{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4b866c37-13ff-4f0f-bff4-61b41719ad6a",
   "metadata": {},
   "source": [
    "# The DAX \"Prophet\"\n",
    "\n",
    "* DAX = German index. Not the ETF named \"DAX\" (pitfall)\n",
    "* Data source: FMP (you can use yfinance)\n",
    "\n",
    "\n",
    "* Purpose of this NB: swing trade support (weeks)\n",
    "* Model can detect seasonalities"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4882e088-c9c9-4004-a984-e6e948dd6c96",
   "metadata": {},
   "source": [
    "## Data retrieval ($)\n",
    "\n",
    "We want historical data for our model. \n",
    "\n",
    "This isn't free."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "ec60e3a9-efcc-4619-9fbd-0d09fedcf4f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from openbb import obb\n",
    "import os\n",
    "from dotenv import load_dotenv\n",
    "\n",
    "def read_api_keys_from_env():\n",
    "    \"\"\"\n",
    "    Read API keys from env\n",
    "    \"\"\"\n",
    "    load_dotenv(\"api_keys\")  # This loads the variables from the file\n",
    "    \n",
    "    obb.user.credentials.nasdaq_api_key = os.getenv('NASDAQ_API_KEY') or print(\"NASDAQ_API_KEY is not set or empty\") or None\n",
    "    obb.user.credentials.fmp_api_key = os.getenv('FMP_API_KEY') or print(\"FMP_API_KEY is not set or empty\") or None\n",
    "    obb.user.credentials.econdb_api_key = os.getenv('ECONDB_API_KEY') or print(\"ECONDB_API_KEY is not set or empty\") or None\n",
    "    obb.user.credentials.fred_api_key = os.getenv('FRED_API_KEY') or print(\"FRED_API_KEY is not set or empty\") or None\n",
    "    obb.user.credentials.intrinio_api_key = os.getenv('INTRINIO_API_KEY') or print(\"INTRINIO_API_KEY is not set or empty\") or None\n",
    "    obb.user.credentials.polygon_api_key = os.getenv('POLYGON_API_KEY') or print(\"POLYGON_API_KEY is not set or empty\") or None\n",
    "    \n",
    "read_api_keys_from_env()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "270e5063-6928-4138-adf9-f25913be521c",
   "metadata": {},
   "source": [
    "## Data download via Quant APIs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c0f83ff6-d1b2-424a-9974-4d005110c9e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dax_data(symbol : str):\n",
    "\n",
    "    df_daily = obb.equity.price.historical(symbol = symbol, provider=\"fmp\",start_date=\"2000-01-01\").to_df()\n",
    "    return df_daily\n",
    "\n",
    "# https://site.financialmodelingprep.com/index-summary/%5EGDAXI \n",
    "# https://de.finance.yahoo.com/quote/%5EGDAXI/ \n",
    "df = get_dax_data(\"^GDAXI\") # Not \"DAX\", which is an ETF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b4e2d572-5349-4644-a82c-083aff2ffcc2",
   "metadata": {},
   "outputs": [
    {
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       "      <th>vwap</th>\n",
       "      <th>adj_close</th>\n",
       "      <th>unadjusted_volume</th>\n",
       "      <th>change</th>\n",
       "      <th>change_percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-08-23</th>\n",
       "      <td>18538.86914</td>\n",
       "      <td>18670.44922</td>\n",
       "      <td>18519.28906</td>\n",
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       "      <td>41122500</td>\n",
       "      <td>18590.43</td>\n",
       "      <td>18633.09961</td>\n",
       "      <td>41122500.0</td>\n",
       "      <td>94.23047</td>\n",
       "      <td>0.005083</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "                   open         high          low        close    volume  \\\n",
       "date                                                                       \n",
       "2024-08-23  18538.86914  18670.44922  18519.28906  18633.09961  41122500   \n",
       "\n",
       "                vwap    adj_close  unadjusted_volume    change  change_percent  \n",
       "date                                                                            \n",
       "2024-08-23  18590.43  18633.09961         41122500.0  94.23047        0.005083  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.tail(1)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4801ef6a-4fb4-4091-8b68-74cce0b05c3c",
   "metadata": {},
   "outputs": [
    {
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       "      <th>unadjusted_volume</th>\n",
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       "      <th>date</th>\n",
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       "      <th>2000-01-03</th>\n",
       "      <td>6961.720215</td>\n",
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       "      <td>43072500</td>\n",
       "      <td>6876.98665</td>\n",
       "      <td>6750.759766</td>\n",
       "      <td>43072500.0</td>\n",
       "      <td>-210.96045</td>\n",
       "      <td>-0.0303</td>\n",
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       "                   open         high          low        close    volume  \\\n",
       "date                                                                       \n",
       "2000-01-03  6961.720215  7159.330078  6720.870117  6750.759766  43072500   \n",
       "\n",
       "                  vwap    adj_close  unadjusted_volume     change  \\\n",
       "date                                                                \n",
       "2000-01-03  6876.98665  6750.759766         43072500.0 -210.96045   \n",
       "\n",
       "            change_percent  \n",
       "date                        \n",
       "2000-01-03         -0.0303  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7d0aca72-041d-461b-8000-dedce6daf3f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"GDAXI_Jan_2k_Aug24.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f91a23d4-73df-44fd-96b7-3f2a0b0545b8",
   "metadata": {},
   "source": [
    "# Load the data (local file)\n",
    "\n",
    "* Data is present in the CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "77d99141-68b3-42ce-871e-b5fc4bbcb087",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "gdax_df = pd.read_csv(\"GDAXI_Jan_2k_Aug24.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2e82c9db-965c-459e-b2f4-5f805f65e117",
   "metadata": {},
   "outputs": [
    {
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       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>vwap</th>\n",
       "      <th>adj_close</th>\n",
       "      <th>unadjusted_volume</th>\n",
       "      <th>change</th>\n",
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       "      <th>6260</th>\n",
       "      <td>2024-08-23</td>\n",
       "      <td>18538.86914</td>\n",
       "      <td>18670.44922</td>\n",
       "      <td>18519.28906</td>\n",
       "      <td>18633.09961</td>\n",
       "      <td>41122500</td>\n",
       "      <td>18590.43</td>\n",
       "      <td>18633.09961</td>\n",
       "      <td>41122500.0</td>\n",
       "      <td>94.23047</td>\n",
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       "            date         open         high          low        close  \\\n",
       "6260  2024-08-23  18538.86914  18670.44922  18519.28906  18633.09961   \n",
       "\n",
       "        volume      vwap    adj_close  unadjusted_volume    change  \\\n",
       "6260  41122500  18590.43  18633.09961         41122500.0  94.23047   \n",
       "\n",
       "      change_percent  \n",
       "6260        0.005083  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdax_df.tail(1)"
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  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "db05b7ff-d860-498c-864c-641583d2699d",
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    {
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       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>vwap</th>\n",
       "      <th>adj_close</th>\n",
       "      <th>unadjusted_volume</th>\n",
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       "      <td>2000-01-03</td>\n",
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       "      <td>6720.870117</td>\n",
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       "         date         open         high          low        close    volume  \\\n",
       "0  2000-01-03  6961.720215  7159.330078  6720.870117  6750.759766  43072500   \n",
       "\n",
       "         vwap    adj_close  unadjusted_volume     change  change_percent  \n",
       "0  6876.98665  6750.759766         43072500.0 -210.96045         -0.0303  "
      ]
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     "execution_count": 24,
     "metadata": {},
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   ],
   "source": [
    "gdax_df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daf6d537-f095-4a27-a2b7-b629f1e17c76",
   "metadata": {},
   "source": [
    "# We use a Kalman Filter for estimates\n",
    "\n",
    "This is one feature we will use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7903e92f-5636-40e6-8b37-202d5f23eeea",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last 5 days of complete data:\n",
      "                  close  KalmanEstimate\n",
      "date                                   \n",
      "2024-08-20  18357.51953    18083.979993\n",
      "2024-08-21  18448.94922    18118.697662\n",
      "2024-08-22  18493.39063    18154.340301\n",
      "2024-08-23  18633.09961    18199.882243\n",
      "2024-08-24          NaN    18199.882243\n",
      "\n",
      "Last complete day in dataset:\n",
      "open    NaN\n",
      "high    NaN\n",
      "low     NaN\n",
      "close   NaN\n",
      "Name: 2024-08-24 00:00:00, dtype: float64\n",
      "\n",
      "Next day estimate: 18199.88\n",
      "Estimate uncertainty: 0.32\n",
      "\n",
      "Last month's maximum price: 18670.45\n",
      "\n",
      "Last month's minimum price: 17024.82\n",
      "\n",
      "Data range: from 2000-01-03 to 2024-08-24\n",
      "\n",
      "Current date and time: 2024-08-25 06:54:51.729938\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pykalman import KalmanFilter\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import timedelta, datetime\n",
    "\n",
    "# Read the DAX data from the CSV file\n",
    "df = pd.read_csv(\"GDAXI_Jan_2k_Aug24.csv\", parse_dates=['date'], index_col='date')\n",
    "\n",
    "# Remove any rows with NaN values and get the latest complete day\n",
    "df = df.dropna()\n",
    "latest_complete_day = df.index[-1]\n",
    "\n",
    "# Ensure we're only working with complete data up to the latest complete day\n",
    "df = df[:latest_complete_day]\n",
    "\n",
    "# Initialize Kalman Filter\n",
    "kf = KalmanFilter(\n",
    "    initial_state_mean=df['close'].iloc[0],\n",
    "    initial_state_covariance=1,\n",
    "    transition_matrices=[1],\n",
    "    observation_matrices=[1],\n",
    "    observation_covariance=1,\n",
    "    transition_covariance=0.01\n",
    ")\n",
    "\n",
    "# Apply Kalman Filter\n",
    "state_means, state_covariances = kf.filter(df['close'])\n",
    "\n",
    "# Add Kalman Filter estimates to the dataframe\n",
    "df['KalmanEstimate'] = state_means\n",
    "\n",
    "# Get the next day estimate\n",
    "last_mean = state_means[-1]\n",
    "last_covariance = state_covariances[-1]\n",
    "next_mean, next_covariance = kf.filter_update(\n",
    "    filtered_state_mean=last_mean,\n",
    "    filtered_state_covariance=last_covariance\n",
    ")\n",
    "\n",
    "# Add the next day estimate to the dataframe\n",
    "next_day = latest_complete_day + timedelta(days=1)\n",
    "df.loc[next_day, 'KalmanEstimate'] = next_mean[0]\n",
    "\n",
    "# Calculate last month's max and min\n",
    "last_month_start = latest_complete_day - timedelta(days=30)\n",
    "last_month = df.loc[last_month_start:]\n",
    "last_month_max = last_month['high'].max()\n",
    "last_month_min = last_month['low'].min()\n",
    "\n",
    "# Plot the results\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(df.index, df['close'], label='Closing Price')\n",
    "plt.plot(df.index, df['KalmanEstimate'], label='Kalman Filter Estimate', color='red')\n",
    "plt.scatter(next_day, next_mean, color='green', s=100, label='Next Day Estimate')\n",
    "\n",
    "# Add horizontal lines for last month's max and min\n",
    "plt.axhline(y=last_month_max, color='orange', linestyle='--', label='Last Month Max')\n",
    "plt.axhline(y=last_month_min, color='purple', linestyle='--', label='Last Month Min')\n",
    "\n",
    "plt.title('DAX (German Stock Index) with Kalman Filter Estimate and Additional Data')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Price')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Print the last few days of data and the next day estimate\n",
    "print(\"Last 5 days of complete data:\")\n",
    "print(df[['close', 'KalmanEstimate']].tail())\n",
    "\n",
    "print(f\"\\nLast complete day in dataset:\")\n",
    "print(df[['open', 'high', 'low', 'close']].iloc[-1])\n",
    "\n",
    "print(f\"\\nNext day estimate: {next_mean[0]:.2f}\")\n",
    "print(f\"Estimate uncertainty: {np.sqrt(next_covariance[0][0]):.2f}\")\n",
    "\n",
    "print(f\"\\nLast month's maximum price: {last_month_max:.2f}\")\n",
    "print(f\"\\nLast month's minimum price: {last_month_min:.2f}\")\n",
    "\n",
    "# Print the date range of the data\n",
    "print(f\"\\nData range: from {df.index[0].date()} to {df.index[-1].date()}\")\n",
    "\n",
    "print(f\"\\nCurrent date and time: {datetime.now()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5267ca35-aced-4b12-bb9b-bfba6cee5a62",
   "metadata": {},
   "source": [
    "# Get foundational features ($)\n",
    "\n",
    "We are going to enrich our dataset with\n",
    "\n",
    "* Export stats\n",
    "* Inflation\n",
    "\n",
    "https://www.econdb.com/series/REXPDE/germany-real-exports-of-goods-and-services/?from=2019-08-25&to=2024-08-25 \n",
    "\n",
    "We know DE is exporting a lot, and that DAX companies are export focused.\n",
    "(For very adv. analysis, we could incorporate the cash flow etc. - all possible)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31cca246-87d6-4952-bf3a-cc481e7bad4b",
   "metadata": {},
   "source": [
    "## RPXDE = Germany - Real exports of goods and services (Level USD)\n",
    "\n",
    "These statistics aren't available since '91. \n",
    "We could go all out and merge certain other stats. But it doesn't seem to be necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de0c2354-e0aa-4034-a4bd-46e25963fd2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "API_TOKEN = os.getenv('ECONDB_API_KEY')\n",
    "\n",
    "export_stats_usd_de_df = pd.read_csv(\n",
    "    'https://www.econdb.com/api/series/REXPDE~TUSD/?token=%s&format=csv' % API_TOKEN,\n",
    "    index_col='Date', parse_dates=['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "0269e523-0a5c-4e09-8805-f6a53016e90a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>REXPDE~TUSD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-01-01</th>\n",
       "      <td>441754.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            REXPDE~TUSD\n",
       "Date                   \n",
       "2024-01-01     441754.0"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "export_stats_usd_df.tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "78ff5b8d-1ab9-4688-86a0-b75e09103cd7",
   "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>REXPDE~TUSD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1991-01-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            REXPDE~TUSD\n",
       "Date                   \n",
       "1991-01-01          NaN"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "export_stats_usd_df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41020bb4-5100-49a7-ba3f-bdc7c0fadb01",
   "metadata": {},
   "source": [
    "## CPI = Germany - Consumer Price Index (Level USD would be better)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2396d267-0797-4225-a5fc-f61f32c93fcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "consumer_price_index_de_df = pd.read_csv(\n",
    "    'https://www.econdb.com/api/series/CPIDE/?token=%s&format=csv' % API_TOKEN,\n",
    "    index_col='Date', parse_dates=['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "196a05a7-3233-416c-b4cc-5de7a1c49a43",
   "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>CPIDE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-03-01</th>\n",
       "      <td>91.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CPIDE\n",
       "Date             \n",
       "2012-03-01   91.7"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "consumer_price_index_de_df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "94260786-8ec3-48ae-ae1b-382df0460882",
   "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>CPIDE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-07-01</th>\n",
       "      <td>119.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CPIDE\n",
       "Date             \n",
       "2024-07-01  119.8"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "consumer_price_index_de_df.tail(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3d0e740-0414-4698-9c44-15041c2cdbed",
   "metadata": {},
   "source": [
    "# Combine the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "714e5a58-e423-45d5-960d-faee3d3312eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           index         open         high          low        close  \\\n",
      "12271 2024-08-19  18312.50977  18454.93945  18284.85938  18421.68945   \n",
      "12272 2024-08-20  18473.23047  18495.27930  18349.98047  18357.51953   \n",
      "12273 2024-08-21  18358.78906  18476.63086  18352.18945  18448.94922   \n",
      "12274 2024-08-22  18459.27930  18551.39063  18458.60938  18493.39063   \n",
      "12275 2024-08-23  18538.86914  18670.44922  18519.28906  18633.09961   \n",
      "\n",
      "           volume      vwap    adj_close  unadjusted_volume     change  \\\n",
      "12271  43769700.0  18368.50  18421.68945         43769700.0  109.17968   \n",
      "12272  42912000.0  18419.00  18357.51953         42912000.0 -115.71094   \n",
      "12273  36068100.0  18409.14  18448.94922         36068100.0   90.16016   \n",
      "12274  40950900.0  18490.67  18493.39063         40950900.0   34.11133   \n",
      "12275  41122500.0  18590.43  18633.09961         41122500.0   94.23047   \n",
      "\n",
      "       change_percent  REXPDE~TUSD  CPIDE  \n",
      "12271        0.005962     441754.0  119.8  \n",
      "12272       -0.006264     441754.0  119.8  \n",
      "12273        0.004911     441754.0  119.8  \n",
      "12274        0.001848     441754.0  119.8  \n",
      "12275        0.005083     441754.0  119.8  \n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12276 entries, 0 to 12275\n",
      "Data columns (total 13 columns):\n",
      " #   Column             Non-Null Count  Dtype         \n",
      "---  ------             --------------  -----         \n",
      " 0   index              12276 non-null  datetime64[ns]\n",
      " 1   open               8987 non-null   float64       \n",
      " 2   high               8987 non-null   float64       \n",
      " 3   low                8987 non-null   float64       \n",
      " 4   close              8987 non-null   float64       \n",
      " 5   volume             8987 non-null   float64       \n",
      " 6   vwap               8987 non-null   float64       \n",
      " 7   adj_close          8987 non-null   float64       \n",
      " 8   unadjusted_volume  8987 non-null   float64       \n",
      " 9   change             8987 non-null   float64       \n",
      " 10  change_percent     8987 non-null   float64       \n",
      " 11  REXPDE~TUSD        8989 non-null   float64       \n",
      " 12  CPIDE              4546 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(12)\n",
      "memory usage: 1.2 MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "# Ensure export_stats_usd_de_df and consumer_price_index_de_df have datetime index\n",
    "export_stats_usd_de_df.index = pd.to_datetime(export_stats_usd_de_df.index)\n",
    "consumer_price_index_de_df.index = pd.to_datetime(consumer_price_index_de_df.index)\n",
    "\n",
    "# Resample export_stats_usd_de_df to daily frequency and forward fill\n",
    "export_stats_daily = export_stats_usd_de_df.resample('D').ffill()\n",
    "\n",
    "# Resample consumer_price_index_de_df to daily frequency and forward fill\n",
    "cpi_daily = consumer_price_index_de_df.resample('D').ffill()\n",
    "\n",
    "# Merge all dataframes\n",
    "combined_df = gdax_df.join([export_stats_daily, cpi_daily], how='outer')\n",
    "\n",
    "# Forward fill any remaining NaN values\n",
    "combined_df = combined_df.ffill()\n",
    "\n",
    "# If you want 'date' as a column instead of index\n",
    "combined_df = combined_df.reset_index()\n",
    "\n",
    "# Display the first few rows of the combined dataframe\n",
    "print(combined_df.tail())\n",
    "\n",
    "# Display info about the combined dataframe\n",
    "print(combined_df.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cb9f86d-5605-4388-9048-67c55ba2cf5c",
   "metadata": {},
   "source": [
    "# Calculation of a volatility-based indicator (Lorentzian) and the Kalman estimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "bd1f6548-d845-4a26-b0be-52951a332ffe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           index         open         high          low        close  \\\n",
      "12271 2024-08-19  18312.50977  18454.93945  18284.85938  18421.68945   \n",
      "12272 2024-08-20  18473.23047  18495.27930  18349.98047  18357.51953   \n",
      "12273 2024-08-21  18358.78906  18476.63086  18352.18945  18448.94922   \n",
      "12274 2024-08-22  18459.27930  18551.39063  18458.60938  18493.39063   \n",
      "12275 2024-08-23  18538.86914  18670.44922  18519.28906  18633.09961   \n",
      "\n",
      "           volume      vwap    adj_close  unadjusted_volume     change  \\\n",
      "12271  43769700.0  18368.50  18421.68945         43769700.0  109.17968   \n",
      "12272  42912000.0  18419.00  18357.51953         42912000.0 -115.71094   \n",
      "12273  36068100.0  18409.14  18448.94922         36068100.0   90.16016   \n",
      "12274  40950900.0  18490.67  18493.39063         40950900.0   34.11133   \n",
      "12275  41122500.0  18590.43  18633.09961         41122500.0   94.23047   \n",
      "\n",
      "       change_percent  KalmanEstimate  LorentzianIndicator  REXPDE~TUSD  CPIDE  \n",
      "12271        0.005962    18055.224170             5.594636     441754.0  119.8  \n",
      "12272       -0.006264    18083.979993             5.592532     441754.0  119.8  \n",
      "12273        0.004911    18118.697662             5.505103     441754.0  119.8  \n",
      "12274        0.001848    18154.340301             5.395760     441754.0  119.8  \n",
      "12275        0.005083    18199.882243             5.234629     441754.0  119.8  \n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12276 entries, 0 to 12275\n",
      "Data columns (total 15 columns):\n",
      " #   Column               Non-Null Count  Dtype         \n",
      "---  ------               --------------  -----         \n",
      " 0   index                12276 non-null  datetime64[ns]\n",
      " 1   open                 8987 non-null   float64       \n",
      " 2   high                 8987 non-null   float64       \n",
      " 3   low                  8987 non-null   float64       \n",
      " 4   close                8987 non-null   float64       \n",
      " 5   volume               8987 non-null   float64       \n",
      " 6   vwap                 8987 non-null   float64       \n",
      " 7   adj_close            8987 non-null   float64       \n",
      " 8   unadjusted_volume    8987 non-null   float64       \n",
      " 9   change               8987 non-null   float64       \n",
      " 10  change_percent       8987 non-null   float64       \n",
      " 11  KalmanEstimate       8987 non-null   float64       \n",
      " 12  LorentzianIndicator  8970 non-null   float64       \n",
      " 13  REXPDE~TUSD          8989 non-null   float64       \n",
      " 14  CPIDE                4546 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(14)\n",
      "memory usage: 1.4 MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pykalman import KalmanFilter\n",
    "import talib\n",
    "\n",
    "# Function to calculate Lorentzian indicator\n",
    "def calculate_lorentzian(data, period=14):\n",
    "    high_low = data['high'] - data['low']\n",
    "    high_close = np.abs(data['high'] - data['close'].shift())\n",
    "    low_close = np.abs(data['low'] - data['close'].shift())\n",
    "    ranges = pd.concat([high_low, high_close, low_close], axis=1)\n",
    "    true_range = np.max(ranges, axis=1)\n",
    "    atr = talib.SMA(true_range, timeperiod=period)\n",
    "    lorentzian = np.log(1 + atr)\n",
    "    return lorentzian\n",
    "\n",
    "# Assuming gdax_df is your DAX data\n",
    "# Ensure gdax_df has 'date' as index and contains 'open', 'high', 'low', 'close' columns\n",
    "\n",
    "# Calculate Kalman Filter estimates\n",
    "kf = KalmanFilter(\n",
    "    initial_state_mean=gdax_df['close'].iloc[0],\n",
    "    initial_state_covariance=1,\n",
    "    transition_matrices=[1],\n",
    "    observation_matrices=[1],\n",
    "    observation_covariance=1,\n",
    "    transition_covariance=0.01\n",
    ")\n",
    "\n",
    "state_means, _ = kf.filter(gdax_df['close'])\n",
    "gdax_df['KalmanEstimate'] = state_means\n",
    "\n",
    "# Calculate Lorentzian indicator\n",
    "gdax_df['LorentzianIndicator'] = calculate_lorentzian(gdax_df)\n",
    "\n",
    "# Ensure export_stats_usd_de_df and consumer_price_index_de_df have datetime index\n",
    "export_stats_usd_de_df.index = pd.to_datetime(export_stats_usd_de_df.index)\n",
    "consumer_price_index_de_df.index = pd.to_datetime(consumer_price_index_de_df.index)\n",
    "\n",
    "# Resample export_stats_usd_de_df to daily frequency and forward fill\n",
    "export_stats_daily = export_stats_usd_de_df.resample('D').ffill()\n",
    "\n",
    "# Resample consumer_price_index_de_df to daily frequency and forward fill\n",
    "cpi_daily = consumer_price_index_de_df.resample('D').ffill()\n",
    "\n",
    "# Merge all dataframes\n",
    "combined_df = gdax_df.join([export_stats_daily, cpi_daily], how='outer')\n",
    "\n",
    "# Forward fill any remaining NaN values\n",
    "combined_df = combined_df.ffill()\n",
    "\n",
    "# If you want 'date' as a column instead of index\n",
    "combined_df = combined_df.reset_index()\n",
    "\n",
    "# Display the first few rows of the combined dataframe\n",
    "print(combined_df.tail())\n",
    "\n",
    "# Display info about the combined dataframe\n",
    "print(combined_df.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab1388dd-a79a-4c1b-be1f-3865e5ab6d86",
   "metadata": {},
   "source": [
    "# Now we fit Prophet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b712cba-df13-4ef7-9a20-4e83fb0ff80a",
   "metadata": {},
   "source": [
    "## Prophet model for the next day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "73e18e29-154b-43ed-947b-c0ac49e54f8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using regressors: ['open', 'high', 'low', 'volume', 'vwap', 'adj_close', 'unadjusted_volume', 'change', 'change_percent', 'KalmanEstimate', 'LorentzianIndicator', 'REXPDE~TUSD', 'CPIDE']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "18:44:45 - cmdstanpy - INFO - Chain [1] start processing\n",
      "18:44:52 - cmdstanpy - INFO - Chain [1] done processing\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model training completed and saved.\n"
     ]
    }
   ],
   "source": [
    "from prophet import Prophet\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "# Assuming your dataframe is named combined_df\n",
    "df = combined_df.copy()\n",
    "\n",
    "# Convert 'index' to datetime if it's not already\n",
    "df['index'] = pd.to_datetime(df['index'])\n",
    "\n",
    "# Filter for the last 10 years of data\n",
    "ten_years_ago = datetime.now() - timedelta(days=365*10)\n",
    "df = df[df['index'] >= ten_years_ago]\n",
    "\n",
    "# Remove rows with NaN values\n",
    "df = df.dropna()\n",
    "\n",
    "# Rename columns to match Prophet's requirements\n",
    "df = df.rename(columns={'index': 'ds', 'close': 'y'})\n",
    "\n",
    "# Define potential regressors (all columns except 'ds' and 'y')\n",
    "regressors = [col for col in df.columns if col not in ['ds', 'y']]\n",
    "\n",
    "def train_prophet_model(df, regressors, include_monthly_seasonality=True):\n",
    "    model = Prophet(\n",
    "        yearly_seasonality=True,\n",
    "        daily_seasonality=True,\n",
    "        weekly_seasonality=True,\n",
    "        changepoint_prior_scale=0.05,\n",
    "        seasonality_prior_scale=10.0,\n",
    "        seasonality_mode='additive'\n",
    "    )\n",
    "    \n",
    "    if include_monthly_seasonality:\n",
    "        model.add_seasonality(name='monthly', period=30.5, fourier_order=5)\n",
    "    \n",
    "    for regressor in regressors:\n",
    "        if regressor in df.columns:\n",
    "            model.add_regressor(regressor)\n",
    "    \n",
    "    model.fit(df)\n",
    "    return model\n",
    "\n",
    "# Filter regressors to only include those present in the dataframe\n",
    "regressors = [reg for reg in regressors if reg in df.columns]\n",
    "print(f\"Using regressors: {regressors}\")\n",
    "\n",
    "# Train the model\n",
    "model = train_prophet_model(df, regressors)\n",
    "\n",
    "# Save model and data for future use\n",
    "df.to_pickle('dax_data.pkl')\n",
    "import pickle\n",
    "with open('prophet_model.pkl', 'wb') as f:\n",
    "    pickle.dump(model, f)\n",
    "\n",
    "print(\"Model training completed and saved.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "93f6d266-070c-4fbb-a638-43c3a28c6096",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Results:\n",
      "Root Mean Squared Error: 0.24804108650748838\n",
      "Mean Absolute Error: 0.2048658067697176\n"
     ]
    },
    {
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          "2015-06-14T00:00:00",
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          "2015-06-25T00:00:00",
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          "2015-06-27T00:00:00",
          "2015-06-28T00:00:00",
          "2015-06-29T00:00:00",
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          "2015-07-05T00:00:00",
          "2015-07-06T00:00:00",
          "2015-07-07T00:00:00",
          "2015-07-08T00:00:00",
          "2015-07-09T00:00:00",
          "2015-07-10T00:00:00",
          "2015-07-11T00:00:00",
          "2015-07-12T00:00:00",
          "2015-07-13T00:00:00",
          "2015-07-14T00:00:00",
          "2015-07-15T00:00:00",
          "2015-07-16T00:00:00",
          "2015-07-17T00:00:00",
          "2015-07-18T00:00:00",
          "2015-07-19T00:00:00",
          "2015-07-20T00:00:00",
          "2015-07-21T00:00:00",
          "2015-07-22T00:00:00",
          "2015-07-23T00:00:00",
          "2015-07-24T00:00:00",
          "2015-07-25T00:00:00",
          "2015-07-26T00:00:00",
          "2015-07-27T00:00:00",
          "2015-07-28T00:00:00",
          "2015-07-29T00:00:00",
          "2015-07-30T00:00:00",
          "2015-07-31T00:00:00",
          "2015-08-01T00:00:00",
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          "2015-08-06T00:00:00",
          "2015-08-07T00:00:00",
          "2015-08-08T00:00:00",
          "2015-08-09T00:00:00",
          "2015-08-10T00:00:00",
          "2015-08-11T00:00:00",
          "2015-08-12T00:00:00",
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          "2015-08-14T00:00:00",
          "2015-08-15T00:00:00",
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          "2015-08-21T00:00:00",
          "2015-08-22T00:00:00",
          "2015-08-23T00:00:00",
          "2015-08-24T00:00:00",
          "2015-08-25T00:00:00",
          "2015-08-26T00:00:00",
          "2015-08-27T00:00:00",
          "2015-08-28T00:00:00",
          "2015-08-29T00:00:00",
          "2015-08-30T00:00:00",
          "2015-08-31T00:00:00",
          "2015-09-01T00:00:00",
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          "2015-09-03T00:00:00",
          "2015-09-04T00:00:00",
          "2015-09-05T00:00:00",
          "2015-09-06T00:00:00",
          "2015-09-07T00:00:00",
          "2015-09-08T00:00:00",
          "2015-09-09T00:00:00",
          "2015-09-10T00:00:00",
          "2015-09-11T00:00:00",
          "2015-09-12T00:00:00",
          "2015-09-13T00:00:00",
          "2015-09-14T00:00:00",
          "2015-09-15T00:00:00",
          "2015-09-16T00:00:00",
          "2015-09-17T00:00:00",
          "2015-09-18T00:00:00",
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          "2015-09-21T00:00:00",
          "2015-09-22T00:00:00",
          "2015-09-23T00:00:00",
          "2015-09-24T00:00:00",
          "2015-09-25T00:00:00",
          "2015-09-26T00:00:00",
          "2015-09-27T00:00:00",
          "2015-09-28T00:00:00",
          "2015-09-29T00:00:00",
          "2015-09-30T00:00:00",
          "2015-10-01T00:00:00",
          "2015-10-02T00:00:00",
          "2015-10-03T00:00:00",
          "2015-10-04T00:00:00",
          "2015-10-05T00:00:00",
          "2015-10-06T00:00:00",
          "2015-10-07T00:00:00",
          "2015-10-08T00:00:00",
          "2015-10-09T00:00:00",
          "2015-10-10T00:00:00",
          "2015-10-11T00:00:00",
          "2015-10-12T00:00:00",
          "2015-10-13T00:00:00",
          "2015-10-14T00:00:00",
          "2015-10-15T00:00:00",
          "2015-10-16T00:00:00",
          "2015-10-17T00:00:00",
          "2015-10-18T00:00:00",
          "2015-10-19T00:00:00",
          "2015-10-20T00:00:00",
          "2015-10-21T00:00:00",
          "2015-10-22T00:00:00",
          "2015-10-23T00:00:00",
          "2015-10-24T00:00:00",
          "2015-10-25T00:00:00",
          "2015-10-26T00:00:00",
          "2015-10-27T00:00:00",
          "2015-10-28T00:00:00",
          "2015-10-29T00:00:00",
          "2015-10-30T00:00:00",
          "2015-10-31T00:00:00",
          "2015-11-01T00:00:00",
          "2015-11-02T00:00:00",
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          "2015-11-04T00:00:00",
          "2015-11-05T00:00:00",
          "2015-11-06T00:00:00",
          "2015-11-07T00:00:00",
          "2015-11-08T00:00:00",
          "2015-11-09T00:00:00",
          "2015-11-10T00:00:00",
          "2015-11-11T00:00:00",
          "2015-11-12T00:00:00",
          "2015-11-13T00:00:00",
          "2015-11-14T00:00:00",
          "2015-11-15T00:00:00",
          "2015-11-16T00:00:00",
          "2015-11-17T00:00:00",
          "2015-11-18T00:00:00",
          "2015-11-19T00:00:00",
          "2015-11-20T00:00:00",
          "2015-11-21T00:00:00",
          "2015-11-22T00:00:00",
          "2015-11-23T00:00:00",
          "2015-11-24T00:00:00",
          "2015-11-25T00:00:00",
          "2015-11-26T00:00:00",
          "2015-11-27T00:00:00",
          "2015-11-28T00:00:00",
          "2015-11-29T00:00:00",
          "2015-11-30T00:00:00",
          "2015-12-01T00:00:00",
          "2015-12-02T00:00:00",
          "2015-12-03T00:00:00",
          "2015-12-04T00:00:00",
          "2015-12-05T00:00:00",
          "2015-12-06T00:00:00",
          "2015-12-07T00:00:00",
          "2015-12-08T00:00:00",
          "2015-12-09T00:00:00",
          "2015-12-10T00:00:00",
          "2015-12-11T00:00:00",
          "2015-12-12T00:00:00",
          "2015-12-13T00:00:00",
          "2015-12-14T00:00:00",
          "2015-12-15T00:00:00",
          "2015-12-16T00:00:00",
          "2015-12-17T00:00:00",
          "2015-12-18T00:00:00",
          "2015-12-19T00:00:00",
          "2015-12-20T00:00:00",
          "2015-12-21T00:00:00",
          "2015-12-22T00:00:00",
          "2015-12-23T00:00:00",
          "2015-12-24T00:00:00",
          "2015-12-25T00:00:00",
          "2015-12-26T00:00:00",
          "2015-12-27T00:00:00",
          "2015-12-28T00:00:00",
          "2015-12-29T00:00:00",
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          "2015-12-31T00:00:00",
          "2016-01-01T00:00:00",
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          "2016-01-08T00:00:00",
          "2016-01-09T00:00:00",
          "2016-01-10T00:00:00",
          "2016-01-11T00:00:00",
          "2016-01-12T00:00:00",
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          "2016-01-14T00:00:00",
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          "2016-01-18T00:00:00",
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          "2016-01-20T00:00:00",
          "2016-01-21T00:00:00",
          "2016-01-22T00:00:00",
          "2016-01-23T00:00:00",
          "2016-01-24T00:00:00",
          "2016-01-25T00:00:00",
          "2016-01-26T00:00:00",
          "2016-01-27T00:00:00",
          "2016-01-28T00:00:00",
          "2016-01-29T00:00:00",
          "2016-01-30T00:00:00",
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          "2016-02-01T00:00:00",
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          "2016-02-04T00:00:00",
          "2016-02-05T00:00:00",
          "2016-02-06T00:00:00",
          "2016-02-07T00:00:00",
          "2016-02-08T00:00:00",
          "2016-02-09T00:00:00",
          "2016-02-10T00:00:00",
          "2016-02-11T00:00:00",
          "2016-02-12T00:00:00",
          "2016-02-13T00:00:00",
          "2016-02-14T00:00:00",
          "2016-02-15T00:00:00",
          "2016-02-16T00:00:00",
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          "2016-02-19T00:00:00",
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          "2016-02-21T00:00:00",
          "2016-02-22T00:00:00",
          "2016-02-23T00:00:00",
          "2016-02-24T00:00:00",
          "2016-02-25T00:00:00",
          "2016-02-26T00:00:00",
          "2016-02-27T00:00:00",
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          "2016-02-29T00:00:00",
          "2016-03-01T00:00:00",
          "2016-03-02T00:00:00",
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          "2016-03-04T00:00:00",
          "2016-03-05T00:00:00",
          "2016-03-06T00:00:00",
          "2016-03-07T00:00:00",
          "2016-03-08T00:00:00",
          "2016-03-09T00:00:00",
          "2016-03-10T00:00:00",
