BPL_YEAST_COB_Batch_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_YEAST_COB_Batch/blob/main/BPL_YEAST_COB_Batch_colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# BPL_YEAST_COB_Batch script with PyFMI\n",
        "\n",
        "The key library PyFMI is installed.\n",
        "\n",
        "After the installation a small application BPL_YEAST_COB_Batch is loaded and run. You can continue with this example if you like."
      ],
      "metadata": {
        "id": "jlDGqS2N64wA"
      },
      "id": "jlDGqS2N64wA"
    },
    {
      "cell_type": "code",
      "source": [
        "!lsb_release -a # Actual VM Ubuntu version used by Google"
      ],
      "metadata": {
        "id": "MHr3A2G47BnX",
        "outputId": "2e6a867b-5722-450f-b12d-67674800b423",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "MHr3A2G47BnX",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No LSB modules are available.\n",
            "Distributor ID:\tUbuntu\n",
            "Description:\tUbuntu 22.04.4 LTS\n",
            "Release:\t22.04\n",
            "Codename:\tjammy\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%env PYTHONPATH="
      ],
      "metadata": {
        "id": "oiwXMGNr7QKj",
        "outputId": "d564ad8e-3b20-45f8-9ff3-7073122b21f7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "oiwXMGNr7QKj",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "env: PYTHONPATH=\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python --version"
      ],
      "metadata": {
        "id": "DkviSA13Rhuk",
        "outputId": "4470d083-c2da-43aa-94fe-5002a7b5faa6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "DkviSA13Rhuk",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python 3.11.11\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://repo.anaconda.com/miniconda/Miniconda3-py311_24.11.1-0-Linux-x86_64.sh\n",
        "!chmod +x Miniconda3-py311_24.11.1-0-Linux-x86_64.sh\n",
        "!bash ./Miniconda3-py311_24.11.1-0-Linux-x86_64.sh -b -f -p /usr/local\n",
        "import sys\n",
        "sys.path.append('/usr/local/lib/python3.11/site-packages/')"
      ],
      "metadata": {
        "id": "qQv_yBOu7TF7",
        "outputId": "2aa1b59d-1608-4772-fe6d-2280c110ed9f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "qQv_yBOu7TF7",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2025-03-26 15:06:31--  https://repo.anaconda.com/miniconda/Miniconda3-py311_24.11.1-0-Linux-x86_64.sh\n",
            "Resolving repo.anaconda.com (repo.anaconda.com)... 104.16.32.241, 104.16.191.158, 2606:4700::6810:20f1, ...\n",
            "Connecting to repo.anaconda.com (repo.anaconda.com)|104.16.32.241|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 145900576 (139M) [application/octet-stream]\n",
            "Saving to: ‘Miniconda3-py311_24.11.1-0-Linux-x86_64.sh’\n",
            "\n",
            "Miniconda3-py311_24 100%[===================>] 139.14M   137MB/s    in 1.0s    \n",
            "\n",
            "2025-03-26 15:06:32 (137 MB/s) - ‘Miniconda3-py311_24.11.1-0-Linux-x86_64.sh’ saved [145900576/145900576]\n",
            "\n",
            "PREFIX=/usr/local\n",
            "Unpacking payload ...\n",
            "\n",
            "Installing base environment...\n",
            "\n",
            "Preparing transaction: ...working... done\n",
            "Executing transaction: ...working... done\n",
            "installation finished.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!conda update -n base -c defaults conda --yes"
      ],
      "metadata": {
        "id": "VHpHcZ1H7Xs1",
        "outputId": "3299b567-4d55-4e3b-a037-ea12cc2e8c41",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "VHpHcZ1H7Xs1",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Channels:\n",
            " - defaults\n",
            "Platform: linux-64\n",
            "Collecting package metadata (repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\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",
            "    ca-certificates-2025.2.25  |       h06a4308_0         129 KB\n",
            "    certifi-2025.1.31          |  py311h06a4308_0         163 KB\n",
            "    openssl-3.0.16             |       h5eee18b_0         5.2 MB\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:         5.5 MB\n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  ca-certificates                     2024.11.26-h06a4308_0 --> 2025.2.25-h06a4308_0 \n",
            "  certifi                         2024.8.30-py311h06a4308_0 --> 2025.1.31-py311h06a4308_0 \n",
            "  openssl                                 3.0.15-h5eee18b_0 --> 3.0.16-h5eee18b_0 \n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages:\n",
            "openssl-3.0.16       | 5.2 MB    | :   0% 0/1 [00:00<?, ?it/s]\n",
            "certifi-2025.1.31    | 163 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\n",
