notebook_fy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interacting with fission product yields in ENDF-6 format (MF=8, MT=454/459)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First let's import `sandy` and the other python packages for formatting and postprocessing that are used in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import yaml\n",
    "import urllib\n",
    "import os\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture --no-stdout\n",
    "import sandy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_context(\"notebook\")\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parsing the data in ENDF-6 format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first thing that we want to do with an evaluated nuclear data  file in ENDF-6 format is to load its content into python using `sandy`.\n",
    "By calling `sandy.Endf6.from_file`, the ENDF-6 file is parsed and its content is split into text sections, each of them indexed by a unique combination of MAT, MF and MT numbers. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we report an example using the JEFF-3.3 fission yield evaluated file available on the [OECD/NEA website](https://www.oecd-nea.org/dbdata/jeff/jeff33/index.html).\n",
    "The file content is loaded into the `Endf6` instance that we name `tape`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://www.oecd-nea.org/dbdata/jeff/jeff33/downloads/JEFF33-nfy.asc\"\n",
    "with urllib.request.urlopen(url) as f:\n",
    "    text_fy = f.read().decode('utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "tape = sandy.Endf6.from_text(text_fy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MAT   MF  MT \n",
       "9040  1   451     9.023200+4 2.300450+2         -1          1  ...\n",
       "      8   454     9.023200+4 2.300450+2          2          0  ...\n",
       "          459     9.023200+4 2.300450+2          2          0  ...\n",
       "9222  1   451     9.223300+4 2.310380+2         -1          1  ...\n",
       "      8   454     9.223300+4 2.310380+2          3          0  ...\n",
       "          459     9.223300+4 2.310380+2          3          0  ...\n",
       "9225  1   451     9.223400+4 2.320300+2         -1          1  ...\n",
       "      8   454     9.223400+4 2.320300+2          1          0  ...\n",
       "          459     9.223400+4 2.320300+2          1          0  ...\n",
       "9228  1   451     9.223500+4 2.330250+2         -1          1  ...\n",
       "      8   454     9.223500+4 2.330250+2          3          0  ...\n",
       "          459     9.223500+4 2.330250+2          3          0  ...\n",
       "9231  1   451     9.223600+4 2.340180+2         -1          1  ...\n",
       "      8   454     9.223600+4 2.340180+2          2          0  ...\n",
       "          459     9.223600+4 2.340180+2          2          0  ...\n",
       "9237  1   451     9.223800+4 2.360060+2         -1          1  ...\n",
       "      8   454     9.223800+4 2.360060+2          2          0  ...\n",
       "          459     9.223800+4 2.360060+2          2          0  ...\n",
       "9346  1   451     9.323700+4 2.350120+2         -1          1  ...\n",
       "      8   454     9.323700+4 2.350120+2          2          0  ...\n",
       "          459     9.323700+4 2.350120+2          2          0  ...\n",
       "9349  1   451     9.323800+4 2.360060+2         -1          1  ...\n",
       "      8   454     9.323800+4 2.360060+2          2          0  ...\n",
       "          459     9.323800+4 2.360060+2          2          0  ...\n",
       "9434  1   451     9.423800+4 2.360050+2         -1          1  ...\n",
       "      8   454     9.423800+4 2.360050+2          2          0  ...\n",
       "          459     9.423800+4 2.360050+2          2          0  ...\n",
       "9437  1   451     9.423900+4 2.369990+2         -1          1  ...\n",
       "      8   454     9.423900+4 2.369990+2          2          0  ...\n",
       "          459     9.423900+4 2.369990+2          2          0  ...\n",
