06_Lab_Assignment_Lab03.ipynb

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      "name": "python"
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    {
      "cell_type": "code",
      "source": [
        "#mount google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gjyy8noMEqZ9",
        "outputId": "c96cfb6e-33f0-49c1-b45d-d24ff52b62c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#upload csv file on google drive then read file using these command\n",
        "import pandas as pd\n",
        "df = pd.read_csv(\"/content/drive/MyDrive/cereal.csv\")\n",
        "df"
      ],
      "metadata": {
        "id": "IQxgc-awEvxl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "ac7c39cc-09bd-4690-d407-4c33492edc49"
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      "execution_count": null,
      "outputs": [
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          "data": {
            "text/plain": [
              "                         name mfr type  calories  protein  fat  sodium  fiber  \\\n",
              "0                   100% Bran   N    C        70        4    1     130   10.0   \n",
              "1           100% Natural Bran   Q    C       120        3    5      15    2.0   \n",
              "2                    All-Bran   K    C        70        4    1     260    9.0   \n",
              "3   All-Bran with Extra Fiber   K    C        50        4    0     140   14.0   \n",
              "4              Almond Delight   R    C       110        2    2     200    1.0   \n",
              "..                        ...  ..  ...       ...      ...  ...     ...    ...   \n",
              "72                    Triples   G    C       110        2    1     250    0.0   \n",
              "73                       Trix   G    C       110        1    1     140    0.0   \n",
              "74                 Wheat Chex   R    C       100        3    1     230    3.0   \n",
              "75                   Wheaties   G    C       100        3    1     200    3.0   \n",
              "76        Wheaties Honey Gold   G    C       110        2    1     200    1.0   \n",
              "\n",
              "    carbo  sugars  potass  vitamins  shelf  weight  cups     rating  \n",
              "0     5.0       6     280        25      3     1.0  0.33  68.402973  \n",
              "1     8.0       8     135         0      3     1.0  1.00  33.983679  \n",
              "2     7.0       5     320        25      3     1.0  0.33  59.425505  \n",
              "3     8.0       0     330        25      3     1.0  0.50  93.704912  \n",
              "4    14.0       8      -1        25      3     1.0  0.75  34.384843  \n",
