Run_RPV_model_Colab.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p7drOtqM4k-H"
      },
      "source": [
        "### This Jupyter notebook is used to run the ensemble neural network machine\n",
        "### learning model to predict embrittlement of reactor pressure vessel (RPV)\n",
        "### steel alloys. If you find the model useful, please cite the original reference:\n",
        "\n",
        "### Jacobs, R., Yamamoto, T., Odette, G. R., Morgan, D. \"Predictions and\n",
        "### Uncertainty Estimates of Reactor Pressure Vessel Steel Embrittlement Using\n",
        "### Machine Learning\", Materials and Design, 236 (2023) (https://doi.org/10.1016/j.matdes.2023.112491).\n",
        "\n",
        "### Note that this model was developed for academic research purposes and should not be used for activities where certified accuracy is required (e.g., regulatory activities) without further discussion.\n",
        "### Current work is ongoing to identify potential issues and update the model accordingly. Therefore, the model may evolve in the future and have newer versions that give somewhat different answers. Any questions/feedback should be sent to:\n",
        "### Ryan Jacobs (rjacobs3@wisc.edu) or Dane Morgan (ddmorgan@wisc.edu)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tWlY3QZJ4wLT"
      },
      "source": [
        "First, we clone the RPV model Github repository to this Colab session:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UtvMNMvf0ypP",
        "outputId": "091f13d7-79bc-4b1e-8ed4-357b0c4e08c0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'RPV_model_Jacobs23'...\n",
            "remote: Enumerating objects: 23, done.\u001b[K\n",
            "remote: Counting objects: 100% (23/23), done.\u001b[K\n",
            "remote: Compressing objects: 100% (22/22), done.\u001b[K\n",
            "remote: Total 23 (delta 0), reused 20 (delta 0), pack-reused 0 (from 0)\u001b[K\n",
            "Receiving objects: 100% (23/23), 108.25 MiB | 21.89 MiB/s, done.\n"
          ]
        }
      ],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "!git clone https://github.com/uw-cmg/RPV_model_Jacobs23.git"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XYdEixBy41E3"
      },
      "source": [
        "Next, we install required software packages:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nxACBsan2Fbv",
        "outputId": "cad18d50-122a-4b8a-c07b-75a7dfff97a4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tensorflow==2.12.0 in /usr/local/lib/python3.11/dist-packages (from -r RPV_model_Jacobs23/requirements.txt (line 1)) (2.12.0)\n",
            "Requirement already satisfied: keras==2.12.0 in /usr/local/lib/python3.11/dist-packages (from -r RPV_model_Jacobs23/requirements.txt (line 2)) (2.12.0)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (from -r RPV_model_Jacobs23/requirements.txt (line 3)) (1.6.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from -r RPV_model_Jacobs23/requirements.txt (line 4)) (1.23.5)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (from -r RPV_model_Jacobs23/requirements.txt (line 5)) (2.2.2)\n",
            "Requirement already satisfied: mastml in /usr/local/lib/python3.11/dist-packages (from -r RPV_model_Jacobs23/requirements.txt (line 6)) (3.2.1)\n",
            "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (1.4.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=2.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (24.12.23)\n",
            "Requirement already satisfied: gast<=0.4.0,>=0.2.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (0.4.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (0.2.0)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (1.69.0)\n",
            "Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (3.12.1)\n",
            "Requirement already satisfied: jax>=0.3.15 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (0.4.30)\n",
            "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (18.1.1)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (3.4.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (24.2)\n",
            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (4.25.5)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (75.1.0)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (1.17.0)\n",
            "Requirement already satisfied: tensorboard<2.13,>=2.12 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (2.12.3)\n",
            "Requirement already satisfied: tensorflow-estimator<2.13,>=2.12.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (2.12.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (2.5.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (4.12.2)\n",
            "Requirement already satisfied: wrapt<1.15,>=1.11.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (1.14.1)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (0.37.1)\n",
            "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn->-r RPV_model_Jacobs23/requirements.txt (line 3)) (1.13.1)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn->-r RPV_model_Jacobs23/requirements.txt (line 3)) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn->-r RPV_model_Jacobs23/requirements.txt (line 3)) (3.5.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas->-r RPV_model_Jacobs23/requirements.txt (line 5)) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas->-r RPV_model_Jacobs23/requirements.txt (line 5)) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas->-r RPV_model_Jacobs23/requirements.txt (line 5)) (2024.2)\n",
            "Collecting scikit-optimize (from mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading scikit_optimize-0.10.2-py2.py3-none-any.whl.metadata (9.7 kB)\n",
            "Collecting citrination-client (from mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading citrination-client-6.5.1.tar.gz (54 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.9/54.9 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting foundry-ml (from mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading foundry_ml-1.0.4-py3-none-any.whl.metadata (7.3 kB)\n",
            "Collecting matminer (from mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading matminer-0.9.3-py3-none-any.whl.metadata (4.9 kB)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (from mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (3.10.0)\n",
            "Collecting mdf-forge (from mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading mdf_forge-0.8.0-py2.py3-none-any.whl.metadata (1.1 kB)\n",
            "Collecting mdf-toolbox (from mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading mdf_toolbox-0.7.1-py2.py3-none-any.whl.metadata (821 bytes)\n",
            "Requirement already satisfied: openpyxl in /usr/local/lib/python3.11/dist-packages (from mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (3.1.5)\n",
            "Collecting pathos (from mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading pathos-0.3.3-py3-none-any.whl.metadata (11 kB)\n",
            "Collecting pykan==0.0.5 (from mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading pykan-0.0.5-py3-none-any.whl.metadata (13 kB)\n",
            "Collecting pymatgen (from mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading pymatgen-2025.1.23.tar.gz (3.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.11/dist-packages (from mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (6.0.2)\n",
            "Collecting scikit-learn-extra (from mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading scikit_learn_extra-0.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.6 kB)\n",
            "Requirement already satisfied: shap in /usr/local/lib/python3.11/dist-packages (from mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (0.46.0)\n",
            "Collecting sphinx-automodapi (from mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading sphinx_automodapi-0.18.0-py3-none-any.whl.metadata (2.9 kB)\n",
            "Requirement already satisfied: statsmodels in /usr/local/lib/python3.11/dist-packages (from mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (0.14.4)\n",
            "Collecting madml (from mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading madml-2.6.7-py3-none-any.whl.metadata (2.5 kB)\n",
            "Collecting udocker (from mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading udocker-1.3.17-py2.py3-none-any.whl.metadata (37 kB)\n",
            "Collecting transfernet (from mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading transfernet-0.4.2-py3-none-any.whl.metadata (1.4 kB)\n",
            "Collecting forestci (from mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading forestci-0.7.tar.gz (12 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.11/dist-packages (from astunparse>=1.6.0->tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (0.45.1)\n",
            "Requirement already satisfied: jaxlib<=0.4.30,>=0.4.27 in /usr/local/lib/python3.11/dist-packages (from jax>=0.3.15->tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (0.4.30)\n",
            "Requirement already satisfied: ml-dtypes>=0.2.0 in /usr/local/lib/python3.11/dist-packages (from jax>=0.3.15->tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (0.4.1)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.13,>=2.12->tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (2.27.0)\n",
