BPL_TEST2_Batch_calibration_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_Batch_calibration/blob/main/BPL_TEST2_Batch_calibration_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_calibration script with FMPy\n",
        "\n",
        "The key library FMPy and scipy and more are installed.\n",
        "\n",
        "After the installation a small application BPL_TEST2_Batch_calibration 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/"
        },
        "id": "ID68dwep2zus",
        "outputId": "c2fe5f5f-69f6-4b29-ca8f-a575f4ca2e6c"
      },
      "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": "0TrVii8d7B7U",
        "outputId": "a135a723-e491-4a7f-82ea-82cdd32f77ee",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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/"
        },
        "id": "04VeBmLU3UFx",
        "outputId": "a8a7a657-9667-4ae1-f15a-fc8d0088cc49"
      },
      "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) (25.3.0)\n",
            "Requirement already satisfied: Jinja2 in /usr/local/lib/python3.11/dist-packages (from fmpy) (3.1.6)\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",
            "Requirement already satisfied: msgpack in /usr/local/lib/python3.11/dist-packages (from fmpy) (1.1.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from fmpy) (2.0.2)\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[31m5.3 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": "markdown",
      "metadata": {
        "id": "mxILgQ103nZ6"
      },
      "source": [
        "Now specific installation and the run simulations. Start with connecting to Github. Then upload the four files:\n",
        "\n",
        "* FMU - BPL_TEST2_Batch_linux_om_me.fmu\n",
        "* Setup-file - BPL_TEST2_Batch_fmpy_explore.py\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RfWPh1B63yBF",
        "outputId": "80313b4d-3382-42e9-8d9b-dacd8b2841cf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Cloning into 'BPL_TEST2_Batch_calibration'...\n"
          ]
        }
      ],
      "source": [
        "%%bash\n",
        "git clone https://github.com/janpeter19/BPL_TEST2_Batch_calibration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i6uOVB44300a",
        "outputId": "6067147b-e568-4758-a926-dfc467f95e71"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/BPL_TEST2_Batch_calibration\n"
          ]
        }
      ],
      "source": [
        "%cd BPL_TEST2_Batch_calibration"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UTETJm3l6heL"
      },
      "source": [
        "# BPL_TEST2_Batch_calibration - demo\n",
        "Author: Jan Peter Axelsson\n",
        "\n",
        "This notebook shows the possibilities for calibration of the model BPL_TEST2_Batch using scipy.optimize.minimize() routine. There are several different methods to choose between. In this notebook we work with simulated data.\n",
        "\n",
        "The text-book model of batch cultivation we simulate is the following where $S$ is substrate, $X$ is cell concentration, and $V$ is volume of the broth\n",
        "\n",
        "$$\n",
        "\\eqalign{\n",
        "{d(VS) \\over dt} =& -q_S(S) \\cdot VX \\cr\n",
        "{d(VX) \\over dt} =& \\mu(S) \\cdot VX }\n",
        "$$\n",
        "\n",
        "and where specific cell growth rate $\\mu$ and substrate uptake rate $q_S$ are\n",
        "\n",
        "$$\n",
        "\\mu(S) = Y \\cdot q_S(S)\n",
        "$$\n",
        "\n",
        "$$\n",
        "q_S(S) = q_S^{max} {S \\over K_s + S}\n",
        "$$\n",
        "\n",
        "where $Y$ is the yield, $q_S^{max}$ is the maximal specific substrate uptake rate and $K_s$ is the corresponding saturation constant.\n",
        "\n",
        "The parameter estimation is done with optimization methods that only require evaluation of the missmatch between simulation with given parameters and data. At start the allowed range for each parameter is given. The method used for optimization is SLSQP but can easily be changed [1].\n",
        "\n",
        "In the near future the FMU may provide first derivative gradient informaion, that will make it possible to choose corresponding method of minimize() for improved performance. This possibility is related to the upgrade to the FMI-standard ver 3.0 for the Modelica compiler.  \n",
        "\n",
        "The Python package PyFMI [2] that is the base for FMU-explore has a simplified built-in\n",
        "functionality for parameter estimation that also use scipy.optimize.minimize(). However, there is estimatation functionaly but the purpose seems to only address smaller examples. There is for instance no support to handle models that takes sub-models from libraries and necesssary changes of default parameters not to be estimated. Therefore we here define a Python function evaluate() that facilitate the formulation of the parameter estimation and also bring flexibility to choice of optimization method, default Nelder-Mead."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s2k4-lXRjQ8G",
