF9Q0HNzCgI2MwDA6c9ewtKlUztUoElNEhERkRbQ3uXCXWdkkEw+Nset2Tccfjj8sb3bPckOuEdbGLVdLjnE/ZuOI77/OVXr9yZXcFD35plt5FywDYKeNzBLwe+C3qL8dXDrdbDsLGhfW38fr6KXI8aWLfMo6E3EkJtE0ax/K88+/5P4TAGL4b/+y0dgAZ+pX3opXHghsLG8znpBz1g7D/rmNkkh636+i/ffDT3iHkeeBKBt0RRTHqroiYiILCjFil532wDJjCp6s8UfdaMH2vRAw23yqUEG4t0sqp5ejZHsYtqD44/YOe/YPNaMPtXMZ91AI9bfWf3C7r81PpZX0bvngRiFAhx6aNNaOfeWPxsAc/gHWbTIULB+Vq708a53zXG7ZpgxrqL90q/8obTuk1/YD2Pz3uA7+4hrXwC/6IE9rsum7XOToBubBqBnWU1Zt3PiP/wKeiIiIguIL+DCXSySIJ5S0JstwTYX9DLDFd3Q/ncpDJZHUyQ7yGCya1TQS+SX0BEaf8TOeaeQA9/ooTETw+4E1oRrRgrKxxsfy6voXf6zdlasgJe9rGmtnHtn/AZeGYdgJ7fcAnfdBddeO9eNmh1+P/SFzy093zm0zC3sS903B+5xj498DW59G2bvTQzE3d+T4WQ7i/ZfXb39ObeAzxuk55Sxuzwr6ImIiCwk/vIofQcfpqA3W8Lt7sQsPbSrvDK9GzZ8FbZdBcmddOQfYDAxOujl/IsJB9Ll+9AWCpujYEJVE8MDJEZcRS8YqZn8OTvG9+8FvRtvifG850FkIf1oB9pKg2wcdBAccwwceODcNmk2ra+4nXUg3u0Wxhq9diF58POQ2umWn/gRPPZtAM769I1c07uHt/9xBwce2lW9T6gLFp/slpc8Y8zDL+CevyIiIvsgf/kMuGu/hXQjU2tr62gjl/OTj9d0wdx4qfuKrcMYWzfoBWLFgVx2Q3ABzYdhc1gC5HJB/KF0aXVyxC2HY9VpLZOME2p0LC8Eb93VzjuePhONlblSGWqLcyvuMxW9uz9Yd/Xqg5Zy1nmLOOu8Bvs9/Sfw1C+gfewrAqroiYiILCS+ipPnriPnrh37mM4uw2CiC5vcVX+D+CYAUrkoXTUX6MOdLuiN7N1Tu9f8VshRsAFyhYq6grWk4q6iF60JejdcU1HR67sT8uVwWKzoxVMxjjhixlosc+Cgg+DpH7+RN33zf0hkvOkD9oV79BqNSgusPbh37H2jK+CQd407YI2CnoiIyEJSUdGjW0FvtnR3w2CyC5NpEPQ865Zvx1dz9tW+eDEA/dsX2IAsNkfeBsjmg1XrkgkX4NraayYDL86VF38K/nA83PHuitdcCIynY3R3z2CbZdYdfjjc9MjT+f71byrd05mK7wNBL59q+NKhhzen06WCnoiIyEJSGfQmMTqbTE9vLwwmuvDnGoS1iBsifTi/etRLPctd0BvcvXfG2jcnClkKNkA2VxH08imySXeCG4mFeWL3mtJLHVGvopf2/h28UQgByMXJ2yDZfGhURVTmtyOOKHfffPmrXNAb7GsQ9BbQgEU2V/4eN+9dVfXa4Yc35z0U9ERERBYSX0WVpG10qJCZ0dsLA4luQgUX9F75lZ9Wb3Denbzhhzdy1eZ/HbXv4uXuvqT4wPCMt3NW5VNkC5Hqil4+TS7tTnBD0TaO+dcHOe1jf+PBrYfRFiqOuumdzFd2S8vFyVo3L2RnzawMMv/ddhs88AAcdqQLesMDdYLe5l/BFT5X8V0Acpny97ilrzronXRSc95DQU9ERGQh8fkrljXm2mzp6XEVvYivH4BUNkIq44XuttVcdd1KfviHp7N8v/CofTt63QAs+dQCG4AinySTj5LKVlSZC2ny3gluMBolTxs3P3oaI6l2gsar6JUmm68MeiOkcu0EgwtsxE0B3IWSww+HSMwFvWxqdNDb++f3uYVHvgb5zGw2b0ZkkuWum9v6V1a91tbWnPdQ0BMRERGZpmAQEtlyn8ICYZJZbw45f4TnPc8t1gspXT1hMrkgNrPwgl46FyWTqxhLM58in0kArqIX8K5FxNMx/Nar6BW8k/jKydZzcVK5GJ2d444/IfNYMegVq76VFkWedAsPfR57V/3RKueTbLL8PYa6V42x5dQp6ImIiCxEHQfPdQv2OelCOeh194ZIZbxU54+Uwslxx43eLxQ2DCU7MbmFF/RS2Wh1181CmoJX0Qu3Rfnnf4alS3EVPbyKXmmQiuqKXjIb0/15C1y03QW9fG3Qq5luYccDt81Wk2ZMpqJqecqzKoLeuX9v2nso6ImIiCw0L34Kzrt9rluxz8macgrpWRQqV/R8EQ48EM45B17zmvr7JjIdmPxCu0cvSSIdHXWPns26il441sYll8DOneALxQj64qX9nOp79OLpdt2ft8BFO7ygl6kOenbo0arn+fT8vyiSS5W7bi5Z5c2l2XkoLDqhae+hzvsiIiILTUyDsMyFgr8c9Dq6wqQqum4ODsIBBzTudpjIdhKw8//ktUo+STwdxZ8vn27+9c+p0miDxW56AJlCO2FfTUWvpuvmSGqJKnoLXKw9QDYXwGarg1527wZClduF5v/vSlX31I6D4Mh/g7Wvbep7qKInIiIi0gQ2UE4hsc4Q6Zw3VHw8wsAAY87/lsp3EDTz/+S1Si7JSLK6ond2/jTIu4peMFIOejlihP01Fb29t8LdH/I2GGEooYreQheLQTIbrZp6ACA3+ETV87Cv4ncln4HE1lloXXPVVi152sehs7ld7hX0RERERJrAFy4HvbZYiEzeBZlrb4iSzTJmNSpjOwmbhdd1czhR03UTiJnNZPMBjL+8Pm/aiQTibp60yomkH/y0e8zFGRiJ0dMzGw2XuRKJQDITxeZrgl5qmHS2XNML+ipev+m18OtVFaO1zg+5TMXP+QzND6igJyIiItIE/mg5yYXawmQLLugVpxcYq6KXtZ1EAgusopdPMhSPkMtX3ym0Mno7yUz1+PEFX4yAP+dG3KwMegC3vwsSm9ndH2P//We60TKXjIFUNoqpCXr59AgjqfbS86AvU55iYfMvihvNVjObojgo0a7wy2DxqTPyHgp6IiIiIk0QipWDXqQtRM66gFccfXOsil7e18G6RY9AcueMtnFW5ZMMjkSxprqit3/7XaRy0ap1BZ93Ep+LVwzG4nnkawAMJ9tZu3amGiutIpOPYArVPwOF9AjxdKx6w+xg9fNCzQWCFlfw7kPctOTS6vlPm0hBT0RERKQJwh3dpeVoW4icra7oxWL19nJ25U8CwD70hRlr36wqZMHm6R+KVnXRLErnamaEDnj/OLmR0RU9z2CyS0FvH5ApRPHZmlE3syOMpNurN7z2+e7nrGieVfSK9yEG602u2STjBj1jzHeNMbuMMfdXrDvfGPOAMaZgjDmhZvt/McZsNMZsMMacW7H+eGPMfd5rXzHGjTtljAkbY37qrb/VGLO2id+fiIiIyKyIdC0qL8fC5HBhphj0AmOMdb6j/W3cv/kIMns2zGgbZ41XlYunoixa7L7x63I/YSDuyprF+xdLikFv+BFIbht1uEfyb+Wyqy9S0NsHZPNR/NRUdXOu6+YjPd/h/r7nunV9f4ehit+XwiwFvY3fgutfOu3D2Jy7oFE5KFGzTaSi9z3gvJp19wMvA66vXGmMORy4ADjC2+e/jTHFWuQ3gIuA9d5X8ZgXAv3W2oOALwGfnfR3ISIiIjLH2nsXl5bb2kPsSBwCwKoVSX76U3jucxvvu2YNbNh+CIXBR2a6mbPDq1Yks1E6ulxF78wzCqS9gJfOV1f0fGGvWnP1c2DjpaMO98dt/0rfyCJWrpzBNktLyNkogZqgZ7ygN7z0LTwZ+1D5BZsrL89WRe+2i2DLr6d9GJtLks0FCEdmbra7cYOetfZ6oK9m3UPW2nqXnF4M/MRam7bWbgI2AicZY1YAndbam621FvgB8JKKfb7vLf8COLtY7RMRERGZL3p6y6cvbe0hNsePB+DgZQ/yylc2nkMPXNB7ZMfBhLOPQyHXeMP5wqvoJTMVXTcL2dKUE7lCdRXDHxqjXyuwbVcXHR1uVEZZ2LK2jaAvARu/DYMPAuArxBlJtdPWBief3l2xceU0C7N8j950R/nMJ0lmozP6M93se/RWApsrnm/x1q30lmvXV+1jrc0Bg8AiREREROaR3l7YM+xOYdo7fOzMHAvAjsSh4+67Zg08uWcNPrKQ2jWj7ZwVFUEv7V/r1oW6SiORZm11Rc8fqbn/qsbmHR0sWdL0VkoLypsYIV8cbnsb/O5IAHwFNxhLWxss3q+7vHHl78psdd0sNTQ5/jZjMPk48VSMcLhJ7amj2UGv3rUqO8b6sfYZfXBjLjLG3G6MuX337t1TbKKIiIhI861YAUddch/nfuYPtLdD97LFHPHB+/nhQ18Zd9+uLrDGO+MrZGa4pbOgIug9Hvs3OO1yWPmiUtDLU13RC0bGrujt2u1X0NtHFHztRIPFOSVdJAjgBmOJRoFgd3njREUdaTa6blbOd5eLT+tQvkKcRKZtXgW9LcDqiuergG3e+lV11lftY4wJAF3UdBUtstZeZq09wVp7whL9touIiEgLCYdhx8AK/nTfubS3w0EHwYNbj2DH7rbxdwaCIe9encqRBOcr76Q7lY0QaQvC2leDMWS9kUjzvo6qzYNtY1f0du9GQW8fYYIxetr2Vq0LMFLqulkauAdmv6JX2VV0ukHPJoin51dF70rgAm8kzXW4QVdus9ZuB4aNMad499+9AfhNxT5v9JZfAVzt3ccnIiIiMi/FYi7oAeyc4NR4wbB3L5tdAPfoeVXJTC7kTs49eS/oEawOeuG2sSt6Cnr7Dl8oRihQcbHDWoKmIugZwzv/uNG9lq7o4Tcb9+ildpSXpxn0AnbmK3rjDvNijLkCeCaw2BizBfgoruL2VWAJ8DtjzN3W2nOttQ8YY34GPAjkgHdaa/PeoS7GjeAZBa7yvgC+A/zQGLPRO+4FTfreREREROZELAbr1k1un1LQWwgVPeu+h2w+WBX0Ml7XTVMT9CId7TDQ+HC7d8PSpc1upLQif7imupsdwmcsOWL4vBKVjR1ALu8nUBn05llFz0+cRKb8Pc2EcYOetfbVDV76VYPtPwV8qs7624Ej66xPAeeP1w4RERGRVvelL8EXv+jmzNtvP/jc5+AlL5nYvqFSRW8BBL1COehFK27Hy+XdrFv+SGfV5m2xEJf+9e284jkPsrhwQ/mFdW9kx+ByMhk4+OAZb7W0gGC0prr76DcAuHf7M0qrli41DCa66E3uKg/2MRv36OVGKpanGfRsgnRuZsefnMEMKSIiIrJvee974amn3LIx8IEPwPr1E9s3FFlAFb1i0MtVV/QMrltqIFpd0Yu1Gy7+7qVszj/frTjwbfDyvXDq9/jSNZ8hGISXTn+OapkHRnXj7b+TbcPr2dBXGfRgKNlJPjHLXTezFUEvua3xdhPgt3Gsf2L3706Vgp6IiIhICwhHF2DQq6noGe/+w1BbTdDzzu1Taa+zWaANwr0AbNgAhxwCPT0z22RpDeH2mq6bw48yku6ks6IIfOihsHHnQQSSG8srZ6PrZmVF7+bXw8jjkEtM6VBBE68eWGYGKOiJiIiItICFGvQqK3p+49b7wvWDXi7rfe++YOm1zZth1SpkH9HWWVvRu5uRdIyurvKqo4+G/7jyQ9XbzXbXTYArD4RfLpvSoUL+BP6Qgp6IiIjIghfxgp5dCEHPu88wkwtVVfSiEbc+FKsf9P70B+97N+Wgt2ULrF6N7CNGBT1gJBmrqugtWgSBaPX9bXt2zXDQ23Mb3HbR6PW5EQqFyR3KWogG4wSj6ropIiIisuBF2ly3xWx64Uyv4PMHq0YVPGS9+94OXB+p2jwYhOc8B3IF929wx72u+146Dbt2qaK3L+noGT2n4lCiOugBHH9ydUh67JGpdaGckIH74E8nN3z5xz+eXNIbGsgSCmQJjTOtyHQp6ImIiIi0gGjMVbES8QVQ0fOqkoFQsGp10O+FWBOs3YP3vAe+8sd38/n/ez+nv+29AGzzxrtQ0Nt3BMKjw89gfHTQO+8F1UHv5LaPw8gTM9Oo6182atWeZR/mJ7e+HoCRvf2TOtz2LS6U1qteNpOCnoiIiEgLKHbdTCcXTtDzB2sCXdtK91gzjx647niJdIwPXvF5khl3El+cbH758hlrqbSaOj8bAyOjg97Tjq3T7bH/rplpU6D6vcxrLbem/p0rbz8PAH9ud729SCbLo/AW/cPbk9x3xX8A0NWrrpsiIiIiC17IC3qZ1MIJeqVJ4ItO/jac+gPoHjW1MovqTCm2d2/j12SBCvWOWhVPx1i6tHpd75LRISk3+NSodU3RNvom0a9+FXYPLQEgmB8d9K6/HtraYM0ad08eQKEA+w9+nPOP/BwAvctGh9pmUtATERERaQGRhRT0bDHoharXh3pg3evr7lIb5rJZ6Otzy72jz/1loaqYciC16kLAzcf4lrfUbOcv/2z98MGvADC4dSMzIrpi1Ko//hF2D7ugF7Kjg96ZZ5aXBwfd49atcNDychu7li5ubjtrKOiJiIiItIBImwt62cw0g949H4ad1zShRdNQrOiFRt+L10h3d/Xzhx6Cxx5zy6ro7UOMKS3+9Beuard2TaFq9NZa6559IXc9cQyL9n4N+u9ufpts/cFWihW9ekEP4M1nfpe/f/IEdu1yzzduhO62gdLrvqiCnoiIiMiCVwx6memOuvnAf8BfnwVDG5rQqinyRt0MR/0T3sVXc1Z69NHw8Y+75doQKPuG7btc0ItExh7VcumKMF/70z8CsOnvNzMw0OSG5FNs2nMQT//4jZz+ieu55BK3+pnnuFJzkPpv+N2LLuSEA+5g184CmQw8+9nQE6sYuCWsoCciIiKy4EW9oJfLZCG1p2EVYUyVc/D936FzF/YKWbL5INGoGX/bCagNgbJv8IVc0ItG8mNut3yFn+9d/yYK1s/l397CS1/a3HbYQpp4MsxNjzydGzecziWXQD4PP7o8QjoXIszYo27u3ZXkD3+Adz7nKxy/7s7yC6GZLVXr10ZERESkBURjbg45k9kDv1wCd//L5A+Si1c/H3ywCS2bAi/otU1yUMFrr52R1sg8tXyl668ZCY990aOjAyJRP/2pZXz4Jf/BOvOj5jRg6+9h99/IplKksuW5Hzs73cUH4zMMp7oJm4ExDzOwN85fr+rnK294T/ULAU2vICIiIrLgFefR8+UG3IqNl07+ILVBL5+cXqOmqpAlmw9NOuideSbccotbnuy+svDsyD8DgC25s8fczhg3BUd7wA3T+t23vh7ufH/9jZM74PZ3jf5dqeeeS+D+T5GKp0nnwlXvVzSc6ibiGxi163Fr7ygtD+5NMLSnTtXPNKfi3YiCnoiIiEgLiLW7oGfyI25FdmjyBxkV9FLTbNUU2SzZXHDMATQa6fBGnO/uhv/8T/ja15raMpkPzvwtrP8HdhVOpfOtg2y1zx93l/32g3AwDcDekUXw6NchvXf0hte9CB75Guy8dvx25OKQ6SOdSJHKROpuMpLpJuIfqFpXKMAdnzqh9HzzpjiZkb7x36/JFPREREREWkC0zXXd9BVGSuuK829NWKsEvUKWzBS6bgLEvN5sp54K//RP8M53NrdpMg+sfAGc+HWiURhOdpJOj7/Lu94Fr/n6j/nhja/j7T/+lfvZ33PL6A37/u4eUzvGP2g+CZk+cpnqil6leLab41b8EZ78aXldza/h7bcmSA9XVPSO+jice9v47z9NCnoiIiIiLcDnN2RzAfwVQS812ZyWG6l6mk7OVdDLkMlOraK3Zg38/vfw/e83v1kyvxR/fpIT6IF8/vlwxU2v4Q3f+CFbBw90K+NPVm9UOcBRfAKTq+eSkOmHfIpMLsKGDW7aj0qm2P3ybxeU1g0PV29TyCTw5ysqesueCYtOHP/9p0lBT0RERKRFZAtBApTDWmJkklMteBW9ax90szVv2jg3Qa+Qm9pgLEXPfW65sif7rrO9W/Oe9awGG7xkCzzfDThkDLz61W71xq3LwRcaHfQqu3ImNo/fgHwCMv34SZEnzMEHw6GHVm+yvNN7j3B5BM2REcjmAqXnTz85Tm+sIujN8GibRQp6IiIiIi0iVwgSNOVyQHJkkoOpeEHvPT/8LwoFQ3xobgZjyWezZHKTH4xFpNLJJ7tukOee22CDtpXQdVjp6Y9+BP/yL9DX58O27T866KV2lpfHC3qFvJsP0uaJBXaTp/49etGg10+z45DSuuFhGEx28ffH3H16Jx2XoLe9Muh1j/3eTaKgJyIiItIiCoUAwYqKXio+taAXT8dIZSOk4nNT0cvnivPozcnbywIymYsFPh8sWeIGQ7n9oTXkh56o3qAY9EK9kNw+9sEqRqyNBQewvvr36H3u5l95x+wurRsehs7oEI/vPgCA550b5/nP9oLeAW+C6IoJfkfTo6AnIiIi0iLyVFf00vHEJA/ggl4yEyOVjZKZo3v0Cjk36qYqejLbLrgAXvtauPexNWQGaip6STcAy52PHUouOTj2gWqmJrGmfkVva/J47njq6VXbb3osTSiQZcfAcgBCvgSnPe1xiK2DU/4HzOxEMAU9ERERkVZhgkQD5YpeOjHJoOdV9JbuFyNnI2QnPZpLcxRU0ZM5smIFfOtb8OSetUTZUT3ybHo3APc8fjCF1MDYB6oJeiZQv6IXDkMiHXUDtwCMbGJt3wcAOPM8r3L3yNdhy6/daKKzSEFPREREpEUYf5COaDnoZaYY9Favi5EjQi4zR0EvP73BWESmIxqFlH+Ne1I5uqY3Ku2WvlWEfCNQGGOwo1zN756vfkUvHIb+oSh7dnlB7+//wFkrvwrAMae4ih6DD7jH9RdP6vuYLgU9ERERkRbh8wernmdTkw96qWyYAw/yu65mubkZjMXmMwp6MqdC3cWgV+6+aXMJMrkgu4eXuBXZMbpv1lT0fMEG8+jFIZmNsndnkr4+qgdaCfVA8d6+9RdXDRwzGxT0RERERFqEP1gd9HLpyQW9bCpJMhNl+XLIE8Vvaip6A/fD9S+FfGa6TR2TzavrpsytUK8bCMXu9SYmf+oXmAc/TSLdxkC8263bcwvccD7k68zInq/+3WsU9O67D5KZKNFQkocegkJ0TfnFQAcEO9xyZHYGYKmkoCciIiLSKmpG48tPMuil4i7oLVkCeRMh6KsJere8yd0rNHDP9No5DlNIkEi3qaInc6ZzxWquuuc87INfAGvhxvMBSGTa6F3e7Ta67gWw+Rcw+ODoA9RU9PYM1Z/7btEiF/QiwRRbt0IingXg8exLofc4CHa6Ddv2a8r3NRkKeiIiIiItwtd7dNXzfGZyQS+dKAc9ayKE/Cny+coDepWLBkPFN4u/MMxwqkMVPZkza9bANQ+ehS83UBXaEuk21hzUXb2xrXOvXk2354efWlX3fX7yEzj7HFfR27oVksNJ9gwv4u6OX7punAFV9ERERET2edH9qoNeITO5e+xyqXLQwxchEkwRj1cesBj0AtNraD3JHaWBLwIMM5zsUEVP5sz++8Ngoss9+XlXaX2+4Gf9EV3VG+dGGKWm6+bi/esHvaVLYf2h5aCXTqRK3acBiHoLgdhUvo1pUdATERERaRG+niOqnheyk6vo5TIVQc/vgt5I5Tlscaj5sUYbnKpfrYDfrAFrCTLMULJTQU/mzMEHw0Ci2z2pqNj5fXkOO2ZJ9cbZYWoVstUXWd7/kZUN38sEogT8eY6NfJ5Vmf8hmYmybJn34knfgvXvhCWnTeXbmBYFPREREZFW0X5g9fP85IJeIZskmfWCXiBKWzhRXdErBj2bnVYzx25EGr/JqeumzKmODnjL27tHrff786w7fD9Wv+spDn3/QwBcd/Xoil42Wb0u3N7R+M387gf9tYd/EKA66MVWw4lfA1+wwc4zR0FPREREpFWEyl3KCgWDnWRFj1ySdC5KRwfkg8vZr3sb8ZFyNWN40Ou6WZjBoJcdAmAk1UF4Zm8FFBlT5+LuUeuC/jw+H2zpW81wyoW3MwOvheT2qu0ycVfluzv9flj+nLHfyBeqepothGlvn3q7m0VBT0RERKQFZQshVkVuntQ+ppCkYKIYA+noEURCaXIDmwA38GDIX+y6OZNBz50gpwsdGDNzbyMynu4l3aPWBfxudKJolFLQAyjc/+mq7XLJIbK5APcFPgfP+tPYb5R4quppJJxvsOHsUtATERERaSXt3vxf+Dlm+V9heOOEd/WTxPpcN7JC++EAxAb/AEAqBeGgN3/eTAa9nAt6GTtGVzeRWbB4RdeodcWgd8cd8NX/Lg+QMjBYPUBRPu3uM21vn8DVio5Dqp6Wfs/mmIKeiIiISCt53v1w/iCXP/J19zyxdcK7BkhiAi7o+boPA+CwxLshnyZR2Qt0BoNeIe2CXlZBT+ZYab68CgG/68p82GHwxjf5S+v7BqrvoSukhxhOdUysC+aBF7Jp5PTSUz8KeiIiIiJSKxCFYCep6DEAjPTtnfCuQX8SX8gFvWhnB1/8/fvcC7kRkpWDCM7gYCz/8y0X9Lbs7Jyx9xCZCH8oQipbfaOo31e/W2U2lap6bjPFit4E3sgYBuxhFe+hoCciIiIiDUS6FgHw0X/pG3fbyy6DP//Z3YMXCLugF4vBA1vcdA3xwfisVfQuPPD5AOweUEVP5l4yW/1z2Cjo+bLVF1RMzlX0YhOc/s4XjJSWO2Iz2DV6EhT0RERERFrQMSf1AhDI7+Xee8fe9u1vh3POsUSDSYIRF/Q6OyGRcRPZxQcTVRW9Qr7JJ6J1guOWvvoTTIvMpgLVXTL/tPmf624XyO+peu7LT6KiBwTC5aDX06WKnoiIiIg0cNyJbRRMmN72Pu67b/ztQ4EMPp8l3OaCXlsbnH2OC3qJkQSJeHmahfhwk4Nevnpy6UPe/zAjKVX0pAVUzF/3vRsu5MaBD1e/fsavyeSChKkOen47xHBygvfoAfjLQc8UFPREREREpBFjsMFeetv7qu+vqxXfzLvP/S+iIbdRJFaepfyIo13QS40kyMb7S+uT8SYHvVz1fH+x5Qfzlrc09y1EpsL4yqNppjIBOmtvHV31Yn5z96uImOqumwE7uYpezpaD3oyOajsJgfE3EREREZG5YApJ3nbWt/nW3n8HltXdxt7yJv7rDVdz7+anAdDWUQ564bY2SEI6niCfLp/IZlK5UceZlnw56GVyQe68UxPoSWswfhd30vkYH/7Zp/jjNaO3yZs2Aqb6akrIDDGS6iAaHb19PeFoFIq/Bqf8zzRa3Dyq6ImIiIi0qshSANaanzbcpGDdEPFHrXb9O2Nd5dEjwjFX0cskEhRS5aCXTs1cRa9gVUeQ1hEMu5/Ht1z6TdYevIgTTqizkS9K0FcR9Kwl6IuTKbRjJnjN4pDDXUUvv//rYM0rp9nq5lDQExEREWlR5ll/AqDHd3/DbbLB1QAcvf89ALT39pRei3a40JdJJSBdHr0zm565e/RqB78QmUuxmPt5TOfCnHZag40CEYL+iukVChl8xpI3EyznAcbvpnHw++xUm9p0CnoiIiIiLcq0r+H6DWeyONA46KV9K4By0Iv1lINeW4er6OVSCUymoutms4NeZUVPQU9aSLHrZiobaRj0TCBKyJ+Bgjf1gnfhouCbeNDD13qVbAU9ERERkRb22O7DWBzeAMkd8JdnQmpX1evZrOtbdsyauwEwoe7Sa7EuF/Ty6Ti+XDno5Zpe0SsHPdOCJ7yyDzPuwsNnPhvmlQ16VPqLc+AV0u6xWKGeTNCj9e5LVdATERERaWEjmV7aAgPwyNdg13Ww8VtVr+cyLrQF/F41IlSu6MU6XdArZBIE8+Wgl83M4KibPlX0pIV4Fx6OfFoQv7/+Jv6QF+iKAa/4GJhM0Gs9CnoiIiIiLSyZ78ZnCpAbqft6LlszgmZFRc8XCFMoGGwuwdZNe9gx4EbuzGdnsKLnV9CTFmK8CrPNN9wkGHEVvcJt74KnfgG5pLfrFIKe1T16IiIiIjIBqUK3W8gO1n09XxH0soUQ+CtOTo0hmW1jqC9B2O5k5+AyMrkguWYHvYqKns+vrpvSQopdiQuNpxQJeXMo+DZfATeeX6ro+YKq6ImIiIjIDMnS5RYyA3Vfr6zo5Xzd1I4Hn8q1kc8kWNa1kyWrl5LNB2egolceddMXUEVPWkjvie4xsqThJqFoxWTnod6pBb3Y/u6x+6jJtnDG6JKLiIiISAvL2G5vYaDu64VctjQORLSje9Tr6VwbJp9gaecu8sGDyKVnIuiVK3r+gE4vpYU87ROw6kXQe1zDTcKxKHjjsND9tFLQC4QjDfcZZekZ8JybYPHJ02hsc6miJyIiItLC8j6vopcdqPt6IVfRJS3YOer1dD6GnwTLOneSCywlXwhW79MMlV03g6roSQvxBWDxKWNu0tZeEeiMKQW9YGSSXTeXnAqmdeJV67REREREREbJ+7sBeHJjg3v0KkNbbO2o17O2jd623cQiCXKBZeRtkEJ+5u7RM0ZBT+aXaHtFoEtshetfAkC4TffoiYiIiMgMKQS6AegID3hrqkf1s5WDTLSvHbV/njbWLnnCbRtaQt4Gsc0OehVdN+u1QaSVtXVUVPSGHyktRtoV9ERERERkhrT3uK6bve39bkUhU71BIctIKobtPhoOfveo/XO0saJ7u3sS6qZAEArNH4xl29Ba/v3qH8JJlzX32CIzrL27fqBr61DQExEREZEZ8rZ3RBiId5WeP/Zoqup1W8jx+K4DMc+7G2KrR+1vfW2Egy4c+kId2JkIerkEQ4k2nuB1EOxo7rFFZlhXT/1BVxT0RERERGTGrF0L0cX7l5737a4Oetgcedt4pEvrayst+8KdrqJns3z967B1a3PamE0lGElEWb++OccTmU2NJkZv71LQExEREZEZFO5ZU1pOjVQHPVPIUhhrxqxAOej5Ix3kaCfEIP/4j/DKVzanfcmRBIlMm4KezE8NBhDq7JrfAwsp6ImIiIi0ulg56I0MpatfsznsGBU9EywHvUC0g5HCKlb2ulJef39zmpdLJUik21i3rjnHE5lV4V5Y/QrskjOrVnd1mzlqUHMo6ImIiIi0uli562YmVVPRI0feNq48+KqCXidJs4rVvZsBS7RJPdNszlX0li9vzvFEZpXxwek/x6x4DgC/u+t5nPLRm+kcPS3lvKKgJyIiItLqKip65FOkK4t6Nocdo+umPxwrLQej7aT9q4lFEnS3DTQt6Jl8kmQmyuLFzTmeyJzwu0FZNu1ex60bT1HQExEREZEZ1lau6EWCKTZvLr/kI0vBNA56gUi5ohcK+8mFVgGwetFmIvUHG5w0n01ifVGC8/uWJtnXeVOXxNPu4sh8/3lW0BMRERFpdRUVvUgwxYc+BPfc454bxq7oBSuCXjgMNrIfAPv1bKNQaE7zfKTxBZuUGkXmSi4OwMXvinHttXPblGZQ0BMRERFpddHyzW+RYIqf/xw+8AH33EcO22DUQIBQtOIevQAEYz0AdLcNMDRU3u7nP4ennppa8wK+FIFQeGo7i7QKL+h19rRz5pnjbDsPKOiJiIiItDpTPmVbv8YNlRnwing+cjBGRS8cc0FvONmOMRBoc5Ovd7UNloJeNuumWnj606fWvJA/RaBZ/UBF5krnwe6x6/C5bUeTKOiJiIiIzAfPfwAO+wC9wUe5+NX3smuXW+0zWfA1Dnqhlc/g8ptezUu/8kcATjrNBb1nnT7Io4/C4CD09bltt2yZQrsKOQK+PMGwKnoyzx30DjjnVtjvvLluSVMo6ImIiIjMB12Hw2HvB+DMQ/7M9u1utd/ksGMMxhJbtJTXfv1y7t56mts+HAPjJ+ofBOCNb4S9e6feLJt3Q4AGwqroyTxnDCw+aa5b0TQKeiIiIiLzRWQpBLtY07uJnTuhUACfyYFvjHn0fBCLQSjkrTAGgp2sWDzIZW99G/vnvjetoJcYcUEvqK6bIi1FQU9ERERkPmlfx/KOTeTzsGcPBHxZGKOiB9DeXhH0AIJdHH/UAG8769t85TVvZu8ey3+/+WLOPOxaPvc5qJmTfUxD/W7jcERdN0VaybhBzxjzXWPMLmPM/RXreo0xfzbGPOo99njr1xpjksaYu72vSyv2Od4Yc58xZqMx5ivGGOOtDxtjfuqtv9UYs3YGvk8RERGRhSG2jkXhTQA8/jj4fTmMf+yg19FRE/RC3fgSj5eebntykIuffSnX/utZ/PM/w1VXTbw5wwMu6IXaVNETaSUTqeh9D6i9I/ES4K/W2vXAX73nRY9Za4/xvt5Rsf4bwEXAeu+reMwLgX5r7UHAl4DPTvq7EBEREdlXtK8jZp4A4K673D16Zoyum+CCXtVYKcEuGHyw9PT+256s2v7OOyfenJEh13UzElPQE2kl4wY9a+31QF/N6hcD3/eWvw+8ZKxjGGNWAJ3W2puttRb4QcU+lcf6BXB2sdonIiIiIjVCvfgKSZYuznDnnRDw5zBjjLoJdSp6wS7IDpaeprbdAYDFcMQRLkBOVHzIVfSiMXXdFGklU71Hb5m1