3. Timeseries Regression.ipynb

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    "Important Disclaimers\n",
    "=====================\n",
    "\n",
    "The analysis presented below is intended for **teaching purposes only**. That is, it has not been vetted for statistical significance, market effectiveness, trading costs, predictive accuracy, or any other validation of the parameters and models selected. We highly recommend that you **do not trade based on this model**, as that is not the intent of this seminar.\n",
    "\n",
    "Further, the views in this document do not represent those of the presenters Sandeep Balasubramanian and Robert Ying, nor those of the Application Development Initiatve, the Columbia Data Science Society, AQR Capital, or D.E. Shaw & Co.\n",
    "\n",
    "That said, we hope you learn something about applied data science and timeseries forecasting!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setup:\n",
    "------"
   ]
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  {
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   "execution_count": 1,
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pandas.tools.plotting import autocorrelation_plot\n",
    "from matplotlib import pyplot as plt\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "import statsmodels.api as sm\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (10, 6)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting with Time Series Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction to Time Series Data ##\n",
    "\n",
    "Much of the data we interact with on a daily basis are drawn from time series. What's the temperature outside today? Is my diet working? How's the stock market doing? A **time series** is a set of observations of a single variable at multiple different points in time. Time series data differs from other data because these observations aren't usually independent from each other. For example, the stock price of Microsoft today is very related to the MSFT price yesterday. In contrast, if you were to sample people in this room to look for a relationship between height and GPA, you could be reasonably confident that each observation $(height, gpa)$ is independent from each other. \n",
    "\n",
    "## Modelling a Time Series##\n"
   ]
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   "metadata": {},
   "source": [
    "### Stationarity ###\n",
    "\n",
    "A process (from which a time series is sampled) is said to be **stationary** if the distribution of the observed values does not depend on time. One example of a nonstationary process is a stock price -- over the decades there's certainly been an upward trend, so the average price of IBM in 1995 is different than the average price of IBM in 2015. What we want is the distribution of the observed variable to be independent of time. We'd like for the mean and our variance of our observations to be constant over time. \n",
    "\n",
    "Taking the trends out of data to make it stationary allows us to properly run regressions against other variables (otherwise we risk spurious results that conflate the time trend with the effect of the other variables). We can make data stationary by taking differences of of the observations. For example, below we have a graph of drug sales. There is clearly a trend over time--by looking at the *difference* in sales instead of the sales numbers themselves, we can more accurately find the relationships between it and a proposed explanatory variable. \n",
    "\n",
    "<img src=\"https://www.otexts.org/sites/default/files/fpp/images/diffa10.png\" width=\"600\"/>\n",
    "\n",
    "A process that becomes stationary after differencing *d* times is said to be **integrated with order d**."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Autoregressive Model ###\n",
    "\n",
    "In an **autoregressive model**, the response variable is regressed against previous values from the same time series. For example, we might think that the price of MSFT today is some linear combination of prices of MSFT over the last few days: \n",
    "\n",
    "$ \\begin{equation*}  price_{t}=\\beta_{0}+\\beta_{1}price_{t-1}+\\beta_{2}price_{t-2}+\\beta_{3}price_{t-3}+\\epsilon_{t}.  \\end{equation*} $\n",
    "\n",
    "We say that a process if **AR(1)** if only the previous observed value is used. A process that uses the previous *p* values is called **AR(p)**. A classic example of an AR(1) process is a **random walk**. In a random walk, a \"walker\" has an equal chance of stepping left or stepping right. At each time observation, the walker randomly takes a step. The position of this walker over time is therefore a direct function of where they were at the previous time and the random step direction.\n",
    "\n",
    "<center> **a single random walker** </center>\n",
    "<img src=\"http://people.duke.edu/~rnau/411rand_files/image004.jpg\" />\n",
    "\n",
    "<center> **various possible random walks** </center>\n",
    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/d/da/Random_Walk_example.svg/720px-Random_Walk_example.svg.png\">"
   ]
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   "source": [
    "### Moving Average Model ###\n",
    "\n",
    "A **moving average model** is similar to an autoregressive model except that instead of being based on the previous observed values $y_t$, the model describes a relationship between an observation and the previous error terms. We say that a process if **MA(1)** if only the previous observed value is used. A process that uses the previous *p* values is called **MA(p)**. \n",
    "\n",
    "<img src=\"https://upload.wikimedia.org/math/9/5/a/95a85ea60c0cd898823ffe17fb509475.png\" />\n",
    "\n",
    "As an example, imagine looking at time series data around Moore's law. \"Moore's law\" is the observation that, over the history of computing hardware, the number of transistors in a dense integrated circuit has doubled approximately every two years. \n",
    "\n",
    "<img src=\"http://www.overclock.net/image/id/10100141\" width=\"600\"/>\n",
    "\n",
    "Notice that the y axis has a log scale. The trend here appears linear after taking the log. Is our transistor count stationary? Not yet! However, if we difference the data once, now we're talking about the slope -- purely by inspection, the slope looks like it's pretty constant through time. Therefore we could guess that the change in log transistor count is stationary over time. We might consider using an MA(1) process to model this. This would suggest that if the change in log transistor count is lower by 2% in period 1, that error would adjust the forecast of period 2. \n"
   ]
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   "source": [
    "## Analysis ##\n",
    "\n",
    "### Our Dataset ###\n",
    "\n",
    "The New York Independent System Operator (NYISO) operates competitive wholesale markets to manage the flow of electricity across New York. We will be using this data, along with weather forecasts, to create a model that predicts electricity prices. \n",
    "\n",
    "### Definitions ###\n",
    "\n",
    "**NYISO**: New York Independent System Operator\n",
    "\n",
    "**LBMP**: Locational Based Marginal Pricing\n",
    "\n",
    "**Day Ahead Market**: The Day-Ahead Energy Market lets market participants commit to buy or sell wholesale electricity one day before the operating day, to help avoid price volatility. \n",
    "\n",
    "**Realtime Market**: The Real-Time Energy Market lets market participants buy and sell wholesale electricity during the course of the operating day. The Real-Time Energy Market balances the differences between day-ahead commitments and the actual real-time demand for and production of electricity.\n",
    "\n",
    "**Vintage Date**: The day that the weather is predicted for is the vintage date.\n",
    "\n",
    "**Forecast Date**: The date on which the above temperature is predicted is the forecast date. \n",
    "\n",
    "**Vintage Category**: A prediction is made on several days leading up to the vintage date. Additionally, the temperature on the vintage date is also recorded after the date has passed. To distinguish between these, there are two types of observations: forecasts (the prediction) and actual (the realized temperature, as documented on a later date). "
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   "source": [
    "# load all the data\n",
    "day_ahead_market = pd.read_csv('datasets/timeseries/day_ahead_market_lbmp.csv.gz')\n",
    "real_time_market = pd.read_csv('datasets/timeseries/real_time_market_lbmp.csv.gz')\n",
    "weather_forecast = pd.read_csv('datasets/timeseries/weather_forecast.csv.gz')"
   ]
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       "      <th>Time Stamp</th>\n",
       "      <th>Name</th>\n",
       "      <th>PTID</th>\n",
       "      <th>LBMP ($/MWHr)</th>\n",
       "      <th>Marginal Cost Losses ($/MWHr)</th>\n",
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       "      <td>GENESE</td>\n",
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       "      <td>20.84</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>-1.11</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01/01/2015 00:00</td>\n",
       "      <td>H Q</td>\n",
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       "      <td>20.25</td>\n",
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       "         Time Stamp    Name   PTID  LBMP ($/MWHr)  \\\n",
       "0  01/01/2015 00:00  CAPITL  61757          33.45   \n",
       "1  01/01/2015 00:00  CENTRL  61754          21.89   \n",
       "2  01/01/2015 00:00  DUNWOD  61760          31.27   \n",
       "3  01/01/2015 00:00  GENESE  61753          20.84   \n",
       "4  01/01/2015 00:00     H Q  61844          20.25   \n",
       "\n",
       "   Marginal Cost Losses ($/MWHr)  Marginal Cost Congestion ($/MWH  \n",
       "0                           1.13                           -11.71  \n",
       "1                          -0.12                            -1.41  \n",
       "2                           1.79                            -8.88  \n",
       "3                          -0.86                            -1.11  \n",
       "4                          -0.35                             0.00  "
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     "execution_count": 5,
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   "source": [
    "day_ahead_market.head()"
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       "      <th>Time Stamp</th>\n",
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       "      <th>LBMP ($/MWHr)</th>\n",
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       "      <td>01/01/2015 00:05:00</td>\n",
       "      <td>CAPITL</td>\n",
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       "      <td>32.51</td>\n",
       "      <td>1.53</td>\n",
       "      <td>-9.07</td>\n",
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       "      <td>01/01/2015 00:05:00</td>\n",
       "      <td>CENTRL</td>\n",
       "      <td>61754</td>\n",
       "      <td>22.84</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-1.09</td>\n",
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       "      <td>30.72</td>\n",
       "      <td>1.95</td>\n",
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       "      <td>21.65</td>\n",
       "      <td>-1.12</td>\n",
       "      <td>-0.86</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01/01/2015 00:05:00</td>\n",
       "      <td>H Q</td>\n",
       "      <td>61844</td>\n",
       "      <td>21.53</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>0.00</td>\n",
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       "            Time Stamp    Name   PTID  LBMP ($/MWHr)  \\\n",
       "0  01/01/2015 00:05:00  CAPITL  61757          32.51   \n",
       "1  01/01/2015 00:05:00  CENTRL  61754          22.84   \n",
       "2  01/01/2015 00:05:00  DUNWOD  61760          30.72   \n",
       "3  01/01/2015 00:05:00  GENESE  61753          21.65   \n",
       "4  01/01/2015 00:05:00     H Q  61844          21.53   \n",
       "\n",
       "   Marginal Cost Losses ($/MWHr)  Marginal Cost Congestion ($/MWHr)  \n",
       "0                           1.53                              -9.07  \n",
       "1                          -0.15                              -1.09  \n",
       "2                           1.95                              -6.87  \n",
       "3                          -1.12                              -0.86  \n",
       "4                          -0.37                               0.00  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "real_time_market.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "# convert string date column to datetime type\n",
    "day_ahead_market['Time Stamp'] = pd.to_datetime(day_ahead_market['Time Stamp'], format='%m/%d/%Y %H:%M')\n",
    "real_time_market['Time Stamp'] = pd.to_datetime(real_time_market['Time Stamp'], format='%m/%d/%Y %H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Forecast Date</th>\n",
       "      <th>Vintage Date</th>\n",
       "      <th>Vintage</th>\n",
       "      <th>Station ID</th>\n",
       "      <th>Max Temp</th>\n",
       "      <th>Min Temp</th>\n",
       "      <th>Max Wet Bulb</th>\n",
       "      <th>Min Wet Bulb</th>\n",
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       "      <th>0</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>Forecast</td>\n",
       "      <td>ALB</td>\n",
       "      <td>31</td>\n",
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       "      <td>2015-01-01</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>Forecast</td>\n",
       "      <td>ART</td>\n",