        "outputId": "ba66af35-557f-4fbb-befa-1c6e25a51ed0"
      },
      "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_TEST2_Batch_fmpy_explore.py"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "3FGDqmUblfY4"
      },
      "outputs": [],
      "source": [
        "# Adjust the size of diagrams\n",
        "plt.rcParams['figure.figsize'] = [15/2.54, 12/2.54]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "process_diagram()"
      ],
      "metadata": {
        "id": "7ZumIhlFPi0f",
        "outputId": "512bd9ad-683e-4b5b-c0ea-823101f03aeb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        }
      },
      "execution_count": 8,
      "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 590.551x472.441 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GTCMYao8lnQk"
      },
      "source": [
        "## 1 Generate data later used for parameter estimation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "-eDPzPtZlmgi"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "jXQP_e_NlxJr",
        "outputId": "3d2b488c-58c1-4592-9cbb-9dcd5b22e489"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 590.551x472.441 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Data generated\n",
        "simulationTime = 6.0\n",
        "par(Y=0.50, qSmax=1.00, Ks=0.1)\n",
        "init(V_start=1.0, VS_start=10, VX_start=1.0)\n",
        "newplot(plotType='Demo_2')\n",
        "simu(simulationTime, options=opts_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "xF_li-0OmAnS",
        "outputId": "f813ae95-02cf-405a-b3e0-e2f5aabf44f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    time         X             S\n",
              "0    0.0  1.000000  1.000000e+01\n",
              "1    0.5  1.280777  9.438447e+00\n",
              "2    1.0  1.640089  8.719823e+00\n",
              "3    1.5  2.099634  7.800732e+00\n",
              "4    2.0  2.686794  6.626412e+00\n",
              "5    2.5  3.435509  5.128981e+00\n",
              "6    3.0  4.385357  3.229286e+00\n",
              "7    3.5  5.559274  8.814516e-01\n",
              "8    4.0  6.000000  4.618858e-07\n",
              "9    4.5  6.000000 -7.022022e-11\n",
              "10   5.0  6.000000 -2.610415e-12\n",
              "11   5.5  6.000000  1.020722e-14\n",
              "12   6.0  6.000000 -1.037244e-17"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-505c65ca-e499-4c3b-80cd-a76d4264a663\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>time</th>\n",
              "      <th>X</th>\n",
              "      <th>S</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.5</td>\n",
              "      <td>1.280777</td>\n",
              "      <td>9.438447e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.640089</td>\n",
              "      <td>8.719823e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.5</td>\n",
              "      <td>2.099634</td>\n",
              "      <td>7.800732e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.0</td>\n",
              "      <td>2.686794</td>\n",
              "      <td>6.626412e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2.5</td>\n",
              "      <td>3.435509</td>\n",
              "      <td>5.128981e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>3.0</td>\n",
              "      <td>4.385357</td>\n",
              "      <td>3.229286e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>3.5</td>\n",
              "      <td>5.559274</td>\n",
              "      <td>8.814516e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>4.0</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>4.618858e-07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>4.5</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>-7.022022e-11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>5.0</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>-2.610415e-12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>5.5</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1.020722e-14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>6.0</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>-1.037244e-17</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 13,\n  \"fields\": [\n    {\n      \"column\": \"time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9472202409246537,\n        \"min\": 0.0,\n        \"max\": 6.0,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          5.5,\n          4.5,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"X\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0452618843853028,\n        \"min\": 1.0,\n        \"max\": 6.000000000035112,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          5.999999999999996,\n          6.000000000035112,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"S\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.090523768770605,\n        \"min\": -7.022021601328924e-11,\n        \"max\": 10.0,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          1.0207220354984522e-14,\n          -7.022021601328924e-11,\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "# Store data in a DataFrame for later use\n",
