svm_sklearn.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "svm - sklearn ",
      "provenance": [],
      "authorship_tag": "ABX9TyPYJq07hrCpCVu/tlpD3vuC",
      "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/joyeeta-19/image-classification-using-distributed-SVM/blob/main/svm_sklearn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5rNXzooSnaQH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "820f0a54-1975-48a1-c9d1-2f9dd2ff72cb"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.activity.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fexperimentsandconfigs%20https%3a%2f%2fwww.googleapis.com%2fauth%2fphotos.native&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "4/1AY0e-g5nfXPELSsa1kzEei1pZc7xp4a20Cvaa4-a9aUHk8oicU_k-GgevOw\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Siz8mc-lnbI4"
      },
      "source": [
        "#inserting the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fBn3FPs5ndl3"
      },
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import cv2\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CWd-55L8ntIi",
        "outputId": "eefb0ceb-b7ef-40ef-d481-8721fa4b9b5b"
      },
      "source": [
        "dir = \"/content/drive/MyDrive/lsdp/damage/data1a/training\"\n",
        "classes = ['damage','nodamage']\n",
        "data = []\n",
        "vector =[]\n",
        "categories=[]\n",
        "\n",
        "for category in classes:\n",
        "  path = os.path.join(dir,category)\n",
        "  label= classes.index(category)\n",
        "  \n",
        "  for img in os.listdir(path):\n",
        "    imgpath = os.path.join(path,img)\n",
        "    car_img=cv2.imread(imgpath,0)\n",
        "    try:\n",
        "      car_img = cv2.resize(car_img,(16,16))\n",
        "      image = np.array(car_img).flatten()\n",
        "\n",
        "      data.append([image,label])\n",
        "      vector.append(image)\n",
        "      categories.append(label)\n",
        "    except Exception as e:\n",
        "      pass\n",
        "\n",
        "print(len(data))"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1840\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NBVi4y8DnwhQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "02cb07bf-6dd7-4c9f-e881-191f52ae9913"
      },
      "source": [
        "# df_read = pd.read_csv('/content/drive/MyDrive/lsdp_dataset/imgvectors.csv')\n",
        "# df_read.head()\n",
        "\n",
        "\n",
        "df = pd.DataFrame()\n",
        "\n",
        "for i in range(len(vector)):\n",
        "  df[i]=vector[i]\n",
        "\n",
        "df_final=df.T\n",
        "\n",
        "df_final.insert(0, 'label', categories)\n",
        "df_final.head()\n",
        "#df.to_csv('/content/drive/MyDrive/lsdp_dataset/imgvectors_final.csv')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>83</td>\n",
              "      <td>64</td>\n",
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              "      <td>61</td>\n",
              "      <td>28</td>\n",
              "      <td>78</td>\n",
              "      <td>72</td>\n",
              "      <td>58</td>\n",
              "      <td>104</td>\n",
              "      <td>23</td>\n",
              "      <td>12</td>\n",
              "      <td>46</td>\n",
              "      <td>69</td>\n",
              "      <td>21</td>\n",
              "      <td>41</td>\n",
              "      <td>10</td>\n",
              "      <td>65</td>\n",
              "      <td>60</td>\n",
              "      <td>47</td>\n",
              "      <td>59</td>\n",
              "      <td>53</td>\n",
              "      <td>31</td>\n",
              "      <td>68</td>\n",
              "      <td>30</td>\n",
              "      <td>37</td>\n",
              "      <td>29</td>\n",
              "      <td>33</td>\n",
              "      <td>26</td>\n",
              "      <td>15</td>\n",
              "      <td>28</td>\n",
              "      <td>78</td>\n",
              "      <td>17</td>\n",
              "      <td>27</td>\n",