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BCFu5juS6lCwBuX5qNemV7kWT1JWZ8L2HvkvdkOpBYpx2zNmJYVlzLy+VJEX60Srl0vkqL9xi4xJw8sBKk/5/MGzKChz46ke17lsEfdqXRJK90Lj9cPU7XDN3eOp1or0Y6tqmMUb0ba/6MYVdTr/XroNm5fjbSevUqBmrVDbIt3uWqN8VCwmQAAmQAAmQAAlYMgGrEsOSOtrpzYa4GxGFfy5cUw/qUla9Pwz1aj2WKoYzS2nU0hUllXfMgI7pYippjPK9JjI0Mfzp+mmoVD5ltliKCDIRZvIALw/yWW2gpaVvS1qwNpvUe9QCNSsswqKkT8psmBQRziLo+77TEn26NjdLDIsokzTYtKXWE9VUirXMdItfC6f0wyvPP51qovmgvVBI+3D9y6HVSrRrRdYVVn/pHSWMPt88Cwb8lwK+fOOnEHG2fsEolSLca+Q8HP/xDL74aA5Kl/LJ9jckok3E+s2wO0q4TVv4oZqt1WanOvefkZLenma2+P4GH0QoZSaGH+Q6SbsuN6ebhSkiUtKkJV06bdq7lq4rL3kWTx2gutF2dBbRJZtuZVY+3HkIs5Z+pL46sGV2puuQsxqzxue3I2tTN87SbE3xI227IaFhakMqybA4dylQvYCRdGRJS9bEsLx02rJsPMr4ZX69PEiM044hOzGsbZ6VdgZXq6vNwP9+ZJ1JOy2H3AhHw7ZDVPX7N82S3axlV+s543vh1TRZC5HRMWqX8MzS47Njl93vVb4z5beTlpHGOG2mTE7XNL8nARIgARIgARIggYJKwKrE8P2QZSZ0+sge6kgaKdnt7iyzHTLrkVkK64zFW7Bl92HsWjMFVSuWSU2Tvl8Ma8faaGI7u/5k9ichMVGleEqRf8usUFaldbMX1Lpmc2aGs1szPH7OOjUDnXZ29f6H/T+/STmeSWYgZVbq/pmwtA/5WfkhR/3IkT+S6ivrrLObTROeMjMus6D3l7RiWFvPKTaSrirrjl+sVxMv1n0idQ3mgwilzMRwblwn2d0MTBGRY2etwadffJu67lTak02sZDMrmVGX1FqtdBkwQ71AkHTx+8//lY20ZF2xdt3JLLpcI6YWuQ5uht9JvYbT1jPFD7GXlxwjp69KTQNO20baWVBtMzP5XtLOaz5aUS0x0JYKyOcPEuO0/WUnhrU0bckwSdun1NfS1rXfhyn8ZJdyeTkku3nLrt5a0Wags2pDSwvXwy6r36u0YcpvJ+1YtBlq7aWWKb7ShgRIgARIgARIgAQKKgGrEsMieuUMT0lDldlGScdNW7ITp9pZvGnX8Wp1tfNHtRm+rGaGNTGsHXukRwxLGqjMgGkbed1/wZQr7as2zMorMTxi6krI2t7MRJMIJlmn/MfX65W4zOrhWpuJlLXEchRRZkVmhWUXZPFXNkHSXgZkZqvNyAtPeRlQzt8XxYsVVqm9Etu06xZlB2VZMyoblGlFxrF52Tg4Otg/kFDKTAznxnWS3c3AFBEpZ2oL67S7XGuzk1m1ff/GT2InO63Ljuuy5nfPF9+q9d/Zza7f37ZcB3ciIlU2w/3FFD/u3I1Eveb9VFURvrLZl38pb0Tci0KbnpPUZ1pKsCwZkDXc8kdbPyv1tNlj+XteimFt12zZtExmZ9MWuZZTzmXOyCGreGjXkezanfa8cO13KC817r9/SVuS3SG7g+thl50YljZz+u2k9YFiuKD+r5zjIgESIAESIAESeBACViWGczrTNDtxKg+EciyNPIT26vJGOpZaiqS2eU1WYljEhDx4ag/xesSwJvxOHpDNbJyzjKUmhk3ZvVZrxJQNtLSZqo2LRqfuUi31ZcfnZ5r1UTs9ayndWT1cy7rhpxr3VGshs0rL1caUk7/Z7VQsazTvF8NauwmJSSpdXWIkKc1airwmlD5aPl7tVmxKyUwM58Z1kl3fOYlITYykZfzX2ctKPMqMaaM0Ke7ST2x8vNp9WtZ37904I7Vr7UihOjWrYc28ETh/OUjNEouoM3U3aS21/8xX69Ptii6d5OSH2Hz17Wn0H7cY73Zsptaea+VqUCiadByZTgynZSYvjY7/+LtKl5dZ7+8/W6Z2v36QGKdtN7uZYVlyIWukxw3qjPYt/tssLPj6LbzSbhiE47oFI025rJRNVmJYO6opp7PR9bDLSQzn9NtJ65SWgbB77dR0u32b7DgNSYAESIAESIAESKAAEaAY/l8wtHNEJQ3xwJY5qbv0Xr8ZjpfbDFGb9xzZMV/NjGYmhmW9rGyMIym9y2YMgsxm6hHD2sxT9/ZNMeS9lB2XtSIP3DLrrKVnPtrgbZN3r5U2TBHD2kZhksIsqcxakSOkBk9cilZNn1cp5FKye7jWNrPKbFdiEacyK+xZrLDaLVd2zZWdjGU2P/WBPCERP5z+G6VKFFcbet0vrLW1sWnTpPcf+RGvvPC0mgHWirazsqz/lnXg2pnTsj46qx2k7/9dZiaGzb1OcvrtZyUiZeOu/Ud+wLjZa9XsoJybLTuTS9E2OZO1wrJm+P6ibaylpfVHREbjtU4jlZBMu2ZbE2KmbqSl7UaddodjrW9TxPCOz77B5Hkb0PftFujzdovUYcvmUvJb0l4qyThl4zR5AZS2DBi/GHIskJa6/CAxTttedmJYWwIg1+OGRaNS10jL2mbhP35wF3VUl6klKzGsbfon/axfOCrdNS0vAeTIN8mAMZVdTr9XU347aX2S609eyGgb4ZnqL+1IgARIgARIgARIoCASoBhOExVNkMoMmzzYyiysbPwkuxqnFVGaGBZx2rD+U5CzOGXzK0nRlfNCtWOO9IhhsZVdiuXBX9JFZc2r9C/nIMs61TEDOqFjq4ZqtDKDLTOUIlAfqVwWwdfDUo8syuwiM0UMy6ZecsyLzKrKWswXnnlcrSVduHqnajJt+nR2Yljb7VnqCMPHqpZXG1/JkTLyoK8JF0nzlBk1ecAXPyQF/Mat2yoNtnwZXyyZNlCtxRQer738DGTX63MXA9VaWSlpxbCWptqiyXNKIMpZviK0ZYdf2RRKZpFll2o5lkpearzzVhN1zNajlculrifPjFtmYljs9F4n968tz+5GoIlI7YxpWdcbdvtu6lFRUldm3UUsSZGZ+wZvDlSc0qZNp+1DezGgnQ+rCZr7ZznlSKoW3capXb9NOWJJ0uolrXfSsLfRpln6tHjND+H9zJOPZHC5VAkv9QKjVffxSty3eLUefH08cfLXv9XGalI0MaztdC6zry/UfULtWv73uasqLV5SliWbQXZpf5AYi8j9+rtfVH+bdx1SvssLGhmTnHP8VI3/UqI1sSw7oMvvU448kjGI7eFtc7PcITyzeGclhsVW60dm82V5gFshV/xz/oo6f7xm9Upqg7R/L1wziZ20l93v1ZTfjjZ+uR/VfaNfuiyRgvg/NY6JBEiABEiABEiABEwlYBViWHtYTjt7mRmAqOhY1G7aK92xPGntZF3iqs371BE8WtGORdLOA5bPNTEsD6uydlMrkqY9un9Hde6vlOz6kzWeImTSrjOUo2LmfbBdHTuUtogIkDRSEelSxN9lGz7FwW9Oqn/LGLPbiEqbRcpsPXTafmQX7snzN6S2K9+JKJs7sQ+qVy2fappT2qWki89a8pFKU05bRNSO7NdBzQxLuXQ1BDOXbEk9O1g+E/HU752WSiCLuBk4fnHq0T/yvcwirt/+RbqzTuet3KGOcxJhrRXZSGv8oC7pxK4IZLHTNozKTMSlHW+qGP5ybboZOlOvE3mRIGIpsx2Is/qBatdy2u+FiYjH+nVqoLmIRu/iqV9rAjC7JQLyMkdeLEgsJwzpqnYnl5c26+aPzJDe/NtfF9Chz1SVLn1o27zUI68yG682S55ZWnxmfqRtQ9udXOIxdcGm1K/E19bNGqjf4NttX8XwPu3U+tgpC2QjtZTrXSsyQyq7tqfdjVxvjLVjzjLzr0ubxhj5v2OM5Hs5rks2L0u7oZtcZ0umDUBAuZQzgk0t8tJHNuXKbFM7WW6wfvsBrNt6IMM1LUs43mhUT3VjCjuxy+73aupvR9rRskfuT2s31WfakQAJkAAJkAAJkEBBI2AVYji3ocoMiKxbdHBwUALC3t4uXRdp06RFpMjMnQiUtMcMmTMmEVtyXIqkXos4kJnnzIqku8pmQ3JOq3YOrjn9anVllvpa8A14FiuS7oxfvW3LQ31waBhcnZ3g7VU0w/E7WntiJ/7K2cYilCUVXSsygyuzdVJE9Li6ZM5CZrbDbkeoPyLkZOwyW3h/ETsRw/LCwtxzUnO6TvTyskR77begrc1+EB/kOg4MvgFXF2eU9ffNNG7SrvwuRNjLSyb5Xcg64cxKbsY4s/ZFFF8JvK4yDjLb5OpBGGTlh7xwkJdU4q929ndaW1PZZTcmU347wr5drynqtygv8OQ3xkICJEACJEACJEAClk6AYvgBIpjVBloP0BSrkIBFE5A07mZdZG2rg9rdO7vN3yzaURsfvLYe+/7dr20cC90nARIgARIgARKwcAIUww8QQIrhB4DGKlZLQEuflbXui6cNSJdSbrVO25Bj2qZeaddn25D7dJUESIAESIAESMCKCVAMP0Bwz/xzCecuXsPL9Z/KMlXzAZplFRKwWAKyjlbSl5996lGULOFpsX5w4BkJHD52Cvcio1H7/4/hkmUjLCRAAiRAAiRAAiRgLQQohq0lkvSDBEiABEiABEiABEiABEiABEjAZAIUwyajoiEJkAAJkAAJkAAJkAAJkAAJkIC1EKAYtpZI0g8SIAESIAESIAESIAESIAESIAGTCVAMm4yKhiRAAiRAAiRAAiRAAiRAAiRAAtZCgGLYWiJJP0iABEiABEiABEiABEiABEiABEwmQDFsMioakgAJkAAJkAAJkAAJkAAJkAAJWAsBimFriST9IAESIAESIAESIAESIAESIAESMJkAxbDJqGhIAiRAAiRAAiRAAiRAAiRAAiRgLQQohq0lkvSDBEiABEiABEiABEiABEiABEjAZAIUwyajoiEJkAAJkAAJkAAJkAAJkAAJkIC1EKAYtpZI0g8SIAESIAESIAESIAESIAESIAGTCVAMm4yKhiRAAiRAAiRAAiRAAiRAAiRAAtZCgGLYWiJJP0iABEiABEiABEiABEiABEiABEwmQDFsMioakgAJkAAJkAAJkAAJkAAJkAAJWAsBimFriST9IAESIAESIAESIAESIAESIAESMJkAxbDJqGhIAiRAAiRAAiRAAiRAAiRAAiRgLQQohq0lkvSDBEiABEiABEiABEiABEiABEjAZAIUwyajoiEJkAAJkAAJkAAJkAAJkAAJkIC1EKAYtpZI0g8SIAESIAESIAESIAESIAESIAGTCVAMm4yKhiRAAiRAAiRAAiRAAiRAAiRAAtZCgGLYWiJJP0iABEiABEiABEiABEiABEiABEwmQDFsMioakgAJkAAJkAAJkAAJkAAJkAAJWAsBimFriST9IAESIAESIAESIAESIAESIAESMJkAxbDJqGhIAiRAAiRAAiRAAiRAAiRAAiRgLQQohq0lkvSDBEiABEiABEiABEiABEiABEjAZAIUwyajoiEJkAAJkAAJkAAJkAAJkAAJkIC1EKAYtpZI0g8SIAESIAESIAESIAESIAESIAGTCVAMm4yKhiRAAiRAAiRAAiRAAiRAAiRAAtZCgGLYWiJJP0iABEiABEiABEiABEiABEiABEwmQDFsMioakgAJkAAJkAAJkAAJkAAJkAAJWAsBimFriST9IAESIAESIAESIAESIAESIAESMJkAxbDJqGhIAiRAAiRAAiRAAiRAAiRAAiRgLQQohq0lkvSDBEiABEiABEiABEiABEiABEjAZAIUwyajoiEJkAAJkAAJkAAJkAAJkAAJkIC1EKAYtpZI0g8SIAESIAESIAESIAESIAESIAGTCVAMm4yKhiRAAiRAAiRAAiRAAiRAAiRAAtZCgGLYWiJJP0iABEiABEiABEiABEiABEiABEwmQDFsMioakgAJkAAJkAAJkAA5qpkgAAAgAElEQVQJkAAJkAAJWAsBimFriST9IAESIAESIAESIAESIAESIAESMJkAxbDJqGhIAiRAAiRAAiRAAiRAAiRAAiRgLQQohq0lkvSDBEiABEiABEiABEiABEiABEjAZAIUwyajoiEJkAAJkAAJkAAJkAAJkAAJkIC1EKAYtpZI0g8SIAESIAESIAESIAESIAESIAGTCVAMm4yKhiRAAiRAAiRAAiRAAiRAAiRAAtZCgGLYWiJJP0iABEiABEiABEiABEiABEiABEwmQDFsMioakgAJkAAJkAAJkAAJkAAJkAAJWAsBimEzIxkcFmNmC6yuh4Czox3cXR0RFhGnpxptbZCAg70BxT2cceNOrA16T5f1EHBysENhN0fcusv7ih5utmhrb2eAVxFnhN7mfcUW46/HZ0u9r5TydNXjJm1JwOIJUAybGUKKYTMB6qxOMawTmA2bUwzbcPB1um6pD6063aR5LhCgGM4FiDbShKXeVyiGbeQCpZupBCiGzbwYKIbNBKizOsWwTmA2bE4xbMPB1+m6pT606nST5rlAgGI4FyDaSBOWel+hGLaRC5RuUgzn1jVAMZxbJE1rh2LYNE60AiiGeRWYSsBSH1pN9Y92uUeAYjj3WFp7S5Z6X6EYtvYrk/7dT4Azw2ZeExTDZgLUWZ1iWCcwGzanGLbh4Ot03VIfWnW6SfNcIEAxnAsQbaQJS72vUAzbyAVKNzkznFvXAMVwbpE0rR2KYdM40Yozw7wGTCdgqQ+tpntIy9wiQDGcWyStvx1Lva9QDFv/tUkP0xPgzLCZVwTFsJkAdVanGNYJzIbNOTNsw8HX6bqlPrTqdJPmuUCAYjgXINpIE5Z6X6EYtpELlG5yZji3rgGK4dwiaVo7FMOmcaIVZ4Z5DZhOwFIfWk33kJa5RYBiOLdIWn87lnpfoRi2/muTHnJmOFevAYrhXMWZY2MUwzkiosH/CHBmmJeCqQQs9aHVVP9ol3sEKIZzj6W1t5Rf95W4sEhEBd5A8ccr5ApSiuFcwchGLIgA06TNDBbFsJkAdVanGNYJzIbNKYZtOPg6Xc+vh1adw6J5ASRAMVwAg1JAh5Qf95WYW/fgOb4VkuGIsFEb4VbW22watiKG70ZEIdmYjKKF3WEwGMzmJg3ci4zG96f+gJ2dHerXqQF7Ozs4OjpkaDs52YiD3/yEurUeQxEPt1zp25Ya+fn3s4pbxfJ+ueI2xbCZGCmGzQSoszrFsE5gNmxOMWzDwdfpen48tOocEs0LKAGK4QIamAI4rPy4rwTv+BIVvp6HZDjjmk8tJJYoi8KvNoRHBd9MicTfiUbkpRAUrxmQJTFrFsNJScnY8dnXWL7hU4TfuacYFHJ1QfsWL2HIe23NuorOXQpEi3fGodYTVVG8aGHcuHUbNapVwIi+7TO0m5CQiCde6YGdqyejWqWyZvWbG5W7DZ6NH3/5WzX1WJXy2P7BxBybXbt1P/xLeqFxg9o52ua2Qd8xC1GjWgDe6/x6rjRNMWwmRophMwHqrE4xrBOYDZtTDNtw8HW6nh8PrTqHRPMCSoBiuIAGpgAOKz/uK7dGj0Gp8F/VTGQiCsHOGIVLLw+GX5uGGYgkxyfAfWBT9XnIwPUoUtU/U2rWLIZXb9mHVZv3YfKwd9Cg7uOIjUvADz//hbkrt+GrjxeYdRWt2LQH5y4GYv6kvqqdS1dD4OrqDF/v4gVeDCcmJWHqgk2Iio7FrLE94WBvnyOLAeMXo2rFsujTtXmOtrltQDGc20TNbI9i2EyAOqtTDOsEZsPmFMM2HHydrufHQ6vOIdG8gBKgGC6ggSmAw8qP+0rMwG4oFheE6x5V4Bt1CXbGWIQ7lUK0sy/i6jZFsXpPwsXbQ9GJCroF72ntYYQ9zj8/CH7tX81A7eax3/F4yzq5QjMpwYjwC8ZcaUtPI3aOBngGZEx7vnsvCnVf74sJg7vgreYvpWsyNi4eLs5OEJs5y7bi0NFT8HB3RetmDdCzUzMlDj879D2O/vCbSs/de+h7VK1YBv26tUSdmtXw1benMXL6Krg4O6JUCS+0a/GSEsaSxtuq6fOqrxOn/sTMJVtw4UowajwSgN//upA6Mxx8/Zb67ofTf+PxRwPQplkDNG5QS9WbtfQjODjY48LlYJz67V+8WPcJ9O/eCqVL+ajvT585iwWrduKf81fVTG3n1o1Un9m1mRnPKUoMx2D22PfU19n5Kyne42avS/W3UgV/TBvZXY3v/eXbcPFqCF55/im0b9kQ1auWV+217zNVsTz+4xn8fe4K2r7RALv2HcPqucPh6uKkbI7/+DvWbzuANfNGYPTMVfj+5B9qBj+gbCn0fadlKhOKYT2/iHywpRjOB8hpuqAYzl/eltwbxbAlRy9/x54fD6356xF7yysCFMN5Rdb62s2P+4pDn9fgaEzArZEfIvzAEVT5fT2S4IxkgzMcjRE498po+LVKEX53/rqCUkt6IBkOuOL9DOwatoT38zVSwYtY9pzaEcU/PpYrwUiIBs4dTsqVtvQ04lgIqPRKxpnNX/44h079puPo7kXwKl4k0yZHTF2pRKWkTIfficDMJR9h0Lut0bFVQ2zY/gXeX7EN77RrgudqV8eBr37En/9eVoL2ZtgdjJz2AYoXK4y33ngRfr5emL54c2oq77XgG3i1wwg0b1xPCezrN8IxfOoKVbdieX80f3sMnni0ohKyl65eV98d2jZXtdN71AIlMge9+6aynb9yB+o8WU2N8WpQKJp0HKnEb6um9XH52nX8+ud5jBvUJds2TRHD2fkbevM2hk5ejjJ+PmjZpD7c3VzhVsgVTTqOwNBebdV66YNfn8TuA8dwZMd8tSb70QZvq247tnoFpXw98cIzT6Dte5MwZfg7aPJSygsYEbki8kf164Atuw8rfz2LFsY3J37FglUf4/u9y1CksJuyY5q0nl9FHttSDOcx4PuapxjOX96W3BvFsCVHL3/Hnh8PrfnrEXvLKwIUw3lF1vrazY37SuDKbaj422acr9Ya/gNSxIRWooLD4DmlA+Ls3WBcthuBG/ei8g9LlBi2R5wyO1u7L/zfaYFbJ/6E9+bxcE1OWScrJQkOuPTycLhVr4IiVfxw49s/4L9lNLx2fJkrwShoYlhmM4dMWo4zX62HnV3GmePomFjUatIL74/vjaYvp4gzmZX98fRf+GTdNCWGvz15BmvmDlffSRp0sy6jUwWaCOmSJTwxuGcb9X1awfbBh59h865DOPbJYiUM064Zltno7kPmYOOi0XAr5KLqTpq7Ac1ffQ4dWr6sxPCT1Svh3Y7N1He7Pj+m2pIxLV33Cbbv/Sq1XS1wP5z+K9s2Mwvw/TPDOfl7f5q0rMPe9+UJzJvYRzWfmJiEdr2nYNeaKWoWXcTwytlDUb9O9dTuZyzegsvXQrDq/WHqhUKDNwdh99qpqBJQGrK++98LV9XLiRu37mDJut1qLbOsaaYYzpWfaO41QjGceyxNaYli2BRKtBECFMO8DkwlkBsPrab2RTvLJkAxbNnxy8/Rm3tfiQkOh/vUd2AHe4QUqwavGdPTDf/WD3/Df8Mg3CpUAe7zVyAqMAy3d+xG+fOfwt5oBIzJOP9oB/j374KgrV+gwrGlqSI5bUOXSjZEiQkjEbRmJwJ+3oDiO47kCqaCJoa1meGvdy6Ej1fRDD5q4nb/5tko619Cfb/v8AlMnr8RJw+szCCGRaC92HoQjnw8X60Lzk4Mj5u9FvHxCZgzvpdqN60YlpTh8XPWoeZjldKN6cV6NdG9fdMMYlhE/fwPPsbBre9j5PQPVB0ttVlrYPf+Y9m2+SBi+H5/7xfDo2aswpHjp5WQTVt6d22OerUeU2J489Kx6fwU31u/OxGHt83F4eM/44uvf8LW5ePV2uVeI+crIfzSczVR0scTst5bvpMUc4rhXPmJ5l4jFMO5x9KUliiGTaFEG4phXgN6CJj70KqnL9paNgGKYcuOX36O3tz7SuCidQj45xMYkIwYe1cYlu5MN/zgT75BhYMzcdWnLnympOz+K7tFh33zE0oeXgLHpHhcqfAqSo3oi8D5a1Dx7E7YGf5LW06ABxxxD5d8XkCJyeOQ0K8DnJMB3+2f5AqmgiaG5Silum/0xZgBnVTac9oSHROnBKp8v2zGIDSo+4T6WmZe93/1A0Qg3z9TqkcMb/z4IA4fPaXE4P1iWNoZNmUFTuxblunGVffPDKcVw3NXbsexE79h78YZ6fw5euK3bNvMNTEcUAZ93m6hmpu3coea5V0yfWCm109mYlgMRQy/8vzT2HPwWzX7LWnXIqpFbH//2bLUo6ekPsVwrvw0c78RiuHcZ5pdixTD+cvbknvjzLAlRy9/x27uQ2v+jpa9PUwCFMMPk75l9W3ufeX6lDkoH3JEiWE5R/h8jU7w790hFcK1D7ah0i/rcaFiS/gNS5lx1Erw7q9R/vBchLv4406l+ihy7hi8Yq/CDv+JYVk7LJtpBXo+BY+BQ+E+sRuSDIkoue1groAuaGJYnFq2/hMs37hHbfbUsP5TkI2zTv76b+pu0rKm2N3NBROHvI3bd+9h8MRlaPRCLbUO1hwxLDOcb/aYoFKwa9esqmacZf2xrBku5euFhm2HKhEo64KlyJgSEhPVGLMTw7ITdvehc9SmYK83qoeQG2Fq06k3GtfLts37A5zZbtI5+btq82dqLbOIX5nJFSHcuf8MzBrTE01ergN5+XD42Ck8XaOK2kgsKzEsad8T3l+X4veBleqoK80vSZmWWffPj/yA6Ys+pBjOlV9mHjRCMZwHULNpkmI4f3lbcm8Uw5Ycvfwdu7kPrfk7Wvb2MAlQDD9M+pbVt7n3lfgBnVE48Q4SjSJZoVKco1cc/k/wzlyEClf243ydXvB/p2U6ODe+/gWld0yAPWKVkJY/dsa41JlhEcLhzqXgGXsF14rVQlLDNvDfOQ03PCrg0bXLcgV0QdtNWpxKSEzC5p2HlCCWNcJS0p4zLKnSA8cvUTs+S5EZYhF3Hu6FsGHHF0poyvpWKdoaVzmSqYR3MZWyLOm8suGWlP5jF6F6tQro2el1JCcbMWLaSrXpltbuN9//mrqeVlK4x85agyuBoaljkn5frv+kEsNP1aiMHh1eU98d/OYk5n+wQ6VJS5FxyQ7OWunV5Q3079YK2bV5f4AzO2c4J3+F1ZBJy3D2YqBKfZZZb0nPlk3HNLaSbr5y9hCU8SuRpRiOjIpBndd6q/XRYwd2VkMTXtK2iGkpL9Wria+++wXbVkxQTNOyzY2LlecMm0mRYthMgDqrUwzrBGbD5hTDNhx8na6b+9CqszuaWzABimELDl4+D92c+0rQps9Q7sQ6hLn6IdK3GspdOqjEcMyKgwjadhCVjs5N9eb861NQqumz6bwL+/lflFwzAgYkqZnlJLjAHlGIXrIfYeMnoPSd07js+wLKh3yJ4MKPI7rSMwj4eS0uVG6FWlmkueYzvjzvLux2hNqkSXaWvn9DLUlddnZ2TE3Rza3B3Aq/C0dHhyzblc20JF3bs1hhtdGWqUX8EH+KFnaDk5NjumoP2qapfUu/hT3c4OiQsoO30WhUY8nOT1PbFl729nYoViTleLC8KhTDZpKlGDYToM7qFMM6gdmwOcWwDQdfp+vmPLTq7IrmFk6AYtjCA5iPwzfnvhI4byUqnd+N83V6A86uqHRsHmRPrPCpOxH+4TaUO38AjsYoGI0GXOu2CF61q6XzLOrKTRSb3VOdO5wMF1z1rQ2Hxi3g9cx/dmEn/4X/2r647v4IIks+ivLnP8el2t1Qa1j7fKTErkjg4ROgGDYzBhTDZgLUWZ1iWCcwGzanGLbh4Ot03ZyHVp1d0dzCCVAMW3gA83H4D3JfiQuLRHTwLcR9uh3lg4/gwksjUKpNQ9waOwH+4T/jUuMRcPzxC/jd+RX2SFTehA7/EB4VfNN5lhAZA9cRnRBlXxi36rWDX7vGGTy/+8dVlFzWHTddKuCu9yPwC/wRQc91xtP9W+UjJXZFAg+fAMWwmTGgGDYToM7qFMM6gdmwOcWwDQdfp+sP8tCqswuaWwkBimErCWQ+uPEg95XbZy7Bb3lPxBoKwcmYgGvtZ8H7+RoInr0UFS/vQTKcYId4yJpfSX12RCTCp++GS/GMaaRhI0bDAANch42Cq0/hDB7LUUxe09rjrpM3wr1qwDv0AkIbtsfTPZrmAx12QQIFhwDFsJmxoBg2E6DO6hTDOoHZsDnFsA0HX6frD/LQqrMLmlsJAYphKwlkPrjxIPeVG9/9gTKbRyLOUFy2uELC8s/VSIPWfYJyJz8EkuNhb5cEOyQiGc647haAonMXPZA3MntceFhzxNm5Iqh4PZQI/xehzXrgqc4vPVB7rEQClkqAYtjMyFEMmwlQZ3WKYZ3AbNicYtiGg6/T9Qd5aNXZBc2thADFsJUEMh/ceJD7yvVDP6HCJ2MRbVcY132ehu/E0akjDVy8HhX//li2KMIdJx+E1e0Ij6eqo3DFkg/sTaHer6i6V13ro0Tsn7jeaQIef/3pB26PFUnAEglQDJsZNYphMwHqrE4xrBOYDZtTDNtw8HW6/iAPrTq7oLmVEKAYtpJA5oMbeu8rt07+C791gwA44EqJZ+E7aUy6UcbciEDxiW1hhB0u+b2EkuNSjvgxpxj7toZb8l1E2FWCkzEIiYt3w7+EuzlNsi4JWBwBimEzQ0YxbCZAndUphnUCs2FzimEbDr5O1/U+tOpsnuZWRIBi2IqCmceu6L2vhEybh/JBXyLCwQe3XnwbpVq9mGGEcvawR2IELjzZGf49Us6zNafEDeyKYvHBiLUrgWDfGvAdPwKlPF3NaZJ1ScDiCFAMmxkyimEzAeqsTjGsE5gNm1MM23Dwdbqu96FVZ/M0tyICFMNWFMw8dkXPfSXs1Dn4rRuijkI6V6cv/N5ukenoAhdtgFvQH0h+swc861Q124OIof3gG/0vEuGKy6+PVucVUwybjZUNWBgBimEzA0YxbCZAndUphnUCs2FzimEbDr5O1/U8tOpsmuZWRoBi2MoCmofu6LmvBM1bhfLn9+KWW3l4zFkMg50hD0f2X9Nho0ah9N2f1QfRKw6r/1IM5wt6dlKACFAMmxkMimEzAeqsTjGsE5gNm1MM23Dwdbqu56FVZ9M0tzICFMNWFtA8dEfPfSWpXxu4JsUhpPtceD5dOQ9Hlb7p0InTUP7GUUTaF4Pd0h1WL4ZfajMYoTdvp4PwWJXy2P7BxHxjrrejwJCbmP/BDswZ3wsO9vZ6q9PeBAIUwyZAys6EYthMgDqrUwzrBGbD5hTDNhx8na7reWjV2TTNrYwAxbCVBTQP3TH1vnLzxF/w2zQakY7ucFq8JQ9HlLHp4JkLUfHq57jtWArOizfahBhu3+JlNKz/VCoMF2cnlCzhma/c9XT297kraP3uRPx6eA0cHR30VKWtiQQohk0ElZUZxbCZAHVWpxjWCcyGzSmGbTj4Ol039aFVZ7M0t0ICFMNWGNQ8csnU+0rw+8tR7uJ+XKzcAv6De+TRaDJvNnDBGlQ+ux03XAPgPn+lTYjhQe+2xhuN6qUDkpSUjHXb9mPrp0dwLzIGL9d/EqP7dUSRwm44fykIY2etwaj+HfDhzkO4cesONi8di+DrtzBzyRb8cPpvPP5oANo0a4DGDWqpdkNCw/D+iu04+evfSsCK+B4zoJOqv377ATU7XbyoB0SY9+7aHAaDAdeCb2DW0o/w0y//wMXZEc8+9SimjeyODn2nQQRxtUplYW9nhzEDO+HxRwLy9Tqx9s4ohs2MMMWwmQB1VqcY1gnMhs0phm04+DpdN/WhVWezNLdCAhTDVhjUPHLJ1PtK5OBe8I69jMC3F+bKplh63AlcuQ0Vf/sQIR5VUGzO/FwVw8boSMR9npJ6nZ/FUMgdzq+1zbRLSZN+snrldGLy9Vfq4vDxU5izbBuG92mHkj7FsWjNLpTy9cTiqQNw5u+LaNd7Ckp4F8ObTZ+Hi4szurRpjOZvj8ETj1ZE59aNcOnqdQyfugKHts2Fj2dRNH9nLHy8iqF7+6ZITjZi9ZZ9SkAfOnoKDg72KF3KG9eCbqD/uMVYPnMwXnj2cfQcPhf29vYQsR5xLwo7Pz+KCYO74tDRkxg3ey3WzB2u6lYOKI0iHm75idTq+6IYNjPEFMNmAtRZnWJYJzAbNqcYtuHg63Td1IdWnc3S3AoJUAxbYVDzyCVT7ysJ/TuhSEIoro/YjMIVSuTRaLJuNvSrX9SGXT4NnshVMZx8KxQRfd7Md3/svEqg8PJdWYphEZKlfL1Svx87oBOGTlmBqhXLYOKQrurzL4//jIHjl+D7vctwNShUieGf9q+EWyEX9f0Pp/9C9yFzsHHR6NTPJs3dgOavPofypX3RY9j72L95Nsr6Z4znhctB+OvsFdwMv4P12w6gR8dm6NqmMTr1mw6v4kXUDLKPV9HU8TFNOu8vIYphMxlTDJsJUGd1imGdwGzYnGLYhoOv03VTH1p1NktzKyRAMWyFQc0jl0y9rxj6toJLUgTCp+6Cq3eRPBqN6c3m1m7SBXVmOLM06fot+mPIe23Rskl9BUrSnBu+NRS7105FfHyCEsN/fL1epTNL2b3/GMbPWYeaj1VKB/bFejVRtLC7Snc+eSAl7Txtkc8lVfqlejVRtrQv9h/5AZ3fbIR32jVR6dGjZnygUqj9S3qjR8fXVOo1xbDp1+6DWlIMPyi5/9WjGDYToM7qFMM6gdmwOcWwDQdfp+umPrTqbJbmVkiAYtgKg5pHLpl6X3Ht3QgGGBG19CAM9nZ5NBrTm80tMWx6j/lnKWnSmYnhlt3GoV7t6hjW6y01mBOn/lSzu1/vXIjQm+EZxPDRE79h2JQVOLFvWYYdnr/5/lf0HbMQR3cvUjO9Wgm7HYHnWw7AugUjUadmNfVxr5HzUKfmI0oMS5G1y5euhqi07aXrPsG+TTMRF5+AN3tMwOlDq+Hs5Jh/sGyoJ4phM4NNMWwmQJ3VKYZ1ArNhc4phGw6+TtdNfWjV2SzNrZAAxbAVBjWPXDLlvhI6YTL8b/6GJEMSkpfvyaOR6GvWFsWwCM/dB45h4eR+KOFdHNMWbkLIjXB8vGoS/vjnUgYxfPdeFBq2Hapmkge9m5IKfvLXf5GQmIinalRGo3bD0eyVZ9G7S3O1znfTxwfRrX1TPNusj9oUq9ELtXDqt3+VoO7TtbkSw/NW7kDrZi+gjJ8P/jl/Ve0gvXP1ZJQrXRJPv9pTiega1QJgNBpRyNVZX1BpnS0BimEzLxCKYTMB6qxOMawTmA2bUwzbcPB1um7KQ6vOJmlupQQohq00sHngVnb3ldCJ0+F16wxcjPcQYyiK8DbDU9fs5sFQdDVpi2I4OiYWY2auweFjpxQrWeu7ZNoABJTzwxkRw70mp0uTFptf/jindpm+Ehiq6hRydcGsMT3VTtQyszx29prUM41l064Pl4zB2q371ZnBUgLKllKzvrKj9NtvvYr+Yxfhq+9+Ud/JZl0dWjZEjw6vqX+LWF+xKeVliWyk9ezTj+qKKY2zJ0AxbOYVQjFsJkCd1SmGdQKzYXOKYRsOvk7XKYZ1ArNhc4phGw6+Ttczu6+EfPoNXL7bhxKR5wAY1RrUCIcicF68SWfreWduzWI4J2oy4xsbG6/EqKlF6iQkJMKzWOHUNcVaXUmNlnOMtY235POo6FhEREarXavvL7Fx8Yi4F51uAy3NJiY2HvEJCdxJ2tTA6LCjGNYBKzNTimEzAeqsTjGsE5gNm1MM23DwdbpOMawTmA2bUwzbcPB1up7ZfeX28EHwjTwPI+xhhwQABlws3RClxgzW2XremduyGM47qmy5IBOgGDYzOhTDZgLUWZ1iWCcwGzanGLbh4Ot0nWJYJzAbNqcYtuHgm+h6bFgUXDzdYIiJxa19x5BYtDhKvPK0qu3QpxkckxNwyq8zvFs3gotjEuxdnODm72li63lvRjGc94zZQ8EiQDFsZjwohs0EqLM6xbBOYDZsTjFsw8HX6TrFsE5gNmxOMWzDwc/B9ZDp8+EfdBIOxkhc83oaJW79DgdDEi4/2QnudesgKTYWpVb1wx1nX7gs2lhgQVIMF9jQcGB5RIBi2EywFMNmAtRZnWJYJzAbNqcYtuHg63SdYlgnMBs2pxi24eDn4HrcgHdQKDEWDsYoJBg84GS8rVKho+yKw8kYiSDP2ih76ztc8n0JvhNHFViQFMMFNjQcWB4RoBg2EyzFsJkAdVanGNYJzIbNKYZtOPg6XacY1gnMhs0phm04+Nm4HhMcDvdpfWA0RiPMvSpgSIZrXBiKxweqWolwhwMiYTQC557tB/+uzQssSIrhAhsaDiyPCFAMmwmWYthMgDqrUwzrBGbD5hTDNhx8na5TDOsEZsPmFMM2HPxsXA/ccRhlvl6D696PwGfKRGUZsv0gAr6Zq/6eDCfYIV79/WrXxfB6plqBBUkxXGBDw4HlEQGKYTPBUgybCVBndYphncBs2Jxi2IaDr9N1imGdwGzYnGLYcoN/79J1eJT3zRMHQqbNg3/wD7hSuxP8306Z9U28GYGIE6cRGxmDoj/tglfcFfV59IrDeTKG3GqUYji3SLIdSyFAMWxmpCiGzQSoszrFsE5gNmxOMWzDwdfpOsWwTmA2bE4xbHnBDzt9HkXWTIe93T0Ylu7MEwfiB3RFoYRI3Oo7D0UfK6f6SHtfuTVmLPxv/4Lzj3WAf99OeTKG3GqUYji3SLIdSyFAMWxmpCiGzQSoszrFsE5gNmxOMWzDwdfpOsWwTmA2bE4xXPCCH3LgBwTsHY8rRWvDe+b0DAMM2nYQZY+uhRNu58msbHRgOIrMeBfxBsCwbFdq//ffV+7+GwTn4h5w8S5c8CCmGRHFcN6G5+69KHx/8g+8+mJtGAyGvO2MrZtEgGLYJExZG1EMmwlQZ3WKYZ3AbNicYtiGg6/TdYphncBs2JxiuOAFP3D+aig7po0AACAASURBVJQ//zmi7d3guGRLhgFeW7AGFc7uhyPuIWzyLrj65K4YDd55BOWOLMHVErXhO2lMlmK44JHLfETWLoaTkpLR4M2BCL9zD0c+ng9f7+I5hmbt1v3wL+mFxg1q52ibk8Gf/15G2/cm4bcja+Fgb5+TOb/PBwIUw2ZCphg2E6DO6hTDOoHZsDnFsA0HX6frFMM6gVmp+b1LoYj6cD0MiXEo8b9NkO53VcRw6ITJsI+OgNOA0XDz97RSGgXTraB1u+H1617cDHge/gO7qUHeGT4QvpHn1QZVEXP2wMGjULrB3xvaF57R1+CAGFwfvAGFK/vlqnOyXrhs0De4WLcX/Du/RjGcq3Rzv7GffvkH7wyeheJFPdC9w2t4u+2rOXYyYPxiVK1YFn1yYRdwiuEccee7AcWwmcgphs0EqLM6xbBOYDZsTjFsw8HX6TrFsE5gVmoeuHIbKv66ATAYcGfK9kzTWe0AOPd6RUxw7d3l8HyykpXSKJhuaWtvA4vVhNeMlJRo116NYYSkmxoR6lYZyfbOSGjaCS5+PnBfOAQORjs4Ge/AYExEYLfF8KxdNVedix/QBR4J4bgxYCWKVPOnGM5Vurnf2MS56xEXn4Cy/iVw6JuT+GTdtNROQkLD8P6K7Tj5699wdHRAw/pP4akalTFu9jq4ODuiVAkvVKrgj4lD30anvtMwZ3wv1Y6U5Rs+hYd7IXRu3Qh37kai9+gFOH8pSH33aJVyGN2/I6oElAbFcO7H1NwWKYbNJEgxbCZAndUphnUCs2FzimEbDr5O1ymGdQKzQvMbR39D0V1z4J4QhWSDPQJL1obv+JEZPE24dQ9FxrdSn1/utAA+9R6zQhoF16X4AZ1ROP4m7jiXhMui9bj1/Z/w+3A47JEAI+xgQDISDe645+CBwgnh6t/yJ9auEFySInGl9Qz4NHw6Vx0s1PsVJBickLD883TtWup9JbfSpGNigMNHk3OVtSmNuboArzSQ11YZS0xsPJ5vOQDzJ/WBf0lvNOsyWonhyhX8kZCQiObvjIWPVzF0b98UyclGrN6yD/Mm9sHQyctRxs8HLZvUh7ubKwLKlkLNRu9i15opqFqxjOpozMzVKF6sMIb1eguyLviTA8fx5GOV4OTkiHVb9+Pi1RDsXD2ZYtiUIOazDcWwmcAphs0EqLM6xbBOYDZsTjFsw8HX6bqlPrTqdJPmWRC4eex3+G8dgySDCxINyXBNikRoocooPH9phhoRf16B79Ie6vOLLafDt5H5awgZmJwJhJ8+hyLVysJjSEoacqzBHSFvjofTwc3wu3dGfWY0GmAwGJEMB9ghEUajPcKcS+Pe671g/PYgyl//BucbjYJfq5dy7tBEi1s//I0yGwYgwsETDku3UQynIRB2Gxg5KcFEkrln5lkMmD3JMdMGDx87hTEz1+D7vUvVzG/rdyfiudrVMejd1jhx6k/0GPY+9m+enTrbqzVyf5p0fHxCtmJY6onw/v3vC7h8NQRn/rmkxPGf32ygGM69UOdaSxTDZqKkGDYToM7qFMM6gdmwOcWwDQdfp+sUwzqBFUBzEbQGJwd4PfOI7tEFLd+CCmc+gj3icfnNmSi7cwzuOPnCefGmDG0F7ziECl8vhB0SkGy0x1XvZ+EzdaLuPlnBNALhv11Ews3bKL9rFC55P4+yN7+DHZKQDCe1RjgRrmot8B3HEigSH6rS1yUudoYkJYavdpgD7+drIHjOMgRc3INrnnUQF/Ak/Lq1NG0A2VgZk42IG/yOyia461wUbgtWUwynIVAQZ4ZF1P5y5hwaPp+SHfDj6b9wLzIa3+xahE+/+Bazln6EkwdWZoi6XjEs6dGyLlnSpms9UVWlZX926HuKYbN/dXnTAMWwmVwphs0EqLM6xbBOYDZsTjFsw8HX6TrFsE5gBcz8xtHfUWb7WMCYjMulGsC1bQdEHj8BxMfCu0MrOBVNv6HS/cMPHzkCJSP+xIVKLVCiRycUGfkGYg2FYJyzCUb3Iqnmgau2o+IvG2GEvfpjj1jcdfTGvS7j4fl05QJGxbKHc+/KTXiU9Ub8gI5wS4yDo/GuEr4w2MPBGKnEsBS1aZZ9CYTWaYdK3y9ScZFiQJL6742RH8G9nDcCl2xCxb8/RrLRQc3+Jy/fg4hzwUhcvQgJLoXg2qkHktYsUHXcJ0yFo7trjgADN+9D2e83IsbBGWGv9EbJ1+tRDOdI7eEZyDrees37oV3zl+BZLGVH8cSkJHzw4WfYsHAUoqJj0XfMQhzdvQhexf/73YudEsMBZdDn7RaqXkJiEp5o2B3bVkxA9WoV1Gdp06RnL9uKv89dwdp5I2Bvb4ff/rqADn2mUgw/vPBn2zPFsJmBoRg2E6DO6hTDOoHZsDnFsA0HX6frFMM6gRUw86C5K1D+/D44GOKRYHBDjF1hFE4KQTIccfWtGfBp8ESWI05KSILzgOawNyYh6v1daifimIHdUDzuKi41mQDf5s+n1g2dMAWlb/4MoyERjsZ41b7R4ICL1dvBr3eHAkbFsocjMfCMv4ZEFIIDolMEiMENdsY42CNRzf7CYIdw51JwnrkcBnsD3Ac2Ted0IhwRv2K/+izifAgSV85Fqcg/Vb3Aoo8jvvrzqHB8qRLU5yq3RcDZT9XfQ0dsgUd5n2wBJsbEwX1wcyQbnHDh2Z4o3aVZBntLva/k1prhgnYF7tx3FIvW7FSzwCJQtfL2oFkoX6YkBnRvhUbthqPZK8+id5fmcHCwx6aPD6oU6lWbP8Op3/7FkukDlWiWnag795+hNtfq1r6pmm0eN3sNmr/6nFozvGz9J/j6+1+xYtZgJCYmYdmGT5kmXdAuiDTjoRg2MzgUw2YC1FmdYlgnMBs2pxi24eDrdN1SH1p1umkx5vF3ojOdzY0JDkfYl9/CuUI5eD9XQ/kTc/0OXKb1gHNSvEqXTYYzkuCo0mXtjDG48PS78Ov+Zpa+h506C7+1A3HDNSB1jfD1KbNQPvhLnK/4JuziYpDs5Qe/995C/IBOKJwQhhttp8J3x2jVpuxifKHcGyg1sp/F8C3oA731w18os3EgkuCiNr8SgSri92KF15Hs7IbSZz+FS3IsjEbgik9dlJgyQbkUObgnfGIvpbp3w6U83BesSv134LyVqHjuU7VbeLzBGcG+dVEu5GskwgNOuK1mlWVG+cLrU1Cy6bPZYpKNu/w/HIZbrmXhMmclHBwymlvqfcVaxbCI15qPVcSQ99qmC5aIZJnJ/W7PEvz8+1mMnb0GoTdvK5snq1fGh0vG4NLVEAyZtAxnLwai5mOVsHnpWHz17WnIztRyXrHsKO3s5IjnatfA0F5tEXIjHP3HLlKzw1Lq16mO4z+eUTPDf529jDY9ec5wQboPUQybGQ2KYTMB6qxOMawTmA2bUwzbcPB1um6pD6063bQI87Af/0Hi5zvgFf4HbrtVQtKrbVDixZSZ3ZDp81E+8AjuORSF45It6rPbI4bAM/I64uyB0IqNYEiIgX31J+F+cD28Yq4gzKUMIhq9g5JNnsngf8hn38Ltq23wjf1XrUctMWV8Sj/T3keFwIO44VYFJaLP4qZLBThOXACP0W+qWUmPbcfx5+c/I+6331Dx9w247F0vy3OJLQJ6ARuknNsbEPQFLvg3AZ5+DuU/naTEa9LS3TDYyRFKwM2x41A67BQuVH4TfkPeTedBQv+OKJJ4A1c868J72uTU7+Qc6dhroSi8cy6KJYQg0q4I3JPvqg23JO1aZqCT4IBQ90oo+v7ibKkErtmNij+vxsXyzVBqRN9MbS31vmKtYljPZR52OwIuzk5wK+SSrpp8XtjDDY4OKen4kmYdFh6BEt7FMm0++PotFC3igUKuznq6p20+E6AYNhM4xbCZAHVWpxjWCcyGzSmGbTj4Ol231IdWnW5ahLkIoTJB36ldgZFsRGjhADgm3FMzflpqbJLBgMuPtYch4jYqXDmgNrM690x/+HV9I9XH8F8uoNSqfkiEG2LsHeG4dGsG/0OnzIZ/0HEY7Z1xtUJDlBrWW9kEz1iAgKuf47ZzaRSPD1SfiTArH3gYoW6VUHntKoTejsWN7/5E2c2DEeL+KIq+n7LelMU0AhIf9/IlM2QARF27Be8Z7VUjN8dshVtpr0wbDJ00HeVCj+PcM33gnybuYhw3sCuKxQfjXMU28BvaM0P961PfR4XgQ6mbb91yLgOvuKswGo0wGAyIsXODcdmn2TpyffIslLt+FBcaDIbfW40ytbXU+wrFsGnXMK2shwDFsJmxpBg2E6DO6hTDOoHZsDnFsA0HX6frlvrQqtNNizCXtaJF42/CoE6NjUOcwQWOxqT/CZdYlTKrFRG6DohSM7fOMxbDIc3sS3J8AgoNfEOt65X0aZnxu/BM73TCKXZgVxSJu4GLFd+A47P1U88MDp61BAGX9yDKvqiaOdT6kRnEi+VeQ81Zw5QYlnWoJeZ1xW2nkgh/rjOMN6+j8KsN4eJVBI6Fc96AySICkgeDDNr0GSqdSJl5jZizR63TlnJj/GR43L2CIom3EepeAcXmzHug3qMGvQvvuMs4W3cQ/DunHMWUtgRu+gyVTyxGAtzVtXGt41wkJyWr1PqyW4cq0/Apu+DinbLJUmZFS8kO6rMKxaqXz9TGUu8rFMMPdNmxkgUToBg2M3gUw2YC1FmdYlgnMBs2pxi24eDrdN1SH1p1ulmgzWPDolB8XAslWhMNjgh3LY+S0X+pMcsmSjL7K0X+KzJZ1pImGQqp9cGXn2gD/55vZfDv+tTZ8A8+DifEKUEb5VAISRNWIvbmHZRa0l3NNCfZ2SN+yWcwpNlQJ2jOclS8uFvNEt5fztUbhCf7v6nEcEJkDDyGt0K8wQkRLiXhFX0ZQUWfgGN8ZKZnFBfoAOTR4GT9960vT6BInccRfSkYzr6eiNy9G+WufKnSkoP7rEHR6mWREBGjdvGWTbLsjXE4X68v/Dtl3JTKlGHeG9pXpbdfajMbJV56MkOVe5du4O6+L2BwLwyngArq6CWtRA1+D96xF3G500L41Hs0y+4S+7WHR+It3BzzEdzLeFMMmxIY2pBAASVAMWxmYCiGzQSoszrFsE5gNmxOMWzDwdfpOsWwTmAPYB59/Q68JrdRNa92WwqvWlXU34NmLYbdo08hOewGKvy4Vq3dDPasDqfOPVFy4dtKHNvBiPNV26DSP9tSe5bNjm66lEGcczEUHjcpy6NwIof0gmdsCOyNKTsShwzeiKjzV1DxswmINxTGTY/yKDZ7bjqPguZ/gErndmbq5cWmE/F4l4ZKDEtx6N0MDkiCHRL/d+SSnRLsl9vNg88L/4msB0Bm8VVib96D+4T26mWElARDYdgZExBv5wDX5Hvqs4stpsG3cR2EnfwXfusG4p6DJ26+0B2lWr/0UPwPnTgN5W8cxbnnh8Kv/atZjsGuT3M4J0fjzsxP4FzMPVM7S72vcGb4oVx67PQhEqAYNhM+xbCZAHVWpxjWCcyGzSmGbTj4Ol231IdWnW4+VPO7/waj5MKuagwXGo1D0Xo14TnxTTVjazTYw9EYgXhDESQY7BHS4F0UebYmvKZ3hBEOCK7ZEklVa8Fh3yYUib4C96Q7qp3AXh+g+OMpZ3xmVYLeX4FKF3err2VNaHDfNYj64SQqn16FcwFvwm9YrwxVgxauQ6V//1tjfMMlAD6xF5TdtU4L8Eizp1PFcNyALmozJiky02xnSFKzm7edfdLtZPxQ4T+kzu+eDUbJBSkxlyIvNgwwpMzuG9Wmzjj7TH+Vuh607SACji7AFd8XUWLiyIc0YiBw8UZU/nszbjuWRKKds0q3vlK0NrxnTk83JtfejSB5A1FLv0iXVZDWyFLvKxTDD+3yY8cPiYDNiWHZ+c3OYAe7/+1IaAr3L4//jMcfCYC3Z9EM5hTDphDMPRuK4dxjae0tUQxbe4Rzzz9LfWjNPQJ531LYz+dRek3KBlWXvJ9DoodPqkiVdb2AAbdcS8N9/srUwdw89rv6e7GaFVPXlcpMcqUrn6nPb4zOOkVVayR499eoeHhGapvn6vSBIfgqKl7bh/P1BqFUp0zWlC7agMr/pOxWLeXsI51Q+a/N6u/BvVehygvVUsXwneGDUs6uzaSkXQ+b94QLXg+3z1yG3/KUnZ61Y4viDK4wwAlOxrvq83OV2sBvSE8EzV+NgHO7cK5GV5TunbKB1sMoQR9/iUpfzVZZA07GiJTrzLUi3OevSB2OrHkO+H4ZEuyckLR8b5bDtNT7CsXww7jy2OfDJGBTYjgmNh5vvTcJPTu9rg7V1sqR46cxYHzGbfRPH1qtzg2r1aQXFk7ph3q1HssQK4rh/L18KYbzl7cl90YxbMnRy9+xW+pDa/5SevDeogPDcXf9OgQEH1SNxBm84GC8o44p0ooI4sBiT8Brxn/CNbMeA5d9iMp/bFJf3Z29B46FUzZfyqqEfH4CAftSzqGVIrtCO0eEwD/iV1x+6334NEg5tiltCVq+BQFnUsSvpD9faDoJAfsnqX/fHLkZFWqWTRXDN8ZPQblbx1N3Jk7bztUui+H1bLUHB2ehNeNuR8G5mBvCfvoH/uv6qbXXkgFw060MEh0LwefuZZUJIDPE2pFW1yfNRIXQr3Ch8TiUbPHCQ/P81k//oMz6/qmz/DKQCHsvOKTZjTypXzu4Jd1DhKMnnBanXIuZFUu9r1AMP7TLjx0/JAI2I4bnrtyO9dsOKMyzx76XTgzLzO/oGauxc/V/59GJXRk/H3UTpxh+SFdnJt1SDBecWBT0kVAMF/QIFZzxWepDa8EhmP1IgqfPR8XAA2ojLNk0SdYFp90VWmqLGL5QpTX8BnXLtrHALZ+j0rdLAGMSYlYezhFB6Jc/o/yuUcrOaDSoI5tk4y1ZQxw6Ygs8yvtkaOPOmStwXTUBxRKD1XdX2s9D3F9/AQnxKNW9LUr6FU0Vw7LzdMUr/80Oyo7TIqClnG8yAaXeqJ/jGK3JICkmHo5D2+Bq5TdgX6UGKuwZjdBCVRHXoiecvIohcu8nKHf1GIKLVkHZ2z/hroMPbjzXFR4nP0OJyH9xua1selXzoSGRDb+Kjm6uxLDRzhkGY8ru5dfdq6rjsxKjYlF42Ou4Z18cIbU6wL9r8yzHaqn3FYrhh3b5seOHRMBmxPCdu5GIjY9Hhz5TMaRn2wxiePK8DTj+6ZJMw5BWDMuB26NnrELdWo/h7bavgjPD+XvlUgznL29L7o1i2JKjl79jt9SH1vyl9GC93Tj6O8puHYZ4O3eEFqkC/zu/KyEs4ldmBpMNLrAzxqp/X2oyCiXfeD7bjkIO/IDye6fgnkMxOC75L5U5q0o3v/0dZbcMRYLBCXbGZLViVXajtkcsoldkLabvDemDEjHnVLNXuy6G1zMpM7z2dgZ4FXFOFcOBy7eg8pkNmXaf0yZMD0a0YNcK3vU1Kn6ZMrsf5uiHognhuOb1FHymTkw38OjgcHhNzbgD+NXuy+D1dOWH7qQcneQVGwgYk2GHJETZF4Fh2U7I2dRlQ47jhlt5FJ6b+TOjNnhLva9QDD/0y48DyGcCNiOGNa6N2w9H/26tMojhgeOXoHnjenB2dsLTj1dB4wa14GBvr6ppYvixquXx9sCZKF+mJOaM76W+pxjO3yuWYjh/eVtybxTDlhy9/B27pT605i+lB+steOZCVLz6OUILVYbD8OmI/fk04i9eRIm/vkAh3MV1t6qIKFtLNe75ZjO4liqebUcyc3f39/Owcy8EzycrmjSomJDbiA68gWIbx6nNt8Kd/GA3eTmcimadYh24cC3soiOR7FEUnq3fgGvJYpmK4eCtX6DCsUVqNlhmhSWx+q6jp9pU62LJV+A7YYRJY7QWo/gBXVAoMQoOyVGIs/eBU3I4LjzRGf7vZRS+jn1eg+P/Zl41/+/O3lsgzmiWbIYKgV/iqlcdlLv1Le44loDT4s2IGNoPPtHncbnFZLULdnbFUu8rFMPW8mukH6YSoBgGcOafSzj4zU8o4uGG4NAw7Nj7NTq0fBljB3ZOFcPTR/XApo8PongxD8yb2BeODilC+U5kytmHLPlDwMHBABdHe0TG/LfWLH96Zi+WRkCODXVzcURENH+jlha7/B6vvDhxdbbHvWjeV3Kb/c2+PeAdcwEX6/RFhfdapzYf0r8vSkb9hfPVOqDi8JRNlvK6XJi+BOUu7MUd55LwXJH1Ws/sxmFnB7i7OiIiKuW+cvWz7+C/eyJuulXEvQop4sgQH4+As9tw16EEiqz67ziovPavQLTf7UUYYYARTipZ/Ga3RfB7LuUYrftLeK8uKB5/TX0sG2xF2heFx+rMj7TKb9/uXr6JqEtBKFTaFwnzRsMz7hpu9VkBn+U9lX+Ji/bB0SP79eqWel8p6i4b2rGQgO0QoBjOJNa79x/D+Dnr8NuRtWr2V2aGpUTHxOLAltko41citVZ0HB+e8vPnYm8wwMHBDnEJSfnZLfuyQAKy3l8yCWLjea1YYPjydch2BgMceV/JdeYJUXFI7pVyVqvdyi/g6Oac2sflvoNQKuJXXKrdC1X6t8v1vjNr8PKe7+H42Rrce+xFVB2U8rJbb5GjgVyc7BCTzX3l6sFT8N08DFF2RVBs4x69XVis/e3z11FoUkcYDElIhjPOV2mFx8a9l6U/FwcMg//tU+p7OY7qjrMP/FavL3D+nx01A2UC/4+96wCPquja792Szab3tpteKIoIFop+giBYQAWkWhBQpHfpvfcOoYoiXZFeBBRFRVERpQvpyZb0bOom2XK/Z2YlEAjJbjbZ7CZ3/ud/HmFn5pzznsv95r3nzDnfIbHZewi5sRcFAjd4ff5NlXra6nvFQUQyHLjBIdBwEODIcAW+/vn36xg6eSX+OrMV9iI7Sobf7NwWyrQsJMlSsXfjTLi5Gpqsc2nSlv3HwqVJWxZvW5bGpUnbsvcsq7utpjNaFiXTpaX9dA2h+yYgXRxermUS2Slz2nQE5fyBmPYTIenT2fTN62jFw3eGK1JDk6+Gy8Q3UcoTQxdtaAPVEEb6j/8geP8UqPkOyHEMg67HR2V3rSuyX3nkR4SfWUiLVRUKXJEa3B6SiYb2W9Y0ZDuPIurSBuTyveCsy4HctTk8lyytUkVbfa9wadJVupabUM8QqDMynJObjx8u/g15aiY6vNASTzQKwcnvL8HT3QWtWzatcZhJf2FWz6Jr/6kY2v8tdH2lDYRCw9evvYe/R6PwQDSNCkFufgEmzttM06B3rDY0fr93Z7jFk5H4aMIy+nefrZwEB7GII8M17qnKN+TIsIUBt2FxHBm2YedZWHVbPbRaGCaTxcm3H0TkX1sQG9gVAdPGlFtPiJAuJRnOHV+Ga9NAk/euqwXGkGGim8OwTlTFiop0FWfko0iRCY/moXVlRq3IpZW+f16PBMkr8Jv5aZUyFMd+QcRpQxeP2LDuCJg4vMo1dTEh8+JNBO0eS0UT4h4f2hUBk0dWqYqtvlc4Mlyla7kJ9QyBOiHDyvRsvPXhNJp2TMaSaZ/QyOvKzV/hyLc/44dv1pQVr6opvMfPiab3gh8cJ740FMNateUrfLbvVNlPTzUNx/KZQyH196Z/R8jwuvmj0ObZJ0CqUr87Yj5tu7Rx0TikqUpqSkVuHyMQ4MiwESBxUygCHBnmHgRjEbDVQ6ux9ll6XurJX6GNu4Oo23vp/cq4l8YjoJ8hXdrWh7Fk2G7YGxCwGuStPgmB2K7M7AJZJnwW9kMpRNBuOmHrcJTTX7b6M0Te+Roxzd6HdMT7VdqWeuZ3hB2ZQYtTZbT9AJK+r1a5pi4maNUlKJg1BQEFN6j4mNajIPnwrSpVsdX3CkeGq3QtN6GeIVAnZDj6iyM4f/FvrJ0/CqSl0Zud2lIyfPNOInoPmYNv9y5DYMCjvf9qE/viklJkZKng7OhQlgJtjDwuTdoYlGpuDkeGaw7L+r4TR4bru4drzj5bPbTWHAI1u1Pm9JmQZl+h3YMBPmSDVsLruYqLKNWs5NrfzVgyrB3ZF05aFdLHfwaXKEmZYqrbKQhYZ+ilXLjhDBhS6a+ejLTZCxGS9jNiOk6EtFfHKq0qySpA3s14CNxd4N4spMr5dTlBvuMQwv/cAUCHpJ4L4duxZZXq2Op7hSPDVbqWm1DPEKgTMtyh1zgMfq8r+nXriE8mrigjw7l5hWj71gjs3zwbzRrbRvoQR4Yt+y+CI8OWxduWpXFk2Ja9Z1ndbfXQalmUjJemmjgGfgWx0MMOaS7hcF+6wvjFVj7TWDKcNmchgtJ+QcLLExDQ+5UyqzL/vIOgHYYU2+yFh2Dv4WyUxbLN+8HotZAMrzriatSGtTApb/xI+BbFIvnDVfBuU/PX3WpBZaO31JeWQj11NJyLU6Fd/RV4dvej/Y/bxFbfKxwZNvqx4CbWEwTqhAz3Gz4fLZ+MxMThfcuR4T//+RcDxi7BhUNr4eXhahMQc2TYsm7iyLBl8bZlaRwZtmXvWVZ3Wz20WhYl46Rl/HIdnvvnwUGbD4YBYhr3hXTMAOMW28AsY8lwyurtaHT3AGKC3oTPiMFlvXMzfrqG4H0TqKUZU/bCMdhwHetxI++ODJkXr+HJP1fTKelT9sKpijW1BWN+fCoEYhH4YlGFPZqZET1grytC1qy9cKiiX3Rt6Vib+2ozs5B/4jTcBxj3QcJW3yscGa7Np4jb2xoRqBMyvH3vSWzZdRwLJn+EA0fP0xTpiBAJJi/cAlcXJ+yLnmmNWFWoE0eGLesqjgxbFm9blsaRYVv2nmV1t9VDq2VRqlpaUVouPOb0gw72ECIfd5t+AOmo/lUvtKEZxpJh2bovEHV7D0oZD8i7fwrfTs9RK1NPX0LYMcMZJzbkbfiMHoz8WCW0OXlwbhIKe+/ykWLFso2ISDhShpBscDQ8WkZaHDG1Ihue8/tQuRrGFUnBL4P1D4JLu9ZwDvZGqaoIrlO7Q8sIoIk+aXH9rFGgrb5XODJsjU8Tp1NtIlAn4SqfSgAAIABJREFUZJhUdp6ycCtOn/+9nG2kYFX04rEID7l/v6Y2ja+JvTkyXBMoGr8HR4aNx6qhz+TIcEN/Aoy331YPrcZbaJmZaecuI+TQNBTyXJDm3wb+MwwR0Po0jCXD8ug9CL++BywjRHy7EXBoGgX1yeNwTb0G75JE6CEEDxooR3wGdudqWpwp7o058H/zhXJwlYz5EO6lCvqBgY9iJPRYVEasLYWrPHo3wm58DT5bBD0E4EFbJjqm3ae08FXWlRhIt45CpjgQjqu3WUo1q5Zjq+8Vjgxb9WPFKVcLCNQJGb5nx407Cfg3JhkFhWoESX3R5pknILav+h5GLeBQ7S05Mlxt6Kq1kCPD1YKtQS7iyHCDdHu1jLbVQ2u1jK3FReRea9TVz3C3UV9Ix35Ui5LqbmtjybBs6wGE/b0fAhQgps1oIE2GkPhzlNASEnxvJPRcCtdj6+BRKkdsp2kI6PFyOeP0I3rBSa+i9695bCliOk6GpNf9O8i1jQRpA8WfNxQu2iww0JWReC0cIEAR7rYZBWn/t0DaJIWfWoBkr1bwXmBol9TQh62+Vzgy3NCf3IZnf52S4foAN0eGLetFjgxbFm9blsaRYVv2nmV1t9VDq2VRqlqafNUWRMYcxN1nh0L60TtVL7DBGcaSYfln3yD08i4IUIiY50eASUtBeOJRaHlO4EMHHlsMlmWQKQ6GW4kCQkJ0/4uyPgiLcHgXCPQlUDo3hX/+bcS2GgHJwG4WQy79wlUE7Z9KCfzdyN5geHxE3tlXFqmOeW44JIO6Q7bta0T+9RliI7pB8ulQi+lnzYJs9b3CkWFrfqo43WoDgTohwxs/P4y/b8Y+1p6180bB0cG+Nuyt8T05MlzjkFa6IUeGLYu3LUvjyLAte8+yutvqodWyKFUtTbFkPSKSjuFu61GQGtGHteodrW+GsWRYtvMowi9tp5HgmGeGgJeahDDZaWTZB0OsKYSjPquccSz4iG0zApL+b5b7e4dhneif7zb/CJH/7EBME1KQzNCayRJDfuAsIn9cTtOj08Z9BteoAGRPngSnQiXcdKm42+JjSD/pA/mSdYhIPIWYNsPrre9NxdtW3yscGTbV09x8W0egTsjw5/tP4/q/CY9gd+bHPxAeHEBbKzmIRTaBLUeGLesmjgxbFm9blsaRYVv2nmV1t9VDq2VRqlqactFqhKecQsyL4yB5742qF9jgDGPJsGL3SYRdjAYPpbjbfBAE6UkIVXyHuFenI6D7y5Ct34moW7vLIRAf0Bl+MyfSv8u+Go/S1AyEHZmBIp4LFC98jIifVyE2sCsCpo2xGHLybV8j4sp2JPi9DL/ZU8rkyjZ8iaibu3D3if6QjvwAWVOmQqr6Gwk95sOvs6FYWEMftvpe4chwQ39yG579dUKGHwdz9BdHcP7i3/hqyxzweIxNeIMjw5Z1E0eGLYu3LUvjyLAte8+yutvqodWyKN2XRtoD6fIKoFflQBgUCPtgf1pROG3eUoQqv8PdlydB2tsQ0axvw1gyLN9/BuEX1tBiU3ebvg9hpgwhaT8gvgspkvUiSOQ46tIGege3lHGHEAXQsTpkODeG3YjJ0K2bC7+iO9CzfKhE/shu1x/hZxciwf+VcqS0tvFVLI9GePxRGt2WftyjTFzKpr2IuroDsU3eg2TMQJBCX24laUgdux2ujaW1rZZN7G+r7xWODNvE48UpWYMIWBUZjkmQodvAGTjx5WKEBvnXoJm1txVHhmsP24p25siwZfG2ZWkcGbZl71lWd1s9tFoCJbUyBwUxKbDz9oBrEwPJUSxchSD5r+CzJdAzdpB5t4RO5IBQ2Y+04nDcazPh//ZLllDP4jKMJcNp3/0F1+OkMJYCMY36wS4zCUGZF5Hw9gL4vd6aRn7V585AnJ4IvYCPoJw/KTEmI+HVSXD/YSc8SmWUDKc6N0Zxu94IPTELiT4vwXfeLIvZnTZ7IULSf0LcK1MQ8M794l7kTnTEn5vAMAxyhP5w1yipTkWbzllMN2sXZKvvFY4MW/uTxelX0whYFRm+dOUWPhq/jEaGn2gUUtO21sp+HBmuFVgfuylHhi2Lty1L48iwLXvPsrrb6qHVEiiRCtHhV3ci1bkJ7MfNhNjfHRkzZiM461daRIlhNeAxOqrKvXZBcW/Og/8bbSyhnsVlGEuGiWLyHYcQ+ecmJPi0B6+0CEE5vyOx51L4vvLMI3rfuxushwi5Qg9KLkkFaRYMZF7PQvvCazRlOsWzDbwWzreY3apJE+CffwOJfZfBt13zMrmyz48g6o+NZX82RLhF0Ecftphu1i7IVt8rHBm29ieL06+mEagTMrzn0DncuptUZgvLssjNL8SPv/6DJpHBOLjNdsryc2S4ph/JyvfjyLBl8bZlaRwZtmXvWVZ3Wz20WgKl1HnLEKz8GXqGj3SnUBS5h8Jb+SdcNWnQgw+GZQGGBwZ66MGAAQ9JPebCt9OzllDP4jJMIcMkFTry0iZk2IdAxxPCv+hfJPVbCe+XnnpEb1I1mlSUfriPL5kYF/gGeK3bIeSrSZC5PwfPxYstZnfxmAFwL1VCOXo73JoElsnN/PUmJLsm0z/rGEcAWuQLXSFeu8Niulm7IFt9r3Bk2NqfLE6/mkagTsjw6q1f469rd8vZ4uzkgHZtmqPDCy3h4+VW03bW2n4cGa41aCvcmCPDlsXblqVxZNiWvWdZ3W310GoJlPImjIRPYQwlvCRqSXrNkgrJJGKZOn4n8r89h6hbu0CqIZPfyEjsuwI+D0QRLaGnpWSYQoaz/ryDwM9GIlskhaMmEyK2GPmrToIvtntEXd3IPnDWZZf9PQkSpDk1QVH7fvDr2hbpv9xA8O6xULo0h9uylUaZS9LZw1LOIj74NQRMHWvUmocn8Ya/TUl67sKvYO/hXO5n5fwVEKtkNDPAr/BfxIZ0Q8DkEdWSUx8X2ep7hSPD9fFp5GyqDIE6IcP1ySUcGbasNzkybFm8bVkaR4Zt2XuW1b2qQ6vy1G9UIYGTI0S+HnBpVP8LBKVfuIbSxASE/r4VfBL9BUuLQbEsHwyjo2Q4f9kRCJwdIF+3E2G3D4KFDqWbTlnWeRaWZgoZLkjMgM+Sd4EH6oE+7k5t4djB8C5JpNbQDwusDneb9Yd0xAf077Iu34V0+3AU852Q9+lWOIf6VGp51rV4SDaNBBEuc38aXosWmowUW6KFeExXsAxQsunbSteTSLFX2ydMllGfF1T1XrFW2zkybK2e4fSqLQQ4MmwmshwZNhNAE5dzZNhEwBrwdI4MN2Dnm2h6VYfWnEkT4V2QRItDJfm2hd+caSZKsL3pmlHvwlmbCx1jDw0DZPSZDc2vFxCeeBJg9NAyQmiiT1LD5Pu+ReRPK1HIdwOz4WvbM9YEjU0hw+qMXHjO7FlGhosZB+ijj1YojVSfZjPSEHz7IOz0ajon9uWJkPTpbCDD/8RBsmWUAe+P1sDz2ahKtVacuIiwE/PAEN/ZB8Np9VYTrDRMzY1Rwm/VAOQKfWC3bpfJ6xv6gqreK9aKD0eGrdUznF61hYDFyPCWXcdx7XacUXYsnzkUDmJ7o+bW9SSODFvWAxwZtizetiyNI8O27D3L6l7VobV4zEC4lSjA8uyR5hgGt+WrLaugBaXJvjwGz8tH4alJBssyYBkhlM5RcF+2GopjvyD81AIaGVYJfWG3rnyfXAuqWWeiTCHDRMl7hbHIf+fzPcDfcKBS3UtH94eTRgUB1EjotRS+HVrS+drCYhTPGA0fdQKSB6yDV+smj90n53oC1KdPISz+GCXDap4jsPGQyZhlHDiJwB/WI805Cq7L15m8vqEvqOq9Yq34cGTYWj3D6VVbCFiMDG/dfRzXb8cbZcfSGUM4MmwUUg1vEkeGG57Pq2sxR4ari1zDW/fgoTU/IQ32ni4QuojLgOAPf5Pe9yTFjUiYL2PSDjiH+tVLoAh508IBAhRR+0jBJ6GXK9yaBiP7agKkmz+hf5/mEAXnlferCddLMCowylQyfO/ZIVtl2QVWWWAqc+pUBKkuU8nKsV/AtZGkTIvsyZMhzb2ChN73SXJFuJOUa88SGRiQ9HZyj5uHgnWnwBfyTXKTctkG+CX/iNRXRsG/WzuT1nKTAY4Mc08Bh4BtIGAxMmwbcJiuJRcZNh0zc1ZwZNgc9Gx/bd4/dyD86zzEAz4B+JUf7B4mw4qvvwPv37/BtH653la6tX0P140F5NDKZqkg37QNbmnX4VYsp4qk24ej2MEb0uzL0JAIqfdztOdqbKdpCOhxv+dq3Whd81KLlDnwmtebbqyDCKU8O7APRBR16lI4j+9Cf5e7tID70mU1r4SV72gqGdaMfBeuugz6ISXZqw185lfeI1i25ySifllDUchbdQICsagMkczpMxGUdQlxb8+H/+utH4sUIeB8llT2JrePSbEzHpRjd8DtAWJtDMyZM2cjMOMi4t6Yg4C3XjRmCTfnAQQ4Msw9DhwCtoFAnZLhgkI11MUljyDl5eFKG7nbwuDIsGW9xJFhy+JtbdJ+m/0NOqZvRmzgGwiYNq5S9R4mw+QgGZj1G+Klr8N/xgRrM43Tpw4RIIfW/H9i4L5xKHSMA+2dS8gLSVW9RwzjGvcAPz8bYbJvEfvypLK7nHWodo2LVt1IRMDGwXRfYn9c496QjBlYTg47shfEugIke7eGz7zZNa6DtW9oKhkuGDcUPsVxFM+Yp/ojcFi/Sk2Uf3Mekd8thprnXO5DBFmUNmchgtN+QVxkd0jGGyL0Dw/5knUITv4FGp4eGb1mQnxsK3yKYpHcb3mFLZ0qUyZ/wgj4Ft5F8qB18Hr+8WnZ1u6zutKPI8N1hTwnl0PANATqhAynZeRg9Ix1uHEnoUJtfz2+Ea7OpG+d9Q+ODFvWRxwZtize1iQtNo4HwfoZaFxyGSq+F+w3fFmpeg+S4YxfbyJw10SA1SPVqRHcVqy1JtM4XeoAAcVX34GxE8Kz/XNgikuh+v4nBP28DixPDB5NiRaCBw3V7F7hI8WiNQhPPoGY/42D9D1DhLQ+jcxDZxB0bgU1iZC3hFenPJIem/vpGHgXJiFJ8r8G+VHJVDKcPXkSpHl/U0zlw7fCvVlopY8MKXwVcXIOMuzD4Lh6S7m5it0nEfrLRmQ4hsFl5YYK92FH9oS9tgjxbYdB0v9NpM5filDFecR1nIyAnh3omiJZNjSF6nIp2BVtph/ZG47aHGTM2AcnqVd9etQtYgtHhi0CMyeEQ8BsBOqEDM9dtRPf/XQZg9/riqUb92HB5I/g7uqMVVu+gp+PBzYuHgehwLS7LWYjUc0NODJcTeCquYwjw9UErh4su3mdxVOb3oYdW0r7mWbNOQixr+tjLSNkWH0jAU7rhoFPWsLAkDZISE5cG8NBkRsNEwGtugQu47vSVGA+SqCFIxiU0Ock1bExHIvT6N3ge31fVUI/Wk1XsXQDwhOOIKbNaEg/fKtegJc6fzmCFRcoDkV8VzjocsvIcNrobXBtUr6NlPLoT9AlJ0LYuGmDvG5gKhlOnz2fptaTkbfqJAQV9Bh+8EEqzshH9i+XwTC8Rz5EZF+Ng2TTEGSIwx5bHVo8tBPA8FG08TQYHgPZmh2IvHMAMc0HQjq0L5THLyL81BwonJrCbfnjPwpmX4mBdNtwA3nedK5ePOuWNoIjw5ZGnJPHIVA9BOqEDHcfNANdO7XFB+90QovOg3Fs5yKEBwfgwm9XMXzqavxxajMcHbhq0tVzaf1exZHh+u3fyqxLOPQbmpybCx500LNCZL8/Ew4vtim3JP3CVTgfWge1vQd8VqxA2v5vIT1vqPxLCLSe5UPLOCLNLQqeixc3XDAbgOWFSRlQnTgHVq8Fw+cB9o5g1UVgGBZwdEHkpfUUBR3I/9Zo6acSEgmOaTYAvh90h2bGSLiXKMBjdEgXR8Bp1SbIl29GRNxBxDw7HNKPe1gViiVZBRB5OpmkU6mqCA5T+4KBAHyUQgt72CGX/jvR8xiUrD9pwI4bZQiYSoYVi9cgItnQgqpg/bfgmfGhP++ODH5rBqKA7wnl0z0h+bhnmV6KJeshypYjMP8vlEIE7aYT9DfZzmOIurQescFvIWDKKMi//g6R55ciV+AD4fo9j/WsdmRfOOnykC9wg3D9Xu4JqAYCHBmuBmjcEg6BOkCgTsjwq/0m4qN3u6D3m+3x3OtDsWzmELzctgVkygyQ3/ZGz0TzpuF1AIfpIrnIsOmYmbOCI8PmoGe7a1PTgLwlC/Fk8e/0Hichw6q23WHf33C/8d4gxWfCf9lAI8Cl644jde02hMQdRincwUMxkv3aIET5I1QiP9iv3Wm7gHCaV4kA6dsadmE99LADCwHskEMjwAIUIo/vCRddFs0SKOI5wEFfCB3EECKfpkBL3n0DBeOHwkdtaAcoc2kJj6VLIVu9HZF39iOmxWBIh/SpUofankDSXUvWLQJfVwyH4izYszrIvZrBd97MSkWTqF9Jshz6mNsIjT+NQoET1EJ3+Knv0nUpbs+guHFbSOpJ9Lsm/WAqGSaR2ag7+6gK5kZY8xPS4bvsPZrCzoMWMc0+hMvrneEc6oOSMR/CSZMLPluKPKFnWV/g1DO/I+TIPAAsErvPgTYtA1G/rgHLAkXRZ2n0+OGRfvEmgnYbrpWkOTWCK3etpFqPEEeGqwUbt4hDwOII1AkZ7jd8Plo8EYFJI/ph/JyNUOUWYOWc4Th+9leaNv3dV6vg7+NhcTCqI5Ajw9VBrfprODJcfexseeXt2zyEbnwPbtos/OH+Jp7POY4CJ1+khXWEMKoxXJ9pgszoHQiSfQ8RCinJkfVdCOGp3fDLuw6ZW0uo/Z+EQ9tnIdk+GnrwULL52xqDRLb7BJg8FTy6d4HY373G9uU2qj4C8sXrEJ50Etl2gbDXF4Kv50PEZtPor4ZxhJAtRIJPe4g/GARtaiake8ZTQpzyziz4vPIssi7HwP3zqXDS59J5vnOnQ75uJyJu7ULMUwMhHf5e9ZUzcqXqVhKKjhwCBHYImDSi3CrZtoNwunMRAYU3yv6ekCTyISh/2SEInB1AouN6ve6RNlCEOLmXKigOPFaHhC5T4BAqQcAGw8elmP+Nh+Td143UsmFNM5kMb/saUVe20msa6k1nzQKrUJ4J7wWGAlz3fJ3QbR78Xn0evOHdIWLJu48PhWtzeC5ZQueV5BRCM28c7U8c9/pM6DLSEfWX4S5y+vSK7wLLNu9HxFXDx8Jkn7bwmVv5xxWzjKrHizkyXI+dy5lWrxCoEzK87rNvcCcuBRsXjcXVW3F4d/j8MlBfbf8cVs0p/z/61ow4R4Yt6x2ODFsW77qURu51krYi8ZsPIStDj/8pPoOOL8Jf7+1Gqy/fAQM9PRAW8p2RJmlLUxH1jD1N8WSgQZ6dF5w0meCzWuRM3w2x1JuaQ1qdkKhg+pTdcA42/F11h2zzPjjF/QHPAiUEbC5k7y+F94tPgRTs0p4/Bb1PMCSfGFrVcMOyCOSPHw7vojgk9l0KvTIVYRfWIF/oAVdtBr0rTNLmU4dvhluzYNoPVPn1OZQyAnh1eJ72GH6wsnJsUFcETB0D2cbdiLz+BWKe6A/pqP61bhApmBR2cQONAuYsOgKR+/3CkqTSr1dRMm2dc2/kCP3hpklHQvd58Ov8PO7dV42J7A3J+PtZFMyIHhCyLMj/aRg+2FUHUJSpokSL/BtK6j4Lvp2eq3X7bFGAqWRYvu9bRP60EjpyK3uTeR/g1Fl58JzxDvURWB39sBPbeToCurcH6Q9NugornFqgqHErSD56pwxe5fwVCJefQWyHSWAz0xB53UB0UwZtgOdzjR5xQ8b0GQjKuoyYJn3h8torcGlU/t64LfqtLnTmyHBdoM7J5BAwHQGLkeG8giJoNFp4urs8omVMggyX/rqFRuFBeO7pRjbTVokYwpFh0x86c1ZwZNgc9GxnrVqRDXbZJOQ06Yzwfz6nZIAQmEy/pyCYtBiY+AGc9Goa3SMpsITYkFiJmueEfHtfShIAHq0KXMK3B7vxSJnxqonj4V9wE4n9lsP3paeqDYomTw2Xyd1pxIfHasEyDOJenY6Abu0gj95DSRO5u1e86ptyvUKrLZBbaBIC4qGd6U3x3CWHYefmAMVX56BPUyLq1i6ohD5QvTWORoDJqOjQSp5Bz/mGVOi7Td6HdPSHkG8/iMi/tiCm1XBIBnQ3SZ/qTJat24mo27vp0owpewGS0rpqKhw0OSjmu8C9NJXaSMrCkeJfMpcWkOReQXyXufB/8wUaLbRjS5Di+Ry8F8wtU0FMiZMQcc8MgmOr5+gHATJk6w0V2j3f6QpxgG1kZ1UHV3PWmEqGFad+Q8TxWdAwdtBEG+4OmzNS5y6BV8YNOOkMWQ53m7wLfkRjhB2fjUK+K3gbvn5ke1L4LSLhKLJFUgh0xfQuMFgtErrMos/Jg4PV6cEfRaLMxUgd9zlcowLMUbdBr+XIcIN2P2e8DSFgMTJ85XoMPhi1EO3bPo23Or+Al1o3h9jezoagqlhVjgxb1oUcGbYs3nUlTbbrJMJ+2w4NnGiVW/L/LMsg+eme8BvyPv5dshuuWXegc/eDR/oVeJUkU+KZNmgljXSQiFrExTVUfYVzM7gtW1VmSuq85QhVnkNsuwmQ9H212iYqT/2G0OPzoWPEELJ5lBTHvTAGAe93gXzFZkTGfWOQP+ozuDUNqrYcbqHpCGRMm46g7D+QLQqEeO2Osg0UX3+HsPOrkOTdCr4P9Mit6NCqKSyB66ddoYUYCZ3GQtKjA8hzGXlxLeKDXod/FX2uTdf60RUPtuVRjv4cpVk5CN4znl4D0DEMhGwpXUSyIUr4Dkj1fJpWLo5/bQaEfr6QfjEWPEaPAr47eBu+onPV6Sp4zOqNEp4j9NGHa0LNBrWHqWQ4/ZcbCNkzDiWMPXTRx2sEK9XEcfAruAMtePQDoIteRT8GZoqC4bhm2yMy5Ku2ITTmJBjyUZFxhEBPKoaziH12yCOF4LL+iUXg5mHIF3iAv+FAjejbUDfhyHBD9Txnt60hYDEynJtfiK+O/YDDp39GkiwNDmJ7dO3UBl1faYMWT0aCV0ERB1sAkyPDlvUSR4Yti7ex0lLP/gGdTA6/fl3Ar6J1iDF7ZkydDt/cm0hzewKlTdtA6qlGcbOXIJL6PrJcucCQApgr8IVwvSGKpilQw25yP9jptUiSvgy/6RPK1sk27ELkzV2Iafq+WamueRNGwrMoBUUCJ9jpC6kskrqtGLoR2BcNae4/VGbKgPXwbNW4nN4k6li8ZhE9kApGTDE7XdsYTBvKHPnyTQhKuAAhCpHo/yL8Zk4uZ7pOXQpSedlBej/y+bhD671IqXv3LnCUekJx5ALCv12IJJ8X4PMAma4NbBWHf0TY2eX/ZR6UQP5JNEoVGQg7ORsaONP0aBIZlDs3Q0D+LSR7PAut2BXh8rOI7TAZ+sx0RFzbRbMqWPAR81R/SIe9i9x/SUXiQVDZ+UO0jisiZ6rvTCXD99Lt7/WqNlVeRfMV+76F/T/fIyD3Gv3Yca8nttz5KbgvW/nIkvQL1xC0bxK0PGeAZWgxOT3LQ1xkD0gmDCk3X3nsJ4Sfno9ErxfgM39OTajbYPfgyHCDdT1nuI0hYDEy/CAut+4m4tT533H021+QrcqHr7c73nnjJbzesTXCgvxtCkKODFvWXRwZtizeD0ojRaI82relJEJ1Rw6/NYPpnV1S3ZkMUqk3vu1gSD/oYpaSpN2L49RelFjmL/i6ynYxhqjHcWTbB8Bp9eYy2coDZ8DPUELUvgNcn7wfmZV9dQ6RPyxHon9H+M4qT5SMVZykEtqPfAM8lqVpuLk34iA4+jkCCm4hwa8dQlMvUGxIRDvxncVl6bj39lcevoCQc6vAsBrEvTgSkvfeMFY0N68KBMh9WDt9KeKa9oKoRUt4v9isSsyMPbSm/vgPQvdPgsL1KbgvXVHlvlVNUBw8D96NS2BbdYD/663LTSdEnKR0k1HKuEMLPeRRbyDi3730KpGOPl9axHdbANH3X6MopAWY3AzD3fn/qg3fm0PIUpZIQqOGmb/dRuDOMbRKsMsKQ3spbhiPgKlkuDgjD+rl86C1cyyXqm68xIpnpp75AyGHZxrIMGNPr4Qker/42I80pMVTWMr3YMFDAd8JzppMJPm9BN8508s/d3tOIvLn1YgP6mKR7AdzcbDm9ca+V6zNhgBPsbWpxOnDIVCrCNQJGb5nkVanw+V/7uDk95dw6JShKf2TjULxxdqpNpNCzZHhWn0+H9mcI8OWxfueNOXCVQiXnaYH8PinPoCoRQsE7RxNfyZ9Wg3FrOxQIHREwYDZ8GgZWW1FSUQs/MyS/wjHsir3Ie1GilNSwXd2hEeL+y3ZBHwGHs4ipKvuFxgim5G0xeDd46F0fgJuyw09iE0dmVOnIkh1GSqhX1kLE9JCJfLOV/T+cgnjCRGbRbeNeXFcObKb8ct1aH46j4iUE/QQGxfeBZIJQ01VgZtfAQIkQyH48ALk2rmb1DrL2EMrSSGVbh6OTHEwHFc/mo5qqlNyJk+CJO9vxIZ2e6RadM6kcZDk30C2nRQMq4NbqQIJ/h0RqvweKZ5t4ZVzB3lib7is3FAmlvSajUg6Rv98L1p478dixommRSu/vYSwI7OQ4vEcvBYtNFXlBj/fVDJcW4CRiLPfhiGUDJNBov9xga8hYNrYCkWmkLZgdw/RommEBAenXqRZA1lzDsDB15Wu0eYXQbVoPn23xUT2gmT8J7WlfoPY19j3irWBwZFha/MIp09tI1CnZPiecYQU/3Dxb0xbvB1F6mL8enwjXJ3vV82sbRDM2Z8jw+agZ/pajgybjpm5KzJ/vQnJrsnQMSII9fko4YmR4dIEgbl/PXLo1rFCxDXpCemYQSiUZaEkPdtkYpw2ZyFCUi8gps1ISM3oc/o4MlyQnAHvRe+hgN6J218teHjD34Y9W4RE7//BZ94suof8wFlE/LiKfhR8oMtmAAAgAElEQVTIEAfBTpcPj1IF7tJ07A/pHBL1dp3agxJm0ueTYYAkz7Y1GjGqlkH1ZBEpXBZ+bRfiGveGZOwgo60y9tBakJgB7yXmPTsPKlU4bgi8i+OR4NMOvnNn0J/y7sqh27YWPoU3wWdZxL88DvZ/naOFsXJEgfAokSGmcb8K7ZOv2ITIuENlIliWB6VTY7irUyDW5yPpvVXQKNMQ/v0yJEg6PpJCbjRgDXiitZBhUm3fZXzXcp6IafwuJGMGVkyGN+1Fo2ufI8X1GbjOnAPVgrnwzbmJtP6L4dX2Cbom7cJVBO2bApZnj/gne1qkfVh9fpSMfa9YGwYcGbY2j3D61DYCdUaGWZbFjX8TaLr0wRMXKAn2cHPG26+9iNGDesDOTljbttfI/hwZrhEYjd6EI8NGQ1VjE9NnzkFg1t/ItJfAVx1D06FpJJjhIUssRV6j/8GxTSuoYxMQcWYRkrxfQKl7AIJjz4DPFiF72i44BnoZpU9+Qhpcl39CiWbm9APl7nUatcEDkx5HhskU+2Gvggc9CtZ/C56Ab9LWubdl8Fs3CLlCbxSPXgGXCMPVjvSfryNoL/lowNCqsaRYU8T5pUj2aA2H0Z/S6Ev21QRIN5ePtpBoewnPBbnDlpdL5zZJKRucnBejQNH+XWBad4RvJ0NVZ3NG6olf4f7tJrjqVYhtOwTS98sThcr2NvbQqi0ohtPE7tAzQGn0aXPUpWu1o/rBRZsJhVMzuC03FHkj95LDziw1pNmzahRFn0XmjBkIzvmD3hVmGBbxbT+q0D7Zqm2IiDlM7xKnuLaE85TZtJJ24djB8ChRQsvwkPzsQET8sQmx4T0gmchlJJjqRGshw1TvEe/AXq+m/ibj7jPDHimIdc8+2c5jiLq0Hgl+r8B39mSQj47BqT8htuUgOMb+CY3IGZrgJxB2+TPaW9hp8DCj39umYthQ5hv7XrE2PDgybG0e4fSpbQQsToZJ8azT53+nhbRkygxq35ud2+LNTm3RqmUTCPimHUxrG6Cq9ufIcFUI1ezvHBmuWTyN2Y0UivItvIOknoshPvUFnItJT10GhQIx2FkbYe9taJeWcz0BARuHIEckQbZPM0T8l1ad8L/RCHj39UpFkWhY/rFjiIg7Cg3jRNOt7deaV9ynMjKsHjOQRtiUY3bArUmgMTBAdSsJmt1b4au6AZG+BAkBHeA3a1LZWlKUKWPvITCOTpAM6gFStCZk/wRDKvTzH4FJk0OadJYSfdIPlAwGQAnjBTt9JhJ7L4Nvhxb07/OTMup9US35l8cR9ttWlDB2yPpw4SNFxoxyygOT5Ms3IzzuME0bjXtr/iN3cCvbz5RDq93w18Fj9chfdgRCZ/Pu1pG9BKwW2XYS2K/9gqoo27wfkVe/oHc7793zJR+kQjIv0t/vRrwD6WPS6uVrP0fkv3tpyixNg144n64hBemCVX/QqtOxTXoj8vZ+xDYfAMmwd02FucHPtyYyTHpNexfGgceQ1nJATIfJkPR6xSgfkbZdkbf3ItG7LUIzfqFrErz/h6D035DQcQICjNzHKGENdJIp7xVrgogjw9bkDU4XSyBgMTKcLE/HxHmbcONOArWrVYsm6Pb6i+jwQks4OZp3oLAEUI+TwZFhy6LPkWHL4V0oy4R68xoEZf1OhWbN/QZiHwPxJanT4kDfRyIHpH8pqVaqcG2OgNyrtPtpQlBnBFTRhiZ78mR45yWBjyIkB7wAh74fwCXSvP6WlZHhzGnTEJT9J2KeGoCAW4eRL/KDa7EC6nm7YO9Z8RUN+cotiIw5iGKeN+z02Yj/3ygEvPf4YmGFSRnA6mlwKklDSsQbAF+IqDv7ab9kElnXMHwkNekFz9gf4VGSgvg354HhMZAcX0r7xuZP20ErGNfXkTVlMgJzr1CCFh/0KtDkaTDX/gTzdGv4vf1SpWaTjwV53/8MvjwOfjMnGp7J6bMgzfodcU8PhHvXTnAwATtTDq3akf3grM1C+tQ9Zn2weDDNlVQaVnSZDF1SPJxjL8FXHUvJcFyLAZB80htpsxciNP1Hamf61L1wCvKuEB8SbWe3LIF/4W0k+LSH71xDcSTlyd8gObkEIr2aZm4EZ1xEzIujTYqe19fn0FS7rIkMp82ai+D031DEd0KuYwj0PT+G5/PlK9c/9uxy8DzCv1+CHLsAeJTK6TQ1zxF2uhKkj9kG1yZSU6Hh5j+EgCnvFWsCjyPD1uQNThdLIGAxMkz6DM9Yuh29urbHax1awd/nflsLSxhaWzI4MlxbyFa8L0eGLYe3YuEqBMkuQs+IkGvvBudV0VUKJxFXN002GFZLS/iQe7HZ4iA4rt5S6VrBsK4QQIcCgSsE66t3j/dhAZWRYVpoKPEYDc1q4YRSnjNE+hwkvTUdfg9V9aVE649/If2ctGciFEVDixMph2+Ee7PQSu0iqbshJ+bSjwPSvL/puiz7QNjNXYO0/ccg/aQPlAuWI0x2FgqXZijyCEFkkqEXaWLflfBp9xRST19C2LGZUDg9Af6wKXAO86vSD3U1gdwTF3k6QSAWVaoCSYd3WDkKTtocgOEj1TEKpfbu9MNLsmerSu9Qy784jPA/dkDL2sEOeRQnj2ejoJk+HO7FcqSO3AK3J0NMgsCUQ2vhuMHwUidBNjQank9HmCTnwcmFKZnwWvguTXsmg9wdD876lf43aXsT324c3P73PL0qkDp/GcIU5+hvWQu+gdjT8FGqokGKcvnm/YsU/xfKVUtXjxlEsyEyxaHwUicg/vUZ8K/io0O1javHC62JDMtXbUH43cNIkL4C/xmfmoR61p93IP1sVNnzR587CFDKiKCPPmLSXtzkihEw5b1iTRhyZNiavMHpYgkELEaG9XrWZnsJV+YIWyDDJEKVffQ0oCtF4LiPLfFc1ZoMjgzXGrSPbJw2dylClOdoG5d4v47wmz2lSuE0UpFxCVrGETliX3gXxdI1BauOPZYgZV68CcnuScgSB0I0ZzWELjWTKVIZGZZvP4iIy6QNE6mn6gghU0D7uZLq0Dy9DnqGj4ymr9G+rGQolm1EWMIJ5PM9kf58H9iFBMP7paeqxCP7SgwCto4sq/iabh+KvP/1Q0CPl8vWypdFIyThOwiRDx3jQCsHM2wpYiJJOuwQKBetRnjKKapniu9zj7RCqVIJC00oiFdCvGocHPRqJHYeD/9u7SqUrE7PQ/bOvYiM/4b+TiLDxG5iPzmMEzsTm7712B7Q2pF9Ya/XgseW0rZepJp5QlBHhCedoonn6s1nTLbYlENrzuRPaX/XxD5L4fuyIa29OiP7ajwkm4aWkZECniuc9LkGTCBE3uKD9L4vGfeeAfLfeStOQuBo91iRyiMXoEtJAj8iqlyquOrTMfAvuA0NYwchW4qkvsvg0/7p6qjeoNdYExnOvHQLxX9ehrBxU5Pv3Wvy1HCd/FY5X+pYAZSuT8Fj6dIG7eOaMt6U90pNyayJfTgyXBMocnvYEgIWI8O2BIoputoCGSb9YSMukoM/i+Tuc+DT+TlTTLSquRwZrj13KHafBJuXA7cur8Ix2Bvps+cjOO0C4l6biYDHEJuHtVHs+xbiK+ehbtkBAf1eQ8G4YfAqTkJskz6Iur0bpL2LrO3HCHj/fnpxyqZ9iLi2G/Hhb0Lyac0V9KmMDCuPX0T4qTlUfZXQBxnNuiHiyjYwNEFZSO9cFgocoZm2CWJ/d+hG9oGjLh+xzd4zqcKqOk0Fzzm9KNkjxcTuPjcc0kHdH3Giw7BOVC6JkpJ+oRrGFUI2F0WbzkE3sjccdYQk8VHMEyPtnRlld4tr72kwbWdNvhpFcyfDoygdIn0W4iSvQtCuM+wD/eAc6lNuM+2ovhDpWIjYbBrtLvRthNC4MxCg0NAOiNUgXvJKhZWOyYc9z8UflN2RvNdHN80hCt6Fsciz84Lduj2mKQ/AlENr+sx5CM74BXGvTTf630VFCqWdv4KQr6aUkWEN4wg+W0I/0BTyncDfcKBsGckOCDy+BEV8RwjXm24f2Shz+nQEZv1J5bEsA9knG+FpRgs0k0GuJwusiQybC6l+ZC84aVWG4gX/jdiQbgiYPMLcrbn1Jr5XrAkwjgxbkzc4XSyBAEeGzUTZFshwypb9aPTPZ9TSmMjekIwfbKbVdbecI8O1h71wOCGoYhTYOaF0zBKUbN+IwKzfEP/2ApOKET2ooWLRWoSkfE8jeHqIoIUIeSI35DsFQxvRDG6vtIN643JIVNeQ1H02/Do/X2MGVkaGs67EInDbMNoGSeb2FEpbdET4D8vKyLAhFdoOcW2GwvH6OXgVKmHHqpA16ytKjk0ZBFdScIkPLZRjPodr40fv4uVMHA+/grv0HrE++ihKR78HV00mEnstQfDXM5AvcIWDLhcMy0eaczjcl1WvP7Ipepsyl9zB9lUlQMRmli0j+CUGvFSO1D54T5ZEgeNaDoJ3ry5QrVwGaeZV8FBC09Dlzk/Bee6iR7IJlMd/QfipuVA4PYkC/6YIjzkKPgiBNKQWk3R0j6VV96Z+2DZTyDCJ0oYln0bMC2Mg/eDxd8arwk95mFSNXlhGhu/dJSf3yRXOzeC+bHm5LXLvyOHaSFLVto/93ZDp8X0ZGTb3znO1FbHxhfWJDKsmjoN//nXqEQ3PFQJ9IWKffBfSkf1t3EvWob4p7xXr0NigBUeGrckbnC6WQIAjw2aibAtkWLZmByL/3Q+WEdBCNQFTx5hpdd0t58hw7WCf9ce/kOwYAzBCmuKc5kLuXDKQqi4jqc9y+FQzHTTtu7/geWQ1nHRZYEmrGBTTVFgS5dQwzijl6eCsy6GpsrmLv4HIveb6i1dGhouzCuExoxvVJa75hxBERiH04GRa+bkUPBQLnOFamobYloPhevs8fNRxSPB5Eb7zZpvsgKKxH8GjRIG4Vp9AMuDRqDDZMHP6TEiy/0GipD38Z0xA0diP4VWShATvl2ixo/jA1+H+8UB4zO6FYp4j2I33e8marFANL1AcPI+I7xejkOcOefthiPyeEDzSSVmMIr64LMKZ83cshF+ugLc6ESU8EbKcImE/dhrEAR7IvZGM/LNnIFSlIjTjJ6qhYtR2uDUNLqetbN0XiLq1BzHh78Djvd7I/vkywn9cBR50hnu2QW8gYJrp7zdTDq2y1Z8h8s4B3G0+AIFmVGOWfXkckb+uN5BT8GnvaTLIf8cGvwHJlNE16imS6h8ef7RMXt6yw2ZXw65RBW1ks/pEhpULVyFcdr9FGK023moopAO72Yg3rFtNU94r1mQJR4atyRucLpZAgCPDZqJsbWQ4+2ocJJtHIJ/vDsGGfdS6e205SDGjhICO8JtV9d1PM2GpteUcGa5ZaBVfnYP48rcQ6IrhUxQLuUtzuBTK4KRT0Qgd6Qee3H8tvNs+UW3BCV+cQdQf22iaMEkBBSmuxQhoFJCkxZIIoo7RQxd9rNoyKlpYGRkm80nEltydVAzfCr0etP8v0SfR93/QBYTTtOlcO184aFSwY0sgH7EN7s1MK8xkrEHk/qj65r8QP9EEHs1DkT1lIiS51wz9nFkhEt+eBd9XW8FheGfoGDESXhgCyQOp5sbKqel5JTmFEM34ACJ9KRJf/RQOjSPgt2YgmP/SLkm0M3PSDpoqrVi6AcFJP9A70XKPJ+G9cMEj6uQnpIPdOB+eRfFIG7wOHi3Cy81RTRwP/4IbiO8yB/5d26IgMQOeSwfS6DAhw7HPfAzp4F4mm2nKoZXcN4/8awvifV+G+5gx1f6AI9+0FxFXvyiLDJOK0oa2WwZCIhlQs4REtuFLRN7YDZZkH9RQn2STga4HC+oTGSZXYyIurqFeiffvBMcPB5pVIb0euLdGTTDlvVKjgs3cjCPDZgLILbc5BDgybKbLrI0Mp5/9EyGHp5VZJXN+BtL8v2jkjUQekr3aVlqp1Uw4an05R4bNh1i+aiu8439GRvO3EHHlM2gYF/psCNk8xLSfCCbuNiJSTlBBJHIq/2gtPJ+NMkuwfM8p2AVK4XpwOZw0+f+lw5KK04ZB7nw6r9xoloyHF1dFhnOuJ8Le14O2iypKy4XXnJ7U3viOE8FqNYi8sMJQ0Ilxhh2bg8zpB2hlX0uM1DmLEJb2g+FjAcOiaPlhCBztoSf3h7U5iHn6I0iH9rWEKpXKUBz6AWHnliPTPghOqzfjwTRow/MjRKJPO+iffh4RZxeRsCfSxI1R0O4d+HdrX+HeJHUzoOAGkt9fA68X7n+E0Wl0cBzdlT6rqgWHaNVqbUExxJP6QsgWUjKc0HU2JcmmDlMOrfJ93yLyp5VURNzb8+H/WmtTxdH5pC9yRNwhWl3cqyQZpIBWKc1IyERyz7nw7diyWvs+bpFsx2FE/rGJ/pwv8Cz7WFqjQhrAZvWJDKtuJSNg/Uf032lshwmQ9urYADxoORNNea9YTquqJXFkuGqMuBn1CwGODJvpT2sjw4q9pxHx8ypasIfHltBGMIYkPAPxIIcg0iNTJfSF3brdZlpv+eUcGa4+5rQ368kzCLpxAEJWRyO/JYwXhHrS3oahdZWVY7+g9xJJ2q5vTgw0jBbFU7fWGAkkxdz4cf+C/0J7lN6+hYhb+5EpDgJ/yrKyHsbVt7D8yqrI8IOzWT1Lo64kopn47iqAx0PI7rF0CvmQxGN0Vba0qSm9yT6ylZsRFfsNtBBD5vUMfOYb0rNln32DyD83ITaqT53f/c+aOoXe9dYyDkgOaoeAKaMMEIzoQfvZkncOifqT29Iso4NQX0oL9ciGboFH87DHwkV6Tkty/0Jin+XlqjWn/3IDQXs+peTRcfW2svX8Ee/QCD/DaqAYvqnKdlcVCTbl0Ko88SvCTxr8EdNhMiS9XqmW65XzVyBccQaJXi8gJPMiLeRWMHAedPmFcGsZCaFTzVRVv6ec7KtziDxvuIecIQ6B0+qt1dK7oS+qT2S4QJYJn4X9aO9z1ezdEPu5NXT31qj9prxXalSwmZtxZNhMALnlNocAR4bNdJm1kWHZup20ai89xMMOPJSilHEF2GLwoSsjxWqes1XdOzTWDRwZNhapR+cpj/6E0G8XU9JLRjHjjTyRK3yKY2kENHX4Zrg1u39HU3nkR7i1am5ywShjNUy/eAPa78+AlYZCMqiHscuMnmcKGab/Xkb2hpMuB4oR22Dv6wmPWT2Qx/eByjUMxX6Rj231Y7RCJkyUb/saIX/vAY/VIX30Jrg2MRTdIpj5756OfJE3HNZsN2HHmp1Kour+0aTirB65Qm8w8zaXtQEivZXD/iOLcqcW8C+4SoXzoMedqD5VtncjH2JI4bbYlydB0qdzmeIkrZ2FPZKDX0bA5JFlf186qj8cdXlIfLInJMPfr5ahphxaiQ9Cdo+jcu62Hgnph29XSyaJ/oemnkdsxDuIjDuEbDsp7Nd+Xq29jFmkPH0J4cdm0qly56cfKdBlzB7cHKA+kWHiz4yfroER8OFlxlUY7rmoGAFT3ivWhCFHhq3JG5wulkCAI8NmomxtZJhEG0Ll55AlDoJnsZxG/+42/QDi1q3gv2Mi/TMhQ6SnqnrTWWp93l05Cm/FQK+QwbNPd9h71lwRIzPhfWQ5R4aNR5TcB9bLEiH9r3q4bPN+RJI7iv8V6kn5KBqez0Yi89JtsKUao/rmGi+97meaSoYf1pg3/G16h/Nu61GQfli+H2dtW5f9dyyKb92BMDSk3H1tckfWe8m7yBN4Q7hhb22r8dj9ZXtOIvyXDfTj2sM9qIsz8qBePg86oRheC+ejYNwn8CqWAaweCW/PqbIyedqseQjKuIwckQ/YCUvgGOiF1DO/I/DoYqjsvIFPl8BR6lmjtptyaFXdSETAxsH0Pn1Mi8GQDulTLV0yZsxGUOZFxHWaDoeoMDiG+tR4NPhBxdIv3izLdkj0/h985s2qlt4NfVF9I8MN3Z+1ab8p75Xa1MPUvTkybCpi3HxbR4Ajw2Z60NrIMEkxDMj9BwlvzAKrKYXd5bNgun0Iz1aNwY7sCaEeELBqGjHOW3WCti4hEYrA9Cu0wJF8yDp4Ph1hJiq1t5wjw8Zhq9hzEiG/bDO0qRmwEtq8AojP7IRPQQxN+c22k8B+7RfGbWajs8wlw6qJYxBQcAuxr0xDwDsvWwUK6sx8eMzogRKeE/TRhy2qEyluRS79Oof6QrlgJUJl3yM2qjvce3WnhPVxg7QiCk35jr5zcpceg9Cl8vTflNXb0ejuAdqKK8n7eWj9wuCY8Be9RxzT8hNIqlEgqyqgTD20yjbsQuSNnYhp9iGkIz6oavsKf8+cOg2BOX8gofsi+L1acy3FHqeM6mYiAjYY2urFBnVBwFTDNQBumIYAR4ZNw6shzzb1vWItWHFk2Fo8welhKQQ4Mmwm0tZGhtVjBsK9RIHUUVvg9kT5yrcF44bBqaQIDqyCRjUypu5D3p/X4fD7EfgX/Et7rJIKqX5z7hfgehgetTIH2bv2QdjqRfi0e8pM9ExfzpFh4zC7RyjI7Li35sPz9AbYa0F75aa4NYfTuMk1fkfXOM0sN8tcMkwyJvgO9jUehTQHAW2JBs5jSREpUmWaD/ngtfB8ppE5Wxq9NnvyJEjz/kbMUwPhfud7eBQroRyxsco7uvK9pxD2czS9G+68KrpKearrSQiI/rjsmgdZQNL4yfspruMkBPTsUOUepk4w9dBKerdH/b0dMY3fhXTsIFPFgbS0CZWdo1k6Cb2XlbsbbfJmRi4olGfCe0E/OpvoLRkz0MiV3LQHEeDIMPc8GIuAqe8VY/et7XkcGa5thLn9rQ0Bjgyb6RFrI8OCYV0hIPf4Fn8NkVv5dGf5mh0QK2LgW3ANAn3Jf71A7WnvV5ZlaIuPFLdn4Ll4yWNR4Q9/EzyIoCN9PS0cmSJKcWS46gc2L1YJ4frp8ChJKWtxQwoxkZ6vqY26wG9ATwicHareyMZnmEuGrdV8/YiecNCTNlVAYtcZ8OtievVkY23LvSOnBdVKVUVwm2q4G0uiihHJJ1HC2EMXfbzKrUhP55L0nLJ7z1UtIIXefJe8S99JesaOtk0iI10UDt2QGUbvU5WcB3839dAq234IkZejERvRE5JPh5oiis7NnDYd0pwrlAw/XDXb5M2MXKAvKYXD2LepzLvPDIH0455GruSmPYgAR4a558FYBEx9rxi7b23P48hwbSPM7W9tCHBk2EyPWBMZVmflwX1GH2jJTb5NhtY4FY2S0R/CXaMo++keEaZtTxwrb3HjMKwTXUciNRmTvgBv7Qw4arKQNWAxPJ+r/QgVR4Yr9qlm1Ptw1mZDB/JxowQaxhkiNou2zLhXWTyuyTuQjh5g5hNvO8vrKxlOm7MQwemXwGOLa53UkKsVIr0OhXwHOGtzaNq9wrEp/AtvQyX0g2jdl7XyQJD3DKmBf+9+O6l2q3B7stIPdeYoYuqhlfRnDf9lNZK8X4TP/Dkmiy4YNxRexUkGMvzRRniZ2brMWAVIETIeq0d8+3HlipMZu56bV/8KaHE+rT0ETH2v1J4mpu3MkWHT8OJm2z4CHBk204fWRIZJ38qwhNPIsau80iwpaONTnPCo5SyQaR8M/uy1ELk7Qp2eh6wjp8FPSwGeeQEuT0TQ4j1ksCwfsS+PR+SPy6GFA/Ls3GC/dqeZaFa9nCPDFWMkGvYaShh/CJFHSQTpi0sGIREGQsEgY9Ln9L5nQxn1lQyT9mmhP2+EnhFAyzKQtRsK/5+3wUFfjLjWn0BSQ8W+Ur+9hOCjC+gHFUKCST1o0E9tGlqAT+nUGG7L19XK40SqaQtSbiM042foGXvERXWH94C+ZRWra1qoqYdW+f4zCLuwEXyoUbTpnMnq6Ef0gr2+iFbYVg6PrjLV3GQBj1mgHdkPTro8JLw1s8pCZjUls77tw0WG65tHa88eU98rtaeJaTtzZNg0vLjZto8AR4bN9KE1kWESbfAsTkZ8iwGQfNL7sZblTRgJv6I7NLp7r81OjtAf7qVK2gc0ps1oSPq/CXLXL/SnaHoYlfm2hC70CYT/vp1GpAgZjgvtivCEY2AhRIHQGYL1+81Es+rlHBm+j1Hq2T8Qdng6NIwrJb0so4a8/XCwqhy4dG4P52BvZF+Nh/r0aZouHTCZtMJpOKO+kmGSsqy6Ggv7Y1vhUxSHDHEo3NVp9N9yis/z8J073WwnZ12JReC2YShhPCBis2l7tmJGCBFbAKFeAz0jhsKjGbwWzjNb1uM2SD17GWGHp9KfUyd8CZcI/1qTZeqhNf3HfxByYCLVx1QyTHpai4e9BobRoyj6LBgeU2t2PbyxfOcxsHkquHZ9Dc6hPhaTW58EcWS4Pnmzdm0x9b1Su9oYvztHho3HiptZPxDgyLCZfrQWMkzS9kIvboCe4UETfbJSq1QTx8GnIA4CqMvmJXu0QVD2b/TQq3RrBO9FCyGP3oPI61/QYlskHVDjHoCIuwfp3WJChvMFbnDS5YPHlqKI7wxsPGQmmlUv58jwfYwMRXi+QykjQpHAFSqvpvCbNalqEBvIjPpKhu+5L3X+UoTKv6MfOvSskH7I0jEMVFN30urOWVMmIzD3CuK7zIVfV9PuFcu2HEDY1f1IdwiCpscn0Px0HsIX24F/Yjf8cmPAQwkUQ9bD4+nwWnua0q4kQfz5QvB4LJzWbqs1OWTj6hxa7Ya9AQE0yFtxEgJHO6P1K0jOgPfi91HEdwGz4Wuj13ETrQMBjgxbhx9sQYvqvFeswS6ODFuDFzgdLIkAR4bNRNsayHBJugrOs/oBjAipLo3gsXRppVYREuWr/I2mVeYJXJHnHArh+0Pgv25gWUpk/qqTSN2yG5H/7gMLFnLXlnApTKb3UjPE4fBWJ4BhdNCydhCgFBqeXZUk3BSoSa9RoboAgiHjykWEODJsQJEt0YI/tgfsoId88Gp4tow0Bd4GMbe+k2H5knWISCQFrHg0bbmUJ4YQelp4KtUlEh75SXBgcxDTagQkA7o91ud5MQqUKDOhkcnBi70OXVRz2F/9ARHCn4YAACAASURBVAGqa0h8Yzr833yhbK1s016EX9uLdMcwuK6onRTpung4q3NoJcUERWwxcpcdg9C58nZRD9qUeysF/usHIUMUAsc1tUvy6wLL+i6TI8P13cM1Z1913is1J736O3FkuPrYcSttEwGODJvpN2sgw4pdRxH223aA1SPmqfcROMzQPqOykXM9Eerrt2HfOAIe/xGp1DO/w/P4GrjqMu/fE2RJZ1EWGeJI+BTH0nTcBGkHRMhO07upLAvat5YMU9MFH6effMUmhMedBFgtknsugM8rz5ZN5ciwAQrFV+cQ8sN65Nm5W+SudlXPkzX+Xu/J8KqtCI85ChZ6ZNkHQu0UgMDM3+l7gMfoUcJ40iJqd6L6IHDcx491Efk4FiK/QPuMk0GyRAKzfgfL8JC76CCtH1DfR3UOrbzhb8OeLULOosMQuTsZDVHmn3cQ9NlIpDo0gsuqDUav4yZaBwIcGbYOP9iCFtV5r1iDXRwZtgYvcDpYEgGODJuJtjWQ4dzJ4+GddxcJLw6H5L03zLIod8Io+Bf9e7+S639kOE/oCxdNGgoEHsjuPQPB+yZADxFkbs3gm3sDIhJlXn0CArHILPlkccaM2QjO+hV6loe4F0dD8n4Xjgw/hGrhuMFwL05DQqtBlUb9zHaGDW9Q78nwjkOI/HMTtHBEYsgrYP2DwY+/gbC082VeI1ccEiSd4DdzcoWeZEZ0g1hfSHv6khrx5N5xMeMAIatBhkMYXFY2DLJWnUMrM6IHxPp8ZC34BmJPF6P/pci/OILI3zdC7twc7stWGL2Om2gdCHBk2Dr8YAtaVOe9Yg12cWTYGrzA6WBJBDgybCbadU2GNZkqOM4gKdI85C84AJGn8RGKikzPnD4TAdnX6U8CFJKwML2LSAY5WGeLgsCbsx6u03rRCrO02NbvX8BBl4echYfNlq/JU0M3cyg8ShWUDMc26QfpmPvtgLjIMMDq9BCPfING5dXrjoNnJzTzKa6fy+s7GVZdT4JgxxJ4FScj4bXJ8H/7JShPX0L4sZm06J2O5HfoSyFzbQGPpcsqdLJ4WGdKhEk7rhLGDSK9ynD3GELERb0D6biP6ufD8ZBV1Tm0siN7wVGnQtacryH2dTMKJ9XtFASsG0TnJrs/D69FC41ax02yHgQ4Mmw9vrB2TarzXrEGmzgybA1e4HSwJAIcGTYT7bomw2m7DiHw1x1QujaD55LFZloDKE/+hvATswzkt1yfTwHpXow0B0MfYtKvks+ySO67GO4Hl8JVk4GMGfvhKPU0S4fcT0fDsyiVtgYiZDgxoGO5olAcGQZUNxPht2EoVHZ+sF/7hVl41+fF9Z0MV+S79Is3ELR7Io3wxr0+E+Gn5iPTPggOaz6r0NWkn6+h/ZYeif7tEaY0tAki/apT+syHT/un6/MjUmZbdQ6tupF94KzLRsaMfXCUeFWIk6ZAjfQvvwHs7CH5uCfSf76OkL3j6dxE7//BZ57hXcsN20GAI8O246u61rQ675W61pnI58iwNXiB08GSCHBk2Ey065oM500ZB6/cGCS8NBKSfq+ZaQ1QJMuG+8L3KPElESNSmIdEjbSMEwRsAeTOzeC+bBXutejw7PUWmMXj4FEiQ8xzQyH56J1q60BajjgM70yr45JByHCqy5NwW7aybE+ODAOKIxcQdmYxUrxaw3v+nGrjXd8XNkQyrM0vQsb3f0JfXAyv116E29TuKGHE0EcffcTdhbIseC/si3y+B9K7jIM42B8B6w13i5MlL8NrxrT6/oiYRYY1I9+Fqy4D6VP3winIu0KscmOU8F/Vn2ZxkDZKyhMXEXF6LrQQIzngRfjN5Kq/29pDxpFhW/NY3enLkeG6w56TzCFgCgIcGTYFrQrm1iUZVv5HiohaqgUHzU5Rvmceaankd+sYxLoSpDuFIKDgZpnlye7PwWvRonJIFIz7BO4l2YBejYLFX5lUcKcgKQM+S96lBbuUzo3gn38XPJSWkeFcO1+I1n3JkeEHEJdt2IWIm3sQ17gPJGMGmvkE19/lDZEMP+xNUvFYyGohH7weni0jyn5WXUuA47bpcNVmIFfgA+H6PdCXamA3pjsE0CEusgck4wfX34fjIcuqc2jVjHofrto0pE7cDZcw3wqxyr6aAOnmT+hvcV3nQf/vNUTGHkRsaDcETGpYfb/ry8PEkeH64snat6M675Xa16pqCVxkuGqMuBn1CwGODJvpz7ogwyl7z0Ity0RY0lHaHinLQUpTl2typF+4htK/fgc/LAoCLw+4frUEbpp0xPt1hN/sKeVEybYegPvN7+FdmoC4V2fAv1s7o1XJ+PUmgneN/S9VU0fTO5PdnoX+tX4I3vcpwLBIn7IXzsGGyAsXGQbSZ89HUPpFxHWaBEmPDkZj3dAmcmQYKB4zEB6lMvrvK65pL0hHfUgfA8XiNQhMuQg+q4XMqzl8/sswIC3NXFRJUHX8kN5BbiijOofWktEfwl2jgHL8l3CN9K8QqvSLNxGyeyz9jXzwI3UWyLj7RH9IR37QUOCtV3ZyZLheubNWjanOe6VWFTJyc44MGwkUN63eIMCRYTNdWRdkWDuyL0R6ht6rVQn/3959x0dRLHAA/92lN9J7CCQhgAhiAZ+gIDYQaeKTJoII0ntv0rtU6dKkCAooihQFn4i9i4qikkASSE9I77nk3mc2JBJIudxekrvb3/0lyezszHfGzf5ud2e9yl05ldmdSjfPmDoOPjn/VHpFQ7zzNDTqBC63n4SAQf+u/lxde2IPnkLwV2LFWpX0jLIF8hDWqAf8Z01A8bi+cCxKRXjIc/CbNhoJ5y6gwfHNyLNzh+vKihcEqm5/5vD7nEmvSLelx4zcDPd7/73aZw59M2QfGIaBtBlT4Zf5u/SYQ6qNBxzWl7zXVqwaL/5/jrO7G4XPvgzPjvcYkt7k6tLnpDV34stwL4hG3MQ34dw8oMI+J3zyI4KOzUGRyr7s1VVSGG43AQGDe5icExsMMAxzFugqoM9xRde6a7Mcw3Bt6rJuYxRgGJY5KnUZhsW7Ze1+OgPfzEvSFYZiWOK6y/3wXFH7K5LemDULDdN/xuWWLyFg7It3qEWv24XQy+8g7IGRCBjeRyfV2LdOwfu7fXAoykKk32NoHPuZ1K/S96JmTBsPr6wwRHm1g2X3fij+8G00vPGDFJyvtB0O/6G9ddpPfRWKPfYZtFlZ8L950isW00n7JQxqezu4twmtsFk50TeQ+tF5qFLi4Ddz3B1lpJWkx3aVvjrI3niKK0lXMbgMw0D8wuUITvgMWq1aWiU6Z8MpqGwsUfpaoKRZh+Bw866L+vr/xBj2q89Ja86kYfDIv4bYcTvhcnfjCrsR++GXCDm9CKrShRBulgrv8ir8anAHjTEYsQ0lAgzDnAm6CuhzXNG17tosxzBcm7qs2xgFGIZljkpdhuH4JWvQOPZTFMIZNrgh3XYXds8gNBw9QGYvqt88cd5iNE7+stIrGtFbD6Lp73tx+e5BCBg/uPoKASQsWoWg+P8h08IV2jUHgWmD0KDoRlngjjvxFYJPLUSeRQPkWjjDSZMKNbTSYjRRfh3gO2+aTvupj0Ixhz9B0PlNUmi92vw5uPTpCc8l/aVVeotVxSjYdAIqC/UdTYtdtRGNIs9Dq1IjsnkPwNoGIb+9Jb3dKtE6EF4F1wBouJK0DoPKMAzErHsDTcLehwpF0mJ414dthr2fBzyW9IMGVijYdloHSfMvos9Jq3jXt2deJK6PfgPu9wSXQ0r57SocAryQ/MnXaHJ+9R1hOHLAGnh1bG3+sGbYQ4ZhMxzUWuqSPseVWmpKjaplGK4RFwubgQDDsMxBrMswnDZ9MryzwqVbidMtPZFl5wdt/5Fwu7/iq4wyu1Zu8/S/olGQlALHpoGw87nznZoxez9A6PdbEN74WfjN1G1hmNQZ0+CXeRFXu8yG77OdEL3vQ9iE/QzrQSPg3Mwf0mq3S/tLV7TEytZiYa1ctQNsi7IQ4SueXZ5tyC4atK6YbYfQ5Pe9Uhi+7nw/cv1boMmlw9Co7GGpzcWV/7wC9wvHkWnvD6cps5AdFY/inFw4v7sKToXp0tVvlbYQuRZiFW+V+Jf4GgBW2mzp6rl4rtpjhfxXaRm000ZWGcMwELP7PYT8uBNqVRGKxNJYKgfkWNrCpTABCXahcFq31chGrX6ao89Ja9aUUfDKvYLrw7fC/bZjsHhllfikWvnCpSD2jjAcN34PnFs0rJ/Ocq+yBBiGZfEpamN9jivGAMQwbAyjwDbUpQDDsEztugzD4r2Wjpp0XHvxNXg+YlzP+IkroU0+ew0R/k/p/LoQsQCNS2E84ifurvSZO3FSqdWqUKwSzxNrEO18P/zTfkakVwd4L14gc/Rqb/PY5esRcu209By0VmWFQpUTrLVpJbe3a62Q4BAC75xwFKnUsCguQrHKVmpMscoKeWoL5Fm5wDPvKsRl8DRrXyQHdQCcPdDkxzekq3xXfR436i8Dak9W95oZhoHc2BRkhUejMDoGDr+chm/232WAV8UXSvPLL4anu655ldTnpDVl1gwEpF/A1X6r4XPL+5iT5i2E740/YK0VX2qJ/4W1d4ThGwuPws77zi8VzUvVPHvDMGye41obvdLnuFIb7ahpnQzDNRVjeVMXYBiWOYJ1FYaLcgvgOKU7NCJYbT0ls9WG3zzu5DcIPrkA193bwWPZYp12YDv6ael6Z+aa47B0KAmDt38Kxw+EsyaxJCjCGlE+j6Bx3Kc397NEp/3UR6HkOXMQmPIj4u2bwS0vDsXQIMmhOWw1KXDPjYFaVbKqrLhaJxY3UmmLYYEcAJaIaPgkbDLj0DDtZ+lEOtKrI7wXz4c2XwOLyf+FrTaHr2bRYVAZhssjJXx2AUFHZkADR1giC5dbDUHAmIE6SJp/EX1OWuOWrUPw9bMI7zAB/gOfKUNKnTkDHplRsNGmVAhX+s5hlfrmC9XNn9esesgwbFbDWaud0ee4UqsN0rFyhmEdoVjMbAQYhmUOZV2F4dSLkfDfOhyJtkFwXL9DZqsNv3nC57+h8dvTENfgHri8trbaHeQmpsFtQT/kq+xRvPX9SsuLW8P9sv6Qfn/5oXGwdnVHo9MLEe3SBu4rV1a7H10KVHcLuC513F7Gakw3WGkLEDVoA6ApgoWzI9xaByPtUhR8No4uC8Ol26VZ+cCpMBmJDqGwmrQQKUfek571FFeSL4f2RcDNd75Gb9oHh+sXkdumC/z6ltyKyU/FAgzD5V2Sf/gLgW9OKHvkILzrfPj17MDpA0Cfk1bxKETo728irNkA+E8aWuaYOXUsXHOTYX1LGE6zC4BWZQnXnEhkWbhAvfko3U1UgGHYRAeuHpqtz3GlHpp5xy4Zho1hFNiGuhRgGJapXVdhWLyio/GxuYh2eQDuRvis6I2fLiNg11gk2YXAcf32alVv/BqOgDfGItk2sOx1LxVtFL9wBYITzqEANtBsO4mUby7Cf/9kxDndA5fV66rdjy4FxO2OXhkR0rOU1hsP6LJJpWVyolPgtHwIbLS5UpmMNSfuuOqtHtMLllBDpdXcvI26EIkjN0Hz/VewbdIYTo93ROzJrxF8apkUhsM6zYB/Pwbfmg4Mw3B5scyIRKg2zIdtURrimz0D197d4BDgXlNWsyyvz0lr9P4TaPrtRoQ36gm/WePLXPLGvwynonRYaTPLfhbWaTrUzg0Qcnwekm0CYb9ht1k6KqFTDMNKGGXD9FGf44ph9iyvFoZheX7c2vQEFBeGNUVFUKvUUFdwi1pmVg7E712dncqN5P++/BmtW4TA0/3OZ7zqKgxLz+SeX41I3yfgPX+m0c20zIh4eK0aBPEGkSv+nWHZsQscmwTAzs+twrbGffwdgo8vwHXXNvBYXvmroWLW7URo2BHkqp2g3XIM6b9chu/OsUi2aQT7DbsM4iCexRavdxILdEWP2gG31kFl9aafOQ+bs28jtdlj8B3Rv9r9iVdMNQ07LF19S7X2ht3re+7YRrxyKfiTNdL+LrcYBMugJvDp3r5cubykTLjM7ws1NLjSYwl8n3mo2n2zQHkBhmHOCF0F9DlpjT5wCk2/2YDwwO7wmz2xbFfFY1+AbXEGLJFf9jNxFd6pVVOknzwDlac3/Pt30bVpLGdkAgzDRjYgRtwcfY4rxtAdhmFjGAW2oS4FFBWGc/MK0G/kQox4sQe6P9WuzDknNw8zl76Bc19fkH52T4sQbFo6AR5uztK/23YdhQ2Lx+Hhti3vDDY3Sq4A1vYnfvFKNIr7BlfbvmyU79fNTUqH27znpTCcr3KBtTYTxVAhMuAp+M6dcgePOJEM/WYjrjZ8Gr5zJlfKF/3mB2j6wxakW3jCavMhqHLzkD99OBpoEpG25F3Yepb/4qKm46DJzIHjjP9KoVO89ijWpWW5K+9ZU8fANTcRltosxDq1hKbnYGjSs6HNy4Prw/eVW1lbk5sP66l9pWd6xedy+4kIGNT9jiZlRiUhd9dWFHg1RsD4lyptcvak4VBpgbyhc8oF9Jr2UanlGYaVOvI177c+J60xB08j9Kv1CG/YDX5zJpXt1Gp0b1ghG4l2wdJq01Hu7WEzeAQaNPWvecO4hdEJMAwb3ZAYbYP0Oa4YQ2cYho1hFNiGuhRQTBhes/0w3nznI8l21dyR5cLwrkOncPTEeRzYNBd2ttYYPWs9ggJ9sWRGyXNgxhCGCyYMRIOCZCTNPginRh51OUd02pdYFMZhzJ238oqAmauyQf6S/bBzty+rK3rDboT+cxRhrV9CwKjK35Msbhducmohbtg0hN2GPbCxUiNp4lh4Z4fh+ksb4PFQc53aV1mhpG/+RMMDU6EW72HVahHn1BIuqzdIxTMux8Br/SvSIl/inc7ilVZh/xkD+0tfwD/zD4Q/OQd+/31MKnvjx3/g9eZ0WEoLYeWjGJaI7LkAPl15RVfWAMnYmGFYBp7CNtXnpDXm7Y8R+sVaXGn4TNkXeoWZubCf8QKKVRoUbT2hMEVldJdhWBnjbIhe6nNcMcR+5dbBMCxXkNubmoBiwnBaehbyCgrwwpglmDKib7kw/PzwBejSqS2GDyy5infm/A+YsnAr/vjsTemVGLeG4RupGZi9fAfat22JIX2fhiFuk1aHX4TVh/sAO3vkj75zJeaCtBw0mP08NOL65baTRjvHLEd3h/UttwaWvtfUujgdCbMOwamxJ2Lf/wwNvjgMr7wryIErbgxbBvc2lb8nWSw4lf/Ofmga3w3/oc9JYTh64So0vP4/XOk0RfbthjGHTiPky41IcGgK3+y/kGLtj6Ipq+HQyBNJX11E4MGpUhgWoV689zc89Fl4R34BZ00Swh+bCb++T0rjEbP1LQRfPAItgGT7QGgs7dBg4VJY2tkY7XiZe8MYhs19hA3XP31OWmPeOYPQz9fgin8X+L46TWqMeDe667JXkGNhC8vNbxuugazJaAQYho1mKIy+IfocV4yhUwzDxjAKbENdCigmDJeidhkwHeOHPlcuDIuwu3TmMCkQi8+ly5HoM2IhvjmxBc5ODmVhuGXzIAyZuEK6avzavFGwtLAwSBi2+P4TqPZuhzUykL78GKxcy9/6K1ZqbvTOLMQ53QXX1wyzaFRtTDLNuAFoUJRcVrW4mioWgBIJMXbSm3BpHoDYFa8j+NoZFMEGeRbWsNh8uEZNEWH42rZ30Oin3Qhv1gcBk1+p0faisCY7D9kRiXBo5IGE7fsQcvU4rgb1QJOID6QrwFeDusH2qadREJOA4FMLcM21LTTWLvCP/xyxXg8iKOkraZ/hHafCb8DTEFeXAw7MhgVyEd3gPriteq3GbeIGhhdgGDa8qbnWqM9Ja/SRT9D0s9dw1bczfOZPl2jS/oyE5+aJSLfx4CJZZjpZGIbNdGBroVv6HFdqoRk1rpJhuMZk3MDEBRQfhsWtsS0fexlbV0zGo+1aS8N5JTIGPYfMxf8Or4Wvt7sUhpfNegX7j56Bm6sT1i4YCytLC6lsgaZY1hT47rN0pB3aiwdzvoStNhHpY7fA9+FWSE0DciKuwbOpD8L3HEPgtzsQ1aQn7l7877NpsnZcCxtfGzoIHnlR0qtDMhwaw6LbQKiO74RX9mWkTdwJv4eaIWzMZPin/Sy9X/d6x3FoMeq5GrVErHt25dhX8Do6D9mWDVA8cR18Hgipto6Yry8h7cwZNAw/C0tkS+WThryO/OP74Zf6K2K7zkTAR8ulK8DidmjxiXpwFAK/fwORjbrCpUcvuG4eiQSHJvDOCZd+H/noZLQY2Rt/vrYHjX49KAX/a0/MQPNhdz4nXG0DWcDgAuItrpYWahQWyft/1OANY4VGJyDWOhAhR1Mk7u3Q7XP54FkEnFqKKP8n4TVsKLKvJ6EgLR1e769EkmMQQnZs060iljIpAR5XTGq46rWx+hxX6rXBN3cuQjw/FFCSgOLDsBjs0rDb+dE20thXdGVY/FwstPXRwVUI9PcumyPJ6f+uGKrPxPlnxS6EXj2FQrUrbIvjkfTMBHx10RHdorfCRpuC5Anbkf3u22gUex6RT0xDYH/jfcVO0qTx8M2+hKv+XRC4cIbEkThlErwyryKqYUc0mj8NWWNehEthnHQF9vrzi+Hf5cEasVlZqpH5TwwcVr4ELdS4GvosGs8YVW0deSOfg21xYcmVaqihRj6uPjELHud3wrEoDZmL3kHqD3+i8amFUl3iyrX4iOd/w0P/C/cBfeCyqL+0QFjpJ/w/49D4ld5ImDoZPhl/I/Kh4fB69inYuctb1KvazrCATgIWFio0sLdCamaBTuVZSLkC4rjiYGuJtCzd58r1Y58h6KOliPB5AsV2jgiJOI4E+2Zwz4lCrOs98HtthXJBzbjn4k0ULo5WSMnQfa6YMQe7VoWAPscVYwD1cObjXcYwDmxD3QkwDAMQzww//diDeOWFbpJ8Rc8M9+jcHnEJNxAVHY9DW+bBxdlRKnvrM8Oq7Eyo0pKg9fCB1ubfxaKqHM5x/4VtUQ4KVG5QawsR3+hB+Ed9igK1J2yL4hHZdxUcP9wK9/xoxI3ZCtdW/772p+6miW57Sp49B4FpP5Z71UjaxSj4bB6JNFtv2L6+D3ajOkOl0iJqwFq43BsKqwZ2ulV+s5S4TdrRzgq5AztKgTrC73FArUaxhSXcBr9Y4auc4k5/i+AT86GFJdKtPOFaGCfVFtbqJYRcPIRClYW02E3CF78j6O2p0u8KVQ6wQBGgLUL43QPgP+ZF2I/pDEAltV98LrcZBb8Xe8BuUm+Ie8FzN3wAtY11jfrDwrUnwNuka8/W3GrW53bGuONfIOTjJbjq/Ri0VrYIiS5ZoFEDG1zzbg+fhXPMjYn9EV+QqlUQYSEhteQOIn4oUJmAPscVY9DkbdLGMApsQ10KKCYMi/cHa4u16D54NkYN7onuT7aDlZWlZL3z4Em8e/JzaTVpezsbjJq5rsLVpO9rGYphU0ueB929doZUtjQMq+OvwXrRKGhVVkjsMx3Ojz1S/TiefR+WH7yFVLULov06olnMl7DSJsJCq4IlSl7Pk2TTGJ75kdJ/52z7pPo667FEwsJlCEo4j/CgZ+E3Y6zUErGgjOfS/ki39EDupHXwXvMS0q29Yb3xgF4tLQ3DF+esQUjUKWSrHeFYnF7yrG9gZ/jNLn8beczqbQi6egZAMfLU1kh6dhaCjs1GMWyQr7aGTXEO4h2bSytI58anIXPz63BNC0OWvTd8M/9Csl0gVBOXSAtqFY4fiAaFSf+G4Xtfgd29rdFw73jEOrSAy5rX9eoTN6odAYbh2nE1x1r1OWmNO/E1Qk4vRIRnR2gtrRAc96lEUwRrRDTsDL85/7572BzNlNonhmGljnzN+63PcaXmezH8FgzDhjdljcYtoJgwLFaHFld8b/2c3F+yGFZ2Th6mLd6GL777Tfp1y2ZB2LRsIrw8XKR/i9uoNy4Zj3Zt7oZYlfqFsUsQ6O+FLcsn48+PfoHnW/OQa+0Ct7zr0rOw/4Q8j8bThlU78vajn0Keygu/+XVDTsgDePiLOdIiWqnqlii0zYdTXjxsisVt2MVIsfEz+gVZYldsQJNrpxAW2hf+U4ZL/c9LyoTbvOeQY+GEhOdeReN3ZyHa+T64r1xVrU9FBUrDcNiXf8Nv+2jplUgFKmdYanOkW6ATZhyAU5CPtKm4Ku29VYRylfQccHjD7tIJamFWLgrmTYJn3lWIFyFdDXz6jhBd0b7Tpk+UAnLplWFRRrxCSaUtwjX3B+G5bKlefeJGtSPAMFw7ruZYqz4nrfGnvkHwyQWI8BRffKoQlPSlRCO+mAtr+hwa6rG4nznamlufGIbNbURrrz/6HFdqrzW618wwrLsVS5qHgGLCsC7DlZ6ZjcJCDTzcnHUpLpW5MHY2GiV9Jz2/Kp4vLYIV4u2bwHXtxirrUOXmwGbKf5Fm0RK/PjoGQQ94InB1H1hAg2x1IK51Hg6rS99J4VIE7HinlnB9bbXO7aqPgtHrdqFp2GFcvmsgAiYMkZoggqfz9J7Sf0e5t0fgje9wJbCrTuGzqjCclJwFh4ndpNcelZ6ASgtYDdsCjzZNpZ+VrvZaWs8/9wxFw9El7zQWwdYv6xKKtRYIf3AkAoaKW52r/iQsWIbGiZ+X7bN0vyqtBhG+j8NnwazqquDv61CAYbgOsU18V/qctIrHL0JOzEeUx8NQFRchMOW7m8ciS1y5dwj8R/YzcRU2vyIBhmHOC10F9Dmu6Fp3bZZjGK5NXdZtjAIMwzJHJa3vI9KVgAwrNzhosmCpzYVWq0Lm2g9h5VD586Oq1CRYzh6GG5ZNED12A1rcVQyLMb1gpS1AocoR0fOOoujQdjQNf09qYdzY3XBuGSiztbW7eWZEAvITUuDaOgQWdiV9LyosgtOEp6XQqVWJK7Qa/HPvMDQc2V+vxpReGb6RkY+sySPgkRctXRFOt/SUbmGO6r8aXp3uleqO3vchQr/dVLbo1eUHxyDg5ZLQzan+DQAAH9VJREFUe2P2bDRM+0laPfpan0Xwfvz+atsTs+4NhIa9Cw0cpBWpS18dpYUFwhs9A/9ZE6qtgwXqToBhuO6sTX1P+py0xp/5HsEfvCp9yacuyodP6p+wUuVBOh60Gwv/wT1MnYXtr0CAYZjTQlcBfY4rutZdm+UYhmtTl3UbowDDsMxREWE41doPqkXbkP7aMtjnJsItLwbXei+Gd+eS1akr+qhio2CxZApSLDxhufoN2NtpkTZtAlxzUlGsyoV247uI3bIXTf86iDyVPYq3HpfZ0vrbPGXWdLhlRsK+OE1apTmy6wz49uyoV4NuDcPxi1aicfwXUKEYkd4dpOeVw9qOgf/Q3ige+zzUWi3sijPKwnDpe4HFjuMXrpDKZ1i6o3jhDti4lyyIVtUn5s0PEPzDbumWaw3sYIECqMQiW+J1XD2XwLfrQ9VVwd/XoQDDcB1im/iu9DlpTTz7Ixq/PwfX3B6CZUEWfDMvolglXs2Wj/DOc+DX+zETV2HzKxJgGOa80FVAn+OKrnXXZjmG4drUZd3GKMAwLHNUEvv1QJJTQ7iuWivVFLN6O4IjPsJ1jwfgtXh+pbWn/fwXvHdNQZxtKNzWl9xSfXXJFgTH/QSNgwqWq/cgesdhBF84iHQbD9ht2COzpfW3eeK8RQhM+hZqVRGSbQJROGIhnFs01KtB5cLw2R/Q+Nh8aFSWiLyrD5pcOoQr9wyC1V13o+GReVJQzVR7IcPJF/lOXrDt3hturUtW4864HIOM736Gfau74XZf9e8pFtvkRN9A6omPoU6OQ0jsGekKUGkYNvbFzfTCNvGNGIZNfADrsPn6nLQm/O9nBL03C1EuD8KmMAPeWWI9AXFEsEFU7/nw6Vyz18bVYXe5KxkCDMMy8BS2qT7HFWMgYhg2hlFgG+pSgGFYpvYPS3fD6fFH4dzMX6op4dwFNDo6F4XixTzbTlRae/RbJ9D0640I9+0Cv/nTpHIRq/cgNOJjJLsFw2XpSsR9cB6BZ1fjmv8T8J07RWZL62/zmB1HYBvxC/JC2sCt+5Ow8ylZmEyfz61hWCyQ5bf1FenqbsLDLyPk/DpE+D8FrbUtgiI/lq7QJNiFwmndVn12VeU2YvEzsXiWGhpkqV2g3nLU4PtghfIEGIbl+Slpa31OWhM/u4DGR2bgmksb2OWlwCP3ivQ4i0urRkqiU1xfGYYVN+R6d1if44reOzPghgzDBsRkVSYhwDAsc5hufc9waVVZU0bBPfc6ovotg8/N51fF71JmzoClJgceuTGw1GZJxS+3m4SAwSXvN47a/Daa/nkQ1wM7wWt2SUAWn9y4VNj5uspsqXlsfmsY1uTmI/XHf6C2s4EmMweNjsxCrMs9sM7PgmdehHTV9rrLA3BfsdLgnVeP6QXr4nxE+XRAYXArBAwuWSSMH+MRYBg2nrEw9pboc9KaeP5XND48Hded74eDeDymIBrxU/ahQaifsXeX7ZMhwDAsA09hm+pzXDEGIoZhYxgFtqEuBRiGZWpXFIajN+5FyF9HcbVJL/hPHSHtQZNbgAZTuqFA1QBqrUZ6j7C4zTZq2CZ4tQmVymRkqoDkBDg3KIbWveT1QPyUF7g1DN/6G3Hbs9e6odI7jMUK084F8dKzwhHeneC9cK7BGYvG9YODJh3RQzfA48HmBq+fFcoXYBiWb6iUGvQ5aU364nc0ensqop3vhVN2LJw1iUiadUh6Jzk/5ivAMGy+Y2vonulzXDF0G/Spj2FYHzVuY8oCDMMyR6+iMHzj+7/ht3ca0q09oV60BdYu9siJS4XH4r5lKxAXqFxhoc1A7sbTUFupZbZCOZtXFoaFgN3ozgC0yFM7wbYoUwrDYY161Moqz+JKdGZkIho08StbOVs5o2AaPWUYNo1xMoZW6nPSmvj1H2j81mSkWvrCrigdttoc3Fj0Huy8GhhDl9iGWhJgGK4lWDOsVp/jijEwMAwbwyiwDXUpwDAsU7uiMCyqzJwxAZ4ZlxHRdQ58e3VERlgsvNcOQYHaBjE+HZDasC2cAtwR8NQ9MlugrM2rCsOacf3hqEkFVGrp6nCsU0vk3vck/AY8rSwk9lYSYBjmRNBVQJ+T1tSLEXDbPhm2RVnSwlnik7biuPTlJz/mK8AwbL5ja+ie6XNcMXQb9KmPYVgfNW5jygIMwzJHr7IwHL1+F5pePozLdw1EwIQhiH33UwR/ug4JDiFwXlOyejQ/NReoKgynT5sA76x/oEYx4hyaw3ntpprvgFuYjQDDsNkMZa13RN+T1rhl6xB8/XRZGM5YdwqWN9+xXuuN5g7qRYBhuF7YTXKn+h5X6ruzDMP1PQLcf10LMAzLFK8sDMe+9xma/G85ojwehvbxXrA9uRveOeEIv38Y/If3kblX5W5eVRhOWLAMjRLEe4eLEOXVscpXWylXUDk9ZxhWzljL7am+J60xKzciNOrftwbwFWtyR8L4t2cYNv4xMpYW6ntcqe/2MwzX9whw/3UtwDAsU7yyMJzyWwQCto9AqpUvUj1aoHHcl1CjAFe6L4Zvt3Yy96rczasKw9fX70Kzy4clnCsBXU36dVTKHWHD9Zxh2HCW5l6Tvietsau3osnV9yUeDaxQsO20uVMpvn8Mw4qfAjoD6Htc0XkHtVSQYbiWYFmt0QowDMscmsrCsKhWvItWfEQgdiy8gQxrb+QOnQe31kEy96rczasKw9H7T6DJN1ugVhXhcqshCBgzULlQ7DmfGeYc0FlA35PW6HW70DTsMDSwR6FaBe2WD3TeJwuapgDDsGmOW320Wt/jSn209dZ9MgzX9whw/3UtwDAsU7yqMJwzaRg88q9JJ0riVUrx0w6gQQhfmSSHvKownBuXBvfFJbegX+2+CD7d2svZFbc1cQFeGTbxAazD5ut70ipeo9f0r4NSS7PULlBvOVqHreau6kOAYbg+1E1zn/oeV+q7twzD9T0C3H9dCzAMyxSvKgzHbNgD7/BzcCy6ATU04PNkMrEBVBWGRe02o5+GCirET9gBl7sayt8hazBZAYZhkx26Om+4viet0VsPounFvVJ706y8Yb3xrTpvO3dYtwIMw3Xrbcp70/e4Ut99Zhiu7xHg/utagGFYpnhVYTj1YiT8tw5HMSwR7XofPJYvl7k3bl5dGL4elgOr1AT4PMhb0ZU+WxiGlT4DdO+/vietCecuIOjoDGlHXA9Cd29TLskwbMqjV7dt1/e4UretvHNvDMP1PQLcf10LMAzLFK8qDKeHx8N37SAUwwrpy4/AxtVR5t64eXVhmEIUKBVgGOZc0FVA35PWG79fRcNtI6XdJM8/AntfV113yXImKsAwbKIDVw/N1ve4Ug9NLbdLhuH6HgHuv64FGIZlilf5zHBcKtwWvwAttMjf9rHMPXFzIcAwzHmgqwDDsK5SLKfvSWv639Hwff1laGGBjDXHYeVgQ0wzF2AYNvMBNmD39D2uGLAJelXFMKwXGzcyYQGGYZmDV1UYllk1N69AgGGY00JXAYZhXaVYTt+T1owr8fBZMwhaqJC77SwhFSDAMKyAQTZQF/U9rhho93pXwzCsNx03NFEBhmGZA8cwLBOwhpszDNcQTMHFGYYVPPg17Lq+J61Z0cnwWjYAuWonaLccq+FeWdwUBRiGTXHU6qfN+h5X6qe1/+6VYbi+R4D7r2sBhmGZ4gzDMgFruDnDcA3BFFycYVjBg1/DrpvqSWsNu8niBhBgGDYAokKqMNXjCsOwQiYou1kmwDAsczIwDMsErOHmDMM1BFNwcYZhBQ9+DbtuqietNewmixtAgGHYAIgKqcJUjysMwwqZoOwmw7Ch5gDDsKEkdauHYVg3J5YCGIY5C3QVMNWTVl37x3KGE2AYNpyluddkqscVhmFzn5ns3+0CvDIsc04wDMsErOHmDMM1BFNwcYZhBQ9+DbtuqietNewmixtAgGHYAIgKqcJUjysMwwqZoOwmrwwbag4wDBtKUrd6GIZ1c2IpXhnmHNBdwFRPWnXvIUsaSoBh2FCS5l+PqR5XGIbNf26yh+UFeGVY5oxgGJYJWMPNGYZrCKbg4rwyrODBr2HXTfWktYbdZHEDCDAMGwBRIVWY6nGFYVghE5Td5JVhQ80BhmFDSepWD8Owbk4sxSvDnAO6C5jqSavuPWRJQwkwDBtK0vzrMdXjCsOw+c9N9pBXhg06BxiGDcpZbWUMw9USscBNAV4Z5lTQVcBUT1p17R/LGU6AYdhwluZek6keVxiGzX1msn+3C/A2aZlzgmFYJmANN2cYriGYgoszDCt48GvYdVM9aa1hN1ncAAIMwwZAVEgVpnpcYRhWyAR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          "2014-09-04T00:00:00",
          "2014-09-05T00:00:00",
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          "2014-09-29T00:00:00",
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          "2014-10-06T00:00:00",
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          "2014-10-09T00:00:00",
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          "2014-10-13T00:00:00",
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          "2014-10-22T00:00:00",
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          "2014-10-24T00:00:00",
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          "2014-11-09T00:00:00",
          "2014-11-10T00:00:00",
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          "2014-11-12T00:00:00",
          "2014-11-13T00:00:00",
          "2014-11-14T00:00:00",
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          "2014-11-16T00:00:00",
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          "2014-11-21T00:00:00",
          "2014-11-22T00:00:00",
          "2014-11-23T00:00:00",
          "2014-11-24T00:00:00",
          "2014-11-25T00:00:00",
          "2014-11-26T00:00:00",
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          "2014-11-29T00:00:00",
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          "2014-12-04T00:00:00",
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          "2014-12-08T00:00:00",
          "2014-12-09T00:00:00",
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          "2014-12-11T00:00:00",
          "2014-12-12T00:00:00",
          "2014-12-13T00:00:00",
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          "2014-12-16T00:00:00",
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          "2014-12-21T00:00:00",
          "2014-12-22T00:00:00",
          "2014-12-23T00:00:00",
          "2014-12-24T00:00:00",
          "2014-12-25T00:00:00",
          "2014-12-26T00:00:00",
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          "2015-01-01T00:00:00",
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          "2015-01-26T00:00:00",
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          "2015-05-13T00:00:00",
          "2015-05-14T00:00:00",
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          "2015-05-16T00:00:00",
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          "2015-05-22T00:00:00",
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          "2015-05-26T00:00:00",
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          "2015-06-07T00:00:00",
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          "2015-06-16T00:00:00",
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          "2015-06-22T00:00:00",
          "2015-06-23T00:00:00",
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          "2015-06-26T00:00:00",
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          "2015-06-29T00:00:00",
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          "2015-07-08T00:00:00",
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          "2015-07-11T00:00:00",
          "2015-07-12T00:00:00",
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          "2015-07-14T00:00:00",
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          "2015-07-16T00:00:00",
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          "2015-07-22T00:00:00",
          "2015-07-23T00:00:00",
          "2015-07-24T00:00:00",
          "2015-07-25T00:00:00",
          "2015-07-26T00:00:00",
          "2015-07-27T00:00:00",
          "2015-07-28T00:00:00",
          "2015-07-29T00:00:00",
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          "2015-08-01T00:00:00",
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          "2015-08-08T00:00:00",
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          "2015-08-11T00:00:00",
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          "2015-08-22T00:00:00",
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          "2015-08-25T00:00:00",
          "2015-08-26T00:00:00",
          "2015-08-27T00:00:00",
          "2015-08-28T00:00:00",
          "2015-08-29T00:00:00",
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          "2015-08-31T00:00:00",
          "2015-09-01T00:00:00",
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          "2015-09-04T00:00:00",
          "2015-09-05T00:00:00",
          "2015-09-06T00:00:00",
          "2015-09-07T00:00:00",
          "2015-09-08T00:00:00",
          "2015-09-09T00:00:00",
          "2015-09-10T00:00:00",
          "2015-09-11T00:00:00",
          "2015-09-12T00:00:00",
          "2015-09-13T00:00:00",
          "2015-09-14T00:00:00",
          "2015-09-15T00:00:00",
          "2015-09-16T00:00:00",
          "2015-09-17T00:00:00",
          "2015-09-18T00:00:00",
          "2015-09-19T00:00:00",
          "2015-09-20T00:00:00",
          "2015-09-21T00:00:00",
          "2015-09-22T00:00:00",
          "2015-09-23T00:00:00",
          "2015-09-24T00:00:00",
          "2015-09-25T00:00:00",
          "2015-09-26T00:00:00",
          "2015-09-27T00:00:00",
          "2015-09-28T00:00:00",
          "2015-09-29T00:00:00",
          "2015-09-30T00:00:00",
          "2015-10-01T00:00:00",
          "2015-10-02T00:00:00",
          "2015-10-03T00:00:00",
          "2015-10-04T00:00:00",
          "2015-10-05T00:00:00",
          "2015-10-06T00:00:00",
          "2015-10-07T00:00:00",
          "2015-10-08T00:00:00",
          "2015-10-09T00:00:00",
          "2015-10-10T00:00:00",
          "2015-10-11T00:00:00",
          "2015-10-12T00:00:00",
          "2015-10-13T00:00:00",
          "2015-10-14T00:00:00",
          "2015-10-15T00:00:00",
          "2015-10-16T00:00:00",
          "2015-10-17T00:00:00",
          "2015-10-18T00:00:00",
          "2015-10-19T00:00:00",
          "2015-10-20T00:00:00",
          "2015-10-21T00:00:00",
          "2015-10-22T00:00:00",
          "2015-10-23T00:00:00",
          "2015-10-24T00:00:00",
          "2015-10-25T00:00:00",
          "2015-10-26T00:00:00",
          "2015-10-27T00:00:00",
          "2015-10-28T00:00:00",
          "2015-10-29T00:00:00",
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          "2015-11-06T00:00:00",
          "2015-11-07T00:00:00",
          "2015-11-08T00:00:00",
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          "2015-11-11T00:00:00",
          "2015-11-12T00:00:00",
          "2015-11-13T00:00:00",
          "2015-11-14T00:00:00",
          "2015-11-15T00:00:00",
          "2015-11-16T00:00:00",
          "2015-11-17T00:00:00",
          "2015-11-18T00:00:00",
          "2015-11-19T00:00:00",
          "2015-11-20T00:00:00",
          "2015-11-21T00:00:00",
          "2015-11-22T00:00:00",
          "2015-11-23T00:00:00",
          "2015-11-24T00:00:00",
          "2015-11-25T00:00:00",
          "2015-11-26T00:00:00",
          "2015-11-27T00:00:00",
          "2015-11-28T00:00:00",
          "2015-11-29T00:00:00",
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          "2015-12-01T00:00:00",
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          "2015-12-07T00:00:00",
          "2015-12-08T00:00:00",
          "2015-12-09T00:00:00",
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          "2015-12-11T00:00:00",
          "2015-12-12T00:00:00",
          "2015-12-13T00:00:00",
          "2015-12-14T00:00:00",
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          "2015-12-19T00:00:00",
          "2015-12-20T00:00:00",
          "2015-12-21T00:00:00",
          "2015-12-22T00:00:00",
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          "2015-12-25T00:00:00",
          "2015-12-26T00:00:00",
          "2015-12-27T00:00:00",
          "2015-12-28T00:00:00",
          "2015-12-29T00:00:00",
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          "2015-12-31T00:00:00",
          "2016-01-01T00:00:00",
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          "2016-01-06T00:00:00",
          "2016-01-07T00:00:00",
          "2016-01-08T00:00:00",
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          "2016-01-11T00:00:00",
          "2016-01-12T00:00:00",
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          "2015-07-16T00:00:00",
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          "2015-07-22T00:00:00",
          "2015-07-23T00:00:00",
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          "2015-07-26T00:00:00",
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          "2015-08-08T00:00:00",
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          "2015-08-26T00:00:00",
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          "2015-09-08T00:00:00",
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          "2015-09-12T00:00:00",
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          "2015-09-22T00:00:00",
          "2015-09-23T00:00:00",
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          "2015-09-25T00:00:00",
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          "2015-10-06T00:00:00",
          "2015-10-07T00:00:00",
          "2015-10-08T00:00:00",
          "2015-10-09T00:00:00",
          "2015-10-10T00:00:00",
          "2015-10-11T00:00:00",
          "2015-10-12T00:00:00",
          "2015-10-13T00:00:00",
          "2015-10-14T00:00:00",
          "2015-10-15T00:00:00",
          "2015-10-16T00:00:00",
          "2015-10-17T00:00:00",
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          "2015-10-19T00:00:00",
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          "2015-10-21T00:00:00",
          "2015-10-22T00:00:00",
          "2015-10-23T00:00:00",
          "2015-10-24T00:00:00",
          "2015-10-25T00:00:00",
          "2015-10-26T00:00:00",
          "2015-10-27T00:00:00",
          "2015-10-28T00:00:00",
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          "2015-11-07T00:00:00",
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          "2015-11-11T00:00:00",
          "2015-11-12T00:00:00",
          "2015-11-13T00:00:00",
          "2015-11-14T00:00:00",
          "2015-11-15T00:00:00",
          "2015-11-16T00:00:00",
          "2015-11-17T00:00:00",
          "2015-11-18T00:00:00",
          "2015-11-19T00:00:00",
          "2015-11-20T00:00:00",
          "2015-11-21T00:00:00",
          "2015-11-22T00:00:00",
          "2015-11-23T00:00:00",
          "2015-11-24T00:00:00",
          "2015-11-25T00:00:00",
          "2015-11-26T00:00:00",
          "2015-11-27T00:00:00",
          "2015-11-28T00:00:00",
          "2015-11-29T00:00:00",
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          "2015-12-01T00:00:00",
          "2015-12-02T00:00:00",
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          "2015-12-07T00:00:00",
          "2015-12-08T00:00:00",
          "2015-12-09T00:00:00",
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          "2015-12-12T00:00:00",
          "2015-12-13T00:00:00",
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          "2015-12-16T00:00:00",
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          "2015-12-19T00:00:00",
          "2015-12-20T00:00:00",
          "2015-12-21T00:00:00",
          "2015-12-22T00:00:00",
          "2015-12-23T00:00:00",
          "2015-12-24T00:00:00",
          "2015-12-25T00:00:00",
          "2015-12-26T00:00:00",
          "2015-12-27T00:00:00",
          "2015-12-28T00:00:00",
          "2015-12-29T00:00:00",
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          "2016-01-01T00:00:00",
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          "2016-01-08T00:00:00",
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          "2016-01-22T00:00:00",
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          "2016-01-26T00:00:00",
          "2016-01-27T00:00:00",
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          "2016-02-01T00:00:00",
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          "2016-02-08T00:00:00",
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      "Regressor Importance:\n",
      "              regressor    importance\n",
      "11          REXPDE~TUSD  4.103350e-06\n",
      "10  LorentzianIndicator  2.511120e-06\n",
      "1                  high  2.672140e-07\n",
      "0                  open  2.117290e-07\n",
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      "2                   low  1.879990e-07\n",
      "5             adj_close  1.869090e-07\n",
      "12                CPIDE  1.220440e-07\n",
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      "7                change  9.281980e-08\n",
      "8        change_percent  7.484040e-08\n",
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ujlrQXvTwtlqV3pEB3Zo99RKFFYateumBo9IurIRhHXauw70dN18cvcDalpdO6+esk4bcMdOWSnh1dSyk5SgQfPh8RG2Xf0cAAQQQQAABBBBAwCEQq8KwY06sBsvW3UeJ9rZqONWQ6tgcvU4dm9cI0XPq+Hf9YV6xTFHnsGmdt6jzF4NvkQnDVl9Pj68LTH2xYK1s2L7PzAN2bGMHdHAurqRDanUBIV2oyvG8Xw398yb0NvN8dSXh0HOk9Tg3b981Pd/Be0ijEnr0WJENw44gGzoE6sJTuoiZlTDsCNRLpvUzc3at1jOqYdgxbF3bQ4fm1eXlF3OaRb10UbLB4+aHGK4cnuOgsfNk8Zod4gjD3QZ+Lht37De9vjpUOqzN8Rzn8ALvc1kzOBfGCu9jLKwwbNUrusKwhmANwzr3XoeI6+YYEh5ePdR+9+oJZii9Y9MbPR+07m/m0mvvvC7MpfvxSCW+1BBAAAEEEEAAAQQiKxArw7Ai3Lh5R2q09DehUuep6rxh3Ryr0c4c/Ym88VrYQzB1v30/HZUWH48M0ZPqwI1MGLb6eqEvnPbkrtv2nRnSrY+k+XKKf4hddA6m9upqb6nOd3bMuazXbpBZkOnAlumSINi8X0dvmq5u3eejRuZYnhSGNfToitx71kwy87yt1jOqYdgR1HVotd5AcGyOueVWeoZDh2Ed4q5D3XWuui6AFtbmGEr8w8Zp4pcoQWTfz2b/sMKwVa+otoun9QxrW9X5+Dokvn3TatKuaTVzno6hzm0bV5XEfglD1FV7h/X5zxMHd5LSbxVy/pvDUG9QaDl9FFP/UXNYTTpKLYVCCCCAAAIIIICAdwvE2jCsl1VXY67W7L9hxY5Qo/Npdf6kziWePa5niHm3ZnGfIydMUHH0pDqGcGqvqy5kpPMb9Yd9WKtJhzVM2urr6UJZCRPEk1zP/bcok27aw629uXpeutKwLjZUIG9O56Jfuo8u8lSrVT+zqJIGNJ2TqnNTtWdRh5k6tqETFsjClducq05HNfRouZjuGdbHSdVs+amZU6pzS3WzWs+IwnAn/wlmDnDwVcT1+I5e3ODzVvX6aw+1OkYlDGuY11Cv4Xry0C5mUS3HpuFPr+GEmStk2vy15vnNjrndjn10VIC2y+ArX4f18RVWGLbqFdV2EV4Y1h7h6QvXmREA2qM/e1wP05OrN6uKV+1ghs3riIHQm+N9E3z+uD4GrW6bAWaV9KXT+pvHUumc7uZdR5jF4VhIy7u/zKg9AggggAACCCAQWYFYHYYVQxc86tB7vHFxrADs6JHSH+IaGBP7JZLf/zxjnutasEBumTCok9lff8SPm77c/L/OadXHuTg2q2FY97fyekvX7jLzU3XV6NdefkESxo8vX+/7xQyrbdekqlk4SQOaPldVH5WjgejuvfuyevMe0xPs6OnWVYzfrv7f+WsvXK7nMpnHQWlY0fqunDUoxCOBIjtnWM9TX097SPXRQDpvWR93pHOWHY9WCr2AVmSHSeu563XR0KM9+/rsZd1mj+0prxfMa/7faj0jCsOOa6zDd/UxW/p6OrT+m+9+loFj55nhzJXLFhMfHzGrduvwXN2iEoa1h7RF15Fm+L4+YqhCmaISEBAo67ftM72gesNDz7dCg0/M3Fr1LVW8oJkDrs9G1hsxvTs1lAY13n3q+zysMGzVSw8clREDjjCsjrlzZJabt+6Kjm7Quuqmjjrf2/F85hXrv5FPP5slXT6sbVZgD70FvxG0b90UE3xrtPA378Hgi21pOf07Ha2hIZtHLEX2K4D9EUAAAQQQQAAB7xWIFWH4xOnzUqVpH+fzeENfTkfg0cWxNBDrD+vZSzbKrC83OufdOgKvPh6nSrnizkNoANm063sTkvRROrq6dO9h080Q1lWzBpv9HL1Y/bo2MY80Cr3p44oiej19zu/wiYvMarvBN329Xp0amCHPei6TZq8KscCSBoDOrWo6h4FrWQ1sPQZPdQY3/TvtjRzco2WIedJRCT0atEJvjtWS9caBWgd/7JDuq4shaaANvZq0Yx7wxoUjzHN9gz9aScs5ngms121At+Yhhitbrac+wun1im3MY51Cr1Ctx9DXHD9juazetMfZFr6aM0SyZ80g/T6bbYb2OjYdrp73+WzmxsKkoR9JqWL/PfopPMch4+fLolXbnStg677asztp1krz945Nr2H1Cm+ZoKvb31dvyOhpS2Td1u9CUGuA1kdg6Xk8bdNrFHxurmPf6GwXjps5weuUNnVys3DY+2XekHLvFAkxFLplt89E52UHX1k6dJ0cw8w/828rx07+ZdpW8GHWwfd3PL9ZH+s1bmCHp/rwjwgggAACCCCAAAIIqECsCMNRvZTaU6e9V9qLpYEraRK/CA/lGD6tvYhj+rePcP/gO1h5PX0m8KW//zHFMqRLHWIlbMex9Bw0nOs8S32ecViPWNJ9tW5X/rkhmTOmlWQW6hapykTDzsF7cT/zb2POX5+TrM8rftrminpq7+vFv/8xNws0nDo2DaZqmDpVMufjuVxRdR1yrdfQ5//PY0+XNmWYz9PVfS5e/scMBdb2GXwhKTvn4AovO69PWQQQQAABBBBAAAEE3EHAq8NwRBdA5+iePX9Z8ufNKWlTJZcr127KxFkrTY/W+EEd5d0Sr0V0CP49EgIRDWmOxKHYFQEEEEAAAQQQQAABBBB4qgBh+Ck8c5ZuMqs5h94a1y4vPdrXo2m5WIAw7GJQDocAAggggAACCCCAAALhChCGn9I4dDjyz7/+KRcuX5UHDx5KhnSpzJxRXVmazfUCOiz4q817zDzTNwqF/9gr178yR0QAAQQQQAABBBBAAAFvEyAMe9sVp74IIIAAAggggAACCCCAAALevYAW1x8BBBBAAAEEEEAAAQQQQMA7BegZ9s7rTq0RQAABBBBAAAEEEEAAAa8WIAx79eWn8ggggAACCCCAAAIIIICAdwoQhr3zulNrBBBAAAEEEEAAAQQQQMCrBQjDXn35qTwCCCCAAAIIIIAAAggg4J0ChGHvvO7UGgEEEEAAAQQQQAABBBDwagHCsFdffiqPAAIIIIAAAggggAACCHinAGHYO687tUYAAQQQQAABBBBAAAEEvFqAMOzVl5/KI4AAAggggAACCCCAAALeKUAY9s7rTq0RQAABBBBAAAEEEEAAAa8WIAx79eWn8ggggAACCCCAAAIIIICAdwoQhr3zulNrBBBAAAEEEEAAAQQQQMCrBQjDXn35qTwCCCCAAAIIIIAAAggg4J0ChGHvvO7UGgEEEEAAAQQQQAABBBDwagHCsFdffiqPAAIIIIAAAggggAACCHinAGHYO687tUYAAQQQQAABBBBAAAEEvFqAMOzVl5/KI4AAAggggAACCCCAAALeKUAY9s7rTq0RQAABBBBAAAEEEEAAAa8WIAx79eWn8ggggAACCCCAAAIIIICAdwoQhr3zulNrBBBAAAEEEEAAAQQQQMCrBQjDXn35qTwCCCCAAAIIIIAAAggg4J0ChGHvvO7UGgEEEEAAAQQQQAABBBDwagHCsFdffiqPAAIIIIAAAggggAACCHinAGHYO687tUYAAQQQQAABBBBAAAEEvFqAMOzVl5/KI4AAAggggAACCCCAAALeKUAY9s7rTq0RQAABBBBAAAEEEEAAAa8WIAx79eWn8ggggAACCCCAAAIIIICAdwoQhr3zulNrBBBAAAEEEEAAAQQQQMCrBQjDXn35qTwCCCCAAAIIIIAAAggg4J0ChGHvvO7UGgEEEEAAAQQQQAABBBDwagHCsFdffiqPAAIIIIAAAggggAACCHinAGHYO687tUYAAQQQQAABBBBAAAEEvFqAMOzVl5/KI4AAAggggAACCCCAAALeKUAY9s7rTq0RQAABBBBAAAEEEEAAAa8WIAx79eWn8ggggAACCCCAAAIIIICAdwoQhr3zulNrBBBAAAEEEEAAAQQQQMCrBQjDXn35qTwCCCCAAAIIIIAAAggg4J0ChGHvvO7UGgEEEEAAAQQQQAABBBDwagHCsFdffiqPAAIIIIAAAggggAACCHinAGHYO687tUYAAQQQQAABBBBAAAEEvFqAMOzVl5/KI4AAAggggAACCCCAAALeKUAY9s7rTq0RQAABBBBAAAEEEEAAAa8WIAx79eWn8ggggAACCCCAAAIIIICAdwoQhr3zulNrBBBAAAEEEEAAAQQQQMCrBQjDXn35qTwCCCCAAAIIIIAAAggg4J0ChGGb1/3CP/dtHoHiCCCAAAIIIIAAAggggEDkBTKlThT5QpRwCrhlGA4KCpLTf12SS39fk5zZM0n6tCnl7PnL4pcooaRJldytLh9h2K0uByeDAAIIIIAAAggggIDXCBCG7V1qtwvDd+89kDY9xsiBw8dMzYb3/lAqlysmnfwnyOmzl+SruUPt1djFpQnDLgblcAgggAACCCCAAAIIIGBJgDBsiSncndwuDC9du0smzlwhn7SrJwtWbJWGNcuaMPz9wd+lWZfhsnP5OEmXJoW9WruwNGHYhZgcCgEEEEAAAQQQQAABBCwLEIYtU4W5o9uF4erN+0r5kq9Lm8ZV5MPuo6Ry2WImDF+7cVtKVOsoi6f2kwJ5c9irtQtLE4ZdiMmhEEAAAQQQQAABBBBAwLIAYdgylWeE4SpNeku1Cm9J87oVQ4ThE6fPS5WmfWTL4lGSOUMae7V2YWnCsAsxORQCCCCAAAIIIIAAAghYFiAMW6byjDA8aOw8+fb7wzJ3Qi/5dOQs0zNcpsRr0n3Q53Lo6AnZtWK8xInja6/W4ZQ+f+mqVGvWV+pVKy1dW39g6TXCDcNBIj7Rc5qWzssVOwUFueIoHAMBBBBAAAEEEEAAAQSiQ4AwbE/V7YZJX795W2q2/FQuX7luapYlY1ozRPre/QcyaehHUqpYQXs1Dqf07Tv3pEH7wXLizAVpUa+irTCsq2FfuOgrD/6NllONsYMmS+YjaVMHxtjr8UIIIIAAAggggAACCCBgXYAwbN0qrD3dLgzrSd5/8FCWrt0pR34/Jbfv3pccWTNI9YolJHeOLPZqG07pxwEB0qH3OMmQNrXcunNPsmRM4wzDwyctkjy5skr1CiVM6Z8OHZPpC9fK1BEfmz+H1zO8ZWsc+fY7n2g535g6aN3aAfJSXu3ijqlX5HUQQAABBBBAAAEEEEDAqgBh2KpU2Pu5XRi+eu2mHPnjtOTL81yIZwrv3n9Y0qRKJi/mzm6vxmGUHjphofx56pxMG/mx9BjyRYgw3LbnWClUILe0alDJlNy192fxHzlTdq+eSBh2+ZXggAgggAACCCCAAAIIIGBVgDBsVcpDwvDoqUtl9abdsmHBCEmaxM951iMmfynrtu6VnSvGSdw4cezVOljpL1dvlzlLNsnSaf0lebLE0rX/lEiF4Vv3Hj1xLoGBQbJmvcjuvZ7dpdqgTqAUesVXfDy7Gi5rKxwIAQQQcLWAfl/4+vIh62pXjocAAgh4i0Ayv3jeUtVoqafb9QzXaT1A3iycTzq3qhWiwqfOXpRKjXuZkJw9S3qXYZSv190c7/nnMptjbv/2gAnh5UsWMb3BEfUM3w4nDK+OJWH4NcKwy9oaB0IAAQRCCxCGaRMIIIAAAnYEkhKG7fCJ24VhfbTSe6Vel3ZNq4Wo2MmzF6Vy416yYsZAyft8NluVDl54yZodcvP2Xedfrd70raRKkUwql31T6lQtHWEYZs6wyy4FB0IAAQQQQAABBBBAAIFICDBMOhJYYezqdmG4x5BpsveHX2XrktGSMEF85ynrQlbzl2+R7zdMlcR+Ce3V+imlwxomfe36LenbpbEk8UsoY6cvk4OHjzNnONquAAdGAAEEEEAAAQQQQAABKwKEYStK4e/jdmH4xOnzUqVpH3PG5Uu+LunTppQd3x6QcxevSPO6FeXjNtae/xtVlrDC8JV/bshvx8+YQxbMn1vOnLtEGI4qMOUQQAABBBBAAAEEEEDAJQKEYXuMbheGtToaPMfPWC7wfGj3AAAgAElEQVQ/HTpuni+cK3smqVO1lBm27MrFs6zQOeYMN65dXgICAsUvUYIQxRgmbUWRfRBAAAEEEEAAAQQQQMDVAoRhe6JuGYaDV+lZLy4SegGt0NyEYXsNkNIIIIAAAggggAACCCAQNQHCcNTcHKXcNgxrCL7/4N8nahed84XDotzzw6/mecd5cmUNU5owbK8BUhoBBBBAAAEEEEAAAQSiJkAYjpqb24bhv6/ekGnzv5ItX/8g127cfqJ2e9dOluRJE9urtQtLE4ZdiMmhEEAAAQQQQAABBBBAwLIAYdgyVZg7ul3P8NAJC2Thym3Svll1yZwhjcSNGyfEiZd7u7DEixfXXq1dWJow7EJMDoUAAggggAACCCCAAAKWBQjDlqk8IwyXqNZRalcuKZ1a1LRXsxgqTRiOIWheBgEEEEAAAQQQQAABBEIIEIbtNQi36xlu02O0ZM2UTvp81MhezWKoNGE4hqB5GQQQQAABBBBAAAEEECAMu7ANuF0Y1gWrOn86STYuHGEWrnL3jTDs7leI80MAAQQQQAABBBBAIHYK0DNs77q6XRjuNvBz2bhjf7i1YgEtexc8MqXr1g6Ql/IGifhEphT7IoAAAggggAACCCCAQEwIEIbtKbtdGN6++4D8deHvcGtVr3oZSRA/nr1au7A0PcMuxORQCCCAAAIIIIAAAgggYFmAMGyZKswd3S4M26tOzJcmDMe8Oa+IAAIIIIAAAggggAACIoRhe63ALcPw1Ws35bfjZ+Xe/QdP1K70W4UkXqjHLdkjsFeaMGzPj9IIIIAAAggggAACCCAQNQHCcNTcHKXcLgwfOnpC6rUbFG6tmDNs74JHpjRzhiOjxb4IIIAAAggggAACCMSsAGHYnrfbheFO/hPkwqV/xL9LY6nfbpCsmjVY0qdNKf4jZ0pQYJBMHPKRvRq7uDQ9wy4G5XAIIIAAAggggAACCCBgSYAwbIkp3J3cLgxXbNhDGtYsJx9UKSmvlGkhy6cPkBdzZ5eDvx6Xhh2GyM7l4yRdmhT2au3C0oRhF2JyKAQQQAABBBBAAAEEELAsQBi2TBXmjm4XhsvX6y5NPnhP6lcvI/r/7ZpWk6rli8uZc5dFg/L8ib2lUIEX7NXahaUJwy7E5FAIIIAAAggggAACCCBgWYAwbJnKM8Jw8y4jJFOGNDK4RwsZMGau7P3hV+nRvp5s2/2TrNm8R/av/1ySJE5kr9YuLE0YdiEmh0IAAQQQQAABBBBAAAHLAoRhy1SeEYY3bN8vp/+6aHqE/756Q2q29JdrN26bk+/Wpo40q1vBXo1dXJow7GJQDocAAggggAACCCCAAAKWBAjDlpjC3cnthkmHPtPHAQFy7MRfkjVTOkmaxM9ebaOhNGE4GlA5JAIIIIAAAggggAACCEQoQBiOkOipO7hdGO4/ao7kzplZGtQoG+LE/zjxl7TtOUZWzBgoKZMntVdrF5YmDLsQk0MhgAACCCCAAAIIIICAZQHCsGWqMHd0uzDcsc94eSnPc9K2cdUQJ3zlnxtSsmZn5+rS9qrtutKEYddZciQEEEAAAQQQQAABBBCwLkAYtm4V1p5uE4Z/O35GHj16LCOnLJYc2TJK7UrvOM9Xh0pv3LFfFq3aLj9u+kISJYxvr9YuLE0YdiEmh0IAAQQQQAABBBBAAAHLAoRhy1Rh7ug2YbhEtY7OhbLCOtNUKZJKi/rvS9MP3rNXYxeXJgy7GJTDIYAAAggggAACCCCAgCUBwrAlpnB3cpswfOL0eXn0OECGjF8gz+fILHWqlHKedLx4cSVH1ozi6+tjr7bRUJowHA2oHBIBBBBAAAEEEEAAAQQiFCAMR0j01B3cJgw7znLmog1y7eYt6d62rr2axVBpwnAMQfMyCCCAAAIIIIAAAgggEEKAMGyvQbhdGP5k0FS5ceuOfPFZN3s1i2TpO3fvy/WbtyVVimSS2C+h5dKEYctU7IgAAggggAACCCCAAAIuFCAM28N0uzC8eM0OGT11qXy3brLEjRPHXu0slL53/4E0aD9Yjp0859y7fvUy0rNDA4kTxzfCIxCGIyRiBwQQQAABBBBAAAEEEIgGAcKwPVS3C8MnzlyQum0GSrO6FaRUsVefqN0LObNaCqlWWbRHeM6STVL1veKSKX0a2fvjr9KmxxiZP7G3FCrwQoSHIQxHSMQOCCCAAAIIIIAAAgggEA0ChGF7qG4XhvU5wzv2HAy3VnvXTpbkSRPbq/VTSutCXlWa9pE1s4eYhbyGT1okeXJlleoVSphSPx06JtMXrpWpIz42fyYMR9ul4MAIIIAAAggggAACCCDwFAHCsL3m4XZh+My5y3Lr9t1wa/XiC9mjZfj0uYtXZOlXO2Xb7p+kYuk3pEPz6uYc2vYcK4UK5JZWDSqZP+/a+7P4j5wpu1dPJAzba3uURgABBBBAAAEEEEAAARsChGEbeCLidmHYXnWiXvq342dk2vy18tOhP+SdN1+Vfl2biD7SKaIwHNYrPg4IkkUrHss3e6J+Pu5QsnG9IClRNL74uN8TrdyBh3NAAAEEbAs8fBwo8eNGvD6F7RfiAAgggAACCCDwhIBbhmGdNzx94To5+sdpuXPvvuTMnklqVHhb3iv1erQ/a/jm7bvy7gcfi3+XRlKlXPEIwzDDpHlXIYAAAggggAACCCCAwLMQoGfYnrrbheHDv5+Sum0GmFq9WTifpEqeVL776Yhcu3HbDFXu3KqWvRpbKF2xYQ8zR1hfL6KeYcKwBVB2QQABBBBAAAEEEEAAAZcLEIbtkbpdGG7fe5z8eeq8rJ49RBIljG9qFxQUJGO/WCYzv9wge9ZMkhTJk9irdbDSB389Lr8dPyvvlnhNUiRLLOu375O+I2bKvAm95bWXXzBh+Nr1W9K3S2NJ4pdQxk5fJgcPH2fOsMuuAAdCAAEEEEAAAQQQQACBqAgQhqOi9r8ybheGS1TrKI1rl3cuWOU41fOXrkq5ut1k/sQ+ZkErV22Hfzsp7XqNNT3Pjq1H+3rmHHTTMHzlnxuic4p1K5g/t5w5d4kw7KoLwHEQQAABBBBAAAEEEEAgSgKE4SixOQu5XRhu2GGI+CVKIF981i1EzdZu2Ss9h34ha+cNk5zZMtqrdajS2vN849Yd0WcOZ0iXWuLFjePcwzFMWsNxQECgObfgG8OkXXopOBgCCCCAAAIIIIAAAghYFCAMW4QKZze3C8PL1u2S/qPmyPtl3jBzhlMmTyo//Py7fLVlj2RKn0YWT/1UfGJweePQc4ZDOxKG7TVASiOAAAIIIIAAAggggEDUBAjDUXNzlHK7MKy9tDMWrZdx05eHqFnp4gWlb+fGkj5tSns1jmTpPT/8KmlSJZc8ubKGWZIwHElQdkcAAQQQQAABBBBAAAGXCBCG7TG6XRh2VOf+g4dy/uIVefDwoWRMl1pSp0xmr6bRVJowHE2wHBYBBBBAAAEEEEAAAQSeKkAYttdA3DYMHzt5Ts5dvKJLSUv2LOkl13OZ7dU0mkoThqMJlsMigAACCCCAAAIIIIAAYTga24DbheHbd+7Jh5+MlkNHT4SodtGCL8rQ3q0kQ9pU0cgR+UMThiNvRgkEEEAAAQQQQAABBBCwL0DPsD1DtwvDA8bMlaVf7ZQuH9Y2z/mNGzeu7D9wVOYu3STZs2SQBZP62Kuxi0sThl0MyuEQQAABBBBAAAEEEEDAkgBh2BJTuDu5XRjW5wwXeTWvjOnfPsRJL1y5TYZOWCDblo6RjOncp3eYMGyvAVIaAQQQQAABBBBAAAEEoiZAGI6am6OU24Xhlt0+k9w5skiP9vVC1EznD5ev113WzB4iz+dwn/nDhGF7DZDSCCCAAAIIIIAAAgggEDUBwnDU3Nw2DG/95kfpPWyGbFs6WpInTeys3e79h6Rr/yny7ZqJkiB+PHu1dmFpwrALMTkUAggggAACCCCAAAIIWBYgDFumCnNHt+sZ1sC7edf3EdZKV5jesGBEhPtF9w6E4egW5vgIIIAAAggggAACCCAQlgBh2F67cLswvH33Afnrwt8R1ipx4oRSu1LJCPeL7h0IwyGFfcQnuslj4PhBEhQDr8JLIIAAAggggAACCCBgR4AwbEdPxO3CsL3qxHxpwnBI89NnfOXRo5i/Dq56RR9fkXRpAyVZUlcdkeMggAACCCCAAAIIIBA9AoRhe65uG4YDA4Pk7r37T9QuaRI/ezV2cWnC8P9AHz0WmT03jpw777m9w4kSijRqGCBZMtE37OK3CodDAAEEEEAAAQQQcLEAYdgeqNuF4fOXrsqUOatly9c/yr37D56o3d61k0MsrGWv+vZLE4YJw/ZbEUdAAAEEEEAAAQQQQCDyAoThyJsFL+F2YVgX0NKVo1s3qiwZ0qYSX1/fEDUs+/ZrEi9eXHu1dmFpwjBh2IXNiUMhgAACCCCAAAIIIGBZgDBsmSrMHd0uDJeo1lHqVi0t7ZtVt1ezGCpNGCYMx1BT42UQQAABBBBAAAEEEAghQBi21yDcLgy37z1OUiZPKoN7tLBXsxgqTRgmDMdQU+NlEEAAAQQQQAABBBAgDLuwDbhdGD5w+Lg06jhEFkzqI+nSpHyiqhnTpRZfX/dZoIkwTBh24fuRQyGAAAIIIIAAAgggYFmAnmHLVGHu6HZh+My5y1KrVb8wF8/SGrCAlr0LHpnSdWsHyEt5g8Tqo4NZTToyuuyLAAIIIIAAAggggIA9AcKwPT+3C8M6TPrQ0RPyUctakj5tyicW0Cpa6EWJGyeOvVq7sDQ9w//DJAy7sGFxKAQQQAABBBBAAAEEIhAgDNtrIm4XhkvX7iK1KpWUdk2q2qtZDJUmDBOGY6ip8TIIIIAAAggggAACCIQQIAzbaxBuF4b10Urx4sWREX1a26tZDJUmDBOGY6ip8TIIIIAAAggggAACCBCGXdgG3C4M79r7s+hQ6SnDuki6NCmeqOoLObNKnDghnz3sQo9IH4owTBiOdKOhAAIIIIAAAggggAACLhCgZ9geotuF4Y59xsuOPQfDrVV0LaB18/Zd+fffR2EG8KcRE4YJw/begpRGAAEEEEAAAQQQQCBqAoThqLk5SrldGNbVpG/dvhturV58IbtLF9C6eu2mNO40VPR1dcuVPZO0alBJKpcrZkmWMEwYttRQ2AkBBBBAAAEEEEAAARcLEIbtgbpdGLZXnciX/vvqDVm9abdUKV9cEidKKPOXb5HZSzbJN6smSKKE8SM8IGGYMBxhI2EHBBBAAAEEEEAAAQSiQYAwbA/VLcLw0WOnZcDoubJkWr9wa/PP9VtSv90gWT59gCRN4mev1k8pfe7iFSlfr7vMn9hbChV4QYZPWiR5cmWV6hVKmFI/HTom0xeulakjPjZ/JgwThqOtMXJgBBBAAAEEEEAAAQSeIkAYttc83CIMHzh8XBp1HCLtm1YLtza3796Xecs2S3TNGXa88KqNu6XviJmye/VESZUiqbTtOVYKFchthk7rpgt8+Y/8798JwyEvF88ZtvdmpDQCCCCAAAIIIIAAApERIAxHRuvJfd0qDFupSnSG4eOnzkn9doOlSe3y0qF5dXM6EYXh2/cfP3HagYFBsnq9yO49Vmrkvvs0qBMor70aR3x8rJ3j/QdBMmV6kJw7b7GAtcPG6F6JEoq0aBIkuXK4z4rlMQrAiyGAQIwKBAUFiY/VD9kYPTNeDAEEEEDAEwSSJorrCafptufoNmG49Sej5YeNU8OF0rm9pWp1jrae4fOXrpre6SKv5pWhPVs5H98UYRi+9yj8MLzXc0OhVsqE4Vd8IxeGZ4jnh+HGGoY9+9q57ScOJ4YAAiEEgsRHfCQIFQQQQAABBKIkkNQvXpTKUeg/AbcIw/pYo9+OnZE3Xnsp3Ovy8OEj2X/wN3mzcD6XriatL/jnqfPSrMtwKf1WIfHv0jjE8SMKw8wZ/t8lY5g0HysIIIAAAggggAACCMScAMOk7Vm7RRi2VwV7pf848ZfUaOEv75d5Qzq2qCG+vv8Nj/VLlEBSJv9vzvC167ekb5fGksQvoYydvkwOHj7OnOEw2AnD9toipRFAAAEEEEAAAQQQiIwAYTgyWk/u6/VheOOO/dJt4OdPyOhzhof3/tCE4Sv/3JDfjp8x+xTMn1vOnLtEGCYM23vnURoBBBBAAAEEEEAAAZsChGF7gF4fhiPicwyTbly7vAQEBJoe4+Abw6T/p0HPcEStiX9HAAEEEEAAAQQQQMB1AoRhe5aE4Qj8Qs8ZDr07YZgwbO8tSGkEEEAAAQQQQAABBKImQBiOmpujFGE4Ar89P/wqaVIllzy5soa5J2GYMGzvLUhpBBBAAAEEEEAAAQSiJkAYjpqbW4dhfe7i6b8uyaW/r0nO7JkkfdqUcvb8ZfFLlNAEU3faCMOEYXdqj5wLAggggAACCCCAgPcIEIbtXWu36xm+e++BtOkxRg4cPmZqpotY6WJWnfwnyOmzl+SruUPt1djFpQnDhGEXNykOhwACCCCAAAIIIICAJQHCsCWmcHdyuzC8dO0umThzhXzSrp4sWLFVGtYsa8Lw9wd/N88C3rl8nKRLk8JerV1YmjBMGHZhc+JQCCCAAAIIIIAAAghYFiAMW6YKc0e3C8PVm/eV8iVflzaNq8iH3UdJ5bLFTBi+duO2lKjWURZP7ScF8uawV2sXliYME4Zd2Jw4FAIIIIAAAggggAAClgUIw5apPCMMV2nSW6pVeEua160YIgyfOH1eqjTtI1sWj5LMGdLYq7ULSxOGCcMubE4cCgEEEEAAAQQQQAABywKEYctUnhGGB42dJ99+f1jmTugln46cZXqGy5R4TboP+lwOHT0hu1aMlzhxfO3V2oWlCcOEYRc2Jw6FAAIIIIAAAggggIBlAcKwZSrPCMPXb96Wmi0/lctXrpsTzpIxrRkife/+A5k09CMpVaygvRq7uDRhmDDs4ibF4RBAAAEEEEAAAQQQsCRAGLbEFO5ObjdnWM/0/oOHsnTtTjny+ym5ffe+5MiaQapXLCG5c2SxV9toKE0YJgxHQ7PikAgggAACCCCAAAIIRChAGI6Q6Kk7uF0Y1lWjkydLLHlyZQ1x4lf+uSH7fjoqFcoUlbhx4tirtQtLE4YJwy5sThwKAQQQQAABBBBAAAHLAoRhy1Rh7uh2Ybhjn/HyUp7npG3jqiFO+MKlq1K2bjdZN2+Y5MiW0V6tXViaMEwYdmFz4lAIIIAAAggggAACCFgWIAxbpvLsMHz02Gmp/WF/2bhwhGTLnN5erV1YmjBMGHZhc+JQCCCAAAIIIIAAAghYFiAMW6Zy7zDcc+gXcuPmbfnp0HFJlSKp5MiWwXnCDx8+lv0Hf5MXc2eX5dMH2Kuxi0sThgnDLm5SHA4BBBBAAAEEEEAAAUsChGFLTOHu5DbDpP1HzpKbt+/IwcPHJWkSP3k+R2bnSSeMH1+KFMwr77zxqqRLk8JejV1cmjBMGHZxk+JwCCCAAAIIIIAAAghYEiAMW2Jy/zDsOMNVG3dLhrSp5M3C+ezVLIZKE4YJwzHU1HgZBBBAAAEEEEAAAQRCCBCG7TUIt+kZtleNZ1eaMEwYfnatj1dGAAEEEEAAAQQQ8GYBwrC9q+92YXjy7FVy8Mif4dZq/MCOktgvob1au7A0YZgw7MLmxKEQQAABBBBAAAEEELAsQBi2TBXmjm4Xhmcv3iiHfz/1xMlu3vW95MqeSRZP7Sd+iRLYq7ULSxOGCcMubE4cCgEEEEAAAQQQQAABywKEYctUnhGGw6vOlDmrZceeg7J0Wn/x9fWxV2sXliYME4Zd2Jw4FAIIIIAAAggggAAClgUIw5apPDsMHz91Tqo16yvr5g2THNky2qu1C0sThgnDLmxOHAoBBBBAAAEEEEAAAcsChGHLVJ4dhvcdOCotuo40PcP58jxnr9YuLE0YJgy7sDlxKAQQQAABBBBAAAEELAsQhi1TeUYYXrhyqxw9dsZ5skFBQXLz9l3ZtfdneTF3dlk+fYC9Gru4NGGYMOziJsXhEEAAAQQQQAABBBCwJEAYtsQU7k5ut4DW2C+WyU+HjoU44aRJ/OSdN1+R0sULSbo0KezV2MWlCcOEYRc3KQ6HAAIIIIAAAggggIAlAcKwJSbPCcP2qmOv9OOAAPH18Y3UAl2EYcKwvVZHaQQQQAABBBBAAAEEoiZAGI6am6OU2/UM26tO1Evff/BQ6rTuLx82rCyVyr5p+UCEYcKw5cbCjggggAACCCCAAAIIuFCAMGwP0y3CcNf+k2Xzrh8s1WTv2smSPGliS/ta3WnU1CWizzfWbUSf1oTh/4OrWztAXsobJGLxSVaPHovMnhtHzp23WMDqBYrB/RIlFGnUMECyZAqKwVflpRBAAAEEEEAAAQQQiLwAYTjyZsFLuEUY3rn3oJy7cMVSTT6oUkoSxI9naV+rO924eUcePHwo9dsNkq4ffhAiDA+ftEjy5Moq1SuUMIfT+czTF66VqSM+Nn+mZ/h/yoRhqy2O/RBAAAEEEEAAAQQQsC9AGLZn6BZh2F4VXFe6fL3u0rF5jRBhuG3PsVKoQG5p1aCSeSFd1dp/5EzZvXoiYTgUPWHYdW2RIyGAAAIIIIAAAgggEJEAYTgioaf/u1uG4X8fPpIN2/fJHyf+kvsP/pUsGdPKe6Vel6yZ0tmrbQSloxKGwzrk44AgWbTisXyzJ1pPN9oP3rhekJQoGl98LI56vn0vUEZPeuzxw6RbN/eR/HniRrsvL4AAAgg8fBwo8eP6AoEAAggggAACz0DA7cLw1Ws3pUH7wXLu4n/Dpv0SJZR79x+Y/x/Tv72UL1kk2piiEoYZJv2/y0HPcLQ1TQ6MAAIIIIAAAggggMATAvQM22sUbheG/UfOkk07v5cpwzrLyy/lMvODT569KKOnLjFDlH/c9IUkShjfXq3DKU0YDgnDAlrR0sw4KAIIIIAAAggggAACLhEgDNtjdLswXLp2F6n07pvStfUHIWr2+59npWbLT2XJtH6SP08Oe7UOVVqfLxwUGCSVGveSNo2rmNePF++/YbI6Z/ja9VvSt0tjSeKXUMZOXyYHDx9nznAYV4CeYZc2Sw6GAAIIIIAAAggggMBTBQjD9hqI24Xh6s37yisvPS/9uzUNUbPvD/4uzboMj5Yw3LX/FNm86/sQr7du3jDJkS2jCcNX/rkhvx0/Y/69YP7ccubcJcIwYdjeO4/SCCCAAAIIIIAAAgjYFCAM2wN0uzA8ZtpSmfnlBhOGX3/1RUmRPIl5nNG0eV/JhctXZcfycRIvbhx7tY5Eacdq0o1rl5eAgEDxS5QgRGnmDP+Pg57hSDQsdkUAAQQQQAABBBBAwKYAYdgeoNuF4fsPHspH/hNkzw+/hqhZqhRJZfygTuYxRzG5hX60UujXJgwThmOyPfJaCCCAAAIIIIAAAgg4BAjD9tqC24VhR3UO/npcjp88J/fu/ytZMqWVYoXzmZWlY3rTUJ4mVXLJkytrmC9NGCYMx3Sb5PUQQAABBBBAAAEEEFABwrC9duB2YfjRo8dy78G/ksQvkcSJ4yu6uNWBQ8clUaIEUiCvaxfOskf3X2nCMGHYFe2IYyCAAAIIIIAAAgggEFkBwnBkxULu73ZheMrcNTJ78UbZtnS0JEviJ3XbDJRf/zhlzlpXmG5Rr6K9Gru4NGGYMOziJsXhEEAAAQQQQAABBBCwJEAYtsQU7k5uF4YbdRwq+fPmkB7t68l3Px6Rlt0+kwHdmsn1m7fly9XbZceysfZq7OLShGHCsIubFIdDAAEEEEAAAQQQQMCSAGHYEpPnhOHy9bqbZ/1Wr1BCHL3E+9ZNkbv3H8ibldrJhgUjJHuW9PZq7cLShGHCsAubE4dCAAEEEEAAAQQQQMCyAGHYMlWYO7plz/CLubNLr471pWrTPib4ThzykVy6ck3K1O4qK2cOCncxK3sUUStNGCYMR63lUAoBBBBAAAEEEEAAAXsChGF7fm4Xhpev+1r6jZptVo6+d/+BzBjVXd4snE+WfrVTBoyZK99vmCqJ/WJ+VenwmAnDhGF7b0FKI4AAAggggAACCCAQNQHCcNTcHKXcLgwHBQXJivXfyI+H/jCPU6pSrrg51z7DZ0jqlMnMIlrutBGGCcPu1B45FwQQQAABBBBAAAHvESAM27vWbheG7VUn5ksThgnDMd/qeEUEEEAAAQQQQAABBHjOsN024JZheN9PR2Xp2l1y6uwFCQgIlOdzZJGa778txYvkt1tfl5cnDBOGXd6oOCACCCCAAAIIIIAAAhYE6Bm2gPSUXdwuDGsQbvHxSHPKGn4TxI8nO/YcNH/u3KqWtGpQyV6NXVyaMEwYdnGT4nAIIIAAAggggAACCFgSIAxbYgp3J7cLw9Wb95Wr127K1iWjJWGC+ObEHz0OkCHj5suydbvk2zUTJWXypPZq7cLShGHCsAubE4dCAAEEEEAAAQQQQMCyAGHYMlWYO7pdGK7YsIeUfbuwdPmwdogT/vPUeanarI/Mn9hbChV4wV6tXViaMEwYdmFz4lAIIIAAAggggAACCFgWIAxbpvKMMDxk/Hw59dcl80il4NvFv6/Jux90lY0LR0i2zOnt1dqFpQnDhGEXNicOhQACCCCAAAIIIICAZQHCsGUq9w3Du/b+LOcvXTUn+PfV6zJj0Xpp3aiypE6Z3HnSR4+dli1f/yh71kyU+PHj2au1C0sThgnDLmxOHAoBBBBAAAEEEEAAAcsChGHLVO4bhjt/Okm2fvOjpZrsXTtZkidNbGnfmNiJMEwYjol2xmsggAACCCCAAAIIIBBagDBsr0243Zxhe9WJ+dKEYcJwzLc6XhEBBBBAAAEEEEAAAZ4zbLcNeEQYfhwQID/+8oes2/qd9OrYQBL7JbRbb5eVJwwThl3WmDgQAggggAACCCCAAAKREKBnOBJYYezq1mH42MlzsmH7Plmx/mu5duO2Of1966ZI0iR+9mrtwtKEYcLwgwcid+76SlCQC0jPSkEAACAASURBVBtWDB/KR0RSpAyUuHFi+IV5OQQQQAABBBBAAIEoCxCGo0xnCrpdGL7yzw3Z8vUPsnzd16JhWLfiRfJLrUolpUTRlyVRwv+ePewuG2GYMPz3FR9Zs9ZX7tzRSOmZW5o0IjWqBUhiPw9O9J5Jz1kjgAACCCCAAAJRFiAMR5nOfcLwvw8fyfbdB+SrLd/K7v2HzYnps4RLFy8oo6YukUVT/OWVl3LZq2k0lSYME4Yv/+0j8xf6yq3bnhuG06cXadIwQJIkth6G79z1kX8fRNMbK4YO6+sbJClTRu7FfHxEgsRzr7WprScPY4jc5WJvBBBAAAEEYrUAYdje5XWLnuEDh49Lo45DJH3alNKwZll5r+TrkilDGrl5+64Uq9yeMGzvGke5dN3aAfJS3iCx+rv/0WOR2XPjyLnznhsUEiUUadQwQLJksh4KvTUMnzztI8tXeva46nx5A6VihQDx0YRrYdNWceyYr0e3ca1mzhyBkuM5621cy/x13lceP45cGQukMbpL8qQiqVJZr8PdeyI68iMwMEZP06Uv5usrkj6tj/j5eXAlXCrCwRBAAIHYJUAYtnc93SIMH/7tpNRtO9DUpNp7b0mld9+UIgXzyt17D9wmDN++c090Ia+U+msq2EbP8P8wCMPWApW9t2z0lI5Kz/CfJ31k3gLPDsOvvhwo1ataD8Oqv/tbX9m6wzd6LkQMHbVq5QB57dVAEYs3AfS09FrrNffUTefDN2kcINmzWg/D/1zzkYVf+sr1655bbw3/DeoFSqqU1uvtqdeY80YAAQS8UYAwbO+qu0UY1iqcu3hFNu7Yb+YK6/+nSpFUyrz1mixbt+uZ9gzfu/9AegyeJjv2HDTSL7+USyYO7iRpUiU3fyYME4a9tWeYMGzvw/dZliYMW9PXMKxTIK55chhOHSSN60cuDN+9K3Lvnmff8PGNEySpU2lveORuZETi/pC1RhTTewXpNA42BBDwJgHCsL2r7TZh2FGNoKAg+fX3U7LerCL9jWgYzZIxrVSvUELKlywiObJltFfjSJaesWi9LFu7S+ZP7GMW72rbc6w5h0GfNCcMh7KkZzhyP7oi2RSjdXd6hq1fO3qGo7UpRtvBo9oz7I1h+K9zPrJqTRwJ9OBUlS1rkFSvoqM+LDapIJHLV3zkzl2L+7vpbn6JRDJkCBQfqzcBgkT+ueEjQY+tQrlnxRMkDJSkIQfuRXyinl3l/+rnwe/RiC8Qe1gVIAxblQp7P7cLw8FP89Gjx7L3xyOyZvMe2bzre/NPe9dOluRJE9urdSRK12rVz4TwVg0qmVJ6Hl37T5Ffd8428wzpGf4fJmHYc79ZCcPWrx1hOBIfoG60K2HY+q/mM2d9ZPa8OB49V/r5XEHSqH4kwrCI/HjQV75a69k94mVLB8pbxQMjcRMgSDZtjSt/HHejN2sUTqVK5SDJkc36vPgH/4pcvCQSEGD9sz8KpxWtReL4iqRJLZI0qfX39oMHPnLvfrSeVrQf3MdXJEVyj1/G0qVOhGF7nG4dhoNXTRfT2vHtAalQuqgkTBBzj1cqUqGNDO7RwgRi3Y4eOy21P+zvDOWEYcIww6TtfQg9y9LMGbb+Q9Bb5wx7Y88wYfhZfirZe+2ohOGVa+LKz4esfxbYO8PoKd2kUYDkymE9FN65IzJ3QRzR729P3ZIl0xs+gZI+nfV6n7sgsni5rzx84Ln1zpwpSOrXCZR48SJ75Ty3zqamPuHPgSAMR7YthNzfY8KwvWpGrbQO2c5fqplMGdZF3nnzFXOQE6fPS5WmfWTbktGSMX3qMA/8OCBI1m19JEd+i9rrukup0u+IvFEwvuU7zLfvBcqXKx7LlavuUoPIn0f8+CLvl/OVl3LHtVz4xJnH8tWmQLl3z3IRt9tRHy9Uu2pcSZvSeq/Ij4cfyuZtbleVSJ1QzhxBUrNSPIkf11q9AwKDZMvXj+TAz5F6GbfbufgbIm8XjSe+vtZ+HDx8HCgLlz+WCxfdriqWT0h7USq85yOv5LH+C+rs+ceyemOg3L5t+WXcbsekyUSqVfCVbJmsf6b98vsjWb8pyKOfwJUpo0j9mnElQTxr720dEv71vkeyd5/1YOF2F1sfS/mqSNm340ncONbe248eB8rydY/l5Cl3rI31c6pQTqRQPusdJX9fC5DlXwXI9evWX8Pd9vTzE6lc3leef876e/voiceyblOgPHrobrWxfj5p04rUrxFXkvhZe2/rkwRPnH8sd+549ns7frwgyZMjnsSx+L1tXZQ9CcMRtAHtGR7Ss6WUe6ew2TN0zzBNCAEEEEAAAQQQQAABBBBAwPMECMMRXDOdM/xeqdelZf33zZ6h5wx73iXnjBFAAAEEEEAAAQQQQAABBAjDEbSB6QvXmcc96WrSfokSSJseY0KsJk0TQgABBBBAAAEEEEAAAQQQ8DwBwnAE1+zuvQfSbeDn8s2+X8ye+fPkkIlDPpJ0aVJ43tXmjBFAAAEEEEAAAQQQQAABBIwAYdhiQ9DVrPVRT2lSJbdYgt0QcL3A44AAefQowDzzmg2B2Cpw5+59SZI4UWytHvVCAAEEEEAAATcRIAy7yYWI6mloOBoyfoHUqVJK8j6fLaqH8bhyFy//IzMWrZdubet6VTBctGq7rNn0rSyZ1s/jrhknjIAVgT9O/CU1WvjL1yvHc/PRChj7IIAAAgi4hUBgYJAMm7hAGtUqJ9kyp3eLc+IkIhYgDEds5NZ7LF27SwaMnmPOsX71MtK5VW1J7JfQrc/ZFSenQ9fPX7oqCyf1tfx4GFe87rM8xvWbt6Vc3e7yaZfGUrlcsWd5Krx2NAtcunJN0qZKIXH0eUBetjXvMsI8tk5X8WeL3QL37v8rS9bskF+OnpC0qZNLhdJFpWD+3OLjY+2xQJ6o483vbU+8XpwzApER+GrLHuk1dLpsXzZGMqRNFZmi7PsMBQjDzxDf7kvr0O13P/hY2jerJuXfKSIDxsyRC5f+kUVT/GP1EMMDh49Jo45DZfHnn0qBF3PaZfSY8kPGz5dDR0/Kl59/6jU3APTinD3/t9y5e0/y5MrmNeFw5OQvJU6cOPJxmw88pn264kS37f5JPvKfKDuXj/OqdRmWrdsl5y9eNSN8wnt+vSt83ekYF/++Jh982E/ixYsrDWqUlfsajL/aIQUL5JahPVvF2u8wb31v6yi2M+cuS6b0aZ4YzaVrs+gCpbHxJsi9+w/k0pXrkj1z+ie+v27fuSdJk/i509vSJefy78NH0rTzcOnXtYlXjVjUdvxe/e7SsGY5ad2ostNS275ucePEcYkvB3G9AGHY9aYxdkT9Ut3+7QFZO3eoxI8fz8xpLl27i3T5sLbUqPh2jJ1HTL5QQECg1GkzQPLkyupVPUfHTp6T6s37il+ihJI9S3rz3OtWDSqF+PEQ2+ZZ6nCjwePnm56jLBnTyrUbt2XS0I+kaMEXY7LJPZPXat97nBR+OY80q1vhmbz+s3jRB/8+lIoNe0j96u86H2X3LM7jWbzmrr0/y9ZvfpTVm76V3p0aSoMa7z6L04ix19TvqnrtBpkgPGtMD2c4unHzjnTsO0GSJfWTCYM6xcqbX9743tbe8G4DPhedAqHhsNp7b5l27hjF1sl/guTMlkk6t6oVY20wJl5Ib9zrzb0H/z4yL9emcRVpVqeCuZmtoyLK1+smQ3t9KCWKFoiJ04mx15i1eIMsWLFVNiwYIQkTeM/6JhNmrjCf4evnj5CgoCD5/uBvsnPvQdmwfb/5zTp5aGdJnixxjF0HXsi6AGHYupVb7Xny7EWp3LiXGRrdq2ND8+Hq6Cnu27mRVC1fXGLjfOJVG3dL3xEzva7nSIeOpkubUnp1aCC/nzgrn46cJfWqlZGmdd4z7fLs+ctSoUEPWT59gLyYO7tbtdWonszMLzfI1HlfydJp/czjzBav2SGjpy6VXSvGxfqpAOXrdZceHepL6eIFDd/3B38X/WHVol5FEyBi46ZrACxatc38gHr46LHM+nKDHD12Wl57OY/UqvSOpE6ZLNZVWz+j/zx1XlKlSGZ6wo+fOif12w2Wz4d3kcKv5Il19XVUaMeeg9Kxz/gwhxJqcKrdqp+sXzBC/r5y3YSnl1/KFWssvO297Zje89rLuWXsgI7y8OEj6T18umjv4YxR3WXfgaPSoutI2bhwRKyaY6mf2c26DJd2TapK+2bV5cTp89Ku1zjzWaY3sqfMWS3rtn0na2YPiVWf6Vf+uSEla3aWsQM6mJv2ut28dVfGzVgu3drUibXf3TqCrUKDT8yNjXhx44p+xr2QM4uUfbuwvPV6AcmXJ0esvLkXWz6YCcMeeiXb9Bgt12/ckfsP/jU1qFjmDdm863tJ7JdIpo382HzgBJ9PXKdqaenQrLqkSpHUQ2ssokOKStfuaoaftKz/fpj10J7j2DbPcvvuA6J3zoMPHZ0yd40JwMN7f+j8sjnw6zF5u+grsaL+2ktYolon6dmhvtR8/79RDnqn9fWKbeWLzz428wp///OspE2dItaFpEePA+TVd1vIV3OGSK7nMosumqZD5NUia6Z05odT8SL5PfZ9HNaJ/331hpSq1dn0BhYrkl/qtxtoPssa1Sor2v5/PPSHrJo1WJInjT131TUIa6+R/mjW7fkcmaVlvfdl0uyV0rh2ealeoUSY11hHidy8dUeKvJrXY9tA1/5TzLmP6d8uzDroEOoMaVNK407DzE0gvbmriyV68veXVvRp7+2GNcvGymHC85dvkS8WrJXtS8eYEWy66Xf59IXrpFPLmlKjub+UKl7QjGiLTZve7IkXL16INq5z48+euyyvvfyClK3bTaaO6Colir4cm6otA8bMlVNnL8rssT2c7Xn4pEVy8PBx5xQv7biJTZ/legH1N5rWUW/c6UifLYtHSeYMaWLVtY3NlSEMe+DV3b3/kLTpMcb8WM6eNYP5MsmQLpWUL/m6WYBE594En0+svUsjJn9pfnR1blVTPqhcyiPvRI6ZttTcSQ1r6I32IA0YPVd+/eOUGUbbsUUNE5g8fdO753q3UXuB9W6ybvoj+v2GPaVlg/eldqWSZvGZ+u0Gyd61k2PNF4x+mVZq3Eu2LRntnEOpPWhVm/WRdfOGmS+Zyk16m//qfMPCr+aJNXV33GHW6/nZlMXy9Xc/y/hBHaVQgRfMM88zpksd6+YS9xk+Q3SF+Flje5jRADoCZNWsQWZagAYInVuq74EPqpTy9Le08/x1/qQOC9+2dIykT5NSvvvpiIyYtEhOnLlgRj+kTplcNu7cL5t27JdUKZNJ3aqlzfw7XS/h+ecyS/9uTT3Wom3PsVIgbw5p17RauHXQm7samudP7CNL1+6UPd8flrkTekvObBk9tt5Pe29/9+MRMzIiceJEUqFUUXnnzVdC1NNTH6vXY8g0iePrK0N7tXriui39aqcJT+nTppTMGdJKkw/Ky7slXvPY6xv8xItUaCO9OzUI86aWtmtt37myZ5LnsmWQ9k2rm2G0nr45blhPG9nVfF/p5vjeXjCpj/lNduDwcWnUcYj5Hs+aOV2smEfrGN2gI/P0Bv07NT6Sr+YONdfXsekNIG3r5UsWMTd/mD/sXq2dMOxe1yPCs9EPm6pN+5jeE+0p0k1/SCZLmlh6tK/nLK/ziZet+1q+WTXBOR9Lv2yHTVwon33aVp7LmkES/N9d2ghf1A12uHzlupkPrasoD+zWzHmHWU/twqWrUrVZXzP8SOfj6CI82pMWG4Zdae/+1HlrQtwA+HL1djPEatOizyRRwgRSr+1AeSVfLjMHK7ZsOj+4RLWOMnd8LzNc9P6Dh9Kx73h58OCh6Jfq3GWbZcbCdVK7ckn54ec/TO+RLhz3SiwYTqnv05bdPpP8eXKYm1Zj+rc3Q2j1x/CbldrLlGGdTa/gwV+PmzUDdOiZJ2+OKR8rZw4yPwh1GGn9Gu9Kk9rlndXS+fKVyhYzw8QXrtwqjwMCQ/y7J9Zfh01WadrH2W51Hq3e7NF66tBKvYE5b9lmM49S59LOWbrJPK5De9qCP3bq6rWbHvcIqqETFsrJsxfMMNmwNn2/v9+oh5lb2qlFTbOL/pC8ePmqTB3xsbnZqzdK4sX1rAVpwntv65zC7oM+N9c6ZfKk5trr/wefO65rJ6zcsNvjHqun8yi/2XdIFk3uG+J723HDvlubD6RMiddEbQaOnSczRnePFZ/jOidev48cv9Mc7fynQ8ekcaehsnRaf0mRPIksW7tLdAXilTMGmT978qafRRoEHZ/lWpcPu4+SlCmSyog+rcWx5svlK9fMd9vtO/dlYPdmphPHk7darfqZxVx1wTDd9LdLz44N5P0ybzirpaOf1m3bK8vXfS1JE/vJuIEdQiyWqHOLEydKJK8X9NwRP558DQnDHnj1dHhs8mRJnD1h+oNp6zc/yfyJvU1tHD8u9ceC3kXv2bG+s5dUw7Su2Kg/rHSIddvGVT1GQO++DR2/wMw10uDnuHPuGJajC7Ho3GnddI7t22+84pxT6zGVDHWiGoC0x0yHx+qmP4p1eJV/l0ZSpVxxWbtlr/kBsXXxKEmSJJH585avfzQ/jvWHlCc/e1qHgn+5apvp+dVrf+SP07JixgDzI1jnRzu+RLVNt+05Rnx8fM1cS0/fNu7Yb3qAdahs19YfOH/wH/79lGnX2mO894dfpV2vsebOs96B9vRNRwLovHDHD4nP/NvKG6+9ZP7sGCXg6FnQGwVvFHopViyypcFWhxDqaJbAoCAzr3DL4tFy5I9T0uSjYSb4ORbXcYwI+qR9PeeNAIeNp60VoPOCy9TuKh2b1zAjXEL3kujoAF0zIPj6AAtXbjM3BzZ/+Zn5/tJexT4fNfKoKQNhvbd14cOi77eVfh83lQ8qlzRtXj/D9Sb3Dxunmj/rnMt363wsjvVAPOn9rjc2G3YYbD6rar7/jukF1nUtNPD/8PPvsmRqP+fUHl2BuEq5YmYBUP1c181TV5jWm5UNOwwxN/DefC2fZMuS3jxqp1arT6VY4fzSvV1dUz+dQ12wXCvTU6qfgbpwpON3jCddZ8e56pD46QvXmyDo4+tj3qeOxwzpiB+9EbZt6Wjz+3X3/sOiU/42Lhwp2TL/9xvHEzf9TZ4ksZ9zGof2/GdMl8p5jfUmgc6lzp0zi6mejgA6fuq8zBnX0/xZPwN0BKBOZdQpjWwxL0AYjnlzl7+i427z4R2zzYeofrjoB+r4QZ1k7tJNMnHWStOjqneaHc890w+lFeu/Mb1snrRpOFyx7msZNXWp6IIcE4d0ltK1OpsPHQ2Hjk3vzH3YsLLpSdm86wf59vvDMuiT5p5U1TDPNfjcG72Zocv461BDHUKqveE6v1R/LF+7fsv8vw6lLPdOEWegmjx7lRnG4ynDTfXH0tf7fjGjGOpUKW16SPuPmiN/nj5vbv44fiipi/5deD1Nnnbh9cs1W+b0IU5bF5g6duIvebNwPvOeLvNWIbPQ1IBuzczoiE9HzTZ3ph03Tjytzo7z1ZtbP/96XGaP7Sm37tyTpp2HmYVINBhqj2Hh9z509jxomFy39TuP6y0Lfm10gaETpy+Y8Ks3ASqWKSq9h02XazdumTo7ts/nrZHVG781Tw/4+cgJmb1ko3zSrq5oeU98Nq/etO3Qe5yZP/ru24UlUYL48lGrWmbV+Hc/6GqqraFIF4nU6/6R/wSp9l4JM0VA//x29U5mcS39jA/d++bObT/0e9sRkH/a/IVz5V3tJerQe7wc2TXHVEU/33785Y8QwdGd6xj63PQa64imvT8eMaO3smRMY0ZFOG5w6f6OIeSO4aXrt++T0VOXSKOa5aRutTJPPI7JE+qv13rBim1y+PeTMm5AB9nzw2EZNnGRMwxqHRw3tL/f8Ln5PtPvt9+On5Hm9SqY725PvBmg569reuhipzqyQ9d5caz50r1tHefvDx0dUKxye3PTS3+XaO9pggTxPH7K05wlm8wIRW3f+htGb/LoTSDtCa9d6R0zPFxH9+1ePdE043HTl8uObw/IylmDGD79jN7YhOFnBO/Kl9W7TtpTpr1mOg/NMZ9YF9/RTRcjGTd9mbz8Yk7Ty6abvvH0DRi8V0kXLfKUZfD1Tvm3Pxw2dx91+HT3tnWdQ20cK5XqMOm0qVOa4Xa6aW+SLm5QtfxbHvnFqnXQH5CPHweYcKDDzzbt/N5cQ33+sM7DmTnmE1NP3Rx3XTUQ69xix4rTwfdxZTuMiWPpl6wOSdI7qo4FhByPqGhR730zEkAfUTNwzNz/epBLF5XWjap47PUObqpfqH9fvW5uDEwb2c18mebL+5yULl7IzBnXqRP+nRvLL0f/NI/t0HbgiYvJadDRGzt6w043XfNgcI+W5pEUeuOvz4gZsnXxaBk/Y7npPdQg6Lipp/XWNRM8cdP3p97I0B+/OgJAh905FhXSz3ANiFOGdTEjYnR0jLZxfYyHJ68VoDc393z/q2mzOmdUF8vTOaanz16ScYM6yvxlm810H8fjePy7NDbfUdr7pDdC1s4dJnfvPzBrB+z54VfJ9Vwm5w1fT2kDKzd8Y+qiC8Q5tp5DvxD9Xtebe6HnXOrNAk9fSExHvvj6+MhI/zbOOnftP9m0a338jG4ann4+8qeZEqM3++aM6+XRzx7XtQ/0xv1HLWuZKV266WddubofS8sGlZyjPTQQamjWBcZ0UT2dJuOp80v18/rV/LnN9+/YL5bJzj0HQwQ+vcG3cft+5+9QDc/6ua/rI+jiap66yJb+TtHh4TtXjDM3BHQ6m96o1ve03hTSkS/62ESd4uT8XTb6E+doKE/57IpN50kYjiVXU4fPPnz0SFp+/JmZW6iLjjztUSQ6b6XzpxPNnSkNlotWb5NZX240q7c65mh5Co0OnZs4a5V85t/GfIHqDwldbEp7wqfNXyv6Y2PgJ83l3IUr5kdUr44NpGSxVz2lemGep15jXThN66I9hTr06NjJv5zDbrRQ8J4kXcVTV7fUYUu6Yq8nbjpsTleXPf3XRcmdI4sM6N7MzK/TxXg0JC6bPkAWrdxm5pTqqAgdKj50wnxT1eA9bJ5Yd0ePqA6n1cW0kibxM8+d7tG+voyausQMO9QeYh0Zoj+i9AtXQ4OuNu6pj6XROt+9dz/EfFhdRO/23ftmaOE/12/Kyy/mMkPMdDSE/vDq5D/R3BQM3avuaddcP6fGz1hhVpvV6zdo7Dy5dfuufPFZN1MVx/u/9FuFYtWzWR1DS4PP/9d5hkES5AwEjvUjdEEmXWVaNw2IOtdcQ7N+7usPTU8JEBr0dOrL4B4tzOimz+euMZ/djl7T4HMuHTe+PX1erY740Ju6jlDveAyRLo6pi4Fu2L7PzC/VqRJ6o6P7oKmSMnkSMyzekzft/c6UIbWzbepUoDWbvjVDpK/fvGN6E1OnTCpvvJbPjOaq3txfuraubRZH9eRNbwTUbOEvfTo3MlNCdHNMlZg09CMpVey/RwjqpuFQXfQZvZ78BAH9TZ4oUQLxHzFTHjx86LypEXxqm84RD/67TD+/9Hd4i/rvx4ob+J7UZgnDnnS1LJyrfpDoCrT7DvwmnVrUMHfYwnouqT7PslqzviY06o9nfVSLvgH1g0rnqOqQzLZNqnrMwiy6AIUOIdQPWB0yXK96GXMXTuelBf+w1Q8bExTnfiV//3PdzN2pUOr1EAt7WGB2i130B7HjB5//yFlmWKXjrnroniTHUHq9CaDPu6v07hseFxgcq8vuWDbWzBnUmyC6aRDUHgbtEdTrrcOR9Lo6buroI3s8uffM0dj+OPGX5MiawbRV7QEtUqG1WWRLew8Gdm8eohdYf0iu2fytaLtwzEVzi0Zr8ySqNOltVlvWxw7pHMteQ78wPeNZM6WVum0HyrDerUJMl7D5cs+suL63Zy/eaELBuYtXTcgLvjqpPnNbpzzoInr6GL3Ysunn99d7f37qvDm92am9pcHnmupIAu0Z1ps/uoaCfueNHdDeY3qJ9fO5/+g5cu7iFTOiZXCP5iYA6SNa2vce55xz2W/UbDl55qLMm9DLOXzW0+eY6meVzqPVRwzpGgk63/LnI8eleJECsmvvQXk13/Omx1hHTejIAK3v/gNHzU1gT96Cf0e/XvBFqdasj1k1XsP/19/9YnqP9TtPF0bVtqC/Z/65fstjV50O/ntFr5uOALl+47a5wac3Qxas3GJW1S9ZrKAZ1VSnzQCz7sn/a+884HM62z9+1abUKGrvvWrVrB2b2DP2JsQKIcSK2GLEHgkxYu89atamNUpVq0tL0aK0qPV/f5f/ed4kb0hUxnM/fvfn837eSk7Oue/vfZ7znGv9rle1mDNl77FnyNpDoKZk0Xy6t3Bw4D312OmL0rE/emy/rJme4b9eHWHI5MuWOa3Uq/GpsdFxU/bHmieNYdN2LILzxZer99RAPTp0dAhR4blLNunLQ5VyRVVECwaFNZDChA8sBmpxUeNgYt1K8FQza23wzKJOrUCerCpvv3LTPnn8+IkETPUwJpIQ1i0ANWW0XEH9HFpTLF27W1+g8UVjRZE+zp9DypcqpMqeeMlYM3+UGo6mDBjA8JZbqf6oN0JWQ4a0qTQiitpRRNQgJhS08TNVm4aIGlJJUY8XJ05sLSFA5AhGpMnDaqcFLQBElOAUQcowIqeZM3wkNSqVkOb1qkjvYX7SsFY5/XI1fVgphagdd21fX59JKJEY1NNFxYZe1cbE9HVv3Pm5iojhvsUILaJn+vreZP7WfR+81hROooYdvfRZB6cuolBeExaqwRw0y8uoNoJIkU32wfvq8EL2A9rHwRjo1sZZ0D6wSZcR+nyzvq/hGOoxaIpmxXyQONGboLSrY/EugjIufKYhJhY43VN78UJhHfoIeLZZ64bje/CY+eoAzJcrs4pEmvh+gvsUbfPwfY3e4cj0+WLXfN17fMbdvWfL5W9/UkE9pBkj3RbHwwmYP1eWEErEdrWZEZiMVR++McBHa4XLOLtKh+a1lJZWwwAAIABJREFUtJYWmjDIcgST4CVdlvirdXqU+uE8KI2y9wGHD9Tz8V6GGmKrPBHZfVZ2DxxhyG6B8yNlimSa3YV+8oiOm1juZO97Enp+NIZN27E3mC8etmu27FdvVJYMaeTgsbMyd+lmfUmAoAFSiP19B2ptmjUso2rF7GHy5OlTVbWtXaW0cX1NrbYleNgicmYN1F3iYQQHAAQqRN6TOm0GiY9HJyM9zXjI+q/YJqiXRZo0DD8IraBmxYokBW/FZEWRYDjDYAreuuMNbi27PBQOnJG+i7TZPYxDeNLhaU2QIJ4K7DhST0sYhrsOnJTaTqV0rVaUAVkQmdKlls27jwrUd+EQ2RAwWtPKHUFIDgYCXhYxrPZbSLXs07mJ1pu+CwMlEWe/+laCZg8zWnX2TfYKqcSIDg7ymad9WdGmxRqor0Y6opUVg59bZQXBjeY3uZ49HAuBy5mL1svWJePVGEI5CAQEUQ5hDYhlfpD4fc2MMT1yiDXBcevSY7Q69Aa7uUiyDxJraQyUqKF98dffj1Q0slih3FoqAs0MRFCDt5W0h7170zkg9b9Om8Hq5EM2H8QxqzUfoGuGLgo6CTTXSGlVuf3HXX2Wo9wJwQxTBxw/uJ+ttX25Z6FN6BOpw/g9nnH3//pbpsxdLau37FfnfbfWzlK3WlnVgoG4GkoiTBl4J791+46kS5NSrOweRIUTv59QQtfMW+UgyN5EyRMyIRyhdaS97hWNYXvdmUieF8Sy1m49oArLeGlEKha8UEP7tLG17rB6wKHXp8+gTjoDq4br2JZZ+uWDY0zxUgVv14K1QNq+YqM+Gi3Al8n2fcelb+cmmlaHnm8li+Y1LnIIgwApZrmzZ5IhvVtJ3DhxpH3fcSochbYlYUWRLFVH0+vOQn9E8CLVpPNwyZcri/Tv1sxWjwaPMmrUnJr2F0ftaXn+0lVNE0ZfcUsrIHitodW3FV+wiKSjFg/3B+qqTR3WswnCJKaoo/8b1rivN+74XJ/bFy5/L826jnSYntoR5QFHFxyzcO4gUgIBQQzUWPb28tM+7DCggg/0eUUJDFqUYaAcKFumdMYYENh3OAFQ/46X6MJOHWVz4Fhtl4gBJmivtmflZI0SWpFDfKfnz51FP+cmDjyjhk/yVwFIvKdgz62+2qFVdy0nYPC+tiauGXOGExvp/lZZBLQCls0Yqstx6TlaswGG9W2j/4YC+bCJAXJ862zjHWLY3+bdRqmTq13TGpqph/Z50A3Ad3nXAZMEZX3TR7vJ3T8fyDi/5eokwX/D4Q9BSXzHQ5yrQunCxryfIkiRNEli7R4QvGbeeo5B6wbK43hnwT2BoAecPtbzzNT73F7nTWPYXncmCuYVumYDL1Vtmla3NQaHih++UNcuGGXrT2vVmkJoC2mmMDaQltKkbkXj0orhaUT9KNaCaBKUmSfMXC6nz12Rwxv9VMgCbV3gfYSyadum1TWFyd4H0oXnBm6SzbuPaLQMKbFD+7bW/QkriuTIMv5g4TN1iaAtB1KhIZwG5054PS3tfY/Dmx9eBvqPnK39aWE0IYoGxco9q3y136ElJLdq3gi5/ftd8fCZJ7FjxTK6JRGYhNWCKjxWpv0eET8X19FaE4+Xoi6t6qjWQ0QGngfDJ/qrhgRqMls1qipFC+bUP0UtW/+Rs2Ty8B6vFVuMyHWi4xi0kIKC+s+/3lRBPHQ/qNXKQ+o4ldZa0+DDigz7jXbTNERLIwPHQBdjUC8Xm0EdHXN/22tYfYYRFcbLM9KHEUlETWGPtvVCRA6hHQFjCa0E0ZrK5IEWY7myZ1QjyUqthcK2VS9sKfGe2DbHVjtveg017ut67YbIpOE9pGCerPpdhnZL6K9tCY7hXW3V5v3q2LeGyeuGA2TJ2l3aYg4ZjCj/QXmftXZkN6GWGsMqlfDz6a1lXzCeIaDZtOtIbbeItHmThuXEt2rmMXcrA8Jqm4mfoUxs76EzukaOyCdAYzjymRpzRkSOIFaAPo5WtDDVh0nl1u/3pF3T6pI6VXKZvmCtpllPGtZdVWsh6AK5+9ixY2tqluWlNmXRSKlDigrqblB/igHvMl40w4scwphGOxd43pG2Zm8DX4ZIKQrejgBfHPHixrHVmIX1QmFv64iM+SALAOUAeGmyUuZf19MyMq4Z0+fAl+rh4+f1xR8OD0R+UWsYlmonavTw+T+1Y55d3ssxzdLero8XZJR3xI8XT/UOlq7dpSmTrxPPgqEAIzp3jkwyyLWlnLlwRUZOXqTpxBDSg+PvytVrIfp129u6w5oPDEEIZEHkEe1agmdDWMdDXE8dYNvnaHQRkSZkzYwf2lV1BSBOhu89vGxCkd6Ecej4ORWXSpI4oaZN4jsb9aSIjIWOHMJQmhO4UR0dceLEkXw5MxsTMQu9F1Y2mpvXdHnx/IXACLJG8H7c6GvrM22poNQLjlAozKPu2MRhBS6stoF4jltaGXB0QkiwWb1KWmfrSOvGcw572LN9Aw1KYM/x+YVhbI3gWjAI1gwcPUfcuzfXZ5qJrZjw3oYsxfIlC2n2JQbaZu45eDpEG6o+w2Zo6zh8r2Mg0JElYxpV1HckEcWY+rzSGI4p8nZwXfTve/jwsb4wQ3hny56jmm727Q+/COqV1Jgolk/aN68lN27+rp5oyxu3cPk2/cDCM2lS6xZEQwaMmq0GA2pm4QyA6Ep4kcN9R74QSOIj0oIet6aOsF4oTF1LROcdkZ6WET2XCcfh5RFOK6fyxdXQDUtIDobE4lU7NEuCwywC2F+ICMHgQ71cHacyYaZKovwD2gF4XsFgwsAzDEYyoqXo171q7ghNqTVxwNE3ZtpSTZeEUYssnufPn2tUFIa+1XrJ6ju/cu5wm3AeSmgmzFqhPZtRo2nKQLr09d9uS6NOw2XUgPZaDhNW5BDfzas375dPCudVZWYYz4iowhFs4sC6kfEDnQ+rbdq5i98JUuGRNg8jAgZisUI5ZSAcP+e+0e90vNs0c66kNZkmDjjqZ/ivkxFw3seNo0uAIwdRVNSS4953xHVbewWH7UepUmiWA4YVFbZ0MPAzvLsGbfhM9q2ZYuw+B783kflSo+VA/bxaGRBWSdCmRT6SPUt6zQzZsPOwlvtBZA29qD8pnMfEW9xu5kxj2G62ImYmAkEa9LeDKiv6l74qLRjiHRDUwgfUGugFt2X3ETWgTRpIKT1z/ooc/+KSptg9efJEnNsNsfV1xFqsCKolQoWWFmu2HNDaRNRt4OXCtIGXaAiy1K/xqXFtld6G9et6WoauM3yb69jj31pfooiCWT06YQi0cRujtUcRTbe1x7W963NCZBB1sHiWQfU/tLhKufq9VCMCCvPWQBQCSq2d3SdpVMF6yTSVJZ5pqBtGFBTqsxiolYURBBVmK4oGJ2aC+HFlYI8WUtuptM15EFqh1hQOWA/67iIdvHoLd12vFTm0yoEsgSW8OLfq6SMVyhTWlGpHGLiPW3QfJYXyZdPnGmptz128qlkOlsAeaiwnz1klaMVnUteE1+2PpXsC46d6xU8cft0w9iAs1allbSlSIKfMCFgvRQvm0s4BGCiLQkaf1UkA6ePnLl0V6EiYOpDJBWc2ov4YlpZP8Y9zqxAoaqxR4pc1Y1qNCFvOsL2rfY1WlI/p/aIxHNM7YCfXhwBPcFXp4NOCkAVUK4N74/B79CN2atZf9q2ZqqqAGNv2HteWRfaYRvwq1OFFDvHFizS0tKk/lKfPnsqFr78XqFRbKS12soWcRgQIhO5pGYE/MfYQ64URNXcXvr4qH6ZIqv0bERHOkjGtKsmH1YPc2AW/oxO3esu//35CFQS0XvwhkNi2aQ1NBw4+dh04JX2Hz5D9a6dqWxMYVFCiRx9XqybRRJQwFKDCa0UOsQYYROgpv2fVZPn8xAUZ67dU0n2UUsUGTcpoetV+hBU5RJsxfDf7T/HQP4PBjxpU1/YN1IByhPHLjdvS3cNXFk8frGnucObD8WNF+S0nCJSmLYcfPicoMzDZMIZo2rJ1u2XuhP621nKvWzci6j/8fF3fXUyNjsPZt/fQaTly6oJqJuxeMUmSJU2st3HwjD4YiZWb9FPHYNO65rcStD6naK03euoSFcrDfY2exVjro8dPVPy2Ue0KWvJkiQgibR7PdUtI0xE+79GxBhrD0UHZ4GvgYVqvnad8WqKgeLq1CrESpByXb+Amq+eNUNU/q4E4XqhwLNKQTej/F17kMHQaGiIQx89c1BeOrm2cjaxTMfiWfOupWz0tHd2ZAccUMhrwJRo3bmxZ/Z/MBvQqLZw/hzSqVd4WQXlroDyBXRBA/dznpy6Ie7dmOh9LEHHW2L5SokheiRM7ljx99jxESxI4QdGLGgMiNhCuQUqmiZkvoTfBiqKhH63Vdgs1mBCiQQmQ74gedrFvkTkJK3U4uLqy5fz4fOMMNSJ2Hzwlo3wXay0m0qy7tnY2ynlt8Qoe1UeqMLpBWNkv6EsMoxEGAvRNJs5aoZ8HZAfAGTJuSBfJ+v+q3JHJPzrOFdF1X/3xV1VghwMYmQSVyhSWYf3aGmsUI8sJejbVKhRXzOixjX1f4jdERQGRLo26evTchngoBhSbsd8ohzB14P3z2bNn4lytrN7DSJPHGuHsRgR53IzlkiFtSi0XsHqTZ82URlrUd5Ii/+Ficv/x6NwzGsPRSdvAa6FXKcR4gnvjrGVApANfMmi79EJeCBqI48NXvFBumTArSDJnSCODe7kYVZMWOnKIFDN4nYOnoWH9cAQgZWfL7qMy0LW5puRZD2CkocLQMrltjYG3KqccigC8yBAPg6OK490ksHP/CZm2YK38eO03Qe/4o6e/Uj0IlH88evSP9livW7WMtiJDtAF1xDAcEW0yfUBgBgbRmvkjbQKCpq8pvPmjJvzy1Z9tDhGrpRpUxJF2CYV5MJnm7abfT2OmL9FTQp3b5GFlr6HvbsE82fSdBe0Sq5YvrlltX13+QeAgwJrxvY268s2LxxorKmbt1avWXaJwXv1sQzxvzvh+ahAjkp45YxqjU4iD36OIhqLFEoRcrbK2gCmDpESRl7WzVtstOANNNoaDrxm9lhcs26ptpxD5tdTUrXVDB2jBf0pnYDgjXRxR4nULR2nWjzVQkxz83yZ/7iNz7jSGI5Omg50LHkj0f7v1x12ZMLSboGbBGnjJbt7dW9o2qS49OzSQlRs/k0lzVsmO5RP0QwppeKSo4cs3uHCJCYiCRw7REL7HIF/Zu8o3zEgaIm1dB07WL9l5E901PcVrgr+gvQVUWzlIgARIIKYJICL6smNAXxVBrFy2iApwoeZ439qpNkfeopU7NB0RzzLTBzJ+5i/dot9DpilHRxZ7KGdbzg+0pqrSpJ+mCZcpXkDcOjbSy6Dd4JHNM43McEIkHE75Ee7t5O69B3rvzgzYoH2XIRwHATk4eGpXKaU9uj1cW6pzvkLD3mJFyiOLdXSeJ7x1w+ERuHqXvo8kgIiia0v5/ufr2poMEUQTBz7PqA1H3SxaDFlZT9hrPMeQGWOVBmB9A73nqMAYnmUQ3kJ5UNIPEkvj2hWMCtAE3ys4NQZ4zxE4OeH4QYYLWkmhhhwt+Gq6eNiE9fB3EFG8e+++/h7DMp5RLoF2dKb2Io+K+5fGcFRQdaBzIjK6YuNnMn3hOu3Xil5oKPBHRBQfRh+PTvLgr4daO4zIArzPnVvVsaVm4Ng0qVIYSwQPWDcvP62XtvrcYTF48IAB0nCw7pYNnaRrK2dVqUYNE9JSTWnZYezmcOIkQAIRJgCDGC9RyGLB8PCZKwnjx1dDwhq9hkyTRIkSiM+gTjJ8YoBs2HFYa2shvIRnv4nDUo4+dfayTWjHxHW86ZzhzG7jNlYVx+H8wPcVjGNEyoM2fqYRpPKlPtY9Pr1znpGp8deu39J6yRTJPtDaYNS+z1u6RdYuGKm144iUL127Wx3ycGyPmb5U7v35QB1DwZ1Ab8o2po8Pb91wyKNcrU/nxrJj3wmZOHuFthYrXCCHjB/SNaan/6+uj+fXqCmLdU+R4j9jTG/tGY4xdPxCfQ+zjL7Q6ssQE0R3BQhmwlnSt0sTm5YCUqsRQTVJTBLRXQRtvKcEagAKkV70okYnGIjIWeWJ0A/QFlX/7/hy9Zwq6FmOEoF12w5K9zb1VKEdPN/1QWP4Xb8DIrh+pFtt2XNEzn71naAHHupPqpQrphEF1CygUfr4od1kytxV+qUzuFdLqVf9U+PTkIDH6me5eNpglbRH7SVeKvGC0bhORSlbooBysOqrUXeHhwwHCZAACdgrAfSNx0vgvAnuqq6Ml8JuHr7aMSBx4kTSvNtI1YP49bffZfCY+SFeIO11Ta+bF1JK0TkAbaXelYG6QuwtBsSXRvoukl0rJun3FRy6M/zXv4wc9mxpLBK8m2zefUT2HDwlT58+k75dm0rBPFl1PTCKugyYrLoJST94X+ss0dMVtZWWU8jUhb9u3XhnOXzivARM8VDDCMcGrNwuFUp9bGxU1NonKEjLC9H9tAbu5QYdhmppHjIYdx04KRVKF9bOHygX+rReL30nbVirvKrOo/86BARxHLIX69f81Mh3NkR64fSxMiAs/R5wgeJ03TaDNaUanQYQ2EG/dUtPAJkS4/yWy4/XbmhEPWfWDKZ+FCJl3jSGIwXju3sSpEujLRHqUqzIAVpdwAPbzLmydG1tfu0ZdhcP4P1HvhDPsQtUwW+ke4f/UaVEahIerDuDJkmihPHZFP3d/Vhw5SRg9wQgAAiVfAiq5ciaQWYGrBeXhk4qfgi13mrN3cVqz4MXJ/+g7Q4pOmX3GxVJE4QTu8l/ektDQwA14pZyuKntpSKKBdExCMX16dJEShTOo8a/pe8R0XOYdhyy9Vr28JYsmdJI+2Y1tWYevcZNEDT9t6wR7T/55deyZc8xdexBQT5e3LgqDgcxyQHes/X5Vb1iCUFkHdkELq7eahyP9uioLSdN5YN1Hz9zSUsWrYFa+UQJE+qarUANBMgmDetuq6HGZx/v68gQsXrR/1v+pv8djWHTdzCG548UpRs3/5CNi3xCPEgePf5HvbSQ88d/37l7X+t4TB7wKm/dc1TFwUoXy6+REmtNUGtEKxOkHKI+iU3RTd5pzp0E3g0CqCPdtf+kXL56TdtuVS1fTH74+Yam0Vlq1JYQEZ5paMUFnQSIsxTIk1Xy5sjsENk/78Zuv3Tq+kxdor1JC+TOKqMGdtDyJ0ceeOFH3TRqxyEkh7aIObKmd+Ql69qQNqwp41sP6OcWytoRUYlHlBUCoabeF0gDhtJ0kQI5pHWvMTJmcGfJlD61plcPGjNPNgeOlWyZ0mpmBGrH0cMYmY0Z06dW558jDCvLB8K36dKklOXr98qUeavFtX19TRMHG4+eLSV75nSOsNxIWQON4UjB+O6eBA+Y1ylHQ+gBdQroiQaVv2nevcL0yqLWFukeJninLHEwCFRYXjbUbly88qMsmzFUHj1+zKbo7+5HgisnAWMJQJW1UadhsnXJOO0dj7qysX7LbWmm1ksV+rceO/2V9q6e7t2LNWeG7TjaTkF8p3Tx/IbN/O2mi7pTZG29a+NN1o362wNHv5TBvVrJx/mzh9BKMY0bMhRRHgCHXpaMaaVa8/7Su1NjwfMreI3ts+fPVZEaRrIjDD//ddpSDFoPEJar2txd68eR+QNnwezAjbJkzS4N3jSpU1Ej5Y8f/yPZMqczNjr+tvtGY/htCfLvQyhHoyajd6dGqq4MzySipT3a1VcFv3rth2ifO0QgQo/ug6bI5e9+kiFurVWYy4QBEQNEvm/evisNO3rJijnD5aOUycNtig7vPHq/mZqSY8LecI4kQAL/jgAUW/cePqPP4r8ePtR64aNbZsm+z1EmMl86tqglbp0aaeZPW7exUsuplHYV4CABErBPAqidLpg3W7jp4dB7gVYAhFCv3/xdW1ChdzPU2E0cyAqAACxU8mHwIV0cmgiICuN9NHiNLdbnKD24sRar/AEOAdSPb1w0RuLGedl/GQMljnHixBEICw6b+FJwDaKv6MFttWPEOeAkgPCYow8aw46+w9G4PhiHSMVoVLuClP2kgF4Zvd7Spn6pJg3Blnhx46iy3W+37siWPUelXdMammaHeqaNOz5XMS6IXwzq5SK5splR0I+HCR428LyF1xQdHCBikDtbRunSuq6R7Syi8ZbipUiABGKAAFqXQJTo3KXvpFeHhio21KKHtz6bb//xp3z7/TUZ0qe1ph4mTfK+DOjRPAZmyUuSAAlEhMDCoG1q2Pbv2vSV2QAoA4OGAAzGYX3b6Gkt8dAvdy/QVGuTB95FEaSBQbho1Q5B1qKlPo11OWIPbhizIycvFvQn7tbGWR2ZoZWj2/UZJ8UK5ZKe7RtoOQFaVqF7Ckpl9h46LUPH+8uBddMcPqOCxrDJn27D5n7m/DfS28tPvPq2ldmLN0jhAjn/pwH8rMUbVcgFw8TekOE1Rd935Avp6TlNDX0IN6A1wIwxfUI8aJDW82mJgqzFM+z+5nRJwJEJIMsHmT1wdJ4+942Mnhqoz7BZY/vaBFkcef1cGwmYTADG4NT5q+Xvvx+p8wplacEH6siROrwzaKJNXA3iSnhnO7t3YbhRZZPYQEUd76Mzx/ZVgS20AH3THtxwHiAAZELU9KvLP8hYv2WqHD2wRwup7VTapjKPdGm0XFs2c6jeE3Cc1KteVtCeC4GeLq3qhGg7BXE2ZEQ62qAx7Gg7aufrQeQUHkoYey4Nq4Yw+KwHEgQPCuXNJhNmrZCr/xFCgOAFFCBNGK9rio52DnXbeqq8P1S2UXvcd/gMSZ82lc0pgNTyHoPRC+6BDO3TRj12HCRAAiQQ0wRe9vJ8LBO9utmyeRA5cCpXnI67mN4cXp8EIkgAKdN4t4IjHuJRMGzwua7ewl1aNnQK0WKo34iZ2pZp9ri+WmMOASqIkFWrUFy6t60nGdKmiuBV7eswiAEO8J4jp89dlu3LJsj+I1++tgc3jt+5/6Q8efpUKpUtImlSpZBNuz4X7ylL5PBGPyO0bhAl3v7ZCRnrt1QzL9N99KGkT5NKUiRPIqOnvBTV2xw4RteGMX3hWlmyZrf+90DX5pod9OuN21LTxUN7lSODwJEGjWFH2k3D14IH7fc/Xpeg2cNsXivU4+JDiRrbga4tjFlhWE3RA1ZslyVrd8nWJePVG4nhO3eVPHz0WOtyrIF08m17j0ncOHG0ZRXSVThIgARIICYJoM7OdfBUTZfEi1G2zGmlSIGcMTklXpsESOBfEIDTHmVqiAounTFE/IO2SdCGvfrZnjSsh/alxbsJ0olXzR0h6JaBVj3eAztoG56la3fLsnV7ZN+aKUZHCSEmhX7Fr+vB3aRuRWnebZRkSJtSBQPRrxeCXKN8F6seTov6ZtVTw7kRN25sTZ/GMx1rQfli5wGTJGvGNPouagWmZo7po0KKSJcf7dFJu6ScufCNlC/5scM5QGkM/4sHCf8k8gmcvfid9sXDgzn4C5Yl6BA43dPIKKnVFN2S8UeNSvWKnyhACGk5Ne0vYz07i1O5Yvqz73+6LnXaDBY/n95SvlQhh0pNivy7hmckARKITgKP/3mijrpdB07JJ4Vzq9AOBwmQgJkEoNUC/Rb0FIfjHcrDo6YEqvgpBJXQeqti6cJSu/UgbZGZ7qOU4unmolFB/AwptxXLFNZ3GWgHmDpe1YMbkeB67YZoBHyat5sGMdDTF3W2aEu0zt/b2Hc07Fn/kbN0z+s4lZbANTulVaOq+kz38Jmr7VDnTXS3ban1jn5k80yj9/pV9yiNYVM/vQ40b9RetOg+SrJmTivjPLvYVoa0DvSJS50yuTYODz5gNKIvXJzY/1XHs2ckaPq+avM+CZjiYVORHj8zSB+sK+cMt3nZoKqNBzAUDzlIgARIgARIgARIIKoIHDtzUTbt/Fz78WIg+gflZfSnRatLpMaiNQ9ElM5/fVXG+S2XEkXyats1BC/QjgdOfUQR0T7T1BFWD+5fbtyWXkOmyZFNMzWCjIGsvxotB+o7mumtyeAEOPnF13Lw+DlJ/WEyadOkuvaYrtS4j4po5cz6UsTWekdHqy1Pt1bqLPEP2i4dW9a2ZTmauu/WvGkMm76DDjB/1F6gfcfe1b62egUsCwbkAO/ZYjUOt5aKAv6aLgNV5bSpcyVjCODBYxnvkLV3bjckRCT80PHzmooU/CE0YWaQ5MiaXuuMOUiABEiABEiABEgguggglbiMs6tsCRyrJVvQPkGK9K6Dp2T5zKEyafZKTae2WvMgrbh142rRNb1Iv07wHtzzl23RFGoY/dZw85ouL56/0Ow9RxzoJDBtwRrZtHiMbXnoGoCMAbyLJ0uaWCBAhl7F6FGMcpl6NT41PlpMY9gR72aD1gQDsVKjPtKqUTUVlbIGvJOVm/TVegzUZQQfaN+Eth/blo6XBPHjaVum58+eS7x4cY1ZOZT60OR8glc3nTPWi753EBaD5w0DSq0NOgxVKXwoFkK0orNLHfYnNmaXOVESIAESIAESMJsAetWeOX9Fxg/pItmzpNfFIHPv+59vSN02g2Wh70AVPd1/5KwcPf2V1hY7woDYq/uo2bJ8lpfWUR//4pJ06Dteti8b/z9q3I6wXqzhzr37WiNdtkRBbcWU7IPEUqPlAFt9NLIG0FnAw7WFpEyRTGvNIfi63n+00XXENIYd5Q42eB1QJ0QqtCUqhaVAuQ/qpfvXTpX3EyWwrQ41uFCzQ9F/1fLFBSk+vYZM17QN1K40rVvJiDYfqL1DDQ6EwWYHbrJ5Wy3PGxaMh27qVMllcE8X+fq7n2TYBH91DrRrVsPg3ebUSYAESIAESIAETCEAwS1ESf3816ljvnubelK3WhlBWVfCBPFDlLE9efpMZvivk+Xr92pfckQN0arHxIFACyLfEBrDum/9fk9bfvbt0iTc5QTPBAz3YDs7AHXkMHJdGjrp/+/Yd0IjxchshMI43l+RFo+BYxG4QqAGwSmkjsPJDqgkAAAcRklEQVRxYNqgMWzajr0j80UaxrUbt0LUEGPpqN+4/9dDWTR1kNYxFKzcXj+wiCwfO/2VjPRdrCk6EHaIFes9u6eFB+biVTtVubFcyYIyZWQvdQogVQXpOPvWTFU1Pwz0YIYzIHhddfAFmtT3zu43hhMkARIgARIgARKwEUA7SPSszZszk3z51bfSzcM3RBkbUqg79p8ov9y4JVNG9lRxppG+i1SgqV/XpsaSROupA0e/FETIkZGYJHGicNfy+ckLsnHHYVv2X7h/YIcH4P20YQcvGdzLRY3cE198Le37jlMGVn9l1I6P9Vsu7t2ayrXrt8V/xTaNGqP+2KRBY9ik3XqH5rpq835ZtHK7pl5AxAFj35EvpKfnNFm30FtyZ88oqO2o2KiPGsafFH4p3IBa3ObdvdVzBw+eKcNqIQVDPkvGNFoTjSgwvG0YeCjVbjVIOrnU1jqN+w/+Nr7vnSl7w3mSAAmQAAmQAAn8lwBqSFOmSCo92taz/dBKK96zcrKk/ehD/Tkc+CMmLRL/KR7G44NeDfoyR2Ss3PiZbNlzTJb4eUbkcLs9xopwW4rb5UoWsjk24BwJnkKNRaB+HMEc09ZNY9hub8F3e2JIIe7Uf6I8e/5cDcK79+6r9wl1xWgUbw30Jj538TsJmDJIPkqVXH+M5uH5cmZWsQd457JnSRdCmMveycIRMCdwo60mGvNFqsqsRRtkx/KJ2gMuvL53UNuG9xJfVhwkQAIkQAIkQAIkEJkEQqcCe46dr4760OJS6G374sVzmb14k9z8/Y6UKV5AalYqYZTOC7hBafnP+3/pu2V4Y+KsFfL3o8cyvF9bPRS6MIdPnhcYk6Z0QQm+RmQebt93XMqXLGSLjE9fuFb2HDwdosVUn2Ez9J0bAre4P9ZtOyQVSn1sez8Pj1tM/Z7GcEyR53XDJYCUm+Ub9sq6rQe1RgG1ss2cK8vTp08lduzYWqwPOfwu7pPk199uq0EM5WVroFE8Cv1RT9ync2Np37ymEQ8hPECu//a7ZEyXWpeCVCO0NvDq21pqVSklzm09w+17B4GuP+7+aavrCBc2DyABEiABEiABEiCBf0nAe0qg3Prjrkz3dgtxhp9+uSk9PadKgTxZpVLZIrJy0z55/PiJBEz1MOKdzFoM9G2GjFugNbFdWtd9rYKyq+dUKVkkr6YL3/7jnsBIxDvZ/EkDJH2alP+SsP38WVgtpr64cEVa9fSRTYt8VGgNvMb6LRV0SkH/4p4dGtgyPe1nJS9nQmPY3naE8wmXwNwlmyVF8iSaLowBryOK+n+8dkM2+I+2eRt9pi3RyDBqbJHSEzduHJky0tWoKDHWN27Gcvni/BUJmj1M9h/9Mty+d5e/+1mSJ00icePG1v/nIAESIAESIAESIIGoJHDl+2tSv/1QLVNDiyUIKqHMrV2fcXLyy69VeKtDi5owPaROm0Hi49HJuF69VoR0wbIt4tKwqtSv+WmYBj0CMUP7tJbkyZJIdw9f+aRwXlXZjhsntuzYf0LQsqp0sfwhAjhRuTeRfW5kKG7be0yNXAyIrDXrNlKKf5xbBvVsGeJy6FU9cPRcjahbSuPgOHxSgGZ6WvXHkT3HNzkfjeE3ocVj7YIAUjOWrNmtNQl5cmTSOaHmFo3CF04eKKWK5RMYhA07esm8ie5S9pMCAoVDrwkL5dvvf5GgWV5qGJsyrv50XZ4+fSa5smVQRcfX9b3DOuu183yp9Njmv7U8pqyV8yQBEiABEiABEjCTwKUrPwpShNGGaED35lLbqZRqu+C9C11CkGrbt3MTDVCgK0jJonlViAvZewVyZzVm0cg4DFixXQ4eOycDejRXI9AaeA8r7NRRS/zwzjYQglKNqwlqjl1cR2v6cMmi+WTL7qPSva2zzaDE36PE7cQXl6RZvcrGsMBEN+78XEZPXSKoF0d6OMRucS9ULltEGtQqL3HjxNGg1cntc+TiNz/ovQDFcRyf9IP3Y3ytNIZjfAs4gTclAI/SpNkrZPHqnfqwLVeqkHqo5gRusqkvoy1RwoTxQ6QJI4JcvEYXbaBepEDON72sXRwfXt+7Zet2K4edQZMkUcL4djFnToIESIAESIAESODdIYAyt1ixY8kfd+5roOLQBj9JkSyJwLk/YeZyOX3uihze6KeKy+gCAs2X9GlSSdum1cWpXDFjQP1y47ZMnrNKShbJYzNgkRYOEdRECRPInPH9pFihXLoelK/BQFziN0S7hpw+9420cRsje1f72jIWw2pXZQIMaPU8e/ZMnKuV1f1EGvUo9/Zy6cpPErByuyCFunrFEtqGC04AD5+56vxAfbnVMSUm10ljOCbp89pvReDoqa9k4Yptgv/HQ9a9e3PtZ7fn0Gnp7eUXQv7dulCLHt4q3GCa7Ls1/9f1vbNqpEe4t5PaVUoJ/j18or8cO3NJRRtaNaoqRQua6QR4qxuFf0wCJEACJEACJBAjBBCcSJcmpQzv307ThDGu3/xDHfZOTftrW54q5YrpuxwixgsmD7D1qkXW37QFa2RonzZqQNrrCC4mhpRhRD4RLbYMPejblKnrKjPG9JZKZYroMvA3peu4yryJ/TVAg9rabh6TZdeKSUbXFcOgz5YprUbMMc5e/E5a9vC21RJPnb9GPjt8RiqWKSxBGz4Lsd8xtb80hmOKPK8baQTwQIkdK5a89957AhXqWq08wuxrZ0WG/Ua7SeVPi0ba9WPiRGH1vYN4xcUrP8qyGUPl2vWbmo6TO0cmGeTaUs5cuCIjJy/SSDkeQBwkQAIkQAIkQAIkENUEoMI8YNRsQU1xjUolpGKZIlq+Nn5mkNYSr5wzXAVRMVBf7FytjDSsVV7baW7edUQd+2ihafJA+nOdNoPlyOaZNuEtq2/v/rVTJVnSJFrihkCGa/sGJi9V06CbdBkhdaqWlmKFckvQ+j1Sqlh+7T9sRc0XTBqg9eJovfVRqhQxLqxFY9joW46TD01gwfKtMmXeajm4frp8mPyDEL8OXL3z5cN3+xxNX3GEYfW9s2qkV8wZLgXzZFXvKtJx8AVi9WnGlwoePI3rVNSaFHhimUrtCHcB10ACJEACJEAC9kvgxYsXcub8Fa0lruNUWp48eSLO7YaEKFuzDKVNi8dI9szpVGBpzZYD0tS5khpSEOQydSAYU76Bm0bBUQ+MNOK2vceqiJbPoE4SusTNz3+dNK5dwdav2bR1Y31HTl7QCPn5r7+31Qa7j5otjx//E6L9FqLm0PQ5euqilCiSRzM3ocQdnYPGcHTS5rWinADSMcZMWyp3/3wgPdrV19qT58+fC2ptUccwZnBnTaV2tHHq7GU5fOK8ilBglKvfS7q0qiutG1ezLRW11k+ePtVUpBkB61X23tPNRRrULOdoOLgeEiABEiABEiABOyUAoyjWe+/JBK9uthlCYAltNJHBhvcVl56jJW3qD+Xps6dy4evvZWOAj63HrZ0u67XTgvgp1p0/dxb56vIPqiSNCCnWDPVpq8TNKvXbt2aqXdTTvg3ra9dv6bsmMgGQxflxlY6y3n+0CsJi4Pcd+02QMp8U0N7Ex89cVEbLZ3nZUuXf5voR/VsawxElxeOMIYB6DTxM5gRulG+uXtN5Z0ibSrq1cX5nDD88WNs2rSEtG1R55b7BS9vba7oM7NFC1acxTKnPMeZm5ERJgARIgARIgARCEPjzwd/aJQN6LxhWyvC2peO11Q4EmUZMWiQ7gybqMXiXswwok1Eimw/BC2TsQVE6Vqz3JHiJ2z9PnmipX8sGTtp2CAPq1LsPnJJqFYsb1Zc59D7dvfdAytbrqaJi0LHBwNp3HTipHV48XFtKtQrFVZUaArju3ZpF21bTGI421LxQTBC49ftdefjosWRK/1FMXD7Grrl++yEZOn6hzBrbV0oUyStxYsdS5cIB3rPl0eMnWrcDQxlOg59/uaneWUeqz4kx8LwwCZAACZAACZBAhAkggNG48zA1kPp1bSpPnjyVyk36agADvXwdeYQucUOp3/L1e2T7sgm2Eje0IJq9eIM6Bkwv8UNmopuXn7i2qy/tmtXQffZ0a6Xv6OP8lqnh/+Cvv6W2U2mbMyA69p/GcHRQ5jVIIAYI7Nx/QqYtWKspKhDV6jxgkrRuXFUa1a4gh46fk3lLN8tvt+7IsL5ttIbFkepzYgA3L0kCJEACJEACJPAvCHx+8oIUyptN06BRY9pjkK/sXeUr8eLF/RdnM+dPQpe4Iauvd6fGUqtKSV3EnXv3pVrzAfqehgy+X2/cloGj58qscX3lg8SJzFlosJmi7daz5y9UHbxx5+EamIFgGsbeQ2dk8tyV4j/Fw9ZuKjoWSWM4OijzGiQQgwSgPI3+b6Xq9Agh6295IJGWFC9uXIerz4lB5Lw0CZAACZAACZDAvyBgRQ83BIw2usXQmy4dqtsQ2Qred9hn2hI5d/GqBM0epinV/UbMEgQ6kEqeNVNajbDmy5XlTS9lN8dDYAtZjL4jXLWuGGuE2Bq6w0TnoDEcnbR5LRKIQQIrNn6m9RntmtaQTOlTq+L0dG83qVKu6Gvrc5DCZLU9iMHp89IkQAIkQAIkQALvAAGrM8jSGUO0B++7MCAaBhGx327fkVaNXqaHD/SeI0GzvKRQvuxa6tbGbYysmjtCUiT/QFsWoU/voQ3TjVbaRjR4xOQAXW+dqmVUOTy6B43h6CbO65FADBKA2va5i99JwMrtkiVDGk1FeVV9zra9x2Ws31Lt8VezckkV2rIayMfgEnhpEiABEiABEiABByeAljtIBY7uKGFMYv374SNZv/2wfPvDL7Jq0z7tfoIuKFZddZniBWRAj+Y6RaRMV23uLkUL5tKABWquYURb7TRjch1vem0oTV+68pP2YEawJroHjeHoJs7rkYAdEICHMVnSxNrLL6z6nP1HvhRXz6kyzrOLFC+cR2b4r1MFxK1LxxmtZmgH6DkFEiABEiABEiABEnglAQhJoSNKM+fKGoRYt+2gjPVbLntWTVaDEWP6wrWy5+BpmTyih/x643cZP3O5FCuUW7wHdiDZNyRAY/gNgfFwEnA0AqHrc1CvUaPlQHGuVkZc2zfQ5VoN4/19B0rBvNnkXfTYOtq+cz0kQAIkQAIkQAL2TQCGceXGfVRYq3GdCjpZ9OeF2Na8ie5aa4uxbN1uFUedM76/fS/IDmdHY9gON4VTIoHoJhC8PufD5B9ITRcPWbfQW3Jnz6hTQYuqio36yOp5I1SsoZP7RE3N6dG2XnRPldcjARIgARIgARIggXeGwE+/3JR0aT60ZeahtvjxP09k5pg+ygD1xk27jpA6VUurLsyjx//I9s+Oay1x6WL5NROQ49UEaAzz7iABElACVrT3+m+/ax0KetplSJtKfzdx1grZvu+47F4xWQ4ePys9Padpv7skiRNK9YolpGf7BvJ+ogQkSQIkQAIkQAIkQAIkEEUEvrhwRVr19JEtgWNVURpj864jKoq6e8Ukefj4H+nj5SdJP3hfUn2YTHYdOKUlbxBL5QibAI1h3hkkQAL/QwD1wvf+/Ev6dG4sB4+dlYVB22TO+H5SskheqdvWU+rV+FQ6taglFy5/L/OXbZXJw3tIooTxSZIESIAESIAESIAESCCKCCBt+vylq1K04EuV7b/+fiQ1Wg6Q7m3rS1PnilKtubsKowZO91RjGWrNg8bMk8Mb/YwU14oijCFOS2M4OijzGiRgGAGk3/iv2Ca79p+UBAniazo0lAoDVmyXJWt3ydYl47VhOgcJkAAJkAAJkAAJkEDMEPDzX6cp0RsXjZFTX36tZWwj3dvLtAVrpEHNclKjUglp0mWEHNk0U6PFHP9LgMYw7woSIIEIEbj9xz2p0LC3NkevXvGTCP0NDyIBEiABEiABEiABEogaAle+vyaPHj+RgnmyClpiLl61Q1bOHa4R4/nLtuj/8ubMLGvmj4yaCTjAWWkMO8AmcgkkEB0E8JBdtXmfBEzxeKf6/kUHW16DBEiABEiABEiABN6GwE+//KYCqDB8YQBjQHzrzr378nG+7G9zaof+WxrDDr29XBwJRC4BNEaPEzt25J6UZyMBEiABEiABEiABEnhrAmu3HpRxM5ZLkzoVpHTx/JI7eybtVczxagI0hnl3kAAJkAAJkAAJkAAJkAAJkIADEEDq9OrNB+TiNz+In4+bJE+axAFWFXVLoDEcdWx5ZhIgARIgARIgARIgARIgARIgATslQGPYTjeG0yIBEiABEiABEiABEiABEiABEog6AjSGo44tz0wCJEACJEACJEACJEACJEACJGCnBGgM2+nGcFokQAIkQAIkQAIkQAIkQAIkQAJRR4DGcNSx5ZlJgARIgARIgARIgARIgARIgATslACNYTvdGE6LBEiABEiABEiABEiABEiABEgg6gjQGI46tjwzCZAACZAACZAACZAACZAACZCAnRKgMWynG8NpkQAJkAAJkAAJkAAJkAAJkAAJRB0BGsNRx5ZnJgESIAESIAESIAESIAESIAESsFMCNIbtdGM4LRIgARIgARIgARIgARIgARIggagjQGM46tjyzCRAAiRAAiRAAiRAAiRAAiRAAnZKgMawnW4Mp0UCJEACJEACJEACJEACJEACJBB1BGgMRx1bnpkESIAESIAESIAESIAESIAESMBOCdAYttON4bRIgARIgARIgARIgARIgARIgASijgCN4ahjyzOTAAmQAAmQAAmQAAmQAAmQAAnYKQEaw3a6MZwWCZAACZAACZAACZAACZAACZBA1BGgMRx1bHlmEiABEiABOyVw/8HfcvLLr22zix8/nmTO8JFkSJvKTmfMaZEACZAACZAACUQ2ARrDkU2U5yMBEiABErB7Apeu/CiNOw//n3nWqVpaRnt0krhxYtv9GjhBEiABEiABEiCBtyNAY/jt+PGvSYAESIAEDCRgGcPzJrpL2U8KyIO/Hkrgml0yM2C9jBncWepVL2vgqjhlEiABEiABEiCBNyFAY/hNaPFYEiABEiABhyAQ2hjGop48fSaFnTpKh+a1pH+3prrOZ8+ey9J1u2XtlgPy3Y+/Sq5sGaRbm3pSveInNg4795+QOYGb5Jur1zTVOnvmdJI6ZXLx6ttGj/Ga4C9ZM6WRnFkzyOZdR+Tm73dlmncvSZwo4WvP/dutOzJtwRo5duai3H/wUHJnzyjNnCtJ3Wpl9Lybdn0ugat3yY/XfpMUyZJIsUK5pG+XJpLqw2Ty5MlTmR24UbbuOSbXrt+SkkXySv9uzSR/7iz6t2cvficTZ62QkQPay7a9x/TflcsWlZYNqjjE/nIRJEACJEACJBARAjSGI0KJx5AACZAACTgUgbCMYRi7zm091VB1KldM1+s7d5UEbfhMWtSvLIXyZZcd+07I9s+Oy/JZXvJxvuyy++Ap6TNshhTInVWaOleSvx8+koCV2yXdRyll6Ywheg6kY+N6GBXLFJbYsWOJ94COsjBo62vP3aqnj/z6223p1aGhxI8XT06e/Vpu3PxDZo/rK0dPfSWd3CfqNT/9pKAeF7Rhr/gM6iRFCuSUEZMWyeot+6VxnQqSN2dmCVy9U43mHcsnSMZ0qeXQ8XPSzcNX5wTjPW+uzPJxvhw0hh3qLudiSIAESIAEwiNAYzg8Qvw9CZAACZCAwxGwjGHXdvUlT45McuPWHVmwfIskiB9PVs8bKe8nSiC/3/lTyjdwk35dm0rHFrWUwdNnz6R0HVdpVLu8DOrZUo1n/Gzb0vE2RkPGLVDDM7gxHDduHJk5po9GcDHCO/eA7s2lUJUOapwO6d3adu6Hj/6RhAniif+KbTJ5zirZt2aqpE6ZTH+PKPbz58/lzr0HUqlxnxAR7rv3HkjZej3FpaGTeLq1shnDYz07i3M1poQ73A3OBZEACZAACUSIAI3hCGHiQSRAAiRAAo5EICwBLShJbwjwUWMT49TZy9K291hVmE6SOJFt+fhbRHinjHCVItU6q6EMg9kaYRnDBfNmk+H92tqOCe/cMJz7jZglSMFGpLdU0XxSofTHgvNgXP7uZ2nY0UsSJUygKduF8+eQWlVK6r+Pf3FJOvQdL3PG95NyJQvZrokIdcIE8WWJn6fNGN6zylfSpk7hSFvLtZAACZAACZBAhAnQGI4wKh5IAiRAAiTgKARCp0kvDNqmKdHBo8CHjp+Xbh6TNZKaKX3qEEtPljSJ5MiSXorX6CKu7RtIj7b13sgYDu/cBfNk1Yjzhu2H5cDRL+XYmUuagt2pZW2tC8b4/qfrmmZ95vw3moYNQ3jTIh/59odfdd4weosWzGWbV7s+4+TxP08kaJYXjWFHuZG5DhIgARIggbciQGP4rfDxj0mABEiABEwkEFbN8NDxC2X99kPiO8JVo60//XJTaroM1IguanODjxcvXsh7770n5er3kuxZ0kvAFA/9N37uNnS6pioHT5MOHRmOyLmR9oz6YgwIYnlN9FcBrnN7/fVn1u/w3xDvatBhqKZuly/1sdRq5SE9OzSQ7m1eGulIr4bhDpVsqGVbNcOMDJt493LOJEACJEACkUWAxnBkkeR5SIAESIAEjCEQljH8zz9PpGP/iRpptQSy3Lymy95DZ2Ske3tVa0at78FjZyVWrFjSp3NjWbRyh0ycvUJVpmGEnvjyazl38TtNbX6dMQxQrzt3++Y1pUX3UdKzfUMpkCer/PX3QxXFevb8uayeN0Kmzl8jDx89ljpOpSVliqRy8Pg58Z4SqHXJSOGGuNblb39S8a3cOTLJ4lU7NeUac8LcaAwbc6tyoiRAAiRAAlFIgMZwFMLlqUmABEiABOyTgGUML5g0QEoXz2+b5J1796V5t1Hyx937mnKcKFECNTxXbdpnOwYiWEidrlm5pDx//kIC1+yU7XuP6+/Lliggl678JC9ePJc54/vrz5p1HSn5cmcJUTOMn9+7/9crz12xTBHpNXSaqkZbo0q5otK7YyONRG/be1zG+i3VeWJAERotlzq71NF/37x9Vwb5zNX6YWuM9ugoDWqW039axvDe1b6SJhVrhu3zLuWsSIAESIAEopoAjeGoJszzkwAJkAAJGE8A9bu3bt+VBAniSfKkLxWhwxpIba7bdrB8WqKQeLq5RGjdrzs3anxv3r4jH6VMLvHixQ1xPqRkwxjGNS1F6dAXhIr0nw/+knRpUkqc2LEjNB8eRAIkQAIkQALvCgEaw+/KTnOdJEACJEACkU4AKdXzlm6WQvlySKIE8TWFGtHYjQE+kiNr+ki/Hk9IAiRAAiRAAiQQeQRoDEceS56JBEiABEjgHSMARecJs1bIj9duaOQ1R9YM0r5ZDVsLpHcMB5dLAiRAAiRAAkYRoDFs1HZxsiRAAiRAAiRAAiRAAiRAAiRAApFBgMZwZFDkOUiABEiABEiABEiABEiABEiABIwiQGPYqO3iZEmABEiABEiABEiABEiABEiABCKDAI3hyKDIc5AACZAACZAACZAACZAACZAACRhFgMawUdvFyZIACZAACZAACZAACZAACZAACUQGARrDkUGR5yABEiABEiABEiABEiABEiABEjCKAI1ho7aLkyUBEiABEiABEiABEiABEiABEogMAjSGI4Miz0ECJEACJEACJEACJEACJEACJGAUARrDRm0XJ0sCJEACJEACJEACJEACJEACJBAZBGgMRwZFnoMESIAESIAESIAESIAESIAESMAoAv8HSANVEHtFf8IAAAAASUVORK5CYII=",
