Boxplot.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Boxplot.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNtrCOKQ+9IoYgTtxogxopg",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SuperDataWorld/Python/blob/main/Boxplot.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rVmc5eFZeAaV"
      },
      "source": [
        "## 1. Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5wMV5JLssb0X"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bfBk364yeEpU"
      },
      "source": [
        "## 2. Simple Boxplots in Matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VkaTrZXQZy24"
      },
      "source": [
        "s = pd.DataFrame({'s':[0,10,20,30,40,50,60,70,80,90,100]})"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "gWOlmYOFZyv6",
        "outputId": "707dbfb9-3bff-4e5f-b853-2d9271d9664f"
      },
      "source": [
        "s.boxplot()\n",
        "plt.title('Simple Boxplot: No Outliers')\n",
        "plt.show()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mlyTwAH5e_W1"
      },
      "source": [
        "s = pd.DataFrame({'s':[0,10,20,30,40,50,60,70,80,90,150]})"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "TsCjdtTXe_OT",
        "outputId": "00420132-a700-43ad-eb53-ecb831fe07e4"
      },
      "source": [
        "s.boxplot()\n",
        "plt.title('Boxplot With One Outlier')\n",
        "plt.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "khlXG7vztg2h"
      },
      "source": [
        "s = pd.DataFrame({'s':[-200,-100,10,20,30,40,50,60,70,80,90,200,300]})"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "hzXV5Oiiyc5I",
        "outputId": "755ff504-0ee4-4755-de49-247a1a9ff353"
      },
      "source": [
        "s.boxplot()\n",
        "plt.title('Boxplot With Outliers')\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "nhrq_EY-yndP",
        "outputId": "8c61fdfb-300d-46d1-c1cd-9fbd74db24b5"
      },
      "source": [
        "s.boxplot(vert=False)\n",
        "plt.title('Horizontal Boxplot: With Outliers')\n",
        "plt.show()\n"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BijJToUReM-d"
      },
      "source": [
        "## 3. Simple Boxplots in Seaborn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "CF6fEDGN3_Jm",
        "outputId": "849605ea-88a3-4fd0-c6f9-61d60c0897b2"
      },
      "source": [
        "sns.set_theme(style=\"whitegrid\")\n",
        "plt.title('Vertical Seaborn Boxplot: With Outliers')\n",
        "ax = sns.boxplot(y=s['s'], orient='v')"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "Rs5wWBIG5MQZ",
        "outputId": "77549a86-e23d-417e-b621-297d50ac9926"
      },
      "source": [
        "sns.set_theme(style=\"whitegrid\")\n",
        "plt.title('Horizontal Seaborn Boxplot: With Outliers')\n",
        "ax = sns.boxplot(x=s['s'])"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MrE9k0kmeRk8"
      },
      "source": [
        "## 4. Import Student Score Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rCIikmbF4rxS",
        "outputId": "fe7c0c24-c1a7-413d-afaa-006484b243f0"
      },
      "source": [
        "! wget https://raw.githubusercontent.com/SuperDataWorld/Python/main/Data/StudentsPerformance.csv\n",
        "df = pd.read_csv('StudentsPerformance.csv')\n"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-09-04 20:30:53--  https://raw.githubusercontent.com/SuperDataWorld/Python/main/Data/StudentsPerformance.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.108.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 72036 (70K) [text/plain]\n",
            "Saving to: ‘StudentsPerformance.csv.3’\n",
            "\n",
            "StudentsPerformance 100%[===================>]  70.35K  --.-KB/s    in 0.01s   \n",
            "\n",
            "2021-09-04 20:30:53 (4.98 MB/s) - ‘StudentsPerformance.csv.3’ saved [72036/72036]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hwFcrgfA4rz5",
        "outputId": "3cfdada6-890a-4d79-b8af-e162e9dee93d"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000 entries, 0 to 999\n",
            "Data columns (total 8 columns):\n",
