{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "217f5465",
   "metadata": {},
   "source": [
    "# Self-Consistent High-Order Buchdahl Model Glass\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "483578bb",
   "metadata": {},
   "source": [
    "**Made by Ziyi Xiong/ 2026.11**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6bf801a",
   "metadata": {},
   "source": [
    "## Theory\n",
    "\n",
    "The model keeps the Zemax-style model-glass inputs independent:\n",
    "\n",
    "$$\n",
    "(n_d, V_d, \\Delta P_{g,F})\n",
    "$$\n",
    "\n",
    "The refractive index is represented by a 6th-order Buchdahl polynomial:\n",
    "\n",
    "$$\n",
    "n(\\lambda) = n_d + \\sum_{k=1}^{6}\\nu_k\\omega^k\n",
    "$$\n",
    "\n",
    "where:\n",
    "\n",
    "$$\n",
    "\\omega(\\lambda) = \\frac{\\lambda - \\lambda_d}{1 + \\alpha(\\lambda - \\lambda_d)}\n",
    "$$\n",
    "\n",
    "The model-glass anchors are:\n",
    "\n",
    "$$\n",
    "n_F - n_C = \\frac{n_d - 1}{V_d} = dn_{FC}\n",
    "$$\n",
    "\n",
    "$$\n",
    "P_{g,F} = 0.6438 - 0.001682V_d + \\Delta P_{g,F}\n",
    "$$\n",
    "\n",
    "$$\n",
    "n_g - n_F = P_{g,F}\\,dn_{FC} = dn_{gF}\n",
    "$$\n",
    "\n",
    "The high-order coefficients are fitted or regressed first:\n",
    "\n",
    "$$\n",
    "\\nu_3, \\nu_4, \\nu_5, \\nu_6\n",
    "$$\n",
    "\n",
    "Then $\\nu_1$ and $\\nu_2$ are solved from the two anchor equations. The high-order terms must be subtracted from the right-hand side:\n",
    "\n",
    "$$\n",
    "\\nu_1(\\omega_F-\\omega_C) + \\nu_2(\\omega_F^2-\\omega_C^2)\n",
    "= dn_{FC} - \\sum_{k=3}^{6}\\nu_k(\\omega_F^k-\\omega_C^k)\n",
    "$$\n",
    "\n",
    "$$\n",
    "\\nu_1(\\omega_g-\\omega_F) + \\nu_2(\\omega_g^2-\\omega_F^2)\n",
    "= dn_{gF} - \\sum_{k=3}^{6}\\nu_k(\\omega_g^k-\\omega_F^k)\n",
    "$$\n",
    "\n",
    "The direct coefficient fit can be unweighted:\n",
    "\n",
    "$$\n",
    "\\min_{\\nu_3..\\nu_6} \\sum_i \\left[n_{true}(\\lambda_i)-n_{model}(\\lambda_i)\\right]^2\n",
    "$$\n",
    "\n",
    "or weighted:\n",
    "\n",
    "$$\n",
    "\\min_{\\nu_3..\\nu_6} \\sum_i w_i\\left[n_{true}(\\lambda_i)-n_{model}(\\lambda_i)\\right]^2\n",
    "$$\n",
    "\n",
    "The first weighted profile increases weight away from the d-line:\n",
    "\n",
    "$$\n",
    "w_i = 1 + s\\left(\\frac{|\\lambda_i-\\lambda_d|}{\\max_j |\\lambda_j-\\lambda_d|}\\right)^p\n",
    "$$\n",
    "\n",
    "with $s=20$ and $p=2$.\n",
    "\n",
    "The heavy-IR profile keeps this base weight and adds extra weight past a chosen near-IR start wavelength:\n",
    "\n",
    "$$\n",
    "w_i^{heavy} = w_i + h\\left[\\max\\left(0, \\frac{\\lambda_i-\\lambda_{start}}{\\lambda_{max}-\\lambda_{start}}\\right)\\right]^q\n",
    "$$\n",
    "\n",
    "with $\\lambda_{start}=1.2\\,\\mu m$, $h=200$, and $q=2$.\n",
    "\n",
    "This intentionally relaxes some accuracy near $n_d$ and spends more of the fit budget at wavelengths far from $\\lambda_d$, especially the bad near-IR points. The $d$, $F$, $C$, and $g$ model-glass anchors remain self-consistent because $\\nu_1,\\nu_2$ are still solved from the anchor equations after $\\nu_3..\\nu_6$ are selected.\n",
    "\n",
    "The notebook now keeps the original unweighted model as a reference and compares it against the weighted and heavy-IR weighted models. For each profile, alpha is checked both at the direct per-glass fit layer and after fitting the runtime regression matrix.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ea0dfeff",
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import annotations\n",
    "\n",
    "from pathlib import Path\n",
    "import copy\n",
    "import importlib.util\n",
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import yaml\n",
    "\n",
    "# Spectral line wavelengths [um]\n",
    "LAMBDA_d = 0.5875618   # d-line, yellow He\n",
    "LAMBDA_g = 0.4358343   # g-line, blue Hg\n",
    "LAMBDA_F = 0.4861327   # F-line, blue H\n",
    "LAMBDA_C = 0.6562725   # C-line, red H"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e05a06c5",
   "metadata": {},
   "source": [
    "## Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8ca8fc45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D:\\Msc.Photonics\\Master_Thesis\\Optiland_Based\\optiland_custom\\optiland\\database\\data-nk\\glass\n",
      "Loaded 640 usable formula-2 glasses\n"
     ]
    }
   ],
   "source": [
    "if \"OPTILAND_DB_ROOT\" in os.environ:\n",
    "    DB_ROOT = Path(os.environ[\"OPTILAND_DB_ROOT\"])\n",
    "else:\n",
    "    _spec = importlib.util.find_spec(\"optiland\")\n",
    "    if _spec is None or _spec.origin is None:\n",
    "        raise RuntimeError(\n",
    "            \"optiland not installed. Run `pip install optiland`, or set \"\n",
    "            \"OPTILAND_DB_ROOT to the absolute path of optiland/database.\"\n",
    "        )\n",
    "    DB_ROOT = Path(_spec.origin).parent / \"database\"\n",
    "\n",
    "GLASS_ROOT = DB_ROOT / \"data-nk\" / \"glass\"\n",
    "print(GLASS_ROOT)\n",
    "\n",
    "\n",
    "def sellmeier(wavelength, B1, C1, B2, C2, B3, C3):\n",
    "    wavelength = np.asarray(wavelength, dtype=np.float64)\n",
    "    wl2 = wavelength**2\n",
    "    n2 = 1 + B1*wl2/(wl2-C1) + B2*wl2/(wl2-C2) + B3*wl2/(wl2-C3)\n",
    "    return np.sqrt(n2)\n",
    "\n",
    "\n",
    "def extract_glass_record(yml_path: Path):\n",
    "    with yml_path.open(\"r\", encoding=\"utf-8\") as f:\n",
    "        doc = yaml.safe_load(f)\n",
    "\n",
    "    data_blocks = doc.get(\"DATA\") or []\n",
    "    if not data_blocks:\n",
    "        return None\n",
    "    formula = data_blocks[0]\n",