       "9440  1   451     9.424000+4 2.379920+2         -1          1  ...\n",
       "      8   454     9.424000+4 2.379920+2          1          0  ...\n",
       "          459     9.424000+4 2.379920+2          1          0  ...\n",
       "9443  1   451     9.424100+4 2.389860+2         -1          1  ...\n",
       "      8   454     9.424100+4 2.389860+2          2          0  ...\n",
       "          459     9.424100+4 2.389860+2          2          0  ...\n",
       "9446  1   451     9.424200+4 2.399790+2         -1          1  ...\n",
       "      8   454     9.424200+4 2.399790+2          1          0  ...\n",
       "          459     9.424200+4 2.399790+2          1          0  ...\n",
       "9543  1   451     9.524100+4 2.389860+2         -1          1  ...\n",
       "      8   454     9.524100+4 2.389860+2          2          0  ...\n",
       "          459     9.524100+4 2.389860+2          2          0  ...\n",
       "9547  1   451     9.524200+4 2.399800+2         -1          1  ...\n",
       "      8   454     9.524200+4 2.399800+2          2          0  ...\n",
       "          459     9.524200+4 2.399800+2          2          0  ...\n",
       "9549  1   451     9.524300+4 2.409730+2         -1          1  ...\n",
       "      8   454     9.524300+4 2.409730+2          2          0  ...\n",
       "          459     9.524300+4 2.409730+2          2          0  ...\n",
       "9634  1   451     9.624300+4 2.409730+2         -1          1  ...\n",
       "      8   454     9.624300+4 2.409730+2          2          0  ...\n",
       "          459     9.624300+4 2.409730+2          2          0  ...\n",
       "9637  1   451     9.624400+4 2.419040+2         -1          1  ...\n",
       "      8   454     9.624400+4 2.419040+2          2          0  ...\n",
       "          459     9.624400+4 2.419040+2          2          0  ...\n",
       "9640  1   451     9.624500+4 2.429600+2         -1          1  ...\n",
       "      8   454     9.624500+4 2.429600+2          2          0  ...\n",
       "          459     9.624500+4 2.429600+2          2          0  ...\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, the information available in each `(MAT,MF,MT)` section in ASCII format can be extracted using method `.read_section`, \n",
    "as shown below.\n",
    "\n",
    "> Recall that MF=8 MT=454 are the indices for independent fission yields, MF=8 MT=459 for cumulative fission yields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "mat = 9437  # This is Pu-239\n",
    "mf = 8\n",
    "mt = 454\n",
    "\n",
    "section = tape.read_section(mat, mf, mt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a better visualization of the FY data structure generated by `.read_section` we resort to the `yaml` package and we print only the first 11 lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AWR: 236.999\n",
      "E:\n",
      "  0.0253:\n",
      "    INTERP: 1\n",
      "    ZAP:\n",
      "      10010:\n",
      "        DFY: 7.0527e-06\n",
      "        FY: 4.08e-05\n",
      "      10020:\n",
      "        DFY: 2.8929e-06\n",
      "        FY: 1.347e-05\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\".join(yaml.dump(section).splitlines()[:11]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data contained in the ENDF-6 section are stored in nested dictionaries, \n",
    "according to the following structure that tries and replicate the ENDF-6 hierarchy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```yaml\n",
    "    AWR : atomic weight ratio\n",
    "    E :   # dictionary of tabulated energies at which fission yields are given\n",
    "        energy :  # energy of the neutron causing fission (eV)\n",
    "            INTERP : interpolation scheme between this and the following energy point\n",
    "            ZAP :   # dictionary of fission products\n",
    "                Z*1000 + A*10 + M :\n",
    "                    FY : fission yield (fraction)\n",
    "                    DFY : uncertainty on fission yield (same unit as FY)\n",