            "\n",
            "ca-certificates-2025 | 129 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
            "\n",
            "ca-certificates-2025 | 129 KB    | : 100% 1.0/1 [00:00<00:00, 18.44it/s]\u001b[A\u001b[A\n",
            "certifi-2025.1.31    | 163 KB    | : 100% 1.0/1 [00:00<00:00, 13.09it/s]\u001b[A\n",
            "\n",
            "ca-certificates-2025 | 129 KB    | : 100% 1.0/1 [00:00<00:00, 12.30it/s]\u001b[A\u001b[A\n",
            "certifi-2025.1.31    | 163 KB    | : 100% 1.0/1 [00:00<00:00,  9.82it/s]\u001b[A\n",
            "                                                                        \n",
            "                                                                        \u001b[A\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\n",
            "Preparing transaction: - \b\bdone\n",
            "Verifying transaction: | \b\b/ \b\b- \b\bdone\n",
            "Executing transaction: | \b\bdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!conda --version\n",
        "!python --version"
      ],
      "metadata": {
        "id": "TQu6KOnT7bTC",
        "outputId": "244fa8a6-d5cf-41d9-a1e0-de9a7a2e4503",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "TQu6KOnT7bTC",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "conda 24.11.1\n",
            "Python 3.11.11\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!conda config --set channel_priority strict"
      ],
      "metadata": {
        "id": "6MM-u1N7Rxwt"
      },
      "id": "6MM-u1N7Rxwt",
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!conda install -c conda-forge pyfmi --yes # Install the key package"
      ],
      "metadata": {
        "id": "Nt_MQjLx7gwt",
        "outputId": "820e0063-f4df-4378-cb0d-6e9b2dfc25e3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "Nt_MQjLx7gwt",
      "execution_count": 8,
      "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\bdone\n",
            "Solving environment: / \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",
            "    - pyfmi\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    _x86_64-microarch-level-3  |      2_broadwell           8 KB  conda-forge\n",
            "    assimulo-3.6.0             |  py311h083bc19_0         1.1 MB  conda-forge\n",
            "    certifi-2025.1.31          |     pyhd8ed1ab_0         159 KB  conda-forge\n",
            "    conda-25.1.1               |  py311h38be061_1         1.1 MB  conda-forge\n",
            "    fmilib-2.4.1               |       hac33072_1         383 KB  conda-forge\n",
            "    gmp-6.3.0                  |       hac33072_2         449 KB  conda-forge\n",
            "    libamd-3.3.3               | haaf9dc3_7100102          49 KB  conda-forge\n",
            "    libblas-3.9.0              |31_h59b9bed_openblas          16 KB  conda-forge\n",
            "    libbtf-2.3.2               | h32481e8_7100102          27 KB  conda-forge\n",
            "    libcamd-3.3.3              | h32481e8_7100102          46 KB  conda-forge\n",
            "    libcblas-3.9.0             |31_he106b2a_openblas          16 KB  conda-forge\n",
            "    libccolamd-3.3.4           | h32481e8_7100102          42 KB  conda-forge\n",
            "    libcholmod-5.3.1           | h59ddab4_7100102         1.1 MB  conda-forge\n",
            "    libcolamd-3.3.4            | h32481e8_7100102          33 KB  conda-forge\n",
            "    libcxsparse-4.4.1          | h32481e8_7100102         118 KB  conda-forge\n",
            "    libgcc-14.2.0              |       h767d61c_2         828 KB  conda-forge\n",
            "    libgcc-ng-14.2.0           |       h69a702a_2          52 KB  conda-forge\n",
            "    libgfortran-14.2.0         |       h69a702a_2          52 KB  conda-forge\n",
            "    libgfortran-ng-14.2.0      |       h69a702a_2          53 KB  conda-forge\n",
            "    libgfortran5-14.2.0        |       hf1ad2bd_2         1.4 MB  conda-forge\n",
            "    libgomp-14.2.0             |       h767d61c_2         449 KB  conda-forge\n",
            "    libklu-2.3.5               | hf24d653_7100102         142 KB  conda-forge\n",
            "    liblapack-3.9.0            |31_h7ac8fdf_openblas          16 KB  conda-forge\n",
            "    libldl-3.3.2               | h32481e8_7100102          24 KB  conda-forge\n",
            "    libopenblas-0.3.29         |pthreads_h94d23a6_0         5.6 MB  conda-forge\n",
            "    libparu-1.0.0              | h17147ab_7100102          91 KB  conda-forge\n",
            "    librbio-4.3.4              | h32481e8_7100102          47 KB  conda-forge\n",
            "    libspex-3.2.3              | had10066_7100102          79 KB  conda-forge\n",
            "    libspqr-4.3.4              | h852d39f_7100102         213 KB  conda-forge\n",