       "      <td>32</td>\n",
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       "      <td>21</td>\n",
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       "      <th>2</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2015-01-01</td>\n",
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       "      <td>BGM</td>\n",
       "      <td>27</td>\n",
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       "      <td>12</td>\n",
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       "      <th>3</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>Forecast</td>\n",
       "      <td>BUF</td>\n",
       "      <td>30</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>18</td>\n",
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       "      <td>2015-01-01</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>Forecast</td>\n",
       "      <td>ELM</td>\n",
       "      <td>32</td>\n",
       "      <td>16</td>\n",
       "      <td>24</td>\n",
       "      <td>13</td>\n",
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      "text/plain": [
       "  Forecast Date Vintage Date   Vintage Station ID  Max Temp  Min Temp  \\\n",
       "0    2015-01-01   2015-01-01  Forecast        ALB        31        18   \n",
       "1    2015-01-01   2015-01-01  Forecast        ART        32        24   \n",
       "2    2015-01-01   2015-01-01  Forecast        BGM        27        15   \n",
       "3    2015-01-01   2015-01-01  Forecast        BUF        30        21   \n",
       "4    2015-01-01   2015-01-01  Forecast        ELM        32        16   \n",
       "\n",
       "   Max Wet Bulb  Min Wet Bulb  \n",
       "0            23            15  \n",
       "1            28            21  \n",
       "2            21            12  \n",
       "3            26            18  \n",
       "4            24            13  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert dates to datetimes on the weather data\n",
    "weather_forecast['Forecast Date'] = pd.to_datetime(weather_forecast['Forecast Date'], format='%m/%d/%Y')\n",
    "weather_forecast['Vintage Date'] = pd.to_datetime(weather_forecast['Vintage Date'], format='%m/%d/%Y')\n",
    "weather_forecast['Vintage'] = weather_forecast['Vintage'].astype('category')\n",
    "\n",
    "weather_forecast.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
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   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>PTID</th>\n",
       "      <th>LBMP ($/MWHr)</th>\n",
       "      <th>Marginal Cost Losses ($/MWHr)</th>\n",
       "      <th>Marginal Cost Congestion ($/MWH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th>Time Stamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>CAPITL</th>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>61757</td>\n",
       "      <td>33.45</td>\n",
       "      <td>1.13</td>\n",
       "      <td>-11.71</td>\n",
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       "    <tr>\n",
       "      <th>CENTRL</th>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>61754</td>\n",
       "      <td>21.89</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-1.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DUNWOD</th>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>61760</td>\n",
       "      <td>31.27</td>\n",
       "      <td>1.79</td>\n",
       "      <td>-8.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GENESE</th>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>61753</td>\n",
       "      <td>20.84</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>-1.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H Q</th>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>61844</td>\n",
       "      <td>20.25</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>0.00</td>\n",
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      ],
      "text/plain": [
       "                    PTID  LBMP ($/MWHr)  Marginal Cost Losses ($/MWHr)  \\\n",
       "Name   Time Stamp                                                        \n",
       "CAPITL 2015-01-01  61757          33.45                           1.13   \n",
       "CENTRL 2015-01-01  61754          21.89                          -0.12   \n",
       "DUNWOD 2015-01-01  61760          31.27                           1.79   \n",
       "GENESE 2015-01-01  61753          20.84                          -0.86   \n",
       "H Q    2015-01-01  61844          20.25                          -0.35   \n",
       "\n",
       "                   Marginal Cost Congestion ($/MWH  \n",
       "Name   Time Stamp                                   \n",
       "CAPITL 2015-01-01                           -11.71  \n",
       "CENTRL 2015-01-01                            -1.41  \n",
       "DUNWOD 2015-01-01                            -8.88  \n",
       "GENESE 2015-01-01                            -1.11  \n",
       "H Q    2015-01-01                             0.00  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# re-index the data by name of region and timestamp. This helps us manipulate and access the data. \n",
    "dam_time_name = day_ahead_market.set_index(['Name', 'Time Stamp'])\n",
    "rtm_time_name = real_time_market.set_index(['Name', 'Time Stamp'])\n",
    "\n",
    "dam_time_name.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# select just the NYC data\n",
    "dam_nyc_lbmp = dam_time_name['LBMP ($/MWHr)']['N.Y.C.']\n",
    "rtm_nyc_lbmp = rtm_time_name['LBMP ($/MWHr)']['N.Y.C.']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12075270>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ERFQiDM4qjm3OiIiIyoXBGREREVGJMDirILYzIyIiKi8GZxXEqkwiIqLyYnBGREREVCIM\nziqI1ZpERETlxeCs4ljFSUREVC4MzoiIiIhKhMEZERERUYkwOCMiIiIqEQZnRERERCXC4KyC2FuT\niIiovBicVRx7axIREZULgzMiIiKiEmFwVnGs4iQiIioXBmdEREREJcLgrOLY5oyIiKhcGJwRERER\nlQiDMyIiIqISYXBWQewEQEREVF4MzoiIiIhKhMEZERERUYkwOKs49tYkIiIqFwZnRERERCXC4IyI\niIioRBicVRB7axIREZUXgzMiIiKiEmFwRkRERFQiDM6IiIiISqTw4ExENhSR0SLyvIiME5FTa8tH\niMg0EXm69u9Aa5uzRORlEXlRRPYvOs3dhm3OiIiIyqunDcdcAuB0pdRYEVkVwH9F5AEACsDFSqmL\n7ZVFZBiAIwEMA7A+gAdFZAul1PKiE05ERESUt8JLzpRSM5RSY2uv3wPwAnTQBQBhZTqHArhZKbVE\nKTUVwEQAOxeRViIiIqKitbXNmYhsAmBHAI/VFp0iIs+IyDUiMri2bD0A06zNpqEezFGLOEMAERFR\nubQtOKtVaf4FwHdqJWiXAxgKYAcAbwD4VcTmDCmIiIioK7WjzRlEZACA2wDcqJS6HQCUUrOs968G\n8Pfan9MBbGhtvkFtWZMRI0Z88Lq3txe9vb1ZJrtrsEMAERFRsfr6+tDX1+e1rqiC67VERABcB+Bt\npdTp1vJ1lVJv1F6fDuATSqkv1zoE3ATdzmx9AA8C2EwFEi4iwUXksNFGwGuv6dfjxgFbb93e9BAR\nEVWNiEApFVpc0o6Ss90BHAPgWRF5urbsbABHicgO0FWWUwB8AwCUUuNFZCSA8QCWAjiZURgRERF1\nq8JLzvLCkjN/LDkjIiJqr6iSM84QUHGMZ4mIiMqFwRkRERFRiTA4qyD21iQiIiovBmcVx2pNIiKi\ncmFwVnEMzoiIiMqFwRkRERFRiTA4qziWnBEREZULgzMiIiKiEmFwVkHsrUlERFReDM4qjtWaRERE\n5cLgrOIYnBEREZULgzMiIiKiEmFwVkFRbc5efRV48cXi0kJERESNetqdAGqvYLXmXnsBkyezupOI\niKhdWHJGDRYtancKiIiIqo3BWcXZJWTz5wPTp7cvLURERMTgrPLs4OzUU8OXExERUXEYnFWQq0PA\nu+/WX//1r8WkhYiIiBoxOKMP2EHb7NntSwcREVGVMTirOFf15fLlxaaDiIiINAZnFWcHZ3bJGYMz\nIiKi9mBwRqEYnBEREbUHg7OKY8kZERFRuTA4qyBXb00GZ0RERO3H4Iw+YAdnHOeMiIioPRicVZyr\nWpOIiIjag8FZxbGEjIiIqFwYnFUcS86IiIjKhcEZfYDBGRERUfsxOKsgn96arO4kIiJqDwZnFcdq\nTSIionJhcEahTNB27rnAlCntTQsREVGVMDiruLjqyx//GLjxxmLSQkRERAzOKilp2zK2PyMiIioO\ng7MKsqdmsgM1tjkjIiJqPwZnFfPqq8DkyfW/fYKz554D9tkn33QRERGR1tPuBFCxZs9u/NunyvL+\n+4G5c/NJDxERETViyVnF9O/f+LcrOLOX9+NVQkREVBjedismGJz5YHBGRERUHN52KyZYUuYahJYl\nZ0RERO3B227FrLpq498+MwQwOCMiIioOb7sUi0NsEBERFYfBWcX4VmvaGJwREREVh8FZxXH0fyIi\nonJhcFYxvsGYT4kaERERZY/BWcVEVWsSERFR+zE4qziWkBEREZULg7OKiSopc41zRkRERMVhcFYx\nrNYkIiIqt8KDMxHZUERGi8jzIjJORE6tLV9TRB4QkQkicr+IDLa2OUtEXhaRF0Vk/6LT3E1GjGj8\nm8EZERFRubSj5GwJgNOVUlsD2BXAt0RkKwBnAnhAKbUFgFG1vyEiwwAcCWAYgAMAXCYiLPFL6YYb\nGv9mcEZERFQuhQc5SqkZSqmxtdfvAXgBwPoAPgfgutpq1wE4rPb6UAA3K6WWKKWmApgIYOdCE10R\n7BBARETUfm0tgRKRTQDsCOBxAEOUUjNrb80EMKT2ej0A06zNpkEHc5QBV8kZS9SIiIjao23BmYis\nCuA2AN9RSs2z31NKKQBR4QFDh4xwKA0iIqJy6WnHQUVkAHRgdoNS6vba4pkiso5SaoaIrAtgVm35\ndAAbWptvUFvWZITV2r23txe9vb0Zp7w6WHJGRESUnb6+PvT19XmtW3hwJiIC4BoA45VSv7HeuhPA\ncAA/r/1/u7X8JhG5GLo6c3MAT4Tte0SwKyLFYhBGRESUv2Ch0Xnnnedctx0lZ7sDOAbAsyLydG3Z\nWQB+BmCkiBwPYCqAIwBAKTVeREYCGA9gKYCTa9WelFDYWXNVa/7zn/ofALzxRr7pIiIiorrCgzOl\n1CNwt3Xb17HNBQAuyC1RFZEkpL377vzSQURERG4cL6zi2CGAiIioXBicVUhctSYRERG1H4OzimNw\nRkREVC4MziqEgRgREVH5MTjrcgsWAP3769es1iQiIio/Bmddbu5cYPly9/vsEEBERFQuDM4qJFhK\nNmhQe9JBREREbgzOupxdGhYMzkRYckZERFQ2DM4qrF8/tjkjIiIqGwZnFRJWckZERETlwuCsy0UF\nYMFqTSIiImo/BmcVEgzEgtWaLEkjIiJqv8InPqf2mDgRWLSocRlLzoiIiMqHwVlFbLGFLimzDRoE\nzJnTnvQQERFROFZrdjlTVakUsGxZ43s9PdED1BIREVHxGJxVWLCNGducERERtR+Dsy4X1aaMbc6I\niIjKh8FZl9l3X+Cyy/zWZckZERFR+TA46zKjRgF/+Uv9b5acERERdRYGZ13IDrjigrOs/e53wNVX\nZ79fIiKiquBQGl2u6ODs1FOBlVcGTjgh+30TERFVAUvOupBvVWVe1ZqsKiUiIkqvUsHZ4sXApEnt\nTkVxpk0D3n7b/T47BBAREZVPpYKziy8GNtus3anInym52nBD4KCD3OsFg7FLL80vTUREROSnUsFZ\nVaYqsqsVX33VvV7Sak1WVxIREeWvUsEZq+0aJT0fDM6IiIjyV6ngrCqSdAjIY79ERESUXqWCs6qU\nnOVVVem7LoM4IiKi9CoVnFVFXkEUgy4iIqL8MTirMAZnRERE5VOp4IzVms3rsbcmERFRuVQqOKsK\nVmsSERF1rkoFZ1UpOcvLN74BjBnT7lQQERF1t0pNfF6V4CyvqsrrrwcGDAB23z15moiIiMhPpUrO\njLFjgQULijvezJnAxz5W3PHaXf3Y7uMTERF1skoGZzvuCFx0UXHHGzcOeOqp4o4Xp3//9Nsy8CIi\nIspXpYIzu1qzyJKzoowdq/+PC6DWWqv+msEWERFRuVQqOOt2n/+8/p8BFxERUeeqVHDWrg4BRR13\nypTk23A4DSIionKpVHBm6+Ygo5s/GxERUberVHDW7UNptNLQ35dP4MfgkIiIKL1KBWe2bgwgemqj\n1vl+NpHuPA9ERESdrLLBWZGKKrFLEpzdfTew6671v+1tNtvMvR2DOSIionxVKjhjtaamFHDggY3r\n20FX2gDs7bfTbUdERER1lQ3OurEEyARbaaZvsreJCmJd+549G1h7bf/jEhERUbhKBWf9uvzT+gZn\nJviyg7Dly5vfT2L+/OTbEBERUbMuD1cadXu15hFH6P/jgrOw9+1lUUGsa9/dfm6JiIiKwuCsi9gN\n/H2FVWu2Gpx1Y5UxERFRUSobnHVjAGE+X5pqzVbPR7cHvkREREVpS3AmIn8QkZki8py1bISITBOR\np2v/DrTeO0tEXhaRF0Vk//THrb8uMjgrKnAxn8luP5Z026zWIyIionTaVXL2RwAHBJYpABcrpXas\n/bsHAERkGIAjAQyrbXOZiKRKd1VKd5IEZ2EBXVQAxmpNIiKifLUlOFNK/QvAuyFvhYVPhwK4WSm1\nRCk1FcBEADunOW63B2cmKFq2zG/9vAIqBmdERETpla3N2Ski8oyIXCMig2vL1gMwzVpnGoD10+y8\nKsFZq9WaBx7oXs/FPre+wSERERE162l3AiyXA/hx7fVPAPwKwPGOdUPLZkaMGPHB697eXvT29ja8\nb/dC7ObSnbjgLGw2APP/dtsBgwY1b7PBBsC0aRxKg4iIKI2+vj709fV5rVua4EwpNcu8FpGrAfy9\n9ud0ABtaq25QW9bEDs7CtCuA8D3umDF6INk0Q2IAyUvOwqo1XdvGfQYGZ0RERG7BQqPzzjvPuW5p\nqjVFZF3rz8MBmJ6cdwL4koisICJDAWwO4Il0x2gtjXnbYw9gzz3Tb+/b5izsPNglaGHvm1LHbi5x\nJCIiKoO2lJyJyM0APg1gbRF5DcC5AHpFZAfoKsspAL4BAEqp8SIyEsB4AEsBnKxUuhChE3oULl3a\n+j7SVGuabZQKPze+Y6gRERFRa9oSnCmljgpZ/IeI9S8AcEGrxy17yVmrsqjWZJsyIiKi9ipNtWYR\nyj4IbU+LobJvcBYVlC1fHp7euJIzlqgRERFlg8FZifTvn81+4tqcmc8edj5c82qy5IyIiKgYDM4C\n5s8HjjyymPQEtRIAvfkmcNll+nWacc4+/GH9f79+LDkjIiJqp0oFZz7jnE2cCIwcWUx6gloJzm64\nAXjqKf06TbWm4Sq9Y8kZERFRMSoVnJU9wGglffa2vr01w443dGj0/llyRkRElK/KBmdF9kr03aer\nvVfSbefOTb+fm2+OrtYkIiKifDE4K5FWAqC0gV3wPAwcmG6cszKeTyIiok7E4KxEsqrW9F03zTZE\nRESULwZnJVJUyVncZ2dvTSIiovapbHDWyjp5KarkzLZ0KbB4sfv9jTdu3D+DMCIionxVNjgr4wwB\nRZWcvflm/Xg//CHwoQ+507Hyyn5pY9BGRESUDQZnJZJVb00A+MpX/LeN6t0ZbJ9WxvNGRETUTRic\nRaxTtKyqNYcMAW68sfX0APWgb8mS6PUYtBEREWWjUsGZzwwB7ZRFtebgwfpfK8ezl5vXL76o/2eH\nACIionxVKjhrV6lYEW3OzLb9+qWbW9NmB1rTp7e2LyIiIkqmssFZGUt6sig569+/9eDM9s47jX+z\n5IyIiChfDM4i1ilaFh0C+vXzD5R8qjWJiIioWAzOSiSras08PxtLzoiIiPLF4KxEsgjOklRrsoSM\niIiofCoVnPno9KE0sig5i0oHS86IiIjyVdngrIzBRFmCsygMzoiIiPLF4KwAvkFXKx0CgiP5J9km\nCwzOiIiIstHT7gQUyQ4gyhhMmGBp2TLddiyNuJKzT34SWH/96H1Ebc+SMyIionyx5CygDG3OvvOd\n9NvGlb5dcQUwcmT0OmnGSbPP5/77J9+eiIiItEoFZ2Uv3TEB1jPPpN9WBJg2LX69tHxKzsp+nomI\niMqsUsGZrcwlZ488AixYkG4fcSVn9udzlZDZ56bHs+KbwRkREVE2KhWcHXNM/XWRwVmaDgFTpqQ7\nRpLgzKcUbM893e+5ljM4IyIiSq9SwZnNFUC00mOyVa0MkuvbWzNpcPbpT/sdn8EZERFRNhicBWRd\ncjZ5MnD88fr1tddGr5tFcJZ1ydmKK7rfcy1ncEZERJReZYOzOFkFGH//O/DCC/r16NHR62YRGOYd\nnLkwOCMiIspGZYOzuAAiqwAjSWlYGas1Bwxwv+fC4IyIiCg9BmcOacb6alVW0zf58m1zts028dvM\nmRO/DhEREcVjcObwpz8Bo0YVkxajlZKzz39e/591tebWWwPPPRd//N12i98vERERxavU9E22uMDk\n2GOBjTYCXnmlteMkCbjswCptyV3W1ZrBYM8n8Fq+XK/XzjHjiIiIOlVlS86KksVE5LvvDixd6reP\nLAehvfpqv2MGjRmjg1siIiJKrrLBWdmHhLCP/eijwPz5ftvFBWd2QBb3+cwQIK50AcA77zT+bYK/\nMWOi901EREThGJzlLG3Vnknfiy9mezw7yPMJUKOMHw+stVay4xMREVE0Bmeey/M8phHWHmzxYr9t\nffmUnEUdy37P7qEZxCCNqFh//Svwi1+0OxVElAUGZwUf8803/dcF6kGOb3pfein6/R13bD5GUHBs\nM9tDD0Xv33e8NSLK1llnAf/7v+1OBRFlobLBWVHsIOXhh4EPf9hv3aBly/yO9/bb/ulxBWeHHALs\nvLPf8aKG+FjiAAAgAElEQVT2T0RERMlVNjgrqlrTDlbiAqewdJjt0wytEdc5wPVZd9kFePzx+P2H\nBWIsOSMiImoNg7MW10kiScBijj14sP7ft+TM1r9/49+TJoUfI4mBA6PfX7JE/8/gjIiIKB0GZ57L\n07KDlDQDxJr/syg5+8hHGv9O81k33TT5NkREROSPwVnOWh1Kw/yfRcmZ6xhJ2NtEfbYkc3wSERFR\nHW+hAUX34hw/Hpg2Lf7YaUrO8gjO7HREBYys1iQiIkqnssFZO9qchdl6a2D//YGnngLGjm0+dp4l\nZ2kCPnub3XdPvj0RERFFq2xwdtdd6ScXT8KnzdmiRcD06Y3LsmhzFhec5Wn8+PYdm6iKWFpN1D0q\nG5wBwHPPNS9r1+C0cWbOTL7fqMFkgXTBW5Lz4ztZO+XvkUd48+527ZwLmIiy1ZbgTET+ICIzReQ5\na9maIvKAiEwQkftFZLD13lki8rKIvCgi+2eVjmBmdsABwO9+l9XeNZ+Ss+XLgVGjwtNm/t9zz+TH\nThqc7bJL/D6LKG2k7NlV5tRdli0DBg1qdyqIKEvtKjn7I4ADAsvOBPCAUmoLAKNqf0NEhgE4EsCw\n2jaXiUgm6banMgKA++4Dbr01iz3X+QRnb78NXHJJ4zITBLXyNPzaa9Hv9/Q0/p11D0s+yRPlb+lS\nYO5c/t6IuklbgjOl1L8AvBtY/DkA19VeXwfgsNrrQwHcrJRaopSaCmAigJSTC/mkLdv92QHPokXh\n67SrbVgwOMsabxZE+TO/MzMANBF1vjK1ORuilDItq2YCGFJ7vR6AadZ60wCsn1ci8hyE1hWcRaUj\nzwAnOAm7z7GS9BplcFYe/C66l/luk+QvRFRuZQrOPqCUUgCibie53WraMWVTWDuuLKo1AeDcc4Gd\ndgLWWaf5vRtvTL6/uBvASSfVXzMg6H6nnQa8/nq7U1FtJq947732poOIspNzxVYiM0VkHaXUDBFZ\nF8Cs2vLpADa01tugtqzJiBEjPnjd29uL3t7efFKagE87rrDgLKvAZsQI4LvfDS/xmjYN2GADv2Ou\nvrpu17LlltHHs6toGZyVR17fxSWXANtuCxx/fD77p3gm/+jW3tFnnqnzsZVWandKiFrT19eHvr4+\nr3XLFJzdCWA4gJ/X/r/dWn6TiFwMXZ25OYAnwnZgB2dh1lgDeDfY0i3AvollcUPzKTkLO07akrPv\nfx/45S8bl62+evi66wcqh6OONWcOcOedwFVX6b+nTm18f/vtgWeeYXBWRRyio72yKmUvq5//HDj6\naP0QQNTJgoVG5513nnPddg2lcTOARwF8VEReE5HjAPwMwH4iMgHA3rW/oZQaD2AkgPEA7gFwcq3a\nM7F2zAowZkz8OmGlWkky3LPOqr9OM+SGL5F6eoYObXxv5ZX1/3Yng269WRCVSRHtU4moWG0pOVNK\nHeV4a1/H+hcAuKD14yZbJ4vM7sor49cJC86SZLg/+1n99YABwOWXA9/8pl/6kujXT4+XtWBB83sm\nnSw5Kyd+F92rW0vOlizhuIpUXaXsEJCXpJlXUZldVMlZUko1NspPum0UET3N1K9+5V7HbmPHjLUa\nWK3ZXuZ31m2/t2uu0W3NiKqIwVnA++8nWz+LdIQdJ21VRZ4BpbkJs+SMqDy6LSgz5s/X/4iqiMFZ\nhutnuV/fqoonnwzf98c+ln26THD2r3/5bcvgrDyKCNqpPbq1zdny5d33mYh8VSo4SyqvjMFnIFff\np+F77mktLTbf4CxqPKWs2+xR+XVKcHbYYd15TXZrm7Ply7u3VJAoTqWCs7DM65ZbgEmTwtfPK2MY\nNw54663odXyfhoOTm5v1