    "                Z2*1000 + A2*10 + M2 : ...\n",
    "        energy_2 : ...\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a `sandy.Fy` object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Class `sandy.Fy` was developed to store and process fission yield data.\n",
    "\n",
    "The ASCII content in a `sandy.Endf6` instance can be directly imported into a `sandy.Fy` instance using \n",
    "classmethod `.from_endf6`, where the `sandy.Endf6` object is passed as an argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "        MAT   MT     ZAM     ZAP           E          FY\n",
       "0      9040  454  902320   10010 4.00000e+05 1.60730e-05\n",
       "1      9040  454  902320   10020 4.00000e+05 4.91210e-06\n",
       "2      9040  454  902320   10030 4.00000e+05 7.00810e-05\n",
       "3      9040  454  902320   20030 4.00000e+05 0.00000e+00\n",
       "4      9040  454  902320   20040 4.00000e+05 1.01610e-03\n",
       "...     ...  ...     ...     ...         ...         ...\n",
       "79489  9640  459  962450  781960 4.00000e+05 5.24140e-12\n",
       "79490  9640  459  962450  781970 4.00000e+05 3.23410e-12\n",
       "79491  9640  459  962450  781971 4.00000e+05 2.43890e-12\n",
       "79492  9640  459  962450  781980 4.00000e+05 1.51570e-12\n",
       "79493  9640  459  962450  791970 4.00000e+05 3.31460e-12\n",
       "\n",
       "[79494 rows x 6 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fy = sandy.Fy.from_endf6(tape)\n",
    "fy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `pd.DataFrame` containing the source data is stored in attribute `.data`, which contains fission yields FY together with the corresponding:\n",
    "- MAT\n",
    "- MT\n",
    "- ZAM\n",
    "- ZAP\n",
    "- E\n",
    "\n",
    "Here ZAM and ZAP are the `Z*10000 + A*10 + M` representation for the parent and daugther nuclides."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to filter the fission yield data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fission yield data can be filtered directly applying the `pandas` functionality to attribute `.data`.\n",
    "However, we recommend using method `.filter_by` to make sure that the returned object be still a `sandy.Fy` instance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below are two examples where `sandy.Fy` instances are created first by filtering only the cumulative yield at thermal energy for the fission of U-235 (`ZAM=922350`), and then keeping only the cumulative fission yields at thermal energy for Cs-137 (`ZAP=551370`) independently of the fissioning nuclide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      MAT   MT     ZAM     ZAP           E          FY\n",
       "0    9228  459  922350   10010 2.53000e-02 1.71100e-05\n",
       "1    9228  459  922350   10020 2.53000e-02 8.40000e-06\n",
       "2    9228  459  922350   10030 2.53000e-02 1.08000e-04\n",
       "3    9228  459  922350   20030 2.53000e-02 1.08000e-04\n",
       "4    9228  459  922350   20040 2.53000e-02 1.70210e-03\n",
       "..    ...  ...     ...     ...         ...         ...\n",
       "978  9228  459  922350  701770 2.53000e-02 1.08900e-12\n",
       "979  9228  459  922350  701771 2.53000e-02 1.08900e-12\n",
       "980  9228  459  922350  711750 2.53000e-02 7.36160e-12\n",
       "981  9228  459  922350  711770 2.53000e-02 1.08900e-12\n",
       "982  9228  459  922350  721770 2.53000e-02 1.08900e-12\n",
       "\n",
       "[983 rows x 6 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cfyU235 = fy.filter_by(\"ZAM\", 922350).filter_by(\"MT\", 459).filter_by(\"E\", 2.53e-2)\n",
    "cfyU235"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     MAT   MT     ZAM     ZAP           E          FY\n",
       "0   9222  459  922330  551370 2.53000e-02 6.26070e-02\n",
       "1   9228  459  922350  551370 2.53000e-02 6.08970e-02\n",
       "2   9231  459  922360  551370 2.53000e-02 6.08310e-02\n",
       "3   9346  459  932370  551370 2.53000e-02 7.03340e-02\n",
       "4   9349  459  932380  551370 2.53000e-02 7.09050e-02\n",
