{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "45919f2e",
   "metadata": {},
   "source": [
    "# Background for q-BIC Kerker metasurfaces \n",
    "\n",
    "**Author: Priyanuj Bordoloi, Stanford University**\n",
    "\n",
    "### Symmetry Breaking and the q-BIC\n",
    "\n",
    "When the symmetry of a metasurface's unit-cell is deliberately broken by a small perturbation (here parameterized by the dimensionless **asymmetry parameter $\\alpha$** that changes the radii of neighbouring disks), a formerly dark bound-state-in-the-continuum (BIC) acquires a finite radiative linewidth. The resulting leaky mode is called a **quasi-BIC (q-BIC)**. It appears in the transmittance spectrum as a sharp, asymmetric **Fano resonance**, arising from the interference between the narrow discrete resonance and the broad radiative continuum background.\n",
    "\n",
    "The **radiative Q factor** of the q-BIC obeys the inverse-quadratic scaling law for small perturbations:\n",
    "\n",
    "$$Q_r = \\frac{B}{\\alpha^{2}}$$\n",
    "\n",
    "where $B$ is a geometry-dependent constant independent of $\\alpha$. This means the resonance becomes arbitrarily sharp as $\\alpha \\to 0$, approaching the ideal BIC limit. For larger $\\alpha$ the scaling breaks down because the perturbation is no longer weak.\n",
    "\n",
    "### Design Inspiration — Hu, Lawrence & Dionne (ACS Photonics 2020)\n",
    "\n",
    "The specific geometry used here, a **biperiodic nanodisk lattice** in which a central disk and four corner disks form the unit cell, with symmetry broken by a diameter offset $\\alpha$ between adjacent disks, is directly inspired by the design introduced by Hu, Lawrence, and Dionne (*ACS Photonics* **7**, 36, 2020). In that work, a biperiodic diamond disk metasurface was designed to support high-Q asymmetric electric ($\\mathbf{p}_\\alpha$) and magnetic ($\\mathbf{m}_\\alpha$) dipole resonances in the **ultraviolet**, achieving optical chirality enhancements exceeding 1000-fold compared to circularly polarized light in free space. Moreover, the achiral design makes the metasurface immune to producing any structural chirality in the far-field emission. \n",
    "\n",
    "Critically for our choice of material and wavelength range, that paper explicitly identified **hafnium oxide (HfO$_2$)** as an alternative UV-transparent material to diamond with sufficiently high refractive index ($n \\sim 2.1$ at 247 nm) to support Mie resonances, and negligible absorption losses down to $\\lambda \\sim 220$ nm. We adopt HfO$_2$ as the disk material here because it is more experimentally accessible than diamond; it can be deposited by atomic layer deposition (ALD) with precise thickness control, while still satisfying the low-loss UV transparency requirement that underpins the high-Q resonance design.\n",
    "\n",
    "The wavelength range of this simulation ($\\lambda_0 \\approx 247$ nm) sits squarely in the **mid-ultraviolet**, where the chiroptical absorption features of most small chiral molecules (pharmaceuticals, amino acids) are located, and where Hu et al. demonstrated that biperiodic dielectric metasurfaces can deliver the largest optical chirality enhancements. This study is broken down into 2 sections:\n",
    "\n",
    "**Part 1 — Resonance Finder (example case).** Demonstrates the q-BIC resonance\n",
    "finding methodology on a representative unit-cell geometry: sweep the asymmetry\n",
    "parameter `alpha`, fit Fano lineshapes, confirm the `Q ∝ α⁻²` radiative scaling\n",
    "law, and visualize near-field and chirality maps.\n",
    "\n",
    "**Part 2 — Geometry Sweep & the Kerker Transition.** Independently sweeps the\n",
    "disk diameter `d0` at fixed `alpha = 0.05` and shows how the transmittance and\n",
    "optical chirality evolve. Within the swept range, one geometry sits close to a\n",
    "(generalized) Kerker-type condition — the disk's electric- and magnetic-dipole\n",
    "responses become comparable, the q-BIC's far-field transmittance signature\n",
    "nearly disappears, and the locally confined optical chirality is strongly\n",
    "enhanced.\n",
    "\n",
    "<img src=\"img/hafnia_qbic.png\" alt=\"diagram\" width=\"500\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2abac797",
   "metadata": {},
   "source": [
    "## 0. Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "521b8584",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web\n",
    "from tidy3d.plugins.dispersion import FastDispersionFitter\n",
    "from scipy.signal import find_peaks\n",
    "from scipy.optimize import curve_fit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd56357b",
   "metadata": {},
   "source": [
    "## 1. Background Medium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "26cddee0",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_air = 1  # refractive index of the background medium\n",
    "air = td.Medium(permittivity=n_air**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6760d308",
   "metadata": {},
   "source": [
    "## 2. Shared Geometry, Source, Monitor & Analysis Utilities"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05cb51b9",
   "metadata": {},
   "source": [
    "These functions are identical between the two parts, so they\n",
    "are defined once here and parameterized explicitly — each part supplies its own\n",
    "geometry (`h, px, py, r0`), material (`medium`), and wavelength config\n",
    "(`freq0, fwidth, freqs, lda0`) without sharing values between parts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2e472b22",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_disks(r0, h, px, py, alpha, medium):\n",
    "    '''\n",
    "    Build the 5-disk unit cell.\n",
    "    Central disk radius  = r1 = r0*(1 - alpha/2)\n",
    "    Corner  disk radius  = r2 = r0*(1 + alpha/2)\n",
    "    alpha=0 -> all equal (symmetric BIC)\n",
    "    alpha>0 -> broken symmetry (q-BIC)\n",
    "    '''\n",
    "    d0    = r0 * 2\n",
    "    delta = alpha * d0\n",
    "    r1    = (d0 - delta / 2) / 2   # central disk\n",
    "    r2    = (d0 + delta / 2) / 2   # corner disks\n",
    "\n",
    "    centers = [\n",
    "        (0,      0,      h / 2),   # center\n",
    "        (-px/2, -py/2,  h / 2),   # lower-left corner\n",
    "        (-px/2,  py/2,  h / 2),   # upper-left corner\n",
    "        ( px/2, -py/2,  h / 2),   # lower-right corner\n",
    "        ( px/2,  py/2,  h / 2),   # upper-right corner\n",
    "    ]\n",
    "    radii = [r1, r2, r2, r2, r2]\n",
    "\n",
    "    structures = []\n",
    "    for center, radius in zip(centers, radii):\n",
    "        cyl = td.Cylinder(center=center, radius=radius, length=h, axis=2)\n",
    "        structures.append(td.Structure(geometry=cyl, medium=medium))\n",
    "    return structures\n",
    "\n",
    "\n",
    "def make_circ_sources(freq0, fwidth, source_z):\n",
    "    '''Left circular polarization = Ex + i*Ey (phase = -pi/2 on Ey).'''\n",
    "    pulse = td.GaussianPulse(freq0=freq0, fwidth=fwidth)\n",
    "    pw_x  = td.PlaneWave(\n",
    "        source_time=pulse,\n",
    "        size=(td.inf, td.inf, 0),\n",
    "        center=(0, 0, source_z),\n",
    "        direction='-',\n",
    "        pol_angle=0,\n",
    "    )\n",
    "    pulse_y = td.GaussianPulse(freq0=freq0, fwidth=fwidth, phase=-np.pi/2)\n",
    "    pw_y    = td.PlaneWave(\n",
    "        source_time=pulse_y,\n",
    "        size=(td.inf, td.inf, 0),\n",
    "        center=(0, 0, source_z),\n",
    "        direction='-',\n",
    "        pol_angle=np.pi/2,\n",
    "    )\n",
    "    return [pw_x, pw_y]\n",
    "\n",
    "\n",
    "def make_monitors(h, freqs):\n",
    "    '''Flux + XY/YZ field monitors. Depends on h since the XY monitor sits at z = h/2.'''\n",
    "    flux_monitor = td.FluxMonitor(\n",
    "        center=[0, 0, -0.75],\n",
    "        size=[td.inf, td.inf, 0],\n",
    "        freqs=freqs,\n",
    "        name='flux',\n",
    "    )\n",
    "    xy_monitor = td.FieldMonitor(\n",
    "        center=[0, 0, h / 2],\n",
    "        size=[td.inf, td.inf, 0],\n",
    "        freqs=freqs,\n",
    "        name='xy_field',\n",
    "    )\n",
    "    yz_monitor = td.FieldMonitor(\n",
    "        center=[0, 0, h / 2],\n",
    "        size=[0, td.inf, td.inf],\n",
    "        freqs=freqs,\n",
    "        name='yz_field',\n",
    "    )\n",
    "    return [flux_monitor, xy_monitor, yz_monitor]\n",
    "\n",
    "\n",
    "def make_sim(h, px, py, r0, alpha, medium, freq0, fwidth, freqs, lda0,\n",
    "             source_z, run_time, Lz, include_structures=True):\n",
    "    '''Build simulation for given geometry, material, and wavelength config.\n",
    "    Set include_structures=False for the normalization run.'''\n",
    "    structures = []\n",
    "    if include_structures:\n",
    "        structures += create_disks(r0, h, px, py, alpha, medium)\n",
    "\n",
    "    return td.Simulation(\n",
    "        size=(px, py, Lz),\n",
    "        grid_spec=td.GridSpec.auto(min_steps_per_wvl=20, wavelength=lda0),\n",
    "        structures=structures,\n",
    "        sources=make_circ_sources(freq0, fwidth, source_z),\n",
    "        monitors=make_monitors(h, freqs),\n",
    "        run_time=run_time,\n",
    "        boundary_spec=td.BoundarySpec(\n",
    "            x=td.Boundary.periodic(),\n",
    "            y=td.Boundary.periodic(),\n",
    "            z=td.Boundary.pml(),\n",
    "        ),\n",
    "    )\n",
    "\n",
    "\n",
    "def get_plot_geom(h, px, py, z_pad=0.3):\n",
    "    '''Return (xy_xlim, xy_ylim, y_pad, z_min_yz, z_max_yz, yz_aspect) for a given geometry.'''\n",
    "    xy_xlim   = (-px/2 * 1e3,  px/2 * 1e3)\n",
    "    xy_ylim   = (-py/2 * 1e3,  py/2 * 1e3)\n",
    "    y_pad     = py / 2 * 1e3\n",
    "    z_min_yz  = (-z_pad) * 1e3\n",
    "    z_max_yz  = (h + z_pad) * 1e3\n",
    "    yz_aspect = (2 * y_pad) / (z_max_yz - z_min_yz)\n",
    "    return xy_xlim, xy_ylim, y_pad, z_min_yz, z_max_yz, yz_aspect\n",
    "\n",
    "\n",
    "def draw_disks(ax, r0, h, px, py, alpha, color='cyan', lw=1.0):\n",
    "    '''Draw disk outlines on an XY axis. Coordinates in nm.'''\n",
    "    d0    = r0 * 2\n",
    "    delta = alpha * d0\n",
    "    r1    = (d0 - delta / 2) / 2\n",
    "    r2    = (d0 + delta / 2) / 2\n",
    "\n",
    "    centers_um = [\n",
    "        (0,      0    ),\n",
    "        (-px/2, -py/2 ),\n",
    "        (-px/2,  py/2 ),\n",
    "        ( px/2, -py/2 ),\n",
    "        ( px/2,  py/2 ),\n",
    "    ]\n",
    "    radii_um = [r1, r2, r2, r2, r2]\n",
    "\n",
    "    t = np.linspace(0, 2*np.pi, 300)\n",
    "    for (cx, cy), r in zip(centers_um, radii_um):\n",
    "        ax.plot((cx + r * np.cos(t)) * 1e3,\n",
    "                (cy + r * np.sin(t)) * 1e3,\n",
    "                color=color, lw=lw, ls='--', alpha=0.8)\n",
    "\n",
    "\n",
    "def get_E_norm(monitor_data, monitor_ref, freq):\n",
    "    '''Field-amplitude enhancement |E|/|E0|, normalized to the reference run.'''\n",
    "    def E_amp(md):\n",
    "        Ex = md.Ex.sel(f=freq, method='nearest')\n",
    "        Ey = md.Ey.sel(f=freq, method='nearest')\n",
    "        Ez = md.Ez.sel(f=freq, method='nearest')\n",
    "        return np.sqrt(np.abs(Ex)**2 + np.abs(Ey)**2 + np.abs(Ez)**2).squeeze()\n",
    "    E     = np.array(E_amp(monitor_data))\n",
    "    E_ref = np.array(E_amp(monitor_ref))\n",
    "    return E / float(E_ref.mean())\n",
    "\n",
    "\n",
    "def get_chirality(monitor_data, monitor_ref, freq):\n",
    "    '''Local optical chirality density C = -(omega/2c^2) * Im(E* . H),\n",
    "    normalized by the spatial mean of |C| in the reference run.\n",
    "    The prefactor cancels in the ratio but is kept for physical correctness.'''\n",
    "    omega     = 2 * np.pi * freq\n",
    "    prefactor = -omega / (2 * td.C_0**2)\n",
    "\n",
    "    def C_field(md):\n",
    "        Ex = md.Ex.sel(f=freq, method='nearest')\n",
    "        Ey = md.Ey.sel(f=freq, method='nearest')\n",
    "        Ez = md.Ez.sel(f=freq, method='nearest')\n",
    "        Hx = md.Hx.sel(f=freq, method='nearest')\n",
    "        Hy = md.Hy.sel(f=freq, method='nearest')\n",
    "        Hz = md.Hz.sel(f=freq, method='nearest')\n",
    "        return (prefactor * np.imag(\n",
    "            np.conj(Ex)*Hx + np.conj(Ey)*Hy + np.conj(Ez)*Hz\n",
    "        )).squeeze()\n",
    "\n",
    "    C     = np.array(C_field(monitor_data))\n",
    "    C_ref = np.array(C_field(monitor_ref))\n",
    "    return C / float(np.abs(C_ref).mean())\n",
    "\n",
    "\n",
    "def get_cos_phi(monitor_data, freq):\n",
    "    '''cos(phi_iE,H) = Im(E*.H) / (|E||H|) — polarization-state indicator in [-1, 1].'''\n",
    "    def fields(md):\n",
    "        Ex = md.Ex.sel(f=freq, method='nearest')\n",
    "        Ey = md.Ey.sel(f=freq, method='nearest')\n",
    "        Ez = md.Ez.sel(f=freq, method='nearest')\n",
    "        Hx = md.Hx.sel(f=freq, method='nearest')\n",
    "        Hy = md.Hy.sel(f=freq, method='nearest')\n",
    "        Hz = md.Hz.sel(f=freq, method='nearest')\n",
    "        EdotH_im = np.imag(np.conj(Ex)*Hx + np.conj(Ey)*Hy + np.conj(Ez)*Hz)\n",
    "        E_amp    = np.sqrt(np.abs(Ex)**2 + np.abs(Ey)**2 + np.abs(Ez)**2)\n",
    "        H_amp    = np.sqrt(np.abs(Hx)**2 + np.abs(Hy)**2 + np.abs(Hz)**2)\n",
    "        return (EdotH_im / (E_amp * H_amp + 1e-30)).squeeze()\n",
    "    return np.array(fields(monitor_data))\n",
    "\n",
    "\n",
    "def fano(f, f0, gamma, q_fano, A, offset):\n",
    "    eps = 2 * (f - f0) / gamma\n",
    "    return offset + A * (eps + q_fano)**2 / (eps**2 + 1)\n",
    "\n",
    "\n",
    "def fit_dips(freqs, ldas, T, prominence, distance, window, Q_MIN, Q_MAX, T_dip_max):\n",
    "    '''Find and fit all dips, return list of successful fit results.'''\n",
    "    dips, _ = find_peaks(-T, prominence=prominence, distance=distance)\n",
    "    fitted  = []\n",
    "    for idx in dips:\n",
    "        i_lo  = max(0, idx - window)\n",
    "        i_hi  = min(len(freqs) - 1, idx + window)\n",
    "        f_win = freqs[i_lo:i_hi]\n",
    "        T_win = T[i_lo:i_hi]\n",
    "\n",
    "        if T_win.min() >= T_dip_max:\n",
    "            print(f'  Dip at {ldas[idx]*1e3:.2f} nm — actual T_min = {T_win.min():.3f} >= {T_dip_max}, phantom, skipped')\n",
    "            continue\n",
    "\n",
    "        best_popt, best_res = None, np.inf\n",
    "        for qg in [-3, -1, 0, 1, 3]:\n",
    "            for gg_frac in [4, 8, 16]:\n",
    "                try:\n",
    "                    p0 = [freqs[idx],\n",
    "                          (freqs[i_hi] - freqs[i_lo]) / gg_frac,\n",
    "                          qg, -0.1, T_win.max()]\n",
    "                    popt, _ = curve_fit(fano, f_win, T_win, p0=p0, maxfev=20000)\n",
    "                    res = np.sum((fano(f_win, *popt) - T_win)**2)\n",
    "                    if res < best_res:\n",
    "                        best_res, best_popt = res, popt\n",
    "                except Exception:\n",
    "                    pass\n",
    "\n",
    "        if best_popt is not None:\n",
    "            Q        = best_popt[0] / abs(best_popt[1])\n",
    "            T_at_res = fano(best_popt[0], *best_popt)\n",
    "\n",
    "            if not (Q_MIN <= Q <= Q_MAX):\n",
    "                print(f'  Dip at {ldas[idx]*1e3:.2f} nm — Q = {Q:.1f} outside [{Q_MIN}, {Q_MAX}], skipped')\n",
    "                continue\n",
    "\n",
    "            fitted.append(dict(\n",
    "                popt     = best_popt,\n",
    "                Q        = Q,\n",
    "                T_at_res = T_at_res,\n",
    "                T_min    = T_win.min(),\n",
    "                lda_nm   = td.C_0 / best_popt[0] * 1e3,\n",
    "                f_win    = f_win,\n",
    "                T_win    = T_win,\n",
    "            ))\n",
    "            print(f'  Dip at {ldas[idx]*1e3:.2f} nm — Q = {Q:.1f},  actual T_min = {T_win.min():.3f}')\n",
    "        else:\n",
    "            print(f'  Dip at {ldas[idx]*1e3:.2f} nm — fit failed')\n",
    "\n",
    "    # ── Deduplicate: if two fits within 1 nm, keep deeper dip ────────────────\n",
    "    deduped = []\n",
    "    for fit in fitted:\n",
    "        too_close = False\n",
    "        for kept in deduped:\n",
    "            if abs(fit['lda_nm'] - kept['lda_nm']) < 1.0:\n",
    "                too_close = True\n",
    "                if fit['T_min'] < kept['T_min']:\n",
    "                    deduped.remove(kept)\n",
    "                    deduped.append(fit)\n",
    "                    print(f\"  Deduped: kept {fit['lda_nm']:.2f} nm over {kept['lda_nm']:.2f} nm\")\n",
    "                else:\n",
    "                    print(f\"  Deduped: kept {kept['lda_nm']:.2f} nm, dropped {fit['lda_nm']:.2f} nm\")\n",
    "                break\n",
    "        if not too_close:\n",
    "            deduped.append(fit)\n",
    "\n",
    "    return deduped"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b98d9a26",
   "metadata": {},
   "source": [
    "## Part 1: qBIC mode generation using lattice perturbation.\n",
    "Demonstrates the q-BIC resonance finding methodology on a representative unit-cell geometry: sweep the asymmetry\n",
    "parameter `alpha`, fit Fano lineshapes, confirm the `Q ∝ α⁻²` radiative scaling law, and visualize near-field and chirality maps.\n",
    "\n",
    "We implement the biperiodic disk unit cell in HfO$_2$ and sweep the asymmetry parameter $\\alpha$ to study the emergence and tunability of the q-BIC resonance. The key parameters are:\n",
    "\n",
    "| Quantity | Symbol | Value |\n",
    "|---|---|---|\n",
    "| Asymmetry parameter | $\\alpha$ | swept: 0, 0.05, 0.10, 0.15, 0.20 |\n",
    "| Unperturbed disk diameter | $d_0$ | 110 nm |\n",
    "| Unit cell period | $p_x = p_y$ | 210 nm |\n",
    "| Disk height | $h$ | 60 nm |\n",
    "| Central wavelength | $\\lambda_0$ | 247 nm |\n",
    "\n",
    "The simulation uses **circularly polarized normal-incidence excitation** (sum of $x$- and $y$-polarized plane waves with a $90°$ phase shift), consistent with the illumination scheme in Hu et al. We consider this to be LCP. The transmittance is normalized against an empty-cell reference run, and resonances are identified by Fano fitting:\n",
    "\n",
    "$$T(f) = T_{\\rm bg} + A \\, \\frac{(\\varepsilon + q)^2}{\\varepsilon^2 + 1}, \\quad \\varepsilon = \\frac{2(f - f_0)}{\\gamma}$$\n",
    "\n",
    "where $f_0$ is the resonance frequency, $\\gamma$ is the linewidth (FWHM), $q$ is the Fano asymmetry parameter, and $Q = f_0 / \\gamma$ is extracted from the fit.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f677a49",
   "metadata": {},
   "source": [
    "### 1. Simulation Parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b4e2f6a",
   "metadata": {},
   "source": [
    "Define all fixed simulation parameters for this part. The wavelength\n",
    "window (240–254 nm) is centred on the expected q-BIC resonance of this example\n",
    "geometry. `alpha_list` drives the asymmetry sweep: `alpha = 0` is the ideal BIC\n",
    "(dark mode, no feature in transmission); each nonzero value introduces\n",
    "progressively stronger symmetry breaking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "14fb8ba6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wavelength range: 240.0 – 254.0 nm\n",
      "Central wavelength: 247.0 nm\n",
      "Disk diameter: 110.0 nm,  height: 60.0 nm\n",
      "Unit cell: 210 x 210 nm\n"
     ]
    }
   ],
   "source": [
    "# ── Wavelength range ──────────────────────────────────────────────────────────\n",
    "p1_lda0   = 0.247          # central wavelength (μm)\n",
    "p1_freq0  = td.C_0 / p1_lda0\n",
    "p1_ldas   = np.linspace(p1_lda0 - 0.007, p1_lda0 + 0.007, 700)   # 240–254 nm\n",
    "p1_freqs  = td.C_0 / p1_ldas\n",
    "p1_fwidth = 0.5 * (np.max(p1_freqs) - np.min(p1_freqs))\n",
    "\n",
    "# ── Geometry (example case) ───────────────────────────────────────────────────\n",
    "p1_h     = 0.060          # disk height (μm)\n",
    "p1_px    = 0.210           # unit cell period x (μm)\n",
    "p1_py    = p1_px           # unit cell period y (μm)\n",
    "p1_d0    = 0.110          # unperturbed disk diameter (μm)\n",
    "p1_r0    = p1_d0 / 2        # unperturbed disk radius (μm)\n",
    "\n",
    "# alpha = 0  -> perfect lattice (BIC, dark mode)\n",
    "# alpha > 0  -> broken symmetry (q-BIC, bright mode)\n",
    "alpha_list = [0.0, 0.05, 0.10, 0.15, 0.2]   # sweep over asymmetry\n",
    "\n",
    "# ── Runtime ───────────────────────────────────────────────────────────────────\n",
    "run_time = 10e-12        # (s)  — shared by both parts\n",
    "Lz       = 2.0           # simulation domain height (μm) — shared by both parts\n",
    "source_z = 0.75          # source z position (μm) — shared by both parts\n",
    "\n",
    "print(f'Wavelength range: {p1_ldas.min()*1e3:.1f} – {p1_ldas.max()*1e3:.1f} nm')\n",
    "print(f'Central wavelength: {p1_lda0*1e3:.1f} nm')\n",
    "print(f'Disk diameter: {p1_d0*1e3:.1f} nm,  height: {p1_h*1e3:.1f} nm')\n",
    "print(f'Unit cell: {p1_px*1e3:.0f} x {p1_py*1e3:.0f} nm')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f393162",
   "metadata": {},
   "source": [
    "### 2. Material Dispersion Fit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbf782fd",
   "metadata": {},
   "source": [
    "HfO2 is dispersive in the UV across this window, so we fit a\n",
    "Sellmeier-like pole expansion (up to 4 poles) to the Siefke ALD-HfO2 dataset\n",
    "using Tidy3D's `FastDispersionFitter`, restricted to a slightly padded range\n",
    "around `p1_ldas` to avoid edge artifacts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fccf5579",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "838dd01ea5d247f2938d085167a2a857",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting over 17 points: 231.3 – 262.7 nm\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dispersion fit RMS error: 0.00020\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1100x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data   = np.loadtxt('./misc/HfO2_Siefke_n_k.csv', skiprows=1, delimiter=',')\n",
    "wl_um  = data[:, 0]\n",
    "n_data = data[:, 1]\n",
    "k_data = data[:, 2]\n",
    "\n",
    "wl_min = p1_ldas.min() - 0.010\n",
    "wl_max = p1_ldas.max() + 0.010\n",
    "mask   = (wl_um >= wl_min) & (wl_um <= wl_max)\n",
    "wl_sel = wl_um[mask]\n",
    "n_sel  = n_data[mask]\n",
    "k_sel  = k_data[mask]\n",
    "print(f'Fitting over {mask.sum()} points: {wl_sel.min()*1e3:.1f} – {wl_sel.max()*1e3:.1f} nm')\n",
    "\n",
    "tmp_fname = 'HfO2_subset_p1.csv'\n",
    "np.savetxt(tmp_fname, np.column_stack([wl_sel, n_sel, k_sel]),\n",
    "           delimiter=',', header='wl,n,k', comments='')\n",
    "\n",
    "p1_fitter = FastDispersionFitter.from_file(tmp_fname, skiprows=1, delimiter=',')\n",
    "p1_hafnia, p1_rms_error = p1_fitter.fit(max_num_poles=4, tolerance_rms=1e-3)\n",
    "print(f'Dispersion fit RMS error: {p1_rms_error:.5f}')\n",
    "\n",
    "eps_complex = p1_hafnia.eps_model(p1_freqs)\n",
    "n_fit = np.real(np.sqrt(eps_complex))\n",
    "k_fit = np.imag(np.sqrt(eps_complex))\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 4))\n",
    "ax1.scatter(wl_sel * 1e3, n_sel, s=15, color='steelblue', label='Raw data', zorder=3)\n",
    "ax1.plot(p1_ldas * 1e3, n_fit, color='tomato', lw=2, label='Fit')\n",
    "ax1.set_xlabel('Wavelength (nm)'); ax1.set_ylabel('n')\n",
    "ax1.set_title('HfO2 Real Refractive Index'); ax1.legend()\n",
    "ax2.scatter(wl_sel * 1e3, k_sel, s=15, color='steelblue', label='Raw data', zorder=3)\n",
    "ax2.plot(p1_ldas * 1e3, k_fit, color='tomato', lw=2, label='Fit')\n",
    "ax2.set_xlabel('Wavelength (nm)'); ax2.set_ylabel('k')\n",
    "ax2.set_title('HfO2 Extinction Coefficient'); ax2.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84661f1c",
   "metadata": {},
   "source": [
    "### 3. Plot Geometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4894fd97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_check = make_sim(p1_h, p1_px, p1_py, p1_r0, 0.10, p1_hafnia,\n",
    "                      p1_freq0, p1_fwidth, p1_freqs, p1_lda0,\n",
    "                      source_z, run_time, Lz)\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))\n",
    "sim_check.plot(z=p1_h/2, ax=ax1)\n",
    "sim_check.plot_grid(z=0, ax=ax1)\n",
    "ax1.set_title('XY cross-section (mid-disk)')\n",
    "sim_check.plot(x=0, ax=ax2)\n",
    "ax2.set_title('XZ cross-section')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "p1_xy_xlim, p1_xy_ylim, p1_y_pad, p1_z_min_yz, p1_z_max_yz, p1_yz_aspect = \\\n",
    "    get_plot_geom(p1_h, p1_px, p1_py, z_pad=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5560810",
   "metadata": {},
   "source": [
    "### 4. Run Simulations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ca90c87",
   "metadata": {},
   "source": [
    "Submit two `Batch` objects — one for the actual runs (disks present) and\n",
    "one for the normalization runs (empty cell). Results are cached to\n",
    "`data/p1_actual/` and `data/p1_norm/` so they can be reloaded without re-running\n",
    "if the kernel restarts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c650f8a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running actual simulations (Part 1)...\n"
     ]
    },
    {
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      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:57:56 EDT </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span> tasks.                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:57:56 EDT\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m5\u001b[0m tasks.                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:58:01 EDT </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.870</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:58:01 EDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.870\u001b[0m for the whole batch.                \n"
      ]
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     "metadata": {},
     "output_type": "display_data"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>completion.                                                        \n",
       "</pre>\n"
      ],
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       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after    \n",
       "\u001b[2;36m             \u001b[0mcompletion.                                                        \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "Output()"
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:02:37 EDT </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:02:37 EDT\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8e6ef6dcd3a7401484fe50c5c13bdf7e",
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      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running normalization simulations (Part 1)...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:03:31 EDT </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span> tasks.                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:03:31 EDT\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m5\u001b[0m tasks.                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:03:36 EDT </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.207</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:03:36 EDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.207\u001b[0m for the whole batch.                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>completion.                                                        \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after    \n",
       "\u001b[2;36m             \u001b[0mcompletion.                                                        \n"
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       "Output()"
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    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:03:56 EDT </span>Batch complete.                                                    \n",
       "</pre>\n"
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       "\u001b[2;36m14:03:56 EDT\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p1_sims_actual = {f'alpha={a:.2f}': make_sim(p1_h, p1_px, p1_py, p1_r0, a, p1_hafnia,\n",
    "                                              p1_freq0, p1_fwidth, p1_freqs, p1_lda0,\n",
    "                                              source_z, run_time, Lz,\n",
    "                                              include_structures=True)  for a in alpha_list}\n",
    "p1_sims_norm   = {f'alpha={a:.2f}': make_sim(p1_h, p1_px, p1_py, p1_r0, a, p1_hafnia,\n",
    "                                              p1_freq0, p1_fwidth, p1_freqs, p1_lda0,\n",
    "                                              source_z, run_time, Lz,\n",
    "                                              include_structures=False) for a in alpha_list}\n",
    "\n",
    "p1_batch_actual = web.Batch(simulations=p1_sims_actual, verbose=True)\n",
    "p1_batch_norm   = web.Batch(simulations=p1_sims_norm,   verbose=True)\n",
    "\n",
    "print('Running actual simulations (Part 1)...')\n",
    "p1_results_actual = p1_batch_actual.run(path_dir='data/p1_actual')\n",
    "\n",
    "print('Running normalization simulations (Part 1)...')\n",
    "p1_results_norm = p1_batch_norm.run(path_dir='data/p1_norm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "34274a32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/1473335795.py:5: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  T_actual = np.array(p1_results_actual[key]['flux'].flux)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/1473335795.py:6: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  T_ref    = np.array(p1_results_norm[key]['flux'].flux)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/1473335795.py:5: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  T_actual = np.array(p1_results_actual[key]['flux'].flux)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/1473335795.py:6: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  T_ref    = np.array(p1_results_norm[key]['flux'].flux)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/1473335795.py:5: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  T_actual = np.array(p1_results_actual[key]['flux'].flux)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/1473335795.py:6: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  T_ref    = np.array(p1_results_norm[key]['flux'].flux)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/1473335795.py:5: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  T_actual = np.array(p1_results_actual[key]['flux'].flux)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/1473335795.py:6: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  T_ref    = np.array(p1_results_norm[key]['flux'].flux)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/1473335795.py:5: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  T_actual = np.array(p1_results_actual[key]['flux'].flux)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p1_T_dict keys: ['alpha=0.00', 'alpha=0.05', 'alpha=0.10', 'alpha=0.15', 'alpha=0.20']\n",
      "  alpha=0.00: T range [0.944, 0.970]\n",
      "  alpha=0.05: T range [0.006, 1.011]\n",
      "  alpha=0.10: T range [0.003, 1.006]\n",
      "  alpha=0.15: T range [0.000, 1.004]\n",
      "  alpha=0.20: T range [0.000, 1.003]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/1473335795.py:6: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  T_ref    = np.array(p1_results_norm[key]['flux'].flux)\n"
     ]
    }
   ],
   "source": [
    "# ── Extract normalized transmittance for each alpha ───────────────────────────\n",
    "p1_T_dict = {}\n",
    "\n",
    "for key in p1_sims_actual.keys():\n",
    "    T_actual = np.array(p1_results_actual[key]['flux'].flux)\n",
    "    T_ref    = np.array(p1_results_norm[key]['flux'].flux)\n",
    "    p1_T_dict[key] = T_actual / T_ref\n",
    "\n",
    "print('p1_T_dict keys:', list(p1_T_dict.keys()))\n",
    "for key, T in p1_T_dict.items():\n",
    "    print(f'  {key}: T range [{T.min():.3f}, {T.max():.3f}]')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a3b8e68",
   "metadata": {},
   "source": [
    "### 5. Fano Fitting and Waterfall Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "03a85eaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Dip at 242.52 nm — Q = 7950.5,  actual T_min = 0.006\n",
      "  Dip at 250.37 nm — actual T_min = 0.953 >= 0.5, phantom, skipped\n",
      "  Dip at 252.24 nm — Q = 8454.2,  actual T_min = 0.013\n",
      "  Dip at 242.66 nm — Q = 2003.0,  actual T_min = 0.003\n",
      "  Dip at 249.01 nm — actual T_min = 0.963 >= 0.5, phantom, skipped\n",
      "  Dip at 252.20 nm — Q = 1917.8,  actual T_min = 0.011\n",
      "  Dip at 242.82 nm — Q = 903.4,  actual T_min = 0.000\n",
      "  Dip at 248.25 nm — actual T_min = 0.974 >= 0.5, phantom, skipped\n",
      "  Dip at 252.30 nm — Q = 857.7,  actual T_min = 0.000\n",
      "  Dip at 243.06 nm — Q = 524.9,  actual T_min = 0.001\n",
      "  Dip at 252.46 nm — Q = 493.2,  actual T_min = 0.000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Q_MIN       = 10\n",
    "Q_MAX       = 1000000\n",
    "T_DIP_MAX   = 0.5\n",
    "PROMINENCE  = 0.02\n",
    "DISTANCE    = 10\n",
    "WINDOW      = 60\n",
    "OFFSET_STEP = 1.2\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "for k, alpha_val in enumerate(alpha_list):\n",
    "    key    = f'alpha={alpha_val:.2f}'\n",
    "    T      = p1_T_dict[key]\n",
    "    offset = k * OFFSET_STEP\n",
    "\n",
    "    ax.plot(p1_ldas * 1e3, T + offset, lw=1.8, color='steelblue')\n",
    "    ax.text(p1_ldas[-1] * 1e3 + 0.3, T[-1] + offset,\n",
    "            f'$\\\\alpha$={alpha_val:.2f}', fontsize=9, va='center', color='steelblue')\n",
    "\n",
    "    if alpha_val > 0.0:\n",
    "        fitted = fit_dips(p1_freqs, p1_ldas, T, PROMINENCE, DISTANCE, WINDOW,\n",
    "                          Q_MIN, Q_MAX, T_DIP_MAX)\n",
    "\n",
    "        for fit in fitted:\n",
    "            popt     = fit['popt']\n",
    "            Q        = fit['Q']\n",
    "            lda_nm   = fit['lda_nm']\n",
    "            T_at_res = fit['T_at_res']\n",
    "            f_win    = fit['f_win']\n",
    "            f_dense  = np.linspace(f_win.min(), f_win.max(), 500)\n",
    "            l_dense  = td.C_0 / f_dense * 1e3\n",
    "\n",
    "            ax.plot(l_dense, fano(f_dense, *popt) + offset,\n",
    "                    color='tomato', lw=1.5, ls='--')\n",
    "            ax.annotate(f'Q={Q:.0f}\\n ',\n",
    "                        xy=(lda_nm, T_at_res + offset),\n",
    "                        xytext=(lda_nm + 0.4, T_at_res + offset + 0.03),\n",
    "                        fontsize=7, color='tomato',\n",
    "                        arrowprops=dict(arrowstyle='->', color='tomato', lw=0.6))\n",
    "\n",
    "ax.set_xlabel('Wavelength (nm)', fontsize=12)\n",
    "ax.set_ylabel('Transmittance', fontsize=12)\n",
    "ax.set_title('Transmittance Waterfall — Alpha Sweep (Example Geometry)', fontsize=13)\n",
    "ax.set_xlim(p1_ldas.min() * 1e3, p1_ldas.max() * 1e3 + 2)\n",
    "ax.set_yticks([])\n",
    "plt.tight_layout()\n",
    "plt.savefig('p1_transmittance_waterfall.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "178be41c",
   "metadata": {},
   "source": [
    "### 6. Mode Grouping and Q ∝ α⁻² Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "874a70eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Dip at 242.52 nm — Q = 7950.5,  actual T_min = 0.006\n",
      "  Dip at 250.37 nm — actual T_min = 0.953 >= 0.5, phantom, skipped\n",
      "  Dip at 252.24 nm — Q = 8454.2,  actual T_min = 0.013\n",
      "  Dip at 242.66 nm — Q = 2003.0,  actual T_min = 0.003\n",
      "  Dip at 249.01 nm — actual T_min = 0.963 >= 0.5, phantom, skipped\n",
      "  Dip at 252.20 nm — Q = 1917.8,  actual T_min = 0.011\n",
      "  Dip at 242.82 nm — Q = 903.4,  actual T_min = 0.000\n",
      "  Dip at 248.25 nm — actual T_min = 0.974 >= 0.5, phantom, skipped\n",
      "  Dip at 252.30 nm — Q = 857.7,  actual T_min = 0.000\n",
      "  Dip at 243.06 nm — Q = 524.9,  actual T_min = 0.001\n",
      "  Dip at 252.46 nm — Q = 493.2,  actual T_min = 0.000\n",
      "Found 2 mode group(s)\n",
      "  Mode 1: mean lda = 242.79 nm,  4 alpha point(s)\n",
      "  Mode 2: mean lda = 252.27 nm,  4 alpha point(s)\n",
      "res_chiral for alpha=0.05: [(np.float64(242.52777156578216), np.float64(7950.547687949862)), (np.float64(252.23545915388863), np.float64(8454.162496843213))]\n"
     ]
    }
   ],
   "source": [
    "p1_all_resonances = []\n",
    "for alpha_val in alpha_list:\n",
    "    if alpha_val == 0.0:\n",
    "        continue\n",
    "    key    = f'alpha={alpha_val:.2f}'\n",
    "    T      = p1_T_dict[key]\n",
    "    fitted = fit_dips(p1_freqs, p1_ldas, T, PROMINENCE, DISTANCE, WINDOW,\n",
    "                      Q_MIN, Q_MAX, T_DIP_MAX)\n",
    "    for fit in fitted:\n",
    "        p1_all_resonances.append((alpha_val, fit['lda_nm'], fit['Q']))\n",
    "\n",
    "CLUSTER_TOL_NM = 2.0\n",
    "p1_groups = []\n",
    "for r in p1_all_resonances:\n",
    "    alpha_val, lda_nm, Q = r\n",
    "    placed = False\n",
    "    for group in p1_groups:\n",
    "        group_lda_mean = np.mean([x[1] for x in group])\n",
    "        if abs(lda_nm - group_lda_mean) < CLUSTER_TOL_NM:\n",
    "            group.append(r)\n",
    "            placed = True\n",
    "            break\n",
    "    if not placed:\n",
    "        p1_groups.append([r])\n",
    "\n",
    "p1_groups      = sorted(p1_groups, key=lambda g: np.mean([x[1] for x in g]))\n",
    "p1_mode_alphas = [[x[0] for x in sorted(g, key=lambda x: x[0])] for g in p1_groups]\n",
    "p1_mode_ldas   = [[x[1] for x in sorted(g, key=lambda x: x[0])] for g in p1_groups]\n",
    "p1_mode_Qs     = [[x[2] for x in sorted(g, key=lambda x: x[0])] for g in p1_groups]\n",
    "\n",
    "colors  = ['steelblue', 'tomato', 'seagreen', 'darkorange']\n",
