1.2.ipynb

{
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  "metadata": {
    "colab": {
      "name": "CS156-Session1.2.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Jp6cSOMV6QR",
        "colab_type": "text"
      },
      "source": [
        "## Download CPU Benchmark Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nHz9iYQuDf5K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!wget -q http://preshing.com/files/specdata20120207.zip\n",
        "!unzip -q specdata20120207.zip\n",
        "!rm specdata20120207.zip"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PvolhOCbWGYu",
        "colab_type": "text"
      },
      "source": [
        "## Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZEmO2jWkDkNN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "%matplotlib inline\n",
        "import matplotlib\n",
        "matplotlib.style.use('ggplot')\n",
        "import seaborn as sns\n",
        "sns.set(rc={'figure.figsize':(12, 8)})\n",
        "from datetime import datetime\n",
        "import warnings\n",
        "warnings.simplefilter('ignore')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-iiYo61gWNKU",
        "colab_type": "text"
      },
      "source": [
        "## Load and Explore Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v9T8NlltDkP3",
        "colab_type": "code",
        "outputId": "cd7c0e9e-7187-4214-97fb-adc3a8811543",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "data = pd.read_csv('benchmarks.txt', low_memory=False)\n",
        "data.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    }\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>testID</th>\n",
              "      <th>benchName</th>\n",
              "      <th>base</th>\n",
              "      <th>peak</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>cpu95-19990104-03254</td>\n",
              "      <td>101.tomcatv</td>\n",
              "      <td>19.40</td>\n",
              "      <td>27.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>cpu95-19990104-03254</td>\n",
              "      <td>102.swim</td>\n",
              "      <td>27.20</td>\n",
              "      <td>34.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>cpu95-19990104-03254</td>\n",
              "      <td>103.su2cor</td>\n",
              "      <td>10.10</td>\n",
              "      <td>9.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>cpu95-19990104-03254</td>\n",
              "      <td>104.hydro2d</td>\n",
              "      <td>8.58</td>\n",
              "      <td>8.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>cpu95-19990104-03254</td>\n",
              "      <td>107.mgrid</td>\n",
              "      <td>8.94</td>\n",
              "      <td>9.44</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 testID    benchName   base  peak\n",
              "0  cpu95-19990104-03254  101.tomcatv  19.40  27.1\n",
              "1  cpu95-19990104-03254     102.swim  27.20  34.8\n",
              "2  cpu95-19990104-03254   103.su2cor  10.10  9.98\n",
              "3  cpu95-19990104-03254  104.hydro2d   8.58  8.61\n",
              "4  cpu95-19990104-03254    107.mgrid   8.94  9.44"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qWRXjKPgDkS7",
        "colab_type": "code",
        "outputId": "331a59a4-8c22-40cb-c9c3-1e63aa4acbc7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "data.info()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 136995 entries, 0 to 136994\n",
            "Data columns (total 4 columns):\n",
            "testID       136995 non-null object\n",
            "benchName    136995 non-null object\n",
            "base         136995 non-null float64\n",
            "peak         131575 non-null object\n",
            "dtypes: float64(1), object(3)\n",
            "memory usage: 4.2+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y7SfemTcDkU9",
        "colab_type": "code",
        "outputId": "cf9919b9-b411-4d62-eaa9-7f9add5dd35d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "pd.unique(data.benchName)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['101.tomcatv', '102.swim', '103.su2cor', '104.hydro2d',\n",
              "       '107.mgrid', '110.applu', '125.turb3d', '141.apsi', '145.fpppp',\n",
              "       '146.wave5', '099.go', '124.m88ksim', '126.gcc', '129.compress',\n",
              "       '130.li', '132.ijpeg', '134.perl', '147.vortex', '168.wupwise',\n",
              "       '171.swim', '172.mgrid', '173.applu', '177.mesa', '178.galgel',\n",
              "       '179.art', '183.equake', '187.facerec', '188.ammp', '189.lucas',\n",
              "       '191.fma3d', '200.sixtrack', '301.apsi', '164.gzip', '175.vpr',\n",
              "       '176.gcc', '181.mcf', '186.crafty', '197.parser', '252.eon',\n",
              "       '253.perlbmk', '254.gap', '255.vortex', '256.bzip2', '300.twolf',\n",
              "       '400.perlbench', '401.bzip2', '403.gcc', '429.mcf', '445.gobmk',\n",
              "       '456.hmmer', '458.sjeng', '462.libquantum', '464.h264ref',\n",
