BPL_CHO_Fedbatch_colab.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/janpeter19/BPL_CHO_Fedbatch/blob/main/BPL_CHO_Fedbatch_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_CHO_Fedbatch script with PyFMI\n",
    "\n",
    "The key library PyFMI is installed.\n",
    "\n",
    "After the installation a small application BPL_CHO_Fedbatch is loaded and run. You can continue with this example if you like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ID68dwep2zus",
    "outputId": "da464136-2f2d-4d1c-b052-af159bf36f6f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No LSB modules are available.\n",
      "Distributor ID:\tUbuntu\n",
      "Description:\tUbuntu 22.04.2 LTS\n",
      "Release:\t22.04\n",
      "Codename:\tjammy\n"
     ]
    }
   ],
   "source": [
    "!lsb_release -a # Actual VM Ubuntu version used by Google"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pVDcSg-j3SSV",
    "outputId": "b46674f9-2bd8-433d-a90f-a3782ba3f97d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: PYTHONPATH=\n"
     ]
    }
   ],
   "source": [
    "%env PYTHONPATH="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "04VeBmLU3UFx",
    "outputId": "9e7164fa-e5fb-4134-aa76-55da8080033f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2023-09-25 13:29:53--  https://repo.anaconda.com/miniconda/Miniconda3-py310_23.1.0-1-Linux-x86_64.sh\n",
      "Resolving repo.anaconda.com (repo.anaconda.com)... 104.16.131.3, 104.16.130.3, 2606:4700::6810:8203, ...\n",
      "Connecting to repo.anaconda.com (repo.anaconda.com)|104.16.131.3|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 74403966 (71M) [application/x-sh]\n",
      "Saving to: ‘Miniconda3-py310_23.1.0-1-Linux-x86_64.sh’\n",
      "\n",
      "Miniconda3-py310_23 100%[===================>]  70.96M   174MB/s    in 0.4s    \n",
      "\n",
      "2023-09-25 13:29:54 (174 MB/s) - ‘Miniconda3-py310_23.1.0-1-Linux-x86_64.sh’ saved [74403966/74403966]\n",
      "\n",
      "PREFIX=/usr/local\n",
      "Unpacking payload ...\n",
      "\n",
      "Installing base environment...\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "\n",
      "Preparing transaction: - \b\b\\ \b\b| \b\b/ \b\bdone\n",
      "Executing transaction: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
      "installation finished.\n"
     ]
    }
   ],
   "source": [
    "!wget https://repo.anaconda.com/miniconda/Miniconda3-py310_23.1.0-1-Linux-x86_64.sh\n",
    "!chmod +x Miniconda3-py310_23.1.0-1-Linux-x86_64.sh\n",
    "!bash ./Miniconda3-py310_23.1.0-1-Linux-x86_64.sh -b -f -p /usr/local\n",
    "import sys\n",
    "sys.path.append('/usr/local/lib/python3.10/site-packages/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5iuP4_vl3WFA",
    "outputId": "91824c9f-90b1-42c1-f0b4-414de99d6604"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_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\bdone\n",
      "Solving environment: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\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",
      "\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",
      "    boltons-23.0.0             |  py310h06a4308_0         430 KB\n",
      "    ca-certificates-2023.08.22 |       h06a4308_0         123 KB\n",
      "    certifi-2023.7.22          |  py310h06a4308_0         153 KB\n",
      "    conda-23.7.4               |  py310h06a4308_0         1.0 MB\n",
      "    conda-package-handling-2.2.0|  py310h06a4308_0         268 KB\n",
      "    conda-package-streaming-0.9.0|  py310h06a4308_0          27 KB\n",
      "    cryptography-41.0.3        |  py310hdda0065_0         2.0 MB\n",
      "    jsonpatch-1.32             |     pyhd3eb1b0_0          15 KB\n",
      "    jsonpointer-2.1            |     pyhd3eb1b0_0           9 KB\n",
      "    libffi-3.4.4               |       h6a678d5_0         142 KB\n",
      "    openssl-3.0.11             |       h7f8727e_2         5.2 MB\n",
      "    packaging-23.1             |  py310h06a4308_0          78 KB\n",
      "    pyopenssl-23.2.0           |  py310h06a4308_0          97 KB\n",
      "    python-3.10.13             |       h955ad1f_0        26.8 MB\n",
      "    requests-2.31.0            |  py310h06a4308_0          97 KB\n",
      "    sqlite-3.41.2              |       h5eee18b_0         1.2 MB\n",
      "    tqdm-4.65.0                |  py310h2f386ee_0         132 KB\n",
      "    tzdata-2023c               |       h04d1e81_0         116 KB\n",
      "    urllib3-1.26.16            |  py310h06a4308_0         201 KB\n",
      "    xz-5.4.2                   |       h5eee18b_0         642 KB\n",
      "    zstandard-0.19.0           |  py310h5eee18b_0         474 KB\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:        39.1 MB\n",
      "\n",
      "The following NEW packages will be INSTALLED:\n",
      "\n",
      "  boltons            pkgs/main/linux-64::boltons-23.0.0-py310h06a4308_0 \n",
      "  jsonpatch          pkgs/main/noarch::jsonpatch-1.32-pyhd3eb1b0_0 \n",
      "  jsonpointer        pkgs/main/noarch::jsonpointer-2.1-pyhd3eb1b0_0 \n",
      "  packaging          pkgs/main/linux-64::packaging-23.1-py310h06a4308_0 \n",
      "\n",
      "The following packages will be UPDATED:\n",
      "\n",
      "  ca-certificates                     2023.01.10-h06a4308_0 --> 2023.08.22-h06a4308_0 \n",
      "  certifi                         2022.12.7-py310h06a4308_0 --> 2023.7.22-py310h06a4308_0 \n",
      "  conda                              23.1.0-py310h06a4308_0 --> 23.7.4-py310h06a4308_0 \n",
      "  conda-package-han~                  2.0.2-py310h06a4308_0 --> 2.2.0-py310h06a4308_0 \n",
      "  conda-package-str~                  0.7.0-py310h06a4308_0 --> 0.9.0-py310h06a4308_0 \n",
      "  cryptography                       38.0.4-py310h9ce1e76_0 --> 41.0.3-py310hdda0065_0 \n",
      "  libffi                                   3.4.2-h6a678d5_6 --> 3.4.4-h6a678d5_0 \n",
      "  openssl                                 1.1.1s-h7f8727e_0 --> 3.0.11-h7f8727e_2 \n",
      "  pyopenssl          pkgs/main/noarch::pyopenssl-22.0.0-py~ --> pkgs/main/linux-64::pyopenssl-23.2.0-py310h06a4308_0 \n",
      "  python                                  3.10.9-h7a1cb2a_0 --> 3.10.13-h955ad1f_0 \n",
      "  requests                           2.28.1-py310h06a4308_0 --> 2.31.0-py310h06a4308_0 \n",
      "  sqlite                                  3.40.1-h5082296_0 --> 3.41.2-h5eee18b_0 \n",
      "  tqdm                               4.64.1-py310h06a4308_0 --> 4.65.0-py310h2f386ee_0 \n",
      "  tzdata                                   2022g-h04d1e81_0 --> 2023c-h04d1e81_0 \n",
      "  urllib3                           1.26.14-py310h06a4308_0 --> 1.26.16-py310h06a4308_0 \n",
      "  xz                                      5.2.10-h5eee18b_1 --> 5.4.2-h5eee18b_0 \n",
      "  zstandard                          0.18.0-py310h5eee18b_0 --> 0.19.0-py310h5eee18b_0 \n",
      "\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "certifi-2023.7.22    | 153 KB    | :   0% 0/1 [00:00<?, ?it/s]\n",
      "tqdm-4.65.0          | 132 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\n",
      "\n",
      "ca-certificates-2023 | 123 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "boltons-23.0.0       | 430 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "conda-package-handli | 268 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "tzdata-2023c         | 116 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "packaging-23.1       | 78 KB     | :   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",