    "markers = ['o', 's', '^', 'D']\n",
    "\n",
    "def inv_square(alpha, C):\n",
    "    return C / alpha**2\n",
    "\n",
    "alpha_chiral = 0.05\n",
    "p1_key_chiral = f'alpha={alpha_chiral:.2f}'\n",
    "\n",
    "p1_res_chiral = []\n",
    "for group in p1_groups:\n",
    "    for (a, lda_nm, Q) in group:\n",
    "        if a == alpha_chiral:\n",
    "            p1_res_chiral.append((lda_nm, Q))\n",
    "\n",
    "print(f'Found {len(p1_groups)} mode group(s)')\n",
    "for i, group in enumerate(p1_groups):\n",
    "    print(f'  Mode {i+1}: mean lda = {np.mean([x[1] for x in group]):.2f} nm,  {len(group)} alpha point(s)')\n",
    "print(f'res_chiral for alpha={alpha_chiral}: {p1_res_chiral}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "98dbd1bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, len(p1_groups), figsize=(5 * len(p1_groups), 4), sharey=False)\n",
    "if len(p1_groups) == 1:\n",
    "    axes = [axes]\n",
    "\n",
    "for i, (alphas_g, ldas_g, Qs_g) in enumerate(zip(p1_mode_alphas, p1_mode_ldas, p1_mode_Qs)):\n",
    "    ax         = axes[i]\n",
    "    alphas_arr = np.array(alphas_g)\n",
    "    Qs_arr     = np.array(Qs_g)\n",
    "    lda_mean   = np.mean(ldas_g)\n",
    "    c          = colors[i % len(colors)]\n",
    "    m          = markers[i % len(markers)]\n",
    "\n",
    "    pert_nm    = alphas_arr * p1_d0 * 1e3\n",
    "    pert_dense = np.linspace(pert_nm.min() * 0.8, pert_nm.max() * 1.2, 200)\n",
    "\n",
    "    ax.scatter(pert_nm, Qs_arr, color=c, marker=m, s=60, zorder=3, label='Simulated Q')\n",
    "\n",
    "    if len(alphas_arr) >= 2:\n",
    "        try:\n",
    "            popt, _ = curve_fit(inv_square, alphas_arr, Qs_arr, p0=[1.0])\n",
    "            C_fit   = popt[0]\n",
    "            alpha_dense = pert_dense / (p1_d0 * 1e3)\n",
    "            ax.plot(pert_dense, inv_square(alpha_dense, C_fit),\n",
    "                    color=c, lw=2, ls='--', label=f'$Q={C_fit:.3f}/\\\\alpha^2$')\n",
    "        except Exception:\n",
    "            print(f'Mode {i+1}: inverse square fit failed')\n",
    "\n",
    "    ax.set_xlabel('Perturbation size $\\\\delta$ (nm)', fontsize=12)\n",
    "    ax.set_ylabel('Quality factor Q', fontsize=12)\n",
    "    ax.set_title(f'Mode {i+1}  |  $\\\\lambda$ = {lda_mean:.1f} nm', fontsize=12)\n",
    "    ax.legend(fontsize=9)\n",
    "\n",
    "fig.suptitle('Q factor vs Perturbation Size (Example Geometry)', fontsize=13)\n",
    "plt.tight_layout()\n",
    "plt.savefig('p1_Q_vs_alpha_modes.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0238f5b3",
   "metadata": {},
   "source": [
    "### 7. Near-Field Maps at Resonance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4c28dd63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:142: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  E     = np.array(E_amp(monitor_data))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:143: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  E_ref = np.array(E_amp(monitor_ref))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3851934302.py:19: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  x_coords = np.array(p1_results_actual[key]['xy_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3851934302.py:21: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  y_coords = np.array(p1_results_actual[key]['xy_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:142: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  E     = np.array(E_amp(monitor_data))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:143: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  E_ref = np.array(E_amp(monitor_ref))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3851934302.py:41: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  y_coords_yz = np.array(p1_results_actual[key]['yz_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3851934302.py:43: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  z_coords_yz = np.array(p1_results_actual[key]['yz_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:142: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  E     = np.array(E_amp(monitor_data))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:143: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  E_ref = np.array(E_amp(monitor_ref))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3851934302.py:19: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  x_coords = np.array(p1_results_actual[key]['xy_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3851934302.py:21: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  y_coords = np.array(p1_results_actual[key]['xy_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:142: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  E     = np.array(E_amp(monitor_data))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:143: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  E_ref = np.array(E_amp(monitor_ref))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3851934302.py:41: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  y_coords_yz = np.array(p1_results_actual[key]['yz_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3851934302.py:43: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  z_coords_yz = np.array(p1_results_actual[key]['yz_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:142: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  E     = np.array(E_amp(monitor_data))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:143: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  E_ref = np.array(E_amp(monitor_ref))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3851934302.py:19: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  x_coords = np.array(p1_results_actual[key]['xy_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3851934302.py:21: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  y_coords = np.array(p1_results_actual[key]['xy_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:142: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  E     = np.array(E_amp(monitor_data))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:143: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  E_ref = np.array(E_amp(monitor_ref))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3851934302.py:41: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  y_coords_yz = np.array(p1_results_actual[key]['yz_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3851934302.py:43: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  z_coords_yz = np.array(p1_results_actual[key]['yz_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:142: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  E     = np.array(E_amp(monitor_data))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:143: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  E_ref = np.array(E_amp(monitor_ref))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3851934302.py:19: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  x_coords = np.array(p1_results_actual[key]['xy_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3851934302.py:21: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  y_coords = np.array(p1_results_actual[key]['xy_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:142: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  E     = np.array(E_amp(monitor_data))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:143: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  E_ref = np.array(E_amp(monitor_ref))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3851934302.py:41: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  y_coords_yz = np.array(p1_results_actual[key]['yz_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3851934302.py:43: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  z_coords_yz = np.array(p1_results_actual[key]['yz_field'].Ex\n"
     ]
    },
    {
     "data": {
      "image/png": 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ry4mIiIiISEAbNmwYQNWIARERERERkWChNTZERERERERERERERCRgKLEhIiIiIiIiIiIiIiIBQ4kNEREREREREREREREJGFpjQ0REREREREREREREAoZGbIiIiIiIiIiIiIiISMBQYkNERERERERERERERAKGEhsiIiIiIiIiIiIiIhIwlNgQEREREREREREREZGAocSGiIiIiIiIiIiIiIgEDCU2REREREREREREREQkYCixISIiIiIiIiIiIiIiAUOJDRERERERERERERERCRhKbIiIiIiIiIiIiIiISMBQYkNERERERERERERERAKGEhsiIiIiIiIiIiIiIhIwlNgQEREREREREREREZGAocSGiIiIiIiIiIiIiIgEDCU2REREREREREREREQkYCixISIiIiIiIiIiIiIiAUOJDRERERERERERERERCRhKbIiIiIiIiIiIiIiISMBQYkNERERERERERERERAKGEhsiIiIiIiIiIiIiIhIwlNgQEREREREREREREZGAocSGiIiIiIiIiIiIiIgEDCU2REREREREREREREQkYCixISIiIiIiIiIiIiIiAcPu6wqIiPhKUVERJSUlHu8nPDycyMjIBqiRiIj4C8UIERExUYwQERETxQjvUWJDRJqkoqIiMjMz2bt3r8f7Sk1NZevWrQo4IiJBQjFCRERMFCNERMREMcK7lNgQkSappKSEvXv3kpW1lbi4uHrvJy8vj7ZtMykpKVGwEREJEooRIiJiohghIiImihHepcSGiDRpcXHRxMVFe7CHsgari4iI+BfFCBERMVGMEBERE8UI79Di4SIiIiIiIiIiIiIiEjA0YkNEmrgyPMuEK4suIhK8FCNERMREMUJEREwUI7xBiQ0RaeIUbERExEQxQkRETBQjRETERDHCG5TYEJEmrhzPAkZ5Q1VERET8jmKEiIiYKEaIiIiJYoQ3aI0NEREREREREREREREJGBqxISJNnIYHioiIiWKEiIiYKEaIiIiJYoQ3KLEhIk2cgo2IiJgoRoiIiIlihIiImChGeIOmohKRJq6sAW4iIhKcvBcjpkyZwmmnnUZsbCzJycmMGjWK9evXV9tm2LBh2Gy2ardx48Z5+iJFRKRe1I4QERETxQhvUGJDRERERMTHFixYwPjx41m8eDFz586ltLSU888/n4KCgmrb3XbbbezZs6fq9uyzz/qoxiIiIiIiIr6jqahEpIkrd948ebyIiAQn78WIL7/8strfM2fOJDk5meXLl3PWWWdVlUdHR5OamupBnUREpGGoHSEiIiaKEd6gERsi0sSV49nQQAUbEZHg1TAxIi8vr9qtuLj4pM+cm5sLQGJiYrXy9957jxYtWtCzZ08efvhhCgsLPX+ZIiJSD2pHiIiIiWKEN2jEhog0cZ7OXah5D0VEglfDxIi2bdtWK500aRKTJ082PsrhcHDvvfdy+umn07Nnz6rya665hnbt2pGWlsaqVav43e9+x/r16/nkk088qKOIiNSP2hEiImKiGOENSmyIiIiIiDSirKws4uLiqv6OiIiodfvx48ezZs0avvvuu2rlt99+e9X/e/XqRatWrTj33HPZvHkzHTp0aNhKi4iIiIiI+DElNkSkiVMWXURETBomRsTFxVVLbNRmwoQJfPbZZyxcuJA2bdrUuu3AgQMB2LRpkxIbIiJep3aEiIiYKEZ4g9bYkIDz7bffkpGR4etqSNDwZM5DTwOViDQ0xQhpWN6LEZZlMWHCBGbPns0333xDZmbmSR+zcuVKAFq1alXn5xFpyhQjpGGpHSESbBQnpOEoRniDEhvSIMrLyxkyZAijR4+uVp6bm0vbtm35/e9/z/DhwxkxYkSNx7788sskJCSwc+dOb1W3QU2bNo2MjAwiIyMZOHAgS5cu9fgxkydPxmazVbt17dq1sV5CE6cFnUQam2JE3WPEwoULufjii0lLS8NmszFnzpwG2a/Ul/dixPjx43n33XeZNWsWsbGx7N27l71793L06FEANm/ezB/+8AeWL1/Otm3b+Oc//8kNN9zAWWedRe/evRvqBYt4nWJEw8YItSO8Se0IEW9QnKhbnJgyZQqnnXYasbGxJCcnM2rUKNavX+/RPsUTihHeoMSGNIjQ0FBmzpzJl19+yXvvvVdVftddd5GYmMikSZOYMWMGS5Ys4dVXX626f+vWrfz2t7/lpZdeOul0C/7oww8/ZOLEiUyaNIkVK1bQp08fRowYwb59+zx+TI8ePdizZ0/V7cR5tkVEAoViRN1jREFBAX369GHatGkNul/xf9OnTyc3N5dhw4bRqlWrqtuHH34IQHh4OF9//TXnn38+Xbt25f777+fyyy/nX//6l49rLuIZxYiGjRGgdoSIBBfFibrFiQULFjB+/HgWL17M3LlzKS0t5fzzz6egoKDe+xTxe5ZIA/rLX/5iNW/e3Nq9e7c1Z84cKywszFq5cmXV/TNnzrSaNWtmbdmyxXI4HNbZZ59tXXbZZW49x/z586127drVq35Lliyxhg4dakVGRlpdunSxli1bZr366qvWxRdfXK/9DRgwwBo/fnzV3+Xl5VZaWpo1ZcoUjx4zadIkq0+fPvWqk9RNbm6uBVi5uV9ZlvVdvW+5uV8595Prg1chElgUI04eI44HWLNnz27w/crJKUaIeJ9iRMPECLUjGp9ihIhvKE64Fyf27dtnAdaCBQsabJ9ycooR3qURG9Kg7rrrLvr06cP111/P7bffzuOPP06fPn2q7r/xxhs599xzGTt2LFOnTmXNmjXVMuqNafHixQwdOpQLL7yQVatW0a1bN5588kmeeeYZnnjiCQD++Mc/0qxZs1pvO3bsAKCkpITly5czfPjwqucICQlh+PDhLFq0yGUd3HnMxo0bSUtLo3379lx77bVVzysNTfMeiniLYkTtMaIuGmu/YqIYIeItihENdyxXO8JbFCNEvElxwr04kZubC0BiYmKD7VPcoRjhDXZfV0CCi81mY/r06XTr1o1evXrx0EMP1djmtddeo0ePHixcuJB//OMftGzZ0it1mzhxIldeeSUPPvggAGPGjGHMmDFceumlnHLKKQCMGzeOq666qtb9pKWlAXDgwAHKy8tJSUmpdn9KSgrr1q1z+di6PmbgwIHMnDmTLl26sGfPHp544gnOPPNM1qxZQ2xsrHsvXE7C04ChYCNSV4oRtceIumis/YqJYoSItyhGNMyxXO0Ib1KMEPEmxYm6xwmHw8G9997L6aefTs+ePRtkn+IuxQhvUGJDGtybb75JdHQ0W7duZefOnWRkZFS7Pzk5mTvuuIM5c+YwatQor9Rp586dLFq0iOeff76qzG63Y1lWVfYcKjLZldlsXxo5cmTV/3v37s3AgQNp164dH330EbfccosPayYi4hnFCBERMVGM8JzaESISzBQn6mb8+PGsWbNGayxJ0NNUVNKgfvjhB1544QU+++wzBgwYwC233IJlWTW2s9vt2O3ey6v98ssvAJx66qlVZevXr2fAgAH06tWrqsydoYEtWrQgNDSU7Ozsas+VnZ1Namqqy3rU5zEACQkJdO7cmU2bNrn3wqUONDxQxFsUI05+vD+ZxtqvmChGiHiLYkTjHMvVjmhMihEi3qQ4Ubc4MWHCBD777DPmz59fbdF0tSO8TTHCG5TYkAZTWFjITTfdxJ133snZZ5/NG2+8wdKlS3nllVd8XTVyc3MJDQ3FZrMBcOjQIZ5//nmio6OrbTdu3DhWrlxZ661yaGB4eDj9+vVj3rx5VY93OBzMmzePwYMHu6xHfR4DkJ+fz+bNm2nVqlW93wMxKcezQFPu/SqLBCDFiLod70+msfYrJooRIt6gGNF4x3K1IxqTYoSItyhOnDxOWJbFhAkTmD17Nt988w2ZmZnV7lc7wtsUI7xBU1FJg3n44YexLIunn34agIyMDJ5//nkeeOABRo4cWWOIoDf17duX8vJynn32Wa688kruueceMjIyWLt2Ldu3b6ddu3aA+0MDJ06cyI033kj//v0ZMGAAL774IgUFBdx8880ATJ06ldmzZ1cLHCd7DMADDzzAxRdfTLt27di9ezeTJk0iNDSUMWPGNNA7Isd4mglXFl2kLhQj6hYj8vPzq11Vu3XrVlauXEliYiLp6el12q80JMUIEW9QjGi4GKF2hDcpRoh4i+LEyePE+PHjmTVrFp9++imxsbHs3bsXgPj4eKKiouq0T2lIihHeoBEb0iAWLFjAtGnTmDFjRrWs9B133MGQIUOMQwS9pWPHjjz55JP85S9/4ZRTTiEtLY3//Oc/tG7dmgsuuKDe+/31r3/N888/z+OPP07fvn1ZuXIlX375ZdViTAcOHGDz5s1uPQYq5mgcM2YMXbp04aqrriIpKYnFixd7beErEZGGpBhR9xjx448/csopp1QtMjhx4kROOeUUHn/88TrvV0QkkChGNGyMUDtCRIKN4kTd4sT06dPJzc1l2LBhtGrVqur24Ycf1nmfIoHGZvny1y9SD99++y033XQT27Zt83VVJIDl5eURHx9Pbu7bxMVFn/wBxv0UEh9/A7m5ucTFxTVgDUWkPhQjpCEoRogEJ8UIaQiKESLBS3FCPKUY4V2aikpEmjgNDxQRERPFCBERMVGMEBERE8UIb1BiQ0SaOAUbERExUYwQERETxQgRETFRjPAGrbEhAScjI4N7773X19UQERE/pBghIiImihEiIlIbxQmRwKIRGxJwFGikYZXjWSa8vKEqIiINQDFCGpZihEgwUYyQhqUYIRJsFCek4ShGeINGbIhIE1feADcREQlOihEiImLi3RixcOFCLr74YtLS0rDZbMyZM6fGNr/88guXXHIJ8fHxxMTEcNppp7Fjx456vj4REak/tSO8QYkNEWniyhrgJiIiwUkxQkRETLwbIwoKCujTpw/Tpk1zef/mzZs544wz6Nq1K99++y2rVq3iscceIzIysj4vTkREPKJ2hDdoKioRERERERERET82cuRIRo4cabz/97//Pb/61a949tlnq8o6dOjgjaqJiIj4hEZsiEgTpyy6iIiYKEaIiIhJw8SIvLy8arfi4mK3a+JwOPj888/p3LkzI0aMIDk5mYEDB7qcrkpERLxB7QhvUGIjSD300ENERERwzTXX+LoqIn6uckGn+t4076EEHsUIkbpSjBBRzBAxaZgY0bZtW+Lj46tuU6ZMcbsm+/btIz8/n6effpoLLriA//znP1x22WWMHj2aBQsWePpCRYwUI0RM1I7wBiU2gtTDDz/Mn/70J95//302bdrk0b6mTJnCaaedRmxsLMnJyYwaNYr169cbt3/66aex2Wzce++9Hu0HYPLkydhstmq3rl27evR6GltWVhZjx44lLS2N8PBw2rVrxz333MPBgwc92m9GRkaN98JmszF+/PiqbY4cOcK9995Lu3btiIqKYsiQISxbtqzq/rq8n3VZlC64KIsuTU8wxIi6HBP9TWPFh7q8d9OmTSMjI4PIyEgGDhzI0qVLa+znZNtMnz6d3r17ExcXR1xcHIMHD+aLL77wqO7+TzFCJBhiBpz8PNkfNVbcqOt7UVtcaJox4UQNEyOysrLIzc2tuj388MNu18ThcABw6aWXct9999G3b18eeughLrroIl555RWPXqVIbfwxRtTn+BSI/SCNFSPq0m90ss8qEPvyGp7aEd6gxEaQio+P55ZbbiEkJITVq1d7tK8FCxYwfvx4Fi9ezNy5cyktLeX888+noKCgxrbLli3j1VdfpXfv3h7t53g9evRgz549VbfvvvvOo9fTmLZs2UL//v3ZuHFjVWB/5ZVXmDdvHoMHD+bQoUP13veyZcuqvQ9z584F4Morr6za5tZbb2Xu3Lm88847rF69mvPPP5/hw4eza9euqm1O9n6ebFE6EQl8wRAj6nJM9CeNGR9O9t59+OGHTJw4kUmTJrFixQr69OnDiBEj2LdvX9U+6rJNmzZtePrpp1m+fDk//vgj55xzDpdeeik///xz/d8YEfF7wRAzoG7nyf6kMeNGXd6Lk8UFxYSGU9n5WnmLiIhwex8tWrTAbrfTvXv3auXdunVjx44dDVVVkRr8MUbU5/gUaP0gjRkj4OT9RnX5rAKpL08CmCVBKy8vz2rWrJn15JNPNuh+9+3bZwHWggULqpUfOXLE6tSpkzV37lxr6NCh1j333FOv/Rxv0qRJVp8+fdyq39ChQ6277rrLevDBB63mzZtbKSkp1qRJk2psM2HCBOuee+6xEhISrOTkZOu1116z8vPzrZtuuslq1qyZ1aFDB+vf//63W899wQUXWG3atLEKCwurle/Zs8eKjo62xo0b59b+anPPPfdYHTp0sBwOh2VZllVYWGiFhoZan332WbXtTj31VOv3v/+9ZVnuv5+ANXv27Iaqsl/Jzc21ACs39wnLsp6p9y039wnnfnJ98CpE6i8YYsTxTjwmutJU4sOJ792AAQOs8ePHV91fXl5upaWlWVOmTKkqq8s2rjRv3tz629/+1mB19xeKESLVBXrMqMt5sivBGDfq+l7UJy4Ea0w4kT/ECFfttMGDB1vXXXddtbJRo0ZZY8aMcXv/Iu7w9xhhWe4dn+raDxKMMcKy6tcPd+JnVZ99BAt/iBFNiUZsBLFHH32U/Px81qxZU638j3/8I82aNav1VttVHbm5uQAkJiZWKx8/fjwXXnghw4cPr1P9TPs50caNG0lLS6N9+/Zce+21dbri5K233iImJoYlS5bw7LPP8uSTT1ZdzXv8Ni1atGDp0qXcdddd3HnnnVx55ZUMGTKEFStWcP7553P99ddTWFhYp9dz6NAhvvrqK37zm98QFRVV7b7U1FSuvfZaPvzwQyzLAjz7HEpKSnj33XcZO3YsNpsNgLKyMsrLy4mMjKy2bVRUVLXMeH3ez+Cm4YHSNAVLjADXx0STYI8PUP29KykpYfny5dXe95CQEIYPH86iRYuq3r+TbXOi8vJyPvjgAwoKChg8eHCd3ofApBghAoEfM+p6nuxKsMWNurwX7saFphMTTuTdGJGfn8/KlStZuXIlAFu3bmXlypVVn+2DDz7Ihx9+yOuvv86mTZuYOnUq//rXv/jNb37j6QsVqZU/x4jGPj4FW4yo5G6/kavPSn1Pakd4g93XFZDGsXz5cl555RUuvPDCGsFl3LhxXHXVVbU+Pi0tzWW5w+Hg3nvv5fTTT6dnz55V5R988AErVqyo8zy1pv2caODAgcycOZMuXbqwZ88ennjiCc4880zWrFlDbGys8XG9e/dm0qRJAHTq1ImpU6cyb948zjvvvKpt+vTpw6OPPgpUzAv59NNP06JFC2677TYAHn/8caZPn86qVasYNGjQSV/Txo0bsSyLbt26uby/W7duHD58mP3795OcnOzR5zBnzhxycnK46aabqspiY2MZPHgwf/jDH+jWrRspKSm8//77LFq0iI4dOwL1fz+Dm6cBQ8FGAk+wxIhKro6JJsEeH05873bv3k15eTkpKSnVtktJSWHdunUAHDhw4KTbVFq9ejWDBw+mqKiIZs2aMXv27BrTXgQXxQiRYIgZdTlPNgm2uFGX96KucaHpxYQTeTdG/Pjjj5x99tlVf0+cOBGAG2+8kZkzZ3LZZZfxyiuvMGXKFO6++266dOnCP/7xD8444wwP6ihSO3+NEd46PgVbjAD3+41cfVbqewJvx4iFCxfy3HPPsXz5cvbs2cPs2bMZNWoUAKWlpTz66KP8+9//ZsuWLcTHxzN8+HCefvpp428wUCixEYQcDgd33HEHEyZMYODAgVx33XWUlpYSFhYGVGRQ63IFrCvjx49nzZo11a5sysrK4p577mHu3Lk1rvxxZz+ujBw5sur/vXv3ZuDAgbRr146PPvqIW265xfi4E+dZbNWqVbV5wk/cJjQ0lKSkJHr16lVVVnkif+LjTqYyK24SHh4OePY5vPHGG4wcObLGAeidd95h7NixtG7dmtDQUE499VTGjBnD8uXLgfq/nyISPIIpRlQyHRNdCfb44O57564uXbqwcuVKcnNz+fvf/86NN97IggULmlhHlkjTEUwx42TnySbBGDfq+16cSDHBu4YNG3bS78TYsWMZO3asl2okTZ0/xwhvHZ+CMUa422/k6rNS35P3Va4TM3bsWEaPHl3tvsLCQlasWMFjjz1Gnz59OHz4MPfccw+XXHIJP/74o49q3DA0FVUQeumllzhw4ABPPvkkvXr1orS0tNqVNfUdDjhhwgQ+++wz5s+fT5s2barKly9fzr59+zj11FOx2+3Y7XYWLFjAX//6V+x2O+Xl5XXaT10kJCTQuXNnNm3aVOt2lYG0ks1mw+FwnHSb48sqpzM58XEmHTt2xGaz8csvv7i8/5dffqFly5YkJCQA9f8ctm/fztdff82tt95a474OHTqwYMEC8vPzycrKYunSpZSWltK+fXuXdarr+xncyvFsaGB5zV2K+LFgixG1HRNdCeb44Oq9a9GiBaGhoWRnZ1fbNjs7m9TU1DpvUyk8PJyOHTvSr18/pkyZQp8+ffjLX/5Sp/chMClGSNMWTDHD3fPkSsEYN072XtQ1LjS9mHAixQhp2vw5Rnjr+BSMMeJEtfUb1TUON82+J+/GiJEjR/LUU09x2WWX1bgvPj6euXPnctVVV9GlSxcGDRrE1KlTWb58ecBPEaYRG0Fm165dPPbYY7z//vvExMTQqVMnIiIiWLNmTVVG2N1haJZlcddddzF79my+/fZbMjMzq2177rnnsnr16mplN998M127duV3v/sdoaGhddpPXeTn57N582auv/56tx/b2JKSkjjvvPN4+eWXue+++6rNdbh3717ee+89xo8fX1VW32GZM2bMIDk5mQsvvND4uJiYGGJiYjh8+DBfffUVzz77rMvt/Pn99J4yINTDx4sEhmCMEXU5JvpaY8eH2t678PBw+vXrx7x586qGIjscDubNm8eECRPqvI2Jw+GguLi4Tu9DYFKMkKYrGGMG1P082Ze81a4wvRf1jQvBHxNOpBghTZc/xwhXgun45K0YUclVv5G7cbhp9j01TIzIy8urVhoREUFERIQH+62Qm5uLzWarSoAFKiU2gszdd9/NyJEjqzp47HY73bp1qzbXobvD0MaPH8+sWbP49NNPiY2NZe/evUBFxi8qKorY2Nga89nGxMSQlJRUrfxk+5k6dSqzZ89m3rx5VY954IEHuPjii2nXrh27d+9m0qRJhIaGMmbMGPffHC+YOnUqQ4YMYcSIETz11FNkZmby888/8+CDD9K5c2cef/zxqm3rMyzT4XAwY8YMbrzxRuz2mj/fr776Csuy6NKlC5s2beLBBx+ka9eu3HzzzUDd3s/8/PxqWfTKRekSExNJT0939y0JAGqQSNMRbDHiZMdEf9KY8eFk793EiRO58cYb6d+/PwMGDODFF1+koKCgKjYAddrm4YcfZuTIkaSnp3PkyBFmzZrFt99+y1dffdUA75C/UoyQpivYYsbJzpP9TWPGjbq8FyeLC00zJpxIMUKaLn+OESc7PrmKEYHWD9KYMaIu/UYn+6wCrS+vcTRMjGjbtm210kmTJjF58mQP9gtFRUX87ne/Y8yYMcTFxXm0L1/z714Acctnn33GN998U2M4Wq9evWos4uSO6dOnAxVzeh5vxowZdVqota77OXDgAJs3b652386dOxkzZgwHDx6kZcuWnHHGGSxevJiWLVu6/Tq8oVOnTixbtozJkydz1VVXsW/fPizLYvTo0bzzzjtER0d7tP+vv/6aHTt2GOdNzc3N5eGHH2bnzp0kJiZy+eWX83//939Vwxzr8n6ebFE6EQlMwRgjTnZM9CeNGR9O9t79+te/Zv/+/Tz++OPs3buXvn378uWXX1ZbFLYu2+zbt48bbriBPXv2EB8fT+/evfnqq6+qLY4oIsEhGGPGyc6T/U1jxo26vBcniwuKCSJNl7/HiJMdn1zFiEDrB2nMGFGXfqOTfVaB1pfnz7KysqolHzwdrVFaWspVV12FZVlVn2Mgs1knW21GRDwyadIk/vznPzN37lwGDRrk6+qIU15eHvHx8eTm/oa4uPoHhry8YuLjXyY3NzfgM90i4l2KD/5LMUJE/JHihn9QjBARf6QY4R/8IUbYbDZmz55dNZ1kpcqkxpYtW/jmm29ISkqqd/38hUZsiDSyJ554goyMDBYvXsyAAQMICQnxdZWkmsoFnTx5vIiI+xQfAoFihIj4D8UNf6MYISL+QzHC3/hXjKhMamzcuJH58+cHRVIDlNgQ8Qp/nbtXoCLQeBLwNTeuiNSf4oO/U4wQEf+iuOFPFCNExL8oRvgT78aI2taJadWqFVdccQUrVqzgs88+o7y8vGpdlMTERMLDwz2op28psSEiIiIiIiIiIiIiEoBqWydm8uTJ/POf/wSgb9++1R43f/78GmulBBIlNkSkidOVViIiYqIYISIiJooRIiJi4t0YMWzYMGpbRjtYl9hWYkNEmjg1SERExEQxQkRETBQjRETERDHCG5TYEJEmrhzPFmXSon8iIsFLMUJEREwUI0RExEQxwhuU2HCTw+Fg9+7dxMbGYrPZfF0dkSbPsiyOHDlCWloaISGeZMNFPKcYIeJfFCPEnyhGiPgXxQjxJ4oRIv5FMSIwKLHhpt27d9O2bVtfV0NETpCVlUWbNm3q8chyPBvipyy6HKMYIeKfFCPEHyhGiPgnxQjxB4oRIv5JMcK/BVRiY+HChTz33HMsX76cPXv2MHv2bEaNGlV1v2VZTJo0iddff52cnBxOP/10pk+fTqdOnaq2OXToEHfddRf/+te/CAkJ4fLLL+cvf/kLzZo1q1MdYmNjAejSrh3NYmI4Y+1aAFp07Ejp0aPk7tpFqN3OhO7dKd+yBSs/n5CkJEJataJszRoAUm5pD2VlsH8HhIRAek/Yvx1G5kJ4c4huCzmrKp6wWSYMdoBje8Xfob3BsQOsHEg4A2gFbAIczv+HAHHO2qYDh4EjQDTQAthFxY8jiYqPP9u5rQM45Nw+GkgDtkHOErJC+nFdzFSuLX6D20v+BiHdYf0BKN0HIdEQ2RGK1oOjGLa1grA4yFlfsduELlCaC0v2QlgEJHeB/Ruh5CjEJkNMEnzxS8W27TpDYT7s3w1h4Rwt6Ypj82YoKIAWLQhJScHx88/8F4ju0AFHcTFFO3diCw2lWY8efLF1K8VHjhCTmEh869bsXr0agA3t21NeVsauHTsIsdno3KsXu7dvJy83l4TmzUlt25Z1qyre7zYZGQB03bYNgLTevTmclcXRw4eJjI/ngnbtOLJ6NVgWkenphNjtFG7ZwplASK9eOHbtgkOHIDaWkMxMHD//TEzPckhuAxERkLW54rV26AFp2ZB/ACJioEUHyF4HZSUwKA3szSBnQ8W2zbtB8UHI3AchURDZ6dj7HZ4KofGQuZ5CIhkZO58LSufxcNGjQAQkjAJ2AkVAc+dtC9AJSAWKnZ+53fmZ73NuGwfEA1nO70c+UOL8vtiB9hXfpZwfwJYIIW2g3PmdDWlf8R2bux1sNojvBYU7oCQHwhPgh3TYvgosILkd2EIgeyvZ/wZ7796UZ2VhHT6MLT6e0HbteGnNGhwOBwnp6YTa7RzcsgWA73v2ZN+ePbRMTWX5zz9X/TbdVwZ4ckWM5j30F/4YI/o6Y0RLZ4zIccaIcd27U7JlC478fEKTkghr1YqiNWvo0Q9o44wRe3dAaAjc1hOObIeSXIhoDs3awsFVcC5gzwQcUOaMEeG9IaMEyKXiN9waWAf0BpKpiBF7nbXNoOK7XwBEUhEj9lARIyKBMI7FiLZADvA9EAO0oeJYUgrZ+WCL5smoXzMv4lzm5dyN3ZENq5wxIqYj5K8HqxgiWkFWHBxyxojELlCcCwV7YV0EtOoC2Ruh+CjEJ0OzJPjUGSPad4aCfMjeDeHhHA6viBFWQQG2Fi2wOWMEwIoOHSgvLqbQGSPie/SgYOtW5jpjRELr1uxyxoiW7dvzfVkZu4+LEbu2b+dIbi7xzZvTykWM6OiMEW169+ZQVhaFhw8TFR/PiHbtyF29GsuyiElPx2a3k79lC0OBUGeMsA4dwuaMEc2LfoaycmjljBHbnDHish6Qmw15ByAyBpI7wJ510LUEmqVBeDM45IwRSd2g9UEoccaImE7H3u/eqRASz7Twc/go+io+zPs9qWVbITUOiHB+B7KAQirOCwZQcZ4AFTGihIpzgzjn3weoiBHNnN8T5/eOtIrvAvuBNUBn536PUBF3WsGGeRWb2p0xomw7fGODxF5w5LgYEZsOrzpjRKt2FedJu7YCsLm0N6VZWZQfPkxIfDzh7dpRtGYNbzgcJDpjxH5njPifYoScwB9jRD8XMSLEbufO7t0pOy5GhLZqRcmaNXQ+HWjtjBHZzhiR0RMGOmNEZHOIccYIgJGZYDmg3PlbDesN5TugdRIVMaIVsIGKdsB5VMSI/c7aplPx+w+hIiYkUREjHFT8rouoGSOOAGudf2+m4riQAkTD5vnsCG3D9a3+znX5H3BbzosV5ds7whHneW1UK7DHVfy9DmjhjBFH9oI9AnK6wB5njEhwxoidv8BPQAdnjNi7GyLCoX1Xdi2qiBEhLVoQmpJCqTNGrDouRhAaSvMePTiydSvzjhyhWWIizVu3Juu4GLHUzRjRzhkj2jpjRMHhwwyPj6dZu3YcdrYjop3tiD7OY1ZYr16U79qFwxkjWg/MhI0uYsQpQHoPyDkuRqR2gPh1UF4CcWkQ1gwOOmPEad2g9CAU7YPQKGjWqeL97VlMYWgGI1u9zwUFc3j48DMQ1gXS46loD4RTcb6/HTgKDKfi2H98jAh3fu525+d8gIq2hQUkcKwdkeL8Lnzt3Lajcz9HKrbbWwqlzu+svT1Y5RXf2a+dMSJ/BxTnQEQCNEuH15wxItUZI3ZXxIhtRyraEZUxwt6uHS+vWYPljBG249oRKxQj5AT+GCMGHhcjSo7raxrfvTulx/U12Z0xot25QKvj+ppsIZDpjBGlzr6mmLZw2Pl7u8AZI07sa2rRDoilZl9TV47FiLZU9CPkU/EbT6SijeGg4vdvB1Yft20ukEdFPNnn3G+Jc79RwBZYtQciu/PPqFPZaG/JfQdfIySiA6xYD+XOGBEeB7nOdkR2FyjKhTxnX1PLLnBgIyw/Cs2TIS4JtjvbEW07w+J82OOMEZ27whZnX1PLFqyPTeGoM0ZEdOiAVVxMyc6dfBcaSlKPHuRu3UrJkSNEJiYS27o1s50xIrV9exxlZexzxgjLECO6ACnOGJHtjBGZvXvTPiuL4sOHCY+PJ75dOw442xHDncesYucxK6pXL0p37aLn0Yq+JjIy4RdnjGjdBvpHwC5nOyLDGSNyDkD/GEjqAPucfU3xaRDRDFKcMSLe2ddUGSNOrehrmhV/GfPiLuK5A38hsXAZ9G5FxbE73/k5R1IRI1o4P8d453dmp/Mz7+0szwVCnd+Rg87PPta5feW2KVS0H07oa6IADmRV72sKdcaIr5x9TQnOGFGaA2EJsCQdttbsa9o+D8J796YsKwvHcTFiqrOvqbmzHXHA+X4vVYwIKDYrgJZF/+KLL/j+++/p168fo0ePrhFsnnnmGaZMmcJbb71FZmYmjz32GKtXr2bt2rVERkYCMHLkSPbs2cOrr75KaWkpN998M6eddhqzZs2qUx3y8vKIj49nQO/e/LxqFeMN291Xyz5SLzXcMcZQPsJQnnCq4Y6etTy7yTrXxTlL2RDSiWuiZzGu5BVuLXmjony1682N5f+r5al/dF1csMJ1+XzDbr5ys7w2prfcVH52LfuKMX1M/Q3lfQzlvWp5kl7gwMaAZksZXvY1Txc9XFGeMMDwgK617MyVNa6LcwwfEpjf+PddF+/91HX5C4bdTHP+a7fbyS0rIzc3l7i4OMPWNVX+lnNzLyYuLqzOj6u5n1Li4//l9vNLw/OnGPF4797sX7XK+LO9ylCeNNJF4WjDxucbytM7uyg0HXCSDOW5hvKVNYv2VpxoPhD7HAvDzmLJoYEVp2+meLDWUG6KE8tcFx8xHJYA/msoX2Aor+VI5pLpsD7UUH6moTzWFK5PM5Sb4kN3Q7nzC7ja3pPNoR04r2QuMVYhpPY2PKCvoTzeRdlBw7aGoL5jQ82y/xh28Ynr4oNfuC7/yEVZ5dev3G7nNcUIwb9iRGU74mbDdqb2RVfTQcbUhnAVU8DNOOHq9w/mOLHYdfHGLWwI68Q1qbMYl/sKt+Y52xQrDbtxFStMccJw2MnKcl1uigXfGMpN25u4+pjOcWNbAONF2+62H0zxoa+z7dB2KcMLv+bpg862Q6f2hgcMclHm7nfDjfgAYDjmm9oS61x8UNNqFgEww/mv2hFSyR9jxO2G7e42lGeYOivcjRHJpjNd04EmwVC+y1D+vevilRXbP5D2HAtjzmLJRmebwnTsN5WvNJQvMZQDKwpdl7sbF0zNHNOh2BQDTDHj1GjDHQMN5X0N5aaP0lTet7XhjtMN5abtcwzlhg9zn6GV5mZ82Gbom/qrYTevOf9VjAgMATViY+TIkYwc6froa1kWL774Io8++iiXXlqROXj77bdJSUlhzpw5XH311fzyyy98+eWXLFu2jP79K84KX3rpJX71q1/x/PPPk5aWVue6nLluHcMw/+5Ta5t+LdFQbkoCmg5exgASUcuTm0S6Lg6DnJAEsEF8SG7FBbzOcnd2Yyyv5b4Yw+amt6mlodwURGpj2pfpuU11Bdx/T0zltR0PwyAEi3BbCUdDo47b1rQzd78jCa6Ljd9NzG+W4ftv+s30cbgur2z0F5eV8ZdaqiFNhz/FiOx162hqs3Dm2uKJt3I9uiZFGkevsjX0KqslExTEyst0pZNU8KcY0W/dOgZgvhTJ1B3gdhvCeILq6uph07mh6VyyyFBuaF6GQK69oiM8zsqjKkiGG3bjqtzN9oep5qa3y5TqN3X1m7jaj+k5jU0kd9sD7ryPACHOtgMlFIVEHvs8jN0Drr4f7n43DFetu9voM/wOXP1uTL+xyqRiSVlZVQeWNG3+FCMGrVvH6Zj7mox7atQYUVu5ux0bhuNMaMU/efY4Yq0j2Jx/u933ZDru1dInFWlIbLjb92SKF+72L7kdG9x9zab3NNRQbowN7n72bn63Gig+mH4zpt9YZVKxuKyMlw3biP8Imn6XrVu3snfvXoYPH15VFh8fz8CBA1m0aBEAixYtIiEhoSrQAAwfPpyQkBCWLHGdvi0uLiYvL6/aDaCspKQRX41/yXVejRNvvPpG/EWsdYQjxqN8cIpvbWz+11FZA9zE3ylGNL7KxIb4r4AZotuAEhQjpA68HSPKm2CMAMgNqWhTJDhyfFsRqdLMkc+RkKbVdjieYoTUhdoR3pUTkkB8udoUvrQxvCMvJU1gS1imr6viU+prCgxBk9jYu7dizvCUlJRq5SkpKVX37d27l+Tk5Gr32+12EhMTq7Y50ZQpU4iPj6+6VS7mFNeqVUO/BL8VTgkdrM20tPaffGPxqZbsZ58t+eQbBpGwqCgP96Bg0xR4O0bEN6EYUSknJIF4hxoh/mhp2GmclbiAzyMu9HVVvE4xQupCMcI7KhMbihX+o0X5AfaHmq4lDn6KEVIX6mvyrtzQeBLKc3xdjSYtK6wtbyXcyLpId6cwDy7hihEBIWgSG43l4YcfJjc3t+qW5ZysNaLei8cEnqGOhXxYfDWnOFb6uipyEsnWPvbbWuJoQpPBHNi0ycM9lONZoCmv9zM//fTT2Gw27r333qqyoqIixo8fT1JSEs2aNePyyy8nOzvbvBPxKcWIYx4seI4xRYaJTcWnmjnyKbRFsy+kaSW+AfYHcIyQwKcYUV2xLYIQHDR3HPZ1VcRpdMEnXJXvaqWipkExQnzJFCMim2iMgIq1f3JD4zWyz8eSy/YBNOnENyhGBIqAWmOjNqmpqQBkZ2fT6rgMd3Z2Nn379q3aZt++fdUeV1ZWxqFDh6oef6KIiAgiImrOKbp/w4Z6rWQh0ph+X/J/RFCMrUlOOBJYli1bxquvvkrv3tUX8b3vvvv4/PPP+fjjj4mPj2fChAmMHj2a7783LLImdeLtGLFvwwbjVKfB6rySr31dBTFIdjgbJyFNu3EiYqIY4R3X5L/PlfkfE6qGut+4ouAfvq6CiN/zRYxoqn1NpbYwrj/8DumlO3xdlSatMrGxz970LoqSwBM0iY3MzExSU1OZN29eVXDJy8tjyZIl3HnnnQAMHjyYnJwcli9fTr9+/QD45ptvcDgcDBw40FdV93uv228l3srlqvKPfV0VOYkkDvm6Cl6X0r07rF3rwR48Hd7n/uPz8/O59tpref3113nqqaeqynNzc3njjTeYNWsW55xzDgAzZsygW7duLF68mEGDBnlY16bL2zHiFiqWQDMtVJbU03BHLxdlplXojFN+ZrixcYKh3HR60KJmUTSUEUoo5dXHipkuNjM9panc0BcfG23YHmhrWACwu2H7WtYSdMn0kbQ1lBvrasozmBZ/TDA9Qe3liRwiPKSEXZGtnWN1XXyOgHnpXFfbm961A66LW2+oWWZ6I139DoCkLNflV7lYF/0M579l3bvzRoDFCPE+b8eI26iIEaYjc+yphjsMvw3jbynJtOpmhouyNoZtmxnKTQzHl5iKY0DYib8J0/Er3kWZ6RBlOGa2NCyO2rbUdbkpRtQSblzKcPWchm1NdTTGAdN74Or9qq3ceEG4KT4kuChz3Vlr/s4YpiBLWu+6vH2B63LD7yB2e82ya1a43naX89+S7t15TTFCTkJ9Td4TYZUw/pCWa/a1pPKDhOAg255y8o2DWGoA9jU1RQGV2MjPz2fTcUOBtm7dysqVK0lMTCQ9PZ17772Xp556ik6dOpGZmcljjz1GWloao0aNAqBbt25ccMEF3HbbbbzyyiuUlpYyYcIErr76atLSTN1Prt3TtStR69aRaprM65xaHtzHUN7JUB5uaq20M5RnGMpr+1G4PtG0YkJ5n2vowGau4pNjdyQYrrQynWzXdpGo6Vhp+Eja73ZdnmPYTX0GcprecVO5sfcSzK/P9J6Y3sOEWp4jJhSAYsJZRW+ac5iObMbc4DA1Xk2HBMN3x/jdBDptcV1u+v4bfjPXfGPY3FHxb8H+/TxirkUdNEywqVzsrZLpChyA8ePHc+GFFzJ8+PBqiY3ly5dTWlpabWG6rl27kp6ezqJFi5TYOAl/ihERXbvCunUN+fL82mz7ZbwYfi/Ti+6kt2O1r6sjJwjBIsPaxmZbB19XxevK9nu6PpgaJMHCn2JEWBOLEZX+HnE5Hco3c0rZSl9XRZxWhfZicsxkbit6nZElX/q6Ol5XrhghTv4UI+7q2pXIdeuMXQzhZxjucLefKcaUXs8wlLvbh1BkKDdkXE9yoU5dd2NMDNfSJ5VmuD7U1NthemWmJLapSqb9G78x7l4U5W5y21keioM21k62Rmc6y0w7cueiKDB3ahkS3zGbXZd32uW63PAbCHeR+AYY84Pr8qHOfwv37+dx15vUkWKENwRUYuPHH3/k7LPPrvp74sSJANx4443MnDmT3/72txQUFHD77beTk5PDGWecwZdffklk5LGrCt977z0mTJjAueeeS0hICJdffjl//etf3a6L43DTmBt2Py3JI5ZMtvm6KlIHh0jkTqZyFX/ntzzv6+p4hcNPGiSVi71VmjRpEpMnT66x9QcffMCKFStYtmxZjfv27t1LeHg4CQkJ1cqPX5hOzPwpRpQfPozpIsxgtDmkA8W2CJKtfSffWHyig2MzX4SOJJ8Yt6+/DmRKbEglf4oRTaUdcbx8WwxPN3uIXxX/m1PyV/q6OuIUax1hR2g6G0M7MRIlNtynGBEsFCP8w5vNbubbyGH8+dBEWjgO+ro6TdpFRz+jwBaDBU1oBdfqFCMCQ0AlNoYNG4ZlmdcOsNlsPPnkkzz55JPGbRITE5k1a5bHdXE0kcV81zoHZffgZx/XROoilb1Ec5RNNJ2rcm3R0VBomHfGi7KysoiLi6v629VojaysLO655x7mzp1b7SRYGoY/xYiyJhIjKv0S0o3m1mFSrKb1ugPJ7WWvcUfZq0Tj++OlN4X4SYwQ3/OnGFHexGIEwLrQrgB0KTNMOyQ+0daRRRilbA5tOm2H4/lLO0J8TzHCPywP78dmeweaO5pucsdfjM2f4esq+JxiRGAwTaQkJ9NEOiXX0g2AbjS94fKByAZ04xd+oRtlhPq6Ol4R2rGjh3sopyITXt9bxbRscXFx1W6uEhvLly9n3759nHrqqdjtdux2OwsWLOCvf/0rdrudlJQUSkpKyMnJqfa47Oxs46Jz4p9sTSRGQMX6GhtDOtGt/JcmezVPIGhr7aSNtYsQzI32YBTuJzFC5HhNKUZUWmuvuFiqW9kvPq6JHM9OOZnlW9kQ2rmJRYcKYYoR4oeaYowAsIBfwrrRpXQ9oTh8XR0RxYgAocRGPdk7d/Z1FbziZ7oTTintMayXIH6nL//jKJFsoGl8R8s8nqPak0BTeaubc889l9WrV7Ny5cqqW//+/bn22mur/h8WFsa8efOqHrN+/Xp27NjB4MGDPXyd4k0RTSRGAGwJaU+JLZxuDnVW+bssWxt+DOnn62p4VXEAxQhpOsKaUIyo9Iu9GzYsupbpYil/06tsNftDWrInpJWvq+J1pYoR4oeaYowA2B2aRl5IHN1K1abwB4W2KO5IeoUX4u71dVV8RjEiMATUVFT+pGzDBl9XwStG8B96sxq7MoUB4xRWArCSPnSn1LeV8YaSEg93UAYeXaNW999GbGwsPXv2rFYWExNDUlJSVfktt9zCxIkTSUxMJC4ujrvuuovBgwdr4fAA06HZBuIGYl7IrZehfIiLMtNCgKEDDHe4eoDpapMYQ7npSrGaC7WtadYCsNEraj1EHTdSqWWx610YFuczrpJnmtrUtNoe0GWN+T5X3B0PZapqd9MDTHU1rdloqpBpwUBTeUz1kWO/43kOksiX/M0wuqaNYUcpLspMiwIahObULOuz1PW2R9zbdZKLlRqTnN+zvLISWO7e/qrzXoyQpiOjxQbihuJ+jDjDUG6KE5zuxgNMUxGZmoum8poLfVrAqujeZLCN6MSj1e90dXgByHFRZoofpjhh2L77Rtflpiv+3Fv21/VL6mraOMNQ3s7NyrgbNxKO/fcU20/8I/RyVsb3Jc24Qpir+GAKYqaOGNN3Jt91cZ//uC53I0bEGt7HrsfHiO/rvr+aFCOk4ZVu2EDNsf/B7+ewHgB0L13r45oIQJR1lKzQtuSGxAPf+Lo6PmEFUF9TU6bERj2lnFNEXBjmBomxgYG5UdIp3s0H9DWUm04yDSeNgOvWA1zCp8BqapxdpxpGcJhONF3vvoKpkWJ4TFvDFHdhhu1N7YLamNoGqabPu0stO8swlLvoiAHcb7AAx7/KXuTSlkNE0wyIPun21TVzs7yWA20nw2JfZ+SaH+OK4X1KdX5vomNbwbt73NunH3vhhReqFpsrLi5mxIgRvPzyy76ulriruKjJRNjL+JTTWE4yWjjc3/Xlf3zE5ewikTbG4BtkWrQCgidGSJBoQjECoAQ7gx2LaKXfol8abC3i9fLb6G6tBS7wdXW8SzFC/FD66UXE2XG/r+lMQ3kv0xTVA918And7VkxJTtcdG8sS+oMdTon+CaKOu8OUoDX1j5iS3bWsA51gOC3uZXiM6dIwU3dYrKHcdO1Tguk1N1Qfkmn/x33nbEDf0JXMtZ9HHu2Jc7lOn+kJ3P2uuJn47vWp6/Ic9xIB4YZqZlQmv+NbwUeKEf5OU1HVV4Lp8qLgUdSUWlxBJIajzOZORjHX11XxjijTaUJd+XZ44LfffsuLL75Y9XdkZCTTpk3j0KFDFBQU8Mknn2h9jUCUFPwxopINaMtOIvD0ihZpbP1ZAcAPTWSqQgBiAjtGSJBKbDoxAiCCMh51/B+3ON70dVXEhXjyOMVa2TTjeIC1IxYuXMjFF19MWloaNpuNOXPmGLcdN24cNputWjtDAkTzphUjKo0vmcafiu4nzVJHsr/oW74SCxsraxsqH8wCLEY0VUps1Fdsc1/XoNGN5Wpu44++roZI7XZ5Oi2cgo00grjgjxEAW0jmcy4gz3gdkviTQSwhjFIW0s3XVfGe7YoR4oeaQDtCAkspdpbYBrAP0wwCQSorsGJEQUEBffr0Ydq0abVuN3v2bBYvXkxamrsTqYlfaNY0Y0QCuQwtX+jrashxBpUvBuA7evq4Jj6yM7BiRFOlxEZ9ZQX3wnfbac4GWpLObl9XReqhmHB+z/381TgeVY4px7NAo3kPxYWtwR0jKv2DgUziMfa4vUqF+EI0RzmN5fxIBwqa5OzN9aEYIY1ge9OIEQBFhHEZDzMj5CZfV0Vqscx2GuNDp/Elp/q6KgHGuzFi5MiRPPXUU1x22WXGbXbt2sVdd93Fe++9R1iYac0U8WtB3tfkylrS2WLL9HU15ATpVhbtHNv5jh4erRTRdKkd4Q1KbIhLc53TVJzHdz6uidRHBCWsoz3/ojtlwf4zb2tcjlFEGpEDG/PoSTpZdMawEqv4nXOZz2ls5rBxduAgk6kYIeJL39ONLFoQSZGvqyK16G/9SBRH+bapXZXbzj9iRF5eXrVbcXFxvfbjcDi4/vrrefDBB+nRo0cD11Kk8fyFUVwX9S6F1RbXEH8wuXgyM3kem68r4gvqawoIWkShvlp3hn2eDkvyTxbwH7oQz1FOY5WvqyP1dCHzeZke/EAGZ2FY7D0YHDns4Q7KwaPrDxwePr8Epds7w9EN5pXlTNOUulqrL6m3YePT3dhJhmFbE9PpwbE5f1fShgMkMpZ/YaNDzU3T1rrehat15wAKDOWmVfhM2wPhpa7LexkOhRmG7U1iTRdAmj7XTm5ub1pvz90FA118LpfyC5fybyCM4z/PCq0N+2ljegIXTF96F0OpkwwdnWcYzj1MM565en8rvx8hh+HvhsfViWKENIJbOkPJBvdXEzWt5xp1luEOU5zo56LMNPLO3WkQqh9X5jIEG3bOta/F5TEmdZfr3bg69pviganccIiJNGzed7Pr8hw3f8YJrq4pchEmAehiKM8wlDfUwrHh1T+LcGAIq/mGX7GXTqSSd8IDXMUHUyVN5xAJhnKDKFOMMExV4+r3ZHp/K78ztsMwy71qVdcwMaJt2+of7KRJk5g8ebLbe3vmmWew2+3cfffdHtRJfK5NZ9gbnH1NrhwgjhV05OzyT4nmqK+rIyfo5VhDLY2N4JavvqZAoMRGfZ1zpGLFVHca2VX3meYuvdBQbmqUmK7CSDCUHzDVCMiu+t/PtGALqVzBekJNLauEXNflbQ+6Lq/tIi3TfW6uX5dq6LBKPeTefgBINJSbGprda9mX6YTatC9TgyUhqZYn6Vij5EK2MR07n3EaZ9XoScww7KeFodzUKjI1DcHYEO73uevyOMN3yvRbqmyQFGZ72CApw7PBawo24kKJqZcleHzpXKfhfH7wcU2kvhzYCAn2geWF2SffplbeixFTpkzhk08+Yd26dURFRTFkyBCeeeYZunQ5diJRVFTE/fffzwcffEBxcTEjRozg5ZdfJiWlaS40GrDKgj9GABQSzn/pRF+ySMbQRhC/cSHzmMev+De9GMv3vq6Od/hJjMjKyiIuLq6qNCLC/ekily9fzl/+8hdWrFiBzdYkr60OHmc7Y4S7fU29Qg13uNvPZMqim/oETB06+Yby6n0O3zAQizCGRyyCCBd9ZWmGfgJ3k921hV7DSzC94sT9rstN129FG8rDWxrucLcPKcNQbspHmMrjXPdVHiSDb+jO5Sw7oe1gOv80VcjUf5RgKDcx9DWdaehrSjBM6XSyvqaj2R5eIKW+Jm8I8jlqGtHRPb6uQaPZQHPCKOdyms5VAsEohRxOYy8LacvhYJ5LPbS25EpdaEEnaQQFwRsjAAoI5wu6041sOrLD19URN2WRwDXcwCyXV20HmQCKEQsWLGD8+PEsXryYuXPnUlpayvnnn09BwbHhSffddx//+te/+Pjjj1mwYAG7d+9m9OjRHr5G8bogjxGV5tKNYuyM4GdfV0XqYAg/0pxCPqNXsKe8j7H7R4yIi4urdqtPYuO///0v+/btIz09Hbvdjt1uZ/v27dx///1kZGR4+DrFq4K4r8mVL+lNBGWcySJfV0UM3mcwz3AxK9yeBSDABVA7oinTiI36CgnH7SEFAWI0GzmP7cQG6etrSi5jA0tpxbekc1mwzoEf2xk0ZZr4myCOEQA2LG7jB9qQ4+uqSD2kcIS9xPEJfbiG5cE9aiMhcGLEl19+We3vmTNnkpyczPLlyznrrLPIzc3ljTfeYNasWZxzzjkAzJgxg27durF48WIGDRrki2pLfYQGd4yo9D0diaKUkazxdVWkDuyUczXLyCWKEuxENIUOlfjAiREnc/311zN8+PBqZSNGjOD666/n5ptv9lGtpF6CvB1xvI2ksIp0LuInorQWk9+6iJ+YyVl8xin0Z6uvq+M9ccETI4KZEhv1FdcVSoLvC25ROcNW0wikwe4cdjCDf9Or1mnIAtwRT0cWaXigNILmXaEg+GJEpWhKuYFlvq6G1FM45VzGKt5iAN+TyZnBvA5Tjn/EiLy86vPVR0REnPSK3NzciqkXEhMr5sdcvnw5paWl1TquunbtSnp6OosWLVJiI5AkdIXC4I0RlZ7mE7aRRIzaFQHjlqYyBVWlXP+IEXWVn5/Ppk2bqv7eunUrK1euJDExkfT0dJKSqk9dHBYWRmpqarUpDSUAxHeFouCPEQAhWAzjF65gqa+rIrXI4AA92ck8enA//ya2qSSh8gIrRjRVSmzUV/6milXWgkgxodzMSK5mHZew6eQPEL8XihXcSQ2Ack+DajmeBYwgvtJZ6u+UTRVrKJsmVDWt0Rw6wEWhaf7bgYbyni7KTGv0mH4/pqs0E9hCc2IppmXVLLIZrjeNMq3FZFgo1lQVU3ltC36bphhOcF0c6+5aTKZ1mExrJJnmb625PFKFDDf3H2X6Qpl2lMCv2cy7DOI9TudMKt8A00zCrvbv7hy5bpxyJhn2PdTQ6HX1PlZ+PfP9I0a4uzCsw+Hg3nvv5fTTT6dnz4rf9N69ewkPDychIaHatikpKezdu9eDOorX5W6CMF9XovGFYNE+2M9Dg1Qh4ZQRQlywd16V+UeMqKsff/yRs88+u+rviRMnAnDjjTcyc+ZMD+ohfiUv+PqaTDqwj+c9WzBTvORKljCJy5lNf27gO19XxzvU1xQQlNior7LCoAs2n9KRDSRSqK9F0FlGKp/Qmaf4L6HBdnCMTAE8WfivjIpxSvUVZO+nNAzLtHRc4JvCmaynBV/wDjG1ZhfEnyVTwPls4gs6sY4WdA3WzsdQ/4gR7i4MO378eNasWcN33zWRhmNTc3ZhxaKwMYb7k0x3mBLd7ibAXWUDTQlw0+KvOYbyZuwllj8xjDtYRMeqY0s715snGfZTVOCizPCUplBkWCvUmPw2LNSbkGPY3iTBRZkpMW1KcJsS4oa30bh/43epg6G8GTtI4AauYRRruJeFzvIEF9uakurNDOWu9gFud0lEGZLfg1yMNunk4nsEUFlcmAL3+T5G1NWwYcOwrLo/Ztu2bW7WR/xCefC2I463mwRSyQ3uKVGDyPms5iXO5wMGcw0/YG8KownU1xQQ1INdX8NbQux+89W44e1refAZhnJTo8S0uKfpzNb0sZo/7hKSmcmpJFLOKA4DLZz3mE56DY2c1P+5Li83nFSC+40RU0LJ1B7LMT+1UYKh3N1GCZgbJqavSKqpAVLbEGLT51TxOf5EB+bSkUHkcyk7MF+Vm+JmeX0WUzJ8DzsZOm/aGaZIqboatzmeBRuRRhDaEtjv61o0uBW04idacSnrlNQIAtfzP+bRng0kBW9iI8Q/YkTlgrB1MWHCBD777DMWLlxImzZtqspTU1MpKSkhJyen2qiN7OxsUlNNcV38UkhwxohKb9Of+XRkNKuOS2xIoGhLDsnk8wm9GMsS4ij2dZUaj5/ECJFqRrSEmP3m5HeMKaloSma7289kTny6lmMoTzCUx1BCKLcyjkwOMI33neWG/q3Whmm5TPkfd0eBg/miesPoynDDYSPc9BymbhNTN4vpI2iopLfpK2TcUQxhwISqZHcMFW9agmF7U+ecaft4Q7m7XdaG7XstcV3e3jCTQFXyWzEiEHgy2VfTZmvp6xo0qI/IZB9RXM8mIo0ZBQlU17KZeEp4jS6UBNvP3rHOwx2UNcBN5AShwRUjoOJ6kdfpRwgWN/GTr6sjDaAzB/mSd7iE9b6uSuMpDZwYYVkWEyZMYPbs2XzzzTdkZmZWu79fv36EhYUxb968qrL169ezY8cOBg8eXO9XKD4QhDGi0gFimENPurOXQWz3dXWkHmzATSyjkHDe5jRfV6dxBVCMkCYkyPqaXPmM3uwjlkFNaSHqIHARq7mI1U1jtAZAuWJEINCIjfpyrPV1DRrMYUL4G51Jo5BfK7AEpRjKuJmNvEgPPiKT63xdIb+i4YHSCErWBt10hd8RzzJacjHraUveyR8gAaHyStwi7ETg2dEwOHkvRowfP55Zs2bx6aefEhsbW7VuRnx8PFFRUcTHx3PLLbcwceJEEhMTiYuL46677mLw4MFaODzQlK6t36DXAPAmAyjBzi0s1fEkgF3AOt6iP+9zCmNYYbz2VtSOkEZQHjx9Ta6UEcJMhhBHEZezwtfVkXrYQgtyiOZUX1fE7ylGeEOQXbot9bGdMMJwcBdrCW8qmdcm6Eq2kkYhr9GF/cbJhQNQSGfPHm85wCr34KbfjAS/MuBFWhNJGb/BsICzBKyltOZiruU74zDwABYWODFi+vTp5ObmMmzYMFq1alV1+/DDD6u2eeGFF7jooou4/PLLOeuss0hNTeWTTz7x7DWKNJCtRPF3+tCb3ZzFZl9XRzwQgsVv+IFi7LxhnN4mCNgDJ0aIBItPOIXdxHMtS4imxNfVETeVEMptXM8TXERpsF/CEKoYEQg0YqO+QjoCm3xdiwbRl2I+ZQFRmoIqqEXg4EFW8wADWEEEI4yTUgYY64ivayBSU+chELcd8+S4GYbyPnUsA+hpKHc1N6q7c+JWn0s0FJjAAQ6xhZZEU32BKdMkrYa1mBIM5W1zXZeblvKo7TzPMB+ucVrXnFr25c5+0gzlpjWVTPPnmqa3TTAlHkw7Mn02Lar9lUkIxUTyHIkMYCMRNa4OSnBVGcO+Td81V0zDqw2np6GG50zf5qLQOTlunuk36H/qsiBsZGQk06ZNY9q0aV6okTQae/C0I473HQk4sDGRBcHe1dEknMVmzmIzPdkLpPu6Oo1D7QjxR6HBGSMA8rDzKmfRknyu0cVSASmccq5jCdMYxhxKuZLdvq5S41GMCAhKbNSXddTXNfBYGbALO+0oI1pJjSbhTLKZw9e0CqZBg9Yezx7voPYO0ro8XqSG2lanCzw24GzyQFfgBqWWHGUcq/kzZzCDloxjn6+r1ID2evZwxQhpDK1SIC4EcyIww1BuSnSbFoDtaCh3lT11t1lYcy6t6znMUBaSThknJlDNr8mQ7G79c82yUkN7xZQANzVvTFNFmnLH7vZrxLooM+WZTYlsU3mGoby1aTS2KfHdxlB+7HOzAX/mB+dfrq5aNX1/Ewzl7iS+wf25xV3sP2mb602TnN+7PA/bwIoR0hiCoK/JxAaMYC292EWU8eAt/u5qlvEh/ZlOBuexj4RgXQvCob6mQKDERn3Ft4C4FMwnbqazUYC+hvIebu7rxAZDJdNB5WC1v94ilr8RxzT2c6rxTD7DUO5mp13r5eb7yovd25dpTuJEQ3mOe7sH3L8aN6OWfZnalK0jDHeYGq1dankSUwVqfq6tALCTh42KJvXxV4iaGhz1OVSYviOmckODLDzDUJ5T8W9ICPBjnWtVQznmhm9dHy9Swx6CYbZHC3iJFC4gl85BlqyR6n7NRv5Ff96iBReQQ0bQTA1girV1pBghjWIvxvOeAFSMDRsQjkW61mAKSjuIYC/hDHA7y+PvPFwQTTFCGkNSZV9TQyW/TeXuJj5NTG2Emn0IscBvqewbOrE/y/C8oYZR3W23u1ed2poypt+iaRS4aeEhU67GtB/TOvHuJr3bulkeatrRyZPeAFHARJbyCKN5iY48VmOEkanTLsHNcpMGSHwDxGwzlDuT35HqawoEgd/r4jOBnRPaQBivE0dbyugZNJ0XUlf7COUqUnnW7QDijzI9e3h5A9xEajCdvQaWr4jnbVrwrpYNDXqhWDzMbsqw8Thtgui6K1OjvY4UI6RRBEeMqDSN1lxHV/YH2euSCmXYGEcnfk8mhwO8DVyTaa7IOlKMkEYRbL+zigvPP6IVxeqCDBrnsYkB5PJPUthhTGQEOvU1BQIdVerNw5MgHyrExkPO4Q2TOeTpdSoSgFpSTjdK+DfRzCXK19Xx0A5fV0DEhU6+roDH9mHnWVrRnHImejqdjwSE3hzlDvYxisNBdC35Vl9XQMQFDxNufmQVMbxPS5pRTpKmFQlKdizuZheHsfN/pNdYhSmwqR0h/ihw+5pMPqYVz9Ket4zz8kmgsQEPsYlXWU160I7sV4wIBEps1NtOX1egXizg/2jODuzcTS7d1QBpkmzAYxymOQ7+QHO2BfRVIR4GUUcD3ERqMAyNDhDlwCO0JY9QHmUXCbpcpMm4lf2M5nAQLfzr4TzVihHSKLJ8XYEGUUAIk8jADjzGDjUsg9gIDnM+h/mWBP4VVKM4Cz17uGKENIrA7Gsy2U0EU8kgjWKuY5evqyMNKJ0iTnVOQRmcrcXA6mtauHAhF198MWlpadhsNubMmVPtfsuyePzxx2nVqhVRUVEMHz6cjRs31v/1+YlA7s30sUICcW7c9YTxFVEMpYgxpsX6pElIxMEfOcR4WvAASbzFPmJ8Xal6MS2uUkea91AaRScqphoxzY1rulrJ1aI8GYZtTR0L7izO6XrCoVdIZCVRXEM2Q9l/3D2u1mMyzcVqWj/JcIKY+j/X5aZ1mGr77Zl611wt6AoNszAsQIqhPMNQbpreNtm0LoRprSXTE5g+G9O6Wsc+m/2E8Txt+B1ZJLo1MZXp++fq+5ph2LbAjecD12uOVZ7jRAMfuLm/4yhGSKMoIBDbEceruFiqHVlE8ABZZAbt1ZoClVfm7mAlMTxPG07liNuz8PunJGBz/R+uGCGNIjD7mlwpw8ajdOEoITzPJqKVzQtKG4jhYbrwOzYzAMOaKAEpsPqaCgoK6NOnD2PHjmX06NE17n/22Wf561//yltvvUVmZiaPPfYYI0aMYO3atURGBu50Ykps1FsC7veE+F5XSvkzB+lLcRBdjSn1dRrF3EMuLxDPRzTjZl9XqF4SPHu4p1dL6dxMXIrD4yvFfcQCigihBwXcpauqmqyfaMY8mnMYOy8TyCeMpiROHSlGSKMYSkUi0ZTENHUZm6awSjWUmxLg7vyiXV8INZdI/kMcZ7OPX1eb8s2U3DRlfk0XWrlIlKRtcL2puwPQTf2FjZn8dneB2AxDeZrpSU3fDdOOTJ+H6fPLJw6YzC+MpyffEMUNxs8uwVBu+t6Zvqem77XptbpKwptiQOVFE8nAUsM2daAYIY0igUDsa3JlOu1YRSzXspuB5Pi6OtJIoilnP+FMpjPv85OnZ99+JMGzh3s5RowcOZKRI0e6vM+yLF588UUeffRRLr30UgDefvttUlJSmDNnDldffbUHFfWtwG2n+lwLAinYZAGJ2IjB4ixdUSXHuYZ8kinnXI7icUbaJ7b4ugIiLjQnUBMbNuB+dlKCjbAgm0lb6u58DrOaGN4nmRcI4cEA/T5rblzxTwkQ4GsXDeMgY9nJdezSxVJNyABymcVPdKIQ16PlAo0u4BB/NAjYhznRaEp+mzKlpiShO6O8wTTS2zTStoRCvieWHhxmAmugql3hbgI8x3VxjCG5mnbQdXltSXBTB7JpQVpTt4mpq810Mbwpr2tKYmcYytsaymNMT2CaPcD9pDdAG/J5gLU8SQ8epgMvUWC4hsD0HTJ1TZue1/SdzjGUm57XlIKp/G6VGO73rry8vGp/R0REEBFhujjGta1bt7J3716GDx9eVRYfH8/AgQNZtGiREhtNUxjQE/OVVhm1PNZ0EEkwlJs+JtOPs/oBfh82fkM0cTTnbXYYDjDuBhfTEds0VqqWaSzSDdOPmKYxMQWFBEN5ffJPpiu2TMfP2sZip5u+I30M5a6mooHav1P1C0BQ0Yl5nvNvB/msIJT+NT5H035qO4QkGMpN33/TVV+m56j8fpQApu9QHTjwbHigrrQSl7ZTEScCx35svEUkd3OUcCBcSY0m7x52sp4oPqQlrXFwjXF6sSCmGCGNIotAixGVCrERjUU4Fr8J8PWkpH46OdelKAGWE85gP+n48QnFCGkUOzH3qAeOcBzMZAn52HWxVBNwCbtZQzyf0IZpOLg7qKakqqcGihFt21bPXE2aNInJkye7tau9eysuqElJqd53mJKSUnVfoFJiI8gdxMY4otiDjdvJCZKZGqWxvEY4fyOcSRRxsVtzqvuSKdtUR5obV4Qj2LiXGNYTyiBKOcPXFRK/YAeeYwu3ksSfiaQt5ZwZMLGhknHelrpRjBCpUoiNsTSnPyXcDxqp0cQ9RRxfEMkfyeW8gE18my4OqyPFCJEa8rCxggiGAZE4iGzKyc8m5gHWs5FY3qYVAyliYMDGhkr+ESOysrKIi4urKnZ3tEawMy2vKSeV7OsKnFQONu4kih2E8FuKuZi8kz9ImrTRlJKKxZNE8q+AyXt6GCwdDXATqaGVrytQZ4XA3c6kxjiKOCPgOq6lMcVTzlTyuYhS+gXkd0MxQvyR/7cjTlQC/I54NmGnBQ4lNYRx5JOEg8eJ48cAHYHk/gItJ1CMkEYReDGiUhnwO5J4gCTWBNFKC1I34Th4lv8xliP0C/ikBng8FVUDxYi4uLhqt/okNlJTKy4Izs7OrlaenZ1ddV+gCpSeSz/k35dXHMTGb4hiCyHcQzFXeXrSJk1CMhavUcgdRPMEkZRSzGi//+4c9nUFRFyIp2J1Unfnzotx4zlMUwLmuChz3SFdxD7uJZXVWNzAAW7hUK3bu349pjm2TfVzszxtlevy2g5NpuGJprfX9dTAZqb9mM4JTYMGjIMJuhjKMwzlpin+TJ+N6Xvp+nNPYS+Tj/t7N/ZaxkGYTi3dWd/L9AYnuLHvyn3oKkHxR/7djjhRGfAw8SwinMs4yo3OqYikaUvDwUvkcCvNmUgCr3OYLgGXAD908k1EvC6wYkQlB/AUzVlGBJdTQA9NRdQktaSY3zgvqraAg4TQImCzuMHT15SZmUlqairz5s2jb9++QMXaHUuWLOHOO+/0beU8pMRGvR3En+c9jMIiEribYq73+45p8SdpWLxOIeOI5o/ONWRG+7hOtfNwgjUNIZdGcQh/jhGVnqUFK4jianK5i0O6AldO6mPi+DNJPEMJZwVEB5ZihPijUioS4KYko7trm9Vt3b3aud7WwS4epw0LsDGS/TzMTmesMD2nqe4NkAQPN2Sh0wwLQJt+f6bDgmmNPXfX63O1n5aGbd1NfIebEtkZhnJ3E9+mz8/V572LTsAL7GcCGdxJFNPZZkzN12Xtv5M/J7i3yGyCYdvK75eHB2nFCGkU/t3X5IoFPEsCnxHN6RTxIDlqVwjPksB8oniTfaQF5AEvsNoR+fn5bNq0qervrVu3snLlShITE0lPT+fee+/lqaeeolOnTmRmZvLYY4+RlpbGqFGjPKik7ymxUW/+OYtXNhWzwEUDf6MwYAcFi2+lOkduPEQkvf0+AJkabHWkBok0Cv+MESe6lcMkU8YdHFbjQ+qkL0U0w8GDxPAHCjnf7y+e8HBaOMUIaRSBESMAdhPO98RyDnlMZmcA1Vy85VQK+RM7+AupJAZEwvt4WqtP/FFzIAxz8tvdbjx3RnnXtr1piPMm/ko6f8fOaezjWdZhx8K9EeBgTrianteQFE3OcV1eWssPznQ6a+pQMyXCTQOFTXmqREO5u6PAk0wd8Ka+kjaG8oYZ7Q0VneunksTf6cTtRPMqa2vpuTGN1nZ3BgATdxLix2+f5ObznMDLMeLHH3/k7LPPrvp74sSJANx4443MnDmT3/72txQUFHD77beTk5PDGWecwZdffklkpOnzDQxKbNRbG6C2leNrO6kzXZVywFBu+pgOVvvrM6L5I815lEP8iiMujsG1/fjdnS7F9Aszve7a3g/Dfek/uy4PN8zV11BXWdW2L9PVVqm1zXHXw1De0VDewVBuCj5g/pxMn6vpu7C96n/JwBtULgzZjF8Ioy1lNMM6bvvaPlfT99ndq7JOZl89H+fk6fy2gTqqUhpZGh5/NxvJLkLIIoRBlJFGGeOCaIitNL5