{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4bb40cf6",
   "metadata": {},
   "source": [
    "# Derivation of the Statistical Dispersion Model for Optiland's AbbeMaterial - 2026 Update\n",
    "\n",
    "Kramer Harrison, 2026\n",
    "\n",
    "## 1. Introduction\n",
    "In first-order optical design, materials are often defined solely by their refractive index ($n_d$) and Abbe number ($V_d$). While sufficient for paraxial achromatization, this two-parameter definition is mathematically under-determined: infinite dispersion curves can satisfy a single ($n_d, V_d$) pair.\n",
    "\n",
    "Standard approximations, such as the \"Normal Line\" rule, assume a fixed linear relationship between partial dispersion ($P_{g,F}$) and Abbe number. While effective for standard crowns and flints, this heuristic fails for \"anomalous\" glasses (e.g., fluor-crowns or dense flints), leading to significant index prediction errors in the deep blue ($<0.45 \\mu m$) and near-infrared.\n",
    "\n",
    "### Objective\n",
    "This study derives a robust, data-driven dispersion model to resolve this ambiguity. By analyzing refractive index data of over 1,000 commercial optical glasses, we aim to:\n",
    "\n",
    "1.  **Determine Dimensionality:** Use Principal Component Analysis (PCA) to quantify the effective degrees of freedom in standard optical glasses.\n",
    "2.  **Select a Basis:** Apply **LassoLarsIC** (Sparse Regression) to the Buchdahl dispersion formula to identify the minimum set of coefficients required for accurate spectral reconstruction.\n",
    "3.  **Validate Stability:** Demonstrate that this sparse, physics-informed model minimizes spectral error compared to standard linear approximations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cc410cc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.linear_model import LassoLarsIC, LinearRegression\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from scipy.optimize import curve_fit\n",
    "from optiland.materials import MaterialFile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4c00ba36",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configuration\n",
    "CATALOG_PATH = '../../database/catalog_nk.csv'  # in Optiland's database dir\n",
    "DATA_DIR = '../../database/data-nk/'  # in Optiland's database dir\n",
    "\n",
    "# We filter for established manufacturers to ensure we are modeling \n",
    "# the physics of stable, amorphous silicate/phosphate glasses.\n",
    "VALID_CATALOGS = [\n",
    "    'schott', 'ohara', 'hoya', 'cdgm', 'sumita', 'hikari'\n",
    "]\n",
    "\n",
    "# Wavelength grid for analysis (Extended Visible: 0.38 - 0.78 µm)\n",
    "WAVE_GRID = np.linspace(0.380, 0.780, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5f71d104",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define standard reference wavelengths for Abbe definition\n",
    "# Helium d-line (yellow), Hydrogen F (blue), Hydrogen C (red)\n",
    "WAVE_D = 0.5875618\n",
    "WAVE_F = 0.4861327\n",
    "WAVE_C = 0.6562725\n",
    "\n",
    "def load_standard_glasses(catalog_path=CATALOG_PATH, wave_grid=WAVE_GRID):\n",
    "    \"\"\"\n",
    "    Ingests the glass database and filters for physically standard optical glasses.\n",
    "    Returns a DataFrame containing the 'Ground Truth' dispersion curves.\n",
    "    \"\"\"\n",
    "    print(f\"Loading glass database from {catalog_path}...\")\n",
    "    \n",
    "    if not os.path.exists(catalog_path):\n",
    "        raise FileNotFoundError(f\"Catalog file not found at: {catalog_path}\")\n",
    "\n",
    "    df_raw = pd.read_csv(catalog_path)\n",
    "    valid_data = []\n",
    "    \n",
    "    # Statistics for the log\n",
    "    stats = {'total': len(df_raw), 'success': 0, 'range_skip': 0, 'error_skip': 0, 'manuf_skip': 0}\n",
    "    \n",
    "    # Standard glass range settings\n",
    "    ND_MIN, ND_MAX = 1.35, 2.6\n",
    "    VD_MIN, VD_MAX = 15.0, 100.0\n",
    "\n",
    "    for idx, row in df_raw.iterrows():\n",
    "        # 1. Manufacturer Check\n",
    "        f_name = str(row.get('filename', '')).lower()\n",
    "        g_name = str(row.get('group', '')).lower()\n",
    "        identifier = f_name + g_name\n",
    "        \n",
    "        is_valid_catalog = any(c in identifier for c in VALID_CATALOGS)\n",
    "        \n",
    "        if not is_valid_catalog:\n",
    "            stats['manuf_skip'] += 1\n",
    "            continue\n",
    "\n",
    "        filepath = os.path.join(DATA_DIR, row['filename'])\n",
    "        if not os.path.exists(filepath):\n",
    "            stats['error_skip'] += 1\n",
    "            continue\n",
    "\n",
    "        try:\n",
    "            # 2. Load material\n",
    "            with warnings.catch_warnings():\n",
    "                warnings.simplefilter(\"ignore\")\n",
    "                mat = MaterialFile(filepath)\n",
    "                \n",
    "                # Abbe Definitions\n",
    "                nd = mat.n(WAVE_D).item()\n",
    "                nF = mat.n(WAVE_F).item()\n",
    "                nC = mat.n(WAVE_C).item()\n",
    "\n",
    "            # 3. Physics & Range Sanity Checks\n",
    "            if np.isnan(nd) or nd <= 1.0:\n",
    "                stats['error_skip'] += 1\n",
    "                continue\n",
    "\n",
    "            dispersion = nF - nC\n",
    "            if dispersion <= 1e-9:\n",
    "                stats['error_skip'] += 1\n",
    "                continue\n",
    "            \n",
    "            Vd = (nd - 1) / dispersion\n",
    "\n",
    "            # Filter out extreme materials\n",
    "            if not (ND_MIN < nd < ND_MAX):\n",
    "                stats['range_skip'] += 1\n",
    "                continue\n",
    "            \n",
    "            if not (VD_MIN < Vd < VD_MAX):\n",
    "                stats['range_skip'] += 1\n",
    "                continue\n",
    "\n",
    "            # 4. Generate full spectral curve\n",
    "            with warnings.catch_warnings():\n",
    "                warnings.simplefilter(\"ignore\")\n",
    "                curve = mat.n(wave_grid)\n",
    "            \n",
    "            if np.any(np.isnan(curve)):\n",
    "                stats['error_skip'] += 1\n",
    "                continue\n",
    "\n",
    "            valid_data.append({\n",
    "                'name': row['name'],\n",
    "                'nd': nd,\n",
    "                'Vd': Vd,\n",
    "                'curve': curve\n",
    "            })\n",
    "            stats['success'] += 1\n",
    "            \n",
    "        except Exception:\n",
    "            stats['error_skip'] += 1\n",
    "            continue\n",
    "\n",
    "    df = pd.DataFrame(valid_data)\n",
    "    \n",
    "    print(\"-\" * 60)\n",
    "    print(f\"Data Ingestion Complete.\")\n",
    "    print(f\"Total entries: {stats['total']}\")\n",
    "    print(f\"Successfully loaded: {stats['success']}\")\n",
    "    print(f\"Skipped (Manufacturer): {stats['manuf_skip']}\")\n",
    "    print(f\"Skipped (Out of Range): {stats['range_skip']}\")\n",
    "    print(\"-\" * 60)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0e6dd3d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading glass database from ../../database/catalog_nk.csv...\n",
      "------------------------------------------------------------\n",
      "Data Ingestion Complete.\n",
      "Total entries: 3201\n",
      "Successfully loaded: 1678\n",
      "Skipped (Manufacturer): 1516\n",
      "Skipped (Out of Range): 6\n",
      "------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Execute data loading\n",
    "df_glass = load_standard_glasses(CATALOG_PATH, WAVE_GRID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "99584994",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization: Glass diagram for dataset\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.scatter(\n",
    "    df_glass['Vd'], \n",
    "    df_glass['nd'], \n",
    "    c=df_glass['nd'], \n",
    "    cmap='viridis', \n",
    "    s=25, \n",
    "    alpha=0.6, \n",
    "    edgecolors='k', \n",
    "    linewidth=0.2\n",
    ")\n",
    "plt.gca().invert_xaxis()\n",
    "plt.title(f\"The Training Set: {len(df_glass)} Standard Optical Glasses\")\n",
    "plt.xlabel(\"Abbe Number ($V_d$)\")\n",
    "plt.ylabel(\"Refractive Index ($n_d$)\")\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f18cbdc",
   "metadata": {},
   "source": [
    "## 2. Determining the Optimal Basis Function\n",
    "To construct a model, we must first choose a mathematical basis function $f(\\lambda)$. While polynomials ($A + B\\lambda + C\\lambda^2$) or Cauchy equations are common, they may not be the most efficient representation for typical glass dispersion.\n",
    "\n",
    "We apply **Principal Component Analysis (PCA)** to the dataset to determine the effective dimensionality of the problem and identifying the optimal functional shape. We subtract the base refractive index ($n_d$) from each curve to isolate the dispersion shape.\n",
    "\n",
    "### Hypothesis\n",
    "The **Buchdahl dispersion model** utilizes a coordinate transformation $\\omega$ designed to linearize the dispersion of silicate glasses:\n",
    "$$\\omega = \\frac{\\lambda - \\lambda_0}{1 + 2.5(\\lambda - \\lambda_0)}$$\n",
    "If this coordinate transformation is physically robust, the first principal component (PC1) of our data should strongly correlate with $\\omega$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a9d2a2d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Constants\n",
    "LAM_D = 0.5875618\n",
    "\n",
    "def buchdahl_coordinate(lam):\n",
    "    \"\"\"\n",
    "    Transforms wavelength to the normalized Buchdahl coordinate.\n",
    "    \"\"\"\n",
    "    return (lam - LAM_D) / (1.0 + 2.5 * (lam - LAM_D))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8d67e04b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       "#sk-container-id-1.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       "#sk-container-id-1.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PCA(n_components=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>PCA</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.decomposition.PCA.html\">?<span>Documentation for PCA</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_components',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.decomposition.PCA.html#:~:text=n_components,-int%2C%20float%20or%20%27mle%27%2C%20default%3DNone\">\n",
       "            n_components\n",
       "            <span class=\"param-doc-description\">n_components: int, float or 'mle', default=None<br><br>Number of components to keep.<br>if n_components is not set all components are kept::<br><br>    n_components == min(n_samples, n_features)<br><br>If ``n_components == 'mle'`` and ``svd_solver == 'full'``, Minka's<br>MLE is used to guess the dimension. Use of ``n_components == 'mle'``<br>will interpret ``svd_solver == 'auto'`` as ``svd_solver == 'full'``.<br><br>If ``0 < n_components < 1`` and ``svd_solver == 'full'``, select the<br>number of components such that the amount of variance that needs to be<br>explained is greater than the percentage specified by n_components.<br><br>If ``svd_solver == 'arpack'``, the number of components must be<br>strictly less than the minimum of n_features and n_samples.<br><br>Hence, the None case results in::<br><br>    n_components == min(n_samples, n_features) - 1</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">3</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.decomposition.PCA.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
       "            copy\n",