            "    libstdcxx-14.2.0           |       h8f9b012_2         3.7 MB  conda-forge\n",
            "    libstdcxx-ng-14.2.0        |       h4852527_2          53 KB  conda-forge\n",
            "    libsuitesparseconfig-7.10.1| h92d6892_7100102          42 KB  conda-forge\n",
            "    libumfpack-6.3.5           | heb53515_7100102         424 KB  conda-forge\n",
            "    metis-5.1.0                |    hd0bcaf9_1007         3.7 MB  conda-forge\n",
            "    mpfr-4.2.1                 |       h90cbb55_3         620 KB  conda-forge\n",
            "    numpy-2.2.4                |  py311h5d046bc_0         8.6 MB  conda-forge\n",
            "    openssl-3.4.1              |       h7b32b05_0         2.8 MB  conda-forge\n",
            "    pyfmi-2.16.3               |  py311h9f3472d_0         5.2 MB  conda-forge\n",
            "    python_abi-3.11            |          2_cp311           5 KB  conda-forge\n",
            "    scipy-1.15.2               |  py311h8f841c2_0        16.4 MB  conda-forge\n",
            "    suitesparse-7.10.1         | ha0f6916_7100102          12 KB  conda-forge\n",
            "    sundials-7.1.1             |       ha52427a_0         907 KB  conda-forge\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:        56.1 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  _x86_64-microarch~ conda-forge/noarch::_x86_64-microarch-level-3-2_broadwell \n",
            "  assimulo           conda-forge/linux-64::assimulo-3.6.0-py311h083bc19_0 \n",
            "  fmilib             conda-forge/linux-64::fmilib-2.4.1-hac33072_1 \n",
            "  gmp                conda-forge/linux-64::gmp-6.3.0-hac33072_2 \n",
            "  libamd             conda-forge/linux-64::libamd-3.3.3-haaf9dc3_7100102 \n",
            "  libblas            conda-forge/linux-64::libblas-3.9.0-31_h59b9bed_openblas \n",
            "  libbtf             conda-forge/linux-64::libbtf-2.3.2-h32481e8_7100102 \n",
            "  libcamd            conda-forge/linux-64::libcamd-3.3.3-h32481e8_7100102 \n",
            "  libcblas           conda-forge/linux-64::libcblas-3.9.0-31_he106b2a_openblas \n",
            "  libccolamd         conda-forge/linux-64::libccolamd-3.3.4-h32481e8_7100102 \n",
            "  libcholmod         conda-forge/linux-64::libcholmod-5.3.1-h59ddab4_7100102 \n",
            "  libcolamd          conda-forge/linux-64::libcolamd-3.3.4-h32481e8_7100102 \n",
            "  libcxsparse        conda-forge/linux-64::libcxsparse-4.4.1-h32481e8_7100102 \n",
            "  libgcc             conda-forge/linux-64::libgcc-14.2.0-h767d61c_2 \n",
            "  libgfortran        conda-forge/linux-64::libgfortran-14.2.0-h69a702a_2 \n",
            "  libgfortran-ng     conda-forge/linux-64::libgfortran-ng-14.2.0-h69a702a_2 \n",
            "  libgfortran5       conda-forge/linux-64::libgfortran5-14.2.0-hf1ad2bd_2 \n",
            "  libklu             conda-forge/linux-64::libklu-2.3.5-hf24d653_7100102 \n",
            "  liblapack          conda-forge/linux-64::liblapack-3.9.0-31_h7ac8fdf_openblas \n",
            "  libldl             conda-forge/linux-64::libldl-3.3.2-h32481e8_7100102 \n",
            "  libopenblas        conda-forge/linux-64::libopenblas-0.3.29-pthreads_h94d23a6_0 \n",
            "  libparu            conda-forge/linux-64::libparu-1.0.0-h17147ab_7100102 \n",
            "  librbio            conda-forge/linux-64::librbio-4.3.4-h32481e8_7100102 \n",
            "  libspex            conda-forge/linux-64::libspex-3.2.3-had10066_7100102 \n",
            "  libspqr            conda-forge/linux-64::libspqr-4.3.4-h852d39f_7100102 \n",
            "  libstdcxx          conda-forge/linux-64::libstdcxx-14.2.0-h8f9b012_2 \n",
            "  libsuitesparsecon~ conda-forge/linux-64::libsuitesparseconfig-7.10.1-h92d6892_7100102 \n",
            "  libumfpack         conda-forge/linux-64::libumfpack-6.3.5-heb53515_7100102 \n",
            "  metis              conda-forge/linux-64::metis-5.1.0-hd0bcaf9_1007 \n",
            "  mpfr               conda-forge/linux-64::mpfr-4.2.1-h90cbb55_3 \n",
            "  numpy              conda-forge/linux-64::numpy-2.2.4-py311h5d046bc_0 \n",
            "  pyfmi              conda-forge/linux-64::pyfmi-2.16.3-py311h9f3472d_0 \n",
            "  python_abi         conda-forge/linux-64::python_abi-3.11-2_cp311 \n",
            "  scipy              conda-forge/linux-64::scipy-1.15.2-py311h8f841c2_0 \n",
            "  suitesparse        conda-forge/linux-64::suitesparse-7.10.1-ha0f6916_7100102 \n",
            "  sundials           conda-forge/linux-64::sundials-7.1.1-ha52427a_0 \n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  conda              pkgs/main::conda-24.11.1-py311h06a430~ --> conda-forge::conda-25.1.1-py311h38be061_1 \n",
            "  libgcc-ng          pkgs/main::libgcc-ng-11.2.0-h1234567_1 --> conda-forge::libgcc-ng-14.2.0-h69a702a_2 \n",
