BPL_CHO_Fedbatch_fmpy_colab.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMA27tYVgeoIOrhB/5c0zqM",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/janpeter19/BPL_CHO_Fedbatch/blob/main/BPL_CHO_Fedbatch_fmpy_colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# BPL_CHO_Fedbatch script with FMPy\n",
        "\n",
        "The key library FMPy is installed.\n",
        "\n",
        "After the installation a small application BPL_CHO_Fedbatch is loaded and run. You can continue with this example if you like."
      ],
      "metadata": {
        "id": "UFcMzs4Y3HwH"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ID68dwep2zus",
        "outputId": "f253e3c0-0f19-4ea3-d8ec-8d070388fcfb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No LSB modules are available.\n",
            "Distributor ID:\tUbuntu\n",
            "Description:\tUbuntu 22.04.3 LTS\n",
            "Release:\t22.04\n",
            "Codename:\tjammy\n"
          ]
        }
      ],
      "source": [
        "!lsb_release -a # Actual VM Ubuntu version used by Google"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%env PYTHONPATH="
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pVDcSg-j3SSV",
        "outputId": "3a7939a8-8aa8-40b8-d6d9-885d5f0bf2a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "env: PYTHONPATH=\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ozI156o5HnMM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://repo.anaconda.com/miniconda/Miniconda3-py312_24.3.0-0-Linux-x86_64.sh\n",
        "!chmod +x Miniconda3-py312_24.3.0-0-Linux-x86_64.sh\n",
        "!bash ./Miniconda3-py312_24.3.0-0-Linux-x86_64.sh -b -f -p /usr/local\n",
        "import sys\n",
        "sys.path.append('/usr/local/lib/python3.12/site-packages/')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "04VeBmLU3UFx",
        "outputId": "e58567b2-6399-433d-cde5-ddd5704cb088"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-05-17 07:06:29--  https://repo.anaconda.com/miniconda/Miniconda3-py312_24.3.0-0-Linux-x86_64.sh\n",
            "Resolving repo.anaconda.com (repo.anaconda.com)... 104.16.32.241, 104.16.191.158, 2606:4700::6810:bf9e, ...\n",
            "Connecting to repo.anaconda.com (repo.anaconda.com)|104.16.32.241|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 143351488 (137M) [application/octet-stream]\n",
            "Saving to: ‘Miniconda3-py312_24.3.0-0-Linux-x86_64.sh’\n",
            "\n",
            "Miniconda3-py312_24 100%[===================>] 136.71M   152MB/s    in 0.9s    \n",
            "\n",
            "2024-05-17 07:06:30 (152 MB/s) - ‘Miniconda3-py312_24.3.0-0-Linux-x86_64.sh’ saved [143351488/143351488]\n",
            "\n",
            "PREFIX=/usr/local\n",
            "Unpacking payload ...\n",
            "\n",
            "Installing base environment...\n",
            "\n",
            "Preparing transaction: ...working... done\n",
            "Executing transaction: ...working... done\n",
            "installation finished.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!conda update -n base -c defaults conda --yes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5iuP4_vl3WFA",
        "outputId": "76cf8669-7c4f-4074-de9e-25397c7a50b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Channels:\n",
            " - defaults\n",
            "Platform: linux-64\n",
            "Collecting package metadata (repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n",
            "Solving environment: | \b\b/ \b\bdone\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - conda\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    conda-24.5.0               |  py312h06a4308_0         1.2 MB\n",
            "    frozendict-2.4.2           |  py312h06a4308_0          36 KB\n",
            "    openssl-3.0.13             |       h7f8727e_1         5.2 MB\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:         6.5 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  frozendict         pkgs/main/linux-64::frozendict-2.4.2-py312h06a4308_0 \n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  conda                              24.3.0-py312h06a4308_0 --> 24.5.0-py312h06a4308_0 \n",
            "  openssl                                 3.0.13-h7f8727e_0 --> 3.0.13-h7f8727e_1 \n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages:\n",
            "openssl-3.0.13       | 5.2 MB    | :   0% 0/1 [00:00<?, ?it/s]\n",
            "conda-24.5.0         | 1.2 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\n",
            "\n",
            "frozendict-2.4.2     | 36 KB     | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
            "\n",
            "frozendict-2.4.2     | 36 KB     | :  44% 0.43853215920344746/1 [00:00<00:00,  3.85it/s]\u001b[A\u001b[A\n",
            "\n",
            "openssl-3.0.13       | 5.2 MB    | :   0% 0.002997347135570501/1 [00:00<00:49, 50.02s/it]\n",
            "conda-24.5.0         | 1.2 MB    | :   1% 0.01293349794914382/1 [00:00<00:12, 12.16s/it]\u001b[A\n",
            "conda-24.5.0         | 1.2 MB    | : 100% 1.0/1 [00:00<00:00,  1.95it/s]                \u001b[A\n",
            "                                                                        \n",
            "                                                                        \u001b[A\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\n",
            "Preparing transaction: \\ \b\bdone\n",
            "Verifying transaction: / \b\b- \b\b\\ \b\bdone\n",
            "Executing transaction: / \b\bdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!conda --version\n",
        "!python --version"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uoBMBSS63Z58",
        "outputId": "d3f19844-d222-4adb-f06d-1bb7849ff505"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "conda 24.5.0\n",
            "Python 3.12.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!conda install -c conda-forge fmpy --yes # Install the key package"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RMuJRdfH3dDq",
        "outputId": "2c679e63-0b32-4049-d3b5-50e66207e9ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Channels:\n",
            " - conda-forge\n",
            " - defaults\n",
            "Platform: linux-64\n",
            "Collecting package metadata (repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
            "Solving environment: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - fmpy\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    _libgcc_mutex-0.1          |      conda_forge           3 KB  conda-forge\n",
            "    _openmp_mutex-4.5          |            2_gnu          23 KB  conda-forge\n",
            "    abseil-cpp-20211102.0      |       h27087fc_1         1.1 MB  conda-forge\n",
            "    anyio-4.3.0                |     pyhd8ed1ab_0         100 KB  conda-forge\n",
            "    argon2-cffi-23.1.0         |     pyhd8ed1ab_0          18 KB  conda-forge\n",
            "    argon2-cffi-bindings-21.2.0|  py312h98912ed_4          34 KB  conda-forge\n",
            "    arrow-1.3.0                |     pyhd8ed1ab_0          98 KB  conda-forge\n",
            "    arrow-cpp-14.0.2           |       h374c478_1        11.7 MB\n",
            "    asttokens-2.4.1            |     pyhd8ed1ab_0          28 KB  conda-forge\n",
            "    async-lru-2.0.4            |     pyhd8ed1ab_0          15 KB  conda-forge\n",
            "    attrs-23.2.0               |     pyh71513ae_0          53 KB  conda-forge\n",
            "    aws-c-auth-0.6.19          |       h5eee18b_0          99 KB\n",
            "    aws-c-cal-0.5.20           |       hff2c3d7_3          43 KB  conda-forge\n",
            "    aws-c-common-0.8.5         |       h166bdaf_0         198 KB  conda-forge\n",
            "    aws-c-compression-0.2.16   |       hf5f93bc_0          18 KB  conda-forge\n",
            "    aws-c-event-stream-0.2.15  |       h6a678d5_0          50 KB\n",
            "    aws-c-http-0.6.25          |       h5eee18b_0         200 KB\n",
            "    aws-c-io-0.13.10           |       h2666983_0         140 KB  conda-forge\n",
            "    aws-c-mqtt-0.7.13          |       h5eee18b_0          67 KB\n",
            "    aws-c-s3-0.1.51            |       hdbd6064_0          71 KB\n",
            "    aws-c-sdkutils-0.1.6       |       hf5f93bc_1          52 KB  conda-forge\n",
            "    aws-checksums-0.1.13       |       hf5f93bc_5          51 KB  conda-forge\n",
            "    aws-crt-cpp-0.18.16        |       h6a678d5_0         213 KB\n",
            "    aws-sdk-cpp-1.10.55        |       h721c034_0         2.5 MB\n",
            "    babel-2.14.0               |     pyhd8ed1ab_0         7.3 MB  conda-forge\n",
            "    beautifulsoup4-4.12.3      |     pyha770c72_0         115 KB  conda-forge\n",
            "    blas-1.1                   |         openblas           1 KB  conda-forge\n",
            "    bleach-6.1.0               |     pyhd8ed1ab_0         128 KB  conda-forge\n",
            "    blinker-1.8.2              |     pyhd8ed1ab_0          14 KB  conda-forge\n",
            "    bokeh-3.4.1                |     pyhd8ed1ab_0         4.5 MB  conda-forge\n",
            "    boost-cpp-1.82.0           |       hdb19cb5_2          11 KB\n",
            "    bottleneck-1.3.8           |  py312hc7c0aa3_0         137 KB  conda-forge\n",
            "    brotli-1.0.9               |       h166bdaf_9          20 KB  conda-forge\n",
            "    brotli-bin-1.0.9           |       h166bdaf_9          20 KB  conda-forge\n",
            "    cached-property-1.5.2      |       hd8ed1ab_1           4 KB  conda-forge\n",
            "    cached_property-1.5.2      |     pyha770c72_1          11 KB  conda-forge\n",
            "    certifi-2024.2.2           |     pyhd8ed1ab_0         157 KB  conda-forge\n",
            "    click-8.1.7                |unix_pyh707e725_0          82 KB  conda-forge\n",
            "    cloudpickle-3.0.0          |     pyhd8ed1ab_0          24 KB  conda-forge\n",
            "    comm-0.2.2                 |     pyhd8ed1ab_0          12 KB  conda-forge\n",
            "    conda-24.5.0               |  py312h7900ff3_0         1.2 MB  conda-forge\n",
            "    contourpy-1.2.0            |  py312hdb19cb5_0         238 KB\n",
            "    cycler-0.12.1              |     pyhd8ed1ab_0          13 KB  conda-forge\n",
            "    cytoolz-0.12.3             |  py312h98912ed_0         385 KB  conda-forge\n",
            "    dash-2.17.0                |     pyhd8ed1ab_0         5.0 MB  conda-forge\n",
            "    dash-bootstrap-components-1.6.0|     pyhd8ed1ab_0         117 KB  conda-forge\n",
            "    dask-2024.5.0              |     pyhd8ed1ab_0           7 KB  conda-forge\n",
            "    dask-core-2024.5.0         |     pyhd8ed1ab_0         861 KB  conda-forge\n",
            "    dask-expr-1.1.0            |     pyhd8ed1ab_0         154 KB  conda-forge\n",
            "    dbus-1.13.18               |       hb2f20db_0         504 KB\n",
            "    debugpy-1.6.7              |  py312h6a678d5_0         2.0 MB\n",
            "    decorator-5.1.1            |     pyhd8ed1ab_0          12 KB  conda-forge\n",
            "    defusedxml-0.7.1           |     pyhd8ed1ab_0          23 KB  conda-forge\n",
            "    distributed-2024.5.0       |     pyhd8ed1ab_0         777 KB  conda-forge\n",
            "    entrypoints-0.4            |     pyhd8ed1ab_0           9 KB  conda-forge\n",
            "    exceptiongroup-1.2.0       |     pyhd8ed1ab_2          20 KB  conda-forge\n",
            "    executing-2.0.1            |     pyhd8ed1ab_0          27 KB  conda-forge\n",
            "    flask-3.0.3                |     pyhd8ed1ab_0          79 KB  conda-forge\n",
            "    fmpy-0.3.20                |       h13ae965_0          10 KB  conda-forge\n",
            "    fmpy-base-0.3.20           |  py312ha770c72_0         3.2 MB  conda-forge\n",
            "    font-ttf-dejavu-sans-mono-2.37|       hab24e00_0         388 KB  conda-forge\n",
            "    font-ttf-inconsolata-3.000 |       h77eed37_0          94 KB  conda-forge\n",
            "    font-ttf-source-code-pro-2.038|       h77eed37_0         684 KB  conda-forge\n",
            "    font-ttf-ubuntu-0.83       |       h77eed37_2         1.5 MB  conda-forge\n",
            "    fontconfig-2.14.2          |       h14ed4e7_0         266 KB  conda-forge\n",
            "    fonts-conda-forge-1        |                0           4 KB  conda-forge\n",
            "    fonttools-4.51.0           |  py312h98912ed_0         2.7 MB  conda-forge\n",
            "    fqdn-1.5.1                 |     pyhd8ed1ab_0          14 KB  conda-forge\n",
            "    freetype-2.12.1            |       h267a509_2         620 KB  conda-forge\n",
