PyWGCNA_2.ipynb

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      "mount_file_id": "1k0bD7AKLiieCmrAjXNOq0Y8L9szGa8um",
      "authorship_tag": "ABX9TyMvdduhIz87e+f3fLUFU7mK",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/YoshifumiMiyagi/PyWGCNA-1/blob/main/PyWGCNA_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "a7ZTJMKRDKNY",
        "outputId": "7b4b161d-923f-477a-c0bc-0052053c2b45"
      },
      "outputs": [
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          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting PyWGCNA==1.2.4\n",
            "  Downloading PyWGCNA-1.2.4.tar.gz (52 kB)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.10/dist-packages (from pexpect>4.3->ipython>=5.3.0->pyvis>=0.3.1->PyWGCNA==1.2.4) (0.7.0)\n",
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            "Building wheels for collected packages: PyWGCNA\n",
            "  Building wheel for PyWGCNA (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for PyWGCNA: filename=PyWGCNA-1.2.4-py3-none-any.whl size=52622 sha256=1200fa438fc0fbbece60fd4de746356781b7e7e99c61e24b168de4ff4e64f958\n",
            "  Stored in directory: /root/.cache/pip/wheels/c3/d4/c7/bf9fd325da2e4edaa808e94f596e9919f49587c0ba6905e8de\n",
            "Successfully built PyWGCNA\n",
            "Installing collected packages: numpy, statsmodels, PyWGCNA\n",
            "  Attempting uninstall: numpy\n",
            "    Found existing installation: numpy 1.23.2\n",
            "    Uninstalling numpy-1.23.2:\n",
            "      Successfully uninstalled numpy-1.23.2\n",
            "  Attempting uninstall: statsmodels\n",
            "    Found existing installation: statsmodels 0.13.5\n",
            "    Uninstalling statsmodels-0.13.5:\n",
            "      Successfully uninstalled statsmodels-0.13.5\n",
            "  Attempting uninstall: PyWGCNA\n",
            "    Found existing installation: PyWGCNA 1.16.8\n",
            "    Uninstalling PyWGCNA-1.16.8:\n",
            "      Successfully uninstalled PyWGCNA-1.16.8\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "numba 0.56.4 requires numpy<1.24,>=1.18, but you have numpy 1.25.0 which is incompatible.\n",
            "tensorflow 2.12.0 requires numpy<1.24,>=1.22, but you have numpy 1.25.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed PyWGCNA-1.2.4 numpy-1.25.0 statsmodels-0.14.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "PyWGCNA",
                  "numpy",
                  "statsmodels"
                ]
              }
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "!pip install PyWGCNA==1.2.4"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import PyWGCNA"
      ],
      "metadata": {
        "id": "P59dLMtyDYBl"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip show PyWGCNA"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "po7N7Lp1ubw4",
        "outputId": "7f5babe2-8549-4a42-875b-8fc93faf0d10"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Name: PyWGCNA\n",
            "Version: 1.2.4\n",
            "Summary: PyWGCNA is a Python package designed to do Weighted correlation network analysis (WGCNA)\n",
            "Home-page: https://github.com/mortazavilab/PyWGCNA\n",
            "Author: Narges Rezaie\n",
            "Author-email: nargesrezaie80@gmail.com\n",
            "License: MIT\n",
            "Location: /usr/local/lib/python3.10/dist-packages\n",
            "Requires: anndata, biomart, gseapy, matplotlib, networkx, numpy, pandas, psutil, pyvis, reactome2py, requests, rsrc, scikit-learn, scipy, seaborn, setuptools, statsmodels\n",
            "Required-by: \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "geneExp = '/content/drive/MyDrive/CC_test.csv'\n",
        "pyWGCNA_CC = PyWGCNA.WGCNA(name='CC',\n",
        "                              species='homo sapiens',\n",
        "                              geneExpPath=geneExp,\n",
        "                              outputPath='',\n",
        "                              save=True)\n",
        "pyWGCNA_CC.geneExpr.to_df().head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "C0_6UrBTDl8W",
        "outputId": "8abbee02-9e91-49f7-be18-f7f5e4356830"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[92mSaving data to be True, checking requirements ...\u001b[0m\n"
          ]
        },
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              "      <th>E5</th>\n",
              "      <th>E6</th>\n",
              "      <th>E7</th>\n",
              "      <th>L1</th>\n",
              "      <th>L2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>SRR11908726</th>\n",
              "      <td>2.887549</td>\n",
              "      <td>0.049133</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.938836</td>\n",
              "      <td>1.321212</td>\n",
              "      <td>0.057450</td>\n",
              "      <td>2.602729</td>\n",
              "      <td>0.034911</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>37.695208</td>\n",
              "      <td>...</td>\n",
              "      <td>19.693493</td>\n",
              "      <td>0.380573</td>\n",
              "      <td>123296.800187</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19091.927917</td>\n",
              "      <td>161893.254693</td>\n",
              "      <td>212976.136384</td>\n",
              "      <td>384944.890664</td>\n",
              "      <td>48014.834587</td>\n",
              "      <td>49782.155568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SRR11908728</th>\n",
              "      <td>0.164905</td>\n",
              "      <td>0.149650</td>\n",
              "      <td>2.567490</td>\n",
              "      <td>3.582004</td>\n",
              "      <td>9.800229</td>\n",
              "      <td>0.262472</td>\n",
              "      <td>2.972785</td>\n",
              "      <td>2.033607</td>\n",
              "      <td>0.103079</td>\n",
              "      <td>33.646869</td>\n",
              "      <td>...</td>\n",
              "      <td>27.985240</td>\n",
              "      <td>0.796919</td>\n",
              "      <td>44002.270221</td>\n",
              "      <td>0.0</td>\n",