t94aGDgwWbrijmXakz39tN+2zFipLJQC7rijO6/JKgRnnfIAQJSVygdn\nRx0FnHOO//pZ2Gkn4KtfbV5uB1O+GW4w0zLrX321nlA9S64M8vbbgf/8p/H4wdd5mDYNWLw432N0\nizy/izlzAM9xFdumW6v+gO79bCw5oyqrfHAGuDOAPDM7u+NBmFZ6awK6RC1pyVkcV3B2+OGNf//i\nF/r/vDPWDTcEIsbwI0ue1/JPfwrstVd++89Ct/ZoBLrzMwG6+Ue3fjaiOAzO0J7gLG7fvk/DWaYx\naSld3HpFZKyzZsWvQ/nqhBtot1b9AZ1x/tOwOwSwWpOqhsEZ3JlbnpleWKZjv057M8nz5uMzTyhQ\nbHDWjTdbyh6Ds87Dak2qskoFZy7tCM4MV8CT9ibSys0nq5Izw6dXaquYefvpxqAkiW6u1mznd7t8\neX4Tri9fXkweQlRGlQrOkpac5ZXp2GmxB23NouRsk01aT5OL7yTuLDmjsmHJWT7OOANYbbV89m2X\nnLm+N6Xi2+8SdSIGZwCmTAE22qh5eZ5PbXkEZ3PmADvu2HraXMrY5qwbb7aUvU4pOXvkEWD27GTb\ntOsznXkm8OijwMKF+ezfLjlz/c6vugpYZRU9l/Brr+WTDqJ2qExw9uCDeiLdMOPGhf+wiwjOWq3W\nLLKhrG/JmVHETaPsN9uy6NQgdtYs4IknWt9Pp5Sc7bkncO65ybZp12f6+c+B8ePz279dZer6jBMn\n6v+vuAL4xz/ySwtR0SoTnO23n/5/o42AYcMa37NLr2xF3Pjjjp0k4201UPMdhNZlxRWBAw5gyRll\n5+STgV12aX0/nRKcAcnT2M4HlDyPnbTNmevhm6gTVSY4MyZNAkaMaFzmCpDyNGuWDmLmzq0vs4Mr\nk0HHVVOWqeTs8ccbx7vKK+O+5556WjrhZkvpZVV63SnVmmm08zPl2S532bL4ak0bgzPqJpULzkSa\ng4x2BGdh1ajz5tVfp8lw0wRqH/pQ/XWn9NYcN67+uhtvtnlo55h9ZdBJJWdJf2ft/A3keT59OwQU\nkRaiolUuOAPcUx6FmTMH+O53s09DXIZaVHD24IP1160GZ8H387pp2NWrzJDbrxO+g04qOUv6O27n\n+c+z5N6nQ4CtE65DIl+VC87CSs6iftSPPgr8+tfZpyOuVKmoDgFhPURd4tqcBasa87oR2iWdzJDj\nnXAC8Mwz+e2/E0ZvZ8lZPrL+7k89VbczBPzanBXdAYmoKD3tTkDRwoKzBQuKT0dWJWeuGQZ8Jdkm\n7U3j1FP1/Iurr55sexc7SGSGHO+aa/Rcq50oq5t/khKYTtNNwdkVV+i2Y5Mm6c5bcd9bN36fREBF\nS858pyFql+22a85wN9wwfN1WgzNbXtWav/sd8OST6dMVxGpNSorVmvnIOjgzDxH336/b4CYJzpgX\nUDcpeZiSDVNMbpS9GqanpzmjySvjSVKt6RuchVVrLl4MvPNO8vSFYXCWXNmv+byxWjMfWV9X9vdj\n99ZMui1Rp6tEcHb55Y1/J8lQDjoo27T46OlpznBdGXCWJWdxWumt+f3vA2utlU067DZnnVASUgYM\nzhr/7ybd9Jnsz+LTIYAlZ9StKhGcfeITjX+X/UbVv39zRjN/fj7HChtbzSVuyJGokrNXXkmeNheW\nnJWDOfed8B10UslZ0mYX7QzOsm4iYn+WpUvZW5OqqxLB2dprN/6dZlyzIjPA/v2bjzdnTvi6WZac\nxX1G3+AsbH9ZZpwMzpLL4zyZ7zfPac6y0knBWSe0OTPHzLNa0yc4a7XkjJOmU1lVIjjrCfRJXWml\n5PvYZx9g+PBifswrrljcOGdJtgmex7h9BasossJqzXLIe8gUILubfydVa3ZCm7O8Sk3tQH/p0mSf\nLU1aVlklv4nbiVrR9UNpjB4N/P3vjctWXDH5fvr69P/f+57uTZmXk07S/4dlNEpl/6S6/vqN+4/i\nW+KY902bJWfJ5VGV30kBT7eWnCmV7xRKLnmdT3t/S5YUMwRKJ5T8UvV0fcnZ1Vc3L0sTnBXl8st1\nyV7YDS8sg2q1WtMeeyzuJutbchY28TmrNdsrj+FjigjOsvp+uzU4O/hgYO+980uLSxHnM+9qzU64\nFqi6uj44C7PCCum3zeoHvcMO7vf69Ws+Ttiyovm2OTOBwGuvtTYy/RNPABdf3Lycg9Aml2fJWSfM\n29lJpXxJvquHHsovHVGK+O7zHkqjmwcmps7H4CyhpD/kVVbR/wcz3EGD3NssXqyHnrCJtD4OWZy4\nzxZXcmaYIG748HoQmiYDPP98XY3s2n/a/VZRp5acZaWTSs6mTQOOOspvXZ/P88ILwKWXtpamoKKD\nM5+Ss+efB/bYw2/fJ51Ub0LSCdcvVU/XB2dhAUsrwVlSpvNBMB1R7UQmTND/L1pUXxYsORs+HHjq\nKeC55+rL8g7OfEvOwtZjtWZ75VlyZm6g77yTbxukVuQdSGY1wDKg28neckt2+7voIuCUU7LbH1Ce\n4Mz20EPAmDF++77ySj2tGcDgjMqp64OzMK3MM5i2XUOw5CKquN7cSIcNqy/r168xE7n+euCQQ4A/\n/KF5u7SyanOWVXDm+jys1kyuiJKztdYCbr89++MAwL33ttZwO89gYubM7AZYzkOndgZJGpyl/W6Z\nh1AZVTI4a0ebs+DNMarkzGSmkyc3LgtmIsGBadNmwm++6bdeK8FZlkOD2OlgyVkyWZ6vsO906tTs\n9m878EDg4YfTb59ncJb18DplHyQbKCagsTsEuNjfZ9o0MTijMur64Cwso2vlKTfpD9k1WKNPyZnN\np0NA2kzdDNKb1VAaeU8sbwdnzFj95DEulWu4lywFByVNK8+Snqz3mXQojaB77sn/oaWoas0ijsM8\nhMqo64OzMP37A1tumW7bdgRnG2/s1yGgVVlVa+66azbpMebOdQ8ZwpKzZLK8hoq+qaUNzpTKt2de\nO4KzZcvc6x10EDBlSrL9JVWWas0s5tZkcEZl1PXBmU/VWBJhP+RJk+IzhmBpUlRVSDDNSoWXnGV5\nozn6aOBrX4tex7dDwLBhwOGHN77XSpuzefPc+xo9uvl9csu7WjNPaduc7b67bp8J5JPmrAM+3+AM\ncH+eoh7kytQhIC0GZ1RGXT9DgOtHnbbqLezpfbPNdHuYPfd0Hz94vIkT3ccIy5zzLjm78cb4dezg\nTCQ6wwwGcq2k/YUXot9/801gtdXS778KgpPSZ6Hom1ratP/7363vI0o7bu4maPEJWPMoOYsLDrM6\nRhGj9zM4ozLq+pIzl7TBmStzDzbOD0qSQfq2OSu6Ss9OV1inCvv9LNqdmf3tt1/j8uDnbsf0NZ0m\njym1fGexaEUW1Vau/WWlHcOHJGkon4eylJxlcXwGZ1RGXR+cuYKitE+TSTMJV5uzKK5qzTJlIkUE\nZy4MztLrtJKzrIOzPNLcjpHm44KzdrdPzQKrNanKuj44c8kieFi0SDdWB9w/cFe1ZpSw4KyIDgFJ\nlCk448TF8Yqq1iz7VE55pC+rACLJQ0bcNZ/3byJNXtTTA/zyl/7r+1Rr2uc87QN3mfJVIqPrgzOf\ngUyTsDODE0+MnoYp7fHSDqVRpLCBfO12ZllWa8Yp03kpu6pXa+ZZctbKvkeNSjY4dicGZ8uWAf/9\nr//6eU98bjA4ozLq+uDMpdWnLJHGqULyrNZcvrx8JWdhNxI7IMsiOHP1Dm1327tOEhzfrNOqNW1l\nLzlr5Xy8+GKy9eOOFfb+xInZBW1p95MkH0za5owlZ9RNKhucpQ0e7B/yW2/VXycdSiPpMctScmZ6\npIYNBWIHU74D1kZxzeRw2GGNf5fhvJSVOTdx1e+t7DtuWZ7Ha8c+grIY8ytpYJGmzdnmmwN//GOy\n4yTZv4+kwVmSjgdZtyMmaqeuD86yrtaMa1vmWh51vDPO0HNkfuMb4fv65CeBGTOAgw9OltY8mPNp\nbva2rEvOgqVzrnO8446tH6tbBW/inVZy1knVmlmVSmXRW9P1ObMaE7CI4Gzp0mKuMdcx5s9vnEKP\nqEhdH5y5ZFEEnuTGEXe8444Drrgi/L0//1n///TT8enLmwm6dtrJ/R6QzU3ANzgjt6KDM5acpWPn\nDz77SRucZTHm2WmnAdtv3/p+4tifMctqzenTG/92navTTgM23dRvn0RZ6/rgzFW9lkXJmU9wlqa3\n5qqrNv7t2va99/z3mRWTAQ4Z0jwRtX2ukzRuDrNoEfD73zcuW76cAVpSwZt4J0/fVPaSs6z27VMC\n187g7J//BBYvTrdt0mpNI6vf/dy5wAYbNC5znat3383mmERpdH1w5mq3lHXj0VaG0shjBO+82GkN\nzohgf8a002MZf/lL87JlyxicJcWSs+z3EVT2krMsGs3bwvYxdWr27cJ8gtSkny1suJKkTVWIilDZ\n4KyoNmeGK+O48Ubge9/zW9cIKzEbPDh6m6xE3TiC0zu14phjmpctXcoMM6luanNWxv1NnKjbgwLZ\nBWc+QUncmGhFl2oOHQr09cWvlyRfSFIz0YpODc6uuAL4whfanQrKS9fPrZmmWvOUU4ApU4B//KP5\nPdcTadgPfOhQYMEC/dqVKR19dPOyuEwhmK511wVefz16m6xEZcBZdwgIYnCWXNHVmll/71mX+mQd\ntGy+efb7zrpa0z5veZbSm7wuD++8E7683cHZ++8Dr7wCbLVV6+lI6rrrgMceK/64VIyuLzlzVa9F\n9Xw87TTgF78If88VnIVlllOn1l+H3bTWWy/8GOZJHAA+97nm94OfqSwj5JctOHvppfASuCopuuQs\nz5t/FvvuhGmAfGYcSFutmYWoHvALF6bbNs4FF4Qvt8+Dz77DzlsrwdkllwDDhsWvlwc+qHa30gVn\nIjJVRJ4VkadF5InasjVF5AERmSAi94uIdyWeq+Tsu9+NSoM7uPjyl4EddtCv40rOgvsMch3DHj9t\nt90a3wsbwmLRouhjZyk4xpjNp1pTBLjqKuAHP0h+7Kjg7KGHmpfdeSfwpz8BM2dWd6DJ4OfOMkMP\n21eeJWdZ7DvP66CVhyT797Jkif4/Kq1pqzXzDJ5/9jNg4MDoddIe//TTw5cn/T7D1r/5ZuDWW5uX\n+5actQuDs+5WuuAMgALQq5TaUSm1c23ZmQAeUEptAWBU7W8vaTL0qOBs4ULgmWdqCU0wlEbY+66M\n6q676q+DweV99zWvP2dO9LGzNHKk/j/uxhyVCZ9+OnDRRcmPHXVD2mcf93vrrKPHkauioqs1RYDZ\ns7M7RnDfreqEkjNznUftr529NV37eOGF9NvGceXHSQPisPV//WvgS19qXp7nwLeteuEF4Ikn2nNs\nKkYZgzMACF7ynwNwXe31dQAiym8apek12K9f8qBuzBjgRz8C/vd/gTfeaH7fZJb2+ECuH7Zd3RlM\nR7t7LEYNkeE78Xlc+seODV+etFrTTs/Mmf7bdZOiqzXHjgUOPTS7Y2StHcHZaafVS9sfeAA466zm\ndZKWnCWZWzPrNmet7CNt6afr89rnyGfi+KxLTtsVnE2c2J7jUnHKGJwpAA+KyJMicmJt2RCllLm9\nzgQwxHdnaaYRiio5s9kZ/eWXAz/5iW6rdued7nX//e966ZfPMcKCs6ATTojfT9bSVOP6busa8b+V\nDgFVrQLwDc4mTgx/qIgS9j2+/74eWT0rSZoO+MizWtO17wceqJe2/+pXuvrPx+LF7u8rSbVmUde+\nz3Gy7CU/dWrjeTCv5893pyVJSZs9L3LZGt4X2ZSlG/z2t8D997c7FcmUsbfm7kqpN0TkQwAeEJGG\nKYGVUkpEQn96e+89Ai+8AAwfDhxwQC96e3sbMoPgcBMLFwIrrdS8HxG/JyLXDyRq/KcBA+rDe2QV\nnJ1ySvx+smae7l2izl/am0UrwVlV25z5Vmtuvjmw9dbAuHH++w7blz1Zdday+A6/9S1deh02jl6r\nsr7GVl8d+OEP9UNfULuqNSdOLE/J2dChjX+b4GzVVfUA1iee2LxNku/Izmueew7YddfmddpVcvbF\nL7bnuJ3qO9/RD/3779/edPT19aHPZ8wZlDA4U0q9Ufv/TRH5G4CdAcwUkXWUUjNEZF0As8K2HT16\nBADgyCPrpS+mXv7885s7Aay4YngafEvO3J+heZnJFPr3r+87OIhrmGA6wp6Y21EqFHdzaKVa04Ul\nZ8klqdacMUNf+77nqgzB2cKF+nfse5OcODG/KiGfXn9Jr0NT4haUNjhr1eabAx/6UPrts67WtNl5\n40svpd+PUdUHuqROOkm3IV599XanJFoZvs/eXl1oZJx33nnOdUtVrSkiK4vIarXXqwDYH8BzAO4E\nMLy22nAAt0ftx/4S/v53/X9PT3gpWXg6WgvOwjIAU9IkUq9qjSrxMk/2wXTceCNwxBGNy9oReMRd\n6FHTu5htL7sMmBUaZofr7QUiruUmSQf27Abz5jXemOKCs0mT6ucpaXVkGYKzgQN1b7syMOmLqnJ0\n/VaTDufQzt6art+2OeYnPwlMm9b4nsn/opqZRKXtnnvcw2kYrbQ5Czt2mTsElMmVV9bbCYvo9tdl\n1GkP6KUKzqDbkv1LRMYCeBzAP5RS9wP4GYD9RGQCgL1rfzuF3RxcP6LTTgtft5XgLCwDsKsBTSeF\nqIzKDGoYTEdZeujEBWfBeTFt5kfyrW+5J3sP89Zb6Xp5Avr8+/Qm63Snnw5suWX977hqTXvYFjNG\nVVlKzkSACROijwcA48dnc7xWLV+u/w0YkHxE+yx6HQbTEnbMVoIJsx/X92DS9O9/63/G/ffXm3Kk\naQMM6IG3/+//0m1rc523sPy+DCUtnWjKlNa2z6u3N4OzFiilpiildqj920YpdWFt+TtKqX2VUlso\npfZXSkV+fUmCsx//uB71f//79XVbycRGjdJPj7bBg+vLfIIz855Pm7MyVmtGcVXzLFniNwWMj5tv\nbpzm6uKL2zdYZJHefLP+evnyxt7BQPO1EtZ20Pe7DbvulizJtuTslVfqr5POa1u0Zcvq5zOrYCvt\nEBJ5BGemja2rvaldcmWnL24w7jy4PqfrvMWVnLnyapacNWvlfqQUsMYa+c6B2ylKFZxlJWxgQNeP\naLXV6jewQYP0/2FDaZx0kv/x77qr8ckR0E+OprjX/NB9grPgOkVPNu3SyoXu6oF3++3AXnul368x\nbpweLPiWW+rLoqpZu4ndSSXsJuoTnPlUDwHh18DixflVa773XniHhdmzgdtuy+eYSSxfXj+frgAm\nabWmK5hJUq2Z5Lf60EPu0vl339X/u35L9vfuCg6VAkaPbt62iCEu7r1XV4+GCTvPDM6ivflm9u03\nzTnPIw95++3O+r66MjhLeyM2P9Cwas2oQU6D4jIan5IzV4/OspScmZvDKquEv+87vlzScYqimLHM\nvvY19/5OO61xkN92mju3fsNL67e/bQwE7Gs/7DoMLgsLIr78ZeDnP48/tqv6PsuM1b62f/ADYNtt\nm9e59dbkE0CPGZP978YOzuxrzxznjjvc17hreRYlZ655NsPssw/w2c82L1+4ULdnDO7PlSb73Nqf\nbfJk/ft84gngtdfCt83LgQfqcSjDhJ3nl1+Ofh8ox81+0qT6+d5mG+D559PvSyngv//1W/eII+pz\ny2b1W/Jpt5mWyWunTUvW1rldujI4S8sMFNnT0/xj/PjH0+/3b38Drr++/rcJXKKK+E1wFvzxh2Vi\nwSFCimDSceWVOrMNDjx6/vnubV3Vmmec0Vqa1llH924z5zXsJnLJJcCll/rtb9kyXR2al913T1/V\nOmmSzkS/8x3g1Vfry+2SsxtuaN4umImGPcj87W/RbQaNdpSc+aYjzh57NDdaT2LhQh0YB9NhzmfY\nzeWww4DHHw/fX5Lqtqj1jfvuA845p56uJMIerD796fg80Cc4M1W/u+wCfPWr9QnNs74ZJw2awuYE\ntavU40rOfAoEFi1y9yINOucc/1lNNttMj7N5ySU6MAvW2iTx1FPu7/mII5qboRg/+Qmw8srpj2uY\n/ccN1dSKjTbyGymh3boyOAuL4n2mVzroIP3aHu7C2GQT4OST06XnsMN0ZmT4VGua4CyuBGr+fGDT\nTdOlK62PfrR+cR99NHDNNbpK0uabOdo3junTW0/bvHnx7Vp80/bWW8D3vtd6mlwmT65Pcj9vXrKn\nuWOPrWei9nVkB2eTJzdvF7xRu8bqe/PN+lOxS9hN/+mni+8Zm/Sp3ZyvVko9TGBsa6VaM2nJWVww\n84c/AD/9qX5tfx9RM3wYYXnOE0/US85cfIKzpUvr52b6dGCttZq3zULWJVpxnU5cDw62X/1Kd9b5\n73/j5+T86U/je6fazj03vHNbUvb3MHu2Po/jxumg8s9/bnzfvjb/8x9gwQL9Our3GDY3tM1sm0fJ\nmX0MuyOUr3/9KzyIz0tXBmc+1TlR+vdPNlF5Uj7VmmYMtrjgLIunlaRefFHPR5cF3xtr8PtYe233\nuibgcO3bN+M233fWT3FK6VI+Ox2f/zwwxHvei8ZAzjy1z57dmFafKnBXcDZvXnx7Ep/qrSKk/X5a\nuYGH/XZd1Zo217lptUNAVD5hp8XnM6eZ8i7IvjbstC5bVr9e7Wuv3SVnxoUX6sGYg1w1AeY4PvmY\nCUw+/nHgkEPi05jke7Cvk7vuAjbcsPH9Rx7xC0js/ZgActtt6+OG2p8z6f3woYfq7bpd8qzWDDtO\nEp/6lF+Ngi+781aYrgzOXn21eboN3+ku9txTj4e22mp6MtzVVqu/V2RwFiw5O/LIbI5dNhMmAF//\nevx6wXPlymj23FMXzQPuDDOuBMAwGcThh/ut72vpUt0+zqTv6qvr7W+OPjr+6dJOG6CfyAHdy+nZ\nZ/XrF17QVZ9BJlNSSt8c4qpto246YRncKqu0Hpz9/ve6ZNBX0uDMpK+VG4Br6IWwak2fG3eSkrML\nLtBjBNp8g7OlS+Nv0j6la3Hsaj77+C+/XH/PDk7KEpw9+KD/0Cx9ffE3WJcnn3S/Z9pG9e/vP/uL\nfZ2MHt1cZb/nnvXRCKKYfPbooxsfwM1vxqf9out69wkO8yw5s9Obto1clg+eH/5w9PtdGZx961vA\nbrs1Lpszx2/bhx+uV2vefHPjjTyr4MynWjPYLs3ueZhlWvLyta+FNywO+sc/gKuuil8vTWbr+gH+\n63AaVgwAACAASURBVF/xmeq8efWqiiw7EOy9tw76gfqT6Ykn1ue1vOmmeoPeCy90j/ljV1mauVpt\nw4bptmNBJnM1mUxc49+oTDIsOFu0qPUM7IorgOuui17n1VeBs8/Wr+3gbNEi4Je/9DuO67PNmxd9\n83Sxh9Kw0+QzQ4DrnIUFXf/3f809KqPyEjstv/99/Aj/5pgLF6a/SbpKxV55pbl0UanyBGdJ0rHX\nXsBvfqNfJy2JsdtW2dfEyy8Da66pX8+fH94+Nuwasu8HrmtJKf+ZXW66qbHNmzkv9ue0j2mn6b33\nwme28An6kwRnV14Z/j0/+2x4R7VWZupoh5Lf4ltjt4PKYigFV0CUNFDy6RBgLjrXj2ngwGTHLNpa\nawF//at+HZVRmmrZuMmg0xRD2w16g+LafAwaBGyxhd9xlNLdtH2MHq0D0iD7HF17LXDwwTr48Jms\nd+nSetu1OO++m2warKhSqbB9LF3aenDm813fcosOXoMmTPDvWOK6AZx7LvCJT+ghdlwTXod99gMO\nAL7yleZ92+u6zo0rLb6lWKa9Txj7O/SZ3N4Eeh/+cLIhhILpMQ8Zwc9m0mPy5C9+sXlqvThx12/a\n4Czqeo/6jfsEE2ElNyus0Hgd280VkvTUtfftumbuuUffe9ZZx93ZwA7yw4Y8cpWc2cvPOafeuc7m\nU01r9uNTGv700/r/s89uzOvHjg3P311DvZRVVwdndnVUFu2Grr02fLmpggyz3XbNy3xKzgzXReQ7\nFVU7mR9jVEZqSobOOit6X1k38I37cfoGLwsW6Osiqg0coEtuo9qn2J/vppuAu+/2SyegG1avu65X\ncrHXXnoOPN/MyWcarqAigjPX95Pkd+5a13zmZ591D4ocdvx58+oDWmdVrenb7ijqQc8+n3EPJfYx\n583TpcxpXHWVHtbhvfcaP5v9UGlKr2+7LfkUXHHXWNqSEfv8BDtaRf3G49IjoqtMw9gzl9hBtrk/\nvPpq47AjYdeKvcx1bzDne+ZM3f4rrE2pqzQsbJm9rs81FnUtK6U7HJh8zycPMce/8EI9nI7hulfY\n52j58vAahzjLljV+F3nq6uDM5tvmLEip+oVnun0HRfXg+OMfm5eZxv5xN3Sg8aI3bYuAzgjOjBtv\nbK6WTSp48/nYx/T8o3abwCRcg/l+9rP+GfukSbrkz1RtRLHbW4QFBXaGkkXbiCgLFrQenL3/vnts\nsXYFZ0olC8582nm1WgKz7bZ+bQijqjV9xsKLetCzz6cp/Zk+3X2u7JKXCRPSnQMzRthqqwG/+119\neVada+JKqtJWk5rqOJFkeYvPNW+Pm+aatSGsBHTLLeslUffdF17lb8Z4BKILC4y77grvje0qOTNc\n1ZquDiA2V4meUnpfRxxRb1t9/fW6Cn7kSPc14wokfWqyFi3SJd1xnntOfx7zvVx+uR6KI6mHH64P\nvvzOO37D+FQmOGulGNP8eHzaULm2ta26arr5C+2ArOzVmsazz+pBTT/zmdb2E/zBnX468D//k/7m\nudlmzcuWLtVVjmHzdx52WPMyU9pnjzPmkiTIsts5zpkT3y7M9yZin0PfEhFXRhv1mVsNznzOlevG\nkeTmP3Ys8MADzct9grO4/MQEB+PG+Q2R4gomnn223gYpStQNKez72GADXX0b1mmkp6deOuBbIhvF\nHmYii7Zl//53/BA3S5b4tzN2MaWgPuxzPHp0/MOoa0gKOzgzVdALFtTP4XHHxVc1+9zr5s/X/2+w\nATBiRHwaw/btCo7CvuO33w5Plz20iu2nPwW+8Q0drD3ySHj6XIGkzz0hrINDmO2207UNpvlN2mtq\nn310e+MNNwT226+5N22YygRnWdh///DlUb0uWumWfuaZerBMww7OOqXkbNtt3RPJuwbPtT/b0KHh\n63z5y/r/LEuWzLHOPLP5vTvuaF7mmsUh6D//CR9zzObKUM46K3xQyHXWqb/27X1qP7n6BssXXRQ+\nNVJUBtiukrOlS+slfT7XxXHHhf+m7c+2aFH4oJ5xbViXLk32QOg6Z2GlZmEl7q5r0HVTBHR1UNhD\nyuuv10sHzE28TK66qrm3atCttxY7OPftt+t2XDNn6t7nRx3VvI59XdmBhR0g21WCYdNH+VzXPtVu\nJi3TpzdW3bum1jNmzqwHjUnaWq+9dni74t12i8+LXLVeruAwyQO7T15ld26w89C33w5/kA9j0jRt\nWvy9wKhEcPbww8AvftH6fuxAy7ehrk+7MpcLL9SD3xp20JJFd/cihf1gXMXv5rNNm1bvrRT8ESUZ\nX8hX3CC49ryOW24J/OlP+rWrunvGDN1raOed4zMg1w3UVJnbgxgX6Te/qQfCtrjzntXcq2H6+sKr\nf5YurWfkrQSI9rV6+eXAJz/ZvE5cVeOSJdFNKR57rJ7Gxx5zNza32yMZSarb1l7bXfLgYpdk+Ayu\nWjSfB97XX6+/njUr/2mWvvc94Pjj9UOTPdG7zdV0wZSsPvpo4wwCO+9cf532PjJ/fngHJPv34Wof\nGVYC9qlP6VKfNddsPMc+zPof/Wh9mI4nn3S364zjqlZNEjQmzafsfd9xh55SbvHi+P24vvsoGQw3\nWH5ZTdVgZwoDB9YzsaibSZbBgx2cpR1fp13CfjCuANMsX289PeAt0Hjx33RT/XUrJZM77VQfE82H\nXaQdNw3LHXfo78inATYQ/+MODp2QJvhJ2+4yLDO5/HL9f09PeCa+bFn64V7iPttee+lrI2j8+Hqv\n1WXLdNqSfL+G/Xld1aRx1adLl0a3Rd1tNz0UwEYb6TkfXVZYoXk/rQ6yHcf+bGXs1ZY00Gplmq40\nXFW3drrtEsmPfET/v/vujevbQYtP56qgWbP0A2RYT1jXbA5xcx2//bbe9t13G0uPkzRFmDBBNyc4\n/fT4baK4qjWDgWxeTTDM97niisCPfgScd178usHXUSpRcpaVYHDmI4shPAw7ONtmm84Yq8UIuyBd\nwZlZV6R+o7B/RHY3bdf34DOl1dNP615iW28d3vYmaP786FHzRfSAsoBuo5Zk6ifXTdA1uXiRN81F\ni5rb0JiiedcTfSuZns9nCys523nn+qT3y5bpm8vHPpb8+HZwknb6pNdf142Jo/zkJ9GBGaB71gaF\n/e6zHBwzbRBfBKWihw0J49vOzacdZpKhYuwOAFEOPjh+nblzdd6TJM8fMkQ3qE8irlqzlbHC7P0l\nyb/McYYPb+xcY+c99uDKwTwp6hy4vssXXwwfJ9KYPLnxwduuVQmTpuSWwVkCaYIzu1qyVZ3SzixM\n2E3Opz3Lpz6lJ122z71P2zsz6XOc73xHl7iEtb0J+va34+ebPPHE+pyGSbgyOju4N8OOrLBCurnh\nWnHUUfWg5dJL68MCuIKz226rVxM/8ECyhrQ+mX7cDXLZsvSly/Y5d7Xni7vhH3OMvm5bFVbyGteL\nrlU+vUuB/KsKDfsa69cvWTXYkiX+wdSnPhW/zjbb6N9C2HdwwgmNfwfHSXQ1f3jllfhJ5VdYQec9\nvm0ATX7p0/4sLiBzreuqFXDlCcF5lJPez66/vvGBx76nXHWVHlxZBLjhBr/0AOG/4zlz9Dyln/98\n83vmmt90U+DUU+vL485b3BAlodv4rUaAOziz52LbeOPGbdZYI7vjmyE4gM4qNQPCgzNXqYT9Y1p5\nZZ0ZP/GEHhkdaGwM7wqSFy4M72EZnNstyVO4aX+z007R6/kGhjafm+uyZX49Q/Nigq1TTqlXtbm+\nw2OOqQ+uuf/+4QPGhjnuOL8BfeNKpJctaw4yfKpZf/ObxqDOdX1kMSSETxBkrrmZM+s35rgSDV+u\n8+Eb6BU1S0mw6UKSHqSzZ2c7+8BLL+kStq9+tfm9sCDwD3+ID2L/85/4HtkHHeQ+RhhzH/LNVwCd\nz8bNp+u6zuzlJugaM0Zfs2aYj+A0XmlKaM3nEWkM1Oz2b6YpgxljL6rd5CGHAL29jcsGD25sN+gq\nLUxSEpimzSCDswTsTMJMD6FU49yQYdUQWbEvxk4LzsLahrku6LALeccddYnUjBmNAdndd4cXP6+9\ndvjy4A0lSYNnky4zMnWcJCULPt/nD37QHPwXafjw9Ns+80x92qoo117rV8oW1Z4L0NdWcB2fTjSn\nn16f2SJKFjf8qOAseO2ss44OXIHw302aas1WHxyLCs6C+UGw/WWcrMZWM2bM0GM3BoV9B6NGxe/P\n51q68079v+9nMdePT3A2d64OovbaSw9PlEZYp4I99tAFF+ZhOmkbtTD253noofB1zGdfbTU97EjU\n/MGPPgr885/Ny12/TVe6kwRnLDnLgZ25H3tsvQHgEUfUMwzTuDMPW21Vf+0a1qOsTEbuM8CqGVMm\nzJAhjX9vvHH4VCFhRdJRx/SRtGFxkuDMJ4O+5ppkx8/a9OnNvQejPqM97ti99zaO4p03e+BII8se\nznlMzGwLO6+zZ+vqG3MNt1qtaA818d57ej7WJIoKznwGVXVZsiT7HqeuDmam85LN59g+15JZxzcI\nN3mVT343c6YeezMr9jHtaZXSBmeu6c9cVbz27+L6693zE0dJOhB4kuDMt2S6Er01s2KX/mywgZ5g\nHdAn/hOf0G2X5s4NHxMrC6b6dPbsfEvo8tSvX/0H5spoPv1p/RSXZJ/mf7vY21h11XomWWRD+iQ3\nr7xv9lmYNKn5Bh4XnAXbpcyY0VgtbcuyhOOoo5rnVs0yOMu6NCbIvpaNd97RpfRmnLP+/Vu7buzf\nYVjg7ZPGvIg0zj+Z1uLFwKGHZpMmY9iw8M4DYSV6Dz8cvS+701MSV12lmzj85Cfh75tSY5/AwmcG\niiRcTQHsasy0D8mzZumBmaPYv5ssfqd2gO1K95tv6gIBV6k7S85yZp/gsAt6q610g1FT/RDsGt0q\nkxkOGFBcY9ysuSbLtQ0cqDMfX3FzldrjQmXZqy1Op1U9pxF1HSrV3K5kzBjdSHnTTXX1sD16vm9D\ndB+jRjW3n4kbdiVJoBNct5UhXcKEBT7mJmE6KQQbyidl2tUsWpTuJpZnHmSX6rUSVIeN8dWqLHuz\nDhiQbriXNdbItrSrCHZJV5KHZHvdE04Att8+ev3gQ1kaSa/tp56K7t1p53NxTTIMBmcJmJGRAfeN\nZJVVdCNQIPuBYk1mbHcM6DQ+N7GkU2SYG5PZd/DJyj5mkcFZ1qVhaa+nVgZCtrmqil2WL28+34sW\n6RK4yZN1xwq7YXWr0+3EiTt/vpkm0PzdZv1bD7s5mKDMBAfBjjNpLVqUruovz9JD+/MnObfBIPWU\nU/yP4yvJdRInbV6+0kq6d2Kcdo+HGTdckg87D3GVyrkeTrIu3XX9TpJ8Ht/fTdcFZ3a7rDzFlYpc\ndpl/DzVf5iLN6mZbtBEj6m3lVl5ZD8QZxnfgVsP0DjJB2Lbb1t+79tr4saTyYn6wWX1fYWN2+WQK\ncQGxb/rCekjGVWsGg7Ojj9bd1A07s/WdhiqtVoKz4O89GJxlcRM49ljgl7/Ur8POa/CB0P5e7d9S\n0kBx0aJ0596+yWRdipY2OEtaBZqmdDtJyVlcCVHaoHqllcJn7igb13WRtj2u63ymnQM3qbiHmDfe\nyC4f67rgrJX2CXGSNGj85jeBXXfN9viDBrknqe0E555bn4d0/nzdXsLMQbfbbsBf/qJfJy1xMj1n\nw4KM4cPbN0m8+RxZlaqElfr5lETGBV++gUXSKqIxY3TAETRhQv21nbYsB2wOE3euooZVCV6Twaff\nLKo1jz1Wj7sHhN9sgjcGc+7+8Q/de9A81CR9GPjjH5NP8RSUNt/1SWuS308RtQpJSs7i7hNp07vi\nivVzbrfhLOLzx43JZnMFTUnyeHvid5dWa0Seflo3VTLDBSV92DDrr7eefgDNAoOzBOxR58Omj8lb\n//46wOlkdkb78Y/Xp2JabTXdjfvhh+tTA/ky37nrB5r3Td/F3CzzDM58bm5ZBWdJvfRS+FACdhBk\nHzvv76mVkrNgMJZHtWb//vUgz+eJ31z3Bx+sH3rMQ0jSG8vZZwM//nGybYLsz58kDzYPVkH2Z0gS\n+CbN/9MM7P3AA8m3cUlbqm63O7aHRCmiLXKSmo0s0nP88fXPmFdHuJ120oMQb7CB/jvp92J/zuCc\no3fdlS5NXRWcfetbydvFJLHvvvpJ6I03gC9+Mb/jdDPXk6S52e25Z/rA11XyYf9wWg3ekw43AGQX\nnIU1HPbJROKCL599uKpfsqxib1cQbUQFZ4sW1a/dxYubq7bSBic2E4RcfTUwdGj8+lm3c0ti2211\nfmjYnzlJwJNVSY/5jfsc284PNtww+bFabUtqf29pgxfTYeLRRxuHEnLtr5U2iUHjx/uvm0Vwtnhx\nMb3Z7cGvkz6whg1Oe8UVukT7kEPSpaergrNLLw0f8ypr66zTub0l223ECOCee5qXZ9EDypXRnn9+\nfeBY15N6mBtv1G0HbUnbwwHuJ/9ddvHfhysI8ilViAsWfDIi13lrtdTNztSCpVNZl+jFVX1EVWuu\nuaa+jgBdgv6rXzW+b6e11Y4bxx/v9726Apsi8qZRo4Af/rD+t8+UamFc58r+/D43ZqX0P5/A2N53\nFm02o4S1P7K/t7Tf1Uc/qv/fbTe//a21VrrjJBW8Jstyn0x6XYQVIkTlR3beZfKZb36zeUqvJLoq\nOAOSRfVUvCFDgAMOaF4+Zkxr+1VKt70xI2nbBg/WQftllyX7sey/vx7l2mZP6+HLDqzszGrJEv9g\n0XXD8ym56umJriZzZTqu+Ux9tvVlZ4LBkrOsh6dw3eSvvFJ3dT/rrOjtX3pJ/x83GLHrO3EFU2Z5\n0qChnTe+nh490fzJJ+u/7SArSXDm+o5POEF35gGS9Qr1uR7tdXzOYdYllK2WnAUDBzvwcO0vz05k\n9vkMXuOtNsgPzmKRdrgdn1J5+3sJy+d9S8TtzxxV6BD70Ox3uM6x/vr11zvu2NgzjMori8xj2DDg\ns591v//Nb7oHQHWlKYt02ZmX3WZi4ULd0NWnS3wrk97vu2/0TcD1Ge3leTU0NjeaZcua26eF3bhb\nCUjskjP7OznpJN15xzUdjGEGfw1jp8t1Pl2ZsbkpuIJ4FzuvMz7zGeDww+O3TSKsyrZ/f93G7f/9\nv+b1N9kk3b5tQ4bUpwuLKznbdls9viSQPKjNu+Qs6He/a3xAzCLAtkvg7evaHgcti3zMNaepfX6C\n36dpKhAX4Lp+G1k/oEUJO0dRn83Ftwo2rqq564KzI47Qg1wCuij3179ub3oo3s03+81nmAU7OIt7\nyu7Xr55prLlm/L7tH7fdQ9Rebk+8bp6qfEoGXMGRvW1YkPfMM8B110Xv284Y7QzITndeHW3mz9fV\nePfdpycdt9kZo/n8rZTU2cFZ8PNMmRK/fVRjZJ/gzJW5m55oSUvOvvKVeu8y4957k82B6nPTts+V\nHZwZa6+th6v53veAceP0lDlRtt8e2Gwz/dp18zXf1coru6v/zffx738Djz2mX/tcH+0Izkx+8O1v\n+z0gJgmmvva15im9brpJH8uwz0va35BrO5/OG3H5h12DkCYgykJYUGWn2/c68B1KI24Uga6bvkmk\n3uOiqLnfqDU+E2Jn5dBD9QjSG2+sS6PC2pCttJJ+4uvfv/504/ohDRxYb6vU01O/qbh6JJof+J//\nXJ8ndLvtgLFjm4vs7Sl8XNeyXVwflnn09MTfgIJVUibgs/eXV3A2bRrwn/+E39CDwdmiRfo7Sdtt\n3s58V1gh+WCiUefRTqtPSWTcPnzyrv79wzvPJLmhDRgQfz7DrgP7s7z8sv6d+F4j11+vf3+DB8cH\nZ/Pn6weMvj499Vcw7cEqvmCQHPbZigrOBg2qD6y88sr1PMFOc1Q1ZJrr3OQXRx3VOKhzsOrRpMVM\nk7XKKu65KsP2YfM5hyusEL3/wYPrs+6suGL9t1pEydkmm+hqzLDgzL7OfQPmuXOB55+PX2/PPYGR\nI93vd3X4wuCMgvr3BzbaSL92VRXa46aZ4Mz1w7Sf+Fw3Rfs6nDxZ//+FL9QnUL733vq8hvYNzqcR\nrx2chWWSPjdqV/WlT3CWdngPwwRIYRmj/XlMulqpnrHTmuaJPKqE066iSDtauU+1pv2duG5crus6\nbH2f82Cn+5139P92WgcPjg/MzPiGgH6gMSXIrioge8Dl7bdvnJ3FCGtHlLRjRp7t9uxrwn79la8A\nv/2tfu1KY9rr/OyzdSALNH4npqQSCC+Z8gmsXWkK66kYFLd/e4o9+zvMegq8ffetF96Y796kOez3\nbR//rbf8jjFvXr2aPcphh0W/39XhC4MziuKqKjSZ18CB9UzVNY+dPQeg62Y5c2b99eqrN1ffDRyo\nb17DhjUGZPfdF19dY2ce5rVdyueT6Zq56Pbbr7EKyadaMyxDSzLob1QVQFhw1sqTtKvNma/Zs8PH\nLBo1Cth77/rfSas1jSQ9NG+4wd2+MklwlufgsbbRo8PnPLRLL3t7gQsu0Nfxpz8dv8+w3rV2Ozyf\n0swsh5gIsn8HBx6oS8gB/Vs300rZD3fDhgG33KJfpw3O1l23fu5efLG+fKutmqs+gXpQlLR622b/\nllzBdtx19uqr+v+LLgK23rq+3Cc4S3Kurruufl5Mmsw1GJaXtTKER1weE9ecpavDFwZnFCWqhOOV\nV3QmZm6oI0cCzz3XvO4vf1kff8z11GgX52+/fX0UeFtPjy4Kv+oq4JprdKa044710bh9SgFM8PHc\nc7qqCWgMQI86Slfrutx/f72qFfBrSxJWNZikVCoq87Mz5uAUXUH2TS6soTzQGJylKTG5/fbwMYv2\n3rvxWnLdLOwALoy93R57hLdNMjeUY45pbL9oCwuOv/EN3SbMtb8g1/kxbS/j8tbJk+vBCKCHudlo\nI3192W0j7d5sW24Z32PWFnbt/PnP9V619rXi6hV9xBE6rTfd1Nwz20g7h6j9PXz2s7p6NsiU4gD6\n+z/yyPrrINf3bTz4YOOUTnbwavKm739fjwdqrLGG/p3Z3/c55+gSuCDXNWFfC66q2LjgbPZs/f8Z\nZ7gfhF2/fZ/f8g9+oP9fddV6WkzeyOCsDRickY+VV24sVl+ypF71KaKrCbbeWhdVBzsGrLGGDqKA\n+g/5k58Edt+9vo5dGhb3gzz4YN3A1zCZdFgVzqqrNpYw9PbqHoebbqqrMZ58svEGf9NN+ublyz6m\n67c0dCjw9a83LsujnYir5KynRw+fY38vrt6v9s0yTXDmOzF72I112TLdKzSK/dkuuaSx5CNq30Fh\nJWeXXw786EfNy12BtL3cBMlDh+rSVR9Dh9Y/z2OP1X9fM2Y0ljbbr5NUYe26q77eg1ZfHdh8c/3a\nvmZd332/fjqtRx0F3HorcOaZzfvcaqt00+bZ31XYTX7kSODii+uf276+zPn/8IfrvYT/+lfgv/91\nH2+ffRqDIPOZBw/WeRIA/OIXjd+hObYJjgDdm/SMM5r3b4LeIPs8ux5QfasEAfc17gqU7OO7gsAV\nV9T52eqr169Lc45NW9+44My3x7ypQo67nuOG9+jq8CVpg1+qlv32013bp07VvfX+8AedoQWHTLAD\noLff1lN9GHZQYJ4ax4wBvvrV+nL7R5q2MbsJFm3z5ukprwwRYK+96n+HTZQ+YIAO/nza5vhMOLze\nenqcMFtWwZl93kyma+/7wgv1QMFbbdW4nf157M9pD1Ls+h7CgjazLCo/cfXW3Gsv/Tn69dOlpuec\n07ytqyeqK8iLY99EzL5F9Dk07ZEM13dv36jMPv5/e2cdLld1tfHfyo0bgQgSXIKFJGhwDSQFgltx\nbQstDkGLluBu7YcHCy0uoXjQAimlFCkUSrECTYGQYIXI+v5492H2nTtz79yQO2fmst/nmWeOz3v2\n7LPP2kvffltmt8xxuyVk1y4XbfnVV437aEsvs7h9Hn64dGkwKPwX8T0cdFBpjXVciWGBBdSnitG1\na+Vl82IzadwnSmnutt9egRHZsfH99+olrfmrrypYCDRBjMeelrDllipJNGVK03EiQxZQErdVx46l\nXT6ysW7y5MYaunjyWW6caG5iM3Jk4/XBg0tPBstNEOPnpNzY417oj9n9Fz9fLWnJinOulUM2OWzp\nWW3p99q1cPbgg3kzSKhVvP22Kkr86ld6mPr2hb32UlHuJ55o/txstv/5540dP8u9oLNBd+TI2SuK\n617ICF6M+OVaqQr+qqvEZYstlLQ3NiXFg1ssjJTKXXX99bpW8blzKvy9JeFs330LZqAY8ez51FMl\nxB57LNx6q3yfoPystdTgnv2XzQ225UqExS+r7t2br2FZ/P+VEs4q0S7FAkKnTo2FqWJfrnKahjFj\nVEYK4K67lCIjQ6ztag4tCendu0tj1ZJjdIbrr5fmByQwtWQZie9t3XULvp7ZfzV1ass54a67Tq4L\nzWHFFWHxxbUc3/Pkyfp2b1nj+PbbjYvPd+6sKOZ+/Qpm+tbmGlxrLXj88abbs3Z77z39t8VoaGg6\nYZg8WROLZZbReHnjjbqvl15SKpwM5cag5nIE/vGPjbWgZ55ZiKKPn4HWpvKItxe3v1njIBWowMxY\nobRUqVIoDtIohXaXSiNGuaR5CQnlahfGPiDlMH68BqtiH5BevQoPZjwwDBoEzz2nQWh2UUnNvGIN\nUnOYMKGwHDtqjxmjF/guuxR+85NPlPIiy9ieYcSIgtn0xRflP7T66s37hrQkXAwYUHipxejUSZrN\nsWML22JtRHzdYuGwZ89C6aVMU1GOR6dOTQfpjh1bFnxLCZJQWqDr108aowceKGz7+c+bmmOzdoxT\nqlQinBW3S7Ew5S6BtXfv8sEbu+6qiUf84m0tKtGgbraZPmYtO+fvvLMEqiOPrMy8O2xYQbtWyt+w\nubx1w4apH8Ya8HK4/Xa9a7bZRlr4rEpNpWZwaDr5KdZAzcmoxUyDXK7cXceOBUGkUyc91/37K6F7\ncVL3IUMamzvLTWBa4n/nnY0Fsuz/XWKJ0oEk5RALUF27yu/u5ZebRk/OmqWxqjWoNH9Zc2XgHd5H\nywAAIABJREFUMkyf3vLz0W41Z+5Nq8MnJMwJ9O/fOKIIlArj8ssLM87hwzUgTJsGhx76w38zm2Xt\ntx/cdpsCB0A54iZN0otgdvxioLEwscQSegmeeGJBo9G3r0xyxdqmWAgaNkznQvlBp5KXdSn/lSef\nlL/cXnsV9l9wQXkfkJhXudlwue2ltH7lBIFSvi4LLtjYtFzKzPPhh/Cb3zTe9tvfNv3tbD2+RiUv\n6Q4dCseVyy3Vs6f6a+yTuPLK+s8//riyVAAtYZ99lDKmEkya1LxWMUPv3nJFaAnujU2mcd9tadL+\n/vvqc8UJfsvVbe7SRZ/77pOGJtPIzE4dXlC7xQlk5zQWW6xpP4rNnnE/LJVHrxjbbVc6ACnGIotI\nc10Oc83V9H/55htp6jJUErEeP9fZ+FBO6G+Ni8l11xXG3FdeaT5penPCWeafXJHrh7u3i49uJSGh\n9vD88yrL/EPw3Xfun302Z/gU49tv3Z94oun2Sy9tyrtQYtp96tTG+6ZM0fblly8cs8EG7pttpuVu\n3RqfX+qzwAKF5ew6MQYNKt2WAwcWzttkk8Ly8cc3PRbcR4xw79Wr6e/36dN021xzleaa3Y+7+5df\nur/8spbvuadwzPDhpdv81VcLx3TuXPqYjKtZ4djsPisBuHfo0PwxJ57oPs88OvbWWyu7br3g+ON1\nX1df7f7NN9oG7hMmuH/0Ueuv9+mn7s8+27QffPxx02PBffvt3U8//YfdQ7UwdmzhfmbOLGxfYYXK\n+9sTT7ifd17hOh066PuUU9zvuEPH3H67+/XXN26/5nDAAYXjBgwoLO+0k/trrzX9L5Zd1v3hh7W8\n4IKlx6kMK63U8ni0887u99/f9Nx//7v8Od27l993++3u06YVrhPklpIyTbvVnCUk1ApWXfWHB6d0\n6lS5Q2pr0blzISFujP33b34WWDxLztY9mskefnihGH1rfdHWWqvpDL/cjDPTclxxRWOtZikN2TPP\niFOpIsqlfNEqSUDco0dB2xRzrmR23pzJ9K23Gv83J5xQOiVDObRkKjzpJAW5tEdkfpp77VXQokyf\nrpxjramxm2GeeZpqzA8+uPRzefLJcMYZpaM/axHHHCMN7euvN9ZQ3XQTPPRQZddYZ51CWxx2WCF9\nxa9/XdDCb721/P9A2rpdd23+mpnm9/77FdSRoaGh4MaxwgqF7e6KWgVp/KdNK2++Lvdsxvffty+M\nGtX0mAUWKK/FbkljGmcGaA7t2ucsIaFW0FaFw9sSZqVNhw0NqgdZfE/dumn7a68p8e7o0Rrws8Fu\nwICCQFTOjysz4f31rwUn6xjlhLNhw+T7su++yuWUoZTT7RprlL4GlBbOyvn7XXllaUfnWBAvx3fQ\nIKU12Xnn5h2Rl1iicI1vv5WAW2kakIsvbjk3VoZllimYXNoLdtmlaWm4HxpJXJyDqzihdIZSaUtq\nHWZNA48GD26diTt71s89V75epaJfs0naNde0LKiMHq1giVGjCr58UPgf331XLh1ZTr1s/Lj2WqVU\nae76hx+udEMXXdTY17RjR40DZ53VcsBIa9EaU2oSzhISEirGlVeWF9pAGqRVVy2sZ07R7hoEH3oI\n7r1XAl4p4WzZZTWADR1a+vrlXq4vvlhYPvhgvVB22qny3EQZMk4PPCCtYXORhEsu2VSTAnJwv+EG\nvaC32KL0uR07ylfn3nsLJZHKYZVVlJ6ltdn8W+O3lJUPa08wa7vajGef3boAnB8LdtihMGFZYYXS\n2ulsUldJJZFtty34w2VatJVX1u+AUgzFAQOZ8LPHHi1fe7fdCqW0unUrCGedOkk4K5XrrRTi+sot\noTXCmXk53Vydwcy8vdxLQkJ7gJkSbW6/fdPt5Qa0Y45pHJFZjMsuU66u5goGt5ZjjCxa1F1awF69\nFNX2/vtNz33jDWnA2hqzZmlQn1MpShJ+GA44QJqh1pQpSyjAXaltSmnVmsNFF2niVeo1/+abEtiW\nW05pPlqDLK1GFiWeFayvRJx4/nlFcY4YUdlv3XST0scUfttw95K68CScJSQktAnee0+CTbEAlKVN\niEPnsxnlmDHKc1QtTJggM+S228rfpl8/8c5MfG+/rYH3X/9qeu7rr5fPP5eQkDBnMWOGUvqUyrkI\nmsSYtb76h5kiRT/6SOu9e0vjV6k4MWVK08oxpXDGGYrEjV0hmhPOUkBAQkJCm2DhhcsPlPH22GG3\nVCWEtsSmmxYSYA4cKAfg2Pdq8cXLZz1vLk9WQkLCnEXHjuUFM5C/2+yUZYPGz3hc77US9OlTSLPR\nHI46qvlkvMVImrOEhISqYpllZBr64ANprP70J+WCmzxZM9BKEozOSUyfLn+u//2vdODG7rvLfBEn\n20xDTUJC+8DccytI6P77tZ4FK7X2GW9JMCx1veY0ZykgICEhoaooLug9aZK+yxUsb2t06qSqD+Uc\n7q+5RjPrCy+EceMUhZaQkNA+MGWKtFqZcNazZ+NC8JXikEMk6FVah7Ul1I3mzMxGARcADcCV7n5m\n0f6kOUtIqENMn666iXvvnTeTlrH22oqcTENNQkL7wY47KsjokUeUR/Cww2b/GS/WoG24odw1rrmm\n1LF17nNmZg3AJcAoYDngp2bWbgKZJ06cmDeFilFPXEuhHvnXI+cYLfHv1Kk2BbNSvJ9+uvo8Zgft\nvc/UIuqRc4x65T8neF93nVJybLghHHjg7GnOMrg3jqxecMHSgllLqAvhDFgNeMvd33H36cB4YMuc\nOc0x1NNDUU9cS6Ee+dcj5xj1yr8U75/9rPo8Zgf12uYZ6pF/PXKOUa/85wTvrl0LwUgdO1aevLkc\nVlsN1lxTy6usMnvXqBefs4FAnGnoA2B4TlwSEhJ+pPjd7/RJSEhIKIfHHpN5c8aM1ifCzlAvwlny\n8EhISEhISEioeWRmzR9SoaIuAgLMbHXgJHcfFdaPAWbFQQFmVvs3kpCQkJCQkJAQUNcVAsysI/AG\nsBHwIfA88FN3b4cV4RISEhISEhJ+zKgLs6a7zzCzXwEPoFQaVyXBLCEhISEhIaE9oi40ZwkJCQkJ\nCQkJPxbUSyqNdgEzGxC+Z7MCWEKlqMc2NrMVzOwHBnHni3prdzObO7hNJFQRZtbPzJbLm8fswsw6\ntXxUbaJeudcr79lF3QhnZjbazA6rxz/IzJY3szuBYwFqvZSBme1gZgflzWN2YWZ7A+vnzaNSmFl/\nM7sEuA5YMm8+rYWZbWVm15tZt1rv2xnMrIeZXQqcDdSdQByEm8vNbNu8ubQWZnYo8CiwZt5cWosw\ngbocGJo3l9bCzLY2s1uAVfPm0hrUK+8fipoXzsxsOTO7BzgUmBSS0NYNzOwy4A/A3e5+SN58moOZ\nLWNmtwO/AurOp8/MtjGzp4BNgFfz5lMJzGwF4D8o0GU1d38hZ0oVw8yWNbP7gf2Am9z9m3rQnJnZ\n5qh/fAsc7O6fhu01zx3AzE4B7gKmAXfmTKdimFl3M3sH2BAY4e5X5kypYphZZzO7EBgHvObuf86b\nU6Uws/nM7HFgf+Sv/UzenCpBvfKeU6hpdb6ZdQMOBhZ296Fh2/dFNOPlGkZP4B13vxrAzOYF/uvu\ns/Kl1RjhxXQ+MNXd1w3bGtx9Zr7MKoOZrQicDNzu7nOo9Gzbw91fDi+sSSHwZV3g3+7+z5ypVYLN\ngE3cvQEUVe3uM3LmVAmWBiYCx7j7t2a2aKg+UutjSRa5fjywm7vfGLbV/HMaxuqvzWwiMNPdJ5vZ\n8oCh8fHLfBm2iI2AlYAT3f3uvMm0EgOBuYFfuPszZtbD3b/Km1QFqFfecwQ1HxBgZsORJucCYDCw\nECrlND5XYmVgZkOA/7j7f8J6Z1Td4EJgA+BTNGM/yt0/zo1ogJl1cfdvw/K2wDbA4cD2wHzAk8Bf\n3H1yfixLo0hQ7wfsBnQCrgf2BP6JBv7nzaxDLQjEZjYUWBaY4O7TwraNgXuB+9BgZMCzwMXu/u+8\nuJaCmc3l7lOj9X8AR6NJyDrAvxDvL3Ki2ARmtgjyJngvrA8C9kDtvAzQHWmKn3b3W2ulr2QIfohf\notyOHsyCWwG7A2OBt4Cn3P2hHGk2gZn1QuPIw1HbdwYmA08DA4C3gd7AYbUWgW9m/d39v2G5D3AQ\nGr+nASOBvwFvuvsd+bEsDTOb390/Css9gJ3R8/kXYHPgFeA5d785P5ZNUa+82wI1ZdY0s03N7E0z\nWyPa/Fr4PA9sAbwLnBpSa9SMKcLM+pjZXagTbRa0frj7d2gAPQw4F9gXCRB7mFmXHPlubmaPAD/P\ntrn7bShVyVPAusDHiO+hZlZrfeVY4LFs3d0/QQLNmshktRCwPHC7mS1cCy9bM9sNeBEN8itl28NL\ndTzwortvgNT4CwJD8uBZCma2sZm9BexfFLRwFHAr0qJdiyYgY8xsgeqzbAwTTgb+AXxfetjd/wG8\njvrKE6iP/w04wcz61EJfATCzrmZ2I3APMCybiLj7+cASaEycCHwCHGlmo/LiWgwzWxk9h2cC6xaN\nh2OQsLmWu++IhMvReXEthpktYmYPAE+aWXcAd/8cCZRboLH8XjTJPt7M1sqNbBHMbHUz+w/wYLYt\naJsmAouh5/Ro4M/AWDObLw+exahX3m2JmtGcmdmqwJHAAsAUdx8d7VscGOTufwzr6wDj3X1gLmRL\nIPgObYhMxQOB6939xWh/vyBAYGYbAmcAa+ZhBgrteSN6aX0J/J+7vxT2LQUMdfdbw/oGSAt1krv/\nq9pcixGExIOBjZHwcqm7nx72dUUz2nei+7kE6OHue+VEmcCjMxIGvkADDciP4t9hfyOToJldhXxb\nzq062SKY2fxICBuI/ONudveno/2buPuDYXkpJGhu6+7v5ED3e5hZb2Tq/gPwa+QXd33Y1xPol3E0\nswbE+4L43vKCKfBpK2AH4Ds0YRrv7lPC/kFIG/hmWL8YmObux+VEuRGCBWFuYBHkyH11PB4WHbst\nMtNuVUWKZWFmpyFNcB9kBRkTthuafDyZ+T6b2alAh1po9yBI/hL4DNgHuMLdrwn7OgKLh4lJdvw9\nwDPZ+JkX6pV3m8Pdc/sgs0LXsNwPWC4svwTsHB3XUHTeQsANQM+c+W8Uce6MzCNdgCuAA4G5y5z3\nC+BioHMVuXYoWl8CzUhOBk5u5rwhwG3F/0EObd0luwdgRTR4LgN8DvSKjys670Dks5AH55FotrdU\n1keiNr0B2LJUu6IXwFPAujm2dwOwQNT2C4fl81HU8XzNnHsPGlDz4D0cWCobG4D5w/e2aNbdMaxb\n0XmjgPuBufJq88Bj4Wh5AWTd2Bg5oq/XzHlHA0fkyHsQcFzoux2y9m1pPAzj0B1owmXV5FzEY/6o\nbyyEIniXBV4Glo2OKx5HrwZG5ci7Y2j77mF98fC9KbI4xWOjRcudQ59aI/GuzU9upiozOxipiS81\ns0Hu/om7vxZ2nwIcFTQhuPtMM+tgZg2mNAl3A697Tk6kZraQmf0VOeZeZGb7ok72tct/63ZgZWBY\nZnY1RSqtZWbPoBnxb10q/mrw3Q94wczOMLNtANz9ny5N2LPAfGa2STi2Q/jubGY/Qw/CJGBWHibk\n8J9fgfzITgzcXwS+cvfX0Qv18nBsJy/4z/UKs9r90MNebd4nAhchv5ozzOyA7P9297+hCcj66AWQ\n3ef8ZjYOaVXPcfcnqs07cNkfeAG4wsy2Q5rH98LumwLnlYN2BzPrZGYdzeyXZvYCMhm+V+rabci5\nmyk1xi1IULkJwIP/CopqfIvQh6Lzlg9t/hukQZ6aUz9fyMweBG4ws7PMbIi7f+jus1xm74+B9cxs\noXB8Nh4uZ0ozsB3wx2rzDlw2RubhuZGG9WigL0DReLhSdM5cZnYYqvryJ3e/0MPbt8rcVzKzl4D/\nA64ys67u/r67T3X5wN2B3kdY8EUM/X1k+L/mRgJc1RHG8g9ROpgbzGxud38bwN0nIMvIcRn38N3H\nzPYBngO+Rm4WiXctIiepeRXgYZTT6QT04t206JgHkCktW29Aqs+7gZXzlGjRTPacsDwCdbJTi445\nBzg2LHcJ35sBO1aZ66pIYzAcaQ+eJZrpAf2BI4CLis7bF9n7c2trNAM/HuX/Whh4HJmn5o+O6Q1M\njXkC86KX8xXAPFXmbEBX4HfAIlF/uRHYLjpuIPLRGg3MAywTtq9ffL0q858HmICCb36CAnHOKjrm\n+NC/+0fb1ka+Z7n0lzCWPBKtP458g7pG24ajF2mmwewCrAEcmgfnIv6HhXGkOxIUrynq00NDH9oi\n2tadoMnMmfuhwB5heVXkZza26Jizw3G9gVWj/2Oe6JgObc21iJOFseXnYX08muh1j46ZF/n2bRLW\nO4Vtvwd2yrHNewTuw8P61cgKsnx0zCAUoJNpj7shreDY7D9IvGv3U80/pUO0vBPwaFg2FB14Bo3V\nx0sjh9K1gNOR70LP+HrVfJjDA5kN6kcDd4blrmGAvy/uOEhjckPY/i4wbxW5NkTLmwFnRuu7oGjX\n+PiVgdOQz98ZyMScqxkz4nYDsE9YXhYJ8j9FL9bMdHI4EiSHAAeGbbHg0EAbCznIhLlUtP40sG9Y\n7gnsimbhsbCwFQogmQqcUXS9jlVs407R8roo8i97xoYhs/bo6Jj+wKXAIUiQW7PoelV5NpEfara8\nBHq5Zibk1QK3VcJ61ldODuPKn4AN8mrzEvdyD7BVWJ4fTZiuLTpmvzAWno38chpxrhZ/JFQNI5gp\nkTA2PutLYf+9WduH7fMhU/0HSHDuFu1r8+ezmXu5Ctg6LPcBHgK2pvH7asvA/VRyFOSB3kXrzwOb\nheXlwv9wUFGfOB4pOm5AaUAS7zr5VMWsaYqsO9fMtgibJgHvmdlQ1z/xALJBr56d4+5voJfaQ8AM\nd3/XgxnTlNdnllchqsrMdg5q74vQbAn0QA80s5Xc/X8oDP8xZFrIsAoSIqYAa3tIrVEFvicjM1oW\nUPEdeuEC4MqN9ImZHRmd9hpyVj8eCQ+feA55k8xsoJmdY2b7BIdikPDSw5Tj5u8otccawIKh74C0\nDOuil/H7AO