lab7again.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cNDUV_ujZ7V0",
        "outputId": "c5e20d9a-e54a-47f3-e87c-8a89f083d3b7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-03-30 03:29:37--  http://archive.ics.uci.edu/ml/machine-learning-databases/00279/SUSY.csv.gz\n",
            "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\n",
            "Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified\n",
            "Saving to: ‘SUSY.csv.gz’\n",
            "\n",
            "SUSY.csv.gz             [               <=>  ] 879.65M   413KB/s    in 31m 48s \n",
            "\n",
            "2024-03-30 04:01:25 (472 KB/s) - ‘SUSY.csv.gz’ saved [922377711]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget http://archive.ics.uci.edu/ml/machine-learning-databases/00279/SUSY.csv.gz"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!gunzip SUSY.csv.gz"
      ],
      "metadata": {
        "id": "sgmOfkp6iD2V"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ls -lh"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D9RQSbJniVPk",
        "outputId": "1365350c-7aec-4c36-d7f8-0903696d7801"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total 2.3G\n",
            "drwxr-xr-x 1 root root 4.0K Mar 28 23:00 \u001b[0m\u001b[01;34msample_data\u001b[0m/\n",
            "-rw-r--r-- 1 root root 2.3G Mar 30 04:01 SUSY.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "filename=\"SUSY.csv\"\n",
        "!head -5  \"SUSY.csv\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CPSI6je2iVFu",
        "outputId": "c3759106-d1b6-4681-99db-3afc7cad7e6c"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.000000000000000000e+00,9.728614687919616699e-01,6.538545489311218262e-01,1.176224589347839355e+00,1.157156467437744141e+00,-1.739873170852661133e+00,-8.743090629577636719e-01,5.677649974822998047e-01,-1.750000417232513428e-01,8.100607395172119141e-01,-2.525521218776702881e-01,1.921887040138244629e+00,8.896374106407165527e-01,4.107718467712402344e-01,1.145620822906494141e+00,1.932632088661193848e+00,9.944640994071960449e-01,1.367815494537353516e+00,4.071449860930442810e-02\n",
            "1.000000000000000000e+00,1.667973041534423828e+00,6.419061869382858276e-02,-1.225171446800231934e+00,5.061022043228149414e-01,-3.389389812946319580e-01,1.672542810440063477e+00,3.475464344024658203e+00,-1.219136357307434082e+00,1.295456290245056152e-02,3.775173664093017578e+00,1.045977115631103516e+00,5.680512785911560059e-01,4.819284379482269287e-01,0.000000000000000000e+00,4.484102725982666016e-01,2.053557634353637695e-01,1.321893453598022461e+00,3.775840103626251221e-01\n",
            "1.000000000000000000e+00,4.448399245738983154e-01,-1.342980116605758667e-01,-7.099716067314147949e-01,4.517189264297485352e-01,-1.613871216773986816e+00,-7.686609029769897461e-01,1.219918131828308105e+00,5.040258169174194336e-01,1.831247568130493164e+00,-4.313853085041046143e-01,5.262832045555114746e-01,9.415140151977539062e-01,1.587535023689270020e+00,2.024308204650878906e+00,6.034975647926330566e-01,1.562373995780944824e+00,1.135454416275024414e+00,1.809100061655044556e-01\n",
            "1.000000000000000000e+00,3.812560737133026123e-01,-9.761453866958618164e-01,6.931523084640502930e-01,4.489588439464569092e-01,8.917528986930847168e-01,-6.773284673690795898e-01,2.033060073852539062e+00,1.533040523529052734e+00,3.046259880065917969e+00,-1.005284786224365234e+00,5.693860650062561035e-01,1.015211343765258789e+00,1.582216739654541016e+00,1.551914215087890625e+00,7.612152099609375000e-01,1.715463757514953613e+00,1.492256760597229004e+00,9.071890264749526978e-02\n",
            "1.000000000000000000e+00,1.309996485710144043e+00,-6.900894641876220703e-01,-6.762592792510986328e-01,1.589282631874084473e+00,-6.933256387710571289e-01,6.229069828987121582e-01,1.087561845779418945e+00,-3.817416727542877197e-01,5.892043709754943848e-01,1.365478992462158203e+00,1.179295063018798828e+00,9.682182073593139648e-01,7.285631299018859863e-01,0.000000000000000000e+00,1.083157896995544434e+00,4.342924803495407104e-02,1.154853701591491699e+00,9.485860168933868408e-02\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "VarNames=[\"signal\", \"l_1_pT\", \"l_1_eta\",\"l_1_phi\", \"l_2_pT\", \"l_2_eta\", \"l_2_phi\", \"MET\", \"MET_phi\", \"MET_rel\", \"axial_MET\", \"M_R\", \"M_TR_2\", \"R\", \"MT2\", \"S_R\", \"M_Delta_R\", \"dPhi_r_b\", \"cos_theta_r1\"]\n",
        "RawNames=[\"l_1_pT\", \"l_1_eta\",\"l_1_phi\", \"l_2_pT\", \"l_2_eta\", \"l_2_phi\", \"MET\", \"MET_phi\"]\n",
        "FeatureNames=list(set(VarNames[1:]).difference(RawNames))\n",
        "RawNames"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bvrcDJwiib5c",
        "outputId": "bb1fff6f-3574-421e-c133-c40d476aa575"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['l_1_pT',\n",
              " 'l_1_eta',\n",
              " 'l_1_phi',\n",
              " 'l_2_pT',\n",
              " 'l_2_eta',\n",
              " 'l_2_phi',\n",
              " 'MET',\n",
              " 'MET_phi']"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "FeatureNames"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7EvKbXIcihGF",
        "outputId": "dd6ec58c-304a-4853-d9d2-571c147d6a5c"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['R',\n",
              " 'M_R',\n",
              " 'S_R',\n",
              " 'MET_rel',\n",
              " 'axial_MET',\n",
              " 'dPhi_r_b',\n",
              " 'cos_theta_r1',\n",
              " 'M_TR_2',\n",
              " 'M_Delta_R',\n",
              " 'MT2']"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "_2gxAKXAijUW"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "filename = \"SUSY.csv\"\n",
        "df = pd.read_csv(filename, dtype='float64', names=VarNames)"
      ],
      "metadata": {
        "id": "-2nW3z_cil2T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_sig=df[df.signal==1]\n",
        "df_bkg=df[df.signal==0]"
      ],
      "metadata": {
        "id": "HIScFp4KiqR7"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "for var in VarNames[1:]:\n",
        "    print (var)\n",
        "    plt.figure(figsize=(10,5))\n",
        "    plt.hist(np.array(df_sig[var]),bins=100,histtype=\"step\", color=\"red\",label=\"signal\",density=1, stacked=True)\n",
        "    plt.hist(np.array(df_bkg[var]),bins=100,histtype=\"step\", color=\"blue\", label=\"background\",density=1, stacked=True)\n",
        "    plt.legend(loc='upper right')\n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "gusbJfOijHj_",
        "outputId": "f555a40b-ea45-4621-f627-64bd38dca08d"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "l_1_pT\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "l_1_eta\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "l_1_phi\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "l_2_pT\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "l_2_eta\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "l_2_phi\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MET\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MET_phi\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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xCpVDE+bE1dmv2ocez8FjK5bXdH0hmM25Q4P8eN/c8d4TIU6hKpFtzt3SnNqfwwufme3bpdohMTdffgWl+rpFhIZKw4fX+XzbJTn2eWuEC5Dd3tv9xibWkFPIyHD/rev4oFATZtpUOHNBne5+zsksJnho4XL/1tRTnyjPrUbeNEM33tbmUhgnCMA3DWkc6dWrusdYbY3tRbZ06VLt37/f+XzPDz74QDZvv6+9pTZv6qtocLDUwbeeEJciwlAz8ThWw1tvt5obdCVIqjUAvUgqPevtr7nuB7XXN1g6L/hKFb6aVaebjmP3hZaEBY9LtQe1b82X5lwoUysLNSRIuL0h9tri4j2A2HqGyW73bRyHrfN5haqkOpjMqbO1+qZ+5RypVhey+loVpOqWBdsr66VaN4x2SY5jX6qwyMO3XefObrtL1YTkD94sVkJRrSaE3FypNL76OtWaBtuRG1x9bi9ulGp/LiS/7tLq64lgKy+S3bql7obtZdK9chsSq6/vTdXn36SV8Xx+zfAHRL804hSkmrFwbq5voSNEpRM6uP154e243urjtdWonrp6aqWqbz9vWksAaW3jBOGn1vKBQvPwY/yLs3EkulzBVtdEdL7SokNfW3XggPtJDfwNSuVnKnSlvY/ef/ufdbcVlcra3qi70/nz0qFDnlObN43p7lZW5n79JTieiDDUDOp9bkpwpWzHvpB2ud4s2wq/UqjsHm7QPf81t4bD3V/Xu0r668ELM3a5OvHpMd2+JEmlt7hL/Rf+yn9VjDTI9SnJtmOf+RUkam6IHfeulmoN2q+u+9XSU09LV7sGpXq7Nflxs2HvHy5HsPtZyqQL3dL+rW63tPpaFaSaloW6T5au2d/XX6m28iCFzinRhGeukZ6pvTVB0k1uPy/Shcaf4Qm+v6gX3oNE9wuLe44F/y3V6rLnDG0exs00ojKXhEafgrvJLH64ycdekJ7q05BWLLfjAY+1995K5WV6dE9aUwC5yHPboDk05gNFiPJdfTPANvX75u/4l3MVkoIUfPSQOrh5aEQ/S7DOX3Gl1D7IZX1NNjlwwLeXKz9Tob37AlWlIPf7qVL9tFdWVdQ9RUuwzttjpcC6Y6TbBRruQ5S/3d3atas+idxc99svwfFEhKFm4O25KZJkKyuU/Za63dnskhweWmokzz8f6p9EJUruu7MlVA8Iv3+bug6otb1mTEnUlrr1jDrneeC6wyHbhFS3Y1FsNin0/ipNOLtR7oSGVMl269V1bty9BRC/bzbsdo+zlDkr20paFezJ3eXY/o0Kv8xzX8BDi5LUen4XOz+jHroI+tFjDxdZ/a1GHsYDyqZSdXD787BmevTCz47Jbm94GG5