{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0fc67c2f",
   "metadata": {},
   "source": [
    "# Processing ERRORR Files with Andalus\n",
    "\n",
    "This notebook demonstrates how to create an `andalus.Covariance` object from ERRORR output files and prepare the data for database storage.\n",
    "\n",
    "### Background\n",
    "We use the `sandy` library to parse ERRORR files and combine covariance matrices. Currently, `andalus` supports reading covariance information from the following sections:\n",
    "\n",
    "* **File 31 (MF31):** $\\bar{\\nu}$ (nubar)\n",
    "* **File 33 (MF33):** Cross sections\n",
    "* **File 34 (MF34):** Angular distributions\n",
    "* **File 35 (MF35):** Energy distributions\n",
    "\n",
    "> **Note on Identifiers:** Since MT numbers in MF34 and MF35 may overlap with those in MF33, we ensure uniqueness in MF34/35 by defining the identifier as:  \n",
    "> `Identifier = (MF * 1000) + MT`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d11dcc5e",
   "metadata": {},
   "source": [
    "### 1. Setup and Imports\n",
    "We import `andalus` to handle the covariance objects. Internally, `andalus` leverages `sandy` to parse the raw text into structured `pandas` DataFrames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "c60e08c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import andalus"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f6bfc0b",
   "metadata": {},
   "source": [
    "### 2. Reading ERRORR Output\n",
    "Raw ERRORR files use a specific nuclear data format that is difficult to parse manually. Below is a snippet of the raw input data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d019bcb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                     1 0  0    0\n",
      " 9.223500+4 2.330248+2          6          0        -11          09228 1451    1\n",
      " 0.000000+0 0.000000+0         33          0         34          09228 1451    2\n",
      " 1.000000-5 1.000000-1 5.400000-1 4.000000+0 8.315290+0 1.370960+19228 1451    3\n",
      " 2.260330+1 4.016900+1 6.790400+1 9.166090+1 1.486250+2 3.043250+29228 1451    4\n",
      " 4.539990+2 7.485180+2 1.234100+3 2.034680+3 3.354630+3 5.530840+39228 1451    5\n",
      " 9.118820+3 1.503440+4 2.478750+4 4.086770+4 6.737950+4 1.110900+59228 1451    6\n",
      " 1.831560+5 3.019740+5 4.978710+5 8.208500+5 1.353350+6 2.231300+69228 1451    7\n",
      " 3.678790+6 6.065310+6 1.000000+7 1.964030+7                      9228 1451    8\n",
      "                                                                  9228 1  099999\n"
     ]
    }
   ],
   "source": [
    "!head -10 \"../data/u235.errorr31\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d802962",
   "metadata": {},
   "source": [
    "The `Covariance.from_errorr` method simplifies this process. You provide a dictionary mapping the error types (keys) to their file paths, specify the **ZAI** (isotope identifier), and optionally provide a list of **MT** numbers to extract."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "15804344",
   "metadata": {},
   "outputs": [],
   "source": [
    "files = {\n",
    "    \"errorr33\": \"../data/u235.errorr33\",\n",
    "    \"errorr31\": \"../data/u235.errorr31\",\n",
    "    \"errorr34\": \"../data/u235.errorr34\",\n",
    "    \"errorr35\": \"../data/u235.errorr35\",\n",
    "}\n",
    "\n",
    "cov = andalus.Covariance.from_errorr(files, zai=922350, mts=[1, 2, 4, 18, 102, 456, 34251, 35018])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a8485d5",
   "metadata": {},
   "source": [
    "### 3. Exploring the Covariance Object\n",
    "The resulting object inherits from `pandas.DataFrame`, meaning you can use standard data manipulation tools. However, it also contains nuclear-specific metadata like `zai`, `nuclide`, and optional physics parameters like `temperature` or `tolerance`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "735b8d5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ZAI: 922350\n",
      "Nuclide: U235\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;31mType:\u001b[0m        Covariance\n",
      "\u001b[1;31mString form:\u001b[0m\n",
      "MT                                  1                                          \\\n",
      "           E_min_eV         <...> 1.00000e+07  3.95538e-03\n",
      "           1.00000e+07 1.96403e+07  1.96065e-02\n",
      "           \n",
      "           [264 rows x 264 columns]\n",
      "\u001b[1;31mLength:\u001b[0m      264\n",
      "\u001b[1;31mFile:\u001b[0m        c:\\users\\dhouben\\documents\\andalus\\src\\andalus\\covariance.py\n",
      "\u001b[1;31mDocstring:\u001b[0m  \n",
      "Subclass of pd.DataFrame for handling individual nuclide covariance data.\n",
      "\n",
      "Attributes\n",
      "----------\n",
      "zai : int\n",
      "    Nuclide identifier (ZAI).\n",
      "temperature : float, optional\n",
      "    Temperature at which the covariance was generated.\n",
      "err : float, optional\n",
      "    Associated error or tolerance."
