{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "21c7ff5c",
   "metadata": {},
   "source": [
    "## Tutorial 6d - Dichroic Mirror Optimization for Polarization Separation\n",
    "\n",
    "We optimize a thin-film stack to create a dichroic mirror that separates s and p polarizations near 600 nm.\n",
    "\n",
    "This version demonstrates the operand-centric architecture in ThinFilmOptimizer with a single API entry point:\n",
    "- standard spectral operands with add_operand(...)\n",
    "- a custom user-defined operand registered with register_operand(...) and used through add_operand(...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4d7f5c3a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T11:09:37.851891Z",
     "iopub.status.busy": "2026-03-24T11:09:37.851422Z",
     "iopub.status.idle": "2026-03-24T11:09:41.055855Z",
     "shell.execute_reply": "2026-03-24T11:09:41.055054Z"
    }
   },
   "outputs": [],
   "source": [
    "from optiland.thin_film.optimization import ThinFilmOptimizer\n",
    "import optiland.backend as be\n",
    "from optiland.thin_film import ThinFilmStack, SpectralAnalyzer\n",
    "from optiland.materials import Material, IdealMaterial\n",
    "\n",
    "SiO2 = Material(\"SiO2\", reference=\"Gao\")\n",
    "TiO2 = Material(\"TiO2\", reference=\"Zhukovsky\")\n",
    "BK7 = Material(\"N-BK7\", reference=\"SCHOTT\")\n",
    "air = IdealMaterial(n=1.0)\n",
    "\n",
    "dichroic_stack = ThinFilmStack(\n",
    "    incident_material=air, \n",
    "    substrate_material=BK7, \n",
    "    reference_wl_um=0.6,\n",
    "    reference_AOI_deg=45.0,\n",
    ")\n",
    "for i in range(10):  # 10 pairs = 20 layers\n",
    "    dichroic_stack.add_layer_qwot(material=TiO2, qwot_thickness=1.0, name=f\"$TiO_2$\")\n",
    "    dichroic_stack.add_layer_qwot(material=SiO2, qwot_thickness=1.0, name=f\"$SiO_2$\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "be178910",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T11:09:41.057870Z",
     "iopub.status.busy": "2026-03-24T11:09:41.057694Z",
     "iopub.status.idle": "2026-03-24T11:09:41.069316Z",
     "shell.execute_reply": "2026-03-24T11:09:41.068879Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ThinFilm Optimizer Information\n",
      "==================================================\n",
      "+--------------+---------+\n",
      "| Property     |   Count |\n",
      "+==============+=========+\n",
      "| Stack layers |      20 |\n",
      "+--------------+---------+\n",
      "| Variables    |      20 |\n",
      "+--------------+---------+\n",
      "| Targets      |       1 |\n",
      "+--------------+---------+\n",
      "\n",
      "Variables:\n",
      "+------+---------+------------------+------------+------------+\n",
      "|   ID |   Layer |   Thickness (nm) |   Min (nm) |   Max (nm) |\n",
      "+======+=========+==================+============+============+\n",
      "|    0 |       0 |             88.2 |         30 |        300 |\n",
      "+------+---------+------------------+------------+------------+\n",
      "|    1 |       1 |            143.6 |         30 |        300 |\n",
      "+------+---------+------------------+------------+------------+\n",
      "|    2 |       2 |             88.2 |         30 |        300 |\n",
      "+------+---------+------------------+------------+------------+\n",
      "|    3 |       3 |            143.6 |         30 |        300 |\n",
      "+------+---------+------------------+------------+------------+\n",
      "|    4 |       4 |             88.2 |         30 |        300 |\n",
      "+------+---------+------------------+------------+------------+\n",
      "|    5 |       5 |            143.6 |         30 |        300 |\n",