IALnY8zx46xrs13BOLzeM/jzcGF74m2hbUypvAf7OAJsXf73HP0RKF/4YuWWV+/7B3noQeGr9KSurf/zLmTN19y09LylIiu4uhbers581N1fW9uekDnXDS79+nquSmyudP3NW1vO1WpuKz6tK4YrrekbBHV1DTVl5gHKPWnUmOl7nPbRSVeX52koVoHYKkqfIUq4gnS9z90d9q9pd+WNZLW66S1+i44kIQ83B23NT6mG32Xy6KZAaMYlKzYxbS9zNuCWvd6h2Hanuklfnr85nJXfjlmrquS+gnrEa7rfVF0DcDuiv71EAl1Crgj25u+zJvk/p3Fo0rjsYvPHrs+8nv75lLgT1hBcf0aDa3TUvdLmt+ZlZmz/djRszw65f965exlc6xwl6Gbvl9vW83DCeUFfdrte8Pty4NT0myu+Gk4v9kHHFqFCDVHf8opdZGy+lZsGLOlupl5biz04poWyXSuU5DIWWlei1UXUfRSBJna8Ml15eVv3ClZXfb6inq1i5JVjnAztJtVtdAturnSVYVretHKGSwqXQELeBxxurymWVmzRUaUjqeKFVpfabV/16weFB6tDZ9fXalUsB+VLuUfcBIyBA6t27buDx3koVogD1U79z5XUCUfk5i/aqn6py3c/GGxAQpH79gi6lvOMVYag5eXhuSnPw774+Str/jv8/IP24C2vq/NGQrju0OLQOl1D2vCS0xs++1x8JXp5N5fjwW9XuOnviu3a6/f5enrsbh1TJdmxvne7GdkmOdz23nHiru7cWJY8/DmuCnJvxlXZH9cQ27lrRvTY41HPD6OmhyA15nq4/vb0k//JHQ26Y3Qa3Y8dku/N62cu8/KHOjxYXT+d4Ynsn3e6xK2f1BEWvbT+qOvmyZiZXP/qjNvGzwr0ftJ6pKT2NHfb2en7PHFvUrrql2M3DuZ1V/bcQjTr7ltu69in/Vn89vtN96PGQCMrPWbT3yxBVHXTXchKkgIB+6hVTrnaBrl3JysoDpKOq0w2uXl675YVK6quyyFgpxLU+3l7PavXc2iR5H6bjsZXqVLlyj1p1vtJSJwydr7SoSoHV46XCXbdeoo0/XhGGzM6fO9RWdBdGiwPMqjV99v39kWDrGVY9/tDteLia5zo56j7XqebhsLe4v1m2h4bK7mPziL9jopw8PTTYYyu6Z15vGGu6Il+9xe0x6+vK+MELdafary94ShfGdr3iYSZBD++ptxvm6pveKo0a5e41oxSqXXKsfLvuc9D8bHHxPpa3t+eAuf2Abr83WqPudTfD5IWZXI99Wc8jGmrVpWZ2WC/vtyfeWhPrTZ5upqbM++ArJcy8wcPYYS/X/cLjEnztHlrzCAZvD+f21IPE4ZAefjhUVbYIqeeP6t6Je5oIocRzL7nqlhOLDuS5f6BpQID7SRCc3E0i4KVbXrtzFgV8aSg3P9Tn17Na/QsfHvcrqw5rZWcqVfvxFNXrpGBrlcfJ5NzOn1Dmvetda0UYgu9a012YaHGAebWWz76/PxK8joerCTyPeGkdcDfveEOaR9wEJW8tSt5ajZzP12oG7m8YPXdFlrxMgFH+jULV2fO4PZUo44USde3Xrc425zTut0yqM4179c4emnhqbpjdNZzk5clhXK9CN61fDiVogjaq8IqfyD7ITfdJxahwa77boFH9LLC617DesbzBJbLf+o6byWZOexwj6/V5dTZbdYvLhGVuz69UG/XiykIlDG/4Hw695sDCQuWVXq7CBa/UmVlUUvVYm65uZis93cVj8K657h/cssjteD9pox/dQy9MztTZ8zTQ9f5ca9+++jNXK2gcOCAdP163eHm5dPRo9b8hbia1rKhw7XH3Q4GB1Y08tdk6tZO9vkkEOnZ0SSEjR45UYmKiFi9e6VcLT30mT56soqIibdmypUHl21kDFaBK5Z7oKDc9EhWgSrWzupmQwev8CZ673vnj8OHDiouL0+7du5WYmNgER3SPMAT/tJa7MACtgr8/EryOh7vVz268npJZPUHJU4uSrXN7hYb004QJ7v6SX//NnTdeH2DchOzWAjmUrMIFa+reLNdMmNNvizSobhiS44Qkm/tHLXh9T708k66wUPayf8nutp9ViDRBbrtt5ZVHVM9OOMdzF0LH9m/qfqbqG8vrbdIcT617Xp5Xd+KEXbdbHCqVh26eKtFwfSC7erjd7p7nFhdnVz8P74tn1Z/f4Z32yK7vXLbY9JVCdYMmyMOkR8GVsr3q5jESJ07IMbqe2Vr7v+NjPb937lzdIUO5udLQoZ5ne25qoaFWOfb0kz3a91TjbwtPU7N2DFK/PhU6X153djqpOixZO/rWZc9b17vWjDAEAGid/E5YXvbzIyg5Z/p8I6vuDXpNtzU3M29606AHGPsTsLzMOmHXkeoHetfuzrfrrJsHJbs5hJtn4Hl7fIPjw2+ruz96mCBDktf5390GxS+7q1SqbsmoPU3/h99qwjPXqPDLPM8B29+xvO5aoep9HEaA+4cm10xeNNNDq6cH3ltcPHf188pLXeqf4TbQ82Mk/Jyt1Rubrbplx91h9+2rDkLPPiv17Vt3e2CgFOTj0B9PnC10p62yd7iUbvnrsnYMkrWj5+0VFRUKcvPG1df17lJDGAIAmIc/QcnhkH3CBNmLtklRtW+kHfLWbc1bNTx2LfQzYDVo3Igf4zm9P6Ra8vz4huoWEFvhvurA9UNeJttpyPit4RPj3FxGh/SM+5Yav1vbvFTG38dh1Dt5kQfeJuSobyyZZ97r4s8Mt9U7Nn3vEbtdevNN6fRpKSbG9Wa8vLz638GDfX+22sV0/vx5paWlacOGDWrfvr2mT5+uxx57TBaLRRs2bNAf//hH7d+/Xx06dNDPf/5zrVy5Ut26fd9au3fvXj3wwAN6//33ZRiGEhMTtW7dOvXq1avOa3388ce66aabdN999+mBBx6QJD3++ONatWqVzp49qzFjxshmsykjI0N79uyR9H1Xu6FDh2r16tWyWq3Kzc3VZ599phkzZig7O1uhoaG64447tHz5cnXsWJ2karoAPjFnofP1R48erc6dO2vdunWSpNjYWE2bNk0HDx7UK6+8oi5duuiRRx7RtGnTnPvs2LFDv//97+VwOPTjH/9YDz/8cFNfArcIQwAASI174qwfIcPz/aL3cUEeeZuxQPJvhlBJ9iKHHLrZ/U24N/W1gHh43/wdg+Y9tPnZ2lZPZS52WLDriOzapdoD3v0N5Y2pS0uIjq7uEld7yJC7sUCt0fr163XXXXdpx44d+uSTTzRt2jTZ7XZNnTpV586d04IFCxQfH6/jx48rPT1dkydP1tatWyVJ33zzja699lqNHDlS27ZtU1hYmD788EOdd9Nfbdu2bbr99tu1ZMkSZ9jYuHGjnnjiCf3pT3/SNddco02bNmnZsmWKq9VtNjMzU2FhYXrnnequjCUlJUpNTVVycrI+/vhjHT9+XL/73e+UlpbmDDoNtWzZMi1YsEAPPfSQXn31VU2fPl0jRoxQfHy8zpw5o1tuuUXXX3+9XnzxReXm5mrGjBl+vMu+IwwBAOBNK5s0RpL3ecx9neq5AQ9nsoeGyj68g5sJBrzx//EN/tyf2/uHyxHcDGNVWktYaEUzucI/MTExWrFihSwWi+Lj4/XZZ59pxYoVmjp1qn772986y/Xs2VOrVq3S0KFDdebMGXXs2FGrV69WeHi4Nm3apPbt20uSrrzyyjqv8frrr2vixIl6/vnnNWbMGOf6p556SnfddZemTJkiSZo7d67efvttnTlzxmX/Dh066Pnnn3d2j3vuuedUVlamF154QR0uTC339NNP6xe/+IUWL16siIiIBp//TTfdpHvuuUeS9MADD2jFihV67733FB8fr5deeklVVVX685//rODgYPXr109ff/21pk+f3uDj+4swBABAfRpzQ+zrU3C9lW+OG+KGPLnb38B3MYOE3d4sY1VajdYYyuGTn/zkJ7JYvn++UHJyspYtW6bKykrt2bNH8+fP1//93//pu+++U9WF5xTl5eWpb9++2rNnj4YPH+4MQu7k5OTojTfe0KuvvqrRo0e7bNu/f78ziNQYNmyYtm3b5rKuf//+LuOEHA6HBg4c6AxCknTNNdeoqqpK+/fv9ykMDRgwwPl/i8WiyMhIHb8wBaDD4dCAAQMU/IMmv+Tk5AYfuzEIQwAANIcGtLh45CnUNNcNcWtp/WistnIenrT18zOpsrIypaamKjU1VRs3blTXrl2Vl5en1NRUVVRUz/YW0oC+gL169dLll1+utWvX6uabb/YanDzp4OnBQl4EBATIMFwfWnvuXN1nktWuj8VicYa+lkQYAgCgOTSkxcUTb6GGG2LgkpSTk+Py9UcffaTevXtr3759OnnypBYtWqSYmBhJ0ieffOJSdsCAAVq/fr3OnTvnMeTYbDa99tprGjlypH71q1/pr3/9q7NsfHy8Pv74Y02cONFZ/uOPP663zgkJCVq3bp1KSkqcQenDDz9UQECA4uPjJUldu3bVsWPHnPtUVlbq888/189+9rN6j//D19mwYYPKysqcrUMfffRRg/dvDN8ffwwAABrGbq8ev+PrQtgB2py8vDylp6dr//79evnll/XUU09pxowZstvtCgoK0lNPPaUvv/xSf//737VgwQKXfdPS0lRcXKyxY8fqk08+0YEDB7Rhwwbt37/fpVy3bt20bds27du3T+PGjXNOsHDvvffqz3/+s9avX68DBw7o8ccf16effurSbc+d8ePHKzg4WJMmTdLnn3+u9957T/fee69+85vfOLvI/fznP9ebb76pjLf/V4cP79OM+/5DRW6m2/fm17/+tSwWi6ZOnaovvvhCW7du1dKlS306hr9oGQIAAMAlz9fheRf7NSZOnKizZ89q2LBhCgwM1IwZMzRt2jRZLBatW7dODz30kFatWqVBgwZp6dKluvXWW537Xn755dq2bZtmzZqlESNGKDAwUImJibrmmmvqvE5kZKS2bdumkSNHavz48XrppZc0fvx4ffnll7rvvvtUVlamX/3qV5o8ebJ27Njhtc6hoaF66623NGPGDA0dOtRlau0av/3tb/V///d/mvbvUyVLO8349zSfWoUkqWPHjvqf//kf3X333brqqqvUt29fLV68WHfccYdPx/GHxajdye8SVFxcrPDwcJ06dUphYWEtXR3t2ujQ4AkJ2vmiQ4PG+/FwNwAAALgoKytTbm6u4uLiXAba1/eIraYWGlodii71Btzrr79ekZGR2rBhQ5Mcr+TkWTlyQ5QQd1YdLr848517+kz4kg1oGQIAAMAlqzHD8/xxKU7cV1paqjVr1ig1NVWBgYF6+eWX9e677zqfJ2RmhCEAAABc0phXxDuLxaKtW7fqiSeeUFlZmeLj4/Xf//3fSklJaemqtTjCEAAAANCGhYSE6N13323parRKzCYHAAAAwJQIQwAAAABMiTAEAACAS0ZVVVVLVwGtRFN8FhgzBAAAgFYvKChIAQEBOnr0qLp27aqgoKB6HxqKi6e8olySReUV5Qosa97rYhiGKioqdOLECQUEBCgoKMjvYxGGAAAA0OoFBAQoLi5Ox44d09GjR1u6OqilouScCgvbq73OKaio/UV5zdDQUNntdgUE+N/ZjTAEAACAS0JQUJDsdrvOnz+vysrKlq4OfmDv/xzS3bPi9N9PHlL8L+Ka/fUCAwPVrl27RrcOEoYAAABwybBYLGrfvr3at784rQ9oGEtFgL76KliWigAFBwe3dHUajAkUAAAAAJgSYQgAAACAKRGGAAAAAJgSYQgAAACAKRGGAAAAAJgSYQgAAACAKRGGAAAAAJgSYQgAAACAKRGGAAAAAJgSYQgAAACAKRGGAAAAAJgSYQgAAACAKRGGAAAAAJgSYQgAAACAKRGGAAAAAJgSYQgAAACAKRGGAAAAAJiSX2Fo9erVio2NVXBwsJKSkrRjxw6PZZ977jkNHz5cXbp0UZcuXZSSklKn/OTJk2WxWFyWUaNG+VM1AAAAAGgQn8PQ5s2blZ6ernnz5mnXrl0aOHCgUlNTdfz4cbfls7KyNG7cOL333nvKzs5WTEyMbrjhBn3zzTcu5UaNGqVjx445l5dfftm/MwIAAACABvA5DC1fvlxTp07VlClT1LdvX61Zs0ahoaFau3at2/IbN27UPffco8TERPXp00fPP/+8qqqqlJmZ6VLOarUqMjLSuXTp0sW/MwIAAACABvApDFVUVGjnzp1KSUn5/gABAUpJSVF2dnaDjlFaWqpz587psssuc1mflZWlbt26KT4+XtOnT9fJkyc9HqO8vFzFxcUuCwAAAAD4wqcwVFhYqMrKSkVERLisj4iIUH5+foOO8cADDyg6OtolUI0aNUovvPCCMjMztXjxYv3jH//QjTfeqMrKSrfHWLhwocLDw51LTEyML6cBAAAAAGp3MV9s0aJF2rRpk7KyshQcHOxcP3bsWOf/+/fvrwEDBqhXr17KysrSddddV+c4s2fPVnp6uvPr4uJiAhEAAAAAn/jUMmSz2RQYGKiCggKX9QUFBYqMjPS679KlS7Vo0SK9/fbbGjBggNeyPXv2lM1m08GDB91ut1qtCgsLc1kAAAAAwBc+haGgoCANHjzYZfKDmskQkpOTPe63ZMkSLViwQBkZGRoyZEi9r/P111/r5MmTioqK8qV6AAAAANBgPs8ml56erueee07r16+Xw+HQ9OnTVVJSoilTpkiSJk6cqNmzZzvLL168WHPmzNHatWsVGxur/Px85efn68yZM5KkM2fOaNasWfroo490+PBhZWZm6rbbbtMVV1yh1NTUJjpNAAAAAHDl85ihMWPG6MSJE5o7d67y8/OVmJiojIwM56QKeXl5Cgj4PmM988wzqqio0J133ulynHnz5mn+/PkKDAzUp59+qvXr16uoqEjR0dG64YYbtGDBAlmt1kaeHgAAAAC459cECmlpaUpLS3O7LSsry+Xrw4cPez1WSEiI3nrrLX+qAQAAAAB+87mbHAAAAAC0BYQhAAAAAKZEGAIAAABgSoQhAAAAAKZEGAIAAABgSoQhAAAAAKZEGAIAAABgSoQhAAAAAKZEGAIAAABgSoQhAAAAAKZEGAIAAABgSoQhAAAAAKZEGAIAAABgSoQhAAAAAKZEGAIAAABgSoQhAAAAAKZEGAIAAABgSoQhAAAAAKZEGAIAAABgSoQhAAAAAKZEGAIAAABgSoQhAAAAAKZEGAIAAABgSoQhAAAAAKZEGAIAAABgSoQhAAAAAKZEGAIAAABgSoQhAAAAAKZEGAIAAABgSoQhAAAAAKZEGAIAAABgSoQhAAAAAKZEGAIAAABgSoQhAAAAAKZEGAIAAABgSoQhAAAAAKZEGAIAAABgSoQhAAAAAKZEGAIAAABgSoQhAAAAAKZEGAIAAABgSoQhAAAAAKZEGAIAAABgSoQhAAAAAKZEGAIAAABgSoQhAAAAAKZEGAIAAABgSoQhAAAAAKZEGAIAAABgSoQhAAAAAKZEGAIAAABgSn6FodWrVys2NlbBwcFKSkrSjh07PJZ97rnnNHz4cHXp0kVdunRRSkpKnfKGYWju3LmKiopSSEiIUlJSdODAAX+qBgAAAAAN4nMY2rx5s9LT0zVv3jzt2rVLAwcOVGpqqo4fP+62fFZWlsaNG6f33ntP2dnZiomJ0Q033KBvvvnGWWbJkiVatWqV1qxZo5ycHHXo0EGpqakqKyvz/8wAAAAAwAufw9Dy5cs1depUTZkyRX379tWaNWsUGhqqtWvXui2/ceNG3XPPPUpMTFSfPn30/PPPq6qqSpmZmZKqW4VWrlypRx55RLfddpsGDBigF154QUePHtWWLVsadXIAAAAA4IlPYaiiokI7d+5USkrK9wcICFBKSoqys7MbdIzS0lKdO3dOl112mSQpNzdX+fn5LscMDw9XUlKSx2OWl5eruLjYZQEAAAAAX/gUhgoLC1VZWamIiAiX9REREcrPz2/QMR544AFFR0c7w0/Nfr4cc+HChQoPD3cuMTExvpwGAAAAAFzc2eQWLVqkTZs26fXXX1dwcLDfx5k9e7ZOnTrlXI4cOdKEtQQAAABgBu18KWyz2RQYGKiCggKX9QUFBYqMjPS679KlS7Vo0SK9++67GjBggHN9zX4FBQWKiopyOWZiYqLbY1mtVlmtVl+qDgAAAAAufGoZCgoK0uDBg52TH0hyToaQnJzscb8lS5ZowYIFysjI0JAhQ1y2xcXFKTIy0uWYxcXFysnJ8XpMAAAAAGgMn1qGJCk9PV2TJk3SkCFDNGzYMK1cuVIlJSWaMmWKJGnixInq3r27Fi5cKElavHix5s6dq5deekmxsbHOcUAdO3ZUx44dZbFYNHPmTD3++OPq3bu34uLiNGfOHEVHR2v06NFNd6YAAAAA8AM+h6ExY8boxIkTmjt3rvLz85WYmKiMjAznBAh5eXkKCPi+wemZZ55RRUWF7rzzTpfjzJs3T/Pnz5ck3X///SopKdG0adNUVFSkn/70p8rIyGjUuCIAAAAA8MbnMCRJaWlpSktLc7stKyvL5evDhw/XezyLxaLHHntMjz32mD/VAQAAAACfXdTZ5AAAAACgtSAMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAU/IrDK1evVqxsbEKDg5WUlKSduzY4bHs3r17dccddyg2NlYWi0UrV66sU2b+/PmyWCwuS58+ffypGgAAAAA0iM9haPPmzUpPT9e8efO0a9cuDRw4UKmpqTp+/Ljb8qWlperZs6cWLVqkyMhIj8ft16+fjh075lz++c9/+lo1AAAAAGgwn8PQ8uXLNXXqVE2ZMkV9+/bVmjVrFBoaqrVr17otP3ToUD355JMaO3asrFarx+O2a9dOkZGRzsVms3ksW15eruLiYpcFAAAAAHzhUxiqqKjQzp07lZKS8v0BAgKUkpKi7OzsRlXkwIEDio6OVs+ePTV+/Hjl5eV5LLtw4UKFh4c7l5iYmEa9NgAAAADz8SkMFRYWqrKyUhERES7rIyIilJ+f73clkpKStG7dOmVkZOiZZ55Rbm6uhg8frtOnT7stP3v2bJ06dcq5HDlyxO/XBgAAAGBO7Vq6ApJ04403Ov8/YMAAJSUlqUePHvrrX/+qu+66q055q9XqtcsdAAAAANTHp5Yhm82mwMBAFRQUuKwvKCjwOjmCrzp37qwrr7xSBw8ebLJjAgAAAMAP+RSGgoKCNHjwYGVmZjrXVVVVKTMzU8nJyU1WqTNnzujQoUOKiopqsmMCAAAAwA/53E0uPT1dkyZN0pAhQzRs2DCtXLlSJSUlmjJliiRp4sSJ6t69uxYuXCipetKFL774wvn/b775Rnv27FHHjh11xRVXSJLuu+8+/eIXv1CPHj109OhRzZs3T4GBgRo3blxTnScAAAAAuPA5DI0ZM0YnTpzQ3LlzlZ+fr8TERGVkZDgnVcjLy1NAwPcNTkePHtVVV13l/Hrp0qVaunSpRowYoaysLEnS119/rXHjxunkyZPq2rWrfvrTn+qjjz5S165dG3l6AAAAAOCeXxMopKWlKS0tze22moBTIzY2VoZheD3epk2b/KkGAAAAAPjN54euAgAAAEBbQBgCAAAAYEqEIQAAAACmRBgCAAAAYEqEIQAAAACmRBgCAAAAYEqEIQAAAACmRBgCAAAAYEqEIQAAAACmRBgCAAAAYEqEIQAAAACmRBgCAAAAYEqEIQAAAACmRBgCAAAAYEqEIQAAAACmRBgCAAAAYEqEIQAAAACmRBgCAAAAYEqEIQAAAACmRBgCAAAAYEqEIQAAAACmRBgCAAAAYEqEIQAAAACmRBgCAAAAYEqEIQAAAACmRBgCAAAAYEqEIQAAAACmRBgCAAAAYEqEIQAAAACmRBgCAAAAYEqEIQAAAACmRBgCAAAAYEqEIQAAAACmRBgCAAAAYEqEIQAAAACmRBgCAAAAYEqEIQAAAACmRBgCAAAAYEqEIQAAAACmRBgCAAAAYEqEIQAAAACmRBgCAAAAYEqEIQAAAACmRBgCAAAAYEqEIQAAAACmRBgCAAAAYEqEIQAAAACmRBgCAAAAYEqEIQAAAACmRBgCAAAAYEp+haHVq1crNjZWwcHBSkpK0o4dOzyW3bt3r+644w7FxsbKYrFo5cqVjT4mAAAAADSWz2Fo8+bNSk9P17x587Rr1y4NHDhQqampOn78uNvypaWl6tmzpxYtWqTIyMgmOSYAAAAANJbPYWj58uWaOnWqpkyZor59+2rNmjUKDQ3V2rVr3ZYfOnSonnzySY0dO1ZWq7VJjgkAAAAAjeVTGKqoqNDOnTuVkpLy/QECApSSkqLs7Gy/KuDPMcvLy1VcXOyyAAAAAIAvfApDhYWFqqysVEREhMv6iIgI5efn+1UBf465cOFChYeHO5eYmBi/XhsAAACAeV2Ss8nNnj1bp06dci5Hjhxp6SoBAAAAuMS086WwzWZTYGCgCgoKXNYXFBR4nByhOY5ptVo9jj8CAAAAgIbwqWUoKChIgwcPVmZmpnNdVVWVMjMzlZyc7FcFmuOYAAAAAFAfn1qGJCk9PV2TJk3SkCFDNGzYMK1cuVIlJSWaMmWKJGnixInq3r27Fi5cKKl6goQvvvjC+f9vvvlGe/bsUceOHXXFFVc06JgAAAAA0NR8DkNjxozRiRMnNHfuXOXn5ysxMVEZGRnOCRDy8vIUEPB9g9PRo0d11VVXOb9eunSpli5dqhEjRigrK6tBxwQAAACApuZzGJKktLQ0paWlud1WE3BqxMbGyjCMRh0TAAAAAJraJTmbHAAAAAA0FmEIAAAAgCkRhgAAAACYEmEIAAAAgCkRhgAAAACYEmEIAAAAgCkRhgAAAACYEmEIAAAAgCkRhgAAAACYEmEIAAAAgCkRhgAAAACYEmEIAAAAgCkRhgAAAACYEmEIAAAAgCkRhgAAAACYEmEIAAAAgCkRhgAAAACYEmEIAAAAgCkRhgAAAACYEmEIAAAAgCkRhgAAAACYEmEIAAAAgCkRhgAAAACYEmEIAAAAgCkRhgAAAACYEmEIAAAAgCkRhgAAAACYEmEIAAAAgCkRhgAAAACYEmEIAAAAgCkRhgAAAACYEmEIAAAAgCkRhgAAAACYEmEIAAAAgCkRhgAAAACYEmEIAAAAgCkRhgAAAACYEmEIAAAAgCkRhgAAAACYEmEIAAAAgCkRhgAAAACYEmEIAAAAgCkRhgAAAACYEmEIAAAAgCkRhgAAAACYEmEIAAAAgCkRhgAAAACYEmEIAAAAgCkRhgAAAACYEmEIAAAAgCkRhgAAAACYkl9haPXq1YqNjVVwcLCSkpK0Y8cOr+VfeeUV9enTR8HBwerfv7+2bt3qsn3y5MmyWCwuy6hRo/ypGgAAAAA0iM9haPPmzUpPT9e8efO0a9cuDRw4UKmpqTp+/Ljb8tu3b9e4ceN01113affu3Ro9erRGjx6tzz//3KXcqFGjdOzYMefy8ssv+3dGAAAAANAAPoeh5cuXa+rUqZoyZYr69u2rNWvWKDQ0VGvXrnVb/o9//KNGjRqlWbNmKSEhQQsWLNCgQYP09NNPu5SzWq2KjIx0Ll26dPHvjAAAAACgAXwKQxUVFdq5c6dSUlK+P0BAgFJSUpSdne12n+zsbJfykpSamlqnfFZWlrp166b4+HhNnz5dJ0+e9FiP8vJyFRcXuywAAAAA4AufwlBhYaEqKysVERHhsj4iIkL5+flu98nPz6+3/KhRo/TCCy8oMzNTixcv1j/+8Q/deOONqqysdHvMhQsXKjw83LnExMT4choAAAAAoHYtXQFJGjt2rPP//fv314ABA9SrVy9lZWXpuuuuq1N+9uzZSk9Pd35dXFxMIAIAAADgE59ahmw2mwIDA1VQUOCyvqCgQJGRkW73iYyM9Km8JPXs2VM2m00HDx50u91qtSosLMxlAQAAAABf+BSGgoKCNHjwYGVmZjrXVVVVKTMzU8nJyW73SU5OdikvSe+8847H8pL09ddf6+TJk4qKivKlegAAAADQYD7PJpeenq7nnntO69evl8Ph0PTp01VSUqIpU6ZIkiZOnKjZs2c7y8+YMUMZGRlatmyZ9u3bp/nz5+uTTz5RWlqaJOnMmTOaNWuWPvroIx0+fFiZmZm67bbbdMUVVyg1NbWJThMAAAAAXPk8ZmjMmDE6ceKE5s6dq/z8fCUmJiojI8M5SUJeXp4CAr7PWFdffbVeeuklPfLII3rooYfUu3dvbdmyRT/+8Y8lSYGBgfr000+1fv16FRUVKTo6WjfccIMWLFggq9XaRKcJAAAAAK78mkAhLS3N2bJTW1ZWVp11v/zlL/XLX/7SbfmQkBC99dZb/lQDAAAAAPzmczc5AAAAAGgLCEMAAAAATIkwBAAAAMCUCEMAAAAATIkwBAAAAMCUCEMAAAAATIkwBAAAAMCUCEMAAAAATIkwBAAAAMCUCEMAAAAATIkwBAAAAMCUCEMAAAAATIkwBAAAAMCUCEMAAAAATIkwBAAAAMCUCEMAAAAATIkwBAAAAMCUCEMAAAAATIkwBAAAAMCUCEMAAAAATIkwBAAAAMCUCEMAAAAATIkwBAAAAMCUCEMAAAAATIkwBAAAAMCUCEMAAAAATIkwBAAAAMCUCEMAAAAATIkwBAAAAMCUCEMAAAAATIkwBAAAAMCUCEMAAAAATIkwBAAAAMCUCEMAAAAATIkwBAAAAMCUCEMAAAAATIkwBAAAAMCUCEMAAAAATIkwBAAAAMCUCEMAAAAATIkwBAAAAMCUCEMAAAAATIkwBAAAAMCUCEMAAAAATIkwBAAAAMCUCEMAAAAATIkwBAAAAMCUCEMAAAAATIkwBAAAAMCUCEMAAAAATIkwBAAAAMCU/ApDq1evVmxsrIKDg5WUlKQdO3Z4Lf/KK6+oT58+Cg4OVv/+/bV161aX7YZhaO7cuYqKilJISIhSUlJ04MABf6oGAAAAAA3icxjavHmz0tPTNW/ePO3atUsDBw5Uamqqjh8/7rb89u3bNW7cON11113avXu3Ro8erdGjR+vzzz93llmyZIlWrVqlNWvWKCcnRx06dFBqaqrKysr8PzMAAAAA8MJiGIbhyw5JSUkaOnSonn76aUlSVVWVYmJidO+99+rBBx+sU37MmDEqKSnRG2+84Vz3k5/8RImJiVqzZo0Mw1B0dLT+8Ic/6L777pMknTp1ShEREVq3bp3Gjh1b55jl5eUqLy93fn3q1CnZ7XYdOXJEYWFhvpxOs9izeb9GTIvXP57dr8Qx8S1dHQAAAKBZtab73+LiYsXExKioqEjh4eHeCxs+KC8vNwIDA43XX3/dZf3EiRONW2+91e0+MTExxooVK1zWzZ071xgwYIBhGIZx6NAhQ5Kxe/dulzLXXnut8R//8R9ujzlv3jxDEgsLCwsLCwsLCwsLi9vlyJEj9eabdvJBYWGhKisrFRER4bI+IiJC+/btc7tPfn6+2/L5+fnO7TXrPJWpbfbs2UpPT3d+XVVVpW+//VaXX365LBaLL6eEH6hJ0a2lhQ1ck9aK69I6cV1aH65J68R1aX24Jk3LMAydPn1a0dHR9Zb1KQy1FlarVVar1WVd586dW6YybVBYWBjfiK0M16R14rq0TlyX1odr0jpxXVofrknTqbd73AU+TaBgs9kUGBiogoICl/UFBQWKjIx0u09kZKTX8jX/+nJMAAAAAGgsn8JQUFCQBg8erMzMTOe6qqoqZWZmKjk52e0+ycnJLuUl6Z133nGWj4uLU2RkpEuZ4uJi5eTkeDwmAAAAADSWz93k0tPTNWnSJA0ZMkTDhg3TypUrVVJSoilTpkiSJk6cqO7du2vhwoWSpBkzZmjEiBFatmyZbr75Zm3atEmffPKJnn32WUmSxWLRzJkz9fjjj6t3796Ki4vTnDlzFB0drdGjRzfdmaJeVqtV8+bNq9MFES2Ha9I6cV1aJ65L68M1aZ24Lq0P16Tl+Dy1tiQ9/fTTevLJJ5Wfn6/ExEStWrVKSUlJkqSRI0cqNjZW69atc5Z/5ZVX9Mgjj+jw4cPq3bu3lixZoptuusm53TAMzZs3T88++6yKior005/+VH/605905ZVXNv4MAQAAAMANv8IQAAAAAFzqfBozBAAAAABtBWEIAAAAgCkRhgAAAACYEmEIAAAAgCkRhkzsiSee0NVXX63Q0FB17ty5QftMnjxZFovFZRk1alTzVtRk/LkuhmFo7ty5ioqKUkhIiFJSUnTgwIHmrajJfPvttxo/frzCwsLUuXNn3XXXXTpz5ozXfUaOHFnn++Xuu+++SDVum1avXq3Y2FgFBwcrKSlJO3bs8Fr+lVdeUZ8+fRQcHKz+/ftr69atF6mm5uHLNVm3bl2d74ng4OCLWNu27/3339cvfvELRUdHy2KxaMuWLfXuk5WVpUGDBslqteqKK65wmREYTcPX65KVlVXne8VisSg/P//iVNhECEMmVlFRoV/+8peaPn26T/uNGjVKx44dcy4vv/xyM9XQnPy5LkuWLNGqVau0Zs0a5eTkqEOHDkpNTVVZWVkz1tRcxo8fr7179+qdd97RG2+8offff1/Tpk2rd7+pU6e6fL8sWbLkItS2bdq8ebPS09M1b9487dq1SwMHDlRqaqqOHz/utvz27ds1btw43XXXXdq9e7dGjx6t0aNH6/PPP7/INW+7fL0mkhQWFubyPfHVV19dxBq3fSUlJRo4cKBWr17doPK5ubm6+eab9bOf/Ux79uzRzJkz9bvf/U5vvfVWM9fUXHy9LjX279/v8v3SrVu3ZqqhiRkwvb/85S9GeHh4g8pOmjTJuO2225q1PqjW0OtSVVVlREZGGk8++aRzXVFRkWG1Wo2XX365GWtoHl988YUhyfj444+d6/73f//XsFgsxjfffONxvxEjRhgzZsy4CDU0h2HDhhn//u//7vy6srLSiI6ONhYuXOi2/K9+9Svj5ptvdlmXlJRk/P73v2/WepqJr9fEl983aDxJxuuvv+61zP3332/069fPZd2YMWOM1NTUZqyZuTXkurz33nuGJOO77767KHUyM1qG4LOsrCx169ZN8fHxmj59uk6ePNnSVTK13Nxc5efnKyUlxbkuPDxcSUlJys7ObsGatR3Z2dnq3LmzhgwZ4lyXkpKigIAA5eTkeN1348aNstls+vGPf6zZs2ertLS0uavbJlVUVGjnzp0un/OAgAClpKR4/JxnZ2e7lJek1NRUvi+aiD/XRJLOnDmjHj16KCYmRrfddpv27t17MaoLD/g+ad0SExMVFRWl66+/Xh9++GFLV6dNatfSFcClZdSoUbr99tsVFxenQ4cO6aGHHtKNN96o7OxsBQYGtnT1TKmm/3BERITL+oiICPoWN5H8/Pw6XRPatWunyy67zOt7/Otf/1o9evRQdHS0Pv30Uz3wwAPav3+/XnvtteaucptTWFioyspKt5/zffv2ud0nPz+f74tm5M81iY+P19q1azVgwACdOnVKS5cu1dVXX629e/fqRz/60cWoNmrx9H1SXFyss2fPKiQkpIVqZm5RUVFas2aNhgwZovLycj3//PMaOXKkcnJyNGjQoJauXptCGGpjHnzwQS1evNhrGYfDoT59+vh1/LFjxzr/379/fw0YMEC9evVSVlaWrrvuOr+OaQbNfV3gn4ZeF3/9cExR//79FRUVpeuuu06HDh1Sr169/D4ucKlKTk5WcnKy8+urr75aCQkJ+q//+i8tWLCgBWsGtC7x8fGKj493fn311Vfr0KFDWrFihTZs2NCCNWt7CENtzB/+8AdNnjzZa5mePXs22ev17NlTNptNBw8eJAx50ZzXJTIyUpJUUFCgqKgo5/qCggIlJib6dUyzaOh1iYyMrDMg/Pz58/r222+d739DJCUlSZIOHjxIGPKRzWZTYGCgCgoKXNYXFBR4vAaRkZE+lYdv/LkmtbVv315XXXWVDh482BxVRAN4+j4JCwujVaiVGTZsmP75z3+2dDXaHMJQG9O1a1d17dr1or3e119/rZMnT7rchKOu5rwucXFxioyMVGZmpjP8FBcXKycnx+eZAs2modclOTlZRUVF2rlzpwYPHixJ2rZtm6qqqpwBpyH27NkjSXy/+CEoKEiDBw9WZmamRo8eLUmqqqpSZmam0tLS3O6TnJyszMxMzZw507nunXfecWmZgP/8uSa1VVZW6rPPPtNNN93UjDWFN8nJyXWmnOf7pHXas2cPvz+aQ0vP4ICW89VXXxm7d+82Hn30UaNjx47G7t27jd27dxunT592lomPjzdee+01wzAM4/Tp08Z9991nZGdnG7m5uca7775rDBo0yOjdu7dRVlbWUqfR5vh6XQzDMBYtWmR07tzZ+Nvf/mZ8+umnxm233WbExcUZZ8+ebYlTaJNGjRplXHXVVUZOTo7xz3/+0+jdu7cxbtw45/avv/7aiI+PN3JycgzDMIyDBw8ajz32mPHJJ58Yubm5xt/+9jejZ8+exrXXXttSp3DJ27Rpk2G1Wo1169YZX3zxhTFt2jSjc+fORn5+vmEYhvGb3/zGePDBB53lP/zwQ6Ndu3bG0qVLDYfDYcybN89o37698dlnn7XUKbQ5vl6TRx991HjrrbeMQ4cOGTt37jTGjh1rBAcHG3v37m2pU2hzTp8+7fy9IclYvny5sXv3buOrr74yDMMwHnzwQeM3v/mNs/yXX35phIaGGrNmzTIcDoexevVqIzAw0MjIyGipU2iTfL0uK1asMLZs2WIcOHDA+Oyzz4wZM2YYAQEBxrvvvttSp9BmEYZMbNKkSYakOst7773nLCPJ+Mtf/mIYhmGUlpYaN9xwg9G1a1ejffv2Ro8ePYypU6c6f+mhafh6XQyjenrtOXPmGBEREYbVajWuu+46Y//+/Re/8m3YyZMnjXHjxhkdO3Y0wsLCjClTprgE1NzcXJfrlJeXZ1x77bXGZZddZlitVuOKK64wZs2aZZw6daqFzqBteOqppwy73W4EBQUZw4YNMz766CPnthEjRhiTJk1yKf/Xv/7VuPLKK42goCCjX79+xptvvnmRa9z2+XJNZs6c6SwbERFh3HTTTcauXbtaoNZtV82UzLWXmuswadIkY8SIEXX2SUxMNIKCgoyePXu6/H5B0/D1uixevNjo1auXERwcbFx22WXGyJEjjW3btrVM5ds4i2EYxkVrhgIAAACAVoLnDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwpf8PO7qy/dyL0XkAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MET_rel\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "axial_MET\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "M_R\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "M_TR_2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MT2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "S_R\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "M_Delta_R\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dPhi_r_b\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cos_theta_r1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "low_level_labels = ['l_1_pT', 'l_1_eta', 'l_1_phi', 'l_2_pT', 'l_2_eta', 'l_2_phi', 'MET', 'MET_phi']\n",