      "conda-23.7.4         | 1.0 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",
      "jsonpointer-2.1      | 9 KB      | :   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",
      "conda-package-stream | 27 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\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "python-3.10.13       | 26.8 MB   | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "requests-2.31.0      | 97 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",
      "xz-5.4.2             | 642 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",
      "jsonpatch-1.32       | 15 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",
      "openssl-3.0.11       | 5.2 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "zstandard-0.19.0     | 474 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",
      "libffi-3.4.4         | 142 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",
      "sqlite-3.41.2        | 1.2 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "pyopenssl-23.2.0     | 97 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",
      " ... (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",
      "certifi-2023.7.22    | 153 KB    | :  73% 0.7318766591790893/1 [00:00<00:00,  7.01it/s]\n",
      "\n",
      "\n",
      "\n",
      "conda-package-handli | 268 KB    | :  60% 0.5979081971520535/1 [00:00<00:00,  5.90it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "tzdata-2023c         | 116 KB    | :  14% 0.13846257859509162/1 [00:00<00:00,  1.36it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "packaging-23.1       | 78 KB     | :  21% 0.20500757016479185/1 [00:00<00:00,  1.80it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "conda-23.7.4         | 1.0 MB    | :   2% 0.01544593733384116/1 [00:00<00:07,  7.78s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "jsonpointer-2.1      | 9 KB      | : 100% 1.0/1 [00:00<00:00,  8.03it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "python-3.10.13       | 26.8 MB   | :   0% 0.0005833059849315943/1 [00:00<03:25, 205.19s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "conda-package-stream | 27 KB     | :  58% 0.5842247896163172/1 [00:00<00:00,  4.68it/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",
      "\n",
      "requests-2.31.0      | 97 KB     | :  16% 0.1642456868465109/1 [00:00<00:00,  1.22it/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",
      "xz-5.4.2             | 642 KB    | :   2% 0.024932699667797344/1 [00:00<00:05,  5.40s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "ca-certificates-2023 | 123 KB    | : 100% 1.0/1 [00:00<00:00,  6.12it/s]\u001b[A\u001b[A\n",
      "\n",
      "ca-certificates-2023 | 123 KB    | : 100% 1.0/1 [00:00<00:00,  6.12it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "jsonpatch-1.32       | 15 KB     | : 100% 1.0/1 [00:00<00:00,  6.80it/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",
      "openssl-3.0.11       | 5.2 MB    | :   0% 0.0030038040729192506/1 [00:00<00:51, 51.35s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "zstandard-0.19.0     | 474 KB    | :   3% 0.033748877371938005/1 [00:00<00:04,  5.06s/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",
      "\n",
      "\n",
      "libffi-3.4.4         | 142 KB    | :  11% 0.11271016207589224/1 [00:00<00:01,  1.63s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "sqlite-3.41.2        | 1.2 MB    | :   1% 0.013323043970761513/1 [00:00<00:15, 15.87s/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",
      "\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",
      "python-3.10.13       | 26.8 MB   | :   6% 0.05774729250822784/1 [00:00<00:03,  3.21s/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",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "pyopenssl-23.2.0     | 97 KB     | :  17% 0.16503817716622682/1 [00:00<00:01,  1.31s/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",
      "openssl-3.0.11       | 5.2 MB    | :  32% 0.3214070358023598/1 [00:00<00:00,  1.54it/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",
      "\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",
      "python-3.10.13       | 26.8 MB   | :  11% 0.11141144312193452/1 [00:00<00:02,  2.49s/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",
      "\n",
      "\n",
      "\n",
      "openssl-3.0.11       | 5.2 MB    | :  71% 0.7119015652818624/1 [00:00<00:00,  2.49it/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",
      "tqdm-4.65.0          | 132 KB    | : 100% 1.0/1 [00:00<00:00,  2.20it/s]               \u001b[A\n",
      "tqdm-4.65.0          | 132 KB    | : 100% 1.0/1 [00:00<00:00,  2.20it/s]\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "python-3.10.13       | 26.8 MB   | :  23% 0.2269060281383902/1 [00:00<00:01,  1.47s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "boltons-23.0.0       | 430 KB    | : 100% 1.0/1 [00:00<00:00,  2.21it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "certifi-2023.7.22    | 153 KB    | : 100% 1.0/1 [00:00<00:00,  7.01it/s]               \n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "python-3.10.13       | 26.8 MB   | :  31% 0.3109020899685398/1 [00:00<00:00,  1.36s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "conda-package-handli | 268 KB    | : 100% 1.0/1 [00:00<00:00,  5.90it/s]               \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "python-3.10.13       | 26.8 MB   | :  43% 0.4333963468041746/1 [00:00<00:00,  1.15s/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",
      "packaging-23.1       | 78 KB     | : 100% 1.0/1 [00:00<00:00,  1.39it/s]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "packaging-23.1       | 78 KB     | : 100% 1.0/1 [00:00<00:00,  1.39it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "jsonpointer-2.1      | 9 KB      | : 100% 1.0/1 [00:00<00:00,  8.03it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "python-3.10.13       | 26.8 MB   | :  55% 0.5523907677302199/1 [00:00<00:00,  1.03s/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",
      "conda-package-stream | 27 KB     | : 100% 1.0/1 [00:00<00:00,  4.68it/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",
      "python-3.10.13       | 26.8 MB   | :  67% 0.6661354347918808/1 [00:00<00:00,  1.00it/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",
      "requests-2.31.0      | 97 KB     | : 100% 1.0/1 [00:00<00:00,  1.10it/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",
      "requests-2.31.0      | 97 KB     | : 100% 1.0/1 [00:00<00:00,  1.10it/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",
      "python-3.10.13       | 26.8 MB   | :  78% 0.7757969599590204/1 [00:00<00:00,  1.02it/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",
      "\n",
      "\n",
      "jsonpatch-1.32       | 15 KB     | : 100% 1.0/1 [00:00<00:00,  6.80it/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",
      "python-3.10.13       | 26.8 MB   | :  90% 0.8994578287645185/1 [00:01<00:00,  1.07it/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",
      "tzdata-2023c         | 116 KB    | : 100% 1.0/1 [00:01<00:00,  1.27s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "tzdata-2023c         | 116 KB    | : 100% 1.0/1 [00:01<00:00,  1.27s/it]\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",
      "zstandard-0.19.0     | 474 KB    | : 100% 1.0/1 [00:01<00:00,  1.25s/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",
      "\n",
      "zstandard-0.19.0     | 474 KB    | : 100% 1.0/1 [00:01<00:00,  1.25s/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",