     ]
    }
   ],
   "source": [
    "print(f\"ZAI: {cov.zai}\")\n",
    "print(f\"Nuclide: {cov.nuclide}\")\n",
    "?cov"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74ed171f",
   "metadata": {},
   "source": [
    "### 4. Correlation Matrix Visualization\n",
    "We can easily derive a correlation matrix from our covariance data and visualize it using a heat map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "plot_cell",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = cov.correlation()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "im = ax.imshow(corr.values, vmin=-1, vmax=1, cmap=\"vlag\", aspect=\"auto\")\n",
    "\n",
    "cbar = fig.colorbar(im, ax=ax)\n",
    "cbar.set_label(\"Correlation Coefficient\")\n",
    "\n",
    "ax.set_title(\"Correlation Matrix\")\n",
    "ax.set_xlabel(\"Index\")\n",
    "ax.set_ylabel(\"Index\")\n",
    "\n",
    "lvl0 = corr.index.get_level_values(0)\n",
    "\n",
    "# find contiguous MT blocks\n",
    "blocks = []\n",
    "start = 0\n",
    "for i in range(1, len(lvl0)):\n",
    "    if lvl0[i] != lvl0[i - 1]:\n",
    "        blocks.append((lvl0[i - 1], start, i - 1))\n",
    "        start = i\n",
    "blocks.append((lvl0[-1], start, len(lvl0) - 1))\n",
    "\n",
    "# tick at block centers\n",
    "tick_pos = [(s + e) / 2 for _, s, e in blocks]\n",
    "tick_labels = [str(mt) for mt, _, _ in blocks]\n",
    "\n",
    "ax.set_xticks(tick_pos)\n",
    "ax.set_xticklabels(tick_labels)\n",
    "ax.set_yticks(tick_pos)\n",
    "ax.set_yticklabels(tick_labels)\n",
    "\n",
    "# optional separators between MT blocks\n",
    "for _, _, e in blocks[:-1]:\n",
    "    ax.axvline(e + 0.5, color=\"white\", lw=0.5, alpha=0.6)\n",
    "    ax.axhline(e + 0.5, color=\"white\", lw=0.5, alpha=0.6)\n",
    "\n",
    "ax.set_xlabel(\"MT\")\n",
    "ax.set_ylabel(\"MT\")\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03308890",
   "metadata": {},
   "source": [
    "### 5. Exporting Data\n",
    "Once the data is processed, it can be exported to a format optimized for use in ANDALUS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "68f0265f",
   "metadata": {},
   "outputs": [],
   "source": [
    "cov.to_hdf5(\"u235_cov.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74fd11d0",
   "metadata": {},
   "source": [
    "And it can then be easily read."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "58cf3028",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>MT</th>\n",
       "      <th colspan=\"10\" halign=\"left\">456</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">35018</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>E_min</th>\n",
       "      <th>1.00000e-05</th>\n",
       "      <th>1.00000e-01</th>\n",
       "      <th>5.40000e-01</th>\n",
       "      <th>4.00000e+00</th>\n",
       "      <th>8.31529e+00</th>\n",
       "      <th>1.37096e+01</th>\n",
       "      <th>2.26033e+01</th>\n",
       "      <th>4.01690e+01</th>\n",
       "      <th>6.79040e+01</th>\n",
       "      <th>9.16609e+01</th>\n",
       "      <th>...</th>\n",
       "      <th>1.11090e+05</th>\n",
       "      <th>1.83156e+05</th>\n",
       "      <th>3.01974e+05</th>\n",
       "      <th>4.97871e+05</th>\n",
       "      <th>8.20850e+05</th>\n",
       "      <th>1.35335e+06</th>\n",
       "      <th>2.23130e+06</th>\n",
       "      <th>3.67879e+06</th>\n",
       "      <th>6.06531e+06</th>\n",
       "      <th>1.00000e+07</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>E_max</th>\n",
       "      <th>1.00000e-01</th>\n",
       "      <th>5.40000e-01</th>\n",