    "    if formula.get(\"type\") != \"formula 2\":\n",
    "        return None\n",
    "\n",
    "    coeffs = formula.get(\"coefficients\", \"\").split()\n",
    "    if len(coeffs) != 7:\n",
    "        return None\n",
    "\n",
    "    wavelength_range = formula.get(\"wavelength_range\", \"\").strip().split()\n",
    "    if len(wavelength_range) != 2:\n",
    "        return None\n",
    "\n",
    "    B1, C1 = float(coeffs[1]), float(coeffs[2])\n",
    "    B2, C2 = float(coeffs[3]), float(coeffs[4])\n",
    "    B3, C3 = float(coeffs[5]), float(coeffs[6])\n",
    "\n",
    "    nd = float(sellmeier(LAMBDA_d, B1, C1, B2, C2, B3, C3))\n",
    "    nF = float(sellmeier(LAMBDA_F, B1, C1, B2, C2, B3, C3))\n",
    "    nC = float(sellmeier(LAMBDA_C, B1, C1, B2, C2, B3, C3))\n",
    "    ng = float(sellmeier(LAMBDA_g, B1, C1, B2, C2, B3, C3))\n",
    "\n",
    "    dn_FC = nF - nC\n",
    "    if dn_FC < 1e-12:\n",
    "        return None\n",
    "\n",
    "    vd = (nd - 1.0) / dn_FC\n",
    "    PgF = (ng - nF) / dn_FC\n",
    "    dPgF = PgF - (0.6438 - 0.001682 * vd)\n",
    "\n",
    "    specs = doc.get(\"SPECS\", {})\n",
    "    return {\n",
    "        \"name\": yml_path.stem,\n",
    "        \"catalog\": yml_path.parent.name,\n",
    "        \"wavelength_range\": (float(wavelength_range[0]), float(wavelength_range[1])),\n",
    "        \"coeffs\": {\"B1\": B1, \"C1\": C1, \"B2\": B2, \"C2\": C2, \"B3\": B3, \"C3\": C3},\n",
    "        \"nd\": float(nd),\n",
    "        \"vd\": float(vd),\n",
    "        \"dPgF\": float(dPgF),\n",
    "        \"nd_stored\": specs.get(\"nd\"),\n",
    "        \"vd_stored\": specs.get(\"Vd\"),\n",
    "        \"dPgF_stored\": specs.get(\"dPgF\"),\n",
    "    }\n",
    "\n",
    "\n",
    "def load_all_glasses(root=GLASS_ROOT):\n",
    "    records = []\n",
    "    for yml_path in sorted(root.glob(\"*/*.yml\")):\n",
    "        try:\n",
    "            rec = extract_glass_record(yml_path)\n",
    "        except Exception:\n",
    "            rec = None\n",
    "        if rec is not None:\n",
    "            records.append(rec)\n",
    "    return records\n",
    "\n",
    "\n",
    "glasses = load_all_glasses()\n",
    "print(f\"Loaded {len(glasses)} usable formula-2 glasses\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f6550820",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After wavelength filtering and dPgF sanity filter: 536 glasses\n",
      "Removed by dPgF sanity filter: 1\n"
     ]
    }
   ],
   "source": [
    "# Filter to glasses covering the full model range and remove known bad dPgF records.\n",
    "glasses_filtered = [\n",
    "    g for g in glasses\n",
    "    if g[\"wavelength_range\"][0] <= 0.36501 and g[\"wavelength_range\"][1] >= 2.3\n",
    "]\n",
    "\n",
    "before = len(glasses_filtered)\n",
    "glasses_filtered = [\n",
    "    g for g in glasses_filtered\n",
    "    if g[\"dPgF_stored\"] is None or abs(g[\"dPgF\"] - g[\"dPgF_stored\"]) < 0.05\n",
    "]\n",
    "\n",
    "print(f\"After wavelength filtering and dPgF sanity filter: {len(glasses_filtered)} glasses\")\n",
    "print(f\"Removed by dPgF sanity filter: {before - len(glasses_filtered)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b8c1840",
   "metadata": {},
   "source": [
    "## Wavelength Grid And Buchdahl Helpers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "62222d85",
   "metadata": {},
   "outputs": [],
   "source": [
    "wavelengths = np.array([\n",
    "    0.36501, 0.40466, 0.43583, 0.48613, 0.54607,\n",
    "    0.58756, 0.58929, 0.6328,  0.64385, 0.65627,\n",
    "    0.70652, 0.85211, 1.01398, 1.060,   1.52958,\n",
    "    1.97009, 2.3,\n",
    "], dtype=np.float64)\n",
    "\n",
    "\n",
    "def get_indices(glass, wavelengths):\n",
    "    c = glass[\"coeffs\"]\n",
    "    return sellmeier(wavelengths, c[\"B1\"], c[\"C1\"], c[\"B2\"], c[\"C2\"], c[\"B3\"], c[\"C3\"])\n",
    "\n",
    "\n",
    "def buchdahl_omega(lam, alpha, lam_d=LAMBDA_d):\n",
    "    lam = np.asarray(lam, dtype=np.float64)\n",
    "    dl = lam - lam_d\n",
    "    return dl / (1.0 + alpha * dl)\n",
    "\n",
    "\n",
    "def buchdahl_eval(nd, coeffs, omega):\n",
    "    omega = np.asarray(omega, dtype=np.float64)\n",
    "    n = np.full_like(omega, nd, dtype=np.float64)\n",
    "    for k, coeff in enumerate(coeffs, start=1):\n",
    "        n += coeff * omega**k\n",
    "    return n\n",
    "\n",
    "\n",
    "def feature_vec_20(nd, vd, dPgF):\n",
    "    return np.array([\n",
    "        1.0, nd, vd, dPgF, nd**2, vd**2, dPgF**2, nd*vd, nd*dPgF, vd*dPgF,\n",
    "        nd**3, vd**3, dPgF**3, nd**2*vd, nd**2*dPgF, vd**2*nd, vd**2*dPgF,\n",
    "        dPgF**2*nd, dPgF**2*vd, nd*vd*dPgF,\n",
    "    ], dtype=np.float64)\n",
    "\n",
    "\n",
    "def feature_vec_10(nd, vd, dPgF):\n",
    "    return np.array([\n",
    "        1.0, nd, vd, dPgF, nd**2, vd**2, dPgF**2, nd*vd, nd*dPgF, vd*dPgF,\n",
    "    ], dtype=np.float64)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c41777cc",
   "metadata": {},
   "source": [
    "## Self-Consistent High-Order Solver\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "17671bc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def solve_nu12_conditioned_high(nd, vd, dPgF, nu_high, alpha):\n",
    "    \"\"\"Solve nu1,nu2 conditioned on nu3..nuN.\"\"\"\n",
    "    wg = float(buchdahl_omega(LAMBDA_g, alpha))\n",
    "    wF = float(buchdahl_omega(LAMBDA_F, alpha))\n",
    "    wC = float(buchdahl_omega(LAMBDA_C, alpha))\n",
    "\n",
    "    dn_FC = (nd - 1.0) / vd\n",
    "    PgF = 0.6438 - 0.001682 * vd + dPgF\n",
