Statistical_Analysis_of_Datasets_Diabetes.ipynb

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        "# The Statisical Analysis of the Diabetes Dataset - Data Science Project 2\n",
        "\n",
        "## Introduction\n",
        "\n",
        "(Thanks to [this article](https://medium.com/@bagusmurdyantoro1997/statistical-analysis-of-breast-cancer-wisconsin-diagnostic-2852a0558bd9) for the inspiration and guidance behind this project!)\n",
        "\n",
        "In the realm of modern healthcare, data-driven approaches are revolutionizing our understanding of complex diseases like diabetes. With the increasing availability of large-scale datasets, statistical analysis has become a crucial tool for uncovering valuable insights and patterns hidden within the data. In this project, I will delve into the world of diabetes research, leveraging the power of various Python libraries to perform an in-depth statistical analysis of a diabetes dataset.\n",
        "\n",
        "From data preprocessing and exploratory analysis to hypothesis testing and correlation studies, join me on this analytical journey as I demonstrate how statistical analysis can empower researchers and healthcare professionals in the ongoing battle against diabetes.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
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        "## Importing Relevant Libraries\n",
        "\n",
        "First, we import the relevant libraries: NumPy, Pandas, scikit-learn, matplotlib, seaborn and SciPy.\n",
        "\n",
        "Then we load the dataset.\n",
        "\n",
        "This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective is to predict based on diagnostic measurements whether a patient has diabetes.\n"
      ],
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy import stats\n",
        "from scipy.stats import pearsonr"
      ],
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      "execution_count": 18,
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        "data = pd.read_csv('diabetes.csv')\n",
        "\n",
        "data"
      ],
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              "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0              6      148             72             35        0  33.6   \n",
              "1              1       85             66             29        0  26.6   \n",
              "2              8      183             64              0        0  23.3   \n",
              "3              1       89             66             23       94  28.1   \n",
              "4              0      137             40             35      168  43.1   \n",
              "..           ...      ...            ...            ...      ...   ...   \n",
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              "766            1      126             60              0        0  30.1   \n",
              "767            1       93             70             31        0  30.4   \n",
              "\n",
              "     DiabetesPedigreeFunction  Age  Outcome  \n",
              "0                       0.627   50        1  \n",
              "1                       0.351   31        0  \n",
              "2                       0.672   32        1  \n",
              "3                       0.167   21        0  \n",
              "4                       2.288   33        1  \n",
              "..                        ...  ...      ...  \n",
              "763                     0.171   63        0  \n",
              "764                     0.340   27        0  \n",
              "765                     0.245   30        0  \n",
              "766                     0.349   47        1  \n",
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      "cell_type": "markdown",
      "source": [
        "Now, we filter the data into two subsets involving those tested positive for diabetes (class 1) or negative (class 0)."
      ],
      "metadata": {
        "id": "Oqun0uVfhVvi"
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      "source": [
        "positive_data = data.loc[data['Outcome'] == 1][['Outcome', 'BMI']]\n",
        "negative_data = data.loc[data['Outcome'] == 0][['Outcome', 'BMI']]"
      ],
      "metadata": {
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      "source": [
        "Observe what is going on here:\n",
        "\n",
        "The .loc method from Pandas allows us to access data rows and columns by labels or a Boolean array.\n",
        "\n",
        "Firstly, we target the individual outcomes (0 and 1) by using:\n",
        "\n",
        "`data.loc[data['Outcome'] == 1]`\n",
        "and\n",
        "`data.loc[data['Outcome'] == 0]`\n",
        "\n",
        "Now, out of all the rows, we only need Outcome and BMI, for our initial analysis, hence the nested list: `[['Outcome', 'BMI']]`"
      ],
      "metadata": {
        "id": "rqf-KLD5puFf"
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    {
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      "source": [
        "## Descriptive Stats of the BMI of Positive and Negative Patients\n",
        "\n",
        "Let's check out the descriptive statistics of BMI across the outcome types."
      ],
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      "source": [
        "# For Outcome = 0 (negative data)\n",
        "print(\"Descriptive Stats for the Negative Patients:\")\n",
        "print(\"Mean: {:.4f}\".format(negative_data.BMI.mean()))\n",
        "print(\"Variance: {:.4f}\".format(negative_data.BMI.var()))\n",
        "print(\"Standard Deviation: {:.4f}\".format(negative_data.BMI.std()))\n",
        "print(\"Description:\")\n",
        "print(negative_data.BMI.describe())"
      ],
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      "execution_count": 21,
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          "text": [
            "Descriptive Stats for the Negative Patients:\n",
            "Mean: 30.3042\n",
            "Variance: 59.1339\n",
            "Standard Deviation: 7.6899\n",
            "Description:\n",
            "count    500.000000\n",
            "mean      30.304200\n",
            "std        7.689855\n",
            "min        0.000000\n",
            "25%       25.400000\n",
            "50%       30.050000\n",
            "75%       35.300000\n",
            "max       57.300000\n",
            "Name: BMI, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Observe how the data is being accessed: first the data subset, `negative_data`, then the column in the susbset that we want to examine (in this case, it is `.BMI`), and then the function we want to call (be it mean, standard deviation, or just a description of the data points).\n",
        "\n",
        "The values are displayed, with their precision limited to 4 decimal places.\n",
        "\n",
        "Now, let's look at the data of patients that tested positive."
      ],
      "metadata": {
        "id": "_R8ekD1eOJ-2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# For Outcome = 1 (positive data)\n",
        "print(\"Descriptive Stats for the Positive Patients:\")\n",
        "print(\"Mean: {:.4f}\".format(positive_data.BMI.mean()))\n",
        "print(\"Variance: {:.4f}\".format(positive_data.BMI.var()))\n",
        "print(\"Standard Deviation: {:.4f}\".format(positive_data.BMI.std()))\n",
        "print(\"Description:\")\n",
        "print(positive_data.BMI.describe())"
      ],
      "metadata": {
        "id": "lQxk8NvOtnSJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9dbc4884-e65d-4ff3-9ae8-c1b52a5261ae"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Descriptive Stats for the Positive Patients:\n",
            "Mean: 35.1425\n",
            "Variance: 52.7507\n",
            "Standard Deviation: 7.2630\n",
            "Description:\n",
            "count    268.000000\n",
            "mean      35.142537\n",
            "std        7.262967\n",
            "min        0.000000\n",
            "25%       30.800000\n",
            "50%       34.250000\n",
            "75%       38.775000\n",
            "max       67.100000\n",
            "Name: BMI, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Description and Outliers in Data of Positive and Negative Patients\n",
        "\n",
        "To achieve this, we use a countplot from Seaborn.\n",
        "\n",
        "First, we write:\n",
        "\n",
        "`sns.countplot(...)  # where '...' represents the parameters to be passed`\n",
        "\n",
        "Then, for our parameters, we pass the following:\n",
        "1. **`x`**: This represents the label of the x-axis, as well as the name of the column you wish to count.\n",
        "2. **`data`**: This is the dataset variable. NOTE: Not the subste, the full dataset.\n",
        "3. **`palette`**: This is the list of colours you want to use to denote each unique section/group of your data.\n",
        "4. **`edgecolor`**: This is the colour of the edge of the bar in your plot.\n",
        "5. **`alpha`**: This sets the transparency of your plot. The lower the value (from 0 to 1), the more transparent your plot will be."
      ],
      "metadata": {
        "id": "FsD2DfwnQzPE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a countplot using Seaborn to get the distribution of positive and negative patients\n",
        "sns.countplot(x='Outcome', data = data, palette = ['blue', 'yellow'], edgecolor = 'black', alpha = 0.8)\n",
        "plt.title(\"Distribution of positive and negative patients\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "YF6TAFYNSbrl",
        "outputId": "4469a61e-f20f-405e-f304-f9b267a5138d"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, let's look at the distribution of BMI values of positive and negative patients.\n",
        "\n",
        "To do this, we first use Matplotlib.pyplot's `plt.hist()` function.\n",
        "This function returns three things: the sequence of data for the histogram bins, the bins themselves, and a container (or list of containers) used to create the histogram.\n",
        "\n",
        "You can check the [docs](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.hist.html) for a better understanding of this function."
      ],
      "metadata": {
        "id": "oPTqxJj4WC1D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# First, create the histogram, and assign variables to the return values of the plt.hist() function\n",
        "p_seq_hist, p_bins, c = plt.hist(positive_data['BMI'], bins = 30, color = 'yellow', alpha = 0.5, label = \"Positive\")\n",
        "# alpha is that low because we need to see through both plots.\n",
        "# Now, plot for the negative data.\n",
        "n_seq_hist, n_bins, c = plt.hist(negative_data['BMI'], bins = 30, color = 'blue', alpha = 0.5, label = \"Negative\")\n",
        "# Now, we add labels, legend and a title to our plot.\n",
        "plt.legend() #That's all you need, really. It takes the label from the label parameter in our hist().\n",
        "plt.xlabel('BMI')\n",
        "plt.ylabel(\"Frequency/Count\")\n",
        "plt.title(\"Distribution of BMI values of positive and negative patients\")\n",
        "plt.show()\n",
        "\n",
        "# ------------------<END OF CODE FOR PLOT CREATION>-----------------------------------------------\n",
        "# Find the bin with the highest frequency count in the histogram and retrieve the corresponding bin value from the bin edges array.\n",
        "# This bin value represents the most frequent BMI.\n",
        "\n",
        "# Find the index of the maximum value in the p_seq_hist array using np.argmax()\n",
        "# This will identify the bin with the highest frequency.\n",
        "p_hist_max = np.argmax(p_seq_hist)\n",
        "n_hist_max = np.argmax(n_seq_hist)\n",
        "# Now, retrieve from the bins array using the indexes above:\n",
        "p_freq_BMI = p_bins[p_hist_max]\n",
        "n_freq_BMI = n_bins[n_hist_max]\n",
        "\n",
        "print(\"Most frequent BMI for positive patients is: {:.4f}\".format(p_freq_BMI))\n",
        "print(\"Most frequent BMI for negative patients is: {:.4f}\".format(n_freq_BMI))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        },
        "id": "LCtlZ-teXYMs",
        "outputId": "501e35e4-3670-4be6-868d-4c9c087aa028"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Most frequent BMI for positive patients is: 33.5500\n",