        "data = pd.DataFrame(data={'time':sim_res['time'], 'X':sim_res['bioreactor.c[1]'], 'S':sim_res['bioreactor.c[2]']})\n",
        "data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SJgJWIahjpkY"
      },
      "source": [
        "## 2 Simulation with initial guess of parameters compared with data\n",
        "\n",
        "Here we define the parameters that should be estimated and specify allowed ranges.\n",
        "Nominal parameters are chosen as the mid-point of the allowed parameter range.\n",
        "\n",
        "Simulation with these nominal parameter set and compare with data give an idea of who well the model fit data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "ikcDT_Pujs3O"
      },
      "outputs": [],
      "source": [
        "# Parameters to be estimated using parDict names and their bounds\n",
        "parEstim = ['Y', 'qSmax', 'Ks']\n",
        "parBounds = [(0.4, 0.8), (0.7, 1.3), (0.05, 0.20)]\n",
        "parEstim_0 = [np.mean(parBounds[k]) for k in range(len(parBounds))]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "vx5UWIT6jxxc",
        "outputId": "dbe3366b-74b7-46b4-b6e7-fe44284289cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 590.551x472.441 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Simulation with nominal parameters\n",
        "newplot(plotType='Demo_1')\n",
        "par(Y=parEstim_0[0], qSmax=parEstim_0[1], Ks=parEstim_0[2])\n",
        "simu(simulationTime)\n",
        "\n",
        "# Show data\n",
        "ax1.plot(data['time'], data['S'],'b*')\n",
        "ax2.plot(data['time'], data['X'],'r*')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "1rqTGUTdjx73",
        "outputId": "eaf159b7-b22a-4f77-bcbd-b18bc5301e09",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 590.551x472.441 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Simulation over the parameter ranges given\n",
        "newplot(plotType='Demo_1')\n",
        "for Y_value in parBounds [0]:\n",
        "    for qSmax_value in parBounds[1]:\n",
        "        for Ks_value in parBounds[2]:\n",
        "            par(Y=Y_value, qSmax=qSmax_value, Ks=Ks_value)\n",
        "            simu(simulationTime)\n",
        "\n",
        "# Show data\n",
        "ax1.plot(data['time'], data['S'],'b*')\n",
        "ax2.plot(data['time'], data['X'],'r*')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VGtOdaTWmf48"
      },
      "source": [
        "Simulation over the diferent parameter combinations of the parameter bounds shows that data is \"covered\" and we have good hope to find a parameter combination that fits data well."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L-Skl_QQmvDx"
      },
      "source": [
        "## 3 Parameter estimation\n",
        "\n",
        "Here we use the scipy.optimize.minimize() procedure which contain a family of different methods [1].\n",
        "The default method is Nelder-Mead and is robust for fitting a model to data. Further we have chosen to work with bounds for the parameters to be estimated and the initial guess is chosen as the middle point in parameter space."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "lWI-xUyvnGwi"
      },
      "outputs": [],
      "source": [
        "# Optimization routine import\n",
        "import scipy.optimize"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "sEQt2957nHPU"
      },
      "outputs": [],
      "source": [
        "# Parameters to be estimated using parDict names and their bounds\n",
        "extra_args = (parEstim, data, fmu_model, simulationTime, parDict, parLocation)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "P3NqjIfUnNy9"
      },
      "outputs": [],
      "source": [
        "# Modified evaluation function tailored for Python optimization algorithms\n",
        "def objective(x, parEstim, data=data, fmu_model=fmu_model, simulationTime=simulationTime,\n",
        "             parDict=parDict, parLocation=parLocation):\n",
        "    \"\"\"The parameter list is tailored for scipy optimization algorithms interface,\n",
        "       where the first parameter x is an array with parameters that are tuned\n",
        "       and evalauted and parEstim is a list of the names of these parameters.\n",
        "       The code can be made 20-30% faster, but loner, using pyfmi-commands directly.\"\"\"\n",