            "    fsspec-2024.5.0            |     pyhff2d567_0         211 KB  conda-forge\n",
            "    gettext-0.21.1             |       h27087fc_0         4.1 MB  conda-forge\n",
            "    gflags-2.2.2               |    he1b5a44_1004         114 KB  conda-forge\n",
            "    glib-2.70.2                |       h780b84a_4         430 KB  conda-forge\n",
            "    glib-tools-2.70.2          |       h780b84a_4         106 KB  conda-forge\n",
            "    glog-0.5.0                 |       h48cff8f_0         104 KB  conda-forge\n",
            "    grpc-cpp-1.48.2            |       he1ff14a_1         4.8 MB\n",
            "    gst-plugins-base-1.14.1    |       h6a678d5_1         2.2 MB\n",
            "    gstreamer-1.14.1           |       h5eee18b_1         1.7 MB\n",
            "    h11-0.14.0                 |     pyhd8ed1ab_0          47 KB  conda-forge\n",
            "    h2-4.1.0                   |     pyhd8ed1ab_0          46 KB  conda-forge\n",
            "    hpack-4.0.0                |     pyh9f0ad1d_0          25 KB  conda-forge\n",
            "    httpcore-1.0.5             |     pyhd8ed1ab_0          45 KB  conda-forge\n",
            "    httpx-0.27.0               |     pyhd8ed1ab_0          63 KB  conda-forge\n",
            "    hyperframe-6.0.1           |     pyhd8ed1ab_0          14 KB  conda-forge\n",
            "    importlib-metadata-7.1.0   |     pyha770c72_0          26 KB  conda-forge\n",
            "    importlib_metadata-7.1.0   |       hd8ed1ab_0           9 KB  conda-forge\n",
            "    importlib_resources-6.4.0  |     pyhd8ed1ab_0          32 KB  conda-forge\n",
            "    ipykernel-6.29.3           |     pyhd33586a_0         116 KB  conda-forge\n",
            "    ipython-8.24.0             |     pyh707e725_0         582 KB  conda-forge\n",
            "    isoduration-20.11.0        |     pyhd8ed1ab_0          17 KB  conda-forge\n",
            "    itsdangerous-2.2.0         |     pyhd8ed1ab_0          19 KB  conda-forge\n",
            "    jedi-0.19.1                |     pyhd8ed1ab_0         822 KB  conda-forge\n",
            "    jinja2-3.1.4               |     pyhd8ed1ab_0         109 KB  conda-forge\n",
            "    jpeg-9e                    |       h0b41bf4_3         235 KB  conda-forge\n",
            "    json5-0.9.25               |     pyhd8ed1ab_0          27 KB  conda-forge\n",
            "    jsonschema-4.22.0          |     pyhd8ed1ab_0          72 KB  conda-forge\n",
            "    jsonschema-specifications-2023.12.1|     pyhd8ed1ab_0          16 KB  conda-forge\n",
            "    jsonschema-with-format-nongpl-4.22.0|     pyhd8ed1ab_0           7 KB  conda-forge\n",
            "    jupyter-lsp-2.2.5          |     pyhd8ed1ab_0          54 KB  conda-forge\n",
            "    jupyter_client-8.6.1       |     pyhd8ed1ab_0         104 KB  conda-forge\n",
            "    jupyter_core-5.7.2         |  py312h7900ff3_0          91 KB  conda-forge\n",
            "    jupyter_events-0.10.0      |     pyhd8ed1ab_0          21 KB  conda-forge\n",
            "    jupyter_server-2.14.0      |     pyhd8ed1ab_0         317 KB  conda-forge\n",
            "    jupyter_server_terminals-0.5.3|     pyhd8ed1ab_0          19 KB  conda-forge\n",
            "    jupyterlab-4.2.0           |     pyhd8ed1ab_1         7.6 MB  conda-forge\n",
            "    jupyterlab_pygments-0.3.0  |     pyhd8ed1ab_1          18 KB  conda-forge\n",
            "    jupyterlab_server-2.27.1   |     pyhd8ed1ab_0          48 KB  conda-forge\n",
            "    kaleido-core-0.2.1         |       h3644ca4_0        59.2 MB  conda-forge\n",
            "    kiwisolver-1.4.4           |  py312h6a678d5_0          68 KB\n",
            "    lark-1.1.9                 |     pyhd8ed1ab_0          90 KB  conda-forge\n",
            "    lcms2-2.15                 |       hfd0df8a_0         235 KB  conda-forge\n",
            "    lerc-3.0                   |       h9c3ff4c_0         216 KB  conda-forge\n",
            "    libboost-1.82.0            |       h109eef0_2        19.5 MB\n",
            "    libbrotlicommon-1.0.9      |       h166bdaf_9          69 KB  conda-forge\n",
            "    libbrotlidec-1.0.9         |       h166bdaf_9          32 KB  conda-forge\n",
            "    libbrotlienc-1.0.9         |       h166bdaf_9         259 KB  conda-forge\n",
            "    libclang-14.0.6            |default_hc6dbbc7_1         137 KB\n",
            "    libclang13-14.0.6          |default_he11475f_1         9.8 MB\n",
            "    libcups-2.4.2              |       h2d74bed_1         4.5 MB\n",
            "    libdeflate-1.17            |       h0b41bf4_0          63 KB  conda-forge\n",
            "    libevent-2.1.12            |       hf998b51_1         417 KB  conda-forge\n",
            "    libexpat-2.5.0             |       hcb278e6_1          76 KB  conda-forge\n",
            "    libgcc-ng-13.2.0           |       h77fa898_7         758 KB  conda-forge\n",
            "    libgfortran-ng-13.2.0      |       h69a702a_7          24 KB  conda-forge\n",
            "    libgfortran5-13.2.0        |       hca663fb_7         1.4 MB  conda-forge\n",
            "    libglib-2.70.2             |       h174f98d_4         3.1 MB  conda-forge\n",
            "    libgomp-13.2.0             |       h77fa898_7         412 KB  conda-forge\n",
            "    libiconv-1.17              |       hd590300_2         689 KB  conda-forge\n",
            "    libllvm14-14.0.6           |       hdb19cb5_3        33.4 MB\n",
            "    libnsl-2.0.1               |       hd590300_0          33 KB  conda-forge\n",
            "    libopenblas-0.3.27         |pthreads_h413a1c8_0         5.3 MB  conda-forge\n",
            "    libpng-1.6.43              |       h2797004_0         281 KB  conda-forge\n",
            "    libpq-12.17                |       hdbd6064_0         2.4 MB\n",
            "    libprotobuf-3.20.3         |       he621ea3_0         2.4 MB\n",
            "    libsodium-1.0.18           |       h36c2ea0_1         366 KB  conda-forge\n",
            "    libsqlite-3.45.3           |       h2797004_0         840 KB  conda-forge\n",
            "    libthrift-0.15.0           |       h1795dd8_2         4.0 MB\n",
            "    libtiff-4.5.1              |       h6a678d5_0         533 KB\n",
            "    libuuid-2.38.1             |       h0b41bf4_0          33 KB  conda-forge\n",
            "    libwebp-base-1.4.0         |       hd590300_0         429 KB  conda-forge\n",
            "    libxcb-1.16                |       hd590300_0         386 KB  conda-forge\n",
            "    libxcrypt-4.4.36           |       hd590300_1          98 KB  conda-forge\n",
            "    libxkbcommon-1.0.3         |       he3ba5ed_0         581 KB  conda-forge\n",
            "    libxslt-1.1.37             |       h873f0b0_0         251 KB  conda-forge\n",
            "    libzlib-1.2.13             |       hd590300_5          60 KB  conda-forge\n",
            "    locket-1.0.0               |     pyhd8ed1ab_0           8 KB  conda-forge\n",
            "    lxml-5.2.1                 |  py312hdbbb534_0         1.4 MB\n",
            "    lz4-4.3.3                  |  py312h03f37cb_0          38 KB  conda-forge\n",
            "    markupsafe-2.1.5           |  py312h98912ed_0          26 KB  conda-forge\n",
            "    mathjax-2.7.7              |       ha770c72_3        21.2 MB  conda-forge\n",
            "    matplotlib-base-3.8.4      |  py312h526ad5a_0         7.6 MB\n",
            "    matplotlib-inline-0.1.7    |     pyhd8ed1ab_0          14 KB  conda-forge\n",
            "    mistune-3.0.2              |     pyhd8ed1ab_0          64 KB  conda-forge\n",
            "    msgpack-python-1.0.3       |  py312hdb19cb5_0          35 KB\n",
            "    munkres-1.1.4              |     pyh9f0ad1d_0          12 KB  conda-forge\n",
            "    mysql-5.7.20               |    hf484d3e_1001        79.2 MB  conda-forge\n",
            "    nbclient-0.10.0            |     pyhd8ed1ab_0          27 KB  conda-forge\n",
            "    nbconvert-core-7.16.4      |     pyhd8ed1ab_0         185 KB  conda-forge\n",
            "    nbformat-5.10.4            |     pyhd8ed1ab_0          99 KB  conda-forge\n",
            "    nest-asyncio-1.6.0         |     pyhd8ed1ab_0          11 KB  conda-forge\n",
            "    notebook-7.2.0             |     pyhd8ed1ab_0         3.8 MB  conda-forge\n",
            "    notebook-shim-0.2.4        |     pyhd8ed1ab_0          16 KB  conda-forge\n",
            "    nspr-4.35                  |       h6a678d5_0         244 KB\n",
            "    nss-3.89.1                 |       h6a678d5_0         2.1 MB\n",
            "    numexpr-2.8.7              |  py312he7dcb8a_0         149 KB\n",
            "    numpy-1.26.4               |  py312h2809609_0          10 KB\n",
            "    numpy-base-1.26.4          |  py312he1a6c75_0         7.7 MB\n",
            "    openblas-0.3.27            |pthreads_h7a3da1a_0         5.5 MB  conda-forge\n",
            "    openjpeg-2.4.0             |       h3ad879b_0         331 KB\n",
            "    openssl-3.3.0              |       hd590300_0         2.8 MB  conda-forge\n",
            "    orc-1.7.4                  |       h6c59b99_1         1.1 MB  conda-forge\n",
            "    overrides-7.7.0            |     pyhd8ed1ab_0          30 KB  conda-forge\n",
            "    pandas-2.2.1               |  py312h526ad5a_0        15.4 MB\n",
            "    pandocfilters-1.5.0        |     pyhd8ed1ab_0          11 KB  conda-forge\n",
            "    parso-0.8.4                |     pyhd8ed1ab_0          73 KB  conda-forge\n",
            "    partd-1.4.2                |     pyhd8ed1ab_0          20 KB  conda-forge\n",
            "    pcre-8.45                  |       h9c3ff4c_0         253 KB  conda-forge\n",
            "    pexpect-4.9.0              |     pyhd8ed1ab_0          52 KB  conda-forge\n",
            "    pickleshare-0.7.5          |          py_1003           9 KB  conda-forge\n",
            "    pillow-10.3.0              |  py312h5eee18b_0         915 KB\n",
            "    pkgutil-resolve-name-1.3.10|     pyhd8ed1ab_1          11 KB  conda-forge\n",
            "    plotly-5.22.0              |     pyhd8ed1ab_0         5.1 MB  conda-forge\n",
            "    ply-3.11                   |     pyhd8ed1ab_2          48 KB  conda-forge\n",
            "    prometheus_client-0.20.0   |     pyhd8ed1ab_0          48 KB  conda-forge\n",
            "    prompt-toolkit-3.0.42      |     pyha770c72_0         264 KB  conda-forge\n",
            "    psutil-5.9.8               |  py312h98912ed_0         475 KB  conda-forge\n",
            "    pthread-stubs-0.4          |    h36c2ea0_1001           5 KB  conda-forge\n",
            "    ptyprocess-0.7.0           |     pyhd3deb0d_0          16 KB  conda-forge\n",
            "    pure_eval-0.2.2            |     pyhd8ed1ab_0          14 KB  conda-forge\n",
            "    pyarrow-14.0.2             |  py312hb107042_0         4.6 MB\n",
            "    pyarrow-hotfix-0.6         |     pyhd8ed1ab_0          13 KB  conda-forge\n",
            "    pygments-2.18.0            |     pyhd8ed1ab_0         859 KB  conda-forge\n",
            "    pyparsing-3.0.9            |     pyhd8ed1ab_0          79 KB  conda-forge\n",
            "    pyqt-5.15.10               |  py312h6a678d5_0         5.0 MB\n",
            "    pyqt5-sip-12.13.0          |  py312h5eee18b_0          88 KB\n",
            "    pyqtgraph-0.13.7           |     pyhd8ed1ab_0         1.4 MB  conda-forge\n",
            "    pyqtwebengine-5.15.10      |  py312h6a678d5_0         153 KB\n",
            "    python-3.12.2              |hab00c5b_0_cpython        30.8 MB  conda-forge\n",
            "    python-dateutil-2.9.0      |     pyhd8ed1ab_0         218 KB  conda-forge\n",
            "    python-fastjsonschema-2.19.1|     pyhd8ed1ab_0         220 KB  conda-forge\n",
            "    python-json-logger-2.0.7   |     pyhd8ed1ab_0          13 KB  conda-forge\n",
            "    python-kaleido-0.2.1       |     pyhd8ed1ab_0          18 KB  conda-forge\n",
            "    python-tzdata-2024.1       |     pyhd8ed1ab_0         141 KB  conda-forge\n",
            "    python_abi-3.12            |          4_cp312           6 KB  conda-forge\n",
            "    pytz-2024.1                |     pyhd8ed1ab_0         184 KB  conda-forge\n",
            "    pyyaml-6.0.1               |  py312h98912ed_1         192 KB  conda-forge\n",
            "    pyzmq-25.1.2               |  py312h6a678d5_0         533 KB\n",
            "    qt-main-5.15.2             |      h53bd1ea_10        53.7 MB\n",
            "    qt-webengine-5.15.9        |       h9ab4d14_7        53.8 MB\n",