    "    dn_gF = PgF * dn_FC\n",
    "\n",
    "    M = np.array([\n",
    "        [wF - wC, wF**2 - wC**2],\n",
    "        [wg - wF, wg**2 - wF**2],\n",
    "    ], dtype=np.float64)\n",
    "\n",
    "    corr_FC = 0.0\n",
    "    corr_gF = 0.0\n",
    "    for power, coeff in enumerate(nu_high, start=3):\n",
    "        corr_FC += coeff * (wF**power - wC**power)\n",
    "        corr_gF += coeff * (wg**power - wF**power)\n",
    "\n",
    "    rhs = np.array([dn_FC - corr_FC, dn_gF - corr_gF], dtype=np.float64)\n",
    "    return np.linalg.solve(M, rhs)\n",
    "\n",
    "\n",
    "def self_consistent_coeffs_high(nd, vd, dPgF, nu_high, alpha):\n",
    "    nu1, nu2 = solve_nu12_conditioned_high(nd, vd, dPgF, nu_high, alpha)\n",
    "    return np.concatenate([[nu1, nu2], np.asarray(nu_high, dtype=np.float64)])\n",
    "\n",
    "\n",
    "def predict_self_consistent_high(nd, vd, dPgF, nu_high, wavelength, alpha):\n",
    "    omega = buchdahl_omega(wavelength, alpha)\n",
    "    coeffs = self_consistent_coeffs_high(nd, vd, dPgF, nu_high, alpha)\n",
    "    return buchdahl_eval(nd, coeffs, omega)\n",
    "\n",
    "\n",
    "def wavelength_weights(wths, strength=20.0, power=2.0, center=LAMBDA_d):\n",
    "    wths = np.asarray(wths, dtype=np.float64)\n",
    "    distance = np.abs(wths - center)\n",
    "    scale = distance.max()\n",
    "    if scale == 0:\n",
    "        return np.ones_like(wths)\n",
    "    return 1.0 + strength * (distance / scale) ** power\n",
    "\n",
    "\n",
    "def heavy_ir_weights(wths, base_strength=20.0, base_power=2.0, ir_start=1.2, ir_strength=200.0, ir_power=2.0):\n",
    "    wths = np.asarray(wths, dtype=np.float64)\n",
    "    weights = wavelength_weights(wths, strength=base_strength, power=base_power)\n",
    "    denom = wths.max() - ir_start\n",
    "    if denom <= 0:\n",
    "        return weights\n",
    "    ir_coord = np.clip((wths - ir_start) / denom, 0.0, 1.0)\n",
    "    return weights + ir_strength * ir_coord**ir_power\n",
    "\n",
    "\n",
    "def fit_self_consistent_nu_high(nd, vd, dPgF, n_true, omega, alpha, order, weights=None):\n",
    "    \"\"\"Fit nu3..nu_order while enforcing anchors exactly.\"\"\"\n",
    "    num_high = order - 2\n",
    "    base = np.zeros(num_high)\n",
    "    coeff_base = self_consistent_coeffs_high(nd, vd, dPgF, base, alpha)\n",
    "    n_base = buchdahl_eval(nd, coeff_base, omega)\n",
    "\n",
    "    cols = []\n",
    "    for j in range(num_high):\n",
    "        nu_high = np.zeros(num_high)\n",
    "        nu_high[j] = 1.0\n",
    "        coeff_j = self_consistent_coeffs_high(nd, vd, dPgF, nu_high, alpha)\n",
    "        cols.append(buchdahl_eval(nd, coeff_j, omega) - n_base)\n",
    "\n",
    "    A = np.column_stack(cols)\n",
    "    target = n_true - n_base\n",
    "\n",
    "    if weights is not None:\n",
    "        sqrt_w = np.sqrt(np.asarray(weights, dtype=np.float64))\n",
    "        A = A * sqrt_w[:, None]\n",
    "        target = target * sqrt_w\n",
    "\n",
    "    nu_high, *_ = np.linalg.lstsq(A, target, rcond=None)\n",
    "    return nu_high\n",
    "\n",
    "\n",
    "def anchor_report_high(nd, vd, dPgF, nu_high, alpha):\n",
    "    coeffs = self_consistent_coeffs_high(nd, vd, dPgF, nu_high, alpha)\n",
    "    nd_model = float(buchdahl_eval(nd, coeffs, buchdahl_omega(LAMBDA_d, alpha)))\n",
    "    nF = float(buchdahl_eval(nd, coeffs, buchdahl_omega(LAMBDA_F, alpha)))\n",
    "    nC = float(buchdahl_eval(nd, coeffs, buchdahl_omega(LAMBDA_C, alpha)))\n",
    "    ng = float(buchdahl_eval(nd, coeffs, buchdahl_omega(LAMBDA_g, alpha)))\n",
    "    vd_model = (nd_model - 1.0) / (nF - nC)\n",
    "    pgf_model = (ng - nF) / (nF - nC)\n",
    "    dpgf_model = pgf_model - (0.6438 - 0.001682 * vd_model)\n",
    "    return nd_model, vd_model, dpgf_model\n",
    "\n",
    "\n",
    "def build_training_targets_high(alpha, order, weights=None):\n",
    "    omega = buchdahl_omega(wavelengths, alpha)\n",
    "    targets = []\n",
    "    fit_errors = []\n",
    "    for g in glasses_filtered:\n",
    "        n_true = get_indices(g, wavelengths)\n",
    "        nu_high = fit_self_consistent_nu_high(\n",
    "            g[\"nd\"], g[\"vd\"], g[\"dPgF\"], n_true, omega, alpha, order=order, weights=weights\n",
    "        )\n",
    "        targets.append(nu_high)\n",
    "        n_pred = predict_self_consistent_high(\n",
    "            g[\"nd\"], g[\"vd\"], g[\"dPgF\"], nu_high, wavelengths, alpha\n",
    "        )\n",
    "        fit_errors.append(np.max(np.abs(n_true - n_pred)))\n",
    "    return np.vstack(targets), np.array(fit_errors)\n",
    "\n",
    "\n",
    "def sweep_alpha_high(order, alphas=np.linspace(0.5, 5.0, 60), weights=None):\n",
    "    results = []\n",
    "    for alpha in alphas:\n",
    "        _, errs = build_training_targets_high(alpha=alpha, order=order, weights=weights)\n",
    "        results.append((alpha, errs.max(), errs.mean(), np.median(errs)))\n",
    "    results = np.array(results)\n",
    "    best_idx = int(np.argmin(results[:, 1]))\n",
    "    return float(results[best_idx, 0]), results\n",
    "\n",
    "\n",
    "def regress_high_order(Y, feature_func=feature_vec_20):\n",
    "    Phi = np.vstack([feature_func(g[\"nd\"], g[\"vd\"], g[\"dPgF\"]) for g in glasses_filtered])\n",
    "    return Phi, np.linalg.lstsq(Phi, Y, rcond=None)[0]\n",
    "\n",
    "\n",
    "def predict_from_regression_high(g, wavelength, alpha, A, feature_func=feature_vec_20):\n",
    "    phi = feature_func(g[\"nd\"], g[\"vd\"], g[\"dPgF\"])\n",
    "    nu_high = phi @ A\n",
    "    return predict_self_consistent_high(g[\"nd\"], g[\"vd\"], g[\"dPgF\"], nu_high, wavelength, alpha)\n",