      "xz-5.4.2             | 642 KB    | : 100% 1.0/1 [00:01<00:00,  1.33s/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",
      "xz-5.4.2             | 642 KB    | : 100% 1.0/1 [00:01<00:00,  1.33s/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",
      "libffi-3.4.4         | 142 KB    | : 100% 1.0/1 [00:01<00:00,  1.64s/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",
      "libffi-3.4.4         | 142 KB    | : 100% 1.0/1 [00:01<00:00,  1.64s/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",
      "pyopenssl-23.2.0     | 97 KB     | : 100% 1.0/1 [00:01<00:00,  1.70s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\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",
      "pyopenssl-23.2.0     | 97 KB     | : 100% 1.0/1 [00:01<00:00,  1.70s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\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",
      "sqlite-3.41.2        | 1.2 MB    | : 100% 1.0/1 [00:01<00:00,  1.82s/it]                 \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "sqlite-3.41.2        | 1.2 MB    | : 100% 1.0/1 [00:01<00:00,  1.82s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "conda-23.7.4         | 1.0 MB    | : 100% 1.0/1 [00:01<00:00,  1.89s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "conda-23.7.4         | 1.0 MB    | : 100% 1.0/1 [00:01<00:00,  1.89s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\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",
      "openssl-3.0.11       | 5.2 MB    | : 100% 1.0/1 [00:02<00:00,  3.10s/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",
      "openssl-3.0.11       | 5.2 MB    | : 100% 1.0/1 [00:02<00:00,  3.10s/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",
      "python-3.10.13       | 26.8 MB   | : 100% 1.0/1 [00:04<00:00,  1.07it/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",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "                                                                        \n",
      "                                                                        \u001b[A\n",
      "\n",
      "                                                                        \u001b[A\u001b[A\n",
      "\n",
      "\n",
      "                                                                        \u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "                                                                        \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",
      "                                                                        \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",
      "                                                                        \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",
      "                                                                        \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",
      "                                                                        \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",
      "                                                                        \u001b[A\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",
      "                                                                        \u001b[A\u001b[A\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",
      "                                                                        \u001b[A\u001b[A\u001b[A\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",
      "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\u001b[A\n",
      "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\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n",
      "Executing transaction: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n"
     ]
    }
   ],
   "source": [
    "!conda update -n base -c defaults conda --yes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uoBMBSS63Z58",
    "outputId": "60b3b4af-9373-4ad3-c671-a6dba49d16ff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "conda 23.7.4\n",
      "Python 3.10.13\n"
     ]
    }
   ],
   "source": [
    "!conda --version\n",
    "!python --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RMuJRdfH3dDq",
    "outputId": "680ff61d-9cc5-4830-fcad-ff17b16f611d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
      "Solving environment: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\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",
      "\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",
      "    _libgcc_mutex-0.1          |      conda_forge           3 KB  conda-forge\n",
      "    _openmp_mutex-4.5          |       2_kmp_llvm           6 KB  conda-forge\n",
      "    assimulo-3.4.3             |  py310hd2a66c7_1         2.0 MB  conda-forge\n",
      "    ca-certificates-2023.7.22  |       hbcca054_0         146 KB  conda-forge\n",
      "    certifi-2023.7.22          |     pyhd8ed1ab_0         150 KB  conda-forge\n",
      "    conda-23.7.4               |  py310hff52083_0        1006 KB  conda-forge\n",
      "    fmilib-2.4.1               |       h59595ed_0         383 KB  conda-forge\n",
      "    gmp-6.2.1                  |       h58526e2_0         806 KB  conda-forge\n",
      "    icu-73.2                   |       h59595ed_0        11.5 MB  conda-forge\n",
      "    libblas-3.9.0              |18_linux64_openblas          14 KB  conda-forge\n",
      "    libcblas-3.9.0             |18_linux64_openblas          14 KB  conda-forge\n",
      "    libgcc-ng-13.2.0           |       h807b86a_2         753 KB  conda-forge\n",
      "    libgfortran-ng-13.2.0      |       h69a702a_2          23 KB  conda-forge\n",
      "    libgfortran5-13.2.0        |       ha4646dd_2         1.4 MB  conda-forge\n",
      "    liblapack-3.9.0            |18_linux64_openblas          14 KB  conda-forge\n",
      "    libopenblas-0.3.24         |pthreads_h413a1c8_0         5.2 MB  conda-forge\n",
      "    libstdcxx-ng-13.2.0        |       h7e041cc_2         3.7 MB  conda-forge\n",
      "    libxml2-2.10.4             |       hf1b16e4_1         753 KB\n",
      "    libxslt-1.1.37             |       h873f0b0_0         251 KB  conda-forge\n",
      "    llvm-openmp-14.0.6         |       h9e868ea_0         4.4 MB\n",
      "    lxml-4.9.3                 |  py310hdbbb534_0         1.5 MB\n",
      "    metis-5.1.0                |    h59595ed_1007         3.7 MB  conda-forge\n",
      "    mpfr-4.2.0                 |       hb012696_0         616 KB  conda-forge\n",
      "    numpy-1.26.0               |  py310hb13e2d6_0         6.6 MB  conda-forge\n",
      "    openssl-3.1.3              |       hd590300_0         2.5 MB  conda-forge\n",
      "    pyfmi-2.11.0               |  py310h278f3c1_0        11.6 MB  conda-forge\n",
      "    python_abi-3.10            |          2_cp310           4 KB  conda-forge\n",
      "    scipy-1.11.2               |  py310hb13e2d6_1        14.9 MB  conda-forge\n",
      "    suitesparse-5.10.1         |       hd8046ac_0         2.4 MB  conda-forge\n",
      "    sundials-6.6.1             |       h777d08e_0         913 KB  conda-forge\n",
      "    tbb-2020.2                 |       h4bd325d_4         1.5 MB  conda-forge\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:        78.6 MB\n",
      "\n",
      "The following NEW packages will be INSTALLED:\n",
      "\n",
      "  assimulo           conda-forge/linux-64::assimulo-3.4.3-py310hd2a66c7_1 \n",
      "  fmilib             conda-forge/linux-64::fmilib-2.4.1-h59595ed_0 \n",
      "  gmp                conda-forge/linux-64::gmp-6.2.1-h58526e2_0 \n",
