BPL_TEST2_Perfusion_fmpy_colab.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/janpeter19/BPL_TEST2_Perfusion/blob/main/BPL_TEST2_Perfusion_fmpy_colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UFcMzs4Y3HwH"
      },
      "source": [
        "# BPL_TEST2_Perfusion script with FMPy\n",
        "\n",
        "The key library FMPy is installed.\n",
        "\n",
        "After the installation a small application BPL_TEST2_Perfusion is loaded and run. You can continue with this example if you like."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ID68dwep2zus",
        "outputId": "708a2615-d2e9-4e3b-cb3a-7c2c9cd5e962"
      },
      "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",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pVDcSg-j3SSV",
        "outputId": "c4802d2c-37a6-4f15-e001-8da9b73676d2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "env: PYTHONPATH=\n"
          ]
        }
      ],
      "source": [
        "%env PYTHONPATH="
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "04VeBmLU3UFx",
        "outputId": "cbc2f2e1-563e-4f06-dadb-78e40d536715"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-05-15 08:14:37--  https://repo.anaconda.com/miniconda/Miniconda3-py312_24.3.0-0-Linux-x86_64.sh\n",
            "Resolving repo.anaconda.com (repo.anaconda.com)... 104.16.191.158, 104.16.32.241, 2606:4700::6810:bf9e, ...\n",
            "Connecting to repo.anaconda.com (repo.anaconda.com)|104.16.191.158|: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  89.5MB/s    in 1.5s    \n",
            "\n",
            "2024-05-15 08:14:39 (89.5 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"
          ]
        }
      ],
      "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/')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5iuP4_vl3WFA",
        "outputId": "c6fcfc4e-ffe4-4ad0-8be0-d85870029eb6"
      },
      "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\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",
            "openssl-3.0.13       | 5.2 MB    | :   0% 0.002997347135570501/1 [00:00<00:50, 50.71s/it]\n",
            "\n",
            "frozendict-2.4.2     | 36 KB     | :  44% 0.43853215920344746/1 [00:00<00:00,  2.77it/s]\u001b[A\u001b[A\n",
            "\n",
            "frozendict-2.4.2     | 36 KB     | : 100% 1.0/1 [00:00<00:00,  2.77it/s]                \u001b[A\u001b[A\n",
            "conda-24.5.0         | 1.2 MB    | : 100% 1.0/1 [00:00<00:00,  1.84it/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"
          ]
        }
      ],
      "source": [
        "!conda update -n base -c defaults conda --yes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uoBMBSS63Z58",
        "outputId": "f2286363-69b3-47ec-ea4b-92a93b049c1a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "conda 24.5.0\n",
            "Python 3.12.2\n"
          ]
        }
      ],
      "source": [
        "!conda --version\n",
        "!python --version"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RMuJRdfH3dDq",
        "outputId": "05b6754f-e55e-46b4-9849-c194fd471ffd"
      },
      "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\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\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-2024.3.1            |     pyhca7485f_0         126 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-4.1.8           |     pyhd8ed1ab_0         7.3 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-7.1.3             |     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:       564.9 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-2024.3.1-pyhca7485f_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-4.1.8-pyhd8ed1ab_0 \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-7.1.3-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-4.1.8     | 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\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
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            "\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",
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            "\n",
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            "\n",
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            "\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",
            "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",
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            "\n",
            "\n",
            "\n",
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            "\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",
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            "\n",
