01_regression_Least_Square.ipynb

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    "# Table of Content <a id='toc'></a>\n",
    "\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;[Convention and definition of the problem for linear model](#0)\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;[1. Least Square Method](#1)\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[1.1 Simple representation of the problem and how it is solved](#2)\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[1.2.Underlying hypotheses of the Least Square fit](#3)\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[1.3. Goodness of fit](#4)\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[1.4. Confidence interval and test statistics](#5)\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[1.5. building and fitting OLS models with `statsmodels`](#4b)\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;[Exercise on a real dataset : model the effect of park frequentation on sparrow nest number with a cubic function](#6)\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[1.6. Model choosing](#8)\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[1.7 What to do when some hypothesis about OLS are not true](#7)\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;[Exercise : model choosing on the sparrow nuisance dataset](#9)\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;[Going further : interaction effects](#11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import Image\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from operator import itemgetter\n",
    "import statsmodels\n",
    "import statsmodels.api as sm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "#matplotlib.use('nbagg') # enables interactive figures, where we can zoom and move"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "# a couple more configuration of the plotting engine\n",
    "import matplotlib.pylab as pylab\n",
    "pylab.rcParams['figure.figsize'] = 10, 10\n",
    "plt.rc(\"font\", size=18)\n",
    "\n",
    "\n",
    "plt.rc('xtick', color='k', labelsize='medium', direction='in')\n",
    "plt.rc('xtick.major', size=8, pad=12)\n",
    "plt.rc('xtick.minor', size=8, pad=12)\n",
    "\n",
    "plt.rc('ytick', color='k', labelsize='medium', direction='in')\n",
    "plt.rc('ytick.major', size=8, pad=12)\n",
    "plt.rc('ytick.minor', size=8, pad=12)"
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    "%%javascript\n",
    "// prevents output into scrollable frames\n",
    "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
    "    return false;\n",
    "}\n"
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   "cell_type": "markdown",
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   "source": [
    "\n",
    "[back to the toc](#toc)\n",
    "\n",
    "<br>\n",
    "\n",
    "# Convention and definition of the problem for linear model <a id='0'></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The problem we are interested in is the following:\n",
    "\n",
    "We have measured a bunch of variables per individual, for $n$ individuals. \n",
    "We are interested in the relationship between one of this variable that we will call the **response variable** \n",
    "and the other variables that we will call **covariables**.\n",
    "\n",
    "Let's illustrate this with the \"diabetes\" dataset, grabbed from Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) \"Least Angle Regression,\" Annals of Statistics (with discussion), 407-499. (https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf).\n",
    "\n",
    "It is described as : \n",
    "\"Ten baseline variables, age, sex, body mass index, average blood pressure, and six blood serum measurements were obtained for each of n = 442 diabetes patients, as well as the response of interest, a quantitative measure of disease progression one year after baseline.\"\n"
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>disease_progression</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>151.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>206.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>135.0</td>\n",
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       "   disease_progression\n",
       "0                151.0\n",
       "1                 75.0\n",
       "2                141.0\n",
       "3                206.0\n",
       "4                135.0"
      ]
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   "source": [
    "from sklearn.datasets import load_diabetes\n",
    "diabetes = load_diabetes()\n",
    "\n",
    "#this is our response variable\n",
    "response=pd.DataFrame(diabetes['target'],columns=['disease_progression'])\n",
    "response.head()"
   ]
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   "cell_type": "code",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>bp</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>s6</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.038076</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.061696</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>-0.044223</td>\n",
       "      <td>-0.034821</td>\n",
       "      <td>-0.043401</td>\n",
       "      <td>-0.002592</td>\n",
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       "      <td>-0.051474</td>\n",
       "      <td>-0.026328</td>\n",
       "      <td>-0.008449</td>\n",
       "      <td>-0.019163</td>\n",
       "      <td>0.074412</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.068332</td>\n",
       "      <td>-0.092204</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.085299</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.044451</td>\n",
       "      <td>-0.005670</td>\n",
       "      <td>-0.045599</td>\n",
       "      <td>-0.034194</td>\n",
       "      <td>-0.032356</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.002861</td>\n",
       "      <td>-0.025930</td>\n",
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       "      <th>3</th>\n",
       "      <td>-0.089063</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.011595</td>\n",
       "      <td>-0.036656</td>\n",
       "      <td>0.012191</td>\n",
       "      <td>0.024991</td>\n",
       "      <td>-0.036038</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>0.022688</td>\n",
       "      <td>-0.009362</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.005383</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.036385</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>0.003935</td>\n",
       "      <td>0.015596</td>\n",
       "      <td>0.008142</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>-0.031988</td>\n",
       "      <td>-0.046641</td>\n",
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       "        age       sex       bmi        bp        s1        s2        s3  \\\n",
       "0  0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
       "1 -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
       "2  0.085299  0.050680  0.044451 -0.005670 -0.045599 -0.034194 -0.032356   \n",
       "3 -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
       "4  0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
       "\n",
       "         s4        s5        s6  \n",
       "0 -0.002592  0.019907 -0.017646  \n",
       "1 -0.039493 -0.068332 -0.092204  \n",
       "2 -0.002592  0.002861 -0.025930  \n",
       "3  0.034309  0.022688 -0.009362  \n",
       "4 -0.002592 -0.031988 -0.046641  "
      ]
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   "source": [
    "## this is our covariable\n",
    "covar=pd.DataFrame(diabetes['data'],columns=diabetes['feature_names'])\n",
    "covar.head()"
   ]
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   "cell_type": "markdown",
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    "\n",
    "From a mathematical standpoint, we note the response variable vector $\\pmb{y}$, and $\\pmb{y}_i$ is a single element of that vector (*ie.* the reponse variable measured on a single individual).\n",
    "\n",
    "Similarly, $\\pmb X$ is the matrix of shape $(n,p)$ which contains the values of covariables. \n",
    "$\\pmb{X}_i$ is a row in this matrix, which corresponds to the measures of the $p$ covariables for individual $i$.\n",
    "\n",
    "\\begin{equation*}\n",
    "\\pmb X = \n",
    "\\begin{bmatrix}\n",
    "X_{1,1} & X_{1,2} & ... & X_{1,p} \\\\\n",
    "X_{2,1} & X_{2,2} & ... & X_{2,p} \\\\\n",
    " ... \\\\\n",
    "X_{n,1} & X_{i,2} & ... & X_{n,p} \\\\\n",
    "\\end{bmatrix}\n",
    " ,\\pmb y = \n",
    "\\begin{bmatrix}\n",
    "y_{1} \\\\\n",
    "y_{2} \\\\\n",
    " ... \\\\\n",
    "y_{n} \\\\\n",
    "\\end{bmatrix}\n",
    "\\end{equation*}\n",
    "\n",
    "Furthermore, we will consider our response variable to actually be a random variable.\n",
    "So each $y_i$ is in fact a realisation of a random variable $\\pmb Y_i$.\n",
    "\n",
    "Indeed our measurements are not perfect so there is some noise associated to it ($\\epsilon$) : this why we decided to consider our response variable as a random variable. In mathematical term we are interested in a class of problem that we can write as :\n",
    "\n",
    "$$\\pmb{Y}_i=f(\\pmb{X}_i)+\\epsilon$$\n",
    "\n",
    "The function $f$ is called the regression function, and today we will be interested in looking at a particular form of those function: **linear combination**.\n",
    "\n",
    "A particular case of linear combination would be a single covariable with an intercept like :\n",
    "\n",
    "$$y_i=\\beta X_{i,1}+c$$\n",
    "\n",
    "A more general case would have more covariables and would be written like:\n",
    "\n",
    "$$f(\\textbf{X}_i,\\pmb{\\beta})=\\sum_{j} \\beta_j X_{i,j}= \\textbf{X}_{i}^{T}\\pmb{\\beta}$$\n",
    "\n",
    "Where *$\\pmb X_i$* is a vector of $p$ covariables associated to point individual $i$. And $\\pmb{\\beta}$ is a vector of $p$ coefficients (one for each covariable).\n",
    "\n",
    "Note that for now nothing is said about the nature of the $X_{i,j}$. For example, some could be constant instead of being a variable (in that specific case they may as well be integrated in the $c$ constant).\n"
   ]
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   "source": [
    "In the case of our diabetes example, the response variable is $diseaseprogression$ and the covariables are \n",
    "$age , \tsex , bmi , bp, s1 , s2 , s3 , s4 , s5 , s6$."
   ]
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   "source": [
    "So in the end, \n",
    "we are interested in finding a set of coefficients $\\pmb \\beta$ so that:\n",
    "\n",
    "$$disease progression_0=\\beta_1 age_0 + \\beta_2 sex_0+\\beta_3 bmi_0+\\beta_4 bp_0+\\beta_5 s1_0 +\\beta_6 s2_0 + \\beta_7 s3_0+\\beta_8 s4_0+\\beta_9 s5_0+\\beta_10 s6_0+\\beta_0$$\n",
    "\n",
    "$$disease progression_1=\\beta_1 age_1 + \\beta_2 sex_1+\\beta_3 bmi_1+\\beta_4 bp_1+\\beta_5 s1_1 +\\beta_6 s2_1 + \\beta_7 s3_1+\\beta_8 s4_1+\\beta_9 s5_1+\\beta_10 s6_1+\\beta_0$$\n",
    "\n",
    "$$......$$\n",
    "\n",
    "$$disease progression_n=\\beta_1 age_n + \\beta_2 sex_n+\\beta_3 bmi_n+\\beta_4 bp_n+\\beta_5 s1_n+\\beta_6 s2_n + \\beta_7 s3_n+\\beta_8 s4_n+\\beta_9 s5_n+\\beta_10 s6_n+\\beta_0$$"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say for now we are only interest in $bmi$ (Body Mass Index) to predict disease progression. \n",
    "Then the kind of outcome we want of this analysis is our ability to understand the data linearly like that, which can be summarized by our ability to produce this kind of plots:"
   ]
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     "text": [
      "slope also called beta in our notation=  949.4352603840384\n",
      "intercept also called c in our notation=  152.13348416289617\n"
     ]
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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pulling all the data together in the same dataFrame\n",
    "df_diabetes = pd.concat( [covar , response] , axis = 1 ) \n",
    "\n",
    "x = df_diabetes['bmi'] # covariable bmi\n",
    "y = df_diabetes['disease_progression'] # response variable disease progression\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots( figsize = (10,10)) #setup graphical windows\n",
    "sns.scatterplot(x=x,y=y) # plot x versus y\n",
    "\n",
    "#linear regression explaining disease progression thanks to bmi\n",
    "slope , intercept , r , pval , stderr = stats.linregress(x,y)\n",
    "##don't mind how we did the regression for now, this is just for showcasing\n",
    "print(\"slope also called beta in our notation= \",slope)\n",
    "print(\"intercept also called c in our notation= \",intercept)\n",
    "\n",
    "#now that we have the outcome iof the regression which is in this case a slope and\n",
    "#an intercept we can calulate what the model will predict as a disease progression given a bmi value\n",
    "yPredict = x * slope + intercept \n",
    "\n",
    "ax.plot( x , yPredict , color = 'red')#the outcome of the regression is this red line"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So of course now the game becomes how to choose the best vector of parameters $\\pmb{\\beta}$. For that we will discuss two main methods (sorry Bayesian people...):\n",
    "- Least Square fit\n",
    "- Maximum Likelihood\n",
    "\n",
    "It is important to realize that these two are just **methods to find a good fit**,\n",
    "they are somewhat disconnected from the actual model we use to represent the data.\n",
    "\n",
    "\n",
    "\n",
    "Indeed, underlying those different methods there are many possible models. We will discuss:\n",
    "\n",
    "- Linear Models\n",
    "- Generalized Linear Models\n",
    "\n",
    "As their name entails, **Generalized Linear Models** (GLMs for short) are just a generalization of the simple linear models, where we can perform a transformation on our linear combination (such as a logarithm for instance), and specify which diststribution to use to generate the \"noise\" (such as a poisson distribution for instance).\n",
    "\n",
    "But, we will se that later. For now, let's focus on the simple **linear model**, which we can solve with the **least square fit** method\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "[back to the toc](#toc)\n",
    "\n",
    "<br>\n",
    "\n",
    "# 1. Least Square Method <a id='1'></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the least square method we are interested in making the smallest overall square error between our model and the response variable.\n",
    "\n",
    "Typically we want to find the vector of parameters $\\beta$ that minimizes the objective function :\n",
    "\n",
    "$$S(\\pmb\\beta)=\\sum_i (y_i-f(\\pmb X_i,\\pmb{\\beta}))^2=\\sum_i \\epsilon_i^2$$\n",
    "\n",
    "Here the sum is over $n$, which counts the number of individuals.\n",
    "\n",
    "in mathematical terms you are looking for :\n",
    "\n",
    "$$\\hat{\\pmb\\beta}=\\text{arg min}_{\\pmb\\beta}S(\\pmb\\beta)$$\n",
    "\n",
    "\n",
    "> The hat $\\hat{.}$, is a notation we use to denote our estimate of the true value of something. So in that sense $\\hat{\\pmb\\beta}$ is the estimate of the \"real\" coefficient values, and $\\hat{Y}$ is the estimation of $Y$ given by our model (also called the model predictions).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how that work on a toy example "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "[back to the toc](#toc)\n",
    "\n",
    "<br>\n",
    "\n",
    "## 1.1 Simple representation of the problem and how it is solved <a id='2'></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Toy example\n",
    "\n",
    "Let's play with a toy example where we know the solution. \n",
    "\n",
    "We simulate a response variable that can be written $y=3x$, so with a single covariable. We also add some gaussian noise on top of that. \n",
    "\n",
    "We are going to check if the Ordinary Least Square (OLS) method could actully find this coefficient of 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Y')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import collections  as mc\n",
    "\n",
    "## let's create some data to play with \n",
    "slopeReal = 3 # the real slope is 3\n",
    "noise = 3 #noise strength\n",
    "\n",
    "x = np.arange(15) # 15 points for x\n",
    "y = slopeReal * x + noise * np.random.randn(len(x)) # y = beta * x + some noise (no intercept here)\n",
    "\n",
    "# plot the data\n",
    "fig, ax = plt.subplots(figsize = (10,5))\n",
    "ax.plot(x,y, 'o' )\n",
    "ax.set_xlabel(\"X\")\n",
    "ax.set_ylabel(\"Y\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The challenge of least square regression is to find the slope that minimizes the squared error\n",
    "let's try 3 possible values for the slope here : 2, 3 and 4**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x700 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "estimatedSlopes = [2,3,4]\n",
    "\n",
    "fig, ax = plt.subplots(ncols=len(estimatedSlopes) , figsize = (14,7))\n",
    "\n",
    "for i,slopeEstimate in enumerate(estimatedSlopes): # for each slope we want to test\n",
    "    \n",
    "    yPredicted = slopeEstimate * x # prediction of y given the estimated slope and values of x\n",
    "\n",
    "    # error of the prediction -> sum of squared difference between observation and prediction\n",
    "    predictionSquaredError = sum( ( yPredicted - y )**2 ) \n",
    "\n",
    "    #base plot\n",
    "    ax[i].plot(x,y, 'o')\n",
    "    ax[i].set_xlabel(\"X\")\n",
    "    ax[i].set_ylabel(\"Y\")\n",
    "    \n",
    "    ax[i].plot(x,yPredicted, 'r--' , linewidth=2,label='Estimated') #plotting the prediction\n",
    "\n",
    "    # now, let's represent the fitting error as segments between real and estimated values\n",
    "    Real = [i for i in zip(x,y)]\n",
    "    Predicted = [i for i in zip(x,yPredicted)]\n",
    "    lc = mc.LineCollection(zip(Real,Predicted) , colors='black',linewidths=2,label='Noise + Fitting Error')\n",
    "    ax[i].add_collection(lc)\n",
    "    \n",
    "    ax[i].set_title('slope : {} \\n squared error : {:.2f}'.format(slopeEstimate,predictionSquaredError) ,\n",
    "                    fontsize=20)\n",
    "    ax[i].legend(fontsize=10)\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**micro-exercise** : based on the 3 plot just above. Which one would you choose and why?\n",