      "  icu                conda-forge/linux-64::icu-73.2-h59595ed_0 \n",
      "  libblas            conda-forge/linux-64::libblas-3.9.0-18_linux64_openblas \n",
      "  libcblas           conda-forge/linux-64::libcblas-3.9.0-18_linux64_openblas \n",
      "  libgfortran-ng     conda-forge/linux-64::libgfortran-ng-13.2.0-h69a702a_2 \n",
      "  libgfortran5       conda-forge/linux-64::libgfortran5-13.2.0-ha4646dd_2 \n",
      "  liblapack          conda-forge/linux-64::liblapack-3.9.0-18_linux64_openblas \n",
      "  libopenblas        conda-forge/linux-64::libopenblas-0.3.24-pthreads_h413a1c8_0 \n",
      "  libxml2            pkgs/main/linux-64::libxml2-2.10.4-hf1b16e4_1 \n",
      "  libxslt            conda-forge/linux-64::libxslt-1.1.37-h873f0b0_0 \n",
      "  llvm-openmp        pkgs/main/linux-64::llvm-openmp-14.0.6-h9e868ea_0 \n",
      "  lxml               pkgs/main/linux-64::lxml-4.9.3-py310hdbbb534_0 \n",
      "  metis              conda-forge/linux-64::metis-5.1.0-h59595ed_1007 \n",
      "  mpfr               conda-forge/linux-64::mpfr-4.2.0-hb012696_0 \n",
      "  numpy              conda-forge/linux-64::numpy-1.26.0-py310hb13e2d6_0 \n",
      "  pyfmi              conda-forge/linux-64::pyfmi-2.11.0-py310h278f3c1_0 \n",
      "  python_abi         conda-forge/linux-64::python_abi-3.10-2_cp310 \n",
      "  scipy              conda-forge/linux-64::scipy-1.11.2-py310hb13e2d6_1 \n",
      "  suitesparse        conda-forge/linux-64::suitesparse-5.10.1-hd8046ac_0 \n",
      "  sundials           conda-forge/linux-64::sundials-6.6.1-h777d08e_0 \n",
      "  tbb                conda-forge/linux-64::tbb-2020.2-h4bd325d_4 \n",
      "\n",
      "The following packages will be REMOVED:\n",
      "\n",
      "  libgomp-11.2.0-h1234567_1\n",
      "\n",
      "The following packages will be UPDATED:\n",
      "\n",
      "  libgcc-ng          pkgs/main::libgcc-ng-11.2.0-h1234567_1 --> conda-forge::libgcc-ng-13.2.0-h807b86a_2 \n",
      "  libstdcxx-ng       pkgs/main::libstdcxx-ng-11.2.0-h12345~ --> conda-forge::libstdcxx-ng-13.2.0-h7e041cc_2 \n",
      "  openssl              pkgs/main::openssl-3.0.11-h7f8727e_2 --> conda-forge::openssl-3.1.3-hd590300_0 \n",
      "\n",
      "The following packages will be SUPERSEDED by a higher-priority channel:\n",
      "\n",
      "  _libgcc_mutex           pkgs/main::_libgcc_mutex-0.1-main --> conda-forge::_libgcc_mutex-0.1-conda_forge \n",
      "  _openmp_mutex          pkgs/main::_openmp_mutex-5.1-1_gnu --> conda-forge::_openmp_mutex-4.5-2_kmp_llvm \n",
      "  ca-certificates    pkgs/main::ca-certificates-2023.08.22~ --> conda-forge::ca-certificates-2023.7.22-hbcca054_0 \n",
      "  certifi            pkgs/main/linux-64::certifi-2023.7.22~ --> conda-forge/noarch::certifi-2023.7.22-pyhd8ed1ab_0 \n",
      "  conda              pkgs/main::conda-23.7.4-py310h06a4308~ --> conda-forge::conda-23.7.4-py310hff52083_0 \n",
      "\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "llvm-openmp-14.0.6   | 4.4 MB    | :   0% 0/1 [00:00<?, ?it/s]\n",
      "fmilib-2.4.1         | 383 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\n",
      "\n",
      "suitesparse-5.10.1   | 2.4 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "libgcc-ng-13.2.0     | 753 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "python_abi-3.10      | 4 KB      | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "icu-73.2             | 11.5 MB   | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libblas-3.9.0        | 14 KB     | :   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",
      "mpfr-4.2.0           | 616 KB    | :   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",
      "libcblas-3.9.0       | 14 KB     | :   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",
      "_openmp_mutex-4.5    | 6 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\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "certifi-2023.7.22    | 150 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\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libopenblas-0.3.24   | 5.2 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "sundials-6.6.1       | 913 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",
      "gmp-6.2.1            | 806 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",
      "libxslt-1.1.37       | 251 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",
      "scipy-1.11.2         | 14.9 MB   | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "tbb-2020.2           | 1.5 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "assimulo-3.4.3       | 2.0 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "_libgcc_mutex-0.1    | 3 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",
      " ... (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",
      "llvm-openmp-14.0.6   | 4.4 MB    | :   0% 0.0035718910660429226/1 [00:00<01:00, 60.57s/it]\n",
      "\n",
      "\n",
      "\n",
      "python_abi-3.10      | 4 KB      | : 100% 1.0/1 [00:00<00:00,  4.43it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "suitesparse-5.10.1   | 2.4 MB    | :   1% 0.006521418105930853/1 [00:00<00:37, 37.82s/it]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "python_abi-3.10      | 4 KB      | : 100% 1.0/1 [00:00<00:00,  4.43it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "fmilib-2.4.1         | 383 KB    | :   4% 0.041796558110583325/1 [00:00<00:05,  6.19s/it]\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "icu-73.2             | 11.5 MB   | :   0% 0.0013552648449229267/1 [00:00<03:13, 193.33s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libblas-3.9.0        | 14 KB     | : 100% 1.0/1 [00:00<00:00,  3.79it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "llvm-openmp-14.0.6   | 4.4 MB    | :  45% 0.4464863832553654/1 [00:00<00:00,  1.76it/s]   \n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libcblas-3.9.0       | 14 KB     | : 100% 1.0/1 [00:00<00:00,  2.99it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "suitesparse-5.10.1   | 2.4 MB    | :  57% 0.573884793321915/1 [00:00<00:00,  2.10it/s]   \u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libblas-3.9.0        | 14 KB     | : 100% 1.0/1 [00:00<00:00,  3.79it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "icu-73.2             | 11.5 MB   | :  15% 0.15314492747629072/1 [00:00<00:01,  1.86s/it]   \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "_openmp_mutex-4.5    | 6 KB      | : 100% 1.0/1 [00:00<00:00,  2.64it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "libgcc-ng-13.2.0     | 753 KB    | : 100% 1.0/1 [00:00<00:00,  3.00it/s]                \u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "llvm-openmp-14.0.6   | 4.4 MB    | :  83% 0.8322506183880011/1 [00:00<00:00,  2.52it/s]\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "certifi-2023.7.22    | 150 KB    | :  11% 0.10653419250801412/1 [00:00<00:03,  3.84s/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",
      "\n",
      "sundials-6.6.1       | 913 KB    | :   2% 0.017523950504252105/1 [00:00<00:23, 23.83s/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",
      "libopenblas-0.3.24   | 5.2 MB    | :   0% 0.0029831989309718285/1 [00:00<02:21, 141.72s/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",
      "icu-73.2             | 11.5 MB   | :  31% 0.3090003846424273/1 [00:00<00:00,  1.14s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "gmp-6.2.1            | 806 KB    | :   2% 0.019840539414665338/1 [00:00<00:23, 23.74s/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",