OlPAqu/kNGTxCNDsp4maK/TgxZopFdaQYIY2iFbDf15WokzaU8DpbyaTY0+sWJYgNJp+BbKpKfB3CRmK1NoO/UowQf5TKiX09/q47BfQnjz+znoiA+O2LN5zHQYoJ4Uk6cBs9eIVC0gMqAe7h79DLMWLYsGFYlvn3Z7PZePLJJ3nyySc9qJT/0UU39ebhSVADKgP+RDyTaU4C5WRoPmlpIJUdVfsJ4Te05HqS2eB344DczdaLeIN/NkaWYud6mvEgMRqjIfXWnlLe4gidcPAykTxPlK+rVAvFCPFH/hkjKuVi4x7i2epMZXSmiDB1VMlJVHYsLCacS2jBv40XWPmTYFjcVoJPYFx0VAYccv7yz+Mg01lLlLJ1coKL2M8f2MgBwrmNlmwJqOvrFSMCQSB9o/xMIf4w7+EuQnmMRFYRzqmU8DQHSdSPTxpYCxzcQR4vEs9NJPMAOVxGgZ90i5qG19SRhpBLozhIxdhl0/HYFH5ddQK4O6d6do0SiyJmkcxfaE00hTzNVpqTV8s+3Bk6bho63ECLitsM5W03GPaDedh4tKHc3XVwTfsxjuozlNs6G+7IMJS7O1e66bNxdzh5zRGqKRzgDUJ4lEwGsx+ciwS6N1LQNPK15ne4gumiEleLU1a+Fg871hQjpFEU4Q/tCFc2Yud+4tlNKP0pJdPXFZKAk4SDeBw8ThxZhHK737QZXDHFyTpSjJBGcRSobUYI3yvCxsMksgs7f2MzcZT78e9cfG0EBwnD4o/G2Uz8lWmqqjpSjPAKJTbqrRnmCfS8owS4lWT2E8K15HMXufpApVHYgKvJpxfFPEQSfySBxUTwEAdI9PlVGQmePdzCs+GBuoBRXIrB1zGiUh6hTCGTuTQngyL+zGbSlQCXBhCNgz+xuaohu4VI9tKMIW4tWNzYEjx7uGKENIpyKhJyDTVHtEmOodxVwi+fr0liMp0po4hH2MJo9uJ+Atw0YZWpcyDBUO4qI2w4tsQY3sc0w8gYUxUbM/ltmqY7xVAeY3pAO0O5KYOeYCg3fR6mN8fV5+06Od2JA7xFGPfRnddpxhaKeYyNNDOO7nN3/n/DYvEu62PaR+V5kIdrbChGSKOIof7TNDe+fYTyAEmsJYwLKSTa5/0BEgjO4RAD2UuM88B3kBCS/P674+FFtIoRXqGpqOrNtMpP46vsKgsH7ieHlznAfUpqiBf0oJT3yOZcjvINUXzvF9OPZHn28PIGuInU4OFCYw1oOTHMpTnnkMNbrFNSQxpUZVLDAp4inbtpx19I8aNVNxQjxB/5T4yAit/vX8ngIboSTTmvstqZ1BCpnxaU8hqrOZeDzCOJm+hDid/EheMpRog/ivd1BYxWEs51JLOWMMZyhEkcdi4ULnJylUmNrdi5glReJN6PU3gAezx7uGKEV6gvvN524O0h5GXA+8TxDnG8xR5aAcM56tU6iMRh8QyHWE44pzovYctyns4E1kJQIo1pB+b5kBrfUWwcxk4apZxNHq+ygVPJ98suBQkONuAptvEIHXmHFiwgjsnspLfOU0Rc8G2MOJENKCSUnhzhWdaR7CcjDiWwReHgadbxEa0ow0a4Oj9F6mg5FTHCnek9wTzKwzSSNsFQ7rqb8EfCGE8/wingGdZwLvtO8rwNMSUvmBM9phFXhtfbcothe6DUUG4K1aYL+d3dT4Kh3N3pbd0ezWd6T02fgekzM33Gpuljc5zPHk4mfXiXBFZhYwqrSXF57mH6buUYyk3T2Zou1jCteVb5GzN9oOJPlNiot41UDNU1/fBdzfdcKcdQ7vpHaNGC70jhJbqzhTDacIDDrKKV8eTQNLS4PvNMuzunXH06tt0MbKnrXZcnGg5K7g4fB/NQ9HDTFXZdatlZR0O5u8Gntqv73P2c3B3yXfPkoB9Q+T1/lkEspSW/Zgu3sZ5YyjAHM1NQcWdYORx7DR4OIde8hxJkFhDLc7QikTJmsoUQoJ9fTQ0kwSqNEt5gC+/RgldI5hbacw0HuZe9PkyqJXv2cMUICVIO4AsSOY/DhGMx0RkvdOWtNCQb8OuqK16bkUMoT5PO3ewizS8SaIoRInXVm1zOIZtb2EpHtS3EA4mU8Bo/MpVOvEs7ruEUnmQDp3PY11U7gWm+yDpSjPAKTUVVb2leeZa1JHErZ3AfA9lJNLeyng+ZT3djB7SI993GerqQyyw6MIrhzKATBV67EtHDUSKOBriJ1GC6zKbx7CCCiXTgftI5QigXKU6ID4QCN3CAWWyiF4WUYvPxSCEPWwSKEdIovB8jjreBKG6lM5PIYKbzApFwLCU1pNEtJIGvac5VdOdDWvrBIVIxQvyRhwm3BvQDzZnqvCAzHAdTWK2khjQIOxb3soE/sxIHNu6hO2s9Xay7wSlGBAKN2Ki3xvuGWRybs/oAUaymOZezjVvZQEu3FxmUE+20tabIFkkJ4dgpI5wSwq0SWlgHCNdQs3rpzWFmspB/05ZX6MI0uvEO6cxmNvGNfjWWabhhHSmLLo1iHRWjjUxhtoWh3NXIJdOVIglARcx4ljP4Bx1wYON8fmEiP9GiWrxwd7ixOyP8TNsmGMpNcczNYfWhtZzCtN7sujzWMFy6IRaGBYiLMNzRwVBuGtHX2lBu+t4kGMrdHanpzmdj2rZiJF8G8DeghFCgnKPk8DvO50p+5gy2O89zcgz7MB3XTSMBXZVXfm88PEgrRkij8E1LtZAQXqUt7zsv0LqafVzr6XmUiBsu4SCplPAU6TxHWz4lifvZQj/yfFQj0zQkdaQYIY3C90nmQ4TxIpn8m5Y0o5yr2W08CxXxxFnsZxY/8SUt6e5MmhURQqRf9Oqb2h51pBjhFUGV2Jg8eTJPPPFEtbIuXbqwbt06AIqKirj//vv54IMPKC4uZsSIEbz88sukpNRneNFeKq5LbDiF2PmKTN6nG4+yiN7s50x2MoevSdMc1XWym1ZssbVni609W8lkl601B6JbcE/xXxhavhCAy2P+QbmLz25m4U30dPzMAVsSd4f+ldbsop21nXbWdjLZSmdrg5dXVQksIcBFZDGCnfybtqzHXpXUWEEKMZTShUON9MwiJ+fdGLGLho4RJyomlAjKsQHlhNCbbO5iMX10BiR+IgSIdH4fV5PCj6TxA23pygFuYzlnsdILozka93cowcO7MeJLcE785Jqp+8iQtDUmwI9d+biRFtzJFeQQRXe28wg/0LVap26CYR+mBKm7vy3TazU9rzsJVYM4w/sVZkjmmOZML3DvaYlxUWa6viDK9NmZEuJtDOWmKWsTDOXudgO4ukLbdL5hupq7onwA8AF23qAvs+jOHbRhOn/ntBoLeZv2407y29QpFZjrAi5cuJDnnnuO5cuXs2fPHmbPns2oUaMAKC0t5dFHH+Xf//43W7ZsIT4+nuHDh/P000+Tluad2SaCmXdjxH58tQ6TA5hNKlPJ4AihDOEwj7CZFroAVBpRK4q5mZ1ARWS5g16kUsw9bCLNpxd3q68pEARVYgOgR48efP3111V/2+3HXuJ9993H559/zscff0x8fDwTJkxg9OjRfP/9976oKgAObPxEa/5JD+bRgyLsxFDKTmLpzX5soKSGgQMbv9AZCxs9qTiheCj0adbauldtE0ceLdlPue1Y42tsyZuE4CCMUsqwU0I4ZdhpbVVcKV1ADPm2ZixgKA7bsQNZOCV8y+WEU8oRZ0MxVsMwawjD4lJ2UNmQsIDnOY0NJNKT/fyKFQxnI4n1WvzElfbAsvo/XFn0JsV7MaLxumvXkcy7nMYKMpnD+4RTzgN8T5gzyWHuEJNKFnCY5iQ653HdENqJf0f8inxbM4ptERQTQYktnFTHXh4qeAaAFfZTWBF2KomOQ7QIPUAbx07aWDs10q+OBrCLObzPO/ThH3TnfkbQgb7cxg8MZ0MjPnNHYG39H64Y0aQEQ4w4XhkhFBJGHMVkcIhMDnEha7mEvWqqi09FU8Zd/Mho1vEJbejn7NDa4UzEpHttOs3OQC0LCp+Ml2NEQUEBffr0YezYsYwePbrafYWFhaxYsYLHHnuMPn36cPjwYe655x4uueQSfvzxRw8qKZWCLUa48gIdeJ82JFPCY2zkbA76eEpRaWoKsJNMMfNI4r8051p2cjM7iPbJSXVbzx6udoRXBF1iw263k5paczHh3Nxc3njjDWbNmsU555wDwIwZM+jWrRuLFy9m0KBBbj5TR2BrverowEaIc3jhKwzmTQYC0IvDXMwmRrCNGHWUuFREOD9wJv9lEN8zgEM05wwW8yKPAnCj4y1yiac9W2hvbSGOIzWmGLmj5LVan6OdtYN/ll1KKXZ20obttnZstnXgIEmEh1R8LrP5FVO5ld6s5XSWMJydtGVfo7zmQGcDHmURf6cLX5HBs5zD85xNf7L4PV/T2jltSf2ZFhevI0/nLvSHEZJSZ96LEZ2BTZ5X2OkIEfyHrnxKL9Y650MfwF5yiCSZAsJ11mNkAVm0ZSW9WR3elU0hHdkS0p6jtii+KziDcErZG5LKu1HXVXtcuFVCevmOqr8Xhw3izeixFX+EVP7j4OLSf/FYyVMA7CKN5hwmWhckuJRMAffzAzfxE2/Tl9l0Ipu4qvt3kkBrchq4Ab3bs4crRjQp3osRXaARE3r5RDCbU3ifwQxgB5P5ijAcvM5Hzi2UAHfXUVskR0JiKSaCWOsICY6K89fdoa2wsNHMyifWcaSqjSd105p87uK7qr9f5Cy+oz3ns57r+C9dPT3PPylXU4C6wcsxYuTIkYwcOdLlffHx8cydO7da2dSpUxkwYAA7duwgPT29vrUUJ+/FiFwqprU1ddeZhoDVrFsF0zG/YnjZBpLpyH5CsLiYZEIZxO0sI7pGn5Rp/QPTyD5T/U0jpkzbm57X9LoMV/iH19JeStvuutw0cMY0La2pG8+0nwRDuWkprtB2hjtMo/lM75HpPXX3MzNd6JtjKDeNvji2nzjgORawjNb8mdOYQTKf0p5b+I5fU5mkNQ2lNI3SM8USU19U5ev1cM0PtSO8IugSGxs3biQtLY3IyEgGDx7MlClTSE9PZ/ny5ZSWljJ8+PCqbbt27Up6ejqLFi1yP9jkfgWOHPMBKubYCIESwlhDT36iLys4hS304J/cThhlDGUtDtZwIfPJNAYE01DkDEN57fOxu+bugc3dH7hp3nEwH2yrzzH+He14hPMpdP66O5LNpSzndLYA50L4Ts6liIqDZQzQq+KB4Tlu1hUggTAgE8iklGHOESFwLgCZtOQMdrOMfqxkMNOIoAf7GMtyhtZIeJlOMEwnJAmG8tre88YOQKYh39sM5XD8dAndgceBB4jgO5rzFWeyhL7E8gmQzxba8ncGcQor6cHPtGLPsY6tAsMJSOVJg6dT8jrwLBOuYBNQvBYj2ONxXSvXW7KAG7mOHTQnilIuZg1Xs4Iujb5+TeDKJY4wSonmKIdIZDQfVNwRZtHcOkwXx3raO7ZQRCThlNK/9Ec+PXwpsdYRIq0iwiit0bl+XdG7DC/5moMhSRyIakGWrS3bQjLItI4d8yfzKP+jFz1ZyxAWM4RFdGW9row+QRJHuY9F3MGxqx4PEMNl3EIGhzifpZzPKtp5Oq8tAEc8e7hiRJPivRjhYcLNYAPJ/Is+fEofCgkniSI6NsjvqGnItcVxxBZLG0dFZ/efo+/j+/DTORDfggLbsfml7sybzi1H3gTgrhYvsd1e0dEUSnnFqD7bAe4qfIkBpRUjirNC2tCaXUp61MEN/EgJdr6kK1/SiV7s4kqWM5xfGukiDv+IEXl51Rs0ERERRETU1n6um9zcXGw2GwkJCR7vS7wZIzxMuNWBBSwjg3cZyA904GlmM5xf6Mw+OvNDoz+/yMmcxi7eYxVz6MvrnMnW4/oNj79YvHF52NmkdoRXBFViY+DAgcycOZMuXbqwZ88ennjiCc4880zWrFnD3r17CQ8PrxHUU1JS2LvXfCVIcXExxcXHFhutOumwcqpt58DGAVqQbUshzdpNErkcJZIbmcE2MnA4u0jCKaUn2zlAc1qxnx5spAcbnXsxLSLaNOUTzj/pxkWsI45iOnGQTA4zkv8ylLW08toQ5ZrOZB1nso4SQvmRDnzJ2XxLJnnHJXC2kUCGD+voj6Ip5ny+43y+4ygRRFHx21pGbz7iCj7iCgASyKU7axnIUq7lXQCO0IxQyoniaPUOR1tzcE4nI1Ibr8YI8nB3/nEHNjbRihVk8j1dSCSfJ/gYG3ATSwAYzvrjrp5KMO2qSconmq8ZxL85i5Vk8BDPM5pPSeIQtzGD9mylT+Eykq39NR4bzVGiHbWPsoizjhBXfgTKNxovajiPecSTy1JOYxU9eYVbackBnuZV+rC+IV5mUDn+SsByQric//E1XXiV4bzKcNI5yBms4z4+92AUh+kiApHqvB8jGjbluYmWXMOtALTnANexhAvYphF9tThAEotCBvNTyCmsCunNtsQMzij5jheP3AeADYsQHPQo+ZkW5QeItY4Q5ThK75JVVfsYXfAJB0JbcMQWy6HQRA6FJLIvIhm7VXFx0RFbMy5rPptmIfn0tNbQ27GKIY4f6G6tVaLDhb7sZiqfsI5kPqIbX9KTx7mEzmTTkf2UEUJIg3ZoJTTQfjzTtm316U4mTZrE5MmTPdpnUVERv/vd7xgzZgxxcXEnf4DUyrsxIpfGmtu/hFC+pifvcgYbSCMEi/P4hQ6afcIteSGxbAvLINuewoHQFuy3t+SmnJnEcYTtYen8OXkidquMcKuEMKuUWI6QVH6Qm3JmEoJFKXZCKVccOIkQLEbzExexiiJnA6yEUK7kPk5nA1eyhExqtu0aTgINccGiNK6gSmwcPyyzd+/eDBw4kHbt2vHRRx8RFRVVr31OmTKlxiJRANgSeDTyfv5n70OhLZojxOJwBp9HS59iFJ8RRRExFHAm39GTNZzCSrqzlnC0eFdt9hHFO3TlU9pQ6Dx4XcP/SCGft/g75oUTvS+ccoawgSGUU4S9KjBtoTlXMYZeZHMLWzj9+BEIAlCV1AC4is8ZzL9YTS9+pjtr6caP9CeKIq51bvNs2G/5InQk4ZSQYOXQLCwfO2VcWPwDMK3+FdHwwCbDqzFiTzYcyTHmNhyxNmxY2ICNto5MCxvPypC+5IdWrFxqp4xhrAY+BTpwCfNc7CXDUKvWLsrcHZ7sauVTMA81d8V0paHpOU37Tqh1P6tpw/sM4lu6UoKdcMo4i+W0pjk4p3q8g9UVD4mNBlwM4Y7LMTyHies6XcVOrmInZfyLVbTnO3qzgD60JRWI5RAxfEUvLuYnmlGM68/q2GuryTRyz92rOk3DwE3lroZ7m64C32kod3X14baq/6UADzGHBwlhGc2Zx9n8wCDWUYqNTwH4e+FFrA/pQnfHWro5fiHdsaNi2i9XSzZV9uUW9TbUp44UI5oMr8YI4jCPnq2dBeygJQvoybf05Gr+y/nsoQP7uZXvGMJmerHLed7p4RQKQezLkBE8GvZU1d9p1m4uKP6SQaWLq8ruK3yR+wpfrHXx8GvzZ9UsPC6ElhLGdUffZXV0L1bYTmWxfRCvcTuJ1iH+WXKJW1G1KenKPh5nC/cyj0W0p6Oz4+oLejKN2zmHVQxhHaeymWiPRrCaRtbXUQPFiKysrGrJB09Ha5SWlnLVVVdhWRbTp0/3aF9SwbsxIh6PRxMZzOQsXuMcIinlapZxDUtJ83hq6OCWY4vHThnNrAKOEsmopDkcTEyqsd2oI3OI4wjFIREsiz6NMuzH1my1QZTjKGNzZgAwt9l5/KHlY7Qli+5la+lVuppeZavpULaZUJ081hBOedWFGnuJJ4oSPmIgHzGQ/mzlUpYzlF+MM4TVXxrwS/0frnaEVwRVYuNECQkJdO7cmU2bNnHeeedRUlJCTk5OtUx6dna2y3kSKz388MNMnDix6u+8vLyKKypC0gm3Skggh1aOPcSRR4qVTYqVTU/HmqrtZzivnJKTO0QEM+nO3+lICSF0JJtrWMkFVSNa/FvkcdM+RVLGpfzCF3TmXtrQg0PcwWoGs1cJDhdsQDpZpJPFhfwbqLh6vfC40NTLsRo7ZRy2NSeHBI4QS4ktnFKHh0N1taBTk9WYMeJQWA/i+B4HNv4eeQW5tnj2hSSTHZJCdkgKu8JbM6doFC04SDmhLA4ZRHfHWk4JXc0p/I9TWEmMy95agcrhxxVW04b/0JNT2c5FrORcfiamURejPjk75ZzKRk5lI3fxD2zOJNSnnMo0zmU653ARK7mGrbTxeD694BKKg0EsZRBLsYD84zpml4f2Y679PGZzWVVZknWQ28tf4/LiTwD4JOIyQnAQW34Eu1VG0XFXQtaLYkST1ajtiCwgfIv5gtzYmsewH+yD+SK+Pytsp5LtnHY2iqMccGwE+0JswDjnOdQxpulsXSW7TVPZNtT83Camrn1Xz+vufPMVdcwhhi/pz5f058+8RmLUT/QmlxEs5Qx+pD+rackhaFZ5TnlCp3KJm8eR8GOPT6SQe3kFaE0pdn6mEz9wKtm0IDKqNdCRzxjIStpzMUvozRZnW8E0j3qCm+Xupk5MWRxX09uaknOmxLe7U9tuJw4YcVxJKEVEOtrwET35iJ7YKaOvtZIrHR9z7kEX0+eYXk5lZ1G+BwuHQ4PFiLi4uAYbVVGZ1Ni+fTvffPONRms0kkaNEbSGqqmw68cC1tGWBfTmB07hZWbQjGIu4ieiKeESVhCnyVJdcmBjta0X/7WdyX+bn8lmewceOfJHRhfNJooiupStJ6nwIJklW0kt20uL8gO0LNtPWlnFNJOdizfyw8bTq/ZVbIvgSEQseaHHfovx5bkMOLqUrdGZfBZ5EZ9FXgTAwJIlTMudUPVYjeioKZ1DvM9U/kc7PmYA8+jJj2TSgX18yCTg2JTOnlvr2cPVjvCKoE5s5Ofns3nzZq6//nr69etHWFgY8+bN4/LLLwdg/fr17Nixg8GDBxv3YZzfsnw1jxevqiWD5t4UJALzaMssOtOFw9zJak7np4BNAqRxhMeYzx0sZSZDmU0H7mYoY9jA/fzk6+oFhBAsmh3XGrmq/OPqB3bnDCZ5pXCzJ0+kYNNkNWaMWG6LIoOKaSz+FH0/5baKmBBulZDiyKa3YxWFtmiwDtLJ2si3RcOIpBjCPJ9POZiVY+MzujKDfrzGyyRzhFGs4Aw2kM4hX1fPpePj2DUsIol8PmAgHzGAvzOEi1jPrSwjrZGuzAtkNiD2uI6zKcWPcF/JC6wN6c6GkM5khbRlhy2dZtaxbaZH38nhkOZUtgPLbfnA2fWvhGJEk9Wo7YiS1RB+7E8HNg6HNCc7NIXs0BR2R6SxObQDZTY7TxZUNNLX2rvzhW0k7a0tXGH9nTOt/3KatYxwSjGP/mraNtOKWZzNF5xGCXaiKWYjaQzkJ9LYx//xJ6/WJ4wy+vILfU+4+nM5HfkXg5jDELqSxa9ZwAj2axoxF37FN4wse4dtZLAoZDCLbYNYbuvHYNuiqm0mxv4Ju1VGW0cWba0s0st2kFq+l+Tyfdgb+j31sxhRmdTYuHEj8+fPJymp5lXl0jAaNUZ4kNSYR1+W0JXv6ME+Z8IzgRI2kUpftpNGDtfxvXNr0wjtpmtWyBhmhNzMYZoDkBhyiJFFX9C2PKtqm7/m3mNemvQEIVhEWUVElReRXH5syqTTj/7A6Ud/gAQ4ZGvOmrCerA7rRXr5jqpt7g19kXJCGen4grOt+bro7Tg2oC/b6ct2HuBz5tGT8OMS8I9wMzk0YxC/MIh1dCLbN0kiP4sRwSqoEhsPPPAAF198Me3atWP37t1MmjSJ0NBQxowZQ3x8PLfccgsTJ04kMTGRuLg47rrrLgYPHlyPxZwAZU4bxPe0pBMlJHOUUWwhmaOcVTV8PvAlU8BvWcGN/MLr9GQk24GKb89RQonWkcpzIZlQY8F2kZq8GSM6l62H8IqTrlfz7iDaKiTZsY94K7fi+BZ7bNtQHITi4VXlQc4C5tKR6Qwki3jiKWYHSSRzhGhK/DapcaIIyriEn7iYn1hOBq9xIf+kK6ewmzQPr8xrKpKt/SSXL2BY+YJjhcfNQvLXvLs5HNKcfKsZ5YSSV5DC1d6vpgQgb8aIO1q+THl8Iq8cGEei4zA/hZ/CHS1fPbaB8/qoNMfuqis2ryj+O1dE/p0ETRlSJ1O4gH/QE4DebOVKFnI2/yPyuLV9/MUk3uNa5vN3zuAzBvIE1/ESZTzH3+ljnN6v6bIBmWwj07GNa3ifEsIodU5f7MDGrpDWbA3NrJgC5rgE4pv7x9K7ZDWHQpozLvkV7JRRfgSomvjW/+Xn57Np06aqv7du3crKlStJTEykVatWXHHFFaxYsYLPPvuM8vLyqvUdEhMTCQ8PN+1W6sCrfU2rdlUMODNcK2vFwsGQJDbbO7AprCOtyvdwTtF8SFzFW5HXsja0O20dWVxXPouhZQvo02yboVPXNDLMNIWtuyP7TCPGGmpkn7uJu5r7OUo4P9GRIeH/BCCElsRRzuXM4Ux+pFvYfwgJs2rmgExVMs3qWsuMu4kc5iz+y1n8t6rYEdqBSCJZyGksCT2LKZTwK77l16yhI7td7Mj0GZjeI3dH85lG6LkazQfmN8LdEX2mGLi96n/NgSv4uOKPPOc5UsQwVoWexTLb2bzE2USXFNKt5Bfuyf0L3Ut/wYGNMuyEFxrOCSq76UoyDc8v/iSoEhs7d+5kzJgxHDx4kJYtW3LGGWewePFiWrZsCcALL7xASEgIl19+OcXFxYwYMYKXX365fk+2uR3YtxsXECWhlg7rVMOQ1wRTQ8V0EHF3juz6dKKbhpm7Oxd6RrW/DhPKFJL5hmZcxF4ms5UwYGi1fTTUvMD1mb3WFBhMQ/prW/i9HSnAoxQCrYBW/JcE/o/OPEo2Z9YYI+1ucKjtPtN356Ch3BQ4thnKa1sM13BfjuG5Teuq5RjKK2NQmYcTD2rewybDmzGi3XFDu/uW/a9B6t9UbaE5zzCU5aQRQyl3sJRr+B8xxuOS/7MB/dlGf+awgjT6OBel20kcP9COy9lLqC6gqJdu5euqXR2VV2JqtNeRYkST4c0YccDelniOUmyruFK3bXkWVxZ8THL5PlLKs0mN2Ev78i0kWMfaBsf/X1zbSxypzun9+rGDI5RwDd/Q87gOEH/Vkd08xEeM51/8k0HM5jwynOfrJYRiA8J0QZRL4ZQ6Ry5VXB39Ye7VlBDG7tA0skrakmVvS3ZoCq3KKmJtgS2GclsopYRRZPNwKh4vx4gff/yRs88+NgqxchqjG2+8kcmTJ/PPf1Z0zvbt27fa4+bPn8+wYcM8qKh4M0YsibuEkOh8Cu3RhFLOr458AcDnsb/ivYRr2RuRSl7IsWmNzipaWJHYAB4qeZp4K5c0a/dxF4uaputrug4SyyzO5hPOoIBIZrOE1mRzJf/m13x23Hvnm/PxECye5RnyaMbXDGEO5zOb85nDRXzOYyTrIodaTSl+hBLCWBnSl8X2Qawp7ckv4d2IsCouJswOTeGS1H+SWrKXtqVZpJRlk1CeQ/Pyw1yX8y4hWOyzt2RFaF88uohW7QivCKrExgcffFDr/ZGRkUybNo1p0zxYaLiKppqqr4XE8BQpHCKUc8jnN03wSqQybBRj4z7SuIg87mc/sTpq1U+xh41VDQ9sMhQjAtNWEllBGpexlvEsJqHWJG/gOfW4q67epD//pCtzyGcyP9C5ruPcxaxcMULqxpsx4h/77ybuyLE1NpLL9/O7nGePbaAQ4pZcopjGMObQl/d4g07s43zWcj5f+LpqbovlKNcyn2uq1tqAmQzhP3TnAf7DIA77tH6BIpxSMsq3k1FUMwa0Ld/JP/ZcAUBevoddvl6OEcOGDcOyzB2ttd0nnvFmjPhtykOExlZcQdui/EBVYqPEFk5+SDM6lG0ms2wrHUs30bFsEx1KN1c9trvDg4WOm4A8onmL4XzIUIoIJ519jOefJDrPuf1t4e448hnNfxjNf1hDZ37i7KqkxhK6EILFaT5eW9BfhVPKAMcyBpQsg/0VI/oqlRPKOUe/IcvWltWRvVhqG1DxGKuE63PeAWBZ9Gk8Gn03MLv+lVA7wiuCKrHhVcVb9O65qQQbz9OST4gnFgf/x15GcAT8cEh4YzuHw/RkO38ghc+IYwVRPMMeumk6Gu9TsJHG8L8tEI15VF+sobyli2NAmmHRsih3RvmZji2mJIFp1FqCizLTuiDujuw7Zj0xRFFOOkWcQzYfsoj2HKH60PjagrC7o9jcZRrWbVog0jRqIKPaXw+yixZE8xbduJ7LuI0sbmIn9pNeLebuCE7T9yHHUO5q2PguF2VgHuG3uWbRUcM+XI2wB9hvKHe1PEnlqYWn0xErRkhjKPNwwWIBKjopPqU7LzGQvP9n77zjo6jTP/7e3WTTkyWdQEISeu8ocCAiCtjw9PT07HpnL8jd2U4FK4KeXeH0FLCXn+X0PGyoiAiIIB3pJZQ0SCM9u/v7Y5NAyDxLJpvsbPm+X6+8lGdnZ56dnZ3PfL9P+RLOEPY26bHtzxzbljecWg4Sxy1cwqns4U6+J0lcFVvhVZRGKNqBO3MfJuFIHZFUEOM4+pDz+9JP+H3pJ/I4QnFC/sFVLKM3XTnIDXzOKayrb9Pl+/Mw/dhKv/rAixN4igvYQUdGs5G/8h0ZYlcOBdCkHVtn+35mHb4bKurbxJsiKLbYKDPHNOrvwMq13FP2OH/05KBKI7yCmppXeI0QnOwjlBFUMIM8kgNk4NFakqnjOfbzIXH8kyRupDP/ZVebNeAKGiIHAOuM9kKhULQBDmABnZlLBv0p4xXWY4L6oEbgE4Gdm/iN8dQwnR7MJYMlxPMCG4hRT7atI0ZphMIHWYcr+C21vJdSyJMEe6oQJEwoFt6gZdfb+tam066317pWoPho0D2fcB5kKCtIJpFiHuUHzmA7JqzHvFea/Jd6fAsJA6F6J7y0nuZtwrZSIsHRz38FuzmDQp5hJN/Qh1V04++sYxI5xwRApPMofX96W98Wt9AGbRL4BjgkfH9S+1qtFu3S40PD2kyhSiMUvsdZZQuJdaKq99qIbaTQvf4G8RcWcjYrOJ3Vxiwm3UaYgKeZyxzOZiHDWUF//sRP/IVviQjCxGFPMAGRzkoi6yqhvj0wQOfa/Uyy613HRWEEHjaVDGIiBxjtgd+wAytOXBfbExzkBfYHfVCjARPwB0p4jRzuJY9oHyt99Atq9nr2fidHex+25s9/n4cU7UmM0gi9lGHhb/TmRbrQhUr+fkwbjmCjF+W8wRquYD+dqCJaBTVaT6XSCIUPEqc0wlP2E8XPJHEeu/mQd5nI9oDWjFSO8Dhf8yTLCcHJXHpTo4bynlOhNELhg0QojWgLKrDyKFO4hFtYSg8ABrCLiazy66BGA2kc5mFe51Wepit5vM7vuJIbqFPa0HaouSa/QFVstJbafXKWlaKRL4llOh25nUNcQjFRauJek95UN7ahyieEOSTxN/LU+WoJdcWevV+VByrag6p9chsqRTN2EsFf6UMO4ZxJAfeynfAgv/9ZcXIbu3HgCoI7gC9IYjIFAT151+YojVD4IpX71CisFdRiooAI0qhgMId4n0VkBVlb23EcZBCHKCCcsHqdLCGUODX70Tpqiz17v9IIRXuwdp+rWkN3S1vBniZUo3USqpUsUrvbYsEuVcdJ5YdSjwq9lX1SxVs4a+nIA5zBfuIYwV6ysLg5rptjW6SymTb68Yr7l85F888wkHze4FPeZSAAIXR0u70LvVV7etv8SteQtL6uUOknrZUnFQa2RTtbOPpYUVksbNBClEZ4BfVI3VrWHIYI5PuNu4ol6ceTLvTES13bcr+A1l39eisobII9pfH/3iaCp4ghGTvDsSKfFOkmKd3k9d5UiwW7O6QvVq+vIIuJ9vn4nEg+I5ot2HiOYhIbJ/e06qsbkErqpfdIgiItsrpdsLu5NnOFB5wcYXtJhKRWkQ1fhcNG677jhvfX/3nyfoXieGoPq8CGDt6gMwcI4052ciEH1cT9MTTkXH1AR54gm5XY+AfbW7DuhgKAEBtKIxQ+R81hNQrTSQHh3MVQCgnnTb4lltr6oEbwYaMGW30vpV9I5A5GchcbOFt8yFaIhNpQGqHwOWrq55oUunECbzCEFxhFCA7+zmIubFxHI3Ax4+RPrGn8dxERPMZ4/sovpKo1mVqP0gi/QNUotRZrrNEe+DT/IoqniCGbOuZRRDcVamwxV1HBTRxhKyFcRwcK1M/UPdEZnr3f3gZ/CsXxWJRG6OEetvMq67hIBTVEziGP0RTxGcncTh/KVePllhGhNELhg4QqjdDDDmK4kjGsI57T2E+UamnbSCR1xFHDDAYzl54BPnXXDkQqjVD4ICFKI1rLfmKYy8lkUsRbvMMfWRvwQQ0tlpDFd3TlUs5nBZ2Mdsd/UXNNfoHKFWotMZlQoRYa0+I1InmFKPpQywsUExuEQuIJJuAaKojBySxiuJ4OzKWIZKMd81UOrzfaA4WiOfmZULZOrtqwCfbDGrYKYdt0oQbXplW9presWI9dKgO3CXbX9h/RgYNYuZk8rEA/qtCucJMq0iQfQX8Fn3QMCSmooHfxXWk/zc9DJPAU+cwihI9I5BaG8jy769dmkvyXSt6k7SW71rW2W9h2i7a5WKMkXUouluxSGXmxhq2hhLxaaYTCB4nMhFo1jmgJq0lgGiOowsLD/MJkVZXQhD4Us4Dvmcpo/k1PDhLJfawhVI2/WkaJ0giFDxKTCVVKI/TgxDWP0pkynuc/9CafyCBqU3g857IJG5U8wGRuZRL38iPnSc/oCplipRH+gApstJaijRBmtBO+SX9qGUwNT1FCjHqobjUXUokJeJwY5hLFA0Y75Ks4PbzGVN9DRXtwaKNah8kNn2LjMdLoTA1XU0CkqrNtERbgHg4Qi535JHI/nXkaDxe1C3SURih8kZX1GiHphNSWPFWwSx2ZqoT2E502am0s7USnM9L2UhBc2k8U64jjFoYRgoPnWMsIMaghVXDoDHLbhWCw3o5XMRr7sUhfquRjy6tSEqjmZdZwL/35nC4cIZInWHOCmu9cwa4nUC7tY7dg36Ft3i/cKPW2r9VyR2rzXlP/3yqlEQofpHij3Blb0Yyd2HiQU5jJItI4wlBxAYbgYiy7WMB/uJVJPMIYDhLNDaxS1fF6UOMIv0AFNlqLQ11hx1OH64IaTi3DKFY3zDbgD1SShp1hjU/fimbEdkFeG6QFqL6HivZAaYTI59h4mE6kUcu/2KWCGjoxATeTRyQOxlBqtDu+j9IIhS+iNKJF9KaUSRzkj+TQM0jX02gpEdh5krU8SF96U6oa2bYUm9IIhQ/iVBrRUjaQxK1MpoJQtpBImhgsDk66UMJ8/sNUJrKeZGoxY1U3npYTpTTCH1CBjdYSkw51qhS6geVEMYsUnqKKLOwqqNGGjDomqPE90YzhiOqs3gQPz4aKoivag5h0qFUacTxL6cCDdCKFWuayixTVJ71VuFoWHu3LtI9wOlKltEELk9IIhQ8Slw7VSiMkcoiiMxCKkwfYZLQ7foMFJw+yoXEcVoMZCyYsqoJeRmmEwhfJSYfKHLlqo62q+sR2t8JEbpQUNJDskkNSBV+Cru03kcrNTMKOiWf5mpM5gGuKU6ocdBf0yNM2Fws/Uqk9qnRo6bu0CPu3Cf7QWeeBbcRTx7/4EhNOrJihftUR7Tm7YmE/etvbShV9QjVNubB/vW1r9VT0gVzV13A6o5VG+AMqmaO1mFWPkQZ2YOVOOlFMiApotCPfEMPf6MxTpBjtim9RutNoD1rMnDlzGDBgALGxscTGxjJy5EgWLlzY+HpVVRU333wzCQkJREdHc8EFF5CXJz3UKHwai9KI46nEzHR6EI2dl9hNWhD3vW1LdhDGZQzicbqpqSstSvxHIxRBhNIIkc3EcQnjeI0so13xSxrGYkcI4RaG8DgDlDa4o0hphMIHCVEacSK2kMDNTKQOE880BjUUEhHUEV4/Sz6f/sxitNKGlnBIaYQ/oCo2WsvSHa6+uFLkNcnNe4sFuxRktUv9cVcJb5AyYN1lxkqvSQuJZAJQjpm/k041Zl5iP5lECtsLnwGQo7xSyZe06NFabbOzFT0WTZ10vsHm5jW9C+tGaVrHUckQqniPePpTw6QmqRfFwr72CXahxy3bBbtwbvdXC9sDkgZIdr2LxTb8Xjzt0uXAs0i4jvLAzp078/jjj9O9e3ecTicLFixgypQp/Prrr/Tt25c77riDzz//nA8++IC4uDhuueUWzj//fJYuXeqBgwpDKN6heuMeRwQOnmITDjqQodrrtRldqKYfZXxMCl2o5DLVV7ht8aJGKIKIw0ojtDhIBFM5CQcwnMNGu+PXhGMnFAcf04VUKriWbUa7FJgojVC0B4U71HquJ+Bt+lGNhWf4mmFiOr7ieOyY+Jk0fqYjSZRzLWuMdimwURrhFVRgQ9FqnMAjJLMXK9MoYCiVIAY2FJ4SAszkIJeSwSOk0I1quqnJQeg0AFjX+vd7se/hOeec0+Tfjz76KHPmzGH58uV07tyZV199lbfffpvx48cDMG/ePHr37s3y5cs5+eSTPXBS4XXW4wp+hwqvxwt2rYptKS4sBcO7aNTUJgvBSd2LxWrZ3c/O1WGilBDiqWUAZSDmB0lBbqlqyV2/U62FcYFy4T1SdbVEghChjZKeXKMFuxT4bvn2IcBsNnMFg3iOTPpQxpDGtTekknsp6C0FRXZr2IQkg1wh4K0VvNbaLchfrZSMV6xhaygISvUfjVAogpkKLNzOyRwinFmsZIBaQ8gjQnDyBGv5M4OZQ2+6UM4EldHcnBSlEQqFP/IAP3AJv9FL90N8cGPByZMs4s9MZg7DSKaCc9hqtFu+S5rSCH9ABTZaS1o/KNxgtBeG8gUxfE0M4znCJWLFgKItScDOLA5yHZ25m468xV7Cgr2IsFiaoGshbdT3sLS06QA8LCyMsDA51cZut/PBBx9QXl7OyJEjWbVqFbW1tUyYMKFxm169epGRkcGyZctUYMPfSO8HecGtEQ28RBc+J5mXWU8XKo12JyCJxMETbOZKBnI3vXiLNSSpwLeLIt/QCIWiCaX9YMsGOfgttRmXihik/ulSjLpW48JMEyY2rFJ0XQqcSgeVhp2J9clSg9lJNLeyntPEMl4pAC4FZYVK5EohOC3dLnQHvzVsnYVjRkjJATbBLgW+mxMJPMtSLmU8DzKQrhSR1XixFAvvks6l1skRotA1wj6kuMpuwS4FufX0Vpe+u4bgd7jSCIUP0rEfHFbjiOOxY+JLujKZ7VhwqqBGK4mkjuf4gquZwiOMIYMSBoqJZEFOidIIf0CtsdFaSlW52wTKuJ1CHiBPra3hRQZSxY0cIp8QtqoaVSj3jVYF6enpxMXFNf7NnDlTc7v169cTHR1NWFgYN9xwAx9//DF9+vQhNzcXq9WKzWZrsn1KSgq5uep+43eUqO8M4Ec68DqdSKOKNHGyS9EWZFHJdLZxmFA+I9lod3yHMt/QCIWiCcVKI44lhyiWkMp4DnCFapnUpiRRxeOsqG/Z0s9od3wPpREKX6RMaYQWLzKcBxjHF3Qz2hW/J5FKnuFLQrFzL+OpUVPD2vjIXJPCPapio7UcKXS1GQlCnLgWpgsFLqfIYG+CkysoYjJlpLhdNyVICI8FT9oVtFF5YE5ODrGxsY1mqVqjZ8+erFmzhpKSEv7v//6PK6+8ksWLF3vggMInKQ1ejWiglBAepjsx2Hmc3wgN9uoyL3Aah1jAWvqImdRBSKRvaIRC0YTSQrlaIwjJoJw3+I5EqlSyVDswhEPMZgVDxIqXIEZphMIXWVbomqmTxhJSS1tpbUq9VX2SPV2okEgoFt6gt7JPIpwfyOJ1+tGf/UxgHa4fj7QfvWu1AoeEzyatDypVoElFjtrLqMrfTbZ0rqXPoHfhrnC6coSHWIiNKqyN90GpCkaqXJDswneQL5QgSOdTqtCTvuLdgl3avxS3aCh8tyqN8AdUYKO1hEWDMzgnDt4li99I4U4KiFK/NEMwQ2NQowALcZixBut3kZCJR30P26g8MDY2tklgQ8JqtdKtmyvLZOjQoaxcuZJnn32WP/7xj9TU1FBcXNykaiMvL4/UVK2FFxQ+TXg0OIJTIxp4gmwOEcqDbCNFtUXyGn3rB5F2oJQwOiCseREsJGbiCxqhUDQhIhrqglsjAGoxU0Q4yVSRqQKy7co4Djb+fyUWIgz0xadIzkRphMLniIiGWnVPbCCfKKZzBnFUMpOFhAbrvEc7MJ4djf/vwKTqNo6nYyZKI3wfFdhoLYezYcc6OTCa4u69gl3sgyvY7cJkRYa0SKy77H657+2xHCKcOXTFhgUzhTQPIUofwl2GkOTvUsG+SNu8Q/jVt6ZdYIrQG7arVBbamnMrIS0m2/zhZhNR3EBPriCPP2suliuFsn8T7MKCu3uFa01oXQwgdhKQkgx2C3bp+2u41EyCzy3FYLFxOBxUV1czdOhQQkNDWbRoERdccAEAW7ZsYe/evYwcOdKzgyi8T3I25HrwEOTn/EwqC0nidxRxJvlGuxN01GHiOvpjpTNzWBTcGdD7/VsjFAFKSjbsD16NaOA1hvMOI5nLEnpRYrQ7QcECuvMeXXmHPcSpFpGQozRC4YOkZcMepRHg6hbyKKdRRhhP8RmpKgjeLqwnlRmczkzm0wPVCq2R3Uoj/AEV2GgtOzcb7YEhvMAAKgjhYfYSoSLlhtOdCpKpZR69OYtddKTCaJe8T53/3O3vueceJk+eTEZGBmVlZbz99tt8//33fPnll8TFxXHttdcybdo04uPjiY2N5dZbb2XkyJFq4XB/5LPNrrivtAxOkmDvpGHTvSislk0ITqYJgyaTnskOWzPLUA5xN6mMJRdTM0elQLcUxVwl2KXgN7BfCE5LAVepdF9C+v66C8HkTm3VNlAazDW9D4YAXUnhY1L4BhunN4tCS0FvIVrt1FhUWCrplna9Q8MmBcelsn9pTVut76/hkrf7j0Yogoh9mwnuiCPsIJ7XGE5XKujqSZsHhS4SqSKfCF5kJPfyndHuGI/SCIUvsjc455q0OIiN9aRyJr8xll1GuxOwhOBgH3E8wB94k5cIUXN9LpRG+AUqsNFa6mrBYrQT3mUT8XxGFieRx1iKjXZHAYTi5E72cBMZPMcgZvKT0S55n5R05OaLLcCLfQ/z8/O54oorOHjwIHFxcQwYMIAvv/yS008/HYCnn34as9nMBRdcQHV1NRMnTuSll17ywDmFYdQGn0YciwUnfxDLsBTe4GY2swgbTzOa0ewhUiz/DHDS0mGnf2iEIohYWevqKypVfkv906XW11IAXPrZawbAhW3ThIhilN5s/7jG/3MCT3ImDhzcx0JCNaOTWgFhKbr5i7a5fJO2XTC3a/C7WNi2j+CM1I9dRAp8Nx3un8k+PiWRj+nN71lG72aZuVK/dI2S63LhgpQC37sFuxQQl74PKfitdas/UaeEVP8ZRyiCiNpaNVNXTxrFfMCbhKrU9XalN/lczS/8m0G8y0guc5dAFkwkK43wB1QLtdaSrJVWG9i8wADMOPkrq4M9ycynGEEpYznA16SzVSNzOuCxerhCs4OjJYKt+dMhNq+++iq7d++murqa/Px8vvnmm8agBkB4eDgvvvgihw8fpry8nI8++kitr+GvpAafRgAcIoIHmMBBYox2JeixUcONrCCfKN5kkNHuGIcfaYQiiAhSjWhgMT1YSSbnspY+uqMGCk8wAXfyIyaczGISjmAf1SmNUPgiScGtEQ2U15e+J1BBbLCvGecFrmYlaRTxMuMpJNpod3yDUKUR/oCKA7eW8EijPfAqFYRQhYXJ7CFblYv7HDewnh9IYy79eYolRrvjXXK0+pvoQEXRFe1BZHBpRAMLGML/6MGp7KSj0c4oOJ+NvMNA3mIQF7MuOAeFu5VGKHyQINUIgDrMPM0EoqjhZr4DYo12KejoShEX8QvvMpzv6cF4cQG8IEBphMIX2R7patcpVfVJLVGlOLHetrZ6136tEqopUoXyKotUfXH0A28jjWu5g7t4n7PEMjupUk24px2SysOA9YJdOrS0K+lcSzlfXQR7uWDvLxw4QdhePEe2ZpYw4G+8zTSu5znG8hCvH/OqcNwawS5di75U0Qeynw2/Aaf/aITdbmfGjBm8+eab5ObmkpaWxlVXXcV9992HyRTYSQyqYqO17JV+SYFJJHW8yiLulUq9FYbSg2IuYwuns9doVxQKBcDO4NIIgEIi+T/60ZNCxolPlQpvYsHJDazgTLaorFw/4YcffuCcc84hLS0Nk8nEJ5980uT1q666CpPJ1ORv0qRJxjiraD1BqBENmHByHUu4g2+ID8a14XyEa1nKIHKIo9JoVxQKxfFsC16NaOB5plCJla4cNNqVoGIMGziVtaRyGKfRzih0MWvWLObMmcMLL7zA5s2bmTVrFrNnz+b555832rV2R1VstJLyta726WL70zQ3by4W7DWCXXc7QSEjM2Ojm/dI6QCdqMVMaP2UyNF1cKXSNKmpvJvouNS/z/6Vtn2FsJs1gl1qFeuOzoJ9kPBlnCT4Cm767EuLyUphe3cXQi5T+ULD/puwvXAt7BWuHWmBV2n3IGc3SHYpWUyI6jckMVT26wdscOPICWgo8/Pk/QqFgtcZTA0WrmOlmkL3Ic5gO2eIN/EgoGc/2Ok/GlFeXs7AgQO55pprOP/88zW3mTRpEvPmzWv8d1hYmOZ2CoUvYsHJWWJqrMJbdKCCf/OG0W4YT69+sN9/NEKhCAY20IWf6MMZrKYXOchzT4q2xgTM5t9qLNdA337wi39oxE8//cSUKVM466yzAMjMzOSdd97h559/9sAB/0AFNlqJqU8f2CTN0AYWDzGeXGJ4ns8IFyfjFb7CTjpgo4r4IMnAcuQev+ihTtSARNEOHA7vQ/WmTWLIOEbqQpKtYZNKkaVycK2SVek6lfaRvlXbbtF6bLBRRjgf0Z0e5DCWH+vt0iOGVN8uBcCF4PduaRFZYKVgXyvY9a4Jly7YpXLm4YKvmXoX5pMC39KVdvS41YSQQzzdyEes97YL37vW+ZHOmVSsoxW8loLjOsvLyzSSvhuusiNF/qURkydPZvLkyW63CQsLU+sv+Ts9+sD24BhHHMtqMrBRQTaFRruiOIYdJJFNQXBOZBX4l0YogoQ+fWBz8GlEAy9zJiac/JmFRrsSlDRoQRkRVGIlmRJD/TGUfN/QiNLSpssBhIWFNUtsGjVqFC+//DJbt26lR48erF27lh9//JGnnnrKAwf8AxXYaC2FwfFAfpgIvqYbA8hVQQ0/YD0pXM0FXMVqbmG50e54B09/i6o3rqI9CBKNaGAxPakilEtYHpwTIz6OE7iSv1BGOJ/yjFxIGIA4fUQjWjIgaSnff/89ycnJdOjQgfHjx/PII4+QkCA2V1b4IAc2FlKS76Z9eqjwwmHBLnV0kgbTegbZ0rZph7Ttsdr9qJ1E8ySXsJ8EvuYerI3jCil6r9UXXKg+KxcmAKVgthT8luYRpZ7gElqV+1LgW0owGCg4I7YLKBbsUqaz6974DuP4Jxcwh+cZzlZA6CdeqvF9S+dFb+BbCmZL1dySfXdzU4FwfhuC32U5vqERCkUTDgXXOOJYttKJn+jD6awmGw8nlRWtpohozucBxrKeB4O5uq/ANzQiPb1pVt306dOZMWNGE9vdd99NaWkpvXr1wmKxYLfbefTRR7n00ks9cMA/UGtstBJnfr7RLniFj+lDHWb+qErG/YK+5JNOCZ/Uf29BQbQqTVX4Ho4g0YgGzmItr/IqZ3jSFk7RbpiA09hEHnF8T2+j3fEqJh/RiPT0dOLi4hr/Zs6c2ar9TJo0iddff51FixYxa9YsFi9ezOTJk7HbVdqvPxFsGgGwjmy20omz+PmYoIbCaMawARNO3mGc0a4Ygq9ohELRhLzg04gGyoggm1wuZ5HRrgQ1HThCNw7wFUM4LK58HgT4iEbk5ORQUlLS+HfPPfc02+b999/nrbfe4u2332b16tUsWLCAJ598kgULFhjgsXfRXbGxa9culixZwp49e6ioqCApKYnBgwczcuRIwsOlvKMAJDISKgJ7wTsn8Dm9iKeSU9hltDuKFmDGyTn8xkucxDIyGGO0Q17AnJ0N69a1fgcBUkKelZWFyaQ/V37q1KncdtttbeaH0oh6gkAjjsUEDNTdz0nhTS7gF15hHJ8ymNN43Wh3vIavaEROTg6xsbGN5tZWa1x88cWN/9+/f38GDBhA165d+f777znttNPE9ymN8C1MQaYRAO9xCgAX8oPBniiOpTOFjGEDS+jHQeLpaLRDXibERzTCF/AFnVAa4WKdKZLIigrCBZlIE6r3bFJVn9QFVsokl65LaXup8kyypwntZ60WhrKd9/jiuApw6bvP0zYfEioK3eXq/iLYVwt2qcKvWLDbBHsfwa73EaG/cE4TtKofAVIE+9GL5RLe4k7u4v/oyXU1H2pvLlXu6a3o2y3Y9a71KlT0FQtVk5KbDWfB3i0bCozXiNjY2CbjCC3+/ve/c/fddzeOFfr378+ePXuYOXMmV155pQdO+IY+uKPFgY233nqLZ599ll9++YWUlBTS0tKIiIjg8OHD7Nixg/DwcC699FLuuusuunSRekAHDuZu3Tx7CPIDNpPJXuK4mHWEqDpZv2ESW3mJk/gfPYIisOHYvNmzHQTIgGT+/Pmtel9mZmabHF9pRFMsQaARDXzIMDpQzqlsVm2ofJh4yhnJdpbRjcPEER8k/XLtPqIRLRmQtIbs7GwSExPZvn2728CG0gjfIiSINAKgjCi+ZwBD2E6WNBGlMIzz+Ikf6M9ChnEN3xntjlep9RGNaCk//PADTzzxBKtWreLgwYN8/PHHnHfeeY2vO51Opk+fziuvvEJxcTGjR49mzpw5dO/e/YT7NlInlEY0JSzINKIBO2YsONR4wkc4hRUkc4hPOY0/MxMzTqNd8jpVfqQRFRUVmM1Nu7ZYLBYcDs/nco0eR5yIFgU2Bg8ejNVq5aqrruLDDz9s1t+rurqaZcuW8e677zJs2DBeeuklLrzwwnZx2FdYsmUL4SAWZWW76cWaLkXMJayCXQpcS3ZrtXyM1Oahzd/ohpkqJvEtLV9ZVfpwUjNUQCozXCFs/q1gX9yKQ0v0FOxSFoQ7RklllNLPr1iwu8tSOdoPNw0YwnAWk045B4nSCvfnCtfCPmH3uwW7FEEHOYvhV21zTrG2XWrHW1b/34paKRWkhTjxrO+hj+j7KaecYtixlUY0x76lNTce/6OGUJ5nAp0oYjwePvgp2p2zWMtSuvM1I/kjXxjtjncIcI3Yt28fhw4domNH93nWSiN8i7og0YgGvmM0NYRwJj8b7YpCg1FswkY5/2MEV0NwTSr6mUaUl5czcOBArrnmGs4///xmr8+ePZvnnnuOBQsWkJWVxf3338/EiRPZtGnTCSsejNIJpRHNqdqyRV5OJ4D5MzNJ5hCzmG20KwrAgoPJLGYB57PGNIghTmEiJ4Bx+pFGnHPOOTz66KNkZGTQt29ffv31V5566imuueYaDxxwYeQ4oiW0KLDx+OOPM3HiRPH1sLAwxo0bx7hx43j00UfZvXt3W/nnsziq3QQJAoTz+ZzxmInTXQenMJpzWEUslZQQqx3YCCDCOnWC/fuNdsNnKSoq4quvvmJ//TlKS0tj4sSJdOjQoc2OoTSiOYurq4lEDn6nCz/LPhpLVFik5ylpBWitRWelJXf07AOgU9NFRZcxhiOYmMR3NI+ASoFuqSR6o7ZZ+n1Li78CYsLpEsHuLkirRTfBrjf4HSp8tk5SZF86d1L/16aLLo7lAH3pTyj70FwgVlqjUStZY7ewrZ7FYYUAeI0w5ytNBWulXjQEv/EzjThy5Ajbtx+9IHft2sWaNWuIj48nPj6eBx98kAsuuIDU1FR27NjBnXfeSbdu3dzeg92hNMIYfqyuJgI3GiHc9/sIiyyL05XSPV4raUraVq89tHlFRhfTWs4I/4bxfEzz+5i0MGdxc1OlcIORkmkknfhRsAu33iqdnRbD0zWMuvVBsPcTPmyEpLk2wV7c+H8hwDn8lwMkU1lZSiSVzTfXKrSREvmEjiiiXdJg4aZfJfwOtM6M9NX5q0ZMnjyZyZMna77mdDp55plnuO+++5gyZQoAr7/+OikpKXzyySdNWhnqob11QmlEc5xBMNd0PHtJZz09OSvIqsZ8nbP4jsWMoJIIo10xhFA/0ojnn3+e+++/n5tuuon8/HzS0tK4/vrreeCBB9rtmN4YR7SEFq0urGewlJCQwNChQ1vtkL8QdoLMuEDBRkVwZe0ECOewmid5kzRxlipwCImM9GwH9jb481FeffVVRo4cyYoVK3A4HDgcDlasWMGoUaN49dVX2+w4SiOaExEkGvEDvwPgNFYZ7ImiJYRTwwJmcj7/MdoVr2HxM4345ZdfGDx4MIMHDwZg2rRpDB48mAceeACLxcK6des499xz6dGjB9deey1Dhw5lyZIlrVqzQ2mEcQSLRjQw0LmOx/gnsWJwVmE0tzOfWczWDmoEML4yjigtLW3yV92Kie1du3aRm5vLhAkTGm1xcXGcdNJJLFu2rFUfzxs6oTSiOaFBphEAX5tPB2CSWofJp8gmhw+4hdHOn4x2xRD8aRwRExPDM888w549e6isrGTHjh088sgjWK1SCyDP8NY4oiXoXjy8gfz8fPLz85v16xowYIDHTvkDIbGxOA8eNNqNdmMRY9hLZy6khGiCL2NA4T+UbxPStlqKA8/KA314+ZnZs2ezevVqoqKaFjM//PDDDBkyhGuvvbbdjh3sGhEaG0tdAGsEuCpjl3ESXcgjDWGRPoXCYMr8TCPGjRuH0ynXnX/55ZceONMUpRHGERobS22Aa0QDVYQRRrVKlFL4JL6iEce3X5o+fTozZszQtavcXFdCW0pK00WBU1JSGl/Ti1E6EewaYYmNhSDRiAZ+NP2OaMoZTvCtLeLrBLN+V/mIRvgiRo4jjkd3YGPVqlVceeWVbN68uXHgZTKZcDqdmEwm7HYfTl9uQ8q3bMHD2J1P8xFn8yv9uYTnjHZF0UqW0pNnuYRHmEkPrZYjChcBsni4FiaTibKysmZiU1ZWhsnUPo8oSiNclAa4RgDsJJt8kriE5Ua7otBBHRbu4nESOMy9qofxiVEa0aYojXBRumVL0DR1mB1yJz+bR/Au9xAd4O1R/Z3/MY551sf5V831xFNktDv+QRtpRE5ODrGxsY3m1lThtQfe1gmlES6+OtF6roK9f4G2PVU6kNTyTrJLid9693OcvcQUy8bEvowzfU+IU2Mi2SL0PiwWrgepLarUthBgtWAX1nfN0WrPh9zVNVVoxZeuN+YoDTClRVksQvKZrVjbrvEbKyaOGTVP0792PddWvNb0ReGa09XeFuRWhdI0ltCqMFfwZ72wmxOt56p3eeRmqHGEV9Ad2Ljmmmvo0aMHr776KikpKV53WNH+ODCxjj70ZzPheLhYjsIwHJjYSSbr6BPQgY3oPn1gk7unlODlySef5JRTTqFfv3506tQJcC00u3HjRv75z3+2yzGVRgQPEVRyHa8xWrUW8StCsLOXdLbS3WhXvEKs0ggRpRGK9sYJLDefTBTlKqjhB9RhYZcpi1/MwzjD8bXR7niFOB/RiNjY2CaBjdaQmuqavs7Ly6PjMa2M8vLyGDRoUKv26W2dUBoRnGwI7YcDM6OCtN2RrxNLKRtC+lFoTmwe2AhwEnxEI3wRI8YREroDGzt37uTDDz+kWzdp5czgIKpnT5xbpGUs/ZsdZFFJBP3dhrQVvk5/9gKwjr78gc8M9qb9qCmUFp1sIQEcRT/77LOZPHkyP//8MwcOuNIj0tLSGDFiBBYp88VDlEa4iO3Zk7oA1YgG0jjIdbwKnGS0KwqdDGAD/+FsCkgkSVy4NzBQGiGjNMI4Ynv2pDbANQIgjxTyTclcYP9QXmxc4TMMqh/7rTMPCJrARnUAaURWVhapqaksWrSoMZBRWlrKihUruPHGG1u1T2/rhNIIF/E9e1IRBBrRwOian1h4aDIRycG1xo+/YMbJ4NpfWRx2ChWmCCKdwfM9VQaQRrQ1RowjJHQHNk477TTWrl0b9GLzfUkJVUCS8Hqxm/eGHta2p0p1UAmCPV6w2wS7VMsIEH+0RG2DaQxY6uhnXwGheis2pMzdtfJbdgi/1jXC9ou1zWXfaNuXyEcWGZOjbRdPofRdAKQIn6+rm3OiSbSb1/Y1s9iADMdO1tEN6o4rQWyrkkHhPAFiXV9usbZdqv6UzlLDR6jJz3fjRAsIoL6HV111FS+99BKRxyxyZbFYGDlypNd8UBrhYnFJCZXIt4Y+OvbVX9IGmw67dPOSypalMueYo2su1WEhBDvESrXDkh4Ua5vLhf1IrU3d3UKFG//uDdp2vflho4T9ZEpvkC4E6SHCJpyLKOkh0SbYtcvPB1Ss5j9hZ7G+qgfj7fuPvqBHG4QSfF16IVzb0lBe0gmtNIyGRy2lEUdRGuE7/FCvEdIjvqQRZsE+SCrMle79cTq2leyiThz9343WvhAC/Y5sgA77hTeUaJvtGvev5o+7LiSdkHK0Vmib1wvPtdK9R2KIxn76Sxvr1gfBni3cUC3SVdZco9PZj622mHWOAc2/Fi05kTRDskuXgDSeEK7r34TNtR4LpEug4eP4m0YcOXKE7du3N/57165drFmzhvj4eDIyMpg6dSqPPPII3bt3Jysri/vvv5+0tDTOO++8Fu3faJ1QGuGiukS4LwYwSY7ATrTxd/rXrue7sFPZFNKHYbWrjHbHa1T4mUa0J0brgzt0Bzb+/e9/c+WVV7Jhwwb69etHaGjTBnnnnntumznny5Tm5ootBv2dTSbXcKqvcyPQw1hnFB7Rx7mJL0yTqCCCSAIzsh4aGQkVHrQ3CKAo+htvvMHs2bMbxebGG29k5syZ2Gy2xm3q6uoICdF9628xSiNclASwRgCUEMvkyIVcVTuf6/jKaHcUOulvd3Wa3Wjuy3j7dwZ7076EKY1oRGmE7xDoGtHAhpB+APSt22iwJ4qWYAL6163np9BR1BCKNQhaEvubRvzyyy+ceuqpjf+eNm0aAFdeeSXz58/nzjvvpLy8nOuuu47i4mJ+97vf8cUXXxAeHt6i/RutE0ojXJTn5tKyb8z/qcbK5+FnMbx2Jeli9FphNAPqXIu6rwsdEFSBDTXXdBSj9cEduo+4bNkyli5dysKFC5u9FkwLOoWEhUF19Yk39ENGOpcR4agkkUJUYMO/yXLuAhPsJpM+bDbanXYhqVs3WLfOaDd8goZF9hp46623+Pvf/94oNnl5eWRlZVHhiTifAKURLkIDWCMAdpi7UmOyEql6pvslGc69WLCz25xptCvtjtKIoyiN8B0CXSMa2GrpQbizikz7buRVZxW+RPe6bSyxjiHHkk5Xu1QyGjgk+5lGjBs3rtm9/FhMJhMPPfQQDz30UKv2b7ROKI1wEchzTcezPaQbj8Xcyw3lc/kzrxrtjkKgZ62rnnp7SHBVU9n8TCPaE6P1wR26Axu33norl112Gffffz8pKSnt4ZNfkNyzJ4UBeoGPd37HeGdgZ3AGC6c7vqa3czNdkFrF+D/5nvYfdeBZJNyHygOPR2vgU1VV1a7HVBrhIqVnT/IDVCMA9pozAOjiCNx7SyATgp3ZVXfS2RH4mXF5SiNElEYYR2rPnuQFsEY08HTZHeSZUzAjT8QqfIvJNQsZVLeGjvaDRrviFXKVRrjF2zqhNMLFnp492bdundiRTvoGpA6z8UJ7OKvUVlRvK3QPWhnuie4Cdsgu2enm9yD8yKS2d1JbVHfDFqFvXY5wjv4n7EYKB2cL9jOF/adLffTSBXsnwS59BzqDhBHFVcyuvZPMmt00KeYrFt4gtN8XuuTK36VwfmqE7aXzL53OE7U9NymNEDFiHCGhO7Bx6NAh7rjjjqAWGoCCbdswGe2EQnECMsghw+luIQz/p87TbJYA6nvYEkym9r1zKY1wkb9NavgdGDQNbEhd3xW+zCn2H4x2wSvUKo3QhdII75AX4BrRgJVa0oMggBpIZNl3k2XfbbQbXkNphH7aUyeURrg4ECQaAbAntAsAGbV7DfZEcSLGlwdf8nON0ghdtPc4QkL3bMT555/Pd98F3wV9PLWVgblewXa6ck7Ip3xuOtNoVxRtRAURHKaD0W60G3EdO3q2A3sb/PkQb7/9NqtXr6a21pi+yEojXNQEqEY0sM/UGTMO0pxSSpTC13Fg4pApnhpCT7yxH2NTGtEEpRG+QaBrBECpKYZloSdTYEo02hWFTiqIoMhkM9oNr6A0ojlG6oTSCBfVQaARDewNdSVLpdcGdjJmIODAxCFLPLX68+P9lnilEU0wehwhofuK7NGjB/fccw8//vgj/fv3b7ag02233dZmzvkya5KTKcrPp4/wulTxBdBFsKdK5VrFOu1lOu1AQ5v0Q5YEDpo7UlUT7ioxs0oHkZazErZ37pePLZVASgleQjXYEmHzf8tH1s2ZUiWau2Q06fNlC+fEJGWo2NwcpFjbXAFnRH/FIPsaXqi89ahd7zUi7F60g1h+mCtsLpUNrhbsDeWEXWJj4WBwlMufiDFjxjB9+nTKysoIDQ2lrq6O6dOnM3r0aAYNGkRSklTQ3HYojXCxITmZw/n5SF1IpTtoqoYtU3huiNGjGdJvu1ywS60xG7QiJAEbxYRU2CFW64DuKNQ26y1Ndjf+2a5t/knY/B03u9JDpnBc0Vfps0nnIkoSFOmpt1jbXAFvhl/Gc1G3Ma/kavrXbWi0a6J1nejVC43rtUy4tqVwmR6daPgqspRGNKI0wnf4rX4cIbWmiBTsaYK9WMjosxULb9D6/ep9NjzB/eK3sF7cGvc8dxc/zh/KP4QaIesxVLBrHVevTgg3kyrhniw9c/5HsOuhu3DMcOmGp1cfpJ9vnPAG4f7rLIfT075maPUqnjt0+9EXtL5vvdeMTt2QrmtJCXdr2NYI2zboidKIphitE0ojXNiSkynPzzfaDa9QEJJEB3sRYc4ao11RnIDXbVfwQvwtvL7/CvpUB+b6rccTpTSiEaP1wR26Axv//ve/iY6OZvHixSxevLjJayaTKXjEJiGBogAUmyKTK7O/g7PIYE8UbUWis5BDJqlRpv+zsy36HgZIeWDDPXnbtm2sWrWK1atXs3r1au69916Ki4u9UhqoNMJFh4QEDgegRjTw8JH7KTXFGu2GwgMSHa4A0yFz4OoDwA6lEY0ojfAdOgToOOJYisyuMUW8Q4rCK3wRE5DgOEShJTgqbZRGNMVonVAa4SI2ISFoAhsZtXvpYFdzT/5AYp1r7BAs+gCQozSiEaP1wR26Axu7du1qDz+8zosvvsgTTzxBbm4uAwcO5Pnnn2fEiBEtfv/2zZuxtKN/RnHY5FoRSgU2Aod4x2H2m6XVpBQel/j5WHkgQPfu3enevTsXX3xxo23Xrl388ssv/Prrr+16bKURLrZt3hzQK0+kOQ6Shspe8WcadL5B9xUCSiPaFKURLrYHuEYAFFlcgQ2bvdhYRxS6SbAf4mCIh+03goUA1AgwTieURrjYu3lzgDcKPcoDBQ8b7YKihcTbXYkKhyyBnRTVpgSgRhg5jpAInuZox/Dee+8xbdo05s6dy0knncQzzzzDxIkT2bJlC8nJyUa7ZygVJlfxe5RT6k+i8DfCqaIGq9FutBvZPXt6VrURgGKjRVZWFllZWVx44YVGu+LzKI04MbssmcQ4ykh0Sj0xFL5OhNPVv7naFGawJ+1L15492as04oQojWg5SiNaRpnJ1Zg3xumuF67CF4lwVga8NjTQtWdPdiuNaBFKJ1pGW2jEFlyL4UrtCtMFu97OgdYq4QWpfb60vdRFStqP3v1LSNtLU1nu5KhY29xWbbQlpP2nFwsv6G0vLJ0jvee6FqIs5eCESkfE0e9Q+i6la6KNrjnpmpZOj1Q7KnULbmxX2LMnKI04IUbrg+7Aht1uZ/78+SxatIj8/Hwcjqa1Md9++22bOddePPXUU/zlL3/h6quvBmDu3Ll8/vnnvPbaa9x9990t2kd2jx7s2bq1Pd00hLr6SyJUvLMo/A0rNQE9ODlSUmK0C4pjUBrhIrtHD3YHoEaAa+G4C20fcEb1Vzx25B9Gu6NoJdb6XsaBHPgGKFUa4VMojXARyBrRQJ2pfkzhVGMKfyPMWR3QY4djURrhWyiNcBEMGgGuMcWTyX+jX+UGzixbaLQ7ihPQOHYwB/bY4ViURvgHugMbt99+O/Pnz+ess86iX79+hvbRag01NTWsWrWKe+65p9FmNpuZMGECy5Yta7Z9dXU11dVHF7UrLS0FoPzIkfZ31gAm1X1BL8dvpDikZdkU/obVWUMNVpy4+uYGGvm5Up5DCwmQvodZWVmtuh9PnTq1TfvVKo0IbI0AqK0vjg+hzmBPFJ4QXp8CFeiTVwVKIwClEW2F0oiWE+GsJMlR0DgRovAfAn3scCxKI47iCzqhNCJ4NAKgxmTlfdtFHDFHq8CGHxDmdF2rNabgCWwojXDhC/rgDt2BjXfffZf333+fM888sz38aXcKCwux2+2kpKQ0saekpPDbb781237mzJk8+OCDzex5Bw4E5BobmY49ZDr2GO2Gog1xYMaMAycmTDiNdqfNCQsLg2MeCHXjwLMSPx8Rm/nz57fqfZmZmW3qh9IIF3kHDgRs/3R7vfpZnH5SG6twSyDqwrFYlUYASiPaCqURLeeqsgVcVbbAaDcUrcBhCuyxw7EojTiKL+iE0ggXwaARAHaTa0wR4lTJUv6EyRnYunAsSiNc+II+uEN3YMNqtdKtW7f28MUnueeee5g2bVrjv0tLS0lPT8dqtWKvCbwMJCeuthSh1GIO8AfZYMFZn2sVqN9nVs+ebF63rvU7sINHT44+Mrd7yimn8MADDzBlyhSGDh1qmB9KI1waERqgGgFHWxUGU7ZOINLQetLsK0/M7UR2z55sVxqhNMIgglEjFP5PoI8djiW7Z0+2Ko0AfEMnlEYEl0Y0tCpUYwr/oKHFZKAHvI9FaYQLX9AHd+g+xX/961959tlncfpplC4xMRGLxUJeXtNWS3l5eaSmpjbbPiwsjNjY2CZ/ANm9ennFX28zz3o1o6OXss3c3WhXFG1ErSkUq7h6k/+zZ9s2o13wGfbt28fkyZPp3LkzN954IwsXLqTGyw/FSiNcGtE1QDUCIJQ6LE47laYIo11ReECtydVSLND73+9WGtGI0gjPURrRcjaH9uLN6EspNCcY7YpCJzVYA14bGlAa0RSjdUJpRPBoBLieQc1OB5VmNabwBxoCUMHUYlJpxFGM1gd36K7Y+PHHH/nuu+9YuHAhffv2JTQ0tMnrH330UZs51x5YrVaGDh3KokWLOO+88wBwOBwsWrSIW265pcX7ydmxo508NJZwp6vndhXhBnuiaCtKTbHEOkuNdqPdqKys9GwHAdL3EOC1117D4XCwdOlSPvvsM6ZOncrBgwc5/fTTmTJlCmeffTbx8fHt6oPSCBd7A1QjGginiiqT0gl/psQUBxDQ+gBQpTSiEaURnqM0ouX8GjaYZ+Km0r9mPYk1h4x2R6GTBEdwfGdKI5pitE4ojXARvmMHfYAk4XXpG4gR7JHigQR7qGCXtpcKLqT91NtNQASVVIZEuGx6hxbS9lGCXTpBADZtc6rQpT3bza70bN883FWPTbBLn0H6zNI50nuuQ6GTcz8PFTxAz+otR79b6TuWrok2uuaka1o6PdJvJv0Eh62trKR5EzkdBJBGGK0P7tAd2LDZbPz+979vD1+8xrRp07jyyisZNmwYI0aM4JlnnqG8vJyrr766xfsoLy8PyDU2op2uhaqOmKIN9kTRVpQQF9ATV4nJyezMz2/9DgKkPLABs9nMmDFjGDNmDLNnz2bz5s189tln/Otf/+K6665jxIgRnHvuuVxyySV06tSpzY+vNMJFRXl5QPfG/frw6Y0tqRT+SaojlysrF9CrzqPHdZ8nMTmZvUojGlEa4TlKI1pGtMM1pigxxxnsiUIvzx26PWgajSQkJ7NbaUQTjNQJpREuKsvLxXneQOOcss/oXLvPaDcULaCDo5gzjwTXIu+25GRQGtGI0eMICd2BjXnz5rWHH17lj3/8IwUFBTzwwAPk5uYyaNAgvvjii2aLPLkjPjGRksLCdvTSGBKdrs9UYE7yuR+RonXMr7gqoDOrbQkJnolNAEXRtejduze9e/fmzjvvpKCggE8//ZRPP/0UgL/97W9tfjylES4SEhMpCkCNaMCqghp+T7Z9F7dWvGC0G+1OB6URblEaoZ+2GkcUB7BGACTbXb+7AouUc6zwZUxGO+AllEacGG/qhNIIF3GJiVQEuEY08LdD/zTaBUULqTGFEuqsDRp9AIhVGuEWb48jJHQHNgKFW265RVc54PGclZLCocJCsTzQXWmaWG4mVe3YdNql+it3pXf1tVxJFIAZ8sOS68vBpIMkCnZhAt3kJlqXsl/b3lnYvqe2eUyOfAi9jJFeEI4t+gogPcOI50S6QtxV0VRpmyMhjlLiOK5iQ+81YtNpB/F6Ti3Ttku/GWHzxrLBms2b3TihOJakpCSuvfZarr32WqNd8Xk81YiJKSkUFhaKt3Xpek/TsMVIKVt6NENv2bJU31tv32buxlZzDybVfYFFvBFIfdWFeseEndp2SWil2mEAYd3JURvcvEcHo3QeV/RV+mxiS3pJUGzSG7SJLBHswvZa14levdC4XqVrO02Im0m/Gy0FzKz/r9KIlqM0ouV4qhGnp6SQX1go/tQzBbt4B5CyAW2CXev3q/fZ8AT3i2RLPlggPyLZZbCGCW8Qnm9jNFohSSdMupdqiSoQLtyTh7TRWGKIjmNKPurWB+l7MglvsB7RNFdGmfg4/PcMqV1NL/uWoy9ofd96rxmduiFd1ynCRE+mhq1COKQaR7QOpRMtw1ONsKWkBE1gQ+E/PB9/Kx/H/J6Pc35Pkj04rs+9SiNajJH60KKimEmTJrF8+fITbldWVsasWbN48cUXPXbM1zm4caPRLrQLydRnV4lP0wp/ooRYfjYNpxjVBkDE3gZ/QY7SiOYcCFCNaOCTkPOYHvYghSYpyK3wdR623sc14a/iCKq8q1agNMJjlEY0Z1+AawRAkqMAgDxLy7OUFcaTY0nnqahp/GAda7Qr/oHSCI9RGtGcPUGgEQ38N/osruw0nwMhHY12RXEC9oV2ps4UQoI9ONZgahOURniFFlVsXHjhhVxwwQXExcVxzjnnMGzYMNLS0ggPD6eoqIhNmzbx448/8r///Y+zzjqLJ554or39VrQTsZTybt3FZLDXaFcUbcBa00CmWZ7ifvvDTHF+arQ77UJyjx6wdWvrdxAgfQ+zsrIwmfRPUE6dOpXbbrvNo2MrjQg+0h2utNY9pi6k4EF5rsIwNlt6U0YM5gDvpJ6qNAJQGqHwPjHOI/yu5key64RqPIVPss/iKkPvbA+OnvcdlUY0YpROKI0IbupMIWwM68t2azfSOGi0Owo37AvpTFrdgYAfOxxLZ6URgLHjiJbQosDGtddey2WXXcYHH3zAe++9x8svv0xJiauNgclkok+fPkycOJGVK1fSu3fvdnXYV0js2pXSHTuMdqPNMQHdCLzPFazsM9UPTgjcwUn1Ee1S+hbjxLPehT6i6/Pnz2/V+zIzMz0+ttKI5iR37UpxAGpEA10drs+2w9yVESqw4Xc4MJFjSqe/Y73RrrQ7VUojAKURvkZK164UBbBGNPBM2R1Gu6DQyW5LJgAZjuBIcqtUGtGIUTqhNKI5Z3ftStWOHWL3Nqk1p2S3Ss04pII6va3wPGipnh2yEyyww9aVsbFLtLe3CG1sLcKsr9TLuotgB+ijbU7P1bafmadtFzYXm42nS9+B4I/4GaS2htKBbcI5tWuf05qqUPZGZDC6ZmnT71Xq9yd9B5Jd5zVqLdC2Zwv2cmH30kq0DW6GKI0AjB1HtIQWr7ERFhbGZZddxmWXXQZASUkJlZWVJCQkEBoqNQAPXOqqq412od04TAc2mfow2Pmr2H5d4R9sNfUAINsZuNlyJQcOGO2CT3DKKacYenylEU2pDWCNgKOBjZ3mbGCZsc4odJNjSqfSFEF3xzajXWl3ipVGAEojfI1A1wiF/7I5pDdmp4PudYGvD6A04liM1AmlEU2xB5FGdLW7xhTbQrob7InCHdtCumPHQu/a4Fpz4ojSCMD4ccSJaHVRTFxcHKmpqUEpNADF+wI3A/4L8ySmWp5ho6mv0a4oPGQTfejIQTpQbLQr7YbFavVsBwHU9/Cqq66iokJKm/Auwa4RRQGsEQAdKKKDs4gt5p5Gu6JoBZssrjSwPvZNBnvS/oQojWhEaYTvcDjANaKBbZZu3B7zDEtCf2e0K4oWssnSh672HYRRY7QrXkFpRFN8RSeCXSPKgkQjAKKcFXSx72FDSD+jXVG44beQXgD0qQv8scOxqLmmo/iKPmjR4ooNRVNMFotYpuXv9HNuAGCdaQAjCK6IbCBRiZXdpkzGOb832pV2JaVXL1i3rvU7sINHa+f60G3gjTfeYPbs2URGRgJw4403MnPmTGw2W+M2dXV1hISoW397c6rFQoTdLpaQpwt2zTCBVFcu7USrFFkqc5bKk6Vy4NgwwPWTOYWllBOFky7CT0jauVDSGyX8mLrv0bYLpcYAHNY2ZwqbZ253sy8tugn2MYJ9oGCXktOipDrzXoI9Wpd9c2R/wETvqJ1A2NEXkoQMQa1nWKmmWyoxL26hDeizRdiHgNbl2uCGtVcvXlMaASiN8CVOaaVGSHcAuurcUScNm3Tf19uSJO7o/4aZqllqHU26NYcxSFmPNm2zJa65rbNQgVws7FrSCUEj+gubd88RXhAI1zrvJwkbS+1GJH3oLNgt0sOCTbAXN7PUYWGY5Rc6Ofc3+R4B7e+7rVqQlOjbvpdQTKLV7UPqkNOw6/BevVigNKIRpRO+gTmA55q0GFS7hv+ETyGfJJLdPuArjOL8qo84qWYFCY7gWjg8Xs01NeLL+qAUqZVM6dsX+7p1uvseAqTadL5Jz+QV6J+oArAefVrtRQFWHKwxn4z8BCqNoKrcHETaldCJcJDwKxYGI9J3cabOCRJAmF0EpAqsQW721VXoXyjOdEkHlzoAalNEB7qZc+jHbrAcNxpJEgRJz4QUiN8FAMIhUoVjDBH2ZRN237Cbak97VDvwrO+hJ+9tY5zOpk0Y33rrLf7+9783ik1eXh5ZWVk+G2kPJOL69qXGk4cgP+A+Hq//P2m2RuGrXM3r9GNTQK+/1MARpRGNKI3wHTr07UtVgGsEQLozh3jnYdaYBgEfG+2O4gSEYOfBuhlGu+FVPF4zM4A0ApRO+AoJfftSFgQa0cCFlR8wofob4uKlKKfCaExAZ8d+o93wOh6vmRlAGuHL+qACG62kYteuY3McAwortfTjN9bRFzvfYPGVFWsUukijiLe5PuC/PXu5lDasOF58AKqqWhF8VOimfNcugrN4XuEPdKCYM/jGaDe8Qp3SCBGlEcZRFiQaYQIGOdbwvWUcpUQTK1XsKRQGocYR7lE6YQwlu3a1vme8H9KrrjWZqApvcZBUfg0bzOiapcQ5S412x6vUKo0Q8SV9UIGNVmIvk1LbA4PBrGc1A9hIOgPYa7Q7Cg/wpPLNHwhNTITCwtbvIIDKA1uCyRToV4RvUFtWFhSTVnP5MzsZzWzmGu2KooXsJZk6sshiV8DrA0CY0ghdKI3wDnVBohEAox1L+dYynqUMZTKLjXZHIeAErmY2IyyLuMk+x2h3vIbSCP0onWh/Ti4rIxq5X4PUuMMmdeiQGkJITTikjqht1TFEw27HzFrLWQxiE+ZmqZnCmbDladuzhdYR7uaodSaZpwtLTaQXC2+wCXapHeEQnduL3QilvpFCn2JL84npbzmXp+Mv5Vn77Yx2/tT0xVph93rt0rJOOvcjXYrhQoczrc6ccLQPTVViIvcrjWgxRumD7kDwlVdeyQ8//NAevvgVofFSx87AYCw/0ZPtVAXNsCuwKCWCO7iCpYww2pV2JyxFWjyghTja4M+HePvtt1m9ejW1tdJTQPuiNMJFWIBrRAM5pPMtgylo1oxb4avMYzIX8Sa54qN/YBGuNKIJSiN8A2uQaATAGMcSTDj5geFGu6Jwwxay2UAPKk0RRrviVZRGNMdInVAa4SIkiDSigVfN13Idj7KBHka7ojiObxhNJBUMc/5itCteJ1RpRBOMHkdI6A5slJSUMGHCBLp3785jjz3G/v3B12cNILyTFNsLDPqyhbe4gRF42FNOYQg/0Jsl9KJAXNkxcDiycaNnO7C3wZ+PMGbMGKZPn86wYcOIjo6moqKC6dOnM3fuXJYvX86RI+3fAkJphItA14gGxrIEgCUMMNgTRUuoxcL3DKYH2+mIsL5VgFGiNKIRpRG+Q2SQaARAPEU8WvsPbuV1o11RuOErxgBwuv1rgz3xLkojmmK0TiiNcBEaRBrRwGjnUsA1ia7wHQ6SxHp6Mtb5A2FiWUXgUqk0ohGj9cEdultRffLJJxQUFPDGG2+wYMECpk+fzoQJE7j22muZMmUKoaHBkeFftn49kUY74SWcBH47o0DjKwZgwcGp/Gi0Kwovsnixq8XDtm3bWLVqFatXr2b16tXce++9FBcXe6U0UGmEi0Hr1xODXEIeIwmIVhlxd2Fbya61j0xhW70l5cfVrI8ijxBC+JrxnE/OcdtKdexS781obXOnOm378FYMdqUEuONdPxHpgn2gYJcSlcWBq7Qgu3ROpSutqX0F3SnDxhWsQ7P/QKpQV6+VlCM9aEuZRTr2YRFuE/13atszNdoHNFxlgd04VB9KI3yHvifQiCTpNGQKdqnNSDfBrnUr0dt6REpijGj+whmsw3WDjNV4Q6KwI40BcoRwxvrouHe5Q9CI8AM696N1zqT2IZI+SNtHSC9IX7agrRxtreEEvuF0kqmkX3gBml9uikbLF0kH9NqluTJhe+l3M2R3c1u6cA0ojdDGaJ1QGuGicv168ZcbqPRxbiKDAyzkFG5jASG+NKMbxHzN7wA43RFcQW9Fc4zWB3e0ao2NpKQkpk2bxrRp01i9ejXz5s3j8ssvJzo6mssuu4ybbrqJ7t2lGZfAYAyuR/MoaQNpEADy4EPvc2qmYO8s2FPdLXfefILjMLHM4Hb6k8tfOL7sTHLKIthtbo4tTFqd9JWb92ggTVjt07cbQD6HgwS7NAcFwGk63ySNLFsm7qWEsYJ+nEQOcdKFmLpK215TrW2X5iHdrQ2kM6CfLqwZli4MJhtaZJZ17Qo7PKgscuBZJNzHygMBunfvTvfu3bn44osbbTt37mTVqlX8+uuv7X58pRHBQwwVjGUL39GbXOJIpcRolxRu+Ip+AJzBTyfYMnAwK41ohtIIhREcJoY1ZDOetUa7ojiOtWRzgAQu5VuN3vaBTYgfaYTdbmfGjBm8+eab5ObmkpaWxlVXXcV9993X5hNKRuqE0ojgxASczbe8xGX8xBDGstJol4IeJ/AJpxNHGSOdy4x2xxDC/EgjvIXR4wgtdLeiOpaDBw/y9ddf8/XXX2OxWDjzzDNZv349ffr04emnn24rH30SU7a0Ok/gYKOM7STwKb1xqJoNv+FrumHHxOlsN9oV71AtBGNaSoD1PZTIzs7mwgsv5LHHHvPaMYNZI8xBoBENnM1anJgaJ80Vvkkp4SyiL/3ZRyfyjXbHeyiNaBFKI7xLSBBpRAOPcAl3cy2FmlUbCiP5tj5zawrBN3Hl9CONmDVrFnPmzOGFF15g8+bNzJo1i9mzZ/P888979hlaiLd1Ipg1IiwINQLgLL7DhJOPOcNoVxRAHSGcz5dcy/tYdZdABgb+pBFGYsQ44lh0V2zU1tby6aefMm/ePL766isGDBjA1KlT+dOf/kRsrOtB9eOPP+aaa67hjjvuaHOHfYY6ocoggDDj5Ey2MJ8hrCaNYQRnj0t/4xP6EEktpwXJ+ijOfa0pyTkGT6tcfaRKNisrq1XZWlOnTuW2225rMz+URtQTBBrRwCi28SxvcXKwBFP9lHLCGM1WTuU3o13xKg6lEYDSCF/DGUQa0cA5rOAH+vMhv+N6/me0O4pjmMrHnMEqsoNk7aVjsfuRRvz0009MmTKFs846C4DMzEzeeecdfv75Zw+dcOELOqE0wkXvkDpiI3HTr1CwS61SpTiJVPgiNZDIFOxStxLJT6v2AVLowFmspQPVOOh+TAWZ1JhL6J2bIMyD9N8j7McNUvtg6VxL/e1iBLt0rqVuLv0Fe4K0I6ldjHROj7aBDAUuo36NCeE7I104p1LXOMkupdxLDWGk7YXfjE1oO2wrEPZT35mk9JD/aER74gv64A7dgY2OHTvicDi45JJL+Pnnnxk0aFCzbU499VRsNlsbuOe7OPfuNdoFr3AOvzGfIXxAPxXY8BP+yf/YRgKRwRJVt1jA7iN3fAOZP39+q96XmZnZpn4ojXDhCBKNAAjBwWi2Ge2G4gR0pITZvG+0G95HaQSgNMLXsAeRRjRwCutI4xAfMJar+JqwYHlO9QPMOOlHKyb8AgEf0YjS0tIm/w4LCyMsrGkb6VGjRvHyyy+zdetWevTowdq1a/nxxx956qmn2sQHX9AJpRH15OwN2kVOZ/Cm0S4ogHzicGIihWKjXTGWEAs+E10wEF/QB3foDmw8/fTTXHjhhYSHS+FjsNls7Nq1yyPHfB6TCZyB34O0C8WMYi/fkc0BYkhTS6z5PMmUk9y4AkXgY+7bF9ata/0OAiSKfsoppxjtAqA0opEg0Yhj2UQaP9KD6/jeaFcUx3GIKDpQEXS90wEsSiMApRE+RxBqhBknf+I7nuQPfM4Izmep0S4FPZVYeYQ/cSnf0ofgC7YBhPqIRqSnN03/nj59OjNmzGhiu/vuuyktLaVXr15YLBbsdjuPPvool156qYdOuPAFnVAaUY85+DTieMqIwIGJOCqMdiUomcPZLGQ4H/IwnThktDvG0bsv/Gy8RrSU/fv3c9ddd7Fw4UIqKiro1q0b8+bNY9iwYR654Qv64A7dgY3LL7+8PfzwO8z9+3v2EORHXMYafiKD5WRwfkMpmsLnOEAMX9ONC9kQPNUagMPTB1sHnmXE+EnfQ2+hNMJF4uT+xG5bB2HCBlJpdicNm1Q+3k2wd9WwSdXJUum4qYfwQqaGzeX0B4znM3oxilL6kSdsC3JNcaJgl1xxMykWKlQYSuddKkOWkPYjlfR30vpiAUYL9qGCPUWwS0+9dTiBv3IBNYTwBu9jwQlS9pVFaI/TZWtzm1RKrqdkXNpHlGAXznuMxvcXU98StxSlEb6E0ggXnU/rT+yWdXKbkXjB3p5tRjKFbSWdiEoQXtASIddBz+UAL+NgPlM4h4OEYkdsJYLW/cgm+CLsYuAmbbt072lPjZC+C6ndSJT0gjQ5IemMNNyP4mNG8CUjGUQpfRpvdEe0N4/V+D4swmSXpAMS0vaSXWoNo/G7STosbNvQZqTaNzQiJyensdUS0KxaA+D999/nrbfe4u2336Zv376sWbOGqVOnkpaWxpVXXumBE76D0oh6+vaHDcEx16TFfhK4nDuZyCruCsZqY4M5QDyfcxKD2R7cQQ2A3b6hES2hqKiI0aNHc+qpp7Jw4UKSkpLYtm0bHTp08MAB/0B3YEPhIiJmD1FDkAck0twDyAMHqf2d9CAsTWplSDNpfSWPgF6CPZXh1PExX5HOEY4OPqRZMqlXn7sJK+EylB5mRy3StqcIEzt5bg4tIX1/XSWnTnOzMym6OVCwS+dKGGjUvzafYXxEV/pgZ3jjwrDSRVKlbc6QAletWDRJOlVWwW4T7ML3F1X/Eez2MljbcreaESDZuAof40DwtXS4nF/5L72Yywhe4DOj3VHUs5LObCCFC9hYH9QIMo54WGmqNELRHuwPPo0AiKSG2/iWUOxYVNTPUCoJ5XVOJpEjTGGN0e4Yh49oRGxsbJPAhhZ///vfufvuu7n44osB6N+/P3v27GHmzJkBE9hQ1LM3ODWigTQO0YV8PmI0l7GITq2Zi1C0mheYggMTf2Gh0a4YT5lvaERLmDVrFunp6cybN6/RlpWV5aED/oG05IriRBwpMdoDr2GC+qAGwTgl4hfkEcGnZDOAQoY1BjWChDgprVGhMJDS4NGIBrIpYjJbWU46q8QUX4U3cQJzGYEZJ1ey2mh3jKGD0giFDxKEGtHAeazhLNYHZWs8X+INTqaQaK5hKdZgjsD6kUZUVFRgNjedvrFYLDgcKkgYcJQEr0aAa/7pZj7FjpkXOddod4KKdWTxFUMYwwaGqTUUIcE3NKK0tLTJX3V182Dfp59+yrBhw7jwwgtJTk5m8ODBvPLKKwZ4631UxUZrie0A5UVGe+FVVpDC0wzmWRa7LUhReJ+59KcOE39mY/CtM5bcCZDqzFuAajOiaA9sHaAsuDQC4Hp+5ku68yIn8yrrgu9+5GN8TTfWkcpFrA/eNbKURih8kbjg1Ihj2UUCm0jjLN19nhSekk8UrzOSTA5xPr8a7Y6xpHaCDf6hEeeccw6PPvooGRkZ9O3bl19//ZWnnnqKa665xgMHFD7JqR2gskjudCDNtUp5RZmCXWrCIbU9lPafLLVqkA7QWbAf7VwxjCLGsp2vGMF5FDCCHI3thS4UUhcRqYMiQH+hSkZqdSh1/5OWOpX2I51TqcVkgnROpW4g0vbNW884gac4GwtmprKYpp1EhHNtEb77TsL5DBcC6VKLSKlDjmSPEexSq0np9l9T/9+MTrDAeI1oyTpMO3fuZM6cOUybNo17772XlStXctttt2G1WgO+qk8FNlpLajrsCK4BiQMT24ljLv2ZHkSLU/s6vxHJf8liBHmMJNdod7zPtvWevd/TSSc1aaXQIjU9KCetOlHKBWzgffqzjn0M9GRCWeERNZh5npOIpobrWGm0O8axRWmEwgdJS4ctwacRDTiBB5jCdpLozydkENzZyd5mBZ2pIoQ7+IaQYL9JbfYfjXj++ee5//77uemmm8jPzyctLY3rr7+eBx54wEMnFD5HcjrsCV6NaOBvfM4KuvE443iXt4O7uswLmIDrWcQW0uhCodHu+AY7fUMjWrIOk8PhYNiwYTz22GMADB48mA0bNjB37lwV2FAIbF2nf2E0P+dkchlBHp+Rxe/ZzQC3az4ovMWbpGLCyTR+VdnRrcGOZz3Wgnw8qBD4Lfg0ooEb+JmJbGOguOaSwhvUYWI8O0mjFJuY0aY4IUojFO1B+DrXGtBShqKUVSpldkrZtVKiZqaOfVultFS9mbi2xv8zAX9lGX/mQmYxiRdY2sJnWJ26ImXI9hMWFbcJ2+tdO1Xr+5NOS4TeRcKlRRndrWfYlHMopyfv051DNP/QkqMaRAkZuV32a9ul612vXcrI1Trv0ndXe8x/BXdbhBc1IiYmhmeeeYZnnnnGgwMq/IId69RMHZBGMbfzBaHEqSCslxjJdkay3Wg3Aoc20oiWrMPUsWNH+vRp+kzRu3dvPvzwQw8c8A/U7VLRYkzAXazij0xmJl14k43BOm/nUzzALs6kkG7Bmu3WKRvW7jTaC4WiKcNwlY9Lg3KbYNeaWNIzMQXaE1ypzbM6XEgTJNLOtSa4mk6mxAIDqQNSqMNESLOnOWliqg0DIZ1WaNttwiRMW0xaAURJX9ZJgn2oYO8q2KWJq+aJBpHAHWwR3idNVAqYNOq9O21pbgMIFRZ41Pot6C0jl/pgahUGNUxa2bLhBaURCoWvMYiDnMsmPmUwn9KFKQT3YrneoBILZpyE4aCHysZ1kZoNKI1QKHyVi1iB7udWhS4cmHiSsZzNZvp4FOkNQDr6j0aMHj2aLVuajs+2bt1Kly7S+DRwUIGN1jIhE8p2y4NyqYcbyFlVUh89yd5JmqiS+ut1E12SZ87imm11Bbt5jQG8Szcupfi47aXZHpubY0vUCXbhsu26Vtue3Yqbs0kST+ncSpNW7t4jfR9SelnTmTcHYMY1dzqKHI7/rlxI36vOc5shnFuLMIEF+vskSr1CpdbLDQnI0XXwP9mNE6LajCjag6RMKNlttBeGsoUo7qEXd7OdEcEaeDUAO/AWnfgDB4k02hlfwCHpXUvf7+nxPXy/IjBJzoSi3UZ7YThTWcJyevEkAxhKIZ1Vq9t25Z8MYDWJ/IslboeqQYVdaYTCB+mYCQW7jfbCpzhIDHMYyd18R2Rj9oqiLVjAUN5nIHWY6RPs6y4djx+NI+644w5GjRrFY489xkUXXcTPP//Myy+/zMsvv+yhE76P2WgHFP7HNWwjmxrV9shg/kEqj5IshiiChsN7PXu/vQ3+WsjMmTMZPnw4MTExJCcnc9555zWLqldVVXHzzTeTkJBAdHQ0F1xwAXl5eZ59RoXCAKKpo5BQZtCDUpVH4TUW0JnnyOQdld3motB/NEKhCDZiqWYGq6jCwndiLyxFW/AlnfmETDpSQYJqT3iUAqURCoU/sIQs/kcvHmSCR519FE1ZQ0fmcDJdOcQ0lhjtju+R5z8aMXz4cD7++GPeeecd+vXrx8MPP8wzzzzDpZde6tln8APUTENrObTblSofhITj4G32qIvHQBYSw9fEMJ4jqh2YyYRHjQu92Bt38eLF3HzzzQwfPpy6ujruvfdezjjjDDZt2kRUlKtS54477uDzzz/ngw8+IC4ujltuuYXzzz+fpUuXeuCkwusU7A5ajWigE9X8jZ08RHdm0pXH2KIC4u3MamKZSxe6UcFlqpTchR9phCKIyN8tV7YGGSMo4D0WkU2Z0a4ELHuJ4lEGk0AVD/OLymw8FrPSCIUPkrjb1X1J6nSg1agBIFWw612fSbJHSd05pGQaySGptaq0/2guZCeryeIbevI6JVzJOvR3BXET1E0Q1o+1CP1qpbap0iGk71I6RTbpXEhthKVOHdIB6sgnkns4ByvwOIsJJxz5O5CQPpjQYjhBGJ+EC+dZ2r3edrZSmaLUVKDhe6wxwRL/0Yizzz6bs88+24MD+idqblrRKhounCIsLCOSM9VgxGvsIZTHSCaROu4mX00UpvUH1hntRYv44osvmvx7/vz5JCcns2rVKsaOHUtJSQmvvvoqb7/9NuPHjwdg3rx59O7dm+XLl3PyyScb4bZC0WrOIZ8lxPM1iQygjEs4YLRLAUsRIfyDnlhx8Di/EaZmS1yk+49GKBTBSkNQowYzv2FjgObCOYrWcIQQ/sbJVGHhKZYRj5s2ssFIhtIIhcIfMAEPsJhd2HiB4XSliN9x0Gi3/JYqLNzBRAqI5DG+JatZi3kFAB2VRvgDKrDRWjoNgAJ1gT9MCj8QRThOxmssXqpoW6oxcTcdqcbMsxwgXtUvw2EPF5tso76HpaWlTcxhYWGEhUnr4LgoKXGlCMTHuxYYWbVqFbW1tUyYMKFxm169epGRkcGyZctUYMOfyBgAuUojTMB0trGDKJ4mi4GU0sdopwKQWkzcTS8KsPIg28ik0miXfIdC39AIhaIJpw2AknVyZV97Z+N20qr37SpsnKnTGb3r7R0djt7DCJaTxCsspY+uHurSGKRY2xwhpNRmCwuE6l2QQitj1CItmiituSdlQEuZztoXzQIy2Ekct7GJYRRz9HzbhP1ITW610mOFjFyrsF5g5g5te6gwntGbkau1Xp+UjVvT8D9KIxQ+SNoAOKTGEccTSR3/5Cuu5Dzu5jRe5X/0VIHwVmHFznAOMI7dnOEni2MbQpHSCH9ABTZaS1GO0R74BHeTzwYymE4KmdSI65wr2oZ/ksQ2wriBQwxRE1cuKj1clNiBZ+WB9e9NT286ezB9+nRmzJghH9bhYOrUqYwePZp+/foBkJubi9VqxWazNdk2JSWF3NxcD5xUeJ3kHEhBbjViE+xakyd6y8RtWpMbUtlypmDXM6EiTGw0vmrnn2zic5LpyRFhH9K+QZ6wkkqu3bwnSmjeF6V3HZsUwd5LsEu+SvuRzoX2LE8Z1ZQRwkUc5Czyj3lF+m4669q/tl3YNnmLtt2qca+WDifMiYmTi8UatoY50Srf0AiFogmlahyhxdVsZRlJ3MbJvMYqMqgw2iW/5zp+I4MjnI265jRRGqHwRYrV71WiM2U8yxe8RX+6iJFLhYTrlmXCAkxlhboFnQgfmWtSuEcFNlpLdhFEIA/K3bWm05tVlSqN8AcKdinzx90kkDTJYRPsrmyeZOp4nBxuoAt/JZXXqKCD5q9P+gwgT+BI/ha72ZcGJmniyB1SFprenoYgfz5350SLOi6lkDDquKbJxBXI35P0vUo/fZ32TmuF7QFLubZd+s3YBLvQbrGx76GjA8wvkv04EQ7wqJ9X/eWek5NDbGxso/lE1Ro333wzGzZs4Mcff/Tg4AqfpbpIvtaDkCwquaU+K9IBVGAmWqWgtBnx1PIq67Cqc9qcsA6A8RqhUDShUmmEFv0o5nF+4W+M4BaG8Bo/k3g0tV6hg5XEM4zDhOLkHBXUkIlQGqHwQSqKwP1QMqjpRwEz+ZaGOYpiwrCpNnsnxAk8zihKsfIIiwjBqdqan4hIpRH+gFo7rLWESTXiwccQKvk7ueRgZSo2KtTtsc0pqz+nXajhr+SrH+6xREsRQe8SGxvb5M9dYOOWW27hv//9L9999x2dOx8NPqWmplJTU0NxcXGT7fPy8khNlYJtCp/EqjRCCyfwIJ24gSwq1J3MYz6kA5/XR4UjcCDUowQ3sb6hEQpFE8KVRkiMJY/7WMMBIriBYRSK/boUEq+RxY0M5f/EBCdFI0ojFL5IhNKIljKf/lzE79mheyHx4MIBPMYoPqInZYThUHN2LcOmNMIfUBUbrSW6C1SovocNXEgxBYSyQbO5qcITFmNlBrH8kxLVfkoLT/uP2vFaFN3pdHLrrbfy8ccf8/3335OVldXk9aFDhxIaGsqiRYu44IILANiyZQt79+5l5MiRHjip8DpxXaBMacTxmIBO1PA5Nm6lC8+xhyhVZdAqPsPGTNLoQg2nswOrSunRxtP10LyoEYogIq6La40NhSbnkkM50XxMJxUC14ETeJVs5tKVHpQxEdXG9ITkKY1Q+CDDu0DVOrmlrbTOjNSyU+oYInWetUjdKKQ3SEFUKUAjtUqVShmltX8gk8OUYuU6JvM0XzKAfDf7cdc+V+jyYSvWttuFtYGEJZzkrhVSWpLUdUT/uavBzH2M51syOZkcnuRrrGI1pLR/aepY6kJi07f/qP3a9u7CGheRwu71rMEEUCbYG1raViqN8AdUYKO1HF6vSsiP40YKsGNuvKgcqJIgT1mGlbuJIxqnWii8vfCi2Nx88828/fbb/Oc//yEmJqZx3Yy4uDgiIiKIi4vj2muvZdq0acTHxxMbG8utt97KyJEj1cLh/kbhelVCLvAXCijHwlskcDOZPM9uYlRwQxefYuMROtGRWl5ilwpqtCdqQKJoD/KVRpyIS9jLBexrbLFXgUWcx1C4xl1P0ov3SacnZbzIKmLdTAYq2gilEYr2oFjNNbWUcezhn3zNXZzGDZzFgyzmdFTiQAPFhHEnE1hNR85gJw/yPaFq3OU9lEZ4BRXYaC1OdYUdj4mjF9QCIllLKI9TogrIW8kyrPyVOCJw8hLFZKoew9rEZgK7W/9+L/Y9nDNnDgDjxo1rYp83bx5XXXUVAE8//TRms5kLLriA6upqJk6cyEsvveSBgwpDUBohYgKmkosVJ/NI5EayeI7dqt6vhbxOIs+RQkdqmcsuUtTElXv8SCMAfvjhB5544glWrVrFwYMH+fjjjznvvPOO7s7pZPr06bzyyisUFxczevRo5syZQ/fu3T1wUuF1ejpd2YbSQ7KUcSglcErdKhOkTMquGrZMYVspQ9em0y7N0knprUdPz/ek8giDmM0yhmguviYNaaXMU5u22SIskhgnLfgmYNLaj3BM3dmx2symK/9HJ4aRzz/5mahGbZDOr97jan1/0vUlfdeCvdMOYXNhvT49GbnSuq8NwypTJv6kEYogQY0jdDGaHF7hv0xlIvcwnkLMXMIao93yCWqwsAcbF7ORaSxTicd6ic5EaYTvowIbrSUmA+r2Gu2FT+IEdhDCD4TxN+KYRQkRRjvlZ3xJGA8QRxQOXqCY7mriKiBwtuAhNTw8nBdffJEXX3zRCx4p2o1BGWDe2zYl5FL5eIQ02dQWE1aJgt2mYZPSjqXJlEJMwE0UYiWdd0njCCXEi9tL1WruUtl0TmbproiT9qO3PFw67hFN6zYcPE8CXSnlBTaS1DgzI507adJJz8SVZNc5cWXTmLgKE8rOpbRsPWXkDSXkfjYgKC8vZ+DAgVxzzTWcf/75zV6fPXs2zz33HAsWLCArK4v777+fiRMnsmnTJsLDVXqn3xCZAahxREux4qAGMzfxO+7jV85W564Zp7OfUkKZwa+NVS4KhcJPicoAh7rP6aE3hbzOJ/yV0+lBgdHuGIoT2EMHMikimQre4UM34yyFwv9RgY3WYlKnTsIEzKAUK04+IYK/0IGnKSFJPWS3mN2EkICdFygmW7Wgck/pbs/er8oDFe2B0ogTYgL+Qg4XkEt8/Sx0GRZi1D1PpDsVzOY3hlKiWoy0lMrdnr3fyxoxefJkJk+erL0rp5NnnnmG++67jylTpgDw+uuvk5KSwieffMLFF1/sgaMKr2IOQT0Wt5xR5PMqP3I7I5jBUNaQwN9ZS1iQn8RVJBJNLT0pYSiHGKpZzaJwS/luz96vxhGK9kCNI1pFCuW8zieY6xOENpNMGWGMIMdgz7xHKWE8zql8S1cW8D49qVRBDU84stuz9yuN8Arqjtla0ne6EiGlbFybm/dK5eI2ofyZnjrtmYJdWtQJQDq2lH0q3RzzANfaGv8gj2QSeZlEriSaZ9lPd812StqZqfqzdPVm6LqjrbJuQf58TQcftUAtJiKp5Trgj1iwNdmvO0FytxCWFnp/+tKiVm5I3aJtDxcGXVIGe7Fgb8jGFarUW4wSG0V7ULlTzjpXNKEhqLGFKK6jP1ewn6vJUaXS9ewigqfJ4hG2Eksdp6qJK+/SRhpRWlraxBwWFkZYmL5FFnbt2kVubi4TJkxotMXFxXHSSSexbNkyFdjwJ44ojdBLd0p5k++4n2F8QibbiGMe3welVtRi4l/0YQHdyaKMd1kUlOfBJ1DjCEV7cGQnquVF62i4Fzow8SAT2E4i57Oe2/lRrGMOFH6iC48ynjxiOJUdpFCGmvI1GKURXkE9AynaDRNwHYU8xAFKsVCpLjeRQ1i4gc7cR8fGNnw2lbXcMiIHePZ+J66sydb+KbFRKNqECOx0pJo5ZHAbfTksZg4EB07gI1K5nEEspwO/Emu0S/5JjG9oRHp6OnFxcY1/M2fO1O1Kbm4uACkpTRdaSElJaXxNoQhk4qnmeZZyI5s4lz1BObLYSQxXM4759KA3xTzJ8qA8D21GnG9ohEKhaFvMOHmK/zKcHD6iPxdyOd8wICB/cvnEcidnchtTKCOM+/mG2XyOTVVqeE4HpRH+gArftZbI/sB6o73wC86klJOpJr5+on43oXSkjjD1KwVgOZHMIIVCQriUInVW9FITPKWlCj8ipj/YlUboIYMqFrCWJ8jmE1K4mMHcxQ5Oq68EDCaKCOFhuvMD8aRSzUNsZQilJ36jojmVvqEROTk5xMYeDU7prdZQBBhx/aFWaURrMAPXcrQquIBwZnAWt7OcHgFe0fYvhvEaJ+MErmEL17GZEDVy8Awf0QiFognD+4Nlvdw0Qar401qrDyA2Tnihi2CXOn2kCHZpbT6p24XeaUhpgr5YsLu0II39vMRmPmYYzzKJu7mEEQzhBV7E3OzeKRzD3s7JptL+LdJnbt4NJIdUviWD8azhb3xOcpMxg97OLDrX0NPdNUWqmxG2twjXbsYeYTcl2vbDwmG11umDow1ZlEb4BSqw0Vpq94PVaCf8h4agRilmbqIz0TiYQS59qDbYM+OoxsTzJPIuNqJw8Ci5TBTvrAqRuiKP3m5H/7LBx79foWhG1X65VaFCJAwH97GdERTzOF25i168wX56IzykBiC7ieYahlJKCJMo4C52qHVHPMFHNCI2NrZJYKM1pKa6epnm5eXRsWPHRnteXh6DBg3yaN8KL5O13zUxJaXaS+P+eMFu7SS8IE1aZWrY9E5YSZMZeoeX0npBWlVIxc0sK+nFSpK5lClMYQ038T3xVNS/Wijsu/l+XAjtY2u1zSJWrf1Ix5R8bH4R2CmjG/u5l2/p2yzobxP2o/d70jPZpXdiTOcEWMIObXvMfm271qSuNLRqWJ7liG9ohEJxLJtNkXQI647VVEOko4IO9iJC1dpqrcIEnM8vnMom5jKSWCoagxqlRBLbqBf+QzWhfMRounKQEWxhKNt5mxfpoambipbiwESpOZYKcyRmk4PUujywK43wB1Rgo7XUHlaBjVYQjYMLKeZfJHAVGVxOEddzCGsQZhs9RyLvYWMQlTxELmnqYaV1hMSBB5OeSmwU7ULtYRXY8IAzKGQYJXxLQmNQ4wARJFFFaIDqhQPXHGcGRxhEKZMo4AxxwkvRYiyBoxFZWVmkpqayaNGixkBGaWkpK1as4MYbb2zDIynaHYeUOqjQy5n8Rjd28k/O4BMG8RV9uJzlXMxKcfk2f+FnMpnPKB7nI2Kp4s/8yPWswxKgOmgIpsDRCEXgcEPa41himwYCJxz5hsfz7gHg64gJFFoS6VK7h+y6naTY8zxq4x8MdKCCe3iv8e5ZQRjn8wAD2cmlfMtgtvv8OSwilg+YzPucQjFRjGITI+orGFVQo2VUmcI4aOlIVt1uAL6LGsf8DldRaEmkMCQRe32Z1NCKVfxr3w0BNY4IZFRgo7VYYpBTQBQSZuBqihhDOQ+RwgI68B39+Bt7GRUEGbnFhBBLHWbgcopIpo7LKVK9cT3B2gVY1+q3N7Qv9OT9CkUz+sdAdJn+EvJYrfY0XYWNMwW7VuauVFIuZeJK6cJSFqYWUgm11CqkaYZrPPAHAAqxY+IO/kgNFm5lGePYhZl9bo4tPdxLxy52sy896M1ereMAMczlJI5g5Sn+hxl4SvRH2r/0fUmPeZJdumC1/Jc+k9RiScP3CKG8vKuQpZsqVHlqPY41jASq/Esjjhw5wvbt2xv/vWvXLtasWUN8fDwZGRlMnTqVRx55hO7du5OVlcX9999PWloa5513ngdeKrzNnQlPERXjwEoNfzzyHoNq1gLwbcSpJNoLyTTtJtapxhktpQf5zOVNvqcnz3Ia/2IsPcjnFLYa7ZpuHJj4kb68wXn8Sjqh2FlLZ8awHSt2VMPtllNLCDkh6eRZUiiwJFFoSWR01VJ61rquizsTZnEoMgW4qtXHUOMIRXtww6EFhNSYqbFYOWKOpsjSgV7VvzW+/knUeawIO6nx3/GOw/SvWc8fze8xwrHSCJf9hobgxRHCGcx2vmMgi+lPNmdyIQuZyBJipeo9gzhAMnP5E98wmhqiSeMQ1/IFv2ep0a75PAdMHVlrGcjauIGsDR3I9tBuhDmr+SF3LGac1Jis5Ickk1RXQM+aLSTWFRLlKCe9ur4FlZpr8gsCKrCRmZnJnj1Ne63NnDmTu+++u/Hf69at4+abb2blypUkJSVx6623cuedd+o/WJ8siFonZ+RGSZMDIJeFdxPseie1pFJym2AH/WXK0oRVy0q+uwHzMPEWWbxCNts5zCh+O+490sSUMOEhTnIVC3Z3tOVCS0eowsJ7dGc+PbmJdVzIdlLZx5Wa29uE/UjfEegvJ5fQM6nlzu7m2Lbtgl3ok1guxKkb2gKUqh7VipbhTY3YHHYqJ/EpAHX1v6sQlXPRahyYmMRWXmMYdzKJbhzmzyxkPJs1euT6Bwew8SZj+Ii+1GHmJPZRjYUwdZ14RBVhHDbHU0U4VaZwDvl8/l1TfvnlF0499dTGf0+bNg2AK6+8kvnz53PnnXdSXl7OddddR3FxMb/73e/44osvCA/XE3RUaOFNjVgTPgxHRBgOzEyuWAhADaHcGT/btYEFOjiK6GLfQ1f7Dq6reJkEp6rycIcJOJUtjGUri+nB2Pqgxq905RNGcRE/0BfhWdNH+J4BPM8U9pBMCCGcy1r+zI+kBUHyl6ccIYpyUxQpznwA7oyaxffWcThim6aPRTnKGwMbG8L6UVajpo0ULcObGnFJ1Wpia9eJQ/OHDj/ArtAsdodksj20Gxus/VgSPoaJ9i8BV/jzYev99HNsYLR9KSlB3P5bIpkSnuDf7CWJ/2MMnzKQWVzPh0ziXW4HXOMPIxJQHZjYQE86k0s8JYRSxxeMpT9buITVnMpaLGrKW5M6LDgwY6WWvaZ0zo/8yPWCBRIdhZxa9R3dardTSyhh1DDxyFdMPPJV8x01rrGh5pr8gYAKbAA89NBD/OUvf2n8d0zM0SLk0tJSzjjjDCZMmMDcuXNZv34911xzDTabjeuuu07fgewb28rloMWCkyvYyWR+I65ebPOJ4AUGcBWbyQ6AcroaLHxGV16hH4WEk0Y5SVQa7VaA4dmkpioPDC68pRE3R9/OL9WuwMa31vHcG/MYcY4SEhyH6Og4SBfLHvrYNzGpfgCicE8oDq5mNVPYzBsM5gP6cTcX0YcDLOBlv5q6dmDiPi7gG/riwEof8rmF5YxwW4GiaMCOmRxTOjtN2ew0Z7PTlE1ubCovl15HCHaWW0/mbzFPNkqD3XIEONXtPt0fz7saMW7cOJxOWddMJhMPPfQQDz30kAdeKSS8pRFf7T+V2BI7DnPTu9djh+/lYEhH9kR0Ybclk52WbNaGDuTWiucB2ERv/m35M6McPzHK+RNpHPTg0wYmFpyMP2Zx8RX04nNG8Dkj6MF+JvELp1NCRwoM9NJFLSFsJosB7AKgiGgOE8NVfM0f2UGSj2UN+xI1hLLSNJylptGsNg1hh6krp0d+zWPl/wCgs2MfY2t/IKt8Fx3tB0m0F5JkL6Bz3VGt/d+Bsyg9AtKyyi1BjSOCC6/NNVVudJufmOA4TEL1YYZVr2q0VZnCMCW6nh/2mTrz39Cz+ZRzARjIZibzNafzPXFNFpZWZFDAND7iRp7lG0Y3Wcvk79xNkcnMySxnuHMlvfiNiDZNgHXhBHJI51cGs9o0hGWmkRymI3/lVS7hM5I4zH/5C8kcQk6GDl4cmPiVAXzORBZFjuae6plMsn9JujOHK2pfp7tjGwMPraWj/WArxoxqrskfCLjARkxMTOPiisfz1ltvUVNTw2uvvYbVaqVv376sWbOGp556Sr/YYOc169VsC+lODGXEOw8T7zxMgvMQvZ2bSSPf8w8TJBw70f89nfkfmSwkk9PI4jLW0Bf/7Bm5kWT+ypkUEkc81dzJan7PDkJVdL1V/GweziZzHwpMSZQRQ7UljCpTOKOrfgHebPV+VXlgcOEtjbjhyJzGir4ExyEmVH/DYXM8heZEfg4dwVLLaIaYVzcGNt4LuYgt5p70YwvDWUW6muTWJJ5KbucnrmA1b5GFA1OjPvxAT2xU0J8cn9OMYiLJJY5eHMSMkypCOZntXMZ2hrPP5/z1JSqIwISTCKooIJHfh39M1XHVgvHOw5SaYol3FtG9bhtXVL5OmL2acKqoq4jgzx4cX2lEcOHNcQTQpOLMSi1nVH7t+kf9heMECk2JRDvLAdhryuBH0+/4wTIWgEx2c4pjMZP5lW7s1ulDcHADnzOeNfwfY/iKITzHFJ5jEtN4lT/xGeA6z966Dx8mjpUMYDEj+JFhVGDjYx4inQLOZCUTWUUk1cgV/oqvTRN40DK9UQuSKOA05yJG1f7UuM1tla5g4Ak7R1sygL2t9kVpRHDhNY0YkAyxZuTpuubhONevweVbOvANM1lOLxYzgO85ibWMZg4VfMXjx6zTI7UylVrV6m25KvkvrS0qBXP1treVxlFy1V5EzR7OYXMTW5TlACvrxrHONICXuQ4zDrLqdnFv2WMMLFyHAxM7rF1JrssnxlHm0vRa4QChUG2ykheSQr4lGYBhVaugCmZG38NHEec3bppdt5PzrJ9wkuMLqA98J7PT9aJVmurWG3CRvhvpu9Tb4UX6jm2CXft3pd1iGaAfecTyEcP5H4M4WL/fXiG7CAsZADgxAbc1JDpEJgFJGvuRKiLr/S/tCPwibHNilEZ4h4ALbDz++OM8/PDDZGRk8Kc//Yk77riDkBDXx1y2bBljx47Faj266vfEiROZNWsWRUVFdOjQoeUHMnVmjWUQP1lGNXtpau0zXMa7AMzgAZIooD8b6McG4iny7AMGOBexjT4c4t/05Ru68g1d6U0BN7GckeQY7Z5bnMAmkkminGTKyaSIWKr5E2v5A9uJVIuDu7vVTjcAAQAASURBVKUOC1vpwRoGspne7CSLruzkIe4H4HPLWXxuOevoG8xgpYYs036Pjqui6MGFtzTiotrljYGNoXWrGXpkdeNrDkwcjOrYZHJ2rWUgX4Wcwaf1UysZ5DCK5ZzLf+nhp62W2pMOVHEL3zT+uxYLD3MuRUTRkWJOZwnjWU0fdhvWqqqQOH5gAIsZxAoGkcFh3uNFTMAs3icUO/LDenBzgI4sYTRLGM0qBvMAjzGZr0ikkJMdy8lw7KWrcwdZjl1kOncTWXY0QaKT4wC3VTzfeHMurc72KLChNCK48No4ouvJEJuPPBRzTSqZaBiG9wBgEmGM5il+pjtL6c1P9GKBeRD72cbjfAxAHWZCGofC0kSE1qSVtK1kd9d2V4tywa6nva30zCdNYrna1/ZgLffyP/5GKMsZype1wxlk/wqcO6nGynnWT+jh3Erf8o30qdtEr7rfSHAe08hOcl0iylWJXkk4taZQYp1lOON2cnnoG/xm7tW4WT/HBk4JWUMkq4Ci41Yokib4pDWzpHGG3na10kSnnn3YBLs0WSq1Ue6JHRMryOIL+nEXXxBFDVmhJvpQzhhWMoZNdCHf9V3ZioDjxubJUpvkhkmrzngS2FAaEVx4TSNIQ27L3TJiqeAMVnMGq6ngK76nN8VENQY13uNkIIyzWUcUNR4dK1B5yD6dukILG0L7sSZ0EJtC+rAxtC8x9Wtg5YakcknndwAwOx3EOkqJth/B4rTz/u6LsODgh6gxPJZyLxWWSCrMRxdZzK7Zyfv7/gjAoNo1hFLLkNrVDKpdQ4LjsPYcvKIJHzOcVxlHMqVcxQ+cyRqyjwtOeY5nU+ZKI7xDQAU2brvtNoYMGUJ8fDw//fQT99xzDwcPHuSpp54CIDc3l6ysrCbvSUlJaXxNS2yqq6uprj7ak7C0tL50zxTGc5W34wg1UUosh03xHDIlUGhKpI9jE+Bql/Atp1JBROP709nHyWzhGv6PJFSvXC36cZhnWMI2avmAfiykJ5X1M4RO4Ge6MYRd9ZNCxpNDAt8xmP/Si5104GpWczPLiaKW93gHkxh9VjTwNaN5kOupOmZIl0wBYcf0A72ybgEX1b1PkrOAWEqx1tZgxklpNdxhhNMKv8OrGpHSG2Jj0ZJZM9CpcXCfCcBjLOKvrGQtg1lOd5bSk3fpTz9i6VE/sNlEKr3IO2ai3iZ8Uq2JA6nRQmvWzzkeaVJK6ucrTTJIE1PSRNbR9XpCgOe4h68YxVeM4nVG8zqjiaWM+fyFDHKow4K5tFI70FEhHEIiUsjuic1lL+ncxww24Zq8smBnGCs5h+9xsgoTzmOW55LOhZSpK01o6V0kXO/3rmWXJr9sgl1LC5uuIfYVvXiTYWwiAYBQ7AxjJ4n0AyIwRRXyJAuPeUdH11+U1nlsmLTyjWcFhe/jVY04bgpbDzFUchrrOA1XtuhaMonE1Q7FgYk/8We6k8/FrKQfJaoi7Bis1DKW5Yyt+0+jrZBEUpx5rDCfxNLI0Y32SGcF3xyegJVatoZ2Z3H4KdgcxUQ5ywlx1mHBTrz9MINr1gCwImwEv4QNI8+SQl54CgfNHTlo6cjllW9wW8XzmIBuzu10te9giGM1ox1LSeQQhEiT+sFNKWF8Qj/eYzh59df36WxiDNvpRi4v81IbHan1QQ1FcOFdjbA229YTIqnhTNY2/tuJK7CxlyTmcgp/YgWXsJJotRZHM0KwM6h2LYNq1zZ7zeqs4S9Fr3DIkkCxxUaRpQNHiMaOBXN9gkG04whptQeIrK0gzlFCSl0eKXV5dKo7Or45s3ohZ1YvbLZ/xVFc84BZLGAkj/MRsZRzEcvpTw4j2daOiWy+vTaXwoXPBzbuvvtuZs2a5XabzZs306tXr8YFFgEGDBiA1Wrl+uuvZ+bMmYSFtW4AMXPmTB588MHmLzhcGUBmnNgoweYsIdu565gNLFhw8B2nsYOubKAv6+nPKobwIZO4kbcByCWRNfThFFYcE/5QAHTnEPeymNtYRkR9Td9mkrmZc4ilkpPYzki2chLbSTFgUb1F9ONfTGAnyUAYsVRzEes5g22N26jBZFOcwBay+R/jWMpQFvB3oqmgM7n0Zz0DWcdA1tGPDcQcl6nW9PfVdjjwLBKuygONxWc1gr3ozWZNoJTxbGQ8G3ECu0iuv7fZ2E4iV3AZnSjhQn7lXDYQ2yqPAxMT0Jud9GYnt/IWGwjnR0axjv50qg+MLGUU/4icTi/Hb3R3bCPdkUO6M4csxy46i8GT5uSaXRNWueZUdpmy2G7uxnZzN2ZUz2AIm0ikkFxSOJ1vOYUfGM0yYsRs1ODGCewhnsz6RI8cbOwmnrNZzTg2MYLtRBqcRag0wr/xXY3YA22wJKkZJ4PZRUPGewkRJHKEL+nDl/ShDwVczUrGsV09kwp04gDza6+mhlC2H+nGxpC+bAvpTqkpFmv9+GOjtS//ir2+2Xv716xnXsE1APwUPoq3oi8FINZUSrIjnwnV39Cr7rfG7WfUaV0LiuN5jRHMYwSVmEmgnOtYwlmspxPFRrvWDKUR/o3vasRu2kIjJEzA27zIQkYwn1H8i7G8wwguYwWXssKD0HtwkWg/xPVFLzc1HteKakjlr7yWcy3HZDUpdLKGzrzEOFaTQQgONpDGKA4RTzmj2Wq0e25RGuEdfD6w8de//pWrrrrK7TbZ2dma9pNOOom6ujp2795Nz549SU1NJS8vr8k2Df+WeiXec889TUSstLSU9PR0sA2FWAtydqNrfxZcxeM9gPPZhJNN5NKLWE4D4L+cxFxGEUEt48jl92xjcLM1JaSyXSkDV2/vQ3fo7X+YJ9jd9YuXoqCubNxjP00KsdzCdhYxlm/oztdkAmcwlLX8q+YqAA7TgRDqiG1oqqo3ExegvkqwDgt76MIOU1e2mnqwLaQ/T3MfZpyYqKGY3zGF9zmFHE5mI1bN8yUt8KQ3I9cdense2gS73p6K0rUJDZnoAIcI53O68l+6srM+yzeVUvZxKb3Ipzcwh+/qt+5Ek/YsUVIZbn2WuDkTeN+NH+5RfQ/9G5/VCA8xAdnHrNUUTTVX8DOf0Y9nGMdcRnMmu7iUtXQxILDry5hxMoANDGBDE3sY1fR3rGeTuQ9rLIMa7UPtq/hX+Q0AvBRxI1+FnYEFOxanHQt2qk1hnFP9GVdXzgfgjpin2Wbt3vh+C3a6OPZQZXLdPyOp5EvOPk7HVWDjWGqw8AXdeJte7CWez5lLByq5mNVcyi+E6wg0nZgecMxCwnpRGuHfBKpGSHSggpd4m50k8h7D+C+D+TvnMJADvML7hrXm8wes1NLHvpk+9s3NCg7PqPiKfjUbKDbbKDdF4TCZqSOEKOfRHlWXHnmL35d/TLI9n8jIShT6qCS0MZEtnDoyKOJSljKBzVjbtUq/J3iwPo3SCP8m2DTiWMKp5fes4RzW8T/6829+x0cM5nKWe+X4CsWJ2Ecs/2Q8S+iGGSdTWMuf+ZGOXh379gYOtvrdSiO8g88HNpKSkkhKal2DuTVr1mA2m0lOdi3OM3LkSP7xj39QW1tLaKgrZPr111/Ts2dPsedhWFiYEIHvSmsegkxAx2NWMbuAdcRSxRf0YiHZLCSb7hRxE78yRi0e24wESrmKd7mKdykijpUMZhnDiTomejHHfCMfm39PLKVkOPeSFn6AeOdhRtctZaTdJdQ/WUZSaYrAjINqwqgwRVJBJKfULSbduY9SYrg6ZB776UTdMT+TSGopIJEUCjiFpYzjx/pBYk9vnwq/4lYmsJX4+qqWNZzJZvqS20bZg1Irl5ah+h76N76rEd2hYZG3NiCVMm7jB25gKd/Qk3cYykf05hAR/JMv2+w4gczJ/MzJVUtwAgWmJPaZOrPXnEG482grLQt2Ip0V2LFgN1moJZRwZxVW59GqgYuq3qfSGUGKM48M5166OPY0ZvU2tJdRmdHa1GLmP/TiVYZSQCSRVHIBa+qX96Odejznn3gTNyiN8G98WyN2tMqvlpBNIffwBdezmgUMI5y6xqBGKWGq4k8nUc4Kutdud7tNsr3AS94EFkcI5TXG8BH9eYc36EgZf+RXLmE1JjGRri1RGhHM+K5G9IRjukC0JyE4OJe1nMl69mNrbPf9DgMYSC59PPyNKBStZRkZLKEbZ7CJG/iBDENa+Xu21o3SCO/g84GNlrJs2TJWrFjBqaeeSkxMDMuWLeOOO+7gsssuaxSSP/3pTzz44INce+213HXXXWzYsIFnn32Wp59+uhVHbJsfVQcquYi1XMRa9tKd9+nFp3Sj8pivpgYzVhWra0YHSjiD7zmD75vYBznXYHdY2GvKIMeUzoaQfq7tnUWNgY1/hv2VPebmFRMdnQdJr9tHNEcIddZyMsvp5tze+JdtLWkcGFrUdyKymgRWkcZfWAfADayhDjNj2EcoB9r4aCpbXXFivK8Rh9r2A9Rjxc6ZbGIym1hLL+LqK5ccmJjLMC5gk6oNOAEmINlZQLKzgCGOX5u8dn3ly1xf+bL2G+v5ffUn+tfMVQDwVybxE+nEU8lUlnEey4lu91ZTxe28f0UgECgacTzxVHAHPzT+u5Qw/sBVjCSfW/mFRFRlgcIYnMAXZPMMIzhEKD0ooJRwOlLWuLixdyjy4rE8Z//+/dx1110sXLiQiooKunXrxrx58xg2bJjRrgU03teILkAycjeFBMEure0pjQ5sjf8XwtF+EoeI4DmGU4eJP3GAm9hDGA7krh1Slw9pzQ69+5G217lun93NegnSFIUUsy4W7LWCXWpFJXUXkfaTJnwGqzQ4ka4haa08vZ1ApO2lhmbu9/MzcWRRSRI1XAD0Yif9qQVGHrd9sbAfqYOMFKCQnscaEt9qgU3CNgpfIWACG2FhYbz77rvMmDGD6upqsrKyuOOOO5qU9sXFxfHVV19x8803M3ToUBITE3nggQe47rrrWnHEItp6ZiODMv7GSm5kDWH1N/UtxHMLp3M5O7iIXYSrmN0JOcv5P85y/q/x3zUVoRSbbIQ5jwrr36qf5IjJtbBTGNVEOiuIcFaS6dgNuFqZvGu/RGPv0kOEAmAjNubQi+UkE0o1F7IFG9WMbdfqo0iP3q2i6MGB9zXiMO05+20CBh3zgLaKjrzGEN5mANewjcv4TQXEFT7BAaJIw9Wu5Q9sZAgH+CMbiKAOvLJ+hjTgahlKI4ID72tEV1yzHNLA3ybYpXalnQR70/1UYqUHEXzOIL6jP7dwgD9QUN/JXe9Ek94JKGn7YsGuNdkgPU8K7eZKhQkLad5Dmt/Q29ZW69FUGkKkCM7ESudLQpq80muX0LqXSlMJkr0vALsIZybprCaGOOq4n12cQwFmhhy3fbGwH6ldodb1Ie2jYVxowZNWVN7UiKKiIkaPHs2pp57KwoULSUpKYtu2bWI1gKLt8L5GlGLkogwJ1PIma3iEbrxFGj/SgQfZSj+vVFEpgpEqzDxPJu/RkfEcYja/YQb6H9Ptxhha087/KGoc4R0CJrAxZMgQli8/cT/AAQMGsGTJkjY4YgTtNSCPOiY8e5hwQnHwHH14n0zuYCPjOajaXOjASi3Jzqah9obKDUXbkEc4T9OPb0jDhJMzyeEvLMcmZmm0JWkevVv1PQwOvK8Rw3FdHZLMSjMcWpNW0oTV0ays4cBz5PMknXmJk/kPQ7mLA4xqHIBUae5B/4SV1n6kfRcLdim7Spqo+E3bbHezWJyUlCNlXbXFpBVAknDPSxUyfSzSeZauG70TVHqzt/Sss+Tex2IsPE8Kn9GBeeykL0WMBcYCcEr9ttIXpWfSCrRnIxvObUdglfC+E6M0IjjwvkaEYcTVkUINz7OZJaTwBOnMJp3vsHE/ezx8mlIoWs4uwvmVaC6ggJs5QCzlJ35Tu5Hs0bu9qRGzZs0iPT2defPmNdqysrI8OLqipQSLRhxLVyp4lXW8QSfm0oVrGMgdWLmEvYb6pQg8NhHL/QxiDxEMopTb2WW0S8fgWS8ENY7wDmajHfBfpImmtmUkB/iERUxjA0cI5S6GcxMj2eVhBqJC0ZY8yGC+IY2x5PIe3/MQv5Lutei6Z+sY2NvgT6FojvcbQo2ilPfYzO3kUYSF2+jCR6gsPoX3WUwMF9Kd/9CBoZQTY+id0rOKQaURivbBuKaBJmAsJbzHJs6nkJXEsIQ4w/xRBAe5hLG3vkJpPMV8yCbuIYdYw++SvqERpaWlTf6qq5snSnz66acMGzaMCy+8kOTkZAYPHswrr7zikf8KX0VqKeVdzMCV7OctfqUvZQxQ7T0VbcxHdOJahnOAcG5jNy+znk5eSY5tKb6hEQr3BEzFhvexAr2QS8ilUnE42r3weDKFI6XyJ2ASR3gJG5+SSSkRyJe5VCIoZdW6e03al1SrLWVfulsgUcjGZaNwCOFGJ2XitmZ+PUawJwmfO9VdNqjeTGmJ1vxcbYJdysqVtm8eSDuAhbR60ZlKKIWUM4pQYED9FtK1sFvnsaXBdsN1UAOsFLZRKIziAEYsIR2Kk8sp5AyKeY5UxlPqdR8UwUs5Zv5JJz7FRix2HmEfEykxuMpU5TopfBFjNOJYInFwL3s5m0P0q8+YLyUEKw7C1e9G0Yb8RAf+QU86Ucfr9e1FMnxm4so3rvX09PQm/54+fTozZsxoYtu5cydz5sxh2rRp3HvvvaxcuZLbbrsNq9XKlVde6UVvFe1PW69J6RnZVPIa6zDVzw2txYYJJwPUWpcKD3AA35NMEtXMZhu9DK3ek1ChBX9ABTZaTS2u4Ib3iMfBfRzmOkpIxg6EsJFQInGSpXuSXKFoHXZgHjG8QiyPUcBpVNCDWnqIK1y1N/EevVuVByrahzqM7I2bQh2PHpNh8ibJVGDhzxxUpZqKdmMX4fwXGyM5wgPsJ8knnk2URih8EWM14lgG1E8kOIB/0JNDhDKL30h3mxClUJwYB/Aa6fyLDKKxc71PPoP4hkbk5OQQGxvbaA8La5486XA4GDZsGI899hgAgwcPZsOGDcydO1cFNgKOWnxFIxpoCMXbMfEA/cgnjBlsZKKYzKhQaFOOhUjsmIFHWI8TE3FiwrjReLbGrhpHeAcV2Gg1HcCgiGJyfdTQDtxHPHmEcDdFnKu7SbhCoY9cLNxDPOux0pU60g0LZhyLZw99DjyLwyuxUWjTAbnizbvYga/owCaiWEcUj7ALm8o+UbQhpViIxU4/KpjHTvpQ6UNrgSmNUPgivqMRxzKIUuaSweUM4kk2M0xl4ypaSQVm7qMnPxBPD8p5gs108rGJWhe+oRGxsbFNAhtadOzYkT59+jSx9e7dmw8//NADDxS+SS9cXR+kLguJgl1fZxB50lY6bjkW4AnsTCWCf3ASuZRyBb8Kz33SunrFgl0KqEtaJAVV9ujbHOQiGek9hwW7tAyvlBMtdReRplmkW1Yn4TOLLextOu1SspIUkNC+RvfSi1tJ5PeUcxVlHL3rRQn7ka4JqYOM9Hn1rl/YcFyplUvLUOMI76ACG60mHKMCGw1YgAcp4l7ieYgOrCWMOyny2Vinwr/5mTDuJZ5izFzOEW6kBKtPZOPmefRuT3sXqulhhTbh+MqklQX4N1uZTTqfkMhl9OYZttNNZeMqPKQO+Cfp/Egcb7KZOOz0pdJot45DaYTCF0nAdXeWBtRSf3Vpjb9Mnds3H/ibOcK1QF9quIs4bmE40yljsthOVprt0dsqt1jH/oWJm3JhgkOaqJLsbdXWVmseRMo/k06XRfhMUXonr6TJIml7PZNC8nU6jyh+IIrJVPEPnITTy82+pWtjv2CX0DMuaXgG8kyzvKkRo0ePZsuWLU1sW7dupUsXaTJb4b+E4b6NuLH0oJb5FHAbCTxPLAfJ4k52+WBFlsKX2EgUt5NEKWY/SrKTgnMtQ40jvIMKbLSag3i7FZUWA6jhTfK5n3j+QyS/EcrTVJHsExPOikChFBN/IwET8BSHGOvDD1oKhW9QgmsyQW+mldaCstKaTdKEla2ZxUod9wEDcPIYsVzLEJ6knOHipIE0YaX125eC/NKDoHTM3dpm51Zte46wG9CfdaU3T0GaJ5ImrqSsqy7CZzNJ101726VJp+aTV5WkcBdR/EQIw6jDQW/AifxoWaxhk7aVnmH0rFnVsK2UOqdQGEk+vjCO0OJkaniVw9yGjfuJxY6Ns9WCsQqd/IVyOmFnClU+VMHn39xxxx2MGjWKxx57jIsuuoiff/6Zl19+mZdfftlo1xRtTj5yJrxvkIydf1PAnSTwf6QwgDLO9HASWBG4rCaG2+iNExNPqvkkRRujgqoBgA0Hz1LIdZSyhVA+wX0Zq0Khl1icPMJh3iDfB0VImvRtGY42+FMo/IVzqeEFygnFqTJAFK2mCDM3EM1PhDReUx1wGu2WQJpH71YaoQhGumFnPkWMo5pRussVFMHKTsKYV5+4YQXO84ugRkeP3u1NjRg+fDgff/wx77zzDv369ePhhx/mmWee4dJLL/XoMygUrSUaJ89QyL3sZJIKaigEfiWG2+mNGSdzKPTB+SR3+I9GBDOqYqPVdEKuVwb3p1bKiNSTwdt0P2bgOmAoMJgYtGugpdJlkMvAJfs+wS6Vq293c+yN2ua91foOLWXitmY8JrXSk/ZVI/gKkCF8PvEa0WsHVzsDLaSsXJtgP1rKXAXMJow/U0MaUYzV3N5dqx0p41Z66Gnt7ai0le9zocoDFe1DR+Ten8YyjDo+pZTI+n9XYyLMZyelFb5GIRauI429WLiGam70+YkrpREKXyQVuUG3b5CMgycpoeEq3kEY2VT7+O9dYRS7COMGsijFwhmU0slvpmM8C9x5WyPOPvtszj77bA+OqPAPUoEio51oEVbgfPIB1yTsf0hmCvkqg1rRSA1mIrDzJFsYILYw9FX8SyOCFXW/aTW+0Tv9eIbi+lIdwCOEsVic8FYoZI4ANxHBp4TyiU8u9HcsbTNp5cmfQtEcY9dgOhENQY18LFxMZz5QlX6KFhKLgy7UcieV3OTzQQ3w9HlNaYSiffBtjTieFURxCd14UUy2UgQze7ByA5mUYGEWe/0oqAFKIxS+iX9pRAMfkMqjZPOMuIi5Ihg5iRL+w68M8NE5VPcojfAHVMVGqynBV3vjAuRhYjEh/JdQZlHJKeonoWgh5cCtRLAeC1dQw43U4MvXulyVolAYSSm+/bs5igmYVV8xeKGHgUJF4FKBiUicWHHyFLmY/GaC07d7VCuClTL8RSMA+lFJXyqZTyLx1PEnH61IVHifPEK4kSyKCOFxcjiFMvCrZAn/+R0qgol9uK5NqdOHTef+9HZxkOzu13H9Pf/P3p3HR1Eefhz/bO6Qk1yECOG+T0XFeFUFQUQrlXqigBcewV+R1oN6gFTFauutqK0FbUWqbdV6YREFRQ4FRTkUAUFACHcSkpB7fn8kWQjZJ2Sz1+zm+3699gV5dnbmmd3Z+e7MM/M88BEO5tCRTFpzJQsNU5q6IjL17GDqssMwbt8+w7kv0xh8YB67z/QaUwyaVs30EZhuCDCNz2e65jTSsM4ZprENTduWaSDBZEO561PKu+nBVFrzew7Qnipinc+YGr3cGaMP3G/8M227poaLunLdCxAM1LDRbPb+EdQWixc4xE3Ecgex/JFSzgp0pcT2SjjcqHEN5dxCeRBcjZvh0as97bswmK5JE3+yd0bUyaCKF9jBjWTxR9JIpopzg/QqMfGdg4RxPVmcTTE3cSAIcuFI6R69WhkhvlEKHMJ8BsR0wsF0YG46pHPnpJirshpxwJOUcx1xPEY26SRxrvEElOkkgWl600mXLQ2Lyg3Tmk48mU5UmXrnNfUy7G5PFK66tDXNw3Ttmemm+w6G9yDKdDLKdDbNVG7aZho2ZldzHLfTmt1E8gAFnOPsDtmz8e8OM23X7pygyjdMW9eNsHf6T/fk9SINBcdxxNGigD+zl+vI4DGSSKcN57Ir0NUSPyshnEmk8gORrCOK9hwKdJU8oOOIYKCGjWbLxDyogz10pprnaxs3phDDs8RyQlDvVMTXXiKKb2vv1AiORg2AnR69Wv0eim9EUHOyxnSCwHRCwTTWkjvzSHZjHjU/156lgmuI4T6OI5UiTjCegHJ1Es4LJ6sAWO+62N0TVo0twnRs5Y2TVmC+eMi0ozBddXWc4b1w+wSVqdx0Aqzhz8IKHNxBRzYRwUjCOHyw7c62CuYTuK6Y3sh8Q7mry+bqTnB5dkCtjBDfyMTT3y/+lojF0+QznhTuI5FUEjlBd/m1aGHAbyhiPRGcRyPjDdpaY5dwH5syQnyjDZ7+fgmUmqzYy3gymEpfOlBM96Dsgkiaoxq4m378QCQ3U8jwoD//qHNNwUD31TRbcByMdKaaZzhEJDCdNvpiSKNuopzplHJr0DRqgPk+zaaxONyS3pyHhlwW1zw7UPa3tlg8RRGRwFx13SO1LOBBsvmSCH5NOVcH5YkrZYTYUXBlRJ1MqnmKfGKw2GBsIJWWoLT2NMIgKrgyqE9cNd61zrEoI8Q37H0B7bFkUsWj7KMNpVQH0VkF8dzLdOIz0jmfEq71cOBtewjejHj44YdxOBxMmjTJg7kEB92x0WwVBMstgt2p5jEOkcbPGkpcXPqWGPpRSiRwvoc7b/9rHegKiLhQjvlyfHvqTjV/5SCddNOr1HqFNrxLKqdRye0cCtJD09RAV0DEhUqCLSPqdKeSt9lHYpBc5CXeN490nqEDT1BN16C/bE7HEWJHwXOuyaQf5fyLlYSr+a7F+JYkZtKFLhTxeyqD9LjhaMmBrkCzfPnll7zwwgv0798/0FXxCzVsNNse4ADNG1jJ1DVJpqHcNEBn0/vTPRGoGUAzmu2Ek0nVUa823R5ougVyi6F8o6H8G0M5sNVwBahpVqZFmy5+yzcv2ijZzXk12sOGYf2yTe+J6XNtrFnK3X5yD5d/SCx3k8JkCrjSOL27fdyC+c0ydU9j+sBNHSHXzd+zfnx1e6D4RmvM+1X76l7bqGEBS0jkVApD5EepuOsAEfyVtnTmEDMIC+ILI2KPPUkjlBHiG8m4P/ClfSTWnqgqJIIFpPKrIO0yRdy3mVgepCtxVJISEhdCmI6hmkYZIb6RBEF9J1SNukaNr2jNdmL5ZZDerShN05ODXMo2LmEbMfQOdHW8JPiOI4qKihgzZgx/+ctfeOCBBzxYevBQw0azxVPTsBFcfiCS60nnIor5LQWBro4E2GYieIDWpFHNMEoIzitbTQ0fTaMDEvGNOIKxYaPOm6TxENncys+M0wmrFqk1lfyV9URTTSu6Bro6Htju0auVEeIbW6j5/WK6oMT0G9106JbsZrmr5bo7AHk8j9OBd0gliUjOcV5w4q2LpTY1LDKdEzPN4kdDuemno+nam0AMHm5iutGno4v3C3D/4ifT+EnxlOLgTnpSRgRPsZkUuhir6ZrpgihX4ySBuTse07qudVG2xTBt3Xbq2V5aGSG+8TM1g8n5OiOS3JzeNH9T14TxVAMP0Y8dxNCXCjpzCPdzwrBzLjbsO0zj8G0xlDf2nCkv9hjKTT2gmvbdprGpyw3lpsEETPOPM7xHcabASzaUmy7CPryNRgG/42dqKtn08fwaZ9pWTPlg+sBc5QMES0YUFtYf0yw6OproaNfdR+fm5jJy5EiGDh2qhg05li00fgW9PXWmgp5U8Brx9KOcYSFwJYA0zyEc3EkqZTh4kr2khcRVV+6r67/Qk9eLNPQujf+oMw247YppHsmGclcHQe7djXUeJcylnGdpx0mU09uZFa4OpEwnjrcYyg0DY+823N1m+n1qOmHV2HPeurMv2VBuOnFl2lGYfkaEG96LTHcHFTeNl5JsnE81UEQ4iVTRAwtw4N7PRdMBiKsDqy2Gad29+9PVfOquhvdsL62MEN/4kWA8jjjaRH5mEUk8RDYDKSIl6LozFXfMJIsfiSWXnzk+iC/e8CZlhPjGFkIhI6DmaGgqG7iO/txHD2bzjU5Chph5pLOaBCayhVjt1erxVka0b9++XvnUqVOZNm1ag+nnzp3LV199xZdffunBUoOPBg9vYSKAGewjlWoepDV5IRKY4r4/kcyPRJBLIScYLwkIBp0DXQGRkNOKah5iW+3ByHGUqUOqFuNfpDCabnznYfcc9tE90BUQCVmpVDKFbeQTwUNkqyf1ELaOVswhg34Uh9idnMF8R6JIcOjHQcaxne+JY5aH3UiLvewlkkfozHzSKAvJ08v2ONe0bds2CgoKnI8pU6a4nOY3v/kNr776KjExoXIc1zShuOX5ScdAV6DZUqlmKvspxsF0WqtNtQXaQxjzieUUyrja7fvr7cazLtWqvPAQaSj4D5S7UsZN7GIz0TxnHOtJQsnPRPIUmcRSTYegbvA+kmfdhiojxDfscaDsDedygGEcYCHJfKiBmENWd0q4iZ1MZUuInUDI9+jVygjxjY6BroDX3cBWOlPCLNqzncRAV0e85E90ppAIprCJ5JC8a9Me55oSExPrPVx1Q7Vy5Up2797NCSecQEREBBERESxatIinnnqKiIgIqqpCN3F0F1izBe+AfwCnUsbFFPMf4lhGNKcGukLiV+lUM5ddRBAKrZumfnmbRn3jim+ERhcNY9nLpyTwJq0Zxx5SAl0h8ZlqYDrHUYqDe/mZViFz2YOpE+SmUUaIb4RGRtS5k618QQL/I4XzAl0Z8YkI4Dpjn+bBzJ2uQRtSRohvBPe5JleisLiLTdxMX74mi3YUHvtFYmsryeIj0jiL/Zzt4TkZ+wqec01Dhgxh9erV9cquueYaevbsyZ133kl4eOj21qOGjebK/6jmLEArw/NRjV2JZdo8TbcLuXsbkeljrd+COol9nEIJp1KCuSVyi6Hc1M+3of/rnw39hYO5K+3v3ZzeNEhUvnnRRsmG8v2G8tJmLMPUh/pxpj7EG2PaRhoOAFhCOK2oJMtli7rpQNvU+t7YwN2mAZpWGsoXuy4uN3SWX1L7b2FsI3U4NvWNK76RRyg0G4YBD7CdCCxSdPgd0ubRgZXEMZoDnBRSB9TKCLGj3YRK/+kASVTxEutpTyO/9yUo7SCeWfTnZiJCdAwVZYTY0R5CKSPqnEAh77CCDOOJHgkW1Tj4E2cQicVtjQ58GOxM4xU2jT8zIiEhgb59+9Yri4uLIzU1tUF5qFHDRrNFQ5D/eG+FxTm1Z4frhgeV0LaGZG4lh6lUcFaoDBwf3g34NtC1EDlKDIRIVz5ZVDj/X4WDcPWiHnJKiOApBpJEFbkh1Xc6QCcaH2leJAC2b4f4MogzPJ9quoDI3Ts93DnUS3VzmfWv6ung/HsvpUQQ0+Ak+HbDfDa5Lv7ZRWP6FsMsTNfZbDCU+/oiqWQXZaZryEw/FUznNCNN5YaLD44zvL/EG8rrdz35BKfwMV0YxnZSGlzJ5e72aLry1fSBmC6G+rzp5fsMDfV1xaX9DfMSCaA930NpIxkRZ/pe+zIjwLzfMF3l2bAxNINKYC9lRBBB1VHHFYacqDLs5E37bFN5Y9dkmnZDprfa9HPZ9FaYrkM19fZbYSg3ZYO710h3M7wZ4UmGFxxX76+9JABlXMU2jnN5QZTpjcg3lJu2XdMHYLoY2JQPy10XF/9sKK/9t0QZEQzUsNFc4T0IlZOpW4hgOqdzC99xYsjeQibVwKP0o4QIOoRKowZAlengv2mq8ez2QF1pJS5tSYCob8139R33g+vy8HwXhaYrJL1xwsr0o7Nh+X9J5a+cyz/4N4n1GvYNPwhNP0TzDWd3TAcbpt+zjX31TSezTAc6prvxTEx9cuUbyk0HJ6abekwnrmIM712yuyeuMuv9FY2Da1lGHL1JdHlwku+izLTtmH5HrHFR5uZJq6ovXJe72gTr7uor9qybEWWE+ERE6BxHHKkKuINfYgGP8d9AV0c8tJJ2fEw3zmEDJxmDLMhV6jhCbCiEzjW5spa2/JZLuIVF/NJ4glrsLIODvMpLVDIy0FXxrSA/17Rw4UIP5xAcgr+fjECpMjXpBp8oLL4niUfppx9XIewD2rGW1lzGj3QKqYMTz+6cqvbCQ6SBitDJiDoRWOwggX+gK1dCTTgWl7KWkW638AQBSxkhNlQZehkBNReSRlDNp3RhrfEyVAkGFvA8pxJONf/HZ4Guju8oI8SOqkMzI+p0YD/lRPBXTqciBLvcCnWFtbeBhGERFfJ38isjgoEaNpqt5NiTBIksqhjDj2wikf8ddYuZhIZywniOXiRRzvUYrhQPVo7MY0/TiCovPEQasEInI+qcx346c4DX6M8Bt8d+EruaR1d2kBDoavhOuDJCbCgEM6LOjSzBgcXznBroqogHVtCerzmOC1hHO2M/WiFAGSF2FMIZARBPGVexnB0k8Y4umAoqZURwKROYHup3atQJU0YEAzVsNJcjI9A18Kqr2UgcFbxID6o02kbIeYf27CKWcWwkMaTu1gAcpn4gRQIoPLQyAmp+MNzICg4RwcscH+jqiBfsIo77OZt7OSfQVfGdMGWE2FBY6GVEnc7sZzjrWUpHvqVtoKsjzTSf7oRTzbUYugAMFcoIsaMQzog6l/MlyRxiNqdSrfNPQeNtBrCXeLqyJ9BV8Q+dawoKGmOjub5Ig8rdGC9y7NbIQJXdTP1Pm/pRNzH1b51sKDf3M53ITsbwLS8ygPdJ5EJnp+Zudm6eZxigrbFxO019oa9zs9y0jOb0rGHqP9300TU2RrC7g/+FG97DzMb69zP1o94RgATi6U0el7CCmu0szzC9aRvMN5SvbaROiwzl77ku3mC4Gsy0fRys/bfEs34PPW0JVyu6uBSeBuwOdC287mw20519vE4fxvM1ycYckmAwm+OpIIzr+CrQVfGdCmWE2FCIZkSd61nGPHryDwbxCO8GujrSDFNYwK/5luNC+W4NUEaIPTlCOyMAWlHOaL7iJU7jM7rxi1DrVSIEVePgNU4miUNcHMrHDkfycIwNZYR/qGGjuQrXmQeFDVJXso5/0pPNqFUy1AxjC+eyRddCuOBp34Xq91BcKl8HUYGuhPeFYTGer3mCHLaSpIaNIFZING/Tk97sIcc4qrooI8Qn3l8HsZgvkOpsuMhlwP9cl8e6uy92dSGLqbsFdy96KaIjRZzOeqqopppiwrAwXiy1z7CurnZLPxkWaboIxnQ+wlRuqGK+m1/kZFd9Ihx0UQbmsxbuXhhl6iEyxvD+pprezD4AOIDuFB1Rnu9i2p8N8zCdYjBdWLXSUL7AdfGhT12XuxqD2HThW93nccjwfBMpI8Qn5h0jI7oZvnv93ja8wN0LaE3Tm3LClEH5hvKa/dKv+Yx36EMx1bVlhv2SaVdj+vnqblYAfG8oN+RFueGGBVMnYqZTh1GmGx9MnWyY+twxZoCbFco2vXl9WUY3tpHEeD4lxtnonW+Y3nRFsCGTjPlgGlzezYtoVxvC9lgX0SojgoIaNsQpngre4d+0cjv4xK4sYA+tyKAkdBs1EntgPkI9NrWii08soOaHZJzh+c6G8gEuunxI9cYJq45uzsP04zKfIXzFOXxNhPOn1hbXkx4yHIWYDkIMs8E0fqLpAATcvrPvoJs99CWYTizlG8pNOwq3T1AZyqMN73Ws6UKFLrzNSZQDl7MMh7Pips/dnZ+LWwzlrk5cGU5a7fvWdbnp2MbV51p33BSrjBAJhD/xryNyQoLFHuJ5mPO4ls/pw85AV8f3kpURIoGSzkHe5U+1jd9id//kFMKwGM2Xga6K/yQpI4KBxthoroRuga6BT9Q1aihaQsNXtOECRvNfuga6Kr5TURjoGog0lByaGQEQjuU8WaU+cYNTNQ7eYCAplDDUgx/rQaFcGSE2lBS6GVHnyEYNHVcEjzc5nkV0Z6/xUvEQo+MIsaPE0M+IOnWNGjqmsLcqHERSxVl8R1vjlVwhSMcRQUF3bDRXlekms+C3hCz+xMk8wQKyA10Z8cg/6Uk1DgayK9BV8Z1Dnl1NplZ08YnK0M2IOg8yjF0k8BQfBboq0gx3sIBCYogK9b1YsTJCbKgFZATAd2RyPxdyPYsZyvuBro4cQzUO3mIgbTjIGY322RJClBFiRy0kI+o8zxA+YAD/5gMi9K2wpXAs/sScltcApXNNQUF3bDRXiamjv+AXQTVbSeR9ugS6KuKB/SSwkGxOYQfZxo59Q0B4tEcvtzjc92FzHroKUVwqCt2MqFNADEvpyG5SAl0VcVMYFqfzI+cb++wKIWHKCLGh4tDPCIAMDvIjabxP30BXRZpgFX3YTQIjWNNyuobRcYTYUQifa3Ilkkp+pjVfMCjQVZFjaDHZUEcZERR0x0ZzvRUJjgqM53MGNPLa0wtclw8yDHRj/JhM/a4fZygvMpTDkf1qn8gu0jmJ98niRja6bpPNNwwGZOo73TRwG5i7rDOdbzH0sZ2331DeyKJNMg3tAJmGj65RpgGE3e1DPcY0ABOQ3LAT+o84n2oquYAVNHwXthhmtNdQbvpxZRrsD4wDN600vImLDbMx9ale93kf1wMw9MfeBGpFF58Ii8Q86ltoOJ91fEx35nE6Y/lvoKsjTVRCDBvJoh87WsY1V62VEWJDLSAjAFIpZjCb+Zyu5JNIMurSwc4+5BcAnMeaANfEj5KVEWJDLSQj6ozgG2YylPcYxqm4GG9QAmoDnbmXifyGeeQYBz8MUYnKiGCgho3matcLfm7+Bm5nYVicx/f8nRP5lp4MaHSEVrGrDzmDGCo4k02Bropv5bWwcJXg0LoXFIdmRtQ5jR9JpJT3OFMNG0FkESdyL1dyH/P4ZUs4eVWgjBAbWtkLfvrWfIFUP0O56Qbc0z91XR5rulrGFdOd2qbDRdNFL/W7Pz2fz1hKNh9X9ORi682Gk5uuQNrhosx0AdUWQ7nh4qlSQy9LpiMedzt0beNizPSehmUaPyHTE6ahL9wur//5VRLORxEn0yVsO11dZoM7V5BXGspNxySfuy4+ZNiuTRdDuSpfbZi27gKpSGWE2NCKXrC1kYwwXUSbbzgNeoa7F9Ca5Ls5vWnvWf9iyiz20o+NLK48icqSoobdUbnKg8bKtxjKm3Gxbd4e92ZlimnTrrizYf6Zhul9ng3JDS9CnR95ChtjU4lmLw0vhDV9xvGGcpOfDOWGfDBdRLvE8B34zDCbY11EG66MCAZq2GiuXY3tFYPfiNqGjQ85Uw0bQaicSMKp5iw20SrUr/Yo9awP0rrb/Dx5vUgDr/wI5XjnpNXphgaSVNMvW1fRXmyYNs5QbvqRerg8EjiHr3mLrmynlHZHn/QwHWyYTkyZfs+a4raR7r+rDAcnphsBTVU1yTLsVnsYlhsVaZiR6e03HWyYpm9lKO/S8CTSfCYQRjlnsIyG24XpxJWrk1Smbcp0AODiwGSfYds2nbRaYih3deKq7oDEw36qlRHiEztC+zjiSKfzHWFUszjsdC6uctGwIbYQThUvVk6gOGpkoKviX4eUEWJDO1tORtQ5nTWsdpzJqrCBnFjdWK8Q4m+LI04nmWIGhvoFs66UKSOCgcbYaK5DjXXrFPy6sYe2FPIF/QNdFWmGKCp4kbu5n3mBrorvJWd49PIqLzxEGigJ7Yyoc3ptw/eX6hM3KFQQwZecyEC20NrYMBFiUpQRYkMhfhxxpAQOMZDNfO04niodetqWA+jCj/Q3XmUQolorI8SGWlBG1DmdtQCsDNcxhZ3sc6TwQ1h3TuG7lje+BigjgoR+XTZXYlqga+BTDuBx3uYVfhfoqogHwltC+CR49l1U2IhPtA7tjKhzCht4nasYpa6ogsI39OcQMZzS2O0uocbD76IyQnwiuWVkRJ17eJ13Ky8gXNce2tbnjlMpIDHQ1fC/pODNiIcffhiHw8GkSZM8mIvYkofbZTDqznbmllzOhIoXA10VOcKy8FMAONV433uI8/C8r44j/EMNG83V2tjrXcjoyl5aGQcoF7sqJ5Jbmcr/OD3QVfGPbS00ZMXeUkI/IwBiqKAzm1vGINQhYAl1Byc/BLgmfrRJGSE21EIyok42e4nDs+4cxHf2kspvwp/k0fDbA10V/9sSnBnx5Zdf8sILL9C/v3pXCEkt4FzT0RxAV2uTjilsZnn4YABOaand0/8UnBnR0miMjWbaNXMNJUCmqWnonGbMNLHhQD0AdDN1Nh1uKDfdumgayA1M/Wp/SyJ7SGcIH9d/wp2B/sDcpzq4PeDftv2uy78yzKY5PVR2NpSfkO+6vL2hrgAkG8pNfe+bpk9obBmHbxv/hhNZSm9OYBHm/tJNCzHtEkx9ppu2TWCDYXs2veRdQ/nHrovzai/6Mw3Q1VTq91B8YtOaFpOwBSSyjJPpxXqyG93ZS6CFU0UPNtCdnYGuStBQRohP/NhyMqLOOnqxxdGR860PAl0VOcoqx0AATq7+AsJ1WtEdgciIoqIixowZw1/+8hceeOABD5YutrWl5WUEQBFxLAk/lU7WZrpVa9BmO7it7HEurHiHlHjTyStpjI4j/KMF7i7FHY8xia1kcw4fq/U8SKymLwCD+AroHtjK+EF4t26wofndqnh6i59uDxRX1q6EeCDL8HyqO+f/TY2av/jCdXl4shszN027t8nlmw614+6oadxa+TTjKl85/MQewyxM5aYGc1P7bCOt1qZra7zVAG5q/DbpZ1pAupfKTcca6WX1/szlKXJ5ChJNV19sN5S7unsz3zDtN66Lq1xsr4ZJjYOEv++6eN+ahmV111lUdOtGoyPNH4MyQnzhh89rMuI4w/MJ7g5zYMqJUz43PBHvosx0AZSracG803adH89X3MSXkScxdP9HRFFx+Al3MsF0AZXp/driutiUEaZdkmE2Rh1dlJlOTpxgmrlxv+7m9MmG8tTD/13Tqi/EQr/81ZBu+uXiKh9M24zpIru1hnLDdmr6QNy4SOqgIfjrflqUBWFG5ObmMnLkSIYOHaqGjRD104Ka3brp2xjlbkYkG7a0fssNLzDt903fedNpRVNOuC7PK8zk9+kPMbboFbodfPrwE+4eOzTjYtt8wzJWG6Y35YjhGlzjLto04l2MoT7JpnXw1rHDUSvQmnxOYgXEm+4OM30Ipm3CtA2Z8sGwja42bNOfGWZjuIi23JAndZtQaRBmREukrqiaKbxLl0BXwS/6s5pCEthKdqCrIk20hr6EU00P0y0vIcYq8axrg2o86/NQrejiSlQLyQiA3tXrCKOab8PUHYLYT7UyQmwosgVlRJ0Bld9Q4Yjk+4iega6KHGVNRF/irGI6VLWwgcOxT0YUFhbWe5SV1b8woc7cuXP56quvmDFjhkf1FnuLaIEZAdC58kfirGK+jdIxhR3sDM/k+8geVBp7igl9dskIaZwaNpqrvDzQNfCL/rVt1N+icAkGFrCGPnRjA9G0jG20+mfT5dz29Omnn3LhhReSlZWFw+Hgrbfeqve8ZVncd999tG3bltjYWIYOHcoGD+5IkcCwWkhGAMRQRg9rPavD+gW6KtKIeeHDeSDqbvY6Uo89cQipCLKMkJahJWVEnX6VNccUayP6BLgmcqQqwvguohd9KtcShhXo6vhdlU0yon379iQlJTkfrhoutm3bxm9+8xteffVVYmJiAlBL8ZsWmBEAYVj0qviOHyK7t8C9kf28E3shV6X9gx8iQ78XEBO7ZIQ0Tg0bzVS1rWX0I96L7wDYQNcA10SaYg/p7Kc1fYw3R4agyEiPXl7thYc7iouLGTBgAM8++6zL5x955BGeeuopnn/+eZYvX05cXBzDhw+ntNRVVzBiVxUtJCPqdKvewH5SKCAx0FURgyURp/JW5CiiLddXgYYqR5BlhLQMlS0sI6DmSlyAzeGdAlwTOVKBI4kuVZsYWLEq0FUJCLtkxLZt2ygoKHA+pkyZ0mBZK1euZPfu3ZxwwglEREQQERHBokWLeOqpp4iIiKCqSp2WhIqWmBF1OlVspsTRij1hpj6UxF82R3TCgUWXik2Brkrg2CQjpHEaY6O5wsOhBfx4aMtOoqhgCx0CXRVpglgOcR8P0NHY2XDoiejVC779ttmv93e/hyNGjGDEiBEun7MsiyeeeIJ77rmHiy66CIBXXnmFNm3a8NZbb3H55Zd7UFPxqxaSEXU6WlsA2BzWiYHVpk6xJZC2OrJJtfaRYOz7PDRFB1lGSAvRwjICINXaR5xVzJbwjoGuihwhxTrAKwXjAl2NgIm0SUYkJiaSmNj4xSFDhgxh9er6Pf5fc8019OzZkzvvvJPw8JbbXUzIaYEZUadT5WYAtkR0JKPcNLiG+MNPER3IrMprMT2BuBJlk4yQxgVNw8aDDz7Ie++9x6pVq4iKiiI/P7/BNFu3buXmm2/mk08+IT4+nnHjxjFjxgwiIg6v5sKFC5k8eTJr166lffv23HPPPYwfP97t+vynTx/2f/stAwxNaFd+3MiL2xvKuxnKOxhGHY3qaHhBcz5W14P+hBVXcmbkItKsvVB5xNcq3zCbfYbyxjJpl6HcMOiTaQxW06k002CxjTloKE82lLc3DVAF5vUzvSem9zC/kWUU13w2CRTwS94+XB5nGswpqZGZuWJoKClvZMhdU+9Jpg/K8J2ZY/iOOWfzo7vD/tbnaUv4kX3jHik6Opro6Gi35rV582by8vIYOnSosywpKYnBgwezdOlSNWw0wm4ZEdOnj0c/goJNz+rvObF6BQ7dOG5LFvBTWAe6Vbe8bu3KbZIRElh2y4ioFpYRAA7gF+WLiLUOBboqIk6VQZQRCQkJ9O3bt15ZXFwcqampDcrFPcoI++hRsZ4Ty1cQrlO6AWUBWyOyGVDesi9YqwiijGjJgqZho7y8nEsuuYScnBxeeumlBs9XVVUxcuRIMjMzWbJkCTt37mTs2LFERkby0EMPATUnDUeOHMlNN93Eq6++yoIFC7j++utp27Ytw4cPd6s+B7Zs8cZqBYWHKxreCiv2VE4kUVQEuhp+VVZkj6uP27ev32I5depUpk2b5tY88vJqGqLatGlTr7xNmzbO58Q1u2XES1u2UA2YRp24dI3r8lRXDd+mRm9TD4HZW1wUphkmNnVxVmAo39uw6CCczJeczJcNyl3KN5TvN5QbGoEPNjKWm+kGflMnfe42gJveNdNN8x0NdU0wNXDnu1lueq9rl1vgSOJgbALZZVtryhJdfI6AuWW90o1pt7gudtVFrelYYbXr4n2G783rjcyi3CYZIYFl14wwnYq80rBTSjDdQN3DUN6t2HV56hYXhaaLXpIN5fmGcsP+pRimF09tWG6KG1flpt2OIT/2GH4Su5sRqwzlJq52+SmGadsb6phuykTTe2B6H035ULtpvNXqInZEZHFD4V+IpBLj59foFVZNnXaL6+J9hu3UzYw46OJ7M8cwi7o4UUYI2C8jXtyyBQsYYHj+iiWuy6NMGWE6luhsGD8gbovhBaaciDeUu7nDOggD+Jbn99/coNyd2bh7TAHGa2rdvqjWlC+ma5tNo+UcZyhPNq2DaZ3dzYba8j3h6ZRWx9Ch+KfaMtOMTJ+xac1M+9wtrouLDduomxfRli92Xf7aMWZjl3NN0rigadi4//77AZg9e7bL5//3v/+xbt06PvroI9q0acPAgQP5wx/+wJ133sm0adOIiori+eefp1OnTvz5z38GoFevXixevJjHH3/c7bApLSzEvWuxRXzvzqg/ss7Rm3ll5+EIdGX8JC4tDfaaDsKOzVu3B27btq3eLeTu3q0hnrFjRkR5tEYi3rMjLAuA46pb3gB48TbJCAksZYSIa5/Ens2XMSdxc+HMQFclIII9IxYuXOjhHATsmRE6kpRAOhieQHrVHo6rbHnHDkcK9oxoKUJm8PClS5fSr1+/elc6Dx8+nMLCQtauXeuc5sguXuqmWbp0qXG+ZWVlFBYW1nsAtEoxXXcTer4OG8h9kfez0dEl0FWRY9hHKuFUtZhGDYCEzEyPXl/lhQcc7hu37tGcho3M2nXZtat+/2W7du1yPifN4++MiGtBGVHnyVb/x/OxNwa6GuJCqrWPW0qe48SKFYGuit8l2iQjmmLatGk4HI56j549e3pUf2kaZYR/fBPVn/ta388PkabLh8XfDoS3pnXVgRZ17HAkuxxHiL0pI/zrqdRbeS7l5mNPKD7TpfxHPthyPmPyTfe9tQzKiOAQMg0beXl5LrtvqXuusWkKCws5dMh1f68zZswgKSnJ+ajrbiapXTtvr4Jt7XGk8374+WxxdAx0VeQY8h3JtOZAoKvhV3lrDH2TNFG1Fx7e0qlTJzIzM1mwYIGzrLCwkOXLl5OTk+PFJbU8/s6I5BaUEXU+izqDBdFDAl0NcaFN9W6uPTSLfpWe7S+D0Y4gy4g+ffqwc+dO52PxYsO98+JVOo7wjz3h6bzf6ny2RHQMdFWk1oGw1qRUm/owCX07gywjJDCUEf61uNXpfBQ/9NgTiviYMiI4BLRh46677mpwZdrRj++//z6QVWTKlCkUFBQ4H9u21fSat7MFDeaUYtX82N3nSA1wTeRYDjhaOz8vsaeioiJWrVrFqlWrgJr+WFetWsXWrVtxOBxMmjSJBx54gP/+97+sXr2asWPHkpWVxahRowJa70AI5oz4uQVlRJ2U6v3KCREPRUREkJmZ6XykpZnGx5FgzogdLTAjAFKqan6jHghrHeCaSJ39YSm0rmpZF0VJy6CMCF6tqw6QH54c6Gq0aF/FHM9rSZdTGJYQ6KqIHFNAx9j47W9/y/jx4xudpnPnzk2aV2ZmJl988UW9srruXOq6cMnMzHTZxUtiYiKxsbEu5xsdHe2yS5m/AA4g11CfcxppWss0nXc+xuCfDUTlG54oMy/cyDAUagW0CiuBSCitisE5LrVpfGrTiKqm8kaeMwwjZ3ybTOMomQYCbIxpcKdjjL/nUpy774mpvLExwSugknAORceSWFV4xLSmmbm7jeS7Lm5k8F7jm2XY/vMM3xnTwFzP1v7buUsX2LSpkYo0rhrPbvFztxV9xYoVnH322c6/J0+eDMC4ceOYPXs2d9xxB8XFxUyYMIH8/HxOP/105s2bR0yMaQCu0BXMGdEStbJKKHPovbCjv8Vew7vRF/Bc4S1kVu869gtCSLpNMqKue4k6pn3Hhg0byMrKIiYmhpycHGbMmEF2drYHNQhdyojgE2PV/C5VVthDJeGUOaKJt1ru4KhpNskI8T5lRPCKsUqVEwG2rNUp/K31tZxasoTEatOJndCnjAgOAW3YSE9PJz093SvzysnJ4cEHH2T37t1kZGQAMH/+fBITE+ndu7dzmvfff7/e6+bPn9+sLl46de7Mlh9/9LziQSC69iS4wsXeKogEILLRFpDQU1Ze7tHr/T2g01lnnYVlWcbnHQ4H06dPZ/r06R7UKjQEc0Ysq82IsYbnTzeUp7pq+DO1nBobFl29wHTSIs5QXtn08tovQVR1OeVEYVXVNPwbd0XulhvaYRtrL3ezXZWNjczLlY5uLtdU1wRTG7O33rvaz+aglcDWsGyqq8JqfyG78fkCrtfAtE0ZNlhX26tp2zZ8UDsMk692UfZK7b/H2SQj6rqXqDN16lSmTZtWr2zw4MHMnj2bHj16sHPnTu6//37OOOMM1qxZQ0KCrpg7WjBnxOe1GXGN4XnTUJ093b04yvQdS3X1/TXtkNy9SMawH6muyYmaV0YfPlo3fUVdlbt5YZWp5qa3a5+h3N0jPlcXSLmbD25f6OTO+wjO93900b/pU772iLMnphxw9Xm7u20YcsPdq9kM3wNX3xtTJyKzav+1S0aI9wVzRiyqzYgJhud/YSjv6K2MiDP9xjOVm04rmvYRhv1M7RciuqqMckcTjilMszd9rRs5eHA3L0wX1ZrywnSJoteywd11PsaxQ4VVe26psqK2zJ1jhMbK3dy2vJQPpmMI00W0L9b+q4wIDgFt2HDH1q1b2b9/P1u3bqWqqsrZjUvXrl2Jj49n2LBh9O7dm6uvvppHHnmEvLw87rnnHnJzc52t4DfddBPPPPMMd9xxB9deey0ff/wxr7/+Ou+9957b9amqajmbWJRV82UuJyrANZHGRFPGP0suI85q7P6R0LO99pbd5vK070K1otuDMiLwoq0yLBxUEkGk8cevBEKFo+bgJKIFfi52yYht27aRmJjoLHd1heaIESOc/+/fvz+DBw+mQ4cOvP7661x33XUe1EKUEfbgPKZw6JjCDiKoYsqBhwNdjYCyS0ZIYCkj7CXKKtcxRYBVOmpOFbfEY4cjKSOCQ9A0bNx33328/PLLzr+PP/54AD755BPOOusswsPDeffdd7n55pvJyckhLi6OcePG1bvquVOnTrz33nvcdtttPPnkk7Rr146//vWvDB8+vMn1qLvSOiY6Goua61HSu3enrKiIwh07iIiKIr1nT4o2bcIqLiYsLY2wNm2oXLsWgPAuXShMKoO92yE8HNr3gd2boeQgVKVAzHFQUHvtYXxnsCqhcCvggPB+UP0TWAXgaA1hXYAfamuWVfvvDmqaa7Opue6oGGgFpAHbAAtIBcKB3bWvaQ9sBwpqp82ipq25Gg62ozQsC6oKKSsvpbCyD1i7oHgvhMdBdBc49D1Y5RCVBdHxkF9bp9a9oGwflOwGYiG9G+xeDxVlkJQJsUmQt76mGbJjDyguhD07ISoaOvag+PuNUFKCIyMD0tKw1tV0KuXo1o3KkhLKfv6ZsMhI4nr1ouTHHykvKiI+LY2EzEznID9pXbrQprycHdu2EREeTtc+ffh5yxYOFhbSOiWFNu3a8X1tH5bZnTtTXVXF9p9+osLhoG2/fhzYupVD+fnEJifTOjubktppYzt0gLAwDm3eDEBR//5Ub9sGBw5AUhJhHTpQvWYNVFdTlZ4NERGwvbb9vmtf2LsTyvfVvF9pnSFvHVRVQvJxEBULBbXXD6f0hkN7ah+tIKbrEe93WwhPgEM/QCEQ1oN06wBYuygkBsK7Q1h47faQAiQCW2o/81TgEDVdTEUCbYFdtVt0EpBQu00AZNZOv5uaXUbn2udK4GAqhLWFqtrrocI6A5VQvRUOhUFSXyj+CSoKIKo1tGoPFbV9hrbpBFY17P4JHHCof3+qtm7Fys/HkZxMeHY2latXU2ZZtO7QAUd4OPtr75Jq278/Kdu3s2//fpKTkynJz2/0LggJfXbNiHIgo3t3youKyK/NiDY9e1KyaRPVxcVEpKUR0aYNpbUZUdi2C5SXwa7ajOjcB6o2Q8VBiEmBuONgX21GlNZ+3yprMyKqH1T+BIXV1HyPj+NwZ3wptf/W3SLfkZqMKAdiqfmO/0xNRqRQs1+ouxapPXCAmitpYmvnu6lm2WTCoS5QuYlB5R+THFlOQUk6UdU7oTQO4rpAUe0+KzoLIuKhtDYjUnvBodqMiIyFzG6wszYjWtdmxI7ajOjcA4oKYfdOiI6GTj04+HNNRoTVZkR1bUaEdesGJSWU1GZEYq9eFP34I5VFRUSlpZGUmcnPtRmR0aULleXlVB+REdu3bKHIkBFVVVX8XJsRx/Xrx74jMiL1iIyI69ABR1gYRbUZUVKbEdaBAzhqM6JqzRqiq6rhuNqM+Kk2I3r0hZidULgPYuIhozP8XJsREcfVvFcHajMitTeU7Kl5hLWCuK5HvN9t4VACVP5AUXUVVZVVlBS1prBqDxRGAh2An6i5oiqNmoyou3yrbW35AWr2+xm120MZNfkQz+GMyKot31c7bc/a+RbXbksZwPc1m0/EkdtsGMT1hYM/QXkBRLeG+Paw79uaTeu4TlBdDTtrMoKu/Tm0fitV+fmEJycTmZ1N6erVYFnEd+hAWHg4e2sz4jgbZkRiYmK9ho2mSE5Opnv37mzc6O49RXI0u2dEWVERBTt2EH5URoSnpRHepg3ldRnRpkvNPnJ3bUZ07AM7N8Oh2oxodRzsr82IhM5QUglVtRkRWZsRVgEUJlDz+66uf/l21OTG3tq/jzyOCKMmI3ZwOCPCjpj2yIyIAboDG6jZeWcC0cBPUARhUZlcunMu3Q99SWFZHER1gdLvobocYrMgPB6KajMioTYjimozIqUbONbXZGTrTGiVBD+vr5m2cw84WAi7ajOicw/YspGD1GREeFoaFbUZEVGbEYd+/hlHZCTJvXpxsDYjomszYnttRrTp0oWK2owIDw+nmxsZ0a5fP/Zv3Upxfj5xycnEZGdz4IiMICyM4s2bOQhE9u9P1bZtVNdmRESHDhTmrwFXGdG2L+TXZkRsPLTpDNvXQWklJB4HEbGwv3afkd4bqvZA2R4IbwXxXeFg7ft9qC2EJUBF7fsd2ROqDtT+hogGOgGbqdm/pwHpHL7mtS4jyqnZ77ehJiPKqcmIDA7fP9GGmmzZX7vddKHmmLQYaF0z34O1dYioPfatqs2IVkdkRExriDsiI7JqMyKvNiM696ckeSvV+fmEJScTkZ1N+erVlFsWKbXHEftqMyLLhhkhgWXXjCijfkZEREWRcdS5pvA2baioy4iM2uOIvdtrzgMcmRFRtccRB44411Rce9x+9LmmmDhq9t9Hn2uqu3q9PTXf6bpzTanATmoyojU13/V91Hz3s2unrcuINrXPV9bONwrnOYrKPlC5i0F5H9M6fwcFVf2JKl0Dh2ozIjIeCmvrlNQLovbBwd0150/SusGeI841xSXB1tqMyK4912TthJho6NYDNtUcR9AmA1LTKFpZkxEx3bpRVVJCRW1GJPTqRUHtuabYtDTiMjPZu2YNFUBWbUbsqc2I7D592GLIiNTac027f/qJMIeDDv36sWfrVkrz84lOTiYxO5s9tdMmduhARVgYZbXHEbH9+1O+bRtVBw5QmJgE2R1gXW1GtM+GyAiorM2ITn1h/04oqM2I1M6wq/Y4Iqn2XNOejTWngpJrzzXVZURiVyiuOY4oio+jKqKSkoMVFFYBhV2oOV+4u/Zz7EzNOcMKIJmaXgDq9vttqcmRQg4fR+yt3X6iqTnmODIjyqn5zXDUuSaSoaTv4XNN4bUZUb0VSsMguS8UHXGuKa49VLo+11TWvz+VWxtmRN25pjBlRFBzWPqE3LJ9+/YG3QmISOBt27aNdu3aNXn6wsJCkpKS+A018dpcZcCTQEFBgdsnrST0KCNE7CkYM6KoqIjs7GymTZvG//3f/3lQC7ELZYSIPQVjRkjoUUaI2JMywt6C5o4Nu8jKymLbtm0kJCTgcDic5YWFhbRv375BVwPBLlTXC0J33UJ1vcD1ulmWxcGDB8nKyjrGq13T7YHiTcqI0BGq6xaq6wXBnxG/+93vuPDCC+nQoQM7duxg6tSphIeHc8UVV3hQA7ETZUToCNV1C9X1guDPCAl9yojQEarrFqrrBcqIYKaGDTeFhYU12lLXnK4GgkGorheE7rqF6npBw3VLSkpq9rw0oJN4kzIi9ITquoXqekHwZsT27du54oor2LdvH+np6Zx++uksW7bMa4OfSuApI0JPqK5bqK4XBG9GSOhTRoSeUF23UF0vUEYEIzVsiIiIiIgE2Ny5cwNdBRERERERkaChhg0RadHUii4iIibKCBERMVFGiIiIiTLCP9Sw4SXR0dFMnTqV6GhPhoaxn1BdLwjddQvV9QLfrJuFZ30XWt6qiIS0UP1ehup6QeiuW6iuFygjJHiF6vcyVNcLQnfdQnW9QBkhwStUv5ehul4QuusWqusFyohg5rAsS++ViLQ4hYWFJCUlcR0Q5cF8yoGXgIKCgpDtZ1JEpKVRRoiIiIkyQkRETJQR/qU7NkSkRdPtgSIiYqKMEBERE2WEiIiYKCP8IyzQFRAREREREREREREREffMmDGDk046iYSEBDIyMhg1ahTr168PdLX8Qg0bItKiVXvhISIioUkZISIiJsoIEREx8WdGLFq0iNzcXJYtW8b8+fOpqKhg2LBhFBcXe2197EpdUYlIi6bbA0VExEQZISIiJsoIEREx8WdGzJs3r97fs2fPJiMjg5UrV3LmmWd6UAv70x0bbnrwwQc59dRTadWqFcnJyS6n2bp1KyNHjqRVq1ZkZGRw++23U1lZWW+ahQsXcsIJJxAdHU3Xrl2ZPXu27yvvpo4dO+JwOOo9Hn744XrTfPvtt5xxxhnExMTQvn17HnnkkQDV1j3PPvssHTt2JCYmhsGDB/PFF18EukpumzZtWoPPp2fPns7nS0tLyc3NJTU1lfj4eEaPHs2uXbsCWGPXPv30Uy688EKysrJwOBy89dZb9Z63LIv77ruPtm3bEhsby9ChQ9mwYUO9afbv38+YMWNITEwkOTmZ6667jqKioiYtX1daiTcpI5QRdqGMOEwZIXahjFBG2IUy4jBlhNiFMkIZYRfKiMOCNSMKCgoASElJ8WAuwUENG24qLy/nkksu4eabb3b5fFVVFSNHjqS8vJwlS5bw8ssvM3v2bO677z7nNJs3b2bkyJGcffbZrFq1ikmTJnH99dfz4Ycf+ms1mmz69Ons3LnT+bj11ludzxUWFjJs2DA6dOjAypUrefTRR5k2bRovvvhiAGt8bP/85z+ZPHkyU6dO5auvvmLAgAEMHz6c3bt3B7pqbuvTp0+9z2fx4sXO52677Tbeeecd3njjDRYtWsSOHTu4+OKLA1hb14qLixkwYADPPvusy+cfeeQRnnrqKZ5//nmWL19OXFwcw4cPp7S01DnNmDFjWLt2LfPnz+fdd9/l008/ZcKECf5aBREnZYQywk6UETWUEWIXyghlhJ0oI2ooI8QulBHKCDtRRtSwQ0YUFhbWe5SVlTU6fXV1NZMmTeK0006jb9++fqplAFnSLLNmzbKSkpIalL///vtWWFiYlZeX5yybOXOmlZiYaJWVlVmWZVl33HGH1adPn3qvu+yyy6zhw4f7tM7u6tChg/X4448bn3/uuees1q1bO9fLsizrzjvvtHr06OGH2jXfySefbOXm5jr/rqqqsrKysqwZM2YEsFbumzp1qjVgwACXz+Xn51uRkZHWG2+84Sz77rvvLMBaunSpn2roPsB68803nX9XV1dbmZmZ1qOPPuosy8/Pt6Kjo63XXnvNsizLWrdunQVYX375pXOaDz74wHI4HNbPP/9sXFZBQYEFWJeCdZUHj0vBAqyCggLvvyEStJQRyohAU0YoI8S+lBHKiEBTRigjxL6UEcqIQFNG2Csjjn5MnTq10XW96aabrA4dOljbtm1r3psVZHTHhpctXbqUfv360aZNG2fZ8OHDKSwsZO3atc5phg4dWu91w4cPZ+nSpX6ta1M8/PDDpKamcvzxx/Poo4/Wu81x6dKlnHnmmURFRTnLhg8fzvr16zlw4EAgqntM5eXlrFy5st77HxYWxtChQ235/h/Lhg0byMrKonPnzowZM4atW7cCsHLlSioqKuqtZ8+ePcnOzg6q9dy8eTN5eXn11iMpKYnBgwc712Pp0qUkJydz4oknOqcZOnQoYWFhLF++/JjLqOZw34fNeegWcnGHMkIZ4U/KCGWEBBdlhDLCn5QRyggJLsoIZYQ/KSPskxHbtm2joKDA+ZgyZYpxmRMnTuTdd9/lk08+oV27dk17M4KcBg/3sry8vHpBAzj/zsvLa3SawsJCDh06RGxsrH8qewz/93//xwknnEBKSgpLlixhypQp7Ny5k8ceewyoWY9OnTrVe82R69q6dWu/1/lY9u7dS1VVlcv3//vvvw9QrZpn8ODBzJ49mx49erBz507uv/9+zjjjDNasWUNeXh5RUVEN+uZs06aNczsMBnV1dfV5Hfl9ysjIqPd8REQEKSkpTVpXT/su1AGJuEMZoYzwF2WEMkKCjzJCGeEvyghlhAQfZYQywl+UEfbKiMTERBITExud1rIsbr31Vt58800WLlzY4PsTytSwAdx111388Y9/bHSa7777rt5gOcHKnXWdPHmys6x///5ERUVx4403MmPGDKKjo31dVTmGESNGOP/fv39/Bg8eTIcOHXj99ddt84NFJBQoI+pTRgQHZYSIfygj6lNGBAdlhIh/KCPqU0YEB2VE8MnNzWXOnDm8/fbbJCQkOBtekpKSQv4zU8MG8Nvf/pbx48c3Ok3nzp2bNK/MzEy++OKLemW7du1yPlf3b13ZkdMkJib6fIPzZF0HDx5MZWUlW7ZsoUePHsb1gMPrajdpaWmEh4e7rLdd69xUycnJdO/enY0bN3LuuedSXl5Ofn5+vZb0YFvPurru2rWLtm3bOst37drFwIEDndMcPRhXZWUl+/fvb9K6VgGe9MlX5cFrJTgoI+pTRgQnZcRhygjxJmVEfcqI4KSMOEwZId6kjKhPGRGclBGH2TUjZs6cCcBZZ51Vr3zWrFnH/F4GOzVsAOnp6aSnp3tlXjk5OTz44IPs3r3becvS/PnzSUxMpHfv3s5p3n///Xqvmz9/Pjk5OV6pQ2M8WddVq1YRFhbmXK+cnBzuvvtuKioqiIyMBGrWo0ePHra8NRAgKiqKQYMGsWDBAkaNGgVAdXU1CxYsYOLEiYGtnIeKiorYtGkTV199NYMGDSIyMpIFCxYwevRoANavX8/WrVv9sp15S6dOncjMzGTBggXOcCksLGT58uXcfPPNQM12mJ+fz8qVKxk0aBAAH3/8MdXV1QwePPiYy9ABiRyLMqJplBH2poxQRohvKCOaRhlhb8oIZYT4hjKiaZQR9qaMsH9GWJblwZKCXKBHLw82P/30k/X1119b999/vxUfH299/fXX1tdff20dPHjQsizLqqystPr27WsNGzbMWrVqlTVv3jwrPT3dmjJlinMeP/74o9WqVSvr9ttvt7777jvr2WeftcLDw6158+YFarUaWLJkifX4449bq1atsjZt2mT94x//sNLT062xY8c6p8nPz7fatGljXX311daaNWusuXPnWq1atbJeeOGFANb82ObOnWtFR0dbs2fPttatW2dNmDDBSk5OtvLy8gJdNbf89re/tRYuXGht3rzZ+vzzz62hQ4daaWlp1u7duy3LsqybbrrJys7Otj7++GNrxYoVVk5OjpWTkxPgWjd08OBB5/cIsB577DHr66+/tn766SfLsizr4YcftpKTk623337b+vbbb62LLrrI6tSpk3Xo0CHnPM477zzr+OOPt5YvX24tXrzY6tatm3XFFVc0utyCggILsM4H6yIPHueDBVgFBQU+fZ8kOCgjlBF2oYxQRoj9KCOUEXahjFBGiP0oI5QRdqGMUEYEEzVsuGncuHEWtRvXkY9PPvnEOc2WLVusESNGWLGxsVZaWpr129/+1qqoqKg3n08++cQaOHCgFRUVZXXu3NmaNWuWf1fkGFauXGkNHjzYSkpKsmJiYqxevXpZDz30kFVaWlpvum+++cY6/fTTrejoaOu4446zHn744QDV2D1PP/20lZ2dbUVFRVknn3yytWzZskBXyW2XXXaZ1bZtWysqKso67rjjrMsuu8zauHGj8/lDhw5Zt9xyi9W6dWurVatW1q9+9Str586dAayxa5988onL79S4ceMsy7Ks6upq695777XatGljRUdHW0OGDLHWr19fbx779u2zrrjiCis+Pt5KTEy0rrnmGucPQBOFjfiCMkIZYRfKiMOUEWIXyghlhF0oIw5TRohdKCOUEXahjDhMGWF/DstqyferiEhLVVhYSFJSEsOBSA/mUwF8CBQUFJCYmOidyomISEApI0RExEQZISIiJsoI//Kkuy8RkaBX7YWHiIiEJmWEiIiY+DMjZsyYwUknnURCQgIZGRmMGjWK9evXe21dRETEu3Qc4R9q2BCRFq2amkGZmvtQ2IiIhC5lhIiImPgzIxYtWkRubi7Lli1j/vz5VFRUMGzYMIqLi722PiIi4j06jvCPiEBXQEREREREREREXJs3b169v2fPnk1GRgYrV67kzDPPDFCtREREAksNGyLSolUBDg9fLyIioUkZISIiJt7KiMLCwnrl0dHRREdHN/ragoICAFJSUjyogYiI+IqOI/xDXVGJSIumfg9FRMREGSEiIibeyoj27duTlJTkfMyYMaPx5VZXM2nSJE477TT69u3r/RUTERGP6TjCP3THhoi0aGpFFxERE2WEiIiYeCsjtm3bRmJiorP8WHdr5ObmsmbNGhYvXuzB0kVExJd0HOEfatgQEREREREREQmAxMTEeg0bjZk4cSLvvvsun376Ke3atfNxzUREROxNDRsi0qKpFV1EREyUESIiYuLPjLAsi1tvvZU333yThQsX0qlTJw+WLCIivqbjCP/QGBsS0l566SWGDRvm8+XMmzePgQMHUl2tXvCCjfo9FGm5lBFyLMoIkZZLGSHH4s+MyM3N5R//+Adz5swhISGBvLw88vLyOHTokNfWR0SaThkhx6LjCP9Qw4aErNLSUu69916mTp3q82Wdd955REZG8uqrr/p8WeJdVV54iEjwUUZIUygjRFomZYQ0hT8zYubMmRQUFHDWWWfRtm1b5+Of//yn19ZHRJpGGSFNoeMI/1DDhoSsf/3rXyQmJnLaaaf5ZXnjx4/nqaee8suyRETEM8oIERExUUaI3ViW5fIxfvz4QFdNpMVRRojYhxo2xPb27NlDZmYmDz30kLNsyZIlREVFsWDBAuPr5s6dy4UXXlivbPz48YwaNYo//elPtG3bltTUVHJzc6moqHBO07FjRx544AHGjh1LfHw8HTp04L///S979uzhoosuIj4+nv79+7NixYp6877wwgtZsWIFmzZt8tKaiz9YeHZroOX/KovIEZQR4kvKCJHgpowQX1JGiAQ3ZYT4kjLCP9SwIbaXnp7O3/72N6ZNm8aKFSs4ePAgV199NRMnTmTIkCHG1y1evJgTTzyxQfknn3zCpk2b+OSTT3j55ZeZPXs2s2fPrjfN448/zmmnncbXX3/NyJEjufrqqxk7dixXXXUVX331FV26dGHs2LFY1uFdTXZ2Nm3atOGzzz7z2rqL7+n2QJHgpowQX1JGiAQ3ZYT4kjJCJLgpI8SXlBH+oYYNCQrnn38+N9xwA2PGjOGmm24iLi6OGTNmGKfPz8+noKCArKysBs+1bt2aZ555hp49e3LBBRcwcuTIBq3x559/PjfeeCPdunXjvvvuo7CwkJNOOolLLrmE7t27c+edd/Ldd9+xa9eueq/Lysrip59+8s5Ki18obESCnzJCfEUZIRL8lBHiK8oIkeCnjBBfUUb4hxo2JGj86U9/orKykjfeeINXX32V6Oho47SHDh0CICYmpsFzffr0ITw83Pl327Zt2b17d71p+vfv7/x/mzZtAOjXr1+DsqNfFxsbS0lJSVNXSUREvEQZISIiJsoIERExUUaIBC81bEjQ2LRpEzt27KC6upotW7Y0Om1qaioOh4MDBw40eC4yMrLe3w6Hg+rqauM0DofDWHb06/bv3096evqxV0Zsw5M+D+seIhJ4ygjxBWWESGhQRogvKCNEQoMyQnxBGeEfatiQoFBeXs5VV13FZZddxh/+8Aeuv/76Bi3YR4qKiqJ3796sW7fOb3UsLS1l06ZNHH/88X5bpnhOtweKBD9lhPiKMkIk+CkjxFeUESLBTxkhvqKM8A81bEhQuPvuuykoKOCpp57izjvvpHv37lx77bWNvmb48OEsXrzYTzWEZcuWER0dTU5Ojt+WKZ5T2IgEP2WE+IoyQiT4KSPEV5QRIsFPGSG+oozwDzVsiO0tXLiQJ554gr///e8kJiYSFhbG3//+dz777DNmzpxpfN11113H+++/T0FBgV/q+dprrzFmzBhatWrll+WJiIgyQkREzJQRIiJioowQCX4Oy7KsQFdCxFcuueQSTjjhBKZMmeLT5ezdu5cePXqwYsUKOnXq5NNliXcUFhaSlJREZyD8mFObVQE/AgUFBSQmJnqnciLiF8oIMVFGiIgyQkyUESKijBATZYR/6Y4NCWmPPvoo8fHxPl/Oli1beO655xQ0Qagaz24N1IBOIsFLGSHHoowQabmUEXIsygiRlksZIceijPAP3bEhIi1SXSt6Np618FYDW1EruohIKFFGiIiIiTJCRERMlBH+pTs2REREREREREREREQkaEQEugIiIoFUBXhy25puDxQRCV3KCBERMVFGiIiIiTLCP9SwISItmsJGRERMlBEiImKijBARERNlhH+oYUNEWjRPw0JhIyISupQRIiJioowQERETZYR/aIwNEREREREREREREREJGrpjQ0RaNN0eKCIiJsoIERExUUaIiIiJMsI/1LAhIi1aNZ6FjSevFRERe1NGiIiIiTJCRERMlBH+oYYNEWnRqgGHB69X2IiIhC5lhIiImCgjRETERBnhHxpjQ0REREREREREREREgobu2BCRFq0KtaKLiIhryggRETFRRoiIiIkywj/UsCEiLZrCRkRETJQRIiJioowQERETZYR/qGFDRFqkqKgoMjMzycvL83hemZmZREVFeaFWIiJiB8oIERExUUaIiIiJMsK/HJZlqRFIRFqk0tJSysvLPZ5PVFQUMTExXqiRiIjYhTJCRERMlBEiImKijPAfNWyIiIiIiIiIiIiIiEjQCAt0BURERERERERERERERJpKDRsiIiIiIiIiIiIiIhI01LAhIiIiIiIiIiIiIiJBQw0bIiIiIiIiIiIiIiISNNSwISIiIiIiIiIiIiIiQUMNGyIiIiIiIiIiIiIiEjTUsCEiIiIiIiIiIiIiIkFDDRsiIiIiIiIiIiIiIhI01LAhIiIiIiIiIiIiIiJBQw0bIiIiIiIiIiIiIiISNNSwISIiIiIiIiIiIiIiQUMNGyIiIiIiIiIiIiIiEjTUsCEiIiIiIiIiIiIiIkFDDRsiIiIiIiIiIiIiIhI01LAhIiIiIiIiIiIiIiJBQw0bIiIiIiIiIiIiIiISNNSwISIiIiIiIiIiIiIiQUMNGyIiIiIiIiIiIiIiEjTUsCEiIiIiIiIiIiIiIkFDDRsiIiIiIiIiIiIiIhI01LAhIiIiIiIiIiIiIiJBQw0bIiIiIiIiIiIiIiISNNSwISIiIiIiIiIiIiIiQUMNGyIiIiIiIiIiIiIiEjTUsCEiIiIiIiIiIiIiIkFDDRsiIn5SVVXFvffeS6dOnYiNjaVLly784Q9/wLIs5zSWZXHffffRtm1bYmNjGTp0KBs2bAhgrUVEREREREREROxFDRsiIn7yxz/+kZkzZ/LMM8/w3Xff8cc//pFHHnmEp59+2jnNI488wlNPPcXzzz/P8uXLiYuLY/jw4ZSWlgaw5iIiIiIiIiIiIvahhg0RET9ZsmQJF110ESNHjqRjx478+te/ZtiwYXzxxRdAzd0aTzzxBPfccw8XXXQR/fv355VXXmHHjh289dZbga28iIj41cMPP4zD4WDSpEnOstLSUnJzc0lNTSU+Pp7Ro0eza9euwFVSREREREQkQCICXQERkUApLS2lvLzc4/lYloXD4ahXFh0dTXR0dL2yU089lRdffJEffviB7t27880337B48WIee+wxADZv3kxeXh5Dhw51viYpKYnBgwezdOlSLr/8co/rKiIiTeOtjIiKiiImJsat13z55Ze88MIL9O/fv175bbfdxnvvvccbb7xBUlISEydO5OKLL+bzzz/3uJ4iItJ0gcwIERGxN2WE/6hhQ0RapNLSUjp16kReXp7H84qPj6eoqKhe2dSpU5k2bVq9srvuuovCwkJ69uxJeHg4VVVVPPjgg4wZMwbAWZc2bdrUe12bNm28Uk8REWkab2ZEZmYmmzdvbvJBSVFREWPGjOEvf/kLDzzwgLO8oKCAl156iTlz5nDOOecAMGvWLHr16sWyZcs45ZRTPK6riIgcWyAzQkRE7E0Z4V9q2BCRFqm8vJy8vDy2bdtMYmJis+dTWFhI+/ad2LZtW735HH23BsDrr7/Oq6++ypw5c+jTpw+rVq1i0qRJZGVlMW7cuGbXQUREvMvbGbF3794GGeEqJwByc3MZOXIkQ4cOrdewsXLlSioqKurd1dezZ0+ys7NZunSpGjZERPzE2xlRXl6uk1YiIiFCGeFfatgQkRYtMTHRo7BxZz633347d911l7NLqX79+vHTTz8xY8YMxo0bR2ZmJgC7du2ibdu2ztft2rWLgQMHelxHERFxj7cyon379vX+dnVXH8DcuXP56quv+PLLLxs8l5eXR1RUFMnJyfXKdVefiEhgeCsjREQk9Cgj/EMNGyLSwlXWPjx5fdOUlJQQFhZWryw8PJzq6moAOnXqRGZmJgsWLHA2ZBQWFrJ8+XJuvvlmD+ooIiLN452MaMpdfdu2beM3v/kN8+fP11VZIiJBwX/HESIiEmyUEf6ghg0RaeH8FzYXXnghDz74INnZ2fTp04evv/6axx57jGuvvRYAh8PBpEmTeOCBB+jWrRudOnXi3nvvJSsri1GjRnlQRxERaR7vZERTrthauXIlu3fv5oQTTnCWVVVV8emnn/LMM8/w4YcfUl5eTn5+fr27Nnbt2uW8409ERPxJJ61ERMREGeEPatgQkRbOf2Hz9NNPc++993LLLbewe/dusrKyuPHGG7nvvvuc09xxxx0UFxczYcIE8vPzOf3005k3b56u3hURCQj/ZcSQIUNYvXp1vbJrrrmGnj17cuedd9K+fXsiIyNZsGABo0ePBmD9+vVs3bqVnJwcD+ooIiLNo5NWIiJioozwBzVsiIj4SUJCAk888QRPPPGEcRqHw8H06dOZPn26/yomIiIBl5CQQN++feuVxcXFkZqa6iy/7rrrmDx5MikpKSQmJnLrrbeSk5OjgcNFRERERKTFUcOGiLRwVXjWEl7lrYqIiIjt2CsjHn/8ccLCwhg9ejRlZWUMHz6c5557zqvLEBGRprJXRoiIiJ0oI/xBDRsi0sLp9kARETEJbEYsXLiw3t8xMTE8++yzPPvssx7NV0REvEHHESIiYqKM8Ac1bIhIC6ewERERE2WEiIiYKCNERMREGeEPYYGugIiIiIiIiIiIiIiISFPpjg0RaeHUii4iIibKCBERMVFGiIiIiTLCH9SwISItXBWeDcqkAZ1EREKXMkJEREyUESIiYqKM8Ac1bIhIC1eFZy3hChsRkdCljBARERNlhIiImCgj/EFjbIiIiIiIiIiI2NSMGTM46aSTSEhIICMjg1GjRrF+/fp605x11lk4HI56j5tuuilANRYREfE93bEhIi2c+j0UERETZYSIiJj4LyMWLVpEbm4uJ510EpWVlfz+979n2LBhrFu3jri4OOd0N9xwA9OnT3f+3apVKw/qJyIizafjCH9Qw4aItHAKGxERMVFGiIiIif8yYt68efX+nj17NhkZGaxcuZIzzzzTWd6qVSsyMzM9qJOIiHiHjiP8QV1RiUgLV+mFh4iIhCZlhIiImHgnIwoLC+s9ysrKjrnkgoICAFJSUuqVv/rqq6SlpdG3b1+mTJlCSUmJ56spIiLNoOMIf9AdGyLSwmlAJxERMVFGiIiIiXcyon379vVKp06dyrRp04yvqq6uZtKkSZx22mn07dvXWX7llVfSoUMHsrKy+Pbbb7nzzjtZv349//nPfzyoo4iINI+OI/xBDRsiIiIiIiIiIgGwbds2EhMTnX9HR0c3On1ubi5r1qxh8eLF9conTJjg/H+/fv1o27YtQ4YMYdOmTXTp0sW7lRYREbEBNWyISAunfg9FRMREGSEiIibeyYjExMR6DRuNmThxIu+++y6ffvop7dq1a3TawYMHA7Bx40Y1bIiI+J2OI/xBDRsi0sIpbERExEQZISIiJv7LCMuyuPXWW3nzzTdZuHAhnTp1OuZrVq1aBUDbtm2bW0EREWk2HUf4gxo2RKSFU9iIiIiJMkJEREz8lxG5ubnMmTOHt99+m4SEBPLy8gBISkoiNjaWTZs2MWfOHM4//3xSU1P59ttvue222zjzzDPp37+/B3UUEZHm0XGEP6hhQ0RERERERETEpmbOnAnAWWedVa981qxZjB8/nqioKD766COeeOIJiouLad++PaNHj+aee+4JQG1FRET8Qw0bItLCqRVdRERMlBEiImLi366oGtO+fXsWLVrkQV1ERMS7dBzhD2rYEJEWrgrPAqPKWxURERHbUUaIiIiJMkJEREyUEf6ghg0RaeHUii4iIibKCBERMVFGiIiIiTLCH8ICXQEREREREREREREREZGm0h0bItLCqRVdRERMlBEiImKijBARERNlhD+oYUNEWjiFjYiImCgjRETERBkhIiImygh/UFdUItLCVXrhISIioUkZISIiJsoIEREx8W9GfPrpp1x44YVkZWXhcDh466236j3vcDhcPh599FHjPKdNm9Zg+p49e7pVL19Tw4aIiIiIiIiIiIiISBAqLi5mwIABPPvssy6f37lzZ73H3/72NxwOB6NHj250vn369Kn3usWLF/ui+s2mrqhEpIWrwrOrpaq8VREREbEdZYSIiJgoI0RExMS/GTFixAhGjBhhfD4zM7Pe32+//TZnn302nTt3bnS+ERERDV5rJ2rYEJEWrgrPDip0QCIiErqUESIiYqKMEBERE+9kRGFhYb3S6OhooqOjPZgv7Nq1i/fee4+XX375mNNu2LCBrKwsYmJiyMnJYcaMGWRnZ3u0fG9SV1Qi0sKpb1wRETFRRoiIiIkyQkRETLyTEe3btycpKcn5mDFjhsc1e/nll0lISODiiy9udLrBgwcze/Zs5s2bx8yZM9m8eTNnnHEGBw8e9LgO3qI7NkREREREREREREREbGTbtm0kJiY6//b0bg2Av/3tb4wZM4aYmJhGpzuya6v+/fszePBgOnTowOuvv851113ncT28QQ0bItLCeXq1lK60EhEJXcoIERExUUaIiIiJdzIiMTGxXsOGpz777DPWr1/PP//5T7dfm5ycTPfu3dm4caPX6uMpdUUlIWf8+PFMmzYt0NWQoFE3oFNzH+obVySYKCPEPcoIkZZEGSHuUUaItCTKCHGPPTPipZdeYtCgQQwYMMDt1xYVFbFp0ybatm3rg5o1jxo2xOcsy2Lo0KEMHz68wXPPPfccycnJRERE4HA4jI+ffvopADX3rf379zNmzBgSExNJTk7muuuuo6ioKNDVaoH81zdux44dXW7fubm5AJSWlpKbm0tqairx8fGMHj2aXbt2eWtFRWxJGeGaMsIu1H+6SCApI1xTRtiFMkIkkJQRrikj7MK/GVFUVMSqVatYtWoVAJs3b2bVqlVs3brVOU1hYSFvvPEG119/vct5DBkyhGeeecb59+9+9zsWLVrEli1bWLJkCb/61a8IDw/niiuucKtuvqSGDfE5h8PBrFmzWL58OS+88IKzfPPmzdxxxx08/fTT7Ny5s8Hju+++IysriwsvvJDs7OwAroFvjBkzhrVr1zJ//nzeffddPv30UyZMmBDoaokPffnll/W28fnz5wNwySWXAHDbbbfxzjvv8MYbb7Bo0SJ27NhxzMGcRIKdMsI1ZYSIiDLCRBkhIqKMMFFGtEwrVqzg+OOP5/jjjwdg8uTJHH/88dx3333OaebOnYtlWcaGiU2bNrF3717n39u3b+eKK66gR48eXHrppaSmprJs2TLS09N9uzJuUMOG+EX79u158skn+d3vfsfmzZuxLIvrrruOYcOGcfXVV5Oenk5mZqbzkZGRwaRJk0hKSuLVV1/F4XD4rG579uwhMzOThx56yFm2ZMkSoqKiWLBggU+W+d133zFv3jz++te/MnjwYE4//XSefvpp5s6dy44dO3yyTDHxXyv60dv5u+++S5cuXfjFL35BQUEBL730Eo899hjnnHMOgwYNYtasWSxZsoRly5Z5cX1F7EcZUZ8ywk50Na5IoCkj6lNG2IkyQiTQlBH1KSPsxL8ZcdZZZ2FZVoPH7NmzndNMmDCBkpISkpKSXM5jy5Yt9bpbq9tuysrK2L59O3PnzqVLly5u1cvX1LAhfjNu3DiGDBnCtddeyzPPPMOaNWvqtaof6a677mL58uW8/fbbJCQk+LRe6enp/O1vf2PatGmsWLGCgwcPcvXVVzNx4kSGDBlifF2fPn2Ij483PkaMGGF87dKlS0lOTubEE090lg0dOpSwsDCWL1/u1fWTYwnMAUl5eTn/+Mc/uPbaa3E4HKxcuZKKigqGDh3qnKZnz55kZ2ezdOnS5q6cSNBQRhymjLAT/2XEzJkz6d+/v3OAwJycHD744APn82eddVaD7hNuuukmb6ykiO0pIw5TRtiJGjZE7EAZcZgywk6UEf4QEegKSMvy4osv0qdPHz799FP+/e9/u7x96bXXXuOxxx7jvffeo1u3bn6p1/nnn88NN9zAmDFjOPHEE4mLi2PGjBmNvub999+noqLC+HxsbKzxuby8PDIyMuqVRUREkJKSQl5ennuVFw95Ghg1ry0sLKxXGh0dTXR0tPFVb731Fvn5+YwfPx6o2SaioqJITk6uN12bNm20TUiLoYyooYywE+9kRFO0a9eOhx9+mG7dumFZFi+//DIXXXQRX3/9NX369AHghhtuYPr06c7XtGrVyoO6iQQXZUQNZYSd+C8jRKRxyogaygg7UUb4gxo2xK8yMjK48cYbeeuttxg1alSD57/66iuuu+46Hn74YZcDQPnSn/70J/r27csbb7zBypUrGz0pDdChQwc/1UyCQfv27ev9PXXq1Hq38B3tpZdeYsSIEWRlZfm4ZiLBQxkhLdmFF15Y7+8HH3yQmTNnsmzZMmfDRqtWrcjMzAxE9UQCThkhIiImygiRlkldUYnfRUREEBHRsE1tz549/OpXv2L06NH87ne/83u9Nm3axI4dO6iurmbLli3HnN6T2wMzMzPZvXt3vbLKykr279+vExZ+V4VntwZWAbBt2zYKCgqcjylTphiX+NNPP/HRRx9x/fXXO8syMzMpLy8nPz+/3rS7du3SNiEtijJCGWEv3smIwsLCeo+ysrLGl1pVxdy5cykuLiYnJ8dZ/uqrr5KWlkbfvn2ZMmUKJSUlXl1bEbtTRigj7MU7GSEi3qGMUEbYizLCH3THhthCRUUFv/71r8nIyOAvf/mL35dfXl7OVVddxWWXXUaPHj24/vrrWb16dYNb+I7kye2BOTk55Ofns3LlSgYNGgTAxx9/THV1NYMHD27+ikgzVALhHr4eZ5/oTTFr1iwyMjIYOXKks2zQoEFERkayYMECRo8eDcD69evZunVrvZNaIi2RMkIZETjeyYim3tW3evVqcnJyKC0tJT4+njfffJPevXsDcOWVV9KhQweysrL49ttvufPOO1m/fj3/+c9/PKifSPBTRigjAsc7GSEivqOMUEYEjjLCH9SwIbYwadIkvvnmGz766KMGV6wDpKSkEBUV5bPl33333RQUFPDUU08RHx/P+++/z7XXXsu7775rfI0ntwf26tWL8847jxtuuIHnn3+eiooKJk6cyOWXX66uifzOv2FTXV3NrFmzGDduXL2rSZKSkrjuuuuYPHkyKSkpJCYmcuutt5KTk8Mpp5ziQf1Egp8yQhkRON7JiG3bttVr/DZ1QdCjRw9WrVpFQUEB//rXvxg3bhyLFi2id+/eTJgwwTldv379aNu2LUOGDGHTpk106dLFgzqKBDdlhDIicHTSSsTulBHKiMBRRviDuqISW3juuecoKCjgpJNOom3btg0eS5Ys8dmyFy5cyBNPPMHf//53EhMTCQsL4+9//zufffYZM2fO9NlyX331VXr27MmQIUM4//zzOf3003nxxRd9tjyxh48++oitW7dy7bXXNnju8ccf54ILLmD06NGceeaZZGZm6kpcEZQRyojgV3dXX93D1LARFRVF165dGTRoEDNmzGDAgAE8+eSTLqetu+pu48aNPqu3SDBQRigjRERMlBHKCAltDsuyrEBXQsSbxo8fT8eOHRsduFmksLCQpKQkCgpuITGx8cG7Gp9PGUlJNT+WmtoVlYgEjjJCmsIuGXHOOeeQnZ3N7NmzGzz3+eefc/rpp/PNN9/Qv3//ZtdRRA5TRkhT2CUjRMS/lBHSFMoI/1JXVCLSwtUN6OTJ60VEJDT5LyOmTJnCiBEjyM7O5uDBg8yZM4eFCxfy4YcfsmnTJubMmcP5559Pamoq3377LbfddhtnnnmmGjVERAJGxxEiImKijPAHNWyISAtXiWe98qnfQxGR0OW/jNi9ezdjx45l586dJCUl0b9/fz788EPOPfdctm3bxkcffcQTTzxBcXEx7du3Z/To0dxzzz0e1E1ERDyj4wgRETFRRviDGjYk5IwaNYrk5ORAV0NERGxIGSF29dJLLxmfa9++PYsWLfJjbURaJmWEiIiYKCNE7EcNGxJyRo0aFegqSFBRK7pIS6KMEPcoI0RaEmWEuEcZIdKSKCPEPcoIf1DDhoi0cAobERExUUaIiIiJMkJEREyUEf6ghg0RaeGq8GxQJg3oJCISupQRIiJioowQERETZYQ/qGHDTdXV1ezYsYOEhAQcDkegqyPS4lmWxcGDB8nKyiIszJPWcBHPKSNE7EUZIXaijBCxF2WE2IkyQsRelBHBQQ0bbtqxYwft27cPdDVE5Cjbtm2jXbt2zXhlFZ7d4qdWdDlMGSFiT8oIsQNlhIg9KSPEDpQRIvakjLA3NWy4KSEhAYCO0dFUlJV5dd7hXp2be9T22LJUB3DZ3t41R0ZHs6WszPnddF8l4MkVMer3UA6r2w4HRUdT6eWM8KdA5lFThcJ1bFagK9BEwfyTOiI6mpXKCLGJuu3wnOhoqgOYEXbax+sYJDACeSxwtEBmTFh0NB8rI8Qm6rbDi6OjIYAZYbf9st3qE8rslA1gg/pER/MfZYTtqWHDTXW3BJaWlTV7B2s6mPD1CQ47HcQcqaUGVSB30qb33B8HFt5edmntj77m366rsBHvqdsOi8rKvLbPteu+O9BC4X0JlgYDf+eVN98XZYTYyZEZEciDsED89g6FfXZLEIhcCuQxUaUyQmykbjss8FNGBGq/bMfzP8GSUXY7dgjU/ttf74MyIjjYcZ8SFOLi4wNdBRFB30WxJ22XIvag76LYUSttlyK2oO+i2FGstksRW9B3MTjojo1mSkxK4lBRUaCrIQESHRtLSnq6R4N6Bfy2Ohfs3EWVZVns37OH0kOH6pUnJiWBR99FtaKL9yUkJVGqjBAJuARlhNhQfFIS+5URIgEXr4wQG2qVlESBMkIk4FopI4KCGjZE3NStb19uvPtu4pOSPGrYsCM79/duWRYHCwp49sEHWb9mjRfnrLARERETZYSIiJgoI0RExEQZ4Q9q2Ggmi+Zf3e7u67zVX1hjV8QHsk9Bu/UT2JiY2Fgm3H037Tt1IjI8WHpibMjODRiNSUlJIffuu/ndtdc679zwfF2q8CxsgmkLFn8prX0Egi/3TKHaf6W771mofut9eddeoN4zz7+HygjxvoNAvhfnF4r7/eD9lW0voThmhjfXKdLjOSgjxPsOAPsCuHxf73+VK94XqD1JMO3vm8Pz9VNG+IMaNkTckJKeTnxSEpHh4R7tnqR5IsLDSUhKIiU9nR1btwa6OiIiIiIiIiIiIhIAathopkMlJYGuggSAw+EIue6ngo3D4SDsiM/A8++ip7f36fZAaahMGSFiC55/F5UR4n06jhCxBx1HiB3pOELEHnQcERxCtVcJnztw4ECgqyDCvdOmseizzwC47qabjNP9vGMHV4wd2+i83n7nHR574okmL/vnHTv4cP5859/fr1/PkmXLmvx6b/H8u1jphYdIffnKCBFb8Py7qIwQ7ytQRojYguffRWWEeJ8yQsQelBHBQXdsNFNVWBiV1d7tUc7U55+pVzVv9hFoWhO1fNVX91nYZYwK64h///r888Z6WUf96w07du7kfx99xLBzzwVg/Q8/sHHTJnJOOcWLS6nPqn1UAOW1ZY6wMPDou6hWdPG+krAwrGZsl97Yr3tjvx0MfdAGQx2PJVh6TfVGPb3xi6k59VBGiB0VNjMjvPW73Bv7Tzvtg1vS8Yqv+zN3h7cyzFvzac57o4wQO9odAhnhzfl4SyhnhZ2yAUIjH0AZESxC+bvtU23atg10FaSFmvnii1w0ejQ33Hwz+/fvd5afNXQoALv37GHcdddx6ZVXcskVV/DDhg31Xr9x40auHDuWbdu3N5j3zzt2cO2ECfxy9Gj+MWeOs3zWyy8z+rLL+PXll/PeBx8A8Mxzz7H8iy+47Mor+dd//sPMF17g3fff57Irr+Szzz/nQH4+v7ntNi694gquv/FGduzYAcB906bx8COPcPX48fzy4ov55ttvufP3v2fU6NE89cwzbr8f+i6KHWm7FLEHfRfFjrRditiDvotiR9ouRexB38XgoDs2RDxwe+fOLst/u20bmRUV5EVG8uf27V1O8+iPPwLwTVwc/2jTpl6ZyZq1a/ls8WLeeO01CgoL+dUll3DZJZfUm2behx9y4qBB3HrLLVRWVlJRWelsAPlhwwamTp/OIzNm0L5duwbzX7tuHa+/9hoR4eFcOXYsvzjzTAoKCvjfRx8x55VXKC0tZcy4cZx04olMvOUW5r7+On/64x8BiIqMZOOmTUyeNAmAhx95hOOPP54nx47lw//9jz/+6U88+dhjABSXlPD32bN5/4MPmDR5Mq/+/e+kp6Xxq1//mquvuorWycmNvg/e5el1AMFyzbWIiLhPGSEiIibKCBERMVFG+IPu2BAJIqu++YZzzj6bqKgo0tPSOPnEExtM06d3b96fN4/nXniBzVu2EBsTA8CePXu46+67edTQqAFwak4OiQkJtGrVitNPO41vv/2WVd98w5BzziE6OpqkpCROPukk1q5bd8y6fr1qFeePGAHAsHPPZe3atc7nfnHmmQB07dqV9tnZZLVtS2RkJO3bt2fXrl1uvy+eUb+HIiJioowQERETZYSIiJgoI/xBd2w0k0Xz+2kztSb5ui2uOX0cBrKvPju2TVZyeJwHgEcaucPCAtpUVBzzLowBxcUMOMY0R3Ic4/lBJ5zA7L/+lc8WL+auu+/m1ltuoVu3biQkJNA6OZk169bRztCw4XA46o/D4Ti8tCPLmzNWh+OIeUVFRgIQ5nA4/2/VTlNVVXXMsUKqObxtej5uSN2n2lx23FIl0CqpGQvGXd7YmrzRn62p7t6YdyC/MXbMWTst15fvT6DG6fD8cEAZId5XzuGxwrzB3X1Ac/KpqcsMxBbv7jLt1Be5u1cZ2ilnArV38+ZyPf8eKiPEPrw1bqqvx0fw1j7YNP/m1N9UJ2/li7f29d6aj7eyxG5Xytsp32soI/zBbtuhiDTi+IED+WTRIioqKti7dy9frlzZYJodO3eSlprKry++mAvOP58NGzcCEBMTwxOPPcbfX32Vz5cscTn/z5cu5WBREYcOHeLzpUvp168fAwcOZMEnn1BeXk5hYSFfrlhB3z59aNWqFSUlJc7XtoqLq/f38QMHMm/ePAA+WrCAPn36ePOtEBERERERERERkRYqaBo2Zs6cSf/+/UlMTCQxMZGcnBw+qB3EGKC0tJTc3FxSU1OJj49n9OjRDbq02bp1KyNHjqRVq1ZkZGRw++23U1nZvGv5dvz8s0frI9IcfXr35rRTT+XXl1/OlHvvpX/fvg2mWbFyJZdeeSWXjRnD50uX8quLLnI+lxAfz1OPPcaTzzzD199843L+v5k8mcuvuorRo0bRvl07+vTuzblDhnDl1Vdz3YQJ3DRhAulpaXTr1o3Kykouu/JK/vPWW5w0aBDfff89l48Zw2eff86NEyaw4quvuPSKK/jnG29wx29/65P3xPPvom4PDAXKCBFxRRkhYL+M2KmMELEFz7+LyohQYLeM0HGEiD3oOCI4OCzL8rwnFz945513CA8Pp1u3bliWxcsvv8yjjz7K119/TZ8+fbj55pt57733mD17NklJSUycOJGwsDA+//xzAKqqqhg4cCCZmZk8+uij7Ny5k7Fjx3LDDTfw0EMPNbkehYWFJCUlkcSxuwQy8VZrkru3jwXq1uXmsuNNV+06dODh558nPS2tyZ9/c7eTQAjkzqApy7aAvXv38rubbmLbTz85ywqAgoICEhMTm7y8uu9yQUEXEhOb/+0oLKwiKWmT28sX7wqVjPBGPvhyX6+uqBpnpy5CmiMUu6JSRgjYLyNa493fh4HY96grKu9QV1Tu8+ZyLeAAyoiWzm4ZkYF3r0C229XM6orq2NQVVfN4O9+rgd0oI+wuaBo2XElJSeHRRx/l17/+Nenp6cyZM4df//rXAHz//ff06tWLpUuXcsopp/DBBx9wwQUXsGPHDtq0aQPA888/z5133smePXuIiopq0jLrNtDOaWns37u3WfVWw0bTqGHD/4KxYSMlLY0f9+71IGw6kpjY/G9lYWE1SUlbFDY2FIwZoYYN31LDRuNCsWFDGSEmgcyIrh4cR7iihg33qGHDfaHasJGSlsZGZYS4EMiM6JWWxgEvZkSonnxWw0bz56OGjaZpnZbGd0GUEZ9++imPPvooK1euZOfOnbz55puMGjXK+fz48eN5+eWX671m+PDhzi7kTZ599lkeffRR8vLyGDBgAE8//TQnn3xys9bJF4Jy8PCqqireeOMNiouLycnJYeXKlVRUVDB06FDnND179iQ7O9sZNktrxwuoCxqo+QBvvvlm1q5dy/HHH+9WHWKio5v9JfbWTiSYGiqaEy7e+tHanJ2b6bNt7tWi7ghkQ4i7y/ZmQ4g7yw7j8GcUEx3txVpIKLBDRiRFR1PqtTUKTqb9pbdOhjU2vbsHJ+7u293NCNP0vr5Awd3fKb48OIn04bwbk6SMkKPYISMyoqOD5sKXQP0ed2e53tq3+/JEvbf233bLDW8sM5DSo6PZGOhKiK3YISPaRUcT67U1cp+vGy19vW/25qDlgWrAbUmZ0ZhA50lqdDTfBbgO7iguLmbAgAFce+21XHzxxS6nOe+885g1a5bz7+hjHCv985//ZPLkyTz//PMMHjyYJ554guHDh7N+/XoyMjK8Wv/mCqqGjdWrV5OTk0NpaSnx8fG8+eab9O7dm1WrVhEVFUVycnK96du0aUNeXh4AeXl59YKm7vm650zKysooKytz/l1YWOiltRERe6jCsyYaO13317IpI0TE+5QRoUIZISLep4wIFcoIEfE+/2bEiBEjGDFiRKPTREdHk5mZ2eR5PvbYY9xwww1cc801QM3daO+99x5/+9vfuOuuu9yqn6/Y7c6hRvXo0YNVq1axfPlybr75ZsaNG8e6det8uswZM2bU9Jde+2jfvr1Plyci/qYBnUKFMkJEvE8ZESqUESLifcqIUKGMEBHv805GFBYW1nsc2SDqroULF5KRkUGPHj24+eab2bdvn3Ha8vJyVq5cWe+OtbCwMIYOHcrSpUubXQdvC6qGjaioKLp27cqgQYOYMWMGAwYM4MknnyQzM5Py8nLy8/PrTb9r1y5nS1RmZia7du1q8HzdcyZTpkyhoKDA+di2bZt3V0pEAkwHJKFCGSEi3qeMCBXKCBHxPv9lxIwZMzjppJNISEggIyODUaNGsX79+nrTlJaWkpubS2pqKvHx8YwePbrBvktcU0aIiPd5JyPat29frxF0xowZzarNeeedxyuvvMKCBQv44x//yKJFixgxYgRVVa47Xdu7dy9VVVUu70hr7G40fwuqrqiOVl1dTVlZGYMGDSIyMpIFCxYwevRoANavX8/WrVvJyckBICcnhwcffJDdu3c7+wGbP38+iYmJ9O7d27iM6Ohol32OlRw4QNOGgGq6QPZP5+v+cwO5bDsOQg72HEsjGMYDcVDTV3vd96/kwIEA1EKCQSAzouLAAeJ8sE6B5s7+1Fv955rKm5MrpnEe3M0Jb/VX625/uL7u9zbQ/dj6QoUyQgwCmREcOECy19fIM976vezLgby9NVCsr/t3d2dfGqiBYAORD7bMmCDKiEWLFpGbm8tJJ51EZWUlv//97xk2bBjr1q0jLq7mV+dtt93Ge++9xxtvvEFSUhITJ07k4osv5vPPPw9w7YNPIDMi8sAB0n2wTk0VDGMrNTZ9IMfS8Nbg4SYtbeyNQOdGuE0yYtu2bfUGDz/WuBgml19+ufP//fr1o3///nTp0oWFCxcyZMgQj+sZKEHTsDFlyhRGjBhBdnY2Bw8eZM6cOSxcuJAPP/yQpKQkrrvuOiZPnkxKSgqJiYnceuut5OTkcMoppwAwbNgwevfuzdVXX80jjzxCXl4e99xzD7m5uc3aKEpKSoLrdhcJSfdOm8a5Q4Zw5hlncP1NN/HX5593Od3PO3Zw+113MeeVV4zz+n79evYdOMCptd+ZYFFSUuLhHCrxLHrVN64d2DEjAv1DTESUEVLDbhlRqowQsYXSIMqIefPm1ft79uzZZGRksHLlSs4880wKCgp46aWXmDNnDueccw4As2bNolevXixbtsy5P5OG7JYRZSUlwXOiTiSEldkkIxITE+s1bHhL586dSUtLY+PGjS4bNtLS0ggPD3d5R5o743T4WtCcm9+9ezdjx46lR48eDBkyhC+//JIPP/yQc889F4DHH3+cCy64gNGjR3PmmWeSmZnJf/7zH+frw8PDeffddwkPDycnJ4errrqKsWPHMn369GbVp1WrVl5ZLxFvMTVqNNX6H35g2bJlXqqN/3j+XazCs1sD3bu+4+eff+aqq64iNTWV2NhY+vXrx4oVK5zPW5bFfffdR9u2bYmNjWXo0KFs2LDBw3UMfcoIEXEl2DJCfMNuGRGjjBCxBc+/i97JiOb0n15QUABASkoKACtXrqSioqJeX+g9e/YkOzvbVn2h25HdMiJaGSFiC55/F+19HLF9+3b27dtH27ZtXT4fFRXFoEGDWLBggbOsurqaBQsWOO9Ys4OgaQh+6aWXGn0+JiaGZ599lmeffdY4TYcOHXj//fe9Up/k1q3J87j1TsR9M198kXkffkhGRgZRkYc7Vjlr6FAWfvQRu/fs4fa77uLQoUNYlsWD06c7b08G2LhxI/dNn84jDz1E+3btAKiqquK5F16grLycZV98wa25ufTt04ep99/Pzrw8EhMTmT51KsdlZXHvtGnExMSwes0aysrKuPuuuzhx0CC/vw91klu3Bo++i5V41qlW0zvwOnDgAKeddhpnn302H3zwAenp6WzYsIHWrVs7p3nkkUd46qmnePnll+nUqRP33nsvw4cPZ926dcTExHhQz9Bmt4xIbN2aPcoIkYBLDKKMEN+xW0YktG7NfmWESMAl2CQjjh40eurUqUybNs34qurqaiZNmsRpp51G3759AcjLyyMqKork5OR609qtL3Q7sltGxLduTYEyQiTg4m2SEU1VVFTExo0bnX9v3ryZVatWkZKSQkpKCvfffz+jR48mMzOTTZs2cccdd9C1a1eGDx/ufM2QIUP41a9+xcSJEwGYPHky48aN48QTT+Tkk0/miSeeoLi4mGuuucaD9fKuoGnYsJtwzH11ezLPQHF32e72ee7NdkY7Xft4R+fOLssnb9tGZkUFeZGRPHbUD9U6j/74IwDfxMXxj9rBeB798cdGd3tr1q5l8eLF/Ou11ygoLORXl1zCZZdc4nyNA5j34YecNGgQt95yC5WVlVRUVrJ//34ANmzYwH3Tp/PojBnORg2oucrklhtvZOOmTfx20iQAHnrkEU44/niuGTuWef/7H4/86U889dhjOIA9e/Yw9+9/58fNm5l0++3891//wuHw34gbERz+/gVTVw5//OMfad++PbNmzXKWderUyfl/y7J44oknuOeee7jooosAeOWVV2jTpg1vvfVWvT4Rxd6iAW81Q5lurQxEBzfu7H/d7ffW3T5gG6uLr/sO93Wf6O6+F4HqP9dT/ti2m9cDbWDMnDmTmTNnsmXLFgD69OnDfffdx4gRI4CaQWF/+9vfMnfuXMrKyhg+fDjPPfdcgwH9xP7igYoALt8bv6W9Na6FN8ZuCtTYG+4IVG64O3935uOtzAjkb/n4AC77SO72n56bm8uaNWtYvHixr6smAZBIYMfj9NZYFybeGnvDm2OsBiovfH3M4utjh0DV318SArx8d61YsYKzzz7b+ffkyZMBGDduHDNnzuTbb7/l5ZdfJj8/n6ysLIYNG8Yf/vCHepmzadMm9u7d6/z7sssuY8+ePdx3333k5eUxcOBA5s2bZ6vjDzVsiASRVd98wzlnn01UVBTpaWmcdOKJDabp27s390ybRnh4OOcOGUK3rl2BmsaIO+++m6cfe4x2RzRqGJe1ahXXP/kkAMPPPZdH/vxn53PDzz0Xh8NBl86daRUby+7du221Y3OPd1rRCwsL65W6Ggzuv//9L8OHD+eSSy5h0aJFHHfccdxyyy3ccMMNQE2Lel5eXr1byJOSkhg8eDBLly5Vw4aIiN/570qrdu3a8fDDD9OtWzcsy+Lll1/moosu4uuvv6ZPnz4aFFZExHa8kxHu9J8+ceJE3n33XT799NN6x3SZmZmUl5eTn59f764Nu/WFLiLScvj3jo2zzjoLyzK/5sMPPzzmPOousDrSxIkTnXdw2JEaNkQ88EjtXRcmmRUVzjszTAYUFzPgGNMc6Vi7xUEnnMDsv/6VTxcv5s677+b/brmFbt26kZCQQOvkZFavW9ekho0Gyz3ijowG//fj3Rre579byH/88UdmzpzJ5MmT+f3vf8+XX37J//3f/xEVFcW4ceOct4kf3UikW8hFRALFfwckF154Yb2/H3zwQWbOnMmyZcto166dBoUVEbEd/2WEZVnceuutvPnmmyxcuLDeXd8AgwYNIjIykgULFjB69GgA1q9fz9atW23VF7qISMuhLm39IWgGD7ebiiYM6CXibccPHMjHixZRUVHB3r17+XLlygbT7Ni5k7TUVC65+GIuOP98fqjtYy8mJoYnH3uMv7/6Kp8vWdLgdXFxcRQf0X/gwIED+WDePADmL1hA3z59nM99+NFHWJbFj5s3U1xSQkZ6urdXtck8/i5a1WBVefCouRF127ZtFBQUOB9TpkxpsKjq6mpOOOEEHnroIY4//ngmTJjADTfcwPMeDvwu9lOujBCxBY+/i17KCHcHhq2qqmLu3LkUFxeTk5OjQWFDjI4jROzBLscRTZGbm8s//vEP5syZQ0JCAnl5eeTl5XHo0CGg5i7v6667jsmTJ/PJJ5+wcuVKrrnmGnJyctT4HWQqlREituDxd9GPGdGS6Y6NZsrfu7fR/t682WIUyH7lTH0N2qnPd38u27TezWmDdfc1Dmq6mTr91FMZffnlZGRk0L9vXxxHzMsBrFi5ktl//zuREREkJCTwxwceoKy8HIDE+Hieeewxbrz1VuLi4jh+wADn/E8eNIi/zZ7NZWPGcOstt3DLhAnce//9vPP++yQlJvKHqVOdy0lPTeWKsWMpLS1l2u9/T5gP79g4uo3aQc3nUPe9yD+i/79Aasot5G3btqV37971ynr16sW///1vAOdt4rt27aJt27bOaXbt2sXAgQO9W2HxqUN793ptjA1v8NbYRN7oezuQueLust3lbp/oprG63O331td9rvt7Ht50yCYZ0dSBYVevXk1OTg6lpaXEx8fz5ptv0rt3b1atWqVBYUOIvzLCtM9zZ5xAd+fhbp/npvm4mt4fYzT5SrCPtRSqY2/YJSOaYubMmUBNVyNHmjVrFuPHjwfg8ccfJywsjNGjR9cbi0mCS1mAjyMCNcaGt44RmnNM4W5e+Hr/5K2xLryVGYE61gj0cUVZEGVES6aGDZEgc8uECdwyYUKD8k8/+giAiy64gIsuuKDB83NfeQWA1NRU/jVnToPnk5KSeK12mjrPPP64yzqccdpp3HPXXW7X3Zaq8ezMqRuvPe2001i/fn29sh9++IEOHToANQOJZ2ZmsmDBAmdDRmFhIcuXL+fmm2/2oJIiItIsXsqIpg4M26NHD1atWkVBQQH/+te/GDduHIsWLfKgAiIi4jN+PI5orN/0OjExMTz77LM8++yzHlRKRES8wo8Z0ZKpK6pmyjzuuEBXQUTwwnexyguPJrrttttYtmwZDz30EBs3bmTOnDm8+OKL5ObmAjXjlUyaNIkHHniA//73v6xevZqxY8eSlZXFqFGjPFtP8at0ZYSILXj8XfRSRtTd1Vf3MDVsREVF0bVrVwYNGsSMGTMYMGAATz75ZL1BYY+kQWGDU5oyQsQWPP4u+vE4QlqO1soIEVvw+LuojPAL3bEhIm55wEXXGUHN08Bw47UnnXQSb775JlOmTGH69Ol06tSJJ554gjFjxjinueOOOyguLmbChAnk5+dz+umnM2/ePGJi7NSxkYhIC+HHjHClurqasrIyDQorImJHAc4IERGxMWWEX6hhQ0TEjy644AIucNFVWB2Hw8H06dOZPn26H2slIiKBNmXKFEaMGEF2djYHDx5kzpw5LFy4kA8//LDeoLApKSkkJiZy6623alBYERERERFpsdSw0Ux1Axg3h7cGAvLW/L25bHcHcAJzfcvdXLY3GzMbG0zJccSjKUzTNWfw8GBy7F5gm8bVeodx+DPy+H1Rv4diI77qH9IfA3N7yh8XpPh6fQM1kKCvB411Z5m+FJDt1Y8ZsXv3bsaOHcvOnTtJSkqif//+fPjhh5x77rmABoUV97nznXZ3gFR3B3l1d7nuTOvuQLTuzicYeOt40ZeDhAd6EFif0HGESMB4KxOaMy9f8/Wg5RqTwE+UEX6hhg0Radl0e6CIiJj4MSNeeumlRp/XoLAiIjaj4wgRETFRRviFGjaaac/OnYGugojghe+iWtHFB/bt3Bl0d1mJhKJ9ygixof07d4bm1esiQWa/MkJsqGDnTp2oE7GBAmVEUNAdSM1UXa0tTOztmhtvZMPGjU2a9vv161mybJnby/hy5UpWr13r9uu8Sd9FsSNtlyL2oO+i2JG2SxF70HdR7EjbpYg96LsYHNQQ3EzJrVtTeOBAs6628lYfqL4eq6M5y2hOf7vu7ircXUZzdkWmedV9YYJljI2m1nP9Dz+wYdMmTnMxAGlVVRXh4a63hC9XriQ5OZl+ffq4XLY73BmT48jaJLduDQcOuLm0I1Tj2S1+yjpxIbF1a4qbsV26szl548oEb8zDjnfImupkGrvJ3a+x6X3z1lgXvuaNvtW9wR+7z0RlhNhQXOvWHPJku/QBdzdzb/1+r3BjenembWx6d+fjjYww7Xcj3Sx3N0+0C2pcnDJCbCiydWtKbZYR4P6m7q0xMLy172+s/qbXNGe8Dne4+5vc3WwwTW9aX3fn7+6xj2m57k7vL9HKiKCgho1mim7VyrMNXKQZSkpKuO3OO9m9Zw8Av/vNb7jz3ntZ/NFHAMx5/XUO5OeTO2ECAP/5739ZvmIFYQ4HD0+fTtcuXfhixQpm/OlPOMLCiIiI4LVZs3jmhRcoLy9n2Rdf8JvcXFavXcvPO3bw09at9OrRgwvPP59HHn+c8vJy4lq14qH778fhcPD6v/9NeEQE/3nrLR6YOpWMjAymP/QQO3ftIiIignvuvJNePXr49D3x+Luofg/FB6JbtWpWw4aIeJcyQuwoqlUr2zVsiLREUcoIsaGoVq0oU0aIBJwyIjioYUPEUw4HWJa57Ij/OxwOrGP8vzGfL1tGclISLzz9NJZlUVxc3Oj0VVVV/GfOHD5fupQHHnmE2S+8wMuvvsrtt93GqYMHc7CoiPDwcCbeeCMbNm3i9kmTAFi9di1bt21j9gsvEBkZSVFREa/85S+Eh4fzyaJFvPDSS9x/zz1cOno0ycnJXHnppQDcec89XD9+PH379OGnrVuZct99zJk9u4lvZICo30MRETFRRoiIiIkyQkRETJQRfqGGDREPxCUkEB4WRmFBAfGJiQAUFRaSmJhIVXU1xQcPkpScTEV5OSXFxSS1bk1paSmlJSUkp6ZSUlREWWkprdPSKCospLysrNHldevShYf//Gf+/NRTDDnrLAb279/o9COGDQPgtJwc7r7/fqqrqzl+wACeeOYZfty8meFDh5IQH+/ytef84hdERtbcjFh48CBTpk5l2/btVFsWiQkJLl+z7Msv2fTjj86/Cw8ebLR+IiIiIiIiIiIiIu5Sw4aNRBnK3e23LpB9eZvulGrsDipTI6S7/QH64y6taA6PW+EASg4eBIeDMKCksBCo+byKCgvBsggDDubnH/7/gQNYtf8v2LcPy7JwAPl79zr/39iYHJ07dODNV19l4eLFPPrEE1xw3nk4apcPUFFejqO2Dg4g/Ijn6uo2Yfx4zjz1VBYuXsyV11zDnJdeci437IhlxcbEOP9+5oUXOPO007j04ovZsHEjd0+f7qzn0XX+5yuvEBFh3rW4M5aGX+j2QPGBSszjOTTG1f7btE837TvdGSfBG3nhbka5+5UxvY+Nradp2aacdfezMs3H3f5nTeWm+Xur31sTd7YHX/bH35zfEiaVzXhNg4UqI8TLDgGN33Prmrd+43tjPt7qa92dPtjd7U+91MfTm7jaV8cYpvV1P+0m/h5TCXzfV31z5mPKW7cWqowQLysGAnl5oLfGLnV38/b1OEqmfTyYjwW8NVaTt8ZZMu2zTBnj7vzdHXvDxFuZFOgbFjzOSmWEXwTiN01IKCooCHQVxC5cdSF1ZNkR/7ea8P/G7N6zh9jYWEZdcAFjL7+c73/4gfj4eH7euZOKykoWLV5cb/oP5s8HYMny5XTu2JGwsDC2bt9Oz+7duenaa+nSqRPbd+wgLi6O4pIS43KLi4tpk5EBwFvvvussj2vVql53WCcPGsQ///1v59/f//BDk9bLEx5/F6u88BA5ykFlhIgtePxdVEaIDxQrI0RswePvojJCfEAZIWIPyojgoDs2mqm4qEitQuJ3P2zcyKNPPklYWBgxMTH84Z57yDn5ZK69+WbSUlPp1LFjvenDwsL41ZVX4nA4+OP06QC8MmcOy1esIDw8nL69ezOwXz8OFhXx19mzuXjMGH5zyy0Nlnvt1Vfz+/vv5+nnn+f0nBxn+VlnnMFtd93FvPnzeXDqVH5/++1MnzGDf7/9NhUVFZx95pn07N7dp+9JcVGRZzNQv4fiAyVFRQG9e05EapQoI8SGSoqKdBAmYgPKCLGjQ0VFbl8hLyLed0gZERT0m7qZomNiqCht7GY2Ee87PSenXsMCQNfOnTm/diyNI738wgsu53HPHXc0KEtOSuL1V15x/v2L006r9/zA/v15/4g7MSbl5gLQsUMH3nzttXrTPvHII8dYC++KjokBfRfFZqJjYqjUdikScMoIsaPomBiqtF2KBJwyQuwoOiaGam2XIgGnjAgOathoptTUVPb8/LPx+cbu5nB3LA13+9p2t79zb3Knn9zmvsabfW+7K46a97fuUcc0LkZj3H1Nc5bhLlOHWKZlN9aBlunzc3c9TMuo255TU1Ohke/iManfQ/GB+GNkhDv7Y3fHT3A1vbdywZ2rx9zdt5vmbeoztrFxMdzt99bdi2HcfT/d7SfX3fkHIve90ReuuznfnP7Zk5URYkNRqansa0ZGeGvf4M48fD29qdzVftxb4w6aTlGY9tXeyAhTXbzVL7q7+ROIu0q9Mc6Ku9Mfaxes4wixo6rUVPb7ISN8PUab3TbvUOhxJVDrEKixkLx1TrC5YzylKCOCgho2RKRls/DsFj/bjYYuIiJeo4wQERETZYSIiJgoI/xCDRsi0rKpFV1EREyUESIiYqKMEBERE2WEXwTN3VgzZszgpJNOIiEhgYyMDEaNGsX69evrTVNaWkpubi6pqanEx8czevRodu3aVW+arVu3MnLkSFq1akVGRga33347lZWV/lwVERHxMmWEiIiYKCNERMREGSEiEryCpmFj0aJF5ObmsmzZMubPn09FRQXDhg2juLjYOc1tt93GO++8wxtvvMGiRYvYsWMHF198sfP5qqoqRo4cSXl5OUuWLOHll19m9uzZ3HfffW7Xp7LC1IO3iPiTx9/FKi88JODslhEVyggRW/D4u6iMCAl2y4gqZYSILXj8XVRGhAS7ZYTONYnYg841BQeHZVlB2WvXnj17yMjIYNGiRZx55pkUFBSQnp7OnDlz+PWvfw3A999/T69evVi6dCmnnHIKH3zwARdccAE7duygTZs2ADz//PPceeed7Nmzh6go0zCehxUWFpKUlERXGh90rbE5uTsYnLuDjjZnkDh3B5ry1qA/jc3L2wP/eENmhw7c9PzztElL8/vg4c2ZlwU8+fzznDp4MCcdfzyz58zhyksuISrS9VZi2hkcXf70iy/SOjmZMZdeytgbb+Se22+ne9euzZrX0eXDfvlL3po7lwP5+dx2113885VXnNNUA3v27uWBm25i508/ATWf+0agoKCAxMREw9wbqvsuFyyBxPgmv6zhfIog6VT3ly++ZZeMcHewPlffTG9kg7u54MvBqL21b2/sZ6a7ueIt3vi8G5ve14OEe2NAVtPn4mqwd9O07pa7qkvVEf8qI+Rogc6IHjSeEabvuru//d0pN83blwOWg3v7fm/lRCAywm754I3Pyd08d5UD4L0scDX/Y20zVcB6lBFSX6Azojc13113v9fuZkGModzdPDDN38Td362BPHbw1uDV3hrA3W7HFO7mpukzcDc3St2cv7u/B6qPeH4dygi7C5o7No5WUFAAQEpKCgArV66koqKCoUOHOqfp2bMn2dnZLF26FIClS5fSr18/Z9AADB8+nMLCQtauXetyOWVlZRQWFtZ7iAST39x0EycdfzwAL8+dqyvJj6ZW9JCkjBARr1BGhCRlhIh4hTIiJCkjRMQrlBF+EZQNG9XV1UyaNInTTjuNvn37ApCXl0dUVBTJycn1pm3Tpg15eXnOaY4Mmrrn655zZcaMGSQlJTkf7du3r3ldVpY3V0mkSbbv2MHFY8c6/374iSf4zzvvAHDWL3/JUy++yIVXXsnFY8eye+9eAO6cNo1PPvuMf7z+Orv37OHy667jpsmTG8z7mzVruPSaa/jllVdy9Y03AlBy6BB3TZvGr8eO5eKrr+bz5cubVM+vv/mG2++5B4DX//Mfho8aBcD6DRvIrV32My+8wKVjx3LRZZfx8GOPNe8NQd9FaUgZISJ19F2UoykjRKSOvotyNDtkRIa2SxFb0HcxOARlw0Zubi5r1qxh7ty5Pl/WlClTKCgocD62bdsGgMPhzY6ERLwjMyODd+bM4cxTT+X1t96q99xVl15KRno6c196ieePakgor6jgt/feywN3381/58zh6UceAeD5v/2Ns844g3+98govPf00Dzz6KE3pva5P796s+/57AL765hsSEhLYs3cvX61axQkDB9bU5/LLef2VV3hr7lx25uXx1TffNGudPf4uqhU95CgjRKSOMkKOpowQkTrKCDmaMkJE6igjgkNEoCvgrokTJ/Luu+/y6aef0q5dO2d5ZmYm5eXl5Ofn12tJ37VrF5mZmc5pvvjii3rz27Vrl/M5V6Kjo4mOjm5QHk7jfeM21s+gqS9Dd8u9NVYHuN/nn7sa+z6anvNWf4belERNa2DdZx8WHk5YeP13qbq6murKSnA4iHAxlkVVeU1PgeERERBWv22xurISqs0jkkQc9W/YEfUBOO+sswgH+vfqxUeffko4NeNvHDmNq+32py1baNe2Lb1qx8lITUoC4PPly1m4eDEzX3oJgEOHDnFg3756y3W4mGdsZCSpKSns3rWL7T//zKjzz+frVav46ptvuPKSSwgDvvjyS/72979TVl7O/v37OT0nh0EDBjjn4TjicWQZ1GzLR66PR6ppfBCYprxebMMuGRFJTf+03ugn3RvZ4G6OuNMfq6/7Ym3OuEreyhUTb/VX662+zwPRV7GpU0N3+rw1TWsqN62nq/5366Z1t9/nBpQRIcUuGRGFeb8M7veH7o1yd48fgnmMpkCM1+frsTGCYawld3PD3XJX741pmXXb9bFHPTgGZURIsUtGxACt8P1YGr4+L2W38R28Oe6ru8vw1m/7YD52AN/ngLfH3mjst1qTKCP8Imju2LAsi4kTJ/Lmm2/y8ccf06lTp3rPDxo0iMjISBYsWOAsW79+PVu3biUnJweAnJwcVq9eze7du53TzJ8/n8TERHr37u2fFZGQEhMfT3LbtvUecbWNAuHh4SS3bUvrox514tPSGjwXFRvb6PLCw8Oxjmj4KD9qvIy6QcnCwsKorvL88Ky6upoXH3uMd+bM4Z05c/jsvfdIT0tr0mtPGDCADz76iPS0NE4YOJCvvvmGNd99R99evSgrK2PGY4/xzJ//zNuvvcaFI0Y0WBe/qcazFnSFjS0oI0TEJ5QRIUEZISI+oYwICcoIEfEJZYRfBM0dG7m5ucyZM4e3336bhIQEZz+FSUlJxMbGkpSUxHXXXcfkyZNJSUkhMTGRW2+9lZycHE455RQAhg0bRu/evbn66qt55JFHyMvL45577iE3N9dlS7nIsZQWFVF+6FC9surahoeqqiryd+40vrZo717Xd2w0IjUlhV179lBUXIzD4WDx8uX07dmzyfWNa9WK4pIS4lq1qlfeuWNHtu/cyQ8bN9K9a1fyCwpITkri9MGDeXnuXKbcdhsA69avp3ePHk1a1qCBA5n60ENcM2YMvbp3586pU0lt3ZqoqCgKDx7E4XCQnJTEwaIiFixaxFVXXNHk9RA5mjJCRERMlBEiImKijBARCV5B07Axc+ZMAM4666x65bNmzWL8+PEAPP7444SFhTF69GjKysoYPnw4zz33nHPa8PBw3n33XW6++WZycnKIi4tj3LhxTJ8+3e367Ku9rVBatuqqKvOdEZZFZXk5pl75qgyNGI314hcVGcmEsWO58KqryMrMpEdt11FNddmoUYy56SY6ZWfz4hHjbERFRvLY9Onc9Yc/UF5RQXJSEv+YOZOJ11/PH/78Zy644goqq6ro06MHf/7DH5q0rOP792f33r2cMGAA4eHhtMnIcDaKJCYkMGrkSC689FLS09MZUDswW3N4/F3U7YEhQRkhIq4oIwTslxH7lREituDxd1EZERKUESLiijIiODispowELE6FhYUkJSUxgMbH2GisTd7UT1ucm9Obyk3LbqwPUXf7RXRXIMfYaM6+wNRHW0qHDox8/nnapqXVaxVszpBCgRwSzPSlN5Wb3sPG3ltvzcs6appde/fy+E03seOnn4Caz/0boKCggMTExEZqVF/dd7ngA0g0ffmaMp9iSBrh/vIlNNVtV4OouXLA3b5xXe3XW7koM01rKvdlX7m+7tOyOX2hB6L/dPB9H+re4o3+cL3R561p2hI35+2qfnXrUgmsRBkh9lC3XZ1J41eXufsb3xvHFt46rvDWWEPu7K/9MXaTpwLVL7q77687x1OuxjcCKDOUu5sbxW5O72q5pmnrVAKfoowQe6jbroZTf0zJo7k7loa7xxLeOi9lqqe3csKkOftyb55L8gZvjUPirffO3ffHW2NpuJsD3jiGgMPrWwF8SPBkxKeffsqjjz7KypUr2blzJ2+++SajRo0CoKKignvuuYf333+fH3/8kaSkJIYOHcrDDz9MVlaWcZ7Tpk3j/vvvr1fWo0cPvv/+++avmJcFzRgbdpNwxKBRIhI4Hn8XPenzsO4hcpREZYSILXj8XVRGiA/EKyNEbMHj76IyQnxAGSFiD8GWEcXFxQwYMIBnn322wXMlJSV89dVX3HvvvXz11Vf85z//Yf369fzyl7885nz79OnDzp07nY/Fixe7VzEfC5quqOwmNi6Okvz8QFdDpMWLjYsDfRfFZmKUESK2EKOMEBuKjYujVNulSMDpOELsKCYujjJtlyIBF2zHESNGjGDEiBEun0tKSmL+/Pn1yp555hlOPvlktm7dSnZ2tnG+ERERZGZmerWu3qSGDRFp2dTvoYiImCgjRETERBkhIiImXsqIwsLCesXR0dFERzc2AELTFBQU4HA4SD7GnSkbNmwgKyuLmJgYcnJymDFjRqMNIf6mho1miqGmr0JTn3WmfgbB3Mehqes1b03fnDE2TOXuauy77K1+/NydT2NMn2uiZfH/7d17fBTV/T/+1+YeLkkI5AoBAiIgd1EgKApKiWitIDepCiiV1g9oIVgRL4AWi8UCFkWs/izRb6WKtqKiRREFaw0oIFVEKCC3AOGeBAK57vz+2Au7ybw3Obuzu7Ob1/Px2Afh7OzszOycee3szDknUtMQhYZvG2ksjUCMsaE6loaRY2wY1bJaq/W3RdMQq2nO/dxTXWsQX5uBswk56WgKW5+cqhmhMsaGSgao9pWrkgn+HifCm77Qg9VPrlF9Bgdr20n9zKqMsSH1havX5600rfR9RaVfXsc6+tCt7aUZMSPIYM1gq59GZAQg50RzoVyvXqjOW6qnRvWprsKoY34wxtiQ+HN7Acb0ly6NsaHax7mUBecMmL/0no71byY832DMCPKDVgDiIR8HpONvsH5n8udYfoD/vxd7EqwqalRmqDLq3EF1LA3puK5yXuHN9FKOObaDWc4jsrKy3Irnzp2LefPm+TBjoLy8HLNmzcL48eM9jt/Rv39/5Ofno3Pnzjh27BiefPJJDBo0CDt27EDz5tI3zcDihQ0iBeUnT6KypARlycloGhnp08WJYF7YUJ3emwsbqq+p74uGBqCypgZlJSUoPXmynqkV8ISEiIgkzAgiIpIwI4iISGJQRhw+fNjt4oOvrTWqqqowduxYaJqG5cuXe5zWtWurnj17on///mjXrh1WrVqFyZMn+7QcRuGFDS9dPH8+2ItAQVBz8SL++/TT6PXYYyhJTAQstssTRl6kMHJeqi0zJNJFB0/zMapViHN6TUNZSQlWPf00Ki9edD7PukhmxP2SyBxYF8mMuF8SmQPrIplROfdLIlMwS11MSEjw2KpCheOixsGDB/HZZ58pzzcpKQmXX3459u7da8jyGIEXNrx0vqSEG6+RKt6xA1/eey/iUlKcFzY8Nf1TbUpuVDNCQG4aWK04vWqTQU+vuahY7mgeaNU0lJ486XZRA7DVRZ9o8K1/GoWrRPPmzcOTTz7pVta5c2fs2rULgK054MyZM/Hmm2+ioqICubm5ePHFF5GWlubDAlIwlDEjiEyhLIQyghqPCyUlhnX3SkTeu8CMIBO6WFLisRtxIgqMi2GWEY6LGnv27MHnn3+Oli1bKs/j/Pnz2LdvH+6++25jF84H/N3FS9HR0dCqpJ+BKdzVXLyIskOHnP/3dDEiWH2rA2r9pQPqfeVKfRh68xrV/ngdoqOjAV/qYoCbkHfr1g2ffvqp8/9RUZcOwzNmzMCHH36It99+G4mJiZg2bRpuv/12/Oc///FhASkYonzdL4nIED7XxQBmxIIFC/DPf/4Tu3btQnx8PAYOHIg//vGP6Ny5s3OawYMHY+PGjW6v+/Wvf42XXnrJh4WkQGNGEJlDKGUENR7MCCJzCLWMOH/+vFtLiv3792P79u1ITk5GRkYGRo8ejW3btmHNmjWoqalBUVERACA5ORkxMbbLqTfeeCNGjhyJadOmAQAeeugh3HrrrWjXrh2OHj2KuXPnIjIyEuPHj/dhxYzFCxteykhNRcmRI+KP09LASoA8AE2iUC4NxyI1GFId1AlQHzxcWm9v6qx0mJAubNY3wI8/qQ7g5ek1qnfqeRpAyoiBYAH1lhml4hLJA/6pXuipb19LTE0FjhzxsCT1sMK3nUfxtVFRUUhPT69TXlJSgldffRUrV67EDTfcAABYsWIFunbtik2bNmHAgAE+LCQFWuvUVJR6yAjpeKx3vJcyQ8oGvXLVwQFVBvtTzQmJ6kCmjXHwcKO2qRHZoHqRWi8TVAeblHJFbx90rHtCCGXExo0bMXXqVFx99dWorq7Go48+imHDhmHnzp1o2vRSbb3vvvvw1FNPOf/fpImnb51kRu1SU1F25IhY16U6YEQeSOVGDRYrZYL0XdmIG3pUc0L1fEX1EKCyTsG6AUr1PEsvH4waBFY6tkv7pDS93vylZXQODBtCGUGNR9vUVFR4yAjVwcNVM0L6nUmaPhRyAvBc3VRzwahrkqrf7UMhGwD1fJCO69LvTdL0Urm3g4fHhlhGbNmyBUOGDHH+Py8vDwAwceJEzJs3D++//z4AoHfv3m6v+/zzzzF48GAAwL59+3Dq1Cnnc4WFhRg/fjxOnz6NlJQUXHvttdi0aRNSUlLU18dPeGGDiMgApaXusRsbG6s7qNOePXuQmZmJuLg45OTkYMGCBWjbti22bt2KqqoqDB061Dltly5d0LZtWxQUFPDCBhFRmFu7dq3b//Pz85GamoqtW7fiuuuuc5Y3adJE9wI5ERERERE1ToMHD4amyf1XeXrO4cCBA27/f/PNN31dLL8zstcbIqLQU2PAA0BWVhYSExOdjwULFtR5q/79+yM/Px9r167F8uXLsX//fgwaNAjnzp1DUVERYmJikJSU5PaatLQ0ZxNBIiIKMIMyorS01O1RUVFR71uX2Pv1TU5Odit/44030KpVK3Tv3h2zZ8/GhQueRrwiIiK/MSgjiIgoDDEjAoItNoiocTOo38PDhw8jIeFSw1291hrDhw93/t2zZ0/0798f7dq1w6pVqxAfH+/DQhARkV8YlBFZWVluxXPnzsW8efPEl1mtVkyfPh3XXHMNunfv7iz/5S9/iXbt2iEzMxPfffcdZs2ahd27d+Of//ynDwtJRERe4RgbREQkYUYEBC9seCnOakUV1Ps3BIAkxXJp7A1peqnvQ6mfUkC9/0NVnuqjUf34edMPu0S170JP20m1r0jVfhY9Ue3zULXv27o/3V8irbdRfco79o84q4+d0xrU72FCQoLbhY2GSEpKwuWXX469e/fiZz/7GSorK1FcXOzWauP48ePsciQENbHvl9J+rdJPepIwrcq4TNI8VPtU1zvW+bs/XOk45k2uGDX2hurxSnV6o7LXqHzVaw8g5UWxUK63T6l+95C2l96yONa9iUkyoiEXv11NnToVO3bswJdffulWPmXKFOffPXr0QEZGBm688Ubs27cPHTt29GFBKZDirVZEQN7XVcZhAow5t1Adu0/KD2nPDoUxNow6l9A7Vvl7DCZVqvmg18ZMdawlqa/0EqFcNSP0yqVzHkfexZokI4hcNbNaEQ+5vgcjIwD1sTek5VTNiUAMsuHvMZlUV0E1G/y9jVSyAZCPvapjaUj5UCyUq47TV984TJHMiJDArqi8dObYsWAvAhEhtOvi+fPnsW/fPmRkZKBv376Ijo7G+vXrnc/v3r0bhw4dQk5OThCXkrxRHML7JVE4MUtddFz8djw8XdiYNm0a1qxZg88//xxt2rTxON/+/fsDAPbu3Wvo8pJ/lZpkvyRq7FgXyYzKuF