7+XxM6uPvM6Ng5zXlNU9TrMchEf0nYdSGwfTC1fomEtQ+RFg0z6wucBHwDbOjuR8fX\n9SoFioT+cr0psSkuU2pnMxsdnq83kel4h8zE4EovsDLSYr7mUXLIajybZraqmT0EXGlmZ5vS7WQu\nDvOE//x54A00GcHd3VQuaHPgK+A4d38suqZVsc3XMbMHzGxs9Jw+irTVuEyx9wFdzGzT6NQv0ERk\nNTQG4cqNZ9Xgb2YDggn4CqQFy1J3XAosGMbD6ajPPIGSQWfBDccijcgR7r6eu3+TXbctn88i/ruZ\n2X1mdoqZZe+aL4FOpuoWnyMBfzcaZzLoh8bHIUAuKRzM7ATgUTM708x2CpvvAAaH/v4aijheiMZV\nRvqgQLX33P3kqpKmfnnXAtpUOAuD6Iuo0V8HfmlmewL/Rblu1gJw91eAj9DsN/NHOBW9mJd091/H\n162G4BB8OnZFaQ0OcIV8DzazvcPL6U7ggHD41MDfTb5aXdEDPdLdd3X396vAd3jw91kIdfZTzGyE\ny1+lm5kdGB0+BtjUFEEIcBYwHWkuc6liEPycJgIz0GzqRFMt0veBxZEmFeRTtBTSLmBmw8K2s9x9\nQXf/PmO6C20pJPRHL/+b3X19FGm0g5kNRP4/H6IZISjKsTsSljMc4u5rufsLob9VzQfUzJY0s2eR\nRnosSv9yZth9FXpB4QpnfwkJNAsHOWDrcD9D3P2IcD0Lx7fps2lm6wG/Ba5EOfm+BrYMk593kNaj\nfzj8fGBrUw48kMntMndfzd0fzYSawLsawkHHMFG9BPlyvg6MM0WkXQ/MNLMsYnEyMsHOG86dF93v\n/kG4eTbm3tb8TWl/dkHRi0PcfQ+gu5nt6vJHnEDo6+7+GRDz6Qjc5u4DPOSntCrWMzX5n44D9kbm\n+C7AXmY2D3L52JzQzu5+FXpfjQjnrok0Zxu4+5Ze5dyUpiz5twROe6Gx/WBTDrmXkUZyvXD442jS\nNCucux76HxZz92MT7zpDW6rlUKfeKVrfheDbhAb/8wj+TygC71kKEXl9o/M6UmV/hPC7qyANTba+\nGwoJBwkMD1EwXW1OkRmiylyHA3tH62egoAOQ4/lHQJ+wviyKFu0R1rtWk2sJ7p2QFmlwWB+IZuNr\no6ipS5G/4YJh/7mECNPQN+aKrlVNc2AXYMWw3BC+b0AaYEOz7b+j1CSgkjtblLhO1c1pSADeOlpf\nEQlhXUL7jweODPvmAh6J+k/n6LyGajybFMySPWnsd7UTcGtYXgQ5n+9JMNUi/5YBNdLm3VFqjHmj\nbROAvcLy7miCkkUNnoeEse/7V878h9DYLH8EwcyH8vJNpOBWcApwWi20e/jdQyiYYZdDk+uBYX08\nmoRnEcm/IUd/siLevWicuWAAEuwHIYHyWDS57hv230bBZFj1d2a9866lT5vM1LMZHRrQH4jW56eg\nOXgIvbjOMOUtOwSpwjsCeKHeXYO7z/B8EkO+7O4fRPyHBs64oktOR0k5fwdchkxueeFV4GZTviYC\nl5mm/FkTkSr5fDPbEZng+nooheEyzeaCoNqejqKl3gh8/o0ESHdlo78TCcOnm8o0rU5IQBv6xtRM\n8+RtaNop1my5ItFeCsszTclZV0Pll9xl7rsSRR6/jcxSE4uv25acm8HbyJSW3Vd34BV3/za0/1lI\n070HMtF+SeHZ/C6c1+AySbWldjJLXurBdPclMvll+ABprLu5+7tIq7YKcKuZvYxm4J9F18u0TVVv\nc3f/Gpjo7v8xRfx1Ctz+GvaPQ1GZVwZN8oZIQ4kHjaQVTMt59JlXi8aKjRBf3P0D9MIdYWZPo5QI\nNxZfoNq8o2f2/9x9ShgPX0PRpFki04uQ0HCOmR2H/ENzicAshqvSxj3xJmAF4HOXtvg25DB/c9AO\nLo60VOT0zsxcBOqOd61hjqmWwx/iUDAReMFHrAP6cwyVv8ClHr7CzBw9DN8gP5BGhc29SibM8FuN\nOkV4+YK0AzPQrP3haP+jplQDqwDnepQor435Nqml503TivwECQnZYHgE0qDtjkyFx7Q1z3KI+Wdt\n7u4fhn2GInqmohxmuPtDwWR7HJrV3hoEzu9RjQc6+w2LyvtE2xpQXqq3PDJju/u5QZW/oIfyNPGz\nUg1YiXJE4SX7v+weTAlbZ2Xc3P0vprQ1K6H/Yr9qP5tmdgzyBzozCI3ZuDIzuqc1UT//Jux70Mwy\n/8+PSvSTarZ7Q+Aaj41ZGbQZQeBcEIjbcV8k2GwGHO7uj8TXrEY/tzI1UiMBsSMayzsRxkMz6+yq\nf7gTCsr4W1vzLAUz6+2FsmgWPadfh+8ZpgS+3xIEsMD7VVQqaBByR3kjD/4xomcxLoXWF/X3yfC9\nb/aBpvQUC6PgqeklLteWPEvWdq113jUP/+HqS6NpYr7i9cwk8SAhsovGYeJxtFjVogTRyzQ2n3Zu\njgeyjc+DtDrHVItnqbYMyyMpSmRLwSRyN/IJAs1YehffY57cw/owGkfoZP1kEPDnaPvSGXcaR1G1\neYQXBTO7hc+RFMLAi/v5higacB6kwt+1+P6r3L+bPJvl2g2l9dgtLK9PCfNTtbhHfXgd9PIfWu4e\nkF/ZumiieShFqTyq3eYx/7DcvZnjlkZ1awl9ZqVK/8M24l1sOu0f9f/ifT1QupqeaNJ0fnPtUCX+\nfQkpPJCv09Jl+sxIggtKGMvXribPCu5jJCEJa8Q5+/4JhUjd0eSYqDpwiMfjFUr0k5rkXQ+fH2TW\nDBKzu2bey5rZvqYkfsWzdA/Ol98A35jZ74HfmFnfMDuYHnxzO3h1owTHAVuZWU9TotNxZnZ04NyI\nhynSqw/yjbqRoHWoNkJbzmtmFyDt16KR2RWk/eiAtE6DzOxWFIXZOZxflcS3MWLH5bC+upldjfyF\nvu+D2X700nrOFOTwJHLq7oC0DbNMyTeron3ywszbw+8tj+rrQWOnZ5C2Zjekzp+MAhXia3m1+nfR\nsznYzE42lRjL+lCWHDlr/wZghpndjASe+YquV7Vn04PWxt2fRA7bewXtYyOEe1gMBeY8hzi/Eu+v\nZptnyPibSp/93hRAkWlXYywFPGWqEzyJECCVIbS5F4+nbQUvaMbWMbM3kKvBdfG+CBshP9t7UU68\nS0tcr1rRrw3h9z5F4+E/kNlsuTKnLAw0BBPmDUjAzAVFY3eG/ZGbz/eIxrp1UBTvVWiimJtLCnyv\ndR9kZveh99HCRftrkndd4IdKdyjP194od8njyH6faRZiLc+iKBrjFeCXeUmj6CWUOW9vhRxyz0LO\ntyuhl8ExYX88K1gTpcU4l+BIXy2+RevzBr6vN3POCqGtn0eRprm0dRn+gwO3sppH9PDOQj6LVS2N\nQpSjC2kthiKBfFDYNhrlO4q1rNnxFyCBbNHifdXkHy13QzPWx1A04I0UHMyLtX6TUT6+/XLuLx1C\nHz8R+Rb2Q356I2mqeZ0/9JObiJJY5sC5mNdqyH/yauCZ0O5ZjkSjoE04KvC/hhzKXRFpFcO42BNF\nM16N0mB0CfyPK9G3dkE+whuV6ntV5B+/Y5ZEfm+f0oxmBk2evkG5HXMpARi1u4V2joNc9kMBUHG+\nykxzfzfwz+w5zot3tN4nHlfK/Ud5867Hzw/9YxqQw/Pfwnp3FKVzEgUzWvbiGhYehu7lrtfGnaqk\neQNlhH4BWCGsDw6dqH/MEQmXi1T1z2maTDaLNtoIzbRHxG0cHbtgGKSqJkSW4B4P5D1Q5G6/sH4r\ncHdYbhIpioSzg8tdrw05x+ao+cL33OiFdQvyLdyGgnq+uN3jKLyqJkkucz+XoFIoWSLWzVAQQFYz\nM+vb86MJVo/o3GolND0POD4sDwjfXcJzmU2SfoHyS8Xtm3FfrVbanEKd4GOBn4Xl9VBqkoOL+0zo\nS+vE91Qt/kU8ukTL16Go+UXD+vIoRUkWqZu1e7+i61XbdBzzH4GEyCNCGx4B3BP2NXFDQqEkAAAQ\nHElEQVSZQelWmpiQq8WbpsL8IBT4sT1SduwBXFeqXZFCoaypvA15F3POoiz7oxyOWaRrSbeZvHjX\n82d2/6ilood1E2AahTQHo5BZZNtSf2rY1rHU9ja5QZk64mzUi4cB6DD0sp0XZQtfk0IR1jvJqYhw\nGMzjWdSGSCN5B9JK/iJsPw5lCs9SBuRWNLiF+9kOaSMfRrOnEci/5muUwy4eNEv5R7XpoB8GwzjT\nfA+kAfszCj5YP2zfG6Vp2Ac5Evdr5pq5VFegsdZp1bD8VnQPcyFBMys9luuzGX5vXRSxuDTwB2Dj\nsH0DJNSMQhOrO8N/UK5webX9mzoUfW9PIa3EjYSKDyilwG4o0XYmFHcqulbV/OLisTCsH4gmeieg\n8m4DUKT3ShS0ffcQpV3Jq92RyWwUKgGVtfsqBM1q0bF/o/AOmiuPPlLEp1fR+kbI9PrzcF8roffm\nhWFMejnrL3E/y4l7sSD8HDJ574YEy4sp8tujIB/k1ub1/mnR58zMzjOzX4flQcFf7AqUVXw1d38Q\nzWqzRLFPouSbm5jZQA//UHS9Dq70B422z2kEv6RTkFS/dNi2OtLaPIryft2AonYeRC/dweH0b8N5\nVYUp6epjwEmmQsiGXl5jgJ8hofhwM5sPDZg9aVyVIDeY2YZmtli03s1UqPY8lH9tBOK8Mxp8TkP1\nJyEkH/TItyb4IJq3ob+QmS2A+sGlgW9nNDj+Fw1CCyDfyAZ3v5pCJYLuSKNWEm3JOUZ4No8PywNC\n+32OJiQbu0LWbwAODqdMQybATcxsWIln06rxbBb93hPo+TsDvayy5LePoajiLVBU4FUof9k8YX8j\njl7lFA1RX+0dvjujJNVrIK3f4DD+fYHGk25II4I3jXr1tu4zZraRmT0KbGRKKIuZ7YJyl22DklCP\nRa4bTyD/oRGmhKAD0GSlCarR7qY0OWeiSerPkK/w2LC7L/Cxuz8Qju0Stp+GxsrLgQlmNle1+0jg\n02BmOwNjwviCme2OJtb3Ic3T/SidyhEo3+BZSFv5/RjjVU4vYWYLmNlQM+sebVsb+XfuhCba56Gx\n5mvku72+mc0T/Le3C7zzSPnSPlCB1LwOmtn2Qrm89gvbJyJBrAvSRv0ZWCPsW42iunXV/CD/lMno\nAY6TyO6NNFGrIen/4rB9bpR37SHk4HozVVTBEs3CkcByP3B22NYz3M8ryLxzOXBl2HckMl3lqi5G\nL8yPQn/IkvJaaOePKdSuWxi9hHcM67NQ6aI8ud8f+sIvw/pCyCx8X+gHjxIVcUYvg79TVLMxJ+4t\naZ1Ghv/hJZRFHyQkrJYX5yL+me/VPChdx/ZoFr572L4WymOWjTmL5sh1Q5SxPFvvgjLiXxvWO4S+\nfTQyBZ6BtMWbI+Hz/HBvfarMuxty1n8WCYfdCGZMpCHeKnD9E6F+KvIjehhNZG8Bdsi5n/w8cMk0\n7EuGfrElSg10Po0TUWcWkM2QX1+TJMRV4p31758iq8emYf14Gidnvx24JCwvirTb35GDHyUyC5+G\nSvrdhSqd/Drs2yBwPwJpW48O2xdEQQz3IY1lLpkM2tun0s51O4Vs86uEB/l8JJBlWcRPAp7I+4YC\nl+HArGh9fTRD3BvNYu8A1gr7eoSBdY8weC5cRZ6bIcfhLEN4b6SV3BUJBplP2QnAnmH5IJRzbfUw\niObmVxbdx1xIqD0g9I29ooH0SOCm6NgrKZhmh1SZ50Kh32b/fd+wfgCaCWZFs48DTg3L+yMBc9Ho\nOhcRBMwc2zx7NseH/rwTMC7afxIS5DsgjdPfaWoKzN0UHvWTE5Hv5wZoIjIUaRfGEaXSyIMz5Scf\nq4e23yhsWxvV3x0V2v0Q5EIxFPk5NUk3UQXuSwD3lWo/pB2bQRQ0FMbJrkiguIPge5lj23dEGtWR\nYb1n+N4jbF82jD0HhfFwGBpDh+XRnwO3zZEwnKWm6RXa+nSkYb0s7gvoffUQhfQZ3Skyg1aJ9yjk\n+3YaCsTphiZI08JzORolO7+Cgs9ZX2ChsLxAHrzb66fSVBr7ATub2aJhQHrI3Q9Fs/OTzGwRpIr9\nGZQND64a3P054A4zuzWknDgHqV8fR1qSS9z9aVO9uotRROB17n6gq05ctTAZmSoPMLPN0UD5D/RA\n3IOEHJBdv5+ZjUJaktOAKe7+uYcs/3nClcV/ChJ0D0L8jzZlQL8JWMTMLjcVeV4dzXohpD2w6tWU\nXBuZ+E41syGu0PsG5BD/IPK/AVgGeD3wnxdpnlYIXDdEM/a/V4lzSzgAaXVmAlODyQQ02I9GyR2v\nBbbyMIJmKF7PA15IRnwyMvH0QRHRFwHfufvu7v5SdHwenGciwfFmYB8zy57LScgEmJkrn0ImwL2A\nJdz9AuBXqN+dRhnTYBvjf6i27vpmtgmq+nCSqaD6fcgX7h2A4IZwMdLY3IyE0u0yc1webe8yi32H\nxkBQlCXufl3YtjjS5iyJJirjgMfc/a/V5hphMrIaHG9mhyDN0u3ICvIT5Mu6Z5baBqX8mOQhaau7\nf+2NE7hWC1NQINxx7v6JqPjTaKI3Fk1O/oWCL740s6GoD2UmzA9z4t0u0exL0d09+Nt8ih7a29BA\n1dXMFke17J5D2puv3f31LDdPmzNvGfugB6Gru6/i7g+6+z9ReZfLzOy3aGD6yN0n5EHQ3Sch7UYP\nNFu9HL1U30eCS4OZjUQDe39khnjK3U/0GshgXYQ7kKPzJCTMjEHmkimo72QFhHd293uhUQ6xauVx\nuhmlTukLDDezI1B/6AH8BVjMlM/uLmQSfB8NqFu5e1aK5A2kycklA3qG6Nn8DPl+HI1eAGPCoLkV\nMsk+H45/o4pCcKsQ8RqDTMjXIBPtcWF/cX6wqqLM5ONYNH7egiZOxweB5xtkCsomeWujieH67t6k\nnFEVkOXbG4cm0Ishc/juwMZh21Fm9jDyPTvK3V8I5x4JPOg55EYswkRgqeBXOTPKeXc/qsf7V3c/\nCAVxDXH3m3JjCrj782hc+RT1g/HIXPkvpCWbhtLxnGBmE9AEK8/Sf8D3So3bzOzasCmbOJ2HBMwV\nkW/5MDRGXoNcg86vPtv2j8w0UtnBSu73FPAm0kCc4+7ntBG3HwwzOwmFqm8UtCAzXUnzFkeakBdc\nNeHy5NgH5ZcajsyuP0E17HYys5+iB3e0u3+eI80WYWa7IU2No8CKs5Ep5xMkuA0H/ufup5nKv8zM\nQ4g3s5WR0DIE+evNQP35WKTlWMPddwz/y/xeKLlUsqRNrcDM3kPPZB9kxnwqE27qASE4wIOQ8Dt3\n/0MQyrxawnsL/LYBlnH3sWZ2INJ+XIkEyqVRCqFewLGRcFO2tE21YWbLoHGmq6vG5H7Asu5+WHCi\nXzzq6x0IcQo5Uv4epnJLBwL/DNrIbPstwGXu/nhu5MrAzOZG7T0IRWJuiYSbvwOPu/vVYYxZz93v\nyo9pY0S813D3V82sh7t/Zap/+Sd3vzwct3QNKgjaFyqxfVJwWN8G+EdYnjvan0vqgAq5vwtsF5Zz\nK13UAsexwP1heU+kceoEDEQCW83b8ZHf2WcEx9awbRDy92tA/gwTkMCTN9c7gDORJuRy5GzcAZkz\nL0PahWziknu+shbuJXs2dwL+HpabLUNWqx8k3NxNUQmmWvigKNLfIy3Uq+E5vQsl912CKF8fVSy5\n9APuZxxFuQRrub+E8eN55H+7BbJ6/JEo3UStfZDF4/Gw3B1Fgk9DkZmL5M2vGd6nAM8UbbsXWDFv\nbj+mT8Was2hm+wgKDvhDnhqQSmEqxDvO3TvnzaU5BM3HQe5+p5n1cffPszbPm1ulMLPzkZD5YLHG\nIDNFeA34JJhKib2DohZfN7Ml3f2tWteOlUOta50qhZmtj/I/neQ1oG2KYWZzIbPUTe7+q7BtKRRA\n9Eh0XE1oyooRxuqFUBDS7kiDc4S7/zdXYq2Ama2JTLKro3Hmdy2ckjvM7F0UNPf7oJFcGz2XuZsx\nm0PgvSfwOqq7+wkKHJmaI60fFTpWemAY/HsBXwFvh201/yJz9/FmNqDWVPUlcBSamXf2YMKsYa7l\nsDjyR2xSh7EWhLIM7v5ZECRvRT4rb4XtWU3EDvUk1ETP5tcUns2aExBagrtPRP5FNQd3n2pm1yE/\np0wIexOZxOPjarLd3X1G6CNDgDGhramnCaC7PwM8U0+c0bh+A/D7MKY8kTOfSjEGlc+bhCqiXJkz\nnx8dKhbOAlZGzt55RsK0Gu5+Ud4cWoK732xm/etAiGwOe7r7lLxJVAJ3P9FUWL0f8FksjNWTYBah\nLp/NOkPJyUe9CAuuIJY4or5qxeznJOqhrTPUkXKgEdz9FjPrjaxO3+bN58eIVgUEJCRUgnrTPCUk\nVAIzm7teJh/NIT2fCQm1jyScJfyoUas+Qgm1iyTcJCQktDWScJaQkJCQkJCQUEOoyaSUCQkJCQkJ\nCQk/ViThLCEhISEhISGhhpCEs4SEhISEhISEGkISzhISEhISEhISaghJOEtISKgLmFlfM3sxfD4y\nsw/C8hdmdkkb/N7SZjYx/MZrZva7sH2omf1kTv9eQkJCQobWJqFNSEhIyAXu/ikqHo2ZnQh84e7n\nteFPXgSc6+73hN8cHLaviJL+3t+Gv52QkPAjRhLOEhIS6hUG39fkPNzdR5vZSahw/WLAwsChwBqo\ncPa/gdGhlNHKwLlAT1Q3cE93/7jo+vOFcwBw91fMrDMqDN3VzNYGTkc1Ny8EugLfAHu5+z/MbE9g\nK1T0eingHKALsCvwLbCpu08xs4mossN6aEze290nzaE2SkhIqEMks2ZCQkJ7w2LABsAWqK7hI+4+\nBAlOm5lZJ+BiYFt3XwW4BjitxHXOBx41swlmdoiZzeXu3wG/Bsa7+4ru/ntUHHodd18JOBEYG11j\neWBrYNXwG1+G4/6ECpADONDN3VcEDgCunmMtkZCQUJdImrOEhIT2BAfud/eZZvYK0ODuD4R9LwOL\nAoOQ0PSwSkzSAHzY5ELu15rZA0jrtiXwczMbijR2Fh3aBxhnZkuG34/H1cfc/SvgKzObCtwTcRkS\nHXdz+M0nzay3mfV292mz2wgJCQn1jSScJSQktDd8Bypgb2bTo+2z0JhnwKvuvmZLF3L3j5Bm7Roz\nexkYjASwGKci7dzWZrYIMDHaFxeNnhWtZ1zK/nRL3BISEtovklkzISGhPcFaPoQ3gP5mtjqAmXUy\ns+WaXMhsZDCBYmbzAX2BD4AvgF7Rob0paN72mg2eBuwYfmdt4HN3/6LC6yQkJLRDJOEsISGhXuHR\nd6llaKqBcnefDmwHnGlmfwVeREEDxdgEeDkc80fgCHefDDwGLBdSbOwAnAWcbmZ/QSbSSrgUc/5f\nOP8yYJ8W7zwhIaFdIxU+T0hISMgRZvYYijb9S95cEhISagNJc5aQkJCQkJCQUENImrOEhISEhISE\nhBpC0pwlJCQkJCQkJNQQknCWkJCQkJCQkFBDSMJZQkJCQkJCQkINIQlnCQkJCQkJCQk1hCScJSQk\nJCQkJCTUEJJwlpCQkJCQkJBQQ/h/KESy4Ef1ZK8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12075230>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "dam_nyc_lbmp.plot(title='NYC Day Ahead LBMP 2015')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22897c90>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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e6D5e1VD+pXPccZ3vHl6IBkSFYxUPQNoCoQ6F8qBRmI42qHXC9gjU39e/Hv6u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\nV+bt+Gn08siJfuR5A0Uvsj4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PuG+zTfnfN8tr3Ljwfa+/vvxYhvW1007uK60U3icVqE88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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12075a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "rtm_nyc_lbmp.plot(title='NYC Realtime LBMP 2015')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time Stamp\n",
       "2015-01-01 00:05:00    30.79\n",
       "2015-01-01 00:10:00    30.75\n",
       "2015-01-01 00:15:00    31.32\n",
       "2015-01-01 00:20:00    30.79\n",
       "2015-01-01 00:25:00    31.26\n",
       "Name: LBMP ($/MWHr), dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Real Time Data Peek\n",
    "rtm_nyc_lbmp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time Stamp\n",
       "2015-01-01 00:00:00    31.45\n",
       "2015-01-01 01:00:00    26.49\n",
       "2015-01-01 02:00:00    24.54\n",
       "2015-01-01 03:00:00    23.64\n",
       "2015-01-01 04:00:00    24.10\n",
       "Name: LBMP ($/MWHr), dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Day Ahead Data Peek\n",
    "dam_nyc_lbmp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now there's a slight problem--the timestamps on the realtime data and day ahead data don't actually line up. More specifically, the realtime data has observations every 5 minutes while the day ahead data has observations every hour. To fix this, we need to align the two series of data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "aligned_dam, aligned_rtm = rtm_nyc_lbmp.align(dam_nyc_lbmp, join='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Remove Duplicates\n",
    "\n",
    "no_dup_al_dam = aligned_dam[~aligned_dam.index.duplicated(keep='first')]\n",
    "no_dup_al_rtm = aligned_rtm[~aligned_dam.index.duplicated(keep='first')]\n",
    "\n",
    "no_dup_al_dam.name = 'dam_lbmp'\n",
    "no_dup_al_rtm.name = 'rtm_lbmp'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next step is to wrap this up nicely in a data frame. However, just constructing the data frame the default way will give us a very *wide* dataframe-- to make it into a *tall* one we'll need to transpose. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Time Stamp</th>\n",
       "      <th>2015-01-01 01:00:00</th>\n",
       "      <th>2015-01-01 02:00:00</th>\n",
       "      <th>2015-01-01 03:00:00</th>\n",
       "      <th>2015-01-01 04:00:00</th>\n",
       "      <th>2015-01-01 05:00:00</th>\n",
       "      <th>2015-01-01 06:00:00</th>\n",
       "      <th>2015-01-01 07:00:00</th>\n",
       "      <th>2015-01-01 08:00:00</th>\n",
       "      <th>2015-01-01 09:00:00</th>\n",
       "      <th>2015-01-01 10:00:00</th>\n",
       "      <th>...</th>\n",
       "      <th>2015-12-31 14:00:00</th>\n",
       "      <th>2015-12-31 15:00:00</th>\n",
       "      <th>2015-12-31 16:00:00</th>\n",
       "      <th>2015-12-31 17:00:00</th>\n",
       "      <th>2015-12-31 18:00:00</th>\n",
       "      <th>2015-12-31 19:00:00</th>\n",
       "      <th>2015-12-31 20:00:00</th>\n",
       "      <th>2015-12-31 21:00:00</th>\n",
       "      <th>2015-12-31 22:00:00</th>\n",
       "      <th>2015-12-31 23:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dam_lbmp</th>\n",
       "      <td>32.94</td>\n",
       "      <td>62.32</td>\n",
       "      <td>37.20</td>\n",
       "      <td>29.75</td>\n",
       "      <td>44.51</td>\n",
       "      <td>25.81</td>\n",
       "      <td>28.04</td>\n",
       "      <td>22.07</td>\n",
       "      <td>17.94</td>\n",
       "      <td>9.05</td>\n",
       "      <td>...</td>\n",
       "      <td>22.00</td>\n",
       "      <td>20.41</td>\n",
       "      <td>14.56</td>\n",