djuA97i04rV1xpi7jDHXGWNO99atBCoH7N3irSu+ttk7Vg4YBBbVe1NjzEXGmNuNMbfv3r17ik0XERERmccCMQCOe9oId98NQX8Wf3DsoHfWWVRPAB3srHr9tINvAcD4Ahx5JDz88MSbkxh2Qa+tQxU9kVbS7MFYtgP7W2uPBf4JuNwY0wnUq9BZ73Gs16pXWnuZtfYEa+0JS5YsaUqDRUREROaVQDsAK5bEefRRCPhyRKJjB72PfQy++MWKFcGOqtcvPH+DWyhk+bcznk8+PvEL6sVRN2MdquiJtJKpBr2dXnfMYrfMXQDW2rS1dq+3fAfwGHAwroK3qmL/VcA2b3kLsNo7VgDoYnRXURERERGBUkVvcXecwUFLMJAjGhv7Hr3Rx6gOeia5rbR8ePfvef5RPyUen9ihct4QnbpHT6S1TDXoXQm80Vt+I/AbAGPMEmOM31s+ADfoyuNe985hY8wp3v13byjuU3OsVwBXe/fxiYiIiEgtr6K3uGsEnykA0BYbu6I3Sk1Fj0z1RHp7hhezc+fEDlWaMD2koCfSSiYyvcIVwM3AIcaYLcaYC4HPAM8xxjwKPMd7DnAGcK8x5h7cwCrvsNYWq3MXA98GNuIqfcWBe78DLDLGbMR196wcwVNEREREKnkVvd6uOF1tbkCVaGf7JI9Rs32mHzoPLT2NheMTDnoUXEUvEFbXTZFWMu7lH2vtqxu8dHadbf8X+N8Gx7kdOLLO+hRw/njtEBERERFKIa2nfYT9elyXy7bFK8faY7Taih647pyn/Rhuei0d0WEefxxOPXX8Qxkv6JmAKnoiraTZg7GIiIiIyEzyKnpd7fFS0It0r5jkMeoFvTbY/5UAdESGed3roFAY/1Cm4Lpu4lPQE2klCnoiIiIi80nQVfS62soVPdO23+SOUey66auYXM/fBr4A+CM87fBhAAYH6+xbo1jRw6+umyKtREFPREREZD7xu4peeyTOft3eaJmRSVb0il03/W3ldQFvOdDB0V7Q27Nn/EMZW6zoKeiJtBIFPREREZH5xKvo+QojrOjezkimGwLRyR2jWNELVAS9YugLdtAedkHvox+Fr3517EMZm3ELldVBEZlzCnoiIiIi84kvDMbHgWvinHDMCNHOzskfo1jRM37weXPwVVT02kIu6F1xBbz73TDWxFfGZr12TXIuPxGZUQp6IiIiIvOJMRBoJ8AIp56cxR+YQsAyxYHXDfi9amBFRS/iH67a/PHHxzhUIUM2H3DtEpGWoaAnIiIiMt8EYpCLQyEztUpa22pY9wY441floFdR0QuaoarN//a3xocyZMnm1W1TpNUo6ImIiIjMN4F2yI1AITu1e+N8fjj1+9B7nOu+CeWgF+zEn68Oenfd1fhQxmbIFdRtU6TVjDthuoiIiIi0mGJFz+anf2+c9SbLK3bdDPVApr9qkzvvbLy7jyy5gip6Iq1GFT0RERGR+Wa6Fb0q3kgrxS6cXtC79FK3/txzYcOGxnv7yJBXRU+k5SjoiYiIiMw3071Hr1Jqp3dMr6IX7gWb5+1vGSGRgCOOgJGRxrsbVfREWpKCnoiIiMh8E4g1r6K33/Pc47Kz3WOoxz1m+olGIRaDRKLxFAt+MuStKnoirUb36ImIiIjMN4F2V9Hzt4Hpmt6xnn6Fu08v1O2eVwQ90ntZ1wXWHksqBdE687L7TYacVUVPpNWooiciIiIy31RW9PzTDFnBznLIg+qg94fjePOK4wBLIlF/d7/JUlBFT6TlKOiJiIiIzDfFil4hA6bJIasU9PpKq45fdwfxeP3N/SZDXhU9kZajoCciIiIy3wRikE+5r+kOxlKrsqLnOXL1/WNW9PKooifSahT0REREROabQLt7zA40YXqFGpFl7jG5HXxhAJZ17mxY0Qv4MhRU0RNpOQp6IiIiIvNNIOYeM/3Nr+j5IxBdASObADeZ+tKuXWPfo6eKnkjLUdATERERmW+KFT1ofkUPILYWhh52g70wgYoequiJtBoFPREREZH5pljRg+ZX9MAFvYH7Sk+Xde1sWNEL+LIKeiItSEFPREREZL6Z8YreGsgNl54u7dzVuKLnz2CbPfKniEybgp6IiIjIfDPTFb1geRL2QnDRmBW9oC+LNaroibQaBT0RERGR+aayojcT1TR/tLRYiB3Eks7dJOL5UZtZC8GAKnoirUhBT0RERGS+qazo+WegmhZoKy36Og/C7yvwxIa+UZvl8xDyZ1TRE2lBCnoiIiIi882MV/Sqgx7A1VftZOfO6s0yGQgGsjPTfVREpkVBT0RERGS+qbpHb2YrenQcCMCSjl3cfnv1ZomEq+j5AqroibQaBT0RERGR+aYyiM1ENa2ioke7q+gt795ZN+gFA1n8AVX0RFqNgp6IiIjIfGMqTuFmqaL3tPWjg158xBIKZPEHVdETaTUKeiIiIiLz2UxU9CqDXngx+IIctX776IpePOc2D6miJ9JqFPRERERE5rOZ7rppfBBdyYH7bWXHDti2rfxSOj7imhCKISKtRUFPREREZD6b6a6bAG2rWN6xBYAHHyyvzo3sAsBElza/DSIyLQp6IiIiIvNR2yr32P205h+7YsL04nu1GRf0duwory4kXdDzxxT0RFpNYK4bICIiIiJTcOZvAQOdBzf/2P7RFb1g9leAZft2U16f2gl+CHYsa34bRGRaFPRERERE5qOeY2bu2LUVvehKTCHN6qV9bN++qLxZZhdEIdypip5Iq1HXTRERERGp5vNXP/e6iR69fgvbt5dX+3O7KBQMka7Fs9g4EZkIBT0RERERqc8fcY9e0DtszZaqe/RChZ30xXtpa1cnMZFWo6AnIiIiIqM9+zp4wQa37AW9Q/bfwqZN5U3azVNs6VtNQDlPpOUo6ImIiIjIaEvPgNj+bjmyHIyPQ1Zv4cknYXjYre4KbGJz/7q5a6OINKSgJyIiIiJj8wUgspxndP87x629g6uugm1bLYvCm9g2dMBct05E6lDQExEREZHxHft5Cv4OvvT69/GqV8FJT9tByJ9iqKCKnkgrUtATERERkfGtfQ2+ledx/JG7ufRSWNrpht8shFfOccNEpB4FPRERERGZGF+YWCTD298Oa/fPAtDdG57jRolIPQp6IiIiIjIx/jDk0wCcekoOgJ5FwblskYg0oKAnIiIiIhPjC0FuGJI7OfUkV9FbvERzK4i0IgU9EREREZkYXxiyQ/Cr5ZxysqvonXKagp5IK1LQExEREZGJ8YVKiwGTAqAtpq6bIq1IQU9EREREJsZfMfBKdsQ9+lTRE2lFCnoiIiIiMjEVFT1yw+7RKOiJtCIFPRERERGZGF9FRS9XrOip66ZIK1LQExEREZGJqazoZVXRE2llCnoiIiIiMjGV9+gVu27qHj2RlqSgJyIiIiITU9l1s1TRU9dNkVakoCciIiIiE1Ov66YqeiItSUFPRERERCbGX2cwFt2jJ9KSFPREREREZGLqTa+gUTdFWpKCnoiIiIhMTN179FTRE2lFCnoiIiIiMjH+ehU9BT2RVqSgJyIiIiITo1E3ReYNBT0RERERmZiqe/SKg7HodFKkFek3U0REREQmxvjLy9lhd3+eMXPXHhFpSEFPRERERCbIVi9rxE2RlqWgJyIiIiITYwvVzzXipkjLUtATERERkYnpOKj6uUbcFGlZCnoiIiIiMjGhbniNhY6D3XN13RRpWQp6IiIiIjI54cXuUV03RVqWgp6IiIiITI6CnkjLGzfoGWO+a4zZZYy5v2JdrzHmz8aYR73HnorX/sUYs9EYs8EYc27F+uONMfd5r33FGDcWrzEmbIz5qbf+VmPM2iZ/jyIiIiLSTMWgp66bIi1rIhW97wHn1ay7BPirtXY98FfvOcaYw4ELgCO8ff7bmNKEK98ALgLWe1/FY14I9FtrDwK+BHx2qt+MiIiIiMyC8CL3WDmvnoi0lHGDnrX2eqCvZvWLge97y98HXlKx/ifW2rS1dhOwETjJGLMC6LTW3myttcAPavYpHusXwNnFap+IiIiItKBiRa+Qmdt2iEhDU71Hb5m1djuA97jUW78S2Fyx3RZv3UpvuXZ91T7W2hwwCCyaYrtEREREZKYVg15mYE6bISKNNXswlnqVODvG+rH2GX1wYy4yxtxujLl99+7dU2yiiIiIiExL1Ltenx2Y02aISGNTDXo7ve6YeI+7vPVbgNUV260CtnnrV9VZX7WPMSYAdDG6qygA1trLrLUnWGtPWLJkyRSbLiIiIiLT0r52rlsgIuOYatC7Enijt/xG4DcV6y/wRtJchxt05Tave+ewMeYU7/67N9TsUzzWK4Crvfv4RERERKQVte0/1y0QkXGMO/mJMeYK4JnAYmPMFuCjwGeAnxljLgSeAs4HsNY+YIz5GfAgkAPeaa3Ne4e6GDeCZxS4yvsC+A7wQ2PMRlwl74KmfGciIiIiMjMC0blugYiMY9ygZ619dYOXzm6w/aeAT9VZfztwZJ31KbygKCIiIiLzSKB9rlsgIg2MG/REREREREZ5RR/NH9dPRJpFQU9EREREJi/UM9ctEJEx6DKMiIiIiIjIAqOgJyIiIiIissAo6ImIiIiIiCwwCnoiIiIiIiILjIKeiIiIiIjIAqOgJyIiIiIissAo6ImIiIiIiCwwCnoiIiIiIiILjIKeiIiIiIjIAqOgJyIiIiIissAo6ImIiIiIiCwwCnoiIiIiIiILjIKeiIiIiIjIAqOgJyIiIiIissAo6ImIiIiIiCwwCnoiIiIiIiILjIKeiIiIiIjIAqOgJyIiIiIissAo6ImIiIiIiCwwCnoiIiIiIiILgn76cAAACttJREFUjIKeiIiIiIjIAqOgJyIiIiIissAYa+1ct2FKjDHDwIa5boeMqQsYnOtGSEP6fFqfPqPWp8+o9ekzam36fFqfPqPWdoi1tqPeC4HZbkkTbbDWnjDXjZDGjDGXWWsvmut2SH36fFqfPqPWp8+o9ekzam36fFqfPqPWZoy5vdFr6ropM+m3c90AGZM+n9anz6j16TNqffqMWps+n9anz2iems9dN29XRU9ERERERPZVY2Wi+VzRu2yuGyAiIiIiIjKHGmaieVvRExERERERkfrmc0VPZpEx5jxjzAZjzEZjzCXeuk8aY+41xtxtjPmTMWa/ie7rre81xvzZGPOo99gzW9/PQjTGv/O7vPUPGGM+N8l99Rk1SYPfoaONMTcbY+4zxvzWGNM50X299fp8msgY811jzC5jzP0V6z5vjHnY+1v3K2NMd4N99RnNsAafz8eMMVu9/4fuNsY8r8G++nxmQYPP6BhjzC3e53O7MeakBvvqM5phxpjVxphrjDEPeecE7/HWn+89LxhjGt4Wpc9oHrLW6ktfY34BfuAx4AAgBNwDHA50VmzzbuDSie7rvfY54BJv+RLgs3P9vc7XrzE+o7OAvwBhb7ul+oxa6vP5O3Cmt81bgE/q85nTz+kM4Djg/op15wABb/mz9f6N9RnN6efzMeD94+ynz2duP6M/Ac/1lp8HXKvPaM4+nxXAcd5yB/CI93/RYcAhwLXACQ321Wc0D79aoqLX4Er3hK4O6OrCrDgJ2GitfdxamwF+ArzYWjtUsU0MqNcPuO6+3msvBr7vLX8feMlMNH4f0ejf+WLgM9baNIC1dtck9gV9Rs3S6N/4EOB6b5s/Ay+fxL6gz6eprLXXA3016/5krc15T28BVtXZVZ/RLKj3+UyQPp9Z0uAzskCxt0IXsK3OrvqMZoG1dru19k5veRh4CFhprX3IWjve3NT6jOahOQ96xhg/8HXgubirCq82xhyOuyLwV2vteuCv3vOJ7stE9pcJWwlsrni+xVuHMeZTxpjNwGuBf/PW7WeM+f14+wLLrLXbwf3xAZbO2Hew8DX6dz4YON0Yc6sx5jpjzImgz2gONPo3vh94kbfufGA16PNpYW8BrgJ9Ri3mH72utd8tXtTV59NS3gt83jtX+ALwL6DPaK4ZY9YCxwK3jrGNPqN5bs6DHo2vEEzk6oCuLswOU2edBbDWfthauxr4MfCP3rpt1trnjbevNFWjf+cA0AOcAnwA+JkxxugzmnWN/o3fArzTGHMHrhtNBvQ71IqMMR8Gcri/dfqMWsc3gAOBY4DtwH+CPp8WczHwPu9c4X3Ad0Cf0VwyxrQD/wu8t6Z3VhV9RvNfKwS9RlcI6l4d0NWFObEFr9LgWcXorheXU7/b2Vj77jTGrADwHut1K5SJafTvvAX4pXVuAwrA4gnuC/qMmqXuv7G19mFr7TnW2uOBK3D3P0xoX29Zn88sMMa8EXgB8Fprbb0TG31Gc8Rau9Nam7fWFoBv4S4A19LnM7feCPzSW/45+ozmlDEmiAt5P7bW/nK87SvoM5qHWiHoTeoKga4uzIm/A+uNMeuMMSHgAuBKY8z6im1eBDw80X29167E/QeA9/ibGWn9vqHRv/OvgWcBGGMOxt1AvWeC+4I+o2Zp9DtUvIDlA/4VuHSi+3qv6fOZYcaY84B/Bl5krU002Eyf0Rwpnlx6XorrDl1Ln8/c2gac6S0/C3i0zjb6jGaBMcbgKqoPWWu/OMnd9RnNR3M9GgxwKvDHiuf/4n1tAFZ461YAGya6r7c87v76mtTn9Dzc6EyPAR/21v0v7j/Ve4Hf4m7oBdgP+P1Y+3rrF+Hun3zUe+yd6+9zPn81+IxCwI+8z+lO4Fn6jFrq83mPt+4R4DOU5zbV5zM3n9EVuO5/WdzV6wuBjbieI3d7X5fqM2qpz+eHwH3e/0NXVvy/r8+ndT6jZwB34EZpvBU4Xp/RnH0+z8AVRO6t+Jv2PNxFki1AGtiJd26tz2j+f835hOnGmADuh+ZsYCvuisFrgDcBe621n/FG0+y11n5wIvtaax8wxnx+vP1FREREREQWojkPegDGTXD6ZdwcHd+11n7KGLMI+BmwP/AUcL61ts+4Sbm/bb3um/X29dbX3X9WvzEREREREZE50BJBT0RERERERJqnFQZjERERERERkSZS0BMREREREVlg5jToGWO+a4zZZYy5v2b9u4wxG4wxDxhjPtdg37W1+4mIiIiIiMjcV/S+B5xXucIYcxbwYuBp1tojgC/MQbtERERERETmrTkNetba64HakTAvBj5jrU172+wa7zhede8GY8yd3tdp3vpnGmOuNcb8whjzsDHmx95kkSIiIiIiIgvWXFf06jkYON0Yc6sx5jpjzIkT2GcX8Bxr7XHAq4CvVLx2LPBe4HDgAODpTW6viIiIiIhISwnMdQPqCAA9wCnAicDPjDEH2LHngQgCXzPGHAPkcWGx6DZr7RYAY8zdwFrgxuY3W0REREREpDW0YtDbAvzSC3a3GWMKwGJvUJZjgW3FydIrvA/YCRyNq1KmKl5LVyznac3vWUREREREpGlaMfT8GngWcK0x5mAgBOyx1r55jH26gC3W2oIx5o2Af+abKSIiIiIi0prmenqFK4CbgUOMMVuMMRcC3wUO8KZO+AnwxgbdNgOUq3X/DbzRGHMLrttmfOZbLyIiIiIi0prM2Le+tS5jzIuB11prXznXbREREREREWklrdh1c1zGmE/g5tp70xw3RUREREREpOXM24qeiIiIiIiI1NeK8+iJiIiIiIjINCjoiYiIiIiILDAKeiIiIiIiIguMgp6IiCwYxphFxpi7va8dxpit3vKIMea/Z/B9n2mMOW2mji8iIjJZ83LUTRERkXqstXuBYwCMMR8DRqy1X5iFt34mMALcNAvvJSIiMi5V9EREZMHzKm7/5y1/zBjzfWPMn4wxTxhjXmaM+Zwx5j5jzB+MMUFvu+ONMdcZY+4wxvzRGLPCW/9uY8yDxph7jTE/McasBd4BvM+rHp5ujHmhMeZWY8xdxpi/GGOWTfK9nzDGfNYYc5v3ddCc/MOJiMi8paAnIiL7ogOB5+PmZP0RcI219iggCTzfC1xfBV5hrT0e+C7wKW/fS4BjrbVPA95hrX0CuBT4krX2GGvtDcCNwCnW2mOBnwAfnOh7V2w3ZK09Cfga8OUmf/8iIrLAqeumiIjsi66y1maNMfcBfuAP3vr7gLXAIcCRwJ+NMXjbbPe2uRf4sTHm18CvGxx/FfBTrwoYAjZN4r2Lrqh4/NKkv0MREdmnqaInIiL7ojSAtbYAZK211ltfwF0ENcADXoXuGGvtUdbac7xtng98HTgeuMMYU++i6VeBr3mVurcDkUm8d5FtsCwiIjIuBT0REZHRNgBLjDGnAhhjgsaYI4wxPmC1tfYaXHfMbqAdGAY6KvbvArZ6y2+cYhteVfF48xSPISIi+yh13RQREalhrc0YY14BfMUY04X7//LLwCPAj7x1Bndf3oAx5rfAL4wxLwbeBXwM+LkxZitwC7BuCs0IG2NuxV2UffV0vycREdm3mHKPEREREWkFxpgngBOstXvmui0iIjI/qeumiIiIiIjIAqOKnoiIiIiIyAKjip6IiIiIiMgCo6AnIiIiIiKywCjoiYiIiIiILDAKeiIiIiIiIguMgp6IiIiIiMgCo6AnIiIiIiKywPx/R2/xwbCi0TgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create an overlay plot that visualizes the two dataframes over a period of one day early in the dataset. \n",
    "# Be sure that the plots include the parameters `legend`, `figsize`, `title`, `color` and `label` \n",
    "bitstamp_sliced[\"Close\"].loc['2018-1-16'].plot(legend=True, figsize=(15, 10), title=\"Bitstamp  v. Coinbase - Jan 16, 2018\", color=\"blue\", label=\"Bitstamp\")\n",
    "coinbase_sliced[\"Close\"].loc['2018-1-16'].plot(legend=True, figsize=(15, 10), color=\"orange\", label=\"Coinbase\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1419.000000\n",
       "mean       23.212135\n",
       "std        67.789186\n",
       "min      -275.920000\n",
       "25%        -8.330000\n",
       "50%        22.280000\n",
       "75%        52.355000\n",
       "max       606.800000\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the early date that you have selected, calculate the arbitrage spread \n",
    "# by subtracting the bitstamp lower closing prices from the coinbase higher closing prices\n",
    "arbitrage_spread_early = coinbase_sliced['Close'].loc['2018-1-16'] - bitstamp_sliced['Close'].loc['2018-1-16']\n",
    "\n",
    "# Generate summary statistics for the early DataFrame\n",
    "arbitrage_spread_early.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Arbitrage Spread - Early Date'}>"
      ]
     },
     "execution_count": 42,
     "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": [
    "# Visualize the arbitrage spread from early in the dataset in a box plot\n",
    "arbitrage_spread_early.plot(kind='box',title=\"Arbitrage Spread - Early Date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Bitstamp  v. Coinbase - Feb 24th, 2018'}, xlabel='Timestamp'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create an overlay plot that visualizes the two dataframes over a period of one day from the middle of the dataset. \n",
    "# Be sure that the plots include the parameters `legend`, `figsize`, `title`, `color` and `label` \n",
    "bitstamp_sliced[\"Close\"].loc['2018-2-24'].plot(legend=True, figsize=(15, 10), title=\"Bitstamp  v. Coinbase - Feb 24th, 2018\", color=\"blue\", label=\"Bitstamp\")\n",
    "coinbase_sliced[\"Close\"].loc['2018-2-24'].plot(legend=True, figsize=(15, 10), color=\"orange\", label=\"Coinbase\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1437.000000\n",
       "mean        3.396131\n",
       "std        22.969472\n",
       "min       -50.180000\n",
       "25%       -10.590000\n",
       "50%        -0.010000\n",
       "75%        12.120000\n",
       "max       121.320000\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the date in the middle that you have selected, calculate the arbitrage spread \n",
    "# by subtracting the bitstamp lower closing prices from the coinbase higher closing prices\n",
    "arbitrage_spread_middle = coinbase_sliced['Close'].loc['2018-2-24'] - bitstamp_sliced['Close'].loc['2018-2-24']\n",
    "\n",
    "# Generate summary statistics \n",
    "arbitrage_spread_middle.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Arbitrage Spread - Middle Date'}>"
      ]
     },
     "execution_count": 45,
     "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": [
    "# Visualize the arbitrage spread from the middle of the dataset in a box plot\n",
    "arbitrage_spread_middle.plot(kind='box',title=\"Arbitrage Spread - Middle Date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Bitstamp  v. Coinbase - Mar 26th, 2018'}, xlabel='Timestamp'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create an overlay plot that visualizes the two dataframes over a period of one day from late in the dataset. \n",
    "# Be sure that the plots include the parameters `legend`, `figsize`, `title`, `color` and `label` \n",
    "bitstamp_sliced[\"Close\"].loc['2018-3-26'].plot(legend=True, figsize=(15, 10), title=\"Bitstamp  v. Coinbase - Mar 26th, 2018\", color=\"blue\", label=\"Bitstamp\")\n",
    "coinbase_sliced[\"Close\"].loc['2018-3-26'].plot(legend=True, figsize=(15, 10), color=\"orange\", label=\"Coinbase\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1432.000000\n",
       "mean       -0.722193\n",
       "std         8.523060\n",
       "min       -32.040000\n",
       "25%        -6.812500\n",
       "50%        -0.730000\n",
       "75%         4.965000\n",
       "max        35.070000\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the date from the late that you have selected, calculate the arbitrage spread \n",
    "# by subtracting the bitstamp lower closing prices from the coinbase higher closing prices\n",
    "arbitrage_spread_late = coinbase_sliced['Close'].loc['2018-3-26'] - bitstamp_sliced['Close'].loc['2018-3-26']\n",
    "\n",
    "\n",
    "# Generate summary statistics for the late DataFrame\n",
    "arbitrage_spread_late.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Arbitrage Spread - Late Date'}>"
      ]
     },
     "execution_count": 48,
     "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": [
    "# Visualize the arbitrage spread from late in the dataset in a box plot\n",
    "arbitrage_spread_late.plot(kind='box',title=\"Arbitrage Spread - Late Date\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: Calculate the Arbitrage Profits\n",
    "\n",
    "Calculate the potential profits for each date that you selected in the previous section. Your goal is to determine whether arbitrage opportunities still exist in the Bitcoin market. Complete the following steps:\n",
    "\n",
    "1. For each of the three dates, measure the arbitrage spread between the two exchanges by subtracting the lower-priced exchange from the higher-priced one. Then use a conditional statement to generate the summary statistics for each arbitrage_spread DataFrame, where the spread is greater than zero.\n",
    "\n",