8SmQLrYmhgiw0iatwC2DzwoYcewq233op27drh6NGjmDt3LiIjIzF+/HgkJiZi8uTJyMvLQ3JyMhISEvDAAw8gJyeHA4cTEQVLADNC0zQ88MADePfdd7FhwwZkZ2fX+5rt27cDADIyMrxcQCIi8hq7GSEiIgkzIiB4YcNLSWlpKDt+PNiLQdToJaWlAb7URSt8CwyF5oGFhYUYP348Tp8+jZSUFFx77bXYtGkTUlJSAABLlixBREQERo0ahYqKCuTm5uLFF1/0YeEoWBLT0nCRGUEUdIkhlBFTp07FypUr8d5776F58+YoKioCACQmJiI+Ph779u3DypUrcfPNN6Nly5b47rvvMGPGDFx33XXo2bOnDwtJgZaQloYKZgRR0CWEUEZQ49E0LQ01zAiioGvKjAgJvLDhpYSoKERC7uNT6mcQAJKF8pZCeYpQbtTYG4DcT65qP9cST3VZ6tNbKlftG9zIMTak7SH2B+nhNUZtW0BeR2mbqI6lUSyUexq3xai+lKXt4Vi3plE+HsYC2O/hm2++6fH5uLg4LFu2DMuWLfNhgcgMEqKiEAW5nkt9lifplEnZIGWJXrnefAH1MZn0ltvfOSFVMen4Bsg54c/+0wH5+CYdD6VtZ9QYTKrZIPWLrpcZUl5I+9QZnTLVnFAZ58qxjk1CKCOWL18OABg8eLBb+YoVKzBp0iTExMTg008/xXPPPYeysjJkZWVh1KhRePzxx31YQAqG5lFRiIF8DJCOwUlCuUoeAPq5YlSf6oYNnqNX4VUP4qonB56CxVeq62/E9gKM22Y6HZEb1Ve6UbuMXrl0zuMcYyOEMoIajyZRUaiBuTICkHMiRnUwJqMqvcSLATMihboYKZ1USIwaZMOogVoN2nYxQjbECINUNBcOvonCyUYw8gGoPyMimREhgRc2iIiIiIiCTNM0j89nZWVh48aNAVoaIiIiIiIic+OFDSJq3NjvIRERSZgRREQkYUYQEZGEGREQvLBBRI0bmwcSEZGEGUFERBJmBBERSZgRAcELG146f/IkLMFeCCLC+ZMnfZsBr6KTH5w7edKr8XKIyFjnmBFkQmUnT/IkjMgEypgRZEIXT570OIYnEQXGRWZESOB3ai/FVVZ6HPQvycNrpcGY0oTydKFcGlRcKk+SFgiQR7JVHa1H4ulKozDgkMrgdYD6YLGeqA4AK24nQN5W0mtUB5YClLeVNEJssTC5dDj3dJhXXQ3Vz8mxypWVqiN6EflffGUlYiFXf2nQVb18kI7pUmboTS/lTnOpoiYJ5XqDAEorqZgT0qB90sE9ztMgr6q5YtTo4arHe2l61QEDBZHSQH9CeVNhBL2U4rpl54TP67SwLHqbwKiLf3rzcaxiBTOCTCi+shJxkOuAlBHSQK+q5wR65UmqJzVSuXROYcSgsNKxWjE/xCzwZ0aoZqVRA8dKVAcP1zl3aF6sP6lU3lSYt7/HC/Y0bTkzgkworrIS8VA/j5AG95ayQ/qdScwD1dHGpQWVcsKg77/KmQCo54Kn8xAVqtng53MH5W0n/K6Ec/rFMcKJQsoZ/fJoxe3sxbjxuhxvG8GMCAne/IRKAGKbS0dpIgokn+tijQEPolpimBFEpuBzXWRGkB8wI4jMgRlBZhTNjCAyBZ/rIjMiIHhhw0vxCQnBXgQiggF10WrAg6iWOGYEkSn4XBeZEeQHMcwIIlPwuS4yI8gPopkRRKbgc11kRgRESF3Y+OKLL3DrrbciMzMTFosFq1evdnte0zTMmTMHGRkZiI+Px9ChQ7Fnzx63ac6cOYM777wTCQkJSEpKwuTJk3H+/PkArgURmYoVvl1BZ9iYBjOCiAzHjAgbzAgiMhwzImwwI4jIcMyIgAipMTbKysrQq1cv3Hvvvbj99tvrPL9w4UIsXboUr732GrKzs/HEE08gNzcXO3fuRFycrXfnO++8E8eOHcO6detQVVWFe+65B1OmTMHKlSuVlqUZbN3bSV3ZSd0PAnK/6K2F8jaK04tvIPV9CMj95Eotr1Q7Q/WmP0PF8SFiVPvR9US170Kpn0jAuL7VPTVDk9ZR6NtQGkwj6bRQfly/3NNq+7NfXODSLhWr+DoKX2bKiKaAV+Mw6fV7Lh3SM4Vy3WxQ7YBdCjG9TNAbdwNQH4tJOgioZoSn5yoU31siHeCkA5KUA6r5oHpglbadMJaGmBk6/d42FwZZkspVdwc9Kh+TY9WNeF8KD2bKiCaw7c+qGaE6xoaUE0313kBl4CZA/dxBygqV2+xU+/5WzQJpeiMyQjreq+aGv/tRV8mHYmFaIQek84loaT4C1a8LnqY1aqynQPniiy/w7LPPYuvWrTh27BjeffddjBgxwvn8pEmT8Nprr7m9Jjc3F2vXrg3wkoYeM2VEvP1f1TE2koRy8TAuHWek8DBqkFdpBYwag9TIcwfVcftUqY7PZ9Q5hUTKBmk7KP7epPodIemofnmNsDxGDaFl1MdLgRFSFzaGDx+O4cOH6z6naRqee+45PP7447jtttsAAK+//jrS0tKwevVq3HHHHfjxxx+xdu1afPPNN7jqqqsAAM8//zxuvvlm/OlPf0JmpnQEJ6Kw5WsTP15FNw1mBBEZjhkRNpgRRGS4AGdEfT++A8BNN92EFStWOP8fG8vbwBqCGUFEhuN5RECEVFdUnuzfvx9FRUUYOnSosywxMRH9+/dHQUEBAKCgoABJSUnOoAGAoUOHIiIiAps3b9adb0VFBUpLS90eAFB1QWg2QEQB5XNd5IBOjUKgM6KaGUFkCj7XRWZEo8CMIGqcQi0jhg8fjvnz52PkyJHiNLGxsUhPT3c+WrRoobhSVBszgqhxCrWMaKzC5sJGUVERACAtzb0ddVpamvO5oqIipKamuj0fFRWF5ORk5zS1LViwAImJic5HVlYWAODi2bNGrwIRecHnusiwaRSYEUSNEzOCGiLQGVHOjCAyBZ/rokEZUfvH7YoKqb+0+m3YsAGpqano3Lkz7r//fpw+fdrreZFNoDOikhlBZAo+10WeRwRESHVFFQyzZ89GXl6e8/+lpaXIyspCs8hIxNTUiF3ZJXmYpzTUhdhfunT5qaNQLg3KkSUuktwvorSw0uAKUh9+nppQSf31SX28ShdNpemNHGND6h/Y02AT0mtU+5D0tB7SNpG+xwp93+KwUJ6gX9x6n7xIVuEzVxw6pd4uMqMjI4EaHvEpOKSMaBoZiVgvMkKv//R0YVpxnKV2OmXS8V8KHulN9bKiuTCtan/qqn26Ssd8QD6gGNVPrmp/uFJGGJUPRvSVDsjZoHc+LvR5K61r64N1y6TFVu0aWS8BHNNGMiMoiDxlRJyHjJAOq0lCufQ1XncsDUD/XEHKCSlsVMf0SxTKpY2gR6rK0jFf9ZzBnxkhrafquYVUrjpYhLSuJUK53jmFMGaG6hiN0iZIKdYvVzmXkHYZxzwiTJIRjh+0HebOnYt58+Ypz+emm27C7bffjuzsbOzbtw+PPvoohg8fjoKCAlsekql4+q0pvqZGeYwNaRymltL3R+k4rnreoDomh5QTiscOkeoPDoD6b0+qYzJJ1c+ocwdpepWMBdTHblX9vUn6jKXlFLZnS+F3q3LF36DqG5PDLBlBnoXNhY30dNuvMMePH0dGRoaz/Pjx4+jdu7dzmhMnTri9rrq6GmfOnHG+vrbY2Fjdfimbpqej6sgRg5aeiLzVND0d8KUust/DRiEYGVHNjCAKOmYENUSgMyI+PR0aM4Io6OJNkhGHDx9GQsKlO8m8HRfjjjvucP7do0cP9OzZEx07dsSGDRtw4403+rCgjVugMyLG1/2SiAzhc13keURAhM2FjezsbKSnp2P9+vXOcCktLcXmzZtx//33AwBycnJQXFyMrVu3om/fvgCAzz77DFarFf379w/WohNRMPnaxM8kF/Czs7NhsViUXzd9+nQ8+OCDflgic2FGEJFXmBHMCGYEEUkMyoiEhAS3CxtG6dChA1q1aoW9e/d6vLDBjPCMGUFEXgmT8whvBSpblC5sWK1WbNy4Ef/+979x8OBBXLhwASkpKejTpw+GDh1apwml0c6fP4+9e/c6/79//35s374dycnJaNu2LaZPn4758+ejU6dOyM7OxhNPPIHMzEyMGDECANC1a1fcdNNNuO+++/DSSy+hqqoK06ZNwx133IHMTKn9HBGFtTAJm/z8fK9e1759e8OWgRlBRGGHGWHYMjAjiCjsmDwjCgsLcfr0abdWBnqYEcwIIvIDk2eEvwUqWxp0YePixYtYtGgRli9fjjNnzqB3797IzMxEfHw89u7di9WrV+O+++7DsGHDMGfOHAwYMMCbZa/Xli1bMGTIEOf/Hf0RTpw4Efn5+Xj44YdRVlaGKVOmoLi4GNdeey3Wrl2LuLhLHba98cYbmDZtGm688UZERERg1KhRWLp0qfKyODacapd4gNzFrNQnoti3rTSWRhehvLP0Bh6ek74/SP0lShvEmzE2pH78ShWnl+bviWqnx55usFHtR1DqA9PTeqiOmbFbKJf6bJR4WKZk4b2lbhil8vq6WwybZmc+uv7664P23syIuqJhq8oxwvMq2ZEkvYl0HNbLjPbCtJcJ5R2Ecr2xnVT7WZf6ya2vI+zapGM+ABQL5ar95EpUwz9JKFfNB6l/XqmDWOnAKvWLLo2b9JNOmbRzS3mvs+2ThOxSzQO9RXEsBnsUt2FGmCsjHN9dpMOhdChRHXtDPLlQyQkpD6RyvXGepPcE1PpUl451quMJFQvl/swIo/JB+q4u7Uyq/aVLPW7ojJOkmw2elkUhHwAgqVi/XCjW3cTSrhGqvxV5+vE9OTkZTz75JEaNGoX09HTs27cPDz/8MC677DLk5uZ6nC8zwlwZEQ3b9xfpe4/qkD3ibxXSd3XVsTdUc0KajzTGn6cf1/QYee4gjTukOr6r6hiu0o+GSUK56jmFRMpBvfH2APn3JikfpC/m0kFZ+syEz6VpsX65al1ykH6ao4YJVLY06DfByy+/HDk5OXjllVfws5/9DNHRdWvlwYMHsXLlStxxxx147LHHcN999xm+sIMHD4amaeLzFosFTz31FJ566ilxmuTkZKxcudLwZSOiEKXBt74L5UOSKZw9exaffPIJjtj7hszMzERubi5atGhh2HswI4gobDEjfMaMIKKwFeCM8PTj+/Lly/Hdd9/htddeQ3FxMTIzMzFs2DD8/ve/93rMDmbEJcwIIlIW5ucR3jI6Wxp0AeqTTz7BqlWrcPPNN+sGDQC0a9cOs2fPxp49e3DDDTd4tTCh5DwHcyIyBZ/rYo0BD5N69dVXkZOTg82bN8NqtcJqtWLz5s0YOHAgXn31VcPehxlRFzOCyByYETJmRPBcYEYQmYLPdTHAGeH48b32Iz8/H/Hx8fj4449x4sQJVFZW4sCBA3j55ZeRlibdfu8ZMyJ4ypkRRKbgc10M4/MIb/kjWxrUYqNr164NnmF0dDQ6dtTrq6Jx8dT1gfSc2AxKanonNR2UunD01BWV1EIofqDizJI8vImkWL84XeorQ2oHJ01/Xm1xAADNhHLpi6HUbtLTa5IavDQAgIRi+blUoW+pDl+pvYfU1E/q6uqoPCvVnlSC1l2IFb5dRffltX62cOFCbNu2DU2burdx/f3vf48rr7wSkydPNuR9mBHG0asHUstlpe6QpO5IpEOX1KXh1Xp3/F0jTCzlhHR8rdYvlo59qZ6+aBYK5VInR6o5Ia2DFMxS35FS3yxJQrn0tU1Y/iQhGzr+R788ukK/XK9Z+hlhUaSI1tlfpX3bVN1HMSN8xoyoy3EDn+ruIdYNqTIZ0e+h9DVWOsR3k14gDZ4r9V2lc5yNEY51TYVje6p0QJIyQjiX0KS+SAQWvT5EpO0i5YMU0FLOCLkkbjO9vqUApG/WL4/T2TZSVyxSPkjntNJ+KuzXkVJXLzqkOuYo9/lmWGaEz5gRdVnh3W+ahmWE1D1SklAuHa46CeXtpT6qpG7GpO/Lgphi/fIE6fciAK0PCE8Ix8pKIRdUu6KKkfqckrKxvVBu0G9OMcL5VdIm/fI4oc8pqUsrKR+kfU41H4TJJVIdc5T7fIgO44zwlj+yxavu6cvLy/Hdd9/hxIkTsFrdt/QvfvELb2YZcuJbtYJ26lSwF4Oo0Ytv1QpgXdRlsVhw7ty5OqFx7tw5WCwWv70vM4L7JZFZsC7KmBHBE8v9ksgUWBdlzIjgieZ+SWQKrIvG80e2KF/YWLt2LSZMmIBTOh+uxWJBTU0YtpXRERkbK91bSkQBFOlln7FOvjbxM/Eh709/+hOuv/56dO/eHa1b2+50KSwsxA8//IBFixb55T2ZETaRsbFm3jWIGg1mhIwZETwRvu6XRGQIn+siM8JQzAgbZgSROTAjjOePbFG+sPHAAw9gzJgxmDNnjtf9NYaDathamVUKzxu6/0ntqaQRUqTmWike3iN+rPDEBKG8m1DeSij3dBlI6hpEaiZ+QCiXmhYWe3hvSZJQLu3z7T3MS2piLjUll6qlpyvFP+gXx7+uX56ySr9c2nekfc3A/kKkOiPVMYVW6PW/cZiGzc9//nMMHz4cX3/9NY4etfUblpmZiX79+iEy0j+dvTAjbCpgO+pJVceQ3UalXkoft9QthPjRPahTdoswrWpXVOVCuZQRB4RyQGw2LuaKUV1RScd71ebkUj5IB2lp+YWuqPChfnHas/rlevuJar+CDRrVzTOVnHDsTV41Ta79pswIwzAjbKph6wJHqhbSdxzl3UklJ6RuMqTuIcReWP9PKJeyQjo+6h3vpGOddA6wVyhXzAhLsTC9JEmnTDUfLhPKpXqjmq3SNhDyIX1u3TJp31DtZ1AxH6R6oFdv6juP8LndATPCUMwIm2rYeqCRtnJ93ef4jVS3pd9+xY/wt0K5lBPSjKTfmLw5d5C+M+/SL44RcidGOuZK3+GldZP6BZbOr9oL5aq/OUl5Kp07CJ+ltE+IfSwbQ7VuSN+3HOU+9wQVxhnhLX9ki/L53vHjx5GXl9eog4aIwkgY9Xs4adIkvPjii2jS5NKvkJGRkcjJyQnYMjAjiCisMCMMxYwgorDCjDAUM4KIwkoYZYS3ApEtyhc2Ro8ejQ0bNjSKQZs8+WDxYpyE+1X0m2fORFJhIYrbtMHfhSY0D44bhyoA3w0YgDdmzHB7LgFA68JCLJw5EwDwzpgxeHf0aETWugFnwH83IW/FEqAKWHzrDGy6vNYgS0nA6J3vYOzOtwEAecMWoTChTZ2LwTOOLkHOedsgQGOrR+ku7yLLb5CFQhxGG8y0uKxTxKUWAqvsd/AWoA+WIK/OPNrgNBbjG/u03fBOndYezTEAp5Bnvxq+GF2wCa1Qu1XIaJzCWJwGcC3y0BKFdXbfGMxABXLslzXHOm8xdb/qtwhVyAJwGMBM4ZLxKvso2gWIxBK3y82V9nWqxmL7nQCr0BTv6FSlATiHPBwFsMllnVwVYjR+wFh7a4s83IRCJAAodZtqBj5ADv4HYDvGYqnu8i6qET4n66XPdVXFONs6RQzAknZv1ZlHm8pCLC6z7XurrhiDd64YfelJ+zFnwP82Ie+DJQDs+97EugN8jf74HYz919uoAvDwokU40sb9DrVSAHcuWYKem2z73qy3bMtSe9zyO2fORMvCQhxt0wYf6dSnGgC3jxsn3gzQGP2///f/sHDhQmdo3H///ViwYAGSkpKc01RXVyMqyud7mEXMCJt/LF6MU3AP2V/MnIkWhYU426YN8oWMWDbOVk9dM6K5y/OZhYVYYM+I928ag3duGV17FhhweBPy3rHX09EzsOmKAbaAcTH623cw9tu3gQtAXs4iFDatdSdpEjDjzBLklNszItN+zKg1yuYizESWZbHt2IPad/tfBACsgm15C9ALSzABtRfGlhH5ADpjFXrgHXSvNZ9mGIATyLMfKxejGzYhFUBv93XCCYzFCQBAHkaiUOf4PgMXkQPbANlj3ZoxXrpddBHOIQtWHEYEZrpt/UtW2e9mKkAslritT5V9naqwGEft0ybiHcTXmccAlCAPX9ZaJ4dj9nXagbH43r5ON6NQ57ZWW0a8b1+n2vvVOfs6/e5SRuBZQLtKZ53GAReAgrgBWJLs/v0EA4E2ZYVYXGDPiA5j8M5dOvvegU3I215r33NfFIz+8B2M/cD2/eShRYtwtE3du5h/oZMRgPu92nfNnIlWhYU41aYNXtGpT9UA7hg3DjF1nmm8mBHm8cZi27dI1xo9duZMJBcW4kybNlguZMQr9oz4dsAAvO5yHuE4UrUpLMRie0asGjMG79yjU0//uwl5m+z19BczsKmzvZ66HMpG73gHY7+3n0d0WYTCJnXr6YyqJciptGdES9fvlZeOvYvwOLJwBIfRGjPRQ3edVuFeAEABemIJ7nJ5xrZAtoz4f/Zpb8U7OvMZgPPIw39t64Re2OQ86bk0YPloFGIsbIOi5mEsCnWODjNQghz7iKdj3VpSuGbESWShGocRhZlCc/hV2G9fpyZY4pzmUtuBNqjEYuyzTxuLd3RacwzAQWGdLt1NOxrfYyy+s6/TdSjUuTN3Bl5Ejn2asVjo8sylkV3dP6f5+utkHyi8IGYAljS373tXXnq+zYVCLN5q3/e6jME73XX2vW82Ie99nX3P5dRn9MfvYOxa+773zCIU6mTEyCVL0MeeETOEjPjVzJlIKSxEYZs2WKVTn6wA7h03zsjG5yGPGWEeryxejDNwz4hJLt97FgoZ8YY9I7YMGICXXTIizn5YbnO0EIsft9fTEWPwzp069fSHTcjbZq+nuTOw6TKX73L2sBm96x2M3WWvpzcuQmFrnYzYtwQ5FfaMaFL7twfbPrYITyMLx3AYGZiJxwDsrDOfVbgfQGsUoDuWYHytZ5vWyogBeAcDULup8QAcQh7+DSAdi3ElNiGjzvuMRiTG2luy5eEqFDqbpN12aZ2wDzk4a1snYaDzRdiDLFTgMGIxUxg9fRV2AAAKkIAlyKzzfBtcxGL8xz5tO7zj1hqjwr5Ox5CHbQBgX6e6LRptGfGBfZ3urpURZfZ1+jty7MszFk/Dcf7mvk5PIQsnXT4nF5G2z3bVBftvTZEDsCR2BmpHdZuLhVj8w0ygyp4RXWrte9cBA/ZuQt7HtfY995/GMPrzdzB2/dtAFZA3fxEKM933vfIqYMqSJbjKnhFT7BlRVmudps2cibTCQhwUzsutsP12S74LRLYov/KFF17AmDFj8O9//xs9evRAdLT7DwcPPqjXTUX4KamoANj3IVHQXThzxrcZhFHzQE1z/9X5jTfewO9+9ztnaBw/fhzZ2dm4cOGCzquNwYywKWVGEJnCRWaEEzPCPHgeQWQO5cwIJ2aEefA8gsgcKpgRPgtEtli02u9Sj1dffRW/+c1vEBcXh5YtW7qNWm6xWPDTTz95vTChoLS0FImJifg1bN3G6d/L6XnEhSuEcv37mIDmWcITUvd6+heGPfRxCLnbcYnUTazUFaC00p6ei7xSeKL23bwOUh+1SR7eXFIslEv90e6QZ1WzTb+87g0JnsuF7h0BGPf5SV0q7hHKpa4oAZw7rF/+vTC9tNoHpPnb/60A8BcAJSUlSEhIEKauy1GXS+4DEny4pbe0Ekh8Rf39/SEiIgJFRUVITbXd+d28eXP897//RYcOHQDYQiMjIwNWq//aNDIjbPvVb2HLCKn7aemwrtejtnRYTUkWntB7QQdhWumwKXXH6uk4VJu0klJGqWZaU2mlADknWgvlSR7mpadYKD8ilAsZUSbUB9VjrjS9cBwWSTubXlfFUhxKVVxn3zkpnCtIu5nUW77eajruzKoA8GcwIwBmhBk49qvHYOtt24iMAOSqGycd4/2ZE9IXOmmwA2msDr3lkfJAWvaW0kJKd4NLX5Cl8SskemOBSF/W9+kXnxb6iJeqiJQD0vRFQrl0vNM7X5O6sTcgHwCgXJiPSkZIMejIiHIAT4MZATAjzMCxXy0CEA95KLy69/jbSIfI9tLwDqrfvaVy1SFFpR8EpN81pbFiVYew83TqkC6135LyQhpbVtrY0tgb0jiqQjYUCb90S1XjgFAuHadPCuXSzij9iKk6jK6UYYrnRAeEzSzN5qhQ7tgVLwKYidDJiC+++ALPPvsstm7dimPHjuHdd9/FiBEjnM9rmoa5c+filVdeQXFxMa655hosX74cnTpJldtm2bJlePbZZ1FUVIRevXrh+eefR79+/Rq0DoHIFuWhHB977DE8+eSTKCkpwYEDB7B//37nI9yDxlVsU+lUhIgCyee6aDXgEUJcTxD8gRlhE8OMIDIFn+siM8JQzAgbZgSROTAj1DAjAiOaGUFkCj7XxQBnRFlZGXr16oVly5bpPr9w4UIsXboUL730EjZv3oymTZsiNzcX5eXShTfgrbfeQl5eHubOnYtt27ahV69eyM3NxYkTJ9QWzgNfs0W5K6rKykqMGzcOERHK10TCSmVSEs6XlYn7We0+3FxJu4z0mubSE7UHJXCQrrJ6asYkPSfdDiBdfZdIywTId3Y1F1o6SOXSxXH9YTQ8qxLKpQ9P+iw8Pedpm+jxtM2l5ZUuQUvlUks7aR087OjSU6r7f4lQXmz/t1lSElDmqcY1LitXrsR1112HHj2k2yf8ixlhU5mUhLKyMrFqJgrlejcqiV8zVCqT6nFImrfe8VS6iypJKJfyRrp7VFqW5h5OcJsKz0l5oJoT0gcrlavmeLFQLm27JMXppc9danCit5yquaDzOUofrXTDXqlQXqxT5li8pswIN8wIcyhPSkK5h4xIEsqluiHt4XFmyQlAzgopEPWOX9L3V2nZ44RbRmOF8piv9ctVd1e9E0SpxUqFUK56ziEd76XWndLNycVCuV4+SMtiQD54mlyqB3pvW1zPtHHMCDfMCHMoS0pCZVmZ2DuI9FOreHgQ6likaqhIX8Sk3xCkkJOOP9JvT1JrAek4KTXV8vT7S7RwEI3+n355pFCuSjp2q55rSNtCKpeyV8pwaeeSzt+k6aV9SPGAL+3T0ttKvynV9/NXTIhlxPDhwzF8+HDd5zRNw3PPPYfHH38ct91mGzvm9ddfR1paGlavXo077rhD93WLFy/Gfffdh3vuuQcA8NJLL+HDDz/EX//6VzzyyCMNWi5/Z4tyYkycOBFvvVV34GEiopBUY8DDJAYNGoS5c+fiqquuQrNmzXDhwgXMnTsXL730EjZt2oTz5/W6RjAWM4KIwgozwlDMCCIKK8wIQzEjiCisGJQRpaWlbo+KCumuCNn+/ftRVFSEoUOHOssSExPRv39/FBQU6L6msrISW7dudXtNREQEhg4dKr6mtkBki3KLjZqaGixcuBAff/wxevbsWWdAp8WLF/u8UKHgDOSrfoB8J4mn5wy5K9fT9NLVXUAeLERqJSBNL10qk/rYA+Q7RFVuxwHk7eFpvSXSlWrp9ghpewDyHQZSCxNpG3p6D2kdVe+kMmpf8/CU9LFK5cVCueOGC+kGuAbztRm4iZqQb9y4EQCwZ88ebN26Fdu2bcO2bdvw6KOPori42O/NxwFmhMMZ2G5IkaqmdAOlXlUT7xFRqWSqdVg61iXplEk5Id0JJJHuopL6gPWmpZxUrnogkfpKlY7TquVSbkh3uKlua2nHVPmConorrc48pFmoNnDRu9uq2P6v+tf+WpgRhmJG2JyGbX+WDsFJQrnq1zelL13+zAlAbrGhMj6ulBNSA75ioVw6L1HewAK97/iq51pJQrk0H2k7StOr9vmttw1UzxsUTwJUG60U65RJu4xjWk+nVw3CjDAUM8LmFGzVQmy0LJRLdUOqemLPIEadyEu/d0gnQklCufT7iGqvFZ5+k5LGGpVeUyyUS8sk/b6UJJRL2SCNm6t6PqbaHEhaftVzCtUfQ4V9VPWnwmKhvL6MkE7JGsygjMjKch99be7cuZg3b57SrIqKbM1r0tLcd6K0tDTnc7WdOnUKNTU1uq/Ztathg28GIluUL2x8//336NOnDwBgxw73wTADEXZmcRq2wJHqtzddUSlf2FBtnuaJdGCTDoRSDZcqbbGH95aaCkqDpEpdVwkjAp32Ynu0lD5YaUwdT4OjS4NySSNDJgnlno6q0t0+qt96jGry6OEp1XOgYqHcceitlhehYXy9W8qH1z7zzDOYPXs2fvvb3+K5554DAJSXl2PmzJl48803UVFRgdzcXLz44ot1wsSTTp06oVOnTm7NCffv348tW7bg22+/9X6BG4AZYXMKctNWQO07nVT9KoXjbYzej/TSTFS/aOkdU6SckL4YS8sibbADQrmUBYA8VreQN9K5jESqjU2l47o0lrmUHe2FcumHQenEUaL6o5PefiLumEKxzjxUo0XlArgjI3y++B3EjPAXZkTwnQZwFvIhWPUeHvGGKqkyqeSE6vdo1XMK6Qd2le6ypFFBpZz4r37x4WL9cuk3MolebypZScLEvYRyKR+kcxHpPEHKYungqPIFXnWfUXxP1V5y9HYP6auFYTdIMSMMxYywOQHbDVJSdVS5QcpTeXPpDVS7yFOtSFJOJCnOR/Wig/T7EgAIvZ2fFC6cS/EiLZJ03UE61KdIA51fqTgjKXsbPg62jXSskg6yqhmjuM+p7uvFQrnUk5ZjtVQ3Ux0GZcThw4fdBg+PjVW5K8Qc/Jktyhc2Pv/8c5/eMFxUeBhchYgCJ1Tr4jfffIO//OUv6Nmzp1v5jBkz8OGHH+Ltt99GYmIipk2bhttvvx3/+c9/fHq/7OxsZGdnY8yYMT7Npz7MCJuqEN0vicJNKNXFBQsW4J///Cd27dqF+Ph4DBw4EH/84x/RufOlOySMuPithxkRWJUhtF8ShTPWxYZhRgRWKH13IQpnZqmLCQkJbhc2vJGeng4AOH78ODIyMpzlx48fR+/evXVf06pVK0RGRuL4cffbAI8fP+6cny+MyhblCxtk87/TpxEB+cqrp64PpIuX4o2z0hNSuXRFUOrCAlAfeFu6u0r1yiwg3yorXAY/IpT/S5jNZx7eWnKDcKfRcOG9W3uaWZJQLt15KzW19NRkXPqcpM9V2hekfUd1H/TwlOrYidLdWo7d5thpT21KGyAId1qdP38ed955J1555RXMnz/fWV5SUoJXX30VK1euxA033AAAWLFiBbp27YpNmzZhwIABPiwoBZIjI6QbJVUa4qneEBmj94S0n0rl0nFI75iimh+qt+lLbYT3CuUADggtM9YI038sz0pXrlD+c+F920vbSMoB1e+Jqhmg2hWi3n6iONihEfu2SrckoZgRGzduxNSpU3H11Vejuroajz76KIYNG4adO3eiaVPb0cRfF78psH48fRqRUG/5rdyoQuUFqi37VHLCU7n0nVSvXPoSWSyUH9Av3iVML2WEVC75uV6Z8J5dDggzkQbRlbaBtB2lnUz189ObXto3VHdUhdzwVK5Xb4qFaR09IdeEUEZQ4/E/e0ZIVLucEn+KUf3CZVQLcNVzB4k0vVSvPHVjK3yH/0aY/F2hXDqnkM4dRgrlN0u9mkit9qR1lraR6rZWbR2umg+K+5xq626V7myBSy01wykjsrOzkZ6ejvXr1zsvZJSWlmLz5s24//77dV8TExODvn37Yv369RgxYgQAwGq1Yv369Zg2bZpxC+cj5QsbZWVleOaZZ7B+/XqcOHECVqv7HvvTT1Inp+Gl8TSEJDI3n+uiQf0elpaWuhXHxsaKTQSnTp2KW265BUOHDnW7sLF161ZUVVW5Dc7UpUsXtG3bFgUFBR4vbGRnZ3vVRHv69Ol48MEHlV8nYUbYMCOIzMEsGdEQa9eudft/fn4+UlNTsXXrVlx33XU+XfxmRpgLM4LIHEIpI/yJGWEuzAgicwi1jDh//jz27r1099/+/fuxfft2JCcno23btpg+fTrmz5+PTp06ITs7G0888QQyMzOdFy0A4MYbb8TIkSOdFy7y8vIwceJEXHXVVejXrx+ee+45lJWV4Z577ql3eQKVLcoXNn71q19h48aNuPvuu5GRkdGo+jp0ldq6NYqOHAmfmyyCuSLSHUPCVWTpYrHUVaN0Q6wn0rzEC9WernhL6xcIYbCD1nejeXrr1jh+RBqBvgGsHt6koa9Hwwd0evPNN7Ft2zZ8803d+z+KiooQExODpKQkt3JPAzo55Ofnqyy1U/v27b16nYQZYdOinoxQaRxlSDVWbeGnOh8Visd88cDrYdyhJKFcugG2hzwrpflI7ysuq+oda0bliernqLefGLAvqDYSVKlPjju50lu3xkETZITKxW+HkpISAEBysq03bV8ufjMjzCWhdWucOHJE+eZX1UwRqYSNURVVml7leCe1XlYcyLz1Qf1yqYty1cOd3nzE1t1JQrm0TtI2kPJBddBdI76gqO4DirNRmX1949qmtm4NmCAjgo0ZYS7xrVvj5JEj4lgahlU91eOAUeWqYzep9mghHT+lDQqIX+57CHkhtQyQhk2SGmOL5yDSyYa0DkZlhmpLiyDtK0Z9T5Jafji+taeEWEZs2bIFQ4YMcf4/Ly8PADBx4kTk5+fj4YcfRllZGaZMmYLi4mJce+21WLt2LeLiLp2U7tu3D6dOnXL+f9y4cTh58iTmzJmDoqIi9O7dG2vXrm1QN7iByhblCxv/+te/8OGHH+Kaa65RfSkRUdhqyIBOhw8fxm9/+1usW7fOLTyMcP3112POnDm47bbb0LdvX0PnrYIZQURUV0MvfjtYrVZMnz4d11xzDbp3t40+78vFb2YEERFJmBFERKFv8ODB0DRNfN5iseCpp57CU089JU5z4MCBOmXTpk3zquupQGWL8oWNFi1aOO8ca8xa2f+VLox6uoFf6i9RvBHTqDs0pT5ZAXmAA6lcdRwPqQkEAGQJ5cLl05bCRh+3TyiX+mf3RGrm0VEov8zDvKT1k7aJ6vgXgPrn52lfUOFh34wQri6rdgEs1THHXQ+thOcbrAa+1TH759KQAZ22bt2KEydO4Morr7z08poafPHFF3jhhRfw8ccfo7KyEsXFxW4/XDV0cKbCwkIMHz4cMTExuPXWW/GLX/wCN954I2JiPA3QYixmhE1LABrkG/KlT0SvHqh0P24YlQ6spVuWpJVUzQnpGOrhbq9av/s63X5AKJc6WJVIu3h7obyDUG5UPkh380ifjWoH/gbQW3TVPJB2Kb165riHSBoLrcEMyoiGXPx2NXXqVOzYsQNffvmlD2/ujhlhHkkAqqHeaCsoeaB6m6NqJ+9S/dJbKWnXkY6xwrybC9/zbz4qlKt2sa134JHuvpWWvb1QLm0DaSdQ7c9c5fPzc8tw1f1dr95IdcyxGZNUFkiPQRlhBswI84iFbd9VHQbB74waN0Ea8MCoFs7SbzmdhXIP75EljHWRJd1PIv3+In3tk07xpXOEdkK5tM5GjXkofWb+Ho/Fz+rrMMDzt/UGCKOM8FYgskV5E//+97/HnDlzcOGC9K2HiCiEWA14NNCNN96I77//Htu3b3c+rrrqKtx5553Ov6Ojo7F+/Xrna3bv3o1Dhw4hJyen3vn/9a9/RVFREf7+97+jefPmmD59Olq1aoVRo0bh9ddfx5kzqr/eqmNGEFFYMSgjHBe/HQ9PFzamTZuGNWvW4PPPP0ebNm2c5enp6c6L364aevGbGUFEZLAAnkf4GzOCiMhgYZQR3gpEtii32Fi0aBH27duHtLQ0tG/fHtHR7te4tm3b5vNChYKWsN0tkig876Hrb+U7eJVu7QXU7+gEgGKhXLoyK52PS7fXe7plMkkol+5gGiSUq14t9sTAvt7F16i2zJDusAXkz6lYKJf2BaNunwUQI3weqptQqmOORidJ8iI0TACvojdv3tzZpYhD06ZN0bJlS2f55MmTkZeXh+TkZCQkJOCBBx5ATk6Ox77TXUVERGDQoEEYNGgQFi5ciB9//BEffPAB/vKXv2DKlCno168ffvGLX2D8+PFo3Vrs8dlrzAiblrAFrHTPmbS/61U15eEW9F6g2o+qdDwt1imTWheoNsOS7jSS8qOTUA6o54FqKzbVXJYOfFK56ucl5UOxYrm03fTeV3GsFL3Jpc0llUv1xtO9nVKGNFgAM0LTNDzwwAN49913sWHDBmRnZ7s937dvX+fF71GjRgFQu/gNMCPMwpER0v6penOqeFev6rgLelRzQmoprXrbo95GkHJCOghId+eq3qmqesek3vdpoz5U1ZYZ0u/DUisU6fPT2zaqP7go7o/RwvxVolaqYzX1PN9gYXY3LjPCHBwZkSQ8b9RwCob1DKJ6/FEdc0k6xkvH1TZCudRyDgB6C+VGjRMiUT3Wq54Yquad9BuzSjZ4el9VwnoZNexKklDuWHxmhDH8nS3KFzZcR0tvzM4cPRrUMaGJyObMUaHfgBC1ZMkSREREYNSoUaioqEBubi5efPFFr+fXtWtXdO3aFQ8//DBOnjyJ999/H++//z4A4KGHHjJqsZ2YETZnjh71b9cgRNQgoZQRU6dOxcqVK/Hee++hefPmznEzEhMTER8fj8TERJ8vftfGjAgOZgSROYRSRgQDMyI4mBFE5sCM8A+js8WieRpZJIwtW7YMzz77LIqKitCrVy88//zz6NevX72vKy0tRWJiIkbDdrE0SZjO042k3YXyK4Ty1tKbqPbZ7elyo3TVXJqXdLVbKvfUQ4Lq3VfS3cEx0q1g3gzSLFx6rhQ6TZRaTADqV72l/ho9HVOl54Q+IcWrciWK8/lJXCIcKdYv3ylMv0Mo3yOUO2ZfBeAdACUlJfWOceHKUZdLbgYSfOjAtLQKSPxI/f3J3HzNiLGw3TEiHa6kw3cXnTLphtNOUj3WGwtItT9W6bYWvZYNqjkh3YSRJpSrtuQA5LyzSCummhNCRmhC0wnp2Kp6x+xxofyIUK6aDdJtTnqrJS3jQaFcZxysPcJ3j93CLHYJ5XpR5IjlSgCrEBoZYbFYdMtXrFiBSZMmAQDKy8sxc+ZM/P3vf3e7+N2QrqjIOEZlRJIwnZQR0rmCVJ4lNXPSewPpWK6aE9Ldm0acU0i7uZQfUk40lX4yTBLKjciIYv1Jy4TbMaV8kHJA9fxBNR/0NoHUWlDKB8XzicPC3d7SuYReuXSqUmz/N5QygkKHrxkxHraMkL7OShnRQyiXMiI9SXhCGj9UOo5Lx1rp5xlpHAqppYV07qA6vZQVgLwOkdITzaQXCOXSrffnhcmFEFDNBukcodCg6VU/Y2n5pXzYq19cVKxfLuXD90K5lBGOU7dKAH8HM8LsGtToINyufbz11lvIy8vD3LlzsW3bNvTq1Qu5ubk4ceJEg+fRjINaEZmCz3WxxoBHI8eMqKs5M4LIFHyuiwHMCE3TdB+OixoAEBcXh2XLluHMmTMoKyvDP//5T9Nf1GBG1MWMIDKHUMqIcMWMqIsZQWQOzIjQ0KCuqLp164Y5c+bg9ttv9zhy+Z49e7B48WK0a9cOjzzyiGELabTFixfjvvvuwz333AMAeOmll/Dhhx/ir3/9a4OXOzU+Hhcg340rlQPqXW2LV0GlN5HqnnTHFSDftSPd/SMtk+odtwDQSZpZf6G8l1DeXihP8vDmkmL94pgD+uUt/yvPquVm4QnhMrZ0lVy6ug3In5NE+g1EusGgWCj30F+y6n4u7c5JQrljUZvEx8sL0RBh0u9hdna2eLevJ9OnT8eDDz7o03szI+pKsWeEdEOrVK5XpcRsUBmwRrVjUalCHtApk/JDWj7pLiopJzpKCy9lBKCeE6o9qApNMCwH9MuThIxIkvJBuA1WuhtLahkofTbSHdVSNujdaST1myzdIa6zP8QJ81AdxitJp8yxyMwIG2aEcYw6jyiHXF2kQ7DqMA1iKyy9GUkVTFoYaXrpbn3VrNBrsSHlRFvpGH6NUN5NKG8vlBuREQf0J236g1D+H/1yq5A/0rmAaj5IrdD1tr10bJTyQXGgyWhhPirnEtIn56gzccwIAMwIIxmRES3tGSEdfqX7tpUzQnUcTWl61Y4zVHv3kfJGaroitQRsKb0AAKQuPaW8kE5opJWWvnwLP/5ECtmQukmYXmh7IGVysVAutcyQfovytEn1GLTPqQ5rKNWZJKHc8VsTM8I3gcqWBl3YeP755zFr1iz83//9H372s5/hqquuQmZmJuLi4nD27Fns3LkTX375JX744QdMmzYN999/v/KCB0plZSW2bt2K2bNnO8siIiIwdOhQFBQUBHHJiCgoNKgPflj79SaQn5/v1evat2/v83szI4gobDEjfH5vZgQRhS1mhM/vzYwgorAVJhnhrUBlS4MubNx4443YsmULvvzyS7z11lt44403cPDgQVy8eBGtWrVCnz59MGHCBNx5551o0aKFN8sdMKdOnUJNTQ3S0tybFqSlpWHXrrq9N1dUVKCi4tKd9aWlpQCArxcvRhGAGPvVpxpNwy2zZiHh8GEUZ2Tgr4sXO8ujIiJg1TRYNQ2/vesuxNXU4Lt+/fD2zJmosVqhAYiOiEATqxWZhYV4ZvZsoKoK/xg9GmvGjgXibDUhKiIC1VYrBny7CXn/bxnQvBKLb8vDlq45qNassACItESgOsGK0Tv+gbE73wVqqjDz5sU4mpSFmpZWWGBBBCyogRUzDi5FTslmQKvGHdevggVADTREwLZOVmhYpM1ClnYYhy0ZeDhmsb1eaoiMagIrNGgAVuFBADUoQG8sxSxneRQsqIGG1jiNxdgOwIpVuAKr0R3V9hoaBQuq0QwDcAp5+B8AKxajC7agFarRzLZOAKoBjMYZjMVZAJmYiUQcRSRqAFhguwhag1jMQDVy7PO+A9H2dYp0XiS1AlgEW/eQhwE8jEvHmkj7v7Z1sl1RL4AFSxHlLI9CBWoAtEYNFtvvxlqFeKy2L6NtnWx/D8B55OE4gD0u64RL69TpHEbjB4zFLgA1mImbcBSJqOl01r5Otu03A2uQg/8B+Bp34CX9z+nCg/qfU1WE/fPQsKrkLtvnFN0PSytnwgqrfZ0iUAMrWlcUYvGrswFrFVZ1H43VPcaiWrPve6URqNasGLBrE/I+XAZU2/e9zBxUW+37nn3/HL32Hxj78buIO16F2YsXoygrC9VWKyIsFkRYLDhjteKXS5ei5+bN0Kqr8dgq275XrGmIdKlPd82aheTDh1GVkYF1i23r5FqfqjQNo++6C9ERvlwCDx/XX3990N6bGVE3IzbrZMTPZ81C4uHDOJuRgdeFjFh8113Qamqwo18/vGvPiGawHf9rrFakFxbi9/aMWDVuNFaPGItqa62MOLgJee8sA6oqsXh8HrZ0z0F1M5eM0KwY/fU/MHbru0BmFWYOXYyjCVmogUtGJFsx49BS5JTaM6KHPSOa1zr2FM9CVnP7sSfO5diDCFijY3UyYgqsSNLJiNcBdDcoIzTMxBU6GQHMQKRORgAR9vtyAp8RX9VaJ3tGdDyPamh1M6KjhhpodTPitjcBVNfNiKqLts+p3CXL4xZDq3T5nBwZcfouYHcNChL7YWnbWhlxxorWpYVY/PlsoKYKq7qOxuosl4yw71cD9m5C3ku19j3H95ML9oz44B8Y++G7QFUV/jB/MU7UyohqqxXDly5Fd3tGPGnPiBpNw0WX+vRLe0acysjAv4SMGMOMcGJGGMOfGfELl4xYIWTEUntGfN+vH95xOY9oYc+IjMJCPG3PiHdHj8a6u3UyYusm5H1pr6fj8rClm72eNnPJiG/sGVFThZl36WQErJhxZilyiu0Z0dflPKKpS0YUzUJW9WEcjsrAw+1rZYTj2BM7E7aM6IOl+I3LeUSyPSNOYTHyAdRgFe7EavSolREaBqBSJyMAC5qZKCMqEGWfn3tGXMRqJOtkxAH9dWpfikj7eo/GDozFTgA1mNn+ahxFSt2MuHUhcvAtbBnxyqXPyb5f2s4jZiHLehiHIzLwcITwOf37LkCrQUGLfljawZ4Rxbbjf42mkxH9dDLi603I+6fOvnfO5TxizT8w9iNbRsxfvBjHdTLi50uXooc9I54QMuJue0ZYMzKwVsiIccwIJ2aEMfz5W9NtLhnxqpARL9oz4rt+/fCWTkbU/q1p7USdjNjmkhFjXOopgMjm9ozY8g+M3WbPiHGLcTQrCzWaPSMsFtRoVszYvBQ5zewZkVPrt6am9mPPwVnIqjyMwzEZeLjdYmhNdI49JXcB6a10MiICNUhyOY+owSoMxGpcg2r7ffiXMuII8vANgC5YjE7YYj/mup9HVGMsSmHLiEwcRbQ9I+JdMuIicuxH6zuQ7JIRNraMiBAywuqSEbZtXgBgKWpcziMcGWHFYuwBoGEVErEaSS4Zcd6eEWeQh32wZUQnbGkZhWr78f9SRvyIsTf8HUA1ZuJeHEWrSxlxw1n7ecQr7hlxsbzub03nZiHrJ5fPCS6fU5n9c9psz4jkflh6+UxYz9nPI1wzYv1s4GIVVl01GquvrpUR56wYsHMT8lbX2vfO1fqt6aN/YOy/3kUc9H9rOmu14i57RqC6GrPtGVFS67emCbNmoeXhw6jxcB5xBzPCZ4HKFqVP6dprr8Xzzz+P7du34+zZsygvL0dhYSE++OADTJs2zfRB440FCxYgMTHR+cjKso2Y1DfF1h63f2oq+qemAgC6DBmCttdeCwAY3Lo1utr7Y/tZVhY6JdoawnaYMAFxXWzDw97coQMymtkGHfrFZZch2d7MKTovD2jVCgAwqmtXxEdFISoiAiMv64qoiAjExMQC/2e7CyA+ril+3vpyAEByTDxyM2wjPTVrlgpcfR8AILVJMq5Pso0c2z4qEQPjs+zTdAXSRtqWPaIVekdkAAB6RKSiR4RtneIihwARtnXqZ2mNjvZ+rgaiNdo7G/eOhmOEqaFIR6q9AVguMpDsbFM2EI4G9yORgnhEIAoWjEQKogDEIAKOZn7xiMLP7Y3FkhGBXPv8miEagG25UhGB6+0nGe1hwUD7382cpye2QXh729+9By4NpuXaPK0fLo2zOxCuDdAjAfvBfCgs9ncFchFljzDA1jg0wr5O8YiHBVH2v23rZIGjHaT7OkUi196wtBmaAehrX6d4XG8P4fZoioFoZZ+mDRxNI7sgGb3tS9MDrdDDPo34OUVmob3F/jnFTwCibPve0CYdkBpp2/dym16G5Ah7E7tr84AmtnmOTOmK+IgoRFkiMDKrK6IsEYiJjgXGuOx72fZ9Ly4eue3s+16rVOAO276XlpyMwe3s+15iIq6x15+Url2RNNK27/Vo1Qr9Mmz73lWpqbjKXp/aDRmCVHt9urF1a3S316fhWVnobK9PfSZMQHIH1faPtYRRv4eTJk3ChQtS+3//Y0Z4zojOQ4Ygy75PD2ndGlfoZETqhAloYs+IW1wy4ubLLkNLe0bE5+XBYs+IkZe7ZMTlLhkx0V5P45vi5+1cMiLNXk8TU4Eh9oyId8mIuEQMTHTJiFR7RsS0Qu9Ye0ZEpqJHpD0j4oYAkS7HHov92GPJEjKinZARt6FxZoTeOkUi134Mr5sRqfZ1qp0RN9nXqVZGWFyy3PVzirB/TlFZaB9h/5yaTgCa6mREk8uQHGXPiP4uGZHZFfGR9ozItGdElLDvxcYjN9u+76WkAhNs+156cjKGuGTEtfb6k9q1K1q6ZER/e0ZcnZqKq+31qb2QETe5ZETvCRPQghnhxIwIPJWMuHzIELTROY8Y6pIRrSZMQLzOecTNLucRsa4Z0cklIzrZMyI2FrjLpZ62FTJicD0Z0bwrkK6TEVGp6BFlP/Y0HQI0sR97IlwyIsLl+ynGAOhkW1e0Q6q9z5FcZLpkxEg4OpKomxGWMM0IaZ2ikWsf5daWEVfb16l5rYxIsU9zOYCb7evkkhGW1EsZET0EiLJ/TtGt0THS/jlFu2RE1gSgmU5GtPCQERG1MiJa2PfiamXE3ZcyQjqPaNGA84gUe326oXVrdNPJiF4TJiCJGeHEjAg8KSOuVMyIy+z7dLLwW9Nw4bemkZcJvzXd6VJPs1y+y6Xa62lCKnC9PSOaJuP6RJeMSLBnREpXoI09I2JboXe8PSPiUtEj2n7sSRwCJNiPPTEu309r/4ahmxHta51HODIiyyUjWtkzIhKA7X3iEYmf23+LsX3ntm2jZoiCo0/3VETievu8bRkRYZ8mEo4+k3zLiEtd8wxFhEtGRLr0Kp8Cx33oIxGHePv/RiLBJSM6u6xTS/s6RbmcRzQFMMy+TrUzorV9mloZofebYEytzynK/jnFZKF9tP1zaj8BaG7PiGYdkBpjz4hkl4zIyQOa2fe9DJeMyHDJCL19Ly4eue1dfmv6peffmlq5/NbUXciItkOGoJWQEZe7ZATPI3wXiGyxaOE2WlM9Kisr0aRJE7zzzjsYMWKEs3zixIkoLi7Ge++95za93lX0rKws/D4pCSguRrNI2xdhraYGEVFRtoEea2rQNioKsP9tiY6GVlMDWK2wREejo8vfWnU1oGmwxMSgc1UVoGlATAzg8ndcu0rbm0fHAFUuf3euBCwWIDIaqHb5u2clAAsQEQVYqwBLBGCJBAZVAbD/rVUBiLS9RqsGBt1ke02da84T7X9ruHQA1gD0waV2URaXv1t68ak4+op1nY8FwDGh/LD9X4t9GS0u0+uVn6m1TtLfrvdaJdv/1ezTONqPZbj87VqeBf3tYcGlgUdql0s9/Umkjg0BwNF/e+3PaaV9Ga2wRaH97y8/t33u0ABLjH1/0IAvY4Aa+9+RMUCNfX/7Lsa2jwFAVMyl/W1XtG2ftFiAKNe/o1C+swqIiAAiI237s/3vXVVVtjKLBaiudv69t7oaFpf6ZLHXoUO16laEvT6dt1oRER2Nmvh4zCktRUlJCRISGr5NS0tLkZiYiJJBQEKD2q4J86kGEv8N5ff3h8jISBw7dgyp9sC+//77sWDBAiQlJTmnqa6uRlSUDysc5ozKiD/YMyJeyIjWLhnh2Kc1qxXZtfJCq67GZTq5gKoqtGxl/7vSXjcdf3fGpbxw1M2OOnkREQV0dckI17wYUCsjHH9fNQx1j5sjUffYE4FLX/FrH/tU+yo/L8xHyggNts57a2eBFbbjqF6548tWQzMiAfoZkQr9jGijs4yALVP0ypt52B56ioXy71D3s7EA+Cfq5AKigG/W2P62RLtnxH9cc8H+9y6dXACAfTH6ufBTtG3/tGcEqqpw+nTdjEBVFfZGRsJisdi+I7n8fVAnI7SaGhTpZMRFl4x4jBkBgBlhBKMy4o/2jGgqZESGcB7RUScjoGnopJMR0DQ0b6+TEQBwhU497aKTETVVQFedjLBWAf11MkKrBgYOsq9tDS71Ul0DYDzcj5uOv7tD/ziYIpTr/X1eKD8F92Oxo/wEAp8RrWoti+PvNKG8lbBOzRqwPVz/dpw/1M6CnS5/u34e79jLa+CWEQXfAFoN3DJic61ccPy9S3PPBcffu1H3+0lVJbDXAkTXzYhzRfoZsUfnPALV1divkxHHhPOIMpeMeIQZAYAZYQSjMmJhUhIsHn5ram1QRsR1FDKiq/BdrotORlgigK46GWGJBHrZc8Fiv9jr+HvI5bh0jNFc/r4LdY9JEQC62reQ6zEuApcGl6197KsSys+5vLb2Mdc1L1yP/2dc/nYtL7W/vnYWVArlTXEpI2r/7uRYlkhc+iU7RWcZAflcozn0syBaKD8N/XOEnS7r6iiPAOytYtw/s2jgsx9xKRdcMmK7/TyidkbstO+HtTPix1q/eTr2vR/tf9fKiPKf9H9r2iP81vST8FvTkXp+a6qOj8csZoRPApEtja5dTUxMDPr27Yv169c7y6xWK9avX4+cnJw608fGxiIhIcHtAQDlxcUAbJVCq7EdfKzV1c6/Nde/q6oAexO/On/brytplZXOv1H7b4cqnb817dIBwfVvaLZAAQDNeulvWO0/YgNAjf1kxF7uPIg6fuSwz8d58Kv9N3T+9oU0T72/tVrLWN/f1gb8XeMyf2utv1HP3yrLXvtvI0ifUzUuLafL344LGQCgVV76u6b2346X6vytae77odvf9n3MatX/u6bGFjS1/natT651yLVuWauqoNnrkLWqChWlji8VXrIa8DCJ2tep33jjDZw5c8b5/+PHj4dsIAZKMDLCdZ+WMqJOLuhlhF5e1K6brvW3Ricj3PLCNSNq54XecdP12NOQ46OqxpQRRpHyWycXVDNCLxcAed+rVMsIzSUjNGaEIZgRvjPreYS5MsL1FkPXv2sfN+s7pzDqb71jsdkywh/nF9LxX8psx9+un1ntXNDJiDrnDoHJCJ5HGI8Z4TujMqIiHDJCc8mF2n+7HWNc/67vGFrfsb2hf9d3/DUqF6SMqJ0XcClv6DLW/tuojLDqlFuh/5nVzoUGZIRj3wuRjKhkRvgsENnSKC+35+XlYeLEibjqqqvQr18/PPfccygrK8M999zT4Hk0iYtDVHk5ooXnDb1iVCmUlwvl54Tywx7eY88n+uWdNgov6CiUt/LwJpJioVxqoVCkX1wmtNOStpMncUJ500jhiXQPM5NasSQ1eHFsTnl4bp9+8Z4K/XKJ8nYXAABTbklEQVRpX5D2HWkbSvumF6Q6I9UxR8PTiLg4oNybDzn86TXIK+e2qpcRGRFvzwjpUCIdSZSyQ2qaWqVTZlReJH9Ut6zjx8LEUk4kCeWSYqG8RCgHoB3XL5dawapWC+mDbSKUW9KEJxKF8iSlxZG3kZAN+4SdRyUbpG2mt/8BSk2ppXog1Ru9j8PxdhZmhIgZ4R2jziOiPZxHSOXKjMiJMqFc+DqOPV/pl3cQyiPbCTNqKpTrkRayWL+4RsgPaTbScU2i9wFKqxOpmgMq2wUQV6rmoH75T8Js9D5vaXv5MR880dvsUl2Ktf/L8wgZM8I7RmRE03oyQvo+JBGrmPSE9GOqVIelY4H0c84P/9Mv7/CYfnm89HuKdPyUVuy8UA6IeaH6G5P01ipfYgEPvzslCeVSa29pPkIOXhQ+NCkbpM9YNU+lfU7YnqqxIW0FqY45PpZIZoTh/JEtjfLCxrhx43Dy5EnMmTMHRUVF6N27N9auXVtnkCdP4lq2RPWRI35cSiJqiLiWLQFf6mINLrW09Pb1IcRi8WVlGwdmBFH4YEaoYUbUz4iMiGdGEJlCPDNCCTOifkadR9QwI4iCjucRgeFrtjS6rqgcpk2bhoMHD6KiogKbN29G//79g71IRBQMYdY8cOXKldi2bRuqqlRvLyRXzAgiAsCMIF3MCCICwIwgXcwIIgIQdhnhLX9ni3KLjRtuuAHXX3895s6d61Z+9uxZjBo1Cp999plhC2dmjl7mpAtonhrSSD1hFAvl6SeFJ44K5VJzNk/7kNBrB1oJXRkl7dQvl9pyeaqQqk3fpa5TpA3rTd2R1qOJ8Ik393AVt6nwnPQ5SZcbPa1HsVAu9V4ldTdyQCiX9jVp34S8SKo9wUh1zFHu89XZMLqKPmjQIMydOxfnzp1DdHQ0qqurMXfuXFxzzTXo3bs3UlJS/L4MzAh3Um9tUrle/ZAOeS3PCE/odRchHW8kUkXVO3YkC5UgQWhmLh1fVQNVWkZAzg+jckLMCKG8qRCyTYRy1T7MpOWXuoWV9h3pu4De5y7liNQ9jc57qsa5an0yBDPCUMwIm/rOV6XDnlQuVfXmUl3X68pYOn5Jxx1pYcSuDIXypkKXSHrHWdUNppoFql0oSfSWXezCUOgOpKlQbtT5g7RtpH1G7zu/dH6get4gvKe0X6vUD2mXsdbzfIMxIwzFjLCp77cmlfMIT+VNi4UnpLqq+v1UqqzS981t0vsK/R1FCuVG/kin+qVUOqhIx+gYoVz63amJsM7iNhLmr7qNpB6Ape/90mesmg/F+sVGfVz1/dbkcxu1MMoIbwUiW5QvbGzYsAHff/89vv32W7zxxhto2tTWz2dlZSU2bpTGYyAiIn9zHIP37NmDrVu3Ytu2bdi2bRseffRRFBcXB6T5ODOCiMicmBFERCRhRhARkdECkS1ejbHx6aef4te//jUGDBiADz74AO3bt/d5QUKNlc0ziUzB57rouCXGl9ebTKdOndCpUyfccccdzrKffvoJW7duxbfffuv392dGMCOIzIIZURczIviYEUTmwIyoixkRfDXMCCJT8LkuhmFGeMuf2eJVLy4ZGRnYuHEjevTogauvvhobNmzwaSFCUdmJE8FeBCKCAXWxkfR72KFDB4wZMwZ/+MMf/P5ezAjgAjOCyBR8rovMCMMxI5gRRGbBjGgYZkRgXWRGEJmCz3WxkWSEt4zKFuUWG45mIrGxsVi5ciXmz5+Pm266CbNmzfJpQUKNY+QJ6eKbp+7Mpa5Lpa5t44SO4pL2CC+QOpbzMB4CfhLKmwvlqv21e7pKqdp/utQnrFH94gJy/+nSejf1MC/xgxXKpX4QPZHWXerAXOiCUewLURgmpNhDH/cK3awDkBdVKnd8rMIoMA3na7+FJun3MDs726tmfNOnT8eDDz5o2HIwI2wuwPY9RDqUSPt1sU6ZdAiR5t1ar7ty6Tio94aAXIH1MkHKCanPWEkgxtiQ5qX6pVG6LUQ1I/ydD1KHsqoHXL3vD4rjf+nFiGJ3ukpjcjh2eU/dKTcIM4IZ4QdlsO0a0qFZOr6rnkPECG+QojcOhnS8KBbKpX6yE4Vy6bimkhVG5YTqICb+HGNDHHtDcXqj8kGlf3XpfEI6uAvnGSeF7as4JIduRki54Vh9ZoQNM8JczsF22JEOPdJhU/q6KX2tj5Z+ZzogvEBaIKmiSW8s/fYUrOMbYNzYS1KdltZB9Xcn1Wzw9/mY9NmrjNcEKP/eJO1axUK5NESItPjltf71WphkhLcClS3KFzY0TXP7/+OPP46uXbti4sSJqrMKac0zM1F5VPpGT0SB0jwzE2BdRH5+vlevM7p5NzPCJoEZQWQKCcwIAMwIs2FGEJkDM8KGGWEuzAgic2BG+CZQ2aJ8YWP//v11Ri0fNWoUunTpgi1btqjOLmQFYvAsIqqfz3UxTK6iX3/99cFeBADMCCdmBJE5MCMAMCNMhxlBZA7MCADMCNNhRhCZAzPCJ4HKFuULG+3atdMt79atG7p16+bzAhERBZQVgC95Feb9HqpiRhBRWGFGGIoZQURhhRlhKGYEEYUVZkRAKF/YIJtzsHXD5003gFI3d1KX3dK+nCb0N5cijL0RI/RbB0B9LA3Vvvqkfg4BueM6afAExX5xvbnIKX5+qv3jAkCs4mtUty2g3o+k0JlgpbBPKXaVC0Du99CI/nKBS6smdTPZYI38Kjr5Ryls+6jUbapEpQtZKRv0Dp0pQgva5lKFlDJBr/Ne6VimGmqqfbp600+ucKysUfzSGCmtm3T8Vu0PV/XLheq2lradNDaJzoH4nGKf6HrlRuVEsU6ZY/FUhwSrgxlBfnAOxmaE6iGgUucY0FJvfCYAcUbkBCCvrLRSenVH6tNcNSeM6jNdordOqv2oq5YbsX0BeRvo5YPwJb1cyGDVIf6MyIhiYVpmBJlZGWyHKdX+/aUskMolVUIdThZ+Z4pUzQnV45hE9TjmaYP6e+wlidmyQaK6TYV8qCnWL5e+40s5oFou5Y+wOMyIEKN6jCMiIiIiIiIiIiIiIgoattjwUklRkfIFZSIyXkmRdF2+gdg8kPzgHDOCyBTOMSPIhJgRRObAjCAzYkYQmQMzIjTwwoaXrDVsE0RkBj7XRV/DgmFDOpgRRObAjCAzYkYQmQMzgsyIGUFkDsyI0MCuqLzUpEWLYC8CEcGAulhjwIOoFmYEkTkwI8iMmBFE5sCMIDOKZ0YQmYLPdZEZERBsseGlqiZNUHb2rDhWjjcX1owYww2QB95pLg2mCqCp8JxRYw15GjvcqHEAjRrvD5DXL1p48zgPA1GpjuPkzdjhRu070kDdxUK5tK8BagPIeppXqVDuWLemTZoAZ896WBKiwHNkhOqArnqMqN9SPUoQZtJEqJBxOuVGNZVXzQJPY/b5fVxY4QNUzQjV8QKDta31viJI+5R0LNcrlwbyU5mHtCyOj4gZQWZUbs8IqU4bdR6rkhMlwrTS+UMToVw6fhkx8Lm0PtJ5hnRMq1Ccj+p5XYTOC2KEhY9V3I7SeYK0L6l+D5H2Pb1tKZ1aSucTRh3zpXMJvflLy8LzCDKz8iZNcOHsWbG+G3UTt3RMlep2sVDeXHiiqVAeK8xH9TemQIwdLs3LsLHDhRlFK2aDv8cOl7a1lKeqvzdJx3spN4z6rUlaHsf3gSbMiJDACxtE1LixeSAREUmYEUREJGFGEBGRhBkRELywQUSNWw0AzYfXM2yIiMIXM4KIiCTMCCIikjAjAoIXNoiocWPYEBGRhBlBREQSZgQREUmYEQHBCxteOnXuHKoh91ln5P6nOk6C1EduEw/vEaz+vD09p9rPoVH94gJyf7Sq/doC6tvWqG0OGNOPOiD3pS7ta4D6uBzS9NJ+7li3i+eknhGJgue4lxmh13+pEf3HSrVEygWV/ltV+2iVqB7bvRljQ7VPcYlqX/FGjbVkVD6o9KEulav2rV7cwDJAvR9cvZxw7B/nmRFkQvWdR6gek6TpVcaekGpKU6FcOq75s+901b7OjcoV5TE2dMqMOt6rlvuzH3UpM/w9lp80vV65lFU8jyAzO2lwRkhUfyuQfhOQckI6z5COe0aNCaR67gQEZhxXPeLYrkK5UWNpGDHuFSDnqXTsNWrsjWKDpq9vHKYyZkRIkPZbqsfFUumwTkSB5HNdtBrwIKqFGUFkDswIMiNmBJE5MCPIjJgRROYQShnRvn17WCyWOo+pU6fqTp+fn19n2rg46dKZubHFhpeiY2KASun6JBEFis910Qrfmgf68loKW8wIInNgRpAZRTEjiEzB57rIjCA/YEYQmUMoZcQ333yDmppLbYl27NiBn/3sZxgzZoz4moSEBOzevdv5f4vF4tViBhtbbHgpISUl2ItARDCgLvJOK/IDZgSROTAjyIyYEUTmEGoZ8cUXX+DWW29FZmYmLBYLVq9e7fa8pmmYM2cOMjIyEB8fj6FDh2LPnj3erx8FBTOCyBxCKSNSUlKQnp7ufKxZswYdO3bE9ddfL77GYrG4vSYtLc2LlQw+ttjw0nnY+mOT+rLz1H+fRLXPayPH2DCqTz7Vfg4B//eX681vCkaOsWFUX7ie+sg1qn9Jo/pC9PScav+69fV3yauzZEYXYatP0v6p0l+rajbo1ePmwrSqfaTrHQNV66B0TFY9XnnKWdU88PcYG0b1oa76vhJpO0jbTaVvdekYr9eQWzUnpHK9ZXGso5HjVhEZpQzenUdIdVcqV/m+p5oTRvXzLTFizCnVcm/OY/ToratqP+qBOH/Qo7KNVcZlAow75hcrTB9u5xFlZWXo1asX7r33Xtx+++11nl+4cCGWLl2K1157DdnZ2XjiiSeQm5uLnTt3hmwXI43RRdhywqjfXIwYrw8IjZwAvDt3UJ1XsMbYMCpL/L2t/Z0PxYrl0nyk378c62uWH8xLa3WJFRsbi9hY6YwdqKysxN/+9jfk5eV5bIVx/vx5tGvXDlarFVdeeSX+8Ic/oFu3boYtd6CEWpYTERmrxoBHAy1fvhw9e/ZEQkICEhISkJOTg3/961/O58vLyzF16lS0bNkSzZo1w6hRo3D8+HEDVpKIiLwSwIwgIqIQE+CMGD58OObPn4+RI0fWeU7TNDz33HN4/PHHcdttt6Fnz554/fXXcfTo0TotO4iIKAAMyoisrCwkJiY6HwsWLPD4tqtXr0ZxcTEmTZokTtO5c2f89a9/xXvvvYe//e1vsFqtGDhwIAoLC31Y4eDghQ0iatwCeELSpk0bPPPMM9i6dSu2bNmCG264Abfddht++OEHAMCMGTPwwQcf4O2338bGjRtx9OhR3buxiIgoQAL8o1V93YxMmjSpzkB/N910k/frR0RE3jMoI0pLS90eFRV6bSU9279/P4qKijB06FBnWWJiIvr374+CggJv15CIiLxlUEYcPnwYJSUlzsfs2bM9vu2rr76K4cOHIzMzU5wmJycHEyZMQO/evXH99dfjn//8J1JSUvCXv/zFlzUOipC5sPH0009j4MCBaNKkCZKSknSnOXToEG655RY0adIEqamp+N3vfofq6mq3aTZs2IArr7wSsbGxuOyyy5Cfn+/V8mgug7IQUfD4XBcD2O/hrbfeiptvvhmdOnXC5ZdfjqeffhrNmjXDpk2bUFJSgldffRWLFy/GDTfcgL59+2LFihX46quvsGnTJt/WsREwW0ZYmRFEpuBzXQxw/+mObkaWLVsmTnPTTTfh2LFjzsff//53xZVqfJgRRKTHLBmhejeunqKiIgCo00d6Wlqa8znSZ7aMqGFGEJmCz3XRoIxw9PjheHjqhurgwYP49NNP8atf/UppUaOjo9GnTx/s3btX6XVmYJYuw+pVWVmJMWPGICcnB6+++mqd52tqanDLLbcgPT0dX331FY4dO4YJEyYgOjoaf/jDHwDY7mK45ZZb8Jvf/AZvvPEG1q9fj1/96lfIyMhAbm6u0vKcKSoKnY1HFMbOmOSLumq/hzU1NXj77bdRVlaGnJwcbN26FVVVVW53WXXp0gVt27ZFQUEBBgwY4LdlDwdmzAj27U8UfGbJiIYaPnw4hg8f7nGa2NhYpKenB2iJwgMzgoj0mCUjDh8+jISEBOf/PZ1DkPHMlhFnmRFEpnDWJBmhYsWKFUhNTcUtt9yi9Lqamhp8//33uPnmm/20ZP4TMr/NP/nkkwAgXvX+5JNPsHPnTnz66adIS0tD79698fvf/x6zZs3CvHnzEBMTg5deegnZ2dlYtGgRAKBr16748ssvsWTJEuWwKYNt40kDgnozYLV0LVB1QB5pABxvBrhWHVRI4uk6p1ED+3kzYLsqb7aTvwdy8kR1W0nl0uDh0r7m6TWqAzdJ+7ljkNtq4fkGqwEgj6dUP832T1ZWllvx3LlzMW/evDqTf//998jJyUF5eTmaNWuGd999F1dccQW2b9+OmJiYOncJ8S6rhjFbRlyALSOk+qySEaqDwup1XiDVR9VB/fTKjRp8TnU9vckV6TWq9+KoDtJq1ACAUjPbYAy2qPodRG8fVJnW0/R6y+JYR7NkhOrFb082bNiA1NRUtGjRAjfccAPmz5+Pli1b+rCQ4S/UMkL1O61UpyuFcr06Y1ROSOdGRlA9dhk1vRH8nQ9Gnj/o0duXjDpHNeqcQWXwcLNlhOMuXF84LngfP34cGRkZzvLjx4+jd+/ePs073JktIy7CVr+k731GHauk796qdVj6NiPlgepxTHV9vfm9SPX8RHV61e/wqtMblQ2qn4G0TaXvH1Ine6rf/UuFctVByKV93fE5miUjGspqtWLFihWYOHEioqLcf+6fMGECWrdu7WwR+NRTT2HAgAG47LLLUFxcjGeffRYHDx5UbulhBiHTFVV9CgoK0KNHD7eml7m5uSgtLXX2X19QUOB2N7RjGk99TlZUVNTp8xIAWvJOOSJT8LkuBrjfw86dO2P79u3YvHkz7r//fkycOBE7d+70bR2oXswIosbJLBlhRDcjgK0bqtdffx3r16/HH//4R2zcuBHDhw9ntxU+YkYQNU5myQgjZGdnIz09HevXr3eWlZaWYvPmzcjJyTHujRohZgRR4xRqGfHpp5/i0KFDuPfee+s8d+jQIRw7dsz5/7Nnz+K+++5D165dcfPNN6O0tBRfffUVrrjiCtW1DLqQabFRn6KiIt3+JB3PeZqmtLQUFy9eRHx8fJ35LliwwHkF31VEpL/viyGihvC5LmpQvhKup6F3WsXExOCyyy4DAPTt2xfffPMN/vznP2PcuHGorKxEcXGxW6uN48ePs8sRAzAjiBons2SEUd2M3HHHHc6/e/TogZ49e6Jjx47YsGEDbrzxRp+Xs7FiRhA1TmbJiIY6f/68W//n+/fvx/bt25GcnIy2bdti+vTpmD9/Pjp16oTs7Gw88cQTyMzMxIgRIwK3kGGIGUHUOIVaRgwbNgyapv+GGzZscPv/kiVLsGTJkgAslf8FtcXGI488AovF4vGxa9euYC4iZs+e7XYX9uHDh4O6PEQUXqxWKyoqKtC3b19ER0e73WW1e/duHDp0qNHeZcWMIKJwoTLon4oOHTqgVatWITnQn6+YEUTU2GzZsgV9+vRBnz59AAB5eXno06cP5syZAwB4+OGH8cADD2DKlCm4+uqrcf78eaxduxZxcVKncuGLGUFE1DgEtcXGzJkzMWnSJI/TdOjQoUHzSk9Px9dff+1Wdvz4cedzjn8dZa7TJCQk6F5BB+Q+kCth6x/Omz5aVfv4DqUxNlT7GvT0GqP6xfVmvBOj+jT09Bqj+lP0xKgxNqR9TSoHjOkj3VO543OV+m9sKF9bgau8dvbs2Rg+fDjatm2Lc+fOYeXKldiwYQM+/vhjJCYmYvLkycjLy0NycjISEhLwwAMPICcnp9EOHB7KGXERtn1dtW9cvXLVvtNVxkMwYgwgo+6QMHKMDaP6yZUYdfw2aowNVarbQe8zUM0LvUxQ/R6jkhOOdfT1p5xAZoQ3CgsLcfr0abf+1BuLUM6ICtj2W3/3J67Sf7pRY2yoHu8kKnloVB/o/hyHyajcMCpnJCrbwKjzBqP6XNebvr7vENJ7N1SgM2Lw4MHi3bgAYLFY8NRTT+Gpp57yYanCQyhnxAXY9mcjxurzNL1RvzOpjrHh7zHjJEaOsaE6H9V1M/L3KJX5SFS3j+oYGyrnD4B/x+kDLq2vr7chmf08IlwE9cJGSkoKUlJSDJlXTk4Onn76aZw4cQKpqakAgHXr1iEhIcHZR1hOTg4++ugjt9etW7eu0d4NTUSBDZsTJ05gwoQJOHbsGBITE9GzZ098/PHH+NnPfgbA1hwwIiICo0aNQkVFBXJzc/Hiiy/6sHShjRlBRMEW6BMST92MJCcn48knn8SoUaOQnp6Offv24eGHH8Zll12mPDBpOGBGEFGw8Ucr82JGEFGwMSMCI2TG2Dh06BDOnDmDQ4cOoaamBtu3bwcAXHbZZWjWrBmGDRuGK664AnfffTcWLlyIoqIiPP7445g6darzKvhvfvMbvPDCC3j44Ydx77334rPPPsOqVavw4YcfKi9P8cmTRq4eEXnJ17pohXetelxf31Cvvvqqx+fj4uKwbNkyLFu2zIclapyYEUSkJ5QyArB1MzJkyBDn//Py8gAAEydOxPLly/Hdd9/htddeQ3FxMTIzMzFs2DD8/ve/N6xrq3DFjCAiPaGWEeQfzAgi0sOMCA0hc2Fjzpw5eO2115z/d/Qr+fnnn2Pw4MGIjIzEmjVrcP/99yMnJwdNmzbFxIkT3ZphZmdn48MPP8SMGTPw5z//GW3atMH/9//9f17d5VZVWelV10BEZKyqSl87o6JwwIwgIj2hlhH1dTPy8ccfB3BpwofZMqKysjJ0TsKIwlhliGUE+Qczgoj0MCNCg0XzdPZEdZSWliIxMRH9EhJwsbRU7PtO6k8QkPtpayqUq/Ztqzo9oN6Pq8Sb/suN6uc2EFczvekP0t996npiRD/qgNz3oKd+KlX7SZT6W5SixLFu8QkJ+Lq0FCUlJUhISPCwRO4cdfkogIa/Smc+ADIB5fen8OTYrwbYM0IiZYRePkjHdJVyo/pI1zsO+bs/XG9yxdOxSXVeelTX2Yjt7Im/+8n1V9/qRvXD7umUIz4hAZuYEWQStTNCta9sI/JAKlfNCX8f1/SOR/4ee8SocwyVrDSqr/lgnMv5e8w+I8rrG2ODGUFm4tivBiYkoNzDeYTqMb+JQfPxd074+5zCyN+kVN/D38d0o+YvMWpcXH/ng1HjuTrEJSTgK2aE6Rk1DmWj07R582AvAhHB97poNeBBVBszgsgcmBFkRswIInNgRpAZMSOIzIEZERrYwo2IGjUO6ERERBJmBBERSZgRREQkYUYEBltsEBERERERERERERFRyGCLDS9VwdancyAGh5X6oZP6OJSm93QVK1j9HHp6jb/7v/WGN30aBqsfRMC4cUpU+0IE5D7PVftIr69vXNW+9Guzwrcr4WweSHoqYKsfRmSEtI+p1FeprhrR961Rd0gYNd6Sp3lJ/D3GhmoOGDnWkh4jtrVq37kq+6U0D5WccCyzlDkNxYwgf6iGbb+t7ztOQ6keJ/XKpfoljQll1PdbIxh1LqE6f4nKNvB3P+pG0dsG0vZV/U6v2ve5ynzqO0+oruf5+jAjyB8qYdx5hCdGjYMgnU+o5odE2g5GjdvnzbyMupM+WOMp+XubquaAUWO9GpUnju3g69DhzIjA4IUNL128IA1LQ0SB5Gtd9LXvQoYN6WFGEJkDM4LM6AIzgsgUfK2LzAjyB2YEkTkwI0IDL2x4qeTs2aDdMUNEl5ScPevT69nvIfkDM4LIHJgRZEalzAgiUyhlRpAJMSOIzIEZERo4xoaXoiIZNURmwLpIZsT9ksgcWBfJjLhfEpkD6yKZEfdLInNgXQwNvLDhpVbp6cFeBCKC73WxxoAHUW3MCCJzYEaQGTEjiMyBGUFmxIwgMgdmRGhgV1Recgz4582gdEYNii1N782AQkYNHi7xtD2MGgw8EJXem+1h1KCx3jBq8HBpek8D8hk1KGx9y2TE4OHs95CM5m1G6E2vWo/1BvCTBu9THTxcT7AGtPbEqFxRZdRgr8EaPFyiMni4ymCBqgMIquQEM4LMzNPA4d6Q9jOpXO+9jRr8NRQGD1edjxFUt4uZti/g38HDjcoClZxp6PP1YUaQPzgyQvUcQCJNH6c4vWoeGHGeYSRvfqMz6j2MWudQyAZAPrYalRtGDSpe3/cHZkRo4IUNImrU2O8hERFJmBFERCRhRhARkYQZERjsioqIiIiIiIiIiIiIiEIGW2x4S9OCvQREBPhcF9k8kPyCGUFkDswIMiGNGUFkCr7WRWYE+QMzgsgcmBGhgRc2vHTs6FFEQN7RAtEURurvTerL0JtlCsQYG/7u/9abg4HqtgrE2BveMGpbSfuap/kb0S8uUP/nd+zo0Xqm8MwK35r4MWxITyAyQqVPcdX+TFWOQ6rHLKP6BfbE37liVF/pRs0/GJ+Bal7o7WtG9bPr6fNmRpAZHbFnhCqjxkjTK1ftJ9vfx0E9/j62m2mMjWBsX0BtG6v2/+/vMTb0lr2+/tGPMCPIhBznEf4eP0iaj/R7kmoehMp4EN4wqu4adew22zY1auxWo35T8ub3LIDnEaGCFzaIqFFjv4dERCRhRhARkYQZQUREEmZEYHCMDS+1Sk0N9iIQEVgXyZy4XxKZA+simRH3SyJzYF0kM2rJ/ZLIFFgXQwNbbHgpKlpqoEdEgeRrXWS/h+QPzAgic2BGkBkxI4jMgRlBZsSMIDIHZkRo4IUNL1XB1q+bN33Z1dfXZ21S8yPpvb1prhSsPvkAc/V/6y1vtl8obXNv+iRU7T/R2/6hVeuT3nzYPJCM5m1G6O3Pqv3f6k1v1BgSwWjmqXps8Ia/x9hQnU+wmtMasa1V8kI1W1SWjxlBZubtfmXUsUGlPpqp73Sj6pOZfigIlf7VVcZaUh17w5/jLdW3z/i6TzEjyB+qYdvPzTZuguoYrxKzfc/1hlF116jPOFS2qep3f6PG5PD23KJaeL6hmBGBwQsbRNSoMWyIiEjCjCAiIgkzgoiIJMyIwOAYG0REREREREREREREFDLYYsNLZ0+fDvYiEBF8r4vs95D8gRlBZA7MCDIjZgSROTAjyIyYEUTmwIwIDbyw4aXy8nI2dyEygfLycp9ez+aB5A/MCCJzYEaQGVUwI4hMoYIZQSbEjCAyB2ZEaOCFDS81adYMF86f92qgTdUBgowamM3T+0qD7BgVqN5caTTjoOLSNvQ0OKnqa4z8EqO63Y0a7M/Ta4wa0MmhSbNmwPnz9Uwl0xrwHvW9nqg2bzPCiAHkPA2k3ND3C8TxqaECkR+qVAekDoXtDKhtayPyIhA5wYwgM4q1Z4RRdV1lMGVAf6BXaVrV45SZBg8P5TsdVfcNMw0erjIPwPjBXlXmwYwgM4pr1gwXPeyXRn3PVT0/kAYJr1ScTzCOV/54TTCobrtgbWvV7enPHPA0f4ljPvHMiJDAC8FeSkhMDPYiEBFYF8mcuF8SmQPrIpkR90sic2BdJDPifklkDqyLoYEtNoioUWPzQCIikjAjiIhIwowgIiIJMyIwQqLFxoEDBzB58mRkZ2cjPj4eHTt2xNy5c1FZ6d7o7bvvvsOgQYMQFxeHrKwsLFy4sM683n77bXTp0gVxcXHo0aMHPvroo0CtBhGZUI0BDwouZgQR+QszIvQxI4jIX5gRoY8ZQUT+wowIjJBosbFr1y5YrVb85S9/wWWXXYYdO3bgvvvuQ1lZGf70pz8BAEpLSzFs2DAMHToUL730Er7//nvce++9SEpKwpQpUwAAX331FcaPH48FCxbg5z//OVauXIkRI0Zg27Zt6N69u9IyBXInk/pkk65KeTPuhxkZ1S+uN+st9UVoxr7ejaS673gzxoZR/SQ29Pn6WD28d0NfT8HVWDNCJRv8mQuqfbcatV1Cqe75eyyNYHwGRuRFIHKCGUFmzAgHaf/w9/hMevOR5qE6loZqP9ZGCOWxN4waS8NM292o7xxGj8vnD8yI0GfGjPC1X/7a/P17kupxyahxH4w81wqV32yM2nbB+gyMyg2jxiGWOObv6xgXzIjAsGiaFpLjkTz77LNYvnw5fvrpJwDA8uXL8dhjj6GoqAgxMTEAgEceeQSrV6/Grl27AADjxo1DWVkZ1qxZ45zPgAED0Lt3b7z00ksNet/S0lIkJiaiU3Iyis+cEafzdKBQPUEw6kQjJJrnuDDjhQ1vhNJ2D8ULG0nJydhz5gxKSkqQkJBQz9SXOOryhwCaNvhVdZUBuAVQfn/yr1DNCL3jhRHZ4M9c4IUN7/HChu/z8DQ9wIwgfcHOiC71ZIRE9bu/NNCryjyCMUi4Kl7YCI5wuLCRlJyMXcwIqsUsGWHU8cHfvycFa0BrXtgI/nzC/cIGMyI0hNJvrm5KSkqQnJzs/H9BQQGuu+46Z9AAQG5uLnbv3o2zZ886pxk6dKjbfHJzc1FQUCC+T0VFBUpLS90eAHDGi5MRIjKer3WRzQPDEzOCiABmBOljRhARwIwgfcwIIgJCKyPmzZsHi8Xi9ujSpYvH14RL93kheWFj7969eP755/HrX//aWVZUVIS0tDS36Rz/Lyoq8jiN43k9CxYsQGJiovORlZUFAIiwWAxZFyLyja91kSck4YcZQUQOzAiqjRlBRA7MCKqNGUFEDqGWEd26dcOxY8ecjy+//FKc1tF93uTJk/Htt99ixIgRGDFiBHbs2KH4rsEX1AsbjzzySJ0rSrUfjqZ9DkeOHMFNN92EMWPG4L777vP7Ms6ePRslJSXOx+HDhwEAKZmZHncyTzumVXio7uDSfKr4cHtI28nTI9jLbLaHUfusN/u/xPF8Smamh6nq583+UfvRUAsWLMDVV1+N5s2bIzU1FSNGjMDu3bvdpikvL8fUqVPRsmVLNGvWDKNGjcLx48d9WsdQ1Rgzwoh6FujjQ7niI9jHs3B8hMJnEOicCMWMIDXhkBGq331U64ZUH4347uvv44sRx7QK4RHsY7Yvyxis470R81Y971LNCD315Q0zInyFckak2/dL1X0pWL8nqR5/VI9jgZi/6jHa3w9/Z0CwPgOjcsOofb2+43J6iGVEVFQU0tPTnY9WrVqJ0/75z3/GTTfdhN/97nfo2rUrfv/73+PKK6/ECy+8oPiuwRfUwcNnzpyJSZMmeZymQ4cOzr+PHj2KIUOGYODAgXj55ZfdpktPT6/z45/j/+np6R6ncTyvJzY2FrGxsfWuCxFRfTZu3IipU6fi6quvRnV1NR599FEMGzYMO3fuRNOmTQEAM2bMwIcffoi3334biYmJmDZtGm6//Xb85z//CfLSBx4zgogamy+++ALPPvsstm7dimPHjuHdd9/FiBEjnM9rmoa5c+filVdeQXFxMa655hosX74cnTp1Ct5CBwkzgoiIJMwIIgoXjm7qHKRjx549e5CZmYm4uDjk5ORgwYIFaNu2re48CwoKkJeX51aWm5uL1atXG7bcgRLUCxspKSlISUlp0LRHjhzBkCFD0LdvX6xYsQIREe6NTXJycvDYY4+hqqoK0dG2ofLWrVuHzp07o0WLFs5p1q9fj+nTpztft27dOuTk5BizQkQUchxX/H15fUOtXbvW7f/5+flITU3F1q1bcd1116GkpASvvvoqVq5ciRtuuAEAsGLFCnTt2hWbNm3CgAEDfFjS0MOMIKJgC2RGAEBZWRl69eqFe++9F7fffnud5xcuXIilS5fitddeQ3Z2Np544gnk5uZi586diIuL82FJQw8zgoiCLdAZQQ3HjCCiYDMqIxzd1DnMnTsX8+bNcyvr378/8vPz0blzZxw7dgxPPvkkBg0ahB07dqB58+Z15u1N93lmFRJjbBw5cgSDBw9G27Zt8ac//QknT55EUVGR2wb/5S9/iZiYGEyePBk//PAD3nrrLfz5z392uwL129/+FmvXrsWiRYuwa9cuzJs3D