              "..    ...     ...     ...       ...    ...     ...   ...        ...  \n",
              "72   21.0       3      60        25      3     1.0  0.75  39.106174  \n",
              "73   13.0      12      25        25      2     1.0  1.00  27.753301  \n",
              "74   17.0       3     115        25      1     1.0  0.67  49.787445  \n",
              "75   17.0       3     110        25      1     1.0  1.00  51.592193  \n",
              "76   16.0       8      60        25      1     1.0  0.75  36.187559  \n",
              "\n",
              "[77 rows x 16 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>mfr</th>\n",
              "      <th>type</th>\n",
              "      <th>calories</th>\n",
              "      <th>protein</th>\n",
              "      <th>fat</th>\n",
              "      <th>sodium</th>\n",
              "      <th>fiber</th>\n",
              "      <th>carbo</th>\n",
              "      <th>sugars</th>\n",
              "      <th>potass</th>\n",
              "      <th>vitamins</th>\n",
              "      <th>shelf</th>\n",
              "      <th>weight</th>\n",
              "      <th>cups</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100% Bran</td>\n",
              "      <td>N</td>\n",
              "      <td>C</td>\n",
              "      <td>70</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>10.0</td>\n",
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              "      <td>6</td>\n",
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              "      <td>25</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.33</td>\n",
              "      <td>68.402973</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100% Natural Bran</td>\n",
              "      <td>Q</td>\n",
              "      <td>C</td>\n",
              "      <td>120</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>15</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8</td>\n",
              "      <td>135</td>\n",
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              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>33.983679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>All-Bran</td>\n",
              "      <td>K</td>\n",
              "      <td>C</td>\n",
              "      <td>70</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>260</td>\n",
              "      <td>9.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>5</td>\n",
              "      <td>320</td>\n",
              "      <td>25</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.33</td>\n",
              "      <td>59.425505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>All-Bran with Extra Fiber</td>\n",