    "2. For each of the three dates, calculate the spread returns. To do so, divide the instances that have a positive arbitrage spread (that is, a spread greater than zero) by the price of Bitcoin from the exchange you’re buying on (that is, the lower-priced exchange). Review the resulting DataFrame.\n",
    "\n",
    "3. For each of the three dates, narrow down your trading opportunities even further. To do so, determine the number of times your trades with positive returns exceed the 1% minimum threshold that you need to cover your costs.\n",
    "\n",
    "4. Generate the summary statistics of your spread returns that are greater than 1%. How do the average returns compare among the three dates?\n",
    "\n",
    "5. For each of the three dates, calculate the potential profit, in dollars, per trade. To do so, multiply the spread returns that were greater than 1% by the cost of what was purchased. Make sure to drop any missing values from the resulting DataFrame.\n",
    "\n",
    "6. Generate the summary statistics, and plot the results for each of the three DataFrames.\n",
    "\n",
    "7. Calculate the potential arbitrage profits that you can make on each day. To do so, sum the elements in the profit_per_trade DataFrame.\n",
    "\n",
    "8. Using the `cumsum` function, plot the cumulative sum of each of the three DataFrames. Can you identify any patterns or trends in the profits across the three time periods?\n",
    "\n",
    "(NOTE: The starter code displays only one date. You'll want to do this analysis for two additional dates)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. For each of the three dates, measure the arbitrage spread between the two exchanges by subtracting the lower-priced exchange from the higher-priced one. Then use a conditional statement to generate the summary statistics for each arbitrage_spread DataFrame, where the spread is greater than zero.\n",
    "\n",
    "*NOTE*: For illustration, only one of the three dates is shown in the starter code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    960.000000\n",
       "mean      53.405344\n",
       "std       53.910409\n",
       "min        0.010000\n",
       "25%       21.602500\n",
       "50%       39.885000\n",
       "75%       68.547500\n",
       "max      606.800000\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For the date early in the dataset, measure the arbitrage spread between the two exchanges\n",
    "# by subtracting the lower-priced exchange from the higher-priced one\n",
    "# arbitrage_spread_early already available from previous step (coinbase closing prices > bitstamp closing prices)\n",
    "\n",
    "# Use a conditional statement to generate the summary statistics for each arbitrage_spread DataFrame\n",
    "arbitrage_spread_early = arbitrage_spread_early[arbitrage_spread_early>0]\n",
    "arbitrage_spread_early.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    713.000000\n",
       "mean      19.880351\n",
       "std       20.699851\n",
       "min        0.010000\n",
       "25%        5.040000\n",
       "50%       12.330000\n",
       "75%       27.250000\n",
       "max      121.320000\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For the date middle in the dataset, measure the arbitrage spread between the two exchanges\n",
    "# by subtracting the lower-priced exchange from the higher-priced one\n",
    "# arbitrage_spread_middle already available from previous step (coinbase closing prices > bitstamp closing prices)\n",
    "\n",
    "# Use a conditional statement to generate the summary statistics for each arbitrage_spread DataFrame\n",
    "arbitrage_spread_middle = arbitrage_spread_middle[arbitrage_spread_middle>0]\n",
    "arbitrage_spread_middle.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    651.000000\n",
       "mean       6.689247\n",
       "std        5.240159\n",
       "min        0.010000\n",
       "25%        2.835000\n",
       "50%        5.230000\n",
       "75%        9.195000\n",
       "max       35.070000\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For the date late in the dataset, measure the arbitrage spread between the two exchanges\n",
    "# by subtracting the lower-priced exchange from the higher-priced one\n",
    "# arbitrage_spread_middle already available from previous step (coinbase closing prices > bitstamp closing prices)\n",
    "\n",
    "# Use a conditional statement to generate the summary statistics for each arbitrage_spread DataFrame\n",
    "arbitrage_spread_late = arbitrage_spread_late[arbitrage_spread_late>0]\n",
    "arbitrage_spread_late.describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inference: The arbitrage spread summary statistics for the above three dates shows that the spread was decreased over time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### 2. For each of the three dates, calculate the spread returns. To do so, divide the instances that have a positive arbitrage spread (that is, a spread greater than zero) by the price of Bitcoin from the exchange you’re buying on (that is, the lower-priced exchange). Review the resulting DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp\n",
       "2018-01-16 00:02:00    0.000731\n",
       "2018-01-16 00:03:00    0.001108\n",
       "2018-01-16 00:04:00    0.000909\n",
       "2018-01-16 00:05:00    0.002485\n",
       "2018-01-16 00:06:00    0.001665\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For the date early in the dataset, calculate the spread returns by dividing the instances when the arbitrage spread is positive (> 0) \n",
    "# by the price of Bitcoin from the exchange you are buying on (the lower-priced exchange).\n",
    "spread_return_early= arbitrage_spread_early / bitstamp_sliced['Close'].loc['2018-1-16']\n",
    "\n",
    "# Review the spread return DataFrame\n",
    "spread_return_early= spread_return_early.dropna()\n",
    "spread_return_early.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp\n",
       "2018-01-16 00:02:00    0.000731\n",
       "2018-01-16 00:03:00    0.001108\n",
       "2018-01-16 00:04:00    0.000909\n",
       "2018-01-16 00:05:00    0.002485\n",
       "2018-01-16 00:06:00    0.001665\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For the date middle in the dataset, calculate the spread returns by dividing the instances when the arbitrage spread is positive (> 0) \n",
    "# by the price of Bitcoin from the exchange you are buying on (the lower-priced exchange).\n",
    "spread_return_middle= arbitrage_spread_middle / bitstamp_sliced['Close'].loc['2018-2-24']\n",
    "\n",
    "# Review the spread return DataFrame\n",
    "spread_return_middle= spread_return_middle.dropna()\n",
    "spread_return_early.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp\n",
       "2018-03-26 00:00:00    0.000738\n",
       "2018-03-26 00:01:00    0.000122\n",
       "2018-03-26 00:02:00    0.000470\n",
       "2018-03-26 00:03:00    0.000576\n",
       "2018-03-26 00:04:00    0.001395\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For the date late in the dataset, calculate the spread returns by dividing the instances when the arbitrage spread is positive (> 0) \n",
    "# by the price of Bitcoin from the exchange you are buying on (the lower-priced exchange).\n",