            "  libgomp              pkgs/main::libgomp-11.2.0-h1234567_1 --> conda-forge::libgomp-14.2.0-h767d61c_2 \n",
            "  libstdcxx-ng       pkgs/main::libstdcxx-ng-11.2.0-h12345~ --> conda-forge::libstdcxx-ng-14.2.0-h4852527_2 \n",
            "  openssl              pkgs/main::openssl-3.0.16-h5eee18b_0 --> conda-forge::openssl-3.4.1-h7b32b05_0 \n",
            "\n",
            "The following packages will be SUPERSEDED by a higher-priority channel:\n",
            "\n",
            "  certifi            pkgs/main/linux-64::certifi-2025.1.31~ --> conda-forge/noarch::certifi-2025.1.31-pyhd8ed1ab_0 \n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages:\n",
            "scipy-1.15.2         | 16.4 MB   | :   0% 0/1 [00:00<?, ?it/s]\n",
            "numpy-2.2.4          | 8.6 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\n",
            "\n",
            "libopenblas-0.3.29   | 5.6 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "pyfmi-2.16.3         | 5.2 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "metis-5.1.0          | 3.7 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libstdcxx-14.2.0     | 3.7 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "openssl-3.4.1        | 2.8 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",
            "libgfortran5-14.2.0  | 1.4 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",
            "conda-25.1.1         | 1.1 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "assimulo-3.6.0       | 1.1 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\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcholmod-5.3.1     | 1.1 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\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "sundials-7.1.1       | 907 KB    | :   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",
            "libgcc-14.2.0        | 828 KB    | :   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",
            "mpfr-4.2.1           | 620 KB    | :   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",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "gmp-6.3.0            | 449 KB    | :   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",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libgomp-14.2.0       | 449 KB    | :   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",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libumfpack-6.3.5     | 424 KB    | :   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",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "fmilib-2.4.1         | 383 KB    | :   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",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libspqr-4.3.4        | 213 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "scipy-1.15.2         | 16.4 MB   | :   8% 0.07623511861659131/1 [00:00<00:01,  1.31s/it]\n",
            "\n",
            "libopenblas-0.3.29   | 5.6 MB    | :  15% 0.14669872457633418/1 [00:00<00:00,  1.46it/s]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "metis-5.1.0          | 3.7 MB    | :  25% 0.24637217221095128/1 [00:00<00:00,  2.45it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "numpy-2.2.4          | 8.6 MB    | :   0% 0.0018194029373407579/1 [00:00<01:01, 62.01s/it]\u001b[A\n",
            "\n",
            "\n",
            "scipy-1.15.2         | 16.4 MB   | :  29% 0.28874051176033955/1 [00:00<00:00,  1.55it/s]\n",
            "\n",
            "libopenblas-0.3.29   | 5.6 MB    | :  59% 0.5923306992327455/1 [00:00<00:00,  3.21it/s] \u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "metis-5.1.0          | 3.7 MB    | : 100% 1.0/1 [00:00<00:00,  5.42it/s]                \u001b[A\u001b[A\u001b[A\u001b[A\n",
            "numpy-2.2.4          | 8.6 MB    | :  38% 0.3802552139042184/1 [00:00<00:00,  2.12it/s]   \u001b[A\n",
            "\n",
            "\n",
            "scipy-1.15.2         | 16.4 MB   | :  51% 0.5088694167657469/1 [00:00<00:00,  1.84it/s] \n",
            "\n",
            "\n",
            "\n",
            "metis-5.1.0          | 3.7 MB    | : 100% 1.0/1 [00:00<00:00,  5.42it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "numpy-2.2.4          | 8.6 MB    | :  94% 0.9406313186051718/1 [00:00<00:00,  3.66it/s]\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libstdcxx-14.2.0     | 3.7 MB    | :   0% 0.0042177278432850495/1 [00:00<01:21, 82.06s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "libopenblas-0.3.29   | 5.6 MB    | : 100% 1.0/1 [00:00<00:00,  2.59it/s]               \u001b[A\u001b[A\n",
            "\n",
            "scipy-1.15.2         | 16.4 MB   | :  72% 0.7156571760132509/1 [00:00<00:00,  1.93it/s]\n",
            "\n",
            "\n",