            "Most frequent BMI for negative patients is: 24.8300\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Observation:*** Patients who test positive for diabetes show higher BMI levels compared to those who test negative."
      ],
      "metadata": {
        "id": "3nSqKNPK-iU4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### A sidenote: np.max() vs np.argmax()\n",
        "\n",
        "I feel there is the need to differentiate between the `max()` and `argmax()` functions in NumPy, so here we go!\n",
        "\n",
        "The np.max() returns the maximum value of a NumPy array.\n",
        "\n",
        "So, for example, an array of `[1,2,5,7,4]`, would have a maximum value of 7.\n",
        "\n",
        "Makes sense, right?\n",
        "\n",
        "The argmax() however, returns the index of the maximum value of the array. Remember that array indexing starts from 0, so for the array example, the index of 7 is...3."
      ],
      "metadata": {
        "id": "WEZpWd_kC1XZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Finding Outliers in the BMI values\n",
        "\n",
        "When we look at the graph, we can see that there are outliers in the BMI data for both positive and negative patients. An outlier is basically an \"unusual\" data point - it could be an error, or some rare event.\n",
        "\n",
        "We need to investigate further. To do this, we can use z-scores (covered [here](https:/https://github.com/samuelbolugee/PythonDSProjects/blob/main/EDADetailedOnPandas.ipynb/)), or we can use box plots, which are basically visual representations of the five-number summary, which I covered [here.](https://github.com/samuelbolugee/PythonDSProjects/blob/main/EDA2.ipynb)\n"
      ],
      "metadata": {
        "id": "t2DbUtjmAEDq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the size of our \"canvas\"\n",
        "plt.figure(figsize=(10,5)) # figsize is a parameter that accepts a tuple of values corresponding to the size parameters of the figure/plot\n",
        "\n",
        "# Next, we create subplots using plt.subplot\n",
        "# This allows us to create multiple plots in one figure.\n",
        "plt.subplot(1,2,1) # 1 row, 2 columns, this plot is on the first column\n",
        "# Call seaborn's boxplot function on the BMI values of the positive data:\n",
        "sns.boxplot(y = positive_data['BMI'], color = 'yellow', width = 0.4) # NO alpha keyword here\n",
        "plt.xlabel('Outcome')\n",
        "plt.ylabel('BMI')\n",
        "plt.title('Boxplot of Positive Patients', fontsize = 14)\n",
        "\n",
        "plt.subplot(1,2,2) # 1 row, 2 columns, this plot is on the second column\n",
        "# Call seaborn's boxplot function on the BMI values of the negative data:\n",
        "sns.boxplot(y = negative_data['BMI'], color = 'blue',  width = 0.4)\n",
        "plt.xlabel('Outcome')\n",
        "plt.ylabel('BMI')\n",
        "plt.title('Boxplot of Negative Patients', fontsize = 14)\n",
        "\n",
        "# This will allow us to have all plots in one image/figure\n",
        "plt.tight_layout()\n",
        "\n",
        "# Display the plot\n",
        "plt.show()\n",
        "\n",
        "# ---------------------------------------<End of Boxplot Creation>------------------------------------------\n",
        "\n",
        "# Now, we calculate to determine the outliers:\n",
        "x = [positive_data, negative_data]\n",
        "\n",
        "for i in range(0,2):\n",
        "  # Set our target to vary between the two outcomes\n",
        "  target = x[i]\n",
        "  desc = target['BMI'].describe() # Get the descriptive stats for BMI\n",
        "  # We can then access them by their index names:\n",
        "  q1 = desc['25%']\n",
        "  q3 = desc['75%']\n",
        "  iqr = q3 - q1 # Calculate the interquartile range\n",
        "  # Now, determine the lower and upper bounds\n",
        "  l_bound = q1 - (1.5 * iqr)\n",
        "  u_bound = q3 + (1.5 * iqr)\n",
        "  # Determine the outliers by selecting data that is less than the lower bound OR higher than the upper bound in the BMI data\n",
        "  outliers = target[(target.BMI < l_bound) | (target.BMI > u_bound)].BMI.values\n",
        "\n",
        "  if i == 0:\n",
        "    print(\"Outliers for negative data:\", outliers)\n",
        "  else:\n",
        "    print(\"Outliers for positive data:\", outliers)\n"
      ],
      "metadata": {
        "id": "33fyZg3LCX8h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        },
        "outputId": "0a9ec6fd-399c-4340-8d60-0f796107103e"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Outliers for negative data: [ 0.  53.2 55.  67.1 52.3 52.9 59.4  0. ]\n",
            "Outliers for positive data: [ 0.   0.   0.   0.  52.3  0.   0.   0.   0.  57.3  0. ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It can be observed that the outliers for negative data are either 0 or some number above 52.3. For positive data, the BMI outliers are also either 0 or some number above 52.3. These values deviate significantly from the range of values for BMIs of both positive and negative patients."
      ],
      "metadata": {
        "id": "32VfeNP63XbQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Using Cumulative Distribution Functions (CDFs) to determine Probablities of Observing Certain Outcomes"
      ],
      "metadata": {
        "id": "QYJs1gdHcAGm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Imagine you have a bag of marbles, and each marble has a number written on it, representing a random value. These values could be anything, like the scores of students in a class, the heights of people in a room, or the weights of apples in a basket.\n",
        "\n",
        "Now, you want to understand how these values are spread out. That's where the CDF comes in. It's like a map that shows you the probabilities of getting a value less than or equal to a certain number.\n",
        "\n",
        "A cumulative distribution function (CDF) is a fundamental concept in statistics that helps us understand and analyze the probability distribution of a random variable.\n",
        "\n",
        "Let's break it down step by step:\n",
        "\n",
        "1. **Random Variables:**\n",
        "\n",
        "In statistics, we often deal with random variables. These are variables whose values are subject to chance or uncertainty. For example, when rolling a fair six-sided die, the outcome (1, 2, 3, 4, 5, or 6) is a random variable.\n",
        "\n",
        "2. **Probability Distribution:**\n",
        "\n",
        "A probability distribution describes how the possible values of a random variable are distributed or spread out. It tells us the likelihood of each value occurring.\n",
        "\n",
        "3. **Probability Mass Function (PMF) or Probability Density Function (PDF):**\n",
        "\n",
        "For discrete and continuous random variables, respectively, we often use probability mass functions (PMFs) or probability density functions (PDFs) to describe the probability distribution. These functions assign probabilities or densities to specific values or ranges of values.\n",
        "\n",
        "4. **Cumulative Distribution Function (CDF):**\n",
        "\n",
        "The CDF takes the concept a step further. It provides a way to find the cumulative probability associated with a random variable taking on a value less than or equal to a specific value.\n",
        "\n",
        "*Mathematically, for a random variable X:*\n",
        "\n",
        "If X is discrete, the CDF is defined as:\n",
        "\n",
        "![cumulative_distribution_function.svg](data:image/svg+xml;base64,<svg xlink="http://www.w3.org/1999/xlink" width="279px" height="37px" viewBox="0 -1402.6 14487.2 1923.3" role="img" focusable="false" aria-hidden="true" style="vertical-align: -1.209ex;" xmlns="http://www.w3.org/2000/svg"><g stroke="currentColor" fill="currentColor" stroke-width="0" transform="matrix(1 0 0 -1 0 0)"><path stroke-width="10" transform="scale(1.73)" d="M48 1Q31 1 31 11Q31 13 34 25Q38 41 42 43T65 46Q92 46 125 49Q139 52 144 61Q146 66 215 342T285 622Q285 629 281 629Q273 632 228 634H197Q191 640 191 642T193 659Q197 676 203 680H742Q749 676 749 669Q749 664 736 557T722 447Q720 440 702 440H690Q683 445 683 453Q683 454 686 477T689 530Q689 560 682 579T663 610T626 626T575 633T503 634H480Q398 633 393 631Q388 629 386 623Q385 622 352 492L320 363H375Q378 363 398 363T426 364T448 367T472 374T489 386Q502 398 511 419T524 457T529 475Q532 480 548 480H560Q567 475 567 470Q567 467 536 339T502 207Q500 200 482 200H470Q463 206 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        ">*where F(x) represents the cumulative probability that X is less than or equal to x.*\n",
        "\n",
        "If X is continuous, the CDF is defined as:\n",
        "\n",
        "> *F(x) = ∫[−∞, x] f(t) dt, where f(t) is the probability density function.*\n",
        "\n",
        "The CDF ranges from 0 to 1, with F(-∞) = 0 and F(∞) = 1.\n",
        "\n",
        "You can find probabilities associated with specific intervals by subtracting CDF values.\n",
        "\n",
        "> ***For example, P(a < X ≤ b) = F(b) - F(a).***\n",
        "\n",
        "\n",
        "\n",
        "Let us use this to find the probability that the BMI of a positive patient is less than 35, and that the BMI of negative patents is less than 27.\n"
      ],
      "metadata": {
        "id": "VARwU76gI1xY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "classes_info = {\"negative_data\":27, \"positive_data\":35}\n",
        "labels_info = {\"Negative\":'blue', \"Positive\":'yellow'}\n",
        "\n",
        "# Set the size of the figure\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "for i in range(0,2):\n",
        "  name = list(classes_info.keys())[i]\n",
        "  target = locals()[name]\n",
        "  label_target = list(labels_info.keys())[i]\n",
        "  # Compute and sort the CDF values for each class.\n",
        "  sorted = np.sort(target.BMI)\n",
        "  y = np.arange(len(sorted)) / float(len(target - 1)) # a range from 0 to a maximum of 1.0\n",
        "  # Calculate cumulative probabilities for each class:\n",
        "  c_probs = np.arange(1, len(sorted) + 1) / len(sorted)\n",
        "  index = np.argmax(sorted > classes_info[name])\n",
        "  prob = 1 - c_probs[index]\n",
        "\n",
        "  # Plot the CDF\n",
        "  plt.plot(sorted, y, color = labels_info[label_target], label = label_target)\n",
        "\n",
        "  # Plot the histograms:\n",
        "  plt.hist(target.BMI, color = labels_info[label_target], bins = 75, alpha = 0.5, label = label_target, density=True, cumulative=True, edgecolor='white')\n",
        "\n",
        "  # Print desired probabilities:\n",
        "  print(\"Desired probability for class {} is:\".format(i), prob)\n",
        "\n",
        "# Set the title and labels\n",
        "plt.title('Cumulative Distribution Function (CDF) of BMI values')\n",
        "plt.xlabel('BMI Values')\n",
        "plt.ylabel('Cumulative Probability')\n",
        "\n",
        "# Display the legend\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "qyego80hiPt5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "outputId": "31194ae9-f75b-4aaa-e7fc-47eae4f39657"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Desired probability for class 0 is: 0.6659999999999999\n",