            " #   Column                       Non-Null Count  Dtype \n",
            "---  ------                       --------------  ----- \n",
            " 0   gender                       1000 non-null   object\n",
            " 1   race/ethnicity               1000 non-null   object\n",
            " 2   parental level of education  1000 non-null   object\n",
            " 3   lunch                        1000 non-null   object\n",
            " 4   test preparation course      1000 non-null   object\n",
            " 5   math score                   1000 non-null   int64 \n",
            " 6   reading score                1000 non-null   int64 \n",
            " 7   writing score                1000 non-null   int64 \n",
            "dtypes: int64(3), object(5)\n",
            "memory usage: 62.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "oYieTZongi5e",
        "outputId": "035c9fe2-cba0-4ac5-c1bf-63a0628edd30"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gender</th>\n",
              "      <th>race/ethnicity</th>\n",
              "      <th>parental level of education</th>\n",
              "      <th>lunch</th>\n",
              "      <th>test preparation course</th>\n",
              "      <th>math score</th>\n",
              "      <th>reading score</th>\n",
              "      <th>writing score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>female</td>\n",
              "      <td>group B</td>\n",
              "      <td>bachelor's degree</td>\n",
              "      <td>standard</td>\n",
              "      <td>none</td>\n",
              "      <td>72</td>\n",
              "      <td>72</td>\n",
              "      <td>74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>female</td>\n",
              "      <td>group C</td>\n",
              "      <td>some college</td>\n",
              "      <td>standard</td>\n",
              "      <td>completed</td>\n",
              "      <td>69</td>\n",
              "      <td>90</td>\n",
              "      <td>88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>female</td>\n",
              "      <td>group B</td>\n",
              "      <td>master's degree</td>\n",
              "      <td>standard</td>\n",
              "      <td>none</td>\n",
              "      <td>90</td>\n",
              "      <td>95</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>male</td>\n",
              "      <td>group A</td>\n",
              "      <td>associate's degree</td>\n",
              "      <td>free/reduced</td>\n",
              "      <td>none</td>\n",
              "      <td>47</td>\n",
              "      <td>57</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>male</td>\n",
              "      <td>group C</td>\n",
              "      <td>some college</td>\n",
              "      <td>standard</td>\n",
              "      <td>none</td>\n",
              "      <td>76</td>\n",
              "      <td>78</td>\n",
              "      <td>75</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   gender race/ethnicity  ... reading score writing score\n",
              "0  female        group B  ...            72            74\n",
              "1  female        group C  ...            90            88\n",
              "2  female        group B  ...            95            93\n",
              "3    male        group A  ...            57            44\n",
              "4    male        group C  ...            78            75\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "twTLItz54r2k",
        "outputId": "4314b268-f611-4b59-a8de-0e3f8c9fd1ed"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>math score</th>\n",
              "      <th>reading score</th>\n",
              "      <th>writing score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1000.00000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>66.08900</td>\n",
              "      <td>69.169000</td>\n",
              "      <td>68.054000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>15.16308</td>\n",
              "      <td>14.600192</td>\n",