        "\n",
        "    # Update parameters and simulate\n",
        "    for i, p in enumerate(parEstim): par(**{p:x[i]})\n",
        "    simu(simulationTime, options=opts_data)\n",
        "\n",
        "    # Calculate loss function V\n",
        "    V={}\n",
        "    V['X'] = np.linalg.norm(data['X'] - np.interp(data['time'], sim_res['time'], sim_res['bioreactor.c[1]']))\n",
        "    V['S'] = np.linalg.norm(data['S'] - np.interp(data['time'], sim_res['time'], sim_res['bioreactor.c[2]']))\n",
        "\n",
        "    return V['X'] + V['S']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "-AExpVp6nOAj"
      },
      "outputs": [],
      "source": [
        "import time"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "OnUBcKG2nXjv",
        "outputId": "9f606918-2aad-461f-ce83-68e2c81a63ba",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimization terminated successfully.\n",
            "         Current function value: 0.045511\n",
            "         Iterations: 39\n",
            "         Function evaluations: 75\n",
            "CPU-time = 5.869832754135132\n"
          ]
        }
      ],
      "source": [
        "# Run minimize()\n",
        "start_time = time.time()\n",
        "result = scipy.optimize.minimize(objective, x0=parEstim_0, args=extra_args,\n",
        "                                 method='Nelder-Mead', bounds=parBounds, options={\"disp\":True})\n",
        "print('CPU-time =', time.time()-start_time)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "tMTXuk1PnXx0",
        "outputId": "84347c60-104e-4eb5-b87e-929592aea9f8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       message: Optimization terminated successfully.\n",
              "       success: True\n",
              "        status: 0\n",
              "           fun: 0.045510651189744136\n",
              "             x: [ 5.001e-01  1.007e+00  1.405e-01]\n",
              "           nit: 39\n",
              "          nfev: 75\n",
              " final_simplex: (array([[ 5.001e-01,  1.007e+00,  1.405e-01],\n",
              "                       [ 5.001e-01,  1.007e+00,  1.405e-01],\n",
              "                       [ 5.001e-01,  1.007e+00,  1.405e-01],\n",
              "                       [ 5.001e-01,  1.007e+00,  1.405e-01]]), array([ 4.551e-02,  4.552e-02,  4.556e-02,  4.559e-02]))"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "result"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DED3EeQknjBF"
      },
      "source": [
        "The estimated parameters result.x are very close to the original values and no surprise."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fzzOCMgFnp-2"
      },
      "source": [
        "## 4 Simulation with estimated parameters compared with data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "SCzQkc8BnX9T",
        "outputId": "fd0ebc54-22ed-4ef8-c4f6-8717124fc7a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 590.551x472.441 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "newplot(plotType='Demo_1')\n",
        "par(Y=result.x[0], qSmax=result.x[1], Ks=result.x[2])\n",
        "simu(simulationTime)\n",
        "\n",
        "# Show data\n",
        "ax1.plot(data['time'], data['S'],'b*')\n",
        "ax2.plot(data['time'], data['X'],'r*')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "QKrkx9mkn3Vv",
        "outputId": "9b04b2b8-a9ec-4f81-a3e2-be4ba5bbd252",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Y : 0.5000549816703198\n",
            "qSmax : 1.0066481313036082\n",
            "Ks : 0.14048278753421406\n"
          ]
        }
      ],
      "source": [
        "# The estimated parameters are\n",
        "for i in range(len(parEstim)): print(parEstim[i],':', result.x[i])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oev7dn9Zn71X"
      },
      "source": [
        "## 5 Analysis of the loss function\n",
        "\n",
        "The problem is small and analysis of the loss function brings some insight. From the diagram above showing parameter sweep over combinations min- and max-parameters we see that the parameter $K_s$ has little influence. Let use set that a fixed value and then plot the loss function in the parameters $Y$ and $qSmax$.  We do this by go through all the parametera combinations and evaluate each of them."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "MElBZgYpoCK4",
        "outputId": "477ad44a-6223-49dc-96c4-bec1ad950951",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 590.551x472.441 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Sweep through Y and qSmax variation and store the value of the loss-function for each\n",
        "nY = 20\n",
        "nqSmax = 20\n",
        "V = np.zeros((nY, nqSmax))\n",
        "\n",
        "Y = np.linspace(parBounds[0][0],parBounds[0][1],nY)\n",
