Day_13_14_Python_Learing_Iris,_time_is_running_out.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyM4E0Mn1sEcPEwYKO9Xsgrm",
      "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/4zmyyy/Belajar-Python-/blob/main/Day_13_14_Python_Learing_Iris%2C_time_is_running_out.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# percobaan klasifikasi bunga iris\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score\n"
      ],
      "metadata": {
        "id": "WVFfenuEk79o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "iris = load_iris()\n",
        "X = iris.data  # Fitur (panjang/sepal, lebar/petal, dll.)\n",
        "y = iris.target  # Label (0: Setosa, 1: Versicolor, 2: Virginica)"
      ],
      "metadata": {
        "id": "3McI1xnjl-2v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "OY5mILPvmD2N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = KNeighborsClassifier(n_neighbors=3)\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "_5Mridk8mExV",
        "outputId": "abbf1700-3cf5-4562-c9cf-283358155350"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=3)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)\n",
        "print(\"Akurasi:\", accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bXZVZyKlmIR2",
        "outputId": "483078e1-0862-4f87-afa5-06532eb8db83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Akurasi: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install matplotlib scikit-learn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zmv3HvSsnyxu",
        "outputId": "5559f56f-99a6-4169-a1d2-2287364ce806"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (3.10.0)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (1.6.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.3.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (4.56.0)\n",
            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.4.8)\n",
            "Requirement already satisfied: numpy>=1.23 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (24.2)\n",
            "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (11.1.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (3.2.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.13.1)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (3.5.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.7->matplotlib) (1.17.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Install dependencies terlebih dahulu\n",
        "# pip install matplotlib scikit-learn pillow\n",
        "%matplotlib inline\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.decomposition import PCA\n",
        "from matplotlib.animation import FuncAnimation\n",
        "\n",
        "%matplotlib inline\n",
        "from IPython.display import HTML\n",
        "HTML(ani.to_jshtml())\n",
        "\n",
        "# Load dataset\n",
        "iris = load_iris()\n",
        "X, y = iris.data, iris.target\n",
        "\n",
        "# Reduksi dimensi ke 2D dengan PCA\n",
        "pca = PCA(n_components=2)\n",
        "X_pca = pca.fit_transform(X)\n",
        "\n",
        "# Setup plot\n",
        "fig, ax = plt.subplots(figsize=(10, 6))\n",
        "colors = ['red', 'green', 'blue']\n",
        "labels = iris.target_names\n",
        "\n",
        "# Fungsi animasi yang DISEDERHANAKAN\n",
        "def animate(frame):\n",
        "    ax.clear()\n",
        "    scale = frame / 20  # Skala 0 → 1 selama 20 frame\n",
        "\n",
        "    for i in range(3):\n",
        "        mask = y == i\n",
        "        scaled_x = X_pca[mask, 0] * scale\n",
        "        scaled_y = X_pca[mask, 1] * scale\n",
        "\n",
        "        ax.scatter(scaled_x,\n",
        "                   scaled_y,\n",
        "                   c=colors[i],\n",
        "                   label=labels[i],\n",
        "                   alpha=0.3 + 0.7*scale,\n",
        "                   s=50 + 150*scale)\n",
        "\n",
        "    ax.set_title(f'Visualisasi Klaster Iris ({int(scale*100)}%)', fontsize=14)\n",
        "    ax.set_xlabel('Principal Component 1')\n",
        "    ax.set_ylabel('Principal Component 2')\n",
        "    ax.legend()\n",
        "    ax.grid(True, linestyle='--', alpha=0.5)\n",
        "\n",
        "# Buat animasi\n",
        "ani = FuncAnimation(fig,\n",
        "                   animate,\n",
        "                   frames=np.arange(0, 21),  # 21 frame (0-20)\n",
        "                   interval=100,             # 100ms per frame\n",
        "                   repeat=False)\n",
        "\n",
        "# Simpan dengan Pillow (tidak perlu ImageMagick)\n",
        "try:\n",
        "    ani.save('iris_animation.gif',\n",
        "             writer='pillow',\n",
        "             fps=10,\n",
        "             dpi=100)\n",
        "    print(\"✅ GIF berhasil disimpan sebagai 'iris_animation.gif'\")\n",
        "except Exception as e:\n",
        "    print(\"❌ Gagal menyimpan GIF:\", str(e))\n",
        "\n",
        "# Tampilkan animasi di notebook (jika menggunakan Jupyter)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "5eSmg_8Yo1Gn",
        "outputId": "c76d44ad-0b34-4e12-a6dd-7ed6d79da943"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "UnboundLocalError",
          "evalue": "cannot access local variable 'y' where it is not associated with a value",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mUnboundLocalError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-16-702b4c059215>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mHTML\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mHTML\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mani\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_jshtml\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;31m# Load dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36mto_jshtml\u001b[0;34m(self, fps, embed_frames, default_mode)\u001b[0m\n\u001b[1;32m   1374\u001b[0m                                     \u001b[0membed_frames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0membed_frames\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1375\u001b[0m                                     default_mode=default_mode)\n\u001b[0;32m-> 1376\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwriter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwriter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1377\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_html_representation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1378\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, filename, writer, fps, dpi, codec, bitrate, extra_args, metadata, extra_anim, savefig_kwargs, progress_callback)\u001b[0m\n\u001b[1;32m   