notebook_lpc.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interact with angular distributions in ENDF-6 format (MF=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First import `sandy` and other python packages for formatting and postprocessing that are used in this notebook.\n",
    "\n",
    "> Notice that, for the moment, `sandy` only provide interaction capabilities for angular distributions \n",
    "> expressed as Legendre polynomial coefficients (lpc)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture --no-stdout\n",
    "import sandy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import filecmp\n",
    "import urllib\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "pd.options.display.float_format = '{:.5e}'.format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract from ENDF-6 file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, create a `endf6` instance from the ENDF-6 file `\"fe56_lpc.endf\"`, \n",
    "which was pre-edited to remove all content except the elastic scattering angular distribution (MF=4, MT=2) \n",
    "and the info section. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MAT   MF  MT \n",
       "2631  1   451     26056.0000 55.4544000          1          0  ...\n",
       "      2   151     26056.0000 55.4544000          0          0  ...\n",
       "      3   1       26056.0000 55.4544000          0          0  ...\n",
       "          2       26056.0000 55.4544000          0          0  ...\n",
       "          3       26056.0000 55.4544000          0          0  ...\n",
       "                                       ...                        \n",
       "      33  808     26056.0000 55.4544000          0        107  ...\n",
       "          809     26056.0000 55.4544000          0        107  ...\n",
       "          810     26056.0000 55.4544000          0        107  ...\n",
       "          849     26056.0000 55.4544000          0        107  ...\n",
       "      34  2       26056.0000 55.4544000          0          1  ...\n",
       "Length: 292, dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = \"https://www.oecd-nea.org/dbdata/nds/JEFF4T0/endf6/files/26-fe-56g.endf2c\"\n",
    "with urllib.request.urlopen(url) as f:\n",
    "    text = f.read().decode('utf-8')\n",
    "    tape = sandy.Endf6.from_text(text)\n",
    "tape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's print the first `nrows=10` lines of the file to screen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 26056.0000 55.4544000          0          3          0          02631 4  2    1\n",
      " 0.0        55.4544000          0          2          0          02631 4  2    2\n",
      " 0.0        0.0                 0          0          1       17822631 4  2    3\n",
      "       1782          2                                            2631 4  2    4\n",
      " 0.0        1.00000E-5          0          0          4          02631 4  2    5\n",
      " 0.0        0.0        0.0        0.0                             2631 4  2    6\n",
      " 0.0        1584.60000          0          0          4          02631 4  2    7\n",
      " 0.0        0.0        0.0        0.0                             2631 4  2    8\n",
      " 0.0        11709.0000          0          0          4          02631 4  2    9\n",
      " .011144000 2.86790E-5-6.45730E-8 1.4044E-11                      2631 4  2   10\n",
      "\n"
     ]
    }
   ],
   "source": [
    "mat = 2631\n",
    "mf = 4 \n",
    "mt = 2\n",
    "\n",
    "nrows = 10\n",
    "ncolumns = 81\n",
    "\n",
    "print(tape.data[mat, mf, mt][:ncolumns * nrows])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Class `sandy.Lpc` can be used to parse MF=4 and extract the legendre polynomial coefficients into variable `lpc`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "P                            0            1            2            3   \\\n",
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       "\n",
       "P                            4           5           6           7   \\\n",
       "MAT  MT E                                                             \n",
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       "MAT  MT E                                                             \n",
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       "MAT  MT E                                                            \n",
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       "\n",
       "[5 rows x 21 columns]"
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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       "      <td>9.24890e-05</td>\n",
       "      <td>2.74920e-05</td>\n",
       "      <td>1.13290e-05</td>\n",
       "      <td>7.65150e-06</td>\n",
       "      <td>0.00000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.90000e+07</th>\n",
       "      <td>1.00000e+00</td>\n",
       "      <td>8.19340e-01</td>\n",
       "      <td>6.86190e-01</td>\n",
       "      <td>5.92550e-01</td>\n",
       "      <td>5.18140e-01</td>\n",
       "      <td>4.34340e-01</td>\n",
       "      <td>3.55260e-01</td>\n",
       "      <td>2.81850e-01</td>\n",
       "      <td>2.23840e-01</td>\n",
       "      <td>1.56220e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>3.94820e-02</td>\n",
       "      <td>1.52910e-02</td>\n",
       "      <td>4.99100e-03</td>\n",
       "      <td>1.49080e-03</td>\n",
       "      <td>4.20250e-04</td>\n",
       "      <td>1.16140e-04</td>\n",
       "      <td>3.41760e-05</td>\n",
       "      <td>1.32530e-05</td>\n",
       "      <td>8.27370e-06</td>\n",
       "      <td>0.00000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.95000e+07</th>\n",
       "      <td>1.00000e+00</td>\n",
       "      <td>8.18830e-01</td>\n",
       "      <td>6.83240e-01</td>\n",
       "      <td>5.89540e-01</td>\n",
       "      <td>5.17300e-01</td>\n",
       "      <td>4.37140e-01</td>\n",