      "scipy-1.11.2         | 14.9 MB   | :   0% 0.0010466103151163262/1 [00:00<07:48, 469.12s/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",
      "libopenblas-0.3.24   | 5.2 MB    | :  31% 0.31025268882107016/1 [00:00<00:00,  1.30s/it]   \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "icu-73.2             | 11.5 MB   | :  43% 0.4296189558405678/1 [00:00<00:00,  1.07s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "fmilib-2.4.1         | 383 KB    | : 100% 1.0/1 [00:00<00:00,  1.90it/s]                 \u001b[A\n",
      "fmilib-2.4.1         | 383 KB    | : 100% 1.0/1 [00:00<00:00,  1.90it/s]\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "tbb-2020.2           | 1.5 MB    | :   1% 0.01070822377730241/1 [00:00<00:54, 55.23s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "scipy-1.11.2         | 14.9 MB   | :  13% 0.12663984812907547/1 [00:00<00:03,  3.54s/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\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "mpfr-4.2.0           | 616 KB    | : 100% 1.0/1 [00:00<00:00,  1.91it/s]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "mpfr-4.2.0           | 616 KB    | : 100% 1.0/1 [00:00<00:00,  1.91it/s]\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",
      "libopenblas-0.3.24   | 5.2 MB    | :  67% 0.6652533616067178/1 [00:00<00:00,  1.48it/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",
      "\n",
      "\n",
      "libxslt-1.1.37       | 251 KB    | :   6% 0.06361878431585732/1 [00:00<00:09, 10.01s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "_openmp_mutex-4.5    | 6 KB      | : 100% 1.0/1 [00:00<00:00,  2.64it/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",
      "libcblas-3.9.0       | 14 KB     | : 100% 1.0/1 [00:00<00:00,  2.99it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "icu-73.2             | 11.5 MB   | :  60% 0.5976717966110107/1 [00:00<00:00,  1.12it/s]\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",
      "scipy-1.11.2         | 14.9 MB   | :  25% 0.25327969625815094/1 [00:00<00:01,  1.93s/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",
      "\n",
      "\n",
      "\n",
      "assimulo-3.4.3       | 2.0 MB    | :   1% 0.007846965024394846/1 [00:00<01:29, 90.70s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\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",
      "_libgcc_mutex-0.1    | 3 KB      | : 100% 1.0/1 [00:00<00:00,  1.40it/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",
      "certifi-2023.7.22    | 150 KB    | : 100% 1.0/1 [00:00<00:00,  1.56it/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",
      "certifi-2023.7.22    | 150 KB    | : 100% 1.0/1 [00:00<00:00,  1.56it/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",
      "\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",
      "icu-73.2             | 11.5 MB   | :  78% 0.7846983452103746/1 [00:00<00:00,  1.34it/s]\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",
      "scipy-1.11.2         | 14.9 MB   | :  37% 0.3705000515511795/1 [00:00<00:00,  1.48s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "assimulo-3.4.3       | 2.0 MB    | :  75% 0.7533086423419053/1 [00:00<00:00,  1.25it/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",
      "icu-73.2             | 11.5 MB   | :  98% 0.9785012180343531/1 [00:00<00:00,  1.52it/s]\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",
      "scipy-1.11.2         | 14.9 MB   | :  50% 0.49504667905002225/1 [00:00<00:00,  1.22s/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\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "scipy-1.11.2         | 14.9 MB   | :  60% 0.6038941518221203/1 [00:01<00:00,  1.12s/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",
      "\n",
      "scipy-1.11.2         | 14.9 MB   | :  77% 0.7723984125558487/1 [00:01<00:00,  1.08it/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",
      "llvm-openmp-14.0.6   | 4.4 MB    | : 100% 1.0/1 [00:01<00:00,  2.52it/s]               \n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "sundials-6.6.1       | 913 KB    | : 100% 1.0/1 [00:01<00:00,  1.56s/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",
      "sundials-6.6.1       | 913 KB    | : 100% 1.0/1 [00:01<00:00,  1.56s/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",
      "gmp-6.2.1            | 806 KB    | : 100% 1.0/1 [00:02<00:00,  1.95s/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",
      "gmp-6.2.1            | 806 KB    | : 100% 1.0/1 [00:02<00:00,  1.95s/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",
      "libxslt-1.1.37       | 251 KB    | : 100% 1.0/1 [00:02<00:00,  2.18s/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",
      "libxslt-1.1.37       | 251 KB    | : 100% 1.0/1 [00:02<00:00,  2.18s/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",
      "suitesparse-5.10.1   | 2.4 MB    | : 100% 1.0/1 [00:02<00:00,  2.93s/it]              \u001b[A\u001b[A\n",
      "\n",
      "suitesparse-5.10.1   | 2.4 MB    | : 100% 1.0/1 [00:02<00:00,  2.93s/it]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "_libgcc_mutex-0.1    | 3 KB      | : 100% 1.0/1 [00:02<00:00,  1.40it/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",
      "tbb-2020.2           | 1.5 MB    | : 100% 1.0/1 [00:02<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\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "tbb-2020.2           | 1.5 MB    | : 100% 1.0/1 [00:02<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\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libopenblas-0.3.24   | 5.2 MB    | : 100% 1.0/1 [00:02<00:00,  3.58s/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",
      "libopenblas-0.3.24   | 5.2 MB    | : 100% 1.0/1 [00:02<00:00,  3.58s/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",
      "\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",
      "\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",
      "assimulo-3.4.3       | 2.0 MB    | : 100% 1.0/1 [00:03<00:00,  1.25it/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",
      "icu-73.2             | 11.5 MB   | : 100% 1.0/1 [00:05<00:00,  1.52it/s]               \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",
      "scipy-1.11.2         | 14.9 MB   | : 100% 1.0/1 [00:08<00:00,  1.07it/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",
      "\n",
      "\n",
      "\n",
      "                                                                        \n",
      "                                                                        \u001b[A\n",
      "\n",
      "                                                                        \u001b[A\u001b[A\n",
      "\n",
      "\n",
      "                                                                        \u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "                                                                        \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",
      "                                                                        \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",
      "                                                                        \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",
      "                                                                        \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",
      "                                                                        \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",
      "                                                                        \u001b[A\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",
