BPL_TEST2_Batch_fmpy_colab.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/janpeter19/BPL_TEST2_Batch/blob/main/BPL_TEST2_Batch_fmpy_colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UFcMzs4Y3HwH"
      },
      "source": [
        "# BPL_TEST2_Batch script with FMPy\n",
        "\n",
        "The key library FMPy is installed.\n",
        "\n",
        "After the installation a small application BPL_TEST2_Batch 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": "041d9478-5411-4243-b1d8-4c3caf418570"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No LSB modules are available.\n",
            "Distributor ID:\tUbuntu\n",
            "Description:\tUbuntu 22.04.3 LTS\n",
            "Release:\t22.04\n",
            "Codename:\tjammy\n"
          ]
        }
      ],
      "source": [
        "!lsb_release -a # Actual VM Ubuntu version used by Google"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pVDcSg-j3SSV",
        "outputId": "045c4a68-5f2f-4ffa-c58f-a53e32d3f15f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "env: PYTHONPATH=\n"
          ]
        }
      ],
      "source": [
        "%env PYTHONPATH="
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "04VeBmLU3UFx",
        "outputId": "070a5c0c-6943-4f71-c6db-4a3183111625"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-05-15 08:02:48--  https://repo.anaconda.com/miniconda/Miniconda3-py312_24.3.0-0-Linux-x86_64.sh\n",
            "Resolving repo.anaconda.com (repo.anaconda.com)... 104.16.32.241, 104.16.191.158, 2606:4700::6810:bf9e, ...\n",
            "Connecting to repo.anaconda.com (repo.anaconda.com)|104.16.32.241|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 143351488 (137M) [application/octet-stream]\n",
            "Saving to: ‘Miniconda3-py312_24.3.0-0-Linux-x86_64.sh’\n",
            "\n",
            "Miniconda3-py312_24 100%[===================>] 136.71M  95.8MB/s    in 1.4s    \n",
            "\n",
            "2024-05-15 08:02:50 (95.8 MB/s) - ‘Miniconda3-py312_24.3.0-0-Linux-x86_64.sh’ saved [143351488/143351488]\n",
            "\n",
            "PREFIX=/usr/local\n",
            "Unpacking payload ...\n",
            "\n",
            "Installing base environment...\n",
            "\n",
            "Preparing transaction: ...working... done\n",
            "Executing transaction: ...working... done\n",
            "installation finished.\n"
          ]
        }
      ],
      "source": [
        "!wget https://repo.anaconda.com/miniconda/Miniconda3-py312_24.3.0-0-Linux-x86_64.sh\n",
        "!chmod +x Miniconda3-py312_24.3.0-0-Linux-x86_64.sh\n",
        "!bash ./Miniconda3-py312_24.3.0-0-Linux-x86_64.sh -b -f -p /usr/local\n",
        "import sys\n",
        "sys.path.append('/usr/local/lib/python3.12/site-packages/')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5iuP4_vl3WFA",
        "outputId": "427581ed-8f7e-49c3-87be-a4ca12b1b43b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Channels:\n",
            " - defaults\n",
            "Platform: linux-64\n",
            "Collecting package metadata (repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
            "Solving environment: \\ \b\b| \b\bdone\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - conda\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    conda-24.5.0               |  py312h06a4308_0         1.2 MB\n",
            "    frozendict-2.4.2           |  py312h06a4308_0          36 KB\n",
            "    openssl-3.0.13             |       h7f8727e_1         5.2 MB\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:         6.5 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  frozendict         pkgs/main/linux-64::frozendict-2.4.2-py312h06a4308_0 \n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  conda                              24.3.0-py312h06a4308_0 --> 24.5.0-py312h06a4308_0 \n",
            "  openssl                                 3.0.13-h7f8727e_0 --> 3.0.13-h7f8727e_1 \n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages:\n",
            "openssl-3.0.13       | 5.2 MB    | :   0% 0/1 [00:00<?, ?it/s]\n",
            "conda-24.5.0         | 1.2 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\n",
            "\n",
            "frozendict-2.4.2     | 36 KB     | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
            "openssl-3.0.13       | 5.2 MB    | :   0% 0.002997347135570501/1 [00:00<00:55, 55.49s/it]\n",
            "\n",
            "frozendict-2.4.2     | 36 KB     | :  44% 0.43853215920344746/1 [00:00<00:00,  2.50it/s]\u001b[A\u001b[A\n",
            "\n",
            "openssl-3.0.13       | 5.2 MB    | : 100% 0.9981165961449769/1 [00:00<00:00,  4.60it/s]  \n",
            "conda-24.5.0         | 1.2 MB    | : 100% 1.0/1 [00:00<00:00,  1.79it/s]                \u001b[A\n",
            "                                                                        \n",
            "                                                                        \u001b[A\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\n",
            "Preparing transaction: - \b\bdone\n",
            "Verifying transaction: | \b\b/ \b\b- \b\bdone\n",
            "Executing transaction: | \b\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": "ff740906-25b0-4306-a894-b1ad5b9f6a60"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "conda 24.5.0\n",
            "Python 3.12.2\n"
          ]
        }
      ],
      "source": [
        "!conda --version\n",
        "!python --version"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RMuJRdfH3dDq",
        "outputId": "340fb574-d592-4230-f667-d676c1eaa67a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Channels:\n",
            " - conda-forge\n",
            " - defaults\n",
            "Platform: linux-64\n",
            "Collecting package metadata (repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
            "Solving environment: / \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",
            "    - fmpy\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    _libgcc_mutex-0.1          |      conda_forge           3 KB  conda-forge\n",
            "    _openmp_mutex-4.5          |            2_gnu          23 KB  conda-forge\n",
            "    abseil-cpp-20211102.0      |       h27087fc_1         1.1 MB  conda-forge\n",
            "    anyio-4.3.0                |     pyhd8ed1ab_0         100 KB  conda-forge\n",
            "    argon2-cffi-23.1.0         |     pyhd8ed1ab_0          18 KB  conda-forge\n",
            "    argon2-cffi-bindings-21.2.0|  py312h98912ed_4          34 KB  conda-forge\n",
            "    arrow-1.3.0                |     pyhd8ed1ab_0          98 KB  conda-forge\n",
            "    arrow-cpp-14.0.2           |       h374c478_1        11.7 MB\n",
            "    asttokens-2.4.1            |     pyhd8ed1ab_0          28 KB  conda-forge\n",
            "    async-lru-2.0.4            |     pyhd8ed1ab_0          15 KB  conda-forge\n",
            "    attrs-23.2.0               |     pyh71513ae_0          53 KB  conda-forge\n",
            "    aws-c-auth-0.6.19          |       h5eee18b_0          99 KB\n",
            "    aws-c-cal-0.5.20           |       hff2c3d7_3          43 KB  conda-forge\n",
            "    aws-c-common-0.8.5         |       h166bdaf_0         198 KB  conda-forge\n",
            "    aws-c-compression-0.2.16   |       hf5f93bc_0          18 KB  conda-forge\n",
            "    aws-c-event-stream-0.2.15  |       h6a678d5_0          50 KB\n",
            "    aws-c-http-0.6.25          |       h5eee18b_0         200 KB\n",
            "    aws-c-io-0.13.10           |       h2666983_0         140 KB  conda-forge\n",
            "    aws-c-mqtt-0.7.13          |       h5eee18b_0          67 KB\n",
            "    aws-c-s3-0.1.51            |       hdbd6064_0          71 KB\n",
            "    aws-c-sdkutils-0.1.6       |       hf5f93bc_1          52 KB  conda-forge\n",
            "    aws-checksums-0.1.13       |       hf5f93bc_5          51 KB  conda-forge\n",
            "    aws-crt-cpp-0.18.16        |       h6a678d5_0         213 KB\n",
            "    aws-sdk-cpp-1.10.55        |       h721c034_0         2.5 MB\n",
            "    babel-2.14.0               |     pyhd8ed1ab_0         7.3 MB  conda-forge\n",
            "    beautifulsoup4-4.12.3      |     pyha770c72_0         115 KB  conda-forge\n",
            "    blas-1.1                   |         openblas           1 KB  conda-forge\n",
            "    bleach-6.1.0               |     pyhd8ed1ab_0         128 KB  conda-forge\n",
            "    blinker-1.8.2              |     pyhd8ed1ab_0          14 KB  conda-forge\n",
            "    bokeh-3.4.1                |     pyhd8ed1ab_0         4.5 MB  conda-forge\n",
            "    boost-cpp-1.82.0           |       hdb19cb5_2          11 KB\n",
            "    bottleneck-1.3.8           |  py312hc7c0aa3_0         137 KB  conda-forge\n",
            "    brotli-1.0.9               |       h166bdaf_9          20 KB  conda-forge\n",
            "    brotli-bin-1.0.9           |       h166bdaf_9          20 KB  conda-forge\n",
            "    cached-property-1.5.2      |       hd8ed1ab_1           4 KB  conda-forge\n",
            "    cached_property-1.5.2      |     pyha770c72_1          11 KB  conda-forge\n",
            "    certifi-2024.2.2           |     pyhd8ed1ab_0         157 KB  conda-forge\n",
            "    click-8.1.7                |unix_pyh707e725_0          82 KB  conda-forge\n",
            "    cloudpickle-3.0.0          |     pyhd8ed1ab_0          24 KB  conda-forge\n",
            "    comm-0.2.2                 |     pyhd8ed1ab_0          12 KB  conda-forge\n",
            "    conda-24.5.0               |  py312h7900ff3_0         1.2 MB  conda-forge\n",
            "    contourpy-1.2.0            |  py312hdb19cb5_0         238 KB\n",
            "    cycler-0.12.1              |     pyhd8ed1ab_0          13 KB  conda-forge\n",
            "    cytoolz-0.12.3             |  py312h98912ed_0         385 KB  conda-forge\n",
            "    dash-2.17.0                |     pyhd8ed1ab_0         5.0 MB  conda-forge\n",
            "    dash-bootstrap-components-1.6.0|     pyhd8ed1ab_0         117 KB  conda-forge\n",
            "    dask-2024.5.0              |     pyhd8ed1ab_0           7 KB  conda-forge\n",
            "    dask-core-2024.5.0         |     pyhd8ed1ab_0         861 KB  conda-forge\n",
            "    dask-expr-1.1.0            |     pyhd8ed1ab_0         154 KB  conda-forge\n",
            "    dbus-1.13.18               |       hb2f20db_0         504 KB\n",
            "    debugpy-1.6.7              |  py312h6a678d5_0         2.0 MB\n",
            "    decorator-5.1.1            |     pyhd8ed1ab_0          12 KB  conda-forge\n",
            "    defusedxml-0.7.1           |     pyhd8ed1ab_0          23 KB  conda-forge\n",
            "    distributed-2024.5.0       |     pyhd8ed1ab_0         777 KB  conda-forge\n",
            "    entrypoints-0.4            |     pyhd8ed1ab_0           9 KB  conda-forge\n",
            "    exceptiongroup-1.2.0       |     pyhd8ed1ab_2          20 KB  conda-forge\n",
            "    executing-2.0.1            |     pyhd8ed1ab_0          27 KB  conda-forge\n",
            "    flask-3.0.3                |     pyhd8ed1ab_0          79 KB  conda-forge\n",
            "    fmpy-0.3.20                |       h13ae965_0          10 KB  conda-forge\n",
            "    fmpy-base-0.3.20           |  py312ha770c72_0         3.2 MB  conda-forge\n",
            "    font-ttf-dejavu-sans-mono-2.37|       hab24e00_0         388 KB  conda-forge\n",
            "    font-ttf-inconsolata-3.000 |       h77eed37_0          94 KB  conda-forge\n",
            "    font-ttf-source-code-pro-2.038|       h77eed37_0         684 KB  conda-forge\n",
            "    font-ttf-ubuntu-0.83       |       h77eed37_2         1.5 MB  conda-forge\n",
            "    fontconfig-2.14.2          |       h14ed4e7_0         266 KB  conda-forge\n",
            "    fonts-conda-forge-1        |                0           4 KB  conda-forge\n",
            "    fonttools-4.51.0           |  py312h98912ed_0         2.7 MB  conda-forge\n",
            "    fqdn-1.5.1                 |     pyhd8ed1ab_0          14 KB  conda-forge\n",
            "    freetype-2.12.1            |       h267a509_2         620 KB  conda-forge\n",
            "    fsspec-2024.3.1            |     pyhca7485f_0         126 KB  conda-forge\n",
            "    gettext-0.21.1             |       h27087fc_0         4.1 MB  conda-forge\n",
            "    gflags-2.2.2               |    he1b5a44_1004         114 KB  conda-forge\n",
            "    glib-2.70.2                |       h780b84a_4         430 KB  conda-forge\n",
            "    glib-tools-2.70.2          |       h780b84a_4         106 KB  conda-forge\n",
            "    glog-0.5.0                 |       h48cff8f_0         104 KB  conda-forge\n",
            "    grpc-cpp-1.48.2            |       he1ff14a_1         4.8 MB\n",
            "    gst-plugins-base-1.14.1    |       h6a678d5_1         2.2 MB\n",
            "    gstreamer-1.14.1           |       h5eee18b_1         1.7 MB\n",
            "    h11-0.14.0                 |     pyhd8ed1ab_0          47 KB  conda-forge\n",
            "    h2-4.1.0                   |     pyhd8ed1ab_0          46 KB  conda-forge\n",
            "    hpack-4.0.0                |     pyh9f0ad1d_0          25 KB  conda-forge\n",
            "    httpcore-1.0.5             |     pyhd8ed1ab_0          45 KB  conda-forge\n",
            "    httpx-0.27.0               |     pyhd8ed1ab_0          63 KB  conda-forge\n",
            "    hyperframe-6.0.1           |     pyhd8ed1ab_0          14 KB  conda-forge\n",
            "    importlib-metadata-7.1.0   |     pyha770c72_0          26 KB  conda-forge\n",
            "    importlib_metadata-7.1.0   |       hd8ed1ab_0           9 KB  conda-forge\n",
            "    importlib_resources-6.4.0  |     pyhd8ed1ab_0          32 KB  conda-forge\n",
            "    ipykernel-6.29.3           |     pyhd33586a_0         116 KB  conda-forge\n",
            "    ipython-8.24.0             |     pyh707e725_0         582 KB  conda-forge\n",
            "    isoduration-20.11.0        |     pyhd8ed1ab_0          17 KB  conda-forge\n",
            "    itsdangerous-2.2.0         |     pyhd8ed1ab_0          19 KB  conda-forge\n",
            "    jedi-0.19.1                |     pyhd8ed1ab_0         822 KB  conda-forge\n",
            "    jinja2-3.1.4               |     pyhd8ed1ab_0         109 KB  conda-forge\n",
            "    jpeg-9e                    |       h0b41bf4_3         235 KB  conda-forge\n",
            "    json5-0.9.25               |     pyhd8ed1ab_0          27 KB  conda-forge\n",
            "    jsonschema-4.22.0          |     pyhd8ed1ab_0          72 KB  conda-forge\n",
            "    jsonschema-specifications-2023.12.1|     pyhd8ed1ab_0          16 KB  conda-forge\n",
            "    jsonschema-with-format-nongpl-4.22.0|     pyhd8ed1ab_0           7 KB  conda-forge\n",
            "    jupyter-lsp-2.2.5          |     pyhd8ed1ab_0          54 KB  conda-forge\n",
            "    jupyter_client-8.6.1       |     pyhd8ed1ab_0         104 KB  conda-forge\n",
            "    jupyter_core-5.7.2         |  py312h7900ff3_0          91 KB  conda-forge\n",
            "    jupyter_events-0.10.0      |     pyhd8ed1ab_0          21 KB  conda-forge\n",
            "    jupyter_server-2.14.0      |     pyhd8ed1ab_0         317 KB  conda-forge\n",
            "    jupyter_server_terminals-0.5.3|     pyhd8ed1ab_0          19 KB  conda-forge\n",
            "    jupyterlab-4.1.8           |     pyhd8ed1ab_0         7.3 MB  conda-forge\n",
            "    jupyterlab_pygments-0.3.0  |     pyhd8ed1ab_1          18 KB  conda-forge\n",
            "    jupyterlab_server-2.27.1   |     pyhd8ed1ab_0          48 KB  conda-forge\n",
            "    kaleido-core-0.2.1         |       h3644ca4_0        59.2 MB  conda-forge\n",
            "    kiwisolver-1.4.4           |  py312h6a678d5_0          68 KB\n",
            "    lark-1.1.9                 |     pyhd8ed1ab_0          90 KB  conda-forge\n",
            "    lcms2-2.15                 |       hfd0df8a_0         235 KB  conda-forge\n",
            "    lerc-3.0                   |       h9c3ff4c_0         216 KB  conda-forge\n",
            "    libboost-1.82.0            |       h109eef0_2        19.5 MB\n",
            "    libbrotlicommon-1.0.9      |       h166bdaf_9          69 KB  conda-forge\n",
            "    libbrotlidec-1.0.9         |       h166bdaf_9          32 KB  conda-forge\n",
            "    libbrotlienc-1.0.9         |       h166bdaf_9         259 KB  conda-forge\n",
            "    libclang-14.0.6            |default_hc6dbbc7_1         137 KB\n",
            "    libclang13-14.0.6          |default_he11475f_1         9.8 MB\n",
            "    libcups-2.4.2              |       h2d74bed_1         4.5 MB\n",
            "    libdeflate-1.17            |       h0b41bf4_0          63 KB  conda-forge\n",
            "    libevent-2.1.12            |       hf998b51_1         417 KB  conda-forge\n",