            "    re2-2022.04.01             |       h27087fc_0         212 KB  conda-forge\n",
            "    referencing-0.35.1         |     pyhd8ed1ab_0          41 KB  conda-forge\n",
            "    retrying-1.3.3             |             py_2          11 KB  conda-forge\n",
            "    rfc3339-validator-0.1.4    |     pyhd8ed1ab_0           8 KB  conda-forge\n",
            "    rfc3986-validator-0.1.1    |     pyh9f0ad1d_0           8 KB  conda-forge\n",
            "    rpds-py-0.18.1             |  py312h4413252_0         901 KB  conda-forge\n",
            "    s2n-1.3.27                 |       h1b5e8cb_0         374 KB  conda-forge\n",
            "    scipy-1.12.0               |  py312h2809609_0        23.3 MB\n",
            "    send2trash-1.8.3           |     pyh0d859eb_0          22 KB  conda-forge\n",
            "    sip-6.7.12                 |  py312h6a678d5_0         553 KB\n",
            "    six-1.16.0                 |     pyh6c4a22f_0          14 KB  conda-forge\n",
            "    snappy-1.1.9               |       hbd366e4_1          35 KB  conda-forge\n",
            "    sniffio-1.3.1              |     pyhd8ed1ab_0          15 KB  conda-forge\n",
            "    sortedcontainers-2.4.0     |     pyhd8ed1ab_0          26 KB  conda-forge\n",
            "    soupsieve-2.5              |     pyhd8ed1ab_1          36 KB  conda-forge\n",
            "    stack_data-0.6.2           |     pyhd8ed1ab_0          26 KB  conda-forge\n",
            "    tblib-3.0.0                |     pyhd8ed1ab_0          17 KB  conda-forge\n",
            "    tenacity-8.3.0             |     pyhd8ed1ab_0          23 KB  conda-forge\n",
            "    terminado-0.18.1           |     pyh0d859eb_0          22 KB  conda-forge\n",
            "    tinycss2-1.3.0             |     pyhd8ed1ab_0          25 KB  conda-forge\n",
            "    tk-8.6.13                  |noxft_h4845f30_101         3.2 MB  conda-forge\n",
            "    tomli-2.0.1                |     pyhd8ed1ab_0          16 KB  conda-forge\n",
            "    toolz-0.12.1               |     pyhd8ed1ab_0          51 KB  conda-forge\n",
            "    tornado-6.4                |  py312h98912ed_0         821 KB  conda-forge\n",
            "    traitlets-5.14.3           |     pyhd8ed1ab_0         108 KB  conda-forge\n",
            "    types-python-dateutil-2.9.0.20240316|     pyhd8ed1ab_0          21 KB  conda-forge\n",
            "    typing-extensions-4.11.0   |       hd8ed1ab_0          10 KB  conda-forge\n",
            "    typing_extensions-4.11.0   |     pyha770c72_0          37 KB  conda-forge\n",
            "    typing_utils-0.1.0         |     pyhd8ed1ab_0          14 KB  conda-forge\n",
            "    uri-template-1.3.0         |     pyhd8ed1ab_0          23 KB  conda-forge\n",
            "    utf8proc-2.6.1             |       h5eee18b_1          93 KB\n",
            "    wcwidth-0.2.13             |     pyhd8ed1ab_0          32 KB  conda-forge\n",
            "    webcolors-1.13             |     pyhd8ed1ab_0          18 KB  conda-forge\n",
            "    webencodings-0.5.1         |     pyhd8ed1ab_2          15 KB  conda-forge\n",
            "    websocket-client-1.8.0     |     pyhd8ed1ab_0          46 KB  conda-forge\n",
            "    werkzeug-3.0.3             |     pyhd8ed1ab_0         237 KB  conda-forge\n",
            "    xorg-libxau-1.0.11         |       hd590300_0          14 KB  conda-forge\n",
            "    xorg-libxdmcp-1.1.3        |       h7f98852_0          19 KB  conda-forge\n",
            "    xyzservices-2024.4.0       |     pyhd8ed1ab_0          45 KB  conda-forge\n",
            "    yaml-0.2.5                 |       h7f98852_2          87 KB  conda-forge\n",
            "    zeromq-4.3.5               |       h6a678d5_0         366 KB\n",
            "    zict-3.0.0                 |     pyhd8ed1ab_0          35 KB  conda-forge\n",
            "    zipp-3.17.0                |     pyhd8ed1ab_0          19 KB  conda-forge\n",
            "    zlib-1.2.13                |       hd590300_5          91 KB  conda-forge\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:       565.4 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  abseil-cpp         conda-forge/linux-64::abseil-cpp-20211102.0-h27087fc_1 \n",
            "  anyio              conda-forge/noarch::anyio-4.3.0-pyhd8ed1ab_0 \n",
            "  argon2-cffi        conda-forge/noarch::argon2-cffi-23.1.0-pyhd8ed1ab_0 \n",
            "  argon2-cffi-bindi~ conda-forge/linux-64::argon2-cffi-bindings-21.2.0-py312h98912ed_4 \n",
            "  arrow              conda-forge/noarch::arrow-1.3.0-pyhd8ed1ab_0 \n",
            "  arrow-cpp          pkgs/main/linux-64::arrow-cpp-14.0.2-h374c478_1 \n",
            "  asttokens          conda-forge/noarch::asttokens-2.4.1-pyhd8ed1ab_0 \n",
            "  async-lru          conda-forge/noarch::async-lru-2.0.4-pyhd8ed1ab_0 \n",
            "  attrs              conda-forge/noarch::attrs-23.2.0-pyh71513ae_0 \n",
            "  aws-c-auth         pkgs/main/linux-64::aws-c-auth-0.6.19-h5eee18b_0 \n",
            "  aws-c-cal          conda-forge/linux-64::aws-c-cal-0.5.20-hff2c3d7_3 \n",
            "  aws-c-common       conda-forge/linux-64::aws-c-common-0.8.5-h166bdaf_0 \n",
            "  aws-c-compression  conda-forge/linux-64::aws-c-compression-0.2.16-hf5f93bc_0 \n",
            "  aws-c-event-stream pkgs/main/linux-64::aws-c-event-stream-0.2.15-h6a678d5_0 \n",
            "  aws-c-http         pkgs/main/linux-64::aws-c-http-0.6.25-h5eee18b_0 \n",
            "  aws-c-io           conda-forge/linux-64::aws-c-io-0.13.10-h2666983_0 \n",
            "  aws-c-mqtt         pkgs/main/linux-64::aws-c-mqtt-0.7.13-h5eee18b_0 \n",
            "  aws-c-s3           pkgs/main/linux-64::aws-c-s3-0.1.51-hdbd6064_0 \n",
            "  aws-c-sdkutils     conda-forge/linux-64::aws-c-sdkutils-0.1.6-hf5f93bc_1 \n",
            "  aws-checksums      conda-forge/linux-64::aws-checksums-0.1.13-hf5f93bc_5 \n",
            "  aws-crt-cpp        pkgs/main/linux-64::aws-crt-cpp-0.18.16-h6a678d5_0 \n",
            "  aws-sdk-cpp        pkgs/main/linux-64::aws-sdk-cpp-1.10.55-h721c034_0 \n",
            "  babel              conda-forge/noarch::babel-2.14.0-pyhd8ed1ab_0 \n",
            "  beautifulsoup4     conda-forge/noarch::beautifulsoup4-4.12.3-pyha770c72_0 \n",
            "  blas               conda-forge/linux-64::blas-1.1-openblas \n",
            "  bleach             conda-forge/noarch::bleach-6.1.0-pyhd8ed1ab_0 \n",
            "  blinker            conda-forge/noarch::blinker-1.8.2-pyhd8ed1ab_0 \n",
            "  bokeh              conda-forge/noarch::bokeh-3.4.1-pyhd8ed1ab_0 \n",
            "  boost-cpp          pkgs/main/linux-64::boost-cpp-1.82.0-hdb19cb5_2 \n",
            "  bottleneck         conda-forge/linux-64::bottleneck-1.3.8-py312hc7c0aa3_0 \n",
            "  brotli             conda-forge/linux-64::brotli-1.0.9-h166bdaf_9 \n",
            "  brotli-bin         conda-forge/linux-64::brotli-bin-1.0.9-h166bdaf_9 \n",
            "  cached-property    conda-forge/noarch::cached-property-1.5.2-hd8ed1ab_1 \n",
            "  cached_property    conda-forge/noarch::cached_property-1.5.2-pyha770c72_1 \n",
            "  click              conda-forge/noarch::click-8.1.7-unix_pyh707e725_0 \n",
            "  cloudpickle        conda-forge/noarch::cloudpickle-3.0.0-pyhd8ed1ab_0 \n",
            "  comm               conda-forge/noarch::comm-0.2.2-pyhd8ed1ab_0 \n",
            "  contourpy          pkgs/main/linux-64::contourpy-1.2.0-py312hdb19cb5_0 \n",
            "  cycler             conda-forge/noarch::cycler-0.12.1-pyhd8ed1ab_0 \n",
            "  cytoolz            conda-forge/linux-64::cytoolz-0.12.3-py312h98912ed_0 \n",
            "  dash               conda-forge/noarch::dash-2.17.0-pyhd8ed1ab_0 \n",
            "  dash-bootstrap-co~ conda-forge/noarch::dash-bootstrap-components-1.6.0-pyhd8ed1ab_0 \n",
            "  dask               conda-forge/noarch::dask-2024.5.0-pyhd8ed1ab_0 \n",
            "  dask-core          conda-forge/noarch::dask-core-2024.5.0-pyhd8ed1ab_0 \n",
            "  dask-expr          conda-forge/noarch::dask-expr-1.1.0-pyhd8ed1ab_0 \n",
            "  dbus               pkgs/main/linux-64::dbus-1.13.18-hb2f20db_0 \n",
            "  debugpy            pkgs/main/linux-64::debugpy-1.6.7-py312h6a678d5_0 \n",
            "  decorator          conda-forge/noarch::decorator-5.1.1-pyhd8ed1ab_0 \n",
            "  defusedxml         conda-forge/noarch::defusedxml-0.7.1-pyhd8ed1ab_0 \n",
            "  distributed        conda-forge/noarch::distributed-2024.5.0-pyhd8ed1ab_0 \n",
            "  entrypoints        conda-forge/noarch::entrypoints-0.4-pyhd8ed1ab_0 \n",
            "  exceptiongroup     conda-forge/noarch::exceptiongroup-1.2.0-pyhd8ed1ab_2 \n",
            "  executing          conda-forge/noarch::executing-2.0.1-pyhd8ed1ab_0 \n",
            "  flask              conda-forge/noarch::flask-3.0.3-pyhd8ed1ab_0 \n",
            "  fmpy               conda-forge/noarch::fmpy-0.3.20-h13ae965_0 \n",
            "  fmpy-base          conda-forge/linux-64::fmpy-base-0.3.20-py312ha770c72_0 \n",
            "  font-ttf-dejavu-s~ conda-forge/noarch::font-ttf-dejavu-sans-mono-2.37-hab24e00_0 \n",
            "  font-ttf-inconsol~ conda-forge/noarch::font-ttf-inconsolata-3.000-h77eed37_0 \n",
            "  font-ttf-source-c~ conda-forge/noarch::font-ttf-source-code-pro-2.038-h77eed37_0 \n",
            "  font-ttf-ubuntu    conda-forge/noarch::font-ttf-ubuntu-0.83-h77eed37_2 \n",
            "  fontconfig         conda-forge/linux-64::fontconfig-2.14.2-h14ed4e7_0 \n",
            "  fonts-conda-forge  conda-forge/noarch::fonts-conda-forge-1-0 \n",
            "  fonttools          conda-forge/linux-64::fonttools-4.51.0-py312h98912ed_0 \n",
            "  fqdn               conda-forge/noarch::fqdn-1.5.1-pyhd8ed1ab_0 \n",
            "  freetype           conda-forge/linux-64::freetype-2.12.1-h267a509_2 \n",
            "  fsspec             conda-forge/noarch::fsspec-2024.5.0-pyhff2d567_0 \n",
            "  gettext            conda-forge/linux-64::gettext-0.21.1-h27087fc_0 \n",
            "  gflags             conda-forge/linux-64::gflags-2.2.2-he1b5a44_1004 \n",
            "  glib               conda-forge/linux-64::glib-2.70.2-h780b84a_4 \n",
            "  glib-tools         conda-forge/linux-64::glib-tools-2.70.2-h780b84a_4 \n",
            "  glog               conda-forge/linux-64::glog-0.5.0-h48cff8f_0 \n",
            "  grpc-cpp           pkgs/main/linux-64::grpc-cpp-1.48.2-he1ff14a_1 \n",
            "  gst-plugins-base   pkgs/main/linux-64::gst-plugins-base-1.14.1-h6a678d5_1 \n",
            "  gstreamer          pkgs/main/linux-64::gstreamer-1.14.1-h5eee18b_1 \n",
            "  h11                conda-forge/noarch::h11-0.14.0-pyhd8ed1ab_0 \n",
            "  h2                 conda-forge/noarch::h2-4.1.0-pyhd8ed1ab_0 \n",
            "  hpack              conda-forge/noarch::hpack-4.0.0-pyh9f0ad1d_0 \n",
            "  httpcore           conda-forge/noarch::httpcore-1.0.5-pyhd8ed1ab_0 \n",
            "  httpx              conda-forge/noarch::httpx-0.27.0-pyhd8ed1ab_0 \n",
            "  hyperframe         conda-forge/noarch::hyperframe-6.0.1-pyhd8ed1ab_0 \n",
            "  importlib-metadata conda-forge/noarch::importlib-metadata-7.1.0-pyha770c72_0 \n",
            "  importlib_metadata conda-forge/noarch::importlib_metadata-7.1.0-hd8ed1ab_0 \n",
            "  importlib_resourc~ conda-forge/noarch::importlib_resources-6.4.0-pyhd8ed1ab_0 \n",
            "  ipykernel          conda-forge/noarch::ipykernel-6.29.3-pyhd33586a_0 \n",
            "  ipython            conda-forge/noarch::ipython-8.24.0-pyh707e725_0 \n",
            "  isoduration        conda-forge/noarch::isoduration-20.11.0-pyhd8ed1ab_0 \n",
            "  itsdangerous       conda-forge/noarch::itsdangerous-2.2.0-pyhd8ed1ab_0 \n",
            "  jedi               conda-forge/noarch::jedi-0.19.1-pyhd8ed1ab_0 \n",
            "  jinja2             conda-forge/noarch::jinja2-3.1.4-pyhd8ed1ab_0 \n",
            "  jpeg               conda-forge/linux-64::jpeg-9e-h0b41bf4_3 \n",
            "  json5              conda-forge/noarch::json5-0.9.25-pyhd8ed1ab_0 \n",
            "  jsonschema         conda-forge/noarch::jsonschema-4.22.0-pyhd8ed1ab_0 \n",
            "  jsonschema-specif~ conda-forge/noarch::jsonschema-specifications-2023.12.1-pyhd8ed1ab_0 \n",
            "  jsonschema-with-f~ conda-forge/noarch::jsonschema-with-format-nongpl-4.22.0-pyhd8ed1ab_0 \n",
            "  jupyter-lsp        conda-forge/noarch::jupyter-lsp-2.2.5-pyhd8ed1ab_0 \n",
            "  jupyter_client     conda-forge/noarch::jupyter_client-8.6.1-pyhd8ed1ab_0 \n",
