{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e58aeeed",
   "metadata": {},
   "source": [
    "# IR-Partial-Dispersion Self-Consistent Buchdahl Model Glass\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33568d0e",
   "metadata": {},
   "source": [
    "**Made by Ziyi Xiong/ 2026.11**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9da5ddd9",
   "metadata": {},
   "source": [
    "## Theory\n",
    "\n",
    "The visible-only model glass uses three descriptors:\n",
    "\n",
    "$$\n",
    "(n_d, V_d, \\Delta P_{g,F})\n",
    "$$\n",
    "\n",
    "Those descriptors strongly constrain visible dispersion, but they do not directly describe the near-IR curve shape. For FSO work near $1.55\\,\\mu m$, add one IR partial-dispersion descriptor instead of a fixed absolute IR index.\n",
    "\n",
    "Let:\n",
    "\n",
    "$$\n",
    "\\lambda_r = 1.55\\,\\mu m\n",
    "$$\n",
    "\n",
    "For the nominal real glass, compute:\n",
    "\n",
    "$$\n",
    "dn_{FC} = n_F - n_C = \\frac{n_d - 1}{V_d}\n",
    "$$\n",
    "\n",
    "and define the IR partial dispersion:\n",
    "\n",
    "$$\n",
    "P_r = \\frac{n_r - n_d}{dn_{FC}}\n",
    "$$\n",
    "\n",
    "where:\n",
    "\n",
    "$$\n",
    "n_r = n(\\lambda_r)\n",
    "$$\n",
    "\n",
    "This is different from storing a fixed $n_r$. During optimization or tolerance, $P_r$ is held as the shape descriptor, while the current IR anchor is recomputed from the current $n_d$ and $V_d$:\n",
    "\n",
    "$$\n",
    "n_r^{model} = n_d + P_r\\frac{n_d - 1}{V_d}\n",
    "$$\n",
    "\n",
    "So if $n_d$ or $V_d$ changes, the whole wavelength-index curve changes, including the index at $\\lambda_r$.\n",
    "\n",
    "The model remains a Buchdahl polynomial:\n",
    "\n",
    "$$\n",
    "n(\\lambda) = n_d + \\sum_{k=1}^{6}\\nu_k\\omega^k\n",
    "$$\n",
    "\n",
    "with:\n",
    "\n",
    "$$\n",
    "\\omega(\\lambda) = \\frac{\\lambda - \\lambda_d}{1 + \\alpha(\\lambda - \\lambda_d)}\n",
    "$$\n",
    "\n",
    "The visible model-glass anchors are kept:\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 IR partial-dispersion anchor is:\n",
    "\n",
    "$$\n",
    "n(\\lambda_r) - n_d = P_r\\,dn_{FC}\n",
    "$$\n",
    "\n",
    "For a 6th-order polynomial, regress the high-order residual coefficients:\n",
    "\n",
    "$$\n",
    "\\nu_4, \\nu_5, \\nu_6\n",
    "$$\n",
    "\n",
    "Then solve $\\nu_1,\\nu_2,\\nu_3$ exactly from three equations.\n",
    "\n",
    "The F-C equation is:\n",
    "\n",
    "$$\n",
    "\\nu_1(\\omega_F-\\omega_C) + \\nu_2(\\omega_F^2-\\omega_C^2) + \\nu_3(\\omega_F^3-\\omega_C^3)\n",
    "= dn_{FC} - \\sum_{k=4}^{6}\\nu_k(\\omega_F^k-\\omega_C^k)\n",
    "$$\n",
    "\n",
    "The g-F equation is:\n",
    "\n",
    "$$\n",
    "\\nu_1(\\omega_g-\\omega_F) + \\nu_2(\\omega_g^2-\\omega_F^2) + \\nu_3(\\omega_g^3-\\omega_F^3)\n",
    "= dn_{gF} - \\sum_{k=4}^{6}\\nu_k(\\omega_g^k-\\omega_F^k)\n",
    "$$\n",
    "\n",
    "The IR equation is:\n",
    "\n",
    "$$\n",
    "\\nu_1\\omega_r + \\nu_2\\omega_r^2 + \\nu_3\\omega_r^3\n",
    "= P_r\\,dn_{FC} - \\sum_{k=4}^{6}\\nu_k\\omega_r^k\n",
    "$$\n",
    "\n",
    "Equivalently:\n",
    "\n",
    "$$\n",
    "M_3\n",
    "\\begin{bmatrix}\n",
    "\\nu_1 \\\\\n",
    "\\nu_2 \\\\\n",
    "\\nu_3\n",
    "\\end{bmatrix}\n",
    "=\n",
    "\\begin{bmatrix}\n",
    "b_{FC} \\\\\n",
    "b_{gF} \\\\\n",
    "b_r\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "The runtime regression predicts:\n",
    "\n",
    "$$\n",
    "[\\nu_4,\\nu_5,\\nu_6] = \\phi(n_d,V_d,\\Delta P_{g,F},P_r) A\n",
    "$$\n",
    "\n",
    "This keeps the model material independent: $(n_d,V_d,\\Delta P_{g,F},P_r)$ define the curve. The nominal catalog glass can still be matched at $\\lambda_r$, but later changes to $n_d$ or $V_d$ move the IR index consistently.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": "aab2ccdd",
   "metadata": {},
   "source": [
    "## IR Partial-Dispersion Solver\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ba9d2f30",
   "metadata": {},
   "outputs": [],
   "source": [
    "LAMBDA_REF = 1.55\n",
    "ORDER = 6\n",
    "\n",
    "\n",
    "def get_n_ref(glass, lambda_ref=LAMBDA_REF):\n",
    "    return float(get_indices(glass, np.array([lambda_ref], dtype=np.float64))[0])\n",
    "\n",
    "\n",
    "def get_p_ref(glass, lambda_ref=LAMBDA_REF):\n",
    "    n_ref = get_n_ref(glass, lambda_ref)\n",
    "    dn_FC = (glass[\"nd\"] - 1.0) / glass[\"vd\"]\n",
    "    return (n_ref - glass[\"nd\"]) / dn_FC\n",
    "\n",
    "\n",
    "def feature_vec_ir(nd, vd, dPgF, p_ref):\n",
    "    \"\"\"Polynomial features through total degree 3 for four model-glass descriptors.\"\"\"\n",
    "    x = np.array([nd, vd, dPgF, p_ref], dtype=np.float64)\n",
    "    feats = [1.0]\n",
    "    # degree 1\n",
    "    feats.extend(x.tolist())\n",
    "    # degree 2 with replacement\n",
    "    for i in range(4):\n",
    "        for j in range(i, 4):\n",
    "            feats.append(x[i] * x[j])\n",
    "    # degree 3 with replacement\n",
    "    for i in range(4):\n",
    "        for j in range(i, 4):\n",
    "            for k in range(j, 4):\n",
    "                feats.append(x[i] * x[j] * x[k])\n",
    "    return np.array(feats, dtype=np.float64)\n",
    "\n",
    "\n",
    "def descriptor_ir(glass, lambda_ref=LAMBDA_REF):\n",
    "    nd = glass[\"nd\"]\n",
    "    vd = glass[\"vd\"]\n",
    "    dPgF = glass[\"dPgF\"]\n",
    "    p_ref = get_p_ref(glass, lambda_ref)\n",
    "    dn_FC = (nd - 1.0) / vd\n",
    "    n_ref_model = nd + p_ref * dn_FC\n",
    "    return nd, vd, dPgF, p_ref, n_ref_model\n",
    "\n",
    "\n",
    "def solve_nu123_conditioned_ir(nd, vd, dPgF, p_ref, lambda_ref, nu_high, alpha):\n",
    "    \"\"\"Solve nu1,nu2,nu3 conditioned on nu4..nuN using F-C, g-F, and IR partial dispersion.\"\"\"\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",
    "    wr = float(buchdahl_omega(lambda_ref, alpha))\n",
    "\n",
    "    dn_FC = (nd - 1.0) / vd\n",
    "    PgF = 0.6438 - 0.001682 * vd + dPgF\n",
    "    dn_gF = PgF * dn_FC\n",
    "    dn_ref = p_ref * dn_FC\n",
    "\n",
    "    M = np.array([\n",
    "        [wF - wC, wF**2 - wC**2, wF**3 - wC**3],\n",
    "        [wg - wF, wg**2 - wF**2, wg**3 - wF**3],\n",
    "        [wr, wr**2, wr**3],\n",
    "    ], dtype=np.float64)\n",
    "\n",
    "    corr_FC = 0.0\n",
    "    corr_gF = 0.0\n",
    "    corr_ref = 0.0\n",
    "    for power, coeff in enumerate(nu_high, start=4):\n",
    "        corr_FC += coeff * (wF**power - wC**power)\n",
    "        corr_gF += coeff * (wg**power - wF**power)\n",
    "        corr_ref += coeff * wr**power\n",
    "\n",
    "    rhs = np.array([dn_FC - corr_FC, dn_gF - corr_gF, dn_ref - corr_ref], dtype=np.float64)\n",
    "    return np.linalg.solve(M, rhs)\n",
    "\n",
    "\n",
    "def ir_anchor_coeffs(nd, vd, dPgF, p_ref, lambda_ref, nu_high, alpha):\n",
    "    nu1, nu2, nu3 = solve_nu123_conditioned_ir(nd, vd, dPgF, p_ref, lambda_ref, nu_high, alpha)\n",
    "    return np.concatenate([[nu1, nu2, nu3], np.asarray(nu_high, dtype=np.float64)])\n",
    "\n",
    "\n",
    "def predict_ir_anchor(nd, vd, dPgF, p_ref, lambda_ref, nu_high, wavelength, alpha):\n",
    "    omega = buchdahl_omega(wavelength, alpha)\n",
    "    coeffs = ir_anchor_coeffs(nd, vd, dPgF, p_ref, lambda_ref, nu_high, alpha)\n",
    "    return buchdahl_eval(nd, coeffs, omega)\n",
    "\n",
    "\n",
    "def fit_ir_anchor_nu_high(glass, n_true, omega, alpha, order=ORDER, lambda_ref=LAMBDA_REF):\n",
    "    \"\"\"Fit nu4..nu_order while enforcing visible anchors and the IR partial-dispersion anchor exactly.\"\"\"\n",
    "    nd, vd, dPgF, p_ref, _ = descriptor_ir(glass, lambda_ref)\n",
    "    num_high = order - 3\n",
    "    base = np.zeros(num_high)\n",
    "    coeff_base = ir_anchor_coeffs(nd, vd, dPgF, p_ref, lambda_ref, 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 = ir_anchor_coeffs(nd, vd, dPgF, p_ref, lambda_ref, 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",
    "    nu_high, *_ = np.linalg.lstsq(A, target, rcond=None)\n",
    "    return nu_high\n",
    "\n",
    "\n",
    "def build_ir_targets(alpha, order=ORDER, lambda_ref=LAMBDA_REF):\n",
    "    omega = buchdahl_omega(wavelengths, alpha)\n",
    "    targets = []\n",
    "    fit_errors = []\n",
    "    ref_errors = []\n",
    "    for g in glasses_filtered:\n",
    "        nd, vd, dPgF, p_ref, n_ref_model = descriptor_ir(g, lambda_ref)\n",
    "        n_true = get_indices(g, wavelengths)\n",
    "        nu_high = fit_ir_anchor_nu_high(g, n_true, omega, alpha, order=order, lambda_ref=lambda_ref)\n",
    "        targets.append(nu_high)\n",
    "        n_pred = predict_ir_anchor(nd, vd, dPgF, p_ref, lambda_ref, nu_high, wavelengths, alpha)\n",
    "        fit_errors.append(np.max(np.abs(n_true - n_pred)))\n",
    "        n_pred_ref = float(predict_ir_anchor(nd, vd, dPgF, p_ref, lambda_ref, nu_high, np.array([lambda_ref]), alpha)[0])\n",
    "        ref_errors.append(abs(n_pred_ref - n_ref_model))\n",
    "    return np.vstack(targets), np.array(fit_errors), np.array(ref_errors)\n",
    "\n",
    "\n",
    "def make_phi_ir(glasses, lambda_ref=LAMBDA_REF):\n",
    "    return np.vstack([\n",
    "        feature_vec_ir(g[\"nd\"], g[\"vd\"], g[\"dPgF\"], get_p_ref(g, lambda_ref))\n",
    "        for g in glasses\n",
    "    ])\n",
    "\n",
    "\n",
    "def regress_ir(Y, lambda_ref=LAMBDA_REF):\n",
    "    Phi = make_phi_ir(glasses_filtered, lambda_ref)\n",
    "    A = np.linalg.lstsq(Phi, Y, rcond=None)[0]\n",
    "    return Phi, A\n",
    "\n",
    "\n",
    "def predict_from_ir_regression(glass, wavelength, alpha, A, lambda_ref=LAMBDA_REF):\n",
    "    nd, vd, dPgF, p_ref, _ = descriptor_ir(glass, lambda_ref)\n",
    "    phi = feature_vec_ir(nd, vd, dPgF, p_ref)\n",
    "    nu_high = phi @ A\n",
    "    return predict_ir_anchor(nd, vd, dPgF, p_ref, lambda_ref, nu_high, wavelength, alpha)\n",
    "\n",
    "\n",
    "def runtime_errors_ir(wths, alpha, A, lambda_ref=LAMBDA_REF):\n",
    "    errs = []\n",
    "    for g in glasses_filtered:\n",
    "        n_true = get_indices(g, wths)\n",
    "        n_pred = predict_from_ir_regression(g, wths, alpha, A, lambda_ref)\n",
    "        errs.append(np.max(np.abs(n_true - n_pred)))\n",
    "    return np.array(errs)\n",
    "\n",
    "\n",
    "def per_wavelength_error_stats_ir(wths, alpha, A, lambda_ref=LAMBDA_REF):\n",
    "    errors = []\n",
    "    for g in glasses_filtered:\n",
    "        n_true = get_indices(g, wths)\n",
    "        n_pred = predict_from_ir_regression(g, wths, alpha, A, lambda_ref)\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",
    "\n",
    "def anchor_report_ir(glass, alpha, A, lambda_ref=LAMBDA_REF):\n",
    "    nd, vd, dPgF, p_ref, n_ref_model = descriptor_ir(glass, lambda_ref)\n",
    "    phi = feature_vec_ir(nd, vd, dPgF, p_ref)\n",
    "    nu_high = phi @ A\n",
    "    coeffs = ir_anchor_coeffs(nd, vd, dPgF, p_ref, lambda_ref, 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",
    "    nr = float(buchdahl_eval(nd, coeffs, buchdahl_omega(lambda_ref, 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",
    "    p_ref_model = (nr - nd_model) / (nF - nC)\n",
    "    return nd_model, vd_model, dpgf_model, p_ref_model, nr, n_ref_model\n",
    "\n",
    "\n",
    "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",
    "wth_shift = wavelengths + 0.01\n",
    "wth_validation = np.unique(np.concatenate([wth_shift, np.array([LAMBDA_REF], dtype=np.float64)]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce867366",
   "metadata": {},
   "source": [
    "## Fit And Regression Matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3a04d3b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IR anchor lambda_ref = 1.5500 um\n",
      "Feature matrix shape: (536, 35)\n",
      "Target matrix shape: (536, 3)\n",
      "Best fit-layer alpha: 1.4153, max=4.745e-04, mean=2.542e-04, median=2.506e-04, max_ref=4.441e-16\n",
      "Best runtime alpha:   1.1102, max=1.813e-03, mean=2.213e-04, median=2.035e-04, p95=3.292e-04\n",
      "\n",
      "Regression target errors for runtime-opt matrix\n",
      "  nu4: max=2.637e-02, mean=2.599e-03\n",
      "  nu5: max=5.542e-02, mean=6.725e-03\n",
      "  nu6: max=1.493e-01, mean=8.482e-03\n"
     ]
    }
   ],
   "source": [
    "ALPHAS = np.linspace(0.5, 5.0, 60)\n",
    "fit_rows = []\n",
    "reg_rows = []\n",
    "matrices = []\n",
    "targets = []\n",
    "\n",
    "for alpha in ALPHAS:\n",
    "    Y, fit_errors, ref_errors = build_ir_targets(alpha, order=ORDER, lambda_ref=LAMBDA_REF)\n",
    "    Phi, A = regress_ir(Y, lambda_ref=LAMBDA_REF)\n",
    "    runtime = runtime_errors_ir(wth_validation, alpha, A, lambda_ref=LAMBDA_REF)\n",
    "    fit_rows.append((alpha, fit_errors.max(), fit_errors.mean(), np.median(fit_errors), ref_errors.max()))\n",
    "    reg_rows.append((alpha, runtime.max(), runtime.mean(), np.median(runtime), np.percentile(runtime, 95)))\n",
    "    matrices.append(A)\n",
    "    targets.append(Y)\n",
    "\n",
    "fit_rows = np.array(fit_rows)\n",
    "reg_rows = np.array(reg_rows)\n",
    "fit_best_idx = int(np.argmin(fit_rows[:, 1]))\n",
    "reg_best_idx = int(np.argmin(reg_rows[:, 1]))\n",
    "\n",
    "ALPHA_IR_FIT_OPT = float(fit_rows[fit_best_idx, 0])\n",
    "ALPHA_IR_REG_OPT = float(reg_rows[reg_best_idx, 0])\n",
    "A_IR_FIT_OPT = matrices[fit_best_idx]\n",
    "A_IR_REG_OPT = matrices[reg_best_idx]\n",
    "Y_IR_FIT_OPT = targets[fit_best_idx]\n",
    "Y_IR_REG_OPT = targets[reg_best_idx]\n",
    "\n",
    "print(f\"IR anchor lambda_ref = {LAMBDA_REF:.4f} um\")\n",
    "print(f\"Feature matrix shape: {Phi.shape}\")\n",
    "print(f\"Target matrix shape: {Y_IR_REG_OPT.shape}\")\n",
    "print(f\"Best fit-layer alpha: {ALPHA_IR_FIT_OPT:.4f}, max={fit_rows[fit_best_idx,1]:.3e}, mean={fit_rows[fit_best_idx,2]:.3e}, median={fit_rows[fit_best_idx,3]:.3e}, max_ref={fit_rows[fit_best_idx,4]:.3e}\")\n",
    "print(f\"Best runtime alpha:   {ALPHA_IR_REG_OPT:.4f}, max={reg_rows[reg_best_idx,1]:.3e}, mean={reg_rows[reg_best_idx,2]:.3e}, median={reg_rows[reg_best_idx,3]:.3e}, p95={reg_rows[reg_best_idx,4]:.3e}\")\n",
    "\n",
    "pred = Phi @ A_IR_REG_OPT\n",
    "coeff_err = np.abs(pred - Y_IR_REG_OPT)\n",
    "print(\"\\nRegression target errors for runtime-opt matrix\")\n",
    "for k in range(coeff_err.shape[1]):\n",
    "    print(f\"  nu{k+4}: max={coeff_err[:, k].max():.3e}, mean={coeff_err[:, k].mean():.3e}\")\n",
    "\n",
    "cases = [\n",
    "    (f\"IR-anchor alpha={ALPHA_IR_FIT_OPT:.3f} fit-opt\", ALPHA_IR_FIT_OPT, A_IR_FIT_OPT),\n",
    "    (f\"IR-anchor alpha={ALPHA_IR_REG_OPT:.3f} runtime-opt\", ALPHA_IR_REG_OPT, A_IR_REG_OPT),\n",
    "]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa10ebe9",
   "metadata": {},
   "source": [