    "\n",
    "\n",
    "def end_to_end_errors_high(wths, alpha, A, feature_func=feature_vec_20):\n",
    "    errs = []\n",
    "    for g in glasses_filtered:\n",
    "        n_true = get_indices(g, wths)\n",
    "        n_pred = predict_from_regression_high(g, wths, alpha, A, feature_func)\n",
    "        errs.append(np.max(np.abs(n_true - n_pred)))\n",
    "    return np.array(errs)\n",
    "\n",
    "\n",
    "def print_regression_coeff_errors(name, Phi, Y, A):\n",
    "    pred = Phi @ A\n",
    "    err = np.abs(pred - Y)\n",
    "    print(f\"\\n{name}\")\n",
    "    for k in range(err.shape[1]):\n",
    "        print(f\"  nu{k+3}: max={err[:, k].max():.3e}, mean={err[:, k].mean():.3e}\")\n",
    "\n",
    "\n",
    "wth_shift = wavelengths + 0.01\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e75e33a0",
   "metadata": {},
   "source": [
    "## 6th-Order Experiment\n",
    "\n",
    "This fits and regresses $\\nu_3..\\nu_6$, then solves $\\nu_1,\\nu_2$ from the anchor equations.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "71817326",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit weights\n",
      "wavelength_um   weighted   heavy_IR\n",
      "     0.36501     1.3378     1.3378\n",
      "     0.40466     1.2282     1.2282\n",
      "     0.43583     1.1570     1.1570\n",
      "     0.48613     1.0702     1.0702\n",
      "     0.54607     1.0117     1.0117\n",
      "     0.58756     1.0000     1.0000\n",
      "     0.58929     1.0000     1.0000\n",
      "     0.63280     1.0140     1.0140\n",
      "     0.64385     1.0216     1.0216\n",
      "     0.65627     1.0322     1.0322\n",
      "     0.70652     1.0965     1.0965\n",
      "     0.85211     1.4773     1.4773\n",
      "     1.01398     2.2401     2.2401\n",
      "     1.06000     2.5223     2.5223\n",
      "     1.52958     7.0523    25.0065\n",
      "     1.97009    14.0361   112.0590\n",
      "     2.30000    21.0000   221.0000\n",
      "\n",
      "6th-order unweighted fit-layer quality, no regression yet\n",
      "alpha=2.5: max=1.420e-03, mean=9.964e-04, median=9.874e-04\n",
      "alpha=1.492: max=4.063e-04, mean=2.204e-04, median=2.177e-04\n",
      "\n",
      "6th unweighted alpha=2.5 20dim\n",
      "  nu3: max=4.201e-02, mean=4.069e-03\n",
      "  nu4: max=7.078e-02, mean=1.165e-02\n",
      "  nu5: max=7.576e-01, mean=1.249e-01\n",
      "  nu6: max=1.070e+00, mean=1.782e-01\n",
      "\n",
      "6th unweighted alpha=1.492 20dim\n",
      "  nu3: max=8.048e-03, mean=1.198e-03\n",
      "  nu4: max=1.142e-01, mean=1.098e-02\n",
      "  nu5: max=6.053e-02, mean=7.439e-03\n",
      "  nu6: max=5.993e-01, mean=6.276e-02\n",
      "\n",
      "6th-order weighted fit-layer quality, no regression yet\n",
      "alpha=2.5: max=3.099e-03, mean=2.171e-03, median=2.150e-03\n",
      "alpha=1.568: max=4.738e-04, mean=2.744e-04, median=2.716e-04\n",
      "\n",
      "6th weighted alpha=2.5 20dim\n",
      "  nu3: max=1.110e-01, mean=1.251e-02\n",
      "  nu4: max=2.715e-01, mean=2.095e-02\n",
      "  nu5: max=1.298e+00, mean=2.058e-01\n",
      "  nu6: max=2.763e+00, mean=3.872e-01\n",
      "\n",
      "6th weighted alpha=1.568 20dim\n",
      "  nu3: max=8.047e-03, mean=1.199e-03\n",
      "  nu4: max=1.993e-01, mean=1.816e-02\n",
      "  nu5: max=5.353e-02, mean=6.844e-03\n",
      "  nu6: max=1.002e+00, mean=1.011e-01\n",
      "\n",
      "6th-order heavy-IR fit-layer quality, no regression yet\n",
      "alpha=2.5: max=5.461e-03, mean=3.835e-03, median=3.801e-03\n",
      "alpha=1.492: max=8.743e-04, mean=4.812e-04, median=4.757e-04\n",
      "\n",
      "6th heavy-IR alpha=2.5 20dim\n",
      "  nu3: max=1.924e-01, mean=2.123e-02\n",
      "  nu4: max=6.627e-01, mean=6.134e-02\n",
      "  nu5: max=2.037e+00, mean=2.849e-01\n",
      "  nu6: max=4.969e+00, mean=6.196e-01\n",
      "\n",
      "6th heavy-IR alpha=1.492 20dim\n",
      "  nu3: max=1.261e-02, mean=2.060e-03\n",
      "  nu4: max=2.442e-01, mean=2.064e-02\n",
      "  nu5: max=1.577e-01, mean=7.384e-03\n",
      "  nu6: max=1.167e+00, mean=1.047e-01\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "BASE_WEIGHT_STRENGTH = 20.0\n",
    "BASE_WEIGHT_POWER = 2.0\n",
    "HEAVY_IR_START = 1.2\n",
    "HEAVY_IR_STRENGTH = 200.0\n",
    "HEAVY_IR_POWER = 2.0\n",
    "\n",
    "UNWEIGHTED_WEIGHTS = None\n",
    "BASE_WEIGHTS = wavelength_weights(wavelengths, strength=BASE_WEIGHT_STRENGTH, power=BASE_WEIGHT_POWER)\n",
    "HEAVY_IR_WEIGHTS = heavy_ir_weights(\n",
    "    wavelengths,\n",
    "    base_strength=BASE_WEIGHT_STRENGTH,\n",
    "    base_power=BASE_WEIGHT_POWER,\n",
    "    ir_start=HEAVY_IR_START,\n",
    "    ir_strength=HEAVY_IR_STRENGTH,\n",
    "    ir_power=HEAVY_IR_POWER,\n",
    ")\n",
    "\n",
    "WEIGHT_PROFILES = [\n",
    "    (\"unweighted\", UNWEIGHTED_WEIGHTS),\n",
    "    (\"weighted\", BASE_WEIGHTS),\n",
    "    (\"heavy-IR\", HEAVY_IR_WEIGHTS),\n",
    "]\n",
    "\n",
    "print(\"Fit weights\")\n",
    "print(\"wavelength_um   weighted   heavy_IR\")\n",
    "for w, base_w, heavy_w in zip(wavelengths, BASE_WEIGHTS, HEAVY_IR_WEIGHTS):\n",
    "    print(f\"{w:12.5f} {base_w:10.4f} {heavy_w:10.4f}\")\n",
    "\n",
    "profile_results = {}\n",
    "cases6 = []\n",
    "Phi20 = None\n",
    "\n",
    "for profile_name, weights in WEIGHT_PROFILES:\n",
    "    Y25, fit25 = build_training_targets_high(alpha=2.5, order=6, weights=weights)\n",
    "    alpha_opt, alpha_results = sweep_alpha_high(order=6, weights=weights)\n",
    "    Yopt, fitopt = build_training_targets_high(alpha=alpha_opt, order=6, weights=weights)\n",