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            "\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<09:23, 563.34s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :   0% 0.00029119184998373156/1 [00:00<16:38, 998.39s/it]\u001b[A\u001b[A\u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :   0% 0.000263832842570537/1 [00:00<19:11, 1152.21s/it]\u001b[A\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :   0% 0.00019718423464384747/1 [00:00<27:37, 1657.73s/it]\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :  14% 0.13912493736437526/1 [00:00<00:01,  2.05s/it]    \u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :   4% 0.03960209159778749/1 [00:00<00:07,  7.69s/it]    \u001b[A\u001b[A\u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :   5% 0.04696224597755559/1 [00:00<00:06,  6.70s/it]   \u001b[A\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :   3% 0.033718504124097916/1 [00:00<00:09,  9.88s/it]    \n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :  24% 0.2407751441255518/1 [00:00<00:01,  1.51s/it] \u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  10% 0.10308191489424097/1 [00:00<00:03,  3.44s/it]\u001b[A\u001b[A\u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  13% 0.12637593159128724/1 [00:00<00:02,  2.85s/it]\u001b[A\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :   7% 0.07433845646073049/1 [00:00<00:04,  5.23s/it] \n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :  33% 0.3339935365010086/1 [00:00<00:00,  1.34s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  18% 0.18415532411423485/1 [00:00<00:01,  2.38s/it]\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  17% 0.17442391814025518/1 [00:00<00:02,  2.45s/it]\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  12% 0.12008519889810311/1 [00:00<00:03,  3.71s/it]\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :  46% 0.4553179768288645/1 [00:00<00:00,  1.16s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  24% 0.24139804363651346/1 [00:00<00:01,  2.05s/it]\u001b[A\u001b[A\u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  26% 0.25882001856169684/1 [00:00<00:01,  1.98s/it]\u001b[A\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  17% 0.17371931072122962/1 [00:00<00:02,  2.86s/it]\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :  56% 0.5583734859876609/1 [00:00<00:00,  1.10s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  31% 0.3072074017328368/1 [00:00<00:01,  1.87s/it] \u001b[A\u001b[A\u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  32% 0.32451439636176055/1 [00:00<00:01,  1.83s/it]\u001b[A\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  22% 0.21986042162788993/1 [00:00<00:02,  2.61s/it]\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :  67% 0.6670502047369372/1 [00:00<00:00,  1.11s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  37% 0.3663193472795343/1 [00:00<00:01,  1.87s/it]\u001b[A\u001b[A\u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  39% 0.3851959501529841/1 [00:00<00:01,  1.85s/it] \u001b[A\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  26% 0.2632409532495364/1 [00:00<00:02,  2.78s/it] \n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :  76% 0.762610767775094/1 [00:01<00:00,  1.20s/it] \u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  42% 0.4236841417263294/1 [00:01<00:01,  2.12s/it]\u001b[A\u001b[A\u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  44% 0.4432391755185022/1 [00:01<00:01,  2.34s/it]\u001b[A\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  30% 0.3028749844129497/1 [00:01<00:02,  3.26s/it]\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :  85% 0.850207950560071/1 [00:01<00:00,  1.28s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  47% 0.4746427154734824/1 [00:01<00:01,  2.18s/it]\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "qt-webengine-5.15.9  | 53.8 MB   | :  49% 0.490089461998149/1 [00:01<00:01,  2.08s/it] \u001b[A\u001b[A\n",
            "mysql-5.7.20         | 79.2 MB   | :  34% 0.3401428047606369/1 [00:01<00:02,  3.10s/it]\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :  93% 0.9317154896220282/1 [00:01<00:00,  1.31s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  52% 0.5232717544207656/1 [00:01<00:01,  2.19s/it]\u001b[A\u001b[A\u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  55% 0.5498276439169992/1 [00:01<00:00,  2.11s/it]\u001b[A\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  38% 0.3809599413319133/1 [00:01<00:01,  2.91s/it]\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  59% 0.5855868103172841/1 [00:01<00:00,  1.99s/it]\u001b[A\u001b[A\u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  61% 0.60919003349537/1 [00:01<00:00,  1.98s/it]  \u001b[A\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  43% 0.42572076259606667/1 [00:01<00:01,  2.77s/it]\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  64% 0.637710151464372/1 [00:01<00:00,  1.99s/it] \u001b[A\u001b[A\u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  66% 0.662748100537189/1 [00:01<00:00,  1.96s/it]\u001b[A\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  47% 0.46712945187127464/1 [00:01<00:01,  2.66s/it]\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  70% 0.6959485214611184/1 [00:01<00:00,  1.99s/it]\u001b[A\u001b[A\u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  73% 0.7345106337163751/1 [00:01<00:00,  1.78s/it]\u001b[A\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  52% 0.5152424051243735/1 [00:01<00:01,  2.53s/it] \n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  78% 0.7760262802066445/1 [00:01<00:00,  1.72s/it]\u001b[A\u001b[A\u001b[A\n",