       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If False, data passed to fit are overwritten and running<br>fit(X).transform(X) will not yield the expected results,<br>use fit_transform(X) instead.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('whiten',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.decomposition.PCA.html#:~:text=whiten,-bool%2C%20default%3DFalse\">\n",
       "            whiten\n",
       "            <span class=\"param-doc-description\">whiten: bool, default=False<br><br>When True (False by default) the `components_` vectors are multiplied<br>by the square root of n_samples and then divided by the singular values<br>to ensure uncorrelated outputs with unit component-wise variances.<br><br>Whitening will remove some information from the transformed signal<br>(the relative variance scales of the components) but can sometime<br>improve the predictive accuracy of the downstream estimators by<br>making their data respect some hard-wired assumptions.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('svd_solver',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.decomposition.PCA.html#:~:text=svd_solver,-%7B%27auto%27%2C%20%27full%27%2C%20%27covariance_eigh%27%2C%20%27arpack%27%2C%20%27randomized%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27auto%27\">\n",
       "            svd_solver\n",
       "            <span class=\"param-doc-description\">svd_solver: {'auto', 'full', 'covariance_eigh', 'arpack', 'randomized'},            default='auto'<br><br>\"auto\" :<br>    The solver is selected by a default 'auto' policy is based on `X.shape` and<br>    `n_components`: if the input data has fewer than 1000 features and<br>    more than 10 times as many samples, then the \"covariance_eigh\"<br>    solver is used. Otherwise, if the input data is larger than 500x500<br>    and the number of components to extract is lower than 80% of the<br>    smallest dimension of the data, then the more efficient<br>    \"randomized\" method is selected. Otherwise the exact \"full\" SVD is<br>    computed and optionally truncated afterwards.<br>\"full\" :<br>    Run exact full SVD calling the standard LAPACK solver via<br>    `scipy.linalg.svd` and select the components by postprocessing<br>\"covariance_eigh\" :<br>    Precompute the covariance matrix (on centered data), run a<br>    classical eigenvalue decomposition on the covariance matrix<br>    typically using LAPACK and select the components by postprocessing.<br>    This solver is very efficient for n_samples >> n_features and small<br>    n_features. It is, however, not tractable otherwise for large<br>    n_features (large memory footprint required to materialize the<br>    covariance matrix). Also note that compared to the \"full\" solver,<br>    this solver effectively doubles the condition number and is<br>    therefore less numerical stable (e.g. on input data with a large<br>    range of singular values).<br>\"arpack\" :<br>    Run SVD truncated to `n_components` calling ARPACK solver via<br>    `scipy.sparse.linalg.svds`. It requires strictly<br>    `0 < n_components < min(X.shape)`<br>\"randomized\" :<br>    Run randomized SVD by the method of Halko et al.<br><br>.. versionadded:: 0.18.0<br><br>.. versionchanged:: 1.5<br>    Added the 'covariance_eigh' solver.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.decomposition.PCA.html#:~:text=tol,-float%2C%20default%3D0.0\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\">tol: float, default=0.0<br><br>Tolerance for singular values computed by svd_solver == 'arpack'.<br>Must be of range [0.0, infinity).<br><br>.. versionadded:: 0.18.0</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('iterated_power',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.decomposition.PCA.html#:~:text=iterated_power,-int%20or%20%27auto%27%2C%20default%3D%27auto%27\">\n",
       "            iterated_power\n",
       "            <span class=\"param-doc-description\">iterated_power: int or 'auto', default='auto'<br><br>Number of iterations for the power method computed by<br>svd_solver == 'randomized'.<br>Must be of range [0, infinity).<br><br>.. versionadded:: 0.18.0</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_oversamples',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.decomposition.PCA.html#:~:text=n_oversamples,-int%2C%20default%3D10\">\n",
       "            n_oversamples\n",
       "            <span class=\"param-doc-description\">n_oversamples: int, default=10<br><br>This parameter is only relevant when `svd_solver=\"randomized\"`.<br>It corresponds to the additional number of random vectors to sample the<br>range of `X` so as to ensure proper conditioning. See<br>:func:`~sklearn.utils.extmath.randomized_svd` for more details.<br><br>.. versionadded:: 1.1</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">10</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('power_iteration_normalizer',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.decomposition.PCA.html#:~:text=power_iteration_normalizer,-%7B%27auto%27%2C%20%27QR%27%2C%20%27LU%27%2C%20%27none%27%7D%2C%20default%3D%27auto%27\">\n",
       "            power_iteration_normalizer\n",
       "            <span class=\"param-doc-description\">power_iteration_normalizer: {'auto', 'QR', 'LU', 'none'}, default='auto'<br><br>Power iteration normalizer for randomized SVD solver.<br>Not used by ARPACK. See :func:`~sklearn.utils.extmath.randomized_svd`<br>for more details.<br><br>.. versionadded:: 1.1</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.decomposition.PCA.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Used when the 'arpack' or 'randomized' solvers are used. Pass an int<br>for reproducible results across multiple function calls.<br>See :term:`Glossary <random_state>`.<br><br>.. versionadded:: 0.18.0</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
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      "text/plain": [
       "PCA(n_components=3)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --- PCA Execution ---\n",
    "# 1. Prepare Data Matrix\n",
    "# Shape: (n_samples, n_wavelengths)\n",
    "X_curves = np.vstack(df_glass['curve'].values)\n",
    "\n",
    "# 2. Center the data per-sample\n",
    "# We subtract nd to focus purely on the dispersion slope and curvature,\n",
    "# removing the \"piston\" term (absolute index).\n",
    "nd_vector = df_glass['nd'].values[:, np.newaxis]\n",
    "X_dispersion = X_curves - nd_vector\n",
    "\n",
    "# 3. Fit PCA\n",
    "pca = PCA(n_components=3)  # choose 3 components\n",
    "pca.fit(X_dispersion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ebf6811d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Effective Degrees of Freedom (Visible Spectrum) ---\n",
      "PC1 (Primary Dispersion):   99.91697%\n",
      "PC2 (Secondary Curvature):  0.08068%\n",
      "PC3 (Residuals):            0.00226%\n",
      "Total Explained (3 terms):  99.999913%\n"
     ]
    }
   ],
   "source": [
    "# Report Explained Variance\n",
    "print(\"--- Effective Degrees of Freedom (Visible Spectrum) ---\")\n",
    "variance = pca.explained_variance_ratio_\n",
    "print(f\"PC1 (Primary Dispersion):   {variance[0]*100:.5f}%\")\n",
    "print(f\"PC2 (Secondary Curvature):  {variance[1]*100:.5f}%\")\n",
    "print(f\"PC3 (Residuals):            {variance[2]*100:.5f}%\")\n",
    "print(f\"Total Explained (3 terms):  {np.sum(variance)*100:.6f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2989fe84",
   "metadata": {},
   "source": [
    "As can be seen, 3 components alone account for >99.9999% of the variance in the refractive index data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "449ced2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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M9DA449gjZpv4pQuzMM8880yC+2BgwswmxxXxc4Dj8jjelG2TXH/PMfEziFmduAJ2BhTMIgd6zielLRnw8XOHr81Aj9nECxcuuF7x1O3PE35+8os3fjHAQDSlpv5wjp3jZpk584VZThGJm4I0kRDhTP7Mb/KJA7d5MccLLs+S8LzIiYn/sbOCIBfO9cQLOA6cZ+AWX8lsXjD279/fLLww40XImDFjzLfo8eHxsMgCC0zwopHfrDMo5DfBDl5EcbA69+fZXSgxk0ezyuCyZctM23h+Q8z5zDzxW3tux4IZTgET4nuLKZBv3/n6MV+LnK5qfDw98dVeDF6Sa/4vflvPoCVmxciYeA7xy4vJkyd7rec55BR7SU58PWZ7PC9a/T2P4zqf2AWR2Sh2ZfactoJd7fzFi3cWquG5zcIWDHycro7J8fccEwMhfiH04Ycfxgq+WJCCRUFYEMRX9iw+Sf1MYJdHFsVgAMljZPDJL7QS83ftdM3kORjXZ2QgnydJCdK473379pn2jgs/Y5hBZnDlGcjF/Azy97OS1SqJv+e0Oq2CSGpTd0eREMBv8Jn14rgSp/sKL454weFZppvdzfhNeczuODE5XeD4TbMvHAsTs5Q2L1r4bW5cz4mJ3Yx43Czvzu5rMedG4/HHzNKwimVCZcd94Rg4BrH8xtnB/XB/MV+TYr4uq77FxEpm5M8FIl+f1Rg5UbeD3T1Z5Y2BS0JjsHxxowR/cuE55jkehe+d44FY/j458CKT2SFWCvz5559jPe78/nydQ/xCwI2xMizrzwwPMzasahnoeRzX+eTrnGSXQWbF/MWLaGYRmSHnwm6Int1K/f17ZiDAc87X9BmeGABxzBwDQX4Z47k4U1ewMmqgkvqZwPOPwTm7dnIMlq8sGn8P/vxN33zzzaYNnWkX4jr/PO/H93mSWPw9cX/MevL3Gt9nELuvenZ75Wci24/BmDMuz9/PykKFCplxsayWyvMiJl/THYiIN2XSRNIhjh1xvgFlv392F+S3syyD7JRTZxlmZsXYrYbftnI7FhxhFxR+o+pgFyp2d+T2/BaV2/ECkOWdPQtdxMyKMOvGrjwMMDjQnN2pmBXz7LIYH14U8DU4Lo1d3FjW3hO7JvGbYXZp4mswwFm6dGlUOe5AsBsXx92xfRiocn/MTMQcE8K2c8bj8EKUxS8Y/PrKWnDOOWLGke+Z3yjzdZyLbU98XV6U8iKRA/V5wczMBvfLjGdiuigFWoKfF15xZUQ43snXcScWzzGeOyw+wIt8XkTy9xZz4t2kYLaXvxueR+w6xy8neLHIIIzZGhZz4DnE85uFWDgFAqcAYADhZAESi12JGdQ4xUjYfZCFcHiu8u/AMyPs73nML0Z4Uc8Agucls83MLvO4Gfgxg8Rzh1+8cH+BdDPlucm/Lxbz4JcDnMcuMX/PnIjeOW/jyooyGHfKvPvCvykGOPw9sLtmIJL6mcB24PvhPHZsa88iGp5/1wxQRo4cac5jBiO+xnnxb5bb8W+evzueY+xSzs9ljiFkD4ZAPk+SwimfHx/+jTCgYsn99evXm98fM5M8d/n36Yyj9Pezkvj/Cf/OOQ9hjx49zN8Vzxn+Xvbs2ePKfIQi6cqVKo8ikk5L8GfNmtWqWrWqNWHCBK/SzzR58mRTCpklxytUqGCeH7P0+bJly0xZ7GLFipky2PzJst3btm2Ls0T54cOHTXln7jNHjhxWnjx5THnrTz75JKD3M3DgQLPf+++/P9ZjLGvNctIFChQwJaJZFv7PP/+MVd7enxL8dOTIEeuhhx6ycufObY6Xt3/55ZdYZaVZOr5169ZW3rx5zXZt27a1/vvvP5/l7l944QWrePHippy5Z/nsmMdI27dvt+677z6zX/7OatSoYS1atMivEu++SsQnVwl+z+N2zq+YZe09y37H3C9//zGPk6Xcx4wZY5UsWdKceyzfzZLqvvbpKWbZ8JiPxXyvO3fuNKX4CxYsaF6nXLly5vnO9AUsDd6/f3+raNGiZlqA2267zZRsZ4l1X1MH+FuC31kyZcpkXrtu3brWiy++aB08eDDR5zGxXD3fQ4YMGbzO6VWrVlm33nqreQ/8+3z66aetr7/+OqCy5kuWLDHbcyqF3bt3ez3m79+zcx7FVyb+iSeeMNvwfI/L8OHDzTbOORHX7z1mGyXlM8Gxbt0681jjxo19Htv+/fut5s2bW7ly5TLbOedJXPtcuXKldeedd5rt2XY33nij17QB/n6eJKYEf3x8tSmnRXDaj5/1LJ/v65z397OS+Hvm32CRIkXM3wM/D++++25r7ty5UduoBL+Ib2H8J7UDRRERSf/4zTu7gHGCZRYUEAk2zO4w88XxsJzkWkQktWhMmoiIiAiASZMmmTFYMbtXi4ikNI1JExERkZDGIjOcNJ7FejheLjnHYIqIJIaCNBEREQlpnGONRS1YvZAl8UVEUpvGpImIiIiIiAQRjUkTEREREREJIgrSREREREREgojGpCUgMjIS//33n5nIkZOEioiIiIhIaLIsC6dOnUKxYsXMxPVuUZCWAAZoJUuWTO3DEBERERGRILF7926UKFHCtf0rSEsAM2jOLyJ37typfThBlWE8dOgQChYs6Oq3CKFK7esuta/71MbuUvu6S+3rLrWvu9S+7jp+/DhKly4dFSO4RUFaApwujgzQFKR5fwCcP3/etIk+AJKf2tddal/3qY3dpfZ1l9rXXWpfd6l93W9fcnsYlH5zIiIiIiIiQURBmoiIiIiISBBRkCYiIiIiIhJENCYtmUpxXr58GREREQil/riXLl0yfZ7V3zn5qX0TlilTJmTIkCG1D0NEREQk2SlIS6KLFy9i3759OHv2LEItMGUgwXkiNH9c8lP7JoztwtK3OXPmTO1DEREREUlWCtKSgBfRO3bsMN/mc0K7zJkzh8wFtZM9zJgxY8i855Sk9k24fVheeM+ePShfvrwyaiIiIpKuKEhLYhaNgRonu86ePTtCiYIId6l9E8b5X/7991/TLVRBmoiIiKQnGuySDDRmSCTlKXgVERGR9ErRhYiIiIiISBBRkCYiIiIiIhJEFKSJhLi6devio48+QjCZOHEi7rnnntQ+DBEREZFUoSAtBHXp0sWM5+HCipTXXHMNnn/+eVOowrNwxXvvvYeaNWuaEud58+ZF9erVMW7cuFjTDbDCHvdTqVIlv49h//79eOKJJ1CuXDlkyZLFFF/hRfmyZcuS9b2mdV27dkWrVq0S3O7777837ccqo/y9LliwwK/9L1y4EAcOHED79u2j1pUpUybq/MiWLZu5f//992P58uWJOtf8Of6YHn74YWzYsAE//PBDwM8VERERSesUpIWopk2bmvnd/vrrL/Tv3x/Dhw/Hq6++GvX4Qw89hL59+6Jly5b49ttvsXHjRjz33HP47LPP8M0333jta+rUqeYi/uTJk1i7dm2Cr82KfNWqVTMX/XzN33//HYsXL0b9+vXRq1cvV95venfmzBlUqVIFb7/9dkDPe/PNN00gGLP4DYN2nh9bt27F9OnTTZDeqFEjvPjii0gJDPo7dOhgjk9ERERC2KVLwMmTCDmWxOvEiRMWm4k/Yzp37pz1xx9/mJ9pSefOna2WLVt6rbvzzjutW2+91dyePXu2ec8LFiyI9dzIyEjr+PHj5ufFixetiIgIq1y5ctbixYutZ555xurRo0eCr3/XXXdZxYsXt06fPh3rsWPHjkXd3rlzp9WiRQsrR44cVq5cuay2bdta+/fvj3p82LBhVpUqVazJkydbJUuWNNs99thj1uXLl62XX37ZKly4sFWwYEFr5MiRXq/B9/bOO+9YTZs2tbJmzWqVLVvWmjNnjtc2v/32m1W/fn3z+FVXXWXe16lTp2K14auvvmoVKVLEbPP444+bNnGcP3/e6t+/v1WsWDEre/bsVo0aNaxvv/026vEPPvjAypMnj2m7ChUqmONv0qSJ9d9//5n2HTJkiDlWz8Xz+XHhdvPnz09wu4MHD1phYWHWpk2bvNaXLl3aGjt2bKzthw4daoWHh1t//vmnuc92fvjhh60yZcqYdrr22mutcePGef1+4jr+p59+2ipfvryVLVs20/58r55tRytWrLAyZ85snT171ufxJ+Xvj+ftvn37zE9xh9rYXWpfd6l93aX2dVeabV/+fx/jWsCaOdOyMma0rL59rWDBa9W4YoPkpHnSkln16uzKl/KvW6QI8PPPiX8+u7UdOXLE3J45cyauu+46k0WLiV3g8uTJY7pDErNs7P7ILEvx4sVRu3ZtjB07Fjly5PD5OkePHjVZM2ZkfG3DjA1x/jm+PrtarlixwnTFZJatXbt2+O6776K23759O7766iuzT96+77778M8//+Daa681z1u9erXpOsfjY9dNB7OCo0ePxhtvvIEPP/zQdPdjRq9ixYomK9WkSRPUqlULP/30Ew4ePIju3bujd+/eJmvo4HsvWrSo+fn333+bY6tatSp69OhhHuf2f/zxB2bNmmW6Ic6fP99kMPk6nICZ2HavvfaaOQZmszp27IgBAwZgxowZ6NevH7Zt22YylB988IHZ/qqrrkJyWblypZnfj+/ZH3369MELL7xgsqlPP/20+R2VKFECc+bMQf78+U1b9+zZ07QJM6t8H1u2bPF5/Lly5TJtyXZhe7DNuI77dbB7LX/vzM7Wq1cv2d63iIiIpKIzZ4A//wT++MN7+ecfYOlSoH796G2LFwc4HGfLFoQaBWnJjAHa3r1IMxhscRzY119/bcaIEbtAMkjzx5QpU0yAw8mEOSaNY8x40c6xSL4wmOFrVqhQId798ph48b5jxw4zXo3Y7e6GG24wgdMtt9xi1jFQ4DHwAv/66683XSbZRe/LL780QQ/fx8svv2wCKc8grW3btibwIgYeS5Yswfjx4/HOO++YIhrnz583r+cEkm+99ZYZ88V9FS5c2KzLly+fWc/3zvfTvHlzc9wMOHbt2mUCE/5kIEIMWhhMcv1LL71k1nEiZhbJuPrqq6MCO3Y1JAaoDJ4vXLiAIozCk9nOnTvNe/F3nj8GWIUKFTLdVSlTpkwYMWJE1ONly5bFmjVr8Mknn5ggLb7jHzJkSNRtjnlj2zCY9QzSGEDyCwEep4iIiKRh/H9/40Zg82aOe4l7OwZjnkHa9dcDlSsD116LUKMgLZm5cC3tyusuWrTIXEQzSGCgw/E/HJdGTpYsIcePH8e8efNMRsbBTNDkyZPjDNL83TczMAzOnACNGIQx08bHnCCNF/gM0BwMOhg0eQYeXMdsmCdmyWLe57g757U5vssz03fbbbeZdmIA6ARpDBj5Wg5mkBhYEn9GRESYjJ4nBizMOnkGIk6A5uwj5rG65dy5c8iaNWtAz+Hvz3MSaY6BY5DMYJT7u3jxoskmJmT27NlmvBmzn6dPnzYZs9y5c8fajkFezEI1IiIiEkTOnbODKwZgXHidM3Cg9zaffmpnz+KSPTvAnj05c3qvL1gQ+O03hCIFacksKV0OUxIzThMmTDAFGpjpyZgx+lRgYPFnfH9IVzDzwYyTZ4aKF/EMZthNL2aAQuzmx4t8f/bvD2ZzPHHfvtbxmJJbfK/DwIMB3Pr1670COWJwHN8+/A1kk6pAgQI4duyY39uzO+yhQ4dMxsz5/TMDNmbMGBPkMlhmIZiEiscw2/bggw+aLBy7lTJbxn1xP766xxbkB7SIiIikrosXgW3b+E00sGlTdFC2fTsvAKO3Y7XvmEHaDTfYQRqvgXibGTLPpVQpwM+ePaFCQVqIYpaIpfd9YVaNXRg59ijmuDQGEBxjxKwHu+1x3BSrA3p6/PHHTXaFY758dZnjhTkzME8++WSscWnMzjFbxnFSu3fvNouTTeP4Lj7OjFpS/fjjj+jUqZPX/Ztuusnc5mtzvBTHpjnHt2rVqqjuk/7gvphJY1asTp06iT5OBnHcjxt4jJwKgYEau24mhOP32AZOSX22Cccg8vftYGbME78EiHn8HLtWunRp/O9//4ta56tLI/fFLwGc34uIiIikAP6/zfFhvP7y7HEzYQLQt2/Cz9+61a7I6PlFNK8JX3/d3qdHjxyJm0JWiYXjiVgE44EHHjBjp37++WdzEc0ukizA4ZTk/+WXX8y4Lo5F81z4vGnTpnnNu+aJARov3GvUqIFPP/3UjIFjF0N2f3O6IfJ1KleubDIunC9r3bp1Jqi64447TEGJpOK4OQaSzPgNGzbM7J/jwYivyW6AnTt3xqZNm8z75Xg9TkvgdHVMCLOI3A+PmV1CObaOrzFq1Ch88cUXfh8nu3P+9ttvppvl4cOHTfdUX5i54+/E6bLJ1+NtdkOMC4MfZtMYbMV06tQpE8AxSOYcbCwIMnLkSFPwxQnumRXlucHxjGxHFmPheMGEjp/P43Exe8ZAjL93FlWJiXOkcYyjZ3dQERERSSbMfu3bB3BqJQZQ/NKd11gcRsLeUOvXe2/vaz5cdlPkczp3Bl55BeA1zl9/AR49tAxeOzBbpgDNf67WjkwHQqUEf0ws2zphwgTrlltuMeXjc+fObVWrVs164403TEn0Xr16WRUrVjSl4mNi2VeWav/ss8/i3D/LzHMfLPfOMussyc9y+54l5v0twZ/Qe7vjjjusPn36RN3n7/Ptt9820w5kyZLFlJDntAOJKcHvia/B13KwpDzL1nP/mTJlsooWLWq1bt3a7NuzBL8nls7n8TlTHBw4cMAcZ86cOeMtwc/1Mcvdc+Fxxoel8Nu3b++1jr8T5/n83ZQqVcq6//77reXLl3ttxykGunTpYt5D3rx5zfQHgwYN8vqdsMy/r+MfOHCglT9/frO+Xbt2puR/zLZo3LixNWrUqDiPXSX4g5va2F1qX3epfd2l9k3F9uV1G6+J6tWzrPz5eVEU9zJxovdzDxywrA4dLOullyyL13jbt/PFrFBzLIVK8IfxnwBiupDDrn0cM3PixIlYhQ3YFYsZC47RCbQAQ1rH04aZMo5l8ywkkRbweJm5cbrthXL7MlvGAijMVrILYrDYvHkzGjRoYDJ0/PvzJSl/fxw7yK6orFbpb3VLCYza2F1qX3epfd2l9nVRRAQi//oLJ1auRN6dOxFWqBBwpXq3V1YrxvCEKLzm4OPMmnXrBjRvniKHnZYcP37cDBPxFRskJ41JEwlhLI3PapzsfhhMQdq+ffvMFAhxBWgiIiIh7+hRu/Kh57JpE8LPnUPUSHOO644ZpLGkPYO0okXt21wYlPEnKyyyC6OkOgVpIiEuGDOKHJMoIiIiVwp5MMPlmXV8663YwZcvrL7IGgGeY8TGjgUmTWKZZ3eOV5KFgjQJOerhKyIiIkHpxAk7I/brr/bCgmAsd//DD3aBDoevCt1XuipalSrh9NVXI8ettyKcc5fGmAoIZcq4/z4kyRSkiYiIiIikNH5p/PnndiDmLDt2+N6Wj3kGaQy+OMVPlSrAjTfaPzn/WI4crD6GMwcPIgfHo2nMX5qlIE1ERERExC2cPmfLFuDsWeDWW70zX5z+Z/fuuJ/rFPKIWUSsSBHg++/dO2ZJdQrSRERERESSw+nTdnfFX36xF6e74oULQI0awNq13tszI+YEaTlyRGfFuPAxFvTImTNV3oqkLgVpIiIiIiKJtXq1XchjwwZg2za7G6MvDN5iFvF48kngoYfsgOzqq9U9UaIoSBMRERERic++fXYQxoVBlWfxjSNHgI8/9v08dlO87jq7FD4DsYsXvYM0VTOWOChIExEREREhZsHY/XD9+uigjMv+/dHbcF5RzyCNARhlzmx3V+R9Z+HcY+zGKBIgBWkiIiIiIh07Al9/DRw+HP92DNo6dYq+X7y43ZWxQgUgUybXD1NCg4I0EREREQmdDNnPP9sVF195xXsbBme+ArR8+YCbb45ePCs0Ol0amTETSUYK0sQ19erVQ9WqVTFu3DjXn5fQcxJ7LIE6cuQIKlasiHXr1qGMn5NFptSxJUb79u1xyy23oH///ql9KCIiIv777z/gp5/sgIwLg7NDh6Ifz5ULGD3au1BHtWr2dvzJhQEZf7J7Y8wS+CIuUwmZENWlSxeEhYVFLfnz50fTpk3xG9P1kmgvvvgiWrZsaQI0z/b1tQwfPhzBbsiQIeY9nThxIrUPRUREJGErVwLFitldEFu1AkaOBBYv9g7Q6NQp4K+/vNcNHQocPGhv/+KLQJs29tgzBWiSCpRJC2EMyj744ANze//+/eaC/O6778auXbtS+9DSpLNnz2Ly5Mn4mv3ZTSGofVGPzZ49G0OHDsXWrVuj1uVMxXlPLl68iMwc4JyASpUq4eqrr8aMGTPQq1evFDk2ERGReOchY7aLWTIuXbvygib6cQZoHv//RsmfH6he3V6YHePPEiW8t8mSxf3jlzidOQPs3et72bMHOHYM4GVUqMTMyqSFsCxZsqBIkSJmYXe7QYMGYffu3Th05dsmZoNidsHjdp4ZoMjISLzyyiu45pprzP5KlSplMi+ejz/99NO46qqrzOvEzB6dOXMGnTp1MgFL0aJFMWbMmFjHuXjxYtx+++3ImzevyfgxkNy+fXus7RJ6LX889thj5rV8KVGiBEaza0QcvvzyS9MGt17pq+60LZc8efKY7JnnOs8gLb5j52OjRo1C2bJlkS1bNlSpUgVz5871eu0LFy7gySefRKFChZA1a1bzHn7if14eXSp79+6Nvn37okCBAmjSpAmmT59u2pPP9dSqVSs8xPLCV9xzzz2YNWtWQO0oIiKSZCxXz4BswgQ7GOPEznny8D81YOBA4JNPgOXLvZ9Ttqw931jDhsAzzwD8/3LHDjuTxgwZM2utWwMlS4bO1X4QDAc8ftye05u/gvffB3iZ06MHcNdd9nC+q66y5+zmbAUNGtizHAwaBIwfD8ybB6xbZyc+Q6ljjzJpbnj9dXtJCPs6L1zova5FC7tqUEL69bOXZHL69GmTLWGwxQt3fw0ePBjvv/8+xo4dawIDZo/+/PPPqMenTZuGfv36Ye3atVizZo3pZnnbbbfhzjvvNI8PHDgQK1aswGeffWYCjGeffRYbNmwwwaBnIMd93HjjjeY4mZFq3bo1Nm7ciHCPvuQJvVZCNm/ejPfeew8//PCDz8c51oyvGRc+rxq/nUsEX8fOYI/ZTgZo/N1MnDgR5cuXx/fff4+OHTuiYMGCuOOOO8zzGeB9+umnZj+lS5c2gTMDsb///tsEfs5rMAhdtWqVuc+AmoHdwoUL0bZtW7Pu4MGD+OKLL/DNN99EHVuNGjVM4M1gjkGoiIiI6x5/HJgyhd9Cxr/dr79632fgxat5BWApGoAx08W6LFx421mc+8ySJUVYGFCokD0lXd68CAlpLkh7++238eqrr5ruecwojB8/3lxE+jJp0iSTLdjE0N2MB62Gl156Kc7tk83Jk3ZuNiH8FicmftPjz3P5Gkm0aNGiqGwOAyFmsrjOM/CJz6lTp/Dmm2/irbfeQufOnc06do3zzEQxsBo2bJi5zQCD2y5btswETgy42D2QAUhDfuN1JZBgxspTG/YJ9zBlyhQToPzxxx+mO54/r+UPnlcsklG7dm2fjzPY4XkXl507d6IYu1kkgq9jX758OerXr2/O2aVLl6JWrVrm8XLlymHlypV49913TZDG392ECRMwdepU3MWvpK6c+0uWLDHty0DY2S+DN08dOnQwXV6dII2/CwZvzLw5+J7YPZLvnQGgiIhIUoUdPRrdbZFf7nIyaM/AKnv22AEaJ4HmPGS33BK9XH+9j50rQEvO3qVO8MWFI2I8gzH+TGoAxtEXvPTjMELnZ3GPheuKFg292Q3SVJDGcT3MNjCjULNmTdMVj9kCjvNhFiam7777Dg888IC56GYXsJdffhmNGzc2GZPi/K27JXdu+6xKSMGCvtf581y+RhIxAODFPR07dgzvvPOOuchnZUJ/LsaZMWN2xQmw4go+PDEQZLaG2GWRF//8XXoGQtcx1+3hr7/+MtkzZpkOHz5suv8Rx87FDNLieq2EXL58GfPmzcNzzz0Xte6RRx4xAX23bt2iglJ2N4zLuXPnzHmWGL6Ond1OmQnjWLeYgSbb7aYrk2eyHS9dumSyho5MmTKZY9+yZUvUOl9Zvh49epjAdO/eveZvgoGeU1TG4bxnHoeIiEiiui2yMNmPP5olbO1aFP77b+9tOJzAsyoyrw3Kl2d3DnthQMZeNvH8PyyB4SwELILpBF786XmbPzkOLCkYazMnwUArroUduBRXp/Eg7fXXXzcXlV3ZLxkwwRq7ZjGzwvFUMc2cOdPrPrvlsUsYsyscB+ULgw7PMTonr2SsGBg4wYGD9y3LilqiPPWUvfjD83n02Wf+Pc/XcwOUI0cOk/lyMPvCcV/s8jdy5EiTUXPeo4PBgHPfCUhivX8PGTNm9HqMF/+e7RbX8z3XcUwUg0YeF7M6fH7lypXN78nzefG9VkLHyWCIQRiDPm7D582ZMwcNGjSIeg4rX95///1x7oNjvRjs+nrc8xgCaSdmG4kZzphfLLDrYSDtmD179liPs1spM9LMYDpfYPC1PLfjtALO+4vr+FOD8958/W0mxDkvAn2e+E9t7C61r7vUvsnkwAGE3XefyZiFeVxb+boej+Sgo1Klolfce69dXTHWhvqd+Hv+Hj8e6RV87doV5nHbDtAiIxMfHWXLZpkAzDsIs8xPZx27JiYUgPHSIoguLxKUUp8LaSZIY+Zg/fr1ZgyUg0FEo0aNzBgefzATwCDDGaPjC8f/jBgxItZ6ZjXOnz/vtY774i+KWRguaYlzYet53LzPNmU7cT0vyplhcbZhwLpjxw6zHd+7U8iC3eoefvjhOC+iY76G87oMvJjxWb16dVQ3QQY527ZtQ506dcw2DBCYKWXGz+lG6YypioiIiNp3Qq/l63FPTiDC98NtWKyEx8Lj431m8dgWLVq0iHMfzIZ99NFHPh93/qB9PRbXsXMduygyGGO7e2bKHE47slIjx6oxc0z8/bBwyBNPPJHg++eXHuy2umfPHpMVZRbPczsGp+yCygA+mM5zHgvbib87/p4CwedxWgG2ib/deyUwamN3qX3dpfYNwLlzyPT778i0fj0iSpbEhbvvjn4sMhKFOH48RrdFK0sWnKtYEZHVq+NytWq4dPPN5rmm/L34hUHN4cPh2LMnA3bvzoC9e+3b0fcL4eTJDInef6ZMFooWjUCxYpEoXpw/I678jDS3ueTNa8UbgDFTF3Pmg/TgRApVL0kzQRq7ufGivHDhwl7red+zUEV8nnnmGRMMMLCLC4NAdql0MDApWbKkGQOVO0YXQwZtzL4wC8IlLeF/OryQZ7sSAxKOg2LmhoEI3w+zSMywcN4vXqBzzFSGDBnMc3lRzEwcC1awzZhVYxDBYJbZGHYRdOYD82wbPpcL13GfDO74fHZX5cJpAPi48zy2OwuZMFvKQIFdHJ1Ancfi7Duh1/L1uCeO8+Ljn3zyifk9MzPbvHlzk1VilUtWRuR54xTq8IVdRXn8PCfy5csXq73J1+vHdexcxy8UOJE0x5XxPgNVfjgwUOVxciwgK0c++uijpl3YXhxTxvF1DLaZeU7o/bMICf82OH6Nv++Y2zCIZnfLYDvHeTxsJ54fgXYz5QUY24PtpQswd6iN3aX2dZfaN57IgKmZ1asRdqXrIjZuRBivxvlw06awYnxpG1azJiymbfiT1Y9r1EBk5crm/zK1b9z43S6Hwf/7r73s3MmfYeYnm5M/z59PfBasYEHLJC6Z7eIIl1KlorNiXM/Lbft3wyW4/v9Pbf5MYZQcQqbVWTqdZcQ5Ti2+CzpmLXxVsHMu+GOu85ycOK1htsjJYOXKlQsVKlQwXfw4Vo1YafHff/813Q0ZCLzwwgsmo+O8V/7kGC4GbAzg/vvvP5OFYcDguY2vtnHWvfbaa6bwBQNDHgMDEn5wO89jIMbfG6sQsosjx6sx68PCFjH3ndBrxfd74nGzgiHPE45NY7EOjuFigMrAjW3AMXvx/Z6ZSbv55ptNG3I8W1zH4Et8x8aupwxgeWz//POPCW75Ovz9OM/heEt+48tuvAwSq1evbuZr88wax/Ua3B+Ls7DrMKtmem7DLyIWLFhgzpVgO8ed9+Prb9Pf5yf2ueIftbG71L7uUvt6WLoUePddE5yZPnJx4Fgz8z+FZ5t99VXU/GNR/4swCD55MqTbl0HYgQP27AAMwpyfzsJAjEP5EiNjRstkusqVy4DSpcNM0GUHYvZPBmbZs8f8Pz24/o8PZil1zoZZwTTIJIHujhxTw/mhOI+Tg5mE48ePmxLucWEgwAtdVsjjxWsgmEljgMLAwVcmjUELu/0ltmBEWuV0nXOyNGJjoMOsFyuKJuWPOKXbl90cb7jhBhMAe2I30/nz53uV5A8WSfn747fkLCrD4DdULxDcpjZ2l9rXXSHbvuybxkCMFX45H5njgw8AH8MajIoVAWbIWIGYyw03JDgIKRTal1fXLGDJ4Cvm4gRiCc0uEF8xDgZbrLPCnzGXQoU4FCB9t29qYtzBHlO+YoOQzKQxtcjMBot+OEEa/8h5n13R4sKS48yQMKsQaIAmEih2kWQ1So5fYzfZYMdurswuc2GmMCZmSTnNhYiIpMNUDoeLcJw3AzP+5PxitGgR/0OL3taZmobT9jAg430GZKzAGKN7fyhhqQInC/bPP9GLE4wldrakXLnsAMwJwjx/ckmoGqJqq6QPaSZII44VY+aMwRbLi7MEP7vKOdUe2dWLFfBY/MPpAsbS7SzmwHFFzhxXnBvMmR9MJLn17dsXaQXL+DNQ499KzKkPqHv37qlyXCIi4gIGY99/bwdkXOKqr87HPIO0a6+1J41mlixD4otRpMVsGGuZMPDavt07EOPiz7S2cWXCypa1A66YP7kw7lUnJUlTQVq7du1MYQoGXgy4WD6cY2WcYiIsKuGZ1mVXLXaTvI/lXz1w/NTw4cNT/PhFgg3HHIqISDpN88TsCv7kk/YE0r6wGALn02Ql4bvu8n6MEUOM+TzTC9Y8YREOBmGeixOIJWaiZtbZYuaLgZfnUq6c/bNAAQVhks6CNGLXxri6N7LLliddgIqIiEhIYKWJH34AVq60f547Z0cbnhiAOUEa+8zxvrMwQEun4+vZFAy4OH82m4Q/ndsM0CIiAt9noUJ20OUEXp63WZgjhBKO4pI0F6SJiIiIhDT2w+N4MnZdZEDGnyyNH9OePXbE4HjwQTsjxqCMXdzTUTqHGS8GXRxW5wRhzsJmCBSn32TXw6uvtoMvz58MxDRqRtymIE1EREQkrdi6Fbj9dk4gG/c2TOPcdJNdrdEzSKtRw17ScEaMQRcDsZjLvn2B74+F+Rh0+VqUDZPUpiAtGaSRWQxE0hX93YlIusWBUj//DKxYAVSoAHhMPWTSOKdPe2+fLZtdbZHBW506dgXGNJrq4VvnaJVt27wXBmK+koUJ4fgvBl3lywPXXGPf5k8uCVVJFElNCtKSgOXJ6ezZs8jGD0gRSTEsCkSc8FxEJE3jhFlr19pBGZc1a3hxYT/WsqV3kMYCH82a2dEMAzIuN99sr08j+B3b/v3h+OMPO/hiEMYEIX9y7FigY8QKFrSDMCcQ48LbDMjy5nXrXYi4S0FaEvDiMG/evGZCRuJk26EysbMms3aX2jd+nCORlV75N8c2EhFJczZvBubOjQ7KWI3RF44548RXnpMSf/op0so4MScA81y2bQvD6dOFAtoXy9JzJgAnGPNcPOfdFkkvdHWTREWKFDE/nUAtlIIIXihzygMFEclP7Zswtk2pUqXUPiKSNjJlzPp7fqnEYh9xTQdUsiRwxx1A3br2EsSfc8yK/fefXcfEc2EwFnf3xLA45w9jIOYEY5632TVRJJQoSEsiXiAWLVoUhQoVwiV2PQgRDCCOHDmC/Pnze81NJ8lD7ZuwzJkzq21EJDixOza7L3JqoG+/tTNlX3wBNGgQvU29etG3WUaQ9xmY8Scn2QqywIyXOCzasWWLvXgGZDGHyMWHH9tly1ooXfoiKlfOjOuuCzOFJhmMFS8edG9bJNUoSEvGro+hNDaGQQTH5GXNmlUXyi5Q+4qIpCGXL9vzjy1fbi+rVtmlCD0xWPMM0lgQZMYMu9gHg7Ig6qLIwIuBGMeMOQEZAzS+zUC6JzL4cha+Xf7kOLFMmSwcPHjMfMEdHq6oTMQXBWkiIiIiifXEE8C0acCpU3Fvw0xZjhze65gy4rxlqeTEiehAzAnG+JOVFf3Ft8BikwzAPBcGYyzmEVdWjEPsRCR+CtJEREREEhp4xZmSWRa/fXvvx1iKMGaAxkm2mDVj18X69e0gLZUcPx4diLFWifNz717/95Elix14VaxoL04wxi6KKm4t4g4FaSIiIiIxhLMg2JIldjfFZcuAXbvsBxh8FfKoTMj7rNLIn87CPn0pPLiKcaITgHHZtCnwYIyTOzMIu/56e3GCMvbGDKERHSJBQUGaiIiICKMcVlxcuhRhS5eiEKMcXzjmzDObxjnM2rRJsaCMhSJZOfH33+1AzFkC6abI8WI33GAvDMKcn8WKqXCHSLBQkCYiIiKhjXOUFS4cVewjVpySNatd4INZsurVvR9zaa5Gjtti4MVgzHPhvGP+TvbsGYxxYXaMP/lWFYyJBDcFaSIiIhIa48pYovCbb+x68n37egdh1aoBK1fam4aH41LVqsjUpAnC7rwTqFXL3sYlx44Bv/1mL04wxuyYv6Xtc+WKDsQqVYq+XbSogjGRtEpBmoiIiKRPR4/a3RMZmHHZudNez1RSnz7eEQwrLd54I3DnnbDq1sXRixdNifiwZJwGhSXsmQlzAjJniXvSZ2+ZM9vdEitXtoMxZylVSsGYSHqjIE1ERETSB0ZBnESaAdnXXwM//eS73vuBA3a6ikGZ49FHo2/zOSwckgzZsV9/jV6YHeOYMn+wtD2DMR4if3IpX9613pUiEmT0py4iIiLpw8aN9tixuNJQfKxxY5MtMymoZOpFybFjfGnPxSkGmZA8eexAzAnGqlSxuyqyC6OIhC4FaSIiIpJ2sLgHqzAuXgxUrQp07hz92M03A/nzA0eO2PcZ7TRpYgdldesC2bMn6aU5lI1l7n/5xQ7E+JMZMk4MnRB2R+S8YgzCuDiBWcmS6qooIrEpSBMREZHgxVQVB3IxKOPy3Xd2NUZitUXPII3jx4YNA3LmtAMzTiqdSGfPAj/+GB2McWEPyYsXE34uX94JxpyFibscORJ9OCISYhSkiYiISHA5c8aeRPqrr+xlxw7f261ZYwdsnpUXn3giUVOkMQjbsMFZwrBlS2FERiac4ipeHLjpJjup5ywcT5aM9UZEJAQpSBMREZHg8sknwMMP+36M2bGmTe2lYcOAS+OzayIDsp9/Btavt4Oyv/6yE3bRwuLsrsgelZ5BWcGCAb43ERE/KEgTERGRlMcyhytWAF9+CbRpA9SpE/0Yx5E5MmWyH7vrLnvhjMx+DuI6edIOwpyAjAsDsoRkymThuusu45ZbMqJatTATlHH8GLsxioikBAVpIiIikjL27LGDsi++AJYts7s1OiXvPYO0YsWAESPsyIjZMj9KHXIMGcePMSBj5X3+3Lo1ZoYstixZ7DFjzJBx4ZzWFStaOHHiiJknLTxcVT1EJOUpSBMRERF3MPhixLRokb0wivKFc5rFNHRovNOhbd4MrFsXvfB+RETCARm7KDIQcxYm5pisi3nYIiKpSUGaiIiIuON//wNGj/b9WKFCQLNm9sJKjHFgJox1QzwDMnZhZCX++DDwYoasevXoxVdAJiISjBSkiYiISNLs3Al8/jlw331AkSLR6xs18g7SGCk1bw7cfbfdt9BHCcRjx+xAbO1ae+Htw4fjf3nuhlOi3XKL/RL8yYmhmTkTEUmLFKSJiIhI4roxMjBbuNCeQIwyZwZ69ozejuPM2rWzM2XMmBUtGqvbIp/K+cichVOiJaRcOaBGDTsY408W9tAcZCKSnihIExERkYSxfyGLfXz2mR2cHTgQexsGbJ5BGoO2WbOi7vIpnNrMCcgY57HgR3zy57cDsZo17YWZsgIFkvONiYgEHwVpIiIiEj9WWnzllbgjKkZRLVoALVt6Zcl++80Oylavtn/GNSe1Z0zHwh4Mxm691f7JrJmfFfdFRNINBWkiIiIS7d9/gVKlvMeL5c3rHaBxAml2YWRgxjFmRYuasWQMxFZ9bAdlHEuWUJasdGmgVi07IOPCAE3jyEREFKSJiIiENpZPZMprwQJg/nzg11/tKIvRk4MZshdftAt+tGwJq9Gd2L4vO1atAlYNg/n5xx/xvwzjOo4h4265MEsWY4iaiIhcoSBNREQk1HBCMQZiDMoYnMXsh8h1HkHapeJlsPHz/Vi5OhwrpwMrewIHD/qXJeNSu7ZdDl/l70VE/KMgTUREJFR89x3w8cd2EBZXlFWjBs6XuhYrlwIrV9oLi3ycORO7XL4jY0a7wuJtt9kBGZfixd17GyIi6Z2CNBERkVDx4YfAlCne6zJmxKXb6mFLhdZYiBb4/JcSWN/HTrbFJU8eOxBjUMaFdUOyZ3f96EVEQoaCNBERkfTk1Cngyy+BefOAd9+1i344Wrc2QZqVNRv2Vm6KpbnvxaR9zbF6RT5gRdy7LFHCnvLs9tvtn5w42sc81CIikkwUpImIiKR1x4/bc5d9+imweDFw4YK9/p57gI4dsXcvsGIFsGpZI2QoNheT/2uKsz/FPfvz9dfbwZizsNijiIikHAVpIiIiaVAYAzNmzBiYLVkCXLoUa5sfh3+FjsM7Yvt2Z01WAG28tmFGjOPJ6taNzpYVLJgy70FERHxTkCYiIpLGhPXrh0LvvIMwH4HZwQxFMCfiXnyKNvh+e11E+CjywVL4d9xhLxxbljt3ih26iIj4QUGaiIhIMDtxwq7U4eFsjoLI4RGg7UFxzMV9ZlkTUQuRyBD1WObM9pxkTlDGkvg54u7pKCIiQUBBmoiISLA5eRJYuBCYPRv4+mucXbkBK45UwvLlMMupDW2xDBNNUDYHbfEjboWF8KhMWa2aQP36QL16dlCmyosiImmLgjQREZFgcOYMsGiRCcysL79EmFP8A8DYW2djiFXJY+NrUQq72PERGTIANaoDDRrYgRm7LypTJiKStilIExERSS0MxL7+GtbHH8P6bCHCz501q8M8NtmLYjhueQ8aq1LFwq23nkXz5tlwxx3hGlMmIpLOpLlZTt5++22UKVMGWbNmRc2aNbFu3bp4t58zZw4qVKhgtq9cuTK+ZCUsERGRVHboEPBvw25Ay5YImzUrKkCj/SiMt9ALdfA9SmI3Pis/EI8+yv/T7Odt2GBh+PBTaN5cRT9ERNKjNBWkzZ49G/369cOwYcOwYcMGVKlSBU2aNMHBgwd9br969Wo88MAD6NatG3755Re0atXKLJs2bUrxYxcRkRBmWbj4/Y9Y/tUFPPMMcPPNQKFCwIBVraI2OYKr8B56oAGW4aaCe7H6gbfQdXId7Pg3HNu2ARMmAPfdBxQokKrvREREUkCYZVkW0ghmzm655Ra89dZb5n5kZCRKliyJJ554AoMGDYq1fbt27XDmzBksYh//K2699VZUrVoVEydO9Os1T548iTx58uDEiRPIra8ro7DtGRwXKlQI4ZxkR5KV2tddal/3qY1NXIadi7fg0LiZKPnDRyhybgdaYx4WoHXUNllxDm+jFz7PfB8u12uE+k0y4847gUqVgDDPPo8xqH3dpfZ1l9rXXWpfdx0/fhz58uVzPTZIM2PSLl68iPXr12Pw4MFR63jiNWrUCGvWrPH5HK5n5s0TM28LFiyI83UuXLhgFs8gzTnhuYiNbcH4Xm3iDrWvu9S+7gvVNj51Clj9yR6cnTIbFTZ8hBsubkQZj8c74KOoIO2mmyzceWdWlLrzfcysDWTlPNOIjArw4vsKNVTbN6Wofd2l9nWX2tddKdWuaSZIO3z4MCIiIlC4cGGv9bz/559/+nzO/v37fW7P9XEZNWoURowYEWv9oUOHcP78+UQff3o8QfkNAj8E9C1N8lP7ukvt675QaWMGUlu3ZsSqL88hbP5XqLV9FppY3yEc3hFWBMLxQ6YG2Hdzc7zd6Tjq1r2IAgWi/6Pn94FXvhP0S6i0b2pR+7pL7esuta+72LYpIc0EaSmFmTrP7BszaexSWbBgQXV3jPEBEBYWZtpFHwDJT+3rLrWv+9JzG58+DSxdCnz1VRgLM2L37jC8hMEYjNGxtt2Suwb21n0AhZ+8H3UaFEHdeLowBiI9t28wUPu6S+3rLrWvuzJnzoyUkGaCtAIFCiBDhgw4cOCA13reL1KkiM/ncH0g21OWLFnMEhNPcp3o3vgBoHZxj9rXXWpf96WnNv77b+CLL4AvFlk4891P+OtyGRxCoajHZ+LBqCDtQJ7yON68I0o83QEVq1yDii4dU3pq32Ck9nWX2tddal/3pFSbhqelqLVatWpYtmyZ1zcFvF+rVi2fz+F6z+1pyZIlcW4vIiJCFy8Cy5cD7Fhx3XVAg/K7cKDvS3hzaUWsulwTXfGB2Y7f6TVuDHQfWwmH+r4I68e1KHxsK66bORQ5qlyT2m9DRETSqDSTSSN2Q+zcuTOqV6+OGjVqYNy4caZ6Y9euXc3jnTp1QvHixc24MurTpw/uuOMOjBkzBs2bN8esWbPw888/47333kvldyIiIsHmyBF2YQQ+/xxYvBiIPHkK92IeJmAa6sF7nFmffNNRZ9rTqN8gDDlyOGufTa1DFxGRdCZNBWksqc8CHkOHDjXFP1hKf/HixVHFQXbt2uWVgqxduzY++ugjDBkyBM8++yzKly9vKjtWYm1jEREJaXbRDzso47JqFWBFRuIOrMCbmIr7MBc5cDb28+64A8U6dUKxZpFAhgypcuwiIpK+pakgjXr37m0WX7777rtY69q2bWsWERGRiAhOzwJ89pm9/PWX9+MPYQamo3PsJ5Yvz+4aQMeOCCvjWVRfREQk+aW5IE1ERCQQZ85wPLIdlC1axCld7PU5cBpFcAr7UdTc59iz0o1bIuK9bMhw4RyQNy/Qvj3QuTNQs2b8s0uLiIgkIwVpIiKSLseXsQvj/PnAN98A0dNcWqiFNeiGyWiH2VhRpB22DpyMe+6xk2VAHqD0C0DJkkCLFs4M0yIiIilKQZqIiKQLe/YACxbYgdmKFXbXRkcR7MND+BDdw6bgWmtr1Prmpz9B80fehEf1D6B//xQ+chEREW8K0kREJE3PX/bpp/by00/ej2XEJTTDl3g882Q0uvQlMlgRTKRFy50beOABuz+kZ5AmIiKSyhSkiYhImqrI+Mcf0YHZb7/53q5uyR34/Ght5D6zH7gY48F69YCHHwbatAGyZ0+JwxYREQmIgjQREQn6wOzXX4G5c+2FZfN9qVIFaN3aXirfUBph12YH/rnyYIkSQJcu9nL11Sl5+CIiIgFTkCYiIkEZmDFL9skn9sJujb60q/wHBuR5DxWz70KOr+d5PBIOPPoo8OOPQPfuQOPGmtNMRETSDAVpIiISVIHZnDl2YBZzDjNiFfwGtc5hYNm5qLftPWT5aWX0g9u2AddeG31/4MCUOXAREZFkpiBNRERS1ZYtwOzZwKxZvrsyhocDdesCPW//Ay32v4ccn04HVh/z3oil8jds8A7SRERE0igFaSIikuL++Sc6MPNV/IMZszvuANq2BTqEz0LemW8DIz2yZo7rrwceeQR46CEgX74UOXYRERG3KUgTEZEUsW+fHZRxWbfOd2DGjNn99wP33gsUKXLlgTsnAytXemfNuFHPnkDt2vYTRURE0hEFaSIi4prjx+0Jpj/6CFi+3B53FtOttwLt2wNtW19GsU3fAE2b2n0cHSwAsnQpULGifVtZMxERSecSFaR9+OGHmDhxInbs2IE1a9agdOnSGDduHMqWLYuWLVsm/1GKiEiacf488PnnwNSpebFsWRguXIi9TdWqdmDGhFjZHAeB998Hbp8I7N4NfPklcNdd0Ru3aAGsWAHUqaOsmYiIhASPryr9M2HCBPTr1w/NmjXD8ePHERERYdbnzZvXBGoiIhJ6IiPtOIrV7gsXZvAVji+/zIoLF6KDKk5PNnSoXSjklw0WnqmzGmWHPGjPYfa//9kBGk2Y4L3zTJnsfpAK0EREJEQEnEkbP348Jk2ahFatWmH06NFR66tXr44BAwYk9/GJiEgQY8D14YfAzJnArl2xHy9c2EL79mHo0AG45RYg7NxZu+/jA28DGzd6b8wgrFkz4PHHU+z4RURE0kWQxi6ON910U6z1WbJkwZkzZ5LruEREJEgdOmTHWQzO1q+P/XjOnCz8YaFZs2No3TovMme+kgHjBGisxHgsRvn8/PmBbt3s8WZly6bMmxAREUlPQRrHnW3cuNGMQ/O0ePFiVOSgbhERSXcuXgS++AKYNs3+efmy9+MZMgCNG9s1PTg0OWtWCwcPXkRGz/9lrrnGO0Bjaq1XL6BdO7tio4iIiCQuSON4tF69euH8+fOwLAvr1q3Dxx9/jFGjRuF9DvwWEZF0gZUYf/mFBUDszNmRI7G3uflmOzB74AF7LJpx+jQiJ0wzPSzw8MPRG7MXRsOG9hg0BmcM0kRERCTpQVr37t2RLVs2DBkyBGfPnkWHDh1QrFgxvPHGG2jPUl0iIpLmuzPOmAF88AHw+++xHy9WDOjYEejUCbjhBo8Hdu4E3noLmDQJ4SdOIOe11wJdu3o/+ZtvvMvri4iISPKU4H/wwQfNwiDt9OnTKFSoUGJ2IyIiQYKFer/+GpgyBVi4ELh0yftx9kZs1Qro0gVo1Mju3hiVblu1CmB13/nz7TKPV2Tatg2RP/1kT4TmUIAmIiLi7mTW2bNnN4uIiKRNf/9tZ8w41mzv3tiP16plB2aczyxv3hiD1D75xA7OYlYPyZIF1gMP4EiHDriqRg3X34OIiEhIBmms5hjm5/w0GzZsSOoxiYiIy5NNz5tneiXiu+9iP86xZZ072z0VK1TwsQPOTs2ujDFr7hcpYpfPf+QRWAUK4PLBg669BxEREYR6kMY50RwsGPLOO+/g+uuvRy1+xQrgxx9/xObNm/G45rYREQlamzfbgRlL5x896v0Yuy/efbdd5+Ouu+z5o+PEgiCcXJoD15zqIX372uk2PkYe3R5FRETEhSBt2LBhXoVDnnzySbzwwguxttm9e3eALy8iIm46e9bulcjgbPXq2I8zIda9u12hkYkwLxxv9v339pO5ZMsW/Vi/fvbOn3oKuO02eyJqERERSZ0xaXPmzMHPP/8ca33Hjh1RvXp1TOGocxERSVV//AG8+6491uzECe/HmOxq2xbo0QOoU8dHfMVJ0ObOBV57LXq8Wb16djTnWU7/00/dfyMiIiIhKOAgjeX3V61ahfLly3ut57qsmoxURCTVcKgYx5pNnGgnwGKqVMkOzFg+/6qrfOzg9Glg8mRg7Fi7nL6n2bO9gzQREREJniCtb9++eOyxx0yBkBpXqnatXbvWZNCee+45N45RRETi8c8/dtaMHRkOH/Z+jN+dcQrLRx4BataMo1fi/v3A+PHAhAnAsWPej3G8Wf/+dupNREREgjNIGzRoEMqVK2cmr55xZdB4xYoV8cEHH+B+DhoXERHXsS4H5zV7+23gyy/t4WOeKlYEHn3UHmuWL188O2K3RqbWmIbzxOohAwfa3Rw13kxERCT450ljMKaATEQk5THRxXnN3nkH2L7d+zFWZGzTxg7OWHzRr9iKE01zJmtnBx06AAMG2H0jRUREJO1NZi0iIinj11/tHokffQScO+f9WKlSdmDWrRtQqFAcO2CqbelS4MgRu/+jo0QJ4LHH7H6RffoAxYu7+j5ERETEhSAtPDw83omtI5xvZEVEJElYZHHhQuCNN3wXArnzTqBXL3t+M85z5hM/k1lNZPRoYMMGoGBBoGVL73L6b77p2nsQERGRFAjS5s+f73X/0qVL+OWXXzBt2jSMGDEiEYcgIiKeONE0iyy+9Rawa5f3Y7lzA126AI8/Dlx3XTw7uXjRnmyawdlff0WvP3QImDUL6NrVteMXERGRFA7SWvIb2Bjuu+8+3HDDDZg9eza6sb+NiIgkam4zZs0+/DB2l8YKFYAnn7QLgeTMGc9O+MT33wdefRXYvTt2pcZBg4B773Xl+EVERCTIxqTdeuut6NmzZ3LtTkQkJHCo2JIl9tRkixfHfrx5czs4Y9fGBAuBMPX2wgvAwYPe6+vXBwYPBho1UqVGERGRUAnSzp07hzfffBPFNeBcRMQv58/bRUAYnG3a5P1Yrlx2b8TevYHy5QPYKTNnngEaB6s9+yxQq1ayHbeIiIgEYZCWL18+r8IhlmXh1KlTyJ49e9S8aSIi4huHhHHOaM5vFjPhVaaMXWDx4YftsWcJ7ihLFu8Nn3rKzqbdc4+dOatSxZX3ICIiIkEWpI0dO9YrSGO1x4IFC6JmzZomgBMRkdj+/hsYMwaYOtXOonmqXRvo1w9o1SqeKo2OAweA116zJ0p75hlg6NDox4oUsSuN5M/vynsQERGRIA3SGjRogJIlS/osw79r1y6U4oQ9IiJirF1r1/BgFXyOP3OEh7Pokp384nzSCfrvP+CVV4B3342O8saNA/r29c6mKUATEREJvSCtbNmy2LdvHwrFmDH1yJEj5jHNkyYioS4yEvjqKzumijm/GSsz9uhhFwNh90a/grOXX7aDswsXotezq+ODD3IelGQ/fhEREUljQRrHoPly+vRpZM2aNTmOSUQkTWK8xCnIGFNt3uz9GHsicrzZI49wbK8fO9u3Lzo48+wfyUmoH3sMGDAAKFo02d+DiIiIpKEgrR8HTIDVm8MwdOhQUyjEwezZ2rVrUbVqVXeOUkQkiHFqsilT7G6NO3d6P8YJpwcOBDp2tJNffuFE1DfdZI8/c/Azt1cvOziL0ZNBREREQjRI++WXX6Iyab///jsyZ84c9RhvV6lSBQN48SAiEiJOnLArNbKMfsxKjax6z7oeLLTI8WcB4ecr553knGcKzkREREKO30Hat99+a3527doVb7zxBnInWB9aRCR9YvV71uxgtfuTJ70fa9rUrn5fp46f80YfPWpHeawgctVV0et5nxk19mJQcCYiIhJSAv1+Fx988EGqBGhHjx7Fgw8+aF47b9686NatmxkHF9/2TzzxBK677jpky5bNVJ188skncYJffYuIJML+/XZCiwU/XnopOkBjMNa2LbBhg10wpG5dPwI0fhaNGMFqTMDIkXZZfU8cuDZ6tAI0ERGREORXJu3ee+/F1KlTTYDE2/GZxzrTLmCAxqqSS5YswaVLl0xGr2fPnvjoo498bv/ff/+Z5bXXXsP111+PnTt34tFHHzXr5s6d68oxikj6tGePXalx0iTvGh6ZMgGdOgFPPw1ce62fOztzBhg/3t7hsWPR61kg5Lnn7MIgIiIiEtL8CtLy5MkTNS8ab6e0LVu2YPHixfjpp59QvXp1s278+PFo1qyZCcKKFSsW6zmVKlXCp59+GnX/6quvxosvvoiOHTvi8uXLyJgx4MKWIhJi/v3XTmZ98IHd89DBAiAso8/grGRJP3fG8vnvvWdnzTwHsPGz6OGHgSFDFKCJiIiIkdHfLo6+bqeUNWvWmC6OToBGjRo1Qnh4uKkq2bp1a7/2w66OzAbGF6BduHDBLI6TV/ozRUZGmkVsbAsWkVGbuEPtm7rty+DspZfCMG0acPlydL/F7NktU0K/f38rqvp9gr8izh354YcIe/55hHmUfrRYTeTBB2Exe3b11X7uLO3QOewuta+71L7uUvu6S+3rrpRq1zSRTtq/f3+sybMZaF111VXmMX8cPnwYL7zwgukiGZ9Ro0ZhBMeJxHDo0CGc9+znFOJ4gjLo5YcAg2VJXmrf1GnfPXvC8eabOTFrVjZcuhQdnOXMGYmuXc+iZ88zKFDAnisyZjXHuISdPImC/fsj7PjxqHXnWrTA6QEDEFG+PALaWRqic9hdal93qX3dpfZ1l9rXXSlV38KvIO2mm26K6u6YkA0cOe+nQYMG4WVO1ppAV8ekYjasefPmZmza8OHD49128ODBUXPCOc8tWbIkChYsqIqWMT4AeE6wXfQBkPzUvinbvhxzNmpUGCZP5oTU0Z91uXNbZgLqJ59k4UXODRk9P6Tf+AXToEFmsZo0gTVyJLLcfDP8nTItrdI57C61r7vUvu5S+7pL7esuz2nIUj1Ia9WqlSsv3r9/f3Tp0iXebcqVK4ciRYrgYIxvmjmujBUc+Vh8Tp06haZNmyJXrlyYP38+MnGkfzyyZMlilph4kutE98YPALWLe9S+7rfvgQPhGD063AwV8xxzlisXTHDWr1+YKbII+PcllSnvyEw8d1i4cPT6J54wE6eF1a3r757SBZ3D7lL7ukvt6y61r7vUvu5JqTb1K0gbNmyYKy/OCJ9LQmrVqoXjx49j/fr1qFatmlm3fPly801BzZo143wes2BNmjQxQdfChQuRNWvWZD1+EUmbjhzhPNE5MWVKmFe1xpw57awZk+n58weww3/+sQt/fPyxfb9UKbuCo4MTUrMuv4iIiIibY9J+/vnnqK6I7EboBE9uqFixosmG9ejRAxMnTjQl+Hv37o327dtHVXbcu3cvGjZsiOnTp6NGjRomQGvcuDHOnj2LGTNmmPtOERAGhhkyZHDteEUkOPEjgPNGjxkThlOnckatz5ED6N3bngOtQIEAZ7VmtcYJE9hPMnr9N9/Y1Rx9ZOVFREREkj1I27NnDx544AGsWrXKVFwkZrlq166NWbNmoUSJEnDDzJkzTWDGQIxpxjZt2uDNN9+MepyB29atW01Q5oyNY+VHuuaaa7z2tWPHDpThbLQiEhL4sfD22wCHwDKL5nRfzJLFQq9eYXjmmQDnjOZcZ4z2ONfZqVPR6xnhsVojS0AqQBMREZGUCtK6d+9uAiJm0a677jqzjsERJ5fmY5zPzA2s5BjXxNXEoItVbBz16tXzui8ioYfJrSlT7GFi+/ZFr8+Y0UL79ufw4otZUapUAKPEWE6fdfkZiP33n3d3RvaRHDiQ1UaS902IiIhIyAk4SFuxYgVWr14dFaARb3Ny6Tp16iT38YmIBIzfz3Au+//9D9i2LXo9i9Q++CAwdKiFXLlOolChrIGn5J59FjhwwL7PbtPdu3PgLqImThMRERFJooDLk7AcPTNpMUVERESNDxMRSS3ffQfceivQtq13gMYitb/9ZuaVjpo7OmAs+/j88/btli2BTZuAiRMVoImIiEjqBmmvvvoqnnjiCVM4xMHbffr0wWuvvZa8Ryci4icGYM2aAfXrA+vWRa9nUcU1a4D584FKlQLYIftHPvoosHu39/qHHwZWrQIWLAAqVEi24xcRERFJdHdHzmvG4hwsfZ8xY8aoOct4++GHHzaLg/OYiYi4iRNRs/r99Ol2N0cHAzIWCrnrLrubo9/OnQNef52zW9sFQlijf+rU6Mf5uVe7drK+BxEREZEkBWnjxo0L9CkiIsmORRVZXHHMGDuucnCKshdesMeeBTTTBiO82bNhSj3u2hW9fuFCfuPE6kXJevwiIiIiyRakde7cOdCniIgkm8uX7YqNQ4dG1++gfPnsQiG9egEBz1vP/pFPPQWsXh29jhEeuzsOH64ATURERNLGZNYHDx40S2RkpNf6G2+8MTmOS0QkFs7wwQmnN2+OXpcpkz0RNbs8BhxL7d1rV2ucMcN7fdOmdoru+uuT5bhFREREXA3S1q9fb7JpnCct5jxkYWFhpsqjiEhy+uMPexqyr7/2Xt+mDTB6NCesT8ROIyMR1qiRdwnIihXt4IwD2URERETSSpDGwiDXXnstJk+ejMKFC5vATETEDRwKxt6G77xjzyPtqFnTjqVuuy0JOw8PhzVkCMI6dbJTcJzx+pFH7NSciIiISFoK0v755x98+umnuCZRX12LiPg37uy994DnnrMDNc+iIKzY2K5dgBUbnXQc5zkrWTJ6XYcO7LsNdO2qcWciIiKSdudJa9iwIX799Vd3jkZEQt6yZcBNN9kFQJwALXt2ew7pP/8E2rcPMEA7fhzo04cDZu0+k564o/79FaCJiIhI2s6kvf/++2ZM2qZNm1CpUiVkitE1qEWLFsl5fCISIv79146hOOm0J5bS57izEiUC3CGLGk2bZpfUP3TIXjd3LrBiBVCnTrIdt4iIiEiqB2lr1qzBqlWr8NVXX8V6TIVDRCRQnCua851x7mjedtxyC/DGG0CtWonY6YYNdiruxx+j12XLBgweDNSokSzHLSIiIhI03R2feOIJdOzYEfv27TPl9z0XBWgiEohFi4AbbgCGDYsO0IoUsRNgjK8CDtCOHbODs+rVvQO0tm2BrVvtQW4M1kRERETSUybtyJEjeOqpp0xlRxGRxNi+Hejb1w7SPOeO5tAxBmy5cydip3PmAI8/Dhw+HL2uQgVg/HiApfZFRERE0msm7d5778W3337rztGISLp27pwdhDF75hmg1asHsB4Ry+onKkBzojwnQMuRwy4DyZ0qQBMREZH0nknjHGmDBw/GypUrUbly5ViFQ5588snkPD4RSSe++cZOdDGL5ihWzA7MElVSP6bWrYEmTYA8eeydBlxpRERERCQNV3fMmTMnVqxYYZaYhUMUpImIp337gKeeAmbPjl6XMaO9jkPEOHVZQCwLmDfPrtXPWa4djPIWLACyZk22YxcRERFJE0Hajh073DkSEUlXWEdo4kTg2WeBkyej17P6Pddff30idvrPP0Dv3oBTXfauu4B77ol+XAGaiIiIhOKYtLhs2bIFAwYMSK7diUgaxgr4rMzIeMoJ0PLnBz74wJ6mLOAA7eJF4MUX7cFsntN/eA5sExEREUknkhSknTlzBpMnT0bt2rVxww03YPHixcl3ZCKS5pw5A/C7Gs5x9tNP0esfftiugN+lSyLGnq1eDdx8MzBkSHSd/uLF7YmpmZITERERSWcSFaRxMuuHH37YlOHv2bOnCdL++OMPbNq0KfmPUETShCVLgMqV7ZodkZH2OmbMvv8emDzZzqQF5MQJu9LI7bcDmzfb68LD7cFsW7YAbdokQ7URERERkTQcpB08eBCvvPIKKlSogPvuuw958+bFd999h/DwcBOwcb2IhJ4jR+wMWePGHLNqr8uSxe6d+Msv9hi0RFUbqVgRmDDBLhRC1arZ6bnXX09EtRERERGRdFg4pHTp0iY4e+ONN3DnnXea4ExEQhdjp08+4bQb/BInev0ddwDvvcfpOpKw8yJFgOrVgc8/t+c8GznSHuDGspAiIiIi6VxAQRrnRitVqpS5rcyZSOjaswd47DHvuh2cnuzVV4Fu3exeiQFHfJ5dF3n77bftao3caenSyXbsIiIiIsHO70upP//8EzNmzMC+fftwyy23oFq1ahg7dmzU/Ggikv4xlpoyxS6y6Bmg3Xsv8McfQI8eiQjQWFGkbl1g4ULv9SVL2qk6BWgiIiISYgK6nLrtttswZcoUE6g9+uijmDNnDiIiIvD4449j0qRJOHTokHtHKiKpavduoFkzO1PmlNUvWhT49FN7KVYswB1evgyMHg1UqQKsXGmn5o4fd+PQRURERNKURA0sy5kzJ3r06IHVq1dj8+bNJqs2ZMgQFAv4Kk1E0kL2jNUZK1UCPGfZYLEQFl1kFi1gGzcCNWsCgwcDFy7Y69i1kZGgiIiISIhLcvWPihUr4rXXXsPevXsxe/bs5DkqEQkKu3YBTZsC3