            "    libexpat-2.5.0             |       hcb278e6_1          76 KB  conda-forge\n",
            "    libgcc-ng-13.2.0           |       h77fa898_7         758 KB  conda-forge\n",
            "    libgfortran-ng-13.2.0      |       h69a702a_7          24 KB  conda-forge\n",
            "    libgfortran5-13.2.0        |       hca663fb_7         1.4 MB  conda-forge\n",
            "    libglib-2.70.2             |       h174f98d_4         3.1 MB  conda-forge\n",
            "    libgomp-13.2.0             |       h77fa898_7         412 KB  conda-forge\n",
            "    libiconv-1.17              |       hd590300_2         689 KB  conda-forge\n",
            "    libllvm14-14.0.6           |       hdb19cb5_3        33.4 MB\n",
            "    libnsl-2.0.1               |       hd590300_0          33 KB  conda-forge\n",
            "    libopenblas-0.3.27         |pthreads_h413a1c8_0         5.3 MB  conda-forge\n",
            "    libpng-1.6.43              |       h2797004_0         281 KB  conda-forge\n",
            "    libpq-12.17                |       hdbd6064_0         2.4 MB\n",
            "    libprotobuf-3.20.3         |       he621ea3_0         2.4 MB\n",
            "    libsodium-1.0.18           |       h36c2ea0_1         366 KB  conda-forge\n",
            "    libsqlite-3.45.3           |       h2797004_0         840 KB  conda-forge\n",
            "    libthrift-0.15.0           |       h1795dd8_2         4.0 MB\n",
            "    libtiff-4.5.1              |       h6a678d5_0         533 KB\n",
            "    libuuid-2.38.1             |       h0b41bf4_0          33 KB  conda-forge\n",
            "    libwebp-base-1.4.0         |       hd590300_0         429 KB  conda-forge\n",
            "    libxcb-1.16                |       hd590300_0         386 KB  conda-forge\n",
            "    libxcrypt-4.4.36           |       hd590300_1          98 KB  conda-forge\n",
            "    libxkbcommon-1.0.3         |       he3ba5ed_0         581 KB  conda-forge\n",
            "    libxslt-1.1.37             |       h873f0b0_0         251 KB  conda-forge\n",
            "    libzlib-1.2.13             |       hd590300_5          60 KB  conda-forge\n",
            "    locket-1.0.0               |     pyhd8ed1ab_0           8 KB  conda-forge\n",
            "    lxml-5.2.1                 |  py312hdbbb534_0         1.4 MB\n",
            "    lz4-4.3.3                  |  py312h03f37cb_0          38 KB  conda-forge\n",
            "    markupsafe-2.1.5           |  py312h98912ed_0          26 KB  conda-forge\n",
            "    mathjax-2.7.7              |       ha770c72_3        21.2 MB  conda-forge\n",
            "    matplotlib-base-3.8.4      |  py312h526ad5a_0         7.6 MB\n",
            "    matplotlib-inline-0.1.7    |     pyhd8ed1ab_0          14 KB  conda-forge\n",
            "    mistune-3.0.2              |     pyhd8ed1ab_0          64 KB  conda-forge\n",
            "    msgpack-python-1.0.3       |  py312hdb19cb5_0          35 KB\n",
            "    munkres-1.1.4              |     pyh9f0ad1d_0          12 KB  conda-forge\n",
            "    mysql-5.7.20               |    hf484d3e_1001        79.2 MB  conda-forge\n",
            "    nbclient-0.10.0            |     pyhd8ed1ab_0          27 KB  conda-forge\n",
            "    nbconvert-core-7.16.4      |     pyhd8ed1ab_0         185 KB  conda-forge\n",
            "    nbformat-5.10.4            |     pyhd8ed1ab_0          99 KB  conda-forge\n",
            "    nest-asyncio-1.6.0         |     pyhd8ed1ab_0          11 KB  conda-forge\n",
            "    notebook-7.1.3             |     pyhd8ed1ab_0         3.8 MB  conda-forge\n",
            "    notebook-shim-0.2.4        |     pyhd8ed1ab_0          16 KB  conda-forge\n",
            "    nspr-4.35                  |       h6a678d5_0         244 KB\n",
            "    nss-3.89.1                 |       h6a678d5_0         2.1 MB\n",
            "    numexpr-2.8.7              |  py312he7dcb8a_0         149 KB\n",
            "    numpy-1.26.4               |  py312h2809609_0          10 KB\n",
            "    numpy-base-1.26.4          |  py312he1a6c75_0         7.7 MB\n",
            "    openblas-0.3.27            |pthreads_h7a3da1a_0         5.5 MB  conda-forge\n",
            "    openjpeg-2.4.0             |       h3ad879b_0         331 KB\n",
            "    openssl-3.3.0              |       hd590300_0         2.8 MB  conda-forge\n",
            "    orc-1.7.4                  |       h6c59b99_1         1.1 MB  conda-forge\n",
            "    overrides-7.7.0            |     pyhd8ed1ab_0          30 KB  conda-forge\n",
            "    pandas-2.2.1               |  py312h526ad5a_0        15.4 MB\n",
            "    pandocfilters-1.5.0        |     pyhd8ed1ab_0          11 KB  conda-forge\n",
            "    parso-0.8.4                |     pyhd8ed1ab_0          73 KB  conda-forge\n",
            "    partd-1.4.2                |     pyhd8ed1ab_0          20 KB  conda-forge\n",
            "    pcre-8.45                  |       h9c3ff4c_0         253 KB  conda-forge\n",
            "    pexpect-4.9.0              |     pyhd8ed1ab_0          52 KB  conda-forge\n",
            "    pickleshare-0.7.5          |          py_1003           9 KB  conda-forge\n",
            "    pillow-10.3.0              |  py312h5eee18b_0         915 KB\n",
            "    pkgutil-resolve-name-1.3.10|     pyhd8ed1ab_1          11 KB  conda-forge\n",
            "    plotly-5.22.0              |     pyhd8ed1ab_0         5.1 MB  conda-forge\n",
            "    ply-3.11                   |     pyhd8ed1ab_2          48 KB  conda-forge\n",
            "    prometheus_client-0.20.0   |     pyhd8ed1ab_0          48 KB  conda-forge\n",
            "    prompt-toolkit-3.0.42      |     pyha770c72_0         264 KB  conda-forge\n",
            "    psutil-5.9.8               |  py312h98912ed_0         475 KB  conda-forge\n",
            "    pthread-stubs-0.4          |    h36c2ea0_1001           5 KB  conda-forge\n",
            "    ptyprocess-0.7.0           |     pyhd3deb0d_0          16 KB  conda-forge\n",
            "    pure_eval-0.2.2            |     pyhd8ed1ab_0          14 KB  conda-forge\n",
            "    pyarrow-14.0.2             |  py312hb107042_0         4.6 MB\n",
            "    pyarrow-hotfix-0.6         |     pyhd8ed1ab_0          13 KB  conda-forge\n",
            "    pygments-2.18.0            |     pyhd8ed1ab_0         859 KB  conda-forge\n",
            "    pyparsing-3.0.9            |     pyhd8ed1ab_0          79 KB  conda-forge\n",
            "    pyqt-5.15.10               |  py312h6a678d5_0         5.0 MB\n",
            "    pyqt5-sip-12.13.0          |  py312h5eee18b_0          88 KB\n",
            "    pyqtgraph-0.13.7           |     pyhd8ed1ab_0         1.4 MB  conda-forge\n",
            "    pyqtwebengine-5.15.10      |  py312h6a678d5_0         153 KB\n",
            "    python-3.12.2              |hab00c5b_0_cpython        30.8 MB  conda-forge\n",
            "    python-dateutil-2.9.0      |     pyhd8ed1ab_0         218 KB  conda-forge\n",
            "    python-fastjsonschema-2.19.1|     pyhd8ed1ab_0         220 KB  conda-forge\n",
            "    python-json-logger-2.0.7   |     pyhd8ed1ab_0          13 KB  conda-forge\n",
            "    python-kaleido-0.2.1       |     pyhd8ed1ab_0          18 KB  conda-forge\n",
            "    python-tzdata-2024.1       |     pyhd8ed1ab_0         141 KB  conda-forge\n",
            "    python_abi-3.12            |          4_cp312           6 KB  conda-forge\n",
            "    pytz-2024.1                |     pyhd8ed1ab_0         184 KB  conda-forge\n",
            "    pyyaml-6.0.1               |  py312h98912ed_1         192 KB  conda-forge\n",
            "    pyzmq-25.1.2               |  py312h6a678d5_0         533 KB\n",
            "    qt-main-5.15.2             |      h53bd1ea_10        53.7 MB\n",
            "    qt-webengine-5.15.9        |       h9ab4d14_7        53.8 MB\n",
            "    re2-2022.04.01             |       h27087fc_0         212 KB  conda-forge\n",
            "    referencing-0.35.1         |     pyhd8ed1ab_0          41 KB  conda-forge\n",
            "    retrying-1.3.3             |             py_2          11 KB  conda-forge\n",
            "    rfc3339-validator-0.1.4    |     pyhd8ed1ab_0           8 KB  conda-forge\n",
            "    rfc3986-validator-0.1.1    |     pyh9f0ad1d_0           8 KB  conda-forge\n",
            "    rpds-py-0.18.1             |  py312h4413252_0         901 KB  conda-forge\n",
            "    s2n-1.3.27                 |       h1b5e8cb_0         374 KB  conda-forge\n",
            "    scipy-1.12.0               |  py312h2809609_0        23.3 MB\n",
            "    send2trash-1.8.3           |     pyh0d859eb_0          22 KB  conda-forge\n",
            "    sip-6.7.12                 |  py312h6a678d5_0         553 KB\n",
            "    six-1.16.0                 |     pyh6c4a22f_0          14 KB  conda-forge\n",
            "    snappy-1.1.9               |       hbd366e4_1          35 KB  conda-forge\n",
            "    sniffio-1.3.1              |     pyhd8ed1ab_0          15 KB  conda-forge\n",
            "    sortedcontainers-2.4.0     |     pyhd8ed1ab_0          26 KB  conda-forge\n",
            "    soupsieve-2.5              |     pyhd8ed1ab_1          36 KB  conda-forge\n",
            "    stack_data-0.6.2           |     pyhd8ed1ab_0          26 KB  conda-forge\n",
            "    tblib-3.0.0                |     pyhd8ed1ab_0          17 KB  conda-forge\n",
            "    tenacity-8.3.0             |     pyhd8ed1ab_0          23 KB  conda-forge\n",
            "    terminado-0.18.1           |     pyh0d859eb_0          22 KB  conda-forge\n",
            "    tinycss2-1.3.0             |     pyhd8ed1ab_0          25 KB  conda-forge\n",
            "    tk-8.6.13                  |noxft_h4845f30_101         3.2 MB  conda-forge\n",
            "    tomli-2.0.1                |     pyhd8ed1ab_0          16 KB  conda-forge\n",
            "    toolz-0.12.1               |     pyhd8ed1ab_0          51 KB  conda-forge\n",
            "    tornado-6.4                |  py312h98912ed_0         821 KB  conda-forge\n",
            "    traitlets-5.14.3           |     pyhd8ed1ab_0         108 KB  conda-forge\n",
            "    types-python-dateutil-2.9.0.20240316|     pyhd8ed1ab_0          21 KB  conda-forge\n",
            "    typing-extensions-4.11.0   |       hd8ed1ab_0          10 KB  conda-forge\n",
            "    typing_extensions-4.11.0   |     pyha770c72_0          37 KB  conda-forge\n",
            "    typing_utils-0.1.0         |     pyhd8ed1ab_0          14 KB  conda-forge\n",
            "    uri-template-1.3.0         |     pyhd8ed1ab_0          23 KB  conda-forge\n",
            "    utf8proc-2.6.1             |       h5eee18b_1          93 KB\n",
            "    wcwidth-0.2.13             |     pyhd8ed1ab_0          32 KB  conda-forge\n",
            "    webcolors-1.13             |     pyhd8ed1ab_0          18 KB  conda-forge\n",
            "    webencodings-0.5.1         |     pyhd8ed1ab_2          15 KB  conda-forge\n",
            "    websocket-client-1.8.0     |     pyhd8ed1ab_0          46 KB  conda-forge\n",
            "    werkzeug-3.0.3             |     pyhd8ed1ab_0         237 KB  conda-forge\n",
            "    xorg-libxau-1.0.11         |       hd590300_0          14 KB  conda-forge\n",
            "    xorg-libxdmcp-1.1.3        |       h7f98852_0          19 KB  conda-forge\n",
            "    xyzservices-2024.4.0       |     pyhd8ed1ab_0          45 KB  conda-forge\n",
            "    yaml-0.2.5                 |       h7f98852_2          87 KB  conda-forge\n",
            "    zeromq-4.3.5               |       h6a678d5_0         366 KB\n",
            "    zict-3.0.0                 |     pyhd8ed1ab_0          35 KB  conda-forge\n",
            "    zipp-3.17.0                |     pyhd8ed1ab_0          19 KB  conda-forge\n",
            "    zlib-1.2.13                |       hd590300_5          91 KB  conda-forge\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:       564.9 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  abseil-cpp         conda-forge/linux-64::abseil-cpp-20211102.0-h27087fc_1 \n",
            "  anyio              conda-forge/noarch::anyio-4.3.0-pyhd8ed1ab_0 \n",
            "  argon2-cffi        conda-forge/noarch::argon2-cffi-23.1.0-pyhd8ed1ab_0 \n",
            "  argon2-cffi-bindi~ conda-forge/linux-64::argon2-cffi-bindings-21.2.0-py312h98912ed_4 \n",
            "  arrow              conda-forge/noarch::arrow-1.3.0-pyhd8ed1ab_0 \n",
            "  arrow-cpp          pkgs/main/linux-64::arrow-cpp-14.0.2-h374c478_1 \n",
            "  asttokens          conda-forge/noarch::asttokens-2.4.1-pyhd8ed1ab_0 \n",
            "  async-lru          conda-forge/noarch::async-lru-2.0.4-pyhd8ed1ab_0 \n",
            "  attrs              conda-forge/noarch::attrs-23.2.0-pyh71513ae_0 \n",
            "  aws-c-auth         pkgs/main/linux-64::aws-c-auth-0.6.19-h5eee18b_0 \n",
            "  aws-c-cal          conda-forge/linux-64::aws-c-cal-0.5.20-hff2c3d7_3 \n",
            "  aws-c-common       conda-forge/linux-64::aws-c-common-0.8.5-h166bdaf_0 \n",
            "  aws-c-compression  conda-forge/linux-64::aws-c-compression-0.2.16-hf5f93bc_0 \n",
            "  aws-c-event-stream pkgs/main/linux-64::aws-c-event-stream-0.2.15-h6a678d5_0 \n",
            "  aws-c-http         pkgs/main/linux-64::aws-c-http-0.6.25-h5eee18b_0 \n",
            "  aws-c-io           conda-forge/linux-64::aws-c-io-0.13.10-h2666983_0 \n",
            "  aws-c-mqtt         pkgs/main/linux-64::aws-c-mqtt-0.7.13-h5eee18b_0 \n",
            "  aws-c-s3           pkgs/main/linux-64::aws-c-s3-0.1.51-hdbd6064_0 \n",
            "  aws-c-sdkutils     conda-forge/linux-64::aws-c-sdkutils-0.1.6-hf5f93bc_1 \n",
            "  aws-checksums      conda-forge/linux-64::aws-checksums-0.1.13-hf5f93bc_5 \n",
            "  aws-crt-cpp        pkgs/main/linux-64::aws-crt-cpp-0.18.16-h6a678d5_0 \n",
            "  aws-sdk-cpp        pkgs/main/linux-64::aws-sdk-cpp-1.10.55-h721c034_0 \n",
            "  babel              conda-forge/noarch::babel-2.14.0-pyhd8ed1ab_0 \n",
            "  beautifulsoup4     conda-forge/noarch::beautifulsoup4-4.12.3-pyha770c72_0 \n",
            "  blas               conda-forge/linux-64::blas-1.1-openblas \n",
            "  bleach             conda-forge/noarch::bleach-6.1.0-pyhd8ed1ab_0 \n",
            "  blinker            conda-forge/noarch::blinker-1.8.2-pyhd8ed1ab_0 \n",
            "  bokeh              conda-forge/noarch::bokeh-3.4.1-pyhd8ed1ab_0 \n",
            "  boost-cpp          pkgs/main/linux-64::boost-cpp-1.82.0-hdb19cb5_2 \n",
            "  bottleneck         conda-forge/linux-64::bottleneck-1.3.8-py312hc7c0aa3_0 \n",
            "  brotli             conda-forge/linux-64::brotli-1.0.9-h166bdaf_9 \n",
            "  brotli-bin         conda-forge/linux-64::brotli-bin-1.0.9-h166bdaf_9 \n",
            "  cached-property    conda-forge/noarch::cached-property-1.5.2-hd8ed1ab_1 \n",
            "  cached_property    conda-forge/noarch::cached_property-1.5.2-pyha770c72_1 \n",
            "  click              conda-forge/noarch::click-8.1.7-unix_pyh707e725_0 \n",
            "  cloudpickle        conda-forge/noarch::cloudpickle-3.0.0-pyhd8ed1ab_0 \n",
            "  comm               conda-forge/noarch::comm-0.2.2-pyhd8ed1ab_0 \n",
            "  contourpy          pkgs/main/linux-64::contourpy-1.2.0-py312hdb19cb5_0 \n",
            "  cycler             conda-forge/noarch::cycler-0.12.1-pyhd8ed1ab_0 \n",
            "  cytoolz            conda-forge/linux-64::cytoolz-0.12.3-py312h98912ed_0 \n",
            "  dash               conda-forge/noarch::dash-2.17.0-pyhd8ed1ab_0 \n",
            "  dash-bootstrap-co~ conda-forge/noarch::dash-bootstrap-components-1.6.0-pyhd8ed1ab_0 \n",
            "  dask               conda-forge/noarch::dask-2024.5.0-pyhd8ed1ab_0 \n",
            "  dask-core          conda-forge/noarch::dask-core-2024.5.0-pyhd8ed1ab_0 \n",
            "  dask-expr          conda-forge/noarch::dask-expr-1.1.0-pyhd8ed1ab_0 \n",
            "  dbus               pkgs/main/linux-64::dbus-1.13.18-hb2f20db_0 \n",
            "  debugpy            pkgs/main/linux-64::debugpy-1.6.7-py312h6a678d5_0 \n",
            "  decorator          conda-forge/noarch::decorator-5.1.1-pyhd8ed1ab_0 \n",
            "  defusedxml         conda-forge/noarch::defusedxml-0.7.1-pyhd8ed1ab_0 \n",
            "  distributed        conda-forge/noarch::distributed-2024.5.0-pyhd8ed1ab_0 \n",
            "  entrypoints        conda-forge/noarch::entrypoints-0.4-pyhd8ed1ab_0 \n",
            "  exceptiongroup     conda-forge/noarch::exceptiongroup-1.2.0-pyhd8ed1ab_2 \n",
            "  executing          conda-forge/noarch::executing-2.0.1-pyhd8ed1ab_0 \n",
            "  flask              conda-forge/noarch::flask-3.0.3-pyhd8ed1ab_0 \n",
            "  fmpy               conda-forge/noarch::fmpy-0.3.20-h13ae965_0 \n",
            "  fmpy-base          conda-forge/linux-64::fmpy-base-0.3.20-py312ha770c72_0 \n",
            "  font-ttf-dejavu-s~ conda-forge/noarch::font-ttf-dejavu-sans-mono-2.37-hab24e00_0 \n",