            "mysql-5.7.20         | 79.2 MB   | :  56% 0.56374972684676/1 [00:01<00:01,  2.38s/it]  \n",
            "\n",
            "qt-webengine-5.15.9  | 53.8 MB   | :  83% 0.8322219037370704/1 [00:01<00:00,  1.57s/it]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  84% 0.8444563649528215/1 [00:01<00:00,  1.65s/it]\u001b[A\u001b[A\u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  87% 0.8685377177422079/1 [00:01<00:00,  1.68s/it]\u001b[A\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  61% 0.6104823904573518/1 [00:01<00:00,  2.37s/it]\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  91% 0.9058978452993888/1 [00:01<00:00,  1.65s/it]\u001b[A\u001b[A\u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  94% 0.9400364180788234/1 [00:01<00:00,  1.62s/it]\u001b[A\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  65% 0.6534685536097106/1 [00:01<00:00,  2.41s/it]\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | : 100% 0.9999212538789505/1 [00:02<00:00,  2.25s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :   0% 0.0005070463002532972/1 [00:03<1:38:37, 5920.31s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :   8% 0.08011331544002097/1 [00:03<00:25, 27.63s/it]      \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  19% 0.19318464039650624/1 [00:03<00:07,  9.74s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  27% 0.2748190947372871/1 [00:03<00:04,  6.27s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  37% 0.3676085676836405/1 [00:03<00:02,  4.25s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  49% 0.4908208186451917/1 [00:03<00:01,  2.77s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  59% 0.585131430492305/1 [00:03<00:00,  2.35s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  67% 0.6713293015353655/1 [00:03<00:00,  2.03s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  75% 0.7549919410771596/1 [00:03<00:00,  1.79s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  85% 0.8467673214230064/1 [00:03<00:00,  1.58s/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 [00:04<1:40:19, 6023.33s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  93% 0.9324581461658136/1 [00:04<00:00,  1.50s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "scipy-1.12.0         | 23.3 MB   | :  12% 0.11643883929900684/1 [00:04<00:22, 25.16s/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   | :  23% 0.22685498001358229/1 [00:04<00:08, 11.24s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "scipy-1.12.0         | 23.3 MB   | :  31% 0.3131803263904322/1 [00:04<00:05,  7.30s/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   | :  44% 0.44434131778471575/1 [00:04<00:02,  4.39s/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 [00:04<1:43:02, 6187.43s/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   | :  55% 0.5500731373780667/1 [00:04<00:01,  3.25s/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   | :  13% 0.12500342771168121/1 [00:04<00:23, 26.33s/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 [00:04<1:37:26, 5851.14s/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   | :  65% 0.646436314728969/1 [00:04<00:00,  2.57s/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   | :  25% 0.25000685542336243/1 [00:04<00:08, 11.31s/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   | :  78% 0.776928117391649/1 [00:04<00:00,  1.90s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :  13% 0.1273492498386048/1 [00:04<00:23, 26.62s/it]       \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",
            "mathjax-2.7.7        | 21.2 MB   | :  35% 0.3463330261894227/1 [00:04<00:04,  7.42s/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   | :  28% 0.28112947605880684/1 [00:04<00:07, 10.17s/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   | :  91% 0.9054123538595187/1 [00:04<00:00,  1.51s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "mathjax-2.7.7        | 21.2 MB   | :  48% 0.47942491098833034/1 [00:04<00:02,  4.51s/it]\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",
            "libboost-1.82.0      | 19.5 MB   | :  43% 0.4309050088878577/1 [00:04<00:03,  5.72s/it] \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",