    "\n",
    "    Phi20, A25 = regress_high_order(Y25)\n",
    "    _, Aopt = regress_high_order(Yopt)\n",
    "\n",
    "    profile_results[profile_name] = {\n",
    "        \"weights\": weights,\n",
    "        \"Y25\": Y25,\n",
    "        \"fit25\": fit25,\n",
    "        \"alpha_opt\": alpha_opt,\n",
    "        \"alpha_results\": alpha_results,\n",
    "        \"Yopt\": Yopt,\n",
    "        \"fitopt\": fitopt,\n",
    "        \"A25\": A25,\n",
    "        \"Aopt\": Aopt,\n",
    "    }\n",
    "\n",
    "    print(f\"\\n6th-order {profile_name} fit-layer quality, no regression yet\")\n",
    "    print(f\"alpha=2.5: max={fit25.max():.3e}, mean={fit25.mean():.3e}, median={np.median(fit25):.3e}\")\n",
    "    print(f\"alpha={alpha_opt:.3f}: max={fitopt.max():.3e}, mean={fitopt.mean():.3e}, median={np.median(fitopt):.3e}\")\n",
    "\n",
    "    print_regression_coeff_errors(f\"6th {profile_name} alpha=2.5 20dim\", Phi20, Y25, A25)\n",
    "    print_regression_coeff_errors(f\"6th {profile_name} alpha={alpha_opt:.3f} 20dim\", Phi20, Yopt, Aopt)\n",
    "\n",
    "    cases6.append((f\"6th {profile_name} alpha={alpha_opt:.3f} 20dim\", 6, alpha_opt, Aopt))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 4.8))\n",
    "for profile_name, result in profile_results.items():\n",
    "    alpha_results = result[\"alpha_results\"]\n",
    "    ax.semilogy(alpha_results[:, 0], alpha_results[:, 1], label=f\"{profile_name} max\")\n",
    "    ax.axvline(result[\"alpha_opt\"], ls=\"--\", alpha=0.6)\n",
    "ax.axvline(2.5, color=\"gray\", ls=\":\", label=\"alpha=2.5\")\n",
    "ax.set_xlabel(\"alpha\")\n",
    "ax.set_ylabel(\"6th-order fit-layer max |n_true - n_model|\")\n",
    "ax.grid(True, which=\"both\", alpha=0.3)\n",
    "ax.legend(fontsize=8)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Named aliases used by the save cell.\n",
    "ALPHA6_REF_OPT = profile_results[\"unweighted\"][\"alpha_opt\"]\n",
    "A6_ref_opt_20 = profile_results[\"unweighted\"][\"Aopt\"]\n",
    "ALPHA6_OPT = profile_results[\"weighted\"][\"alpha_opt\"]\n",
    "A6_opt_20 = profile_results[\"weighted\"][\"Aopt\"]\n",
    "ALPHA6_HEAVY_OPT = profile_results[\"heavy-IR\"][\"alpha_opt\"]\n",
    "A6_heavy_opt_20 = profile_results[\"heavy-IR\"][\"Aopt\"]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75daf513",
   "metadata": {},
   "source": [
    "## 6th-Order Regression-Level Alpha Sweep\n",
    "\n",
    "The fit-level alpha sweep optimizes the direct per-glass fit. This sweep optimizes alpha after fitting the runtime regression matrix, which is the quantity used by `ModelMaterial`.\n",
    "\n",
    "The validation grid includes the shifted wavelengths and 1.55 um, but does not give 1.55 extra weight.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6282b14b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Best 6th-order unweighted alpha after regression by worst validation error: 1.4915\n",
      "runtime max=3.971e-03, mean=7.600e-04, median=5.458e-04, p95=1.995e-03\n",
      "\n",
      "Best 6th-order weighted alpha after regression by worst validation error: 1.4915\n",
      "runtime max=3.892e-03, mean=7.184e-04, median=4.839e-04, p95=1.996e-03\n",
      "\n",
      "Best 6th-order heavy-IR alpha after regression by worst validation error: 1.4915\n",
      "runtime max=3.856e-03, mean=7.980e-04, median=5.781e-04, p95=2.016e-03\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wth_validation = np.unique(np.concatenate([wth_shift, np.array([1.55], dtype=np.float64)]))\n",
    "\n",
    "def sweep_alpha_high_regression(order, alphas=np.linspace(0.5, 5.0, 60), validation_wths=wth_validation, weights=None):\n",
    "    results = []\n",
    "    matrices = []\n",
    "    for alpha in alphas:\n",
    "        Y, fit_errors = build_training_targets_high(alpha=alpha, order=order, weights=weights)\n",
    "        _, A = regress_high_order(Y)\n",
    "        runtime_errors = end_to_end_errors_high(validation_wths, alpha, A)\n",
    "        results.append((\n",
    "            alpha,\n",
    "            runtime_errors.max(),\n",
    "            runtime_errors.mean(),\n",
    "            np.median(runtime_errors),\n",
    "            np.percentile(runtime_errors, 95),\n",
    "            fit_errors.max(),\n",
    "        ))\n",
    "        matrices.append(A)\n",
    "    results = np.array(results)\n",
    "    best_idx = int(np.argmin(results[:, 1]))\n",
    "    return float(results[best_idx, 0]), results, matrices[best_idx]\n",
    "\n",
    "reg_profile_results = {}\n",
    "\n",
    "for profile_name, result in profile_results.items():\n",
    "    alpha_reg_opt, alpha_reg_results, Areg = sweep_alpha_high_regression(order=6, weights=result[\"weights\"])\n",
    "    best_idx = int(np.argmin(alpha_reg_results[:, 1]))\n",
    "    reg_profile_results[profile_name] = {\n",
    "        \"alpha_reg_opt\": alpha_reg_opt,\n",
    "        \"alpha_reg_results\": alpha_reg_results,\n",
    "        \"Areg\": Areg,\n",
    "    }\n",
    "\n",
    "    print(f\"\\nBest 6th-order {profile_name} alpha after regression by worst validation error: {alpha_reg_opt:.4f}\")\n",
    "    print(\n",
    "        f\"runtime max={alpha_reg_results[best_idx,1]:.3e}, \"\n",
    "        f\"mean={alpha_reg_results[best_idx,2]:.3e}, \"\n",
    "        f\"median={alpha_reg_results[best_idx,3]:.3e}, \"\n",
    "        f\"p95={alpha_reg_results[best_idx,4]:.3e}\"\n",
    "    )\n",
    "\n",
    "    cases6.append((f\"6th {profile_name} alpha={alpha_reg_opt:.3f} 20dim reg-opt\", 6, alpha_reg_opt, Areg))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 4.8))\n",