              "      <td>126</td>\n",
              "      <td>148</td>\n",
              "      <td>165</td>\n",
              "      <td>111</td>\n",
              "      <td>105</td>\n",
              "      <td>110</td>\n",
              "      <td>108</td>\n",
              "      <td>...</td>\n",
              "      <td>194</td>\n",
              "      <td>196</td>\n",
              "      <td>196</td>\n",
              "      <td>196</td>\n",
              "      <td>196</td>\n",
              "      <td>196</td>\n",
              "      <td>198</td>\n",
              "      <td>196</td>\n",
              "      <td>152</td>\n",
              "      <td>145</td>\n",
              "      <td>148</td>\n",
              "      <td>167</td>\n",
              "      <td>188</td>\n",
              "      <td>189</td>\n",
              "      <td>193</td>\n",
              "      <td>194</td>\n",
              "      <td>196</td>\n",
              "      <td>197</td>\n",
              "      <td>197</td>\n",
              "      <td>196</td>\n",
              "      <td>198</td>\n",
              "      <td>198</td>\n",
              "      <td>200</td>\n",
              "      <td>195</td>\n",
              "      <td>158</td>\n",
              "      <td>158</td>\n",
              "      <td>157</td>\n",
              "      <td>146</td>\n",
              "      <td>179</td>\n",
              "      <td>191</td>\n",
              "      <td>200</td>\n",
              "      <td>195</td>\n",
              "      <td>196</td>\n",
              "      <td>198</td>\n",
              "      <td>197</td>\n",
              "      <td>198</td>\n",
              "      <td>201</td>\n",
              "      <td>199</td>\n",
              "      <td>202</td>\n",
              "      <td>201</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 257 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   label    0    1    2    3    4    5  ...  249  250  251  252  253  254  255\n",
              "0      0  242  248  212  243  172  226  ...  110  105  106  117  119  123  129\n",
              "1      0  154  212  111   71   97   65  ...  116  119  128  123  134  130  121\n",
              "2      0  174   72  200  197  198  139  ...   30   33   35   39   44   53   59\n",
              "3      0  220  216  250  254  177  169  ...   87   91   96   40   32   37   45\n",
              "4      0   35   48   61   28   78   72  ...  198  197  198  201  199  202  201\n",
              "\n",
              "[5 rows x 257 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aA-CL54rvn23"
      },
      "source": [
        "#reading from csv file"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "isebAh2yn78F",
        "outputId": "0a73d2d2-c94a-49fa-86ae-e9a8192d0dde"
      },
      "source": [
        "df_second = pd.DataFrame()\n",
        "df_second['vectors'] = data\n",
        "#df_second.to_csv('/content/drive/MyDrive/lsdp_dataset/imgvectors_final2.csv')\n",
        "df_second.head()"
      ],
      "execution_count": 10,
      "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>vectors</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>[[242, 248, 212, 243, 172, 226, 213, 225, 219,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>[[154, 212, 111, 71, 97, 65, 136, 73, 77, 76, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>[[174, 72, 200, 197, 198, 139, 137, 132, 127, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>[[220, 216, 250, 254, 177, 169, 237, 236, 243,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>[[35, 48, 61, 28, 78, 72, 58, 104, 23, 12, 46,...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                             vectors\n",
              "0  [[242, 248, 212, 243, 172, 226, 213, 225, 219,...\n",
              "1  [[154, 212, 111, 71, 97, 65, 136, 73, 77, 76, ...\n",
              "2  [[174, 72, 200, 197, 198, 139, 137, 132, 127, ...\n",
              "3  [[220, 216, 250, 254, 177, 169, 237, 236, 243,...\n",