    "\n",
    "<br>\n",
    "\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We could try to generalize this approach and test a lot of possible slope and then choose the best one (the one giving the lowest SSE)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "slope estimate with the smallest squared error (noise + fitting error) :  2.802\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "possibleSlopes = np.linspace(0,6,1001)# trying a hundred slopes between 0 and 6\n",
    "errors = []\n",
    "for sl in possibleSlopes: # we compute the sum of squared error for each slopes\n",
    "    yPred = sl*x\n",
    "    errors.append( sum( yPred - y )**2 )\n",
    "\n",
    "fitted_slope=possibleSlopes[ np.argmin( errors ) ] # we use np.argmin to find the best slope\n",
    "min_err=min(errors)\n",
    "\n",
    "#plotting \n",
    "fig, ax = plt.subplots(figsize = (12,6))\n",
    "ax.plot(possibleSlopes , errors )\n",
    "ax.plot(fitted_slope , min_err,'ro' ,label='Best estimated slope')\n",
    "ax.legend(loc='best',fontsize=10)\n",
    "\n",
    "plt.xlabel('estimated slope')\n",
    "plt.ylabel('sum of squared errors')\n",
    "print( 'slope estimate with the smallest squared error (noise + fitting error) : ', fitted_slope )\n",
    "#plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Mini-exercice : what are the defaults of this method to find the best $\\beta$ ?**\n",
    "\n",
    "<br>\n",
    "\n",
    "\n",
    "<br>\n",
    "\n",
    "\n",
    "<br>\n",
    "\n",
    "\n",
    "\n",
    "While we could use various optimization algorithms to find the best value for $\\beta$, \n",
    "when the system is overdetermined (*i.e.*, you have more points than coefficients $\\beta_i$) an analytical solution exists. It is of the form:\n",
    "\n",
    "$$\\hat{\\pmb\\beta}=(\\pmb X^T \\pmb X)^{-1}\\pmb X^T \\pmb Y$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[back to the toc](#toc)\n",
    "\n",
    "<br>\n",
    "\n",
    "## 1.2.Underlying hypotheses of the Least Square fit <a id='3'></a>\n",
    "\n",
    "There are a couple of important hypothesis behind this method:\n",
    "\n",
    "- **Correct specification** : have a good incentive for the function you use\n",
    "- **Strict exogeneity** : the errors are centered around 0\n",
    "- **No linear dependance** : you can not reconstruct one of your covariable by summing a subset of your covariables with some set of constant weights \n",
    "- **Spherical errors**: \n",
    "    - Homoscedasticity : the spread of the error is the same along the curve.\n",
    "    - No autocorrelation : error are not correlated along the curve.\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "        \n",
    "To illustrate **Strict exogeneity** and **Spherical errors**, here is a plot of the errors on the diabetes model with $bmi$ :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "error\n",
      "mean    -0.000\n",
      "median  -8.649\n",
      "Q1 : -43.572 , Q3 : 46.344\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df_diabetes['bmi'] # covariable bmi\n",
    "y = df_diabetes['disease_progression'] # response variable disease progression\n",
    "slope , intercept , r , pval , stderr = stats.linregress(x,y)\n",
    "fitted_y = (x*slope+intercept)\n",
    "errors = y - fitted_y\n",
    "\n",
    "fig, ax = plt.subplots(ncols=2, figsize = (14,7)) #setup graphical windows\n",
    "sns.scatterplot(x=fitted_y,y=errors , ax=ax[0]) # plot the errors along x\n",
    "ax[0].axhline(0,color='red')\n",
    "ax[0].set_ylabel('errors')\n",
    "ax[0].set_xlabel('fitted values')\n",
    "sns.histplot(errors , ax=ax[1]) # plot an histogram of the errors\n",
    "ax[1].vlines( np.median( errors ) ,0,70 , color=\"red\", linewidth = 2)\n",
    "ax[1].set_xlabel('errors')\n",
    "\n",
    "print( \"error\" )\n",
    "print( \"mean    {:.3f}\".format(errors.mean()) )\n",
    "print( \"median  {:.3f}\".format(np.median( errors )) )\n",
    "print( \"Q1 : {:.3f} , Q3 : {:.3f}\".format(*np.quantile( errors , [0.25,0.75] ) ) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The errors seems centered around 0 and there is no definite pattern that would allow us to reject outright the hypotheses of the Least Square fitting. We shall see later on how to actually test these using specific statistical tests.\n",
    "\n",
    "The right panel also seems to suggest that our errors could follow something that looks like a normal distribution.\n",
    "\n",
    "**Normality is not strictly needed for Least Square fitting, neither for the variables nor for their errors.** \n",
    "However you may need that hypothesis of normality of errors downstream in your analysis, in particular when using a test statistic to determinate the significance of your parameter\n",
    "\n",
    "\n",
    "Additionnaly, if your errors are normally distributed, then Least Square fitting and Maximum Likelihood are equivalent, showing that your method for choosing $\\pmb\\beta$ is efficient and sound.\n",
    "\n",
    "<br>\n",
    "\n",
    "A model where things go wrong, for instance where there is heteroskedascticity, could look something like that instead:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'fitted values')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.random.random(1000)\n",
    "y = 3 * x + x * np.random.randn(len(x))  # the noise strength now depends on x.\n",
    "slope , intercept , r , pval , stderr = stats.linregress(x,y)\n",
    "fitted_y = (x*slope+intercept)\n",
    "errors = y - fitted_y \n",
    "\n",
    "fig, ax = plt.subplots(figsize = (10,6))\n",
    "sns.scatterplot(x=fitted_y,y=errors , ax=ax) # plot the errors along x\n",
    "ax.axhline(0,color='red')\n",
    "ax.set_ylabel('errors')\n",
    "ax.set_xlabel('fitted values')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>   \n",
    "\n",
    "---  \n",
    "\n",
    "<br>\n",
    "\n",
    "Finally, within that set of constraints and even if the method is called Linear Models, it is possible to **fit polynomials**. To do so you just have to precompute the monomials and add them to your set of covariables.\n",
    "\n",
    "For example :\n",
    "\n",
    "$y=\\beta x +c$ is a linear combination of x\n",
    "\n",
    "$y=\\beta_{1}x+\\beta_{2}x^{2}+\\beta_{3}x^{3}$ is still a linear combination of covariables x, $x^{2}$ and $x^{3}$, and **X** becomes {$x,x^2,x^3$\\}\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "[back to the toc](#toc)\n",
    "\n",
    "<br>\n",
    "\n",
    "## 1.3. Goodness of fit <a id='4'></a>\n",
    "\n",
    "By definiton, the least square fit minimizes the Sum of Square Errors : $SSE=\\sum (y_i-\\hat{y_i})^2)$.\n",
    "\n",
    "It is still important to assess the goodness of fit of your model, for instance to get an idea if you capture most of the variance of your response variable or to compare models between one-another.\n",
    "\n",
    "The first metric use to do so is the **Mean Square Error** (MSE), which is defined as follow:\n",
    "\n",
    "<br>\n",
    "\n",
    "$$MSE=\\frac{SSE}{n-2}$$ \n",
    "\n",
    "Why $n-2$? here are [elements of answer](https://stats.stackexchange.com/questions/378407/why-error-sum-of-squares-has-n-2-df-possibly-not-duplicate-please-read-on-r). Anyway this accounts for what your model is missing. \n",
    "That could be the simple inherent variance induced by the noise term or the noise term and a missing term that your model doesn't take into account. By its nature, this metric makes it hard to compare between different hypothetical fitting models or different dataset.\n",
    "\n",
    "A better normalized metric is the **adjusted coefficient of determination $\\pmb R^2_a$**. \n",
    "The adjusted part is very necessary when we work in the context of multiple linear regression (more than one covariable). \n",
    "\n",
    "Let's start by defining the coefficient of determination $\\pmb R^2$. \n",
    "This coefficient partitions the variance present in your data between what is taken into account by your model and what is not.\n",
    "\n",
    "$$R^2=1-\\frac{SSE}{SST}$$ <br>, where $SSE=\\sum_i (y_i-\\hat{y_i})^2$) and SST in the Sum of Squares Total ($\\sum_i (y_i-\\bar{y})^2$)\n",
    "\n",
    "For the adjusted coefficient of determination you have to take into account that SSE and SST don't have the same degree of freedom and you should adjust for that.\n",
    "\n",
    "$$R^2_a=1-\\frac{n-1}{n-p}(1-R^2)$$, with $p$ the number of covariables and $n$ the number of individuals.\n",
    "\n",
    "> Note : you can see that when there is only one covariable then $R^2_a = R^2$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bmi and disease progression model \n",
      " - SSE: 1719581.811\n",
      " - MSE: 3908.140\n",
      " - R2:  0.34392\n",
      " - R2a: 0.34243\n"
     ]
    }
   ],
   "source": [
    "SSE = sum( errors**2 )\n",
    "MSE = SSE/(len(y)-2)\n",
    "SST = sum( ( y - np.mean(y) )**2 )               # sum of square total\n",
    "R2 =  1 - SSE/SST                                # coefficient of determination\n",
    "R2a = 1 - ( (len(y)-1)/(len(y) - 2) ) * (1 - R2) # here, we have 2 parameters : intercept and slope for bmi\n",
    "print( \"\"\"bmi and disease progression model \n",
    " - SSE: {:.3f}\n",
    " - MSE: {:.3f}\n",
    " - R2:  {:.5f}\n",
    " - R2a: {:.5f}\"\"\".format(SSE,MSE,R2,R2a))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "[back to the toc](#toc)\n",
    "\n",
    "<br>\n",
    "\n",
    "## 1.4. Confidence interval and test statistics <a id='5'></a>\n",
    "\n",
    "After your fitting, you would probably like to know the confidence interval for each of your estimated $\\beta$, as well as if they are truly necessary (significantly different from zero). \n",
    "For both **you can't truly do anything without making an hypothesis about the statistic of the noise** : here comes the part where assuming your noise to be normally distributed ($N(0,\\sigma^2)$) becomes important, but potentially wrong too.\n",
    "\n",
    "For the confidence interval, if you have an infinite amount of data it is demonstrated that the estimators are well described by a normal statistic. \n",
    "There is a convergence in the distribution so that $(\\hat{\\pmb\\beta}-\\pmb\\beta)\\rightarrow N(0,\\sigma^2 (\\pmb X^T \\pmb X)^{-1})$). \n",
    "\n",
    "So for a big amount of points relative to the number of estimated parameters, you are not making a big mistake by writing the confidence interval of $\\beta_i$ as:\n",
    "\n",
    "$$\\beta_j \\in [\\hat{\\beta_j} \\pm z_{1-\\frac{\\alpha}{2}}\\sqrt{\\hat{\\sigma}^2 [(\\pmb X^T \\pmb X)^{-1}]_{j,j}}]$$ \n",
    "\n",
    "So in this formula, \n",
    " * $j$ is indexing one of the covariable, \n",
    " * $z_{1-\\frac{\\alpha}{2}}$ would be 1.96 for a 95% confidence interval, as you usually do with a normal distribution\n",
    "\n",
    "If you don't have a huge amount of data you need to show that you have an incentive about your noise statistic to use these kind of confidence intervals.\n",
    "\n",
    "\n",
    "For the significance of the coefficients, **if you know that your noise is normally distributed then you can use a t-test**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's examine all these metrics for our simple model on the diabetes dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>const</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.061696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.051474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.044451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.011595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.036385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.019662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.015906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.015906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.039062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.073030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>442 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     const       bmi\n",
       "0      1.0  0.061696\n",
       "1      1.0 -0.051474\n",
       "2      1.0  0.044451\n",
       "3      1.0 -0.011595\n",
       "4      1.0 -0.036385\n",
       "..     ...       ...\n",
       "437    1.0  0.019662\n",
       "438    1.0 -0.015906\n",
       "439    1.0 -0.015906\n",
       "440    1.0  0.039062\n",
       "441    1.0 -0.073030\n",
       "\n",
       "[442 rows x 2 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## We'll be using the statsmodel package, which computes a lot of these metrics for you\n",
    "\n",
    "X = sm.add_constant(df_diabetes['bmi'])## adding the intercept to the model\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             OLS Regression Results                            \n",
      "===============================================================================\n",
      "Dep. Variable:     disease_progression   R-squared:                       0.344\n",
      "Model:                             OLS   Adj. R-squared:                  0.342\n",
      "Method:                  Least Squares   F-statistic:                     230.7\n",
      "Date:                 Tue, 28 Nov 2023   Prob (F-statistic):           3.47e-42\n",
      "Time:                         10:02:15   Log-Likelihood:                -2454.0\n",
      "No. Observations:                  442   AIC:                             4912.\n",
      "Df Residuals:                      440   BIC:                             4920.\n",
      "Df Model:                            1                                         \n",
      "Covariance Type:             nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const        152.1335      2.974     51.162      0.000     146.289     157.978\n",
      "bmi          949.4353     62.515     15.187      0.000     826.570    1072.301\n",
      "==============================================================================\n",
      "Omnibus:                       11.674   Durbin-Watson:                   1.848\n",
      "Prob(Omnibus):                  0.003   Jarque-Bera (JB):                7.310\n",
      "Skew:                           0.156   Prob(JB):                       0.0259\n",
      "Kurtosis:                       2.453   Cond. No.                         21.0\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "model = sm.OLS( df_diabetes[ 'disease_progression' ], X)  ## defining an Ordinary Least Square variable\n",
    "results = model.fit() ## fitting it\n",
    "res=results.summary()\n",
    "print( res )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's go at these panels one by one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             OLS Regression Results                            \n",
      "===============================================================================\n",
      "Dep. Variable:     disease_progression   R-squared:                       0.344\n",
      "Model:                             OLS   Adj. R-squared:                  0.342\n",
      "Method:                  Least Squares   F-statistic:                     230.7\n",
      "Date:                 Tue, 28 Nov 2023   Prob (F-statistic):           3.47e-42\n",
      "Time:                         10:02:15   Log-Likelihood:                -2454.0\n",
      "No. Observations:                  442   AIC:                             4912.\n",
      "Df Residuals:                      440   BIC:                             4920.\n",
      "Df Model:                            1                                         \n",
      "Covariance Type:             nonrobust                                         \n",
      "===============================================================================\n"
     ]
    }
   ],
   "source": [
    "print(res.tables[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first panel gives you an overview of the fit quality:\n",
    "* You recognize the good old **$R^2$** and **$R_a^2$**\n",
    "* The **F-statistic** and its associated P-value (**Prob (F-statistic)**) tests the hypothesis that all the coefficients are 0 (under a normality assumption)\n",
    "* **Log-likelihood** (normality assumption + this is the next big part so keep it on a corner of your mind)\n",
    "* AIC and BIC, respectively **Aikike Information Criterion** and **Bayesian Information Criterion** are used (like the log-likelihood) for model comparison. You can use them to compare non nested models, although it is not always clear what constitutes a \"meaningful\" difference in AIC or BIC between two models.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const        152.1335      2.974     51.162      0.000     146.289     157.978\n",
      "bmi          949.4353     62.515     15.187      0.000     826.570    1072.301\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "print(res.tables[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second panel presents the coefficient values and associated statistics.\n",
    " * **const** is the intercept. It represents the value of the response variable when all covariable are at 0.\n",
    " * **bmi** is the coefficient of the $bmi$. It represents the increase of the response variable when $bmi$ increases by 1.\n",
    "\n",
    "Regarding the columns:\n",
    " * **std err** : standard errors of the estimated coefficients.\n",
    " * **t** : t-test statistics.\n",
    " * **P>|t|** : p-values of the t-test testing if the coefficient is significantly different from 0.\n",
    " * **\\[0.025 0975\\]** : 95% condidence interval aournd the coefficient\n",
    "\n",
    "Be careful with this **t-test which again makes the assumption that errors are normally distributed**, same for the standard error and the 95% confidence interval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============================================================================\n",
      "Omnibus:                       11.674   Durbin-Watson:                   1.848\n",
      "Prob(Omnibus):                  0.003   Jarque-Bera (JB):                7.310\n",
      "Skew:                           0.156   Prob(JB):                       0.0259\n",
