{
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   "source": [
    "# Silver nanoparticle dimer with a 5 nm gap: how fine must the mesh be?\n",
    "\n",
    "**Author**: Seokwoo Kim, POSTECH\n",
    "\n",
    "Two metal nanoparticles separated by a nanometre-scale gap support a *bonding* (gap) plasmon: the\n",
    "charges on the facing surfaces attract each other, the resonance red-shifts as the gap closes, and the\n",
    "electric field in the gap is strongly enhanced. These structures are popular for sensing, surface-enhanced\n",
    "spectroscopy and nonlinear optics, and they are a classic stress test for FDTD: the gap is one to two\n",
    "orders of magnitude smaller than the wavelength, and the metal surface is curved, so on a Cartesian grid it is\n",
    "represented by a staircase.\n",
    "\n",
    "In this example we simulate a periodic array of silver nanosphere dimers (diameter 50 nm, gap 5 nm)\n",
    "under normal incidence and compare two local mesh sizes in the gap, 1 nm and 0.5 nm. We look at\n",
    "**two different outputs of the same simulation**:\n",
    "\n",
    "* the far-field transmission and reflection spectrum, and\n",
    "* the near-field intensity enhancement in the gap.\n",
    "\n",
    "The point of the example is that these two outputs do not converge at the same rate. A mesh that is good\n",
    "enough for the spectrum is not necessarily good enough for the field enhancement, so the mesh should be\n",
    "chosen for the quantity you want to report.\n",
    "\n",
    "<img src=\"img/silver_dimer_array.png\" width=500, alt=\"Illustration of silver NP dimer array\">\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "320ef3a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-02T02:16:56.520078Z",
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     "shell.execute_reply": "2026-10-02T02:17:01.639308Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tidy3d as td\n",
    "from scipy.optimize import curve_fit\n",
    "from tidy3d import web"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88119c72",
   "metadata": {},
   "source": [
    "## Simulation Setup\n",
    "\n",
    "**Geometry.** Two Ag spheres (diameter $D$ = 50 nm) are separated by a gap $g$ = 5 nm along $x$. The dimers\n",
    "form a square array with period 220 nm, which places the first diffraction order at $n \\times 220 = 330$ nm, outside\n",
    "our 380–900 nm band. The particles sit on a glass substrate and are covered by an index-matching liquid, so the\n",
    "surrounding medium is homogeneous with $n$ = 1.5. This keeps the example free of a sphere–substrate contact point,\n",
    "which would be a second small feature competing with the gap.\n",
    "\n",
    "**Material.** We use the Johnson–Christy silver model from the Tidy3D material library,\n",
    "`td.material_library[\"Ag\"][\"JohnsonChristy1972\"]`.\n",
    "\n",
    "**Source.** A plane wave at normal incidence, polarized along the dimer axis ($E \\parallel x$), which excites the\n",
    "bonding mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ad1cad48",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-02T02:17:01.641312Z",
     "iopub.status.busy": "2026-10-02T02:17:01.641312Z",
     "iopub.status.idle": "2026-10-02T02:17:01.646589Z",
     "shell.execute_reply": "2026-10-02T02:17:01.646589Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ag permittivity at 480 nm: -8.54+0.16j\n",
      "Ag permittivity at 520 nm: -10.83+0.20j\n",
      "Ag permittivity at 600 nm: -15.81+0.32j\n"
     ]
    }
   ],
   "source": [
    "nm = 1e-3  # Tidy3D length unit is um\n",
    "\n",
    "# geometry (lengths written in nm, converted with \"* nm\")\n",
    "D_nm, gap_nm = 50.0, 5  # sphere diameter, surface-to-surface gap\n",
    "D, gap = D_nm * nm, gap_nm * nm\n",
    "R = D / 2\n",
    "period = 220.0 * nm  # array period in x and y\n",
    "n_bg = 1.5  # index-matched glass environment\n",
    "Lz = 1.2  # simulation size along z (um)\n",
    "\n",
    "# materials\n",
    "Ag = td.material_library[\"Ag\"][\"JohnsonChristy1972\"]\n",
    "background = td.Medium(permittivity=n_bg**2)\n",
    "\n",
    "# wavelength / frequency range\n",
    "lam = np.linspace(380, 900, 261) * nm\n",
    "freqs = td.C_0 / lam\n",
    "freq0 = 0.5 * (freqs.max() + freqs.min())\n",
    "fwidth = freqs.max() - freqs.min()\n",
    "\n",
    "for wl in (480, 520, 600):\n",
    "    print(\n",
    "        f\"Ag permittivity at {wl} nm: {complex(Ag.eps_model(td.C_0 / (wl * nm))):.2f}\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27a9b0b6",
   "metadata": {},
   "source": [
    "**Mesh.** The background mesh is automatic (20 steps per wavelength in the medium). On top of it we place two\n",
    "`MeshOverrideStructure` boxes: one covering both particles with 1 nm steps, and a smaller box around the gap\n",
    "(24 nm wide laterally) with the step `dl_gap`. With `dl_gap` = 1 nm the gap box is not needed.\n",
    "\n",
    "**Symmetry.** For $E \\parallel x$ the fields are antisymmetric (PEC-like) about the $x = 0$ plane and symmetric\n",
    "(PMC-like) about $y = 0$, so `symmetry=(-1, 1, 0)` cuts the computational cost by four.\n",
    "\n",
    "**Subpixel treatment.** Tidy3D's default for metals is `Staircasing`; we keep the default here so that the\n",
    "mesh size is the only thing we change.\n",
    "\n",
    "**Monitors.** Two flux monitors give $T$ and $R$. A point field monitor sits at the centre of the gap, and a\n",