            "  font-ttf-inconsol~ conda-forge/noarch::font-ttf-inconsolata-3.000-h77eed37_0 \n",
            "  font-ttf-source-c~ conda-forge/noarch::font-ttf-source-code-pro-2.038-h77eed37_0 \n",
            "  font-ttf-ubuntu    conda-forge/noarch::font-ttf-ubuntu-0.83-h77eed37_2 \n",
            "  fontconfig         conda-forge/linux-64::fontconfig-2.14.2-h14ed4e7_0 \n",
            "  fonts-conda-forge  conda-forge/noarch::fonts-conda-forge-1-0 \n",
            "  fonttools          conda-forge/linux-64::fonttools-4.51.0-py312h98912ed_0 \n",
            "  fqdn               conda-forge/noarch::fqdn-1.5.1-pyhd8ed1ab_0 \n",
            "  freetype           conda-forge/linux-64::freetype-2.12.1-h267a509_2 \n",
            "  fsspec             conda-forge/noarch::fsspec-2024.3.1-pyhca7485f_0 \n",
            "  gettext            conda-forge/linux-64::gettext-0.21.1-h27087fc_0 \n",
            "  gflags             conda-forge/linux-64::gflags-2.2.2-he1b5a44_1004 \n",
            "  glib               conda-forge/linux-64::glib-2.70.2-h780b84a_4 \n",
            "  glib-tools         conda-forge/linux-64::glib-tools-2.70.2-h780b84a_4 \n",
            "  glog               conda-forge/linux-64::glog-0.5.0-h48cff8f_0 \n",
            "  grpc-cpp           pkgs/main/linux-64::grpc-cpp-1.48.2-he1ff14a_1 \n",
            "  gst-plugins-base   pkgs/main/linux-64::gst-plugins-base-1.14.1-h6a678d5_1 \n",
            "  gstreamer          pkgs/main/linux-64::gstreamer-1.14.1-h5eee18b_1 \n",
            "  h11                conda-forge/noarch::h11-0.14.0-pyhd8ed1ab_0 \n",
            "  h2                 conda-forge/noarch::h2-4.1.0-pyhd8ed1ab_0 \n",
            "  hpack              conda-forge/noarch::hpack-4.0.0-pyh9f0ad1d_0 \n",
            "  httpcore           conda-forge/noarch::httpcore-1.0.5-pyhd8ed1ab_0 \n",
            "  httpx              conda-forge/noarch::httpx-0.27.0-pyhd8ed1ab_0 \n",
            "  hyperframe         conda-forge/noarch::hyperframe-6.0.1-pyhd8ed1ab_0 \n",
            "  importlib-metadata conda-forge/noarch::importlib-metadata-7.1.0-pyha770c72_0 \n",
            "  importlib_metadata conda-forge/noarch::importlib_metadata-7.1.0-hd8ed1ab_0 \n",
            "  importlib_resourc~ conda-forge/noarch::importlib_resources-6.4.0-pyhd8ed1ab_0 \n",
            "  ipykernel          conda-forge/noarch::ipykernel-6.29.3-pyhd33586a_0 \n",
            "  ipython            conda-forge/noarch::ipython-8.24.0-pyh707e725_0 \n",
            "  isoduration        conda-forge/noarch::isoduration-20.11.0-pyhd8ed1ab_0 \n",
            "  itsdangerous       conda-forge/noarch::itsdangerous-2.2.0-pyhd8ed1ab_0 \n",
            "  jedi               conda-forge/noarch::jedi-0.19.1-pyhd8ed1ab_0 \n",
            "  jinja2             conda-forge/noarch::jinja2-3.1.4-pyhd8ed1ab_0 \n",
            "  jpeg               conda-forge/linux-64::jpeg-9e-h0b41bf4_3 \n",
            "  json5              conda-forge/noarch::json5-0.9.25-pyhd8ed1ab_0 \n",
            "  jsonschema         conda-forge/noarch::jsonschema-4.22.0-pyhd8ed1ab_0 \n",
            "  jsonschema-specif~ conda-forge/noarch::jsonschema-specifications-2023.12.1-pyhd8ed1ab_0 \n",
            "  jsonschema-with-f~ conda-forge/noarch::jsonschema-with-format-nongpl-4.22.0-pyhd8ed1ab_0 \n",
            "  jupyter-lsp        conda-forge/noarch::jupyter-lsp-2.2.5-pyhd8ed1ab_0 \n",
            "  jupyter_client     conda-forge/noarch::jupyter_client-8.6.1-pyhd8ed1ab_0 \n",
            "  jupyter_core       conda-forge/linux-64::jupyter_core-5.7.2-py312h7900ff3_0 \n",
            "  jupyter_events     conda-forge/noarch::jupyter_events-0.10.0-pyhd8ed1ab_0 \n",
            "  jupyter_server     conda-forge/noarch::jupyter_server-2.14.0-pyhd8ed1ab_0 \n",
            "  jupyter_server_te~ conda-forge/noarch::jupyter_server_terminals-0.5.3-pyhd8ed1ab_0 \n",
            "  jupyterlab         conda-forge/noarch::jupyterlab-4.1.8-pyhd8ed1ab_0 \n",
            "  jupyterlab_pygmen~ conda-forge/noarch::jupyterlab_pygments-0.3.0-pyhd8ed1ab_1 \n",
            "  jupyterlab_server  conda-forge/noarch::jupyterlab_server-2.27.1-pyhd8ed1ab_0 \n",
            "  kaleido-core       conda-forge/linux-64::kaleido-core-0.2.1-h3644ca4_0 \n",
            "  kiwisolver         pkgs/main/linux-64::kiwisolver-1.4.4-py312h6a678d5_0 \n",
            "  lark               conda-forge/noarch::lark-1.1.9-pyhd8ed1ab_0 \n",
            "  lcms2              conda-forge/linux-64::lcms2-2.15-hfd0df8a_0 \n",
            "  lerc               conda-forge/linux-64::lerc-3.0-h9c3ff4c_0 \n",
            "  libboost           pkgs/main/linux-64::libboost-1.82.0-h109eef0_2 \n",
            "  libbrotlicommon    conda-forge/linux-64::libbrotlicommon-1.0.9-h166bdaf_9 \n",
            "  libbrotlidec       conda-forge/linux-64::libbrotlidec-1.0.9-h166bdaf_9 \n",
            "  libbrotlienc       conda-forge/linux-64::libbrotlienc-1.0.9-h166bdaf_9 \n",
            "  libclang           pkgs/main/linux-64::libclang-14.0.6-default_hc6dbbc7_1 \n",
            "  libclang13         pkgs/main/linux-64::libclang13-14.0.6-default_he11475f_1 \n",
            "  libcups            pkgs/main/linux-64::libcups-2.4.2-h2d74bed_1 \n",
            "  libdeflate         conda-forge/linux-64::libdeflate-1.17-h0b41bf4_0 \n",
            "  libevent           conda-forge/linux-64::libevent-2.1.12-hf998b51_1 \n",
            "  libexpat           conda-forge/linux-64::libexpat-2.5.0-hcb278e6_1 \n",
            "  libgfortran-ng     conda-forge/linux-64::libgfortran-ng-13.2.0-h69a702a_7 \n",
            "  libgfortran5       conda-forge/linux-64::libgfortran5-13.2.0-hca663fb_7 \n",
            "  libglib            conda-forge/linux-64::libglib-2.70.2-h174f98d_4 \n",
            "  libiconv           conda-forge/linux-64::libiconv-1.17-hd590300_2 \n",
            "  libllvm14          pkgs/main/linux-64::libllvm14-14.0.6-hdb19cb5_3 \n",
            "  libnsl             conda-forge/linux-64::libnsl-2.0.1-hd590300_0 \n",
            "  libopenblas        conda-forge/linux-64::libopenblas-0.3.27-pthreads_h413a1c8_0 \n",
            "  libpng             conda-forge/linux-64::libpng-1.6.43-h2797004_0 \n",
            "  libpq              pkgs/main/linux-64::libpq-12.17-hdbd6064_0 \n",
            "  libprotobuf        pkgs/main/linux-64::libprotobuf-3.20.3-he621ea3_0 \n",
            "  libsodium          conda-forge/linux-64::libsodium-1.0.18-h36c2ea0_1 \n",
            "  libsqlite          conda-forge/linux-64::libsqlite-3.45.3-h2797004_0 \n",
            "  libthrift          pkgs/main/linux-64::libthrift-0.15.0-h1795dd8_2 \n",
            "  libtiff            pkgs/main/linux-64::libtiff-4.5.1-h6a678d5_0 \n",
            "  libwebp-base       conda-forge/linux-64::libwebp-base-1.4.0-hd590300_0 \n",
            "  libxcb             conda-forge/linux-64::libxcb-1.16-hd590300_0 \n",
            "  libxcrypt          conda-forge/linux-64::libxcrypt-4.4.36-hd590300_1 \n",
            "  libxkbcommon       conda-forge/linux-64::libxkbcommon-1.0.3-he3ba5ed_0 \n",
            "  libxslt            conda-forge/linux-64::libxslt-1.1.37-h873f0b0_0 \n",
            "  libzlib            conda-forge/linux-64::libzlib-1.2.13-hd590300_5 \n",
            "  locket             conda-forge/noarch::locket-1.0.0-pyhd8ed1ab_0 \n",
            "  lxml               pkgs/main/linux-64::lxml-5.2.1-py312hdbbb534_0 \n",
            "  lz4                conda-forge/linux-64::lz4-4.3.3-py312h03f37cb_0 \n",
            "  markupsafe         conda-forge/linux-64::markupsafe-2.1.5-py312h98912ed_0 \n",
            "  mathjax            conda-forge/linux-64::mathjax-2.7.7-ha770c72_3 \n",
            "  matplotlib-base    pkgs/main/linux-64::matplotlib-base-3.8.4-py312h526ad5a_0 \n",
            "  matplotlib-inline  conda-forge/noarch::matplotlib-inline-0.1.7-pyhd8ed1ab_0 \n",
            "  mistune            conda-forge/noarch::mistune-3.0.2-pyhd8ed1ab_0 \n",
            "  msgpack-python     pkgs/main/linux-64::msgpack-python-1.0.3-py312hdb19cb5_0 \n",
            "  munkres            conda-forge/noarch::munkres-1.1.4-pyh9f0ad1d_0 \n",
            "  mysql              conda-forge/linux-64::mysql-5.7.20-hf484d3e_1001 \n",
            "  nbclient           conda-forge/noarch::nbclient-0.10.0-pyhd8ed1ab_0 \n",
            "  nbconvert-core     conda-forge/noarch::nbconvert-core-7.16.4-pyhd8ed1ab_0 \n",
            "  nbformat           conda-forge/noarch::nbformat-5.10.4-pyhd8ed1ab_0 \n",
            "  nest-asyncio       conda-forge/noarch::nest-asyncio-1.6.0-pyhd8ed1ab_0 \n",
            "  notebook           conda-forge/noarch::notebook-7.1.3-pyhd8ed1ab_0 \n",
            "  notebook-shim      conda-forge/noarch::notebook-shim-0.2.4-pyhd8ed1ab_0 \n",
            "  nspr               pkgs/main/linux-64::nspr-4.35-h6a678d5_0 \n",
            "  nss                pkgs/main/linux-64::nss-3.89.1-h6a678d5_0 \n",
            "  numexpr            pkgs/main/linux-64::numexpr-2.8.7-py312he7dcb8a_0 \n",
            "  numpy              pkgs/main/linux-64::numpy-1.26.4-py312h2809609_0 \n",
            "  numpy-base         pkgs/main/linux-64::numpy-base-1.26.4-py312he1a6c75_0 \n",
            "  openblas           conda-forge/linux-64::openblas-0.3.27-pthreads_h7a3da1a_0 \n",
            "  openjpeg           pkgs/main/linux-64::openjpeg-2.4.0-h3ad879b_0 \n",
            "  orc                conda-forge/linux-64::orc-1.7.4-h6c59b99_1 \n",
            "  overrides          conda-forge/noarch::overrides-7.7.0-pyhd8ed1ab_0 \n",
            "  pandas             pkgs/main/linux-64::pandas-2.2.1-py312h526ad5a_0 \n",
            "  pandocfilters      conda-forge/noarch::pandocfilters-1.5.0-pyhd8ed1ab_0 \n",
            "  parso              conda-forge/noarch::parso-0.8.4-pyhd8ed1ab_0 \n",
            "  partd              conda-forge/noarch::partd-1.4.2-pyhd8ed1ab_0 \n",
            "  pcre               conda-forge/linux-64::pcre-8.45-h9c3ff4c_0 \n",
            "  pexpect            conda-forge/noarch::pexpect-4.9.0-pyhd8ed1ab_0 \n",
            "  pickleshare        conda-forge/noarch::pickleshare-0.7.5-py_1003 \n",
            "  pillow             pkgs/main/linux-64::pillow-10.3.0-py312h5eee18b_0 \n",
            "  pkgutil-resolve-n~ conda-forge/noarch::pkgutil-resolve-name-1.3.10-pyhd8ed1ab_1 \n",
            "  plotly             conda-forge/noarch::plotly-5.22.0-pyhd8ed1ab_0 \n",
            "  ply                conda-forge/noarch::ply-3.11-pyhd8ed1ab_2 \n",
            "  prometheus_client  conda-forge/noarch::prometheus_client-0.20.0-pyhd8ed1ab_0 \n",
            "  prompt-toolkit     conda-forge/noarch::prompt-toolkit-3.0.42-pyha770c72_0 \n",
            "  psutil             conda-forge/linux-64::psutil-5.9.8-py312h98912ed_0 \n",
            "  pthread-stubs      conda-forge/linux-64::pthread-stubs-0.4-h36c2ea0_1001 \n",
            "  ptyprocess         conda-forge/noarch::ptyprocess-0.7.0-pyhd3deb0d_0 \n",
            "  pure_eval          conda-forge/noarch::pure_eval-0.2.2-pyhd8ed1ab_0 \n",
            "  pyarrow            pkgs/main/linux-64::pyarrow-14.0.2-py312hb107042_0 \n",
            "  pyarrow-hotfix     conda-forge/noarch::pyarrow-hotfix-0.6-pyhd8ed1ab_0 \n",
            "  pygments           conda-forge/noarch::pygments-2.18.0-pyhd8ed1ab_0 \n",
            "  pyparsing          conda-forge/noarch::pyparsing-3.0.9-pyhd8ed1ab_0 \n",
            "  pyqt               pkgs/main/linux-64::pyqt-5.15.10-py312h6a678d5_0 \n",
            "  pyqt5-sip          pkgs/main/linux-64::pyqt5-sip-12.13.0-py312h5eee18b_0 \n",
            "  pyqtgraph          conda-forge/noarch::pyqtgraph-0.13.7-pyhd8ed1ab_0 \n",
            "  pyqtwebengine      pkgs/main/linux-64::pyqtwebengine-5.15.10-py312h6a678d5_0 \n",
            "  python-dateutil    conda-forge/noarch::python-dateutil-2.9.0-pyhd8ed1ab_0 \n",
            "  python-fastjsonsc~ conda-forge/noarch::python-fastjsonschema-2.19.1-pyhd8ed1ab_0 \n",
            "  python-json-logger conda-forge/noarch::python-json-logger-2.0.7-pyhd8ed1ab_0 \n",
            "  python-kaleido     conda-forge/noarch::python-kaleido-0.2.1-pyhd8ed1ab_0 \n",
            "  python-tzdata      conda-forge/noarch::python-tzdata-2024.1-pyhd8ed1ab_0 \n",
            "  python_abi         conda-forge/linux-64::python_abi-3.12-4_cp312 \n",
            "  pytz               conda-forge/noarch::pytz-2024.1-pyhd8ed1ab_0 \n",
            "  pyyaml             conda-forge/linux-64::pyyaml-6.0.1-py312h98912ed_1 \n",
            "  pyzmq              pkgs/main/linux-64::pyzmq-25.1.2-py312h6a678d5_0 \n",
            "  qt-main            pkgs/main/linux-64::qt-main-5.15.2-h53bd1ea_10 \n",
            "  qt-webengine       pkgs/main/linux-64::qt-webengine-5.15.9-h9ab4d14_7 \n",
            "  re2                conda-forge/linux-64::re2-2022.04.01-h27087fc_0 \n",
            "  referencing        conda-forge/noarch::referencing-0.35.1-pyhd8ed1ab_0 \n",
            "  retrying           conda-forge/noarch::retrying-1.3.3-py_2 \n",
            "  rfc3339-validator  conda-forge/noarch::rfc3339-validator-0.1.4-pyhd8ed1ab_0 \n",
            "  rfc3986-validator  conda-forge/noarch::rfc3986-validator-0.1.1-pyh9f0ad1d_0 \n",
            "  rpds-py            conda-forge/linux-64::rpds-py-0.18.1-py312h4413252_0 \n",
            "  s2n                conda-forge/linux-64::s2n-1.3.27-h1b5e8cb_0 \n",
            "  scipy              pkgs/main/linux-64::scipy-1.12.0-py312h2809609_0 \n",
            "  send2trash         conda-forge/noarch::send2trash-1.8.3-pyh0d859eb_0 \n",
            "  sip                pkgs/main/linux-64::sip-6.7.12-py312h6a678d5_0 \n",
            "  six                conda-forge/noarch::six-1.16.0-pyh6c4a22f_0 \n",
            "  snappy             conda-forge/linux-64::snappy-1.1.9-hbd366e4_1 \n",
            "  sniffio            conda-forge/noarch::sniffio-1.3.1-pyhd8ed1ab_0 \n",
            "  sortedcontainers   conda-forge/noarch::sortedcontainers-2.4.0-pyhd8ed1ab_0 \n",
            "  soupsieve          conda-forge/noarch::soupsieve-2.5-pyhd8ed1ab_1 \n",
            "  stack_data         conda-forge/noarch::stack_data-0.6.2-pyhd8ed1ab_0 \n",
            "  tblib              conda-forge/noarch::tblib-3.0.0-pyhd8ed1ab_0 \n",
            "  tenacity           conda-forge/noarch::tenacity-8.3.0-pyhd8ed1ab_0 \n",
            "  terminado          conda-forge/noarch::terminado-0.18.1-pyh0d859eb_0 \n",
            "  tinycss2           conda-forge/noarch::tinycss2-1.3.0-pyhd8ed1ab_0 \n",
            "  tomli              conda-forge/noarch::tomli-2.0.1-pyhd8ed1ab_0 \n",
            "  toolz              conda-forge/noarch::toolz-0.12.1-pyhd8ed1ab_0 \n",
            "  tornado            conda-forge/linux-64::tornado-6.4-py312h98912ed_0 \n",
            "  traitlets          conda-forge/noarch::traitlets-5.14.3-pyhd8ed1ab_0 \n",
            "  types-python-date~ conda-forge/noarch::types-python-dateutil-2.9.0.20240316-pyhd8ed1ab_0 \n",
            "  typing-extensions  conda-forge/noarch::typing-extensions-4.11.0-hd8ed1ab_0 \n",
            "  typing_extensions  conda-forge/noarch::typing_extensions-4.11.0-pyha770c72_0 \n",
            "  typing_utils       conda-forge/noarch::typing_utils-0.1.0-pyhd8ed1ab_0 \n",
            "  uri-template       conda-forge/noarch::uri-template-1.3.0-pyhd8ed1ab_0 \n",
            "  utf8proc           pkgs/main/linux-64::utf8proc-2.6.1-h5eee18b_1 \n",
            "  wcwidth            conda-forge/noarch::wcwidth-0.2.13-pyhd8ed1ab_0 \n",
            "  webcolors          conda-forge/noarch::webcolors-1.13-pyhd8ed1ab_0 \n",
            "  webencodings       conda-forge/noarch::webencodings-0.5.1-pyhd8ed1ab_2 \n",
            "  websocket-client   conda-forge/noarch::websocket-client-1.8.0-pyhd8ed1ab_0 \n",
            "  werkzeug           conda-forge/noarch::werkzeug-3.0.3-pyhd8ed1ab_0 \n",
            "  xorg-libxau        conda-forge/linux-64::xorg-libxau-1.0.11-hd590300_0 \n",
            "  xorg-libxdmcp      conda-forge/linux-64::xorg-libxdmcp-1.1.3-h7f98852_0 \n",
            "  xyzservices        conda-forge/noarch::xyzservices-2024.4.0-pyhd8ed1ab_0 \n",
            "  yaml               conda-forge/linux-64::yaml-0.2.5-h7f98852_2 \n",
            "  zeromq             pkgs/main/linux-64::zeromq-4.3.5-h6a678d5_0 \n",
            "  zict               conda-forge/noarch::zict-3.0.0-pyhd8ed1ab_0 \n",
            "  zipp               conda-forge/noarch::zipp-3.17.0-pyhd8ed1ab_0 \n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  libgcc-ng          pkgs/main::libgcc-ng-11.2.0-h1234567_1 --> conda-forge::libgcc-ng-13.2.0-h77fa898_7 \n",
            "  libgomp              pkgs/main::libgomp-11.2.0-h1234567_1 --> conda-forge::libgomp-13.2.0-h77fa898_7 \n",
            "  libuuid              pkgs/main::libuuid-1.41.5-h5eee18b_0 --> conda-forge::libuuid-2.38.1-h0b41bf4_0 \n",
            "  openssl              pkgs/main::openssl-3.0.13-h7f8727e_1 --> conda-forge::openssl-3.3.0-hd590300_0 \n",
            "  tk                        pkgs/main::tk-8.6.12-h1ccaba5_0 --> conda-forge::tk-8.6.13-noxft_h4845f30_101 \n",
            "  zlib                    pkgs/main::zlib-1.2.13-h5eee18b_0 --> conda-forge::zlib-1.2.13-hd590300_5 \n",
            "\n",
            "The following packages will be SUPERSEDED by a higher-priority channel:\n",