        "high_level_labels = ['MET_rel', 'axial_MET', 'M_R', 'M_TR_2', 'R', 'MT2', 'S_R', 'M_Delta_R', 'dPhi_r_b', 'cos_theta_r1'] #separated column names into low and high levels\n",
        "\n",
        "fig, ax = plt.subplots(1, 1, figsize=(10, 10)) #subplot with figure size set to 10,10\n",
        "\n",
        "ax.bar(range(8), df[df.columns[1]][0:8])\n",
        "ax.set_xticks(range(8))\n",
        "ax.set_xticklabels(low_level_labels, rotation='vertical', fontsize=10)\n",
        "ax.set_ylabel(df.columns[1])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 892
        },
        "id": "tLzxH19AjKcb",
        "outputId": "78047ae7-277d-4b61-8e86-b33b932597e6"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'l_1_pT')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n",
        "\n",
        "ax.bar(range(10), df[df.columns[1]][8:18])\n",
        "ax.set_xticks(range(10))\n",
        "ax.set_xticklabels(high_level_labels, rotation='vertical', fontsize=10)\n",
        "ax.set_ylabel(df.columns[1])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 923
        },
        "id": "zMQ9WFixjYQO",
        "outputId": "6ad7c5f4-88b6-4770-c475-9ddd5865353d"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'l_1_pT')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def makepairplots(df, labels):\n",
        "    num_features = len(labels)#number of features\n",
        "\n",
        "    fig, axes = plt.subplots(num_features, num_features, figsize=(15, 15))#creates figure and array of subplots\n",
        "\n",
        "    fig.subplots_adjust(hspace=0.5, wspace=0.5)\n",
        "\n",
        "    for i in range(num_features):\n",
        "        for j in range(num_features):#loop per column\n",
        "            if i == j:#on diagonal\n",
        "                axes[i, j].hist(df[df.columns[i]], bins=30, color='skyblue', edgecolor='black')#plots histogram of column val\n",
        "                axes[i, j].set_xlabel(labels[i])\n",
        "                axes[i, j].set_ylabel('Frequency')\n",
        "            else:#nondiagonal scatterplot\n",
        "                axes[i, j].scatter(df[df.columns[j]], df[df.columns[i]], s=1, alpha=0.5, color='orange') #plots scatterplot to value of 2 columns\n",
        "                axes[i, j].set_xlabel(labels[j]) #sets x axis label to feature name\n",
        "                axes[i, j].set_ylabel(labels[i])\n",
        "\n",
        "    plt.tight_layout() #this changes subplot paramters\n",
        "    plt.show()\n",
        "\n",
        "labels = ['l_1_pT', 'l_1_eta', 'l_1_phi', 'l_2_pT', 'l_2_eta', 'l_2_phi', 'MET', 'MET_phi',\n",
        "          'MET_rel', 'axial_MET', 'M_R', 'M_TR_2', 'R', 'MT2', 'S_R', 'M_Delta_R', 'dPhi_r_b', 'cos_theta_r1'] #labels for the features\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "w3Cw_q-yjdn_"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "makespairplots(df, labels)\n",
        "#tried to run this and \"session crashed after using all available RAM\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        },
        "id": "ebTlE2vXkBeC",
        "outputId": "6ffe0960-147c-497f-8f87-00edbd878f18"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-9-bc2b7433f0d5>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcreate_pair_plots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\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      2\u001b[0m \u001b[0;31m#tried to run this and \"session crashed after using all available RAM\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-8-cc0db1a620b7>\u001b[0m in \u001b[0;36mcreate_pair_plots\u001b[0;34m(df, labels)\u001b[0m\n\u001b[1;32m     15\u001b[0m                 \u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Frequency'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\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---> 17\u001b[0;31m                 \u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'orange'\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     18\u001b[0m                 \u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\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     19\u001b[0m                 \u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\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/python3.10/dist-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1440\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\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[1;32m   1441\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\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[0;32m-> 1442\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\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[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   1443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1444\u001b[0m         \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\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[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[1;32m   4709\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ymargin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.05\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4710\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4711\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_collection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcollection\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   4712\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_request_autoscale_view\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   4713\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36madd_collection\u001b[0;34m(self, collection, autolim)\u001b[0m\n\u001b[1;32m   2262\u001b[0m             \u001b[0;31m# pre-lazy-autoscale behavior, which is not really better).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2263\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_unstale_viewLim\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-> 2264\u001b[0;31m             \u001b[0mdatalim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcollection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_datalim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransData\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   2265\u001b[0m             \u001b[0mpoints\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatalim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_points\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   2266\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misinf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdatalim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminpos\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\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/usr/local/lib/python3.10/dist-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mget_datalim\u001b[0;34m(self, transData)\u001b[0m\n\u001b[1;32m    293\u001b[0m             \u001b[0moffsets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0moffset_trf\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mtransData\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moffsets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m             \u001b[0;31m# note A-B means A B^{-1}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m             \u001b[0moffsets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmasked_invalid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moffsets\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    296\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0moffsets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\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    297\u001b[0m                 \u001b[0mbbox\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransforms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnull\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/python3.10/dist-packages/numpy/ma/core.py\u001b[0m in \u001b[0;36mmasked_invalid\u001b[0;34m(a, copy)\u001b[0m\n\u001b[1;32m   2358\u001b[0m     \"\"\"\n\u001b[1;32m   2359\u001b[0m     \u001b[0ma\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[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubok\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[0;32m-> 2360\u001b[0;31m     \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmasked_where\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\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[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2361\u001b[0m     \u001b[0;31m# masked_invalid previously never returned nomask as a mask and doing so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2362\u001b[0m     \u001b[0;31m# threw off matplotlib (gh-22842).  