            "Requirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.13,>=2.12->tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (1.0.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.13,>=2.12->tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (3.7)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.13,>=2.12->tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (2.32.3)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.13,>=2.12->tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (0.7.2)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.13,>=2.12->tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (3.1.3)\n",
            "Collecting pypif (from citrination-client->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading pypif-2.1.2.tar.gz (14 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting globus-sdk<4,>=3 (from foundry-ml->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading globus_sdk-3.50.0-py3-none-any.whl.metadata (3.3 kB)\n",
            "Collecting dlhub-sdk>=1.0.0 (from foundry-ml->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading dlhub_sdk-2.1.0-py2.py3-none-any.whl.metadata (1.2 kB)\n",
            "Requirement already satisfied: pydantic>=2.7.2 in /usr/local/lib/python3.11/dist-packages (from foundry-ml->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (2.10.5)\n",
            "Collecting mdf-connect-client>=0.5.0 (from foundry-ml->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading mdf_connect_client-0.5.0-py2.py3-none-any.whl.metadata (792 bytes)\n",
            "Collecting json2table (from foundry-ml->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading json2table-1.1.5-py2.py3-none-any.whl.metadata (6.0 kB)\n",
            "Requirement already satisfied: tqdm>=4.19.4 in /usr/local/lib/python3.11/dist-packages (from mdf-forge->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (4.67.1)\n",
            "Collecting fair-research-login>=0.2.4 (from mdf-toolbox->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading fair_research_login-0.3.1-py3-none-any.whl.metadata (3.1 kB)\n",
            "Requirement already satisfied: jsonschema>=4.3.0 in /usr/local/lib/python3.11/dist-packages (from mdf-toolbox->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (4.23.0)\n",
            "Requirement already satisfied: et-xmlfile in /usr/local/lib/python3.11/dist-packages (from openpyxl->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (2.0.0)\n",
            "Requirement already satisfied: pytest in /usr/local/lib/python3.11/dist-packages (from madml->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (8.3.4)\n",
            "Collecting docker (from madml->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading docker-7.1.0-py3-none-any.whl.metadata (3.8 kB)\n",
            "Collecting scikeras (from madml->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading scikeras-0.13.0-py3-none-any.whl.metadata (3.1 kB)\n",
            "Collecting kneed (from madml->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading kneed-0.8.5-py3-none-any.whl.metadata (5.5 kB)\n",
            "Collecting pymongo~=4.5 (from matminer->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading pymongo-4.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (22 kB)\n",
            "Requirement already satisfied: sympy~=1.11 in /usr/local/lib/python3.11/dist-packages (from matminer->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (1.13.1)\n",
            "Collecting monty>=2023 (from matminer->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading monty-2025.1.9-py3-none-any.whl.metadata (3.6 kB)\n",
            "Requirement already satisfied: networkx>=2.7 in /usr/local/lib/python3.11/dist-packages (from pymatgen->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (3.4.2)\n",
            "Collecting palettable>=3.3.3 (from pymatgen->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading palettable-3.3.3-py2.py3-none-any.whl.metadata (3.3 kB)\n",
            "Requirement already satisfied: plotly>=4.5.0 in /usr/local/lib/python3.11/dist-packages (from pymatgen->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (5.24.1)\n",
            "Collecting pybtex>=0.24.0 (from pymatgen->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading pybtex-0.24.0-py2.py3-none-any.whl.metadata (2.0 kB)\n",
            "Collecting ruamel.yaml>=0.17.0 (from pymatgen->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading ruamel.yaml-0.18.10-py3-none-any.whl.metadata (23 kB)\n",
            "Collecting spglib>=2.5.0 (from pymatgen->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading spglib-2.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)\n",
            "Requirement already satisfied: tabulate>=0.9 in /usr/local/lib/python3.11/dist-packages (from pymatgen->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (0.9.0)\n",
            "Collecting uncertainties>=3.1.4 (from pymatgen->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading uncertainties-3.2.2-py3-none-any.whl.metadata (6.9 kB)\n",
            "INFO: pip is looking at multiple versions of pymatgen to determine which version is compatible with other requirements. This could take a while.\n",
            "Collecting pymatgen (from mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading pymatgen-2025.1.9-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n",
            "  Downloading pymatgen-2024.11.13-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n",
            "  Downloading pymatgen-2024.10.29-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n",
            "  Downloading pymatgen-2024.10.27-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n",
            "  Downloading pymatgen-2024.10.25-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n",
            "  Downloading pymatgen-2024.10.22-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n",
            "  Downloading pymatgen-2024.10.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n",
            "INFO: pip is still looking at multiple versions of pymatgen to determine which version is compatible with other requirements. This could take a while.\n",
            "  Downloading pymatgen-2024.9.17.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n",
            "  Downloading pymatgen-2024.9.17-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n",
            "  Downloading pymatgen-2024.9.10-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n",
            "  Downloading pymatgen-2024.8.9-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n",
            "  Downloading pymatgen-2024.7.18-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n",
            "INFO: This is taking longer than usual. You might need to provide the dependency resolver with stricter constraints to reduce runtime. See https://pip.pypa.io/warnings/backtracking for guidance. If you want to abort this run, press Ctrl + C.\n",
            "  Downloading pymatgen-2024.6.10-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n",
            "  Downloading pymatgen-2024.6.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n",
            "  Downloading pymatgen-2024.5.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n",
            "  Downloading pymatgen-2024.4.13-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n",
            "  Downloading pymatgen-2024.4.12-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n",
            "  Downloading pymatgen-2024.3.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
            "  Downloading pymatgen-2024.2.23-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
            "  Downloading pymatgen-2024.2.20-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
            "  Downloading pymatgen-2024.2.8-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
            "  Downloading pymatgen-2024.1.27.tar.gz (7.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.2/7.2 MB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Downloading pymatgen-2024.1.26-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
            "  Downloading pymatgen-2023.12.18-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n",
            "  Downloading pymatgen-2023.11.12-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
            "  Downloading pymatgen-2023.11.10-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n",
            "  Downloading pymatgen-2023.10.11-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
            "  Downloading pymatgen-2023.10.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
            "  Downloading pymatgen-2023.10.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
            "  Downloading pymatgen-2023.9.25-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (1.3.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (4.55.3)\n",
            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (1.4.8)\n",
            "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.11/dist-packages (from matplotlib->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (11.1.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (3.2.1)\n",
            "Collecting ppft>=1.7.6.9 (from pathos->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading ppft-1.7.6.9-py3-none-any.whl.metadata (12 kB)\n",