              "       '471.omnetpp', '473.astar', '483.xalancbmk', '410.bwaves',\n",
              "       '416.gamess', '433.milc', '434.zeusmp', '435.gromacs',\n",
              "       '436.cactusADM', '437.leslie3d', '444.namd', '447.dealII',\n",
              "       '450.soplex', '453.povray', '454.calculix', '459.GemsFDTD',\n",
              "       '465.tonto', '470.lbm', '481.wrf', '482.sphinx3'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SWYd-JQlWVfJ",
        "colab_type": "text"
      },
      "source": [
        "## Select Specific Benchmark"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uTfUBS7KDkbq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "443b8156-785c-45f1-af07-66e8d6ed749a"
      },
      "source": [
        "mesa_data = data[data['benchName'] == '177.mesa']\n",
        "mesa_data.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>testID</th>\n",
              "      <th>benchName</th>\n",
              "      <th>base</th>\n",
              "      <th>peak</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9954</th>\n",
              "      <td>cpu2000-19991130-00002</td>\n",
              "      <td>177.mesa</td>\n",
              "      <td>130.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9980</th>\n",
              "      <td>cpu2000-19991130-00004</td>\n",
              "      <td>177.mesa</td>\n",
              "      <td>198.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10006</th>\n",
              "      <td>cpu2000-19991130-00006</td>\n",
              "      <td>177.mesa</td>\n",
              "      <td>502.0</td>\n",
              "      <td>502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10032</th>\n",
              "      <td>cpu2000-19991130-00008</td>\n",
              "      <td>177.mesa</td>\n",
              "      <td>195.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10058</th>\n",
              "      <td>cpu2000-19991130-00010</td>\n",
              "      <td>177.mesa</td>\n",
              "      <td>386.0</td>\n",
              "      <td>366</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       testID benchName   base peak\n",
              "9954   cpu2000-19991130-00002  177.mesa  130.0  NaN\n",
              "9980   cpu2000-19991130-00004  177.mesa  198.0  NaN\n",
              "10006  cpu2000-19991130-00006  177.mesa  502.0  502\n",
              "10032  cpu2000-19991130-00008  177.mesa  195.0  NaN\n",
              "10058  cpu2000-19991130-00010  177.mesa  386.0  366"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GmzYOHjiWmHv",
        "colab_type": "text"
      },
      "source": [
        "## Extract Date and Base Speed of Benchmark"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JxXoWRxaDkhM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mesa_data['date'] = [datetime.strptime(ID.split('-')[1], '%Y%m%d') for ID in mesa_data.testID]\n",
        "mesa_data['year'] = pd.DatetimeIndex(mesa_data['date']).year\n",
        "mesa_data['constant'] = 1\n",
        "mesa_data['peak'] = mesa_data.peak.fillna(0)\n",
        "mesa_data.peak = mesa_data.peak.astype(float)\n",
        "mesa_data['timeStamp'] = [i.timestamp() for i in mesa_data['date']]\n",
        "mesa_data['baseLog2'] = np.log2(mesa_data.base)\n",
        "mesa_data['peakLog2'] = np.log2(mesa_data.peak)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6QJR4S9xnF_K",
        "colab_type": "text"
      },
      "source": [
        "## Fit a Linear Model and Plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r3bsAAPlDkm9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error as MSE\n",
        "from sklearn.metrics import r2_score as R2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M_53gwREUuhO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "febd5621-d869-4dd2-9edf-4d5e8cdfbefb"
      },
      "source": [
        "X = mesa_data[['timeStamp', 'constant']]\n",
        "y = mesa_data.baseLog2\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X[:1385], y, random_state=1, test_size=0.2)\n",
        "linreg = LinearRegression()\n",
        "linreg.fit(X_train, y_train)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ldkp9dISUumf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = linreg.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g6TFn-kf0h9y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "b61f6987-811d-4c64-f9a2-68307502fd9b"
      },
      "source": [
        "print('Coefficients: \\n', linreg.coef_)\n",
        "print(\"Root Mean Squared Error: %.2f\"\n",
        "      % np.sqrt(MSE(y_test, y_pred)))\n",
        "# Explained variance score: 1 is perfect prediction\n",
        "print('Variance score: %.2f' % R2(y_test, y_pred))\n",
        "r2= R2(y_test, y_pred)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Coefficients: \n",
            " [1.21138149e-08 0.00000000e+00]\n",
            "Root Mean Squared Error: 0.52\n",