            "Desired probability for class 1 is: 0.4253731343283582\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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C3kfXXHMNderUYe7cuSd1/pKaO3cuYWFh3HXXXUHt9957L4ZhMG/evKD2nj17Bl11PPPMM4mNjS3xe++WW25h4cKFQdPRBQxKY+DAgSQkJASWC66+FcSyb98+li5dyk033RR4fxU42XL3c+fO5ZxzzqFLly6BtujoaG655Ra2bNnCqlWrgrYfPnx40H1xx8ZYnAMHDmCz2QpdkSnrz5yC4x/7+xAREVHo53Rs92Eg8Pofe/XqaD6fjwULFnDFFVcEde+tU6cOgwYNYtmyZYHndTKOfk99+OGH3HHHHbzxxhuMGTOm2H0GDRrERx99hMfjYdasWYSFhRW6mlhaAwcOxOv18tFHHwXaFixYwKFDhxg4cGCg7ejPVq/Xy4EDB2jatCnx8fEn/fl6rP/85z+0atWKli1bBn2OFlyBK/gcLYvPOpGqRF37RCqRgq4sZVVSuyjH/gNYUOr46BvHC9oPHDhQZuf9/PPPeeqpp/jll1+C+tSfynhLAwcOZPbs2axYsYK0tDQ2btzIypUrmThxYmCb9evXYxhGsSWKy6JYQlZW1nG78AwcOJC3336bkSNH8uCDD9KjRw+uuuoqrrnmmhJXcKtXr16pCksc+3wtFgtNmzYt93G4tm7dSt26dQv9Y96qVavA+qMd+34E85/pjIyMEp2vWbNmhe77OVnHxlLwT31BLAXJyhlnnFEm5wPz9Tj33HMLtR/9eh19vhPFWFpHf+acShfXAllZWUDhxCwsLKxEP6eCL0WO97mwb98+srOzadGiRaF1rVq1wu/38+eff9KmTZvShB5w7HvqqquuwmKxMHHiRG666Sbatm1baJ/rrruO++67j3nz5jFt2jT69u17yslpu3btaNmyJTNmzAh80TVjxgxq1aoVSGDArEyYnp7O5MmT2bFjR9AXSy6X65RiKLB+/XpWr15dbBfVvXv3AmXzWSdSlSiREqlEYmNjqVu3bokHpSzun41jxzs5WlGV74qrhnf0H+STOVeB//73v/Tv359u3brx2muvUadOHcLDw5k8eTLTp08/4f7F6devH1FRUcycOZO0tDRmzpyJ1WplwIABgW38fj8Wi4V58+YV+TxLcs/E8Wzfvh2Xy0XTpk2L3SYyMpKlS5eyePFi5syZwxdffMGMGTPo3r07CxYsKFE1wtLc11RSx/uZVlSFxJK8906WxWIp8jjFvWfLM5aycrIx1qxZk/z8fA4fPhz0D37Lli0B+P333wNXt05FwWfX8X4fjqcgITz2i51Q69GjB//6179YunRpkYlUnTp1uPDCC3nhhRdYvnx5mVXqGzhwIE8//TT79+8nJiaGTz/9lOuvvz6o8MWdd97J5MmTGT16NJ07dw4MPHzddded8KpQST8D/H4/bdu2ZcKECUVuX1Duviw+60SqEiVSIpVM3759efPNN1mxYgWdO3c+7rYF30YfO/Dnsd/6l4VTOdeHH35IREQE8+fPDyr/PHny5ELbluYKldPppG/fvvznP/9hwoQJzJgxg65du1K3bt3ANk2aNMEwDBo1akTz5s1LfOySeu+99wACN34Xx2q10qNHD3r06MGECRMYP348Dz/8MIsXL6Znz56ndGWuKOvXrw9aNgyDDRs2BI13lZCQUOSgsVu3bg3qMlWa2Bo2bMiXX35Z6B/2NWvWBNZXlISEhCK7vJ3s70fBa3KiLzpK+3qtXbu2UHtZv14FCdPmzZuD3gP9+vUjPT2d999/v0wSqZL+PhRn8+bNWK3W4/6uJiYmEhUVVezrZrVaC41jdary8/OBI1fcijJo0CBGjhxJfHx8obHNTtbAgQN5/PHH+fDDD0lKSiIzM5PrrrsuaJtZs2YxdOjQoK6Subm5JRoQuqSfAU2aNOHXX3+lR48eJ3x/n+izTqQ60XVWkUrm/vvvx+l0MnLkSPbs2VNo/caNG3nppZcA8wpWrVq1WLp0adA2r732WpnHVXAfy9Hn8vl8JRoYOCwsDIvFEnQlYMuWLYFKT0dzOp0l+gegwMCBA9m5cydvv/02v/76a9C9A2B2ywkLC+Pxxx8v9K29YRin1H3xq6++4sknn6RRo0YMHjy42O0OHjxYqK1gYNuCbo5OpxMonKierKlTpwZ1EZ01axa7du2iT58+gbYmTZrw7bffBg22+vnnnxcqk16a2C699FJ8Ph//+te/gtpffPFFLBZL0PnLW5MmTVizZk1gLCGAX3/99aQriCUmJtKtWzfeeecdtm3bFrTu6PdWaV+v77//nhUrVgTa3G43b775JqmpqbRu3fqkYj1WwZcyP/74Y6H23r178/bbbxf5++jxeLjvvvtKdI7p06fz9ttv07lzZ3r06HFSca5cuZI2bdoUquZ3tLCwMC655BI++eSToK6qe/bsYfr06XTp0uW4Ff9OxmeffQZw3PvvrrnmGsaNG8drr71WZuO7tWrVirZt2zJjxgxmzJhBnTp1ClVSDQsLK/TZ9sorr5Sot0BJPwOuvfZaduzYwVtvvVXoGDk5ObjdbqBkn3Ui1YmuSIlUMk2aNGH69OmBMuVDhgzhjDPOwOPx8M033/Cf//wnaNyPkSNH8swzzzBy5Eg6derE0qVLWbduXZnH1aZNG8477zzGjh3LwYMHqVGjBh988EHgm9rjueyyy5gwYQK9e/dm0KBB7N27l1dffZWmTZvy22+/BW3bsWNHvvzySyZMmEDdunVp1KhRkfeQFLj00kuJiYnhvvvuIywsrNBYOE2aNOGpp55i7NixbNmyhSuuuIKYmBg2b97Mxx9/zC233FKifxTnzZvHmjVryM/PZ8+ePXz11VcsXLiQhg0b8umnnxYa5PRoTzzxBEuXLuWyyy6jYcOG7N27l9dee4369esHigw0adKE+Ph4Jk2aRExMDE6nk3PPPZdGjRqdMLai1KhRgy5dujB8+HD27NnDxIkTadq0aVCJ9pEjRzJr1ix69+7Ntddey8aNG3n//fcLlZwvTWz9+vXjoosu4uGHH2bLli20a9eOBQsW8MknnzB69Ohiy9mXh5tuuokJEybQq1cvRowYwd69e5k0aRJt2rQ56WIEL7/8Ml26dKFDhw7ccsstNGrUiC1btjBnzhx++eUXwHwPAzz88MNcd911hIeH069fv0CCdbQHH3yQ//u//6NPnz7cdddd1KhRg3fffZfNmzfz4Ycfltl9JY0bN+aMM87gyy+/5KabbgpaN3XqVC655BKuuuoq+vXrR48ePXA6naxfv54PPviAXbt2FRpLatasWURHR+PxeNixYwfz589n+fLltGvXjv/85z8nFaPX6+Xrr7/m9ttvP+G2Tz31VGC8ottvvx2bzcYbb7xBXl4ezz333Emdv8BPP/0UGBfu8OHDLFq0iA8//JC0tDQuueSSYveLi4sLGruvrAwcOJBHH32UiIgIRowYUeg90bdvX9577z3i4uJo3bo1K1as4MsvvyzR8AEl/Qy48cYbmTlzJrfeeiuLFy/m/PPPx+fzsWbNGmbOnMn8+fPp1KlTiT7rRKqVCq8TKCIlsm7dOuPmm282UlNTDbvdbsTExBjnn3++8corrxi5ubmB7bKzs40RI0YYcXFxRkxMjHHttdcae/fuLbb8+bFlaYcOHWo4nc5C5z+6tG+BjRs3Gj179jQcDoeRlJRkPPTQQ8bChQtLVP783//+t9GsWTPD4XAYLVu2NCZPnlyoTLhhGMaaNWuMbt26GZGRkQYQKM1bXPl1wzCMwYMHG4DRs2fPYl/PDz/80OjSpYvhdDoNp9NptGzZ0rjjjjuMtWvXFrvP0ectmOx2u5GcnGxcfPHFxksvvRRUYrzAsc9r0aJFxuWXX27UrVvXsNvtRt26dY3rr7/eWLduXdB+n3zyidG6dWvDZrMFlRsv6mdRoLjy5//3f/9njB071qhdu7YRGRlpXHbZZYVKdhuGYbzwwgtGvXr1DIfDYZx//vnGjz/+WGTZ8OJiK+pnffjwYeOee+4x6tata4SHhxvNmjUznn/++ULlowHjjjvuKBRTcSWZj1bSstbvv/++0bhxY8Nutxvt27c35s+fX2z586KOdezvkWEYxv/+9z/jyiuvNOLj442IiAijRYsWxiOPPBK0zZNPPmnUq1fPsFqtQe/bop7bxo0bjWuuuSZwvHPOOcf4/PPPg7Ypqoz20bEXVZr+WBMmTDCio6OLLJednZ1t/POf/zTOPvtsIzo62rDb7UazZs2MO++8M6g8fcF7u2CKiIgw6tevb/Tt29d45513gj6bChT3GXOsefPmGYCxfv36E25rGIbx008/Gb169TKio6ONqKgo46KLLjK++eaboG1Otfy5zWYzGjdubPz9738vVDr+eL+XBU6l/HmB9evXB+JZtmxZofUZGRnG8OHDjVq1ahnR0dFGr169jDVr1hR6rxVV/twwSv4Z4PF4jGeffdZo06aN4XA4jISEBKNjx47G448/brhcLsMwSv5ZJ1JdWAyjEt1FKyIiIuXC5XLRuHFjnnvuuaDhDiqLK664AovFwscffxzqUERESkSJlIiIyGni2WefZfLkyaxatapSlaNevXo1bdu25ZdffinT8vIiIuVJiZSIiIiIiEgpVZ6vo0RERERERKoIJVIiIiIiIiKlpERKRERERESklJRIiYiIiIiIlNJpNyCv3+9n586dxMTEYLFYQh2OiIiIiIiEiGEYHD58mLp165a6mulpl0jt3LmTlJSUUIchIiIiIiKVxJ9//kn9+vVLtc9pl0jFxMQA5osVGxsb4mhERERERCRUMjMzSUlJCeQIpXHaJVIF3fliY2OVSImIiIiIyEnd8qNiEyIiIiIiIqWkREpERERERKSUlEiJiIiIiIiU0ml3j1RJGIZBfn4+Pp8v1KFIKYSFhWGz2VTWXkRERETKnRKpY3g8Hnbt2kV2dnaoQ5GTEBUVRZ06dbDb7aEORURERESqMSVSR/H7/WzevJmwsDDq1q2L3W7X1Y0qwjAMPB4P+/btY/PmzTRr1qzUg6qJiIiIiJSUEqmjeDwe/H4/KSkpREVFhTocKaXIyEjCw8PZunUrHo+HiIiIUIckIiIiItWUvrIvgq5kVF362YmIiIhIRdB/nSIiIiIiIqWkREpERERERKSUlEhJhUlNTWXixImhDkNERERE5JQpkaomhg0bhsVi4Zlnnglqnz17doVXHpwyZQrx8fGF2n/44QduueWWCo1FRERERKQ8KJGqRiIiInj22WfJyMgIdShFSkxMVDVEEREREakWlEidgGGA2x2ayTBKF2vPnj1JTk4mPT292G2WLVtG165diYyMJCUlhbvuugu32x1Yv2vXLi677DIiIyNp1KgR06dPL9Qlb8KECbRt2xan00lKSgq33347WVlZACxZsoThw4fjcrmwWCxYLBYee+wxILhr36BBgxg4cGBQbF6vl1q1ajF16lTAHNcrPT2dRo0aERkZSbt27Zg1a1bpXhQRERERkXKgROoEsrMhOjo0U3Z26WINCwtj/PjxvPLKK2zfvr3Q+o0bN9K7d2+uvvpqfvvtN2bMmMGyZcsYNWpUYJshQ4awc+dOlixZwocffsibb77J3r17g45jtVp5+eWX+eOPP3j33Xf56quvuP/++wFIS0tj4sSJxMbGsmvXLnbt2sV9991XKJbBgwfz2WefBRIwgPnz55Odnc2VV14JQHp6OlOnTmXSpEn88ccf3HPPPdxwww18/fXXpXthRERERETKWEgTqaVLl9KvXz/q1q2LxWJh9uzZJ9xnyZIldOjQAYfDQdOmTZkyZUq5x1mVXHnllbRv355x48YVWpeens7gwYMZPXo0zZo1Iy0tjZdffpmpU6eSm5vLmjVr+PLLL3nrrbc499xz6dChA2+//TY5OTlBxxk9ejQXXXQRqampdO/enaeeeoqZM2cCYLfbiYuLw2KxkJycTHJyMtHR0YVi6dWrF06nk48//jjQNn36dPr3709MTAx5eXmMHz+ed955h169etG4cWOGDRvGDTfcwBtvvFHGr5qIiIiISOnYQnlyt9tNu3btuOmmm7jqqqtOuP3mzZu57LLLuPXWW5k2bRqLFi1i5MiR1KlTh169epVLjFFRcNRFkwp1srcTPfvss3Tv3r3QlaBff/2V3377jWnTpgXaDMPA7/ezefNm1q1bh81mo0OHDoH1TZs2JSEhIeg4X375Jenp6axZs4bMzEzy8/PJzc0lOzu7xPdA2Ww2rr32WqZNm8aNN96I2+3mk08+4YMPPgBgw4YNZGdnc/HFFwft5/F4OOuss0r1eoiIiIiIlLWQJlJ9+vShT58+Jd5+0qRJNGrUiBdeeAGAVq1asWzZMl588cVyS6QsFnA6y+XQ5aZbt2706tWLsWPHMmzYsEB7VlYWf/vb37jrrrsK7dOgQQPWrVt3wmNv2bKFvn37ctttt/H0009To0YNli1bxogRI/B4PKUqJjF48GAuuOAC9u7dy8KFC4mMjKR3796BWAHmzJlDvXr1gvZzOBwlPoeIiIiISHkIaSJVWitWrKBnz55Bbb169WL06NHF7pOXl0deXl5gOTMzs7zCq1SeeeYZ2rdvT4sWLQJtHTp0YNWqVTRt2rTIfVq0aEF+fj4///wzHTt2BMwrQ0dXAVy5ciV+v58XXngBq9XsGVrQra+A3W7H5/OdMMa0tDRSUlKYMWMG8+bNY8CAAYSHhwPQunVrHA4H27Zt44ILLijdkxcREZFKyzDA74f8fPB6zcdj5092XVHbDRkCiYmhftZSHVWpRGr37t0kJSUFtSUlJZGZmUlOTg6RkZGF9klPT+fxxx+vqBArjbZt2zJ48GBefvnlQNsDDzzAeeedx6hRoxg5ciROp5NVq1axcOFC/vWvf9GyZUt69uzJLbfcwuuvv054eDj33nsvkZGRgbGomjZtitfr5ZVXXqFfv34sX76cSZMmBZ07NTWVrKwsFi1aRLt27YiKiir2StWgQYOYNGkS69atY/HixYH2mJgY7rvvPu655x78fj9dunTB5XKxfPlyYmNjGTp0aDm8aiIiIpWD328mAUdPBYlBcdPx1pd037JMYI63riJ1765EKpgB5ANewPPX49HTsW0l2ebYtkzA/Vdb/lHnyz9qGwvwARBbvk+3HFWpROpkjB07ljFjxgSWMzMzSUlJCWFEFeeJJ55gxowZgeUzzzyTr7/+mocffpiuXbtiGAZNmjQJKkM+depURowYQbdu3QKl1P/44w8iIiIAaNeuHRMmTODZZ59l7NixdOvWjfT0dIYMGRI4RlpaGrfeeisDBw7kwIEDjBs3LlAC/ViDBw/m6aefpmHDhpx//vlB65588kkSExNJT09n06ZNxMfH06FDBx566KEyfJVERERKzueDgwfhwAHYvx+2boXt2yEjw5wOHQKX60iC4vdDXl5wInF0QuHzBSczBZPfH+pnWvGsVrDZzCkszHwMDzfnC5aPXWexwOHDR/YPCzvyaLdD/foQH19WEWYALsxk4Oik4ehEwQJkUzhpKJj3AX4gt5htjk44CrbLL+ZY+X8dL6+IfYs65tHLlUU2VTmRshhGaUcrKh8Wi4WPP/6YK664othtunXrRocOHYLGNJo8eTKjR4/G5XKV6DyZmZnExcXhcrmIjQ3+weXm5rJ582YaNWoUSBxOd9u3byclJYUvv/ySHj16hDqcE9LPUEREytKGDXDvvfDbb0eSpFD952S1HkkSCpKJyEgz8bJYgpOIsDCIiDCTiMOHzZgL2gu2cTohORn27DETt2PXx8RAw4awZYuZ8B19fqvVPHbz5rB2LXg8R9oLtqlZE9q2hV9+MZPJsDBwOMwpLAxq1YJ27eCnnyA319wPzOdiGOb+Z54J331XeEgYiwVq1DDXf/21l7y8HGy24CkmJpvzz8+hdu0c4HhT9gnWF2xTMFXHLNcKhGMmglYg7K/p6HkHEId5pYmj2o/e1gY4gWRgB2aid/RxCh6jgLOBu/7aPnSOlxucSJW6ItW5c2fmzp0b1LZw4UI6d+4cooiqn6+++oqsrCzatm3Lrl27uP/++0lNTaVbt26hDk1ERKTCZGTA5Mnwz3/Crl2F10dGmolGcrKZRDgcZltUlJnA2GyQkABt2sDvvwcnGgWPiYnQqZOZKBQkEkdvk5wMXbvCV1+ZicRfvewDkpLM9V98UXSF4Ypa7/EUvT4xEZo1g40bzdgjIvLp3n0DsbGbsNkOEBV1gPj4wyQn5wK5WCy5hIXlERXlIS/Pjc2WS0RELvXr5wG5WK1HprAwc3uLJZeWLU98X3b5snIkEYnATB6KS0gigJrAIcwudkUlGVYgBkgFtv51vKKOlQC0Bv7AvEJ2bOJjBRKBc4AVmFeujj1XwXwy0BX4AiiuXHVSCbYp6XbRwDWEOok6VSFNpLKystiwYUNgefPmzfzyyy/UqFGDBg0aMHbsWHbs2MHUqVMBuPXWW/nXv/7F/fffz0033cRXX33FzJkzmTNnTqieQrXj9Xp56KGH2LRpEzExMaSlpTFt2rRAEQgREZHqJCPDvMpUYNUqmDoVPvnETG7AvPLRpw/ExZmJUmSkmeyUNNGwWIpf36qV2T2wqPWRkeYUFlY4iapIdjsUMSQkUVEGdvth6tTZi9W6l8jIPURF7Q1MCQl7SUraw0037SUiYi9RUQeKPH5Rt1EfXTHZbi95rD5fOH6/OUEEdnssFosXM2EIx/zXN/yoKQ5oCOzCTGzCi5lqA2dhJiSev45TkIQU/HBKkkCUZTJSsE3YCbZpCmw4wfnkZIQ0kfrxxx+56KKLAssF9zINHTqUKVOmsGvXLrZt2xZY36hRI+bMmcM999zDSy+9RP369Xn77bfLrfT56ahXr156PUVE5LThcsG8eWb3twULYNGiI+tSUmD4cPNqk8cTuhhDyWqFzp0zCA93YbPto3btSURH/4jNth+bbT9Wq4fmzY9/jKMTJcOw4vVG4fFE4fM5cTgiyMwMJz/fht9vIywsnri4phw4sBW/309Cgo3du8PxeGz4/eH4fDYcjiTq1TuXPXu+p0YNLxs32sjLC8fvD+NIUgPx8Um0adOV8PCySGxqYiZcq0+wnZxOQppIXXjhhRzvFq0pU6YUuc/PP/9cjlGJiIhIdef3w7598Mcf8MMP8O23sHq1ua51azj3XLPbXbdu5hWn0zmRio7eQnz8g9SuvZiwsMIl93y+CKAWubnh5OVF4vHE4PVGY7FEExsbzc6d0eTkRGOzNaRevW5s3LgQtzsLp9O8B2vDhiOvr9OZhMPRld27zcQmMtIs6nH06+90JuHzNcfj2YTfn4XPd3oW55DQq1L3SImIiIiU1nffwfTpsGOHOe3cad73dGwZbqsV+vWD9u3N5VB2p6sIdrt5L5fzmNtUwsLyqFt3BS1bLqJhw6+IiPgei8Ws9OZy1Wf79vPIzY3HZnNSp46TnTvr06BBVzZu/AK3+8jVGqfTPMfBg2Yi5HQWdIcTqR6USImIiEi1lZEBgwebBQ+OZbGYleEcDrNwRFqa2Z2vejNwODKJi8ugS5et1K27ncsuc2GxHMJm20909Pc4nT9iteYF7eV212PTpm4cONCMgu5zxyZgIqcbJVIiIiJSbWVkwN695vyFF5qJUlycOTVqZHbhK65YREVzOMyxkQpERRV9xcgsMZ5DrVoHiYrKoGnTgxhGBhERGUREHCQiIoPIyIPExmZQu/ZBbrwxg/DwgnWHsFqDq9zFxBSOxeOJJiurGXb7Wfh817Ju3e9BV5tERImUiIiIVEM+H8ydC089ZRaSsFqhQ4fgpKEy3ffkcEC3bhk4nUdKCEZEmJX9unXLIz7+fWrU+BCb7QA2mwuL5cgVo8aNj3/soiru+f0OIBa/30p2tgOPJ4L8/EgOH65DRkYjsrMTcTotNG6chNVaD/i9bJ6oSDWiREpERESqrILy5YcOwc8/w6+/HpkyMsxtbDbo37/oKy+VRXg4OJ0uvN55eL0FA576sNl+oFmzL7HbXYX2MQwrhhGJxxOJ1xtFfn4k+flRQA2io5uQmXmAqKgwdu+OIjc3Ep8vCq/XfIyIqEujRh3Jz/+CtWuzKlVSKVJVKJEqoWPHmShvcXHmQH7VSWpqKqNHj2b06NGhDkVERKqJgwfhiSfg//6vcPEIpxMGDIA6dUo3FlEoeb1uPB6zC12LFh8RE2NeCcrNjWXr1q54vWdSv/6FbNz4PX6/l4YNLUFV78CsatekSVf27fuC2rWz2Lfv2KtveYSFZVfckxKpppRIlVDBOBNu94m3PVVOpznwX2kSqWHDhvHuu++Snp7Ogw8+GGifPXs2V1555XHLzJe1KVOmMHr0aA4dOhTU/sMPP+DUnakiInKS8vPNqnvbtsG6dfDll+b9TQV/bmrUMO+BqlsX6tWDtm2he/fKcw9UadSu/Rs1a/6OYVjYsuUS/vyzE36/DaczCb+/Ln5/BJAf6jBFTmtKpErB7a7cH8QRERE8++yz/O1vfyOhEl7OSkxMDHUIIiJSRaxfD+++ayZMmzebJcv37DHvfTpWRAScc45ZTOLokuVHF26oKqKjt5CSMoeEhC0AZGd3Zteu8zROkkglZA11AFJ2evbsSXJyMunp6cVus2zZMrp27UpkZCQpKSncdddduI+6zLZr1y4uu+wyIiMjadSoEdOnTyc1NZWJEycGtpkwYQJt27bF6XSSkpLC7bffTtZfGeaSJUsYPnw4LpcLi8WCxWLhscceAwg6zqBBgxg4cGBQbF6vl1q1ajF16lQA/H4/6enpNGrUiMjISNq1a8esWbPK4JUSEZHKIiMDtmwpPA0cCE8/Df/5D/z4o5lI+XwQFmZeeWra1BzzaepUeOwxuOiiyj3uk8NhFn04eiqoypecvJnOnSfStOm1nHnmqyQkbMHvD2P37nNxuy8KdegiUgxdkapGwsLCGD9+PIMGDeKuu+6ifv36Qes3btxI7969eeqpp3jnnXfYt28fo0aNYtSoUUyePBmAIUOGsH//fpYsWUJ4eDhjxoxhb0Hd2L9YrVZefvllGjVqxKZNm7j99tu5//77ee2110hLS2PixIk8+uijrF27FoDoIsoFDR48mAEDBpCVlRVYP3/+fLKzs7nyyisBSE9P5/3332fSpEk0a9aMpUuXcsMNN5CYmMgFF1xQ5q+fiIhUvGO7zhsGfP21WTgCzK55deqYiUh8vJmAFCRMSUlw1lnmlarKzG6Hzp2PrshnEBm5mpo1FxAfv4CWLVcHtjUMK7t3t2PLlguw2eJo2DA0MYvIiSmRqmauvPJK2rdvz7hx4/j3v/8dtC49PZ3BgwcHij00a9aMl19+mQsuuIDXX3+dLVu28OWXX/LDDz/QqVMnAN5++22aNWsWdJyji0Wkpqby1FNPceutt/Laa69ht9uJi4vDYrGQnJxcbJy9evXC6XTy8ccfc+ONNwIwffp0+vfvT0xMDHl5eYwfP54vv/ySzp07A9C4cWOWLVvGG2+8oURKRKQa2bnT7MpXcP/Ttm1me5cuMHEizJ9febvWW63FD0xbcMUpLg5iYg5gt79KbOyP1KjxBxERGYHtDMNKVlYLrNbL2bDBSkaG2SfRpv/SRCo1/YpWQ88++yzdu3fnvvvuC2r/9ddf+e2335g2bVqgzTAM/H4/mzdvZt26ddhsNjp06BBY37Rp00L3W3355Zekp6ezZs0aMjMzyc/PJzc3l+zsbKKiokoUo81m49prr2XatGnceOONuN1uPvnkEz744AMANmzYQHZ2NhdffHHQfh6Ph7POOqtUr4eIiFROGzbAlVfC//4X3G6xQK9eZte9ytxdz26HxMQMunZ1kV9E3YeICIN69b7j8ss/JjZ2AVbrocA6n89GZmZTwsJasGZNcyyWRjRp0hWP5wugkmaNIhJEiVQ11K1bN3r16sXYsWMZNmxYoD0rK4u//e1v3HXXXYX2adCgAevWrTvhsbds2ULfvn257bbbePrpp6lRowbLli1jxIgReDyeEidSYHbvu+CCC9i7dy8LFy4kMjKS3r17B2IFmDNnDvXq1Qvaz+FwlPgcIiJSOS1ZAldfbZYvt1ggMfFItb1GjaBmzcqdRIF5xchmc5GfP4/s7CP3G4eHZ1K79kqSk7/H4dgfaPd6o9i/vzn797cgI6MJkZHhNGxoViOsioUxRE53SqSqqWeeeYb27dvTokWLQFuHDh1YtWoVTZs2LXKfFi1akJ+fz88//0zHjh0B88pQRsaR7gcrV67E7/fzwgsvYLWatUpmzpwZdBy73Y6vqLJKx0hLSyMlJYUZM2Ywb948BgwYQPhff0lat26Nw+Fg27Zt6sYnIlKFFTUO4969ZnEIgDPPNAfLDQur+NhOxG43k6Wiuu5ZrV4SE7cQHr6chIT51Kq1i8jIg39NGVgs5rAjfr+dgwfPxm4fwdq1e3G7cyr4WYhIeVEiVQoVNQRSWZynbdu2DB48mJdffjnQ9sADD3DeeecxatQoRo4cidPpZNWqVSxcuJB//etftGzZkp49e3LLLbfw+uuvEx4ezr333ktkZCSWv74WbNq0KV6vl1deeYV+/fqxfPlyJk2aFHTu1NRUsrKyWLRoEe3atSMqKqrYK1WDBg1i0qRJrFu3jsWLFwfaY2JiuO+++7jnnnvw+/106dIFl8vF8uXLiY2NZejQoaf+IomISLkrKCaxb59ZQGLNGnMq8O9/m1enKts9UA4HdOyYQVLSbnr3/onw8HU4HFv+mrZit2/HYjG/NCyiphIuV30OHuxAdHQbduxIoUGDs4EvKvZJiEi5UiJVQnFx5iC5FXm+U/XEE08wY8aMwPKZZ57J119/zcMPP0zXrl0xDIMmTZoElSGfOnUqI0aMoFu3boFS6n/88QcREREAtGvXjgkTJvDss88yduxYunXrRnp6OkOGDAkcIy0tjVtvvZWBAwdy4MABxo0bFyiBfqzBgwfz9NNP07BhQ84///ygdU8++SSJiYmkp6ezadMm4uPj6dChAw899NCpvzgiIlIh9u83x4NauZKg+4iio+Gyy8wxoELB4ThSDOLoLzCtVi/JyT/QtOkimjefS2TkjyQkFD3wrd9vBxqSmekgMzOWnJwa5OTUIDu7Fh5PDE5nxX0JKyIVT4lUCSUkmFNlNWXKlEJtqamp5OXlBbWdffbZLFiwoNjj1KlTh7lz5waWt2/fzt69e4O6A95zzz3cc889QfsVVN4r8Prrr/P6668HtW3ZsqXQ+Vq1aoVhGEXGYrFYuPvuu7n77ruLjVdERCoPnw/+/NMsIlEw/d//mVX5AGrXhrZtoVkzc/44xV3LlcMB3bplULOmi6Qk6NoVYA/1648jJmYZYWHuoO3z8mLIzKx/VKJkPsbGNqVZs25s2fIFhw5VsktqIlLulEhJkK+++oqsrCzatm3Lrl27uP/++0lNTaVbt26hDk1ERCqhjAxzUNyXXzaTKK+38Da1akHfvtCgQeUoIBEeDk6nWSTC53OTk2PQrNmbxMdvAMyiEG53axyO3uTmeli9OgKPp6jAK8GTEZGQUSIlQbxeLw899BCbNm0iJiaGtLQ0pk2bFigCISIiUsDvh88+g6OGFyQszKy4V7MmpKTAOeeYRRtKUIOowuXnu7FYtpGaupj4+A34fDZ+++1GXK4UnM5kmjfvit+vcuQiUjQlUhKkV69e9OrVK9RhiIhIFdC3r1lIosDgwdC4sTlILRDoNvfFF5WvmERYWAZ16syiRo0fsFj8AGzadDEuV4MQRyYiVYUSKRERETmhNWtg6VL4448j0+7d5rpmzcwBdGvWDG2MJeenUaPbiI7+DoCMjKZs3NidrKw6IY5LRKoSJVIiIiJSrIMHzfufnnzS7Mp3rLZt4frr4ZjaRiHjcBwZ3LaoqnwAnTq9Q3T0d/h84WRmDmL16lQ8noqPVUSqNiVSIiIiUqx77jGLSQA0amTe95SUZFbca90aunQxu+5VhkSqoBqf02mOABwRcaR7YUHpdZttL61a3QvAnj29sNlSQxStiFR1SqRERESkSL//biZRFgv06AFpacFV94oZvSJkCqrxeb3z8HrNEuY+H+TkHKkm2Ljxh4SFZZKf34aDB7tQu3ZOCCMWkapMiZSIiIgU6dAh87FWLThmzPRKzet14/FkER5udkf0egl03XM49gCQmzsYCAtdkCJS5VlDHYCIiIhUThkZoY6gbFmtXiIjDwBgGFEhjkZEqjolUnJcS5YswWKxcKjga8lipKamMnHixAqJSUREyt++fWY5c4DatUMbS1lJTV1CREQmHk88Xu95oQ5HRKo4JVLVxLBhw7BYLFgsFux2O02bNuWJJ54gv+Du2pOUlpbGrl27iIuLA2DKlCnEx8cX2u6HH37glltuOaVziYhIaBkG/Pe/8OCDZvKUlWXedzRgQKgjO1UGsbF/kpKyAoAtW64HnMffRUTkBHSPVDXSu3dvJk+eTF5eHnPnzuWOO+4gPDycsWPHnvQx7XY7ycnJJ9wuMTHxpM8hIiKhk5EBLpdZde/BB2H27OD1t91mlg+vDAPqHl3aHAqXN3c6wWbLJCZmC3b7FuLj9xIfv5ezz95LeLhZVGLPnjNwudpSq1YInoCIVCu6InVCBuAO0VS6ckgOh4Pk5GQaNmzIbbfdRs+ePfn000/JyMhgyJAhJCQkEBUVRZ8+fVi/fn1gv61bt9KvXz8SEhJwOp20adOGuXPnAsFd+5YsWcLw4cNxuVyBq1+PPfYYENy1b9CgQQwcODAoNq/XS61atZj6Vw1dv99Peno6jRo1IjIyknbt2jFr1qxSPV8RETl1LhfMnGkOqDt7Nlit0KEDDBpkjh91882hjtBUUNq8Z88tgencc7eQnPwDV189mptv7s6IEfU544x2tG37Ki1azCEp6Qfs9q2Eh+dgGBYOHWrIhg29Q/1URKSa0BWpE8oGokN07ixOpetBZGQkBw4cYNiwYaxfv55PP/2U2NhYHnjgAS699FJWrVpFeHg4d9xxBx6Ph6VLl+J0Olm1ahXR0YWfc1paGhMnTuTRRx9l7dq1AEVuN3jwYAYMGEBWVlZg/fz588nOzubKK68EID09nffff59JkybRrFkzli5dyg033EBiYiIXXHDBST9nEREpnTVr4JlnzCtTERFmN77Gjc11tkr0X8Kxpc3DwrJJTv6auLj/YrF4g7b1eOI4fDgRj6c2TmdtNm+ujctVC7/fvJxlt4fiGYhIdVOJPiKlrBiGwaJFi5g/fz59+vRh9uzZLF++nLS0NACmTZtGSkoKs2fPZsCAAWzbto2rr76atm3bAtC44C/oMex2O3FxcVgsluN29+vVqxdOp5OPP/6YG2+8EYDp06fTv39/YmJiyMvLY/z48Xz55Zd07tw5cM5ly5bxxhtvKJESEakgn38O110HbjfUqAHXX0+l7/Lm9bqJiFhD27bTCA/PBeDw4Xrs2tUOn68lyclXsWHDCtzuLJxOaNjQfH5+f4gDF5FqR4nUCUVhXhkK1blL7vPPPyc6Ohqv14vf72fQoEFcddVVfP7555x77rmB7WrWrEmLFi1YvXo1AHfddRe33XYbCxYsoGfPnlx99dWceeaZJx21zWbj2muvZdq0adx444243W4++eQTPvjgAwA2bNhAdnY2F198cdB+Ho+Hs84666TPKyIihRXcA3U0w4B//xvGjzfnmzSBq6+GyMjQxFgaFouXli1nEx6eS3Z2Ih5Pd1ataoHHY8HpTMIw4kIdooicJpRInZCFqlLZ56KLLuL111/HbrdTt25dbDYbn3766Qn3GzlyJL169WLOnDksWLCA9PR0XnjhBe68886TjmXw4MFccMEF7N27l4ULFxIZGUnv3ma/9Ky/7lieM2cO9erVC9rP4XCc9DlFRKQwlwvmzTOvygDk58PHH8P335vLV18NZ58NOTmhi7E06tdfRFTUAfLyolmz5ibq148IdUgicppSsYlqxOl00rRpUxo0aIDtr47trVq1Ij8/n++++y6w3YEDB1i7di2tW7cOtKWkpHDrrbfy0Ucfce+99/LWW28VeQ673Y7P5zthLGlpaaSkpDBjxgymTZvGgAEDCP+r1FLr1q1xOBxs27aNpk2bBk0pKSmn8hKIiEgR3G6z6l5W1pEkymKBa66BceMgLCzUEZZMRMRa6tVbDMD69Zfi8ymJEpHQ0RWpaq5Zs2Zcfvnl3HzzzbzxxhvExMTw4IMPUq9ePS6//HIARo8eTZ8+fWjevDkZGRksXryYVq1aFXm81NRUsrKyWLRoEe3atSMqKoqoqKK7IA4aNIhJkyaxbt06Fi9eHGiPiYnhvvvu45577sHv99OlSxdcLhfLly8nNjaWoUOHlv0LISIiZGQcuRLVrRt0724mVFVFfPxnWK1+Dhxoxv79rQJlz0VEQkFXpE4DkydPpmPHjvTt25fOnTtjGAZz584NXCHy+XzccccdtGrVit69e9O8eXNee+21Io+VlpbGrbfeysCBA0lMTOS5554r9ryDBw9m1apV1KtXj/PPPz9o3ZNPPskjjzxCenp64Lxz5syhUaNGZffERUQkyJw55mNMDPxV66dSsNshOrrwVDBOVHS0j1at5hIXtwQAt7uSV8QQkdOCrkhVE1OmTCl2XUJCQmD8pqK88sorxa678MILMYzg8axef/11Xn/99aC2LVu2FNq3VatWhfYtYLFYuPvuu7n77ruLPbeIiJStgqITl19ujstUGVit0LlzBuHhrkLroqIySEmZwogR/yE8fFegPSurTkWGKCJSJCVSIiIip4mCEuCVaXwoqxUiI13k5JjjQx2tYcP3iYz8FYD8fCf5+VeyYUMS+/eHanxHEZEjKtFHqYiIiJQHnw/mzoWDB83lynhvkdfrxuMJHm7EZssAYPv2LuzePYDGjbuTk/MFoRuWRETkCCVSIiIi1VhmJrzzDqxbZy736FH5B921WHw0bfoFMTHbAMjIaIphhIc4KhGRYEqkREREqrFbbzWTqPBwuPJKKKYoayVicMYZM6hZcz0Abnc3Dh9uQLjyKBGpZJRIiYiIVFPffQcrVpjzAwdCkyahjackkpN/pWbN9fh8NjZuHEB8fPNQhyQiUiSVPxcREamGfD6zOh9AgwbQuHFo4ymplJTlAGzdegEul5IoEam8lEiJiIhUM4YB6emwZ4+5fNNNVWfgXZstD4CDB6vA5TMROa2pa5+IiEgVlpFxZHwogLw8+Mc/YNYsc/nmm80qfVkqdCciUqaUSImIiFRhLhfMmwdut3kl6s03YcMG8wrU4MFw113wxRehjlJEpPpRIlViGUDhUdfLTxyQUIHnK9qSJUu46KKLyMjIID4+vtjtUlNTGT16NKNHj66w2ERExOR2m1ec1q0zkyiAQYMgLS20cYmIVGdKpErMBcwD3CfasAw4gT6UJpEaNmwY7777LgDh4eE0aNCAIUOG8NBDD2E7hSHs09LS2LVrF3FxcQBMmTKF0aNHc+jQoaDtfvjhB5yVcYRHEZHTxJYt8PHH5nyjRtC0aUjDOSkOhwubLQcAvz+sytzXJSKnJyVSpeKmMo+m3rt3byZPnkxeXh5z587ljjvuIDw8nLFjx570Me12O8nJySfcLjEx8aTPISIip+bQIXjvPfD7oV49c7yoqsegWbO5hIXl43LVJzs7EX0/JyKVmar2VSMOh4Pk5GQaNmzIbbfdRs+ePfn000/JyMhgyJAhJCQkEBUVRZ8+fVi/fn1gv61bt9KvXz8SEhJwOp20adOGuXPnAmbXPovFwqFDh1iyZAnDhw/H5XJhsViwWCw89thjgNm1b+LEiQAMGjSIgQMHBsXm9XqpVasWU6dOBcDv95Oenk6jRo2IjIykXbt2zCq4M1pEREpl1y4ziUpIgKFDISYm1BGVXs2av1Kr1jr8fitr1/YHdDlKRCo3XZGqxiIjIzlw4ADDhg1j/fr1fPrpp8TGxvLAAw9w6aWXsmrVKsLDw7njjjvweDwsXboUp9PJqlWriI6OLnS8tLQ0Jk6cyKOPPsratWsBitxu8ODBDBgwgKysrMD6+fPnk52dzZV/fU2anp7O+++/z6RJk2jWrBlLly7lhhtuIDExkQsuuKAcXxURkeolLw9++smcj46G8PDQxnMsq5ViryxFRUFYGERFfUXduh8CsHVrV7Kz1ctBRCo/JVLVkGEYLFq0iPnz59OnTx9mz57N8uXLSfvrruNp06aRkpLC7NmzGTBgANu2bePqq6+mbdu2ADQuZtRGu91OXFwcFovluN39evXqhdPp5OOPP+bGG28EYPr06fTv35+YmBjy8vIYP348X375JZ07dw6cc9myZbzxxhtKpERESmjvXrjhBvjlF7NK37nnhjqiYHY7JCZm0LWri/z8wusjIqB+/TeJjEwH4NChBmzb1qWCoxQROTlKpKqRzz//nOjoaLxeL36/n0GDBnHVVVfx+eefc+5Rf11r1qxJixYtWL16NQB33XUXt912GwsWLKBnz55cffXVnHnmmScdh81m49prr2XatGnceOONuN1uPvnkEz744AMANmzYQHZ2NhdffHHQfh6Ph7POOuukzysicjrZswfOO88sMhERAddcA00q2Ri2NhvYbC7y8+eRnV24WJPVmkNExLMA7NmTxpo13TGMsIoOU0TkpCiRqkYuuugiXn/9dex2O3Xr1sVms/Hpp5+ecL+RI0fSq1cv5syZw4IFC0hPT+eFF17gzjvvPOlYBg8ezAUXXMDevXtZuHAhkZGR9O7dG4Csv0aFnDNnDvXq1Qvaz+FwnPQ5RUROJ88/byZRDgfceafZTa6y8nrdeDyFizX5fFlYLH4Mw8K2bVdiGJW3oJOIyLGUSFUjTqeTpsfUu23VqhX5+fl89913ga59Bw4cYO3atbRu3TqwXUpKCrfeeiu33norY8eO5a233ioykbLb7fh8vhPGkpaWRkpKCjNmzGDevHkMGDCA8L867rdu3RqHw8G2bdvUjU9E5AQyMsxBd4/2yy8wYYI5P2IE1K5tjiMlIiIVR4lUqVRUHdayO0+zZs24/PLLufnmm3njjTeIiYnhwQcfpF69elx++eUAjB49mj59+tC8eXMyMjJYvHgxrVq1KvJ4qampZGVlsWjRItq1a0dUVBRRxXwNOmjQICZNmsS6detYvHhxoD0mJob77ruPe+65B7/fT5cuXXC5XCxfvpzY2FiGDh1aZs9fRKSqc7lg3jxz0N0CCxeCYUDz5nDTTXDUR2yVERW1j4YNCwJXEWERqXqUSJVYHOYguRV5vrIxefJk7r77bvr27YvH46Fbt27MnTs3cIXI5/Nxxx13sH37dmJjY+nduzcvvvhikcdKS0vj1ltvZeDAgRw4cIBx48YFSqAfa/DgwTz99NM0bNiQ888/P2jdk08+SWJiIunp6WzatIn4+Hg6dOjAQw89VGbPW0SkunC74eBB+N//YONGcwJISjK79lUlNlsuTZrMJzn5VywWA8OwkJc3KNRhiYiUmhKpEkv4a6qcpkyZUuy6hISEwPhNRXnllVeKXXfhhRdiGEZQ2+uvv87rr78e1LZly5ZC+7Zq1arQvgUsFgt33303d999d7HnFhER06FD8PbbsG/fkbbISGjfPlQRnRyncw9t2swkKuogABkZLbFYrsEwBgJfhDY4EZFSUiIlIiJSyS1caCZR0dHQsSM0bQp160KdOqGOrOQcjkw6dPg3YWFecnPjWLXqany+FBo3TsKqnn0iUgUpkRIREankDh82Hy+4ADp1Cm0sJys2dhthYV5ychJYuXIk+flRxQ7UKyJSFeg7IBERkUrs889hzRpzPiYmtLGcisjIQwDk5CSQn1+Ja7WLiJSQEikREZFKbMYMs0Jf27bQrFmoozk54eEuGjT4LwD79hVdFVZEpKpRIlWE4gokSOWnn52IVCduN+zaZc43b06VvZcoLm4dNpuHrKwkdu3qGOpwRETKRBX9SC4fBeXAs7OzQxyJnKyCn13Bz1JEpKravh26djVLnYeFQb16oY7oVPgByM2NAyyhDUVEpIyo2MRRwsLCiI+PZ+/evQBERUVhsegDvyowDIPs7Gz27t1LfHw8YWFhoQ5JRKREMjLMQXePNWwY/PwzxMXBDTdAQuUdgUNE5LSkROoYycnJAIFkSqqW+Pj4wM9QRKQqcLlg3jyzG1+BP/6Ar7825x97DPLzISsrJOGdMqt1O7VrrwDA71dvARGpPpRIHcNisVCnTh1q166N1+sNdThSCuHh4boSJSJVktt9JFFaudKs1AfQsiV07w4LFoQutuOx28Fmo9gy5nXq/ERMTH+s1gy83kj+/DOtYgMUESlHSqSKERYWpn/KRUSkwi1ZYj526ADDh1feAhMOB3TsmEFSkouuXc2rZkezWDy0bHk9VmsGbnd9fvvtavLy4kMSq4hIeVAiJSIiEmJ+v1mdb8MGKKh3lJZmFpmorMLDISLChd8/j5wcN0d34rBa82jadCYOxzoMowabNo0kL09VVUWkelEiJSIiEkI+H7z8MuzYcaTNbi++u1xlYxhuvN4sPB6wWPJJSNhMkyYLcTr3YRhW8vMfxefzAlX0Ji8RkWIokRIREQmhgwePJFEtWkCTJuZjRERo4yopiyWHWrV+Jz5+LTVqrMdm8wCQlxfNjh1/o169HsAXoQ1SRKQcKJESEREJoaVLzUeLBa67LrSxlFZi4rPUqPEWNWv6Am15edHs39+SrVsvIDa2SQijExEpX0qkREREQiQjAx56yJxv1y60sZRW48afUbPmJACys2uxb19L9u9vyeHDddGguyJyOlAiJSIiEiIHDoDHYxZu6Ncv1NEc4XCYMUVFFV3evGHDBVx66TAAsrMv4JdfLsTjqfg4RURCSYmUiIhIiFmtlafMucMB3bpl4HS6iIiApCSCypsnJr5B3brPYrEY5OefSU7OJYCyKBE5/SiREhERCZHDh0MdQWHh4eB0uvB65wFufD7IyQGvF+Li1lGv3lsA7N9/AeHhLwCLUSIlIqejkH//9eqrr5KamkpERATnnnsu33///XG3nzhxIi1atCAyMpKUlBTuuececnNzKyhaERGRsvHzz0e68yUmhjaWoni9Zllzvz/rr/Lmh2nceBYAO3aczebNAwFHaIMUEQmhkF6RmjFjBmPGjGHSpEmce+65TJw4kV69erF27Vpq165daPvp06fz4IMP8s4775CWlsa6desYNmwYFouFCRMmhOAZiIiIHF9GBrhcwW2HDkG3bpCVBY0bw4ABIQmtVMLD3URGZgCwaVOPKlOeXUSkvIQ0kZowYQI333wzw4cPB2DSpEnMmTOHd955hwcffLDQ9t988w3nn38+gwYNAiA1NZXrr7+e7777rkLjFhERKSmXC+bNA7fbXPb74ZNPzCTKYoH33oNvvjGXKyOLJYfk5N+oXftnAAzDgs+nK1EiIiFLpDweDytXrmTs2LGBNqvVSs+ePVmxYkWR+6SlpfH+++/z/fffc84557Bp0ybmzp3LjTfeWOx58vLyyMvLCyxnZmaW3ZMQEREpAbfbvAr13Xfwww9HrlBddhnExlZsLAUV+aDoqnxOJ9hsbmrU+I3ExJ+pUWM1iYnmOFF+fxh//tm5YgMWEamkQpZI7d+/H5/PR1JSUlB7UlISa9asKXKfQYMGsX//frp06YJhGOTn53PrrbfyUMEgHEVIT0/n8ccfL9PYRURESiMrC6ZOhT//NJcjI82ufX36VGwcR1fkA4Kq8hnGAeLiviQ+fiExMf/FYjlSQMLtTmLnzg7s2dOW/PzIig1aRKSSqlJV+5YsWcL48eN57bXXOPfcc9mwYQN33303Tz75JI888kiR+4wdO5YxY8YEljMzM0lJSamokEVE5DS3eze8/LJ5r1REBFxyCZxxhnlVyFLB49YeXZHP6zX7Gvp8EBn5M40afYDV6g9sm5tbg0OHWmC3t2HNmrp4PBpkV0TkaCFLpGrVqkVYWBh79uwJat+zZw/JyclF7vPII49w4403MnLkSADatm2L2+3mlltu4eGHH8ZaxCAcDocDh0N9uUVEJDQmTTKTKLsdRoyAWrVCHZFZkc/jyfqri18mDRt+iNXq5/DhZFyus4mJGc66dRsANw0bhjhYEZFKKmTlz+12Ox07dmTRokWBNr/fz6JFi+jcuej+19nZ2YWSpbCwMAAMwyi/YEVERE7Cm2/Cu++a8127Vo4k6liRkd9js+WRmVmPlStvZteuPvh8zQBdgRIROZ6Qdu0bM2YMQ4cOpVOnTpxzzjlMnDgRt9sdqOI3ZMgQ6tWrR3p6OgD9+vVjwoQJnHXWWYGufY888gj9+vULJFQiIiIVqajy5gCffQZ33WXOn3UWnHdexcZVUhaLORbjgQPNqATDS4qIVBkhTaQGDhzIvn37ePTRR9m9ezft27fniy++CBSg2LZtW9AVqH/84x9YLBb+8Y9/sGPHDhITE+nXrx9PP/10qJ6CiIic5o4tbw7g8UDB8IZpaXDFFcHrKwur1YPDsRFARSREREop5MUmRo0axahRo4pct2TJkqBlm83GuHHjGDduXAVEJiIiUjJud/A4UIsXw4EDZmnxu+46Uq2vMomL20qzZl8SFnaIvLw4du9uH+qQRESqlJAnUiIiItVNRob52KcPtGlTuRIph2MjDRu+RULCOgAMw8aGDf3x+ewhjkxEpGpRIiUiIlLGCuof2StZbhIefpgmTYZgt+/E77eyf/9ZWK3dcLkqeFRgEZFqQImUiIhIGdq4EVavNuejo0Mby7HOP/8x7Pad5ObW4JdfbiAsLEHlzUVETpLK84iIiJShlSvNQW6bNYMOHUIdTbBGjeYBsHXrpeTmJoQ4GhGRqk2JlIiISBnx+Y6UQm/Rgr8GvK1MzD6HXm9MiOMQEan61LVPRESkDGRnw8yZsHMnWCxQr16oIxIRkfKkREpERKQMfPEFbN1qFpi4+mpITq7Y8zscha+ARUWZJdidTj/nnptOjRprAcjPd1RscCIi1ZASKRERkTJw4ID52K8fNG9esee226Fz5wycTldQe0QEJCUZXH/9aOLjPwUgO/t6cnPrApVwhGARkSpEiZSIiEgZCkXJ8/BwcDpdeL3z8HqDEySb7SdiYj7F7w9j+/ahREffQ1jYFxUfpIhINaNESkRE5BT9+KN5bxRAZGTo4vB63Xg8WYHl8HCIipoDwJYtF3DgQKdKV5JdRKSqUtU+ERGRU7R0qfnYvj3Urx/SUAqxWg8DsHt3+9AGIiJSzSiREhEROQVutzkBtGtnVuyrLKKjt2GxGBgG+HyVrha7iEiVpkRKRETkFNx/P2RmmtXxEhNDHc3RDBo2NLv17d17Fj5fRIjjERGpXpRIiYiInIKffjIfr7oKnM7QxnI0qzWfqKi9AGzb1j3E0YiIVD8qNiEiIlIG4uNDHcERUVH7aNRocWDZ59O4USIiZU2JlIiIyEmaMgX27TPnbZXgL2pYWA4tWnxCcvKvgXujcnLOwe/X/VEiImWtEnzsi4iIVE4ZGeByFb3u1Vfhn/805886C2rVqri4ihIRsZYWLV4mMnI/APv2tWL37gtJSqod2sBERKopJVIiIiLFcLlg3rwjVfkKeL1Hkqgbb4QzzoDs7IqPr0Bk5F6aNbuasDA3ubmxrFp1DZmZKZXqni0RkepGiZSIiMhxuN2QdWSMWw4fhhkzzPnISLjrLliyJCShBSQm/khYmJu8vHhWrrwZrzcqtAGJiJwGlEiJiIiUkN8P771n3hcVGQl/+xtEVKKq4l5vtJIoEZEKokRKRESkhLKzjxSXGDkSmjevmPPa7RAdXbg9KsoscuFwGBUTiIiIBCiREhERKaE1a8xHiwVq1KiYc1qt0LlzBuHhhateRERAUhIkJ78LQH6+rkaJiFQUJVIiIiIlsGgRLFtmznfsWHHntVohMtJFTs48vN5jql5gYLd/jdM5B8OwsHPnxRUXmIjIaU6JlIiIyAlkZBxJoi64wJwqmtfrxuM5UvXCYsmnQYOPcTpXAZCbexNudyqQVfQBRESkTFlDHYCIiEhlV1D+PDoaLrzQ7NoXarVrr6JGjVUYhpU//7yOnJy7Qx2SiMhpRYmUiIjIcXi98PHH5nx8fEhDCRIRcRCAvLzWHDjQDagE2Z2IyGlEXftERESO49tv4aCZs9C/f2hjMRk0aLCc1NSlAOTn1wxxPCIipyclUiIiIsXYuRPmzDHn27eHxMSQhgNAUtJvNG68CIB9+9pjGOeHOCIRkdOTuvaJiIgUwTDgmWfA5wOHAy69NNQRmZzOvQDs3t2OLVsuB8JDG5CIyGlKiZSIiEgRli+Hzz4z53v0gPBKka8YxMbuACA3Ny7EsYiInN6USImIiBRh+3bzMTUVzj47pKEE1K79P+Ljt+Lz2di9u12owxEROa3pHikRETltZWSAy1W4PTMTHn7YnI+OrtiYjicubisAu3Z1JDe3Bk5niAMSETmNKZESEZHTlssF8+YdGSeqwIIFsGmTOV5U796hia0odrsZqNcbGeJIREREiZSIiJzW3G7IyjqyvHcvLDKL4nHDDZCUFLw+VGJjN5KYuAaAgwebhDgaERHRPVIiIiJH2bQJ/H5o0ABGjQp1NEckJq4EYNeu9hw+XD/E0YiIiBIpERGRIiQlQVRUqKM4IizMA4DbXTvEkYiICJxEIuU+tiO5iIhINeH1wrp15ry1En3VaLdvokaNPwDIzq4Z4mhERAROIpFKSkripptuYtmyZeURj4iISMj8+its3gw2G5x/fqijOSIx8Q2s1nwOHmzCwYPNQh2OiIhwEonU+++/z8GDB+nevTvNmzfnmWeeYefOneURm4iISIUqKCpx5pnQtGloYzlaWFgGAPv2tQIsoQ1GRESAk0ikrrjiCmbPns2OHTu49dZbmT59Og0bNqRv37589NFH5Ofnl0ecIiIi5WrPHlixwpyPiQltLEezWg8QFfUTAPn5ESGORkRECpx0D/DExETGjBnDb7/9xoQJE/jyyy+55pprqFu3Lo8++ijZ2dllGaeIiEi5+v138Higbl0477xQR3NErVpPYLMdIDs7if37W4Y6HBER+ctJjyO1Z88e3n33XaZMmcLWrVu55pprGDFiBNu3b+fZZ5/l22+/ZcGCBWUZq4iISLkxDPOxYUOIqEQXfhyO/wGwY8cFGEZYiKMREZECpU6kPvroIyZPnsz8+fNp3bo1t99+OzfccAPx8fGBbdLS0mjVqlVZxikiIlJu8vNh9+7QnNtqBaez6HXR0R7Cw7cA4PM5Ki4oERE5oVInUsOHD+e6665j+fLlnH322UVuU7duXR5++OFTDk5ERKS8ud3w7ruwfTtYLNCiRcWd226HxMQMunZ1UdQtxvXqvYzdvgm/vwaHDzcF/BUXnIiIHFepE6ldu3YRdYIRCiMjIxk3btxJByUiIlJRvvrKTKIiImDAALNrX0Wx2cBmc5GfP4/s7OBxGiMj91Cz5ssAZGc/iFmtL6vighMRkeMqdbGJmJgY9u7dW6j9wIEDhIWp77aIiFQtmZnmY5cu0LhxaGLwet14PFmBKSxsG82bv4vV6sPjOQOvt3doAhMRkWKV+oqUUXA37jHy8vKw2+2nHJCIiEhF+e9/zWp9ANHRoY2lQI0a62nd+kNstjw8nmjc7uvQ2FEiIpVPiROpl182uxdYLBbefvttoo/6i+Pz+Vi6dCktW6osq4iIVA2GAePHg88HdepAmzahjggiIw/Sps1MwsLycblS2Lx5ACkpNbCe9GAlIiJSXkqcSL344ouAeUVq0qRJQd347HY7qampTJo0qewjFBEROUkZGeByFb1u0iRYs8ac793bvF8p1Jo2nUdYWD4ZGY347bfBREWpy7yISGVV4j8bmzdvBuCiiy7io48+IiEhodyCEhERKQsuF8ybZ1bmO9ann5qPPXpAgwYVG1dxatTYCMCGDb00ZpSISCVX6u/fFi9eXB5xiIiIlAu3G7KKKHbn/6uSeHJyxcZTHJstB4vFvA/Z6y1mYCkREak0SpRIjRkzhieffBKn08mYMWOOu+2ECRPKJDAREZHyYBiwaZNZ8hygchScNWjSZAEAbnctPB4lUiIilV2JEqmff/4Zr9cbmC+OxaKqQiIiUnmtXQuLFsG+feZy06bmALweTyijyqJJk/epUeM3ANat64eq9ImIVH4lSqSO7s6nrn0iIlJVzZ9vFqAID4e0NHjmGVi2rHwSKYcDoqLMIhbOYi4w1a69mtjYKwgL24jfb2X9+j64XJXkhi0RETmuSlCjSEREpGLk55uPQ4ZAx47lN3aUwwHdumVQs6aLpCTo2vXIuQvExc2jYcO/Y7W68Xhi+d//riEzM6V8AhIRkTJXokTqqquuKvEBP/roo5MORkREpLzk5sJfvdTL/b6o8HBwOl3k58/D53OTk3Pk3AB2ewapqc9gsfjx+89m3bo+ZGaqO5+ISFVSokQqLi6uvOMQEREpVx9+aCZTDgdU1Age+flu/P4svN7g7oORkTuxWPx4PDUwjDfJz/8SKKK0oIiIVFolSqQmT55c3nGIiIiUqz//NB+vvRYiIkIbSwHDCEe97EVEqiZrqAMQERGpSJWhk4XF4gt1CCIicopK9DVYhw4dWLRoEQkJCZx11lnHLXP+008/lVlwIiIi1Y9BgwbfAJCXl4TDEeJwRETkpJQokbr88stx/PVJf8UVV5RnPCIiItVabOwOatTYiN8fxu7dV9GwYagjEhGRk1GiRGrcuHFFzouIiFQFPh/4/aGOwmSz5QCQlZWEx1M7xNGIiMjJOuk7XH/88UdWr14NQOvWrenYsWOZBSUiIlKWPvrILD9uliUPdTQiIlIdlDqR2r59O9dffz3Lly8nPj4egEOHDpGWlsYHH3xA/fr1yzpGERGRU/LX935ceWXoK/ZZrd4TbyQiIpVeqav2jRw5Eq/Xy+rVqzl48CAHDx5k9erV+P1+Ro4cWR4xioiInJR162DWLDAMczklJbTxWCw+Gjb8L2B27RMRkaqr1Fekvv76a7755htatGgRaGvRogWvvPIKXbt2LdPgRERETtaUKXDLLWaXPoAGDULfrS8hYRMxMbvxeiPYvLl7pSjFLiIiJ6fUiVRKSgpeb+FuCT6fj7p165ZJUCIiIiWRkQEuV9HtBUlUy5Zw3nlmInWc0TsqhM2WB0BWVjJeb3RogxERkVNS6kTq+eef58477+TVV1+lU6dOgFl44u677+af//xnmQcoIiJSHJcL5s0Dtzu4ff9+M4mKjIThwyE7OzTxBTOoXft3ALxeVbwQEanqSpRIJSQkBA3C63a7Offcc7HZzN3z8/Ox2WzcdNNNGmdKREQqlNsNWVnBbQWJk9VqTpVBfPwWatVah99vZcuWbqEOR0RETlGJEqmJEyeWcxgiIiLVW2RkBgAZGY3Jztb4USIiVV2JEqmhQ4eWdxwiIiJl5tChUEcQLDJyP/XrrwAgPz8yxNGIiEhZOOkBeQFyc3PxeDxBbbGxsacUkIiIyKlYvRo+/ticb9cutLEA2Gx/cuaZ7xMW5iEvL4Zt27qEOiQRESkDpe457na7GTVqFLVr18bpdJKQkBA0iYiIhMqyZTBzplloolUreP750MZjsXiJjZ1NWJiHQ4ca8uOPt+B2q1ufiEh1UOpE6v777+err77i9ddfx+Fw8Pbbb/P4449Tt25dpk6dWh4xioiInNDu3bBokTl/9tlw++0Q6k4SyckLsdkOkpcXw++/X6eS5yIi1Uipu/Z99tlnTJ06lQsvvJDhw4fTtWtXmjZtSsOGDZk2bRqDBw8ujzhFRESO6/Bh8zEpCS69FMLCQhtPRMQqatf+GoBNmy7F54sIbUAiIlKmSn1F6uDBgzRu3Bgw74c6ePAgAF26dGHp0qVlG52IiEgJ+XzmY6gTqAI1anyIxeInL68FGRktQx2OiIiUsVInUo0bN2bz5s0AtGzZkpkzZwLmlar4+PgyDU5ERKQkcnJg7lxzPiYmtLEUsFrzAPB6k0MciYiIlIdSJ1LDhw/n119/BeDBBx/k1VdfJSIignvuuYe///3vpQ7g1VdfJTU1lYiICM4991y+//77425/6NAh7rjjDurUqYPD4aB58+bMLfjrKSIip6VNm8yufTEx0KtXqKMREZHTQanvkbrnnnsC8z179mT16tX89NNPNG3alDPPPLNUx5oxYwZjxoxh0qRJnHvuuUycOJFevXqxdu1aatcuXNXI4/Fw8cUXU7t2bWbNmkW9evXYunWrroSJiJzmDMN8rFkTKksB2fDwPX/NWUIah4iIlI9TGkcKIDU1ldTU1JPad8KECdx8880MHz4cgEmTJjFnzhzeeecdHnzwwULbv/POOxw8eJBvvvmG8PDwwPlFREQqksMB4eEQFQU2GzidwetbtFhIXNyXGIYFj6dpaIIUEZFydVKJ1KJFi3jxxRdZvXo1AK1atWL06NH07NmzxMfweDysXLmSsWPHBtqsVis9e/ZkxYoVRe7z6aef0rlzZ+644w4++eQTEhMTGTRoEA888ABhxdxdnJeXR15eXmA5MzOzxDGKiEjVUHBFqiI4HNCtWwZOp4uICLNKYNeukJ9vrrdYcmjVagQABw6k4ffXq7jgRESkwpT6HqnXXnuN3r17ExMTw913383dd99NbGwsl156Ka+++mqJj7N//358Ph9JSUlB7UlJSezevbvIfTZt2sSsWbPw+XzMnTuXRx55hBdeeIGnnnqq2POkp6cTFxcXmFJSUkoco4iIVG7798OsWfDpp+ZyRVTsCw8Hp9OF1zuP3NxZ+HyzyMmZRXa2OdWqdRt2+5/4fMns3du7/AMSEZGQKPUVqfHjx/Piiy8yatSoQNtdd93F+eefz/jx47njjjvKNMCj+f1+ateuzZtvvklYWBgdO3Zkx44dPP/884wbN67IfcaOHcuYMWMCy5mZmUqmRESqgd9/hyuugB07zOWCK0MVxet1A1n4/eD1gscD0dE7qVvXHAokO/sR/P5sIKvighIRkQpT6kTq0KFD9O5d+Bu2Sy65hAceeKDEx6lVqxZhYWHs2bMnqH3Pnj0kJxddKrZOnTqEh4cHdeNr1aoVu3fvxuPxYLfbC+3jcDhwOBwljktERCqPjAxwuQq3r14NAwaA2w21akHfvtCgAVhCWNfBYvHRosVnWCwGBw50wmLpBnwRuoBERKRclbprX//+/fn4448LtX/yySf07du3xMex2+107NiRRYsWBdr8fj+LFi2ic+fORe5z/vnns2HDBvx+f6Bt3bp11KlTp8gkSkREqjaXC+bNM7vvHT09/7yZRJ1xBtx5JzRsGNokCqB+/e+IidmN1xvJtm0DQhuMiIiUuxJdkXr55ZcD861bt+bpp59myZIlgYTn22+/Zfny5dx7772lOvmYMWMYOnQonTp14pxzzmHixIm43e5AFb8hQ4ZQr1490tPTAbjtttv417/+xd13382dd97J+vXrGT9+PHfddVepzisiIlWH2w1Zx/SOK6gh1KmTWTnv2PWhUKuWWYBp8+YLyc+vJKMCi4hIuSlRIvXiiy8GLSckJLBq1SpWrVoVaIuPj+edd97hH//4R4lPPnDgQPbt28ejjz7K7t27ad++PV988UWgAMW2bduwWo9cNEtJSWH+/Pncc889nHnmmdSrV4+77767VF0KRUREyoPFYvaWyM2ND20gIiJSIUqUSG3evLncAhg1alRQ4YqjLVmypFBb586d+fbbb8stHhERERERkRMp9T1SRzMMA6MiB+8QERGphKxWDxERZlUMw6iAGuwiIhJyJ5VITZ06lbZt2xIZGUlkZCRnnnkm7733XlnHJiIiUqTK9R2en0aN5mG3u8nJicflahDqgEREpAKUuvz5hAkTeOSRRxg1ahTnn38+AMuWLePWW29l//793HPPPWUepIiISIFdu2DlSnM+ISG0sdhsbuLjP8RuN7vAr1t3GX5/eGiDEhGRClHqROqVV17h9ddfZ8iQIYG2/v3706ZNGx577DElUiIiUi4OH4aNG+GTT8zlWrXMsaSWLw9NPFarl5Ytp2C378fnC2fNmsvJyGgammBERKTClTqR2rVrF2lpaYXa09LS2LVrV5kEJSIicrRly+CoYQcBcxDemBBWGa9ffyGRkfvx+aL57bchuFyJoQtGREQqXKnvkWratCkzZ84s1D5jxgyaNWtWJkGJiIgcbdMm8zExEbp1gxEj4JxzQhdPZOQq6tX7GoDDh/uSk6MkSkTkdFPqK1KPP/44AwcOZOnSpYF7pJYvX86iRYuKTLBERETKSrducMYZoY4C6td/GIvFz8GDrcnPbxHqcEREJARKfUXq6quv5vvvv6dWrVrMnj2b2bNnU6tWLb7//nuuvPLK8ohRREROY3l55lRZ2GzZOJ2/APDnn5eENhgREQmZUl2R8nq9/O1vf+ORRx7h/fffL6+YRERE+M9/4MUX4fvvwecz22yl7kdRvvLzI0MdgoiIhEip/iSFh4fz4Ycf8sgjj5RXPCIichrJyACXq3D7pEnw7LNHluPjoWVLaNKkwkITERE5rlJ/t3fFFVcwe/ZslTkXEZFT5nLBvHngdge3/+tf5uPgwVCvHkREgMVS8fEVpV69ZQD4/WEYRliIoxERkVApdSLVrFkznnjiCZYvX07Hjh1xOp1B6++6664yC05ERKo/txuysoLb8vPNx+uvh9WrC68PFYvFz8UX3wbAnj3nKZESETmNlTqR+ve//018fDwrV65kZcHQ8n+xWCxKpEREpNqKitpHfLxZi33r1t6EheWHOCIREQmVUidSmzdvLo84REREqhS/P4KwsEpyqUxERCpcqRKpb7/9ls8++wyPx0OPHj3o3bt3ecUlIiKnKcMwp1BwOCA83JyPijKrBB7dgz0qKjRxiYhI5VPiRGrWrFkMHDiQyMhIwsPDmTBhAs8++yz33XdfecYnIiKnCcOAn3+G5cvB4zHbCpKaiuBwQLduGTidZhnBiAhISoKuXY/cs5WQ8CEAfn801lKPxCgiItVJif8MpKenc/PNN+NyucjIyOCpp55i/Pjx5RmbiIicRubNg88+g4MHzaSmd2+oU6fizh8eDk6nC693HtnZs8jNnYXPN4ucnFlkZ8/C651C3bqPApCbO4Iw1ZkQETmtlTiRWrt2Lffddx9hf/3luPfeezl8+DB79+4tt+BEROT0kJMDP/xgzvfoAWPGQP/+oSl57vW68Xiy8Hqz8PvNR48niwYNPiI8PJvs7Hrk5g6t+MBERKRSKXEilZ2dTWxsbGDZbrcTERFBVmWpSSsiIlXW4cNH5rt0Abs9dLEUpUaN9SQl/Y5hWNiyZTBQgX0ORUSkUipVsYm3336b6OjowHJ+fj5TpkyhVq1agTaVPxcRkdLIz4f/+z9zPikptLEUxWr10rz5HAC2bz8Xtzs1tAGJiEilUOJEqkGDBrz11ltBbcnJybz33nuBZY0jJSIipXXokDmFhZkD8FY2TuduIiJceL2RbN58EZGRoY5IREQqgxInUlu2bCnHMERE5HQXFgZxcaGOoih+ALzeKPz+StbnUEREQkbFW0VEREREREpJiZSIiMhxREXtA8DvV71zERE5QomUiIiEVMFgt5WRxZJDgwaLANizp12IoxERkcpEiZSIiIREVhZMnw6TJ5vL8fEhDadI4eHbCQ/PJScnnu3bzwt1OCIiUomUqvy5iIhIaWRkgMtVuH3nTnPA3QMHzOXYWLjyyoqNrTS83igMQ989iojIESeVSG3cuJHJkyezceNGXnrpJWrXrs28efNo0KABbdq0KesYRUSkinK5YN48cLuD2z/55EgS1b8/tGoFEREVH5+IiMjJKvXXa19//TVt27blu+++46OPPiIrKwuAX3/9lXHjxpV5gCIiUrW53WY3voJp/XpYtsxcd/vt0LVr5U2ibLadAPj96sAhIiLBSp1IPfjggzz11FMsXLgQu/3IeBrdu3fn22+/LdPgRESk+tm713xs0ABuvTW0sRxPRMR+nM7/ArBzZ8cQRyMiIpVNqb9i+/3335k+fXqh9tq1a7N///4yCUpERKqfw4dh4UL44w9zOToaLJbQxlQ8g4YNP8di8ZGR0YS9e9uGOiAREalkSn1FKj4+nl27dhVq//nnn6lXr16ZBCUiItWLywVvvQW//w5+P9SvD5ddFuqoilejxgZiY7diGOFs2tQXqLQZn4iIhEipr0hdd911PPDAA/znP//BYrHg9/tZvnw59913H0OGDCmPGEVEpIr7/XfzilSNGnD11VC3LiQlhTqq4jkchwHweBqRlxcf2mBERKRSKvUVqfHjx9OyZUtSUlLIysqidevWdOvWjbS0NP7xj3+UR4wiIlLF+XzmY6NGZhIlIiJS1ZX6ipTdbuett97ikUce4X//+x9ZWVmcddZZNGvWrDziExERERERqXRKnUgtW7aMLl260KBBAxo0aFAeMYmIiIiIiFRqpe7a1717dxo1asRDDz3EqlWryiMmERGpRnJzYcMGc95a6r86oeFwZP41pyITIiJStFL/Sdu5cyf33nsvX3/9NWeccQbt27fn+eefZ/v27eURn4iIVHHffgvbt4PdDu3bhzqaE3M4MkhJ+QaAvLwWIY5GREQqq1InUrVq1WLUqFEsX76cjRs3MmDAAN59911SU1Pp3r17ecQoIiJV2GGzAB4dOlSNQhMJCasIC/OSlVWP3Nz2oQ5HREQqqVPqZNGoUSMefPBBnnnmGdq2bcvXX39dVnGJiEg18PPPsGKFOR8TE9pYSspi8QOQl1cDde0TEZHinHQitXz5cm6//Xbq1KnDoEGDOOOMM5gzZ05ZxiYiIlXcxx+D1wsNGkCnTqGORkREpOyUumrf2LFj+eCDD9i5cycXX3wxL730EpdffjlRUVHlEZ+IiFRR+fmwZYs536iReY+UiIhIdVHqRGrp0qX8/e9/59prr6VWrVrlEZOIiFQDL74I//2vOd+wYWhjKR0j1AGIiEgVUOpEavny5eURh4iIVDO//WY+XnSReUWqavBTs+bvAOTnR2LRLVIiIlKMEiVSn376KX369CE8PJxPP/30uNv279+/TAITEZGqa906WLzYnI+LC20spVGz5nRiY7eQn29n9+406tQJdUQiIlJZlSiRuuKKK9i9eze1a9fmiiuuKHY7i8WCz+crq9hERKSSy8gAl6tw+0MPwY4dZpe+9u3BqAK95aKjt1O37rMAbN7cHY+nCmWAIiJS4UqUSPn9/iLnRUTk9OZywbx54HYHt69ZYz4OHQpOJ2RlVXxspdW27TuEhWVx+HAKO3acjdMZ6ohERKQyK3X586lTp5KXl1eo3ePxMHXq1DIJSkREqg6320yUjp7y8811lekeI4cDoqOPTFFRYLOZiV50NDid5sjBmZmNOcVhFkVE5DRQ6mITw4cPp3fv3tSuXTuo/fDhwwwfPpwhQ4aUWXAiIlL1+Hxw+HCoowjmcEC3bhk4nUf6IUZEQFISdO0Kfr+bli0/AMDrrSIjB4uISEiVOpEyDANLEV8xbt++nbiqdEexiIiUi6++gp07zatRZ5xxZCypUAoPB6fThdc7D6/3SD9Enw9ycqBevU+x27fj89XlwIHzAG/oghURkSqhxInUWWedhcViwWKx0KNHD2y2I7v6fD42b95M7969yyVIERGpOnbtMh+vugpatqwciVQBr9eNx2PesBUeDn4/OBw7qFPHHPAqO/sR/P4slEiJiMiJlDiRKqjW98svv9CrVy+io6MD6+x2O6mpqVx99dVlHqCIiFQdu3ebE0CNGqGNpaSSklZiscCBA52wWLoAX4Q6JBERqQJKnEiNGzcOgNTUVAYOHEhERES5BSUiIlXTl1+aXeVq14bWrUMdTclYrebVJ7e7IUd9RygiInJcpb5HaujQoeURh4iIVAM5OeZjjx5mgYfKzmp1kZCwFgCPp4pcQhMRkUqh1ImUz+fjxRdfZObMmWzbtg2PxxO0/uDBg2UWnIiIVB07d1aN8aKO5nT+l7AwLy5XChkZ7apMd0QREQm9Ug+U8fjjjzNhwgQGDhyIy+VizJgxXHXVVVitVh577LFyCFFERCq7zz+Ht96CzExzOTY2tPGUlNVqZn67d7dDY0eJiEhplPqvxrRp03jrrbe49957sdlsXH/99bz99ts8+uijfPvtt+URo4iIVHLr15uPLVvCiBGQnBzaeERERMpbqROp3bt307ZtWwCio6NxuczBDfv27cucOXPKNjoREalSunWD+vVDHUVp+EMdgIiIVFGlTqTq16/Prr8GCWnSpAkLFiwA4IcffsBRFe4sFhGRMuP3w++/g/uvMW6tVah3XGTkbuz2DQBkZ9cKcTQiIlLVlPpP3pVXXsmiRYsAuPPOO3nkkUdo1qwZQ4YM4aabbirzAEVEpHJ6/32zOt/UqeDzQWqqWfa8qqhb979YLAb797fG5WoY6nBERKSKKXXVvmeeeSYwP3DgQBo0aMCKFSto1qwZ/fr1K9PgREQktDIy4K8e3EE8Hhg2zEygIiKgQwe46CKwWCo8xJNms5m12g8ebBniSEREpCoqdSJ1rM6dO9O5c+eyiEVERCoZlwvmzTvSda+A220mUQAPPFDxcZUlw6hC2Z+IiFQaJUqkPv300xIfsH///icdjIiIVD5ud/D4UIYBU6aY84mJEBl5ZCBeERGR00WJEqkrrriiRAezWCz4Cr6iFBGRasnthm3bzPkXX4S/6g+JiIicVkqUSPn9Kg8rIiKm7duPzLdrV/USKYfDRcOGXxMTs+WvlipUalBERCqNU75HSkRETh95efDhh+b8WWeFNpaTUafOUho2nIvVavaeyMtrzqFDjUMclYiIVEWlTqSeeOKJ465/9NFHTzoYERGp3BYuhPx8c37YsJCGclLq1v0vVquPQ4casGtXT2rWTEE90kVE5GSUOpH6+OOPg5a9Xi+bN2/GZrPRpEkTJVIiItVURgasXGnO9+oF4eGhjefkGABs2NAbw6hDzZohDkdERKqsUidSP//8c6G2zMxMhg0bxpVXXlkmQYmISOWTl2c+RkfDeeeFNhYREZFQK5M7bGNjY3n88cd55JFHyuJwIiJSiVWlQXdFRETKS5mVKnK5XLhcrrI6nIiIiIiISKVV6q59L7/8ctCyYRjs2rWL9957jz59+pRZYCIiUrlUtTLnIiIi5anUidSLL74YtGy1WklMTGTo0KGMHTu2zAITEZHKIzMTPvvMnG/WLLSxiIiIVAalTqQ2b95cHnGIiEglZRgwb5756HDAZZeFOqLCHI7CVQSjosBmA6fTXLbZ3Fit+RUfnIiIVEsakFdERI5r2zZYs8ac790brGV2d23ZsNuhc+cMnM7g+3QjIiApyeCSS34jIWEG8fGfYLVmA+D3h6tohoiInJJSJ1K5ubm88sorLF68mL179+L3+4PW//TTT2UWnIiIhN7hw+ZjnTrQvn1IQylSeDg4nS683nl4ve6j1vhxOt8iIWFDoCU3txZ//nk22dk1A1eqRERETkapE6kRI0awYMECrrnmGs455xws+kpPRKTacrlg9mxzPjY2pKGckNfrxuPJCixHRmZht5tJ1P7952C338ratftxu93FHUJERKTESp1Iff7558ydO5fzzz+/zIJ49dVXef7559m9ezft2rXjlVde4Zxzzjnhfh988AHXX389l19+ObML/tKLiEiZWbbMTKZiYqBXr1BHc/I2bx5GkyZnA1+EOhQREakmSt3TvV69esTExJRZADNmzGDMmDGMGzeOn376iXbt2tGrVy/27t173P22bNnCfffdR9euXcssFhERCbZunflYsyYkJIQ2FhERkcqk1InUCy+8wAMPPMDWrVvLJIAJEyZw8803M3z4cFq3bs2kSZOIiorinXfeKXYfn8/H4MGDefzxx2ncuHGZxCEiIkf4fDBmDBQMHaiPWhERkWCl7trXqVMncnNzady4MVFRUYQfU2/24MGDJT6Wx+Nh5cqVQeNPWa1WevbsyYoVK4rd74knnqB27dqMGDGC//73v8c9R15eHnl5eYHlzMzMEscnIlLdZWSYXfeOdvgw3H03LF5sLl9yCZx3XsXHJiIiUpmVOpG6/vrr2bFjB+PHjycpKemUik3s378fn89HUlJSUHtSUhJrCmrtHmPZsmX8+9//5pdffinROdLT03n88cdPOkYRkerM5TLHiCqov3D4MLz5JuzebVbDGz/ebM/KKv4YIiIip6NSJ1LffPMNK1asoF27duURz3EdPnyYG2+8kbfeeotatWqVaJ+xY8cyZsyYwHJmZiYpKSnlFaKISJXjdh9JlJYuNZOo6Gi4/XZz3KgvVJ9BRESkkFInUi1btiQnJ6dMTl6rVi3CwsLYs2dPUPuePXtITk4utP3GjRvZsmUL/fr1C7QVjGNls9lYu3YtTZo0CdrH4XDgcDjKJF4RkerO4zEf27aF1NSQhiIiIlKplbrYxDPPPMO9997LkiVLOHDgAJmZmUFTadjtdjp27MiiRYsCbX6/n0WLFtG5c+dC27ds2ZLff/+dX375JTD179+fiy66iF9++UVXmkRETsHevbB9e6ijEBERqRpKfUWqd+/eAPTo0SOo3TAMLBYLPp+vVMcbM2YMQ4cOpVOnTpxzzjlMnDgRt9vN8OHDARgyZAj16tUjPT2diIgIzjjjjKD94+PjAQq1i4hIyf34I8yZc2S5hL2nRURETlulTqQWF5RxKiMDBw5k3759PProo+zevZv27dvzxRdfBApQbNu2Dau11BfORESkFP7803xMSYHu3dWtT0RE5ERKnUhdcMEFZR7EqFGjGDVqVJHrlixZctx9p0yZUubxiIicrlq1qj5JVETEAQD8fn0ZJyIiZa/UidTSpUuPu75bt24nHYyIiEjZMGjY0OyruH//mSGORUREqqNSJ1IXXnhhobajx5Iq7T1SIiIiZS0i4hBRUfswDCtbtlzMKQx5KCIiUqRS93fIyMgImvbu3csXX3zB2WefzYIFC8ojRhERKWf5+aGOoGxZLAYAhmEjPz8qxNGIiEh1VOorUnFxcYXaLr74Yux2O2PGjGHlypVlEpiIiFSMX36BVavM+SI+4kVERKQIZXYHblJSEmvXri2rw4mISAX55Rfz8cwzoWXLkIZSZqKjd/81V+rvC0VEREqk1H9hfvvtt6BlwzDYtWsXzzzzDO3bty+ruEREpII1aADVYbQJqzWfpk2/ACAn56wQRyMiItVVqROp9u3bY7FYMAwjqP28887jnXfeKbPARESk/O3eDbt2mfPVpSBDZOR+HI7D5Oc7cLvLfsgOEREROIlEavPmzUHLVquVxMREIiIiyiwoERGpGM89BwcPQlQUNGsW6mjKgkGtWusA8PvtQHhowxERkWqr1IlUw4YNyyMOEREJgT17zMdLLoGYmNDGcur8tGr1EUlJ/wNg//522HSLlIiIlJMS94b/6quvaN26NZmZmYXWuVwu2rRpw3//+98yDU5ERMqX12s+hoWFNo6yEBOzhaSk/+H3W1m/vg87dnQPdUgiIlKNlfi7uokTJ3LzzTcTGxtbaF1cXBx/+9vfmDBhAl27di3TAEVE5ORlZIDLVfS66dPhhx/M+ap/NQpsthwAsrKS2bHjHJzOEAckIiLVWokTqV9//ZVnn3222PWXXHIJ//znP8skKBERKRsuF8ybB2534XX//rf5eP75ZsW+qs2gTp1lAOTmJoQ4FhEROR2UOJHas2cP4eHF37Rrs9nYt29fmQQlIiJlx+2GrKzgtuxs82oVQIsWVb9iX3T0t8THb8Dns7F5s7r0iYhI+SvxPVL16tXjf//7X7Hrf/vtN+rUqVMmQYmISPn6/HMzkYqOhpSUUEdz6my2AwAcPlyPnJwaIY5GREROByVOpC699FIeeeQRcnNzC63Lyclh3Lhx9O3bt0yDExGR8lHQgeCpp8xkqrowjCp+aU1ERKqMEnft+8c//sFHH31E8+bNGTVqFC1atABgzZo1vPrqq/h8Ph5++OFyC1RERMrG2rVHuvXVrg07doQ2HhERkaqoxIlUUlIS33zzDbfddhtjx47FMAwALBYLvXr14tVXXyUpKancAhURkbLx5Zfg80HbtnDGGUqkRERETkaphips2LAhc+fOJSMjgw0bNmAYBs2aNSMhQRWSRESqCo/HfLzssqpfZKKA3f4nAIZR4h7rIiIip+SkxnxPSEjg7LPPLutYRESknHk8RwbhrS6czh0kJb0KwP79rUIcjYiInC701Z2IyGnko48gJwfsdqhVK9TRlI369RcQFubG7a7Dzp0dQx2OiIicJpRIiYicJnbvNgtNAPTrB1FRoY2nrFitPgDy