    "for profile_name, result in reg_profile_results.items():\n",
    "    alpha_reg_results = result[\"alpha_reg_results\"]\n",
    "    ax.semilogy(alpha_reg_results[:, 0], alpha_reg_results[:, 1], label=f\"{profile_name} runtime max\")\n",
    "    ax.semilogy(alpha_reg_results[:, 0], alpha_reg_results[:, 4], ls=\"--\", label=f\"{profile_name} runtime p95\")\n",
    "    ax.axvline(result[\"alpha_reg_opt\"], ls=\":\", alpha=0.6)\n",
    "ax.axvline(2.5, color=\"gray\", ls=\":\", label=\"alpha=2.5\")\n",
    "ax.set_xlabel(\"alpha\")\n",
    "ax.set_ylabel(\"6th-order runtime regression error\")\n",
    "ax.grid(True, which=\"both\", alpha=0.3)\n",
    "ax.legend(fontsize=8)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "ALPHA6_REF_REG_OPT = reg_profile_results[\"unweighted\"][\"alpha_reg_opt\"]\n",
    "A6_ref_reg_opt_20 = reg_profile_results[\"unweighted\"][\"Areg\"]\n",
    "ALPHA6_REG_OPT = reg_profile_results[\"weighted\"][\"alpha_reg_opt\"]\n",
    "A6_reg_opt_20 = reg_profile_results[\"weighted\"][\"Areg\"]\n",
    "ALPHA6_HEAVY_REG_OPT = reg_profile_results[\"heavy-IR\"][\"alpha_reg_opt\"]\n",
    "A6_heavy_reg_opt_20 = reg_profile_results[\"heavy-IR\"][\"Areg\"]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bf8b8bc",
   "metadata": {},
   "source": [
    "## End-To-End Regression Validation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7f06b6df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "========================================================================\n",
      "training wavelengths\n",
      "========================================================================\n",
      "6th unweighted alpha=1.492 20dim               max=3.9135e-03 mean=7.4434e-04 median=5.3789e-04 p95=1.9772e-03\n",
      "6th weighted alpha=1.568 20dim                 max=3.8552e-03 mean=7.1772e-04 median=4.8127e-04 p95=1.9779e-03\n",
      "6th heavy-IR alpha=1.492 20dim                 max=3.8103e-03 mean=7.8986e-04 median=5.7159e-04 p95=1.9836e-03\n",
      "6th unweighted alpha=1.492 20dim reg-opt       max=3.9135e-03 mean=7.4434e-04 median=5.3789e-04 p95=1.9772e-03\n",
      "6th weighted alpha=1.492 20dim reg-opt         max=3.8410e-03 mean=7.0835e-04 median=4.7903e-04 p95=1.9753e-03\n",
      "6th heavy-IR alpha=1.492 20dim reg-opt         max=3.8103e-03 mean=7.8986e-04 median=5.7159e-04 p95=1.9836e-03\n",
      "\n",
      "========================================================================\n",
      "shifted wavelengths\n",
      "========================================================================\n",
      "6th unweighted alpha=1.492 20dim               max=3.9706e-03 mean=7.5998e-04 median=5.4580e-04 p95=1.9950e-03\n",
      "6th weighted alpha=1.568 20dim                 max=3.9192e-03 mean=7.2788e-04 median=4.9138e-04 p95=2.0048e-03\n",
      "6th heavy-IR alpha=1.492 20dim                 max=3.8563e-03 mean=7.9801e-04 median=5.7811e-04 p95=2.0158e-03\n",
      "6th unweighted alpha=1.492 20dim reg-opt       max=3.9706e-03 mean=7.5998e-04 median=5.4580e-04 p95=1.9950e-03\n",
      "6th weighted alpha=1.492 20dim reg-opt         max=3.8922e-03 mean=7.1836e-04 median=4.8395e-04 p95=1.9964e-03\n",
      "6th heavy-IR alpha=1.492 20dim reg-opt         max=3.8563e-03 mean=7.9801e-04 median=5.7811e-04 p95=2.0158e-03\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_cases = cases6\n",
    "\n",
    "for test_name, wths in [(\"training wavelengths\", wavelengths), (\"shifted wavelengths\", wth_shift)]:\n",
    "    print(\"\\n\" + \"=\"*72)\n",
    "    print(test_name)\n",
    "    print(\"=\"*72)\n",
    "    for name, order, alpha, A in all_cases:\n",
    "        errs = end_to_end_errors_high(wths, alpha, A)\n",
    "        print(f\"{name:<46} max={errs.max():.4e} mean={errs.mean():.4e} median={np.median(errs):.4e} p95={np.percentile(errs, 95):.4e}\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 4.8))\n",
    "for name, order, alpha, A in all_cases:\n",
    "    errs = end_to_end_errors_high(wth_shift, alpha, A)\n",
    "    ax.hist(errs, bins=40, alpha=0.4, label=name)\n",
    "ax.set_xlabel(\"per-glass max |n_true - n_model| on shifted wavelengths\")\n",
    "ax.set_ylabel(\"count\")\n",
    "ax.grid(alpha=0.25)\n",
    "ax.legend(fontsize=8)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f3fc70d",
   "metadata": {},
   "source": [
    "## Per-Wavelength Error\n",
    "\n",
    "This plot shows the 6th-order runtime regression error at each wavelength, summarized over all filtered glasses.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "69e1eec8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6th unweighted alpha=1.492 20dim reg-opt\n",
      "wavelength_um   max_abs_err   mean_abs_err   median_abs_err\n",
      "     0.36501  2.728746e-04   3.605045e-05     2.360046e-05\n",
      "     0.40466  4.045961e-04   2.920249e-05     2.587852e-05\n",
      "     0.43583  1.760664e-04   5.333755e-05     5.249194e-05\n",
      "     0.48613  1.761170e-04   5.334670e-05     5.250133e-05\n",
      "     0.54607  1.908250e-05   1.314931e-06     1.272374e-06\n",
      "     0.58756  3.077878e-09   6.656749e-10     6.523735e-10\n",
      "     0.58929  3.023734e-06   6.643914e-07     6.511326e-07\n",
      "     0.63280  1.097079e-04   3.072227e-05     3.029435e-05\n",
      "     0.64385  1.411227e-04   4.110432e-05     4.053291e-05\n",
      "     0.65627  1.760982e-04   5.334164e-05     5.249635e-05\n",
      "     0.70652  2.955121e-04   1.013547e-04     1.000461e-04\n",
      "     0.85211  3.124035e-04   1.275990e-04     1.270390e-04\n",
      "     1.01398  4.613244e-04   6.121422e-05     4.438171e-05\n",