            "  jupyter_core       conda-forge/linux-64::jupyter_core-5.7.2-py312h7900ff3_0 \n",
            "  jupyter_events     conda-forge/noarch::jupyter_events-0.10.0-pyhd8ed1ab_0 \n",
            "  jupyter_server     conda-forge/noarch::jupyter_server-2.14.0-pyhd8ed1ab_0 \n",
            "  jupyter_server_te~ conda-forge/noarch::jupyter_server_terminals-0.5.3-pyhd8ed1ab_0 \n",
            "  jupyterlab         conda-forge/noarch::jupyterlab-4.2.0-pyhd8ed1ab_1 \n",
            "  jupyterlab_pygmen~ conda-forge/noarch::jupyterlab_pygments-0.3.0-pyhd8ed1ab_1 \n",
            "  jupyterlab_server  conda-forge/noarch::jupyterlab_server-2.27.1-pyhd8ed1ab_0 \n",
            "  kaleido-core       conda-forge/linux-64::kaleido-core-0.2.1-h3644ca4_0 \n",
            "  kiwisolver         pkgs/main/linux-64::kiwisolver-1.4.4-py312h6a678d5_0 \n",
            "  lark               conda-forge/noarch::lark-1.1.9-pyhd8ed1ab_0 \n",
            "  lcms2              conda-forge/linux-64::lcms2-2.15-hfd0df8a_0 \n",
            "  lerc               conda-forge/linux-64::lerc-3.0-h9c3ff4c_0 \n",
            "  libboost           pkgs/main/linux-64::libboost-1.82.0-h109eef0_2 \n",
            "  libbrotlicommon    conda-forge/linux-64::libbrotlicommon-1.0.9-h166bdaf_9 \n",
            "  libbrotlidec       conda-forge/linux-64::libbrotlidec-1.0.9-h166bdaf_9 \n",
            "  libbrotlienc       conda-forge/linux-64::libbrotlienc-1.0.9-h166bdaf_9 \n",
            "  libclang           pkgs/main/linux-64::libclang-14.0.6-default_hc6dbbc7_1 \n",
            "  libclang13         pkgs/main/linux-64::libclang13-14.0.6-default_he11475f_1 \n",
            "  libcups            pkgs/main/linux-64::libcups-2.4.2-h2d74bed_1 \n",
            "  libdeflate         conda-forge/linux-64::libdeflate-1.17-h0b41bf4_0 \n",
            "  libevent           conda-forge/linux-64::libevent-2.1.12-hf998b51_1 \n",
            "  libexpat           conda-forge/linux-64::libexpat-2.5.0-hcb278e6_1 \n",
            "  libgfortran-ng     conda-forge/linux-64::libgfortran-ng-13.2.0-h69a702a_7 \n",
            "  libgfortran5       conda-forge/linux-64::libgfortran5-13.2.0-hca663fb_7 \n",
            "  libglib            conda-forge/linux-64::libglib-2.70.2-h174f98d_4 \n",
            "  libiconv           conda-forge/linux-64::libiconv-1.17-hd590300_2 \n",
            "  libllvm14          pkgs/main/linux-64::libllvm14-14.0.6-hdb19cb5_3 \n",
            "  libnsl             conda-forge/linux-64::libnsl-2.0.1-hd590300_0 \n",
            "  libopenblas        conda-forge/linux-64::libopenblas-0.3.27-pthreads_h413a1c8_0 \n",
            "  libpng             conda-forge/linux-64::libpng-1.6.43-h2797004_0 \n",
            "  libpq              pkgs/main/linux-64::libpq-12.17-hdbd6064_0 \n",
            "  libprotobuf        pkgs/main/linux-64::libprotobuf-3.20.3-he621ea3_0 \n",
            "  libsodium          conda-forge/linux-64::libsodium-1.0.18-h36c2ea0_1 \n",
            "  libsqlite          conda-forge/linux-64::libsqlite-3.45.3-h2797004_0 \n",
            "  libthrift          pkgs/main/linux-64::libthrift-0.15.0-h1795dd8_2 \n",
            "  libtiff            pkgs/main/linux-64::libtiff-4.5.1-h6a678d5_0 \n",
            "  libwebp-base       conda-forge/linux-64::libwebp-base-1.4.0-hd590300_0 \n",
            "  libxcb             conda-forge/linux-64::libxcb-1.16-hd590300_0 \n",
            "  libxcrypt          conda-forge/linux-64::libxcrypt-4.4.36-hd590300_1 \n",
            "  libxkbcommon       conda-forge/linux-64::libxkbcommon-1.0.3-he3ba5ed_0 \n",
            "  libxslt            conda-forge/linux-64::libxslt-1.1.37-h873f0b0_0 \n",
            "  libzlib            conda-forge/linux-64::libzlib-1.2.13-hd590300_5 \n",
            "  locket             conda-forge/noarch::locket-1.0.0-pyhd8ed1ab_0 \n",
            "  lxml               pkgs/main/linux-64::lxml-5.2.1-py312hdbbb534_0 \n",
            "  lz4                conda-forge/linux-64::lz4-4.3.3-py312h03f37cb_0 \n",
            "  markupsafe         conda-forge/linux-64::markupsafe-2.1.5-py312h98912ed_0 \n",
            "  mathjax            conda-forge/linux-64::mathjax-2.7.7-ha770c72_3 \n",
            "  matplotlib-base    pkgs/main/linux-64::matplotlib-base-3.8.4-py312h526ad5a_0 \n",
            "  matplotlib-inline  conda-forge/noarch::matplotlib-inline-0.1.7-pyhd8ed1ab_0 \n",
            "  mistune            conda-forge/noarch::mistune-3.0.2-pyhd8ed1ab_0 \n",
            "  msgpack-python     pkgs/main/linux-64::msgpack-python-1.0.3-py312hdb19cb5_0 \n",
            "  munkres            conda-forge/noarch::munkres-1.1.4-pyh9f0ad1d_0 \n",
            "  mysql              conda-forge/linux-64::mysql-5.7.20-hf484d3e_1001 \n",
            "  nbclient           conda-forge/noarch::nbclient-0.10.0-pyhd8ed1ab_0 \n",
            "  nbconvert-core     conda-forge/noarch::nbconvert-core-7.16.4-pyhd8ed1ab_0 \n",
            "  nbformat           conda-forge/noarch::nbformat-5.10.4-pyhd8ed1ab_0 \n",
            "  nest-asyncio       conda-forge/noarch::nest-asyncio-1.6.0-pyhd8ed1ab_0 \n",
            "  notebook           conda-forge/noarch::notebook-7.2.0-pyhd8ed1ab_0 \n",
            "  notebook-shim      conda-forge/noarch::notebook-shim-0.2.4-pyhd8ed1ab_0 \n",
            "  nspr               pkgs/main/linux-64::nspr-4.35-h6a678d5_0 \n",
            "  nss                pkgs/main/linux-64::nss-3.89.1-h6a678d5_0 \n",
            "  numexpr            pkgs/main/linux-64::numexpr-2.8.7-py312he7dcb8a_0 \n",
            "  numpy              pkgs/main/linux-64::numpy-1.26.4-py312h2809609_0 \n",
            "  numpy-base         pkgs/main/linux-64::numpy-base-1.26.4-py312he1a6c75_0 \n",
            "  openblas           conda-forge/linux-64::openblas-0.3.27-pthreads_h7a3da1a_0 \n",
            "  openjpeg           pkgs/main/linux-64::openjpeg-2.4.0-h3ad879b_0 \n",
            "  orc                conda-forge/linux-64::orc-1.7.4-h6c59b99_1 \n",
            "  overrides          conda-forge/noarch::overrides-7.7.0-pyhd8ed1ab_0 \n",
            "  pandas             pkgs/main/linux-64::pandas-2.2.1-py312h526ad5a_0 \n",
            "  pandocfilters      conda-forge/noarch::pandocfilters-1.5.0-pyhd8ed1ab_0 \n",
            "  parso              conda-forge/noarch::parso-0.8.4-pyhd8ed1ab_0 \n",
            "  partd              conda-forge/noarch::partd-1.4.2-pyhd8ed1ab_0 \n",
            "  pcre               conda-forge/linux-64::pcre-8.45-h9c3ff4c_0 \n",
            "  pexpect            conda-forge/noarch::pexpect-4.9.0-pyhd8ed1ab_0 \n",
            "  pickleshare        conda-forge/noarch::pickleshare-0.7.5-py_1003 \n",
            "  pillow             pkgs/main/linux-64::pillow-10.3.0-py312h5eee18b_0 \n",
            "  pkgutil-resolve-n~ conda-forge/noarch::pkgutil-resolve-name-1.3.10-pyhd8ed1ab_1 \n",
            "  plotly             conda-forge/noarch::plotly-5.22.0-pyhd8ed1ab_0 \n",
            "  ply                conda-forge/noarch::ply-3.11-pyhd8ed1ab_2 \n",
            "  prometheus_client  conda-forge/noarch::prometheus_client-0.20.0-pyhd8ed1ab_0 \n",
            "  prompt-toolkit     conda-forge/noarch::prompt-toolkit-3.0.42-pyha770c72_0 \n",
            "  psutil             conda-forge/linux-64::psutil-5.9.8-py312h98912ed_0 \n",
            "  pthread-stubs      conda-forge/linux-64::pthread-stubs-0.4-h36c2ea0_1001 \n",
            "  ptyprocess         conda-forge/noarch::ptyprocess-0.7.0-pyhd3deb0d_0 \n",
            "  pure_eval          conda-forge/noarch::pure_eval-0.2.2-pyhd8ed1ab_0 \n",
            "  pyarrow            pkgs/main/linux-64::pyarrow-14.0.2-py312hb107042_0 \n",
            "  pyarrow-hotfix     conda-forge/noarch::pyarrow-hotfix-0.6-pyhd8ed1ab_0 \n",
            "  pygments           conda-forge/noarch::pygments-2.18.0-pyhd8ed1ab_0 \n",
            "  pyparsing          conda-forge/noarch::pyparsing-3.0.9-pyhd8ed1ab_0 \n",
            "  pyqt               pkgs/main/linux-64::pyqt-5.15.10-py312h6a678d5_0 \n",
            "  pyqt5-sip          pkgs/main/linux-64::pyqt5-sip-12.13.0-py312h5eee18b_0 \n",
            "  pyqtgraph          conda-forge/noarch::pyqtgraph-0.13.7-pyhd8ed1ab_0 \n",
            "  pyqtwebengine      pkgs/main/linux-64::pyqtwebengine-5.15.10-py312h6a678d5_0 \n",
            "  python-dateutil    conda-forge/noarch::python-dateutil-2.9.0-pyhd8ed1ab_0 \n",
            "  python-fastjsonsc~ conda-forge/noarch::python-fastjsonschema-2.19.1-pyhd8ed1ab_0 \n",
            "  python-json-logger conda-forge/noarch::python-json-logger-2.0.7-pyhd8ed1ab_0 \n",
            "  python-kaleido     conda-forge/noarch::python-kaleido-0.2.1-pyhd8ed1ab_0 \n",
            "  python-tzdata      conda-forge/noarch::python-tzdata-2024.1-pyhd8ed1ab_0 \n",
            "  python_abi         conda-forge/linux-64::python_abi-3.12-4_cp312 \n",
            "  pytz               conda-forge/noarch::pytz-2024.1-pyhd8ed1ab_0 \n",
            "  pyyaml             conda-forge/linux-64::pyyaml-6.0.1-py312h98912ed_1 \n",
            "  pyzmq              pkgs/main/linux-64::pyzmq-25.1.2-py312h6a678d5_0 \n",
            "  qt-main            pkgs/main/linux-64::qt-main-5.15.2-h53bd1ea_10 \n",
            "  qt-webengine       pkgs/main/linux-64::qt-webengine-5.15.9-h9ab4d14_7 \n",
            "  re2                conda-forge/linux-64::re2-2022.04.01-h27087fc_0 \n",
            "  referencing        conda-forge/noarch::referencing-0.35.1-pyhd8ed1ab_0 \n",
            "  retrying           conda-forge/noarch::retrying-1.3.3-py_2 \n",
            "  rfc3339-validator  conda-forge/noarch::rfc3339-validator-0.1.4-pyhd8ed1ab_0 \n",
            "  rfc3986-validator  conda-forge/noarch::rfc3986-validator-0.1.1-pyh9f0ad1d_0 \n",
            "  rpds-py            conda-forge/linux-64::rpds-py-0.18.1-py312h4413252_0 \n",
            "  s2n                conda-forge/linux-64::s2n-1.3.27-h1b5e8cb_0 \n",
            "  scipy              pkgs/main/linux-64::scipy-1.12.0-py312h2809609_0 \n",
            "  send2trash         conda-forge/noarch::send2trash-1.8.3-pyh0d859eb_0 \n",
            "  sip                pkgs/main/linux-64::sip-6.7.12-py312h6a678d5_0 \n",
            "  six                conda-forge/noarch::six-1.16.0-pyh6c4a22f_0 \n",
            "  snappy             conda-forge/linux-64::snappy-1.1.9-hbd366e4_1 \n",
            "  sniffio            conda-forge/noarch::sniffio-1.3.1-pyhd8ed1ab_0 \n",
            "  sortedcontainers   conda-forge/noarch::sortedcontainers-2.4.0-pyhd8ed1ab_0 \n",
            "  soupsieve          conda-forge/noarch::soupsieve-2.5-pyhd8ed1ab_1 \n",
            "  stack_data         conda-forge/noarch::stack_data-0.6.2-pyhd8ed1ab_0 \n",
            "  tblib              conda-forge/noarch::tblib-3.0.0-pyhd8ed1ab_0 \n",
            "  tenacity           conda-forge/noarch::tenacity-8.3.0-pyhd8ed1ab_0 \n",
            "  terminado          conda-forge/noarch::terminado-0.18.1-pyh0d859eb_0 \n",
            "  tinycss2           conda-forge/noarch::tinycss2-1.3.0-pyhd8ed1ab_0 \n",
            "  tomli              conda-forge/noarch::tomli-2.0.1-pyhd8ed1ab_0 \n",
            "  toolz              conda-forge/noarch::toolz-0.12.1-pyhd8ed1ab_0 \n",
            "  tornado            conda-forge/linux-64::tornado-6.4-py312h98912ed_0 \n",
            "  traitlets          conda-forge/noarch::traitlets-5.14.3-pyhd8ed1ab_0 \n",
            "  types-python-date~ conda-forge/noarch::types-python-dateutil-2.9.0.20240316-pyhd8ed1ab_0 \n",
            "  typing-extensions  conda-forge/noarch::typing-extensions-4.11.0-hd8ed1ab_0 \n",
            "  typing_extensions  conda-forge/noarch::typing_extensions-4.11.0-pyha770c72_0 \n",
            "  typing_utils       conda-forge/noarch::typing_utils-0.1.0-pyhd8ed1ab_0 \n",
            "  uri-template       conda-forge/noarch::uri-template-1.3.0-pyhd8ed1ab_0 \n",
            "  utf8proc           pkgs/main/linux-64::utf8proc-2.6.1-h5eee18b_1 \n",
            "  wcwidth            conda-forge/noarch::wcwidth-0.2.13-pyhd8ed1ab_0 \n",
            "  webcolors          conda-forge/noarch::webcolors-1.13-pyhd8ed1ab_0 \n",
            "  webencodings       conda-forge/noarch::webencodings-0.5.1-pyhd8ed1ab_2 \n",
            "  websocket-client   conda-forge/noarch::websocket-client-1.8.0-pyhd8ed1ab_0 \n",
            "  werkzeug           conda-forge/noarch::werkzeug-3.0.3-pyhd8ed1ab_0 \n",
            "  xorg-libxau        conda-forge/linux-64::xorg-libxau-1.0.11-hd590300_0 \n",
            "  xorg-libxdmcp      conda-forge/linux-64::xorg-libxdmcp-1.1.3-h7f98852_0 \n",
            "  xyzservices        conda-forge/noarch::xyzservices-2024.4.0-pyhd8ed1ab_0 \n",
            "  yaml               conda-forge/linux-64::yaml-0.2.5-h7f98852_2 \n",
            "  zeromq             pkgs/main/linux-64::zeromq-4.3.5-h6a678d5_0 \n",
            "  zict               conda-forge/noarch::zict-3.0.0-pyhd8ed1ab_0 \n",