      "Kurtosis:                       2.453   Cond. No.                         21.0\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "print(res.tables[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The third panel is a summary of a few statistical tests that will give you a sense of how all of the hypothesis needed for OLS are plausible:\n",
    "* **Omnibus and Prob(omnibus)**: this is a test for normality of residuals. Low P-values means that your linear model is not adapted\n",
    "* **[Skew](https://en.wikipedia.org/wiki/Skewness)** : should be at 0\n",
    "* **[Kurtosis](https://en.wikipedia.org/wiki/Kurtosis)** : should be at 3\n",
    "* **Durbin-Watson** : tests autocorrelation in the error terms (2 is no autocorrelation, less than 1 is bad)\n",
    "* **Jarque-Bera** : tests if the [skewness](https://en.wikipedia.org/wiki/Skewness) and [kurtosis](https://en.wikipedia.org/wiki/Kurtosis) of your errors are looking like a normal distribution. If the Pvalue is high then they look normal.\n",
    "* **Condition Number** : tries to evaluate dependence between the co-variables. Close to infinity ($\\gt 10^{15}$) mean there is a linear relationship between some co-variables (you can read more on this [here](https://medium.com/@docintangible/multicollinearity-in-pythons-statsmodels-ols-vs-r-s-lm-6fc9a994154c))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is one important hypothesis of OLS which is missing from this summary : **homoskedasticity**.\n",
    "\n",
    "Thankfully, statsmodels provide several tests for this as well, we will use White's test, whose null hypothesis is homoscedasticity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "White test for heteroscedasticity p-value: 0.4302870419459758\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.stats.diagnostic import het_white\n",
    "\n",
    "LMstat , LMpval , Fstat , Fpval = het_white( results.resid , X )\n",
    "print(\"White test for heteroscedasticity p-value:\" , LMpval)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "Providing a visual impression of the goodness of fit is also important.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5221333430>]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "O = np.argsort( results.fittedvalues )\n",
    "plt.plot( results.fittedvalues[O] ,\n",
    "gaussian_filter1d( results.resid[O] , sigma = 10 ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.ndimage import gaussian_filter1d\n",
    "fig, ax = plt.subplots(ncols=3, figsize = (14,5)) #setup graphical windows\n",
    "# plot the residuals along fitted values\n",
    "sns.scatterplot(x=results.fittedvalues,\n",
    "                y=results.resid , \n",
    "                ax=ax[0]) \n",
    "# adding a smoothed average line\n",
    "O = np.argsort( results.fittedvalues ) ## sorting by X values\n",
    "ax[0].plot( results.fittedvalues[O] ,\n",
    "            gaussian_filter1d( results.resid[O] , sigma = 10 ) , color='orange',linestyle='dashed' )\n",
    "\n",
    "ax[0].axhline(0,color='red')\n",
    "ax[0].set_ylabel('errors')\n",
    "ax[0].set_xlabel('fitted values')\n",
    "\n",
    "# histogram of residuals\n",
    "sns.histplot(results.resid , ax=ax[1]) # plot an histogram of the errors\n",
    "ax[1].vlines( np.median( results.resid ) ,0,70 , color=\"red\", linewidth = 2)\n",
    "ax[1].set_xlabel('errors')\n",
    "\n",
    "\n",
    "sns.scatterplot(x=results.fittedvalues,\n",
    "                y=df_diabetes[\"disease_progression\"] , \n",
    "                ax=ax[2]) \n",
    "m,M = min(df_diabetes[\"disease_progression\"]) , max(df_diabetes[\"disease_progression\"])\n",
    "ax[2].plot([m,M],[m,M], color='red')\n",
    "ax[2].set_ylabel('real values')\n",
    "ax[2].set_xlabel('fitted values')\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Mini-exercise : what is your conclusion on the $bmi$ model based on these results ?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[back to the toc](#toc)\n",
    "\n",
    "<br>\n",
    "\n",
    "## 1.5. building and fitting OLS models with `statsmodels`  <a id='4b'></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There  are two main approach to building a model with `statsmodels`.\n",
    "\n",
    "The first is the one we juste demonstrated: giving an iterable representing the target variable (`y`), and a 2D numpy array representing all covariables, including the intercept (`X`).\n",
    "\n",
    "The second is to use a *formula*. If you have been doing some regression in R, you would have come across these : instead of different dataframes for dependent variable covariables, on gives a single `DataFrame` along with a formula of the type `y ~ x`.\n",
    "\n",
    "As long as you have regrouped all your variables of interest in the same `DataFrame`, this second solution offers a fairly simple and elegant way of specifying your model, with an efficient handling of intercepts, as well as interaction effects.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "## option 1 : using different matrices for target / covariables\n",
    "\n",
    "y = df_diabetes['disease_progression'] # response variable disease progression\n",
    "X = sm.add_constant( df_diabetes[['bmi']] )## adding the intercept to the model\n",
    "\n",
    "model1 = sm.OLS(y, X)\n",
    "\n",
    "## option 2 : using a formula\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "model2 = smf.ols(\"disease_progression ~ bmi\" , data = df_diabetes) \n",
    "## NB : the intercept is implicit here. To have a model without intercept: disease_progression ~ 0 + bmi\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Whichever method you end up using, you need to fit you model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "results1 = model1.fit()\n",
    "results2 = model2.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "method 1\n",
      "const    152.133484\n",
      "bmi      949.435260\n",
      "dtype: float64\n",
      "method 2\n",
      "Intercept    152.133484\n",
      "bmi          949.435260\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "## both method give the same result:\n",
    "print(\"method 1\")\n",
    "print(results1.params)\n",
    "print(\"method 2\")\n",
    "print(results2.params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Going into details on these formula is beyond the scope of this course, but we encourage you to go though this [very well made tutorial](https://www.statsmodels.org/devel/example_formulas.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's now proceed to an OLS fitting on a toy model produced thanks to the relation** $\\pmb Y=-3x+6x^3+1$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'y')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "###making toy data\n",
    "nsample = 200\n",
    "x = np.linspace(0, 10, nsample)#this is our covariable\n",
    "X = np.column_stack((x, x**3)) #here with have only one real covariable, \n",
    "#but we will increase that number by also considering it's cube in our model\n",
    "\n",
    "\n",
    "#the true value of our parameters associated to our covariables \n",
    "# bonus : try 1,-100,2\n",
    "beta = np.array([1, -3, 6])\n",
    "\n",
    "#for now noise is low, but you can play with it by increasing the scale\n",
    "e = nsample*np.random.normal(size=nsample , scale=1)\n",
    "\n",
    "\n",
    "X = sm.add_constant(X)  ##adding the intercept\n",
    "y = np.dot(X, beta) + e ## making y=1-3x+6x^3 + noise\n",
    "y_true=np.dot(X, beta)\n",
    "\n",
    "plt.subplots(figsize = (9,6)) #setup graphical windows\n",
    "plt.plot(x,y,'o')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.987\n",
      "Model:                            OLS   Adj. R-squared:                  0.987\n",
      "Method:                 Least Squares   F-statistic:                     7292.\n",
      "Date:                Thu, 07 Dec 2023   Prob (F-statistic):          1.95e-185\n",
      "Time:                        11:09:04   Log-Likelihood:                -1338.7\n",
      "No. Observations:                 200   AIC:                             2683.\n",
      "Df Residuals:                     197   BIC:                             2693.\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         46.5828     36.830      1.265      0.207     -26.049     119.214\n",
      "x1            -5.4425     11.958     -0.455      0.650     -29.024      18.139\n",
      "x2             5.9367      0.122     48.861      0.000       5.697       6.176\n",
      "==============================================================================\n",
      "Omnibus:                        0.259   Durbin-Watson:                   1.918\n",
      "Prob(Omnibus):                  0.879   Jarque-Bera (JB):                0.407\n",
      "Skew:                           0.048   Prob(JB):                        0.816\n",
      "Kurtosis:                       2.801   Cond. No.                     1.05e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.05e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "\n",
      "\tWhite test for heteroscedasticity p-value: 0.4302870419459758\n"
     ]
    }
   ],
   "source": [
    "model = sm.OLS(y, X)  ##defining an Ordinary Least Square variable\n",
    "results = model.fit() ##fitting it\n",
    "\n",
    "res=results.summary()\n",
    "print(res)\n",
    "LMstat , LMpval , Fstat , Fpval = het_white( results.resid , X )\n",
    "print(\"\\n\\tWhite test for heteroscedasticity p-value:\" , LMpval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1, -3,  6])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also plot the result :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f5220f57d00>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nT5p/+7l69Wqbx3Xs2FEBlMaNG1u8HxYWZo4/ePCgzfiaNWtavb8FCxaY423Ne09PT1c8PDwUQPnggw9y8HS25XSkIzFRUdQU4MH+JCbm7Lnq169v/o19165dlcmTJytOTk5KfHy8EhUVpQDKiRMnFEVRMv0GPquajrFjx2b4LBIVjUajbN682eZ9WKvpMI2mZPwtvaJYrzfIONLx77//KhqNRrl8+bLFMc8++6wyZswYq9f/9ddfFQcHB6sjc4qiKH369FHatm1r8d6oUaOUgIAAu68ZFxdnHs0wsfbevZ9zkyZNlL59+1q9L0Wx/Oy2b9+ueHp6Zqqnqly5svLVV18piqL+uRUtWtQ8smF6lqefftr82tpn3LlzZ2XgwIE27yMjGekQQgjx2Js8WVFAueRVRvEftc5ilMP043dnxCPdYLuO+UG4byMdt2/f5s8//wSgZcuWnDt3jtdee828CpSPjw9dunRh8+bNmWLDw8PN27Vr17Z5DdO+iIgIm/G1atXKNv748eNW4729vW3+1lur1VKjRg2r8cJSq1at2LlzJ4qisHv3brp06ULt2rXZs2cPv/32G6VLlzZ/ljnxxBNPmLfd3Nzw8PAgOjo6x+dxcnKyOJc9jhw5gqIoVKtWDXd3d/PPrl27OHv2rNWYo0ePUq5cOapVq2Z1/4kTJ2jWrJnFe82aNePMmTMYDIZcXdNeR48e5dlnn7Xr2MOHD5OYmEiJEiUs7iMyMtLiPvz8/PDw8DC/9vX1zfbPJygoiFWrVlG3bl1Gjx7NH3/8kbsHEkIIIR51ly/DJ58AML1ZH4wOWquHFdTO47bkuCP5+fPnSU1NBeC///7jiSeeICkpCScnJ4oWLcq1a9dYv34969evZ+jQocyfP98ce+XKFfN22bJlbV7DtC8+Pp7ExETc3d0t4osXL55l8aspPuP1Mr7O6tqm/YcOHcoUn1fx8fGZ3ktNTcVoNGIwGDAYDAC4uGhITHzwLVRyWk/cqlUrlixZwrFjx3BwcCAgIICWLVuya9cu4uLirE6tsse90940Gk2uisBdXV3RaCyXi3NwcMi0JHNaWpp522g0otVqOXz4sHl6lInp76G162RFUZRM95HxHnJzTXtld28ZGY1GfH192blzZ6Z9Gacy5ubPp2PHjly4cIFffvmFbdu28eyzz/Lmm28y07TuuBBCCCFUH3wASUnEPNmQ9TWz/y5V0DqP25LjpCMuLs68PWXKFDw9PVm5ciXdu3fH0dGRS5cuMXr0aFatWsWCBQuoUaMGw4YNAyAhIcEcm1XSkHFfQkKC+YuXKT671XZM+zNeLz/i86p8+fKZ3qtYsSILFizg1q1b5vfKlClDmTJl8vXa94OprmPOnDm0bNkSjUZDy5YtmTJlCnFxceY/d2ucnJzMSVZe5eRcpUqVsqj7MBgMhIeH07p1awDq1auHwWAgOjqa5s2b23XOJ554gv/++4/Tp09bHe0ICAgw1xKZ/PHHH1SrVg2tVpura9rriSeeYPv27QwcODDbY+vXr8/Vq1cpUqQIfn5+ub6mrT+PUqVKMWDAAAYMGEDz5s0ZNWqUJB1CCCFERvv2QWgoaDRcDpkK+7JPKFz//g9jQGkcnHL8tf6ByvGv0zP+RtNoNLJgwQJ69+5t/u1n+fLlWbFiBfXq1QPg448/Jj09PZ9ut3C7dOmSeXlf08+ff/5J+fLlqVmzJvXq1aNevXr4+Pg87Fu1i06no27duoSGhtKqVStATUSOHDnC6dOnze9Z4+fnR2RkJEePHuXGjRvcvn071/fh5+fH77//zuXLl7lx40aWxz7zzDP88ssv/PLLL5w8eZI33njDooFltWrV6Nu3L/379+enn34iMjKSQ4cOMW3aNDZt2mT1nC1btqRFixZ0796drVu3EhkZyebNmwkLCwNgxIgRbN++nUmTJnH69Gm++eYb5s2bx8iRI3N9TXsFBwezcuVKgoODOXHiBP/88w/Tp0+3emybNm1o0qQJXbt25ddff+X8+fP88ccfjB071jyl0h5+fn4cOHCA8+fPc+PGDYxGI+PHj2fdunX8+++/HD9+nI0bN1KzZs08PZsQQgjxSDEa4Z131O2BA6nV+ZlsO49XLKLhiVdf5IJnHS7u+PdB3Wmu5DjpyDiXu3z58harDplP6uDAiBEjALhx4waHDx/OFJucnGzzGhn3ZYwxbWcVm3F/xtj8iM8rT0/PTD/u7u44ODig1WrNPzlZDexha926NQaDwZxgFC9enICAAEqVKpXll8ru3bvToUMHWrduTalSpVi5cmWu72HixImcP3+eypUrU6pUqSyPffXVV3nllVfo378/LVu2xN/f3zzKYbJ06VL69+/PiBEjqF69Oi+88AIHDhywOlJlsmbNGp566ilefvllAgICGD16tPm3/fXr12f16tWsWrWK2rVrM378eCZOnMiAAQPydE17tGrVih9++IH169dTt25dnnnmGQ4cOGD1WI1Gw6ZNm2jRogWvvvoq1apVo3fv3pw/f57SpUvbfc2RI0ei1WrNfw8uXryIk5MTY8aM4YknnqBFixZotVpWrVqVp2cTQgghHikrV8Kff4KHB3zyiV2dx4dv34OP4QoO6bcpWbfcA73dnNIo905wz0ZsbCwlSpQAoG3btmzZssXqcUeOHKFBgwYAfP/99/Ts2ZM1a9bw0ksvAWqRuK0vpaNHj2bGjBl4enqi1+vN78+aNcv82+GkpCSb06R69uzJDz/8QJ06dfj777/N77/99tvMmzcPb29vrl27ZvMZGzVqxKFDh+jcuTPr16+3eZy94uPj0el06PV6PD09LfalpKQQGRmJv78/Li4ueb6WEIWd/JsQQgjxWEpOhhkzwMsL3n7b/HZYeBQhGyIsls311bnwfpUStO3ZDDeS+f2t1bSY2+Nh3HWW33MzyvHkLy8vL8qWLcvly5czFcdmlDGXMR2XccWq8PBwm0mHaZWpgIAAi/czxh8/fpynnnoqy/h7V7gyxUdHR3P9+nWrvxU3GAycPHnSarwQQgghhBD3RdGiEByc6e0OtX1pG+DDwchYohNSKOnmDBpIajMcN5I55taUwDkvPYQbzplczeNp164doC4Famug5MSJE+ZtUxPA6tWrU6FCBQDzfPd7JSUlsXv3bovrmAQGBppX47EVf+HCBfO1741v27atedtW/N69e80F5PfGCyGEEEIIka/i4iCbBXG0DhqaVC6BcxEHRv54jJCRa2lzQW2iPaXNYLacuPog7jRPcpV0mFbCuXTpEt9//32m/UajkU8//RRQl5+tX7++eV///v0BWLVqFefPn88U+8UXX5CYmIhWq6Vv374W+9zc3OjevTsA8+fPt5h6ZTJt2jRArcfo2rWrxb5KlSoRGBgIqFO1Mi6VamLqrFyxYkVatGiR+eGFEEIIIYTIL6+8Ak89BUePZnlYWHiU2p385i0+/HUZDiis9ujKgRolC0V38lwlHc2bNzfXZgQFBfH999+bv8BfunSJvn378tdffwEwefJki8LokSNH4uPjQ3JyMp06dTIXmaempjJ//nzGjRsHwODBg60uPzpx4kTc3NyIioqic+fOnDlzBlBHSCZOnMiCBQsAGDt2LMWLF88UP336dLRaLceOHaN3795cvnwZUGtV3njjDXNTQ9NxQgghhBBC3Be//gobNsA//0AWdYwGo0LIhggUoMXek7RK2UsKzsx8viemOUchGyIwGHNUqv1A5biQ3CQpKYnnnnuO33//HQBnZ2eKFi1q0cdj/PjxhISEZIo9fPgw7du3JyYmBlBHJVJSUsyJS7t27Vi/fj3Ozs5Wr71p0yZ69OhhXmVKp9ORmJhoXi1owIABfP311zZrThYvXkxQUJB5Kd9ixYqh1+vNU8WCg4OZMGFCTj8Sm6SQXAj7yb8JIYQQj4W0NHjiCTh5Et57D2bNsnnovrMxvLxoP0XS0tk8ZwRVjWeZ5RvE3P6dLI5bOagxTSqXuN93bsHeQvJcr83q5ubGb7/9xqJFi2jRogVubm4kJiZStmxZevfuzd69e60mHAANGjTg+PHjDB8+nKpVq5KWloabmxuBgYEsWrSIzZs320w4AJ577jn+/vtvBg0ahJ+fH7du3aJYsWK0bduWH3/8kaVLl2ZZ5P76669z4MAB+vTpQ9myZUlOTsbb25uuXbuyffv2fE04hBBCCCGEyOSLL9SEo1QpuDPTxxZT1/Eev+yjqvEs1/BmSZc2No8riHI90iHsJyMdQthP/k0IIYR45F2/DlWrgl4PCxfCoEFZHr7vbAyDZ/7GzgVDKEEsw2uM4+cuT2c6riCPdBTsfulCCCGEEEI8asaOVROOevXg1VezPbyRvxdvbQijBLGEa2uy7jnLthEawEfnQiN/r/t0w3lXeFpfCyGEEEIIUdilpUFEhLr92Wdgx8JFUb+fZeDl5QBMajwUo+Pdr/CmgoLgzgFoHWyXFzxsknSIB2LZsmUUK1Ys2+M0Gg1r16697/dT2O3cuRONRsPNmzcf9q0IIYQQIiccHWHXLvjtN2je3K6QK31H4kQae3VtufCcZfNqH50L8/vVp0Nt3/txt/lGkg7xQPTq1YvTp0+bX0+YMIG6devm2/mPHz9Oz549KVWqFM7OzlStWpVx48aZVzgz8fPzY86cOTbPs2bNGp5++ml0Oh0eHh7UqlWLESNG5Nt93i/2JnVCCCGEKAAcHKBVK7sOPTZrG42urCMdLSWXzmHvB8+wclBjPutdl5WDGrPn/WcKfMIBknSIB8TV1RVvb+/7cu79+/fz9NNPk5qayi+//MLp06f55JNP+Oabb2jbti2pqal2nWfbtm307t2bl156iYMHD3L48GEmT55sd7yJtaaTQgghhHjMxcWpq1QlJNgdYridjtvYdwHYXfsNqncLMHcn71K3LE0qlyjQU6oykqRD5MqGDRsoVqwYRqMRgKNHj6LRaBg1apT5mCFDhvDyyy8Dlr+JX7ZsGSEhIRw7dgyNRoNGo2HZsmXmuBs3btCtWzeKFi1K1apVWb9+vc37UBSF1157jZo1a/LTTz/RqFEjKlasSI8ePdiwYQP79u1j9uzZdj3Txo0bCQwMZNSoUVSvXp1q1arRtWtX5s6dazPm/PnzaDQaVq9eTatWrXBxcSE0NBSApUuXUrNmTVxcXKhRowZffvmlOS41NZW33noLX19fXFxc8PPzY8qUKRbnPJqhM+nNmzfRaDTs3Lkz0z3s3LmTgQMHotfrzZ+nLPsshBBCFDDjxsHHH8OLL2Z7qMGosO9sDD90nUOVlOPEaryos2bC/b/H+0hWryqIFAXumRb0QBQtCln0N8moRYsWJCQk8Ndff9GgQQN27dpFyZIl2bVrl/mYnTt3Mnz48EyxvXr1Ijw8nLCwMLZt2waoDR5NQkJCmD59OjNmzGDu3Ln07duXCxcu4OWVeUWGo0ePEhERwXfffUfGzvcATz75JG3atGHlypW8//772T6Tj48P3333HeHh4dSuXduuz8Hk/fffZ9asWSxduhRnZ2cWLVpEcHAw8+bNo169evz1118MGjQINzc3XnnlFT7//HPWr1/P6tWrqVChApcuXeLSpUs5uqZJ06ZNmTNnDuPHj+fUqVMAuLu75+pcQgghhLgPjh6F+fPV7Q8/zPLQsPAoQjZEkHghjp1h6i8kPw14i8DU23S4z7d5P0nSURAlJ8PD+NKYmAhubnYdqtPpqFu3Ljt37qRBgwbmBCMkJISEhASSkpI4ffo0razMV3R1dcXd3Z0iRYrg4+OTaf+AAQPMIySffPIJc+fO5eDBg3TokPmfmqlOpGbNmlbvs2bNmuzZs8euZ3r77bfZvXs3derUoWLFijRu3Jh27drRt2/fLJtVArz77ru8mOE3F5MmTWLWrFnm9/z9/YmIiOCrr77ilVde4eLFi1StWpXAwEA0Gg0VK1a06x6tcXJyQqfTodForH6eQgghhHiIjEZ48031v716YWjZioNnY4hOSMHbQ13m1jRFKiw8iqDQIyjA+z9vvLNEbgAr2zdkReiRQlEwbotMrxK51qpVK3bu3ImiKOzevZsuXbpQu3Zt9uzZw2+//Ubp0qWpUaNGjs/7xBNPmLfd3Nzw8PAgOjo6V/eoKEqW3ekzcnNz45dffuHff/9l7NixuLu7M2LECBo1apSpIP1eDRs2NG9fv36dS5cu8dprr+Hu7m7++fjjjzl79iygJlZHjx6levXqvPPOO2zZsiVXzyeEEEKIAm75cvjjD3Bz47fXRxE4bQcvL9rPsFVHeXnRfgKn7SAsPAqDUSFkQwQK4PfvdQZFq9O1JzQLwnBnidyQDREYjIWzr7eMdBRERYuqow4P47o50KpVK5YsWcKxY8dwcHAgICCAli1bsmvXLuLi4mjZsmWubsPR0dHitUajMdeO3KtatWoAREREWF0N6+TJk1StWjVH169cuTKVK1fm9ddf56OPPqJatWp8//33DBw40GaMW4YRItO9Llq0iKeftuwWqr2zFnf9+vWJjIxk8+bNbNu2jZ49e9KmTRt+/PFH8zQxRbn7PypSnC6EEEIUQjdvwujRAJwcMpxXt13l3pThqj6FoNAjvNumKlH6FFAUPtr0DUUwsKFoB/5s4g+AAkTpUzgYGfvAu47nB0k6CiKNxu5pTg+Tqa5jzpw5tGzZEo1GQ8uWLZkyZQpxcXEMGzbMZqyTkxMGgyHP91C3bl1q1KjB7Nmz6d27t0Vdx7Fjx9i2bZu5QDs3/Pz8KFq0KElJSXbHlC5dmrJly3Lu3Dn69u1r8zhPT0969epFr169eOmll+jQoQOxsbGUKlUKgKioKOrVqwdgUVRuTX59nkIIIYTIRxMnwvXrKDVqMKhYM5SkzP9fraA2+Fu69zwAgXtO0/bWTm7jxNTn+2U6Pjoh5f7e830iSYfINVNdR2hoKJ999hmgJiI9evQgLS3Naj2HiZ+fH5GRkRw9epRy5crh4eGRbd2ENRqNhsWLF9OuXTu6d+/OmDFj8PHx4cCBA4wYMYImTZrw7rvvWsRcvnw505f4ChUq8Pnnn5OcnMxzzz1HxYoVuXnzJp9//jlpaWm0bds2R/c1YcIE3nnnHTw9PenYsSO3b9/mzz//JC4ujvfee4/Zs2fj6+tL3bp1cXBw4IcffsDHx4dixYrh4OBA48aNmTp1Kn5+fty4cYOxY8dmeT0/Pz8SExPZvn07Tz75JEWLFqVoDkeuhBBCCJHPRoyAq1c50eElLkXY/uWgAty8lUaR1HTG71dXu/zSZyCX/YtlOtbbw+U+3ez9JTUdIk9at26NwWAwJxjFixcnICCAUqVK2SzuBujevTsdOnSgdevWlCpVipUrV+b6Hpo1a8b+/fvRarU899xzVKlShTFjxvDKK6+wdevWTMnMzJkzqVevnsXP+vXradmyJefOnaN///7UqFGDjh07cvXqVbZs2UL16tVzdE+vv/46ixcvZtmyZdSpU4eWLVuybNky/P3VIVJ3d3emTZtGw4YNeeqppzh//jybNm0yj9R8/fXXpKWl0bBhQ4YNG8bHH3+c5fWaNm3K0KFD6dWrF6VKlWL69Ok5ul8hhBBC3Adly8J333HmiaezPxbov+kPqhnPcpXSLHyxvcU+DeCrUwvPCyONknHiuLgv4uPj0el06PV6PD09LfalpKQQGRmJv78/Li6FM3MVIj/JvwkhhBCF3vXrcGe6NMC+szG8vGh/liHFbiSxa8kgdMTzdu0QNnRqYN5nWhIn0+pV587BkiXwwQfg4ZGfT2C3rL7nZiQjHUIIIYQQQuQXvR7q1IEePSA2FoBG/l746lywtZ6mBhixfh064vnHpT5/9mxisd9H52J9udwPPoBPPoFBg/L/OfKZ1HQIIYQQQgiRX4KD4do1+Ocfc981rYOG4M4BBIUeQQMWK1hpgKonouh7XZ1qbpj5OXuCmnIwMtZqLw+zPXvghx/AwSHbhoMFgSQdQgghhBBC5Ie//4a5c9XtuXPBycm8q0NtX+b3q0/Ihgh1adw7Sns488lv3+CAwp4KLxP4ZjOArJfFNRrhvffU7ddegww9zgoqSTqEEEIIIYTIq4ydx7t3BysrX3ao7UvbAB+LUQy+2E7DhD0k44rfqmn2XevaNUhOVus4Jk3K5we5PyTpEEIIIYQQIq+WLVOnPLm5YZg5i4NnY6xOj9I6aMyjGLdikon5XG0eeKDl+7RuUt6+a/n6wtGjEB4OpUvfh4fJf5J0FBC2Om4L8biRfwtCCCEKnevXYdQoAE4GjWDgyn8tplD56lwI7hyQqRD8wIvTaGW4wGVteRr9MCpn1yxSBOrWzeudPzCSdDxkTk5OODg4cOXKFUqVKoWTkxMaja21DYR4dCmKQmpqKtevX8fBwQGnDPNghRBCiALtwgVwcyO+lA/PaxqQrrfsGn5Vn0JQ6BGLFagu7jxH49/V6VQXhs2mbCk7mvpeuQKhofDOO1DIlpWXPh0PQHbrF6emphIVFUVycvJDuDshCpaiRYvi6+srSYcQQohCxRCfQO+JP3OoiPUCcA3q0rd73n8GrYOGg74v0OjqBg4Xb0P9G1vQ3Ls6lTWvvgpLl6rL8a5enb8PkEv29umQkY4CwMnJiQoVKpCeno7BYHjYtyPEQ6PVailSpIiM9gkhhCh0Dl5PtZlwgLpMbpQ+hYORsTiG7qfR1Q2kUQTdN5+jcdBgMCpZL5N75IhaNwJ3V64qRCTpKCA0Gg2Ojo44Ojo+7FsRQgghhBD2mDsXXF3h1VeJTkjJ/ngg6spNGkweBsDuhu/iGuDNdxuOs/boFWKTUs3HWdSBKIqaaCgKvPwyNG58Xx7nfpKkQwghhBBCiJw6dw5Gj4aUFPD1xbuGfYmAdsy3VEw7S5SDLyHPtubCov1Wj7OoAzmzH3btUus4pk7Nz6d