       "      <td>19.25</td>\n",
       "      <td>27.65</td>\n",
       "      <td>57.16</td>\n",
       "      <td>17.77</td>\n",
       "      <td>22.02</td>\n",
       "      <td>14.00</td>\n",
       "      <td>16.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rtm_lbmp</th>\n",
       "      <td>26.49</td>\n",
       "      <td>24.54</td>\n",
       "      <td>23.64</td>\n",
       "      <td>24.10</td>\n",
       "      <td>26.32</td>\n",
       "      <td>31.48</td>\n",
       "      <td>30.57</td>\n",
       "      <td>31.34</td>\n",
       "      <td>33.21</td>\n",
       "      <td>29.85</td>\n",
       "      <td>...</td>\n",
       "      <td>24.88</td>\n",
       "      <td>25.54</td>\n",
       "      <td>31.94</td>\n",
       "      <td>41.37</td>\n",
       "      <td>32.24</td>\n",
       "      <td>30.76</td>\n",
       "      <td>27.99</td>\n",
       "      <td>24.12</td>\n",
       "      <td>22.61</td>\n",
       "      <td>19.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 8758 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Time Stamp  2015-01-01 01:00:00  2015-01-01 02:00:00  2015-01-01 03:00:00  \\\n",
       "dam_lbmp                  32.94                62.32                37.20   \n",
       "rtm_lbmp                  26.49                24.54                23.64   \n",
       "\n",
       "Time Stamp  2015-01-01 04:00:00  2015-01-01 05:00:00  2015-01-01 06:00:00  \\\n",
       "dam_lbmp                  29.75                44.51                25.81   \n",
       "rtm_lbmp                  24.10                26.32                31.48   \n",
       "\n",
       "Time Stamp  2015-01-01 07:00:00  2015-01-01 08:00:00  2015-01-01 09:00:00  \\\n",
       "dam_lbmp                  28.04                22.07                17.94   \n",
       "rtm_lbmp                  30.57                31.34                33.21   \n",
       "\n",
       "Time Stamp  2015-01-01 10:00:00         ...           2015-12-31 14:00:00  \\\n",
       "dam_lbmp                   9.05         ...                         22.00   \n",
       "rtm_lbmp                  29.85         ...                         24.88   \n",
       "\n",
       "Time Stamp  2015-12-31 15:00:00  2015-12-31 16:00:00  2015-12-31 17:00:00  \\\n",
       "dam_lbmp                  20.41                14.56                19.25   \n",
       "rtm_lbmp                  25.54                31.94                41.37   \n",
       "\n",
       "Time Stamp  2015-12-31 18:00:00  2015-12-31 19:00:00  2015-12-31 20:00:00  \\\n",
       "dam_lbmp                  27.65                57.16                17.77   \n",
       "rtm_lbmp                  32.24                30.76                27.99   \n",
       "\n",
       "Time Stamp  2015-12-31 21:00:00  2015-12-31 22:00:00  2015-12-31 23:00:00  \n",
       "dam_lbmp                  22.02                14.00                16.14  \n",
       "rtm_lbmp                  24.12                22.61                19.84  \n",
       "\n",
       "[2 rows x 8758 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this dataframe is too wide\n",
    "pd.DataFrame([no_dup_al_dam, no_dup_al_rtm])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dam_lbmp</th>\n",
       "      <th>rtm_lbmp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time Stamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01 01:00:00</th>\n",
       "      <td>32.94</td>\n",
       "      <td>26.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01 02:00:00</th>\n",
       "      <td>62.32</td>\n",
       "      <td>24.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01 03:00:00</th>\n",
       "      <td>37.20</td>\n",
       "      <td>23.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01 04:00:00</th>\n",
       "      <td>29.75</td>\n",
       "      <td>24.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01 05:00:00</th>\n",
       "      <td>44.51</td>\n",
       "      <td>26.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     dam_lbmp  rtm_lbmp\n",
       "Time Stamp                             \n",
       "2015-01-01 01:00:00     32.94     26.49\n",
       "2015-01-01 02:00:00     62.32     24.54\n",
       "2015-01-01 03:00:00     37.20     23.64\n",
       "2015-01-01 04:00:00     29.75     24.10\n",
       "2015-01-01 05:00:00     44.51     26.32"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transpose dataframe to make it tall\n",
    "dam_rtm_df = pd.DataFrame([no_dup_al_dam, no_dup_al_rtm]).transpose()\n",
    "dam_rtm_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have our pricing data for the NYC region, we need to get the weather data ready. The weather data comes from a different data source, and unfortunately it's not split into the same exact regions as the pricing data. To remedy this, we'll pick two weather stations nearby -- the ones located at JFK airport and LGA airport -- and average the temperatures together. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Forecast Date</th>\n",
       "      <th>Vintage Date</th>\n",
       "      <th>Vintage</th>\n",
       "      <th>Station ID</th>\n",
       "      <th>Max Temp</th>\n",
       "      <th>Min Temp</th>\n",
       "      <th>Max Wet Bulb</th>\n",
       "      <th>Min Wet Bulb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>Forecast</td>\n",
       "      <td>ALB</td>\n",
       "      <td>31</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>Forecast</td>\n",
       "      <td>ART</td>\n",
       "      <td>32</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>Forecast</td>\n",
       "      <td>BGM</td>\n",
       "      <td>27</td>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>Forecast</td>\n",
       "      <td>BUF</td>\n",
       "      <td>30</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>Forecast</td>\n",
       "      <td>ELM</td>\n",
       "      <td>32</td>\n",
       "      <td>16</td>\n",
       "      <td>24</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Forecast Date Vintage Date   Vintage Station ID  Max Temp  Min Temp  \\\n",
       "0    2015-01-01   2015-01-01  Forecast        ALB        31        18   \n",
       "1    2015-01-01   2015-01-01  Forecast        ART        32        24   \n",
       "2    2015-01-01   2015-01-01  Forecast        BGM        27        15   \n",
       "3    2015-01-01   2015-01-01  Forecast        BUF        30        21   \n",
       "4    2015-01-01   2015-01-01  Forecast        ELM        32        16   \n",
       "\n",
       "   Max Wet Bulb  Min Wet Bulb  \n",
       "0            23            15  \n",
       "1            28            21  \n",
       "2            21            12  \n",
       "3            26            18  \n",
       "4            24            13  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a reminder about what the weather data looks like\n",
    "weather_forecast.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Max Temp</th>\n",
       "      <th>Min Temp</th>\n",
       "      <th>Max Wet Bulb</th>\n",
       "      <th>Min Wet Bulb</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forecast Date</th>\n",
       "      <th>Vintage Date</th>\n",
       "      <th>Vintage</th>\n",
       "      <th>Station ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2015-01-01</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">2015-01-01</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Forecast</th>\n",
       "      <th>JFK</th>\n",
       "      <td>39</td>\n",
       "      <td>27</td>\n",
       "      <td>31</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGA</th>\n",
       "      <td>37</td>\n",
       "      <td>26</td>\n",
       "      <td>29</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2015-01-02</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Forecast</th>\n",
       "      <th>JFK</th>\n",
       "      <td>42</td>\n",
       "      <td>31</td>\n",
       "      <td>34</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGA</th>\n",
       "      <td>41</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-29</th>\n",
       "      <th>Actual</th>\n",
       "      <th>JFK</th>\n",
       "      <td>46</td>\n",
       "      <td>36</td>\n",
       "      <td>37</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Max Temp  Min Temp  \\\n",
       "Forecast Date Vintage Date Vintage  Station ID                       \n",
       "2015-01-01    2015-01-01   Forecast JFK               39        27   \n",
       "                                    LGA               37        26   \n",
       "              2015-01-02   Forecast JFK               42        31   \n",
       "                                    LGA               41        32   \n",
       "              2014-12-29   Actual   JFK               46        36   \n",
       "\n",
       "                                                Max Wet Bulb  Min Wet Bulb  \n",
       "Forecast Date Vintage Date Vintage  Station ID                              \n",
       "2015-01-01    2015-01-01   Forecast JFK                   31            22  \n",
       "                                    LGA                   29            21  \n",
       "              2015-01-02   Forecast JFK                   34            27  \n",
       "                                    LGA                   32            27  \n",
       "              2014-12-29   Actual   JFK                   37            30  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get every temperature from the LGA and JFK stations\n",
    "lga_and_jfk_indexed = weather_forecast[(weather_forecast['Station ID'] == 'LGA') |\n",
    "                                       (weather_forecast['Station ID'] == 'JFK')].set_index(['Forecast Date',\n",
    "                                                                                             'Vintage Date',\n",
    "                                                                                             'Vintage',\n",
    "                                                                                             'Station ID'])\n",
    "lga_and_jfk_indexed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Max Temp</th>\n",
       "      <th>Min Temp</th>\n",
       "      <th>Max Wet Bulb</th>\n",
       "      <th>Min Wet Bulb</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forecast Date</th>\n",
       "      <th>Vintage Date</th>\n",
       "      <th>Vintage</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2015-01-01</th>\n",
       "      <th>2014-12-29</th>\n",
       "      <th>Actual</th>\n",
       "      <td>44.5</td>\n",
       "      <td>36.0</td>\n",
       "      <td>36.5</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30</th>\n",
       "      <th>Actual</th>\n",
       "      <td>35.5</td>\n",
       "      <td>28.5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <th>Actual</th>\n",
       "      <td>32.5</td>\n",
       "      <td>27.5</td>\n",
       "      <td>25.5</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <th>Forecast</th>\n",
       "      <td>38.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>21.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <th>Forecast</th>\n",
       "      <td>41.5</td>\n",
       "      <td>31.5</td>\n",
       "      <td>33.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Max Temp  Min Temp  Max Wet Bulb  \\\n",
       "Forecast Date Vintage Date Vintage                                      \n",
       "2015-01-01    2014-12-29   Actual        44.5      36.0          36.5   \n",
       "              2014-12-30   Actual        35.5      28.5          30.0   \n",
       "              2014-12-31   Actual        32.5      27.5          25.5   \n",
       "              2015-01-01   Forecast      38.0      26.5          30.0   \n",
       "              2015-01-02   Forecast      41.5      31.5          33.0   \n",
       "\n",
       "                                     Min Wet Bulb  \n",
       "Forecast Date Vintage Date Vintage                 \n",
       "2015-01-01    2014-12-29   Actual            30.0  \n",
       "              2014-12-30   Actual            23.0  \n",
       "              2014-12-31   Actual            22.5  \n",
       "              2015-01-01   Forecast          21.5  \n",
       "              2015-01-02   Forecast          27.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# average together LGA and JFK temperatures (you'll notice that it's no longer in our data!)\n",
    "mean_nyc_indexed = lga_and_jfk_indexed.mean(level=[0,1,2])\n",
    "mean_nyc_indexed.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to massage our data a bit more. First step is to unindex, which flattens our dataframe. Next, we pick out only the rows with the Vintage being 'Actual'. We've got a few different kinds of dates floating around and it's important to stay consistent. Our end goal is to have our data like:\n",
    "\n",
    "**Time stamp**:        today \n",
    "\n",
    "**Temperature**:       the actual temperature today **(x)**\n",
    "\n",
    "**Day ahead price**:   today's price for electricity tomorrow **(x)**\n",
    "\n",
    "**Realtime price**:    tomorrrow's electricity price observed tomorrow **(y)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dam_lbmp</th>\n",
       "      <th>rtm_lbmp</th>\n",
       "      <th>Actual Forecast Date</th>\n",
       "      <th>Actual Vintage</th>\n",
       "      <th>Actual Max Temp</th>\n",
       "      <th>Actual Min Temp</th>\n",
       "      <th>Actual Max Wet Bulb</th>\n",
       "      <th>Actual Min Wet Bulb</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time Stamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02 00:00:00</th>\n",