       "      <td>3.61510e-01</td>\n",
       "      <td>2.89570e-01</td>\n",
       "      <td>2.31950e-01</td>\n",
       "      <td>1.65070e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>4.43810e-02</td>\n",
       "      <td>1.75940e-02</td>\n",
       "      <td>5.87300e-03</td>\n",
       "      <td>1.79320e-03</td>\n",
       "      <td>5.15470e-04</td>\n",
       "      <td>1.44420e-04</td>\n",
       "      <td>4.23120e-05</td>\n",
       "      <td>1.56160e-05</td>\n",
       "      <td>9.02390e-06</td>\n",
       "      <td>0.00000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.00000e+07</th>\n",
       "      <td>1.00000e+00</td>\n",
       "      <td>8.18730e-01</td>\n",
       "      <td>6.80810e-01</td>\n",
       "      <td>5.86720e-01</td>\n",
       "      <td>5.16200e-01</td>\n",
       "      <td>4.39420e-01</td>\n",
       "      <td>3.66990e-01</td>\n",
       "      <td>2.96650e-01</td>\n",
       "      <td>2.39420e-01</td>\n",
       "      <td>1.73460e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>4.94380e-02</td>\n",
       "      <td>2.00420e-02</td>\n",
       "      <td>6.83960e-03</td>\n",
       "      <td>2.13430e-03</td>\n",
       "      <td>6.25610e-04</td>\n",
       "      <td>1.77860e-04</td>\n",
       "      <td>5.21110e-05</td>\n",
       "      <td>1.84890e-05</td>\n",
       "      <td>9.92440e-06</td>\n",
       "      <td>0.00000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "P                            0           1           2           3   \\\n",
       "MAT  MT E                                                             \n",
       "2631 2  1.80000e+07 1.00000e+00 8.21540e-01 6.93540e-01 5.98950e-01   \n",
       "        1.85000e+07 1.00000e+00 8.20250e-01 6.89640e-01 5.95710e-01   \n",
       "        1.90000e+07 1.00000e+00 8.19340e-01 6.86190e-01 5.92550e-01   \n",
       "        1.95000e+07 1.00000e+00 8.18830e-01 6.83240e-01 5.89540e-01   \n",
       "        2.00000e+07 1.00000e+00 8.18730e-01 6.80810e-01 5.86720e-01   \n",
       "\n",
       "P                            4           5           6           7   \\\n",
       "MAT  MT E                                                             \n",
       "2631 2  1.80000e+07 5.18790e-01 4.26990e-01 3.40440e-01 2.64590e-01   \n",
       "        1.85000e+07 5.18660e-01 4.30970e-01 3.48240e-01 2.73510e-01   \n",
       "        1.90000e+07 5.18140e-01 4.34340e-01 3.55260e-01 2.81850e-01   \n",
       "        1.95000e+07 5.17300e-01 4.37140e-01 3.61510e-01 2.89570e-01   \n",
       "        2.00000e+07 5.16200e-01 4.39420e-01 3.66990e-01 2.96650e-01   \n",
       "\n",
       "P                            8           9   ...          11          12  \\\n",
       "MAT  MT E                                    ...                           \n",
       "2631 2  1.80000e+07 2.05710e-01 1.37410e-01  ... 3.03740e-02 1.11880e-02   \n",
       "        1.85000e+07 2.15080e-01 1.46970e-01  ... 3.47960e-02 1.31510e-02   \n",
       "        1.90000e+07 2.23840e-01 1.56220e-01  ... 3.94820e-02 1.52910e-02   \n",
       "        1.95000e+07 2.31950e-01 1.65070e-01  ... 4.43810e-02 1.75940e-02   \n",
       "        2.00000e+07 2.39420e-01 1.73460e-01  ... 4.94380e-02 2.00420e-02   \n",
       "\n",
       "P                            13          14          15          16  \\\n",
       "MAT  MT E                                                             \n",
       "2631 2  1.80000e+07 3.48700e-03 9.96120e-04 2.70090e-04 7.29360e-05   \n",
       "        1.85000e+07 4.19560e-03 1.22570e-03 3.38860e-04 9.24890e-05   \n",
       "        1.90000e+07 4.99100e-03 1.49080e-03 4.20250e-04 1.16140e-04   \n",
       "        1.95000e+07 5.87300e-03 1.79320e-03 5.15470e-04 1.44420e-04   \n",
       "        2.00000e+07 6.83960e-03 2.13430e-03 6.25610e-04 1.77860e-04   \n",
       "\n",
       "P                            17          18          19          20  \n",
       "MAT  MT E                                                            \n",
       "2631 2  1.80000e+07 2.20630e-05 9.77590e-06 7.13560e-06 0.00000e+00  \n",
       "        1.85000e+07 2.74920e-05 1.13290e-05 7.65150e-06 0.00000e+00  \n",
       "        1.90000e+07 3.41760e-05 1.32530e-05 8.27370e-06 0.00000e+00  \n",
       "        1.95000e+07 4.23120e-05 1.56160e-05 9.02390e-06 0.00000e+00  \n",
       "        2.00000e+07 5.21110e-05 1.84890e-05 9.92440e-06 0.00000e+00  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lpc = sandy.Lpc.from_endf6(tape).filter_by(\"MT\", 2)\n",
    "lpc.data.head() # print first 5 rows of dataframe\n",
    "lpc.data.tail() # print last 5 rows of dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert Legendre poynomial coefficients into distibutions tabulated over cosine "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Legendre polynomials are a good way to store most of the angular distribution information in just a few coefficients.\n",
    "\n",
    "However, for analyses or plotting purposes, one could be interested in having the angular distributions \n",
    "tabulated oven a cosine grid.\n",
    "\n",
    "Legendre polynomial coefficients can be easily converted into tabulated probability distributions using method `.to_tpd`.\n",
    "Here the method's output is stored in variabe `tpd`.\n",
    "Then, the tabulated distributions can be accessed via attribute `.data` as a `pandas.DataFrame`.\n",
    "\n",
    "By default the distributions are tabulated over 101 cosine bins equally spaced between -1 and 1.\n",
    "Should we want more bins, say 201, we should pass argument `cosines=np.linspace(-1, 1, 201)` to `.to_tpd`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>COSINE</th>\n",
       "      <th>-1.00000e+00</th>\n",
       "      <th>-9.80000e-01</th>\n",
       "      <th>-9.60000e-01</th>\n",
       "      <th>-9.40000e-01</th>\n",
       "      <th>-9.20000e-01</th>\n",
       "      <th>-9.00000e-01</th>\n",
       "      <th>-8.80000e-01</th>\n",