So use shrink=False:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/ma/core.py\u001b[0m in \u001b[0;36mmasked_where\u001b[0;34m(condition, a, copy)\u001b[0m\n\u001b[1;32m   1934\u001b[0m                          \" (got %s and %s)\" % (cshape, ashape))\n\u001b[1;32m   1935\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'_mask'\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-> 1936\u001b[0;31m         \u001b[0mcond\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmask_or\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcond\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mask\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   1937\u001b[0m         \u001b[0mcls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1938\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/usr/local/lib/python3.10/dist-packages/numpy/ma/core.py\u001b[0m in \u001b[0;36mmask_or\u001b[0;34m(m1, m2, copy, shrink)\u001b[0m\n\u001b[1;32m   1755\u001b[0m         \u001b[0m_recursive_mask_or\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mm2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnewmask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1756\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnewmask\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1757\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mmake_mask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mumath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogical_or\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mm2\u001b[0m\u001b[0;34m)\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[0mshrink\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshrink\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   1758\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1759\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error in callback <function flush_figures at 0x79a6c7228280> (for post_execute):\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib_inline/backend_inline.py\u001b[0m in \u001b[0;36mflush_figures\u001b[0;34m()\u001b[0m\n\u001b[1;32m    124\u001b[0m             \u001b[0;31m# ignore the tracking, just draw and close all figures\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\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--> 126\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mshow\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    127\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m                 \u001b[0;31m# safely show traceback if in IPython, else raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib_inline/backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m     88\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[1;32m     89\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mfigure_manager\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mGcf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_all_fig_managers\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---> 90\u001b[0;31m             display(\n\u001b[0m\u001b[1;32m     91\u001b[0m                 \u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m                 \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_fetch_figure_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\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/python3.10/dist-packages/IPython/core/display.py\u001b[0m in \u001b[0;36mdisplay\u001b[0;34m(include, exclude, metadata, transient, display_id, *objs, **kwargs)\u001b[0m\n\u001b[1;32m    318\u001b[0m             \u001b[0mpublish_display_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetadata\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[1;32m    319\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--> 320\u001b[0;31m             \u001b[0mformat_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmd_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexclude\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    321\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mformat_dict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    322\u001b[0m                 \u001b[0;31m# nothing to display (e.g. _ipython_display_ took over)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mformat\u001b[0;34m(self, obj, include, exclude)\u001b[0m\n\u001b[1;32m    178\u001b[0m             \u001b[0mmd\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    179\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--> 180\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\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    181\u001b[0m             \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m                 \u001b[0;31m# FIXME: log the exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<decorator-gen-2>\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mcatch_format_error\u001b[0;34m(method, self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    222\u001b[0m     \u001b[0;34m\"\"\"show traceback on failed format call\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\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--> 224\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\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[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    225\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m         \u001b[0;31m# don't warn on NotImplementedErrors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    339\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\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--> 341\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\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    342\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\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/python3.10/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m    149\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\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    152\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\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    153\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\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/python3.10/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2364\u001b[0m                 \u001b[0;31m# force the figure dpi to 72), so we need to set it again here.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2365\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdpi\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-> 2366\u001b[0;31m                     result = print_method(\n\u001b[0m\u001b[1;32m   2367\u001b[0m                         \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2368\u001b[0m                         \u001b[0mfacecolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfacecolor\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/python3.10/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   2230\u001b[0m                 \"bbox_inches_restore\"}\n\u001b[1;32m   2231\u001b[0m             \u001b[0mskip\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptional_kws\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minspect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmeth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2232\u001b[0;31m             print_method = functools.wraps(meth)(lambda *args, **kwargs: meth(\n\u001b[0m\u001b[1;32m   2233\u001b[0m                 *args, **{k: v for k, v in kwargs.items() if k not in skip}))\n\u001b[1;32m   2234\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Let third-parties do as they see fit.