       "      <td>16.87</td>\n",
       "      <td>28.77</td>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>Actual</td>\n",
       "      <td>43</td>\n",
       "      <td>35.5</td>\n",
       "      <td>33.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02 01:00:00</th>\n",
       "      <td>26.47</td>\n",
       "      <td>27.61</td>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>Actual</td>\n",
       "      <td>43</td>\n",
       "      <td>35.5</td>\n",
       "      <td>33.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02 02:00:00</th>\n",
       "      <td>15.07</td>\n",
       "      <td>24.41</td>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>Actual</td>\n",
       "      <td>43</td>\n",
       "      <td>35.5</td>\n",
       "      <td>33.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02 03:00:00</th>\n",
       "      <td>14.24</td>\n",
       "      <td>24.01</td>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>Actual</td>\n",
       "      <td>43</td>\n",
       "      <td>35.5</td>\n",
       "      <td>33.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02 04:00:00</th>\n",
       "      <td>12.95</td>\n",
       "      <td>24.03</td>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>Actual</td>\n",
       "      <td>43</td>\n",
       "      <td>35.5</td>\n",
       "      <td>33.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     dam_lbmp  rtm_lbmp Actual Forecast Date Actual Vintage  \\\n",
       "Time Stamp                                                                    \n",
       "2015-01-02 00:00:00     16.87     28.77           2015-01-03         Actual   \n",
       "2015-01-02 01:00:00     26.47     27.61           2015-01-03         Actual   \n",
       "2015-01-02 02:00:00     15.07     24.41           2015-01-03         Actual   \n",
       "2015-01-02 03:00:00     14.24     24.01           2015-01-03         Actual   \n",
       "2015-01-02 04:00:00     12.95     24.03           2015-01-03         Actual   \n",
       "\n",
       "                     Actual Max Temp  Actual Min Temp  Actual Max Wet Bulb  \\\n",
       "Time Stamp                                                                   \n",
       "2015-01-02 00:00:00               43             35.5                 33.5   \n",
       "2015-01-02 01:00:00               43             35.5                 33.5   \n",
       "2015-01-02 02:00:00               43             35.5                 33.5   \n",
       "2015-01-02 03:00:00               43             35.5                 33.5   \n",
       "2015-01-02 04:00:00               43             35.5                 33.5   \n",
       "\n",
       "                     Actual Min Wet Bulb  \n",
       "Time Stamp                                \n",
       "2015-01-02 00:00:00                   30  \n",
       "2015-01-02 01:00:00                   30  \n",
       "2015-01-02 02:00:00                   30  \n",
       "2015-01-02 03:00:00                   30  \n",
       "2015-01-02 04:00:00                   30  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_nyc = mean_nyc_indexed.reset_index()\n",
    "actual_temp_df = mean_nyc[mean_nyc['Vintage'] == 'Actual'] \\\n",
    "    .groupby(['Vintage Date']).first() \\\n",
    "    .rename(columns=lambda x: 'Actual ' + x) # prepend the word Actual to column names\n",
    "\n",
    "dam_rtm_act_df = dam_rtm_df.join(actual_temp_df, how='left').fillna(method='ffill').dropna()\n",
    "\n",
    "dam_rtm_act_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, observe that there is a different day ahead price every single hour, but only a single temperature estimate per day. We'll do something similar to the frequency mismatch we saw with day ahead/realtime data -- resample at the lower frequency and average the data in those lower frequency buckets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dam_lbmp</th>\n",
       "      <th>rtm_lbmp</th>\n",
       "      <th>Actual Max Temp</th>\n",
       "      <th>Actual Min Temp</th>\n",
       "      <th>Actual Max Wet Bulb</th>\n",
       "      <th>Actual Min Wet Bulb</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time Stamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>47.749583</td>\n",
       "      <td>32.220833</td>\n",
       "      <td>43.0</td>\n",
       "      <td>35.5</td>\n",
       "      <td>33.5</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-03</th>\n",
       "      <td>33.246667</td>\n",
       "      <td>27.447917</td>\n",
       "      <td>43.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>42.5</td>\n",
       "      <td>27.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-04</th>\n",
       "      <td>29.820417</td>\n",
       "      <td>23.439583</td>\n",
       "      <td>55.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>66.288333</td>\n",
       "      <td>42.450000</td>\n",
       "      <td>51.0</td>\n",
       "      <td>22.5</td>\n",
       "      <td>43.0</td>\n",
       "      <td>17.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>64.860833</td>\n",
       "      <td>60.502917</td>\n",
       "      <td>22.5</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             dam_lbmp   rtm_lbmp  Actual Max Temp  Actual Min Temp  \\\n",
       "Time Stamp                                                           \n",
       "2015-01-02  47.749583  32.220833             43.0             35.5   \n",
       "2015-01-03  33.246667  27.447917             43.0             31.0   \n",
       "2015-01-04  29.820417  23.439583             55.0             42.0   \n",
       "2015-01-05  66.288333  42.450000             51.0             22.5   \n",
       "2015-01-06  64.860833  60.502917             22.5             19.0   \n",
       "\n",
       "            Actual Max Wet Bulb  Actual Min Wet Bulb  \n",
       "Time Stamp                                            \n",
       "2015-01-02                 33.5                 30.0  \n",
       "2015-01-03                 42.5                 27.5  \n",
       "2015-01-04                 53.0                 41.0  \n",
       "2015-01-05                 43.0                 17.5  \n",
       "2015-01-06                 20.0                 15.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resample to daily because temperature and prices don't have the same frequency\n",
    "daily_df = dam_rtm_act_df.resample('D', how='mean')\n",
    "daily_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next question is whether to use the max temp or min temp--let's take a look at the data to see if one or the other makes a difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2336fcd0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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jxT8zMzM655xzmnp85XsZGxtTMpksfr5v376abdJCI8QAAAAAS+jWW2/V/fffX9ZoFFhr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O206c4MsbN9apKo0wpyh0lZWJXput5neKqsrPZme5wd0NMxJpd5XEnO9202uzcSSTYX86zeV+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\nYgCmp05nYtJEn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2UzkdVpNgHbgBv05SuBh5GW3RZgrsV7fwK0Iis9Gf+S9dfmIG8yezFV\nnkLmX1To7zuCXoEEWKh/zib9/+uH4ViXoFezQNrCu/Tj+wUyTAK8VMpChln16/toHK9RVSYReB/Y\njYyxjje9bz9wFHmzr0CGZUQCXwCb9fP+MPJLOBx/9w+BM12WfQdp8S/R//5vADuB33s7h6bX9iAH\nfsZ5ML/vx/rffSdwlmn5A/rx9+r//5e+/Jf6OdgEfABMHIHr3ue1YFr3K9fzNxa+Az7OSTAymRng\n1/p1+w4yhO4afbm9Gox+LB8eL8c/wPOwEuvqZD3698A47p+YXntcP+7N5vciXcAqpMNVYRwjMrl7\ni77+v5E5dsN53J7uCU/4OBfn6d+HL5DhPc+N9WsCOXP+CVIUbNaP17juj8vno5dzkY0UGLv1fXsU\nR9U2Q9Rs1/fjVn357ch7/QfHw3cE0/MCSAXacDyrcpHPqM3IQXu2ad2LkY71yR62+x5QbToX/za9\ndr1+nvagV4fTl39XPwfdSLfiKdM5N56ZnwILAnwOevV9ND7jTrBXGRXI3LAt+jX8AbJQBcjKrNv1\n9/7PWD4Hx+KfanapUAwQIZMRf6Bp2vnHel9GI0KITGClpmkB66MxGhCy6tofNU1b4HPl4xh1HgaG\nECJK07Q2/ecngN2apj3q423HHUKIlcj7pqccIoVCoRgQKpxMoRg4Gn5WYRpvCNlw63Oks3TcIIRY\njszBGvvNwYaAOg+D4iYhqyluR4YQ/fFY75BCoVAcDygnRqFQKBQKhUKhUIwplBOjUCgUCoVCoVAo\nxhRKxCgUCoVCoVAoFIoxhRIxCoVCoVAoFAqFYkyhRIxCoVAoFAqFQqEYUygRo1AoFAqFQqFQKMYU\nSsQoFAqFQqFQKBSKMcX/B76hJDtIQ1KiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x232e8dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,10))\n",
    "plt.plot_date(daily_df.index, daily_df['rtm_lbmp'], '-', label='RTM LBMP')\n",
    "plt.plot_date(daily_df.index, daily_df['dam_lbmp'], '-', label='DAM LBMP')\n",
    "plt.plot_date(daily_df.index, daily_df['Actual Min Temp'], '-', label='Min Temp')\n",
    "plt.plot_date(daily_df.index, daily_df['Actual Max Temp'], '-', label='Max Temp')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Min and max temperature seem to be so highly correlated with each other that it most likely won't matter one way or another which we used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Estimation ##\n",
    "\n",
    "Now that our data is in the format we want, we can finally put into practice some of those theoretical concepts we discussed at the beginning. Our tool of choice here is a type of regression called **ARIMA**. It's a combination of 3 types of models -- AutoRegressive (a combination of the previous values), Integrated (differencing the data), Moving Average (smoothing the data). It's typically represented as: \n",
    "\n",
    "arima(# of AR terms, # of differences, # of smoothing terms) = $ arima(p,d,q) $.\n",
    "\n",
    "So how can we go about picking our parameters p, i, and q? There's several approaches of varying sophistication.  A common approach is to use the <a href=\"https://en.wikipedia.org/wiki/Box%E2%80%93Jenkins#Identify_p_and_q\"> Box-Jenkins Method </a> for parameter selection. First, you pick parameter d (does the data need differencing to make it stationary?) You can do this using a **Dickey-Fuller test**. Then, you can use autocorrelation function (ACF) and partial autocorrelation function (PACF) to identify the AR parameter (p) and the MA parameter (q). \n",
    "\n",
    "For our example, we'll just pick ARIMA(0,0,1) for it's simplicity (this is therefore just an MA(1) process). At the end we discuss these tools / the procedure in more detail."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before fitting the model, we'll be splitting our data into two chunks -- one to train the model on and one to test it. This gives us a chance to see how the model would do in the \"real world\" on data it hasn't seen before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x23211250>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HAAAAQOYQdAAAAABkDkEHAAAAQOYQdAAAAABkDkEHAAAAQOYQdAAAAABkDkEHAAAAQOYQ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LZKq9aPXPkRzhs9j9/+wW9Tnnw5ExOVgbzZsQz7hnnkxCNt78sYG7nv6H0U3FMcOLyypaxro2tm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\nOpOYxDhgqeiAmIiPheh4uHowM2Im8X7xuMhdyMyE4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OYa4Le/BQ4eBDJ50ZFo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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x232fbad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "exog_data = np.array([daily_df['Actual Max Temp'].values, daily_df['dam_lbmp'].values])\n",
    "\n",
    "# fit the model on a portion of the data\n",
    "k = 250\n",
    "m = ARIMA(daily_df['rtm_lbmp'].values[0:k], [0,0,1], exog=np.transpose(exog_data[:,0:k]), dates=daily_df.index.values[0:k])\n",
    "results = m.fit(trend='nc', disp=True)\n",
    "\n",
    "# what does our model predict?\n",
    "predicted_prices = results.predict(10, 364, exog=np.transpose(exog_data), dynamic=True)\n",
    "\n",
    "# let's see it graphically\n",
    "plt.figure(figsize=(14, 10))\n",
    "plt.plot(predicted_prices, label='prediction')\n",
    "plt.plot(daily_df['rtm_lbmp'].values, label='actual RTM')\n",
    "#plt.plot(daily_df['dam_lbmp'].values, label='actual DAM')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARMA Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>y</td>        <th>  No. Observations:  </th>    <td>250</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARMA(0, 1)</td>    <th>  Log Likelihood     </th> <td>-1029.430</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>  <td>14.855</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Mon, 29 Feb 2016</td> <th>  AIC                </th> <td>2066.860</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>17:07:58</td>     <th>  BIC                </th> <td>2080.946</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>           <td>01-02-2015</td>    <th>  HQIC               </th> <td>2072.529</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                 <td>- 09-08-2015</td>   <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>      <td>    0.0571</td> <td>    0.027</td> <td>    2.126</td> <td> 0.035</td> <td>    0.004     0.110</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>      <td>    0.8586</td> <td>    0.033</td> <td>   25.844</td> <td> 0.000</td> <td>    0.793     0.924</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1.y</th> <td>    0.4423</td> <td>    0.061</td> <td>    7.239</td> <td> 0.000</td> <td>    0.323     0.562</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Roots</caption>\n",
       "<tr>\n",
       "    <td></td>   <th>           Real</th> <th>         Imaginary</th> <th>         Modulus</th> <th>        Frequency</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.1</th>              -2.2608                    +0.0000j                    2.2608                    0.5000     \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                              ARMA Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                  250\n",
       "Model:                     ARMA(0, 1)   Log Likelihood               -1029.430\n",
       "Method:                       css-mle   S.D. of innovations             14.855\n",
       "Date:                Mon, 29 Feb 2016   AIC                           2066.860\n",