              "      <td>K</td>\n",
              "      <td>C</td>\n",
              "      <td>50</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>140</td>\n",
              "      <td>14.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0</td>\n",
              "      <td>330</td>\n",
              "      <td>25</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.50</td>\n",
              "      <td>93.704912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Almond Delight</td>\n",
              "      <td>R</td>\n",
              "      <td>C</td>\n",
              "      <td>110</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>200</td>\n",
              "      <td>1.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>8</td>\n",
              "      <td>-1</td>\n",
              "      <td>25</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.75</td>\n",
              "      <td>34.384843</td>\n",
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              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>Triples</td>\n",
              "      <td>G</td>\n",
              "      <td>C</td>\n",
              "      <td>110</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>250</td>\n",
              "      <td>0.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>3</td>\n",
              "      <td>60</td>\n",
              "      <td>25</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.75</td>\n",
              "      <td>39.106174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>Trix</td>\n",
              "      <td>G</td>\n",
              "      <td>C</td>\n",
              "      <td>110</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>140</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>12</td>\n",
              "      <td>25</td>\n",
              "      <td>25</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>27.753301</td>\n",
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              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>Wheat Chex</td>\n",
              "      <td>R</td>\n",
              "      <td>C</td>\n",
              "      <td>100</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>230</td>\n",
              "      <td>3.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>3</td>\n",
              "      <td>115</td>\n",
              "      <td>25</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.67</td>\n",
              "      <td>49.787445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>Wheaties</td>\n",
              "      <td>G</td>\n",
              "      <td>C</td>\n",