       "5   9434  459  942380  551370 2.53000e-02 5.84950e-02\n",
       "6   9437  459  942390  551370 2.53000e-02 6.57960e-02\n",
       "7   9443  459  942410  551370 2.53000e-02 6.40440e-02\n",
       "8   9543  459  952410  551370 2.53000e-02 7.20690e-02\n",
       "9   9547  459  952420  551370 2.53000e-02 5.65360e-02\n",
       "10  9549  459  952430  551370 2.53000e-02 7.31680e-02\n",
       "11  9634  459  962430  551370 2.53000e-02 7.03410e-02\n",
       "12  9637  459  962440  551370 2.53000e-02 6.88910e-02\n",
       "13  9640  459  962450  551370 2.53000e-02 5.37540e-02"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cfyCs137 = fy.filter_by(\"ZAP\", 551370).filter_by(\"MT\", 459).filter_by(\"E\", 2.53e-2)\n",
    "cfyCs137"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting fission yields as a function of mass or charge can be cumbersome with the current information available in a `sandy.Fy` instance.\n",
    "In fact, to do so columns ZAM or ZAP should be *expanded* to their source components Z, A, M, where Z is the charge number, A is the number of nucleons and M is the metastate number.\n",
    "\n",
    "Here we introduce two new methods that do exactly what required: `._expand_zap` and `._expand_zam`.\n",
    "\n",
    "The first method returns a copy a of the `.data` attribute with three extra columns: the Z, A and M of the fission product.\n",
    "The second method returns a copy a of the `.data` attribute with three extra columns: the Z, A and M of the fissioning nuclide."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Methods `._expand_zap` and `._expand_zam` return a `pd.DataFrame` and not a `sandy.Fy` instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAT</th>\n",
       "      <th>MT</th>\n",
       "      <th>ZAM</th>\n",
       "      <th>ZAP</th>\n",
       "      <th>E</th>\n",
       "      <th>FY</th>\n",
       "      <th>Z</th>\n",
       "      <th>A</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9228</td>\n",
       "      <td>459</td>\n",
       "      <td>922350</td>\n",
       "      <td>10010</td>\n",
       "      <td>2.53000e-02</td>\n",
       "      <td>1.71100e-05</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9228</td>\n",
       "      <td>459</td>\n",
       "      <td>922350</td>\n",
       "      <td>10020</td>\n",
       "      <td>2.53000e-02</td>\n",
       "      <td>8.40000e-06</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9228</td>\n",
       "      <td>459</td>\n",
       "      <td>922350</td>\n",
       "      <td>10030</td>\n",
       "      <td>2.53000e-02</td>\n",
       "      <td>1.08000e-04</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9228</td>\n",
       "      <td>459</td>\n",
       "      <td>922350</td>\n",
       "      <td>20030</td>\n",
       "      <td>2.53000e-02</td>\n",
       "      <td>1.08000e-04</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9228</td>\n",
       "      <td>459</td>\n",
       "      <td>922350</td>\n",
       "      <td>20040</td>\n",
       "      <td>2.53000e-02</td>\n",
       "      <td>1.70210e-03</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    MAT   MT     ZAM    ZAP           E          FY  Z  A  M\n",
       "0  9228  459  922350  10010 2.53000e-02 1.71100e-05  1  1  0\n",
       "1  9228  459  922350  10020 2.53000e-02 8.40000e-06  1  2  0\n",
       "2  9228  459  922350  10030 2.53000e-02 1.08000e-04  1  3  0\n",
       "3  9228  459  922350  20030 2.53000e-02 1.08000e-04  2  3  0\n",
       "4  9228  459  922350  20040 2.53000e-02 1.70210e-03  2  4  0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cfyU235._expand_zap().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2, figsize=(10, 5))\n",
    "ax[0] = sns.scatterplot(x=\"A\", y=\"FY\", data=cfyU235._expand_zap(), ax=ax[0])\n",
    "ax[1] = sns.scatterplot(x=\"Z\", y=\"FY\", data=cfyU235._expand_zap(), ax=ax[1])\n",
    "\n",
    "ax[0].set_title(\"by mass\")\n",
    "ax[1].set_title(\"by charge\")\n",
    "\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>MAT</th>\n",