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, metadata, pil_kwargs)\u001b[0m\n\u001b[1;32m    507\u001b[0m             \u001b[0;34m*\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mincluding\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdefault\u001b[0m \u001b[0;34m'Software'\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    508\u001b[0m         \"\"\"\n\u001b[0;32m--> 509\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_print_pil\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"png\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpil_kwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\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    510\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    511\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprint_to_buffer\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/python3.10/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36m_print_pil\u001b[0;34m(self, filename_or_obj, fmt, pil_kwargs, metadata)\u001b[0m\n\u001b[1;32m    455\u001b[0m         *pil_kwargs* and *metadata* are forwarded).\n\u001b[1;32m    456\u001b[0m         \"\"\"\n\u001b[0;32m--> 457\u001b[0;31m         \u001b[0mFigureCanvasAgg\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[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    458\u001b[0m         mpl.image.imsave(\n\u001b[1;32m    459\u001b[0m             \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfmt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morigin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"upper\"\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/python3.10/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    398\u001b[0m              (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar\n\u001b[1;32m    399\u001b[0m               else nullcontext()):\n\u001b[0;32m--> 400\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    401\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    402\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/python3.10/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     93\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     94\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---> 95\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     96\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     97\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/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\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     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\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     73\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     74\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/python3.10/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3138\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3139\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\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[0;32m-> 3140\u001b[0;31m             mimage._draw_list_compositing_images(\n\u001b[0m\u001b[1;32m   3141\u001b[0m                 renderer, self, artists, self.suppressComposite)\n\u001b[1;32m   3142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\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    132\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    133\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\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     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\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     73\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     74\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/python3.10/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3062\u001b[0m             \u001b[0m_draw_rasterized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists_rasterized\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[1;32m   3063\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3064\u001b[0;31m         mimage._draw_list_compositing_images(\n\u001b[0m\u001b[1;32m   3065\u001b[0m             renderer, self, artists, self.figure.suppressComposite)\n\u001b[1;32m   3066\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\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    132\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    133\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\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     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\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     73\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     74\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/python3.10/dist-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    970\u001b[0m     \u001b[0;32mdef\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    971\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_sizes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sizes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 972\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\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    973\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    974\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\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     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\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     73\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     74\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/python3.10/dist-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    403\u001b[0m             \u001b[0mgc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_antialiased\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_antialiaseds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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    404\u001b[0m             \u001b[0mgc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_url\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_urls\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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--> 405\u001b[0;31m             renderer.draw_markers(\n\u001b[0m\u001b[1;32m    406\u001b[0m                 \u001b[0mgc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpaths\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcombined_transform\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrozen\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    407\u001b[0m                 mpath.Path(offsets), offset_trf, tuple(facecolors[0]))\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#don't know how to make this run faster since it doesn't run at all"
      ],
      "metadata": {
        "id": "6snDjVHfnZnW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#as far as observables go i can't figure it out since it wont run"
      ],
      "metadata": {
        "id": "XzwUaxvSncn9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import HTML, display\n",
        "import tabulate"
      ],
      "metadata": {
        "id": "jnspoa_2qGXd"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#4.2.2\n",
        "import numpy as np\n",
        "\n",
        "all = np.random.rand(100, 10)  # 100 samples, 10 observables\n",
        "low_high_features = np.random.rand(100, 5)  # 100 samples, 5 low-level features and 5 high-level features\n",
        "\n",
        "covariance_all = np.cov(all, rowvar=False)#covariance and correlation matrices\n",
        "correlation_all = np.corrcoef(all, rowvar=False)\n",
        "\n",
        "low_features = low_high_features[:, :5]  # takes low-level features\n",
        "high_features = low_high_features[:, 5:]\n",
        "\n",
        "covariance_low = np.cov(low_features, rowvar=False) #computes covariance and correlation coefficient matrixs with the feature(low_features in this case)\n",
        "correlation_low = np.corrcoef(low_features, rowvar=False)#rowvar is the argument that distinguishes between row and column so in this each volumn represents a variable, and if it was true it would be viceversa\n",
        "\n",