            "  zipp               conda-forge/noarch::zipp-3.17.0-pyhd8ed1ab_0 \n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  libgcc-ng          pkgs/main::libgcc-ng-11.2.0-h1234567_1 --> conda-forge::libgcc-ng-13.2.0-h77fa898_7 \n",
            "  libgomp              pkgs/main::libgomp-11.2.0-h1234567_1 --> conda-forge::libgomp-13.2.0-h77fa898_7 \n",
            "  libuuid              pkgs/main::libuuid-1.41.5-h5eee18b_0 --> conda-forge::libuuid-2.38.1-h0b41bf4_0 \n",
            "  openssl              pkgs/main::openssl-3.0.13-h7f8727e_1 --> conda-forge::openssl-3.3.0-hd590300_0 \n",
            "  tk                        pkgs/main::tk-8.6.12-h1ccaba5_0 --> conda-forge::tk-8.6.13-noxft_h4845f30_101 \n",
            "  zlib                    pkgs/main::zlib-1.2.13-h5eee18b_0 --> conda-forge::zlib-1.2.13-hd590300_5 \n",
            "\n",
            "The following packages will be SUPERSEDED by a higher-priority channel:\n",
            "\n",
            "  _libgcc_mutex           pkgs/main::_libgcc_mutex-0.1-main --> conda-forge::_libgcc_mutex-0.1-conda_forge \n",
            "  _openmp_mutex          pkgs/main::_openmp_mutex-5.1-1_gnu --> conda-forge::_openmp_mutex-4.5-2_gnu \n",
            "  certifi            pkgs/main/linux-64::certifi-2024.2.2-~ --> conda-forge/noarch::certifi-2024.2.2-pyhd8ed1ab_0 \n",
            "  conda              pkgs/main::conda-24.5.0-py312h06a4308~ --> conda-forge::conda-24.5.0-py312h7900ff3_0 \n",
            "  python                pkgs/main::python-3.12.2-h996f2a0_0 --> conda-forge::python-3.12.2-hab00c5b_0_cpython \n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages:\n",
            "mysql-5.7.20         | 79.2 MB   | :   0% 0/1 [00:00<?, ?it/s]\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\n",
            "\n",
            "qt-webengine-5.15.9  | 53.8 MB   | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "scipy-1.12.0         | 23.3 MB   | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mathjax-2.7.7        | 21.2 MB   | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pandas-2.2.1         | 15.4 MB   | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "arrow-cpp-14.0.2     | 11.7 MB   | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libclang13-14.0.6    | 9.8 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "numpy-base-1.26.4    | 7.7 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "matplotlib-base-3.8. | 7.6 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "jupyterlab-4.2.0     | 7.6 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "babel-2.14.0         | 7.3 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "openblas-0.3.27      | 5.5 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
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            "\n",
            "\n",
            "libopenblas-0.3.27   | 5.3 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "plotly-5.22.0        | 5.1 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            " ... (more hidden) ...\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :   0% 0.00046843413253998403/1 [00:00<09:22, 562.55s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :   0% 0.00019718423464384747/1 [00:00<24:25, 1465.84s/it]\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :   0% 0.00029119184998373156/1 [00:00<16:59, 1019.75s/it]\u001b[A\u001b[A\u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :   0% 0.000263832842570537/1 [00:00<19:43, 1184.17s/it]\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :  12% 0.11991913793023591/1 [00:00<00:02,  2.38s/it]    \u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :   5% 0.046535479375948/1 [00:00<00:06,  6.51s/it]       \n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :   3% 0.033195870898145394/1 [00:00<00:09,  9.32s/it]    \u001b[A\u001b[A\u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :   5% 0.053821899884389554/1 [00:00<00:05,  5.94s/it]  \u001b[A\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :  21% 0.20657945245013296/1 [00:00<00:01,  1.76s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :   9% 0.08754980018186828/1 [00:00<00:03,  4.24s/it]\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  11% 0.10774098449398067/1 [00:00<00:02,  3.28s/it] \u001b[A\u001b[A\u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  11% 0.10949062966677287/1 [00:00<00:03,  3.50s/it] \u001b[A\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  15% 0.14946564986003638/1 [00:00<00:02,  2.81s/it]\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :  28% 0.2843395184517703/1 [00:00<00:01,  1.67s/it] \u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  17% 0.17063842409046667/1 [00:00<00:02,  2.47s/it]\u001b[A\u001b[A\u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  16% 0.1625210310234508/1 [00:00<00:02,  2.78s/it] \u001b[A\n",
            "\n",
            "qt-webengine-5.15.9  | 53.8 MB   | :  31% 0.3136456284080681/1 [00:00<00:01,  1.52s/it] \u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :  38% 0.3780263449597671/1 [00:00<00:00,  1.43s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  25% 0.24605711323625315/1 [00:00<00:01,  1.96s/it]\u001b[A\u001b[A\u001b[A\n",
            "mysql-5.7.20         | 79.2 MB   | :  19% 0.19422647112418975/1 [00:00<00:02,  2.85s/it]\n",
            "\n",
            "qt-webengine-5.15.9  | 53.8 MB   | :  39% 0.3903856153401626/1 [00:00<00:00,  1.45s/it]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :  46% 0.4623444888169642/1 [00:00<00:00,  1.36s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  24% 0.23602952886868542/1 [00:00<00:02,  2.70s/it]\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  27% 0.267262669523954/1 [00:00<00:01,  2.33s/it]  \u001b[A\n",
            "\n",
            "qt-webengine-5.15.9  | 53.8 MB   | :  47% 0.46654423873489276/1 [00:00<00:00,  1.43s/it]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  28% 0.2788185077864003/1 [00:00<00:01,  2.59s/it] \n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  33% 0.33348471300915883/1 [00:00<00:01,  2.04s/it]\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  40% 0.39747687522779357/1 [00:00<00:01,  1.74s/it]\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  32% 0.3241708817544852/1 [00:01<00:01,  2.46s/it]\n",
            "\n",
            "qt-webengine-5.15.9  | 53.8 MB   | :  54% 0.541249453286212/1 [00:01<00:00,  1.52s/it]  \u001b[A\u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  39% 0.3917917712172475/1 [00:01<00:01,  2.01s/it] \u001b[A\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  37% 0.367157044906844/1 [00:01<00:01,  2.55s/it] \n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :  84% 0.8394339655116514/1 [00:01<00:00,  1.10s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  44% 0.44429450688878436/1 [00:01<00:01,  2.10s/it]\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  52% 0.5209422196208957/1 [00:01<00:00,  1.82s/it]\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  42% 0.416255919333162/1 [00:01<00:01,  2.43s/it]\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  50% 0.495214245504898/1 [00:01<00:01,  2.06s/it]  \u001b[A\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :  94% 0.9354629626823481/1 [00:01<00:00,  1.11s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "qt-webengine-5.15.9  | 53.8 MB   | :  67% 0.6729282944992379/1 [00:01<00:00,  1.74s/it]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  48% 0.48290419064278245/1 [00:01<00:01,  2.12s/it]\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  57% 0.5651299487860904/1 [00:01<00:00,  1.85s/it]\u001b[A\n",
            "\n",
            "qt-webengine-5.15.9  | 53.8 MB   | :  73% 0.7328087388477661/1 [00:01<00:00,  1.77s/it]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  54% 0.5369326709351967/1 [00:01<00:00,  2.04s/it] \n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  63% 0.6339903206970005/1 [00:01<00:00,  1.72s/it]\u001b[A\n",
            "\n",
            "qt-webengine-5.15.9  | 53.8 MB   | :  81% 0.805188499249628/1 [00:01<00:00,  1.65s/it] \u001b[A\u001b[A\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  59% 0.5897780458197478/1 [00:01<00:00,  2.00s/it]\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  69% 0.6933527102753714/1 [00:01<00:00,  1.72s/it]\u001b[A\n",
            "\n",
            "qt-webengine-5.15.9  | 53.8 MB   | :  88% 0.8798937138009473/1 [00:01<00:00,  1.55s/it]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  79% 0.7943713667556196/1 [00:01<00:00,  1.61s/it]\u001b[A\u001b[A\u001b[A\n",
            "mysql-5.7.20         | 79.2 MB   | :  64% 0.6404543941232166/1 [00:01<00:00,  2.12s/it]\n",
            "\n",
            "qt-webengine-5.15.9  | 53.8 MB   | :  95% 0.9458784752918012/1 [00:01<00:00,  1.62s/it]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  86% 0.8581423819020568/1 [00:01<00:00,  1.65s/it]\u001b[A\u001b[A\u001b[A\n",
            "mysql-5.7.20         | 79.2 MB   | :  69% 0.6883701631416715/1 [00:01<00:00,  2.32s/it]\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  92% 0.9201662459485916/1 [00:01<00:00,  1.65s/it]\u001b[A\u001b[A\u001b[A\n",
            "mysql-5.7.20         | 79.2 MB   | :  73% 0.7335253528751126/1 [00:01<00:00,  2.36s/it]\n",
            "mysql-5.7.20         | 79.2 MB   | :  85% 0.8486809459071195/1 [00:02<00:00,  2.17s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  91% 0.9058643739538352/1 [00:02<00:00,  2.09s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  95% 0.9543716956762217/1 [00:02<00:00,  2.20s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  21% 0.20839602940410515/1 [00:02<00:05,  6.85s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  36% 0.35746764167857453/1 [00:02<00:02,  3.55s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  50% 0.49842651314899117/1 [00:02<00:01,  2.37s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  64% 0.6373571994183946/1 [00:02<00:00,  1.76s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  76% 0.7600624040796925/1 [00:02<00:00,  1.51s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  88% 0.8761760068376976/1 [00:02<00:00,  1.32s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "scipy-1.12.0         | 23.3 MB   | :   0% 0.0006691887316034876/1 [00:02<1:14:34, 4477.29s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "scipy-1.12.0         | 23.3 MB   | :  13% 0.12982261393107658/1 [00:03<00:14, 16.98s/it]      \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "scipy-1.12.0         | 23.3 MB   | :  25% 0.2475998306932904/1 [00:03<00:05,  7.87s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mathjax-2.7.7        | 21.2 MB   | :   0% 0.0007353142806569484/1 [00:03<1:13:23, 4407.06s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "scipy-1.12.0         | 23.3 MB   | :  38% 0.37876082208757395/1 [00:03<00:02,  4.54s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mathjax-2.7.7        | 21.2 MB   | :  11% 0.11029714209854226/1 [00:03<00:19, 21.56s/it]      \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :   0% 0.0008009386782302188/1 [00:03<1:13:03, 4387.37s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pandas-2.2.1         | 15.4 MB   | :   0% 0.0010174177522631242/1 [00:03<58:35, 3519.42s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :   2% 0.016018773564604377/1 [00:03<02:39, 162.27s/it]    \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pandas-2.2.1         | 15.4 MB   | :   7% 0.07223666041068182/1 [00:03<00:33, 36.24s/it]    \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :   3% 0.025630037703367/1 [00:03<01:29, 91.65s/it]    \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "arrow-cpp-14.0.2     | 11.7 MB   | :   0% 0.001340927777322181/1 [00:03<46:32, 2796.55s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :   4% 0.035241301842129626/1 [00:03<00:57, 59.50s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "arrow-cpp-14.0.2     | 11.7 MB   | :   9% 0.08850123330326394/1 [00:03<00:28, 30.96s/it]   \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mathjax-2.7.7        | 21.2 MB   | :  17% 0.17206354167372592/1 [00:03<00:13, 16.13s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :   4% 0.04485256598089225/1 [00:03<00:39, 41.82s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "scipy-1.12.0         | 23.3 MB   | :  48% 0.483154264217718/1 [00:03<00:02,  5.11s/it]  \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :   5% 0.05446383011965488/1 [00:04<00:29, 31.41s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pandas-2.2.1         | 15.4 MB   | :  11% 0.11496820600573303/1 [00:04<00:20, 23.51s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :   6% 0.06407509425841751/1 [00:04<00:23, 24.69s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "arrow-cpp-14.0.2     | 11.7 MB   | :  14% 