       "      <th>MT</th>\n",
       "      <th>ZAM</th>\n",
       "      <th>ZAP</th>\n",
       "      <th>E</th>\n",
       "      <th>FY</th>\n",
       "      <th>Z</th>\n",
       "      <th>A</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9222</td>\n",
       "      <td>459</td>\n",
       "      <td>922330</td>\n",
       "      <td>551370</td>\n",
       "      <td>2.53000e-02</td>\n",
       "      <td>6.26070e-02</td>\n",
       "      <td>92</td>\n",
       "      <td>233</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9228</td>\n",
       "      <td>459</td>\n",
       "      <td>922350</td>\n",
       "      <td>551370</td>\n",
       "      <td>2.53000e-02</td>\n",
       "      <td>6.08970e-02</td>\n",
       "      <td>92</td>\n",
       "      <td>235</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9231</td>\n",
       "      <td>459</td>\n",
       "      <td>922360</td>\n",
       "      <td>551370</td>\n",
       "      <td>2.53000e-02</td>\n",
       "      <td>6.08310e-02</td>\n",
       "      <td>92</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9346</td>\n",
       "      <td>459</td>\n",
       "      <td>932370</td>\n",
       "      <td>551370</td>\n",
       "      <td>2.53000e-02</td>\n",
       "      <td>7.03340e-02</td>\n",
       "      <td>93</td>\n",
       "      <td>237</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9349</td>\n",
       "      <td>459</td>\n",
       "      <td>932380</td>\n",
       "      <td>551370</td>\n",
       "      <td>2.53000e-02</td>\n",
       "      <td>7.09050e-02</td>\n",
       "      <td>93</td>\n",
       "      <td>238</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    MAT   MT     ZAM     ZAP           E          FY   Z    A  M\n",
       "0  9222  459  922330  551370 2.53000e-02 6.26070e-02  92  233  0\n",
       "1  9228  459  922350  551370 2.53000e-02 6.08970e-02  92  235  0\n",
       "2  9231  459  922360  551370 2.53000e-02 6.08310e-02  92  236  0\n",
       "3  9346  459  932370  551370 2.53000e-02 7.03340e-02  93  237  0\n",
       "4  9349  459  932380  551370 2.53000e-02 7.09050e-02  93  238  0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cfyCs137._expand_zam().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(10, 5), sharey=True)\n",
    "\n",
    "ax[0] = sns.scatterplot(x=\"A\", y=\"FY\", data=cfyCs137._expand_zam(), ax=ax[0])\n",
    "ax[0].set_title(\"by mass\")\n",
    "\n",
    "ax[1] = sns.scatterplot(x=\"Z\", y=\"FY\", data=cfyCs137._expand_zam(), ax=ax[1])\n",
    "ax[1].set_title(\"by charge\")\n",
    "\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tabulated fission yields as a function of energy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is sometimes important to have access to the fission yield data (either cumulaitve or independent, not both) as a tabulated function of energy.\n",
    "This can be done by calling method `.energy_table` where the fissioning nuclide is specified as an argument either as a ZAM or MAT number. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we report an example for the cumulative yields of U-235."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>ZAP</th>\n",
       "      <th>10010</th>\n",
       "      <th>10020</th>\n",
       "      <th>10030</th>\n",
       "      <th>20030</th>\n",
       "      <th>20040</th>\n",
       "      <th>20060</th>\n",
       "      <th>30060</th>\n",
       "      <th>30080</th>\n",
       "      <th>30090</th>\n",
       "      <th>40080</th>\n",
       "      <th>...</th>\n",
       "      <th>761940</th>\n",
       "      <th>761950</th>\n",
       "      <th>771910</th>\n",
       "      <th>771911</th>\n",
       "      <th>771930</th>\n",
       "      <th>771940</th>\n",
       "      <th>771950</th>\n",
       "      <th>771951</th>\n",
       "      <th>781940</th>\n",
       "      <th>781950</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2.53000e-02</th>\n",
       "      <td>1.71100e-05</td>\n",
       "      <td>8.40000e-06</td>\n",