            "pyfmi-2.16.3         | 5.2 MB    | : 100% 1.0/1 [00:00<00:00,  2.53it/s]               \u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "pyfmi-2.16.3         | 5.2 MB    | : 100% 1.0/1 [00:00<00:00,  2.53it/s]\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "openssl-3.4.1        | 2.8 MB    | :   1% 0.0055741049077571376/1 [00:00<01:16, 77.16s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libstdcxx-14.2.0     | 3.7 MB    | :  73% 0.7296669168883136/1 [00:00<00:00,  2.13it/s]   \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "scipy-1.15.2         | 16.4 MB   | :  92% 0.9195861183126326/1 [00:00<00:00,  1.91it/s]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libgfortran5-14.2.0  | 1.4 MB    | : 100% 1.0/1 [00:00<00:00, 41.60s/it]                 \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "numpy-2.2.4          | 8.6 MB    | : 100% 1.0/1 [00:00<00:00,  3.66it/s]               \u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "conda-25.1.1         | 1.1 MB    | :   1% 0.013622478419712683/1 [00:00<00:42, 42.85s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "openssl-3.4.1        | 2.8 MB    | : 100% 1.0/1 [00:00<00:00,  2.17it/s]                  \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "openssl-3.4.1        | 2.8 MB    | : 100% 1.0/1 [00:00<00:00,  2.17it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "assimulo-3.6.0       | 1.1 MB    | :   1% 0.014703493605362324/1 [00:00<00:39, 39.93s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libstdcxx-14.2.0     | 3.7 MB    | : 100% 1.0/1 [00:00<00:00,  2.13it/s]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcholmod-5.3.1     | 1.1 MB    | :   1% 0.014870549794649543/1 [00:00<00:41, 42.07s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "conda-25.1.1         | 1.1 MB    | : 100% 1.0/1 [00:00<00:00, 42.85s/it]                 \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "sundials-7.1.1       | 907 KB    | :   2% 0.01763373830085844/1 [00:00<00:36, 37.32s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "assimulo-3.6.0       | 1.1 MB    | : 100% 1.0/1 [00:00<00:00, 39.93s/it]                 \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libgcc-14.2.0        | 828 KB    | :   2% 0.01932337522187561/1 [00:00<00:36, 37.13s/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\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcholmod-5.3.1     | 1.1 MB    | : 100% 1.0/1 [00:00<00:00, 42.07s/it]                 \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "sundials-7.1.1       | 907 KB    | : 100% 1.0/1 [00:00<00:00, 37.32s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mpfr-4.2.1           | 620 KB    | :   3% 0.025811696239942908/1 [00:00<00:27, 28.49s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mpfr-4.2.1           | 620 KB    | : 100% 1.0/1 [00:00<00:00, 28.49s/it]                 \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libgomp-14.2.0       | 449 KB    | :   4% 0.03562807972826631/1 [00:00<00:20, 21.41s/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",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "gmp-6.3.0            | 449 KB    | :   4% 0.03561313321233331/1 [00:00<00:20, 21.52s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "gmp-6.3.0            | 449 KB    | : 100% 1.0/1 [00:00<00:00, 21.52s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libgomp-14.2.0       | 449 KB    | : 100% 1.0/1 [00:00<00:00, 21.41s/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",
            "metis-5.1.0          | 3.7 MB    | : 100% 1.0/1 [00:00<00:00,  5.42it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libgcc-14.2.0        | 828 KB    | : 100% 1.0/1 [00:00<00:00, 37.13s/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\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libumfpack-6.3.5     | 424 KB    | :   4% 0.037731330084655984/1 [00:00<00:21, 21.91s/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\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libspqr-4.3.4        | 213 KB    | :   8% 0.07503068271326775/1 [00:00<00:10, 11.15s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libumfpack-6.3.5     | 424 KB    | : 100% 1.0/1 [00:00<00:00, 21.91s/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\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            " ... (more hidden) ...\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "fmilib-2.4.1         | 383 KB    | :   4% 0.04180391656566945/1 [00:00<00:19, 20.22s/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",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libspqr-4.3.4        | 213 KB    | : 100% 1.0/1 [00:00<00:00, 11.15s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            " ... (more hidden) ...\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "scipy-1.15.2         | 16.4 MB   | : 100% 1.0/1 [00:00<00:00,  1.91it/s]               \n",
            "\n",
            "\n",