              "4  [[35, 48, 61, 28, 78, 72, 58, 104, 23, 12, 46,..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IhpUuEZvicxj"
      },
      "source": [
        "# Performing PCA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AUZG_ZCIiHSe",
        "outputId": "49838372-d280-4bf7-ac31-f1e95b03afc9"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "ft_arr = np.array(output)\n",
        "\n",
        "ft_t=ft_arr.transpose().reshape(3,-1)\n",
        "\n",
        "# ft_t = ft_arr.transpose().reshape(3,-1)\n",
        "print(ft_t.shape)\n",
        "# x = StandardScaler().fit_transform(ft_t)\n",
        "# print(x)\n",
        "# pca = PCA(n_components=2)\n",
        "# pc = pca.fit_transform(x)\n",
        "# pDf = pd.DataFrame(data = pc)\n",
        "# pDf.head()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(3, 196608)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VuI1p73zuqJR"
      },
      "source": [
        "import random\n",
        "\n",
        "dataread = df_second['vectors']\n",
        "\n",
        "random.shuffle(dataread)\n",
        "features = []\n",
        "labels = []\n",
        "\n",
        "\n",
        "for feature,label in dataread:\n",
        "  features.append(feature)\n",
        "  labels.append(label)\n",
        "\n",
        "xtrain, xtest, ytrain, test = train_test_split(features,labels,test_size=0.25)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FZgjqPbTvG1j"
      },
      "source": [
        "#!!!!!!!!!!!Training the svm model!!!!!!!!!! using svm from sklearn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cy5NpVsOvOks",
        "outputId": "2e924521-a231-418b-f496-6fb7126acd04"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "\n",
        "model = SVC(C=10,kernel='poly', gamma='auto')\n",
        "model.fit(xtrain,ytrain)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=10, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='auto', kernel='poly',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dOyBVgVNvRsG",
        "outputId": "dcb94e94-e5f7-4f94-ef41-9bfa6041fdec"
      },
      "source": [
        "prediction = model.predict(xtest)\n",
        "\n",
        "accuracy = model.score(xtest,test)\n",
        "print(\"accuracy: \" ,accuracy) "
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "accuracy:  0.5782608695652174\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "id": "htighYnVRaMS",
        "outputId": "b3b2797f-e4f1-4e40-d3cc-626fc91687d6"
      },
      "source": [
        "from sklearn import svm, datasets\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import plot_confusion_matrix\n",
        "\n",
        "titles_options = [(\"Confusion matrix, without normalization\", None),\n",
        "                  (\"Normalized confusion matrix\", 'true')]\n",
        "for title, normalize in titles_options:\n",
        "    disp = plot_confusion_matrix(model, xtest, test,\n",
        "                                 display_labels=labels,\n",
        "                                 cmap=plt.cm.Blues,\n",
        "                                 normalize=normalize)\n",
        "    disp.ax_.set_title(title)\n",
        "\n",
        "    print(title)\n",
        "    print(disp.confusion_matrix)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix, without normalization\n",
            "[[134  89]\n",
            " [105 132]]\n",
            "Normalized confusion matrix\n",
            "[[0.60089686 0.39910314]\n",
            " [0.44303797 0.55696203]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O3O8qUDlSUKC",
        "outputId": "b76c158f-0620-4839-9b70-792f7c0f4977"
      },
      "source": [
        "from sklearn.model_selection import cross_val_predict\n",
        "ytrain_pred = cross_val_predict(model, xtrain, ytrain, cv=3)\n",
        "from sklearn.metrics import classification_report\n",
        " \n",
        "# # Constructing the confusion matrix.\n",
        "# from sklearn.metrics import confusion_matrix\n",
        "# confusion_matrix(ytrain, ytrain_pred)\n",