              "      <td>112335.030705</td>\n",
              "      <td>217368.782794</td>\n",
              "      <td>182292.648561</td>\n",
              "      <td>438514.687901</td>\n",
              "      <td>2523.422117</td>\n",
              "      <td>2963.157701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SRR11908729</th>\n",
              "      <td>2.494948</td>\n",
              "      <td>0.406838</td>\n",
              "      <td>2.427819</td>\n",
              "      <td>1.725933</td>\n",
              "      <td>9.431120</td>\n",
              "      <td>0.186145</td>\n",
              "      <td>0.390426</td>\n",
              "      <td>0.640993</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12.977781</td>\n",
              "      <td>...</td>\n",
              "      <td>21.767833</td>\n",
              "      <td>0.068506</td>\n",
              "      <td>54825.195660</td>\n",
              "      <td>0.0</td>\n",
              "      <td>119196.237787</td>\n",
              "      <td>147116.380442</td>\n",
              "      <td>176229.801934</td>\n",
              "      <td>475209.754331</td>\n",
              "      <td>11416.090483</td>\n",
              "      <td>16006.539362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SRR11908731</th>\n",
              "      <td>1.167995</td>\n",
              "      <td>0.728711</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.864071</td>\n",
              "      <td>6.978196</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.228252</td>\n",
              "      <td>0.621336</td>\n",
              "      <td>0.048673</td>\n",
              "      <td>15.129879</td>\n",
              "      <td>...</td>\n",
              "      <td>17.370438</td>\n",
              "      <td>0.179596</td>\n",
              "      <td>42084.182762</td>\n",
              "      <td>0.0</td>\n",
              "      <td>84783.219452</td>\n",
              "      <td>163109.870654</td>\n",
              "      <td>252809.040204</td>\n",
              "      <td>449908.379365</td>\n",
              "      <td>3679.524702</td>\n",
              "      <td>3625.782861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SRR11908732</th>\n",
              "      <td>1.908663</td>\n",
              "      <td>0.118097</td>\n",
              "      <td>0.771868</td>\n",
              "      <td>0.024007</td>\n",
              "      <td>1.624353</td>\n",
              "      <td>0.345220</td>\n",
              "      <td>0.781999</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.216921</td>\n",
              "      <td>19.798059</td>\n",
              "      <td>...</td>\n",
              "      <td>16.425055</td>\n",
              "      <td>0.304919</td>\n",
              "      <td>21029.479284</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5814.570857</td>\n",
              "      <td>15590.717462</td>\n",
              "      <td>318653.673402</td>\n",
              "      <td>621738.914531</td>\n",
              "      <td>11646.820554</td>\n",
              "      <td>5525.823910</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 2008 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-57fdf4c0-6fcd-4d81-9047-3bff34651d93')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "          document.querySelector('#df-57fdf4c0-6fcd-4d81-9047-3bff34651d93 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-57fdf4c0-6fcd-4d81-9047-3bff34651d93');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pyWGCNA_CC.preprocess()"
      ],
      "metadata": {
        "id": "0KlKCqh3D4AR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "outputId": "550eb115-3311-4f1c-e816-3aa582e75a7d"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m\u001b[94mPre-processing...\u001b[0m\n",
            "\tDetecting genes and samples with too many missing values...\n",
            "\tDone pre-processing..\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x1000 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pyWGCNA_CC.findModules()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "68Qlj6phlrDx",
        "outputId": "de92b4f4-70fa-4bf6-bb8b-05dff438cbab"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m\u001b[94mRun WGCNA...\u001b[0m\n",
            "\u001b[96mpickSoftThreshold: calculating connectivity for given powers...\u001b[0m\n",
            "will use block size  1673\n",
            "    Power  SFT.R.sq     slope truncated R.sq     mean(k)   median(k)  \\\n",
            "0       1  0.017472  0.123611       0.733161  288.340322  288.425466   \n",
            "1       2  0.404283  -0.70364        0.92777  102.734741    94.89349   \n",
            "2       3  0.673702 -1.152143       0.968234   44.708513   37.255009   \n",
            "3       4  0.755768 -1.493568       0.978045   22.139495   17.278734   \n",
            "4       5  0.770761 -1.763171       0.968369   12.032733    8.645089   \n",
            "5       6  0.816038 -1.864219       0.983261    7.025828    4.628054   \n",
            "6       7  0.822298 -2.033877       0.981922     4.34721    2.644642   \n",
            "7       8  0.839694 -2.093455       0.988444    2.824217    1.569788   \n",
            "8       9  0.353801 -3.371678       0.341182    1.914211    1.075661   \n",
            "9      10  0.365541 -3.363012       0.355253    1.347501    0.706975   \n",
            "10     11  0.362354 -3.214607       0.364447    0.981972    0.448292   \n",
            "11     13  0.868293 -1.909472       0.973002    0.573098    0.203491   \n",
            "12     15  0.282156 -2.588905       0.096268    0.374554    0.102003   \n",
            "13     17  0.270725 -2.340049       0.062824    0.270082    0.050576   \n",
            "14     19  0.279212  -2.27436        0.07401    0.211375    0.026312   \n",
            "\n",
            "        max(k)  \n",
            "0   558.232129  \n",
            "1   269.033273  \n",
            "2   151.317235  \n",
            "3    95.008366  \n",
            "4    63.430234  \n",
            "5    44.292747  \n",
            "6    32.004351  \n",
            "7    23.754103  \n",