      "     1.06000  5.917811e-04   9.492670e-05     7.803041e-05\n",
      "     1.52958  1.615619e-03   2.377441e-04     1.707954e-04\n",
      "     1.97009  2.524598e-03   4.933208e-04     3.947206e-04\n",
      "     2.30000  3.913542e-03   6.626389e-04     4.581106e-04\n"
     ]
    }
   ],
   "source": [
    "def per_wavelength_error_stats(wths, alpha, A, feature_func=feature_vec_20):\n",
    "    errors = []\n",
    "    for g in glasses_filtered:\n",
    "        n_true = get_indices(g, wths)\n",
    "        n_pred = predict_from_regression_high(g, wths, alpha, A, feature_func)\n",
    "        errors.append(np.abs(n_true - n_pred))\n",
    "    errors = np.vstack(errors)\n",
    "    return {\n",
    "        \"max\": errors.max(axis=0),\n",
    "        \"mean\": errors.mean(axis=0),\n",
    "        \"median\": np.median(errors, axis=0),\n",
    "    }\n",
    "\n",
    "fig, axes = plt.subplots(3, 1, figsize=(8, 9), sharex=True)\n",
    "stat_names = [\"max\", \"mean\", \"median\"]\n",
    "\n",
    "for name, order, alpha, A in cases6:\n",
    "    stats = per_wavelength_error_stats(wavelengths, alpha, A)\n",
    "    for ax, stat_name in zip(axes, stat_names):\n",
    "        ax.semilogy(wavelengths, stats[stat_name], marker=\"o\", ms=3.5, label=name)\n",
    "        ax.set_ylabel(f\"{stat_name} |error|\")\n",
    "        ax.grid(True, which=\"both\", alpha=0.3)\n",
    "\n",
    "axes[-1].set_xlabel(\"wavelength [um]\")\n",
    "axes[0].set_title(\"6th-order runtime regression error by wavelength\")\n",
    "axes[0].legend(fontsize=8)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Numeric table for the regression-optimized 6th-order candidate.\n",
    "reg_opt_case = next((case for case in cases6 if \"reg-opt\" in case[0]), cases6[-1])\n",
    "name, order, alpha, A = reg_opt_case\n",
    "stats = per_wavelength_error_stats(wavelengths, alpha, A)\n",
    "print(name)\n",
    "print(\"wavelength_um   max_abs_err   mean_abs_err   median_abs_err\")\n",
    "for w, max_err, mean_err, med_err in zip(wavelengths, stats[\"max\"], stats[\"mean\"], stats[\"median\"]):\n",
    "    print(f\"{w:12.5f} {max_err:13.6e} {mean_err:14.6e} {med_err:16.6e}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22e41ee3",
   "metadata": {},
   "source": [
    "## H-LAF3B Diagnostic\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6426642d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cdgm H-LAF3B\n",
      "true n(1.55): 1.7185450039\n",
      "\n",
      "6th unweighted alpha=1.492 20dim\n",
      "  n=1.7177329103, err=-8.1209e-04\n",
      "  predicted nu3..nu6 = [-0.086996  0.255581 -0.164489 -1.337315]\n",
      "  with nu5..nu6=0: n=1.7241514567, delta=+6.4185e-03\n",
      "\n",
      "6th weighted alpha=1.568 20dim\n",
      "  n=1.7178401472, err=-7.0486e-04\n",
      "  predicted nu3..nu6 = [-0.073411  0.384365 -0.145019 -2.131093]\n",
      "  with nu5..nu6=0: n=1.7251512200, delta=+7.3111e-03\n",
      "\n",
      "6th heavy-IR alpha=1.492 20dim\n",
      "  n=1.7178513452, err=-6.9366e-04\n",
      "  predicted nu3..nu6 = [-0.107803  0.435068  0.034151 -2.121427]\n",
      "  with nu5..nu6=0: n=1.7245264176, delta=+6.6751e-03\n",
      "\n",
      "6th unweighted alpha=1.492 20dim reg-opt\n",
      "  n=1.7177329103, err=-8.1209e-04\n",
      "  predicted nu3..nu6 = [-0.086996  0.255581 -0.164489 -1.337315]\n",
      "  with nu5..nu6=0: n=1.7241514567, delta=+6.4185e-03\n",
      "\n",
      "6th weighted alpha=1.492 20dim reg-opt\n",
      "  n=1.7178359588, err=-7.0905e-04\n",
      "  predicted nu3..nu6 = [-0.09335   0.327457 -0.09668  -1.653753]\n",
      "  with nu5..nu6=0: n=1.7245048559, delta=+6.6689e-03\n",
      "\n",
      "6th heavy-IR alpha=1.492 20dim reg-opt\n",
      "  n=1.7178513452, err=-6.9366e-04\n",
      "  predicted nu3..nu6 = [-0.107803  0.435068  0.034151 -2.121427]\n",
      "  with nu5..nu6=0: n=1.7245264176, delta=+6.6751e-03\n"
     ]
    }
   ],
   "source": [
    "def find_glass(name):\n",
    "    key = name.upper()\n",
    "    return [g for g in glasses_filtered if key in g[\"name\"].upper()]\n",
    "\n",
    "\n",
    "matches = find_glass(\"H-LAF3B\")\n",
    "if matches:\n",
    "    g = matches[0]\n",
    "    n_true_155 = float(get_indices(g, np.array([1.55]))[0])\n",
    "    print(g[\"catalog\"], g[\"name\"])\n",
    "    print(\"true n(1.55):\", f\"{n_true_155:.10f}\")\n",
    "\n",
    "    for name, order, alpha, A in all_cases:\n",
    "        phi = feature_vec_20(g[\"nd\"], g[\"vd\"], g[\"dPgF\"])\n",
    "        nu_high = phi @ A\n",
    "        n_pred = float(predict_self_consistent_high(g[\"nd\"], g[\"vd\"], g[\"dPgF\"], nu_high, np.array([1.55]), alpha)[0])\n",
    "\n",
    "        nu_tail_zeroed = nu_high.copy()\n",
    "        nu_tail_zeroed[2:] = 0.0\n",
    "        n_pred_without_tail = float(predict_self_consistent_high(g[\"nd\"], g[\"vd\"], g[\"dPgF\"], nu_tail_zeroed, np.array([1.55]), alpha)[0])\n",
    "\n",
    "        print(\"\\n\" + name)\n",
    "        print(f\"  n={n_pred:.10f}, err={n_pred - n_true_155:+.4e}\")\n",
    "        print(f\"  predicted nu3..nu{order} =\", np.array2string(nu_high, precision=6, suppress_small=False))\n",
    "        print(f\"  with nu5..nu{order}=0: n={n_pred_without_tail:.10f}, delta={n_pred_without_tail - n_pred:+.4e}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16501f84",
   "metadata": {},
   "source": [
    "## Anchor Consistency Test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ec6c7abc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6th unweighted alpha=1.492 20dim               max anchor errors: nd=0.000e+00, Vd=8.043e-12, dPgF=1.051e-13\n",