              "      <td>100</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>200</td>\n",
              "      <td>3.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>3</td>\n",
              "      <td>110</td>\n",
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              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>51.592193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>Wheaties Honey Gold</td>\n",
              "      <td>G</td>\n",
              "      <td>C</td>\n",
              "      <td>110</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>200</td>\n",
              "      <td>1.0</td>\n",
              "      <td>16.0</td>\n",
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              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.75</td>\n",
              "      <td>36.187559</td>\n",
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              "<p>77 rows × 16 columns</p>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def mean(l):\n",
        "  sum=0\n",
        "  for i in l:\n",
        "    sum+=i\n",
        "  length=len(l)\n",
        "  return sum/length"
      ],
      "metadata": {
        "id": "5LDZYWZ8E2kl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def variance(l):\n",
        "  s=0\n",
        "  for i in range(len(l)):\n",
        "    s+=(l[i]-mean(l))**2\n",
        "  s/=(len(l)-1)\n",
        "  return s"
      ],
      "metadata": {
        "id": "IL850emrFAoF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def pearson(x,y):\n",
        "  sum=0\n",
        "  for i in range(len(x)):\n",
        "    sum+=(x[i]-mean(x))*(y[i]-mean(y))\n",
        "  return sum/((len(x)-1)*(variance(x)**0.5)*(variance(y)**0.5))"
      ],
      "metadata": {
        "id": "hYGbgPqHFC2N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#data frame to python list\n",
        "List1=df['calories'].values.tolist()\n",
        "List2=df['protein'].values.tolist()\n",
        "List3=df['fat'].values.tolist()\n",
        "List4=df['sugars'].values.tolist()\n",
        "List5=df['carbo'].values.tolist()"
      ],
      "metadata": {
        "id": "QYh0FesrEy8l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "List=[List1,List2,List3,List4,List5]"
      ],
      "metadata": {
        "id": "HoJs36fCjtXk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text=['calories','protein','fat','sugars','carbo']"
      ],
      "metadata": {
        "id": "vM9K4NaMnE1d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(List)):\n",
        "  for j in range(i+1,len(List)):\n",
        "   print(text[i]+\" and \"+text[j]+\" correlation is %f\"%pearson(List[i],List[j]))\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J1bQKvNAnBDG",