4YBwe9g0IIYQQQghRqCiK2pMjJQWeeQaee45G/l746lywNUFYAwToU2i7dzoAE558iwvYLq027Zny01GUkSPVF++9BxUq5NtjPEiSdAghhBBCCJETP/4IYWFqx/EvvwSNBq2DhuDOAQCZEg/T61E/raAot9jj3JSwNtl3EVeA53Z8j+bcOShTBsaMydfHeJAk6RBCCCGEEMJe8fEwTK3J4IMPoHp1864OtX2Z368+PjrL5WxLezrzbkw8raN/Jh0twR1et2+1KiCsWlOutmgD06aBu3u+PcaDJjUdQgghhBBC2GvcOIiKgipVrI48dKjtS9sAH/NKVOdvJPP97+do9/UEABZ5/4+zNbztvlykV1kiv16FTyWv/HqCh0JGOoQQQgghhLDHf/+p06lA/a+NBn1aBw1NKpfAuYgDc7ad5tkV26hpPE00pfjixeftupSjIQ0N6ipWjfy9oJAvJy8jHUIIIYQQQtijXDnYuxc2bYK2bbM81GBUCNkQgdfVBEafWwDApDpvkqjLvpO4RjGy6rsx/FuyAiXnzbLs11FISdIhhBBCCCGEvRo1Un+ycTAylih9CtN+/h4PEtnv2JANHerbdYmux3fS4MpJnrx5iSJVbDccLExkepUQQgghhBBZiYqC06dzFBKdkELdPy/QK34tRjQEtx8CDll/9fZyc2RofW+mHVoBQJHx48DXN9e3XZDISIcQQgghhBBZeest2LgRFiyAgQPtCvFyLMKknWr9x9ISL3OqlvXkYVynmpT0cMbbQ63d0I79CKKvQeXK8O67+fUED50kHUIIIYQQQtjy88/w009QpAg0aGB3mGHsSuoYjhODF3Ne7JZpvwbw0bkwoJn/3ZqNc+dg1ix1+9NPwdk5Hx6gYJDpVUIIIYQQQlhz86baeRxg9Gh4IvuGfgBXj1yh/trxAHwS8CaJXq4W+01l4cGdAyyLxEeOhNRUtUi9c+c83nzBIkmHEEIIIYQQ1nzwgVrPUa2a2p/DTue6vocnCRx3a8SzK4Zkahboo3Nhfr/6dKidYcrVtWvw+++g1cKcOYV+idx7yfQqIYQQQggh7rV7N3z1lbq9cKHNnhz3+vOTLTS99D0GHNAuWsBzdcvS/oky5maB5tqNe5fBLV0azpyB336DgIB8fpiHT5IOIYQQQgghMkpJgUGD1O1Bg6BlS7vCbsWlUHKCOh1rb923aPFyPeBus8BsFS8OL76Yq1su6GR6lRBCCCGEEBlpteoqVf7+MH263WH7u07FL+1frjr4Um/jJPuCYmLUQnVFyeXNFg6SdAghhBBCCJGRoyO8/z6cPAnFitkVErnlDE1/n6JuvzMHj7Ke9l0rOBi6d79bsP6IkqRDCCGEEEIIAIMBbt+++9rJya4wxagQ8/KbOJPKvmJtUN54BoPRjpGLv/+G+fPV7ZdeysUNFx6SdAghhBBCCAHwxRdQvz788UeOwlb+72saxm4lBWdGtn+FPksOEDhtB2HhUbaDFEUd3TAa1YTjmWfyePMFmyQdQgghhBBCXLwIH34IERFw7JjdYeu2nqbVyrEAzK4wiMt+xQG4qk8hKPSI7cRjxQrYsweKFlUbAT7iJOkQQgghhBCPN0WBoCBISoJmzWDIELvCDEaF6/2nUEa5yhmHyizt1ubuKe/8N2RDROapVvHxMGqUuj12LJQvnw8PUbDlKulYtmwZGo0m259t27bZPMe1a9cYMWIE1atXx9XVFS8vL5o3b87ixYtR7KjeP3v2LEOGDMHf3x8XFxe8vb1p3749a9assesZjhw5Qr9+/ShXrhzOzs74+vrSrVs3duzYYffnIIQQQgghHgHffw+bNqk1HIsWgYN9X5E3zv6dgVeXA/Bh83dIdbHsRqEAUfoUDkbGWgZOmABXr0LVqvDee/nwAAVfnvp0ODg4UKpUKZv7nZ2drb5/+PBh2rdvT0xMDADu7u4kJCSwZ88e9uzZww8//MD69ettxm/atIkePXqQnJwMgKenJzExMWzZsoUtW7YwcOBAlixZgsZGJ8fFixcTFBREeno6ADqdjmvXrrF27VrWrl1LcHAwEyZMsPdjEEIIIYQQhVVMDLzzjrr90UdQs6ZdYYZUA35jR6LFyA+eL3CocWWbx0YnpFi+0akTbN4Ms2eDje+7j5o8Ta8qX748V69etfnTvHnzTDF6vZ7nn3+emJgYatSowaFDh0hISCApKYl58+bh6OjIli1bGD58uNVrRkZG0rNnT5KTk2nWrBmnTp1Cr9ej1+sZP348AEuXLmXGjBlW4/ft28fQoUNJT0+na9euXLp0iZs3b3L9+nWG3BlKCwkJYfXq1Xn5aIQQQgghRGEwYgRcvw61asEHH9gdtrv/Qp5M+RM9nkx78eUsj/X2uKeb+bPPwj//QIcOubnjQumB13TMnDmTq1ev4urqyqZNm2jYsCEATk5OvPnmm4SEhACwcOFCTp8+nSl+/PjxJCUl4ePjw8aNG6lWrRqgjpaEhIQwePBgACZPnkxcXFym+NGjR2MwGKhTpw6rV6+mXLlyAJQoUYIFCxbQvn17i+OEEEIIIcQj6vZtuHwZNBp1WpWdS+RG/3ONut+PAWB27beJKe1h9TgN4KtzoZG/l/pGxu+WRfI04ajQeeBJx/Ll6ry33r174+/vn2n/22+/jbu7OwaDgRUrVljsS0pKMtdsBAUFUcxKs5YxY9S/APHx8axdu9Zi37lz59izZw8AI0eOxNHR0Wb8hQsX+P3333P2cEIIIYQQovBwdoYtW2DvXmjSxO6wUy+MpBh6ThStz1PfDAXUBCMj0+vgzgFoHTSQkKCOpsyYAWlp+XP/hcgDTTpOnTrFxYsXAejYsaPVY9zd3c3TsrZs2WKxb8+ePdy6dSvLeD8/P2remYt3b/zWrVvN2x1sDGcFBgbi4eFhNV4IIYQQQjxiNJocJRx/Tt1G8/OhGNHA/AV0ql+O+f3q46OznELlo3Nhfr/6dKjtq74xcSKcOgVffWU54vGYyFPScf36dRo0aIC7uzuurq5UqlSJfv36sXPnTqvHh4eHm7dr165t87ymfRERETbja9WqlW388ePHrcZ7e3vj7e1tNVar1VKjRg2r8UIIIYQQ4hGwbZu6LG58fI7Ckq4nU2qcWgO8t04QNfs/BUCH2r7sef8ZVg5qzGe967JyUGP2vP/M3YQjIgLmzFG3P/sMXFysnP3RlqfJZMnJyRw5coTixYuTlJREZGQkkZGRrFixgoEDB7Jw4UKKZJivduXKFfN22bJlbZ7XtC8+Pp7ExETc3d0t4osXL07RokWzjc94vYyvs7q2af+hQ4cyxedVvJ1/sZ2dnW2u3CWEEEIIIfIgIQFefx0uXICSJWHyZLtDD3SexDPp54jSlqXu5ikW+7QOGppULpE5SFHU1bHS06FzZ3XlqsdQrkY6ypQpQ3BwMMeOHSMlJYXY2FiSk5PZu3cvbdqoTVGWLl2aaQWqhIQE83ZWSUPGfRljTNtZxWbcnzE2P+Lzqnz58uh0umx/pkyZkv3JhBBCCCFEzn3wgZpw+PnBnVpee5z4/m9aHFBXR/3vgy/wKOuJwaiw72wM645eZt/ZmMxNAAF+/BG2b1frR0yjHY+hXI10tGvXjnbt2lm8p9Vqadq0Kb/++isvvvgi69at48svv+Sdd96hatWq+XKzhd2lS5fw9PTM9jgZ5RBCCCGEuA927oQvv1S3Fy+GO7NpspN+24Dx1dcpgoEDZV/EODCQiRuOs/boFWKTUs3H+epcCO4ccHdaVWLi3eZ/H3wAlSrl48MULvleSO7g4MDMmTMBMBqNbNiwwbzPVKANmBv7WZNxX8YY03ZWsRn3Z4zNj/i88vT0tOtHkg4hhBBCiHyWlASvvaZuDx6s9sqw0+7eX1Ar+RB6PBnfrR8vL9rP13vPWyQcAFf1KQSFHiEsPOpO4G6187i/P7z/fn49SaF0X1avqlKlCiVLlgTUZWpNypQpY96+fPmyzXjTPk9PT3M9R8b4uLi4LBMHU3zG62V8ndW1s4oXQgghhBCF1IcfwrlzUL68umytnS7tvchTaz8EYFLA25xys93LwzS5KmRDhDrVqmNH+PtvWL4cXF3zcveF3gNdMjfjilUZV6K6l2lfQECAzfisVpYyxd+7wpUpPjo6muvXr1uNNRgMnDx50mq8EEIIIYQohG7ehJUr1e1Fi8CO6e4AilHhSrc3cSeJfc5P82Onp7OPAaL0KRyMjFXfqFkTAgNzd9+PkPuSdJw9e5YbN24AWDQArF69OhUqVAAgLCzMamxSUhK7d+8GyFQ3EhgYiOudLNFW/IULFzhx4oTV+LZt25q3bcXv3bvXXEB+b7wQQgghhCiEihWD8HC1R0b79naH/fHejzx9fSOpODLm+aHgYN9X5+aRR7h98FAub/bRlOOkQ1GsVOXfs3/UqFHqyR0ceP755y329+/fH4BVq1Zx/vz5TPFffPEFiYmJaLVa+vbta7HPzc2N7t27AzB//nz0en2m+GnTpgFqPUbXrl0t9lWqVInAO5nmrFmzSLPSDXLq1KkAVKxYkRYtWmT5rEIIIYQQopDw9lZrOWy4dyWqa6djqPL52wCsfuIdzlcpZddlPFMSmfXLbFr26wS//povt/4oyHHSceHCBRo1asRXX33FuXPnzEmI0Whk//79dOzYkZ9//hmAIUOGUL16dYv4kSNH4uPjQ3JyMp06deLw4cMApKamMn/+fMaNGwfA4MGDqVatWqbrT5w4ETc3N6KioujcuTNnzpwB1BGSiRMnsmDBAgDGjh1L8eLFM8VPnz4drVbLsWPH6N27t7l+IzY2ljfeeIPNmzdbHCeEEEIIIQqpgwdhzZpsDwsLjyJw2g5eXrSfYauO8vKi/fzS5B1KK9c45VCVic/YPz1q5O5v8U6KgypVoFWrPNz8o0WjZDd0cY/z589bTJlydnbGw8ODhIQEbt++bX7fWnNAk8OHD9O+fXtiYmIAdVQiJSXFPPLQrl071q9fb3MVp02bNtGjRw9zMblOpyMxMRHDnZbyAwYM4Ouvv0aj0ViNX7x4MUFBQaSnpwNQrFgx9Hq9OYEKDg5mwoQJOflYshQfH49Op0Ov19u1ZK4QQgghhMijlBSoVw9OnoR58+DNN60eFhYeRVDoETJ+Ia576CJrd7wBQNc2X3C0QUW7Lvlk1Gl+Xj4CBxS1N8czz+T1KQo8e7/n5niko3Tp0sydO5c+ffoQEBCAp6cnN2/exNHRkRo1avDqq6+yZ88evv76a6sJB0CDBg04fvw4w4cPp2rVqqSlpeHm5kZgYCCLFi1i8+bNWS4b+9xzz/H3338zaNAg/Pz8uHXrFsWKFaNt27b8+OOPLF261GbCAfD6669z4MAB+vTpQ9myZUlOTsbb25uuXbuyffv2fE04hBBCCCHEQzB+vJpw+PjAyy9bPcRgVAjZEGGRcBS5ZWDqztkALC3xst0Jh4PRwLRt89WEo1+/xyLhyIkcj3SInJORDiGEEEKIB2jPHmjRAhQF1q6FLl2sHrbvbAwvL9pv8d6QFb8y5r+5XNWUpu2QeSTosl7q1tOlCD0alKPfnxvwnzhGLVo/eRJKl86nhynY7P2em6uO5EIIIYQQQhRIiYnwyitqwjFggM2EAyA6IcXidYUzMYz4T60P/uipd7NNOABCXqhFtzJFoO8n6htTpjw2CUdOPNA+HUIIIYQQQtxXo0apTQArVIA5c7I81NvDxbytSTcwdcMXOJHGJre2bG9VO4vIu3x0rlCqFEyfri7Hm8UKWY8zSTqEEEIIIcSj4cQJuLOSKUuXgk6X5eGN/L3w1bmgAXqv20fTtIPE48GEFwdAFvXBABrAV+dCI38v0Gph6FDYvNnuXh6PG/lUhBBCCCHEo6FmTQgLg4kT7Srk1jpoCO4cgO/5m4z993MAJgQMI7pM1smKKR0JaVcJbcqtDDuyTlQeZ5J0CCGEEEKIR0f79nCn75s92tX0YfYvi3AjmV3OzfipUyMAHLLIH3x0LszvV59265ZCrVqwdWte7/qRJ4XkQgghhBCicNu1C/z91TqOnIb2W0TrxF0k40rK15/zWUBpvD1caFCxOIcvxBGdkEJJN2fQwI3E23h7qFOqtGdOw9SpkJYG8fH34aEeLZJ0CCGEEEKIwuvaNXjpJUhNVRvyNWxod+ilPy7RYNVIAA53/4T2fepb7G9SuYT1QEWBN95QE47nnoMXX8z17T8uZHqVEEIIIYQonBQFhgyBGzfAzw/q1LE/1KhwpctQPEkg3KMJzVa+bf91v/sOduwAFxe127nUcmRLkg4hhBBCCFE4LV8O69aBoyN8+y04O9sd+vvgUJ6+sYnbOOH+/RIcHLX2BcbFwXvvqdvjxqnTukS2JOkQQgghhBCFz8WL8M476nZICDzxhN2hUX9d5YmvhwFwoOME/DrWtP+6o0ZBdLS6UtbIkTm548eaJB1CCCGEEKJwMRrh1VfVAu7GjdVEwE6KAuc6vUVxJY6TReuhnTGQdUcvs+9sDAajknVwejrcvKluL1wITk65f4bHjBSSCyGEEEKIwuXbb9WicVdX+OYbKJL1V1qDUeFgZCzRCSncnrODnlFrSKMI47u+y8FvD5uP89W5ENw5gA61fa2fqEgR+PFHOHoU6tbNv+d5DEjSIYQQQgghCpdevSA8XC0er1YNsEwszMvaOmgIC48iZEMEUfoUPG6ksO0bdUrU7PKDOVjecnWqq/oUgkKPML9ffduJB0jCkQuSdAghhBBCiMLFxQVmzDC/zJhYmPjqXHjhSV8W/h6JadLUhz98R2miidDWYFH3dplOq6B2Gw/ZEEHbAB+0pg6B4eEwfTrMnAne3vfvuR5hUtMhhBBCCCEKh+3b1bqKDMLCowgKPWKRcABE6VP4KkPC0XTXv7wc/xMGHBjZ/l3SnK3/7l25E3swMlZ9w2CAQYPUKV1SOJ5rknQIIYQQQoiCb+dOaNsWWraEW7cAdUpVyIYIsin/pujNVKYfmA7AF74DiahTLtvLRSfcSWLmz4f9+8HDA6ZMycMDPN4k6RBCCCGEEAVbXBz0768uPVWjhlpADhyMjM00wmHNmFWrKKdc4YxDZb7o0cmuS3p7uMClSzBmjPrG1KlQtmyuH+FxJzUdQgghhBCi4FIUeOMNNQGoXBk++8y8yzwakYWmu87wP/1qjGgY0fY9brtmvcytBvDRudDIrzh0GwiJidC0KQwdmtcneazJSIcQQgghhCi4VqyAVatAq1W33d3Nu7w9XLIM9biRwqz90wCY5/sqf9etaNclgzsHoP35J9iwQe12vnAhOMjX5ryQT08IIYQQQhRM58/Dm2+q28HB8PTTFrsb+Xvhq3NBYyVUUWDc6lB8ucoJbXXm9eyc7eWKFXVUl8ut5QPT1GSFDz6AWrXy9hxCkg4hhBBCCFFADRmidh1v2vRubUUGWgcNwZ0DADIlHm22HadnwlrS0TKu6yhSXbKvKvji5Tv9OTQa2LZNTXQ+/DA/nuSxJ0mHEEIIIYQomGbPhhYtIDTUatdxg1FB5+rEwGZ+FHe7W6tR7Foy046oq1XtazmG71YNxMvNdi2HBrWvR+PKGZoF6nQwYYLaE0TkmRSSCyGEEEKIgikgAHbtsrrLWkNALzdHuj5RhpavfURJYjjt+iSNfxmHYxEHPulWm6DQIwAWS+yaRkiCOwegTb0Na9eqHc811iZtidySkQ4hhBBCCFFwJCXBwYNZHmKrIWBcUhpR49fTMmY9qTjy78y5bDpznX1nY2gb4MP8fvXx0VmOXPjoXNQ6jtq+8PHH8PLL8L//5ftjPe5kpEMIIYQQQhQcw4fD11/Dp5/CO+9k2p1VQ8ASlxOZ8rc6rWpO1bf48mI8XDwKqNOngjsHsOf9ZzgYGUt0QgreHi408vdC66CBv/5Se3EAdOt2nx7u8SVJhxBCCCGEKBjWrYNFi9SpTbVrWz3EVkNAxagw+ceFFEPPIcd6LHihlcX+q/oUgkKP3B3VyCgtDV59FQwG6N5d/RH5SqZXCSGEEEKIh+/SJfWLP8CIEfDMM1YPs9UQsNvGP2mfsoMUnBnZ5R2MRbQW+00jIyEbIjAY7xknmT4djh4FLy/44os8PISwRZIOIYQQQgjxcKWnQ9++EBsLDRrA5Mk2D7XWELD0xXgmnZgFwOSqb3OhcimrsQoQpU/hYGTs3TePH4eJE9Xtzz+H0qVz/RjCNkk6hBBCCCHEw/Xxx7B7t9ptfNUqcLK9vG2mhoAGI9PWfIkHifzh3IjQLs2zvZx5tERRYNAgSE2F55+HPn3y/izCKkk6hBBCCCHEw/PPPzBpkrr91VdQpUqWh9/bELDXuv20St1DEkUZ/eJbKFptlvGQYbREo1EL1ps0gQULZJnc+0iSDiGEEEII8fDUrq3WUQQF2T3S0KG2L/P71eeJywlMOPMpACE1h5Fe25diRR0zdSc3MTUBbOTvdffNxo1h714oWzZvzyGypFEUxdqKYyIfxcfHo9Pp0Ov1eHp6PuzbEUIIIYQo9FLiUzlXugkBKUfYX+xZjAdW8nSVkmyNuJplE8D5/erTIaA0XLgA/v4P/L4fNfZ+z5WRDiGEEEII8eBt3gx6fa7D97QLISDlCLEaLyrvXE7TaqXQOmjMoyBZNgH88ku12/ncuXl9CmEn6dMhhBBCCCEerCNHoGtXKFMG/vgDfH2zDbEIn7uX1gfURn7nRn9FwyfLWOzvUNuXtgE+1psARkbCBx9ASgo4yO/fHxRJOoQQQgghxIOTkAC9e6srRj35JPj45Cg87kI8JYb/Dy1G/qjan6ZTX7J6nNZBQ5PKJSzfNK1WlZQELVqodSTigZD0TgghhBBCPDhvvQVnzkC5cvD11zleMerv1sOoaIjkUhE/ntiZw+lRS5bA9u3g6qpuy0jHAyMjHUIIIYQQ4sH49ltYvlz9sv/dd2oH8DsMRsX6dKgMdg//iZaRyzCiIWHecsqXycECPf/9p3Y6B7UvSDZL84r8JUmHEEIIIYS4/06fvjudKTgYmt9t4hcWHkXIhgii9Cnm93x1LgR3DlALv4HLf0YR8NlgAPY2e5/mQ7JvAmimKDBkCMTHw9NPw7BheX8ekSMypiSEEEIIIe6/UaPUWoqWLeGjj8xvh4VHERR6xCLhALiqTyEo9Ahh4VEYDQqX2w+khBLDqaL1aPxrSM6urSgQGAgeHuqULjsaCIr8JUmHEEIIIYS4/5YsUZv/hYaav/QbjAohGyKw1jTO9F7Ihgh2vDSPRrG/cgsXnH9cgaObU86u7eAAY8aovTkCAvL0GCJ3ZHqVEEIIIYS4/0qWhBUrLN46GBmbaYQjIwVwPHSZZmtHA3Ck9wyadaxp/zUVRV0ly9lZfV28eE7vWuQTGekQQgghhBD3x6lTsGyZzd3RCbYTDoAitw3M2fAprqRwpFR7mq54E4NRYd/ZGNYdvcy+szEYjNbGSe746iuoXx8OH87lA4j8IiMdQgghhBAi/yUnw0svQXg4xMTcXTkqA28PFyuBdw39Poz66ceIwYsPew+mZdgJ1h+LyrLg3OzsWRg5Uq0j2bMHGjTIl8cSuSMjHUIIIYQQIv+99ZaacJQurdZyWNHI3wtfnQvWOnU8cegSw6O+AmBk4/c5WdSZr36PzLLg3MxohIED1YSjVSt4++18eiiRW/mWdEydOhWNRmP+ycq1a9cYMWIE1atXx9XVFS8vL5o3b87ixYtRlCyGyO44e/YsQ4YMwd/fHxcXF7y9vWnfvj1r1qyx616PHDlCv379KFeuHM7Ozvj6+tKtWzd27NhhV7wQQgghhMjC0qXqj4MDrFwJvr5WD9M6aAjurBZ2Z/z26B57m3m/TUaLkVCvl/itZS2bl8pYcG6eavXZZ7B7N7i7q6tVSRPAh06j2PMtPxunTp2ibt26pKTczTxtnfbw4cO0b9+emJgYANzd3UlJSSE9PR2Adu3asX79epxNBT/32LRpEz169CA5ORkAT09PEhMTMRqNAAwcOJAlS5bYTHwWL15MUFCQ+Xo6nY74+Hjz/QYHBzNhwoQcfgJZi4+PR6fTodfr8fTMQRMbIYQQQojC5p9/1F4Yt27BpEkwdqzFbmtNALdGXDX36VCMCnO+XEC3pF8441CZF96Yxi23rKdhmawc1JgmqdFQrx7cvg0LF8KgQffjKcUd9n7PzXPaZzQaee2110hJSaFJkyZZHqvX63n++eeJiYmhRo0aHDp0iISEBJKSkpg3bx6Ojo5s2bKF4cOHW42PjIykZ8+eJCcn06xZM06dOoVer0ev1zN+/HgAli5dyowZM6zG79u3j6FDh5Kenk7Xrl25dOkSN2/e5Pr16wwZMgSAkJAQVq9enYdPRAghhBDiMZWQAD16qAlH+/bw4YcWu8PCowictoOXF+1n2KqjvLxoP4HT1Jkme95/hrdaV+HFDX/SLekXUnHkrU7v251wAFyPS4RXXlETjg4d4PXX8/XxRO7lOemYO3cue/fupW/fvrRr1y7LY2fOnMnVq1dxdXVl06ZNNGzYEAAnJyfefPNNQkLURi8LFy7k9OnTmeLHjx9PUlISPj4+bNy4kWrVqgHqaElISAiDB6tdKidPnkxcXFym+NGjR2MwGKhTpw6rV6+mXLlyAJQoUYIFCxbQvn17i+OEEEIIIUQObN2qdh4vW1btx5FhWlN2TQC3Rlyl6tk4Jp+cDsCkqsM4FVAmR5f3JVWdUlWsGCxeDNlM+RcPTp6SjsjISD766CNKlCjB7Nmzsz1++fLlAPTu3Rt/f/9M+99++23c3d0xGAysuGcd56SkJHPNRlBQEMWKFcsUP2bMGEAd5lm7dq3FvnPnzrFnzx4ARo4ciaOjo834Cxcu8Pvvv2f7PEIIIYQQIoMXX4SwMPjhB7Uvxx32NAH8eNXf1HhvMEW5xdairfm2awu7L6tBXcWqfv0qsG0b7N+vJj6iwMhT0jFo0CCSkpL49NNPKVWqVJbHnjp1iosXLwLQsWNHq8e4u7vTvHlzALZs2WKxb8+ePdy6dSvLeD8/P2rWrGk1fuvWrebtDh06WI0PDAzEw8PDarwQQgghhLBDu3Zwz5T7/edism0C2G/BSqrf/odojTfvvzwkZ8XfikJw5wC0Dho1rnr1XN68uF9ynXQsWrSI7du306ZNG/r375/t8eHh4ebt2rVr2zzOtC8iIsJmfK1atlcwMMUfP37cary3tzfe3t5WY7VaLTVq1LAaL4QQQgghrLh5E7p3V/tiWBEWHsWbK45keYpmO08zNOYbAK588g2fvNsCH0/riwrdy1fnwra4bXT4chIkJubo1sWDk6vmgJcvX2bUqFG4urry1Vdf2RVz5coV83bZLIa7TPvi4+NJTEzE3d3dIr548eIULVo02/iM18v4Oqtrm/YfOnQoU3x+iI+Pt+s4Z2dnm6t3CSGEEEIUGEYjDBgA69bBuXNw5IhFHYWpjiOrpVJLXk7kswOTAdj85Dt0/ECdkdI2wId5O/5l9rbMdb4mw9tU5S33OLRj56r30qEDvPBCfjyZyGe5SjqGDBmCXq9n2rRpVKpUya6YhIQE83ZWSUPGfQkJCeakwxSfVWzG/Rmvlx/x+aF8+fJ2HXc/lu0VQgghhMh3U6aoCYezMyxaZJFwZFXHYaJJNzJz9TxKEsNfjk/SYtc08z6tg4ZhbapS3cfdvJyuibkLeSUd1H9BTTj69JGEowDLcdIRGhrKL7/8Qt26dXnvvffuxz09si5dumRXnw4Z5RBCCCFEgRcWBuPGqdtffAF3ViU1ORgZm2UdB8CAH3fSKnUPibjxzovDSf9yr5pM1L7bTLBDbV/aBvhk6u2hddDAm2/CqVNQpgzMnZvvjyjyT46SjujoaN599120Wi2LFi2iSBH7w00F2gDJyck2v3ybmv7dG2Pazrg/q/iMsfkRnx88PT2lOaAQQgghCr/ISHVkQVHU5nuvvQZYNv47cy3r+oqAv67w0YXPABhTdxSX/EqiubN87vx+9S0SD62DhiaVS1ieYNMm+PJLdfubb8DLK/+eT+S7HCUd77//PjExMQQFBVGjRg0S7ynWSU1NNW+b9jk5OeHk5ESZMnfXWb58+bLNL9+XL18G1C/opqlVgDk+Li6O5ORkm9OkTPEZr5fxtWm/LbbihRBCCCEEkJysLo0bFweNGplHGMLCozJNg7Kl6M1U5m39hCIYWK3rwoZ2DQB1FSsNELIhgrYBPupohjXR0TBwoLr97rvQpk3en0vcVzlavSoyMhKA+fPn4+HhkelnypQp5mNN740ePRqwXLEq40pU9zLtCwgIsHg/Y3xWK0uZ4u9d4coUHx0dzfXr163GGgwGTp48aTVeCCGEEEKgdh13coJSpeDHH8HZ2WbjP2sUo8KE75ZTSTnPOQc/Qvr+z6IWRAGi9CkcjIy1fZIzZyA9HWrVUutKRIGX547k9qpevToVKlQAICwszOoxSUlJ7N69GyBTd/PAwEBcXV2zjL9w4QInTpywGt+2bVvztq34vXv3mgvIs+uuLoQQQgjxWCpdGn7/HX77DcqXt6tgPKMXN/xJz4S1pKPl7Q7vk+ThYvW46IQsEphmzeCff9Skx8V6vChYcpR07Ny5E0VRbP4EBwebjzW9N2fOHPN7pn4eq1at4vz585nO/8UXX5CYmIhWq6Vv374W+9zc3OjevTugjrTo9fpM8dOmqSseeHh40LVrV4t9lSpVIjAwEIBZs2aRlpaWKX7q1KkAVKxYkRYt7O+CKYQQQgjxyMv43cvZWR1lwL6CcZPKx68z5aT6fWtSlWEcr2N7ZU9vG8mIWZkycKe/mij4HthIB8DIkSPx8fEhOTmZTp06cfjwYUCtBZk/fz7j7qyAMHjwYKpVq5YpfuLEibi5uREVFUXnzp05c+YMoI6QTJw4kQULFgAwduxYihcvnil++vTpaLVajh07Ru/evc31G7Gxsbzxxhts3rzZ4jghhBBCCAFcvaomGR9+CAaDxa4sRyQyCKrty8LNn+DCbcLc27K8Wyurx2lQl8Rt5H9PYXh6OnTuDD//nPP7Fw/dA006dDodGzdupESJEkRERNCwYUNzwfgbb7xBamoq7dq1Y/bs2Vbj/f39Wb16NUWLFmX37t1Uq1aNYsWKodPpCA4ORlEUBgwYwKhRo6zGN2nShAULFlCkSBF++uknypUrR/HixSlZsiTz588H1B4ZPXv2vG+fgRBCCCFEoZKWBj17wuXLak+OW7csdmc7IoFax9Fw1DQqG85wRVue1NVzwMGBe8vETa+DOwdkLiKfPBk2boRXX1WL2EWh8kCTDoAGDRpw/Phxhg8fTtWqVUlLS8PNzY3AwEAWLVrE5s2bs+xT8dxzz/H3338zaNAg/Pz8uHXrFsWKFaNt27b8+OOPLF26FI3GxkoHwOuvv86BAwfo06cPZcuWJTk5GW9vb7p27cr27dulKZ8QQgghREajR8Pu3eDhAT/9BBlWFwVo5O+Fr84lUwJhogH6hh3h2aurSaMI+oXf80LHAOb3q4+PzjJh8dG5ZFouF4ADB2DSJHX7yy/ByowWUbBpFEWxt+5H5FJ8fDw6nQ69Xi99OoQQQghReKxcqfbjAHVa0z01syam1asAi4JyDVDleDQbNgbhwm32dJlB4NqR5v0Z+3pYNP3LKDER6tWDf/9V72XFivx7PpFn9n7PzXFHciGEEEII8Rg4dgxef13d/vBDmwkHqF3D5/ern6lPR0VFw4LNU3DhNn/6PE+zNe9ZxFlt+nev995TE47y5dXO56JQkqRDCCGEEEJYSk5Wk4zkZGjXDiZOzDakQ21f2gb4mEcuSrk5Y2z9FpUNZ7isrUDVP75Bo83hzP5162DRIrWPx/LlUKxYrh5HPHwPvKZDCCGEEEIUcEWLwscfQ506sGoV2Lmqp2nkokvdsqSMW0Pg5e9JowjxC1ehu3c1Knvs36/+d+RIaNUq5/GiwJCajgdAajqEEEIIUSgZDHYnHBkdX3GUyv0aq3UcXWcS+POI3N/D1q3QooXaG0QUOPZ+z5WRDiGEEEIIofr5Z7Unh0kuEo6bF+NxG9ADF25zyLdzpjqOHGvbVhKOR4AkHUIIIYQQAnbtUvtxNGyo9uTIBcWoEN5sMH7p/3K5SAWq/7EMzb2rUWXnzz+hQ4dc34MomCTpEEIIIYR43F24AC+9pHb9bt4cypTJ1Wl29ppP4H9qHUfCou/x9MthHUdCAvTuDb/+CsHBuboHUTBJ0iGEEEII8ThLSoIuXeDGDahfH5YsUVeLyqFjX+2n2Y/vAnCg2zRqDGic83t56y04exYqVIAZM3IeLwosWTJXCCGEEOJxpSgwcKDak8PbG9auVVeuyqFr4dcp9UYPnEjjQPmXaPbjcPsa/2UUGqoui+vgAN99J13HHzGSdAghhBBCPK4mT4YffgBHR/jpJ7UBXzbuTSbq+XjyX/PeNDD+xzmn6tQ++DW/RlzN1CjQV+dCcOcAOtT2zXzSs2chKEjdDg6GZs3y6wlFASFJhxBCCCHE4yglBVauVLe//NKuL/ph4VGZkokRK37h7Zs7SMQNh59/YveNRIJCj3BvT4ar+hSCQo8wv199y8QjNRVefhkSE9WlcT/6KB8eThQ0UtMhhBBCCPE4cnGBffvUjt+vv57t4WHhUQSFHrFIOJr/dpK3/5sPwC/9P6N8h5qEbIjIlHAA5vdCNkRgMGY44to1tYC8eHF1ilUulukVBZ+MdAghhBBCPEYMaekcvKi/W2vx6mtk9zXfYFQyJRPlzsUx7+BEAL4oPYDQGn6UOxdjkZTcSwGi9CkcjIylSeUS6pvly8PhwxARYdf0LlE4SdIhhBBCCPGY2HI4klIvdWFbpadZ0rALaDRZ11rccTAy1iKZcE5KY/5PU9ERz17nxnzatxsGfQr7zsbYdR/RCSlqEbtplayiRdX+IOKRJdOrhBBCCCEeA2H/XCG5/0Dqnf+Hd/aupFRSHHC31iIsPMpmbHTC3YRDMSpMWP4tdQzHuaopzTt9hmFwNI2VWJtYlZm3mxN07QrTp4PRmNtHEoWIJB1CCCGEEI84g1Hh0vAP6RqxizQHLUFdx3DdXW3cZ7PWIgNvDxfzds91+3k5/ifS0TL02fHEeHuY9zWpVBJfnQu2FsbVoK5i9fSGb2H9enWlqnPn8uEJRUEnSYcQQgghxCPu7NzFDNr+DQDj2gbxh19di/0Zay2saeTvha/OhVpHLzP59HQAJlR9j6MNKgJ3k4nGlUsQ3DnA/F5Gptef+t3G4YMP1BezZ0OVKnl8OlEYSE2HEEIIIcSjbN8+Ko9+G4CFT3VjVd0ONg/NOI3q3n4c71UrTZOP+uNEGj95dOLbri3MiYQC9H5KLQLvUNuX+f3qZ1pa10fnwqQWZWjS7zlIS4Pu3WHIkHx/XFEwSdIhhBBCCPGoiomBrl3Rpt5ma5WnmdpqQJaHm6ZR3duPwyHNyNdfzqSccoWT2mp81P9VNPd0F5+97QyrDl0yF6W3DfCx7EjuVxxt9xfh/HmoVAmWLLlbSC4eeTK9SgghhBDiUVWiBIwdi9KgAVP6jkVxsL44rml6VCN/L6v9ON4LXUurlN9JxI2/5yxiaNc6Vs+TsShd66ChSeUSdKlbliaVS6Cd+zmsWwdOTmoXdJ3ufjyxKKAk6RBCCCGEeJS9/TaaffsY/VIDwHathakW495+HM9t/ou3or9WT/X0eD6NT2XlwYtWL5VlUbqzMzg6qnUc9evn/nlEoSRJhxBCCCHEo2b+fIiLu/va0dFca+Gjc7E41Efnwvx+9elQ2zdTP44a/1zl078/BmBGuSB+a1WLq/G3uRpvXwNAC0FBcPy4+l/x2JGaDiGEEEKIR8m8efD22zB3Lhw5Ai53kwyrtRb+Xmjv1GdkLCQvFp3Mos3BuHCbTW5t+eLljjm6DXMDwKQkcHdX36xaNe/PJwolGekQQgghhHhUbNoEw4ap26+8YpFwmGSqtchQEG4qJC9y28AXKz6lvHKZUw5VGdV/CDjk7Gujt4eLOpWqXj04ejTXjyQeDZJ0CCGEEEI8Cv78E3r0UDt8DxwIo0fn+BSN/L3w8XThg+U/0Cx1P3o8GdT9I5I8MycvtpiL0q+dhvffh3//hX37cnwv4tEi06uEEEIIIQq7yEjo1AmSk6FtW/jqq1wtR6t10DDsj3Bejg3FiIY3AoO5WKmk3fGmK37cwhdt7w6Qng69esHQoTm+F/FokaRDCCGEEKIwi4mBjh0hOhqefBJ+/FFdJcoO9zYAdN58gu6/jAJgWuV32duseo5uxUfnQnCnGjz70VC4dEmt4Vi4UPpxCEk6hBBCCCEKtdhYuHULypdXazo8Pe0Ku7cBYMkriWwMHYYTaewv9xLNN43hq68PZnuecZ1qUtLD+W5R+qezYONGdYnc1avtvh/xaJOkQwghhBCiMKtaFfbvB70eypSxK8TUANDUScPxVjrzV03HR7nGP9oArn87leeqlMRX58JVfQqKlXNoUEc2BjTzv1uMvm8ffPCBuv3ZZ1C3bh4fTjwqpJBcCCGEEKIwOnPm7ravL9SoYfNQg1Fh39kY1h29zN4zN5iw/m4DQMWoELw8lKfSjhCDF0N6jeGTfeeBuw0Ds2oomHH1K6pWhWeegZdfhsGD8/R44tEiIx1CCCGEEIXNnDnq6lTLlkGfPlkeeu80qnv1XbuHfjd/xIADQ1sFc6V8cbjT3M/UUPDeeB+dC8GdA+hQ29fyZCVLwubNkJoqdRzCgiQdQgghhBCFyZo18N57auO9//7L8tB7p1Hdq8meM0w6MwOA8VVHcujpyuZ9pkaB2TUUBNTlehs2VLe1WnB1zfXjiUeTJB1CCCGEEIXF3r3Qt6+acLzxBowaZfNQg1EhZEOEzYTD7/R1vto7Hi1GlhfvSWjX5hbTqEyNAuFuQ0Gr1qyBl16CQYNgwYIcNxEUjwf5WyGEEEIIUcAZjAp/bd1Paqfn4fZtlM4vwOefZzmF6WBkrM0pVbqYZL5eG4InCex2bsLEAX3Q3Bm5MDf38/fK/sZOnlQbEQLodJJwCJvkb4YQQgghRAEWFh5F149WU7JHV5z0NznqW43WDQYTdiI6yzjT9Kh7OaYY+HL5p1RSznPWwZ83+w8n3Umd/GKzQNyaxER48UVISICWLWHKlJw+mniMSNIhhBBCCFFAmWoyWu/ZQHn9Nc4X8+W17uO5cAuCQo8QFh5lMzbj9CgTxagwbvkKmqXuR48nr3UNJt6rqHm/j86F+f3qZy4Qz3QiBV5/HU6cUJfp/f57KCKz9oVt8rdDCCGEEKIAyliT8XnT3hg1GtYGtCLGrRigjkqEbIigbYCP1VGJRv5emfpsvLJmJ/3jVmPAgSHNJ5LSoCwretblRuJt6wXitnz22d1E44cfoHTp/Hps8YiSpEMIIYQQogA6eCaa6LgkcNCCRsO8pr0t9itAlD6F/WdjcHDQZFpZSuugIbhzAEGhR9AAgb+fZMK5TwH4qNr7HGhahfkv1KJZlZI5u7H//oP331e3Z82Cpk3z/rDikadRFMXWogYin8THx6PT6dDr9Xh6ej7s2xFCCCFEQacoXOzSi4hT/zGs8yhuF3GyeWgxV0du3kozv/a9p4dGWHgUiz7Zzjcrh+JOEotL9GXx8AFM6FIr+2lUtmzeDBs2wBdfSD+Ox5y933Ml6XgAJOkQQgghRI6MGgUzZ5KucaDPy59wsHxtu0NNKYCpNuN6+DVu121EOcNF9nu2Jn3PdzSpVdq+aVRCZMPe77lSSC6EEEIIUZDMmAEzZwIwpftIDuUg4QDM9RshGyJIir3FtSZdKWe4yDnHalT/aw2BdazXgGRr7lyIjMx5nBBI0iGEEEIIUXAsXQqjR6vbM2bwVPC7AOQ0RVCAK3G3OPDkq9RO3E+cpjis30DxSsVzd18rVsA776hdx2/cyN05xGNNkg4hhBBCiIJg/Xq1qzeo06tGjqRDbV/m96uPj85y+dtiro7Zni5oVRjP/LeKNIpwbtqPVOpQLXf3dfiwujwuwJAhUDKHhedCIDUdD4TUdAghhBAiSwkJ4OcHsbFqh+8lSywKtA1GhYORseYVqoyKQt/FB2ye7vnNh5n3dzAAu/oupGXooNzdV3S0Orpx6RI895yaGGm1uTuXeCTd15qOI0eOEBISwgsvvECNGjUoUaIEjo6OlChRgmbNmjF58mRiY2OzPMe1a9cYMWIE1atXx9XVFS8vL5o3b87ixYuxJw86e/YsQ4YMwd/fHxcXF7y9vWnfvj1r1qyx+xn69etHuXLlcHZ2xtfXl27durFjxw674oUQQggh8o2Hh7oaVP/+sHBhphWhtA4amlQuQZe6ZWlSuQSNK5XAV+diddpVwwPnmP33JADW1x3JzZHPse9sDAZjDn/PnJYGL72kJhzVqsF330nCIXItVyMdb731Fl988YX5tYuLC46OjiQkJJjfK1myJOvXr6dJkyaZ4g8fPkz79u2JiYkBwN3dnZSUFNLT0wFo164d69evx9nZ2er1N23aRI8ePUhOTgbA09OTxMREjEYjAAMHDmTJkiVobCzhtnjxYoKCgszX0+l0xMfHm5Od4OBgJkyYkJOPJEsy0iGEEEIIqxQl10vOmrqVw93icf/T0az9+V10xPODrhujBg8EB/V3zPcupZutN9+EL78ET084cABq1MjVfYpH230d6WjUqBEzZsxg3759xMXFcevWLeLj40lISGDZsmWUKlWKGzdu0LVrV/R6vUWsXq/n+eefJyYmhho1anDo0CESEhJISkpi3rx5ODo6smXLFoYPH2712pGRkfTs2ZPk5GSaNWvGqVOn0Ov16PV6xo8fD8DSpUuZMWOG1fh9+/YxdOhQ0tPT6dq1K5cuXeLmzZtcv36dIUOGABASEsLq1atz89EIIYQQQtjn3DmoXx+OHs1V+L31HiWvJfDN2vHoiGePUxM+HDDAnHAAXNWnEBR6hLDwqOxPnpwMhw6pCdGKFZJwiDy7LzUdW7ZsoX379gCEhobSt29f875x48bx8ccf4+rqyvHjx/H397eInTJlCh9++CFarZaIiAiqVbMsevrf//5HaGgoPj4+nDhxgmLFilnsHzJkCAsXLsTT05Pz589TvLjlKg3Nmzdnz5491KlTh8OHD+PoaFmI1aFDB3799VcqVqzI2bNn0ebDMKKMdAghhBDCwuXL0Ly5ugRts2awe3euRzwMRoU9hy5SsmVXat0+ymmHqnR/9RMSSrhmOlYD+Ohc2PP+M9kvm5uSAlu3QufOubov8Xh4qH06GjdubN7+77//LPYtX74cgN69e2dKOADefvtt3N3dMRgMrFixwmJfUlKSuWYjKCgoU8IBMGbMGED9ANauXWux79y5c+zZsweAkSNHZko4MsZfuHCB33//PavHFEIIIYTIuehoaNNGTTgqV4YffshbV+80A0VfeINat49yXVOKV3oGW004QJ2GFaVP4WCkjdrb1NS72y4uknCIfHNfko7du3ebtytXrmzePnXqFBcvXgSgY8eOVmPd3d1p3rw5oI6YZLRnzx5u3bqVZbyfnx81a9a0Gr9161bzdocOHazGBwYG4uHhYTVeCCGEECJP4uKgXTs4eRLKl4ft28HXzhoLKxSjwh8N3+ap6E3cwoWwj74hqmKxbOOiE1Iyv5mSAi1awEcfwZ06WSHyS74lHbdv3+b8+fPMmzeP//3vfwBUqVKFzhky5PDwcPN27dq2u2ua9kVERFi8nzG+Vq1a2cYfP37cary3tzfe3t5WY7VaLTXuzFu8N14IIYQQItcSEqBjRzh2DEqXhm3boGLFHJ/GYFTYdzaGdUcvs77tJzQPX4ARDX+NWEGVAY3sOoe3h2XfDxQFgoLgwAHSv/iSX7cezt2KV0LYUCSvJ3BxceH27duZ3m/WrBnfffedxQpUV65cMW+XLVvW5jlN++Lj40lMTMTd3d0ivnjx4hQtWjTb+IzXy/g6q2ub9h86dChTfF7Fx8fbdZyzs7PNlbuEEEIIUUgFB6urQHl5qbUS1XLerC8sPIqQDRFE6VNos+MfFh8aC8APLSfTa+aLGIwKvjoXrupTsJYumGo6Gvl7We6YNw+WLcOgceCVDiPZ+1s0/Bad8xWvhLAhzyMdPj4+lC5dGjc3N/N7rVu3Zs6cOVSoUMHi2IxL6maVNGTclzHGtJ1VbMb9GWPzIz6vypcvj06ny/ZnypQp+XpdIYQQQhQAkyZBt24QFgZ16uQ43LREbpQ+hSeOXmTeoQkALCg5gPcb1SEsPAqtg4bgzgEAmXp4mF4Hdw6wLCL/7TeMd1YN/aTVQPb61TXvytGKV0JkIc9Jx/nz57l69SqJiYlcu3aNmTNncvToURo1amRewlaoLl26ZF7eN6sfUzG7EEIIIQq5jIuEurnBTz/BU0/l+DQGo0LIhggUwO/sdZb9+hEu3GZj0fZMe+VFNA4aQjZEYDAqmZbSNfHRuTC/X33LUYt//0Xp3h0Hg4GfA1qx5Kmulrd/57+mcwuRW3meXpWRt7c3I0aMoHnz5jRp0oRJkybRqFEjnn/+eQBzgTZAcnKyzWW1TE3/7o0xbWfcn1V8xtj8iM8rT09PWTJXCCGEeFwYjTBwoLpC1bhxeVqh6mBkLFH6FEpdjefbNWPxIo4Djg0ZMXAoShH1d8hR+hT2n43BwUHD7XQjM196EjRwI/E23h7qlCqLEY7bt6FzZzRxcRz1rcYHHd62eo8ZV7xqUrlErp9BPN7yNekwadSoEYGBgfz+++8sXLjQnHSUKVPGfMzly5dtfgG/fPkyoH5JN9VzZIyPi4sjOTnZ5jQpU3zG62V8bdpvi614IYQQQgi7GI3w+uuwfDlotdC9O2SxCE52ohNS8NDfYlnoJMorlznpUJVB/d/ntrvl8v9vfneEm7fSzK9NNRlWkwVnZxg7lsQxYxn0wlhuO2ZdT2p1xSsh7HRflsyFu8Xa//77r/m9jCtWZVyJ6l6mfQEBARbvZ4zPamUpU/y9K1yZ4qOjo7l+/brVWIPBwMmTJ63GCyGEEEJky2iEwYNh6VI14fjuuzwlHABeRoWvls6kluEElzVl6N87hPiSmXtxZEw4wI6ajL59Cf/1D667e1nfn0GmFa+EyIH7lnScO3cOsJyiVL16dXNxeVhYmNW4pKQkc5+Pdu3aWewLDAzE1dU1y/gLFy5w4sQJq/Ft27Y1b9uK37t3r7mA/N54IYQQQogsGY0wdCgsWQIODhAaCj175umUhtvpuDz/Jk1vH+AmOvp1nkx0efuma1utyfjxR4i6m4Q8Vd0HX51LpsJzEw3qiEmmFa+EyIEcJx0GgwFFybqQaPv27Rw8eBCAVq1aWezr378/AKtWreL8+fOZYr/44gsSExPRarX07dvXYp+bmxvdu3cHYP78+ej1+kzx06ZNA9Rkp2vXrhb7KlWqRGBgIACzZs0iLS3t3nCmTp0KQMWKFWnRokWWzymEEEIIYWY0whtvwKJFasLx7bfQu3eeTqkYFQ7UD+KpqPWk4MyA1lM4X7NUzs5Bhi7kW7eq99SoEVy9CpC7Fa+EyKEcJx2XLl2iXr16fPXVV5w7d84iAbl06RJTp06lS5cuKIqCl5cXw+8swWYycuRIfHx8SE5OplOnThw+fBiA1NRU5s+fz7hx4wAYPHgw1aysXz1x4kTc3NyIioqic+fOnDlzBlBHSCZOnMiCBQsAGDt2LMWLF88UP336dLRaLceOHaN3797m+o3Y2FjeeOMNNm/ebHGcEEIIIYRddu+Gr75Si7G/+Qb69MnzKfe2CaZpxGIMOHDovZUM/bxrplWpirk62oi2lPx3OPToAQYDPPOM2qDwjhyteCVELmiU7IYt7nH+/Hn8/f3Nr52cnPD09OTWrVskJSWZ3/f392fNmjXUq1cv0zkOHz5M+/btiYmJAdRRiZSUFPPIQ7t27Vi/fr3NBnmbNm2iR48e5lWmdDodiYmJGAwGAAYMGMDXX3+NxsYqEYsXLyYoKIj09HQAihUrhl6vNydQwcHBTJgwIScfS5bi4+PR6XTo9XpZvUoIIYR4lC1YAEWLwp2ZHfYwGBUORsYSnZBCSTdn84pThPxAl7XqL2+391jAs6uHZDre28MFo6LQd/GBLK9R7FY8+9d+hMvFSGjWDLZvVwvJs7gXqyteCXEPe7/n5jjpSE1NZd26dezcuZMDBw4QFRXFjRs30Gq1lCpViieffJIuXbrQp08fc/2FNdeuXWPatGls3LiRS5cu4eLiQu3atXnllVd49dVXcXDIehDm7NmzTJs2ja1bt3LlyhU8PDyoX78+Q4YMMU/BysqRI0eYNWsWu3bt4vr16xQvXpwmTZrw9ttv88wzz+TkI8mWJB1CCCHEI0pRIDERcrnMfsYO4xk9s+sfFu//EAcUVtf7kJ5HJts8h8GoEDhth80u5E6GNFatCaZ+5N/g5wcHD0KpnE3REsKW+5Z0iJyTpEMIIYR4BCkKDB8OO3fCtm1QsmSOwk0dxu/9Ilb/8DlWbhuJM6ksKd6XSa/3ZkH/BllOcTKdC7A4n0ZRmBo2l15/b1EToz/+gAyrgQqRV/Z+z71vq1cJIYQQQjyyFAVGjIDPPoNjx+C333IUnrHDeEbVTkWxdNtYnEllbdFOfDywl0W3cVts1WRUcTbQKTFSLWxftUoSDvHQ3JfmgEIIIYQQjyxTwjF7tvr6q6/UAu0cMHUYz6jC+RuErvsQHfH87tSUUa8NRnG82208u47gHWr70jbAJ3NNxohWsGMHPPdcju5RiPwkSYcQQgghhL2MRnjrLZg/X309f77aCDCH7u3uXfrKTVas/ghv5TpHtHUZOnAkaUW1WcZYo3XQqIlJQsLdOhOdDrp1y/E9CpGfZHqVEEIIIYQ9DAYYNEhNNDQaWLxYbQRo63Cjwr6zMaw7epl9Z2Mspkdl7O5dPCaR0BXjKa9cJsKhOq/0H0tyMadM57O7I3hkJFSvrk79EqKAkJEOIYQQQgh7XL+uNtdzcFD7cPTrZ/NQa6tS+epcCO4cQIfavjTy98JX50LixVi+WTaJqsZzRGoq8r8+ISR4Z04uvNwcaVAxc/+xTOLi1GlUUVGwdCkMGQIudiYrQtxHsnrVAyCrVwkhhBCPiH//hfBw6NrV5iG2VqUyGfZsFRr5l2DH/kjavfYGT6f+SRQ+vNhjFlGVdDbPmzFpser2bWjfHnbtgnLlYP9+KFvW/mcTIhdkydwCRJIOIYQQopC6fRv+/FNtqGeH1HQjjadsJzYpNcvjnG6nseCrT3nm1m5i8KJ759mcD7BdJA5gatFntUO4osD//gcrVqi1HHv2wBNP2HXPQuSFLJkrhBBCCJEXt25Bly7QujVs2pTt4WHhUTSesi3bhEObns6sJfN55tZuEnCnX7vpnA8owWvN/Ph2YCO83DLXc8Dd/htWl88dP15NOLRa+PFHSThEgSNJhxBCCCHEvRIToVMn+PVXcHTMti7CNKUqNikt6/MajUxeuozOCVtIwZn/tZjBiXo+aIBN4Vdx0GqyTFoU7i6fa7Z/P3z8sbq9cCG0a2ffMwrxAEkhuRBCCCFERvHxajH23r3qVKVNmyAw0Obhthr9ZaIofLR8Fb1j15JGEV5tNI2jTcqru1CTiX1nY+y6RYvlcxs3hjlzIDYWXn3VrnghHjRJOoQQQggh7jBcv0Fy2/Z4HDtCuqcOza+/om38dJYx1hr9ZaIoDFu1jkHXvsOIhjee+Jg/WlexdqBd95lp+dxhw+yKE+JhkelVQgghhBDAtr0nOF/7KTyOHSHW1ZMu3UII/C2RsPCoLOPsado39IfNDL+4GIAR1caztWNtq8c1qVQSX52LuWj8XhrUVawaOSarS/bevJnttYUoCCTpEEIIIcRjLyw8isHr/+WIdxWuunvRs89UjvtU4ao+haDQI1kmHtk17Xt9za98EPklAB/6jeHnbk9lOsaUTDSuXILgzgHm9+49BmBSy7Jon++kFo6//rq9jyjEQyVJhxBCCCEea6aaDKPGgQ86vk2X/p/yb8kKQDYrRt3RoGJxmytOvbJ2O2P/nQvA+PKjWNEz89K7pmQiuHMAWgcNHWr7Mr9ffXx0lsmMj86Fr3oE0GbMEPjnH/DxgRkzcv7AQjwEUtMhhBBCiMfX3r3EzPmSaP9e4KDF4KDlmkdJi0MyrhjVpLJlLw1T53FrK0712bCLkFOzAZhSbgTfvNwSjZV5Uz5Wmv51qO1L2wAfDkbGEp2QgreHC40q6ND27gW//w6enrB5M/j75/0zEOIBkKRDCCGEEI+nsDB48UW8b93i9VZOfPX0S1kefm/tRladx3tu2s0nEeooxMpaHzDq2Ce0Oq8mECXdnEEDNxJvq8mEvxdah8zZiNZBczfJURR44w346SdwcoJ166Bu3dw8tRAPhSQdQgghhHj8fP+92sE7LY24lm34pv7z2YaUdHM2b2e1TG63X/cz/Z9pAPzyxPv0OvIJDlpNplGSHJk+HRYsAI1GreVo1Sr35xLiIZCaDiGEEEI8Xr76Cl5+GdLSoHdvPMM2UrxkMZsrRpmM+OGYuaDc1jK5nbcfZNbRyQB8XmIQxX4YiYM2uzPb4fnnoVw5mDcPXsp6REaIgkiSDiGEEEI8HhQFpkyBoUPV7aFDITQUrYuzzRWjMroan8LQ0CN8tu00V+MzJxzP/XaYz/6chAMKXxYfyKyBL3Aj+Xb+3HutWnD8uDrFSohCSJIOIYQQQjweLlyASZPU7Y8+gi+/BK0WwLxiVGlP5yxOoJq97QwTNxy3eK/N7qPMPRiCAwqLdP2Z9lp3NFpNtsvpZmn3bvjtt7uvPT1zfy4hHjKp6RBCCCHE48HPD9asgRMn4L33Mu3uUNsXDxdH+i4+kO2p4pLTzNvP7vmbBX8Eo8XIMs8+TH69Bw5adVWqRv5eubvXf/6BF16A5GTYuhVatMjdeYQoICTpEEIIIcSj6+ZN+O8/qH2nA3jHjuqPDTcSczYdqt3vf/HlvgkUwUCoe08mvN4HzZ1vV6a+Gzl24QJ06KDee7Nm8FTmZoJCFDYyvUoIIYQQj6b//oPmzeHZZ+HcObtCcjId6rnf/mT+vmCKYOBb996MG/Q/cFRHOOb3q2/Rd8NuN25A+/Zw5Ypax7F+Pbi65vw8QhQwMtIhhBBCiEfP8ePqaMF//6mduxMS7Apr5O+Fr86Fq/oUq8vhmnTeftBcNL7WZwAVtk5nTnpqln03sqXXq/d86hSUL6/2EfHK5fQsIQoYGekQQgghxKPl998hMFBNOGrUgH374Mkn7QrVOmjMK1nZ0nXrPnPCsVD3Cl7bptOydim61C1Lk8olcpdw3Lql1nAcPgylSsGWLeoSuUI8IiTpEEIIIcSj44cfoG3bu/UQe/eqBeQ5YFrJysfKSlY9ft3DnCOT1WVxi73KkhH/o1nNknm/7yJF1NENT0/49Vc1WRLiESJJhxBCCCEeDevXQ69ekJoK3bqpqz7lcnpSh9q+7P3gWYa3qWZ+r/fmXcw4OhWAucUHMf21Fwnplsti8Xs5OsLy5XDoENSrl/fzCVHASE2HEEIIIR4Nzz4LDRtCo0bw2WfmHhy5pXXQMKxNVar7uHOk/2w+/HsGAJ+WCOLbN7vyWsOy6FydMBiV3CUeigIrVqjd0bVacHCAatWyjxOiENIoipJVnZTIB/Hx8eh0OvR6PZ7S2EcIIYTIPykp4OwMmjtf+pOSoGjRu6/vMBgVDkbGEp2QkuNi77/6zKDeytEALK74Hl8MaE9cyt0+Hb46F4I7B+RstSpFgZEj4dNPoW9f+PbbTPcsRGFg7/dcGekQQgghRKFjMCr8dfAElV/vQ2KbDpT5dIqaRLi5ZTo2LDyKkA0RROlTzO/Zmyj81fMT6v3wEQDfVv+Qjzs3gQwJB8BVfQpBoUdytkzuxx+rCQeoIzSScIhHnNR0CCGEEKJQCQuP4pXhi/Fp34rix4/htnA+zwX/TFh4lNVjg0KPWCQccDdRsBYDgKLwV4cPzAnHz/Un8sX/ngFt5uTANGUkZEMEBqMdE0g++wzGj1e358yBgQOzjxGikJOkQwghhBCFRlh4FKtCFjJ/wTDKxV/nrFdZuv1vJqfTnDMlEQajQsiGCKv9NrJMFIxGjjZ7k3q/TgPg56Yz8F71BlcTLBOXe88XpU/hYGRs1g+wbBm8++6di4fAsGFZHy/EI0KSDiGEEEIUCgajwvEPP2HJmol4pN7ijwpP8GK/mVwoXsZqEnEwMjbTCEdG1hIFJS2do3Vfoe6++RjR8HOHr+i6ZyQ3kmyfJ6PoLBIT1qyB115Tt4cPh3Hj7DqnEI8CqekQQgghRKFwbfBbjNjwJQCr67Tho/ZvkqZ1NO83JRHL9kZS0sOZM9cS7TrvVf0t9p2NITr6JmV6vctTlzaSRhE29VpOt1UvA+Dt4WLXubI8ztVVXRq3Xz+YNUvqOMRjRZIOIYQQQhQK18pXxgcNM1r2Z/7TL9n80j7plxM5Ou+kX05wK0bPl4vn8FTSXlJwZlmPZQxd1dt8TCN/L3x1LlzVp1idrqUBfHTqqlg2Pfec2h39iSck4RCPHUk6hBBCCFFwKYr5C3pKv1dof9GFM6Uq5usl0qJjWbp4Bo1TDpOAO/97+lOOVfLALzzKvBqV1kFDcOcAgkKPoAGLxMOUPgR3ttIo8Lff1E7jVaqor6Xxn3hMSU2HEEIIIQqmvXuhcWOIjgbU0YbEKtXJzzGC4vp4QhdOonHKYWIpTo8W8zjaqgyQuci8Q21f5verj4/OcgqVj87F+nK5O3dCp07QsiWcP5+Pdy1E4SMjHUIIIYQoeBYvhjfegLQ0dXnZBQuyHG3IKS83RxwvXOPbb0Koln6Wq5Smd7tZnK9XDLAsMm9SuYQ5rkNtX9oG+GTfaHDPHnj+ebh1C555Bnxz0DhQiEeQJB1CCCGEKDjS0tSVnb74Qn3do4dadH2HabTh3mZ/OTG8TVUqRF2jfsiHVDT8x3kq0vuF6VytmbmxoLXVqLQOGotEJJN9+6BjR7U7ert28OOPatd0IR5jMr1KCCGEEAXDjRvql3RTwvHxx/D995m6jHeo7cuuUa3xcnPK8SU0wKHlu2gx4EUqGv7jpKYa3V+aaTXhAPtXrTL780/o0AESE9URjrVrwSWH5xDiESQjHUIIIYR4+E6dUr+snz8P7u6wYgW88ILNww9fiCM2KTXHl2n8VwQLtoTgThKHtfUZ2Gsc8eUdMx1n12pU9/rnH2jbFuLjoXlzWL9eXSZXCCFJhxBCCCEeLoNR4XCyIwFGDUUq+OO4cT3aOrWzjMmyCZ8NnXYfZM4fn+BIOrtc23Ll+wUk7D2Rs9WoslKmDFSqpE6l+uWXTCM0QjzOJOkQQgghxMNhNBJ2/CohG08QpU+hQocP0bu4o117hW7nNbQJ8LFepE3Opz0N2LSFCf98DsBK1x4U3/YZfqWdGIgfa49esRg18dG5ENw5IPNqVNkpUQK2b1eX+PXwyFmsEI84jaIoeVn8QdghPj4enU6HXq/H09PzYd+OEEII8fDFx3Ota0++1PrxTYPONg/ztZEAGIwKgdN22GzWZ6JRjIxa/QNvnP8WgM90Q/j6zRdxddNyNf7uaImXmyPd6pbNMtGx6p9/4I8/YMgQ+44X4hFj7/dcKSQXQgghxIN1/DjKU09R+rdfGb3rG7yS9TYPvapPISj0CGHhURbvm5bPBWz27SiSnsa0ZYvMCcdHPh/x6aAX0BvSLBIOgLikNL7eex79rVT7E46jR6F1axg6VC14F0LYlKukIyYmhqVLl9KvXz8CAgJwc3PD2dmZcuXK0bVrV37++edsz3Ht2jVGjBhB9erVcXV1xcvLi+bNm7N48WLsGXw5e/YsQ4YMwd/fHxcXF7y9vWnfvj1r1qyx6xmOHDlCv379KFeuHM7Ozvj6+tKtWzd27NhhV7wQQgghcmHVKmjUCM3p01zxKEnf3pOJLaqzebjpG8G9jfrAdrM+X50Lb9b3ZvHi2fSM3kAaRRhceQbb3mpFcQ/rM8uzuo5Vhw+rq1PFxECjRtC+ffYxQjzGcjW9ytHRkfT0dPNrFxcXtFotSUlJ5vc6duzIjz/+SNGiRTPFHz58mPbt2xMTEwOAu7s7KSkp5nO2a9eO9evX42xjTetNmzbRo0cPkpOTAfD09CQxMRGj0QjAwIEDWbJkCRqN9d9ULF68mKCgIPP1dDod8fHx5mQnODiYCRMm5OQjyZJMrxJCCPHYS02FUaPgc7Wu4nqjQNo/HZRlwnGvlYMaW+2PYTAqFs366jqmE1W3E5XiDpOIG4u6L6PxtNYYFYW+iw/k+jpmBw6oSYZeD02awObNoLP/OYR4lNzX6VXp6ek0atSIL7/8krNnz3Lr1i0SExOJjIzktddeA2Dz5s0MsTK/Ua/X8/zzzxMTE0ONGjU4dOgQCQkJJCUlMW/ePBwdHdmyZQvDhw+3eu3IyEh69uxJcnIyzZo149SpU+j1evR6PePHjwdg6dKlzJgxw2r8vn37GDp0KOnp6XTt2pVLly5x8+ZNrl+/br7fkJAQVq9enZuPRgghhBD3MhrV/ht3Eg4+/JB/v12To4QDbK9YZWrW16VuWWrrY4mtEUiluMNcpyR7J+5g+I8v0aRyCW4k3s7TdQC1fqNtWzXhaN4cfv1VEg4h7JCrpGPHjh0cOHCAoKAgKlWqZH7fz8+PxYsXm7+8h4aGcunSJYvYmTNncvXqVVxdXdm0aRMNGzYEwMnJiTfffJOQkBAAFi5cyOnTpzNde/z48SQlJeHj48PGjRupVq0aoI6WhISEMHjwYAAmT55MXFxcpvjRo0djMBioU6cOq1evply5cgCUKFGCBQsW0P7O8KjpOCGEEELkkYOD2nNDp4N162DyZBpUKpnj5n7ZrVh1bc0e0hs1oeytfzmv8ePUkr20H9fI7vhsj7t8WR3hSEiAVq3UEQ5ZpUoIu+Qq6WjdunWW+02jHQB//vmnxb7ly5cD0Lt3b/z9/TPFvv3227i7u2MwGFixYoXFvqSkJHPNRlBQEMWKFcsUP2bMGEAd6lm7dq3FvnPnzrFnzx4ARo4ciaNj5mZApvgLFy7w+++/Z/WYQgghhLBFUeD69buvhw+HiAh44QXCwqNoOeM3u5v7aVDrNLJq1Hd+6iqKvfQsxQ0x/OX4FPqw/QS+Ws3imEb+XvjqXGwWnmd7nbJl4f334dlnpQ+HEDl0X1avcnG5+xuCjKMFp06d4uLFi4Ba82GNu7s7zZs3B2DLli0W+/bs2cOtW7eyjPfz86NmzZpW47du3Wre7tChg9X4wMBAPO781uLeeCGEEELYIT4eXnoJWrRQRwVA7V1Rpgxh4VEEhR4hSm9fc79sG/UpCqcGfILfmJdxJpVtHt0o+c9OnmxXOtOhWa14leV1Mpa/jh2rjnBYqVkVQth2X5KOnTt3mrfr1Klj3g4PDzdv165tu9OoaV9ERITF+xnja9WqlW388ePHrcZ7e3vj7e1tNVar1VKjRg2r8UIIIYTIxtGj0LAh/PQTnD0L+/aZdxmMCiEbIrLuq3HP930fnQvz+9W33qgvLY0TzQdR/ZuPAPihwns8df4Hyle3nRDYWvHK5nW2bFGnVCUm3n3PykwJIUTW8r0j+c2bN5kyZQoAzZs3p3r16uZ9V65cMW+XLVvW5jlM++Lj40lMTMTd3d0ivnjx4lZXxbo3PuP1Mr7O6tqm/YcOHcoUn1fx8fF2Hefs7Gxz5S4hhBCiQFIUmD8f3nsPbt+G8uXhhx/g6afNhxyMjM12hENRYFynmpT0cMbbw8Vmoz5jnJ5/6/eg5vmtGHDgu6c/p9fvb+JkR5lIh9q+tA3wsVjxyup11q6FXr3UlbdmzIA7dadCiJzL16TDaDTyv//9j6ioKJydnZk7d67F/gTTECtkmTRk3JeQkGBOOkzxWcVm3J/xevkRn1fly5e367j8XrJXCCGEuK9u3oTXXwdTr6zOnWHpUihhuexslqtCZVDSw5kudW3/gjDl9EWin+pEtfhwEnFjY99V9Pv2+UyjJFkxrXhl07ffwsCBYDCoU8U++sj+kwshMsnXpGPYsGFs3LgRgC+//JInn3wyP09f6F26dMmuPh0yyiGEEKJQGT5cTTgcHWH6dBg2LPM8KfJh9SggbvthDB2fp0LaVa7gy18TN9J7XP1c37pVX34Jb76pbg8YAIsWQZF8nxwixGMl3/4FjRw5knnz5gEwe/ZsXn311UzHeGRYVi45OdnmF3BT0797Y0zbGfdnFe9xzzJ2eY3PK09PT2kOKIQQ4pGQsSFf2SGjaHDqFJrPPoOnnrIZY1o96qo+xWpdhwa1tsLa6lEGo8K+j7+j/oTBFFWSOe5QB/13v9Cpl32zCOw2ZQp8+KG6/c47MHu2uuSvECJP8uVf0ejRo5k1axYAM2bM4N1337V6XJkyZczbly9ftnk+0z5PT0/z1KqM8XFxcVkmDqb4jNfL+Dqra2cVL4QQQgjYvjucWS8O5+VF+xm26igvrY2k6fMTCXMtl2VcblePCvvnCp8//TZNg/9HUSWZX7VtGDR4NvG18nn04fp1NckAGDcO5syRhEOIfJLnf0mjRo0yd/+ePn06I0eOtHlsxhWrMq5EdS/TvoCAAJvxWa0sZYq/d4UrU3x0dDTXM64dnoHBYODkyZNW44UQQojH3YFv1hLwXCtGr/uM50/c7Wd1Nf42QaFHCAuPyjI+p6tHbfnzHAntBjH8zy9wQGF+0dcZOmgEUboUu66XI6VKqatVzZkDEydanSImhMidPP2KYOTIkeYRjunTpzNq1Kgsj69evToVKlTg4sWLhIWF0aNHj0zHJCUlsXv3bgDatWtnsS8wMBBXV1du3bpFWFgYT1kZwr1w4QInTpywGt+2bVvzdlhYGP/73/8yxe/du9dcQH5vvBBCCPGoyDg9KqtVoszS0zF+/DENJ05Cqxg561WOf0vcndqkoI5WhGyIoG2AT5bnsnf1qNvnL1Oq1Yu0SzpGGkUY4fsx6/o8gaaI2gPM3utlKS0Njh+HunXV13Xr3t0WQuSbXI90ZEw4Zs6cmW3CYdK/f38AVq1axfnz5zPt/+KLL0hMTESr1dK3b1+LfW5ubnTv3h2A+fPno9frM8VPmzYNUOsxunbtarGvUqVKBAYGAjBr1izS0tIyxU+dOhWAihUr0qJFC7ueSQghhChMwsKjCJy2wzw96uVF+wmctsP2qMHZs9C8OQ4hIWgVI2tqtabzK7M56e1vcZgCROlTOBgZm+09mFaP6lK3LE0ql8iUNMTv+BN99UbUSzpGDF50e3IJ6/5XG00RY66uZ1VKiroyVdOmsHdv7s4hhLBLrpKO999/35xwfPrpp4wYMcLu2JEjR+Lj40NycjKdOnXi8OHDAKSmpjJ//nzGjRsHwODBg6lWrVqm+IkTJ+Lm5kZUVBSdO3fmzJkzgDpCMnHiRBYsWADA2LFjKV68eKb46dOno9VqOXbsGL179zbXb8TGxvLGG2+wefNmi+OEEEKIR4mtjuBX9TamK33/vfqb//+3d9/xTVZtA8d/Sbp3C5SWWfaQjTIElA2CyFARxY2IPICKgr6PCxyPioo4UFTAiaLiQFBAkKUgyt4bWmZb6KB7Jvf7xyFp2iZt0iYttNf384lNcp/7zkkKeK6cc67rn3/ICwjk0WHTePLmJ8n08rX7Go6mxrXn/Nvf4tW/F+G55zlAawYPWsj+wTXsrnYq0+ulp8PNN8OyZWAyqbS/Qgi30WmaVlJh0GJOnz5Nw4YNAdDr9dSqVavE9tOmTSu2z2PHjh0MGjSIxMREQM1KZGdnW2YeBg4cyLJly+ymjl2xYgW33367ZTN5cHAw6enpGI1quvX+++/n008/RWfnX6cFCxYwceJE8vPzAQgJCSElJQXzR+HqOhmpqakEBweTkpIi2auEEEJUGqNJo+esdXYL9JmzR216um/BzMOvv6q6G716seOV97h1RcnJWAC+HteVHs1qOt9Bk4mT98yg8TevALDaeyiTb5tEbj1TiactHt+t5JobRV28CEOHwrZtEBCgAo8+fZzvrxDC4XGu0zMdJpOp0P34+PgSb+np6cWu0blzZw4cOMDUqVNp1qwZeXl5+Pv707NnT+bPn8/KlStLrFUxZMgQ9u7dy/jx44mKiiIrK4uQkBAGDBjADz/8wGeffWY34AB46KGH+Pfff7nrrruoW7cumZmZhIeHM2LECNauXSuF+YQQQlRJpVUENy9X2rnzeMGTN98Mq1bB+vV06NmeyGCfYpmninpyyR6nN3hraemc6HibJeBYVOcp2hxfSo1rvOy+ng6ItJNi167oaOjRQwUcNWrA2rUScAhRAZye6RDOk5kOIYQQV4Jfdp/jsW932z3ulZ/H9D+/4L6jG/DatxfqF6+BYV6eBdistWFtav9mTO7brNRN3rmHT3Khx0jqJe0lBy8+77GA+9feg7e3/dczX9FWxiu7YmKge3eIi4OGDeH336FFC8fOFULY5LaZDiGEEEJcnUqq9N3s4il++XIq47ctxSvlEixdarOdOeVt7SD7KxLM5vxxjGtfWcPLyw+w5UQiRlPxMCXpm1Vkt72Wekl7iaM2P0zeyMN/qYDD+vUcTbFbovr1oVs3aNsW/v5bAg4hKpDMdFQAmekQQghxJTDv6bCuCK43GXlo21Ke/GsR3sY8kv2CCfr6Cwwjhpd4rc3HExi74F+nXj8y2IcZw1qrQMFk4tTE16j/yfPo0dhm6Erqwh/od19BgUHrtL41/b1BBwnpOY6l+LWmaQU1N7KyICcHQkKc6rsQwjZHx7kuLuUphBBCiCuVuSL4xEU70QFRSed467c5dD6viuKub9wZbeFC+vZuX+q1EtJznH59c4asT25pStP/PE2jPb8A8F3oBDr/9S7XXVMwe7JqfywvLj9YaA+KOWhxatP43LmwezfMn68CD19fdRNCVChZXiWEEEJUI9bLlcbs+Z3O5w+T5uXL/0Y9Sc7SZQ4FHFDyUi17NKBJ/Fka9R1Coz2/kIMX8zovYOjpj2haJOBwKq2vzRfT4LnnYMoUWLgQfvnF6f4KIVxHZjqEEEKI6kTTLBXBt9/SgpjnvUmc+jT/16OdU1W9uzQKIzLYp9BSrdIM2rWV2WtmE6BlcIZ6rJ/8E4+8d12h+htGk8aLyw/avKbDVc/z82HCBPj0U/X4pZdgeMnLxYQQ7iUzHUIIIUR1YDLBBx/A4MFgNGLQ6+h6TT2ifvqGzr3aOxVwQMFSLYfamoxMW7aYj1e/RICWwTp9b269bSHhE+oVK/jnaFpfu1XIMzJg1CgVcOj18Mkn8Pzz2K0sKISoEBJ0CCGEEFVdTAz07w+TJ8Pq1arKuAtYlmqVkMkqJCOFhQteY/KhrwGY7TeF+8c9w4UmeTaXSjlaXdxmu7g4uPFGWL4cfHzgxx9h/HjH35AQwm0k6BBCCCGqKpMJ5s1TKWLXrwc/P3j/fRgzpkyXM5o0tpxI5Jfd5ywpcAe3iWTz//Vjav/mxdp3jD7Crx9No3fyP2Tgx311P+TdR25CH5ZrafPi8oOFUuk6ulfEZrsjR2DPnoKifyNGOP0ehRDuIXs6hBBCiKro0CH1Lf/mzepxz57w2WfQtGmZLldSNqnBbSJ5rH8zWkQEqDaXsnhw/Qr+u20+nuRzlGY80PUNzt7oiV5nspxvvVTKnJGqtL0iOlSNDptVyG+8Eb79Ftq3L/P7FEK4h8x0CCGEEFWROeAICID33oMNG8oVcDiSTWpwm0g2je/AyuUf8sK2eXiSz3cetzFk9Aec6+1pd1uF9VIp670iRZubH88Y1rpgD8qXX6oAy+zWWyXgEOIKJEGHEEIIUVVY1/v94AOVsenAAZU21mAo0yVLyyYFBUuksjf+S3KjzrQ6tJIcvJjV5D2mPTKe/Ea5Ns4uUHSplENVyDVNbRC/7z4YMgSSk8v0/oQQFUOWVwkhhBBXu7Q0ePZZCA6Gl19Wz7VvD0uXlvvSDmWTupTFnkmv0fbjmdTU8jhBY9ZPXMIT73Vk6ZvriEvJc3qplDmtr7kieaEq5Dk58OCD8M03qvHdd0uFcSGucBJ0CCGEEFez336DiRPhzBnw8ICHH4b69V12+dKySQVlp/Pqdx/TKW49AL9630rg9wt56JZggEIV0K0DD5tLpYow6HXFq48nJsLIkfDXX+r9fvIJPPBAGd6ZEKIiyfIqIYQQ4moUGwt33gk336wCjkaNYOVKlwYcUHI2qTZnj/PLvOncHLeeXDx5t+n7dD65hBsvBxzg4FIpRx0/Dt27q4AjKAhWrZKAQ4irhMx0CCGEEFeT/Hz48EO1nyE1VRXAe+IJmDkT/P1d/nK2sknpNBP3/bmS//6zEG9yiSaK3+7/nknzr8PDxsiixKVSzvi//4Njx6BBA1ixAq65ptzvTwhRMSToEEIIIa4mFy7AM8+oytvXXafqcHTu7LaXM2eTMi+RqpmezOvfz6Pfxb8BWKq/hWNvzGH6k41LvU6xpVLO+vhj8PSEOXMgIqJ81xJCVCidpmm29nYJF0pNTSU4OJiUlBSCgoIquztCCCGuNunpKvWt2UcfgU4HDz1U5qxUzlq1P5aVr37Bc9+/RS1jIln48N+w/9H527u4Z4CbAoC8PPj5Zxg92j3XF0KUm6PjXJnpEEIIIa5UJhN8+qlaVvTdd9Cvn3r+kUcqth/Z2XR4+XUGf/8eAHtox0/3fMobCzvh5enkEilHJSbC7berSurJyTBhgnteRwhRIWQjuRBCCHEl2rULevRQRf4SE9XsRiUw7TvAhUZdiLgccHwa+BiZ6/7lxS87uy/gOHgQunZVAUdAANSp457XEUJUGAk6hBBCiCtJYiJMmgTXXgv//KMG3XPmwOLFFdsPTePSa/PI63At4XH7uEAtXuv1G6NOv0P3PvYzWpXbihXQrRucOAFRUbBlCwwb5r7XE0JUCFleJYQQQlwpFi2CRx8tqK49ZgzMnl3x3/QnJBB38zgi/l0GwGr9YBLf+oz/ezwCnZsmN9A09V6fekrdv+EG+OEHqFXLTS8ohKhIMtMhhBBCXCl8fFTA0a4drFunZjcqOODI/uk3Uhq2JeLfZeTgxZt15tBw/2/cOdWNAQeo5WTmgGP8eFizRgIOIaoQmekQQgghKsvRo3DyJAwerB7feiv8+CMMH15hWaksUlO5cM8ThC9biA9wkFasGPsNjy7sgLd3Bbx+p07w+uvg6wuTJ+PeCEcIUdEkZW4FkJS5QgghCklJwfTSS/D+++T5B7Jn3TY6t2/kfLE8F8lfu5G02+4n9FIMJnTM959K429eYcAtvu594XXroGFDaNLEva8jhHAbR8e5srxKCCGEqChGI8yfT07jJujffht9Xh6bwxrz6Py/6DlrHav2x7r35U0aW04k8svuc2w5kYgxI5PE+59A378PoZdiiCaK6d1/59ZTb7k34NA0eOstGDAARo2CzEz3vZYQ4oogy6uEEEIId9M0lZXp6afhwAG8gRNh9Xi570NsaHItALqUbCYu2sm8uzsxuE2ky7uwan8sLy4/SGxKNgBtzh9jzs9zaZZ+AoAFhgd5ZdD90DaVvz9Zx4xhrd3SD9LTYdw4+P579bhDB1lKJUQ1IMurKoAsrxJCiGru2DFo0QI0jVTfQN7pPoYvOw0l31D4uz8dEBHsw6an+5Z5qZXRpLE1OokLadmEB/rQpVEYaw7GMXHRTjTAw5jPpPU/MmXHN3hgJJYI/lP7LbbdWhuPwBxLPwDXB0DHjsHIkXDgAHh4wLvvwsSJEnQIcRWTiuRCCCFEZUpOhtBQdb9ZM3j4Yc5pXtzk24NUnwCbp2hAbEo2W6OT6N6khtMvWXQ2AyAiyJvsfBMa0OxCDG/++AEdUg8B8K1uNM/dOJm8Lql46HIK9UMHvLj8IANaR7hmr8ny5XD33ZCaCpGRsGSJKn4ohKgWJOgQQgghXOniRXjlFViwAHbvVgEHwEcfsX33OVK/3V3qJTYfv1hopsKRQf+q/bGW2Qxrcak5eBrzeHTDT0ze/i1e5JFEKI+GvsH625rgGZaKrauXNwAqxGSCV19VAUePHirgiHTD0i0hxBVLgg4hhBDCFTIz4Z13YNYsNbgGNbh+5hlLk/BAxyp5z11/wnI/zN+TkR3q0r91hN0AxGjSeHH5wWIBB0Db80eZ9fOHtE4/DsAvDOP/uk4j84Z0PPWlb+C+kJZdaptS6fVqD8eHH8KLL4KXV/mvKYS4qkjQIYQQQpRHTg7Mnw//+x/ExannOnaEN96A/v0LNe3SKIzIYB/iUrJtBgi2JGXksXBzDAs3xxAZ7GNzg/fW6KRCS6oAfPKyeXztd4zf8yMGTFygFlNDXmf9yMZ4hafZnN2wxdFAqZjNm9XtqafU4/r14bXXynYtIcRVT1LmCiGEEGWladC1K0yZogKOqCj4+mvYvr1YwAFg0OuYMaw1gMODfmtxlzNcFU2tW3Q2ouupvayYN5VH9izBgIlFurvofv1SNo2PwCs8w6HX0gGRwWp5l1NMJnjzTbjxRpWt6/ffnTtfCFElyUyHEEII4QyjUS0X0unU7fbb1T6O555TqWBLWTo0uE0k8+7uVGzDtyPsbfA2z0YE5GTy9O9fcc+h5QCcpS6Ta7zFtlEReIYlOxzomNvNGNbauU3kSUlw333w66/q8Z13wvXXO36+EKLKkpS5FUBS5gohRBVgMsFPP8ELL8Ds2XDTTer5rCz109e5YnrWqW2Pxaczd/1xp85fPL6bZYO30aQx/f5XefL7udTNUUu8PtaP59UbHoAuSZaMtHodmKz+rx8Z7MMt7SNZtie2UABkXsY1oHVEsfS7doOQf/6BO+6A06fB21ulw334YUmHK0QVJylzhRBCCFfQNPXN/QsvqGxUUDjocCLYsFVDw6DXseVEotNBh2VJ1dmzpD3wOG//8SMAx2nCf2rP5sCIQDxDkgqdYw44xvWIKrQx/anBrWzW9ug5a53NYKRY7Y558+DRRyE/H5o2VZvGO3Z06v0IIao2CTqEEEIIW0wm+OUXlf525071XGAgPPEETJ3q9OVs1dCwnlFwdoN5uK8HOW+8i/bcc4TkpZOPgbmej/PJoDvJaB2Hpy7L5nk6YMX+OJ4ZWrB0yqDXFUqLazf9rr2q6SEhKuC4/XaVKlhm9YUQRcjyqgogy6uEEOIqdOed8O236r6/P0yapDIx1Shb0T5bg3jryt+AzTZF6YC+qTHM/n0hISd3AfA33fmx/0c88Xk7YrISGLvg31L7ZL08y5rRpBWb4Sj6+hHBPmya0g1DgH/BgfXroXdvWU4lRDXj6DhXslcJIYQQoL6pzymoys2oUWpm45lnICZG1d8oQ8BRUg0N83PmjeHz7u5EZLD9FLWBORnM+O0T5s+bQsjJXSQTwv+FfUzSL5uYvaYddetCQnqO3fOt2au/YSv9rjWdyciolV+Q37QZxMcXHOjTRwIOIYRdEnQIIYSo3sx1Npo3h7lzC56/9VY4dUrV36hZs8yXL20Qb135e3CbSDY93ZfF47vxYI8owvwvZ8LSNG4+sJE/PpzE/fuXoUfjK8ZyQ9c/6LbyFm6+peB/547W1bDXLi7Vfl8jUy+y+Ntnmf7XV3jHx8KiRQ69lhBCyJ4OIYQQ1VNGBnz6qSrid/aseu7LL9WeDZ1OpcUNDS33yzha0dvczry/onuTGjw7tDV712/De9KTtD6yCYAjNOfRsDfYNzwU7/A4pv4Uh5dPR0L9vbmQlk1Nf28ignyIT7W9P8S8PMpW/Y1V+2N5+dcDNvs3+MhmXl/1PiHZ6aR7+RL7v7do9sREh96bEEJI0CGEEKJ6uXAB3n8fPvxQ1ZUAiIxU+zXckOLV0ZmHhLQcftl9riCrVUY6ec+9Qtu5c/Aw5ZGNN68ZnuaT3sPx6hSLlz7NElRMXryrUCrcED9PS00P68CjpPob9vad+OZm8/y6+dy1RxX52xPRjFfGPse3T9wjy6mEEA6ToEMIIUT1Mm0afPWVut+kCTz5JDzwAPg4Fhw4q0ujsFIzU+l18PJvh9QDTeP+k5uZ+vvnBKepmhsrGczTjZ4l6aZsvANji51vKnLhlMw8AIL9PLl0+T6oGQ5bKW9L2ncyZcu33LXnd0zo+KjbrbzTcyzv3dfVuaKBQohqT4IOIYQQVds//0Dt2tCokXo8dSocOQLTp8PIkWAwuPXlDXodM4a1ZuKincVmHszMQUObuOO8sGIBXS7uB1TNjVdqv8XKGyLwbZzo8P+0zbMcPh56vn6oKwnpOSUW9ytp38kH3UbT6dxh3u1xJ0dbX8d7I9sUr9MhhBClkI3kQgghqh6TCZYvh169oHt3ePXVgmMdO8K//8Jtt7k94DAb3CaSeXd3IqJIZirz+D8sM4X//foBy76YSpeL+0nHn/8a/sdNA3/h/nU98W2c6PRrakBcag56nY7hHerSvUkNu7MT1vtOIlITeOLPr1RRRCDD248xd73OlobteW5oKwk4hBBlUqagIzMzk5UrV/LKK68watQoGjZsiE6nQ6fTMXPmTIeuER8fz5NPPkmLFi3w9fUlLCyMXr16sWDBAhwpHXLixAkmTJhAo0aN8PHxITw8nEGDBvHjjz869Po7d+7k7rvvpl69enh7exMZGcnIkSNZt26dQ+cLIYS4AqWmwnvvQYsWcMstsGkTeHqCh4dlEF1ZrDNTvTumA88PbYXOaOS+7ctZN+8Rxh5YiR6NRYylY/P1fDWhC3kdY/D0VEUEy7qYyZGN7OGBPqBpjNy/jtWfTuLRLd9x964VxdpFBDtefV0IIayVaXnV1q1bGTJkSJlfdMeOHQwaNIjERPXNTUBAAGlpaWzatIlNmzaxZMkSli1bhre3t83zV6xYwe23305mZiYAQUFBJCYmsnr1alavXs0DDzzAwoUL0dnZ4LZgwQImTpxIfn4+AMHBwcTHx7N06VKWLl3KjBkzHA6ehBBCXCFefhnefBPS0tTj4GCYMAEefRTq1q3cvl1mqfytaWz58Gt+++QFWqZEA7CLDkwNeY3DQ4PwqXfB8j/ohIwcy/KssnBkI3uXACOf/TaLPgdUhqxdkS3YHNXBcrykjFdCCOGIMi+vCg0NpV+/fkyfPp3FixcTERHh0HkpKSncfPPNJCYm0rJlS7Zt20ZaWhoZGRnMnTsXT09PVq9ezdSpU22eHx0dzejRo8nMzKRHjx4cOXKElJQUUlJSeOGFFwD47LPPePPNN22ev2XLFh555BHy8/MZMWIEZ86c4dKlS1y8eJEJEyYA8OKLL/L999+X4VMRQghRYTRNLaMyMxpVwNGypcpMdfasKujnpoDDaNLYciKRX3afY8uJRIxFd3Pbs2cPmTcMovvke2iZEk0CNZjoMZeB/T8nerwRn3rJhZqHB/pYlmeF+Xs63D8daoak1EDhl18wtGtLnwObyNMbeKvXPdx29xtEh9W1XAdsZ7wSQghH6TRH1jIVYTQaMRRZBxsVFcWpU6dKnSV4/vnneeWVV/D19eXAgQM0Mm/su+y1117jmWeewWAwcPDgQZo3b17o+D333MOiRYuIiIjg0KFDhISEFDo+YcIEPvnkE4KCgoiJiSG0SI71Xr16sWnTJtq2bcuOHTvw9Cz8D/jgwYP5/fffadiwISdOnCj2PsvC0fLwQgghHJCerrJPvfeeqrExbJh6Pj4e9uyB/v1VjQ03WrU/lheXHyy0+TrSTmYoi3PnyHn6eTy//hw9Gjl48R5TeO+ae6FvHAa/vELNzbMLm57uaxns/7zrHFO/2+1QH3XAvLs7lbwHY+ZMePFFdb9NGza/MJtpxw3OvS8hRLXm6Di3TP8ql2cg/uWXXwIwZsyYYgEHwJQpUwgICMBoNPL1118XOpaRkWHZszFx4sRiAQfAf//7X0B9AEuXLi107OTJk2zapKaOp02bVizgsD7/1KlT/Pnnn869OSGEEO6zbx9MnqxmLv7zHzh8GD75pOB47dowcGCFBBwTF+0slu0pLiWbiYt2smp/kZS26ekYn5tBXuPmeH/9GXo0FjOG8T0PE/Dj03jcfAYPGwEHFJ9diAhyLK1vDX+v0gMOgKFDwctL1SjZvp0etw8stO9k8fhubHq6rwQcQohyq9DsVUeOHOH06dMA3HTTTTbbBAQE0KtXLwBWr15d6NimTZvIysoq8fyoqChatWpl8/w1a9ZY7g8ePNjm+T179iQwMNDm+UIIISqYpqlZjR49oF07+OADtVm8aVN4910o8uWUu5VUz8L83IvLD6qlVvn5aJ/MJ7t+Uwz/ewnP3Ew20YMxUf8QsmIxX/zZiImjatnMahUR7GMzaDDX/ChpkVOYvydb/tvPdqCQkQHWCVOuuw5OnlTL0C7vozTvOykt45UQQjijQut07N+/33K/TZs2dtu1adOGlStXcvDgQbvnX3PNNSWef+jQIQ4cOGDz/PDwcMLDw22eazAYLHtNip4vhBCigul0MHcubN2q0tuOGAGPPAJ9+7p9RsOWkupZgAo8Yi9lcXThYqJmvYrviQP4AMdoyqtBs+jy2kgWPazDw+r/voPbRDKgdQRbo5O4kJZdYj2Nkmp+mFu/OrItXh42Ppt162D8eLXXZccOMP9/+ArZZC+EqNoqNOg4f/685X7dEv6RMx9LTU0lPT2dgICAQueHhobi5+dX6vnWr2f9uKTXNh/ftm1bsfPLKzU11aF23t7edjN3CSFElZWTA0uXwmefwbffgnkJ7dNPw6FD8OCDEOnYMh+jSXNoEO+s0tLPdj29j2nrFtEqXn1plUgY/zPMwHfqI7zznBfBwbbPs2S1coB5U3nRPSX2qo2TkqIKIc6frx7Xrw/JhTerCyGEu1Vo0JFmTmMIJQYN1sfS0tIsQYf5/JLOtT5u/XquOL+86tev71A7SdkrhKg2NA127VKBxjffQFKSev6rr2DKFHV/1CinLlmWTd6OBin20s+2iTvO9PVfcePpHQBk4sv7TOHoyP/jv2+GcJEkNkS7LgByeHZk+XI1M2T+Em3iRHj9dZCkJkKIClahQUd1d+bMGYeyV8kshxCiyktNhU8/VcHG3r0Fz9etCw89BCNHlumy5k3eRfdcmDd529on4UyQYt5TEZeSjQY0STjDkxsXMeT4ZgDy8OATHubdBpOY9Ho4t7fN5p7v17klG1SpsyMPPQQLF6r7TZuq+zfcUK7XFEKIsqrQoMO8QRtUVXN7A3Bz0b+i55jvWx8v6Xzrc11xfnkFBQVJylwhhADIyoJp01RtDW9vtVfjgQdUutsyZkgsbZO3DrXJe0DrCMuMgLNBinlPxSsfrOLRvxZz64G1GDBhQsci7ubVmk+Q3C8b36ho5uyJhj3F+1JSAORSLVqofS/TpqnUuL5STVwIUXkqNOioU6eO5f65c+fsDsDPnTsHqEG6eWmV9fnJyclkZmbaXSZlPt/69awfm4/bY+98IYQQTtI02L1bZZk6cwa++049X7u2Ggw3aAB33glFaiqVhUObvFOy2RqdRPcmNcoUpHD+PAM/fp3+8z/Gw5gLwM+M4MWg/3KujwG/FufwLWXllN1rl9e5c2p5Wtu26vHUqSqFcPv2rrm+EEKUQ4UGHdYZq/bv329JbVuUOctU69at7Z5/4MABrrvuuhLPL5rhynz+hQsXuHjxIrVq1Sp2rtFo5PDhwzbPF0II4aCTJ9UejW++UZvAzV57DRo3Vvdff92lL1naJu+i7ZwKUnyy0V6fhenjTzDk5aAH1tKX531f4MiNoQS0PYu/3vFau0UDoHIxGlUF9mefhXr11B4Zb2/w8JCAQwhxxajQfIMtWrSgQYMGAKxatcpmm4yMDP766y8ABg4cWOhYz5498b08PWzv/FOnTnHo8v/gip4/YMAAy31752/evNmygbzo+UIIIUrx66/QvTs0aQLPP68CDm9vuO02lZnKwYQazjKaNBLSchxqa94M7kiQEpGaQPgzT2KMaoxu7vsY8nLYRA9GBqxh0dSfODsxk8D2Z9A5EXBYczRQsmvHDujaFR59FNLS1AbxhITyXVMIIdygwpOc33vvvQB8++23xMTEFDv+wQcfkJ6ejsFgYOzYsYWO+fv7c+uttwIwb948UlJSip0/a9YsQO3HGDFiRKFjjRs3pmfPngDMnj2bvLy8oqfz+uVv3ho2bMgNsuFOCHGVMpo0tpxI5Jfd59hyIlEVq3OHtDS1Kdzs0iX45x+1l2DAALVRPD4eliyB4cPB09PlXVi1P5aes9bx8m+HSmynQ23i7tIoDLCfiQqgTuoFXv79QzZ+PJ4m33+BIT+XP+nFLf5/sHbGX3x2pj8PT8pH72kqV99L6kOJUlJUoNGliwo8goNh3jzYvFnqbgghrkhlXl6VnJyM0Wi0PDaZ1D+8mZmZJFh9y+Lj41NoX8a0adNYsGABcXFxDB06lC+//JLOnTuTm5vLwoULef755wF4+OGHad68ebHXfemll/j555+JjY1l2LBhLFy4kGbNmpGRkcHs2bP56KOPAHjuuecItbFG+I033qBXr17s2bOHMWPG8N5771G3bl2SkpJ47rnnWLlypaWdoYybGYUQojKVJWWsU1JS1IzGkiWwahW89BI89ZQ6NmIEzJkDd9zhcE0NZxRNbZuckcukb4pvBC/KvGtixrDWlj0URTNRAdRNucDELd8zeu8feGn5AGzgRt70m8l103vz5eMF5UO6BBU/31E6VF0NcwDklLNnVbARG6se33UXzJ4NERHOX0sIISqITtO0Mn39FRUVxalTp0ptd9999/H5558Xem7Hjh0MGjSIxMREQM1KZGdnW2YeBg4cyLJly+ymjl2xYgW33367JctUcHAw6enpliDo/vvv59NPP0Wns705b8GCBUycOJH8fPU/lJCQEFJSUjB/FK6uk5GamkpwcDApKSmSvUoI4Vb2sjGZ/zUsc8ak5GRYtgx++AFWr4bc3IJjI0bAzz+XsceOsxVM6XXgyCSOvaDL/Hk1TjzL+H9/YtT+dZZgYy19ecP3ObpM603PEclk67OK1cOw93mXpNy/C01TG8RjYtReDqulw0IIUdEcHedWStABEB8fz6xZs/j11185c+YMPj4+tGnThvvuu48HH3wQvb7klV8nTpxg1qxZrFmzhvPnzxMYGEinTp2YMGGCZQlWSXbu3Mns2bPZuHEjFy9eJDQ0lO7duzNlyhT69u1b6vnOkKBDCFERjCaNnrPW2d0cbf52fdPTfZ3LmJSXB+HhaumUWcuWcPvt6tamDdj5ksdVyjK4N3t+aCvu79HI9nveupXY/75I7XUr0V+++hr687Lns+hHXsPAO1JZdjiGuFT7s0ar9scyc9kB4lId21Pi9KxTbi689x6MG1eQ5Ss+Xi2p8inj8iwhhHARtwcdwnESdAghKsKWE4ncOf+fUtstHt/NfsakkyfVjMauXfDFFwXPjx4NBw+qIOO226ACs/uVFkyV5t0xHRjewWqfg6bBmjUYX30dw8b1lqeXMpy3vaZxtFsdPDscx+BbfN8f2J6pMJo05q47zpw/jtrtx7geUfRvHeFcRfIVK+Dxx+HYMVVZfN48x84TQogK4ug4VyqSCyFEFeFsylgATCbYvh1++UUFG5dTjgPwzDOqwByoAMQFxeWK7slwZABeWmrb0lg2a+fnw48/YnxtFoY9uzCgKogv4m7mB0+n6d31ifH4Gx+fkjekm7+pe+bnffRtWRsvDz0GvY7H+jejRUSAa/bTHD+ugo3fflOPa9eGy4lQhBDiaiRBhxBCVBGOZkKytPvqK3j66YINyaCqgd9wA9xyC9Swmg1xQcBR1g3uZU0ra9msXdsHPvoI4xtvYYg+gQFIx5/5jGdxxBOM/b/6rHpQY9DcdehT8h2+flJGHt1eW8urI9tY+j+4TSQDWkc4HVhZpKfD//4Hb7+tllV5eKjg4/nnVTpcIYS4SknQIYQQVYStbExmUUnn6HNyB8daX1uQMSkkRAUcgYEweLBKaXvTTRBWhoxKpbC3JyMuJZuJi3bywV0dCfX3tjlQL0taWR1QOy2BRUk7ocEdkJyIAUigBu/xKL83mcSEZ2qw6W7w8oItJ8o2m5KUkcvERTsLLbUy6HVlL/g3c6bKRAUwaBC8847aPyOEEFc5CTqEEMINyrKMqLwMeh0zhrVm4qKd+OZl0+30Pnqf3M6NJ3cSdUnNZpxo5F3Qj/79Vcrb3r1VAT83MZo0Xlx+0OYmcPNzkxfvKpSFynoGpKRgysw6i1Xb2GNM2vsrA/ZtxGBUMxfRRDGHqWxvN44nnvdnxkg1qWNW3iJ9Ly4/yIDWEWX7HRuNBZ15+mnYuBFeeAFuvtntG/SFEKKiSNAhhBAu5vY6GSUYXNuDbX++QeC2LXjnF2yEzjN4kHptN5rc2KWgsa+v+jbdzf45mVjqLELRtLfmGRDzDII5mNJBocDDPCSfO7odTf5eS40F86i5e6vl+J/0Yg5Tyeh7C09MN/DuINvj+DIX6bvcn9iUbLZGJzk3wxEfr4KLc+dg+XLVsVq1YOtWCTaEEFWOBB1CCOFCpS0jKnNtBluio2HtWpXSduJE9VyNGtQ8dhDy88iuW5/Y7r3J6T+QZmNuoUZwxe8JWLU/lv/7cZ/T52mogMI8gzC4TSTz7u5ULJhr4p3P+1m7aTFyMvpTMYDaHP4tY5jr8Tgt7+rMzCegffuSX8+R2ZTSODxbkpmpCii+/rrawwGwcyd07qzuS8AhhKiCJOgQQggXKW0ZkfUgukzLcC5cgHXrVKCxdq0KOgDq1lXpVHU60OtVpqlGjfBp2ZJGlTiALU9tDSg+g2C9STt7+w5aL/2aWst+RJ+ZAaj9Gh/xCN8E/4eRk+rw8ySoU8ex17JemlZ0NsVRpc6WmExq8/6zz6rZDYDrrlObxs0BhxBCVFESdAghqqTK2FNRWmrXMi/DARgzBr77rvBzHh7QtSv066cyHZn3ZQwZ4ty1S1GWz7KkAMxZm49fVK+Zm4Phhx/o9uGH6LZssRw/QGve4XH+aTyWiU/6se0+8Pd3/nXszabUDvQiI9dEeo7tzFaWLFmNStiAf+qUqty+e7d63LChmukYPVoFikIIUcVJ0CGEqHIqa09FmepkmGmaKsz311/w55+wZQvs2AF+fup4pOq31q4dsdf24FSHbuhvuIFr2za0BADuCLTK+lmWt7aGtaU/byb81ZncsXcN3peS0KGWUP3EKD7kP9DrBp54UsdHNxfeHF4W9lLerjkYx8RFOwHbe0pmDGtd8mcdGamWUgUHq5mOKVOkmrgQolqRiuQVQCqSC1Fx7C3psVVF2tUcrQj+9biu9GhWU1WZXrkS/v5bBRvnzxduuG4d9Omj7p8/z9rjSTy3Od5mAAC4PNAqz2f5y+5zPPbt7lJfQ6dT8VZRepOR3id3cPeuFfQ+uQP95V6coR4fM4HFvuMYcG8kkyZB27aOv6fycCoAi4uDuXNhxgzw9FTP7doF9etDzZoV02EhhKgAjo5zJeioABJ0CFExjCaNnrPW2f2G3bwMZtPTfd2y1Mr8+rY2I4enJdLp/GF21mmJvm5dZt7SmsHrlsBjjxU08vSELl2gVy9VoK9XLwgIAEoOAOz9I16eQKu8n6WjAdjj/Zrx7tpjgHof9S/FMXrvGm7b9weR6YmWdr8zkHlMZFVobyKuv8BbzwRx2/URTr0nVyh1Nik5Gd58E959V20Yf/99mDy5wvsphBAVxdFxriyvEkJUGW7dU+EA82bkJz/dzDXxJ2gbd5yO54/Q8dxh6qZdBGD6TY/xQ2ANJi7ayaJrW9FjyBDo1k0FGF272qz87UidC1vKs3m9vJ9ladmgzEHLlH7NaB3qweY3FzDon9+4/vReS5tEwvic+/mICZxrGkRgp1PUjtoMOpi+DAKC3DdrZY/dwn8ZGSrQeOMNSElRz3XrBp06VWj/hBDiSiVBhxCiyijXnoqyysiAnBxLFe/BiUcZ9M5odEUmkY06PUdqNSTX4GEJBqYd07Np+a+lBgPl2R9R1kDL0c8oLiXL5vMlZYMyv9s3Gxu5eN94ev/yAwPT1EDdhI4/6M9CxrHM5ya82l8gsONpwoOLv87MZQcI9PEkIT3HqQ3uLt33omnwwQfwyiuq7gZAmzbwv//BsGGS/lYIIS6ToEMIUWU4WuCtzIXgkpJgzx51270btm+HQ4dUFelXX1VtWrdGp2nkRNRhQ2AD9kQ2Z1edluyNaEqGt5/lUs4EA64Ikpy9hqOf0cu/HcLXy2BzxsFWNqiwzBTGxmxh7MG1RMw6ZGl7igZ8xgN8xgNciPIjoP1pwpttQmewPZejAXGpOYxd8K/ludL2sLglwYBOB7/+qgKOxo3hpZdUprHy7mgXQogqRoIOIUSV4UiBtzB/Tzo3DC35QkYjpKZC6OV2Fy+qZTJnz9puf/Jkwf1atSA+nlXn8xzaSO1IMFCeatllvYajxfKSM3JLLHo4uE0kAxoFc2LhYoJ+/Jbwvzegz1epZ3Pw4mdGspBxbPS/Ht925wlod4LaIbZnT0pTUgFGlxVtNJng55+hZ0+oXVs99/rrKh3uuHEFm8aFEEIUIsnBhRBVhnlJDxQs4SkqKSOPG99cz6r9sSq4OHECli+HWbPg/vvVRu7AQBg/vuCkmjVVEALQqJEaYM6Yoc47fx6+/bbwi4SHu3TWxRwAlGWhjg71bX6JNSRssP4sS2IexL+4/CBGk9WQ3mSC9eth3Dj0dSJp/thDRPz5B/r8fLZxLY/yLnU4x4NN3mXPqBAi/7OR0BuO4lnGgKOkvpS2J0YDnvl5H7n5JvsXN5nghx+gQwe47baCmS1Qzz3yiAQcQghRApnpEEJUKbaW9HgY82mYHEtgbia767RQ325/tYMjH9+LV0qy7QsdPVpwX6dTKW0bNlR1Fhzg6EZqR4KB0vZHaDbumx+DAzUk7DB/ls/8vI+kjDy77QotFcuOU1W3v/7aMjOkA2JoyCLu5mvGcjy0Af7XnCWg7R4Cg2zP9IT6eZKcaf81HerL5WVrjuyJScrIo9tra3l1ZJvCMx7mYOPll2H/fvVcYKCa0RJCCOEwCTqEEFXLX38x+NAhBiQeY+OKLdS/cJqo5PN4mowcqhXFTQ/OVRu5dTpi/MJolp2JrmVLaN1a3Vq1UoUfmjQpfN127ZzqhiMbqZ0JBuxVy44ooU5HhAsKIg5uE0lWnomp3+2226ZR0jmGHv6Lpt89ATEFwdolgvme0SzibvYH92T0GD33d7vIh4c2lLq/+oWbW3M6KYs5fxwtuaEd1svWHN3PklR0qdhPP8Hzz8PBg6pBUJBKcfz445bEAUIIIRwjQYcQ4uqRnw9nzsDx4wW3/HyVqtTsP/+B/fsxAH2tTk338iXV219lG9Lp0IAxt73EB5P70715uFu6W1qg4GwwYK9atjlwKelYeUQEFV8C1iA5lpsP/8XQw5u45kLBnpZcPFnBEBZxNyv0Q+k3xJcp96pETj4+sOWEnnmHHXjNYF9GdqpHi4iAYp+fI6yXrTm7n8WSYnjTJhVwhISoQOOxx9R9IYQQTpOgQwhx5cjOVkFFQgJ0717w/MMPw9q16lhekSU3/v7wzjsFqUl794YGDTgRHMEXFz2JCa3DsZoNiA2sWSx9aZJfMBfKsITHGaUFCs6yWyeilGPlYV4qZjh9iqGXA412ccctx/Pw4A/68z2jWcoIMmvr8b/mLDVbb2LShMLLlZxddlb086vp782TS/YQn+r4sjVHNsV7GPMZfnAjR2o1ZD9N1fKsp55SMxpTpji8rE4IIYRtEnQIISpGfj54WP2T8/33sG0bnDpVcDPXOQgIUBu3zUFCbGxBhihvb7X0qWnTglt+fsEm3vffB+DCiUS+dKAidnkzQzlS98FdwYCjylybQtPg8GEMS5ey8pvvCdm/23IoHwPr6Mv3jOZnRpIa4oN/q/P4tdpLcK10wHZhwrIsOyv6+c28xflla2Oua2BzqZZvbjZj9v7OQ1uXUjftIhsbdeK+0S+pJVlN6sJzz5X+OQkhhCiVBB1CCNf58084cEBldLK+xcZCZiakpRUEEosWqexPRfn5Qf36qr2/v3ruhRfgqafURu569UBfeuI9V27ktsctdR9czOk+mkywdatKC7t0qWVDfQhgRM8GevM9o/mJUSQHBuDfMha/VoeoE5FSbJ+GvVok5V125sz5tt4/QGhmCvft/JX7dvxKaHYaABf9Q9jSoB06zeSSNMVCCCEK6DRNszfbLFwkNTWV4OBgUlJSCAoKquzuCGGfpqklTr6+Bc/9/rvK2nPxorpduFBwPzVVPTaPNocPh2XL7F//0qWCZSqffKIK6zVsqG4NGqifNWq4rIqzuTYD2P5G3OHaDCVcu+g/oK64NrimcrbDfczJUeltly6FX36BuDhL2xy8WEs/ljKCXxiOViucXgNz6Nw3DY/IJD7ceJzSvDumA8M71HX5eyztfHvvf8K/P/DY5sX45eUAEBMSycddb+WnNn3J9fAiItiHTU/3dcl+GCGEqOocHefKTIcQVYmmqQFkWlrBLTUVMjJg8OCCdnPnwr//QnKyqrKdnFxwM5kgN7dg4P/RR2owak9amsrqA9Cjh5qFqFPH9s36H6OHH3b52y/K1Ru5zUqr+2BrWZH1uaUNtF0xg1JaH2ulJ7Nt5hwGEY1u9e/q93hZKoH8xlB+ZiSrGIxPrSBGjICvboO+fcHDwwfwYcsJvUNBh71Zg/IuOyvp/GLv/3ICAYAU7wD88nLYV7sJ87rdzqrm3THpDeVOMSyEEMI+CTqqA01Tg0gPDzUgdNG3yKKMjEbIyiq4ZWcX3DeZ4PrrC9r++itERxcOIsw3UCk9zUaMgN9+U/sbivLwKBxIrFunls/Yk5Gh9lWA2pjt56fqEtSqBeHhBfdr1SpYAgVqCZQblOcbcVdv5IbS6z7YW1bkSDDhqsrZRfuo00y0jz1GnxPb6X1yO+3jjhVqf55IfmE4SxnBBnpjDDRRq+1Fnh+fxxP3gMFQ/DUqYglbWW2NTiL2UhY3Ru9k3LalrGpxPd90uAmAn9r043RIBH83bF/o30NXpBgWQghhmwQd1cGpU6qKspnBoG4eHurnww/DW2+pY4mJ0LlzwbGiP4cNU+vrQQ2Whw+337ZbN5g8ueB1H3us+Gubg6AWLeCuuwrazpqlBuc6XfFbw4YwenRB248/Vn0p2k7T1ADZuu28eeqbf5Op+C08HCZNKmj75ptqY7PJpDIm5eWpgXtenhpsv/12Qdvx4+HIkYJ21rdateDvvwvaXn+9WjNvS2iomnkwmzNHBQi2eHgU+vYWKBxw+PmpImaBgeq6OTkqZynA3XerfoSGqltYWMH90NDCgcRjj9l+/XJwJohwxbf+rt7I7WjdB+t2jgQTA1pHlHkGxdZrB2Wnc0P0Tvqc2MaN0TupmZlSqM12OrOCIfzGULZxHYawTPyaxxHWfBteESnodfDhYWh7yHag4+paJC6TmYnvZwtYs/ADmiWeASAiLZFv2g8GnY5cD0/+juoAwOQ+TWhWO9ClKYaFEEIUJ0FHdWA0Fn9sNKoBNKjBqFlengpS7GnbtuB+bi6sXm2/bX5+QdBhMsF779lve/PNhYOOGTMK98tanz6FA4lnnik8ULfWtWvhtq++aqmSXEzbtoWDjoULVSBhS6NGhYOOnTvVzZZLlwo9TDIZMH/vm2PwIMfDm1wvb/yC/PELr1n43BtvVHsczMFDYKCagTDftw46PvxQLZsyt7H11bTZqFH2j7mIvcDCmSDCVd/6u5qjm4zN7Upb6gTwzM/78PfyKNMMikVuLmzZAn/8Qd/lK7l57y4MmslyOIUgVjOQFQxhJTcRT21at88ntdZpIhr8hWfN9EIxrCOBjruWsJXJuXPwwQfw8cd0uPxvQpqXL9+3G8jnnYfZnOXt0bRWpWYWE0KI6kKCjuqgUSO4dAljbh47oxNIupRBTV9POtQJUAOSwEBL099jc/h+4lyS07IwmIwYNBPhvgbu7VKfa+sFQV2rzaA+PvDllyqAyc8v/rN584K2mgbPPmu7raYVDmYAHnxQDaA0rfitVavCbUeNUsuNirYzGFQ6VWujR6t9C3p9wSyL+X6dOoXbjhun6kXodCodq6cneHmpn0WrEb/2mppBMbezvvkUDFBX7Y9lWs+p5N/wJDkeXmg6lYWp0MZe6+uaZ5UcUbT/lcheYHFL+0g++TPaoSCiPPsmXK1oANW5YahTy4pKW44FkJSRxyNf73CoP5YZFE1T2cL++APWrIGNG9XSOMD8t/ogrfiNofzGUDbTA6OnDp9GCfg1iadTh2O892Arxi44jL0QtdRAB/csYSuTSZPURnhAa9SId1sPZmGzPqR5+xVrWplLv4QQojqS7FUV4ErIXlWeteSuysZT3RlNGj1nrbM7+DQPgq72rDn2/hyVpuj733IikTsdqLOxeHw3t35TXVoABaVnxvpl9zke+3a3S/pTLyWezxtl0nT/VhVsxMYWOp6gr8VqU3/WMIA/6M9Z6mMIysSv6QV8m8Tj0yAJvYfJ0secfJNDfbOXgarSZGfDDz9Az54QFaWeW7cOZs5U1cOHD2fVoQtuy14mhBBCkexVwqKi15IL28q6+fhqUtLsRGmKvv+y7JtwNXt/d2JTsvn4z2hubhfJ9phk4lJLXlZU5poPmkb9lHi6nd5LtzP76XZ6H3VTLxZqkqP3YaPpBlZfDjL2mtrh6aWnZ0+YMggCm17ki8N77fZxy4lEh7pyxdStiI5W+7gWLlQzkdOnwxtvqGN9+6rbZVfU0i8hhKjmJOio4hxdohLo41nlB8SV7UoYRLubI8uISmN+/zUDvB1q767BsCMB1K97Y6kd6M3U/s2Iqulvd1lRaVmeLDSNqOTzdL0cYHQ9s586aQmFmuRjYKfhOtYae/MH/dls6kEOPjRvDoMGwf8GqYRjBbkAavGwqa/dpU9XcgYqC6MRVq1S+5ZWrlTLykAViqxXr8RTr5ilX0IIUc1J0FHFOfrtuqPfdl7NA+LK5uzm46uRK/58hAf6sGp/LDOXHSixnbsHw44GUPFpObzzxzHm3d3JbkBuneXJmnd+Lm3ijtP53CGuPXeITucOFcswlYsn2/Sd2WDqy0Zu5G+uJ8MYQFAQ9O8P7w5UwYZ5hZG913ekb67MQOWK4oaACjCuvRZ27y54buBA+M9/YOhQlcWtFK7OXiaEEMJ5EnRUcY4PAh1bEOOOAbHLBidXOGe/UXbn5+Kua5fnz4f5/Sdn5DLpm5L3hFREOlZnA6jSlh8ObhPJwsH1WPHxjzQ/sZ9rzx2kTdwJvEyF66rk6LzYqruODaY+bKA3W+hOlskPPz+NXr10PN9HrSDq2NGh8bZDXL0MqVxpjo1GtSG+T5+C9Nc33qiy6j34IEyYAM2aOdUfIYQQlU+CjirO0UFg98Y1+XHnuQpfYuGKGgyu5M6BvjPfKLvzc3H1ta0/s5r+3kQE+RCfWvIyInvv//mhrXj5t9L3hFTEmnxnAiibyw/T0mDHDti2Td22bqXvqVP0LXJuvKEmf2s92GzqyWZ6sFPrRK7mjbe3xvXX6/jv5SDjuut0eHm57O0V46plSGVOcxwTA599Bp9/DqdPw4YNKtgAlcXttdfA17cM70wIIcSVQIKOKq5zw1DC/L1Iysi1edwcTHRrUqPCi3xdaTUYKiIAcuQbZXd+Lq6+tq3PLMTP07JfyF7wYK7daBbq78nIDnWJTcl2aEnTW7e1p0ezmqW2Kw+H92EAXvl5tLpwEv/5uyD2mAoyDh8u/CYBEzpO+rdlXVZ3/jL1YDM9iDY2AnToPPPxrnMJ37qneGlcDR67q4Z1tmWbXB0kl3cZktNpjrOzYelStSl87dqCzys0FM6cKTi5aIpqIYQQVx1JmVsBKitlrq0BoTVbaSMraubhSksfW9Hpgu0NFkv7XABCfD35YGwnujWu4dRn4+rPvKTPTEMFH5cy80q8Rr+Wtdh1JsVuUGxPRaVvtfUeA3MyaHUhmmviT9A6PpprLpygacKZYsukABL86rNd14X1GdexjevYQWdSCQYgOMyIPiIRaifgXS8Jr9qp1Anzdvjv2pU2Swg4l+ZYl6L2aiQnFxzo10/Vxxk5klIjLiGEEFcESZlbzTlSK8HWEpWKyvRyJaWPrYwidPa+UXZk8/KlrDzGLvjX6QGmKz9zRz4zb4OuxFk2gLWHL9o9VpLy7B1xeHZA0xgcnM8P9RPZvnQ9DU4fpfWFkzS8FGfzukkeoRzx78q69C5sMaog40JmbcvxFi1gZDfo1UuVlmje3IBJq8XWaAMX0oKd+rtWEbOEZZlFKWkfTGTqRZolnObPxp1Vu/aN1AyGvz888IC6NWpUrj4LIYS4cknQUQU5kuoz1M+DN25tR1JmLltOJBYaUFREppcrKX3slRQAOfN+nR1guvIzd+Qzi09zbvbCEeXdW2RzdiDIm1e71aCPMUFV9z5wAPbvh4MHITWVzkDnItc57VWHPYZ27MrpzE7TteymA6fyG0KK+jsUGGyiTcd8JvbW6N5NR5cuasVQUQad83/XKiJILussStFgMDAng8FHNjPywAa6nd5Hqo8/XSZ9pdrpdKq4Yf36YLBXD10IIURVIUFHFeTIt+XJmfnc8+lWy2N7AwpHvu0syzei7kof6+pvZ8vSrjyc3bzszADTlXUvKiN1cnn3Fq3ad57nP1lH04TTDEw4TYuLp2iWcJrmCacIzsmweY5R78HZwGbs8+jI32kd+Se3E3toT1JuQaCg8zDSvJWRYW0yOWSMJjPkIh4hmZzXwe9ePnSr25rQUNctd3J3kFyeWZQujcKoF+BBy92bGXFgA/2P/4tPfkHwebhWFNfoMwuCxpLy/AohhKhSJOiogsoyIIxNyeaRRTuZ2r8Zk/s2cziDUlm/EXV0g7sz32i76tvZ8rYrD2c2L4PjA0xX172ojFoiDmesSkmBY8fg6FH189gxtKNHuX7fIbZlp9s8xYieaL8oEkM7sCPnGv5KbM0+rQ1HTc3JSylIGeXto9GxI9RrmkVkk2yuu1bH7f2D2XDsgmWg7ml1XVctd7IOpo/F234PRZXl34HyzqIY9Do+P7+apj++bXnuWI36/HxNH5a17s254HDm3d2pSqbEFkIIUTIJOqqg8gwI5/xxjMVbzzC8QySf/Bld4redQJm+ETUHByUFHFDyN9pFZzTs1XZw9NvZK6Uic0lpdUtS0gDTkf09zs4iOPKZ1Q7yBnSlps8tyfNDW1Ez0Lv4rFVaGpw8aQkqrAMMLlyw2Z8gVPaoaN/6HPJswQFTW/ZmtWe/sT1HaEFOpg9kFpyj98nFs1YageGpeEWk4F07Bc8aGcy4t+PlP0sqfau7lzuVlhDCHkf+HSj698ikaY7PokSFwObN8P33MGyYqlAINJ3yEDnff8XPLXvxVdMbOFC7Ceh0RAb7MK8SN7kLIYSoXBJ0VEHOflteVFxqNh//GW3zmHkQpb411zk90CrrBndrtgZhOp3tAbqj385WdLrgkthLq1sSewNMR/b3gPN1Lxz5zGbecg2A0wEUgH9uFh2Ml7g/2YRhzymIjlZ1HMy3pKQSz88Oqc3F0ObEeDZjf05zNl9swp7MFhynKdlZvpBl/WaMeNZIx79WAoGRGZhCU/CslYohIAddkV+5rT9L7lzu5Mjfl6JKC5LNgcaag3Es3X2+UPAf4utp8xzLtTUTnc8dovZzP8LGVRAbqw4kJlqCDlq1wjs+ltt1ehpWg6KfQgghHCNBRxVU1m/LHaUBcak5pbaJTcnmnxOJ6PU6S+G4mctKHgCH+XuycXofvDz0No/bG4SVlPjZkUGfsxWZ3V1F3ZxF7J8TiUz6ZieXsmynni1tgOnI/h5wvu6F0aQR7OvFAz2iig1ci35mRT9XT2MebXQZjAzX2PrXfmqnJ1I7LZF6KfHUS71AvZQLhGWlqou9Z78PeUFhXKrVjLM+zThsas72lGZsutCMQ/nNSLsUBJeKn2MIysS3VjyetdLwqpWKZ600PMMy0Bkc+1ti68+Su/YEORowWistSC5t1sTenzO9ychz6xZy05HNRKYnWp7XQkK42H8Ix3rfgt46IYXBgAHcnnhBCCHE1UOCjiqqLN+Wu0NJA2ZbkjLy2HEq2eZgpSyDMGulDfocTRdcUfURDHodPZrV5PVb2zJx0U7A+VkYRwe6CRkqiHQkmLL1/sP8PRnVNoLBkZ509M7FcG4vbF0J584x+Nw5Bp05S2b0aTzizuOdXDBovbeEPhlDwsioGUVCQBRnPaI4lhfF3tQotl6I4kBGQ9JSgyC1+Hm+vtChhUpR27KlujVrrjFx+QYuZmW6JAi3/lzdtSfI0YDRWkkzVs7MmvjkZdP6QjQ767YCwKQ30OXsASLTE0n39sNv9G3s6tafxxNrcSbDCNHA/H8qvU6IEEKIK1e1DTrS0tKYPXs2P/74I9HR0RgMBpo3b86YMWOYMmUKXl5epV/kCld0EF3T35snl+wp1xp7ZzkTcJjZGyiXZRBmzZFBX2npgiujirqzszDWnBkQFwsmNI1GfjCje216h2hw4QL7dh5h79q9jM+4RI3MFGpmJlPz8v2wzFT0dv5k6QB/q8eatzf54XXIDKtHsm9djuWHczSvPidyG3MotSn/xEZxyc5shVmdOoUDi5Yt1eP69UFfbKJMx0teLV02+2f9ubpyT1BZNoxP7tOEZrUDS5xxcyRgD8lKpd/xbQw8toUboneh10x0mvI1Gd5+ALzb4y4Abnv2QTQv78t/D4yFruHOvwdCCCGubtUy6Dh16hS9e/cmJiYGAD8/P3Jycti+fTvbt2/n66+/Zu3atYTaSqx/lSk6iJ55S2vLt+ZXKnsD5bKmaXXVRnBnNwy7cgmWrQASHSSk5xSrs2LdX5NJo6aXjvyUFAJyMgnKySQ4O42QrDRCL/+sq2VR78gi0g7E8E5WGiHZaYRmpRGcnYa3sXCV7baXb/aYdDp0NWtirBlBRmhdkn3rEu9Rl9NaPY5n1eVQSl12XajLwfgamM7o4Iz9a/n4QOPGBbcmTQp+RkWpGQ1nP0NbwZszQYitP0uu2hNU1g3jPZrWKnUZk72AvV5KPIOO/E3/E1u57swBPDST5dj54No0SInjUHhjAPZfeyMzhrWmf+sIes5aV6HFNIUQQlz9ql3QYTQaGTZsGDExMURGRvLll1/Sv39/TCYTS5YsYfz48ezatYuxY8eyYsWKyu6uy5kHXjOXHSh1X0ZFKy04KE9WLmcyYdkLDpzZMJySlVu2JVgmE2RnQ1YWZGYWuhmysuiekcGeg2dY/c9RTCmpBORkcik3k0umHDqF6KlNLqSmkpGQTE5iMp1zMtme71iRvvp2ns8xeHApIBS/enXYmubNRc8aXDDU4oKuFvFaBPHGCOLy6hCbXZcLWZFolwLJu1j6YNPLC+rVgwYN1OxEo0aFA4yICFszFuVjHbytORjHp5tjnJ71sPVnqTyzUeCeDePWzAG7d34uOs1Etqf6u9T/2L88v36hpd3B8EasbtaN1c268+x/x/CCQV/s78WWE4lXTDFNIYQQV49qF3R8/vnn7Nu3D4Aff/yR7t27A6DX67njjjswmUzcddddrFy5krVr19KvX7/K7G75xcfD22+rir96PRgMDDYYGKjTseXiJf48mYQJPSa9HqNOj0mnw6Q3YNLp6dmiFv+eSiEtX8Ok01++6VQ7vXpstH7+8nl+Pp6k5Jks5xS0V+cadQY0nQ7j5dfUdOafOl4d1A5DfJzNt9LZy0RzUxrJmeYlW6UPbEP9PPm/IS3pG2qC06fVoN5otNw2HY7j4/XHSErNwqCZMJhMhPt78GC3BnRtGFKore74RQYcO46nMR9PYx5exnw8Tfl4GvPxys/D05SPlzEP44mlnD2VwARjvmpjzMfTlId3fh4en+aTUNObmnqjJZjQsrLIT89Al5WJR3bp33K3v3wriT+FlzMBZHl4k+7tyyWfQJJ9A8kOCKZ+owZk+9Xk5+N5JOvCSNBqkGSsSaKxJgn54STk1CI1KxRjpg/aQW80zbFvrnU6FTSYA4r69Qvum3+Gh7s+qHCEQa+jS6Mwnvh+t1PnlRY0OronqCh3bBgv5NQp2i77ngU/LKHHqb3M7P8w37VXmabWNbmOfse3sr7Jdaxp1pUzIRGWYKZb05o2r30lFdMUQghx9dBpWkl5f6qeG264gb/++os+ffqwbt26Ysc1TaNJkyZER0dz77338sUXX5T7NVNTUwkODiYlJYWgoKByX88p+/dD25IWxIgrlpcX+Pmpm68vmp8fBy7lkWTwJd3Ll3RvP9K8/Ejz8CdNH0iaLhCTTygpBHE+K4AUUzCpxmBS8kNJyQshN9cPU7YnZHvja/Qj9ZIOk8nJ5S86DYN/jroFZF/+Wfj+h+NbM7RrKGXZFlWWJWllOWfLiUTunP+Pw/2yLprpas72BUoJgHJyYO1aWLNG3Q4ULgr5fdv+PDXkcZvXNb+7kvZkONrfxeO7yUyHEEJUA46Oc6vVTEdmZiabN28G4KabbrLZRqfTMXjwYObNm8fq1asrsnvuUaMGPPFE4W/4i9w35RtJTM0kJycfHz3U8DGg01QbLd/IPycukp+bj14zotc09CYTBs2E3nLTMFx+zlMHjcN8OHMxDZPxcjuTamco1N5YcF8z4aVT1zVHwNrl/5jQbC64d2Top9OpVLo6TcN4eWZG0+vx8PTA4OlBaq6JPKvZGKO+YNbFpNejGQxoej25mq7Q8Vy9B3kGD3INnuQZPMgzeJJr8CBP74HOx5t0k149vnw81/JTtcn28ObBvm1JzPdi7qZ4MjR/MjU/Mk3+ZJr8yDQFkJnvz5C2jajjH0RaGqSnw6m4HLakp2DK9sSU6ql+5niA0eD0HwvrhXUBgSayDVno/XIx+OWi97X+mYPBLxdDQA4fjW/Nq+t3E59W8obpW3qEYCjDDEZZsoKVNZOYo9/Ch/h58vqotm7dFO1oX+xuGM/PVwUR69RRj7OyVLE+0+X9GXo9XH89Rzr34vHUOhyuFWX3NRxZDnYlFdMUQghx9ahWQcehQ4cwXf4fcZs2bey2Mx+Li4sjKSmJsLCr+H+ekZEwe3aJTfRALTvHdECKk+vNF4/vRkpWrkPnmAcom57uy6qDcWXaSGsOLswig324pb2qqG7SUEGLSY9m1F3+qWfc9Y2Zv/EUmHRo5mNGfcH9yz81k/7y8zo0o161zzeg5esL/8wzoBn1NDAEEp2QbbuNVdt3fys9bPpifdFnvIFwOx+CCb1PPnqfPPTeeYV/Fn3eN5dnRjXi9p61qVkTDB46es76p9RB5NBuYXgFuaeIYlmygtk7JzYlm0cW7SxxdsLR/UEf3NnJqfolZeFoXywbxjUNjhyBdevgjz/Uz9at4e+/VcOQELjtNggOhgEDoF8/CAujBfCYnXTHIzvUpX/rCIdmia60YppCCCGuDtVqedXy5cu55ZZbANizZw/t2rWz2e6XX35hxIgRAOzbt6/EAMUR5mmnM2fOOLS8ytvbG29v73K9ptnx42r8UZqilZeLSsvJ43xyFkYH/rjUC/MlxNeL1Kw8YlOyyDNePkdn+9yGYSol56mkTDSTDjTzDbWHQNOpwb6G3WPeHgY0k3pOM0Fevqau5dCcSGXR0Hnlo/cyqp+el3965aPzNKL3ymdIp1o0r+dLQABczErn823HbQYXOi9jqb9Da0WXvpgH8KpXBWwtt3F1nRKjSaPnrHV2g03rwNScFay0oolmEUE+zLzFdnHHnrPWlRpomV/TnRzty+a659H/9its3Kj2almrWRPOngUH/t1wVVa1iqpXI4QQ4somy6tsSEtLs9z38/Oz2876mPU55VW/vr38QIXNmDGDmTNnuuQ1s7Nhzx5XXMnz8q10Jy3jIcfOOWbZN1722ihZxRJxlTKIMhjR6TV0BhNc/qnTa2AwodObVIXqyz9VG/NxDZ2HEZ3BRJemQQQH6gkJNODlrZGvyyMsyIOf9p4mLS8bPIzoPEyqvdXPkCAdd/WM5JO/j5caKNw1pgPDO9QFwGjyZ/OsRLuDU0fYW/pSWvalAa0j2HIi0TJQ3Ti9DztOJbskHXB5s4KVJC7V9kzJlfRtfdG+oJlonnCaTucO8237QaBTx/UvfgQ//KBO8vaG7t2hf381m9G5s0oW4eDruWKvRVk3zgshhKieqlXQUdmcmelwlagoKG1rSmmTF+bjRpPGk9/vJjkj18agVw00wvw9eev2DsUGHpqmzj8an8alzFxC/LxoXjsQg17HodhUZq08fPkyGug10IFOp6nH1vf1mhqoFzk2Z0x7OkeFYjDAH4fimPnr/kLnWQcR6NQ1ylsk7rExHfD20BceBGdCSDtPjJl5dq+fAczfUnrAAYWX3pQ0UHZEaYNpe7VA1h6K55mf95OUUZB61/yNtjkgKg9H9zSsORjHZ2VIcQu260aUN82ty2RmMjjpGL9nb+bC7+u5JuYAodnqy47j11zLuAcGqr7ccw+0awc33ghduqhCJpXMVQGMEEKIqq9aBR2BgYGW+5mZmXbbWR+zPqe8goKCKjx7VUCA+iLUNXTo60eUuAxnzt2dGNzG3mhaBxR//7m70/A9erGMPVKDxFv7FGxevsboiUdg6TVIyruuMCYhk3f+OFrsOimXU/oG+3lyKdP5iuxmYf6edG5YuEBleeqsODKYNg8iV+2PZdoPe+zOKLiy8rSjexqW7j5fpt9ZSXUjKuXbek0rWM/43nvw5JOQn09zoPnlJvk+vqR37sq3d7XFYP58b7lF3YQQQoirUCVkya88dczZXYBz587ZbWd9zPocUTDojQguPFCMCPYp8wC0rEX/7H1zb86u4+iwseiMQ0SQNyF+nnbP111us3jr6RKrMvt46PnqwS6E+Dq2LK2opIw8bnxzPav2xxZ6fnCbSGaP7uD09d66rb1Dvx/z/o7SljyBmkEwmsoXvpX2+9KhAjDrmZaysDejYg60hneoS/cmNVwbcKSlqT0Yb74Jo0dDw4aFpx6jolT2qTp14PbbYc4c+OcfPFJTCNm0HkPnTq7rixBCCFGJqtVMR6tWrdDr9ZhMJvbv3283be7+/fsBiIiIuLozV7mJq78dLi0Fpz32vrm3XobkCE2DQB8PRneuZ8ngs+ZgXInr/e/s0oA5fxyzf00gLjVHLScrZbNzSezNKCSkO19NPiGj9HOcKVTnqsrTjuyvGNmhLgs3x5T5NaB8Fe2dcvgwvP46bNsGhw4VX7/4998wSBXno18/iIlRFROdyQQghBBCXGWq1UyHn58fPXr0AGDVqlU222iaxu+//w7AwIEDK6xvVxtXfjtsHnRC6bmmwvw9GdcjisXju7Hp6b4lVoeed3cnh2cZ0rLz+XRzDClZuRj0ulJndKJqFq33bdupJPvL+KwFeNveBGxvRqEsA2hHziltU7ctrqg8Xdrn3b91RJmvrUPtQXFp3YiMDNiyBebNg4cfhiVLCo7l58MXX8DBgyrgqF8fRo2C115TRfumTy9o6++vZj8k4BBCCFHFVauZDoD77ruPv/76i/Xr1/Pvv//StWvXQseXLFnCyZMnAbj33nsro4vVkt1NvUHe3NmlAVE1/Z2eURncJpJAH0/GLvjX4X5YbzguaUZny4lEh65nTgdcmvQco91jtmYUnJkdcqZYW1kCCFfNIJT0eRtNWplmw1yWiSo1VQUYu3fDrl1w9GjhGYzsbLU8CqBVK5g5U2WUuu46qF277K8rhBBCVBHVMuh499132bdvH7feeitffPEF/fr1w2Qy8eOPPzJ+/HhAVSzv169fJfe2enHHpt5ujWs4PFi1Nbi3l53H0arM93SPYsGm6BLbBft6OrQEyzogcDSTlbODbmcCCHdUnrb3eTuyBOvhGxqxbE9s2TNRXboEBw4U3Bo3hsceU8c8POCZZwqqfIMqvNmhA3TsqDJKWTprgBkzHHzHQgghRPVQrYoDmsXExNCnTx9iYmIAtezKZDKRna0GKx07dmTt2rWEhoaWcBXHOVo0RbiHvcrV9rxrVRvDkeuC7UG/uSK2eX9I0XbmwfLj/ZuVuD/ErGhBP3MfSqpb4WyxttIK1RXte9G9Jq4qPGdPaQXpnHp9kwmmTYP9+1WQcf584eM9esCmTQWPp05VsxYdO6pgQ2YwhBBCCIfHudUy6ABV9O+tt97ip59+Ijo6Gr1eT/PmzbnzzjuZMmUKXl5lL1RXlAQdlW/V/lie+XkfSRmlzyjYGtyXdF1HBv2A3cHygNYR5aqObT3QNtfWSEjPKfOgv7Rgyrrv1gFHRVWodiiwyM9XG7SPHoVjxwp+hofDokUF7Ro0gDNnCh7XqwfXXANt2sC118KYMS7rtxBCCFEVSdBxBZGg48qQm2+i22tr7aZeLW1wb4/RpDF33XHm/HHU5jVBzQiUtHTM3kDf3oyCu9kKIML8PRnZoa4lw5f1Z2RvNsmt/TeZ4OxZSEiATlapZW+8UWWIys8vfk7t2hAXV/D444/VcqhrroHWrSE42LV9FEIIIao4CTquIBJ0XDncMbg3L0myN9vhaDBTUTMFRdmbOXB0qZKr3n+JfvtNLYOKiSl8y85WsxPWsxW9e6vaGL6+0LQpNG8OzZqpW4sWcP31ki1KCCGEcBFHx7nVbiO5qN7sZskqx+C+tDSzjtazqIzq2KUFOo4sMyvz+8/NVfsozp1Tt6LBxLp1BW3feAP+/LP4xT09ITAQ8vLUfVCzF/7+quCevlplBRdCCCGuWBJ0iGqnrIN7e9/8O5pm1pF29rI3uYO9JVH2ChLaU+x9aRpBORnUTkskMi2B2umJRKQlUjN2OXzxUUG7QYNgwwbbF9XpVFBi3ls1aJCqZxEVVfjWoIHKLGWtRYtS+yyEEEKIiiVBh3A5d2cwcgVnB/clzQg4mma2wipiO6CkyuMaakmUdc0STCZITob4+MK3lBTCx06ynDtn+VvcdPRvfPKL75vRNutgwfsFMxJ166qgom5ddbMOKho1KrwE6plnXPjuhRBCCFHRJOgQLlVZ+xLcqbQZgQ/u6uhQzQ6XVsQup507jhFw/AhdstMIyVK30OxUQrLS8c3LZuaARwqWRD07CX780fbGbKDL0/9nef+AJeC45BNAXEAN4gNrkBoWztAh16GzXgY1fz589ZXsrxBCCCGqAQk6hMu4arnOlcSRGYGXfzvE80NbM+kb+4Xryl0RG9S+hYwMSEuDlBTIylIVr82++07Vm0hNVbeUlIL7ubmqkvZlDR6fwJq/N9h9qVf6PkS+wUMtnfLwKAg4QkNVBqjwcMtPQ16upXDf7Bvu5e1ed3PRP4RsT59CG/T1RX/3vr7l+zyEEEIIcdWQoKM60DQ1YNXr1U2nc/m3y04v17lKFNokrWkYNBOexjw8TCbSvf0sm6QjL57l604efLr+KGkpGXga8/Ey5lHbG27vGkUn6wH3559DdDRkZha/eXvDDz8UtB05Um2ozswsPtPg7w/p6QWPv/gCVq60/2by8y37H3S1a5PoG8Ql30Au+QSqn+b7PgEYNBP5XF4SNmuWutWqVbDHoojBbQJcvkFfCCGEEFWHBB3VQUwMNG5c/HlzEPKf/8C776rnEhKgSZOC4MTcxny74w6YM0e1zcyEdu1ApyPXqPFVSg4mnR6TToem07GxUSde7/MgAJqm8eH7k8hcEkSgn1fx63brBq+8UtC3YcNUBiN1cuGfHTrA7NkFbYcPV9/qa1rxtq1awSefFLQdPFhlTDIa1S0/v+B+8+bwxx8FbTt14rpDhziam49BM2HQTJZD0aGR9Hl4vuVx4//cT/DRg1xv6/NfFwGPPlDweP58VUfClsDAwo+zstRMhTVPT1VPIihI7bUwZ2gaMkTthQgKUjdzG/PNKtCs8cPiUgsSRpqXhDkYJFZG9i0hhBBCXB0k6KgO7JViMZkKbmZGY/FBrrWUlMJtT5wAwBdoWqTp8Rr1Lfd1aHSMPQKxdq5bdKnNH38UBB1FFX0/mzdDYqLttrlFNjQfOgSnT9tuW3TAn52Nh50+eBoLzzpo4bUhI0XNBHh7F/z09oaaNQufPGKECpz8/IrfAgIKt/3oIzVLZd3G29t2/ydPtv28DQa9zrIkypVLwioy+5YQQgghrh4SdFQHDRuqzEMmkxqwWwcbJpNapmNWowYcPWq7ncmk1vSb+fqqAb+msf9MMi8v249e09BpJnRAol9BdWcNHeNHPceTA5rTMty/8DU1DSKLLL/59FN1zPztvPXP8PDCbRcsKAgurJeO6XSF+wtqCVJenqpC7eGhfprv+/kVbvv77xjzjYz8+B9i0/Mx6vUYdXryDB7kGtRmaPMm8cCNax2eEWD6dMfagcrk5CbuqFkihBBCCGGLVCSvANWhIrm5KnVpGZzKVZW6krijivmV5GpIcSyEEEKIK5Oj41wp1ytcwrxcBwoG42YuzeBUCcwzAhHBhetsRAT7XPUBBxQsiRreoS7dm9S4Kn9HQgghhLiyyUxHBagOMx1mVbFOh5nMCAghhBBCFOboOFeCjgpQnYIOkMG5EEIIIUR14eg4VzaSC5eTDEZCCCGEEMKa7OkQQgghhBBCuJUEHUIIIYQQQgi3kqBDCCGEEEII4VYSdFQDOTk5zJw5k5ycnMruiqhg8ruvvuR3Xz3J7736kt999XW1/O4le1UFqOzsVZX9+qLyyO+++pLfffUkv/fqS3731Vdl/+6lOKAQQgghhBDiiiBBhxBCCCGEEMKtJOgQQgghhBBCuJUEHUIIIYQQQgi3kqBDCCGEEEII4VYSdAghhBBCCCHcSoIOIYQQQgghhFtJ0CGEEEIIIYRwK4/K7kB1YK6/mJqaWimvb37dynp9UXnkd199ye++epLfe/Ulv/vqq7J/9+bXLa3euFQkrwBnz56lfv36ld0NIYQQQggh3OLMmTPUq1fP7nEJOiqAyWTi/PnzBAYGotPpKrs7QgghhBBCuISmaaSlpVGnTh30evs7NyToEEIIIYQQQriVbCQXQgghhBBCuJUEHUIIIYQQQgi3kqBDCCGEEEII4VYSdAghhBBCCCHcSoIOIYQQQgghhFtJ0CGEEEIIIYRwKwk6hBBCCCGEEG4lQYcQQgghhBDCrSToEEIIIYQQQriVBB1CCCGEEEIIt5KgQwghhBBCCOFWEnQIIYQQQggh3EqCDiGEEEIIIYRbSdAhhBBCCCGEcCsJOoQQQgghhBBuJUGHEEIIIYQQwq0k6KjC0tLSmDlzJm3btiUgIIDg4GCuu+46Zs+eTW5ubmV3T7hBYmIin332GXfffTetW7fG398fb29v6tWrx4gRI/j5558ru4uiAr3++uvodDrLTVRtqampzJo1i+uvv55atWpZ/u736dOHmTNncunSpcruonCDNWvWMHr0aBo2bIiPjw++vr40btyYsWPHsnHjxsruniiDzMxMVq5cySuvvMKoUaNo2LCh5d/xmTNnOnSN+Ph4nnzySVq0aIGvry9hYWH06tWLBQsWoGmae9+AHTqtsl5ZuNWpU6fo3bs3MTExAPj5+WE0GsnJyQGgY8eOrF27ltDQ0ErspXA1T09P8vPzLY99fHwwGAxkZGRYnrvpppv44Ycf8PPzq4wuigpy5MgROnToQHZ2tuU5+ee+6lq/fj133nkn8fHxAHh4eBAQEFAo0Ni1axcdOnSonA4Kl9M0jYkTJ/Lxxx9bnvPx8UGn05GVlWV5burUqbz99tuV0UVRRhs2bKBPnz42j82YMaPUwGPHjh0MGjSIxMREAAICAsjOzraMDwYOHMiyZcvw9vZ2ab9LIzMdVZDRaGTYsGHExMQQGRnJmjVryMjIIDMzk2+//ZbAwEB27drF2LFjK7urwsXy8/Pp0qULH374ISdOnCArK4v09HSio6MZN24cACtXrmTChAmV3FPhTiaTiXHjxpGdnU337t0ruzvCzTZv3szQoUOJj4+nf//+bNq0iZycHJKTk8nMzGT79u08++yzBAcHV3ZXhQt9/vnnloDjtttu4+jRo2RlZZGZmcnhw4cZPnw4AHPmzJFZ7qtQaGgo/fr1Y/r06SxevJiIiAiHzktJSeHmm28mMTGRli1bsm3bNtLS0sjIyGDu3Ll4enqyevVqpk6d6uZ3YIMmqpwFCxZogAZof//9d7Hj33zzjeX4H3/8UQk9FO6ybt26Eo9PmDDB8rs/ffp0BfVKVLR33nlHA7SxY8dqM2bMsPzORdWTkZGhNW7cWAO0W2+9VTMajZXdJVFBevfurQFa06ZNtby8vGLHc3NzLX82xowZUwk9FGWVn59f7LmGDRtqgDZjxowSz33uuec0QPP19dVOnjxZ7Pirr76qAZrBYNCOHDniqi47RGY6qqAvvvgCgD59+tj8lnPMmDE0atQIgC+//LJC+ybcy950rJl5tgNg+/bt7u6OqATR0dE8++yz1KhRgzlz5lR2d4SbffXVV5w8eRJfX18++ugj9Hr533p1ERsbC0D79u3x8PAodtzT09OynC49Pb0iuybKyWAwlPlc87jOeqxnbcqUKQQEBGA0Gvn666/L/DplIf86VTGZmZls3rwZUGv3bdHpdAwePBiA1atXV1jfROXz8fGx3DcajZXYE+Eu48ePJyMjg7fffptatWpVdneEm5kHGMOHD6dmzZqV3BtRkRo3bgzAnj17Cu3lM8vLy2P37t0AXHvttRXZNVFJjhw5wunTpwH7Y8CAgAB69eoFVPwYUIKOKubQoUOYTCYA2rRpY7ed+VhcXBxJSUkV0jdR+TZs2GC537Zt28rriHCL+fPns3btWvr378+9995b2d0RbpaTk2OZsbzxxhs5efIk48aNo169enh7exMREcHw4cNZuXJlJfdUuMPEiRMBOH78OHfeeSfHjx+3HDty5AijR4/m5MmTNGnSpHLW74sKt3//fst9R8aABw8edHufrEnQUcWcP3/ecr9u3bp221kfsz5HVF2XLl3itddeA6BXr160aNGiknskXOncuXNMnz4dX1/fQtlsRNUVExNjSX9+9uxZ2rVrx6effsrFixfx8/MjPj6eZcuWMWTIEMsAVVQdw4YNY86cOXh5efHDDz/QrFkz/Pz88PPzo2XLlmzYsIGJEyeydetWgoKCKru7ogI4OwZMTU2t0KV3EnRUMWlpaZb7JaVEtT5mfY6omkwmE/fccw+xsbF4e3vz/vvvV3aXhItNmDCBlJQUZs6caVl2Iaq25ORky/3XXnsNT09PFi9eTHp6OsnJyZw+fZoxY8YA8NFHH/Huu+9WVleFmzz++OP89NNPhIeHA5CVlWVJl5uTk0NaWhopKSmV2UVRga70MaAEHUJUA4899hi//vorAB9++CHt27ev5B4JV1q0aBG//fYbHTp04Iknnqjs7ogKYl5Ka77/0UcfMWbMGDw9PQGoX78+X3/9NR07dgTglVdesbn2X1ydMjMzueOOO7j55ptp0KABq1evJiEhgYsXL7J69WquueYaFi1aRJcuXdi7d29ld1cICTqqmsDAQMv9zMxMu+2sj1mfI6qeadOmMXfuXEDla3/wwQcruUfClS5cuMDjjz+OwWBg/vz5NrPYiKrJ+t/u+vXrc8cddxRro9frefLJJwFISEhgx44dFdY/4V7Tp0/n+++/p3nz5vz5558MGDCAGjVqULNmTQYMGMCff/5J8+bNSUhIYNKkSZXdXVEBrvQxoAQdVUydOnUs98+dO2e3nfUx63NE1fLUU08xe/ZsAN58800ef/zxyu2QcLmnn36axMREHn74YVq2bEl6enqhm3nNP2DzOXH1sl6z3bJlS7vtWrVqZbl/6tQpt/ZJVIy0tDQ++eQTACZPnoyvr2+xNr6+vkyePBmATZs2ceHChQrto6h4zo4Bg4KCCAgIcHu/zCToqGJatWplydNuncWgKPOxiIgIwsLCKqRvomJNnz6dN998E4A33niDadOmVXKPhDtER0cDMG/ePAIDA4vdzMkDAMtzTz31VGV1V7hQWFiYJfDQ6XR222maZrlfUjtx9Th69KhlqVyTJk3stmvWrJnlvvnfClF1WWescmQM2Lp1a7f3yZoEHVWMn58fPXr0AGDVqlU222iaxu+//w7AwIEDK6xvouJMmzaNt956C1ABx/Tp0yu5R0IIdzD/G37o0KFCwYW1Q4cOWe7bKhYmrj7WRSBLmr2Kj4+33Jel1FVfixYtaNCgAWB/DJiRkcFff/0FVPwYUIKOKui+++4DYP369fz777/Fji9ZsoSTJ08CSC7/KmjatGmWJVVvvfWWBBxV3IYNG9A0ze5txowZlrbm5955553K67BwqQceeACAM2fO8N133xU7bjKZePvttwG1HKtTp04V2j/hHi1btrQsqVqwYIHNBAFGo9GyBCs0NFTSpFcT5nHdt99+S0xMTLHjH3zwAenp6RgMBsaOHVuhfZOgowq67777aNu2LZqmceutt7J27VpA/c9nyZIljB8/HlDVKvv161eZXRUu9vTTT1sCjrffftuygVQIUTX16tWL2267DVDF4r777jvy8vIAFYiMHTuWXbt2AfC///2v0Dfk4url6+vLQw89BMDOnTsZNmwY+/btw2QyYTKZ2Lt3L0OGDOHvv/8GsCSbEFeP5ORkEhISLDdztrrMzMxCzxetszFt2jQiIiLIzMxk6NChluQRubm5zJs3j+effx6Ahx9+mObNm1foe9Jp9uZjxVUtJiaGPn36WKJcPz8/TCYT2dnZAHTs2JG1a9cSGhpaib0UrnT69GkaNmwIqKn3WrVqldh+2rRpss+jGpg5cyYvvvgigN3lN+LqlpGRwZAhQ/jzzz8B8Pb2xs/Pr1AdjxdeeMHy50BUDVlZWYwaNarQMhpvb29A1egwu/POO/nqq68k6LjKREVFOZT44b777uPzzz8v9NyOHTsYNGgQiYmJgFpal52dbflCYuDAgSxbtszy56WiyFceVVRUVBR79+7lhRdeoE2bNuh0Ojw9PencuTNvvfUW//zzjwQcVUzRnP3x8fEl3iqyCqkQwn38/f1Zv3498+fP54YbbsDf35/09HTq1q3LmDFj2Lx5swQcVZCvry8rVqxgyZIlDB8+nHr16lm+WKhfvz633norv/76K998840EHNVM586dOXDgAFOnTqVZs2bk5eXh7+9Pz549mT9/PitXrqzwgANkpkMIIYQQQgjhZjLTIYQQQgghhHArCTqEEEIIIYQQbiVBhxBCCCGEEMKtJOgQQgghhBBCuJUEHUIIIYQQQgi3kqBDCCGEEEII4VYSdAghhBBCCCHcSoIOIYQQQgghhFtJ0CGEEEIIIYRwKwk6hBBCCCGEEG4lQYcQQgghhBDCrSToEEIIIYQQQriVBB1CCCGEEEIIt/p/tX8H8l32v34AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "###Plotting the fit\n",
    "from statsmodels.sandbox.regression.predstd import wls_prediction_std\n",
    "\n",
    "# we obtain the predicted values for our model, as well as their 95% intervals\n",
    "prstd, iv_l, iv_u = wls_prediction_std(results) \n",
    "\n",
    "fig, ax = plt.subplots(figsize=(9,6))\n",
    "\n",
    "ax.plot(x, y, 'o', label=\"data\")\n",
    "ax.plot(x, y_true, 'b-', label=\"with true coefficients\")\n",
    "ax.plot(x, results.fittedvalues, 'r', label=\"with OLS result\")\n",
    "ax.plot(x, iv_u, 'r--')\n",
    "ax.plot(x, iv_l, 'r--')\n",
    "ax.legend(loc='best',fontsize=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Mini-exercise : given these results (plot + result summary), what is your conclusion on this model ?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "[back to the toc](#toc)\n",
    "\n",
    "<br>\n",
    "\n",
    "# Exercise on a real dataset : model the effect of park frequentation on sparrow nest number with a cubic function <a id='6'></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Using data from [TESTING THE RISK-DISTURBANCE HYPOTHESIS IN A FRAGMENTED LANDSCAPE: NONLINEAR RESPONSES OF HOUSE SPARROWS TO HUMANS\n",
    "Fernández-Juricic(2003),BioOne](https://bioone.org/journals/the-condor/volume-105/issue-2/0010-5422(2003)105%5b0316%3aTTRHIA%5d2.0.CO%3b2/TESTING-THE-RISK-DISTURBANCE-HYPOTHESIS-IN-A-FRAGMENTED-LANDSCAPE/10.1650/0010-5422(2003)105[0316:TTRHIA]2.0.CO;2.short)\n",
    "\n",
    "In this data set we would like to model the relation between the density of house sparrow nests in a park and how popular a park is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Breeding</th>\n",
       "      <th>Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.974940</td>\n",
       "      <td>0.851146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33.316187</td>\n",
       "      <td>3.973849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60.407007</td>\n",
       "      <td>5.955376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89.562266</td>\n",
       "      <td>5.959888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>74.250691</td>\n",
       "      <td>6.039171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Breeding    Number\n",
       "0   7.974940  0.851146\n",
       "1  33.316187  3.973849\n",
       "2  60.407007  5.955376\n",
       "3  89.562266  5.959888\n",
       "4  74.250691  6.039171"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"data/Human_nuisance.csv\", index_col=0)\n",
    "df = df.rename( columns = {\"Breeding density(individuals per ha)\" : \"Breeding\" , \n",
    "            \"Number of pedestrians per ha per min\" : \"Number\" } )\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Number of pedestrians per ha per min')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(9,6))\n",
    "ax.plot( df['Number'], df['Breeding'],'ro')\n",
    "ax.set_ylabel(\"Breeding density(individuals per ha)\")\n",
    "ax.set_xlabel(\"Number of pedestrians per ha per min\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform an OLS regression analysis on this data, to explain the swallow breeding density (response variable) with the number of pedestrian (covariable).\n",
    "\n",
    "> The relationship between the two variable implies at most a cubic (\\*\\*3) relation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load solutions/solution_01_01.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "[back to the toc](#toc)\n",
    "\n",
    "<br>\n",
    "\n",
    "## 1.6. Model choosing <a id='8'></a>\n",
    "\n",
    "Most of the time you are not sure of the model you want to fit. You might have a broad idea of the different forms of the function but you don't really know for example what would be the best degree for your poynomial or if all the covariables are actually necessary. Of course you could say \"I am keeping the model that fit the best in term of $R^2$\". But the question really is : is that bunch of extra parameters that are complexifying my model worth the increase in $R^2$?\n",
    "\n",
    "We touched that question in 1.4 by asking about the significance of parameters values. Again if you are confident on the noise distribution you are dealing with (let's say it is normally distributed), and you have a function in mind but you don't know if you should include 1,2 or $p$ covariables then the problem is easy: you can use a log-likelihood ratio test.\n",
    "\n",
    "\n",
    "### Likelihood ratio test (LRT)\n",
    "\n",
    "We already mentioned the likelihood, or log-likelihood, several time. \n",
    "\n",
    "We will not spend too long here on its precise definition, in part because we will discuss it in some details in the next chapter. Suffice to know for now the **likelihood is a metric of goodness of fit of your model to your data** which possess interesting mathematical properties when it comes to comparing different models. \n",
    "\n",
    "Furthermore, it is computed for you by `statsmodels.OLS` and is part of the fit summary.\n",
    "\n",
    "\n",
    "To perform a Likelihood Ratio Test you just have to calculate the difference between the maximised log-likelihood of the two models you are comparing. \n",
    "You can estimate the significance of that difference either by using a test statistic (approximate method, which we will show) or by using simulations.\n",
    "\n",
    "LRT are to be used in the case of **nested function comparisons**. Nested functions are functions that have the same form but differ in the number of parameters used : for example comparing $y=\\beta_1 x_1 +c$ and $y=\\beta_1 x_1 +\\beta_2 x_2 +c$. In this course this will always be the case (but just remember that outside of this course you might want to do other comparison, so be careful).\n",
    "\n",
    "**Briefly :**\n",
    "\n",
    "You want to compare model $M_0$ and $M_1$, respectively having $\\{\\beta_{1,0}\\}$ and $\\{\\beta_{1,2},\\beta_{2,2}\\}$ as parameters. You want to see if adding this extra parameter $\\beta_{2,2}$ is worth it.\n",
    "\n",
    "The LRT statistics is :\n",
    "\n",
    "$$2*(l(X;\\hat{\\beta}_{1,2},\\hat{\\beta}_{2,2})-l(X;\\hat{\\beta}_{1,0}))$$\n",
    "\n",
    "Where the hat represents the maximum likelihood estimates. \n",
    "\n",
    "The LRT statistic asymptoptically, for your sample size going to infinity, follows a **chi-square distribution with a number of degree of freedom equal to the difference between the number of parameters in your models**. You have thus access to a P-value which will help you to decide if complexifying your model is worth it. \n",
    "\n",
    "Typically this LRT statistic could be viewed as a difference in terms of goodness of fit. If this difference is big compared to the goodness of fit of the model with fewer parameters then it is interesting to keep the complexified model. \n",
    "\n",