      "+------+---------+------------------+------------+------------+\n",
      "|    6 |       6 |             88.2 |         30 |        300 |\n",
      "+------+---------+------------------+------------+------------+\n",
      "|    7 |       7 |            143.6 |         30 |        300 |\n",
      "+------+---------+------------------+------------+------------+\n",
      "|    8 |       8 |             88.2 |         30 |        300 |\n",
      "+------+---------+------------------+------------+------------+\n",
      "|    9 |       9 |            143.6 |         30 |        300 |\n",
      "+------+---------+------------------+------------+------------+\n",
      "|   10 |      10 |             88.2 |         30 |        300 |\n",
      "+------+---------+------------------+------------+------------+\n",
      "|   11 |      11 |            143.6 |         30 |        300 |\n",
      "+------+---------+------------------+------------+------------+\n",
      "|   12 |      12 |             88.2 |         30 |        300 |\n",
      "+------+---------+------------------+------------+------------+\n",
      "|   13 |      13 |            143.6 |         30 |        300 |\n",
      "+------+---------+------------------+------------+------------+\n",
      "|   14 |      14 |             88.2 |         30 |        300 |\n",
      "+------+---------+------------------+------------+------------+\n",
      "|   15 |      15 |            143.6 |         30 |        300 |\n",
      "+------+---------+------------------+------------+------------+\n",
      "|   16 |      16 |             88.2 |         30 |        300 |\n",
      "+------+---------+------------------+------------+------------+\n",
      "|   17 |      17 |            143.6 |         30 |        300 |\n",
      "+------+---------+------------------+------------+------------+\n",
      "|   18 |      18 |             88.2 |         30 |        300 |\n",
      "+------+---------+------------------+------------+------------+\n",
      "|   19 |      19 |            143.6 |         30 |        300 |\n",
      "+------+---------+------------------+------------+------------+\n",
      "\n",
      "Targets:\n",
      "+------+--------------------------+--------+---------+--------------+-------+----------+-------+\n",
      "|   ID | Prop                     | Type   | Value   | Wavelength   | AOI   |   Weight | Pol   |\n",
      "+======+==========================+========+=========+==============+=======+==========+=======+\n",
      "|    0 | Rs-Rp @ 595-605nm, 45deg | custom |         | -            | -     |        1 | -     |\n",
      "+------+--------------------------+--------+---------+--------------+-------+----------+-------+\n",
      "\n",
      "Initial RSS: 3.058333e-01\n"
     ]
    }
   ],
   "source": [
    "optimizer = ThinFilmOptimizer(dichroic_stack)\n",
    "\n",
    "# Add all thicknesses as optimization variables\n",
    "for i in range(len(dichroic_stack.layers)):\n",
    "    optimizer.add_variable(\n",
    "        layer_index=i,\n",
    "        min_nm=30,\n",
    "        max_nm=300\n",
    "    )\n",
    "\n",
    "# we want to maximize the polarization contrast (Rs-Rp) at 600 nm and 45° AOI, so we \n",
    "# minimize the negative contrast, averaged over a wavelength range to make it more \n",
    "# robust to fabrication variations. The contrast is normalized to be between 0 and 1,\n",
    "# where 1 corresponds to Rs=1 and Rp=0\n",
    "wl_nm = be.linspace(595, 605, 11)\n",
    "\n",
    "# Register and add a custom operand through add_operand\n",
    "def polarization_contrast(stack: ThinFilmStack, wavelength_nm: be.ndarray, aoi_deg:float):\n",
    "    \"\"\"Calculate the polarization contrast (Rs-Rp) averaged over a wavelength range.\n",