      "                                                                        \u001b[A\u001b[A\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",
      "                                                                        \u001b[A\u001b[A\u001b[A\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",
      "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\u001b[A\n",
      "\n",
      "\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\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",
      "\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",
      "\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",
      "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\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\b- \b\b\\ \b\b| \b\bdone\n",
      "Executing transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n"
     ]
    }
   ],
   "source": [
    "!conda install -c conda-forge pyfmi --yes # Install the key package"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UTETJm3l6heL"
   },
   "source": [
    "# BPL_CHO_Fedbatch 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_CHO_Fedbatch_linux_om_me.fmu\n",
    "* Setup-file - BPL_CHO_Fedbatch_explore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RfWPh1B63yBF",
    "outputId": "447aa56e-186f-4b88-e7b2-e45b0500ad9c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Cloning into 'BPL_CHO_Fedbatch'...\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "git clone https://github.com/janpeter19/BPL_CHO_Fedbatch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "i6uOVB44300a",
    "outputId": "febf7420-2d35-4d3b-9a48-354a99572201"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content/BPL_CHO_Fedbatch\n"
     ]
    }
   ],
   "source": [
    "%cd BPL_CHO_Fedbatch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dsjD0XLpQysI"
   },
   "source": [
    "# BPL_CHO_Fedbatch - demo\n",
    "\n",
    "Author: Jan Peter Axelsson\n",
    "\n",
    "This notebook deals with CHO fedbatch cultivation and recombinant protein production is included. First we make a check of the model by comparing a simulation result with corresponding published diagram. Then we take a closer look at the start-up strategy to keep the by-product formation low. After that we investigate at a whole cultivation and see the impact of feeding strategy on both cell growth and protein production where a trade-off is needed in this case.\n",
    "\n",
    "The model used takes its inspiration from the microbial bottleneck models as described in the original papers [1] and [2] and reformulated and studied in [3]. The laboratory cultures used for model validation in [1] did produce MAb (against part of IgG) but no MAb-data was presented. The paper focus on viable and non-viable cell concentrations only. The original model is in section 5 expanded with the classical empirical Luedeking-Piret model recombinant protein production, see chapter 5 in [4]. In this way can get more insight into choice of feeding profile.\n",
    "\n",
    "Interaction with the compiled model as FMU is mainly through the simplified commands: par(), init(), newplot(), simu() etc. The last simulation is always available in the workspace and called 'sim_res'. The command describe() brings mainly up description infomration from the actual Modelica code from the FMU but is complemented with information given in the dedicated Python setup-file.\n",
    "\n",
    "The idea here is to demonstrate how simulations and varyiing conditions can provide some process insight that can support the experimental work. I hope that at the end of this session you are ready to formulate your own questions you want to address with simulations - and you can just go on in this notebook! Just press the field \"+Code\" in the upper left part of notebook interface and you get a new \"cell\" where you write your own code. You can copy and paste from cells above using ctrl-c and ctrl-v as usual and edit the cell. When your are ready to execute the cell just press the \"play button\" to the left in the cell or press shift-enter as in \"ordinary\" Jupyter notebooks.\n",
    "\n",
    "After a session you may want to save your own notebook. That you can do on your Google Drive account and I refer to Colab instructions for how to do this. It is easy.\n",
    "\n",
    "Good luck!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "b-y82jZY33cO",
    "outputId": "cedba313-41de-4e63-ef92-91be4f4aea72"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linux - run FMU pre-comiled OpenModelica 1.21.0\n",
      "\n",
      "Model for bioreactor has been setup. Key commands:\n",
      " - par()       - change of parameters and initial values\n",
      " - init()      - change initial values only\n",
      " - simu()      - simulate and plot\n",
      " - newplot()   - make a new plot\n",
      " - show()      - show plot from previous simulation\n",
      " - disp()      - display parameters and initial values from the last simulation\n",
      " - describe()  - describe culture, broth, parameters, variables with values/units\n",
      "\n",
      "Note that both disp() and describe() takes values from the last simulation\n",
      "and the command process_diagram() brings up the main configuration\n",
      "\n",
      "Brief information about a command by help(), eg help(simu)\n",
      "Key system information is listed with the command system_info()\n"
     ]
    }
   ],
   "source": [
    "run -i BPL_CHO_Fedbatch_explore.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HUFhuE-H4RCw"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = [25/2.54, 20/2.54]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dx9oMnP0RJkM"
   },
   "source": [
    "## 1 About the process model\n",
    "\n",
    "We can get information about the process and liquid phase by the command describe(). Here is no gas-phase included. This command can also be used to bring up information about a specific variable or parameter. However, you should use describe() after a simulation to get the valued used during the simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VgLBZgp0mPIi"
   },
   "outputs": [],
   "source": [
    "process_diagram()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-Y3NHWIj4TZ6",
    "outputId": "c46e7ea7-f8d0-4a81-fe4a-ff6921d037f9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reactor culture CHO-MAb - cell line HB-58 American Culture Collection ATCC\n",
      "\n"
     ]
    }
   ],
   "source": [
    "describe('culture'); print(); #describe('liquidphase')                                  # Pump schedule parameter\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ywb8yKJ-I3PJ"
   },
   "source": [
    "The molecular weight of the recombinant protein (MAb) is somewhat arbitrarly chosen and the value not used in the simulations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e0HywTQuRTuK"
   },
   "source": [
    "## 2 Simulation reproducing the original paper\n",
    "\n",
    "The simulation below reproduce diagrams in Figure 5 in the original paper. There are several simulation in the paper showing how well the model describe different experiments and here I just choose one of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 708
    },
    "id": "3BkbO1cprB44",
    "outputId": "78e8fd6d-4fc2-4a60-b6cd-3033e6331fea"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 984.252x787.402 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data from Table 1 and 2 for experiment 4 shown in Figure 5 in paper [1]\n",
