ARIMA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports and Defines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports\n",
    "    Imports all modules and submodules that were necessary for this study.\n",
    "    Special mention to timeseries-cv (tsxv) which was created with the purpose to be used in this thesis (https://github.com/DidierRLopes/timeseries-cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas_datareader as dr\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.gridspec as gridspec\n",
    "import seaborn as sns\n",
    "import ast\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "\n",
    "# pip install timeseries-cv\n",
    "from tsxv import splitTrain\n",
    "from matplotlib.lines import Line2D\n",
    "from collections import OrderedDict\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from statsmodels.tsa.stattools import adfuller, kpss, bds\n",
    "from scipy.stats import skewtest, kurtosistest, skew, kurtosis\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "from statsmodels.stats.diagnostic import het_arch, acorr_ljungbox, acorr_breusch_godfrey\n",
    "from statsmodels.graphics.gofplots import qqplot\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Functions\n",
    "    Defines helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Parse string into tuple\n",
    "def parse_tuple(string):\n",
    "    try:\n",
    "        s = ast.literal_eval(str(string))\n",
    "        if type(s) == tuple:\n",
    "            return s\n",
    "        return\n",
    "    except:\n",
    "        return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Compute MAE over an array of different horizons\n",
    "def horizon_mae(y_true, y_pred, horizonSteps):\n",
    "    ae  = abs(y_pred - y_true.T);\n",
    "    list_MAE = list()\n",
    "    for i in horizonSteps:\n",
    "        list_MAE.append(round(np.mean(ae[0,:i]), 2))\n",
    "    return list_MAE;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Define Mean Absolute Percentage Error\n",
    "def absolute_percentage_error(y_true, y_pred):\n",
    "    if (len(y_true[y_true == 0])):\n",
    "        print(\"Division by zero!\")\n",
    "        return None;\n",
    "    else:\n",
    "        return 100*(abs((y_pred - y_true.T) / y_true.T));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Compute MAPE over an array of different horizons\n",
    "def horizon_mape(y_true, y_pred, horizonSteps):\n",
    "    ape = absolute_percentage_error(y_true, y_pred);\n",
    "    if ape is not None:\n",
    "        list_MAPE = list()\n",
    "        for i in horizonSteps:\n",
    "            list_MAPE.append(round(np.mean(ape[0,:i]), 2))\n",
    "        return list_MAPE;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Evaluate combinations of p, d and q values for an ARIMA model\n",
    "def ARIMA_grid(dataset, arimaCfg, isLog):\n",
    "    df_IC = pd.DataFrame( columns=['ARIMA(p, d, q)', 'AIC', 'BIC', 'HQIC'])\n",
    "    df_IC = df_IC.set_index('ARIMA(p, d, q)')\n",
    "    dict_arimaFit  = {} \n",
    "    dict_arimaPred = {} \n",
    "    modelNum = 0;\n",
    "    totalModelNum = len(arimaCfg.p_range)*len(arimaCfg.d_range)*len(arimaCfg.q_range)\n",
    "    for p in arimaCfg.p_range:\n",
    "        for d in arimaCfg.d_range:\n",
    "            for q in arimaCfg.q_range:\n",
    "                order = (p,d,q)\n",
    "                modelNum+=1;\n",
    "                print(\"%d/%d \" % (modelNum, totalModelNum))\n",
    "\n",
    "                try:\n",
    "                    model = ARIMA(dataset, order=order);\n",
    "                    model_fit = model.fit(disp=0);\n",
    "                    \n",
    "                    if (~np.isnan(model_fit.mle_retvals.get('fopt'))):\n",
    "                        \n",
    "                        model_str = 'ARIMA' + str(order)\n",
    "                        df_IC = df_IC.append(pd.DataFrame({'AIC':model_fit.aic, 'BIC':model_fit.bic, \\\n",
    "                                                           'HQIC':model_fit.hqic}, index =[model_str]), ignore_index=False)\n",
    "\n",
    "                        if (isLog):\n",
    "                            dict_arimaFit[model_str]  = np.e**(model_fit.predict(typ='levels'));\n",
    "                            dict_arimaPred[model_str] = np.e**(model_fit.forecast(steps=stepsToForecast[-1])[0]);\n",
    "                        else:\n",
    "                            dict_arimaFit[model_str]  = model_fit.predict(typ='levels');\n",
    "                            dict_arimaPred[model_str] = model_fit.forecast(steps=stepsToForecast[-1])[0];\n",
    "                except:\n",
    "                    continue\n",
    "    return dict_arimaFit, dict_arimaPred, df_IC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Univariate Time-Series\n",
    "    df: Dataframe with an index and a value column\n",
    "    title: Title of data being used\n",
    "    serie: Serie 'Name'\n",
    "    xlab: Label of df.index\n",
    "    ylab: Label of df.values\n",
    "    \n",
    "    stepsToForecast: Steps to forecast out-of-sample (and in-sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "previousStepsToShow = 15;\n",
    "\n",
    "#stepsToForecast = [1, 3, 12]\n",
    "#df = dr.DataReader('CPIAUCSL', \"fred\", start='1947-01-01', end='2019-09-01')\n",
    "#df_train = df[['CPIAUCSL']][:-stepsToForecast[-1]].rename(columns={'CPIAUCSL': 'train'})\n",
    "#df_test = df[['CPIAUCSL']][-stepsToForecast[-1]:].rename(columns={'CPIAUCSL': 'test'})\n",
    "#title = \"CPIAUCSL: All Items in U.S. City Average\"\n",
    "#serie = \"CPIAUCSL\"\n",
    "#xlab = \"Months/Year\"; \n",
    "#ylab = \"Consumer Price Index\";\n",
    "\n",
    "#stepsToForecast = [1, 5, 21]\n",
    "#df = pd.read_csv(\"PSI_20_Data_1992_Stooq.csv\") \n",
    "#df = pd.DataFrame(df, columns= ['Date','Close'])\n",
    "#df.set_index('Date', drop=True, inplace=True)\n",
    "#df.index = pd.to_datetime(df.index)\n",
    "#df = df.truncate(before='2002-01-01', after='2019-09-27')\n",
    "#df_train = df[['Close']][:-stepsToForecast[-1]].rename(columns={'Close': 'train'})\n",
    "#df_test = df[['Close']][-stepsToForecast[-1]:].rename(columns={'Close': 'test'})\n",
    "#title = 'PSI20: Historical Closing Prices'\n",
    "#serie = \"PSI20\"\n",
    "#xlab = 'Business Days/Year'; \n",
    "#ylab = 'Closing Price (Euro)';\n",
    "\n",
    "stepsToForecast = [1, 5, 21]\n",
    "df = dr.data.get_data_yahoo('SPY', start= '1993-01-01', end='2019-09-27')\n",
    "df_train = df[['Close']][:-stepsToForecast[-1]].rename(columns={'Close': 'train'})\n",
    "df_test = df[['Close']][-stepsToForecast[-1]:].rename(columns={'Close': 'test'})\n",
    "title = 'SPY: Historical Closing Prices'\n",
    "serie = \"SPY\"\n",
    "xlab = 'Business Days/Year'; \n",
    "ylab = 'Closing Price (Dollar)';"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stationarity of the Time-Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data transformation and its graphical representation\n",
    "    This section allows to understand the representation of the series after its transformation to achieve stationarity\n",
    "\n",
    "    Box-Cox transformations such as the logarithmic one can help to stabilise the variance of a time series. \n",
    "    Differencing can help stabilise the mean of a time series by removing changes in the level of a time series, and therefore eliminating (or reducing) trend and seasonality.\n",
    "    Note: The order Box-Cox transformation, and then, differencing, is important!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,10))\n",
    "\n",
    "fig.add_subplot(221)\n",
    "plt.title(serie + ' (Time Series)')\n",
    "plt.plot(df_train)\n",
    "plt.xlabel(xlab)\n",
    "plt.ylabel(ylab)\n",
    "plt.grid()\n",
    "\n",
    "fig.add_subplot(222)\n",
    "plt.title('Log(' + serie + ')')\n",
    "plt.plot(np.log(df_train))\n",
    "plt.xlabel(xlab)\n",
    "plt.ylabel(ylab)\n",
    "plt.grid()\n",
    "\n",
    "fig.add_subplot(223)\n",
    "plt.title('Dif(' + serie + ')')\n",
    "plt.plot(df_train.diff().dropna())\n",
    "plt.xlabel(xlab)\n",
    "plt.ylabel(ylab)\n",
    "plt.grid()\n",
    "\n",
    "fig.add_subplot(224)\n",
    "plt.title('DifLog(' + serie + ')')\n",
    "plt.plot(np.log(df_train).diff().dropna())\n",
    "plt.xlabel(xlab)\n",
    "plt.ylabel(ylab)\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normality tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Jarque-Bera test\n",
    "    Perform the Jarque-Bera goodness of fit test on sample data.\n",
    "    The Jarque-Bera test tests whether the sample data has the skewness and kurtosis matching a normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jarque-Bera Test for SPY\n",
      "Statistic: 726.0377\n",
      "p-value: 0.0000\n",
      "\n",
      "Jarque-Bera Test for Log(SPY)\n",
      "Statistic: 135.9271\n",
      "p-value: 0.0000\n",
      "\n",
      "Jarque-Bera Test for Diff(SPY)\n",
      "Statistic: 12828.4972\n",
      "p-value: 0.0000\n",
      "\n",
      "Jarque-Bera Test for Diff(Log(SPY))\n",
      "Statistic: 29088.8787\n",
      "p-value: 0.0000\n"
     ]
    }
   ],
   "source": [
    "jb, jbpval = stats.jarque_bera(df_train)\n",
    "print(\"Jarque-Bera Test for \" + serie)\n",
    "print(\"Statistic: %.4f\" % jb)\n",
    "print(\"p-value: %.4f\" % jbpval)\n",
    "print(\"\")\n",
    "jb, jbpval = stats.jarque_bera(np.log(df_train))\n",
    "print(\"Jarque-Bera Test for Log(\" + serie + \")\")\n",
    "print(\"Statistic: %.4f\" % jb)\n",
    "print(\"p-value: %.4f\" % jbpval)\n",
    "print(\"\")\n",
    "jb, jbpval = stats.jarque_bera(df_train.diff().dropna())\n",
    "print(\"Jarque-Bera Test for Diff(\" + serie + \")\")\n",
    "print(\"Statistic: %.4f\" % jb)\n",
    "print(\"p-value: %.4f\" % jbpval)\n",
    "print(\"\")\n",
    "jb, jbpval = stats.jarque_bera(np.log(df_train).diff().dropna())\n",
    "print(\"Jarque-Bera Test for Diff(Log(\" + serie + \"))\")\n",
    "print(\"Statistic: %.4f\" % jb)\n",
    "print(\"p-value: %.4f\" % jbpval) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kolmogorov-Smirnov test\n",
    "    Perform the Kolmogorov-Smirnov test for goodness of fit.\n",
    "    This performs a test of the distribution G(x) of an observed random variable against a given distribution F(x). Under the null hypothesis the two distributions are identical, G(x)=F(x). The alternative hypothesis can be either 'two-sided' (default), 'less' or 'greater'. The KS test is only valid for continuous distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kolmogorov-Smirnov Test for SPY\n",
      "Statistic: 1.0000\n",
      "p-value: 0.0000\n",
      "\n",
      "Kolmogorov-Smirnov Test for Log(SPY)\n",
      "Statistic: 1.0000\n",
      "p-value: 0.0000\n",
      "\n",
      "Kolmogorov-Smirnov Test for Diff(SPY)\n",
      "Statistic: 1.0000\n",
      "p-value: 0.0000\n",
      "\n",
      "Kolmogorov-Smirnov Test for Diff(Log(SPY))\n",
      "Statistic: 0.5539\n",
      "p-value: 0.0000\n"
     ]
    }
   ],
   "source": [
    "ks, kspval = stats.kstest(df_train.values, 'norm')\n",
    "print(\"Kolmogorov-Smirnov Test for \" + serie)\n",
    "print(\"Statistic: %.4f\" % ks)\n",
    "print(\"p-value: %.4f\" % kspval)  \n",
    "print(\"\")\n",
    "ks, kspval = stats.kstest(np.log(df_train), 'norm')\n",
    "print(\"Kolmogorov-Smirnov Test for Log(\" + serie + \")\")\n",
    "print(\"Statistic: %.4f\" % ks)\n",
    "print(\"p-value: %.4f\" % kspval)\n",
    "print(\"\")\n",
    "ks, kspval = stats.kstest(df_train.diff().dropna(), 'norm')\n",
    "print(\"Kolmogorov-Smirnov Test for Diff(\" + serie + \")\")\n",
    "print(\"Statistic: %.4f\" % ks)\n",
    "print(\"p-value: %.4f\" % kspval)\n",
    "print(\"\")\n",
    "ks, kspval = stats.kstest(np.log(df_train).diff().dropna(), 'norm')\n",
    "print(\"Kolmogorov-Smirnov Test for Diff(Log(\" + serie + \"))\")\n",
    "print(\"Statistic: %.4f\" % ks)\n",
    "print(\"p-value: %.4f\" % kspval) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unit Root and Stationarity Tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Augmented Dickey-Fuller test \n",
    "    Augmented Dickey-Fuller unit root test.\n",
    "    The Augmented Dickey-Fuller test can be used to test for a unit root in a univariate process in the presence of serial correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Dickey Fuller Test for SPY\n",
      "Statistic: 0.3972\n",
      "p-value: 0.9814\n",
      "Used lags: 17\n",
      "Num obs: 6676\n",
      "Critical Values:\n",
      "\t1%: -3.431\n",
      "\t5%: -2.862\n",
      "\t10%: -2.567\n",
      "\n",
      "Augmented Dickey Fuller Test for Log(SPY)\n",
      "Statistic: -1.1596\n",
      "p-value: 0.6907\n",
      "Used lags: 34\n",
      "Num obs: 6659\n",
      "Critical Values:\n",
      "\t1%: -3.431\n",
      "\t5%: -2.862\n",
      "\t10%: -2.567\n",
      "\n",
      "Augmented Dickey Fuller Test for Diff(SPY)\n",
      "Statistic: -20.1409\n",
      "p-value: 0.0000\n",
      "Used lags: 16\n",
      "Num obs: 6676\n",
      "Critical Values:\n",
      "\t1%: -3.431\n",
      "\t5%: -2.862\n",
      "\t10%: -2.567\n",
      "\n",
      "Augmented Dickey Fuller Test for Diff(Log(SPY))\n",
      "Statistic: -14.7617\n",
      "p-value: 0.0000\n",
      "Used lags: 33\n",
      "Num obs: 6659\n",
      "Critical Values:\n",
      "\t1%: -3.431\n",
      "\t5%: -2.862\n",
      "\t10%: -2.567\n"
     ]
    }
   ],
   "source": [
    "result = adfuller(df_train['train'].values, regression='c')\n",
    "print(\"Augmented Dickey Fuller Test for \" + serie)\n",
    "print(\"Statistic: %.4f\" % result[0])\n",
    "print(\"p-value: %.4f\" % result[1])\n",
    "print(\"Used lags: %d\" % result[2])\n",
    "print(\"Num obs: %d\" % result[3])\n",
    "print(\"Critical Values:\")\n",
    "d = OrderedDict(sorted(result[4].items(), key=lambda t: t[1]))\n",
    "for key, value in d.items():\n",
    "    print(\"\\t%s: %.3f\" % (key, value))\n",
    "print(\"\")\n",
    "result = adfuller(np.log(df_train['train'].values), regression='c')\n",
    "print(\"Augmented Dickey Fuller Test for Log(\" + serie + \")\")\n",
    "print(\"Statistic: %.4f\" % result[0])\n",
    "print(\"p-value: %.4f\" % result[1])\n",
    "print(\"Used lags: %d\" % result[2])\n",
    "print(\"Num obs: %d\" % result[3])\n",
    "print(\"Critical Values:\")\n",
    "d = OrderedDict(sorted(result[4].items(), key=lambda t: t[1]))\n",
    "for key, value in d.items():\n",
    "    print(\"\\t%s: %.3f\" % (key, value))\n",
    "print(\"\")\n",
    "result = adfuller(df_train['train'].diff().dropna(), regression='c')\n",
    "print(\"Augmented Dickey Fuller Test for Diff(\" + serie + \")\")\n",
    "print(\"Statistic: %.4f\" % result[0])\n",
    "print(\"p-value: %.4f\" % result[1])\n",
    "print(\"Used lags: %d\" % result[2])\n",
    "print(\"Num obs: %d\" % result[3])\n",
    "print(\"Critical Values:\")\n",
    "d = OrderedDict(sorted(result[4].items(), key=lambda t: t[1]))\n",
    "for key, value in d.items():\n",
    "    print (\"\\t%s: %.3f\" % (key, value))\n",
    "print(\"\")\n",
    "result = adfuller(np.log(df_train['train']).diff().dropna().values, regression='c')\n",
    "print(\"Augmented Dickey Fuller Test for Diff(Log(\" + serie + \"))\")\n",
    "print(\"Statistic: %.4f\" % result[0])\n",
    "print(\"p-value: %.4f\" % result[1])\n",
    "print(\"Used lags: %d\" % result[2])\n",
    "print(\"Num obs: %d\" % result[3])\n",
    "print(\"Critical Values:\")\n",
    "d = OrderedDict(sorted(result[4].items(), key=lambda t: t[1]))\n",
    "for key, value in d.items():\n",
    "    print(\"\\t%s: %.3f\" % (key, value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kwiatkowski-Phillips-Schmidt-Shin test\n",
    "    Kwiatkowski-Phillips-Schmidt-Shin test for stationarity.\n",
    "    Computes the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test for the null hypothesis that x is level or trend stationary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kwiatkowski-Phillips-Schmidt-Shin Test for SPY\n",
      "Statistic: 13.2820\n",
      "Critical Values:\n",
      "\t1%: 0.739\n",
      "\t2.5%: 0.574\n",
      "\t5%: 0.463\n",
      "\t10%: 0.347\n",
      "\n",
      "Kwiatkowski-Phillips-Schmidt-Shin Test for Log(SPY)\n",
      "Statistic: 13.2698\n",
      "Critical Values:\n",
      "\t1%: 0.739\n",
      "\t2.5%: 0.574\n",
      "\t5%: 0.463\n",
      "\t10%: 0.347\n",
      "\n",
      "Kwiatkowski-Phillips-Schmidt-Shin Test for Diff(SPY)\n",
      "Statistic: 0.2234\n",
      "Critical Values:\n",
      "\t1%: 0.739\n",
      "\t2.5%: 0.574\n",
      "\t5%: 0.463\n",
      "\t10%: 0.347\n",
      "\n",
      "Kwiatkowski-Phillips-Schmidt-Shin Test for Diff(Log(SPY))\n",
      "Statistic: 0.1275\n",
      "Critical Values:\n",
      "\t1%: 0.739\n",
      "\t2.5%: 0.574\n",
      "\t5%: 0.463\n",
      "\t10%: 0.347\n"
     ]
    }
   ],
   "source": [
    "result = kpss(df_train['train'].values, regression='c')\n",
    "print(\"Kwiatkowski-Phillips-Schmidt-Shin Test for \" + serie)\n",
    "print(\"Statistic: %.4f\" % result[0])\n",
    "print(\"Critical Values:\")\n",
    "d = OrderedDict(sorted(result[3].items(), key=lambda t: t[1], reverse=True))\n",
    "for key, value in d.items():\n",
    "    print(\"\\t%s: %.3f\" % (key, value));\n",
    "print(\"\")\n",
    "result = kpss(np.log(df_train['train'].values), regression='c')\n",
    "print(\"Kwiatkowski-Phillips-Schmidt-Shin Test for Log(\" + serie + \")\")\n",
    "print(\"Statistic: %.4f\" % result[0])\n",
    "print(\"Critical Values:\")\n",
    "d = OrderedDict(sorted(result[3].items(), key=lambda t: t[1], reverse=True))\n",
    "for key, value in d.items():\n",
    "    print(\"\\t%s: %.3f\" % (key, value));\n",
    "print(\"\")\n",
    "result = kpss(df_train['train'].diff().dropna(), regression='c')\n",
    "print(\"Kwiatkowski-Phillips-Schmidt-Shin Test for Diff(\" + serie + \")\")\n",
    "print(\"Statistic: %.4f\" % result[0])\n",
    "print(\"Critical Values:\")\n",
    "d = OrderedDict(sorted(result[3].items(), key=lambda t: t[1], reverse=True))\n",
    "for key, value in d.items():\n",
    "    print(\"\\t%s: %.3f\" % (key, value));\n",
    "print(\"\")\n",
    "result = kpss(np.log(df_train['train']).diff().dropna().values, regression='c')\n",
    "print(\"Kwiatkowski-Phillips-Schmidt-Shin Test for Diff(Log(\" + serie + \"))\")\n",
    "print(\"Statistic: %.4f\" % result[0])\n",
    "print(\"Critical Values:\")\n",
    "d = OrderedDict(sorted(result[3].items(), key=lambda t: t[1], reverse=True))\n",
    "for key, value in d.items():\n",
    "    print(\"\\t%s: %.3f\" % (key, value));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation plots\n",