    "    The contrast is normalized to be between 0 and 1, where 1 corresponds to Rs=1 and Rp=0.\"\"\"\n",
    "    rs = stack.reflectance_nm_deg(wavelength_nm, aoi_deg, \"s\")\n",
    "    rp = stack.reflectance_nm_deg(wavelength_nm, aoi_deg, \"p\")\n",
    "    return (1 + be.mean(rs - rp))/2\n",
    "\n",
    "ThinFilmOptimizer.register_operand(\n",
    "    \"polarization_contrast\", polarization_contrast, overwrite=True\n",
    ")\n",
    "\n",
    "optimizer.add_operand(\n",
    "    property=\"polarization_contrast\",\n",
    "    min_val=0.99,\n",
    "    input_data={\"wavelength_nm\": wl_nm, \"aoi_deg\": 45.0},\n",
    "    label=\"Rs-Rp @ 595-605nm, 45deg\",\n",
    ")\n",
    "\n",
    "# Display optimization information\n",
    "optimizer.info()\n",
    "contrast_initial=optimizer.rss()\n",
    "print(f\"Initial RSS: {contrast_initial:.6e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c5715cbf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T11:09:41.071169Z",
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     "shell.execute_reply": "2026-03-24T11:09:43.329563Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Merit: 0.093534002618582 -> 0.000329856133348 in 46 iterations\n",
      "Improvement: 0.093204146485235\n"
     ]
    }
   ],
   "source": [
    "# Launch optimization\n",
    "result = optimizer.optimize(\n",
    "    method=\"L-BFGS-B\",\n",
    "    max_iterations=1000,\n",
    ")\n",
    "\n",
    "print(f\"Merit: {result['initial_merit']:.15f} -> {result['final_merit']:.15f} in {result['iterations']} iterations\")\n",
    "print(f\"Improvement: {result['improvement']:.15f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8d67491c",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2026-03-24T11:09:43.341462Z",
     "shell.execute_reply": "2026-03-24T11:09:43.341023Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variable</th>\n",
       "      <th>Initial</th>\n",
       "      <th>Final</th>\n",
       "      <th>Change</th>\n",
       "      <th>Unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Layer 0 thickness</td>\n",
       "      <td>88.2</td>\n",
       "      <td>71.4</td>\n",
       "      <td>-16.8 (-19.1%)</td>\n",
       "      <td>nm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Layer 1 thickness</td>\n",
       "      <td>143.6</td>\n",
       "      <td>145.8</td>\n",
       "      <td>+2.2 (+1.5%)</td>\n",
       "      <td>nm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Layer 2 thickness</td>\n",
       "      <td>88.2</td>\n",
       "      <td>78.3</td>\n",
       "      <td>-9.9 (-11.2%)</td>\n",
       "      <td>nm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Layer 3 thickness</td>\n",
       "      <td>143.6</td>\n",
       "      <td>153.6</td>\n",
       "      <td>+10.0 (+7.0%)</td>\n",
       "      <td>nm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Layer 4 thickness</td>\n",
       "      <td>88.2</td>\n",
       "      <td>199.6</td>\n",
       "      <td>+111.4 (+126.3%)</td>\n",
       "      <td>nm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Layer 5 thickness</td>\n",
       "      <td>143.6</td>\n",
       "      <td>146.2</td>\n",
       "      <td>+2.6 (+1.8%)</td>\n",
       "      <td>nm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Layer 6 thickness</td>\n",
       "      <td>88.2</td>\n",
       "      <td>74.2</td>\n",
       "      <td>-14.1 (-16.0%)</td>\n",
       "      <td>nm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Layer 7 thickness</td>\n",
       "      <td>143.6</td>\n",
       "      <td>135.6</td>\n",
       "      <td>-8.0 (-5.6%)</td>\n",
       "      <td>nm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Layer 8 thickness</td>\n",