            "\n",
            "  _libgcc_mutex           pkgs/main::_libgcc_mutex-0.1-main --> conda-forge::_libgcc_mutex-0.1-conda_forge \n",
            "  _openmp_mutex          pkgs/main::_openmp_mutex-5.1-1_gnu --> conda-forge::_openmp_mutex-4.5-2_gnu \n",
            "  certifi            pkgs/main/linux-64::certifi-2024.2.2-~ --> conda-forge/noarch::certifi-2024.2.2-pyhd8ed1ab_0 \n",
            "  conda              pkgs/main::conda-24.5.0-py312h06a4308~ --> conda-forge::conda-24.5.0-py312h7900ff3_0 \n",
            "  python                pkgs/main::python-3.12.2-h996f2a0_0 --> conda-forge::python-3.12.2-hab00c5b_0_cpython \n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages:\n",
            "mysql-5.7.20         | 79.2 MB   | :   0% 0/1 [00:00<?, ?it/s]\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\n",
            "\n",
            "qt-webengine-5.15.9  | 53.8 MB   | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "scipy-1.12.0         | 23.3 MB   | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mathjax-2.7.7        | 21.2 MB   | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pandas-2.2.1         | 15.4 MB   | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "arrow-cpp-14.0.2     | 11.7 MB   | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libclang13-14.0.6    | 9.8 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "numpy-base-1.26.4    | 7.7 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "matplotlib-base-3.8. | 7.6 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "jupyterlab-4.1.8     | 7.3 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
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            "\n",
            "babel-2.14.0         | 7.3 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "openblas-0.3.27      | 5.5 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
            "libopenblas-0.3.27   | 5.3 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "plotly-5.22.0        | 5.1 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\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",
            "qt-webengine-5.15.9  | 53.8 MB   | :   0% 0.00029068176868217617/1 [00:00<12:28, 748.32s/it]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :   0% 0.00046843413253998403/1 [00:00<08:02, 483.09s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :   0% 0.00029119184998373156/1 [00:00<16:23, 984.27s/it]\u001b[A\u001b[A\u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :   0% 0.000263832842570537/1 [00:00<19:17, 1157.55s/it]\u001b[A\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :   0% 0.00019718423464384747/1 [00:00<27:53, 1673.53s/it]\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :  11% 0.11476636247229609/1 [00:00<00:01,  2.26s/it]    \u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :   3% 0.03377825459811286/1 [00:00<00:08,  8.97s/it]    \u001b[A\u001b[A\u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :   3% 0.029021612682759074/1 [00:00<00:10, 10.85s/it]  \u001b[A\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :   2% 0.022873371218686305/1 [00:00<00:14, 14.53s/it]    \n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :  18% 0.18222087755805377/1 [00:00<00:01,  1.97s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :   7% 0.06726531734624198/1 [00:00<00:05,  5.49s/it]\u001b[A\u001b[A\u001b[A\n",
            "mysql-5.7.20         | 79.2 MB   | :   5% 0.05383129605777036/1 [00:00<00:06,  7.10s/it] \n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :  29% 0.28668168911447023/1 [00:00<00:01,  1.48s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "qt-webengine-5.15.9  | 53.8 MB   | :  14% 0.13807384012403368/1 [00:00<00:02,  3.37s/it]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  11% 0.11123528669378545/1 [00:00<00:03,  3.84s/it]\u001b[A\u001b[A\u001b[A\n",
            "mysql-5.7.20         | 79.2 MB   | :  10% 0.09839493308727988/1 [00:00<00:03,  4.31s/it]\n",
            "\n",
            "qt-webengine-5.15.9  | 53.8 MB   | :  19% 0.18981519494946103/1 [00:00<00:02,  2.74s/it]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :  39% 0.39114250067088663/1 [00:00<00:00,  1.26s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  18% 0.17646226109014132/1 [00:00<00:02,  2.66s/it]\u001b[A\u001b[A\u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  18% 0.17835100157768305/1 [00:00<00:02,  2.84s/it]\u001b[A\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  13% 0.1287613052224324/1 [00:00<00:03,  4.25s/it] \n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :  48% 0.47920811758840365/1 [00:00<00:00,  1.29s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  22% 0.2215969978376197/1 [00:00<00:01,  2.51s/it] \u001b[A\u001b[A\u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  24% 0.2443092122203173/1 [00:00<00:01,  2.26s/it] \u001b[A\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  18% 0.17845173235268197/1 [00:00<00:02,  3.23s/it]\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :  56% 0.5625893931805208/1 [00:00<00:00,  1.35s/it] \u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  28% 0.2789617922844148/1 [00:00<00:01,  2.22s/it]\u001b[A\u001b[A\u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  29% 0.29390978662357825/1 [00:00<00:01,  2.20s/it]\u001b[A\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  22% 0.21552236846572528/1 [00:00<00:02,  3.06s/it]\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :  67% 0.6689239412670972/1 [00:00<00:00,  1.23s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  33% 0.3287555986316329/1 [00:01<00:01,  2.15s/it]\u001b[A\u001b[A\u001b[A\n",
            "mysql-5.7.20         | 79.2 MB   | :  27% 0.26935366452349563/1 [00:01<00:01,  2.57s/it]\n",
            "\n",
            "qt-webengine-5.15.9  | 53.8 MB   | :  42% 0.4150935656781476/1 [00:01<00:01,  1.95s/it] \u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :  75% 0.7537105192568343/1 [00:01<00:00,  1.25s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  38% 0.38175251532867205/1 [00:01<00:01,  2.17s/it]\u001b[A\u001b[A\u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  39% 0.3933747682726707/1 [00:01<00:01,  2.28s/it] \u001b[A\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  31% 0.3111567222679913/1 [00:01<00:01,  2.58s/it] \n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :  84% 0.8380286631140315/1 [00:01<00:00,  1.24s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  44% 0.4405732690253858/1 [00:01<00:01,  2.04s/it] \u001b[A\u001b[A\u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  44% 0.4392816828799442/1 [00:01<00:01,  2.25s/it]\u001b[A\n",
            "\n",
            "qt-webengine-5.15.9  | 53.8 MB   | :  52% 0.5206110477097775/1 [00:01<00:01,  2.10s/it] \u001b[A\u001b[A\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  35% 0.35197385883926774/1 [00:01<00:02,  3.13s/it]\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :  92% 0.9200046363085286/1 [00:01<00:00,  1.36s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  48% 0.48492476464464707/1 [00:01<00:01,  2.44s/it]\u001b[A\n",
            "\n",
            "qt-webengine-5.15.9  | 53.8 MB   | :  57% 0.5723524025352049/1 [00:01<00:00,  2.05s/it]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  39% 0.3910163372987495/1 [00:01<00:01,  3.08s/it] \n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  53% 0.5279295179836446/1 [00:01<00:01,  2.44s/it] \u001b[A\n",
            "\n",
            "qt-webengine-5.15.9  | 53.8 MB   | :  62% 0.622058984979857/1 [00:01<00:00,  2.08s/it] \u001b[A\u001b[A\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  43% 0.42572076259606667/1 [00:01<00:01,  3.39s/it]\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  57% 0.56987893995236/1 [00:01<00:01,  2.57s/it]  \u001b[A\n",
            "\n",
            "qt-webengine-5.15.9  | 53.8 MB   | :  67% 0.6708935221184626/1 [00:01<00:00,  2.21s/it]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  46% 0.4649604252901923/1 [00:01<00:01,  3.16s/it] \n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  62% 0.6171050187724861/1 [00:01<00:00,  2.49s/it]\u001b[A\n",
            "\n",
            "qt-webengine-5.15.9  | 53.8 MB   | :  73% 0.7304832846983087/1 [00:01<00:00,  2.08s/it]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  51% 0.5103127992582772/1 [00:01<00:01,  2.84s/it]\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  68% 0.6827993965725498/1 [00:01<00:00,  2.16s/it]\u001b[A\n",
            "\n",
            "qt-webengine-5.15.9  | 53.8 MB   | :  79% 0.7921078196589301/1 [00:01<00:00,  1.94s/it]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  55% 0.5479749880752521/1 [00:01<00:01,  2.86s/it]\n",
            "kaleido-core-0.2.1   | 59.2 MB   | :  75% 0.754561929751736/1 [00:01<00:00,  1.90s/it] \u001b[A\n",
            "\n",
            "qt-webengine-5.15.9  | 53.8 MB   | :  86% 0.8615807623739702/1 [00:01<00:00,  1.86s/it]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  83% 0.8348470339033583/1 [00:01<00:00,  1.74s/it]\u001b[A\u001b[A\u001b[A\n",
            "mysql-5.7.20         | 79.2 MB   | :  60% 0.5982569679094332/1 [00:02<00:01,  2.67s/it]\n",
            "\n",
            "qt-webengine-5.15.9  | 53.8 MB   | :  92% 0.9211705249538163/1 [00:02<00:00,  1.84s/it]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  90% 0.9041506941994865/1 [00:02<00:00,  1.70s/it]\u001b[A\u001b[A\u001b[A\n",
            "mysql-5.7.20         | 79.2 MB   | :  64% 0.6443980788160936/1 [00:02<00:00,  2.55s/it]\n",
            "\n",
            "qt-webengine-5.15.9  | 53.8 MB   | :  98% 0.983376423451802/1 [00:02<00:00,  1.79s/it] \u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  98% 0.9778222322453705/1 [00:02<00:00,  1.62s/it]\u001b[A\u001b[A\u001b[A\n",
            "mysql-5.7.20         | 79.2 MB   | :  97% 0.9701464344477295/1 [00:03<00:00,  3.01s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :   0% 0.0005070463002532972/1 [00:03<1:40:37, 6040.67s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :   8% 0.08366263954179404/1 [00:03<00:24, 26.95s/it]      \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  15% 0.15414207527700236/1 [00:03<00:11, 13.10s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  25% 0.25048087232512883/1 [00:03<00:05,  6.90s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  33% 0.32755190996363004/1 [00:03<00:03,  4.85s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  44% 0.4360598182178356/1 [00:03<00:01,  3.20s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  53% 0.5334127078664687/1 [00:03<00:01,  2.45s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  62% 0.6180894400087693/1 [00:03<00:00,  2.11s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  71% 0.7088507277541095/1 [00:03<00:00,  1.80s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  81% 0.813809311906542/1 [00:04<00:00,  1.55s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | :  90% 0.9020353681506158/1 [00:04<00:00,  1.47s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "scipy-1.12.0         | 23.3 MB   | :   0% 0.0006691887316034876/1 [00:04<1:45:37, 6341.58s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "scipy-1.12.0         | 23.3 MB   | :  11% 0.10640100832495451/1 [00:04<00:25, 29.05s/it]      \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "scipy-1.12.0         | 23.3 MB   | :  23% 0.22819335747678926/1 [00:04<00:08, 11.52s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "scipy-1.12.0         | 23.3 MB   | :  32% 0.3232181573644845/1 [00:04<00:04,  7.30s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "scipy-1.12.0         | 23.3 MB   | :  45% 0.4470180727111297/1 [00:04<00:02,  4.50s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "mathjax-2.7.7        | 21.2 MB   | :   0% 0.0007353142806569484/1 [00:04<1:48:13, 6498.72s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "scipy-1.12.0         | 23.3 MB   | :  59% 0.5922320274690864/1 [00:04<00:01,  2.94s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "mathjax-2.7.7        | 21.2 MB   | :  11% 0.11323839922117004/1 [00:04<00:27, 30.60s/it]      \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "scipy-1.12.0         | 23.3 MB   | :  70% 0.699971413257248/1 [00:04<00:00,  2.40s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mathjax-2.7.7        | 21.2 MB   | :  24% 0.24118308405547906/1 [00:04<00:09, 12.20s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "scipy-1.12.0         | 23.3 MB   | :  80% 0.7996805342661676/1 [00:05<00:00,  2.09s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "mathjax-2.7.7        | 21.2 MB   | :  36% 0.3632452546445325/1 [00:05<00:04,  7.00s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "scipy-1.12.0         | 23.3 MB   | :  89% 0.8947053341538629/1 [00:05<00:00,  1.80s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "mathjax-2.7.7        | 21.2 MB   | :  46% 0.4647186253751914/1 [00:05<00:02,  4.93s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :   0% 0.0008009386782302188/1 [00:05<1:49:05, 6551.18s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "scipy-1.12.0         | 23.3 MB   | :  99% 0.9870533791151441/1 [00:05<00:00,  1.70s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "mathjax-2.7.7        | 21.2 MB   | :  56% 0.5617801104219086/1 [00:05<00:01,  3.73s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :   3% 0.02723191505982744/1 [00:05<02:16, 139.95s/it]     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
            "mathjax-2.7.7        | 21.2 MB   | :  65% 0.6529590812233702/1 [00:05<00:01,  2.96s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "libboost-1.82.0      | 19.5 MB   | :  10% 0.0977145187440867/1 [00:05<00:27, 30.70s/it]  \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "mathjax-2.7.7        | 21.2 MB   | :  79% 0.7875215945835917/1 [00:05<00:00,  2.10s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :  20% 0.20183654691401515/1 [00:05<00:09, 12.16s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "pandas-2.2.1         | 15.4 MB   | :   0% 0.0010174177522631242/1 [00:05<1:31:29, 5495.25s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "mathjax-2.7.7        | 21.2 MB   | :  96% 0.9603204505379745/1 [00:05<00:00,  1.48s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :  31% 0.314768900544476/1 [00:05<00:04,  6.66s/it]  \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
            "pandas-2.2.1         | 15.4 MB   | :  19% 0.19432679068225672/1 [00:05<00:16, 20.69s/it]      \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :  48% 0.4765585135469802/1 [00:05<00:01,  3.64s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
            "pandas-2.2.1         | 15.4 MB   | :  43% 0.4283328737027753/1 [00:05<00:04,  7.87s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :  59% 0.5926946218903619/1 [00:05<00:01,  2.69s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
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            "\n",
            "pandas-2.2.1         | 15.4 MB   | :  60% 0.5972242205784539/1 [00:05<00:02,  5.05s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "libboost-1.82.0      | 19.5 MB   | :  71% 0.7104326075902041/1 [00:05<00:00,  2.12s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
            "pandas-2.2.1         | 15.4 MB   | :  75% 0.7539065544269751/1 [00:06<00:00,  3.54s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
            "libboost-1.82.0      | 19.5 MB   | :  84% 0.837781857428809/1 [00:06<00:00,  1.69s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "libclang13-14.0.6    | 9.8 MB    | :   0% 0.0016025206836573897/1 [00:06<1:03:35, 3822.02s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "arrow-cpp-14.0.2     | 11.7 MB   | :   0% 0.001340927777322181/1 [00:06<1:16:41, 4607.70s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "libclang13-14.0.6    | 9.8 MB    | :  35% 0.34934950903731093/1 [00:06<00:08, 12.58s/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",
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            "\n",
            "\n",
            "\n",
            "arrow-cpp-14.0.2     | 11.7 MB   | :  20% 0.19845731104368278/1 [00:06<00:17, 22.34s/it]     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "numpy-base-1.26.4    | 7.7 MB    | :   0% 0.0020306342695560406/1 [00:06<51:31, 3098.04s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "libclang13-14.0.6    | 9.8 MB    | :  60% 0.6009452563715211/1 [00:06<00:02,  6.40s/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",
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            "\n",
            "\n",