       "      <th>-8.60000e-01</th>\n",
       "      <th>-8.40000e-01</th>\n",
       "      <th>-8.20000e-01</th>\n",
       "      <th>...</th>\n",
       "      <th>8.20000e-01</th>\n",
       "      <th>8.40000e-01</th>\n",
       "      <th>8.60000e-01</th>\n",
       "      <th>8.80000e-01</th>\n",
       "      <th>9.00000e-01</th>\n",
       "      <th>9.20000e-01</th>\n",
       "      <th>9.40000e-01</th>\n",
       "      <th>9.60000e-01</th>\n",
       "      <th>9.80000e-01</th>\n",
       "      <th>1.00000e+00</th>\n",
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       "      <th>MAT</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <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",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2631</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">2</th>\n",
       "      <th>1.00000e-05</th>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.58460e+03</th>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "      <td>5.00000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.17090e+04</th>\n",
       "      <td>4.83356e-01</td>\n",
       "      <td>4.83686e-01</td>\n",
       "      <td>4.84016e-01</td>\n",
       "      <td>4.84346e-01</td>\n",
       "      <td>4.84677e-01</td>\n",
       "      <td>4.85007e-01</td>\n",
       "      <td>4.85337e-01</td>\n",
       "      <td>4.85668e-01</td>\n",
       "      <td>4.85999e-01</td>\n",
       "      <td>4.86329e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>5.13744e-01</td>\n",
       "      <td>5.14081e-01</td>\n",
       "      <td>5.14419e-01</td>\n",
       "      <td>5.14757e-01</td>\n",
       "      <td>5.15096e-01</td>\n",
       "      <td>5.15434e-01</td>\n",
       "      <td>5.15772e-01</td>\n",
       "      <td>5.16110e-01</td>\n",
       "      <td>5.16449e-01</td>\n",
       "      <td>5.16787e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.50340e+04</th>\n",
       "      <td>5.06502e-01</td>\n",
       "      <td>5.06387e-01</td>\n",
       "      <td>5.06271e-01</td>\n",
       "      <td>5.06155e-01</td>\n",
       "      <td>5.06038e-01</td>\n",
       "      <td>5.05920e-01</td>\n",
       "      <td>5.05803e-01</td>\n",
       "      <td>5.05685e-01</td>\n",
       "      <td>5.05566e-01</td>\n",
       "      <td>5.05447e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>4.94179e-01</td>\n",
       "      <td>4.94024e-01</td>\n",
       "      <td>4.93867e-01</td>\n",
       "      <td>4.93711e-01</td>\n",
       "      <td>4.93554e-01</td>\n",
       "      <td>4.93397e-01</td>\n",
       "      <td>4.93239e-01</td>\n",
       "      <td>4.93080e-01</td>\n",
       "      <td>4.92922e-01</td>\n",
       "      <td>4.92762e-01</td>\n",
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       "    <tr>\n",
       "      <th>1.93050e+04</th>\n",
       "      <td>4.52482e-01</td>\n",
       "      <td>4.53936e-01</td>\n",
       "      <td>4.55375e-01</td>\n",
       "      <td>4.56800e-01</td>\n",
       "      <td>4.58211e-01</td>\n",
       "      <td>4.59607e-01</td>\n",
       "      <td>4.60988e-01</td>\n",
       "      <td>4.62355e-01</td>\n",
       "      <td>4.63707e-01</td>\n",
       "      <td>4.65045e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>5.21140e-01</td>\n",
       "      <td>5.21126e-01</td>\n",
       "      <td>5.21093e-01</td>\n",
       "      <td>5.21042e-01</td>\n",
       "      <td>5.20974e-01</td>\n",
       "      <td>5.20887e-01</td>\n",
       "      <td>5.20782e-01</td>\n",
       "      <td>5.20660e-01</td>\n",
       "      <td>5.20519e-01</td>\n",
       "      <td>5.20360e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "COSINE               -1.00000e+00  -9.80000e-01  -9.60000e-01  -9.40000e-01  \\\n",
       "MAT  MT E                                                                     \n",
       "2631 2  1.00000e-05   5.00000e-01   5.00000e-01   5.00000e-01   5.00000e-01   \n",
       "        1.58460e+03   5.00000e-01   5.00000e-01   5.00000e-01   5.00000e-01   \n",
       "        1.17090e+04   4.83356e-01   4.83686e-01   4.84016e-01   4.84346e-01   \n",
       "        1.50340e+04   5.06502e-01   5.06387e-01   5.06271e-01   5.06155e-01   \n",
       "        1.93050e+04   4.52482e-01   4.53936e-01   4.55375e-01   4.56800e-01   \n",
       "\n",
       "COSINE               -9.20000e-01  -9.00000e-01  -8.80000e-01  -8.60000e-01  \\\n",
       "MAT  MT E                                                                     \n",
       "2631 2  1.00000e-05   5.00000e-01   5.00000e-01   5.00000e-01   5.00000e-01   \n",
       "        1.58460e+03   5.00000e-01   5.00000e-01   5.00000e-01   5.00000e-01   \n",
       "        1.17090e+04   4.84677e-01   4.85007e-01   4.85337e-01   4.85668e-01   \n",
       "        1.50340e+04   5.06038e-01   5.05920e-01   5.05803e-01   5.05685e-01   \n",
       "        1.93050e+04   4.58211e-01   4.59607e-01   4.60988e-01   4.62355e-01   \n",
       "\n",
       "COSINE               -8.40000e-01  -8.20000e-01  ...  8.20000e-01   \\\n",
       "MAT  MT E                                        ...                 \n",
       "2631 2  1.00000e-05   5.00000e-01   5.00000e-01  ...   5.00000e-01   \n",
       "        1.58460e+03   5.00000e-01   5.00000e-01  ...   5.00000e-01   \n",
       "        1.17090e+04   4.85999e-01   4.86329e-01  ...   5.13744e-01   \n",
       "        1.50340e+04   5.05566e-01   5.05447e-01  ...   4.94179e-01   \n",
       "        1.93050e+04   4.63707e-01   4.65045e-01  ...   5.21140e-01   \n",
       "\n",
       "COSINE               8.40000e-01   8.60000e-01   8.80000e-01   9.00000e-01   \\\n",
       "MAT  MT E                                                                     \n",
       "2631 2  1.00000e-05   5.00000e-01   5.00000e-01   5.00000e-01   5.00000e-01   \n",
       "        1.58460e+03   5.00000e-01   5.00000e-01   5.00000e-01   5.00000e-01   \n",