            "mathjax-2.7.7        | 21.2 MB   | :  61% 0.6139874243485519/1 [00:05<00:01,  3.05s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :  56% 0.5566523813700021/1 [00:05<00:01,  4.00s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "mathjax-2.7.7        | 21.2 MB   | :  77% 0.7742859375317667/1 [00:05<00:00,  2.09s/it]\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",
            "pandas-2.2.1         | 15.4 MB   | :   0% 0.0010174177522631242/1 [00:05<1:25:10, 5115.88s/it]\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",
            "libboost-1.82.0      | 19.5 MB   | :  68% 0.6848025698868371/1 [00:05<00:00,  2.90s/it]\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",
            "mathjax-2.7.7        | 21.2 MB   | :  93% 0.9294372507503827/1 [00:05<00:00,  1.58s/it]\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",
            "libboost-1.82.0      | 19.5 MB   | :  84% 0.8401846734634996/1 [00:05<00:00,  2.06s/it]\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",
            "pandas-2.2.1         | 15.4 MB   | :  21% 0.20551838595715108/1 [00:05<00:14, 18.33s/it]      \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",
            "pandas-2.2.1         | 15.4 MB   | :  39% 0.3917058346213028/1 [00:05<00:05,  8.29s/it] \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",
            "pandas-2.2.1         | 15.4 MB   | :  60% 0.5951893850739277/1 [00:05<00:01,  4.64s/it]\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",
            "pandas-2.2.1         | 15.4 MB   | :  84% 0.8373348101125512/1 [00:05<00:00,  2.79s/it]\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",
            "libclang13-14.0.6    | 9.8 MB    | :   0% 0.0016025206836573897/1 [00:05<58:44, 3530.32s/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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            "\n",
            "libclang13-14.0.6    | 9.8 MB    | :  27% 0.27242851622175623/1 [00:05<00:10, 14.93s/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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            "\n",
            "arrow-cpp-14.0.2     | 11.7 MB   | :   0% 0.001340927777322181/1 [00:05<1:11:32, 4298.19s/it]\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",
            "libclang13-14.0.6    | 9.8 MB    | :  67% 0.6730586871361036/1 [00:05<00:01,  4.99s/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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            "\n",
            "arrow-cpp-14.0.2     | 11.7 MB   | :  16% 0.1635931888333061/1 [00:05<00:21, 25.35s/it]      \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",
            "matplotlib-base-3.8. | 7.6 MB    | :   0% 0.002050079549442966/1 [00:05<47:53, 2879.63s/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",
            "numpy-base-1.26.4    | 7.7 MB    | :   0% 0.0020306342695560406/1 [00:05<48:29, 2915.26s/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",
            "arrow-cpp-14.0.2     | 11.7 MB   | :  52% 0.5229618331556506/1 [00:05<00:03,  6.34s/it]\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",
            "matplotlib-base-3.8. | 7.6 MB    | :  52% 0.5207202055585134/1 [00:06<00:03,  8.14s/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",
            "numpy-base-1.26.4    | 7.7 MB    | :   5% 0.04873522246934497/1 [00:06<01:23, 88.28s/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",
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            "\n",
            "\n",
            "arrow-cpp-14.0.2     | 11.7 MB   | :  79% 0.7911473886200868/1 [00:06<00:00,  3.67s/it]\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",
            "matplotlib-base-3.8. | 7.6 MB    | :  92% 0.9184356381504488/1 [00:06<00:00,  4.02s/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",
            "jupyterlab-4.1.8     | 7.3 MB    | :   0% 0.0021490437384966347/1 [00:06<47:15, 2841.91s/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",
            "numpy-base-1.26.4    | 7.7 MB    | :  20% 0.20306342695560403/1 [00:06<00:13, 16.36s/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",
            "jupyterlab-4.1.8     | 7.3 MB    | :  39% 0.3889769166678909/1 [00:06<00:06, 11.28s/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",
            "numpy-base-1.26.4    | 7.7 MB    | :  46% 0.45892334491966513/1 [00:06<00:03,  5.81s/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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            "\n",
            "jupyterlab-4.1.8     | 7.3 MB    | :  70% 0.7048863462268962/1 [00:06<00:01,  5.40s/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",
            "numpy-base-1.26.4    | 7.7 MB    | :  84% 0.8366213190570887/1 [00:06<00:00,  2.59s/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",