    "spread_return_late= arbitrage_spread_late / bitstamp_sliced['Close'].loc['2018-3-26']\n",
    "\n",
    "# Review the spread return DataFrame\n",
    "spread_return_late= spread_return_late.dropna()\n",
    "spread_return_late.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. For each of the three dates, narrow down your trading opportunities even further. To do so, determine the number of times your trades with positive returns exceed the 1% minimum threshold that you need to cover your costs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp\n",
       "2018-01-16 02:56:00    0.012962\n",
       "2018-01-16 02:57:00    0.010838\n",
       "2018-01-16 02:59:00    0.014171\n",
       "2018-01-16 03:00:00    0.010675\n",
       "2018-01-16 03:06:00    0.019704\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For the date early in the dataset, determine the number of times your trades with positive returns \n",
    "# exceed the 1% minimum threshold (.01) that you need to cover your costs\n",
    "profitable_trades_early = spread_return_early[spread_return_early > .01]\n",
    "# Review the first five profitable trades\n",
    "profitable_trades_early.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp\n",
       "2018-02-24 08:32:00    0.012726\n",
       "2018-02-24 09:32:00    0.010974\n",
       "2018-02-24 09:39:00    0.010431\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For the date middle in the dataset, determine the number of times your trades with positive returns \n",
    "# exceed the 1% minimum threshold (.01) that you need to cover your costs\n",
    "profitable_trades_middle = spread_return_middle[spread_return_middle > .01]\n",
    "# Review the first five profitable trades\n",
    "profitable_trades_middle.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: Close, dtype: float64)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For the date late in the dataset, determine the number of times your trades with positive returns \n",
    "# exceed the 1% minimum threshold (.01) that you need to cover your costs\n",
    "profitable_trades_late = spread_return_late[spread_return_late > .01]\n",
    "# Review the first five profitable trades\n",
    "profitable_trades_late.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Generate the summary statistics of your spread returns that are greater than 1%. How do the average returns compare among the three dates?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    73.000000\n",
       "mean      0.016720\n",
       "std       0.007628\n",
       "min       0.010109\n",
       "25%       0.012061\n",
       "50%       0.014794\n",
       "75%       0.018124\n",
       "max       0.051633\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For the date early in the dataset, generate the summary statistics for the profitable trades\n",
    "# or you trades where the spread returns are are greater than 1%\n",
    "profitable_trades_early.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3.000000\n",
       "mean     0.011377\n",
       "std      0.001199\n",
       "min      0.010431\n",
       "25%      0.010703\n",
       "50%      0.010974\n",
       "75%      0.011850\n",
       "max      0.012726\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For the date middle in the dataset, generate the summary statistics for the profitable trades\n",
    "# or you trades where the spread returns are are greater than 1%\n",
    "profitable_trades_middle.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    0.0\n",
       "mean     NaN\n",
       "std      NaN\n",
       "min      NaN\n",
       "25%      NaN\n",
       "50%      NaN\n",
       "75%      NaN\n",
       "max      NaN\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For the date late in the dataset, generate the summary statistics for the profitable trades\n",
    "# or you trades where the spread returns are are greater than 1%\n",
    "profitable_trades_late.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inference: The average returns declined from early to late dates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. For each of the three dates, calculate the potential profit, in dollars, per trade. To do so, multiply the spread returns that were greater than 1% by the cost of what was purchased. Make sure to drop any missing values from the resulting DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp\n",
       "2018-01-16 02:56:00    162.01\n",
       "2018-01-16 02:57:00    135.00\n",
       "2018-01-16 02:59:00    176.13\n",
       "2018-01-16 03:00:00    134.01\n",
       "2018-01-16 03:06:00    241.54\n",
       "                        ...  \n",
       "2018-01-16 19:23:00    125.09\n",
       "2018-01-16 19:26:00    131.78\n",
       "2018-01-16 19:38:00    124.01\n",
       "2018-01-16 19:48:00    117.63\n",
       "2018-01-16 20:07:00    113.69\n",
       "Name: Close, Length: 73, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For the date early in the dataset, calculate the potential profit per trade in dollars \n",
    "# Multiply the profitable trades by the cost of the Bitcoin that was purchased\n",
    "profit_early =  profitable_trades_early * bitstamp_sliced['Close'].loc['2018-1-16']\n",
    "\n",
    "# Drop any missing values from the profit DataFrame\n",
    "profit_per_trade_early = profit_early.dropna()\n",
    "\n",
    "# View the early profit DataFrame\n",
    "profit_per_trade_early"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp\n",
       "2018-02-24 08:32:00    121.32\n",
       "2018-02-24 09:32:00    107.00\n",
       "2018-02-24 09:39:00    101.75\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For the date middle in the dataset, calculate the potential profit per trade in dollars \n",
    "# Multiply the profitable trades by the cost of the Bitcoin that was purchased\n",
    "profit_middle =  profitable_trades_middle * bitstamp_sliced['Close'].loc['2018-2-24']\n",
    "\n",
    "# Drop any missing values from the profit DataFrame\n",
    "profit_per_trade_middle = profit_middle.dropna()\n",
    "\n",
    "# View the early profit DataFrame\n",
    "profit_per_trade_middle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: Close, dtype: float64)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For the date late in the dataset, calculate the potential profit per trade in dollars \n",
    "# Multiply the profitable trades by the cost of the Bitcoin that was purchased\n",
    "profit_late =  profitable_trades_late * bitstamp_sliced['Close'].loc['2018-3-26']\n",
    "\n",
    "# Drop any missing values from the profit DataFrame\n",
    "profit_per_trade_late = profit_late.dropna()\n",
    "\n",
    "# View the early profit DataFrame\n",
    "profit_per_trade_late"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6. Generate the summary statistics, and plot the results for each of the three DataFrames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     73.000000\n",
       "mean     193.796849\n",
       "std       88.701429\n",
       "min      112.520000\n",
       "25%      139.420000\n",
       "50%      171.310000\n",
       "75%      210.060000\n",