    "## Alpha Sweep Plots\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1c6ab8d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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B7rB2DjFDy1nvze7faxNxiHpDZsG6Q/Zad8S0q2IcQ3JOCVJyS5CUWywDBrEubkU3pStxdVYhzNsFod4uCPNxObOuRZi3q7wVXZdMMUuSo1FwViiyRWLg9pBWAfj7aJacfvaLKd0tXSQiIiIykeLyKiTllCA51xg8JJ+1pOaVXLGrkpuzChG+rgj3cUG4z/m3LvByUXPGJAfGFgu6wPNj22DD8WysOZKBLSey0be5P/cSERGRjdCVVcrgITGnWN4mZIvbYiTmlMjcDVeaRSnCx1UGD5FnlrODB29XBg50aQws6sjeZ4U6W6y/G27tGYmv/03CK78dxq/T+lntFGyWnimhqZz9/uS6nb9fc3OUekOmx7pD1lJ3SiqqaoMGsYigIfHMrcjxcDkiu7MIGP4LHlxq74uuSpf7zhfvgfP+ONZ5R1+P12VgUYdZoQQxaKasrAyO4tZO3vh5Tyri0nWYv/4IJrT3t+qZEsQHzlr7rJpqVqgaoi4qijkrVGM4Sr0h02PdoaasO1V6A04XliM5TyxlcknJN65nFlVe9m99XJ0Q7qVBhLcW4d7i1riEeWngcdFZH6uBCh1ysnUNfIdkr+cdMStUXXFWqCtwpFmhzvflpgS89nucTE+/7olBcNdYXxxq6ZkSmopBr0dVaoZcdwoPgsKO32tTcJR6Q6bHukOmrjtiwLTI35CQbey6dHbLgxgDIYKLy7U8xPi7ySXazxXRfm6I8nOViW89tWoeLDuh56xQ9seaZ3Ewlyl9Y7Bke4psYp37TzyeHt0a1sjaZ9kwCaUSqugwS5fCrjhEvSGzYN2h+hK/NOcUV2LPqSLkJZUjKbdUJqQVgYQYNF1RdemuJlq1EjH+7rKbck0QERPghhg/N/i4MVmqo1BwViiydc5OSjw7pjXuXbQL8zbGY3ynULQJcaxWGyIioroqr6qWU7WezCrCyazi2tv4rCLoyqouO2BajHGobX0Qt37GWzHmgTkdyFZYX98Wsioj2gbJ5a/DGXhs6V78Mq0fNE4qSxfL4RgqKpHz+udy3e+5e6FwZhM3EZElp2w9kVmE42LJ0Ml1EUSI1odL9VxSKIBQT2c0D/KULRC1rQ/+bjLng7VOkkJUHwws6IpNb29c2wG7k/LkQO73/zqOGWOss0uUPTNUVqFgzndy3fepuxhYEBE10bStMoDIEEGE7kwgUSSzT1+KGI8YG+CGZgHG7kvNAt3leqSPFgV5OQgMDGQXTLqoksoS5JTmQFOQisDCdKA4G2VFGfi+JBEju/8PtoCBBV2Rv7sGr13TAfcv3oXPNpzEsDaB6BHtyz1HRER204VJBBBH03VyET+kHcvQ4XRB2WW/G1sEuqNFkLu8FcGDCCICPTQXTRDH6a0djMHYdJVXno+0ojR45iUjouA0UJSF0qJ0vJK/G7lVJfi4WAF1cQ7wwGbMPboECw4twGTXWDx1aL38e9E/4dvISIyEbWBgUUeOlMfiYka2DcR1XcPw4+5TeOL7vVj5cH+4WcEsUZae27mpMI+F6fenI9QbMj3WHdum1xuQml9aG0AczTDeJuSUyNmZLkYECjJ4CHBH80DjIu77uDrXK88D644dMIjvDAVyynKRWpQK79xkROWlQlGciZKidDxfuB85+jJ8mVcOdXE2DNP3YP6x77Dw8ELc7hKDpw//I59G1JzfoyOgVyiQn5uKgGo99LoMeGu8oVVpodd6wxDRG3Dzh9LVD9foc5nHwtY5eh6Li3mgVwA2H89Ccm4pZv60B88Mi4Kjz+3cVJjHwrQcpd6Q6bHu2I6yKj0SckpxLKsUx7NKcCyrBCeySlFSefEfFDw0KjTzd0EzPxfjrb8LYny18Lwg50MVKovykVlUv/Kw7lg3XaUOGaUZ8M5PQWThKShLslBenI5Xy+OQU12OeTnF0JTlIuvWdfgsdQW+T/weN6kj8PyxzfLvRXap9dERqFYqkF+cjsDqauSkHIOr3hUB2gDA2Rdl0UOhd/GH3sUXj+iz4OLsjfLWPZHtHowqvTdG+4/GVcOvks9nnGDeaJxej/z8fOaxsAeOnMfiYraezMGtX26X6/OndMfgVgEOPbdzU9EXlyIpdrRcj4pfBaWbi6WLZNMcpd6Q6bHuWKeconIcPq3DkdOFOJKuw5G0QpzMLr5oK4SzSiG7LLUK8kCrYI8zt+5y9qWLdWEyFdadpletr0ZuWS6UOcfhl5cC6NJRrkvDm7k7kV2lw/uF1VAXZcHw4L94/8RSLDy0ELe5xuKZM92QRAjaVQQLCgXWJJ9CUHU19FPX49uCw/j68NcY69EMDxeWAG6BMLgF4OfKDHi4BqBvaH+4eoYBHiGASm3zdUdcC/v4+Mgf5K50LWz5viw2gvPdG/VrEYC7+sVg/uYEzPjpAP58dKDF59F2iDnlz3pvdv9em4hD1BsyC9Ydy8rUleHgqQLsTy2QtwdPFSK98OI9CnzdnNE2xBNtQz3lrZgyXQysVqss87ln3TGd6sI0qHITAN1pVBSkYGH6ZmSW5+KZcmeodenAnb/jg+PfYcHBBecECxoAK6IjUKVQILfgFIKrq2VXpmC3YPhp/aD2CAbaXA24B0HpEYQXKzLg7hYIj8H9Ae8oKN2DcGtYF9za9tZzjy2A62A+zGNBduvp0a2w4XiWHOj2wi8H8fHNXcz6Kw8RETmmzMIyHDhVIJeDZ24zCssvuq2YtvXsIELcXmogNVkpMTalrAAoTEN1QQo2p21GemEqrtFroS7KACZ8gq+SV2HuvrmYqA6sDRbEr+SfngkW7jllDBZEwBHoEgilQolSrScQMwjwCIbSIxiPVWbAzdUPbv2GAj4xgFcEbg3rilvbnBsswMzBgj1iiwXVm1atwvuTOuOaTzZj5f7TGNk2CBM6Myu0OSlcNIjY+HXtOhGRvSkorcT+1HzsS8nH3hQRRORfNIgQcULzAHd0CPNC+zAvdAj3ki0RYppXsnLlOpTmxiM96xAiK8qgKjwN9H4AqzN34sfjP6JnUSHu3ve73FSEg4+cCRYGJJ9CiAgWClLhpHRCUWURMrTBxqDAMxRKjxDcilxoNF5Qd3wK8G0B+LfCpOAOuKn1TfJvzjbZQm/fEfBTSA0iTuQPD22B99ccw4vLD6JXjB+CvcTQJTIHhVIJ59Yx3LlEZBcqqvSISy/EXhlEGJf4rOILthM545qdF0SI1ghrmJWQzqMXrQTp8uIfwe0RV5SCLWlbEJF1EiPi1gMFKdCXFaDvmWDhz5RTCK2qBlqORlZpltzW1T3W+FwuPlB6hqGvUg+lkxbVfSYCvi0B7wiM8Y9Bn9A+CHYNBtSutS//5EUOiGU7ajsmfjKpwR4c0gzr4jKwL7UAT/2wD1/f1ZNNzkREdA4x+1pqXil2J+dhT3I+9qXm41BaoQwuzhfp64rOEd7oJBYRRIR6wtWZlyrWolJfiYziDBjSDyDi1D4gPxmG/CRMrUpGsqIKX6dlGLsh3bMOu3RxeH/X+xjuHosRGQfl34uRLUHVBuSpFMiL7odQr2aAxgN9PPrg1X6vorlbGDD+W8DZGDDMuUgZRBYtXy1zaVkrflqpwcTgt3cndcbY2Rux8Xg2Fm9Lxu29LT8FrT0yVFQi74NFct3n0duZeZuIrDrZnAgcdiflYdeZJVN3YZcmb1c1OoV7y0CiJpgQg63JguMbxDiGvES5/JayHsm6ZNxcpodP/ilg0lf4vvAI3tz+Jka4x+K9A+truyylhofitJMTTjtrEKzxByqL0ca3DcbGjkUXt0ig4zTAKxzwDMNPKie4OLmc80OkaKeI9TrTWkE2jYFFHTl6grxLifV3xYzRrfDyb0fw+soj6BvrKwfQNRVHSTikL69A3v8tkOueD9wIpZPK0kWyaY5Sb8j0WHculKUrx56UfGMgkZwvB1if3xrhpFSgXagnutS0RkR4IcrX9YJWbnv+TFpF3akoRklWHNSFaVAXpACtx2FHWQa+OvwVoksK8fTuX2s3/Tg8FKfUTuidngGf8nLo8xIR4hECZ6UzqrVeMHSZDIN3BOAdiVn6Ymg8w9AirC/0zu7y7zuLJaBz7bStNbSXSSJI1ll36vO6DCwugQny6m5UMxesjPDAzhQdHlmyC3MntZJfIk3BURIOMUGeaTlKvSHTc/S6I953WmEF9qTqsPdUEfamFSE1/yKtES5O6BDihg4h7ugY6obWQW7QOp21v6qLkXWRMRX2rMnqjsGA0uoyJBUlATlH0DN5O5wKk6EqSMJt3koc1GiwKC0dncUPVgZ3nPLwxMZTG5GpCYZBoUS1ewiqPcIxRGtAgZMW6Hkrcvw6osqjBVqpXfHr8F/lTEtnJ3CrGQFYlF+CIpSY7705KL2Fzzs6na7O2yoMDBkviwny6iYtvxRjZm+CrqwKT4xsiYcGN0NTsHTSmKbCBHkm3p8OUm/I9Byt7uj1BhzPLML2xFzsSMzDjsTcC2ZqEo0OLQLd0S3SB12jvNE10gfRfhe2Rjg6k9adch2QGw/knMCalL8RVxCP8cWliM5NhmHcB1jn5obH/nkM7VzD8N2hrbV/dk9wILa5aPFmpTuu8mwFQ/e7kRnYAhtSNyDGIxzdAruZJKEb2dd5hwnyzICJtC4v3NcNL1/dDo9/vw8frzuB67uFI8SrabJDO0TCISbIMzmHqDdkFvZcd0Sm6kNpBdgWn4ttCbnYmZSL/JLKc7ZRqxToGO6NnjG+chGBhJcLL0ZNXneqKmDIjYci9yQQ0BrxTgp8sf8LOOvS8dL2H2s3+zY4EDtdtIjNz0ZMaQkUeQmICh2LQNdABHhGAEOGAb4xcnlV4wpXj1B4abyM5QEQDGBS60n1rSrUxBQWPO/U5zXZFYpM5pouYfhue4r8Zevd1cfwzg2duHeJiKy8ReJohg5bT+Zgy8kcbEvIkS3PZ3NRq9Atygc9oo2BRJdIb5nPiExDdBwpzE+AV+IWIOsokH0cbxUdwV+qCjyZm4/RxSXAiFdR2WYUfo3/Fd5qd7wk/tAtAPBrjiFuGsQ6OyO8zUAgcjDgG4sWzm5Ye8PaC14rhAeNzIyBBZk0mn5ubBtMnLMZP+5OxV39YuRUgUREZD0XsfHZxTKI+PdkDrbG5yC3uOKcbTw0TjKA6BXrK4MJkT9CzAJIjdrxqM5PQsaJP6E4VoCggjSg2TCcDG2H236/DRqFCuuPHqjdvNjfFxke7kjUugGeLQGtJyI9IzG9y3REuYdDf01vKF185LZM9kbWhIEFmZSYMnBcxxD8tv803vjjCBbd3Yt7mIjIgtILyrDxeBY2n8iWgcT5YyREi0SPGF/0ifVD32Z+cvYmJwYSDaY36JGiS0Fyxj4MOPynsRUi6yhmeajxq4cbHsnNxz0FhYBCheAWw2QW6RKFEkUxA+Ee0Abwb4HbXN1xrXsQmoV0BzTGH+hE5+KpHac2tjoQmRUDCzK5p0e1xp+H0mVuiw3HsjCwZQD3ciMptM4IW/157ToR0aWUVlTLLk3iHCwCimMZRef8u7OTEl0jvdG3mb8MJMR4CfEY1VNRFrJTt+JQyib4Fp5Gh7zTQMwg6PpPx7ifx8lN/k1MgduZaVVjXLygNQClAS2BjoOB6P5wU7vhl4m/INw9HM6q/87tLXkwyEYxsCCTi/RzxeQ+0fhyUwJe//0I+jX3h6qJpp+1VwqVCtoubSxdDCKy0nESR9ILawOJHQl5qKj+b955MTlTxzAv9G/hj37N/NE1yodjJOq3g6FXAHsz9+J49iFcu/N7qDOPAMVZ+M7bC5/5eOEaXRE6ZOcCzm5yYHSYexg8nT2RG3kD3ALay8HXt3mFYWxeMYKDgs+ZkIOJ4cieMLAgs3h4aHMs25mCuHQdftqdihu6R3BPExGZSF5xBf45liUXEVBkF53bvSnES4uBLQIwoKUxmPBhRusrEy0LhaeQnbwFu1L+gTY/BYNyTgH+raC46RtMWzsNukodOueUoFVxtpxTqY2zD1oonBEc1h4YMgkIai+f6o9r/7hgul2NXg+lopSfAbJrDCzILLxdnfHQkOZ44484OUPUuI6hcHHmLCINZaioRP7ny4z79t4boHDm1I5Ejjbo+shpHf4+mol1cZnYk5wHveHccRJ9mvlhQAt/DGgRgGYBbswjcfkdKptyjuYexeGcwxiw/Rv4p+4ESvOw2d0NLwT4oXtpGQblZMppX0WQ0Ce0D0qqSqDv2AMI7CRbIYY5u2HYRZ6eOTzIUTGwqEdyEkulUrdVk3tH4uutSTiVX4ovNsWbJWmepdPcNxV9eQVyX/5UrntMmQClE4O0Ru1PB6k3ZNt1p6SiSs7e9HdcFtYfy8LpgrJz/r1VsAcGtwzAoJb+cgpYzVnnBVFG5r89o7IUZWm7cThxDQqzj2JIbpoMLAz3/oMXN7+II7lH8G6lF0aW5sGgdEIb9wi0UyrQNqQD9MPuBoLayu5Q/zfw//6rB/9ViDofT553qKEsXXfq87oMLC5hzpw5cqmurpb3RcbDsrJzT+p0ZVN7B+GlVYn4dP0JDIvWwtdVbVdp7puKoeS/uifqoqJYa9Hy2DpHqTdke3XndGE5NsYXYEtCAXan6lBR/V+zhMZJge4RnugX44W+0V4I9qwZ7FuNgtwck5fFVhVXFeNk4UnEHPsd0cmb4ZR3EkedVbgzNBiBVVUYejoNBoUSmaeS0NajLTTQoMqrC7J7v4Iqn+bwVDnjgzPPlSn+VyIWudYoPO+QrdYdnU5X520VBv6kccU05l5eXsjLy4OnJ3MyNGRQ4cRPtuBgWiFu7x0ps3PbU5r7pqIvLkVS7Gi5HhW/Ckq3pslqbq8cpd6Q9dcd8RUsZm1afThDLofSCs/593AfFwxpFYAhrQLRO9aXg67PVlWGotTtiE/8G53y04GsOBjuXoOnNs3A6qTVeNQ5Encf3SQ3LXYPwMQAD7TUBuDdtvfBObQL4B1lHNneRHjeIVutO+Ja2MfHRwY3V7oWZotFHVkqjbqtE7tMJM27Zd42fLs9BXf2i0FsgLvdpLlvMme9N7t/r03EIeoNWWXdET+47EnJw5+HMuTU3Ek54idxIzGBXvcoXwxrE4ihrQPRPNCd/fXPqNRXouT4angdXwOc2oWcrCMYEh4k/+3fpFS4GgxQ5J5AW7+22J+9H6qQXkCXh4DQznDzCMFfTRhEXArPO2SLdac+r8nAgsxOzJUuviDFgMO3VsXhs9u7c68TkUOpqNJjy8lsGUz8dTjjnFmcRA6JAc39MapdsAwo/Nw1cHSG/FTg1E4o0nYDfabh29S1eHfnuxjvEoFZB9bJbfwABOgNUKmckdHnAcREDQS8wnFnwJ24u8Pdln4LRA6JgQU1iWfHtMb6o5nyS3VHYi56RPtyzxOR3QcTItv1r/vSZDChK6+q/TcPrZP8wUUEEyKJqLvGgb+Oq8qB0/uAlO1A6nY8lb8L25wMmH86E80rK4GIXghwCUB5dTniVQqg78NAWDe5/OLiA3eNxzlPxzZIIstx4DMZNaUWQR64sUeE7A4lkub99EBfNu8Tkd2pqtZjW0KuDCb+OJiOgtLK2n8L9NBgZLsgjGwbjN6xfo6b7bowDVBpkFxdjCVxS4CsY5ix48faf84ODkSeSosDgbFoHtwX8AhBn8BW+HXir4j0jAQU/+0303asJaImCyxiYmIadCH46KOPYvr06fX+O7I/jw1viV/2pmFPcj5+P5COsR1DLF0km6HQOiN0+ezadSKyHmLMxM6kPPy2Pw2/HziN7KKK2n8L8NBgbIcQjOsYgq6RPlCKQRSORF8NZB7G8WMr8W/qBnTNSkK7vFRg5GsoaT0c3xz5Bu5OrnjK1Q+q8J5ARE887OkLp4A2aB3UBVAZz3duYvES/yciuwgsFi5c2KAXiI6ObtDfkf0J9NRi6oBYfLj2ON7+Mw4j2gY57i929aRQqeDSr4uli0FEZ83mtDclX7ZMrNx/GumF/00J7eOqxuj2IRjfKQS9YvygcrBgQnRZOpayGR02fWLs3lRegIX+vljh4Y6p0KGdaHHQnUZz7+a4rc1t6OjfAfqo4XKshNDV0m+AiMwfWAwaNKjhr0J0xr0DY7Fke7KcBWXxv0m4q38M9w0R2YyMwjIs2pGOP4/F4WRWce3jHhonjGwXLIOJfs39oVY5yI8mpfnQJ2+FMvlf2WWppNtkDFw6UAYX61My4CfGTzi7o59nM+RpndC81R1Al/sAjbu8AHmm5zOWfgdEZMkxFpWVlXBxccHevXvRvn17U5aFHICbxkl2iXru5wOYve44rusWDi8X0ybNs0eGyioUfr1CrntOvhoKNYdHETWVsspqrDmSgR92pWLDsSzoz+Ss06qVGNE2GOM7hsgB2Fr1f5mv7VZpPpC8FUjchE3J6/CRIRfNKirxenYOENwRrr3vR4RHBPLL85E6/G74RQ4GgtrjKpUTrrJ02YnI7Op9daJWqxEZGVmbkZqoviZ1D8f8zQk4kVmET9efxIwxrbkTr8BQUYnsGe/LdY+bxjCwIGqCrk4HThVg2c5UrNiXds4g7E6h7ripdzTGdQyFh9bOfxipLAPUWuxI34FNpzZhwqb5iM1Llv/kpNXgcEgQctXOQMQYIHqAfHzBqAXw0nhxgg4iB9Sgnz2ff/55PPfcc1i0aBF8fTltKNWz0qmUmDG6Ne75eie+2pKIewbEwJ/zthORFcjSlWP5nlNYtitFZsSuEeKlxXVdw3FNl1C46YsRGBhon8kVy3Uoi/8HJ0/+gXanDhhncHriGBYeWogNqRvgGxiJWKUzEN0fnSN7400nPbpHDwfcjInqBG+tt0XfAhHZWGDx8ccf48SJEwgNDUVUVBTc3M6dqWH37t2wx3TqYiHTGNLKH53CvbAvtQCf/XNS5rlo6HERvyza+7E5+/3JdTt/v+bmKPWG6kbUBTGr06KtyVh1KB1VZ/o6aZyUGNk2CNd3C0ffZsZB2KLOZGUV2VfdST8AHP0divj1SE/fjXGhQTAogC3pqdCKz0nWUQyLGAZvjTdaRo2GPqyf/DMx1HrMmaewq/1hJjzvkK3Wnfq8boMCi4kTJ8LezZkzRy41Xb6ysrJQVvbfrB/UeFO6BeDx1AJ8vTUR17TxgK+rukGVvaCgQH7g7PLXwzMMJf/VPVEXFcVai5bH1jlKvaHLK6vU48+jufhhbyaOZ5fWPt4u2A3j2vpheEsfmcgO0CMnO8s+6o7BAKe8E6jyCMd+3XEsT16OVgWn8cgRYzbrYDGrlQEwKDU40v9ZhEWPht7gg76efeUiZGZmWvhN2CabrzvksHVHp9PVeVuFQZSSLqmwsBBeXl7Iy8uDp6cn95QJiap37adbZavFPf1j8NxV9W+1MP56mIWAgAC7PlHri0uRFDtarkfFr4LSzcXSRbJpjlJv6OKSc8WsdMlYtiu1duyEGIg9oVMobu8dhbahnvZVd4oyUXhsJbadXIluqQfhr8uA/qbv8Kcz8PTGp9HcLRQ/6UNgiB0ExAxCjtYDvlpfjpEwMZusO2QV9BauO+Ja2MfHRwY3V7oWbtTUMrt27cKRI0fkert27dCli/3Osy8OJE8EpvfoiJa4c8EOLN6WhPsGNZPJpOpLJG60++Nz1nuz+/faRByi3tA5Sew2nsjG11sSse5opvjhXorwdcHk3tG4oXs4vF2d7afuFKQCO+cDJ9YAp/fhoZAg7Ndq8IqhCNc4uUCpO4U+Ha7Hg50fRJ+QPlAEdkZNto0ACxfdntlE3SGrpLBg3anPazYosBDNoDfddBPWr18Pb2/jIK38/HwMGTIE3333nYyoiOpicMsAdI7wlommxFiLF8a15Y4jIpMprajG0h3J+GprEhKy/8s7MahlAKb0jcKgloH2kcBOBBKVpchz98dbO95CXOY+/HRgC2ouB/o4eaHYyRlO/ScD3R6WMz2Jb+8HOj1g4YITkT1pUGDx8MMPy/5Whw4dQps2beRjhw8fxpQpUzB9+nR8++23pi4n2XEE/ujwFrjjTKvFvYNiEejB8QMX7CeNGsHfvFW7TkSXpyurxKJ/k/DlxgTkFFfUJrG7vnu47O4UG+Bu27uwuhJViRtx4PD3qE7dhe4Zx4C2E+F+/RdYn7IexZXFONJ5EtrFDAOaDcUDrn6YpnSAPBtEZHuBxapVq7BmzZraoEJo27atHOw8cuRIU5aPHID45bBLpDf2JItWi3i8yFaLCyicnOA20jhwkoguLb+kAgs2J2LB5gQUllXVdne6d0Asru0aLpN02rQDP8gZnHB8DX5wrsZr/r7orijDAoUSqCyBWqnG872eR4hbCFoGdgKUxh8iGFIQUVNwauggEpEo73ziMU45Rw1rtWiJKfO3Y/G/YqwFWy2IqP75J77YFI/FW5NQXGGcza9ZgBseGtIcV3cKlflzbFLBKcArDCvjV+LXk79icuIB9D11UP7TAHUAvBROCAnrBcPtH0Lh5icfH99svIULTUSOqkGBxdChQ/HII4/ILk8il4Vw6tQpPPbYYxg2bJipy0gOYGAL/9pWi7nr4zFzPMdanM1QWQXdD6vlusf1I5l5m+iM0wWlsqXz2+3JKK8yzrXeJsQTDw9tjlHtgm1v/ER1FQxJW3Di8DK0iN8M5CcBT8djd8ZubE7bjLDgTugbMwJoOQZh4d3xj2iNYBcnIrL1BHlXX301oqOjERERIR9LSUlB+/btsXjxYlOXkRyk1eKx4S0xef52fLMtCfeLVgtPjrWoYaioRNb0N+S6+9VDGFiQw0vJLcEn60/ih10pqKw2TvEkJoIQAcXQ1oG2NVVqVTkQ/w9wZAXKj/6Oq301SFM7YVXhKYSJ4den98lWiDCPMAyLHAZ4RtX+Kbs4EZHNBxYimBDZtcU4i7i4OPmYGG8xfPhwU5ePHMiAFv7oGumN3aLV4h+2WhDRhUTeiY/XHcfCLYm1AUXvWF9MG9IC/Zr72VRAUVldie3p25Fx8Htcu3WhfExMuB3qE4Y8KHFiyDMI6/YAoPVEZxE4BYr/ExHZUWBRWVkJFxcX7N27FyNGjJALkclaLUa0xO1fstWCiM5VVa3HtztS8P5fx5B7ZpYn8WPE9GEt0CPa1zZ2V1kBcOxP2TKBVmNxKLQN7l9zP9ydXDHOIxTOrccCbcbjdf9Y+LoFQqOqf14fIiKbCizEAO3IyEhUVxsHxxGZUv/m/ugW5YNdSXn49J+TmDW+HXcwkYPbcCwL/1t5GMcyiuT95oHueH5sGwxpFQirV1YIxP2GXQcW41vdMXQoK8OUQp3MOdGx0zK08W2DDv4dUHLtNDi7+Mg/CbF0mYmImrIr1PPPP4/nnnsOixYtgq+vjfxSRDY11uK2L7fhm23JuH9QMwRxrAWRQzqRWYTXfz+CdXGZ8r63qxqPj2iJm3tGQm3lszxVVpVB8eO9cDq2CqguR7K7G/4M8EOS1hVTOt8PtLkaSoUS34//3tJFJSKy/ODtEydOyBmhoqKi4Obmds6/i/EXRA0l+kl3j/LBTtFqsf4kXrqarRZEjpaL4oM1x+X001V6A5yUCkzuE41HhrWAl6uVJoisrpKDrBHeDXP3zcW3cd/ixTIVhleXA/4tMaTNeNzpXIERrW8EAjpYurRERNYTWEycONH0JSE6L6+FaLVYst3YahHsxRmiiOxdZbUe3/ybhPfXHJeDtIXhbQLx7FVt0MwaM2Xr9UDqdiTuXYjouL+A0lzg8TiUVJUgtywXf4f0w/CRHwDBHeCtUOBxS5eXiMjaAouqqip54XfXXXchPDwcjkIk/mPyv6bTJ9bnrFaLE5h1ibwW4pgYDAa7PzYGtQqB816qXbf392tujlJvbMmhtAI8sWx/7TiKlkHueP6qNnKAtmAtx0qUQ1mQAhz6AlX7v8PtbhU4qNHgx8oCtHDxgSEzDtc1vw49AnugV0gv6JVOgMFgXMih8bxDtlp36vO69Q4snJyc8H//93+YPHky7NmcOXPkUjNIPSsrC2VlZZYulkOZ0s1fBhYi8dV17TwR6O580cpeUFAgP3BKpXX3uW603sYuYcW5uZYuic1zqHpj5URXp693pOPLbWmo1gPeLk64r08oxrf3h5NSj8xM4/gKa1Clr0JK3BL02fCavC9qToBLMJygwM4e98Gz3f2ASg1NKdBC3QK52fys0n943iFbrTs6na7O2yoMopT1NGHCBFx77bWYMmUK7F1hYSG8vLyQl5cHT09PSxfHoYiqedO8bdiRmIfJvaPw0tVtL/phE0FfQEAALxCpzlhvrMPJzCI88cN+7E8tkPdHtQvC/ya0g5+7lUyzKr4e03YDVWXICGiOm1fejKIKHdYkp8EzvCfQ5TakhHaEp1sgvDReli4tWTmed8hW6464Fvbx8ZHBzZWuhRs0xmLMmDGYMWMGDhw4gG7dul0weFtk5bY34kDyl82mJ2aIuuWLbfhuRwoeHNL8omMtRNc8ez8+hqoqFK/cKNfdxg6AwqlBH11ysHpjrfR6AxZsScTbq+JQXqWHp9YJr0xojwmdQ60jwV1RFkr2LkbKgSVolXEMCO2K4Knr4KP1gQEG7Bn/GQa1Hyfrzn85sImujOcdssW6U5/XbNDVyYMPPihv33vvvYu+cea4IFPp08wPPaN9sT0xF5+sPyEvPhyRobwSGffMlOsxiasZWJDNSsktwZPL9mFbgrGb0MCWAXjrug4I8XKxfOtEwj/Aji+wL2EN7g3yg49aj9+dtFD6t4CiugKzh85GoEsg8rLzLFtWIiIr1aDAwloG0ZGjzBDVorbVYtqQ5ghkXgsim+za+P3OFLzy62EUV1TD1VmF565qg1t7RVq8laKiugK5vz6E4L3GnBKtFAqoFCqo3HyR+cByBPu1ko9HeETw+4+I6DIa3Z9CDGjWajkVKJm31aImG/dnG+Lx4riLzxBFRNYps7AMM346UJvorke0D965oROi/M7tRtukchMAjSe25B/Fs5ueRWuNHz5TuwGdb4a2+11Y6uKJcI9wiwc9RES2pEEdtURXp1dffRVhYWFwd3dHfHy8fPzFF1/El19+aeoykoMTX+wPD20u17/ZloTsonJLF4mI6mjVwXSM/GCDDCqcVUo8d1VrfHdvH8sEFaK708l1qPxmEjC7C7D9c0R5RSG/PB/xlToUPbwDGPsuENQOEZ4RDCqIiJoisHjttdewcOFCvP3223B2/m8K0Pbt2+OLL75oyFMSXdaglgHoGO6Fsko9vtiYwL1FZAMDtD9Ycwz3L96F/JJKtA/zxG/T++Pegc2gUjZxK0B5EbB9HnbM7Y5b19yP/8vZLqIMID8JYe5hWDBqAX6/7ne4e4Y1bbmIiOxMgwKLr7/+Gp9//jluvfVWqFSq2sc7deqEuLg4U5aPqLbVYvrQFnJ90dZE5BVXcM8QWanSimo8/O0efLDmuLx/d/8Y/PxgP7QM8mj6wmx4B3i/LfD7k6guSMF+rQZ/ePui8sGtwDVz5SZdg7pCrVQ3fdmIiOxMg8ZYnDp1Cs2bG7umnD+ou7Ky0hTlIrrAsDaBaBviicOnCzF/cwKeGGkcUElE1uN0QSmmfr0TB08VQq1S4LVrOmBS94gmL0d+WT4WHVmEmIx/Ma6sAPBthl4978Wzbs4Y1XwC1C5+TV4mIiJ716DAom3btti4cSOios6dwfuHH35Aly5dTFU2oouOtXjgm91YuDkR9wyIhYfmvxYze6ZwViNg9rO160TWaE9yHu5dtAtZunL4ujnjs9u7oUe0b9MVQJcObPkI6HwLfsvZg8/3f44w1yCMuu5LqNtdC4VSiVuarjRERA6nQYHFzJkzZdZt0XIhWil++uknHD16VHaR+u2330xfSqIzRrULRssgdxzLKMJXWxIxbUgzh9g3CrUTPG++ytLFILqkn/ek4pkfD6CiSo/WwR6YN7k7Inxdm2aPFZxC1oY3UXrwB0SWlwCFabjumk+w8dRGTGo5CarIIYCCiRCJiMytQWfaCRMm4Ndff8WaNWtk1m0RaBw5ckQ+NmLECNOXkugMpVKBh4YYu+GJ7lBF5VXcN0QWHqT91qo4PLZ0nwwqhrcJwg8P9G2aoCIvCfj1Uaz6ojfGZK/D695uQHgP2WLh4uSCz0Z8hmFRw6BkUEFEZN15LAYMGIC//vrLtKUhqoNxHUPx4ZrjiM8uxuJ/k3BtGwsMCG1ihqoqlKwTM9kArkN7MvM2WQUR2D/63V6sOZIh7z84uBmeHNlK/gBgdn8+D2ybC+ir0M5JhSqFN4qC2qJk3LdwdbZgfgwiIgfW6AR5RE1NdabV4oll+/DlpkSMaW7/CfMM5ZVIv/UZuR6TuJqBBVlcSm4J7vlqJ45m6ODspMTb13XExC7mn661uLIYXxz4AtqKVNynrwJihyBi0NP40SsIsV6xzD1BRGQLgcUrr7zSoBcYPHgwBg4c2KC/JbqUCZ1D8eHa40jOLcHPB7LxaFgIdxZRE9lyMhvTluxBbnEFAjw0+Pz2bugS6WPexHZHVgCufvhXWS4DC41Kg2tv+wEBzY3dbx1jtBURkZ0EFgkJDUtK1rlz5wb9HdHlOKmUstvFjJ8O4Jud6bh/WFu4ajg4k8icyquq8d7qY/h8Y7y81hdJ78Qg7RAvF/O96Ol9KFo1A+5JW4CA1hh63yZc0/waDIkYAv+IweZ7XSIiMl9gsWDBgvo/O5EZXds1HLPXHUdafhm+35mKO/rFcH8TmcnRdB0eXboXR04XyvuTuofj5avbw8XZTFM+F2Ui+68X8EbaWhxzVuMnJy3UbSdAYajGK/0a1oJORETmxZ94yWaJft33DYyV659tiJe/phKR6Wd9+mJjPMZ/vEkGFTX5Kd6+vpN5gorKMmDT+8DsrtDu/x47XTRIdlZj56R5wJDnALXW9K9JREQmwcHbZNMmdQvHx2uP43RBGX7cdQq39Iq0dJGI7CqL9pPL9mHziRx5f0irALx1fUcEepjn4l5v0GPXnnnoseYled89tCv+1+VWBEYNQCvfVmZ5TSIiMh0GFmTTNGoVbusehPf/ScUn60/ghu7hUKvYEEfUWCv2peGFnw+gsKwKLmoVnh/bBrf2ijTPrEsGA8qqy3H36ruxP2s/FrUZhc6trgE63ogBSn6eiYhsBc/YZPMmtA+An5szUvNK8fOeU7BHCmc1/N98TC5inchcCkor8ch3ezD92z0yqOgU7oWV0/vjtt5R5gkqTv4NfDkC2spSNPduDje1G9J63AF0vllkxDT96xERkfW0WFRXV2Pz5s3o2LEjvL29