Run_RPV_model_Colab.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "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": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UtvMNMvf0ypP",
    "outputId": "f93e3657-3549-44f4-c6c9-7fcbf09529ee"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fatal: destination path 'RPV_model' already exists and is not an empty directory.\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "!git clone https://github.com/uw-cmg/RPV_model.git"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XYdEixBy41E3"
   },
   "source": [
    "Next, we install required software packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nxACBsan2Fbv",
    "outputId": "0f70022f-7754-4498-800d-c17607edbfce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Building wheels for collected packages: citrination-client, pypif\n",
      "  Building wheel for citrination-client (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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      "Installing collected packages: texttable, nameparser, json2table, websockets, uncertainties, tblib, spglib, slicer, ruamel.yaml.clib, pyrsistent, pypif, pydantic, pyaml, ppft, pox, pika, palettable, monty, latexcodec, dnspython, dill, deprecated, scikit-learn, ruamel.yaml, pynacl, pymongo, pybtex, multiprocess, jsonschema, globus-compute-common, citrination-client, shap, scikit-optimize, scikit-learn-extra, PyGithub, pathos, globus-sdk, globus-nexus-client, globus-compute-sdk, fair-research-login, mdf-toolbox, mdf-forge, mdf-connect-client, dlhub-sdk, foundry-ml, emmet-core, mp-api, pymatgen, sphinx-automodapi, matminer, mastml\n",
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      "Successfully installed PyGithub-1.56 citrination-client-6.5.1 deprecated-1.2.14 dill-0.3.6 dlhub-sdk-2.1.0 dnspython-2.4.2 emmet-core-0.66.0 fair-research-login-0.3.0 foundry-ml-0.6.3 globus-compute-common-0.2.0 globus-compute-sdk-2.2.4 globus-nexus-client-0.4.1 globus-sdk-3.27.0 json2table-1.1.5 jsonschema-4.3.0 latexcodec-2.0.1 mastml-3.1.8 matminer-0.9.0 mdf-connect-client-0.4.1 mdf-forge-0.8.0 mdf-toolbox-0.6.0 monty-2023.8.8 mp-api-0.34.3 multiprocess-0.70.14 nameparser-1.1.2 palettable-3.3.3 pathos-0.3.0 pika-1.3.2 pox-0.3.3 ppft-1.7.6.7 pyaml-23.7.0 pybtex-0.24.0 pydantic-1.10.12 pymatgen-2023.8.10 pymongo-4.5.0 pynacl-1.5.0 pypif-2.1.2 pyrsistent-0.19.3 ruamel.yaml-0.17.32 ruamel.yaml.clib-0.2.7 scikit-learn-1.1.3 scikit-learn-extra-0.3.0 scikit-optimize-0.9.0 shap-0.42.1 slicer-0.0.7 spglib-2.0.2 sphinx-automodapi-0.16.0 tblib-1.7.0 texttable-1.6.7 uncertainties-3.1.7 websockets-10.3\n"
     ]
    }
   ],
   "source": [
    "!pip install -r RPV_model/requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zGurmUNM48Mi"
   },
   "source": [
    "Restart the runtime (Runtime -> Restart Runtime) for the new package installs to take effect"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jdPTpPKF5B0-"
   },
   "source": [
    "Add the path of the RPV_model repo so python can find it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "2qZC19C62ZOc"
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('RPV_model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the model type you'd like to run. Currently, 3 are available:\n",
    "\n",
    "fullfit: The full fit model to the entire database of UCSB+PLOTTER data. This is the current production model\n",
    "\n",
    "fullfit_ATR2anchors: A modified version of the full fit model in the paper. This model is fit to the same full UCSB+PLOTTER database, but includes additional synthetic \"anchor\" points of ATR2 flux values to try fixing some known extrapolation issues.\n",
    "\n",
    "ATR2_only: Model fit to only the small ATR2 subset of data. For testing purposes only. Error bars are from fullfit production model.\n",
    "\n",
    "highfluence_lowflux: Model fit to subset of high fluence, low flux data. For testing purposes only. Error bars are from fullfit production model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_folder = 'fullfit' # Or 'fullfit_ATR2anchors', ATR2_only', 'highfluence_lowflux', 'fullfit_newfeatures_noanchors', 'fullfit_newfeatures_noanchors_noPF'"
   ]
  },
  {