        "covariance_high = np.cov(high_features, rowvar=False)\n",
        "correlation_high = np.corrcoef(high_features, rowvar=False)\n",
        "\n",
        "\n",
        "print(\"Covariance matrix for all observables:\")#prints matrices\n",
        "print(covariance_all)\n",
        "print(\"\\nCorrelation matrix for all observables:\")\n",
        "print(correlation_all)\n",
        "\n",
        "print(\"\\nCovariance matrix for low-level features:\")\n",
        "print(covariance_low)\n",
        "print(\"\\nCorrelation matrix for low-level features:\")\n",
        "print(correlation_low)\n",
        "\n",
        "print(\"\\nCovariance matrix for high-level features:\")\n",
        "print(covariance_high)\n",
        "print(\"\\nCorrelation matrix for high-level features:\")\n",
        "print(correlation_high)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oQrVolcZpU9b",
        "outputId": "bcec7dba-ba94-4b40-e884-772ef996c647"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Covariance matrix for all observables:\n",
            "[[ 0.08469659  0.01196607  0.00252939 -0.01160059  0.00468522  0.00374874\n",
            "   0.01660509  0.0108433   0.00134604  0.00028744]\n",
            " [ 0.01196607  0.08581347  0.00739688  0.01217437 -0.00103154  0.00084888\n",
            "   0.00441411 -0.00136206  0.00979531  0.0042584 ]\n",
            " [ 0.00252939  0.00739688  0.08349413 -0.00409494 -0.00253645  0.00258271\n",
            "  -0.00649496  0.01316204  0.0075742   0.00377076]\n",
            " [-0.01160059  0.01217437 -0.00409494  0.07842982  0.00064001 -0.0051285\n",
            "   0.00573935 -0.00819277  0.00432461  0.00731707]\n",
            " [ 0.00468522 -0.00103154 -0.00253645  0.00064001  0.07424163  0.00226116\n",
            "   0.00487632 -0.0019647   0.00660158  0.00314328]\n",
            " [ 0.00374874  0.00084888  0.00258271 -0.0051285   0.00226116  0.07327075\n",
            "  -0.00284417 -0.00778933 -0.0065829   0.00253518]\n",
            " [ 0.01660509  0.00441411 -0.00649496  0.00573935  0.00487632 -0.00284417\n",
            "   0.07871923 -0.00408559 -0.00511622  0.01004255]\n",
            " [ 0.0108433  -0.00136206  0.01316204 -0.00819277 -0.0019647  -0.00778933\n",
            "  -0.00408559  0.07469276  0.00110135 -0.00680706]\n",
            " [ 0.00134604  0.00979531  0.0075742   0.00432461  0.00660158 -0.0065829\n",
            "  -0.00511622  0.00110135  0.09519024  0.01235445]\n",
            " [ 0.00028744  0.0042584   0.00377076  0.00731707  0.00314328  0.00253518\n",
            "   0.01004255 -0.00680706  0.01235445  0.09570591]]\n",
            "\n",
            "Correlation matrix for all observables:\n",
            "[[ 1.          0.14035924  0.03007837 -0.14233326  0.05908448  0.0475868\n",
            "   0.20336106  0.13632934  0.01499091  0.00319259]\n",
            " [ 0.14035924  1.          0.08738616  0.14839808 -0.01292359  0.01070543\n",
            "   0.05370629 -0.01701291  0.10837881  0.04698936]\n",
            " [ 0.03007837  0.08738616  1.         -0.0506033  -0.03221619  0.03302046\n",
            "  -0.08011391  0.1666694   0.08495968  0.04218245]\n",
            " [-0.14233326  0.14839808 -0.0506033   1.          0.00838726 -0.06765253\n",
            "   0.07304346 -0.10704122  0.05005066  0.0844553 ]\n",
            " [ 0.05908448 -0.01292359 -0.03221619  0.00838726  1.          0.03065787\n",
            "   0.06378642 -0.0263836   0.07852861  0.03728982]\n",
            " [ 0.0475868   0.01070543  0.03302046 -0.06765253  0.03065787  1.\n",
            "  -0.03744984 -0.10529206 -0.07882347  0.03027433]\n",
            " [ 0.20336106  0.05370629 -0.08011391  0.07304346  0.06378642 -0.03744984\n",
            "   1.         -0.0532814  -0.05910345  0.11570019]\n",
            " [ 0.13632934 -0.01701291  0.1666694  -0.10704122 -0.0263836  -0.10529206\n",
            "  -0.0532814   1.          0.01306138 -0.08051019]\n",
            " [ 0.01499091  0.10837881  0.08495968  0.05005066  0.07852861 -0.07882347\n",
            "  -0.05910345  0.01306138  1.          0.12943678]\n",
            " [ 0.00319259  0.04698936  0.04218245  0.0844553   0.03728982  0.03027433\n",
            "   0.11570019 -0.08051019  0.12943678  1.        ]]\n",
            "\n",
            "Covariance matrix for low-level features:\n",
            "[[ 0.08200184 -0.01016233 -0.00707398  0.01552654  0.00103028]\n",
            " [-0.01016233  0.09276399 -0.00790467  0.00338834  0.01105871]\n",
            " [-0.00707398 -0.00790467  0.07883713 -0.00737401 -0.00845924]\n",
            " [ 0.01552654  0.00338834 -0.00737401  0.08361152 -0.00325187]\n",
            " [ 0.00103028  0.01105871 -0.00845924 -0.00325187  0.08619198]]\n",
            "\n",
            "Correlation matrix for low-level features:\n",
            "[[ 1.         -0.11651767 -0.08798052  0.18751236  0.01225494]\n",
            " [-0.11651767  1.         -0.0924333   0.03847377  0.12367482]\n",
            " [-0.08798052 -0.0924333   1.         -0.09082501 -0.10262014]\n",
            " [ 0.18751236  0.03847377 -0.09082501  1.         -0.03830604]\n",
            " [ 0.01225494  0.12367482 -0.10262014 -0.03830604  1.        ]]\n",
            "\n",
            "Covariance matrix for high-level features:\n",
            "[]\n",
            "\n",
            "Correlation matrix for high-level features:\n",
            "[]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from tabulate import tabulate\n",
        "#import libraries\n",
        "\n",
        "\n",
        "\n",
        "covariance_all = np.cov(all, rowvar=False)\n",
        "correlation_all = np.corrcoef(all, rowvar=False)\n",
        "covariance_low = np.cov(low_features, rowvar=False)\n",
        "correlation_low = np.corrcoef(low_features, rowvar=False)\n",
        "covariance_high = np.cov(high_features, rowvar=False)\n",
        "correlation_high = np.corrcoef(high_features, rowvar=False)\n",
        "\n",
        "#copied from above, those were the coorelation and covariance matrices\n",
        "\n",
        "\n",
        "headers = [\"Feature\"] + [f\"Feature {i+1}\" for i in range(len(all))]#creates header per feature for the amount of features in the data\n",
        "table_cov_all = tabulate(covariance_all, headers=headers, floatfmt=\".3f\", tablefmt=\"fancy_grid\")#table for covariance\n",
        "table_corr_all = tabulate(correlation_all, headers=headers, floatfmt=\".3f\", tablefmt=\"fancy_grid\")#correlation table formats the values in the table with three decimal places and specific format fancy grid\n",
        "table_cov_low = tabulate(covariance_low, headers=headers, floatfmt=\".3f\", tablefmt=\"fancy_grid\")\n",
        "table_corr_low = tabulate(correlation_low, headers=headers, floatfmt=\".3f\", tablefmt=\"fancy_grid\")#low level feature table\n",
        "table_cov_high = tabulate(covariance_high, headers=headers, floatfmt=\".3f\", tablefmt=\"fancy_grid\")#high level feature table\n",
        "table_corr_high = tabulate(correlation_high, headers=headers, floatfmt=\".3f\", tablefmt=\"fancy_grid\")#high level correlation\n",
        "\n",
        "print(\"Covariance Matrix of All Observables:\")#all of these embed them into the notebook with nic headings\n",
        "print(table_cov_all)\n",
        "print(\"\\nCorrelation Matrix of All Observables:\")\n",
        "print(table_corr_all)\n",
        "\n",
        "print(\"\\nCovariance Matrix of Low-level Features:\")\n",
        "print(table_cov_low)\n",
        "print(\"\\nCorrelation Matrix of Low-level Features:\")\n",
        "print(table_corr_low)\n",
        "\n",
        "print(\"\\nCovariance Matrix of High-level Features:\")\n",
        "print(table_cov_high)\n",
        "print(\"\\nCorrelation Matrix of High-level Features:\")\n",
        "print(table_corr_high)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9-tSl_NXqIdi",
        "outputId": "ae2037e2-edad-45b9-f386-2de702421625"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Covariance Matrix of All Observables:\n",
            "╒═══════════╤═════════════╤═════════════╤═════════════╤═════════════╤═════════════╤═════════════╤═════════════╤═════════════╤═════════════╕\n",
            "│   Feature │   Feature 1 │   Feature 2 │   Feature 3 │   Feature 4 │   Feature 5 │   Feature 6 │   Feature 7 │   Feature 8 │   Feature 9 │\n",
            "╞═══════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╡\n",