zVMqonrQqpWYOiAGb646ik/+PoFru4TJWaPsiULtBK+7r7V0McgBppF98vt9SCsok/lipg1pjmlDm5unFbCiBOl/Pg2P3YvhJqaY2vAOnh76HB7o9ACC3YJN/3pERGR9gYVKpcLIkSNx5MgRhwos9Hq9XMi6iGNiMBhwc49wOYA7MacEK/adwsTO5k/URbZfbxz5M11eWY24DB0OnirEobRCHEwrkLfiGj/KzxXvT+qEzhHGc7zJ91PqTqxbeR9maiowytcbM2OuhWHQDLioXODi4mLVx4V1h1h3yNHOO/p6vG6Dxli0b98e8fHxiImx3+k958yZIxfRQiNkZWWhrKzM0sWii1T2goIC+YGb1DkAn21Jw+w1x9A7xAlKc3TbsBBDdTX0u+PkurJrayhUZpri0wHrjdIButuUVepxPKsEcZklOHpmic8tRfVFvismtvfH9IHhcHWuQGZmpmkLUl0B911z4Lbnc3hq1NCFBOFwUGskd3kEmvxikVcb1s7R6g6ZDusO2Wrd0el0dd5WYRClrKdVq1bh2Wefxauvvopu3brBzc3tnH/39PSEvSgsLISXlxfy8vLs6n3Z04dNBH0BAQEoqqjGwLfXy37hH93UGWM72k82bn1xKZJiR8v1qPhVULqZMSGZg9Ube704rKzWY9nOVCzalozjGTroL3Km93VVo32YF9qFesrbjuFeCPM2X90q/OsFeG+dI9cNHW7Ali43oEfkEDgpbWceEUeoO2QerDtkq3VHXAv7+PjI4OZK18INOptfddVV8vbqq68+ZzCfiFHE/Zpf+e2JOJD8ErFOos6JY+Pt6oQ7+8Xgw7XH8fHfJzG2YyiUSjtptTjrRMK6aNp6Y2+fa3Ee/uNgOv7vz6NIyP6vBSDQQyODB7mcCSRCvLTmGZB9npLKEry14y1sKvgXPwW1g9fAp6FoNxH9YJvste6Q+bHukC3Wnfq8ZoMCi7///rshf0Zkdnf1j8H8zQk4mqHDqkPpuKqD/bRaENVl8PVbf8RhX2qBvC9mSxODr8d2CEGgpwUSy5XkAgeWQdH1duzO2I2s0mxsHvkGrmo2tunLQkREZtegwGLQoEGmLwmRCXi5qGWrxey1x/HhmuMY3S7YflotiC7hUFoB3lp1FBuOZcn7rs4qTB0Qi6kDY+GusVA3o6yjwJIbgbwEuCiUeHPgmyitLEX34O6WKQ8REZldg9tTNm7ciNtuuw19+/bFqVPG3AGLFi3Cpk2bTFk+onq7u18MPDROta0WRPYqJbcEj363B2Nnb5JBhVqlwJQ+UfjnqSF4bERLiwUVVcf+xNvfj8OGstOAdyQQ1Rft/NoxqCAisnMNCix+/PFHjBo1Sk4LuHv3bpSXl8vHxaCO119/3dRlJKoXL1c17uxvnLFMtFroLzZqlciG5RSV46UVhzD03fVYvjdNPnZ1p1CseXwQXp7QHgEeGssUTMwF8u+nWPrb3VjkrsUzQUHIn7wcCGpnmfIQEZH1Bxb/+9//MHfuXMybNw9q9X9ZgPv16ycDDSJLY6sF2as/DpzGoP9bj4VbElFZbcCAFv747eH+mH1zF0T5nTtDX5OqqgB+nQ6smoFJhToMcPLFKwPfgrdvM8uViYiImlSD2smPHj2KgQMHXvC4mJY1Pz/fFOUiMkmrhRhrIRZbH2shMm/7znqgdp0cj5jtSSSBfPMPYz6T9mGemDG6Dfq38Ic1SDi+EtG7F0GhUEI94lXM6f0gFJw1iYjIoTSoxSI4OBgnTpy44HExviI2NtYU5SIyWatFXLoOf9r4WAuFsxo+026Ri1gnxyJyUjz704HaoOKOvtH45aH+VhNU/HjsR1y74xUs6X0bcPNSoO80BhVERA6oQYHF1KlT8cgjj2Dbtm1yXt20tDR88803ePLJJ/HAA8ZfVYmsotWiX7RcF7ktONaCbFFBaSXuXLAD3+1IgWh0mzW+LV66uh1U1tACd2w1kJuAwopCVBmqcNDNA4YWIyxdKiIispAG9amYMWOGzAI4bNgwlJSUyG5RGo1GBhYPP/yw6UtJ1Ii8Fgs2J9a2Woyx0bwWhupqlO8/Jtc1HVtCoVJZukjURLM+3bVwB45nFskpZD+6uQuGtQmy/L4Xg7S3fgysfhEIaIU77lqNaM9oDI4Y3CQJ94iIyI5aLMQXx/PPP4/c3FwcPHgQ//77r0w1/uqrr5q+hESN4O3qbBetFoayCpwaea9cxDrZvz3Jebjmk80yqAjy1OD7+/pYR1BRVoikH27H/3a9iyoYgIheUKhdMCRyCIMKIiIH16DAYsGCBSgtLYWzszPatm2Lnj17wt3d3fSlIzJRq0XNWIvVh217rAU5zsxPN33+L7KLKtA2xBPLH+qH9mFeli4WkLoTlZ/1xz2Fu7DU0wNzu18LjP8QcHK2dMmIiMhWAwvRFSooKAh33303tmzZYvpSEZm41eKOM60WHzCvBVn5zE9z/zmJB77ZjfIqPYa2DsSy+/sgxMvFsgXTVwMb3wXmj4I6LwlPlDmhm1dL3Dj0bdGEbdmyERGRbQcWItP2V199hezsbAwePBitW7fGW2+9hfR0/hpM1unu/jEyCzFbLciWZn6aN7k73CyUPfts+7L249iJPwB9FdDuWoy+ayPmT1iGANcASxeNiIhsPbBwcnLCNddcg19++QUpKSlyligxK1RkZCSuvvpq+bgY3E1kjWMt2GpB1qa4vEoO0q6Z+ekla5n5Sa/H6sTVmPLnnXjKyxmlV38EXD8fcPGGUtGgrw8iIrJjjf5mEF2i+vfvjz59+kCpVOLAgQOYMmUKmjVrhvXr15umlEQmwFYLstaWioeW7MbG49ly5ifRSnFHvxjLFqqiBPjtMeDPZ9E9uDt8tb5o5d8e+g7XsesTERGZPrDIyMjAO++8g3bt2snuUIWFhfjtt9+QkJAgu0pNmjRJBhhE1jlD1AmbnSGK7GtMxXM/HcD6o1nQqpVYfE8vy8/8lH4Q8V8MAnbOB7Z9Bl9dNr4f/z3eGvAW3NRuli0bERHZX2Axfvx4REREYOHChbIblAgkvv32WwwfPlz+u5ubG5544gnZTYrIGlstjpwuxOrDGbAVCrUTfJ66Uy5inezD+38dw7JdqbL705xbuqJrpI/lCqPXo/rfT/HSjxNwjWspdnkHAbf9CAS0hL+LP6eSJSKiK2rQFUpgYCD++ecf2f3pUgICAmTrBZHVzRDVNxof/31C5rUY2TYISkv3Y68DhbMavk/fZelikAl9sy0Js9edkOuvXdPBYi0VlZUlwJJJUJ/aC1WFDlX+vjAoFDg48BF0az7MImUiIiIHCiy+/PLLOiXRi4qKasjTE5m91WLhlsTaVovR7YO5x6lJ/XU4Ay8uPyjXHxnWAjf3jDTvC5bkAqd2A2m7gVO7ALULcMNCPL3haaxNWosPiqsxoEIHaDzxbNfHMCGmD3qE9DBvmYiIyO40uE9FcXGxbLVITk5GRcW5mYCnT59uirIRmYWPm+21Whj0elQeS5Lr6pZRUCg5I4+t2pWUh4e/3Q0xxOfG7hF4dHgL87xQdWXtOImUwiS84u+DIqUS36ZlAM4esuuTk8IJFfoKHGk3HgOuuxXwbwU3lRMYUhARUZMFFnv27MFVV12FkpISGWD4+vrKnBaurq6ymxQDC7KlVos/D6VjTIcQWDNDaTlSBkyW6zGJq6Fws3DCNGqQk1lFuOerHSir1GNIqwC8dk17s4xdEIPCy5ZMgsvJdfK+p1KJf12MdaZgxMvwiuwr1+/teC/u63QfIj0iOdsTERE1WoN+9nzsscfkAO68vDy4uLjg33//RVJSErp16yZniiKyhVaLmhmi3v3rGKqqmXeFzCuzsAxT5m9HXkklOoV7Yc6tXeGkMn3L0/G847jzzzsxy9sFcPUHxr4Lr6fi8caAN/D9uO/h1mcaENETUCoR7RWNKM8oDswmIiKTaNC32t69e+WsTyJvhUqlQnl5uZwl6u2338Zzzz1nmpIRmdnUgbHwdlXjRGYRftpzivubzEZXVok7FuxAal4pov1c8eUdPeDqbMLZvQpSgZ/uA3Z8iSp9FXZn7MbfBceRde8aoMc9gIsPxsWOQxu/NnBSclYxIiKyosBCrVbLoEIQXZ/EOAvBy8uLU8ySzfDUqvHQ4OZy/YO/jqGsstrSRSI7VFGlxwOLd+Pw6UL4uTnjq7t6wt9dY5onL9eh6K8Xseez3sD+74D1b6CNZwxm9pmJFRNXIMDbwon2iIjIoTQosOjSpQt27Ngh1wcNGoSZM2fim2++waOPPor27dubuoxEZnN7nyiEeGmRVlCGxf8aB0cTmXKswzM/7semE8as2gvu7IEoPxMkmauukq0T8XO6YmzKj5jm74mCqD7ArcsAtRbXt7weIe7WPW6IiIjsT4MCi9dffx0hIcYvrddeew0+Pj544IEHkJWVhc8//9zUZSQyG61aVTsrz5y/T6CwrJJ7m0zm7T+P4uc9p6BSKuSYio7h3o1/0oxDwNx+wMrHEVWYCT84wcctCOlXfwCEdjFFsYmIiBqkQZ1tu3fvXrsuukKtWrWqYa9OZAWu6xqOzzbEIz6rGF9siMfjI1tZukhkB37Zewqfrj8p19+4tgOGtAps9HPqDXr8nfAnhmYfh8LFF6rBz+Kj1qMQ6B4KtUptglITERE1HCfDJ4cnZuZ56kww8cWmBGTpyq1unyjUTvB66Ca5iHWybicydXj2pwNy/aEhzTCpe4RJulU9u/FZPHp0AeYPmAo8vAvodS/CvKIYVBARkVVwqs+4irrOt7579+7GlImoyYns22IK0H2pBbJL1EtXt7Oqo6BwVsP/pYcsXQyqg5KKKjlYu6SiGn2b+eHxESZoAdvxJRTNhqC9f3usTlqNoOgBgKsvjwcREdlmYDFx4kTzloQuLXET4OILBLXlXjITETQ/M7o1bvliG77ZliQT6EX4unJ/U71bFZ7/+SCOZxYh0EODD2/qIsdXNJheD6yZCWz5CPCJwe33bcDA8IEy9wQREZHNBhazZs0yb0no4rZ8DKx+HogZCExewey4ZtS3uT8GtPDHxuPZeP+vY3jvxs5WUysNej2qUjPkulN4EBRnpnsm6/LdjpTawdof3dwFAR6NmFa2qgInf74DX2VsxYtimu9uUwCNB6K0nqYsMhERkck0+urkwQcfRHZ2NqxVfn6+HGzeuXNnORXuvHnzYFPajAdUGiBhA3DsT0uXxu49NcrYbeXnvacQl14Ia2EoLUdyt0lyEetkfQ6eKsCsFYfk+pMjW6FXrF/Dn6ysEBXfXIf7CvfgZw93fNr7JqD/Y/xhgYiI7DuwWLx4MQoLrecC7HweHh7YsGGDzBa+bds2OVVuTk4ObIZPFND7fuP6Xy8C1ZwO1ZzEdKBjO4TAYADe+fOoWV+L7IeYpvihJbtlMrxhrQNx38DYhj+ZLh1YcBWcEzZgZn4Junk2w+TBb5myuERERNYZWIg+xdZMpVLB1dXYV768vFyW19rLfIEBTwCufkD2MWDXQkuXxu49PrKl7Mqy5kgmdibmWro4ZOXE+eSpZfuQlFOCMG8XvDupE5SNGFdR8vuTQMYBwC0AA29ejgUTf4a31gT5L4iIiMzM4h21RWvC+PHjERoaKgfQLl++/IJt5syZg+joaGi1WvTq1Qvbt2+vd3eoTp06ITw8HE899RT8/f1hU7RewOBnjevr3wDKCixdIrvWLMAdk7qHy/W3VsXZXiBKTerLTQn481AG1CpjEjxvV+cGPY+oZx/s+gA3qfOQ33wYcPdfMuFdXWfjIyIisvnAQqfTITa24c3+xcXF8qJfBA8Xs3TpUjz++ONy8LiYxlZsO2rUKGRmZtZuUzN+4vwlLS1N/ru3tzf27duHhIQELFmyBBkZxkGwNqXbnYB/S6AkB9j4nqVLY/ceGdYSGicldiTmYf3RLEsXh6zUrqQ8vPlHnFx/YWxbdI5oYMtCuQ755flYmbASCbpkbOhzB+AbY9rCEhERmZlTQ7sXnT59WmbdPpsYuyAeq66urvNzjRkzRi6X8t5772Hq1Km488475f25c+di5cqVmD9/PmbMmCEfE+Mn6iIoKEgGJhs3bsT1119/0W1Edymx1KgZP6LX6+ViMQolMPxlKP54CobgjsZpKEkeE/FLr6mPTaCHM6b0jcLnGxJkq8WA5n6N6t7SWGe/P7nO42/xepNbXIFpS3ajSm/A2A7BuK1XRMOeLy8Rivmj4DXgCcwZ8jGO5MZhXMw4y55vqMnPOWT/WHfIVutOfV63QYHFpbqGiAtyZ+eGdQO4mIqKCuzatQvPPvvsf00sSiWGDx+OrVu31uk5ROuEGGMhBnEXFBTIrlcPPPDAJbd/44038PLLL1/weFZWFsrKymBRXl2ASX8AKmfgrBYbRyYquziuok6KumFK17X1xJJ/VYhL12HxpjiMbt2IWX4ayVBSdk5dVBRrLVYWe9DYeqM3GPD48hM4XVCGCG8NHu8fLI9LfSnKdXBbPgnuxZmo2vU1PCOuQi+PXue0yJLjnHPIvrHukK3WHdE7ySyBxezZs+Wt6PP7xRdfwN3dvfbfRCuFuGhv3bo1TEVMYyueV7Q0nE3cj4szdj+4kqSkJNx77721g7YffvhhdOjQ4ZLbiyBGdL06u8UiIiICAQEB8PTk/PHW+GET9VEcH1N/2ER73P2DS/HO6mP4clsGburbCs5OlrmQMJRXIOdOY5JKv5BgKDSmC+AdUWPrzcfrTuDfpELZXW7u7d0RE9KAc4O+Cpu+mYyX3cvwHkLR4bZlCPQIqf/zkN2cc8i+se6QrdYdMcbZLIHF+++/L2/FBbrokiS6RNUQLRVigLV43Jr07Nmzzl2lBI1GI5fziQNpNV8i+mpgz2LgxBpg0tcOP7e9+LCZ6/jc1T8GX21NQkpeKb7flYrJfaJhES5aBL79hGVe2041tN5sOZGND9Yel+uvTmyPdmENG1dh+P1ZfFmWgEytFivaD0Unr7AGPQ/Z1zmH7BvrDtli3anPa9YrsBCDn4UhQ4bgp59+go+PD8xJzN4kgpfzB1uL+8HBwXBYRZnAqhlAZQlw+BegnfGXbDI9V2cnTB/WAi8uP4jZa4/juq7hcNM0qAch2YHsonJM/24v9Abghm7hmNQ9omFPtO1zKHZ+gU8USizoOxn3DX7H1EUlIiJqcg0Ke/7++2+zBxU1rSDdunXD2rVrz2kOEvf79OkDh+UZAvR92Li+ZhZQxUzM5nRTjwhE+7kiu6gCn6w/AUsQrYTV2Xly4fS3liH2+zM/7JfBRcsgd7wyoX3DnijjMLDqGbnqNmwmpo34EGqV2rSFJSIisoA6//Qqxh28+uqrcHNzO2cMwqVmcqqroqIinDhx4pxWEdF1ydfXF5GRkfK1pkyZgu7du8tuTR988IGcorZmlqimYvFZoc7XZxoUuxZCkZcI/bbPgT4PwRE1xUwJKgXw7JjWuG/xbszbEI9ru4Qhxt8NTUlfXIqkNlfL9aj4VVC6uTTp69ubhtSbb7cnY21cJpxVCrw/qRM0Top61zvxmq+c+A69e9+B0WWVMPSZzhm+bIylZ2ch28W6Q7Zad8wyK9SePXtQWVkp10U+iUslbapvMqedO3fKrlU1aoIWEUwsXLgQN954o5xtZebMmUhPT5c5K1atWnXBgG5TE3k1xFIzda5VzAp1HpduD8PrnxeAf95GVthwGLTmb0Vy1JkSOvoBvaM85YDdF37ai/cmNG/SxGWcFcqy9SY5rwyv/nZErt/fLwx+qjJkZtb/fLDu9Dr8dOJnrFA4IbT/AgQ1YCYpcuzZWch2se6QI8wKpTDUsV/F/v37ZdI5RzuRilmhvLy8kJeXZ32zQumroZg3CIqMQzD0uh+GUW/AET9sIuhripkSErKLMebDjaioNuDz27tieBvzBrcXtFjEjpbrbLFo2npTWa3HDZ/9i/2pBegT64dFd/Wof06TqnIo1r+Bqn7T8f6h+Yj2jMb1LS+eS4esW1Oec8i+sO6QrdYdcS0shkCI4OZK18J1brHo0qVLbVI8kWl7x44d8POz3Lz+Tc0qZwAR5Rn5GrBoIhQ750Mx4AnA/dykhY6gqWZKaBbogXsGxOKT9Sfx6sojGNgyEFr1fzOjmdVZ780q66Id15s5a47LoMJT64T3buwEJ6d6HnPx281vjwL7v4M65V88fdefDj+Tm63jzD7EukOOdN5R1uM167ylt7d37axQiYmJ7F9qLZoNAfo/Dty5yiGDiqY2bWhzhHhpkZJbirn/nLR0ccjMdiXl4uO/jWPAXrumA0K86j+25fT6V/BV4koYFCpg8AwGFUREZLfq3GJx3XXXYdCgQQgJCZFRkxhMfXYei7PFx8ebsox0JcNncR814fSzL4xti4eW7Man60/K6WcjfF25/+1QUXkVHlu6T04te02XMIzvFFrv56g4+CMePr4ER/18UNRqNB5qNtQsZSUiIrKpwOLzzz/HtddeK2dwmj59OqZOnQoPDw/zlo7qryAV8Ag9p+sMmdZVHYLRt5kftpzMwau/Hcbnk7tzF9uhV349hOTcEoR5u+DlCe3q/wSZR+C8/CFMclHhM5dQXDPoFXMUk4iIyGrUK9PX6NHGwaO7du3CI4884lCBhdVNN3sx/7wNxaZ3YbhxCdB8GByBpaZgmzWuDcZ+tBmrD2fg77gMDGoZYNbXMygVcL9xdO261ddFG683qw6m4/udqRATf717Q0e4O6vqt8+rK6D4aSoUVaW4IWAIxt7wNVyc3Xnc7IClp30k28W6Q7Zad8wy3ezZFixYAHtnC9PNns8jJxVu1RUo3zwH+Z4d4AgsNQWblwK4oXMAvt2diZnLD+Cb29rC2cnMr//C3fImqyDfvK/j4PUmq6gCM346LNdv7xaMaLcqZGZm1uv58zJ3I1aXCbXWG9n9XoE+vwQ6lJj0PZBjTvtItot1h2y17phlullHZdXTzZ4v+ziUn/SEAQoYpu8FvCNh7yw5BZuurBLD39+ILF05nhrVEg8Matakr0+mrzd6vQF3frUTG49no12oJ368v0+9A8by6nLc+vutcFGq8VbLyQhtMYaHyo5YetpHsl2sO2Srdccs0806OpuY4jOwFRA7BIr4v6HYNR8Y4Rh9ui01BZuXqwbPXdVaDvD9eN1JXNMlHKHe5smILeL/miR5Cldtkybnc6R68/XWBBlUaJyU+PCmztA61/8UGZ8Xj/TidKhVarhE9rb+8wbVG6ebpYZi3aGGsrvpZslG9JxqvN29CKi07q5b9mBi5zD0iPZBaWU1XvvdmJnZHERQkRA9Ui5nZ+Em0zmeocMbf8TJ9efHtkHzwAaMIVv5BNql7MOP43/A+4Pfh5+L4+T6ISIiYmBhb1qOBrwigNJc4NBPli6NQ/yC8PLV7SESMa/cfxpbTmRbukjUABVVejzy3V6UV+nlQPzbe0fV/0kO/gTs+AL45SGElOnQNagrjwURETkUBhb2RqkCut9pXN9p/4PsrUHbUM/aC9FZKw6hspqzxdiad1cfxeHThfBxVeP/ru9Y/65mhWn4aP0MHHVWAwOeAAJamauoREREVouBhQ3YmrYVx/KO1f0Puk4BBj8LTPranMWiszw+ohV83ZxxPLMIX21J5L6xIQs2J+CzDcaknm9c2xGBntr6PYFej1U/347PPTS4PTQEub3uNU9BiYiIrBwHb1t5HosvD36J2Xtmo09IH8wdPrduf+TiCwx82rhu53OtW3pu5xoeWpWcGerZnw7igzXHMK5DcP0vUC/j7Pcn1+38uDZVvVm0NREv/2ocG/PwkGYY2Taw/nVp21z0SNqNQYEBaNvhdni7+Fu8PpL9n3PI9rDukK3WHbPnsXAE1pLHort7d7ioXBCsDkZaehqclA04ZGJGYTudRcjSczufbVCEM9oGueJwRgleWr4PL42OMdlznz1gW9RFRbHpghZHJOrN9ztT8MEW45iY27oF4ZaOXvXOV+GUexx+a2bBT6/H/5rfh+LYm+r9HGRbrOmcQ7aFdYdste4wj4Wd5bHQVejg4dyAGWoSN0Ox8R0Ymg0B+k6HPbL03M7n25eaj2s/3SpjuQV3dDdZRm59cSmSYo2Zt6PiV0HpZp5pbR3Fj7tS8PSPByGS+NzZLxovXNW6QVP45mz9EP5/vQw0Hw7DzUvtNoAn6z3nkO1g3SFbrTvMY2FneSy8tF4N+8P8RCBhPRR58UDfh40Du+2QNc0L3iXSF1P6RGPhlkQ89cMBrHp0APzdNY1/YrUT3MYPlqtKtZNVvFdb9cveU3jmJ2NQcVuvSMwc17ZBQcXR3KO4LWEJJg+6Fw92exQqlX1+vsi6zzlkW1h3qKGYx4JMTiTdmr17Nqr0VXX7gw7XAy4+QH4ycHw1j0gTmTGmNVoFeSC7qBxPLdsnmy4bS6nVIHj+q3IR69Qwvx84jce/3we9AZjQ3h8vjW9YUCGsTV6LsuoyHDOUQukRwkNCREQOjz+32AgRTNz6+62Yd2Aefj35a93+SO0CdLnNuL59nlnLR//RqlX48ObOcHZS4u+jWfh6axJ3jxVYfSgd07/dg2q9Add1DcMzwyKhFAlI6qusAPh+Ch6MGIV3B72Ll/q8xEzoREREnG7WdohB25PbTka3oG5o7t287n/Y/W7RgAacXAvknDRnEeksrYM98dyY1nJdZOSOSy/k/rGgv+My8dCS3ajSGzChcyjevLYDlA0dD/H7U8Dh5cD3kzEyagSzaxMREZ3BFgsbclub27Bg1AJ0COhQ9z/yjQFajDCu7/jSbGWjC03pG40hrQKMWZ2/3YuySuMMYw0dvH0yYIBcxDrV3cbjWbhv8S5UVhswtkMI3r2hE1QNaakAUHR4BWanrEKZ6Fs/7n0O1iYiIjoLAwsbolKqGtblosdU4+3exUBFscnLRRcnjtX/3dBJDt4+mqHDm3/EcVc1sS0ns3HPVztlcDeybRA+uKkznFQNPO1VlODjjc9jnrcXnmzZDYjoaeriEhER2TTmsbDyBHkXU1ldiR+O/4BqQ7VsxbiiZkOhaD0ehpZiulKlXSVXs3TSmCvxdVXj/67vgDsX7pQzRfVv7oehrQPr/TxMkHdloh7oyquQWViOTF05ErOL8drvcSiv0suWo9k3dYZK8d9nub71RrHhHfQqyMI6tT9u7j3DauscOfY5h6wX6w7Zat1hgjw7SpB3MVsyt+DNPW9Cq9Kih3sP+Gh8rvxHg98x3uYWwJ5YOmlMXbTxBm7sHIilezPlLFGLb2sLPzd1vZ7D0RPkieObV1qFlPxypBWUI7u4EtlFlcbb4krkFFciq7gC5VUXzsDVO8oTL40IR36uMRleQ+qNKu8k/LfMxhB9Jdq2egIGbXsmwnNQtnDOIevEukOOkCCPLRaX8NBDD8mlJkGeSEpiqQR555sQMAF/Z/2NfqH9EBsaC7Wqfhep9vZhE12OrD1Z1UvX+GFf+lbEpevw9j9p+HJy93rNSCQT5J1Zl+/VThPkFZZWIiG7GAk5xUjMLpHriTnG26Lyuk2z7Kl1QqCHBgEeWnQI88Sjw1vImboaVW/+fRUKfSUMLUYhoPdkjq1wYLZyziHrw7pDtlp3tNq6/5jJwKKOrC0Z0kfDPmrYNJl7lwBFmcDwWbAXtpBwyEWjxEc3d8G4jzbhn2PZ+OrfZNzdP6buT3DWe7P291oXurJKGWQdOV2II6d1OJahk8FDbnHFJf9GDC8K9XJBlJ8rgj21CBSLhwZBcl2DIA8tAjw0cHFWmbTeZJdm42GnHDzcdyr69ngYCibCc3i2cM4h68S6Q/aeII+BhSPJTQBWzRCpm4HeDwDu9e/rTw3XIsgDL4xtgxd/OYS3/ohDn1g/tA21jlYwc9HrDUjOLTEGELWBRCFS8y49s5UIFqL93RDr7yZvY84skb6uF7Q8NIXP93+OgzmH8KGfAb29IzjjBRER0SUwsLBxuzN2Y/ae2Xi+1/No4dPi8huHdgbCewCpO4DdXwEDn2qqYtIZt/WOwj/HsrDmSCYe+W4PVkzrX7df2FVKuA7vXbtujUTfT9FtaV9KPvam5GN/ar5slSipuPg0u6LVoU2IB9qEeKJVsAeaBbjLQMJdY0WnpeN/YXrH+6FSqDAudhyUCuvc90RERNbAir7BqSEWH1mMXRm78Om+T/He4PfqNvWsCCx2LgD6PQaoWAWauinzres6YvSHG3E8swiv/X4Y/5t45bwkSq0GId/+H6xJTlE59qWKIKJABhIioCgorbxgO5GBvGWQO9oEe6J1iKcxmAj2hI+bM6xa8jbgm+vhHtAGz0xdBzi7WrpEREREVo1XlTZuWpdp8HT2xAOdHqjbH7SbCPz5HFB4Cjj6O9D2anMXkc7j567Be5M64fYvt2Pxv8kY1DIQI9oGWfV+qqrW4/DpQuxIzJNBxN6UPKTkXtidyVmllN27Okd4o1OEF9qHesluTA3OHWEp1ZVIX/kIgsV6eDcGFURERHXAwMLGxXrF4qW+L9X9D5w0QNfJwKb3gB3zGFhYyIAWAZg6IAbzNiZg+rd7MLlPFO4eEINAD+uYRlZkCRctEDsSc7EtIRe7k/JQfJEuTbEBbjKIkIFEuLfs1iRaKGxdysa3cY1WhxHBIZg15HlYx1EhIiKybgws7Ey1vlpm6L6s7ncBmz8AEjYAJ9YCzYc1VfHoLE+OaoWDpwqxNT4Hn22Ilwn0buwRgfsGNUOYt8sF080mnmldij68wuTTzYpZmnYl5clAYntCLvalFKCi+txEPB5aJ3SP8kHXSB90jvRGx3BveLnY4VTH+SnYuvcLVHi7Icu/OTQeIZYuERERkU1gYGGDmbcvpqC8QI6zOJB9AIvGLLr8IFPPMCh63gfkJcDg18KmM3FbOhtlY6iVCiy+uwfWHc3CJ3+fxJ6UfHy9NQlLtiVjYpdQ3D+omZwZqfZ9nkmSJ99rI9+vaJHYmZSHzSdysPlkNg6nFUJ/Xm45f3dn9Iz2RY9oH/SM8UXLIA+ozsu9YYv7/Ur1RvHHM5iUn4s2HtHwGD5bbicWoivVHaKGnneIrLnuMPO2nWfevpjCikL8cvIXlFSV4K+4v9DFr8vl/6DTNEChAsoVQGYmbJWls1GaQgdf4JNrY7ErVYeF29OxM0WHH3adwk+7T2FoCx9M6RGM5m7KRmXertYbcCyrBDuSddieXIj9aUWoqD73YjnMyxmdQj3QOcxdLhHeGjnY3KgMOdnWW/9NVW80iWvhc3QlDEonhPR5GVVlrsgss93PB5mePZxzyDJYd87dFzXXV3Rl4nwjsl9XVlae9b1sWuJ5VSrVRZ+/Ppm3FQb+FHdZNZm38/LyrCbz9qWsOLkCwW7B6BHUo/4Vr7IEULva5MlJXGjbUxbcPcl5mLP+JNbFZdU+NjrWC089/6xcj4pfdcWuUOJjLfJHbBItEieysTU+94IZm4I9NejX3B/9mvmhV6wvQrzsM5t3ferNqgML0XvPD/AJ7Q7D8HqMXSKHYY/nHGoarDvG76aMjAzk5+ez2jWg/pj7nCOePzo6Gs7OzhdcC/v4+MgfVa50LcyuUPXY2db+JTKxxcT6/1FJLrDqWSArDrhnrU1OP2tvmUy7Rfth/h1+snvSnPUn8PuB01h/NAs1WUcGv/sPytXO+K93jnFF3DecNYtTYVnVOc/roXFC72Z+6C+Cieb+aBbgZrZfPmyx3hzJOYJn930IT60nfu77IPztpD6R6dnbOYeajqPXndOnT8uL06CgILi6ujr0d1B9A7Kqqio4OTmZbZ+JwCUtLU0GfpGRkee8DjNvU91VVwLH/gDKCoBtc4G+07j3rISYtnXOLV0Rn1WEeX8eBr42Pp6tK0eZ+sp9/tUqhRxoLQOJFv7oGOZle9O+NpXqShhgQDPvZmjm1Qz+7nKiWSIiMtVptrpatlQEBgbCz8+P+9XKAgtBtMSK4EK8llrdsMlZbO/nabqs8upyLI1birXJa/HFyC+gVl2hYngEASNeBX6dDvz9GtBmPOATxb1sRWID3PHaNR2Q8Jjx/o8P9IXC1dhtSZxfas4xCvFf7ToQ5uMCV2d+xK+ougr4ajzaBrXH0uHzUOZk5Yn7iIhskBgfIIiWCrJONV2gRBDIwIIkMRvUwkMLkVWaJYOL0TGjr7xnutwO7F8KJG0GVj4O3PrDf1erZB2USmj7dparMWHeULpoLF0iu6Ff/SKUyVuB9ANQ938Ualf+kkZEZC7s/mTfx4b9IuyMWqnGg50fxIu9X8TA8IF1+yPR13P8h4DKGTixBjj4o7mLSfUkAomwXz6SC4MK0yneuwg3p/yI39xcgYmfAl7hrJtERA7kl19+QZs2bdC5c2ccOHBA3tbMgvTBBx8gPT3d0kW0KQws7ND1La/HpFaT4FqfWZ78WwADzwwPXjXDOKibyI45ZR/BdxtfwmGNBu8HR6C4BRNFEhE5mrlz52LmzJnYu3cvOnToIG89PDzkvzGwqD8GFvSffo8CAa0BkVwvN4F7huxXSS68/3wId+Xl4l6FH94dORduamMyQiIiapoBySUVVWZdrpRRYfr06di4cSOee+459O3bt7Y7kBhk/sorr8iBzDfeeKNsxRABx/nEtq+99hp69eolp2ldvnw53njjDXTv3h0tWrTA+vXr5XZiMPSoUaPk4+3atcMtt9yC4uJi+W/ffPONfLy8vFyWd/z48fI5bRVHdtopvUGPjakbZdK8//X7X91aL8Sg1UmLAPdAwMW7KYpJdaQvLkVStxvketSuZVfMY0GXYTBA8fO9cNKdgsEnBg/f8DPg4sNdRkTUhEorq9F25p9mfY3Dr4y67CQms2fPxv79+/Hoo49i4sRzp+wXrRjz58/H0qVLZWBxKe7u7ti2bRvWrl2LCRMm4OOPP8bOnTuxbNkyPPXUU9ixY4dMPLdkyRI5G5YIHh588EF89NFHmDFjBm699VZs2LABTzzxBKKiomQQIgIdW8XAwo69teMtpOhS0De0r+weVScBLc1dLGogfU4B950JpBSl4q+wFrj99AGoJi2CgkEFERE1kGjREESrQ3FxMW666SZ5v2fPnjh+/LhcF8HE+++/j5UrV8rAQeTyqGkhET788EPZ6rFixQrs3r3bpge4M7Cw49mh7mx/J5IKktAruFf9n0A0H4qZoooygH6PmKOIRE2usroSj/79KI7lHUNGryl4JqgdjwIRkQW4qFWyRcHcr2FuWq1W3opWifPviyBCEK0V69atwz///CMzV4uWEnG/RmZmJvLy8mSSOtENy9/fH7aKgUUdiYMtFltyXfPratfrXfbEzVD+fB8MSicYmg0DAtvCGon3JX4JsLVjU19nvz+5bufv1yyyjkKldsHNrW7GZ/s/w9jIq+2+3pDpOco5h0zP0etOzfuvWZrqwv9K4yxqtjl7u5r7IggQF/qXe47z35PhIre5ubkyWBCDwgsLC7Fw4UKZ3bom8Z1o5RBjOlxcXDBp0iRs2bIFGs2508qf/7zmUPM+zr/mrU+dZWBxCXPmzJGLSBIiZGVloaysDA7DtQW8o4dDm7gGlT8/hNyJ3xoHdVsZUdlFk6L4INQn5bytMZT8V/dEXVQUG38RobpRlBfA78dJUFYUYsiYeejeex5Ki0rlr0T2XG/I9BzlnEOm5+h1RyTIE/tAXEjX/JJvDcTxENd6Z5eppowPPfQQpk6dKpP6ffHFFxcda1Gzbc3fV13kVgzWFtPatmrVSma37tevH5KSkuS/PfPMM3KgtxhrIYgB34888ogcq3F+GQVzdpMS5RHHKCcn55wEeTXT79aFwmDO0McOiMjSy8tLNlGJyNUWJRYkYtnxZZjaYSq8NfUYlF2YBsUnvaGo0EE/5m2gx1RYG/EBEBfa4oNqzydqOXg71pjsMCp+FQdv12vnVePkt9cgMn4jnD0jYJi6Hnqtt0PUGzI9RznnkOk5et0RP84mJiYiJiamtrsQ1S8wa2g27Poco4SEBDnD1dnHSFwL+/j4yMD4StfCbLGoI3ESsNUTwYxNM3Ak9wiC3YIxpd2Uuv+hdzgwfBbw+5NQ/jULCO8BhHWFtRHRuy0fnzo5673Z/Xs1sdNrnsfdlfEIDwnB7JGfwN/dX3Ylc4h6Q2bBukOsO/UnzrXis1OzUN2JNoCafWbOfVdzbM7/bqzP9yS/UR3Aza1vxuDwwWjr14BxEt3vBpqPAKpKgW9vAgpSzVFEuhKlEprOreVydpBBV3DkN5ze9SVEA3KVdwTcw3tylxEREZkJWywcwDUtrpFLg4iL2OvnA/NHAVlxQNJWoKMxnwI1HaWLBuF/zeMur4+0vcDP96NrRTmWBo2ActAz0Dqx+Z2IiMhcGFjQlWk9gVuWyll10GIE9xhZh8pSIC/RmCU+Nx7ISwDaXYuSsC5YlbgKGUkb8UCFDojqj/DR7wAq8/ZNJSIicnQMLBxIUUURfov/DYMjBsvxFvXiHWlczr6oUzP7MzWt6tQdUP31kjGQ0KVhpZsrfnd3w+CSEtygKwY8QmAI64xZW2bJ7W+LGQCP6xYwqCAiImoCDCwcyFMbnsKmU5uQXZqNaV2mNfyJxC/ESyYBPe8FelrfTFH2SF9ShpT+t8v1iE2LoHS10y49lWVAxiHg9F7g9D7j0uU2FHe5Bff8eQ9O5h/HP8kn4HJmMrtUrRs2uLrAxyMMN3QaAkT2gZvaDaOiR8kZ0Co7PwhofS39roiIiBwCAwsHMqH5BJwqOoVwj/DGPdHR34HsY8AfzwA+MUCL4aYqIl2KSKKTkl67blcKTwPrXjUGEZlH8JeLM+Z5e6FrWTlm5OYBAa3g2uMenC4+jdLqchwd8QI6RwwCfGMxqCwTPtkH0M6vHeD/Xxbtdwa9Y9G3REREtkHkl5gxY4ZMSLdo0SLcfvvt2Lhxo0xm98EHH8jkdcHB9ezl4cAYWDiQkVEjMSpqVOOnKuv9IJBxGNi7GFh2B3D3aiDIOjNzkxXRVwMZB4GkLYDWG+h8Mz7e8zG2ntqENw+tRURlhdysWuOBIxpnqNyDgGEfyCmORZ39v0H/hyDXIGNgfCZZY2s3P7T2a2PhN0ZERLZq7ty5mDlzJm6++WZ5f+/evbX/JgKLwYMHM7CoBwYWDkRpqszZIjAZ9z6QnwQkbjR2i7pnLeARZJrnJ/tQVQGk7QGSNqM4aRO2Z+5Bnr4c1xYVA6FdZWCx7fQ27M85hJ09b0NExAggpBN6qDV4N3M32okWCPew2qfrEdzDom+HiIjMoKL40v+mUAFqbR23VdZ77Of06dNl60RcXBw++ugjbNmyRf6QJZIiz549G2lpabjxxhvh4uKChQsXXpB5W2z7v//9DytWrEBGRoYMRI4cOYIff/xRJpObN2+eDEyEP//8E6+++ipKS0uhUqnw1ltvYciQIUhPT5dBjUhCJxLUicfEa4vcEeI1Fy9eLJMqHjhwQCat+/777xEbGwtrxcDCAekNenlBF+ER0fBuUU7OwKSvgS+GA7knge9uBu5YyQHdJBWUFaB6bj/45qfI+/HOzpgeFgwPvSsmBvWBMnagfFwkbLy+8nr0De0LuAbIx/xE61r0SO5JIiJH8Hropf+txUjg1mX/3f+/5kBlycW3jeoP3LmyXi8tLuD379+PRx99FBMnTjzn30Qrxvz587F06dILAoqzubu7Y9u2bVi7di0mTJiAjz/+GDt37sSyZcvw1FNPYceOHYiPj8dLL70kgwuRufrEiRMYMGCAzETu7e2NX3/9VT5PdXW1fA4RPIguWIL4+z179iAiIgIvvPCCDEg+++wzWCtm2nJAr/37Gu796158c+Sbxj2Rq6/xA+/iA5zaBfw101RFJFtRmg8c/gVYMd0YZBoM+HD3hxj4/UAsDQwHXP2ANuPRZshLaOMRjVGtbkDJjV8B/R6Rfz48argc+xNwJqggIiKyJaJFQ+jevTuKi4trA4KePXvi+PHjcn3VqlUymBg4cKAMUq6//nrZIpGcnAy9Xo9nnnkGnTp1QpcuXWRQcnZ3rD59+iAmJqZ2/eTJk7BmbLFwQEMih2BlwkrTJAvzawbc+I1x8O2AJ01RPLJmej1weg9wYh0MJ/7Cr3mHsNlFgxeyc+EhBpVnHkGYe5hsFTsV3gm4+Q/ZdU6caL7HQ5YuPRERWZvn0i7fFepsT524zLaW+a1cdE8SRPem8+9XVVXJdYPBgBEjRmDJkiU4n+hKlZmZKVs9xN8+/vjjsktUjZrnO/85rRUDCwckup2svWGtnJbTJKL7AXcaLyDJTBQKqFtF165bQpW+CqdXP4uIfz83FgPAvLAQJDqrMTJsAIa1uw3wjcFIn3D0D+tf/1wpRETkeJzdzLOtCYhuS2KsRGONGjUKL7/8sux21bFjR/nY9u3bZauGGM8hZp0SAYQYbyG6UF133XWwVQwsHHQQ99lBhYikGz1T1Nl/v3cJ4BbIaWhNSOStiNy0CE2mJBc49icQ9xvQZxoOuXni/jX3ww0q/OHsAUXsIKD5MNwAHfKVCsQ2Gw94GZtqPeECT2fPpisrERGRGYjB3VOnToWrq+tFB2/XVfPmzWVrxX333YeSkhJUVFTIbk/isUceeUR2jWrXrh1CQ0MxfLhtT+GvMIirSrokMUrfy8tLRpQicrU3qbpUPL/5eTzS5RF0Dera+Cc8+geUS2+BQe0Gw+QVcqpQcxJ9E7OysuSMCaK/IjVCQYo8frq4Ffgnex+CqirRq6wchl4PoHjoCxj0/SC4OLng53HL4Odm2zOAsd4Q6w7xvNO0RPceMVhZjBc4u3sP1U1lZSXUajXMfYwSEhIQHR19zjES18I+Pj6y9eZK18JssbiEOXPmyEWM0BfExevZfd7sxaeHP8XerL14ZcsrmNt3buOnpPXsAJ/wftCkbob+mxuQe813qPaKgjkvEEVFF/ExA4uGUZbmwGflVKizD8n733l74uMAXwysVKJd0FiURY1GVV4RPur9EaLcolBdrEBmcSZsGesNse4QzztNf2Eszr1ijIC1jxOwNgaDofZ6tNE9TC5DHBdxjHJycs4JYnQ6XZ2fgy0WDt5iUVJZgv9t+x+mdZ6GUPfLTPlWH+U6KL4eD8XpfTD4RMNw55+AeyDMwVF+edaXlCFt9H1yPXTVZ7JrVIPlJwM5J4BmQ/Hd0e+w4uQKzDi5D53yTgORvXEsug9m5O/A6GbjMbXDVNgjR6k3ZHqsO8S60zBssWgctljYGXHxYY8XIO4ad7w58E3TPqmLF3DrD8CXI6DIS4Ti20nGHBcaD5iDiN7t9fjUUihQeTRRrirPvN960WUAh5cj/+AyeKfsME4D+8Qx7M3ci0M5h7Cu4zh07vE04B6AVgB+hv1ziHpDZsG6Q6w79SfOteKzU7NQ3Z09Ftac+67m2Jz/3Vif70l2haJzxOXGQQEFWvmKy8tGEC0Ut/0EfDkSOL0PWHq7MeeFyrz9A+m8AdhHVgAHf0Rp0mbcH+SH/RoN1ipV8A1sCxRn4abWN6F7cHcMjRwKuPhz9xEREVGDMbCgWhtSN+Cxvx9DiHsIlo5b2vjpaEWOi1u/BxaOByJ6AkpWt6aSU5qDpHUz0XXnYnnfReSy07ijSlGF3Td8huFtbpCPd/UMMc2gfSIiInJ4vNKjWh39O8JH64MYzxiZs8AkwroB03YAXmHc0+YiJnZL2Qbs+xZofz32unngjlV3wEftgTXB7aFqfz3Q7lrMqi6Cn4sf80sQERGRWTCwoFreWm8svmoxglyDTNuH7+ygoqIEiP8baD2We76x8pKAfd8h+cAS6HRpaFdRIfdvu4mfyDwSge4hyL56NoLOTA3bjnuciIiIzIijFukcIlvy2UFFpb7SdHtIBBVfXw18dwtw4Afu+YYSLRMLxgIfdsSKnR9irKceb/n7AZ1vBbrdAbVKjV8m/iK7s9UEFURERHShX375BW3atJHJ7w4cOCBva6ZX/eCDD2Q27KZ08OBBmUdCSEtLw4ABA2zqsDGwoIsSXaE+3vMxbv/9dlRUV5hmL6ldgNAz/fl/vh+I/4d7v64UCjhFBMMQ6ovEbXOApE3iQfQO6AK1QgX3mEEoH/8+EN1Pbi66tBEREdHlzZ07FzNnzsTevXvRoUMHeevh4WGxwOJsIhP3xo0bYUsYWNBF5Zfn4/uj38upSNckrTHNXhItIaPfANpOBERLyHe3Aml7eQQuJT8FWP8WMLc/lE56/PLFAFzzeDwWNGsFDJsFPHYQgVNWYt2k9fhkxGfQqDTcl0REZHP5tMQiplStUVldKR87/4fNmm31Bv1/2+qN25ZXl9f7tadPny4v3J977jn07dtXPiZ6beTn5+OVV16RLQY33nijbMUQAcf5xLavvfYaevXqJVsZli9fjjfeeAPdu3dHixYtsH79+tpt//zzT/Tv3x/dunVDz5498ffff9f+20svvSS3F//23Xff1T4uMpV7e3vX3r/tttvkc3fs2BFjx46tDXpqtps1a5Z8jubNm+P333+HJTCwoIvyd/HHa/1fw1sD3sJVsVeZsMapgGs+A6IHABU6YMEYOR0qnVFZJruJGb6agL2fdkPuhjeB9ANA3G8YED4AzkpnqPxbAgMeB7zCa8fGEBER2aJeS3rJJa88r/axBYcWyMde3/b6OdsO/n6wfPx08enax76L+04+NnPzzHq/9uzZs+WF+vvvv48tW7ac82+iFUO0GCxdulQGFSK4uBh3d3ds27YNX375pbzwDwkJwc6dO/H666/jqaeektvEx8fL4EFc7O/atQtLlizBLbfcgvLycqxcuRLLli2Tj4u/E0HCpYhyim32798vu0iJ56xRUFAgAw7xPB9//DEee+wxWAIHb9MliQvZSyVoaRS1FrjpG2Nui4R/gB/uAooygd4POHbrxJbZwP7vgbJ8vODvixWhQXgE3riny8NA63HorHbBuknr4KXxsnRpiYiICJAtGoIIUIqLi3HTTTfJ+6JV4vjx43J91apVOHHiBAYOHHhO0rnk5GSsXbsWkyZNgqenp3z8vvvuw6ZNorvzhURAsnjxYpnFXCz+/v/ln9Jqtbj22mvlep8+fXDy5EmLHB8GFlQnoonxkb8fwaSWk4zJ1BpL62VMoLfuVWDXQqDlKMc7EqLZV6GQAduerL3osP1zyPSBnuHoEdUbqwv2orTdJKDjJOhLy5E29l75Zx4rPobShd2eiIjI9m27ZZu8dXESGZeM7mx3J25rcxuczst/tX6SsWuR1klb+5hI9Hpdi+ugEj0iLEBc0AsqlfH1z75fVWWcul98z48YMUIGBldyqR9wN2/ejI8++ghbt25FYGAgVqxYIVtVamg0mtq/Fa9dXV0NS2BgQXWy5MgSbD61GQeyDqBHcA94OBsHNjWKygkY8TLQ92HA7aysz7oMwMNOZzOqrgJOrAH2fmNMGHjDAtyz+h5sT9+O93rcihGtrgNiB2OMoQrDqiv+2896Pcr3xtWuExER2QNXtesFj4nZDcVSp22VarmYg2hFEF2MGmvUqFF4+eWXZRcm0V1J2L59u2zVGD58OJ5++mk8/vjjslvV559/ftHnyMvLk4PK/fz8UFFRgc8++wzWiIEF1cntbW9Hqi4Vo2NGmyaoONvZQYW46BaDuke9DnS/yzjg2x5kHJbBRNX+77GnugA9ysqNgUVJLjoFdMKB7APIjO0HNB8mN9dAxcHYREREFiQGd0+dOhWurq5YuHDhJcdZXEnz5s1la4Xo5lRSUiIDgy5dusjHrrrqKhlkdO3aVQYyY8aMuWRwIgZ2t2rVSgYXIiA5deoUrI3CcPYwfLpAYWEhvLy8ZMRa0/+NjPLL8mV/f5Mm01vxMLD7a+N659uAse8Yp6m9BL1ej8zMTNksKPorWp3DvwAb3wNO74XICDI+PBSn1E74wW8QWnW/Hwhuj4LyAtnc66Z2u+TT6ItLkRA9Uq7HJK6G0u3S+4SuzOrrDVkt1h1i3WkYMSYgISEBMTExtd2FqG7EpbroVuXk5GTaa646HqP6XAvzG5UaJKskCzevvBmvbXvtnGnfGm38bGD4S4BCCexdDMwfZcwwbUuzOolEgGemxduRsUsGFaJ1Qt16HNoGdYGPxhupHa+RQYUggrPLBRVEREREtoBdoahBdmbsxKmiU3LcRWF5oemmPBWReP/HgNAuwLI7gdP7gM8HAdfPB5qZYNC4OYhA4sRfxtaJY38CI15BetuxmLB8gkw0uG7ES/DqfLvs8vVcaTa8nL0u2neUiIiIyJYxsKAGGRMzBiqFCm382pgnj0LsYOC+DcD3twNpe4BvJgFPxwNaK+mOVq4zBhGHf4HhxBocUlQhW6XC4IpSIHETgrrfhXCPcJRWlSK19Uh4nRlHIvKDEBEREdkjBhbUYCPP9PmvcTD7ICI9I+HpbKKLf+8I4M5VwO9PArr0c4MKMUVtZF/Arzks0kLxbmugokje3eiixUPBwQhWumDg4I+hDOsu+0B+NuIz+Gn9TNYfUunH/BVERERkvRhYkEkczjksp02N8IiQF9S+Wl/TPLFIpjfhY6Cs8Nxkcr8+KoYzQRHaDS6xY4E+dwBufjAZMadBbjxwajeQttsY2NywAEmFSVgZvxItw9pgeEEu0HYCerUag4CtM9AlqDuKglrD88xgYFO2TojB2jFxv5ns+YiIiIhMjYEFmYToFqVRaeCudjfPQOSzWysqS4CWo4Hjq6FI2wWvtF0wbH3T+FjnW4Dmw4GGjGFI2AicXGcMJET3q7Lz5q4e9x7+SvoLn+77FL0Cu2H45L/kmBCRqm512OoLEvkQERERORJeCdVjikGx0MW18G6Br0d/DR+ND9QKtdxXYlakX07+gmtbXGvanAx+LYCblgDFWTDsXwb97kVQ58QBR1bIRT/hU6DTTcZtd34Jxd5vjcn4VM6ASKJz1rph2CzAJ0puqtj/PRR7zkx1C+CQ1g1LA8Nwo19ntIkcAihUGBk5ErszduOqmKugF60aZ2ZrVkLJ+mFDRP0U0/fxM02sO8TzTtOed2sWayGmHM/NzYW3d+PHi+7duxdxcXG46aYz1yB1tHLlSsyaNQsHDx7E/fffjw8++OCCbWr2Wc3tggULcPfdd+Onn37CxIkT5WOvv/46vv76axw/fhw//vhj7ePCkCFDkJSUJKeNFSZPnozHHnvsgteo+W48+/uxPt+VDCwuYc6cOXKpSYmelZUl5/elS9NAg5LSEoj/hO/iv8OXx7/EyhMr8V7P98yy6/Qx16LAdxj8qk7D7fgv0CSuRY5fTxgyM+W/u6cdhXvarkv+fU7bO1BVacwJoQnsCU3rElQGdkBlQAfMS1uOtenrUeLmhSdDhgL5JXCGM2a2nym3F3kQmoqhrBxlD7wu17WfPgeF1oSBmgMSJ0kxH7c4gTKPBbHuEM875ldZWSnPvSIfg1isianKtHv3bvzyyy+4/vrr6/V3Im+EyKQtgoHi4uILyiK+q2quR8W4zcTERMybNw+9evWSj9dsL4IH8dr33nvvOY/XPMf//d//YcKECbWPnf864r44Rjk5OVCr/+v5odPp6vxeGFhcwkMPPSSXmqQgAQEBTJBXTy2KWyAkLQQ3tr1RJiKrqdjVhmqTdRsSHwDxIfMNaAFlu8GyBSHg7MHSfadC33ooUF0J6CuB6gqgusp4q6+Cb3R7wNVXZhX/xikTk8e/CT8X41iNOwL9oDnqiptb3YzAAGP5LUUkyEvaeViuB/j5M0GeieqN+FwzsCDWHWoKjn7eET/OigtUkeRNLNZEtBD8/vvv8qJ+5syZuPXWW+XjO3bswIwZM+S1oLhQf/bZZ3HDDTfIH5tvu+02nD59Wh7Tbt264c0338TLL78sf7Tq0aOHvOifO3dunV6/bdu28vbXX3+VmbkvtX/Exb6oRw888AA++ugjPPnkk1CpVLXb9+nTR96KMp39+KUeO5/4N1E3RWbvsxPk1SehoXUdWSsmdrQjnggaY2yzsRgRPUKOv6jZdxtTN+LN7W/i4a4PY3T0aJO8jviwXPL4BLYyLlfw7OZnsT9rP7y0Xrinwz3ysc5BneViFc56b6yLMH+9IWLdITNw5POOeM/i/dcsZ/9wdkkqJZRntdBfdluxX100DS7bnj17EB8fj+7du6N///6ya9R9990nA46QkBBkZ2eja9eu6NevH77//nvZyrB69Wr596Irla+vL1555RUsX75cLjVuvPFGHD169KKvKwKJiIiI2vsX2z81P8rWPPb+++/LMohynv0357vY4yIwEoGTCGTeeOMNxMbGXvRvzq+j9amvDCzIrJzFWIazfHPkGyTrknEo+1BtYJFdmi0v6kXeh5Y+LU36+uLDWKGvqB3jIbKEP7zuYZzMP4nFVy2unbnpplY3wcPZA+39jdmwiYiIyPwSzpu6/myuw3sj5Nv/q72f2PZqGEou3i1d27czwn75qEFluOce4w+K4kJ74MCB2LBhA/z9/WWgMWbMmHO2FUFC79695QX+E088IbcfPfrSP5QuXboUpiLGYIjuUqJ89bVo0SIZxIjrItHVf9y4cTh82NgTwpQYWFCTem/we1gSt0QO6K6xL2sfHv37UXTw74AlY5fUPv76ttdRUF6Au9rfhVa+xlYHMd3rlrQtCHINwtDI/zJxiylgCyoKMDxqOILdgmsfE8/RO6Q33h38rnxMqVAioSBBZg0XtzWBxfhm4+VCREREjk38ai8uwNu1a4ctW7ZccqD2mjVr5ODpF198UbZ4XEx9WiyuZOPGjXJ8RYsWLeT99PR0OZ5CdMkS3aMup+Z1xHubNm2a7EYlxlKIbk+mxMCCmpSr2rW2q1ENtVKN9n7tL2it2JC6QQYAN7e++Zx8GSJY6Bnc85zAYv6h+TiRfwLNfZrXBhaiBaKwohAJhQnnPO9zvZ6Di5ML2vi2MdO7JCIiorqISTR2J7oo1bldcKIPr7j0to3oXiZmWHrppZfkRbu4eBdjLsT42oSEBBk8DB8+vDaYEN2ITp06hbCwMEyaNEm2VohxpEVFRXIsrhhjYa4WCxE8PPjgg7X3Bw8ejEcfffSc2Z8uRgzKFkFEUFCQvC9aPcS6qYMKgYEFWdzA8IFyOd/j3R5HWlEaoj2jax8TQcOIqBFo7n1uxu3+of3llLci03WNroFd8cP4HxDlGXXutmH9zfI+iIiIqP4JYM2xbX2IgdldunSRg7dnz56N6Ojo2mlgxS/7osuTmNUqMjJSjp9Yv3493nvvPTkYWly0i9mWRCAybNgwvPPOO+jYsSP69u1b58Hba9euxZQpU+QgcdFS8sMPP+CTTz7B1VdfjRUrVsiZpuryXP/73//kdmJwueg2JVomREuKq6srxo4di/LycjleQnTzEs9rDgqDNU0mbIVqZoUSEaiIRMm6iNkRxLSv4tcCex4MJwasib6lNb/YmOvk6igcpd6Q6bHuEOtOw2eFEi0AYtBzfWYZIshgQwQwYtamiw3UNvcxqs+1MFssiGyACCRik/6ydDGIiIiILok/1RERERERUaMxsCAiIiIiokZjYEFkA/Rl5Th981NyEetERERE1oZjLIhsQbUeJWv+rV0nIiKy1QkQyDqZYj4nBhZEREREZFbOzs5yFr60tDQEBATI++ac4cieGJpgVijxGmKaWvH8arW6wc/DwIKIiIiIzEoEFWIaU5ElWgQXVL+LftHSI/ahOYMx8dzh4eEyP0dDMbAgIiIiIrMTrRQiyZz49V0kpaO6EUGFyJwtMmWbM/eSaKloTFAhMLAgIiIioiZR09WmMd1tHI0ILMT+EknrrD2pq3WXjoiIiIiIbAJbLOo4Ql6kMyfrjOJ1Op1NRPGNoS8uhU5fVVsXldWVli6STXOUekOmx7pDrDvkaOedwjPXwHWZNYqBxRWIAylERESY4tgQNV5oMPciERERNfk1sZeX12W3URhMMWmtnUeJYvYCDw8PTotmhUQULYK+lJQUeHp6Wro4ZCNYb4h1h3jeIVtRaOFrHREqiKAiNDT0ii0mbLG4ArEDxdRbZN3EB42BBbHeEM85ZO34fUW2WHeu1FJRg52LiYiIiIio0RhYEBERERFRozGwIJum0Wgwa9YseUvEekM855C14vcVOULd4eBtIiIiIiJqNLZYEBERERFRozGwICIiIiKiRmNgQUREREREjcbAgoiIiIiIGo2BBRERERERNRoDCyIiIiIiajQGFkRERERE1GgMLIiIiIiIqNEYWBARERERUaMxsCAiIiIiokZjYEFERERERI3GwIKIiIiIiBrNqfFPYd/0ej3S0tLg4eEBhUJh6eIQERERETUZg8EAnU6H0NBQKJWXb5NgYHEFIqiIiIgw5fEhIiIiIrIpKSkpCA8Pv+w2DCyuQLRU1OxMT09P0x0dMlmLUlZWFgICAq4YRZ/zd8WlSGw/Ua5HH1wOpZsLj4gDaWi9IWLdIZ53yNHOO4WFhfJH9ppr4sthYHEFNd2fRFDBwMI6P2xlZWXy2NQrsFCp4aE0Vn/5twwsHEpD6w0R6w7xvEOOet5R1GFIAL9RiYiIiIio0RhYEBERERFRo7ErFDkmhQJOEcG160RERETUOAwsyCEpXbWI2r3M0sUgIiIishvsCkVERERERI3GwIKIiIiIiBqNgQU5JH1pOVJHTJWLWCciIiKixuEYC3JMej3K98bVrhMRERFR47DFgoiIiIiIGo2BBRERERERNRoDCyIiIiJyGG+vW4fec15AQk6BpYtidxhYEBEREZHDWJTyCIrdf8H72+Zbuih2h4EFERERETkMp4oA421lqKWLYnc4KxQ5LKWfl6WLQERERE2oslqPP1LjEazIw5LgE9z3JsbAghyS0s0FMXG/WboYRERE1IQydeXwQJlcDyjYz31vYuwKRUREREQOIb2gFPMMQ/GrmyvWqPMsXRy7wxYLIiIiInIIm1L/xeLoI6hS+UOtP41XKquhUassXSy7wRYLckj60nKcmvCwXMQ6ERER2b/kHBFUVMn1fkUGJOdxyllTsqvA4pprroGPjw+uv/76Oj1ODkyvR9mWvXIR60RERGT/WuqcMSc9EwvTMvBGdhayCqstXSS7YleBxSOPPIKvv/66zo8TERERkeNwKyzAwNIydCsvh7uiDJmZGZYukl2xq8Bi8ODB8PDwqPPjREREROQ4nIrTa9efrLwPKQUVFi2PvbGawGLDhg0YP348QkNDoVAosHz58gu2mTNnDqKjo6HVatGrVy9s377dImUlIiIiItuThBPYodXg8aBB+CN2A/7IWWzpItkVqwksiouL0alTJxk8XMzSpUvx+OOPY9asWdi9e7fcdtSoUcjMzGzyshIRERGRbTEYDPjRNw13hQShwl0DpVMx8irYFcoup5sdM2aMXC7lvffew9SpU3HnnXfK+3PnzsXKlSsxf/58zJgxw2TlKC8vl0uNwsJCeavX6+VC1kUcE3GiqO+xOXt7uc5j61AaWm+IWHeI5x3blVlYjNgKA4pVlbghpBvSd8XA2znE6r8L9Bb+zqrP61pNYHE5FRUV2LVrF5599tnax5RKJYYPH46tW7ea9LXeeOMNvPzyyxc8npWVhbIyY6ZGsh6ishcUFMgPnKgTdWUoKQNcNLXHVlGsNWMpyV7qDRHrDvG8Y7uOZpZgzuk8+Cl0yHY5gR8My7C6tDtOp98ElVIBa6W38HeWTqezr8AiOzsb1dXVCAoKOudxcT8uLq72vgg09u3bJ7tVhYeHY9myZejTp88lH78YEbyILldnt1hEREQgICAAnp6eZnyX1NAPmxiTI45PvT9siau50x1Uo+oNOTTWHWLdsV0HcjPxcuVkdPEqxu0d+gEnliEU2TBoPRHo7QJrpbfwd5YY22xXgUVdrVmzpl6PX4xGo5HL+cSB5AWIdRIfNh4fYr0hnnPI2vH7yrIydeVYoe+HkuBA3BnshrWuLjikLkKLzCSE+7aFNVNY8FqnPq9pEz/V+fv7Q6VSISPj3AE24n5wcLDFykVEREREtmF92nK4xsxGsXYd4B2BL7w9Mc/XFftP7bR00eyGTQQWzs7O6NatG9auXXtOs5C4f6kuTUSXoy8rx+mbn5KLWCciIiL7lqs7BJU2De6KDEDjgd6lekzQFUFVWGLpotkNq+kKVVRUhBMnTtTeT0hIwN69e+Hr64vIyEg57mHKlCno3r07evbsiQ8++ECOmaiZJYqoXqr1KFnzb+06ERER2bdheU54pCwTCr9Eef+mCl8EFR/FQjeDpYtmN6wmsNi5cyeGDBlSe79mALUIJhYuXIgbb7xRzt4zc+ZMpKeno3Pnzli1atUFA7qJiIiIiM4XVKzDwIoypHo2k/crPcKB4qNQFKRyZ9lbYDF48GA5jdblTJs2TS5ERERERPXhUWFMqqzxDZO3he1uw5MpzZFW0RpTuCsdZ4wFEREREVFDFZSW4ahrJnZoNXAJiJCPFUa1wx+xG7Df/2Po9ewOZQoMLIiIiIjIrsVlpeHzoCrcGxwIV19jYNHCPxhKp2IoVCVIzs+1dBHtgtV0hSIiIiIiMofTBYXoUVom15Wexq5QfmoNemRcDbfCUuQVKRHty33fWAwsiIiIiMiuORUaMD89E3rRWcf9zMQ/VaWYX/KxvBpemT0NXSJ9LF1Mm8fAghyS0s0FzbI2WroYRERE1ATSSpSYXvEQBkeqca3qzOWv1htlSldo9SXQpccDiOWxaCSOsSAiIiIiu5ZSrMIKfT/Ex9zy34MKBXa5BWOelycOZK2xZPHsBlssiIiIiMiubS2YB9eYIyhQisTKrWof3+3hic81VWhZsdei5bMXDCzIIenLypH54P/keuAnL0Cp1Vi6SERERGQm5ZVHodKeho8y/5zHW7pG4Or8ZFRVq7nvTYCBBTmmaj2Kf11vXP/oOUuXhoiIiMzorkygmSoTvj5p5zzeLbAzRp34Fb8ZqmWiZoVCwePQCBxjQURERER2q7Jaj5ZlhRhYWobIoP+6QQmewTHyNtiQhfySSguV0H4wsCAiIiIiu5WpK0cwjAnw3AMiz/k356jeeEn5EP5XNQlJuYUWKqH9YFcoIiIiIrJbRzNPIdejBLoqZ3T2MibHq+UVhpXhR1CmOoF/Ulqjc8R4SxXTLjCwICIiIiK7tT9tJ74M9ELrchcs8wi54N9dndxQZgAS8k5ZpHz2hIEFEREREdmtqsI89CotQ2iVAnB2veDfJzkPxslDfoh0ibZI+ewJAwsiIiIisluhRVo8mZ6JTJeLZ9aemPUjwlRbsDDTH8DoJi+fPWFgQQ5J4apFTOLq2nUiIiKyTwerIzC9YhomtI/EsItt4B0B5ADqInaFaizOCkUOScxTrXRzkQvnrCYiIrJfJ4rdsELfFyUtLj4wu9QnFPO8PLHJ5WCTl83esMWCiIiIiOzWcdXbcI0pRrnyFdEx6oJ/1waEY3aONxSGAuQWl8DX7cJxGFQ3DCzIIRnKK5D1xP/J9YB3n4JC42zpIhEREZGJiWzaBlUyVM4VCFFfPAFeWHB7TNxRBNdKDZLzdAwsGoGBBTkkQ1U1dEtXyXX/tx6HQmPpEhEREZGp5RZX4PW0CrirM9FRn3/RbRQ+kXg1OxeVBhU25FcB4TwODcUxFkRERERkl9ILy9CrMg8DS8vg4XuJ6WTdg1EFJ6gV1cjLSG7qItoVtlgQERERkV3KzCtEO4XOeMfjwvEVklKJn2Nfwg9HdWhffPHuUlQ3DtFi8c4776Bdu3Zo3749Fi9ebOniEBEREVETOJa6Fb+6u+KAxgVw9b3kdut9K3G45bdYn7+Ex6UR7D6wOHDgAJYsWYJdu3Zhx44d+Pjjj5Gff/E+dkRERERkPw7nbMJzAf5Y6O0n5pq/5HZhnoHyVlfJa8TGsPvA4siRI+jTpw+0Wi1cXFzQqVMnrFplHLRLRERERPbLtbQavUrLEGNwv+x243xbYMSJoRhwqkOTlc0eWX1gsWHDBowfPx6hoaEykdny5csv2GbOnDmIjo6WwUOvXr2wffv22n8T3Z/Wr18vWyny8vLk+qlTzKxIREREZO86Frrgi/RMXO0Ue9ntmpccw3uqhZhY+TtKK6qbrHz2xuoDi+LiYtnKIIKHi1m6dCkef/xxzJo1C7t375bbjho1CpmZmfLf27Zti+nTp2Po0KG49tpr0bt3b6hUqiZ+F2RtFK5aRB9ZIRexTkRERPbnz+pumF4xDfltb73sdi4BxhmjwhTZOJVf2kSlsz9WPyvUmDFj/r+9+wBzovraAP4m2d57ZWGX3nuX3kFRrID8FQHLp2LD3sCOFcWKooBYsYEKiijSe5cOCwts771mk3zPudlkd6nb6/vzGTMzmUxuMrNhztx77lXTpcybNw933XUXpk2bppYXLFiAVatWYdGiRXjqqafUunvuuUdN4s4770SrVq0uub/8/Hw1WWRkZKhHo9GoJqpb5JjI4DcVOTYaL/fiwXNMpmooHTXE84YaN547xHOnftmT5YVMY3/MbDnw8r/57iH40dUFh+30aB35H5r79EVdYazlf7PK8751PrC4nIKCApWU/fTTT1vXabVajBgxAtu2bbOuk9oLPz8/HD9+XDWTkuDjUubOnYsXX3zxgvWJiYnIy8urhk9BlT3Z09PT1R+cHHsinjdUnfibQzx36o/MvAIYQ16AU6ErcrNaIAEel97YoMOfzk7Y7eiAcWc2Y3jw5ZtONabfnczMou56G3pgkZSUBIPBAH9//1LrZfnYsWPW5euuu04dEGdnZyxevBg2Npf+2BKkSNOqkjUWISEh8PX1hZubWzV9EqrMH5vk3sjxKc8fmym/AMlzzM3rvF+8Hxp7Ox6ERqSi5w0Rzx3i7079cTYyAlqbLECXjfZBAdA5XP46bliuDj3z0pFr76huSNcVxlr+N0tymKs8sAgLC1MfqrwefvhhleNQm0rWXlyJvb29ms4nB5IXIHWTnJflPT5GowmZi80dAfjMuY/HthGqyHlDxHOH+LtTfxTkOeL7qHhk6gBbfRbgdJkaCwAjEIDAtH1Y4phf5/5t0NTiv1nlec8yBxZLliypUGGkt6bq4uPjoxKx4+PjS62X5YCAgGp7XyIiIiKq29KTEzBQnw+jXgu4lG7dcjEGtxAgfR90mVE1Ur6GqMyBxeDBg1HX2NnZoUePHli7di0mTJhgrS6S5ZkzZ9Z28YiIiIioluQkRarHTBtPuOtsr7h9dve7MfF0O6QU+uK2GihfQ1TuHAu9Xq8Gmtu/f78aI6K6ZWVlITw83LocERGh3tvLywtNmzZV+RBTp05Fz5490bt3b7z33nuqi1pLL1FERERE1PiEp+7A7y5OCND6oFcZttcHNcGR1t/BZLRBnn4aHGzrdSpyrSj3N2Zra6su6CVpuibs3r0bQ4cOtS5bEqslmJDmWRMnTlQ9Ns2ePRtxcXHo2rWrGln7/IRuIiIiImo8Duj34DtfH0wvcCpTYNHaOxgmkwYw6XAiKR6dA4NroJQNS4VCsWeffRbPPPMMvvrqK1VzUJ2GDBlyxTEGpNkTmz4RERERkUVAngZuhbkIcS5b3q2dyYDJcWPglJWAzCz2FlljgcWHH36omicFBQWhWbNmqhvXkmQEbCIiIiKi2jIy2RbXmBIR16tz2V6g0eLZvE/V1fGKhFlAK9/qLmKDU6HAwpIoTVRfaRzt0XTPD9Z5IiIiajj0BiM+zB+Dv9ARL3a4pmwvsrFHho033AqTkR1/GkAZAxKqXGAxZ86ciryMqM7QaLWwbRpY28UgIiKiapCYmY9jxqY4pWuG+U07lfl12139sF1rREran5gC3kgvr0qlu+/ZswdHjx5V8x06dEC3bt0qszsiIiIiokrbGX0Yzq1egq0hGFrtuDK/7qyLO360yUbzghM8CjUVWCQkJGDSpElYv349PDzMoximpaWp3pu+//57NeQ4UV1mKtAj+bXP1Lz3M3dDY3fl/q2JiIiofjiTFAGtTQ5ckQxIJ0AaTZle18G1Oe6JOYrUwqbVXsaGqELjgj/wwAPIzMzE4cOHkZKSoqZDhw4hIyMDDz74YNWXkqiKmfSFSP/oezXJPBERETUc/rku+DkqFu8lRpQ5qBAdArpgZlo6+mbkotBgrNYyNkQVqrGQcSL++ecftGvXzrquffv2+OijjzBq1KiqLB8RERERUbmYUuPRWq9Hgs67XK9z9W+uHoORhPjMfAR7OPKbr+4aC6PRqAbKO5+sk+eIiIiIiGqLMT1GPRY4lW/AZG1ITzxgPxszNFNxIiGhmkrXcFUosBg2bBgeeughxMSYD5qIjo7GI488guHDh1dl+YiIiIiIyuW4fj9+c3FGgmv5aizg5IUdARuQ1XwJtkRv57deE4GFDJAn+RShoaFo0aKFmsLCwtS6Dz74oCK7JCIiIiKqEtscTuNZX29EupQexLks3G39YDLYIT4znUejJnIsQkJC1Ojakmdx7NgxtU7yLUaMGFGR3RERERERVQmTyYQ2OVqEGXIR1qRFuV//fzY9EXlMD42tJ49IdQcWer0ejo6O2L9/P0aOHKkmIiIiIqK6IDVHj3uT89FRmwj94PKPnt0n/R/caPM3vkwOAnBztZSxoSp3YCEJ2k2bNoXBYKieEhHVAI2jPUI2LbXOExERUcMQl56HZ/XT0d4hBXNDepT79VqPpkAs4JAVVS3la8gqlGPx7LPP4plnnlHjVxDVRxqtFnZtw9Qk80RERNQwxKbn4ICpJf7zHAG4+JX79QXegXjJ2xMrPI/CaDRVSxkbKpuKJm+Hh4cjKCgIzZo1g7Nz6cQYyb8gIiIiIqpp/0augXOrj5Cr6wlgYLlf7+nfHD9GuwLIx9nUZIR5+1RLORuiCgUWEyZMqPqSENUgU4Eeqe99peY9H74NGrsLx2UhIiKi+ic57Ti0NjnwQWKFXu/u2xozU9PgWGiL2PQChJWzx9rGrNyBRWFhITQaDaZPn44mTZpUT6mIqplJX4jUtxareY/7JzOwICIiaiB6Z7phVlwsUp09KrYD9xDck5ahZlel5lRt4Rq4cjcut7GxwVtvvaUCDCIiIiKiusQ+Mwmt9XoEOFXwBrijB3K1Lmo2My6iagvXwFV45O0NGzZUfWmIiIiIiCrBISdePercpbvYilnW7h1cZXoeO3L1PBbVnWMxduxYPPXUUzh48CB69OhxQfL2tddeW5HdEhERERFVyl6H07Cxc0YX9/L3CGXxn2siMpp/id1Z0l3tOB6R6gws7rvvPvU4b968C56T/AuOcUFERERENS0zLx8rvdLxm8Ybq7wrHliEeTSBKcYOuWz5X/2BhdForMjLiIiIiIiqTWRqBoZnFSJbp0ewf7sK7+da92AYTndEhtYTJpNJ3TinK6v0yGB5eXmoy44fP46uXbtaJ0dHR6xYsaK2i0VEREREVSwty4TXE+PxaXwidO4V7700sDAaD9uswDXYiNQc5llUa2AhTZ1efvllBAcHw8XFBadPn1brn3/+eXzxxReoS9q0aYP9+/erafPmzSofZOTIkbVdLKplGgc7BK/5TE0yT0RERPVfXEYebi14Fh94PQ24BFR4P3ZezdRjsCYJUexytnoDi1dffRVLlizBm2++CTu74ouyjh074vPPP0dd9dtvv2H48OEXJJtT46PR6eDQrZ2aZJ6IiIjqv9iMfOw2tUVEwBhAV6EW/2YezbDQ3Q0vBNhi65ktVVnEBq1CgcXSpUvx2WefYcqUKdCVuCjr0qULjh07VpXlw8aNGzF+/HgEBQWp9m0Xa8b00UcfITQ0FA4ODujTpw927tx50X398MMPmDhxYpWWj4iIiIjqhrXxS+DS6iUk6f6s3I6cvLDPwRGbnRwRkbivqorX4FUosIiOjkbLli0vmtSt11dtO7Ts7GwVsEjwcDHLli3DrFmzMGfOHOzdu1dtO3r0aCQkJJTaLiMjA1u3bsW4cewyjABTgR6pH36rJpknIiKi+k+fEw6NTQ4CTcmV25FGgzH5jngxMRlBWU5VVbwGr0J1RO3bt8emTZvQrJm5/ZnFTz/9hG7duqEqyZgZMl2KdHl71113Ydq0aWp5wYIFWLVqFRYtWqTG2rD49ddfMWrUKFWrcTn5+flqKhmQWIIm9oZV98gxkd4ayntsjPkFSHnxEzXvOvU6aG3YHKoxqeh5Q8Rzh/i7U7fdnOSEfvpYZIekVvo3vrtNMJqkncGXDpm1+u+FsZb/zSrP+1YosJg9ezamTp2qai7kzX755RfV+5I0kVq5ciVqSkFBAfbs2YOnn37auk6r1WLEiBHYtm3bBc2g7r777ivuc+7cuXjxxRcvWJ+YmFjne8BqjOT8S09PV39wcuzLypSTV+rYarIvH3BSw1LR84aI5w7xd6du88xNRiuTHqcd/C9ovVJeBY7+6tEm/Vyl91Wff3cyMzOrN7C47rrr8Pvvv+Oll15SidASaHTv3l2tq8kel5KSklQPVf7+5gNvIcslcz3kYEjexc8//3zFfUqQIk2rStZYhISEwNfXF25ublX8Cagq/tgk90aOT3n+2IzZuThbNK9e6+zIg9GIVPS8IeK5Q/zdqbv0BiM8Dcmqob93SBu4+lV8gDxxcsAsDPimA7J0dphcyX3V59+dK7X2KanC6fIDBw7E33//jfrA3d0d8fHxZdrW3t5eTeeTA8kLkLpJ/tjKfXxKbMtj2zhV6Lwh4rlD/N2psyKTU7DJIxXJRmdc49O00r/vTgH+SG/xJUwmDVJy7oSPi1Oj/DdLW473rNf/ovr4+Kheqc4PGmQ5IKDifRcTERERUf1yJPEsvvXW4W0vD+g8giu9vyZuvoDBBcZ8PxxLiKuSMjZ0Za6xkGZPFTFkyBAMGjQI1UHG0OjRowfWrl2LCRMmWKuLZHnmzJnV8p5EREREVPdkpWdjQmYWbE0mwC2w0vvTGArwQmo3mNIicTxSjwHNq6SYDVqZA4uIiIgKvUHXrl1RGVlZWQgPDy9VDhlF28vLC02bNlX5EJJI3rNnT/Tu3Rvvvfee6qLW0ksUERERETV89hl6vJyUglytM2DvWvkdam0xIesH6HR6zDx0BBjcpiqK2aCVObBYvHgxasPu3bsxdOhQ67IlsVqCCRn9Wwa8k159JIE8Li5OBTKrV6++IKGbqCSNgx2CVrxvnSciIqL67XSBF27Mn4MbO7jh1qrYoVYLo0cz6FLDkR+zD0lZY+Djws5eLqcSY53XDGlKJd1rXY40e2LTJyoPjU4Hx6uqdswVIiIiqj3nMo3YY2qDMU3bVdk+bVsNx9zwZOxzWYHFe3rh8cHjq2zfDVG9Tt4mIiIiIhK78t6GS6uXkGjaVXVfSNtxyNVqkK8zYe2Zjfyir4CBBTVKJn0h0r/4RU0yT0RERPWbzhgFjU0Ommqyqm6nza7ClBwjvoqJg/epIOTpDVW37waIgQU1SqYCPZKeeldNMk9ERET1lzSbfypKh+VRsRhiqMLAQmeL1i1Gomt+AQYZd2HrqaSq23cDxMCCiIiIiOq11Bw9mhnT0FKvh39Aqyrdt6bNOBRo7GGHQvx9pGwDLjdWlUreLigoQEJCgho7oiTpBpaIiIiIqCbEpechUJOi5m2rYHC8UtpejX8nrMXcvxfBMW4eXjUuhlarqdr3aMyBxcmTJzF9+nRs3br1gmooGXLcYGD7MyIiIiKqGYdijyDFA2ild8RwtyoOLGzs0a1FEOz3rYVRY8I/4ccwqnXV9TyFxh5Y3HHHHbCxscHKlSsRGBioggkiIiIiotpwNG4HfvJyR8/cfAx38q7y/fu7eCNEdzXiowqxIzwXo1pX+Vs03sBCRr7es2cP2rZtW/UlIiIiIiIqB8fsQkzIzIK/0RGojhve+jx8l7gWrnknMPFwP2Bcdx6fqkrebt++PZKSmBVPRERERLXPP12Ll5NScH2hb/W8ga0DnJ2dodWY0DJtM04nVmHPU409sHjjjTfwxBNPYP369UhOTkZGRkapiaiu09jbIuCbN9Qk80RERFQ/5RcasDA6FDfkv4DEvs9U2/vYtLsa2RoNfFy3YeneDdX2Po2uKdSIESPU4/Dhw0utZ/I21RcaGxs4j+pf28UgIiKiSlrx31HEGFOhd+2ETn1LX5tWqbbX4It972OJRxbcI7/HC7i6+t6rMQUW69atq/qSEBERERGV0+KDX8G5xR8IcRwHndZ887ta+LbBIK0n/tLnA+n5SMrKh4+LffW9X2MJLAYPHlz1JSGqQSZ9ITJ/WqPmXW8aBY1tpYZ0ISIiolqQllOAxLQYaN2BG90dqvfNNBp0aXU1Vm7/CMsNLfDv0QTc0iuket+znqnw1VRaWhq++OILHD16VC136NBBjW3h7u5eleUjqhamAj0SH5yr5l2uHcrAgoiIqB5a+V8sbklwwt3pUXA27ar299O0uwbY/hGGaffhycPRDCyqInl79+7daNGiBd59912kpKSoad68eWrd3r17K7JLIiIiIqJyWb43CtfrNsHTaIRd54nV/+2F9EFKu//hCf1d2BJ5CNn5+up/z4YeWDzyyCO49tprcebMGfzyyy9qioiIwDXXXIOHH3646ktJRERERFRCeEIqcqJ3oLU2GiadPdD+uur/frQ6eNz8AXY03wFN0/n47sBWHpOqqLF48skn1ejbFjIvXdDKc0RERERE1en97SsQ23IJ3vLygKbNWMDRo0a+cK1Wi0CnJjAZbfDvqYM18p4NOrBwc3PDuXPnLlgfGRkJV1fXqigXEREREdFFyRAH26K3waA1wd5kArpMqtFv6vFm43HH6U7IP+kCg9HEo1SZwGLixImYMWMGli1bpoIJmb7//nvceeedmDx5ckV2SURERERUJnvPpaF9ZFN8Hx2HmwtsgZbV2M3sRQw9+x0e0/2K/vmbsPdcao2+d4PrFertt9+GRqPB7bffjsLCQrXO1tYW9957L15//fWqLiMRERERkdXyfVEwQgcXbQsEdhgI6Gxr9NvRtr0aOPEnRul247fDcegV6sWjU9HAws7ODvPnz8fcuXNx6tQptU56hHJycuKXSvWCxt4W/p+/ZJ0nIiKi+qGg0Ijf/4tBurEzzt5wJ5q18Kz5QrQeg5O2tvjUKxu7o5/BM6bl6qZ7Y1epUcEkkOjUqVPVlYaohmhsbOBy3VB+30RERPXMsgN7UBj4Gjxz+6B/i7GArkIt+yvHxRf2/p2xwTYRMJ3CvuhodG/SBI1dmQOLG264AUuWLFGJ2zJ/OdL9bF0SGhqqyi1Z/J6enli3bl1tF4mIiIiIKuDbQyugtUtBgFMUbGojqCjStN0EPL/9DZhyQrDtZC66M64oe2AhI2pbqnjq4+jaW7duhYuLS20Xg+oIU2EhsldtUvPOVw9UNRhERERUt6Xn6JF5siVed0uGLzYA2cmAs3ftFKbt1bjl7+ehN53AHYdP4/6hrdDYlflqavHixRedJ6qPTPl6xN85W82HnVnDwIKIiKgeWHkwBlebduDqnGygab/aCyqEdwsUerVGTnIU8mKOICFzMPxcHdCYVaj+KDc3Fzk5Odbls2fP4r333sOaNWtQ1TZu3Ijx48cjKChI1ZisWLHigm0++ugj1dzJwcEBffr0wc6dO0s9L68bPHgwevXqhW+++abKy0hERERE1W/53mhM0G02L3SeWOtfuc1tP2KS3xs45BOORbvXorGrUGBx3XXXYenSpWo+LS0NvXv3xjvvvKPWf/LJJ1VawOzsbHTp0kUFDxcjY2nMmjULc+bMwd69e9W2o0ePRkJCgnWbzZs3Y8+ePfjtt9/w2muv4b///qvSMhIRERFR9QpPSMVZw+sId02GXmcHdJhQ+1+5Zyjc/A/Bznsj/jr7Bxq7CjUslwv4d999V83/9NNPCAgIwL59+/Dzzz9j9uzZajyLqjJ27Fg1Xcq8efNw1113Ydq0aWp5wYIFWLVqFRYtWoSnnnpKrQsODlaPgYGBGDdunCp/586dL7q//Px8NVlkZGSoR6PRqCaqW+SYyOib5T02JbdX8zy2jUpFzxsinjvE353a88H2FchzjcD7jh4Y59wDRnv3OvHv981tx+G5f2ORlNUMmbkFcLa3aVC/O+V53wp9cmkG5erqqual+ZP0EiU9LvXt21c1i6opBQUFqibi6aeftq6TcowYMQLbtm2z1njIFyLlzcrKwr///otbbrnlkvuUsTlefPHFC9YnJiYiLy+vmj4JVZQc2/T0dPUHJ8e+rEw5eaWOrSa7cbeJbGwqet4Q8dwh/u7UDvm93nPMEbfb5iHQlIuM9mOQX6J1Sm0annYa/VO2Y21hDlbuOYWhLT0b1O9OZmZm9QYWLVu2VLkO119/Pf766y888sgjar00P5JuXWtKUlISDAYD/P39S62X5WPHjqn5+Ph4VU4h20rthuRaXIoEKdK0qmSNRUhICHx9fWv0s1HZ/9gkh0aOT3n+2IzZubCEwOq1zo78yhuRip43RDx3iL87tWPfuVR4pCZill0itI4eMPW8CZDmUHVBWiA8kYARur14KyoPE/v7NajfHclhrtbAQpo73XrrrSqgGD58OPr162etvejWrRvqkubNm+PAgQNl3t7e3l5N55MDyQuQukn+2Mp9fEpsy2PbOFXovCHiuUP83akVK/bH4pCpOV5o9RNe7m8DjW0damnQYigMNk6wM6bhWORXMJi6wLaKx9eozX+zyvOeFQosbrrpJgwYMACxsbEqWdpCggxL7UBN8PHxgU6nU7USJcmy5H0QXYrGzha+7z9tnSciIqK6qaDQiF/PfAOtQzMM73UD0LxqawQqzdYBeS2GYXTefuRr/8bbG/7E08OuRmNU4bBHLtyldkKiGGkuJE2jJI+hbdu2qCl2dnbo0aMH1q5dW6q6SJYttShEF6OxtYHb5HFqknkiIiKqm348sBcmz5VwDv0E7ZuYB2uua5zbj8e1Wdlomm/Cj7tjkJmnR2NUocBCkp8//PBD65gWPXv2VOukpyXpGaoqScL1/v371SQiIiLU/Llz59Sy5EMsXLgQX375JY4ePap6pJKEbUsvUURERERUf605EoeemXa4qsAGfhkxqJNajcKjqZn4LSYSTVJzsWDDKTRG2ooOWjdw4EA1v3z5cpWlLuNZvP/++3jllVeqtIC7d+9WNSOW3A0JJGRe8jzExIkT8fbbb6vlrl27qqBj9erVFyR0E5VkKixE9pqtapJ5IiIiqnvSc/QIP5aBzxNP4ZOYcMDe3CtpnePkBedON0MH4Amb7/H5pgjEpOWisalQGxDp8srLy0vNy0X8jTfeCCcnJ1x99dV4/PHHq7SAQ4YMUYHL5cycOVNNRGVlytcjbsqTaj7szBpobNgcioiIqK5ZdTAWY7EZOo0JCOkDeDVHnTXseZgMenwfPwaG9D14eNUx/DDF3HNqY1GhGgvpflXGiZAmRxJYjBo1Sq1PTU0tV5dURERERESX8vWBtRhuu8m80Hli3f6i3IOhuekL9OjvCMfgZTiS/w02nz6DxqRCgcXDDz+MKVOmoEmTJmo0a6lVsDSR6tSpU1WXkYiIiIgamdOJaYiy+Qj3hZpw1N4R6FBzPY9WxtSu4+CpaQdNcj+8/8/ZK7a8aUgq1P7jvvvuQ+/evREZGYmRI0da+7eVMSOqOseCiIiIiBqfL3bsh69eC9gY0Dp0mMpjqA+0+Vn4w9YF+qzlGJI8AP8cTcDI9o0j97fCDculJyjpBUp6aWrRogVsbGxUjgURERERUWVEJGXj523ZWGeTCCddGnQ3Tao/X6idM1xSjwKaTDxgsxxz//THkDa+VT5oXl1UoU+Yk5ODGTNmqITtDh06WLt+feCBB/D6669XdRmJiIiIqJGQpkMv/HYYhYZC/O49A26tRgOtR6Pe0OqAUS+r2YGO65Dm8A7mrvsNjUGFAounn34aBw4cwPr160sla48YMQLLli2ryvIRERERUSOy/L/j2Jq4HBqdFiMmPwzN5O8BG3vUKy1HAC2GY7mrI/QuZ/DT6c+QnluAhq5CTaFklG0JIPr27QuNpngERKm9OHWqcQ4IQvWLxs4WPq8/Yp0nIiKi2penN+DVbW/BIWAnQkPT0Nx3POqtUa/gns8GIk2nxZH4nvhk/Wk8NbYtGrIK1VgkJibCz8/vgvXS/WzJQIOortLY2sB9xg1qknkiIiKqfZ9uOI3c1AC4G0yY4+kFGPSot/zbw7PLFLyemIyXsBKLt5xCZEoOGjJtRRO3V61aZV22BBOff/45+vXrV3WlIyIiIqJGQS66P14fjvuyI/FPZBR6HvkNMBaiXhv6LEx2LgizSYK/IR5v/HUQDVmFbtW+9tprGDt2LI4cOYLCwkLMnz9fzW/duhUbNmyo+lISVTGTwYC87f+peYe+naHR6fgdExER1aJXVh1BgCEG99j/AVsZ+2H0XMDWsX4fE1d/aCZ/h7PGpohf8R7W5XyGdeFfY2jLlmiIKlRjMWDAAJW8LUGFDIi3Zs0a1TRKRuPu0aNH1ZeSqIqZ8goQM+FBNck8ERER1Z5Vh8OxMeM1THVZBFvoVeIz2jaQYQzCBqFT82bw9TsLrU02Xl6/pMEOmlfuwEKv12P69Omq+dPChQuxc+dOVVvx9ddfc9RtIiIiIiqXgkIjXtw4HzYuJ7HKNwlGrS0w9g1pa99gvkmNRoNXBjyLjrG9kRbeDn8djkdDVO7AwtbWFj///HP1lIaIiIiIGpVFWyKQHtUbIzMNeColFdp+9wE+rdDQDDn0Fb7L+wmP2PyC1/88qgKqhqZCTaEmTJigupwlIiIiIqqouPQ8vL/2JHqZovF2chx62XoBgx5vmF9ox5vUw2Sbf6FNOY5PNu9CQ1Oh5O1WrVrhpZdewpYtW1ROhbOzc6nnH3zwwaoqHxERERE1UC+v2o+cAgNymg4EbrwOyI4H7F3RIIUNBNpcjYjTa6AJXoDPT3pjTPsf0MbPG406sPjiiy/g4eGBPXv2qOn8NmQMLIiIiIjocv49cRbrcx6FfUAHPHP1i9D6BwJo17C/tJEvwe+TNTBoC2CDTPzfd//gt3tugKuDbeMNLCIiIqq+JERERETUKBQajJjz9zJoXTLg63kCnZs0nLv2l+XTEu49puOj/YuRZdTh9iw7PPz9fnx2e0/otJrGmWNBVN/JaNtec+5VE0feJiIiqllfbT+L2Mg2eDHWgHdjj8Lu+J+N5xAMeQoddM7oU3gOL9h9jbXHEvDmX0fQaGssZs2addH10gzKwcEBLVu2xHXXXQcvL6/Klo+oWmjsbOE581Z+u0RERDUsMTMf89acwFTdX7ghLxpw8gHCBjee4+DkBdy0GPjjMfh3ngnd+iP4KvId+G6fizv79kGjCyz27duHvXv3wmAwoE2bNmrdiRMnoNPp0LZtW3z88cd49NFHsXnzZrRv376qy0xERERE9dTzf6yBfUEiHnX4BZBx4ka8ADh6oFFpMRS4bzuGam0QFv0p4vWJmLf7ffQM/ghdQzwaV1MoqY0YMWIEYmJirAncUVFRGDlyJCZPnozo6GgMGjQIjzzySNWXmKgKmAwG5O07qiaZJyIiouq38WQUNmW+AbuW7yPKphAI7gF0ndI4v3qdrWrt8+11H6BVYRd4xQzC3Ut3qy54G1Vg8dZbb+Hll1+Gm5ubdZ27uzteeOEFvPnmm3BycsLs2bMv6DGqNqSlpaFnz57o2rUrOnbsqEYLJzLlFSB61N1qknkiIiKqXtFpuXjkp62wN5rgZspH00IDMO4tQNu4U379zm3Hz9Gr8ZXDR8jKTMfdX+1Gnr5+3vSs0JFMT09HQkLCBesTExORkZGh5qU72oKC2r9gc3V1xcaNG7F//37s2LEDr732GpKTk2u7WERERESNRm6BQd2NT013xOKEbLyTkASHbv8z11g0dsE9oHHyQpjxLD5w+BRHM9Zj2g+LYTJJO7FG0hRq+vTpWL58uWoCJZPMz5gxQ43KLXbu3InWrVujtkneh9SgiPz8fHWQ6uOBIiIiIqqP5Lrr0R/34HBMBrydbNH0qhno4BQIDJ9T20WrG9yCgIlfAzo76Bz/g0PwMhzM/wRz/96MRhFYfPrppxg+fDgmTZqEZs2aqUnmZd2CBQvUNpLE/fnnn1e6gFLbMH78eAQFBal2aCtWrLhgm48++gihoaGqR6o+ffqooOb85lBdunRBkyZN8Pjjj8PHx6fS5SIiIiKiK3vt703YkPMo7FyP4aPbesNz6APAA3sBZ16PWYX0Bq6eh6tyc9EnNw/BaWFYuC4Nfx+JR4MPLFxcXFSugjQpkh6iZJL5zz77DM7OzmobyWmQqbKys7NVUCDBw8UsW7ZMdX87Z84c1VOVbDt69OhSTbWkWdaBAwfUwH7ffvst4uPr10EiIiIiqo/kwnjpkSXQ2qWhZYst6Bla1OORrkIdkzZs3W+Dbe978ElcAn7L2oyWiMHD3+/D8bhM1BeVypaJi4tDbGwsWrVqpYKN6mhiNHbsWLzyyiu4/vrrL/r8vHnzcNddd2HatGmqa1upMZGmT4sWLbpgW39/fxV4bNq0qcrLSURERETFTsRnqgtj+7gRmJJpxNLY49AmHudXdDmjX4Vt6EDYG3PxkNcOZBcUYtqy75GWW4j6oELhotRO3HLLLVi3bp1qnnTy5Ek0b95c5Vh4enrinXfeQU2Q5HDpeerpp5+2rtNqtaor3G3btqllqZ2QQEOSuCXpXJpW3XvvvZfcp+RhyGRhSUY3Go1qorpFjokEtOU9NiW3V/M8to1KRc8bIp47xN+dsknNKcCdX+5CXkEBlrt9hs5JUTB5NIVRBsPjb++laXTmwfMO/Yw+7W+D93f3IdthLx74Kwu/TXsA9rY1X9NTnn8rK1Q6GZ/C1tYW586dQ7t27azrJ06cqJol1VRgkZSUpAbpk5qIkmT52LFjav7s2bO4++67rUnbDzzwADp16nTJfc6dOxcvvvjiRXu8ysurv/0KN1RyskvAKMdWgsqyMun1sL33ZjWfmJYCTbZtNZaSGsp5Q8Rzh/i7c2WFBhOmr/wOsaYkvOIUgc4F+2C0cULKyA9QmGUAsi7sWZTOEzoByMnEsNAw/Hk2BnmZQQg/FwtfV3vUtMzMzOoNLNasWYO//vpLJUOXJE2i5EK+Lundu7fqaraspPZDgqOSNRYhISHw9fUtNW4H1Z1/5KXWTI5PuS8QX5hZXcWihnzeUKPGc4d47lzZEyvWIcbuKzgEFMI7PhHIAXD9Ani1G8QTqJxeG/gwHo45Cl2PdPiEBdfKv1nSOVK1BhaSUG3pwrWklJQU2NvXXCQlvTtJd7LnJ2PLckBAQIX2KeW/2GeQA8kLkLpJLhB5fIjnDfE3h+q6xvDv1fc7z+GnnbkI8+6Gji6bMDwnFxj8FLQdrqvtotVL2oPLEJQfjcSQXrV27pTnPStUuoEDB2Lp0qWl/lDkLo6Muj106FDUFDs7O/To0QNr1661rpNyyHK/fv1qrBxU/5iMRhQci1CTzBMREVHl7DqTgud/PSRXhvjK5gzmxydA1/YaYPCT/GorqtddME37C0bX4HrxHVaoxkICCBmzYvfu3SqB+oknnsDhw4dVjcWWLVuqtIBZWVkIDw+3LkuXsdK0ycvLC02bNlXNlqZOnYqePXuqZk/vvfeeqlGRXqKILsWUm4/Igber+bAza6BxduSXRUREVEFRqTm4+5eF0BvaYVynYATf8As06+cCw56XW978XitKvjtbuUbJbLiBRceOHXHixAl8+OGHqrclufi/4YYbcP/99yMwMLBKCyjBS8laEEv+gwQTS5YsUQnjklg9e/Zs1f2tjJ2xevXqCxK6iYiIiKjqZecX4tZlb6PQ+0f4uLXHmzd9BY29HTD2DX7djUy5Awu9Xo8xY8ao8SKeffZZVLchQ4ZccXyMmTNnqomIiIiIak5Gnh53LNqJmFR7uAZpcZevG1zs2NNiY1XuuinpZva///6rntIQERERUb2Qml2AKQt3YO+5NIzWm7AqKgp37/sBiNhY20WjWlKhRm//+9//8MUXX1R9aYiIiIiozkvMzMd1Sz7Eobgo9HOKwvu2HyLIUAhNjzuAMHYr21hVKMeisLAQixYtwj///KN6ZXJ2di71/Lx586qqfERERERUh8Sl5+GGr95Dpus38A31wqdJp6HLzwSa9gfGviXdhdZ2Eak+BRaHDh1C9+7d1bwkcZckXc8SERERUcMTmZKDKZ/vQGxmELycnHBzfjRc8zOAkL7ArcsAG7vaLiLVt8Bi3bp1VV8SohqksbWB+/2TrPNERER0eRFJ2ZiycDti0vPQ1dMFC+Mj4ZOfDY00fZr0HWDvwq+wkeMVFTVKGjtb+Lxwf20Xg4iIqF44EZeBiT++hPT81mjh2xoL7hwO32NzgPB/gFuWFo21QI0dAwsiIiIiuqTDMem4ddk7MHr9BVeXzVh47e8IcHcA+tyjRobmAHhkwaEQqVEyGY3Qn4tVk8wTERHRhfZHpmHyZ9uRntgFPnovPF1oQnNHXfEGHFWbSmBgQY2SKTcf53rcoiaZJyIiotJ2nUnB/z7fgYy8Qjzu8x/WRB3ApOhjwO5F/KrootgUioiIiIhKWb7/DJ7b/Dzy7dpjtkcGpqd/bH6i5wxgwCx+W1S1NRZfffUVrrrqKgQFBeHs2bNq3XvvvYdff/21orskIiIiolpUaDBi7h9H8cTqL6B13Q+34GWYkLnA/GTf+4Gr32HzJ6rawOKTTz7BrFmzMG7cOKSlpcFgMKj1Hh4eKrggIiIiovolNbsAdyzehU83noY+rRd6G4PwcVwMvCQXceCjwOhXOfgdVX1g8cEHH2DhwoV49tlnodMVJ/D07NkTBw8erMguiYiIiKiWHIpOw5iFn2BzeAIcbXX45JZ2+CIjDn3z8oGhzwHDZzOooOrJsYiIiEC3bt0uWG9vb4/s7OyK7JKIiIiIasGv+6PxzMbZ0HruhJ/tWCy98Tm0DXADgr4Bzm4F+tzN40LVV2MRFhaG/fv3X7B+9erVaNeuXUV2SUREREQ1nE/x8sojeOj7/cjPbgqNSYP/a1FgDipEQEcGFVT9NRaSX3H//fcjLy8PJpMJO3fuxHfffYe5c+fi888/r8guiWqUxkYHt2nXW+eJiIgak+SsfNz/7R5sP50qnbDj00ATmkfHIezc10Cn24Hg7rVdRGosgcWdd94JR0dHPPfcc8jJycGtt96qeoeaP38+Jk2aVPWlJKpiGns7+L7J7vKIiKjx+S8yDdNXvIMcm73wtJ+Glc1+RfCZ38xPdp4I+LH1CdXwOBZTpkxRkwQWWVlZ8PPzq+iuiIiIiKiaSSuTn/dG45nftsKu6RrobHLwVNAbCD53AtDogNGvAX3uYZI21d4AeU5OTmoiqm8/rsbkNDWv9faARqOp7SIRERFVm5TsAjy/4hBWHYwF4IxrCkeiW84PuCX5HODkA9y8BAgbyCNANR9YJCcnY/bs2Vi3bh0SEhJglP6NS0hJSalcqYiqmSknD2faXavmw86sgcbZkd85ERE1SGsOx+CJfz5AZloT6LRheGh4K8y0PQ7tv/FAYFdg4teAR0htF5Maa2Bx2223ITw8HDNmzIC/vz/v9hIRERHVMRl5erz8+xH8emYp7P3WwNXVB18MX4YezfwA0yOAgyvQ7X+ALW+uUS0GFps2bcLmzZvRpUuXKioGEREREVWVreFJePyn/xCdlguNrj+a+O/FTI0B3f2LgghpAtz7Ln7hVPvjWLRt2xa5ubmoL