0.13945648884150683/1 [00:04<00:17, 19.90s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :   7% 0.07368635839718013/1 [00:04<00:18, 20.17s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pandas-2.2.1         | 15.4 MB   | :  14% 0.1434559030691005/1 [00:04<00:16, 18.98s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :   8% 0.08329762253594276/1 [00:04<00:15, 17.25s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mathjax-2.7.7        | 21.2 MB   | :  21% 0.21029988426788723/1 [00:04<00:11, 14.95s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "arrow-cpp-14.0.2     | 11.7 MB   | :  17% 0.17432061105188354/1 [00:04<00:12, 15.74s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :   9% 0.09290888667470538/1 [00:04<00:14, 15.47s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pandas-2.2.1         | 15.4 MB   | :  17% 0.16583909361888924/1 [00:04<00:13, 16.35s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :  10% 0.102520150813468/1 [00:04<00:12, 14.08s/it]  \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "arrow-cpp-14.0.2     | 11.7 MB   | :  20% 0.20248009437564932/1 [00:04<00:10, 13.36s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pandas-2.2.1         | 15.4 MB   | :  18% 0.18415261315962547/1 [00:04<00:11, 14.57s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :  11% 0.11213141495223064/1 [00:04<00:11, 12.97s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mathjax-2.7.7        | 21.2 MB   | :  24% 0.23677119837153737/1 [00:04<00:10, 14.27s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "arrow-cpp-14.0.2     | 11.7 MB   | :  23% 0.2252758665901264/1 [00:04<00:09, 11.79s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :  12% 0.12174267909099326/1 [00:04<00:10, 12.32s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pandas-2.2.1         | 15.4 MB   | :  20% 0.20043129719583547/1 [00:04<00:10, 13.23s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :  13% 0.13135394322975588/1 [00:04<00:10, 11.96s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "arrow-cpp-14.0.2     | 11.7 MB   | :  25% 0.2467307110272813/1 [00:04<00:07, 10.56s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "scipy-1.12.0         | 23.3 MB   | :  56% 0.5560958359624981/1 [00:04<00:03,  7.16s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pandas-2.2.1         | 15.4 MB   | :  22% 0.21569256347978233/1 [00:04<00:09, 12.19s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mathjax-2.7.7        | 21.2 MB   | :  26% 0.25735999822993194/1 [00:04<00:10, 13.83s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :  14% 0.1409652073685185/1 [00:04<00:09, 11.59s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "arrow-cpp-14.0.2     | 11.7 MB   | :  27% 0.26550369990979183/1 [00:05<00:07,  9.68s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pandas-2.2.1         | 15.4 MB   | :  23% 0.22993641201146606/1 [00:05<00:08, 11.36s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :  15% 0.15057647150728115/1 [00:05<00:09, 11.32s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "arrow-cpp-14.0.2     | 11.7 MB   | :  28% 0.2829357610149802/1 [00:05<00:06,  8.96s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mathjax-2.7.7        | 21.2 MB   | :  27% 0.27353691240438477/1 [00:05<00:09, 13.53s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pandas-2.2.1         | 15.4 MB   | :  24% 0.24316284279088668/1 [00:05<00:08, 10.82s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :  16% 0.16018773564604377/1 [00:05<00:09, 11.39s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "arrow-cpp-14.0.2     | 11.7 MB   | :  30% 0.30036782212016855/1 [00:05<00:05,  8.41s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pandas-2.2.1         | 15.4 MB   | :  26% 0.2563892735703073/1 [00:05<00:07, 10.32s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :  17% 0.1697989997848064/1 [00:05<00:09, 11.59s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mathjax-2.7.7        | 21.2 MB   | :  29% 0.2875078837368668/1 [00:05<00:09, 13.34s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "arrow-cpp-14.0.2     | 11.7 MB   | :  32% 0.3164589554480347/1 [00:05<00:05,  8.01s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pandas-2.2.1         | 15.4 MB   | :  27% 0.26859828659746476/1 [00:05<00:07,  9.89s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :  18% 0.179410263923569/1 [00:05<00:09, 11.23s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mathjax-2.7.7        | 21.2 MB   | :  30% 0.299272912227378/1 [00:05<00:09, 13.05s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "arrow-cpp-14.0.2     | 11.7 MB   | :  33% 0.33255008877590087/1 [00:05<00:05,  7.62s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pandas-2.2.1         | 15.4 MB   | :  28% 0.28080729962462225/1 [00:05<00:06,  9.62s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :  19% 0.18902152806233163/1 [00:05<00:09, 11.17s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "arrow-cpp-14.0.2     | 11.7 MB   | :  35% 0.3486412221037671/1 [00:05<00:04,  7.34s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "scipy-1.12.0         | 23.3 MB   | :  61% 0.6069541795643631/1 [00:05<00:03,  8.41s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mathjax-2.7.7        | 21.2 MB   | :  31% 0.3103026264372322/1 [00:05<00:08, 12.82s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pandas-2.2.1         | 15.4 MB   | :  29% 0.2930163126517798/1 [00:05<00:06,  9.41s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :  20% 0.19863279220109428/1 [00:05<00:09, 11.25s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "arrow-cpp-14.0.2     | 11.7 MB   | :  36% 0.36473235543163324/1 [00:05<00:04,  7.19s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pandas-2.2.1         | 15.4 MB   | :  31% 0.3052253256789373/1 [00:05<00:06,  9.20s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mathjax-2.7.7        | 21.2 MB   | :  32% 0.3205970263664295/1 [00:05<00:08, 12.68s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "scipy-1.12.0         | 23.3 MB   | :  64% 0.6437595598025551/1 [00:05<00:02,  7.68s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :  21% 0.21144781105277777/1 [00:05<00:08, 10.34s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "arrow-cpp-14.0.2     | 11.7 MB   | :  47% 0.4706656498400855/1 [00:05<00:01,  2.66s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mathjax-2.7.7        | 21.2 MB   | :  43% 0.4331001113069426/1 [00:05<00:02,  3.70s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pandas-2.2.1         | 15.4 MB   | :  46% 0.46394249503198465/1 [00:05<00:01,  2.06s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "scipy-1.12.0         | 23.3 MB   | :  73% 0.7287465287161979/1 [00:05<00:01,  5.30s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :  33% 0.32598204203969905/1 [00:05<00:01,  2.65s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "arrow-cpp-14.0.2     | 11.7 MB   | :  66% 0.6637592497744795/1 [00:05<00:00,  1.27s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mathjax-2.7.7        | 21.2 MB   | :  55% 0.5536916533346822/1 [00:05<00:00,  2.17s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pandas-2.2.1         | 15.4 MB   | :  66% 0.6582692857142414/1 [00:05<00:00,  1.16s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "scipy-1.12.0         | 23.3 MB   | :  85% 0.8478621229416187/1 [00:05<00:00,  3.41s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :  47% 0.4669472494082176/1 [00:05<00:00,  1.50s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "arrow-cpp-14.0.2     | 11.7 MB   | :  91% 0.9078081052471165/1 [00:05<00:00,  1.24it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mathjax-2.7.7        | 21.2 MB   | :  68% 0.6750185096430786/1 [00:06<00:00,  1.59s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pandas-2.2.1         | 15.4 MB   | :  83% 0.8332651391034988/1 [00:06<00:00,  1.08it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "scipy-1.12.0         | 23.3 MB   | :  95% 0.9482404326821419/1 [00:06<00:00,  2.60s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :  58% 0.5838842964298295/1 [00:06<00:00,  1.27s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mathjax-2.7.7        | 21.2 MB   | :  83% 0.8294345085810377/1 [00:06<00:00,  1.17s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pandas-2.2.1         | 15.4 MB   | :  98% 0.9797732954293886/1 [00:06<00:00,  1.17it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :  71% 0.7056269755208228/1 [00:06<00:00,  1.11s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mathjax-2.7.7        | 21.2 MB   | :  97% 0.968408907625201/1 [00:06<00:00,  1.01s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :  84% 0.8441893668546506/1 [00:06<00:00,  1.01it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | : 100% 0.9963677157183922/1 [00:06<00:00,  1.10it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libclang13-14.0.6    | 9.8 MB    | :   0% 0.0016025206836573897/1 [00:06<1:08:16, 4103.53s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "libclang13-14.0.6    | 9.8 MB    | :  28% 0.2804411196400432/1 [00:06<00:12, 16.80s/it]       \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "libclang13-14.0.6    | 9.8 MB    | :  55% 0.5496645944944847/1 [00:06<00:03,  7.30s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "libclang13-14.0.6    | 9.8 MB    | :  80% 0.7980553004613801/1 [00:06<00:00,  4.32s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "numpy-base-1.26.4    | 7.7 MB    | :   0% 0.0020306342695560406/1 [00:06<56:26, 3393.00s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "numpy-base-1.26.4    | 7.7 MB    | :  36% 0.35536099717230707/1 [00:06<00:08, 13.90s/it]    \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "numpy-base-1.26.4    | 7.7 MB    | :  71% 0.7107219943446141/1 [00:07<00:01,  5.90s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
            "matplotlib-base-3.8. | 7.6 MB    | :   0% 0.002050079549442966/1 [00:07<57:57, 3484.30s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
            "matplotlib-base-3.8. | 7.6 MB    | :  34% 0.33621304610864644/1 [00:07<00:10, 15.21s/it]   \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "jupyterlab-4.2.0     | 7.6 MB    | :   0% 0.00205255410441918/1 [00:07<58:50, 3537.29s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "babel-2.14.0         | 7.3 MB    | :   0% 0.0021530273662078254/1 [00:07<56:43, 3411.11s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "jupyterlab-4.2.0     | 7.6 MB    | :  21% 0.21346562685959472/1 [00:07<00:19, 24.35s/it]  \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
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            "\n",
            "\n",
            "\n",
            "matplotlib-base-3.8. | 7.6 MB    | :  53% 0.5268704442068424/1 [00:07<00:04,  8.70s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "babel-2.14.0         | 7.3 MB    | :  36% 0.35955557015670686/1 [00:07<00:09, 14.68s/it]    \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
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            "\n",
            "\n",
            "jupyterlab-4.2.0     | 7.6 MB    | :  46% 0.45771956528547714/1 [00:07<00:05,  9.52s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
            "matplotlib-base-3.8. | 7.6 MB    | :  73% 0.7298283196016959/1 [00:07<00:01,  5.39s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "openblas-0.3.27      | 5.5 MB    | :   0% 0.0028590374480857153/1 [00:07<43:32, 2619.76s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "jupyterlab-4.2.0     | 7.6 MB    | :  83% 0.8251267499765104/1 [00:07<00:00,  4.23s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "babel-2.14.0         | 7.3 MB    | :  72% 0.7212641676796215/1 [00:07<00:01,  6.21s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "openblas-0.3.27      | 5.5 MB    | :  26% 0.2601724077758001/1 [00:07<00:15, 20.61s/it]     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "libopenblas-0.3.27   | 5.3 MB    | :   0% 0.0029263690607916376/1 [00:07<43:21, 2609.47s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "openblas-0.3.27      | 5.5 MB    | :  76% 0.7576449237427145/1 [00:07<00:01,  5.66s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libopenblas-0.3.27   | 5.3 MB    | :  61% 0.605758395583869/1 [00:07<00:03,  9.01s/it]      \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
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            "\n",
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            "\n",