8hKAatJXUUREKj0lUiIip4mZM83H2rWhdevQxlK2jFAHICIip6GTukdKRESqlu+/P1LyfOBAsFaRr9GsVnA6i14XE5ND7dqv0rTpM4AKTYiISMVSIiUiUo3l55v3Ra1YYS536gQ1aoQ2ppKy2yExMYOuXV3k5wevi4z8ncaNRxIevheA/PxG7Np1YcUHKSIipy0lUiIi1ZRhwG23HUmievSA888PbUylYbOBzeYiP38e2dnuoHW1as0lPHwvPl8cu3ZdQ1TUKHJyvgSyQhOsiIicdpRIiYhUU9u2mYPvWixw7bXQsmWoIzo5Xq8bjyc4QfL78wDIzW3N/v0dadBAf85ERKRiqUO5iEg1tXGj+Wi3V90kSkREpLLSV3giIlVYRga4XIXb58+He+4x5xs0qNiYRERETgdKpEREqjCXC+bNA/dRtxAdOgTp6eD3Q1oa9O4NPl/IQhQREamW1LVPRKSKc7shK+vItGOHmUTVqAGvvgqRkaGOUEREpPrRFSkRkWrEMOCbb8x5p9OsfFedREYeJDn5Z5KTf/mrxRLKcERE5DRWzf7Eioic3lavhnXrzPm+fUMbS1mKidlJ48YLSUjYEmjzep3k5amKhoiIhIYSKRGRaiInB+bMMeebNoW2bUMbT1lKTV1MQsIWDAMOHmzKrl0dyM1tToMGYaEOTURETlNKpEREqokVKyA72xw36tJLQx1N2bJazWoZa9f2Z/fuswCz66KIiEioqNiEiEgVZxjw7bewbJm5fN55kJAQ2pjKi98fHuoQREREAF2REhGp8tatM8eNAjjrLOjRI7TxiIiInA6USImIVHF795qPTZtCv35m1z4REREpX+raJyJSxRmG+RgZqSRKRESkoiiREhGpwnbvPnJvlMMR2lhEREROJ0qkRESqsNmzISMD4uMhLS3U0YiIiJw+lEiJiFRhu3ebj02aVN9KfSIiIpWRik2IiFRRc+bAu++a80lJoY3lZNjtYLMVPx5UwT1fdnsGdntWxQYnIiJyAkqkRESqqK+/Nh/btIFOnUIbS2k5HNCxYwZJSS66doX8/OD1FouH2rW/Iibmfc488xssFrOihtcbFYJoRUREClMiJSJSxdWsWfWq9YWHQ0SEC79/Hjk5brze4PVt2kwiLm5jYDkjoxE7dnQiI6NRBUcqIiJSNCVSIiJV0M6d8Nln5rytCn+SG4YbrzcLjye43encDkB+/g2sW1efvXtVklBERCqXKvznV0Sk+svIAJercPtzz8GaNZCYCOecU/FxVRS//zo8ntWA7pESEZHKRYmUiEgl5nLBvHngdge3//yz+XjNNWbXvizlGSIiIhVKiZSISCXndhdOlAruKapq90aJiIhUFxpHSkREREREpJSUSImIVDFeL+zfH+ooRERETm/q2iciUkVkZ8M338BPP0FODlitcP75ZgW/6iQqaj9hYWYZP8NQtT4REamclEiJiFQRc+bAqlXmfFwcDB0KZ55Z3RIpg+bNP8NiMTh8uA12e3KoAxIRESmSEikRkSoiJ8d8vOAC6NYN6tQJbTzloW7dlcTHb8PnC2fnzutJTQ11RCIiIkX7//buPbyK6t7/+Hv2Nfd7yIX7HQQBDULRolZog4oFa1t/XioWWmuLrR7b8yh9Wu2pTw+2VlvbeqTVCuf8qsVyevC0+gNEFGolioBUKYig3CFcQy47l32Z9ftjzE5CAhJIMjvh83qe/ezZs9bMfCcrm6wvs2aN7pESEelm8vKcYX09TSBQxaBBKwH46KMpRCI5LkckIiJyaj3wT7GISM9jDITDbkfRuYYOXYbPF6aqqjf791/idjgiIiKnpURKRCTBRSKwdCns3+98zumBF2p8vjry898HYNu269CfJxERSXS6R0pEJMEtXQrvvecM57vmGigudjuijuf1RuLLoVCBi5GIiIicGSVSIiIJbvdu5/3zn4exY92NpbP06VMGQG1tD7zcJiIiPZLGToiIdBOZmW5H0Dl8vlr69HkLgB07prkcjYiIyJlRIiUiksBeeQWOHXOW/X53Y+ksSUkVWJYhGg1w/PhQt8MRERE5IxraJyKSoIyBefMgFoP+/XvevVFe71FSU/+X0aOdYX3RaJLLEYmIiJw5JVIiIi6qqIDKyrbLfv5zOHrUWZ45Eyyry8LqdBkZuxg06Cq83moATpzoz44dpS5HJSIicuaUSImIuKiyEpYtg1CoddmiRc77tddCVlZXRtX5cnO34PVWY9upbN16PUeODAJ6UKYoIiI9nhIpERGXhUJQU9N6vW0776NGdW08XSkWy6aycrDbYYiIiLSbJpsQERERERFpJyVSIiIiIiIi7aRESkQkAb33HtTXO8s+DcIWERFJOEqkREQSjDHwwgvO+2c/C9nZbkckIiIiJ1MiJSKSYF56qWmiiR//2N1YzlYwCCkpztW01FRIS2v5Ki7e/HFNXW4TEZHuSX/BREQSSDQKGzY4y7fc4iQd3U0wCJdfXkFubiUFBTB5snNejfz+ckaO/BEADQ3j3QlSRETkHCmREhFJIMY0LZeUuBfHufD7ITW1kmh0GbFYiLo6iESaynNz/4HHU0csNoSGhkuAWtdiFREROVtKpEREpFNEoyFsu4ZIBMLh5uvrADAmG40wFxGR7kp/wURERERERNpJiZSISALZuNF5tyzw6F9oERGRhKWhfSIiCWLFCnjzTWf5kksgEHA3ns5iWeaTK4mIiCQ4JVIiIgng2LGmJGrqVLj0Unfj6TyG4uL1ANh2kcuxiIiInD0NHBERcZltw5/+5CxnZ8NllzlD+3oajyfMyJFLycraQywWpL5+rtshiYiInLWESKSeeOIJBgwYQFJSEhMnTmTdunWnrPvUU08xefJksrOzyc7OZurUqaetLyKS6MrL4fBh5+G1N9zgdjSdw++v5eKLf09BwXsYY7F79//BmGK3wxIRETlrridSzz//PPfeey8PPvggGzduZOzYsZSWlnL48OE2669evZqbbrqJ1157jbKyMvr27cvnPvc59u/f38WRi4h0jMZnRyUnQ+/e7sbSWQoKNpGWdphwOJVNm2Zx7NhEt0MSERE5J64nUo899hhf//rX+epXv8oFF1zAggULSElJ4Zlnnmmz/rPPPsu3vvUtxo0bx4gRI3j66aexbZtVq1Z1ceQiInKmvF7nibxHj46gsrK/y9GIiIicO1cTqXA4zIYNG5g6dWp8ncfjYerUqZSVlZ3RPmpra4lEIuTk5LRZ3tDQQFVVVYuXiEgiefdd513TnYuIiHQfrv7ZPnr0KLFYjIKCghbrCwoKKC8vP6N93HfffRQXF7dIxpqbP38+mZmZ8Vffvn3POW4RkY6yYwe8+qqzPGGCu7F0pmCwEgBjeuAsGiIicl7q1v//+fDDD7N48WKWLl1KUlJSm3XmzZtHZWVl/LV3794ujlJE5NSOH3fec3J67pTnqakHKCraBDhD+0RERHoCV58jlZeXh9fr5dChQy3WHzp0iMLCwtNu+/Of/5yHH36YV155hTFjxpyyXjAYJBgMdki8IiIdra7Oee+J0503ys3dimUZjhwZQUXFYLfDERER6RCuXpEKBAKUlJS0mCiiceKISZMmnXK7n/3sZzz00EMsX76c8ePHd0WoIiIdrqwMvvtdZzk7291YOpNl2QDU1fXgkxQRkfOOq1ekAO69915mzZrF+PHjmTBhAr/85S8JhUJ89atfBeC2226jd+/ezJ8/H4Cf/vSnPPDAAzz33HMMGDAgfi9VWloaaWlprp2HiEhbKiqgsrL1+n37YOpUaGiAoiKYPr3rYxMREZGz53oideONN3LkyBEeeOABysvLGTduHMuXL49PQLFnzx48zaayevLJJwmHw3zxi19ssZ8HH3yQH/3oR10ZuojIJ6qshGXLIBRquX7TJieJGjAAvvY1iETciE5ERETOluuJFMBdd93FXXfd1WbZ6tWrW3zetWtX5wckItKBQiGoqWn6bNvw3nvOckEBBIM9PZEybgcgIiLS4br1rH0iIt3Rjh3Os6MsC066uN4DRcjJ2eYsRVJcjkVERKTjKJESEeli1dXO+wUXwLRp7sbS2ZKT15OcfJyGhjQOHNDkQCIi0nMokRIR6ULV1bBmjbOckeFuLF3B6z0GQHn5RcRibT/vT0REpDtSIiUi0oW2bnWSqawsuOYat6PpXGlpO0hK2gpAJKIkSkREepaEmGxCROR8YTuPVKJvX8jNdTeWzpSR8TIDB/4OyzLU1BRSXj7O7ZBEREQ6lBIpERHpcDk5/4NlGerrR7B58xeIRv1uhyQiItKhNLRPREQ6nGU5l97C4SHYtpIoERHpeZRIiYh0kbo65x4pAK/X3VhERETk3Ghon4hIF6iuhkWL4PhxCASgpMTtiDqPz1cbn2TCGF2NEhGRnkmJlIhIF9iwwUmiMjLg5puhoMDtiM5OMAj+j3OjlBTw+SA1tWWdK674N4LBfYTDmYTDw7s+SBERkS6gREpEpAtEIs77qFHdO4m6/PIKUlMrAUhKcs5l8mSIRp06ycmbGTbsUQD27/8CqalBt8IVERHpVEqkREQ6mTFQU+N2FOfO74fU1EoikWVEIiEAYjHn3q9IBNLS9jBgwH9hWTEaGqYRCl1AamoPOHEREZE2KJESEelkGzfCu+86y8XF7sbSESKREOFwDX6/81ysaDRKQcE6Bg5chcdjU1dXQH39/cDbbocqIiLSaZRIiYh0oupqWLHCWR42zBna13PYJCVt4uKL1xAMVgFw5MhI9uz5GgMH9uCnDYuIiKBESkSk0+zZAzfcABUVzqQMn/kMWJbbUXUUw8iRC0lL2wdAQ0M6u3ZdycGDF5GamuxybCIiIp1PiZSISCf5/vdh+3ZIToZbb4XCQrcj6jgeTzSeRO3aNZU9eybowbsiInJeUSIlInIOKiqgsrL1+r//HZ591lm+9tqecW9Uc0lJJ+LL5eVKokRE5PyjREpE5BxUVsKyZRAKtVz/+98775de6jx8t76+62PrTEOH/j8AGhqGKIkSEZHzkhIpEZFzFAq1nN7ctuHECWd55syedF+Uw+cLkZ29C4Dq6mvcDUZERMQlHrcDEBHpaV55BcrLneUhQ9yNpTNYlh1ftu1sFyMRERFxjxIpEZEOUl0Nr74KZWXO59JSGDrU3ZhERESkc2hon4hIB9izBxYvhro653NWFlx5pZsRdQ7LCtOnz0oAYjH9CRERkfOX/gqKiHSAv/zFSaIKCuDyy2H4cMjMdDuq9gkGwd9s3oiUFOf5V6mpzueBA99iyJA7SU3dBMD+/VeRlNT1cYqIiCQCJVIiIufImKbJJr74RcjLczeesxEMwuWXV5Ca2jSXe1KSkxhOnbqVXr0eIzPzFQCi0SS2bp1Jff1w+vd3K2IRERF3KZESETlHL70EDQ3g8TgP3+2O/H5ITa0kEllGJNJ8Lvd9DBnyayzLxhiLcHgG779/ARUV/viVKhERkfOREikRkXP0978775/9LN0+uYhEQoTDzuU1vx88nn1Ylk1dXRY7dnyH3r2/RDi8HKg5/Y5ERER6OM3aJyJyjuyPZwMfPdrdODpTfX0W9fWFbochIiKSMJRIiYicgy1bnHukeq4efXIiIiJnTYmUiMhZisXga19zlouKuv+wvtYMycnvABAOp7sci4iISGLRPVIiImehrg5+8Qs4eND5fNNNYFnuxtTR8vI2EQjsJBbzsXPnlXi9bkckIiKSOJRIiYicRkUFVDbNCE5DA/zqV/Dcc3DihLPu05+G9B52wSYYrKJv35cB2Lv3Surrc3rgFTcREZGzp0RKROQ0Kith2TIIhZxJJX77W/joI6csNxe+9S0niQqFTr+f7iQzcw8XXLAEn6+eSKSQAwcmuR2SiIhIwlEiJSLyCUIh54G7//xnUxL1xS/ClVfCFVfA8uWuhtehsrM3M3z4/8Xjsamtzae29svodloREZHWlEiJiJyBujp48UVneehQGDXKeQBvT+L1VjBkyH/j8dgcPjyKvXs/T9++AbfDEhERSUg9rBsgItI59u2D+npnGN/nP+92NJ0jP38Rfn+IUCifrVuvx7aVRImIiJyKrkiJiJyBXbuc9/R0SEtzNZR2CwTajjklBXy+xmnbDcHgbgCOHLkAYzRFn4iIyOkokRIR+QQ7d8Latc7y6NHuxtJeHg9MmlSB31/ZqiwpCQoKoLT0TQoKHiU1dT0A0Wiwq8MUERHpdpRIiYicRmUl/M//OMuFhTCpm01g5/FAcnIldXXLiERaTy0YCKxk0CBnmnNjgpSXf4oDB8Z3dZgiIiLdjhIpEZFTOHoUrr8eysvB74fSUrcjOnuRSIhwuKbFOr8f/P7tABw/fhGW9XP27duIbde0tQsRERFpRpNNiIicwi9+4Qzr83phzhwYMMDtiDpPRcVFGNPL7TBERES6DV2REpHzWkWFM3zvZJs3w09/6ixfcYVzL5GIiIhIIyVSInJeq6yEZcuch+4CGAPbt8PTTzvLffrAlCkQDrsbZ2fw+6vw+fYDEI2muByNiIhI96JESkTOe6EQ1Hx8W9Bbb8Hy5U1ld97pTB/eExOpPn1exeMJU1XVh6qqEeTnux2RiIhI96F7pEREmjlyxHkfOBB++EOYMcPdeDpTUpJzsgcOXIr+HIiIiLSP/nKKiHzsxAn48ENnedAgKCpyNZxO5fWGCQadm8NisYDL0YiIiHQ/SqRERD62dq2TTGVmwpgxbkfTufr3X43fHyIWy6K6up/b4YiIiHQ7SqRERD7W0OC8T5gAGRnuxtLZcnN3AFBTMwXb9rscjYiISPejySZERE5iWW5H0HksK0ZR0TskJZ0AwLbT3A1IRESkm1IiJSICxGJQVeV2FJ0rN3cbQ4asIDm5AoD6+myiUT0gS0RE5GwokRKR886xY7B6Nfzzn7BuHaxf78zWZ9tOee/erobXKVJS9jN69PNYliEcTmX37slUVpbQr5/+DIiIiJwN/QUVkR6tosJ56G6jdeucZ0NVVLSum5IC114L/brR3AseD6Smtl2WkgJeL1jWEQYOdJKoo0eHsWXLDdh24JTbiYiIyCdTIiUiPVplJSxb5jx0F+AnP3Fm5svNhZEjnYklTpyA9HRntr7udH9UIAD5+RVMnlxJNNq6PCkJCgs3kZLyDbzew0QiyXzwwXXYtqY7FxEROVdKpESkxwuFoKbGWW68OvWVr8CwYTB5Mixf3lTenfh84PNVEo0uo7Y2dFKpIS9vLWlpf8WyYtTV9eK9975EOKzJJURERDqCEikROW/YNhjjLHu97sbSkSKREOFwUyZoWTbDh/+FwsJ/ABAOf44tWyZTW9vGZSsRERE5K3qOlIicN5Yudd4DAefVU/Xps5bCwn9gjEUodAOh0M+x7SS3wxIREelRlEiJSI9XXw9r18Lmzc7n667ruYlUcvIxBgxYA8CuXddRXz8F6EY3fomIiHQTGtonIj1abS38+McQiTifs7Nh1Ch3Y+osHk+UYcNexOuNcvz4II4eHUdGhttRiYiI9ExKpESkx3ntNXj1VXj7bVixomn91VfD2LHdZ2a+QMCZUOJU05QnJ4PHU0lOzkb69/8Hubnb8XojxGI+PvhgOl5vNzlRERGRbkiJlIh0a82fE2UMPP88zJvXso7XC5dc4kx13l0Eg1BSUkFBQSWTJ9NqevNgcAf9+v2YlJQyMjObChsa0tmxo5T6+mw9J0pERKQTKZESkW6tshL++Ed48UV4/304fryp7Lbb4JprYOdO5z6p7sTvh6SkSmx7GXV1ofjQRADLijJs2C9ISTkMQF1dAYcPD+Po0RFUVxehe6JEREQ6nxIpEenW3nsPHn4Yqqqczx4P9OsHn/mMcxVq1CjYu9fdGM+FMSEikRrCYeezcx/UX0lJOUwkkoEx/5dt2z7gxIlu+CAsERGRbkyJlIh0OzU1sHq1c//T0087V5vy82HKFBg4sOfOyBcMVjJq1J/IyDiAMXDgwP+hsHAQ8IHboYmIiJx3lEiJSEJrfg8UwLPPOrPwNV6hARgxAmbOdO4r6qksK8aFF/6RtLRDRCLJbNnyBbzecRQWuh2ZiIjI+UmJlIgktMpKWLYMQiHn86JFThKVmQkXXwzXXw91dc405z1VMHicgQNfjydRGzZ8nfr6bPLy3I5MRETk/KVESkQSXijkDOerqYGjR511U6c6Q/kmT4bly92NrzN4vfWkp/+FjIwFlJR8GF/fOCOfiIiIuEuJlIh0G8uWwYkTzj1QAwa4Hc3ZCwadWflSUlo/J8rrbWDMmKf41Kd+QmpqOeBM6378+BAOHLiEY8eGuRS1iIiINKdESkS6hQ8/hC1bnOVp0yAtzd14zlYwCJdfXkFqaiVJSVBQ4FxVM+YYWVnLKSj4DwKBAwDEYkU0NIxmy5ZR1NRkuhy5iIiINKdESkQSWkUFrFzpvAD69oUxY9yN6Vz4/ZCaWkkksoxAYC9+/2b69fsn6ek7sSwDQENDBocPzyQj4x6i0VWEw5raXEREJNEokRIR19k2HDwIe/bA7t3Oa88e50G6r74KDQ1OvbFjYfp08Hrdjfd0GoftQVtD9wz9+2+iqGgRaWlLSE092GLb6upCDh0aw4ED40lO7kNGhr9LYxcREZEzp0RKRFxx9Cj8/vewcCF89BFEIqeu27u383DdMWPAsrouxvZqPmwP+HjoXoxp094mLe1lMjNfJhDYH69vjEUk0p99+0Zw6NBwGhqyXIpcRERE2kuJlIh0qpOfAwXwzjtw883Og3QbeTzOlOZZWZCd7bz69oXSUti7t2n680TWfNheJFJDZuZm0tNXkJ19KF7HtgNEo59m3758TpzoR58+KZSXt3wuloiIiCQ+JVIi3YQxrV+2fWbr2ru+I+vu3w9lZU3PeTIG/vEPJ4nKzYW773aWvV4nmWquoABGj4Z9+7r+530uAoFtDB++hIwMZ9KIaDTIkSMjOXp0OOHwJAYOnMKxY8sB3fskIiLSXSmRclFDg/P8m8rK7tUx1vG6/njGuP3b2jkGD3YeqLt8ufOMqJ7A6z3B8OG/JxCoIRbzU1//KTZvnkRdXTIAqakBlyMUERGRjqBEykX/8R9w771uRyHnK8tyXh5P0/Kp1jeWnbzuk14ej5ME1tU1JYONZT4fTJzozrl3puLifycQqCEUyuODD26nd+9UYjG3oxIREZGOpkTKReXOszbp3du5N8S2W3Zmwbl5PTsbjh+HWKx1ZzcpCXJy4MgR4p215h3ZpCTIz4dDhyAabb3/lBRn+NSBA63LLcspLyx0hmdFoy23tSxnNrLevZ0Z1sLh1h3ytDTo0wd27WpZ3lgnPd25D2bnzqZ7RJp32tPTnQevbt/esryxTkYGDBoE27Y17b/5PrKynKseW7c6VwBPPr/MTBg2DDZvbipvXicrC0aMgHffdYafnbx9djZccAFs2uQkCydvn5MDo0bBxo1N9wM1L8/NhQsvhPXrne1PPr+8PGemurfeatp/833k58NFFzUNnTs5vvx8uPhieOONpvLmkzXk50NJCbz+etv3IHVU+amuOBUUtF7XnfXr9wq5uUswxmLbts8Ti6V+8kYiIiLSLSmRSgBXXw3Dh5+6ozl58uk7oip37q85XXkkcuryyy6D6upTl5eUOInqqcpHjXImQ2irPCcHhgyBHTta3/8DTYnm5s1NiWpzgYCTTDVO9914Vaf5UL+0NCeJbmvWu3DYmQChvr7pHqXmGqflDoXajr+jynuC5tOaN3KmN48xcuQKhg59msGD/wpAefkkqqr69qjzFxERkZaUSCWAxis7bWl8Dk1a2unLU1Odjrptn7q8UfN6bZU3r3Oq8sY6pys/3f7P5vxOjr2zzw+c9R1xfmfaPh11fo31UlIMPp8hLc3G47GxLINl2ViWDdhkZRkCAZucHJvkZBtoKrcsm9xcQ1KSTVGRTW1t6/KcHENKik3fvjbhsI0xLfefnW1IT7cZPNimocHG4zEY07R9VpYhM9Nm5EinvHH/Xq+NMTaZmYbsbJuxY53y5vFblrPv3Fyb8eNtwuGWsYFTnp9vc+mlNpGIicfVuH1qqk1hoc2VVzaVN98+NdU592nTbPLy6vB66z/e3gAGny9KRsbrjBjR9DyohoZL2bfvaqCNzFhERER6jIRIpJ544gkeeeQRysvLGTt2LL/+9a+ZMGHCKesvWbKEH/7wh+zatYuhQ4fy05/+lGuuuaYLI+5YwWA9kyeXn3RFwgA2SUk2BQUwY4aPo0frMcaOl1mWIRi06dXLcNVVhowMHxUV9RjjlDvbG/LyDFOm2MRiTkc2K8vPiRPOvoJBm9xc+OxnnXKnEwlZWV4qKxvw+53O8rRphljM/ni/Bo/HkJHhpaGhgcxMwzXX2Ni2iZc3jy8nx0t6ephotKkD2hhfIGBITzfcdJOHmprwx+dHvMPr99ukpRmuv95gjCElxUNdXTjeYff5nM7uF79osG1n/8nJFvX1EYxxkoiUFJsvf9n++OfibJeUZNHQEMXjMSQn2xQU2K2ObVmGpCQLjydKcbHd7Gff1CH3eiEpydC3b6xF2zTW8XgMfr9N//4Gr7flzxCccp/PZuBAZ53XC7Yda7YPg9frJCKNnz0eg23H4j9Lj8dm2LDG/dIiWWg0YsTpfweHDTt9+ZAhpy8fNOj05f37n768b9/Tl/fuffryoqLTl3/SEMK8vNOX5+ScvjwaTeHw4RKqqkrp1WsGoBn5REREejrXE6nnn3+ee++9lwULFjBx4kR++ctfUlpayrZt2+jVq1er+mvXruWmm25i/vz5TJ8+neeee46ZM2eyceNGRo8e7cIZnL1Bg17j0KEbyc6uwuuNxjvGzntrp+ssZmWdvk5mZsvPJ3csMzJab5Of37Scnn7qY8Opryg1+qSOMLSOsbmUlNPXS05u+fnkeJOSWm/T/CpOMHj62AKfMNHaJw3h8n3CN61x6N6pnDws8OTPHfmQWmOsZu8WluUBvNi2+XhIobPeGAvLsvB4LGIxD15vkGg00mz4oVPu83k+Hnbow+9PIRyu+zjpdsoDAYuGBgvbdvbr8fgJBNJpaKgGDMEg1NU1lRtj4fUGSUrKIhyuwO+3qa1tWd74HggkkZaWS1XVESKRGMY0/uCccp/PIi0tBehFdXU5kUi0xfn5fBbp6RahUCppaUUcP76TSCQcP4bfb5GensuWLcNpaPCRmppDG/9siYiISA/keiL12GOP8fWvf52vfvWrACxYsICXXnqJZ555hvvvv79V/ccff5xp06bxr//6rwA89NBDrFy5kt/85jcsWLCgS2M/V8OHL6dXryMdtr/GTmJjZ7epI+x0Mp2OrQePJ0AsFonX83otolEr3oFu7PBGInUYY/D7LcLhxo4qH+8zQDCYTjhchd9vqK9vKm/ekfX7g6SkZFNTc5xIxI5v31jH67VISUnCsvKpqTlMNBptsb3PZ5GaalFVBR5PCunpRVRV7f+4w0u8o3vihEUsZuH3p5KZ2Y8TJ3YRDofx+y0yMy2OH3fKnZjSyckZyPHj2zEmTHa2xdGjzs+geWyBQAa9eg0lFttCeXk4vn1THUhLy6KwcCT7979LKNRAy448BIMWvXpZHDuWQ69eozlwYAP19Q3x7ZOSLAoKLPbts/D5ciguvoj9+9+irq4eYyA52aKoyGLPHotIxCIpKY++fcezd+/fqa2tIznZorjYYvdup40aj5+Skk///p+ivHw1eXm17NzplDePLSWlgKFDJxONrmTbttDH2zvt0yg1FQYNKsDjmcyOHcsJhVpeZUlNda42HThQQL9+k/nww5Z1Gst37AC/v4DBg1vWaV7eOJlIampTPahpVd68zoEDy+nVq6ZVeaO8POccd+9ezokTra8QNT+/Xbs++fwOHGh9foFAz52eXkRERE7N1UQqHA6zYcMG5s2bF1/n8XiYOnUqZWVlbW5TVlbGvSfNGV5aWsoLL7zQZv2GhgYaGhrinysrKwGoqqo6x+jPXSjUQFUVvPnmzeTkDKG+vj7e0QUnIXE62rl4POM4cGAd9fW1LTrzyclOR/zYsXzy88dx8OBa6uqaZhVITnauPu3f70xGkJycR1FRU72Ty51tmupAbavy5nWOH19LVlZtq/JGWVl59Okzjt2711Jd3Xq2g+RkKCzMw+sdx/79LWM/OX6fr2Xs53J+sVjT+UWjrc+vsZ7XO45YLJvy8tpTnl9S0jiOHvWd8vz8fqioyMPnG0dFRW2r9gkGnWeJ+Xx5pKQMpqrqULxONOpcjaupceKLxfKorOxFKFRIXV0txjgTZdTVtYzf48miqiqJ+voMamp8hMOtzy8aDVJdHSYW82PbgTaTgVgMqqstvN4aYjEPxvhalYdCEI1aVFe3rtNYbtsQi7Wu07y88fjN64GvVXnzOpGIh1DI16q8USRiUVVVQzTaOvaOPj9jWp/j2Z7fyfVs29ep53em7XMm53em7dNR59eRv3/tPT/9jup3tKt/R8+0fTrq/BL19+9Mv1/