    "# -culture parameters taken from Table 5 identified parameters for cultures 1,2,and 3\n",
    "\n",
    "# Initial process conditions\n",
    "V_start=0.30\n",
    "init(V_start=V_start, VXv_start=V_start*0.172, VXd_start=V_start*0.020)\n",
    "init(VG_start=V_start*17.83, VGn_start=V_start*3.74, VL_start=V_start*0.12, VN_start=V_start*0.24)\n",
    "\n",
    "# Feeding\n",
    "Feed=0.1/24\n",
    "par(G_in=15, Gn_in=9.3)\n",
    "par(t0=0, F0=0, t1=63.5, F1=Feed, t2=300, F2=Feed)\n",
    "\n",
    "# Simulation\n",
    "newplot(title='Figure 5 - Fedbatch cultivation')\n",
    "simu(200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rWDmNFwbJWEn"
   },
   "source": [
    "**Comment:** The simulation results looks very similar to the published diagram Figure 5 in [1]. The model pass this quality check."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "s9Ico4G1Je3Q"
   },
   "source": [
    "## 3 Simulation of different start-up feeding strategies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 708
    },
    "id": "btfm5EDZJohy",
    "outputId": "e817623e-5def-4e28-b719-b1e721eb368f"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 984.252x787.402 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figur 5\n",
    "V_start=0.30\n",
    "init(V_start=V_start, VXv_start=V_start*0.172, VXd_start=V_start*0.020)\n",
    "init(VG_start=V_start*17.83, VGn_start=V_start*3.74, VL_start=V_start*0.12, VN_start=V_start*0.24)\n",
    "\n",
    "# Feeding\n",
    "Feed=0.1/24\n",
    "par(G_in=15, Gn_in=9.3)\n",
    "par(t0=0, F0=0, t1=63.5, F1=Feed, t2=300, F2=Feed)\n",
    "\n",
    "newplot(title='Figure 5 - Fedbatch cultivation')\n",
    "simu(120)\n",
    "\n",
    "init(VG_start=0.35*V_start*17.83, VGn_start=0.35*V_start*3.74)\n",
    "par(t0=0, F0=0, t1=40.0, F1=0.5*Feed, t2=63.5, F2=Feed,t3=300, F3=Feed)\n",
    "simu(120)\n",
    "\n",
    "init(VG_start=0.35*V_start*17.83, VGn_start=0.35*V_start*3.74)\n",
    "par(t0=0, F0=0, t1=40.0, F1=0.5*Feed, t2=63.5, F2=0.7*Feed, t3=88.0, F3=Feed, t4=300, F4=Feed)\n",
    "simu(120)\n",
    "\n",
    "# Reset time table to avoid problems below\n",
    "par(t1=1001, t2=1002, t3=1003, t4=1004, t5=1005, t6=1006)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FfIxEErSJuDI"
   },
   "source": [
    "**Comment:** We see that starting the feed a day erlier at lower rate and then increase decreaes lactate formation to half, while the cell conentation is just slightlly lower. With a more careful design of the feedprofile the ammonia formation can be decreased more than shown here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "05aIcbn2JxUb"
   },
   "source": [
    "## 4 Simulation of optimal feed profile for cell growth\n",
    "\n",
    "At the end of the original paper section 5 in [1], the derived model is used to find an optimal feeding profile for high final cell concentration. It is stated that protein productivity is assumed to be mainly positively growth associated and therefore optimization of cell concentration is very similar to optimization of protein product. The optimization of feed profile is done with differnt structures of the feed profile. All of them have a start-time and all of them has a fixed amount of substrate and concentrations in the media are also the same.\n",
    "\n",
    "* The first optimzation is for a feed profile similar to the experimental, i.e. after start the feed rate remains constant throughout the cultivation. Thus the start time and the actual feed rate are optimized. The result was that the start time was about the same as expermentally but the feed rate was 50% higher, see Figure 7 and Figure 10 in [1].\n",
    "\n",
    "* The second optimization is for a feed profile with not just one increase but three steps of increase of feed rate. The results is a somwehat higher final cell concentration, see Figure 11.\n",
    "\n",
    "* The third optimization is for a feed profile with five steps of increase of feed rate. The results is a slightly higher final cell concentration than for three steps, see Figure 12.\n",
    "\n",
    "* The fourth optimization is for a feed profie with continuous exponential increase of the reed rate. The result is a bit higher final concentration than the previous with five steps, see Figure 13 but not shown in the figure below.\n",
    "\n",
    "Below we just show the results of the original experimetnal cultivation, compared with results from three and five steps.\n",
    "It is possible to do the optimization in Python with the FMU, but we save that for a future notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 743
    },
    "id": "JDD59RgUKA9g",
    "outputId": "929b609d-4ef0-4c68-dcaa-aee93708909a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error - the following requirements do not hold:\n",
      "parDict['t4'] < parDict['t5']\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 984.252x787.402 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Culture parameters taken from Table 5 identified parameters for cultures 1,2,and 3\n",
    "\n",
    "# Data chosen\n",
    "V_start=0.35\n",
    "init(V_start=V_start, VXv_start=V_start*0.20, VXd_start=V_start*0.0)\n",
    "init(VG_start=V_start*18.0, VGn_start=V_start*2.4, VL_start=V_start*0, VN_start=V_start*0)\n",
    "\n",
    "# Feeding n=1 - experimental and lower feed rate\n",
    "par(G_in=15, Gn_in=4.0)\n",
    "par(t0=0, F0=0, t1=49, F1=0.00417)\n",
    "par(t2=1002, t3=1003, t4=1004, t5=1005)\n",
    "\n",
    "# Simulation\n",
    "newplot(title='Figure 12 - Fedbatch with optimal step-wise feed')\n",
    "simu(125)\n",
    "\n",
    "# Feeding n=1\n",
    "par(G_in=15, Gn_in=4.0)\n",
    "par(t0=0, F0=0, t1=52, F1=0.00625)\n",
    "par(t2=1002, t3=1003, t4=1004, t5=1005, t6=1006)\n",
    "\n",
    "# Simulation\n",
    "simu(125)\n",
    "\n",
    "# Feeding n=3\n",
    "par(G_in=15, Gn_in=4.0)\n",
    "par(t0=0, F0=0, t1=52, F1=0.002, t2=74, F2=0.0045, t3=98.0, F3=0.010)\n",
    "par(t4=99.0, F4=0.010, t5=106, F5=0.010, t6=150, F6=0.010)\n",
    "\n",
    "# Simulation\n",
    "simu(125)\n",
    "\n",
    "# Feeding n=5\n",
    "par(G_in=15, Gn_in=4.0)\n",
    "par(t0=0, F0=0, t1=52, F1=0.0012, t2=64, F2=0.0020, t3=78.0, F3=0.0040)\n",
    "par(t4=92.0, F4=0.0080, t5=106, F5=0.012, t6=150, F6=0.012)\n",
    "\n",
    "# Simulation\n",
    "simu(125)\n",
    "\n",
    "# Reset feeding parameters since the table need time in strict increasing value\n",
    "par(t3=1004, t4=1005, t5=1006, t6=1007)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xYXCy9o-KIU1"
   },
   "source": [
    "**Comment:** We see that that already the better tuned constant feed rate (dahsed) compared to the experimental (solid) gives higher final cell concnetration.\n",
    "\n",
    "Breaking up the constant feed rate in three (dotted) and five (dash-dotted) steps with a more gradual increase of the feed rate gives even higher final cell concentration. The difference between n=3 and n=5 is small. The change to continuous exponential feed is even smaller and not shown here.\n",
    "\n",
    "The results shown here are similar to what is presented in Table 7 in [1] but our simulation are slightly longer and here are small differences in the final cel concentration too. The qualitative result is the same though. The difference we see to the result in the original paper is most likely due to the fact that we here use the full model with 17 parameters while in the paper they have reduced the model to 15 parameters for the optimization work."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EDBp6v1EKP6x"