            "Collecting dill>=0.3.9 (from pathos->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading dill-0.3.9-py3-none-any.whl.metadata (10 kB)\n",
            "Collecting pox>=0.3.5 (from pathos->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading pox-0.3.5-py3-none-any.whl.metadata (8.0 kB)\n",
            "Collecting multiprocess>=0.70.17 (from pathos->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading multiprocess-0.70.17-py311-none-any.whl.metadata (7.2 kB)\n",
            "Collecting pyaml>=16.9 (from scikit-optimize->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading pyaml-25.1.0-py3-none-any.whl.metadata (12 kB)\n",
            "Requirement already satisfied: slicer==0.0.8 in /usr/local/lib/python3.11/dist-packages (from shap->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (0.0.8)\n",
            "Requirement already satisfied: numba in /usr/local/lib/python3.11/dist-packages (from shap->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (0.60.0)\n",
            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.11/dist-packages (from shap->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (3.1.0)\n",
            "Requirement already satisfied: sphinx>=4 in /usr/local/lib/python3.11/dist-packages (from sphinx-automodapi->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (8.1.3)\n",
            "Requirement already satisfied: patsy>=0.5.6 in /usr/local/lib/python3.11/dist-packages (from statsmodels->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (1.0.1)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.11/dist-packages (from transfernet->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (2.5.1+cu121)\n",
            "Collecting globus-compute-sdk>=2.0.0 (from dlhub-sdk>=1.0.0->foundry-ml->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading globus_compute_sdk-2.34.0-py3-none-any.whl.metadata (3.0 kB)\n",
            "Collecting PyGithub (from dlhub-sdk>=1.0.0->foundry-ml->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading PyGithub-2.5.0-py3-none-any.whl.metadata (3.9 kB)\n",
            "Requirement already satisfied: pyjwt<3.0.0,>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from pyjwt[crypto]<3.0.0,>=2.0.0->globus-sdk<4,>=3->foundry-ml->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (2.10.1)\n",
            "Requirement already satisfied: cryptography!=3.4.0,>=3.3.1 in /usr/local/lib/python3.11/dist-packages (from globus-sdk<4,>=3->foundry-ml->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (43.0.3)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (5.5.0)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.11/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (0.4.1)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.11/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (4.9)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.11/dist-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (1.3.1)\n",
            "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.11/dist-packages (from jsonschema>=4.3.0->mdf-toolbox->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (24.3.0)\n",
            "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.11/dist-packages (from jsonschema>=4.3.0->mdf-toolbox->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (2024.10.1)\n",
            "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.11/dist-packages (from jsonschema>=4.3.0->mdf-toolbox->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (0.35.1)\n",
            "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.11/dist-packages (from jsonschema>=4.3.0->mdf-toolbox->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (0.22.3)\n",
            "Collecting nameparser>=1.0.4 (from mdf-connect-client>=0.5.0->foundry-ml->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading nameparser-1.1.3-py2.py3-none-any.whl.metadata (6.1 kB)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from plotly>=4.5.0->pymatgen->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (9.0.0)\n",
            "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.11/dist-packages (from pydantic>=2.7.2->foundry-ml->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (0.7.0)\n",
            "Requirement already satisfied: pydantic-core==2.27.2 in /usr/local/lib/python3.11/dist-packages (from pydantic>=2.7.2->foundry-ml->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (2.27.2)\n",
            "Collecting dnspython<3.0.0,>=1.16.0 (from pymongo~=4.5->matminer->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading dnspython-2.7.0-py3-none-any.whl.metadata (5.8 kB)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (3.4.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (2.3.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (2024.12.14)\n",
            "Collecting ruamel.yaml.clib>=0.2.7 (from ruamel.yaml>=0.17.0->pymatgen->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading ruamel.yaml.clib-0.2.12-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.7 kB)\n",
            "Requirement already satisfied: sphinxcontrib-applehelp>=1.0.7 in /usr/local/lib/python3.11/dist-packages (from sphinx>=4->sphinx-automodapi->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (2.0.0)\n",
            "Requirement already satisfied: sphinxcontrib-devhelp>=1.0.6 in /usr/local/lib/python3.11/dist-packages (from sphinx>=4->sphinx-automodapi->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (2.0.0)\n",
            "Requirement already satisfied: sphinxcontrib-htmlhelp>=2.0.6 in /usr/local/lib/python3.11/dist-packages (from sphinx>=4->sphinx-automodapi->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (2.1.0)\n",
            "Requirement already satisfied: sphinxcontrib-jsmath>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from sphinx>=4->sphinx-automodapi->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (1.0.1)\n",
            "Requirement already satisfied: sphinxcontrib-qthelp>=1.0.6 in /usr/local/lib/python3.11/dist-packages (from sphinx>=4->sphinx-automodapi->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (2.0.0)\n",
            "Requirement already satisfied: sphinxcontrib-serializinghtml>=1.1.9 in /usr/local/lib/python3.11/dist-packages (from sphinx>=4->sphinx-automodapi->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (2.0.0)\n",
            "Requirement already satisfied: Jinja2>=3.1 in /usr/local/lib/python3.11/dist-packages (from sphinx>=4->sphinx-automodapi->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (3.1.5)\n",
            "Requirement already satisfied: Pygments>=2.17 in /usr/local/lib/python3.11/dist-packages (from sphinx>=4->sphinx-automodapi->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (2.18.0)\n",
            "Requirement already satisfied: docutils<0.22,>=0.20 in /usr/local/lib/python3.11/dist-packages (from sphinx>=4->sphinx-automodapi->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (0.21.2)\n",
            "Requirement already satisfied: snowballstemmer>=2.2 in /usr/local/lib/python3.11/dist-packages (from sphinx>=4->sphinx-automodapi->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (2.2.0)\n",
            "Requirement already satisfied: babel>=2.13 in /usr/local/lib/python3.11/dist-packages (from sphinx>=4->sphinx-automodapi->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (2.16.0)\n",
            "Requirement already satisfied: alabaster>=0.7.14 in /usr/local/lib/python3.11/dist-packages (from sphinx>=4->sphinx-automodapi->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (1.0.0)\n",
            "Requirement already satisfied: imagesize>=1.3 in /usr/local/lib/python3.11/dist-packages (from sphinx>=4->sphinx-automodapi->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (1.4.1)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy~=1.11->matminer->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (1.3.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.11/dist-packages (from werkzeug>=1.0.1->tensorboard<2.13,>=2.12->tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (3.0.2)\n",
            "Requirement already satisfied: llvmlite<0.44,>=0.43.0dev0 in /usr/local/lib/python3.11/dist-packages (from numba->shap->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (0.43.0)\n",
            "Collecting latexcodec>=1.0.4 (from pybtex>=0.24.0->pymatgen->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading latexcodec-3.0.0-py3-none-any.whl.metadata (4.9 kB)\n",
            "Requirement already satisfied: iniconfig in /usr/local/lib/python3.11/dist-packages (from pytest->madml->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (2.0.0)\n",
            "Requirement already satisfied: pluggy<2,>=1.5 in /usr/local/lib/python3.11/dist-packages (from pytest->madml->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (1.5.0)\n",
            "INFO: pip is looking at multiple versions of scikeras to determine which version is compatible with other requirements. This could take a while.\n",
            "Collecting scikeras (from madml->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading scikeras-0.12.0-py3-none-any.whl.metadata (4.0 kB)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from torch->transfernet->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (3.16.1)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from torch->transfernet->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (2024.10.0)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.11/dist-packages (from torch->transfernet->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.11/dist-packages (from torch->transfernet->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.11/dist-packages (from torch->transfernet->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /usr/local/lib/python3.11/dist-packages (from torch->transfernet->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (9.1.0.70)\n",
            "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.11/dist-packages (from torch->transfernet->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (12.1.3.1)\n",
            "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.11/dist-packages (from torch->transfernet->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (11.0.2.54)\n",
            "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.11/dist-packages (from torch->transfernet->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (10.3.2.106)\n",
            "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.11/dist-packages (from torch->transfernet->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (11.4.5.107)\n",
            "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.11/dist-packages (from torch->transfernet->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (12.1.0.106)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch->transfernet->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (2.21.5)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.11/dist-packages (from torch->transfernet->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (12.1.105)\n",
            "Requirement already satisfied: triton==3.1.0 in /usr/local/lib/python3.11/dist-packages (from torch->transfernet->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (3.1.0)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.11/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch->transfernet->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (12.6.85)\n",
            "Requirement already satisfied: cffi>=1.12 in /usr/local/lib/python3.11/dist-packages (from cryptography!=3.4.0,>=3.3.1->globus-sdk<4,>=3->foundry-ml->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (1.17.1)\n",
            "Collecting globus-compute-common==0.5.0 (from globus-compute-sdk>=2.0.0->dlhub-sdk>=1.0.0->foundry-ml->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading globus_compute_common-0.5.0-py3-none-any.whl.metadata (1.4 kB)\n",
            "Collecting pika>=1.2 (from globus-compute-sdk>=2.0.0->dlhub-sdk>=1.0.0->foundry-ml->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading pika-1.3.2-py3-none-any.whl.metadata (13 kB)\n",
            "Collecting tblib==1.7.0 (from globus-compute-sdk>=2.0.0->dlhub-sdk>=1.0.0->foundry-ml->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading tblib-1.7.0-py2.py3-none-any.whl.metadata (24 kB)\n",
            "Collecting texttable>=1.6.7 (from globus-compute-sdk>=2.0.0->dlhub-sdk>=1.0.0->foundry-ml->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading texttable-1.7.0-py2.py3-none-any.whl.metadata (9.8 kB)\n",
            "Collecting colorama==0.4.6 (from globus-compute-sdk>=2.0.0->dlhub-sdk>=1.0.0->foundry-ml->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading colorama-0.4.6-py2.py3-none-any.whl.metadata (17 kB)\n",
            "Collecting rich==13.7.1 (from globus-compute-sdk>=2.0.0->dlhub-sdk>=1.0.0->foundry-ml->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading rich-13.7.1-py3-none-any.whl.metadata (18 kB)\n",
            "Requirement already satisfied: psutil<6 in /usr/local/lib/python3.11/dist-packages (from globus-compute-sdk>=2.0.0->dlhub-sdk>=1.0.0->foundry-ml->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (5.9.5)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.11/dist-packages (from rich==13.7.1->globus-compute-sdk>=2.0.0->dlhub-sdk>=1.0.0->foundry-ml->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (3.0.0)\n",
            "Requirement already satisfied: pyasn1<0.7.0,>=0.4.6 in /usr/local/lib/python3.11/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (0.6.1)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.11/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tensorflow==2.12.0->-r RPV_model_Jacobs23/requirements.txt (line 1)) (3.2.2)\n",
            "Collecting pynacl>=1.4.0 (from PyGithub->dlhub-sdk>=1.0.0->foundry-ml->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6))\n",
            "  Downloading PyNaCl-1.5.0-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl.metadata (8.6 kB)\n",
            "Requirement already satisfied: Deprecated in /usr/local/lib/python3.11/dist-packages (from PyGithub->dlhub-sdk>=1.0.0->foundry-ml->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (1.2.15)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.11/dist-packages (from cffi>=1.12->cryptography!=3.4.0,>=3.3.1->globus-sdk<4,>=3->foundry-ml->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (2.22)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.11/dist-packages (from markdown-it-py>=2.2.0->rich==13.7.1->globus-compute-sdk>=2.0.0->dlhub-sdk>=1.0.0->foundry-ml->mastml->-r RPV_model_Jacobs23/requirements.txt (line 6)) (0.1.2)\n",
            "Downloading pykan-0.0.5-py3-none-any.whl (33 kB)\n",
            "Downloading foundry_ml-1.0.4-py3-none-any.whl (42 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.5/42.5 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading mdf_forge-0.8.0-py2.py3-none-any.whl (17 kB)\n",
            "Downloading mdf_toolbox-0.7.1-py2.py3-none-any.whl (35 kB)\n",
            "Downloading madml-2.6.7-py3-none-any.whl (18.8 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m18.8/18.8 MB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading matminer-0.9.3-py3-none-any.whl (5.5 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.5/5.5 MB\u001b[0m \u001b[31m14.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading pymatgen-2023.9.25-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (9.8 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.8/9.8 MB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading pathos-0.3.3-py3-none-any.whl (82 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m82.1/82.1 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading scikit_learn_extra-0.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.1 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m72.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading scikit_optimize-0.10.2-py2.py3-none-any.whl (107 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m107.8/107.8 kB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading sphinx_automodapi-0.18.0-py3-none-any.whl (88 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m88.5/88.5 kB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading transfernet-0.4.2-py3-none-any.whl (8.5 kB)\n",
            "Downloading udocker-1.3.17-py2.py3-none-any.whl (119 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119.6/119.6 kB\u001b[0m \u001b[31m10.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading dill-0.3.9-py3-none-any.whl (119 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119.4/119.4 kB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading dlhub_sdk-2.1.0-py2.py3-none-any.whl (56 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.0/56.0 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading fair_research_login-0.3.1-py3-none-any.whl (39 kB)\n",
            "Downloading globus_sdk-3.50.0-py3-none-any.whl (392 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m392.8/392.8 kB\u001b[0m \u001b[31m24.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading mdf_connect_client-0.5.0-py2.py3-none-any.whl (19 kB)\n",
            "Downloading monty-2025.1.9-py3-none-any.whl (51 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m51.5/51.5 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading multiprocess-0.70.17-py311-none-any.whl (144 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m144.3/144.3 kB\u001b[0m \u001b[31m11.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading palettable-3.3.3-py2.py3-none-any.whl (332 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m332.3/332.3 kB\u001b[0m \u001b[31m21.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading pox-0.3.5-py3-none-any.whl (29 kB)\n",
            "Downloading ppft-1.7.6.9-py3-none-any.whl (56 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.8/56.8 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading pyaml-25.1.0-py3-none-any.whl (26 kB)\n",
            "Downloading pymongo-4.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.7 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m47.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading ruamel.yaml-0.18.10-py3-none-any.whl (117 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m117.7/117.7 kB\u001b[0m \u001b[31m8.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading spglib-2.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m40.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading uncertainties-3.2.2-py3-none-any.whl (58 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading docker-7.1.0-py3-none-any.whl (147 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m147.8/147.8 kB\u001b[0m \u001b[31m11.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading json2table-1.1.5-py2.py3-none-any.whl (8.7 kB)\n",