        "qSmax = np.linspace(parBounds[1][0],parBounds[1][1],nqSmax)\n",
        "\n",
        "for j in range(nY):\n",
        "    for k in range(nqSmax):\n",
        "        V[k,j] = objective([Y[j], qSmax[k], 0.1], parEstim)\n",
        "\n",
        "# Contour plot\n",
        "plt.figure()\n",
        "plt.clf\n",
        "plt.subplot(1,1,1)\n",
        "plt.contourf(Y, qSmax, V, 20, cmap='RdGy')\n",
        "plt.plot(result.x[0], result.x[1],'k+')\n",
        "plt.colorbar()\n",
        "plt.ylabel('qSmax')\n",
        "plt.xlabel('Y')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GMCkRwMdoLWF"
      },
      "source": [
        "We see the following in the contour diagram of the loss function simplified:\n",
        "* The minima is unique in the range of parmaters we study. This is good news.\n",
        "* The contour plot is ellipsoid and rather narrow. The more narrow the ellipsoid the more difficult and more time it takes to converge to the minima.\n",
        "* The direction of the ellipsoid axis indicate the correlation you may get between the two parameters during the minimization process.\n",
        "\n",
        "Note that the form of the contour plot change with the parameters (and initial values) of the actual proces.\n",
        "You can see the impact by changing the parameters in \"cell # 4\" where data is generated and then just choose to run that cell and the cells below. No need to restart the notebook."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w9R7WisroQ9L"
      },
      "source": [
        "## 6 Summary\n",
        "\n",
        "A choice was made to work with allowed ranges of parameters to be estimated and a start value was defined as the center point in this parameter space. There are only three methods available in optimize.minimize() that can handle bounds on parameters.\n",
        "\n",
        "An evaluate() function was created that define how the difference beween simulation and data is measured. The function is rather transparent and easy to modify and you may want to change weight on the loss in S and X, for instance. Here they have so far equal weight.\n",
        "\n",
        "The FMU-explore workspace dictionaries partDict[] and parLocation[] are useful also here and simplify the code for the evaluation() function. But we also use the detailed PyFMI-functions to administrate and set parameters of the actual simulation.\n",
        "\n",
        "The call optimize.minimize() has several parameters and can easily be modified, for instance change of method. For fitting a model to data Nelder-Mead is ao a robust and good choice, but can be somewhat slow.\n",
        "\n",
        "The estimated parameters were close to perfect!\n",
        "\n",
        "The contour plot of the simplified loss function shows that the minima is unique and should not be difficult too difficut to obtain. More narrow elliptical contour plots would indicate difficulties. Multiple local minima would also be a problem."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jocY1wc4oU3T"
      },
      "source": [
        "## 7 References\n",
        "\n",
        "[1] Scipy Reference guide on optimize.minimize()\n",
        "[here](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html?highlight=minimize)\n",
        "\n",
        "[2] Andersson, C., Åkesson, J., Fuhrer C. : \"PyFMI: A Python package for simulation of coupled dynamic models with the functional mock-up interface\", Centre for Mathematical Sciences, Lund University, Report LUTFNA-5008-2016, 2016."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6W-ZrGbiokih"
      },
      "source": [
        "## Appendix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "ff0MCegVKSC0",
        "outputId": "19c356cb-10b6-44cf-a685-4c6ffc223196",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['bioreactor', 'bioreactor.culture']\n"
          ]
        }
      ],
      "source": [
        "describe('parts')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "l5zDoRbOKoW5",
        "outputId": "b8ec2f6a-0ff2-4120-dff4-76d092a55145",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSL: 3.2.3 - used components: none\n"
          ]
        }
      ],
      "source": [
        "describe('MSL')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "mUFH0C7DKpek",
        "outputId": "ceaeac72-4d4f-42d0-a02e-c92f30e14a26",
        "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: 1.14.1\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.Examples_TEST2.Batch\n",
            " -Generated: 2024-11-06T21:35:34Z\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": [],
      "metadata": {
        "id": "BTEq_feFoPKn"
      },
      "execution_count": 26,
      "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": "3.10.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}