    "small planar field monitor ($z = 0$, 40 nm × 40 nm) records the field map around the gap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "89051781",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-02T02:17:01.648592Z",
     "iopub.status.busy": "2026-10-02T02:17:01.648592Z",
     "iopub.status.idle": "2026-10-02T02:17:01.835812Z",
     "shell.execute_reply": "2026-10-02T02:17:01.835812Z"
    }
   },
   "outputs": [],
   "source": [
    "def make_sim(dl_gap, dl_body=1 * nm, run_time=300e-15):\n",
    "    # two Ag spheres along x, gap centred at the origin\n",
    "    xc = (D_nm / 2 + gap_nm / 2) * nm\n",
    "    spheres = [\n",
    "        td.Structure(\n",
    "            geometry=td.Sphere(center=(s * xc, 0, 0), radius=D_nm / 2 * nm), medium=Ag\n",
    "        )\n",
    "        for s in (-1, 1)\n",
    "    ]\n",
    "\n",
    "    # local mesh refinement: whole dimer at dl_body, gap region at dl_gap\n",
    "    overrides = [\n",
    "        td.MeshOverrideStructure(\n",
    "            geometry=td.Box(\n",
    "                center=(0, 0, 0),\n",
    "                size=((2 * D_nm + gap_nm + 4) * nm, (D_nm + 4) * nm, (D_nm + 4) * nm),\n",
    "            ),\n",
    "            dl=(dl_body,) * 3,\n",
    "        )\n",
    "    ]\n",
    "    if dl_gap < dl_body:\n",
    "        overrides.append(\n",
    "            td.MeshOverrideStructure(\n",
    "                geometry=td.Box(\n",
    "                    center=(0, 0, 0), size=((gap_nm + 6) * nm, 24 * nm, 24 * nm)\n",
    "                ),\n",
    "                dl=(dl_gap,) * 3,\n",
    "            )\n",
    "        )\n",
    "    grid_spec = td.GridSpec.auto(\n",
    "        min_steps_per_wvl=20, wavelength=lam.min(), override_structures=overrides\n",
    "    )\n",
    "\n",
    "    source = td.PlaneWave(\n",
    "        center=(0, 0, -0.45 * Lz),\n",
    "        size=(td.inf, td.inf, 0),\n",
    "        direction=\"+\",\n",
    "        pol_angle=0,  # E along x\n",
    "        source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
    "    )\n",
    "    monitors = [\n",
    "        td.FluxMonitor(\n",
    "            center=(0, 0, 0.42 * Lz), size=(td.inf, td.inf, 0), freqs=freqs, name=\"T\"\n",
    "        ),\n",
    "        td.FluxMonitor(\n",
    "            center=(0, 0, -0.48 * Lz), size=(td.inf, td.inf, 0), freqs=freqs, name=\"R\"\n",
    "        ),\n",
    "        td.FieldMonitor(\n",
    "            center=(0, 0, 0), size=(0, 0, 0), freqs=freqs, name=\"gap_center\"\n",
    "        ),\n",
    "        td.FieldMonitor(\n",
    "            center=(0, 0, 0),\n",
    "            size=(40 * nm, 40 * nm, 0),\n",
    "            freqs=freqs[::4],\n",
    "            name=\"gap_plane\",\n",
    "        ),\n",
    "    ]\n",
    "    return td.Simulation(\n",
    "        size=(period, period, Lz),\n",
    "        medium=background,\n",
    "        structures=spheres,\n",
    "        sources=[source],\n",
    "        monitors=monitors,\n",
    "        grid_spec=grid_spec,\n",
    "        run_time=run_time,\n",
    "        shutoff=1e-5,\n",
    "        boundary_spec=td.BoundarySpec(\n",
    "            x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n",
    "        ),\n",
    "        symmetry=(-1, 1, 0),\n",
    "        subpixel=td.SubpixelSpec(\n",
    "            metal=td.Staircasing()\n",
    "        ),  # the default, written out explicitly\n",
    "    )\n",
    "\n",
    "\n",
    "sims = {\"dl = 1 nm\": make_sim(dl_gap=1 * nm), \"dl = 0.5 nm\": make_sim(dl_gap=0.5 * nm)}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1490395e",
   "metadata": {},
   "source": [
    "Let us look at the structure and at the grid around the gap. The right-hand panels show the permittivity as it is\n",
    "assigned on the grid cells: the spheres become staircases, and with 1 nm cells the surface-to-surface distance on\n",
    "the dimer axis is quantised by the grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1404221e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-02T02:17:01.837816Z",
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     "shell.execute_reply": "2026-10-02T02:17:02.206890Z"
    }
   },
   "outputs": [
    {
     "data": {
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IkSNHYtmyZRg/fjxuvfVWXH311XjkkUfwpz/9CTt27OBI5Bj685//HPi/rus466yz8Ouvv+Kmm24KLM/MzMQpp5wS9Lfrq6++itNOOw2nnnpq0PfqhRdeCAD48MMPcc4559QZOykpKfB/v9+P8vJydOnSBZmZmfjqq69w3XXXhb0fUkqsWrUKf/jDH2qN9gB++3v41VdfxXnnnYdmzZoF5T1w4EA8+OCD+Pjjj3HNNdeEFbPmDoI333wTvXr1gt1uDztfAOjWrRvOO++8wPusrCzLzzncfI/8PA8ePAjTNHHeeefhb3/7W63Y/fv3R7du3SLKN5QxY8YETUly3nnn4bHHHsMvv/yCnj17Ys2aNSgtLcVVV10VtA+6riMvLw8ffvhhndtfvXo1+vXrh969eweWNW/eHNdccw3mz58f1PfIz+DQoUPwer0477zz8PTTT2PTpk3o1atXoN1ms+GWW24JvHc4HLjlllswduxYbNiwAb/73e/C/gx2796NjRs3YtKkSUF3Z/zP//wPunXrBrfbHfa2jrRmzZqw+tX83RBKfedC5eXlda7fvn17vPvuu0HLrrvuOnTr1g0TJkyo9XdGKLfeemvQ+/POOw8vvvhirX4jRowI+hzz8vIAANdee23Q7YR5eXn429/+hp07d+Kkk04KK4eQjruMRRRFkY6EuvXWW2v17dChg7rhhhsC7493JNTgwYNVmzZt6uwzduzYOivoPXv2rHMf0QBXbidNmqQ0TVPvvPPOcW0nUhwJRRSMV8B5BZxXwBPnCvjRjnUk1GmnnRY0qvDxxx9XANR//vOfwLKaz/bll18OLNu0aVPg++7zzz8PLH/33XePKX9qGDXHgeLi4qDlo0aNUi6Xq1b//v37qx49egTen3baaXV+Xx45Or+u48CUKVMCx54j1z9yVGk4x4Hi4mIFQP3v//5vnfudlJRUZ95z584N9K3vOCClVJdffrkCoNLT09WwYcPU4sWLwxqV0aFDBzV48OBay/v3768GDBhwTPn+85//VHl5ecrpdAb1EUIExQCgbrzxxnpzrFHfceDIn2ulfvt6rV27Viml1EMPPVTnPqSnp9cZ3+FwqOuvv77W8prfQUceB7799ls1fPhwlZ6eXivORx99FOjXv39/1b59+1rbLCoqUgDU3/72tzpzOlrN3y/PPfdcrbbLLrss5iNij3ckVCiTJk1SANSOHTvq7Bfq98rR5881I6GO/DtUqd++p5YvXx60vOZ78Msvvzym/I/EkVAU146efLzG0RPe1Qj11DulVIPlFA4pJYQQeOeddyxzSk1NjWr8VatW4aGHHsLMmTMxePDgsNYpKysLa4SSw+FA8+bNQ7Y3b94cTqcTu3fvrtVWsyw3NzesnIiaEl4Br8Yr4MeOV8Dj/wp4Qxk9ejQcDkfgfc33788//xw0z1RqaipGjhwZeH/KKacgMzMTbdq0CVzNBn67st2QI8MpclZ/E4bzt6uUEqeffjrmzp1r2bddu3b1xr799tuxZMkSjB8/Hv369UNGRgaEEBg5cuQxTRIeDikl/ud//gcTJ060bD/55JPD3pYQAq+99ho+//xz/POf/8S7776LG2+8EXPmzMHnn39e79/W4X7O4eT7ySefYNiwYTj//PPx1FNPoXXr1rDb7ViyZAlefvnlWusd+fvyeNW3HzVfyxdffBE5OTm1+h05suV4lJaWon///khPT8eMGTPQuXNnuFwufPXVV/jrX/8ate+paCouLg6rX0ZGRp1f09atWwOoPl4d/bO5e/du9O3b95jyq9nWgQMH6h35G8mT4EP1jeZ5NYtQFNeaNWuG0tLSWst/+eWXBo0TqthlpXPnznj33Xdx4MCBkMWYzp07QymFTp06RXSAbQg//vgjRo0aheHDh0d0y9+dd96JpUuX1tuvvkdNa5qG008/vdaTAgFg/fr1OOmkk3grACWk9u3bB73PyMiAy+VCy5Ytay3fv39/4P1PP/2E//73v8jKyrLc7p49e+qNXVVVhdmzZ2PJkiXYuXNn0B8QZWVlkewG9u7di/Ly8nonXP7pp5/wzTffHFfeNfr374/LL78c06dPx2OPPYYBAwZg+PDhuPrqq8O6Nerozx6oPr4cPHjwmPJ98803MWvWLGzcuLHWk6mO1qlTp3rzC9fR+1Fze3TNfvz0008AfitQHi09Pb3O7f/yyy/o169freVHP/ACAL777jtMnjwZH3zwQa1bC47+nsrNza11S3jNsXHbtm0RFaFqjv9du3at1XbKKafgq6++CntbRxo4cOAxrRct9X2ta7Rt27bW911GRkatE5+agt3R69OJoXPnzvj3v/+Niy66qN6/WUO1v/baaxg1alTQk/c8Ho/l39n1ycrKQnp6Or799tt6866oqGjQn6/f/e53+N3vfof7778fL7/8Mq655hosX7486ELPsQo337///e9wuVx49913g45BS5YsOe4cIjknsVJzG36rVq2O6XPv0KEDNm/eXGv50cvWrl2L/fv34/XXXw9MHwAAW7dutdzurl274Ha7g44FP/74IwDUeZt4qByB3455R/rhhx8i2taRaopH9VmyZEmdTx2vuZDz5ZdfBhWcdu3ahV9//TUwZUKkai4ihPo75UTCIhTFtc6dO6OsrAzffPMNevbsCaC6gtzQj+BMSUkJ+yB8+eWXY8GCBZg+fToef/zxoDalFIQQ+OMf/4jCwkJMnz4dL730UtABRSmFAwcOhHxa3/GoqKjAZZddhjZt2gQezxuuhpoTCgD+9Kc/YdKkSfjyyy8DIyV++OEHfPDBB7j77rvDzomoKeEV8GC8Ah45XgGPnoa6At5Qwr0CHYsr2NT4rrjiCrz99tt49tlna53AVlVVQUoZOLkP9Tetruu1vv7z588PeXdBXTRNw/Dhw/HSSy8F/a1Xo+bv4SuuuAL33Xcf3n33XeTn5wf1KS0tRWpqati/lw4ePIjMzMygv21rTvaPvBhwPMLNV9d1CCFqPe6+Zo7G4xHJOYmV/Px8pKen44EHHsAFF1xQa+Tw3r176yxi5OfnY8GCBdi4cWPg8z1w4ACWLVsW1K/md8yR31M+nw9PPfWU5XYNw8DTTz+NgoKCQN+nn34aWVlZ6NOnT0T72Lp1a/Tu3RtLly4NGhW7Zs0afP/994EiVaQaakRs9+7dceqpp+KZZ57BLbfcEvisFi5cCCEE/vSnPwX6lpWVYffu3WjdunVgP6y+Rjt37sTixYvRs2fPsItl8YxFKIprI0eOxF//+ldcdtlluOOOOwITrJ588snHfLXTSp8+fbBw4ULMmjULXbp0QatWrUJeSb7gggtw3XXX4YknnsBPP/2EwYMHQ0qJTz75BBdccAHGjRuHzp07Y9asWSgsLMS2bdswfPhwpKWlYevWrVi5ciXGjBkTcTFGCFHvKKTp06fj+++/x+TJk/GPf/wjqK1z586WV7hrdOvWrcFuGbntttvw7LPPYujQobj77rtht9sxd+5cZGdnY8KECQ0SgyhR8Ar4b3gFvG68Ah77K+BE0XDdddfhlVdewa233ooPP/wQv//972GaJjZt2oRXXnkF7777bqAQ1KdPH7z//vuYO3cucnNz0alTJ+Tl5eGSSy7Biy++iIyMDHTr1g3r1q3D+++/f8wXRR944AG899576N+/f+ChGbt378arr76KTz/9FJmZmbjnnnvwxhtv4JJLLgk8DMLtduM///kPXnvtNWzbtq3WaOBQli5diqeeegqXXXYZOnfujEOHDuHZZ59Feno6Lr744mPah6OFm+/QoUMxd+5cDB48GFdffTX27NmDBQsWoEuXLvjmm2+OK4dIzkmspKenY+HChbjuuutw5plnYuTIkcjKysL27dvx1ltv4fe//z2efPLJkOtPnDgRL730Ev7nf/4Ht99+e+ABFe3bt8eBAwcCx6lzzjkHzZo1w6hRo3DHHXdACIEXX3wxZKE7NzcXDz30ELZt24aTTz4ZK1aswMaNG/HMM88EFcoGDBiAjz76qN6C+ezZszF06FCce+65uPHGG3HgwAHMnz8f3bt3R0VFRdif15Ea8u+VRx55BMOGDcOgQYMwcuRIfPvtt3jyySfx5z//Gaeddlqg38qVKzF69OigY8vEiROxZcsWXHTRRcjNzcW2bdvw9NNPw+121xoAcaJiEYriWosWLbBy5UoUFBRg4sSJ6NSpE2bPno2ffvqpQYtQU6dOxS+//IKHH34Yhw4dQv/+/ev8hb9kyRL07NkTzz33HO655x5kZGTgrLPOCpqXZdKkSTj55JPx2GOPYfr06QCqRywMGjQIw4YNiyi/ml+m9f2RvHfvXgDArFmzarWNGjWqziJUQ0pLS8PatWtx1113YdasWZBSYsCAAXjssceaxBBSosbEK+C8Ah4uXgGP/RVwomjQNA2rVq3CY489hhdeeAErV65EcnIyTjrpJNx5551BI0vnzp2LMWPGYPLkyaiqqsKoUaOQl5eHxx9/HLquY9myZfB4PPj973+P999/v9bvu3C1adMG69evx5QpU7Bs2TKUl5ejTZs2GDJkSOCJq8nJyfjoo4/wwAMP4NVXX8ULL7yA9PR0nHzyyZg+fXrQvG716d+/P7744gssX74cJSUlyMjIQN++fbFs2bIGu+053HwvvPBCPPfcc3jwwQcxfvx4dOrUKVBgOd4iVKTnJFauvvpq5Obm4sEHH8QjjzwCr9eLNm3a4LzzzsPo0aPrXLddu3b48MMPcccdd+CBBx5AVlYW/vKXvyAlJQV33HFH4EnXLVq0wJtvvokJEyZg8uTJaNasGa699lpcdNFFlt9TzZo1w9KlS3H77bfj2WefRXZ2Np588kncfPPNQf0qKiosR/IebfDgwXj11VcxefJkFBYWonPnzliyZAn+8Y9/1HnBvrFccskleP311zF9+nTcfvvtyMrKwr333oupU6fWu+6gQYOwaNEiLFiwIPD3z/nnn4/JkyfjzDPPbITsG8FxT21ORFH31ltvKSGE+uabb2KdChFFqOZpJHv37g1aPmrUKJWSklKrf//+/VX37t0D703TVBdffLESQqiRI0eq+fPnq3nz5qlbb71VNW/ePOiJcRdffLFKSUlRc+bMUX/7298CT9G5/vrrla7r6s4771RPP/20uuGGG1Tbtm1VixYtgp6iFO7T8X799VeVk5OjkpOT1fjx49XTTz+t7rvvPtW9e3d18OBBpZRSbrdbnXnmmcpms6k///nPauHCherRRx8N7PeRnwfqeSrSY489prp27aomTpyonn76afXoo4+qU045RaWnp6uff/65zlw7dOighg4davk5H/kEonDzrXmaz3nnnacWLlyopk+frlq1aqV69uypjv6zCoD6y1/+Umd+R7rtttuUEELNnDlT/e1vf1NFRUVBn8fRTwe0+notW7ZMaZqmevTooWbNmqWefvpp9b//+7+qd+/e9eayfft2lZmZqVq2bKmmT5+uHn30UXXqqaeq3r17KwBq27ZtSiml9u3bp5o1a6Y6dOig5syZo+bOnavOOOMM1atXr1r59O/fX+Xm5qpWrVqp22+/Xc2fP1+de+65CoB65plnguLXPO2tPu+8805gH+fOnasmT56sMjIyVPfu3WP+VCSllProo4/UzJkz1cyZM1WrVq1Ux44dA++PfGKUlZqv6auvvhq0vOYpRkc+3e7o3xU1Qn3PR/r9SERU484771Qul0sZhhHxuqF+Vx2tvLxc2Ww29eSTTx5LinQC4UgoohPAhx9+iJEjR+L000+PdSpE1Mh4BZxXwCPBK+Brw/+wouSDDz4IjICuMWXKFADAtGnTgm5hJCKKN1VVVUFz3+3fvx8vvvgizj333Iieuhapjz/+GG3atKl1bKCmRyjFGQqJiIiIyNr48ePx9NNPo6KiIuITkAEDBmDfvn31ziF26NAhNG/eHPPmzcNf/vKX40mXiIiOQ+/evTFgwACcdtppKCkpwXPPPYddu3ahqKjomIro4R4HKHFwJBQRERERAeAVcCKiRHfxxRfjtddewzPPPAMhBM4880w899xzHMVJDYYjoYiIiIgIAK+AExERUXRpsU6AiIioIXz88cf4wx/+gNzcXAghaj2t7IYbboAQIug1ePDg2CRLFKcuvvhivP3227jrrrvw0EMPoX379njnnXd4BZyIiIgaBEdCERFRk/DOO+/g//7v/9CnTx/88Y9/xMqVKzF8+PBA+w033ICSkhIsWbIksMzpdKJZs2YxyJaIiIiIKPFwTqgwSSmxa9cupKWlQQgR63SIiMKilMKhQ4eQm5sLTWvag1+HDBmCIUOG1NnH6XSG9fQtIiKiY8XzBiJKBMd6nsEiVJh27dqFvz1wBpqnNe2TOIqdA4ck7n/hAMrcMtapUBO0Y8cOtG3bNtZpxNzatWvRqlUrNGvWDBdeeCFmzZqFFi1axDotIiJqQnbt2oV27drFOg0iokYR6XkGi1BhSktLQ/M0DQMuyIfdlRrrdKipMT1Q0os/T5sKPal1rLOhJqS8vBzt2rVDWlparFOJucGDB+OPf/wjOnXqhC1btuDee+/FkCFDsG7dupBP/fJ6vfB6vYH3UkocOHAALVq04NVtImryEmk0bUOqOebefPPNcDgcMc6m6XrggQdinQLFuXvvvTfWKTRpPp8Pzz77bMTnGSxChanmZMPuSoUzOTO2yVCTI/1uSK+J1NRU2FPSY50ONUEsmAAjR44M/P/0009Hz5490blzZ6xduxYXXXSR5TqzZ8/G9OnTGytFIqK4xNG0kak55jocDjidzhhn03Slp/NvZqobf/4aR6TnGSxCERFRQjrppJPQsmVLbN68OWQRqrCwEAUFBYH3ZWVlaN++Pa9uRxmvblO4eJU7uo71KjcREVEoLEJFQNcElFEF6bfHOhVqYqRRCaWMWKdBlFB+/fVX7N+/H61bh74F1ul0Wl5F49Xt6OLVbQoXfw4bB0fTEhFRQ2ERKgKZqRqMii1QHl79poalpB+anVcZiY5HRUUFNm/eHHi/detWbNy4Ec2bN0fz5s0xffp0XH755cjJycGWLVswceJEdOnSBfn5+THMmoiIiIgocbAIFYHSCglbamc4kniFlhqWNCqhjMpYp0F0Qvvyyy9xwQUXBN7X3EY3atQoLFy4EN988w2WLl2K0tJS5ObmYtCgQZg5cyZHUhARERERNRIWoSJgSgVhS4JmT4l1KtQESdMX6xSITmgDBgyAUipk+7vvvtuI2RARERER0dH4rFUiIiIiIiIiIoo6joQiIiIiIiKiE8qECRNCts2ZM6cRM6FYquv7gOJTXI6EWrBgATp27AiXy4W8vDx88cUXIft+9913uPzyy9GxY0cIITBv3rzj3iYRERERERERETWsuCtCrVixAgUFBZg2bRq++uor9OrVC/n5+dizZ49l/8rKSpx00kl48MEHkZOT0yDbJCIiIiIiIiKihhV3t+PNnTsXN998M0aPHg0AWLRoEd566y0sXrwYkyZNqtX/7LPPxtlnnw0Alu3Hsk0iIiKKnXCH1vN2i6aLt1cQERE1TXFVhPL5fNiwYQMKCwsDyzRNw8CBA7Fu3bpG3abX64XX6w28Ly8vR2aqBqP8R6gq1zHlQhSKkj5oOr+viIiIiIiIqOmKqyLUvn37YJomsrOzg5ZnZ2dj06ZNjbrN2bNnY/r06UHLXp6SDc2eDt2Zcky5EIUiTQ9E6CfLExEREREREZ3w4qoIFU8KCwtRUFAQeF9eXo535/eBlpQDe3Jm7BKjJkn63ZDe0linQURERERERBQ1cVWEatmyJXRdR0lJSdDykpKSkJOOR2ubTqcTTqfzmGISEREREREREVGwuHo6nsPhQJ8+fVBUVBRYJqVEUVER+vXrFzfbJCIiIiIiIiKiyMTVSCgAKCgowKhRo3DWWWehb9++mDdvHtxud+DJdtdffz3atGmD2bNnA6ieePz7778P/H/nzp3YuHEjUlNT0aVLl7C2SURERERERERE0RV3Ragrr7wSe/fuxdSpU1FcXIzevXtj9erVgYnFt2/fDk37bQDXrl27cMYZZwTeP/roo3j00UfRv39/rF27NqxtEhERERERERFRdMVdEQoAxo0bh3Hjxlm21RSWanTs2BFK1f9Ysbq2SURERERERERE0RWXRah4lewUkFXF8MtDsU6FmhhpeiDqr6USERERERERnbBYhIqAwy4g/eUw4Yt1KtTEKOmDprtinQYRERERERFR1LAIFYHSCglb+slwJmfGOhVqYqTfDektjXUaRERERERERFGj1d+FiIiIiIiIiIjo+HAkFBERERERETUZEyZMsFw+Z86cRs6EGkqorymdeDgSioiIiIiIiIiIoo5FKCIiIiIiIiIiijoWoYiIiIiIiIiIKOo4J1QEdE1AGVWQfnusU6EmRhqVUMqIdRpEREREREREUcMiVAQyUzUYFVugPI5Yp0JNjJJ+aPa0WKdBREREREREFDUsQkWgtELCltoZjqT0WKdCTYw0KqGMylinQURERERERBQ1LEJFwJQKwpYEzZ4S61SoCZKmL9YpEBGdUMJ9XDMfyR0/+IhtIiKixMaJyYmIiIiIiIiIKOpYhCIiIiIiIiIioqjj7XhERERERETU5FndEsxbtuMPb91u2jgSioiIiIiIiIiIoo5FKCIiIiIiIiIiijrejheBzFQNRvmPUFWuWKdCTYySPmg6v6+IiIiIiIio6WIRKgI+v4JmT4fuTIl1KtTESNMDoWKdBREREREREVH0sAgVgUqvgpaUA3tyZqxToSZG+t2Q3tJYp0FEREREREQUNZwTioiIiIiIiIiIoo5FKCIiIiIiIiIiijoWoYiIiIiIiIiIKOrisgi1YMECdOzYES6XC3l5efjiiy/q7P/qq6/i1FNPhcvlwumnn4633347qL2iogLjxo1D27ZtkZSUhG7dumHRokXR3AUiIiIiIiIiIjpC3E1MvmLFChQUFGDRokXIy8vDvHnzkJ+fjx9++AGtWrWq1f+zzz7DVVddhdmzZ+OSSy7Byy+/jOHDh+Orr75Cjx49AAAFBQX44IMP8NJLL6Fjx4547733cNtttyE3NxfDhg1r7F0kIiIiIiKiODBhwoRay+bMmRODTBKT1edPTVvcFaHmzp2Lm2++GaNHjwYALFq0CG+99RYWL16MSZMm1er/+OOPY/DgwbjnnnsAADNnzsSaNWvw5JNPBkY7ffbZZxg1ahQGDBgAABgzZgyefvppfPHFFxEVoZKdArKqGH556Dj3kiiYND0QKtZZEBERETVtH3/8MR555BFs2LABu3fvxsqVKzF8+PBAu1IK06ZNw7PPPovS0lL8/ve/x8KFC9G1a9fYJU1E1ITEVRHK5/Nhw4YNKCwsDCzTNA0DBw7EunXrLNdZt24dCgoKgpbl5+dj1apVgffnnHMO3njjDdx4443Izc3F2rVr8eOPP+Kxxx6LKD+HXUD6y2HCF9F6RPVR0gdNd8U6DaITGk8siIioPm63G7169cKNN96IP/7xj7XaH374YTzxxBNYunQpOnXqhClTpiA/Px/ff/89XC7+rUZEdLziqgi1b98+mKaJ7OzsoOXZ2dnYtGmT5TrFxcWW/YuLiwPv58+fjzFjxqBt27aw2WzQNA3PPvsszj///JC5eL1eeL3ewPvy8nKUVkjY0k+GMznzGPaOKDTpd0N6S2OdBtEJjScWRERUnyFDhmDIkCGWbUopzJs3D5MnT8all14KAHjhhReQnZ2NVatWYeTIkY2ZKhFRkxSXE5M3tPnz5+Pzzz/HG2+8gQ0bNmDOnDn4y1/+gvfffz/kOrNnz0ZGRkbg1a5du0bMmIiIIjVkyBDMmjULl112Wa22o08sevbsiRdeeAG7du0KGjlLRESJa+vWrSguLsbAgQMDyzIyMpCXlxfyrgwiIopMXBWhWrZsCV3XUVJSErS8pKQEOTk5luvk5OTU2b+qqgr33nsv5s6diz/84Q/o2bMnxo0bhyuvvBKPPvpoyFwKCwtRVlYWeO3YseM4946IiGKFJxZERFSfmjsp6rvL4mherxfl5eVBLyIishZXRSiHw4E+ffqgqKgosExKiaKiIvTr189ynX79+gX1B4A1a9YE+vv9fvj9fmha8K7qug4pZchcnE4n0tPTg15ERHRi4okFERFFC++gICIKX1zNCQUABQUFGDVqFM466yz07dsX8+bNg9vtDjwt7/rrr0ebNm0we/ZsAMCdd96J/v37Y86cORg6dCiWL1+OL7/8Es888wwAID09Hf3798c999yDpKQkdOjQAR999BFeeOEFzJ07N2b7SURE8W/27NmYPn16rNOg4xTu45/5SO5jx0dsU1NQcydFSUkJWrduHVheUlKC3r17h1yvsLAw6EFJ5eXlLEQREYUQVyOhAARuk5s6dSp69+6NjRs3YvXq1YGr19u3b8fu3bsD/c855xy8/PLLeOaZZ9CrVy+89tprWLVqFXr06BHos3z5cpx99tm45ppr0K1bNzz44IO4//77ceuttzb6/hERUeM78sTiSHXd7g3w1mwiokTSqVMn5OTkBN1lUV5ejvXr14e8KwPgHRRERJGIu5FQADBu3DiMGzfOsm3t2rW1lo0YMQIjRowIub2cnBwsWbLkuPPSNQFlVEH67ce9LaIjSaMSShmxToOoyTryxKLmanbNicXYsWNDrud0OuF0OhspSyIiiraKigps3rw58H7r1q3YuHEjmjdvjvbt22P8+PGYNWsWunbtGniSam5uLoYPHx67pImImpC4LELFq8xUDUbFFiiPI9apUBOjpB+aPS3WaRCd0HhiQURE9fnyyy9xwQUXBN7X3EY3atQoPP/885g4cSLcbjfGjBmD0tJSnHvuuVi9ejVcLlesUiYialJYhIpAaYWELbUzHEkcYksNSxqVUEZlrNMgOqHxxIKIiOozYMAAKKVCtgshMGPGDMyYMaMRs6J4YzXPHecNPH6cP5AAFqEiYkoFYUuCZk+JdSrUBEnTF+sUiE5oPLEgIiIiIopvcTcxORERERERERERNT0sQhERERERERERUdSxCEVERERERERERFHHIhQREREREREREUUdi1BERERERERERBR1fDpeBDJTNRjlP0JV8XHe1LCU9EHT+X1FRERERERETReLUBHw+RU0ezp0Z0qsU6EmRpoeiNBPliciIiIiIiI64bEIFYFKr4KWlAN7cmasU6EmRvrdkN7SWKdBREREREREFDWcE4qIiIiIiIiIiKKORSgiIiIiIiIiIoo6FqGIiIiIiIiIiCjqWIQiIiIiIiIiIqKoYxGKiIiIiIiIiIiijk/Hi0CyU0BWFcMvD8U6FWpipOmBULHOgoiIiIiIrEyYMKHWsjlz5sQgkxOD1edFBLAIFRGHXUD6y2HCF+tUqIlR0gdNd8U6DSIiIiIiIqKoYREqAqUVErb0k+FMzox1KtTESL8b0lsa6zSIiIiIiIiIooZzQhERERERERERUdSxCEVERERERERERFHHIhQREREREREREUUdi1BERERERERERBR1LEIREREREREREVHUxeXT8RYsWIBHHnkExcXF6NWrF+bPn4++ffuG7P/qq69iypQp2LZtG7p27YqHHnoIF198caD9hhtuwNKlS4PWyc/Px+rVqyPKS9cElFEF6bdHtkNE9ZBGJZQyYp0GERERERERUdTEXRFqxYoVKCgowKJFi5CXl4d58+YhPz8fP/zwA1q1alWr/2effYarrroKs2fPxiWXXIKXX34Zw4cPx1dffYUePXoE+g0ePBhLliwJvHc6nRHnlpmqwajYAuVxHNvOEYWgpB+aPS3WaRARERERERFFTdwVoebOnYubb74Zo0ePBgAsWrQIb731FhYvXoxJkybV6v/4449j8ODBuOeeewAAM2fOxJo1a/Dkk09i0aJFgX5OpxM5OTnHlVtphYQttTMcSenHtR2io0mjEsqojHUaRERERERERFETV0Uon8+HDRs2oLCwMLBM0zQMHDgQ69ats1xn3bp1KCgoCFqWn5+PVatWBS1bu3YtWrVqhWbNmuHCCy/ErFmz0KJFi5C5eL1eeL3ewPvy8nKYUkHYkqDZU45h74jqJk1frFMgIkpoEyZMCKvfnDlzopxJ/Aj3MyEiIiIKR1xNTL5v3z6Ypons7Oyg5dnZ2SguLrZcp7i4uN7+gwcPxgsvvICioiI89NBD+OijjzBkyBCYphkyl9mzZyMjIyPwateu3XHsGRERERERERFRYourkVDRMnLkyMD/Tz/9dPTs2ROdO3fG2rVrcdFFF1muU1hYGDTCqry8HO/O7xP1XImIiIiIiIiImqK4GgnVsmVL6LqOkpKSoOUlJSUh53PKycmJqD8AnHTSSWjZsiU2b94cso/T6UR6enrQi4iIiIiIiIiIjk1cjYRyOBzo06cPioqKMHz4cACAlBJFRUUYN26c5Tr9+vVDUVERxo8fH1i2Zs0a9OvXL2ScX3/9Ffv370fr1q0bMn0iIiIiIiJKEFbz5iXSvIE1OH8gRSKuRkIBQEFBAZ599lksXboU//3vfzF27Fi43e7A0/Kuv/76oInL77zzTqxevRpz5szBpk2bcN999+HLL78MFK0qKipwzz334PPPP8e2bdtQVFSESy+9FF26dEF+fn5M9pGIiIiIiIiIKNHE1UgoALjyyiuxd+9eTJ06FcXFxejduzdWr14dmHx8+/bt0LTfamfnnHMOXn75ZUyePBn33nsvunbtilWrVqFHjx4AAF3X8c0332Dp0qUoLS1Fbm4uBg0ahJkzZ8LpdEaUW2aqBqP8R6gqV8PtMBEAJX3QdH5fERERERERUdMVd0UoABg3blzI2+/Wrl1ba9mIESMwYsQIy/5JSUl49913GyQvn19Bs6dDd6Y0yPaIakjTA6FinQURERERERFR9MRlESpeVXoVtKQc2JMzY50KNTHS74b0lsY6DSIiIiIiIqKoibs5oYiIiIiIiIiIqOlhEYqIiIiIiIiIiKKORSgiIiIiIiIiIoo6FqGIiIiIiIiIiCjqWIQiIiIiIiIiIqKo49PxIpDsFJBVxfDLQ7FOhZoYaXogVKyzICIiIiIiIooeFqEi4LALSH85TPhinQo1MUr6oOmuWKdBRERElPDuu+8+TJ8+PWjZKaecgk2bNsUoIyKipoNFqAiUVkjY0k+GMzkz1qlQEyP9bkhvaazTIGrSeFJBRETh6t69O95///3Ae5uNp01ERA2Bv02JiChh8KSCiIjCYbPZkJOTE+s0iIiaHP71TURECYMnFUREFI6ffvoJubm5cLlc6NevH2bPno327dtb9vV6vfB6vYH35eXljZUmEdEJh0/HIyKihFFzUnHSSSfhmmuuwfbt2+vs7/V6UV5eHvQiIqKmLS8vD88//zxWr16NhQsXYuvWrTjvvPNw6JD1w4lmz56NjIyMwKtdu3aNnDER0YmDRSgiIkoIkZ5UADyxICJKREOGDMGIESPQs2dP5Ofn4+2330ZpaSleeeUVy/6FhYUoKysLvHbs2NHIGRMRnTh4Ox4RESWEIUOGBP7fs2dP5OXloUOHDnjllVdw0003Wa5TWFiIgoKCwPvy8nIWooiIEkxmZiZOPvlkbN682bLd6XTC6XQ2clZERCcmFqEioGsCyqiC9NtjnQo1MdKohFJGrNMgSij1nVQAPLEgIiKgoqICW7ZswXXXXRfrVIiITngsQkUgM1WDUbEFyuOIdSrUxCjph2ZPi3UaRAmFJxVERGTl7rvvxh/+8Ad06NABu3btwrRp06DrOq666qpYp0ZEdMJjESoCpRUSttTOcCSlxzoVamKkUQllVMY6DaImjScVREQUjl9//RVXXXUV9u/fj6ysLJx77rn4/PPPkZWVFevUiIhOeCxCRcCUCsKWBM2eEutUqAmSpi/WKRA1aTypICKicCxfvjzWKRARNVksQhERUULgSQURERERUWxpsU6AiIiIiIiIiIiaPhahiIiIiIiIiIgo6ng7HhEREREREVEDmDBhQq1lc+bMiUEm0WG1f0SR4EgoIiIiIiIiIiKKurgcCbVgwQI88sgjKC4uRq9evTB//nz07ds3ZP9XX30VU6ZMwbZt29C1a1c89NBDuPjiiwPt9913H5YvX44dO3bA4XCgT58+uP/++5GXlxdRXpmpGozyH6GqXMe8b0RWlPRB0/l9RURERERERE1X3BWhVqxYgYKCAixatAh5eXmYN28e8vPz8cMPP6BVq1a1+n/22We46qqrMHv2bFxyySV4+eWXMXz4cHz11Vfo0aMHAODkk0/Gk08+iZNOOglVVVV47LHHMGjQIGzevDmiR3P7/AqaPR26M6XB9pcIAKTpgVCxzoKIiIiIiIgoeuKuCDV37lzcfPPNGD16NABg0aJFeOutt7B48WJMmjSpVv/HH38cgwcPxj333AMAmDlzJtasWYMnn3wSixYtAgBcffXVtWI899xz+Oabb3DRRReFnVulV0FLyoE9OfMY947ImvS7Ib2lsU6DiIiIiIiIKGriak4on8+HDRs2YODAgYFlmqZh4MCBWLduneU669atC+oPAPn5+SH7+3w+PPPMM8jIyECvXr0aLnkiIiIiIiIiIgoprkZC7du3D6ZpIjs7O2h5dnY2Nm3aZLlOcXGxZf/i4uKgZW+++SZGjhyJyspKtG7dGmvWrEHLli1D5uL1euH1egPvy8vLI90diiUF+EwTHp8Jr2HAMCVMqSCVgoCApgnYNA0OmwaX3QaXXYfQRKyzjhmpFErdXuw9VImySh8qPH54DRNSSmhCwGHXkeKwIyPZgZZpSWiRmgQtgT8vIiIiIiIiilxcFaGi6YILLsDGjRuxb98+PPvss7jiiiuwfv16y3mmAGD27NmYPn160LL/N9G6L8WeUgqHqnw4WOnFoarqIophyrDXF0Ig2WlDmsuBjGQHMpNdsNviaqBgg/IZJjYXl+KH3QexZU8pft1fAa9hhr2+XdfQplkqOrVKx8mtm+OU1s2Q5EiYXydERERERER0DOLqrLFly5bQdR0lJSVBy0tKSpCTk2O5Tk5OTlj9U1JS0KVLF3Tp0gW/+93v0LVrVzz33HMoLCy03G5hYSEKCgoC78vLy/Hu/D7HslsULQoorfRib3kl9lV4YEZQdKq1KaXg9vjh9vhRXOoGAKQnO5GVloSs9CTY9BO/IGVKif/s2I9/bSnGt7/ug8849s/Lb0ps21eObfvK8eH3v0LXBE7NbY6zT8rGGR1bwWHTGzBzIiIiIiIiagriqgjlcDjQp08fFBUVYfjw4QAAKSWKioowbtw4y3X69euHoqIijB8/PrBszZo16NevX52xpJRBt9sdzel0wul0RrwPFH2mlCgprcSuUjc8PiNqccorvSiv9GLrnjJkZSSjTbMUJDvtUYsXLRUeHz7676/4eNNOlFf5ohLDlArf/bof3/26Hys+/xHndM3Fhd3boXmqKyrxiIiIiIiI6MQTV0UoACgoKMCoUaNw1llnoW/fvpg3bx7cbnfgaXnXX3892rRpg9mzZwMA7rzzTvTv3x9z5szB0KFDsXz5cnz55Zd45plnAAButxv3338/hg0bhtatW2Pfvn1YsGABdu7ciREjRkSUW7JTQFYVwy8PNexOU1iUAg66PdhfUQXDVHAAcERhWiKB2iOEPGXAljIgLcmBVmlJsDfwSB9peiGgANMLmJ4G2WaVz8D7/9mOjzb9GrjV7ugfeAN2AA37IVb5DBR9tx1r/7sD55yci6G9OyEjmQVdIiIiIiKiRBd3Ragrr7wSe/fuxdSpU1FcXIzevXtj9erVgcnHt2/fDk377daoc845By+//DImT56Me++9F127dsWqVavQo0cPAICu69i0aROWLl2Kffv2oUWLFjj77LPxySefoHv37hHl5rALSH85TERnNAmF5vEZOOj2wJASdgD2KM2JLQCkoI5J6D1AmQdw2XW4HDYI0TCJKGlC02zAjr8Dzozj25ZS+HlPGb7+ZQ+8fhNnAECImtnn5iAYcBxXvFBMqfDJpp34YnMxLu7dCRf1aAddO/FvayQiIiIiIqJjE3dFKAAYN25cyNvv1q5dW2vZiBEjQo5qcrlceP311xskr9IKCVv6yXAmZzbI9qh+himxpaQUe8urGiWegES2+LXefm4fYDM1ZGckN8iE3NL0QRrHv49ujx/rNu9GSZn7uLfVULyGiZVfbsa/fi7GDed3R5vmqbFOiYiIiIiIiGIgLotQRABQXunDD7sPwOsP/6ltDSldHKi7gwQOHQSk04aU450rSpqANID9XwC2Y7t17YDbi59LypAmJdLqGaC1WZ1+TDGOx68HKvDgP/+FEXldcd4pbRpsFBkRERERERGdGHhvDMWl4lI3/rNjX8wKUJFwew2UVnqhYhRfKmDHgQr8tPsgTHnsT7xrDIYp8bfPfsBLn/4XxnE8zZCIiIiIiIhOPBwJRfFFAb/sK8eO/fEz+XuFSoeqr17rBw65dbRulgLbscx7ZPqqJyRvdibgTA97NSklPt9Sgl/2l9XbV4eBDuKnyHOLgs9+2o39FR7cOrAnXHb+GiIiIiIiIkoEHAlF8UMBW/aUxlUBCgAUtLBeXkNh58FKGBKA0CJ/QQCaDdDsYb1M6Pjkpz3Yus8NCVu9LzPOas4/7D6Ix1d/jSqfEetUiIiIiIiIqBGwCEXxQQFb95Zh98H4mVD7WPgNiZ0HK6J+W5yUCp/9uBu/HqiIapxo27a3HAve2wifEf+3XRIREREREdHxia+hEXFO1wSUUQXpP85JqKmW4lI39hw4hFh/sgISGmpG5lQXkjQY9d+OdwTTAHYfKEVOZgq0MCfflqYfSoVfuNqwtQTb95eH3T+ebdlThufWfotbLuwJTeNk5URERERERE0Vi1ARyEzVYFRsgfI4Yp1Kk+L1m/C6PWgVJ+PyklE9usgmvIffH0OxxwTKykqR7AivrKaUCU0P76l4PxUfxI/FByPPKY59s30f/rFhCy47u0usUyEiIiIiIqIoibgItXXrVnzyySf45ZdfUFlZiaysLJxxxhno168fXC5XNHKMG6UVErbUznAkhT9xNNXNZ5jY/Ov+uHmqm4BES1EMAEhFKQCgMpyJya34Ad3pQnpS/UVLafqhpK/efvsrqvDlzyWR53ICeO8/v+Ck7Az0ap8V61ToOCTyMYKIiMLH4wURUWIKuwi1bNkyPP744/jyyy+RnZ2N3NxcJCUl4cCBA9iyZQtcLheuueYa/PWvf0WHDh2imXPMmFJB2JKg2VNinUrToICfdu+Dx4yfkWUCEjLwY1FdeJKwHVsRCsB+t4FkVxIcNr3evlLVPUG3YUr83w+7IJU6plxOBC9+8l90vCwdGcnhjQqj+MFjBBERhYPHCyKixBZWEeqMM86Aw+HADTfcgL///e9o165dULvX68W6deuwfPlynHXWWXjqqacwYsSIqCRMTcfuMjfKKr2xTiOqlAJKyirRrnkacJzTHX2zfS8OeeofLXUic3v9WPH5DxhzYc9Yp0IR4DGCiIjCweMFERGFVYR68MEHkZ+fH7Ld6XRiwIABGDBgAO6//35s27atofKjJspvSGzb2zQm1q6P12+irMp7XKN7Dro92LSrac0DFcrX2/biu1/3o3vbFrFOhcLEYwQREYWDxwsiIgqrCFXXweJoLVq0QIsWPHmkum3fVw7TjI95oBrD/goP0lyOY37629fb9kCh6d6Gd7TXvvgRp+bmQdfiZLZ6qhOPEUREFA4eL4iI6Jifjrdnzx7s2bMH8qgJpXv25G00VDePz0BxWWWs02hUUiocrPSiRWrkE22WlLmxu9QdhaziV3FpJb7YUoJ+XVvHOhU6RjxGEBFROHi8ICJKLBEXoTZs2IBRo0bhv//9L9ThCZKFEFBKQQgB0zQbPElqWn49UBH43kkkZZVeNEtxQhORjYb6dsf+KGUU31b/exvyuuRE/HlRbPEYQURE4eDxgogoMUVchLrxxhtx8skn47nnnkN2djZEAp0gZqZqMMp/hKriY2OPlZQKKK9AthafRSgBIAXVc1XZhAcAkIxSHPes4gCggENl5XDaa//YKWlC02ovP+j2oLgssUZB1dhTXolvd+xDz/ZZsU6FIpDIxwgiIgofjxdERIkp4iLUzz//jL///e/o0qVLNPKJaz6/gmZPh+5MiXUqJ6yyCg8q47P+BAAQkHCguvhkR/WT6AzlgGqIIhQAt19DclJSreVSGpYRNheXNkjcE9Unm3ayCHWCSeRjBBERhY/HCyKixBRxEeqiiy7Cv//974Q8YFR6FbSkHNiTM2Odyglr1749cKvkWKcRkoBE0uERUC54AQA+JEOhYSbI9pmAqaXCddRoKGn6II2qoGWmlNi2LzGeIBjKdzv3o6zy+J4sSI0rkY8RREQUPh4viIgSU8RFqP/3//4fRo0ahW+//RY9evSA3W4Pah82bFiDJUdNS5XPgNvjj3UaMVfh8dcqQlkpLq2Ez0js+RCUAjb+shf9T2sb61QoTDxGEBFROHi8ICJKTBEXodatW4f/+7//wzvvvFOrjZMIUl0OVHhinUJccHv9aJlW+5a8o+08WNEI2cS/b7bvYxHqBMJjBBERhYPHCyKixBTxPUa33347rr32WuzevRtSyqAXDxZUl4NuFqEAwG9IGKast9/u0sSckPxoPxUfhD+Mz4viA48RREQUDh4viIgSU8RFqP379+Ouu+5CdnZ2NPKhpkoBh6p8sc4iblT5jDrbK31+VHj4eQGA35TYnuBzY51IeIwgIqJw8HhBRJSYIi5C/fGPf8SHH34YjVyoCav0+WHKOH4sXiPzGHUXofbz1sUgv7AIdcLgMYKIiMLB4wURUWKKeE6ok08+GYWFhfj0009x+umn15pE8I477miw5OJNslNAVhXDLw/FOpUTTlWVF+ki/m8vE5BwoDpPDdWTqDtQCQXRoHGkzwO/97eRTlIaQRHK3N4GjXei23kw/r93qFoiHyOIiCh8PF4QESWmY3o6XmpqKj766CN89NFHQW1CiCZ9wHDYBaS/HCZ4m1SkTI8XSSL+CysCgP3w11cT1fMR2OA73NJwlBQwjcMjw5SCMquqhyVW/AzYe6Gcty4G2VteGesUKEyJfIwgIqLw8XhBRJSYIi5Cbd26NRp5BFmwYAEeeeQRFBcXo1evXpg/fz769u0bsv+rr76KKVOmYNu2bejatSseeughXHzxxYF2pRSmTZuGZ599FqWlpfj973+PhQsXomvXrhHlVVohYUs/Gc7kzGPdtYS1w1+KEnf8j2YRkMgWvwIAdFQXiSpVJlTkd67WzQRykjMA336gYguk9ENCA7Y8CzjSkIKLALRr2JgnMD5Z8cTRGMeI4xXpMYaIiBoejxdERImpwc6sd+/ejYcffvi4t7NixQoUFBRg2rRp+Oqrr9CrVy/k5+djz549lv0/++wzXHXVVbjpppvw9ddfY/jw4Rg+fDi+/fbbQJ+HH34YTzzxBBYtWoT169cjJSUF+fn58Hh4YttY/AafcnI06dkHlP8XkEeNevKVoqfv72gnNscmsTh0iJO0UwOJ9BhDRESJiccLIqLoiHgk1I033mi5/JdffsEXX3yBiRMnHldCc+fOxc0334zRo0cDABYtWoS33noLixcvxqRJk2r1f/zxxzF48GDcc889AICZM2dizZo1ePLJJ7Fo0SIopTBv3jxMnjwZl156KQDghRdeQHZ2NlatWoWRI0ceV74UHk5KfjQFzf1znT366Guxw+iMhr4V8ETkMyRMKaFrDTwijRqcaZp4/vnnUVRUhD179kBKGdT+wQcfxCizapEeY4iIKDp4vCAiSkwRF6EOHjwY9N40Tfz888/473//i6eeeuq4kvH5fNiwYQMKCwsDyzRNw8CBA7Fu3TrLddatW4eCgoKgZfn5+Vi1ahWA6qG+xcXFGDhwYKA9IyMDeXl5WLduXcgilNfrhdf72xxG5eXlEPZ0+JAGqJRj3cWEZWhewOaKdRphkDBRPf+QIaoLHqZKb/Db8VzCDa/W7IioAhIaquCAH9V/hAkALYUb+1VOg8QUMGGK5tX/V6kAAKewwQa9QbYfbeWVPjjtEf/KSngeX+OOQrzzzjvx/PPPY+jQoejRoweEiJ8i6rEcY6yOBUREdPya0vGCxwoiovBFfEa3cuVKy+X3338/Vq1ahVtuueWYk9m3bx9M00R2dnbQ8uzsbGzatMlyneLiYsv+xcXFgfaaZaH6WJk9ezamT58etGz53xZgu34qNNMGU9mgoMMWYrJtpTQYcMAGH4SQln0M5YSACV0Ylu1NKYYvU0EzQswJJTRASwJkFaCsY0BPBpRR+9a1GpoDEDbADDGBdQQxKlT1Hw6ew0/HM1D9tBYlnFDCBk1a74eCBqWnQJhuCFjHkFoKhDJQpdyoFJ4j1q22Q9gBYYdDlgIATNkSTqQeuSMQugvK9Byx1tG76oSCCcjgr4eARBn6QWlOuJAGJb04TbWoVWATArBrOvzShAoxgM2ua5BKhRzhpmsCmhDwm9afw7HE+H7rQWjab3+gGqaEVAoOm3URTSoFvyFht2nQQvxh6zNMaELAplsXGZtCDMOw/rmMluXLl+OVV14JmpcvXhzLMcbqWEBERMevKR0veKwgIgpfgw0ruOqqqzBr1qyG2lzMFRYWBo2wKi8vx7ovPoZQBmwwIJEKAza4UGa5vgkbTKTAgQrosD4J9CIFdpiwwXpuqqYUQwkbYFZZtkPYIfRMKLMMUH7rLno6lPLXvQ1hgzruGD5oZgUAQKt5Sh7sAARMmwNKOKCZJZbrK+GAYWsJXe6HUNbFMlNvBqH8EGYFbKLit3UBKAgoW3MYcCHFrB5xKGUa1JEjlYQNwpYGJcuri3KW+5ECSKPWZ6GgIFABJeyAZoMyS+FXRvD2AehCIMlmg8cvYYaoEDltenVxxLQeZaNrtsNFqBDtxxDDYdeCrpIapoRhKqS4Qhd3Kg0Jl0MPWQCq9Bpw2ATsttDbONFjyBA112hxOBzo0qVL4waNIqtjQbt2fGgAEdHxakrHCx4riIjC12BFqH//+98444wzjmsbLVu2hK7rKCkJPskvKSlBTo71LUk5OTl19q/5t6SkBK1btw7q07t375C5OJ1OOJ3OoGVCswHKgFIC8nDZQKlQt7poEDAByDr6KMg6ttGUYlSP4AixvtABGNUjrULFENWjZ0Jv43Cf444hgMAoJnXEvwoQ4qh2i/VhVOcSYrSVENU7IqEdHoEkD0c4vAOwVY/aAmDABg+Sa68PCSFEyBFE1XlqIcZJofqzqGvIuxDV6wqBUEE0ISDqmKtKQIQctXMsMbTAZ/+bcGYZO3LkVCh1bacpxAj5fRIlEyZMwOOPP44nn3wyrm6tAI7tGGN1LCAiouPXlI4XPFYQEYUv4iLU0fMvAdW/kP/xj39g6NChQe1z586NaNsOhwN9+vRBUVERhg8fDgCQUqKoqAjjxo2zXKdfv34oKirC+PHjA8vWrFmDfv36AQA6deqEnJwcFBUVBYpO5eXlWL9+PcaOHRtRfjZXOqqQAS1wi5EXbqTXsYYXVUgCkBSy3QBg1LONphBDgw/C1aqOGAbgyKy7XbcDej3baIAYUm8PAIG5mWpuV6suE0lIZ/uQW9AgoeytQhYENEhAS4J0todb4KiCmIINJgQUyhzdUanSDo/Cqk3YM+uerlxzQnNmWexhxhFdspCGJISa+DzFYR27hl3XYNfr/oMr3VV3e7gxdE2gzG09uizU8hpuT923o/kNiTKj7m2cyDEMs3Fvx/v000/x4Ycf4p133kH37t1htwd/jV9//fVGzedIx3KMISKi6ODxgogoMUVchPr6668tl5999tnYs2dP4LGlx3pFo6CgAKNGjcJZZ52Fvn37Yt68eXC73YEnU1x//fVo06YNZs+eDaB6UsP+/ftjzpw5GDp0KJYvX44vv/wSzzzzTCCP8ePHY9asWejatSs6deqEKVOmIDc3N3BQCZfhKUdGkoRNF/AiHSacSMZey74SdniQCRdKocH69q9KZEGHF05YT17YtGI4YLp/tWyH5gDsLQH/vtBzPjlzq+dz8h+0brc3q57zybvruGPYjJ0AANtRc0KZ9iwoLQU27zbL1ZXmgmnPge4vhpDWtyYazo4Q0g2bsQ/JDjsgvYDhhoICIKBsWTD0TAizDF6LCaWFZoewZ0L5S6Gk9ddDc2YB0gvpP/rroWBDGaS9JaA5Ib17cUjptW/H0zSkOOxw+/wwQ9zLle5ywm9KVPmtc0iy22HXNZR7rOcaizRGqsuOjBRHUHul14DfkLWW1zBMCbfHQIrLFvI2tjK3D3abhmSn9a/CphAjxJcgajIzM3HZZZc1btAI1HeMISKixsHjBRFRYoq4CPXhhx9GI4+AK6+8Env37sXUqVNRXFyM3r17Y/Xq1YGJAbdv3w7tiMe0n3POOXj55ZcxefJk3HvvvejatStWrVqFHj16BPpMnDgRbrcbY8aMQWlpKc4991ysXr0aLldkT2tT0oAmFGwaYCoJpRRsmvWYF1NJQGnQhYQuQtxuJBVsQsIWor0pxfALBRnqNjhpQkCDkmbIW+WEMqGkEfpWOmlACDP0LYNhx/BDHJ4zSkP12bs4XCAS0gclnIH22jG0QL+QfZQBIX3QhQGbEICuA3oalPRASQPK1QxSbw74JOCtPb+VkjV3+/lDzm2Fw/tRu11BgxdKegBhAsoPU6nDBbAj96O6KGTK0PM1mUrBVHW1S2hKhGyPNIbLUbsAY9METC30hN+BfroWso+mCdjq2EZTiKGHcStfQ1qyZEmjxotUfccYIiJqHDxeEBElprh83vm4ceNCDnVdu3ZtrWUjRozAiBEjQm5PCIEZM2ZgxowZDZUiRajO+YESkB70cQhAOABNA2zVt8c57NZPSktUKc66