              "      <td>15.195657</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.00000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>10.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>57.00000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>57.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>66.00000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>69.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>77.00000</td>\n",
              "      <td>79.000000</td>\n",
              "      <td>79.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>100.00000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       math score  reading score  writing score\n",
              "count  1000.00000    1000.000000    1000.000000\n",
              "mean     66.08900      69.169000      68.054000\n",
              "std      15.16308      14.600192      15.195657\n",
              "min       0.00000      17.000000      10.000000\n",
              "25%      57.00000      59.000000      57.750000\n",
              "50%      66.00000      70.000000      69.000000\n",
              "75%      77.00000      79.000000      79.000000\n",
              "max     100.00000     100.000000     100.000000"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-9-ZtqIreX5h"
      },
      "source": [
        "## 5. Intermediate Boxplots in Seaborn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "q6E4uFf54r45",
        "outputId": "511ad39a-590d-4f66-e235-2ed5bc5007bd"
      },
      "source": [
        "sns.set_palette(\"Paired\")\n",
        "ax = sns.boxplot(x=\"gender\", y =\"math score\",data = df)\n",
        "plt.title('Math score Boxplot By Gender')\n",
        "plt.show()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "AK0hZEGjQ9uD",
        "outputId": "71b4d173-d993-4c06-cbcf-33ab9589a8a5"
      },
      "source": [
        "sns.set_palette(\"pastel\")\n",
        "ax = sns.boxplot(x=\"gender\", y =\"math score\", hue = 'lunch',data = df)\n",
        "plt.title('Math score Boxplot By Gender / Lunch') \n",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "b9M70SthR8dY",
        "outputId": "32d24129-accd-4337-91e6-b719b3386b38"
      },
      "source": [
        "\n",
        "sns.set_palette(\"icefire\")\n",
        "f , axes = plt.subplots(nrows=1, ncols=3, figsize = (15,4))\n",
        "sns.set_style(\"darkgrid\")\n",
        "\n",
        "fig1 = sns.boxplot(x=\"gender\", y =\"math score\",data = df, ax = axes[0] )\n",
        "fig2 = sns.boxplot(x=\"gender\", y =\"reading score\", data = df, ax = axes[1] )\n",
        "fig3 = sns.boxplot(x=\"gender\", y =\"writing score\",data = df, ax = axes[2] )\n",
        "plt.show()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x288 with 3 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "fQ1eaG9BW941",
        "outputId": "27f495de-5082-4368-9b6b-8b1727f33e01"
      },
      "source": [
        "sns.set_palette(\"bone_r\")\n",
        "f , axes = plt.subplots(nrows=1, ncols=3, figsize = (20,4))\n",
        "sns.set_style(\"darkgrid\")\n",
        "\n",
        "fig1 = sns.boxplot(x=\"gender\", y =\"math score\",hue= 'test preparation course', data = df, ax = axes[0] )\n",
        "fig2 = sns.boxplot(x=\"gender\", y =\"reading score\",hue= 'test preparation course', data = df, ax = axes[1] )\n",
        "fig3 = sns.boxplot(x=\"gender\", y =\"writing score\",hue= 'test preparation course',data = df, ax = axes[2] )\n",
        "plt.show()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x288 with 3 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "p6ptyb5sXpTV",
        "outputId": "ea0d8e84-c9cd-4567-ef3c-98094b69bd56"
      },
      "source": [
        "f , axes = plt.subplots(nrows=1, ncols=3, figsize = (20,4))\n",
        "sns.set_style(\"dark\")\n",
        "\n",
        "fig1 = sns.boxplot(x=\"gender\", y =\"math score\",hue= 'test preparation course', data = df, ax = axes[0] )\n",
        "fig2 = sns.boxplot(x=\"gender\", y =\"reading score\",hue= 'test preparation course', data = df, ax = axes[1])\n",