        "# from sklearn.metrics import precision_score, recall_score\n",
        "# precision_score(ytrain, ytrain_pred)\n",
        "# recall_score(ytrain, ytrain_pred)\n",
        "target_names = ['damage', 'nodamage']\n",
        "print(classification_report(ytrain, ytrain_pred, target_names=target_names))"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "      damage       0.64      0.64      0.64       722\n",
            "    nodamage       0.60      0.60      0.60       658\n",
            "\n",
            "    accuracy                           0.62      1380\n",
            "   macro avg       0.62      0.62      0.62      1380\n",
            "weighted avg       0.62      0.62      0.62      1380\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        },
        "id": "B8WTf37pv4UA",
        "outputId": "32768102-8234-4fc6-c717-2030841353d4"
      },
      "source": [
        "categories = ['damage','nodamage']\n",
        "print(\"Prediction is: \",categories[prediction[0]])\n",
        "\n",
        "grbg= xtest[0].reshape(16,16)\n",
        "plt.imshow(grbg,cmap=\"gray\")\n",
        "print(xtest[0])"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Prediction is:  nodamage\n",
            "[ 20 239 118 117 117 110 109 106  99  96  94  92  94  94 105 123  34 213\n",
            " 180 216 212 218 190 203 230 229 221 214 200 183 175 202   6 226 170 129\n",
            " 171 191 171 124 217 224 226 151 171 165 172 180  12  79 103 160 188  90\n",
            " 132  51 102 109 104 179 160 165 124 216  22 220 233 139 192  95 107 196\n",
            " 181 205 209 148 151 144 198 185  57 222 132 138 182 201 114 223  77 204\n",
            " 208 131 141 241 253 175  84 199 200 182 150 138 188 163  53 190 173 122\n",
            " 116 168  42  53 105 125 153 209 234 178 173 122 139 138 132 128 100 148\n",
            "  44 213 104 106 110 114 117 189 212 225 237 159 163 150 141 142 198 200\n",
            " 114 110 100  95 100 103 102 108 134 203 206 198 195 191 191 188 122 129\n",
            " 145 123  96  90  79  89 104  80  98  99 105 110 123 138 127 129 137 132\n",
            "  32   9  40  73  78  75  75  79  89  88  95 100 139 137 126 117  34  21\n",
            "  10  12  42  36  76  70  66  66  70  74 124 123 114  69  32  30  22   8\n",
            "  16  19  13  24  44  41  40  56 121 133 110  67  47  26  34  40  20  16\n",
            "  14  11  16  23   4  13  98 120 101  62  61  48  50  36  37  27  29  28\n",
            "  27  54  37  39]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vkqhCw29mc83sV2a2xcwWD3J7k5n9JLt9jZmdXYMeppjZL8xso5m9a2a3D7LNV82s28zWZ19/m3cfZWN9ZGZvZ+OsHeR2M7N/zvbJBjO7JOfxzyv7f643s/1mdseAbWq2P8zsYTPbYWbvlF3XamarzOy97N/xQ9QuyrZ5z8wW1aCPH5rZ5my/P2Vmg36U8Ynuwxz6+J6ZdZTt/3lD1B43X5/j7oV8AfXA+8A0oBF4C7hgwDa3AA9klxcCP6lBH5OAS7LLLcCvB+njq8AzBe2Xj4AJx7l9HvAcYMBlwJoa30ddlN4oUsj+AGYBlwDvlF33d8Di7PJi4AeD1LUCH2T/js8uj8+5jzlAQ3b5B4P1Ucl9mEMf3wP+uoL77rj5GvhV5JF/JrDF3T9w917gcWDBgG0WAI9kl58AZpuZ5dmEu3e6+7rs8gFgEzA5zzFytgD4Dy95DRhnZpNqNNZs4H13H+pdmLlz91eAPQOuLv89eAT42iClVwKr3H2Pu+8FVgFz8+zD3Z9398PZt69RWpS2pobYH5WoJF/HKDL8k4FtZd+38/nQ/WabbKd3A79Tq4aypxUXA2sGuflyM3vLzJ4zswtr1QPgwPNm9oaZ3TTI7ZXst7wsBJYPcVtR+wNgort3Zpe7gMFWbSlyvwDcSOkR2GBOdB/m4dbs6cfDQzwNCt4fyZ7wM7PRwE+BO9x9/4Cb11F66Pt7wL8AK2rYylfc/RLgKuAvzGxWDccakpk1AtcA/zXIzUXuj2N46THtSX092szuAg4Djw2xSa3vwx8DXwR+H+gE/iGPH1pk+DuAKWXft2XXDbqNmTUAY4HdeTdiZiMoBf8xd39y4O3uvt/d/y+7/Cwwwswm5N1H9vM7sn93AE9RevhWrpL9loergHXuvn2QHgvbH5ntR5/aZP/uGGSbQvaLmd0AzAf+LPtD9DkV3IdVcfft7n7E3fuBfxvi5wfvjyLD/0vgHDObmh1lFgJPD9jmaeDoWduvAy8NtcNjZecQHgI2ufuPhtjmzKPnGsxsJqX9VIs/Qs1m1nL0MqUTTO8M2Oxp4PrsrP9lQHfZQ+I8XccQD/mL2h9lyn8PFgE/G2SbnwNzzGx89jB4TnZdbsxsLnAncI27Hxxim0ruw2r7KD/H86dD/PxK8nWsPM5QBpzJnEfp7Pr7wF3ZdUso7VyAkZQedm4BXgem1aCHr1B6GLkBWJ99zQO+DXw72+ZW4F1KZ0xfA75co/0xLRvjrWy8o/ukvBcD7s/22dvAjBr00UwpzGPLritkf1D6g9MJ9FF6nvotSud5XgTeA14AWrNtZwAPltXemP2ubAG+WYM+tlB6Hn309+ToK1FfAJ493n2Ycx//md33GygFetLAPobK1/G+9PZekUQle8JPJHUKv0iiFH6RRCn8IolS+EUSpfCLJErhF0nU/wNlF3KkqiyQxwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}