       "      <th>4.00000e+00</th>\n",
       "      <th>8.31529e+00</th>\n",
       "      <th>1.37096e+01</th>\n",
       "      <th>2.26033e+01</th>\n",
       "      <th>4.01690e+01</th>\n",
       "      <th>6.79040e+01</th>\n",
       "      <th>9.16609e+01</th>\n",
       "      <th>1.48625e+02</th>\n",
       "      <th>...</th>\n",
       "      <th>1.83156e+05</th>\n",
       "      <th>3.01974e+05</th>\n",
       "      <th>4.97871e+05</th>\n",
       "      <th>8.20850e+05</th>\n",
       "      <th>1.35335e+06</th>\n",
       "      <th>2.23130e+06</th>\n",
       "      <th>3.67879e+06</th>\n",
       "      <th>6.06531e+06</th>\n",
       "      <th>1.00000e+07</th>\n",
       "      <th>1.96403e+07</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT</th>\n",
       "      <th>E_min</th>\n",
       "      <th>E_max</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 rowspan=\"5\" valign=\"top\">456</th>\n",
       "      <th>1.00000e-05</th>\n",
       "      <th>1.00000e-01</th>\n",
       "      <td>2.05760e-05</td>\n",
       "      <td>2.05760e-05</td>\n",
       "      <td>1.79154e-05</td>\n",
       "      <td>1.76400e-05</td>\n",
       "      <td>1.76400e-05</td>\n",
       "      <td>1.76400e-05</td>\n",
       "      <td>1.76400e-05</td>\n",
       "      <td>1.76400e-05</td>\n",
       "      <td>1.76400e-05</td>\n",
       "      <td>1.76400e-05</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>1.00000e-01</th>\n",
       "      <th>5.40000e-01</th>\n",
       "      <td>2.05760e-05</td>\n",
       "      <td>2.05760e-05</td>\n",
       "      <td>1.79154e-05</td>\n",
       "      <td>1.76400e-05</td>\n",
       "      <td>1.76400e-05</td>\n",
       "      <td>1.76400e-05</td>\n",
       "      <td>1.76400e-05</td>\n",
       "      <td>1.76400e-05</td>\n",
       "      <td>1.76400e-05</td>\n",
       "      <td>1.76400e-05</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>5.40000e-01</th>\n",
       "      <th>4.00000e+00</th>\n",
       "      <td>1.79154e-05</td>\n",
       "      <td>1.79154e-05</td>\n",
       "      <td>2.00769e-05</td>\n",
       "      <td>2.03007e-05</td>\n",
       "      <td>1.84709e-05</td>\n",
       "      <td>1.76400e-05</td>\n",
       "      <td>1.76400e-05</td>\n",
       "      <td>1.76400e-05</td>\n",
       "      <td>1.76400e-05</td>\n",
       "      <td>1.76400e-05</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+00</th>\n",
       "      <th>8.31529e+00</th>\n",
       "      <td>1.76400e-05</td>\n",
       "      <td>1.76400e-05</td>\n",
       "      <td>2.03007e-05</td>\n",
       "      <td>2.05760e-05</td>\n",
       "      <td>1.85570e-05</td>\n",
       "      <td>1.76400e-05</td>\n",
       "      <td>1.76400e-05</td>\n",
       "      <td>1.76400e-05</td>\n",
       "      <td>1.76400e-05</td>\n",
       "      <td>1.76400e-05</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>8.31529e+00</th>\n",
       "      <th>1.37096e+01</th>\n",
       "      <td>1.76400e-05</td>\n",
       "      <td>1.76400e-05</td>\n",
       "      <td>1.84709e-05</td>\n",
       "      <td>1.85570e-05</td>\n",
       "      <td>2.71709e-05</td>\n",
       "      <td>1.95905e-05</td>\n",
       "      <td>1.76400e-05</td>\n",
       "      <td>1.76400e-05</td>\n",
       "      <td>1.76400e-05</td>\n",
       "      <td>1.76400e-05</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>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">35018</th>\n",
       "      <th>1.35335e+06</th>\n",
       "      <th>2.23130e+06</th>\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",
       "      <td>...</td>\n",