btHZ8/4XcwXX9gTU+fLF+AOGZD97392URDOeE3sH9mvH/Dbb3YNfxEREZEQl2yl0jJmzIh7E/WVuogEY/aMQVjfvsCpU9Hru3a1K+DnzZuInbJ8PtNvHLzm4OA2pumYVRMRERERQ/WsRcTLgQNAz57edTyKFwcmTQLuuiuRO50+HXj4YSAiwr6fKZOdUWN3x8yZk+W4RURERNILBWkiEmX+fDtAO3w4mbJnjnr1gOzZ7bTczTfbabobb0yOQxYRERFJdxSkiQhOnAD69AGmTYteV6iQnT1r0SIZXqBUKeCNN4C9e4FnnrEzaSIiIiLik4I0kRD37bf2UDEWCXG0agW89x5QsGAidsiCIEOGALNmAblze6fkRERERCR5grRACoLMmzfP721FJPU4hRbHjIlelysXMH480KkTJ6lPxLxno0YBzz9v3x4wwI70RERERCT5g7Q8efJE3bYsC/PnzzfrqrOMNoD169fj+PHjqu4okkb8+SfwwAP2dGWew8amTgVKl07kDhnZsYKj4+efgXPngGzZkuWYRUREREKFX0HaBxzkf8UzzzyD+++/HxMnTkSGDBnMuoiICDz++OPI7dm1SUSCsrQ+k1tPPWXHT8TiikyAsdw+pywLSGQk8NZb9jiz8+ftdfxcePZZu8ujKjeKiIiIuD8mbcqUKVi5cmVUgEa83a9fP9SuXRuvvvpq4EchIq47csSelHrBguh1FSsCH30EVK2aiB1yEBsHs3FQm+O66+xy+zVqJMsxi4iIiISiQL83x+XLl/EnuzbFwHWR/FZdRILO8uV2xXvPAO2xx+weiYkK0BjZVa7sHaCxPCSLhihAExEREUnZTFrXrl3RrVs3bN++HTWuXIytXbsWo0ePNo+JSPC4dAkYOhR4+WW7qyPlz8+MeBJL6x88CJw8GV1en12iGzRIlmMWERERCXUBB2mvvfYaihQpgjFjxmDfvn1mXdGiRTFw4ED079/fjWMUkUTYvRto3x5YvTp6XaNG9lxoxYolcedPPgl88QVQvLg9/5lHcSERERERSeEgLTw8HE8//bRZTl75Jl0FQ0SCy+ef28PFjh6172fMaBcH6dcvEcVBWKt/2TKgWbPoddwJXyRr1mQ9bhERERFJxJg0Z1za0qVL8fHHHyPsymRK//33H06fPp3cxyciAbh4EWBCm10ZnQCtTBlg1Sp72rKAA7TNm4GaNYG77wa++877MQVoIiIiIsERpO3cuROVK1dGy5Yt0atXLxw6dMisf/nllzGAV4EuOXr0KB588EGTtcubN68ZF+dvUMi53e666y4TUC7wrJwgko7s2AHUqQO8/nr0Ok5d+MsviajlwQFsb78NcC7EX3+17/foYU9SLSIiIiLBFaT16dPHTGJ97NgxZPOYpLZ169ZYxi5RLmGAtnnzZixZsgSLFi3C999/j549e/r13HHjxkVl/ETSo/nzgZtuAtats+9zerLx44G5c4G8eRNRq79VK6B37+i5z264wd4Z+02KiIiIiKsCvuL64YcfsHr1amSOMUltmTJlsHfvXrhhy5YtWLx4MX766ScTINL48ePRrFkzU8ikWDxVEDZu3GiKnPz888+mwIlIesLE1qBBwJgx0euuvhqYPRuoVi0RO2SXxo4dAc+/5SeesMtDenwpIyIiIiJBFKRxLrSIiIhY6/fs2YNcuXLBDWvWrDFdHJ0AjRo1amSKmLD8P7N4vpw9exYdOnTA22+/bSpS+uPChQtmcTjFUfi+NQ9cNLYFu5GqTVKvfVlctUOHMHz/fXSWuG1bC++9Z4G1fAL61Vy6hLDnnzfVRcKu1Oq3ChSAxVr9zZs7B4X0Quev+9TG7lL7ukvt6y61r7vUvu5KqXYNOEhr3Lix6T743nvvmfvsRsixYcOGDTOZLTfs378fhQoV8lqXMWNGXHXVVeaxuDz11FOoXbu2GT/nr1GjRmHEiBGx1nPs3Xmn65eYE/TEiRPmQ4DBsqRs+65ZkwmPPpoXBw/aj2XKZGHEiFPo0uWs6aEY6Kmae8AAZJ85M+r+hTp1cGL8eEQWLmzPiZbO6Px1n9rYXWpfd6l93aX2dZfa111s26AM0th1sEmTJrj++utN0MJM1V9//YUCBQqYao+BGDRokCk4klBXx8RYuHAhli9fjl9YNSEAgwcPRj/WKffIpJUsWRIFCxbUVAMxPgAYoLNd9AGQcu3LJNfYsfzbCUNEhJ1BK1HCwuzZFm69NScALonw7LOwOLDt4kVYL7yATAMGoEA6/r3q/HWf2thdal93qX3dpfZ1l9rXXTGHfAVNkFaiRAn8+uuvmD17tvnJLBorLbKwh2chEX9w8usunMwpHuXKlTNdFQ/G+Daf0wCw4mNc3RgZoG3fvt10k/TUpk0b1KlTB9/FLCd+RZYsWcwSE09yneje+AGgdkm59mXP265dgXnzordp2BD4+GN+ECexMM711wNTpwKlSiGMJfdDgM5f96mN3aX2dZfa111qX3epfd2TUm2aqFJt7GrIoIxLUjDC55KQWrVq4fjx41i/fj2qXamGwCCM3xTUjOOCklm67t27e63j1AFjx47FPffck6TjFklpTCiz4OK2bdHr/vc/gD1zM2QIcGd//QWMHAm8+673XGdt2ybb8YqIiIhI4gUcCmbIkAH169c3WSxPBw4cMI+5oWLFimjatCl69OiBdevWYdWqVejduzfat28fVdmRlSUrVKhgHidm2CpVquS1UKlSpVC2bFlXjlPEDQsX2vNJOwEak8Off27HWQH/yc2aBdx8MzB9uj3rtYiIiIik/SCNgxBZ/ZCVFjlvWczH3DJz5kwThDVs2NAUKLn99tujipfQpUuXsHXrVlPRUSQ9YPGgF14AWPfm1Cl73Y03AuvXA3ffHeDOzp0DHnkEeOABwJkEfvny6B2LiIiISNDImJg+rp9++ilGjx5tuiF++OGHUdUT3ZwwmpUcP/roozgf5zxtCQWJbgaRIsmJsVO3bnmxeHH09yj33w+wIn6OHAHujCk4dmX87bfodQ89BLzzDpAzkYVGRERERCS4Mmns1vjGG2+YiaTbtWuHkSNHKgASSSYcMla7dhgWL7bHi/G7j9Gj7Z6KAQdoc+fas1o7ARqL+zDSmzZNAZqIiIhIkEpU4RBHz549Ub58ebRt2xbff/998h2VSIhavBho355zcNhZ6Tx5LFO98a67AtzRpUvA008D48ZFr6tYEZgzB7jhhuQ9aBERERFJ3Uxa6dKlvQqEsIjIjz/+iN27dyfvkYmEEGf+s+bNGaDZ68qXv4y1a63AAzTieE3PAK1DB4BFdRSgiYiIiKS/IG3Hjh3Inz+/17prrrnGTBr9zz//JOexiYSEixftmh6cQ53FQqhFCwtffnkE5csncqfc4R13AJky2WPPZsxQ90YRERGRUOju6Clr1qwmyyYi/uNMFm3aAJ5zq3P+s+HDLRw+nIRxnhkz2oPYdu0CatRIlmMVERERkSAK0lhZcdu2bShQoADy5csXbxXHmPOniYhvW7fapfT//tu+nyWLXdODPROdjJpfjh8HOHH7oEFA9erR64sUsRcRERERSX9B2tixY5ErVy5ze5znOBcRSZSlS+2q+IyvqFAhYMECoFatAHfEuQpbt7ZLQv78M7BhA79VceOQRURERCSYgrTOnTv7vC0igZs4EejdG4iIsO9Xrgx8/jmL8iSivH6XLsCZM9GTq23friBNREREJBSCtJMnT/q9w9y5cyfleETSLXZhZFX8MWOi191zDzBzJnAlUe0fRnccuPbyy9HrbroJmDePs7on6zGLiIiISJAGaXnz5o13HBpxMmtuE+GkB0QkyrlzQKdOdvLLMXAgMGoU4DGjRcKOHAEeeABYsiR63UMPAe++a09ULSIiIiKhEaR9++237h+JSDp1+DDQsiWwerV9n0HZhAlAjx4B7mjjRnv82b//RldwfP11u+9kAl+iiIiIiEg6C9Lu4HxLIhIwDhHjZNSs60E5cgBz5tjrAsKqqfw7dLoes9IId1S3brIfs4iIiIik0XnSzp49i127duEiZ+L1cOONNybHcYmkeT/+aI85YyaNWA3/iy+Am29OxM5YDOSll+ysWc2awKefAsWLJ/chi4iIiEhaDNIOHTqErl274quvvvL5uMakiQDz59vznZ0/b9+//nrgyy8TUcHR0+OPszIPcP/99qRqIiIiIpIuhQf6hL59++L48eNYu3YtsmXLhsWLF2PatGkoX748Fi5c6M5RiqQh77wDtGkTHaDVrw+sWhVggPb338jGso+eOO6MRUIUoImIiIikawFn0pYvX47PPvsM1atXR3h4OEqXLo0777zTlN4fNWoUmjdv7s6RigQ5ywKGDweefz56XceOwOTJQObMAexo6VKE3X8/8hw7hshy5exiISIiIiISMgLOpJ05cwaFWLQAQL58+Uz3R6pcuTI2bNiQ/Ecokgawly97I3oGaIMGAdOnBxCgMcp7802gaVOEHTtmVoWNHm1PsCYiIiIiISPgIO26667D1q1bze0qVarg3Xffxd69ezFx4kQULVrUjWMUCWoXLgDt2wMTJ0avGzvWngPN78r4ly4Bjz0G9OljR3wAzt95J6yvvwbCA/4zFREREZFQ6u7Yp08f7Nu3z9weNmwYmjZtipkzZyJz5syYOnWqG8coErRYEb9VK84lGD11Gf8MHnwwgJ0wa9a2LbBsWdQqa9AgHO/dG4VYKEREREREQkrAQVpHDrK5olq1ati5cyf+/PNPlCpVCgUKFEju4xMJWgcO2POd/fKLfT97drsyftOmAezk77+Bu+8GrmSnTd/IKVNgPfAAcPCgK8ctIiIiIul0njRH9uzZcXOiJn4SSbt27ADuvNOerNqZxoxzoN16awA7YcnHFi3siaqpYEG7dv9tt2kcmoiIiEgICzhIsywLc+fOxbfffouDBw8iMsbF5Lx585Lz+ESCzp9/Ao0aAXv32vdLlAC++QaoWDHAHTHz7Pz9cCK1RYuAsmWT/XhFREREJATmSXvooYewY8cO5MyZE3ny5PFaRNKzX38F6taNDtAqVABWr05EgEbXXWf3j7znHnsnCtBEREREJDGZtA8//NBky5o1a+bOEYkEqbVr7fFmx4/b96tWtTNo7KXol3PngAwZvGvyN2hgLyIiIiIiic2kMVtWjhPsioSQ776zuzg6ARrHnrGio98B2uHD9g569rTnQxMRERERSa4gbfjw4RgxYgTOMSsgEgK++squ4nj6tH2/fn1gyRIgb94AKjjWqmV3aZw2DXjpJTcPV0RERERCrbvj/fffj48//hiFChVCmTJlkClTJq/HN2zYkJzHJ5KqOGSM1fA51zQ1bw7MmQNky+bnDhiYsYLjkSP2fU74rq7CIiIiIpKcQVrnzp2xfv16M19a4cKFERYW5s6RiaSymTOBTp2iCzByvukZM7yHlMVr7lxOLAhcuGDfr1TJrtNfqpRrxywiIiIiIRikffHFF/j6669x++23u3NEIkHgww/5hUT08DHefv99IKM/fzF80pgxwMCB0es4Ho1BmyqgioiIiEhyj0krWbIkcufOHejTRNIMDhvzDNAeewyYMsXPAI1pt6ee8g7QunSxM2gK0ERERETEjSBtzJgxePrpp/Hvv/8G+lSRoDd1KtC1a3SA1qsX8PbbQLi/fykjRwJvvBF9f8QIO8Lzu4+kiIiIiIS6gLs7ciza2bNncfXVVyN79uyxCoccPXo0OY9PJMV88AHQrVt0gPbEE3a8FdCwS0Z1H38M/PUXMGmSHfGJiIiIiLgZpI0bNy7Qp4gEPSa7unePDtCefJLneoABGuXPDyxeDGzZYs98LSIiIiLiZpB26dIlrFixAs899xzKli0b6GuJBKXJk4EePaIDtD59gLFj/QzQtm0DrroKKFAgel3p0vYiIiIiIuL2mDR2bfyUE0eJpKMAzTOD1rdvAAHa2rVA7drAPfcAZ8+6fagiIiIiEiICLhzSqlUrLFiwwJ2jEUnhedCYQXP06we8/rqfAdpXXwH169uTVP/4I/Dcc24eqoiIiIiEkIDHpJUvXx7PP/88Vq1ahWrVqiFHjhxejz/JwTwiQY4JYc8y+6ya/9prfgZoLAzCWa4vX7bvN2gADB3q6vGKiIiISOgIOEibPHky8ubNi/Xr15vFU1hYmII0CXqcsuyBB4CIiOh50Dj3tF8B2oQJdgVHJ7pr29ae+TpLFlePWURERERCR8BB2o4dO9w5EpEUsGwZ0KYNi+BEzzP91lt+BGgMyl56CRgyJHpdz57AO+8AGTK4eswiIiIiEloCHpPmybIss4ikBStXAi1aABcu2PfbtQPef9+Piap5jg8Y4B2gDRoETJyoAE1EREREgiNImz59OipXroxs2bKZ5cYbb8SH7PIlEqR++glo1iy6CGPLlnYvRb9iLE6ixooijldeAUaNSsQkaiIiIiIiLnR3fP311808ab1798Ztt91m1q1cuRKPPvooDh8+jKdYgUEkiPz6K9CkCXDqlH2ft2fP5pQSfu6ARUJY0fTLL4F337Vr9ouIiIiIBEuQNn78eEyYMAGdeOF6RYsWLXDDDTdg+PDhCtIkqPz1F9C4MXDsmH2/Xj1g3rwA63wwmvvkE2D1aqBhQ7cOVUREREQkcd0d9+3bh9qcwDcGruNjbjl69CgefPBB5M6d21SX7NatG06fPp3g89asWYMGDRqYqQL43Lp16+LcuXOuHacEj//+swO0gwft+7feCixcCGTPnsATT54E/vnHe122bArQRERERCQ4g7RrrrkGnzCrEMPs2bPNHGpuYYC2efNmLFmyBIsWLcL333+Pnqyul0CA1rRpUzRu3Bjr1q3DTz/9ZLpphidYKULSOmbO2K3x33/t+5Ur270Vc+VK4IlHjwJ33mmn3Jwni4iIiIgEc3fHESNGoF27diZIcsakcWLrZcuW+QzeksOWLVuwePFiE2RVr149qttls2bN8Nprr6FYsWI+n8eul5y3bRAr8V1x3XXXuXKMEjxYHOSee4BNm+z7ZcoAixcD+fIl8ESm3Jh64yA2p/zjjz+qQIiIiIiIBHeQ1qZNG6xduxZjx47FAhZTAFCxYkWTqbrpppvcOEaTEWMXRydAo0aNGpmMGI+ldevWsZ5z8OBB8xgzcOyKuX37dlSoUAEvvvgibr/99jhf68KFC2ZxnGTXNwCRkZFmERvbgtMvBFubcP6z++4Lw6pVdmBVqJCFr7+2UKQIjzmeJ/73H8LuvBNhf/5p7lqFC8OaNMkuv58K00wEa/umF2pf96mN3aX2dZfa111qX3epfd2VUu0acJBG1apVw4wZM5BS9u/fj0KFCnmty5gxI6666irzmC//XBlTxGImzLZVrVrVTB3QsGFDbNq0Kc6umaNGjTLZwpgOHTqE8+fPJ8v7SS8n6IkTJ8yHQLB0H+XfzBNP5MFXX2Uz93PlisSMGUeRO/flqHFpvoTv3o2r7r8fGa90b4woVgxHP/kEETzn4ntiiLVveqL2dZ/a2F1qX3epfd2l9nWX2tddbNugDdKSC7shvvzyywl2dUxKlPvII4+ga9eu5jYzfeyWOWXKFBOM+TJ48GD069fPK5NWsmRJFCxY0BQekej2DQsLM+0SDB8ATHb16xeGefPsDFqWLBY++wy4446r4n/i338jrE0bhO3ebe+nbFmELVmC/GXLIjUFW/umN2pf96mN3aX2dZfa111qX3epfd2VOXNmBFWQxl8yf+Hx4eOXL1/2+8X79++PLl26xLtNuXLlUKRIEdN90RNfhxUf+ZgvRYsWNT+vv/56r/Xsmrlr1644Xy9Llixm8fX+daLH/n0HS7u8+CLw5pv2bR7OrFlhqF8/gbFk27bZBUKcqqTXXouwZcsQVqIEgkEwtW96pPZ1n9rYXWpfd6l93aX2dZfa1z0p1aZ+B2nz58+Pd8zYm2++GXAfTUb4XBJSq1YtHD9+HOvXrzddLWn58uXm9WrWrOnzOWXKlDEFRbZu3eq1ftu2bbjrrrsCOk4JbtOmAUOGRN/nULJWrRJ40qFD3gFapUrA0qVA4cKuHquIiIiISLIFaS1btoy1jgEQuyx+/vnnpkDH888/Dzcw+8VS+j169MDEiRNx6dIlU0q/ffv2UZUd9+7da8abcdxZjRo1zDcIAwcOxLBhw1ClShUzJm3atGn4888/MXfuXFeOU1Ie46ru3aPvjx4NPPywH08sUADghOzsblulir0jrhMRERERSWWJGpP233//meCHQU+TJk2wceNGVGImwkUzZ840gRkDMaYZWWWS2TsHAzcGjWdZf/2Kvn37mmIfLMXPrpEM1jjP2tVXX+3qsUrK+O034N572fXVvt+rF/D0034+mV13OS6RQf6DDwL587t5qCIiIiIi7gRprGby0ksvmTnKmJliEY46deogJbCS40cffRTn4+zeyCo2MTHT5zlPmqQPe/YAzZoBp07Z91u0AN54I4EpzVifP1Om6Pvc+MknXT9WEREREZFA+D3y7ZVXXjFFPBYtWoSPP/4Yq1evTrEATcQTp65r3pxdXO37NWoAH38MZMgQz5M4s3WFCsDKlSl1mCIiIiIi7mbSmI3Kli0brrnmGtPNkYsv8+bNS9yRiPg9WbXd1ZFYKf/zz4Hs2eN50u+/Aw0aAIcPAywas3w5cMstKXXIIiIiIiLuBGmdOnVKsAS/iJvYm/WRR4AlS+z7V10FfPUVEGOec2+bN0cHaFSxIhDHROYiIiIiImkqSJs6daq7RyKSgBdeAD74wL7NqewWLgSuuy6eJ3D6hYYNowM09ov85hsgT54UOV4RERERkcTQDHeSJrBmzLBh9m0mdGfMAG67LZ4nbN9uZ9AOHLDvV68OfP21AjQRERERCXoK0iTo/fij99xnr75qj0uL07//2gHaf//Z96tWtQO0vHldP1YRERERkaRSkCZBbdcuoFUr4MIF+36PHkC/fvE8YfduO0DjE4nz93EQGwewiYiIiIikAQrSJGidPm3Pf+b0WKxXD3jrrQTmQluzxs6kEUvuL10KFCiQIscrIiIiIpLik1mLpJTISOChh4Bff7XvX301MHcukDlzAk+8/37g4kVg5Ehg2TKgcOGUOFwRERERkWSjTJoEpSFDgAUL7Nus9bFoEZA/v59P7tjRnkitWDE3D1FERERExBUK0iTofPghMGqUfTs8HPjkE7vnok+nTgGLF8den2DKTUREREQkOClIk6CyejXQvXv0/XHjgMaN49j4/HmgZUugeXNg8uSUOkQREREREVcpSJOgwYKMrVvbQ8ro0UeB3r3j2PjSJXv82bff2gPYnn4aOHo0JQ9XRERERMQVCtIkKJw7B9x7L3DwoH2fVfTffDOOSo4REUDnzsDnn9v3c+QAvvhCZfZFREREJF1QkCapzrKAxx8H1q+375ctC8yZA2TKFMfGTK99/LF9P0sWYOFC4NZbU/SYRURERETcoiBNUt2ECcDUqfbt7Nntqo4+k2IM0AYNAiZOtO9nyGBXFWHaTUREREQknVCQJqlq5UqgT5/o+6z/ceONcWzMko+vvGLfZj/I6dPt2a5FRERERNIRBWmSavbuBe67D7h82b7fvz/Qvn0cG7/3HvC//3mn3zp0SJHjFBERERFJSQrSJFVcuGAHaAcO2PfZY3H06HiecM01QK5c9u2XXwYeeSRFjlNEREREJKVlTPFXFIHdxfHHH+3bpUoBs2YBGeM7GxnFffcd8OWXdrl9EREREZF0SkGapLj33wfefTe6OOO8eUDBgn488eab7UVEREREJB1Td0dJUT/9BPTq5T3UrFo1Hxvu2AG89ppd0VFEREREJIQokyYp5tgxoG1b4OJF+z6nO+vUyceGnNG6cWPg77+Bf/4Bxo+3y+2LiIiIiIQAZdIkRTAh1qULsHOnfb9WLWDMGB8bnjoFNG9uB2jEcWhcJyIiIiISIhSkSYp4/XVg4UL7dv78wOzZQObMMTZiiq1NG+Dnn+37JUoAX38N5M2b4scrIiIiIpJaFKSJ61avBp55Jvr+hx8CJUv6SLV17w4sWWLfz5fPDtBibSgiIiIikr4pSBNXHT4MtGsHRETY9wcPBu66y8eGw4bZ0RtlzQosWgRcf32KHquIiIiISDBQkCauiYwEHnoI2LPHvl+3LvD88z42nDwZeOEF+3ZYGPDRR0Dt2il6rCIiIiIiwUJBmrjm5ZeBxYvt25wH7eOPfUxY/c03wCOPRN8fNw5o3TpFj1NEREREJJgoSBNXrFgBDBninRwrVszHhmXLAmXK2Lefegp48skUPU4RERERkWCjedIk2R04ADzwgN3dkYYOBRo1imPj8uWBNWuAt96yx6WJiIiIiIQ4BWmSrBiYcT60ffvs+w0bAs89l8CT2BdyxIiUODwRERERkaCn7o6SrN58M3ocWuHCwMyZQIYMMeZCGzUKOH8+tQ5RRERERCSoKUiTZPPrr97zoU2bZgdqXnOh9ewJPPss0KQJcPRoahymiIiIiEhQU5AmyeLsWXscGhNl1K+fHYfFKvfIyI3WrQO2b0/x4xQRERERCXYK0iRZ9O8PbNli365aFXjppRgbzJtnz2TtmD4duOWWFD1GEREREZG0QEGaJNmCBcDEifbtbNns+dCyZPHYYP16oGPH6PsjRwJt26b4cYqIiIiIpAUK0iRJ9u4FunWLvv/GG0CFCh4b7NkD3HMPcO6cfZ/BGsekiYiIiIiITwrSJNEiIoDOncOi6n+0bg107+6xwenTdoDm1OO/7Tbg/fft2a1FRERERMQnBWmSaBMm5MC339oBV/HiwKRJHvEXJ0xj1mzjRvt+2bLA/Pkx+kGKiIiIiEhMCtIkUX7+mcUac5rbDMw+/BDInz9GucdTp+zbuXMDixbZk1aLiIiIiEi8FKRJwDi8rFOnMFy+bKfNBg0C6tePsVHOnPas1o8+CnzyCXD99alyrCIiIiIiaU3G1D4ASXtY92PrVjtAq17dwogRcYwxy5SJfSJT9uBERERERNK4NJNJO3r0KB588EHkzp0befPmRbdu3XCahSnisX//fjz00EMoUqQIcuTIgZtvvhmffvppih1zevTdd8C4cfbtLFksTJ1qmVjMYIGQw4dT8/BERERERNK8NBOkMUDbvHkzlixZgkWLFuH7779Hz549431Op06dsHXrVixcuBC///477r33Xtx///345ZdfUuy40xMOMevaNfr+oEGnULHilTvnzwOtWtkTVP/2W2odooiIiIhImpcmujtu2bIFixcvxk8//YTq1aubdePHj0ezZs3w2muvoVixYj6ft3r1akyYMAE1atQw94cMGYKxY8di/fr1uOmmm3w+58KFC2ZxnDx50vyMjIw0Syjr1y8M//5rd228/XYL3bufQWRkdsCyEPbIIwhbt848ZnXoAItVHcPTzHcAQYfnmmVZIX/OuUXt6z61sbvUvu5S+7pL7esuta+7Uqpd00SQtmbNGtPF0QnQqFGjRggPD8fatWvRmhN0+VC7dm3Mnj0bzZs3N8//5JNPcP78edSrVy/O1xo1ahRGjBgRa/2hQ4fMc0PVsmWZ8f77V5nb2bNH4pVXDuHUqeMID7eQc9Ik5J4+3TwWmS0bjo4bh8vq9pjkD4ATJ06YD1me55K81L7uUxu7S+3rLrWvu9S+7lL7uottmxLSRJDGsWWFChXyWpcxY0ZcddVV5rG4MChr164d8ufPb7bPnj075s+fj2uuuSbO5wwePBj9+vXzyqSVLFkSBQsWNOPhQhEnqx44MLo4yGuvsVdjfhw6FIlCv/6K8Oefj974gw9wVYMGqXOg6ewDNiwszJx3+oBNfmpf96