            "8    18.016211  \n",
            "9    13.910226  \n",
            "10   10.902154  \n",
            "11    6.946714  \n",
            "12    5.361981  \n",
            "13    4.867579  \n",
            "14    4.477932  \n",
            "\u001b[92mNo power detected to have scale free network!\n",
            "Found the best given power which is 13.\u001b[0m\n",
            "\u001b[96mcalculating adjacency matrix ...\u001b[0m\n",
            "\tDone..\n",
            "\n",
            "\u001b[96mcalculating TOM similarity matrix ...\u001b[0m\n",
            "\tDone..\n",
            "\n",
            "\u001b[96mGoing through the merge tree...\u001b[0m\n",
            "..cutHeight not given, setting it to 0.996  ===>  99% of the (truncated) height range in dendro.\n",
            "\tDone..\n",
            "\n",
            "\u001b[96mCalculating 1 module eigengenes in given set...\u001b[0m\n",
            "\tDone..\n",
            "\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "UnboundLocalError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mUnboundLocalError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-616561ee041c>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpyWGCNA_CC\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfindModules\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/PyWGCNA/wgcna.py\u001b[0m in \u001b[0;36mfindModules\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    335\u001b[0m                                             self.datExpr.var['moduleColors']]\n\u001b[1;32m    336\u001b[0m         \u001b[0;31m# Eigengenes of the new merged modules:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 337\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMEs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmerge\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'newMEs'\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    338\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    339\u001b[0m         \u001b[0;31m# Recalculate MEs with color labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mUnboundLocalError\u001b[0m: local variable 'merge' referenced before assignment"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pyWGCNA_CC.analyseWGCNA()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "OdxrvwKzlvKx",
        "outputId": "af6c202f-f762-456e-b55d-b731a335f7ca"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m\u001b[94mAnalysing WGCNA...\u001b[0m\n",
            "\u001b[96mCalculating module trait relationship ...\u001b[0m\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-18-acf2f6ff62f3>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpyWGCNA_CC\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0manalyseWGCNA\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/PyWGCNA/wgcna.py\u001b[0m in \u001b[0;36manalyseWGCNA\u001b[0;34m(self, order, geneList, show)\u001b[0m\n\u001b[1;32m    402\u001b[0m         \u001b[0;31m# Select module probes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    403\u001b[0m         \u001b[0mmodules\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatExpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'moduleColors'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\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--> 404\u001b[0;31m         \u001b[0mmetadata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatExpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    405\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0morder\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    406\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmetadata\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0morder\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/seaborn/matrix.py\u001b[0m in \u001b[0;36mheatmap\u001b[0;34m(data, vmin, vmax, cmap, center, robust, annot, fmt, annot_kws, linewidths, linecolor, cbar, cbar_kws, cbar_ax, square, xticklabels, yticklabels, mask, ax, **kwargs)\u001b[0m\n\u001b[1;32m    457\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0msquare\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    458\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_aspect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"equal\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 459\u001b[0;31m     \u001b[0mplotter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcbar_ax\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    460\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/seaborn/matrix.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, ax, cax, kws)\u001b[0m\n\u001b[1;32m    338\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    339\u001b[0m         \u001b[0;31m# Possibly rotate them if they overlap\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 340\u001b[0;31m         \u001b[0m_draw_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\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[0m\u001b[1;32m    341\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    342\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0maxis_ticklabels_overlap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxtl\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/seaborn/utils.py\u001b[0m in \u001b[0;36m_draw_figure\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m     78\u001b[0m     \u001b[0;34m\"\"\"Force draw of a matplotlib figure, accounting for back-compat.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m     \u001b[0;31m# See https://github.com/matplotlib/matplotlib/issues/19197 for context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     81\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\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/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",