            "Downloading kneed-0.8.5-py3-none-any.whl (10 kB)\n",
            "Downloading pybtex-0.24.0-py2.py3-none-any.whl (561 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m561.4/561.4 kB\u001b[0m \u001b[31m30.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading scikeras-0.12.0-py3-none-any.whl (27 kB)\n",
            "Downloading dnspython-2.7.0-py3-none-any.whl (313 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m313.6/313.6 kB\u001b[0m \u001b[31m20.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading globus_compute_sdk-2.34.0-py3-none-any.whl (73 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m73.0/73.0 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading colorama-0.4.6-py2.py3-none-any.whl (25 kB)\n",
            "Downloading globus_compute_common-0.5.0-py3-none-any.whl (35 kB)\n",
            "Downloading rich-13.7.1-py3-none-any.whl (240 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m240.7/240.7 kB\u001b[0m \u001b[31m17.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading tblib-1.7.0-py2.py3-none-any.whl (12 kB)\n",
            "Downloading latexcodec-3.0.0-py3-none-any.whl (18 kB)\n",
            "Downloading nameparser-1.1.3-py2.py3-none-any.whl (24 kB)\n",
            "Downloading ruamel.yaml.clib-0.2.12-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (739 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m739.1/739.1 kB\u001b[0m \u001b[31m36.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading PyGithub-2.5.0-py3-none-any.whl (375 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m375.9/375.9 kB\u001b[0m \u001b[31m22.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading pika-1.3.2-py3-none-any.whl (155 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m155.4/155.4 kB\u001b[0m \u001b[31m13.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading PyNaCl-1.5.0-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (856 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m856.7/856.7 kB\u001b[0m \u001b[31m33.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading texttable-1.7.0-py2.py3-none-any.whl (10 kB)\n",
            "Building wheels for collected packages: citrination-client, forestci, pypif\n",
            "  Building wheel for citrination-client (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for citrination-client: filename=citrination_client-6.5.1-py2.py3-none-any.whl size=116067 sha256=d5f3ef9da94f2eec913223008cc04f395b5f227fb2dee5af1615a3264f1d36a9\n",
            "  Stored in directory: /root/.cache/pip/wheels/4d/f2/e2/8509e5230fe491425ecf0c0c5c0aea55f6003017ceb609e1eb\n",
            "  Building wheel for forestci (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for forestci: filename=forestci-0.7-py3-none-any.whl size=12016 sha256=44c141347fd18f546a48307695fc0c96e677dc9ca01207e1685af26b7f861322\n",
            "  Stored in directory: /root/.cache/pip/wheels/f3/8e/ad/88ee48b8b9bd2ded707a0979332029ad6d183a6a84eb574ce5\n",
            "  Building wheel for pypif (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pypif: filename=pypif-2.1.2-py2.py3-none-any.whl size=29472 sha256=1c9e288e4fc445a34ae2c61098d3b28b826d1b58700ca38557917eac31bc1a9e\n",
            "  Stored in directory: /root/.cache/pip/wheels/c6/c3/af/2da785dca1de65c7082ae1763760236c4c26f90d542adca2c0\n",
            "Successfully built citrination-client forestci pypif\n",
            "Installing collected packages: udocker, texttable, nameparser, json2table, uncertainties, tblib, spglib, ruamel.yaml.clib, pypif, pykan, pyaml, ppft, pox, pika, palettable, latexcodec, dnspython, dill, colorama, ruamel.yaml, rich, pynacl, pymongo, pybtex, multiprocess, kneed, docker, citrination-client, sphinx-automodapi, scikit-optimize, scikit-learn-extra, scikeras, pathos, monty, globus-compute-common, forestci, pymatgen, PyGithub, globus-sdk, transfernet, matminer, globus-compute-sdk, fair-research-login, mdf-toolbox, madml, mdf-forge, mdf-connect-client, dlhub-sdk, foundry-ml\n",
            "  Attempting uninstall: rich\n",
            "    Found existing installation: rich 13.9.4\n",
            "    Uninstalling rich-13.9.4:\n",
            "      Successfully uninstalled rich-13.9.4\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "bigframes 1.31.0 requires numpy>=1.24.0, but you have numpy 1.23.5 which is incompatible.\n",
            "pymc 5.19.1 requires numpy>=1.25.0, but you have numpy 1.23.5 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed PyGithub-2.5.0 citrination-client-6.5.1 colorama-0.4.6 dill-0.3.9 dlhub-sdk-2.1.0 dnspython-2.7.0 docker-7.1.0 fair-research-login-0.3.1 forestci-0.7 foundry-ml-1.0.4 globus-compute-common-0.5.0 globus-compute-sdk-2.34.0 globus-sdk-3.50.0 json2table-1.1.5 kneed-0.8.5 latexcodec-3.0.0 madml-2.6.7 matminer-0.9.3 mdf-connect-client-0.5.0 mdf-forge-0.8.0 mdf-toolbox-0.7.1 monty-2025.1.9 multiprocess-0.70.17 nameparser-1.1.3 palettable-3.3.3 pathos-0.3.3 pika-1.3.2 pox-0.3.5 ppft-1.7.6.9 pyaml-25.1.0 pybtex-0.24.0 pykan-0.0.5 pymatgen-2023.9.25 pymongo-4.10.1 pynacl-1.5.0 pypif-2.1.2 rich-13.7.1 ruamel.yaml-0.18.10 ruamel.yaml.clib-0.2.12 scikeras-0.12.0 scikit-learn-extra-0.3.0 scikit-optimize-0.10.2 spglib-2.5.0 sphinx-automodapi-0.18.0 tblib-1.7.0 texttable-1.7.0 transfernet-0.4.2 udocker-1.3.17 uncertainties-3.2.2\n"
          ]
        }
      ],
      "source": [
        "!pip install -r RPV_model_Jacobs23/requirements.txt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zGurmUNM48Mi"
      },
      "source": [
        "Restart the runtime (Runtime -> Restart Session) for the new package installs to take effect (you may get prompted with a pop-up to do this)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jdPTpPKF5B0-"
      },
      "source": [
        "Add the path of the RPV_model_Jacobs23 repo so python can find it"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "2qZC19C62ZOc"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "sys.path.append('RPV_model_Jacobs23')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cqkvd-Bh7zQH"
      },
      "source": [
        "This repo is meant to run the Jacobs '23 neural network model. For other models, see our development repo at https://github.com/uw-cmg/RPV_model\n",
        "\n",
        "fullfit: The full fit model to the entire database of UCSB+PLOTTER data. This is the current production model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "yjxQbuwG7zQH"
      },
      "outputs": [],
      "source": [
        "model_folder = 'fullfit'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "suwfBBIC5FV-"
      },
      "source": [
        "Import the prediction function and your test data (an example is given in test_data.csv) and make predictions! Note the input features of your test data need to be in the same order as shown in this test file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aMZavzc9174I",
        "outputId": "43a70d74-add9-4c03-c91e-7807cd085c76"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "scikit-lego is an optional dependency, enabling use of the LowessRegression model. If you want to use this model, do \"pip install scikit-lego\"\n",
            "linear-tree is an optional dependency, enabling use of Linear tree, forest, and boosting models. If you want to use this model, do \"pip install linear-tree\"\n",
            "gplearn is an optional dependency, enabling the use of genetic programming SymbolicRegressor model. If you want to use this model, do \"pip install gplearn\"\n",
            "Could not find designated model type in scikit-learn model library. Note the other supported modeltype is the keras.wrappers.scikit_learn.KerasRegressor model\n",
            "1/1 [==============================] - 0s 92ms/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/sklearn/base.py:380: InconsistentVersionWarning: Trying to unpickle estimator StandardScaler from version 0.24.1 when using version 1.6.0. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
            "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.11/dist-packages/sklearn/utils/validation.py:2732: UserWarning: X has feature names, but StandardScaler was fitted without feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 49ms/step\n",
            "1/1 [==============================] - 0s 44ms/step\n",
            "1/1 [==============================] - 0s 50ms/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7be3fc996c00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 49ms/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7be3eff54a40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 52ms/step\n",
            "1/1 [==============================] - 0s 51ms/step\n",
            "1/1 [==============================] - 0s 52ms/step\n",
            "1/1 [==============================] - 0s 53ms/step\n",
            "1/1 [==============================] - 0s 69ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n"
          ]
        }
      ],
      "source": [
        "from model_predict_df import make_predictions_DNN\n",
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('RPV_model_Jacobs23/test_data.csv')\n",
        "preds = make_predictions_DNN(df, model_folder)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ANOkZu9U5OqW"
      },
      "source": [