            "pyfmi-2.16.3         | 5.2 MB    | : 100% 1.0/1 [00:01<00:00,  2.53it/s]\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "libopenblas-0.3.29   | 5.6 MB    | : 100% 1.0/1 [00:01<00:00,  2.59it/s]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libgfortran5-14.2.0  | 1.4 MB    | : 100% 1.0/1 [00:01<00:00,  1.23s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libgfortran5-14.2.0  | 1.4 MB    | : 100% 1.0/1 [00:01<00:00,  1.23s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "openssl-3.4.1        | 2.8 MB    | : 100% 1.0/1 [00:01<00:00,  2.17it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libstdcxx-14.2.0     | 3.7 MB    | : 100% 1.0/1 [00:01<00:00,  2.13it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "conda-25.1.1         | 1.1 MB    | : 100% 1.0/1 [00:02<00:00,  2.08s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "conda-25.1.1         | 1.1 MB    | : 100% 1.0/1 [00:02<00:00,  2.08s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "assimulo-3.6.0       | 1.1 MB    | : 100% 1.0/1 [00:02<00:00,  2.26s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "assimulo-3.6.0       | 1.1 MB    | : 100% 1.0/1 [00:02<00:00,  2.26s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcholmod-5.3.1     | 1.1 MB    | : 100% 1.0/1 [00:02<00:00,  2.30s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcholmod-5.3.1     | 1.1 MB    | : 100% 1.0/1 [00:02<00:00,  2.30s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "sundials-7.1.1       | 907 KB    | : 100% 1.0/1 [00:02<00:00,  2.49s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "sundials-7.1.1       | 907 KB    | : 100% 1.0/1 [00:02<00:00,  2.49s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "numpy-2.2.4          | 8.6 MB    | : 100% 1.0/1 [00:02<00:00,  3.66it/s]\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "gmp-6.3.0            | 449 KB    | : 100% 1.0/1 [00:02<00:00,  2.54s/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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            "gmp-6.3.0            | 449 KB    | : 100% 1.0/1 [00:02<00:00,  2.54s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
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            "\n",
            "\n",
            "mpfr-4.2.1           | 620 KB    | : 100% 1.0/1 [00:02<00:00,  2.55s/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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            "mpfr-4.2.1           | 620 KB    | : 100% 1.0/1 [00:02<00:00,  2.55s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
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            "\n",
            "libgomp-14.2.0       | 449 KB    | : 100% 1.0/1 [00:02<00:00,  2.66s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
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            "\n",
            "libgomp-14.2.0       | 449 KB    | : 100% 1.0/1 [00:02<00:00,  2.66s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libgcc-14.2.0        | 828 KB    | : 100% 1.0/1 [00:02<00:00,  2.69s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libgcc-14.2.0        | 828 KB    | : 100% 1.0/1 [00:02<00:00,  2.69s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
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            "\n",
            "libumfpack-6.3.5     | 424 KB    | : 100% 1.0/1 [00:02<00:00,  2.69s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
            "libumfpack-6.3.5     | 424 KB    | : 100% 1.0/1 [00:02<00:00,  2.69s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "libspqr-4.3.4        | 213 KB    | : 100% 1.0/1 [00:02<00:00,  2.73s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libspqr-4.3.4        | 213 KB    | : 100% 1.0/1 [00:02<00:00,  2.73s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            " ... (more hidden) ...\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            " ... (more hidden) ...\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
            "fmilib-2.4.1         | 383 KB    | : 100% 1.0/1 [00:03<00:00,  2.79s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "scipy-1.15.2         | 16.4 MB   | : 100% 1.0/1 [00:04<00:00,  1.91it/s]\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "                                                                        \n",
            "                                                                        \u001b[A\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\n",
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            "                                                                        \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",
            "\n",
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            "                                                                        \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\n",