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   "source": [
    "import pandas as pd\n",
    "import pickle\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "from datetime import datetime\n",
    "\n",
    "# Load the trained model and data\n",
    "df = pd.read_pickle('dax_data.pkl')\n",
    "with open('prophet_model.pkl', 'rb') as f:\n",
    "    model = pickle.load(f)\n",
    "\n",
    "# Get current date\n",
    "current_date = datetime.now().date()\n",
    "\n",
    "# Make predictions up to the current date\n",
    "future = model.make_future_dataframe(periods=0)  # No future predictions\n",
    "future = future.merge(df[['ds'] + [col for col in df.columns if col not in ['ds', 'y']]], on='ds', how='left')\n",
    "forecast = model.predict(future)\n",
    "\n",
    "# Evaluate the model\n",
    "y_true = df['y'].values\n",
    "y_pred = forecast['yhat'].values\n",
    "mse = mean_squared_error(y_true, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "mae = mean_absolute_error(y_true, y_pred)\n",
    "\n",
    "print(f\"\\nModel Results:\")\n",
    "print(f\"Root Mean Squared Error: {rmse}\")\n",
    "print(f\"Mean Absolute Error: {mae}\")\n",
    "\n",
    "# Plot the results with Plotly\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(x=df['ds'], y=df['y'], mode='lines', name='Actual'))\n",
    "fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat'], mode='lines', name='Forecast'))\n",
    "fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat_lower'], mode='lines', line=dict(width=0), showlegend=False))\n",
    "fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat_upper'], mode='lines', fill='tonexty', line=dict(width=0), name='Confidence Interval'))\n",
    "fig.update_layout(title='Prophet Forecast for DAX (Last 10 Years)', xaxis_title='Date', yaxis_title='Value', hovermode='x unified')\n",
    "fig.update_xaxes(range=[df['ds'].min(), current_date])\n",
    "fig.show()\n",
    "\n",
    "# Plot component-wise effects including daily trend\n",
    "component_names = ['trend', 'yearly', 'monthly', 'daily'] + [col for col in df.columns if col not in ['ds', 'y']]\n",
    "fig = make_subplots(rows=len(component_names), cols=1, subplot_titles=component_names)\n",
    "\n",
    "for i, component in enumerate(component_names, 1):\n",
    "    if component == 'daily':\n",
    "        daily_trend = forecast[['ds', 'daily']].copy()  # Use .copy() to avoid SettingWithCopyWarning\n",
    "        daily_trend['day_of_week'] = daily_trend['ds'].dt.day_name()\n",
    "        fig.add_trace(go.Scatter(x=daily_trend['day_of_week'], y=daily_trend['daily'], mode='lines', name='Daily Trend'), row=i, col=1)\n",
    "    else:\n",
    "        fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast[component], mode='lines', name=component), row=i, col=1)\n",
    "\n",
    "fig.update_layout(height=300*len(component_names), title_text=\"Prophet Components for DAX\", hovermode='x unified')\n",
    "fig.update_xaxes(range=[df['ds'].min(), current_date])\n",
    "fig.show()\n",
    "\n",
    "# Analyze regressor importance\n",
    "if regressors:\n",
    "    beta_values = model.params['beta'].flatten()\n",
    "    regressor_importance = pd.DataFrame({\n",
    "        'regressor': regressors,\n",
    "        'importance': np.abs(beta_values[:len(regressors)])\n",
    "    })\n",
    "    regressor_importance = regressor_importance.sort_values('importance', ascending=False)\n",
    "    print(\"\\nRegressor Importance:\")\n",
    "    print(regressor_importance)\n",
    "\n",
    "    # Plot regressor importance\n",
    "    fig = go.Figure([go.Bar(x=regressor_importance['regressor'], y=regressor_importance['importance'])])\n",
    "    fig.update_layout(title='Regressor Importance for DAX', xaxis_title='Regressor', yaxis_title='Absolute Importance')\n",
    "    fig.show()\n",
    "else:\n",
    "    print(\"\\nNo regressors were included in the model.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f1ccf7d-1f3a-4348-9dbf-bef44e9f5d2d",
   "metadata": {},
   "source": [
    "## Prediction of the DAX price of the next day based on the features: Real Exports DE, Volatility (Lorentzian) mostly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "60e5c126-07b4-48d0-addd-a016247fda4c",
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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     "text": [
      "\n",
      "Future Prediction for the next day:\n",
      "          ds          yhat    yhat_lower    yhat_upper\n",
      "0 2024-08-26  18170.678657  18163.911752  18176.962993\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import pickle\n",
    "from datetime import datetime, timedelta\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "# Load the trained model and data\n",
    "df = pd.read_pickle('dax_data.pkl')\n",
    "with open('prophet_model.pkl', 'rb') as f:\n",
    "    model = pickle.load(f)\n",
    "\n",
    "# Get current date\n",
    "current_date = datetime.now().date()\n",
    "\n",
    "# Predict for the next day\n",
    "future_dates = pd.date_range(start=current_date + timedelta(days=1), periods=1)\n",
    "future_df = pd.DataFrame({'ds': future_dates})\n",
    "\n",
    "# Initialize future_df with estimated regressor values (mean of the last 30 days)\n",
    "regressors = [col for col in df.columns if col not in ['ds', 'y']]\n",
    "for regressor in regressors:\n",
    "    future_df[regressor] = df[regressor].tail(30).mean()\n",
    "\n",
    "# Make the prediction for the next day\n",
    "future_forecast = model.predict(future_df)\n",
    "\n",
    "# Plot historical data and the next day's forecast with a violet dot\n",
    "fig = go.Figure()\n",
    "\n",
    "# Plot the actual historical data\n",
    "fig.add_trace(go.Scatter(x=df['ds'].tail(365), y=df['y'].tail(365), mode='lines', name='Historical'))\n",
    "\n",
    "# Plot the forecast line (without predictions)\n",
    "fig.add_trace(go.Scatter(x=df['ds'].tail(365), y=model.predict(df.tail(365)[['ds'] + regressors])['yhat'], mode='lines', name='Forecast'))\n",
    "\n",
    "# Plot the violet dot for the next day's prediction\n",
    "fig.add_trace(go.Scatter(x=future_forecast['ds'], y=future_forecast['yhat'], mode='markers', name='Next Day Prediction', marker=dict(color='violet', size=10, symbol='circle')))\n",
    "\n",
    "# Plot the confidence interval for the next day's prediction\n",
    "fig.add_trace(go.Scatter(x=future_forecast['ds'], y=future_forecast['yhat_lower'], mode='lines', line=dict(width=0), showlegend=False))\n",
    "fig.add_trace(go.Scatter(x=future_forecast['ds'], y=future_forecast['yhat_upper'], mode='lines', fill='tonexty', line=dict(width=0), name='Confidence Interval'))\n",
    "\n",
    "# Update layout\n",
    "fig.update_layout(title='Next Day Forecast for DAX with Violet Dot',\n",
    "                  xaxis_title='Date',\n",
    "                  yaxis_title='Value',\n",
    "                  hovermode='x unified')\n",
    "fig.update_xaxes(range=[current_date - timedelta(days=365), future_dates[-1]])\n",
    "\n",
    "# Show the plot\n",
    "fig.show()\n",
    "\n",
    "# Print the future prediction\n",
    "print(\"\\nFuture Prediction for the next day:\")\n",
    "print(future_forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6860d7f6-c25b-4584-afb0-e63048cb90d6",
   "metadata": {},
   "source": [
    "## Prophet model for the next month (Swing trade)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "fdf0c527-26fe-4e39-8f24-3e77ffcfd6a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "19:49:05 - cmdstanpy - INFO - Chain [1] start processing\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using regressors: ['open', 'high', 'low', 'volume', 'vwap', 'adj_close', 'unadjusted_volume', 'change', 'change_percent', 'KalmanEstimate', 'LorentzianIndicator', 'REXPDE~TUSD', 'CPIDE']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "19:49:05 - cmdstanpy - INFO - Chain [1] done processing\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model training completed and saved.\n"
     ]
    }
   ],
   "source": [
    "from prophet import Prophet\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "# Assuming your dataframe is named combined_df\n",
    "df = combined_df.copy()\n",
    "\n",
    "# Convert 'index' to datetime if it's not already\n",
    "df['index'] = pd.to_datetime(df['index'])\n",
    "\n",
    "# Filter for the last 10 years of data\n",
    "ten_years_ago = datetime.now() - timedelta(days=365*10)\n",
    "df = df[df['index'] >= ten_years_ago]\n",
    "\n",
    "# Remove rows with NaN values\n",
    "df = df.dropna()\n",
    "\n",
    "# Filter the data to include only the 1st day of each month\n",
    "df = df[df['index'].dt.day == 1]\n",
    "\n",
    "# Rename columns to match Prophet's requirements\n",
    "df = df.rename(columns={'index': 'ds', 'close': 'y'})\n",
    "\n",
    "# Define potential regressors (all columns except 'ds' and 'y')\n",
    "regressors = [col for col in df.columns if col not in ['ds', 'y']]\n",
    "\n",
    "def train_prophet_model(df, regressors, include_monthly_seasonality=True):\n",
    "    model = Prophet(\n",
    "        yearly_seasonality=True,\n",
    "        daily_seasonality=False,  # Daily seasonality is unnecessary if we only have one day per month\n",
    "        weekly_seasonality=False, # Weekly seasonality is unnecessary if we only have one day per month\n",
    "        changepoint_prior_scale=0.05,\n",
    "        seasonality_prior_scale=10.0,\n",
    "        seasonality_mode='additive'\n",
    "    )\n",
    "    \n",
    "    if include_monthly_seasonality:\n",
    "        model.add_seasonality(name='monthly', period=30.5, fourier_order=5)\n",
    "    \n",
    "    for regressor in regressors:\n",
    "        if regressor in df.columns:\n",
    "            model.add_regressor(regressor)\n",
    "    \n",
    "    model.fit(df)\n",
    "    return model\n",
    "\n",
    "# Filter regressors to only include those present in the dataframe\n",
    "regressors = [reg for reg in regressors if reg in df.columns]\n",
    "print(f\"Using regressors: {regressors}\")\n",
    "\n",
    "# Train the model\n",
    "model = train_prophet_model(df, regressors)\n",
    "\n",
    "# Save model and data for future use\n",
    "df.to_pickle('dax_data_month.pkl')\n",
    "import pickle\n",
    "with open('prophet_model_month.pkl', 'wb') as f:\n",
    "    pickle.dump(model, f)\n",
    "\n",
    "print(\"Model training completed and saved.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "8a930cb1-7b3f-4dc3-8dd2-30090c107916",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             ds         open         high          low            y  \\\n",
      "8644 2014-09-01  9484.530273  9500.200195  9424.780273  9479.030273   \n",
      "\n",
      "          volume        vwap    adj_close  unadjusted_volume  change  \\\n",
      "8644  43071900.0  9468.00358  9479.030273         43071900.0    -5.5   \n",
      "\n",
      "      change_percent  KalmanEstimate  LorentzianIndicator  REXPDE~TUSD  CPIDE  \n",
      "8644        -0.00058      9408.71774             4.904607     448849.0   94.3  \n"
     ]
    }
   ],
   "source": [
    "df = pd.read_pickle('dax_data_month.pkl')\n",
    "print(df.head(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "41d36764-33b8-44b5-9232-bee0d281237a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pickle\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "import numpy as np\n",
    "\n",
    "# Load the trained model and data\n",
    "df = pd.read_pickle('dax_data_month.pkl')\n",
    "with open('prophet_model_month.pkl', 'rb') as f:\n",
    "    model = pickle.load(f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "d115e213-d02d-42e7-8b0b-65300ce201c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Evaluation Results:\n",
      "Root Mean Squared Error (RMSE): 0.5615036063953492\n",
      "Mean Absolute Error (MAE): 0.42772182765000555\n"
     ]
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   ],
   "source": [
    "import pandas as pd\n",
    "import pickle\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "import numpy as np\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "# Load the trained model and data\n",
    "df = pd.read_pickle('dax_data_month.pkl')\n",
    "with open('prophet_model_month.pkl', 'rb') as f:\n",
    "    model = pickle.load(f)\n",
    "\n",
    "# Ensure all regressors are present in the future dataframe\n",
    "regressors = ['open', 'high', 'low', 'volume', 'vwap', 'adj_close', 'unadjusted_volume', 'change', 'change_percent', 'KalmanEstimate', 'LorentzianIndicator', 'REXPDE~TUSD', 'CPIDE']\n",
    "future = df[['ds'] + regressors].copy()\n",
    "\n",
    "# Make predictions on the existing data (no future dates, just evaluation)\n",
    "forecast = model.predict(future)\n",
    "\n",
    "# Extract the true values and the predicted values\n",
    "y_true = df['y'].values\n",
    "y_pred = forecast['yhat'].values\n",
    "\n",
    "# Calculate evaluation metrics\n",
    "mse = mean_squared_error(y_true, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "mae = mean_absolute_error(y_true, y_pred)\n",
    "\n",
    "# Print the results\n",
    "print(f\"\\nModel Evaluation Results:\")\n",
    "print(f\"Root Mean Squared Error (RMSE): {rmse}\")\n",
    "print(f\"Mean Absolute Error (MAE): {mae}\")\n",
    "\n",
    "# Plot the evaluation results with Plotly\n",
    "fig = go.Figure()\n",
    "\n",
    "# Plot the actual historical data\n",
    "fig.add_trace(go.Scatter(x=df['ds'], y=df['y'], mode='lines', name='Actual'))\n",
    "\n",
    "# Plot the forecasted line\n",
    "fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat'], mode='lines', name='Forecast'))\n",
    "\n",
    "# Plot the confidence interval\n",
    "fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat_lower'], mode='lines', line=dict(width=0), showlegend=False))\n",
    "fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat_upper'], mode='lines', fill='tonexty', line=dict(width=0), name='Confidence Interval'))\n",
    "\n",
    "# Update the layout\n",
    "fig.update_layout(title='Prophet Forecast vs Actual',\n",
    "                  xaxis_title='Date',\n",
    "                  yaxis_title='Value',\n",
    "                  hovermode='x unified')\n",
    "\n",
    "# Show the plot\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "c1cfde03-c238-456f-b12a-404e6e8470ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pickle\n",
    "from datetime import datetime\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "# Load the trained model and data\n",
    "df = pd.read_pickle('dax_data_month.pkl')\n",
    "with open('prophet_model_month.pkl', 'rb') as f:\n",
    "    model = pickle.load(f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "55dcaecb-9f35-468d-81e7-6f929ab6a2d9",
   "metadata": {},
   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Prediction for the next first day of the month:\n",
      "          ds          yhat    yhat_lower    yhat_upper\n",
      "0 2024-09-01  18082.128156  18074.147232  18090.528226\n"
     ]
    }
   ],
   "source": [
    "# Make a prediction for the next first day of the month\n",
    "future_dates = pd.date_range(start=datetime.now().date(), periods=1, freq='MS')  # MS = Month Start\n",
    "future_df = pd.DataFrame({'ds': future_dates})\n",
    "\n",
    "# Include all regressors, set them to a default value or use last known values\n",
    "for regressor in ['open', 'high', 'low', 'volume', 'vwap', 'adj_close', 'unadjusted_volume', 'change', 'change_percent', 'KalmanEstimate', 'LorentzianIndicator', 'REXPDE~TUSD', 'CPIDE']:\n",
    "    if regressor in df.columns:\n",
    "        future_df[regressor] = df[regressor].iloc[-1]  # Use last known value as a simple approach\n",
    "\n",
    "# Make the prediction\n",
    "forecast = model.predict(future_df)\n",
    "\n",
    "# Display the prediction\n",
    "print(\"\\nPrediction for the next first day of the month:\")\n",
    "print(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']])\n"
   ]
  },
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   "execution_count": 255,
   "id": "b4e247da-1d82-4349-8218-32a75f3f2fab",
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   "source": [
    "# Plot the historical data and forecast\n",
    "fig = go.Figure()\n",
    "\n",
    "# Plot the actual historical data\n",
    "fig.add_trace(go.Scatter(x=df['ds'], y=df['y'], mode='lines', name='Historical'))\n",
    "\n",
    "# Plot the forecasted line\n",
    "fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat'], mode='lines+markers', name='Forecast'))\n",
    "\n",
    "# Add a violet dot for the next month's prediction\n",
    "fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat'], mode='markers', name='Next Month Prediction', marker=dict(color='violet', size=12, symbol='circle')))\n",
    "\n",
    "# Plot the confidence interval\n",
    "fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat_lower'], mode='lines', line=dict(width=0), showlegend=False))\n",
    "fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat_upper'], mode='lines', fill='tonexty', line=dict(width=0), name='Confidence Interval'))\n",
    "\n",
    "# Update the layout\n",
    "fig.update_layout(title='Prediction for Next First Day of the Month with Violet Dot',\n",
    "                  xaxis_title='Date',\n",
    "                  yaxis_title='Value',\n",
    "                  hovermode='x unified')\n",
    "\n",
    "# Show the plot\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7fda70a-555e-4503-8aff-2142f32ca046",
   "metadata": {},
   "source": [
    "# Time-series AutoML with H2O (alternative to Prophet)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee5e0c74-2fec-4662-b5be-cc757a14e587",
   "metadata": {},
   "source": [
    "## Day range "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "401eaf48-91bb-42c3-a9a0-c85e89f3a690",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54321. connected.\n"
     ]
    },
    {
     "data": {
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       "  <table class=\"h2o-table\">\n",
       "    <caption></caption>\n",
       "    <thead></thead>\n",
       "    <tbody><tr><td>H2O_cluster_uptime:</td>\n",
       "<td>6 mins 46 secs</td></tr>\n",
       "<tr><td>H2O_cluster_timezone:</td>\n",
       "<td>Etc/UTC</td></tr>\n",
       "<tr><td>H2O_data_parsing_timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O_cluster_version:</td>\n",
       "<td>3.46.0.4</td></tr>\n",
       "<tr><td>H2O_cluster_version_age:</td>\n",
       "<td>1 month and 15 days</td></tr>\n",
       "<tr><td>H2O_cluster_name:</td>\n",
       "<td>H2O_from_python_marius_2e9dqw</td></tr>\n",
       "<tr><td>H2O_cluster_total_nodes:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O_cluster_free_memory:</td>\n",
       "<td>2.910 Gb</td></tr>\n",
       "<tr><td>H2O_cluster_total_cores:</td>\n",
       "<td>6</td></tr>\n",
       "<tr><td>H2O_cluster_allowed_cores:</td>\n",
       "<td>6</td></tr>\n",
       "<tr><td>H2O_cluster_status:</td>\n",
       "<td>locked, healthy</td></tr>\n",
       "<tr><td>H2O_connection_url:</td>\n",
       "<td>http://localhost:54321</td></tr>\n",
       "<tr><td>H2O_connection_proxy:</td>\n",
       "<td>{\"http\": null, \"https\": null}</td></tr>\n",
       "<tr><td>H2O_internal_security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>Python_version:</td>\n",
       "<td>3.11.9 final</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n"
      ],
      "text/plain": [
       "--------------------------  -----------------------------\n",
       "H2O_cluster_uptime:         6 mins 46 secs\n",
       "H2O_cluster_timezone:       Etc/UTC\n",
       "H2O_data_parsing_timezone:  UTC\n",
       "H2O_cluster_version:        3.46.0.4\n",
       "H2O_cluster_version_age:    1 month and 15 days\n",
       "H2O_cluster_name:           H2O_from_python_marius_2e9dqw\n",
       "H2O_cluster_total_nodes:    1\n",
       "H2O_cluster_free_memory:    2.910 Gb\n",
       "H2O_cluster_total_cores:    6\n",
       "H2O_cluster_allowed_cores:  6\n",
       "H2O_cluster_status:         locked, healthy\n",
       "H2O_connection_url:         http://localhost:54321\n",
       "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
       "H2O_internal_security:      False\n",
       "Python_version:             3.11.9 final\n",
       "--------------------------  -----------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features used: ['open', 'high', 'low', 'volume', 'vwap', 'adj_close', 'unadjusted_volume', 'change', 'change_percent', 'KalmanEstimate', 'LorentzianIndicator', 'REXPDE~TUSD', 'CPIDE', 'close_lag_1', 'close_lag_7', 'close_lag_30', 'close_return_1', 'close_return_7', 'close_return_30', 'REXPDE~TUSD_lag_1', 'REXPDE~TUSD_lag_7', 'REXPDE~TUSD_lag_30', 'REXPDE~TUSD_return_1', 'REXPDE~TUSD_return_7', 'REXPDE~TUSD_return_30', 'LorentzianIndicator_lag_1', 'LorentzianIndicator_lag_7', 'LorentzianIndicator_lag_30', 'LorentzianIndicator_return_1', 'LorentzianIndicator_return_7', 'LorentzianIndicator_return_30', 'volume_lag_1', 'volume_lag_7', 'volume_lag_30', 'volume_return_1', 'volume_return_7', 'volume_return_30', 'CPIDE_lag_1', 'CPIDE_lag_7', 'CPIDE_lag_30', 'CPIDE_return_1', 'CPIDE_return_7', 'CPIDE_return_30']\n",
      "\n",
      "DataFrame column types:\n",
      "open                                    float64\n",
      "high                                    float64\n",
      "low                                     float64\n",
      "close                                   float64\n",
      "volume                                  float64\n",
      "vwap                                    float64\n",
      "adj_close                               float64\n",
      "unadjusted_volume                       float64\n",
      "change                                  float64\n",
      "change_percent                          float64\n",
      "KalmanEstimate                          float64\n",
      "LorentzianIndicator                     float64\n",
      "REXPDE~TUSD                             float64\n",
      "CPIDE                                   float64\n",
      "close_lag_1                             float64\n",
      "close_lag_7                             float64\n",
      "close_lag_30                            float64\n",
      "close_return_1                          float64\n",
      "close_return_7                          float64\n",
      "close_return_30                         float64\n",
      "REXPDE~TUSD_lag_1                       float64\n",
      "REXPDE~TUSD_lag_7                       float64\n",
      "REXPDE~TUSD_lag_30                      float64\n",
      "REXPDE~TUSD_return_1                    float64\n",
      "REXPDE~TUSD_return_7                    float64\n",
      "REXPDE~TUSD_return_30                   float64\n",
      "LorentzianIndicator_lag_1               float64\n",
      "LorentzianIndicator_lag_7               float64\n",
      "LorentzianIndicator_lag_30              float64\n",
      "LorentzianIndicator_return_1            float64\n",
      "LorentzianIndicator_return_7            float64\n",
      "LorentzianIndicator_return_30           float64\n",
      "volume_lag_1                            float64\n",
      "volume_lag_7                            float64\n",
      "volume_lag_30                           float64\n",
      "volume_return_1                         float64\n",
      "volume_return_7                         float64\n",
      "volume_return_30                        float64\n",
      "CPIDE_lag_1                             float64\n",
      "CPIDE_lag_7                             float64\n",
      "CPIDE_lag_30                            float64\n",
      "CPIDE_return_1                          float64\n",
      "CPIDE_return_7                          float64\n",
      "CPIDE_return_30                         float64\n",
      "date                             datetime64[ns]\n",
      "dtype: object\n",
      "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n",
      "\n",
      "H2OFrame column types:\n",
      "{'open': 'real', 'high': 'real', 'low': 'real', 'close': 'real', 'volume': 'int', 'vwap': 'real', 'adj_close': 'real', 'unadjusted_volume': 'int', 'change': 'real', 'change_percent': 'real', 'KalmanEstimate': 'real', 'LorentzianIndicator': 'real', 'REXPDE~TUSD': 'int', 'CPIDE': 'real', 'close_lag_1': 'real', 'close_lag_7': 'real', 'close_lag_30': 'real', 'close_return_1': 'real', 'close_return_7': 'real', 'close_return_30': 'real', 'REXPDE~TUSD_lag_1': 'int', 'REXPDE~TUSD_lag_7': 'int', 'REXPDE~TUSD_lag_30': 'int', 'REXPDE~TUSD_return_1': 'real', 'REXPDE~TUSD_return_7': 'real', 'REXPDE~TUSD_return_30': 'real', 'LorentzianIndicator_lag_1': 'real', 'LorentzianIndicator_lag_7': 'real', 'LorentzianIndicator_lag_30': 'real', 'LorentzianIndicator_return_1': 'real', 'LorentzianIndicator_return_7': 'real', 'LorentzianIndicator_return_30': 'real', 'volume_lag_1': 'int', 'volume_lag_7': 'int', 'volume_lag_30': 'int', 'volume_return_1': 'real', 'volume_return_7': 'real', 'volume_return_30': 'real', 'CPIDE_lag_1': 'real', 'CPIDE_lag_7': 'real', 'CPIDE_lag_30': 'real', 'CPIDE_return_1': 'real', 'CPIDE_return_7': 'real', 'CPIDE_return_30': 'real', 'date': 'time'}\n",
      "AutoML progress: |\n",
      "19:25:03.665: Stopping tolerance set by the user is < 70% of the recommended default of 0.01989286700048564, so models may take a long time to converge or may not converge at all.\n",
      "\n",
      "███████████████████████████████████████████████████████████████| (done) 100%\n",
      "\n",
      "Best model: Model Details\n",
      "=============\n",
      "H2OGradientBoostingEstimator : Gradient Boosting Machine\n",
      "Model Key: GBM_grid_1_AutoML_1_20240825_192503_model_10\n",
      "\n",
      "\n",
      "Model Summary: \n",
      "    number_of_trees    number_of_internal_trees    model_size_in_bytes    min_depth    max_depth    mean_depth    min_leaves    max_leaves    mean_leaves\n",
      "--  -----------------  --------------------------  ---------------------  -----------  -----------  ------------  ------------  ------------  -------------\n",
      "    265                265                         180294                 6            6            6             17            64            49.4604\n",
      "\n",
      "ModelMetricsRegression: gbm\n",
      "** Reported on train data. **\n",
      "\n",
      "MSE: 0.21206737272249743\n",
      "RMSE: 0.46050773361855435\n",
      "MAE: 0.34181078724772457\n",
      "RMSLE: 3.671399543919675e-05\n",
      "Mean Residual Deviance: 0.21206737272249743\n",
      "\n",
      "ModelMetricsRegression: gbm\n",
      "** Reported on validation data. **\n",
      "\n",
      "MSE: 39.610729351137486\n",
      "RMSE: 6.293705534193468\n",
      "MAE: 2.792280000891665\n",
      "RMSLE: 0.0004400475320151746\n",
      "Mean Residual Deviance: 39.610729351137486\n",
      "\n",
      "Scoring History: \n",
      "     timestamp            duration    number_of_trees    training_rmse        training_mae         training_deviance    validation_rmse     validation_mae      validation_deviance\n",
      "---  -------------------  ----------  -----------------  -------------------  -------------------  -------------------  ------------------  ------------------  ---------------------\n",
      "     2024-08-25 19:33:32  22.322 sec  0.0                2267.8070877442683   1831.7651450271476   5142948.98722314     2167.37146448559    1727.5369870745326  4697499.06506641\n",
      "     2024-08-25 19:33:32  22.384 sec  5.0                1339.4290444956703   1081.8047211212654   1794070.1652385842   1280.086231604434   1020.2502613844603  1638620.7603432408\n",
      "     2024-08-25 19:33:32  22.438 sec  10.0               791.1037600180482    638.9889297153245    625845.1591146935    756.3318833032002   602.8763082947705   572037.9177009657\n",
      "     2024-08-25 19:33:32  22.490 sec  15.0               467.2540165384569    377.4190457898447    218326.31597132055   446.80941625421246  356.0965450093761   199638.6544534301\n",
      "     2024-08-25 19:33:32  22.540 sec  20.0               275.9894821285658    222.902427996389     76170.19424559394    263.99987481691744  210.28850343236857  69695.93390334808\n",
      "     2024-08-25 19:33:32  22.587 sec  25.0               163.02393409515125   131.66268286876732   26576.803087860215   156.19649878065823  124.21944497011569  24397.346231336167\n",
      "     2024-08-25 19:33:32  22.636 sec  30.0               96.30989622211466    77.77900896876237    9275.596110314496    92.4303735515898    73.35618719650957   8543.37395488643\n",
      "     2024-08-25 19:33:32  22.682 sec  35.0               56.914897549685065   45.96736766360803    3239.3055630911467   54.950025285150154  43.38128926287381   3019.505278838641\n",
      "     2024-08-25 19:33:32  22.724 sec  40.0               33.660746942232926   27.186723619286703   1133.045884709043    32.96765657283606   25.71040371955034   1086.8663799044607\n",
      "     2024-08-25 19:33:32  22.769 sec  45.0               19.948569567649148   16.112147401810446   397.9454277953377    20.23255715365955   15.543300604837833  409.35636897610016\n",
      "---  ---                  ---         ---                ---                  ---                  ---                  ---                 ---                 ---\n",
      "     2024-08-25 19:33:33  24.176 sec  220.0              0.5774730392218062   0.43098327129501385  0.3334751110280697   6.293659693034171   2.8169700225909162  39.61015233172298\n",
      "     2024-08-25 19:33:33  24.215 sec  225.0              0.5605945566221089   0.41884599450929955  0.3142662569143388   6.296492897239999   2.813847540411579   39.64582280499376\n",
      "     2024-08-25 19:33:33  24.261 sec  230.0              0.5470047646209211   0.4076891447368421   0.2992142125179893   6.298017085944119   2.810778540880968   39.66501921484405\n",
      "     2024-08-25 19:33:34  24.302 sec  235.0              0.5334259621145044   0.3975111761723387   0.2845432570577847   6.2948378182961955  2.805359711464768   39.62498315865201\n",
      "     2024-08-25 19:33:34  24.342 sec  240.0              0.5230718751480744   0.38953675306687774  0.2736041865709227   6.2946263141075365  2.8042309477880623  39.62232043425503\n",
      "     2024-08-25 19:33:34  24.379 sec  245.0              0.5137845356151425   0.3816628073060942   0.26397454903726764  6.29695562568025    2.8024185682891063  39.65165015178615\n",
      "     2024-08-25 19:33:34  24.421 sec  250.0              0.5030422046621342   0.3730978865749901   0.2530514596713405   6.296888610241514   2.8008790858133437  39.650806169789305\n",
      "     2024-08-25 19:33:34  24.460 sec  255.0              0.4944384601924157   0.36583414745745946  0.24446939091744704  6.297635582539977   2.7996777626978813  39.66021393047364\n",
      "     2024-08-25 19:33:34  24.496 sec  260.0              0.4808029471585699   0.3563606796596755   0.2311714739963666   6.2955211429398314  2.796881209253175   39.63358646120244\n",
      "     2024-08-25 19:33:34  24.536 sec  265.0              0.46050773361855435  0.34181078724772457  0.21206737272249743  6.293705534193468   2.792280000891665   39.610729351137486\n",
      "[54 rows x 10 columns]\n",
      "\n",
      "\n",
      "Variable Importances: \n",
      "variable               relative_importance    scaled_importance       percentage\n",
      "---------------------  ---------------------  ----------------------  ----------------------\n",
      "adj_close              65597067264.0          1.0                     0.9590094247547796\n",
      "high                   1523658112.0           0.0232275340278237      0.022275424046495275\n",
      "vwap                   1004545152.0           0.015313872919914812    0.0146861484596953\n",
      "low                    256683648.0            0.0039130354253027605   0.0037526378522646743\n",
      "open                   17375846.0             0.0002648875433724756   0.0002540296505943446\n",
      "CPIDE_return_1         586555.0               8.941786949702619e-06   8.575257958914104e-06\n",
      "close_lag_1            551760.6875            8.411362131166633e-06   8.066575558814249e-06\n",
      "KalmanEstimate         144279.390625          2.1994792853213613e-06  2.1093213641760925e-06\n",
      "unadjusted_volume      34905.69140625         5.32122743624644e-07    5.103107262624049e-07\n",
      "close_return_30        32185.701171875        4.906576241029403e-07   4.705452858425076e-07\n",
      "---                    ---                    ---                     ---\n",
      "volume_return_30       789.420166015625       1.2034382007331947e-08  1.154108576613068e-08\n",
      "REXPDE~TUSD_lag_7      738.735107421875       1.1261709375646074e-08  1.080008543009385e-08\n",
      "CPIDE_lag_30           676.3101806640625      1.0310067337952849e-08  9.887451746953205e-09\n",
      "CPIDE_return_30        548.4481811523438      8.360864350003264e-09   8.018147710749374e-09\n",
      "REXPDE~TUSD_return_30  431.3699645996094      6.576055646871082e-09   6.3064993430612565e-09\n",
      "REXPDE~TUSD_lag_30     402.5090637207031      6.1360832200131315e-09  5.88456163907225e-09\n",
      "REXPDE~TUSD            311.0453796386719      4.741757407947034e-09   4.5473900441219995e-09\n",
      "REXPDE~TUSD_return_7   234.057861328125       3.568114720527714e-09   3.421855645592344e-09\n",
      "REXPDE~TUSD_lag_1      129.0416717529297      1.9671866004861533e-09  1.8865504901175363e-09\n",
      "REXPDE~TUSD_return_1   75.47683715820312      1.1506129817426335e-09  1.1034486937363847e-09\n",
      "[43 rows x 4 columns]\n",
      "\n",
      "\n",
      "Model saved to: /home/marius/finance_code/GBM_grid_1_AutoML_1_20240825_192503_model_10\n",
      "DataFrame and model path saved.\n"
     ]
    }
   ],
   "source": [
    "import h2o\n",
    "from h2o.automl import H2OAutoML\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime, timedelta\n",
    "import pickle\n",
    "\n",
    "# Initialize H2O\n",
    "h2o.init()\n",
    "\n",
    "# Assuming your dataframe is named combined_df\n",
    "df = combined_df.copy()\n",
    "\n",
    "# Convert 'index' to datetime if it's not already\n",
    "df['index'] = pd.to_datetime(df['index'])\n",
    "df.set_index('index', inplace=True)\n",
    "\n",
    "# Filter for the last 10 years of data\n",
    "ten_years_ago = datetime.now() - timedelta(days=365*10)\n",
    "df = df[df.index >= ten_years_ago]\n",
    "\n",
    "# Function to calculate returns for CPIDE\n",
    "def calculate_cpide_returns(series, periods):\n",
    "    returns = series.pct_change(periods)\n",
    "    mask = returns != 0\n",
    "    idx = np.arange(len(returns))\n",
    "    idx_valid = idx[mask]\n",
    "    returns = pd.Series(np.interp(idx, idx_valid, returns[mask]), index=returns.index)\n",
    "    return returns\n",
    "\n",
    "# Feature Engineering\n",
    "for col in ['close', 'REXPDE~TUSD', 'LorentzianIndicator', 'volume']:\n",
    "    df[f'{col}_lag_1'] = df[col].shift(1)\n",
    "    df[f'{col}_lag_7'] = df[col].shift(7)\n",
    "    df[f'{col}_lag_30'] = df[col].shift(30)\n",
    "    df[f'{col}_return_1'] = df[col].pct_change(1)\n",
    "    df[f'{col}_return_7'] = df[col].pct_change(7)\n",
    "    df[f'{col}_return_30'] = df[col].pct_change(30)\n",
    "\n",
    "# Special handling for CPIDE\n",
    "df['CPIDE_lag_1'] = df['CPIDE'].shift(1)\n",
    "df['CPIDE_lag_7'] = df['CPIDE'].shift(7)\n",
    "df['CPIDE_lag_30'] = df['CPIDE'].shift(30)\n",
    "df['CPIDE_return_1'] = calculate_cpide_returns(df['CPIDE'], 1)\n",
    "df['CPIDE_return_7'] = calculate_cpide_returns(df['CPIDE'], 7)\n",
    "df['CPIDE_return_30'] = calculate_cpide_returns(df['CPIDE'], 30)\n",
    "\n",
    "# Remove rows with NaN values\n",
    "df = df.dropna()\n",
    "\n",
    "# Set date as a separate column for H2O\n",
    "df['date'] = df.index\n",
    "\n",
    "# Select all features except 'close' and 'date'\n",
    "features = [col for col in df.columns if col not in ['close', 'date']]\n",
    "target = 'close'\n",
    "\n",
    "print(\"Features used:\", features)\n",
    "print(\"\\nDataFrame column types:\")\n",
    "print(df.dtypes)\n",
    "\n",
    "# Convert to H2OFrame\n",
    "h2o_df = h2o.H2OFrame(df)\n",
    "\n",
    "print(\"\\nH2OFrame column types:\")\n",
    "print(h2o_df.types)\n",
    "\n",
    "# Split the data into train, validation, and test sets\n",
    "train, valid, test = h2o_df.split_frame(ratios=[0.7, 0.15], seed=1234)\n",
    "\n",
    "# Run AutoML\n",
    "aml = H2OAutoML(max_runtime_secs=3600,  # 1 hour\n",
    "                seed=1234,\n",
    "                sort_metric='RMSE',\n",
    "                stopping_rounds=20,\n",
    "                stopping_tolerance=0.01,\n",
    "                stopping_metric='RMSE',\n",
    "                max_models=100,\n",
    "                exclude_algos=['DeepLearning'],\n",
    "                nfolds=0)\n",
    "\n",
    "aml.train(x=features, y=target, training_frame=train, validation_frame=valid)\n",
    "\n",
    "# Get the best model\n",
    "best_model = aml.leader\n",
    "\n",
    "print(\"\\nBest model:\", best_model)\n",
    "\n",
    "# Save the model\n",
    "model_path = h2o.save_model(model=best_model, path=\"./\", force=True)\n",
    "print(f\"\\nModel saved to: {model_path}\")\n",
    "\n",
    "# Save the DataFrame to a pickle file for later use\n",
    "df.to_pickle('dax_data.pkl')\n",
    "with open('model_path.txt', 'w') as f:\n",
    "    f.write(model_path)\n",
    "\n",
    "print(\"DataFrame and model path saved.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "d3d6cc09-23ad-4e34-a962-4218b183030b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54321. connected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
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       "#h2o-table-26.h2o-container {\n",
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       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
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       "#h2o-table-26 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-26 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-26 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-26 .h2o-table th,\n",
       "#h2o-table-26 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-26 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-26\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption></caption>\n",
       "    <thead></thead>\n",
       "    <tbody><tr><td>H2O_cluster_uptime:</td>\n",
       "<td>42 mins 19 secs</td></tr>\n",
       "<tr><td>H2O_cluster_timezone:</td>\n",
       "<td>Etc/UTC</td></tr>\n",
       "<tr><td>H2O_data_parsing_timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O_cluster_version:</td>\n",
       "<td>3.46.0.4</td></tr>\n",
       "<tr><td>H2O_cluster_version_age:</td>\n",
       "<td>1 month and 15 days</td></tr>\n",
       "<tr><td>H2O_cluster_name:</td>\n",
       "<td>H2O_from_python_marius_2e9dqw</td></tr>\n",
       "<tr><td>H2O_cluster_total_nodes:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O_cluster_free_memory:</td>\n",
       "<td>2.498 Gb</td></tr>\n",
       "<tr><td>H2O_cluster_total_cores:</td>\n",
       "<td>6</td></tr>\n",
       "<tr><td>H2O_cluster_allowed_cores:</td>\n",
       "<td>6</td></tr>\n",
       "<tr><td>H2O_cluster_status:</td>\n",
       "<td>locked, healthy</td></tr>\n",
       "<tr><td>H2O_connection_url:</td>\n",
       "<td>http://localhost:54321</td></tr>\n",
       "<tr><td>H2O_connection_proxy:</td>\n",
       "<td>{\"http\": null, \"https\": null}</td></tr>\n",
       "<tr><td>H2O_internal_security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>Python_version:</td>\n",
       "<td>3.11.9 final</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n"
      ],
      "text/plain": [
       "--------------------------  -----------------------------\n",
       "H2O_cluster_uptime:         42 mins 19 secs\n",
       "H2O_cluster_timezone:       Etc/UTC\n",
       "H2O_data_parsing_timezone:  UTC\n",
       "H2O_cluster_version:        3.46.0.4\n",
       "H2O_cluster_version_age:    1 month and 15 days\n",
       "H2O_cluster_name:           H2O_from_python_marius_2e9dqw\n",
       "H2O_cluster_total_nodes:    1\n",
       "H2O_cluster_free_memory:    2.498 Gb\n",
       "H2O_cluster_total_cores:    6\n",
       "H2O_cluster_allowed_cores:  6\n",
       "H2O_cluster_status:         locked, healthy\n",
       "H2O_connection_url:         http://localhost:54321\n",
       "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
       "H2O_internal_security:      False\n",
       "Python_version:             3.11.9 final\n",
       "--------------------------  -----------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n",
      "AutoML progress: |\n",
      "20:00:35.757: Stopping tolerance set by the user is < 70% of the recommended default of 0.01989286700048564, so models may take a long time to converge or may not converge at all.\n",
      "\n",
      "███████████████████████████████████████████████████████████████| (done) 100%\n",
      "\n",
      "Model saved to: /home/marius/finance_code/GBM_grid_1_AutoML_2_20240825_200035_model_10\n",
      "DataFrame and model path saved.\n"
     ]
    }
   ],
   "source": [
    "import h2o\n",
    "from h2o.automl import H2OAutoML\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime, timedelta\n",
    "import pickle\n",
    "\n",
    "# Initialize H2O\n",
    "h2o.init()\n",
    "\n",
    "# Assuming your dataframe is named combined_df\n",
    "df = combined_df.copy()\n",
    "\n",
    "# Convert 'index' to datetime if it's not already\n",
    "df['index'] = pd.to_datetime(df['index'])\n",
    "df.set_index('index', inplace=True)\n",
    "\n",
    "# Filter for the last 10 years of data\n",
    "ten_years_ago = datetime.now() - timedelta(days=365*10)\n",
    "df = df[df.index >= ten_years_ago]\n",
    "\n",
    "# Function to calculate returns for CPIDE\n",
    "def calculate_cpide_returns(series, periods):\n",
    "    returns = series.pct_change(periods)\n",
    "    mask = returns != 0\n",
    "    idx = np.arange(len(returns))\n",
    "    idx_valid = idx[mask]\n",
    "    returns = pd.Series(np.interp(idx, idx_valid, returns[mask]), index=returns.index)\n",
    "    return returns\n",
    "\n",
    "# Feature Engineering\n",
    "for col in ['close', 'REXPDE~TUSD', 'LorentzianIndicator', 'volume']:\n",
    "    df[f'{col}_lag_1'] = df[col].shift(1)\n",
    "    df[f'{col}_lag_7'] = df[col].shift(7)\n",
    "    df[f'{col}_lag_30'] = df[col].shift(30)\n",
    "    df[f'{col}_return_1'] = df[col].pct_change(1)\n",
    "    df[f'{col}_return_7'] = df[col].pct_change(7)\n",
    "    df[f'{col}_return_30'] = df[col].pct_change(30)\n",
    "\n",
    "# Special handling for CPIDE\n",
    "df['CPIDE_lag_1'] = df['CPIDE'].shift(1)\n",
    "df['CPIDE_lag_7'] = df['CPIDE'].shift(7)\n",
    "df['CPIDE_lag_30'] = df['CPIDE'].shift(30)\n",
    "df['CPIDE_return_1'] = calculate_cpide_returns(df['CPIDE'], 1)\n",
    "df['CPIDE_return_7'] = calculate_cpide_returns(df['CPIDE'], 7)\n",
    "df['CPIDE_return_30'] = calculate_cpide_returns(df['CPIDE'], 30)\n",
    "\n",
    "# Remove rows with NaN values\n",
    "df = df.dropna()\n",
    "\n",
    "# Set date as a separate column for H2O\n",
    "df['date'] = df.index\n",
    "\n",
    "# Ensure data types\n",
    "df['volume_return_7'] = df['volume_return_7'].astype('float')\n",
    "\n",
    "# Select all features except 'close' and 'date'\n",
    "features = [col for col in df.columns if col not in ['close', 'date']]\n",
    "target = 'close'\n",
    "\n",
    "# Convert to H2OFrame\n",
    "h2o_df = h2o.H2OFrame(df)\n",
    "\n",
    "# Split the data into train, validation, and test sets\n",
    "train, valid, test = h2o_df.split_frame(ratios=[0.7, 0.15], seed=1234)\n",
    "\n",
    "# Run AutoML\n",
    "aml = H2OAutoML(max_runtime_secs=3600,  # 1 hour\n",
    "                seed=1234,\n",
    "                sort_metric='RMSE',\n",
    "                stopping_rounds=20,\n",
    "                stopping_tolerance=0.01,\n",
    "                stopping_metric='RMSE',\n",
    "                max_models=100,\n",
    "                exclude_algos=['DeepLearning'],\n",
    "                nfolds=0)\n",
    "\n",
    "aml.train(x=features, y=target, training_frame=train, validation_frame=valid)\n",
    "\n",
    "# Get the best model\n",
    "best_model = aml.leader\n",
    "\n",
    "# Save the model\n",
    "model_path = h2o.save_model(model=best_model, path=\"./\", force=True)\n",
    "print(f\"\\nModel saved to: {model_path}\")\n",
    "\n",
    "# Save the DataFrame to a pickle file for later use\n",
    "df.to_pickle('dax_data.pkl')\n",
    "with open('model_path.txt', 'w') as f:\n",
    "    f.write(model_path)\n",
    "\n",
    "print(\"DataFrame and model path saved.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "cfad0910-1eb6-4acf-ab79-a18144858114",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54321. connected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "\n",
       "#h2o-table-27.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-27 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-27 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-27 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-27 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-27 .h2o-table th,\n",
       "#h2o-table-27 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-27 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-27\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption></caption>\n",
       "    <thead></thead>\n",
       "    <tbody><tr><td>H2O_cluster_uptime:</td>\n",
       "<td>54 mins 08 secs</td></tr>\n",
       "<tr><td>H2O_cluster_timezone:</td>\n",
       "<td>Etc/UTC</td></tr>\n",
       "<tr><td>H2O_data_parsing_timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O_cluster_version:</td>\n",
       "<td>3.46.0.4</td></tr>\n",
       "<tr><td>H2O_cluster_version_age:</td>\n",
       "<td>1 month and 16 days</td></tr>\n",
       "<tr><td>H2O_cluster_name:</td>\n",
       "<td>H2O_from_python_marius_2e9dqw</td></tr>\n",
       "<tr><td>H2O_cluster_total_nodes:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O_cluster_free_memory:</td>\n",
       "<td>2.150 Gb</td></tr>\n",
       "<tr><td>H2O_cluster_total_cores:</td>\n",
       "<td>6</td></tr>\n",
       "<tr><td>H2O_cluster_allowed_cores:</td>\n",
       "<td>6</td></tr>\n",
       "<tr><td>H2O_cluster_status:</td>\n",
       "<td>locked, healthy</td></tr>\n",
       "<tr><td>H2O_connection_url:</td>\n",
       "<td>http://localhost:54321</td></tr>\n",
       "<tr><td>H2O_connection_proxy:</td>\n",
       "<td>{\"http\": null, \"https\": null}</td></tr>\n",
       "<tr><td>H2O_internal_security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>Python_version:</td>\n",
       "<td>3.11.9 final</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n"
      ],
      "text/plain": [
       "--------------------------  -----------------------------\n",
       "H2O_cluster_uptime:         54 mins 08 secs\n",
       "H2O_cluster_timezone:       Etc/UTC\n",
       "H2O_data_parsing_timezone:  UTC\n",
       "H2O_cluster_version:        3.46.0.4\n",
       "H2O_cluster_version_age:    1 month and 16 days\n",
       "H2O_cluster_name:           H2O_from_python_marius_2e9dqw\n",
       "H2O_cluster_total_nodes:    1\n",
       "H2O_cluster_free_memory:    2.150 Gb\n",
       "H2O_cluster_total_cores:    6\n",
       "H2O_cluster_allowed_cores:  6\n",
       "H2O_cluster_status:         locked, healthy\n",
       "H2O_connection_url:         http://localhost:54321\n",
       "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
       "H2O_internal_security:      False\n",
       "Python_version:             3.11.9 final\n",
       "--------------------------  -----------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n"
     ]
    }
   ],
   "source": [
    "import h2o\n",
    "import pandas as pd\n",
    "import pickle\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "import numpy as np\n",
    "\n",
    "# Initialize H2O\n",
    "h2o.init()\n",
    "\n",
    "# Load the trained model and data\n",
    "df = pd.read_pickle('dax_data.pkl')\n",
    "model_path = open('model_path.txt', 'r').read().strip()\n",
    "best_model = h2o.load_model(model_path)\n",
    "\n",
    "# Convert the DataFrame to an H2OFrame\n",
    "h2o_df = h2o.H2OFrame(df)\n",
    "\n",
    "# Split the data back into train, validation, and test sets\n",
    "train, valid, test = h2o_df.split_frame(ratios=[0.7, 0.15], seed=1234)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "7aba7967-c3d7-4b1b-97dc-88301eaebf2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/marius/miniconda3/envs/openbb/lib/python3.11/site-packages/h2o/frame.py:1981: H2ODependencyWarning:\n",
      "\n",
      "Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using multi-thread, install polars and pyarrow and use it as pandas_df = h2o_df.as_data_frame(use_multi_thread=True)\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Evaluation Results on Validation Set:\n",
      "Root Mean Squared Error (RMSE): 6.293713231950466\n",
      "Mean Absolute Error (MAE): 2.7922842229304963\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/marius/miniconda3/envs/openbb/lib/python3.11/site-packages/h2o/frame.py:1981: H2ODependencyWarning:\n",
      "\n",
      "Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using multi-thread, install polars and pyarrow and use it as pandas_df = h2o_df.as_data_frame(use_multi_thread=True)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Generate predictions on the validation set\n",
    "predictions = best_model.predict(valid)\n",
    "\n",
    "# Convert H2OFrame to Pandas DataFrame\n",
    "y_true = valid['close'].as_data_frame().values.flatten()  # Actual values\n",
    "y_pred = predictions.as_data_frame().values.flatten()      # Predicted values\n",
    "\n",
    "# Calculate evaluation metrics\n",
    "mse = mean_squared_error(y_true, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "mae = mean_absolute_error(y_true, y_pred)\n",
    "\n",
    "# Print the evaluation metrics\n",
    "print(f\"\\nModel Evaluation Results on Validation Set:\")\n",
    "print(f\"Root Mean Squared Error (RMSE): {rmse}\")\n",
    "print(f\"Mean Absolute Error (MAE): {mae}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "446990f5-8a4d-4f65-a3f3-7e2433ce4c0e",
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   "outputs": [
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s01Xbx8zAin7XavyRFGBnvv9Mrxs8ozBUPrYI0bFh0RjdyPrC1TvkKLAQHkKAKLkk1nvPmdAnXd6JGQpChAl/2vScKAVz2ntDn7WqisE//rmCboNnyo8cS6cN1rUjPo6IvBBtCx9EEWKhYnlfnTBJTk7BkIlLceTXc9i7YQYqlEu9N/MjhvWNpXL/HNg0SzdyKMTWV/uOyBFeN5eiWcZHfFwSH5mEOJ84tJv8mCOu7Td2oXzOKB+iFDEs4i2mir/z5iuyrrLpVdoPBdklQ89hc+RHoa/XTkXlCqVlVWUpiLi3f9qxUD5f8vOcyXhP5eZ+FL4EBoXq4if8E2K0Rdex8uOHkuvZTZPO2L7YXbrZ/6Yup42RMjU/LTvl+SNycNygLnKjNpHbvUfOl9yUeGTHWIw+r9iwR1YRs1FefK4yKvmVljMo0j630z6TxD0o1mq7uhSR1ykfI8R6aLEumtOk9XrEsRIJkAAJkAAJmAQBixHDygtJ2h2ZlQ2LBnR/D/27vyeB//3fdXTuP02OXE4f81GmQcjuZSY3YlgxLl4QhZgJDX8oR0anL96cbsQ6N2I44+isaEMZ7ZowpKucTqqIYSEwt6yYqFv3q2/GKWJYTEd2KVpEHgG094dfpHgSU2m/WDJOjgIpL6PKCFVa+/3GLpKjOQe3zYOXx9M120IQiN2+B/Rojf4ftsK2b36SPJQpn4qNzNYMZ3xxzm8sDeFjZozFRwgxS0G8RCsjmTnFIqMwVETm4qkD5XR+pSjCS7EtRIAQUWKq87xP+8GlaOFnmhLTOzv2nSJF3SdDuqb7XYwWiw8e5w6t0X1EycxXsWNuw7ZDZD7/dmCl3GhNFGUE/PS3q+RsCqUIAXzzjlguIKZKR8kp9WIWR9pR6PyIYX1jqexynXZmRk6xUH4XeStmdmQc3VdErjIFXolJ2g9ewoYYiXyxeT+UcHd5ZrQ3Mx/ERyMx40NMkxbTpUURzMQMGHHviHsobcnLcybjPZWb+1FpW0wLv3YrEPdDIvAgMkoujRDPipP7V8j8y40YFrkn8kBMOxfPn7RFyQ8l35Tnzzcbpssj2JSi9GHh5AFyZDm7Inxftm43Nu744ZlqYibFgB7vwfN/o9mijpjyL9bJv51mqUl0TCzqtRyg27CRYljfO4r1SIAESIAESKDgCViMGFZGUcQXe8//HeUiBJUYHRajjT9snStHUcQatbEzP8fkkd3RvkXDTCOglhgWL6diJFTZKThtY2l3is2NGE476vvjVwvliJM4lknY+HXvct1ohbLjr2hTjJbXrlZBjgoq076zSz1FDGesI8TT8N7tdaOSWb2MiuvEqG7Go0rS2hPrCsXRV8pa2oziRB8xnN9YGsLHzLgq+SQ+Tpz4JnX6Z04lozBUpsGnHS1TbDTvMkbOdBC73oqpvI3aDZUiVRQxklarWgW0bf66bqRL+aCTnQ+Ht8+HdzZThsW1yqi0Mu1XmcafUQSKaa9TFmzU+ZS23bQ7UudHDOsby6+/PY5P562XLogpyGIt6hv1a+ONes/luMFbo/bD5KhfxhgquaqMkmclhpX7IvHJkxyXKoi6ymyXtKPAyr2ZdkQ9P8+ZjGI4N/ej+CCy+sv9upHVjPmkPI9yI4aVe1pM5xdiNG1RfFNGyrN6/ij5nXZ2Qk73mxjlFvsuiCUF5y/dkP+dEEXcs8e+XiI//ik7eGdlS1mCQjGcE23+TgIkQAIkQAKmQ8AixLCYJiheVLMryujljv3H5It5duv21BLDysu9WH8pxF9ZX08Uc3OW0wjF5j/KsSm5EcOij2Kjrq/2HpEjtN4l3fFWx5FySmraabFiuqCY1in+X1nTJq4V55aK80uzK8oL96zxveBR3A0l3F3lBwUx/TltyU4Mi2nbQrgLwZtZKVvKU25Opoj2jLu56iOG8xtLQ/iYFVdFsCqjZTk9AjIKQ+Xs58zWtbbu+Ylco/jv0Q1S0EVFx+DzL/fju59OySm0Slk0ZaDcYE2ZMSE+joj15JkVsR5eTPPNroi8EptlNXi5FlbNHiY3PBJTTtMKXCW3ha2BPVujZpXy8PEsITegEjMC1BLD+sZS9Ef4JNbFpj3rWaxf/nLFJ9keGSbaEBvRZVxzL8SoGBlUpvCqJYaFr4MmLJHTybev+hRi86kXm/d9Zh+E/DxnMorhvNyPYnmG2AxLTIUXzzWxtl1swJUXMazkZmbPZzEqK0ZnlWd5Vs8fZcp8bsRwxjx/GBktPzKKHN+8bILc8Eu5B8UovehnxiJyXNw3FMM5Pd34OwmQAAmQAAmYDgGLEMPKzqtCEFb2S11bpxQxfU+cO6xMwz31x3/4aMRc9OvWSr6cZ1aUl5nM1vaJHZ3FTrYZRy4ybqAldlp9tdUguUnQV6snpWtGbCiUHzGsTHMVIyfi6KPFa3alW/+csU9ClIodaIX4ECOGOQmyrDbQymg3OzGsvKBntttwWjuKDXEetDgXWin6iOH8xtIQPmZ1aysiQ+ScyL3MiphGm5ScLNfWZhTDy9bvxmeb9skPIMoO58KGGJ17uUX/LKfeCo4HfvxNTu9U1tMr03ozm2qb20eTIvLFaGmnflNlfv2yd5nuw4nYjE7svJvxOC5lun92Ylj5yCVEuxBHaUvGDbT0jWVaG2IUXay/FzkopucqZzxnxUBZb/znoTXpPgxdvn4XbT6aKJcoiKUKaophRdiJj1jV/MvKZ8+ofh3R/f3UHfLz+5zJKIZzcz8qU+IzPk+UDb4yimFlXXtavhnbVz6eZLaLtjgpQJwYIDaqEut58yuGxb0jPh4pu4BnjLu438R9N2lEd3Ro2VDuIr5i4zdYt2C03B8hq5Ldfz9ye3+xPgmQAAmQAAmQgGEJWIQYzniUS1pkyqiN+GovzuQUX/xfbzNEjnr9uGNBujWVYhSgmKuz3HG5WsPume7kquxeLDaGEpvjiCJEzLZvjmDGks263aQVcSzW2H65fILOJTGdVQiIvE6TVgwpL6JiGp/oizgGSXmpE4JEbNQlxHzaouy2nXY35szSSw0xvHTd11i9eX+mRwndCw6TG+2IKdvKeueMOwYrG+Zkt5u0IpjzGktD+JjV7apsTiV+z0x0CSaTF2zEhx3ezvScYYVHxhkA4mirYZOW63aKFnEXR9EoG8aJ9oToEyOX4qOImEqtCCjBTUy7FvmuFLG299jJc2j06vN6PXnECPSStV/LmQ9iFoJYeiCWIChFiDfxAWndwtF4+flUASFmLYjRwy27f8x2ZFgRFWI684FNs3X5Laazduw3Nd0abH1jKabQimOdxLE/ShF+zFz6pVyjKtaqZlUWfLYD67d/J2c7iP4qRVwrbCijmWqK4ZjYeNRt1kdO1xUfM8SxPmnPTs/vcyajGM3N/aiMxp86sFK3dELMSugzeoH8yKCIYXH0UtuPP9V9LEjLN2P7yj0tclI805TZKMru2mmnjOdXDIs8Eh8Ixw7qLPdCSFvELuM9h8+VH0nEqLzYUEusFxfrxkXdDYvHpsshcW+JY6DEbviiZPXfD71uKlYiARIgARIgARIwGgGzF8OKyBAbBgmRkVlRRKBYTyxeVsTxPOIlTEz97dzmTbn5j5gyKdaJKVPwlHW4YvS1qn8Z3AsOlzv2ChH3ZocRUlh0bdcELs6FcfzUP7qjhZRNdMSLvJi6LYTpO41fRtVKZXH1RoAcpRYlv2JY2SRG2BLTnsXIkVKUF2RxBNPr9Z6DZwk3XLx6R04NFaOvYnQxq9EQYUMNMSwEgdgVVvRfTKMV6zIFE2U9nrLRmdg9VhyfI+o1a/QSKvmVwl8XrsmXflFyOlopP7E0hI/Z3blCSAlBJYpYX1q7WkUkJD6RUzEPHz8r/64I5Ywjw+KDS+cB02WeiZHS11+uJddki1kBoijTp5Wp46KOOCrG0d4eP5/6WwrStCPB4sxjsQZTCGKRO+JIIXEv/fzbX3LKtRDN+hTleB2lrjjyS+SdUsR0fjGtX9xrLd+qBysrQKwhVo4uy25kWNhQdoRvVL82nqteEX//d01uIiVK2g3J9I2lEHBCWL7X7FV53JE4F1sclyN2PxZT9TOb/qr0JTwiCg1ap+6cLTbl8yvrjVN/XsSOfUflTu6710+To/pqimHRljLSKv63uH/FLAql5Pc5k9luzvrej8pzQohDcX/LWQiHT+rWhitiWDwrX28zVD4zxcwI8by1sbGR5zpndrSS8mFDiH9RR/Rx5RffyCn/aY/yUkMMi48qoog+CMErdqS+cOUWTp75V/Yj46wEZdq6iLf4IFK4kBMuXbstz1KvXaOibmf1rP77oc89xTokQAIkQAIkQALGI2D2Ylg5E3PmuF7yaJTMijLVUBl9FCNTO/Ydk9PsxAuaUoRAEUemiDWyQlCKKXFiWp4oaY/FEcJl6KfLddeJF/0P2rwpzycWxxYN691e/vbn+avyCBllMyPlJXrDVz/A16u4bs2wMt1X2Q1an/Arm+uIuse+Xix9VooY/Z66SGzcleq7UsSHAHFeaylvj2ybUEbzxE7QylFHmV0ghJjgn3Yzn7T1xC7KC1Z/JUd/0xYhlob2aidHukQR00zF8TTK+lbBuneXFlLopd31W6xHFetSd6+bJkWzKPmNpdo+5hQ7MdI0d+X2dOcyi2vEx5F2LRrK/oqzSjM7Z1jEfMrCjeniKuIzf1J/1KhcTjYtzrKdvWyrHNFKW8SI7bjBH+hG2oS4Fi/w81ZtT7euWLB/v9UbOa4rT2v7/T5TZLtCZB79enG6s11FfCbN26D7CCSuE3EXRwUJEbl85hC8Ua+2NJdZn8V9KM6CVjZjE/4N79MeC1fvlGI27RIEfWIpPkaI85HT3vdi5Fnc98qoXnYxFCJ+zPTPdGJe1BUzMKaP+VgnpB/HxMm1vRk3EhN1xawQMT1X3Fv6FnH+c98xC2T1zDb+y89zJrN7St/7UfAW4lA5P1n4Jz78CVEsPi6e3LdCfiwURcxsEM8KJS9FPbErc1b3tDjmTjnySFyvHFOV9nzmrJ4/yvM+41nwGXmLjxZiNoNYRpB2XwWlPSHchRhPu1eC2H16w1ffY/2275/JIbHr97tNUv8blN1/P/SNO+uRAAmQAAmQAAkYnoDZi+H8IBKCIDQ8EnHx8XKjKEcH+2fMiWl/UY8eo2RxN9jZ2ep+F9MX794LkaMcXiWfHh2U0YAY+RRTo0URItTJ8dk28tOH7K4VQkQITPFyLqYXZnbMjqHaTmtX+CHOLxVTcIUfmXEWvwmeycnJKO1TUu7empuSn1iKdozhY0YmYmp0QsITlPj/9Y+5iY14iRes3N1cnjkLVWlD7I4bHBIu/+np4Z5t3onZDiFhEfKMXXc35xx3Vc5NXJS6QjiJo8XciznrjqrR146IzZ3AEFiJe8jHI53YzsxGTrEUuSJGecX/CwEvOGY3UyKzNoTgE/3x8SqhO1ZK3/4Yop7azxl970elnvi44F2yuE78ZtVHkQeCv/h4lxNzMSIsZh7Y2trKj3K5fSbkhrMQuSKe4lkpjlJSzhDOyobog8gB8YFKPNOK/u9osYz1s/rvR258Y10SIAESIAESIAHDEdC0GDYcVlomARIgARIgARIgARIgARIgARIwZQIUw6YcHfpGAiRAAiRAAiRAAiRAAiRAAiRgEAIUwwbBSqMkQAIkQAIkQAIkQAIkQAIkQAKmTIBi2JSjQ99IgARIgARIgARIgARIgARIgAQMQoBi2CBYaZQESIAESIAESIAESIAESIAESMCUCVAMm3J06BsJkAAJkAAJkAAJkAAJkAAJkIBBCFAMGwQrjZIACZAACZAACZAACZAACZAACZgyAYphU44OfSMBEiABEiABEiABEiABEiABEjAIAYphg2ClURIgARIgARIgARIgARIgARIgAVMmQDFsytGhbyRAAiRAAiRAAiRAAiRAAiRAAgYhQDFsEKw0SgIkQAIkQAIkQAIkQAIkQAIkYMoEKIZNOTr0jQRIgARIgARIgARIgARIgARIwCAEKIbHIFsUAAAgAElEQVQNgpVGSYAESIAESIAESIAESIAESIAETJkAxbApR4e+kQAJkAAJkAAJkAAJkAAJkAAJGIQAxbBBsNIoCZAACZAACZAACZAACZAACZCAKROgGDbl6NA3EiABEiABEiABEiABEiABEiABgxCgGDYIVholARIgARIgARIgARIgARIgARIwZQIUw6YcHfpGAiRAAiRAAiRAAiRAAiRAAiRgEAIUwwbBSqMkQAIkQAIkQAIkQAIkQAIkQAKmTIBi2JSjQ99IgARIgARIgARIgARIgARIgAQMQoBi2CBYaZQESIAESIAESIAESIAESIAESMCUCVAMm3J06BsJkAAJkAAJkAAJkAAJkAAJkIBBCFAMGwQrjZIACZAACZAACZAACZAACZAACZgyAYphU44OfSMBEiABEiABEiABEiABEiABEjAIAYphg2ClURIgARIgARIgARIgARIgARIgAVMmQDFsytGhbyRAAiRAAiRAAiRAAiRAAiRAAgYhQDFsEKw0SgIkQAIkQAIkQAIkQAIkQAIkYMoEKIZNOTr0jQRIgARIgARIgARIgARIgARIwCAEKIYNgpVGSYAESIAESIAESIAESIAESIAETJkAxbApR4e+kQAJkAAJkAAJkAAJkAAJkAAJGIQAxbBBsNIoCZAACZAACZAACZAACZAACZCAKROgGDbl6NA3EiABEiABEiABEiABEiABEiABgxCgGDYIVholARIgARIgARIgARIgARIgARIwZQIUw6YcHfpGAiRAAiRAAiRAAiRAAiRAAiRgEAIUwwbBSqMkQAIkQAIkQAIkQAIkQAIkQAKmTIBi2JSjQ99IgARIgARIgARIgARIgARIgAQMQoBi2CBYaZQESIAESIAESIAESIAESIAESMCUCVAMm3J06BsJkAAJkAAJkAAJkAAJkAAJkIBBCFAMGwQrjZIACZAACZAACZAACZAACZAACZgyAYphU44OfSMBEiABEiABEiABEiABEiABEjAIAYphg2ClURIgARIgARIgARIgARIgARIgAVMmQDFsytGhbyRAAiRAAiRAAiRAAiRAAiRAAgYhQDFsEKw0SgIkQAIkQAIkQAIkQAIkQAIkYMoEKIZNOTr0jQRIgARIgARIgARIgARIgARIwCAEKIYNgpVGSYAESIAESIAESIAESIAESIAETJkAxbApR4e+kQAJkAAJkAAJkAAJkAAJkAAJGIQAxbBBsNIoCZAACZAACZAACZAACZAACZCAKROgGDbl6NA3EiABEiABEiABEiABEiABEiABgxCgGDYIVholARIgARIgARIgARIgARIgARIwZQIUwypE5154rApWaELLBFyL2CMhMQkx8UlaxsC+q0DA3dkB0bGJiE9MVsEaTWiZgIerIx48iseTpBQtY2DfVSDgVcwJwQ9iwUxSAaaGTVhZAZ5uTgh6oO57t7e7k4apsusUwyrkAMWwChA1boJiWOMJoGL3KYZVhKlxUxTDGk8AFbtPMawiTA2bohjWcPAN2HWKYRXgUgyrAFHjJiiGNZ4AKnafYlhFmBo3RTGs8QRQsfsUwyrC1LApimENB9+AXacYVgEuxbAKEDVugmJY4wmgYvcphlWEqXFTFMMaTwAVu08xrCJMDZuiGNZw8A3YdYphFeBSDKsAUeMmKIY1ngAqdp9iWEWYGjdFMazxBFCx+xTDKsLUsCmKYQ0H34BdpxhWAS7FsAoQNW6CYljjCaBi9ymGVYSpcVMUwxpPABW7TzGsIkwNm6IY1nDwDdh1imEV4FIMqwBR4yYohjWeACp2n2JYRZgaN0UxrPEEULH7FMMqwtSwKYphDQffgF2nGFYBLsWwChA1boJiWOMJoGL3KYZVhKlxUxTDGk8AFbtPMawiTA2bykwM/7v7P1RvUzUdlahHVnAuqv9BXjxaScNJBYBiWIX4UwyrAFHjJiiGNZ4AKnafYlhFmBo3RTGs8QRQsfsUwyrC1LCpjGI4OiACHjM64HrRlxH3ZmfERMQh7tJlFHl4A14TRsK5uL1etCiG9cJksZUohlUILcWwChA1boJiWOMJoGL3KYZVhKlxUxTDGk8AFbtPMawiTA2byiiGQ09fRpmNAzMlcr72IPj1flf3W1i4NcKvBOPRdwdhV8getca9r/uNYtg4SXXw2BnUqVUJ7m7OxmlQz1YohvUElV01imEVIGrcBMWwxhNAxe5TDKsIU+OmKIY1ngAqdp9iWEWYGjaVUQwHffsb/A58KonEWzvhUdEysLK2gnvERUTZuMN2+XZER1vh8uKdcCvpAJtHYahydausf63DQni/UUP+by2I4aSkZDRsOwQPHj7CTzsXwrNEsRwz6eff/sb5izcwsGfrHOvqU6Faw+7YtHQ8Xqjpr091o9WhGFYBNcWwChA1boJiWOMJoGL3KYZVhKlxUxTDGk8AFbtPMawiTA2byiiGA77YB/9Ty3C1TEv4jB2sIxM7tCfc4+/ihk8TOIbdgnf8FVzzaAzrJ3Eo/+BXWS/K1h22y7ZrRgz/fu4SegybjWKuRfFR53fQvcPbOWbSlt0/4oejv2PzsvE51tWnAsWwPpTMtA7FsJkGzoTcphg2oWCYuSsUw2YeQBNyn2LYhIJh5q5QDJt5AAvI/cDtB4EHobJ1p/r14P5ceXi6OSHoQaz8W8Di9fC/vA2Xa/REqf6ddF4GfnUIFY/NS+d1mH0p+e/iCXfx2NoZhZOjEPzxYji/UE0TI8OT5m9AfEIiyviWxKFjZ7Bn/fSnHw/iErBy4zc49PMZxMTGoe5zldG1bRMMnrhUjiRXr1RO1v1i6TjMXrYVLz1fBc0avST/dvTkOSmY50zoI/89ZsZqnDzzr7zOr4w3BvRojaYN68rfKIYL6EYyRrMUw8agbNltUAxbdnyN2TuKYWPStuy2KIYtO77G7B3FsDFpW0ZbT2LjUWjEe7BNeSI7dLVKF/gO+TCdGL4/dQ7KBf2Iyw1Go1Snt9J1PGDFZiA5dUfpche/gl1Kgu73C6XbodqdXbj7Qhe4f/yh6mL40NFkxMUbPw5N37CGg8Oz7cbGJaBB68FYOLk/fL1KoEW3cVIM+5f3lZUnzl2PX8+cx6CebaRY/vrb42jV9FUcPn4Gp/+8iInDusl6z9fwR/ehs6UQ7ty6sfzbnu9PYNPOgzpxvWX3YVQo5wt3V2cc++0vLPp8J07uWwEX58IUw8ZPCeO1SDFsPNaW2hLFsKVG1vj9ohg2PnNLbZFi2FIja/x+UQwbn7m5txi452dUPPR09PJ2iVfhMW1SOjEcMWY0fKLO4XrbufB6s3aWXY4aOQiejy/J3yMcvHGn4VDUOjgaoa5VUXjWEtXF8IiJiYiMMn4EFkyzg0sme1MdPn4W42etxcl9y2FnZ4t2vSbh1RdrYGivdhBCuc7bvTF9zEdo3ey1dE5nNk2666CZ2YphsTb58vU7uHTtDkLCHmLZ+t34avUkObrMkWHj54TRWqQYNhpqi22IYthiQ2v0jlEMGx25xTZIMWyxoTV6xyiGjY7c7BsMmjwLfveP4KbHaygXcgIP7H3htHRDOjEcN6Q7iiUE4u7A9XCvljoNOrMSNHsJ/G4fkD/dLf4K4ntNRPlZLWGFJMQt3gcv75w3k8oNUFMbGRbTnc+dv4o3G9SR3Tj95394FB2DY18vwZ3A+3Kk+MCmWShX2itfYvhxTBz6jlkohXCjV2vDy8Mda7YcwLaVE1Gzqh/FcG6SyNzqUgybW8RMz1+KYdOLibl6RDFsrpEzPb8phk0vJubqEcWwuUauYPxOfAI4DG4Fx5QYBA1YB68VH0lHYj87nE4MO/Z7G9ZIQvis/XBydczS2XtbDqDCL0vk79cqvQ/PQR8jdPhwlEs4j8e9p8LnzUYF01EjtPowMhr1Ww1Ex1aNdEcaPUlKwurN+7Fx8Vj4+5VCvZYDsGTaILz52gvpPNq65yd899MpfLl8gu7vPYfNQYOXa6H7+6kbcKWdJv3TiT/lOuOT+1fApWhh+bsYDaYYNkKgC7oJiuGCjoD5t08xbP4xNJUeUAybSiTM3w+KYfOPoan0gGLYVCJhHn4E/HAW/nvHIdSxLAovWoO4wd1RLDEQQf1WoXLDGnIDrScPH8F5XBvEWhdGyopvsu1Y+JnLKLU+9Tziaw1HwPv9t3F+2ia8dG8zwl5+HxWGDzIPMHnwcteBn7Fk7S45CmxjY62zINb+ipHgScM/RJeBM2BlZYUJQ7qgbClPfPvjKTxXzQ+Rjx6jz+iF+H7LHHmtq3MRrNq0D7+fuyjFc2BQKGYt24roxzFyzfCpP/7DRyPmYve6afLopm9/OoUZSzZTDOchbmZ3CcWw2YXM5BymGDa5kJitQxTDZhs6k3OcYtjkQmK2DlEMm23oCsTxgGmL4X/vW1yp3g2+A7oi5JPJKBv+K268NRa1P24hxXDUhVvwXN4LIfZlUWTJmmz9TIl/gkJDm8MKKbjbYyncX6yCf9cdxYtnZyK0VD1UXDC3QPppjEbFGt/a1StgeJ8O6ZoTInnOim34de8yBIdGYPysNTj371VZR2yytWb+KHh7umPg+MU4cfq8/PvZHz5HcEg4hk9egSs3AlDIyREv1KyIsAdR2LVmCpKTU+RvYo2yKI3q18aRX89h+6pPUaNKeTlKLI5pEhtxmVLhOcMqRINiWAWIGjdBMazxBFCx+xTDKsLUuCmKYY0ngIrdpxhWEaYGTCUPaIciyZEIGrQeLlVL4e6Sjah0aQuuV+mM5yf3l2I4/NhZlPpqHG65vAiP2TNypBJ26iJSEhLh8UplpNjZ49qRG6i5sw8eFi6Nshu25ni9FipEP45FQuITeRZx2iJGiO3t7ODkaK/7c3DoAxQv5gJbG5tn0IQ9iJQjyW4u6e2YKkOKYRUiQzGsAkSNm6AY1ngCqNh9imEVYWrcFMWwxhNAxe5TDKsI08JNBRy9AP8dQ/HA3geOSzbK3gbsOgr/n2bKza+qr5gnxXDwV9+j/LGFuOLdDL4Th+eaSkhAHMrOaCmvc93xS66v5wWWQ4BiWIVYUgyrAFHjJiiGNZ4AKnafYlhFmBo3RTGs8QRQsfsUwyrCtHBTt+Z8jqq3duJK1a7wHZR6vm34P7dQalUvRNh7IX7SDjgXi0PQ6i3w+2sj/qvUFWWHptbLbUkY0AmuyWEUw7kFZ2H1KYZVCCjFsAoQNW6CYljjCaBi9ymGVYSpcVMUwxpPABW7TzGsIkwLN2XXvwXsUuIR1H8tXGqU0fW2UL+3dP/7L6928Hr8H0pG/YeLr4xAmW6pOxvntoSNGo3S0ecohnMLzsLqUwyrEFCKYRUgatwExbDGE0DF7lMMqwhT46YohjWeACp2n2JYRZgWbCrwq8OoeGwuwhxKo9Didel6evO3QFj/ehB+N3bDPiVe/vbQujhufrAYleqVzBOVgBlL4R+wn2I4T/Qs5yKKYRViSTGsAkSNm6AY1ngCqNh9imEVYWrcFMWwxhNAxe5TDKsI04JNRQ/rB4+4a7j6xij4dGjyTE+trADHq5eRsGg0YhzcEdhpLsq+UBzWT08MyhWdgPXfwP/MCorhXFGzvMoUwyrElGJYBYgaN0ExrPEEULH7FMMqwtS4KYphjSeAit2nGFYRpoWaCjt5AaU3D0WsdVGkrNidaS+FGPZ0c8L9v88jxc0DKUWc80Uj5MfTKPv1JxTD+aJo/hdTDKsQQ4phFSBq3ATFsMYTQMXuUwyrCFPjpiiGNZ4AKnafYlhFmBZqKnDtLlT8YzWulm8Ln1F9sxXDYjdpNUr8nXtwm/UhxbAaMM3YBsWwCsGjGFYBosZNUAxrPAFU7D7FsIowNW6KYljjCaBi9ymGVYRpoaaCZi6C393vcPXVYfD5oLlRxLBoxPa3g/Bo8Z6FUmW39CFAMawPpRzqUAyrAFHjJiiGNZ4AKnafYlhFmBo3RTGs8QRQsfsUwyrCtFBTD0ePgPejf3Cr03x4NKhlNDEsGvJ2d7JQqobvVtiDSPx5/iqavF5HNhYTGw97e1vY2tgYrPGMbea3IYrh/BIEQDGsAkSNm6AY1ngCqNh9imEVYWrcFMWwxhNAxe5TDKsI00JNJQ3siKJJ4QiZsA1FfItTDKsY50bth+F+aIS0WMy1KN5sUAej+r2PQk6O+W7l93OX0GPYbFw4thGxcQmo83ZvLJsxBI3q187SdkBQKBau3oG5E/vmSTSnbTPfHQBAMawCRYphFSBq3ATFsMYTQMXuUwyrCFPjpiiGNZ4AKnafYlhFmBZoKjk+AUWGvoMnsEPCqu+y7KGygZZaa4aVhix9ZFiI4W7tmqLxa8/jTmAIJs5dh3p1qmP6mI/ynU1phWlycgouXbsNX28POBcplKXti1dvo12vSfjr8FrY2dnm2geK4VwjM/wFFMOGZ2zpLVAMW3qEjdc/imHjsbb0liiGLT3CxusfxbDxWJtjS+F/XkWpNf0R4lgORRZ9TjGschCFGB7aqx3ebVJfWv5i50F8seMHHNm5CLOXb0Vpn5KIfBSNk2cuoNN7jdGs0YvYse+orPcoOgZtmjdAp9aN4VmiGFJSUrBp1yF5vRht9i/viys3AuTIsChdBs7AhCFdUKViGVn362+PY8vuwwgICkPlCqUxvE97zFjyJYQgFnVsrK0xfkgX+JcvhSVrd+HbH3+Dm0tRvN/qDbRp/jqcHO1zbDO/uDgynF+CnCatAkGaoBhmDqhFgGJYLZK0QzHMHFCLAMWwWiQt0869b46jwsFpuFXiVXhMnWT2Yjj+wHakxMYYPViOLTsCjs+OyGYUw9MWbcLf/13HrjVT0G/sIhw/9TeaNnwRtar5oUbl8ggKCcfk+RsxZWQPlCvtiVWb9sKlaBFMG90T3/10GqOmrcKA7u+hwSu1cPjns1i79VudGK7WsDs2LR2PF2r6Y/+hkxg783MM/qgtXnmhKk6evQDnooWlwP1kzjqsnT8KtrY28PcrhUWrd0qBPKxPe1hZWWHKgo3o160VWjapl2Ob+QVNMZxfghTDKhCkCYph5oBaBCiG1SJJOxTDzAG1CFAMq0XSMu0ErNwC//MbccX/ffgO+9jsxXBkn/eQEhFm9GC5rP4GVm7PrrcWYrhOzUpyJPb8pZs4eOx3LJs+GI1efV6K4Up+peTIsVLE6G4Z35Lo0vYt+SchUmct24rfDqxA75Hz4VHCDbPH95a/ZZyynFYMCzu+3iV0dRX7GadJK2uNJwzpitrVK8hqu787jvthEVg6bTB6DpuTbZv5BU0xnF+CFMMqEKQJimHmgFoEKIbVIkk7FMPMAbUIUAyrRdIy7QRPnYfyQYeyPVZJ9Nxc1gyb4shwMVdnlC/tBW/P4nLn56r+ZWUyCTH8fI2K6PVBC11yvfbeILm5Vgl313QJt3jqQLTu+QmGfNwO7Vq8nqMYrtusL8YO7Iy27zRIZyejGL55Jwgtuo2TYt3RwV5X16O4KxZOHgDhT3Zt5veuoBjOL0GKYRUI0gTFMHNALQIUw2qRpB2KYeaAWgQohtUiaZl2ooYPhGfsZdz+YAFKvFozy06aixg2tShlnCad1r/MxLDY3KpV0/ro2q7JM10ZMH4xqlQog4E9W+cohoVwfun5qlIQpy2Xrt1B248/xZ+H1sDB3g6Rjx6jXssB2Pn5ZJ1IT1s/pzbzy5tiOL8EKYZVIEgTFMPMAbUIUAyrRZJ2KIaZA2oRoBhWi6Tl2bn/4x8o9/VY2bEHU3fDsURRimGVw5xbMfz5l/uxedchrJw1TIrTwOAw7DpwDMP7dMDWPT/JzbNmje+NEu4uWL5hDw4c/i3TNcMrNuzB9r1HMHNcL7xSpxrO/nUZ0TGxqF+3hjyCaf2iMahZxU9ukDVw/GIkPkmSxy0VL+aCy9fv4I9/ruDD9k1zbDO/uCiG80uQYlgFgjRBMcwcUIsAxbBaJGmHYpg5oBYBimG1SFqWneQnSYgb2QfF42/jcs2eKNWvU7Yd5Mhw3uKfkxgWm1193PkdnfGEhEQsWrMLm3Ye1P2t7nOVsXHxWIQ9iESvkfPkDtKi1K9bHb+e+TedGN68bDyer+Evzx2evngTvvnhF1lXTL2e80kfeQbx8vV75MZcooiNtMqX8cbkBRvlZl5K6dO1pdx8K6c280bl6VUUw/klSDGsAkGaoBhmDqhFgGJYLZK0QzHMHFCLAMWwWiQty07Q96fgt28iQh3KovDiNTl2jmI4R0SqVniSlITwB1G6HaAV42IkNzjkAdxci6Zb45tV40JcP4x6DHc3Z9jYWOuqCbGckJgIl6KFdX+Li09ApKhbzBm2Nja6v+e2zdyAoBjODa0s6vKcYRUgatwExbDGE0DF7lMMqwhT46YohjWeACp2n2JYRZgWZCpw4RpUvLoDV6p/CN8BXXLsGcVwjohYIQ8EKIbzAC3jJRTDKkDUuAmKYY0ngIrdpxhWEabGTVEMazwBVOw+xbCKME3QVMSM6Yi3K4roV9vBvaov3FxTMvUy7sFjOBZ7Ogr4cNQweEf/i5vtZqNk4xdy7BnFcI6IWCEPBCiG8wCNYlgFaDSRjgDFMBNCLQIUw2qRpB2KYeaAWgQohtUiaXp2Hv51Fd6r+0vH4q2cEGBXCXYOVkgsWgJJbp5IKuaF2LhklD33JR541IT3xJGybkr8EzgObQEbJOHhrL2wdy2UY+cohnNExAp5IEAxnAdoFMMqQKMJimHmgEEIUAwbBKsmjVIMazLsBuk0xbBBsJqE0bDx41E64gweOpWCa+zdHH262/tzuNcuh7Azl1F6/UCEO5SC0+L1OV4nKlAM64WJlXJJgGI4l8Ayq85p0ipA1LgJjgxrPAFU7D7FsIowNW6KYljjCaBi9ymGVYRpQqZCT11CmS8GIcrGHVbzNsHGyR7W9wPw+M59xAWEIiH4PhB+H7bR4XCMf4gScTcQWKQ63OYtQsCGb+D/+wrc8G4Kz/+NFufUNYrhnAjx97wQoBjOC7UM11AMqwBR4yYohjWeACp2n2JYRZgaN0UxrPEEULH7FMMqwjQhU5ETRsPrwTlceWUofLs9PZonKxefDOwI56RwXG82CVZ/nkD5+0dwrf5QeHfJ+Vphk2LYhIJvQa5QDKsQTIphFSBq3ATFsMYTQMXuUwyrCFPjpiiGNZ4AKnafYlhFmCZiyvrKP3BcNAJR1sUQN2MLirja5uhZ0O4j8Ds8K129oAHr4FK9dI7XUgzrhYiV8kCAYjgP0DJeQjGsAkSNm6AY1ngCqNh9imEVYWrcFMWwxhNAxe5TDKsI00RMpcwcicJ3/8ZR34F4aUIrvb2KHt4PHrHXZP1r5VvDe1Tq5lv6FI4M60OJdXJLgGI4t8QyqU8xrAJEjZugGNZ4AqjYfYphFWFq3BTFsMYTQMXuUwyrB/P4zkDEWxXC828Ug7t75kcYqdda5pas4mLhNOxdJMABP7Xbg9cb2+ndZMTZS/BZNwj3C/mj0OylsLGz0ftaimG9UbFiLghQDOcCVlZVKYZVgKhxExTDGk8AFbtPMawiTI2bohjWeAKo2H2KYXVgRgQnwH1qezimxOC/NyaibIcG6hjOpRWbC2fgsHw8btjXQNKYRfDxzp0ovzdnKVw++ACFfd1z1TLFcK5wsbKeBCiG9QSVXTWKYRUgatwExbDGE0DF7lMMqwhT46YohjWeACp2n2I4a5ihx/9Bwr//wLZyNZRsVDtb6tdW7kHN8ytlnf8qdUXZod1UjJL+phJ3bILL0c34xaUDnp/dS/8L81mTYjifAHl5pgQohlVIDIphFSBq3ATFsMYTQMXuUwyrCFPjpiiGNZ4AKnafYjhrmGETPkXpB7/hdvH6KDFt8jMVQ384iRJv10NcTApsxnSCy5NwWeeG+2vwnP6pilHS31TM1LEoHvQHfnl+Mp7vVV//C/NZk2I4nwB5OcWwoXKAYthQZLVjl2JYO7E2dE8phg1NWDv2KYbNO9YBXx5AydPbEFKrNXw+blegnaEYzhx/SlIybAe9B4eUWDy084L90k3pKsaGRMF9UlvE2RTFYxtXuCfcRZx1YTgmP0aoQxkUXry2QOJqO6AV7JNj8E+f3ajwXFGj+UAxbDTUmmqowEaGT5+7iD3fn8DtgPvo2/VdvP5KLcz/7Cu4uzqjR8dmZhUEimGzCpdJOksxbJJhMUunKIbNMmwm6TTFsEmGRS+ngr75GX4Hp8u6kbYlYLdsq17XGaoSxXDmZMP/uoZSq/vpfoyavx+2hR11/w779QJKfzlU9+8H9t6I7zsdJZd+DCukIHrpD7CxszZU2J6x++hWKOL//Relv52JCBsPOCzfYrS2RUMUw0bFrZnGCkQMX7h8Cx36TEbJEm54FB2LT4d1Q8sm9bB1z0+YsWQz/jj4ORwd7M0mCBTDZhMqk3WUYthkQ2N2jlEMm13ITNZhimGTDU2OjkUP6w2PuJu6etGLv4V1Ab5XUQxnHrKAL/bC/9Ry3Y93ui5Gwt1AeL/fRP7t3o4fUeHoHIQ4+uFRcX8U698XDm6FED+kB9wSAnC372q41yqfYz6oUSExKhaY8BFcnoRKczeKN4DntIlqmNbbBsWw3qhYMRcECkQMT5y7HpGPorFk6iD0Gb0ALd+qJ8XwzTtBaNFtHPZtnAG/sj656EbBVqUYLlj+ltA6xbAlRNE0+kAxbBpxsAQvKIaNG8XA9bthe+tf2L7bCe51Kua58Uc376Pk3C6IsyqEWFsXuCUGIWjAOrhUL51nm/m9kGI4c4Ihn05B2dBfkGDlAPuUeFz3aQK/wEO42W4OSjZ+HoFLNqDipa24UuUD+A7urjMSNnESSoedxPUmE+DVumF+w6PX9fcnz0C5+8fw0NYDj528kFDvHZR87w29rlWrEsWwWiRpJy2BAhHDr703CMN6t0eb5g3Qe9R8nRh+8PARxG+71kxBlYplzCZSFMNmEyqTdZRi2GRDY3aOUQybXchM1mGKYeOGplC/t2SDN7waw5iTWSAAACAASURBVOWjjxAT9ADudSrl2onAjXtQ8fRK3PBsBNuEWLk50/UWU+H1zivP2IqItELSL8dh9+9JWD0MRXwCENP4A5Rpnv2uxrl1imL4WWIP/7sN9+UD4ZAShxvl3kX5m/t0leKsiiBy1GrEbPgM5UJP4GqjcfBp30j3+70lG1Dh0lZcq9oF3oM+zDEcVvExSHEolGO9rCoE7/8F5b+bIn++NWAzPKp75tlWfi6kGM4PPV6bFYECEcMfj5wHdzdnzJnQJ50YPnD4N4yZsRqnDqxE0SJ5v2mNHW6KYWMTt7z2KIYtL6YF1SOK4YIib3ntUgwbL6ahv19GmQ0DdQ3GWheFU/IjXK3ZAz79OufKkYjRo+Dz6C9ce3M8Um5cRsUbX+Nq7V7w6d1BZyc62gpHV/2FRrcXoURSYDr71z3egNeU8blqM6fKFMPpCYmNsWym94NrYgjuuNaBVfP2KLV1TLpK94pUg92TWJSIu4E73Zej+EtPP4zc23MEFQ7NwoXS7VBuXJ8s8SclA/d3HITr2T0oNP+znMKU5e/xg7vJGQYXag9Aud7v5dlOfi+kGM4vQV6fGYECEcOHj5/F0E+Xo3Prxjj950U0rPccirk6Y96q7Xjv7VcxY+zHZhUtimGzCpdJOksxbJJhMUunKIbNMmwm6TTFsHphCVi2CTZRYbBv8g7c6z472ntn3R5UPpt6fmzGElyoEqwGfoqi5TxydCjhYQycx7WW9aJm7UHI3h/hf2oZrpdqDq/xwxCwdCMKB/wLJMTBJ/6yrBfmWBb/Vv0IhZyAF3+dKKdXJ6/cm2NbualAMfyUltWTBESOGQ7PmMsIty8F2xkrYJWcBOcxqXG72ngCSh9ZKHeYVkr4jL1wKvZ0kOjRzWCUnNsV4qNJyordslpc+GOEb9gMj1s/416ZN/CwzEtwPnsA1R4dk78/nL0P9i5OuQmbrBu4dhcq/rEaYXa+cFiwzqgbdmV0lmI41+HjBXoQKBAxLPzasf8Y5q3cjpjYOJ2b7zR+GROGdoVL0cJ6uG46VSiGTScW5uoJxbC5Rs70/KYYNr2YmKtHFMP5i1zYyQuI//U4nNu2Rsl5XaWxO8VeQfEZU1NFxuL18OjZCXbOTgj6dAb8Qo/hhm9TlA84iJseryHlxTfh/sNyuWGREKgBDQfCu0PqVOqsSsCOw/A/OheBRZ+D29x5uP/Tnyi3awwCnJ9DsTnzYDXgPTglP5aXR9l7AG17wLbBmzpzUSMHwfPxJVxrOQ3ezV/OH4A0V1MMP4Vx/9NpKBd6HNE2rogd+xkK+7rLH61iHyPqWjCK1vDD/UkzUC4kVcRGW7vCesXOZ2IRNWKgFNRXXhkEj7M74ZoYnG28brafi5KNcjf9PVnMmx/eGUWSI3Gl+RT4tqynWk7kxRDFcF6o8ZqcCBSYGBaOJSQkIiA4TApiX88ScHUpkpO/Jvk7xbBJhsWsnKIYNqtwmbSzFMMmHR6zco5iOH/hCpz3mZyiLISsY0qMzlj4lK8RH/IQ3is+Qoy1MwLrdkGJs1/DNek+ggesxqNTf+rOBY6PeIyo+bNQ5sFpef2VlwfC98NWWToWMnEqyoadwJU6feH7UVs8uhEshXikTXE87PwJymweigf2vnjUdhjc6laGrVP6kzsCVn8F/7/W4oZ3U3hOHJk/ABoXw2KU3t41dTT33u6jSL57C9bxcahwczfirRwR1m8Z3GqUzZRxwM4f4X9kjvwtsEgNuM1b+Ey9exv2oMLvT2cTCHEd7uyPhJeaIf7vv+EYGw7XSl6IuXsfpe/9jCs1esC3f+6m3N/dehCVTsxHlIMnbBd+AVgb7xinzMBQDKt2S9JQGgIFIoZ/OvEnVm3ai3kT+6JcaS+dO6OnfYbChZ0waXjOmwGYUhQphk0pGubpC8WwecbNFL2mGDbFqJinTxTD+Yvbk0Ed4fwkXGck1LG8XP959aX+sCpcFBX+J3aUColW9khc+W3m4mjpF/C/+CUCi1SH27xFz9QJ2nccKREPUfrU57BHPO6P3oyi5VI3OVI25rpavq0U51cqtIXviL6ZtqOI57TTb/NHIfVqSx4ZTgiPQvTNeyhWp3I6VAEL18Dt7llEvdQGZX9eBBskyd+TYY3b7WdnO0obFxoFt4lt5bm613yaw/uTYc+EIeZ+JNwntZN1xMeO5Glr4eD27MzKu7t/RqXD03G72EsoMSP17Gl9SkoKEDu0J4on3MWt1wfBo+O7+lxm0DoUwwbFq1njBSKGB01YguSUFKyY+fQgcRGBI7/8iUGfLMXJfSvg4mw+U6UphjV7/6jWcYph1VBq3hDFsOZTQDUAFMN5Rxl26j+U/mKInAob7lIJKXaOSKrdAH4/TEOwUyU88qqGijd245ZPYzhHXEOxmNsIdHsebjNTRwMzlieP41BkZCtYpSQjbPpeFC7+dP3o/aPnUG7HaN0l4Q6l4LR4ve7fcUO6o1hCoG6E+tb78+DR8LksO2c1oI3cvCt41JdwLl8y7xDSXGnJYjho3gr43fgGQT71kfhqK1gVcoKzfxnYT+ikm5IuUIgp8kWi7yL8lY7w6dg0R66RI4fA6/F/uFyrF0r1fbr5WdoL7637Gja3/4VNs3Yo/kq1TG0+vBYM7wVdc70WPPDQOVTcMxpx1kWRvGgbYO+Qo8+GrkAxbGjC2rRfIGK4eZcx6NiqEbq1T/8wiIh8hFdbDcLOzyejqn/mU0dMMUwUw6YYFfPyiWLYvOJlyt5SDJtydMzLN4rhvMfr3ryVqHBjD66VeRfeYwchPjwatoXsYT+irTxKJ6hQJXjFXMbNDnNR8vWaiN7zDRJinqBY1/ezbDR8/HiUijiDi6+PQZmOT9f5BqzYDP9/N+muu1LxffgOf7oR6b2ZS1Dh7gH5e7yVE5JWPj3CJ7PGIsaMhk/UOVxvNQ1eb6uzbthSxHDUtSB4LugmsYVO2I6kuHh4Lnh2NqPYCdo7+oIcsXVJCsMdt7ooPnNmrhIq8mowomNt4VK+BIoUScnVtRkrK2vFg4ZvgkvFpzMyszN6f+wElIv8HddrdIVX/9Q+F3ShGC7oCFhm+wUihrsPnQ0nRwesmp1+2sf3R05j5NRVOLx9Prw9i5sNcYphswmVyTpKMWyyoTE7xyiGzS5kJuswxXDeQ5M4sJMUQbc/WIASr9bUGQqbMAGlH/yu+/fDWXt160pzai3gywPw/3UJrhVvCO9pE3TVw8eORanIP3DDpwmSK9WG22t14OTpqvs9JSkZIVNmyE2bbrvXQ4npqefFZlUUIX+l1kfw7dsxJ7f0+t1SxPC9Ld+iwi+LZZ8v+XWAfXgAyj88iSDnGoh1cod9TAR8H/2tY3K70wJY2drApVp52OVhJ2e94OpRKWz8eJSOOINrb41HckgwCt/4A7F13oZ3h6cfVdKaCbkQjDLLuiLJ2haPp22HnbuLHq0YvgrFsOEZa7GFAhHDX+w8iLkrtmF4nw54/eVaKF7MBafP/Ycla7+WMTiwaTasra3MJh4Uw2YTKpN1lGLYZENjdo5RDJtdyEzWYYph/UOTFJuAqMkTgJQUxDf5AOW/Ho3HNi6wWr4rnZF7G3ajwu+r5N8i7L3hsOQLvRuJDY6A+5QOSIAD7raZAq+3XoAQuvaDWsIuJQER0/fAwT3zjUhTklMQPHMhkspUhm/Xd7JtM3Dr96h4YiFueDaG56SxevuXXUVLEcP3J01FuZATz3Q18tOtsPMqkSqSV+zF8/8uR4hDeRRZvFoVfvk1ErByC/zPb0SIkx88Yq9LcyGO5VBk0eeZmr4zbSkq39uP66WawWv88Pw2r9r1FMOqoaShNAQKRAw/SUrC6GmrcfDY06+jwqdirkWxcvZw1KhczmBBEm1bW1nnSmz/eOIP1KrqhxLuT7+0pnWQYthg4dKMYYphzYTa4B2lGDY4Ys00QDGsf6iDvjkGv4Mz5AWPrZxROCUK10q/A+9x6fdGibl8A8UX95H1bpR8A56Tx+vfiNhZeMFqVLyWKrCvvtAHjs8/h1Jr+uGBvQ8cl2zM0VZidCzsimR/1mzY7xdResPgbMVSjg1lqGApYlhZTy3OB3ZPuAux6dmDtmNQtHEDXY/FxlMB0xbCqkot+LZvnFtUBqkvdiV3+KQrCiU/Smf/bp9VcH+uQrq/xYREw3VSB9ikJCJo6Ea4VvYxiE95MUoxnBdqvCYnAgUihhWn/vnvOi5du4PHMXEo41sSL9augiKFc38geE6dVH6PjUvA+30mo3eXlmjx1iu6y8Tu1oMnLn3GzJ+H1sDB3g51m/XF4qkDUb9u9UybohjWNwKslxUBimHmhloEKIbVIkk7FMP650DohE90xx8pV91sNxslG7/wjBHrAW3gmPwIV17oA9+P2+nfyP9q3t24D5VOL5P/emhXEq6J93Hd5214fTIi17Yyu0AIZpdR7yIFVohZ/gOsbPJ/nI4liOHws1dRal1/RNkWR/i7w+B6YCWSB02FU4XSqnA3tJF7O3/U7WAeUtgfHo+v4Faxenhc7gV4vF0fDw+dQMqjh0i2skHli5tyvfu0of0X9imGjUFZe20UqBg2Ju75n32FDdu/l03OmdAnnRgWI7/jZq7BrjXp19GU9vGAlZUVxbAxA6XRtiiGNRp4A3SbYtgAUDVqkmJYv8DHP3gMtwnvycpigyqHlFhkdzRRzJHjiI9NgtOLL8CxhLN+jWSode+H0yizb6qcHi3K1ddHwKfj23myldlFCYO7wTUxCPcGrIFr9fxvaGrOYjjxYSzCFy1E+ZBjEtW1su/Be8wAJEbFws7ZcAM4qgUzjaHQ4//INcwOjlYouWaI7perDUfB/p9fUObBb7q/3ey0ECUb1DCEG3m2STGcZ3S8MBsCRhPD12/fw/5DJ/Fhh6Y49+81BNwLydKt91s1kiOyapaHkdGIS0hA5/7TMLx3h2fE8JQFG3Him9QvrRlL2pHh8IgojJv5OerVrY7uHVL/w8ORYTUjpU1bFMPajLshek0xbAiq2rRJMaxf3O99dQgVjs1DYNGaiHX2QYXA73Hduym8Jo7Uz0Aea0VevYeEoDB5tXOtCnBweXrcUh5N6i4L/WQSyoSfxPUmn8Cr9ev5NWdW5wyHnb0Gxy1z4RwfKvv9xMoGRZIj5f8OK1IBsZ1Hwr22X76ZFLSBx9MnwiYiGMVibuGabzP4Bv4Mx5QY6ZY4E7vwItNY75yWE8VwQWeNZbZvNDH8829/o/+4Rfjuyzlyo6yM64XT4j25fwVcihrmnOGmnUZhUM82z4jhIROXoVXT+nBwsEedWpXQtGFd2NrYSLcUMVy9cjl0HzIL5Up7Ye7EvrrfKYYt8+YwZq8oho1J27Lbohi27Pgas3cUw/rRDpz3GSre+BpXa38Mn95ZH42knzXTqBWwdCP8L27BlSofwHdw93w7ZU4jw3cXr0ely9vS9fle4aqw7zUchSqVyTcLUzIQdu4GSn/eB3FWReCYEi2n3YfW7wZrN1d4NXnRlFyVvlAMm1xILMIho4nhpKRkxCckwsnRXk49LqiSmRg+f+mmFOdCgN+7H44d+46ic+vGmDCkq04Mzxj7MTbtPIhibkWxYNIA2NmmCmVRkpLyd/5bQbFguyoQUCmV5S2RIv+PhQTyRUBsxC82cGEu5Quj7uKExGTY2+V/zaQ63hjXisilZCZSjtAv9hsG34izuN9hNiq0qZ9jfXOocGXbEXjtnYS7Hq+i6tJZ+XbZ2soKyeLBZAblRo/uKBF7HXdeG4Ii/n6wsrFC6UbPmYHneXMxtFNT3Yjwbd+mqD7/k7wZMtJVhsglGzM6wcZImDXVjNHEcFqqn8xZh5CwCHw+z7BTiDKLZGZiOGO93d8dx8S56/H3T+vk6K8YGRYlJjYO32+Zg9I+JdNdEhwRq6mkYWfTEFDpv+0uhe2R8CQJsfFJxEsC+SLgVtQBj+MSIUQciwoExD2u0kcvFbwxqoniLo54GB2PJ/zgmy33+AGd4ZIUiuBhG+FayXR23s1PskRcuAWvZb0QYecNp2X6H/+UVZsl3ZwQEhFr8h/pHgeEw316R3l81ZOle2Ct8pK9/MTEUNeGjB6H0lFnpfmrrw5FqS7ZH71lKD/0sSsGDjxcnXBf5fduz2LmtfZbH1asoz+BAhHDQmjevReCjYvVOb9O/+4C+ojhE6fPo++YBfjj4OdwdLCXYrhlk3oIuh+O2wHB2LpiIlxdnp7lx2nSuYkA62ZGgNOkmRdqEeA0abVI0g6nSeecA08excB5dCs8sbJF3NIDsE4zayznq023hji/2GHQO7BNeYLoJd/lWxSayzTpgM3fwv/kYtx2r4cS09Nvqmq60cqfZ7eXbkGVi6nHct0buAau1fK/YVr+PMr6ak6TNhRZbdstEDF85NdzGDRhCQy5NjhjWMX5wuLQ+RbdxqFvt3fR4s1XYGdnK6tt3fMTKvmVQlX/soh8FI1RUz+T06DXLxojf1fWDNeuXhEfjZgr/7ZuwWgUcnJIfXiEc2RY27dR/ntPMZx/hrSQSoBimJmgFgGK4ZxJhp25jNLrB6p6Jm/OrRqnRvSwvvCIu447PZah+IuV89WouYjhsAkTUfrBKVx9dRh8Pmierz6by8WBh/9Bxd0jEGdVCMkr95q02xTDJh0es3WuQMTwsZN/YdS0z/Bi7cqoV+fZs3vbt3gd9ipPTRk+eeUzm3Yd2JS6GdbC1Tuwbtt3uiDWrOqHeRP7wterhE4ML502CK/UqQaxK3XnAdMgjl1aMXMYbGysKYbNNv1Nx3GKYdOJhbl7QjFs7hE0Hf8phnOORcCOw/A/Ohc3PRqi5JQJOV9gRjXuT52DckE/4mqDEfDplL9jm8xFDCcO6gyXJ6EIHv4FnCt6m1G08u5qQiLwMMIKbm4psFP3IJe8O5XFlRTDqiOlQbESKiXF+DsaDP10OQ4fT12fkFkx5oix0n5cfAJCwx+iaOFC6aZA65MlHBnWhxLrZEeAYpj5oRYBimG1SNIOxXDOORCweD38L2/Dlerd4DsgddNNSykBq7+C/19rcbV8W/iMSt07Ja/FvYgTHkSb9pphMXuwUP8mcsfiR0t/gI3d041S89pvXqcuAYphdXnSWiqBAhHDlgafYtjSImr8/lAMG5+5pbZIMWypkTV+vyiGs2aeFBuP4G3fwvWf71Ei/hauN58Er5avGj9IBmwx+ODvKP/NBNx1eQHus2fnuaXExBTcmzwPboMGwtlTvbOQ8+xQFhc+DgxDiemdEGtdFCkrdqttnvZUIEAxrAJEmniGgNHF8MFjZ/DHP5dhY2ODN197AS/U9Df7sFAMm30IC7wDFMMFHgKLcYBi2GJCWeAdoRjOOgT3Nu5BhdMrdRUC+38OtxrlCjxmajrw+HYoSszujEjbErBbtjXPpq/P+Bw1Anbiol9nlBnZI892DH2hJa//NjQ7Y9mnGDYWaW21Y1Qx/NmmfVi2Pv3XtpnjeqFVU/M+l49iWFs3jSF6SzFsCKratEkxrM24G6LXFMNZUE1MRNKwLiia9EBWSIY1YpZ9ZzE7SaftdaF+b8l/xqw6rPtz5MUAOBR3gf2s/igSG4zAluPg1rxRprBuf/EdqpxaJH+Lt3JC1NTtKFy8YEaH46MT8TAiCSVLOWbqa9CBk/D7dhLuuL2I4jNnGOKWos18EqAYzidAXp4pAaOJYXFGr9iVuU3zBpg04kMkJiZhwuy1OHH6H/yydxkcVN4wy5jxphg2Jm3LbIti2DLjWhC9ohguCOqW2SbFcOZxDdm2F2WPL0ewQwUUXbRKrjG11OLUrwmskILI+QdgV9gBoRM+QZkHpxFctCo8H/0nu32j7DvwHDM0UwTW/VvBMSUGYYX9UPzxdVz274RSw3oWCK5by7ai9KXdCHqxK3w/eAcptqkniigl4It98D+1DNdLNYfX+GEF4iMbzZ4AxTAzxBAEjCaGr90MRKseE6Ds4CwfoHeC0LLbOOzfNAvlS3sZon9GsUkxbBTMFt0IxbBFh9eonaMYNipui26MYjjz8MaM6I3iMTdxock0lGv9skXnQPLADiiSFIHQidtR2NsdUcMHwjP2cro+P3AohfBXOsLa0REl3qgLW2cn+XvklSB4LeqGB/becJkwHzaTOsu/Xy7xFgr1GQx3n8xHaA0B9NHdB3Cb2Q32iH/GvLITeOCSDah4aSuuVOsG34GWtRmaIZgWhE2K4YKgbvltGk0M/3n+KroOmoHfDqyEc5HUKTIxsfGo26yPPM/3pdpVzJY2xbDZhs5kHKcYNplQmL0jFMNmH0KT6QDF8LOhsEqIg+OQd1PF3sLvYe9k2TsOxw7pAfeEANwbvB6uVUohcdAHcHkSkmWOXm8+GV4tU5e+3dt9DBUOz5DHTj23bDr+WbULZY4tlr9dqdkTvv06GS3XgybNgF/IMQQXrgzn2EAUSn6ka/uBvS8cl2xA8NR5KB90CFdfGw6fzs2M5hsb0p8AxbD+rFhTfwJGF8NTRvaAk5OD9PDJkySMn7UGH3d+B/5+pXReN2lQB3Z26aev6N8l49ekGDY+c0trkWLY0iJacP2hGC449pbWMsXwsxGNOPMffNYPwX1HPxRd9JmlhfyZ/kSNHATPx5dwp8dSFH+xCuz6vwO7lATcdKyFlCIusE2MQenIp0dlXq3UET5DP5J2Ahasgf+1HbhSuxfqjv0QwQ9iEbj2K1T4Yy2ulG8D31H9jMLv0a1QlJyTOiodMWEzop28YG0LuLmkwLZfCzlaHDV/PyKnTUKpyD9xq+0seLxZxyi+sZHcEaAYzh0v1taPgNHFsD5uFcQ5w/r4lVUdiuH80OO1ggDFMPNALQIUw2qRpB2K4Wdz4N7mvahwcjmuer0Nn09HWHyShI8dh1KRZ3GjzSy41a0Ct3HvyY2wklbuk32/u2obKv2zXschwPl5FJszR/47fMxolIo6h5sd5uK5tvVSxfCOQ6hwdB5ueL0Fz09HG4Vf8KGzKL9nHO64vIDiGY6Iihw5GF6PL+JO18UotGMhisffQWD/NXCrUdYovrGR3BGgGM4dL9bWj4DRxHBCQiLCHz6dlpKdeyWLu8Ha2nx2pKAY1i/ZWCtrAhTDzA61CFAMq0WSdrQqhhOfAIEB1rB3SIG3V0q6RAieuRDl736Pi3UHoUzP1OnSllzuT5qOciE/41rTiShctQK8Fn2ICDtvOCz9QnY78uo9PN65E0iIRYX7PyHOqhCSV+5FSgpgPfA9OCU/xsNZe1GmvLsUw0E/nEL5vRNxx/VFFJ9lnB2bA9btgf/ZlbhWthW8xwxMF657s5aiwp39uPryIJT5P/bOO6Cp6+3j3xD2JqyQBJDpqlo7rLW1rfVXrdZRra171VFFRcS9cOAWcWPr3qPuXau2tmqrtmpbtSpLRhJG2COBAMn73psmEAEJEEhCzv0Lc895znO+zwXzueec57mznV4lzlp2BpbONk05rAY7NwLDBhs6vXa80WBYr1Wop3MEhuspIOlOVobJM6A1BQgMa01KozdkrDAcd+QXtPl1GSQMOxSv/R7mNuXHtgpCJ8JNEov4IRvA7ty6yT8jwhUb4J98EdHvh8DC1wfe+6cixaYVHCI2Vpp7yeQhcCgTISV0H8rKGOBtHKECZw+WFQ3DGXefwnNvsOLsbsTmRtFPsGoTAhLPI/qdyeCN6qsOwwcvwv/2Brxw/QA+ol/pexXLSDWKg2QQjRUgMKyxVKRhLRQgMFwLsaprSmBYCyIauQmyMmzkD4AWp09gWItiGrkpY4RhalVYNm04nEpT6ehHdwqBvG1HOAe6wNq0BBbBvcBEGXJWX4C5vSL/SVO+BJE7EBDzPaLfGA+mB5euw5vo3Amuy5ZUmnb6gsVolnkbcdxusMrhg1P4L+LdPgR7yQIoYTjnGR8eG0errS43tH5Zc2aBl/sQLwasgnvXN9WGy7r7DLy9U+gVbaoEVJY5F5Yb9za0S8R+HRUgMFxH4Ui3VypAYFgLDwiBYS2IaOQmCAwb+QOgxekTGNaimEZuyhhhOHnnaTS/H4Uihi0s5QWqJyDLjIMCblt4JfyADHMvWG/cZRRPB//bowj8exeiWw4Bw8UdATfXI9brM3DmVq4rnLz1EJo/LgfJHDM2sj4LBrv72yoYFmfkwnnhAFpfWdTpRtFQuWKdNusQ7Hzc1MaUS4pgE9pb9ZnAvh2cVkc0il9kkNorQGC49pqRHjUrQGC4Zo1qbEFguEaJSIMaFCAwTB4RbSlAYFhbShI7xgbDWRd/Bu/CCjrwVMIo1vmNcCxRrBBXvOLcP4bH4rlG8YAI9p9HwO+bEOvzOeR2Tgj4Zw+iWwwFb+qoSvNPvfIHfM/MQ66ZO0Q9JoPTo7wGs3JlWC6Xw2piN1BQU7j1RzAaOD+MrFgK25DPUAozSLddqjJmRVNHgSUVKOLO7gr2ojlGEVtDnCSBYUOMmv77TGBYCzEiMKwFEY3cBIFhI38AtDh9AsNaFLMJmyoUM2BjrZ4c6uXpGgsMZ9/7FzZHI+AoSaYleNQuCH4T+iFh1zkw4v8F6+tRSLl2H6YJT4D2ncDr/hbMHSyb8NNRPjXhyZ/hf20F4j26QmZhB/+EM3SyKe7IysnDJCnZyLr8M7hf96+kjQqGqTuTvoC1LA+Zi76HFdvplTqWSaQoiEuFjbcLTO2sa6155sNYeG6fCJGlL2zWf1dl/9Rla8EoKUZZu/fg8vE7MHes/Ti1dox0qJMCBIbrJBvpVIMCOodhSZEUpqZMmJkabuF6AsPk96y+ChAYrq+CpL9SAQLD5FmoSYGiPCnEi2Ygl9UcHmMGwpLjUmUXY4Hhomlfg1WUjAITRzxtOQqtJ39G61EsZcDC/NUvDGrS2tDvp/54D76n5yOJ9S7KzCzgk3YDcZ8uhEffD2o1tYowLJk6Gs5SPoRTdsGxldcr7RRPHQknqRDRrUeAN3l4rcakGgtP/wr/H8Pxwu1DuC9ZUOv+pIN+KUBghwbUJAAAIABJREFUWL/i0VS80QkMl5aVYfvBCzhy+hqycvKxat549O7WCRNmr4O5uRk2hQcblL4Ehg0qXHrpLIFhvQyLQTpFYNggw9aoTqfNDYNPzu+qMdNGR8KuQ5tKPhgDDGccOQ2vX6MgMbFD0eqjsLA1b9RY6PtgGb89gdeBEAhtX4PMxBS8vL+QOHgdXD9oWyvXK8Jwzsyp4BT8i8RhG+D6XvUZuSWpOXBe8qXixQTDEpIVx2q9asvfegiBj/cipuUwcINH1spn0lj/FCAwrH8xaQoe6QSGb/z2FybN24D+PT/AvYdPMXl0PxqGf/zlT0xbtAW/nd8KBzvDqfFGYLgp/Crodg4EhnWrf1MancBwU4qm9ufC//YYAv/eiWKGNXLN2XArjkd8j4Vg96m80mcMMMwM6gsLuRgxnaeBO6Sn9gU3cIs5jxLBiRpLbzOGXAbX4gSkTNkNh1aetZpZRRgWzQ+Dd9bviO+9FOye71ZrR3jiOvyvr1Ldj24xGLypX9dq3OT1O9E8+hhiu80Dp1+XWvUljfVPAQLD+heTpuCRTmCYWgH25Lhh/tThGD8zAr0/6UTDcKooC12/DMWJHUvQMsDbYPQlMGwwodJbRwkM621oDM4xAsMGF7IGdTjnzt+wtTeBaSvFyq9o/gJ4Z91F4schKImOhj//EmI7h4IzpEclP5o6DBc8TYTbprHIZ7LA3HKsQeNgqMYLkzPgumIwcszcYCYrhk1ZLrKWnoKlq12tplQRhlOWRcBPcAWxH04HZ9Cn1dpJCY+An/AK4ty6wC/9Z6TYtIRDxKZajVscPBJOJUKNtmTXyjBprBMFCAzrRPYmP6hOYPjjL6dh4si++LLXR1XC8Ll9K+DnzTEY8QkMG0yo9NZRAsPaCU3qhd9QlhRPG7Pr9jHs/Q3n74h2FAAIDGtLScO3k37rMZodmkZPJHf5SZix7KE8r5k8LgpF168hIP4Uot8OAu/rfkYHw6lnfobvlRVIcuoAlxXLDT/gDTCD0nwx7Gf1hcTEBpZlhfQI4qjaZ4GuCMP8ddsRGHscMe3HgTv+q0pe83edgkXCP/DOuE3fezEqCj57g1DMsEJZ1DmNZ1kkygMr7AuUMMxREnVR436kof4qQGBYf2NjyJ7pBIZDwrYgJ68AuyNn0+eElSvDm3adxHcHzuPBjztgYW5mMLoSGDaYUOmtowSG6x8aWWkZMHUgbGW5ii//pq6QztgIG2/X+ht/hYWizEJYOuvPsQ4Cww0aboMyLly9Gf4JCnhI6B0Gt56dYTmxO0wgQ37kRaRsP4zAZ4cQ3XYUeBOHGh0Mp2zeA79/DyM6cBB408YYVGwb01m6FBIUicTquopeEYaTvz2K5n/vQkzzgeCGjK00FeULG+pGAdMRJluOo3TyYNiXZSBj/lFY85w1mn7KD3fgd3YhBPbt4bR6jUZ9SCP9VoDAsH7Hx1C90wkMP49LRv8xC+HNc0d+gRivt/ZHaZkMv975GyHjBmDc0F4GpSeBYYMKl146S2C4/mFJu/YnfE7ORZY5F2JLVzrRC/Wz5ca99TdehYXc2FQwti0Ds6wYzHlrIUnLQeHvd8EbP7BBxtPUKIFhTZVqWu0YxWLILcpLwlAvh5hTv4SVLJ+eaFzzr2DTpw/Ya4fRWZNNth5HOZQMAjekMgw2pW3S6b/8A7tAT1h5OCHzzxgU/f0PvfrolfEbYj+ZB05/cp60ut8IxqT+qudIaNsajms31PqXRw2GD11G81uRiPfsAfa8UDVb6TcfodnhUEgY1nRSsxy3tmCHzULmrFnwzH+IF4PWwv3D1zUaX7BpHwKeHkR04EDwplWGbo2MkEZ6pQCBYb0KR5NxRicwTKlHATG1Enzv4TOIJUUI9OVhaP9P6KRaJg1chF3b0SMwrG1Fjc8egeH6xzx1ySr4pl5HdIcgsAd0h3TBZLhIkxHXJxwePTrWf4D/LORNnwy2+LmaPbGJPV03Ux+24xEY1lqo9dIQDXLRsbB7+3XYB3iofMxeugTgeoE1fDCyFswHq/CFCmCoRkL7tpD2HIFmR2dACTTJe86i+b0tiPX5HJxZkyrNtynBsDR4GBxL0ugXZCypgJ5rvqkz7EozIQzaCcc2hpOnpLEfTGnwCDiWpNDDxrQYAu7U0bV2oSIMC87dQsDlJUh0eQ+u4YvVbKUtXQ2flGsqgBWn5cLa3QH8lZsRmHQO0e8Ggzeit0bjZ86ZA8/c+4jtEQZOn84a9SGN9FsBAsP6HR9D9U5nMFxRMLlcDgb1hBvoRWDYQAOnR24TGK5fMMoKxbCc+SWY8lJkLzkOKzd78PeeReDdLUhw7Qy3pWH1G+C/3tIcMRzn9lXZiuV+Cqs8Prj5j1WfJQ2JgEvndloZry5GCAzXRTX970OdfyxavRCcwn9VzuaYuiHT4014zAsFY9LnsJIpznRWvJ75D0SL2GP0i5rE94Lgf2sD4jndwF44E/wjVxD4awTiud3BXjCjUl99hGFGSTEKbt5FfnoB2IM0y/6c9us/8Dkyvcogy8GAeMsPYDBN9P8h0JGHBaET4CaJo0ePH7gW7I80W5mt6G5FGE799R/4HpkOoV0bOK6JVDWr+Pc1dfp+2PuXv+xJ2nUaLf6MqvbFzcvSSIXpsFw2DpZyMURzDjf4cRkdhcbohiUwbHQhb5QJ6wSGqazRz2KS8Fa75rC1sUIiPw0Xr9+BtZUFBvb5GFaWhlXnj8BwozyrTXoQAsP1C2/K6Z/h9+MKJLHehcvypbQx6iyv44L+oL7sFq45BVO78i2kFUcT87NQGM+nP7Jp7k2vQlR3Cc/dgv/lJZXOoAlP3YDJP7/DN+0nxLUaAo8ptV85qZ8C5b0JDGtLSf2yk3LiGvyur6adEti1gb2YD7uybPrfca+Pgt9f5ccBxCZ2SO87F6V5ebB7uy3MImeBJeUj2fFNeObcR3Tb0eBNHALBmV8QcGUZEtw+gNuShXoLw3kxQpjZWMKKw0Lm9XvwPDGf9vVF36Vw/7T60jzPzj1Cs+5tkRWxDv78y4jx6QfwmgGFhQh4sJ22kW7pB9v13+pXsPXMm+xZM8HN/wulMEPRhjMwsaj9d7SKMJz5TwI8t41DhqU3rNfvVM029cpd+J5ZQNc0dly7Xk0F4ZX78D8zB3yHN8FaVV5uqSqpsk5eAO/aRvqW8syxnklK3KmjAgSG6ygc6fZKBXQCw8s3HsCvd/7BhQOrUFZWhk8GTkdWjuJcE7VNOnxW7erI6TrGBIZ1HQHDH5/AcP1imB62BM1EtxDzwXRwB5eX