       "      <td>-8.24541e-04</td>\n",
       "      <td>-6.39354e-04</td>\n",
       "      <td>-3.54139e-04</td>\n",
       "      <td>-1.32770e-04</td>\n",
       "      <td>4.91341e-05</td>\n",
       "      <td>2.42313e-04</td>\n",
       "      <td>1.20013e-04</td>\n",
       "      <td>4.55191e-06</td>\n",
       "      <td>-9.22334e-05</td>\n",
       "      <td>-2.44922e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.23130e+06</th>\n",
       "      <th>3.67879e+06</th>\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",
       "      <td>...</td>\n",
       "      <td>-7.69174e-04</td>\n",
       "      <td>-6.44898e-04</td>\n",
       "      <td>-4.72111e-04</td>\n",
       "      <td>-2.48225e-04</td>\n",
       "      <td>-3.08282e-05</td>\n",
       "      <td>1.20013e-04</td>\n",
       "      <td>2.92605e-04</td>\n",
       "      <td>2.06564e-04</td>\n",
       "      <td>4.78707e-05</td>\n",
       "      <td>-3.31639e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.67879e+06</th>\n",
       "      <th>6.06531e+06</th>\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",
       "      <td>...</td>\n",
       "      <td>-6.33792e-04</td>\n",
       "      <td>-5.84774e-04</td>\n",
       "      <td>-4.77911e-04</td>\n",
       "      <td>-3.00563e-04</td>\n",
       "      <td>-8.00144e-05</td>\n",
       "      <td>4.55191e-06</td>\n",
       "      <td>2.06564e-04</td>\n",
       "      <td>5.42362e-04</td>\n",
       "      <td>6.37521e-04</td>\n",
       "      <td>7.84379e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.06531e+06</th>\n",
       "      <th>1.00000e+07</th>\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",
       "      <td>...</td>\n",
       "      <td>-5.45683e-04</td>\n",
       "      <td>-5.36010e-04</td>\n",
       "      <td>-4.44288e-04</td>\n",
       "      <td>-2.73286e-04</td>\n",
       "      <td>-1.02590e-04</td>\n",
       "      <td>-9.22334e-05</td>\n",
       "      <td>4.78707e-05</td>\n",
       "      <td>6.37521e-04</td>\n",
       "      <td>2.10911e-03</td>\n",
       "      <td>3.95538e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.00000e+07</th>\n",
       "      <th>1.96403e+07</th>\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",
       "      <td>...</td>\n",
       "      <td>-1.32210e-03</td>\n",
       "      <td>-8.83098e-04</td>\n",
       "      <td>-4.94075e-04</td>\n",
       "      <td>-3.63020e-04</td>\n",
       "      <td>-2.68487e-04</td>\n",
       "      <td>-2.44922e-06</td>\n",
       "      <td>-3.31639e-06</td>\n",
       "      <td>7.84379e-04</td>\n",
       "      <td>3.95538e-03</td>\n",
       "      <td>1.96065e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>231 rows × 231 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "MT                                  456                                        \\\n",
       "E_min                         1.00000e-05 1.00000e-01 5.40000e-01 4.00000e+00   \n",
       "E_max                         1.00000e-01 5.40000e-01 4.00000e+00 8.31529e+00   \n",
       "MT    E_min       E_max                                                         \n",
       "456   1.00000e-05 1.00000e-01 2.05760e-05 2.05760e-05 1.79154e-05 1.76400e-05   \n",
       "      1.00000e-01 5.40000e-01 2.05760e-05 2.05760e-05 1.79154e-05 1.76400e-05   \n",
       "      5.40000e-01 4.00000e+00 1.79154e-05 1.79154e-05 2.00769e-05 2.03007e-05   \n",
       "      4.00000e+00 8.31529e+00 1.76400e-05 1.76400e-05 2.03007e-05 2.05760e-05   \n",
       "      8.31529e+00 1.37096e+01 1.76400e-05 1.76400e-05 1.84709e-05 1.85570e-05   \n",