            "babel-2.14.0         | 7.3 MB    | :   0% 0.0021530273662078254/1 [00:06<49:56, 3003.19s/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",
            "openblas-0.3.27      | 5.5 MB    | :   0% 0.0028590374480857153/1 [00:06<37:37, 2264.11s/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\u001b[A\n",
            "\n",
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            "\n",
            "plotly-5.22.0        | 5.1 MB    | :   0% 0.0030781225781906824/1 [00:06<35:25, 2132.36s/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",
            "libopenblas-0.3.27   | 5.3 MB    | :   0% 0.0029263690607916376/1 [00:06<37:17, 2244.36s/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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            "babel-2.14.0         | 7.3 MB    | :  35% 0.35309648805808336/1 [00:06<00:08, 13.16s/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",
            "openblas-0.3.27      | 5.5 MB    | :  53% 0.531780965343943/1 [00:06<00:04,  8.75s/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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            "plotly-5.22.0        | 5.1 MB    | :  69% 0.6894994575147129/1 [00:06<00:02,  6.82s/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",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libopenblas-0.3.27   | 5.3 MB    | :  47% 0.46529268066587043/1 [00:06<00:05, 10.13s/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",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "babel-2.14.0         | 7.3 MB    | :  72% 0.7234171950458294/1 [00:06<00:01,  5.43s/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",
            "\n",
            "\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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            "\n",
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            "\n",
            "\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",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | : 100% 1.0/1 [00:07<00:00,  1.31s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "qt-webengine-5.15.9  | 53.8 MB   | : 100% 1.0/1 [00:09<00:00,  1.44s/it]              \u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | : 100% 1.0/1 [00:18<00:00,  1.53s/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,  1.50s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | : 100% 1.0/1 [00:33<00:00,  1.62s/it]               \u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | : 100% 1.0/1 [00:48<00:00,  2.25s/it]               \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | : 100% 1.0/1 [01:01<00:00, 125.95s/it]              \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | : 100% 1.0/1 [01:01<00:00, 125.95s/it]\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",
            "pandas-2.2.1         | 15.4 MB   | : 100% 1.0/1 [01:03<00:00,  2.79s/it]               \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",
            "libclang13-14.0.6    | 9.8 MB    | : 100% 1.0/1 [01:04<00:00, 81.32s/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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            "\n",
            "libclang13-14.0.6    | 9.8 MB    | : 100% 1.0/1 [01:04<00:00, 81.32s/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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            "\n",
            "matplotlib-base-3.8. | 7.6 MB    | : 100% 1.0/1 [01:04<00:00,  4.02s/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",
            "mathjax-2.7.7        | 21.2 MB   | : 100% 1.0/1 [01:04<00:00,  1.58s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "arrow-cpp-14.0.2     | 11.7 MB   | : 100% 1.0/1 [01:05<00:00,  3.67s/it]               \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 [01:06<00:00,  2.59s/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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            "jupyterlab-4.1.8     | 7.3 MB    | : 100% 1.0/1 [01:06<00:00, 88.48s/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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            "jupyterlab-4.1.8     | 7.3 MB    | : 100% 1.0/1 [01:06<00:00, 88.48s/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",
            "openblas-0.3.27      | 5.5 MB    | : 100% 1.0/1 [01:06<00:00, 75.44s/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",
            "openblas-0.3.27      | 5.5 MB    | : 100% 1.0/1 [01:06<00:00, 75.44s/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    | : 100% 1.0/1 [01:07<00:00, 74.02s/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",
            "libopenblas-0.3.27   | 5.3 MB    | : 100% 1.0/1 [01:07<00:00, 74.02s/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",