        "fig3 = sns.boxplot(x=\"gender\", y =\"writing score\",hue= 'test preparation course',data = df, ax = axes[2] )\n",
        "axes[1].legend(loc='upper left')\n",
        "axes[2].legend(loc='lower right')\n",
        "plt.show()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VpKSk0LBhQywWCwAWi4XQ0FBSUlIICgqq8P6Dg/2qKvQq5+lZ8tpDQvwNjsRYOg6uwVXeh9I43Jmnp8Xw90FERERERORihieZiouLWblyJcuXL6dDhw7s2bOHKVOmMH/+/ErZf2ZmHjabvVL2Vd2KiqwAZGTkGhyJsXQcXIOrvA+lcbizoiKrU++D2Wyq0Ql4ERGRi6nGq4iIazA8yXT48GHS09Pp0KEDAB06dKBu3bp4e3uTlpaG1WrFYrFgtVpJT08nPDzc4IhFRERERMRVqMariIjrMLwmU1hYGKmpqfz8888AHD16lMzMTK677jpat25NYmIiAImJibRu3fqqhsqJiIiIiEjtpxqvIiKuwfCeTCEhIcTGxjJ58mRMJhMAc+fOJTAwkNjYWGJiYli+fDkBAQHEx8cbHK2IiIiIiLiSqq7xKiIiFWd4kglg8ODBDB48+JL1zZs3Z926dQZEJCIirkS1NkSkIj75ZAOpqSmGxpCSchKA1atXGhZDWFg4/fpd+tu6tqrKGq81vYahK0zcoklbSmjiFtf4PBrNHY6BSySZRERErkS1NkSkolJTU0j+NRnfevUMi8HkWfLzOiv3jCHt558+bUi7RqrKGq/OTiJkdOKzNOk5f/5Cw2JITT1JUXExB7/8wrAYjJZ/Oodsk8nwCXSM5goTCek7WcLZmxHlTSSkJJOUy+gvIeiOYCmj3wtXeR/EPZVXa2P16tVASa2N2bNnk5WVVW3DIPS9hMzMU5i8vAxrX+RivvXqcUu37kaHYRh3vJC/sMbrDTfccNkar0OGDDGkxmtqagonU04QHBJSbW1eqI5PXQAKi88Z0n5mRgY2q82QtkUux+ibEUbfiICqvxmhJJOUy+gvIRj/RXSVO4L6kZJhSLtivKquteHMUIjMzHS3/15azGY8lWTSMAhcYwiAhuWUcLfPo6vXeA0OCWHwsGHV3q4r2LB2LVkZp/D283P75K+fn/t8J12dbkZU7c0IJZnkd+lL6Dp3BN39R4q4p6qstQHODYUoKrK6/fcyK+OU0WG4hKIiq4ZBuMAwiNIY3J2zn8fyhkG4KtV4FRFxDWajAxARESlPRWptANdUa0NERERERCqPkkwiIuLSLqy1AVy21gZgSK0NERERERE5T8PlRETEpbl6rQ13Z7VayT+d41JDi6tb/ukcvP7vsykiIiLizpRkKodmDdKsQeJ68vJy3fqC1l0vZlVrQ0RERETE9SnJVA7N5pWBCRPeSjKJiMgVWCwWzRqkWYNEREREACWZfpdmDdKsQeJa/Pz8OWe3u+0FrS5mRURERETEVanwt4iIiIiIiIiIOE1JJhERERERERERcZqSTCIiIiIiIiIi4jQlmURERERERERExGlKMomISKUoKipi9+7dbNq0CYCCggIKCgoMjkpERERERKqLZpcTERGn/fDDDzz00EN4eXmRlpZG//792bVrFx9++CGLFy82OjwRERExUP7p0xz88gtD2j5XeBYAL+86hrQPJa8/yD/AsPZFqpOSTCIi4rTY2FgeffRR7rjjDjp27AhAx44defrppw2OTEREXInNZuPUqVOEhoYaHYpUEy8vL8KDGxjWfkpeHgBBDYxL8gT5BxAWFm5Y+yLVSUkmERFx2k8//cSQIUMAMJlMAPj4+FBYWGhkWCIi4iLOnDlDXFwcW7ZswcPDg6SkJLZu3cr+/ft57LHHjA5PqlBwcAPGjHnQsPZXr14JYGgMIu5ESSYREXFa48aNOXjwIG3atHGs279/P82aNTMwKhH38sknG0hNTTGs/ZSUk8D5CzojZGaewuTlZVj7cmUzZ84kICCAbdu2MWDAAACioqKIj49XkklEpBZRkknKZbVayT+dY9gYaleQfzoHr//rmWGkvLxczpw5zYa1a40OxRCZ6ekEBNTDz8/f6FDkMiZPnsyDDz7IsGHDKCoqYuXKlaxdu5bZs2cbHZqI20hNTeFkygmCQ0IMab+OT10ACovPGdJ+ZkYGJkx4K8nkkr755hu+/PJLPD09HT1eg4KCyMzMNDgyERGpTEoyiYiI02677TZefvll3n33XTp27MiJEydYsmQJt9xyi9GhibiV4JAQBg8bZnQYhtiwdi1ZGaeMDkOuwN/fn+zs7DK1mE6ePEmIQUnR6qKbhCU3CUVcRV5erjpRVHEniqtKMqlQn/uxWCx4+/lxS7fuRodimINffuESvWf8/PzxrOPt1hcP3h66O+2KrFYrffr0YdOmTcTGxhodjoiIuKChQ4fy6KOPMmXKFGw2G3v37uX5559nmJv+rhERqa0qlGRSoT4REbkSi8WCxWKhsLAQLw1TERGRyxg3bhze3t7MmjWL4uJipk+fzj333MOoUaOMDq1K6SahbhK6EtXug8LCQnzrBaoTRRV2oqhQkkmF+kREpDwjR45kypQpPPjgg4SFhTnqbQA0bdrUwMiqloZBpGOz2Y0OQ8RBtSRdp5bkhaxWK9OnT2f27Nm1Pqkk4spUu0+1+6pDhZJMKtQnIiLlKS3wvWPHjjLrTSYThw8fNiIkERFxERaLhR07dpS5ASEixlDtPtXuq2oVSjK5a6E+ERGpmCNHjhgdgiE0DKLkx1r+6dOG9Rw5V3gWAC/vOoa0D5B/+jRB/gGGtS/nqZak69SSvNioUaNYsmQJkyZN0tBqEZFarEJJJhXqExGRijh58iRpaWmEhYURHh5udDhSDby8vAgPbmBY+yl5eQAENTAuyRPkH0BYmD7vIuV54403OHXqFKtXryYoKKhMr6bt27cbF5iIiFSqCiWZ3LVQn4iIVEx6ejqPP/44SUlJBAYGkpOTQ7t27Xj++edp2LCh0eFJFQoObsCYMQ8a1n5p8VAjYxCR37dgwQKjQxARkWrwu0kmFeoTEZHfExsbS6tWrVi1ahU+Pj4UFBTw/PPPM3PmTF588UWjwxMREYN16tTJ6BBERKQa/G6SyZ0L9WnWIM0aJK7Jneu/uGrtlz179pCQkICnpycAPj4+TJs2jW7duhkcmYiIuIKioiJWrFjB+vXrSU9PJzQ0lCFDhjBhwgTVaBIRqUUqNFyuqgv1FRYWMnfuXL755hu8vb2JjIxk9uzZHDt2jJiYGHJycggMDCQ+Pp6IiIhKb19Eag6j654YXf/FVWu/1KtXj6NHj9KqVSvHup9//pmAANdLiImISPVbsGAB+/fvJy4ujkaNGnHy5EmWL19OXl4e06dPNzo8ERGpJBVKMlV1ob4FCxbg7e3Nli1bMJlMnDpVMq3gzJkzGT58OEOGDGH9+vXMmDGDNWvWON1eRWnWIE3xKK6nX7/Bhrav+i+XN3bsWEaPHs1dd93luHj44IMPmDx5stGhiYiIC9i8eTPr16+nfv36ANxwww3cdNNNDBkyREkmEZFapEJJpqos1Jefn89HH33E559/7kheNWjQgMzMTA4dOsTq1asBGDhwILNnzyYrK4ugoKAqi0dERK7e3XffTdOmTUlMTOSHH34gNDSU5557ji5duhgdmoiIuAC7/fIlGK60XkREaqYKJZmqslBfcnIygYGBLF26lJ07d+Lr68vkyZOpU6cODRs2xGKxACW1oUJDQ0lJSVGSSUTEBXXp0kVJJRERuay+ffvy0EMP8fDDD9OoUSNOnDjBihUr6Nevn9GhiYhIJapQkqkqC/VZrVaSk5O56aabePLJJ9m3bx8TJkwgISHBqf2WCg72u+ZtPT0tFBZXShg1ltnsfgXfL8fT00JIiL/hMbj759FV3gfA8DhczaRJkxg9ejTR0dGOdbt372bNmjW88MILBkYmIiKuYOrUqaxYsYJZs2aRnp5Ow4YN6d+/PxMnTjQ6NBERqUQVHi5XVYX6wsPD8fDwYODAgQC0a9eO+vXrU6dOHdLS0rBarVgsFqxWK+np6YSHX13B28zMvGueIa2oyHpN29UmNpvd0Jm8wDVm8yryDyAjI9eQ9kvp81hyDFzlfTA6DmeYzSanEvCXs2vXrktuDkRGRvLwww9XajsiIlIzeXl5MXnyZNXqExGp5SqUZKrKQn1BQUF07tyZHTt20LVrV44dO0ZmZiYRERG0bt2axMREhgwZQmJiIq1bt9ZQuWrm5eVFeHADQ2PQbF4irs/Ly4vffvsNP7/zyauCggI8PCp0mhERkVpu1apV/OEPf6Bt27aOdfv372fnzp2MGzfOwMhERKQyVejXf1UX6ouLi2P69OnEx8fj4eHB/PnzCQgIIDY2lpiYGJYvX05AQADx8fGV0p5UXHBwA8Nn0dJsXudlZmSwYe1aQ9ouyM8HwMfX15D2MzMyaBTe2JC25fd17dqVGTNmMGvWLPz8/MjLy2PWrFl069bN6NBERMQFrFmzhhEjRpRZ17x5cyZOnKgkk4hILVKhJFNVF+pr2rQpr7/++iXrmzdvzrp16yqlDZGazujeVFkFpwCoX6++Ie03Cm9s+DGQK4uJiWHq1Kl06tSJevXqcfr0abp37878+fONDk1ERFxAUVHRJb1bPT09OXfunEERiYhIVahQkkmF+kSM16/fYEPbV48yKU+9evVYtWoVGRkZpKSkEB4eTkhIiNFhiYiIi7j55pt56623GD16tGPd2rVruemmmypl/4WFhcydO5dvvvkGb29vIiMjmT17NseOHSMmJoacnBwCAwOJj48nIiKiUtoUEZFLVSjJpEJ9IiJSnqysLLy9vQkJCSEoKIiPPvoIi8XC4MGDMZvNRocnIiIG+8c//sGYMWPYsGEDTZs2JTk5mYyMDFavXl0p+1+wYAHe3t5s2bIFk8nEqVMlPbBnzpzJ8OHDGTJkCOvXr2fGjBmsWbOmUtoUEZFLVSjJpEJ9IiJSngcffJC4uDhuuukmFi1axGeffYaHhweHDh1yeoIIERGp+W688Ua2bNnC9u3bSUlJoXfv3vTo0QPfSqj1mJ+fz0cffcTnn3+OyWQCoEGDBmRmZnLo0CFHImvgwIHMnj2brKwsTSYk4saMnD3d6JnToeT1B/lX3aRaFUoyqVCfiIiU55dffqF169YAbNiwgbVr1+Lj48PAgQOVZBKpJnl5uZw5c9qwCSKMlpmejs1WOZPSSNXw9fVlwIABACQnJ5OdnV0pSabk5GQCAwNZunQpO3fuxNfXl8mTJ1OnTh0aNmyIxWIBwGKxEBoaSkpKSrUmmTRxiyZucRU6T6RjNptp2qSpYTEYPXM6VP3s6RVKMqlQn4iIlMdsNlNUVMSxY8fw9/enUaNG2Gw28v/vx62IiLi3xx9/nBEjRtC+fXvef/994uLiMJvNPPXUUwwdOtSpfVutVpKTk7npppt48skn2bdvHxMmTCAhIcHpuIOD/Zza/vrrr8PT0+J0HNcqJzMTgNAGDQxp369ZM5o0aUJIiL8h7QOO429kDK7CYlEJg3r16jFt2hOGtb9o0SIAHnvsMcNiqGoVSjJVdaE+ERGp2bp3787kyZPJycmhf//+APz00080bNjQ4MhE3Iefnz+edbwZPGyY0aEYYsPatWRlnDI6DLmCb775hmeffRaA1157jdWrVxMQEMDDDz/sdJIpPDwcDw8PBg4cCEC7du2oX78+derUIS0tDavVisViwWq1kp6eTnh4xe/gZ2bmOdVDrkePvte8bWUonbhlxIixhsaRkZFrWNtFRVbDY3AVdev6Yvb0dOvzhLeHlz6PlcBsNl0xCV+hJFNVF+oTEZGabc6cOXz44Yd4eHhwxx13AJCdnc0jjzxicGQiIuIKioqK8PLyIi0tjZycHDp06ADgKNDtjKCgIDp37syOHTvo2rUrx44dIzMzk4iICFq3bk1iYiJDhgwhMTGR1q1bqx6TiEgVqlCSqSoL9YmISM3n5eXFPffcU2Zd586dK23/mppaRKRma926NStXruTEiRP06NEDgLS0NPz8nBuOViouLo7p06cTHx+Ph4cH8+fPJyAggNjYWGJiYli+fDkBAQHEx8dXSnsiInJ5FUoyQdUV6hMREfk9rjw1tQq6qqCruBYjZw0C42cOqupZg67VnDlzSEhIwMPDg2nTpgGwd+9eBg0aVCn7b9q0Ka+//vol65s3b866desqpQ0REfl9FUoyVWWhPhERkfK48tTUVTkzR0VkFZQk2+rXq29I+43CGxt+DEQu5D4xPOoAACAASURBVOXlRXiwMQWOSxk9c1BVzxp0rZo1a8Zzzz1XZl3fvn3p29fYmkUiIlK5KpRkqspCfa5Od6h1h1pEjOXKU1P36ze4Wtq5ktKCrmPGPGhoHCKuIji4geHfB30vRUTEnVUoyVSVhfpcmdF3gXSHWkSkaqemBuenpzaSpmXWMbiQp6eFwmKjozCWp6fF8M+CPpMiIuLOKpRkqupCfa5Kd6hFRCpm+PDhjqFsF/Ly8iIsLIxevXpx++23X9O+q3JqanB+emoj1ZZpcJ2hY3Be6bFwZ0VFVsM/C7XhM1ne1NQiIiLlMVfkSXPmzOF///d/KSwsZMqUKUDlFuoTEZGarVOnTpw4cYKOHTsyePBgOnbsyMmTJ7nlllsIDg5m+vTpvPTSS9e07wunpgYuOzU1oKmpRUREREQMVqGeTCrUJyIi5dmxYwevvPIKzZs3d6wbNGgQMTExrFu3jt69e/P4448zbty4a9q/pqYWEanZrjTEubTHa7du3WjQwNii7SIi4rwKJZlERETK8/PPP9O0adMy6xo3bsyxY8cAaNu2LZmZmde8f01NLSJSs/3yyy/8+9//pm3btoSHh5OSksL+/fu5/fbb+eyzz4iLi+OFF16ge/fuRocqIiJOUJJJRESc1rFjR/7xj3/w6KOPEhYWRmpqKkuWLHFMFPHDDz8QEhJicJQiImIUm83GokWL6NWrl2Pdf/7zHxITE3n33Xf58MMPee6555RkkkskJe1h797d17x9SspJ4Hy922sRFRVNZGSHa95exJ1UqCaTiIhIeZ599llsNhsDBgwgMjKSAQMGYLPZmDdvHgCenp6XDLsWERH38dVXX10yAcRtt93GF198AcDgwYNJTk42IjSp5fz9/fH312yPItVFPZlERMRpgYGBLFq0CJvNRlZWFkFBQZjN5+9j3HDDDQZGJyIiRmvWrBlvv/02I0aMcKxbu3YtzZo1AyA7O5u6desaFZ64sMjIDupFJFKDVCjJdO7cOT788EMOHz5MQUFBmcfmz59fJYGJiEjNkpuby7Fjx8jPzy+zvkuXLgZFJCIiruKZZ57hkUce4aWXXqJhw4akpaVhsVhYsmQJUDJz6OTJkw2OUkREnFWhJFNMTAxHjhzhtttu06wPIiJyiQ8++IBZs2bh4+NDnTp1HOtNJhNbt241MDIREXEFN998M1u2bCEpKYmMjAxCQkKIjIzE09MTKKnt17FjR4OjFBERZ1UoyfTll1+ydetWAgICqjoeERGpgRYtWkRCQgK33nqr0aGIiIiL8vT0pGPHjthsNsc6m81WZni1iIjUbBVKMoWHh3Pu3LmqjkVERGooq9VK165djQ5DRERc1Pfff8+sWbP44YcfKCwsBMBut2MymTh8+LDB0Ym4j8yMDDasXWtI2wX/V1LBx9fXkPYzMzJoFN7YkLbdyRWTTN98843j7zvuuIOJEycycuRIgoODyzxPtTZERGTcuHGsWLGCiRMn6o60iIhcIiYmhttuu425c+eWGVYtItUnLCzc0PazCk4BUL9efUPabxTe2PBj4A6umGR66qmnLln3/PPPl1lWrQ0REQF47bXXOHXqFC+//DKBgYFlHtu+fbsxQYm4Id2h1h1qV3XixAkee+wxTCaT0aGIuK1+/QYb2v7q1SsBGDPmQUPjkKp1xSTTtm3bqjMOERGpwRYsWGB0CCJuz+i7s7pDLeXp1asXX331Fd26dTM6FBERqUIVqsn00EMPsWLFikvWT5o0iaVLl1Z6UCIiUrN06tTJ6BBE