            "│     0.079 │       0.009 │      -0.005 │       0.015 │       0.001 │      -0.013 │       0.007 │      -0.003 │       0.009 │       0.012 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│     0.009 │       0.083 │       0.014 │       0.001 │       0.002 │       0.010 │       0.005 │      -0.012 │      -0.002 │       0.016 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│    -0.005 │       0.014 │       0.081 │       0.006 │      -0.012 │       0.010 │      -0.001 │      -0.006 │      -0.013 │      -0.005 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│     0.015 │       0.001 │       0.006 │       0.076 │      -0.005 │       0.009 │      -0.011 │       0.012 │      -0.014 │      -0.008 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│     0.001 │       0.002 │      -0.012 │      -0.005 │       0.071 │      -0.001 │       0.001 │       0.006 │      -0.010 │       0.006 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│    -0.013 │       0.010 │       0.010 │       0.009 │      -0.001 │       0.080 │      -0.011 │      -0.004 │      -0.008 │      -0.005 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│     0.007 │       0.005 │      -0.001 │      -0.011 │       0.001 │      -0.011 │       0.086 │      -0.003 │       0.004 │      -0.000 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│    -0.003 │      -0.012 │      -0.006 │       0.012 │       0.006 │      -0.004 │      -0.003 │       0.098 │      -0.003 │       0.002 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│     0.009 │      -0.002 │      -0.013 │      -0.014 │      -0.010 │      -0.008 │       0.004 │      -0.003 │       0.082 │      -0.003 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│     0.012 │       0.016 │      -0.005 │      -0.008 │       0.006 │      -0.005 │      -0.000 │       0.002 │      -0.003 │       0.088 │\n",
            "╘═══════════╧═════════════╧═════════════╧═════════════╧═════════════╧═════════════╧═════════════╧═════════════╧═════════════╧═════════════╛\n",
            "\n",
            "Correlation Matrix of All Observables:\n",
            "╒═══════════╤═════════════╤═════════════╤═════════════╤═════════════╤═════════════╤═════════════╤═════════════╤═════════════╤═════════════╕\n",
            "│   Feature │   Feature 1 │   Feature 2 │   Feature 3 │   Feature 4 │   Feature 5 │   Feature 6 │   Feature 7 │   Feature 8 │   Feature 9 │\n",
            "╞═══════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╡\n",
            "│     1.000 │       0.116 │      -0.068 │       0.200 │       0.015 │      -0.159 │       0.090 │      -0.037 │       0.107 │       0.141 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│     0.116 │       1.000 │       0.165 │       0.007 │       0.032 │       0.120 │       0.053 │      -0.132 │      -0.028 │       0.187 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│    -0.068 │       0.165 │       1.000 │       0.082 │      -0.164 │       0.121 │      -0.014 │      -0.063 │      -0.164 │      -0.062 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│     0.200 │       0.007 │       0.082 │       1.000 │      -0.065 │       0.118 │      -0.139 │       0.141 │      -0.182 │      -0.102 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│     0.015 │       0.032 │      -0.164 │      -0.065 │       1.000 │      -0.014 │       0.018 │       0.077 │      -0.135 │       0.074 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│    -0.159 │       0.120 │       0.121 │       0.118 │      -0.014 │       1.000 │      -0.130 │      -0.050 │      -0.102 │      -0.058 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│     0.090 │       0.053 │      -0.014 │      -0.139 │       0.018 │      -0.130 │       1.000 │      -0.029 │       0.053 │      -0.004 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│    -0.037 │      -0.132 │      -0.063 │       0.141 │       0.077 │      -0.050 │      -0.029 │       1.000 │      -0.033 │       0.017 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│     0.107 │      -0.028 │      -0.164 │      -0.182 │      -0.135 │      -0.102 │       0.053 │      -0.033 │       1.000 │      -0.035 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│     0.141 │       0.187 │      -0.062 │      -0.102 │       0.074 │      -0.058 │      -0.004 │       0.017 │      -0.035 │       1.000 │\n",
            "╘═══════════╧═════════════╧═════════════╧═════════════╧═════════════╧═════════════╧═════════════╧═════════════╧═════════════╧═════════════╛\n",
            "\n",
            "Covariance Matrix of Low-level Features:\n",
            "╒═══════════╤═════════════╤═════════════╤═════════════╤═════════════╕\n",
            "│   Feature │   Feature 1 │   Feature 2 │   Feature 3 │   Feature 4 │\n",
            "╞═══════════╪═════════════╪═════════════╪═════════════╪═════════════╡\n",
            "│     0.083 │       0.026 │       0.022 │       0.035 │      -0.004 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│     0.026 │       0.045 │      -0.040 │       0.019 │      -0.005 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│     0.022 │      -0.040 │       0.085 │      -0.018 │       0.026 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│     0.035 │       0.019 │      -0.018 │       0.119 │      -0.081 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│    -0.004 │      -0.005 │       0.026 │      -0.081 │       0.067 │\n",
            "╘═══════════╧═════════════╧═════════════╧═════════════╧═════════════╛\n",
            "\n",
            "Correlation Matrix of Low-level Features:\n",
            "╒═══════════╤═════════════╤═════════════╤═════════════╤═════════════╕\n",
            "│   Feature │   Feature 1 │   Feature 2 │   Feature 3 │   Feature 4 │\n",
            "╞═══════════╪═════════════╪═════════════╪═════════════╪═════════════╡\n",
            "│     1.000 │       0.428 │       0.258 │       0.349 │      -0.058 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│     0.428 │       1.000 │      -0.653 │       0.265 │      -0.094 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│     0.258 │      -0.653 │       1.000 │      -0.175 │       0.342 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│     0.349 │       0.265 │      -0.175 │       1.000 │      -0.911 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│    -0.058 │      -0.094 │       0.342 │      -0.911 │       1.000 │\n",
            "╘═══════════╧═════════════╧═════════════╧═════════════╧═════════════╛\n",
            "\n",
            "Covariance Matrix of High-level Features:\n",
            "╒═══════════╤═════════════╤═════════════╤═════════════╤═════════════╕\n",
            "│   Feature │   Feature 1 │   Feature 2 │   Feature 3 │   Feature 4 │\n",
            "╞═══════════╪═════════════╪═════════════╪═════════════╪═════════════╡\n",
            "│     0.036 │      -0.020 │      -0.011 │       0.033 │       0.006 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│    -0.020 │       0.037 │       0.045 │       0.016 │       0.022 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│    -0.011 │       0.045 │       0.123 │      -0.013 │       0.063 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│     0.033 │       0.016 │      -0.013 │       0.245 │       0.005 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│     0.006 │       0.022 │       0.063 │       0.005 │       0.038 │\n",
            "╘═══════════╧═════════════╧═════════════╧═════════════╧═════════════╛\n",
            "\n",
            "Correlation Matrix of High-level Features:\n",
            "╒═══════════╤═════════════╤═════════════╤═════════════╤═════════════╕\n",
            "│   Feature │   Feature 1 │   Feature 2 │   Feature 3 │   Feature 4 │\n",
            "╞═══════════╪═════════════╪═════════════╪═════════════╪═════════════╡\n",
            "│     1.000 │      -0.555 │      -0.167 │       0.347 │       0.150 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│    -0.555 │       1.000 │       0.678 │       0.164 │       0.597 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│    -0.167 │       0.678 │       1.000 │      -0.077 │       0.919 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│     0.347 │       0.164 │      -0.077 │       1.000 │       0.051 │\n",
            "├───────────┼─────────────┼─────────────┼─────────────┼─────────────┤\n",
            "│     0.150 │       0.597 │       0.919 │       0.051 │       1.000 │\n",
            "╘═══════════╧═════════════╧═════════════╧═════════════╧═════════════╛\n"
          ]
        }
      ]
    }
  ]
}