            "babel-2.14.0         | 7.3 MB    | : 100% 1.0/1 [01:08<00:00, 90.24s/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 [01:08<00:00, 90.24s/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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            " ... (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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            "plotly-5.22.0        | 5.1 MB    | : 100% 1.0/1 [01:16<00:00,  6.82s/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",
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            "                                                                        \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\n",
            "\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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            "                                                                        \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",
            "\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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            "\n",
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            "\u001b[A\n",
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            "\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\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\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\b\\ \b\b| \b\b/ \b\bdone\n"
          ]
        }
      ],
      "source": [
        "!conda install -c conda-forge fmpy --yes # Install the key package"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#!conda install -c conda-forge matplotlib --yes"
      ],
      "metadata": {
        "id": "9GP_mxWcXRTn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#!conda install -c conda-forge scipy --yes"
      ],
      "metadata": {
        "id": "N9-uz4b9XRhJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#!conda install -c conda-forge openpyxl --yes"
      ],
      "metadata": {
        "id": "K7MWhWlLXRvR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#!conda install -c conda-forge xlrd --yes"
      ],
      "metadata": {
        "id": "oUgrzoQbXSI0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UTETJm3l6heL"
      },
      "source": [
        "# Notes of BPL_TEST2_Perfusion\n",
        "\n",
        "This notebook explore perfusion cultivation in comparison with ordinary continuous cultivation (chemostat) and use comparable settings to earlier notebook. Further you see here examples of interaction with the simplified commands par(), init(), simu() etc as well as direct interaction with the FMU which is called \"model\" here. The last simulation is always available in the workspace and called \"sim_res\". Note that describe() brings mainly up from descriptive information from the Modelica code from the FMU but is complemented by some information given in the Python setup file."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mxILgQ103nZ6"
      },
      "source": [
        "Now specific installation run a simulation and notebook for that\n",
        "Start with connecting to Github. Then upload the two files:\n",
        "\n",
        "* FMU - BPL_TEST2_Perfusion_linux_om_me.fmu\n",
        "* Setup-file - BPL_TEST2_Perfusion_fmpy_explore.py"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RfWPh1B63yBF",
        "outputId": "85968295-b07e-4bcc-8c66-3f4b1a74e852"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Cloning into 'BPL_TEST2_Perfusion'...\n"
          ]
        }
      ],
      "source": [
        "%%bash\n",
        "git clone https://github.com/janpeter19/BPL_TEST2_Perfusion"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i6uOVB44300a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "04b4f3e5-1d9e-4cab-98d5-72e31cb17448"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/BPL_TEST2_Perfusion\n"
          ]
        }
      ],
      "source": [
        "%cd BPL_TEST2_Perfusion"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b-y82jZY33cO",
        "outputId": "6faf8723-83a9-4761-ef0d-3569a9ffabc6"
      },
      "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"
          ]
        }
      ],
      "source": [
        "run -i BPL_TEST2_Perfusion_fmpy_explore.py"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HUFhuE-H4RCw"
      },
      "outputs": [],
      "source": [
        "%matplotlib inline\n",
        "plt.rcParams['figure.figsize'] = [25/2.54, 20/2.54]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "process_diagram()"
      ],
      "metadata": {
        "id": "QzLWGB4_2Vo5",
        "outputId": "a69c0045-aeab-4ffc-fcda-101d8538c4ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        }
      },
      "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",
      "execution_count": null,
      "metadata": {
        "id": "-Y3NHWIj4TZ6"
      },
      "outputs": [],
      "source": [