1u2bMG0adOCsVpEZAJGNQ+sPfBbRUVFve9dUlICAM7B6bZu3Yqqqiq3gee6dOmCtm3behx4rrFjRhCRvwS6Cfnw4cMxf/58jBw5ss5zmqbhueeew+OPP47bbrsNPXv2xOuvv46jR4+G5N1VgcKMICJ/YVdUoY8ZQUT+YlRGHD582K3butmzZ9d5r+HDh2PMmDHo2bMncnNz8dFHH6G4uBirVq3y70qaQFBbbDTUunXrsHfvXuzduxdt2rRxe07TNABAYmIiPvnkE0ydOhV9+/ZFq1atMGfOHEyZMsU57cCBA7Fy5Uo8/vjjePTRR9GpUyesXr0a3bt393rZfLn65ivpvSMVp/f0mkAI5jYMBNXPKRBUt7k3n5H0Rd1Tf+jBUF9/1g15PdCwq+iurFYrpk+fjmuuucZ5DCoqKkJMTAySkpLcpg3VK+eB0lgzQqpjKncsqC6fP49bgTgG+Ps9VLePmbY/YMz2keah8uONmXLCqIxoaBNyT/bv34+ioiK3i9+JiYno378/CgoKcMcdd/iwpOHLjBnh637lK733Vj1/kKY36odaf24fM517eHPepsKfP5z7+3xC9VzCm2l93c5GZQQFTyhmhFHHX6N+T/J3HkiMrD9GHZ9Uqd5prnouUGXQfIwSrNzwdv5myYiEhAQkJCQovTYpKQmXX3459u7dq/u8N93nmZVFcxytqUFKS0uRmJiIFgAsXs5DOohI5f4+2Hn7GqM01i90obTNzXxhQwNwFrbWDyoHe0ddXgmgiZfvDQAXAPwStqvoru9f349W999/P/71r3/hyy+/dH6JXrlyJe655546rT369euHIUOG4I9//KMPS0qB4GtG6B0XVI8Veplh1PGGFzY88/dxnRc21JklI2qr7+I3AFgsFrcxNr766itcc801OHr0KDIyMpzTjR07FhaLBW+99ZYPS0qB0NCMUK3rRpxDGPWeRmnsFzZCYf7hcGHDLBmh+v4Unhz7VSsYmxH+/j0pVH4kN3JeoXJhw9/zURVqFzY0AKcQmhlx/vx5tG3bFvPmzcODDz5Y5/lx48bhwoUL+OCDD5xlAwcORM+ePfHSSy/5sNSBFxItNoiI/CUYV9GnTZuGNWvW4IsvvnC7Myg9PR2VlZUoLi52a7URqlfOiYhCnVEZoXfxm4iIQhtbbJA/aPD+JloiMo6vrQACmREPPfQQbr31VrRr1w5Hjx7F3LlzERkZifHjxwMAJkyYgNatW2PBggUAbN3nXX/99Vi0aBFuueUWvPnmm9iyZQtefvllH5Y4OEJijA0zSm7ZMtiLQETwvS4Gsm9cTdMwbdo0vPvuu/jss8+QnZ3t9nzfvn0RHR2N9evXO8t2796NQ4cOceC5EMOMIDIHs2SE4+K34+HNhQ3HBe5waTbemDEjiMzBLBlB5KolM4LIFHyti4HMiMLCQowfPx6dO3fG2LFj0bJlS2zatAkpKSkAgEOHDuHYsWPO6R3d57388svo1asX3nnnHZ+74Q4WttjwUmxcXLAXgYjge10M5FX0qVOnYuXKlXjvvffQvHlz57gZiYmJiI+PR2JiIiZPnoy8vDwkJycjISEBDzzwAHJycjBgwAAflpICjRlBZA6hlBH1yc7ORnp6OtavX4/evXsDsDV137x5M+6//34D34n8jRlBZA7hlBEUPpgRROYQShnx5ptvenx+w4YNdcrGjBmDMWPGqC2UCfHChpc0eL565qkpjBm/wJhxmYzizZ0wgWjKFErb3JttaFQf6fW9dygNErR8+XIAwODBg93KV6xYgUmTJgEAlixZgoiICIwaNQoVFRXIzc3Fiy++GOAlJV95mxH+Oi4YMdA4YK7jlhnvclRdpsa0/Y3sE702T8sSShkB2PrDdR3kb//+/di+fTuSk5PRtm1bTJ8+HfPnz0enTp2QnZ2NJ554ApmZmc5xOCg8GDWgqz+F8vFI4s91MmpwXeaGcdMThapwHsRbhZHrFazjjepYF6GQd0ZqbOtL3uGFDSJq1AJ5FV3T6v+JLS4uDsuWLcOyZcu8XygiIjJEoO/G3bJlC4YMGeL8f15eHgBg4sSJyM/Px8MPP4yysjJMmTIFxcXFuPbaa7F27VrE8e5OIqKAY4sNIiKSMCMCgxc2iKhRq+/O+oa8noiIwlOgM2Lw4MEeL4JbLBY89dRTeOqpp3xYKiIiMgLPI4iISMKMCAxe2PBSSXFxsBeBiOB7XeRVdPIHZgSROTAjyIxKmRFEpuBrXWRGkD/wPILIHHgeERp4YcNLZWVlsHh43kx9qzd23K6+82YbGj2WhqSsrMzLVxL5j7cZoZcPodDXejA0lvU0KyO2fyByghlBZnSunowIxrmCatYEg1Hrb6YxSVS3Lz8nNfXlxjlmBJlQWVmZV2N+qtaZYI3JoSqYxxSjxgZS3daq3wPCMUs8CVSO8zwiNARijOSwFB8fH+xFICL4XhetBjyIamNGEJkDM4LMiBlBZA7MCDKjJswIIlPwtS4yIwKDFza81CI5OdiLQETwvS7WGPAgqo0ZQWQOzAgyI2YEkTkwI8iMkpgRRKbga11kRgQGu6IiokaN/R4SEZGEGUFERBJmBBERSZgRgcELG16qbwf11DedUf3okf9wm7szcnsY1U9lQ58nCgZHRqj2U+qv5qbhOB5HKC97OFP5XPzZvLqm1r9EoUT1XEGisv+HQk4YtSzs2iE4/L0v8XOlcOHteYQ37xPKAjFWW6DGDQ20UMj8YOJ5RGjhhQ0iatR87bswVL68EBGROmYEERFJmBFERCRhRgQGL2x4qbKiItiLQETwvS6yeSD5AzOCyByYEWRGFcwIIlPwtS4yI8gfeB5BZA48jwgNvLDhpVOnTsES7IUgIpw6dcqn11vhW2DwKjrpYUYQmQMzgszoNDOCyBROMyPIhJgRRObAjAgNql21EhERERERERFRgMybNw8Wi8Xt0aVLl2AvFhERUVCxxYaXMlu3xrEjR8TnvRlYXPVqXCAGGzdqwCo2oWo4IwcJC6VB+rxd1szWrfE/D3WxPuz3kPzBkRGq+7VK/VcZXJbHYBt/11d/Duwb6ozY9t5sL2YEmVF95xESo84V9ITy8SiU65kZz//MIhCfa2br1igJoYzo1q0bPv30U+f/o6L4c044ymjdGkV+Po8wo3A4joXKnePctxomo3VrnA2hjGismIRE1Kix30MiIpIwI4iISBLojIiKikJ6eroP70hERIHC84jA4IUNImrUeBWdiIgkzAgiIpIYlRGlpaVu5bGxsYiNja0z/Z49e5CZmYm4uDjk5ORgwYIFaNu2rQ9LQERE/sLziMAIlZZSRERERERERERhJSsrC4mJic7HggUL6kzTv39/5OfnY+3atVi+fDn279+PQYMG4dy5c0FYYiIiInNgi40gkJoTqfZb589+dh3M2PTJjFctjbxCGMxtHohta7Z9is0DyUz09id/Z4OeYNz1YMZjuyqzjeFhlEB/NmY6rjIjKJSp1F2z3e1mpkwwU1/kgTj/8xczfaZGMSojDh8+jISEBGe5XmuN4cOHO//u2bMn+vfvj3bt2mHVqlWYPHmyD0tB4cJMxyoz8nQ8lI5PZttGZjqmB1OofL/meURg8MKGl04UFQV7EYgIvtdFhg35AzOCyByYEWRGx5kRRKbga100KiMSEhLcLmw0RFJSEi6//HLs3bvXhyUgM+J5BJE58DwiNPCCn5eqa7iLEZmBr3XRasCDqDZmBJE5MCPIjGqYEUSm4GtdDGZGnD9/Hvv27UNGRoYPcyEzYkYQmUMoZ0RjwgsbXmrRokWwF4GIwLpI5sT9ksgcWBfJjLhfEplDKNXFhx56CBs3bsSBAwfw1VdfYeTIkYiMjMT48eODvWhksFDaL4nCGetiaAiZCxu/+MUv0LZtW8TFxSEjIwN33303jh496jbNd999h0GDBiEuLg5ZWVlYuHBhnfm8/fbb6NKlC+Li4tCjRw989NFHXi1PfJMmXr3OkxrhYRQjrhaa4WFGwd4mZty2/t6fHXyti1bIy9qQh1n3ycYmnDNCdZ80Qqgff8JVKGdDoPdhB2YEAebLiJgmTUzXvUCwv4OaORMiFR9mEuzP0ayfqeMYHRNCGVFYWIjx48ejc+fOGDt2LFq2bIlNmzYhJSXFp3Ug82VEnB9+a9ITjHMMs4oIkUe4Muu+6Gtd5HlEYIRM3RgyZAhWrVqF3bt34x//+Af27duH0aNHO58vLS3FsGHD0K5dO2zduhXPPvss5s2bh5dfftk5zVdffYXx48dj8uTJ+PbbbzFixAiMGDECO3bsCMYqEZEJ+BI0jeFLXqhgRhCRPzAjwgMzgoj8IZAZ8eabb+Lo0aOoqKhAYWEh3nzzTXTs2NGwdWnMmBFE5A88jwgMi6ZpWrAXwhvvv/8+RowYgYqKCkRHR2P58uV47LHHUFRUhJiYGADAI488gtWrV2PXrl0AgHHjxqGsrAxr1qxxzmfAgAHo3bs3XnrppQa9b2lpKRITE3F569Y4duSI8Sumw2x3/hA1RKAOwhmtW+N/R46gpKREadA9R13OAxDrw/tXAFgMKL8/+VdjyojamBlkRsH6Ys6MID1myQger4mCw5FJzAjSE+yMuKJ1axQF6TzCE39nFn/ENa/G+tmnt26NncwI0wuZFhuuzpw5gzfeeAMDBw5EdHQ0AKCgoADXXXedM2gAIDc3F7t378bZs2ed0wwdOtRtXrm5uSgoKBDfq6KiAqWlpc5HSUkJAKDGaoUG8MEHH0F+1FhtDfRC9Bot+QEzgg8++HA8mBFUGzOCDz74cDyYEVQbM0J++Lu7umCvHx/87Gs/mBGhISrYC6Bi1qxZeOGFF3DhwgUMGDDA7Wp4UVERsrOz3aZPS0tzPteiRQsUFRU5y1ynKSoqEt9zwYIFePLJJ+uU7zt2zJdVISKDnLfXxXPnziExMVH59b428TPr3QWNETOCiGpjRpADM4KIamNGkIOZMmI3M4LIFM4yI0JCUC9sPPLII/jjH//ocZoff/wRXbp0AQD87ne/w+TJk3Hw4EE8+eSTmDBhAtasWQOLxeK3ZZw9ezby8vKc/7darThz5gxatmzp9r6lpaXIysrC4cOHw6qJULiuFxC+6xau6wXor5umaTh37hwyMzO9mqevAxpyQCf/YUaYX7iuFxC+6xau6wUwIxobZoT5het6AeG7buG6XgAzorFhRphfuK4XEL7rFq7rBTAjQllQL2zMnDkTkyZN8jhNhw4dnH+3atUKrVq1wuWXX46uXbsiKysLmzZtQk5ODtLT03H8+HG31zr+n56e7vxXbxrH83piY2MRG+veK1pSUpI4fUJCQthVcCB81wsI33UL1/UC6q6bN1fPHXgV3byYEaEjXNcLCN91C9f1ApgRjQUzInSE63oB4btu4bpeADOisWBGhI5wXS8gfNctXNcLYEaEoqBe2EhJSUFKSopXr7Xa+zqrqKgAAOTk5OCxxx5DVVWVsy/EdevWoXPnzmjRooVzmvXr12P69OnO+axbtw45OTk+rAUREfkDM4KIiCTMCCIikjAjiIgah5AYPHzz5s144YUXsH37dhw8eBCfffYZxo8fj44dOzqD4pe//CViYmIwefJk/PDDD3jrrbfw5z//2a1p329/+1usXbsWixYtwq5duzBv3jxs2bIF06ZNC9aqEVGQ1RjwoOBiRhCRvzAjQh8zgoj8hRkR+pgRROQvzIjACIkLG02aNME///lP3HjjjejcuTMmT56Mnj17YuPGjc6me4mJifjkk0+wf/9+9O3bFzNnzsScOXMwZcoU53wGDhyIlStX4uWXX0avXr3wzjvvYPXq1ejevbvPyxgbG4u5c+fWaUoY6sJ1vYDwXbdwXS/AP+um4VLfh948NMOWhLzFjAiecF0vIHzXLVzXC2BGkD5mRPCE63oB4btu4bpeADOC9DEjgidc1wsI33UL1/UCmBGhzKJpGrcVETU6paWlSExMxGQAMT7MpxLAqwBKSkrCtp9JIqLGhhlBREQSZgQREUmYEYEVEi02iIiIiIiIiIiIiIiIAF7YIKJGjv0eEhGRhBlBREQSZgQREUkCmRELFizA1VdfjebNmyM1NRUjRozA7t27Pb4mPz8fFovF7REXF6e2kibACxtE1Kj50ueh46Hiiy++wK233orMzExYLBasXr3a7XlN0zBnzhxkZGQgPj4eQ4cOxZ49e7xePyIi8l4gM2LevHl1Ti66dOli2LoQEZGxAn0eQUREoSOQGbFx40ZMnToVmzZtwrp161BVVYVhw4ahrKzM4+sSEhJw7Ngx5+PgwYNqK2kCUcFeACKiYPL1binV15aVlaFXr1649957cfvtt9d5fuHChVi6dClee+01ZGdn44knnkBubi527twZklfPiYhCWaAzolu3bvj000+d/4+K4ld1IiKzCnRGEBFR6AhkRqxdu9bt//n5+UhNTcXWrVtx3XXXia+zWCxIT0/3cgnNgS02FD399NMYOHAgmjRpgqSkJN1pDh06hFtuuQVNmjRBamoqfve736G6utptmg0bNuDKK69EbGwsLrvsMuTn5/t/4RW1b9++zp2DzzzzjNs03333HQYNGoS4uDhkZWVh4cKFQVpaNcuWLUP79u0RFxeH/v374+uvvw72Iimr787O8vJyTJ06FS1btkSzZs0watQoHD9+PIhLrM+IFgxnzpzBnXfeiYSEBCQlJWHy5Mk4f/58ANei4YYPH4758+dj5MiRdZ7TNA3PPfccHn/8cdx2223o2bMnXn/9dRw9erTOdiFzYkYwI8yCGXFJKGVEVFQU0tPTnY9WrVoFe5HIQMwIZoRZMCMuCaWMoPDGjGBGmAUz4hIzZERpaanbo6Kiot7XlJSUAACSk5M9Tnf+/Hm0a9cOWVlZuO222/DDDz8YssyBxAsbiiorKzFmzBjcf//9us/X1NTglltuQWVlJb766iu89tpryM/Px5w5c5zT7N+/H7fccguGDBmC7du3Y/r06fjVr36Fjz/+OFCr0WBPPfWUW7OkBx54wPlcaWkphg0bhnbt2mHr1q149tlnMW/ePLz88stBXOL6vfXWW8jLy8PcuXOxbds29OrVC7m5uThx4kSwF01Zt27d3D6fL7/80vncjBkz8MEHH+Dtt9/Gxo0bcfToUd0WAsHmaMGwbNky3ecdLRheeuklbN68GU2bNkVubi7Ky8ud09x555344YcfsG7dOqxZswZffPEFpkyZ0qD3N6p5oDdhU9v+/ftRVFSEoUOHOssSExPRv39/FBQUKM+PAo8ZwYwwE2aETShlxJ49e5CZmYkOHTrgzjvvxKFDhxq0nBQamBHMCDNhRtiYISOIAGYEM8JcmBE2ZsiIrKwsJCYmOh8LFizw/L5WK6ZPn45rrrkG3bt3F6fr3Lkz/vrXv+K9997D3/72N1itVgwcOBCFhYUNWj/T0MgrK1as0BITE+uUf/TRR1pERIRWVFTkLFu+fLmWkJCgVVRUaJqmaQ8//LDWrVs3t9eNGzdOy83N9esyq2rXrp22ZMkS8fkXX3xRa9GihXO9NE3TZs2apXXu3DkAS+e9fv36aVOnTnX+v6amRsvMzNQWLFgQxKVSN3fuXK1Xr166zxUXF2vR0dHa22+/7Sz78ccfNQBaQUFBgJZQHQDt3Xffdf7farVq6enp2rPPPussKy4u1mJjY7W///3vmqZp2s6dOzUA2jfffOOc5l//+pdmsVi0I0eOiO9VUlKiAdDGAtpdPjzGAhp0HnPnzlVe3//85z8aAO3o0aNu040ZM0YbO3ZsvfMj82BGMCOCjRkRehnx0UcfaatWrdL++9//amvXrtVycnK0tm3baqWlpb5vPDIVZgQzItiYEebKiJKSEuM3CIUsZgQzItiYEebKiMOHD2slJSXOR3l5ucd1/c1vfqO1a9dOO3z4sNI2qqys1Dp27Kg9/vjjSq8LNrbYMFhBQQF69OiBtLQ0Z1lubi5KS0udTXoKCgrc7sh2TGPGO7KfeeYZtGzZEn369MGzzz7r1syxoKAA1113HWJiYpxlubm52L17N86ePRuMxa1XZWUltm7d6rb9IyIiMHToUFNu//pId3Zu3boVVVVVbuvZpUsXtG3bNqTWsyEtGAoKCpCUlISrrrrKOc3QoUMRERGBzZs31/seVlzq+9Cbh+Mq+uHDh1FSUuJ8zJ4926d1p/DEjGBGBBIzIrQyYvjw4RgzZgx69uyJ3NxcfPTRRyguLsaqVavUNgyFLGYEMyKQmBHmyQiihmBGMCMCiRlhnoxISEhwe8TGxorvOW3aNKxZswaff/452rRp07CNYRcdHY0+ffpg7969Sq8LNo5IaLCioiK3oAHg/H9RUZHHaUpLS3Hx4kXEx8cHZmHr8eCDD+LKK69EcnIyvvrqK8yePRvHjh3D4sWLAdjWIzs72+01ruvaokWLgC9zfU6dOoWamhrd7b9r164gLZV3+vfvj/z8fHTu3BnHjh3Dk08+iUGDBmHHjh0oKipCTExMnb4509LSnPthKHAsq97n5VqfUlNT3Z6PiopCcnJyQNfVETK+cAzadPz4cWRkZDjLjx8/jt69e/s0bzIHZgQzIlCYEaGfEUlJSbj88stD7uSCvMeMYEYECjPCXBlB1BDMCGZEoDAjQi8jNE3DAw88gHfffRcbNmyoU38aoqamBt9//z1uvvlmPyyh/7DFBoBHHnmkzsA4tR+hdiCSqKxrXl4eBg8ejJ49e+I3v/kNFi1ahOeff96rsQPIeLyz0xhm6hs3Ozsb6enpWL9+vbOstLQUmzdvRk5OjoHvRCqYEcyIUMSMMEYwM+L8+fPYt2+f24VuMh9mBDMiFDEjjGGm8wgyJ2YEMyIUMSOMEciMmDp1Kv72t79h5cqVaN68OYqKilBUVISLFy86p5kwYYJbq/GnnnoKn3zyCX766Sds27YNd911Fw4ePIhf/epXPqx14LHFBoCZM2di0qRJHqfp0KFDg+aVnp6Or7/+2q3s+PHjzucc/zrKXKdJSEjw+xV0X9a1f//+qK6uxoEDB9C5c2dxPYBL62o2rVq1QmRkpO5ym3WZG8r1zs6f/exnqKysRHFxsduV9FBbz4a0YEhPT68zGFd1dTXOnDnToHWtgW9XeGsUpz9//rzb3bf79+/H9u3bkZycjLZt22L69OmYP38+OnXqhOzsbDzxxBPIzMzEiBEjfFhK8gUzwh0zIjQxIy4xa0Y89NBDuPXWW9GuXTscPXoUc+fORWRkJMaPH+/DEpC/MSPcMSNCEzPiErNmBIUmZoQ7ZkRoYkZcYtaMWL58OQBg8ODBbuUrVqxw1stDhw4hIuLSEp09exb33XefsxVU37598dVXX+GKK67wYakDjxc2AKSkpCAlJcWQeeXk5ODpp5/GiRMnnE2W1q1bh4SEBOfOkZOTg48++sjtdevWrQvIHdm+rOv27dsRERHhXK+cnBw89thjqKqqQnR0NADbenTu3NmUTQMBICYmBn379sX69eudPxRbrVasX78e06ZNC+7C+chxZ+fdd9+Nvn37Ijo6GuvXr8eoUaMAALt378ahQ4dC6s5/1xYMjnBxtGC4//77Adj2w+LiYmzduhV9+/YFAHz22WewWq3o379/ve8R6BOSLVu2YMiQIc7/5+XlAQAmTpyI/Px8PPzwwygrK8OUKVNQXFyMa6+9FmvXrkVcXJwPS0m+YEY0DDPC3JgR5s+IwsJCjB8/HqdPn0ZKSgquvfZabNq0ybDjD/kHM6JhmBHmxowwf0ZQaGJGNAwzwtyYEebPCE3T6p1mw4YNbv9fsmQJlixZorZQZhTs0ctDzcGDB7Vvv/1We/LJJ7VmzZpp3377rfbtt99q586d0zRN06qrq7Xu3btrw4YN07Zv366tXbtWS0lJ0WbPnu2cx08//aQ1adJE+93vfqf9+OOP2rJly7TIyEht7dq1wVqtOr766ittyZIl2vbt27V9+/Zpf/vb37SUlBRtwoQJzmmKi4u1tLQ07e6779Z27Nihvfnmm1qTJk20v/zlL0Fc8vq9+eabWmxsrJafn6/t3LlTmzJlipaUlKQVFRUFe9GUzJw5U9uwYYO2f/9+7T//+Y82dOhQrVWrVtqJEyc0TdO03/zmN1rbtm21zz77TNuyZYuWk5Oj5eTkBHmp6zp37pyzHgHQFi9erH377bfawYMHNU3TtGeeeUZLSkrS3nvvPe27777TbrvtNi07O1u7ePGicx433XST1qdPH23z5s3al19+qXXq1EkbP368x/ctKSnRAGg/B7SRPjx+DmgAtJKSEr9uJwoNzAhmhFkwI5gRZD7MCGaEWTAjmBFkPswIZoRZMCOYEaGEFzYUTZw4UYN953J9fP75585pDhw4oA0fPlyLj4/XWrVqpc2cOVOrqqpym8/nn3+u9e7dW4uJidE6dOigrVixIrArUo+tW7dq/fv31xITE7W4uDita9eu2h/+8AetvLzcbbr//ve/2rXXXqvFxsZqrVu31p555pkgLbGa559/Xmvbtq0WExOj9evXT9u0aVOwF0nZuHHjtIyMDC0mJkZr3bq1Nm7cOG3v3r3O5y9evKj93//9n9aiRQutSZMm2siRI7Vjx44FcYn1ff7557p1auLEiZqmaZrVatWeeOIJLS0tTYuNjdVuvPFGbffu3W7zOH36tDZ+/HitWbNmWkJCgnbPPfc4vwBKHGFzM6Dd5sPjZoYNuWBGMCPMghlxCTOCzIIZwYwwC2bEJcwIMgtmBDPCLJgRlzAjzM+iaQ1or0JEFGZKS0uRmJiIXADRPsynCsDHAEpKSpCQkGDMwhERUVAxI4iISMKMICIiCTMisDjGBhE1alb7w5fXExFReGJGEBGRhBlBREQSZkRg+DKOCRERERERERERERERUUCxxQYRNWpWADU+vp6IiMITM4KIiCTMCCIikjAjAoMXNoioUasBYPHx9UREFJ6YEUREJGFGEBGRhBkRGLywQUSNGvs9JCIiCTOCiIgkzAgiIpIwIwKDY2wQEREREREREREREVHIYIsNImrU2DyQiIgkzAgiIpIwI4iISMKMCAy22KCw9uqrr2LYsGF+f5+1a9eid+/esFrZWCzU1BjwIKLQxIyg+jAjiBovZgTVhxlB1HgxI6g+zIjA4IUNClvl5eV44oknMHfuXL+/10033YTo6Gi88cYbfn8vMpbVgAcRhR5mBDUEM4KocWJGUEMwI4gaJ2YENQQzIjB4YYPC1jvvvIOEhARcc801AXm/SZMmYenSpQF5LyIi8g0zgoiIJMwIIiKSMCOIzIMXNsj0Xn/9dbRs2RIVFRVu5SNGjMDdd98tvu7NN9/Erbfe6lY2adIkjBgxAn/605+QkZGBli1bYurUqaiqqnJO0759e8yfPx8TJkxAs2bN0K5dO7z//vs4efIkbrvtNjRr1gw9e/bEli1b3OZ96623YsuWLdi3b58Ba02BwuaBRKGNGUH+xIwgCm3MCPInZgRRaGNGkD8xIwKDFzbI9MaMGYOamhq8//77zrITJ07gww8/xL333iu+7ssvv8RVV11Vp/zzzz/Hvn378Pnnn+O1115Dfn4+8vPz3aZZsmQJrrnmGnz77be45ZZbcPfdd2PChAm46667sG3bNnTs2BETJkyApmnO17Rt2xZpaWn497//7ftKU8Bo8K1poFZ3lkQUQMwI8idmBFFoY0aQPzEjiEIbM4L8iRkRGLywQaYXHx+PX/7yl1ixYoWz7G9/+xvatm2LwYMH676muLgYJSUlyMzMrPNcixYt8MILL6BLly74+c9/jltuuQXr1693m+bmm2/Gr3/9a3Tq1Alz5sxBaWkprr76aowZMwaXX345Zs2ahR9//BHHjx93e11mZiYOHjzo+0pTwPAqOlFoY0aQPzEjiEIbM4L8iRlBFNqYEeRPzIjA4IUNCgn33XcfPvnkExw5cgQAkJ+fj0mTJsFisehOf/HiRQBAXFxcnee6deuGyMhI5/8zMjJw4sQJt2l69uzp/DstLQ0A0KNHjzpltV8XHx+PCxcuNHi9iIjId8wIIiKSMCOIiEjCjCAKbVHBXgCihujTpw969eqF119/HcOGDcMPP/yADz/8UJy+ZcuWsFgsOHv2bJ3noqOj3f5vsVhgtVrFaRyBpldW+3VnzpxBSkpKA9eKzMDXq+C8ik4UfMwI8hdmBFHoY0aQvzAjiEIfM4L8hRkRGGyxQSHjV7/6FfLz87FixQoMHToUWVlZ4rQxMTG44oorsHPnzoAtX3l5Ofbt24c+ffoE7D3Jd770eeh4EFHwMSPIH5gRROGBGUH+wIwgCg/MCPIHZkRg8MIGhYxf/vKXKCwsxCuvvOJxICeH3NxcfPnllwFYMptNmzYhNjYWOTk5AXtP8h37PSQKD8wI8gdmBFF4YEaQPzAjiMIDM4L8gRkRGLywQSEjMTERo0aNQrNmzTBixIh6p588eTI++ugjlJSU+H/hAPz973/HnXfeiSZNmgTk/YiI6BJmBBERSZgRREQkYUYQhS6LpmlasBeCqKFuvPFGdOvWDUuXLm3Q9GPGjMGVV16J2bNn+3W5Tp06hc6dO2PLli3Izs7263uRMUpLS5GYmIjLAETWO7WsBsBeACUlJUhISDBm4YjIK8wIMgozgij8MCPIKMwIovDDjCCjMCMCiy02KCScPXsW7777LjZs2ICpU6c2+HXPPvssmjVr5sclszlw4ABefPFFBk0IYr+HRKGPGUH+wowgCn3MCPIXZgRR6GNGkL8wIwKDLTYoJLRv3x5nz57FE088gYceeijYi0NhwHEVvT18u8JrBXAAvIpOFEzMCDIaM4IofDAjyGjMCKLwwYwgozEjAisq2AtA1BAHDhwI9iIQEZFJMSOIiEjCjCAiIgkzgii08cIGETVqvjbvY/NAIqLwxYwgIiIJM4KIiCTMiMDghQ0iatRqAPjSHx/DhogofDEjiIhIwowgIiIJMyIweGGDiBo1hg0REUmYEUREJGFGEBGRhBkRGL6MY0JERERERERERERERBRQbLFBRI0a+z0kIiIJM4KIiCTMCCIikjAjAoMXNoioUWPzQCIikjAjiIhIwowgIiIJMyIweGGDiBo1K3wLG19eS0RE5saMICIiCTOCiIgkzIjA4BgbREQBtmzZMrRv3x5xcXHo378/vv7662AvEhERERERERERhSjV35refvttdOnSBXFxcejRowc++uijAC2pcXhhg4gaNasBDxVvvfUW8vLyMHfuXGzbtg29evVCbm4uTpw4Ycj6EBGRcQKdEUREFDqYEUREJDH7b01fffUVxo8fj8mTJ+Pbb7/FiBEjMGLECOzYsUN9ZYPIomkaW7cQUaNTWlqKxMRENANg8WE+GoDzAEpKSpCQkFDv9P3798fVV1+NF154AQBgtVqRlZWFBx54AI888ogPS0JEREYJVkYQEZH5MSOIiEgSKr81jRs3DmVlZVizZo2zbMCAAejduzdeeuklH5Y8sNhig4gatRoDHoAtvFwfFRUVdd6rsrISW7duxdChQ51lERERGDp0KAoKCvy0hkRE5C2jMoKIiMIPM4KIiCSBzAhvfmsqKChwmx4AcnNzQ+63KQ4eTkSNUkxMDNLT01FUVOTzvJo1a4asrCy3srlz52LevHluZadOnUJNTQ3S0tLcytPS0rBr1y6fl4OIiIxhZEakp6cjJibGgKUiIiIzYEYQEZHE6IwoLy93K4uNjUVsbKxbmTe/NRUVFelOb8RyBxIvbBBRoxQXF4f9+/ejsrLS53lpmgaLxb2RYe2gISKi0GFkRsTExCAuLs6ApSIiIjNgRhARkcTIjFi4cGGdiw96N9E2ZrywQUSNVlxcXEBPJFq1aoXIyEgcP37crfz48eNIT08P2HIQEVH9Ap0RREQUOpgRREQkMSojnnjiCTz88MNuZXo30XrzW1N6enpY/DbFMTaIiAIkJiYGffv2xfr1651lVqsV69evR05OThCXjIiIiIiIiIiIzCI2NhYJCQluD70LG9781pSTk+M2PQCsW7cu5H6bYosNIqIAysvLw8SJE3HVVVehX79+eO6551BWVoZ77rkn2ItGREREREREREQhpr7fmiZMmIDWrVtjwYIFAIDf/va3uP7667Fo0SLccsstePPNN7Flyxa8/PLLwVwNZbywQUQUQOPGjcPJkycxZ84cFBUVoXfv3li7dm2dfhOJiIiIiIiIiIjqU99vTYcOHUJExKWOmwYOHIiVK1fi8ccfx6OPPopOnTph9erV6N69e7BWwSsWTdO0YC8EERERERERERERERFRQ3CMDSIiIiIiIiIiIiIiChm8sEFERERERERERERERCGDFzaIiIiIiIiIiIiIiChk8MIGERERERERERERERGFDF7YICIiIiIiIiIiIiKikMELG0REREREREREREREFDJ4YYOIiIiIiIiIiIiIiEIGL2wQEREREREREREREVHI4IUNIiIiIiIiIiIiIiIKGbywQUREREREREREREREIYMXNoiIiIiIiIiIiIiIKGTwwgYREREREREREREREYWM/x9NtHugs7bUmgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1600x800 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:142: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  E     = np.array(E_amp(monitor_data))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:143: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  E_ref = np.array(E_amp(monitor_ref))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3851934302.py:19: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  x_coords = np.array(p1_results_actual[key]['xy_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3851934302.py:21: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  y_coords = np.array(p1_results_actual[key]['xy_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:142: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  E     = np.array(E_amp(monitor_data))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:143: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  E_ref = np.array(E_amp(monitor_ref))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3851934302.py:41: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  y_coords_yz = np.array(p1_results_actual[key]['yz_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3851934302.py:43: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  z_coords_yz = np.array(p1_results_actual[key]['yz_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:142: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  E     = np.array(E_amp(monitor_data))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:143: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  E_ref = np.array(E_amp(monitor_ref))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3851934302.py:19: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  x_coords = np.array(p1_results_actual[key]['xy_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3851934302.py:21: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  y_coords = np.array(p1_results_actual[key]['xy_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:142: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  E     = np.array(E_amp(monitor_data))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:143: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  E_ref = np.array(E_amp(monitor_ref))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3851934302.py:41: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  y_coords_yz = np.array(p1_results_actual[key]['yz_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3851934302.py:43: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  z_coords_yz = np.array(p1_results_actual[key]['yz_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:142: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  E     = np.array(E_amp(monitor_data))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:143: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  E_ref = np.array(E_amp(monitor_ref))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3851934302.py:19: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  x_coords = np.array(p1_results_actual[key]['xy_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3851934302.py:21: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  y_coords = np.array(p1_results_actual[key]['xy_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:142: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  E     = np.array(E_amp(monitor_data))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:143: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  E_ref = np.array(E_amp(monitor_ref))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3851934302.py:41: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  y_coords_yz = np.array(p1_results_actual[key]['yz_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3851934302.py:43: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  z_coords_yz = np.array(p1_results_actual[key]['yz_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:142: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  E     = np.array(E_amp(monitor_data))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:143: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  E_ref = np.array(E_amp(monitor_ref))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3851934302.py:19: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  x_coords = np.array(p1_results_actual[key]['xy_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3851934302.py:21: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  y_coords = np.array(p1_results_actual[key]['xy_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:142: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  E     = np.array(E_amp(monitor_data))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:143: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  E_ref = np.array(E_amp(monitor_ref))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3851934302.py:41: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  y_coords_yz = np.array(p1_results_actual[key]['yz_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3851934302.py:43: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  z_coords_yz = np.array(p1_results_actual[key]['yz_field'].Ex\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i, group in enumerate(p1_groups):\n",
    "    group_sorted = sorted(group, key=lambda x: x[0])\n",
    "    n_cols       = len(group_sorted)\n",
    "    lda_mean     = np.mean([x[1] for x in group_sorted])\n",
    "\n",
    "    fig, axes = plt.subplots(2, n_cols, figsize=(4 * n_cols, 8))\n",
    "    if n_cols == 1:\n",
    "        axes = axes.reshape(2, 1)\n",
    "\n",
    "    for j, (alpha_val, lda_nm, Q) in enumerate(group_sorted):\n",
    "        key      = f'alpha={alpha_val:.2f}'\n",
    "        freq_res = td.C_0 / (lda_nm / 1e3)\n",
    "\n",
    "        ax_xy = axes[0, j]\n",
    "        ax_yz = axes[1, j]\n",
    "\n",
    "        E_xy = get_E_norm(p1_results_actual[key]['xy_field'],\n",
    "                          p1_results_norm[key]['xy_field'], freq_res)\n",
    "        x_coords = np.array(p1_results_actual[key]['xy_field'].Ex\n",
    "                            .sel(f=freq_res, method='nearest').x) * 1e3\n",
    "        y_coords = np.array(p1_results_actual[key]['xy_field'].Ex\n",
    "                            .sel(f=freq_res, method='nearest').y) * 1e3\n",
    "\n",
    "        im1 = ax_xy.pcolormesh(x_coords, y_coords, E_xy.T, cmap='hot', shading='auto')\n",
    "        fig.colorbar(im1, ax=ax_xy, label=r'$|E|/|E_0|$')\n",
    "        draw_disks(ax_xy, p1_r0, p1_h, p1_px, p1_py, alpha_val, color='cyan', lw=1.2)\n",
    "\n",
    "        uc = plt.Rectangle((p1_xy_xlim[0], p1_xy_ylim[0]),\n",
    "                            p1_xy_xlim[1] - p1_xy_xlim[0],\n",
    "                            p1_xy_ylim[1] - p1_xy_ylim[0],\n",
    "                            edgecolor='white', facecolor='none', lw=1.0, ls=':')\n",
    "        ax_xy.add_patch(uc)\n",
    "        ax_xy.set_xlim(p1_xy_xlim); ax_xy.set_ylim(p1_xy_ylim)\n",
    "        ax_xy.set_xlabel('x (nm)', fontsize=10); ax_xy.set_ylabel('y (nm)', fontsize=10)\n",
    "        ax_xy.set_aspect('equal')\n",
    "        ax_xy.set_title(f'XY  |  $\\\\alpha$={alpha_val:.2f}\\n'\n",
    "                        f'$\\\\lambda$={lda_nm:.1f} nm  Q={Q:.0f}', fontsize=10)\n",
    "\n",
    "        E_yz = get_E_norm(p1_results_actual[key]['yz_field'],\n",
    "                          p1_results_norm[key]['yz_field'], freq_res)\n",
    "        y_coords_yz = np.array(p1_results_actual[key]['yz_field'].Ex\n",
    "                               .sel(f=freq_res, method='nearest').y) * 1e3\n",
    "        z_coords_yz = np.array(p1_results_actual[key]['yz_field'].Ex\n",
    "                               .sel(f=freq_res, method='nearest').z) * 1e3\n",
    "\n",
    "        im2 = ax_yz.pcolormesh(y_coords_yz, z_coords_yz, E_yz.T, cmap='hot', shading='auto')\n",
    "        fig.colorbar(im2, ax=ax_yz, label=r'$|E|/|E_0|$')\n",
    "\n",
    "        ax_yz.axhline(0,          color='cyan', lw=1.2, ls='--', alpha=0.8, label='disk bottom')\n",
    "        ax_yz.axhline(p1_h * 1e3, color='cyan', lw=1.2, ls='--', alpha=0.8, label='disk top')\n",
    "        ax_yz.axhline(0,          color='white', lw=0.8, ls=':',  alpha=0.5, label='substrate')\n",
    "        ax_yz.axvline(-p1_y_pad,  color='white', lw=1.0, ls='--', alpha=0.6, label='unit cell wall')\n",
    "        ax_yz.axvline( p1_y_pad,  color='white', lw=1.0, ls='--', alpha=0.6)\n",
    "\n",
    "        ax_yz.set_xlim(-p1_y_pad, p1_y_pad)\n",
    "        ax_yz.set_ylim(p1_z_min_yz, p1_z_max_yz)\n",
    "        ax_yz.set_aspect(p1_yz_aspect)\n",
    "        ax_yz.set_xlabel('y (nm)', fontsize=10); ax_yz.set_ylabel('z (nm)', fontsize=10)\n",
    "        ax_yz.set_title('YZ  |  x = 0', fontsize=10)\n",
    "        if j == 0:\n",
    "            ax_yz.legend(fontsize=7, loc='upper right')\n",
    "\n",
    "    fig.suptitle(f'Mode {i+1}  |  $\\\\lambda$ $\\\\approx$ {lda_mean:.1f} nm', fontsize=13)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f'p1_fields_mode{i+1}.png', dpi=300, bbox_inches='tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78a9beab",
   "metadata": {},
   "source": [
    "### 8. Optical Chirality Density Maps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba466a43",
   "metadata": {},
   "source": [
    "For `alpha = 0.05` we compute the local optical chirality density,\n",
    "normalized by the spatial mean of `|C|` in the reference run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cee8d520",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resonances for alpha=0.05:\n",
      "  Res 1: 242.53 nm  Q=7950.5\n",
      "  Res 2: 252.24 nm  Q=8454.2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:165: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  C     = np.array(C_field(monitor_data))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:166: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  C_ref = np.array(C_field(monitor_ref))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3645847515.py:10: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  x_coords = np.array(p1_results_actual[p1_key_chiral]['xy_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3645847515.py:12: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  y_coords = np.array(p1_results_actual[p1_key_chiral]['xy_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:165: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  C     = np.array(C_field(monitor_data))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:166: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  C_ref = np.array(C_field(monitor_ref))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3645847515.py:17: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  y_coords_yz = np.array(p1_results_actual[p1_key_chiral]['yz_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3645847515.py:19: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  z_coords_yz = np.array(p1_results_actual[p1_key_chiral]['yz_field'].Ex\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:165: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  C     = np.array(C_field(monitor_data))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:166: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  C_ref = np.array(C_field(monitor_ref))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3645847515.py:10: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  x_coords = np.array(p1_results_actual[p1_key_chiral]['xy_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3645847515.py:12: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  y_coords = np.array(p1_results_actual[p1_key_chiral]['xy_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:165: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  C     = np.array(C_field(monitor_data))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:166: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  C_ref = np.array(C_field(monitor_ref))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3645847515.py:17: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  y_coords_yz = np.array(p1_results_actual[p1_key_chiral]['yz_field'].Ex\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3645847515.py:19: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  z_coords_yz = np.array(p1_results_actual[p1_key_chiral]['yz_field'].Ex\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f'Resonances for {p1_key_chiral}:')\n",
    "for j, (lda_nm, Q) in enumerate(p1_res_chiral):\n",
    "    print(f'  Res {j+1}: {lda_nm:.2f} nm  Q={Q:.1f}')\n",
    "\n",
    "for j, (lda_nm, Q) in enumerate(p1_res_chiral):\n",
    "    freq_res = td.C_0 / (lda_nm / 1e3)\n",
    "\n",
    "    C_xy = get_chirality(p1_results_actual[p1_key_chiral]['xy_field'],\n",
    "                         p1_results_norm[p1_key_chiral]['xy_field'], freq_res)\n",
    "    x_coords = np.array(p1_results_actual[p1_key_chiral]['xy_field'].Ex\n",
    "                        .sel(f=freq_res, method='nearest').x) * 1e3\n",
    "    y_coords = np.array(p1_results_actual[p1_key_chiral]['xy_field'].Ex\n",
    "                        .sel(f=freq_res, method='nearest').y) * 1e3\n",
    "\n",
    "    C_yz = get_chirality(p1_results_actual[p1_key_chiral]['yz_field'],\n",
    "                         p1_results_norm[p1_key_chiral]['yz_field'], freq_res)\n",
    "    y_coords_yz = np.array(p1_results_actual[p1_key_chiral]['yz_field'].Ex\n",
    "                           .sel(f=freq_res, method='nearest').y) * 1e3\n",
    "    z_coords_yz = np.array(p1_results_actual[p1_key_chiral]['yz_field'].Ex\n",
    "                           .sel(f=freq_res, method='nearest').z) * 1e3\n",
    "\n",
    "    clim = max(np.abs(C_xy).max(), np.abs(C_yz).max())\n",
    "\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 4))\n",
    "\n",
    "    im1 = ax1.pcolormesh(x_coords, y_coords, C_xy.T,\n",
    "                         cmap='RdBu', shading='auto', vmin=-clim, vmax=clim)\n",
    "    fig.colorbar(im1, ax=ax1, label=r'$C / \\langle|C_0|\\rangle$')\n",
    "    draw_disks(ax1, p1_r0, p1_h, p1_px, p1_py, alpha_chiral, color='black', lw=1.2)\n",
    "    ax1.set_xlim(p1_xy_xlim); ax1.set_ylim(p1_xy_ylim); ax1.set_aspect('equal')\n",
    "    ax1.set_xlabel('x (nm)', fontsize=11); ax1.set_ylabel('y (nm)', fontsize=11)\n",
    "    ax1.set_title(f'XY  |  z = {p1_h/2*1e3:.0f} nm (mid-disk)', fontsize=11)\n",
    "\n",
    "    im2 = ax2.pcolormesh(y_coords_yz, z_coords_yz, C_yz.T,\n",
    "                         cmap='RdBu', shading='auto', vmin=-clim, vmax=clim)\n",
    "    fig.colorbar(im2, ax=ax2, label=r'$C / \\langle|C_0|\\rangle$')\n",
    "    ax2.axhline(0,          color='black', lw=1.2, ls='--', alpha=0.8, label='disk bottom')\n",
    "    ax2.axhline(p1_h * 1e3, color='black', lw=1.2, ls='--', alpha=0.8, label='disk top')\n",
    "    ax2.axvline(-p1_y_pad,  color='black', lw=1.0, ls='--', alpha=0.6, label='unit cell wall')\n",
    "    ax2.axvline( p1_y_pad,  color='black', lw=1.0, ls='--', alpha=0.6)\n",
    "    ax2.set_xlim(-p1_y_pad, p1_y_pad); ax2.set_ylim(p1_z_min_yz, p1_z_max_yz)\n",
    "    ax2.set_aspect(p1_yz_aspect)\n",
    "    ax2.set_xlabel('y (nm)', fontsize=11); ax2.set_ylabel('z (nm)', fontsize=11)\n",
    "    ax2.set_title('YZ  |  x = 0', fontsize=11)\n",
    "    ax2.legend(fontsize=8, loc='upper right')\n",
    "\n",
    "    fig.suptitle(f'Optical Chirality  |  $\\\\alpha$={alpha_chiral:.2f}  |  '\n",
    "                 f'Res {j+1}  |  $\\\\lambda$={lda_nm:.1f} nm  Q={Q:.0f}', fontsize=13)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f'p1_chirality_alpha{alpha_chiral:.2f}_res{j+1}_{lda_nm:.1f}nm.png',\n",
    "                dpi=300, bbox_inches='tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62626860",
   "metadata": {},
   "source": [
    "### 9. Spatially Averaged Chirality Spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8d88230b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2922822606.py:16: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  C_actual_phys = prefactor * np.array(EdotH_actual)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2922822606.py:17: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  C_ref_phys    = prefactor * np.array(EdotH_ref)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p1_xy_actual = p1_results_actual[p1_key_chiral]['xy_field']\n",
    "p1_xy_ref    = p1_results_norm[p1_key_chiral]['xy_field']\n",
    "\n",
    "freqs_xr  = p1_xy_actual.Ex.f.values\n",
    "omega_arr = 2 * np.pi * freqs_xr\n",
    "prefactor = -omega_arr / (2 * td.C_0**2)\n",
    "\n",
    "EdotH_actual = (np.conj(p1_xy_actual.Ex) * p1_xy_actual.Hx +\n",
    "                np.conj(p1_xy_actual.Ey) * p1_xy_actual.Hy +\n",
    "                np.conj(p1_xy_actual.Ez) * p1_xy_actual.Hz).imag.mean(dim=['x', 'y']).squeeze()\n",
    "\n",
    "EdotH_ref    = (np.conj(p1_xy_ref.Ex) * p1_xy_ref.Hx +\n",
    "                np.conj(p1_xy_ref.Ey) * p1_xy_ref.Hy +\n",
    "                np.conj(p1_xy_ref.Ez) * p1_xy_ref.Hz).imag.mean(dim=['x', 'y']).squeeze()\n",
    "\n",
    "C_actual_phys = prefactor * np.array(EdotH_actual)\n",
    "C_ref_phys    = prefactor * np.array(EdotH_ref)\n",
    "p1_C_norm     = C_actual_phys / float(np.abs(C_ref_phys).mean())\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "ax.plot(p1_ldas * 1e3, p1_C_norm, lw=2.0, color='steelblue',\n",
    "        label=r'$\\langle C \\rangle / \\langle|C_0|\\rangle$')\n",
    "ax.axhline(0, color='gray', lw=0.8, ls='--', alpha=0.5)\n",
    "for j, (lda_nm, Q) in enumerate(p1_res_chiral):\n",
    "    ax.axvline(lda_nm, color='tomato', lw=1.0, ls=':', label=f'Res {j+1}: {lda_nm:.1f} nm')\n",
    "ax.set_xlabel('Wavelength (nm)', fontsize=12)\n",
    "ax.set_ylabel(r'$\\langle C \\rangle / \\langle|C_0|\\rangle$', fontsize=12)\n",
    "ax.set_title(f'Spatially Averaged Optical Chirality vs Wavelength  |  '\n",
    "             f'$\\\\alpha$={alpha_chiral:.2f} (Example Geometry)', fontsize=12)\n",
    "ax.legend(fontsize=9)\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'p1_chirality_avg_spectrum_alpha{alpha_chiral:.2f}.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "937077b3",
   "metadata": {},
   "source": [
    "### Summary\n",
    "\n",
    "In this section, we have demonstrated computationally how quasi-bound states in the continuum emerge in a biperiodic HfO₂ nanodisk metasurface through controlled symmetry breaking. By sweeping the asymmetry parameter $\\alpha$, we confirmed the key theoretical prediction — that the radiative Q factor scales as $Q \\propto \\alpha^{-2}$, and showed how the q-BIC manifests as a sharp Fano resonance in the transmittance spectrum that becomes progressively narrower and weaker as the lattice approaches the ideal symmetric BIC limit.\n",
    "\n",
    "The near-field electric field maps at resonance confirm that the q-BIC mode produces large local field enhancements $|E|/|E_0| \\gg 1$ concentrated within and immediately around the HfO₂ nanodisks. This strong field confinement is the fundamental mechanism that makes q-BIC metasurfaces attractive for enhancing light-matter interactions at the nanoscale.\n",
    "\n",
    "However, the optical chirality maps reveal a critical limitation for chiroptical applications: the local chirality density $C$ exhibits **regions of alternating sign** across the unit cell, with hotspots of opposite handedness in close spatial proximity. For circular dichroism spectroscopy, where chiral analyte molecules are randomly distributed throughout the near-field volume, contributions from regions of opposite $C$ sign partially cancel when averaged, significantly reducing the net chiroptical enhancement relative to the local peak values.\n",
    "\n",
    "Achieving large, **spatially uniform-sign** chirality enhancements, where $C/C_{\\rm CPL}$ is everywhere positive (or everywhere negative) across the unit cell, requires an additional design ingredient beyond the q-BIC alone. As shown by Hu et al., this is accomplished by spectrally overlapping the asymmetric electric dipole mode $\\mathbf{p}_\\alpha$ and magnetic dipole mode $\\mathbf{m}_\\alpha$ of the biperiodic lattice, inducing a Kerker-like condition in which the scattered fields preserve the helicity of the incident circular polarization throughout the near-field volume.\n",
    "\n",
    "This is precisely the design challenge addressed in the next section, where we tune the disk aspect ratio to bring $\\mathbf{p}_\\alpha$ and $\\mathbf{m}_\\alpha$ into spectral alignment and evaluate the resulting improvement in spatially averaged chirality enhancement."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54c12ddd",
   "metadata": {},
   "source": [
    "## Part 2: Geometry Sweep & the Kerker Transition.\n",
    "\n",
    "In the previous section we established that the HfO2 biperiodic nanodisk metasurface supports a q-BIC resonance whose radiative Q factor scales as $Q \\propto \\alpha^{-2}$, and that this resonance produces large but spatially non-uniform near-field chirality enhancements. The next design challenge is to find the geometry that maximises the spatially *averaged* optical chirality $\\langle C \\rangle$, the quantity that governs sensitivity for molecules randomly distributed around the metasurface.\n",
    "\n",
    "The averaged chirality is not simply maximised by maximising Q. It requires a **Kerker-like condition** in which the asymmetric electric dipole mode $\\mathbf{p}_\\alpha$ and magnetic dipole mode $\\mathbf{m}_\\alpha$ of the biperiodic lattice are spectrally overlapped. When these two modes are degenerate, the scattered fields from the metasurface preserve the helicity of the incident circularly polarized light throughout the near-field volume, producing chirality enhancements of a single sign everywhere in the unit cell. The geometry, specifically the disk diameter $d_0$, height $h$, or lattice period $p_x$, is the tuning knob that shifts $\\mathbf{p}_\\alpha$ and $\\mathbf{m}_\\alpha$ relative to each other, because the electric and magnetic Mie resonances of a disk depend differently on the aspect ratio $d/h$.\n",
    "\n",
    "### The Kerker Condition in a Biperiodic Lattice\n",
    "\n",
    "For a single dielectric disk, the electric dipole **p** and magnetic dipole **m** Mie resonances can be brought into spectral overlap by tuning the aspect ratio $d/h$. When **p** and **m** are spectrally degenerate and of equal amplitude, the forward-scattered field is that of a Huygens source and the backward-scattered field vanishes — this is the first Kerker condition, and it corresponds to unity transmission with fully preserved incident polarization in the scattered near field.\n",
    "\n",
    "In the biperiodic lattice with asymmetry $\\alpha > 0$, the two broad Mie modes split into high-Q asymmetric counterparts $\\mathbf{p}_\\alpha$ and $\\mathbf{m}_\\alpha$. As shown by Hu, Lawrence and Dionne (ACS Photonics 7, 36, 2020), when $\\mathbf{p}_\\alpha$ and $\\mathbf{m}_\\alpha$ are spectrally overlapped the condition\n",
    "\n",
    "$$\\cos(\\phi_{iE,H}) = \\frac{\\text{Im}(\\mathbf{E}^* \\cdot \\mathbf{H})}{|\\mathbf{E}||\\mathbf{H}|} \\approx -1$$\n",
    "\n",
    "is satisfied uniformly across the unit cell, meaning the near-field ellipticity matches that of the incident LCP wave everywhere. This directly maximises the plane-averaged chirality:\n",
    "\n",
    "$$\\langle C \\rangle = -\\frac{\\omega}{2c^2} \\left\\langle \\text{Im}(\\mathbf{E}^* \\cdot \\mathbf{H}) \\right\\rangle_{xy}$$\n",
    "\n",