mN3aX2dZfa111qX3epfd2VOXNmpPsgbdCgQXj55ZcT7OqYWM899xyOHz+OpUuXokCBAliwYIEZk/bDDz+gcuXKPp+TJUsWs8TEkzxUT/Q+feyaINSkCavqh7OHIzL++y/CO3RAmJP2HTIE4e3apeqxpif8gA3l885tal/3qY3dpfZ1l9rXXWpfd6l93ZNSbZqqQVr//v3RpUuXeLcpV66cqc548OBBr/WXL182FR/5mC/bt2/HW2+9hU2bNuGGG24w66pUqWICtLfffhsTJ05MxneSfs2dC3z0kX07b15g8mR78mrrxEnk7dIFYceO2Q/ecw/go5uoiIiIiIikoSCNaVguCalVq5bJiLHgR7Vq1cy65cuXm3RuzZo1fT7n7NmzPqPdDBkyaCClnw4csOeidowfDxQvzsogFsK6dkWmbdvsBzhR9YwZKhQiIiIiIpIM0sRVdcWKFdG0aVP06NED69atw6pVq9C7d2+0b98+qrLj3r17UaFCBfM48TbHnj3yyCNmHTNrY8aMMSX8W7FUvMSL3Rkfeww4csS+f++9nAbhyoMLFyJswQJ7O6bXPvsMCNHxeiIiIiIiIRmk0cyZM03g1bBhQ1N6//bbb8d7770X9filS5fMnGhOBi1Tpkz48ssvTabunnvuwY033ojp06dj2rRp5vkSP875PX++fZvJzgkT7G6ORosWiHzjDViZM8P68EMgnkIsIiIiIiKSDqs7Eis5fuQMjvKhTJkyptSop/Lly+NTRhsScDXH3r29uzl6FddktNa7Nw7dfjsKVK2aGocoIiIiIpJupZlMmqScAQPs8WjUogVw//2+t4uMYxJxERERERFJPAVp4mXpUjPVmcFhZu+8c6Wb48CBwFdfpfbhiYiIiIikewrSJMqZM0DPntH3X3nlSjVH1t3nDNbNmwOjR6fmIYqIiIiIpHsK0iTK0KHAjh327bp1gR49AKxfD/TqZa/kmD8TtYmIiIiIiFsUpInx00/AuHH27SxZgEmTgPBjR4A2bYALF+wHGKw99FCqHqeIiIiISHqXZqo7insuXgS6dQOcOb6HDweuvSYSaN4R2LnTXnnrrcDrr6fqcYqIiIiIhAJl0sSMPfv9d/s2K+r37w977NnixdETpc2ZA2TOnKrHKSIiIiISChSkhbgtW4AXXrBvZ8hg1wjJtHoF8Nxz9kqWdvz4Y6BEiVQ9ThERERGRUKHujiGM3RtZzZHdHZ350W4ufgCo2j667+OwYUDDhql6nCIiIiIioUSZtBA2bRqwcqV9+5pr7HjMpNbOnrVXNmoEDBmSqscoIiIiIhJqFKSFqCNH7PmpHRMmANmyAahXzy6736wZMGOG3QdSRERERERSjLo7hqjBg+1Ajdq3t5NmUZhW++KL1Do0EREREZGQpkxaCPrxR3seNMqVCxgz8lxqH5KIiIiIiFyhIC3EXL4MPPZY9P2RIyJQrEdzoGvX6LFoIiIiIiKSatTdMcS8/TawcWP0nGi9jz4PfPutveK//4Cvv07V4xMRERERCXUK0kIIYzBn+jOa0WMFwntfmSQtPFyVHEVEREREgoC6O4aQ/v2BU6fs230fOoIbRnUELMte8fzzQJ06qXp8IiIiIiKiIC1kLF0KzJpl385/lYWXj3QH9uyxV9SvDwwalKrHJyIiIiIiNgVpIeDCBaBXr+j7nzV/F5m/XGDfyZ8f+PBDzYcmIiIiIhIkFKSFgFdfBbZts293rLoJtec8Ff3glClA8eKpdmwiIiIiIuJNQVo6t2sX8OKL9u0c4efw3ukHEHb+vL2C6bUWLVL1+ERERERExJuCtHTu6acBJybr2+MMspUubN+pXNlOsYmIiIiISFBRCf507IcfgNmz7dsFCwIDRhcAcn8DvPYa0Lw5kC1bah+iiIiIiIjEoCAtnYqIAPr0ib4/ciSQNy9vhdvpNRERERERCUrq7phOTZ0K/PILEIZI1Kp0Ct26pfYRiYiIiIiIPxSkpUMnTwLPPmvffgpjsfxQZWRY9X1qH5aIiIiIiPhBQVo6xK6NBw8ClfA7Roc/i6wHdgINGgA7dqT2oYmIiIiISAIUpKUzf/0FjBsHZMYFzAzriEyRF+0H+vYFypZN7cMTEREREZEEqHBIOjNgAHDpEjAaQ3Gj9Zu9slIlO70mIiIiIiJBT5m0dGTJEmDhQqAOvsdAXJkDLVMmYMYMIGvW1D48ERERERHxg4K0dOLyZbtHYy6cxHR0Qjgs+wFm0KpUSe3DExERERERP6m7YzoxcSLwxx/AFPRBGey0V9apA/Tvn9qHJiIiIiIiAVAmLR04dgwYNgxojXnoiqn2yly5gOnTgQwZUvvwREREREQkAArS0oFRo4CjR4E8OIELGbLZK998EyhTJrUPTUREREREAqQgLY3btcuOx+jjLF1x5JsN9kzWnTun9qGJiIiIiEgiaExaGjd0KHDhgn37ySeBYg0qAA1eTO3DEhERERGRRFImLQ379VdgxrQIcztfPmDw4NQ+IhERERERSSpl0tKwQc9YWIh7sAUVkXHgC8iXL3tqH5KIiIiIiCSRgrQ0avlyoPjXk9EMX5kl4utfgMHLU/uwREREREQkiRSkpUGRkcCYvrvwMfpFrcvw9IBUPSYREREREUkeGpOWBn0y28KTv/dAbpwy963OXYBmzVL7sEREREREJBkoSEtjWMlx45OT0QTfmPvnCxRH2LixqX1YIiIiIiISakHaiy++iNq1ayN79uzImzevX8+xLAtDhw5F0aJFkS1bNjRq1Ah//fUX0rKZo3bh2cPR3RyzTJsE+NkeIiIiIiIS/NJMkHbx4kW0bdsWjz32mN/PeeWVV/Dmm29i4sSJWLt2LXLkyIEmTZrg/PnzSItOHLdQ9qXuUd0cD9/TFWHN7krtwxIRERERkVAsHDJixAjzc+rUqX5n0caNG4chQ4agZcuWZt306dNRuHBhLFiwAO3bt0das7zD+2h9aYm5fSRbcRSY/npqH5KIiIiIiIRqkBaoHTt2YP/+/aaLoyNPnjyoWbMm1qxZE2eQduHCBbM4Tp48aX5GRkaaJbXs3WMhz9ezo+5ffOtdRObObZd6TAVsCwbCqdkm6Zna111qX/epjd2l9nWX2tddal93qX3dlVLtmm6DNAZoxMyZJ953HvNl1KhRUVk7T4cOHUrVbpIvjcqFSZGLMQCvoWnF7ajQrBoOHjyYqifoiRMnzIdAeHia6TWbZqh93aX2dZ/a2F1qX3epfd2l9nWX2tddbNt0H6QNGjQIL7/8crzbbNmyBRUqVEixYxo8eDD69evnlUkrWbIkChYsiNzMXKWS114DypQBJkx4Bv2+s1CgAFL9AyAsLMy0iz4Akp/a111qX/epjd2l9nWX2tddal93qX3dlTlzZqT7IK1///7o0qVLvNuUK1cuUfsuUqSI+XngwAFT3dHB+1WrVo3zeVmyZDFLTDzJU/NEz5EDeOYZthmQMWMYggE/AFK7XdIzta+71L7uUxu7S+3rLrWvu9S+7lL7uiel2jRVgzRG+FzcULZsWROoLVu2LCooY1aMVR4DqRAZbDKm2w6qIiIiIiJCaSa83rVrFzZu3Gh+RkREmNtcTp8+HbUNu0XOnz8/6huEvn37YuTIkVi4cCF+//13dOrUCcWKFUOrVq1S8Z2IiIiIiIjELc3kZTgp9bRp06Lu33TTTebnt99+i3r16pnbW7du9RrM9/TTT+PMmTPo2bMnjh8/jttvvx2LFy9G1qxZU+EdiIiIiIiIpKMgjfOjJTRHGqvYeGI27fnnnzeLiIiIiIhIWpBmujuKiIiIiIiEAgVpIiIiIiIiQURBmoiIiIiISBBRkCYiIiIiIhJEFKSJiIiIiIgEEQVpIiIiIiIiQURBmoiIiIiISBBRkCYiIiIiIhJEFKSJiIiIiIgEEQVpIiIiIiIiQSRjah9AsLMsy/w8efJkah9KUImMjMSpU6eQNWtWhIcr1k9ual93qX3dpzZ2l9rXXWpfd6l93aX2dZcTEzgxglsUpCWAJzmVLFkytQ9FRERERESCwJEjR5AnTx7X9h9muR0GpoNvI/777z/kypULYWFhqX04QfUtAgPX3bt3I3fu3Kl9OOmO2tddal/3qY3dpfZ1l9rXXWpfd6l93XXixAmUKlUKx44dQ968eV17HWXSEsA0cYkSJVL7MIIW//j1AeAeta+71L7uUxu7S+3rLrWvu9S+7lL7usvtrqTqqCoiIiIiIhJEFKSJiIiIiIgEEQVpkihZsmTBsGHDzE9Jfmpfd6l93ac2dpfa111qX3epfd2l9k0f7avCISIiIiIiIkFEmTQREREREZEgoiBNREREREQkiChIExERERERCSIK0kRERERERIKIgjSJ8vbbb6NMmTLImjUratasiXXr1vn1vFmzZiEsLAytWrXyWs+aNEOHDkXRokWRLVs2NGrUCH/99RdCVXK3b5cuXcx6z6Vp06YIVYG079SpU2O1HZ/nSeevu+2r8zdpnw/Hjx9Hr169zPnJCmPXXnstvvzyyyTtMz1L7vYdPnx4rPO3QoUKCFWBtG+9evVitR2X5s2bR22jz19321efv0n7fBg3bhyuu+46c26WLFkSTz31FM6fP5+kffrE6o4is2bNsjJnzmxNmTLF2rx5s9WjRw8rb9681oEDB+J93o4dO6zixYtbderUsVq2bOn12OjRo608efJYCxYssH799VerRYsWVtmyZa1z585ZocaN9u3cubPVtGlTa9++fVHL0aNHrVAUaPt+8MEHVu7cub3abv/+/V7b6Px1t311/ia+fS9cuGBVr17datasmbVy5UrzOfHdd99ZGzduTPQ+0zM32nfYsGHWDTfc4HX+Hjp0yApFgbbvkSNHvNpt06ZNVoYMGcznhkOfv+62rz5/E9++M2fOtLJkyWJ+8rPh66+/tooWLWo99dRTid5nXBSkiVGjRg2rV69eUfcjIiKsYsWKWaNGjYrzOZcvX7Zq165tvf/+++YP3jOIiIyMtIoUKWK9+uqrUeuOHz9uTuyPP/7YCjXJ3b7ka12oCrR9+Z8VLwDiovPX3fYlnb+Jb98JEyZY5cqVsy5evJhs+0zP3GhfBmlVqlRx5XjTmqSea2PHjrVy5cplnT592tzX56+77Uv6/E18+3LbBg0aeK3r16+fddtttyV6n3FRd0fBxYsXsX79etOdwBEeHm7ur1mzJs7nPf/88yhUqBC6desW67EdO3Zg//79XvvMkyePSfnGt8/0yI32dXz33XdmG6bdH3vsMRw5cgShJrHte/r0aZQuXdp0VWjZsiU2b94c9ZjOX3fb16HzN3Htu3DhQtSqVct0xytcuDAqVaqEl156CREREYneZ3rlRvs62P2uWLFiKFeuHB588EHs2rULoSY5zrXJkyejffv2yJEjh7mvz19329ehz18kqn1r165tnuN0X/znn39MV+hmzZolep9xUZAmOHz4sPnPh/8ZeeJ9flD6snLlSvOHP2nSJJ+PO88LZJ/plRvtS+w/Pn36dCxbtgwvv/wyVqxYgbvuuivWhUR6l5j25X9KU6ZMwWeffYYZM2YgMjLSfPDu2bPHPK7z1932JZ2/iW9fXhTMnTvXPI8XB8899xzGjBmDkSNHJnqf6ZUb7UsMGDj2cvHixZgwYYIJLOrUqYNTp04hlCT1XOOF7qZNm9C9e/eodfr8dbd9SZ+/iW/fDh06mC/Rb7/9dmTKlAlXX321GQf47LPPJnqfcckY0NYigPlP6KGHHjIBRIECBVL7cEK2ffnNmKNy5cq48cYbzYcFvx1r2LBhCh1t2sRvybk4GEBUrFgR7777Ll544YVUPbZQaV+dv4nHoJffgL/33nvIkCEDqlWrhr179+LVV1/FsGHDUvvwQqJ9eUHr4LnLoI2Z408++STe3g/ijV9G8u+/Ro0aqX0oIdW++vxNPLYRM+vvvPOO+bv/+++/0adPH/N/G7/QSU4K0sQEAvyP6MCBA17reb9IkSKxtt++fTv+/fdf3HPPPV7/qVHGjBmxdevWqOdxH6zO5LnPqlWrIpS40b78MI2JXW74WvzACKUP2UDb1xd+G3bTTTeZtiOdv+62ry86f/1vX56TbFM+z8EgmN/SsqtNcvzO0gs32jdz5syxnpM3b15TATK+czw9Ssq5dubMGVO9mFkJT/r8dbd9fdHn7wG/25eBGL9Id7KTDHLZ1j179sT//ve/ZP38VXdHMf/h8JtCpr09gwLe9/w23MEyw7///js2btwYtbRo0QL169c3tzkGpWzZsuZk9NznyZMnsXbtWp/7TM/caF9f2JWMfco9/1MLBYG2ry/smsA2d9pO56+77euLzl//2/e2224zF1POlze0bds203bcX3L8ztILN9o3rjGY/IJN56//59qcOXNw4cIFdOzY0Wu9Pn/dbV9f9Pm7zO/2PXv2rBlj5sn5QocFGZP18zegMiOSbrFcKCsnTZ061frjjz+snj17mnKhTtnshx56yBo0aFCcz/dVKYgldLmPzz77zPrtt9/M46FcQjc52/fUqVPWgAEDrDVr1pgSsEuXLrVuvvlmq3z58tb58+etUBNo+44YMcKUzd2+fbu1fv16q3379lbWrFlNqVyHzl/32lfnb9Lad9euXaZaW+/eva2tW7daixYtsgoVKmSNHDnS732GEjfat3///qYsP8/fVatWWY0aNbIKFChgHTx40Ao1if3/7fbbb7fatWvnc5/6/HWvffX5m7T2ZWVXfj6w0ug///xjffPNN9bVV19t3X///X7v018K0iTK+PHjrVKlSpm5HVg+9Mcff4x67I477jCBQiBBGsvoPvfcc1bhwoXNydqwYUPzH16oSs72PXv2rNW4cWOrYMGCVqZMmazSpUubeThC8QIsMe3bt2/fqG15fnI+pA0bNnjtT+eve+2r8zfpnw+rV6+2atasac5Nlot/8cUXzbQd/u4z1CR3+/Lil3MjcX+cy5L3//77bytUBdq+f/75p8U8AS9wfdHnr3vtq8/fpLXvpUuXrOHDh5vAjF8+lixZ0nr88cetY8eO+b1Pf4Xxn8BybyIiIiIiIuIWjUkTEREREREJIgrSREREREREgoiCNBERERERkSCiIE1ERERERCSIKEgTEREREREJIgrSREREREREgoiCNBERERERkSCiIE1ERERERCSIKEgTEREREREJIgrSREREREREgoiCNBERSZfq1auHvn37Iq0fz5EjR1CoUCH8+++/cFv79u0xZswY119HRETipyBNREQSbeLEiciVKxcuX74cte706dPIlCmTCUo8fffddwgLC8P27duRniV3cPjiiy+iZcuWKFOmDNw2ZMgQ83onTpxw/bVERCRuCtJERCTR6tevb4Kyn3/+OWrdDz/8gCJFimDt2rU4f/581Ppvv/0WpUqVwtVXX51KR5v2nD17FpMnT0a3bt1S5PUqVapkfj8zZsxIkdcTERHfFKSJiEiiXXfddShatKjJkjl4m5mfsmXL4scff/Raz6COFi9ejNtvvx158+ZF/vz5cffdd3tl2N577z0UK1YMkZGRXq/H/T788MPmNh8bNWqUeZ1s2bKhSpUqmDt3bpzH6s/2zII9+eSTePrpp3HVVVeZYHP48OFe25w6dQoPPvggcuTIYd772LFjo7JnXbp0wYoVK/DGG2+YrCEXz26KPIb49h3Tl19+iSxZsuDWW2/1Wr9y5UqTrfQMgvk6fL2dO3dG3f70009Rt25d835vueUW7Nq1ywTR3F/27NnRsGFDHD9+3Gvf99xzD2bNmhXvcYmIiLsUpImISJIw8GKWzMHbDFruuOOOqPXnzp0zmTUnSDtz5gz69etnMnDLli1DeHg4WrduHRWUtW3b1ozF8tzv0aNHTXDHAIkYcE2fPt10udy8eTOeeuopdOzY0QRJvvi7/bRp00wAxuN95ZVX8Pzzz2PJkiVRj/O4V61ahYULF5r1DHo2bNhgHmNwVqtWLfTo0QP79u0zS8mSJf3ed0zcd7Vq1WKt37hxIypWrIisWbNGrfvll1+QL18+lC5dGr/++qtZN2HCBLz00ktYvXo1Dhw4YN7v6NGj8dZbb5m25XYffPCB175r1KiBdevW4cKFC3Eel4iIuCujy/sXEZF0joEXs0gcl8ZgjMECA7RLly6ZgIjWrFljLvqdIK1NmzZe+5gyZQoKFiyIP/74w3S5Y7Bx11134aOPPjLZHmLWq0CBAmYf3BeDj6VLl5qgiMqVK2cyTO+++655fU+BbH/jjTdi2LBh5nb58uVNQMNA8s477zRZNAZansfFIIdZP8qTJw8yZ85sslTMlMUU3759YVbM2bcnBlc33XRTrMCN2UHnNrN1s2fPNplK4nvk+2WAyuMjZtf279/vtR++3sWLF816BnwiIpLylEkTEZEkYdaMmbGffvrJZH6uvfZaE3AxKHDGpbGrI4Mijkmjv/76Cw888IBZlzt37qiiGOyO52DGjN31nIzOzJkzTfVBZt3+/vtvM16LwU3OnDmjFmbKfBUmCWR7BlKe2KXx4MGD5vY///xjgk9mmxwMzNjt0x/x7dsXBr2e2TIHg7CqVat6rWNw7KxjEMfMpBOgOW3brl27qADNWcfun57YNZLYXiIikjqUSRMRkSS55pprUKJECdN97tixY1FZKWZk2NWPXe34WIMGDbzGPTFLM2nSpKixZ8ygMYPjuY1lWfjiiy9MxocBIMd/EYuVEB8rXry41/FwDFdMgWzPsV6eOLYr5ti4xAp038wcsk09RUREYNOmTbEyaexy6WQoGcQNHjzY63EGbuzi6WDwvHXr1qjsm2e3UmKgLSIiqUNBmoiIJBm7IDJbxoBi4MCBUetZtOKrr74yY5wee+wxs45jzRgcMECrU6eOWcdueDExg3TvvfeaDBozYcxW3Xzzzeax66+/3gRXzATF7NroS6Dbx4WZPwZazBo6WUGWq9+2bZt5r8TujgykkgMDsZiVFtl2DLA8u0GyO+nevXtNJu3kyZOmcIhnELdjxw5znJ7rfv/9dxMEV65c2Wv/DAAZdDNAFBGR1KEgTUREkiVI69Wrl+kK6BkE8Xbv3r1NhswZj8bxZuyGxwqO7O7HwGnQoEE+98suj6z8yHFULHrh4NxsAwYMMJkhZqJYKZJBCAt6sPtk586dvfYT6PZx4X64LQNRjvniJNMcY8YumMyKEbtuspsnAyV2qeR2fDwxmjRpYjJiDH7Zbk6WjMaPH28qUTKA5U9iOzNjliFDBpOZdDhj1DzHmHEdy+3zGD0xY9m4ceNEHa+IiCQPjUkTEZEkYwDG8VPs+li4cGGvII3FNpxS/cSAhSXe169fbwIJBk6vvvqqz/2yiySDC2aPOnTo4PXYCy+8gOeee85UbWSlw6ZNm5rujDHHWCV2+7i8/vrrpvgIg8dGjRrhtttu86q0yGCQQRKzd+wy6DnOLlDMcjF7+Mknn3gFVwzeOD6Oj//vf//DiBEjTLD55ptvmiCN7e05ls1XoRGui9nVkRm6BQsWmOqUIiKSesIs9nUQERGRRGHRFI5zGzNmjCuTTjOQZOaO3RAZ4DJA4xi9kSNHJvtrsWT//Pnz8c033yT7vkVExH/q7igiIhIAVlH8888/TYVHdpnkXGfORNtuaN68uamGyTFnLMTCDJgzoXdy43g7dqMUEZHUpUyaiIhIgEFa9+7dTRdMFgnhZNPsAhmzAIcbOHcZu41yjB67U4qISPqkIE1ERERERCSIqHCIiIiIiIhIEFGQJiIiIiIiEkQUpImIiIiIiAQRBWkiIiIiIiJBREGaiIiIiIhIEFGQJiIiIiIiEkQUpImIiIiIiAQRBWkiIiIiIiJBREGaiIiIiIhIEFGQJiIiIiIiguDxf2aawbSeee0BAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- Visualization: Buchdahl vs. Data ---\n",
    "w_vec = buchdahl_coordinate(WAVE_GRID)\n",
    "\n",
    "# Align signs for visual comparison (PCA sign is arbitrary)\n",
    "pc1 = pca.components_[0]\n",
    "if np.corrcoef(pc1, w_vec)[0, 1] < 0:\n",
    "    pc1 = -pc1\n",
    "\n",
    "# Normalize for plotting shape comparison\n",
    "pc1_norm = pc1 / np.max(np.abs(pc1))\n",
    "w_vec_norm = w_vec / np.max(np.abs(w_vec))\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(WAVE_GRID, pc1_norm, 'b-', linewidth=2, label='PCA Component 1 (Data)')\n",
    "plt.plot(WAVE_GRID, w_vec_norm, 'r--', linewidth=2, label=r'Buchdahl $\\omega$ (Theory)')\n",
    "plt.title(\"Basis Validation: Empirical Data vs. Analytical Model\")\n",
    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
    "plt.ylabel(\"Normalized Amplitude\")\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d599b441",
   "metadata": {},
   "source": [
    "## 3. Analyzing the Coefficients\n",
    "Having validated that the Buchdahl coordinate is a suitable proxy for the primary dispersion vector, we adopt the 3-term Buchdahl model:\n",
    "$$n(\\omega) = n_d + v_1 \\omega + v_2 \\omega^2 + v_3 \\omega^3$$\n",
    "\n",
    "We fit this equation to every glass in the dataset to extract the \"True\" coefficients. This allows us to inspect how these coefficients relate to the input parameters ($n_d, V_d$).\n",
    "\n",
    "### The \"Partial Dispersion\" Problem\n",
    "Standard 2-parameter models typically assume:\n",
    "1.  $v_1$ is linear with $1/V_d$.\n",
    "2.  $v_2$ is linear with $1/V_d$.\n",
    "3.  $v_3$ is zero.\n",
    "\n",
    "We visualize the calculated $v_3$ term on the Abbe diagram. A non-random pattern would indicate that $v_3$ is not noise, but a systematic property of the glass chemistry that we can predict."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "70558d32",