       "      <td>1.08000e-04</td>\n",
       "      <td>1.08000e-04</td>\n",
       "      <td>1.70210e-03</td>\n",
       "      <td>2.66800e-05</td>\n",
       "      <td>2.66800e-05</td>\n",
       "      <td>7.29200e-07</td>\n",
       "      <td>4.07100e-07</td>\n",
       "      <td>1.34180e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.00000e+05</th>\n",
       "      <td>2.68910e-05</td>\n",
       "      <td>8.21820e-06</td>\n",
       "      <td>1.08000e-04</td>\n",
       "      <td>1.08000e-04</td>\n",
       "      <td>1.70000e-03</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.40000e+07</th>\n",
       "      <td>2.63690e-05</td>\n",
       "      <td>8.05870e-06</td>\n",
       "      <td>1.74200e-04</td>\n",
       "      <td>1.74200e-04</td>\n",
       "      <td>1.66700e-03</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>3.31630e-12</td>\n",
       "      <td>1.04300e-12</td>\n",
       "      <td>1.94260e-11</td>\n",
       "      <td>9.16910e-12</td>\n",
       "      <td>6.63920e-12</td>\n",
       "      <td>3.31630e-12</td>\n",
       "      <td>1.40570e-12</td>\n",
       "      <td>1.35380e-12</td>\n",
       "      <td>3.31630e-12</td>\n",
       "      <td>2.69180e-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 1205 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "ZAP              10010       10020       10030       20030       20040   \\\n",
       "E                                                                         \n",
       "2.53000e-02 1.71100e-05 8.40000e-06 1.08000e-04 1.08000e-04 1.70210e-03   \n",
       "4.00000e+05 2.68910e-05 8.21820e-06 1.08000e-04 1.08000e-04 1.70000e-03   \n",
       "1.40000e+07 2.63690e-05 8.05870e-06 1.74200e-04 1.74200e-04 1.66700e-03   \n",
       "\n",
       "ZAP              20060       30060       30080       30090       40080   ...  \\\n",
       "E                                                                        ...   \n",
       "2.53000e-02 2.66800e-05 2.66800e-05 7.29200e-07 4.07100e-07 1.34180e-06  ...   \n",
       "4.00000e+05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00  ...   \n",
       "1.40000e+07 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00  ...   \n",
       "\n",
       "ZAP              761940      761950      771910      771911      771930  \\\n",
       "E                                                                         \n",
       "2.53000e-02 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "4.00000e+05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "1.40000e+07 3.31630e-12 1.04300e-12 1.94260e-11 9.16910e-12 6.63920e-12   \n",
       "\n",
       "ZAP              771940      771950      771951      781940      781950  \n",
       "E                                                                        \n",
       "2.53000e-02 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00  \n",
       "4.00000e+05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00  \n",
       "1.40000e+07 3.31630e-12 1.40570e-12 1.35380e-12 3.31630e-12 2.69180e-12  \n",
       "\n",
       "[3 rows x 1205 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fy.energy_table(922350, by=\"ZAM\", kind=\"cumulative\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write to HDF5 file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Codes like ALEPH read fission yields from nuclear data files in HDF5 format.\n",
    "To produce such files we can use `sandy` and method `Fy.to_hdf5`, which extracts and stores energy tables for each set of fission yields found in the `Fy` object."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As an example, we can transfer the whole content of the `fy` instance to a HDF5 file `'fy.hdf5'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = 'fy.hdf5'\n",
    "if os.path.exists(file):\n",
    "    os.remove(file)\n",
    "fy.to_hdf5(file, \"jeff_33\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The group key for each set of fission yields is `\"library/fy/kind/zam\"`, where\n",