      "6th weighted alpha=1.568 20dim                 max anchor errors: nd=0.000e+00, Vd=9.052e-12, dPgF=1.291e-13\n",
      "6th heavy-IR alpha=1.492 20dim                 max anchor errors: nd=0.000e+00, Vd=6.679e-12, dPgF=1.094e-13\n",
      "6th unweighted alpha=1.492 20dim reg-opt       max anchor errors: nd=0.000e+00, Vd=8.043e-12, dPgF=1.051e-13\n",
      "6th weighted alpha=1.492 20dim reg-opt         max anchor errors: nd=0.000e+00, Vd=5.940e-12, dPgF=1.588e-13\n",
      "6th heavy-IR alpha=1.492 20dim reg-opt         max anchor errors: nd=0.000e+00, Vd=6.679e-12, dPgF=1.094e-13\n"
     ]
    }
   ],
   "source": [
    "for name, order, alpha, A in all_cases:\n",
    "    max_nd = 0.0\n",
    "    max_vd = 0.0\n",
    "    max_dpgf = 0.0\n",
    "    for g in glasses_filtered:\n",
    "        phi = feature_vec_20(g[\"nd\"], g[\"vd\"], g[\"dPgF\"])\n",
    "        nu_high = phi @ A\n",
    "        nd_m, vd_m, dpgf_m = anchor_report_high(g[\"nd\"], g[\"vd\"], g[\"dPgF\"], nu_high, alpha)\n",
    "        max_nd = max(max_nd, abs(nd_m - g[\"nd\"]))\n",
    "        max_vd = max(max_vd, abs(vd_m - g[\"vd\"]))\n",
    "        max_dpgf = max(max_dpgf, abs(dpgf_m - g[\"dPgF\"]))\n",
    "    print(f\"{name:<46} max anchor errors: nd={max_nd:.3e}, Vd={max_vd:.3e}, dPgF={max_dpgf:.3e}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9096d8e7",
   "metadata": {},
   "source": [
    "## Save Regression Matrices\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c13c6beb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved unweighted, weighted, and heavy-IR 6th-order regression matrices.\n",
      "ALPHA6_REF_OPT = 1.491525\n",
      "ALPHA6_REF_REG_OPT = 1.491525\n",
      "ALPHA6_OPT = 1.567797\n",
      "ALPHA6_REG_OPT = 1.491525\n",
      "ALPHA6_HEAVY_OPT = 1.491525\n",
      "ALPHA6_HEAVY_REG_OPT = 1.491525\n"
     ]
    }
   ],
   "source": [
    "np.save(\"regression_buchdahl_nu3456_20dim_self_consistent_unweighted_opt.npy\", A6_ref_opt_20)\n",
    "np.save(\"regression_buchdahl_nu3456_20dim_self_consistent_unweighted_reg_opt.npy\", A6_ref_reg_opt_20)\n",
    "np.save(\"regression_buchdahl_nu3456_20dim_self_consistent_weighted_opt.npy\", A6_opt_20)\n",
    "np.save(\"regression_buchdahl_nu3456_20dim_self_consistent_weighted_reg_opt.npy\", A6_reg_opt_20)\n",
    "np.save(\"regression_buchdahl_nu3456_20dim_self_consistent_heavy_ir_opt.npy\", A6_heavy_opt_20)\n",
    "np.save(\"regression_buchdahl_nu3456_20dim_self_consistent_heavy_ir_reg_opt.npy\", A6_heavy_reg_opt_20)\n",
    "\n",
    "with open(\"regression_buchdahl_self_consistent_high_order_metadata.txt\", \"w\", encoding=\"utf-8\") as f:\n",
    "    f.write(f\"BASE_WEIGHT_STRENGTH={BASE_WEIGHT_STRENGTH:.12g}\\n\")\n",
    "    f.write(f\"BASE_WEIGHT_POWER={BASE_WEIGHT_POWER:.12g}\\n\")\n",
    "    f.write(f\"HEAVY_IR_START={HEAVY_IR_START:.12g}\\n\")\n",
    "    f.write(f\"HEAVY_IR_STRENGTH={HEAVY_IR_STRENGTH:.12g}\\n\")\n",
    "    f.write(f\"HEAVY_IR_POWER={HEAVY_IR_POWER:.12g}\\n\")\n",
    "    f.write(f\"ALPHA6_REF_OPT={ALPHA6_REF_OPT:.12g}\\n\")\n",
    "    f.write(f\"ALPHA6_REF_REG_OPT={ALPHA6_REF_REG_OPT:.12g}\\n\")\n",
    "    f.write(f\"ALPHA6_OPT={ALPHA6_OPT:.12g}\\n\")\n",
    "    f.write(f\"ALPHA6_REG_OPT={ALPHA6_REG_OPT:.12g}\\n\")\n",
    "    f.write(f\"ALPHA6_HEAVY_OPT={ALPHA6_HEAVY_OPT:.12g}\\n\")\n",
    "    f.write(f\"ALPHA6_HEAVY_REG_OPT={ALPHA6_HEAVY_REG_OPT:.12g}\\n\")\n",
    "    f.write(\"Algorithm=6th-order self-consistent nu12 solved conditioned on predicted/fitted nu3..nu6\\n\")\n",
    "    f.write(\"Profiles=unweighted, weighted distance-from-d-line, heavy-IR weighted\\n\")\n",
    "    f.write(\"6th-order coefficients: nu3..nu6\\n\")\n",
    "\n",
    "print(\"Saved unweighted, weighted, and heavy-IR 6th-order regression matrices.\")\n",
    "print(f\"ALPHA6_REF_OPT = {ALPHA6_REF_OPT:.6f}\")\n",
    "print(f\"ALPHA6_REF_REG_OPT = {ALPHA6_REF_REG_OPT:.6f}\")\n",
    "print(f\"ALPHA6_OPT = {ALPHA6_OPT:.6f}\")\n",
    "print(f\"ALPHA6_REG_OPT = {ALPHA6_REG_OPT:.6f}\")\n",
    "print(f\"ALPHA6_HEAVY_OPT = {ALPHA6_HEAVY_OPT:.6f}\")\n",
    "print(f\"ALPHA6_HEAVY_REG_OPT = {ALPHA6_HEAVY_REG_OPT:.6f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18177d17",
   "metadata": {},
   "source": [
    "## Runtime Formula For `ModelMaterial`\n",
    "\n",
    "For the 6th-order runtime model:\n",
    "\n",
    "1. Choose one trained regression matrix: unweighted reference, distance-weighted, or heavy-IR weighted.\n",
    "2. Compute $[\\nu_3, \\nu_4, \\nu_5, \\nu_6] = \\phi(n_d,V_d,\\Delta P_{g,F})A_{3456}$.\n",
    "3. Solve $\\nu_1,\\nu_2$ with `solve_nu12_conditioned_high`, subtracting every high-order contribution from the F-C and g-F anchor equations.\n",
    "4. Return $[\\nu_1, \\nu_2, \\nu_3, \\nu_4, \\nu_5, \\nu_6]$.\n",
    "5. Evaluate:\n",
    "\n",
    "$$\n",
    "n(\\lambda) = n_d + \\sum_{k=1}^{6}\\nu_k\\omega^k\n",
    "$$\n",
    "\n",
    "`ModelMaterial._get_coefficients()` must return all six coefficients. The final polynomial evaluator must use every returned coefficient; otherwise the extra fitted terms are silently discarded.\n",
    "\n",
    "The weighting only changes how the regression matrix is trained. Runtime evaluation is unchanged: after a selected matrix predicts $\\nu_3..\\nu_6$, the same self-consistent anchor solve gives $\\nu_1,\\nu_2$.\n",
    "\n",
    "Use the unweighted matrix as the reference. If far-from-d-line wavelengths are more important, compare the distance-weighted and heavy-IR weighted regression-optimized matrices.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv_optiland",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