       "        1.17090e+04   5.14081e-01   5.14419e-01   5.14757e-01   5.15096e-01   \n",
       "        1.50340e+04   4.94024e-01   4.93867e-01   4.93711e-01   4.93554e-01   \n",
       "        1.93050e+04   5.21126e-01   5.21093e-01   5.21042e-01   5.20974e-01   \n",
       "\n",
       "COSINE               9.20000e-01   9.40000e-01   9.60000e-01   9.80000e-01   \\\n",
       "MAT  MT E                                                                     \n",
       "2631 2  1.00000e-05   5.00000e-01   5.00000e-01   5.00000e-01   5.00000e-01   \n",
       "        1.58460e+03   5.00000e-01   5.00000e-01   5.00000e-01   5.00000e-01   \n",
       "        1.17090e+04   5.15434e-01   5.15772e-01   5.16110e-01   5.16449e-01   \n",
       "        1.50340e+04   4.93397e-01   4.93239e-01   4.93080e-01   4.92922e-01   \n",
       "        1.93050e+04   5.20887e-01   5.20782e-01   5.20660e-01   5.20519e-01   \n",
       "\n",
       "COSINE               1.00000e+00   \n",
       "MAT  MT E                          \n",
       "2631 2  1.00000e-05   5.00000e-01  \n",
       "        1.58460e+03   5.00000e-01  \n",
       "        1.17090e+04   5.16787e-01  \n",
       "        1.50340e+04   4.92762e-01  \n",
       "        1.93050e+04   5.20360e-01  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tpd = lpc.to_tpd()\n",
    "tpd.data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check that all distributions are normalized to one by calling property `.integrals`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MAT   MT  E          \n",
       "2631  2   1.00000e-05   1.00000e+00\n",
       "          1.58460e+03   1.00000e+00\n",
       "          1.17090e+04   1.00000e+00\n",
       "          1.50340e+04   1.00000e+00\n",
       "          1.93050e+04   9.99997e-01\n",
       "Name: integral, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tpd.integrals.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try and make some plots!\n",
    "\n",
    "For instance, we can plot the Legendre polynomial coefficients from P0 to P6  as a function of energy. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we define the order of the highest polynomial that we want to consider, i.e. 6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "pmax = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "lpc.data.loc[(mat, mt), :pmax + 1].plot(ax=ax, logx=True);\n",
    "plt.xlim([1e3, 2e7]);\n",
    "plt.xlabel(\"Energy / $eV$\");\n",
    "plt.ylabel(\"Coefficient value\")\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot the tabulated distribution that we created before and stored in variable `tpd`!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we have to do is to select the energy values of interest for us (in eV).\n",
    "\n",
    "Make sure that these values exist for the given reaction.\n",
    "You can always check it by printing the index `tpd.data.loc[(mat,mt)].index`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "energies = [1e-5, 72000.0, 407620.0, 1000000.0, 2000000]\n",
    "tpd.data.loc[(mat,mt)].loc[energies].T.plot(ax=ax)\n",
    "ax.set_xlim([-1, 1])\n",
    "plt.xlabel(\"Angle / $cos$\");\n",
    "plt.ylabel(\"Tabulated distribution\")\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write to ENDF-6 file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suppose we applied modifiations to our object `lpc` and we would like to test what we changed in our daily reactor simulations.\n",
    "\n",
    "First we need to reinsert the Legendre polynomial coeffcients in `lpc` back into the endf6 tape object `tape`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "tape1 = lpc.to_endf6(tape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that a new endf6 object `tape1` was created."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we test that the Legendre polynomial coeffcients in `tape1` are exactly the same as those in `lpc`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "lpc1 = sandy.Lpc.from_endf6(tape1).filter_by(\"MT\", 2)\n",
    "\n",
    "pd.testing.assert_frame_equal(lpc1.data, lpc.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Last steps, write first the `endf6` object content into a string using method `write_string`, \n",
    "then open a new file and dump the string into it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "string = tape1.write_string()\n",
    "\n",
    "newfile = \"new_lpc.endf\"\n",
    "with open(newfile, \"w\") as f:\n",
    "    ncharacters = f.write(string)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you open the ASCII file `\"new_lpc.endf\"` you'll see that it is formatted according to the ENDF-6 rules and \n",
    "that it contains the same information that was initially retained in object `lpc`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change energy grid of the coefficients "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Legendre polynomial coeffcients are reported in the ENDF-6 format over a number of incident neutron energies.\n",
    "For several reasons (including applying perturbations to such coeffcients, as we will see later in the tutorial) \n",
    "we might be interested in extending this energy grid by adding some extra points.\n",
    "This can be done using method `.reshape`, to which the additional energy points are passed.\n",
    "\n",
    "> Notice that the new `sandy.Lpc` instance that is returned by this method contains \n",
    "> Legendre polynomial coefficients that are tabulated over the union of the original \n",
    "> grid and the additional energy points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>P</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
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       "      <th>7</th>\n",
       "      <th>8</th>\n",
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       "      <th>MAT</th>\n",