1107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1108\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0manim\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mall_anim\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1109\u001b[0;31m                 \u001b[0manim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_draw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Clear the initial frame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1110\u001b[0m             \u001b[0mframe_number\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1111\u001b[0m             \u001b[0;31m# TODO: Currently only FuncAnimation has a save_count\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_init_draw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1768\u001b[0m                 )\n\u001b[1;32m   1769\u001b[0m                 \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1770\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1771\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1772\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drawn_artists\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_draw_frame\u001b[0;34m(self, framedata)\u001b[0m\n\u001b[1;32m   1787\u001b[0m         \u001b[0;31m# Call the func with framedata and args. If blitting is desired,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1788\u001b[0m         \u001b[0;31m# func needs to return a sequence of any artists that were modified.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1789\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drawn_artists\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframedata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1790\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1791\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_blit\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-13-bc1a4190a0f9>\u001b[0m in \u001b[0;36manimate\u001b[0;34m(frame)\u001b[0m\n\u001b[1;32m     28\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m         \u001b[0;31m# Ambil data per kelas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m         \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     31\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_pca\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_pca\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mUnboundLocalError\u001b[0m: cannot access local variable 'y' where it is not associated with a value"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#proses iterasi K-mean, regresi model iris ( iris cluster animation )\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import imageio\n",
        "\n",
        "# Muat dataset Iris\n",
        "iris = load_iris()\n",
        "X = iris.data[:, [0, 2]]  # Sepal length dan Petal length\n",
        "X = StandardScaler().fit_transform(X)  # Normalisasi fitur\n",
        "\n",
        "# Inisialisasi K-means\n",
        "k = 3\n",
        "max_iter = 100\n",
        "np.random.seed(42)  # Untuk reprodulibilitas\n",
        "centroids = np.random.randn(k, X.shape[1])  # Centroid awal\n",
        "assignments = np.zeros(X.shape[0], dtype=int)  # Inisialisasi assignmen\n",
        "frames = []  # Simpan frame animasi\n",
        "\n",
        "# Fungsi untuk menghitung jarak Euclidean\n",
        "def euclidean_distance(X, centroid):\n",
        "    return np.sqrt(np.sum((X - centroid) ** 2, axis=1))\n",
        "\n",
        "# Fungsi untuk update centroid\n",
        "def update_centroids(X, assignments, k):\n",
        "    new_centroids = np.zeros((k, X.shape[1]))\n",
        "    for i in range(k):\n",
        "        cluster_points = X[assignments == i]\n",
        "        if len(cluster_points) > 0:\n",
        "            new_centroids[i] = np.mean(cluster_points, axis=0)\n",
        "        else:\n",
        "            new_centroids[i] = np.random.randn(X.shape[1])\n",
        "    return new_centroids\n",
        "\n",
        "# Fungsi untuk membuat frame animasi\n",
        "def plot_clusters(X, centroids, assignments, iteration):\n",
        "    fig, ax = plt.subplots(figsize=(10, 6))\n",
        "    colors = ['#98FB98', '#FFFACD', '#87CEEB']  # Warna cluster\n",
        "\n",
        "    for i in range(k):\n",
        "        cluster_points = X[assignments == i]\n",
        "        ax.scatter(cluster_points[:, 0], cluster_points[:, 1],\n",
        "                   color=colors[i], label=f'Cluster {i+1}', alpha=0.8)\n",
        "\n",
        "    ax.scatter(centroids[:, 0], centroids[:, 1],\n",
        "               color='black', marker='X', s=200, label='Centroid')\n",
        "    ax.set_title(f'K-means Iterasi {iteration}', fontsize=14)\n",
        "    ax.legend()\n",
        "    plt.tight_layout()\n",
        "\n",
        "    # Simpan frame\n",
        "    fig.canvas.draw()\n",
        "    image = np.frombuffer(fig.canvas.tostring_rgb(), dtype='uint8')\n",
        "    image = image.reshape(fig.canvas.get_width_height()[::-1] + (3,))\n",
        "    frames.append(image)\n",
        "    plt.close()\n",
        "\n",
        "# Eksekusi algoritma K-means\n",
        "for iteration in range(max_iter):\n",
        "    distances = np.array([euclidean_distance(X, centroid) for centroid in centroids])\n",
        "    new_assignments = np.argmin(distances, axis=0)\n",
        "\n",
        "    new_centroids = update_centroids(X, new_assignments, k)\n",
        "\n",
        "    plot_clusters(X, new_centroids, new_assignments, iteration + 1)\n",
        "\n",
        "    if np.all(new_centroids == centroids):\n",
        "        print(f\"Konvergen pada iterasi {iteration + 1}\")\n",
        "        break\n",
        "    centroids = new_centroids\n",
        "\n",
        "# Simpan animasi sebagai GIF\n",
        "imageio.mimsave('iris_cluster_animation.gif', frames, fps=5)\n",
        "\n",
        "print(\"Animasi K-means berhasil disimpan sebagai iris_cluster_animation.gif\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 921
        },
        "id": "q6jbLXaSmkXe",