       "...                                   ...         ...         ...         ...   \n",
       "35018 1.35335e+06 2.23130e+06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "      2.23130e+06 3.67879e+06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "      3.67879e+06 6.06531e+06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "      6.06531e+06 1.00000e+07 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "      1.00000e+07 1.96403e+07 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "\n",
       "MT                                                                             \\\n",
       "E_min                         8.31529e+00 1.37096e+01 2.26033e+01 4.01690e+01   \n",
       "E_max                         1.37096e+01 2.26033e+01 4.01690e+01 6.79040e+01   \n",
       "MT    E_min       E_max                                                         \n",
       "456   1.00000e-05 1.00000e-01 1.76400e-05 1.76400e-05 1.76400e-05 1.76400e-05   \n",
       "      1.00000e-01 5.40000e-01 1.76400e-05 1.76400e-05 1.76400e-05 1.76400e-05   \n",
       "      5.40000e-01 4.00000e+00 1.84709e-05 1.76400e-05 1.76400e-05 1.76400e-05   \n",
       "      4.00000e+00 8.31529e+00 1.85570e-05 1.76400e-05 1.76400e-05 1.76400e-05   \n",
       "      8.31529e+00 1.37096e+01 2.71709e-05 1.95905e-05 1.76400e-05 1.76400e-05   \n",
       "...                                   ...         ...         ...         ...   \n",
       "35018 1.35335e+06 2.23130e+06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "      2.23130e+06 3.67879e+06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "      3.67879e+06 6.06531e+06 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "      6.06531e+06 1.00000e+07 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "      1.00000e+07 1.96403e+07 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00   \n",
       "\n",
       "MT                                                     ...        35018  \\\n",
       "E_min                         6.79040e+01 9.16609e+01  ...  1.11090e+05   \n",
       "E_max                         9.16609e+01 1.48625e+02  ...  1.83156e+05   \n",
       "MT    E_min       E_max                                ...                \n",
       "456   1.00000e-05 1.00000e-01 1.76400e-05 1.76400e-05  ...  0.00000e+00   \n",
       "      1.00000e-01 5.40000e-01 1.76400e-05 1.76400e-05  ...  0.00000e+00   \n",
       "      5.40000e-01 4.00000e+00 1.76400e-05 1.76400e-05  ...  0.00000e+00   \n",
       "      4.00000e+00 8.31529e+00 1.76400e-05 1.76400e-05  ...  0.00000e+00   \n",
       "      8.31529e+00 1.37096e+01 1.76400e-05 1.76400e-05  ...  0.00000e+00   \n",
       "...                                   ...         ...  ...          ...   \n",
       "35018 1.35335e+06 2.23130e+06 0.00000e+00 0.00000e+00  ... -8.24541e-04   \n",
       "      2.23130e+06 3.67879e+06 0.00000e+00 0.00000e+00  ... -7.69174e-04   \n",
       "      3.67879e+06 6.06531e+06 0.00000e+00 0.00000e+00  ... -6.33792e-04   \n",
       "      6.06531e+06 1.00000e+07 0.00000e+00 0.00000e+00  ... -5.45683e-04   \n",
       "      1.00000e+07 1.96403e+07 0.00000e+00 0.00000e+00  ... -1.32210e-03   \n",
       "\n",
       "MT                                                                    \\\n",
       "E_min                          1.83156e+05  3.01974e+05  4.97871e+05   \n",
       "E_max                          3.01974e+05  4.97871e+05  8.20850e+05   \n",
       "MT    E_min       E_max                                                \n",
       "456   1.00000e-05 1.00000e-01  0.00000e+00  0.00000e+00  0.00000e+00   \n",