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       "      <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>...</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>1.58460e+03</th>\n",
       "      <td>1.00000e+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",
       "      <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>6.64680e+03</th>\n",
       "      <td>1.00000e+00</td>\n",
       "      <td>5.57200e-03</td>\n",
       "      <td>1.43395e-05</td>\n",
       "      <td>-3.22865e-08</td>\n",
       "      <td>7.02200e-12</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>1.17090e+04</th>\n",
       "      <td>1.00000e+00</td>\n",
       "      <td>1.11440e-02</td>\n",
       "      <td>2.86790e-05</td>\n",
       "      <td>-6.45730e-08</td>\n",
       "      <td>1.40440e-11</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>1.40655e+04</th>\n",
       "      <td>1.00000e+00</td>\n",
       "      <td>1.21431e-16</td>\n",
       "      <td>-9.58473e-05</td>\n",
       "      <td>1.04676e-07</td>\n",
       "      <td>2.09666e-10</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>1.50340e+04</th>\n",
       "      <td>1.00000e+00</td>\n",
       "      <td>-4.58040e-03</td>\n",
       "      <td>-1.47030e-04</td>\n",
       "      <td>1.74240e-07</td>\n",
       "      <td>2.90070e-10</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>1.93050e+04</th>\n",
       "      <td>1.00000e+00</td>\n",
       "      <td>2.28420e-02</td>\n",
       "      <td>-5.43220e-03</td>\n",
       "      <td>-9.25960e-05</td>\n",
       "      <td>2.79840e-07</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>2.18750e+04</th>\n",
       "      <td>1.00000e+00</td>\n",
       "      <td>-6.43930e-04</td>\n",
       "      <td>-2.55650e-04</td>\n",
       "      <td>6.12790e-07</td>\n",
       "      <td>6.91230e-10</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>2.35790e+04</th>\n",
       "      <td>1.00000e+00</td>\n",
       "      <td>1.21020e-01</td>\n",
       "      <td>4.11800e-03</td>\n",
       "      <td>-2.24810e-05</td>\n",
       "      <td>2.20890e-08</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "P                            0            1            2            3   \\\n",
       "MAT  MT E                                                                \n",
       "2631 2  1.00000e-05 1.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00   \n",
       "        1.00000e+00 1.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00   \n",
       "        1.58460e+03 1.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00   \n",
       "        6.64680e+03 1.00000e+00  5.57200e-03  1.43395e-05 -3.22865e-08   \n",
       "        1.17090e+04 1.00000e+00  1.11440e-02  2.86790e-05 -6.45730e-08   \n",
       "        1.40655e+04 1.00000e+00  1.21431e-16 -9.58473e-05  1.04676e-07   \n",
       "        1.50340e+04 1.00000e+00 -4.58040e-03 -1.47030e-04  1.74240e-07   \n",
       "        1.93050e+04 1.00000e+00  2.28420e-02 -5.43220e-03 -9.25960e-05   \n",
       "        2.18750e+04 1.00000e+00 -6.43930e-04 -2.55650e-04  6.12790e-07   \n",
       "        2.35790e+04 1.00000e+00  1.21020e-01  4.11800e-03 -2.24810e-05   \n",
       "\n",
       "P                            4           5           6           7   \\\n",
       "MAT  MT E                                                             \n",
       "2631 2  1.00000e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "        1.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "        1.58460e+03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "        6.64680e+03 7.02200e-12 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "        1.17090e+04 1.40440e-11 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "        1.40655e+04 2.09666e-10 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "        1.50340e+04 2.90070e-10 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "        1.93050e+04 2.79840e-07 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "        2.18750e+04 6.91230e-10 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "        2.35790e+04 2.20890e-08 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "\n",
       "P                            8           9   ...          11          12  \\\n",
       "MAT  MT E                                    ...                           \n",
       "2631 2  1.00000e-05 0.00000e+00 0.00000e+00  ... 0.00000e+00 0.00000e+00   \n",
       "        1.00000e+00 0.00000e+00 0.00000e+00  ... 0.00000e+00 0.00000e+00   \n",
       "        1.58460e+03 0.00000e+00 0.00000e+00  ... 0.00000e+00 0.00000e+00   \n",
       "        6.64680e+03 0.00000e+00 0.00000e+00  ... 0.00000e+00 0.00000e+00   \n",
       "        1.17090e+04 0.00000e+00 0.00000e+00  ... 0.00000e+00 0.00000e+00   \n",
       "        1.40655e+04 0.00000e+00 0.00000e+00  ... 0.00000e+00 0.00000e+00   \n",
       "        1.50340e+04 0.00000e+00 0.00000e+00  ... 0.00000e+00 0.00000e+00   \n",
       "        1.93050e+04 0.00000e+00 0.00000e+00  ... 0.00000e+00 0.00000e+00   \n",
       "        2.18750e+04 0.00000e+00 0.00000e+00  ... 0.00000e+00 0.00000e+00   \n",
       "        2.35790e+04 0.00000e+00 0.00000e+00  ... 0.00000e+00 0.00000e+00   \n",
       "\n",
       "P                            13          14          15          16  \\\n",
       "MAT  MT E                                                             \n",
       "2631 2  1.00000e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "        1.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "        1.58460e+03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "        6.64680e+03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "        1.17090e+04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "        1.40655e+04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "        1.50340e+04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "        1.93050e+04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "        2.18750e+04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "        2.35790e+04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "\n",