       "      <td>88.2</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-16.2 (-18.4%)</td>\n",
       "      <td>nm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Layer 9 thickness</td>\n",
       "      <td>143.6</td>\n",
       "      <td>132.5</td>\n",
       "      <td>-11.1 (-7.7%)</td>\n",
       "      <td>nm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Layer 10 thickness</td>\n",
       "      <td>88.2</td>\n",
       "      <td>70.8</td>\n",
       "      <td>-17.4 (-19.8%)</td>\n",
       "      <td>nm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Layer 11 thickness</td>\n",
       "      <td>143.6</td>\n",
       "      <td>132.5</td>\n",
       "      <td>-11.1 (-7.7%)</td>\n",
       "      <td>nm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Layer 12 thickness</td>\n",
       "      <td>88.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>-20.9 (-23.7%)</td>\n",
       "      <td>nm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Layer 13 thickness</td>\n",
       "      <td>143.6</td>\n",
       "      <td>161.4</td>\n",
       "      <td>+17.8 (+12.4%)</td>\n",
       "      <td>nm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Layer 14 thickness</td>\n",
       "      <td>88.2</td>\n",
       "      <td>196.0</td>\n",
       "      <td>+107.7 (+122.1%)</td>\n",
       "      <td>nm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Layer 15 thickness</td>\n",
       "      <td>143.6</td>\n",
       "      <td>161.7</td>\n",
       "      <td>+18.1 (+12.6%)</td>\n",
       "      <td>nm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Layer 16 thickness</td>\n",
       "      <td>88.2</td>\n",
       "      <td>77.6</td>\n",
       "      <td>-10.7 (-12.1%)</td>\n",
       "      <td>nm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Layer 17 thickness</td>\n",
       "      <td>143.6</td>\n",
       "      <td>137.2</td>\n",
       "      <td>-6.4 (-4.5%)</td>\n",
       "      <td>nm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Layer 18 thickness</td>\n",
       "      <td>88.2</td>\n",
       "      <td>71.3</td>\n",
       "      <td>-16.9 (-19.1%)</td>\n",
       "      <td>nm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Layer 19 thickness</td>\n",
       "      <td>143.6</td>\n",
       "      <td>140.0</td>\n",
       "      <td>-3.6 (-2.5%)</td>\n",
       "      <td>nm</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Variable Initial  Final            Change Unit\n",
       "0    Layer 0 thickness    88.2   71.4    -16.8 (-19.1%)   nm\n",
       "1    Layer 1 thickness   143.6  145.8      +2.2 (+1.5%)   nm\n",
       "2    Layer 2 thickness    88.2   78.3     -9.9 (-11.2%)   nm\n",
       "3    Layer 3 thickness   143.6  153.6     +10.0 (+7.0%)   nm\n",
       "4    Layer 4 thickness    88.2  199.6  +111.4 (+126.3%)   nm\n",
       "5    Layer 5 thickness   143.6  146.2      +2.6 (+1.8%)   nm\n",
       "6    Layer 6 thickness    88.2   74.2    -14.1 (-16.0%)   nm\n",
       "7    Layer 7 thickness   143.6  135.6      -8.0 (-5.6%)   nm\n",
       "8    Layer 8 thickness    88.2   72.0    -16.2 (-18.4%)   nm\n",
       "9    Layer 9 thickness   143.6  132.5     -11.1 (-7.7%)   nm\n",
       "10  Layer 10 thickness    88.2   70.8    -17.4 (-19.8%)   nm\n",
       "11  Layer 11 thickness   143.6  132.5     -11.1 (-7.7%)   nm\n",
       "12  Layer 12 thickness    88.2   67.3    -20.9 (-23.7%)   nm\n",
       "13  Layer 13 thickness   143.6  161.4    +17.8 (+12.4%)   nm\n",
       "14  Layer 14 thickness    88.2  196.0  +107.7 (+122.1%)   nm\n",
       "15  Layer 15 thickness   143.6  161.7    +18.1 (+12.6%)   nm\n",
       "16  Layer 16 thickness    88.2   77.6    -10.7 (-12.1%)   nm\n",
       "17  Layer 17 thickness   143.6  137.2      -6.4 (-4.5%)   nm\n",