        "outputId": "4c6da6f3-0eca-4320-9e22-323c6a3e3965"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "calories and protein correlation is 0.019066\n",
            "calories and fat correlation is 0.498610\n",
            "calories and sugars correlation is 0.562340\n",
            "calories and carbo correlation is 0.250681\n",
            "protein and fat correlation is 0.208431\n",
            "protein and sugars correlation is -0.329142\n",
            "protein and carbo correlation is -0.130864\n",
            "fat and sugars correlation is 0.270819\n",
            "fat and carbo correlation is -0.318043\n",
            "sugars and carbo correlation is -0.331665\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "tg1AsQDOrboV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def scatter(x,y,xlabel,ylabel):\n",
        "  plt.xlabel(xlabel)\n",
        "  plt.ylabel(ylabel)\n",
        "  plt.scatter(x,y)\n",
        "  plt.title(xlabel+\" vs \"+ylabel)\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "JovBXW0st7EK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(List)):\n",
        "  for j in range(i+1,len(List)):\n",
        "    scatter(List[i],List[j],text[i],text[j])\n",
        "    print(\"\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "k8u5yCXeu8zm",
        "outputId": "6f1842d2-788b-417e-cf6a-31e220fc91ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEWCAYAAABsY4yMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAa8UlEQVR4nO3de5xV5X3v8c/XAXW8TtHRlBGCJQmtERU7ESipNRqDRqvz4vQSC02ai5y0ObmcJqQSOUltNZhwXjamSZOXmqiNBGMsTj3NiWiamAtB0kEaJhE5SkRg8DIWR9FOFcff+WOtwWGYPbM3s9ds9sP3/XrtF3s96/I8zyz2d9Y8a+21FBGYmVl6Dql1A8zMrBgOeDOzRDngzcwS5YA3M0uUA97MLFEOeDOzRDng7YAgaYukt+/nur8raVO121TvJE2W9IKkhlq3xWrDAW91LyJ+HBHTat2Ookk6R9L2cpePiK0RcVRE9BXZLjtwOeCtrkkaV+s2VEtKfbEDgwPeqkrSJEkrJXVL+g9JX8rLp0r6fl72jKTlkppKbOMwSV+QtCN/fUHSYfm8cyRtl/RXkp4Ebh58ZCtpoqR/ytvwmKSPDJh3lqQOSc9LekrSdSXasFHSxQOmx+XbO1PS4ZJuy/vSI+nfJJ1YYjtbJC2W9JCkZyXdLOnwYfoyZN8lHQl8F5iYD7u8kPfzEElXSNqct+cOSRPy7U+RFP2/OCTdL+lvJa2WtEvSvZKOr2T/Wn1xwFvV5GO9/wI8DkwBWoDb+2cDS4GJwG8Bk4C/LrGpK4FZwBnA6cBZwJIB818HTABeDywc1IZDgP8D/Dyv/zzgY5Lm5otcD1wfEccAU4E7SrRhBXDZgOm5wDMR8SDwHuDYvA/HAR8EektsB2B+vv5U4E0j9GXIvkfEi8CFwI582OWoiNgBfBhoA36P7Gf7LPDlYdryJ8B7gROAQ4FPDLOs1buI8MuvqryA2UA3MK6MZduA9QOmtwBvz99vBt45YN5cYEv+/hzgZeDwAfPPAbbn72cCWwfVtRi4OX//I+Aq4PgR2vcGYBdwRD69HPh0/v59wE+B08ro5xbggwOm3wlsHqYvI/V9+6DtbwTOGzD968BuYBzZL9no3x/A/WS/LPqX/Qvgnlr/v/GruJeP4K2aJgGPR8Qrg2dIOlHS7ZK6JD0P3AaUGh6YSPZXQL/H87J+3RHxXyXWfT3ZMEZP/wv4FNA/hPJ+sqPoh/OhlYuH2khEPEoWnr8v6QjgEuCb+exvAKuA2/NhlM9LGl+iPQDbKujLSH0fqr93DejrRqCP1/o72JMD3v8ncNQw27Y654C3atoGTC5xsvCzZEeT0