        "Display your predictions. The values in the \"preds\" column are predicted TTS values, in degrees Celsius. The \"ebars\" column is the calibrated, one sigma error bar from the ensemble model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "rsoL0Y2u2Cm4",
        "outputId": "da34077f-0ae8-40da-f94a-8b02015f1a70"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       preds      ebars\n",
              "0  14.001073   8.925599\n",
              "1  14.001073   8.925599\n",
              "2  27.393063  13.632395\n",
              "3  27.393063  13.632395\n",
              "4  12.671827  11.352390\n",
              "5  21.425608  12.390108\n",
              "6  18.096539  12.848585\n",
              "7  18.096539  12.848585\n",
              "8  28.334789  11.072144\n",
              "9  33.445641  11.628885"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fc1dbd00-941b-4381-ba59-5bbda0e13072\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>preds</th>\n",
              "      <th>ebars</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14.001073</td>\n",
              "      <td>8.925599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>14.001073</td>\n",
              "      <td>8.925599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>27.393063</td>\n",
              "      <td>13.632395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>27.393063</td>\n",
              "      <td>13.632395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12.671827</td>\n",
              "      <td>11.352390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>21.425608</td>\n",
              "      <td>12.390108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>18.096539</td>\n",
              "      <td>12.848585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>18.096539</td>\n",
              "      <td>12.848585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>28.334789</td>\n",
              "      <td>11.072144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>33.445641</td>\n",
              "      <td>11.628885</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fc1dbd00-941b-4381-ba59-5bbda0e13072')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fc1dbd00-941b-4381-ba59-5bbda0e13072 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-fc1dbd00-941b-4381-ba59-5bbda0e13072');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-bf450c55-651d-4ab6-83f9-6865fb3935bf\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bf450c55-651d-4ab6-83f9-6865fb3935bf')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bf450c55-651d-4ab6-83f9-6865fb3935bf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_1095ebe7-ae73-43e6-aff2-2333e63e92a4\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('preds')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_1095ebe7-ae73-43e6-aff2-2333e63e92a4 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('preds');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "preds",
              "summary": "{\n  \"name\": \"preds\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"preds\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          14.001072883605957,\n          27.393062591552734,\n          28.334789276123047\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ebars\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          8.925599098205566,\n          13.632394790649414,\n          11.0721435546875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "preds"
      ]
    },
    {
      "source": [
        "# @title preds\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "preds['preds'].plot(kind='hist', bins=20, title='preds')\n",
        "plt.gca().spines[['top', 'right',]].set_visible(False)"
      ],
      "cell_type": "code",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "metadata": {
        "cellView": "form",
        "id": "y1h5RehB-4qV",
        "outputId": "31f69dca-99fd-4958-d3d8-36b65bd3edc1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        }
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K68bbnExy5qV"
      },
      "source": [
        "Another approach is to simply edit a dictionary of the feature values below and make predictions. Note that each feature list needs to be the same length."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 806
        },
        "id": "v9hbvFug3Dbp",
        "outputId": "c5a2d7e6-feed-42c0-fb1d-92fc65cbc25a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Could not find designated model type in scikit-learn model library. Note the other supported modeltype is the keras.wrappers.scikit_learn.KerasRegressor model\n",
            "1/1 [==============================] - 0s 41ms/step\n",
            "1/1 [==============================] - 0s 43ms/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/sklearn/base.py:380: InconsistentVersionWarning: Trying to unpickle estimator StandardScaler from version 0.24.1 when using version 1.6.0. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
            "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.11/dist-packages/sklearn/utils/validation.py:2732: UserWarning: X has feature names, but StandardScaler was fitted without feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 54ms/step\n",
            "1/1 [==============================] - 0s 53ms/step\n",
            "1/1 [==============================] - 0s 48ms/step\n",
            "1/1 [==============================] - 0s 57ms/step\n",
            "1/1 [==============================] - 0s 47ms/step\n",
            "1/1 [==============================] - 0s 48ms/step\n",
            "1/1 [==============================] - 0s 50ms/step\n",
            "1/1 [==============================] - 0s 45ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       preds      ebars\n",
              "0  14.001073   8.925599\n",
              "1  18.109430  12.839308\n",
              "2  28.369940  11.057064"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fd275acb-3e36-4d62-ad84-759191a01821\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>preds</th>\n",
              "      <th>ebars</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14.001073</td>\n",
              "      <td>8.925599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18.109430</td>\n",
              "      <td>12.839308</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28.369940</td>\n",
              "      <td>11.057064</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fd275acb-3e36-4d62-ad84-759191a01821')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fd275acb-3e36-4d62-ad84-759191a01821 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-fd275acb-3e36-4d62-ad84-759191a01821');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-5e153b79-7706-40b1-b0a5-282b7e4f3668\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5e153b79-7706-40b1-b0a5-282b7e4f3668')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5e153b79-7706-40b1-b0a5-282b7e4f3668 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_f19435bf-9ee7-41b6-95ae-e98f68c02682\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('preds_new')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_f19435bf-9ee7-41b6-95ae-e98f68c02682 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('preds_new');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "preds_new",
              "summary": "{\n  \"name\": \"preds_new\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"preds\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          14.001072883605957,\n          18.10943031311035,\n          28.36993980407715\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ebars\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          8.925599098205566,\n          12.83930778503418,\n          11.057064056396484\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "d = {'temperature_C': [287, 291.1, 292.2],\n",
        "     'wt_percent_Cu': [0.04, 0.15, 0.15],\n",
        "     'wt_percent_Ni': [0.74, 0.52, 0.52],\n",
        "     'wt_percent_Mn': [0.6, 1.32, 1.32],\n",
        "     'wt_percent_P': [0.012, 0.01, 0.01],\n",
        "     'wt_percent_Si': [0.23, 0.2, 0.2],\n",
        "     'wt_percent_C': [0.21, 0.21, 0.21],\n",
        "     'log(fluence_n_cm2)': [18.664547962246548, 17.861534410859036, 19.012837224705173],\n",
        "     'log(flux_n_cm2_sec)': [11.165741355311178, 10.389599950323971, 10.966255912381982]}\n",
        "\n",
        "df_fromd = pd.DataFrame(d)\n",
        "preds_new = make_predictions_DNN(df_fromd, model_folder)\n",
        "\n",
        "preds_new"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iH3sD2_X0BUO"
      },
      "source": [
        "Here, we can take a designated temperature, composition, and set of flux values and make an embrittlement curve (TTS vs. fluence)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "60Euq1sHy_bl",
        "outputId": "64e3531f-29b1-427f-83fd-532fa78af531"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicting TTS embrittlement curve for flux = 30000000000\n",
            "Could not find designated model type in scikit-learn model library. Note the other supported modeltype is the keras.wrappers.scikit_learn.KerasRegressor model\n",
            "1/1 [==============================] - 0s 50ms/step\n",