       "P                            17          18          19          20  \n",
       "MAT  MT E                                                            \n",
       "2631 2  1.00000e-05 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00  \n",
       "        1.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00  \n",
       "        1.58460e+03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00  \n",
       "        6.64680e+03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00  \n",
       "        1.17090e+04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00  \n",
       "        1.40655e+04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00  \n",
       "        1.50340e+04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00  \n",
       "        1.93050e+04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00  \n",
       "        2.18750e+04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00  \n",
       "        2.35790e+04 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lpc.reshape([1e0, 6646.8, 14065.4523924601]).data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply custom perturbations to Legendre polynomial coefficients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Custom energy-dependent perturbations can be applied to specific Legendre polynomial coefficients.\n",
    "\n",
    "In the following example we increase the P1 coefficient of Fe56*(n,n)* by 10% for neutron energies between 10 and 20 MeV.\n",
    "At the same time, we also reduce the P2 coefficient of the same reaction by 30% over all energies lower than 20 MeV.\n",
    "The two tabulated perturbation coefficients are stored in variables `pert_p1` and `pert_p2`, respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> To learn how to produce energy-dependent tabulated **perturbation coefficients** with `sandy` \n",
    "> you can check [this tutorial](https://github.com/luca-fiorito-11/sandy/blob/develop/notebooks/pert_notebook.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "pert_p1 = sandy.Pert(pd.Series([1, 1.1], index=[1e7, 2e7]))\n",
    "pert_p2 = sandy.Pert(pd.Series([0.7], index=[2e7]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, the perturbations are applied in sequence to the `sandy.Lpc` instance using method `.custom_perturbation`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "lpc1 = lpc.custom_perturbation(mat, mt, 1, pert_p1).custom_perturbation(mat, mt, 2, pert_p2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A new object `lpc1` is created and the content of its `data` attribute is dispayed below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>P</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
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       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAT</th>\n",
       "      <th>MT</th>\n",
       "      <th>E</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">2631</th>\n",
       "      <th rowspan=\"10\" valign=\"top\">2</th>\n",
       "      <th>1.55000e+07</th>\n",
       "      <td>1.00000e+00</td>\n",
       "      <td>9.15904e-01</td>\n",
       "      <td>5.01746e-01</td>\n",
       "      <td>6.13440e-01</td>\n",
       "      <td>5.11550e-01</td>\n",
       "      <td>3.97110e-01</td>\n",
       "      <td>2.91630e-01</td>\n",
       "      <td>2.13080e-01</td>\n",
       "      <td>1.51850e-01</td>\n",
       "      <td>8.82620e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>1.32790e-02</td>\n",
       "      <td>4.19890e-03</td>\n",
       "      <td>1.15060e-03</td>\n",
       "      <td>2.92180e-04</td>\n",
       "      <td>7.24270e-05</td>\n",
       "      <td>1.97400e-05</td>\n",
       "      <td>7.87460e-06</td>\n",
       "      <td>5.65010e-06</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.60000e+07</th>\n",
       "      <td>1.00000e+00</td>\n",
       "      <td>9.12989e-01</td>\n",
       "      <td>4.98414e-01</td>\n",
       "      <td>6.11120e-01</td>\n",
       "      <td>5.14230e-01</td>\n",
       "      <td>4.04500e-01</td>\n",
       "      <td>3.02500e-01</td>\n",
       "      <td>2.24110e-01</td>\n",
       "      <td>1.63310e-01</td>\n",
       "      <td>9.79430e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>1.59850e-02</td>\n",
       "      <td>5.22990e-03</td>\n",
       "      <td>1.47350e-03</td>\n",
       "      <td>3.83420e-04</td>\n",
       "      <td>9.65680e-05</td>\n",
       "      <td>2.59050e-05</td>\n",
       "      <td>9.46970e-06</td>\n",
       "      <td>6.14550e-06</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.65000e+07</th>\n",
       "      <td>1.00000e+00</td>\n",
       "      <td>9.10228e-01</td>\n",
       "      <td>4.95026e-01</td>\n",
       "      <td>6.08390e-01</td>\n",
       "      <td>5.16240e-01</td>\n",
       "      <td>4.11150e-01</td>\n",
       "      <td>3.12880e-01</td>\n",
       "      <td>2.34830e-01</td>\n",
       "      <td>1.74510e-01</td>\n",
       "      <td>1.07790e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.90490e-02</td>\n",
       "      <td>6.43660e-03</td>\n",
       "      <td>1.86250e-03</td>\n",
       "      <td>4.96380e-04</td>\n",
       "      <td>1.27210e-04</td>\n",
       "      <td>3.39010e-05</td>\n",
       "      <td>1.15560e-05</td>\n",
       "      <td>6.76560e-06</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.70000e+07</th>\n",
       "      <td>1.00000e+00</td>\n",
       "      <td>9.07720e-01</td>\n",
       "      <td>4.91680e-01</td>\n",
       "      <td>6.05370e-01</td>\n",
       "      <td>5.17630e-01</td>\n",
       "      <td>4.17100e-01</td>\n",
       "      <td>3.22710e-01</td>\n",
       "      <td>2.45190e-01</td>\n",
       "      <td>1.85370e-01</td>\n",
       "      <td>1.17730e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>2.24760e-02</td>\n",