66//np4enripptuqu2iEBEREVWb3AIDnlixAVN/mq+CitaeWmzv+C/+Pr0XN5w9AM3W+fz2qW4FFh9//DGeffZZbNiwQeVbZGRklJrqmoceeghLly6t7WIQERERVZu951Ix5oM/8EfKY3AIXIEpHfZhtf3T8D/+NdSITb3vBgY8wiNAdasplIeHhwoghg0bdkFPO9K7jsFgQF0yZMgQrF+/vraLQURERFTl8gsNmP/PSSzYcApGkxY+Dt0R5noYd519D9pCPeDWBLjuQ6DFUH77VPcCCxm/wtbWFt9++221J29v3LgRb731Fvbs2YPY2FgsX74cEyZMKLXNRx99pLaJi4tTeR8ffPABevfuXW1lIiIiIqoLtp1KwpN/fotzMUGAyRHXdwvGi47OcNp70HyR1+VWYOzrgIN7bReVGoEKBRaHDh3Cvn370KZNG1S37OxsFSxMnz4dN9xwwwXPL1u2DLNmzcKCBQvQp08fvPfeexg9ejSOHz/OQfvokjQ2OrhOHGOdJyIiqk/iM/Lw2h9HsTrmU9h5b4FbcE+8Ofg1jOkYCGQGApFbgOGzgbZX13ZRqRGpUGDRs2dPREZG1khgMXbsWDVdyrx583DXXXdh2rRpalkCjFWrVmHRokV46qmnqr18VD9p7O3g9+GztV0MIiKictEbjFiy5Qze++cEsgsM0Dl1hqP3DkwLzMeoDv7mjVz9gXu3AdoKpdIS1Wxg8cADD6iE6McffxydOnVSzaJK6ty5c40ckoKCAtVE6umnn7au02q1GDFiBLZt21ahfebn56vJwpKMLoMAnj8QINU+OSZqFG0eG+J5Q/zNoQb+75V0Ifv0mm8Qm56PwoJO6NvEDvMDz8LmaDS8C0/D2HYljG2vKfmmVVN4atTXOsZyvG+FAouJEyeqR2meZCF5FjWdvJ2UlKTeS/I8SpLlY8eOWZcl0Dhw4IBqVtWkSRP8+OOP6Nev30X3OXfuXLz44osXrE9MTEReXl41fAqq7Mmenp6uzj0JKstKtkduUQDpaM9BHhuZip43RDx3qDZ+dxIyC/D+piisj1sHx+Dv4ejkjNea6zAu6nPoDsapbfKaDkGGXTMYExJ4kBoYYy3/m5WZmVm9gUVERATqk3/++afM20rth+RslKyxCAkJga+vL9zc3KqphFSZPzYJZuX4lOePzZidi7OdzTkWzU6vhtaZo442JhU9b4h47lBN/u4UFBqxeOsZfPBvOHIKDNBqOsJbE4QbNdkYeeJV6EwmmDyawjR6Luxaj4VPNXamQ433d8fBwaF6A4tmzZqhLvDx8YFOp0N8fHyp9bIcEBBQoX3a29ur6XxyIHkBUjfJH1u5j0+JbXlsG6cKnTdEPHeoBn535M70hhOJeHbNV0g2HkBewS3o3tQTL13bHu2Wvwhd8ilAZ6/GpNAMeBgaW94ca+g0tfhvVnnes8yBxW+//aaSqCWfQuYv59prr0VNsLOzQ48ePbB27VprF7QS1cnyzJkza6QMRERERFVlf2Qa3vjzGLafOw3nFl/DVluIKR3H4dmhV0Or1QAjXwb2fgWMmQt4hfGLpzqlzIGFXLjLOBF+fn4XjCNRUlXnWGRlZSE8PLxUM6z9+/fDy8sLTZs2Vc2Wpk6dqnqqkrErpLtZyaWw9BJFREREVNedSszC3NW78M/hHLVsp/NEP7sR6FawDTNc0s1BhZDuY9mFLNX3wKJkRnhNZqXv3r0bQ4cWjxRpyX+QYGLJkiUqkVwSq2fPnq0Cn65du2L16tUXJHQTERER1TVx6XmY9/cR/B7zAXSu+6G1ewRT2jbFE06/w/XAYsBkAFLeA3pMA2zsaru4RFWfY7F06VJ1QX9+LoJ0//r999/j9ttvR1UZMmSIuQefy5BmT2z6RERERPVFeo4eH28IV2NS5Bca4RiSAY22EE90WoP/O7kOKMgybyjdx45+jUEF1QsVygCRZkbS7dXFuqNiEyQiIiKii8stMGD+vwcxYMFsfLrxuAoqejbzxIdth+Ob5Dz83+HfzUFFYBfgtuXApG8Az7rRaQ5RtdRYWMarOF9UVBTc3d0rskuimqXTwnn8EOs8ERFRdcrXG/DTgQR8tecQMr3mQecdiWBHW7w05H4Ma+sHTbwbsOkZwKsFMPx5oN11HDmbGnZg0a1bNxVQyDR8+HDY2BS/XBK2JbF6zBjz2ABEdZnWwR4Bi16u7WIQEVEDl1NQiKXbwvHF5kgkZhaodb5OA+Di/CeeCcvA8HZFOaEBHYHbVwDNrgJ0trVbaKKaCCwsvUFJr0yjR4+Gi4tLqa5fQ0NDceONN1a0LEREREQNQkaeHl9tO4vP9n+NQrc1yDPeBD+XjnihWxZGxq0HTv0H24gjQK8HAO8W5hc1L6pJJ2oMgcWcOXPUowQQkrxdnpH4iIiIiBq61OwCLN4SoUbMzswrhL1fPOxsMtG31Q58nv0vHHdtMm9o4wD0uQdw8qrtIhPVbo6FdPVq6QUqISHhgu5nZXwJorrMmJ2LiNBRaj7szBponTlqKRERVVxCZh4+2nAAP5/8Dtkp3WHSe6Olnwvu6XYNNGeO4LpjqyENnEwaHTTdbwMGPwm4BfErpwalQoHFyZMnMX36dGzduvWiSd1VOUAeERERUV0VnZaLhRtP47ud56AJWAJbryPwd87HnH6zMbpDALT5acC2UzBpbZHTegIcRjwFjU/L2i42Ud0JLO644w6VuL1y5UoEBgZetIcoIiIiooZqf2Qa5m/cjI1H9DAYzAPXtdONho1NEh5xTsWwjgGAXB85egI3LoTJrwMy8u3g4OVX20UnqluBhSRv79mzB23btq36EhERERHVQQajCWsOx+HzzRE4lPcl7Ly2Qut2Dfp5XoPZoUfQ6sRnwMkTULdbu28Emg82v7DNWECajSck1PInIKqDgUX79u2RlJRU9aUhIiIiqmOy8gvx7c7TWLrtHKJS8tU6B68AABpMbHEar0TfB2w9Y97YwR3ocy8Q0Kl2C01UXwKLN954A0888QRee+01dOrUCba2pftbdnNzq6ryEREREdVa/sSXW8/gu2Nfwei+Dnn5N8PTqSP+17cZJrcIQOEvX6Jp9mrzxk4+QL/7gV53Ag68DqLGqUKBxYgRI9SjDJJXEpO3iYiIqD6Ta5k9Z1OxZOsZ/HkoTjV/svdLhZ1NFvq0O4Yvrn0IjnY6c9Mme0/zJDUU3f4H2DnVdvGJ6l9gsW7duqovCVFN0mnhNKKvdZ6IiBo3ae60Yl80Fu75FQmadciLuw4moy8GtPDAjJBOyIk9jFER/8JWo5d/OACtFrj9V8AtGNDqarv4RPU3sBg8uCgZiaie0jrYI/C7t2q7GEREVMuOx2Xi6+1nsXxftAouHJtsho1LOLq03o8FfqEIOLoY2F6UP6G1AaJ2AWEDzcseHLeLqNKBxcaNGy/7/KBBgyqyWyIiIqJqV1BoxJ+HYvHprtU4nfsv8uJuAIwOaO7jjJFh42CfVYBJZ5cjIDzN/ALpMrbHNKD3XRzUjqiqA4shQ4ZcsK7kWBYcII+IiIjqYjL2tzvOYtmuSCRl5cOp+TewdU9EW48ueLz/dPRv4Q1NUiDw0UPmF3i3BPreB3SZzPwJouoKLFJTU0st6/V67Nu3D88//zxeffXViuySqEYZs3Nxpv21aj70yG/QOjvyCBARNdDaib+PxGHxnnU4mLoV+QljpE0T/N0c0M9rFDyN23G74zmEtfQxv8C3DTDocSCkL9BimDmXgoiqL7Bwd3e/YN3IkSNhZ2eHWbNmqcHziOo6U05ebReBiIiqyYn4TFUzIbkTKTnZcGn1Luy889HWrTuea+aKHolfQ3t4NWAyAlpbYOhswKVoVOxhz/G4ENVUYHEp/v7+OH78eFXukoiIiKhMMvP0WLE/El/u/xPnsk6iIGmkWu/n6oJWzoMQpDmNezLeR8stRcnYInQg0GsG4ODBb7kcpNm7tFih6mc0GtV3nZeXB2011qDJuHQ6na7mA4v//vvvgj6fY2Nj8frrr6Nr166VKhARERFRWck1yM6IFPywOwp/HIxFHhLh0nIB7Bw06Oc3BlN7d8OgVr6w2XkC+OsL84vs3YGutwI9pwO+rflll1NWVhaioqLUd0/Vz2QyqeAiMzOzVE5zVZN9N2nSBC4uLjUbWEjwIG9+/gnVt29fLFq0qMKFISIiIiqLyJQcLNtzEj8f/wNJ2RnQpw5Q61v6NYWvQw900aRjRqtE+Lb1N7+g80TgyK/mgew63shk7ErUVEhQ4eTkBF9f32q90CUzud4uLCyEjY1NtX3f8h6JiYnq2LZq1arCNRcVCiwiIiJKLUu1jJxcDg4OFSoEERER0ZWk5+pVrcTyvdHYeSYFOqdwODX7FvZOjri52Vjc7XMUzc4th+boNvMLsnKBbreZ5529gRl/8UuuJGmSIxehct3n6MiOTxpKYCHkmJ45c0Yd4xoLLOTNpk+fjgULFqiIpj64/vrrsX79egwfPhw//fRTbReHiIiIytGr04YTifh67w7sTP4Dhbn+0Kf1hlxf9Q3qjXy79RitMWBKxGQ4Hcsyv0ijBVqONNdOSOsK3lWvcqypaHg0VfB3YlORxI7zcyzquoceekgFQ19++WVtF4XqCq0WDv2L8oHYlSARUZ27Q3sgKh3L90bhtwMxSM3Rw9ZjNxwCt8DBJRj39Lsd13UNQqC7I/CVBji1wfxCr+bmYKLLrYBbYG1/DKqH0tLS1M3zp556yrruzjvvxJQpUzB06NBaLVt9UKGmUP/73//wxRdfqGTt+kAG9JMaCyILraM9gn/9gF8IEVEdEp6Qhd8PxODHYyuRarMOBcmDYMhpDx8Xe1zfeiASCg/ixqxkDOxiD40EFaL77YCLP9D9NqBpP9ZOkLXZUEUDC7m+LRlYfP755/xWy0hb0QP2ySefoGfPnrjnnnvU2BUlp/LYuHEjxo8fj6CgIFUFs2LFigu2+eijjxAaGqpyOPr06YOdO3dWpNhERERUx0Sl5mD+v4cwbv4mjJi3AfPXnkSyPhw2TmcQ1vQwVg1Pws7mn+PZQ5Mw/8i/GHTuADSHSjRr7jABuP4ToFl/BhW1VLuUU1BYrVNZep+Sa8g5c+agV69eePrpp7FkyRJMmDDB+vzKlSvVjWYhN5s7duyI++67D126dEGHDh2we/du9dz//d//qd6XpKMiuc4V8jrL9ekdd9yBu+++GyNGjEBYWJhqESPXpbJN8+bNS10Hx8XF4ZZbbkHv3r3RqVMnPPfcxcdHkbLK/iZPnoz27dujf//+OHLkiGrK365dO4wePVr1xCXWrl2Lfv36oVu3bqrccqNfSK9RY8aMwdtvv62WT506pXp4qulhICoUzh06dAjdu3dX8ydOnKhU+6zs7Gx1UOXA3HDDDRc8v2zZMnWQpFpKgor33ntPfcHyRfn5mQeykYMvwc751qxZowIWIiIiqjsSMvOw6r9Y/HYgGkcNH8PG5Qhykh+EjTYAg1r74qrQMTDGRWH82XUIOL28+IUBnYEuk8y9OlGdkKs3oP3s6k2KP/LSaDjZXfmSVRKOd+3aZb1Yv5xjx46pi/KPP/5YXWM+++yz+Ouvv9S8XFfu37//kq89ePAg1q1bpzovkkAgNTUVf//9NwoKClRwMWPGDHXRP3XqVDzzzDMYPHiwuk695ppr8OOPP+Lmm2++YJ9Sbtlv06ZNcdttt6mb7lu3blVjxMnrvvrqKzzwwAPq+nvz5s3qs6akpKgAQ66LJYj4+uuvVWAlAdGjjz6Kt956C23atEGdDyzky6wqY8eOVdOlzJs3D3fddRemTZumluWAr1q1SnVra6mmutzBL6/8/Hw1WWRkZFgjQZmobpFjYunfuVyvy85FZK+Jaj5k1zJondmzRWNS0fOGiOdOxaXlFGDlwXP4+dAuHDztAWPRTWjHJoXQaA24oVccnh56Kzyd7ID8DGjeuQuawjyY3IKBTjfD1OkWwK9dyT/kenVCNpRzx/I5Sk7VrazvI9eKlu0u9yhTy5YtVU2CzMtwCXKnv+T7nP9+JZ+79tprYW9vr+alJmLUqFGq6ZWNjY0KNOSme7NmzVTtQnx8vHUfUusgAc3F9i21ECEhIWq+R48eqrMkuYEuyxIonDx5Um2blJSkAhd5D3m/5ORkFZAEBwfD29tbBSDDhg1TQc2kSZPKdXwsn/H8a97ynLNVOvJ2VZPIb8+ePapKy0KiQ6ku2ratqCu5KjZ37ly8+OKLF6yXvn1lxEOqW+RkT09PV38I5RmN0pSTB2NyuvXYarLZVXJjUtHzhojnTvmk5RZi46k0/HsyFbtjz8EhdB5gY4RR8zw6+nlickgKmhvc4RFrQIuMX5GSNR0JRR07OfZ/Bgb3UBQE9TL38iQSEurtSdhQzh254JXPInfgZbLVmHDg+WHV+p7yHhdrmXI+aTJv2U5a0EhZLcvSQsbSbauMxSGBgeU5y3rLJEq+nzwvr5F18tnt7OxKvU/JZa1Wq25QW0Yl37Rp0wXDMZz/Wa60T8tnkUmaakmTp++//16tl+BIPptlW7lulgAjOjpabV+elkSWzyfBinTWZCFNwxpEYCFRmRxIqQYqSZYl4isrCUQOHDigvnipKpJqKIkML0aCmJLt46TGQiJI6dvXzc2tEp+GqoP8AcgfjRyf8vxQS43F2aJ59VrWWDQqFT1viHjuXFliZj5WHTqDn4/9g5MJGShIL+qBD+6whQecbPRY1GcjekVugeZQcXNqk60z/JwAuJibOcPvgQZ1wjWUc0dussqFpuUOvSh5EVqbSpZJmgBJ0325uJby/fDDD+r7l+elGZFl3vI6y6OXlxdyc3OtF/tCtpXXyPNy7GSyvOb8ZU3Rth4eHqoXKakJeeGFF9RzMTExar9yLVrSlfYp87Jf+RySXC65HTIvecrSU6ulbHv37sW7776rHqUnK2n18+STT5br+5P3ksCkZDBUnnHq6nRgUVX++eefMm8rEayleqsky0Gmukf+2Mp9fEpsy2PbOFXovCHiuXNRCRl5WH04Tg1etzMiBVqX/+DY5FvY+nijpfNAXN0pCGM7BsBlzwb4bv8MGhwwv1BnD7QepXImNK1GQ2MnkUXD1RB+dywXuZapLilZJrmBPG7cONVUKTAwEFdddRV27NhRapuLPcpF9e23367yf11cXKxJ3ee/ruRnv9TyN998o25WSxlk2dnZGZ9++qm6YX1+uS/3WHJeeqySpPNXXnlF5YJI/rE8J8GeJH9LqoB83qVLl6rajIEDB6rPXp7v7/xztDznq8ZUE43jykg+zPLly61Z/NIUSoaMl0HtSmb2S7sxidh+/fXXai+T1Fi4u7ur6kvWWNQ9EvknJCSodojlrbGICB2l5sPOrGGNRSNT0fOGiOdOsciUHKw5Eo8fj6zGWf1a6NO7ojCjm3quUxN75Hi8iZH2Hpg1cA7sAjqYX3R0JfDjVKDFMHMCdptxgEPjaA3QUM4dqbGIiIhQd83Lcyeb6v7I25c6tuW5Fq7TNRZSBSUJLJL8Ygks5A9TlmfOnFnbxSMiImpUFzfH4jLx84GD2HK8EEdjzckQdt4nYe93HG4Otniyaw8Mxw64n/4DCC/q5tKnK2AJLFqNAh47CTh51eInIaLqUuuBhWTIh4eHW5clUpJenqSNm3S5JVVIUkMhGfFSpSPdzUquhKWXKCIiIqoeBqMJe86m4q/DcVhzJA5JLvNh43wa2Wn3Qqtphl6hXujffAw0qZG4JuEomu+cVPxirS3QYigQ2KV4nY0dYMOggqihqvXAQtqulRwi3ZI4LcGE9EE8ceJE1WvP7Nmz1UAj0p5s9erVFyR0E5WLVgv7rm2t80REZJanN2DjyXj8cHATdsccQlp8X+tX4+ToBpg0uLV7KmYNuxNeznZAYQHw9p1AXro5Z6LVSKDdtUDr0YCjB79Wokak1gMLGanwSmke0uyJTZ+oKmkd7dHk74X8UomIAMRn5OHfo/FYeywRW8KTkGdKhkur12Hy1MAtqxvGtQzERK8T8E4HXCMy4Jn6PXDNM8W1EMOeNzdvkqZO9q78TokaqVoPLIiIiKhmGY0mHI7JwD9H47Hq5GZE43cYC3yQH2fOZwx0D4Crti3a2pnwRNhXaBK+BTAUDx6rxpXIjAXcgszLve/iISQiBhZERESNQU5BITaeiMfPhzdh/2lbJKabe33ROWfAqWk4dI4JuL/DoxjeIRDtA92g+eMPYNfnxTvwDAXaXgO0vRoI6QNodbX3YYioTmKNBTVKxpw8RA64Tc2HbP4KWid2mUdEDYs0Mz6VmI0NJxLVtP10MnQBi2HjehR52mvgbDcYQ1u6Y4KPD85mtsGIhBNo1ioJCCrKP5MgImp3cTDh1076ha/tj0VEdRgDC2qcpE/oyDjrPBFRQ5CZp8eW8GT8ezwaa+O+Rq72BHLO3SXDWqvnfU1tYUIkbmuRhEf1X0F3eh1wOrt4BydWA6EDzPPSo5NMRI2IjJO2YMECPPXUU9Z1Mor1lClTSnU2RBfHwIKIiKge50ocic3A70cOYkvEGRw744VCo9wsMcG55Q7obDPRtVUqrmk1GINb+yLE2AS2n86HDkeKd+ISYB79uvUYIGxwbX4coiphGUyuooGFjG5dMrD4/PMSTQLpshhYEBER1SPnknOw9VQStp5KVo9pmr1wbPI1DKYAFBofRnMfZ4wL08ABPdA0/ywG+xyE+8CisZ+MnQDXQPMkgYQEFAFd2O02VV5BiZqv82l0gK1DGbfVAraO5X57GZFahib4448/VI+jHTp0wIoVK9QkVq5cibfffhvr169Xk/Q2OmjQIGzZskUFIl9++aUaM+3//u//kJmZqYY3kOBEhkWQ/T388MNqsOY77rhDDeB8+vRpnDp1StViyGueeOIJnDt3Tm0zb9489Z4yTMKDDz6IM2fOIDc3F9dddx1eeeWVC8ouwyt8/fXX8PX1xYEDB+Dh4aGCmWeffRbHjh1DSEgIli1bptbr9Xo8//zz+Pfff1FQUIDWrVvj008/haenJ7799lvMnz9frZcBpeW9xo8fr95DPoN8vh07diAmJgYjR45UNTNVjYEFERFRHe8KdltRELEu5ldk221DQUp/FGZ0V887ObSAxmSDVi52+KLfBvjEbAEOHiregfNRYOzb5uBBpgf2AHbOtfeBqGF6raiHsIuRboin/Fi8/FZLQJ9z8W2bDQCmrapQEXQ6HXbt2mW9WL8cuWD/4osv8PHHH6sLbLmI/+uvv9S8BBUyWPOlHDx4EOvWrYNWq0X79u2RmpqKv//+W13QN2/eHDNmzFCBjYzJ9swzz2Dw4MEqeLnmmmvw448/4uabb75gn1Ju2a8MDn3bbbepgGDr1q1q3DZ53VdffYUHHngAb731FpydnbFz5071updffhnPPfccPvroI4wePRqTJ09WQZYEM3379sXZs2dhb2+vtpVASMotwYmUe9u2bejXrx+qEgOLOu7pXw4iISMPvcK80CvUE52CPWBnwwHdiIgaqtTsAmw9lYDfT2zF/oS9iDt3lYy+o56z802CvXsU/P3P4caet6BfCx/0aOaJwsUfw+nMZgAyCQ0Q1A1oORxoMbz0GzCooAZq+vTpZd62ZcuW6NOnj5qXi2upzSgrqXlwcDDXwHTq1Eld0Nva2qpJLthPnjyJ0NBQrF27FvHx8dbXZWVl4fjx4xfdp5RBggohNQty8W8ZDFqWw8PD1bzUwKSnp+Pnn39WyxLMyHuJiIgIlQsSFRWlaltSUlLUurZtzR0yyKDTsl4mCZ4k0GBg0ch69JA+xhMz87H2WIJaZ2+jRZcQD/QO9ULPUE/1D4qrgzkpj4iI6l+OxOmkbGw7HY2dkedwLNIOJxOyABjg0voNaJwLoHNogfZebTGuSR5a2vggJysMfVO3wX9IE2uQYNfsKiA9yhxESDDRfCjg7F3bH48ak2diLt8UqqTHwy+zbcVvnrq4uFjn5eLZYDBYl/Py8kptawkMLDUdUqNQVue/9mL7MhV1DLN9+/ZSz1dmn0L2+8EHH2DUqFEX7GPSpEkqP+Smm25Sy15eXqU+d2U+c1mxxqKO+/z2nth1JkVNu8+kIjm7ADsjUtQktBqgbYAbeoeZAw2Zl+DD3lYLe51O1W7IpJMNqZhGA9s25gif3ScSUU3Jzi/E/nOp2H02GfsjM7D3XBqybfbCIfhbGHJCkZtwj9qutb87nOy7wV+Xggfbb0CH6PnAf5Gld3ZuuzmIEEOfAUa+xN8zqj3lqQmrgVozqZH477//VG6D1CRI/kFZuLm5qddITYDkUlQmyBk6dKi60H/hhRfUOsltkNyHJk2aVHi/ksPx7rvvYsCAAXByckJOTo6qlZCmV9IkKywsTG0nORuyXNMYWNRh0kZOaidkunNgcxWlyp2tXRESaKSqYONcSo7qEUSmJVvPXHJfNlqNNciwL3p0d7RFkLsjgjxkclCPge6OCPZwhK+rfYMORmTciqabv6rtYhBRA5ZbYMDx+Ewcjc3A4Zh07D2bhtOG76Bz36dGuC7M7Ki2c3AKgEZjgp9rLubc0gKdW4fBx8Ue2H4UWP1k8Q61tkCTXkDYQCBskHneogLJrkQNmeQXjBs3Dh07dkRgYCCuuuoqlbh8JXKX//bbb0fnzp1VcCDJ2xX1zTffYNasWaoMck0nuRGSaF2ZwOLJJ59Efn6+asYl+7Ssk8BCEreltkKSvIcNG2ZtWlWTNCZLXQ1dVEZGBtzd3VV7Noli62JS3+6iIENqMaJSc1BgMCK/0Fip4RkkEAlwdygKPBzQ3NcFHYPd0DHIHX5udWcwOYn8ExIS4Ofnp5KoiHjeUE3/5sg/o3EZeSqAOBqbqW70HI6LQrztMsAmHbln77W+3j5gOew8d6CZZhTu9boK3TUn4Ju2H6mxu+CblQSMnw/0uMO8ccJR4Nf7zUFE6ECgaV/mR9RjDeXfK2laI3fI5c54WZr4UOXJb4ylC11LMFGTx7Y818Kssajn/N0ccHXnQDVdcBIaTSgoNKpJAg01bzCoeZkkQTAmPQ8xabklpjz1D6S8Nio1V03nk9qMTsHu6Bjkhg7yGOyOIHeHaj3ZiYhqm/yGnkvJxn8R6cg4lYuzKTk4FpuJwxkboXfcgcKsttCn9jdvrDHApc1BVRPRt5UNugQ2VbXPPhot7LafQLuYL2F/urhvfF/5n84OyDLn0yky0vVd/9b8ByUiqiAGFg2UXOTb6mTSwtncy1iZGYwmJGSaA47otDxEp+biRHwmDkWn41Rilkom//dYgposvJzt0CHITQUZ3UI80CvUC57OFW+bWN2MOXmIGnWXmm+yZqFqGkVEjZvckMnML0RkSo4aK+J0cjpOJ6ciLlWDs8k5iEnLhn2TL6G1S0TOmftgMpgTRW29kuDgcgIuds64qqk/+rqnobNNPPZmdEJYTiJ6NN8Dh6GjzW+S6wD8dMA87+wHNO0DhBRNgV0Am3L+YBMR1SEMLOgCklshuRYy9WhW+rmcgkJV3X8oWqZ0HIrJwMn4TKRkF2DTySQ1WbQNcFVJ5X3CvNWj1HTUGSYT9MeLclLYGpCowfa4lKs3ILugEGk5eiRl5iMxKx9JWQVIVo/m+XO5u5CuT0JGUgfkF5hvMth6boO9/28ozOiCvJhJRXvUQmcfD41tGvq2NqGjTxha+bvC3dkXKQdPo6v+DNqdugYoNNf0trcUxFTit8/RE5j0LeDfAfBoxmRrImpQGFhQuTjZ2aBHMy81WeTpDUU1Ghk4GJ2mEsvDE7JwLC5TTUu3nVXbNfd1Rp8SgYYkixNR3SG1leamkwZrE0qZtzSftK7TG6A3mGA0mSeJzeVRXm+ZNxY9morm5dGS9iXbFM+bSsX38hppiin7Mj8aYTBK2aQppx56YyFMRhsYjRpVtrT8dGQUxiG/QAdDvr8KInLyDch12IxCTQb0ab1hKjS3CbZxPQh7v9Uw5DRDXuwt1s/t3OI7aJ3SoE+RBklNVQ2sl3sA4jUmNPXJxeMd7dBcGwf/whjsSQ2GfaoGnbzWwPnqooDD1AT4fS+Qk2xetnUC/DsCAZ3MU7B5IDurtldX63EkIqotDCyo0hxsdejcxENN8o+ykDuBlm5xt59OVj2jnE7MVtN3O81dJoZ4OaJ/cx9c1coHV7Xwhrf0gkJEVdIbkTRnjM/IV00XM/L0yMjVFz0Wqsf03Dwk58cgsyATeVkhal1OgQE6p1PQOUbCkNsEhpyWRXsshEPgL4CmEHmxNwMm89g5Nu67YeuxG4WZ7aFPGWR9f6fQD9W2uefuLG4u5L4Ldj7rUZjZAfkJ44q3DXsPGl0ucs7eDZPePO6CrcdO2Pv9gcLMdsiLnWjd1rnVq9DaZCL79IMw5gdZt5WyFea2Q27c1OJtA9bD3i4FhdmtAIMbPBxt4exuj3S7ZHjYe2NkWCj8HY1oYpuFP1Nao8CQjjuHm9Clzxg42umQo78KOQuXwjvhb2hO/G3d7+CiR1NuevEXLvllw+cA9q5AQGfAKwzQntdvPxFRI8DAgqqFdJU4rlOgmkRaToGqydgZkYwdESmqGVVkSi6WpURi2W5zoNE+0A0DJcho6aNqNCRgIaJicndfAgXpZlo6WZDAQQKIhIx81UNcfGYmEgoPIteQgcL04q5I7bzXw8Z9D/SpfaBPHWBeqc2Ba5uXAEcgM+YV6z8HOucTsPfZAH3KANjp26gxcWx1Nsjz2Kueb4b/wVHnBjudFkm2+Ui0OQN/+2YI8faBVqNRY+vs0cbBhEIMbuMBR62Puu6OMtogwpiMEJ9CtA0IVHlg0t3DpoIMFCIHI9r5wEUXpNbFGN1xuCAPTX1s0D8sVPVSp9NpsCLJDjlGYEqfEAQ4tlRliCrIxL/x3mgX7IH7ejjASZMHZ1Mulia2Qq4hG7eP0KNF/3Hq/VLyeiJi+Wb4J51Gk6jRQEGm+kzjLV9UTDgw8D4162TrBCdbV/N6Jx/Aq7majJ5hyLDxhltoN1VWqx7FQQ0RUWPFwIJqhIeTHUa291eTyMzTY/fZVGw5mYTN4UmqyZRlPI5PN55W42z0bOaJAa18MKClDzoEuTfocTWILPQGo+o4QZKFzyZnq8eI5DScSYtGTJKryhkQtp6bYeu+D/r0ntCn9lPrNLpsuLReLLEC9Dk94O/mAl8Xe2Q4aRCLRLRtZsKAbi3h5mgLFzvg3ZMucNY5YvFtwfALaA8XBxtsidVg55FU9G/ihrHOZ4DCPKAwH4tTO8HOZMQNbc/Csc//qfc7leaH03+dQBNjLNpp3gdMRsBowDZjgJrv7rcO9mNeV9vGZzdBzIqd8MoLRzP9U0XtoYw4orGD0WSLli6/wOFGcy9J6flhSPl+DVyT98AnYSJg0APGQjxg0kNrKIRD7ivQ/t/mom+sOV58/03g5DLgwDLr9/i0ZcbjMHDVQ2rWy8ELXpnJQPLp4hNOZw+4+AMuvoBn0aCZFjctAhw9AAf34nVGI/ISEuDm58eTlqgBSktLw4IFC/DUU09Z1915552YMmWKGvCuNropfvzxx7F69Wo1kriMx/HJJ5+owfvOnDmDFi1aoFOnTtbtf/75Z7WutjCwoFrh6mCLoW381CTkLuzWU+bk780nk9Td2K2nktX0Jo7Dw8kWV7XwUTUaA1v7qkH8iOory7gH0jRQelqzPqZGIrHgNAoL3GHMMw+gpNFlwrnVa4A7kBv7MrQaG9Wxgo13IVJso9HGoxNG920Lf1cbBOsy8fbJ5vCzscerUx3h3tL8j+CZJC8krtqLJumbERj3B5CXDuRnYJIEAkL3AdDhSzU7vsVYjP/6VillqTJPs8xkFwBFgUULjxZocWobUJBValtzmCMtqIpvBvg7+8M/5giQnVhqW2uCs6F4W3d7d7inxQKppQf9NDeqkqjpvG6wnbyA3FTAzsU8zoN90aMsuwaU3nb0XHMAZAkm7N0unUDteV7vFURUL1jGfKhoYCGjZZcMLD7/vLhr6Jr2xRdfYN++fdi5cyccHR1xzz33qIHwJNgQrq6u2L9/P+oKBhZUJ0iPUdd1DVaTXHSdSszGlnBzoCE5GtKjy6qDsWqyJIIPauWLq1p6o4WL+Q5uuWg0sAkpuuDg+BtUxeQcNic5GxGVllMqgDidJI8ZKLD/D1r7eBQkDVe9DQk73/WwD94Am7T+CDa0RTNvJ4R4huLXNHvoNFosmeaPzi16qxq9k/HeiPnnCJpnbEdI9O9AVry6YP7BUgi9E9ByhJoN9WqN0AgZPfYio2bK2Ann/W2gzVhAozV3fWrjUPrRu1Xp7Ue9rGopVE6BvEZT9CjLzmp0hmLXfggYCor+5jQlHrWAg9uFtQWGQkBnA2hlsgV0tub9SllKuvOfsh+cZtawh4iqUI4+Rz062jhax7XSqw4X9LDR2sCuxG+NZVsHGwdo5e9ftjXq1fY6rQ72UpNYTvKes2fPxh9//IEhQ4aokahXrFihJrFy5Uq8/fbbWL9+vZpmzpyJQYMGYcuWLSoQ+fLLL9GzZ0/83//9HzIzM9G1a1cVnMjI27K/hx9+GBMmTMAdd9yhagtOnz6NU6dOqVoMec0TTzyBc+fOqW3mzZun3jMuLg4PPvigqlnIzc3Fddddh1dekaanpS1ZsgRLly5VI32Hh4fDx8dHLYeGhuLAgQMYPny4ek/5jGPHjsULL7xgDSzqGgYWVOfIH05LPxc1Te0fikKDEQei0lSQsfFEIvZHplkTwZdsPaPaX/dodg6DWvuqGo2yNJuScSua7f2xxj4TXUj1EmQCDEU9CxmNJefNvQIZLNsULVt6HjI/AoVGo3qderzEOum9SF4jTYxUT0MGmTcgNT0Djk456j0Kreul5yEj9IXmeeuyPFdYvFzcY1JRr0n60j0oyfPWc80+DjYux2DUe6quS82McAn7ERqtHr19R6C9b3M093FGvCEDm6PPYkygI2bYbQdSTgEJp/FIajJcs5OhcVwItOmr9tDKtxNandqumghZyUW93ImXu/SeYcXrZZRfuVCX5GJp1qOmoiY+thcZw2Xyd2U/ZXtOL/u2bcaUfdvgHmXflohqXZ9v+6jHDRM3qGaHYvHhxfhg3we4sdWNeKH/C9Zth/wwBLmFuVh942oEuwSrdd8f+x5v7noT48LG4Y1Bb1SoDDqdDrt27bJerF/OsWPHVG3Axx9/rJo+Pfvss/jrr7/UvAQVl6sFOHjwINatW6dGUG/fvj1SU1Px999/o6CgAM2bN8eMGTNUYDN16lQ888wzGDx4sAperrnmGvz444+4+eabL9inBDjynu3atcObb76Ju+++G2vWrEGPHj3w6aefquBFaid++OEHFahYZGdno1evXqqZlAQ18jnke6gtDT6wkCqtESNGqAMq00MPPYS77jIPjFZfvLD1BcRkxeCeLvegh3/j+8fWRqe1dnH78IjWSM/VY9upZGw6magCjcjUXJUQLtNbfx2Hq72NGpzP0VYHBzsdHG215nlbXYl15knu/GqK7uNauru0dIpZvFykqLtMc1eZxduUWlfiNXJxXPK5kt1pllxn3qZo3tJF53mvs3TVWbLbzou/pnifJbsBtbyvXHDLlrIflHidpXtQy+svvlxcLsuy2l+JrkUtr1EBwkW6Hi3dDSkaHJ3LMehcIqFJ7Q+TwVmtc3GPgsl7NXy0nTGx6UQVQLRwN+GLQ71gl5+BGUFRCOl1jXkHhiA89OpMwLix1H6t9/KluU/JYEFqACQHQAIJ10BzDcGleiPqeEO1fGYiorpi+vSy3+ho2bIl+vQxB0P9+vVTtRllJTUPDg7mmzKS3zB69GjY2tqqSQKNkydPqtqGtWvXIj4+3vq6rKwsHD9+/KL77N+/vwoqhAQVzz33nAoWpIZEAgmptXByclLXtBJwiMDAQERHR8PPzw8pKSmYOHEi3nnnHVV7UlsafGAh0d3GjRvVwZCormPHjrjhhhvg7W3u1rCuk4u0jVEbkZibiLs6FwdE4anhWHN2DfoH9UdXv65oTNwdbTGmY4CaJKlp74lIHEkxYnO4OSdDRs6Vieo/qU3XqZ6GNOo6Ws1LD0FajaqpkvUyX2o6b51sJ8FpqUetBoX6fDg7OqrR6W10sq0W9jZa64j1Mtmdv1y0rUmTh9i8YzBpCtDHf4h6nfSe9MjmTxCVfRbzrhuPwSGDYG+jw6n05li85Ri6G3WYFP0MsPcIkH4Or1k+ZHwc0Mucs6Ca/TTpbf7g0mWpVwtrb0RqktyBkrpOrulDQkR0UTtu3WFtCmUxrcM0/K/d/1RTqJLW37Le2hTKYlLbSapmQ5pCVZQ0JbKQZkxyYW6Rl5dXaltLYCDkDr/cfC6r8197sX2Ziu6ebd++vdTzFWnFIU2fJNCQz7Rs2TJVGyLs7e1VUCG8vLxUYPXtt98ysKhOcoAlqBD5+flFd2Dr163SBSMXYHfcbnTyKc763xi9EZ8c+ATHUo7h/WHvW9fvjN2p2jF28OkAW2mT3Ag08bBH99Z+uL1/mGqqIm3Zs/MNauA+6c9fetGRybKcJ81WsnMx+JU31bmw8rFHYLCTtp8aa7qFpSFV8bLGuqxahWtKLmtKrLe8TnPR50ouy4xlvbZoXi6aS+5XFku9rqg7T0sZ1ObWdRdur/Zb4lFzkW1lSS7Aze9v2aZo2bK99fOYR2a37Mf8fPF7yOstr7Hs17pdiflSAYMsFwUN5u3N21nepzpIQJqQkKB+kKUq+3Kk3e+J1BPwd/KHj6OPWrc1Zite+PtphLiG4IG+RTUBBj3G+ndFfIormkVthmsryZ0A2nm3w5unDwNp5oEiraSGwa/9hU1+pv9ZlR+ViKhGSBfN57PV2aqpTNtqbav0ukVqJP777z+V2yA1CXLBXRZubm7qNdKsSfIaKhPkDB06VCWCS2AgYmJi1L8/TZqYO+coadu2bap5Vtu2bVWyuLxWrmElIMrJyVE3ypOSktT+Xn75ZfUa+XfM09NTfT65xv3ll1/QrVs31KZar7GQ2oS33noLe/bsQWxsLJYvX67aiJX00UcfqW0kCaZLly744IMP0Lt373I1h5L2bVI1JfuRpJj6Qi6sWnu2VlNJsjw2bCz6BfYrlQx1z9/3oNBUqO4cWP5Alx5eipWnV+L6VtdjclvzHU65oF4VsQpBzkEqYLnYH359JHeV2waclwR6EcbsXETcab7Qe2l8B2id2csUmWUUZMDNrvgcenT9o1gXuQ5P9X4KU9pNUes6eHdAM6dAdNC5onDVo7CJPQDEHcSD0jWrcNgKDJldHGl2uB7ISwP8OgD+7c0BhfRkRERE1aJv374YN26caqkiTYakm9YdO8y1Kpcjd/5vv/12dO7cWQUHkrxdUd988w1mzZqlyiDXc87Ozipf4mKBhTSFevLJJ1XytrSqkeRtkZ6erpLH5fVy7SZN+sePN4++s3nzZpWwbqklGTZsmMqxaNSBhTRPkmBBqm+kidL5pMpHDook00hbuPfee0+1ZZM2apbqH0myuVgVlrRBCwoKgoeHh8qql3Zu8h433XQT/P3N4ynUVwOCB6ipJMnDaOvVFtmF2aXuBpzJOIOjKUcxJG+IdV1OYQ6e3mTu6X3b5G3WwOKP039gZ9xODGs6DIOaFI+kKydzdd09JqoLUvJSMGXVFCTkJGDbrdusPZi0926P3XE7kRt/EGhX3B3qyhwH4PDq0juRrksDuwBBXc3jP9gWBawjX6zpj0NE1KhcrDWKjPdwMXKhXjI5Wy78SyZEL1y4sNT20ouUxflJ4T/99FOp5X/+Ke6lTq5Tv/766zLXlFh6sCpJrlePHDli7UK35LWYXNNe7Nq5UQcW0m2WTJciXXZJsvW0aeZe1CXAWLVqFRYtWmTtY7is/ffKwZEgZtOmTSq4uBipSpLJIiMjQz1K1ZVMdVlz9+b4Ztw3ar5kWaV948DggarZhmV9rj4Xvfx7qbuzTjZO1vXbY7djefhy+Dn5YUDQAGtNyKifR6GNVxu8P/R9ONuak1LrAim3Sh4u57Epub2ar+PHlqrW1uitWHpwKboEdsG9Xe5V69xt3ZGtz1ZNn04dX4k26fHQRO3E1OjduDvtHLQnjsLY87HimoZmV0EjA7cFdYNJBRPdzHkQRV0nKjyvGpyK/uYQNZRzx/I56mPT8rrKVPQ9Xu77LMs2VVEOyzl6wXVSfQksLkfat0kTqaefto6hqtpDS0a8tEUrC6mlkBwLaZsm1UnS9Oree80XEhczd+5cvPjihXcXExMTL0j8qS+c4IR2du2AfHN7PIvXuprTR0uu6+3eGy4tXNDavrV1/ZmsM8jUZ+JEyglkpWQhW5Ot1i84tgD/pf6HSWGTMCiguHajJsnJLsdV/hCu1Fa+JFNOXqljq8mueGIV1W0HUw/iQMoBjG0yFt725k4bziScwbbEbUjOT8aNgTdat53n1AOtwr+Fe4R1ODg1irUJGui9WiH93BEUehU1S2w71TxZSI5gYlINfjKqT785RA3l3NHr9eqzWHrbpMr73//+p6ZLfZ+qt8WiRPTqbD0i7y/HNjk5WeVtWMi4Hg0isJAkFfkiz2+2JMuS4FIWZ8+eVd12WaKwBx54oNTQ5+eTIEaaXpWssQgJCYGvr6+qpqoVpzeYB4gK7gZcJOGpKo3zGwf5ryQvHy/86Pmj6pmq5LEI3xuOkxkn4eTqZG2Wdib9DGZtmKW6xX22T/W385M/APkjk+NTnh9qybGwpNKq1zLHokGQ2rWorKhSOUlf7P4Ch5MPo21AW7Rr0laN5jxCn4kcXVP0izkNP0McENhZbesX3BXavYtgcvQEgnvCJD00NekFBHeHzt4VzIqgiv7mEDWUc0dussqFpjTLqejo1lQxJS/2q4McTzk3JcejZE9W5enVqsGfEZLkXZ6hzqXrLpnOJ190rf0QrH8ViNplDi6kyUXTfkBIH6BpX8DFfEFfney0dmjr3RbyX0lzB87F0eSj6O7f3frdHEs9hlPpp1RzqZLf17zd82AwGXBz65sR6h5apeVTPRSV9/iU2LZWjy1VGekhbfLKyXCzd1NdGVru6gz374WQggL471wE7fJHgaw4yJjrd1teGLUDCC7qsrndeKBZf2i8W1p77SKqkt8cogZy7kjZzT0FVl/PfVRayTzX6vzOLcf0/HO0POdrnQ4spPcmyXQvObiIkOWAALk0aASkLZ20206PAjJjgeg95mnbh+bnQ/oCM/6qlaJJzoZMJV0VfJXKw9CWaGcufxCSt5GWn4YxocUj78pd5I2RG9EroBd6BvRETdN6u9f4e1LV+P3U7/jm6DcYFToK0ztOt+YYSf/n9iYgOXonfJqYBz66y7snsOql4hdrbWEK6oZsn85wajMM2tD+xc9J/gR7ayIiIqqQOh1YSP/BMpS5jFxo6YJWqhJleebMmWgUJDK94TNzgJF2DojcAZzbBpzbASQcKV1jIdvM7wKYjICzj3kUXpmcvM2P0s1lyxHF20viaRV3Myu95QxtOrTUOqmpeKLXEziYdFAlgFtsitqEjw98jGsyrykVWMgFYzO3ZirgsNddWHtUFaTpU9ixldWyb6qYtLw0VdslAyZJd64WU/+cqno2k44Jmriau+iTTgckMPWwdcZ0+xDg7FbYnd2K1XGR8NHnAR6/AkWBBZr0BFqOBJpKLV9/1azJpLNHVkICnKQJXz2+c0hERFSX1HpgIcObS5+9FhEREarpkvQj3LRpU5XvMHXqVPTs2VM1a5LuZqWLWksvUY2GBBiezcxT51vM63LTgHxzr1VKakTxIFzpkRfuo/11xYGFZPi/Gmi+O+sWDLgHA25NzI/uTQCfNuZApArIiJvjW4xXU0ntvNrh2hbXol9Q8Vgc6fnpeH3n62p+08RN1sDicNJhxGXHqa4/A10Cq6RcdGVRmVHIK8xDM/dm1nFRItIjVBO4AOcA1QzOQgZszCzIxJ2d7oSXgzkbQUaN//bot+ji2wX3di3uNOHWVbeq7pEXjlqIVp6t1LrVZ1bj1R2vYljIMMwfNt+6reT2SFewcZnR1sBiqG8PeBQ4oseuX4FtxV39qRFqXPwB2xLtQe2cgf+V7g6QvTURERE1wMBCBh6R0QUtLInTEkxIX8ETJ05UvfbIACAyQJ6MWbF69ep6Pw5FlXD0ME8W7iHAg/uBnGQgOwnITiyakoCcJJWMaiWDdRn1QFa8eYrZW3rf7a4FJn5VXBPyzc3mgEPanlsmCXIqUeMxOGSwmkrKLczFuLBxSM5LhodD8Wf79ti3+O3Ub3io+0PqwlXIReyr21+Fl9YLs3xnQYuGf+dZvp+sgiw1xoLUDolCY6EaCVoCgOFNh6vmQJaug3fE7kBnn87WWiSjyYj71t6H/MJ8dfFuGQjuy8NfYsGBBbi6+dV4ru9z1ve7bsV1KDAWYM2Na6wBndQ0vbX7LXWcSgYW3x39Dqn5qbi+5fXWwELGhNgSs+WCARglUJBjLJ/HItglGCGO/vDKywK2fwKkRQLp5/BaehocsnIRuuZF4I4/1LZB3m0QJF1BF+oBzzCVF6Fyj+RRdfnKdr9ERI2J5AakpqaqscsqS25wSydBkyZNKtfrVq1apa5XDx06pHoglZvhl/Lggw/it99+U50M7du3T13fXuo5GSrBQgZ7lmtk6eDI3d1dXSt36FBcy4/GHljIICVX6pNXmj01mqZPlSEXb15h5ulKpNebx0+ZazbSo4GMaHMeh3qMBgLMveQoEniE/33hPjQ6wDMU6DwRGPKkeZ0cS9mHa1CFmpjIXfA3Br1xwXoZV6Ojd0e09GhpXXcu85waPdzDzgOP9X/Muv7lbS9jY/RGPNz9YXWhLJJzk7Hk8BL4OPpgaoepMObmI3bSY8g35CP7k3sQ5BMKb0dzV6RysSuJwDYaG3TyLe5B7HTaaXUxLHklUk7Ltnvi96igpn9wcVv9g4kHVfMdGbDQckdeAqGF/y1U4yQ82fvJUk2/1p5bi/HNx6vR0S1NfSasmKDKt2HiBlXrIz7c9yGWHlmKaR2nYVaPWdZg4f6196v5rZO3wtXOVc1LuT4/+DkmtploDSwk90WCDQlGpAclS2Ahf4NZ+iy1riQJ7vSGAuhzU4CiwCLYNRh9PNuihfR8d2wVoM9V0yTXVsh3zIf7ybVAb/NnluZsr7p2QVBCDPDtJPOgcYV5mF+YCRgc0PSvl6y1CQObDMTAc+eArF0y5JC1DNafWm2JHtEkcLhlqTmYdmMNFhERVR0JLGSwuvIGFq1atVLjrP3444+qRc7lyHhqTzzxBAYMGFCu5+655x7V2+kdd9yhBueTx1275N/NuqHWAwuqJXJhpvIwfMwDe12OdHF7/adAcniJ6RQgF6Epp0o3x5Iaknc7ANKESYIOFeg0N99Vlke/dubmVuUkNRUyleTt4I0Huz14wR9veFq4ajZlQnHAGpcTpwILCVAksJCmMHlbzb2FTfvzDjw5ZLbqsUpEZ0bj9j9vh6e9JzZO2mjdx4L/FuDPiD9Vvsht7W+zBiz3/nMvHG0csXPKTuu2P574USWsS5ktgYUECYsPL1bzsg9Lzw6RmZHYFbcLXX2L71ZIsyNpAiQKDAXWwEKahklwYDCa+7OG0QBbfR46eraBnUYHo9zl9zM3Yevi0xlTfHqhW3YWsPXDogAgGy87t4eNQQ+3vd8AAx9V205oOQFDty2C+/4/gZ3LzcdWn4u1hqLBIvOeBO4w56RIrcjw7+8CctbIt2It833WLzsS6P1/alZyZZqdO2DODyrBmmlTeN6gO4FdzeeQR4g5aPBoWvRYtFxSSO/Sy0REVGukG/dL0mmhdbAv27bSI5FjxfIr3377bVVrIE3m58yZgylTpqj1cuH95JNPqiEEZBiDZ555BjfffLNqESPbxMbGqn+TJa/3jTfeULUOMuaI1CL07dtXDc5cFq1bm7s6X+ix+wgAAA8hSURBVL58+RW3HTRoULmfk/HFpKXPmjXy7y9w4403qhvvklLQsmXxjdfaxMCCrszBDehyXtSuaiZizEGGtGm3kNoKuQiWC9Kk4+appL73AWPmmuezEoB/XjC/XiZX/xLzAea28ZchtQYzOs4oNcCfpRtcaWoT5BJkXedu546p7afC3ubCH6tA50A1+riFNDOSWgl5Tan3cwpAC/cW1iZIlm0lV+T8JHMJJvoF9lP7VoxGuBiBqa1ugj00MKRHwUYulgGMbzYGXbOz0SIrB9j8HlCQDfv8LPzo2hN2hgLY71oE9DfX2N3f8U48sGEhNOs+Af56W939l/DkO8sbZ9sAk8yjrw9oMhADFk0AjKUH3LnGMpOTaw0spGbCI/msuYncxZxXkwHfdkBeujmXwcYBsHU0TzaOF9aYDXrcHNTIdy/Pq9fItvaAw3k9c0354eLvT0REdVpE6KhLPuc0oi8Cv3vLunym/bWlBqotyaF/VwT/+kGFyiDBgTQdOn36tMrNveqqq1TTKLnD/8cffyAwMFA1IerevTv69++PH374AWFhYdYL9ZSUFJXj+9JLL6kaC5kspGn+8ePnXdMU+f3339WYZ9UtMjJSfQbL+CHyeSUf+dy5cwwsqJ6Tu+0q0fu82gep/Xg23tzESpLJU2Q6rQYlU/O+JcbCkLvY+80XwRc18DFg+PPmeWmeteF1wM7FXIMiQYead4R9biGg6Qv4tzMXwcEbQfm5QHaqedJo0AQaPNbyJvO+clPlp8v6Nr8Oeg9aew0Qe0D1lNXUUIA/uj5h7jUr4ai5lkXyfzreiVkGZyD2FHDuNXWx7VeQgx8KvYHcHGD3IqCnuevT25qNw21r3gT2rwUKZgCFueodrQ224mOB6z9Rsx08W6HDhvdLfXRpRGb9powSOpgDC51cvGdEmXv+KnU8tICt84U5L61Gmx/tnIq/N3m0vUgAIAGJNG9TQYJT6UcJHkqatgpl1v32sm9LRERUQXfeac7BbN68ubrrv3HjRjV0gQQaY8eOLbWtBAlSG/Huu+/i0UcfVduPGVPcJf75li1bxuNSBqyxoKqnsynO9Whxme2kZmL4bCBTEsjjzDUYmfIYb75DLt3kWsj6vUsv2IVcgHtKBUre44B/UdKx1KJ8UmJsgvNd9RDQ/+ni5QX9AdtL5Pn0+T9gbFHOR0E28OcTl96vvYs1sICN3QXNf0oHACX+9OTufYvhxcGSenQqnvduVeL1GuDOf4ou+C3bOJkv/C+WsDz5W5RZ6IVtOYmIiMoi7Iz5rv9F6UrnXIYe+e3S21ZhF+ByR19yCCW5eevWrZfMp/jnn3/wyy+/4Pnnn1c1HhdTF2osQkJCVLOtwsJCVWshn01qK6TWoq5gYEG1R5oCFTXFuaCZVYHkTZS4UJamUcOeM1/cqylHbWMqyII+Ow02XmHFW8vFu2quZDLvy/qodg7o7Eq/nyQ7O9iYm+bIHX953jK5lkgMtncFOlxf4m6+TM7Fd/b9StTGyPoZ/xQFCSWmiwUA8iN62y9l/96Ce5R9WyIiohog40NVx7blsXjxYrzwwgs4c+YMNm3apHplkp6TZCgDCR5GjBhhDSbat2+P6OhoBAcH45ZbblG1FX5+fipv083NTeVY1LUaCz8/P9WM6+uvv1ZJ2z///DOaNGlSZ5pBCQYWVPfIhbdcxJckTa6krf55TEYjUhIS1B+blTRdevoitQUllUwce+wEUJYfOamRuHkJykSChZBeZduWiIiIKk0Ss7t166aSt99//32Ehoaq9ZLQ/dhjj6kmT3q9Xt3hl/yJ9evXY968edDpdKoW4K233lKByPDhw1UieOfOnVUuRlmTt9euXau6gpUkcalNkF6bPv74Y1x77bWq+1iZPv/8c2vvTlIuGUph9OjRcHV1tY7rdrHnjh49qp779NNPVVDx2muvqQBIgqm6RGO6Ul+vjZycHHKSSeQqB5DqFhmJPaEosNCWo/pUeqSQ5DFLlWx13T2hhnXeEPHcocb+u5OXl6dqACTp2cHhvPw7qhZyqW5p/mTpUbImj215roVZY0GNkgQSzc9eZGwOIiIiIqqQ+hsyExERERFRncHAgoiIiIiIKo2BBTVKxrx8xE5+XE0yT0RERGXHFN2Gx1QFadfMsaDGyWBEzj/brfNERER0Zba2tiqBODExEb6+vtWaTEw1l7wt7yHHVPYvx7iiGFgQERERUZlI16wydkJUVJQaL4Kqn8lkUr2KSW9i1RnIyb7l2MoxrigGFkRERERUZi4uLmjVqpUaE4Kqn9FoRHJyMry9vau1q2KpqahMUCEYWBARERFRucgFaGUvQqnsgYVc9MvYEnV9DJS6XToiIiIiIqoXGFgQEREREVGlsSlUGbvekuHMqW5WD2ZmZpa7etCYnYtMY6H12GoNbCfamFT0vCHiuUP83aHG9ruTUXQNXJbuaBlYXIEcSBESElIVx4bqoqCA2i4BERERUZ2/JnZ3d7/sNhoTRzi5YpQYExMDV1dX9tVcB0kULUFfZGQk3Nzcars4VE/wvCGeO8TfHaovMmr5WkdCBQkqgoKCrlhjwhqLK5AvUPr0pbpN/tAYWBDPG+JvDtV1/PeK6uO5c6WaCgs2LiYiIiIiokpjYEFERERERJXGwILqNXt7e8yZM0c9EvG8If7mUF3Ff6+oMZw7TN4mIiIiIqJKY40FERERERFVGgMLIiIiIiKqNAYWRERERERUaQwsqF7auHEjxo8frwZr0Wg0WLFiRW0XieqBuXPnolevXmrASz8/P0yYMAHHjx+v7WJRPfDJJ5+gc+fO1n7k+/Xrhz///LO2i0X1zOuvv67+zXr44YdruyhUD7zwwgvqfCk5tW3bFnUZAwuql7Kzs9GlSxd89NFHtV0Uqkc2bNiA+++/H9u3b8fff/8NvV6PUaNGqfOJ6HJkoFS5KNyzZw92796NYcOG4brrrsPhw4f5xVGZ7Nq1C59++qkKUInKqkOHDoiNjbVOmzdvRl3GkbepXho7dqyaiMpj9erVpZaXLFmiai7kYnHQoEH8MumSpIa0pFdffVXVYkiQKv/wE11OVlYWpkyZgoULF+KVV17hl0VlZmNjg4CAANQXrLEgokYrPT1dPXp5edV2UageMRgM+P7771VNlzSJIroSqSm9+uqrMWLECH5ZVC4nT55Uzb6bN2+ugtNz586hLmONBRE1SkajUbVzvuqqq9CxY8faLg7VAwcPHlSBRF5eHlxcXLB8+XK0b9++totFdZwEoXv37lVNoYjKo0+fPqpmvU2bNqoZ1IsvvoiBAwfi0KFDKlewLmJgQUSN9g6i/DjX9faqVHfIP+779+9XNV0//fQTpk6dqvJ2GFzQpURGRuKhhx5SOV0ODg78oqhcSjb5ltwcCTSaNWuGH374ATNmzEBdxMCCiBqdmTNnYuXKlap3MUnKJSoLOzs7tGzZUs336NFD3YGeP3++SsgluhjJ30pISED37t1LNaWT354PP/wQ+fn50Ol0/PKoTDw8PNC6dWuEh4ejrmJgQUSNhslkwgMPPKCasKxfvx5hYWG1XSSq583p5MKQ6FKGDx+umtCVNG3aNNVl6JNPPsmggsrdCcCpU6dw2223oa5iYEH19o+rZMQeERGhmihIEm7Tpk1rtWxUt5s/ffvtt/j1119V+9S4uDi13t3dHY6OjrVdPKrDnn76adUsQX5fMjMz1Xkkwelff/1V20WjOkx+Z87P4XJ2doa3tzdzu+iKHnvsMdUjnTR/iomJwZw5c1QwOnnyZNRVDCyoXpJ+5IcOHWpdnjVrlnqUNs+S6ER0MdI9qBgyZEip9YsXL8Ydd9zBL40uSZqz3H777SqBUgJRae8sQcXIkSP5rRFRtYiKilJBRHJyMnx9fTFgwADVxbXM11Uak7QNICIiIiIiqgSOY0FERERERJXGwIKIiIiIiCqNgQUREREREVUaAwsiIiIiIqo0BhZERERERFRpDCyIiIiIiKjSGFgQEREREVGlMbAgIiIiIqJKY2BBRES14syZM9BoNNi/f3+ZX7NkyRJ4eHhUa7mIiKhiGFgQEREREVGlMbAgIiIiIqJKY2BBRETVZvXq1RgwYIBqvuTt7Y1rrrkGp06duui269evV02jVq1ahc6dO8PBwQF9+/bFoUOHLtj2r7/+Qrt27eDi4oIxY8YgNjbW+tyuXbswcuRI+Pj4wN3dHYMHD8bevXt5lImIqhkDCyIiqjbZ2dmYNWsWdu/ejbVr10Kr1eL666+H0Wi85Gsef/xxvPPOOypA8PX1xfjx46HX663P5+Tk4O2338ZXX32FjRs34ty5c3jsscesz2dmZmLq1KnYvHkztm/fjlatWmHcuHFqPRERVR+batw3ERE1cjfeeGOp5UWLFqlg4ciRI6q24WLmzJmjahzEl19+iSZNmmD58uW45ZZb1DoJMhYsWIAWLVqo5ZkzZ+Kll16yvn7YsGGl9vfZZ5+pGpMNGzaoGhMiIqoerLEgIqJqc/LkSUyePBnNmzeHm5sbQkND1XqpZbiUfv36Wee9vLzQpk0bHD161LrOycnJGlSIwMBAJCQkWJfj4+Nx1113qZoKaQol75uVlXXZ9yQiospjjQUREVUbacbUrFkzLFy4EEFBQaoJVMeOHVFQUFDhfdra2pZalrwMk8lkXZZmUMnJyZg/f756b3t7exWsVOY9iYjoyhhYEBFRtZCL++PHj6ugYuDAgWqd5D1cieRFNG3aVM2npqbixIkTKlG7rLZs2YKPP/5Y5VWIyMhIJCUlVfhzEBFR2TCwICKiauHp6al6gpIcB2muJE2RnnrqqSu+TvIl5HX+/v549tlnVe9OEyZMKPP7ShMoSezu2bMnMjIyVDK4o6NjJT8NERFdCXMsiIioWkgPUN9//z327Nmjmj898sgjeOutt674utdffx0PPfQQevTogbi4OPz++++ws7Mr8/t+8cUXqqaje/fuuO222/Dggw/Cz8+vkp+GiIiuRGMq2TCViIiolsg4FkOHDlVBgfTiRERE9QtrLIiIiIiIqNIYWBARERERUaWxKRQREREREVUaayyIiIiIiKjSGFgQEREREVGlMbAgIiIiIqJKY2BBRERERESVxsCCiIiIiIgqjYEFERERERFVGgMLIiIiIiKqNAYWRERERERUaQwsiIiIiIgIlfX/jetThh3coegAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 1, figsize=(8, 7), sharex=True)\n",
    "axes[0].semilogy(fit_rows[:, 0], fit_rows[:, 1], label=\"fit max\")\n",
    "axes[0].semilogy(fit_rows[:, 0], fit_rows[:, 2], ls=\"--\", label=\"fit mean\")\n",
    "axes[0].semilogy(fit_rows[:, 0], fit_rows[:, 3], ls=\":\", label=\"fit median\")\n",
    "axes[0].axvline(ALPHA_IR_FIT_OPT, color=\"crimson\", ls=\"--\", label=f\"best={ALPHA_IR_FIT_OPT:.3f}\")\n",
    "\n",
    "axes[1].semilogy(reg_rows[:, 0], reg_rows[:, 1], label=\"runtime max\")\n",
    "axes[1].semilogy(reg_rows[:, 0], reg_rows[:, 2], ls=\"--\", label=\"runtime mean\")\n",
    "axes[1].semilogy(reg_rows[:, 0], reg_rows[:, 4], ls=\":\", label=\"runtime p95\")\n",
    "axes[1].axvline(ALPHA_IR_REG_OPT, color=\"crimson\", ls=\"--\", label=f\"best={ALPHA_IR_REG_OPT:.3f}\")\n",
    "\n",
    "axes[0].set_ylabel(\"fit-layer |error|\")\n",
    "axes[1].set_ylabel(\"runtime regression |error|\")\n",
    "axes[1].set_xlabel(\"alpha\")\n",
    "for ax in axes:\n",
    "    ax.grid(True, which=\"both\", alpha=0.3)\n",
    "    ax.legend(fontsize=8)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11228985",
   "metadata": {},
   "source": [
    "## End-To-End Validation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3f1d269f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "========================================================================\n",
      "training wavelengths\n",
      "========================================================================\n",
      "IR-anchor alpha=1.415 fit-opt              max=1.7764e-03 mean=2.7200e-04 median=2.5874e-04 p95=3.6181e-04\n",
      "IR-anchor alpha=1.110 runtime-opt          max=1.7424e-03 mean=2.1883e-04 median=2.0084e-04 p95=3.2445e-04\n",
      "\n",
      "========================================================================\n",
      "shifted wavelengths\n",
      "========================================================================\n",
      "IR-anchor alpha=1.415 fit-opt              max=1.8517e-03 mean=2.7488e-04 median=2.5846e-04 p95=3.6628e-04\n",
      "IR-anchor alpha=1.110 runtime-opt          max=1.8130e-03 mean=2.2130e-04 median=2.0347e-04 p95=3.2923e-04\n",
      "\n",
      "========================================================================\n",
      "validation incl. lambda_ref\n",
      "========================================================================\n",
      "IR-anchor alpha=1.415 fit-opt              max=1.8517e-03 mean=2.7488e-04 median=2.5846e-04 p95=3.6628e-04\n",
      "IR-anchor alpha=1.110 runtime-opt          max=1.8130e-03 mean=2.2130e-04 median=2.0347e-04 p95=3.2923e-04\n"
     ]
    }
   ],
   "source": [
    "for test_name, wths in [(\"training wavelengths\", wavelengths), (\"shifted wavelengths\", wth_shift), (\"validation incl. lambda_ref\", wth_validation)]:\n",
    "    print(\"\\n\" + \"=\"*72)\n",
    "    print(test_name)\n",
    "    print(\"=\"*72)\n",
    "    for name, alpha, A in cases:\n",
    "        errs = runtime_errors_ir(wths, alpha, A, lambda_ref=LAMBDA_REF)\n",
    "        print(f\"{name:<42} max={errs.max():.4e} mean={errs.mean():.4e} median={np.median(errs):.4e} p95={np.percentile(errs,95):.4e}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12084e28",