    "Indeed if this LRT statistic is big then the P-value will be low, and you will tend to reject the null hypothesis that the two models have the same (or non-significatively different) goodness of fit to your data.\n",
    "\n",
    "> Note how in the LRT statistic I have put the complex model first then the simple model.\n",
    "\n",
    "\n",
    "To calulate this P-value you can use $1-scipy.stats.chi2.cdf(LRT,df_{M_1}-df_{M_0})$, where $df$ is the number of degree of freedom of the models, equivalent to the number of parameters in our case.\n",
    "\n",
    "<br>\n",
    "\n",
    "From these conepts, many strategies can be devised to find the smallest set of features  which adequately describes your response variable.\n",
    "\n",
    "\n",
    "\n",
    "For example, we can use **Forward Sequential Feature Selection** : a greedy strategy which starts from an empty model and adds features 1 by 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accepted co-variable        bmi\t\tlog-likelihood: -2454.02\tLRT p-value: 0.0e+00\n",
      "accepted co-variable         s5\t\tlog-likelihood: -2411.20\tLRT p-value: 0.0e+00\n",
      "accepted co-variable         bp\t\tlog-likelihood: -2402.61\tLRT p-value: 3.4e-05\n",
      "accepted co-variable         s1\t\tlog-likelihood: -2397.48\tLRT p-value: 1.4e-03\n",
      "accepted co-variable        sex\t\tlog-likelihood: -2394.04\tLRT p-value: 8.7e-03\n",
      "accepted co-variable         s2\t\tlog-likelihood: -2387.30\tLRT p-value: 2.4e-04\n",
      "did not accept co-variable         s4\t\tlog-likelihood: -2386.66\tLRT p-value: 2.6e-01\n",
      "\n",
      "accepted covariables: ['bmi', 's5', 'bp', 's1', 'sex', 's2']\n"
     ]
    }
   ],
   "source": [
    "## simple forward Sequential Feature Selection implementation:\n",
    "def score_variables( data, response , current_covar , candidates ):\n",
    "    '''Takes:\n",
    "            - data: a pandas DataFrame containing the data\n",
    "            - response: column name of the response variable\n",
    "            - current_covar: list of the column names of the currently accepted co-variables (ie, part of the base model)\n",
    "            - candidates: list of the column names of the covariables to test\n",
    "    \n",
    "        Returns:\n",
    "            (dict): a dictionnary associating each candidate variable to the log-likelihood of\n",
    "                    a linear model with the corrent_covar and the candidate variable\n",
    "    '''\n",
    "\n",
    "    loglikelihood_dict = {}\n",
    "    \n",
    "    formula_base =response + ' ~ ' + ' + '.join(current_covar)\n",
    "    for c in candidates:\n",
    "        \n",
    "        model = smf.ols( formula_base + ' + ' + c , data = data )\n",
    "        results = model.fit() # fitting\n",
    "    \n",
    "        loglikelihood_dict[c] = results.llf\n",
    "    return loglikelihood_dict\n",
    "\n",
    "\n",
    "def LR_test( loglkh_ref , loglkh_candidate , ddf=1 ):\n",
    "    LRT = 2*( loglkh_candidate - loglkh_ref  )\n",
    "    return 1-stats.chi2.cdf( LRT , ddf )\n",
    "\n",
    "\n",
    "## setup the base model without any co-variable\n",
    "base_model = smf.ols( 'disease_progression ~ 1' , data = df_diabetes )\n",
    "base_model = base_model.fit()\n",
    "loglkh_ref = base_model.llf\n",
    "\n",
    "\n",
    "threshold = 10**-2 ## p-value threshold for the LRT\n",
    "accepted_covariables = []\n",
    "candidate_covariables = [c for c in df_diabetes.columns if c != 'disease_progression' ]\n",
    "\n",
    "\n",
    "while True:\n",
    "\n",
    "    ## score each covariable with its log-likelihood\n",
    "    loglkh_dict = score_variables(df_diabetes , 'disease_progression' , accepted_covariables , candidate_covariables)\n",
    "\n",
    "    ## get the best likelihood\n",
    "    best_candidate = max( loglkh_dict , key= loglkh_dict.get )\n",
    "\n",
    "    ## compute LRT pv-alue\n",
    "    pval = LR_test( loglkh_ref , loglkh_dict[best_candidate] , ddf=1 )\n",
    "    \n",
    "    ## compare p-value to threshold\n",
    "    if pval > threshold :\n",
    "        print(\"did not accept co-variable {:>10}\\t\\tlog-likelihood: {:.2f}\\tLRT p-value: {:.1e}\".format(best_candidate,\n",
    "                                                                                            loglkh_dict[best_candidate],\n",
    "                                                                                            pval))\n",
    "        break\n",
    "    else:\n",
    "        ## accepted a new co-variable\n",
    "        accepted_covariables.append( best_candidate )\n",
    "        loglkh_ref = loglkh_dict[best_candidate]\n",
    "        candidate_covariables.remove(best_candidate)\n",
    "        print(\"accepted co-variable {:>10}\\t\\tlog-likelihood: {:.2f}\\tLRT p-value: {:.1e}\".format(best_candidate,\n",
    "                                                                                            loglkh_dict[best_candidate],\n",
    "                                                                                            pval))\n",
    "print('\\naccepted covariables:', accepted_covariables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final model:\n",
      "                             OLS Regression Results                            \n",
      "===============================================================================\n",
      "Dep. Variable:     disease_progression   R-squared:                       0.515\n",
      "Model:                             OLS   Adj. R-squared:                  0.508\n",
      "Method:                  Least Squares   F-statistic:                     76.95\n",
      "Date:                 Tue, 28 Nov 2023   Prob (F-statistic):           3.01e-65\n",
      "Time:                         11:15:38   Log-Likelihood:                -2387.3\n",
      "No. Observations:                  442   AIC:                             4789.\n",
      "Df Residuals:                      435   BIC:                             4817.\n",
      "Df Model:                            6                                         \n",
      "Covariance Type:             nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept    152.1335      2.572     59.159      0.000     147.079     157.188\n",
      "bmi          529.8796     65.620      8.075      0.000     400.908     658.852\n",
      "s5           804.1874     80.173     10.031      0.000     646.613     961.762\n",
      "bp           327.2150     62.693      5.219      0.000     203.996     450.434\n",
      "s1          -757.9303    160.435     -4.724      0.000   -1073.254    -442.606\n",
      "sex         -226.5066     59.857     -3.784      0.000    -344.151    -108.863\n",
      "s2           538.5797    146.738      3.670      0.000     250.176     826.983\n",
      "==============================================================================\n",
      "Omnibus:                        1.187   Durbin-Watson:                   2.043\n",
      "Prob(Omnibus):                  0.552   Jarque-Bera (JB):                1.172\n",
      "Skew:                           0.016   Prob(JB):                        0.557\n",
      "Kurtosis:                       2.750   Cond. No.                         85.7\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "formula = 'disease_progression ~ ' + ' + '.join(accepted_covariables)\n",
    "model = smf.ols( formula , data = df_diabetes )\n",
    "res = model.fit()\n",
    "print('final model:')\n",
    "print( res.summary() )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "[back to the toc](#toc)\n",
    "\n",
    "<br>\n",
    "\n",
    "## 1.7. What to do when some hypothesis about OLS are not true <a id='7'></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When the homoscedasticity of your data is not true you have a few possibilities:\n",
    "- you can transform your data so your data become homoscedastic (for example you could use a variance stabilizing transformation, or a simple log transform or other...)\n",
    "- you can change $S(\\beta)$, your loss function, to reweight the different members of that equation by taking into account the discrepancy in terms of variance. That only works if there is no correlation between the error terms. In that case the method is called [**Weighted Least Square**](https://www.statsmodels.org/stable/generated/statsmodels.regression.linear_model.WLS.html#statsmodels.regression.linear_model.WLS) and the loss function becomes \n",
    "$$S(\\pmb\\beta)=\\sum_i (y_i-f(\\pmb X_i,\\pmb{\\beta}))^2 \\rightarrow S(\\pmb\\beta)=\\sum_i \\frac{1}{\\sigma_i^2} (y_i-f(\\pmb X_i,\\pmb{\\beta}))^2$$\n",
    "- if there is a correlation between the different error terms then it becomes more complicated, but technics exist such as [Generalized Least Square model](https://www.statsmodels.org/stable/generated/statsmodels.regression.linear_model.GLS.html#statsmodels.regression.linear_model.GLS)\n",
    "\n",
    "Finally **if you know  what statistics your measurement follow**, you can bypass all of those problems (and encounter others :-)) by using a **Maximum Likelihood approach** rather than an Least-Square method. \n",
    "\n",
    "By doing so you will have to put yourself in the framework of Generalized Linear Models.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "[back to the toc](#toc)\n",
    "\n",
    "<br>\n",
    "\n",
    "# Exercise : model choosing on the sparrow nuisance dataset <a id='9'></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Come back to the sparrow dataset (`df_nuisance`).\n",
    "\n",
    "Find the best model possible (i.e., change the degree of the polynomial we try to fit)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load -r -13 solutions/solution_01_02.py\n",
    "df=pd.read_csv(\"data/Human_nuisance.csv\", index_col=0)\n",
    "df = df.rename( columns = {\"Breeding density(individuals per ha)\" : \"Breeding\" , \n",
    "            \"Number of pedestrians per ha per min\" : \"Number\" } )\n",
    "\n",
    "\n",
    "## let's build a reference model with only the number \n",
    "\n",
    "model = smf.ols( 'Breeding ~ Number' , data=df)\n",
    "results = model.fit()\n",
    "\n",
    "ref_model = 'Breeding ~ Number'\n",
    "ref_results = results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Breeding ~ Number+Number2 -> pval: 0.008\n",
      "Breeding ~ Number+Number2+Number3 -> pval: 0.000\n",
      "Breeding ~ Number+Number2+Number3+Number4 -> pval: 0.165\n",
      "Best model: Breeding ~ Number+Number2+Number3\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               Breeding   R-squared:                       0.764\n",
      "Model:                            OLS   Adj. R-squared:                  0.729\n",
      "Method:                 Least Squares   F-statistic:                     21.63\n",
      "Date:                Thu, 07 Dec 2023   Prob (F-statistic):           1.73e-06\n",
      "Time:                        13:22:14   Log-Likelihood:                -120.22\n",
      "No. Observations:                  24   AIC:                             248.4\n",
      "Df Residuals:                      20   BIC:                             253.2\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept   -103.6962     36.066     -2.875      0.009    -178.929     -28.463\n",
      "Number        54.2835      7.523      7.216      0.000      38.591      69.976\n",
      "Number2       -3.0726      0.441     -6.965      0.000      -3.993      -2.152\n",
      "Number3        0.0462      0.007      6.279      0.000       0.031       0.062\n",
      "==============================================================================\n",
      "Omnibus:                        1.440   Durbin-Watson:                   1.365\n",
      "Prob(Omnibus):                  0.487   Jarque-Bera (JB):                1.218\n",
      "Skew:                           0.381   Prob(JB):                        0.544\n",
      "Kurtosis:                       2.202   Cond. No.                     7.33e+04\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 7.33e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "# %load -r 14- solutions/solution_01_02.py\n",
    "# incrementally adding columns of increasing power\n",
    "maxPow = 10\n",
    "threshold = 0.05\n",
    "coVariables = ['Number']\n",
    "for current_power in range( 2,maxPow ) :\n",
    "\n",
    "    # create the column we need\n",
    "    df[ 'Number'+str(current_power) ] = df.Number ** current_power\n",
    "    \n",
    "    coVariables.append('Number'+str(current_power))\n",
    "    \n",
    "    formula = 'Breeding ~ ' + '+'.join(coVariables)\n",
    "    \n",
    "    model = smf.ols( formula , data=df)\n",
    "    results = model.fit()\n",
    "    \n",
    "    # LRT test\n",
    "    LRT = 2*(results.llf - ref_results.llf)\n",
    "    pval = 1-stats.chi2.cdf( LRT , 1 )\n",
    "    \n",
    "    print( '{:>20} -> pval: {:.3f}'.format(formula,pval) )\n",
    "    \n",
    "    if pval < threshold :\n",
    "        ## switch the reference \n",
    "        ref_model = formula\n",
    "        ref_results = results\n",
    "    \n",
    "    else:\n",
    "        break ## stop here\n",
    "\n",
    "print(\"Best model:\",ref_model)\n",
    "print(ref_results.summary())\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "[back to the toc](#toc)\n",
    "\n",
    "<br>\n",
    "\n",
    "# Going further : interaction effects <a id='11'></a>\n",
    "\n",
    "So far the models we have created consider each co-variable effects independently. \n",
    "However it is legitimate to want to consider interactions effect between in certain cases.\n",
    "\n",
    "While you could add interaction effects yourselves by adding columns consisting of the multiplication of several covariables into your dataframe of covariables, we highly recommend you use the syntax offered by formulas. For instance,  a `*` or `:` instead of a `+` when specifying covariables will, respectively, add each variables and their interaction effect, or add only the interaction effect.\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             OLS Regression Results                            \n",
      "===============================================================================\n",
      "Dep. Variable:     disease_progression   R-squared:                       0.344\n",
      "Model:                             OLS   Adj. R-squared:                  0.341\n",
      "Method:                  Least Squares   F-statistic:                     115.1\n",
      "Date:                 Thu, 07 Dec 2023   Prob (F-statistic):           6.46e-41\n",
      "Time:                         14:02:50   Log-Likelihood:                -2454.0\n",
      "No. Observations:                  442   AIC:                             4914.\n",
      "Df Residuals:                      439   BIC:                             4926.\n",
      "Df Model:                            2                                         \n",
      "Covariance Type:             nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept    152.1335      2.977     51.107      0.000     146.283     157.984\n",
      "bmi          950.6781     62.827     15.132      0.000     827.198    1074.158\n",
      "sex          -14.0978     62.827     -0.224      0.823    -137.577     109.382\n",
      "==============================================================================\n",
      "Omnibus:                       11.253   Durbin-Watson:                   1.849\n",
      "Prob(Omnibus):                  0.004   Jarque-Bera (JB):                7.108\n",
      "Skew:                           0.153   Prob(JB):                       0.0286\n",
      "Kurtosis:                       2.459   Cond. No.                         22.0\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "#df_diabetes \n",
    "\n",
    "model_noInteraction = smf.ols( 'disease_progression ~ bmi + sex' , data = df_diabetes )\n",
    "result_noInteraction = model_noInteraction.fit()\n",
    "print( result_noInteraction.summary() )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             OLS Regression Results                            \n",
      "===============================================================================\n",
      "Dep. Variable:     disease_progression   R-squared:                       0.353\n",
      "Model:                             OLS   Adj. R-squared:                  0.348\n",
      "Method:                  Least Squares   F-statistic:                     79.56\n",
      "Date:                 Thu, 07 Dec 2023   Prob (F-statistic):           4.23e-41\n",
      "Time:                         14:05:57   Log-Likelihood:                -2451.0\n",
      "No. Observations:                  442   AIC:                             4910.\n",
      "Df Residuals:                      438   BIC:                             4926.\n",
      "Df Model:                            3                                         \n",
      "Covariance Type:             nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept    152.2464      4.073     37.381      0.000     144.242     160.251\n",
      "bmi          819.4475     82.576      9.924      0.000     657.153     981.742\n",
      "sexI          -1.6125      5.957     -0.271      0.787     -13.320      10.095\n",
      "bmi:sexI     306.9567    126.292      2.431      0.015      58.743     555.170\n",
      "==============================================================================\n",
      "Omnibus:                       12.317   Durbin-Watson:                   1.845\n",
      "Prob(Omnibus):                  0.002   Jarque-Bera (JB):                8.213\n",
      "Skew:                           0.197   Prob(JB):                       0.0165\n",
      "Kurtosis:                       2.461   Cond. No.                         53.3\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "df_diabetes['sexI'] = (df_diabetes.sex > 0).astype(int)\n",
    "\n",
    "model_noInteraction = smf.ols( 'disease_progression ~ bmi * sexI' , data = df_diabetes )\n",
    "result_noInteraction = model_noInteraction.fit()\n",
    "print( result_noInteraction.summary() )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_diabetes['sexI'] = (df_diabetes.sex > 0).astype(int)"
   ]
  }
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