            "1/1 [==============================] - 0s 50ms/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/sklearn/base.py:380: InconsistentVersionWarning: Trying to unpickle estimator StandardScaler from version 0.24.1 when using version 1.6.0. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
            "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.11/dist-packages/sklearn/utils/validation.py:2732: UserWarning: X has feature names, but StandardScaler was fitted without feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 46ms/step\n",
            "1/1 [==============================] - 0s 50ms/step\n",
            "1/1 [==============================] - 0s 45ms/step\n",
            "1/1 [==============================] - 0s 50ms/step\n",
            "1/1 [==============================] - 0s 51ms/step\n",
            "1/1 [==============================] - 0s 48ms/step\n",
            "1/1 [==============================] - 0s 46ms/step\n",
            "1/1 [==============================] - 0s 51ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 72ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 44ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 64ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 74ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "Predicting TTS embrittlement curve for flux = 3680000000000.0\n",
            "Could not find designated model type in scikit-learn model library. Note the other supported modeltype is the keras.wrappers.scikit_learn.KerasRegressor model\n",
            "1/1 [==============================] - 0s 79ms/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/sklearn/base.py:380: InconsistentVersionWarning: Trying to unpickle estimator StandardScaler from version 0.24.1 when using version 1.6.0. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
            "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.11/dist-packages/sklearn/utils/validation.py:2732: UserWarning: X has feature names, but StandardScaler was fitted without feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 80ms/step\n",
            "1/1 [==============================] - 0s 79ms/step\n",
            "1/1 [==============================] - 0s 80ms/step\n",
            "1/1 [==============================] - 0s 87ms/step\n",
            "1/1 [==============================] - 0s 67ms/step\n",
            "1/1 [==============================] - 0s 84ms/step\n",
            "1/1 [==============================] - 0s 69ms/step\n",
            "1/1 [==============================] - 0s 77ms/step\n",
            "1/1 [==============================] - 0s 79ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 51ms/step\n",
            "1/1 [==============================] - 0s 49ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 14ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "Predicting TTS embrittlement curve for flux = 100000000000000\n",
            "Could not find designated model type in scikit-learn model library. Note the other supported modeltype is the keras.wrappers.scikit_learn.KerasRegressor model\n",
            "1/1 [==============================] - 0s 50ms/step\n",
            "1/1 [==============================] - 0s 49ms/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/sklearn/base.py:380: InconsistentVersionWarning: Trying to unpickle estimator StandardScaler from version 0.24.1 when using version 1.6.0. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
            "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.11/dist-packages/sklearn/utils/validation.py:2732: UserWarning: X has feature names, but StandardScaler was fitted without feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 48ms/step\n",
            "1/1 [==============================] - 0s 61ms/step\n",
            "1/1 [==============================] - 0s 53ms/step\n",
            "1/1 [==============================] - 0s 47ms/step\n",
            "1/1 [==============================] - 0s 44ms/step\n",
            "1/1 [==============================] - 0s 45ms/step\n",
            "1/1 [==============================] - 0s 48ms/step\n",
            "1/1 [==============================] - 0s 50ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 44ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 41ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 42ms/step\n",
            "1/1 [==============================] - 0s 41ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 48ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "# Edit the desired temperature, composition and fluxes below\n",
        "# Note the number of points to grid fluence can also be provided.\n",
        "# Plotting a given flux value with 50 points will take about 45-60 seconds\n",
        "\n",
        "########################################\n",
        "########################################\n",
        "\n",
        "d = {'temperature_C': 287,\n",
        "     'wt_percent_Cu': 0.04,\n",
        "     'wt_percent_Ni': 0.74,\n",
        "     'wt_percent_Mn': 0.6,\n",
        "     'wt_percent_P': 0.012,\n",
        "     'wt_percent_Si': 0.23,\n",
        "     'wt_percent_C': 0.21}\n",
        "\n",
        "fluxes = [3*10**10, 3.68*10**12, 1*10**14]\n",
        "num_points = 50\n",
        "\n",
        "########################################\n",
        "########################################\n",
        "\n",
        "all_preds = list()\n",
        "all_errs = list()\n",
        "colors = ['blue', 'green', 'red', 'purple', 'orange', 'grey']\n",
        "for flux in fluxes:\n",
        "    print('Predicting TTS embrittlement curve for flux =', flux)\n",
        "    log_fluence = np.arange(16, 21, 5/num_points)\n",
        "    temp = [d['temperature_C'] for i in log_fluence]\n",
        "    Cu = [d['wt_percent_Cu'] for i in log_fluence]\n",
        "    Ni = [d['wt_percent_Ni'] for i in log_fluence]\n",
        "    Mn = [d['wt_percent_Mn'] for i in log_fluence]\n",
        "    P = [d['wt_percent_P'] for i in log_fluence]\n",
        "    Si = [d['wt_percent_Si'] for i in log_fluence]\n",
        "    C = [d['wt_percent_C'] for i in log_fluence]\n",
        "    f = [np.log10(flux) for i in log_fluence]\n",
        "\n",
        "    df_test = pd.DataFrame({'temperature_C': temp,\n",
        "                            'wt_percent_Cu': Cu,\n",
        "                            'wt_percent_Ni': Ni,\n",
        "                            'wt_percent_Mn': Mn,\n",
        "                            'wt_percent_P': P,\n",
        "                            'wt_percent_Si': Si,\n",
        "                            'wt_percent_C': C,\n",
        "                            'log(fluence_n_cm2)': log_fluence,\n",
        "                            'log(flux_n_cm2_sec)': f})\n",
        "\n",
        "    preds_test = make_predictions_DNN(df_test, model_folder)\n",
        "    all_preds.append(preds_test['preds'])\n",
        "    all_errs.append(preds_test['ebars'])\n",
        "\n",
        "plt.clf()\n",
        "data = dict()\n",
        "for i, flux in enumerate(fluxes):\n",
        "    flux_val = str(round(np.log10(flux),3))\n",
        "    data['Preds for Flux = '+flux_val] = all_preds[i]\n",
        "    data['Ebars for Flux = '+flux_val] = all_errs[i]\n",
        "    plt.plot(log_fluence, all_preds[i], label='log10(flux) = '+flux_val, color=colors[i])\n",
        "    plt.fill_between(log_fluence, all_preds[i]+all_errs[i], all_preds[i]-all_errs[i], color=colors[i], alpha=0.2)\n",
        "\n",
        "data['log_fluence'] = log_fluence\n",
        "pd.DataFrame(data).to_csv('embrittlement_curve_data.csv', index=False)\n",
        "plt.xlabel('log10(fluence) (n/cm2)')\n",
        "plt.ylabel('ML predicted TTS (degC)')\n",
        "plt.legend(loc='best')\n",
        "plt.savefig('embrittlement_curve.png', dpi=300, bbox_inches='tight')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Bonus: the AI-based code generation in Google Colab is getting quite good. Try entering the following text into the generate command below:\n",
        "\n",
        "\"Create an interactive line plot using the embrittlement_curve_data.csv where x-axis is the 'log_fluence' data from the .csv file and the y-axis is the 'Preds for Flux = 10.477' data from the .csv file\""
      ],
      "metadata": {
        "id": "GxVB9I4G_7af"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: Create an interactive line plot using the embrittlement_curve_data.csv where x-axis is the 'log_fluence' data from the .csv file and the y-axis is the 'Preds for Flux = 10.477' data from the .csv file\n",
        "\n",
        "import pandas as pd\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "# Load the data from the CSV file\n",
        "df = pd.read_csv('embrittlement_curve_data.csv')\n",
        "\n",
        "# Create the interactive line plot\n",
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    x=df['log_fluence'],\n",
        "    y=df['Preds for Flux = 10.477'],\n",
        "    mode='lines+markers',\n",
        "    name='Flux = 10.477'\n",
        "))\n",
        "\n",
        "fig.update_layout(\n",
        "    title=\"Embrittlement Curve\",\n",
        "    xaxis_title=\"log10(fluence) (n/cm2)\",\n",
        "    yaxis_title=\"ML predicted TTS (degC)\"\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "HlzPwnvc_yLh",