        "outputId": "610b0898-0486-4a19-97d8-9f40f527149c"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "'FigureCanvasAgg' object has no attribute 'tostring_rgb'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-3eadefee9747>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0mnew_centroids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mupdate_centroids\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_assignments\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m     \u001b[0mplot_clusters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_centroids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_assignments\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miteration\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_centroids\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mcentroids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-2-3eadefee9747>\u001b[0m in \u001b[0;36mplot_clusters\u001b[0;34m(X, centroids, assignments, iteration)\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0;31m# Simpan frame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m     \u001b[0mimage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrombuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtostring_rgb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'uint8'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m     \u001b[0mimage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_width_height\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0mframes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'FigureCanvasAgg' object has no attribute 'tostring_rgb'"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import imageio\n",
        "from PIL import Image  # Tambahkan dependensi Pillow\n",
        "\n",
        "# Muat dataset Iris\n",
        "iris = load_iris()\n",
        "X = iris.data[:, [0, 2]]  # Sepal length dan Petal length\n",
        "X = StandardScaler().fit_transform(X)  # Normalisasi data\n",
        "\n",
        "# Inisialisasi Parameter\n",
        "k = 3\n",
        "max_iter = 100\n",
        "np.random.seed(42)  # Untuk reproduktibilitas\n",
        "centroids = np.random.randn(k, X.shape[1])  # Centroid awal\n",
        "assignments = np.zeros(X.shape[0], dtype=int)\n",
        "frames = []  # Simpan frame animasi\n",
        "\n",
        "# Fungsi untuk menghitung jarak Euclidean\n",
        "def euclidean_distance(X, centroid):\n",
        "    return np.sqrt(np.sum((X - centroid) ** 2, axis=1))\n",
        "\n",
        "# Fungsi untuk update centroid\n",
        "def update_centroids(X, assignments, k):\n",
        "    new_centroids = np.zeros((k, X.shape[1]))\n",
        "    for i in range(k):\n",
        "        cluster_points = X[assignments == i]\n",
        "        if len(cluster_points) > 0:\n",
        "            new_centroids[i] = np.mean(cluster_points, axis=0)\n",
        "        else:\n",
        "            new_centroids[i] = np.random.randn(X.shape[1])\n",
        "    return new_centroids\n",
        "\n",
        "# Fungsi untuk membuat frame animasi\n",
        "def plot_clusters(X, centroids, assignments, iteration):\n",
        "    fig, ax = plt.subplots(figsize=(10, 6))\n",
        "    colors = ['#98FB98', '#FFFACD', '#87CEEB']  # Warna cluster\n",
        "\n",
        "    for i in range(k):\n",
        "        cluster_points = X[assignments == i]\n",
        "        ax.scatter(cluster_points[:, 0], cluster_points[:, 1],\n",
        "                   color=colors[i], label=f'Cluster {i+1}', alpha=0.8)\n",
        "\n",
        "    ax.scatter(centroids[:, 0], centroids[:, 1],\n",
        "               color='black', marker='X', s=200, label='Centroid')\n",
        "    ax.set_title(f'K-means Iterasi {iteration}', fontsize=14)\n",
        "    ax.legend()\n",
        "    plt.tight_layout()\n",
        "\n",
        "    # Simpan frame sebagai gambar\n",
        "    fig.canvas.draw()\n",
        "    img = Image.frombytes('RGB', fig.canvas.get_width_height(), fig.canvas.tostring_rgb())\n",
        "    frames.append(np.array(img))\n",
        "    plt.close()\n",
        "\n",
        "# Eksekusi K-means\n",
        "for iteration in range(max_iter):\n",
        "    distances = np.array([euclidean_distance(X, centroid) for centroid in centroids])\n",
        "    new_assignments = np.argmin(distances, axis=0)\n",
        "\n",
        "    new_centroids = update_centroids(X, new_assignments, k)\n",
        "\n",
        "    plot_clusters(X, new_centroids, new_assignments, iteration + 1)\n",
        "\n",
        "    if np.all(new_centroids == centroids):\n",
        "        print(f\"Konvergen pada iterasi {iteration + 1}\")\n",
        "        break\n",
        "    centroids = new_centroids\n",
        "\n",
        "# Simpan animasi sebagai GIF\n",
        "imageio.mimsave('iris_cluster_animation.gif', frames, fps=5)\n",
        "\n",
        "print(\"Animasi K-means berhasil disimpan sebagai iris_cluster_animation.gif\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 921
        },
        "id": "H9D9vcdxnJD9",
        "outputId": "3c47af6c-26f5-4caa-e7ab-78774c77df9d"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "'FigureCanvasAgg' object has no attribute 'tostring_rgb'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-919249197313>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0mnew_centroids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mupdate_centroids\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_assignments\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m     \u001b[0mplot_clusters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_centroids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_assignments\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miteration\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_centroids\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mcentroids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-3-919249197313>\u001b[0m in \u001b[0;36mplot_clusters\u001b[0;34m(X, centroids, assignments, iteration)\u001b[0m\n\u001b[1;32m     52\u001b[0m     \u001b[0;31m# Simpan frame sebagai gambar\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m     \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrombytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'RGB'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_width_height\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtostring_rgb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     55\u001b[0m     \u001b[0mframes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'FigureCanvasAgg' object has no attribute 'tostring_rgb'"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Animasi garis yang menunjukkan gelombang sinus yang bergerak secara dinamis.\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.animation as animation\n",