RqNM+fr+zr7ZZOEOxT1fnTOt1VJ3ufLwzrecBqgH3++ONOYE5i/8VdTWROnr0KLFYjIKTxpkVFBTw/vvvt7lNeXl5m/XLG+cSP8n8+fP5t3/7t1br+37STRld6jm3AxAREREROW9VV1eTebr7TNrg+tC+zjZv3rwWV7Bs2+b48ePk5uZideSNJWepqqqKvn37snfvXjLaulFJXKc2Snxqo8SnNkp8aqPEpzZKfGqjxHdyGxljqK6upri4uN37cjWRysvLw+v1cujQoRbrDx06RGFhYZvbFBYWtqt+MBgkeNJMAlmNMzMkkIyMDH3hEpzaKPGpjRKf2ijxqY0Sn9oo8amNEl/zNmrvlahGbTwitOsEAgFKSkpYtWpVfJ1t26xatYpJkya1uc2kSZNa1AdYuXLlKeuLiIiIiIh0NNeH9t17773MmjWL8ePHM2HCBH75y18SCoXis/jddttt9O7dm/nz5wNw9913c8UVV/Doo49y7bXXsnjxYtavX8/vfvc7N09DRERERETOI64nUjfeeCNHjhzhgQceoLy8nHHjxrF8+fL4hBJ79uzB0+yhOZdeeinPPfccP/jBD/j+97/P0KFDeeGFF7rdM6QaBYNBHnzwwVbDDyVxqI0Sn9oo8amNEp/aKPGpjRKf2ijxdWQbWeZs5voTERERERE5j7l6j5SIiIiIiEh3pERKRERERESknZRIiYiIiIiItJMSKRERERERkXZSIuWiJ554ggEDBpCUlMTEiRNZt26d2yGdt/72t79x3XXXUVxcjGVZvPDCCy3KjTE88MADFBUVkZyczNSpU9m+fbs7wZ6n5s+fzyWXXEJ6ejq9evVi5syZbNu2rUWd+vp65s6dS25uLmlpadxwww2tHuAtnefJJ59kzJgx8YccTpo0iWXLlsXL1T6J5eGHH8ayLO655574OrWR+370ox9hWVaL14gRI+LlaqPEsH//fm699VZyc3NJTk7mwgsvZP369fFy9RvcNWDAgFbfI8uymDt3LtBx3yMlUi55/vnnuffee3nwwQfZuHEjY8eOpbS0lMOHD7sd2nkpFAoxduxYnnjiiTbLf/azn/GrX/2KBQsW8NZbb5GamkppaSn19fVdHOn5a82aNcydO5c333yTlStXEolE+NznPkcoFIrX+Zd/+Rf++te/smTJEtasWcOBAwf4whe+4GLU55c+ffrw8MMPs2HDBtavX89VV13FjBkz+Oc//wmofRLJ22+/zW9/+1vGjBnTYr3aKDGMGjWKgwcPxl9///vf42VqI/dVVFRw2WWX4ff7WbZsGVu2bOHRRx8lOzs7Xkf9Bne9/fbbLb5DK1euBOBLX/oS0IHfIyOumDBhgpk7d278cywWM8XFxWb+/PkuRiXGGAOYpUuXxj/btm0KCwvNI488El934sQJEwwGzR//+EcXIhRjjDl8+LABzJo1a4wxTpv4/X6zZMmSeJ2tW7cawJSVlbkV5nkvOzvbPP3002qfBFJdXW2GDh1qVq5caa644gpz9913G2P0HUoUDz74oBk7dmybZWqjxHDfffeZT3/606csV78h8dx9991m8ODBxrbtDv0e6YqUC8LhMBs2bGDq1KnxdR6Ph6lTp1JWVuZiZNKWnTt3Ul5e3qK9MjMzmThxotrLRZWVlQDk5OQAsGHDBiKRSIt2GjFiBP369VM7uSAWi7F48WJCoRCTJk1S+ySQuXPncu2117ZoC9B3KJFs376d4uJiBg0axC233MKePXsAtVGi+Mtf/sL48eP50pe+RK9evbjooot46qmn4uXqNySWcDjMH/7wB2bPno1lWR36PVIi5YKjR48Si8UoKChosb6goIDy8nKXopJTaWwTtVfisG2be+65h8suu4zRo0cDTjsFAgGysrJa1FU7da333nuPtLQ0gsEgd955J0uXLuWCCy5Q+ySIxYsXs3HjRubPn9+qTG2UGCZOnMiiRYtYvnw5Tz75JDt37mTy5MlUV1erjRLERx99xJNPPsnQoUNZsWIF3/zmN/nOd77Df/7nfwLqNySaF154gRMnTnD77bcDHftvna+DYhQR6TJz585l8+bNLe4bkMQwfPhwNm3aRGVlJf/93//NrFmzWLNmjdthCbB3717uvvtuVq5cSVJSktvhyClcffXV8eUxY8YwceJE+vfvz5/+9CeSk5NdjEwa2bbN+PHj+fd//3cALrroIjZv3syCBQuYNWuWy9HJyX7/+99z9dVXU1xc3OH71hUpF+Tl5eH1elvNDnLo0CEKCwtdikpOpbFN1F6J4a677uLFF1/ktddeo0+fPvH1hYWFhMNhTpw40aK+2qlrBQIBhgwZQklJCfPnz2fs2LE8/vjjap8EsGHDBg4fPszFF1+Mz+fD5/OxZs0afvWrX+Hz+SgoKFAbJaCsrCyGDRvGjh079D1KEEVFRVxwwQUt1o0cOTI+BFP9hsSxe/duXnnlFb72ta/F13Xk90iJlAsCgQAlJSWsWrUqvs62bVatWsWkSZNcjEzaMnDgQAoLC1u0V1VVFW+99ZbaqwsZY7jrrrtYunQpr776KgMHDmxRXlJSgt/vb9FO27ZtY8+ePWonF9m2TUNDg9onAUyZMoX33nuPTZs2xV/jx4/nlltuiS+rjRJPTU0NH374IUVFRfoeJYjLLrus1eM3PvjgA/r37w+o35BIFi5cSK9evbj22mvj6zr0e9TBk2LIGVq8eLEJBoNm0aJFZsuWLeaOO+4wWVlZpry83O3QzkvV1dXmnXfeMe+8844BzGOPPWbeeecds3v3bmOMMQ8//LDJysoy//u//2veffddM2PGDDNw4EBTV1fncuTnj29+85smMzPTrF692hw8eDD+qq2tjde58847Tb9+/cyrr75q1q9fbyZNmmQmTZrkYtTnl/vvv9+sWbPG7Ny507z77rvm/vvvN5ZlmZdfftkYo/ZJRM1n7TNGbZQIvvvd75rVq1ebnTt3mjfeeMNMnTrV5OXlmcOHDxtj1EaJYN26dcbn85mf/OQnZvv27ebZZ581KSkp5g9/+EO8jvoN7ovFYqZfv37mvvvua1XWUd8jJVIu+vWvf2369etnAoGAmTBhgnnzzTfdDum89dprrxmg1WvWrFnGGGcq0x/+8IemoKDABINBM2XKFLNt2zZ3gz7PtNU+gFm4cGG8Tl1dnfnWt75lsrOzTUpKirn++uvNwYMH3Qv6PDN79mzTv39/EwgETH5+vpkyZUo8iTJG7ZOITk6k1Ebuu/HGG01RUZEJBAKmd+/e5sYbbzQ7duyIl6uNEsNf//pXM3r0aBMMBs2IESPM7373uxbl6je4b8WKFQZo8+feUd8jyxhjzuGKmYiIiIiIyHlH90iJiIiIiIi0kxIpERERERGRdlIiJSIiIiIi0k5KpERERERERNpJiZSIiIiIiEg7KZESERERERFpJyVSIiIiIiIi7aRESkREREREpJ2USImIiHyCK6+8knvuucftMEREJIEokRIRkS5x++23Y1lW/JWbm8u0adN49913W9RrLH/zzTdbrG9oaCA3NxfLsli9enWL+i+88EKbx7zuuuuYNm1am2Wvv/46lmW1Or6IiMiZUCIlIiJdZtq0aRw8eJCDBw+yatUqfD4f06dPb1Wvb9++LFy4sMW6pUuXkpaW1q7jzZkzh5UrV7Jv375WZQsXLmT8+PGMGTOmfSchIiKCEikREelCwWCQwsJCCgsLGTduHPfffz979+7lyJEjLerNmjWLxYsXU1dXF1/3zDPPMGvWrHYdb/r06eTn57No0aIW62tqaliyZAlz5szh2LFj3HTTTfTu3ZuUlBQuvPBC/vjHP552v21dBcvKympxnL179/LlL3+ZrKwscnJymDFjBrt27YqXr169mgkTJpCamkpWVhaXXXYZu3fvbtf5iYiIe5RIiYiIK2pqavjDH/7AkCFDyM3NbVFWUlLCgAED+POf/wzAnj17+Nvf/sZXvvKVdh3D5/Nx2223sWjRIowx8fVLliwhFotx0003UV9fT0lJCS+99BKbN2/mjjvu4Ctf+Qrr1q0763OLRCKUlpaSnp7O66+/zhtvvEFaWhrTpk0jHA4TjUaZOXMmV1xxBe+++y5lZWXccccdWJZ11scUEZGu5XM7ABEROX+8+OKL8eF5oVCIoqIiXnzxRTye1v+vN3v2bJ555hluvfVWFi1axDXXXEN+fn67jzl79mweeeQR1qxZw5VXXgk4w/puuOEGMjMzyczM5Hvf+168/re//W1WrFjBn/70JyZMmHBW5/n8889j2zZPP/10PDlauHAhWVlZrF69mvHjx1NZWcn06dMZPHgwACNHjjyrY4mIiDt0RUpERLrMZz7zGTZt2sSmTZtYt24dpaWlXH311W0Oabv11lspKyvjo48+YtGiRcyePfusjjlixAguvfRSnnnmGQB27NjB66+/zpw5cwCIxWI89NBDXHjhheTk5JCWlsaKFSvYs2fPWZ/nP/7xD3bs2EF6ejppaWmkpaWRk5NDfX09H374ITk5Odx+++2UlpZy3XXX8fjjj3Pw4MGzPp6IiHQ9JVIiItJlUlNTGTJkCEOGDOGSSy7h6aefJhQK8dRTT7Wqm5uby/Tp05kzZw719fVcffXVZ33cOXPm8Oc//5nq6moWLlzI4MGDueKKKwB45JFHePzxx7nvvvt47bXX2LRpE6WlpYTD4VPuz7KsFkMFwRnO16impoaSkpJ40tj4+uCDD7j55psB5wpVWVkZl156Kc8//zzDhg1rNVOhiIgkLiVSIiLiGsuy8Hg8LSaVaG727NmsXr2a2267Da/Xe9bH+fKXv4zH4+G5557jv/7rv5g9e3Z8yN0bb7zBjBkzuPXWWxk7diyDBg3igw8+OO3+8vPzW1xB2r59O7W1tfHPF198Mdu3b6dXr17xxLHxlZmZGa930UUXMW/ePNauXcvo0aN57rnnzvocRUSkaymREhGRLtPQ0EB5eTnl5eVs3bqVb3/729TU1HDddde1WX/atGkcOXKEH//4x+d03LS0NG688UbmzZvHwYMHuf322+NlQ4cOZeXKlaxdu5atW7fyjW98g0OHDp12f1dddRW/+c1veOedd1i/fj133nknfr8/Xn7LLbeQl5fHjBkzeP3119m5cyerV6/mO9/5Dvv27WPnzp3MmzePsrIydu/ezcsvv8z27dt1n5SISDeiREpERLrM8uXLKSoqoqioiIkTJ/L222+zZMmS+CQQJ7Msi7y8PAKBwDkfe86cOVRUVFBaWkpxcXF8/Q9+8AMuvvhiSktLufLKKyksLGTmzJmn3dejjz5K3759mTx5MjfffDPf+973SElJiZenpKTwt7/9jX79+vGFL3yBkSNHxocoZmRkkJKSwvvvv88NN9zAsGHDuOOOO5g7dy7f+MY3zvk8RUSka1jm5EHeIiIiIiIiclq6IiUiIiIiItJOSqRERERERETaSYmUiIiIiIhIOymREhERERERaSclUiIiIiIiIu2kREpERERERKSdlEiJiIiIiIi0kxIpERERERGRdlIiJSIiIiIi0k5KpERERERERNpJiZSIiIiIiEg7/X+Zt/gdHbc6+QAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###***So, that's a lot of code, right?***\n",
        "\n",
        "Let's break it down.\n",
        "\n",
        "There are a few things of interest:\n",
        "\n",
        "1. I kept all information regarding the classes and desired values for calculating probabilities inside one dictionary. Dictionaries are a great way to keep your sets of values ordered, whch is what I was aiming for.\n",
        "To access the keys of your dictionary (say `dict`), then use the `dict.keys()` method.\n",
        "\n",
        "But  `dict.keys()` returns a key object which contains a list of keys. How then do we get the keys in a list for easier access?\n",
        "\n",
        "Simple. We use `list()` to achieve this. In this case, our indexes `i` are being generated by a `range()` function.\n",
        "\n",
        "2. There is one problem, however: the keys are **strings**! This may come in handy most of the time, but if you noticed, in the `plt.hist()` plot, we can't pass a string as the name of our DataFrame holder! So, we must convert it to a variable name. I did this using the `locals()` method. Ideally, `locals()` returns a dictionary with all the loacl variables and symbols for the current program.\n",
        "\n",
        "When we use the `locals()[name]` in the code above, what we are trying to do is to take the string which is stored the the variable `name`, and use the `locals()` method to \"get\" it as a variable.\n",
        "\n",
        "3. Next, we sorted the BMI values.\n",
        "4. To obtain the range of values from 0 to a maximum of 1.0, we use the `np.arange()` function.\n",
        "\n",
        "`np.arange(len(sorted))`: This part of the code creates a NumPy array that contains a sequence of numbers from 0 to one **less than** the length of the sorted array. Here, `len(sorted)` returns the number of elements in the sorted array, and `np.arange(`) generates a sequence of integers starting from 0 and ending at one less than the specified length.\n",
        "\n",
        "`float(len(target - 1))`: This part calculates the length of the target array minus 1 and converts it to a floating-point number.\n",
        "\n",
        "5. Consider this piece of code:\n",
        "```\n",
        "c_probs = np.arange(1, len(sorted) + 1) / len(sorted)\n",
        "index = np.argmax(sorted > classes_info[name])\n",
        "prob = 1 - c_probs[index]\n",
        "```\n",
        "This is the part of the code used to calculate the cumulative probabilities. Let's see what it does:\n",
        "\n",
        "**a.** `c_probs = np.arange(1, len(sorted) + 1) / len(sorted)`\n",
        "\n",
        "`np.arange(1, len(sorted) + 1)` generates an array of integers starting from 1 up to the length of the `sorted` array. This array represents the indices or positions of elements in the `sorted` array.\n",
        "\n",
        "`/ len(sorted)` divides each element of the generated array by the length of the `sorted` array. This operation normalizes the values in the array to be in the range `[0, 1]`, effectively converting the indices into probabilities.\n",
        "\n",
        "So, `c_probs` is an array of probabilities, where each element represents the probability of an element in `sorted` being selected.\n",
        "\n",
        "**b.** `index = np.argmax(sorted > classes_info[name])`\n",
        "\n",
        "`sorted > classes_info[name]` creates a boolean array where each element is `True` if the corresponding element in `sorted` is greater than the value stored in `classes_info[name]`, and `False` otherwise.\n",
        "\n",
        "`np.argmax()` finds the index of the first occurrence of `True` in the boolean array. This effectively identifies the first element in `sorted` that is **greater than** `classes_info[name]`.\n",
        "\n",
        "So, `index` will hold the index of the first element in `sorted` that is greater than `classes_info[name]`. However, since we need the probability that such a value is less than what was intended, we subtract from 1."
      ],
      "metadata": {
        "id": "i0XYUvpWcyHQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Cohen's Effect Size - A Gauge of \"Practical\" Significance\n",
        "\n",
        "Cohen's effect size, named afted the statistician Jacob Cohen (1923 - 1998), helps answer the question, \"Is the observed effect large enough to be meaningful in the real world?\"\n",
        "\n",
        "But hold up - what **exactly** is an \"*effect size*\"?\n",
        "\n",
        "An effect size is a quantitative measure used in statistics to describe the size or magnitude of a phenomenon, or how strong the relationship between two variables is.\n",
        "\n",
        "Cohen's effect size is **standardized**, meaning that it is expressed in a common metric that allows for comparisons across different studies, experiments, or data sets. This standardization makes it easier to assess the practical importance of findings.\n",
        "\n",
        "Examples of unstandardized measures are the *difference between group means* and *regression coefficients*.\n",
        "\n",
        "In this instance, I attempt to use Cohen's size to evalaute the practical significance of observable differences in BMI values of positive and negative patients."
      ],
      "metadata": {
        "id": "agu0UsRiClSm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1: Calculate difference between means of groups:\n",
        "av_diff = positive_data.BMI.mean() - negative_data.BMI.mean()\n",
        "\n",
        "# Step 2: Calculate variances for each group:\n",
        "var_positive = positive_data.BMI.var()\n",
        "var_negative = negative_data.BMI.var()\n",
        "\n",
        "# Step 3: Calculate the pooled variance\n",
        "pooled_var = (len(positive_data) * var_positive) + (len(negative_data) * var_negative) / float(len(positive_data) + (len(negative_data)))\n",
        "\n",
        "# Step 4: Divide the mean difference with the square root of the pooled variance to obtain the Cohen's effect size:\n",
        "effect_size = av_diff / np.sqrt(pooled_var)\n",
        "\n",
        "# Step 5: Print the effect size\n",
        "print(\"Effect size:\", effect_size)"
      ],
      "metadata": {
        "id": "F0HmSNcyFajf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "73ea4243-46bd-4366-eb6c-b1abdeb1459f"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Effect size: 0.040637232631926194\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A Cohen's ***d*** of 0.04 indicates that the difference between the means of the two groups is very small, and likely not **practically significant.**\n",
        "It suggests that while there is minimal overlap between the two groups, the difference is not substantial enough to have practical implications."