        "# Process parameters used throughout\n",
        "par(Y=0.5, qSmax=0.75, Ks=0.1)                                      # Culture\n",
        "par(filter_eps=0.10, filter_alpha_X=0.02, filter_alpha_S=0.10)      # Filter\n",
        "par(S_in=30.0)                                                      # Inlet substrate conc\n",
        "init(V_start=1.0, VX_start=1.0)                                     # Process initial conditions that are common\n",
        "eps = parDict['filter_eps']                                         # Pump schedule parameter"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "QoyqEIHU6xDV",
        "outputId": "406d2079-81d6-46cb-9a5f-cf84ee88207d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 984.252x787.402 with 5 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Simulation of process with flow rate clot to wash-out for chemostat\n",
        "\n",
        "init(VS_start=20)                                        # Process initial\n",
        "par(pump1_t1=10, pump2_t1=10)                            # Pump schedule - recycle flow 10 times perfusion flow\n",
        "par(pump1_F1=2.5*0.155, pump2_F1=2.5*0.155/eps)\n",
        "par(pump1_t2=940, pump2_t2=940, pump1_t3=950, pump2_t3=950, pump1_t4=960, pump2_t4=960)\n",
        "\n",
        "newplot(title='Perfusion cultivation - flow rate corresponding to maximal rate for chemostat')\n",
        "simu(60)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5W4m7VfKW0Zw",
        "outputId": "6b9f1d63-6657-4377-852c-11093c38a9cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 984.252x787.402 with 5 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Simulation of process with flow rate close to wash-out for chemostat\n",
        "\n",
        "init(VS_start=20)                                        # Process initial\n",
        "par(pump1_t1=10, pump2_t1=10)                            # Pump schedule - recycle flow 10 times perfusion flow\n",
        "par(pump1_F1=2.5*0.155, pump2_F1=2.5*0.155/eps)\n",
        "par(pump1_t2=940, pump2_t2=940, pump1_t3=950, pump2_t3=950, pump1_t4=960, pump2_t4=960)\n",
        "\n",
        "newplot(title='Perfusion cultivation - flow rate corresponding to maximal rate for chemostat')\n",
        "simu(30)\n",
        "simu(30,'cont')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Viu05MEF60Q-",
        "outputId": "85f61c5d-731f-494d-b37f-101a10513737"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Conc factor of perfusion filter = 1.186\n"
          ]
        }
      ],
      "source": [
        "# Concentration factor of the filter\n",
        "c=model_get('filter.retentate.c[1]')/model_get('filter.inlet.c[1]')\n",
        "print('Conc factor of perfusion filter =', np.round(c,3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bO9Ur8dp64K6",
        "outputId": "adffb957-11d8-455b-a218-0e026867685a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Conc factor variation 1.186 1.186\n"
          ]
        }
      ],
      "source": [
        "c_data=sim_res['filter.retentate.c[1]']/sim_res['filter.inlet.c[1]']\n",
        "print('Conc factor variation', np.round(min(c_data[151:]), 3), np.round(max(c_data[151:]),3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "HH4wlMf-68Cm",
        "outputId": "ca8ae1a4-e107-400f-a200-153f6dc6b521"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 984.252x787.402 with 5 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Simulation of process with step-wise increase of pefusion rate until wash-out.\n",
        "# This means that re-circulation rate change at the same time as the perfusion rate.\n",
        "\n",
        "init(VS_start=150)                                       # Process initial varied\n",
        "\n",
        "par(pump1_t1=12, pump2_t1=12)                            # Pump schedule - recycle flow 10 times perfusion flow\n",
        "par(pump1_F1=2.5*0.155, pump2_F1=2.5*0.155/eps)\n",
        "par(pump1_t2=22, pump2_t2=22)\n",
        "par(pump1_F2=2.5*0.35, pump2_F2=2.5*0.35/eps)\n",
        "par(pump1_t3=32, pump2_t3=32)\n",
        "par(pump1_F3=2.5*0.63, pump2_F3=2.5*0.63/eps)\n",
        "par(pump1_t4=42, pump2_t4=42)\n",
        "par(pump1_F4=2.5*0.83, pump2_F4=2.5*0.83/eps)\n",
        "\n",
        "newplot(title='Perfusion cultivation - step wise increase of perfusion rate and max productivity 4x chemostat')\n",
        "simu(60)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "40WwRSFD7ADP"
      },
      "outputs": [],
      "source": [
        "# Simulation without a plot and just to check typical values at high production rate\n",
        "#simu(40)\n",
        "#c_data=sim_res['filter.retentate.c[1]']/sim_res['filter.inlet.c[1]']\n",
        "#print('Conc factor variation', np.round(min(c_data[190:]), 3), 'to', np.round(max(c_data[190:]),3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-HRUnkPA7J-q"