        "from matplotlib import style\n",
        "\n",
        "style.use('ggplot')\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "x_vals = []\n",
        "y_vals = []\n",
        "\n",
        "def animate(i):\n",
        "    x_vals.append(i)\n",
        "    y_vals.append(np.sin(i/10))\n",
        "\n",
        "    ax.clear()\n",
        "    ax.plot(x_vals, y_vals, 'b-')\n",
        "\n",
        "ani = animation.FuncAnimation(fig, animate, frames=100, interval=100)\n",
        "ani.save('weather_animation.gif', writer='imagemagick')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "MlGKpYj1n75s",
        "outputId": "def0b062-b5d9-493d-fc51-77c2c6abf805"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.animation:MovieWriter imagemagick unavailable; using Pillow instead.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#visualisasi animasi titik sederhana , visualisasi pergerakan data, animasi dasar python\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.animation import FuncAnimation\n",
        "\n",
        "# Data titik yang akan di animasi\n",
        "x = [0, 1, 2, 3, 4, 5]\n",
        "y = [0, 1, 4, 9, 16, 25]\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax.set_xlim(0, 5)\n",
        "ax.set_ylim(0, 20)\n",
        "point, = ax.plot([], [], 'ro')\n",
        "\n",
        "def update(num):\n",
        "    point.set_data(x[num], y[num])\n",
        "    return point,\n",
        "\n",
        "ani = FuncAnimation(fig, update, frames=6, interval=200, blit=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 757
        },
        "id": "hM-BCgrnoaom",
        "outputId": "f381f305-a67e-4c3c-8541-a540c59f7982"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "x must be a sequence",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-7-b87670f3915b>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     17\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0mani\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFuncAnimation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupdate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fig, func, frames, init_func, fargs, save_count, cache_frame_data, **kwargs)\u001b[0m\n\u001b[1;32m   1714\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_save_seq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1715\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1716\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1717\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1718\u001b[0m         \u001b[0;31m# Need to reset the saved seq, since right now it will contain data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fig, interval, repeat_delay, repeat, event_source, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1438\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mevent_source\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1439\u001b[0m             \u001b[0mevent_source\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew_timer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_interval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1440\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent_source\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mevent_source\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1441\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1442\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fig, event_source, blit)\u001b[0m\n\u001b[1;32m    902\u001b[0m                                                       self._stop)\n\u001b[1;32m    903\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_blit\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 904\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_blit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    905\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    906\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__del__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_setup_blit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1226\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drawn_artists\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1227\u001b[0m         \u001b[0;31m# _post_draw needs to be called first to initialize the renderer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1228\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_post_draw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_blit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1229\u001b[0m         \u001b[0;31m# Then we need to clear the Frame for the initial draw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1230\u001b[0m         \u001b[0;31m# This is typically handled in _on_resize because QT and Tk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_post_draw\u001b[0;34m(self, framedata, blit)\u001b[0m\n\u001b[1;32m   1181\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_blit_draw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drawn_artists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1182\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1183\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_idle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1185\u001b[0m     \u001b[0;31m# The rest of the code in this class is to facilitate easy blitting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mdraw_idle\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1889\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_idle_drawing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1890\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_idle_draw_cntx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1891\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1892\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1893\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    380\u001b[0m         with (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar\n\u001b[1;32m    381\u001b[0m               else nullcontext()):\n\u001b[0;32m--> 382\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    383\u001b[0m             \u001b[0;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    384\u001b[0m             \u001b[0;31m# don't forget to call the superclass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     