6siYNx9e2fcQ/9kSsHt1UhuEUZALua0DUlZEwi/5Mn0v19QN0tmbYFNNghbBqk0ISDyP521Hw3PiEDV7KZfvwO/cQois/WGzblv9JlSP3gSG6yGeHndNDV8DX+FVxHQKBne4Ypsof/0OBEZ/r/I6yaUTzCTZKB06Dc7tfcohI3wtfIU/qv4d220+OP0+QsqVP+B3Zh6SnTrAuYpsyvqwMqzcNpvs+BacV65EStR++D068N/vqyuYa/fAxNKiysiJQ8ZCJgfsSjPoVXPlCqG8uBSM0IH00YYX7P/BfdFsPY687l0TLVxMZ3bmO7QHa1XdElFVhOF8QRbclw1EIdMRjC3HVRPkr9+FwOijiG41HLwpI9QmnhOTBk7kMOSZusB085FXiiJaHA7vtF+R6NwJ5sPHw6E5V/ciEg+0ogCBYa3ISIy8pIBOYHjgN0vw0XuvY+KIvrj8013MWLqNri1MATGVafr3C1thymQaTLAIDBtMqPTWUQLDdQ9NqaQY1tM/h4lchtxlJ2HhbKsylnLuJvwuL0W2OQcWG/fRn5fwhSg4cRxuMT8ic8Ry2BxYQa+wZZt5wOm/c3EVvUmffQC2zdj0R8VTR8JJKkTS6E1w6dBSzemSAgnsZvaDCcpQsOY0TOzK/aj77DTvWVpYBFMbS1JaSXPJDKqlbNIAOlO6IGg7nNooQDfjzlN47QtWzUOZMfrliaX98hd8js5UfZw0ejNcOrRA2q+P4HMktNJ2VWVDfYDhoqmjVGd8E4dvAPPCQfCy/4QUFjBHMWI/nAHOoO5VxlKZgZi6KbRrC8c161TtBHvOgMmPAeO9/8H94/YG9Sw0trP8bYfB/vccUtv0q3OCwIowTPnPDOqNfBMWTMK3wdJJsWsne1YouPmP8GLAKrh3fbPSNE2DPoO5XIqkIWvA8neDiUfVkCudOhKOUiGSx26B85vNG1suMl4DKkBguAHFNWLTOoHh7oNnYvyw3vjisw+weusRXLlxDz8dXw+xpBhv9/iGBuOWAYaTzILAsBH/Bmlp6gSGNRcy47cnYNpZw9TGCkwbK+T+8gf8fl4N5crRy5aUCa9SOJ1QJjcBL+WWqkmmhSeci5PppDqMlTuREb4CATk31UxQqwuuy5YgP1EE91VDUMywROmWc2BUkeiP+jLnWvAccX1WwvvTtppPSgst+btPw+mfSzAPWgTb1l4oLpFpwSoxoQ8K5D5JhMeWsSgwcYDJ1hNqLpVN/op+mSOXA4Vrq38JkzdjCtiFz+i+mctOwsrZHhl/xsBrVxDSrfxhG1l5N4OuYfjlM/qpVs3hWJwMS5kYMW1GIuDRPiSy3kGJNQtWuUKU9hkJ1/fbqPQxCepLnxmlygGldRoFzpAe+hBOo/ThZRhOnb8Evlm3EPO/ueB+8THKSspgE/wZGJAhb/VZmNlbVdKpMOQbuBYr6si/4HSD+8LyFzzKxnJJEWxCe9PHY8QbLoFhYWqUejfVSRMYbqqR1e28dALDk+ZtgEwmx4yJAzFq6kp81Kk9vTU6PikFvUfMxYX9K+HjVZ44QbcS1Tw6geGaNSItXq0AgWHNnpCcp8ngbKr6GEX0+yHgDf2skiEq+67nriDV59R5yljXrmibdkb12fPAgfCcNhZFxQzk5gIsJzlKs/Ngt3gIzOXFSBq8GsV/3ENA7MkqM6AqDfG3H0Pgw51IcHkPbi9lSdVshnVvJQ0eCseSdNpAXOcQeAyprEXdrZOeulSAv/c0Au9GIZ7zCdgLZ6m5IlwbBf/400i3/f8cHGu3VOum6LcncDs0H1k2PnBaoziPmfOUD86m0dWWINM1DIuu/wnvE3MhsvaDbXE6rMoUx6kouC1dsgtOC/qpzTe2WV9wZk+mP5OXyWAzuTsNRZJtP4LauWFmWxmwdBlXYxr7ZRhO3HcJLe+sRzy7K9iL5iDj96fw2h9c7YsZSivqGS7+5ToCk84jyfkduCxbVknCjN8fw2v/NHIWvIk+XASGm2hgdTwtncDwH389w6iQ8gQISviN/O57HDnzE26f3QxzczMdS6P58ASGNdeKtKxaAQLDmj0Z/EMXEXirco1LalUsZ/lptS3SFS1S9SblslKYNm8N5/91hJkZkLJwOfwybtDNEkdsguu76tueqc8FO08g4P53as4JJn4Hp7a+VTosSckGa6miznBW2DG6pmljXMKTP8P/2gpQoG8ty6czB+ctPkJn1SaX4SsgXL0V/glnEPu/eeB8oV4PV5KaA5kgEXZcFmRsz1pNtiApE24rByGf6QzmlqOV+jYWDGdlMcBiySuNrzwfHBswACYsN/jejaLbJLi8D7fwRciaMwu83IeqfnmmzjDdrJiHRJQP57D+dK1a+dZTtdKFNNa+Ai/DcG5cBjwiBkNiYgPZljNI3X4Yfn/toZOccWeVv7x82RPR3Wfw3jsFadaBsFu3tZKjgn1nEXBnC+K4n8JjQdUZxLU/O2KxsRQgMNxYShvXODqBYUrimBd8PH72Am+2DYQX151W/dCpq3B1dkK3D98yqCgQGDaocOmls/oOwyV5Eogu/gJZfm6tz4wxMlMhd1acua3vpapB2WkqeMN71ctc+s3HcD8ahiybZnCqUN7jZaOpkVHwjTlNfyzoFgqnfq/eailasAjemb8huu0o8CYOrZePmnYumDYebkUvENtlFiz+vgHPrHuI8esP7oyJ1ZooyStE5o0HYJP6m5rKrJN21Jl4q+n9YSqTvvKFT12ck2QVwHl+PxQxrCGLOlvJRGPB8IVTUtgVJOPDEX5qPmTPmwNu9n3E9VgI9qedUDp9OBxKMxDTfhy447+C8OBF+N9WvBwrZljBQi5ByqRdcHjNC3lxaWBHDEOOmQfMN+2vizykjxYVeBmGKdOFU8fBVZoAavs7W6Ko3U7F2qPPB9WOrEyklWPmDvNNByu1S12+Dr78HxD9zmTwRpWXwdPiVIgpHSpAYFiH4jfhoXUGw0pNM7PzUFwshZurk0Elzar4TBAYbsK/IY00NX2G4fRrf6LZybkqJYRT98Kxxauzc1LJnNK37wcv/gdYluYjMaA37IePgoVr/VYqS6YMg0NpmloSocYIkTTuBYpv34TdCPUMp1WNnXb1D/icmocCUycwNh6r8myxNn1OvfonfE/NRR7TGYz1hyBLSIFDpKK0U+aSE7Byq1wqSl5UBPHCUJhLsmG68RBdYzV18Uqwsp5A1Gk4uNUkJNKm38SWZgqUl/NqB6fVEZp10rCVcisx1Vy87WqlXo0Bw8oa4WZyKeLaDofHRMXvmEwkgunisfT54MwFR2DFdUF+fCrAZMLO25VuQ72ky/jpHuRSKZgxf8OHfwXRr48F75uByPwrDp7fTSDbZTV8Fhq6WVUwLIj4FgFxJ+mhqRcyyW+NgseIPjAxrT6BanGeBE6z+6AMTBRv+0HltjgtF4XPE2FzehNcihKRNHwDXDq1buhpEfuNrACB4UYW3EiG0xkMn758E9S2aGVJJUrvr/p0Qci4AXCwszEo+QkMG1S49NJZfYbhtEVL4ZN+E9kmrnCSifDC5X24hy+qVsesv1/AZteiSpmZE1zfh9vS6vvVFBiJMAvO4QPpFaCyqHM1NdfZfblMjpKQYfT53fjPl4PdvUOD+qLcKqpcCaFKK0VPmkbXk43rvRQePdXrsDJKS5AfNhtu2Y9ov+I+XQi3bh1hOb0faCDhdIfHwhkN6jMxrrkC1EsKqn718w6T4Tla+ytdZhM/gxmkyIu4AFMb9RJFjQHDqSevwvdaebmemH5rwOvSGkUzxsNJKqh2O+zLCqZc/B1+F8KQw3RF9oRIMHIy0OzQNAjs2sKpQhZpzZUnLbWpQFUwTB0rKYhJpoexDfSCFdtRoyEtJn4KJspQuO4sGNaKTNT8HccR+F/taOrfeRHn6ez65GpaChAYblrx1JfZ6ASGL1z9HbOXf4e3X2+B995+DSxHe9x98C8uXr+DDzq2Q9TKEDCoJ95ALgLDBhIoPXZTX2G4jC4X1IdWTjhlF1w2B9HlTMQm9mBAjixbX0icvCHnNIMpj4fS2BgEPNxBt6eyMEsHhaAkLx+tDoxBljkHlv+VN6ptKHKfC5B3+w6a//EtbZfK7qzPl+C7wwj4a0+D+ppy5DJKUwVoHn2MzjIsizgEUysLurTS40Ub4Rd7EtHtx4A3fpCaVJm7D8Pzjz2qz4Q2rSDu9Dn8r65QxNn2NTiuVSRYIterFUj8PQne73ppXaasOTPBoArkAnDNf0b/zokWHocNRzNYqI1D8kkDYCPLRdaS47B0U7ffGDCcNW8OeNn3IbRvC07eP/R591xzNlyKk5Bh4Q3GvEiNz74rjyhQ54T573+DgF8jkMR6Fy7Ll9ZGEtK2ARSoCobrOoxyh1DqzAOw91UcwUlZtg5+gh8gsmyGfAdfsBeX72aq6zikn/4pQGBY/2LSFDzSCQwPm7yc1u7glvlqGh6/cAOLI/bi6tEIcNguBqMvgWGDCZXeOqqvMCw4cR0B11chwbED3FYuB3/TXgQ+PfRKHansrbFtR4E7cYiqnXlQD5jKS2v9tj7txt+wObsNbkVxKlsxb00Ad8wXehtLyrGitGw4LW64RFqivcfgfXenSoPodmPAm6CAXhqGvzsNv1/XI57bHewF5au8ZfmFMJszhN56+qxrGLx/Xg8rWT6UJaao/tR2a9MqkinpteA6cK60RI6iGeMgbP05AsfX7/x6Rfcz/ngOr92KjMjKK8WmFRwiNjbILKsCC+VADQ3DUlEuHMMG0MPlrjyN9F0H6Yzt1CWyaAaTOWs1Xi1U+lw0dTRYUj5ivHsjIPG8Kltxg4hHjGqsgDZhWFkur2K9d2XpsKSRm+DSsXIyRI0dJQ31WgECw3odHoN1Ticw3PnzKRg9qAe+HtRT/T/89Cz876tQ7N0wh141NpSLwLChREp//dRXGKZWqHi5f+HpR/PgPbALXZ4k50GMSkjqrF5JYiJMUhNhWpCJEjs3MDt+APeP26uJrfryMmwDXN7T/BxX1uzZ4OU9ULNV8QuQ/kYUEC1aAu/0W4hpNxrcCeUvBnL/TYYkWYiytDRYvd4GrLY+tZpG1pkr4F1RnB2N8eoNua0j2KMGwNROsV2Q3iZ9+Q94HpyOFNtWcFhbDlHCzfvg/+9BGn6tNuwGf8sBBD6pnFwoL/ICvcpMruoVSNh7Ga3uRqIE5hBN3QnHFh4QnvkFsoQYcIPH1PmsuGDvGQTc3UrvKkA3xUsfcw8XOARwGiQchSHj4FqcAH7Qd2C1Uc+Srg0YTktnQMbng+XtUCnbu2D3SQT88S2SXd6Fc/hSuhxSZtgiMEvEsF2wrMpaszWJkLFgEbwyf0OywxvwzH2AWO8+4MyZUlM3cr+BFdAmDIvmzYd39j3E9Q2Hx6cdac+ZQX3oBGp5a86q/hY28JSIeR0oQGBYB6IbwZA6geEJs9dBmJqJM3uWw8SkfDv09oPnsXHnSfx8YgPcXLS/Hayh4klguKGUNR67+gTD/E17YJqbAfPCDPByH0DCsIF4+RFYOdW9RmfKivXwS76E6HeDwRvRW6PASlJy4Lz0SzqxinTTWZiaatRNbxpl/PIAXkdn04m0Ut8fA9Zvh8GSCtX8o7ab507eCIeWPI38LrhxC67HltJb1KNbDwNv8shK/SgYTo1Lg0OYQjtllmDh4cvwvxlJt3/Rbxncu70DaY4YGVd/gzw5HnK5DE7J92kwokCMlfMMaZ4fgDN7kka+GVMjaV4xzOYNhU1ZLj3tNEt/mC9YC5MlE+BQkoZEpw5wmju/xi/liZsOQlzEhG/wEFhYKrZFpy1aBp/0XxDz/jRwh6q/MG4IjXOnB8ND/BRJozbB5R31FTVtwHD2zOngFvyDmM6h4A5Rz8SeHxoEd0kMEj9bCNdeigzC9a0HnLIiEn7Jl0FlG3YsSUN080HghYxpCOmIzVoooE0YTg2PgK/wCmI+nA7uoE9RkCCC2+ohyDN1genmI7XwijQ1NAUIDBtaxAzDX53A8P1/ojEieAVYjnZ4r0MbuLAccPveI0TH89G/5wcIn/W1Yaj3n5cEhg0qXHrprC5huDRfrPrSTiV/Mp1MvWEvonXKZzpBOCwCnh3rdy6Sv+cMAu9tRazXZ+DMDdEoBvy9ZxF4dwsSXDrDLTxMoz761qh46gg4SVPU3CpgOiLPigfzkjz6XCT174LgDbAPrD5Dd/pPD+B2chldQ5i6XqUjBcMFkhKUjutFr5Rkhx1D5v4jdJ1a6npVySXlmcuKDicNXAWXj95UfZT7OAnWXq51WrXTt/jUxh9pIh+lsbEw8fVD9sFj8BNeQap1c5jIyuBWFItsMw+1pHEv3LvAffG8aodQZh2nGjzz7gevOYraqqWTB8G+LBMpk3fCobV3bVysU1vl7ouEr1bDrcsbaja0AcOSqaPhTG1b9v8S3OnjVfapzNDua4ej2MQKpZEnwLAwr5P/L3cSbNyNgGflQBTdfmyty8FpxRFiRE0BbcKwYP1OBEQfU2UOV/4uJTu+BeeVK4nyTVgBAsNNOLg6nJpOYJia74NH0YjadxZ/P4mDWFIEP28Ovuz9EQb1/RhmZoa1BKQrGM5+LoRYagknf2dYWylWFchlmAroCoZl0hKUzBiDvEGz4dqpNTLvPoPn3inIMXNDekB3OPXtCRuv+p/fF936B96HpiPd0ge57XqAYecIm+Z+dD3Q6q6cmSHgFDxBXPf58Pj8I4MMbOqu7+H7pyKhWJxPb7DGfK3aKkppnxM2j96GTr10EE/fDDsfRc116ipMzoAkah1sgkLBiJhBryrnmrpCxH4LnPmh1eqhhOG0kMngFDyl+ziUiiBlWELQYxbce3eutq8gcjsCYo6r3aeSGFlvUJxPLpz2DVyL4hHr1QucuVMNIiZFmQXIf56IkiQ+5Fki2mfTgBZw/6R29exTN+6A77Pv1eacODgCFmwXOGycBCtZIX0vxrc/AuJP0aBsv25LtRopE/4oG8S+ORb2H3aCW+TX1db9bQjBlS9A4notgcdnndSG0AYMM4L6wkouphNZOS6Yj4IXqbDmuCJ9/3EEPj2IRK/ucJ2rvezlyq3XyolEd5wC3khFEkBy6U4BrcLwjuMIeLAdMQEDwA39BvxdJxH457eI9e0HzkzFSyVyNU0FCAw3zbjqelY6g+GKE5fL5QaVPfrloOkChstKysCYNgi2ZTl42nEavEc2/HY6XT+sTXl8XcGw8nyiFBZIGb4axX//jcB/9iDG9wtwZ07QmuRFmYVgLfi8kr0U6xYoGxIM1psBqnsyGRC95QzeeLoVpQxTiNedMdjzq9LMPDxduR/OloXghs/Gy0nyyyRS5C2aDW7+Y/Ad2oO1qrzEjGDDbgQ8L1/hEln5wyZyW40xUcJwwqLV8BP8SLenVt8yx6+HYzu/V/YXHrwI/9sb6DbxnG7gpNyCpVyMrKUnAROmWgyTR22G8zv6m9sh888YmHy/Ddx8RQmpileKTUs4RGyqUcuKDWSTv6T/3lIXlYGbMW4WHJorVvPTb/wF72OzQNk1Cw2Da/gg5DNZYG45Vu0YJkH9YCkvgND9XXDSfldrl+TUAS4rFIkmG/pKWbIKfqnXEdN1DrgDuqoNV18YLipmgBXyP9pmljkXDGorfkkKYt4JgtWz3+gXQamjImD/TjutTVN44jr8r69S2Ysc18RRAAAgAElEQVT+eA54X6rPS2uDEUMaK6BVGP7+KgJ+XoN4j0/ADpsF5TGcmPdCwB32mcY+kYaGpwCBYcOLmSF43GgwnCRIQ36BRCNNWvh7gck00aitPjTSBQynnL4Bvx8VX5aiOZ+Bt1Czraf6oBfxobICuoBhCsQYM4bAVqY490jV7y00c6JXIBP6rYBbt7e1GqqSKUPoFUqxiR1yrL3BKXhM2y9kOkA851vY8FzA//YobOPuqu7FdpgIzuj+WvWjsY1d+4mBTh0Ba+uqd29Q53Yd5yrqxwqCdsCpTTPIJEUwmTFEtS2aupcybBUc3ivfrlzdPJQwHLf5MAL/3kU3e/HaYLhPqvn4SdpPD+FzfBbdJ/Z/82Dx5xW6XnF8n3CgTAbfi+V1opOc3obLCkU5Jn24irMLYRI2Fg6lGZXcUZRbaYZSJy4CnymyoeesPAtzR0XSsVddxZkFEL/gg7trCr3Kbrb5cJXN+duPweqtN8F63Q9Wk3qAIS9DwdYfwWRWLhOY9uOf8Dk9V1FubP1uSK5eg/ml/bCTptO2Y+hs7ENrck0r94UrN8I/6QJiOoWAO1wdJOoLw7kxqfCIHF7JT4mJDb2SnmfuDtONB7UyD6WR9Gt/otnJ8rI6cb3D4dFTkWSJXLpTQJswnPbjPficno8kx7fhsnIFcmZOBafgXyQMXQ+391/T3STJyA2uAIHhBpfYKAdoNBieMn8jfrr9UCORfzu/FQ52Nhq11YdGuoDh7FnT6NUk6kqz9IPd+m/1QQriQx0V0AUMC3afQcAfW+lanmIbDryyylenGiKbcMa8+TAtFcMiZCGsOCyU5EtQsCwM3Ly/kG7pj+Kh0+C5S5GsidqmndMrROtAXsfwNHi3tFWR8Em8jFhuD3AWhEJ46CL8b22gY5Pv2xHWouewC1+rkR9KGE6+9hDsY2GQM5goXXUQzP+yTb/KSG5sKjzWKeAlY/EJZB09TcNjTPPBYBSL4Z9wFrGePeGffIk+I2uxqXImao2cbIBGGfMXwivrDqhdDmIzBxSbOiCX1x7sb4bD1MZSNWLOrFBw8h8h7rMl8Oilvi34ZbdSLv0OrwvLkGPBo7eHR7cYDN7Uml8qlEwZCofSdKTNOQw7b9dKs1UmAIpuPQK8yQq9GaWlSD9xCc63DyJjwHy4fai91dJXyS1Y9x0CYk8g+o3x4I37Uq1pfWE47dYT+Byq/kVtfIfxYI9WH7O+j0bWg2jwdpQnfXsxeB3cP2hbX7Okfz0V0CYMZ/0VA953QUizCkDBW73gdWsrzORSjV9w1XMqpLsOFSAwrEPxm/DQjQbDSYJ0FBSKNZKyuR9ZGa5OKGo7nuOpSDiWpKDQxAE2/63q5Wy8CnPt5B/RKEakkXYVaEgYTj1/G9a/HIVZSQEsy/JhVlaE9HHrwdo1m155jOuxEG6fdETR3El0JmGhXVs4rlmn3QkCyHwQC+c3/NXsUqt5zEXjYV+STp+TpLbkxrQcAm7waK2Pr88GC5++gOsmRXKhjCUngRXT6ORaMe8GgzuiNxhFEsgtNcvmrYTh4hIZbS8/IRV2zdgaT79IlA+xUARWO1+kXrkL3zMLILBrCwtpDu0TVcfTa18wbU+87arGdhuyoeDYjwi4sRZUdm7Jwp2wYjtVO5xg6wEEPN6P2GZ9wZmtqOdLrc4rV4mpn3P/iUVZXgE8Ly2nv2QrL+XKfU1zyZ0xFR6F/9JavVzzVF5cCua0L+hnPTV0H+xfKplUJimGqSkDcrPG+YPO37wfgf8eQEzrEeD+B+bK+dUXhoXnbsL/8lI1uUoY5rSm1EuLotXfw9S+5tX5mvSueL8wSQTXleWlzJLHRcH5jfJjGLWxRdpqTwFtwrCEL4Lz8vIYU16SrOHai5U+WyIwrM/RMVzfGg2GKYly8wtRUCiBm7NjpSRZYkkxsnPz4eRgB2sDq2/ZmCvDRVNHgSUVIMeKh7z+IbA49R1dmuLFsE1wf48UmjfUX8WGguHs67+De6L6TMxUQivb9dtp2XKf8iGOTYRz5/YabR/VltZ5vz8Ae/9s2hz1Rblg+fewcDKcnSHa0iF/zjS45z5GssOb8My9T2shWXtSbVVTk7FehmFN+lTXpkiUB1aYotYtdSlLNZVMHgKHMhFSZxyAvZ/moF0fX17VN3vWDHDz/0Zst7ng9Pv4lcNk3XkM3r5pyDT3hNXG3RDsOwv2H4fA3KJIjiU4dAkBt9arbFBlpizEmTCVSWC9QbHtvKYrPWwpmoluIqbHInD7vK/WPOWHO/A7uxDU1m2b9Yrkarq8+Nu/R+DDHYgJ+Arc0HFqrtQXhpN3n0XzP9STiMV+OBMyUSpMuJ7g9O+i9alTielsp5bn0EiZfgAO/rp/RrU+UQMzqE0YZpRIYRVcvqU/4fNwuHUnW+EN7JGok7sEhuskG+lUgwKNCsPDJi9HIj8Vlw6uhp2t+tvg+KQU9B4xF1/16YJFoZVrZ+pzJBsLhgUnriPg+irkmrvD7L9zVsIVG+CffBHPOkyB12iSMVOfn5NX+aZtGE774Xd4XFwLy1JFKZ54bjcwP+hO/2x3MpJ+oUJdcb2XwqPnuzqXLX3pKjhmPIKobT9wxw7QuT+6cCDjx1vwOr1ENXSM3xfgzqh9EjNtwjDlTHHwCFXJoBiv3uDODUbOzFBwCh4hfuBasD96XRdyqcakVrKdFvaHnGGCwjWnNdoObhrUG+byIlWmbcpY4pAIuHZuh9SF4fDN+BUiS1/kuTSHx3+Zu6lM65omDBOujYJ//Gk8e2cKvEap/11ODV8DX+FVRLcZBV5Q45wLflWAkvedR/M7mxDr3RucOYoVf+VVbxjesBvNnx9BljkPLCmfNpsfeRFMq4Zd9TYJ6kuvvFNX5pLjsHJz1OkzSgYHtAnDlJ7K32GBeyc4LS7/u0m0btoKEBhu2vHV1ewaDYYT+WnoOWw21iycgM+6Vv0Gb8OOE9hx6AL+urYLZqZMXWlS63EbGoZTzt2E5a2zcBQnwKYsFwkfT4fbl5/SfvIPXUTgrQ2IY3eDx6KZtfaddNAPBbQJw5L0PFgvGQUrWT6ymW5IcuwA36VTocxJl3nvGXi7p0Bk5QdbctZcPx4Aygu5HNKpI+BYkgo5GMiYfwQ2POda+6dtGE5bvBw+aTfo7NLu86aDwTRB6tK18E35ETEfTAd3sOJvka4uwZEfEPDrOiQ7vQ1nDRN65YXNAVt0X83lWL/+4MyYCGWW56wFh2HJrXzeV5N5CrZ/j4CHO/A8cDA8p5WfMS7LzoHdPMUZ2bSZB2Dnq/sVS/6xqwi8sQYvPP4H9zDFDg3lVV8YTlkWAT/BFfo8solICJiagjOr/DyvJlrWpY00eCQcS4R018ItV+hnlly6VUDbMFxy5zaKb9+E5bCvYeruptvJkdEbTQECw40mtVEN1GgwfP3mAwQv3IQ7F6IqrQorFX/4OAbU6vGZPcsQ4MMzmEA0NAynLlkJ39SfaD2oxEJmkfvB+O9lQeb95/DcORkiC2/Y/FcL1GCEI46qFNAWDEtLgNxFC+GdfQd82zZgrY2sUmXh6i1gvP4OPLprN2M0CWn9FBCe+hlWdy6ikNMSvJAxdTKmbRjm7zoFsxwh3KcrztdSF3/LAQQ+2Y/olkPBCx5VJz+11UmZOCv2/WBwhvbWyCy1zVIaG4fMP59DVgb4391K/23NHrIEPvsmIsvCE5Ybdmtkq6pGwpM/w//aClAZt5nvfQJLe3NYdX4PqVF74PvoMPj27cFaXV5Gq84DaaGj4NxtBFxejESX9+AavljNYn1hmEqa55V9D/H9V4Jdy7rO9Zla7vRgeIif0kcNSqIu1scU6aslBbQNw1pyi5gxMAUIDBtYwAzE3UaD4dOXb2LFpkP443L1WY9TRVno+mUojkQtRNtWr66HqU/6NjQMS4OH06tFsV3nwLZtC9gHKmpbUheVjMUypBdMIEPumgswt2vY7Wf6pHtT8qWuMJxx5ymKH9wH0zcADC8fWH87H+4lCZAwbJExfRec/VhVylRSUAQz2/IMu01JS2Ofi7ZhuCxfAqadevIuwbGrCLixBvHsrmAvmtPokot+ewKUlsGpvT/sZynKUmUvOgYLdtXPe00OmkzqC0uZGAkeXdEs5Tri/T4He0bdVzBFv/4D7yPTVcNSUJY3ewes10ymd2wkDImEW+c2NbnVKPdTr/4J31NzwXd4E6xV5fV5qcHrC8MF0ybArSgOyd98C+fXG+//dOULkgKmE0z+OwveKGKSQapVgMAweTi0oQCBYW2oSGy8rECjwfD9f6IxIngFfju3FQ72VSfHufvwKb6ethq/nNoIF5aDwUSrIWG4OCULTksH0m+4pZvPV7ndK3/6JLiLoxE/KBLsD9tAnJ6P/G1b4a6DL6kGEzQ9c7SuMCyaOx/eOfeQa+oGoVsHtBReQK4FG6mDwuHZsZmezZK40xgKaBuGq/I5/eYjNDscCqFtKziu3dgY01KNIU7LhctixbnyGO/eCEg8jzTb5rBbq56oqTZOpYevRDOhYvcNdSV9EQ6X/9U9IU/ecz7YG9QzoiuzKAttX4Pj2vIEXbXxsyHaim4/gffBkCpjWV8Ylk36ErayHGSEfQ9rj+ozfGt7XqnL1sFX8AOyzLmw3LhX2+aJvTooQGC4DqKRLpUUIDBMHoqGUKDRYJjKJN2p96RqE2SVlJRixNSVSBNl4afj+vNFQRPRGxKGhWd/hf8P4XSGWeeX3torfROs3ISApPOIfmsinP7XGczIGWBJhVWW7dBkPqRN4ytQFxguFuXBqUKmX6XXWfMPwpLn3viTICPqhQKNAcMFCSK4rR6CfCYLzC3HGnXewuPX4P/TarUx498aC/aYgXX2Q3T+J3hfWkn3l4EJSeRpMKw0K2VV1aClhUWwn6HYsq0sGUb9TL20Kpy4Eo6tvOrsq7Y7Zj6Mg+f2CRBZ+sDmv8zyyjHqA8NymRzWQd1oU+KoH8EwYWjb9Wrt8dfvRGD0MaRaNYd9ZN1fkjSaw0YwEIFhIwhyI0yRwHAjiGyEQzQaDFPaHj59Hcs3HsA77Vti+Jfd4MV1BwXBsS8EiNp3BlSSrS0rpqJLp/YGFYqGgOH0RynIK5DD6tY5BMSfREybkeAGDatSF8HRKwj4JYKuBWov5sOuLItuF912NHgT1WvxGZSwRuSspjAsXLUZltnJKGrXBbLCQgQ++E5NpRSHdnBYFWFEypGpvqxAY8AwNablxO708YzGyA5ccY5pS1bDJ/Wa2rSzpu+Epb93nR8GhrQIJgnPFf3NzFHmU/8ydcpEXDG8njQIWuSmwG7OokYtW6aJIDnRKeCsH4EcMw+Yb9qv1qU+MFzIz4Tr8kHIY7rAdMsRTVzRWhv+zhMIvP8d+A7twVqlH2eztTY5AzVEYNhAA6dnbhMY1rOANBF3GhWGZTI5Tly4gbXbjkEsKVKTkOVohwUhw9H9ow4GJ21DwHDSnvNocW+TalXhxYDVcO/6RpXaZP0dD96336juFZrYw0aWhwwLb1jXIqlW/os0lG5bi1IL20qJVAwuKAbmsCYwTJV28dw7pdLMYvz6IyDuFP15fPd5YH+u/dqdBianUbvbWDAsCfkazsXJENq2hJxhTv/dkNqwYP5BV7h2ao3c5wIUH9wBZokE1iFzYcWue3kbwbEfIU9LAS94JGSTBsBWlotCEwfYyHKRY+EB8w3qEKcPD0BaWDjcM/9Cau9ZYHVso3cQrNSoUJgJ1/BBKGQ6grHluNZgOPOP5/DcPRmpNi1gH7G5UUPC//4qAn9eU2VSsEZ1hAymUoDAMHkYtKEAgWFtqEhsvKxAo8KwcvD8AjFiEwRISE6FubkZvHnu8PPmwsrSMJM/NQQMC5ZvQABfkQWTKrOSt/oszOyr3rYnL5PBcnJPMFGGYoYV8mbthP2asbCQS8CfuB2stj6quPM37Ebg8yOI8+wJj3nTIIl+AdtNocix5KDgw8Hwu6So19fYqz3G/qv5Khh+9u0lWCf+BXbBEziWpiOJ1RHFLB5cEm/DqjQXsqizEEQdBPUc8KaMMHYpjX7+jQXDwtVb4Sy4C5uSLJijWKV7gsv7kHXqDt9zC1WfSUzskDFihcZ1eisGkX/wAgJvK84lU8++V9Yduj6w6J2hsPv3Z5S9/h4cv+pn9HGvqwDSfDEcZ/WtMvNyfVaGlSWv4jmfgL1wVl3dI/2aiAIEhptIIHU8DQLDOg5AEx1eJzDc1LRsCBjOmR4Mjvgp/eXPUiyqsR5s6tJVYKf9AcE7I8Ed0Qcpy9bBT/ADot+eBN7Xn6skFy1YBO/M3yC0bQ3HtRuQvm03mv2j2MIW7/4xfNMUSWQEQTvg1IYkYGqsZ7U6GC4rkQHTBsGuLFvxkoLpBNmKfaoXI/mJIth5160WamPNjYzTuAo0FgxXnJU0R4yCpDTwto2nX8gJXd6Ej+gWku3bQ2ZqDu+su0hivQuX5UtrLYZg7bf0UZGKVyyvJzjzp9XaFulQtQLWEz+hb4i3XVVrUB8YFq6Ngn/8acS8OR7csYrayuQyXgUIDBtv7LU5cwLD2lST2FIqQGBYC8+CtmFYLgcsJvWAqbwUBRsvoVRcUustdqJb/8D70HQ6wY3p+gOQmylW3YumjgJLKqC/sJZFnUNJyHA4FKfS95TZTqmf43stAfuzTlpQh5jQRIHqYJh/+hYCf1yCHDN3ZHDeAaPtW/Do+a4mJkkbI1VAFzCslDo/NAjukhiV8qI5h2HhYgfLGQNgBimywo7BSoOswmlX/4T8zs9gdukOxuUT8Mr6HbE+/SCzsqVtW3XqBOc3/Y00wtqfNjOoNyzkRchZfRbm9taqAeoDw9mzZoKb/xcSBqyAW1dSz1z7UTMsiwSGDSte+uotgWF9jYxh+0VgWAvx0zYMZz8TgLtxFDLNebDauKfOHubNmAJ24TM6yzRvTH/ajnIFgPo55aul8Pg+rEr7MW9+A+5YRfkScjW8AtXBcPasGeDm/43otyaAN+aLhneEjGDwCugShvmROxEYo8gunW7pp9rRIly5Cf5UxvtWw2vcyi/4/hoCflZki6byHjDkMjhLkyGcshOOreqeJMvgA9uAE1CWQBItOAIbrku9YNgknQ+ZGw+MSf3pmso5i47CnO3cgN4T04agAIFhQ4iS/vtIYFj/Y2SIHhIY1kLUtA3DwjM34X9lKRLcPoDbkvJzd7V1VXTrEbwPhUJsYoeyiKPIixWAGzVeZSbTuhmcxQnIMWPDsUSxOqy8Yj17gTNvam2HJO3rqMDLMEzVUs3ZdxD+L87QFrNXnIGFU9X1ues4JOnWRBXQJQynXb8PnxNzaGWjWw8Hb7LiDHvuk0R4bBmLPFNnmG4++krl80MnwV0SXakNyWPQcA9scfBIOJUIkRKyFw7NufWCYfmyEIjdW8D1wUlIGZYojTrfcI4TywajAIFhgwmVXjtKYFivw2OwzhEY1kLotA3D/M37EfjvAUS3GQVe0NB6eZg5ZzY8cx8g5o3xMHF1g9+VZZXsxfVYCL/L4Wqf8+3bg7VaOyUpinPEyPn+FKzszGE/+Kt6zaepdn4ZhpVnu6n5xvj2B3fmxKY6dTIvLSugSxiWFUthHdIHJihD8pitcH4rUDU76eShcCxLR/rsw7BtVv05d7OJn9FbqrPNOXCSChUwbeoGs82HtKwUMadUoHDaN3Atikfy+Cg4tw+oMwy/OPozWv+yQtVfaN8GjqsjidBEARAYJg+BNhQgMKwNFYmNlxUgMKyFZ0LbMKwEodjeS8Gp5/lQZXkLiYkN+L49EBB7AgVMR9iW5fz3JdMVZpsPgzHpc1jJCumtjW5FcVr98pm09zxa3N1Ej1ew7hxMrKvOiq2FUBisiZdhWBo8nF6tT/56K5zfLgcKg50gcbzRFNAlDL9qkunzwtAs+3fE9Q6HR8+OVTbNjRHCI3IkvYKcwekA36TLdDttvpxrtEAY0EA5M0LAKXyChKHr4fb+a9XCcMZvTyAvLYPrB20rzU6aK4HZvGF0WT/lFe/TB+xZlcvBGZA0xFUtKUBgWEtCGrkZAsNG/gA00PQJDGtBWG3CcHFmAcwWjaZhNW3WQdj5uNfbQ9G8+fDOvqeyE9vsc/gnKLbfPg8cCM9pY5Exbx68sv9AdPNBCHyu2Mb4cmbRujhCJQMrmDYe7sUv6O7JI9bD+d3yL1t1sdkU+1SE4ZICCRxm9kEpwxTSKAUMkIsooKkC+grDSRv3o8WzA4h+bQR4k4ZXOZ2Ui7/D70IYkh3ehLRtZ/jd3EC3I8c2NI1+3dplzpkLz9w/8aL/Crh/Up7sqmICLUl6HpwXKfIWvNyO+owfdRiBj/bQ57wtS3Pp/8P4XUPAGvBZ3ZwivZqUAgSGm1Q4dTYZAsM6k75JD0xgWAvh1RYMl+aLIVk8E+7iaGSZc2G5ca8WvAOyH72Ay7ZgOlsodSUMW49mBxVlSfgTtoPVzgd5z/koTsuCjS8PJmtD6YzTwim74NjKq84+ZD1KQN7jeLT6daXKRnznELCHkC9HL4taEYYzf38Cz/0hSLFpAYeIzXXWn3Q0TgX0FYb5Z24h8MoSJDp3gusyRT3zly9+1CEEPtqLGL8vYPlhF3junkw3iX5jPHjjSHmehnqi0xcuQbOMW4jtEQZOn86qYSrCsLJuMHWTKsPHXjxXzZ2M+QvpGtCx3RfAvl1z2DmYQM5yayiXiV0DU4DAsIEFTE/dJTCsp4ExcLcIDGshgNqCYeUXQaqMTtnsDbCqkNWzvm5mPoyD9d5lyAz4CLzJI0HVBc2PF8L5jcrlSZQryfH9loPdrUOdhi5ISIXb6vLVHwnTDlZl+Yjz6QuPWYovuOQqV6AiDAsPXoD/7Y2I5fYEZwGppUqek9opoK8wnBObBs66YfQRDMaa3ciPFcKpjY/a5FIXL4dv2g3Edg6FxxefwDqkJxiQQxtHRmqnonG1Tl26Br4pVxHbZSY4X3WrEobTw5aimegmfY8qwydZfRymduVlmJRl+4STdsDxNVKj3rieoJpnS2C4Zo1Ii5oVIDBcs0akRe0VIDBce80q9dAWDItDxsKlOBGJQyLg2rmdFjyrmwnhmq10FuPoNyeCN1ZRkqm2V86COeBk3ofIyh9S79aQcv3hc30dhHZt4LimckKVkrxCiJMz4BjooaqJXNsxDbl9RRhOWbMJfi/O49lbQfAa08+Qp0V814EC+grDlBQmQX1hKRdDZOkLiZULXFYsV1OoMGQcXIsTkDRqE1zeaQn+vrNAXi5Y/XrBmsfSgZrGMaRg1SYEJJ5HdMcp4I3sUyUMK/NKZJt5wKkkBTHvTwV3aC8IV2+Fxcf/U63ia+N4jXGoblyzJDBsXPFuqNkSGG4oZY3bLoFhLcRfGzCc9SgevKhvIDGxg2zzSTBMGFrwrG4m+HvOIPDeVsR69wFnTu2Sn6QtWg5LcRo8Cp6imGGFgoX7YOXhhIIXaXBbM6zaUhsJa3ai1YtjENq2geNa48s+WhGGc2dNg0f+Y8QOWAtO19frFkTSy2gV0GcYzpkZCk7BIzo2VD6BrLBjsOKwkPs0CeKncfC7qshEnLv2HMxsSaK9xnqIlfWho9uPAW/8oEownHrzMbwOhCDLnIestwfA/7biLLfIshlcixIQ690b/onnQYGyxab9jeU2GceAFCAwbEDB0mNXCQzrcXAM2DUCw1oInjZgWLBpDwKeHq4TgGphCmomUn64C7+zC5Ds8BacV5Wf961pHJm0BLZTe6qaxXQKBnd4b9W/mUG96XPLGfOPwprnrPo8/okYgVu/hLlcSn+5slm/o6ahGvV+6g93YN++JazdHeo9rry4FMJjl+B8/xTyek6AW3dFVt2KMGwa1Bvm8iKkhZ2BnQepLVxv0Y3MgD7DsGBNFAJenEYRw5peIY5pMxLcoGEomDaBzmJPg7CpIsM9uRpPAf7WAwh8vB8xLYeBGzyyEgwnbDuKwIc7QSVf5MyehNSwcPiKflW1U8Yz0akDXF9a7W+8WZCR9FkBAsP6HB3D8Y3AsOHEypA8JTCshWjVF4YFhy+De3sHrGX5SBy6Dq7vVy5boQU3NTaR8zQZnE1fI9uMA4tN+zTv93ccON9OgMiiGZjz18HS1V6tb+7MqfAo+Bex/VaD0+0N1b2HYXvxnkhRQzSP6QLTLUc0HrOhG5bkSWAybyQKLNmwWbQKBUvmQuzsC/uBA2Hryy7/MijKh6WrnZo7JaWA8FEmbJ3M4dxMcU+5DVTZMN3pNTDNmDBhMFDW/A0w3usC55XDkcd0hukWRVZvchEFaqOAPsMw//APYMY/gUmnLvA5Phv5TBZSW/RCwJPy1cRkp7fhvKK8Vm1t5k7a1k0B/s4TCLz/HWJ8vwB35gSVEWUCLeH8JfR54ZgPZ4A7qDt9P3tmKLW+D27BY1X7aP8vwJte3r9u3pBeTVEBAsNNMaqNPycCw42vuTGMSGBYC1GuDwwLT/8M/x8VX/wSHTvAdaX6GTotuFdrE7LSMthO+RQymEC8+RJMTJka2Ug5+RP8rq2sNluscG0U/ONPQ2xihzjf/rDO56Oo19fw3z0WFnIJPYYUFijddkGj8RqjkTBiG/zjTtFDUX5TLywqXvHsrnAaPxZOSweD+tl6yAjYB3CQFLED3vGXYCUvwBO/wfCZ8TXSfrgDn7MLIbLwRqb7G2iRdLrSFFLs28Aj7xGesnvCexFJntUYMW5qY+gzDFMvl8zsrSCXyVEWPBj2ZZkq+aksxG7dOkKSng27ZuUvmppafPRxPvyDFxF4ewPiPHvCY1753x0lDIsnUXXPU5D8zbdwft2PnkJxjhgWjtYonjISTqVC+rPoTiHgDSfVAvQxxrr2icCwriPQNMYnMNw04qhvsyAwrIWI1AeG+ZE7EBjzPWJeHwfuN19pwRvtmJAGD4NjSRpSpu2DQyBHI6PCzXvg/+9hRDcfAl7I6Ep9SsXFyF0aBs/cB6p71LliCoRTeSjI9ZwAACAASURBVJ3B5isylepLAhaqJBU3arzaPHKZzsh2boVm6QpfqYvv/DZ4mX+o/h0d8BUdU+WVbcqGxeYDyJozE7zcv/C880x4DumGmNsCiAUZdDOX5z8hQHiJ/pnaciiadQDOzdRX1jUKAmlk9AroMwxXDA5/y34EPjlAf5TE6giX5eFGHztdCcA/8RMCr69EvHsXsBfPQ87MEHAKntDuxHl9Br+ki9X+bRYsW48AgeJv14tBEXD/UHfJH3WlHxm3ZgUIDNesEWlRswIEhmvWiLSovQIEhmuvWaUe9YHhrNmzwct7gBf9V8D9k7e14I12TGTPngVu3sNa+SUKWwxv0W1EfzwHvC+7VuuIcOUGyCxs4Bl3GVb/rbRmhXwLi81zYFOWA9GCI7DRYlmpuiqSsjwSfvzL9DlumSsPgX9GIW7kd/Do6EubTAmPgJ/wisp8ssOb8My9X/7vj6bA/tYxOJSm49n7M9DiVgS9ulyy4RTMzNS9sjVjIG3yWDoZzZN2E+Az4Yu6uk36GbkChgLDRh4mvZp+yoXf4HdxkeqlhGTqaDhL+Wo+ptq0gH0Vdc8Tj/2EljcUuSVE84/CpkI+CL2aJHFGpwoQGNap/E1mcALDTSaUejURAsNaCEd9YNgkqB8s5QV6A4BKOYQrNsA/+WKlUhuvkqso5GuwipORNCYKLm8F1Khs1t0n4OwNBd/pLbrEivILmHDyDji21n2dSvnkAbApy1VtDcy8+wzO77RQzSsvRgj3yFF0HdR4zidwnzcDKas3wj/5EgROb8JpxSrw1+9GYHT5GehHgaPgN21oJW2oBFpp/yRCunkJrNdspc8Rk4soUBcFCAzXRTXj7pP20wP6DLfA/nU4rV6rKoGVa+YGh5J0WpwYr97gzg2uJFR2Qg6kG5eBaWYClzVrjFtIMvtqFSAwTB4ObShAYFgbKhIbLytAYFgLz0RdYbggQQS31UNQxLCFLKry+VEtuFZnE/zvjiHwr52I9esPzoyJNdphlJbAYkovmECGgo2XYGL+0tJnNRYyjp1DsUcguB+0QO70YHiInyJp2Aa4vNe6xjEbogGV9Orp1gvgftAKntu/QZ6pM0w3V5/ISnD0CuSiNDj06gY7H8U5x5Rdx+HYsyusPFjIuPccXnsmKz63CIB5+GZY2VUGXWU26bzMApiSkjINEVqjsUlg2GhCrbWJZtx5Cq99wUi1bg77dVtgPfET2nZi56nwvrlRAcOdp4E7pLxagNYGJ4aMQgECw0YR5gafJIHhBpfYKAcwOhguLSuDCcMEJlXU8c0vEIO67+SgnhX42s37aNfKD67OjlU+JHWF4ZTLd+B3biGSHd+C80rNSxg1xpMqPHcT/peXIpH1DlyXL6txyLx/X4C9eXytM1BXNJwxbz68su8hrk84PHooSg419pVw5Dpa/boKGWaecClJRqxXb3CqWA2pjV/UuXDLzGSYj5sK+2blJaUq2qhYWqk2tklbosDLChAYJs9EbRXIeZwEztYxyLDwRlnISrivHoIcM3e4bNwHcVBfmKMYyWOi4KzBjp/ajk3aG4cCBIaNI84NPUsCww2tsHHaNyoYlhRJMfCbxRg/rDd6ffKuKuJiSRFmL/sOP91+SH/WtpUfNi8LhgtLUVf27R4TsGHpZLz39mtahWH+1oMIfLwP0QEDwQsdq1dPYO7jJHhsHaM447ryEJ0B9lWX6MIv8L64DIlOHeG6om6JcFKXrIJv6nVEd5kF3leKlQlNrrznfBSnZcHGlwdrHkuTLlW2KSuVoSR0FFglKar7cX2WwqNH+bNSZ+M1dCQw3FDKGp9dAsPGF/P6zjj/RRrc1wxDjpkb8ocvgufuSUixboGAXTuQlV+M0jJ5fYcg/Y1cAQLDRv4AaGn6BIa1JCQxo6aA0cBwxLfHsOfoZXryq+d/owbDOw9fxPHzN3Bg83xYWZpj4pz18PHyQPisrxsUhtPDqNqNtxDzv7ngfvGx3j2aeTOmgF34DNEth4IXPKpa/5gJz4CI2bAoE+NZy+HwCh5Rp7kIV2+Bf8JZRHcIAm90P41tKFeUX66RqbGB/xomf38dzX9ehQKmI2zLcpBjxoZFxA7IzS1ra6rW7QkM11oy0qEaBQgMk0ejtgoUifLACvsCEhM7CPvMgd+Z+UhivYtWW9cQGK6tmKR9lQoQGCYPhjYUIDCsDRWJjZcVMBoYzsktQJFUiiFB4Qgd/5UaDA8YtwjdP3ob44b2ovW5cuMeQhdH4fHPe8BgMNRWhjOz8zB3xXZ0evs1jPrqU7p9XbdJZ88KBTf/EVKm7IZDK0+9ezpFvz2B94EQlDDMkRd2CFbsytvEmfFPwNwwF6YlEkRbdQBryVJYV3EmVpPJCTbtQ8DTg4huPQK8ycM16UK3kU3+kobXVKvmsI/conE/uqFcjrRdR+B9fx993pm64vutgJUPFw4BmpWUqt2AVbcmMKwNFYkNSgECw+Q5qK0CZRIp7EIV9YFjPpiBgF8jEMfrgXZr5xEYrq2YpD2BYfIMNJgCBIYbTFqjNmw0MKyMcvfBMzHl6/5qMExtg142ewwNxNT1b3QCvhy/GL+d3woHOxsVDL/Wwgejpq6kV43XLJwAU6YiEVJdYDj95iM0OxyKTHNPWG3crbcPoWj+Anhn3UV0q+HgTVFf8S159DdsvpsH0zIpnlh0QvE3i9GiJaPOc+HvPIXA+9sQ6/M5OLMmaWSnUJgJ1/BBCq4FAwWRF8C0Mqf/LQn5Ggy5DHmfT4Vbl/ZV2stZsRyc5Bv0PSpZVobTa2AvXaDR2NpsRGBYm2oaty0Cw8Yd/7rOXpk063m7MWj+9y5EtxiMNxYFERiuq6Ckn5oCZGWYPBDaUIDAsDZUJDZeVsDoYVgul+O1LqMRtXIaPny3Ha1PXIIAfUbNx7Vj6+Dh7kzD8PI5Y7H/+BWwnOywbtEkmJmWZwTOzJfW+slKXLQWvvzLiHljLHwnDal1/8bqkPEwFu5bxuP/2rsP6CjK/Y3jTxIICS0JPXQIgiJgRYVrvQqogFe5ioKiKIJcQLqgINIE6SBdEAtN6YgogoIg9oZgBUSpCQFDSwiBtP+ZyT9raEncnWWXfb9zzj0Xw8w78/u8v7Psk2mJIVEqPGOJkuOOqnB0hHa+951ilj5jH8aW8Ft09JFBaniDZ/eV7Zr/gaqvHaU/y9+hykP75avE2NXfqtLCPq51/2jUT0HpacqI3aOY37JeafRn9O2q/GL/s8bLTE1TSMcmSg8K0e6mA1TlvpvytU9vrFQ0rIDS0jOUkpp1dpoFAXcFihcuqJSTaTrFfZ7uEp6+XWamZH0DC/DlVPvmCs84rj8qNFb1fWu0o0EXXdntISWeOKV0PpYCfPa9X17JYqE6lHhKnn1L8P5xsgf/FrA+iUsUC5U737tzq8zqTxZzBYwPw9bUZ4fdxrdca3fCuc4MWz+3HrS1at5IVa5Q9rSOOXkqPd8ddPJosoILFNDxjvcoNDNFacMXq3jlUvne3hcr7vhfF5VP/FHxRWqp7PGtShuxRIkDuigqNU7bS9+hii8MUIkoz4/s92WfqsKS/tpT8gbVfHlkvgb8bcJ8Vfn2lVzXTQkqrKg5WfeL51z2f/GzIqZ00oFCVVVl1pv52p+3VipQIFgZGZn2/1gQ8ESgYIFgpWVkKpNe8oTRta31O4WQwM/Cin+shYqnJyi2WD2VT9yifc1eUO02jZSalinrl8YsCHgiEFowRKdS8/9dyZN9sW0ACwRJoQWc76VCoWe/8jKAFSntDAHCsCTrnuE7b7tOT7bOumfqXPcMN2/cUHHxCdq1d7/mTxmgyIiiLsr8XiYdP3iEUqNrKCS6nGLeH6w9Edeo5IgRft+UcSs+Ucyqv58Qva1hd9X8fIJOBYUpbeq7jh1/9qXjsUVrK3J01rstz7ekJSXbl0UfGjNO1eLXa3fJBqqc8IW9+rGQkgrLOK64yCtVMDVJ5ZN+0o57hym6yXWnDRc7e7lqfDFFv5dvovIDejtWhzsDcZm0O2pscy4BLpOmL9wRSOn2uEqc2mvfLlI8LUE7HxqjOv+5nsuk3cFkm7MEuEyapnBCgMuknVBkjDMFjAnD1vuDrTMlzR59Th0fvUfN7migggUL2B4z563U4pUb7KdJFw4vpI59x53zadJX1blE7XqNsreZNbaPva615BWG046dUOKw51Xh2Bb7IU/HS1RTzL4PtP26zqrw+L0XRVfGDhungscTVOXw19pZsqGqJnyuuGJ1FDFqvGPHf/jHP1Vhaod83Ucdu2C1Km6YqrDMZHv/u1qNVsZH76jE4W1K6TFWxaqXk/WE1IT31uqSr6a6Am/Its32+plh4Yp7c7mqxX6o367vqsptmztWhzsDEYbdUWMbwjA94JRAYq/OKpu8zTVcXOdZuuTGmoRhp4ANH4cwbHgDOFQ+YdghSIY5TcCYMGw9Hdo645tzWTk762FYx5NT1HvINH3yZVZQqlOrmiYN66YypbKenmxdRj1x6NNqcO3lsp5K3brzUFWuUEZThvdQSEhwnmF4/6CXVD1+nWvXKUFFFZaZpLiuryvisooXTUvGf7JF1d7q5TreHTX+q+heHR07/qS9f6nMsFZKColS8OSFuY57uG8fVTiW9V7odIUoZfL7CgoJPmubY9v2qdz4tvbZ4qRWfVR+bl/XOtmvUNrddrJKXV/LsTrcGYgw7I4a2xCG6QGnBA7262//sjN7OTRkiSpfUoYw7BSw4eMQhg1vAIfKJww7BMkwZobh/Mz70cTjSk1NU6kSEflZ3bVOXmeGT3Vto8jU/dpXtK4qJP1ob3ekYBmFTpz3j/bj65Wt128U7dlMQf//CIw/mvRTuXtvc+yw0pJSVPyZ5tbbjnRi+ofnHfdE3CGVHPKg/ffWu4ULHd6jUsOHnXf9oM4tFJ6RqN1lb1Tl+E/PWi952vn35VhxeQxEGL5Q0oG/Hy6TDvw59kaFe+e8Z9/+Yi3WZ3Dy1DUqWyKcMOwNbAPHJAwbOOleKJkw7AVUhpQxZ4a9Ode5heGTh48rql/WpdA77hmqmBUD7D//Xrmpyj/X3ZuH5ZWxc15KF99rtorViHZ0P+EdG9kPbj06aoUKFgs/59h7Zy1RzW+na3fpf6nUkEF57j9u4IuKObDBtd7vN/VUpU+nqVDmCR0Ir6Gi46blOYa3VyAMe1vYnPEJw+bMtZOVnkxIUsTz/7Xft54UHKngKYtUJjKMMOwkssFjEYYNnnwHSycMO4jJUC4BwrADzZBbGI7/eJOqLeyjuCK1VWz4GBXofr99n+uOuwcpuvm/HNj7hR0idtQU1fhzuawnNGdMfcfxnad3eVDF0g/ZPiVvukqhkYXP2kd2IP+j+UCVu/vGPI9h12srdNk3k+z1MhSixNHLdHDNlwre8qV0xQ0qd59zZ7fzPJjzrEAYdleO7c4UIAzTE+4KHOrbVxWPfa8DYdVUdPwMwrC7kGx3lgBhmKZwQoAw7IQiY5wpQBh2oCdyC8N7Zy5Sze9n6PfK96j8c0/ryC+7VKhEhMLLZd2PfLEt8Wu/V+jq+UouE6MKvf/n+OEnd2+nUid32+PuLna1ooaPUEiBv99rkpacouK9mutkULjSp67I1/4PbNqpqjPa2+vGFr9CkSPH5Gu7C7kSYfhCagf2vgjDgT2/3qwudv4qlf38dcVXvEnln32aMOxNbMPGJgwbNuFeKpcw7CVYw4clDDvQALmF4bjBIxSzf6223thLlR6+04G9BfYQsSMnKzxhp4on78k6Q1z2dkU+1VHh0Vm/PDj07W+qOOtp7S9cS8XHTs4XRnqGFPJ0CxXOSNS2q9qpYoeH8rXdhVyJMHwhtQN7X4ThwJ7fC1kdl0lfSO3A3hdhOLDn90JVRxi+UNJm7Ycw7MB85xaGs8907npiikrXr+nA3swY4ugve1Ricmf7vl5r2XFLD0U/dLf2L16j6mtH6/foJir/Qv7fDbx91W8K2bddpe+or2JVy/kdImHY76bkoj0gwvBFO3V+d+CEYb+bkov2gAjDF+3U+dWBE4b9ajoC5mAIwx5O5d42LRQy/BVlFi7qGulEtyd0pEwd+37UmPcG2j8/OvFDFSzo4c4M2zzh2+3KWD5XVRI+tyuPe+hFJf/wo2J+W6Bfr+igKh0fCBgRwnDATKXPCyEM+3wKAuYACMMBM5U+L4Qw7PMpCIgDIAwHxDT6XRGEYQ+n5EjLG7Wv7QRFXX+5PdLBT7eoyry/38Vr/exAWIyKjp/u4Z7M3XzPhFmqtfVtnQoK06GImip3ZIu2NhuuSk3rBwwKYThgptLnhRCGfT4FAXMAhOGAmUqfF0IY9vkUBMQBEIYDYhr9rgjCsIdTYoXhP27poXIP3W2PFDtmmmrsWKpDoRWVWKSSjpaqpaC69RXTiEukPaE+2rOLok9sdQ2xs+s8lbmsjCdD+tW2hGG/mo6L+mAIwxf19PnVwROG/Wo6LuqDIQxf1NPnNwdPGPabqQioAyEMezidVhjeEXOvont3ljIzlf609Wqgw9rXfpKirr7Uw9HZPFvgz+lLdPnmrLPraUEFlDjhAxUKzQwYIMJwwEylzwshDPt8CgLmAAjDATOVPi+EMOzzKQiIAyAMB8Q0+l0RhGEPp8QKw3sjrlKJEaP015e/qPKb3ZQYUkIhkxd4ODKb5xSI/fWIYl5+QNYHYWyhmoqcMCWggAjDATWdPi2GMOxT/oDaOWE4oKbTp8UQhn3KHzA7JwwHzFT6VSGEYQ+nwwrDSQWiFDxpoQ4MGaGqcWv1R/VmKvdMNw9HZvOcApmZ0sEevVT15BZtLdVIlYb2CSggwnBATadPiyEM+5Q/oHZOGA6o6fRpMYRhn/IHzM4JwwEzlX5VCGHYw+mIf7CR/fqfXVXvVJWdH9ijHew0SUXqcom0h7Rnbb5x1s86un2filxZR7c9VN7p4X06HmHYp/wBtXPCcEBNp0+LIQz7lD+gdk4YDqjp9FkxhGGf0Qf0jgnDHk7vtraPq0zydnuUlKDC2vfgS4q+pbaHo7L5uQSOHQtSeoYUFRk49wpn10kYpuedEiAMOyXJOIRhesApAcKwU5Jmj0MYNnv+vVU9YdhD2c09BqnKvo/sUbY0Hasazep5OCKbmyhAGDZx1r1TM2HYO64mjkoYNnHWvVMzYdg7rqaNShg2bcYvTL2EYQ+d93+3Q7uXrFRmdBVVat3Ew9HY3FQBwrCpM+983YRh501NHZEwbOrMO183Ydh5UxNHJAybOOver5kw7IBxbMIJB0ZhCJMFCMMmz76ztROGnfU0eTTCsMmz72zthGFnPU0djTBs6sx7t27CsAO+hGEHEA0fgjBseAM4WD5h2EFMw4ciDBveAA6WTxh2ENPgoQjDBk++F0snDDuASxh2ANHwIQjDhjeAg+UThh3ENHwowrDhDeBg+YRhBzENHoowbPDke7F0wrADuIRhBxANH4IwbHgDOFg+YdhBTMOHIgwb3gAOlk8YdhDT4KEIwwZPvhdLJww7gEsYdgDR8CEIw4Y3gIPlE4YdxDR8KMKw4Q3gYPmEYQcxDR6KMGzw5HuxdMKwA7iEYQcQDR+CMGx4AzhYPmHYQUzDhyIMG94ADpZPGHYQ0+ChCMMGT74XSycMO4BLGHYA0fAhCMOGN4CD5ROGHcQ0fCjCsOEN4GD5hGEHMQ0eijBs8OR7sXTCsAO4hGEHEA0fgjBseAM4WD5h2EFMw4ciDBveAA6WTxh2ENPgoQjDBk++F0snDHsRl6ERQAABBBBAAAEEEEAAAQT8U4Aw7J/zwlEhgAACCCCAAAIIIIAAAgh4UYAw7EVchkYAAQQQQAABBBBAAAEEEPBPAcKwB/OSmJSstPR0RUUU82AUNjVFwOqVAiEh5yz3r0NHVaRwuMLDQs/6+4yMTB1IOKxSJSLOu70phqbXafXQwYSjKhFZTIVCC/7jXjp1KlWHjyapTKlIBVk3X7EYK2D1kvW5k5mRqTKlohQSEnyaRV6fO/SSsa3zjwvPq5f4LvWPSY3dgF4yduq9Wjhh2A3e5BMp6vviK1r32SZ763q1YzTpxa52WGFB4FwCu/cd0F0P99GHb49R+XKlXKvs3hevjn3HadfeePtnLe6+WS/0fEwFC2SF5g1fbFbvIdNk9Zy1DOzVVi2b3wqygQIz563UhJmLXZU3ubW+BvZsq4jiReyf5dZLmZmZmjZ7haa8vsxe1wrTk4d31xW1YwyUpOQF76zTkPGzXRBlS0dp4otdVadWtTw/d+gl+udcAtYvR9r1Gq0TKSe1eOZg1yq5/RvGdyl6KafAyClvafai1aehXFXnEs2d3D/PzyV6iV7yRIAw7Ibeq/Pf06J312vOpP72mbz/PTte1SpHa2ifJ9wYjU0CXaBVp6Ha8ssOu8wzw3CHZ8aoaJFwDXu2vfYfSFDLpwbrhR6PqnnjhjqRcko339dVXZ64Tw+3uEPrP/9B3QZM0uq3RqtidOlAZ6O+MwQWrVyvSuXL6IraNbQn9oDa9Rypdq2aqu2Dd9pr5tZLm37arke6DNOcSf1U99Lqmjhrqd5b+4U+WjBOwcGcITat2d5d87kiI4rqmnq17Kubeg+eqrS0dL02vm+enzv0kmndkne91i9Inh85S8s/+FSXXVLFFYbz+jeM71J525q0xojJ8+1/2/p0auUqu1ChgipXukSen0v0kkmd4nythGE3TO9vP1DWWZn2Dzezt169/mv1HDRVP338OpceuuEZ6Jsc+OuIHXStUJwzDB9NPK6GzTvbv/W0fvtpLcNenqP9Bw5p0rBu9lnhTs+N16Y1MxX6/5fE3v1IXzsYP9yiUaCzUV8eAgNGvaZ9cQftAJNXL42dvlC//r5Lr455xh7V6snb7u9uf2m1vryymC1gXX1iXX44blCnPD936CWze+Vc1VtXrby/9ks1a9RQq9Z95QrDef0bxncpeimngBWGjxxL0oh+Hc6CoZfoFW8KEIbd0K1/V0e92LedHYit5ZdtO/VAh0H6/N0piiiWdckiCwI5BeIPHta/H+hxWhjesXOf7mnbX+uXTFDpkpH26nMWr9E7qz+zv0wsfHe93liwSu/PHeka6un+L6tqpWj16tgSYIMFUtPS1aRVbzW9vYHdC3n1khV2oiKKqn+3Ni61y29tq6kv9dAtDa4wWNLs0les+UzrPt2kbX/s0bhBnXVpjcp5fu7QS2b3zJnVr9nwrYaOf1OLZg7WJ19stvsn+zLpvP4N47sUvXRmGF6z4RvdcHVt+1k8/77xal1Tr6a9Cr1Er3hTgDD8D3Wty4Hq3Pb4aV8is7+IfrRgrKLLlvyHI7K6CQLnCsPZlxvm/CWK9YE/ffY7WrdovKzLfj74+OvT7r+yvogWLRyuQb3bmsBGjecRGDjmdb2/9iu9N2eE/TCsvHrJuoS6Vkzl036JYn0Rtfqo6e034GyogHUP+ndbtunAX4c1tE87XXfVpXl+7tBLhjbLOcr+8bc/9USPkfbVKXUvraaFKz4+LQzn9m/YwF6P8V2KVjpNwLp9Y+fe/fbDIX/a+qfWbvzevlqlya3X5fq5RC/RSJ4KEIbdELS+RA579kk1vuVae2vODLuBaNgmuZ0Z3rD0ZdfD1zgzbFhjuFHu1DeWa8oby/X29IH2F1Bryf6F3Pl6yfolivXQrH5dH3HtkTPDbuAH6CavzHlXc5es0cblk/I8A0MvBWgTuFHW0PGz9cV3P+vWBldmfRfavks/b92pB5rdov899h+t+vjrXK9u4ruUG+gGbfLs8Bk6cjRR00f2yvNziV4yqDG8UCph2A1U6z6XO2+7Tk+2bmpvzT3DbiAatsm5wvC57vO0vlxYZ2ly3jP8w4evqmDBArZYk1bP6NEHGnPPsGH9Y5Vr3dM5dvoC+0vBmy8/q9o1q7oU8uol6z7PrTt2a8bo3vY23DNsYAPlUrJ1qWuPgZO1ee0sffb1T/azCs73uUMv0TvZAhu/2qJft+9ygWz+ZYf9sMg29zfWI/9tpG9+2JprL/Fdil7KTSD7yhXrwY/Z9wyf73OJXqKXPBEgDLuhZz0sYvHKDfbTpAuHF7JfjcPTpN2ANGQT6/5O6wFad7buY9//a71aKfvVSU/2Hq3iRYvYVxqc+TTp5BMnVf+up9S3cyu15mnShnTL+cu0nta6bNVG+7fk1atEu1a0Xotjvb86t176+wnA/VX3sup6+dXF9gNveJq0mW1lXV3wr+vqqlZMJSUcPma/vi28UKh9uWtenzv0kpk9k5+qz7xMOq9e4rtUflTNWWf8jEW6p3F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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "# Create a plot to visualize the predictions vs actual values\n",
    "fig = go.Figure()\n",
    "\n",
    "# Plot the actual values\n",
    "fig.add_trace(go.Scatter(x=np.arange(len(y_true)), y=y_true, mode='lines', name='Actual'))\n",
    "\n",
    "# Plot the predicted values\n",
    "fig.add_trace(go.Scatter(x=np.arange(len(y_pred)), y=y_pred, mode='lines', name='Predicted'))\n",
    "\n",
    "# Update layout\n",
    "fig.update_layout(title='Actual vs Predicted Close Values on Validation Set',\n",
    "                  xaxis_title='Index',\n",
    "                  yaxis_title='Close Price',\n",
    "                  hovermode='x unified')\n",
    "\n",
    "# Show the plot\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "22d0f533-c280-4f9f-a8c5-eb1e0dd463f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ensure 'volume_return_7' is correctly set as a float\n",
    "next_day_df['volume_return_7'] = next_day_df['volume_return_7'].astype('float')\n",
    "\n",
    "# Ensure all other columns match their training data types\n",
    "for col in next_day_df.columns:\n",
    "    if training_types[col] == 'enum':  # If the column was treated as categorical during training\n",
    "        next_day_df[col] = next_day_df[col].astype('category')\n",
    "    elif training_types[col] == 'real':  # If the column was treated as real (float) during training\n",
    "        next_day_df[col] = next_day_df[col].astype('float')\n",
    "    elif training_types[col] == 'int':  # If the column was treated as integer during training\n",
    "        next_day_df[col] = next_day_df[col].astype('int')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "96d1be6c-516d-4d65-8747-460bc3a429c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n"
     ]
    }
   ],
   "source": [
    "# Convert the DataFrame to an H2OFrame\n",
    "next_day_h2o = h2o.H2OFrame(next_day_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "c1e6c26e-2a7a-4991-89c7-0c8431681be2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n",
      "\n",
      "H2OFrame column types:\n",
      "{'open': 'real', 'high': 'real', 'low': 'real', 'close': 'real', 'volume': 'int', 'vwap': 'real', 'adj_close': 'real', 'unadjusted_volume': 'int', 'change': 'real', 'change_percent': 'real', 'KalmanEstimate': 'real', 'LorentzianIndicator': 'real', 'REXPDE~TUSD': 'int', 'CPIDE': 'real', 'close_lag_1': 'real', 'close_lag_7': 'real', 'close_lag_30': 'real', 'close_return_1': 'real', 'close_return_7': 'real', 'close_return_30': 'real', 'REXPDE~TUSD_lag_1': 'int', 'REXPDE~TUSD_lag_7': 'int', 'REXPDE~TUSD_lag_30': 'int', 'REXPDE~TUSD_return_1': 'real', 'REXPDE~TUSD_return_7': 'real', 'REXPDE~TUSD_return_30': 'real', 'LorentzianIndicator_lag_1': 'real', 'LorentzianIndicator_lag_7': 'real', 'LorentzianIndicator_lag_30': 'real', 'LorentzianIndicator_return_1': 'real', 'LorentzianIndicator_return_7': 'real', 'LorentzianIndicator_return_30': 'real', 'volume_lag_1': 'int', 'volume_lag_7': 'int', 'volume_lag_30': 'int', 'volume_return_1': 'real', 'volume_return_7': 'real', 'volume_return_30': 'real', 'CPIDE_lag_1': 'real', 'CPIDE_lag_7': 'real', 'CPIDE_lag_30': 'real', 'CPIDE_return_1': 'real', 'CPIDE_return_7': 'real', 'CPIDE_return_30': 'real', 'date': 'time'}\n"
     ]
    }
   ],
   "source": [
    "import h2o\n",
    "import pandas as pd\n",
    "\n",
    "# Load the DataFrame and model\n",
    "df = pd.read_pickle('dax_data.pkl')\n",
    "\n",
    "# Convert the DataFrame to an H2OFrame\n",
    "h2o_df = h2o.H2OFrame(df)\n",
    "\n",
    "# Print data types to confirm\n",
    "print(\"\\nH2OFrame column types:\")\n",
    "print(h2o_df.types)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "369db021-ab2b-49ec-a612-5c745b9b0a94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n"
     ]
    }
   ],
   "source": [
    "import h2o\n",
    "import pandas as pd\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "# Prepare the data for the next day prediction\n",
    "last_row = df.iloc[-1].copy()\n",
    "\n",
    "# Increment the date to predict for the next day\n",
    "next_day = last_row['date'] + timedelta(days=1)\n",
    "last_row['date'] = next_day\n",
    "\n",
    "# Convert to DataFrame\n",
    "next_day_df = pd.DataFrame([last_row])\n",
    "\n",
    "# Ensure 'volume_return_7' and all similar columns are set as float\n",
    "next_day_df['volume_return_7'] = next_day_df['volume_return_7'].astype('float')\n",
    "\n",
    "# Convert the DataFrame to an H2OFrame with specified types\n",
    "types_dict = {\n",
    "    'open': 'real',\n",
    "    'high': 'real',\n",
    "    'low': 'real',\n",
    "    'close': 'real',\n",
    "    'volume': 'int',\n",
    "    'vwap': 'real',\n",
    "    'adj_close': 'real',\n",
    "    'unadjusted_volume': 'int',\n",
    "    'change': 'real',\n",
    "    'change_percent': 'real',\n",
    "    'KalmanEstimate': 'real',\n",
    "    'LorentzianIndicator': 'real',\n",
    "    'REXPDE~TUSD': 'int',\n",
    "    'CPIDE': 'real',\n",
    "    'close_lag_1': 'real',\n",
    "    'close_lag_7': 'real',\n",
    "    'close_lag_30': 'real',\n",
    "    'close_return_1': 'real',\n",
    "    'close_return_7': 'real',\n",
    "    'close_return_30': 'real',\n",
    "    'REXPDE~TUSD_lag_1': 'int',\n",
    "    'REXPDE~TUSD_lag_7': 'int',\n",
    "    'REXPDE~TUSD_lag_30': 'int',\n",
    "    'REXPDE~TUSD_return_1': 'real',\n",
    "    'REXPDE~TUSD_return_7': 'real',\n",
    "    'REXPDE~TUSD_return_30': 'real',\n",
    "    'LorentzianIndicator_lag_1': 'real',\n",
    "    'LorentzianIndicator_lag_7': 'real',\n",
    "    'LorentzianIndicator_lag_30': 'real',\n",
    "    'LorentzianIndicator_return_1': 'real',\n",
    "    'LorentzianIndicator_return_7': 'real',\n",
    "    'LorentzianIndicator_return_30': 'real',\n",
    "    'volume_lag_1': 'int',\n",
    "    'volume_lag_7': 'int',\n",
    "    'volume_lag_30': 'int',\n",
    "    'volume_return_1': 'real',\n",
    "    'volume_return_7': 'real',\n",
    "    'volume_return_30': 'real',\n",
    "    'CPIDE_lag_1': 'real',\n",
    "    'CPIDE_lag_7': 'real',\n",
    "    'CPIDE_lag_30': 'real',\n",
    "    'CPIDE_return_1': 'real',\n",
    "    'CPIDE_return_7': 'real',\n",
    "    'CPIDE_return_30': 'real',\n",
    "    'date': 'time'\n",
    "}\n",
    "\n",
    "# Convert the DataFrame to an H2OFrame with explicitly specified column types\n",
    "next_day_h2o = h2o.H2OFrame(next_day_df, column_types=types_dict)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "bc4bdca0-b3fb-424f-8c71-f95d6288e22f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n",
      "Prediction for 2024-08-24: 18633.202683363976\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/marius/miniconda3/envs/openbb/lib/python3.11/site-packages/h2o/frame.py:1981: H2ODependencyWarning:\n",
      "\n",
      "Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using multi-thread, install polars and pyarrow and use it as pandas_df = h2o_df.as_data_frame(use_multi_thread=True)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Predict the 'close' value for the next day\n",
    "try:\n",
    "    prediction = best_model.predict(next_day_h2o)\n",
    "    predicted_close = prediction.as_data_frame().iloc[0, 0]\n",
    "    print(f\"Prediction for {next_day.date()}: {predicted_close}\")\n",
    "except Exception as e:\n",
    "    print(f\"An error occurred during prediction: {str(e)}\")\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 274,
   "id": "b5e585bf-d251-41d5-83c1-4d86235c409d",
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          "2015-06-14T00:00:00",
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          "2015-06-25T00:00:00",
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          "2015-06-27T00:00:00",
          "2015-06-28T00:00:00",
          "2015-06-29T00:00:00",
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          "2015-07-05T00:00:00",
          "2015-07-06T00:00:00",
          "2015-07-07T00:00:00",
          "2015-07-08T00:00:00",
          "2015-07-09T00:00:00",
          "2015-07-10T00:00:00",
          "2015-07-11T00:00:00",
          "2015-07-12T00:00:00",
          "2015-07-13T00:00:00",
          "2015-07-14T00:00:00",
          "2015-07-15T00:00:00",
          "2015-07-16T00:00:00",
          "2015-07-17T00:00:00",
          "2015-07-18T00:00:00",
          "2015-07-19T00:00:00",
          "2015-07-20T00:00:00",
          "2015-07-21T00:00:00",
          "2015-07-22T00:00:00",
          "2015-07-23T00:00:00",
          "2015-07-24T00:00:00",
          "2015-07-25T00:00:00",
          "2015-07-26T00:00:00",
          "2015-07-27T00:00:00",
          "2015-07-28T00:00:00",
          "2015-07-29T00:00:00",
          "2015-07-30T00:00:00",
          "2015-07-31T00:00:00",
          "2015-08-01T00:00:00",
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          "2015-08-06T00:00:00",
          "2015-08-07T00:00:00",
          "2015-08-08T00:00:00",
          "2015-08-09T00:00:00",
          "2015-08-10T00:00:00",
          "2015-08-11T00:00:00",
          "2015-08-12T00:00:00",
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          "2015-08-14T00:00:00",
          "2015-08-15T00:00:00",
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          "2015-08-21T00:00:00",
          "2015-08-22T00:00:00",
          "2015-08-23T00:00:00",
          "2015-08-24T00:00:00",
          "2015-08-25T00:00:00",
          "2015-08-26T00:00:00",
          "2015-08-27T00:00:00",
          "2015-08-28T00:00:00",
          "2015-08-29T00:00:00",
          "2015-08-30T00:00:00",
          "2015-08-31T00:00:00",
          "2015-09-01T00:00:00",
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          "2015-09-03T00:00:00",
          "2015-09-04T00:00:00",
          "2015-09-05T00:00:00",
          "2015-09-06T00:00:00",
          "2015-09-07T00:00:00",
          "2015-09-08T00:00:00",
          "2015-09-09T00:00:00",
          "2015-09-10T00:00:00",
          "2015-09-11T00:00:00",
          "2015-09-12T00:00:00",
          "2015-09-13T00:00:00",
          "2015-09-14T00:00:00",
          "2015-09-15T00:00:00",
          "2015-09-16T00:00:00",
          "2015-09-17T00:00:00",
          "2015-09-18T00:00:00",
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          "2015-09-21T00:00:00",
          "2015-09-22T00:00:00",
          "2015-09-23T00:00:00",
          "2015-09-24T00:00:00",
          "2015-09-25T00:00:00",
          "2015-09-26T00:00:00",
          "2015-09-27T00:00:00",
          "2015-09-28T00:00:00",
          "2015-09-29T00:00:00",
          "2015-09-30T00:00:00",
          "2015-10-01T00:00:00",
          "2015-10-02T00:00:00",
          "2015-10-03T00:00:00",
          "2015-10-04T00:00:00",
          "2015-10-05T00:00:00",
          "2015-10-06T00:00:00",
          "2015-10-07T00:00:00",
          "2015-10-08T00:00:00",
          "2015-10-09T00:00:00",
          "2015-10-10T00:00:00",
          "2015-10-11T00:00:00",
          "2015-10-12T00:00:00",
          "2015-10-13T00:00:00",
          "2015-10-14T00:00:00",
          "2015-10-15T00:00:00",
          "2015-10-16T00:00:00",
          "2015-10-17T00:00:00",
          "2015-10-18T00:00:00",
          "2015-10-19T00:00:00",
          "2015-10-20T00:00:00",
          "2015-10-21T00:00:00",
          "2015-10-22T00:00:00",
          "2015-10-23T00:00:00",
          "2015-10-24T00:00:00",
          "2015-10-25T00:00:00",
          "2015-10-26T00:00:00",
          "2015-10-27T00:00:00",
          "2015-10-28T00:00:00",
          "2015-10-29T00:00:00",
          "2015-10-30T00:00:00",
          "2015-10-31T00:00:00",
          "2015-11-01T00:00:00",
          "2015-11-02T00:00:00",
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          "2015-11-04T00:00:00",
          "2015-11-05T00:00:00",
          "2015-11-06T00:00:00",
          "2015-11-07T00:00:00",
          "2015-11-08T00:00:00",
          "2015-11-09T00:00:00",
          "2015-11-10T00:00:00",
          "2015-11-11T00:00:00",
          "2015-11-12T00:00:00",
          "2015-11-13T00:00:00",
          "2015-11-14T00:00:00",
          "2015-11-15T00:00:00",
          "2015-11-16T00:00:00",
          "2015-11-17T00:00:00",
          "2015-11-18T00:00:00",
          "2015-11-19T00:00:00",