            "\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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            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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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",
            "\n",
            "\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\n",
            "Preparing transaction: - \b\b\\ \b\b| \b\bdone\n",
            "Verifying transaction: - \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\bdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install optlang"
      ],
      "metadata": {
        "id": "blXY9zkDsfvs",
        "outputId": "38e9b703-eef7-4816-ee8d-267886f9b217",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "blXY9zkDsfvs",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting optlang\n",
            "  Downloading optlang-1.8.3-py2.py3-none-any.whl.metadata (8.2 kB)\n",
            "Collecting swiglpk>=5.0.12 (from optlang)\n",
            "  Downloading swiglpk-5.0.12-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.5 kB)\n",
            "Collecting sympy>=1.12.0 (from optlang)\n",
            "  Downloading sympy-1.13.3-py3-none-any.whl.metadata (12 kB)\n",
            "Collecting mpmath<1.4,>=1.1.0 (from sympy>=1.12.0->optlang)\n",
            "  Downloading mpmath-1.3.0-py3-none-any.whl.metadata (8.6 kB)\n",
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            "\u001b[?25hInstalling collected packages: swiglpk, mpmath, sympy, optlang\n",
            "Successfully installed mpmath-1.3.0 optlang-1.8.3 swiglpk-5.0.12 sympy-1.13.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "43a36d05",
      "metadata": {
        "id": "43a36d05"
      },
      "source": [
        "# Notes YEAST_COB_Batch constraint-based approach"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now specific installation and the run simulations. Start with connecting to Github. Then upload the two files:\n",
        "\n",
        "* FMU - BPL_YEAST_AIR_Fedbatch_linux_jm_cs.fmu\n",
        "* Setup-file - BPL_YEAST_AIR_Fedbatch_explore"
      ],
      "metadata": {
        "id": "tfZ-xRIG7vAD"
      },
      "id": "tfZ-xRIG7vAD"
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "git clone https://github.com/janpeter19/BPL_YEAST_COB_Batch"
      ],
      "metadata": {
        "id": "ljtfYAJy7xOj",
        "outputId": "824af396-8b54-475e-b330-84fdd50476e1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "ljtfYAJy7xOj",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Cloning into 'BPL_YEAST_COB_Batch'...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd BPL_YEAST_COB_Batch"
      ],
      "metadata": {
        "id": "MSOqmo0C756c",
        "outputId": "dc4d3818-6e6b-4129-db40-e0cf9608efb0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "MSOqmo0C756c",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/BPL_YEAST_COB_Batch\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "de3cd8b4",
      "metadata": {
        "id": "de3cd8b4",
        "outputId": "ac7d5bbf-1dc7-4fea-f6b5-12e3e57e0e3f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Linux - run FMU pre-compiled OpenModelica\n",
            "\n",
            "Model for the process 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_YEAST_COB_Batch_explore.py"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "6783bef1",
      "metadata": {
        "id": "6783bef1"
      },
      "outputs": [],
      "source": [
        "plt.rcParams['figure.figsize'] = [20/2.54, 16/2.54]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "process_diagram()"
      ],
      "metadata": {
        "id": "7v7g0_1UlCMt",
        "outputId": "50cf59ed-ab5c-47e1-cd72-b0d91ee912bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "id": "7v7g0_1UlCMt",
      "execution_count": 14,
      "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 787.402x629.921 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bccc8620",
      "metadata": {
        "id": "bccc8620"
      },
      "source": [
        "## Try using LP in each step"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "f84aae91",
      "metadata": {
        "id": "f84aae91"
      },
      "outputs": [],
      "source": [
        "from optlang import Model, Variable, Constraint, Objective"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "ae0a7f7b",
      "metadata": {
        "id": "ae0a7f7b"
      },
      "outputs": [],
      "source": [
        "# Define culture constraint-based model\n",
        "def culture(G, E):\n",
        "\n",
        "    # LP calculation of the optimal qGr, qEr based on G and E values\n",
        "\n",
        "    # - parameters\n",
        "    qO2max = 6.9e-3; kog = 2.3; koe = 1.6; YGr = 3.5; YEr = 1.32;\n",
        "    alpha = 0.01; beta = 1.0\n",
        "\n",
        "    # - transfer data from dynamic reactor model to static LP model\n",
        "    qGr_opt = Variable('qGr_opt', lb=0)\n",