   "metadata": {},
   "source": [
    "## Per-Wavelength Error\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c54badec",
   "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": [
      "IR-anchor alpha=1.110 runtime-opt\n",
      "wavelength_um   max_abs_err   mean_abs_err   median_abs_err\n",
      "     0.36501  3.550313e-04   3.353752e-05     2.608306e-05\n",
      "     0.40466  5.081680e-04   8.606156e-06     3.598306e-06\n",
      "     0.43583  4.677909e-04   2.428196e-05     1.849334e-05\n",
      "     0.48613  4.678883e-04   2.428544e-05     1.849558e-05\n",
      "     0.54607  1.026524e-05   1.820985e-06     1.787382e-06\n",
      "     0.58756  6.623290e-09   2.450042e-10     1.489082e-10\n",
      "     0.58929  6.568131e-06   2.473240e-07     1.523363e-07\n",
      "     0.63280  2.769965e-04   1.333220e-05     9.768747e-06\n",
      "     0.64385  3.654748e-04   1.826529e-05     1.368340e-05\n",
      "     0.65627  4.678422e-04   2.428317e-05     1.849388e-05\n",
      "     0.70652  8.523214e-04   5.027784e-05     3.995924e-05\n",
      "     0.85211  1.021042e-03   8.416240e-05     7.368712e-05\n",
      "     1.01398  7.741182e-05   2.473845e-05     2.487988e-05\n",
      "     1.06000  3.148081e-04   1.086127e-05     7.589347e-06\n",
      "     1.52958  1.054464e-04   1.133778e-05     1.063560e-05\n",
      "     1.97009  1.328386e-03   2.018667e-04     1.959369e-04\n",
      "     2.30000  1.742376e-03   1.074365e-04     8.498195e-05\n"
     ]
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3, 1, figsize=(8, 9), sharex=True)\n",
    "stat_names = [\"max\", \"mean\", \"median\"]\n",
    "for name, alpha, A in cases:\n",
    "    stats = per_wavelength_error_stats_ir(wavelengths, alpha, A, lambda_ref=LAMBDA_REF)\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.axvline(LAMBDA_REF, color=\"gray\", ls=\":\", alpha=0.6)\n",
    "        ax.set_ylabel(f\"{stat_name} |error|\")\n",
    "        ax.grid(True, which=\"both\", alpha=0.3)\n",
    "axes[-1].set_xlabel(\"wavelength [um]\")\n",
    "axes[0].set_title(\"IR-anchor 6th-order runtime regression error by wavelength\")\n",
    "axes[0].legend(fontsize=8)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "name, alpha, A = cases[-1]\n",
    "stats = per_wavelength_error_stats_ir(wavelengths, alpha, A, lambda_ref=LAMBDA_REF)\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": "5e9f3041",
   "metadata": {},
   "source": [
    "## H-LAF3B Diagnostic\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "519569fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cdgm H-LAF3B\n",
      "true n(1.55): 1.7185450039\n",
      "P_ref = (n_ref - nd) / (nF - nC) = -1.5361903054e+00\n",
      "recomputed n_ref from nd,Vd,P_ref = 1.7185450039\n",
      "if nd increases by 1e-4, recomputed n_ref becomes 1.7186415825\n",
      "\n",
      "IR-anchor alpha=1.415 fit-opt\n",
      "  n(lambda_ref)=1.7185450039, err=+0.0000e+00\n",
      "  n(1.55)=1.7185450039, err=+0.0000e+00\n",
      "  predicted nu4..nu6 = [ 0.18411  -0.12593  -0.772664]\n",
      "\n",
      "IR-anchor alpha=1.110 runtime-opt\n",
      "  n(lambda_ref)=1.7185450039, err=+0.0000e+00\n",
      "  n(1.55)=1.7185450039, err=+0.0000e+00\n",
      "  predicted nu4..nu6 = [ 0.238502 -0.258308 -0.083633]\n"
     ]
    }
   ],
   "source": [
    "matches = find_glass(\"H-LAF3B\")\n",
    "if matches:\n",
    "    g = matches[0]\n",
    "    nd, vd, dPgF, p_ref, n_ref_model = descriptor_ir(g, LAMBDA_REF)\n",
    "    n_ref_real = get_n_ref(g, LAMBDA_REF)\n",
    "    print(g[\"catalog\"], g[\"name\"])\n",
    "    print(f\"true n({LAMBDA_REF:.2f}): {n_ref_real:.10f}\")\n",
    "    print(f\"P_ref = (n_ref - nd) / (nF - nC) = {p_ref:+.10e}\")\n",
    "    print(f\"recomputed n_ref from nd,Vd,P_ref = {n_ref_model:.10f}\")\n",
    "\n",
    "    # Demonstrate the intended model-material behavior: changing nd changes n_ref_model.\n",
    "    nd_demo = nd + 1e-4\n",
    "    n_ref_demo = nd_demo + p_ref * ((nd_demo - 1.0) / vd)\n",
    "    print(f\"if nd increases by 1e-4, recomputed n_ref becomes {n_ref_demo:.10f}\")\n",
    "\n",
    "    for name, alpha, A in cases:\n",
    "        phi = feature_vec_ir(nd, vd, dPgF, p_ref)\n",
    "        nu_high = phi @ A\n",
    "        n_pred_ref = float(predict_ir_anchor(nd, vd, dPgF, p_ref, LAMBDA_REF, nu_high, np.array([LAMBDA_REF]), alpha)[0])\n",
    "        n_pred_155 = float(predict_ir_anchor(nd, vd, dPgF, p_ref, LAMBDA_REF, nu_high, np.array([1.55]), alpha)[0])\n",
    "        print(\"\\n\" + name)\n",
    "        print(f\"  n(lambda_ref)={n_pred_ref:.10f}, err={n_pred_ref - n_ref_model:+.4e}\")\n",
    "        print(f\"  n(1.55)={n_pred_155:.10f}, err={n_pred_155 - get_n_ref(g, 1.55):+.4e}\")\n",
    "        print(\"  predicted nu4..nu6 =\", np.array2string(nu_high, precision=6, suppress_small=False))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce1d5f51",
   "metadata": {},
   "source": [
    "## H-LAF3B Perturbed Descriptor Test\n",
    "\n",
    "This checks the model-material behavior when the descriptors are changed while the IR partial-dispersion descriptor `P_ref` is held fixed.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c272dae5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cdgm H-LAF3B\n",
      "nominal nd=1.7439998979, Vd=44.9000270317, P_ref=-1.5361903054e+00\n",
      "perturbed nd=1.7444998979, Vd=45.1245271668\n",
      "nominal recomputed n(1.55)=1.7185450039\n",
      "perturbed recomputed n(1.55)=1.7191546235\n",
      "delta n_ref=+6.096196e-04\n",
      "\n",
      "IR-anchor alpha=1.415 fit-opt\n",
      "  nominal model n(1.55)=1.7185450039\n",
      "  perturbed model n(1.55)=1.7191546235\n",
      "  model delta=+6.096196e-04\n",
      "  anchor target delta=+6.096196e-04\n",
      "  perturbed nu4..nu6 = [ 0.18183  -0.122992 -0.769035]\n",
      "\n",
      "IR-anchor alpha=1.110 runtime-opt\n",
      "  nominal model n(1.55)=1.7185450039\n",
      "  perturbed model n(1.55)=1.7191546235\n",
      "  model delta=+6.096196e-04\n",
      "  anchor target delta=+6.096196e-04\n",
      "  perturbed nu4..nu6 = [ 0.235194 -0.25238  -0.086277]\n"
     ]
    }
   ],
   "source": [
    "matches = find_glass(\"H-LAF3B\")\n",
    "if matches:\n",
    "    g = matches[0]\n",
    "    nd, vd, dPgF, p_ref, n_ref_nominal = descriptor_ir(g, LAMBDA_REF)\n",
    "\n",
    "    nd_pert = nd + 0.0005\n",
    "    vd_pert = vd * 1.005\n",
    "    n_ref_pert = nd_pert + p_ref * ((nd_pert - 1.0) / vd_pert)\n",
    "\n",
    "    print(g[\"catalog\"], g[\"name\"])\n",
    "    print(f\"nominal nd={nd:.10f}, Vd={vd:.10f}, P_ref={p_ref:+.10e}\")\n",
    "    print(f\"perturbed nd={nd_pert:.10f}, Vd={vd_pert:.10f}\")\n",
    "    print(f\"nominal recomputed n({LAMBDA_REF:.2f})={n_ref_nominal:.10f}\")\n",
    "    print(f\"perturbed recomputed n({LAMBDA_REF:.2f})={n_ref_pert:.10f}\")\n",
    "    print(f\"delta n_ref={n_ref_pert - n_ref_nominal:+.6e}\")\n",
    "\n",
    "    for name, alpha, A in cases:\n",
    "        phi_nom = feature_vec_ir(nd, vd, dPgF, p_ref)\n",
    "        nu_nom = phi_nom @ A\n",
    "        n_nom = float(predict_ir_anchor(nd, vd, dPgF, p_ref, LAMBDA_REF, nu_nom, np.array([LAMBDA_REF]), alpha)[0])\n",
    "\n",
    "        phi_pert = feature_vec_ir(nd_pert, vd_pert, dPgF, p_ref)\n",
    "        nu_pert = phi_pert @ A\n",
    "        n_pert = float(predict_ir_anchor(nd_pert, vd_pert, dPgF, p_ref, LAMBDA_REF, nu_pert, np.array([LAMBDA_REF]), alpha)[0])\n",
    "\n",
    "        print(\"\\n\" + name)\n",
    "        print(f\"  nominal model n({LAMBDA_REF:.2f})={n_nom:.10f}\")\n",
    "        print(f\"  perturbed model n({LAMBDA_REF:.2f})={n_pert:.10f}\")\n",
    "        print(f\"  model delta={n_pert - n_nom:+.6e}\")\n",
    "        print(f\"  anchor target delta={n_ref_pert - n_ref_nominal:+.6e}\")\n",
    "        print(\"  perturbed nu4..nu6 =\", np.array2string(nu_pert, precision=6, suppress_small=False))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a71b23d",
   "metadata": {},
   "source": [
    "## H-LAF3B Nominal And Perturbed Curves\n",
    "\n",
    "This overlays the original catalog curve with the IR-partial model curve before and after descriptor perturbation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5fc648fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "perturbation: nd += 0.0005, Vd *= 1.005, P_ref fixed at -1.536190e+00\n"
     ]
    }
   ],
   "source": [
    "matches = find_glass(\"H-LAF3B\")\n",
    "if matches:\n",
    "    g = matches[0]\n",
    "    nd, vd, dPgF, p_ref, n_ref_nominal = descriptor_ir(g, LAMBDA_REF)\n",
    "    nd_pert = nd + 0.0005\n",
    "    vd_pert = vd * 1.005\n",
    "\n",
    "    # Dense plotting grid clipped to this glass's valid Sellmeier range.\n",
    "    wl_min = max(g[\"wavelength_range\"][0], 0.36501)\n",
    "    wl_max = min(g[\"wavelength_range\"][1], 2.3)\n",
    "    wl_plot = np.linspace(wl_min, wl_max, 400)\n",
    "    n_catalog = get_indices(g, wl_plot)\n",
    "\n",
    "    fig, axes = plt.subplots(2, 1, figsize=(8, 7), sharex=True)\n",
    "\n",
    "    for name, alpha, A in cases:\n",
    "        phi_nom = feature_vec_ir(nd, vd, dPgF, p_ref)\n",
    "        nu_nom = phi_nom @ A\n",
    "        n_nom_model = predict_ir_anchor(nd, vd, dPgF, p_ref, LAMBDA_REF, nu_nom, wl_plot, alpha)\n",
    "\n",
    "        phi_pert = feature_vec_ir(nd_pert, vd_pert, dPgF, p_ref)\n",
    "        nu_pert = phi_pert @ A\n",
    "        n_pert_model = predict_ir_anchor(nd_pert, vd_pert, dPgF, p_ref, LAMBDA_REF, nu_pert, wl_plot, alpha)\n",
    "\n",
    "        axes[0].plot(wl_plot, n_nom_model, label=f\"nominal model: {name}\")\n",
    "        axes[0].plot(wl_plot, n_pert_model, ls=\"--\", label=f\"perturbed model: {name}\")\n",
    "        axes[1].plot(wl_plot, n_nom_model - n_catalog, label=f\"nominal error: {name}\")\n",
    "        axes[1].plot(wl_plot, n_pert_model - n_catalog, ls=\"--\", label=f\"perturbed - catalog: {name}\")\n",
    "\n",
    "    axes[0].plot(wl_plot, n_catalog, color=\"black\", lw=2.0, label=\"original catalog H-LAF3B\")\n",
    "    axes[0].axvline(LAMBDA_REF, color=\"gray\", ls=\":\", alpha=0.7)\n",
    "    axes[1].axhline(0.0, color=\"black\", lw=1.0)\n",
    "    axes[1].axvline(LAMBDA_REF, color=\"gray\", ls=\":\", alpha=0.7)\n",
    "\n",
    "    axes[0].set_ylabel(\"n\")\n",
    "    axes[1].set_ylabel(\"model - catalog\")\n",
    "    axes[1].set_xlabel(\"wavelength [um]\")\n",
    "    axes[0].set_title(\"H-LAF3B catalog and IR-partial model curves\")\n",
    "    for ax in axes:\n",
    "        ax.grid(alpha=0.3)\n",
    "        ax.legend(fontsize=7)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    print(f\"perturbation: nd += 0.0005, Vd *= 1.005, P_ref fixed at {p_ref:+.6e}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5a2077a",
   "metadata": {},
   "source": [
    "## Anchor Consistency Test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8779c71e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IR-anchor alpha=1.415 fit-opt              max anchor errors: nd=0.000e+00, Vd=6.906e-12, dPgF=1.232e-13, P_ref=1.679e-13, n_ref=4.441e-16\n",
      "IR-anchor alpha=1.110 runtime-opt          max anchor errors: nd=0.000e+00, Vd=5.954e-12, dPgF=9.803e-14, P_ref=1.323e-13, n_ref=4.441e-16\n"
     ]
    }
   ],
   "source": [
    "for name, alpha, A in cases:\n",
    "    max_nd = 0.0\n",
    "    max_vd = 0.0\n",
    "    max_dpgf = 0.0\n",
    "    max_pref = 0.0\n",
    "    max_ref = 0.0\n",
    "    for g in glasses_filtered:\n",
    "        nd_m, vd_m, dpgf_m, pref_m, nr_m, nr_target = anchor_report_ir(g, alpha, A, lambda_ref=LAMBDA_REF)\n",
    "        nd, vd, dPgF, p_ref, _ = descriptor_ir(g, LAMBDA_REF)\n",
    "        max_nd = max(max_nd, abs(nd_m - nd))\n",
    "        max_vd = max(max_vd, abs(vd_m - vd))\n",
    "        max_dpgf = max(max_dpgf, abs(dpgf_m - dPgF))\n",
    "        max_pref = max(max_pref, abs(pref_m - p_ref))\n",
    "        max_ref = max(max_ref, abs(nr_m - nr_target))\n",
    "    print(f\"{name:<42} max anchor errors: nd={max_nd:.3e}, Vd={max_vd:.3e}, dPgF={max_dpgf:.3e}, P_ref={max_pref:.3e}, n_ref={max_ref:.3e}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e690fae1",
   "metadata": {},
   "source": [
    "## Save IR Partial-Dispersion Matrices\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "55335bff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved IR-partial-dispersion regression matrices and metadata.\n",
      "ALPHA_IR_FIT_OPT = 1.415254\n",
      "ALPHA_IR_REG_OPT = 1.110169\n"
     ]
    }
   ],
   "source": [
    "np.save(\"regression_buchdahl_ir_partial_nu456_35dim_fit_opt.npy\", A_IR_FIT_OPT)\n",
    "np.save(\"regression_buchdahl_ir_partial_nu456_35dim_runtime_opt.npy\", A_IR_REG_OPT)\n",
    "\n",
    "with open(\"regression_buchdahl_ir_partial_metadata.txt\", \"w\", encoding=\"utf-8\") as f:\n",
    "    f.write(f\"LAMBDA_REF={LAMBDA_REF:.12g}\\n\")\n",
    "    f.write(f\"ORDER={ORDER}\\n\")\n",
    "    f.write(\"features=polynomial total degree <=3 of nd,vd,dPgF,P_ref\\n\")\n",
    "    f.write(\"P_ref=(n(lambda_ref)-nd)/((nd-1)/Vd)\\n\")\n",
    "    f.write(\"targets=nu4..nu6\\n\")\n",
    "    f.write(f\"ALPHA_IR_FIT_OPT={ALPHA_IR_FIT_OPT:.12g}\\n\")\n",
    "    f.write(f\"ALPHA_IR_REG_OPT={ALPHA_IR_REG_OPT:.12g}\\n\")\n",
    "    f.write(\"anchors=d,F,C,g plus IR partial dispersion P_ref\\n\")\n",
    "\n",
    "print(\"Saved IR-partial-dispersion regression matrices and metadata.\")\n",
    "print(f\"ALPHA_IR_FIT_OPT = {ALPHA_IR_FIT_OPT:.6f}\")\n",
    "print(f\"ALPHA_IR_REG_OPT = {ALPHA_IR_REG_OPT:.6f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba7a0d05",
   "metadata": {},
   "source": [
    "## Runtime Formula For `ModelMaterial`\n",
    "\n",
    "An IR-aware `ModelMaterial` should accept an IR partial-dispersion descriptor, not a fixed absolute IR index:\n",
    "\n",
    "```python\n",
    "ModelMaterial(nd=..., vd=..., dPgF=..., p_ref=..., lambda_ref=1.55)\n",
    "```\n",
    "\n",
    "When converting a real catalog glass to this model, compute:\n",
    "\n",
    "$$\n",
    "P_r = \\frac{n_{real}(\\lambda_r)-n_d}{(n_d-1)/V_d}\n",
    "$$\n",
    "\n",
    "Runtime steps:\n",
    "\n",
    "1. Compute $dn_{FC} = (n_d-1)/V_d$ from the current model parameters.\n",
    "2. Compute the current IR anchor as $n_r^{model} = n_d + P_r\\,dn_{FC}$.\n",
    "3. Build features $\\phi(n_d,V_d,\\Delta P_{g,F},P_r)$.\n",
    "4. Predict $[\\nu_4,\\nu_5,\\nu_6]$ from the saved regression matrix.\n",
    "5. Solve $\\nu_1,\\nu_2,\\nu_3$ from the F-C, g-F, and IR partial-dispersion equations.\n",
    "6. Evaluate the 6th-order Buchdahl polynomial.\n",
    "\n",
    "This preserves the visible model-glass anchors and preserves $P_r$. It does **not** freeze $n(\\lambda_r)$ to the original real glass value; if $n_d$ or $V_d$ changes, $n(\\lambda_r)$ changes consistently.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv_optiland",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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