    "    These plots are useful for identifying non-stationary time series \n",
    "\n",
    "    This also gives an idea of the order of the model by:\n",
    "        AR(p) - p from the partial autocorrelation plot\n",
    "        I(d)  - d from the number of Diffs your dataset is using\n",
    "        MA(q) - q from the autocorrelation plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zGipfT5txh8rX85hT5/XLANl643+RMI+Zfsi+57HtqP085uPN1hbWu6NVoL4+yaljl0/pr1s3znryo3LmqSfkY1fflqWWPKT/T/msJz9q3bZN5rOwPo9tp20/j8F/Xj8ozWMA34wfduYxgM/rh51R+3kL0Zvxww5MYENn62nbT9N2HnPqvH4ZIFtv/C8S5jHTD9n3PLad13y82dqO38e85eN5bDuL9pvR0SpQX5DkVVX1nnQncLlrPa2RlyRbt1TOf8Uzc85bP5L7di3mZ1/w1DW/gIZqm8xnYX0e207bfh6D/7x+UJrHAL4ZP+zMYwCf1w878xiiN+OHnVH7zRbAhffDtqGz9bTtp2k7jzl1Xr8MkK03/hcJ85jph+x7HtvOaz7ebG2T+czH89h2Vu03Y7beMos7qap3J/lokidX1XVV9YqqemVVvbLf5MIkVye5KslvJPn3s+h31rZuqTziIdvz2Eccm+c85dETPYlDtj3/Fc/MEx/1sJxywrH5f1/89DW/6LVde9tp248C+GjTwwnvm6XttO1HQXjcpCF6s7Qdsu+DBeGN2HbIvsfDbMv+YXYjtp3XcY+C7Kvf/an88l/8Q1797k/l+97x8SwuHXpFhqHaDt33RiZbT9/+cNvOY04dMuMO1XYeM+6Q+Xge227GfDyPbec1H2+2tsl85uN5bDtt+82crWdSoG6tvbi19pjW2oNaa6e01t7RWvu11tqv9be31toPtdae0Fr7R621i2fRL515LazPW9tp2s9j8J/XD0rzGMA344edeQzg8/phZx5D9Gb8sLMZA/i0fW9ksvWw5jGnzuOXAdO0nceMuxm/SJjHTD9k3/PYdl7z8WZrm8xnPp7HttO238zZeiYFauDQ5i34D9l2mvbzGMA344edeQzg8/phZx5D9Gb8sLMZA/i0fQOb2zxm3M32RcI0bTdjPp7HtvOajzdb22Q+8/E8tp22/WbO1grUwIYzbwF8yLZD9T2PAXxeP+zMY4jejB92NmMAn7ZvADa2zZaP57HtvObjzdY2mc98PI9tp22/mbO1AjUAg5i3AD5t26H6nscQvRk/7GzGAD5t3wDA8OYxH2/WtvOWj+ex7bTtN3O23jb0AACAI2sUZh/xkKzpLN/z3nbIvg+37SjInvPWj+S+XYv52Rc8dc1n3R6q7dB9AwCwdvOWj+e17TTtN3O2VqAGAFgHNlsAn0XfAACwkWzWbG2JDwAAAAAABqFADQAAAADAIBSoAQAAAAAYhAI1AAAAAACDUKAGAAAAAGAQCtQAAAAAAAxCgRoAAAAAgEEoUAMAAAAAMAgFagAAAAAABqFADQAAAADAIBSoAQAAAAAYhAI1AAAAAACDUKAGAAAAAGAQCtQAAAAAAAxCgRoAAAAAgEEoUAMAAAAAMAgFagAAAAAABqFADQAAAADAIBSoAQAAAAAYhAI1AAAAAACDmEmBuqrOrqorq+qqqnrNCrefVlUfqqpPVdVnqup5s+gXAAA2IvkaAIDNYuoCdVVtTfK2JOckOSPJi6vqjGWb/UyS97XWnp7kRUn+x7T9AgDARiRfAwCwmcxiBvUzklzVWru6tbY7yXuSvGDZNi3J8f3vD09ywwz6BQCAjUi+BgBg05hFgfqxSa4du3xdf924NyT53qq6LsmFSV690h1V1blVdXFVXXzLLbfMYGgAADB35GsAADaNo3WSxBcnOa+1dkqS5yU5v6oO6Lu19vbW2o7W2o6TTjrpKA0NAADmjnwNAMCGMIsC9fVJTh27fEp/3bhXJHlfkrTWPprkwUlOnEHfAACw0cjXAABsGrMoUH8iyZOq6vFVtT3dSVouWLbNl5I8J0mq6inpArRjDAEA4EDyNQAAm8bUBerW2kKSVyX5QJIr0p1N/LKqemNVPb/f7MeT/EBVfTrJu5O8vLXWpu0bAAA2GvkaAIDNZNss7qS1dmG6k7OMX/e6sd8vT/JNs+gLAAA2OvkaAIDN4midJBEAAAAAAPajQA0AAAAAwCAUqAEAAAAAGIQCNQAAAAAAg1CgBgAAAABgEArUAAAAAAAMQoEaAAAAAIBBKFADAAAAADAIBWoAAAAAAAahQA0AAAAAwCAUqAEAAAAAGIQCNQAAAAAAg1CgBgAAAABgEArUAAAAAAAMQoEaAAAAAIBBKFADAAAAADAIBWoAAAAAAAahQA0AAAAAwCAUqAEAAAAAGIQCNQAAAAAAg1CgBgAAAABgEArUAAAAAAAMQoEaAAAAAIBBKFADAAAAADAIBWoAAAAAAAYxkwJ1VZ1dVVdW1VVV9ZpVtnlhVV1eVZdV1e/Ool8AANhoZGsAADaTbdPeQVVtTfK2JM9Ncl2ST1TVBa21y8e2eVKSn0ryTa21O6rqUdP2CwAAG41sDQDAZjOLGdTPSHJVa+3q1truJO9J8oJl2/xAkre11u5Iktbazhn0CwAAG41sDQDApjKLAvVjk1w7dvm6/rpxX53kq6vq/1TVx6rq7JXuqKrOraqLq+riW265ZQZDAwCAuTKzbJ3I1wAArH9H6ySJ25I8KclZSV6c5Deq6oTlG7XW3t5a29Fa23HSSScdpaEBAMBcWVO2TuRrAADWv1kUqK9PcurY5VP668Zdl+SC1tqe1toXkvxDulANAADsI1sDALCpzKJA/YkkT6qqx1fV9iQvSnLBsm3+KN0Mj1TViekOS7x6Bn0DAMBGIlsDALCpTF2gbq0tJHlVkg8kuSLJ+1prl1XVG6vq+f1mH0hyW1VdnuRDSf5Ta+22afsGAICNRLYGAGCz2TaLO2mtXZjkwmXXvW7s95bkx/ofAABgFbI1AACbydE6SSIAAAAAAOxHgRoAAAAAgEEoUAMAAAAAMAgFagAAAAAABqFADQAAAADAIBSoAQAAAAAYhAI1AAAAAACDUKAGAAAAAGAQCtQAAAAAAAxCgRoAAAAAgEEoUAMAAAAAMAgFagAAAAAABqFADQAAAADAIBSoAQAAAAAYhAI1AAAAAACDUKAGAAAAAGAQCtQAAAAAAAxCgRoAAAAAgEEoUAMAAAAAMAgFagAAAAAABqFADQAAAADAIBSoAQAAAAAYhAI1AAAAAACDUKAGAAAAAGAQCtQAAAAAAAxiJgXqqjq7qq6sqquq6jUH2e67qqpV1Y5Z9AsAABuRfA0AwGYxdYG6qrYmeVuSc5KckeTFVXXGCtsdl+SHk3x82j4BAGCjkq8BANhMZjGD+hlJrmqtXd1a253kPUlesMJ2/znJLyZ5YAZ9AgDARiVfAwCwacyiQP3YJNeOXb6uv26vqvq6JKe21v7kYHdUVedW1cVVdfEtt9wyg6EBAMDcka8BANg0jvhJEqtqS5JfSvLjh9q2tfb21tqO1tqOk0466UgPDQAA5o58DQDARjKLAvX1SU4du3xKf93IcUm+JslFVXVNkm9IcoETuQAAwIrkawAANo1ZFKg/keRJVfX4qtqe5EVJLhjd2Fq7q7V2Ymvt9Nba6Uk+luT5rbWLZ9A3AABsNPI1AACbxtQF6tbaQpJXJflAkiuSvK+1dllVvbGqnj/t/QMAwGYiXwMAsJlsm8WdtNYuTHLhsutet8q2Z82iTwAA2KjkawAANosjfpJEAAAAAABYiQI1AAAAAACDUKAGAAAAAGAQCtQAAAAAAAxCgRoAAAAAgEEoUAMAAAAAMAgFagAAAAAABqFADQAAAADAIBSoAQAAAAAYhAI1AAAAAACDUKAGAAAAAGAQCtQAAAAAAAxCgRoAAAAAgEEoUAMAAAAAMAgFagAAAAAABqFADQAAAADAIBSoAQAAAAAYhAI1AAAAAACDUKAGAAAAAGAQCtQAAAAAAAxCgRoAAAAAgEEoUAMAAAAAMAgFagAAAAAABqFADQAAAADAIBSoAQAAAAAYxEwK1FV1dlVdWVVXVdVrVrj9x6rq8qr6TFV9sKoeN4t+AQBgo5GtAQDYTKYuUFfV1iRvS3JOkjOSvLiqzli22aeS7GitfW2S9yf5r9P2CwAAG41sDQDAkdBaS2tt6GGsaNsM7uMZSa5qrV2dJFX1niQvSHL5aIPW2ofGtv9Yku+dQb8AAExpFFJbkl0Li2ktWWotS/2/rXXbLO29vu23TWstuxaWkrRcc+uX0/r73PtvS//72P1ldL/Jl3ctJEkuvfbOvf0k+9o96VEPywkP2T7ErhmKbA0AMGeWltp+GXqpz9j37lroMvDSvutHGbplLHcv7cvYD+xZTJJctfPesVzdZeilpX1ZOhnL2Onu894+W198ze0HZPAkOeURx+bURz7kqO6btZhFgfqxSa4du3xdkmceZPtXJPnTGfQLADDXVguy9zywpwuUKwTZ1QrFD+xZTEvyuZvvWXWbpX7WxN4QnC603v3AniTJJ79452E9jl0LXYi+8a4HJm672D/m+3cvrnj7Op3kcSTJ1gAAE1ieqUf5d7Gb+ZA779u95skXo7x8/55u4sblN9y93zajovJ4pl5aIa+OCsV/d91dEz+e3YtLSZJb7tk1+b7ow/OexfkK0bMoUK9ZVX1vkh1JnrXK7ecmOTdJTjvttKM4MgBgM5okzB4QXpfGL+8rIN+/uysUX3bDXQctDh8syH72+rsnfiyjIHvrvbsPe38wXw6Vrftt5GsA4IhaLVOPrlvog++t9+5ac6Ye3dd9uxfSknzmujvXnKlHvry7y9ZX3HjPxI9pT5+t77p/z8RtmdwsCtTXJzl17PIp/XX7qapvTfLTSZ7VWlvxK4DW2tuTvD1JduzYMV+lfgDgsByqSHzHl3evaQbxeFhdXGq5b/diWtrRD7NLXZi9+/6FyXcGzDBbJ/I1AGwW4zl5cZWi78JSl4d33vNAWp+ZD3rEXeuWlFhqbe+ybJ/80h0rHpF3MPf12fpzN9878eMaFbe/vGvlo+3YGGZRoP5EkidV1ePThecXJfk34xtU1dOT/HqSs1trO2fQJwAwYystNzEq9ib7CsWLB5n1sLgs0I6KxH933V0H3r7GIvHf3zR5kThJFvpCsTDLnJGtAWCOLZ9QsbS08mziW+7Zterki8WllSdnfHn3QtKST33pjgNuX8uyaKNC8ed3fnnixzValm3XnqWJ28KhTF2gbq0tVNWrknwgydYk72ytXVZVb0xycWvtgiT/LcnDkvxeVSXJl1prz5+2bwDYqFYqFi+27qxxd923J4t7Q+7+QXa8cLy4tK8YfN/uxWSsUDw+k2LxEDMfpikUj4rEo6UrgIOTrQFgtpaW2t7sPD5reJSBb7t31wHF3gNnIO8/o3hUKL702jsPOIpvrbOJr9o5+Wzi0cSRBxSJ2WBmsgZ1a+3CJBcuu+51Y79/6yz6AYChtLGg2tKthbYvpB5klsTS/sF2+ck29g+7B58BMTqs7vIbJ1+fWKEY5odsDcBGNpqIMX5UXmvdWr+jzL13IsWyI/b2FZpbFpdWn4Qxnq1XM5qE8Q+HsezEqFC82kmegckc1ZMkAsCsLS61vbMgVgqk47OIF/vrdy10ReLP33LvfkXhxVWC73jB+J5d3UkyPn3t5GdjTpxsAwCA9akl2b2wdMBSbwcrGi+1lgf2dCeI/tzN9/R5OxNNxBhNoLj8BpMwYLNSoAbgiNjv0Lmxou944XjP4lJakuvvvH/fTIpls5H3tl9WhL77ga7A+7dfuH3ise1a6ILszrtXPa8YAACsCwfL1ePZeffiUtKSa2+/74DsPH4yvJVuG2XrS754x8Tj291PwLj13t0zfdzA5qFADbAJja/DtrSUvWsb33nf7v0C7PIC8egEd6NZxYtL3dmcW7owOz67Yi3u39MdEvel2+47cg8WAACOkOW5enxt49u/vHvVXL3vuv2P5BvNBL74mtsnytVJ8kCfra+74/4j8EgBjhwFaoB1aFToXWz7F4fHZx/vm23csmthKa21XLXz3n2H3Y2F3eUBePlhdaO1ja+4cfKT4I3O5rx7wYk6AABYX8YnUCyNZ+x+ksbyAvJo8sZo2Yq/v+nuiXJ1sm9t4ysP4wTTS/0d7lmcoDINMOcUqAEO0/iZmveG3WVngh6/fvnhdXu37w/Tu3fXQlpLPnb1bYc88/Nyuxa62RK33GPJCgAA5s/y84qMZ+tb7tm1YhF5teLy4lLLPQ8sJGn52NWTLweX7Fu24o4vO28IwJGmQA1sCuOBd3w28gGzKcZOoHfVznuyuJSDtltumjNBj2ZLTFqcBgCAo2W0pMV+R/QtKxiPZ+/RTOQrb7rnwILyGmciX7Vz8mzdlbYBmAcK1MC6M76OW9KdDG9UFB4tXTFeKN77+1gYHq3d9rdfuH3VwLua0Qn0brnHST4AAJhf48vGjTLxXfftWbWYPF40XhzL2tMc6TeaiXz7l2VrAFamQA1MbRR8F5aF3IXFpbQkN951/wEzK1aa0Ty+5EWy7wQhl11/98RjGhW3Fyc5qwgAAAxsPCvvLST3WXvP4lJaS669/b4DJmosLO5fZO4u77vfUba+/MbDz9aO9APgSFCghk2qrVAYXh6GFxb3LXfxuZvvWbG4vDz4jruvP4v0NbfedxQfGQAAHF1LY5M1FpZWyMx9kXml5S4WlvafuHGwIvD9fb6+7o77j84DA4CjQIEa5lA3W3lpv+A7Cr0Ly69rLfftXkxLy6XX3rlfm7UYLXdx670OyQMAYGNp7cD8vH/GXsrSUrp/+22/vHshacknv3TH3vy91pnFlrsAgAMpUMMAdi0srlhUXlhqWVwczWpeysJSN0N5tKby4a77trDUBeH7dy8egUcDAADDaEke2LO4X3F5pYkci2O5et+ayi0fu/r2ifscTfTYtWdpxo8GADYnBWo4DK213Ld7YW9BeXkYXh6CR9ff/cCeJMknv3jnxH1a9w0AgI2ktZbW/3vvroU+Vy/L00sHTtwY3TbK1p/60p0T970kVAPAuqFAzaY1msW8Z1kReWHxwCC89/LiviD86WvvGvgRAADA8FYqMi+MHR24ZyxnLy7tf/mePlv/3XWyNQBsVgrUzLVRYXlhcVRI3heGFxb3FZj3LC7t3faeB/ak5fBmMQMAwEY1Wi5jlKVHEznGs/SB1ykyAwDTUaBm3RgF3fFAPCo+j67fs9hdHq0Z97dfmHzNOAfzAQCw0S0sy897xiZ17FmWsRcWl6ZaLgMAYBoK1Bwx46F4z2IXfHcv7gvD9+1eTGstl3zxjiwsLmVpgsqxNeMAANhMRoXmLk93OXv3wr68fd/u7oTal3zx9uxZbM5bAgDMDQVqJtIVm5eyZ6ELx7sXltLSctXOew8IzYcqOC8sdWe93r3g7NcAAGwurXXnQhnl61G2Xmotn7v5nr1HE44mexyq4LzQh+/dCyrTAMB8UaAmSTcj+Z4H9uwNybsXupA8Xoxeqej8wMJikuSWe3YNMGoAAFh/llp3Yu3dC/vn6T37/RxYdB5l61vv3T3AqAEAhqFAvcEtLXVheNeepexaXOwKzwv7ZmjsXti33txnr7974NECAMD6tbjUsmthX6betSxX7x5by/ky2RoAYE0UqOfcroXF7OpnZiy15Opb7t0vJO9ZdIgfAACsxa6FxTywZ1+2/vwt9+5XfF6QrQEAZk6Bep1bWmrZtbCUB/Ys7v33gYXF7NrT/T5acuP+Pd3hgDffbakNAABYydJS25el+2L0qCi9a4VsvVO2BgA44hSo14E9i0tZbC1LSy3X33l/V4Tes2/2hjNwAwDA2nRLbyzunQV91c579072cHJuAID1R4H6KFg+C3rX2GyNXQvdoYJf3rWQJPnSbfcNPFoAAFi/RutAj2ZBj/+7a2Epi/006NEsaCfzBgBY3xSoZ2TXwmIWl1qWWsu1t983VoQ2UwMAANaqtdZP6lgamwV9z94JHrsXHF4IALCRKFBPaPfCUm69d9f+M6L79eq+vLubBX3dHfcPPEoAAFj/lpZabrz7gezaszhWgF46YC3oW+7ZPeAoAQA4krbM4k6q6uyqurKqrqqq16xw+zFV9d7+9o9X1emz6HcI9+9ezBdvuy833fVA7rxvT+7fve9kKgAAMAubJV8vtZYv3XZfbr57V+66f08e2LMkWwMAbDJTF6iramuStyU5J8kZSV5cVWcs2+wVSe5orT0xyS8n+cVp+wUAgI1IvgYAYDOZxQzqZyS5qrV2dWttd5L3JHnBsm1ekORd/e/vT/KcqqoZ9A0AABuNfA0AwKZRrU13DF1VfXeSs1tr399f/r4kz2ytvWpsm8/221zXX/58v82tq93vIx/3lPbc175zqrEdjstvvDtJcsZjjl/x9sWltnet6eW+eNt9SZLHfcVDJu53mrZD9r3Z2g7Z92ZrO2Tfm63tkH1vtrZD9r3Z2g7Z92Zre6T7fsj2bdm2ZZi66/te+U8uaa3tONr9bqR8fahs3ZLc88CeVdvP43tiHtsO2be289H3Zms7ZN+bre2QfW+2tkP2vdnaDtn3odoes21rjtk2kxWfJ3awbL2uCtRVdW6Sc5PkYY95wj9+3uvPn2psR8LBCtQAAMyWAvXGzteHKlADADA767VAvW0G9399klPHLp/SX7fSNtdV1bYkD09y2/I7aq29Pcnbk2THjh3tvf/uG2cwvNm66749e2eCAABwZP1fX3lcHvHQ7YP0/b5XDtJtsony9cLiUj5xzR1DDwMAYFM45RHH5tRHHt6s8GkdLFvPomT+iSRPqqrHV9X2JC9KcsGybS5I8rL+9+9O8ldt2qnbAACwMcnXAABsGlPPoG6tLVTVq5J8IMnWJO9srV1WVW9McnFr7YIk70hyflVdleT2dCF7Lm3Zkjxoa2XPovwPAMDsbaZ8XVV58IO2ZNfCUpTXAQA2p1ks8ZHW2oVJLlx23evGfn8gyb+eRV9DO+7BD8qO0x+ZhcWl7Frofh7Ys7jfv7v2LGZJwAYA4DBtlny9dUvl6ac9Iq21vdl61yhTLyzmgT3dv7sXhGsAgI1qJgXqzWjb1i3ZtnVLHnrMyrfvWhgrWu/Zv5C9e2Hp6A4WAADWsW4m9dY8+EFbk2MfdMDti0uty9d7lvLAsn93LSxl0ewQAIC5pUB9hHRnxdya4x98YMBeWmoHzAoZ/1fABgCAfbZuqTxk+7Y8ZJXzZe5eJVuPJodYPgQAYP1SoB7Ali2VY7dvzbHbt654+57Fbrb1eMA2+xoAAFa2fduWbN+2Jcc9+MDbRpNDxpfl2zsL29J8AACDU6Behx60dUsetHX1gD1+WON4EXu3wxsBAGA/h5occuDSfPsy9p7FZvY1AMARpkA9Z7Yc4vDGPYtdoXr3wlJ297/vWnZZERsAADoHW5pvdPLG3eMZe1nO3rNoCREAgGkoUG8wo9nXq528MUkWFvcP2aNgvWex7Q3cC4tLDncEAGBT2+/kjatore1fwO5/7yaOtD5nd1kbAIADKVBvQtu2bsm2rVtWnYU9sjdML3She8/Yzyhsd8VsYRsAgM2pqvbOwj6YUSF7z2LLnlEBe3R5cWlsokhzxCMAsKkoULOq0WzsHKKQvbTUsmepC9Oj2SELS11he89SF74XFpvADQDAprWvkJ3kIEc7Jsni0r6Z13sz9lLLwths7L2525GPAMCcU6Bmalu2VI7Z0oftNVha6ovVS93skYWlrqA9Ct8LfSBfGBW6nZwGAIBNZOuWytYtB19aZNxCn6FHE0IWxgraC0ttv5w9uk6+BgDWCwVqjrotWyoP3tKH7UPMHhlZWFa4Hs3YHs0u6f7tLy+ZqQ0AwObRLeGXNRe0W2v7CtdL+xe1F/vrludsM7UBgCNFgZq5MGnoTrrgPV60XlxW2B6fQbL8shklAABsVFWVB22tPGhrcmzWnq9HS/uNF627rN0VtheW9p84srC0L2vL1wDAahSo2bCqKtu3VZLJgneSfYF6se0rYC8rZi8u7Ttkct91Zm4DALAxjZb2Oxx7lxtZXsxedvmAvL3YzNwGgA1OgRpWMFr3b63rao8bHTK5uEIAX6nAvbh0YIHbDBMAADaS0RGRh2Npvxy9Qq7u8/bS2NIlJpAAwPxQoIYZGz9k8nAdEL4XWxbbvqC9NFbkXlzK3m3Hf+RwAAA2gi1bKtu3VH9p8pDdWjugqD1e9F5sKxe5R7+PtjWJBACODAVqWIdGM7inMQrSo2A9KnIvLC1laSl7g3hX+N5X6F4a/duaQyoBAJh7VZVtW+uwZ3CPjE8GGc/No4kki0tt/yy9bAKJiSQAsDIFatig9p9pcvjGlywZn0WyOD7jZFkYHy92jxfAHV4JAMC86iaRHP5M7pGlsQw9ytN7J5f0l0czuMcz+EqZW74GYCNQoAYOahZLloxbXLG4vSykLy+GjxXBF8eCuvW6AQCYN1u2VLZkNvm6tW5G9v6TQ/bP1Mvzt3wNwHqjQA0cVfvPPJneajNQ9v2eFa4TygEAmH9Vla2VqZcHHHdAcVu+BuAIU6AG5tosZ6CMjAL2UlsWsFcJ4Ett9WC+NBbeHYEJAMB6N+sJJcn+k0r2LV2SVfP1fjlavgbY8BSoAZYZFb1nrY2H8WUzUpZalgX2/Yvjra1eFB+1BQCA9ehITCpJ9uXrxdbN0l4+43u/3N3P5B4vlC8pfgOsCwrUAEfJ6AzyR8r44ZgrzkxZdkjm0goF89E2owJ460/As9QcmgkAwPoyytdHorAxWt971vl6aex2+Rqgo0ANsEEcicMxx60YxJfN4h7N9B4F9/2C+XjAVwQHAGAd27e+9/rI13tngI8VzOVrYKNQoAZgTY7U0ifjlsaK2SvNVNkXuveF8ZWC+tJBQvwo8AMAwJCOZr5enqH3m9U9dnmlYvdKhfDlRXKFcGAaCtQArBtHI6QnBx6yOVqPcKm1tPHZK+MBvQ/eq4X1tt+hnPuvOQ4AAEMY5esjXfxZaUmUvZNOlg78fbTcyX45fGzm94ETUfZvD2wsCtQAbDpH45DNceOHb65UyB4/hHN5YF9eEF9eLF8p+JvBAgDA0XQ08/V4Lj5wVvf+R2WutAThvsx88IL4eE4HjqypCtRV9cgk701yepJrkrywtXbHsm3OTPI/kxyfZDHJz7fW3jtNvwAwT47WzPCR8dnb+xXF+xniB8xqGQvtqxXFDwj+47ebyQIzI18DwMHtLYYfxXy90lKEezPxsiMw95tIskJRfKWjME06YbObdgb1a5J8sLX2X6rqNf3ln1y2zX1JXtpa+1xVnZzkkqr6QGvtzin7BgBWMDqj/dG0fCbLwcL2SsH9UIXxtuzDQGvCOxuWfA0A68zRnnCS5CBHUB6Yhw+atddwNKaJJwxt2gL1C5Kc1f/+riQXZVmAbq39w9jvN1TVziQnJblzyr4BgHViiJksI6vNGF8xvB+kOL6m7QV4jjz5GgDYL18/aOvR7ftQM8YPKzubfMJBTFugfnRr7cb+95uSPPpgG1fVM5JsT/L5KfsFAEgyzIzxcQceujn7ArkQv6nI1wDAoIaYMT5yyIL3ESiQJyagDO2QBeqq+sskX7nCTT89fqG11qpq1aexqh6T5PwkL2utLa2yzblJzk2S00477VBDAwAY3JABPlk9lI+H87UUyVsOHuST1WeSt0ShfALyNQDAyoY8MnNkrRNQlv+7epH84NusdD+bLVsfskDdWvvW1W6rqpur6jGttRv7gLxzle2OT/InSX66tfaxg/T19iRvT5IdO3ZssqcCAGBy6yHEJ12g7grdY0E8+8L10tjto/CdsW2W2r5Cd+tuyFJLjt1+lI9pPQrkawCA9WvoCSgj+yaCtP0ydpL9Jpi08Ry9QuYeZeuW5KHHTLuYxpEx7aguSPKyJP+l//d/L9+gqrYn+cMkv9Vae/+U/QEAsA5VVarP8UMXy+ecfA0AQLZsGWXqjZ+tt0zZ/r8keW5VfS7Jt/aXU1U7qup/9du8MMm3JHl5VV3a/5w5Zb8AALARydcAAGwq1dbpoiY7duxoF1988dDDAABgk6qqS1prO4Yex6zI1wAADOVg2XraGdQAAAAAAHBYFKgBAAAAABiEAjUAAAAAAINQoAYAAAAAYBAK1AAAAAAADEKBGgAAAACAQShQAwAAAAAwiGqtDT2GFVXVLUm+OFD3Jya5daC+55H9NRn7azL219rZV5OxvyZjf03G/prMet1fj2utnTT0IGZlwHy9Xp/f9cr+moz9NRn7azL219rZV5OxvyZjf01mve6vVbP1ui1QD6mqLm6t7Rh6HPPC/pqM/TUZ+2vt7KvJ2F+Tsb8mY39Nxv7a2Dy/k7G/JmN/Tcb+moz9tXb21WTsr8nYX5OZx/1liQ8AAAAAAAahQA0AAAAAwCAUqFf29qEHMGfsr8nYX5Oxv9bOvpqM/TUZ+2sy9tdk7K+NzfM7GftrMvbXZOyvydhfa2dfTcb+moz9NZm521/WoAYAAAAAYBBmUAMAAAAAMAgF6mWq6uyqurKqrqqq1ww9nvWuqq6pqr+rqkur6uKhx7PeVNU7q2pnVX127LpHVtVfVNXn+n8fMeQY14tV9tUbqur6/vV1aVU9b8gxridVdWpVfaiqLq+qy6rqh/vrvb5WcJD95TW2gqp6cFX9bVV9ut9fP9tf//iq+nj/N/K9VbV96LEO7SD76ryq+sLYa+vMgYe6rlTV1qr6VFX9cX/Za2uDkq0nI1sfnGw9Gfl67WTrycjWk5GtJyNfT24jZGsF6jFVtTXJ25Kck+SMJC+uqjOGHdVc+GettTNbazuGHsg6dF6Ss5dd95okH2ytPSnJB/vLrLyvkuSX+9fXma21C4/ymNazhSQ/3lo7I8k3JPmh/v8rr6+Vrba/Eq+xlexK8uzW2tOSnJnk7Kr6hiS/mG5/PTHJHUleMdwQ143V9lWS/Kex19alQw1wnfrhJFeMXfba2oBk68MmW6/uvMjWkzgv8vVaydaTka0nI1tPRr6e3NxnawXq/T0jyVWttatba7uTvCfJCwYeE3OstfaRJLcvu/oFSd7V//6uJN95NMe0Xq2yr1hFa+3G1ton+9/vSffH6LHx+lrRQfYXK2ide/uLD+p/WpJnJ3l/f73XVw66r1hFVZ2S5NuS/K/+csVra6OSrZkp2Xoy8vXaydaTka0nI1tPRr6ezEbJ1grU+3tskmvHLl8X/8keSkvy51V1SVWdO/Rg5sSjW2s39r/flOTRQw5mDryqqj7TH6LokLoVVNXpSZ6e5OPx+jqkZfsr8RpbUX+Y2KVJdib5iySfT3Jna22h38TfyN7yfdVaG722fr5/bf1yVR0z3AjXnbck+YkkS/3lr4jX1kYlW09Otp6c7DM52ecgZOvJyNZrI1tPRr6eyFuyAbK1AjXT+ubW2telO3Tzh6rqW4Ye0DxprbX4JvBg/meSJ6Q7rOfGJG8edDTrUFU9LMnvJ/mR1trd47d5fR1ohf3lNbaK1tpia+3MJKekmwX5fw07ovVr+b6qqq9J8lPp9tnXJ3lkkp8cboTrR1V9e5KdrbVLhh4LrFOy9RRknzWRfQ5Ctp6MbL12svVk5Ou12UjZWoF6f9cnOXXs8in9dayitXZ9/+/OJH+Y7j9aDu7mqnpMkvT/7hx4POtWa+3m/g/TUpLfiNfXfqrqQekC4e+01v6gv9rraxUr7S+vsUNrrd2Z5ENJvjHJCVW1rb/J38hlxvbV2f2hr621tivJb8Zra+Sbkjy/qq5Jt9zDs5O8NV5bG5VsPSHZ+rDIPhOQfVYnW09Gtj48svVk5OtD2jDZWoF6f59I8qT+bJfbk7woyQUDj2ndqqqHVtVxo9+T/PMknz14K9K9pl7W//6yJP97wLGsa6Mw2PuX8fraq19X6h1Jrmit/dLYTV5fK1htf3mNrayqTqqqE/rfj03y3HRrC34oyXf3m3l9ZdV99fdjH2Yr3ZpvXltJWms/1Vo7pbV2erqc9VettZfEa2ujkq0nIFsfNtlnArLPymTrycjWk5GtJyNfr91GytbVHaXCSFU9L936LVuTvLO19vPDjmj9qqqvSjezI0m2Jfld+2t/VfXuJGclOTHJzUlen+SPkrwvyWlJvpjkha21TX/yklX21VnpDg9rSa5J8u/G1oDb1Krqm5P8dZK/y761pl6bbu03r69lDrK/XhyvsQNU1demO5nG1nRfZr+vtfbG/v/996Q7pO5TSb63n8GwaR1kX/1VkpOSVJJLk7xy7GQvJKmqs5L8x9bat3ttbVyy9drJ1ocmW09Gvl472XoysvVkZOvJyNeHZ96ztQI1AAAAAACDsMQHAAAAAACDUKAGAAAAAGAQCtQAAAAAAAxCgRoAAAAAgEEoUAMAAAAAMAgFagAAAAAABqFADQAAAADAIBSoAQAAAAAYhAI1AAAAAACDUKAGAAAAAGAQCtQAAAAAAAxCgRoAAAAAgEEoUAMAAAAAMAgFamBDq6p7q+qr1rDd6VXVqmrb0RjXelVVL6+qv5mi/Z9W1ctmOaY19HlMVV1eVY85wv38bVU99Uj2AQAwCzLwZOYxA0/Sf1WdV1U/dzTHtMo4fqGqfuQI9/HqqvrFI9kHMHsK1MCgquqaqrq/D9E39+HpYYd5XxdV1fePX9dae1hr7erZjHZvH3dU1TETtmtV9cRZjWM9qKo3VNVvj1/XWjuntfauozyUc5N8pLV2Yz+uU6rq96vq1qq6q6o+W1Uv728bfQi7t/+5pqpeU1WP7rc/a/yOq+qdVfWe/uJ/T/LGo/ewAICNSgaeX+soA6/Y/wyK7UfkS4uqOinJS5P8+th1r62qL/Tvg+uq6r1jt11UVQ/0t91aVX9QVY+pqt+uqt9cdt/Pqqrb+gkrv5HkJVX1qFmOHziyFKiB9eA7WmsPS/J1SXYk+ZlJGlfniP9/VlWnJ/mnSVqS5x/p/qa1UqjcoLNjXpnk/LHL5ye5NsnjknxFku9LcvOyNif0r7kXJ3ldkqcn+dEkv1FVxyZJVT0nybcneXXf5oIk/6yqvvIIPQ4AYHORgY+ATZSB583Lk1zYWrs/SfoZ39+X5Fv798GOJB9c1uZV/W1fneSEJL+c5IeTnFNVz+3v58HpitI/3lq7sbX2QJI/TVcMB+aEAjWwbrTWrk8XJr6mqh5RVX9cVbf0szX+uKpOGW3bf6P+81X1f5Lcl64o+U+T/Gr/Lfuv9tvtnbVRVd9WVZ+qqrur6tqqesOEQ3xpko8lOS/JfofQLZ+5Mj5zoao+0l/96X5s39Nf/wNVdVVV3V5VF1TVyWPtn1pVf9HfdnNVvba//piqektV3dD/vGU0k6WqzupnHvxkVd2U5Df7GR7v72ca3J3k5VX18Kp6R1XdWFXXV9XPVdXWlR5wVb2131d3V9UlVfVP++vPTvLaJN/TP6ZPL98PVbWlqn6mqr5YVTur6req6uH9baOZGS+rqi/1syJ+eqzfZ1TVxX2/N1fVL60yvtOSfFWSj49d/fVJzmutfbm1ttBa+1Rr7U9Xat9a+2iSy5J8TWvt/CRXJnljX6T+9ST/obV2S7/tA0kuSfIvVrovAIDDIQPLwKuM4fFVdWf1X0JU1W9U1c6x28+vfrmMUf9V9ZQkv5bkG/vx3Tl2l4+oqj+pqnuq6uNV9YSV+j2Yqjq5f85u75/DHxi77diqelf/ur2iqn6iqq4ba35Okg+PXf76JB9orX0+SVprN7XW3r5Sv62125P8frrMflu6CSRvr6qHJnl9ks+31s4ba3JRkm+b9PEBw1GgBtaNqjo1yfOSfCrd/0+/mW4W7GlJ7k/yq8uafF+65R2OS/eN/F+n/5a9tfaqFbr4crqAfUK6wPKDVfWdEwzxpUl+p//5F1X16LU0aq19S//r0/qxvbeqnp3kF5K8MMljknwxyXuSpKqOS/KXSf4syclJnph9swl+Osk3JDkzydOSPCP7z7b5yiSPTLffzu2ve0GS96d73L+T7sPFQn+/T0/yz5Psd1jomE/0fT0yye8m+b2qenBr7c+SvCnJe/vH9LQV2r68//ln6YrID8uBz+E3J3lykuckeV0fqpPkrUne2lo7PskTkrxvlfH9oyRXt9YWxq77WJK3VdWL+gL2iqrzTUmemu41l3Szsf/vdM/FZ1tr71nW7Ip0+x0AYCZkYBl4LAPv1Vr7QpK7+7EmybckuXds22dl/4JvWmtXpMuzH+3Hd8LYzS9K8rNJHpHkqiQ/v8pjP5j3JLku3fPz3Une1D+nSVcoPj3dY35uku9d1vYfpZsMMvKxJC+tqv9UVTtW+7IgSarqxCTflT6zt9Z+L8knk7w73fN97rImMjvMGQVqYD34o/7b/b9JF7Le1Fq7rbX2+621+1pr96QLUM9a1u681tpl/SzZPYfqpLV2UWvt71prS621z6QLNMvvc0VV9c3pAu/7WmuXJPl8kn+z5kd4oJckeWdr7ZOttV1JfirdTIfT0y0rcVNr7c2ttQdaa/e01j4+1u6NrbWd/czen033IWVkKcnrW2u7RofPpQuof9RaW0pyfLoPQD/SzzDeme5QuRetNMjW2m/3z8VCa+3NSY5JF6bX+hh/qbV2dWvt3v4xvqj2P8TyZ1tr97fWPp3k09kXJPckeWJVndhau7e19rFV+jghyT3LrvvX6T6o/T9JvlBVl1bV1y/b5tYktyf5X0le01r7YP94r0u35Me3JvnBFfq7p+8TAGBaMrAMvDwDL/fhJM+qfUvMvb+//Pj+MX16jWNKkj9srf1tP7Hjd9IV4Nes/yLlm5L8ZP/8XJouS4+W0nhhutfwHX2m/pVld3FCxnJ7a+23082E/hfpHufOqvrJZW1+pX+PfDrJjUl+bOy2f5/k2eleF9cua3dPkodP8viAYSlQA+vBd7bWTmitPa619u9ba/dX1UOq6tf7Q+PuTvKRJCcs+2Z9eRA5qKp6ZlV9qLpDJu9KN7vgxDU2f1mSP2+t3dpf/t0sO8RxQienmzGSJOnD621JHpvk1HTh/5Dt+t9PHrt8S78Uxbjx/fS4JA9KcmN/yOCd6ZayWPEkIlX1H/tD9O7qt3141r7PVhrrtiTjs25uGvv9vnQzTJLkFenWmvv7qvpEVX37Kn3ckW720F59KH5Na+2pfV+XpvsAWGObndhae0Rr7SmtteXh+bIkd7T+pIvLHJfkzlXGAgAwCRlYBk72z8DLfTjJWelmT38k3dIVz+p//rovvq/VWvtczclJbu+/OBn5YrrnbnT7+D5f/jpdKbf/TmvtW9MVr1+Z5D9X1fhyev+hf488trX2kv7LiVHbm9NNOrlshbEel+SuNT8yYHAK1MB69ePpZik8s3XLPIwOERwvMrZlbZZfXu53053o7tTW2sPTrc9WB2/SraeWbkbAs6rqpurWtvvRJE+rqtFshy8nechYs0OdSO+GdEF51MdD053Q7/p0Ye6r1tIu3aGfN4xdXmkfjF93bZJd6Qq0J/Q/x/fF3P1Ut9beT6R77I/oDxG8K/v22aH290pjXciBJyw8cMCtfa619uJ0Hxp+Mcn7+3203GeSPL5WOfFN/2Hqv6cLzI88VL9r8JRMNlMFAGASMvAa2mWDZuAVfDjdGuNn9b//TbpZzAcs7zHmUOM7XDckeWS/FMvIaemeu6Sb4XzK2G2nLmv/mXQTUA7QWtvTumU7PpPka2YwVpkd5owCNbBeHZduzb07q+qR6dY0O5Sbs3qoHd3n7a21B6rqGVn74YnfmWQxyRnpDoU7M13o+evsO6Tt0iT/qp/18sR0M4APNrZ3J/m3VXVmdSd4eVOSj7fWrknyx0keU1U/Ut0JYY6rqmeOtfuZqjqpX4vtdUl+e42PI/2s4D9P8uaqOr66k7g8oapWOszzuHRh+pYk26rqdekOJRx/TKfX6mePf3eSH63uBC8Py771+hZW2X6vqvreqjqpnxVyZ3/1ATNE+sMHr0q3DuGo7S9W1ddU1bY+QP9gkqtad0KVw1bdGcL/cZK/mOZ+AAAOQgbexBl4hXF/Lt3r4XuTfLi1dnff/3dl9QL1zUlOqartk/a3zDFV9eDRT7pC9P+X5Bf667423fM9eh7el+SnqjvR52OTLF8P/cKMLS1T3Qk1v61/nrdU1Tnpzg3z8UzvWelOPArMCQVqYL16S5Jj0x229bF0J0s5lLcm+e7qzhy9fNmGpFun7I1VdU+6ULvaifeWe1mS32ytfal1Z5e+qbV2U7qTnbykn737y0l2pwuE70q3rtu4NyR5V39I4Qtba3+Zbo3k30832+AJ6dfA6w+be26S70h3KN7n0p1kJUl+LsnF6WYX/F26k4P83Bofx8hLk2xPcnm6Q+3en+4kNct9IN1+/4d0h+89kP0P1fu9/t/bquqTK7R/Z7ozy38kyRf69q9e4xjPTnJZVd2b7nl90dh6gsv9evZfg/AhSf4wXWH76nQzWJ6/xn4P5juSXNRau+GQWwIAHJ63RAbezBl4JR9OctvYOssfTjebe6W+k+Sv0i17cVNV3brKNmtxb7ri+Ojn2UlenO5EiDeky9uv75/TJHljuhMofiHdyS7fn27W+shvJXlePzM/6U4A+dokX0qX2/9rkh9srf3NFGMeTSp5XrrXIzAnqrUjdfQHABx5/eybTyV5zirrRs+qn48neUVr7bNHqg8AANgIquoH000yGZ81/aYkO1trbzmC/b463XI2P3Gk+gBmT4EaAAAAgMNWVY9Jt5zLR5M8KcmfJPnVI1mMBjaOFU8qBQAAAABrtD3d0nuPT7dkx3uS/I8hBwTMDzOoAQAAAAAYhJMkAgAAAAAwiHW7xMeJJ57YTj/99KGHAQDAJnXJJZfc2lo7aehxzIp8DQDAUA6Wrddtgfr000/PxRdfPPQwAADYpKrqi0OPYZbkawAAhnKwbG2JDwAAAAAABqFADQAAAADAIBSoAQAAAAAYhAI1AAAAAACDUKAGAAAAAGAQMylQV9U7q2pnVX12ldurqn6lqq6qqs9U1dfNot9ZW1xq+eAVN+dXPvi5fPCKm7O41IYeEgAAm4xsDQDAZrJtRvdzXpJfTfJbq9x+TpIn9T/PTPI/+3/XjcWllu97x8dz6bV35v7dizl2+9aceeoJOf8Vz8zWLbWm9hdduTOX3XB3nnry8TnryY9aUzsAAFjmvGzybD26D/kaAGDjm0mBurX2kao6/SCbvCDJb7XWWpKPVdUJVfWY1tqNs+h/Fi66cmcuvfbO3Ld7MUly3+7FXHrtnbnoyp15zlMefdC2swjgAACQyNaJfA0AsJkcrTWoH5vk2rHL1/XX7aeqzq2qi6vq4ltuueUoDa1z2Q135/4+QI/cv3sxl99w9yHbjgfwlv0DOAAAzNiasnUyXL6eJlsn8jUAwGayrk6S2Fp7e2ttR2ttx0knnXRU+37qycfn2O1b97vu2O1bc8bJxx+y7bQBHAAAjoSh8vU02TqRrwEANpOjVaC+PsmpY5dP6a9bN8568qNy5qknZHTE4EP6wwjPevKjDtl22gAOAAAT2NDZOpGvAQA2k6NVoL4gyUv7M45/Q5K71tMaeUmydUvl/Fc8M0981MNyygnH5v998dPXvMbdtAEcAAAmsKGzdSJfAwBsJjM5SWJVvTvJWUlOrKrrkrw+yYOSpLX2a0kuTPK8JFcluS/Jv51Fv7O2dUvlEQ/Znkc8JGs6ect4u/Nf8cyc89aP5L5di/nZFzzVWcYBADgsmz1bj9rK1wAAm8NMCtSttRcf4vaW5Idm0dd6NU0ABwCAEdm6I18DAGwO6+okiQAAAAAAbB4K1AAAAAAADEKBGgAAAACAQShQAwAAAAAwCAVqAAAAAAAGoUANAAAAAMAgFKgBAAAAABiEAjUAAAAAAINQoAYAAAAAYBAK1AAAAAAADEKBGgAAAACAQShQAwAAAAAwCAVqAAAAAAAGoUANAAAAAMAgFKgBAAAAABiEAjUAAAAAAINQoAYAAAAAYBAK1AAAAAAADEKBGgAAAACAQShQAwAAAAAwCAVqAAAAAAAGoUANAAAAAMAgFKgBAAAAABiEAjUAAAAAAINQoAYAAAAAYBAK1AAAAAAADEKBGgAAAACAQShQAwAAAAAwCAVqAAAAAAAGoUANAAAAAMAgFKgBAAAAABjETArUVXV2VV1ZVVdV1WtWuP20qvpQVX2qqj5TVc+bRb8AALARydcAAGwWUxeoq2prkrclOSfJGUleXFVnLNvsZ5K8r7X29CQvSvI/pu0XAAA2IvkaAIDNZBYzqJ+R5KrW2tWttd1J3pPkBcu2aUmO739/eJIbZtAvAABsRPI1AACbxrYZ3Mdjk1w7dvm6JM9cts0bkvx5Vb06yUOTfOtKd1RV5yY5N0lOO+20GQwNAADmzszyNQAArHdH6ySJL05yXmvtlCTPS3J+VR3Qd2vt7a21Ha21HSeddNJRGhoAAMydNeXrqjq3qi6uqotvueWWoz5IAAA4lFkUqK9PcurY5VP668a9Isn7kqS19tEkD05y4gz6BgCAjWZm+doEEAAA1rtZFKg/keRJVfX4qtqe7iQtFyzb5ktJnpMkVfWUdAHaFA4AADiQfA0AwKYxdYG6tbaQ5FVJPpDkinRnE7+sqt5YVc/vN/vxJD9QVZ9O8u4kL2+ttWn7BgCAjUa+BgBgM5nFSRLTWrswyYXLrnvd2O+XJ/mmWfQFAAAbnXwNAMBmcbROkggAAAAAAPtRoAYAAAAAYBAK1AAAAAAADEKBGgAAAACAQShQAwAAAAAwCAVqAAAAAAAGoUANAAAAAMAgFKgBAAAAABiEAjUAAAAAAINQoAYAAAAAYBAK1AAAAAAADEKBGgAAAACAQShQAwAAAAAwCAVqAAAAAAAGoUANAAAAAMAgFKgBAAAAABiEAjUAAAAAAINQoAYAAAAAYBAK1AAAAAAADEKBGgAAAACAQShQAwAAAAAwCAVqAAAAAAAGoUANAAAAAMAgFKgBAAAAABiEAjUAAAAAAINQoAYAAAAAYBAK1AAAAAAADEKBGgAAAACAQShQAwAAAAAwCAVqAAAAAAAGoUANAAAAAMAgZlKgrqqzq+rKqrqqql6zyjYvrKrLq+qyqvrdWfQLAAAbjWwNAMBmsm3aO6iqrUneluS5Sa5L8omquqC1dvnYNk9K8lNJvqm1dkdVPWrafgEAYKORrQEA2GxmMYP6GUmuaq1d3VrbneQ9SV6wbJsfSPK21todSdJa2zmDfgEAYKORrQEA2FRmUaB+bJJrxy5f11837quTfHVV/Z+q+lhVnb3SHVXVuVV1cVVdfMstt8xgaAAAMFdmlq0BAGAeHK2TJG5L8qQkZyV5cZLfqKoTlm/UWnt7a21Ha23HSSeddJSGBgAAc2VN2ToxAQQAgPVvFgXq65OcOnb5lP66cdcluaC1tqe19oUk/5AuVAMAAPvMNFubAAIAwHo3iwL1J5I8qaoeX1Xbk7woyQXLtvmjdDM8UlUnpjss8eoZ9A0AABuJbA0AwKYydYG6tbaQ5FVJPpDkiiTva61dVlVvrKrn95t9IMltVXV5kg8l+U+ttdum7RsAADYS2RoAgM1m2yzupLV2YZILl133urHfW5If638AAIBVyNYAAGwmR+skiQAAAAAAsB8FagAAAAAABqFADQAAAADAIBSoAQAAAAAYhAI1AAAAAACDUKAGAAAAAGAQCtQAAAAAAAxCgRoAAAAAgEEoUAMAAAAAMAgFagAAAAAABqFADQAAAADAIBSoAQAAAAAYhAI1AAAAAACDUKAGAAAAAGAQCtQAAAAAAAxCgRoAAAAAgEEoUAMAAAAAMAgFagAAAAAABqFADQAAAADAIBSoAQAAAAAYhAI1AAAAAACDUKAGAAAAAGAQCtQAAAAAAAxCgRoAAAAAgEEoUAMAAAAAMAgFagAAAAAABqFADQAAAADAIBSoAQAAAAAYhAI1AAAAAACDUKAGAAAAAGAQCtQAAAAAAAxiJgXqqjq7qq6sqquq6jUH2e67qqpV1Y5Z9AsAAAAAwPyaukBdVVuTvC3JOUnOSPLiqjpjhe2OS/LDST4+bZ8AALCRmQACAMBmMYsZ1M9IclVr7erW2u4k70nyghW2+89JfjHJAzPoEwAANiQTQAAA2ExmUaB+bJJrxy5f11+3V1V9XZJTW2t/crA7qqpzq+riqrr4lltumcHQAABg7pgAAgDApnHET5JYVVuS/FKSHz/Utq21t7fWdrTWdpx00klHemgAALAemQACAMCmMYsC9fVJTh27fEp/3chxSb4myUVVdU2Sb0hygXXyAABgciaAAACwkcyiQP2JJE+qqsdX1fYkL0pywejG1tpdrbUTW2unt9ZOT/KxJM9vrV08g74BAGCjMQEEAIBNY+oCdWttIcmrknwgyRVJ3tdau6yq3lhVz5/2/gEAYJMxAQQAgE1j2yzupLV2YZILl133ulW2PWsWfQIAwEbUWluoqtEEkK1J3jmaAJLk4tbaBQe/BwAAmB8zKVADAACzYwIIAACbxSzWoAYAAAAAgIkpUAMAAAAAMAgFagAAAAAABqFADQAAAADAIBSoAQAAAAAYhAI1AAAAAACDUKAGAAAAAGAQCtQAAAAAAAxCgRoAAAAAgEEoUAMAAAAAMAgFagAAAAAABqFADQAAAADAIBSoAQAAAAAYhAI1AAAAAACDUKAGAAAAAGAQCtQAAAAAAAxCgRoAAAAAgEEoUAMAAAAAMAgFagAAAAAABqFADQAAAADAIBSoAQAAAAAYhAI1AAAAAACDUKAGAAAAAGAQCtQAAAAAAAxCgRoAAAAAgEEoUAMAAAAAMAgFagAAAAAABqFADQAAAADAIBSoAQAAAAAYhAI1AAAAAACDmEmBuqrOrqorq+qqqnrNCrf/WFVdXlWfqaoPVtXjZtEvAAAAAADza+oCdVVtTfK2JOckOSPJi6vqjGWbfSrJjtba1yZ5f5L/Om2/AACwEZn8AQDAZjKLGdTPSHJVa+3q1truJO9J8oLxDVprH2qt3ddf/FiSU2bQLwAAbCgmfwAAsNnMokD92CTXjl2+rr9uNa9I8qcr3VBV51bVxVV18S233DKDoQEAwFwx+QMAgE3lqJ4ksaq+N8mOJP9tpdtba29vre1ore046aSTjubQAABgPZjZ5A8AAJgH22ZwH9cnOXXs8in9dfupqm9N8tNJntVa2zWDfgEAYNMam/zxrINsc26Sc5PktNNOO0ojAwCAtZvFDOpPJHlSVT2+qrYneVGSC8Y3qKqnJ/n1JM9vre2cQZ8AALARTTr54/kHm/zhCEUAANa7qQvUrbWFJK9K8oEkVyR5X2vtsqp6Y1U9v9/svyV5WJLfq6pLq+qCVe4OAAA2M5M/AADYVGaxxEdaaxcmuXDZda8b+/1bZ9EPAABsZK21haoaTf7YmuSdo8kfSS5urV2Q/Sd/JMmXWmvPX/VOAQBgHZtJgRoAAJgNkz8AANhMZrEGNQAAAAAATEyBGgAAAACAQShQAwAAAAAwCAVqAAAAAAAGoUANAAAAAMAgFKgBAAAAABiEAjUAAAAAAINQoAYAAAAAYBAK1AAAAAAADEKBGgAAAACAQShQAwAAAAAwCAVqAAAAAAAGoUANAAAAAMAgFKgBAAAAABiEAjUAAAAAAINQoAYAAAAAYBAK1AAAAAAADEKBGgAAAACAQShQAwAAAAAwCAVqAAAAAAAGoUANAAAAAMAgFKgBAAAAABiEAjUAAAAAAIPYNvQAAGZtcanloit35rIb7s5TTz4+Zz35Udm6pYYeFgAAAADLKFADG8riUsv3vePjufTaO3P/7sUcu31rzjz1hJz/imcqUgMAAACsM5b4WAcWl1o+eMXN+ZUPfi4fvOLmLC61oYcEc+uiK3fm0mvvzH27F9OS3Ld7MZdee2cuunLn0EMDAAAAYBkzqAdmtud8mWbpCMtOHB2X3XB37t+9uN919+9ezOU33J3nPOXRA40K2Cz8Xw/rg/ciAMD8UKAe2Phsz2T/2Z6KaSsb6gPHNF8m+CLi6Hnqycfn2O1b976nkuTY7VtzxsnHDzgqYDPwfz2sD96LALAx+MJ581CgPgwf/fxtK15/9wN7Dnr7Sv7sszetONvzzz57Ux6yfW1Pz9JSy6XX3plrbvtyTv+Kh+bMU0/IljW+YadpO43D7XdpqeVNf3pFrtp5b3YvLGX7ti154qMeltee85QjPu5PfvGOXPLFO7JrYSlJ92XCJV+8I7920efzdY97xBFry2QevG1rHn/iQ3P5jXenteSYbVvy+BMfmgdv2zrRe/NwDPV+YjKeJ44U/9cfGd/4hK8YegjMmWkngPgwDPvzngCG4AvnzUWBemCnf8VDs33blr0fZpNk+7YtOf0rHrqm9tMUbIcq9k7T76XX3pmrdt67d3/tWljKVTvvzaXX3nnEP/xfc9uXs3vseUqS3QtLuea2Lx+y72nablaHW0TcsqXy2nOekp/8g89k157FvPyfPP6oFCCH/PKEtZvn52keC+vzOOZpzOv/9ZvteWLjm2a5r3n9MKyAyJEyr++JeeW9DPtYcWBzmUmBuqrOTvLWJFuT/K/W2n9ZdvsxSX4ryT9OcluS72mtXTOLvufdmaeekCc+6mH7zfZ84qMeljNPPWFN7acp2A5V7J2m3yE//E/zZcIsvoiYp5nus+h3miLili2V4x68Lcc9eNtRKwoN+eUJazft8zTte2Iejx45XNOOeR6LpkP+X7+ZXltsPAc7wulwjlDcWrXie3FL1SHvZ9ojIYb4v2uzvo/n8e/EkA53fw15dNBme469lzfH88zazWLFAQ60Xo9OnPoZraqtSd6W5LlJrkvyiaq6oLV2+dhmr0hyR2vtiVX1oiS/mOR7pu17I5h2tuc8zuqdpt9pP/xPY5ovE6ZpO48z3ac1j8XeeZ05OaQhinHTPE+zKLgOdfTIEMF/mjHP4v+fIV5fQ/1fP/SRST5YHhkmgBy+ad6LQ/6dOFzz/D725drRMc3+GirjbsbneB4/A01rMz7PrN2Q9Z9pyceTm8VXDs9IclVr7eokqar3JHlBkvEC9QuSvKH//f1JfrWqqrXWVrvTq2/5cr7n1z86g+FN5vIb706Sg/Y9msmx3Bdvuy9J8sY/vmzifm//8u4kyR//3Q3547+7Yc3t7nlgIakk43uyko9efVs+e8NdR6ztNKbpt7W23yFOVcnWLZX/32eun2i/Ha7WWrZv3ZKl1nLSccdkcWkpP3fh5YduOEXbex5YyPV33p/Ru2XXwlIuv/Hu/OQffCbHPfjIfWs4VL9Jcss9u/b7IzTq/7z/7wtrfp6neT8ejqHeT7PQWsu9uxbzwJ7FPPhBW/OwY7amam1/PA+3bWstX7r9/ty/ZzGtde/lYx+0Nac98thDtp+m7bT/Z07znpim/TTviWn216j94TzH04x52n091Otr1P5o/18/1GsrmX5/rcXxD37QTO5nnpgAMp1pJoBM82F4qOLStAXEoY54GfrLtc1kmv01VIFoHr+cn9aQE16GOq/VvD7P89rvvI172hUHhrIZjySdhVlUmR6b5Nqxy9cleeZq27TWFqrqriRfkeTW8Y2q6twk5ybJwx7zhBkMbXJnPOb4w277uK94yFFv+7BjtubYB2094IPhw47ZekTbjoyKgJOMf5p+qyqnPfLY3LtrMbv2LOaYwyimfeHW+7LUWh59/IMnajvq/6tOOrwwdrhtH+j307jWkl17FtdcKD6c52mofpPkwQ/amqrs139VcsyD1v7aPNz31OG+RoZ6P03bdlRcGq3rdTiFvMNpe++uxb37qruv5P49i7l316FfX9O0neZ5mvY9MU37ad4T0+yvaZ7jacY87b4e6vWVDPN//VCvrWT6/TXt3+UN7IhMANlMDne5r6FmXyeH/4F02gLiUEe8DLns32b78D/N/hqqQDSPRzNMa6ilwoY8r9XQRzfO0xEcQx7NOWp/tPfXtCsOzOMXL/P6/9cs1LQZtqq+O8nZrbXv7y9/X5JnttZeNbbNZ/ttrusvf77f5taV7jNJduzY0S6++OKpxnakTLIG3tEw1Jsu2Tc79XXf/tSjNubDNXqjLw9X6/2N/skv3pFf+avP7RdUjtm2Jf/h2U9a8weuw3mepu13aanlJ//gM3lgz2L+7WH8IRnyj/7hvkaGfD8d7r6e5nmepu0ffPK6vP+S65ZPZM53/+NT8q++7pQj1jaZbg3Gad4T07Sf5j0xzf4aaszT7ushX1+Ha6j34rT/306zv9b6f+6Q6+RV1SWttR0D9HtE8vUjH/eU9tzXvvPIDn6Z0dGJB5sEstrRiclwX94ezhcny49mSLovfB57wrFr/kJwmiM/DufLxKQ7kuLWe3cfcP1JD9ueE4875qBtp3nMQ/V7NI78OFjfh3vU2jSm2V/JMF8mTjPmaR9vMuwRhtNMHDncIwyH2tdD9T3U/prGkPt66P11OH/Phzyicqi/b2s15NGJ73vlP1k1W8/i0V2f5NSxy6f01620zXVVtS3Jw9OtlccMbNlS+brHPeKwDvuZpu3SUss9DyzkgT2L+eQX75ioKDZNv4dr9C3W+CHQ83Ao4GjWwvLiwZGetTBNv6Oiw+g/1l/5q89NfJLD157zlMHWzD3c18gQ76dp9/VQ69gPedLRw32epn0vTtN+mvfENPtrmud4mjFPu6+HfH0drmke81CvrWQ2yyHM29/leTP0EYrTHJ2YDHOE4uEeCTHNUTrTHI0w7RGGQx3xMk2/Q+3rkWmKJYf7RcLoPoY40m+ao0iHGPO0R2INdYThNO/laV7XQx3FlQz3PA+1v7ptD+89MeTRnEPur+Tw/p4PeUTlkEeSzvPRibMoUH8iyZOq6vHpCtEvSvJvlm1zQZKXJfloku9O8lcOP5xv0xbFhjCvJ7Ebqlg7Tb+zKDoM8SXG0CeBOZz307T7eqhC3lDFuGlM+16cRfujXVgf6suAaffVZnt9DfXaSqbbX/P6d/komdkEkNba25O8PemOUHzvv/vGIzLgaay3oxOncbhH6YyORthPS77xq77iiB69kRyZI15e/k8ev24Pz592X48fufbt/+jkiY/E2ttlSxaXWr7jax870VrhuxeX0lo3Q+/hx67vNU2HGvM0r8vx9iOTPFfTPs+Ha5rX9TT7a9p9nQzzPA+1v6Z5T8zqdT1P+2sa04x5Fn8nhvy7eqjX15BHJ77vlavfNnWBul9T+lVJPpDuLOPvbK1dVlVvTHJxa+2CJO9Icn5VXZXk9nRFbObYPM56muczwM7bTPd5LToMfRKYw3k/Tbuv53HW5lBf2oz6nuaLkyG+eBlyJvO0457m6KDN9voa4rU16neI2f2bgAkgc+pw34tDvh+G+jsx1Jdr0+zraSYVTJvZhjzS73ANNeZp88tQRxhOY54njszbJIxp+p3mPTHk0ZxD7a9pDH3E7hB/V+exTjduJguYtNYuTHLhsuteN/b7A0n+9Sz6Wg+G/LZhvfjENbev+Id3qbV1u3+e8fhH5v98/tZceu2duX/3Yo7dvjVnnnpCXnnWE7J1nc76ntbiUsv3vePjueHO+7PUkrdddFXOPPWEnP+KZx7Rx3zf7oX8yd/duPfQtiQ5dvvWnP01X7luXx/JcK+Rad5Ps9jXFzzhm3PRlTtz+Q1354yTj89ZT37Umh/vNG2T5JuedOKat51l283ocPfXtM/xkLy+jp7D2V+b8e/yWpkAsvkM+YVgMtwRL0MUTYf68D9twWMeJ4AMNeYhl6+ax6XC5vmL/Xn7cm2o5fOmbT/kl5GHa16/eBnq9bUeHLkV3NnQnnry8Tl2+9YDimJnnDzdOoNH0tYtlfNf8cy5LbQcjouu3JlLr70zS32Ivm/3Yi699s5cdOXOPOcpjz5i/Z715EflzFNPOKDocNaTH3XE+pyFoV4j07yfZrGvt26pPOcpjz6s18Q0bZkPnmOOlM34d3kSm20CyGY3ZJFnWkMdwXG4hvrwP+T5FYYy9JEBQyxfNY9LhY3az9tRXNP0PY9HcEzT77Tt5/HLyHn94mXU/7wdjTULtV6PBNyxY0e7+OKLhx4GqxjNzF1eFDvSM3OZzK988HP55b/4h4y/yyvJjz33q/Pq5zzpiPa9uNQUHdZo2veTfQ1wZFTVqmcan0fyNUzng1fcnFe/+1P7TSp4yPat+X9f/PQ1fZE7TWabx89f8zjmkWmfK9mclczze4L1bx5eXwfL1grUHDZ/eNe/aUM0R4/3E8D6o0ANjBv6w/885sV5HDMcSd4THEnr/fWlQA2b1NAhGgDmmQI1sNx6//APAOvVwbK1NahhA7O+JwAAzI5zMwDA7ClQwwYnRAMAAACwXm0ZegAAAAAAAGxOCtQAAAAAAAxCgRoAAAAAgEEoUAMAAAAAMAgFagAAAAAABlGttaHHsKKquiXJFwfq/sQktw7U9zyyvyZjf03G/lo7+2oy9tdk7K/J2F+TWa/763GttZOGHsSsDJiv1+vzu17ZX5OxvyZjf03G/lo7+2oy9tdk7K/JrNf9tWq2XrcF6iFV1cWttR1Dj2Ne2F+Tsb8mY3+tnX01GftrMvbXZOyvydhfG5vndzL212Tsr8nYX5Oxv9bOvpqM/TUZ+2sy87i/LPEBAAAAAMAgFKgBAAAAABiEAvXK3j70AOaM/TUZ+2sy9tfa2VeTsb8mY39Nxv6ajP21sXl+J2N/Tcb+moz9NRn7a+3sq8nYX5OxvyYzd/vLGtQAAAAAAAzCDGoAAAAAAAahQL1MVZ1dVVdW1VVV9Zqhx7PeVdU1VfV3VXVpVV089HjWm6p6Z1XtrKrPjl33yKr6i6r6XP/vI4Yc43qxyr56Q1Vd37++Lq2q5w05xvWkqk6tqg9V1eVVdVlV/XB/vdfXCg6yv7zGVlBVD66qv62qT/f762f76x9fVR/v/0a+t6q2Dz3WoR1kX51XVV8Ye22dOfBQ15Wq2lpVn6qqP+4ve21tULL1ZGTrg5OtJyNfr51sPRnZejKy9WTk68lthGytQD2mqrYmeVuSc5KckeTFVXXGsKOaC/+stXZma23H0ANZh85Lcvay616T5IOttScl+WB/mZX3VZL8cv/6OrO1duFRHtN6tpDkx1trZyT5hiQ/1P9/5fW1stX2V+I1tpJdSZ7dWntakjOTnF1V35DkF9PtrycmuSPJK4Yb4rqx2r5Kkv809tq6dKgBrlM/nOSKscteWxuQbH3YZOvVnRfZehLnRb5eK9l6MrL1ZGTrycjXk5v7bK1Avb9nJLmqtXZ1a213kvckecHAY2KOtdY+kuT2ZVe/IMm7+t/fleQ7j+aY1qtV9hWraK3d2Fr7ZP/7Pen+GD02Xl8rOsj+YgWtc29/8UH9T0vy7CTv76/3+spB9xWrqKpTknxbkv/VX654bW1UsjUzJVtPRr5eO9l6MrL1ZGTrycjXk9ko2VqBen+PTXLt2OXr4j/ZQ2lJ/ryqLqmqc4cezJx4dGvtxv73m5I8esjBzIFXVdVn+kMUHVK3gqo6PcnTk3w8Xl+HtGx/JV5jK+oPE7s0yc4kf5Hk80nubK0t9Jv4G9lbvq9aa6PX1s/3r61frqpjhhvhuvOWJD+RZKm//BXx2tqoZOvJydaTk30mJ/schGw9Gdl6bWTrycjXE3lLNkC2VqBmWt/cWvu6dIdu/lBVfcvQA5onrbUW3wQezP9M8oR0h/XcmOTNg45mHaqqhyX5/SQ/0lq7e/w2r68DrbC/vMZW0VpbbK2dmeSUdLMg/69hR7R+Ld9XVfU1SX4q3T77+iSPTPKTw41w/aiqb0+ys7V2ydBjgXVKtp6C7LMmss9ByNaTka3XTraejHy9NhspWytQ7+/6JKeOXT6lv45VtNau7//dmeQP0/1Hy8HdXFWPSZL+350Dj2fdaq3d3P9hWkryG/H62k9VPShdIPyd1tof9Fd7fa1ipf3lNXZorbU7k3woyTcmOaGqtvU3+Ru5zNi+Ors/9LW11nYl+c14bY18U5LnV9U16ZZ7eHaSt8Zra6OSrSckWx8W2WcCss/qZOvJyNaHR7aejHx9SBsmWytQ7+8TSZ7Un+1ye5IXJblg4DGtW1X10Ko6bvR7kn+e5LMHb0W619TL+t9fluR/DziWdW0UBnv/Ml5fe/XrSr0jyRWttV8au8nrawWr7S+vsZVV1UlVdUL/+7FJnptubcEPJfnufjOvr6y6r/5+7MNspVvzzWsrSWvtp1prp7TWTk+Xs/6qtfaSeG1tVLL1BGTrwyb7TED2WZlsPRnZejKy9WTk67XbSNm6uqNUGKmq56Vbv2Vrkne21n5+2BGtX1X1VelmdiTJtiS/a3/tr6reneSsJCcmuTnJ65P8UZL3JTktyReTvLC1tulPXrLKvjor3eFhLck1Sf7d2Bpwm1pVfXOSv07yd9m31tRr06395vW1zEH214vjNXaAqvradCfT2Jruy+z3tdbe2P+//550h9R9Ksn39jMYNq2D7Ku/SnJSkkpyaZJXjp3shSRVdVaS/9ha+3avrY1Ltl472frQZOvJyNdrJ1tPRraejGw9Gfn68Mx7tlagBgAAAABgEJb4AAAAAABgEArUAAAAAAAMQoEaAAAAAIBBKFADAAAAADAIBWoAAAAAAAahQA0AAAAAwCAUqAEAAAAAGIQCNQAAAAAAg1CgBgAAAABgEArUAAAAAAAMQoEaAAAAAIBBKFADAAAAADAIBWoAAAAAAAahQA1whFXVy6vqb6Zo/6dV9bJZjmmW/VfVeVX1czPu87KqOqv/varqN6vqjqr62/66H6yqm6vq3qr6iv66d1fVd85yHCuM681V9YNHsg8AgI1OPj6sPtdlPu77+T9V9fQj3MfvV9U5Y5cfXVVXVNUxR7Jf4OhQoAYGV1UX9eFqonBRVa2qnnikxjWEqnpDVf32+HWttXNaa+8aakzj/c/gw8Tp/fN2b/9zc1X9cVU9d1mfT22tXdRf/OYkz01ySmvtGVX1oCS/lOSft9Ye1lq7raq+NsnTkvzvvp/tfTH5ur6fa6rqLWPjuKaq7h8bw3lVdVxVfaSqXr9szC+tqs9X1UOS/Pckr62q7Ye7DwAADkU+3kc+3tvntPl4qnEeZPzfkeSe1tqn+ssnVNU7q+qmqrqnqv6hql4ztn2rqi/3j/X6qvqlqnpoVX1uedG/ql7XF7+3JPnFJHuL/q21m5N8KMm5s35MwNGnQA0MqqpOT/JPk7Qkzx92NIdWVdvWch2HdEJr7WHpQvNfJPnDqnr5Kts+Lsk1rbUv95cfneTBSS4b2+bfJfmd1lrrL/9Ukh1JnpHkuCRnJfnksvv9jn4MX9dv+9NJvj/Jj1bVU5Okqk5K8uYk399au6+1dmOSv88cvFYBgPkkH29aRzofHymvTHL+2OVfTvKwJE9J8vB0r+GrlrV5Wv9Yn5Pk3yT5viSvSPLfq+rRSVJVT0ny40le0Vpbaq39bZLjq2rH2P38TrrHCcw5BWpgaC9N8rEk5yVZ/o35RVX1/WOX937rX1Uf6a/+dP/t+/f01/9AVV1VVbdX1QVVdfJY+6dW1V/0t91cVa/trz+mqt5SVTf0P28ZzVapqrP6Wbg/WVU3JfnNfhbH+6vqt6vq7iQvr6qHV9U7qurGfibAz1XV1pUecFW9taquraq7q+qSqvqn/fVnJ3ltku/pH9Onl++HqtpSVT9TVV+sqp1V9VtV9fD+ttHsi5dV1Zeq6taq+ulVxvD4qrqzn42QqvqNqto5dvv5VfUj4/33IfHXknxjP747x+7yEVX1J/0siY9X1RNW6ne51tpNrbW3JnlDkl8cG881VfWtVfWKJP9rrM93J7myb35nVf1V//s5ST48dtdfn+QPW2s3tM41rbXfWmUM1yf50yRf01r7hyQ/n+Qd/Vh+Jcnvt9Y+NNbkoiTftpbHBwBwGORj+fhI5ONVVdU/qapPVNVd/b//ZNl++Uj/OP6yqt5W/Yz26o4qfHYOzOG/21q7oy8s/31r7f2rPNa/T/LX6XL4R5K8L8mvVlX1j/EX+m1GLsr+OfzjSb6qqh63lscJrF8K1MDQXprum+/fSfIvqv/G/FBaa9/S//q0/jC291bVs5P8QpIXJnlMki8meU+SVNVxSf4yyZ8lOTnJE5N8sL+Pn07yDUnOTDdj4RlJfmasu69M8sh0MxVGh5C9IMn7k5zQj/28JAv9/T49yT9PNxt3JZ/o+3pkkt9N8ntV9eDW2p8leVOS9/aP6WkrtH15//PPknxVutkJv7psm29O8uR0MxJe1wfn/bTWvpDk7n6sSfItSe4d2/ZZWRZoW2tXpJsh8dF+fCeM3fyiJD+b5BHpZkj8/CqPfTV/kORR/bjH+3zHsj5fnOSp/c0ntNaeXVUPTfL47AvmSfeh7seq6t9X1T/qQ+6KqurUJM9L8qn+ql9KUume329K8p+WNbki3esEAOBIkI/l42T2+XhFVfXIJH+SbmLGV6TLwn9S/TrW6Z6Pv+1ve0O62c4jT0qy1Fq7buy6jyX5+ar6t1X1pEP0fUa6owVGOfwn0xW4fz/JMUn+27Im++Xw1tpCun0rm8OcU6AGBlNV35wu1L6vtXZJks+nO8TrcL0kyTtba59sre1Kt8zDN1Z3mOS3J7mptfbm1toDrbV7WmsfH2v3xtbaztbaLemC5HjwWkry+tbartba/f11H22t/VFrbSnJ8ekKnD/SWvtya21nukPbXrTSIFtrv91au621ttBae3O68PXklbZd5TH+Umvt6tbavf1jfFHtfxjlz7bW7m+tfTrJp7N6YPtwkmdV1Vf2l9/fX358/5g+vcYxJd1s5b/tQ+LvpPuAMYkb+n8fOWG7pPsQlCT3jF33C+nWqXtJkouTXF8Hnsjmj/pZLn+Tbl+8KUlaa4tJ/u8k/zLJq1tr9yxrd89YnwAAMyMfy8djZp2PV/NtST7XWju/3//vTrek3XdU1WnpCsava63tbq39TZILlvWzvI9Xp3u8r0pyeXWz989Zts0nq+qOJP+/dDOlfzNJ+ufvh9Ll8Ff0uXzcSjlcNocNQIEaGNLLkvx5a+3W/vLvZtlhjBM6Od2skCR7A85tSR6b5NR0Af+Q7frfTx67fEtr7YFlba4d+/1xSR6U5Mb+sMA7k/x6uhkPB6iq/1jdGafv6rd9eJITD/7QDjrWbenWnRu5aez3+9LNIlnJh9OtzfwtST6S7pC5Z/U/f91/uFirtfa5msf2/94+YbskubP/97jRFa21xdba21pr35QusP58kncumy3zna21E1prj2ut/fuxD1dprY3W7xtfx2/kuLE+AQBmST6Wj0dmmo8PYvn+S3/5sf1tt7fW7hu7bfx5vmN5H/0XAW9qrf3jdLOu35duRvx4of3rWmuPaK09obX2M8v266Q5XDaHDUCBGhhEVR2b7lDDZ1V3huebkvxokqdV1WhGw5eTPGSs2Vfm4G5IF4ZHfTw0XSi6Pl2Q+qq1tEtyWvbNWEi6E9QsN37dtUl2JTmxL3ie0Fo7vrX21OWN+vX0fiLdY39EfxjgXemWlFitr0ONdSHJzYdot5IPpzuk7qz+979Jt6TFAYcvjjlSJ1n5l0l2Zg2HIS7XupPDfD7JV69y+/2ttbelC9BnTDPI3lMy2ewZAIBDko/l42WOWD5eZvn+S7p9eH2SG5M8sqrGX3Onjv1+VZKqqsdmBa21u9MdpThacmRa++Xwfpb8EyObw9xToAaG8p1JFtMVDM/sf56S7iQZL+23uTTJv6qqh1TVE9Od2Xnczdk/VL87yb+tqjOrO4nLm5J8vLV2TZI/TvKYqvqR6k76clxVPXOs3c9U1UlVdWKS1yX57bU+kNbajUn+PMmbq+r46k7U8oSqetYKmx+XLjDfkmRbVb0u3eGC44/p9NHJUFbw7iQ/2p+s5GHZtybfwlrHOzbuzyW5P8n3JvlwHyBvTvJdWT2A35zklP6EKFOrqkdX1auSvD7JT004K2Xchek+OIzu90eqO4HPsVW1rV/e47jsW99uGs9Kd1JFAIBZ+s7Ix/LxEcrH++6+Hjz+02/31VX1b/rc/D3pXoN/3Fr7Yrrl8t5QVdur6huTfMfozlpru9OtYz6ew/+fqvr6fvsHJ/nhdDOcJy60r2B5Dn9Gkmv6cQJzTIEaGMrLkvxma+1LrTtT9U2ttZvSndDkJf234b+cZHe60PeudGuZjXtDknf1hw2+sLX2l0n+n3Qn1bgxyRPSr3PXryP83HSB6qYkn0t3IpUk+bl0weszSf4uySf76ybx0iTbk1yebqbu+9OdiGa5D6Q7Ec0/pDt07oHsf5jc7/X/3lZVn1yh/TuTnJ/ukMMv9O1fPeFYx304yW2ttWvHLle6fbCSv0p3uN1NVXXrKtusxZ1V9eV0+/t5Sf51a+2dU9zf29O9bkYzbe5L8uZ0z/Wt6day+67W2tVT9JGqeky6wP5H09wPAMAK5GP5+Ejm4yT5J+kK8OM/d6Vbj/zH0y3/8hNJvn1smZmXJPnG/rafS/LedLPjR349+69P3tKtKX1rutnZz03ybf3yMoetqr4+yb2ttb8du/olSX5tmvsF1odq7UgdjQIAR09V/W66Ewr90RHs481JPt9a+x9Hqg8AAJiFI5GPq+q9Sf6+tfb6sev+T5JXtdZmcbTiav3+fpJ3tNYu7C8/Kt2XB09fYT10YM4oUAMAAABwgH7m8u3pZqf/83RHEn7jkSxGA5vPtqEHAAAAAMC69JVJ/iDdyTWvS/KDitPArJlBDQAAAADAIJwkEQAAAACAQShQAwAAAAAwiHW7BvWJJ57YTj/99KGHAQDAJnXJJZfc2lo7aehxzIp8DQDAUA6Wrddtgfr000/PxRdfPPQwAADYpKrqi0OPYZbkawAAhnKwbG2JDwAAAAAABqFADQAAAADAIBSoAQAAAAAYhAI1AAAAAACDmEmBuqreWVU7q+qzq9xeVfUrVXVVVX2mqr5uFv0CAMBGI1sDALCZzGoG9XlJzj7I7eckeVL/c26S/zmjfmdqcanlg1fcnF/54OfywStuzuJSG3pIAABsPudFtgYAYJPYNos7aa19pKpOP8gmL0jyW621luRjVXVCVT2mtXbjLPqfhcWllu97x8dz6bV35v7dizl2+9aceeoJOf8Vz8zWLTX08AAA2CRkawAANpOjtQb1Y5NcO3b5uv66deOiK3fm0mvvzH27F9OS3Ld7MZdee2cuunLn0EMDAIBxsjUAABvGujpJYlWdW1UXV9XFt9xyy1Ht+7Ib7s79uxf3u+7+3Yu5/Ia7j+o4AABgVobK17I1AABrdbQK1NcnOXXs8in9dftprb29tbajtbbjpJNOOkpD6zz15ONz7Pat+1137PatOePk44/qOAAA4BDWlK2T4fK1bA0AwFodrQL1BUle2p9x/BuS3LWe1shLkrOe/KiceeoJGS2J95B+nbyznvyoYQcGAAD7k60BANgwZnKSxKp6d5KzkpxYVdcleX2SByVJa+3XklyY5HlJrkpyX5J/O4t+Z2nrlsr5r3hmznnrR3LfrsX87AuemrOe/CgncQEA4KiSrQEA2ExmUqBurb34ELe3JD80i76OpK1bKo94yPY84iHJc57y6KGHAwDAJiRbAwCwmayrkyQCAAAAALB5KFADAAAAADAIBWoAAAAAAAahQA0AAAAAwCAUqAEAAAAAGIQCNQAAAAAAg1CgBgAAAABgEArUAAAAAAAMQoEaAID/f3t/H2/ZWdcH/5/vzGSSgSTkaRJIMpAAAQlWA44B1NYUxAZEsK2V4CP90aK9xZdPbUVqkVK19e6tIJU+YEGUKpAiYm6MUhuItv54yAARSGIkhIQ8kUlCQp4zmTPX/cfeJ5w5OWfm7LP2OdfZM+/36zWvOXvvtfb67mtfe+3v/q5rXQsAAKALBWoAAAAAALpQoAYAAAAAoAsFagAAAAAAulCgBgAAAACgCwVqAAAAAAC6UKAGAAAAAKALBWoAAAAAALpQoAYAAAAAoAsFagAAAAAAulCgBgAAAACgCwVqAAAAAAC6UKAGAAAAAKALBWoAAAAAALpQoAYAAAAAoAsFagAAAAAAulCgBgAAAACgCwVqAAAAAAC6UKAGAAAAAKALBWoAAAAAALpQoAYAAAAAoAsFagAAAAAAulCgBgCADaaqzq+qq6vqmqp67RKPP7GqPlJVn66qz1TVi3vECQAAQ02lQC2BBgCA6aiqzUnemuRFSc5O8oqqOnvRYr+Q5MLW2rOSXJDkP61vlAAAMB2DC9QSaAAAmKpzk1zTWru2tbYnyXuSvGzRMi3JseO/H5fk5nWMDwAApmYaI6gl0AAAMD2nJblhwe0bx/ct9IYkP1hVNya5OMlPLPVEVfXqqtpVVbtuu+22tYgVAAAGmUaBWgINAADr6xVJ3tlaOz3Ji5O8q6oeldu31t7WWtvZWtu5ffv2dQ8SAAAOZr0ukiiBBgCAlbkpyY4Ft08f37fQq5JcmCSttY8mOSrJSesSHQAATNE0CtQSaAAAmJ7LkpxVVWdW1daMruFy0aJlvpTkBUlSVc/IKL92CiIAADNnGgVqCTQAAExJa21vktck+VCSqzK62PgVVfXGqnrpeLGfTfJPq+qvkrw7yStba61PxAAAsHpbhj5Ba21vVc0n0JuTvGM+gU6yq7V2UUYJ9G9V1U9ndMFECTQAACyjtXZxRtduWXjf6xf8fWWSb13vuAAAYNoGF6gTCTQAAAAAAJNbr4skAgAAAADAfhSoAQAAAADoQoEaAAAAAIAuFKgBAAAAAOhCgRoAAAAAgC4UqAEAAAAA6EKBGgAAAACALhSoAQAAAADoQoEaAAAAAIAuFKgBAAAAAOhCgRoAAAAAgC4UqAEAAAAA6EKBGgAAAACALhSoAQAAAADoQoEaAAAAAIAuFKgBAAAAAOhCgRoAAAAAgC4UqAEAAAAA6EKBGgAAAACALhSoAQAAAADoQoEaAAAAAIAuFKgBAAAAAOhCgRoAAAAAgC4UqAEAAAAA6EKBGgAAAACALhSoAQAAAADoQoEaAAAAAIAuFKgBAAAAAOhCgRoAADaQqjq/qq6uqmuq6rXLLPN9VXVlVV1RVb+/3jECAMC0bOkdAAAAMFJVm5O8NckLk9yY5LKquqi1duWCZc5K8vNJvrW1dmdVndwnWgAAGG4qI6iN8gAAgKk4N8k1rbVrW2t7krwnycsWLfNPk7y1tXZnkrTWdq9zjAAAMDWDR1Ab5QEAAFNzWpIbFty+MclzFi3ztCSpqr9MsjnJG1prf7o+4QEAwHRNYwS1UR4AALB+tiQ5K8l5SV6R5Leq6rilFqyqV1fVrqraddttt61fhAAAsELTKFAvNcrjtEXLPC3J06rqL6vqY1V1/hS2CwAAh5qbkuxYcPv08X0L3Zjkotbaw621Lyb5m4wK1o/SWntba21na23n9u3b1yRgAAAYYipzUK/AikZ5GOEBAMBh7rIkZ1XVmVW1NckFSS5atMwHMsqrU1UnZTQY5Np1jBEAAKZmGgXqqY3yMMIDAIDDWWttb5LXJPlQkquSXNhau6Kq3lhVLx0v9qEkd1TVlUk+kuRftNbu6BMxAAAMM/giiVkwyiOjwvQFSb5/0TIfyGjk9G8b5QEAAMtrrV2c5OJF971+wd8tyc+M/wEAwEwbPILaKA8AAAAAAFZjGiOojfIAAAAAAGBi63WRRAAAAAAA2I8CNQAAAAAAXShQAwAAAADQhQI1AAAAAABdKFADAAAAANCFAjUAAAAAAF0oUAMAAAAA0IUCNQAAAAAAXShQAwAAAADQhQI1AAAAAABdKFADAAAAANCFAjUAAAAAAF0oUAMAAAAA0IUCNQAAAAAAXShQAwAAAADQhQI1AAAAAABdKFADAAAAANCFAjUAAAAAAF0oUAMAAAAA0IUCNQAAAAAAXShQAwAAAADQhQI1AAAAAABdKFADAAAAANCFAjUAAAAAAF0oUAMAAAAA0IUCNQAAAAAAXShQAwAAAADQhQI1AAAAAABdKFADAMAGU1XnV9XVVXVNVb32AMv9w6pqVbVzPeMDAIBpUaAGAIANpKo2J3lrkhclOTvJK6rq7CWWOybJTyb5+PpGCAAA0zOVArURHgAAMDXnJrmmtXZta21PkvckedkSy/3bJL+a5MH1DA4AAKZpcIHaCA8AAJiq05LcsOD2jeP7HlFVz06yo7X2xwd6oqp6dVXtqqpdt9122/QjBQCAgaYxgtoIDwAAWCdVtSnJryf52YMt21p7W2ttZ2tt5/bt29c+OAAAmNA0CtRTG+EBAADkpiQ7Ftw+fXzfvGOSfH2SS6vquiTPTXKRafQAAJhFa36RxElGeDgFEQAAclmSs6rqzKramuSCJBfNP9ha+2pr7aTW2hmttTOSfCzJS1tru/qECwAAqzeNAvXURng4BREAgMNda21vktck+VCSq5Jc2Fq7oqreWFUv7RsdAABM15YpPMcjIzwyKkxfkOT75x9srX01yUnzt6vq0iT/3AgPAABYWmvt4iQXL7rv9csse956xAQAAGth8AhqIzwAAAAAAFiNaYygNsIDAAAAAICJrflFEgEAAAAAYCkK1AAAAAAAdKFADQAAAABAFwrUAAAAAAB0oUANAAAAAEAXCtQAAAAAAHShQA0AAAAAQBcK1AAAAAAAdKFADQAAAABAFwrUAAAAAAB0oUANAAAAAEAXCtQAAAAAAHShQA0AAAAAQBcK1AAAAAAAdKFADQAAAABAFwrUAAAAAAB0oUANAAAAAEAXCtQAAAAAAHShQA0AAAAAQBcK1AAAAAAAdKFADQAAAABAFwrUAAAAAAB0oUANAAAAAEAXCtQAAAAAAHShQA0AAAAAQBcK1AAAAAAAdKFADQAAAABAFwrUAACwgVTV+VV1dVVdU1WvXeLxn6mqK6vqM1V1SVU9qUecAAAwDQrUAACwQVTV5iRvTfKiJGcneUVVnb1osU8n2dla+4Yk70vyf69vlAAAMD0K1AAAsHGcm+Sa1tq1rbU9Sd6T5GULF2itfaS1dv/45seSnL7OMQIAwNQoUAMAwMZxWpIbFty+cXzfcl6V5E/WNCIAAFhDUylQmycPAADWV1X9YJKdSf7DAZZ5dVXtqqpdt9122/oFBwAAKzS4QG2ePAAAmJqbkuxYcPv08X37qarvSPKvkry0tfbQck/WWntba21na23n9u3bpx4sAAAMNY0R1ObJAwCA6bgsyVlVdWZVbU1yQZKLFi5QVc9K8l8zKk7v7hAjAABMzTQK1FObJ88piAAAHM5aa3uTvCbJh5JcleTC1toVVfXGqnrpeLH/kOToJP+jqi6vqouWeToAANjwtqznxhbMk/ftSz3eWntbkrclyc6dO9s6hgYAABtCa+3iJBcvuu/1C/7+jnUPCgAA1sg0CtSTzpP37QeaJw8AAAAAgMPDNKb4ME8eAAAAAAATG1ygNk8eAAAAAACrMZU5qM2TBwAAAADApKYxxQcAAAAAAExMgRoAAAAAgC4UqAEAAAAA6EKBGgAAAACALhSoAQAAAADoQoEaAAAAAIAuFKgBAAAAAOhCgRoAAAAAgC4UqAEAAAAA6EKBGgAAAACALhSoAQAAAADoQoEaAAAAAIAuFKgBAAAAAOhCgRoAAAAAgC4UqAEAAAAA6EKBGgAAAACALhSoAQAAAADoQoEaAAAAAIAuFKgBAAAAAOhiS+8AAAAAYBbM7Wu59OrdueLmu/PMU4/NeU8/OZs3Ve+wAGCmKVADAADAQczta/mht388l99wVx7YM5dtWzfnnB3H5V2ves4hXaRWlAdYP4frPleBGgAAAA7i0qt35/Ib7sr9e+aSJPfvmcvlN9yVS6/enRc845TO0a2Nw7UoD9DD4bzPNQc1AAAAHMQVN9+dB8bF6XkP7JnLlTffvaL15/a1XHLVrXnLJZ/PJVfdmrl9bS3CnKqFRfmW/Yvy0NssfqbgQA7nfa4R1AAAAHAQzzz12GzbuvmREdRJsm3r5px96rEHXXdWR8UdqCi/kUeNH66nyB9Oen6m9K/ZMIvv06zuc6dBgRoOcbO4Ux7qcHzNAL3Y5zJEay233fvQKleeYhzTeyrWydy+lo9+4Y5cfes9efopx+R5Tzlxzfc9z3jCsTn7CcfmU1+6M/tasu2IzTn7Ccfm6x5/bL781QcPuO7/+fzt+fSX7soDD39tepBPf+mu/OGnbsq3nXXSmsY9xKmP25ajjtj8SNxJctQRm/P4xx2VW776QMfIlje3r+Wn3nN5rrj57jz48FyOOmJznnnqsXnzBeds+O+n+X79N7fek6etU7+eVct9pt7/qRvzrU9du8/U3L6Wn3rv5blyQf86+9Rj8+aXr6x/ze1r+di1X3uPn/tk7/G8pb6LV9tec/tafnqJ9+lNK3yfenn8445aYp+7KScfe2RuvPP+qWzj2G1H5NijjpjKc02TAjUcwmZ1pMYQh+NrBujFPpeh5va1fGH3fb3DYIB9+1ouv+GuXHfHfTnjxMfmnB3HZdMaf/737Wv5lT+5Ktfsvjd79u7L1i2b8tSTj87rXvSMNd/2T3/H0/Jz7/9MHnp4Lq/8ljNzzo7j8qWvHLxo8PEv3pEHH95/VNyDD8/l41+8I6cdv22twh3sCY87Kk/e/thcecvdaS05csumPHn7Y3Pq47blutunUyyZtk9df2c+e9NX89DefUmSBx6ey2dv+mr+6NM359lPOn5Fz3G49eueVtvWn/jiV5b8TH3ii1/J6cc/Zq3CzaeuvzOfW9S/PnfTV3PR5QfvX7P8Hs/aZ2K59+n/XcH71NNpj9u2xD736Jx+3GNyw1emc1Dw9OOjQA2sr8PxQi5DX7ORgAArdzh+z6yXqjo/yW8k2Zzkv7XW/v2ix49M8rtJvinJHUle3lq7br3jPBT1+BE+VK+YexVbLr/hrlyz+95HCg8P7d2Xa3bfm8tvuGvNCw+bNlWOOWpLjjlqy0TbOuPEx2brlk2PxJwkW7dsyhknPnYtwpyaTZsqr3vRMx5VlN/In4nr7rgvexa0c5Ls2bsv191x34res8OxX8/iPqTXZ2pI/+r5Hg8xi5+JofuBoVb7mRq6z53FHGaeAjUcwg7H+YuGvGYjAYGeZvEA2eH4PbMeqmpzkrcmeWGSG5NcVlUXtdauXLDYq5Lc2Vp7alVdkORXk7x8/aM9tMzi6LaeMfcqtvQuPKzGOTuOy1NPPnq/UXFPPfnonLPjuN6hHdRqi/JJn2LJ0MLlrPbr1bb10H3IkPd4SFv3+kwN6V+93uOh68/iZ6LnQcGhn6nV7nNnMYdZqFrbmDOu7dy5s+3atWvdt/vy//rRJMl7f/R5yy6z3m22Qd8iZsAlf31rfvI9l+93IZfHbN2c37jgnDz/6w7NwsGHl3nNb375OXn+1518kHV356feu7p1gUPPen79zu1r+ce/fVn+6savHSD7xtOPy2//42/e0EXqD//17vz0EvvNN01xv7m5qltSXVWfbK3t7LDd5yV5Q2vt741v/3yStNb+3YJlPjRe5qNVtSXJl5NsbwdIVE940jPaC1/3jrUNfpErb7k7SXL2E5a+iFxLcs+DD69jRAd2z4N7c9NdD+yXf1clpx23LccctTHH9vSM+bZ7Hsrt9+551P3bj96ak4458qDrt9Zy70Nzj8wPevSRm1N18M/70Ne82u3Ou/6O0dQWTzpxsmkEWmv54u33Z19rOeXYoybebk+rec2ttXzpKw/kgYfn0troPdp2xOY88YRta/q657c7/9006XaH9uvVGtKvh7R1r+0m09mHrPYztdr9wJD+1bOth6w/y5+J1e4HhpjG9/Jq9rkr3e6RWzbnyC2bVvy803Thj33Lsrn1xsyyNrCv3v/wI4k2fczyKQvr7TFHbMmZJ+0/f9GZJz02jzliSz7xxa8cdP1ZbOvlXvNjt27JZdfdecB1/+zKW5ccCfhnV96aY9Z4jqZZbGtgej51/Z351JfufGSUx/175vKpL92Zt//vL27Y0YBJcvTWpfe5R2/dkl0H2eeu1Nc9/pgc/9itU3muGXJakhsW3L4xyXOWW6a1treqvprkxCS3L1yoql6d5NVJcvQTnrJW8S5rucL0Sq22ELjadR8c/3BfqLXkoYfnVlz4HFKA7BHzkLiPOmJzqvKoH8NHHrF5RdtcbfHg6CM3Z9v4IlILCy1HH7m22523mv442lblyduHjd5b78/EvNWsc+9Dc4+8R8monzzw8FzufWjlfXM1MVdVnnjCttz70FweenguR054EGJIvx4S95B+PaSth+xDhr7HQ9t6tZ+pIfuBIf2r13s8dP1Z/EwM3Q+sNuZkOt/Lq9nnTmO7PU0lQnPk9XU4FbZm+ZSFHu/TkPmLZrWth7zmXqcBzWpbJ8P69Szuu3qdVjer6/bUcy7F1Wx3Fk9XTw7vefJmRWvtbUnelozOUDzQWYI97J3bd8ADyG/84BVJkte/5JkTP/dq1v3U9XfmLR/+/H65wJFbNuWV33Lmii9+tWduX1objTh73LbJvs/XO+ahcQ+9gNVbPvz5R263Njqb5Lu/4bQ1PdV96HZ7W+/PxBDv/9SNed8nb9z/zpY878kn5h88+/QVPcd6x5xMJzdfbdyr7ddD2nrIPmToe9zrd1DP/UCP93jo+rP4mRhq376Wn3v/Z/Lgw3N5yd86daLtDv1eXq2Vbvf047dlxwlrdyHRA7nwx5Z/bHCB2hx5fc1yYWs1XFRgcqudv2hW2zpZ/WvuNY/ZrLb1kH49i/uuaczP16O9Drf3KekX9yxe6CcZnvgfrvPkrbGbkuxYcPv08X1LLXPjeIqPx2U0EIQB5nOBxf1yJbnA/Pf5/Oil9fo+HxLz0LjnD1L1ODC3aVPl2U86fuK2ndUDgrNo1i8M2aMottp+PaSth+xDhr7Hvdq6536gx3s8dP1Z/EwMMZ+jzk+X8ZYPf36iHHXo9/Jq9drutExjBPW5Sa5prV2bJFX1niQvS7KwQP2yJG8Y//2+JL9ZVXWgOfJYmVktbK1W74Sy10UFehw17N3WPfS6SvmstvWQfj2Ln4mhMfdqr57v0xC9LrgzxCxe6OdwvLjajLgsyVlVdWZGhegLknz/omUuSvIjST6a5HuTfFhuPVzPgutqDS0c9CoU9ypezmrRNBnts+95cG8efHgun7r+zonPWFntuqs1tFjSI+Z5Q4pis9bWQ/Yh0yiI9ShAzuJ+YGhbD12/x/vUy9ADzr0K+j0PJEzD4IskVtX3Jjm/tfZPxrd/KMlzWmuvWbDM58bL3Di+/YXxMrcv9ZxJn4u4JAe/kMvcvpb79uxdz5AOqNdk9UP1uiDKEL0uKjCNi4us5QT7G1Wv+flWo/eFflZrSL/u+ZlYbXsN3d/2aq9e6w7R+4I7q9XzQj+rNa19/Wr2myttr8ds3ZItnRLrA13IZa1V1YuTvDmjKfTe0Vr75ap6Y5JdrbWLquqoJO9K8qwkX0lywfyAkeX0ugj5gczta/nMjXct+/gvfOBzSZJf+p6vn/i559f9t6tYN5n8QuW7rvtK3vRnf5MHFxQ8jtqyKT/9wqdl5xknrOg5Xv9Ho5jf+LLVxbwa04h7Neb2tfzbD16Zz996Tx7auy9HbtmUs045Jv/6JWev6cVhe2330SbrYPNxf+7m0YHMo7ZsylmnHL2iuIesO9TcvpZPf2lULHnSiY/Ns3Yct6Jtzu1r+aU/fnTMv/Bd6/0+TaZn3HP7Wj59w1257vb7csZJK2/rWd3uEPPv0+dvvXfBfmB179N6X1T78gVtfc6EbT10/SF+8Y9GU3z8m5etbIqPnofc3/fJG/M/dt2w33tbSb7vm3fkH65weqKN7LTjt+W047Z12faBLkC+oapMvS/ikgy7kEuPgtjQyep7XMil1wVR5re92tfb66IC07i4yGr65NC2ToYXetf7Qi7TWHe9L/4wjQv99LgwUq/PxJD2Grq/7dVevdadt5rPRO8L7qw27l4X+pnX6+Jqk25z3jTep0NZa+3iJBcvuu/1C/5+MMk/Wu+4pm3zpsqznrj8aKSjjxz1wwMtc7B1n72KdVfjnB3H5c//5rZcfsNdeWDPXLZt3ZxzdhyXf/K3n7zigtzDc/ty/0Nzuev+PTnv6SevS+FgaNxDfODHvzWXXr07V958d84+9dh1e829tjvEJVfdmi/cdt8j+8wH9+7LF267L/c8uDcveMYpa7buNJx75okTr7NczPc+tD4xr1bvuJ/z5Mnbepa3O8QHfvzbZm4/kCTPHdjWQ9dfrfm88pvX8MDntNzz4MP54GdufuQ3Y5Js27o533n2KTn3zI0f/6yaRoF6anPkbfSLuCTJV+9/+JFR1ov1uGDFNOYW7XUhl3nrdUGUoa+310UFpnFxkdUaOo3C0Iua9LgoyhBDLqTQ60I/vS6M1OszMaS9zEG9ute8ms/ERrjgzmr2Pz2ny1htW/e6iEuy8vb6uscfk+Mfu3VNY1nOgS7kAott3lR516ues6qCx9y+lh96+8dzze57s68lP/HuT+ecHcflXa96zpoXTIbEPY1tv+AZp6x7wbHXdoe44ua788CCYkmSPLBnLlfefPdBX8eQdXuZxZiT2Y37cDSL+wHWx3lPPznn7DjuUQduz3v6yb1DO6RNo0BtjryOhswx0+tCLr3muRv6entdVKDn/FiH0zxTQw29kEKvC/30ujBSr8/EkPYaOqdXr/bqte6Qz8SsXnCn13aHtHXPi6nM+jx5bGxz+1ruvH9P7n9oLpdcdeuGL7heevXuXH7DXdk3/j6+f89cLr/hrlx69e51KZ4o1Gx8zzz12GzbuvlRI/rOPvXgZwAPWbeXWYw5md24ga/peeD2cDa4QN1a21tVr0nyoXxtjrwrFs6Rl+TtSd5VVddkPEfe0O3yNb0KW6u9+EOvguvQ19vrogKzeiXWoRcH6XlRlNXodcBn6Oep1wGjIev2vtL4kIM2Pdqr17pDPhOzesGdXtvtdaBpGhwIZS30HI28WkZdcjBDRvTN4mjAWYw5md24gf05cLv+pjIH9eEyR16SHLV1U848aelCxlHjORPPOGl18zCudt15k45JP/fME/LBz9ySBx7+WjJ81BGbc+6ZJxx0Lsm5fS0/9d7Lc/NdD2RfS37zI9fk7FOPzZtffs5BE//Tj9+WD4+PRM1fpOzsU4/NS885dU1/NAx5vfP+yw9+Uz527R35m1vvydNOOSbPffKJ6/JDp9d2F5qke83ta/npJfrHm1bQP6ax/jRMepLHJVfdumSh984H9mTHCWt3AYJTjztq/Hn6ah58eF+OOmJTzj71cXnJNz5hRW2184zj88HP3JwHHl5wYaQjNmXnGcevadxD/acfeHY+du0d+fzue3PWyUev+DMxtL1YuaGfidW+x9Ny5JbR9/KBYt0o54L9r+Xa+v49Of34lX2On7hOF4VdjW1bzUl9uFrtKOjeo5FXw6hLDmbIiL5ZHA04izEnsxs3QG8b6iKJs+DILZvz+Mct/UNp6+ZNSZInPG6yos7cvpb7Htqb+x8ajZJYry+wf/Ds0/OHn77pUUd3/8GzTz/o9i+56tZcdcvdjyT+Dzw8l6tuuTt/c+s9K0r8L/zR5637l/aQ17vQjhP6/Ijvtd3VWK5/fH6F/WPo+j087ykn5vc/8aVH/bB83pNPzOnHr+17N+Tz9L3ftCN/dPnNj/pcfO837djwifSTVnnWRY/9z+FoGp+J1b7HQ83ta7l/z+h7+eov37Ph+8i3POXEvHuptn7KiTP13QELDRkFPYujkY26ZCWGjOibxdGAsxhzMrtxA/SkQD0lqx3hMasXRBma+Pf40nY0e/0M7R9+WE5m6I+Vw+1z4UfD+pjVYsssTg0wq20NBzJkFPQsjkY+HL+PATg89LouBLNFgXoKhvyY7X0K4moLNbOY+CcKU+tlaP+Yxf41yz8sfS5YC7P6mej9vbwas9rWcCBDDlbP6kEb38cAHGpmcfAHfShQT8GQH7OzOFI0md3En/UxtH/Mav/ywxL2N4ufiVn9Xp7FtoYDGXKw2kEbANgYZnHwB30oUE/BkB+zszhSNJH4c2BD+4f+BfQyq9/LcKgZerDaQRsA6G9WB3+w/hSop2DIj9lZHSmaSPw5sKH9Q/8Cepjl72U4lDhYDQCzz+APVkqBegqG/JiVfAPAxuF7GTYOB6sBYLYZ/MFKVWutdwxL2rlzZ9u1a1fvMFZsbl/zYxYA4BBSVZ9sre3sHce0zFp+DQDMPvUy5h0otzaCekqM8AAAAACAr1EvYyU29Q4AAAAAAIDDkwI1AAAAAABdKFADAAAAANCFAjUAAAAAAF0oUAMAAAAA0EW11nrHsKSqui3J9Z02f1KS2zttexZpr8lor8lor5XTVpPRXpPRXpPRXpPZqO31pNba9t5BTEvH/Hqjvr8blfaajPaajPaajPZaOW01Ge01Ge01mY3aXsvm1hu2QN1TVe1qre3sHces0F6T0V6T0V4rp60mo70mo70mo70mo70Obd7fyWivyWivyWivyWivldNWk9Fek9Fek5nF9jLFBwAAAAAAXShQAwAAAADQhQL10t7WO4AZo70mo70mo71WTltNRntNRntNRntNRnsd2ry/k9Fek9Fek9Fek9FeK6etJqO9JqO9JjNz7WUOagAAAAAAujCCGgAAAACALhSoAQAAAADoQoF6kao6v6qurqprquq1vePZ6Krquqr6bFVdXlW7esez0VTVO6pqd1V9bsF9J1TVn1XV58f/H98zxo1imbZ6Q1XdNO5fl1fVi3vGuJFU1Y6q+khVXVlVV1TVT47v17+WcID20seWUFVHVdUnquqvxu31b8b3n1lVHx9/R763qrb2jrW3A7TVO6vqiwv61jmdQ91QqmpzVX26qj44vq1vHaLk1pORWx+Y3Hoy8uuVk1tPRm49Gbn1ZOTXkzsUcmsF6gWqanOStyZ5UZKzk7yiqs7uG9VM+LuttXNaazt7B7IBvTPJ+Yvue22SS1prZyW5ZHybpdsqSd407l/ntNYuXueYNrK9SX62tXZ2kucm+fHx/kr/Wtpy7ZXoY0t5KMnzW2vfmOScJOdX1XOT/GpG7fXUJHcmeVW/EDeM5doqSf7Fgr51ea8AN6ifTHLVgtv61iFIbr1qcuvlvTNy60m8M/LrlZJbT0ZuPRm59WTk15Ob+dxagXp/5ya5prV2bWttT5L3JHlZ55iYYa21v0jylUV3vyzJ74z//p0k37OeMW1Uy7QVy2it3dJa+9T473sy+jI6LfrXkg7QXiyhjdw7vnnE+F9L8vwk7xvfr3/lgG3FMqrq9CTfleS/jW9X9K1DldyaqZJbT0Z+vXJy68nIrScjt56M/Hoyh0purUC9v9OS3LDg9o2xkz2YluR/VtUnq+rVvYOZEae01m4Z//3lJKf0DGYGvKaqPjM+RdEpdUuoqjOSPCvJx6N/HdSi9kr0sSWNTxO7PMnuJH+W5AtJ7mqt7R0v4jtybHFbtdbm+9Yvj/vWm6rqyH4RbjhvTvIvk+wb3z4x+tahSm49Obn15OQ+k5P7HIDcejJy65WRW09Gfj2RN+cQyK0VqBnq21prz87o1M0fr6q/0zugWdJaa3Ek8ED+c5KnZHRazy1Jfq1rNBtQVR2d5A+S/FRr7e6Fj+lfj7ZEe+ljy2itzbXWzklyekajIL+ub0Qb1+K2qqqvT/LzGbXZNyc5IcnP9Ytw46iqlyTZ3Vr7ZO9YYIOSWw8g91kRuc8ByK0nI7deObn1ZOTXK3Mo5dYK1Pu7KcmOBbdPH9/HMlprN43/353kDzPa0XJgt1bVE5Jk/P/uzvFsWK21W8dfTPuS/Fb0r/1U1REZJYS/11p7//hu/WsZS7WXPnZwrbW7knwkyfOSHFdVW8YP+Y5cZEFbnT8+9bW11h5K8tvRt+Z9a5KXVtV1GU338PwkvxF961Alt56Q3HpV5D4TkPssT249Gbn16sitJyO/PqhDJrdWoN7fZUnOGl/tcmuSC5Jc1DmmDauqHltVx8z/neQ7k3zuwGuRUZ/6kfHfP5LkjzrGsqHNJ4Njfz/61yPG80q9PclVrbVfX/CQ/rWE5dpLH1taVW2vquPGf29L8sKM5hb8SJLvHS+mf2XZtvrrBT9mK6M53/StJK21n2+tnd5aOyOjPOvDrbUfiL51qJJbT0BuvWpynwnIfZYmt56M3HoycuvJyK9X7lDKrWt0lgrzqurFGc3fsjnJO1prv9w3oo2rqp6c0ciOJNmS5Pe11/6q6t1JzktyUpJbk/xikg8kuTDJE5Ncn+T7WmuH/cVLlmmr8zI6PawluS7Jjy6YA+6wVlXfluR/J/lsvjbX1OsymvtN/1rkAO31iuhjj1JV35DRxTQ2Z3Qw+8LW2hvH+/33ZHRK3aeT/OB4BMNh6wBt9eEk25NUksuT/NiCi72QpKrOS/LPW2sv0bcOXXLrlZNbH5zcejLy65WTW09Gbj0ZufVk5NerM+u5tQI1AAAAAABdmOIDAAAAAIAuFKgBAAAAAOhCgRoAAAAAgC4UqAEAAAAA6EKBGgAAAACALhSoAQAAAADoQoEaAAAAAIAuFKgBAAAAAOhCgRoAAAAAgC4UqAEAAAAA6EKBGgAAAACALhSoAQAAAADoQoEa2BCq6t6qevIKljujqlpVbVmPuDaqqnplVf2fAev/SVX9yDRjmub2q+qdVfVLU97mFVV13vjvqqrfrqo7q+oT4/v+WVXdOu6LJ47ve3dVfc8041girl+rqn+2ltsAAGaP/Hgy8uNVbXND5sfj7fxlVT1rjbfxB1X1ogW3T6mqq6rqyLXcLvBoCtTAilTVdVX1wDg5uXWcIB29yue6tKr+ycL7WmtHt9aunU60j2zjzkmTi3Fy/9RpxbERVNUbquq/L7yvtfai1trv9Ipp4fan8GNi/kfZvQv65wer6oWLtvnM1tql45vfluSFSU5vrZ1bVUck+fUk3znui3dU1Tck+cYkfzTeztZxMfnG8Xauq6o3L4hjqc/IMVX1F1X1i4ti/uGq+kJVPSbJ/5PkdVW1dbVtAACsP/nx7JIfP7LNofnxoDgPEP93J7mntfbp8e3jquodVfXlqrqnqv6mql67YPlWVfeNX+tNVfXrVfXYqvr84qJ/Vb1+XPzelORXkzxS9G+t3ZrkI0lePe3XBByYAjUwie9urR2d5NlJdib5hUlWHh+VX/P9TlWdkeRvJ2lJXrrW2xtqqdEuh/sImFU6btw/vzHJnyX5w6p65TLLPinJda21+8a3T0lyVJIrFizzo0l+r7XWxrd/PqN+f26SY5Kcl+RTi5538WfkXyX5J0l+uqqemSRVtT3JryX5J621+1trtyT568xAXwUAHkV+vAbkx1Oz1vnxWvmxJO9acPtNSY5O8owkj8uoD1+zaJ1vHL/WFyT5/iQ/lORVSf6fqjolSarqGUl+NsmrWmv7WmufSHJsVe1c8Dy/l9HrBNaRAjUwsdbaTUn+JMnXV9Xx46Pxt41HZHywqk6fX3Y8UuOXq+ovk9yfUaLxt5P85vgI92+Ol3tkZEZVfVdVfbqq7q6qG6rqDROG+MNJPpbknUkWHzHfb3TKwqP+VfUX47v/ahzby8f3/9OquqaqvlJVF1XVqQvWf2ZV/dn4sVur6nXj+4+sqjdX1c3jf2+eH61SVefVaBTuz1XVl5P89ngUx/uq6r9X1d1JXllVj6uqt1fVLeORAL9UVZuXesFV9Rvjtrq7qj5ZVX97fP/5SV6X5OXj1/RXi9uhqjZV1S9U1fVVtbuqfreqHjd+bH70xY9U1Zeq6vaq+lfLxHBmVd01/yOrqn6rqnYvePxdVfVTC7c/ThL/S5LnjeO7a8FTHl9Vf1yjURIfr6qnLLXdxVprX26t/UaSNyT51QXxXFdV31FVr0ry3xZs891Jrh6vfldVfXj894uS/PmCp/7mJH/YWru5jVzXWvvdZWJ45DPSWvubJL+c5O3jWN6S5A9aax9ZsMqlSb5rJa8PANh45Mfy42ViONTz42VV1bdU1WVV9dXx/9+yqF3+Yvw6/ldVvbXGI9prdFbh8/PoPPz3W2t3jgvLf91ae98yr/Wvk/zvjPLwv0hyYUafrRq/xn83Xmbepdk/D/94kidX1ZNW8jqB6VCgBiZWVTuSvDjJpzPaj/x2Rkfcn5jkgSS/uWiVH8roNKljkrwyo4ThNeNTxV6zxCbuyyiJPi6jZOGf1WTznP1wRke+fy/J36vxEfODaa39nfGf3ziO7b1V9fwk/y7J9yV5QpLrk7wnSarqmCT/K8mfJjk1yVOTXDJ+jn+V5LlJzsloxMK52X9EzeOTnJBRu82fQvayJO/L6HX/XkY/IPaOn/dZSb4zo9G4S7lsvK0Tkvx+kv9RVUe11v40ya8kee/4NX3jEuu+cvzv7yZ5ckajExa/h9+W5OkZjUh4/Thx3k9r7YtJ7h7HmiR/J8m9C5b99ixKaFtrV2U0QuKj4/iOW/DwBUn+TZLjMxoh8cvLvPblvD/JyeO4F27z7Yu2+Yokzxw/fFxr7flV9dgkZ+ZriXky+lH3M1X1f1XV3xonuUta9BlJRqdHVkbv77cm+ReLVrkqo34CAMwg+bH8+DDNj5dUVSck+eOMBmacmFEu/Mc1nsc6o/fjE+PH3pDR52HeWUn2tdZuXHDfx5L8clX946o66yDbPjujAz7zefjPZVTg/oMkRyb5D4tW2S8Pb63tzaht5eawjhSogUl8YHwE//9klEj9SmvtjtbaH4ynKrgnoyTp2xet987W2hWttb2ttYcPtpHW2qWttc+Oj45/Jsm7l3jOJVXVt2WU1F7YWvtkki9kdIrXav1Akne01j7VWnsoo2kenlej0yRfkuTLrbVfa6092Fq7p7X28QXrvbG1tru1dltGieTCxGtfkl9srT3UWntgfN9HW2sfaK3tS3JsRj9yfqq1dl9rbXdGp7ZdsFSQrbX/Pn4v9rbWfi2j5OvpSy27zGv89dbata21e8ev8YLa/zTKf9Nae6C19ldJ/irLJ2x/nuTbq+rx49vvG98+c/ya/mqFMSWj0cqfGCeJv5fRD4xJ3Dz+/4QJ10tGP4KS5J4F9/27jOap+4Eku5LcVI++kM2jPiNJ0lqbS/L/S/L3k/zE+LOy0D0LtgkAzA75sfz4cM6Pl/NdST7fWnvXuP3fndGUdt9dVU/MqGD8+tbantba/0ly0aLtLN7GT2T0el+T5Moajd5/0aJlPlVVdyb5fzMaKf3bSTJ+/348ozz8VeO8fKGl8nC5OawzczgBk/ie1tr/WnhHjS7y9qYk52d0JD9JjqmqzQu+/G+YZCNV9Zwk/z7J1yfZmlEy+T9WuPqPJPmfrbXbx7d/f3zfmyaJYYFTs2Ce4dbavVV1R5LTkuzIKMFfbr3rF9y+fnzfvNtaaw8uWmdhOz0pyRFJblkwUHdTlmnLqvrnGc2xdmpGcwsem+SkZV/VwWPdktG8c/O+vODv+zMaRbKUP89oTrgbk/xFRqfM/VCSB5P87/GPi5Va6TaXc9r4/69MuF6S3DX+/5iMYp8vMr81yVuraltGBed3VNUnxiNdkiU+I/Naa1eM38srlnj4mAXbBABmh/xYfpwcpvnxASxuv4xvnzZ+7CuttfsXPHZDRn0nSe4cb+MR4wMWv5LkV6rq2CSvzWhE/BNba/Ov5dmttcXzUs+7YtH/Cy2Vh8vNYZ0ZQQ0M9bMZjUR4Tmvt2IxOW0tG0xnMW3wRjYNdVOP3MzqKvqO19riM5mBbdjqFeeOi4fdlNCLhyzWav+6nk3xjVc2PaLgvyWMWrPb4HNjNGSXD89t4bEanot2UUSL15JWsl9HpnTcvuL1UGyy874YkDyU5qbV23Pjfsa21Zy5eqUbz6f3LjF778ePTAL+ar7XZwdp7qVj3Jrn1IOst5c8zOqXuvPHf/yejKS0edfriAmt1kZW/n2R3VnAa4mJtdHGYLyR52jKPP9Bae2tGCfTZQ4Ice0YmGz0DAGxc8uMVrBf58SGVHy+yuP2SURvelOSWJCeMD+TM27Hg72syun7oaVlCa+3ujIrV81OODLVfHj4eJf/UyM1hXSlQA0Mdk9G8eneN5xr7xRWsc2uWT1znn/MrrbUHq+rcrPwUxO9JMpdRwfCc8b9nZDSn3w+Pl7k8yT+oqsfU6KIzrzpIbO9O8o+r6pwaXcTlV5J8vLV2XZIPJnlCVf1UjS76csx4dMv8er9QVdur6qQkr0/y31f4OtJauyXJ/0zya1V1bI0u1PKUqlrqVM5jMkqYb0uypapen9EIkYWv6Yxa/grx707y0+OLlRydr83Jt3el8S6I+/MZ9YcfTPLn4wTy1iT/MMsn4LcmOX18QZTBquqUqnpNRn3x5ycclbLQxVlw6uz4fT6vqrZV1Zbx9B7H5Gvz2w3x7RldWAkAmH3yY/nxwrgP2fz4a09fRy38N17uaVX1/eO8+eUZ9cEPttauz2i6vDdU1daqel6S755/stbanozmMV+Yh//rqvrm8fJHJfnJjEY4T1xoX8LiPPzcJNeN4wTWiQI1MNSbk2xLcntGF6/40xWs8xtJvrdGVzV/yxKP/19J3lhV92SUuF64wlh+JMlvt9a+1EZXqv5ya+3LGV3Q5AfGR8PflGRPRknf72Q0l9lCb0jyOzW62vb3jU/Z/NcZXVTjliRPyXieu/Gcgi/MKKH6cpLPZ3QhlST5pYwSr88k+WxGp0H+0gpfx7wfzugUziszGqn7vowuRLPYhzJq97/J6NS5B7P/qY7zp3/eUVWfyqO9I6Orx/9Fki+O1/+JCWNd6M+T3NFau2HB7cqCU0EX+XBGp9t9uapuX2aZlbirqu7LqL1fnOQftdbeMeD53pZRv5kfaXN/kl/L6L2+PaO57P5ha+3aAdtIVT0ho4T9A0OeBwDYMN4c+bH8eH+Han6cJN+SUQF+4b+vZjQf+c8muSOj0ewvWTDNzA8ked74sV9K8t6MRsfP+6/Zf37yltGc0rdnNDr7hUm+azy/9KpV1Tcnube19okFd/9ARmcoAOuoWlurM0cAYLZV1e9ndEGhD6zhNn4tyRdaa/9prbYBAADTsBb5cVW9N8lft9Z+ccF9f5nkNa21aZytuNx2/yDJ21trF49vn5zRwYNnLTEfOrCGFKgBAAAAWBfjkctfyWh0+ndmdCbh89ayGA1sbFt6BwAAAADAYePxSd6f0cU1b0zyzxSn4fBmBDUAAAAAAF24SCIAAAAAAF0oUAMAAAAA0MWGnYP6pJNOameccUbvMAAAOEx98pOfvL21tr13HNMivwYAoJcD5dYbtkB9xhlnZNeuXb3DAADgMFVV1/eOYZrk1wAA9HKg3NoUHwAAAAAAdKFADQAAAABAFwrUAAAAAAB0oUANAAAAAEAXCtQAAAAAAHQxlQJ1Vb2jqnZX1eeWebyq6i1VdU1Vfaaqnj2N7U7b3L6WS666NW+55PO55KpbM7ev9Q4JAIDDjNwaAIDDyZYpPc87k/xmkt9d5vEXJTlr/O85Sf7z+P8NY25fyw+9/eO5/Ia78sCeuWzbujnn7Dgu73rVc7J5U/UODwCAw8c7I7cGAOAwMZUR1K21v0jylQMs8rIkv9tGPpbkuKp6wjS2PS2XXr07l99wV+7fM5eW5P49c7n8hrty6dW7e4cGAMBhRG4NAMDhZL3moD4tyQ0Lbt84vm8/VfXqqtpVVbtuu+22dQpt5Iqb784De+b2u++BPXO58ua71zUOAAA4iBXl1km//FpuDQDASm2oiyS21t7WWtvZWtu5ffv2dd32M089Ntu2bt7vvm1bN+fsU49d1zgAAGBaeuXXcmsAAFZqvQrUNyXZseD26eP7Nozznn5yztlxXOanxHvMeJ68855+ct/AAABgf3JrAAAOGetVoL4oyQ+Przj+3CRfba3dsk7bXpHNmyrvetVz8tSTj87px23Lf3zFs1zEBQCAjUhuDQDAIWPLNJ6kqt6d5LwkJ1XVjUl+MckRSdJa+y9JLk7y4iTXJLk/yT+exnanbfOmyvGP2ZrjH5O84Bmn9A4HAIDDkNwaAIDDyVQK1K21Vxzk8Zbkx6exLQAAOJTJrQEAOJxsqIskAgAAAABw+FCgBgAAAACgCwVqAAAAAAC6UKAGAAAAAKALBWoAAAAAALpQoAYAAAAAoAsFagAAAAAAulCgBgAAAACgCwVqAAAAAAC6UKAGAAAAAKALBWoAAAAAALpQoAYAAAAAoAsFagAAAAAAulCgBgAAAACgCwVqAAAAAAC6UKAGAAAAAKALBWoAAAAAALpQoAYAAAAAoAsFagAAAAAAulCgBgAAAACgCwVqAAAAAAC6UKAGAAAAAKALBWoAAAAAALpQoAYAAAAAoAsFagAAAAAAulCgBgAAAACgCwVqAAAAAAC6UKAGAAAAAKALBWoAAAAAALpQoAYAgA2mqs6vqqur6pqqeu0Sjz+xqj5SVZ+uqs9U1Yt7xAkAAENNpUAtgQYAgOmoqs1J3prkRUnOTvKKqjp70WK/kOTC1tqzklyQ5D+tb5QAADAdgwvUEmgAAJiqc5Nc01q7trW2J8l7krxs0TItybHjvx+X5OZ1jA8AAKZmGiOoJdAAADA9pyW5YcHtG8f3LfSGJD9YVTcmuTjJT6xPaAAAMF3TKFBLoAEAYH29Isk7W2unJ3lxkndV1aNy+6p6dVXtqqpdt91227oHCQAAB7NeF0mUQAMAwMrclGTHgtunj+9b6FVJLkyS1tpHkxyV5KTFT9Rae1trbWdrbef27dvXKFwAAFi9aRSoJdAAADA9lyU5q6rOrKqtGV3D5aJFy3wpyQuSpKqekVF+bYQHAAAzZxoFagk0AABMSWttb5LXJPlQkqsyutj4FVX1xqp66Xixn03yT6vqr5K8O8krW2utT8QAALB6W4Y+QWttb1XNJ9Cbk7xjPoFOsqu1dlFGCfRvVdVPZ3TBRAk0AAAso7V2cUbXbll43+sX/H1lkm9d77gAAGDaBheoEwk0AAAAAACTW6+LJAIAAAAAwH4UqAEAAAAA6EKBGgAAAACALhSoAQAAAADoQoEaAAAAAIAuFKgBAAAAAOhCgRoAAAAAgC4UqAEAAAAA6EKBGgAAAACALhSoAQAAAADoQoEaAAAAAIAuFKgBAAAAAOhCgRoAAAAAgC4UqAEAAAAA6EKBGgAAAACALhSoAQAAAADoQoEaAAAAAIAuFKgBAAAAAOhCgRoAAAAAgC4UqAEAAAAA6EKBGgAAAACALhSoAQAAAADoQoEaAAAAAIAuFKgBAAAAAOhCgRoAAAAAgC4UqAEAAAAA6EKBGgAAAACALhSoAQAAAADoQoEaAAAAAIAuFKgBAGADqarzq+rqqrqmql67zDLfV1VXVtUVVfX76x0jAABMy1QK1JJoAAAYrqo2J3lrkhclOTvJK6rq7EXLnJXk55N8a2vtmUl+ar3jBACAadky9AkWJNEvTHJjksuq6qLW2pULllmYRN9ZVScP3S4AAByCzk1yTWvt2iSpqvckeVmSKxcs80+TvLW1dmeStNZ2r3uUAAAwJdMYQf1IEt1a25NkPoleSBINAAAHd1qSGxbcvnF830JPS/K0qvrLqvpYVZ2/3JNV1auraldV7brtttvWIFwAABhmGgXqqSbRAADAAW1JclaS85K8IslvVdVxSy3YWntba21na23n9u3b1y9CAABYocFTfEywnfkk+vQkf1FVf6u1dtfCharq1UlenSRPfOIT1yk0AADYMG5KsmPB7dPH9y10Y5KPt9YeTvLFqvqbjHLty9YnRAAAmJ5pjKBeaRJ9UWvt4dbaF5PMJ9H7McIDAIDD3GVJzqqqM6tqa5ILkly0aJkPZDTwI1V1UkZnK167jjECAMDUTKNALYkGAIApaK3tTfKaJB9KclWSC1trV1TVG6vqpePFPpTkjqq6MslHkvyL1todfSIGAIBhBk/x0VrbW1XzSfTmJO+YT6KT7GqtXTR+7DvHSfRcJNEAALCk1trFSS5edN/rF/zdkvzM+B8AAMy0qcxBLYkGAAAAAGBS05jiAwAAAAAAJqZADQAAAABAFwrUAAAAAAB0oUANAAAAAEAXCtQAAAAAAHShQA0AAAAAQBcK1AAAAAAAdKFADQAAAABAFwrUAAAAAAB0oUANAAAAAEAXCtQAAAAAAHShQA0AAAAAQBcK1AAAAAAAdKFADQAAAABAFwrUAAAAAAB0oUANAAAAAEAXCtQAAAAAAHShQA0AAAAAQBcK1AAAAAAAdKFADQAAAABAFwrUAAAAAAB0oUANAAAAAEAXCtQAAAAAAHShQA0AAAAAQBcK1AAAAAAAdKFADQAAAABAFwrUAAAAAAB0oUANAAAAAEAXCtQAALDBVNX5VXV1VV1TVa89wHL/sKpaVe1cz/gAAGBaFKgBAGADqarNSd6a5EVJzk7yiqo6e4nljknyk0k+vr4RAgDA9ChQAwDAxnJukmtaa9e21vYkeU+Sly2x3L9N8qtJHlzP4AAAYJqmUqB2CiIAAEzNaUluWHD7xvF9j6iqZyfZ0Vr74/UMDAAApm1wgdopiAAAsH6qalOSX0/ysytY9tVVtauqdt12221rHxwAAExoGiOonYIIAADTc1OSHQtunz6+b94xSb4+yaVVdV2S5ya5aKmzFFtrb2ut7Wyt7dy+ffsahgwAAKszjQL11E5BNMIDAAByWZKzqurMqtqa5IIkF80/2Fr7amvtpNbaGa21M5J8LMlLW2u7+oQLAACrt+YXSZzkFEQjPAAAONy11vYmeU2SDyW5KsmFrbUrquqNVfXSvtEBAMB0bZnCc0xyCmKSPD6jUxCN8gAAgCW01i5OcvGi+16/zLLnrUdMAACwFqYxgtopiAAAAAAATGxwgdopiAAAAAAArMY0pvhwCiIAAAAAABNb84skAgAAAADAUhSoAQAAAADoQoEaAAAAAIAuFKgBAAAAAOhCgRoAAAAAgC4UqAEAAAAA6EKBGgAAAACALhSoAQAAAADoQoEaAAAAAIAuFKgBAAAAAOhCgRoAAAAAgC4UqAEAAAAA6EKBGgAAAACALhSoAQAAAADoQoEaAAAAAIAuFKgBAAAAAOhCgRoAAAAAgC4UqAEAAAAA6EKBGgAAAACALhSoAQAAAADoQoEaAAAAAIAuFKgBAAAAAOhCgRoAAAAAgC4UqAEAAAAA6EKBGgAAAACALhSoAQAAAADoQoEaAAAAAIAuFKgBAGADqarzq+rqqrqmql67xOM/U1VXVtVnquqSqnpSjzgBAGAaFKgBAGCDqKrNSd6a5EVJzk7yiqo6e9Fin06ys7X2DUnel+T/Xt8oAQBgehSoAQBg4zg3yTWttWtba3uSvCfJyxYu0Fr7SGvt/vHNjyU5fZ1jBACAqZlKgdppiAAAMBWnJblhwe0bx/ct51VJ/mRNIwIAgDU0uEDtNEQAAFh/VfWDSXYm+Q8HWObVVbWrqnbddttt6xccAACs0DRGUDsNEQAApuOmJDsW3D59fN9+quo7kvyrJC9trT203JO11t7WWtvZWtu5ffv2qQcLAABDTaNA7TREAACYjsuSnFVVZ1bV1iQXJLlo4QJV9awk/zWj4vTuDjECAMDUbFnPjS04DfHbl3n81UlenSRPfOIT1zEyAADor7W2t6pek+RDSTYneUdr7YqqemOSXa21izKa0uPoJP+jqpLkS621l3YLGgAABphGgXrS0xC/fbnTEFtrb0vytiTZuXNnm0JsAAAwU1prFye5eNF9r1/w93ese1AAALBGpjHFh9MQAQAAAACY2OACdWttb5L50xCvSnLh/GmIVTV/quHC0xAvr6qLlnk6AAAAAAAOE1OZg9ppiAAAAAAATGoaU3wAAAAAAMDEFKgBAAAAAOhCgRoAAAAAgC4UqAEAAAAA6EKBGgAAAACALhSoAQAAAADoQoEaAAAAAIAuFKgBAAAAAOhCgRoAAAAAgC4UqAEAAAAA6EKBGgAAAACALhSoAQAAAADoQoEaAAAAAIAuFKgBAAAAAOhCgRoAAAAAgC4UqAEAAAAA6EKBGgAAAACALhSoAQAAAADoQoEaAAAAAIAuFKgBAAAAAOhiS+8AAAAAYBbM7Wu59OrdueLmu/PMU4/NeU8/OZs3Ve+wAGCmKVADAADAQczta/mht388l99wVx7YM5dtWzfnnB3H5V2veo4iNQAMYIoPAAAAOIhLr96dy2+4K/fvmUtLcv+euVx+w1259OrdvUNbU3P7Wi656ta85ZLP55Krbs3cvtY7JEiib8KhxAhqAAAAOIgrbr47D+yZ2+++B/bM5cqb784LnnHKQdefxelBjBpno9I3OVTN4nfFNChQwyHucN25AQDAND3z1GOzbevm3L+gSL1t6+acfeqxB113VotpC0eNJ/uPGl9JUR7WSs++6Tc2a2VWvyumQYEaDmGH884NAIBDV48C0XlPPznn7DguH7v2juxryWPGufV5Tz/5oOvOaqF36KjxXhQQD329+ubQ39j65vqZxbae1e+KaVCghkPY4bpzG/JFNItfYgA92W8y1Ee/cEfvEJgx+/a1/MqfXJVrdt+bPXv3ZeuWTXnqyUfndS96Rjat8f7nx897ar70lfvz0MNzeeW3nJlzdhyXT3zxKwdd708/9+Uli2l/+rkv5zFbN+7P8s1V2bplUx7au++R+7Zu2ZRNVRv2s9uzfwy1b1/L5TfclevuuC9nnPjYnLPjuA0fcy+9+uanrr8zn7z+zke2e/+euXzy+jvzXy79Qp79pOMPuK6+uX5mta2Hfles5H163lNOnGrM07JxvwmBwWZ1xMMQQ45oG3EOMBn7zbVTVecn+Y0km5P8t9bav1/0+JFJfjfJNyW5I8nLW2vXrXec0KNocfkNd+Wa3fc+UiB6aO++XLP73lx+w10HLRANtWlT5ZijtuSYo7ZMtK0zTnzsksW0M0587FqEOTXn7DguTz356Fx5y91pLTlyXOQ5Z8dxK1p/VvtHj7hntaA21GrbemjfXK3r7rgvexZ8jpNkz959ue6O+w7av4b2zV5F4p59c7Wvuef3RLL6uId8V8z6PkSBGg5hQ+bJm1VDRo0friPOAVbLfnNtVNXmJG9N8sIkNya5rKouaq1duWCxVyW5s7X21Kq6IMmvJnn5+kd76Jm1UWI99foxPKRA1EuvYtpQmzZVXveiZ+Tn3v+Z/UaNr+T9ndX+0SvunsXLWSx8DumbQwwpIA7pm9Pol7NW7B3ymnt+TwyJe8h3Re+i/FDVWusdw5J27tzZdu3ate7bffl//WiS5L0/+rxll9mopzLBYvM7xsU7t5V+ic3iD7T3f+rGvO+TN2bhnq2SfO83nZ5/8OzT12xdWIlZ/Eyxfmaxf6zHfrPnaYhV9cnW2s4O231ekje01v7e+PbPJ0lr7d8tWOZD42U+WlVbknw5yfZ2gOT+hCc9o73wde9Y2+AXufKWu5MkZz9h+YPjdz/48HqFc1CttXzpKw/kgYfn0lpSlWw7YnOeeMK2VG3cz2NrLfc+NJcHH57LUUdsztFHbl6XeO95cG9uuuuBLOx1Vclpx23LMUet3Vioodsd2l7X33F/kuRJJz5morhba/ni7fdnX2s55dij1u19mobVvOZZ7R+94r7tnody+717HnX/9qO35qRjjjzgukP2XUP3e0M+T9No6yGfx9XEPd9e8wfnJ2mvIa93Gvu91b7PQ/rmED3ba4hpvFer+a5Y6ft07FFHrOyFrIELf+xbls2tp/KuOAWR9TSLP+B7mcURD0MNOaLd89RL/Xo2DB2ZMoufKX1zfcxq/xi639S/lnVakhsW3L4xyXOWW6a1treqvprkxCS3L1yoql6d5NVJcvQTnrJW8S7rQIXplVht4WG169770NwjP96TpLXkgYfncu9Dc2v6o3JIzEOKJUO3/eCCtvpaPMlDD69tex195OZsO2LzowotRx+5eUXbHNpeq+mPo21Vnrx9WF653p+JeatZZ2j/SFYX85D+0TPuo47YnKo8qqh15BEHj3vIvmvIukM/T9No69X0zSFxV1WeeMK23PvQXB56eC5HTlDcHtI3h7bVkPd5SN+ct97fMUP3A0O+z4e+V6v9rpjG+9TT4AK1UxBZT7P6Az7p9yN8tfPkzerpIUNOiel16uXh2q9nrTA19H2axdM2D9e+2WO7h+M+d5b71yxprb0tyduS0RmKBzpLsJcDnZ34xg9ekSR5/UueOfHzrmbd+bMC9tOS5z35xIOeFTDfp/fM7Utro5FMj9s2WZ9eTcyfuv7OvOXDn/9auG00P/x3f8NpE823+3Pv/0wefHguL/lbp6543zW/7YUHqY7csimv/JYzV3y6+mrba7X722m0V0/r/ZkYYkj/mLfamIfkAb3iHvK9OGTfNWTdoZ+nabT1avTcDwzdd622rabx/TYkZxvy/bba1zz0N9Bqv5969euVvk89z0688MeWf2waI6jPTXJNa+3aJKmq9yR5WZKFBeqXJXnD+O/3JfnNqqoDnYLIys1akWeIWf0BP4s/wmdxbr9k2KjxXvOYHY79euhnYhYvuNNrzrkh6/bsm7M4Wn2W58lbbVsP2W/O6r5vndyUZMeC26eP71tqmRvHU3w8LqMzFRlgyFkB8316/hfOevXpac23O3868ls+/PmJ58pcvN+bZK7M1bbXpk2VZz/p+InbdlZz3GT0Xt3z4N48+PBcPnX9nRv+d9+Q/jHUavtH0i/u+e/U9b6wWq85lZN+bd1zP7Davjm0rYa8z0P65hBDX/Nq23ro99Ms7kM2gmkUqKd2CuJC19523yPzQa+n+XnyDrRt8+T1c9s9D+23Q01GO4t3/v+/mA9+9uY13/5q56laPAfRQ3v35cpb7s7Pvf8z6zpP3vxRy5W458G9o4lEF00s+tFr78jnbv7qip+nl6/cN5p76YOfvXnivjFk3dUY2q83yryTk/TrIev2midv6Ps05DPVq6177XOHvsdD97mrNfR96rHPnVYesZr95kr7V8958jq6LMlZVXVmRoXoC5J8/6JlLkryI0k+muR7k3zY4I/hhvyo7FXwGDrVzpAf4kN+DM9qe/Uy5EDC/PrrXdweWizpVZDvGXeP4uWQdYd+nnoV1GZxPzC0rXoVe4fo1T+Gfj/1LBT3eJ+mZW1nBp9Q7znykmHz5PWYD2zoPHlDtt1j3V5zHyXD5qkaMgdRr3nyhs7ZlAzrH0PXX+02h6y72nmqhvTrafSP1cY9pF8PWbfXPHlD9z+95pwbsu7Q17zavjX0u63X3JGzOE/eNPKIZHX7zVmfJ28tjQd0vCbJhzK6xss7WmtXVNUbk+xqrV2U5O1J3lVV1yT5SkZFbAbqNYJxiKFFh2n8EF/Nj+FZba9ehhxIGFrcHmK1/aNnzMnsxT1k3zVk3Wl8nnoU1GZ1PzCkrWZ1dG2P/jGN76dZLhT3Mo0C9dROQZyFOfKS5efJ6zEf2JB5hJLVzzc3NO7Vbnca0wKs9vUOmadqyBxEszg/1ryh89yt9zx5QwyZp2pIvx7aP4bEPY1+vZp1e82TN41pI3rMOTeN+UGHTC2ymr419Lut19yRszhP3tC2HmLW58lba621i5NcvOi+1y/4+8Ek/2i941oLB3qP50fQr6YfDFn3W886aeJ1zj3zhPzlF27P5TfclQf2zGXb1s05Z8dx+bHznpLNK/yeWG3MFz3l23Lp1btz5c135+xTj815Tz95xdu8f8/e/PFnb3nk4G2SbNu6Oed//ePX9PM3jfZarSHt1ctl131lyQMJ+1o76Pt0yVW35ou337dfcfuLt9+XB/fO5QXPOGWtQh5kFmNO+se9mn3X0HVn8fOUzG7cQw3pI0MM+U5ebz2/nw5n0yhQOwWxoyFHdnod3R2y3SFH/Ya+3iGjS2bxdNHEUb9JzOrpsUPinsVTCYe01zRGHczaaZtDXvOQvjV01EKvUTGzOE9ez1NcZ3UkDyxn86bKu171nFUXPOb2tdx5/57c/9BcLrnq1onW3byp8oJnnLKqAth5Tz855+w47lE/xM97+skTP9ckhrbX0G2vtr16eeapx2bb1s2POpBw9qkHPwP4ipvvzgML1kuSB/bM5cqb796wbTCLMSezG/cQs/h5SmY3btZez++nw9ngArVTEPvqeWGS1ep1QZSh2+11UYFZnB8rmb2LuAw1q6fH9irY9jqVcBrz5PU4aNOrrefXX++LUE2j0Nuj8DmL8+T1PsXVgVAONasteMzta/mht3881+y+N/ta8hPv/nTO2XFc3vWq56z5D2KF4tkw5EDCkOJ2L7MYczK7cQP78/20/qYyB/XhdAriRtNz5OVqC5C9RgT3vsJwr6v19jCN0fmzVuCe1XkUexZsV7tu73nyeunR1kP0vlJ4z4MJh1tbw0Y0ZDRyD5devTuX33BX9o0HUty/Zy6X33BXLr1697r8OPZDfOMbciCh1yj5IWYx5mR24wbobUNdJHFWLDdnTq858pLVzSM0ZL65+VEeN9/1QPa15K2XXrPiUR695rmbxnZ7zVM1a/NjDZ17bUj/6mVW51Gc5fm1Drd58mbNNPpWrznyksNvnryebQ3LWW2Ruedo5NU6HKcFYHKrPZAwi6erz2LMyezGDdCbAvWUDBml0WuEx5Cju0NGefQ6qjyN7fYaXTJro1qG/sjqPYpoNWb19NjDMYmetc/TrJrlvjVrIy9nua1hOUOKzLOYR5gWgLU2i/nPLMaczG7csFZmLbemDwXqKRiSQM/qfHNDCpC9fkj7Ab9+hv7ImtVRRLOajM5q3Gx8s9i3ZnHkZTKbbQ0HMqTIPIt5hGkBADgUzWpuzfpToJ6CIQl07xEeq/1BO7QAaSTyoW3ojyyjiIBeen8vAyNDisyzmEcYSAHAoUhuzUpt6h3AoeBACfRartvTfAHyMVs3p5I8xigPFpj/kfUfX/Gs/MwLn5b/+IpnTXSEVP8CepnV72U41MwXmRdaaZF5VvOI+YEUP/GCs/KCZ5yiOA3AzJNbs1JGUE/BkFEaszjCIzHKg4MzLzIwi2b1exkONUPOxpJHAMDGILdmpRSop2BIAj3L882ZLoO1pH8BPczy9zIcSoYWmeURANCf3JqVqtZa7xiWtHPnzrZr167eYazY3L626gR6yLoAwHT5XmZeVX2ytbazdxzTMmv5NQAw++TWzDtQbq1ADQAAS1CgBgCA6ThQbu0iiQAAAAAAdKFADQAAAABAFwrUAAAAAAB0oUANAAAAAEAXCtQAAAAAAHShQA0AAAAAQBfVWusdw5Kq6rYk13fa/ElJbu+07VmkvSajvSajvVZOW01Ge01Ge01Ge01mo7bXk1pr23sHMS0d8+uN+v5uVNprMtprMtprMtpr5bTVZLTXZLTXZDZqey2bW2/YAnVPVbWrtbazdxyzQntNRntNRnutnLaajPaajPaajPaajPY6tHl/J6O9JqO9JqO9JqO9Vk5bTUZ7TUZ7TWYW28sUHwAAAAAAdKFADQAAAABAFwrUS3tb7wBmjPaajPaajPZaOW01Ge01Ge01Ge01Ge11aPP+TkZ7TUZ7TUZ7TUZ7rZy2moz2moz2mszMtZc5qAEAAAAA6MIIagAAAAAAulCgXqSqzq+qq6vqmqp6be94Nrqquq6qPltVl1fVrt7xbDRV9Y6q2l1Vn1tw3wlV9WdV9fnx/8f3jHGjWKat3lBVN4371+VV9eKeMW4kVbWjqj5SVVdW1RVV9ZPj+/WvJRygvfSxJVTVUVX1iar6q3F7/Zvx/WdW1cfH35HvraqtvWPt7QBt9c6q+uKCvnVO51A3lKraXFWfrqoPjm/rW4coufVk5NYHJreejPx65eTWk5FbT0ZuPRn59eQOhdxagXqBqtqc5K1JXpTk7CSvqKqz+0Y1E/5ua+2c1trO3oFsQO9Mcv6i+16b5JLW2llJLhnfZum2SpI3jfvXOa21i9c5po1sb5Kfba2dneS5SX58vL/Sv5a2XHsl+thSHkry/NbaNyY5J8n5VfXcJL+aUXs9NcmdSV7VL8QNY7m2SpJ/saBvXd4rwA3qJ5NcteC2vnUIkluvmtx6ee+M3HoS74z8eqXk1pORW09Gbj0Z+fXkZj63VqDe37lJrmmtXdta25PkPUle1jkmZlhr7S+SfGXR3S9L8jvjv38nyfesZ0wb1TJtxTJaa7e01j41/vuejL6MTov+taQDtBdLaCP3jm8eMf7Xkjw/yfvG9+tfOWBbsYyqOj3JdyX5b+PbFX3rUCW3Zqrk1pORX6+c3HoycuvJyK0nI7+ezKGSWytQ7++0JDcsuH1j7GQPpiX5n1X1yap6de9gZsQprbVbxn9/OckpPYOZAa+pqs+MT1F0St0SquqMJM9K8vHoXwe1qL0SfWxJ49PELk+yO8mfJflCkrtaa3vHi/iOHFvcVq21+b71y+O+9aaqOrJfhBvOm5P8yyT7xrdPjL51qJJbT05uPTm5z+TkPgcgt56M3Hpl5NaTkV9P5M05BHJrBWqG+rbW2rMzOnXzx6vq7/QOaJa01locCTyQ/5zkKRmd1nNLkl/rGs0GVFVHJ/mDJD/VWrt74WP616Mt0V762DJaa3OttXOSnJ7RKMiv6xvRxrW4rarq65P8fEZt9s1JTkjyc/0i3Diq6iVJdrfWPtk7Ftig5NYDyH1WRO5zAHLrycitV05uPRn59cocSrm1AvX+bkqyY8Ht08f3sYzW2k3j/3cn+cOMdrQc2K1V9YQkGf+/u3M8G1Zr7dbxF9O+JL8V/Ws/VXVERgnh77XW3j++W/9axlLtpY8dXGvtriQfSfK8JMdV1ZbxQ74jF1nQVuePT31trbWHkvx29K1535rkpVV1XUbTPTw/yW9E3zpUya0nJLdeFbnPBOQ+y5NbT0ZuvTpy68nIrw/qkMmtFaj3d1mSs8ZXu9ya5IIkF3WOacOqqsdW1THzfyf5ziSfO/BaZNSnfmT8948k+aOOsWxo88ng2N+P/vWI8bxSb09yVWvt1xc8pH8tYbn20seWVlXbq+q48d/bkrwwo7kFP5Lke8eL6V9Ztq3+esGP2cpozjd9K0lr7edba6e31s7IKM/6cGvtB6JvHark1hOQW6+a3GcCcp+lya0nI7eejNx6MvLrlTuUcusanaXCvKp6cUbzt2xO8o7W2i/3jWjjqqonZzSyI0m2JPl97bW/qnp3kvOSnJTk1iS/mOQDSS5M8sQk1yf5vtbaYX/xkmXa6ryMTg9rSa5L8qML5oA7rFXVtyX530k+m6/NNfW6jOZ+078WOUB7vSL62KNU1TdkdDGNzRkdzL6wtfbG8X7/PRmdUvfpJD84HsFw2DpAW304yfYkleTyJD+24GIvJKmq85L889baS/StQ5fceuXk1gcnt56M/Hrl5NaTkVtPRm49Gfn16sx6bq1ADQAAAABAF6b4AAAAAACgCwVqAAAAAAC6UKAGAAAAAKALBWoAAAAAALpQoAYAAAAAoAsFagAAAAAAulCgBgAAAACgCwVqAAAAAAC6+P8ARymBcH35r+gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x1080 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "maxLags = 40\n",
    "\n",
    "fig = plt.figure(figsize=(20,15), constrained_layout=True)\n",
    "spec = gridspec.GridSpec(ncols=2, nrows=4, figure=fig)\n",
    "\n",
    "# Auto-correlation function for original time series\n",
    "ax_acf = fig.add_subplot(spec[0, 0])\n",
    "sm.graphics.tsa.plot_acf(df_train, lags=maxLags, ax=ax_acf, title='Autocorrelations (' + serie + ')')\n",
    "# Partial auto-correlation function for original time series\n",
    "ax_pacf = fig.add_subplot(spec[1, 0])\n",
    "sm.graphics.tsa.plot_pacf(df_train, lags=maxLags, ax=ax_pacf, title='Partial Autocorrelations (' + serie + ')');\n",
    "\n",
    "# Auto-correlation function for time series transformed with Log\n",
    "ax_acfLog = fig.add_subplot(spec[0, 1])\n",
    "sm.graphics.tsa.plot_acf(np.log(df_train), lags=maxLags, ax=ax_acfLog, title='Autocorrelation with Log('+ serie + ')')\n",
    "# Partial auto-correlation function for time series transformed with Log\n",
    "ax_pacfLog = fig.add_subplot(spec[1, 1])\n",
    "sm.graphics.tsa.plot_pacf(np.log(df_train), lags=maxLags, ax=ax_pacfLog, title='Partial Autocorrelation with Log('+ serie + ')');\n",
    "\n",
    "# Auto-correlation function for time series transformed with Diff\n",
    "ax_acfDiff = fig.add_subplot(spec[2, 0])\n",
    "sm.graphics.tsa.plot_acf(df_train.diff().dropna(), lags=maxLags, ax=ax_acfDiff, title='Autocorrelation with Diff('+ serie + ')')\n",
    "# Partial auto-correlation function for time series transformed with Diff\n",
    "ax_pacfDiff = fig.add_subplot(spec[3, 0])\n",
    "sm.graphics.tsa.plot_pacf(df_train.diff().dropna(), lags=maxLags, ax=ax_pacfDiff, title='Partial Autocorrelation with Diff('+ serie + ')');\n",
    "\n",
    "# Auto-correlation function for time series transformed with Log\n",
    "ax_acfDiffLog = fig.add_subplot(spec[2, 1])\n",
    "sm.graphics.tsa.plot_acf(np.log(df_train).diff().dropna(), lags=maxLags, ax=ax_acfDiffLog, title='Autocorrelation with Diff(Log('+ serie + '))')\n",
    "# Partial auto-correlation function for time series transformed with Log\n",
    "ax_pacfDiffLog = fig.add_subplot(spec[3, 1])\n",
    "sm.graphics.tsa.plot_pacf(np.log(df_train).diff().dropna(), lags=maxLags, ax=ax_pacfDiffLog, title='Partial Autocorrelation with Diff(Log('+ serie + '))');\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ARIMA Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-processing\n",
    "    From now on, we must choose whether we go with the Log(Time-Series) or not. This should be done to remove stationarity of the data, so that ARIMA can work. In addition, if the model is multiplicative we need to use log to make it additive as ARIMA relies on that assumption. The previously seen differentiated data enters in the I component of the ARIMA model it will take care of differenciating for us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "isLog = True\n",
    "\n",
    "if (isLog):\n",
    "    df_Train = pd.DataFrame(np.log(df_train), index = df_train.index).copy()\n",
    "else:\n",
    "    df_Train = df_train.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model training\n",
    "    In this section we will train several ARIMA models based on distinct (p,d,q) combinations.\n",
    "    Not only train but also forecast, and extract their AIC, BIC and HQIC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/4 \n",
      "2/4 \n",
      "3/4 \n",
      "4/4 \n"
     ]
    }
   ],
   "source": [
    "# Range of ARIMA models to compute\n",
    "class arimaCfg:\n",
    "    p_range = range(1, 3) # [1,7)\n",
    "    d_range = range(1, 2) # [1,3)\n",
    "    q_range = range(1, 3) # [1,7)\n",
    "\n",
    "dict_arimaFit, dict_arimaPred, df_IC = ARIMA_grid(df_Train.values, arimaCfg, isLog)\n",
    "#dict_arimaFit, dict_arimaPred, df_IC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Comparison based on Information Criteria\n",
    "    AIC - Akaike information criterion (AIC) is an estimator of out-of-sample prediction error and thereby relative quality of statistical models for a given set of data.\n",
    "    BIC - Bayesian information criterion (BIC) is a criterion for model selection among a finite set of models. It is based, in part, on the likelihood function and it is closely related to AIC.\n",
    "    HQIC - Hannan–Quinn information criterion (HQIC) is a criterion for model selection. It is an alternative to AIC and BIC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   TOP 5 AIC\n",
      "ARIMA(1, 1, 1) -40829.244481\n",
      "ARIMA(1, 1, 2) -40827.275726\n",
      "ARIMA(2, 1, 1) -40827.272424\n",
      "ARIMA(2, 1, 2) -40826.227822\n",
      " \n",
      "                   TOP 5 BIC\n",
      "ARIMA(1, 1, 1) -40802.009211\n",
      "ARIMA(1, 1, 2) -40793.231638\n",
      "ARIMA(2, 1, 1) -40793.228336\n",
      "ARIMA(2, 1, 2) -40785.374917\n",
      " \n",
      "                  TOP 5 HQIC\n",
      "ARIMA(1, 1, 1) -40819.838455\n",
      "ARIMA(1, 1, 2) -40815.518193\n",
      "ARIMA(2, 1, 1) -40815.514892\n",
      "ARIMA(2, 1, 2) -40812.118783\n"
     ]
    }
   ],
   "source": [
    "print(df_IC[['AIC']].sort_values('AIC').head().rename(columns={'AIC': 'TOP 5 AIC'}))\n",
    "print(\" \")\n",
    "print(df_IC[['BIC']].sort_values('BIC').head().rename(columns={'BIC': 'TOP 5 BIC'}))\n",
    "print(\" \")\n",
    "print(df_IC[['HQIC']].sort_values('HQIC').head().rename(columns={'HQIC': 'TOP 5 HQIC'}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selected Models Information Criteria Comparison\n",
    "    Evaluate models based on their Information Criteria (IC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['ARIMA(1, 1, 1)',\n",
    "          'ARIMA(1, 1, 2)']\n",
    "\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "params = {'legend.fontsize': 15,\n",
    "          'legend.handlelength': 2}\n",
    "plt.rcParams.update(params)\n",
    "spacing = 0.05\n",
    "barWidth = 0.025\n",
    "r = np.arange(3) - ((len(labels)/2)-1)*spacing\n",
    "\n",
    "for i in np.arange(len(labels)):\n",
    "    plt.bar(r, df_IC.loc[labels[i]].values, width=barWidth, edgecolor='white', label=labels[i])\n",
    "    r = [x + spacing for x in r]\n",
    " \n",
    "plt.xticks([r + barWidth for r in range(3)], ['AIC', 'BIC', 'HQIC'], fontsize='20')\n",
    "plt.axis((-0.5, 3.0, min(df_IC.min())-0.05*(max(df_IC.max())-min(df_IC.min())), max(df_IC.max())+0.05*(max(df_IC.max())-min(df_IC.min()))))\n",
    "plt.legend(loc='best')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selected Models Cross-Validation\n",
    "    Although IC provides an estimate of the quality of each model, relative to each of the other models. It does so by analyzing the models itself, and not having a look into the data. Another raised question is, which IC should be followed in this case.\n",
    "    By applying cross-validation and having a look at the data itself, and the model, simultaneously. It will be possible to estimate the prediction performance, but also, which model is the most likely to perform better with un-seen data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(1, 1, 1)\n",
      "1/32\n",
      "2/32\n",
      "3/32\n",
      "4/32\n",
      "5/32\n",
      "6/32\n",
      "7/32\n",
      "8/32\n",
      "9/32\n",
      "10/32\n",
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      "\n",
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      "\n"
     ]
    }
   ],
   "source": [
    "labels = ['ARIMA(1, 1, 1)',\n",
    "          'ARIMA(1, 1, 2)']\n",
    "\n",
    "# Select data to be used for cross-validation\n",
    "Xcv, ycv = splitTrain.split_train_variableInput(sequence = df_Train.values[-540:], \n",
    "                                                minSamplesTrain = 200, \n",
    "                                                numOutputs = stepsToForecast[-1], \n",
    "                                                numJumps = 10)\n",
    "\n",
    "dict_MAE = {} \n",
    "for model_str in labels:\n",
    "    list_conv = list()\n",
    "    list_mae = list()\n",
    "    list_mape = list()\n",
    "    print(\"%s\" % model_str);\n",
    "    for i in np.arange(len(Xcv)):\n",
    "        try:\n",
    "            model = ARIMA(Xcv[i], order=parse_tuple(model_str[5:]));\n",
    "            model_fit = model.fit(disp=0);\n",
    "\n",
    "            if (~np.isnan(model_fit.mle_retvals.get('fopt'))):\n",
    "                if (isLog):\n",
    "                    predictions_array = np.e**(model_fit.forecast(steps=stepsToForecast[-1])[0]);\n",
    "                else:\n",
    "                    predictions_array = model_fit.forecast(steps=stepsToForecast[-1])[0];\n",
    "\n",
    "                list_conv.append(i+1)\n",
    "\n",
    "                mae = horizon_mae(ycv[i], predictions_array, stepsToForecast)\n",
    "                list_mae.append(mae)\n",
    "\n",
    "                mape = horizon_mape(ycv[i], predictions_array, stepsToForecast)\n",
    "                list_mape.append(mape)\n",
    "\n",
    "                print(\"%d/%d\" % (i+1, len(Xcv)));\n",
    "        except:\n",
    "            continue\n",
    "    print(\"\")\n",
    "\n",