            "Variance score: 0.69\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "57X1QsPay-VL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "baseReg = linreg.fit(X, y)\n",
        "mesa_data['baseReg'] = baseReg"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kStaNSeGUuwb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "outputId": "7be5006e-01dd-4722-c90f-f85c6b35f321"
      },
      "source": [
        "ax = sns.lmplot(x='timeStamp', y='baseLog2', data=mesa_data, size=7, aspect=2)\n",
        "plt.xlabel('year')\n",
        "plt.ylabel('performance plot')\n",
        "plt.xticks([]);"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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f/MMk/KBznXiyH/jeIezfk8PYzkEYTXt9d+qE/rEP3IXjL72FuYoLgej9DKXC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XwmiJWDsrUrAwWDqhMKUNU2JMgJIgUqsVx5JJEhpplLhntJ4tIe2lQSG7Jsx51mNUSm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\ngg==\n",
            "text/plain": [
              "<Figure size 1008x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j8qwUj8eEoTj",
        "colab_type": "text"
      },
      "source": [
        "# MNIST K-nearest Neighbors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "07v4-9v7Dkqu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "35620d2e-65d5-4638-94f3-bda22b2bdeb3"
      },
      "source": [
        "from sklearn import datasets, neighbors\n",
        "\n",
        "digits = datasets.load_digits()\n",
        "X_digits = digits.data / digits.data.max()\n",
        "y_digits = digits.target\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_digits, y_digits, random_state=1)\n",
        "\n",
        "knn = neighbors.KNeighborsClassifier()\n",
        "\n",
        "print('KNN score: %f' % knn.fit(X_train, y_train).score(X_test, y_test))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "KNN score: 0.993333\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "18LtcHnDDkwu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8bff3b36-30d1-45f2-f496-07237bb77c0d"
      },
      "source": [
        "for i in range(3):\n",
        "    plt.gray() \n",
        "    plt.matshow(digits.images[i]) \n",
        "    plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8+uijY7kTADSUSlEUxWi+oLe3N55++un47W9/O1Y7AUBDGXWMIyK+/e1vx/PPPx/t7e0j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\nZGIMAMnEGACS/R+eRiOjRS81dAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZGANAMjEGgGRiDADJxBgAkokxACQTYwBIJsYAkKxhJA+66aabYu/evTFp0qQ45ZRT4q67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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AuSE_1s0Dz-E",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "outputId": "9408c0c4-51fa-4afe-d0af-07d14d13ff1c"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score\n",
        "mnist_preds = knn.predict(X_test)\n",
        "print(classification_report(mnist_preds, y_test))\n",
        "print(confusion_matrix(mnist_preds, y_test))\n",
        "print(accuracy_score(mnist_preds, y_test))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        53\n",
            "           1       1.00      1.00      1.00        42\n",
            "           2       0.98      1.00      0.99        40\n",
            "           3       1.00      1.00      1.00        52\n",
            "           4       1.00      1.00      1.00        47\n",
            "           5       0.97      1.00      0.99        38\n",
            "           6       1.00      1.00      1.00        43\n",
            "           7       0.98      0.98      0.98        48\n",
            "           8       1.00      1.00      1.00        37\n",
            "           9       1.00      0.96      0.98        50\n",
            "\n",
            "    accuracy                           0.99       450\n",
            "   macro avg       0.99      0.99      0.99       450\n",
            "weighted avg       0.99      0.99      0.99       450\n",
            "\n",
            "[[53  0  0  0  0  0  0  0  0  0]\n",
            " [ 0 42  0  0  0  0  0  0  0  0]\n",
            " [ 0  0 40  0  0  0  0  0  0  0]\n",
            " [ 0  0  0 52  0  0  0  0  0  0]\n",
            " [ 0  0  0  0 47  0  0  0  0  0]\n",
            " [ 0  0  0  0  0 38  0  0  0  0]\n",
            " [ 0  0  0  0  0  0 43  0  0  0]\n",
            " [ 0  0  1  0  0  0  0 47  0  0]\n",
            " [ 0  0  0  0  0  0  0  0 37  0]\n",
            " [ 0  0  0  0  0  1  0  1  0 48]]\n",
            "0.9933333333333333\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rO6_MLZGD0BE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "outputId": "8c3d937d-5eec-4f96-b769-8f48d452f11c"
      },
      "source": [
        "sns.heatmap(confusion_matrix(mnist_preds, y_test), annot=True);"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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nn0ljdSYTc5mYSSIXAq9BgwZ68803z/u9Sy+9VG+99VaFzlfqBPG7774759j4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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hCARTjqiD0Eo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}