       "18  Layer 18 thickness    88.2   71.3    -16.9 (-19.1%)   nm\n",
       "19  Layer 19 thickness   143.6  140.0      -3.6 (-2.5%)   nm"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from optiland.thin_film.optimization import ThinFilmReport\n",
    "\n",
    "report = ThinFilmReport(optimizer, result)\n",
    "report.summary_table()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b28715cd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T11:09:43.342914Z",
     "iopub.status.busy": "2026-03-24T11:09:43.342708Z",
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     "shell.execute_reply": "2026-03-24T11:09:43.771235Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:25: SyntaxWarning: invalid escape sequence '\\l'\n",
      "<>:38: SyntaxWarning: invalid escape sequence '\\l'\n",
      "<>:25: SyntaxWarning: invalid escape sequence '\\l'\n",
      "<>:38: SyntaxWarning: invalid escape sequence '\\l'\n",
      "C:\\Users\\kdani\\AppData\\Local\\Temp\\ipykernel_3984\\3268724171.py:25: SyntaxWarning: invalid escape sequence '\\l'\n",
      "  axes[0].set_xlabel('$\\lambda$ (nm)')\n",
      "C:\\Users\\kdani\\AppData\\Local\\Temp\\ipykernel_3984\\3268724171.py:38: SyntaxWarning: invalid escape sequence '\\l'\n",
      "  axes[1].set_xlabel('$\\lambda$ (nm)')\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "analyzer = SpectralAnalyzer(dichroic_stack)\n",
    "fig, axes = plt.subplots(1, 3, figsize=(12, 4))\n",
    "\n",
    "wl_range = be.linspace(500, 700, 201)  # Around 600 nm\n",
    "\n",
    "# Calculate before optimization (reset and recalculate)\n",
    "optimizer.reset()\n",
    "Rs_before = dichroic_stack.reflectance_nm_deg(wl_range, 45, 's')\n",
    "Rp_before = dichroic_stack.reflectance_nm_deg(wl_range, 45, 'p')\n",
    "\n",
    "# Restore optimized state\n",
    "for i, var_info in enumerate(optimizer.variables):\n",
    "    final_thickness = result['thickness_changes'][i]['final_nm'] / 1000  # nm → μm\n",
    "    dichroic_stack.layers[i].update_thickness(final_thickness)\n",
    "\n",
    "Rs_after = dichroic_stack.reflectance_nm_deg(wl_range, 45, 's')\n",
    "Rp_after = dichroic_stack.reflectance_nm_deg(wl_range, 45, 'p')\n",
    "\n",
    "axes[0].plot(wl_range, Rs_before, 'b:', label='$R_s$ before', alpha=0.7)\n",
    "axes[0].plot(wl_range, Rp_before, 'g:', label='$P_p$ before', alpha=0.7)\n",
    "axes[0].plot(wl_range, Rs_after, 'b-', label='$R_s$ after', linewidth=2)\n",
    "axes[0].plot(wl_range, Rp_after, 'g-', label='$P_p$ after', linewidth=2)\n",
    "axes[0].set_xlabel('$\\lambda$ (nm)')\n",
    "axes[0].set_ylabel('Reflectance')\n",
    "axes[0].set_title('Spectral Performance (AOI=45°)')\n",
    "axes[0].legend()\n",
    "axes[0].grid(True, alpha=0.3)\n",
    "axes[0].fill_betweenx([0, 1], 595, 605, color='gray', alpha=0.2, label='Optimization Range')\n",
    "\n",
    "# 2. Polarization contrast (Rs - Rp)\n",
    "contrast_before = Rs_before - Rp_before\n",
    "contrast_after = Rs_after - Rp_after\n",
    "\n",
    "axes[1].plot(wl_range, contrast_before, 'g--', label='Before', alpha=0.7)\n",
    "axes[1].plot(wl_range, contrast_after, 'g-', label='After', linewidth=2)\n",
    "axes[1].set_xlabel('$\\lambda$ (nm)')\n",
    "axes[1].set_ylabel('Contrast ($R_s - R_p$)')\n",
    "axes[1].set_title('Polarization Contrast')\n",
    "axes[1].legend()\n",
    "axes[1].grid(True, alpha=0.3)\n",
    "\n",
    "# 3. Optimized stack structure\n",
    "dichroic_stack.plot_structure_thickness(axes[2])\n",
    "axes[2].set_title('Optimized Stack Structure')\n",
    "fig.tight_layout()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "optics_venv (3.11.7)",
   "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": "3.13.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}