      ],
      "metadata": {
        "id": "YkjpHbPUtPIH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exploring the Nature and Strength of Relationships - Correlation and Covariance\n",
        "\n",
        "This involves assessing the degree and nature of associations between pairs of variables within a dataset. Two essential statistical concepts for exploring these relationships are ***correlation and covariance.***\n",
        "\n",
        "Correlation measures the linear relationship between two variables, indicating whether they move together or in opposite directions. It is expressed as a coefficient, typically the Pearson correlation coefficient, which ranges from -1 (perfect negative correlation) to 1 (perfect positive correlation), with 0 indicating no linear correlation.\n",
        "\n",
        "Covariance, on the other hand, measures the degree to which two variables change together. A positive covariance suggests that the variables tend to increase or decrease simultaneously, while a negative covariance implies an inverse relationship.\n",
        "\n",
        "What I plan on doing here is to use jointplots from Seaborn to examine the relationship between two sets of features - Age and BP in one, and Glucose and Insulin in the other."
      ],
      "metadata": {
        "id": "w2Ux68ymtn23"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy.stats as stats\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Set figure size:\n",
        "plt.figure(figsize=(12, 6))  # Adjust the figure size as needed\n",
        "\n",
        "# First plot: Age and BP\n",
        "jp = sns.jointplot(data=data, x='Age', y='BloodPressure', kind = \"reg\", color = 'g')\n",
        "# Calculate Pearson's correlation coefficient and p-value:\n",
        "corr, p = stats.pearsonr(data['Age'], data['BloodPressure'])\n",
        "# Annotate the plot with correlation coefficient and p-value\n",
        "jp.ax_joint.annotate(f'$\\\\rho = {corr:.4f}, p = {p:.3f}$',\n",
        "                     xy=(0.1, 0.9), xycoords='axes fraction',\n",
        "                     ha='left', va='center',\n",
        "                     bbox={'boxstyle': 'round', 'fc': 'powderblue', 'ec': 'blue'})\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "id": "E_L382fBvHUn",
        "outputId": "2bf9bfba-d98d-4528-fc73-509368ecf2fe"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So, what have we done so far?\n",
        "\n",
        "Let's examine! Here's a step-by-step explanation:\n",
        "\n",
        "1. **Set Figure Size:**\n",
        "   - `plt.figure(figsize=(12, 6))` sets the size of the figure where the plot will be displayed. In this case, it's set to 12 inches in width and 6 inches in height.\n",
        "\n",
        "2. **Create a Joint Plot:**\n",
        "   - `sns.jointplot(data=data, x='Age', y='BloodPressure', kind=\"reg\", color='g')` creates a joint plot using Seaborn. It displays a scatter plot of 'Age' (on the x-axis) against 'BloodPressure' (on the y-axis) with regression lines ('kind=\"reg\"') and sets the color to green ('color='g'').\n",
        "\n",
        "3. **Calculate Pearson's Correlation Coefficient and P-Value:**\n",
        "   - `corr, p = stats.pearsonr(data['Age'], data['BloodPressure'])` calculates Pearson's correlation coefficient (corr) and its associated p-value (p) for the 'Age' and 'BloodPressure' variables using the `pearsonr` function from the 'stats' module in SciPy. The correlation coefficient measures the linear relationship between the two variables, and the p-value assesses the significance of this relationship.\n",
        "\n",
        "4. **Annotation:**\n",
        "   - `jp.ax_joint.annotate(...)` adds an annotation to the joint plot. It displays the calculated correlation coefficient (ρ) and p-value (p) on the plot at the specified location (xy=(0.1, 0.9), which corresponds to the top-left corner of the plot, in axes fraction coordinates).\n",
        "   - The annotation is formatted using **LaTeX-style notation** (e.g., `$\\\\rho = {corr:.4f}, p = {p:.3f}$`), which allows for mathematical symbols and formatting. This is made possible, thanks to - yup, you got it: **f-strings**!\n",
        "   - It uses a rounded rectangle ('boxstyle': 'round') with a light blue fill color ('fc': 'powderblue') and a blue edge color ('ec': 'blue') to highlight the annotation.\n",
        "\n",
        "   The rest is fairly self-explanatory!"
      ],
      "metadata": {
        "id": "d-GvEofv7SLb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Second plot: Insulin and Glucose levels\n",
        "jp_2 = sns.jointplot(data=data, x='Insulin', y='Glucose', hue = 'Outcome')\n",
        "jp_2.plot_joint(sns.kdeplot, zorder=0, levels=6)\n",
        "# Calculate Pearson's correlation coefficient and p-value:\n",
        "corr_2, p_2 = stats.pearsonr(data['Insulin'], data['Glucose'])\n",
        "# Annotate the plot with correlation coefficient and p-value\n",
        "jp_2.ax_joint.annotate(f'$\\\\rho = {corr_2:.4f}, p = {p_2:.3f}$',\n",
        "                       xy=(0.1, 0.9), xycoords='axes fraction',\n",
        "                       ha='left', va='center',\n",
        "                       bbox={'boxstyle': 'round', 'fc': 'powderblue', 'ec': 'blue'})\n",
        "\n",
        "\n",
        "# Display the plot\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "b8ERWes30ntf",
        "outputId": "3d41cc11-9fe7-4916-c9c0-2a90e9d2c4c3"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Notice anything different?\n",
        "\n",
        "> ***That's because it already is.***\n",
        "\n",
        "But what changed?\n",
        "\n",
        "Let's examine!\n",
        "\n",
        "```\n",
        "jp_2 = sns.jointplot(data=data, x='Insulin', y='Glucose', hue='Outcome')\n",
        "```\n",
        "\n",
        "1. `jp_2` is being assigned the result of the `sns.jointplot` function. This function is used to create a scatter plot with histograms along the x and y axes. In this case, the following parameters are used:\n",
        "   - `data`: This specifies the dataset (`data`) from which the 'Insulin' and 'Glucose' columns will be used for plotting.\n",
        "   - `x='Insulin'`: This specifies that the 'Insulin' column will be used for the x-axis.\n",
        "   - `y='Glucose'`: This specifies that the 'Glucose' column will be used for the y-axis.\n",
        "   - `hue='Outcome'`: The 'Outcome' column is used to color-code the points in the scatter plot based on the values in the 'Outcome' variable.\n",
        "\n",
        "Let's move on to the next line of code:\n",
        "\n",
        "```\n",
        "jp_2.plot_joint(sns.kdeplot, zorder=0, levels=6)\n",
        "```\n",
        "\n",
        "2. `jp_2.plot_joint(sns.kdeplot, zorder=0, levels=6)`: This line of code is customizing the joint plot created earlier. Here's what each part does:\n",
        "\n",
        "   - `jp_2.plot_joint(...)`: This part modifies the central scatter plot. Instead of plotting points, it will now overlay a kernel density estimate (KDE) plot. A KDE plot is a smoothed representation of the data's distribution.\n",
        "   - `sns.kdeplot`: The `sns.kdeplot` function is used to create the KDE plot.\n",
        "   - `zorder=0`: The `zorder` parameter specifies the layering order of the plot elements. A `zorder` of 0 means that the KDE plot will be drawn behind the scatter points, ensuring that the points are visible on top of the KDE estimate.\n",
        "   - `levels=6`: The `levels` parameter sets the number of contour levels in the KDE plot. In this case, it is set to 6, which means that the KDE plot will have 6 contour lines representing different levels of density.\n",
        "\n",
        "For more information, visit [here](https://seaborn.pydata.org/generated/seaborn.JointGrid.plot_joint.html) for the `plot_joint` method under `Seaborn.JointGrid` or [here](https://seaborn.pydata.org/generated/seaborn.jointplot.html) for information on the `jointplot` method."
      ],
      "metadata": {
        "id": "7v70Zen58feu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "So, enough on pretty plots.\n",
        "\n",
        "What do the results tell us?\n",
        "\n",
        "1. Both pairs of features are weakly correlated.\n",
        "2. Most negative patients have a glucose level between 100-150. For positive patients, most glucose levels fall between 120-200.\n",
        "3. The most common insulin level is 0."
      ],
      "metadata": {
        "id": "acj0o1sM8fal"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Statistical Significance with Hypothesis Testing, ANOVA and Tukey's Test\n",
        "\n",
        "Imagine you have collected data on various aspects of a scientific experiment, a marketing campaign, or even a medical study. You've generated intriguing patterns and differences among groups or treatments, but you need to answer a crucial question: Are these differences ***statistically significant***, or could they have occurred by random chance alone?\n",
        "\n",
        "But wait, what is statistical significance?\n",
        "\n",
        "Statistical significance is a fundamental concept in data analysis and hypothesis testing. It allows us to determine whether observed differences or relationships in data are likely to be real or if they could have occurred by random chance.\n",
        "\n",
        "We can determine statistical significance with ***hypothesis testing.***\n",
        "\n",
        "Look at it this way - you've asked the questions, and from your questions, you made observations. You spotted some insights/anomalies.\n",
        "\n",
        "What happens next?\n",
        "\n",
        "You start getting ideas! You're exposed to the \"small picture\", and you want to understand the \"big picture\", so you start making guesses based on your ideas to try and answer the questions you want to answer.\n",
        "\n",
        "That's where a hypothesis comes in.\n",
        "\n",
        "Remember the **scientific method** from chemistry?\n",
        "\n",
        "A hypothesis is basically **\"an intelligent guess\"**.\n",
        "\n",
        "But in the world of data, guesses are not enough, which is why we need to rigorously test and validate them, and reject the ones that do not work!\n",
        "  \n",
        "Hypothesis testing is a systematic approach to making inferences about a population based on a sample of data. It involves defining a null hypothesis (H0) and an alternative hypothesis (H1). The null hypothesis typically represents a default or no-effect assumption, while the alternative hypothesis suggests the presence of an effect or difference. By comparing sample statistics to expected values under the null hypothesis, we can assess whether there is enough evidence to reject the null hypothesis in favor of the alternative.\n",
        "\n",
        "Furthermore, we'll embark on a journey into the world of ANOVA, a powerful statistical technique that assesses whether the means of multiple groups are significantly different. ANOVA helps us tackle questions involving more than two groups, such as comparing the effectiveness of multiple drug treatments or analyzing the impact of various marketing strategies. To unravel the nuances of ANOVA, we'll also introduce Tukey's Test, a post-hoc analysis that uncovers which specific groups differ significantly from one another.\n",
        "\n",
        "***So, without further ado...***"
      ],
      "metadata": {
        "id": "e-nuVG1RuPt7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Covariance"
      ],
      "metadata": {
        "id": "ZDPgucQwJ8TL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cov_age_bmi = np.cov(data.Age, data.BMI)[0,1]\n",
        "cov_bp_glucose = np.cov(data.BloodPressure, data.Glucose)[0,1]\n",
        "cov_dpf_insulin = np.cov(data.DiabetesPedigreeFunction, data.Insulin)[0,1]\n",
        "\n",
        "print(\"Covariance between Age and BMI:\", cov_age_bmi)\n",
        "print(\"Covariance between BP and Glucose:\", cov_bp_glucose)\n",
        "print(\"Covariance between DPF and Insulin:\", cov_dpf_insulin)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8fLvXN6RHNrU",
        "outputId": "d194daad-29b0-43d4-c1b4-e948e7b7b9d3"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Covariance between Age and BMI: 3.3603299176988295\n",
            "Covariance between BP and Glucose: 94.43095563070403\n",
            "Covariance between DPF and Insulin: 7.066680509153629\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Positive covariance indicates that when one variable increases, the other tends to increase as well, while negative covariance suggests that when one variable increases, the other tends to decrease. A covariance of zero means there is no linear relationship between the variables.\n",
        "\n",
        "Let's interpret the results:\n",
        "\n",
        "1. **Covariance between Age and BMI (3.360):** This positive covariance suggests that there is a tendency for age and BMI to increase together on average. In other words, as individuals get older, they tend to have slightly higher BMI values on average.\n",
        "\n",
        "2. **Covariance between BP (Blood Pressure) and Glucose (94.431):** This relatively high positive covariance indicates that there is a positive association between blood pressure and glucose levels. On average, as blood pressure increases, glucose levels also tend to increase. This relationship can have very important implications in healthcare, as elevated blood pressure and glucose levels are risk factors for various medical conditions.\n",
        "\n",
        "3. **Covariance between DPF (Diabetes Pedigree Function) and Insulin (7.067):** This positive covariance suggests that there is a tendency for diabetes pedigree function and insulin levels to increase together on average. It implies that individuals with higher diabetes pedigree function values also tend to have higher insulin levels."
      ],
      "metadata": {
        "id": "IGC7rkt_JSQy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Analysis of Variance (ANOVA)\n",
        "ANOVA test is used to compare the means of more than 2 groups (as against a t-test or Z-test, which can be used to compare 2 groups).\n",
        "\n",
        "***ANOVA Hypotheses:***\n",
        "\n",
        "I'll expand further on the null and alternative hypotheses in the context of a one-way analysis of variance (ANOVA) test:\n",
        "\n",
        "**Null Hypothesis (H0):**\n",
        "The null hypothesis in a one-way ANOVA test states that there is no significant variation in the means of the groups being compared. In mathematical notation, it is often expressed as:\n",
        "\n",
        "H0: μ1 = μ2 = μ3 = ... = μp\n",
        "- μ1, μ2, μ3, ..., μp represent the population means of each of the p groups you are comparing.\n",
        "- The null hypothesis essentially states that all of these population means are equal, indicating that there are no meaningful differences between the groups.\n",
        "\n",
        "**Alternative Hypothesis (H1):**\n",
        "The alternative hypothesis in a one-way ANOVA test is the opposite of the null hypothesis. It suggests that there is at least one group mean that is different from the means of the other groups. Mathematically, it is expressed as:\n",
        "\n",
        "H1: At least one μ is not equal to the others\n",
        "\n",
        "- This implies the existence of variability in the population means, and at least one group stands out as having a different mean compared to the rest.\n",
        "- It does not specify which group(s) have different means or the direction of the differences; it only asserts that there are differences.\n",
        "\n",
        "In practical terms:\n",
        "- Researchers use the one-way ANOVA test to determine if there are statistically significant differences in means among multiple groups.\n",
        "- If the p-value from the ANOVA test is less than a chosen significance level (e.g., 0.05), it provides evidence against the null hypothesis, suggesting that at least one group differs significantly from the others.\n",
        "- If the p-value is greater than the significance level, there is insufficient evidence to reject the null hypothesis, indicating that the group means are not significantly different.\n",
        "\n",
        "This is a very nice [article](https://www.reneshbedre.com/blog/anova.html) on the subject of ANOVA and Tukey's tests. It'll be worth your time.\n",
        "\n",
        "I will be carrying out a two-way ANOVA test with some factors on the dependent variable (Outcome)."
      ],
      "metadata": {
        "id": "iXtl0dnlJ_De"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.api as sm\n",
        "from statsmodels.formula.api import ols\n",
        "\n",
        "model = ols('Outcome ~ BMI + Glucose + BMI:Age', data = data).fit()\n",
        "\n",
        "anova_table = sm.stats.anova_lm(model, typ =2)\n",
        "print(anova_table)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T7CuhcMoKMlC",
        "outputId": "a661ee77-948c-4b65-a324-653abbd20e59"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              sum_sq     df           F        PR(>F)\n",
            "BMI         6.590809    1.0   39.737390  4.908710e-10\n",
            "Glucose    23.501432    1.0  141.695146  4.311052e-30\n",
            "BMI:Age     3.188190    1.0   19.222280  1.326474e-05\n",
            "Residual  126.716367  764.0         NaN           NaN\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The p-value obtained from ANOVA analysis for BMI, Glucose, and BMI + Age are statistically significant (p<0.05). That means, they all significantly affect the outcome."
      ],
      "metadata": {
        "id": "UAy-lRK2VYL6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Why a two-way ANOVA?\n",
        "\n",
        "A two-way analysis of variance (ANOVA), often referred to as a two-way ANOVA, is a statistical technique used to analyze the influence of two categorical independent variables (factors) on a continuous dependent variable. It allows you to explore how these two factors interact and affect the outcome variable. Two-way ANOVA is valuable in various research and experimental settings for several reasons:\n",
        "\n",
        "1. In many real-world situations, the outcome variable may be influenced by multiple factors simultaneously.\n",
        "\n",
        "2. Two-way ANOVA allows you to assess whether there is an interaction effect between the two factors. An interaction effect occurs when the impact of one factor on the dependent variable depends on the levels or categories of the other factor.\n",
        "\n",
        "3. Instead of conducting separate one-way ANOVA tests for each factor, which can lead to inflated Type I error rates (false positives), a two-way ANOVA considers both factors simultaneously while properly controlling for multiple comparisons.\n",
        "\n",
        "4.  If you have two categorical variables (e.g., gender and treatment type) and you want to compare multiple groups formed by the combinations of these variables (e.g., male vs. female within each treatment group), two-way ANOVA is the way to go."
      ],
      "metadata": {
        "id": "XtDVSGokVypO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Tukey's Test\n",
        "\n",
        "Remember when I said that ANOVA \"does not specify which group(s) have different means or the direction of the differences; it only asserts that there are differences\"?\n",
        "\n",
        "Well, then how do we find out more about those differences?\n",
        "\n",
        "We can use Tukey's test for that.\n",
        "\n",
        "To perform Tukey's test, I will be using the `bioinfokit` package. To install it, use this command: `pip install bioinfokit`.\n",
        "\n",
        "\n",
        "![tukey.webp](data:image/webp;base64,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)\n",
        "\n",
        "> Image source: https://www.reneshbedre.com/blog/anova.html"
      ],
      "metadata": {
        "id": "y71t2rcpWbzf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install bioinfokit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K2cZzYMaWfCf",
        "outputId": "72d69470-459d-44ef-a4a3-97397492df26"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: bioinfokit in /usr/local/lib/python3.10/dist-packages (2.1.3)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from bioinfokit) (1.5.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from bioinfokit) (1.23.5)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from bioinfokit) (3.7.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from bioinfokit) (1.11.2)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from bioinfokit) (1.2.2)\n",
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.10/dist-packages (from bioinfokit) (0.12.2)\n",
            "Requirement already satisfied: matplotlib-venn in /usr/local/lib/python3.10/dist-packages (from bioinfokit) (0.11.9)\n",
            "Requirement already satisfied: tabulate in /usr/local/lib/python3.10/dist-packages (from bioinfokit) (0.9.0)\n",
            "Requirement already satisfied: statsmodels in /usr/local/lib/python3.10/dist-packages (from bioinfokit) (0.14.0)\n",
            "Requirement already satisfied: textwrap3 in /usr/local/lib/python3.10/dist-packages (from bioinfokit) (0.9.2)\n",
            "Requirement already satisfied: adjustText in /usr/local/lib/python3.10/dist-packages (from bioinfokit) (0.8)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->bioinfokit) (1.1.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->bioinfokit) (0.11.0)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->bioinfokit) (4.42.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->bioinfokit) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->bioinfokit) (23.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->bioinfokit) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->bioinfokit) (3.1.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->bioinfokit) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->bioinfokit) (2023.3.post1)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->bioinfokit) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->bioinfokit) (3.2.0)\n",
            "Requirement already satisfied: patsy>=0.5.2 in /usr/local/lib/python3.10/dist-packages (from statsmodels->bioinfokit) (0.5.3)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from patsy>=0.5.2->statsmodels->bioinfokit) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from bioinfokit.analys import stat\n",
        "# Test to perform multiple pairwise comparison (Tukey HSD)\n",
        "# For an unequal sample size data, tukey_hsd uses Tukey-Kramer test\n",
        "res = stat()\n",
        "# The main effect under consideration is Pregnancies\n",
        "res.tukey_hsd(df=data, res_var='Outcome', xfac_var= 'Pregnancies', anova_model='Outcome ~ Pregnancies + Age + Pregnancies:Age')\n",
        "res.tukey_summary"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "4byZ_oE4hdI-",
        "outputId": "f74eb912-e280-4b5c-c77a-ed0c258b9c5f"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     group1  group2      Diff     Lower     Upper   q-value   p-value\n",
              "0         6       1  0.105185 -0.158457  0.368827  1.958124  0.900000\n",
              "1         6       8  0.258947 -0.083777  0.601672  3.708229  0.416718\n",
              "2         6       0  0.022342 -0.248894  0.293578  0.404280  0.900000\n",
              "3         6       5  0.048421 -0.260147  0.356989  0.770166  0.900000\n",
              "4         6       3  0.040000 -0.250750  0.330750  0.675212  0.900000\n",
              "..      ...     ...       ...       ...       ...       ...       ...\n",
              "131      15      12  0.555556 -1.123093  2.234204  1.624308  0.900000\n",
              "132      15      14  0.000000 -1.950413  1.950413  0.000000  0.900000\n",
              "133      17      12  0.555556 -1.123093  2.234204  1.624308  0.900000\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's get the pairings of pregnancies that are statistically significant.\n",
        "res.tukey_summary[res.tukey_summary['p-value'] < 0.05]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "ylmVWa7_erYI",
        "outputId": "a3a7b90f-0738-4fc3-d700-e5488a254a8e"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    group1  group2      Diff     Lower     Upper   q-value   p-value\n",
              "16       1       8  0.364133  0.071687  0.656578  6.111034  0.002101\n",
              "23       1       7  0.340741  0.066619  0.614863  6.100712  0.002170\n",
              "24       1       9  0.428042  0.097346  0.758738  6.352700  0.001005\n",
              "35       8       2  0.394481  0.092222  0.696741  6.405409  0.001000\n",
              "92       2       7  0.371090  0.086521  0.655658  6.400181  0.001000\n",
              "93       2       9  0.458391  0.118986  0.797797  6.628538  0.001000"
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5d7f90f3-892f-4293-810d-361080e78a10 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5d7f90f3-892f-4293-810d-361080e78a10');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-159987a5-dc20-483e-b33e-f2f2ea9b377f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-159987a5-dc20-483e-b33e-f2f2ea9b377f')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-159987a5-dc20-483e-b33e-f2f2ea9b377f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**A note of warning**: If you see a p-value of 0.001 from the output of your Tukey's test (at least when usng this package), it should be interpreted as p-value <=0.001.\n",
        "\n",
        "Observe that of the 136 pairings of different pregnancy occurences, only 6 are statistically significant."
      ],
      "metadata": {
        "id": "nTgLL_uxbiDf"
      }
    }
  ]
}