   "metadata": {},
   "outputs": [],
   "source": [
    "def buchdahl_3term(w, v1, v2, v3, nd_fixed):\n",
    "    \"\"\"\n",
    "    3-term Buchdahl model with fixed intercept.\n",
    "    \"\"\"\n",
    "    return nd_fixed + v1 * w + v2 * (w**2) + v3 * (w**3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5e343a9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting coefficients for all glasses...\n"
     ]
    }
   ],
   "source": [
    "# --- Extract Coefficients ---\n",
    "results = []\n",
    "w_grid_calc = buchdahl_coordinate(WAVE_GRID)\n",
    "\n",
    "print(\"Extracting coefficients for all glasses...\")\n",
    "\n",
    "for idx, row in df_glass.iterrows():\n",
    "    # Define objective function for this specific glass\n",
    "    nd_glass = row['nd']\n",
    "    \n",
    "    def objective(w, v1, v2, v3):\n",
    "        return buchdahl_3term(w, v1, v2, v3, nd_glass)\n",
    "    \n",
    "    try:\n",
    "        # Initial guess [0,0,0] is important for convergence stability\n",
    "        popt, _ = curve_fit(objective, w_grid_calc, row['curve'], p0=[0, 0, 0])\n",
    "        \n",
    "        results.append({\n",
    "            'nd': row['nd'],\n",
    "            'Vd': row['Vd'],\n",
    "            'v1': popt[0],\n",
    "            'v2': popt[1],\n",
    "            'v3': popt[2]\n",
    "        })\n",
    "    except Exception:\n",
    "        continue\n",
    "\n",
    "df_coeffs = pd.DataFrame(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "96e81c70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- Visualization ---\n",
    "fig, axes = plt.subplots(1, 3, figsize=(16, 5))\n",
    "\n",
    "# Plot 1: v1 vs 1/Vd (Check Linearity)\n",
    "axes[0].scatter(1.0/df_coeffs['Vd'], df_coeffs['v1'], s=5, alpha=0.4, c='blue')\n",
    "axes[0].set_title(\"$v_1$ (Slope) vs Inverse Abbe\")\n",
    "axes[0].set_xlabel(\"$1/V_d$\")\n",
    "axes[0].set_ylabel(\"$v_1$\")\n",
    "axes[0].grid(True, alpha=0.3)\n",
    "\n",
    "# Plot 2: v2 vs 1/Vd (Check Curvature)\n",
    "axes[1].scatter(1.0/df_coeffs['Vd'], df_coeffs['v2'], s=5, alpha=0.4, c='green')\n",
    "axes[1].set_title(\"$v_2$ (Curvature) vs Inverse Abbe\")\n",
    "axes[1].set_xlabel(\"$1/V_d$\")\n",
    "axes[1].grid(True, alpha=0.3)\n",
    "\n",
    "# Plot 3: v3 Map (Check for Structure)\n",
    "# We plot v3 color-coded on the Glass Map\n",
    "sc = axes[2].scatter(\n",
    "    df_coeffs['Vd'], \n",
    "    df_coeffs['nd'], \n",
    "    c=df_coeffs['v3'], \n",
    "    cmap='coolwarm', \n",
    "    s=20, \n",
    "    edgecolors='k', \n",
    "    linewidth=0.2,\n",
    "    vmin=-0.010, vmax=0.025\n",
    ")\n",
    "cbar = plt.colorbar(sc, ax=axes[2])\n",
    "cbar.set_label(\"Value of $v_3$ Term\")\n",
    "axes[2].invert_xaxis()\n",
    "axes[2].set_title(\"The High-Order Correction Map ($v_3$)\")\n",
    "axes[2].set_xlabel(\"$V_d$\")\n",
    "axes[2].set_ylabel(\"$n_d$\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecd91021",
   "metadata": {},
   "source": [
    "## 4. Model Discovery via LASSO Regression\n",
    "The visualization above reveals that $v_3$ is non-zero and structured. Additionally, $v_2$ exhibits a slight parabolic relationship with $1/V_d$.\n",
    "\n",
    "To capture these behaviors without overfitting, we employ **LASSO Regression** (Least Absolute Shrinkage and Selection Operator). We construct a feature matrix containing potential physical terms (e.g., $1/V_d^2$, $n_d/V_d$) and allow LASSO to select only the terms that provide a statistically significant improvement to the fit.\n",
    "\n",
    "This results in a sparse set of equations that relate the inputs ($n_d, V_d$) to the Buchdahl coefficients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ebbc906c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- Feature Engineering ---\n",
    "# Construct candidate features based on physical intuition\n",
    "df_train = df_coeffs.dropna().copy()\n",
    "X = pd.DataFrame()\n",
    "\n",
    "# Basic Dispersion Terms\n",
    "X['inv_V'] = 1.0 / df_train['Vd']\n",
    "X['inv_V2'] = (1.0 / df_train['Vd']) ** 2\n",
    "\n",
    "# Index Interaction Terms (suggested by the v3 map structure)\n",
    "X['nd'] = df_train['nd']\n",
    "X['nd_sq'] = df_train['nd'] ** 2\n",
    "X['nd_div_V'] = df_train['nd'] / df_train['Vd']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e58a402e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- LASSO Model Discovery ---\n",
      "\n",
      "Target: v1 (R^2 = 1.0000)\n",
      "Intercept: 0.004160\n",
      " + 4.462559 * (inv_V)\n",
      " + 2.326660 * (inv_V2)\n",
      " + 0.002330 * (nd)\n",
      " + -0.003697 * (nd_sq)\n",
      " + -4.697604 * (nd_div_V)\n",
      "\n",
      "Target: v2 (R^2 = 0.9896)\n",
      "Intercept: 0.066434\n",
      " + -7.636396 * (inv_V)\n",
      " + 12.597434 * (inv_V2)\n",
      " + -0.037014 * (nd_sq)\n",
      " + 5.551013 * (nd_div_V)\n",
      "\n",
      "Target: v3 (R^2 = 0.5213)\n",
      "Intercept: -0.032218\n",
      " + 2.230357 * (inv_V)\n",
      " + -103.318994 * (inv_V2)\n",
      " + -0.009654 * (nd_sq)\n",
      " + 1.934983 * (nd_div_V)\n"
     ]
    }
   ],
   "source": [
    "discovered_equations = {}\n",
    "\n",
    "print(\"--- LASSO Model Discovery ---\")\n",
    "\n",
    "targets = ['v1', 'v2', 'v3']\n",
    "\n",
    "for target in targets:\n",
    "    y = df_train[target].values\n",
    "    \n",
    "    # 1. Feature Selection via LASSO (BIC criterion)\n",
    "    # We use a pipeline with StandardScaler to ensure fair regularization\n",
    "    model = make_pipeline(StandardScaler(), LassoLarsIC(criterion='bic', max_iter=5000))\n",
    "    model.fit(X, y)\n",
    "    \n",
    "    # Identify selected features\n",
    "    lasso = model.named_steps['lassolarsic']\n",
    "    active_mask = lasso.coef_ != 0\n",
    "    selected_features = X.columns[active_mask].tolist()\n",
    "    \n",
    "    # 2. Final Fit (OLS)\n",
    "    # We refit using simple Linear Regression on only the selected features\n",
    "    # to obtain un-regularized physical coefficients.\n",
    "    final_model = LinearRegression()\n",
    "    final_model.fit(X[selected_features], y)\n",
    "    score = final_model.score(X[selected_features], y)\n",
    "    \n",
    "    # Store results\n",
    "    discovered_equations[target] = {\n",
    "        'intercept': final_model.intercept_,\n",
    "        'coeffs': dict(zip(selected_features, final_model.coef_))\n",
    "    }\n",
    "    \n",
    "    # Display Result\n",
    "    print(f\"\\nTarget: {target} (R^2 = {score:.4f})\")\n",
    "    print(f\"Intercept: {final_model.intercept_:.6f}\")\n",
    "    for name, val in zip(selected_features, final_model.coef_):\n",
    "        print(f\" + {val:.6f} * ({name})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49f23afa",
   "metadata": {},
   "source": [
    "## 5. Model Validation and Error Analysis\n",
    "\n",
    "To quantify the predictive fidelity of our derived model, we evaluate the reconstruction error across the full dataset of standard optical glasses.\n",
    "\n",
    "**Methodology:**\n",
    "We test the model's **generative capacity** by reconstructing the refractive index of each glass in the catalog using *only* its $n_d$ and $V_d$ parameters. This simulates the real-world usage of the data-based model implementation.\n",
    "1.  **Input:** Extract $n_d$ and $V_d$ for every glass.\n",
    "2.  **Prediction:** Calculate the Buchdahl coefficients ($v_1, v_2, v_3$) using the regression weights derived in Section 4.\n",
    "3.  **Reconstruction:** Generate the full spectral curve $n(\\lambda)$.\n",
    "4.  **Validation:** Compare against the reference catalog measurements.\n",
    "\n",
    "**Metric:**\n",
    "We track the **Root Mean Square Error (RMSE)**:\n",
    "\n",
    "$$RMSE = \\sqrt{\\frac{1}{N_\\lambda} \\sum (n_{pred}(\\lambda) - n_{measured}(\\lambda))^2}$$\n",
    "\n",
    "We compare this against a **Baseline Linear Model**, which assumes a simple linear dispersion slope between the F and C lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e5c50a55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validating model against 1678 catalog glasses...\n",
      "Validation Loop Complete.\n"
     ]
    }
   ],
   "source": [
    "# --- 1. Helper Function: The Optimized Model Logic ---\n",
    "def predict_abbe_index(nd, Vd, wave_grid):\n",
    "    \"\"\"\n",
    "    Reconstructs the refractive index curve using the LASSO-derived coefficients \n",
    "    from Section 4. This replicates the logic that will go into the new AbbeMaterial class.\n",
    "    \"\"\"\n",
    "    # Buchdahl Constants\n",
    "    WAVE_D = 0.5875618\n",
    "    ALPHA = 2.5\n",
    "    \n",
    "    # Feature Engineering (Must match the regression input exactly)\n",
    "    inv_V = 1.0 / Vd\n",
    "    inv_V2 = 1.0 / (Vd**2)\n",
    "    nd_sq = nd**2\n",
    "    nd_div_V = nd / Vd\n",
    "    \n",
    "    # --- COEFFICIENTS FROM SECTION 4 LASSO ANALYSIS ---\n",
    "    \n",
    "    # Target: v1\n",
    "    v1 = (0.004160 \n",
    "          + 4.462559 * inv_V \n",
    "          + 2.326660 * inv_V2 \n",
    "          + 0.002330 * nd \n",
    "          - 0.003697 * nd_sq \n",
    "          - 4.697604 * nd_div_V)\n",
    "          \n",
    "    # Target: v2\n",
    "    v2 = (0.066434 \n",
    "          - 7.636396 * inv_V \n",
    "          + 12.597434 * inv_V2 \n",
    "          - 0.037014 * nd_sq \n",
    "          + 5.551013 * nd_div_V)\n",
    "          \n",
    "    # Target: v3\n",
    "    v3 = (-0.032218 \n",
    "          + 2.230357 * inv_V \n",
    "          - 103.318994 * inv_V2 \n",
    "          - 0.009654 * nd_sq \n",
    "          + 1.934983 * nd_div_V)\n",
    "    \n",
    "    # --- Spectral Reconstruction ---\n",
    "    # Calculate Buchdahl Coordinate omega\n",
    "    omega = (wave_grid - WAVE_D) / (1 + ALPHA * (wave_grid - WAVE_D))\n",
    "    \n",
    "    # Buchdahl Polynomial: n = nd + sum(vi * omega^i)\n",
    "    n_pred = nd + v1 * omega + v2 * (omega**2) + v3 * (omega**3)\n",
    "    \n",
    "    return n_pred\n",
    "\n",
    "# --- 2. Validation Loop ---\n",
    "rmse_baseline = []\n",
    "rmse_prediction = []\n",
    "max_errors = []\n",
    "\n",
    "print(f\"Validating model against {len(df_glass)} catalog glasses...\")\n",
    "\n",
    "for idx, row in df_glass.iterrows():\n",
    "    # Ground Truth\n",
    "    n_real = row['curve']\n",
    "    \n",
    "    # Baseline Model (Linear Assumption)\n",
    "    # Slope defined by F and C lines (Normal Line assumption)\n",
    "    slope = (row['nd'] - 1) / (row['Vd'] * (WAVE_F - WAVE_C))\n",
    "    n_baseline = row['nd'] + slope * (WAVE_GRID - WAVE_D)\n",
    "    \n",
    "    # Optimized Model Prediction\n",
    "    n_pred = predict_abbe_index(row['nd'], row['Vd'], WAVE_GRID)\n",
    "\n",
    "    # Compute Errors\n",
    "    error_base = np.sqrt(np.mean((n_baseline - n_real)**2))\n",
    "    error_pred = np.sqrt(np.mean((n_pred - n_real)**2))\n",
    "    \n",
    "    rmse_baseline.append(error_base)\n",
    "    rmse_prediction.append(error_pred)\n",
    "    \n",
    "    # Track worst-case spectral error\n",
    "    max_errors.append(np.max(np.abs(n_pred - n_real)))\n",
    "\n",
    "# Convert to arrays\n",
    "rmse_baseline = np.array(rmse_baseline)\n",
    "rmse_prediction = np.array(rmse_prediction)\n",
    "max_errors = np.array(max_errors)\n",
    "\n",
    "print(\"Validation Loop Complete.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "29a8e5f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------------------------------\n",
      "GLOBAL VALIDATION STATISTICS (N=1678)\n",
