BPL_YEAST_COB_Batch_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_YEAST_COB_Batch/blob/main/BPL_YEAST_COB_Batch_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_Batch script with FMPy\n",
        "\n",
        "The key library FMPy 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."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "ID68dwep2zus",
        "outputId": "7fe28809-9958-4cd8-f3e7-4ba4795ff9f8"
      },
      "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"
          ]
        }
      ],
      "source": [
        "!lsb_release -a # Actual VM Ubuntu version used by Google"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python --version"
      ],
      "metadata": {
        "id": "EP1IDpTBD4wJ",
        "outputId": "797e484d-0180-46cd-b945-b9367c6bbb53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python 3.11.11\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "04VeBmLU3UFx",
        "outputId": "128f2a9a-9ce9-4f14-eb17-8a7d4baa0f32"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting fmpy\n",
            "  Downloading FMPy-0.3.22-py3-none-any.whl.metadata (1.9 kB)\n",
            "Requirement already satisfied: attrs in /usr/local/lib/python3.11/dist-packages (from fmpy) (25.1.0)\n",
            "Requirement already satisfied: Jinja2 in /usr/local/lib/python3.11/dist-packages (from fmpy) (3.1.5)\n",
            "Collecting lark (from fmpy)\n",
            "  Downloading lark-1.2.2-py3-none-any.whl.metadata (1.8 kB)\n",
            "Requirement already satisfied: lxml in /usr/local/lib/python3.11/dist-packages (from fmpy) (5.3.1)\n",
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            "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from fmpy) (1.26.4)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from Jinja2->fmpy) (3.0.2)\n",
            "Downloading FMPy-0.3.22-py3-none-any.whl (4.9 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.9/4.9 MB\u001b[0m \u001b[31m19.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading lark-1.2.2-py3-none-any.whl (111 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m111.0/111.0 kB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: lark, fmpy\n",
            "Successfully installed fmpy-0.3.22 lark-1.2.2\n"
          ]
        }
      ],
      "source": [
        "!pip install fmpy"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# For some reason conda installation does not work for optlang\n",
        "!pip install optlang"
      ],
      "metadata": {
        "id": "HN0b6HVUN_eA",
        "outputId": "9d9d669c-d247-4b92-bed2-7f5505afa135",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "execution_count": 4,
      "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",
            "Requirement already satisfied: sympy>=1.12.0 in /usr/local/lib/python3.11/dist-packages (from optlang) (1.13.1)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy>=1.12.0->optlang) (1.3.0)\n",
            "Downloading optlang-1.8.3-py2.py3-none-any.whl (141 kB)\n",
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            "\u001b[?25hDownloading swiglpk-5.0.12-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.3 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m26.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: swiglpk, optlang\n",
            "Successfully installed optlang-1.8.3 swiglpk-5.0.12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UTETJm3l6heL"
      },
      "source": [
        "# BPL_YEAST_COB_Batch setup\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mxILgQ103nZ6"
      },
      "source": [
        "Now specific installation and the run simulations. Start with connecting to Github. Then upload the two files:\n",
        "\n",
        "* FMU - BPL_YEAST_COB_Batch_linux_om_me.fmu\n",
        "* Setup-file - BPL_YEAST_COB_Batch_fmpy_explore.py"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "RfWPh1B63yBF",
        "outputId": "7dc89e59-c90d-4801-9768-4ccc196549d0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Cloning into 'BPL_YEAST_COB_Batch'...\n"
          ]
        }
      ],
      "source": [
        "%%bash\n",
        "git clone https://github.com/janpeter19/BPL_YEAST_COB_Batch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "i6uOVB44300a",
        "outputId": "68bdcea7-bfe2-4396-a208-829e47bcd35a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/BPL_YEAST_COB_Batch\n"
          ]
        }
      ],
      "source": [
        "%cd BPL_YEAST_COB_Batch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "b-y82jZY33cO",
        "outputId": "abc89272-0c26-4b03-da48-30a73435f047",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Linux - run FMU pre-comiled OpenModelica\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_YEAST_COB_Batch_fmpy_explore.py"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "HUFhuE-H4RCw"
      },
      "outputs": [],
      "source": [
        "%matplotlib inline\n",
        "plt.rcParams['figure.figsize'] = [25/2.54, 20/2.54]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "cMO4OlAgJIlb"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dx9oMnP0RJkM"
      },
      "source": [
        "## BPL_YEAST_COB_Batch - demo\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "UeMOu19LJT6b"
      },
      "outputs": [],
      "source": [
        "from optlang import Model, Variable, Constraint, Objective"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "process_diagram()"
      ],
      "metadata": {
        "id": "8PxV2IHQ_y7T",
        "outputId": "284e3946-fca3-45a5-d9b6-6f1047d2a695",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "execution_count": 11,
      "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": 12,
      "metadata": {
        "id": "-Y3NHWIj4TZ6",
        "outputId": "8975bbab-1996-45af-c77e-41eca66df8e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saccharomyces cerevisae - default parameters for strain H1022\n",
            "\n"
          ]
        }
      ],
      "source": [
        "describe('culture'); print(); #describe('liquidphase')                                  # Pump schedule parameter\n"
      ]
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "id": "nJpSSL2HO0Gj"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "mtWJ3rfbJaVN"
      },
      "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",
      "source": [
        "# Loop of simulations\n",
        "t_final = 8.0\n",
        "t_samp = 0.0333\n",
        "n_samp = t_final/t_samp + 1"
      ],
      "metadata": {
        "id": "GIxPx4GzO-5F"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "IOLRVcsFJ5NX",
        "outputId": "4b64207f-03bf-4ab5-e0cb-ac972a7157b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 984.252x787.402 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",
      "execution_count": 17,
      "metadata": {
        "id": "D7lCWmdiKNLm",
        "outputId": "996485f1-e68e-46b7-bbbf-bb2a99a9b2d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "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",
            " -FMPy: 0.3.22\n",
            " -FMU by: OpenModelica Compiler OpenModelica 1.25.0~dev-133-ga5470be\n",
            " -FMI: 2.0\n",
            " -Type: ME\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 for FMPy version 1.0.1\n"
          ]
        }
      ],
      "source": [
        "system_info()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip show optlang|grep Version"
      ],
      "metadata": {
        "id": "Rsp0QFz7j6BJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "77b12994-b4cb-469d-8a33-0d4238422fa7"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Version: 1.8.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DxD3D4-moilC"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "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
}