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            "\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   3127\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3129\u001b[0;31m         \u001b[0martists\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_draw_artists\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   3130\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   3131\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_gid\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/figure.py\u001b[0m in \u001b[0;36m_get_draw_artists\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    219\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0max\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_localaxes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    220\u001b[0m             \u001b[0mlocator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_axes_locator\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--> 221\u001b[0;31m             \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_aspect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlocator\u001b[0m \u001b[0;32melse\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[0m\u001b[1;32m    222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mchild\u001b[0m \u001b[0;32min\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_children\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/axes/_base.py\u001b[0m in \u001b[0;36mapply_aspect\u001b[0;34m(self, position)\u001b[0m\n\u001b[1;32m   1931\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_aspect\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1932\u001b[0m             \u001b[0mpb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mposition\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[0m\n\u001b[0;32m-> 1933\u001b[0;31m             \u001b[0mpb1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshrunk_to_aspect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_aspect\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfig_aspect\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   1934\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpb1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0manchored\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_anchor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'active'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1935\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0maspect\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"auto\"\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/transforms.py\u001b[0m in \u001b[0;36mshrunk_to_aspect\u001b[0;34m(self, box_aspect, container, fig_aspect)\u001b[0m\n\u001b[1;32m    543\u001b[0m         \"\"\"\n\u001b[1;32m    544\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mbox_aspect\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfig_aspect\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[0m\n\u001b[0;32m--> 545\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"'box_aspect' and 'fig_aspect' must be positive\"\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    546\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcontainer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    547\u001b[0m             \u001b[0mcontainer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: 'box_aspect' and 'fig_aspect' must be positive"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error in callback <function _draw_all_if_interactive at 0x7f5eab857910> (for post_execute):\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36m_draw_all_if_interactive\u001b[0;34m()\u001b[0m\n\u001b[1;32m    118\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_draw_all_if_interactive\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    119\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_interactive\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--> 120\u001b[0;31m         \u001b[0mdraw_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    121\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/_pylab_helpers.py\u001b[0m in \u001b[0;36mdraw_all\u001b[0;34m(cls, force)\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmanager\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcls\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[1;32m    131\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mforce\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mmanager\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[0mstale\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m                 \u001b[0mmanager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_idle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    133\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\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;36mdraw_idle\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2080\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_idle_drawing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2081\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_idle_draw_cntx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2082\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2084\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/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   3127\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3129\u001b[0;31m         \u001b[0martists\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_draw_artists\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   3130\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   3131\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_gid\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/figure.py\u001b[0m in \u001b[0;36m_get_draw_artists\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    219\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0max\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_localaxes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    220\u001b[0m             \u001b[0mlocator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_axes_locator\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--> 221\u001b[0;31m             \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_aspect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlocator\u001b[0m \u001b[0;32melse\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[0m\u001b[1;32m    222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mchild\u001b[0m \u001b[0;32min\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_children\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/axes/_base.py\u001b[0m in \u001b[0;36mapply_aspect\u001b[0;34m(self, position)\u001b[0m\n\u001b[1;32m   1931\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_aspect\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1932\u001b[0m             \u001b[0mpb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mposition\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[0m\n\u001b[0;32m-> 1933\u001b[0;31m             \u001b[0mpb1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshrunk_to_aspect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_aspect\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfig_aspect\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   1934\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpb1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0manchored\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_anchor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'active'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1935\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0maspect\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"auto\"\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/transforms.py\u001b[0m in \u001b[0;36mshrunk_to_aspect\u001b[0;34m(self, box_aspect, container, fig_aspect)\u001b[0m\n\u001b[1;32m    543\u001b[0m         \"\"\"\n\u001b[1;32m    544\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mbox_aspect\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfig_aspect\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[0m\n\u001b[0;32m--> 545\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"'box_aspect' and 'fig_aspect' must be positive\"\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    546\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcontainer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    547\u001b[0m             \u001b[0mcontainer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: 'box_aspect' and 'fig_aspect' must be positive"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\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   2340\u001b[0m                 )\n\u001b[1;32m   2341\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"_draw_disabled\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnullcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2342\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[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   2343\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2344\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbbox_inches\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, *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   3127\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3129\u001b[0;31m         \u001b[0martists\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_draw_artists\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   3130\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   3131\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_gid\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/figure.py\u001b[0m in \u001b[0;36m_get_draw_artists\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    219\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0max\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_localaxes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    220\u001b[0m             \u001b[0mlocator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_axes_locator\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--> 221\u001b[0;31m             \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_aspect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlocator\u001b[0m \u001b[0;32melse\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[0m\u001b[1;32m    222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mchild\u001b[0m \u001b[0;32min\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_children\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/axes/_base.py\u001b[0m in \u001b[0;36mapply_aspect\u001b[0;34m(self, position)\u001b[0m\n\u001b[1;32m   1931\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_aspect\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1932\u001b[0m             \u001b[0mpb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mposition\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[0m\n\u001b[0;32m-> 1933\u001b[0;31m             \u001b[0mpb1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshrunk_to_aspect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_aspect\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfig_aspect\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   1934\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpb1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0manchored\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_anchor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'active'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1935\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0maspect\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"auto\"\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/transforms.py\u001b[0m in \u001b[0;36mshrunk_to_aspect\u001b[0;34m(self, box_aspect, container, fig_aspect)\u001b[0m\n\u001b[1;32m    543\u001b[0m         \"\"\"\n\u001b[1;32m    544\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mbox_aspect\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfig_aspect\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[0m\n\u001b[0;32m--> 545\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"'box_aspect' and 'fig_aspect' must be positive\"\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    546\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcontainer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    547\u001b[0m             \u001b[0mcontainer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: 'box_aspect' and 'fig_aspect' must be positive"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x0 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Y4WhcwVCqvsd"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}