92\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 94\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     95\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rasterizing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 71\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     72\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3262\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3263\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3264\u001b[0;31m             \u001b[0mDrawEvent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"draw_event\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_process\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3265\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3266\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_without_rendering\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36m_process\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1187\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_process\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1188\u001b[0m         \u001b[0;34m\"\"\"Process this event on ``self.canvas``, then unset ``guiEvent``.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1189\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1190\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mguiEvent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1191\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/cbook.py\u001b[0m in \u001b[0;36mprocess\u001b[0;34m(self, s, *args, **kwargs)\u001b[0m\n\u001b[1;32m    364\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    365\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexception_handler\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 366\u001b[0;31m                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexception_handler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    367\u001b[0m                     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    368\u001b[0m                         \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/cbook.py\u001b[0m in \u001b[0;36m_exception_printer\u001b[0;34m(exc)\u001b[0m\n\u001b[1;32m    108\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_exception_printer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    109\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0m_get_running_interactive_framework\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"headless\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 110\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    111\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    112\u001b[0m         \u001b[0mtraceback\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_exc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/cbook.py\u001b[0m in \u001b[0;36mprocess\u001b[0;34m(self, s, *args, **kwargs)\u001b[0m\n\u001b[1;32m    359\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    360\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 361\u001b[0;31m                     \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    362\u001b[0m                 \u001b[0;31m# this does not capture KeyboardInterrupt, SystemExit,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    363\u001b[0m                 \u001b[0;31m# and GeneratorExit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_start\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    926\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    927\u001b[0m         \u001b[0;31m# Now do any initial draw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 928\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_draw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    930\u001b[0m         \u001b[0;31m# Add our callback for stepping the animation and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_init_draw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1768\u001b[0m                 )\n\u001b[1;32m   1769\u001b[0m                 \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1770\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1771\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1772\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drawn_artists\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_draw_frame\u001b[0;34m(self, framedata)\u001b[0m\n\u001b[1;32m   1787\u001b[0m         \u001b[0;31m# Call the func with framedata and args. If blitting is desired,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1788\u001b[0m         \u001b[0;31m# func needs to return a sequence of any artists that were modified.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1789\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drawn_artists\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframedata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1790\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1791\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_blit\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-7-b87670f3915b>\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(num)\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m     \u001b[0mpoint\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnum\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnum\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36mset_data\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    664\u001b[0m             \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    665\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 666\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xdata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    667\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ydata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    668\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36mset_xdata\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m   1288\u001b[0m         \"\"\"\n\u001b[1;32m   1289\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1290\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'x must be a sequence'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1291\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_xorig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1292\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalidx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: x must be a sequence"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}