3J7uUIsrKywsZNKkSXTo0OGS2arnz59vUFQiIlLZKpRk2rlz52XXf/vtt5UajIiI1BwrVqzgoYceAiAhIeGKz5s8eXJ1hSQiIi6qRYsWtGjRwugwRESkipWbZCq9aCgqKrrkAiI5OZlGjRpVXWQiIuLSUlNTL/u3iIjIxSZNmmR0CCIiUg3KTTKVXjTY7fZLLiDCw8N55JFHqi4yERFxaXFxcY6/582bZ2AkIiLiinbt2kXHjh2BsjNXX0yzVYuI1B7lJplKLxqioqK4++67qyUgERGpGZKTkyv0vKZNm1ZxJCIi4ori4uJITEwELj9zNWi2ahGR2qZCNZlKE0x5eXlkZ2eXeawyLx6WLl3KkiVL2LhxIy1btiQpKYkZM2ZQWFhI48aNWbBgAcHBwZXWnoiIXLtevXphMpmw2+1lpqS+ePnw4cNGhCciIgYrTTCBZq4WEXEXFUoyHT16lL///e8cOXLkkguKyrp4+P7770lKSqJx48YA2Gw2pk6dyrx584iOjmb58uUsXLhQQzJERFzEkSNHHH+///77fP311zzyyCM0atSIkydPsmzZMg2BEBERQLNVi4i4C3NFnhQbG0vnzp359ttv8fPzY9euXdxzzz08++yzlRLEuXPnmDVrFrGxsY51Bw8exNvbm+joaACGDRvG5s2bK6U9ERGpXAkJCcyZM4eIiAi8vLyIiIhg1qxZLF682OjQRETEBWi2ahER91ChnkxHjhzh1VdfxdPTE7vdjr+/P9OmTWPgwIEMGTLE6SASEhIYPHgwTZo0caxLSUkpM3tdUFAQNpuNnJwcAgMDK7zv4GA/p+MziqenBYCQEH+DIzGWjoNr0PtQQsfh8mw2GydOnKB58+aOdSdPnsRmsxkYlYiIGE2zVYuIuJcKJZm8vb0pLi7G09OT+vXrc/LkSQICAsjJyXE6gL1793Lw4EGeeOIJp/d1OZmZedhs9irZd1UrKrICkJGRa3AkxtJxcA215X1IStrD3r27r3n7lJSTAMyfv/Ca9xEVFU1kZIdr3t5ZZrOp0hPwo0ePZtSoUdx5552EhYWRmprKBx98wKhRoyq1HRERqVk0W7UYLSXlBKtXr+T++x8iLCzc6HBEar0KJZk6dOjAJ598wp133kmfPn0YN24cXl5e/OEPf3A6gF27dnH06FF69uwJlJyIHnjgAe677z5OnjzpeF5WVhZms/mqejGJiFQ2f3/1YLqcsWPH0rJlSzZv3syhQ4cICQlh7ty5dO/e3ejQRETEQJqtWoz2/vtrKSws5L333mbSpMeNDkek1qtQkunCrq2PP/44LVq0oKCggDvuuMPpAMaPH8/48eMdy7fffjsvvvgiLVq04N1332X37t1ER0ezdu1a+vbt63R7IuLeIiM7GNqLqDbr3r27kkoiIuLw66+/OsphdOnSheTk5Ms+rzJnqxa5UErKCTIy0gHIyEgjNTVFvZlEqliFkkwXMpvNlZJcqkg78+fPZ+bMmRQWFtK4cWMWLFhQ5e2KiMi1OXz4MLt37yY7Oxu7/fww5cmTJxsYlYiIGGXQoEHs3bsXgF69ejlmqb6QyWSqtNmqRS72/vtryyyrN5NI1atQkik3N5c1a9Zw+PBhCgoKyjz26quvVmpA27Ztc/zdvn17Nm7cWKn7FxGRyvfOO+8wb948/vSnP/HFF1/QvXt3duzY4RgKLXIllVUnbfXqlU7FYXStNJHaqDTBBCUTCYlUt9JeTOeX0wyKRMR9VCjJNHnyZKxWK7169cLb27uqYxIRkRrm5Zdf5uWXXyY6OpqOHTuybNkyPv/8czZt2mR0aFLLqU6aiOuzWq306dOHTZs24eXlZXQ44kZCQkLLJJpCQhoaGI2Ie6hQkikpKYn/+Z//0UlBREQuKzMzk+joaKBkuLPNZuPWW29l6tSpBkcmrk510kRqP4vFgsVi4ezZs7qekGr1178O48UXX3As33XX3wyMRsQ9mCvypA4dOvDzzz9XdSwiIlJDhYWF8euvvwIQERHB1q1b2b17N56engZHJiIirmDkyJE89thjfPvttxw/fpzk5GTHP5GqEh7emDp16gBQp05dFf0WqQYV6sn07LPPMm7cONq1a0dwcHCZxyZNmlQlgYlI5XKFuieqeVJ7jR07lqNHj9KkSRMmTpzI5MmTKSoq4qmnnjI6NBERcQGzZ88GYMeOHWXWq/C3VKXc3DMUFRUBUFRURG5uroZZi1SxCiWZFi1aRGpqKk2aNCEvL8+x3mQyVVlgIuJadEKW8tx5552Ov2+99Va+/fZbioqK8PX1NTAqERFxFdVV+Hvp0qUsWbKEjRs30rJlS5KSkpgxY0aZ2aovvmkutdf27VsvWLLz+ef/YeDAvxgWj4g7qFCS6eOPP2bLli2EhoZWdTxSyzjbewbUg6ayqO6JVLXs7Gw+//xzMjIyGDduHNnZ2eTm5hIWFmZ0aFKL5eaeYd26txg69F4lw0Vc2JEjR2jVqlWVtvH999+TlJRE48aNAbDZbEydOpV58+YRHR3N8uXLWbhwIfPmzavSOMR1HDiwF6vVCpQUoN+/f6+STCJVrEI1mZo2bYqHR4XyUSKVztfXB6vVSnFxsdGhiMgVfPvtt/Tt25eNGzeyfPlyAP773/8SGxtrbGBS623fvpXjx3/h88//Y3QoIlKOCRMm0LlzZyZOnMhrr73G999/j91ur7T9nzt3jlmzZpU57xw8eBBvb2/HxBTDhg1j8+bNldamuL42baKwWCxASQH6tm2jDI5IpParUOZoyJAhTJw4kREjRlzSvbRLly5VEpjUDpXRe2bjxg/Zs2cn4eGNdOdBxEXNnTuXxYsX06VLFzp27AhAu3bt2L9/v8GRSW2Wm3uGpKTd2O129u7dw623/lm9mURc1Pbt20lOTmbXrl3s2rWLN954g5ycHDp06MDKldfeW71UQkICgwcPpkmTJo51KSkpNGrUyLEcFBSEzWYjJyeHwMBAp9sU19ejR0+SkkpGVZhMZm699c8GRyRS+1UoyfTmm28C8Pzzz5dZbzKZ2Lp16+U2EakUuoAQqRlOnDjhuOlQWq/P09PT0UVdpCps377V0RPCbrep1oaIi2vatCnFxcUUFRVx7tw5vvrqKzIzM53e7969ezl48CBPPPFEJURZVnCwX6Xvszp5epb04gkJcc/fzyEh/nTp0oWvvvqKP/6xCzfc0Oj3N5Iq4+6fR3CPY1ChJNO2bduqOg6Ry9IFhEjN0Lx5c7788ku6devmWPf111/TsmVLA6OS2k61NkRqjilTppCUlERoaCidOnVi8ODBxMXF4efnfBJn165dHD16lJ49ewKQmprKAw88wH333cfJkycdz8vKysJsNl9VL6bMzDxstsob1lfdiopK/o/MyMg1OBLjdO7cnePHf6VTp+5ufRxcgT6PtecYmM2mKybhK1STScQol7uAEGOkpJxg7twZpKamGB2KuKCYmBieeOIJnnzySc6ePcuMGTOIiYlh6tSpRocmtZhqbYjUHIcOHcJsNtOqVSvHv8pIMAGMHz+er776im3btrFt2zbCwsJ45ZVXGDt2LGfPnmX37pLhUmvXrqVv376V0qbUHP7+Adx//wSNhhCpJqrmLS6tTZso9u7dhdVq1QWEwd5/fy2FhYW8997bTJr0uNHhiItp27YtGzZsYMOGDfz1r38lPDyc9957TzPLSZVSrQ2RmuPTTz8lPT2d3bt3s2vXLlatWkVhYSHR0dHMmTOnSto0m83Mnz+fmTNnUlhYSOPGjVmwYEGVtCUi7sHZ2dMrY+Z0cO3Z05VkEpfWo0dPx5fYZDLpAsIgKSknyMhIByAjI43U1BTCwsINjkpchdVqJSoqit27dzNu3DijwxE34u8fQGRkNHv27CQqqoPuUou4uNDQUK6//nrS09NJTU1l586dfPHFF5XezoWlPtq3b8/GjRsrvQ2pOXJzz7Bu3VsMHXqvzhNiOHf4DCrJJC7N3z+AoKAgMjLSqV8/2C2+lK7o/ffXlllWbya5kMViISIiguzsbBo2bGh0OOJmevToSUZGmm5CiLi4CRMm8N133+Hr60vHjh25/fbbefLJJ4mIiDA6NKnltm/fyvHjv6i2q1SKypg9vbZTkklcWm7uGbKySmYdycrKJDc3V4kmA5T2Yjq/nGZQJOKqBg0axIQJExg5cuQlQ+RKZ50TqQqltTZExLX17t2bp556iqZNmxodirgRzVQtUv2UZBKXtn371guW7LoDYZCQkNAyiaaQEPVWkbLefvttAJYsWVJmvclkYuvWrZfb5JosXbqUJUuWsHHjRlq2bElSUhIzZswoU2sjODi40toT16dhECI1w5133ml0COKGNFO1SPXT7HLi0jS7nGvo23dQmeX+/QcbFIm4qtIZfS7+V5kJpu+//56kpCQaN24MgM1mY+rUqcyYMYMtW7YQHR3NwoULK609qRkuHAYhIiJyIV1LiFQ/JZnEpWl6atdw+PD3ZZYPHTpgUCTirs6dO8esWbOIjY11rDt48CDe3t5ER0cDMGzYMDZv3mxQhGKEi4dB5ObmGh2SiIi4EF1LiFQ/JZnEpfXo0ROTyQRoemoj7d//XZnlffu+u8IzRapGQkICgwcPpkmTJo51KSkpNGrUyLEcFBSEzWYjJyfHiBDFAJcbBiEiIlJK1xIi1U81mcSlaXpq11CvXmCZmkz16tU3MBpxN3v37uXgwYM88cQTVbL/4GC/KtlvdfD0LLk7GxLinv83HjyYVGYYxIEDSYwZM9LgqNyXu38eS+k4iLgOXUuIVD8lmcTlaXpq450+nXPRcrZBkYg72rVrF0ePHqVnz54ApKam8sADD3Dfffdx8uRJx/OysrIwm80EBgZe1f4zM/Ow2eyVGnN1KSoqSbBkZLjnMLFbbolk795dWK1WLBYLbdpEuu2xcAXu/nksVRuOg9lsqtEJeJEL6VpCpHppuJy4vNLpqXXnwTht27Yvs9yuXfsrPFOk8o0fP56vvvrKUUw8LCyMV155hbFjx3L27Fl2794NwNq1a+nbt6/B0Up10jAIERH5PbqWEKleSjKJyO/q0aPnBUUTPXQhJy7BbDYzf/584uLi6N27N7t27eLvf/+70WFJNSodBmEymTQMQkRERMQFaLiciPwuf/8AoqI6smfPTtq3j9aFnBhq27Ztjr/bt2/Pxo0bDYxGjKZhECIiIiKuQ0kmcXm5uWdYt+4thg69V8kNA+lCTkRcUekwCBGR2igpaQ979+6+5u1TUkpqF65evfKa9xEVFU1kZIdr3l5E3IuSTOLytm/fyvHjv/D55/9h4MC/GB1OlbFai8nOzqC4+JzRoVxR//79yM/PIj8/y+hQpAI8PLyoXz8Ei0X/1YuIVISzF/Sgi3pxLb6+Ppw5k0txcTEeHvo9ICJVT//TiEvLzT1DUtJu7HY7e/fu4dZb/1xrezNlZ2dQp44Pvr5hjkK2ItfKbreTn3+G7OwMGjQINzocERG3UVt/p4gxIiM7OJVw3LjxQ/bs2Ul4eKNafbNWRFyHkkzi0rZv34rdXjK1uN1uq9W9mYqLzynBJJXGZDLh6xtAXl6O0aGIiNQYzl7Qi7gSd7pZKyKuQ7PLiUs7cGAvVqsVAKvVyv79ew2OqGopwSSVSZ8nERER93W5m7UiIlVNPZmqkAr1Oa9Nmyj27t2F1WrFYrHQtm2U0SGJiIiIiLi8y92sra0jAkTEdRjekyk7O5tx48bRp08fBg0axKRJk8jKKikqnJSUxODBg+nTpw/3338/mZmZBkdbvfz9/d2+S2uPHj0dvTFMJrNmNpOrsmDBXF577WWjwxARERGpdm3aRGGxWAB0s1ZEqo3hPZlMJhNjx46lc+fOAMTHx7Nw4UKeeeYZpk6dyrx584iOjmb58uUsXLiQefPmGRxxxWlcv/P8/QOIjIxmz56dREV1cMuk2113DeLJJ5+mY8fOTu1n06aNbNz4EStWvFJJkbmWy72+qVOnGxiRiIiIiHF69OjJnj07AbDZbLpZKyLVwvAkU2BgoCPBBBAZGcnbb7/NwYMH8fb2Jjo6GoBhw4bRs2fPGpVkclZu7hnWrXuLoUPvdcvkSqkePXqSkZGmE6OLqc6pcN1x2l13fM3uSMOqRUSkKp2vyWRwICLiNgwfLnchm83G22+/ze23305KSgqNGjVyPBYUFITNZiMnx31mStq+fSvHj//i9kX6/P0DuP/+CW6ZaJs9+5+kpaXy5JOP06tXN958818AHDx4gAkT7qdv3x6MGvU3vvvu/EXqpk0bGTp0CL16dWfo0MF8+ukn/PLLMRYunMf33x+gV69u9O3b47LtTZo0nhdfXMq4cSPp3ftWYmIe58yZ00DJxWzXrtEkJn7EnXcOYPLkhwBITFzPvffeRd++t/H445NITU1x7K9r12jWrVvL0KFDGDCgJ8uWJWCz2QA4ceJXHn10Av3792TAgJ7ExT1Nbm6uY9u77hrEG2+8xqhRw+jVqxvFxcW8/vpr3H13yWsbMWIon3/+GcAVX9+cObGsWrXcsc8NGz7knnvuoF+/23nyycc4dSqjTKwfffQew4b9hb59e/Dcc/GOH2YXs1qtrFnzqiOW++8fQVpaKgAHDuxj7NiR9OlzK2PHjuTAgX1lXtOuXTsdy6+8spJZs/55xeNbWFjIrFn/pH//nvTt24OxY0eSlVUybDgvL49582YxZEgf7rijH6tWLXfUXRD3oWHVIiJyJdu3b71gye721xQiUj1c6jb57Nmz8fHxYcSIEfz73/+ulH0GB/tVyn6q2+nTpy+YcnQ3d945hHr16hkdllSh9HQzHh5l875xcXPYty+J6dNn0KlT5/97XjrTpk0hNnY2f/jDH9m161uefvpJ3nnnA+rUqcPixQtZvfp1rrsuglOnMjhz5gw33NCcadOms2HDR6xa9eoVYzCZTGzZ8jGLFy+jUaPGxMX9k4SEhcTFzcFiKYlt3769vPPOB5hMJr7++gveeGM1CxYspmnTZqxZ8xpxcU/x0kuvOfb55Zfbee21N/jttwIeeeQhIiIiGDLkL5jNMGrU/URFtSc/P59//OMJXnttFY89NtWx7datn/L88y9Qr14gdep40axZU1aufJXg4GC2bv03s2f/k3bt1tOixeVfn8lkwmw24eFhZvfub1m5chkJCcu44YbmvPDCImJjp/Pii+eH133zzVesXv0G+fl5jB59L927d6dLlz9dcpzWrn2drVs/ZdGiJTRrdh0//fQjfn4+5OfnMm3aFB5/fCq9evVl27b/MG3aFN57bz316gUCYLGcf5/NZhMmE3h4mC97fD/5JJH8/Dw2bNiEp6cXP/74A76+dfHwMDNvXhz169fnvfc28Ntvv/HEE5MJDw/jL3+5q0ysZrOZkBAlIVyVs8OqS3u83nXXcCWbRESkjP37vyuzvG/fdyr8LSJVzmWSTPHx8fz3v//lxRdfxGw2Ex4ezsmTJx2PZ2VlYTabCQwMvKr9ZmbmYbPVvP6hGzeuLzMbxAcfrNdJoZaz2WwUF9su+5jVev6xTZsS6dLlj3Tq9EdsNujQoROtWrXmq6++pEePnpjNJn788UcaNAglMDCYwMBgiott2Gx27Hb7FduAki7VvXv357rrbgDggQcmMGbMcKZPj8VqLdlu9OhxeHp6A/D+++8xYsRomjaNAGDEiNH861+v8OuvJwgLCwdg+PCR+Pr64+vrz9Chf+PTTzczYMAQwsObEB7eBAB//3rcffe9rF69qkx8f/3rPQQHhwJQXGzj1lt7/t+xgttu68W//rWaAwcO0K1bj8u+Prvdjs1Wsu6TTzYxYMAgWrT4fwCMH/8w/frdRnLyr4SHN/q/WEdTt64vdev6EhUVzZEjP9CxY5dLjtP69R8yceKjNG7cDKvVzvXXtwBg8+aPadKkKb169Qfg9tt78847b/P555/Tv/+gS97LkphLXtvljq/JZOH06dP88stxWrS4kRYtWgGQnp7B119/xebNn+Hp6Y2npzdDh/6NDRs+ZNCgO8vEarPZyMjILbPObDbV2AS8lHVhj1edI0RE5EL16gWSkZF+wXJ9A6OR2kDD/KUiXCLJ9Pzzz3Pw4EFWrVqFl5cXALfccgtnz55l9+7dREdHs3btWvr27WtwpNVn//7vLhhDbdedB3FITU3ls8+2smPHl451xcXFREVFU7duXeLi5vH226/z7LOzadOmHZMmPcZ110VUeP+hoQ0df4eFhVNcXMzp0+eHqTZsGOb4Oy0thYSE51i6dLFjnd0OGRnpjiTTxfsrHaKWlZVJQsJC9u1LoqCgALvdhr9/QJlYGjZsWGb5k08Seeedt0hNLTlB/fbbb2ViK8+pU6do2bKVY9nHx8fx46s0yRQcHOx4vE6dOvz2W8Fl95Wenkbjxk0u00YGDRuGl1kXFhZW5gfe77nw+PbtO4D09DRiY6eTm5tLnz79GD/+YVJTUyguLmbIkPP/J9ps9jLHWmq/3Nwz7N27C7vdznff7ebWW/+s3kwiIuJw8W+k06ezDYpEpIR+p7gHw5NMP/74IytXriQiIoJhw4YB0KRJE5YtW8b8+fOZOXMmhYWFNG7cmAULFhgcbfXRnQcpZTKZyiw3bNiQPn368+STT1/2+Z07d6Fz5y4UFp5l1aoVxMc/w/LlL1+ynytJT09z/J2WloqHhwf16gWWWV8qNLQhI0feT+/e/crd3w03NHfsr0GDEABWrlwGmFizZi0BAfX44ovtLFo0/6Ktz8ecmprC/PlzWLx4Bbfc0gaLxcLo0cMdhSx/7/U1aNCAtLTz9aJKE1QhIaHlbnc5oaENOXHiV264ocVFbYSQlratzLq0tDQ6d/4jAHXq1KWw8KzjsdL6Slfi4eHB/feP5/77x5OScpKpUyfTrNl1/OEPXfH09CIx8T8qDu7Gtm/fekGP12L1ZhIRkTLatm3Pnj07sdvtmEwm2rVrb3RIUsNp9nSpCMMLf99444388MMPbNmyhfXr17N+/XqWLVsGQPv27dm4cSOffvopq1evpkGDBgZHW31050FK1a8fxMmTJxzLvXv3Y8eOL9m58xusViuFhYV8991u0tPTyMrK5Msvt/Pbb7/h6emFj48PZrPZsZ+MjHSKiorKbe/TTz/h2LGfOXv2LC+//CI9evTEYrFc9rlDhvyV119fzc8/HwVKIHw62wAAHoJJREFUilFv21a2qOTbb7/OmTNnSEtLZd26t7n99l4AFBQU4OPjg6+vHxkZ6bz99ppy4/rtt98wmUzUr18yZPbjjzdw7NjRMsepvNf35z/3YdOmjfz44w+cO3eOlSuXcdNNtzh6MV2NQYPu4OWXXyQ5+Th2u52ffvqR06dz6NLlTyQnH+fTTzdTXFzM1q2f8ssvP/PHP3YD4MYbW/Kf/3xKcXExR44cuqgg56W++243R4/+hNVqxdfXF4vFA5PJTIMGDejUqTNLly4mPz8Pm83GiRO/snfvnqt+LVJzXa7WhoiISKmSMgolv+EsFotmahaRaqFb4C6qbdv27N79P45l3XlwX/fdN5pFixawYsULjBz5AMOH38e8ec+xYsULxMY+hcVipnXrm/n73/+BzWbnnXfe4plnZmIymWjRoiV//3sMAB06dOT6629g8OA+mM0mPv748gmOPn36M3duLP/973+JjGzPE0/844qx3Xrrbfz2WwGxsdNJTU3Fz8+P6OhO3H77+R8xXbveygMP3Ed+fh79+g1k4MAhAIwZM45nnplJ3749aNy4KX369Ofdd9+6YlvXX38D99xzLw8+eD9ms4m+fQfQpk07x+O/9/o6duzM2LETeOqpaeTm5tKmTVvi4ub+/htwGffccy/nzp3j8ccnkZOTw3XXRTB37gJCQxsSH7+YhISFPPfcPBo3bkp8/GJHLbmxYycQG/sU/frdRmRke3r16suZM2eu2E5m5ikWLJhLRkY6dev60LNnL/r0Kan39PTTs3jxxSWMGHE3BQX5NGrUmHvvHXVNr0dqJvV4FRGR8vj7BxAVFc2ePTuJiorWUCURqRYm+5Xm6K4lamrh79zcMyxa9CxWqxWLxYPHHovRiaGWS039L2Fh1xkaw6RJ4+nTpz+DBt1RKfvr2jWatWs/pEmTppWyP7l6l/tcqfB3WTX1PDFnzj85d+6cY/n/t3fvcVGW+f/H3zPgYIAGGh46mK7fPKxgoLhuZYlSaa1mVqZbWq4/c7N8uDwqzbXSPKSirB0MUzNrXVM7W2rteja/WaYEIpVmrWmuLKAiDCrgzFy/P/w6RSoip3uGeT3/Yuae+5rPPd7OZ67PfV3X7XA49NRTUyyMCIHuzEKuf/rTny2OBFVFnviZv+aIM87chXTAgPvpSwCoNuXlCcuny+HcTl956CKbzaZOnbjyAAAoq2PHsiNcGfEKAAAAq1Fk8mEJCYlq0aIl86cBAGdJSEjUmcXxbTYbuQIAcJZNm9brwIEftXnzugu/GACqAUUmH9agQUMNG/Ywo5hQa15+eUG1TZWTpP/93x1MlQNqUAVvGgkACEBOZ6EyMnbIGKP09DQ5nU6rQ0KAczoLtWjRPM7FOo6FvwEA8EObNq2X3W6X2+2W3W7X5s3r1KdPf6vDAgD4iE2b1uvM8rvGeMgTsNwvR9ZV5Fx0u13Kz8+Ty1V6wdeiZgQHOxQZGaWgoIqXjigyAQDgh3btSpfb7ZYkud1uZWam03kAAHiRJ+BLfj2yrnv3my84Yyc/P0/164cqLKyZbAzfrnXGGB0/Xqj8/DxddlnzCu/HdDkAAPxQTEycgoKCJElBQUHq2DHO4ogAAL6EPAFfcq6RdRficpUqLKwhBSaL2Gw2hYU1vOiRZBSZAADwQwkJid4fXTabnYW/AQBlkCfgS841sq4iKDBZqzKfP9PlgAD35tI3dOLkiWpvN/SSUN1/39BqbxfAaQ0aNFRsbLzS0rYpLq4zN4kAELDy8/M1duxYHThwQA6HQ1dffbUmT56sRo0aKSMjQxMmTFBJSYmuuOIKzZo1S40bN7Y65FpBnoAviYmJU3r6drnd7kqPrKPf4h8oMgEB7sTJE/rDvQOqvd3Vb79T7W0CKCshIVF5eTlcnQYQ0Gw2m4YPH66uXbtKkpKTk5WSkqKpU6dqzJgxmj59uuLj4zV37lylpKRo+vTpFkdce8gT8BUJCYnKyNghqfIj6+i3+AeKTAAs1a1bvEaMeESffrpJBQUFevTR0UpISJQkffHFVs2f/7I8Ho8iIiI1Zsx4XXnlVfrqqx166aXZ+u1vO+jrr3dJsmnSpGlq2bKVJOmTT1bp/fffkdvtVnh4uJ54YpxatGhp3UECNaRBg4YaNuxhq8MAAEtFRER4C0ySFBsbq2XLlikrK0shISGKj4+XJA0aNEiJiYkBVWQiT8BXNGjQUL/9bUdlZn6l6OgYvxxZR7+lYigyAbBcWFiYFi5crMzMDE2Y8FclJCQqP/+opk6doDlzFqhVq99o1aoVmjTpab366t8lSfv2/aDx4ydo7Nin9Pe/v6a///01TZw4VTt3pmvDhrVKTX1VDodDn3/+maZPn6xXXllk8VECgG/LyEhTevqOSu+fnX1IkvT66/Mr3UZcXLxiYztXen/A4/Fo2bJl6tmzp7Kzs3X55Zd7tzVq1Egej0fHjh1TREREhdpr3Di8pkIFAs4ll9STJIWE1FNU1IWLTLm5dgUH/7yMdE2tzmSTyrxPeRo0CNcbbyzRzp0ZevrpJ3Xzzbfo6NHT/ZZXXlmoVq1+o48+WqHJk5/RokWLFRRk1759/9Yzzzyr8eOf0euvL9TixYs0efJzysj4Shs3rtP8+a/J4XBo69bPNH36FL366us1dKSVY7fbK/TvdQZFJgCWS0zsJUnq0CFGhw/nqaSkRF9/naXWrduoVavfSJJuv/0O/e1vyTpx4rgkqUWLq9WmTTvvfp99tkWS9Nlnn+r77/dqxIihkk7fetPpLKzlIwKAwOOPV6VR90yZMkWhoaEaPHiw1q5dW+X2jhwpksdjqiEyILA5nYVKS0uTJKWlfaUbb7zlgnnD4/HI5fJ4H9fU/0QjlXmf8vTocatcLo/ateugvLw8HT9+UpmZmWrduo2uuqqlXC6Pevfuo1mzpquw0Cm326MWLVqodes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            "text/plain": [
              "<Figure size 1440x288 with 3 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}