   "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": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aMZavzc9174I",
    "outputId": "041ad0fe-74eb-49ab-ed1a-37392dd3a4d0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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",
      "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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator StandardScaler from version 0.24.1 when using version 1.1.3. 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/python3.10/dist-packages/sklearn/base.py:443: UserWarning: X has feature names, but StandardScaler was fitted without feature names\n",
      "  warnings.warn(\n",
      "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x79800025df30> 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",
      "WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_predict_function.<locals>.predict_function at 0x79800025ea70> 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"
     ]
    }
   ],
   "source": [
    "from model_predict_df import make_predictions_DNN\n",
    "import pandas as pd\n",
    "\n",
    "# Note, if using model \"fullfit_newfeatures_noanchors\" you need to use the \"test_data_newfeatures.csv\" file\n",
    "# Note, if using model \"fullfit_newfeatures_noanchors_noPF\" you need to use the \"test_data_newfeatures_noPF.csv\" file\n",
    "\n",
    "df = pd.read_csv('RPV_model/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": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "rsoL0Y2u2Cm4",
    "outputId": "318148ac-22e0-4df3-d0ac-ffc35e20934f"
   },
   "outputs": [
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      "text/plain": [
       "       preds      ebars\n",
       "0  14.001073   8.925599\n",
       "1  14.001073   8.925599\n",
       "2  27.393063  13.632395\n",
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   "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."
   ]
  },
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   "execution_count": 6,
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    {
     "name": "stdout",
     "output_type": "stream",
     "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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator StandardScaler from version 0.24.1 when using version 1.1.3. 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/python3.10/dist-packages/sklearn/base.py:443: UserWarning: X has feature names, but StandardScaler was fitted without feature names\n",
      "  warnings.warn(\n"
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      ],
      "text/plain": [
       "       preds      ebars\n",
       "0  14.001073   8.925599\n",
       "1  18.109430  12.839308\n",
       "2  28.369940  11.057064"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "# Use the data below if running the \"fullfit_newfeatures_noanchors\" model\n",
    "'''\n",
    "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",
    "     'fluence_n_cm2': [4619000000000000000, 727000000000000000, 10300000000000000000],\n",
    "     'flux_n_cm2_sec': [146467529173.008, 24524488055.5285, 92524322218.9165],\n",
    "     'Product Form_0': [1, 0, 0],\n",
    "     'Product Form_1': [0, 0, 0],\n",
    "     'Product Form_2': [0, 1, 1],\n",
    "     'Product Form_3': [0, 0, 0],\n",
    "     'Product Form_4': [0, 0, 0],\n",
    "     'Product Form_5': [0, 0, 0]}\n",
    "# Product form key:\n",
    "# 0 = F, 1 = HAZ, 2 = P, 3 = PCE, 4 = SRM, 5 = W\n",
    "'''\n",
    "\n",
    "# Use the data below if running the \"fullfit_newfeatures_noanchors_noPF\" model\n",
    "'''\n",
    "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",
    "     'fluence_n_cm2': [4619000000000000000, 727000000000000000, 10300000000000000000],\n",
    "     'flux_n_cm2_sec': [146467529173.008, 24524488055.5285, 92524322218.9165]}\n",
    "'''\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": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 576
    },
    "id": "60Euq1sHy_bl",
    "outputId": "cb23026f-1563-4e59-dd81-752a1af26f43"