      },
      "outputs": [],
      "source": [
        "#describe('cstrProdMax')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y2_XnnSJ7MxO",
        "outputId": "d4e92113-3827-47d9-c7fa-c67c31c79888"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25.2"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "# The maximal biomass productivity before washout is obtained aroudn 40 hours\n",
        "np.round(model_get('harvesttank.inlet.F')*model_get('harvesttank.inlet.c[1]'),1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nByd9ytr7P1Q",
        "outputId": "a66d5c10-7131-4960-9b81-150ae00f6d53"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.2"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "# Thus perfusion (with this filter) brings a productivity improvement of about\n",
        "np.round(23.5/5.6,1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xlYHIR7s7UGB",
        "outputId": "b4fcb794-129e-4bb4-94a7-397cf57153fb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20.749999999999996"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "# Finally we check the filter flow rates at time 40 hour - note the negative sign for outlfow\n",
        "model_get('filter.inlet.F')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LUdmmQ767Xiz",
        "outputId": "f9c92cde-af0e-4e17-8e50-096b84c2b21e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-2.0749999999999997"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "model_get('filter.filtrate.F')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qgZ6VrOh7aRi",
        "outputId": "9803f5e9-b83e-46e1-adfe-165270c736af"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-18.674999999999997"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "model_get('filter.retentate.F')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jn90LYlW7hBw"
      },
      "source": [
        "## Summary\n",
        "* The perfusion filter had a concentration factor of cells around 1.08 and re-cycling flow was set to a factor 10 higher than the perfusion rate and changed when perfusion rate was change to keep the ratio factor 10.\n",
        "* The first simulation showed that by cell retention using perfusion filter the process could be run at a perfusion flow rate at the maximal flow rate possible for corresponding chemostat culture and cell concetration increased steadily.\n",
        "* The second simulation showed that with a proper startup cell concentration, the cell concentration remained constant when perfusion rate increased in a similar way as what we see in a chemostat.\n",
        "* The second simulation also showed that biomass productivity in this case was increased by a factor 4.2 compared to chemostat.\n",
        "* If the perfusion rate increased to higher levels washout started but the decrase of cell concentration was slow.\n",
        "\n",
        "Some of you who read this may have your perfusion experience with CHO-cultures. For such cultures the cell concentration do increase with increase of perfusion rate and there are understood reasons for that. But for this simplifed process as well as microbial processes they typically keep cell concentration constant when flow rate is chaged, and that under quite wide conditions. I will try come back to this phenomena in a later notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TWAYSaP87ctd",
        "outputId": "3e48436f-b1ef-4c74-e2ad-13e961f64463"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['bioreactor', 'bioreactor.culture', 'D', 'feedtank', 'filter', 'harvesttank', 'schemePump1', 'schemePump2']\n"
          ]
        }
      ],
      "source": [
        "# List of components in the process setup and also a couple of other things like liquidphase and D\n",
        "describe('parts')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ktx57IhTj6ld",
        "outputId": "8c506a8f-f35b-4df4-9809-562167e2bdd5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSL: 3.2.3 - used components: RealInput, RealOutput, CombiTimeTable, Types\n"
          ]
        }
      ],
      "source": [
        "describe('MSL')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fr8YZScu7nNy",
        "outputId": "3e6fd76b-466e-4229-8a5e-d552b53476ec"
      },
      "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_TEST2.Perfusion\n",
            " -Generated: 2024-03-05T09:10:09Z\n",
            " -MSL: 3.2.3\n",
            " -Description: Bioprocess Library version 2.2.0\n",
            " -Interaction: FMU-explore for FMPy version 1.0.0\n"
          ]
        }
      ],
      "source": [
        "system_info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FHx0NsS47ppj"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.16"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}