       "      <td>7.82890e-03</td>\n",
       "      <td>2.32410e-03</td>\n",
       "      <td>6.34150e-04</td>\n",
       "      <td>1.65520e-04</td>\n",
       "      <td>4.41150e-05</td>\n",
       "      <td>1.42560e-05</td>\n",
       "      <td>7.54720e-06</td>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.75000e+07</th>\n",
       "      <td>1.00000e+00</td>\n",
       "      <td>9.05531e-01</td>\n",
       "      <td>4.88474e-01</td>\n",
       "      <td>6.02190e-01</td>\n",
       "      <td>5.18460e-01</td>\n",
       "      <td>4.22370e-01</td>\n",
       "      <td>3.31920e-01</td>\n",
       "      <td>2.55130e-01</td>\n",
       "      <td>1.95790e-01</td>\n",
       "      <td>1.27630e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>2.62570e-02</td>\n",
       "      <td>9.41250e-03</td>\n",
       "      <td>2.86410e-03</td>\n",
       "      <td>7.99780e-04</td>\n",
       "      <td>2.12730e-04</td>\n",
       "      <td>5.69740e-05</td>\n",
       "      <td>1.77070e-05</td>\n",
       "      <td>8.53400e-06</td>\n",
       "      <td>6.70590e-06</td>\n",
       "      <td>0.00000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.80000e+07</th>\n",
       "      <td>1.00000e+00</td>\n",
       "      <td>9.03694e-01</td>\n",
       "      <td>4.85478e-01</td>\n",
       "      <td>5.98950e-01</td>\n",
       "      <td>5.18790e-01</td>\n",
       "      <td>4.26990e-01</td>\n",
       "      <td>3.40440e-01</td>\n",
       "      <td>2.64590e-01</td>\n",
       "      <td>2.05710e-01</td>\n",
       "      <td>1.37410e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>3.03740e-02</td>\n",
       "      <td>1.11880e-02</td>\n",
       "      <td>3.48700e-03</td>\n",
       "      <td>9.96120e-04</td>\n",
       "      <td>2.70090e-04</td>\n",
       "      <td>7.29360e-05</td>\n",
       "      <td>2.20630e-05</td>\n",
       "      <td>9.77590e-06</td>\n",
       "      <td>7.13560e-06</td>\n",
       "      <td>0.00000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.85000e+07</th>\n",
       "      <td>1.00000e+00</td>\n",
       "      <td>9.02275e-01</td>\n",
       "      <td>4.82748e-01</td>\n",
       "      <td>5.95710e-01</td>\n",
       "      <td>5.18660e-01</td>\n",
       "      <td>4.30970e-01</td>\n",
       "      <td>3.48240e-01</td>\n",
       "      <td>2.73510e-01</td>\n",
       "      <td>2.15080e-01</td>\n",
       "      <td>1.46970e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>3.47960e-02</td>\n",
       "      <td>1.31510e-02</td>\n",
       "      <td>4.19560e-03</td>\n",
       "      <td>1.22570e-03</td>\n",
       "      <td>3.38860e-04</td>\n",
       "      <td>9.24890e-05</td>\n",
       "      <td>2.74920e-05</td>\n",
       "      <td>1.13290e-05</td>\n",
       "      <td>7.65150e-06</td>\n",
       "      <td>0.00000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.90000e+07</th>\n",
       "      <td>1.00000e+00</td>\n",
       "      <td>9.01274e-01</td>\n",
       "      <td>4.80333e-01</td>\n",
       "      <td>5.92550e-01</td>\n",
       "      <td>5.18140e-01</td>\n",
       "      <td>4.34340e-01</td>\n",
       "      <td>3.55260e-01</td>\n",
       "      <td>2.81850e-01</td>\n",
       "      <td>2.23840e-01</td>\n",
       "      <td>1.56220e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>3.94820e-02</td>\n",
       "      <td>1.52910e-02</td>\n",
       "      <td>4.99100e-03</td>\n",
       "      <td>1.49080e-03</td>\n",
       "      <td>4.20250e-04</td>\n",
       "      <td>1.16140e-04</td>\n",
       "      <td>3.41760e-05</td>\n",
       "      <td>1.32530e-05</td>\n",
       "      <td>8.27370e-06</td>\n",
       "      <td>0.00000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.95000e+07</th>\n",
       "      <td>1.00000e+00</td>\n",
       "      <td>9.00713e-01</td>\n",
       "      <td>4.78268e-01</td>\n",
       "      <td>5.89540e-01</td>\n",
       "      <td>5.17300e-01</td>\n",
       "      <td>4.37140e-01</td>\n",
       "      <td>3.61510e-01</td>\n",
       "      <td>2.89570e-01</td>\n",
       "      <td>2.31950e-01</td>\n",
       "      <td>1.65070e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>4.43810e-02</td>\n",
       "      <td>1.75940e-02</td>\n",
       "      <td>5.87300e-03</td>\n",
       "      <td>1.79320e-03</td>\n",
       "      <td>5.15470e-04</td>\n",
       "      <td>1.44420e-04</td>\n",
       "      <td>4.23120e-05</td>\n",
       "      <td>1.56160e-05</td>\n",
       "      <td>9.02390e-06</td>\n",
       "      <td>0.00000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.00000e+07</th>\n",
       "      <td>1.00000e+00</td>\n",
       "      <td>9.00603e-01</td>\n",
       "      <td>4.76567e-01</td>\n",
       "      <td>5.86720e-01</td>\n",
       "      <td>5.16200e-01</td>\n",
       "      <td>4.39420e-01</td>\n",
       "      <td>3.66990e-01</td>\n",
       "      <td>2.96650e-01</td>\n",
       "      <td>2.39420e-01</td>\n",
       "      <td>1.73460e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>4.94380e-02</td>\n",
       "      <td>2.00420e-02</td>\n",
       "      <td>6.83960e-03</td>\n",
       "      <td>2.13430e-03</td>\n",
       "      <td>6.25610e-04</td>\n",
       "      <td>1.77860e-04</td>\n",
       "      <td>5.21110e-05</td>\n",
       "      <td>1.84890e-05</td>\n",
       "      <td>9.92440e-06</td>\n",
       "      <td>0.00000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "P                            0           1           2           3   \\\n",
       "MAT  MT E                                                             \n",
       "2631 2  1.55000e+07 1.00000e+00 9.15904e-01 5.01746e-01 6.13440e-01   \n",
       "        1.60000e+07 1.00000e+00 9.12989e-01 4.98414e-01 6.11120e-01   \n",
       "        1.65000e+07 1.00000e+00 9.10228e-01 4.95026e-01 6.08390e-01   \n",
       "        1.70000e+07 1.00000e+00 9.07720e-01 4.91680e-01 6.05370e-01   \n",