            " ... (more hidden) ...\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " ... (more hidden) ...\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " ... (more hidden) ...\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "plotly-5.22.0        | 5.1 MB    | :   0% 0.0030781225781906824/1 [00:08<45:31, 2739.62s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "plotly-5.22.0        | 5.1 MB    | :  53% 0.5294370834487974/1 [00:08<00:05, 11.38s/it]     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | : 100% 1.0/1 [00:09<00:00,  1.11s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "qt-webengine-5.15.9  | 53.8 MB   | : 100% 1.0/1 [00:12<00:00,  1.62s/it]               \u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | : 100% 1.0/1 [00:17<00:00, 70.03s/it]               \u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | : 100% 1.0/1 [00:17<00:00, 70.03s/it]\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | : 100% 1.0/1 [00:20<00:00, 45.94s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | : 100% 1.0/1 [00:20<00:00, 45.94s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | : 100% 1.0/1 [00:34<00:00, 155.58s/it]              \u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | : 100% 1.0/1 [00:34<00:00, 155.58s/it]\u001b[A\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "arrow-cpp-14.0.2     | 11.7 MB   | : 100% 1.0/1 [00:35<00:00,  1.24it/s]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pandas-2.2.1         | 15.4 MB   | : 100% 1.0/1 [00:37<00:00,  1.17it/s]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | : 100% 1.0/1 [00:46<00:00,  2.20s/it]               \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "libboost-1.82.0      | 19.5 MB   | : 100% 1.0/1 [01:00<00:00,  1.10it/s]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "libclang13-14.0.6    | 9.8 MB    | : 100% 1.0/1 [01:01<00:00,  4.32s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "numpy-base-1.26.4    | 7.7 MB    | : 100% 1.0/1 [01:02<00:00, 81.81s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "numpy-base-1.26.4    | 7.7 MB    | : 100% 1.0/1 [01:02<00:00, 81.81s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "matplotlib-base-3.8. | 7.6 MB    | : 100% 1.0/1 [01:03<00:00, 93.88s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "matplotlib-base-3.8. | 7.6 MB    | : 100% 1.0/1 [01:03<00:00, 93.88s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "jupyterlab-4.2.0     | 7.6 MB    | : 100% 1.0/1 [01:04<00:00,  4.23s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
            "openblas-0.3.27      | 5.5 MB    | : 100% 1.0/1 [01:05<00:00,  5.66s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "babel-2.14.0         | 7.3 MB    | : 100% 1.0/1 [01:06<00:00, 86.49s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "babel-2.14.0         | 7.3 MB    | : 100% 1.0/1 [01:06<00:00, 86.49s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "libopenblas-0.3.27   | 5.3 MB    | : 100% 1.0/1 [01:06<00:00, 76.47s/it]              \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "libopenblas-0.3.27   | 5.3 MB    | : 100% 1.0/1 [01:06<00:00, 76.47s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " ... (more hidden) ...\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " ... (more hidden) ...\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "mathjax-2.7.7        | 21.2 MB   | : 100% 1.0/1 [01:11<00:00,  1.01s/it]              \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "plotly-5.22.0        | 5.1 MB    | : 100% 1.0/1 [01:17<00:00, 86.97s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "plotly-5.22.0        | 5.1 MB    | : 100% 1.0/1 [01:17<00:00, 86.97s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\u001b[A\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\u001b[A\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\u001b[A\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\u001b[A\n",
            "\n",
            "\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "Preparing transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n",
            "Verifying transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
            "Executing transaction: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#!conda install matplotlib --yes"
      ],
      "metadata": {
        "id": "1aE1Oo5W5fS2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#!conda install scipy --yes"
      ],
      "metadata": {
        "id": "JXoeQYUX5ff4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#!conda install openpyxl --yes"
      ],
      "metadata": {
        "id": "ufpCSnbY5fq1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#!conda install xlrd --yes"
      ],
      "metadata": {
        "id": "UXe0t8Bd5f35"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# BPL_CHO_Fedbatch setup\n"
      ],
      "metadata": {
        "id": "UTETJm3l6heL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now specific installation and the run simulations. Start with connecting to Github. Then upload the two files:\n",
        "\n",
        "* FMU - BPL_CHO_Fedbatch_linux_om_me.fmu\n",
        "* Setup-file - BPL_CHO_Fedbatch_fmpy_explore"
      ],
      "metadata": {
        "id": "mxILgQ103nZ6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter out DepracationWarnings for 'np.float as alias' is needed - wish I could make filter more narrow\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "metadata": {
        "id": "RrYshFe13jmO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "git clone https://github.com/janpeter19/BPL_CHO_Fedbatch"
      ],
      "metadata": {
        "id": "RfWPh1B63yBF",
        "outputId": "f72de254-e8b0-42ab-873a-ad26ed37d407",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Cloning into 'BPL_CHO_Fedbatch'...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd BPL_CHO_Fedbatch"
      ],
      "metadata": {
        "id": "i6uOVB44300a",
        "outputId": "a74b81e3-b32d-4b41-e90a-13efbc240370",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/BPL_CHO_Fedbatch\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# BPL_CHO_Fedbatch - demo\n",
        "\n",
        "Author: Jan Peter Axelson\n",
        "\n",
        "This notebook deals with CHO fedbatch cultivation and recombinant protein production is included. First we make a check of the model by comparing a simulation result with corresponding published diagram. Then we take a closer look at the start-up strategy to keep the by-product formation low. After that we investigate at a whole cultivation and see the impact of feeding strategy on both cell growth and protein production where a trade-off is needed in this case.\n",
        "\n",
        "The model used takes its inspiration from the microbial bottleneck models as described in the original papers [1] and [2] and reformulated and studied in [3]. The laboratory cultures used for model validation in [1] did produce MAb (against part of IgG) but no MAb-data was presented. The paper focus on viable and non-viable cell concentrations only. The original model is in section 5 expanded with the classical empirical Luedeking-Piret model recombinant protein production, see chapter 5 in [4]. In this way can get more insight into choice of feeding profile.\n",
        "\n",
        "Interaction with the compiled model as FMU is mainly through the simplified commands: par(), init(), newplot(), simu() etc. The last simulation is always available in the workspace and called 'sim_res'. The command describe() brings mainly up description infomration from the actual Modelica code from the FMU but is complemented with information given in the dedicated Python setup-file.\n",
        "\n",
        "The idea here is to demonstrate how simulations and varyiing conditions can provide some process insight that can support the experimental work. I hope that at the end of this session you are ready to formulate your own questions you want to address with simulations - and you can just go on in this notebook! Just press the field \"+Code\" in the upper left part of notebook interface and you get a new \"cell\" where you write your own code. You can copy and paste from cells above using ctrl-c and ctrl-v as usual and edit the cell. When your are ready to execute the cell just press the \"play button\" to the left in the cell or press shift-enter as in \"ordinary\" Jupyter notebooks.\n",
        "\n",
        "After a session you may want to save your own notebook. That you can do on your Google Drive account and I refer to Colab instructions for how to do this. It is easy.\n",
        "\n",
        "Good luck!"
      ],
      "metadata": {
        "id": "dsjD0XLpQysI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "run -i BPL_CHO_Fedbatch_fmpy_explore.py"
      ],
      "metadata": {
        "id": "b-y82jZY33cO",
        "outputId": "3e6d9f02-08af-4815-bbc0-2a6b45125c87",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Linux - run FMU pre-comiled OpenModelica 1.21.0\n",
            "\n",
            "Model for bioreactor has been setup. Key commands:\n",
            " - par()       - change of parameters and initial values\n",
            " - init()      - change initial values only\n",
            " - simu()      - simulate and plot\n",
            " - newplot()   - make a new plot\n",
            " - show()      - show plot from previous simulation\n",
            " - disp()      - display parameters and initial values from the last simulation\n",
            " - describe()  - describe culture, broth, parameters, variables with values/units\n",
            "\n",
            "Note that both disp() and describe() takes values from the last simulation\n",
            "and the command process_diagram() brings up the main configuration\n",
            "\n",
            "Brief information about a command by help(), eg help(simu)\n",
            "Key system information is listed with the command system_info()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "plt.rcParams['figure.figsize'] = [25/2.54, 20/2.54]"
      ],
      "metadata": {
        "id": "HUFhuE-H4RCw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1 About the process model\n",
        "\n",
        "We can get information about the process and liquid phase by the command describe(). Here is no gas-phase included. This command can also be used to bring up information about a specific variable or parameter. However, you should use describe() after a simulation to get the valued used during the simulation."
      ],
      "metadata": {
        "id": "dx9oMnP0RJkM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "process_diagram()"
      ],
      "metadata": {
        "id": "3P0yJq2W8Ca6",
        "outputId": "91743b4f-62e9-49a1-c9d2-874fe4235afa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No processDiagram.png file in the FMU, but try the file on disk.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 984.252x787.402 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "describe('culture'); print(); #describe('liquidphase')                                  # Pump schedule parameter\n"
      ],
      "metadata": {
        "id": "-Y3NHWIj4TZ6",
        "outputId": "23c06d63-b4ef-419b-c5df-d39afde7bf81",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reactor culture CHO-MAb - cell line HB-58 American Culture Collection ATCC\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The molecular weight of the recombinant protein (MAb) is somewhat arbitrarly chosen and the value not used in the simulations."
      ],
      "metadata": {
        "id": "Ywb8yKJ-I3PJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2 Simulation reproducing the original paper\n",
        "\n",
        "The simulation below reproduce diagrams in Figure 5 in the original paper. There are several simulation in the paper showing how well the model describe different experiments and here I just choose one of them."
      ],
      "metadata": {
        "id": "e0HywTQuRTuK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Data from Table 1 and 2 for experiment 4 shown in Figure 5 in paper [1]\n",
        "# -culture parameters taken from Table 5 identified parameters for cultures 1,2,and 3\n",
        "\n",
        "# Initial process conditions\n",
        "V_start=0.30\n",
        "init(V_start=V_start, VXv_start=V_start*0.172, VXd_start=V_start*0.020)\n",
        "init(VG_start=V_start*17.83, VGn_start=V_start*3.74, VL_start=V_start*0.12, VN_start=V_start*0.24)\n",
        "\n",
        "# Feeding\n",
        "Feed=0.1/24\n",
        "par(G_in=15, Gn_in=9.3)\n",
        "par(t0=0, F0=0, t1=63.5, F1=Feed, t2=300, F2=Feed)\n",
        "\n",
        "# Simulation\n",
        "newplot(title='Figure 5 - Fedbatch cultivation')\n",
        "simu(200)"
      ],
      "metadata": {
        "id": "3BkbO1cprB44",
        "outputId": "372d4ece-cbee-493d-ae90-9229d89b5e63",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 984.252x787.402 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Comment:** The simulation results looks very similar to the published diagram Figure 5 in [1]. The model pass this quality check."