    "- library: is the lowercase name of the library\n",
    "- fy: is the key \"fy\"\n",
    "- kind: is either \"independent\" or \"cumulative\"\n",
    "- zam: is the ZAM number of the parent nuclide"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The energy values in the HDF5 file are converted into MeV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ZAP</th>\n",
       "      <th>10010</th>\n",
       "      <th>10020</th>\n",
       "      <th>10030</th>\n",
       "      <th>20030</th>\n",
       "      <th>20040</th>\n",
       "      <th>190500</th>\n",
       "      <th>190510</th>\n",
       "      <th>200500</th>\n",
       "      <th>200510</th>\n",
       "      <th>200520</th>\n",
       "      <th>...</th>\n",
       "      <th>761930</th>\n",
       "      <th>761940</th>\n",
       "      <th>761950</th>\n",
       "      <th>771910</th>\n",
       "      <th>771911</th>\n",
       "      <th>771930</th>\n",
       "      <th>771940</th>\n",
       "      <th>771950</th>\n",
       "      <th>781940</th>\n",
       "      <th>781950</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4.00000e-01</th>\n",
       "      <td>2.35370e-05</td>\n",
       "      <td>7.19320e-06</td>\n",
       "      <td>1.02620e-04</td>\n",
       "      <td>1.02620e-04</td>\n",
       "      <td>1.48800e-03</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.40000e+01</th>\n",
       "      <td>1.30120e-05</td>\n",
       "      <td>3.97660e-06</td>\n",
       "      <td>6.49900e-05</td>\n",
       "      <td>6.49900e-05</td>\n",
       "      <td>8.22600e-04</td>\n",
       "      <td>2.14670e-12</td>\n",
       "      <td>1.16000e-12</td>\n",
       "      <td>2.85210e-11</td>\n",
       "      <td>2.28820e-11</td>\n",
       "      <td>2.47380e-11</td>\n",
       "      <td>...</td>\n",
       "      <td>3.79010e-12</td>\n",
       "      <td>2.95410e-12</td>\n",
       "      <td>1.00010e-12</td>\n",
       "      <td>1.31880e-11</td>\n",
       "      <td>6.22490e-12</td>\n",
       "      <td>3.79010e-12</td>\n",
       "      <td>2.95410e-12</td>\n",
       "      <td>1.00010e-12</td>\n",
       "      <td>2.95410e-12</td>\n",
       "      <td>1.00010e-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 1147 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "ZAP              10010       10020       10030       20030       20040   \\\n",
       "E                                                                         \n",
       "4.00000e-01 2.35370e-05 7.19320e-06 1.02620e-04 1.02620e-04 1.48800e-03   \n",
       "1.40000e+01 1.30120e-05 3.97660e-06 6.49900e-05 6.49900e-05 8.22600e-04   \n",
       "\n",
       "ZAP              190500      190510      200500      200510      200520  ...  \\\n",
       "E                                                                        ...   \n",
       "4.00000e-01 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00  ...   \n",
       "1.40000e+01 2.14670e-12 1.16000e-12 2.85210e-11 2.28820e-11 2.47380e-11  ...   \n",
       "\n",
       "ZAP              761930      761940      761950      771910      771911  \\\n",
       "E                                                                         \n",
       "4.00000e-01 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "1.40000e+01 3.79010e-12 2.95410e-12 1.00010e-12 1.31880e-11 6.22490e-12   \n",
       "\n",
       "ZAP              771930      771940      771950      781940      781950  \n",
       "E                                                                        \n",
       "4.00000e-01 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00  \n",
       "1.40000e+01 3.79010e-12 2.95410e-12 1.00010e-12 2.95410e-12 1.00010e-12  \n",
       "\n",
       "[2 rows x 1147 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_hdf(file, key='jeff_33/fy/cumulative/922380')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply custom perturbations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "to be written"
   ]
  }
 ],
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