       "      1.00000e-01 5.40000e-01  0.00000e+00  0.00000e+00  0.00000e+00   \n",
       "      5.40000e-01 4.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00   \n",
       "      4.00000e+00 8.31529e+00  0.00000e+00  0.00000e+00  0.00000e+00   \n",
       "      8.31529e+00 1.37096e+01  0.00000e+00  0.00000e+00  0.00000e+00   \n",
       "...                                    ...          ...          ...   \n",
       "35018 1.35335e+06 2.23130e+06 -6.39354e-04 -3.54139e-04 -1.32770e-04   \n",
       "      2.23130e+06 3.67879e+06 -6.44898e-04 -4.72111e-04 -2.48225e-04   \n",
       "      3.67879e+06 6.06531e+06 -5.84774e-04 -4.77911e-04 -3.00563e-04   \n",
       "      6.06531e+06 1.00000e+07 -5.36010e-04 -4.44288e-04 -2.73286e-04   \n",
       "      1.00000e+07 1.96403e+07 -8.83098e-04 -4.94075e-04 -3.63020e-04   \n",
       "\n",
       "MT                                                                    \\\n",
       "E_min                          8.20850e+05  1.35335e+06  2.23130e+06   \n",
       "E_max                          1.35335e+06  2.23130e+06  3.67879e+06   \n",
       "MT    E_min       E_max                                                \n",
       "456   1.00000e-05 1.00000e-01  0.00000e+00  0.00000e+00  0.00000e+00   \n",
       "      1.00000e-01 5.40000e-01  0.00000e+00  0.00000e+00  0.00000e+00   \n",
       "      5.40000e-01 4.00000e+00  0.00000e+00  0.00000e+00  0.00000e+00   \n",
       "      4.00000e+00 8.31529e+00  0.00000e+00  0.00000e+00  0.00000e+00   \n",
       "      8.31529e+00 1.37096e+01  0.00000e+00  0.00000e+00  0.00000e+00   \n",
       "...                                    ...          ...          ...   \n",
       "35018 1.35335e+06 2.23130e+06  4.91341e-05  2.42313e-04  1.20013e-04   \n",
       "      2.23130e+06 3.67879e+06 -3.08282e-05  1.20013e-04  2.92605e-04   \n",
       "      3.67879e+06 6.06531e+06 -8.00144e-05  4.55191e-06  2.06564e-04   \n",
       "      6.06531e+06 1.00000e+07 -1.02590e-04 -9.22334e-05  4.78707e-05   \n",
       "      1.00000e+07 1.96403e+07 -2.68487e-04 -2.44922e-06 -3.31639e-06   \n",
       "\n",
       "MT                                                                   \n",
       "E_min                         3.67879e+06  6.06531e+06  1.00000e+07  \n",
       "E_max                         6.06531e+06  1.00000e+07  1.96403e+07  \n",
       "MT    E_min       E_max                                              \n",
       "456   1.00000e-05 1.00000e-01 0.00000e+00  0.00000e+00  0.00000e+00  \n",
       "      1.00000e-01 5.40000e-01 0.00000e+00  0.00000e+00  0.00000e+00  \n",
       "      5.40000e-01 4.00000e+00 0.00000e+00  0.00000e+00  0.00000e+00  \n",
       "      4.00000e+00 8.31529e+00 0.00000e+00  0.00000e+00  0.00000e+00  \n",
       "      8.31529e+00 1.37096e+01 0.00000e+00  0.00000e+00  0.00000e+00  \n",
       "...                                   ...          ...          ...  \n",
       "35018 1.35335e+06 2.23130e+06 4.55191e-06 -9.22334e-05 -2.44922e-06  \n",
       "      2.23130e+06 3.67879e+06 2.06564e-04  4.78707e-05 -3.31639e-06  \n",
       "      3.67879e+06 6.06531e+06 5.42362e-04  6.37521e-04  7.84379e-04  \n",
       "      6.06531e+06 1.00000e+07 6.37521e-04  2.10911e-03  3.95538e-03  \n",
       "      1.00000e+07 1.96403e+07 7.84379e-04  3.95538e-03  1.96065e-02  \n",
       "\n",
       "[231 rows x 231 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "andalus.Covariance.from_hdf5(\"u235_cov.h5\", zai=922350)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "andalus",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}