        "    qEr_opt = Variable('qEr_opt', lb=0)\n",
        "\n",
        "    # - LP model constraint and objective\n",
        "    mu_max = Objective(YGr*qGr_opt + YEr*qEr_opt, direction='max')\n",
        "    qO2lim = Constraint(kog*qGr_opt + koe*qEr_opt, ub=qO2max)\n",
        "    qGlim = Constraint(qGr_opt, ub=alpha*max(0,G))\n",
        "    qElim = Constraint(qEr_opt, ub=beta*max(0,E))\n",
        "\n",
        "    # - put together the LP model\n",
        "    yeast_model = Model(name='Yeast bottleneck model')\n",
        "    yeast_model.objective = mu_max\n",
        "    yeast_model.add(qO2lim)\n",
        "    yeast_model.add(qGlim)\n",
        "    yeast_model.add(qElim)\n",
        "\n",
        "    # - do LP optimization\n",
        "    yeast_model.optimize()\n",
        "\n",
        "    return (yeast_model.objective.value, yeast_model.variables.qGr_opt.primal, yeast_model.variables.qEr_opt.primal, qO2lim.primal)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "9bfacccb",
      "metadata": {
        "id": "9bfacccb"
      },
      "outputs": [],
      "source": [
        "# Initialization\n",
        "V_start=1.0\n",
        "init(V_start=V_start, VX_start=V_start*2.0, VG_start=V_start*10, VE_start=V_start*3.0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "90790a9f",
      "metadata": {
        "id": "90790a9f"
      },
      "outputs": [],
      "source": [
        "# Loop of simulations\n",
        "t_final = 8.0\n",
        "t_samp = 0.0333\n",
        "n_samp = t_final/t_samp + 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "50bad686",
      "metadata": {
        "id": "50bad686",
        "outputId": "069b2062-cba4-4cef-d380-ee3769806c65",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Could not find cannot import name 'dopri5' from 'assimulo.lib' (/usr/local/lib/python3.11/site-packages/assimulo/lib/__init__.py)\n",
            "Could not find cannot import name 'rodas' from 'assimulo.lib' (/usr/local/lib/python3.11/site-packages/assimulo/lib/__init__.py)\n",
            "Could not find cannot import name 'odassl' from 'assimulo.lib' (/usr/local/lib/python3.11/site-packages/assimulo/lib/__init__.py)\n",
            "Could not find ODEPACK functions.\n",
            "Could not find RADAR5\n",
            "Could not find GLIMDA.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 787.402x629.921 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Simulate n sample steps\n",
        "newplot(title='Batch cultivation', plotType='TimeSeries2')\n",
        "ax1.set_xlim([0, t_final]); ax2.set_xlim([0, t_final]); ax3.set_xlim([0, t_final])\n",
        "\n",
        "simu(t_samp, options=opts_fast)\n",
        "for i in range(int(n_samp)):\n",
        "    (mum_opt, qGr_opt, qEr_opt, qO2_opt) = culture(sim_res['bioreactor.c[2]'][-1], sim_res['bioreactor.c[3]'][-1])\n",
        "    par(mum=mum_opt, qGr=qGr_opt, qEr=qEr_opt, qO2=qO2_opt)\n",
        "    simu(t_samp, 'cont', options=opts_fast)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "describe('MSL')"
      ],
      "metadata": {
        "id": "a3Nn_QQ0Skt0",
        "outputId": "0c11c5d2-b09e-4f8b-dcff-7f2c8fe5d0b1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "a3Nn_QQ0Skt0",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSL: 3.2.3 - used components: none\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "0ccb67b4",
      "metadata": {
        "id": "0ccb67b4",
        "outputId": "422325eb-dbdb-4c8e-c8f5-addc1ff51729",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "System information\n",
            " -OS: Linux\n",
            " -Python: 3.11.11\n",
            " -Scipy: not installed in the notebook\n",
            " -PyFMI: 2.16.3\n",
            " -FMU by: OpenModelica Compiler OpenModelica 1.25.0~dev-133-ga5470be\n",
            " -FMI: 2.0\n",
            " -Type: FMUModelME2\n",
            " -Name: BPL_YEAST_COB.Batch\n",
            " -Generated: 2024-11-08T08:21:20Z\n",
            " -MSL: 3.2.3\n",
            " -Description: Bioprocess Library version 2.3.0\n",
            " -Interaction: FMU-explore version 1.0.0\n"
          ]
        }
      ],
      "source": [
        "system_info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "b869782a",
      "metadata": {
        "id": "b869782a",
        "outputId": "be3650bc-643f-4f39-c5c9-1dd0e1ce51a7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# packages in environment at /usr/local:\n",
            "#\n",
            "# Name                    Version                   Build  Channel\n",
            "optlang                   1.8.3                    pypi_0    pypi\n"
          ]
        }
      ],
      "source": [
        "!conda list optlang"
      ]
    }
  ],
  "metadata": {
    "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"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}