            "arrow-cpp-14.0.2     | 11.7 MB   | :  35% 0.3499821498810892/1 [00:06<00:07, 11.02s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "numpy-base-1.26.4    | 7.7 MB    | :  39% 0.3878511454852037/1 [00:06<00:07, 11.63s/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\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "arrow-cpp-14.0.2     | 11.7 MB   | :  56% 0.5645305942526382/1 [00:06<00:02,  5.60s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "numpy-base-1.26.4    | 7.7 MB    | :  75% 0.751334679735735/1 [00:06<00:01,  5.13s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "matplotlib-base-3.8. | 7.6 MB    | :   0% 0.002050079549442966/1 [00:06<53:09, 3196.28s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "arrow-cpp-14.0.2     | 11.7 MB   | :  82% 0.8219887274984969/1 [00:06<00:00,  3.18s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "matplotlib-base-3.8. | 7.6 MB    | :  36% 0.36491415980084796/1 [00:06<00:08, 12.88s/it]   \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "jupyterlab-4.1.8     | 7.3 MB    | :   0% 0.0021490437384966347/1 [00:06<51:42, 3109.20s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "matplotlib-base-3.8. | 7.6 MB    | :  76% 0.7564793537444545/1 [00:06<00:01,  5.24s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "jupyterlab-4.1.8     | 7.3 MB    | :  40% 0.3954240478833808/1 [00:06<00:07, 12.11s/it]     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "openblas-0.3.27      | 5.5 MB    | :   0% 0.0028590374480857153/1 [00:06<39:26, 2373.52s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "jupyterlab-4.1.8     | 7.3 MB    | :  84% 0.8424251454906808/1 [00:06<00:00,  4.78s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "openblas-0.3.27      | 5.5 MB    | :  58% 0.5775255645133145/1 [00:06<00:03,  8.43s/it]     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "babel-2.14.0         | 7.3 MB    | :   0% 0.0021530273662078254/1 [00:06<53:49, 3236.37s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "libopenblas-0.3.27   | 5.3 MB    | :   0% 0.0029263690607916376/1 [00:07<39:59, 2406.11s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "babel-2.14.0         | 7.3 MB    | :  33% 0.32941318702979727/1 [00:07<00:10, 15.16s/it]    \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "plotly-5.22.0        | 5.1 MB    | :   0% 0.0030781225781906824/1 [00:07<38:22, 2309.64s/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\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libopenblas-0.3.27   | 5.3 MB    | :  50% 0.5004091093953701/1 [00:07<00:05, 10.07s/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",
            "babel-2.14.0         | 7.3 MB    | :  66% 0.6631324287920103/1 [00:07<00:02,  6.37s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "plotly-5.22.0        | 5.1 MB    | :  53% 0.5325152060269881/1 [00:07<00:04,  9.55s/it]     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
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            "\n",
            " ... (more hidden) ...\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
            "\n",
            "babel-2.14.0         | 7.3 MB    | :  96% 0.9645562600611058/1 [00:07<00:00,  3.76s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "plotly-5.22.0        | 5.1 MB    | :  98% 0.9819211024428277/1 [00:07<00:00,  4.45s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " ... (more hidden) ...\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " ... (more hidden) ...\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | : 100% 1.0/1 [00:10<00:00, 33.56s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | : 100% 1.0/1 [00:10<00:00, 33.56s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "qt-webengine-5.15.9  | 53.8 MB   | : 100% 1.0/1 [00:12<00:00,  1.79s/it]              \u001b[A\u001b[A\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | : 100% 1.0/1 [00:27<00:00,  1.62s/it]               \u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | : 100% 1.0/1 [00:29<00:00, 82.97s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.12.2        | 30.8 MB   | : 100% 1.0/1 [00:29<00:00, 82.97s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | : 100% 1.0/1 [00:44<00:00, 210.54s/it]              \u001b[A\n",
            "kaleido-core-0.2.1   | 59.2 MB   | : 100% 1.0/1 [00:44<00:00, 210.54s/it]\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | : 100% 1.0/1 [01:01<00:00,  3.01s/it]               \n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "pandas-2.2.1         | 15.4 MB   | : 100% 1.0/1 [01:04<00:00, 106.12s/it]              \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "pandas-2.2.1         | 15.4 MB   | : 100% 1.0/1 [01:04<00:00, 106.12s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "libboost-1.82.0      | 19.5 MB   | : 100% 1.0/1 [01:18<00:00, 170.69s/it]             \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "libboost-1.82.0      | 19.5 MB   | : 100% 1.0/1 [01:18<00:00, 170.69s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "libclang13-14.0.6    | 9.8 MB    | : 100% 1.0/1 [01:18<00:00, 100.33s/it]              \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "libclang13-14.0.6    | 9.8 MB    | : 100% 1.0/1 [01:18<00:00, 100.33s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "numpy-base-1.26.4    | 7.7 MB    | : 100% 1.0/1 [01:19<00:00,  5.13s/it]              \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "matplotlib-base-3.8. | 7.6 MB    | : 100% 1.0/1 [01:20<00:00,  5.24s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "mathjax-2.7.7        | 21.2 MB   | : 100% 1.0/1 [01:20<00:00,  1.48s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "openblas-0.3.27      | 5.5 MB    | : 100% 1.0/1 [01:21<00:00, 94.51s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "openblas-0.3.27      | 5.5 MB    | : 100% 1.0/1 [01:21<00:00, 94.51s/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",
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            "\n",
            "\n",
            "arrow-cpp-14.0.2     | 11.7 MB   | : 100% 1.0/1 [01:21<00:00,  3.18s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "libopenblas-0.3.27   | 5.3 MB    | : 100% 1.0/1 [01:23<00:00, 93.80s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "libopenblas-0.3.27   | 5.3 MB    | : 100% 1.0/1 [01:23<00:00, 93.80s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "jupyterlab-4.1.8     | 7.3 MB    | : 100% 1.0/1 [01:24<00:00,  4.78s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "babel-2.14.0         | 7.3 MB    | : 100% 1.0/1 [01:25<00:00,  3.76s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " ... (more hidden) ...\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "plotly-5.22.0        | 5.1 MB    | : 100% 1.0/1 [01:34<00:00,  4.45s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \n",
            "                                                                         \u001b[A\n",
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            "\n",
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            "                                                                         \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                         \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                         \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\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",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\u001b[A\n",
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            "\n",
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            "\n",
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            "\u001b[A\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\u001b[A\n",
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            "\n",
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            "\n",
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            "\n",
            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
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            "\u001b[A\n",
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            "\n",
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            "\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",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
            "\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",
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            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
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            "\n",
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            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\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",
            "Preparing transaction: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
            "Verifying transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
            "Executing transaction: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n"
          ]
        }
      ],
      "source": [
        "!conda install -c conda-forge fmpy --yes # Install the key package"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#!conda install matplotlib --yes"
      ],
      "metadata": {
        "id": "zGfSEtcfMVF_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#!conda install scipy --yes"
      ],
      "metadata": {
        "id": "P331PfESMWM-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#!conda install xlrd --yes"
      ],
      "metadata": {
        "id": "QObtV8VAMuYE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#!conda install openpyxl --yes"
      ],
      "metadata": {
        "id": "z4p1aZ0BM3Pb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UTETJm3l6heL"
      },
      "source": [
        "# BPL_TEST2_Batch setup\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mxILgQ103nZ6"
      },
      "source": [
        "Now specific installation and the run simulations. Start with connecting to Github. Then upload the two files:\n",
        "\n",
        "* FMU - BPL_TEST2_Batch_linux_om_me.fmu\n",
        "* Setup-file - BPL_TEST2_Batch_fmpy_explore.py"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RfWPh1B63yBF",
        "outputId": "74550da4-638c-4a7c-d607-f38896b2bf02"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Cloning into 'BPL_TEST2_Batch'...\n"
          ]
        }
      ],
      "source": [
        "%%bash\n",
        "git clone https://github.com/janpeter19/BPL_TEST2_Batch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i6uOVB44300a",
        "outputId": "ffd31303-d358-4253-c43e-0c11266545ba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/BPL_TEST2_Batch\n"
          ]
        }
      ],
      "source": [
        "%cd BPL_TEST2_Batch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b-y82jZY33cO",
        "outputId": "68fcd047-0791-40b5-fb66-3b1c050cc975"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Linux - run FMU pre-compiled OpenModelica 1.21.0\n",
            "\n",
            "Model for bioreactor has been setup. Key commands:\n",
            " - par()       - change of parameters and initial values\n",
            " - init()      - change initial values only\n",
            " - simu()      - simulate and plot\n",
            " - newplot()   - make a new plot\n",
            " - show()      - show plot from previous simulation\n",
            " - disp()      - display parameters and initial values from the last simulation\n",
            " - describe()  - describe culture, broth, parameters, variables with values/units\n",
            "\n",
            "Note that both disp() and describe() takes values from the last simulation\n",
            "and the command process_diagram() brings up the main configuration\n",
            "\n",
            "Brief information about a command by help(), eg help(simu)\n",
            "Key system information is listed with the command system_info()\n"
          ]
        }
      ],
      "source": [
        "run -i BPL_TEST2_Batch_fmpy_explore.py"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HUFhuE-H4RCw"
      },
      "outputs": [],
      "source": [
        "%matplotlib inline\n",
        "plt.rcParams['figure.figsize'] = [25/2.54, 20/2.54]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cMO4OlAgJIlb"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dx9oMnP0RJkM"
      },
      "source": [
        "## BPL_TEST2_Batch - demo\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Y3NHWIj4TZ6",
        "outputId": "229420b2-4634-46e0-bdb3-71fb15bc8e65"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Simplified text book model - only substrate S and cell concentration X\n",
            "\n"
          ]
        }
      ],
      "source": [
        "describe('culture'); print(); #describe('liquidphase')                                  # Pump schedule parameter\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "UeMOu19LJT6b",
        "outputId": "cfe36b95-be05-478c-8d45-73022f9b44d5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 984.252x787.402 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Simulation with default values of the process\n",
        "newplot(plotType='TimeSeries')\n",
        "simu()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Simulation were initial value of biomass VX_start is varied\n",
        "newplot(plotType='TimeSeries')\n",
        "for value in [1.0, 0.7, 1.3]: init(VX_start=value); simu(5)\n",
        "\n",
        "# Restore default value of VX_start\n",
        "init(VX_start=1.0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "nJpSSL2HO0Gj",
        "outputId": "ca020981-3929-4f0f-994f-db0c71b4c361"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 984.252x787.402 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "mtWJ3rfbJaVN",
        "outputId": "e804bb8a-d3ec-4696-b680-5e8b563ced34"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 984.252x787.402 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Simulation were initial value of substrate VS_start is varied\n",
        "newplot(plotType='TimeSeries')\n",
        "for value in [10, 7, 13]: init(VS_start=value); simu(5)\n",
        "\n",
        "# Restore default value of VS_start\n",
        "init(VS_start=10)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Simulation where metabolism is changed after 3 hours\n",
        "newplot(plotType='TimeSeries')\n",
        "simu(5)\n",
        "\n",
        "simu(3)\n",
        "par(Y=0.4, qSmax=1.0/(0.4/0.5)); simu(2, 'cont')\n",
        "\n",
        "# Restore default value of Y and qSmax\n",
        "par(Y=0.5, qSmax=1.0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "GIxPx4GzO-5F",
        "outputId": "c8bebeb5-ae83-4452-d460-106391fc1506"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 984.252x787.402 with 2 Axes>"
            ],
            "image/png": 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na9euBAYGejsc8WHqK+IJ9RPxlPqKeMLX+ol79pcn8k0iVL58eQAOHz5MhQoVUtoPHz5MkyZNLnhecHAwwcHB6doDAwN94h+rTp1oevZ0EBiYWrfi0CH48Uc4fBj274f16+HPPyEhwUFsLGzd6mDrVvj2Wz+GDoXAQGjcGHr1gqZN7fK/t0sKEF/ps+L71FfEE+on4in1FfGEr/STzMSQbxKhGjVqUL58eRYsWJCS+MTExLBy5UoGDx7s3eByWIUKMGhQ2raEBFiwAH7+GVatgu3b4dgxcDohMRFWr7aLm58flCkD9epBu3Zw001wkXxRRERERKRQ8alE6PTp0+zYsSPl9q5du1i3bh2lSpWiatWqDBs2jBdffJHatWtTo0YNnnvuOSpWrEjv3r29F3QeCQqCHj3scq4tW+CHHyAuDnbsgDVrrM3ptBGlw4dh0SJ44QVLjiIi4Prr4YYbbOSoUiVwOLzykkREREREvManEqHVq1fTqVOnlNvutT39+/dn0qRJPPHEE8TGxjJo0CBOnjxJ27ZtmTt3LiEhId4K2evq1rXLuQ4dgu++g19+gb/+ggMHID7ekqOoKPj4Y7sAhIdDcDDUqQNXX23T61q1sqRJRERERKSg8qlEqGPHjrhcrgve73A4GDNmDGPGjMnDqPKfChVgyBC7uEVFwfffw/z5EBJiCdKmTeBeT3bkCCxdCq++aiNEERHQoAE88AB06WK3RUREREQKCp9KhCT3lC1rSc0DD6S2xcXZyNEPP8C6dbBvH5w9Cy6XJUaLFtkFoFYtK8BQpgz8619w661QqpQXXoiIiIiISA5QIlSIFSkCd91lF7eTJ2HaNJg7FzZutKRo2zbYudMuADNmwP/9H4SGQo0aNpXuhhvguusgQD1KRERERPIBfWyVNEqUgPvus4vbiRNWkW78eFi71qbZOZ1w5oxNr9u0CT79FIoWhRYtoGVLe5zOne26iIiIiIivUSIkl1SyJHTtahewJOiPP2zk6LffYOtWOH0aYmPTTqcD8Pe3NUvNm1u1uttug2LFvPEqRERERERSKRGSTPPzg6uusotbcjJs3mwJ0rJlMHmyjRglJ9umsPv325S6gQNtvVL//tC6tV20+auIiIiI5DUlQpIj/P2hYUO73Hsv/Pe/NoVu8mSYM8eKMRw5YmuOoqLg9ddTzw0MtP2MWrWy8t033miV7UREREREcosSIck1ZcvCsGF2AZtSN3++le7esQOWL4cNGyAxEXbvtsvUqXZsiRJWvvvWW+Gee6B4cW+8AhEREREpqJQISZ7x84Pu3e3idvAgfPYZzJtnSdGxY9Z+8iT8/rtdHn0UGjWyQgxhYZYYNWrkjVcgIiIiIgWFEiHxqooVYcQIu4CNDs2aBdOn24hRfLytL1q/3i4Ab71l0+mqV4e2baFvX+jUyRItERERERFPKBESnxIYCL1728Xt0CGrTvfJJ7B4sW36mpgI27fb5dNPLQnq0MHWF7VrB1dcYeuWREREREQyokRIfF6FClZ2+7bb7PaePTBpkm36+vffcOqUrT9auNAuYMUWihWDZs3g5ptt1KhIEa+9BBERERHxMZpMJPlOtWowcqRNnYuJsXVFEybACy9At26WAJ09C0ePws8/w6BBttlrmTLQs6clUQkJ3n4VIiIiIuJNGhGSfK9UKXjwwdTbSUm22evUqbByJURGWtnuo0etlPecOfDww9C+va0tql8fOneGoCDvvQYRERERyVtKhKTACQiAO+6wC1hi9N138PXXsGKFjSCdOmVFGWbNSj2vfHnby+jOO206XYD+d4iIiIgUWJoaJwVeQICtL/r+eyu8cOYMrF4Nb7xhU+ncIiNh5kxLoIKCrKJd//62Dsnl8l78IiIiIpLzlAhJoePvb0UUHnvM1hCdPWvrhnr0sHVEYInPoUPw+efQsCFUqgR33QWDB8Off3o1fBERERHJAUqEpNALDraRn9mzISoK4uLgo49stKhpU6tAd+gQfPUVTJyY2nbllTBqlG0KKyIiIiL5ixIhkfOEhsJ999lo0Zo1cOIELFhga4fcJbjj42HdOhg92kaLiheHAQOskl1SklfDFxEREREPKBESuYSQELjmGpg8GWJjYetWGD4c6tZNLagQE2PT69q0gdKlrSLdrbfaXkdOp1fDFxEREZEMKBESyaTLL4c334TNmyEx0TZx7dsXbroJSpa0pOi336yEd48eNvWuYUN49lmbYiciIiIi3qdESCSbOnaEL7+E6dPhyBFYtcrKb5csafcnJVnluZdeskp0JUva5q8bNqganYiIiIi3KBESyUH+/tC8uY0GHT9uJbmff95GhNzT6E6etLZGjaBKFbj6aqtgt2ePV0MXERERKVSUCInkonLlrKDChg2QkADz58NDD0HPnlaU4cABWLYMxo2D6tWt6ELXrjbCpKILIiIiIrlHiZBIHnE4oEsXeOcdmDULjh2D776zMtxBQXZMTAz88gvcfbetLWrWDL77zsGZMwHeDV5ERESkgFEiJOIloaFw442wdq2V4/7tN+jTBypUsPudTrvvjjsCuPvuHnTr5s8tt8CSJd6NW0RERKQg0NfMIj6ibVu7AJw+bZu3bt4MS5a42LHDj0WL7L7p0220qEkTGzm67z67LSIiIiKe04iQiA8qVgwefxw+/hg2bUpiwoQF3HtvMsWL2/3x8bByJQwdaiNLNWrA+PEQHe3VsEVERETyDSVCIvlApUqnmTjRycmT8M8/lgBVr27rjlwu2L0bHn0UIiJs89cnnrD9jUREREQkY0qERPKZGjXg3Xdh1y6Ii7PrHTpA3bpWaW7hQnj9dUuIQkOhfXuYPNnWHImIiIiIUSIkko+FhNjo0KJFtp5o+3YrxV26tN1/9qwVYejb1yrTXXmlTaFTaW4REREp7JQIiRQgl11mU+SOHrWkaNAgqFjR7ktOhnXr7P5y5eCee2DmTNvgVURERKSwUSIkUkBddhl8+KFt2nr0KDz9tLUVLQrHj8Nnn0Hv3lCypCVLgwbBzp3ejlpEREQkbygREikESpeGl16yUaKTJ20q3SOPpE6hO3QIPvrIEqXSpeGuu2DTJm9GLCIiIpK7lAiJFDIBAVZcYfx4iIqCKVPsdkiI3X/8OHz1FTRoAI0awQcfQGSkV0MWERERyXFKhEQKMT8/uP12GyE6cwbmz4fu3aFIEbt/wwZ48EGbOtewIdx2m60zEhEREcnvlAiJSIouXWDuXIiNhRUr4LXXoGVL26vo77/h22+t8lzx4nDTTbBqlbcjFhEREckaJUIikqFWreDf/4aVK23Pol69oFgxuy8mBr7/3pKk8HC45RYryiAiIiKSXygREpFLql4dZsyAU6csMerd2xIgsLbp06FKFejYESZOtMRJRERExJcpERKRTGnZ0kaDoqNh9Wq4+WYrrOByweLFMHgw1KwJZcvC//0fHDzo7YhFRERE0st3iVBycjLPPfccNWrUIDQ0lFq1avHCCy/gcrm8HZpIodOsGUybBhs3wp498PrrULWq3XfkiO1jVKkSVK5s0+yOH/duvCIiIiJu+S4RevXVV/nggw9477332Lx5M6+++iqvvfYa7777rrdDEynUqlaFxx+3hGj2bJsmFxRk9x04AG+8YXsU9ehhBRkSE70aroiIiBRy+S4RWrZsGb169eLaa6+levXq3HLLLXTr1o0//vjD26GJyP/06AELF1pJ7q+/tul0/v5239y5dn/FinD99bZPkdPp3XhFRESk8AnwdgCZ1aZNG/7zn/+wbds2Lr/8ctavX8/SpUsZN25chsfHx8cTHx+fcjsmJgaAxMREEr38lbT7+b0dh/i2/N5Pbr7ZLklJMH26g2XLHEyf7kdUlIOffoKffoKHH3bRurWLp59OpnNnb0ecf+X3viJ5Q/1EPKW+Ip7wtX6SmTgcrny2uMbpdPL000/z2muv4e/vT3JyMi+99BIjRozI8PhRo0YxevTodO2TJ0+miHvXSBHJU8nJDtauLcPHH19BZGRRwJFyX0hIIs2bH6ZPn81UrBjnvSBFREQk34mLi6NPnz5ER0cT7i5xewH5LhGaMmUK//73v3n99ddp0KAB69atY9iwYYwbN47+/funOz6jEaEqVapw9OjRS745uS0xMZH58+fTtWtXAgMDvRqL+K6C3k+iouCFF/yYNs2PY8fAnRQ5HC5uuMFF375OevRwERzs1TDzhYLeVyRnqJ+Ip9RXxBO+1k9iYmKIiIjwKBHKd1Pj/v3vf/PUU09xxx13AHDFFVewZ88exo4dm2EiFBwcTHAGn6ACAwN94h8LfCsW8V0FtZ9UqmR7D02cCOvWwciRMH8+nDnjYOZMBzNn+hEebsc9/DAMGgR++W51Y94qqH1Fcpb6iXhKfUU84Sv9JDMx5LuPE3Fxcfid9ynI398fp1Zbi+R7TZrAzJkQFwfr11vJ7YoVISYGNm+2PYqCg+Gaa2DZMm9HKyIiIvlZvkuErr/+el566SVmzZrF7t27+f777xk3bhw33nijt0MTkRzUqBG89hrs3QtvvQW1all7UpJVpLv6aivH/dBDtrmriIiISGbku0To3Xff5ZZbbuHBBx+kXr16PP744zzwwAO88MIL3g5NRHKBvz8MGwY7dsDhw/DAA1CypN13/Di89x5UqwZDhsCaNZC/Vj2KiIiIt+S7RCgsLIzx48ezZ88ezpw5w86dO3nxxRcJcu/cKCIFVtmytpbo+HFYvBjat4fixW1E6P33oXlzKFECeveGTZu8Ha2IiIj4snyXCImIgCVBixdbUjR/Ptx5JwQG2nqimTOhQQMrsDByJJw96+1oRURExNcoERKRfM3PD7p0gcmTYds2uOMOcG8RdvAgjBkDRYtCmzawZIl3YxURERHfoURIRAqM6tXh668hNhamTYOmTcHhAKcTli+HDh2gc2eYOhUSErwdrYiIiHiTEiERKZBuvtmKJ5w6BY8/btPkAH791UaNSpaEVq1gwQLvxikiIiLeoURIRAq0okXh9ddh/37Ysweefx4qVLC9iv74w6bVlSoFjz4Kp097O1oRERHJK0qERKTQqFoVRo+Gf/6BESOgTBlrP3ECxo+H8HAbJfrlF6+GKSIiInlAiZCIFDohIfDyyxAVBUuXQrt2VnTB5bJRoq5dbaTo22+1lkhERKSgUiIkIoXa1VdbNbnYWHj66dRRogUL4LbbbBpdhw623khEREQKDiVCIiLYKNFLL9ko0e7d8OyzUL687VO0ZIlt1lqlCrz9tlWhExERkfxNiZCIyHmqVYMXXoC9e62IQni4te/fD8OG2T5Fd9wBhw55NUwRERHJBiVCIiIXEBgI48ZBdLStF6pXz9rj420vosqV4eGHYcsW78YpIiIimadESETEA7fcAps22bS5G2+0JMnphHfftQSpUyd48kk4e9bbkYqIiIgnlAiJiGRCtWrw3Xe2D9GsWdCrl1WcW7QIXnsNihWD666zhElERER8lxIhEZEsCAiAnj1hxgzbl+hf/wKHA5KTLUGqUQMaNYKff/Z2pCIiIpIRJUIiItlUrRrMmQPHjsE991gFOoANGyxBKlcOZs+2fYpERETENygREhHJISVLwqef2p5Eb7xhCRBYSe5rr4UrroD//hdOnfJunCIiIqJESEQkx/n5wWOPQWQk/PIL9Olja4f+/hvuvx+KF7eNXFev9nakIiIihZcSIRGRXNS5M3z1le1BNG6cjRq5XLBsGbRoYWuJJk/2dpQiIiKFjxIhEZE8ULy4bc4aGWlltosXt/bdu6FvXyhVCl56yUpyi4iISO5TIiQikoeCguCVV+DkSfjySyu0AHDiBDz7LDRsCJ9/DgkJXg1TRESkwFMiJCLiJX372ojQ77/DlVfaJq2bN0P//lCzpq0t2rfP21GKiIgUTEqERES8rE0bWLvWqsuNHQvly8OBA/D111C1KrRsCatWeTtKERGRgkWJkIiIjyhRAp56ykaJHn/cptGBJUEtW8Jll8F333kzQhERkYJDiZCIiI8JDobXX7f9iJ57LrWwws6dcPPNtj/Rjz96N0YREZH8TomQiIiPCgiAMWOssMLHH0PlytYeFQU33AAdO8K8eVaOW0RERDJHiZCISD5w771WOGHePOjZ0worLF4M3btb6e0nn4SkJG9HKSIikn8EZOWkm266KdPnTJw4kbJly2bl6URE5H+6drXL/v3w5pswYYKNGL32GowfH0CnTo3o1MkSJREREbmwLI0IzZgxg6CgIIoXL+7RZdasWZw+fTqnYxcRKbQqV4a33oL16+Hqq60tIcHBzz/XoHTpAO680xIkERERyViWRoQA3nnnHY9HeKZNm5bVpxERkYuoVw+WLoW9e+G++5z88ouDpCQHU6bAN99YsYXHH4dixbwdqYiIiG/J0ojQwoULKVWqlMfHz5kzh0qVKmXlqURExANVq8KsWcl89tkcbrrJib8/OJ0wejRUqwYvvwwnTng7ShEREd+RpUSoQ4cOBARcfDApLi6OZcuWAdC2bVuCg4Oz8lQiIpIJ4eGJTJmSzMmT8MYbULs2HD8OzzwDERHQpYutLxIRESnscq1q3Pbt22nXrl1uPbyIiFxEsWLw2GOweTN89ZUlQU4nLFhgo0ddulgVOhERkcJK5bNFRAowf3/o0wcOHIAhQyAoyPYdWrDApsxdc42tLxIRESlslAiJiBQCQUHw3ntw6hQMHZqaEC1cCNWrw/PPQ3S0t6MUERHJO0qEREQKkaAgePddOH0aHn4YgoMtIXrhBUuIXngBIiO9HaWIiEjuy3L57B9++OGi9+/atSurDy0iIrksMBDeftsKKnz1lW3IunmzjQyNHGlT5r74AipU8HakIiIiuSPLiVDv3r0veYzD4cjqw4uISB4IDIR77oG774Zp02DwYCuzvWABVKoE118Pn34KmdgxQUREJF/I8tQ4p9N5yUtycnJOxioiIrnE3x9uv92mxQ0ZYgmSywU//ABlyljBhdOnvR2liIhIzslyIvT888+zZs2anIxFRES8zF1UIToa+vcnZWPWr7+2EtyffQb6jktERAqCLCdC+/fvp0ePHlSuXJnBgwczZ84cEhIScjI2ERHxktBQmDTJNmO95Rbw84P4eJtGd8UV8M03kJTk7ShFRESyLsuJ0CeffEJkZCRff/01YWFhDBs2jIiICG6++WY+//xzjh8/npNxpnHgwAHuuusuSpcuTWhoKFdccQWrV6/OtecTESmswsPh22/h0CF48UUoWdKKKtx+u903apSNGImIiOQ32Sqf7efnR7t27XjttdfYunUrK1eupFWrVnz44YdUrFiR9u3b88Ybb3DgwIGcipcTJ05w9dVXExgYyJw5c9i0aRNvvvkmJUuWzLHnEBGRtMqWhWeegV27rLKcnx+cOQOjR1ty9J//eDtCERGRzMly1biM1KtXj3r16vHEE08QFRXFjz/+mFJm+/HHH8+R53j11VepUqUKn376aUpbjRo1Lnh8fHw88fHxKbdjYmIASExMJDExMUdiyir383s7DvFt6ifiqbzoK0WKwLPPwm23wT33+LN6tYOYGAcPPACjRrn44IMkevbMtaeXHKDfKV525AiOdetwbN4MBw/iOHDASjWeOQNnzuA4cwYSEqxaiZv7usuV9vr5bTnM3+Wi69mz+IeE4FIlYLkAdz9J/uADuOEGb4eTqd9tDpcrl/735JL69evTvXt39u/fz+LFi6lUqRIPPvgg999/f4bHjxo1itGjR6drnzx5MkWKFMntcEVECrTt20vw1ltNOXiwGOAAXFx22QlGjFhF6dJnvR2eiNcFnjpFmfXrKbd2LREbNlDkyBFvhySSK1Y9/jgH27b1dhjExcXRp08foqOjCQ8Pv+ix2U6Ebrzxxgz3C3I4HISEhFC7dm369OnD5Zdfnp2nSRESEgLA8OHDufXWW1m1ahWPPPIIEydOpH///umOz2hEqEqVKhw9evSSb05uS0xMZP78+XTt2pXAwECvxiK+S/1EPOXNvvLjj/DggwEcPmx/D0JDXTz0kJN//9tJ8eJ5Gopcgn6n5IFTp3D88AN+U6bg+OUXHOeVWnTVro2rYUNcVatChQq4IiKsQon7EhwMDoddIP31C7XlsKSkpJRlDwEBOTqJSAoQdz9pcccdBJYr5+1wiImJISIiwqNEKNu9unjx4syYMYMSJUrQrFkzANauXcvJkyfp1q0bU6ZM4ZVXXmHBggVcffXV2X06nE4nzZs35+WXXwbgyiuvZOPGjRdMhIKDgwkODk7XHhgY6DN/AHwpFvFd6ifiKW/0lZtussukSbZeaPlyB6+95s977/nTpQt88YUVVxDfod8pOczlgl9/hf/+F2bOtKlubg0aQI8e0L07tGyJIzyc/DDRzJWYyMnjx/G/6ioC1FfkAtz9JLBcOZ/4nZKZGLJVLAGgfPny9OnTh3/++Yfp06czffp0du7cyV133UWtWrXYvHkz/fv358knn8zuUwFQoUIF6tevn6atXr167N27N0ceX0REsu6ee+D332HGDLjsMoiLs01ZS5WC//s/ldyWAujYMRg3DurWhS5dYMoUS4IuuwxGjoStW2HjRnj9dbtf3wiI+IxsJ0Iff/wxw4YNw88v9aH8/Px46KGH+M9//oPD4WDo0KFs3Lgxu08FwNVXX83WrVvTtG3bto1q1arlyOOLiEj2OBzQqxds2AD9+lmFueRk+PBD+wz45pvejlAkB6xcabsOV6oEjz0G27ZBsWIweDD88YfdHjUKcmhpgIjkvGwnQklJSWzZsiVd+5YtW0j+35zYkJCQDNcRZcWjjz7KihUrePnll9mxYweTJ0/mP//5D0OGDMmRxxcRkZwREgKffQZRUTYjCOyL8scfh/LlYcEC78YnkmlJSTBtGrRpA1ddBZ9/bjsNN2limf7Bg/D++9CiRa6t2xGRnJPtNUJ33303AwcO5Omnn6ZFixYArFq1ipdffpl+/foBsHjxYho0aJDdpwKgRYsWfP/994wYMYIxY8ZQo0YNxo8fT9++fXPk8UVEJGeVLg1z59oMoVtvtZGiw4ctOXrwQZs9VLq0t6MUuYiYGPj4Y3jnHdi929qCguCOO6wTt2ypxEckH8r2iNBbb73FsGHDeO2112jfvj3t27fntdde49FHH2XcuHEAKUUTcsp1113Hhg0bOHv2LJs3b75g6WwREfEdderAX3/BrFnQqJFNl3v3XVtKMWiQfdYU8Sm7d9u0t8qVYfhwux0eDs89B3v22JDnjBnQti3Mnp163rZtUL8+tGuX9vEefdSmyn32WWrbvn3W1qRJ2mOfecbaJ05MbTt2zNrOn2734ovW9tZbqW2xsanHnlu44c03re1/RacAK/TgPvb48ZRmv4kT6fzgg/g9/3za52vUyI49cCC17dNPre38fSNbtbL2HTtS26ZOtbYHH0x7bMeO1r5hQ2rbzJnWNmBA2mN79LD2P/5IbZs/39ruvDPtsTfdZO2LFqW2LV1qbb16pT22b19rnzs3tW31amvr1i3tsfffb+3ff5/atmmTtZ3/b//QQ9b+1Vepbbt2WVvz5mmPfeIJa//449S2yEhrO39gwT398t13U9uio1P/Pc9dmPnKK9b22mupbQkJqceeOpXa/s471nb+FjT16ln74cMpTY6PP6bzgw/imDqV/CbbI0L+/v4888wzPPPMMymblZ5fqq5q1arZfRoRESkgeva0y4IF9hlz/Xr46CP7HDVsGLz6qq0rEvGaFSusAML06eB0WluVKpa0lCwJY8akHrt5MyxbZve5xcdbe5kyaR83MhK2b4eTJ1PbkpKsrWjRtMcePmzt5yQmJCdb2/mOHLH2Y8dS21yu1GPP3Snl2DFrP38/I/ex7tcLcPw4xQ4exHn+sTt2WHJ17ofskyftMSIj0x67c6c9Z0JCalt0tB17/of6Xbtg7157/9xOnbJjq1dPe+yePdZ+bpJ3+rS1lS2b9ti9e609Li61LS4u4/d9/35rP306te3s2Yzf9wMHrP3cb3Hi49M/F8ChQ9YeHZ3alphobSVKpD3W/W9/4kRqm7ufnF8RLSoqfT9xOjOO9+hRaz96NG17Rv/2F+snycl2+R/HiRMUO3iQ5Hz4bVa+21A1u2JiYihevLhHtcVzW2JiIrNnz6Znz54+UW5QfJP6iXgqP/aV5GT497/h7bdT/waHh9uXkRnsiCA5ID/2kzyRlGTf7I8bZ4mQW9euNpLTsiVUrGjf3i9dmjoVbulS+zDatGnqh/XTp2HVKps+d+7WIVu32gfLGjWsyALYh+zVqy37b9Mm9djt2+0DcbVqloSBJRPuEZBzN6785x9bn1Slih0P9p9r+XK73ro1+Pvb9d277cN+xYpQs6a1uVxW7hFsBOd//SJx505WfPMNV/XqReC5FXuXLbP/sM2b22JAsKRg1y5L/urUST12xQp7b5s2BfdG9pGRlkyVKmUjZ26rVlki0bgxhIVZW1SUjbCVKAENG6Yeu2aNJUFXXEHKZmXHjlkCGh5uo1Zu69bZv0n9+vacYEnG339bInTllanH/vWXJTZ160JEhLVFR9soVUhI2tGbv/+2x6ldG9z755w+bc8XFGR9xm3zZouvVi2oUMHazpyx1+Hvb/9Gbtu22euuXt1GI8Hel1WrrN+d26d27LD3s2pVu4C93+4+fPXVqX111y77d6pUyfog2L/jsmV2/aqrwL1f1J49ltxXqGAxuy1daj9btLD9roDEXbtYMWUKV/XtS6APDH5k5rN+lhKhpk2bsmDBAkqWLOnR8W3btmXq1KlUcv+n9yIlQpLfqJ+Ip/JzX4mMhNtug99+S22rUQO++Sb9rBHJnvzcT3JFTIwNSb7zjo0cgCUlTid06JB2OlV8fMqHv8JAfUU84Wv9JDOf9bM0NW7dunWsX7+eUu6s2oPj488d5hQRETlH+fKwZIl94XnHHfYF965d9oXqmDG27MD9xbNIjti714YiJ0xInYoVEWHrVq67zjZHvfXWtOcUoiRIpDDI8hqhzp074+lgUk6VzhYRkYKtRQtbUvD55zBkiM0yee45mDTJ1nhff73WD0k2rV5tnenbb9Osc+Caa+CnnyA01G7/rxKuiBRcWUqEdu3alelzKrvnOIqIiFxCv35w113w5Zfw1FOWHPXubWW2P//cii2IeMzphB9+gKeftrUabtdcYx2rWDEbinQnQSJSKGQpEarmXognIiKSS/z8LCG68UZ46SWrJnfsGFx7LTRrBt99l7o2WCRDcXGWOb/1li1Ad7vySvjkk/Qlq0WkUNEEAxER8WlhYbb9xS+/pBZmWrPGCirde2/aCr4igFVbu/9+G0IcPNiSoOLFbc+YRx6xKXBKgkQKPSVCIiKSL3TubNXlXnjBKtO6XLb3UIkStoZIhE2b4L77bKjwv/+10tRly1pRhP37bfPT8eOtdLSIFHpKhEREJF959lmbInf99XY7NtY2nR84MP0+gVIIuFxW+KBRI9ug8+OPbb+dSpVsD5U5c+Dhh20dkIjIOZQIiYhIvlOsmK19/+uv1D0LP/nE9nEcNco2bJcCLiHB1v80amSbUG3YYO033WSbg+7fb5s/Nm3q3ThFxGflaCJ09uxZPvvsM95//322b9+ekw8tIiKSzhVXwMqV9rm3USM4fhxGj4aSJeGLL7wdneSKw4dh6FDbcbd/f9i4Efz9bQHZtGkwfTq0aePtKEUkH8hyIjR8+HAeeuihlNsJCQm0bt2a+++/n6effporr7yS5cuX50iQIiIiF9OmjRVQGDzYbsfGWsW5K66A3bu9GprklL//hr59bffdCRPg4EGoUAFeftn+kSMj4eabvR2liOQjWU6E5s2bR9euXVNuf/XVV+zZs4ft27dz4sQJbr31Vl588cUcCVJERORSAgLg/fdh3TqrKAc2WFCzJgwaZFvJSD7jdMLkydC1KzRsaNfB/rGffRZ27YIRI0B7FYpIFmQ5Edq7dy/169dPuT1v3jxuueUWqlWrhsPh4JFHHuHPP//MkSBFREQ81bixfT5+443U6nIffQSlSlmSJPnAqVPw3HNQpIiNAv3yi20sdeONlgydPm3lA4ODvR2piORjWU6E/Pz8cLlcKbdXrFjBVVddlXK7RIkSnDhxInvRiYiIZNFjj1l1uX/9y25HR0Pz5vD44/Y5WnzQjh3w6KM2wvPiixAfb+133w07d9ouunfeqQRIRHJElhOhevXq8eOPPwLw999/s3fvXjp16pRy/549eyjn3vlORETEC4oVs+rJy5bBdddBcjK8+SZcfjk8/7y3oxPAhuy+/NKSn9q1bZ+fmBgrAfjQQ5YAff556nxHEZEckuVE6IknnmDEiBF07tyZzp0707NnT2rUqJFy/+zZs2nprmkqIiLiRa1bw48/wqxZUK0aHDpkM6uqVNF0Oa85c8Y2PW3c2EZ8Dhyw9vbtLXvdtAneeccWeYmI5IIsJ0I33ngjs2fPplGjRjz66KNMnTo1zf1FihThwQcfzHaAIiIiOaVnT/jzT2jRwm7v3w9XXmnLUJKSvBtbobFypZX5K10a7r/f9v8pUsQ2P50+HRYvtvmMftrqUERyV0B2TnaPBmVk5MiR2XloERGRXFGyJPzxB0ydCvfeC3Fxtv7+xx9t76FevbwdYQHkdMK8eVb2+qefUturVIGHH4aBA+0fRkQkD+nrFhERKZRuvx1OnLBCZGCFynr3tj06z571amgFx549trdPxYrQo0dqElShAjzxBGzbZtUrlASJiBcoERIRkUIrKMgKkS1fDu76Pp9/Do0awcKF3o0tX1u7Fu67z4offPcdHD4M4eHwyCOwZYtthvrqqxAS4u1IRaQQUyIkIiKF3lVX2Wfzzz6zwYrt2+Gaaywh2rnT29HlE9HRMHiwbXzarBl8/DEkJkJoqA2/7dljFeHq1PF2pCIigBIhERERwNbm9+sHmzfb53mwdfyXX26zuOQC9u6Fp5+2DHLiRPj7bwgMtP1+li6F2FiYMgVKlPB2pCIiaSgREhEROUfx4vD++/aZPijI1vm//rqt69+40dvR+Yj4eHjuORs2q1EDxo61ctj+/tCliyVHkydbJTiHw9vRiohkKEtV46688kocHv5iW7t2bVaeQkRExKseeMBmdF17rW3Iun+/TZX7v/+D994rpNWd9+2zvX/eeMPK7bl17gxDhtibFRTkvfhERDIhS4lQ7969U66fPXuW999/n/r169O6dWsAVqxYwd9//619hEREJF8rUQJ+/x2+/tpKbZ89Cx98AL/8YsUUKlXydoR5ID4eXn4ZVqywF+50WrvDYSM+H39s8wdFRPKZLCVC5+4RdN999/Hwww/zwgsvpDtm37592YtORETEB9x5p+0v1KuX5QLbt0P9+vDmm7YFToGc/eUe/Xn1VUuG3Dp1so1Qe/TQuh8RydeyPbD/7bff0q9fv3Ttd911F9OnT8/uw4uIiPiEIkVg/nwbGGnZEmJiLB+oXx+WLPF2dDnkzBl4/nm47jqoXh3GjLEkyOGwBGjrVvj1V8sMlQSJSD6X7UQoNDSU33//PV3777//Toj2BxARkQKmVStbM/Tmm7YcZssW6NABbrkFkpK8HV0W7d5tCVB4OLzwAsyaZVPgOnWCSZNs59lff9UUOBEpULI0Ne5cw4YNY/Dgwaxdu5aWLVsCsHLlSj755BOee+65bAcoIiLia/z9YfhwaNwYbr7ZttCZPh0iIuCHH6B9e29H6IGTJ21fn5UrYcGC1HaHw4oevPmmEh8RKdCynQg99dRT1KxZk7fffpsvv/wSgHr16vHpp59y2223ZTtAERERX9W5Mxw9CnffbVvlREfb6FDfvvD55z5YWc7lwjF1Ku2fe46APXvA5Uq9r0sXuO02m/ZWrJj3YhQRySPZToQAbrvtNiU9IiJSKAUEWFW5QYOgd29bO/TVVzbIsnq1j1SW274dRoyA2bMJOHOGku52f3/L2kaPtjVBIiKFSI59V5WQkMD+/fvZu3dvmouIiEhh0KkTHDtmleUAIiOhYUP48su0Ay95JjnZqr5VqWJT3KZPhzNncAFxEREkvfSSFUf47DMlQSJSKGU7Edq+fTvt2rUjNDSUatWqUaNGDWrUqEH16tWpUaNGTsQoIiKSLwQEwIwZVmK7eXNbhnP33ZYkbdyYR0H8/Tc88wxUq2Zl7fbvt/bQULjlFpI2bWL+f/+L69//hsDAPApKRMT3ZHtq3D333ENAQAA//fQTFSpUwFEgN1MQERHxXOfOsHw5vPIKjBoFixdDo0bw2GPw+uu58IQ7d8Jzz8FPP8GpU6ntpUvDFVfA//0f3H67tSUmwrZtuRCEiEj+ku1EaN26daxZs4a6devmRDwiIiIFQkAAPPusJUC33w5nz8Ibb8C0abZ+qGbNbD7B6dPw8ss2te3gwbT3de5syc/110NwcDafSESkYMr21Lj69etz9OjRnIhFRESkwLnhBoiKgjZt7Pbu3VC7Nowdm4UHc7ngjz+sPnd4uD2IOwkKCbGNUNeutbl5t9yiJEhE5CKynQi9+uqrPPHEEyxatIhjx44RExOT5iIiIlLYhYXB779b7YLAQNur9Omn4corbWDnkpYutalv9erZjq6//WZJkZ+fLUb65hsrfPDjj/agIiJySdlOhLp06cKKFSvo3LkzZcuWpWTJkpQsWZISJUpQsmTJSz9ANrzyyis4HA6GDRuWq88jIiKSEwYOtAGchg3t9rp1cNVV8NdfGRy8bx8MGAAlSkC7dvDii7B1qxU9uPVWeOkliIuDVavstoiIZEq21wgtXLgwJ+LItFWrVvHhhx/SqFEjrzy/iIhIVkREwIYN8MIL8M47VuStRQsrqvDv+08S8PpY24jowIG0J1aqZMnQzTfbEJOIiGRLthOhDh065EQcmXL69Gn69u3LRx99xIsvvnjRY+Pj44mPj0+57Z6ul5iYSGJiYq7GeSnu5/d2HOLb1E/EU+or+ctTT8G998IDg/yYNdufZ55OZu7T6/mKr6iMJUGuoCBcHTqQ/NxzNnTklo1/Y/UT8ZT6injC1/pJZuJwuFw5s81bXFwce/fuJSEhIU17bozY9O/fn1KlSvHWW2/RsWNHmjRpwvjx4zM8dtSoUYwePTpd++TJkylSpEiOxyYiInJJTifV5s+nysKFFDl4iNdiHmYUowEHJTjBU8XfovPAWA60bWvrgERExCNxcXH06dOH6OhowsPDL3psthOhI0eOMGDAAObMmZPh/cnJydl5+HSmTJnCSy+9xKpVqwgJCblkIpTRiFCVKlU4evToJd+c3JaYmMj8+fPp2rUrgdrUTi5A/UQ8pb7i41wu+P57/N96C8fq1TjO+fvoKl2aOa2e544FD3Aq3iq91azpYsGCJCpVytkw1E/EU+or4glf6ycxMTFERER4lAhle2rcsGHDOHnyJCtXrqRjx458//33HD58mBdffJE333wzuw+fxr59+3jkkUeYP38+ISEhHp0THBxMcAblQwMDA33iHwt8KxbxXeon4in1FR+zdCmMHm2V3s75Yg6AGjXg6adx9O9Pz8BADsVC9+5WYe6ffxzUrh3Iu+/alkA5Tf1EPKW+Ip7wlX6SmRiynQj9+uuvzJw5k+bNm+Pn50e1atXo2rUr4eHhjB07lmuvvTa7T5FizZo1REVF0bRp05S25ORklixZwnvvvUd8fDz+/v459nwiIiJZsmMHTJ8O334La9akva9CBdth9ZlnrHLCOYoWtbzp/ffhkUcgKQkGD7ZKcyNHgv7EiYjknGwnQrGxsZQtWxaAkiVLcuTIES6//HKuuOIK1q5dm+0Az9W5c2c2bNiQpm3AgAHUrVuXJ598UkmQiIh4z6JF8OqrsGSJlbV2czigdGm4/nrLZqpVu+RDPfigHd6xI/zzj1WYW7zYislVrpxrr0BEpFDJdiJUp04dtm7dSvXq1WncuDEffvgh1atXZ+LEiVSoUCEnYkwRFhZGQ/fmC/9TtGhRSpcuna5dREQk1y1cCK+9lj75AejSxfb3ufFGKFMm0w9dpQrs3AlffmmjQkuWQN26NlL00ks5FL+ISCGW7UTokUce4dChQwCMHDmSf/3rX3z11VcEBQUxadKk7D68iIiIb/nnH/jmG8tGTp9Oe1/JktCjh017q18/R57urrugVSvo3Rs2bYKXX4affrIRohIlcuQpREQKpWwnQnfddVfK9WbNmrFnzx62bNlC1apViThv7nNuWLRoUa4/h4iIFHJz59rCnQMH4Pxp36VKWfLz9NM5lvycr3ZtWL4cOnWyp//rL1tq9N139tQiIpJ52U6EzlekSJE0xQxERETyHafTFuRMnAirV8O5e+T5+VlG0qmTTXvLpeTnfOHhVndh1CgYMwbOnoWePWHgQPjPf7TdkIhIZuV4IiQiIpIvJSXB5Mkwbhxs3Ajn74NXujQMGgTDhsH/igR5w6hR0KsXdO4MJ07Axx/Dr7/CunWWLImIiGeUCImISOF18iTMmQPz58MPP8CxY2nvr1TJso4nnvCo2lteufJKiIyEa6+FX36BXbugWTOYMsV+iojIpSkREhGRwmXvXitz/cMPsH9/2vtKlbK9fW69FYYPt9s+KijI8rdPP4Xnn7eti1q3tr1bn3xSU+VERC5FvyZFRKTgW7kS7rjDkpxq1azwgTsJCgqChx6y+WWHD8PWrfDiiz6dBJ1rwABYv96WKyUmWs2GqlUt3xMRkQvLciL03HPPkZSUdMH79+7dS9euXbP68CIiIlnndFry8+yz0KgRXHUVTJ2aOvUtKAjatIFJk+DMGXjnHSt+EJA/J0qUKgXTp8PQoXb7wAG47DKbKiciIhnLciL02Wef0aJFCzZu3Jjuvg8//JCGDRsSkE//oIiISD4UFWXDIXXqQHCwJT8vvQQbNtj94eFWa3r2bIiPh99/h/79C8wcMocD3n3Xiif4+9vo0J132j5ETqe3oxMR8T1Z/u2/ceNGrrjiCpo3b87YsWNxOp3s3buXLl268MQTT/DGG28wZ86cnIxVREQkrT/+sE/7ZctCuXIwdixs22YV4IoUsbU+n39uSVJ0tCVBBXzjnXvvtdl97sJ2X30FtWpZcQUREUmV5SGb8PBwPv/8c26++WYeeOABpk6dyq5du2jZsiV//fUX1Xyouo6IiBQQTqdtpvPTTzb0ceBA2vsDAqBBA0uOHnwQwsK8E6eX1aplb80NN1hRvN27baBs+XLbnFVERHKgatxVV13FFVdcwYIFCyhatCjPPvuskiAREck5O3bYGp5Zs2yNT3R02vvDwmy9z+DBcP31BWaqW3YFBNgA2IQJtvVRTAy0aAHvvefIL3UgRERyVbb+Wnz99dfUr18fp9PJ5s2bGTx4MN26dePRRx/l7NmzORWjiIgUJklJNp3tmmssyald2xa//POPJUFhYXDTTTYi9Pff9gl/7lzb70dJUDpDhsC+fdClC8TFwb33BvD8863TbZkkIlLYZPkvxs0338z999/PqFGjWLBgAXXq1OG1115j4cKFzJ49m8aNG7N8+fKcjFVERAqqY8fg66/httusolv//rBwIZw+bfcHB9twxrvvwtGjViLt3nuhfn3vxp1PlC9vueKoUQAu/vqrLHXrBrB0qZcDExHxoixPjYuMjOTPP/+k9nmTjdu0acO6det46qmn6NChAwkJCdkOUkRECpikJFvF/9lnVtzg0KH0pc0qVIDOnW1I46qrvBNnAeLvDyNHQkiIk6efdhAd7UeHDjawds893o5ORCTvZTkR+u233/C7wBSE0NBQ3n77bW6++eYsByYiIgXMmjXwwQewYAHs2QMuV9r7Gza0im6NGkHv3lCsmFfCLOiGD3fici1n1Kh2xMc7GDDAZh2OHGnJkohIYZHlROhCSdC52rdvn9WHFxGR/O70aVi8GObPt41Lzy9yADbq06ULPPecypnloXr1TrB+fRJvvBHIf/8LL7wAq1bZIJ0KKYhIYaEdT0VEJGckJMDkyfZpetUqiI21KXDnKloUGje2/X3uv99ui1fUrAkffQTt28OgQbaGqHFj+PFHaNLE29GJiOQ+JUIiIpJ1S5bAf/4DixbBwYPpp7tVrw7dutkan3bt4LLLvBGlXMTdd9vg3YMPwv799k/13//CXXd5OzIRkdylREhERDx3+LAlPQsXwowZdvt8FSrYMMPQoXD11eBw5HWUkkk33QRvv22VyA8dsuRo5Up4800r4iciUhApERIRkQvbvdtGfGbPhq1bIaM94sLDoWlTuPNO6NcPQkLyPEzJnnLlYNkym6n40ku2Zui992DtWvj2W6hY0dsRiojkPCVCIiKS6vhx+wT800+2WWlcXPpjGjWyzU47dYJmzaBSpbyPU3Kcu0jCmDG2ZdOtt1py1KyZJUNt23o3PhGRnKZESESkMDt+3JKebdvg119tPtT5+/kEBdnanm7d4L77oEED78QqeebYMYiPt3LakZGW8771lm3ppJmOIlJQKBESESlMjh+Hzz+HmTPhzz8zLmldtKiN8lxzjSU+zZrlfZziVTfeCB9+aNs6bd4MU6bAQw/BunUwYQIEB3s7QhGR7FMiJCJSkEVH2/ymN96wktanTqU/xs8PbrvNRnw6dbJKb1KoFS9uW0AFBVkhwGbN4Mkn4eOPLTGaPh3Kl/d2lCIi2aNESESkACm2bx9+Tz9tVd2cTli/Pv1UNz8/qFzZFn30728bmnqwSbYULu5qcQ4HDBtmMyd/+83y6ubNrWhg8+bejFBEJHuUCImI5GerVsGkSbBwIQE7d9I5ISH9MbVqweWXW1nru+6CDh2U+EimvPQSzJkDZcpYBblt22xbqP/+F/r29XZ0IiJZo0RIRCS/SEqCTZvgjz9g6VL44Qc4cSLlbvcadldwMI7LLoOBA23Km6q6STY9+qgNMj7+uCVAffvCrFmWV69fD2PHWmEFEZH8RImQiIivioqCyZPtE+f69XD0qC3YOF+RIlCvHsmdO/NrtWp0vP9+AgMD8z5eKbDCwy0RcleMmzkTnnvOEqDXX4cNG+Drr6FECa+GKSKSKUqERER8xf798P33tiJ9+/aM9/AJCLC1PW3aQOvWcOWVKSM+zsRE4mbPzuOgpbA4t2x2dLRVkHv9dXj+eZg7F1q1sgSpbl2vhSgikilKhEREvOHMGSu9NWOGjfzs3g379qU/zs/PEp0WLaBXL7jlFhsBEvGixx6zNUP79sGSJXDTTbZuqFUrG8S89lpvRygicmlKhERE8sKff8I338CiRbBlC5w8mf4Yf39o1Mi+er/6aluI0apVXkcqcklvvmkbrb7xhu2vu3o13HyzLV27/nqbMvfEE9p8VUR8mxIhEZGcduQI/PKLTXVbuRJWrIADB9If53BARIQlO8OH26hPsWJ5H69IJpUqZSNCbmXLWpd/5BHbiPWpp6yux3/+o81XRcR3KRESEcmOpCQrZjBjhiU9u3bB2bMZHxscDNWqWeJz8802fyhAv4Yl/9uyBe6+26bFNWoEDz8Mn38O//xjy94iIrwdoYhIevoLLCKSGZGRlvCsXAmffmq3M+LvD9ddZwUNWrWCJk1UUksKrIcesulxDz9sI0WXXQa33mpT5Vq2hJ9+gvr1vR2liEhaSoRERC5k2zaYNs3W9fz9t015S0zM+NjixW3T0g4dbO+e5s21QEIKja++sr2G3n7bbnfrBsuX23qhf/6x7wO++Qa6d/dunCIi51IiJCICcPw4rFkDkybB4sU20pOcnPGxDRvaKE/lyrZS/PrrISQkT8MV8SVly1oydK66dW3g9Kab4LffoGdPS5SGDvVOjCIi51MiJCKFz65dNtKzcKHtBOnnB3v3ZnxsYCBUqGALHzp3hj597FOfiFzQ3Lm2v9CcOTB/Pvzf/9l3DA89ZOuJxo/X8jgR8T79GhKRgu34cfjxR5uXs2EDHDpkBQ4yUqsW1KhhRQ2uucYKGlSrlrfxiuRzCQkweLBtjfXqq/Daa/DJJ1CvnlWTmzDB9gv+5hubUSoi4i1KhESkYHA6bVHCrFmwbJmN8uzceeGRnoAAKF8errgC7rzTChuULJm3MYsUQEFBMHs2vPMOvPSStTkctq/Q5Zfb9ljz5tm6oZ9+gpo1vRuviBReSoREJP9JTITff7davWvW2Grs6GhwuTI+vlIl+9mwYepIT61aeRevSCFTrx588EHatuRk6N07ddPVzZutotz330O7dl4JU0QKuXyXCI0dO5bvvvuOLVu2EBoaSps2bXj11VepU6eOt0MTkdxw8KDt0bNwIZw5Y1PbNm60+TcZCQmxxKdbN7jjDlvbo7LVIl41bpytFZoxA668Ev74A3r1spLbXbpYJfo+fbwdpYgUNvkuEVq8eDFDhgyhRYsWJCUl8fTTT9OtWzc2bdpE0aJFvR2eiGRVcrKt4fnqK1i1yhYRXKxcdViYzbepUQOaNrXEp2dPCA/P27hF5KL274fnnoO4OKtR0rcvVKxoxRn7909t++cfeOYZVZ0XkbyT7xKhuXPnprk9adIkypYty5o1a2jfvn264+Pj44mPj0+5HRMTA0BiYiKJF/qAlUfcz+/tOMS3Fch+smkTfrNn41ixwqa0nT6NY9MmHGfOZHi4y98fSpfG1bw5zn79cDVpAtWr2zqg8xWk9ymTCmRfkRyX1/2kXDn47jsHq1Y5uO02Z8p/0cBA+PJLqFrVj3Hj/HnuOdi508mECckEBuZJaHIJ+p0invC1fpKZOBwu14Um1ecPO3bsoHbt2mzYsIGGDRumu3/UqFGMHj06XfvkyZMpUqRIXoQoUmj5x8cTtns31efNo/iuXRSJiiIwNhbHBX7tJAcF4fT3J75ECU5VqcLxunWJbNGC01Wq5HHkIpKbnE776f4uY86c6nz0USOcTgeNG0fxxBOrKFr0AtUdRUQuIi4ujj59+hAdHU34JWaJ5OtEyOl0csMNN3Dy5EmWLl2a4TEZjQhVqVKFo0ePXvLNyW2JiYnMnz+frl27Eqivv+QC8kU/iYuDhQvxW7IEx5o1cPw4jvh42LEjw6THBfZ1cEQEroYNcQ4ciKthQytg4O+f5+EXFPmir4jXebufJCfDoEH+hIW5eOstZ8pUuDlzHPTp409srIP69V388EMSVavmeXhyDm/3FckffK2fxMTEEBER4VEilO+mxp1ryJAhbNy48YJJEEBwcDDBwcHp2gMDA33iHwt8KxbxXT7RTxISYNs22LQJ/vMf2LrV1vGc82VDOhERVsAgIgIaN4YOHXBce23KpqQOIIMJbpINPtFXxOd5q58sX25T4vz84L77/LnySmu/4Qb47Te49lrYtMlB27aB/PQTNGuW5yHKefQ7RTzhK/0kMzHk20Ro6NCh/PTTTyxZsoTKlSt7OxyRguXkSfjlF1iyBNats/14YmKsalty8oXPK1rU9uZp2BAefNAqtpUrp9XPIpKifXsrrV2uHClJkNuVV8LKlZYMbdhgx06datt8iYjktHyXCLlcLh566CG+//57Fi1aRI0aNbwdkkj+dfw47NljIzybNtknjr17L15wICwMGjSwKWwlS9pGIF26QIsWtkmpiMglPPBA2ttOZ+p6oSpVbK+hW26xktu9esG779p3KyIiOSnffWoZMmQIkydPZubMmYSFhREZGQlA8eLFCQ0N9XJ0Ij4oKcnKUS9ebJuPbt0KBw5YtbaLje6AJT0VK9ruiC1b2r481atrhEdEcszJkzYt7qGH4NZbrS08HGbNgsGD4eOPYcgQG5h+/fWMi0WKiGRFvkuEPvjfVtUdO3ZM0/7pp59yzz335H1AIr7i8GFYsMAm4JcqZQnP1q02v+RiCU/JkjbC06CBTW0rV8725GnUSJ84RCTXvf++rQ3auhV69IBixaw9MBA++ghq1rT9hcaNg927bX2RvvcUkZyQ7xKhfFzkTiT7nE7bnXDWLEt6tm2z2zExlx7dCQqyBKlqVahfH1q1gmuugcsvz5vYRUQy8OSTcPAg3H9/ahLk5nDA00/bvsn33APffWczcX/80X6diYhkR75LhEQKvKQkWLvWJsn/+Sf+W7bQfedOAsqUsfU7Z89e+FyHwz5JdOtmiU7dulCpkv3Uvlki4oP8/eG999K2uVxpZ+DeeafN0u3VC5Ytg6uvhrlzoVq1vI1VRAoWJUIi3pCUZOt1Tp60YgXbt9sIz6ZN6UpR+wEhACdOWENAgE1fS062TwENGqSO7lx2WR6/EBGRnLV9O/TtC198AXXqpLZ36GDfD/XoAVu2QOvWMHs2NGnitVBFJJ9TIiSSW5xOW5/zyy/w5582AX7/fktoLrbvjltgIJQogbNCBQ4VLUq5QYMIaNfOprb5QJ1+EZHcMGyY1XcZOtSqxp2rYUNbBtmjB2zcaOW1v/8eOnf2Sqgiks8pERLJjgMHbJ7GmjU2mrNrF5QoYZuM7tplG5BeTPny0LSpjeSULm3FCVq3hjZtUlYDJycmsnr2bHr27KkESEQKvEmTrILcO+9kfH/lylZc4cYbYdEiS4o+/dRGkUREMkOJkMjFnD0Lq1fbfjtRUfDPP5b4rF4NcXE2kf1i/PxsAnyJErZWp3ZtaNw4NdkJCcmTlyEikl+UKQNTpqRtO3/NUIkStkaoXz/45hu46y77Xurf/1Z1fxHxnBIhKdxcLkty1qyxv6pbtlh91sOHrRJbUtKlH8PhsEIEpUtbstOlC3TqZDVfK1XSJqMiItnwyy/wxhswfbpV+HcLDoavv7YRonHjrPrc/v3w1lv2/ZOIyKXoE5oUbElJNpH8jz/s5/btsG+fje6EhFgSFBt76ccpWxaaNbPkpmxZGw1q1sxGdSpVyv3XISJSCMXFwd13Q2QkvPoqjBmT9n4/P3jzTfs1/Nhj8O67Vor7yy814C4il6ZESPK3s2dtNGfNGvj7b1tDc/asjeps3mx/ET1RurSt54mIgCpVUqewXXUVXHmlRnVERLygSBHbO2jiRNtU9UKGD7fy2v3728hRVBTMnGn7RYuIXIg+3Ylvi4xMrbS2Z4/trzN3Lhw7ZiM5l9pE1M3f3+ZUlCplfy1r1YJ27awea9Wq+upQRMRHtW5tl0u54w6rP9O7txVTaNsW5syxX/EiIhlRIiTec+aMTVP7+2+YNctGcQ4csCTn1CkrMX2pYgRugYG2kWjDhrZGp3p1mysRFGRT2LSZqIhIgTBhgn0/NnZs+vs6drQkqEcPK+TZurUlQ40a5XmYIpIPKBGS3BEbCytW2D46W7ZYKen9++HoUStC4Odnk789ERRk5aWrVbPRnP37beraFVdA8+b2F05T10RECrz1621/IbA9pLt2TX/MFVek7jX09982+D9zpiVJIiLn0qdHyRyn06ao/fWXfd22cyfs3QuHDtn0MqfTRnmOHPHs8YoWteTm2DFbp1Ohgo3mXH65/TVr2tRKAomISKHXuDG8/rpNGujS5cLHValiI0O9etnP7t3hiy/gttvyLlYR8X1KhCTV0aP2ddumTbBtm01VCw620ZaDBy0B2rs3c4/pnrJWqpQlOdWqWZLTooXNWSheXJs+iIiIxx5/3LPjSpaEefNsj6Hp020N0aFD8MgjuRufiOQfSoQKg9hYKx29caMlOMnJNt3s4EEb0fnjD6uYlhkOhz2GO8kpU8aqq/3rXzaCU6WK/RXy88ud1yQiIoVecrIlNtdea1PhzhcSAlOnwrBh8N579nP/fivFrT9PIqJEKL9KToZ//rEJ0Nu22X45QUFWZW3XLli0yBKghATPCw64BQZacYESJaBuXZtTUKmSjegkJ9vcBNUkFRERL5s40YonfPaZ/UksUyb9Mf7+8M479mdsxAjbnPXQIfjkE/uzKSKFlxIhXxMVZSM3W7da1bTAwNTkZv58OH3a2j0tG30uf38IDbXpaZ06pSY3hw9D/fpWdKByZX1NJiIi+cKgQfDLL3DnnRknQW4OBzz1lC1JHTgQvvrK/vR99x2EheVdvCLiW5QIeUtUFH5PPUW3H34gIDHRSkknJmb+cdxT1CpUgKuvtk0UypWzr8aqV7fqavXq2U9VVhMRkQIkMNCSGU+XmvbrB2XLwi23WALVoQPMnm1/OkWk8NEnY29xufD/9FNCL3R/QID9tr7qqtTkZudOG82pVcuSm3r19FWWiIgUaucmQTExVl775ZcvXHD0X/+y2ePXXgt//ml1e37+2er4iEjhokTIWyIiSH7ySSJ/+43yzZvjX7s21KkDDRpY4qPpaSIiIpnywAMwZYotnV2+/MIjRc2bw7JltgR2505o08b29W7VKm/jFRHvUiLkLf7+OF94gdWzZ9OzZ0/8AwO9HZGIiEi+9sortsR2woRLT5erVcuSoWuvhdWrbensN9/AddflTawi4n0adhAREZECoVo1WLMGmjXz7PiyZWHhQpsud+YM9O4NH3+cqyGKiA9RIiQiIiIFxrkjQf/8Y2uGkpIufHyxYvDDD9C/vxVkve8+GDMm8ztPiEj+o6lxIiIiUuAkJEDXrpYMFStm0+YuJDAQPv3UdpV4+WUYORIOHLApdiq4KlJwaURIRERECpygIBg/Hq68Eh555NLHOxzw0kvw3nt2/T//gZtvhri4XA9VRLxEiZCIiIgUSNdfD6tW2VZ7nhoyBKZNg+BgmzLXpQscO5Z7MYqI9ygREhERkQLL3z/1+uLF8MEHlz7npptg/nwoUcLKcLdtC3v25FqIIuIlSoRERESkwNu2zarDPfggzJ596ePbtYOlS21j1i1bbOPV9etzP04RyTtKhERERKTAq10b/u//4IYb4JprPDunQQMbEWrYEA4dgvbt4ddfczdOEck7SoRERESkwHM44M03Yfp0CAnx/LzKleG33ywJiomxUaUpU3IvThHJO0qEREREpFDw80tbDvuTT6yYwqWUKAE//wy33AKJiXDnnfDWW7kWpojkESVCIiIiUuhMnQoDB0L37rB//6WPDwmxkaCHHrLbw4fDY4/ZJqwikj8pERIREZFCp2dPK4Bw//22kaon/P3h7bdTN2cdNw5uvBFOncq9OEUk92i/ZBERESl0wsJgwQIb6XE4PD/P4YAnn4QqVeDee+HHHy2h+uEHqFkz9+IVkZynESEREREplEJDU5MgpxOGDbMqcZ7o0weWLLHNWv/+G1q2tH2KRCT/UCIkIiIihd4779i0t+uus+pwnmjZ0ootNG8Ox45Bly4wfjy4XLkaqojkECVCIiIiUujdfz907myJTHi45+dVqmQjQ3feCUlJ8OijcNttnidTIuI9SoRERESk0CtaFObNg7vvTm2LifFsdCc0FL76Ct59FwIDYdo0aNoUli3LvXhFJPuUCImIiIhg+wy5nT0LnTpBv34QF3fpcx0OGDrUNl+tUgV27oR27WDECIiPz72YRSTrlAiJiIiInGfJEli/HubMsfU/nmrVCv76yxIop9NKbTdqBDNnau2QiK/Jt4nQhAkTqF69OiEhIbRq1Yo//vjD2yGJiIhIAdGtG/zyC3zzjY3wuHmyZ1CJEvDZZ/Ddd1C2LGzbBr17wzXXwKJFSohEfEW+TISmTp3K8OHDGTlyJGvXrqVx48Z0796dqKgob4cmIiIiBUTHjpa8uP36qyVFL7zg2fk33gjbt9v0uOBgS4I6dYIGDWDkSJg/H06ezIXARcQj+XJD1XHjxnH//fczYMAAACZOnMisWbP45JNPeOqpp9IcGx8fT/w5k3Nj/lfGJTExkcTExLwLOgPu5/d2HOLb1E/EU+or4gn1k6z77DN/oqP9OHYsmcREJ2Drh3r39iciAiZPTk459ptvHCxf7uBf/3IxerSLe++Fl1/24/PP/di82cGYMamPW7q0C39/qFzZRfXqVnDB3x/Wr3fg5+eiTRsXgYF27J49sHevg4oVXdSqlfoYS5c6cLmgVSsXwcHWtm8f7N7toFw5uPzy1GGoZcscJCdD8+YuihSxtgMHYOdOB2XKQL16dmxyMsyf346RI/1o1iyZsDA7NjIStm1zUKoUNGyY+rh//OHg7Flo0sSVUnkvKgq2bHFQvDg0bpx67Jo1DmJj4YorXJQsaW3HjsHffzsIC4Mrr0w99s8/HZw6BfXru4iIsLYTJ2DDBgdFitjrcPvrLwcnT0Ldui7KlrW2mBhYt85BSAi0bJl67MaNDo4ft/emfHlri4212AIDoXXr1GM3b3Zw5AjUquWiUiVrO3MGVq1y4O8PV1+deuzWrQ4OH4bq1V1UrWpt8fGwcqVtWtW+feqxO3bAwYMOqla1f3uw6oPLltmxV19tfQPgn39g/34HlSql/tu7XPDbb3Zs69Zp+8mePQ4qVHBRu3bK07F0qQOn096HkBBr278f/vnHQdmy9r65LVvmICkJmjVzUbSotR08CDt2OChdGho0OLeftOXwYScDBnj/90pmfrc5XK78NUCbkJBAkSJFmDZtGr17905p79+/PydPnmTmzJlpjh81ahSjR49O9ziTJ0+miPt/v4iIiMglOJ3w559lKV8+lkqVYgE4dKgIgwd3JTAwmW+//Snl2A8+aMTPP9fgzjs3c/vt2wCIiQmkX7+eALRtu58dO0oQGVks71+ISC7o2nU3Q4as93YYxMXF0adPH6Kjowm/RC38fDcidPToUZKTkylXrlya9nLlyrFly5Z0x48YMYLhw4en3I6JiaFKlSp069btkm9ObktMTGT+/Pl07dqVQHcKL3Ie9RPxlPqKeEL9JHuuuy7t7ehoKFIkidhY6NmzZ0p7crKDpk2T6dSpNh07XgbY6NFTT9mo0ahR5fDzg6NHE5k82Y/ly+2b/po1ITHRqtb9/LN9e9+mjYuA/31i27XLvumvVCntN/2LF9uI0FVXpY4I7d0Lu3Y5KFfORd26qccuXWojQi1auAgNtbYDB+yb/jJlbOQFwOl0MmdONEWLlqRZMyj2v5wtv44IBQfbiJmbe0Sodm0XFSpY2+nTsHbthUeEatZ0UbmytblHhPz8oG3bc0eE4PBhR5oRoYQEWLHiwiNCVaq4qFHD2i40IrRrF+zbd+ERoauuchEUZO3uEaHy5V1cfnnK0+XKiJDT6eTnn09y++3l6dmzEt4Wk4lNvPJdIpRZwcHBBLt/I5wjMDDQZ/4A+FIs4rvUT8RT6iviCfWTnBERYRXiznfTTXY5V/HiMHas+5Z9uq1QAR57LOPHfvbZHAszSxITnbRt+zs9e/ZUX5ELSkx00q7dMp/pJ5mJId8VS4iIiMDf35/Dhw+naT98+DDl3RM8RURERERELiLfJUJBQUE0a9aMBQsWpLQ5nU4WLFhA69atvRiZiIiIiIjkF/lyatzw4cPp378/zZs3p2XLlowfP57Y2NiUKnIiIiIiIiIXky8Todtvv50jR47w/PPPExkZSZMmTZg7d266AgoiIiIiIiIZyZeJEMDQoUMZOnSot8MQEREREZF8KN+tERIREREREckuJUIiIiIiIlLoKBESEREREZFCR4mQiIiIiIgUOvm2WEJWuVwuAGJiYrwcCSQmJhIXF0dMTIxP7MQrvkn9RDylviKeUD8RT6mviCd8rZ+4P+O7P/NfTKFLhE6dOgVAlSpVvByJiIiIiIjkhlOnTlG8ePGLHuNweZIuFSBOp5ODBw8SFhaGw+HwaiwxMTFUqVKFffv2ER4e7tVYxHepn4in1FfEE+on4in1FfGEr/UTl8vFqVOnqFixIn5+F18FVOhGhPz8/KhcubK3w0gjPDzcJzqO+Db1E/GU+op4Qv1EPKW+Ip7wpX5yqZEgNxVLEBERERGRQkeJkIiIiIiIFDpKhLwoODiYkSNHEhwc7O1QxIepn4in1FfEE+on4in1FfFEfu4nha5YgoiIiIiIiEaERERERESk0FEiJCIiIiIihY4SIRERERERKXSUCImIiIiISKGjREhERERERAodJUJeMmHCBKpXr05ISAitWrXijz/+8HZI4oOWLFnC9ddfT8WKFXE4HMyYMcPbIYkPGjt2LC1atCAsLIyyZcvSu3dvtm7d6u2wxMd88MEHNGrUKGX399atWzNnzhxvhyU+7pVXXsHhcDBs2DBvhyI+ZtSoUTgcjjSXunXrejusTFEi5AVTp05l+PDhjBw5krVr19K4cWO6d+9OVFSUt0MTHxMbG0vjxo2ZMGGCt0MRH7Z48WKGDBnCihUrmD9/PomJiXTr1o3Y2FhvhyY+pHLlyrzyyiusWbOG1atXc80119CrVy/+/vtvb4cmPmrVqlV8+OGHNGrUyNuhiI9q0KABhw4dSrksXbrU2yFlivYR8oJWrVrRokUL3nvvPQCcTidVqlThoYce4qmnnvJydOKrHA4H33//Pb179/Z2KOLjjhw5QtmyZVm8eDHt27f3djjiw0qVKsXrr7/OwIEDvR2K+JjTp0/TtGlT3n//fV588UWaNGnC+PHjvR2W+JBRo0YxY8YM1q1b5+1QskwjQnksISGBNWvW0KVLl5Q2Pz8/unTpwvLly70YmYgUFNHR0YB9yBXJSHJyMlOmTCE2NpbWrVt7OxzxQUOGDOHaa69N83lF5Hzbt2+nYsWK1KxZk759+7J3715vh5QpAd4OoLA5evQoycnJlCtXLk17uXLl2LJli5eiEpGCwul0MmzYMK6++moaNmzo7XDEx2zYsIHWrVtz9uxZihUrxvfff0/9+vW9HZb4mClTprB27VpWrVrl7VDEh7Vq1YpJkyZRp04dDh06xOjRo2nXrh0bN24kLCzM2+F5RImQiEgBMmTIEDZu3Jjv5mlL3qhTpw7r1q0jOjqaadOm0b9/fxYvXqxkSFLs27ePRx55hPnz5xMSEuLtcMSH9ejRI+V6o0aNaNWqFdWqVeObb77JN9NtlQjlsYiICPz9/Tl8+HCa9sOHD1O+fHkvRSUiBcHQoUP56aefWLJkCZUrV/Z2OOKDgoKCuOyyywBo1qwZq1at4u233+bDDz/0cmTiK9asWUNUVBRNmzZNaUtOTmbJkiW89957xMfH4+/v78UIxVeVKFGCyy+/nB07dng7FI9pjVAeCwoKolmzZixYsCClzel0smDBAs3TFpEscblcDB06lO+//55ff/2VGjVqeDskySecTifx8fHeDkN8SOfOndmwYQPr1q1LuTRv3py+ffuybt06JUFyQadPn2bnzp1UqFDB26F4TCNCXjB8+HD69+9P8+bNadmyJePHjyc2NpYBAwZ4OzTxMadPn07zzcquXbtYt24dpUqVomrVql6MTHzJkCFDmDx5MjNnziQsLIzIyEgAihcvTmhoqJejE18xYsQIevToQdWqVTl16hSTJ09m0aJF/Pzzz94OTXxIWFhYuvWFRYsWpXTp0lp3KGk8/vjjXH/99VSrVo2DBw8ycuRI/P39ufPOO70dmseUCHnB7bffzpEjR3j++eeJjIykSZMmzJ07N10BBZHVq1fTqVOnlNvDhw8HoH///kyaNMlLUYmv+eCDDwDo2LFjmvZPP/2Ue+65J+8DEp8UFRVFv379OHToEMWLF6dRo0b8/PPPdO3a1duhiUg+tH//fu68806OHTtGmTJlaNu2LStWrKBMmTLeDs1j2kdIREREREQKHa0REhERERGRQkeJkIiIiIiIFDpKhEREREREpNBRIiQiIiIiIoWOEiERERERESl0lAiJiIiIiEiho0RIREREREQKHSVCIiIiIiJS6CgREhERERGRQkeJkIiIiIiIFDpKhEREREREpNBRIiQiIiIiIoWOEiERERERESl0lAiJiIiIiEiho0RIREREREQKHSVCIiIiIiJS6CgREhERERGRQkeJkIiIiIiIFDpKhEREREREpNBRIiQiIiIiIoWOEiERERERESl0lAiJiIiIiEiho0RIREREREQKHSVCIiIiIiJS6CgREhERERGRQkeJkIiIiIiIFDpKhEREREREpNBRIiQiIiIiIoWOEiERERERESl0lAiJiIiIiEiho0RIREREREQKnQBvB5DXnE4nBw8eJCwsDIfD4e1wREREREQkh7hcLk6dOkXFihXx87v4mE+hS4QOHjxIlSpVvB2GiIiIiIjkkn379lG5cuWLHlPoEqGwsDDA3pzw8HCvxpKYmMi8efPo1q0bgYGBXo1FfJf6iXhKfUU8oX4inlJfEU/4Wj+JiYmhSpUqKZ/5L6bQJULu6XDh4eE+kQgVKVKE8PBwn+g44pvUT8RT6iviCfUT8ZT6injCV/uJJ0tgVCxBREREREQKHSVCIiIiIiJS6CgREhERERGRQkeJkIiIiIiIFDo+kQhNmDCB6tWrExISQqtWrfjjjz8ueOykSZNwOBxpLiEhIXkYrYiIiIiI5HdeT4SmTp3K8OHDGTlyJGvXrqVx48Z0796dqKioC54THh7OoUOHUi579uzJw4hFRERERCS/83r57HHjxnH//fczYMAAACZOnMisWbP45JNPeOqppzI8x+FwUL58eY8ePz4+nvj4+JTbMTExgJX6S0xMzGb02eN+fm/HIb5N/UQ8pb4inlA/EU+pr4gnfK2fZCYOh8vlcuViLBeVkJBAkSJFmDZtGr17905p79+/PydPnmTmzJnpzpk0aRL33XcflSpVwul00rRpU15++WUaNGiQ4XOMGjWK0aNHp2ufPHkyRYoUybHXIiIiIiIi3hUXF0efPn2Ijo6+5J6hXh0ROnr0KMnJyZQrVy5Ne7ly5diyZUuG59SpU4dPPvmERo0aER0dzRtvvEGbNm34+++/qVy5crrjR4wYwfDhw1Nuu3eb7datm3c3VI2Lw/nFF0R+9x0VK1XCL+B//xTnbP7kKlMG6tZNue1YscKu+PmlHvu/i6tkSahXL+V8x+rV4HKlPt4557iKF7fH/d+5jj//hOTkNI+X8rjFiqU5lo0bcSQmpo3hf9ddoaEWg7t92zaIj7f7z43D4YCgIKhTJ7V91y479rzjXA4HBAbCZZelPu6+fXD2bIYx4O8PtWqlth8+DGfOpD7v+Zdq1VKf8/hxe1z3fec/fvnyqY8THZ362tzxnnu9VKnUY+PiwP2enfvc7uOLFbO4wY5LTk59Lf7+JCYns2DRIjp37UpgaGja91PkHImJicyfP5+uXbv61KZ24lvUT8RT6iviCV/rJ+7ZX57w+tS4zGrdujWtW7dOud2mTRvq1avHhx9+yAsvvJDu+ODgYIKDg9O1BwYGevcf68wZeOghqnkvAsknAoHrs3JiSAgEBFjilJBgidv53AlVyZJ2vDtxi45Om2yde718eUveHA44dQqiolLvP/9n1apQvLjdjomxJDajYx0OqF4dIiJSE81//kl7/7nH16gBFSqkxrBpU8aJrvvYKlXsemwsrF+fmriee3E4LImuWdNux8XB2rVpj/H3T71erRpcfrldj4+HNWtSj3Ff3LcrVoTate16UpLFcO4xgYGpx5YpYzG7E+WtW1OPDQiwn4GB9jM83B7bHf++fZTcvJnAiAhLmoOCIDjYfhYpAmFhqY+jZLrQ8/rfQck31FfEE77STzITg1cToYiICPz9/Tl8+HCa9sOHD3u8BigwMJArr7ySHTt25EaIuSc4GGf37iSsWEFwUBApH0nOnalYsqR9IHK57LJ2bdr7z70eFmYf4NzHbtgATmf641wuKFrUPnS6b2/ZYqMQ58+SdLkgNNQ+zLpv79xpH+TOf1ywD1uVK6e279tnH8DP53LZB7GKFVPjPXw442PBPuSVKZN67vHjNnKS0fvgcNj75m4/derC8YJ9oHe3nz2bOhqTEfeonctl7633ZpV65uzZSx/jfg3Hjnn+uPv2eX7sBUZ2M3TihOfHHjrk+bE7d3p+7Jo1nh/rgwKB9tl5AHdyFBxs/+fcSdP+/akjzO7RTPfPYsVsJNidpG3caP8/z00a3dfDwqBVq9Rj1661//cBAZbcBQWl/gwPh7Zt7XZgoPWlpCSLzX0JDbUE3h2D+9hTp+wxixSxY4oUsYs7uRQREcHLiVBQUBDNmjVjwYIFKWuEnE4nCxYsYOjQoR49RnJyMhs2bKBnz565GGkuKFmS5B9/5OfZs+nZs6dPZNCSA9yJ3bm3k5NTE033xZ1IuVz2oc/hsOtnz9qHSKfTLsnJJMbHs+jXX+nYrh2BpUvbhzmXy0ZYTp9OOe7cc3A67YNsUJBdP3HCkh2Xyz5MumNwH1+5sn1gdDrh6FGIjEz7WO7X4XRaEh0WZtejomDPntRj3I/pvl2zJpQoYdePHIHt29Mf475+2WU2IuRyWQx//532mHN/1qplI1PuxHjdurTv7bnvb40aqQn6iROW7Jwbw7mXqlVt9MjptPd31aqMY3W5LJF3Hxsbm/pFRUbxRkRApUp2++xZG8E6tz+c22/Cwux4p9P6wt69qX3p/J/ukR6nE5KScMXGpnS9LI33nPulgKcJb0wMHDzo+XOsXev5sZMmeX5sZoSHp02aEhLSjuQFBKQmZ+3b2/+N4GBLqk+dsushIakXd7LVvr190RQcbH04IcFGREuVsktEBJQunfqlioiIeJXXfxsPHz6c/v3707x5c1q2bMn48eOJjY1NqSLXr18/KlWqxNixYwEYM2YMV111FZdddhknT57k9ddfZ8+ePdx3333efBki5twpXG7u9T+eyKiAR2IiceXL29SqcxPmsmWzFqMUWEmJicw+98sVl8uSqfh4S4DdU/OSky15SUhIvbiPS0xMHbF1H/vnn6mPkZCQ9mdIiI3GuI/9/XebVpiUlNrmXvsWEgJXXpnavmxZ6qjt+ZfAQKhf385NTLSRptjYjJNSh8MSDvexZ85c/I3KaP74haoMff215/8AEyZ4fmzJkjaSVbRo6pca7tEwd6IVGmqXzp0tgSpWDE6etPe+TBn7HVChgl3Kl1eCJSKSSV7/rXn77bdz5MgRnn/+eSIjI2nSpAlz585NKaCwd+9e/M6ZznDixAnuv/9+IiMjKVmyJM2aNWPZsmXUr1/fWy9BRMQ3uQuTBAWlv8893dQTV1/t+bH/+xLL65xOS8ji4iwxcl+Pj7fX7k6aNm+20ZvYWLuce05CAjRvbiNk8fGwcqWNlp6bQCYl2eMkJUGDBqnH7t2bOmKb0TTaEyc8nw66bJnnr7t8eRt9Cg+3JPPkSUu2wsIs+YqIwC8igsuio+111q5tBXGKFfP8OURECgivJ0IAQ4cOveBUuEWLFqW5/dZbb/HWW2/lQVQiIpJvudcvXeoDfuPGeRNPUpIlPkeP2jTVkiVTk64NG2D3bktcYmLSJmVnzliM8fGWWG3caGsq3aNq5ydZkZF2uQh/oAHAF1+kNoaG2qhT0aIWR7lylgCXL28jTmXK2BTT8uVtJCozI90iIj7KJxIhERGRAi0gwJKJjEbiOnTI+uM6nTbqc/CgFREJD7ckKiYGVq+2bQxOnLBKjCdPwunTuGJjcZ45g5/LhcNdTObMGSuK4XbwoE2JzIi7gE2ZMpY41a1r2yFUq5Z6qVBByZKI+DwlQiIiIvmVe9+yUqWgYcO0952zUfm50qwlc5e2P3nSRqpWr4b58206Za1allxFRlpbUpJNt3Q6bVTKXfF15cr0T+Lvb8VEatSwQih16qReatTQeiYR8Qn6TSQiIlJYBQSkJlI1a0KLFjB4cPrjjh2zja/r1rUpepGRMGUK/Pe/lvA0amQVJPfssWl+ycn2c/duWLgw/XPWqmVJUaNGNvWvSZPUPbxERPKIEiERERG5uNKl7QK27qp8eUteXnnFRojcCUxiItx4I/z1F3zyiSVM27bBjBm2FsrPz0aWtm61yw8/pD5HsWKWGDVtCm3a2Bol92bIIiK5QImQiIiIZN25oziBgfDTT+mPKVoUxo2Dp56Cm2+25GjdOnjmGds2IC7ORpqWLbPLe+/ZeZUqWVLUpg106mSJkhIjEckhSoREREQkdz35JDzxhI0YBQXZ5sUOh5Ugr1DBNmfevt2So59+gi1bbATpwAH49lu7gI1Ede0K3bvbJSLCqy9LRPI3JUIiIiKS+9z7Wrldc41VpouMTN1At149ePFF2LEDvvvONsr9/Xf47TdYvNiO/eILu/j7Q8eOcOutVhjif/sPioh4SomQiIiI5D2Hw9YZnev4cQgOtiSnQwcrB96+Pdx9t02hW78efv4Z5s616wsW2OXBBy2xuv9+S4oy2kRYROQ8Ks8iIiIivqF0aRsl2r7dkiC3oUOhdm0r9f3KKzaFbscOu968uRVs+OUXuP122/j1ySdh506vvQwRyR+UCImIiIhvqVw59fqZM7Bpk+11dNllqe21alnCs2qVJT3PPmvrjY4cgddes8Tp9ttt5EhEJANKhERERMR3hYbC5s2waBE0aJDaPmOGtYPtQfTCC7aP0fffWyEFlwu++cam3/XubQUYRETOoURIREREfJu/P7Rrl3r7wAHo18+SnCVLUtsDAy3pca8huuMOK+89cyY0bAhDhsDRo3kdvYj4KCVCIiIikv+0bw9XXWV7DGWkUSP4+mv4+2+44QZITob337fKdFOn2oiRiBRqSoREREQkf6lUCX78EebMgYBzCuAeOpT+2Lp1bURo4UK44gobEbrjDrjxRtu/SEQKLSVCIiIikv84HFZS223cOBvtWbgw4+M7doTVq2HUKEueZs6EZs1g5cq8iFZEfJASIREREcnfkpPhhx8gOhrWrr3wcUFBMHIkrFkDderA/v229mjixLyLVUR8hhIhERERyd/8/a1AwqRJMHz4pY9v1Aj++ANuvhkSE2HwYHjmGa0bEilklAiJiIhI/hcSAv3725Q5sKQmozVDbuHh8O238NJLdvvll2HYMNucVUQKBSVCIiIiUrCcPQt9+1pVucOHL3ycwwFPP23V5ADeeQcGDVIyJFJIKBESERGRgiUuzgojHDhgG7FeyuDB8PnntufQxx/Dk0/meogi4n0Blz5EREREJB8pVSq1eEKrVp6dc/fdNp2uf3944w2oXt02YBWRAksjQiIiIlLw1K3reRLk1q+frRUCWy/kyWiSiORbSoRERESkYDt40Mpme7L256mn4M47ISnJfh45kvvxiYhXKBESERGRgishAVq0gDFj4JNPLn28wwH//S/Urw+RkTBggMpqixRQSoRERESk4AoKgsceg5YtoVkzz84pUgS+/hqCg2HWLPjss9yNUUS8QomQiIiIFGwPPwzLlsGVV3p+TqNGNooElkhFReVObCLiNUqEREREpGALCAB//8yfN3w4NGkCx4+rpLZIAaRESERERAqHhASYOBEmTPDs+IAAOx5setyff+ZebCKS55QIiYiISOHwww+2eeoLL1hS5IlWrax6nMsFjz+eu/GJSJ5SIiQiIiKFQ69e0K6dTXNLTvb8vLFjrejCr7/C4sW5F5+I5CklQiIiIlI4BAbCkiXw6KMQGur5edWqwcCBdv2FF3InNhHJc0qERERERC7lySdtzdCCBbBihbejEZEcoERIREREChenExYuhClTPD+nWjW4+267/vbbuROXiOQpJUIiIiJSuMyfD9dcA488AklJnp/38MP2c9o0OHgwd2ITkTyjREhEREQKl2uugcsvt+IJp055fl6TJtC2rSVPH36Ya+GJSN5QIiQiIiKFS2AgbNkC//kPlCyZuXOHDrWfn35qU+xEJN9SIiQiIiKFj8ORtfN69YLixWHfPpXSFsnnlAiJiIhI4XXggF08FRICt99u1z//PHdiEpE8oURIRERECqfnnoPKleH99zN3Xr9+9nPaNIiLy/m4RCRP+EQiNGHCBKpXr05ISAitWrXijz/+8Oi8KVOm4HA46N27d+4GKCIiIgVP7do2RS4zI0IAbdpYOe3Tp60CnYjkS15PhKZOncrw4cMZOXIka9eupXHjxnTv3p2oqKiLnrd7924ef/xx2rVrl0eRioiISIFy000QFQWTJmXuPIcD3F/CzpiRw0GJSF7xeiI0btw47r//fgYMGED9+vWZOHEiRYoU4ZNPPrngOcnJyfTt25fRo0dTs2bNPIxWRERECoxixSAiImvnuhOhH3/M3F5EIuIzArz55AkJCaxZs4YRI0aktPn5+dGlSxeWL19+wfPGjBlD2bJlGThwIL/99ttFnyM+Pp74+PiU2zExMQAkJiaSmJiYzVeQPe7n93Yc4tvUT8RT6iviCfWTHNKqFQGlSuE4doykxYtxtW/v7YhynPqKeMLX+klm4vBqInT06FGSk5MpV65cmvZy5cqxZcuWDM9ZunQpH3/8MevWrfPoOcaOHcvo0aPTtc+bN48iRYpkOubcMF/zi8UD6ifiKfUV8YT6iSly+DD1vvySwNhYVjz/fKbOvbJxY6ouXMiu995j0+nTuRSh96mviCd8pZ/EZaKAiVcTocw6deoUd999Nx999BERHg5ljxgxguHDh6fcjomJoUqVKnTr1o3w8PDcCtUjiYmJzJ8/n65duxIYGOjVWMR3qZ+Ip9RXxBPqJ+c5fJjABx7A5XDQs3XrTG2w6oiOhoULuWz3bqr37JmLQXqH+op4wtf6iXv2lye8mghFRETg7+/P4cOH07QfPnyY8uXLpzt+586d7N69m+uvvz6lzfm/XZ0DAgLYunUrtWrVSnNOcHAwwcHB6R4rMDDQJ/6xwLdiEd+lfiKeUl8RT6if/E/lyvD22zgaNiSwRAnIzHvSvTsAjnXrCIyOzvp6Ix+nviKe8JV+kpkYvFosISgoiGbNmrFgwYKUNqfTyYIFC2jdunW64+vWrcuGDRtYt25dyuWGG26gU6dOrFu3jipVquRl+CIiIlIQPPwwXHMNBAVl7rxy5aBhQ7u+cGHOxyUiucrrU+OGDx9O//79ad68OS1btmT8+PHExsYyYMAAAPr160elSpUYO3YsISEhNHT/wvmfEiVKAKRrFxEREcl1XbrAxo3wyy9w663ejkZEMsHridDtt9/OkSNHeP7554mMjKRJkybMnTs3pYDC3r178fPzepVvERERKaiSkmDlSli/HgYPtn2CPNW5M4wfD+fMbhGR/MHriRDA0KFDGTp0aIb3LVq06KLnTsrsJmgiIiIi50pOhk6dIDERevaE6tU9P/fqq+3nzp22OWvZsrkSoojkPA21iIiISOEWHAxdu8L118OZM5k7t2RJqFfPrq9cmfOxiUiu8YkRIRERERGvmjUr6+dedRVs3gzLl1syJSL5gkaERERERLLDXel2xQrvxiEimaJESERERMQtOTnz57gToT/+sMILIpIvKBESEREROXMGGjeGYsUgEzvTA1C/PoSHQ2ysldIWkXxBiZCIiIhIaKhVfTt7FrZsydy5fn7QtKld//PPnI9NRHKFEiERERERgOnTrQx2s2aZP7dxY/u5fn3OxiQiuUZV40REREQA2rTJ+rlKhETyHY0IiYiIiGTXuYmQy+XdWETEI0qERERERACio+Gjj+CVVzJ/bv36EBAAJ07A/v05H5uI5DglQiIiIiJgVd8GDYJnn4XExMydGxICdevadU2PE8kXlAiJiIiIAFSoADfcAA8+aOW0M0vrhETyFRVLEBEREQFwOGDmzKyf36gRfPUVbNiQczGJSK7RiJCIiIhITnBPjdu61btxiIhHlAiJiIiInMvlsvVCmVWnjv3ctg2czpyNSURynBIhEREREbcpU6BYMbjzzsyfW7OmVY6Li4ODB3M+NhHJUUqERERERNxKlbJE5p9/Mn9uYKAlQ6DpcSL5gBIhEREREberr7apbWvXZu189/Q4JUIiPk+JkIiIiIhb0aJQuzYEBWXtfCVCIvmGEiERERGRnKJESCTfUCIkIiIicq7vv4dnnoE1azJ/rhIhkXxDG6qKiIiInOvLL+G776B8eWjWLHPnXn65/dyzB+LjITg45+MTkRyhREhERETkXD17QoUKcMUVmT+3bFkoUsQqz+3bB5ddlvPxiUiOUCIkIiIicq6BA7N+rsMB1avDpk2we7cSIREfpjVCIiIiIjmpWjX7uXu3V8MQkYtTIiQiIiJyPpcLTpzI2rnVq9tPJUIiPk2JkIiIiMi5oqJsP6EyZSApKfPnuxOhPXtyNCwRyVlKhERERETOVbo0JCRAcrIlRZmlESGRfEHFEkRERETO5e8P//xjI0KhoZk/X2uERPIFJUIiIiIi56taNevnukeEDhywkaWgoBwJSURylqbGiYiIiOSksmUhJMQKLuzf7+1oROQClAiJiIiInO/XX+GZZ+CnnzJ/rnsvIdD0OBEfpkRIRERE5HwLFsDLL8PPP2ftfK0TEvF5SoREREREzte2LQwZAh07Zu38ypXt58GDORaSiOQsFUsQEREROV+PHnbJqooV7eeBAzkTj4jkOI0IiYiIiOQ0dyKkESERn6VESERERCQjLhecOJG1cytVsp9KhER8lhIhERERkfPFxVkJ7FKl4NSpzJ+vESERn6dESEREROR8RYqkboQaGZn5892JUGQkJCfnXFwikmOUCImIiIhkZNs2GxmqXTvz55YtC35+4HRCVFTOxyYi2eYTidCECROoXr06ISEhtGrVij/++OOCx3733Xc0b96cEiVKULRoUZo0acIXX3yRh9GKiIhIoVChAoSGZu1cf38oX96ua3qciE/yeiI0depUhg8fzsiRI1m7di2NGzeme/fuRF3g25NSpUrxzDPPsHz5cv766y8GDBjAgAED+DmrG56JiIiI5AatExLxaV7fR2jcuHHcf//9DBgwAICJEycya9YsPvnkE5566ql0x3c8b2OzRx55hM8++4ylS5fSvXv3dMfHx8cTHx+fcjsmJgaAxMREEhMTc/CVZJ77+b0dh/g29RPxlPqKeEL9xHOOefNw/PYbrnbtcHXrlunz/cuXxw9I3rcPZz58v9VXxBO+1k8yE4dHidBNN92U6SAmTpxI2bJlL3pMQkICa9asYcSIESltfn5+dOnSheXLl1/yOVwuF7/++itbt27l1VdfzfCYsWPHMnr06HTt8+bNo0iRIpd8jrwwf/58b4cg+YD6iXhKfUU8oX5yaQ0+/ZTLZs5k+9atbEpKyvT5jZKSqAFsX7KEre5y2vmQ+op4wlf6SVxcnMfHepQIzZgxg9tuu41QD+fJTp48mdOnT18yETp69CjJycmUK1cuTXu5cuXYsmXLBc+Ljo6mUqVKxMfH4+/vz/vvv0/Xrl0zPHbEiBEMHz485XZMTAxVqlShW7duhIeHe/R6cktiYiLz58+na9euBAYGejUW8V3qJ+Ip9RXxhPqJ5xzJySRXrkyNTp2o3rNnps/3+/NPmDuXy4sWpVYWzvc29RXxhK/1E/fsL094PDXunXfeuWRi4zZt2jSPA8iKsLAw1q1bx+nTp1mwYAHDhw+nZs2a6abNAQQHBxMcHJyuPTAw0Cf+scC3YhHfpX4inlJfEU+on3jgppvsklVVqgDgFxmJXz5+r9VXxBO+0k8yE4NHidDChQspVaqUxw86Z84cKnkwBBwREYG/vz+HDx9O03748GHKuyutZMDPz4/LLrsMgCZNmrB582bGjh2bYSIkIiIi4hUVKthPFUsQ8UkeVY3r0KEDAQGe11Vo27ZthqMw5wsKCqJZs2YsWLAgpc3pdLJgwQJat27t8fM5nc40BRFEREREcoTLBadOZe1c90yaI0dyLh4RyTFZqhrndDrZsWMHUVFROJ3ONPe1b98+U481fPhw+vfvT/PmzWnZsiXjx48nNjY2pYpcv379qFSpEmPHjgWs+EHz5s2pVasW8fHxzJ49my+++IIPPvggKy9FREREJGM7dkC9elC0KJw8mfnz3YlQVJQlVA5HjoYnItmT6URoxYoV9OnThz179uByudLc53A4SE5OztTj3X777Rw5coTnn3+eyMhImjRpwty5c1MKKOzduxc/v9SBq9jYWB588EH2799PaGgodevW5csvv+T222/P7EsRERERubCSJSEpCaKjITERMrv+oUwZ+5mYCDExULx4zscoIlmW6UTo//7v/2jevDmzZs2iQoUKOHLg242hQ4cydOjQDO9btGhRmtsvvvgiL774YrafU0REROSiSpaE/fuhdOnMJ0EAoaFQrBicPm2jQkqERHxKphOh7du3M23atJRiBSIiIiIFkp8fZHf/n7JlLRE6cgRq186ZuEQkR3hULOFcrVq1YseOHbkRi4iIiEjB4p4eFxXl3ThEJB2PRoT++uuvlOsPPfQQjz32GJGRkVxxxRXpanU3atQoZyMUERER8Zavv4YNG+DWW+HKKzN//rkFE0TEp3iUCDVp0gSHw5GmOMK9996bct19X1aKJYiIiIj4rK++glmzoGbN7CVCKqEt4nM8SoR27dqV23GIiIiI+J7rr4datayMdlZoapyIz/IoEapWrRrPP/88vXr1olmzZrkdk4iIiIhveOCB7J2vESERn+VxsYT9+/fTo0cPKleuzODBg5kzZw4JCQm5GZuIiIhI/qYRIRGf5XEi9MknnxAZGcnXX39NWFgYw4YNIyIigptvvpnPP/+c48eP52acIiIiIt7hcsHZs1k7V8USRHxWpspn+/n50a5dO1577TW2bt3KypUradWqFR9++CEVK1akffv2vPHGGxw4cCC34hURERHJO1Om2GaqvXpl7XxNjRPxWZneR+hc9erV44knnuD3339n37599O/fn99++42vv/46p+ITERER8Z6iRSE5GU6cyNr57qlxR46A05lzcYlItnlULMETZcqUYeDAgQwcODCnHlJERETEuzp3hgMHoGTJrJ3vToTcyVTp0jkXm4hkS7ZGhM61c+dOrrnmmpx6OBERERHvK1IEKlaE0NCsnR8UBMWL23WtExLxKTmWCJ0+fZrFixfn1MOJiIiIFAwREfZThaVEfIrHU+Peeeedi96vAgkiIiJS4Did8OabNq3t2WdthCizSpWCnTuVCIn4GI8ToWHDhlGhQgWCgoIyvF97ComIiEiB4+cHzz0H8fG2uWq1apl/jFKl7OexYzkbm4hki8eJULVq1Xj11Ve57bbbMrx/3bp1NGvWLMcCExEREfEJ991nCVFwcNbOdxdI0IiQiE/xOBFq1qwZa9asuWAi5HA4cLlcORaYiIiIiE94773sna8RIRGf5HEiNGbMGOLi4i54f/369dm1a1eOBCUiIiJSYGhESMQneZwI1a9f/6L3BwYGUi0r82ZFREREfJ3LZYUT/P0zf657REiJkIhPybHy2SIiIiIF0v33Q2AgXKKC7gW5R4Q0NU7Ep3iUCDVt2pQTJ054/KBt27ZVOW0REREpGAICIDkZoqOzdr5GhER8kkdT49atW8f69esp5f6P7MHx8fHx2QpMRERExCeMGWMltD38HJSOiiWI+CSP1wh17tzZ46pwDocjywGJiIiI+JQyZbJ3vooliPgkjxKhrFSDq1y5cqbPERERESlw3CNCp09DQgJcYHN6EclbHiVCqgYnIiIihda2bTBzpo0M3XNP5s8vUQIcDqs8d/w4lC+f0xGKSBaoapyIiIjIxWzeDE88ARMnZu18Pz8oWdKua3qciM/weI2QiIiISKFUqxb06weXX571xyhVypIgJUIiPkOJkIiIiMjFNGwIn32WvccoXRp27FDlOBEfoqlxIiIiIrlNewmJ+BwlQiIiIiKecLnskhXuEtoaERLxGZlOhPz8/PD397/gRURERKRASUy0EZ3AQIiOztpjaFNVEZ+T6TVC33//fZrbiYmJ/Pnnn3z22WeMHj06xwITERER8QmBgRAXB8nJlgiVKJH5x3AnQidP5mRkIpINmU6EevXqla7tlltuoUGDBkydOpWBAwfmSGAiIiIiPuOvv6BYMShXLmvnu5OnEydyLCQRyZ4cWyN01VVXsWDBgpx6OBERERHfcfnlULEiZHUZgHsfIY0IifiMHEmEzpw5wzvvvEOlSpVy4uFEREREChaNCIn4nExPjStZsiQOhyPltsvl4tSpUxQpUoQvv/wyR4MTERER8Qk//QRbtkC3btCoUebP14iQiM/JdCI0fvz4NLf9/PwoU6YMrVq1oqT7P7mIiIhIQfLf/8LMmRAWlrVESCNCIj4n04lQ//79/7+9uw+Oqr73OP7ZhDwASYgQSHhIiAiCVJIAgTTWVqUQChbN3IoUvRCjg1YIJZPbqaa1CcztCCo6OIWi0lKsVwrVjjBXazClBK4VyzMSLFSoCkIeoeRhI0nY3fvHYfMgEc5uQs6Bfb9mdrI5e/a33939KfvJ95zfXo06AAAA7Ouuu6SoKOmmm/y7f9uOkMcjtTm6BoA1fA5CknT+/Hl99NFHqqyslNvtbnfbPffc0yWFAQAA2MaiRZ27v7cj1NxsLMXdu3enSwLQOT4HoaKiIs2ZM0dnOvhCMIfDIZfL5XMRq1at0nPPPafy8nIlJyfrV7/6lSZOnNjhvmvWrNHvf/97lZaWSpLGjx+vp59++mv3BwAAsFzv3lKPHtKFC0ZXiCAEWM7nVeMWLlyo+++/X2VlZXK73e0u/oSgjRs3Ki8vT4WFhdq3b5+Sk5M1depUVVZWdrh/SUmJZs+erW3btmnnzp2Kj49XRkaGTp065fNjAwAAdAuHg/OEAJvxOQhVVFQoLy9Psf5+odhXvPDCC5o3b56ys7M1evRovfTSS+rVq5fWrl3b4f6vv/665s+fr5SUFI0aNUq/+c1v5Ha7+Q4jAABw9bz+utSvnzRzpv9jeIMQK8cBtuDzoXH33XefSkpKdJO/Jwu20dTUpL179yo/P79lW1BQkCZPnqydO3eaGqOhoUHNzc3q27dvh7c3NjaqsbGx5ffa2lpJUnNzs5qbmztRfed5H9/qOmBvzBOYxVyBGcwT/zjcbvU4e1bu6mq5/HztgqOjFSTpQlWVPNfA689cgRl2mye+1OHweDweXwZvaGjQzJkz1b9/f40ZM0YhISHtbv/xj39seqzTp09r8ODB+uCDD5Sent6y/ac//am2b9+uv//971ccY/78+dqyZYsOHz6s8PDwS25fvHixlixZcsn29evXq1evXqZrBQAAgSukvl7h//63miIj1ejt7PgovbBQAw4e1N5Fi/TFXXd1bYEAJBlZ5YEHHlBNTY2ioqIuu6/PHaE//OEPeu+99xQeHq6SkpJ2X67qcDh8CkKdtWzZMm3YsEElJSUdhiBJys/PV15eXsvvtbW1LecVXenFudqam5tVXFysKVOmXBIoAS/mCcxirsAM5ol1gv/nf6SDB5UydKiSpk+3upwrYq7ADLvNE+/RX2b4HIR+/vOfa8mSJXryyScVFOTzKUbtxMTEKDg4WBUVFe22V1RUKC4u7rL3Xb58uZYtW6a//OUvSrrMF5uFhYUpLCzsku0hISG2eLMke9UC+2KewCzmCsxgnljg4mH8wXV1Cr6GXnvmCsywyzzxpQafk0xTU5NmzZrV6RAkSaGhoRo/fny7hQ68Cx+0PVTuq5599ln993//t4qKipSamtrpOgAAAC6roUF65RXphRf8H8P7paqsGgfYgs9pJisrSxs3buyyAvLy8rRmzRq9+uqr+sc//qHHH39cTqdT2dnZkqS5c+e2W0zhmWee0S9+8QutXbtWiYmJKi8vV3l5uerr67usJgAAgHaamqTHHpP+67+kNosw+YRV4wBb8fnQOJfLpWeffVZbtmxRUlLSJe2nF3z8S8msWbNUVVWlgoIClZeXKyUlRUVFRS3Lc584caJd92n16tVqamrSfffd126cwsJCLV682NenAwAAcGWRkdI990hRUcaXonZw2P0V0RECbMXnIHTo0CGNHTtWklRaWtrutrYLJ/giJydHOTk5Hd5WUlLS7vfPPvvMr8cAAADwW3CwtHlz58agIwTYis9BaNu2bVejDgAAgOsbHSHAVjq/4gEAAACujI4QYCsEIQAAADPuvVeKiZHefde/+9MRAmyFIAQAAGBGXZ105oz/HR1vR6iuzlhwAYClfD5HCAAAICCtXm0EmIQE/+7vDUKSVFMj9evXJWUB8A9BCAAAwIyRIzt3/5AQqXdvyek0ukoEIcBSHBoHAADQXThPCLANghAAAIAZH30krVkjdearRFg5DrANghAAAIAZ77wjPfqo9Npr/o9BRwiwDYIQAACAGbfcIs2YIaWk+D8GHSHANlgsAQAAwIzMTOPSGXSEANugIwQAANBd6AgBtkEQAgAA6C50hADbIAgBAACY8fHH0k03SWPH+j8GHSHANjhHCAAAwIzgYOlf/5L69PF/DDpCgG0QhAAAAMwYOlT64AMpKsr/MegIAbZBEAIAADAjPFxKT+/cGHSEANvgHCEAAIDuQkcIsA2CEAAAgFmbN0u//a109qx/92/bEfJ4uq4uAD7j0DgAAACzFi6UTp6UkpOlvn19v7+3I9TcLH35pdSrV5eWB8A8ghAAAIBZkyZJZ85IPXv6d/+ICGP1OZfL6AoRhADLEIQAAADMWreuc/d3OIyu0JkzxnlCgwd3QVEA/ME5QgAAAN2JleMAWyAIAQAAdCdWjgNsgSAEAABg1uLF0vDh0q9+5f8YdIQAWyAIAQAAmHXunHT8uFRW5v8YdIQAW2CxBAAAALMWLJDuv18aOtT/MegIAbZAEAIAADBrxAjj0hl0hABb4NA4AACA7kRHCLAFOkIAAABmlZVJ779vfDHqtGn+jUFHCLAFghAAAIBZe/ca5wilpvofhLwdIYIQYCmCEAAAgFmDBknf+Y40erT/Y3g7QhwaB1iKIAQAAGDWuHHS9u2dG4OOEGALLJYAAADQnegIAbZAEAIAAOhO3iBUWyu5XJaWAgQyghAAAIBZFy4Yh8eNGCHV1Pg3hjcISf6PAaDTCEIAAABm9eghlZZKx44ZHR1/hIZKvXoZ1zlPCLAMiyUAAAD44p13jCDTv7//Y9xwg9TQwHlCgIUIQgAAAL6YMqXzY0RHS6dO0RECLMShcQAAAN3Nu4Q2HSHAMpYHoVWrVikxMVHh4eFKS0vTrl27vnbfw4cP6wc/+IESExPlcDi0YsWK7isUAABAkvbuld54Qzp+3P8xvAsm0BECLGNpENq4caPy8vJUWFioffv2KTk5WVOnTlVlZWWH+zc0NGjYsGFatmyZ4uLiurlaAAAASb/8pXT//VJxsf9j0BECLGdpEHrhhRc0b948ZWdna/To0XrppZfUq1cvrV27tsP9J0yYoOeee04//OEPFRYW1s3VAgAASLr1Vun226V+/fwfg44QYDnLFktoamrS3r17lZ+f37ItKChIkydP1s6dO7vscRobG9XY2Njye+3FpS6bm5vV3NzcZY/jD+/jW10H7I15ArOYKzCDedIFCgqMiyT5+ToGRUUpWJLrzBm5bfpeMFdght3miS91WBaEqqur5XK5FBsb2257bGysjhw50mWPs3TpUi1ZsuSS7e+99556edfwt1hxZ1rrCBjME5jFXIEZzBNrDSsr0xhJZR9/rL1//rPV5VwWcwVm2GWeNDQ0mN73ul8+Oz8/X3l5eS2/19bWKj4+XhkZGYqKirKwMiOxFhcXa8qUKQoJCbG0FtgX8wRmMVdgBvPEHhzV1dLatRrUs6dip0+3upwOMVdght3mSa0PX3RsWRCKiYlRcHCwKioq2m2vqKjo0oUQwsLCOjyfKCQkxBZvlmSvWmBfzBOYxVyBGcyTTigulvLzjXOF1q3zb4yLn3WCqqsVZPP3gbkCM+wyT3ypwbLFEkJDQzV+/Hht3bq1ZZvb7dbWrVuVnp5uVVkAAACXd/68sYT2xx/7P0b//sbPqqquqQmAzyw9NC4vL09ZWVlKTU3VxIkTtWLFCjmdTmVnZ0uS5s6dq8GDB2vp0qWSjAUWPr74P52mpiadOnVKBw4cUEREhIYPH27Z8wAAAAEkLU16552Wro5f2gYhj0dyOLqmNgCmWRqEZs2apaqqKhUUFKi8vFwpKSkqKipqWUDhxIkTCgpqbVqdPn1aY8eObfl9+fLlWr58ue644w6VlJR0d/kAACAQDRggdfa8Hm8QOn9ecjqliIjO1wXAJ5YvlpCTk6OcnJwOb/tquElMTJTH4+mGqgAAAK6i3r2lsDCpsdHoChGEgG5n6ReqAgAAXHPcbukvf5HeeENqavJvDIeD84QAi1neEQIAALimOBzS974nuVzSyZPSkCH+jdO/v/TFFwQhwCIEIQAAAF84HNJttxnXXS7/x6EjBFiKIAQAAOCrHTs6PwZBCLAU5wgBAABYgSAEWIogBAAAYAWCEGApghAAAICvnnlGGj9e+u1v/R+DIARYiiAEAADgq7Iyad8+6dgx/8cgCAGWYrEEAAAAXz38sDRlinTLLf6PQRACLEUQAgAA8FVSknHpDIIQYCkOjQMAALCCNwjV10vnz1tbCxCACEIAAAC+cjqlrVul//1f/8eIjpZCQ43rFRVdUhYA8whCAAAAvjp1Spo8WfrP//R/DIdDGjzYuH7yZNfUBcA0ghAAAICvBgyQRo+Wxo2TXC7/x4mPN34ShIBux2IJAAAAvoqOlg4f7vw4BCHAMnSEAAAArOINQl98YW0dQAAiCAEAAFiFjhBgGYIQAACAP/LzjXOENm3yfwyCEGAZghAAAIA/PvtM2r9f+vRT/8cgCAGWYbEEAAAAf/z4x9LcuVJysv9jeINQZaXU2CiFhXVNbQCuiCAEAADgj/T0zo/Rt6/Us6f05ZfGggk33dT5MQGYwqFxAAAAVnE4pCFDjOscHgd0K4IQAACAPxobpW3bpA0bOjcO5wkBluDQOAAAAH/U10uTJhnXMzOl8HD/xklIMH5+/nmXlAXAHIIQAACAP/r2NRZKGDBAqqvzPwiNGmX8/PjjrqsNwBURhAAAAPzhcEgHDnR+nFtvNX6WlnZ+LACmcY4QAACAlbxB6MgRqbnZ2lqAAEIQAgAAsFJCghQRYYSgTz6xuhogYBCEAAAA/PXmm1JKijR/vv9jOBwcHgdYgCAEAADgrx49pIMHpb//vXPjEISAbsdiCQAAAP66/XZp8+bWIOMvghDQ7QhCAAAA/oqJke65p/PjeIPQ/v2dHwuAKRwaBwAAYLW0NCkkRPrsM+nYMaurAQICQQgAAKAzvvhC+t3vjIUT/BURYRxmJ0lFRV1TF4DLIggBAAB0xrZt0sMPS88+27lxpk41fhKEgG5BEAIAAOiMSZOMQ9umT5c8Hv/H+d73jJ/btkmNjV1TG4CvxWIJAAAAnTF4sPThh50fJylJGjhQKiuT3n5b+sEPOj8mgK9FRwgAAMAOHA7jEDtJWrasc90lAFdEEAIAAOgKHo+0ZYux8pu/cnOlXr2kPXuk4uKuqgxABwhCAAAAXSEnxzjPZ9Ei/7s5MTHSo48a1x99VKqo6Lr6ALRjiyC0atUqJSYmKjw8XGlpadq1a9dl93/jjTc0atQohYeHa8yYMfrzn//cTZUCAAB8jccek8LCpIwM4zA3fxUUSCNGSJ9/Lk2bJn3ySdfVCKCF5UFo48aNysvLU2Fhofbt26fk5GRNnTpVlZWVHe7/wQcfaPbs2XrkkUe0f/9+ZWZmKjMzU6Wlpd1cOQAAQBtJSdLx49KCBa3b3n9fKi2VXC7z49xwg7FYQt++0v79xrhz5kgbNkgffyydOSO53V1fPxBgHB6PtWfipaWlacKECVq5cqUkye12Kz4+XgsXLtSTTz55yf6zZs2S0+nU22+/3bLtm9/8plJSUvTSSy9dsn9jY6Ma2yxBWVtbq/j4eFVXVysqKuoqPCPzmpubVVxcrClTpigkJMTSWmBfzBOYxVyBGcyTbnT+vHokJMhx7pyaT52S+veXJAUtX66gVavkzsqSe/Hilt17jB4tSbrwf/8n9esnff65gr//fQUdPXrJ0B7J6DpFRUmhoVJwsLHkdl2dcY5R377Gjg6HdPq0EZzi4qSQEGNbfb109qzUs6c0YEDrwKdOSRcuSAMHyhMWpganU708Hjmqq6XwcGOMtvs2N0uxscY4ktTQIFVWGp2xgQNb9y0rM+obMMCoT5K+/NI49C80VBo0qHXf8nLp/Hnj9erdu+W1VHm5Uf/gwa37VlQY4/TrJ0VGGtuamoznHBwsxce37ltVJTmdxmvj/QzY3Gw8j6AgKSGhdd/qauM1uuEGqU8fY9uFC8aX5zoc0tChrfueOWO87tHRxkUyXu8TJ4zrQ4e2dgj//W+ppsZ4fO975PEY3T/JqDc42Lh+7pxxiYw0np/X558b9xkyROpxcQHomhpj7IgI4/BKrxMnjFoGDzZeO0mqrTXe+969W+akJOnkSSOwDxxovH+S8RpUVxvvb2xs677e9z4uTp7wcDU4neqxcqWCv/99Wa22tlYxMTGqqam54md9S4NQU1OTevXqpTfffFOZmZkt27OysnTu3Dlt3rz5kvskJCQoLy9Pubm5LdsKCwu1adMmHTx48JL9Fy9erCVLllyyff369erl/Q8RAACgi0WePKmk1avV57PP9O5rr8lz8QPuLa+9ppv/9CcdnzFDpY880rL/vRc/C727bp2aLn6gHvHGGxr9+uuqjY+XKzxcvU+fVqjT2d1PBbii3T/5iU7ffrvVZaihoUEPPPCAqSBk6fcIVVdXy+VyKbZtwpQUGxurI0eOdHif8vLyDvcvLy/vcP/8/Hzl5eW1/O7tCGVkZNARwjWBeQKzmCswg3nSzR57TPJ4NK3tOUNjx6r5pz9VQkyMEtp0LC6UlEiSJqemGl0SSRozRhfmzVPPAQOk4cMlSc1NTXJs2SJ9+aU8I0caHQSXSyork+PkSaOLkZDQumDD4cPG7cOHt3ZuqquNv+pHRbXvbhw5YnRUbrpJF0JDtXv3bk0YPlw9ysuNbsONN7bu+89/Gl2eG280bpOMzsSJE8bjXKxXknTsmNG5GTq0tRtTV2essBcebpwT5fWvfxmdm/j41g6L02lsDw2VRo5s3ffTT42uxeDBrR2WL780Hq9HD+mWW1r3PXHCqG/gwNauSWOj8TyCgqRvfKN135MnjW5MXFxr16SpSfJ258aMad331Cmjw9K/f2vHzOUyDmOUjHGDLp6NUlZmvPYxMa0dM7fbeI8kafTo1o5QRYXRXevbt30X7NAh4+fIka3zpKrK6JhFR7fvgh0+bIx/882tXZ4zZ4yO2Vff+3/8w+h6tZ0nZ88az++r7/3Ro8brMWyYLoSFaffu3Rr/wANKadvZs0htba3pfa/7L1QNCwtTmPeNbyMkJMQ2/wDYqRbYF/MEZjFXYAbzxEIJCe0Pw/K6445Ltw0bZlzaCgmR/uM/zD+eL3+lb1ODp7lZZ51OBU+frh4dzZU77zQ/rh32/TpTp5rfNyPD/L7f/a75fSdNMr+vHV7LNvt650nIoEG2+H+KLzVYulhCTEyMgoODVfGVpSErKioU1/YY1Dbi4uJ82h8AAAAAvsrSIBQaGqrx48dr69atLdvcbre2bt2q9PT0Du+Tnp7ebn9JKi4u/tr9AQAAAOCrLD80Li8vT1lZWUpNTdXEiRO1YsUKOZ1OZWdnS5Lmzp2rwYMHa+nSpZKkRYsW6Y477tDzzz+vu+++Wxs2bNCePXv0yiuvWPk0AAAAAFxDLA9Cs2bNUlVVlQoKClReXq6UlBQVFRW1LIhw4sQJBQW1Nq5uu+02rV+/Xk899ZR+9rOfacSIEdq0aZNuvfVWq54CAAAAgGuM5UFIknJycpSTk9PhbSUXV1Bpa+bMmZo5c+ZVrgoAAADA9crSc4QAAAAAwAoEIQAAAAABhyAEAAAAIOAQhAAAAAAEHFssltCdPB6PJKm2ttbiSqTm5mY1NDSotrbWFt/EC3tinsAs5grMYJ7ALOYKzLDbPPF+xvd+5r+cgAtCdXV1kqT4+HiLKwEAAABwNdTV1alPnz6X3cfhMROXriNut1unT59WZGSkHA6HpbXU1tYqPj5eJ0+eVFRUlKW1wL6YJzCLuQIzmCcwi7kCM+w2Tzwej+rq6jRo0KB230XakYDrCAUFBWnIkCFWl9FOVFSULSYO7I15ArOYKzCDeQKzmCsww07z5EqdIC8WSwAAAAAQcAhCAAAAAAIOQchCYWFhKiwsVFhYmNWlwMaYJzCLuQIzmCcwi7kCM67leRJwiyUAAAAAAB0hAAAAAAGHIAQAAAAg4BCEAAAAAAQcghAAAACAgEMQssiqVauUmJio8PBwpaWladeuXVaXBBvasWOHZsyYoUGDBsnhcGjTpk1WlwQbWrp0qSZMmKDIyEgNGDBAmZmZOnr0qNVlwWZWr16tpKSkli89TE9P17vvvmt1WbC5ZcuWyeFwKDc31+pSYDOLFy+Ww+Fodxk1apTVZfmEIGSBjRs3Ki8vT4WFhdq3b5+Sk5M1depUVVZWWl0abMbpdCo5OVmrVq2yuhTY2Pbt27VgwQJ9+OGHKi4uVnNzszIyMuR0Oq0uDTYyZMgQLVu2THv37tWePXs0adIk3XvvvTp8+LDVpcGmdu/erZdffllJSUlWlwKb+sY3vqGysrKWy/vvv291ST5h+WwLpKWlacKECVq5cqUkye12Kz4+XgsXLtSTTz5pcXWwK4fDobfeekuZmZlWlwKbq6qq0oABA7R9+3Z95zvfsboc2Fjfvn313HPP6ZFHHrG6FNhMfX29xo0bp1//+tf65S9/qZSUFK1YscLqsmAjixcv1qZNm3TgwAGrS/EbHaFu1tTUpL1792ry5Mkt24KCgjR58mTt3LnTwsoAXC9qamokGR9ygY64XC5t2LBBTqdT6enpVpcDG1qwYIHuvvvudp9XgK/65JNPNGjQIA0bNkwPPvigTpw4YXVJPulhdQGBprq6Wi6XS7Gxse22x8bG6siRIxZVBeB64Xa7lZubq29961u69dZbrS4HNnPo0CGlp6fr/PnzioiI0FtvvaXRo0dbXRZsZsOGDdq3b592795tdSmwsbS0NK1bt04jR45UWVmZlixZom9/+9sqLS1VZGSk1eWZQhACgOvIggULVFpaes0dp43uMXLkSB04cEA1NTV68803lZWVpe3btxOG0OLkyZNatGiRiouLFR4ebnU5sLFp06a1XE9KSlJaWpqGDh2qP/7xj9fM4bYEoW4WExOj4OBgVVRUtNteUVGhuLg4i6oCcD3IycnR22+/rR07dmjIkCFWlwMbCg0N1fDhwyVJ48eP1+7du/Xiiy/q5Zdftrgy2MXevXtVWVmpcePGtWxzuVzasWOHVq5cqcbGRgUHB1tYIewqOjpaN998s44dO2Z1KaZxjlA3Cw0N1fjx47V169aWbW63W1u3buU4bQB+8Xg8ysnJ0VtvvaW//vWvuvHGG60uCdcIt9utxsZGq8uAjXz3u9/VoUOHdODAgZZLamqqHnzwQR04cIAQhK9VX1+v48ePa+DAgVaXYhodIQvk5eUpKytLqampmjhxolasWCGn06ns7GyrS4PN1NfXt/vLyqeffqoDBw6ob9++SkhIsLAy2MmCBQu0fv16bd68WZGRkSovL5ck9enTRz179rS4OthFfn6+pk2bpoSEBNXV1Wn9+vUqKSnRli1brC4NNhIZGXnJ+YW9e/dWv379OO8Q7fzkJz/RjBkzNHToUJ0+fVqFhYUKDg7W7NmzrS7NNIKQBWbNmqWqqioVFBSovLxcKSkpKioqumQBBWDPnj266667Wn7Py8uTJGVlZWndunUWVQW7Wb16tSTpzjvvbLf9d7/7nR566KHuLwi2VFlZqblz56qsrEx9+vRRUlKStmzZoilTplhdGoBr0BdffKHZs2frzJkz6t+/v26//XZ9+OGH6t+/v9Wlmcb3CAEAAAAIOJwjBAAAACDgEIQAAAAABByCEAAAAICAQxACAAAAEHAIQgAAAAACDkEIAAAAQMAhCAEAAAAIOAQhAAAAAAGHIAQAsJ2HHnpImZmZ3f6469atk8PhkMPhUG5ubsv2xMRErVix4rL39d4vOjr6qtYIAOgaPawuAAAQWBwOx2VvLyws1IsvviiPx9NNFbUXFRWlo0ePqnfv3j7dr6ysTBs3blRhYeFVqgwA0JUIQgCAblVWVtZyfePGjSooKNDRo0dbtkVERCgiIsKK0iQZQS0uLs7n+8XFxalPnz5XoSIAwNXAoXEAgG4VFxfXcunTp09L8PBeIiIiLjk07s4779TChQuVm5urG264QbGxsVqzZo2cTqeys7MVGRmp4cOH69133233WKWlpZo2bZoiIiIUGxurOXPmqLq62q+6Gxoa9PDDDysyMlIJCQl65ZVXOvMyAAAsRhACAFwTXn31VcXExGjXrl1auHChHn/8cc2cOVO33Xab9u3bp4yMDM2ZM0cNDQ2SpHPnzmnSpEkaO3as9uzZo6KiIlVUVOj+++/36/Gff/55paamav/+/Zo/f74ef/zxdp0sAMC1hSAEALgmJCcn66mnntKIESOUn5+v8PBwxcTEaN68eRoxYoQKCgp05swZffTRR5KklStXauzYsXr66ac1atQojR07VmvXrtW2bdv0z3/+0+fHnz59uubPn6/hw4friSeeUExMjLZt29bVTxMA0E04RwgAcE1ISkpquR4cHKx+/fppzJgxLdtiY2MlSZWVlZKkgwcPatu2bR2eb3T8+HHdfPPNfj++93A+72MBAK49BCEAwDUhJCSk3e8Oh6PdNu9qdG63W5JUX1+vGTNm6JlnnrlkrIEDB3bJ43sfCwBw7SEIAQCuS+PGjdOf/vQnJSYmqkcP/rkDALTHOUIAgOvSggULdPbsWc2ePVu7d+/W8ePHtWXLFmVnZ8vlclldHgDAYgQhAMB1adCgQfrb3/4ml8uljIwMjRkzRrm5uYqOjlZQEP/8AUCgc3is+upuAABsZt26dcrNzdW5c+csuT8AoPvwJzEAANqoqalRRESEnnjiCZ/uFxERoR/96EdXqSoAQFejIwQAwEV1dXWqqKiQJEVHRysmJsb0fY8dOybJWNr7xhtvvCr1AQC6DkEIAAAAQMDh0DgAAAAAAYcgBAAAACDgEIQAAAAABByCEAAAAICAQxACAAAAEHAIQgAAAAACDkEIAAAAQMAhCAEAAAAIOP8PSnGXrxa5secAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IOLRVcsFJ5NX",
        "outputId": "5d1502cb-c004-42e6-88bf-54caa3cf81f8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Y : 0.4\n",
            "qSmax : 1.25\n",
            "Ks : 0.1\n"
          ]
        }
      ],
      "source": [
        "disp('culture')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yjcgKrvpJ96A",
        "outputId": "7818ff75-7c4f-4dc1-9be8-095e69a53d67"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cell specific growth rate variable : -0.0 [ 1/h ]\n"
          ]
        }
      ],
      "source": [
        "# Growth rate variable at the end of the cultivation\n",
        "describe('mu')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sLF6NdXvKBY4",
        "outputId": "f0d26041-c3a4-43a0-c853-0980181fa52c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['bioreactor', 'bioreactor.culture']\n"
          ]
        }
      ],
      "source": [
        "describe('parts')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cZKKtkydKHH-",
        "outputId": "bf906364-daa5-4510-8f55-4393d21a9b02"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSL: 3.2.3 - used components: none\n"
          ]
        }
      ],
      "source": [
        "describe('MSL')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D7lCWmdiKNLm",
        "outputId": "a0cb6bc8-e107-4fa4-8100-a3806e381bf6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "System information\n",
            " -OS: Linux\n",
            " -Python: 3.10.12\n",
            " -Scipy: not installed in the notebook\n",
            " -FMPy: 0.3.20\n",
            " -FMU by: OpenModelica Compiler OpenModelica 1.21.0\n",
            " -FMI: 2.0\n",
            " -Type: ME\n",
            " -Name: BPL_TEST2.Batch\n",
            " -Generated: 2024-03-05T08:05:44Z\n",
            " -MSL: 3.2.3\n",
            " -Description: Bioprocess Library version 2.2.0\n",
            " -Interaction: FMU-explore for FMPy version 1.0.0\n"
          ]
        }
      ],
      "source": [
        "system_info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lowen2GTLKMi"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.16"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}