    "since $\\text{Im}(\\mathbf{E}^* \\cdot \\mathbf{H}) = |\\mathbf{E}||\\mathbf{H}|\\cos(\\phi_{iE,H})$ and a uniform negative $\\cos(\\phi_{iE,H})$ prevents cancellation between regions of opposite chirality sign when spatially averaged.\n",
    "\n",
    "### What This Section Does\n",
    "\n",
    "We fix $\\alpha = 0.05$, small enough to maintain high Q while still allowing far-field coupling, and sweep one geometric parameter (`h`, `px`, or `d0`) across a range chosen to straddle the Kerker condition. For each geometry we run a pair of FDTD simulations (disks present and empty reference) and extract:\n",
    "\n",
    "**Transmittance $T(\\lambda)$** — the normalized flux spectrum. The q-BIC appears as a sharp Fano dip. As the geometry is tuned, the positions of $\\mathbf{p}_\\alpha$ and $\\mathbf{m}_\\alpha$ shift relative to each other; when they overlap the two dips merge and transmission approaches unity at the resonance — a hallmark of the Kerker condition.\n",
    "\n",
    "**Spatially averaged XY chirality $\\langle C(\\lambda) \\rangle$** — computed as:\n",
    "\n",
    "$$\\langle C(\\lambda) \\rangle = -\\frac{\\omega}{2c^2} \\left\\langle \\text{Im}(\\mathbf{E}^* \\cdot \\mathbf{H}) \\right\\rangle_{xy}$$\n",
    "\n",
    "normalized by the mean $|C|$ of the reference run. This spectrum peaks sharply at the Kerker wavelength and its sign is predominantly single-valued — in contrast to the mixed-sign maps seen in the previous section where no Kerker condition was imposed.\n",
    "\n",
    "The sweep value that produces the globally largest $|\\langle C \\rangle|$ is then identified and its near-field chirality $C$ and polarization state $\\cos(\\phi_{iE,H})$ maps are plotted on XY and YZ cross-sections to confirm spatial uniformity of the enhancement.\n",
    "\n",
    "### Key Parameters\n",
    "\n",
    "| Quantity | Symbol | Value |\n",
    "|---|---|---|\n",
    "| Fixed asymmetry | $\\alpha$ | 0.05 |\n",
    "| Swept parameter | `SWEEP_PARAM` | `d0` / `h` / `px` |\n",
    "| Central wavelength | $\\lambda_0$ | 258 nm |\n",
    "| Base disk height | $h$ | 80 nm |\n",
    "| Base period | $p_x = p_y$ | 210 nm |\n",
    "| Base disk diameter | $d_0$ | 110 nm |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58bda718",
   "metadata": {},
   "source": [
    "### 1. Base Parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2657dc23",
   "metadata": {},
   "source": [
    "`SWEEP_PARAM` and `SWEEP_VALUES` are the only knobs needed to switch\n",
    "between sweeping disk height (`h`), unit cell period (`px`), or disk diameter\n",
    "(`d0`). The base geometry values are used for all parameters that are not\n",
    "currently being swept. This part's geometry and wavelength window are\n",
    "independent of Part 1's example case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "49bd8e52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sweeping: d0 over [106.   106.75 107.5  108.25 109.   109.75 110.5  111.25 112.  ] nm\n",
      "Alpha fixed at: 0.05\n",
      "Wavelength range: 251.0 – 265.0 nm\n"
     ]
    }
   ],
   "source": [
    "# ── Wavelength range ──────────────────────────────────────────────────────────\n",
    "p2_lda0   = 0.258\n",
    "p2_freq0  = td.C_0 / p2_lda0\n",
    "p2_ldas   = np.linspace(p2_lda0 - 0.007, p2_lda0 + 0.007, 500)\n",
    "p2_freqs  = td.C_0 / p2_ldas\n",
    "p2_fwidth = 0.5 * (np.max(p2_freqs) - np.min(p2_freqs))\n",
    "\n",
    "# ── Base geometry (used as default when not swept) ────────────────────────────\n",
    "p2_h_base  = 0.080          # disk height (μm)\n",
    "p2_px_base = 0.210          # unit cell period (μm)\n",
    "p2_d0_base = 0.110          # disk diameter (μm)\n",
    "\n",
    "p2_alpha = 0.05              # fixed asymmetry\n",
    "\n",
    "# ── Sweep config — change SWEEP_PARAM and SWEEP_VALUES ───────────────────────\n",
    "# Options: 'h', 'px', 'd0'\n",
    "p2_SWEEP_PARAM  = 'd0'\n",
    "p2_SWEEP_VALUES = np.linspace(0.106, 0.112, 9)   # μm\n",
    "\n",
    "print(f'Sweeping: {p2_SWEEP_PARAM} over {p2_SWEEP_VALUES*1e3} nm')\n",
    "print(f'Alpha fixed at: {p2_alpha}')\n",
    "print(f'Wavelength range: {p2_ldas.min()*1e3:.1f} – {p2_ldas.max()*1e3:.1f} nm')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0618e67",
   "metadata": {},
   "source": [
    "### 2. Material Dispersion Fit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49492d1b",
   "metadata": {},
   "source": [
    "Independent fit over Part 2's own wavelength window."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "896c4995",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eb1ddaf1402345c99a26f7026729e16b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting over 16 points: 242.2 – 274.3 nm\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dispersion fit RMS error: 0.00019\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1100x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data   = np.loadtxt('./misc/HfO2_Siefke_n_k.csv', skiprows=1, delimiter=',')\n",
    "wl_um  = data[:, 0]\n",
    "n_data = data[:, 1]\n",
    "k_data = data[:, 2]\n",
    "\n",
    "wl_min = p2_ldas.min() - 0.010\n",
    "wl_max = p2_ldas.max() + 0.010\n",
    "mask   = (wl_um >= wl_min) & (wl_um <= wl_max)\n",
    "wl_sel = wl_um[mask]\n",
    "n_sel  = n_data[mask]\n",
    "k_sel  = k_data[mask]\n",
    "print(f'Fitting over {mask.sum()} points: {wl_sel.min()*1e3:.1f} – {wl_sel.max()*1e3:.1f} nm')\n",
    "\n",
    "tmp_fname = 'HfO2_subset_p2.csv'\n",
    "np.savetxt(tmp_fname, np.column_stack([wl_sel, n_sel, k_sel]),\n",
    "           delimiter=',', header='wl,n,k', comments='')\n",
    "\n",
    "p2_fitter = FastDispersionFitter.from_file(tmp_fname, skiprows=1, delimiter=',')\n",
    "p2_hafnia, p2_rms_error = p2_fitter.fit(max_num_poles=4, tolerance_rms=1e-3)\n",
    "print(f'Dispersion fit RMS error: {p2_rms_error:.5f}')\n",
    "\n",
    "eps_complex = p2_hafnia.eps_model(p2_freqs)\n",
    "n_fit = np.real(np.sqrt(eps_complex))\n",
    "k_fit = np.imag(np.sqrt(eps_complex))\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 4))\n",
    "ax1.scatter(wl_sel * 1e3, n_sel, s=15, color='steelblue', label='Raw data', zorder=3)\n",
    "ax1.plot(p2_ldas * 1e3, n_fit, color='tomato', lw=2, label='Fit')\n",
    "ax1.set_xlabel('Wavelength (nm)'); ax1.set_ylabel('n')\n",
    "ax1.set_title('HfO2 Real Refractive Index'); ax1.legend()\n",
    "ax2.scatter(wl_sel * 1e3, k_sel, s=15, color='steelblue', label='Raw data', zorder=3)\n",
    "ax2.plot(p2_ldas * 1e3, k_fit, color='tomato', lw=2, label='Fit')\n",
    "ax2.set_xlabel('Wavelength (nm)'); ax2.set_ylabel('k')\n",
    "ax2.set_title('HfO2 Extinction Coefficient'); ax2.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4fa5747",
   "metadata": {},
   "source": [
    "### 3. Geometry Builder for the Sweep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3c36c1a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_geometry(sweep_val):\n",
    "    '''Return (h, px, py, d0, r0) for a given sweep value.'''\n",
    "    h_g  = p2_h_base\n",
    "    px_g = p2_px_base\n",
    "    d0_g = p2_d0_base\n",
    "    if p2_SWEEP_PARAM == 'h':\n",
    "        h_g  = sweep_val\n",
    "    elif p2_SWEEP_PARAM == 'px':\n",
    "        px_g = sweep_val\n",
    "    elif p2_SWEEP_PARAM == 'd0':\n",
    "        d0_g = sweep_val\n",
    "    py_g = px_g\n",
    "    r0_g = d0_g / 2\n",
    "    return h_g, px_g, py_g, d0_g, r0_g\n",
    "\n",
    "\n",
    "def p2_sweep_key(val):\n",
    "    return f'{p2_SWEEP_PARAM}={val*1e3:.1f}nm'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe554437",
   "metadata": {},
   "source": [
    "### 4. Run Simulations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4584ef2f",
   "metadata": {},
   "source": [
    "Two `Batch` objects are submitted: one with disks present (`p2_sims_actual`)\n",
    "and one with an empty unit cell (`p2_sims_norm`). Results are cached to disk so\n",
    "the kernel can be restarted without re-running."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9226b37b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d117db0aa3db4c6bbb2dac1b4261d5f1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:04:32 EDT </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Simulation has </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.47e+06</span><span style=\"color: #800000; text-decoration-color: #800000\"> time steps. The </span><span style=\"color: #008000; text-decoration-color: #008000\">'run_time'</span><span style=\"color: #800000; text-decoration-color: #800000\"> may be </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">unnecessarily large, unless there are very long-lived resonances.  </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:04:32 EDT\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Simulation has \u001b[0m\u001b[1;36m1.47e+06\u001b[0m\u001b[31m time steps. The \u001b[0m\u001b[32m'run_time'\u001b[0m\u001b[31m may be \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31munnecessarily large, unless there are very long-lived resonances.  \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Simulation has </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.33e+06</span><span style=\"color: #800000; text-decoration-color: #800000\"> time steps. The </span><span style=\"color: #008000; text-decoration-color: #008000\">'run_time'</span><span style=\"color: #800000; text-decoration-color: #800000\"> may be </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">unnecessarily large, unless there are very long-lived resonances.  </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Simulation has \u001b[0m\u001b[1;36m1.33e+06\u001b[0m\u001b[31m time steps. The \u001b[0m\u001b[32m'run_time'\u001b[0m\u001b[31m may be \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31munnecessarily large, unless there are very long-lived resonances.  \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running actual simulations (Part 2)...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:04:35 EDT </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">9</span> tasks.                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:04:35 EDT\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m9\u001b[0m tasks.                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:04:43 EDT </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.312</span> for the whole batch.                \n",
       "</pre>\n"
      ],
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       "\u001b[2;36m14:04:43 EDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m1.312\u001b[0m for the whole batch.                \n"
      ]
     },
     "metadata": {},
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>completion.                                                        \n",
       "</pre>\n"
      ],
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       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after    \n",
       "\u001b[2;36m             \u001b[0mcompletion.                                                        \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "Output()"
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:28:22 EDT </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
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       "\u001b[2;36m14:28:22 EDT\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
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      ],
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       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running normalization simulations (Part 2)...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:28:43 EDT </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">9</span> tasks.                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:28:43 EDT\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m9\u001b[0m tasks.                       \n"
      ]
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     "metadata": {},
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:28:51 EDT </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.246</span> for the whole batch.                \n",
       "</pre>\n"
      ],
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       "\u001b[2;36m14:28:51 EDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.246\u001b[0m for the whole batch.                \n"
      ]
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       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>completion.                                                        \n",
       "</pre>\n"
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       "\u001b[2;36m             \u001b[0mcompletion.                                                        \n"
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       "Output()"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:29:57 EDT </span>Batch complete.                                                    \n",
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       "\n"
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     "metadata": {},
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   ],
   "source": [
    "p2_sims_actual = {}\n",
    "p2_sims_norm   = {}\n",
    "for v in p2_SWEEP_VALUES:\n",
    "    key = p2_sweep_key(v)\n",
    "    h_g, px_g, py_g, d0_g, r0_g = get_geometry(v)\n",
    "    p2_sims_actual[key] = make_sim(h_g, px_g, py_g, r0_g, p2_alpha, p2_hafnia,\n",
    "                                    p2_freq0, p2_fwidth, p2_freqs, p2_lda0,\n",
    "                                    source_z, run_time, Lz, include_structures=True)\n",
    "    p2_sims_norm[key]   = make_sim(h_g, px_g, py_g, r0_g, p2_alpha, p2_hafnia,\n",
    "                                    p2_freq0, p2_fwidth, p2_freqs, p2_lda0,\n",
    "                                    source_z, run_time, Lz, include_structures=False)\n",
    "\n",
    "p2_batch_actual = web.Batch(simulations=p2_sims_actual, verbose=True)\n",
    "p2_batch_norm   = web.Batch(simulations=p2_sims_norm,   verbose=True)\n",
    "\n",
    "print('Running actual simulations (Part 2)...')\n",
    "p2_results_actual = p2_batch_actual.run(path_dir='data/p2_actual')\n",
    "\n",
    "print('Running normalization simulations (Part 2)...')\n",
    "p2_results_norm = p2_batch_norm.run(path_dir='data/p2_norm')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaadd884",
   "metadata": {},
   "source": [
    "### 5. Extract Transmittance & Average XY Chirality"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c23c84da",
   "metadata": {},
   "source": [
    "For each sweep value we extract transmittance `T(lambda)` and the\n",
    "spatially-averaged XY chirality `<C(lambda)>`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "11cf4a53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:7: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  T_actual = np.array(p2_results_actual[key]['flux'].flux)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:8: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  T_ref    = np.array(p2_results_norm[key]['flux'].flux)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:22: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  return np.array(EdotH_im)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:22: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  return np.array(EdotH_im)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:7: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  T_actual = np.array(p2_results_actual[key]['flux'].flux)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "d0=106.0nm: T=[0.033, 1.007]  C=[-237.475, 326.499]\n",
      "d0=106.8nm: T=[0.079, 1.000]  C=[-451.117, 432.416]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:8: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  T_ref    = np.array(p2_results_norm[key]['flux'].flux)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:22: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  return np.array(EdotH_im)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:22: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  return np.array(EdotH_im)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:7: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  T_actual = np.array(p2_results_actual[key]['flux'].flux)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:8: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  T_ref    = np.array(p2_results_norm[key]['flux'].flux)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:22: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  return np.array(EdotH_im)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:22: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  return np.array(EdotH_im)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:7: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  T_actual = np.array(p2_results_actual[key]['flux'].flux)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:8: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  T_ref    = np.array(p2_results_norm[key]['flux'].flux)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:22: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  return np.array(EdotH_im)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:22: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  return np.array(EdotH_im)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "d0=107.5nm: T=[0.001, 1.000]  C=[-275.534, 270.167]\n",
      "d0=108.2nm: T=[0.008, 1.005]  C=[-507.142, 529.769]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:7: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  T_actual = np.array(p2_results_actual[key]['flux'].flux)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:8: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  T_ref    = np.array(p2_results_norm[key]['flux'].flux)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:22: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  return np.array(EdotH_im)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:22: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  return np.array(EdotH_im)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:7: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  T_actual = np.array(p2_results_actual[key]['flux'].flux)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "d0=109.0nm: T=[0.726, 1.004]  C=[1.401, 4248.611]\n",
      "d0=109.8nm: T=[0.472, 1.007]  C=[1.416, 3552.322]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:8: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  T_ref    = np.array(p2_results_norm[key]['flux'].flux)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:22: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  return np.array(EdotH_im)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:22: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  return np.array(EdotH_im)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:7: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  T_actual = np.array(p2_results_actual[key]['flux'].flux)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:8: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  T_ref    = np.array(p2_results_norm[key]['flux'].flux)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:22: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  return np.array(EdotH_im)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:22: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  return np.array(EdotH_im)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:7: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  T_actual = np.array(p2_results_actual[key]['flux'].flux)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:8: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  T_ref    = np.array(p2_results_norm[key]['flux'].flux)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "d0=110.5nm: T=[0.031, 1.007]  C=[-593.129, 604.261]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:22: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  return np.array(EdotH_im)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:22: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  return np.array(EdotH_im)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:7: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  T_actual = np.array(p2_results_actual[key]['flux'].flux)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:8: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  T_ref    = np.array(p2_results_norm[key]['flux'].flux)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:22: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  return np.array(EdotH_im)\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2948041685.py:22: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  return np.array(EdotH_im)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "d0=111.2nm: T=[0.070, 1.001]  C=[-193.123, 148.056]\n",
      "d0=112.0nm: T=[0.114, 0.999]  C=[-149.903, 170.825]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p2_T_dict = {}\n",
    "p2_C_dict = {}\n",
    "\n",
    "for val in p2_SWEEP_VALUES:\n",
    "    key = p2_sweep_key(val)\n",
    "\n",
    "    T_actual = np.array(p2_results_actual[key]['flux'].flux)\n",
    "    T_ref    = np.array(p2_results_norm[key]['flux'].flux)\n",
    "    p2_T_dict[key] = T_actual / T_ref\n",
    "\n",
    "    xy_actual = p2_results_actual[key]['xy_field']\n",
    "    xy_ref    = p2_results_norm[key]['xy_field']\n",
    "\n",
    "    freqs_xr  = xy_actual.Ex.f.values\n",
    "    omega_arr = 2 * np.pi * freqs_xr\n",
    "    prefactor = -omega_arr / (2 * td.C_0**2)\n",
    "\n",
    "    def avg_chirality(xy):\n",
    "        EdotH_im = (np.conj(xy.Ex) * xy.Hx +\n",
    "                    np.conj(xy.Ey) * xy.Hy +\n",
    "                    np.conj(xy.Ez) * xy.Hz).imag.mean(dim=['x', 'y']).squeeze()\n",
    "        return np.array(EdotH_im)\n",
    "\n",
    "    C_actual_raw = avg_chirality(xy_actual)\n",
    "    C_ref_raw    = avg_chirality(xy_ref)\n",
    "    C_actual_phys = prefactor * C_actual_raw\n",
    "    C_ref_phys    = prefactor * C_ref_raw\n",
    "\n",
    "    p2_C_dict[key] = C_actual_phys / float(np.abs(C_ref_phys).mean())\n",
    "\n",
    "    print(f'{key}: T=[{p2_T_dict[key].min():.3f}, {p2_T_dict[key].max():.3f}]  '\n",
    "          f'C=[{p2_C_dict[key].min():.3f}, {p2_C_dict[key].max():.3f}]')\n",
    "\n",
    "LDA_MIN = 254\n",
    "LDA_MAX = 262\n",
    "\n",
    "lda_nm = p2_ldas * 1e3\n",
    "sweep_colors = plt.cm.viridis(np.linspace(0.1, 0.9, len(p2_SWEEP_VALUES)))\n",
    "\n",
    "fig, (ax_T, ax_C) = plt.subplots(1, 2, figsize=(12, 5))\n",
    "for k, val in enumerate(p2_SWEEP_VALUES):\n",
    "    key   = p2_sweep_key(val)\n",
    "    label = f'{p2_SWEEP_PARAM} = {val*1e3:.1f} nm'\n",
    "    color = sweep_colors[k]\n",
    "    ax_T.plot(lda_nm, p2_T_dict[key], lw=1.8, color=color, label=label)\n",
    "    ax_C.plot(lda_nm, p2_C_dict[key], lw=1.8, color=color, label=label)\n",
    "\n",
    "ax_T.set_xlabel('Wavelength (nm)', fontsize=12); ax_T.set_ylabel('Transmittance', fontsize=12)\n",
    "ax_T.set_title('Transmittance vs d0', fontsize=12)\n",
    "ax_T.set_xlim(LDA_MIN, LDA_MAX)\n",
    "ax_C.set_xlabel('Wavelength (nm)', fontsize=12)\n",
    "ax_C.set_ylabel(r'$\\langle C \\rangle / \\langle|C_0|\\rangle$', fontsize=12)\n",
    "ax_C.set_title('Averaged Chirality vs d0', fontsize=12)\n",
    "ax_C.set_xlim(LDA_MIN, LDA_MAX)\n",
    "ax_C.legend(fontsize=8, loc='best')\n",
    "fig.suptitle(f'Geometry Sweep (alpha = {p2_alpha})', fontsize=13)\n",
    "plt.tight_layout()\n",
    "plt.savefig('p2_geometry_sweep_T_and_C.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a8f88c1-8f09-40dd-866d-e66f59b6b558",
   "metadata": {},
   "source": [
    "### 6. The Kerker Transition in terms of T_min and Peak Chirality vs d0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "613c95b8",
   "metadata": {},
   "source": [
    "For each sweep value, extract the minimum transmittance `T_min` (how deep\n",
    "the Fano dip is — small `T_min` means strong far-field coupling) and the peak\n",
    "`|<C>|` (the strongest chirality reached anywhere in the window). If a Kerker-like\n",
    "condition is crossed within the sweep, `T_min` should rise sharply (the dip\n",
    "nearly disappears) right where `|<C>|` spikes, then both should relax back to\n",
    "their normal Fano-resonance behaviour on either side — a visible transition\n",
    "through the Kerker regime rather than a single isolated point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c72bb3b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shallowest Fano dip (closest approach to the Kerker condition): d0 = 109.0 nm  (T_min = 0.726, peak |<C>| = 4248.6)\n"
     ]
    }
   ],
   "source": [
    "p2_Tmin_vs_d0  = np.array([p2_T_dict[p2_sweep_key(v)].min()        for v in p2_SWEEP_VALUES])\n",
    "p2_Cpeak_vs_d0 = np.array([np.abs(p2_C_dict[p2_sweep_key(v)]).max() for v in p2_SWEEP_VALUES])\n",
    "p2_d0_nm       = p2_SWEEP_VALUES * 1e3\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(8, 5))\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "l1, = ax1.plot(p2_d0_nm, p2_Tmin_vs_d0, 'o-', color='steelblue', lw=2, ms=7,\n",
    "               label=r'$T_{min}$ (dip depth)')\n",
    "l2, = ax2.plot(p2_d0_nm, p2_Cpeak_vs_d0, 's-', color='tomato', lw=2, ms=7,\n",
    "               label=r'peak $|\\langle C\\rangle|$')\n",
    "\n",
    "ax1.set_xlabel('d0 (nm)', fontsize=12)\n",
    "ax1.set_ylabel(r'$T_{min}$', fontsize=12, color='steelblue')\n",
    "ax2.set_ylabel(r'peak $|\\langle C\\rangle / \\langle|C_0|\\rangle|$', fontsize=12, color='tomato')\n",
    "ax1.tick_params(axis='y', labelcolor='steelblue')\n",
    "ax2.tick_params(axis='y', labelcolor='tomato')\n",
    "ax1.set_title('Transmittance Dip Collapsing as Chirality Spikes — the Kerker Transition',\n",
    "              fontsize=12)\n",
    "ax1.legend(handles=[l1, l2], loc='upper right', fontsize=9)\n",
    "plt.tight_layout()\n",
    "plt.savefig('p2_kerker_transition.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "p2_kerker_idx = np.argmax(p2_Tmin_vs_d0)\n",
    "p2_kerker_val = p2_SWEEP_VALUES[p2_kerker_idx]\n",
    "print(f'Shallowest Fano dip (closest approach to the Kerker condition): '\n",
    "      f'd0 = {p2_kerker_val*1e3:.1f} nm  (T_min = {p2_Tmin_vs_d0[p2_kerker_idx]:.3f}, '\n",
    "      f'peak |<C>| = {p2_Cpeak_vs_d0[p2_kerker_idx]:.1f})')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cf99c82",
   "metadata": {},
   "source": [
    "### 7. Near-Field Maps at the Near-Kerker Geometry"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91bcbaa2",
   "metadata": {},
   "source": [
    "At the geometry identified above (the largest `T_min` in the sweep — the\n",
    "point closest to the Kerker condition, which also coincides with the peak\n",
    "chirality), compute and plot four near-field maps: local chirality `C` and the\n",
    "polarization-state indicator `cos(phi_iE,H)`, in both the XY and YZ planes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5c6bb131",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Near-Kerker geometry : d0 = 109.0 nm\n",
      "Peak |<C>| wavelength: 258.55 nm\n",
      "Peak <C> value       : 4248.611\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:165: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  C     = np.array(C_field(monitor_data))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:166: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  C_ref = np.array(C_field(monitor_ref))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:183: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  return np.array(fields(monitor_data))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3260157534.py:19: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  x_coords  = np.array(res_act['xy_field'].Ex.sel(f=p2_best_freq, method='nearest').x) * 1e3\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3260157534.py:20: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  y_coords  = np.array(res_act['xy_field'].Ex.sel(f=p2_best_freq, method='nearest').y) * 1e3\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:165: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  C     = np.array(C_field(monitor_data))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:166: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  C_ref = np.array(C_field(monitor_ref))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/2740503689.py:183: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  return np.array(fields(monitor_data))\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3260157534.py:24: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  y_coords_yz = np.array(res_act['yz_field'].Ex.sel(f=p2_best_freq, method='nearest').y) * 1e3\n",
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_37396/3260157534.py:25: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword\n",
      "  z_coords_yz = np.array(res_act['yz_field'].Ex.sel(f=p2_best_freq, method='nearest').z) * 1e3\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x900 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p2_best_val = p2_kerker_val\n",
    "p2_best_key = p2_sweep_key(p2_best_val)\n",
    "C_norm_best = p2_C_dict[p2_best_key]\n",
    "ki          = np.argmax(np.abs(C_norm_best))\n",
    "p2_best_lda = p2_ldas[ki] * 1e3\n",
    "p2_best_freq = td.C_0 / (p2_best_lda / 1e3)\n",
    "\n",
    "print(f'Near-Kerker geometry : {p2_SWEEP_PARAM} = {p2_best_val*1e3:.1f} nm')\n",
    "print(f'Peak |<C>| wavelength: {p2_best_lda:.2f} nm')\n",
    "print(f'Peak <C> value       : {C_norm_best[ki]:.3f}')\n",
    "\n",
    "res_act  = p2_results_actual[p2_best_key]\n",
    "res_norm = p2_results_norm[p2_best_key]\n",
    "\n",
    "h_best, px_best, py_best, d0_best, r0_best = get_geometry(p2_best_val)\n",
    "\n",
    "C_xy      = get_chirality(res_act['xy_field'], res_norm['xy_field'], p2_best_freq)\n",
    "cos_xy    = get_cos_phi(res_act['xy_field'], p2_best_freq)\n",
    "x_coords  = np.array(res_act['xy_field'].Ex.sel(f=p2_best_freq, method='nearest').x) * 1e3\n",
    "y_coords  = np.array(res_act['xy_field'].Ex.sel(f=p2_best_freq, method='nearest').y) * 1e3\n",
    "\n",
    "C_yz        = get_chirality(res_act['yz_field'], res_norm['yz_field'], p2_best_freq)\n",
    "cos_yz      = get_cos_phi(res_act['yz_field'], p2_best_freq)\n",
    "y_coords_yz = np.array(res_act['yz_field'].Ex.sel(f=p2_best_freq, method='nearest').y) * 1e3\n",
    "z_coords_yz = np.array(res_act['yz_field'].Ex.sel(f=p2_best_freq, method='nearest').z) * 1e3\n",
    "\n",
    "p2_xy_xlim, p2_xy_ylim, p2_y_pad, _, _, _ = get_plot_geom(h_best, px_best, py_best, z_pad=0.08)\n",
    "z_pad_best    = 0.08\n",
    "y_lim_best    = p2_y_pad\n",
    "z_min_yz_best = -z_pad_best * 1e3\n",
    "z_max_yz_best = (h_best + z_pad_best) * 1e3\n",
    "yz_asp_best   = (2 * y_lim_best) / (z_max_yz_best - z_min_yz_best)\n",
    "\n",
    "clim_C   = max(np.abs(C_xy).max(), np.abs(C_yz).max())\n",
    "clim_cos = 1.0\n",
    "\n",
    "fig, axes = plt.subplots(2, 2, figsize=(11, 9), gridspec_kw=dict(hspace=0.38, wspace=0.35))\n",
    "ax_C_xy,  ax_cos_xy = axes[0, 0], axes[0, 1]\n",
    "ax_C_yz,  ax_cos_yz = axes[1, 0], axes[1, 1]\n",
    "\n",
    "im1 = ax_C_xy.pcolormesh(x_coords, y_coords, C_xy.T, cmap='RdBu', shading='auto',\n",
    "                          vmin=-clim_C, vmax=clim_C)\n",
    "fig.colorbar(im1, ax=ax_C_xy, label=r'$C/\\langle|C_0|\\rangle$')\n",
    "draw_disks(ax_C_xy, r0_best, h_best, px_best, py_best, p2_alpha, color='black', lw=1.2)\n",
    "ax_C_xy.set_xlim(p2_xy_xlim); ax_C_xy.set_ylim(p2_xy_ylim); ax_C_xy.set_aspect('equal')\n",
    "ax_C_xy.set_xlabel('x (nm)', fontsize=11); ax_C_xy.set_ylabel('y (nm)', fontsize=11)\n",
    "ax_C_xy.set_title(f'XY — Local Chirality $C$\\n'\n",
    "                  f'{p2_SWEEP_PARAM}={p2_best_val*1e3:.1f} nm  |  '\n",
    "                  f'$\\\\lambda$={p2_best_lda:.1f} nm', fontsize=11)\n",
    "\n",
    "im2 = ax_cos_xy.pcolormesh(x_coords, y_coords, cos_xy.T, cmap='PiYG', shading='auto',\n",
    "                            vmin=-clim_cos, vmax=clim_cos)\n",
    "fig.colorbar(im2, ax=ax_cos_xy, label=r'$\\cos(\\phi_{iE,H})$')\n",
    "draw_disks(ax_cos_xy, r0_best, h_best, px_best, py_best, p2_alpha, color='black', lw=1.2)\n",
    "ax_cos_xy.set_xlim(p2_xy_xlim); ax_cos_xy.set_ylim(p2_xy_ylim); ax_cos_xy.set_aspect('equal')\n",
    "ax_cos_xy.set_xlabel('x (nm)', fontsize=11); ax_cos_xy.set_ylabel('y (nm)', fontsize=11)\n",
    "ax_cos_xy.set_title(f'XY — $\\\\cos(\\\\phi_{{iE,H}})$\\n'\n",
    "                    f'{p2_SWEEP_PARAM}={p2_best_val*1e3:.1f} nm  |  '\n",
    "                    f'$\\\\lambda$={p2_best_lda:.1f} nm', fontsize=11)\n",
    "\n",
    "im3 = ax_C_yz.pcolormesh(y_coords_yz, z_coords_yz, C_yz.T, cmap='RdBu', shading='auto',\n",
    "                          vmin=-clim_C, vmax=clim_C)\n",
    "fig.colorbar(im3, ax=ax_C_yz, label=r'$C/\\langle|C_0|\\rangle$')\n",
    "ax_C_yz.axhline(0,           color='black', lw=1.2, ls='--', alpha=0.8, label='disk bottom')\n",
    "ax_C_yz.axhline(h_best*1e3,  color='black', lw=1.2, ls='--', alpha=0.8, label='disk top')\n",
    "ax_C_yz.axvline(-y_lim_best, color='black', lw=1.0, ls='--', alpha=0.6, label='unit cell wall')\n",
    "ax_C_yz.axvline( y_lim_best, color='black', lw=1.0, ls='--', alpha=0.6)\n",
    "ax_C_yz.set_xlim(-y_lim_best, y_lim_best); ax_C_yz.set_ylim(z_min_yz_best, z_max_yz_best)\n",
    "ax_C_yz.set_aspect(yz_asp_best)\n",
    "ax_C_yz.set_xlabel('y (nm)', fontsize=11); ax_C_yz.set_ylabel('z (nm)', fontsize=11)\n",
    "ax_C_yz.set_title('YZ — Local Chirality $C$  |  $x=0$', fontsize=11)\n",
    "ax_C_yz.legend(fontsize=7, loc='upper right')\n",
    "\n",
    "im4 = ax_cos_yz.pcolormesh(y_coords_yz, z_coords_yz, cos_yz.T, cmap='PiYG', shading='auto',\n",
    "                            vmin=-clim_cos, vmax=clim_cos)\n",
    "fig.colorbar(im4, ax=ax_cos_yz, label=r'$\\cos(\\phi_{iE,H})$')\n",
    "ax_cos_yz.axhline(0,           color='black', lw=1.2, ls='--', alpha=0.8, label='disk bottom')\n",
    "ax_cos_yz.axhline(h_best*1e3,  color='black', lw=1.2, ls='--', alpha=0.8, label='disk top')\n",
    "ax_cos_yz.axvline(-y_lim_best, color='black', lw=1.0, ls='--', alpha=0.6, label='unit cell wall')\n",
    "ax_cos_yz.axvline( y_lim_best, color='black', lw=1.0, ls='--', alpha=0.6)\n",
    "ax_cos_yz.set_xlim(-y_lim_best, y_lim_best); ax_cos_yz.set_ylim(z_min_yz_best, z_max_yz_best)\n",
    "ax_cos_yz.set_aspect(yz_asp_best)\n",
    "ax_cos_yz.set_xlabel('y (nm)', fontsize=11); ax_cos_yz.set_ylabel('z (nm)', fontsize=11)\n",
    "ax_cos_yz.set_title(r'YZ — $\\cos(\\phi_{iE,H})$  |  $x=0$', fontsize=11)\n",
    "ax_cos_yz.legend(fontsize=7, loc='upper right')\n",
    "\n",
    "fig.suptitle(\n",
    "    f'Near-field Chirality & Polarization State — Near-Kerker Geometry\\n'\n",
    "    f'{p2_SWEEP_PARAM} = {p2_best_val*1e3:.1f} nm  |  '\n",
    "    f'$\\\\lambda$ = {p2_best_lda:.1f} nm  |  $\\\\alpha$ = {p2_alpha}',\n",
    "    fontsize=12\n",
    ")\n",
    "plt.savefig(f'p2_nearfield_chirality_kerker_{p2_SWEEP_PARAM}.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05a8de5a",
   "metadata": {},
   "source": [
    "### Summary\n",
    "\n",
    "In this section we have systematically swept the HfO2 nanodisk geometry, fixing the asymmetry at $\\alpha = 0.05$ — to identify the unit-cell dimensions that maximise the spatially averaged optical chirality $\\langle C \\rangle$ in the mid-ultraviolet. The key finding is that the averaged chirality is not monotonically related to the resonance Q factor, but instead peaks sharply at a specific geometry where the asymmetric electric dipole mode $\\mathbf{p}_\\alpha$ and magnetic dipole mode $\\mathbf{m}_\\alpha$ are brought into spectral overlap — the Kerker condition.\n",
    "\n",
    "At this optimal geometry, the transmittance spectrum shows the two Fano dips associated with $\\mathbf{p}_\\alpha$ and $\\mathbf{m}_\\alpha$ merging toward unity transmission, while the averaged chirality spectrum exhibits a sharp peak of single sign. The near-field maps confirm this picture: the local chirality density $C$ and the polarization state indicator $\\cos(\\phi_{iE,H})$ are spatially uniform in sign across the unit cell, in stark contrast to the alternating-sign patterns seen in the previous section where no Kerker condition was imposed. Regions where $\\cos(\\phi_{iE,H}) \\approx -1$ extend throughout the near-field volume above and below the disk layer, indicating that the scattered fields preserve the helicity of the incident LCP wave everywhere a chiral analyte molecule might sit.\n",
    "\n",
    "These results validate the design strategy proposed by Hu, Lawrence and Dionne: the biperiodic disk geometry simultaneously provides high-Q resonances through the q-BIC mechanism and uniform-sign chirality enhancement through the Kerker condition, with the two effects independently controlled by $\\alpha$ and the disk aspect ratio respectively. The HfO2 material system delivers this functionality in the mid-ultraviolet, where the chiroptical absorption features of most pharmaceutically and biochemically relevant small chiral molecules are found, and where conventional high-index materials such as silicon are too lossy to sustain high-Q resonances."
   ]
  }
 ],
 "metadata": {
  "applications": [
   "Metamaterials, gratings, and other periodic structures",
   "Nanophotonics",
   "Sensors"
  ],
  "description": "This notebook demonstrates how to design and simulate biperiodic hafnium oxide (HfO2) nanodisk metasurfaces that support quasi-bound-states-in-the-continuum (q-BIC) with tunable Q-factors in the mid-ultraviolet.",
  "feature_image": "./img/hafnia_qbic.png",
  "features": [
   "Parameter sweep",
   "Resonance finder"
  ],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "quasi-BIC, Kerker condition, metasurface, chirality, hafnium oxide, Fano resonance, ultraviolet, Tidy3D, FDTD",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  },
  "title": "How to simulate q-BIC Kerker metasurfaces in Tidy3D FDTD"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}