    "    dict_MAE[model_str] =  {'conv': list_conv, 'mae': list_mae, 'mape': list_mape}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nu9/9Lpdccsn22++88w7f+ta36N+/PwUFBQwZMqTZ+7Yk29tat24dZWVlrFy5cpf3IyIiIiIiIjn29jNw1wnBGdJqtmD4p2dMu+uEYH0m9R9FytL370lZHPqfl9kcO0mFpAhWb6rkhrnL6DfxaQ669nH6TXyaG+YuY/WmyqxlWLBgAatWrQJg5syZzba7//77WbBgATNnzqRTp06cffbZvPDCCy1uv127drz77rssWrSowfJXXnmFVatW0a5du13OtWbNGqZOnco111yzfdny5ct54okn6Nu3LwcffHCL+dLJ9ra6d+/Oeeedx09/+tPd2peIiIiIiIjkSP0zpjWepyhVGyyfdWHQLkPWHXopn6TSn+n7k1QB6/7jGxnLsCtUSGpBxYqNDJ88n/KFa9hancCBrdUJyheuYfjk+VSs2JiVHDNnzqS0tJRjjjkmbSGpf//+HHvssZxyyinMmTOH/fffnxkzZrS4/dLSUoYNG0Z5eXmD5eXl5QwbNqzZs49FyXX33XczcOBADjnkkO3LTj/9dNasWcPs2bM57LDDWsyXTi62dckllzBz5kw2bdq0W/sTERERERGRHMiDM6bdtTTFVYkJbPNiarxhQanGC9jmxVyVmMDdSz1jGXaFCklprN5UybgZS6iqTZJINXzhEimnqjbJuBlLWPOvqozmSCaTzJo1izPOOINvfOMbvPXWW7z++ust3q99+/Z84QtfYM2aNZH2M3r0aGbNmoV78FjdnVmzZjF69OjdyjV9+nTOPffcBstisdZ76+ViWyeccAIdO3bcofAmIiIiIiIibUAenDFt7qvreS55OMNrbmZmchhbvISUG1u8hJnJYQyvuZnnkofzh1fXZSzDrlAhKY2p81dSm0ylbVObTDH9r2szmqOiooINGzYwevRozj33XAoLC9P2SqqTSqVYu3YtBx10UKT9nH322WzYsIEXX3wRgPnz5/PBBx9w9tln73KuFStWsHbtWo4//vhIGdoKM+PYY4/l2WefzXUUERERERER2Vl5cMa0yuoEAP/0rkxMXMIXq6fRu/p+vlg9jYmJS/indw3a1SQylmFXqJCUxtxX1+/QE6mxRMp57I3MDm+bOXMm++23H8OHD6djx4587Wtfo7y8fHvPofqSySSJRIIPPviAa665hsrKSsaPHx9pP3X7qOtlU15ezvDhw9l33313OdfixYsB6Nev384+7Lx3+OGHs3DhwlzHEBERERERkZ0V9UxoGTxjWmlx+om2t7critYuW1RISqOuOthyu2TGMtTU1PDwww9z1llnUVRUBARD0FavXs2CBQt2aH/EEUdQWFhIly5duPXWW/nd735H3759I+9v9OjRzJkzh+rqaubMmdPssLZ0ueoXV95//33atWvX7BxLbVnnzp3ZuHFjkwU9ERERERERyV+bDz57h3mJGqvxAjYffE7GMowY0I14zNK2iceMswZ0z1iGXaFCUhqRq4PF6d98u+PJJ5/k448/5pRTTuHjjz/m448/ZsiQIRQXFzc5vK28vJxXXnmFhx56iL59+3LJJZewfv36yPs744wz2Lp1K9dffz2VlZWcfvrpO51r9uzZ29t98sknFBcX7/wDbwOKi4tJJBIkEvnVzVBERERERETSuyd5CgnSf+dPEOeexMkZy3D54N4UFqQvyxQWxLhscLTparIlY4UkM+thZhVm9qaZLTez8eHyw81sgZktM7NHzWyfeve5zszeMbMVZvb1TGWLKmp18LR+XTKWoa5YNHLkSPbff3/2339/evToQXV1NbNnzyaZbNgb6rDDDuPII4/k7LPP5tFHH2Xbtm1MmjQp8v5KS0s57bTTuO222zj99NPTnq2tuVxz587dnqtjx45s3ryZVCr9XFNt0ccff0xZWRmFhYW5jiIiIiIiIiI74d43jXG149OeMW1c7XjufTN9TWB39OxUypSxAykpLNih9hCPGSWFBUwZO5CenfJrhE8meyQlgO+7+6HAscCVZnYocA9wrbt/EfgD8EOAcN1o4DBgODDFzDLX1SeCqNXBC4/5XEb2X1lZyaOPPsqYMWOoqKhocLn11lvZsGEDzz33XLP3//znP89ll13G7373OzZujD6P0xVXXMHpp5/Ot7/97V3KtXHjxu25+vbti7uzevXqnXvwbcCqVas4+OCDcx1DREREREREdlJldYJ5qSPSnjFtXuqIjE90PbRvF56aMJgxRx9IWXEcA8qK44w5+kCemjCYoX0z13FlV2VsxiZ3fw94L7y+xczeAroDBwMvhM2eAZ4G/h9wJlDu7tXAu2b2DnA0sONEQFlSVx0cN2MJtclUg4m34zGjsCDGlLED6bF/SUb2/8c//pFt27Yxfvx4jjnmmAbrTjjhBG666SZmzpzJSSed1Ow2fvSjHzF16lR+/etfR+6ZNGTIEIYMGbLLuX72s59tz3X00UcTj8dZvHhxg7PHbdu2jSeeeAKAdevWsXnzZubMmQPAKaecQvv27bdnAZg3b16zeZra1ty5cykpKWmVbTWVC2DRokWccMIJzW5LRERERERE8lNpcZyt1YntZ0ybmLik6XZZmOi6Z6dSJo3ox6QR/Zg3b17a7+P5ICtTf5tZL2AA8FdgOUHRaC4wEugRNusO/KXe3daGy3Kqrjp4z/x3+cOr66isSVBaFOesAd25bPBB9OxUypYtWzKy75kzZ9KnT58dijUAhYWFjBo1igceeIC77rqr2W307NmTsWPHMmXKFK699tpWmfS6pVxnnXUWc+bM4a677qK0tJSvf/3rPPnkk5x77rnb223cuJGRI0c2uG/d7XfffZdevXoBQWGn7npzmtrWhRde2GrbairXBx98wOLFi/n5z3+ednsiIiIiIiKSf0YM6Eb5wjVpz9SejxNd5wPL9BmnzKwMeB64yd0fNrNDgF8BnYBHgO+6eyczuwP4i7vPCO83DXjS3ec02t43gW8CdO3adVDdqeqbsu+++/KFL3whEw+rgWQySUFBTkfh5XWORx99lKuuuoq///3vOzXxdnV1Nd27d2fu3LmceOKJu5Vhd7bVlHvvvZdf/epXvPrqq5g1P2b27bffZvPmzbu9v921detWysoyd9pK5Wi7OfIhg3IoR75nUA7laAs58iGDcihHvmdQDuWob+O2FDe8VEVNmpOwFxXAz04ooUv77J2nLF9ek6FDhy529yObXOnuGbsAhQRD177XzPqDgYXh9euA6+qtexo4Lt32Bw0a5Om8+eabade3ls2bN2dlPy3J1xzJZNL79evnU6dO3antvPjii97Saxw1w+5sq7FUKuX/8R//4b/97W9bbLtkyZJW2efuqqioyHUEd1eOxvIhRz5kcFeOxpQjvzK4K0djytFQPuTIhwzuytGYcuRJhk3/cH/0avebuntq4r7uN3UPbm/6R84i5cNr4q4cz/1tgx9yw5P++ese957XPLb98vnrHvdDbnjSn/vbhqxnypfXBFjkzdRiMnnWNgOmAW+5+631lncJf8aAG4C7w1WPAKPNrNjMDgL6AAszlU+yJxaLMXXq1J0+u9kJJ5zAokWLWiVDa27r/fff5z//8z+54IILWmV7IiIiIiKSIW8/A3edAEumQ80WDIeaLcHtu04I1steqy1OdJ0PMjlH0gnABcAyM3stXPZfQB8zuzK8/TDwWwB3X25ms4A3Cc74dqW7p+lkJm3Jsccey7HHHpvrGK3igAMO4Prrr891DJFdtnpTJVPnr2Tuq+vZWp2grOJpRgzoxuWDe+fdqUVFREREdtlHK2HWhVC7bcd1qdrgMutCuOIl6Ng7+/n2dh+thJfvgKWz+HLNVni5DPqPguOvyurr0dYmus4HmTxr24tAc5PH3N7MfW4CbspUJhGRvV3Fio38z4wnuJDHWBB7kdLiKiop4Y+LT+Tyxadx3dhT9J8XERER2TO8fAcka9O3SdbCgjvh1Fuyk0kCbz8TFPGSQUHP4NOeYq/PhFHToU/zZyeX3MrejFEiIpJTqzdVMnPGNObGfsSo2HN0sCpiBh2silGx55gb+xEzZ0xj9abKXEcVERER2X1LZwW9jtJJ1cLSB7OTRwL1e4o1fn1StcHyWRcG7SQvZXJom4iI5JGHnp3P5NhttLfqHdYVWZIikkyO3cbdzw7ge+cNz3iedSuXs+bxX9Dvw6f4ElVsrSjhjc7D6XHqD+ne+7CM77+OhvqJiIjsoWq2tm47aR3qKdbmqUeSiMhe4oA3pxEnkbZNnASfffPejGd5vWI2+/9+KAM/fISysGdUmVUx8MNH2P/3Q3m9YnbGM0Aw1O/yybM5ZPFPWMBFrCw+nwVcxCGLf8Llk2dTsWJjVnKIiIhI60sVRvuHUKow96da36uop1ibp0KSiMhe4jTmU2Tpz2FQZElO54WM5li3cjl95l1Je6veIU+RJWlv1fSZdyXrVi7PaA4N9RMREdmzLdznJGq8IG2bGi9gYQfNxZNNHrEHmFerp1i+UiFJRGQvUconrdpuV615/BeRekatefyXGc1Rf6hfcwWtybHbeOjZ+RnNISIiIpnxkw+HkmhhNpcEcX7y4ZDsBBIAqmMlEdu1z3AS2VUqJImI7CVqCqL90a4pyOwf7X4fPhWpZ9RhHz6Z0Rz5NNRPREREWt/fqjszrnY827x4h55JNV7ANi9mXO14/lbTOUcJ905/SJ4YqafYw8kTs5RIdpYKSW3IQQcdhJnxzjvv7LBu3rx57LPPPpgZZsa+++7LMcccw9y5c3do26tXL37wgx9sv33jjTdiZvTp06fJ/fbp0wcz48Ybb9zpXHW++93vcskll2y//c477/Ctb32L/v37U1BQwJAhQ5q9b0tac1uzZ8/mjDPOoHv37pSVlTFo0CBmzpzZoE1VVRVdunRh/nz1UpC2pebQkZH+aNccNjKjOdpTFaldpntG5ctQPxERkT3WRyvhse/Bf3+OL88bAf/9ueB2ls7GVVocZ17qCIbX3MzM5DC2eAkpN7Z4CTOTwxheczPzUkdQWqRzUGXT3TUnR+op9puazJ/8RXaNCklR1DsAcuN+WT8AAixYsIBVq1YB7FDYqO/+++9nwYIFzJw5k06dOnH22Wfzwgstfwlq164d7777LosWLWqw/JVXXmHVqlW0a9dul3OtWbOGqVOncs0112xftnz5cp544gn69u3LwQcf3GK+dFpzW7feeitlZWXcdtttPPLIIwwdOpTzzz+fX//619vblJSU8J3vfIf/9//+327tSyTb9hk2gYJ4Udo2BfEi9hk6PqM5thGtZ1QlTR93Wku+DPUTERHZI739DNx1AiyZDjVbMBxqtgS37zohWJ9hIwZ0Ix4z/uldmZi4hC9WT6N39f18sXoaExOX8E/vSjxmnDWge8azyKc2FXWP1FNsU5Fel3ylQlJLGh0AycEBEIIiTWlpKcccc0zaQlL//v059thjOeWUU5gzZw77778/M2bMaHH7paWlDBs2jPLy8gbLy8vLGTZsGKWlTZ/xIEquu+++m4EDB3LIIYdsX3b66aezZs0aZs+ezWGH7d5pvltzW48++igPPPAAo0aNYtiwYfzyl79kzJgx3HrrrQ3aXXzxxbzwwgssW7Zst/YnklUde1Mw+j5S8ZId/guUIE4qXkLB6PugY++Mxnij8/BIPaOWdz45oznyZaifiIjIHuejlTDrQqjdtuPZuVK1wfJZF2b8H/OXD+5NYUH6r7yFBTEuG3xQRnNIQyMGdONFBqTtKfYiA1Tgy2MqJKUT8QBoH6/KaIxkMsmsWbM444wz+MY3vsFbb73F66+/3uL92rdvzxe+8AXWrFkTaT+jR49m1qxZuDsA7s6sWbMYPXr0buWaPn065557boNlsVjrvfVac1udO+84PnrAgAGsX7++wbIePXpw1FFHMX369Fbbt0hW9DmJ2LiXiR91MRR3wDEo7kD8qIuJjXsZ+mT+rCU9Tv1hpO7MPU79Qdo2uytfhvqJiIjscV6+A5ItnN49WQsL7sxojJ6dSpkydiAlhQXEY9ZgXTxmlBQWMGXsQHp2avqf5pIZdQW+dD3FVODLbyokpRPxAFi0aGpGY1RUVLBhwwZGjx7NueeeS2FhYdpeSXVSqRRr167loIOi/QKeffbZbNiwgRdffBGA+fPn88EHH3D22Wfvcq4VK1awdu1ajj/++EgZ8tGCBQuaHDJ3/PHH8+yzz+Ygkchu6tgbTr0FrlvL80PmwnVrg9sZ7olUp3vvw3h7yJ1puzO/PeROuvfevR6GLcmXoX4iIiJ7nKWzdvxHfGOpWlj6YMajDO3bhacmDGbM0QdSVhzHgLLiOGOOPpCnJgxmaN8uGc8gDanA1/apkJROxANg4VsPZzTGzJkz2W+//Rg+fDgdO3bka1/7GuXl5dt7DtWXTCZJJBJ88MEHXHPNNVRWVjJ+fLQvQXX7qBveVl5ezvDhw9l33313OdfixYsB6Nev384+7Lzw5z//mblz5/L9739/h3WHH344y5Yt45NPNH+KyM46fOhI/nVRBUs6n9mgO/OSzmfyr4sqOHxoFnoB5clQPxERkT2N12yN1q46Wrvd1bNTKZNG9OONn3yd3w4v5Y2ffJ1JI/qpUJFDKvC1bZqePp2IB8DI7XYlQk0NDz/8MGeddRZFRcF/zkePHs0FF1zAggULdujpc8QRR2y/HovFeOihh+jbt2/k/Y0ePZoJEyZw6623MmfOHH71q1/tdK6FCxfy1a9+FYD333+fdu3aNTvHUj5btWoV559/PmeeeSYXX3zxDus7d+5MMpnkgw8+oEePHtkPKNLGde99GN2/81sgOPPkkCFDODbbIcKhfrEFd8LSB/HqrVhxGfH+58FxV6qIJCIibddHK4MRFktn8eWarfByGfQfBcdflfG/b9WxEtqltkVo1z7Dp9aQfFZX4Js0ot/2z4LSNqhHUjpFZa3bbhc8+eSTfPzxx5xyyil8/PHHfPzxxwwZMoTi4uImh7eVl5fzyiuvbC8gXXLJJTvM75POGWecwdatW7n++uuprKzk9NNP3+lcs2fP3t7uk08+obi4eOcfeI599NFHnHzyyfTs2ZP777+/yTZ1j0s9kkTauBwP9RMREWl1OT5j2h+SJ0aah/Dh5IkZzSEimaFCUjr9R0GsMH2bWCG1/9H0HEKtoa5YNHLkSPbff3/2339/evToQXV1NbNnzyaZTDZof9hhh3HkkUdy9tln8+ijj7Jt2zYmTZoUeX+lpaWcdtpp3HbbbZx++ulpz9bWXK65c+duz9WxY0c2b95MKpXalYefE9u2beO0006jpqaGxx57jPbtmz5j08cffwwEj1FEREREJC/kwRnT7q45OdKJNX5TMzxjGUQkc1RISuf4q6CghUJSQSE1R16ekd1XVlby6KOPMmbMGCoqKhpcbr31VjZs2MBzzz3X7P0///nPc9lll/G73/2OjRs3Rt7vFVdcwemnn863v/3tXcq1cePG7bn69u2Lu7N69eqde/A5kkgkGDlyJG+//TZPPfUUXbo0PzZ31apVdOrUiU6dOmUxoYiIiIhIGnlwxrRNRd0ZVzs+7Yk1xtWOZ1ORTu8u0hZpjqR0OvaGUdODin2ytmFFP1YYFJlGTcf365WR3f/xj39k27ZtjB8/nmOOOabBuhNOOIGbbrqJmTNnctJJzZ+u+0c/+hFTp07l17/+deSeSUOGDEk7PrWlXD/72c+25zr66KOJx+MsXry4wdnjtm3bxhNPPAHAunXr2Lx5M3PmzAHglFNO2d4LqC7HvHnzms3T1Lbmzp1LSUnJTm9r3LhxPPHEE9x+++1s2rSJTZs2bV83YMCABsP0Fi1a1KbPRiciIiIie6CdOWPaqbdkJMKIAd0oXziA4TU3c2nBE5xd8CKlfEIl7Xg4eSLTkqew3j7LmAEqJIm0RSoktaTPSXDFS0HFfumDwcTaRWVQfyLWLVsysuuZM2fSp0+fHYo1AIWFhYwaNYoHHniAu+66q9lt9OzZk7FjxzJlyhSuvfbaVpn0uqVcZ511FnPmzOGuu+6itLSUr3/96zz55JOce+6529tt3LiRkSMbnpWp7va7775Lr169gKBIVHe9OU1t68ILL9ylbf3pT38CaPJMd/W3lUgk+POf/8wtt2Tmj6+IiIiIyK7wmq1Yy82CE0xkKMPlg3vz0OJ1/LO2KxMTlzAxcckObUriMS4bfFAT9xaRfKdCUhR1E7FmqGLfnEcffTTt+ilTpjBlyhQg6G2zefNmOnTosEO7e++9t8HtVatWNbh94403cuONN6bd14cffhg512233cY999yz/fall17KZZddxpQpU7b36OnVqxfunnY71dXVvP766/ziF79I266pbW3ZsqXBcxF1W42fm+b8+c9/pra2tkFxTEREREQk1/LhjGk9O5UyZexAxs1YQm0yRSL16Wf1eMwoLIgxZexAenZqe2d2FhHNkSRZcOaZZ9KtWzfuu+++nbrfokWL+OIXv8iXv/zl3c7QmtuCoFh29dVXt0oPLxERERGR1pIvZ0wb2rcLT00YzJijD6SsOI4BZcVxxhx9IE9NGMzQvs3PRSoi+U09kiTjYrEYU6dOZcWKFTt1vxNOOIFFixa1SobW3FZVVRXHHXccV199datsT0RERESktdxdczJnFj1PEclm29SdMe38DGfp2amUSSP6MWlEP+bNm5d2HlYRaTvUI0my4thjj+Wiiy7KdYxWUVJSwsSJE9lnn31yHUVEREREpAGdMU1EMk2FJBERERERkT3EiAHdeJHgjGkzk8PY4iWk3NjiJcxMDmN4zc28yADO0hnTRGQX7fFD29wds0ydj0CkeS1NJi4iIiIi0tp0xjQRybQ9ukdSYWEhVVVVuY4he6mqqioVk0REREQkq+rOmFZSWEA81vAf6vGYUVJYoDOmichu2aMLSV26dGHdunVs27ZNX+gla9ydbdu2sW7dOiorK3MdR0RERET2Mjpjmohk0h49tK1uMuT169dTW1ubsf188skntGvXLmPbV462l6GwsJCuXbuybt26nGUQERERkb2XzpgmIpmyRxeSICgmZfrsWvPmzWPAgAEZ3YdytL0MIiIiIiIiInuaPb6QJCIiIiIiIpJr61YuZ83jv6Dfh0/xJarYWlHCG52H0+PUH9K992G5jicSWcbmSDKzHmZWYWZvmtlyMxsfLj/CzP5iZq+Z2SIzOzpcbmb2KzN7x8yWmtnATGUTERERERERyZbXK2az/++HMvDDRyizKmIGZVbFwA8fYf/fD+X1itm5jigSWSYn204A33f3Q4FjgSvN7FDg58BP3P0I4MfhbYCTgT7h5ZvAXRnMJiIiIiIiIpJx61Yup8+8K2lv1RRZssG6IkvS3qrpM+9K1q1cnqOEIjsnY4Ukd3/P3ZeE17cAbwHdAQfqJi3aF1gfXj8TmO6BvwD7mdkBmconIiIiIiIikmlrHv8FcRJp28RJsObxX2YpkcjuycocSWbWCxgA/BWYADxtZr8kKGQdHzbrDqypd7e14bL3spFRREREREREpLX1+/CpHXoiNVZkSQ778MksJRLZPebumd2BWRnwPHCTuz9sZr8Cnnf3h8xsFPBNd/+qmT0G3OzuL4b3+zNwjbsvarS9bxIMfaNr166DysvLM5o/iq1bt1JWVpbrGMqRZxmUQznaQo58yKAcypHvGZRDOdpCjnzIoBzKke8ZcpXjSxVnErOW26XceGHo3IznqW9vfl3yNUc+ZAAYOnToYnc/ssmV7p6xC1AIPA18r96yf/NpAcuAzeH13wBj6rVbARyQbvuDBg3yfFBRUZHrCO6uHPmWwV05GlOOhvIhRz5kcFeOxpQjvzK4K0djytFQPuTIhwzuytGYcuRXBvfc5Njy467uE/dp8bL5x12znm1vfl2akg858iGDuzuwyJupxWTyrG0GTAPecvdb661aD3w5vD4MeDu8/ghwYXj2tmOBf7u7hrWJyB5l9aZKbpi7jH4Tn+bipyrpN/Fpbpi7jNWbKnMdTUREREQy4I3Ow6nxgrRtaryA5Z1PzlIikd2TyTmSTgAuAJaZ2Wvhsv8CLgduN7M48AnhMDXgCeAU4B1gG3BJBrOJiGRdxYqN/M+MJ7iQx1gQe5HS4ioqKeGPi0/k8sWncd3YUxjat0uuY4qIiIhIK+px6g9J/P4Jimh+nqQEcXqc+oMsphLZdRkrJHkw11FzI0EHNdHegSszlUdEJJdWb6pk5oxpzI3dRpzE9gkXO1DFqNhznM0LTJjxHr0nfJeenUpznFZEREREWkv33ofx+pA76TPvygafAyHoiZQgzttD7uTw3oflMKVIdBkb2iYiIp966Nn5TI7dRnur3uGsHUWWpL1VMzl2Gw89Oz9HCUVEREQkUw4fOpJ/XVTBks5nssVLSLmxxUtY0vlM/nVRBYcPHZnriCKRZXJom4gIqzdVMnX+Sua+up6t1QnKKp5mxIBuXD64d1Z73uQ6xwFvTiNOIm2bOAk+++a9wPCM5xERERGR7Ore+zC6f+e3AMybN48hQ4ZwbI4ziewKFZJEJGPyZU6gfMhxGvN36InUWJElOd1fyGgOEREREdm75fofrNL2qZAkIhmRL3MC5UuOUj5p1XYiIiIiIjurYsVGxs1YQm0yRSLlAGytTlC+cA0PLV7HlLEDdfIXaZHmSBKRjMiXOYHyJUdNQUnEdu0zmkOkLVi9qZIb5i6j38SnufipSvpNfJob5i5j9abKXEcTERFps1ZvqmTcjCVU1Sa3F5HqJFJOVW2ScTOW6O+ttEiFJBHJiJ2bE2jPz1Fz6EhqvCB9Gy+g5jBNtCh7t4oVG7l88mwOWfwTFnARK4vPZwEXccjin3D55NlUrNiY64giIiJt0tT5K6lNptK2qU2muGf+u1lKJG2VCkkikhGR5wQis3MC5UuOfYZNoCBelLZNQbyIfYaOz2gOkXz26VDUHzEq9hwdrIqYQQcLhqLOjf2ImTOm6T+lIiIiu2Duq+t36InUWCLl/OHVdVlKJG2VCkkikhH5MidQvuSgY28KRt9HKl5CotH0dAnipOIlFIy+Dzr2zmwOkTyWL0NRRURE9kSV1UEv/QNtA5Pi97Ks+FJWFp/PsuJLmRS/lwNtQ9CuJn1vfhFNti0iGVFTUEK71LYI7drTbi/IAUCfk4iNe5nYgjth6YN49VasuIx4//PguCtVRJK93s4NRR2enVAiIjtJZ8SSfFVaHOfI2kVMKbx9h5PQnFdQwTkF8xlXO55FhUfmOKnkOxWSRCQjag4dSWzZjLTDyurmBMpoISlPcmzXsTecegucegvPz5vHkCFDsrFXkTYh8lBUz+xQVBHZOSqcfEpnxJJ89o1DnW+/eTvtrXqHdUWWpIgkUwpv5+5Dp+cgnbQlGtomIhmRL3MC5UsOEWlZ3gxFFZHIKlZsZPjk+ZQvXMPWcNhMXeFk+OT5e9UE+XVnxPpMYj0TY9MaDBuaGJvGZxLrdUYsyanLCp6I1PP3sviTWUokbZUKSSKSGfkyJ1C+5BCRFtUUlERs1z7DSUQkChVOGpo6fyXHpZbwVNG1nFdQ0eCEAecVVPBU0bUcl1qiM2JJzuzz94cj9fzd5+8PZSmRtFUqJIlI5oRzAsWPuhiKO+AYFHcgftTFxMa9DH1O2rtyiEhaNYeOpMYL0rcJh6KKSO6pcNLQ4leXcEd8ctoTBtwRn8yiVxfnKKHs9Wq2tm472WtpjiQRyax8mRMoX3KISLP2GTaB5FuzIFnVbBsNRRXJH4tfXcJDYeGksbr5Vu6IT+acV7vDiH45SJhd5ycfIV7Q8rChMclHgYuyE0qkvqIyqNkSrZ1IGuqRJCIiIvlBQ1FF2pTzk49Emm8lKJzs+c4qeCnSsKGzCl7MUiKRRvqPglhh+jaxQuh/XnbySJulQpKIiIjkDw1FFWkzVDhpqNQinjAgYjuRVnf8VVDQQiGpoBCOuzI7eaTNUiFJRERE8kvdUNTr1vL8kLlw3drgtnoiieQVFU4a8sLSaA01bEhypWNvGDUdCtvv2DMpVhgsHzVdf2+lRWkLSWYWM7NR2QojIiIiIiJtgwonDcUOP4+UpZ+CNmVxYodr2JDkUJ+T4IqXYNBFDXr+MuiiYLl6/koEaQtJ7p4CfpSlLCIiIiIi0kaocNLI8VcRixelbRKLF2nYkOSeev7KbooytO1ZM/uBmfUws451l4wnExERERGR/KXCSUMaNiQie4n0/0II1P0Lof5fAAd0BJS8snpTJVPnr2Tuq+vZWp2grOJpRgzoxuWDe9OzU8Su1yIiIiISTV3hZNaFkKyFVO2n62KFwaS9e1vhpG7Y0II7YemDePVWrLgsOAvWcVfuXc+FiOyxWiwkuftB2QgisjsqVmzkf2Y8wYU8xoLYi5QWV1FJCX9cfCKXLz6N68aewtC+XbIT5qOV8PIdsHQWX67ZCi+XBafaPP4qfXgQERGRPYsKJzuqGzZ06i08P28eQ4YMyXUiEZFW1WIhycwKgSuAL4WL5gG/cffaZu8kkkWrN1Uyc8Y05sZuI05i+2loO1DFqNhznM0LTJjxHr0nfDfzPZPefobUgxeQStQSJ4EB1Gwhsej3xF57gNh592kCOxEREdmzqHAiIrJXiTJH0l3AIGBKeBkULhPJCw89O5/Jsdtob9Xbi0h1iixJe6tmcuw2Hnp2fmaDfLSSZPkFxBJVxEk0WBUnQSxRRbL8gqDHkoiIiIiIiEgbFKWQdJS7X+Tuz4WXS4CjMh1MJKoD3py2Q+GmsTgJPvvmvRnNsfm5ySQTNWnbJBM1bK64PaM5RERERERERDIlymTbSTP7vLv/A8DMegPJFu4jkjWnMX+HnkiNFVmS0/2FjOYoenN2pByp5bPhHBWTRERERFqTTrwiIpIdUQpJPwAqzGwlYEBP4JKMphLZCaV80qrtdlVRsir4DWmx3baM5hARERHZ21Ss2Mi4GUuoTaZIpByArdUJyheu4aHF65gydmD2TrwiIrKHSzu0zcwKgMOBPsB3ge8Afd29IgvZRCKpKSiJ2K59RnNU0q5V24mIiIhIy1ZvqmTcjCVU1Sa3F5HqJFJOVW2ScTOWsHpTZY4SiojsWdIWktw9CYxx92p3XxpeqrOUTSSSmkNHUuMF6dt4ATWHjcxojscYHCnHo9tPgCgiIiIiu2vq/JXUJlNp29QmU9wz/90sJRIR2bNFmWz7JTO7w8wGm9nAuktLdzKzHmZWYWZvmtlyMxsfLn/QzF4LL6vM7LV697nOzN4xsxVm9vVdf1iSbas3VXLD3GX0m/g0Fz9VSb+JT3PD3GVZ+c/PPsMmUBAvStumIF7EPkPHZzTHe4deSqKF0aIJ4rx/6DcymkNERERkbzL31fUkUs6BtoFJ8XtZVnwpK4vPZ1nxpUyK38uBtoFEyvnDq+tyHVVEZI8QZY6kI8KfP623zIFhLdwvAXzf3ZeYWQdgsZk94+7n1TUws1uAf4fXDwVGA4cB3YBnzezgsFeU5LGcj0nv2JuC0feRevACUonaBmdwSxAnFi+k4Lz7oGPvzGUAzvnqYCa8cTWTY7cRJ9Fg4u0aLyBBnAmpq7n+q4MzmkNERERkb1JZnWBI7DWmFN7e4DNYB6o4r6CCcwrmM652PM/XHJHboCIie4gocyQ94u5DG11aKiLh7u+5+5Lw+hbgLaB7vW0bMAqYGS46EygPh9G9C7wDHL1Lj0qypm5M+mcS65kYm9bgP0ATY9P4TGJ9dsak9zmJ2LiXiR91MRR3wDEo7kD8qIuJjXsZ+pyU2f0DPTuVMmbspYxI/ZwHU8PY4iWk3NjiJTyYGsaI1M8ZM/ZSnTVEREREpBUdUvwhUwpvp71V73AG3SJL0t6qmVJ4O4cUfZijhCIie5a0PZLcPWlmY4DbdmcnZtYLGAD8td7iwcAGd387vN0d+Eu99WupV3iS/DR1/kqOSy3hjqLJzf4H6KrEBO6Z/zkmjeiX2TAde8Opt8Cpt/D8vHkMGTIks/trwtC+Xeg9YST3zD+S415dR2V1gtLiOGcN6s7UwQepiCQiIiLSyiZ2riD+YSJtmzgJJnaeB1yUlUwiInsyc/f0DcxuAwqBB4Ht3Urqehu1uAOzMuB54CZ3f7je8ruAd9z9lvD2HcBf3H1GeHsa8KS7z2m0vW8C3wTo2rXroPLy8igxMmrr1q2UlZXlOkZOctz0zEr+GL+W9tb8HOzbvJgRyZv5r69mdmhZfXvza6IcytHWMihHoF3Ve/RY80e6bphHQbKKZEEJG7oOYU2PM/mk5ICcZMqH1yUfMiiHcrSFHPmQIVc5TnhhNIWpqhbb1RaU8NLg7H532Jtfl3zMoBzK0RZy5EMGgKFDhy529yObWpfJOZIws0LgIeD+RkWkOHA2MKhe83VAj3q3Pxcua8Dd/w/4P4AjjzzSc9HrpLF5Oer9kg851jz76wZzEjUlToKxPMGQIXPStmtNe/NrohzK0dYyKAfw9jMw63uQrIVULQDxZBXd33+W7h88D6OmZ2WIbmP58LrkQwblUI62kCMfMuQsx7xPIjUrTH2S9Wx79euShxmUQznaQo58yNCSFgtJ7j50VzYczoE0DXjL3W9ttPqrwN/cfW29ZY8AD5jZrQSTbfcBFu7KviV7zip4aYex6I0VWZKzCl7MUiIRkTbmo5Uw60Ko3bbjulRYWJp1IVzxUsZPGiAi0iYVlUHNlmjtRERktzU72baZTa53fXyjdb+LsO0TgAuAYWb2Wng5JVw3mk8n2QbA3ZcDs4A3gaeAK3XGtvxXatH+AxS1nYjIXuflO4KeSOkka2HBndnJIyLS1vQfBbHC9G1ihdD/vPRtREQkknRnbftSveuNZ6Xr39KG3f1Fdzd37+/uR4SXJ8J1F7v73U3c5yZ3/7y793X3JyM9AskpL4w4ebT+AyQi0rSls7YPZ2tWqhaWPpidPCIibc3xV0FBC4WkgkI47srs5BER2cOlKyRZM9dFtosdfh4pSz9CMmVxYofrP0AiIk3xmq3R2lVHaycistfp2DuYS66w/Y49k2KFwfJR0zU8WESklaQrJMXMbH8z61Tvekcz6wgUZCmf5LvjryIWL0rbJBYv0n+ARESaUR0ridiufYaTiIi0YX1OCuaSG3QRFHfAMSjuENy+4qWcnLBARGRPla4ryb7AYj7tjbSk3jrPWCJpW+r+AzTrwgZnGwKC/wAVFOo/QDmyelMlU+evZO6r69lanaCs4mlGDOjG5YN707NTxCGJIpJxf0ieyDn8Oe2JC2q8gIeTJ3J+FnOJSP7T3/pGOvaGU2+BU2/h+TZw1iMRkbaq2R5J7t7L3Xu7+0FNXFQVkE/pP0B5p2LFRoZPnk/5wjVsrU4AsLU6QfnCNQyfPJ+KFRtznFBE6txdczKJFk6imiDOb2qGZymRiLQF+lsvIiK5km5om0h0df8Bum4tzw+ZC9etDW6rJ1LWrd5UybgZS/hMYj0TY9NYVnwpK4vPZ1nxpUyMTeMzifWMm7GE1Zsqcx1VRIBNRd0ZVzuebV5MjTccOV7jBWzzYsbVjmdTUfccJRSRfFP3t76qNkki1XCgQCLlVNUm9bdeREQyRoUkkT3M1PkrOS61hKeKruW8ggo6WBUxgw5WxXkFFTxVdC3HpZZwz/x3cx1VRIARA7rxIgMYXnMzM5PD2OIlpNzY4iXMTA5jeM3NvMgAzhqgQpKIBKbOX0ltMpW2TW0ypb/1IiKSEen70otIm7P41SU8FJ9Me6veYV2RJSkiyR3xyZzzancY0S8HCUWkvssH9+ahxev4Z21XJiYuYWLikh3alMRjXDb4oOwE+mglvHwHLJ3Fl2u2wstl0H9UcHpt9TIVyQtzX12/Q0+kxhIp5w+vrmOS/taLiEgri9QjycxONLNLwuufMbMsfZoVkZ11fvIR4iTStomTYEzy0SwlEpF0enYqZcrYgZQUFhCPWYN18ZhRUljAlLEDszNx7tvPkJpyPIlFv4eaLRgONVtILPo9qSnHw9vPZD6DiLSosjr93/nt7WqitRMREdkZLRaSzGwicA1wXbioEJiRyVAisuvOKngp7dmfIOiZdFbBi1lKJCItGdq3C09NGMyYow+krDiOAWXFccYcfSBPTRjM0L5dMh/io5Ukyy8glqjaoRgdJ0EsUUWy/IKgx5KI5FRpcTCo4EDbwKT4vQ3mQ5wUv5cDbUPQrkiDD0REpPVF+etyFjAAWALg7uvNrENGU4nILiu1T1q1nYhkR89OpUwa0Y9JI/oxLwenrd783GTaJWoosObbJBM1VFbczj7n3J69YCKygxEDurH+lUe5Iz6ZOInt/0DqQDAf4jkF87kqMYHuA87IcVIREdkTRRnaVuPuDjiAmWWhb72I7CovjPgrWlSW2SAi0qYUvTk7Um/GouWzs5RIRJpzRf8Yd4TzITb+vS2yJO2tmjvik/l2/zSVYRERkV0UpZA0y8x+A+xnZpcDzwL3ZDaWiOyq2OHnkbL0nQ1TFid2+HlZSiQibUFRsipiu20ZTiIiLen+5jTaxdIXftvFknR/694sJRIRkb1Ji4Ukd/8lMAd4COgL/Njdf5XpYCKyi46/ili8KG2TWLwIjrsyS4FEpC2opF2rthORDFo6i5inn0g75glY+mCWAomIyN4kymTb/+vuz7j7D939B+7+jJn9bzbCicgu6NgbRk2HwvYQK2y4LlYYLB81XafxFpEGHmMwNV6Qtk2NF/AoX8pSIhFpVs3W1m0nIiKyE6IMbTupiWUnt3YQEWlFfU6CK16CQRdBcQccg+IOwe0rXgrWi4jU896hl5Jo4RwcCeK8f+g3spRIRJoVdZ5DzYcoIiIZ0GwhycyuMLNlQF8zW1rv8i6wNHsRRWSXdOwNp94C163l+SFz4bq1wW31RBKRJpzz1cFMSF3NNi/eoWdSjRewzYuZkLqac746OEcJRWS7/qN27HXcWKwQ+ms+RBERaX3peiQ9AJwOPBL+rLsMcvexWcgmIiIiWdKzUyljxl7KiNTPeTA1jC1eQsqNLV7Cg6lhjEj9nDFjL6VnJ528VSTnjr8KClooJBUUaj5EERHJiGb7sLv7v4F/m9k1jVaVmVmZu/8zs9EkitWbKpk6fyVzX13P1uoEZRVPM2JANy4f3Fsf9kVEZKcM7duF3hNGcs/8Iznu1XVUVicoLY5z1qDuTB18kP6uiOSLuvkQZ10IyVpI1X66LlYYFJE0H6KIiGRI+skQAo8DDhjQDjgIWAEclsFc+e+jlfDyHbB0Fl+u2QovlwXdjI+/Kmt/tCtWbOR/ZjzBhTzGgtiLlBZXUUkJf1x8IpcvPo3rxp7C0L5dspJFRET2DD07lTJpRD8mjejHvHnzGDJkSK4jiUhT6uZDXHAnLH0Qr96KFZcFw9mOu1JFJBERyZgWC0nu/sX6t81sIDAuY4nagrefIfXgBaQStcRJYAA1W0gs+j2x1x4gdt59GZ/MePWmSmbOmMbc2G3ESVBkSQA6UMWo2HOczQtMmPEevSd8V/9BFhEREdkT1c2HeOotPK/Cr4iIZEmUs7Y14O5LgGMykKVt+GglyfILiCWqiJNosCpOgliiimT5BUGPpQx66Nn5TI7dRnur3l5EqlNkSdpbNZNjt/HQs/MzmkNERERERERE9h4t9kgys+/VuxkDBgLrM5Yoz21+bjLtEjUUWPNtkokaKituZ59zbs9YjgPenLZDIauxOAk+++a9wPCM5RARERERERGRvUeUHkkd6l2KCeZMOjOTofJZ0Zuzd+gBtEMbS1K0fHZGc5zG/Eg5TueFjOYQERERERERkb1HlDmSfpKNIG1FUbIqmHa8xXbbMpqjlE9atZ2IiIjkJ52hVURERPJJs4UkM3uU4GxtTXL3MzKSKM9V0o4OVEVslzk1BSW0S7VcrKopaE+7DOYQERGRzKlYsZFxM5ZQm0yRSAUfy7ZWJyhfuIaHFq9jytiBOkOriIiIZFW6Hkm/zFqKNuQxBnOO/zntsLIaL+BRvsT5GcxRc+hIYstmtJij5rCRKiSJiIi0Qas3VTJuxhKqanf8W59IOYlUknEzlvDUhMHqmZQD6ikmIiJ7q2bnSHL35+suwAJgU3h5OVy2V3rv0EtJtDAiMEGc9w/9RkZz7DNsAgXxorRtCuJF7DN0fEZziIiISGZMnb+S2mQqbZvaZIp75r+bpURSp2LFRoZPnk/5wjVsrQ5OflLXU2z45PlUrNiY44QiIiKZ0+Jk22Y2BHgbuBOYAvzdzL6U2Vj565yvDmZC6mq2eTE1XtBgXY0XsM2LmZC6mnO+OjizQTr2pmD0faTiJTsUthLEScVLKBh9H3TsndkcIiIie6jVmyq5Ye4y+k18moufqqTfxKe5Ye4yVm+qzMr+5766fvtwtuYkUs4fXl2XlTwSqN9TrPHrk0g5VbVBT7FsvU9ERESyLcpZ224BvubuX3b3LwFfB27LbKz81bNTKWPGXsqI1M95MDWMLV5Cyo0tXsKDqWGMSP2cMWMvzU6X5j4nERv3MvGjLobiDjgGxR2IH3UxsXEvQ5+TMp9BRERkD5QPPU4qw/222K4mWjtpHeopJiIie7sohaRCd19Rd8Pd/w4UtnQnM+thZhVm9qaZLTez8fXWfcfM/hYu/3m95deZ2TtmtsLMvr6zDyZbhvbtwtQJI/n7oBs5jt/z+er7OY7f8/dBNzJ1wsjsTnrZsTecegtct5bnh8yF69YGt9UTSUREZJfkS4+T0uIWT64btCuK1k5ah3qKiYjI3i7KJ49FZnYPMCO8PRZYFOF+CeD77r7EzDoAi83sGaArcCZwuLtXm1kXADM7FBgNHAZ0A541s4PdvfnZpHOoZ6dSJo3ox6QR/Zg3bx5DhgzJdSQRERFpBTvT42TSiH4ZyzFiQDfKF66hm7/P5QWPM6LgJUqpopIS5iZPYGryVNbbZzlrQPeMZZAdqaeYiIjs7aL0SLoCeBP4bnhZHi5Ly93fc/cl4fUtwFtA9/C+N7t7dbiurm/4mUC5u1e7+7vAO8DRO/dwRERERHZPvvQ4uXxwb4YVvM5TRddyXkEFHayKmEEHq+K8ggqeKrqWYQWvc9nggzKaQxpSTzEREdnbtVhICgs7t7r72cBlwJ/rikBRmVkvYADwV+BgYLCZ/dXMnjezo8Jm3YE19e62NlwmIiIikjX50uOkp23grqLbaW/VFFnDDtpFlqS9VXNX0e30tA0ZzSENjRjQjXjM0raJx0w9xUREZI9l7un/42Zm84AzCIbBLQY2Ai+7+9WRdmBWBjwP3OTuD5vZG0AFQe+mo4AHgd7Ar4G/uPuM8H7TgCfdfU6j7X0T+CZA165dB5WXl0d7pBm0detWysrKch1DOfIsg3IoR1vIkQ8ZlEM58i3Dt5+p5JMIA+tL4nDXVzN3co0+f7+bA9b/iRjNh0lRwPpuX+edg7+VsRxNyYf3Rq5ybNyW4oaXqqhJ8x4pKoCfnVBCl/ZROv+3jr35NVGOtpEjHzIoh3K0hRz5kAFg6NChi939yKbWRelzu6+7bzazy4Dp7j7RzJZG2bGZFQIPAfe7+8Ph4rXAwx5UsBaaWQroDKwDetS7++fCZQ24+/8B/wdw5JFHej7MTZQvcyQpR35lUA7laAs58iGDcihHvmU49+NllC9ck3Z4WzxmnHvkgQwZkrk5knh5LKQpIgHESPK5TfP53JCZmcvRhHx4b+Qyx2c+v5FxM5ZQm0w1eJ/EY0ZhQYwpYwdm9+Qr6DVRjvzPkQ8ZlEM52kKOfMjQkiiFpLiZHQCMAq6PumEzM2Aa8Ja731pv1VxgKFBhZgcDRcCHwCPAA2Z2K8Fk232AhVH3JyIiItIaLh/cm4cWr6Obr292kusPCrplfm6imq2t205azdC+XXj2kh6sffwXHPbhU7Snim2UsLzzcD536g/p3ju7RSQREZFsilJI+inwNPCSu79iZr2BtyPc7wTgAmCZmb0WLvsv4F7g3nCIWw1wUdg7abmZzSKY2DsBXJmvZ2wTERGRPVfPTqWUD9tCn3nXEiexfX6iDgSTXJ9TMJ+3h9xJz06ZG9YGQFEZ1GyJ1k6y6+1n6D7rQrona8FqASijimP+9RjMfBpGTYc+J+U4pIiISGa0WEhy99nA7Hq3VwLnRLjfi0BzMxGObeY+NwE3tbRtERERkYz5aCWHv/xdsB3PLVJkSYpIBusPHwQde2cuR/9RsGQ6pGqbbxMrhP7nZS6D7OijlTDrQqjdtuO6VG1wmXUhXPFSZt8fIiIiOdLiDIBm1tvMHjWzD8xso5n9MeyVJCIiIrLnefkOSKYp3kCwfsGdmc1x/FVQUJi+TUEhHHdlZnNIQ/ny/hAREcmRKKeSeACYBRxAMHfRbCC7MzqKiIiIZMvSWel7AUGwfumDmc3RsXcwRKqwfdDzqL5YYbB81HT1esm2fHl/iIiI5EiUQlJ7d7/P3RPhZQbQLtPBRERERHIinya57nNSMERq0EVQ3AHHoLhDcPuKlzQPTy7k0/tDREQkB5qdI8nMOoZXnzSza4FywIHzgCeykE1EREQk+/JtkuuOveHUW+DUW3i+DZwSeI+Xb+8PERGRLEs32fZigsJR3YTZ36q3zoHrMhVKREREJGc0ybWko/eHiIjs5Zod2ubuB7l77/BngwvQN4sZRURERLJHk1xLOnp/iIjIXi7KHEkAWOArZjYNWJvBTCIiIiK5o0muJR29P0REZC+XbmgbAGZ2LHA+MALoCFwJ/CCzsURERERyqG6S6wV3wtIH8eqtWHFZMFzpuCtVJMih1ZsqmTp/JXNfXc/W6gRlFU8zYkA3Lh/cm56dSrMTQu8PERHZi6WbbPu/gZHAP4GZwE+ARe7++yxlExEREckdTXKddypWbGTcjCXUJlMkUg7A1uoE5QvX8NDidUwZO5ChfbtkJ4zeHyIispdKN7TtMmADcBdwn7tvIphkW0REREQkq1ZvqmTcjCVU1Sa3F5HqJFJOVW2ScTOWsHpTZY4SioiI7B3SFZIOAH4GnA78w8zuA0rMrMXhcCIiIiIirWnq/JXUJlNp29QmU9wz/90sJRIREdk7pTtrW9Ldn3L3i4DPA3OBl4B1ZvZAlvKJiIiIiDD31fU79ERqLJFy/vDquiwlEhER2TtF6l3k7tXAQ8BDZrYPwcTbIiIiIiJZUVmdiNauJlo7ERER2TXphrY1yd03u/v0TIQREREREWlKaXG02RVKizQLg4iISCbtdCFJRERERCTbRgzoRjxmadvEY8ZZA7pnKZGIiMjeSYUkEREREcl7lw/uTWFB+o+uhQUxLht8UJYSiYiI7J0i9f01s+OBXvXba3ibiIiIiGRLz06lTBk7kHEzllCbTDWYeDseMwoLYkwZO5CenUpzmFJERGTP12IhyczuIzhr22tAMlzsgApJIiIiIpI1Q/t24akJg7ln/rv84dV1VFYnKC2Oc9aA7lw2+CAVkURERLIgSo+kI4FD3T39+VZFRERERDKsZ6dSJo3ox6QR/Zg3bx5DhgzJdSQREZG9SpQ5kt4APpvpICIiIiIiIiIikt+i9EjqDLxpZguB6rqF7n5GxlKJiIiIiDTlo5Xw8h2wdBZfrtkKL5dB/1Fw/FXQsXeu04mIiOzxohSSbsx0CBERERGRFr39DMy6EJK1kKrFAGq2wJLp8PpMGDUd+pyU65QiIiJ7tBYLSe7+fDaCiIiIiIg066OVQRGpdtuO61JBYYlZF8IVL6lnkoiISAa1OEeSmR1rZq+Y2VYzqzGzpJltzkY4EREREREgGM6WrE3fJlkLC+7MTh4REZG9VJTJtu8AxgBvAyXAZYD+QouIiIhI9iydFfQ6SidVC0sfzE4eERGRvVSUQhLu/g5Q4O5Jd/8tMDyzsURERERE6qnZ2rrtREREZJdEmWx7m5kVAa+Z2c+B94hYgBIRERERaRVFZcHE2lHaiYiISMZEKQhdELa7CqgEegDnZDKUiIiIiEgD/UdBrDB9m1gh9D8vO3lERET2UlHO2rbazEqAA9z9J1nIJCIiIiLS0PFXwesz08+TVFAIx12ZvUwiIiJ7oShnbTsdeA14Krx9hJk9EuF+PcyswszeNLPlZjY+XH6jma0zs9fCyyn17nOdmb1jZivM7Ou7/KhEREREZM/SsTeMmg6F7XfsmRQrDJaPmh60ExERkYyJMkfSjcDRwDwAd3/NzA6KcL8E8H13X2JmHYDFZvZMuO42d/9l/cZmdigwGjgM6AY8a2YHu3sy0iMRERERkT1bn5PgipdgwZ2w9EG8eitWXBYMZzvuShWRREREsiBKIanW3f9tZvWXeUt3cvf3CCbmxt23mNlbQPc0dzkTKHf3auBdM3uHoIC1IEJGEREREdkbdOwNp94Cp97C8/PmMWTIkFwnEhER2atEmWx7uZmdDxSYWR8z+zXw8s7sxMx6AQOAv4aLrjKzpWZ2r5ntHy7rDqypd7e1pC88iYiIiIiIiIhIFpl7+s5FZtYeuB74GmDA08Akd/8k0g7MyoDngZvc/WEz6wp8SNCraRLBJN7fMLM7gL+4+4zwftOAJ919TqPtfRP4JkDXrl0HlZeXR36wmbJ161bKynJ/qlnlyK8MyqEcbSFHPmRQDuXI9wzKoRxtIUc+ZFAO5cj3DMqhHG0hRz5kABg6dOhidz+yyZXunrELUEhQePpeM+t7AW+E168Drqu37mnguHTbHzRokOeDioqKXEdwd+XItwzuytGYcjSUDznyIYO7cjSmHPmVwV05GlOOhvIhRz5kcFeOxpQjvzK4K0djytFQPuTIhwzu7sAib6YW0+wcSS2dmc3dz0i33oJJlaYBb7n7rfWWH+DB/EkAZwFvhNcfAR4ws1sJJtvuAyxMtw8REREREREREcmedJNtH0cwZ9FMgrmNLE3bppwAXAAsM7PXwmX/BYwxsyMIhratAr4F4O7LzWwW8CbBGd+udJ2xTUREREREREQkb6QrJH0WOAkYA5wPPA7MdPflUTbs7i/SdPHpiTT3uQm4Kcr2RUREREREREQku5o9a5u7J939KXe/CDgWeAeYZ2ZXZS2diIiIiIiIiIjkjXQ9kjCzYuBUgl5JvYBfAX/IfCwREREREREREck36Sbbng70IxiK9hN3f6O5tiIiIiIiIiIisudL1yNpLFAJjAe+G5yEDQjmPXJ33yfD2UREREREREREJI80W0hy92bnTxIRERERERERkb2PikUiIiIiIiIiIhKJCkkiIiIiIiIiIhKJCkkiIiIiIiIiIhKJCkkiIiIiIiIiIhKJCkkiIiIiIiIiIhKJCkkiIiIiIiIiIhKJCkkiIiIiIiIiIhKJCkkiIiIiIiIiIhKJCkkiIiIiIiIiIhKJCkkiIiIiIiIiIhKJCkkiIiIiIiIiIhKJCkkiIiIiIiIiIhKJCkkiIiIiIiIiIhKJCkkiIiIiIiIiIhKJCkkiIiIiIiIiIhKJCkkiIiIiIiIiIhKJCkkiIiIiIiIiIhKJCkkiIiIiIiIiIhKJCkkiIiIiIiIiIhKJCkkiIiIiIiIiIhKJCkkiIiIiIiIiIhKJCkkiIiIiIiIiIhKJCkkiIiIiIiIiIhKJCkkiIiIiIiIiIhJJxgpJZtbDzCrM7E0zW25m4xut/76ZuZl1Dm+bmf3KzN4xs6VmNjBT2UREREREREREZOfFM7jtBPB9d19iZh2AxWb2jLu/aWY9gK8B/6zX/mSgT3g5Brgr/CkiIiIiIiIiInkgYz2S3P09d18SXt8CvAV0D1ffBvwI8Hp3OROY7oG/APuZ2QGZyiciIiIiIiIiIjsnK3MkmVkvYADwVzM7E1jn7q83atYdWFPv9lo+LTyJiIiIiIiIiEiOmbu33Gp3dmBWBjwP3AQ8BVQAX3P3f5vZKuBId//QzB4Dbnb3F8P7/Rm4xt0XNdreN4FvAnTt2nVQeXl5RvNHsXXrVsrKynIdQznyLINyKEdbyJEPGZRDOfI9g3IoR1vIkQ8ZlEM58j2DcihHW8iRDxkAhg4dutjdj2xypbtn7AIUAk8D3wtvfxHYCKwKLwmCeZI+C/wGGFPvviuAA9Jtf9CgQZ4PKioqch3B3ZUj3zK4K0djytFQPuTIhwzuytGYcuRXBnflaEw5GsqHHPmQwV05GlOO/MrgrhyNKUdD+ZAjHzK4uwOLvJlaTCbP2mbANOAtd781LFotc/cu7t7L3XsRDF8b6O7vA48AF4ZnbzsW+Le7v5epfCIiIiIiIiIisnMyeda2E4ALgGVm9lq47L/c/Ylm2j8BnAK8A2wDLslgNhERERERERER2UkZKyR5MNeRtdCmV73rDlyZqTwiIiIiIiIiIrJ7snLWNhERERERERERaftUSBIRERERERERkUhUSBIRERERERERkUhUSBIRERERERERkUhUSBIRERERERERkUhUSBIRERERERERkUhUSBIRERERERERkUhUSBIRERERERERkUhUSBIRERERERERkUhUSBIRERERERERkUhUSBIRERERERERkUhUSBIRERERERERkUhUSBIRERERERERkUhUSBIRERERERERkUhUSBIRERERERERkUhUSBIRERERERERkUhUSBIRERERERERkUhUSBIRERERERERkUhUSBIRERERERERkUhUSBIRERERERERkUhUSBIRERERERERkUhUSBIRERERERERkUhUSBIRERERERERkUhUSBIRERERERERkUhUSBIRERERERERkUhUSBIRERERERERkUhUSBIRERERERERkUhUSBIRERERERERkUhUSBIRERERERERkUgyVkgysx5mVmFmb5rZcjMbHy6fZGZLzew1M/uTmXULl5uZ/crM3gnXD8xUNhERERERERER2XmZ7JGUAL7v7ocCxwJXmtmhwC/cvb+7HwE8Bvw4bH8y0Ce8fBO4K4PZRERERERERERkJ2WskOTu77n7kvD6FuAtoLu7b67XrBTw8PqZwHQP/AXYz8wOyFQ+ERERERERERHZOfFs7MTMegEDgL+Gt28CLgT+DQwNm3UH1tS729pw2XvZyCgiIiIiIiIiIumZu7fcand2YFYGPA/c5O4PN1p3HdDO3Sea2WPAze7+Yrjuz8A17r6o0X2+STD0ja5duw4qLy/PaP4otm7dSllZWa5jKEeeZVAO5WgLOfIhg3IoR75nUI5A5UfrKPn7XI6umk97qthGCQtLBlN18AhKO3bPSSa9LvmVQTmUI98zKIdytIUc+ZABYOjQoYvd/cgmV7p7xi5AIfA08L1m1h8IvBFe/w0wpt66FcAB6bY/aNAgzwcVFRW5juDuypFvGdyVozHlaCgfcuRDBnflaEw58iuDu3K89twsr/zxZ7z6x/u7T9xn+6X6x/t75Y8/4689Nysnufb21yXfMrgrR2PKkV8Z3JWjMeVoKB9y5EMGd3dgkTdTi8nkWdsMmAa85e631lvep16zM4G/hdcfAS4Mz952LPBvd9ewNhEREZEcWrdyOX3mXUl7q6bIkg3WFVmS9lZNn3lXsm7l8hwlFBERkWzK5FnbTgAuAIaZ2Wvh5RTgZjN7w8yWAl8DxoftnwBWAu8AU4FxGcwmIiIiIhGsefwXxEmkbRMnwZrHf5mlRCIiIpJLGZts24O5jqyJVU80096BKzOVR0RERER2Xr8Pn9qhJ1JjRZbksA+fzFIiERERyaVM9kgSERERkTauPVWR2pXySYaTiIiISD5QIUlEREREmrWNkkjtKmmX4SQiIiKSD1RIEhEREZFmvdF5ODVekLZNjRewvPPJWUokIiIiuaRCkoiIiIg0q8epPyTRwrSaCeL0OPUHWUokIiIiuaRCkoiIiIg0q3vvw3h7yJ1s8+IdeibVeAHbvJi3h9xJ996H5SihiIiIZJMKSSIiIiKS1uFDR/KviypY0vlMtngJKTe2eAlLOp/Jvy6q4PChI3MdUURERLIkfT9lERERERGCnkndv/NbAObNm8eQIUM4NseZREREJPvUI0lERERERERERCJRIUlERERERERERCJRIUlERERERERERCJRIUlERERERERERCJRIUlERERERERERCJRIUlERERERERERCIxd891hl1mZh8Aq3OdA+gMfJjrEChHvmUA5WhMORrKhxz5kAGUozHlyK8MoByNKUdD+ZAjHzKAcjSmHPmVAZSjMeVoKB9y5EMGgJ7u/pmmVrTpQlK+MLNF7n6kcuRPjnzIoBzK0RZy5EMG5VCOfM+gHMrRFnLkQwblUI58z6AcytEWcuRDhpZoaJuIiIiIiIiIiESiQpKIiIiIiIiIiESiQlLr+L9cBwgpx6fyIQMoR2PK0VA+5MiHDKAcjSnHp/IhAyhHY8rRUD7kyIcMoByNKcen8iEDKEdjytFQPuTIhwxpaY4kERERERERERGJRD2SREREREREREQkEhWSdoOZ3WtmG83sjRxm6GFmFWb2ppktN7PxOcrRzswWmtnrYY6f5CJHvTwFZvaqmT2WwwyrzGyZmb1mZotymGM/M5tjZn8zs7fM7LgcZOgbPg91l81mNiEHOa4O359vmNlMM2uX7QxhjvFhhuXZfB6aOmaZWUcze8bM3g5/7p+jHCPD5yNlZlk5S0UzOX4R/q4sNbM/mNl+OcoxKczwmpn9ycy6ZTtDvXXfNzM3s86ZzNBcDjO70czW1Tt+nJKLHOHy74Tvj+Vm9vNc5DCzB+s9F6vM7LUc5TjCzP5S9zfOzI7OQYbDzWxB+Lf2UTPbJ5MZwn02+bkr28fSNDmyeixNkyNrx9I0GbJ9HE37mTxbx9I0z0dWj6Xpno9sHkvTPB9ZPZamyZG1Y2maDFk9lloz3xvN7CAz+6uZvRO+PkU5ynFVmCFbn32ay3G/ma2w4DvDvWZWmOksO8XdddnFC/AlYCDwRg4zHAAMDK93AP4OHJqDHAaUhdcLgb8Cx+bwefke8ADwWA4zrAI652r/9XL8HrgsvF4E7JfjPAXA+0DPLO+3O/AuUBLengVcnIPH3w94A2gPxIFngS9kad87HLOAnwPXhtevBf43Rzn+A+gLzAOOzOHz8TUgHl7/3xw+H/vUu/5d4O5sZwiX9wCeBlZn43jWzHNxI/CDbLwnWsgxNPx9LQ5vd8lFjkbrbwF+nKPn40/AyeH1U4B5OcjwCvDl8Po3gElZeC6a/NyV7WNpmhxZPZamyZG1Y2maDNk+jjb7mTybx9I0z0dWj6VpcmT1WJrudanXJuPH0jTPR9aOpWkyZPVYSjPfGwk+m48Ol98NXJGjHAOAXmTpu1yaHKeE6wyYmennY2cv6pG0G9z9BeCjHGd4z92XhNe3AG8RfGHOdg53963hzcLwkpMJuMzsc8CpwD252H8+MbN9CT58TwNw9xp3/zinoeArwD/cfXUO9h0HSswsTlDIWZ+DDP8B/NXdt7l7AngeODsbO27mmHUmQbGR8OeIXORw97fcfUWm9x0hx5/C1wXgL8DncpRjc72bpWT4eJrm79ltwI8yvf8IObKqmRxXADe7e3XYZmOOcgBgZgaMIvhwmYscDtT913pfMnw8bSbDwcAL4fVngHMymSHM0dznrqweS5vLke1jaZocWTuWpsmQ7eNous/kWTuW5tF3g+ZyZPVY2tLzka1jaZocWTuWpsmQ1WNpmu+Nw4A54fJsHEebzOHur7r7qkzuO2KOJ8J1DiwkC59Jd4YKSXsQM+tFUEH9a472XxB2C90IPOPuOckBTCb4Y53K0f7rOPAnM1tsZt/MUYaDgA+A31ow1O8eMyvNUZY6o8nCF5/G3H0d8Evgn8B7wL/d/U/ZzkHQG2mwmXUys/YE/23okYMcdbq6+3vh9feBrjnMkm++ATyZq52b2U1mtgb4T+DHOdj/mcA6d3892/tuwlXhEJV7LQvDL5txMMHv7l/N7HkzOypHOeoMBja4+9s52v8E4Bfhe/SXwHU5yLCcoIADMJIsH0sbfe7K2bE015//IuTI2rG0cYZcHUfr58jlsbSJ1yQnx9JGOXJ2LG3mPZr1Y2mjHBPIwbG0UYasH0sbf28E/gF8XK8AvZYsFEDz5ftruhzhkLYLgKdyka05KiTtIcysDHgImNDoPzBZ4+5Jdz+CoFp6tJn1y3YGMzsN2Ojui7O97yac6O4DgZOBK83sSznIECcYCnCXuw8AKgm63OdEONb5DGB2Dva9P8EfyYOAbkCpmY3Ndg53f4ugm/+fCP4gvAYks52jKeF/PHQqT8DMrgcSwP25yuDu17t7jzDDVdncd1jk/C9yUMBqwl3A54EjCIrAt+QoRxzoSNDd/IfArPA/2bkyhhwU5eu5Arg6fI9eTdjzNcu+AYwzs8UEwzRqsrXjdJ+7snkszYfPf+lyZPNY2lSGXBxH6+cgeOw5OZY28Xzk5FjaRI6cHEvT/K5k9VjaRI6sH0ubyJD1Y2nj743AIZneZ5Qcufj+GiHHFOAFd5+fi2zNUSFpDxBWKR8C7nf3h3Odx4OhUxXA8Bzs/gTgDDNbBZQDw8xsRg5y1PWAqeuy+weCg2S2rQXW1qtqzyEoLOXKycASd9+Qg31/FXjX3T9w91rgYeD4HOTA3ae5+yB3/xLwL4Ix6rmywcwOAAh/Zny4Tr4zs4uB04D/DL8Q5tr9ZGHITiOfJyi6vh4eTz8HLDGzz2Y5B+6+IfyAlQKmkptjKQTH04fDXuYLCXq9ZnwSzqaEw3PPBh7Mxf5DFxEcRyH450DWXxd3/5u7f83dBxF8EfxHNvbbzOeurB9L8+XzX3M5snksjfBcZOU42kSOnBxLm3o+cnEsbeZ1yfqxNM17NKvH0mZyZPVY2sx7IyfH0nDfHxN8bzwO2C98TSD4XVmXgxy5+P7abA4zmwh8hmD+37yiQlIbF1bwpwFvufutOczxGQvPyGFmJcBJwN+yncPdr3P3z7l7L4IhVM+5e9Z7nZhZqZl1qLtOMOlk1s/u5+7vA2vMrG+46CvAm9nOUU8u/4P+T+BYM2sf/t58hWBseNaZWZfw54EEH2AeyEWO0CMEH2IIf/4xh1lyzsyGEwyNPcPdt+UwR596N88ky8dTd1/m7l3cvVd4PF1LMEHn+9nMAdu/lNc5ixwcS0NzCSaJxcwOJjh5wYc5yvJV4G/uvjZH+4dgHo8vh9eHAVkfYlfvWBoDbiCYnDXT+2zuc1dWj6V59PmvyRzZPJamyZDV42hTOXJxLE3zfGT1WJrmPTqXLB5LW/hdydqxNE2OrB1L07w3snosbeZ741sEBZRzw2bZOI7mxffX5nKY2WXA14ExYQE4v3gezPjdVi8EX4jfA2oJ/jBcmoMMJxJ0n15KMETmNeCUHOToD7wa5niDLJxFJkKmIeTorG1Ab+D18LIcuD6Hz8MRwKLwtZkL7J+jHKXAJmDfHD4XPyH4A/EGcB/hGUNykGM+QUHvdeArWdzvDscsoBPwZ4IPLs8CHXOU46zwejWwAXg6RzneAdbUO55m9Cw/aXI8FL5PlwKPEkwcm9UMjdavIjtnLmnqubgPWBY+F48AB+QoRxEwI3xdlgDDcpEjXP474NuZ3n8Lz8eJwOLwOPZXYFAOMown6NH5d+BmwLLwXDT5uSvbx9I0ObJ6LE2TI2vH0jQZsn0cbfEzeTaOpWmej6weS9PkyOqxNN3rQhaPpWmej6wdS9NkyOqxlGa+NxJ8f1oYHj9mk+HP6WlyfJfgOJogKPTdk6McCYLeYXWvVc6/X9e/WBhSREREREREREQkLQ1tExERERERERGRSFRIEhERERERERGRSFRIEhERERERERGRSFRIEhERERERERGRSFRIEhERERERERGRSFRIEhEREWmBmf2PmQ01sxFmdl0zbW40s3Vm9pqZvWlmY+qtm2dmR9a73cvM3givDzEzN7PT661/zMyGhNdPM7NXzez1cLvfytTjFBEREWmJCkkiIiIiLTsG+AvwZeCFNO1uc/cjgDOB35hZYcTtrwWub7wwvP//Aae7++HAAGBe9NgiIiIirUuFJBEREZFmmNkvzGwpcBSwALgMuMvMfpzufu7+NrAN2D/irl4H/m1mJzVa3gGIA5vC7Va7+4qdeAgiIiIirUqFJBEREZFmuPsPgUuB3xEUk5a6e393/2m6+5nZQOBtd9+4E7u7Cbih0f4/Ah4BVpvZTDP7TzPT5zcRERHJGX0QEREREUlvIEGPoUOAt1poe7WZLQf+SlAYquNNtG2wzN1fADCzExstvwz4CrAQ+AFw786EFxEREWlN8VwHEBEREclHZnYEQU+kzwEfAu2DxfYacJy7VzVxt9vc/ZdmdgYwzcw+7+6fEAxNqz/MrWO4zcbqeiUl6i9092XAMjO7D3gXuHjXH5mIiIjIrlOPJBEREZEmuPtr4cTZfwcOBZ4Dvu7uRzRTRKp/30eARcBF4aJ5wFgzs/D2RUBFE/f7E0HBqT+AmZXVnb0tdASwepcekIiIiEgrUCFJREREpBlm9hngX+6eAg5x9zd34u4/Bb4Xzmn0f8AW4HUzex0oA37ZzP1uAnrURQB+ZGYrwp5QP0G9kURERCSHzL2pIfsiIiIiIiIiIiINqUeSiIiIiIiIiIhEokKSiIiIiIiIiIhEokKSiIiIiIiIiIhEokKSiIiIiIiIiIhEokKSiIiIiIiIiIhEokKSiIiIiIiIiIhEokKSiIiIiIiIiIhEokKSiIiIiIiIiIhE8v8Be6Xp4z0W3OMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(1, 1, 1)\n",
      "Prediction for 21 Business Days/Year: MAE = 273.25 (4851.1%)\n",
      "ARIMA(1, 1, 2)\n",
      "Prediction for 21 Business Days/Year: MAE = 272.97 (4846.02%)\n"
     ]
    }
   ],
   "source": [
    "labels = ['ARIMA(1, 1, 1)',\n",
    "          'ARIMA(1, 1, 2)']\n",
    "\n",
    "# Select index of stepsToForecast to assess cross-validation from\n",
    "PRED_IX = 3\n",
    "\n",
    "\n",
    "plt.figure(figsize=(20,5));\n",
    "for model_str in labels:\n",
    "    plt.plot(dict_MAE[model_str]['conv'], [mae[PRED_IX-1] for mae in dict_MAE[model_str]['mae']], 'o', markersize=10)\n",
    "plt.legend(labels)\n",
    "plt.xlabel('# RUNS')\n",
    "plt.ylabel('Mean Absolute Error')\n",
    "plt.title('Mean Absolute Error at each Cross-Validation run')\n",
    "if (len(Xcv) > 500):\n",
    "    plt.xticks(np.arange(0, len(Xcv), 100))\n",
    "if (len(Xcv) > 250):\n",
    "    plt.xticks(np.arange(0, len(Xcv), 50))\n",
    "elif (len(Xcv) > 50):\n",
    "    plt.xticks(np.arange(0, len(Xcv), 10))\n",
    "else:\n",
    "    plt.xticks(np.arange(1,len(Xcv)+1))\n",
    "plt.xlim([0, len(Xcv)+1])\n",
    "plt.grid()\n",
    "plt.show();\n",
    "\n",
    "plt.figure(figsize=(20, 5))\n",
    "y_bp = []\n",
    "x_bp = np.empty((0))\n",
    "for model_str in labels:\n",
    "    if (len(dict_MAE[model_str]['conv'])>0):\n",
    "        maeVals = [mae[PRED_IX-1] for mae in dict_MAE[model_str]['mae']]\n",
    "        y_bp.extend(maeVals)\n",
    "        x_bp = np.append(x_bp, np.repeat(model_str, len(maeVals)))\n",
    "        df_bp = pd.DataFrame(data={'ARIMA Model': x_bp, 'Mean Average Error': y_bp})\n",
    "bplot=sns.boxplot(y='Mean Average Error', x='ARIMA Model', data=df_bp, width=0.5) \n",
    "plt.title('Boxplot of Mean Absolute Error of Cross-Validation associated with each ARIMA model');\n",
    "plt.grid(linewidth=.3)\n",
    "plt.show();\n",
    "\n",
    "for i in np.arange(len(labels)):\n",
    "    if (len(dict_MAE[labels[i]]['conv'])>0):\n",
    "        print(labels[i])\n",
    "        print('Prediction for ' + str(stepsToForecast[PRED_IX-1]) + ' ' + xlab + ': MAE = ' \\\n",
    "              + str(round(np.mean([mae[PRED_IX-1] for mae in dict_MAE[labels[i]]['mae']]), 2)) \\\n",
    "              + ' (' + str(round(np.mean([mape[PRED_IX-1] for mape in dict_MAE[labels[i]]['mape']]), 2)) + '%)' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Validation\n",
    "    This section allows to validate the selected model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARIMA Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>D.y</td>       <th>  No. Observations:  </th>    <td>6693</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>          <td>ARIMA(1, 1, 1)</td>  <th>  Log Likelihood     </th>  <td>20418.622</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>    <td>0.011</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Sun, 30 May 2021</td> <th>  AIC                </th> <td>-40829.244</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>16:33:50</td>     <th>  BIC                </th> <td>-40802.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                <td>1</td>        <th>  HQIC               </th> <td>-40819.838</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                       <td> </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>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>     <td>    0.0003</td> <td>    0.000</td> <td>    2.464</td> <td> 0.014</td> <td> 5.75e-05</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1.D.y</th> <td>    0.6412</td> <td>    0.075</td> <td>    8.561</td> <td> 0.000</td> <td>    0.494</td> <td>    0.788</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1.D.y</th> <td>   -0.7077</td> <td>    0.069</td> <td>  -10.268</td> <td> 0.000</td> <td>   -0.843</td> <td>   -0.573</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>AR.1</th> <td>           1.5597</td> <td>          +0.0000j</td> <td>           1.5597</td> <td>           0.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.1</th> <td>           1.4130</td> <td>          +0.0000j</td> <td>           1.4130</td> <td>           0.0000</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                             ARIMA Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:                    D.y   No. Observations:                 6693\n",
       "Model:                 ARIMA(1, 1, 1)   Log Likelihood               20418.622\n",
       "Method:                       css-mle   S.D. of innovations              0.011\n",
       "Date:                Sun, 30 May 2021   AIC                         -40829.244\n",
       "Time:                        16:33:50   BIC                         -40802.009\n",
       "Sample:                             1   HQIC                        -40819.838\n",
       "                                                                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.0003      0.000      2.464      0.014    5.75e-05       0.001\n",
       "ar.L1.D.y      0.6412      0.075      8.561      0.000       0.494       0.788\n",
       "ma.L1.D.y     -0.7077      0.069    -10.268      0.000      -0.843      -0.573\n",
       "                                    Roots                                    \n",
       "=============================================================================\n",
       "                  Real          Imaginary           Modulus         Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "AR.1            1.5597           +0.0000j            1.5597            0.0000\n",
       "MA.1            1.4130           +0.0000j            1.4130            0.0000\n",
       "-----------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pick you favourite's models order\n",
    "order = (1, 1, 1)\n",
    "\n",
    "model = ARIMA(df_Train.values, order=order)\n",
    "model_fit = model.fit(disp=0);\n",
    "df_res = pd.DataFrame({\"resid\": model_fit.resid}, index = df_Train.index[order[1]:])\n",
    "model_fit.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Residual Analysis\n",
    "    Plots residual and its histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 0.000000\n",
      "Variance: 0.000131\n"
     ]
    }
   ],
   "source": [
    "# Plot model residual\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.plot(df_res.index, df_res.values)\n",
    "plt.xlabel(xlab)\n",
    "plt.title('ARIMA' + str(order) +' Residuals')\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "# Plot histogram\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.title('ARIMA' + str(order) + ' Residuals (Histogram)')\n",
    "hist_res = df_res['resid'].hist(bins=50,edgecolor='black')\n",
    "df_res['resid'].plot(kind='kde', linewidth=2, linestyle='--')\n",
    "plt.xlabel('Residuals')\n",
    "limX = np.mean(df_res.values)+5*np.std(df_res.values);\n",
    "plt.xlim((-limX, limX))\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "print (\"Mean: %.6f\" % np.mean(df_res))\n",
    "print (\"Variance: %.6f\" % np.var(df_res))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normality Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kurtosis and Kurtosis Test\n",
    "    Compute the kurtosis (Fisher or Pearson) of a dataset.\n",
    "    Kurtosis is the fourth central moment divided by the square of the variance. If Fisher's definition is used, then 3.0 is subtracted from the result to give 0.0 for a normal distribution. If bias is False then the kurtosis is calculated using k statistics to eliminate bias coming from biased moment estimators\n",