       "Time:                        17:07:58   BIC                           2080.946\n",
       "Sample:                    01-02-2015   HQIC                          2072.529\n",
       "                         - 09-08-2015                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
       "------------------------------------------------------------------------------\n",
       "x1             0.0571      0.027      2.126      0.035         0.004     0.110\n",
       "x2             0.8586      0.033     25.844      0.000         0.793     0.924\n",
       "ma.L1.y        0.4423      0.061      7.239      0.000         0.323     0.562\n",
       "                                    Roots                                    \n",
       "=============================================================================\n",
       "                 Real           Imaginary           Modulus         Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "MA.1           -2.2608           +0.0000j            2.2608            0.5000\n",
       "-----------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis ##\n",
    "\n",
    "Our model seems to work well from the graph of the the forecast. Looking at the coefficients from the ARIMA model, we can see that that increasing temperatures and increasing day ahead prices are both associated with higher realtime prices the next day. Just by looking at the coefficients, one can see that our forecast is extremely similar to the day ahead values, with a small adjustment based on temperature and another adjustment based on the moving average term.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2305cc90>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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S0da1qbau7d+vdPWAMpepVO0MOpGNVPbzig6tawAAAOh2BJ0imSLotO6jc+CA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\n+Ai6BvZFDgyNIoIPfwSvcnEXn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L1v/DgACaNyjiSTHnGK6OTHJhg2T3Bb123oVDrStjS6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\nlr6N9Ss9vGZpYvSZZyq7LOa5+5G7afxGI8u+u4zPbvzsnKV40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ekf/k/dbd9y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CsqJlWHVW/qnhn2h1tjLLMovgbx7jQe83uO468ViFRdXoYn4S8ZRoiDs9CAYDl9xSwM5EPUUf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\n6IiIiIiIyLijQEdERERERMad/wd4KLsGz1ayAwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x235ad3f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 10))\n",
    "plt.plot(predicted_prices, label='prediction')\n",
    "plt.plot(daily_df['rtm_lbmp'].values, label='actual RTM')\n",
    "plt.plot(daily_df['dam_lbmp'].values, label='actual DAM')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How Did We Do? ##\n",
    "\n",
    "Our data and model helps us predict the realtime electricity cost the day before. If you ran a company with significant power demands, you might be able to use such a model to decide whether or not to buy electricity in advance or on demand. Let's see how it would work!\n",
    "\n",
    "1. Learn about Autoregressive Integrated Moving Average models\n",
    "2. Apply it to a relevant dataset\n",
    "3. ????\n",
    "4. PROFIT!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Trading Log ---\n",
      "('Buy DAM, +', 4.7812499999999964)\n",
      "('Lose $$, -', 2.4829166666666715)\n",
      "('Buy RTM, +', 17.576249999999991)\n",
      "('Buy DAM, +', 0.96041666666667069)\n",
      "('Buy DAM, +', 18.397499999999997)\n",
      "('Buy DAM, +', 4.6199999999999939)\n",
      "('Buy RTM, +', 4.8508333333333411)\n",
      "('Lose $$, -', 0.82583333333333542)\n",
      "('Buy RTM, +', 13.429583333333326)\n",
      "('Buy RTM, +', 25.946249999999988)\n",
      "('Buy RTM, +', 14.424166666666668)\n",
      "('Buy RTM, +', 9.9324999999999974)\n",
      "('Buy RTM, +', 3.8166666666666558)\n",
      "('Buy RTM, +', 1.2629166666666691)\n",
      "('Buy DAM, +', 6.7224999999999966)\n",
      "('Lose $$, -', 0.052083333333332149)\n",
      "('Buy DAM, +', 4.0216666666666612)\n",
      "('Buy DAM, +', 13.397499999999994)\n",
      "('Buy DAM, +', 10.117500000000003)\n",
      "('Buy RTM, +', 42.542500000000004)\n",
      "('Buy RTM, +', 8.0600000000000058)\n",
      "('Buy DAM, +', 5.0695833333333304)\n",
      "('Lose $$, -', 2.4054166666666674)\n",
      "('Buy RTM, +', 3.8545833333333306)\n",
      "('Buy DAM, +', 2.8687499999999986)\n",
      "('Buy RTM, +', 3.5899999999999999)\n",
      "('Buy DAM, +', 5.9754166666666606)\n",
      "('Buy DAM, +', 8.5354166666666629)\n",
      "('Lose $$, -', 1.1391666666666609)\n",
      "('Lose $$, -', 3.2083333333333428)\n",
      "('Buy DAM, +', 1.1404166666666633)\n",
      "('Buy DAM, +', 14.305416666666668)\n",
      "('Buy DAM, +', 6.8320833333333297)\n",
      "('Buy DAM, +', 4.9129166666666713)\n",
      "('Buy DAM, +', 1.5162499999999994)\n",
      "('Buy DAM, +', 4.5512499999999889)\n",
      "('Buy DAM, +', 6.9862500000000054)\n",
      "('Lose $$, -', 5.7316666666666585)\n",
      "('Lose $$, -', 6.875833333333329)\n",
      "('Lose $$, -', 5.0062500000000014)\n",
      "('Buy RTM, +', 2.0275000000000034)\n",
      "('Buy RTM, +', 8.3766666666666616)\n",
      "('Buy DAM, +', 2.2762499999999939)\n",
      "('Lose $$, -', 0.70875000000000199)\n",
      "('Buy DAM, +', 1.1291666666666629)\n",
      "('Buy DAM, +', 6.8625000000000007)\n",
      "('Buy DAM, +', 4.0633333333333432)\n",
      "('Lose $$, -', 1.0916666666666721)\n",
      "('Buy DAM, +', 2.980833333333333)\n",
      "('Buy DAM, +', 9.6670833333333412)\n",
      "('Buy RTM, +', 7.178333333333331)\n",
      "('Buy RTM, +', 5.9908333333333381)\n",
      "('Buy DAM, +', 9.9066666666666627)\n",
      "('Buy RTM, +', 14.988749999999992)\n",
      "('Buy DAM, +', 4.6975000000000016)\n",
      "('Buy DAM, +', 1.5120833333333401)\n",
      "('Buy DAM, +', 5.6191666666666755)\n",
      "('Buy DAM, +', 8.5974999999999948)\n",
      "('Buy DAM, +', 6.176666666666673)\n",
      "('Buy DAM, +', 10.897083333333331)\n",
      "('Lose $$, -', 1.960833333333337)\n",
      "('Buy RTM, +', 4.7395833333333357)\n",
      "('Buy RTM, +', 3.5533333333333275)\n",
      "('Buy DAM, +', 10.574999999999999)\n",
      "('Buy DAM, +', 12.364166666666662)\n",
      "('Buy DAM, +', 2.3991666666666713)\n",
      "('Buy DAM, +', 11.910416666666665)\n",
      "('Lose $$, -', 0.45500000000000185)\n",
      "('Buy DAM, +', 5.4337500000000034)\n",
      "('Lose $$, -', 1.5620833333333337)\n",
      "('Lose $$, -', 0.65583333333334082)\n",
      "('Buy DAM, +', 11.046250000000001)\n",
      "('Buy DAM, +', 9.148750000000005)\n",
      "('Buy DAM, +', 9.275416666666672)\n",
      "('Lose $$, -', 2.66458333333334)\n",
      "('Buy RTM, +', 0.58750000000000213)\n",
      "('Buy RTM, +', 13.678750000000008)\n",
      "('Buy DAM, +', 4.9604166666666689)\n",
      "('Buy DAM, +', 8.8345833333333381)\n",
      "('Buy RTM, +', 7.3291666666666622)\n",
      "('Lose $$, -', 1.1929166666666688)\n",
      "('Lose $$, -', 2.8512499999999967)\n",
      "('Buy DAM, +', 1.0904166666666661)\n",
      "('Buy RTM, +', 3.7537499999999859)\n",
      "('Buy DAM, +', 5.2866666666666653)\n",
      "('Buy DAM, +', 3.8229166666666714)\n",
      "('Buy RTM, +', 19.479166666666671)\n",
      "('Lose $$, -', 0.14541666666666586)\n",
      "('Buy RTM, +', 44.968749999999986)\n",
      "('Lose $$, -', 4.2724999999999937)\n",
      "('Buy DAM, +', 3.6150000000000055)\n",
      "('Buy DAM, +', 1.1541666666666615)\n",
      "('Lose $$, -', 1.9920833333333334)\n",
      "('Lose $$, -', 6.7012499999999982)\n",
      "('Buy DAM, +', 1.1079166666666662)\n",
      "('Buy DAM, +', 0.061666666666667425)\n",
      "('Buy RTM, +', 15.491250000000004)\n",
      "('Buy DAM, +', 4.5175000000000018)\n",
      "('Buy DAM, +', 5.1470833333333346)\n",
      "('Buy DAM, +', 9.932916666666662)\n"
     ]
    }
   ],
   "source": [
    "# Profit Check\n",
    "len(predicted_prices)\n",
    "len(daily_df['rtm_lbmp'].values[10:])\n",
    "\n",
    "print(\"--- Trading Log ---\")\n",
    "\n",
    "i = 251\n",
    "PnL = np.zeros(100)\n",
    "\n",
    "\n",
    "while i < 351:\n",
    "    if (predicted_prices[i] < daily_df['dam_lbmp'].values[i]) and (daily_df['rtm_lbmp'].values[i+1] < daily_df['dam_lbmp'].values[i]):\n",
    "        \n",
    "        # if our model says the DAM is overpriced, then don't pre-buy and buy at the realtime price\n",
    "        \n",
    "        print(\"Buy RTM, +\", daily_df['dam_lbmp'].values[i] - daily_df['rtm_lbmp'].values[i+1])\n",
    "        PnL[i-251] = daily_df['dam_lbmp'].values[i] - daily_df['rtm_lbmp'].values[i+1]\n",
    "        \n",
    "    elif (predicted_prices[i] > daily_df['dam_lbmp'].values[i]) and (daily_df['rtm_lbmp'].values[i+1] > daily_df['dam_lbmp'].values[i]):\n",
    "        \n",
    "        # if our model says the DAM is underpriced, pre-buy the electricity so you don't have to pay realtime price \n",
    "        \n",
    "        print(\"Buy DAM, +\", daily_df['rtm_lbmp'].values[i+1] - daily_df['dam_lbmp'].values[i] )\n",
    "        PnL[i-251] = daily_df['rtm_lbmp'].values[i+1] - daily_df['dam_lbmp'].values[i]\n",
    "        \n",
    "    else:\n",
    "        \n",
    "        # if we were wrong, we lose money :(\n",
    "        \n",
    "        print(\"Lose $$, -\", max(daily_df['rtm_lbmp'].values[i+1] - daily_df['dam_lbmp'].values[i],daily_df['dam_lbmp'].values[i] - daily_df['rtm_lbmp'].values[i+1]))\n",
    "        PnL[i-251] = min(daily_df['rtm_lbmp'].values[i+1] - daily_df['dam_lbmp'].values[i],daily_df['dam_lbmp'].values[i] - daily_df['rtm_lbmp'].values[i+1])\n",
    "    i = i+1\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x22f3f970>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ddim6GkkqD5cOpRr36dV2jz8Of/0rDBmSg8oWW8DDDxddXctce23ek3XPPYYs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\nFWLQkiRJqhCDliRJUoX8f86fjzQkzHdPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f3f870>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cumPnL = np.cumsum(PnL)\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.plot(cumPnL, label='PnL')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like the model works because it predicts every so slightly closer to the true RTM than the DAM!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x23c04b30>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZI66MGZgSxoQQQohzxNIi0S233NLQ8datTamUyiileua+7gKuBh4HvgW8bW63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\nQbuj0AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f3f7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dam_adj = daily_df['rtm_lbmp'].values[10:]-daily_df['dam_lbmp'].values[:-10]\n",
    "mod_adj = daily_df['rtm_lbmp'].values[10:]-predicted_prices[:-1]\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.plot(dam_adj, label='DAM error')\n",
    "plt.plot(mod_adj, label='Model error')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problems #\n",
    "\n",
    "There are MANY potential problems with this approach that need to be mentioned. Though the results seem okay, this is a good example of a model that might fit but not be meaningful. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Seasonality**: Data with simple trends can be differenced once to achieve stationarity. However, you will often find data that varies by season. In our case, weather data and electricity usage are strong candidates for having some sort of seasonal trend. To deal with this, we would need to grab several years of data and incoporate the yearly differences (between Jan 1 2015 and Jan 1 2016, for example) in addition. Seasonal ARIMA (SARIMA) is a tool for this purpose."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Parameter Estimation**: It could easily take a whole workshop to discuss choosing model form. The parameters chosen ARIMA(0,0,1) may be inappropriate. When doing ARIMA, we want the y variable (RTM in our case) to be stationary (that is, the distribution doesn't change over time). This assumption of stationary may seem suspect, especially given the winter activity (discussed in *seasonality* section below). You should use of the Dickey Fuller test to make sure stationarity is appropriate. \n",
    "\n",
    "Additionally, the AR(p) and MA(q) terms need to be determined by using ACF and PACF. The ACF tells you how much an observation at time i is related to observations at times i-1, i-2, etc. The PACF is similar to an ACF but it controls for the values of the time series at all shorter lags (how much does the 3rd lag tell me beyond what the 1st and 2nd ones do?). See it in action below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python27\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": 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sAJs2fWOf0e89o+LdxWh47fbh6e7ubiK6Ri1rGb3cJUZtN9p0o5IXgO3bt9DV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23b71c70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "ax1 = fig.add_subplot(211)\n",
    "fig = sm.graphics.tsa.plot_acf(np.diff(daily_df['rtm_lbmp'].values), lags=40, ax=ax1)\n",
    "ax2 = fig.add_subplot(212)\n",
    "fig = sm.graphics.tsa.plot_pacf(np.diff(daily_df['rtm_lbmp'].values), lags=40, ax=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpreting these plots is a bit of an art as well as a science. There are many different rules that you can use to help you determine the \"correct\" parameters for an ARIMA process, though it's important to note that there isn't always a right answer; in fact, it's not always possible to model some data using ARIMA. \n",
    "\n",
    "<img src=\"http://www.civildatanalytics.com/uploads/4/6/2/5/46259103/980794_orig.png\" />"
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