      ],
      "metadata": {
        "id": "rWDmNFwbJWEn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3 Simulation of different start-up feeding strategies"
      ],
      "metadata": {
        "id": "s9Ico4G1Je3Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Figur 5\n",
        "V_0=0.30\n",
        "init(V_start=V_start, VXv_start=V_start*0.172, VXd_start=V_start*0.020)\n",
        "init(VG_start=V_start*17.83, VGn_start=V_start*3.74, VL_start=V_start*0.12, VN_start=V_start*0.24)\n",
        "\n",
        "# Feeding\n",
        "Feed=0.1/24\n",
        "par(G_in=15, Gn_in=9.3)\n",
        "par(t0=0, F0=0, t1=63.5, F1=Feed, t2=300, F2=Feed)\n",
        "\n",
        "newplot(title='Figure 5 - Fedbatch cultivation')\n",
        "simu(120)\n",
        "\n",
        "init(VG_start=0.35*V_start*17.83, VGn_start=0.35*V_start*3.74)\n",
        "par(t0=0, F0=0, t1=40.0, F1=0.5*Feed, t2=63.5, F2=Feed,t3=300, F3=Feed)\n",
        "simu(120)\n",
        "\n",
        "init(VG_start=0.35*V_start*17.83, VGn_start=0.35*V_start*3.74)\n",
        "par(t0=0, F0=0, t1=40.0, F1=0.5*Feed, t2=63.5, F2=0.7*Feed, t3=88.0, F3=Feed, t4=300, F4=Feed)\n",
        "simu(120)\n",
        "\n",
        "# Reset time table to avoid problems below\n",
        "par(t1=1001, t2=1002, t3=1003, t4=1004, t5=1005, t6=1006)"
      ],
      "metadata": {
        "id": "btfm5EDZJohy",
        "outputId": "aacc5bf6-f846-42f1-985c-eb770511e983",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 984.252x787.402 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Comment:** We see that starting the feed a day erlier at lower rate and then increase decreaes lactate formation to half, while the cell conentation is just slightlly lower. With a more careful design of the feedprofile the ammonia formation can be decreased more than shown here."
      ],
      "metadata": {
        "id": "FfIxEErSJuDI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4 Simulation of optimal feed profile for cell growth\n",
        "\n",
        "At the end of the original paper section 5 in [1], the derived model is used to find an optimal feeding profile for high final cell concentration. It is stated that protein productivity is assumed to be mainly positively growth associated and therefore optimization of cell concentration is very similar to optimization of protein product. The optimization of feed profile is done with differnt structures of the feed profile. All of them have a start-time and all of them has a fixed amount of substrate and concentrations in the media are also the same.\n",
        "\n",
        "* The first optimzation is for a feed profile similar to the experimental, i.e. after start the feed rate remains constant throughout the cultivation. Thus the start time and the actual feed rate are optimized. The result was that the start time was about the same as expermentally but the feed rate was 50% higher, see Figure 7 and Figure 10 in [1].\n",
        "\n",
        "* The second optimization is for a feed profile with not just one increase but three steps of increase of feed rate. The results is a somwehat higher final cell concentration, see Figure 11.\n",
        "\n",
        "* The third optimization is for a feed profile with five steps of increase of feed rate. The results is a slightly higher final cell concentration than for three steps, see Figure 12.\n",
        "\n",
        "* The fourth optimization is for a feed profie with continuous exponential increase of the reed rate. The result is a bit higher final concentration than the previous with five steps, see Figure 13 but not shown in the figure below.\n",
        "\n",
        "Below we just show the results of the original experimetnal cultivation, compared with results from three and five steps.\n",
        "It is possible to do the optimization in Python with the FMU, but we save that for a future notebook."
      ],
      "metadata": {
        "id": "05aIcbn2JxUb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Culture parameters taken from Table 5 identified parameters for cultures 1,2,and 3\n",
        "\n",
        "# Data chosen\n",
        "V_start=0.35\n",
        "init(V_start=V_start, VXv_start=V_start*0.20, VXd_start=V_start*0.0)\n",
        "init(VG_start=V_start*18.0, VGn_start=V_start*2.4, VL_start=V_start*0, VN_start=V_start*0)\n",
        "\n",
        "# Feeding n=1 - experimental and lower feed rate\n",
        "par(G_in=15, Gn_in=4.0)\n",
        "par(t0=0, F0=0, t1=49, F1=0.00417)\n",
        "par(t2=1002, t3=1003, t4=1004, t5=1005)\n",
        "\n",
        "# Simulation\n",
        "newplot(title='Figure 12 - Fedbatch with optimal step-wise feed')\n",
        "simu(125)\n",
        "\n",
        "# Feeding n=1\n",
        "par(G_in=15, Gn_in=4.0)\n",
        "par(t0=0, F0=0, t1=52, F1=0.00625)\n",
        "par(t2=1002, t3=1003, t4=1004, t5=1005, t6=1006)\n",
        "\n",
        "# Simulation\n",
        "simu(125)\n",
        "\n",
        "# Feeding n=3\n",
        "par(G_in=15, Gn_in=4.0)\n",
        "par(t0=0, F0=0, t1=52, F1=0.002, t2=74, F2=0.0045, t3=98.0, F3=0.010)\n",
        "par(t4=99.0, F4=0.010, t5=106, F5=0.010, t6=150, F6=0.010)\n",
        "\n",
        "# Simulation\n",
        "simu(125)\n",
        "\n",
        "# Feeding n=5\n",
        "par(G_in=15, Gn_in=4.0)\n",
        "par(t0=0, F0=0, t1=52, F1=0.0012, t2=64, F2=0.0020, t3=78.0, F3=0.0040)\n",
        "par(t4=92.0, F4=0.0080, t5=106, F5=0.012, t6=150, F6=0.012)\n",
        "\n",
        "# Simulation\n",
        "simu(125)\n",
        "\n",
        "# Reset feeding parameters since the table need time in strict increasing value\n",
        "par(t3=1004, t4=1005, t5=1005, t6=1006)"
      ],
      "metadata": {
        "id": "JDD59RgUKA9g",
        "outputId": "5b4821f0-93d3-4246-8228-d8d30b099ca4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 984.252x787.402 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Comment:** We see that that already the better tuned constant feed rate (dahsed) compared to the experimental (solid) gives higher final cell concnetration.\n",
        "\n",
        "Breaking up the constant feed rate in three (dotted) and five (dash-dotted) steps with a more gradual increase of the feed rate gives even higher final cell concentration. The difference between n=3 and n=5 is small. The change to continuous exponential feed is even smaller and not shown here.\n",
        "\n",
        "The results shown here are similar to what is presented in Table 7 in [1] but our simulation are slightly longer and here are small differences in the final cel concentration too. The qualitative result is the same though. The difference we see to the result in the original paper is most likely due to the fact that we here use the full model with 17 parameters while in the paper they have reduced the model to 15 parameters for the optimization work."
      ],
      "metadata": {
        "id": "xYXCy9o-KIU1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5 Simulation of different feed profiles to increase recombinant protein production\n",
        "\n",
        "In this section we take a closer look at recombinant protein production. The original model is extended with the empirical model for specific protein production, see chapter 5 in [4]\n",
        "\n",
        "$$\n",
        "q_P = \\alpha \\cdot \\mu + \\beta\n",
        "$$\n",
        "\n",
        "Here we choose a negative value of growth-associated protein production production $\\alpha$ while keeping the non-growth associated $\\beta$ positive. The culture produced recombinant protein in the form of monoclonal antibodies for a specific IgG1 molecule,see section 2 in [1]. However, no experimental results were given. The only information we have is that feed rate was kept constant at a low level during fedbatch production and this choice indicates that the the growth-associated protein production is negative. The consequence of this observation for the feed profile we take a look at there by simulation."
      ],
      "metadata": {
        "id": "EDBp6v1EKP6x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Slide 3\n",
        "newplot('CHO fedbatch cultivation - protein expression', plotType='Textbook_3')\n",
        "\n",
        "# Data from Table 1 and 2 for experiment 3\n",
        "V_start=0.35\n",
        "init(V_start=V_start, VXv_start=V_start*0.29, VXd_start=V_start*0.010)\n",
        "init(VG_start=V_start*17.17, VGn_start=V_start*3.02, VL_start=V_start*1.12, VN_start=V_start*0.29)\n",
        "\n",
        "# Feeding\n",
        "Feed=0.1/24\n",
        "par(G_in=15, Gn_in=9.3)\n",
        "par(t0=0, F0=0, t1=35, F1=Feed, t2=100, F2=Feed, t3=300, F3=Feed)\n",
        "\n",
        "# Culture parameters\n",
        "par(alpha=-1.0, beta=0.01)\n",
        "\n",
        "# Simulation\n",
        "simu(200)\n",
        "par(t2=100, F2=0.7*Feed, t3=300, F3=0.7*Feed); simu(200)\n",
        "par(F2=Feed, F3=Feed)"
      ],
      "metadata": {
        "id": "Az_GoYonKV2O",
        "outputId": "96e54830-497e-4e10-c90f-7cf4c59babeb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 984.252x787.402 with 13 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Comment:** The simulation results show that actually a decease in the feed rate can lead to an increase in recombinant protein produced, although the cell concentration is a bit lower. This is a result due to the fact that growth-associated protein production here is set to a negative value. The main point is that the model can actually capture this phenomena."
      ],
      "metadata": {
        "id": "i35mjf0lKglp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6 Summary\n",
        "\n",
        "In short we have done the following:\n",
        "\n",
        "* The model was checked by comparing the simulation results with one of the published diagrams [1].\n",
        "\n",
        "* The common startup-procedure with 3 days batch cultivation can be questioned. We found that by shorten it to 2 days, and giving smaller feed rate day 3, byproduct formation can be kept lower at the prize of just a bit lower cell concentration. Similar idea was shown in section 2.1 in [3].\n",
        "\n",
        "* In the original paper the experimental feeding strategy was to keep the substrate feed at a constant lower level. The authors made a point of that the optimal feeding strategy should be exponential for maximal cell production. This is an insight derived from the bottle-neck model and they showed that through simulation optimization [1]. However, there was no experimental support to confirm the results. The optimal cell growth feedprofile simulation was just reproduced here.\n",
        "\n",
        "* To optimize recombinant protein production we must include production in the model. Here we do that with the empirical model that distinguish between growth-associated and non-growth-associated protein production, see chapter 5 in [4]. For a class of CHO-processes the recombinant protein productivity is acutally negatively affected by cell growth. Simulation of the original model extended with such a protein production model shows that keeping the substrate feed rate constant as the cell culture grows, giving less and less feed per cell, actually can give higher protein production than an increaeing feed rate. Simulation confirms this idea. The results gives some possible background to why the constant feed rate was used experimetnally in the original paper [1].\n"
      ],
      "metadata": {
        "id": "LAll0XdHRt6d"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 7 References\n",
        "\n",
        "[1] Amribt, Z., Niu, H. and Bogaerts P.: \"Macroscopic modelling of overflow metabolism and model based optimization of hybridoma cell fed-batch cultures.\", Biochem. Eng. Journal, 2013.\n",
        "\n",
        "[2] Niu,H., Amribt, Z., Fickers, P., Tan, W. and Bogaerts P.: \"Metabolic pathway analysis and reduction for mammalian cell cultures - towards macroscopic modelling\", Chem. Eng. Science, 2013.\n",
        "\n",
        "[3] Axelsson, J. P.: \"Simplified model of CHO-cultivation in Bioproces Library for Modelica - some experience\", conference paper 22nd NPCW Lyngby, Denmark, August 22-23, 2019.\n",
        "\n",
        "[4] Hu, W-S: \"Cell culture bioprocess engineering\", 2nd edition, CRC Press, 2020."
      ],
      "metadata": {
        "id": "pmOKR1hSRjeu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Appendix"
      ],
      "metadata": {
        "id": "5XsXzuE7QT4S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# List of components in the process setup and also a couple of other things like liquidphase and gasphase etc\n",
        "describe('parts')"
      ],
      "metadata": {
        "id": "UIfWCS7_QZpy",
        "outputId": "3f3ff8fe-cfee-4298-f343-70d4609061d2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['bioreactor', 'bioreactor.broth_decay', 'bioreactor.culture', 'dosagescheme', 'feedtank']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "describe('MSL')"
      ],
      "metadata": {
        "id": "Ze_Kl3qLQhHX",
        "outputId": "97dda43e-a5d1-466d-c8fa-08a588da6859",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSL: 3.2.3 - used components: RealInput, RealOutput, CombiTimeTable, Types\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "system_info()"
      ],
      "metadata": {
        "id": "fr8YZScu7nNy",
        "outputId": "2f7a8137-3a83-47a8-a605-3cf91f356e88",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "System information\n",
            " -OS: Linux\n",
            " -Python: 3.10.12\n",
            " -Scipy: not installed in the notebook\n",
            " -FMPy: 0.3.20\n",
            " -FMU by: OpenModelica Compiler OpenModelica 1.21.0\n",
            " -FMI: 2.0\n",
            " -Type: ME\n",
            " -Name: BPL_CHO.Fedbatch\n",
            " -Generated: 2024-03-06T12:05:46Z\n",
            " -MSL: 3.2.3\n",
            " -Description: Bioprocess Library version 2.2.0\n",
            " -Interaction: FMU-explore for FMPy version 1.0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FHx0NsS47ppj"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}