   },
   "source": [
    "## 5 Simulation of different feed profiles to increase recombinant protein production\n",
    "\n",
    "In this section we take a closer look at recombinant protein production. The original model is extended with the empirical model for specific protein production, see chapter 5 in [4]\n",
    "\n",
    "$$\n",
    "q_P = \\alpha \\cdot \\mu + \\beta\n",
    "$$\n",
    "\n",
    "Here we choose a negative value of growth-associated protein production production $\\alpha$ while keeping the non-growth associated $\\beta$ positive. The culture produced recombinant protein in the form of monoclonal antibodies for a specific IgG1 molecule,see section 2 in [1]. However, no experimental results were given. The only information we have is that feed rate was kept constant at a low level during fedbatch production and this choice indicates that the the growth-associated protein production is negative. The consequence of this observation for the feed profile we take a look at there by simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 882
    },
    "id": "Az_GoYonKV2O",
    "outputId": "19d09bd2-36f2-45be-8c55-6763f5fa4f6f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error - the following requirements do not hold:\n",
      "parDict['t4'] < parDict['t5']\n",
      "Error - the following requirements do not hold:\n",
      "parDict['t4'] < parDict['t5']\n",
      "Error - the following requirements do not hold:\n",
      "parDict['t4'] < parDict['t5']\n",
      "Error - the following requirements do not hold:\n",
      "parDict['t4'] < parDict['t5']\n",
      "Error - the following requirements do not hold:\n",
      "parDict['t4'] < parDict['t5']\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 984.252x787.402 with 13 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Slide 3\n",
    "newplot('CHO fedbatch cultivation - protein expression', plotType='Textbook_3')\n",
    "\n",
    "# Data from Table 1 and 2 for experiment 3\n",
    "V_start=0.35\n",
    "init(V_start=V_start, VXv_start=V_start*0.29, VXd_start=V_start*0.010)\n",
    "init(VG_start=V_start*17.17, VGn_start=V_start*3.02, VL_start=V_start*1.12, VN_start=V_start*0.29)\n",
    "\n",
    "# Feeding\n",
    "Feed=0.1/24\n",
    "par(G_in=15, Gn_in=9.3)\n",
    "par(t0=0, F0=0, t1=35, F1=Feed, t2=100, F2=Feed, t3=300, F3=Feed)\n",
    "\n",
    "# Culture parameters\n",
    "par(alpha=-1.0, beta=0.01)\n",
    "\n",
    "# Simulation\n",
    "simu(200)\n",
    "par(t2=100, F2=0.7*Feed, t3=300, F3=0.7*Feed); simu(200)\n",
    "par(F2=Feed, F3=Feed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i35mjf0lKglp"
   },
   "source": [
    "**Comment:** The simulation results show that actually a decease in the feed rate can lead to an increase in recombinant protein produced, although the cell concentration is a bit lower. This is a result due to the fact that growth-associated protein production here is set to a negative value. The main point is that the model can actually capture this phenomena."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LAll0XdHRt6d"
   },
   "source": [
    "## 6 Summary\n",
    "\n",
    "In short we have done the following:\n",
    "\n",
    "* The model was checked by comparing the simulation results with one of the published diagrams [1].\n",
    "\n",
    "* The common startup-procedure with 3 days batch cultivation can be questioned. We found that by shorten it to 2 days, and giving smaller feed rate day 3, byproduct formation can be kept lower at the prize of just a bit lower cell concentration. Similar idea was shown in section 2.1 in [3].\n",
    "\n",
    "* In the original paper the experimental feeding strategy was to keep the substrate feed at a constant lower level. The authors made a point of that the optimal feeding strategy should be exponential for maximal cell production. This is an insight derived from the bottle-neck model and they showed that through simulation optimization [1]. However, there was no experimental support to confirm the results. The optimal cell growth feedprofile simulation was just reproduced here.\n",
    "\n",
    "* To optimize recombinant protein production we must include production in the model. Here we do that with the empirical model that distinguish between growth-associated and non-growth-associated protein production, see chapter 5 in [4]. For a class of CHO-processes the recombinant protein productivity is acutally negatively affected by cell growth. Simulation of the original model extended with such a protein production model shows that keeping the substrate feed rate constant as the cell culture grows, giving less and less feed per cell, actually can give higher protein production than an increaeing feed rate. Simulation confirms this idea. The results gives some possible background to why the constant feed rate was used experimetnally in the original paper [1].\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pmOKR1hSRjeu"
   },
   "source": [
    "## 7 References\n",
    "\n",
    "[1] Amribt, Z., Niu, H. and Bogaerts P.: \"Macroscopic modelling of overflow metabolism and model based optimization of hybridoma cell fed-batch cultures.\", Biochem. Eng. Journal, 2013.\n",
    "\n",
    "[2] Niu,H., Amribt, Z., Fickers, P., Tan, W. and Bogaerts P.: \"Metabolic pathway analysis and reduction for mammalian cell cultures - towards macroscopic modelling\", Chem. Eng. Science, 2013.\n",
    "\n",
    "[3] Axelsson, J. P.: \"Simplified model of CHO-cultivation in Bioproces Library for Modelica - some experience\", conference paper 22nd NPCW Lyngby, Denmark, August 22-23, 2019.\n",
    "\n",
    "[4] Hu, W-S: \"Cell culture bioprocess engineering\", 2nd edition, CRC Press, 2020."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5XsXzuE7QT4S"
   },
   "source": [
    "## Appendix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UIfWCS7_QZpy",
    "outputId": "a853680b-bb43-400e-d8b5-6f9d8cec31b0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['bioreactor', 'bioreactor.broth_decay', 'bioreactor.culture', 'dosagescheme', 'feedtank']\n"
     ]
    }
   ],
   "source": [
    "# List of components in the process setup and also a couple of other things like liquidphase and gasphase etc\n",
    "describe('parts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ze_Kl3qLQhHX",
    "outputId": "45fb4bd7-53b3-4a96-8620-9bc93b86a4c7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSL: 3.2.3 - used components: RealInput, RealOutput, CombiTimeTable, Types\n"
     ]
    }
   ],
   "source": [
    "describe('MSL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fr8YZScu7nNy",
    "outputId": "a1de2300-3d0c-4e3c-e9f0-41c245560f74"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "System information\n",
      " -OS: Linux\n",
      " -Python: 3.10.12\n",
      " -Scipy: not installed in the notebook\n",
      " -PyFMI: 2.11.0\n",
      " -FMU by: OpenModelica Compiler OpenModelica 1.21.0\n",
      " -FMI: 2.0\n",
      " -Type: FMUModelME2\n",
      " -Name: BPL_CHO.Fedbatch\n",
      " -Generated: 2023-04-21T09:45:30Z\n",
      " -MSL: 3.2.3\n",
      " -Description: Bioprocess Library version 2.1.1\n",
      " -Interaction: FMU-explore version 0.9.8\n"
     ]
    }
   ],
   "source": [
    "system_info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "EMlexLjHj0nN"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "include_colab_link": true,
   "provenance": []
  },
  "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": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}