{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ba1b8a37",
   "metadata": {},
   "source": [
    "# Visualizing Serpent Sensitivity Profiles\n",
    "\n",
    "This example demonstrates how to use the `andalus` package to process and visualize sensitivity profiles generated by the **Serpent-2** Monte Carlo neutron transport code. Sensitivity analysis is crucial in nuclear data assimilation to understand how uncertainties in specific cross-sections affect integral parameters like $k_{\\text{eff}}$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "setup-markdown",
   "metadata": {},
   "source": [
    "### 1. Setup and Imports\n",
    "First, we import `matplotlib` for figure customization and the `andalus` core package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "941d3504",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import andalus"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "parsing-markdown",
   "metadata": {},
   "source": [
    "### 2. Reading Serpent Output\n",
    "We use the `Sensitivity.from_serpent` method to parse the `.m` file. \n",
    "\n",
    "In this example, we examine the **HMF001** (Godiva) benchmark. We filter the perturbations (`pertlist`) for specific reaction types (MT) as denoted in the sens0.m output file:\n",
    "* `mt 2`: Elastic scattering\n",
    "* `mt 4`: Inelastic scattering\n",
    "* `mt 18`: Fission\n",
    "* `mt 102`: Radiative capture\n",
    "* `nubar prompt`: Average number of outgoing prompt neutrons per fission\n",
    "* `chi prompt`: Outgoing prompt neutron energy distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2386e159",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Reading ../data/hmf001.ser_sens0.m\n",
      "  - done\n"
     ]
    }
   ],
   "source": [
    "file_path = \"../data/hmf001.ser_sens0.m\"\n",
    "hmf001 = andalus.Sensitivity.from_serpent(\n",
    "    sens0_path=file_path, title=\"HMF001\", pertlist=[\"mt 2 xs\", \"mt 4 xs\", \"mt 18 xs\", \"mt 102 xs\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "plotting-markdown",
   "metadata": {},
   "source": [
    "### 3. Plotting Energy-Dependent Sensitivities\n",
    "We can now plot the sensitivity profiles for specific isotopes. Below, we focus on U-235 (ZAI 922350) for Fission (MT 18) and Capture (MT 102) reactions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bb91eb79",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dhouben\\Documents\\andalus\\src\\andalus\\sensitivity.py:230: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  subset = self.loc[zai, pert]\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "zais = [922350]  # U-235\n",
    "perts = [18, 102]  # Fission and Capture\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5, 4), layout=\"constrained\")\n",
    "\n",
    "for zai in zais:\n",
    "    for pert in perts:\n",
    "        hmf001.plot_sensitivity(zais=[zai], perts=[pert], ax=ax)\n",
    "\n",
    "ax.set(\n",
    "    xlim=(1e2, 2e7),\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d82ff9b",
   "metadata": {},
   "source": [
    "Next, we plot the sensitivity profiles for U-234. As $k_{\\text{eff}}$ is less sensitive to U-234, the sensitivity estimates obtained via Serpent also suffers more from statistical noise. When plotting sensitivity profiles using ANDALUS, the 1 $\\sigma$ uncertainty is included by default and is visualized as a shaded region around the nominal values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d28619d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "zais = [922340]  # U-235\n",
    "perts = [2, 4, 18, 102]  # Fission and Capture\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5, 4), layout=\"constrained\")\n",
    "\n",
    "for zai in zais:\n",
    "    for pert in perts:\n",
    "        hmf001.plot_sensitivity(zais=[zai], perts=[pert], ax=ax)\n",
    "\n",
    "ax.set(\n",
    "    xlim=(1e2, 2e7),\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df-markdown",
   "metadata": {},
   "source": [
    "### 4. Data Inspection\n",
    "The `Sensitivity` object behaves like a MultiIndexed Pandas DataFrame, allowing for easy energy-bin inspection and statistical uncertainty analysis (`HMF001_std`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "20ee2455",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>HMF001</th>\n",
       "      <th>HMF001_std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZAI</th>\n",
       "      <th>MT</th>\n",
       "      <th>E_min_eV</th>\n",
       "      <th>E_max_eV</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">922340</th>\n",
       "      <th rowspan=\"10\" valign=\"top\">2</th>\n",
       "      <th>1.00000e-05</th>\n",
       "      <th>1.00000e-01</th>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
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       "    <tr>\n",
       "      <th>1.00000e-01</th>\n",
       "      <th>5.40000e-01</th>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
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       "    <tr>\n",
       "      <th>5.40000e-01</th>\n",
       "      <th>4.00000e+00</th>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
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       "    <tr>\n",
       "      <th>4.00000e+00</th>\n",
       "      <th>8.31529e+00</th>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
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       "    <tr>\n",
       "      <th>8.31529e+00</th>\n",
       "      <th>1.37096e+01</th>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.37096e+01</th>\n",
       "      <th>2.26033e+01</th>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.26033e+01</th>\n",
       "      <th>4.01690e+01</th>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.01690e+01</th>\n",
       "      <th>6.79040e+01</th>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.79040e+01</th>\n",
       "      <th>9.16609e+01</th>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
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       "    <tr>\n",
       "      <th>9.16609e+01</th>\n",
       "      <th>1.48625e+02</th>\n",
       "      <td>0.00000e+00</td>\n",
       "      <td>0.00000e+00</td>\n",
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      ],
      "text/plain": [
       "                                       HMF001  HMF001_std\n",
       "ZAI    MT E_min_eV    E_max_eV                           \n",
       "922340 2  1.00000e-05 1.00000e-01 0.00000e+00 0.00000e+00\n",
       "          1.00000e-01 5.40000e-01 0.00000e+00 0.00000e+00\n",
       "          5.40000e-01 4.00000e+00 0.00000e+00 0.00000e+00\n",
       "          4.00000e+00 8.31529e+00 0.00000e+00 0.00000e+00\n",
       "          8.31529e+00 1.37096e+01 0.00000e+00 0.00000e+00\n",
       "          1.37096e+01 2.26033e+01 0.00000e+00 0.00000e+00\n",
       "          2.26033e+01 4.01690e+01 0.00000e+00 0.00000e+00\n",
       "          4.01690e+01 6.79040e+01 0.00000e+00 0.00000e+00\n",
       "          6.79040e+01 9.16609e+01 0.00000e+00 0.00000e+00\n",
       "          9.16609e+01 1.48625e+02 0.00000e+00 0.00000e+00"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying the top of the sensitivity matrix\n",
    "hmf001.head(10)"
   ]
  }
 ],
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