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (2143034782.py, line 55)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Cell \u001b[0;32mIn [1], line 55\u001b[0;36m\u001b[0m\n\u001b[0;31m    df_test['Product Form_0'= = pf0\u001b[0m\n\u001b[0m                            ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "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 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",
    "# Example of input d if using fullfit_newfeatures_noanchors model\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",
    "    'Product Form': 'P'}\n",
    "'''\n",
    "\n",
    "fluxes = [3*10**10, 3.68*10**12, 1*10**14]\n",
    "num_points = 50\n",
    "\n",
    "adding_new_features = False # Set to true if want to inclue flux, fluence and product form. Needed if using \"fullfit_newfeatures_noanchors\" model\n",
    "include_PF = True # Set to False if using new features but don't want to include PF. Needed if using \"fullfit_newfeatures_noanchors_noPF\" model\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",
    "    if adding_new_features == True:\n",
    "        f2 = [10**i for i in log_fluence]\n",
    "        f3 = [flux for i in log_fluence]\n",
    "\n",
    "        if include_PF == True:\n",
    "            pf = d['Product Form']\n",
    "            # Product form key:\n",
    "            # 0 = F, 1 = HAZ, 2 = P, 3 = PCE, 4 = SRM, 5 = W\n",
    "            \n",
    "            if pf == 'F':\n",
    "                pf0 = [1 for i in log_fluence]\n",
    "                pf1 = [0 for i in log_fluence]\n",
    "                pf2 = [0 for i in log_fluence]\n",
    "                pf3 = [0 for i in log_fluence]\n",
    "                pf4 = [0 for i in log_fluence]\n",
    "                pf5 = [0 for i in log_fluence]\n",
    "            elif pf == 'HAZ':\n",
    "                pf0 = [0 for i in log_fluence]\n",
    "                pf1 = [1 for i in log_fluence]\n",
    "                pf2 = [0 for i in log_fluence]\n",
    "                pf3 = [0 for i in log_fluence]\n",
    "                pf4 = [0 for i in log_fluence]\n",
    "                pf5 = [0 for i in log_fluence]\n",
    "            elif pf == 'P':\n",
    "                pf0 = [0 for i in log_fluence]\n",
    "                pf1 = [0 for i in log_fluence]\n",
    "                pf2 = [1 for i in log_fluence]\n",
    "                pf3 = [0 for i in log_fluence]\n",
    "                pf4 = [0 for i in log_fluence]\n",
    "                pf5 = [0 for i in log_fluence]\n",
    "            elif pf == 'PCE':\n",
    "                pf0 = [0 for i in log_fluence]\n",
    "                pf1 = [0 for i in log_fluence]\n",
    "                pf2 = [0 for i in log_fluence]\n",
    "                pf3 = [1 for i in log_fluence]\n",
    "                pf4 = [0 for i in log_fluence]\n",
    "                pf5 = [0 for i in log_fluence]\n",
    "            elif pf == 'SRM':\n",
    "                pf0 = [0 for i in log_fluence]\n",
    "                pf1 = [0 for i in log_fluence]\n",
    "                pf2 = [0 for i in log_fluence]\n",
    "                pf3 = [0 for i in log_fluence]\n",
    "                pf4 = [1 for i in log_fluence]\n",
    "                pf5 = [0 for i in log_fluence]\n",
    "            elif pf == 'W':\n",
    "                pf0 = [0 for i in log_fluence]\n",
    "                pf1 = [0 for i in log_fluence]\n",
    "                pf2 = [0 for i in log_fluence]\n",
    "                pf3 = [0 for i in log_fluence]\n",
    "                pf4 = [0 for i in log_fluence]\n",
    "                pf5 = [1 for i in log_fluence]\n",
    "\n",
    "        df_test['fluence_n_cm2'] = f2\n",
    "        df_test['flux_n_cm2_sec'] = f3\n",
    "        \n",
    "        if include_PF == True:\n",
    "            df_test['Product Form_0'] = pf0\n",
    "            df_test['Product Form_1'] = pf1\n",
    "            df_test['Product Form_2'] = pf2\n",
    "            df_test['Product Form_3'] = pf3\n",
    "            df_test['Product Form_4'] = pf4\n",
    "            df_test['Product Form_5'] = pf5\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",
    "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": "code",
   "execution_count": null,
   "metadata": {
    "id": "0hXdqowu4Sb8"
   },
   "outputs": [],
   "source": []
  }
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