       "max      606.800000\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate the summary statistics for the early profit per trade DataFrame\n",
    "profit_per_trade_early.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      3.000000\n",
       "mean     110.023333\n",
       "std       10.129246\n",
       "min      101.750000\n",
       "25%      104.375000\n",
       "50%      107.000000\n",
       "75%      114.160000\n",
       "max      121.320000\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate the summary statistics for the middle profit per trade DataFrame\n",
    "profit_per_trade_middle.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    0.0\n",
       "mean     NaN\n",
       "std      NaN\n",
       "min      NaN\n",
       "25%      NaN\n",
       "50%      NaN\n",
       "75%      NaN\n",
       "max      NaN\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate the summary statistics for the middle profit per trade DataFrame\n",
    "profit_per_trade_late.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Profit Per Trade - Early Date'}, xlabel='Timestamp'>"
      ]
     },
     "execution_count": 67,
     "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": [
    "# Plot the results for the early profit per trade DataFrame\n",
    "profit_per_trade_early.plot(title=\"Profit Per Trade - Early Date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Profit Per Trade - Middle Date'}, xlabel='Timestamp'>"
      ]
     },
     "execution_count": 68,
     "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": [
    "# Plot the results for the middle profit per trade DataFrame\n",
    "profit_per_trade_middle.plot(title=\"Profit Per Trade - Middle Date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Profit Per Trade - Late Date'}, xlabel='Timestamp'>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEPCAYAAABP1MOPAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAAZcklEQVR4nO3de7xcdX3u8c9DwkWFgkqAQKJBDWpERdwg6LFyRISAEGy9QLUgqClWqq3HA6hHi556qZeDh4tgtChUK9aKEGwQAfECRy47oFBEJGCEkIABjlwUjIGnf6zfhmF3djKzZ7LHvX/P+/WaV2bdv7+Z7PWs9VtrZmSbiIio10aDLiAiIgYrQRARUbkEQURE5RIEERGVSxBERFQuQRARUbkEQWwwkl4m6SZJD0g6WNL5kg4fdF0bkqS3SLp00HVEdCNBEI8jabmkB8vO+05JX5K0+ThX9xHgZNub2z7H9nzbZ5TtrHeHKen7kh4qtdwl6WxJM8dZS+t6ry/rfEDSwy3beEDS+3td/4Yi6XhJXxnHcj2FU1n+4ZbX6Jfl/8VOXazjy5L+Ybw1xIaVIIh2DrS9ObArsBvwv0bPIGl6B+t5OnB9j7UcXWrZCdgKOKHbFUia1jps+3klnDYHfjSyjfL4WMtynbSxFj8ur9eWwKuAB4GlknYebFnRDwmCGJPt24HzgZ0BJFnSOyXdBNxUxr1d0jJJ90haLGn7Mv5m4BnAeeUoctNyhP82Sc8FTgP2LNN+00Et9wDfbKnlOZIuLNu9UdIbRuYtR5+nSloi6bfAf++kvZLmlDa+VdKtwPfK+G9IukPSvZJ+KOl5Lcs8tbT7PklXAs8ctc4x6+wnScdJulnS/ZJ+Jum1ZXzb17q8H5+WdGs58ztN0hPWtx3bD9u+2fZfAz8Ajm+poe3rJGkh8CbgmFLDeWX89pK+KWl1Oct4V39flehUgiDGJGk2sD9wTcvog4GXAPMkvRL4OPAGYCbwK+AsANvPBG6lnF3Y/v3ICmzfABxFOcq0vVUHtWwN/DlwjaQnARcC/wJsAxwKfK51Bw38BfBRYAug226RVwDPBfYtw+cDc8u2rga+2jLvKcBDNO0/sjxGau6kzn65GXg5zRH7h4GvSJq5jtf6H2nOsnYBngXsAHyoy22eXbY5ou3rZHtRef7JUsOBkjYCzgN+Wra9N/C3kvYlJlyCINo5pxw5Xkpz1Pexlmkft32P7QdpjvJOt3112dG/j+bIc04fazmx1PJTYBXwHuA1wHLbX7K91vbVNGcLr2tZ7lzbl9l+xPZDXW7zeNu/LW3E9um27y9tPB54oaQtS5fTnwMfKvP/B3BGy3o6qbMvbH/D9srS3q/TnLHt3m5eSQLeDvxdeS/vp3mPD+lysyuBp7TU0PZ1GmPZ3YAZtj9ie43tW4AvjKOG6IP0gUY7B9u+aIxpt7U8357myA8A2w9IupvmCG95n2p5l+0vto6Q9HTgJaO6lKYD/zxGnd16dNmys/8o8HpgBvBImbQ18ISy3dZt/arleSd1jmzn5TRH1AC/st3VWYOkw2hCck4ZtXmpsZ0ZwBNp+vgfXQUwbYz5x7IDcE/Z/rpep3vbLPt0YPtRr800mms2McESBNGt1q+rXUnzBw082hXyVOD2LtfTrduAH9jeZwOtv3XZvwAW0FwgXU7T9fL/aXacq4G1wGzg52X+p3VZZ7NB+0c0O++ulWD8Ak33yo9tPyzpJ6XG0e0BuIvmYu/zynWg8Xotj+241/U6tavhNuCXtuf2sP3ok3QNRS/+BThC0i6SNqXpXrjC9vIOlr0TmCVpk3Fs99vATpL+UtLG5bFbuTDab1sAvwfupjmKfrSbzPbDNP3kx0t6oqR5QOvnJDZEnRtJ2qzlsSnwJJod7WoASUdQLqoXj3utbT9CExwnSNqmLLNDJ/3zkqZJ2lHSScBeNNcjYB2vU0sNz2gZvhK4T9Kxkp5Q1ruzpN06fymiXxIEMW62LwY+SNPvvYrmjplO+3i/R3Nr6R2S7upyu/cDry7bWgncQXPxc9Nu1tOhM2m6e24HfgZcPmr60TRH8ncAXwa+tIHrPJTmaH7kcbPtnwGfAX5Ms8N9PnBZyzLtXutjgWXA5ZLuAy4Cnr2O7e4p6QHgPuD7wJ8Au9m+rkxf3+v0TzQ3GPxG0jklRA+kuVj9S5qzlC/SnEnEBFN+mCYiom45I4iIqFyCICKicgmCiIjKJQgiIiqXIIiIqNyk/EDZ1ltv7Tlz5gy6jIiISWXp0qV32Z4xevykDII5c+YwPDw86DIiIiYVSb9qNz5dQxERlUsQRERULkEQEVG5BEFEROUSBBERlUsQRERULkEQEVG5BEFEROUSBBERlUsQRERULkEQEVG5BEFEROUSBBERlUsQRERULkEQEVG5BEFEROUSBBERlUsQRERULkEQEVG5BEFEROUSBBERletLEEjaT9KNkpZJOq7NdEk6sUy/VtKuo6ZPk3SNpG/3o56IiOhcz0EgaRpwCjAfmAccKmneqNnmA3PLYyFw6qjp7wZu6LWWiIjoXj/OCHYHltm+xfYa4Cxgwah5FgBnunE5sJWkmQCSZgEHAF/sQy0REdGlfgTBDsBtLcMryrhO5/kscAzwSB9qiYiILvUjCNRmnDuZR9JrgF/bXrrejUgLJQ1LGl69evV46oyIiDb6EQQrgNktw7OAlR3O8zLgIEnLabqUXinpK+02YnuR7SHbQzNmzOhD2RERAf0JgquAuZJ2lLQJcAiweNQ8i4HDyt1DewD32l5l+322Z9meU5b7nu0396GmiIjo0PReV2B7raSjgQuAacDptq+XdFSZfhqwBNgfWAb8Djii1+1GRER/yB7dnf/Hb2hoyMPDw4MuIyJiUpG01PbQ6PH5ZHFEROUSBBERlUsQRERULkEQEVG5BEFEROUSBBERlUsQRERULkEQEVG5BEFEROUSBBERlUsQRERULkEQEVG5BEFEROUSBBERlUsQRERULkEQEVG5BEFEROUSBBERlUsQRERULkEQEVG5BEFEROUSBBERlUsQRERULkEQEVG5BEFEROUSBBERlUsQRERUri9BIGk/STdKWibpuDbTJenEMv1aSbuW8bMlXSLpBknXS3p3P+qJiIjO9RwEkqYBpwDzgXnAoZLmjZptPjC3PBYCp5bxa4H/Yfu5wB7AO9ssGxERG1A/zgh2B5bZvsX2GuAsYMGoeRYAZ7pxObCVpJm2V9m+GsD2/cANwA59qCkiIjrUjyDYAbitZXgF/3Vnvt55JM0BXgRc0W4jkhZKGpY0vHr16l5rjoiIoh9BoDbj3M08kjYHvgn8re372m3E9iLbQ7aHZsyYMe5iIyLi8foRBCuA2S3Ds4CVnc4jaWOaEPiq7bP7UE9ERHShH0FwFTBX0o6SNgEOARaPmmcxcFi5e2gP4F7bqyQJ+CfgBtv/pw+1REREl6b3ugLbayUdDVwATANOt329pKPK9NOAJcD+wDLgd8ARZfGXAX8JXCfpJ2Xc+20v6bWuiIjojOzR3fl//IaGhjw8PDzoMiIiJhVJS20PjR6fTxZHRFQuQRARUbkEQURE5RIEERGVSxBERFQuQRARUbkEQURE5RIEERGVSxBERFQuQRARUbkEQURE5RIEERGVSxBERFQuQRARUbkEQURE5RIEERGVSxBERFQuQRARUbkEQURE5RIEERGVSxBERFQuQRARUbkEQURE5RIEERGVSxBERFQuQRARUbm+BIGk/STdKGmZpOPaTJekE8v0ayXt2umyERGxYfUcBJKmAacA84F5wKGS5o2abT4wtzwWAqd2sWxERGxA/Tgj2B1YZvsW22uAs4AFo+ZZAJzpxuXAVpJmdrhsRERsQP0Igh2A21qGV5RxnczTybIREbEB9SMI1GacO5ynk2WbFUgLJQ1LGl69enWXJUZExFj6EQQrgNktw7OAlR3O08myANheZHvI9tCMGTN6LjoiIhr9CIKrgLmSdpS0CXAIsHjUPIuBw8rdQ3sA99pe1eGyERGxAU3vdQW210o6GrgAmAacbvt6SUeV6acBS4D9gWXA74Aj1rVsrzVFRETnZLftkv+jNjQ05OHh4UGXERExqUhaanto9Ph8sjgionIJgoiIyiUIIiIqlyCIiKhcgiAionIJgoiIyiUIIiIqlyCIiKhcgiAionIJgoiIyiUIIiIqlyCIiKhcgiAionIJgoiIyiUIIiIqlyCIiKhcgiAionIJgoiIyiUIIiIqlyCIiKhcgiAionIJgoiIyiUIIiIqlyCIiKhcgiAionIJgoiIyiUIIiIq11MQSHqKpAsl3VT+ffIY8+0n6UZJyyQd1zL+U5J+LulaSd+StFUv9URERPd6PSM4DrjY9lzg4jL8OJKmAacA84F5wKGS5pXJFwI7234B8AvgfT3WExERXeo1CBYAZ5TnZwAHt5lnd2CZ7VtsrwHOKsth+7u215b5Lgdm9VhPRER0qdcg2Nb2KoDy7zZt5tkBuK1leEUZN9qRwPljbUjSQknDkoZXr17dQ8kREdFq+vpmkHQRsF2bSR/ocBtqM86jtvEBYC3w1bFWYnsRsAhgaGjIY80XERHdWW8Q2H7VWNMk3Slppu1VkmYCv24z2wpgdsvwLGBlyzoOB14D7G07O/iIiAnWa9fQYuDw8vxw4Nw281wFzJW0o6RNgEPKckjaDzgWOMj273qsJSIixqHXIPgEsI+km4B9yjCStpe0BKBcDD4auAC4AfhX29eX5U8GtgAulPQTSaf1WE9ERHRpvV1D62L7bmDvNuNXAvu3DC8BlrSZ71m9bD8iInqXTxZHRFQuQRARUbkEQURE5RIEERGVSxBERFQuQRARUbkEQURE5RIEERGVSxBERFQuQRARUbkEQURE5RIEERGVSxBERFQuQRARUbkEQURE5RIEERGVSxBERFQuQRARUbkEQURE5RIEERGVSxBERFQuQRARUbkEQURE5RIEERGVSxBERFQuQRARUbmegkDSUyRdKOmm8u+Tx5hvP0k3Slom6bg2098ryZK27qWeiIjoXq9nBMcBF9ueC1xchh9H0jTgFGA+MA84VNK8lumzgX2AW3usJSIixqHXIFgAnFGenwEc3Gae3YFltm+xvQY4qyw34gTgGMA91hIREePQaxBsa3sVQPl3mzbz7ADc1jK8ooxD0kHA7bZ/2mMdERExTtPXN4Oki4Dt2kz6QIfbUJtxlvTEso5Xd7QSaSGwEOBpT3tah5uOiIj1WW8Q2H7VWNMk3Slppu1VkmYCv24z2wpgdsvwLGAl8ExgR+CnkkbGXy1pd9t3tKljEbAIYGhoKN1IERF90mvX0GLg8PL8cODcNvNcBcyVtKOkTYBDgMW2r7O9je05tufQBMau7UIgIiI2nF6D4BPAPpJuornz5xMAkraXtATA9lrgaOAC4AbgX21f3+N2IyKiT9bbNbQutu8G9m4zfiWwf8vwEmDJetY1p5daIiJifPLJ4oiIyiUIIiIqlyCIiKhcgiAionIJgoiIyiUIIiIqlyCIiKhcgiAionIJgoiIyiUIIiIqlyCIiKhcgiAionIJgoiIyiUIIiIqlyCIiKhcgiAionIJgoiIyiUIIiIqlyCIiKhcgiAionIJgoiIyiUIIiIqJ9uDrqFrklYDvxp0HeOwNXDXoIuYQLW1F9LmWkzWNj/d9ozRIydlEExWkoZtDw26jolSW3shba7FVGtzuoYiIiqXIIiIqFyCYGItGnQBE6y29kLaXIsp1eZcI4iIqFzOCCIiKpcgiIgYgyQNuoaJkCDoE0n/5d7cqU7SkwZdw0STtJWk3SRNH3QtE0HSlpJeKGnjQdcyUUqb3y5pY9uuIQwSBH0g6dnAnZL+pgxP+f84kj4ELJF0nKRXlHFT+v+TpIXAjcDxwCJJ8wZb0YYl6X8Cw8A/AKdL2n7AJU2UjwGfAt4y4DomzJT+w51ATwBWAodKerKn8BV4SXMkLQHmAMcCAo6StIXtRwZa3AYk6QnAnsDLbR8A3A4slLRzmT6lwl/SW4BX0rT3QOBJwMvLtCnV1hGSppWny4DPAq+R9IxyVjBt7CUnvwTBOIycJrccAW8EvAe4GfjfZdqU+mNp6Rq4H/iW7SNtXw58F/g9zY5iSpG03ch7bPtB4KXAtmXymcA9wOvL9Ekf/q3tBS4E/sr2HWX4MmABTI22jhj1Hj9cRj8HuJXmbOhto6ZNSQmCLkh6qqRTgY9J2qXlCPg5wEuAdwL7lq6SFwyqzn4a1eZdbd8NnNGyw7gTeC7wwMCK7DNJG0s6GfgR8HlJbyqTTgcOBrB9E3Al8GRJuwyizn4Z1d5Fkl5v+3bbt7Yc0DwFuHRwVfZXm/f4jS2TVwMXA18FXiTpdEl7TXyVEydB0CFJmwFfAn5L0y3w95LeWibfCgzbvo+mi+gSYO+BFNpHbdr8QUlH2l7TEoI7ActtT5kgAA4EnmZ7LnAO8H5Jz6HZ8W8uaZ8y3y+A7YGHBlJl/7S291vAh0t74bF9xCzgjnYLT1Kj3+O/l/TcMm1LYDtgPrB7eUyZEGwnQdC5mcC2tt9r+7M0XQO7SdqTplvk45KuAO6luaB49cAq7Z+x2rxPyzxPp9khImmfyXx03HL0u5byzZK2/x34d+BImjD8KfBWSdNt3wI8keZ1mnTW0d7zgHeU610jXSIzgEsk/amkD0uaqm3enuZv+Fyas7+3Av8BvGHCi51ACYI22vXv2/4lcK+kg8uoy4CfA39Gc3Hph8DHbB9Ec9fBX0+mC0xdtnnfcrYA8CLgTySdDhwDTKq+1NbbQFv6vjcF7pa0Qxn+JDAEbAN8kebv5muSzqc5CPjFxFXcmw7b+yng+ZTuTUkvpLk2cgbwaZqz31UTVnSPumjzzsBsmu6iw2zvY/sc4Os01/+mrATBKJI2ab0YNrKDlLQJcDawt6RNbf+a5uhwOvCI7cNsnwtg+59tv2GyXGDqss3X0rR5uxIGfwq8Griq/OFcN/Et6J6k6ZI+DXxG0qtGTf4+zXWfXUq77wK+Bxxjew3wZuAk4Gzbr7B9+0TWPh7jaO/FwHvL9I3L9Att7277vImquxfjaPMlNO/xBba/W9Yh29+yfcWEFj/BEgQt1HwO4EpJH5F0UOu0sgO4huY1e3MZfQWwF+UoeOQMYDLdMTTONr+imeyHaG6zG7J96sRV3Zvy/pxI0w98JXCspHdK2hTA9mpgCfBaYJey2FnAakmblWskP7T9hYmvvnvjbO/Xado7neYscFvbJ0107eM1zjZ/DfiNpE1G/oan0h1S61LFpyM7Ue70eTNNX/BOwIck3W37MjWfMPwDzRnA+TQX066j2UHeRXPE9OgtZpPlP0+PbR75g/ryQIrvzRY0f/z72r5f0l3A/jS3gn4FwPapkj4AHCPpx8ChwLkl/Cab8bZ3se21TM47wnp5j9cMqOaByRnBY7YGvmP7attn0fQFfw7A9h/KjvEh298GPg+8g6bP9FTbk7X/sJc2T5p+8dHK3V3LeeyTo5fRnPnsqcd/evYzwAk0/cb/1/ZHJrDMvumhvR+ewDL7qrb3uFcJgsdsRNPfDYDt0+DRrxUY2TFOL88XAe+w/WzbZw+i2D6psc0jvkXTPzyz3Pp6Lc0H47YGkPQymq9pv9T2u22fOcBa+6G29kKdbR6X6oNgpF/f9jdoLoC+qWXy+2n6EEf6HD9fulOYpF0EQJ1tbuNS4G7KEaPtq2nuF99M0kuAucBU+sKx2toLdbZ5XKq9RlDefI3060t6Kk3XyEckne3mKwVWATeouWd8raT32L53gGX3pMY2j8X2KknnAJ+QtAy4ClgD/MH2NTQXxaeM2toLdbZ5vKr8hTJJ01p2htsBHwdeDLwK+ATwB+AimgtL99l+26Bq7Zca29wJSfNp2vxS4GTbJw+4pA2qtvZCnW3uVpVBMKLcMfBG4N9oPgy2VtKWwH8DjgBusP3BQdbYbzW2eX3UfKGeyx0yU15t7YU629yNKoNAzacJvw1cB7zf9ooyfuSWycc9nwpqbHNEdKbWIBCwq+2lZXgjmqOFKfti1NjmiOhMlUEwouXi6ZT9QZXRamxzRKxb1UEQERH5HEFERPUSBBERlUsQRERULkEQEVG5BEFEROUSBBERlUsQxJQn6amSflIed0i6vTx/QNLnNuB295L00g21/oh+qfbbR6Metu+m/ByhpOOBB2x/egI2vRfNr3v9vwnYVsS45YwgqlWO2L9dnh8v6QxJ35W0XNKfSfqkpOskfad8aRmSXizpB5KWSrpA0swy/l2SfibpWklnSZoDHAX8XTn7eLmkAyVdIekaSRdJ2rbLbS+X9I+SriyPZw3khYspJ0EQ8ZhnAgcAC2h+1/YS288HHgQOKDvkk4DX2X4xcDrw0bLsccCLbL8AOMr2cuA04ATbu9j+Ec0Ppexh+0XAWcAxnW67Zb77bO8OnAx8ts/tj0qlayjiMeeXn+e8DpgGfKeMvw6YAzwb2Bm4sPyo1TSaH/KB5mcQv1p+COWcMdY/C/h6OYvYBPhlF9se8bWWf0/ouoURbeSMIOIxvwcoX8j3h5ZvZn2E5qBJwPXlCH8X28+3/eoyzwHAKTQ/9rNU5beeRzmJ5odRng/8FbBZF9se4TGeR4xbgiCiczcCMyTtCc3vN0h6XvlK79m2L6Hp7tkK2By4H9iiZfktgdvL88PHWcMbW/798TjXEfE46RqK6JDtNZJeB5xYftVtOk0//S+Ar5Rxorku8BtJ5wH/JmkB8DfA8cA3JN0OXA7sOI4yNpV0Bc1B3KG9tikC8jXUEZOGpOXAkO27Bl1LTC3pGoqIqFzOCCIiKpczgoiIyiUIIiIqlyCIiKhcgiAionIJgoiIyiUIIiIq95/0k49+trZZQAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the results for the late profit per trade DataFrame\n",
    "profit_per_trade_late.plot(title=\"Profit Per Trade - Late Date\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7. Calculate the potential arbitrage profits that you can make on each day. To do so, sum the elements in the profit_per_trade DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14147.169999999998"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the sum of the potential profits for the early profit per trade DataFrame\n",
    "profit_per_trade_early.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "330.0699999999997"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the sum of the potential profits for the middle profit per trade DataFrame\n",
    "profit_per_trade_middle.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the sum of the potential profits for the late profit per trade DataFrame\n",
    "profit_per_trade_late.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8. Using the `cumsum` function, plot the cumulative sum of each of the three DataFrames. Can you identify any patterns or trends in the profits across the three time periods?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the cumsum function to calculate the cumulative profits over time for the early profit per trade DataFrame\n",
    "cumulative_profit_early = profit_per_trade_early.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Cumulative Sum - Early Date'}, xlabel='Timestamp'>"
      ]
     },
     "execution_count": 74,
     "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": [
    "# Plot the cumulative sum of profits for the early profit per trade DataFrame\n",
    "cumulative_profit_early.plot(title=\"Cumulative Sum - Early Date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Cumulative Sum - Middle Date'}, xlabel='Timestamp'>"
      ]
     },
     "execution_count": 75,
     "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": [
    "# Use the cumsum function to calculate the cumulative profits over time for the middle profit per trade DataFrame\n",
    "cumulative_profit_middle = profit_per_trade_middle.cumsum()\n",
    "# Plot the cumulative sum of profits for the early profit per trade DataFrame\n",
    "cumulative_profit_middle.plot(title=\"Cumulative Sum - Middle Date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Cumulative Sum - Late Date'}, xlabel='Timestamp'>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use the cumsum function to calculate the cumulative profits over time for the late profit per trade DataFrame\n",
    "cumulative_profit_late = profit_per_trade_late.cumsum()\n",
    "# Plot the cumulative sum of profits for the early profit per trade DataFrame\n",
    "cumulative_profit_late.plot(title=\"Cumulative Sum - Late Date\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question:** After reviewing the profit information across each date from the different time periods, can you identify any patterns or trends?\n",
    "    \n",
    "**Answer:** From the Early and the Middle Dates, the trend shows that the profitable trades are higher towards the end of the time cycle. There are no profitable trades towards the late dates\n",
    "\n",
    "\n",
    "## Analysis Report\n",
    "\n",
    "### We assumed a 0.5% fee for each buy and sell which totals to 1%. hence a profitable trade should have a return of over 1% to cover the fee.\n",
    "### From the initial visualization, it was evident that Coinbase generally had higher Bitcoin value compared to Bitstamp.\n",
    "### The above observation was the basis for the calculation of assumed that the calculation of arbitrage spread, spread return and profit.\n",
    "### The average returns were best during the early months of the date range and towards the end of the month or day. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}