    "\n",
    "    Test whether a dataset has normal kurtosis.\n",
    "    This function tests the null hypothesis that the kurtosis of the population from which the sample was drawn is that of the normal distribution: kurtosis = 3(n-1)/(n+1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kurtosis Test\n",
      "Statistic: 33.7533\n",
      "p-value: 0.0000\n",
      "Kurtosis value: 9.4961\n"
     ]
    }
   ],
   "source": [
    "k, kpval = kurtosistest(df_res['resid'].values)\n",
    "kurtosis_val = kurtosis(df_res['resid'].values, fisher=True)\n",
    "print(\"Kurtosis Test\")\n",
    "print(\"Statistic: %.4f\" % k)\n",
    "print(\"p-value: %.4f\" % kpval)\n",
    "print(\"Kurtosis value: %.4f\" % kurtosis_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Skew and Skewness Test\n",
    "    Compute the skewness of a data set.\n",
    "    For normally distributed data, the skewness should be about 0. For unimodal continuous distributions, a skewness value > 0 means that there is more weight in the right tail of the distribution. The function `skewtest` can be used to determine if the skewness value is close enough to 0, statistically speaking.\n",
    "\n",
    "    Test whether the skew is different from the normal distribution.\n",
    "    This function tests the null hypothesis that the skewness of the population that the sample was drawn from is the same as that of a corresponding normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skew Test for\n",
      "Statistic: -11.5797\n",
      "p-value: 0.0000\n",
      "Skewness value: -0.3568\n"
     ]
    }
   ],
   "source": [
    "s, spval = skewtest(df_res['resid'].values)\n",
    "skew_val = skew(df_res['resid'].values)\n",
    "print(\"Skew Test for\")\n",
    "print(\"Statistic: %.4f\" % s)\n",
    "print(\"p-value: %.4f\" % spval) \n",
    "print(\"Skewness value: %.4f\" % skew_val) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Jarque-Bera and Kolmogorov-Smirnov tests\n",
    "    Tests for goodness of fit (previously mentioned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jarque-Bera Test\n",
      "Statistic: 25289.9268\n",
      "p-value: 0.0000\n",
      " \n",
      "Kolmogorov-Smirnov Test\n",
      "Statistic: 0.4807\n",
      "p-value: 0.0000\n"
     ]
    }
   ],
   "source": [
    "jb, jbpval= stats.jarque_bera(df_res['resid'].values)\n",
    "print(\"Jarque-Bera Test\")\n",
    "print(\"Statistic: %.4f\" % jb)\n",
    "print(\"p-value: %.4f\" % jbpval)\n",
    "\n",
    "print(\" \")\n",
    "\n",
    "ks, kspval = stats.kstest(df_res['resid'].values, 'norm')\n",
    "print(\"Kolmogorov-Smirnov Test\")\n",
    "print(\"Statistic: %.4f\" % ks)\n",
    "print(\"p-value: %.4f\" % kspval) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Engle's Test for Autoregressive Conditional Heteroscedasticity (ARCH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lagrange multiplier Test\n",
      "LM Statistic: 2033.7359\n",
      "LM-Test p-value: 0.0000\n",
      "\n",
      "fstatistic for F test\n",
      "F Statistic: 83.2095\n",
      "F-Test p-value: 0.0000\n"
     ]
    }
   ],
   "source": [
    "lm, lmpval, fval, fpval = het_arch(df_res['resid'].values)\n",
    "print(\"Lagrange multiplier Test\")\n",
    "print(\"LM Statistic: %.4f\" % lm)\n",
    "print(\"LM-Test p-value: %.4f\" % lmpval)\n",
    "print('')\n",
    "print('fstatistic for F test')\n",
    "print(\"F Statistic: %.4f\" % fval)\n",
    "print(\"F-Test p-value: %.4f\" % fpval)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test for No Autocorrelation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Brock–Dechert–Scheinkman test\n",
    "    Calculate the BDS test statistic for independence of a time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Brock Dechert and Scheinkman Test\n",
      "Dim 2: z-static 16.7631 Prob 0.0000\n",
      "Dim 3: z-static 24.3408 Prob 0.0000\n",
      "Dim 4: z-static 28.5794 Prob 0.0000\n",
      "Dim 5: z-static 32.6203 Prob 0.0000\n",
      "Dim 6: z-static 36.1071 Prob 0.0000\n"
     ]
    }
   ],
   "source": [
    "result = bds(df_res['resid'].values, max_dim=6);\n",
    "print(\"Brock Dechert and Scheinkman Test\")\n",
    "print(\"Dim 2: z-static %.4f Prob %.4f\" % (result[0][0], result[1][0]))\n",
    "print(\"Dim 3: z-static %.4f Prob %.4f\" % (result[0][1], result[1][1]))\n",
    "print(\"Dim 4: z-static %.4f Prob %.4f\" % (result[0][2], result[1][2]))\n",
    "print(\"Dim 5: z-static %.4f Prob %.4f\" % (result[0][3], result[1][3]))\n",
    "print(\"Dim 6: z-static %.4f Prob %.4f\" % (result[0][4], result[1][4]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Breusch-Godfrey test\n",
    "    Breusch Godfrey Lagrange Multiplier tests for residual autocorrelation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lagrange multiplier test statistic\n",
      "LM Statistic: -0.2583\n",
      "LM-Test p-value: 1.0000\n",
      "\n",
      "fstatistic for F test\n",
      "F Statistic: 0.0058\n",
      "F-Test p-value: 0.9392\n"
     ]
    }
   ],
   "source": [
    "lm, lmpval, fval, fpval = acorr_breusch_godfrey(model_fit, nlags = max(order[0], order[2]))\n",
    "print('Lagrange multiplier test statistic')\n",
    "print(\"LM Statistic: %.4f\" % lm)\n",
    "print(\"LM-Test p-value: %.4f\" % lmpval)\n",
    "print('')\n",
    "print('fstatistic for F test')\n",
    "print(\"F Statistic: %.4f\" % fval)\n",
    "print(\"F-Test p-value: %.4f\" % fpval)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Box-Pierce and Ljung-Box tests\n",
    "    Ljung-Box and Box-Pierce statistic differ in their scaling of the autocorrelation function. Ljung-Box test is reported to have better small sample properties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Test</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Box-Pierce</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Ljung-Box</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Statistics</th>\n",
       "      <th>Stats</th>\n",
       "      <th>p-value</th>\n",
       "      <th>Stats</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lags</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.005822</td>\n",
       "      <td>0.939176</td>\n",
       "      <td>0.005825</td>\n",
       "      <td>0.939163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.986652</td>\n",
       "      <td>0.610592</td>\n",
       "      <td>0.987241</td>\n",
       "      <td>0.610412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.258738</td>\n",
       "      <td>0.153802</td>\n",
       "      <td>5.262520</td>\n",
       "      <td>0.153553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.340199</td>\n",
       "      <td>0.254137</td>\n",
       "      <td>5.344054</td>\n",
       "      <td>0.253781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10.889278</td>\n",
       "      <td>0.053620</td>\n",
       "      <td>10.898941</td>\n",
       "      <td>0.053421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11.041598</td>\n",
       "      <td>0.087099</td>\n",
       "      <td>11.051443</td>\n",
       "      <td>0.086799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11.878678</td>\n",
       "      <td>0.104620</td>\n",
       "      <td>11.889650</td>\n",
       "      <td>0.104247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12.343041</td>\n",
       "      <td>0.136542</td>\n",
       "      <td>12.354708</td>\n",
       "      <td>0.136065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12.664266</td>\n",
       "      <td>0.178395</td>\n",
       "      <td>12.676462</td>\n",
       "      <td>0.177801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14.058083</td>\n",
       "      <td>0.170359</td>\n",
       "      <td>14.072781</td>\n",
       "      <td>0.169698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14.330440</td>\n",
       "      <td>0.215245</td>\n",
       "      <td>14.345668</td>\n",
       "      <td>0.214453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>24.548045</td>\n",
       "      <td>0.017116</td>\n",
       "      <td>24.584684</td>\n",
       "      <td>0.016918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>27.320495</td>\n",
       "      <td>0.011242</td>\n",
       "      <td>27.363359</td>\n",
       "      <td>0.011090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>32.009310</td>\n",
       "      <td>0.003994</td>\n",
       "      <td>32.063407</td>\n",
       "      <td>0.003924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>36.963819</td>\n",
       "      <td>0.001281</td>\n",
       "      <td>37.030528</td>\n",
       "      <td>0.001253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>42.989631</td>\n",
       "      <td>0.000280</td>\n",
       "      <td>43.072585</td>\n",
       "      <td>0.000273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>43.031041</td>\n",
       "      <td>0.000475</td>\n",
       "      <td>43.114113</td>\n",
       "      <td>0.000462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>49.596185</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>49.698927</td>\n",
       "      <td>0.000084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>51.152734</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>51.260374</td>\n",
       "      <td>0.000085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>51.351622</td>\n",
       "      <td>0.000141</td>\n",
       "      <td>51.459918</td>\n",
       "      <td>0.000136</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Test       Box-Pierce            Ljung-Box          \n",
       "Statistics      Stats   p-value      Stats   p-value\n",
       "lags                                                \n",
       "1            0.005822  0.939176   0.005825  0.939163\n",
       "2            0.986652  0.610592   0.987241  0.610412\n",
       "3            5.258738  0.153802   5.262520  0.153553\n",
       "4            5.340199  0.254137   5.344054  0.253781\n",
       "5           10.889278  0.053620  10.898941  0.053421\n",
       "6           11.041598  0.087099  11.051443  0.086799\n",
       "7           11.878678  0.104620  11.889650  0.104247\n",
       "8           12.343041  0.136542  12.354708  0.136065\n",
       "9           12.664266  0.178395  12.676462  0.177801\n",
       "10          14.058083  0.170359  14.072781  0.169698\n",
       "11          14.330440  0.215245  14.345668  0.214453\n",
       "12          24.548045  0.017116  24.584684  0.016918\n",
       "13          27.320495  0.011242  27.363359  0.011090\n",
       "14          32.009310  0.003994  32.063407  0.003924\n",
       "15          36.963819  0.001281  37.030528  0.001253\n",
       "16          42.989631  0.000280  43.072585  0.000273\n",
       "17          43.031041  0.000475  43.114113  0.000462\n",
       "18          49.596185  0.000087  49.698927  0.000084\n",
       "19          51.152734  0.000089  51.260374  0.000085\n",
       "20          51.351622  0.000141  51.459918  0.000136"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maxLags = 21\n",
    "lbvalue, pvalue, bpvalue, bppvalue = acorr_ljungbox(df_res['resid'].values, lags = range(1, maxLags), boxpierce=True)\n",
    "\n",
    "index = pd.MultiIndex.from_product([range(1, maxLags)], names=['lags'])\n",
    "columns = pd.MultiIndex.from_product([['Box-Pierce', 'Ljung-Box'], ['Stats', 'p-value']], names=['Test', 'Statistics'])\n",
    "data = np.array([bpvalue, bppvalue, lbvalue, pvalue])\n",
    "\n",
    "boxTests = pd.DataFrame(data.T, index=index, columns=columns)\n",
    "boxTests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QQplot\n",
    "    Q-Q plot of the quantiles of x versus the quantiles/ppf of a distribution.\n",
    "    Can take arguments specifying the parameters for dist or fit them automatically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "qqplot(df_res['resid'].values, stats.t, fit=True, line='45')\n",
    "plt.title('QQ plot');\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Auto-correlation and Partial Auto-correlation functions\n",
    "    Plots lags on the horizontal and the correlations on vertical axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,10))\n",
    "\n",
    "# Auto-correlation function\n",
    "ax1 = fig.add_subplot(221)\n",
    "ax1.grid()\n",
    "sm.graphics.tsa.plot_acf(df_res['resid'].values, lags=maxLags, ax=ax1)\n",
    "ax2 = fig.add_subplot(222)\n",
    "ax2.grid()\n",
    "sm.graphics.tsa.plot_acf(df_res['resid'].values, lags=maxLags, ax=ax2, title = 'Zoom Autocorrelation')\n",
    "ax2.axis((0, maxLags, -0.2, 0.2))\n",
    "\n",
    "# Partial auto-correlation function\n",
    "ax3 = fig.add_subplot(223)\n",
    "ax3.grid()\n",
    "sm.graphics.tsa.plot_pacf(df_res['resid'].values, lags=maxLags, ax=ax3);\n",
    "ax4 = fig.add_subplot(224)\n",
    "ax4.grid()\n",
    "sm.graphics.tsa.plot_pacf(df_res['resid'].values, lags=maxLags, ax=ax4, title = 'Zoom Partial Autocorrelation')\n",
    "ax4.axis((0, maxLags, -0.2, 0.2));\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Prediction       \n",
    "    This section allows to see the forecast of the model that was selected and validated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(1, 1, 2)\n",
      "Prediction for 1 Business Days/Year: MAE = 3.63 (1.24%)\n",
      "Prediction for 5 Business Days/Year: MAE = 4.44 (1.51%)\n",
      "Prediction for 21 Business Days/Year: MAE = 7.91 (2.65%)\n"
     ]
    }
   ],
   "source": [
    "cmap = plt.get_cmap(\"tab10\")\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.subplot(211)\n",
    "plt.title('Real versus Predicted ' + ylab + ' for ARIMA' + str(order)) \n",
    "plt.plot(range(0, stepsToForecast[-1]+1), np.concatenate((df_train.values[-1], df_test.values.flatten()), axis=0), 'k-o', linewidth=2)\n",
    "plt.plot(range(1, stepsToForecast[-1]+1), dict_arimaPred['ARIMA' + str(order)], color = cmap(0), linewidth=2)\n",
    "plt.plot([0,1], [df_train.values[-1][0], dict_arimaPred['ARIMA' + str(order)][0]], '--', color = cmap(0), linewidth=2)\n",
    "\n",
    "plt.xlim([0,stepsToForecast[-1]])\n",
    "plt.xticks(np.arange(0, stepsToForecast[-1]+1, step=1))\n",
    "plt.grid(color='k', linestyle='--', linewidth=.5)\n",
    "plt.xlabel(xlab)\n",
    "plt.ylabel(ylab)\n",
    "plt.legend(['Real value', 'Predicted value'])\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.axhline(y=0, color='k', linestyle='--', linewidth=2)\n",
    "plt.plot(range(0,stepsToForecast[-1]+1), np.concatenate((np.array([0]), df_test.values.flatten()-dict_arimaPred['ARIMA' + str(order)]), axis=0), 'g-*', linewidth=2)\n",
    "plt.xticks(np.arange(0, stepsToForecast[-1]+1, step=1))\n",
    "plt.xlim([0, stepsToForecast[-1]])\n",
    "plt.xlabel(xlab)\n",
    "plt.ylabel('Prediction error')\n",
    "plt.grid(color='k', linestyle='--', linewidth=.5)\n",
    "plt.show()\n",
    "\n",
    "for i in np.arange(len(labels[1:])):\n",
    "    mae  = horizon_mae(df_test.values, dict_arimaPred['ARIMA' + str(order)], stepsToForecast)\n",
    "    mape = horizon_mape(df_test.values, dict_arimaPred['ARIMA' + str(order)], stepsToForecast)\n",
    "    print(labels[i+1])\n",
    "    for i in np.arange(len(stepsToForecast)):\n",
    "        print('Prediction for ' + str(stepsToForecast[i]) + ' ' + xlab + ': MAE = ' \\\n",
    "              + str(round(mae[i], 2)) + ' (' + str(round(mape[i], 2)) + '%)' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([288.94672026, 289.01222313, 289.08337565, 289.15815951,\n",
       "       289.2352804 , 289.3139083 , 289.39351089, 289.4737468 ,\n",
       "       289.55439707, 289.63532131, 289.71642946, 289.79766376,\n",
       "       289.87898719, 289.96037599, 290.04181493, 290.12329424,\n",
       "       290.20480767, 290.2863512 , 290.36792226, 290.4495192 ,\n",
       "       290.53114098])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Forecasted values of ARIMA model\n",
    "dict_arimaPred['ARIMA' + str(order)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Prediction Overview\n",
    "    Compare: Fitted values of the model with the training data\n",
    "             Predicted values of the model with the testing data\n",
    "      \n",
    "    This section allows to see the forecast of the model that was selected and validated, but also to compare it against other models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(1, 1, 1)\n",
      "Prediction for 1 Business Days/Year: MAE = 3.63 (1.24%)\n",
      "Prediction for 5 Business Days/Year: MAE = 4.44 (1.51%)\n",
      "Prediction for 21 Business Days/Year: MAE = 7.91 (2.65%)\n",
      "\n",
      "ARIMA(1, 1, 2)\n",
      "Prediction for 1 Business Days/Year: MAE = 3.63 (1.24%)\n",
      "Prediction for 5 Business Days/Year: MAE = 4.44 (1.51%)\n",
      "Prediction for 21 Business Days/Year: MAE = 7.92 (2.65%)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "labels = ['Real Closing price', \n",
    "          'ARIMA(1, 1, 1)',\n",
    "          'ARIMA(1, 1, 2)']\n",
    "\n",
    "plt.figure(figsize=(20,7))\n",
    "plt.title(title + ' fitting and forecasting')\n",
    "plt.plot(1+np.arange(stepsToForecast[-1]), df_test.values, 'k-o', linewidth = 3, label='Real values')\n",
    "for i in np.arange(len(labels)-1):\n",
    "    arimaPlot = plt.plot(1+np.arange(stepsToForecast[-1]), dict_arimaPred[labels[i+1]], '--*' , linewidth = 3, label = labels[i+1] )\n",
    "    plt.plot(1+np.arange(previousStepsToShow)-previousStepsToShow, dict_arimaFit[labels[i+1]][-previousStepsToShow:], '--' , linewidth = 2, color=arimaPlot[0].get_color())\n",
    "plt.legend(loc='upper left', framealpha=0.95)\n",
    "plt.plot(1+np.arange(previousStepsToShow)-previousStepsToShow, df_train.values[-previousStepsToShow:], 'k', linewidth = 4 ) # label = labels[i+1]  legenda\n",
    "plt.grid(color='k', linestyle='--', linewidth=.2)\n",
    "plt.xlim([1-previousStepsToShow,+stepsToForecast[-1]])\n",
    "plt.xticks(1+np.arange(-previousStepsToShow, stepsToForecast[-1]))\n",
    "plt.xlabel('Past and future working days')\n",
    "plt.ylabel(ylab);\n",
    "plt.show()\n",
    "\n",
    "for i in np.arange(len(labels[1:])):\n",
    "    mae  = horizon_mae(df_test.values, dict_arimaPred[labels[i+1]], stepsToForecast)\n",
    "    mape = horizon_mape(df_test.values, dict_arimaPred[labels[i+1]], stepsToForecast)\n",
    "    print(labels[i+1])\n",
    "    for i in np.arange(len(stepsToForecast)):\n",
    "        print('Prediction for ' + str(stepsToForecast[i]) + ' ' + xlab + ': MAE = ' \\\n",
    "              + str(round(mae[i], 2)) + ' (' + str(round(mape[i], 2)) + '%)' )\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}