       "        1.75000e+07 1.00000e+00 9.05531e-01 4.88474e-01 6.02190e-01   \n",
       "        1.80000e+07 1.00000e+00 9.03694e-01 4.85478e-01 5.98950e-01   \n",
       "        1.85000e+07 1.00000e+00 9.02275e-01 4.82748e-01 5.95710e-01   \n",
       "        1.90000e+07 1.00000e+00 9.01274e-01 4.80333e-01 5.92550e-01   \n",
       "        1.95000e+07 1.00000e+00 9.00713e-01 4.78268e-01 5.89540e-01   \n",
       "        2.00000e+07 1.00000e+00 9.00603e-01 4.76567e-01 5.86720e-01   \n",
       "\n",
       "P                            4           5           6           7   \\\n",
       "MAT  MT E                                                             \n",
       "2631 2  1.55000e+07 5.11550e-01 3.97110e-01 2.91630e-01 2.13080e-01   \n",
       "        1.60000e+07 5.14230e-01 4.04500e-01 3.02500e-01 2.24110e-01   \n",
       "        1.65000e+07 5.16240e-01 4.11150e-01 3.12880e-01 2.34830e-01   \n",
       "        1.70000e+07 5.17630e-01 4.17100e-01 3.22710e-01 2.45190e-01   \n",
       "        1.75000e+07 5.18460e-01 4.22370e-01 3.31920e-01 2.55130e-01   \n",
       "        1.80000e+07 5.18790e-01 4.26990e-01 3.40440e-01 2.64590e-01   \n",
       "        1.85000e+07 5.18660e-01 4.30970e-01 3.48240e-01 2.73510e-01   \n",
       "        1.90000e+07 5.18140e-01 4.34340e-01 3.55260e-01 2.81850e-01   \n",
       "        1.95000e+07 5.17300e-01 4.37140e-01 3.61510e-01 2.89570e-01   \n",
       "        2.00000e+07 5.16200e-01 4.39420e-01 3.66990e-01 2.96650e-01   \n",
       "\n",
       "P                            8           9   ...          11          12  \\\n",
       "MAT  MT E                                    ...                           \n",
       "2631 2  1.55000e+07 1.51850e-01 8.82620e-02  ... 1.32790e-02 4.19890e-03   \n",
       "        1.60000e+07 1.63310e-01 9.79430e-02  ... 1.59850e-02 5.22990e-03   \n",
       "        1.65000e+07 1.74510e-01 1.07790e-01  ... 1.90490e-02 6.43660e-03   \n",
       "        1.70000e+07 1.85370e-01 1.17730e-01  ... 2.24760e-02 7.82890e-03   \n",
       "        1.75000e+07 1.95790e-01 1.27630e-01  ... 2.62570e-02 9.41250e-03   \n",
       "        1.80000e+07 2.05710e-01 1.37410e-01  ... 3.03740e-02 1.11880e-02   \n",
       "        1.85000e+07 2.15080e-01 1.46970e-01  ... 3.47960e-02 1.31510e-02   \n",
       "        1.90000e+07 2.23840e-01 1.56220e-01  ... 3.94820e-02 1.52910e-02   \n",
       "        1.95000e+07 2.31950e-01 1.65070e-01  ... 4.43810e-02 1.75940e-02   \n",
       "        2.00000e+07 2.39420e-01 1.73460e-01  ... 4.94380e-02 2.00420e-02   \n",
       "\n",
       "P                            13          14          15          16  \\\n",
       "MAT  MT E                                                             \n",
       "2631 2  1.55000e+07 1.15060e-03 2.92180e-04 7.24270e-05 1.97400e-05   \n",
       "        1.60000e+07 1.47350e-03 3.83420e-04 9.65680e-05 2.59050e-05   \n",
       "        1.65000e+07 1.86250e-03 4.96380e-04 1.27210e-04 3.39010e-05   \n",
       "        1.70000e+07 2.32410e-03 6.34150e-04 1.65520e-04 4.41150e-05   \n",
       "        1.75000e+07 2.86410e-03 7.99780e-04 2.12730e-04 5.69740e-05   \n",
       "        1.80000e+07 3.48700e-03 9.96120e-04 2.70090e-04 7.29360e-05   \n",
       "        1.85000e+07 4.19560e-03 1.22570e-03 3.38860e-04 9.24890e-05   \n",
       "        1.90000e+07 4.99100e-03 1.49080e-03 4.20250e-04 1.16140e-04   \n",
       "        1.95000e+07 5.87300e-03 1.79320e-03 5.15470e-04 1.44420e-04   \n",
       "        2.00000e+07 6.83960e-03 2.13430e-03 6.25610e-04 1.77860e-04   \n",
       "\n",
       "P                            17          18          19          20  \n",
       "MAT  MT E                                                            \n",
       "2631 2  1.55000e+07 7.87460e-06 5.65010e-06 0.00000e+00 0.00000e+00  \n",
       "        1.60000e+07 9.46970e-06 6.14550e-06 0.00000e+00 0.00000e+00  \n",
       "        1.65000e+07 1.15560e-05 6.76560e-06 0.00000e+00 0.00000e+00  \n",
       "        1.70000e+07 1.42560e-05 7.54720e-06 0.00000e+00 0.00000e+00  \n",
       "        1.75000e+07 1.77070e-05 8.53400e-06 6.70590e-06 0.00000e+00  \n",
       "        1.80000e+07 2.20630e-05 9.77590e-06 7.13560e-06 0.00000e+00  \n",
       "        1.85000e+07 2.74920e-05 1.13290e-05 7.65150e-06 0.00000e+00  \n",
       "        1.90000e+07 3.41760e-05 1.32530e-05 8.27370e-06 0.00000e+00  \n",
       "        1.95000e+07 4.23120e-05 1.56160e-05 9.02390e-06 0.00000e+00  \n",
       "        2.00000e+07 5.21110e-05 1.84890e-05 9.92440e-06 0.00000e+00  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lpc1.data.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The energy points that define the energy bounds of the perturbation coefficients were automatically added to the \n",
    "`lpc1` energy grid, but what actually happened under the hood was a call to `sandy.Lpc` method `.reshape`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The perturbed Legendre polynomial can be graphically compared to the original ones into \n",
    "a `matplotlib` plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "ax = lpc.data[1].loc[(mat,mt)].plot(logx=True, label=\"P1 (original)\", style=\"b-\", ax=ax)\n",
    "ax = lpc1.data[1].loc[(mat,mt)].plot(logx=True, label=\"P1 (perturbed)\", style=\"b--\", ax=ax)\n",
    "ax = lpc.data[2].loc[(mat,mt)].plot(logx=True, label=\"P2 (original)\", style=\"r-\", ax=ax)\n",
    "ax = lpc1.data[2].loc[(mat,mt)].plot(logx=True, label=\"P2 (perturbed)\", style=\"r--\", ax=ax)\n",
    "ax = lpc.data[3].loc[(mat,mt)].plot(logx=True, label=\"P3 (original)\", style=\"c-\", ax=ax)\n",
    "ax = lpc1.data[3].loc[(mat,mt)].plot(logx=True, label=\"P3 (perturbed)\", style=\"c--\", ax=ax)\n",
    "ax.set_xlim([1e6, 2e7]);\n",
    "ax.set_xlabel(\"Energy / eV\");\n",
    "ax.set_ylabel(\"Coefficient value\");\n",
    "ax.legend()\n",
    "plt.tight_layout();"
   ]
  }
 ],
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