          "2015-11-20T00:00:00",
          "2015-11-21T00:00:00",
          "2015-11-22T00:00:00",
          "2015-11-23T00:00:00",
          "2015-11-24T00:00:00",
          "2015-11-25T00:00:00",
          "2015-11-26T00:00:00",
          "2015-11-27T00:00:00",
          "2015-11-28T00:00:00",
          "2015-11-29T00:00:00",
          "2015-11-30T00:00:00",
          "2015-12-01T00:00:00",
          "2015-12-02T00:00:00",
          "2015-12-03T00:00:00",
          "2015-12-04T00:00:00",
          "2015-12-05T00:00:00",
          "2015-12-06T00:00:00",
          "2015-12-07T00:00:00",
          "2015-12-08T00:00:00",
          "2015-12-09T00:00:00",
          "2015-12-10T00:00:00",
          "2015-12-11T00:00:00",
          "2015-12-12T00:00:00",
          "2015-12-13T00:00:00",
          "2015-12-14T00:00:00",
          "2015-12-15T00:00:00",
          "2015-12-16T00:00:00",
          "2015-12-17T00:00:00",
          "2015-12-18T00:00:00",
          "2015-12-19T00:00:00",
          "2015-12-20T00:00:00",
          "2015-12-21T00:00:00",
          "2015-12-22T00:00:00",
          "2015-12-23T00:00:00",
          "2015-12-24T00:00:00",
          "2015-12-25T00:00:00",
          "2015-12-26T00:00:00",
          "2015-12-27T00:00:00",
          "2015-12-28T00:00:00",
          "2015-12-29T00:00:00",
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          "2015-12-31T00:00:00",
          "2016-01-01T00:00:00",
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          "2016-01-08T00:00:00",
          "2016-01-09T00:00:00",
          "2016-01-10T00:00:00",
          "2016-01-11T00:00:00",
          "2016-01-12T00:00:00",
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          "2016-01-14T00:00:00",
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          "2016-01-18T00:00:00",
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          "2016-01-20T00:00:00",
          "2016-01-21T00:00:00",
          "2016-01-22T00:00:00",
          "2016-01-23T00:00:00",
          "2016-01-24T00:00:00",
          "2016-01-25T00:00:00",
          "2016-01-26T00:00:00",
          "2016-01-27T00:00:00",
          "2016-01-28T00:00:00",
          "2016-01-29T00:00:00",
          "2016-01-30T00:00:00",
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          "2016-02-01T00:00:00",
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          "2016-02-04T00:00:00",
          "2016-02-05T00:00:00",
          "2016-02-06T00:00:00",
          "2016-02-07T00:00:00",
          "2016-02-08T00:00:00",
          "2016-02-09T00:00:00",
          "2016-02-10T00:00:00",
          "2016-02-11T00:00:00",
          "2016-02-12T00:00:00",
          "2016-02-13T00:00:00",
          "2016-02-14T00:00:00",
          "2016-02-15T00:00:00",
          "2016-02-16T00:00:00",
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          "2016-02-19T00:00:00",
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          "2016-02-21T00:00:00",
          "2016-02-22T00:00:00",
          "2016-02-23T00:00:00",
          "2016-02-24T00:00:00",
          "2016-02-25T00:00:00",
          "2016-02-26T00:00:00",
          "2016-02-27T00:00:00",
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          "2016-02-29T00:00:00",
          "2016-03-01T00:00:00",
          "2016-03-02T00:00:00",
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          "2016-03-04T00:00:00",
          "2016-03-05T00:00:00",
          "2016-03-06T00:00:00",
          "2016-03-07T00:00:00",
          "2016-03-08T00:00:00",
          "2016-03-09T00:00:00",
          "2016-03-10T00:00:00",
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",
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Price\"},\"showgrid\":true,\"gridcolor\":\"LightGray\"},\"title\":{\"text\":\"Next Day Prediction with Violet Dot\"},\"hovermode\":\"x unified\",\"plot_bgcolor\":\"rgba(0,0,0,0)\"},                        {\"responsive\": true}                    ).then(function(){\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "# Historical data\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(x=df['date'], y=df['close'], mode='lines', name='Historical'))\n",
    "\n",
    "# Plot the prediction with a violet dot\n",
    "fig.add_trace(go.Scatter(x=[next_day], y=[predicted_close], mode='markers', name='Next Day Prediction', marker=dict(color='violet', size=12, symbol='circle')))\n",
    "\n",
    "# Update layout for better style\n",
    "fig.update_layout(\n",
    "    title='Next Day Prediction with Violet Dot',\n",
    "    xaxis_title='Date',\n",
    "    yaxis_title='Close Price',\n",
    "    hovermode='x unified',\n",
    "    plot_bgcolor='rgba(0,0,0,0)',\n",
    "    xaxis=dict(showgrid=True, gridcolor='LightGray'),\n",
    "    yaxis=dict(showgrid=True, gridcolor='LightGray')\n",
    ")\n",
    "\n",
    "# Show the plot\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8750240-2e84-4d6e-8f9a-a4ed82f7ea56",
   "metadata": {},
   "source": [
    "# Kalman estimate (high, low, close)\n",
    "\n",
    "Useful for stop-loss and take-profit estimation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "461af6cf-498b-413c-aa24-9ec87e050549",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "index                  datetime64[ns]\n",
      "open                          float64\n",
      "high                          float64\n",
      "low                           float64\n",
      "close                         float64\n",
      "volume                        float64\n",
      "vwap                          float64\n",
      "adj_close                     float64\n",
      "unadjusted_volume             float64\n",
      "change                        float64\n",
      "change_percent                float64\n",
      "KalmanEstimate                float64\n",
      "LorentzianIndicator           float64\n",
      "REXPDE~TUSD                   float64\n",
      "CPIDE                         float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "df = combined_df.iloc[-365:]\n",
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "a3240957-f544-46c4-9a9c-374145777e19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           index      open      high       low     close      volume  \\\n",
      "11911 2023-08-12  15927.79  15962.45  15803.08  15832.17  60296700.0   \n",
      "\n",
      "           vwap  adj_close  unadjusted_volume  change  change_percent  \\\n",
      "11911  15881.37   15832.17         60296700.0  -95.62       -0.006003   \n",
      "\n",
      "       KalmanEstimate  LorentzianIndicator  REXPDE~TUSD  CPIDE  \n",
      "11911    16021.801098             5.274866     441556.0  117.5  \n"
     ]
    }
   ],
   "source": [
    "print(df.head(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "26570013-fa43-48f9-8d8b-1175fa1ae643",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int64\n"
     ]
    }
   ],
   "source": [
    "print(df.index.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "49e265a1-1d6b-4f48-bc19-7146403d17cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimate for the next value: 18199.13689976488\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from pykalman import KalmanFilter\n",
    "\n",
    "# Assuming df is your DataFrame with a 'close' column\n",
    "\n",
    "# Initialize Kalman Filter\n",
    "kf = KalmanFilter(\n",
    "    initial_state_mean=df['close'].iloc[0],\n",
    "    initial_state_covariance=1,\n",
    "    transition_matrices=[1],\n",
    "    observation_matrices=[1],\n",
    "    observation_covariance=1,\n",
    "    transition_covariance=0.01\n",
    ")\n",
    "\n",
    "# Filter the data\n",
    "filtered_state_means, filtered_state_covariances = kf.filter(df['close'])\n",
    "\n",
    "# Get the last filtered state\n",
    "last_state_mean = filtered_state_means[-1]\n",
    "last_state_covariance = filtered_state_covariances[-1]\n",
    "\n",
    "# Predict the next state\n",
    "next_state_mean, next_state_covariance = kf.filter_update(\n",
    "    filtered_state_mean=last_state_mean,\n",
    "    filtered_state_covariance=last_state_covariance\n",
    ")\n",
    "\n",
    "print(f\"Estimate for the next value: {next_state_mean[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "9b952d5e-51cd-4858-b226-26c75c1c7890",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimate for the next value: 18271.87958586028\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from pykalman import KalmanFilter\n",
    "\n",
    "# Assuming df is your DataFrame with a 'close' column\n",
    "\n",
    "# Initialize Kalman Filter\n",
    "kf = KalmanFilter(\n",
    "    initial_state_mean=df['high'].iloc[0],\n",
    "    initial_state_covariance=1,\n",
    "    transition_matrices=[1],\n",
    "    observation_matrices=[1],\n",
    "    observation_covariance=1,\n",
    "    transition_covariance=0.01\n",
    ")\n",
    "\n",
    "# Filter the data\n",
    "filtered_state_means, filtered_state_covariances = kf.filter(df['high'])\n",
    "\n",
    "# Get the last filtered state\n",
    "last_state_mean = filtered_state_means[-1]\n",
    "last_state_covariance = filtered_state_covariances[-1]\n",
    "\n",
    "# Predict the next state\n",
    "next_state_mean, next_state_covariance = kf.filter_update(\n",
    "    filtered_state_mean=last_state_mean,\n",
    "    filtered_state_covariance=last_state_covariance\n",
    ")\n",
    "\n",
    "print(f\"Estimate for the next value: {next_state_mean[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "83038d7b-3aa4-4802-b19e-d19891f63352",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimate for the next value: 18199.13689976488\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from pykalman import KalmanFilter\n",
    "\n",
    "# Assuming df is your DataFrame with a 'close' column\n",
    "\n",
    "# Initialize Kalman Filter\n",
    "kf = KalmanFilter(\n",
    "    initial_state_mean=df['close'].iloc[0],\n",
    "    initial_state_covariance=1,\n",
    "    transition_matrices=[1],\n",
    "    observation_matrices=[1],\n",
    "    observation_covariance=1,\n",
    "    transition_covariance=0.01\n",
    ")\n",
    "\n",
    "# Filter the data\n",
    "filtered_state_means, filtered_state_covariances = kf.filter(df['close'])\n",
    "\n",
    "# Get the last filtered state\n",
    "last_state_mean = filtered_state_means[-1]\n",
    "last_state_covariance = filtered_state_covariances[-1]\n",
    "\n",
    "# Predict the next state\n",
    "next_state_mean, next_state_covariance = kf.filter_update(\n",
    "    filtered_state_mean=last_state_mean,\n",
    "    filtered_state_covariance=last_state_covariance\n",
    ")\n",
    "\n",
    "print(f\"Estimate for the next value: {next_state_mean[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73da180b-a0a8-488c-b377-fc4591ea0e61",
   "metadata": {},
   "source": [
    "# Buy / sell signals for the Dax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "69aa7d54-7d05-4a82-9d4a-4269023fb7cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import talib as ta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "d5c705f3-7041-42d6-88c0-1ecdf7882d73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "open                   float64\n",
      "high                   float64\n",
      "low                    float64\n",
      "close                  float64\n",
      "volume                 float64\n",
      "vwap                   float64\n",
      "adj_close              float64\n",
      "unadjusted_volume      float64\n",
      "change                 float64\n",
      "change_percent         float64\n",
      "KalmanEstimate         float64\n",
      "LorentzianIndicator    float64\n",
      "REXPDE~TUSD            float64\n",
      "CPIDE                  float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "df = combined_df.iloc[-365:].copy()\n",
    "print(df.dtypes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "47c6f776-ce9f-484c-90b9-8aa2a38b87fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ma(ma_type, src, length, volume=None):\n",
    "    if ma_type == 'SMA':\n",
    "        return ta.SMA(src, timeperiod=length)\n",
    "    elif ma_type == 'EMA':\n",
    "        return ta.EMA(src, timeperiod=length)\n",
    "    elif ma_type == 'DEMA':\n",
    "        return ta.DEMA(src, timeperiod=length)\n",
    "    elif ma_type == 'TEMA':\n",
    "        return ta.TEMA(src, timeperiod=length)\n",
    "    elif ma_type == 'WMA':\n",
    "        return ta.WMA(src, timeperiod=length)\n",
    "    elif ma_type == 'VWMA':\n",
    "        if volume is None:\n",
    "            raise ValueError(\"Volume data is required for VWMA\")\n",
    "        return ta.SMA(src * volume, timeperiod=length) / ta.SMA(volume, timeperiod=length)\n",
    "    elif ma_type == 'SMMA':\n",
    "        return ta.SMA(src, timeperiod=length)\n",
    "    elif ma_type == 'HMA':\n",
    "        return ta.WMA(2 * ta.WMA(src, timeperiod=length // 2) - ta.WMA(src, timeperiod=length), timeperiod=int(np.sqrt(length)))\n",
    "    elif ma_type == 'LSMA':\n",
    "        return ta.LINEARREG(src, timeperiod=length)\n",
    "    elif ma_type == 'PEMA':\n",
    "        ema1 = ta.EMA(src, timeperiod=length)\n",
    "        ema2 = ta.EMA(ema1, timeperiod=length)\n",
    "        ema3 = ta.EMA(ema2, timeperiod=length)\n",
    "        ema4 = ta.EMA(ema3, timeperiod=length)\n",
    "        ema5 = ta.EMA(ema4, timeperiod=length)\n",
    "        ema6 = ta.EMA(ema5, timeperiod=length)\n",
    "        ema7 = ta.EMA(ema6, timeperiod=length)\n",
    "        ema8 = ta.EMA(ema7, timeperiod=length)\n",
    "        return 8 * ema1 - 28 * ema2 + 56 * ema3 - 70 * ema4 + 56 * ema5 - 28 * ema6 + 8 * ema7 - ema8\n",
    "    else:\n",
    "        raise ValueError(f\"Unknown MA Type: {ma_type}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "e9289c53-f7b6-44ef-a10b-6f9a5ab4eb53",
   "metadata": {},
   "outputs": [],
   "source": [
    "def kalman_filter(df, sharpness, k_period):\n",
    "    df.loc[:, 'kfilt'] = 0.0\n",
    "    df.loc[:, 'velocity'] = 0.0\n",
    "    for i in range(1, len(df)):\n",
    "        distance = df['close'].iloc[i] - df['kfilt'].iloc[i - 1]\n",
    "        error = df['kfilt'].iloc[i - 1] + distance * np.sqrt(sharpness * k_period / 100)\n",
    "        velocity = df['velocity'].iloc[i - 1] + distance * k_period / 100\n",
    "        df.loc[df.index[i], 'kfilt'] = error + velocity\n",
    "        df.loc[df.index[i], 'velocity'] = velocity\n",
    "    return df['kfilt']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "8bc4ef6b-bc67-4735-8e1e-042f7782cdad",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_roc(df, length):\n",
    "    return ta.ROC(df['kfilt'], timeperiod=length)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "1002d3da-c2c9-4c79-8f73-b9bfecb9df0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_stochastic(df, periodK, smoothK, periodD):\n",
    "    k, d = ta.STOCH(df['high'], df['low'], df['close'],\n",
    "                    fastk_period=periodK,\n",
    "                    slowk_period=smoothK, \n",
    "                    slowk_matype=0,\n",
    "                    slowd_period=periodD,\n",
    "                    slowd_matype=0)\n",
    "    return k, d\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "a86a97b2-0327-4717-b0f1-8bcd32d5624d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_blend(df, ma_type, smoothlen):\n",
    "    roc = calculate_roc(df, length=9)  # Assuming length=9\n",
    "    _, d = calculate_stochastic(df, periodK=14, smoothK=1, periodD=3)  # Default values\n",
    "    blend = (roc + d) / 2\n",
    "    blend = ma(ma_type, blend, smoothlen)\n",
    "    blend = blend.dropna()  # Drop NaN values that could arise from insufficient data\n",
    "    return blend\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "03c913a3-453b-401c-87b8-a0bf41fa66a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_signals(df, blend):\n",
    "    df.loc[:, 'buy_signal'] = np.where((blend > blend.shift(1)) & (blend.shift(1) <= blend.shift(2)), 1, 0)\n",
    "    df.loc[:, 'sell_signal'] = np.where((blend < blend.shift(1)) & (blend.shift(1) >= blend.shift(2)), 1, 0)\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "95208370-2aec-4a2e-8424-6fe5a80a06c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def analyze(df, ma_type='TEMA', smoothlen=12, sharpness=25.0, k_period=1.0):\n",
    "    df['kfilt'] = kalman_filter(df, sharpness, k_period)\n",
    "    blend = calculate_blend(df, ma_type, smoothlen)\n",
    "    df['blend'] = blend\n",
    "    df = generate_signals(df, blend)\n",
    "    df = df.dropna(subset=['blend'])  # Drop rows where 'blend' is NaN\n",
    "    return df[['buy_signal', 'sell_signal']]  # Return only the buy and sell signals\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "a35b5788-c54e-419c-9327-fce714d5ca6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    open          high           low         close  \\\n",
      "2023-08-19  15597.120000  15612.880000  15468.650000  15574.260000   \n",
      "2023-08-20  15597.120000  15612.880000  15468.650000  15574.260000   \n",
      "2023-08-21  15573.950000  15710.210000  15571.820000  15603.280000   \n",
      "2023-08-22  15684.640000  15799.340000  15677.440000  15705.620000   \n",
      "2023-08-23  15761.250000  15820.950000  15672.430000  15728.410000   \n",
      "...                  ...           ...           ...           ...   \n",
      "2024-08-26  18470.006463  18582.574769  18450.405407  18537.821152   \n",
      "2024-08-27  18470.149396  18582.209304  18451.668874  18537.641506   \n",
      "2024-08-28  18470.316624  18582.192740  18451.403439  18537.936833   \n",
      "2024-08-29  18470.645896  18583.090019  18451.158720  18538.491185   \n",
      "2024-08-30  18471.650998  18582.831214  18451.680800  18538.901426   \n",
      "\n",
      "                  volume          vwap     adj_close  unadjusted_volume  \\\n",
      "2023-08-19  5.990950e+07  15563.230000  15574.260000       5.990950e+07   \n",
      "2023-08-20  5.990950e+07  15563.230000  15574.260000       5.990950e+07   \n",
      "2023-08-21  4.525110e+07  15614.820000  15603.280000       4.525110e+07   \n",
      "2023-08-22  4.280740e+07  15727.470000  15705.620000       4.280740e+07   \n",
      "2023-08-23  5.342540e+07  15745.760000  15728.410000       5.342540e+07   \n",
      "...                  ...           ...           ...                ...   \n",
      "2024-08-26  4.042621e+07  18509.926913  18536.299033       4.042621e+07   \n",
      "2024-08-27  4.042621e+07  18509.730243  18536.621775       4.042621e+07   \n",
      "2024-08-28  4.042621e+07  18509.744616  18537.703402       4.042621e+07   \n",
      "2024-08-29  4.042621e+07  18510.383842  18537.549513       4.042621e+07   \n",
      "2024-08-30  4.042621e+07  18510.479391  18537.659088       4.042621e+07   \n",
      "\n",
      "               change  change_percent  KalmanEstimate  LorentzianIndicator  \\\n",
      "2023-08-19 -22.860000       -0.001466    15906.008411             5.249547   \n",
      "2023-08-20 -22.860000       -0.001466    15906.008411             5.249547   \n",
      "2023-08-21  29.330000        0.001883    15877.211395             5.223702   \n",
      "2023-08-22  20.980000        0.001338    15860.888777             5.188841   \n",
      "2023-08-23 -32.840000       -0.002084    15848.286743             5.163152   \n",
      "...               ...             ...             ...                  ...   \n",
      "2024-08-26  66.122684        0.974946    18160.294818             6.115079   \n",
      "2024-08-27  66.189169        0.942071    18159.784202             6.065491   \n",
      "2024-08-28  65.839109        0.664471    18160.138380             6.988810   \n",
      "2024-08-29  66.436632        1.605050    18160.260280             6.453768   \n",
      "2024-08-30  65.675039        0.881043    18159.978241             5.691005   \n",
      "\n",
      "              REXPDE~TUSD       CPIDE         kfilt    velocity      blend  \\\n",
      "2023-08-19  441556.000000  117.500000      0.000000    0.000000        NaN   \n",
      "2023-08-20  441556.000000  117.500000   7942.872600  155.742600        NaN   \n",
      "2023-08-21  441556.000000  117.500000  12005.422974  232.346674        NaN   \n",
      "2023-08-22  441556.000000  117.500000  14124.870131  269.348644        NaN   \n",
      "2023-08-23  441556.000000  117.500000  15212.024109  285.384043        NaN   \n",
      "...                   ...         ...           ...         ...        ...   \n",
      "2024-08-26  441754.098877  120.530011  18551.078791    6.902298  53.161177   \n",
      "2024-08-27  441753.978259  120.758387  18551.128073    6.767925  51.591696   \n",
      "2024-08-28  441754.154286  121.043271  18551.168466    6.636013  49.939906   \n",
      "2024-08-29  441753.528517  121.267125  18551.339065    6.509240  48.342753   \n",
      "2024-08-30  441754.250399  121.588486  18551.505109    6.384863  46.728891   \n",
      "\n",
      "            buy_signal  sell_signal  \n",
      "2023-08-19           0            0  \n",
      "2023-08-20           0            0  \n",
      "2023-08-21           0            0  \n",
      "2023-08-22           0            0  \n",
      "2023-08-23           0            0  \n",
      "...                ...          ...  \n",
      "2024-08-26           0            0  \n",
      "2024-08-27           0            0  \n",
      "2024-08-28           0            0  \n",
      "2024-08-29           0            0  \n",
      "2024-08-30           0            0  \n",
      "\n",
      "[365 rows x 19 columns]\n"
     ]
    }
   ],
   "source": [
    "# Analyze and print only the buy and sell signals\n",
    "signal_df = analyze(df)\n",
    "print(signal_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "1340b1ea-482a-49aa-b259-58449490c862",
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objs as go\n",
    "import plotly.express as px\n",
    "\n",
    "# Example: Assuming 'df' is the DataFrame after analysis and contains the 'buy_signal' and 'sell_signal' columns\n",
    "# and 'close' column to plot the price\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "c3931367-6cb3-43c6-a65f-57a713bbc511",
   "metadata": {},
   "outputs": [
    {
     "data": {
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   "source": [
    "def plot_signals(df):\n",
    "    # Create a candlestick chart\n",
    "    fig = go.Figure(data=[go.Candlestick(x=df.index,\n",
    "                                         open=df['open'],\n",
    "                                         high=df['high'],\n",
    "                                         low=df['low'],\n",
    "                                         close=df['close'])])\n",
    "\n",
    "    # Add Buy signals as green up arrows\n",
    "    buy_signals = df[df['buy_signal'] == 1]\n",
    "    fig.add_trace(go.Scatter(x=buy_signals.index,\n",
    "                             y=buy_signals['close'],\n",
    "                             mode='markers',\n",
    "                             marker=dict(symbol='triangle-up', color='green', size=10),\n",
    "                             name='Buy Signal'))\n",
    "\n",
    "    # Add Sell signals as red down arrows\n",
    "    sell_signals = df[df['sell_signal'] == 1]\n",
    "    fig.add_trace(go.Scatter(x=sell_signals.index,\n",
    "                             y=sell_signals['close'],\n",
    "                             mode='markers',\n",
    "                             marker=dict(symbol='triangle-down', color='red', size=10),\n",
    "                             name='Sell Signal'))\n",
    "\n",
    "    # Update layout for better visuals\n",
    "    fig.update_layout(title='Buy and Sell Signals',\n",
    "                      xaxis_title='Date',\n",
    "                      yaxis_title='Price',\n",
    "                      xaxis_rangeslider_visible=False)\n",
    "\n",
    "    # Show the plot\n",
    "    fig.show()\n",
    "\n",
    "# Assuming signal_df contains the 'buy_signal' and 'sell_signal' columns along with price data\n",
    "plot_signals(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "80903fc5-738c-4258-8fe4-b3c6ac1925fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_signal_list(df):\n",
    "    # Create an empty list to store the signals\n",
    "    signals = []\n",
    "    \n",
    "    # Iterate through the DataFrame and check for buy and sell signals\n",
    "    for index, row in df.iterrows():\n",
    "        if row['buy_signal'] == 1:\n",
    "            signals.append({'Date': index, 'Signal': 'Buy', 'Price': row['close']})\n",
    "        elif row['sell_signal'] == 1:\n",
    "            signals.append({'Date': index, 'Signal': 'Sell', 'Price': row['close']})\n",
    "    \n",
    "    return signals\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "b0ac3c11-8f1d-4a08-bc9f-8d35732abc7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date: 2023-10-14 00:00:00, Signal: Sell, Price: 15186.66016\n",
      "Date: 2023-10-24 00:00:00, Signal: Buy, Price: 14879.94043\n",
      "Date: 2023-10-28 00:00:00, Signal: Sell, Price: 14687.41016\n",
      "Date: 2023-10-30 00:00:00, Signal: Buy, Price: 14716.54004\n",
      "Date: 2023-11-11 00:00:00, Signal: Sell, Price: 15234.38965\n",
      "Date: 2023-11-14 00:00:00, Signal: Buy, Price: 15614.42969\n",
      "Date: 2023-11-18 00:00:00, Signal: Sell, Price: 15919.16016\n",
      "Date: 2023-11-19 00:00:00, Signal: Buy, Price: 15919.16016\n",
      "Date: 2023-11-20 00:00:00, Signal: Sell, Price: 15901.33008\n",
      "Date: 2023-11-25 00:00:00, Signal: Buy, Price: 16029.49023\n",
      "Date: 2023-11-26 00:00:00, Signal: Sell, Price: 16029.49023\n",
      "Date: 2023-12-01 00:00:00, Signal: Buy, Price: 16397.51953\n",
      "Date: 2023-12-06 00:00:00, Signal: Sell, Price: 16656.43945\n",
      "Date: 2023-12-09 00:00:00, Signal: Buy, Price: 16759.2207\n",
      "Date: 2023-12-13 00:00:00, Signal: Sell, Price: 16766.05078\n",
      "Date: 2023-12-27 00:00:00, Signal: Buy, Price: 16742.07031\n",
      "Date: 2024-01-03 00:00:00, Signal: Sell, Price: 16538.39062\n",
      "Date: 2024-01-08 00:00:00, Signal: Buy, Price: 16716.4707\n",
      "Date: 2024-01-11 00:00:00, Signal: Sell, Price: 16547.0293\n",
      "Date: 2024-01-12 00:00:00, Signal: Buy, Price: 16704.56055\n",
      "Date: 2024-01-16 00:00:00, Signal: Sell, Price: 16571.67969\n",
      "Date: 2024-01-20 00:00:00, Signal: Buy, Price: 16555.13086\n",
      "Date: 2024-01-31 00:00:00, Signal: Sell, Price: 16903.75977\n",
      "Date: 2024-02-12 00:00:00, Signal: Buy, Price: 17037.34961\n",
      "Date: 2024-02-13 00:00:00, Signal: Sell, Price: 16880.83008\n",
      "Date: 2024-02-14 00:00:00, Signal: Buy, Price: 16945.48047\n",
      "Date: 2024-02-15 00:00:00, Signal: Sell, Price: 17046.68945\n",
      "Date: 2024-02-16 00:00:00, Signal: Buy, Price: 17117.43945\n",
      "Date: 2024-03-01 00:00:00, Signal: Sell, Price: 17735.07031\n",
      "Date: 2024-03-09 00:00:00, Signal: Buy, Price: 17814.50977\n",
      "Date: 2024-03-10 00:00:00, Signal: Sell, Price: 17814.50977\n",
      "Date: 2024-03-13 00:00:00, Signal: Buy, Price: 17961.38086\n",
      "Date: 2024-03-15 00:00:00, Signal: Sell, Price: 17936.65039\n",
      "Date: 2024-03-19 00:00:00, Signal: Buy, Price: 17987.49023\n",
      "Date: 2024-03-31 00:00:00, Signal: Sell, Price: 18492.49023\n",
      "Date: 2024-04-15 00:00:00, Signal: Buy, Price: 18026.58008\n",
      "Date: 2024-04-30 00:00:00, Signal: Sell, Price: 17932.16992\n",
      "Date: 2024-05-06 00:00:00, Signal: Buy, Price: 18175.21094\n",
      "Date: 2024-05-14 00:00:00, Signal: Sell, Price: 18716.41992\n",
      "Date: 2024-05-27 00:00:00, Signal: Buy, Price: 18774.71094\n",
      "Date: 2024-05-29 00:00:00, Signal: Sell, Price: 18473.28906\n",
      "Date: 2024-06-03 00:00:00, Signal: Buy, Price: 18608.16016\n",
      "Date: 2024-06-10 00:00:00, Signal: Sell, Price: 18494.89062\n",
      "Date: 2024-06-12 00:00:00, Signal: Buy, Price: 18630.85938\n",
      "Date: 2024-06-13 00:00:00, Signal: Sell, Price: 18265.67969\n",
      "Date: 2024-06-19 00:00:00, Signal: Buy, Price: 18067.91016\n",
      "Date: 2024-07-07 00:00:00, Signal: Sell, Price: 18472.05078\n",
      "Date: 2024-07-12 00:00:00, Signal: Buy, Price: 18748.17969\n",
      "Date: 2024-07-17 00:00:00, Signal: Sell, Price: 18437.30078\n",
      "Date: 2024-07-24 00:00:00, Signal: Buy, Price: 18387.46094\n",
      "Date: 2024-07-25 00:00:00, Signal: Sell, Price: 18298.7207\n",
      "Date: 2024-07-30 00:00:00, Signal: Buy, Price: 18411.17969\n",
      "Date: 2024-08-01 00:00:00, Signal: Sell, Price: 18083.05078\n",
      "Date: 2024-08-07 00:00:00, Signal: Buy, Price: 17615.15039\n",
      "Date: 2024-08-25 00:00:00, Signal: Sell, Price: 18537.529187856657\n"
     ]
    }
   ],
   "source": [
    "# Assuming df contains the 'buy_signal' and 'sell_signal' columns\n",
    "signal_list = get_signal_list(df)\n",
    "\n",
    "# Display the signal list\n",
    "for signal in signal_list:\n",
    "    print(f\"Date: {signal['Date']}, Signal: {signal['Signal']}, Price: {signal['Price']}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb0458cb-fc41-4c57-b2d9-b7d870463efd",
   "metadata": {},
   "source": [
    "## Backtest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "f5620cc4-3d06-453d-beb4-d015bb95e0f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_depot = 10000  # Initial depot in Euros\n",
    "depot = initial_depot  # Current depot value\n",
    "position_size_percentage = 0.03   # 3% of the depot for each trade\n",
    "stop_loss_points = 100  # Stop loss of 100 points\n",
    "dax_data = df['close']  # Assuming 'close' is the DAX price column\n",
    "\n",
    "# Generate the signal list as in the previous steps\n",
    "signal_list = get_signal_list(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "061a831e-ba43-48c6-bd4f-b5cb51e714e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import timedelta\n",
    "\n",
    "# Initialize variables\n",
    "current_position = None  # Track whether in a buy or sell position\n",
    "entry_price = None  # Track the price at which the position was entered\n",
    "entry_date = None  # Track the date at which the position was entered\n",
    "results = []  # Store results of each trade\n",
    "\n",
    "for signal in signal_list:\n",
    "    signal_date = signal['Date']\n",
    "    signal_type = signal['Signal']\n",
    "    signal_price = signal['Price']\n",
    "    \n",
    "    if signal_type == 'Buy':\n",
    "        if current_position is None:  # Enter a new long position\n",
    "            position_size = depot * position_size_percentage\n",
    "            entry_price = signal_price\n",
    "            entry_date = signal_date\n",
    "            current_position = 'long'\n",
    "            results.append({\n",
    "                'Date': signal_date,\n",
    "                'Action': 'Buy',\n",
    "                'Entry Price': entry_price,\n",
    "                'Position Size': position_size,\n",
    "                'Depot': depot\n",
    "            })\n",
    "    \n",
    "    elif signal_type == 'Sell':\n",
    "        if current_position == 'long':  # Exit the long position\n",
    "            profit = (signal_price - entry_price) * (position_size / entry_price)\n",
    "            depot += profit\n",
    "            results.append({\n",
    "                'Date': signal_date,\n",
    "                'Action': 'Sell',\n",
    "                'Exit Price': signal_price,\n",
    "                'Profit': profit,\n",
    "                'Depot': depot\n",
    "            })\n",
    "            current_position = None\n",
    "    \n",
    "    # Implement stop loss logic\n",
    "    if current_position == 'long' and signal_price <= entry_price - stop_loss_points:\n",
    "        # Sell the position if stop loss is hit\n",
    "        loss = (entry_price - (entry_price - stop_loss_points)) * (position_size / entry_price)\n",
    "        depot -= loss\n",
    "        results.append({\n",
    "            'Date': signal_date,\n",
    "            'Action': 'Stop Loss Triggered',\n",
    "            'Exit Price': entry_price - stop_loss_points,\n",
    "            'Loss': loss,\n",
    "            'Depot': depot\n",
    "        })\n",
    "        current_position = None\n",
    "    \n",
    "    # Automatically close the position after 2 days\n",
    "    if current_position == 'long' and (signal_date - entry_date).days >= 2:\n",
    "        profit = (signal_price - entry_price) * (position_size / entry_price)\n",
    "        depot += profit\n",
    "        results.append({\n",
    "            'Date': signal_date,\n",
    "            'Action': 'Auto Close After 2 Days',\n",
    "            'Exit Price': signal_price,\n",
    "            'Profit': profit,\n",
    "            'Depot': depot\n",
    "        })\n",
    "        current_position = None\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "fc54c9b2-c8ad-4698-b3fc-d539ef847901",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date: 2023-10-24 00:00:00, Action: Buy, Entry Price: 14879.94043, Exit Price: N/A, Profit/Loss: N/A, Depot: 10000.00\n",
      "Date: 2023-10-28 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 14687.41016, Profit/Loss: -3.88, Depot: 9996.12\n",
      "Date: 2023-10-30 00:00:00, Action: Buy, Entry Price: 14716.54004, Exit Price: N/A, Profit/Loss: N/A, Depot: 9996.12\n",
      "Date: 2023-11-11 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 15234.38965, Profit/Loss: 10.55, Depot: 10006.67\n",
      "Date: 2023-11-14 00:00:00, Action: Buy, Entry Price: 15614.42969, Exit Price: N/A, Profit/Loss: N/A, Depot: 10006.67\n",
      "Date: 2023-11-18 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 15919.16016, Profit/Loss: 5.86, Depot: 10012.53\n",
      "Date: 2023-11-19 00:00:00, Action: Buy, Entry Price: 15919.16016, Exit Price: N/A, Profit/Loss: N/A, Depot: 10012.53\n",
      "Date: 2023-11-20 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 15901.33008, Profit/Loss: -0.34, Depot: 10012.19\n",
      "Date: 2023-11-25 00:00:00, Action: Buy, Entry Price: 16029.49023, Exit Price: N/A, Profit/Loss: N/A, Depot: 10012.19\n",
      "Date: 2023-11-26 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 16029.49023, Profit/Loss: 0.00, Depot: 10012.19\n",
      "Date: 2023-12-01 00:00:00, Action: Buy, Entry Price: 16397.51953, Exit Price: N/A, Profit/Loss: N/A, Depot: 10012.19\n",
      "Date: 2023-12-06 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 16656.43945, Profit/Loss: 4.74, Depot: 10016.94\n",
      "Date: 2023-12-09 00:00:00, Action: Buy, Entry Price: 16759.2207, Exit Price: N/A, Profit/Loss: N/A, Depot: 10016.94\n",
      "Date: 2023-12-13 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 16766.05078, Profit/Loss: 0.12, Depot: 10017.06\n",
      "Date: 2023-12-27 00:00:00, Action: Buy, Entry Price: 16742.07031, Exit Price: N/A, Profit/Loss: N/A, Depot: 10017.06\n",
      "Date: 2024-01-03 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 16538.39062, Profit/Loss: -3.66, Depot: 10013.40\n",
      "Date: 2024-01-08 00:00:00, Action: Buy, Entry Price: 16716.4707, Exit Price: N/A, Profit/Loss: N/A, Depot: 10013.40\n",
      "Date: 2024-01-11 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 16547.0293, Profit/Loss: -3.04, Depot: 10010.36\n",
      "Date: 2024-01-12 00:00:00, Action: Buy, Entry Price: 16704.56055, Exit Price: N/A, Profit/Loss: N/A, Depot: 10010.36\n",
      "Date: 2024-01-16 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 16571.67969, Profit/Loss: -2.39, Depot: 10007.97\n",
      "Date: 2024-01-20 00:00:00, Action: Buy, Entry Price: 16555.13086, Exit Price: N/A, Profit/Loss: N/A, Depot: 10007.97\n",
      "Date: 2024-01-31 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 16903.75977, Profit/Loss: 6.32, Depot: 10014.29\n",
      "Date: 2024-02-12 00:00:00, Action: Buy, Entry Price: 17037.34961, Exit Price: N/A, Profit/Loss: N/A, Depot: 10014.29\n",
      "Date: 2024-02-13 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 16880.83008, Profit/Loss: -2.76, Depot: 10011.53\n",
      "Date: 2024-02-14 00:00:00, Action: Buy, Entry Price: 16945.48047, Exit Price: N/A, Profit/Loss: N/A, Depot: 10011.53\n",
      "Date: 2024-02-15 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 17046.68945, Profit/Loss: 1.79, Depot: 10013.32\n",
      "Date: 2024-02-16 00:00:00, Action: Buy, Entry Price: 17117.43945, Exit Price: N/A, Profit/Loss: N/A, Depot: 10013.32\n",
      "Date: 2024-03-01 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 17735.07031, Profit/Loss: 10.84, Depot: 10024.16\n",
      "Date: 2024-03-09 00:00:00, Action: Buy, Entry Price: 17814.50977, Exit Price: N/A, Profit/Loss: N/A, Depot: 10024.16\n",
      "Date: 2024-03-10 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 17814.50977, Profit/Loss: 0.00, Depot: 10024.16\n",
      "Date: 2024-03-13 00:00:00, Action: Buy, Entry Price: 17961.38086, Exit Price: N/A, Profit/Loss: N/A, Depot: 10024.16\n",
      "Date: 2024-03-15 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 17936.65039, Profit/Loss: -0.41, Depot: 10023.75\n",
      "Date: 2024-03-19 00:00:00, Action: Buy, Entry Price: 17987.49023, Exit Price: N/A, Profit/Loss: N/A, Depot: 10023.75\n",
      "Date: 2024-03-31 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 18492.49023, Profit/Loss: 8.44, Depot: 10032.19\n",
      "Date: 2024-04-15 00:00:00, Action: Buy, Entry Price: 18026.58008, Exit Price: N/A, Profit/Loss: N/A, Depot: 10032.19\n",
      "Date: 2024-04-30 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 17932.16992, Profit/Loss: -1.58, Depot: 10030.62\n",
      "Date: 2024-05-06 00:00:00, Action: Buy, Entry Price: 18175.21094, Exit Price: N/A, Profit/Loss: N/A, Depot: 10030.62\n",
      "Date: 2024-05-14 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 18716.41992, Profit/Loss: 8.96, Depot: 10039.58\n",
      "Date: 2024-05-27 00:00:00, Action: Buy, Entry Price: 18774.71094, Exit Price: N/A, Profit/Loss: N/A, Depot: 10039.58\n",
      "Date: 2024-05-29 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 18473.28906, Profit/Loss: -4.84, Depot: 10034.74\n",
      "Date: 2024-06-03 00:00:00, Action: Buy, Entry Price: 18608.16016, Exit Price: N/A, Profit/Loss: N/A, Depot: 10034.74\n",
      "Date: 2024-06-10 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 18494.89062, Profit/Loss: -1.83, Depot: 10032.91\n",
      "Date: 2024-06-12 00:00:00, Action: Buy, Entry Price: 18630.85938, Exit Price: N/A, Profit/Loss: N/A, Depot: 10032.91\n",
      "Date: 2024-06-13 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 18265.67969, Profit/Loss: -5.90, Depot: 10027.01\n",
      "Date: 2024-06-19 00:00:00, Action: Buy, Entry Price: 18067.91016, Exit Price: N/A, Profit/Loss: N/A, Depot: 10027.01\n",
      "Date: 2024-07-07 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 18472.05078, Profit/Loss: 6.73, Depot: 10033.74\n",
      "Date: 2024-07-12 00:00:00, Action: Buy, Entry Price: 18748.17969, Exit Price: N/A, Profit/Loss: N/A, Depot: 10033.74\n",
      "Date: 2024-07-17 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 18437.30078, Profit/Loss: -4.99, Depot: 10028.75\n",
      "Date: 2024-07-24 00:00:00, Action: Buy, Entry Price: 18387.46094, Exit Price: N/A, Profit/Loss: N/A, Depot: 10028.75\n",
      "Date: 2024-07-25 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 18298.7207, Profit/Loss: -1.45, Depot: 10027.29\n",
      "Date: 2024-07-30 00:00:00, Action: Buy, Entry Price: 18411.17969, Exit Price: N/A, Profit/Loss: N/A, Depot: 10027.29\n",
      "Date: 2024-08-01 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 18083.05078, Profit/Loss: -5.36, Depot: 10021.93\n",
      "Date: 2024-08-07 00:00:00, Action: Buy, Entry Price: 17615.15039, Exit Price: N/A, Profit/Loss: N/A, Depot: 10021.93\n",
      "Date: 2024-08-25 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 18537.529187856657, Profit/Loss: 15.74, Depot: 10037.68\n",
      "\n",
      "Final Depot Value: 10037.68 Euros\n"
     ]
    }
   ],
   "source": [
    "# Display the results\n",
    "for trade in results:\n",
    "    profit_loss = trade.get('Profit', trade.get('Loss', 'N/A'))\n",
    "    # Only format profit_loss if it's a number\n",
    "    if isinstance(profit_loss, (int, float)):\n",
    "        profit_loss = f\"{profit_loss:.2f}\"\n",
    "    \n",
    "    print(f\"Date: {trade['Date']}, Action: {trade['Action']}, \"\n",
    "          f\"Entry Price: {trade.get('Entry Price', 'N/A')}, \"\n",
    "          f\"Exit Price: {trade.get('Exit Price', 'N/A')}, \"\n",
    "          f\"Profit/Loss: {profit_loss}, \"\n",
    "          f\"Depot: {trade['Depot']:.2f}\")\n",
    "\n",
    "# Print the final depot value\n",
    "print(f\"\\nFinal Depot Value: {depot:.2f} Euros\")\n"
   ]
  }
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