        "outputId": "515cf9ae-9046-4e98-9c51-9d2d7b93f1a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.35.2.min.js\"></script>                <div id=\"135f87d0-e549-4f0a-9ea9-cc8d95698ab9\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"135f87d0-e549-4f0a-9ea9-cc8d95698ab9\")) {                    Plotly.newPlot(                        \"135f87d0-e549-4f0a-9ea9-cc8d95698ab9\",                        [{\"mode\":\"lines+markers\",\"name\":\"Flux = 10.477\",\"x\":[16.0,16.1,16.200000000000003,16.300000000000004,16.400000000000006,16.500000000000007,16.60000000000001,16.70000000000001,16.80000000000001,16.900000000000013,17.000000000000014,17.100000000000016,17.200000000000017,17.30000000000002,17.40000000000002,17.50000000000002,17.600000000000023,17.700000000000024,17.800000000000026,17.900000000000027,18.00000000000003,18.10000000000003,18.20000000000003,18.300000000000036,18.400000000000038,18.50000000000004,18.60000000000004,18.70000000000004,18.80000000000004,18.90000000000004,19.000000000000043,19.100000000000044,19.200000000000045,19.300000000000047,19.40000000000005,19.50000000000005,19.60000000000005,19.700000000000053,19.80000000000005,19.900000000000052,20.000000000000057,20.10000000000006,20.20000000000006,20.30000000000006,20.400000000000063,20.500000000000064,20.600000000000065,20.700000000000067,20.800000000000068,20.90000000000007],\"y\":[0.2872924,0.29804975,0.31032404,0.3245107,0.3533528,0.3945227,0.46515006,0.59842366,0.78572,1.0963154,1.491174,2.005548,2.5489535,3.133967,3.8161998,4.6329536,5.5886307,6.630693,7.7233553,8.76771,9.848004,10.913114,11.951659,13.016208,13.985684,14.911807,15.810318,16.732836,17.692003,18.606668,19.533747,20.48986,21.445053,22.568747,24.025375,25.907812,28.401356,31.75651,35.982975,41.18035,47.211884,54.089806,61.815277,70.36479,79.74046,89.91984,100.846664,112.48087,125.02074,138.14072],\"type\":\"scatter\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"title\":{\"text\":\"Embrittlement Curve\"},\"xaxis\":{\"title\":{\"text\":\"log10(fluence) (n\\u002fcm2)\"}},\"yaxis\":{\"title\":{\"text\":\"ML predicted TTS (degC)\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('135f87d0-e549-4f0a-9ea9-cc8d95698ab9');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can build upon the previous plot! In the \"generate\" command, add the following:\n",
        "\n",
        "\"Now add the Preds for Flux = 12.566 and Preds for Flux = 14.0 from the csv file to the previously made interactive plot as additional lines with different colors\""
      ],
      "metadata": {
        "id": "FT8nKrOjBe_d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: Now add the Preds for Flux = 12.566 and Preds for Flux = 14.0 from the csv file to the previously made interactive plot as additional lines with different colors\n",
        "\n",
        "import pandas as pd\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "# Load the data from the CSV file\n",
        "df = pd.read_csv('embrittlement_curve_data.csv')\n",
        "\n",
        "# Create the interactive line plot\n",
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    x=df['log_fluence'],\n",
        "    y=df['Preds for Flux = 10.477'],\n",
        "    mode='lines+markers',\n",
        "    name='Flux = 10.477'\n",
        "))\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    x=df['log_fluence'],\n",
        "    y=df['Preds for Flux = 12.566'],\n",
        "    mode='lines+markers',\n",
        "    name='Flux = 12.566',\n",
        "    line=dict(color='red')\n",
        "))\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    x=df['log_fluence'],\n",
        "    y=df['Preds for Flux = 14.0'],\n",
        "    mode='lines+markers',\n",
        "    name='Flux = 14.0',\n",
        "    line=dict(color='green')\n",
        "))\n",
        "\n",
        "\n",
        "fig.update_layout(\n",
        "    title=\"Embrittlement Curve\",\n",
        "    xaxis_title=\"log10(fluence) (n/cm2)\",\n",
        "    yaxis_title=\"ML predicted TTS (degC)\"\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "6ndxX5qeBAvu",
        "outputId": "bb8bf6c9-7ede-4a1f-9ba4-481a2c6c0ea0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.35.2.min.js\"></script>                <div id=\"e8e1ed15-4e9e-446b-b4e7-0b371c3e37d5\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"e8e1ed15-4e9e-446b-b4e7-0b371c3e37d5\")) {                    Plotly.newPlot(                        \"e8e1ed15-4e9e-446b-b4e7-0b371c3e37d5\",                        [{\"mode\":\"lines+markers\",\"name\":\"Flux = 10.477\",\"x\":[16.0,16.1,16.200000000000003,16.300000000000004,16.400000000000006,16.500000000000007,16.60000000000001,16.70000000000001,16.80000000000001,16.900000000000013,17.000000000000014,17.100000000000016,17.200000000000017,17.30000000000002,17.40000000000002,17.50000000000002,17.600000000000023,17.700000000000024,17.800000000000026,17.900000000000027,18.00000000000003,18.10000000000003,18.20000000000003,18.300000000000036,18.400000000000038,18.50000000000004,18.60000000000004,18.70000000000004,18.80000000000004,18.90000000000004,19.000000000000043,19.100000000000044,19.200000000000045,19.300000000000047,19.40000000000005,19.50000000000005,19.60000000000005,19.700000000000053,19.80000000000005,19.900000000000052,20.000000000000057,20.10000000000006,20.20000000000006,20.30000000000006,20.400000000000063,20.500000000000064,20.600000000000065,20.700000000000067,20.800000000000068,20.90000000000007],\"y\":[0.2872924,0.29804975,0.31032404,0.3245107,0.3533528,0.3945227,0.46515006,0.59842366,0.78572,1.0963154,1.491174,2.005548,2.5489535,3.133967,3.8161998,4.6329536,5.5886307,6.630693,7.7233553,8.76771,9.848004,10.913114,11.951659,13.016208,13.985684,14.911807,15.810318,16.732836,17.692003,18.606668,19.533747,20.48986,21.445053,22.568747,24.025375,25.907812,28.401356,31.75651,35.982975,41.18035,47.211884,54.089806,61.815277,70.36479,79.74046,89.91984,100.846664,112.48087,125.02074,138.14072],\"type\":\"scatter\"},{\"line\":{\"color\":\"red\"},\"mode\":\"lines+markers\",\"name\":\"Flux = 12.566\",\"x\":[16.0,16.1,16.200000000000003,16.300000000000004,16.400000000000006,16.500000000000007,16.60000000000001,16.70000000000001,16.80000000000001,16.900000000000013,17.000000000000014,17.100000000000016,17.200000000000017,17.30000000000002,17.40000000000002,17.50000000000002,17.600000000000023,17.700000000000024,17.800000000000026,17.900000000000027,18.00000000000003,18.10000000000003,18.20000000000003,18.300000000000036,18.400000000000038,18.50000000000004,18.60000000000004,18.70000000000004,18.80000000000004,18.90000000000004,19.000000000000043,19.100000000000044,19.200000000000045,19.300000000000047,19.40000000000005,19.50000000000005,19.60000000000005,19.700000000000053,19.80000000000005,19.900000000000052,20.000000000000057,20.10000000000006,20.20000000000006,20.30000000000006,20.400000000000063,20.500000000000064,20.600000000000065,20.700000000000067,20.800000000000068,20.90000000000007],\"y\":[0.23329219,0.25516525,0.27773672,0.30036068,0.32170194,0.34413257,0.34009725,0.3210118,0.3055598,0.2958179,0.29223692,0.29385155,0.3199334,0.39217323,0.49763155,0.64879084,0.84888136,1.0891069,1.3370553,1.629096,1.9596357,2.3197405,2.763123,3.3103714,3.8765419,4.4316144,5.031967,5.720351,6.6029778,7.7488356,9.077797,10.612313,12.554716,15.115236,18.606022,23.442024,29.907787,38.4652,48.59501,59.778027,72.019485,85.36949,99.41783,114.2568,129.58199,145.5383,161.91576,178.72435,195.7209,213.15738],\"type\":\"scatter\"},{\"line\":{\"color\":\"green\"},\"mode\":\"lines+markers\",\"name\":\"Flux = 14.0\",\"x\":[16.0,16.1,16.200000000000003,16.300000000000004,16.400000000000006,16.500000000000007,16.60000000000001,16.70000000000001,16.80000000000001,16.900000000000013,17.000000000000014,17.100000000000016,17.200000000000017,17.30000000000002,17.40000000000002,17.50000000000002,17.600000000000023,17.700000000000024,17.800000000000026,17.900000000000027,18.00000000000003,18.10000000000003,18.20000000000003,18.300000000000036,18.400000000000038,18.50000000000004,18.60000000000004,18.70000000000004,18.80000000000004,18.90000000000004,19.000000000000043,19.100000000000044,19.200000000000045,19.300000000000047,19.40000000000005,19.50000000000005,19.60000000000005,19.700000000000053,19.80000000000005,19.900000000000052,20.000000000000057,20.10000000000006,20.20000000000006,20.30000000000006,20.400000000000063,20.500000000000064,20.600000000000065,20.700000000000067,20.800000000000068,20.90000000000007],\"y\":[0.09811262,0.12221426,0.14178596,0.14087787,0.1418812,0.14280154,0.14738333,0.08569388,0.009500051,-0.069329426,-0.15726621,-0.25101554,-0.34976536,-0.46739554,-0.5914432,-0.72516954,-0.84524953,-0.9606592,-1.0488735,-1.0748377,-1.0546396,-1.0001087,-0.8869001,-0.7128087,-0.48948002,-0.19350997,0.12368008,0.5364614,1.0316031,1.7210115,2.660344,3.9643435,5.668165,8.083799,11.420117,16.040693,22.351595,30.572346,40.71238,52.740227,66.25723,80.94789,96.39424,112.711105,129.48117,146.86972,164.70673,182.88527,201.43082,220.26457],\"type\":\"scatter\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"title\":{\"text\":\"Embrittlement Curve\"},\"xaxis\":{\"title\":{\"text\":\"log10(fluence) (n\\u002fcm2)\"}},\"yaxis\":{\"title\":{\"text\":\"ML predicted TTS (degC)\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('e8e1ed15-4e9e-446b-b4e7-0b371c3e37d5');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oFS7qKJ5BRk7"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.15"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}