      "-----------------------------------------------------------------\n",
      "Baseline (Linear) Mean RMSE:    5.94e-03\n",
      "Optimized Model Mean RMSE:      6.84e-05\n",
      "IMPROVEMENT FACTOR:             86.9x reduction in error\n",
      "-----------------------------------------------------------------\n",
      "Avg Max Spectral Error:         2.85e-04\n",
      "-----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Compute Aggregate Statistics\n",
    "mean_rmse_base = np.mean(rmse_baseline)\n",
    "mean_rmse_pred = np.mean(rmse_prediction)\n",
    "improvement_factor = mean_rmse_base / mean_rmse_pred\n",
    "\n",
    "print(\"-\" * 65)\n",
    "print(f\"GLOBAL VALIDATION STATISTICS (N={len(rmse_prediction)})\")\n",
    "print(\"-\" * 65)\n",
    "print(f\"Baseline (Linear) Mean RMSE:    {mean_rmse_base:.2e}\")\n",
    "print(f\"Optimized Model Mean RMSE:      {mean_rmse_pred:.2e}\")\n",
    "print(f\"IMPROVEMENT FACTOR:             {improvement_factor:.1f}x reduction in error\")\n",
    "print(\"-\" * 65)\n",
    "print(f\"Avg Max Spectral Error:         {np.mean(max_errors):.2e}\")\n",
    "print(\"-\" * 65)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d4d2156f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Log-spaced bins for wide dynamic range\n",
    "bins = np.logspace(np.log10(1e-6), np.log10(1e-2), 50)\n",
    "\n",
    "plt.hist(rmse_baseline, bins=bins, alpha=0.5, color='gray', label='Baseline (Linear Assumption)')\n",
    "plt.hist(rmse_prediction, bins=bins, alpha=0.8, color='#007acc', label='Optimized Model Prediction')\n",
    "\n",
    "# Mean Markers\n",
    "plt.axvline(mean_rmse_pred, color='#007acc', linestyle='--', linewidth=2, label=f'Mean Prediction Error ({mean_rmse_pred:.1e})')\n",
    "\n",
    "plt.xscale('log')\n",
    "plt.xlabel('Root Mean Square Error (RMSE)', fontsize=12)\n",
    "plt.ylabel('Count of Glasses', fontsize=12)\n",
    "plt.title('Distribution of Model Fidelity', fontsize=14)\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True, which=\"both\", ls=\"-\", alpha=0.2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "13874316",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select a stress-test glass (Low Abbe number = High Dispersion)\n",
    "sample_idx = df_glass['Vd'].idxmin() \n",
    "sample = df_glass.iloc[sample_idx]\n",
    "\n",
    "# 1. Get Ground Truth\n",
    "n_real_sample = sample['curve']\n",
    "\n",
    "# 2. Generate Prediction\n",
    "n_pred_sample = predict_abbe_index(sample['nd'], sample['Vd'], WAVE_GRID)\n",
    "\n",
    "# 3. Calculate Residual\n",
    "residual = n_pred_sample - n_real_sample\n",
    "\n",
    "# 4. Plot\n",
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8), sharex=True, gridspec_kw={'height_ratios': [3, 1]})\n",
    "\n",
    "# Top: Absolute Index\n",
    "ax1.plot(WAVE_GRID, n_real_sample, 'k-', linewidth=2, label=f\"{sample['name']} (Measured)\")\n",
    "ax1.plot(WAVE_GRID, n_pred_sample, 'r--', linewidth=2, label='Model Prediction')\n",
    "ax1.set_ylabel('Refractive Index')\n",
    "ax1.set_title(f\"Spectral Reconstruction: {sample['name']} (Vd={sample['Vd']:.1f})\")\n",
    "ax1.legend()\n",
    "ax1.grid(True, alpha=0.3)\n",
    "\n",
    "# Bottom: Residual\n",
    "ax2.plot(WAVE_GRID, residual, 'k-')\n",
    "ax2.fill_between(WAVE_GRID, residual, 0, color='red', alpha=0.1)\n",
    "\n",
    "ax2.axhline(0, color='r', linestyle='--', alpha=0.5)\n",
    "\n",
    "ax2.set_ylabel('Residual ($n_{pred} - n_{real}$)')\n",
    "ax2.set_xlabel(r'Wavelength ($\\mu m$)')\n",
    "ax2.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e367e32",
   "metadata": {},
   "source": [
    "## 6. Production Implementation\n",
    "We now implement the final `OptimizedAbbeMaterial` class. This class incorporates the specific LASSO-derived coefficients found in the discovery phase.\n",
    "\n",
    "**Key Features:**\n",
    "* **Hardcoded Physics:** The regression weights are baked into the class.\n",
    "* **Buchdahl Engine:** Uses the robust coordinate transformation $\\omega$ for spectral stability.\n",
    "* **Vectorized:** Built on the Optiland backend to support efficient array operations during ray tracing.\n",
    "\n",
    "The code below defines the class and demonstrates its usage by comparing the \"Model N-SF11\" (generated purely from $n_d=1.785, V_d=25.7$) against the actual measured data for N-SF11."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "150a262c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import optiland.backend as be\n",
    "from optiland.materials.base import BaseMaterial\n",
    "from optiland.materials import Material\n",
    "\n",
    "class OptimizedAbbeMaterial(BaseMaterial):\n",
    "    \"\"\"\n",
    "    A High-Accuracy Data-Driven Model Glass.\n",
    "\n",
    "    Predicts the full dispersion curve from only n_d and V_d using a\n",
    "    statistical prior derived from catalog glass data.\n",
    "\n",
    "    Attributes:\n",
    "        index (float): Refractive index at d-line (587.56 nm)\n",
    "        abbe (float): Abbe number (Vd)\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, n, abbe):\n",
    "        super().__init__()\n",
    "        self.index = be.array([n])\n",
    "        self.abbe = be.array([abbe])\n",
    "        self._coeffs = self._predict_coefficients()\n",
    "\n",
    "    def _predict_coefficients(self):\n",
    "        \"\"\"\n",
    "        Calculates Buchdahl coefficients (v1, v2, v3) using the\n",
    "        equations found via LASSO regression.\n",
    "        \"\"\"\n",
    "        # 1. Feature Engineering\n",
    "        inv_V = 1.0 / self.abbe\n",
    "        inv_V2 = inv_V ** 2\n",
    "        nd = self.index\n",
    "        nd_sq = nd ** 2\n",
    "        nd_div_V = nd * inv_V\n",
    "\n",
    "        # 2. The Discovered Physics Equations\n",
    "        \n",
    "        # Target: v1 (Primary Slope)\n",
    "        v1 = (0.004160 \n",
    "              + 4.462559 * inv_V \n",
    "              + 2.326660 * inv_V2 \n",
    "              + 0.002330 * nd \n",
    "              - 0.003697 * nd_sq \n",
    "              - 4.697604 * nd_div_V)\n",
    "\n",
    "        # Target: v2 (Curvature)\n",
    "        v2 = (0.066434 \n",
    "              - 7.636396 * inv_V \n",
    "              + 12.597434 * inv_V2 \n",
    "              - 0.037014 * nd_sq \n",
    "              + 5.551013 * nd_div_V)\n",
    "\n",
    "        # Target: v3 (Deep Blue Correction)\n",
    "        v3 = (-0.032218 \n",
    "              + 2.230357 * inv_V \n",
    "              - 103.318994 * inv_V2 \n",
    "              - 0.009654 * nd_sq \n",
    "              + 1.934983 * nd_div_V)\n",
    "\n",
    "        return v1, v2, v3\n",
    "\n",
    "    def _calculate_n(self, wavelength, **kwargs):\n",
    "        wavelength = be.array(wavelength)\n",
    "        \n",
    "        # Buchdahl Coordinate Transformation (d-line centered)\n",
    "        # w = (lam - 0.58756) / (1 + 2.5 * (lam - 0.58756))\n",
    "        lam_norm = wavelength - 0.5875618\n",
    "        w = lam_norm / (1.0 + 2.5 * lam_norm)\n",
    "        \n",
    "        v1, v2, v3 = self._coeffs\n",
    "        \n",
    "        # n(w) = nd + v1*w + v2*w^2 + v3*w^3\n",
    "        return self.index + v1 * w + v2 * (w**2) + v3 * (w**3)\n",
    "\n",
    "    def _calculate_k(self, wavelength, **kwargs):\n",
    "        return be.zeros_like(wavelength)\n",
    "    \n",
    "    def to_dict(self):\n",
    "        material_dict = super().to_dict()\n",
    "        material_dict.update({\"index\": float(self.index), \"abbe\": float(self.abbe)})\n",
    "        return material_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1099019",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Demonstration: Modeling N-SF11\n",
      "Inputs: nd=1.7847, Vd=25.68\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We verify the model against a difficult high-index flint (N-SF11)\n",
    "glass_name = \"N-SF11\"\n",
    "real_glass = Material(glass_name)\n",
    "\n",
    "# 1. Create the Model Glass using ONLY nd and Vd\n",
    "nd_val = real_glass.n(0.5875618).item()\n",
    "Vd_val = real_glass.abbe().item()\n",
    "model_glass = OptimizedAbbeMaterial(nd_val, Vd_val)\n",
    "\n",
    "print(f\"Demonstration: Modeling {glass_name}\")\n",
    "print(f\"Inputs: nd={nd_val:.4f}, Vd={Vd_val:.2f}\")\n",
    "\n",
    "# 2. Calculate Curves\n",
    "wave_grid = np.linspace(0.38, 0.78, 500)\n",
    "n_real = real_glass.n(wave_grid)\n",
    "n_model = model_glass.n(wave_grid)\n",
    "residual = n_model - n_real\n",
    "\n",
    "# 3. Visualize\n",
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8), sharex=True, gridspec_kw={'height_ratios': [3, 1]})\n",
    "\n",
    "# Top: Absolute Index\n",
    "ax1.plot(wave_grid, n_real, 'k-', linewidth=2, label=f'{glass_name} (Measured)')\n",
    "ax1.plot(wave_grid, n_model, 'r--', linewidth=2, label='Optimized Model (Predicted)')\n",
    "ax1.set_ylabel('Refractive Index')\n",
    "ax1.set_title(f'Model Reconstruction of {glass_name} (Vd={Vd_val:.1f})')\n",
    "ax1.legend()\n",
    "ax1.grid(True, alpha=0.3)\n",
    "\n",
    "# Bottom: Residual Error\n",
    "ax2.plot(wave_grid, residual, 'k-')\n",
    "ax2.fill_between(wave_grid, residual, 0, color='red', alpha=0.1)\n",
    "\n",
    "ax2.axhline(0, color='r', linestyle='--', alpha=0.5)\n",
    "\n",
    "ax2.set_ylabel('Residual ($n_{pred} - n_{real}$)')\n",
    "ax2.set_xlabel(r'Wavelength ($\\mu m$)')\n",
    "ax2.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
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   "id": "20c41bd6",
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   "source": [
    "# 6. Conclusions\n",
    "\n",
    "This notebook has successfully derived and validated the **AbbeMaterial** implementation for Optiland. By applying data science techniques to the physics of optical dispersion, we have moved beyond traditional heuristic models.\n",
    "\n",
    "## Key Results\n",
    "\n",
    "**Sparse Representation:** The LassoLarsIC analysis demonstrated that the full spectral behavior of standard glasses can be accurately reconstructed using a sparse subset of Buchdahl terms.\n",
    "\n",
    "**Basis Validity:** The PCA decomposition confirms that the refractive index curves of glasses in this study lie on a low-dimensional manifold. This justifies the use of a constrained model (the optimized Buchdahl form) rather than a free-form interpolation.\n",
    "\n",
    "## Justification of Approach\n",
    "We utilize a data-driven optimization of the **Buchdahl Dispersion Model** rather than direct polynomial interpolation (e.g., solving for Sellmeier coefficients) for two primary reasons:\n",
    "\n",
    "1.  **Numerical Stability (Convexity):**\n",
    "    solving for polynomial coefficients (like Sellmeier $B_i, C_i$) from limited data points is a non-convex problem. Small changes in inputs ($n_d, V_d$) can result in large, chaotic jumps in coefficients, leading to singularities. The Buchdahl coordinate $\\omega$, by contrast, orthogonalizes the spectral space, ensuring that the mapping from ($n_d, V_d$) to the dispersion curve remains smooth and continuous.\n",
    "\n",
    "2.  **Parsimony (Occam's Razor):**\n",
    "    By using Lasso regression, we strictly enforce sparsity. The model includes only the dispersion terms necessary to describe the variance in the training data, discarding higher-order terms that contribute to overfitting (ringing) at the spectral edges. This ensures the `AbbeMaterial` behaves predictably even when simulating glasses outside the standard catalog range."
   ]
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