yMbHllANmwzlB1kwdVvcl7Wb7hboG4DHouIpgGvoyPinQAR8UhEXEY2RPE54M58fHso/cM0lwIP5aFPROyOiKsi4hTgd4CLgXcP06ZJFfRluL4P1e9twIWD+nt4RHQN0x47SDjgrZp+BjwBXCvpyPxk5Jx83tHAC8BzklqARcNsZwWwRFJzfhLw02RH/OW2YVd+4rJRUoOkUyW9BUDSAknNEfEq0JOv82qJbd0OvAP4c147ekfS2yRNz885PE82JFJqGwAfknRSfvLzSuBbwyw7XN+fAo6TdOyA5b8KXCPp9XnbmiVdOsz27SDigLeqiex6698nG7/eCmwH/jiffRVwJvAc8B1g5TCbuhroADYAncCDeVm5bbiY7CTlY8AzwE1kJ0UBLgB+KekFshOu74qIIU+QRsQTwBqyo/SBofw64E6ycN8I/JBs2KaUbwL3Ar8iG2Mfri8l+x4RD5P9AvhVPiQzMe/D3cC9knYBD5CdhzBDEX7gh1lRJG0BPhAR36t1W+zg4yN4M7NEOeDNzBLlIRozs0T5CN7MLFEH1M2Njj/++JgyZUqtm2FmVjfWrVv3TEQ0DzXvgAr4KVOm0NHRUetmmJnVDUmPl5rnIRozs0Q54M3MEuWANzNLlAPezCxRDngzs0QVehVN/ki2m4BTyW51+r6IWFNknWYHgiXtnaxYu42+CBokLps5iavbpte6WXaQKfoyyevJnhjzB5IOBY4ouD6zmlvS3sltD2zdM90XsWfaIW9jqbAhmvye1WcDXwOIiJcjomf4tczq34q12yoqNytKkWPwJ5M9n/NmSesl3TTUk3MkLcyfct/R3d1dYHPMxkZfifs7lSo3K0qRAT+O7AEPX4mIGcCLwBWDF4qIGyKiNSJam5uH/LatWV1p0NBPIixVblaUIgN+O9kT4Nfm03eSBb5Z0i6bOamicrOiFBbwEfEksE3StLzoPOChouozO1Bc3TadBbMm7zlib5BYMGuyT7DamCv0fvCSziC7TPJQsudRvjcini21fGtra/hmY2Zm5ZO0LiJah5pX6GWSEfHvwJAVm5lZsfxNVjOzRDngzcwS5YA3M0uUA97MLFEOeDOzRDngzcwS5YA3M0uUA97MLFEOeDOzRDngzcwS5YA3M0uUA97MLFEOeDOzRDngzcwS5YA3M0uUA97MLFEOeDOzRDngzcwS5YA3M0uUA97MLFEOeDOzRDngzcwS5YA3M0uUA97MLFEOeDOzRDngzcwS5YA3M0uUA97MLFEOeDOzRDngzcwS5YA3M0vUuCI3LmkLsAvoA16JiNYi6zMrpX19F8tWbWJHTy8TmxpZNHcabTNaat0ss0IVGvC5t0XEM2NQj9mQ2td3sXhlJ727+wDo6ull8cpOAIe8Jc1DNJa8Zas27Qn3fr27+1i2alONWmQ2NooO+ADulbRO0sKhFpC0UFKHpI7u7u6Cm2MHox09vRWVm6Wi6IB/a0ScCVwIfEjS2YMXiIgbIqI1Ilqbm5sLbo4djCY2NVZUbpaKQgM+Irryf58G7gLOKrI+s6EsmjuNxvENe5U1jm9g0dxpNWqR2dgoLOAlHSnp6P73wDuAXxRVn1kpbTNaWDpvOi1NjQhoaWpk6bzpPsFqySvyKpoTgbsk9dfzzYi4p8D6zEpqm9HiQLeDTmEBHxG/Ak4vavtmZjY8XyZpZpYoB7yZWaIc8GZmiXLAm5klygFvZpYoB7yZWaIc8GZmiXLAm5klygFvZpYoB7yZWaIc8GZmiXLAm5klygFvZpYoB7yZWaIc8GZmiXLAm5klygFvZpYoB7yZWaIc8GZmiXLAm5klygFvZpYoB7yZWaIc8GZmiXLAm5klygFvZpYoB7yZWaIc8GZmiXLAm5klygFvZpYoB7yZWaLGFV2BpAagA+iKiIurvf329V0sW7WJHT29TGxqZNHcabTNaKl2NTWxpL2TFWu30RdBg8RlMydxddv0WjerLs2/cQ2rN+/cMz1n6gSWXz67hi2yA1Fqn7mxOIL/KLCxiA23r+9i8cpOunp6CaCrp5fFKztpX99VRHVjakl7J7c9sJW+CAD6Irjtga0sae+sccvqz+BwB1i9eSfzb1xToxbZgSjFz1yhAS/pJOAi4KYitr9s1SZ6d/ftVda7u49lqzYVUd2YWrF2W0XlVtrgcB+p3A5OKX7mij6C/wLwSeDVUgtIWiipQ1JHd3d3RRvf0dNbUXk96T+KKLfczEYnxc9cYQEv6WLg6YhYN9xyEXFDRLRGRGtzc3NFdUxsaqyovJ40SBWVm9nopPiZK/IIfg5wiaQtwO3AuZJuq2YFi+ZOo3F8w15ljeMbWDR3WjWrqYnLZk6qqNxKmzN1QkXldnBK8TNXWMBHxOKIOCkipgDvAr4fEQuqWUfbjBaWzptOS1MjAlqaGlk6b3oSV9Fc3TadBbMm7zl6aJBYMGtyXZ/Rr5Xll8/eJ8x9FY0NluJnTjEG40uSzgE+MdJlkq2trdHR0VF4e8zMUiFpXUS0DjWv8OvgASLifuD+sajLzMwyZQW8pMOA/wZMGbhORPxNMc0yM7PRKvcI/p+B54B1wEvFNcfMzKql3IA/KSIuKLQlZmZWVeVeRfNTSfV7KtnM7CBU7hH8W4E/k/QY2RCNgIiI0wprmZmZjUq5AX9hoa0wM7OqGzbgJR0TEc8Du8aoPWZmViUjHcF/E7iY7OqZIBua6RfAbxTULjMzG6VhA77/m6cRcfLYNMfMzKqlrKtolFkg6X/l05MlnVVs08zMbDTKvUzyH4DZwJ/k07uALxfSIjMzq4pyr6KZGRFnSloPEBHPSjq0wHaZmdkolXsEvzt/eHYASGpmmKc0mZlZ7ZUb8F8E7gJOkHQN8BNgaWGtMjOzUStriCYilktaB5xHdqlkW0RsLLRlZmY2KuXeLvgbEfGnwMNDlJmZ2QGo3CGaNw+cyMfjf7v6zTEzs2oZNuAlLZa0CzhN0vOSduXTT5PdI97MzA5QwwZ8RCyNiKOBZRFxTEQcnb+Oi4jFY9RGMzPbD+WeZF0s6RLg7Lzo/oj4l+KaZWZmo1XurQqWAh8FHspfH5X02SIbZmZmo1PuN1kvAs6IiFcBJN0KrAc+VVTDzMxsdMq9igagacD7Y6vdEDMzq65yj+A/C6yX9AOyLzqdDVxRWKvMzGzURgx4SYeQ3XdmFvCWvPivIuLJIhtmZmajM2LAR8Srkj4ZEXcAd49Bm8zMrArKHYP/nqRPSJokaUL/q9CWmZnZqJQ7Bv/HZLcK/otB5X4mq5nZAarcgD+FLNzfShb0Pwa+WlSjzMxs9MoN+FuB58nuCw/Zo/tuBf6oiEaZmdnolRvwp0bEKQOmfyDpoSIaZGZm1VFuwD8oaVZEPAAgaSbQMdwKkg4HfgQcltdzZ0R8ZjSNtXScf939PPL0i3um33jCkdz3l+cUVt8bFn+HV+K16XGCR5deVFh9Y9m/0z5zD8+/1Ldn+pjDGthw1QWF1AWwpL2TFWu30RdBg8RlMydxddv0wuqbf+MaVm/euWd6ztQJLL98dmH1jaX29V0sW7WJHT29TGxqZNHcabTNaKna9su9iua3gZ9K2iJpC7AGeIukTkkbSqzzEnBuRJwOnAFcIGnWqFtsdW9w+AE88vSLnH/d/YXUNzjcAV6JrLwIY9m/weEO8PxLfZz2mXuqXhdk4X7bA1vpi+wH2hfBbQ9sZUl7ZyH1DQ53gNWbdzL/xjWF1DeW2td3sXhlJ109vQTQ1dPL4pWdtK/vqlod5R7BV3w4EBEBvJBPjs9fUXoNO1gMDr+RykdrcLiPVD5aY9m/weE+UvlorVi7rWR5EUfxg8N9pPJ6smzVJnp3772fenf3sWzVpqodxZd7u+DH92fj+ZOf1gFvAL4cEWuHWGYhsBBg8uTJ+1ONmY2R/iP3csuttB09vRWV749KbjZWsYjoi4gzgJOAsySdOsQyN0REa0S0Njc3F9kcMxulBqmicittYlNjReX7o9CA7xcRPcAP2I+hHkvPG084sqLy0RpXIntKlY/WWPbvmMMaKiofrctmTqqofLTmTB36C/OlyuvJornTaBy/935qHN/AornTqlZHYQEvqVlSU/6+ETgfeLio+qx+3PeX5+wTdkVeZfLo0ov2CfMir6IZy/5tuOqCfcK8yKtorm6bzoJZk/ccsTdILJg1ubCraJZfPnufME/lKpq2GS0snTedlqZGBLQ0NbJ03vSqXkWjKGjsTNJpZF+GaiD7RXJHRPzNcOu0trZGR8ewV1+amdkAktZFROtQ88q9iqZiEbEBmFHU9s3MbHhjMgZvZmZjzwFvZpYoB7yZWaIc8GZmiXLAm5klygFvZpYoB7yZWaIc8GZmiXLAm5klygFvZpYoB7yZWaIc8GZmiXLAm5klygFvZpYoB7yZWaIc8GZmiXLAm5klygFvZpYoB7yZWaIc8GZmiXLAm5klygFvZpYoB7yZWaIc8GZmiXLAm5klygFvZpYoB7yZWaIc8GZmiXLAm5klygFvZpaocUVtWNIk4B+BE4EAboiI66tdT/v6Lpat2sSOnl4mNjWyaO402ma0VLuag8JY/izn37iG1Zt37pmeM3UCyy+fXUhdADOvuY+ndr28Z/rEow9l7ZXnF1bflCu+s0/ZlmsvKqQufwaslCKP4F8BPh4RpwCzgA9JOqWaFbSv72Lxyk66enoJoKunl8UrO2lf31XNag4KY/mzHBzuAKs372T+jWuqXhfsG+4AT+16mZnX3FdIfUOF+3Dlo+HPgA2nsICPiCci4sH8/S5gI1DVw4plqzbRu7tvr7Le3X0sW7WpmtUcFMbyZzk43EcqH63B4T5SeT3xZ8CGMyZj8JKmADOAtUPMWyipQ1JHd3d3Rdvd0dNbUbmV5p9lffJ+s+EUHvCSjgL+CfhYRDw/eH5E3BARrRHR2tzcXNG2JzY1VlRupflnWZ+832w4hQa8pPFk4b48IlZWe/uL5k6jcXzDXmWN4xtYNHdatatK3lj+LOdMnVBR+WidePShFZXXE38GbDiFBbwkAV8DNkbEdUXU0TajhaXzptPS1IiAlqZGls6b7isI9sNY/iyXXz57nzAv8iqatVeev0+YF3kVTamrZYq4isafARuOIqKYDUtvBX4MdAKv5sWfioj/W2qd1tbW6OjoKKQ9ZmYpkrQuIlqHmlfYdfAR8RNARW3fzMyG52+ympklygFvZpYoB7yZWaIc8GZmiXLAm5klygFvZpYoB7yZWaIc8GZmiXLAm5klygFvZpYoB7yZWaIc8GZmiXLAm5klygFvZpYoB7yZWaIc8GZmiXLAm5klygFvZpYoB7yZWaIc8GZmiXLAm5klygFvZpYoB7yZWaIc8GZmiXLAm5klygFvZpYoB7yZWaIc8GZmiXLAm5klygFvZpYoB7yZWaLGFbVhSV8HLgaejohTi6qnfX0Xy1ZtYkdPLxObGlk0dxptM1qKqs6sLCdf8R1iwLSAx669qJC65t+4htWbd+6ZnjN1Assvn11IXVZfijyCvwW4oMDt076+i8UrO+nq6SWArp5eFq/spH19V5HVmg1rcLgDRF5ebYPDHWD15p3Mv3FN1euy+lNYwEfEj4CdIy44CstWbaJ3d99eZb27+1i2alOR1ZoNa3C4j1Q+GoPDfaRyO7jUfAxe0kJJHZI6uru7K1p3R09vReVmZgeTmgd8RNwQEa0R0drc3FzRuhObGisqNzM7mNQ84Edj0dxpNI5v2KuscXwDi+ZOq1GLzLITqpWUj8acqRMqKreDS10HfNuMFpbOm05LUyMCWpoaWTpvuq+isZp67NqL9gnzoq6iWX757H3C3FfRWD9FFHHqByStAM4BjgeeAj4TEV8bbp3W1tbo6OgopD1mZimStC4iWoeaV9h18BFxWVHbNjOzkdX1EI2ZmZXmgDczS5QD3swsUQ54M7NEFXYVzf6Q1A08Xut2lOF44JlaN6JAKffPfatfKfdvNH17fUQM+S3RAyrg64WkjlKXJaUg5f65b/Ur5f4V1TcP0ZiZJcoBb2aWKAf8/rmh1g0oWMr9c9/qV8r9K6RvHoM3M0uUj+DNzBLlgDczS5QDvgySmiTdKelhSRslzZY0QdJ9kh7J//21Wrdzf0j6n5J+KekXklZIOlzSyZLWSnpU0rckHVrrdpZL0tclPS3pFwPKhtxXynwx7+cGSWfWruUjK9G3Zfn/yw2S7pLUNGDe4rxvmyTNrU2ryzdU/wbM+7ikkHR8Pl33+y4v/3C+/34p6fMDyquy7xzw5bkeuCcifhM4HdgIXAH8a0S8EfjXfLquSGoBPgK0RsSpQAPwLuBzwN9FxBuAZ4H3166VFbuFfR/2XmpfXQi8MX8tBL4yRm3cX7ewb9/uA06NiNOA/wcsBpB0Ctm+fHO+zj9IauDAdgv79g9Jk4B3AFsHFNf9vpP0NuBS4PSIeDPwv/Pyqu07B/wIJB0LnA18DSAiXo6IHrIdc2u+2K1AW21aOGrjgEZJ44AjgCeAc4E78/l11bcSD3svta8uBf4xMg8ATZJ+fWxaWrmh+hYR90bEK/nkA8BJ+ftLgdsj4qWIeAx4FDhrzBq7H0rsO4C/Az7J3s8tr/t9B/w5cG1EvJQv83ReXrV954Af2clAN3CzpPWSbpJ0JHBiRDyRL/MkcGLNWrifIqKL7KhhK1mwPwesA3oGhMZ2oN4fkVVqX7UA2wYsV+99fR/w3fx9En2TdCnQFRE/HzQrhf69CfjdfDj0h5LekpdXrW8O+JGNA84EvhIRM4AXGTQcE9m1pnV3vWk+Fn0p2S+xicCRDPEnckrqdV+NRNKVwCvA8lq3pVokHQF8Cvh0rdtSkHHABGAWsAi4Q1JVH93rgB/ZdmB7RKzNp+8kC/yn+v8kzP99usT6B7K3A49FRHdE7AZWAnPI/tztf9rXSUBXrRpYJaX2VRcwacByddlXSX8GXAzMj9e+2JJC36aSHXz8XNIWsj48KOl1pNG/7cDKfJjpZ8CrZDcdq1rfHPAjiIgngW2SpuVF5wEPAXcD78nL3gP8cw2aN1pbgVmSjsiPHPr79gPgD/Jl6rVvA5XaV3cD786vyJgFPDdgKKcuSLqAbHz6koj4zwGz7gbeJekwSSeTnYz8WS3auL8iojMiToiIKRExhSwQz8w/k3W/74B24G0Akt4EHEp2R8nq7buI8GuEF3AG0AFsyHfKrwHHkV2R8QjwPWBCrdu5n327CngY+AXwDeAw4Dfy/1CPAt8GDqt1Oyvozwqy8wm7yQLh/aX2FSDgy8BmoJPsaqKa96HCvj1KNl777/nrqwOWvzLv2ybgwlq3f3/6N2j+FuD4hPbdocBt+WfvQeDcau8736rAzCxRHqIxM0uUA97MLFEOeDOzRDngzcwS5YA3M0uUA96M7MtCkr5U4TqXSKq7m8zZwWPcyIuY2WCSxkXE3WRfSjE7IDngLWmS3g18guz+MxuAO4AlZF8y+Q+yr/c/NWidKcDXyb423g28NyK2SroF+C9gBrBa0gayL9j8D0nNwFeByflmPhYRqyX9HtntpsnbcHZE7Cqou2Z7ccBbsiS9mSzMfycinpE0gSxkZ0VESPoA2df8Pz5o1b8Hbo2IWyW9D/gir91i+KR8e335PWD6XU92D/2fSJoMrAJ+i+yXy4fysD+K7BeE2ZhwwFvKzgW+HRHPAETETknTgW/lNx07FHhsiPVmA/Py998APj9g3rcjom+Idd4OnDLgZoDH5IG+GrhO0nKyG0ttH22nzMrlk6x2sPl74EsRMR3478DhFa7/YonyQ8j+Mjgjf7VExAsRcS3wAaCRbFjnN/e75WYVcsBbyr4P/KGk4yB7NitwLK/devU9Jdb7Kdkj0wDmAz8uo657gQ/3T0g6I/93amR3Rfwc8G+AA97GjAPekhURvwSuAX4o6efAdcBfA9+WtI7s1qxD+TDw3vwk6p8CHy2juo8ArfkDoB8CPpiXf0zZA803kN1J8Lslt2BWZb6bpJlZonwEb2aWKAe8mVmiHPBmZolywJuZJcoBb2aWKAe8mVmiHPBmZon6//hj+wwoKbJ2AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        }
      ]
    }
  ]
}