b9sjikRdxxgiIqIaPF4QETW8ui+9HzZ48GB8/vnn9fY7dOgQHnroISxYsOC4E6OmxWpi6USma3X/6DltLEIdKc1lfasaxQceI4iIKBw8XhARUVgjoUaMGIHLL78cGRkZ+MMf/oCzzjoLubm5cLlcOHjwIL7//nt8+umnePvttzF06FA88sgj0c6bTkAOmwavP9QT9hJLfUUom67Bruvwm/y8NCGQ6uJIqHjGYwQREYWDxwsiIgqrCHXTTTfh2muvxauvvooVK1bgmWeeQVlZGYDqW926deuG/Px8/Otf/8Jpp50W1YTpxOWw6SxCARAivNsTk502lFXy88pIdnAUXZzjMYKIiMLB4wUREYU9J5TT6cS1116La6+9FgBQVlaGqqoqtGjRotYjVYmsuOw6DtWeazvh1DcKqkaK046yykZ+tFkcap6aFOsUKAw8RhARUTh4vCAiSmzHPDF5RkYGMjIyGjKXuGdzZaBKpcPrl5DQAfhxyEyx7CthB+BBBVKhwfopZhJ+mNDhQ6htNK0YlUiDw5Va62lsStgB+ABXi5BPlVNCAVoShM36W7Z6GwrC1bKO9vpjCC0Zpq1d9fvDeUqIw+0OCACms12I9R0QMGE620Ion2UfITSYIh2HkHTUugpCCGgyGUJoOOhPB4SC7kqFOvLpcaJ6rijNmRX6SYFCh7ClQuhHF28U/Mg4/Fno0JzZSIcL6qip4fTDo47SXc6QT67ThYBus8GuW89ddeQ26muvL4ZD03CwonYxTsrq9azajmwvc/ughXhCnJQKVT4TXkPWuY0TOYZhGJbrRFsiHiOIiChyPF4QESWWuHw6XrxS0g9deaELCb9wAQBsqsKyrxSACRd0eKAp65PL+rbRFGMoIeE7+kRZk1BaEoSsgpLWMZTtcDHDtB5KVV97uDEUAN2s3g8N1UWe6iKNgLK1AAAI03o/obmgtBQI6YaQnhAxWkDTNNikO3i5qi51SVTHcGrVRSyX7kel97eimdDsgOYEpBdKWhfThC21epu1PgsFDRUwbc0D7T4kA0cVBaXQYNc1+E0JU1kXTvTDxcBQc1bV1x5uDKdNR5LD+tdUla96206b9cgyQyr4DQm7TYMtRPGmvm00hRhC8VZGIiIiIiKKDyxCRcD0VcKh2eGwC2jKB79yIUmzHmVgKsCt0uASZdBFiD5Sg1144AzR3hRjOHWFvZ6qo0b3+CGcaVC+CiDUCCItA8qsBAy3ZTuUHUJPhvKHaA87hhtO8yAAwIbqYpWCA4CAgg6pp0EzDlqnIJwwnGnQ/QcgQhTshL0ZnMoNJw4FrwsTShlQWiv4kYxkvbqIZXOZcLvLj4hhh+bMgvSXAyFGdAk9CcqsgjLKj2pR0HE4d70VlFEOj0qHQvBoJl0I2HUnqvz+kKOUaiZNr/KHHmVj1/WQ7eHGSEtxILmOScm9hgzZbpgSZYYPyU4bbLp1gcdrSDhtWpOOIWBd5CMiIiIiImps4U1OQ9RANE2EHNnS5AlADzGSJRSHXUeyIzHnR7DrGlKdibnvRERERERETRGLUNToUpy26kfEJRinTQcQ+X43S3U1fDIngHSX4xg+LSIiIiIiIopXERehRo0ahY8//jgauVCC0HUNyYk2GkoADpv1JN71SXLYkJxgI4KSHTYkOxPse6SJ4DGCiIjCweMFEVFiirgIVVZWhoEDB6Jr16544IEHsHPnzmjkRU1cqssOEeGtaScyl80GcRzjelqmJuFYRlGdqDq1ykjI0XJNAY8RREQUDh4viIgSU8RFqFWrVmHnzp0YO3YsVqxYgY4dO2LIkCF47bXX4PdbT5RMdDRNE0hzOWKdRqPQNHHMo6BqOOw6mqU4Gyij+NYyLQnNUhLzFsSmgMcIIiIKB48XRESJ6Zjud8nKykJBQQEKCgrw1VdfYcmSJbjuuuuQmpqKa6+9Frfddhu6du3a0LnGnO5Ihk86Yfol/MIBQKLKtP4IpXAC8MMDJzTrh39BCgkvHJDKehtNPYbQbHA67TDhgBIGhCMVkNZPlVOaALQUCGH9R4mypVRv055inaTmDDNGKqRoBgAwUP3YewkNgICyZUIAkLZmIdZ3QUBC2ptDSE9guU3XUHV4FJOEBq9IgzzqyXYKEkIAUiZDCA2VZu0ijCvJBbtZnZVmT4eSIf5AEzqELdUqQ5jQYNqaA9AhbOlwwY6ja9G6qH6fZLfDVNZPVtOFgG4L/evDdbgtyW7dJ1QMTRPIzUxBpccPKRWqfGbIGDVtlR7rz8GQ1d+wlV4DthCj7hIhhs8fOna0JOoxgoiIIsPjBRFR4jmuSVd2796NNWvWYM2aNdB1HRdffDH+85//oFu3bnj44Ydx1113NVSecUFodpjCCQUJpapP7AxYj06Ryg7AhAF7yOFm9W0jEWLoNkBKHYAENCeECBWlulAh9KRjalfCXv2o+npiCABKTz38rvrkXwVugzv8Xrcq8ABKOAAoKC0ZENU/WpoQMI+4rUwdLrgYSDlqXQUBEfiMvdJ6lFiSy4kKj//wftT941v7s1CH9/C3dgd0qFpFqOo+dl2Dpuq+Jc6u1z3CK1R7qBhZaUkwJWDK3wpTXsO6EFZfuzxcvPEbEmY9t3425RiGGaJ63AgS7RhBRETHhscLIqLEEXERyu/344033sCSJUvw3nvvoWfPnhg/fjyuvvpqpKenAwBWrlyJG2+8sckdMAxPGZJSAYddwKvS4FcupGqlln1NZYNbtUSK2AddGJZ9KmQW7MIDp3BbtidKDI9fQ5nZHMqzG1A+yz7CmQtlVgKG9TZgy4TQk6G8+6zbhQPC2RrKW18MN5LN6jkJ7KgeMeWHA4CAaWsJqafB5t1hub4SThjODrB5f4FQXth0DcmO4AnFK0Q72JUbThwIXleZUMqA0pLgRzKa2cstYxi6DlMmw31oN6CsR85ozmwoswrKOHobCnYchGlrAeitIL0lKFd2KAQXinQhkO5yotzjhamsCxjpLif8pokqv/X3RJLdBruuo9xjPerMKka75mlo3fy34tzBCi+cNg3JLutJ2Ss9fngNiWap1sVPw5Qoc/uQkeKATbcuPCZCjCqPxQpRlMjHCCIiCh+PF0REiSniIlTr1q0hpcRVV12FL774Ar17967V54ILLkBmZmYDpEeJwG7TYIcG69LQiUnTRNSeAOiw6dCSnDhU2XTmS2iR6kL7rLRYp0ENgMcIIiIKB48XRESJKeKz5MceewwjRoyAyxV64uDMzExs3br1uBKjxGLTNehOG6o8J34pSgiBFKcd0XyaXfNUJ5T0oaIJfF7pSQ6cktv8uJ4eSPGDxwgiIgoHjxdERIkp4iLUddddF408iJDqtEODHe4QEzSfCASAJIcNAtHdBwGB7IxkKAW4vSduISrFZUf3ti2gCRagmgoeI4iIKBw8XhARJabo3C9EdCwEkOZyQADVk2/HDXXEv+qI98GEVj2iS5MI2Sf0Ng6/V4fbQjyVrrrMpVA9ibpCToYLe8oVDgWNiLKOoaHuibEbm8Omo31WBnQt1ETxRERERERE1JSwCEXxRQCpSQ7omoayKt/hokxs2QOjmgwImLBbzF6lawK6boNbGYBZBcB6Qm4IA5BeQFYGL1eq+uUvBzQFmCUhsnEAsAPmPgA+CADZTiAV/sAIMj8yoKECOg5GvrONJCs9Cc2SnCxAERERERERJRAWoSIgNBukEjCkgAENEtX/tyKhAZAwlQYV4jH3EgKG0qCHaE/kGHa7HZlCxyGPD0oqQOgQmg0QuuU2cLhNhGzXqzPVdECG6CN0CM0OJe2H8wqmNBeUcECK4KeY2XQBTdNgiuo5DUzhClk7k7DDgBM6grehoAChoOCEhA0GHJbr1yw/ut3pdEBoJg55/FDCDqW5auV55H5A6ICwQ4eAOmoupprCkK5pgLQePaULASk06CFuo9MPtx3drgmB9llpaJbsgttjwDBDj86SUsGQKmQfQ6rqPqHaDy9P9BimjH0hl4iIiIiICGARKiI2VzqqkAEtcKLnhRvpdazhRRWSACSFbDcAGPVsI2FjCMDhAkyloJQB6HZAb1XHNgzAVU+7I7Pudt0OqbcHgJBP6/M5O/+WoqgeHxWggCqtdegQCjBEKgw9tXaDELDZ0iGEhjJ0tly9hhvJtRfqgOZUkD4DPjgBZ7OQ61chDXCmoa7n0aU47HXmYNc12HXrQleNdNdv7XZdQ0ayEzZNg9tjVO/H4X9D8RsSZUbdc16VuetuT/QYhln3ekRERERERI2FRagIGJ5yZCRJ2HQBL9Jhwolk7LXsK2GHB5lwoRRaiEmqK5EFHV44UW7Zzhi/qTBbwu+vhPLtt2yHvRmgJQHeXdbtmgOwtwT8+wAZ4mTfmQvIKtiMnZbNpj0LSkuBzbsNdpsOh64HTbskhRMeLQsuuReasr4dr1JvC11VwSmD90MpCSgJ5ewKAy5k2A5Zrm8oHW4zGSl6JWzCtN5VkQZpelBRcQCmxUgmzZ4OaGko91jnqGsaUhx2uH1+y/WB6uKS35So8lt/vZLsdth1DeUeL3RNILd5KrLTkyEOj4wyTAm3x0CKywabbn1LXpnbB7tNQ7LT+tdUpdeA35DISAkxaowxAAAhvsxERERERESNLq6KUEopTJs2Dc8++yxKS0vx+9//HgsXLkTXrl3rXG/BggV45JFHUFxcjF69emH+/Pno27cvAODAgQOYNm0a3nvvPWzfvh1ZWVkYPnw4Zs6ciYyMjMjykwY0oWDTAFNJKKVg06xvdTGVBJQGXUjowrqPJhVsQsIWop0xfmMDkKRrMDUdFV4/5NG3GEkDQphQyrowA2lCQIOSJhCij1AmlDRQJa2LAcIUsOk6UlOtJ9M2lQ2AC7rugi6sf7Q0ZYMNdthEStByJf1Q0geV1AJS2mFLDTGiy5SA24AtpWXIgoRW4UOSXUPzlm2x80AFdpW6g4pJLmmHXSiYIe8ZlIdDyZB9TKVgqrraJXQl0CojGe1bpsGuW98CadO10PuhCdg0EbLdpgmYdbQzRjVd45MHiYiIiIgoPsTVrMAPP/wwnnjiCSxatAjr169HSkoK8vPz4fF4Qq6zYsUKFBQUYNq0afjqq6/Qq1cv5OfnY8+ePQCAXbt2YdeuXXj00Ufx7bff4vnnn8fq1atx0003NdZuUUMRQLLLjqz0ZKQnO+o9aT+uQEe8hNCQ7HQgLdkJp90GXder78OzegGh28ThbVq2aYfjadX/F3odL1FPuwZAg67b0T6rGc46KRftWzaD3eaAgo5o/9jbdIEWqS7kNktB5+zMkAUoIiIiIiIiSixxMxJKKYV58+Zh8uTJuPTSSwEAL7zwArKzs7Fq1SqMHDnScr25c+fi5ptvxujRowEAixYtwltvvYXFixdj0qRJ6NGjB/7+978H+nfu3Bn3338/rr32WhiGAZstbj4CCpMQQLLTjmSHHT7DRJXfhFcJNOj0ywJw6DpcDhuS7DqEJuBVGk7EOZ5tuoZ2LdLQtnkqDrq92FdeBa8/xIixYySEQLNkJ7IyktAiNQkerwGvEXoibSIiIiIiIko8cVOB2bp1K4qLizFw4MDAsoyMDOTl5WHdunWWRSifz4cNGzagsLAwsEzTNAwcOBDr1q0LGausrAzp6el1FqC8Xi+83t8mUykvt57viGJIAA67Doddh1c64ZF26MIBv2nCb8rat+zVuS0BoQk4NB1JTiccNh1aE7uNSQiB5qkuuGw6vH4T7bQ0lFV6UV7lRYXXH9FT1ASAJIcNzVJdyEx2ICPZaXmLIhEREREREVGNuClCFRcXAwCys7ODlmdnZwfajrZv3z6Ypmm5zqZNm0KuM3PmTIwZM6bOfGbPno3p06cHLXvl78vqXIdiSFTPfZOaZAdQ/VQ3KRVMqWBKCUPZ4dM02IQdOg7faCcEdK3mpaFC2WEXJpwh5nNqUoRAZooTmSk1T69T8BkSHr+BKp8Jn89E+uGJsoUAdFE935DDpsNl11HpNeC06Uh21f0EPSIiIiIiIqIaMRu6sGzZMqSmpgZe/hBP2WpI5eXlGDp0KLp164b77ruvzr6FhYUoKysLvHbs2BH1/KhhaZqA3aZV31Ln0GHTNaQ4bUhx2ZHssiPJaYPDrkPXteqqVEITcNh0pCc50SLVhWSnHa0zUw7fxpeG1s1SkZWejIzD82LxAyMiIiIiIqJIxWzIx7Bhw5CXlxd4X3PrW0lJCVq3bh1YXlJSgt69e1tuo2XLltB1HSUlJUHLS0pKkJOTE7Ts0KFDGDx4MNLS0rBy5UrY7XWP4HA6nXA6nUHLbK4MVKl0eP0SEjoAPw6ZKZbrS9gBeFCBVGiwLrBJ+GFChw+htsEYiRJDqeqRWZpfQWgKByu81usfvmWuzO0LebuglApVPjPknEw122CMxIhhGIblOkRERERERI0tZkWotLQ0pKWlBd4rpZCTk4OioqJA0am8vBzr16/H2LFjLbfhcDjQp08fFBUVYfjw4QAAKSWKioowbty4QL/y8nLk5+fD6XTijTfegMvlOqaclfRDV17oQsIvqrdhUxWWfaUATLigwwNNWZ9c1rcNxkicGAoSQgE1JQSnzXqQoiEV/IaE3abBFqIgUeUz69xGfe2M0bRiCMVRa0REREREFB/iZvIbIQTGjx+PWbNmoWvXrujUqROmTJmC3NzcQIEJAC666CJcdtllgSJTQUEBRo0ahbPOOgt9+/bFvHnz4Ha7A0/LKy8vx6BBg1BZWYmXXnoJ5eXlgUnGs7KyoEfw+HjTVwmHZofDLqApH/zKhSTNepSBqQC3SoNLlEEXIfpIDXbhgTNEO2MkTgwlDSjTC2UX8CsRcq4lw5QoM3xIdtpg062LFl5DwmnT6pyvyWtIxkiQGAJ8SiEREREREcWHuClCAcDEiRPhdrsxZswYlJaW4txzz8Xq1auDRi5t2bIF+/btC7y/8sorsXfvXkydOhXFxcXo3bs3Vq9eHZis/KuvvsL69esBAF26dAmKt3XrVnTs2DH6O0ZERERERERElODiqgglhMCMGTMwY8aMkH22bdtWa9m4ceOCbr870oABA6BU+I+eJyIiIiIiIiKihhdXRSgiIiIiIiKiE9WcOXNinUJUWe3fhAkTYpAJnaisJxghIiIiIiIiIiJqQCxCERERERERERFR1PF2vAjojmT4pBOmX8IvHAAkqkzrj1AKJwA/PHBCCzEllRQSXjgglfU2GCNxYiilQcAB6VcQmkKlx2+5viGrN1zpNWDThHUMqVDlM60TAAJtjJEYMXz+0LGJiIiIiIgaE4tQERCaHaZwQkFCqeoTOwNOy75S2QGYMGAPOdysvm0wRuLEUKiemL9mPa8hrWMcLkj4DQkzREGiRqht1NfOGE0rhmHywQxERERERBQfWISKgOEpQ1Iq4LALeFUa/MqFVK3Usq+pbHCrlkgR+6ALw7JPhcyCXXjgFG7LdsZInBhKGlCmF8reHn4l0CzVutBlmBJlbh8yUhyw6dalroMVXjhtGpJddsv2So8fXkMyRoLEqPJYrpKQOnbsiF9++SVo2ezZszFp0qQYZURERPGIxwsiouhhEYqIiBLGjBkzcPPNNwfep6WlxTAbIiKKVzxeEBFFB4tQRESUMNLS0pCTkxPrNIiIKM7xeEFEFB18Oh4RESWMBx98EC1atMAZZ5yBRx55BIZhfetsDa/Xi/Ly8qAXERE1fZEeL4iIKDwcCUVERAnhjjvuwJlnnonmzZvjs88+Q2FhIXbv3o25c+eGXGf27NmYPn16I2ZJRESxFunxwuv1wuv1Bt7zggURUWgcCUVERCesSZMmQQhR52vTpk0AgIKCAgwYMAA9e/bErbfeijlz5mD+/PlBJw5HKywsRFlZWeC1Y8eOxto1IiJqQNE8XsyePRsZGRmBV7t27Rpz14iITigcCRUBodkglYAhBQxokKj+vxUJDYCEqTQoFaqPgKE06CHaGSNxYigpAGWHkoBUCoYprXM4vDxUOwBIqWDIOrYhVXUfxkiIGKZUIddrCiZMmIAbbrihzj4nnXSS5fK8vDwYhoFt27bhlFNOsezjdDrhdFo/uZCIiE4c0TxeFBYWoqCgIPC+vLychSgiohBYhIqAzZWOKmRAC5zoeeFGeh1reFGFJABJIdsNAEY922CMBIihFCA02EwFoQFlbl8dMQC3p+55CfyGRJlR9zYYIzFiGGbTnsMiKysLWVlZx7Tuxo0boWkaWrVq1cBZERFRvInm8YIXLIiIwsciVAQMTzkykiRsuoAX6TDhRDL2WvaVsMODTLhQCg1+yz6VyIIOL5ywvm+cMRInhpIGIE0oPReGAjJSHJbrG6aE22MgxWWDTbe+m7bM7YPdpiHZaf3jXek14DckYyRIDE/oO80Syrp167B+/XpccMEFSEtLw7p163DXXXfh2muvRbNmzWKdHhERxQkeL4iIootFqAgoaUATCjYNMJWEUgo2zfpWF1NJQGnQhYQurPtoUsEmJGwh2hkjcWIoKCjlh9IAqUTIYkMNm66F7KNpAjYt9DZsmoBZRztjNK0YumZ9a2iicTqdWL58Oe677z54vV506tQJd911V9DtE0RERDxeEBFFF4tQRETU5J155pn4/PPPY50GERHFOR4viIiii0/HIyIiIiIiIiKiqGMRioiIiIiIiIiIoo5FKCIiIiIiIiIiijoWoYiIiIiIiIiIKOo4MXkEbK4MVKl0eP0SEjoAPw6ZKZZ9JewAPKhAKjT4Q/Txw4QOH0JtgzESJYZSgBACml9BaAoHK7zW68vqp+qVuX3QQjz1TEqFKp8JryHr3AZjJEYMwzAs1yEiIiIiImpsLEJFQEk/dOWFLiT8wgUAsKkKy75SACZc0OGBpqxPLuvbBmMkTgwFCaGAmhKC02Y9SNGQCn5Dwm7TYAtRkKjymXVuo752xmhaMYSyXoeIiIiIiKixsQgVAdNXCYdmh8MuoCkf/MqFJM16lIGpALdKg0uUQRch+kgNduGBM0Q7YyRODCUNKNMLZRfwK4Fkl91yfcOUKDN8SHbaYNOtixZeQ8Jp00Juo6YPYyRGDAHrEVhERERERESNjXNCERERERERERFR1MVVEUophalTp6J169ZISkrCwIED8dNPP9W73oIFC9CxY0e4XC7k5eXhiy++CGofMGAAhBBBr1tvvTVau0FEREREREREREeJq9vxHn74YTzxxBNYunQpOnXqhClTpiA/Px/ff/89XC6X5TorVqxAQUEBFi1ahLy8PMybNw/5+fn44Ycf0KpVq0C/m2++GTNmzAi8T05Ojvr+EBERERERUdM0Z86cWKcQF6w+hwkTJsQgEzoRxM1IKKUU5s2bh8mTJ+PSSy9Fz5498cILL2DXrl1YtWpVyPXmzp2Lm2++GaNHj0a3bt2waNEiJCcnY/HixUH9kpOTkZOTE3ilp6dHeY+IiIiIiIiIiKhG3BShtm7diuLiYgwcODCwLCMjA3l5eVi3bp3lOj6fDxs2bAhaR9M0DBw4sNY6y5YtQ8uWLdGjRw8UFhaisrKyzny8Xi/Ky8uDXkREREREREREdGzi5na84uJiAEB2dnbQ8uzs7EDb0fbt2wfTNC3X2bRpU+D91VdfjQ4dOiA3NxfffPMN/vrXv+KHH37A66+/HjKf2bNnY/r06UHL/v7PlfBJJ0y/hF84AEhUmdYfoRROAH544ISmrGNIIeGFA1JZb4MxEieGUhoEHJB+BaEpVHr8lusbsnrDlV4DNk1Yx5AKVT7TOgEg0MYYiRHD5w8dm4iIiIiIqDHFrAi1bNky3HLLLYH3b731VtRijRkzJvD/008/Ha1bt8ZFF12ELVu2oHPnzpbrFBYWoqCgIPC+vLwc//rm3zCFEwoSSlWf2BlwWq4vlR2ACQP2kMPN6tsGYyRODAVACBFYz2tI6xiHCxJ+Q8IMUZCoEWob9bUzRtOKYZghKqJEVAvn9qgt3M+Ec38QERFROGJWhBo2bBjy8vIC771eLwCgpKQErVu3DiwvKSlB7969LbfRsmVL6LqOkpKSoOUlJSXIyckJGbsm7ubNm0MWoZxOJ5zO4EKB4SlDUirgsAt4VRr8yoVUrdRyfVPZ4FYtkSL2QReGZZ8KmQW78MAp3JbtjJE4MZQ0oEwvlL09/EqgWap1ocswJcrcPmSkOGDTrUtdByu8cNo0JLvslu2VHj+8hmSMBIlR5bFchYiIiIiIqNHFbE6otLQ0dOnSJfDq1q0bcnJyUFRUFOhTXl6O9evXo1+/fpbbcDgc6NOnT9A6UkoUFRWFXAcANm7cCABBxS4iIiIiIiIiIoqeuJkTSgiB8ePHY9asWejatSs6deqEKVOmIDc3F8OHDw/0u+iii3DZZZdh3LhxAICCggKMGjUKZ511Fvr27Yt58+bB7XZj9OjRAIAtW7bg5ZdfxsUXX4wWLVrgm2++wV133YXzzz8fPXv2jMWuEhERERERERElnLgpQgHAxIkT4Xa7MWbMGJSWluLcc8/F6tWr4XK5An22bNmCffv2Bd5feeWV2Lt3L6ZOnYri4mL07t0bq1evDkxW7nA48P777weKU+3atcPll1+OyZMnN/r+ERERERERERElqrgqQgkhMGPGDMyYMSNkn23bttVaNm7cuMDIqKO1a9cOH330UUOlSERERERERERExyBmc0IREREREREREVHiiKuRUPFOaDZIJWBIAQMaJKr/b0VCAyBhKg1KheojYCgNeoh2xkicGEoKQNmhJCCVgmFK6xwOLw/VDgBSKhiyjm1IVd2HMRIihilVyPWIiIiIiIgaE4tQEbC50lGFDGiBEz0v3EivYw0vqpAEIClkuwHAqGcbjJEAMZQChAabqSA0oMztqyMG4PYYdbb7DYkyo+5tMEZixDDMutcjIiIiIiJqLCxCRcDwlCMjScKmC3iRDhNOJGOvZV8JOzzIhAul0OC37FOJLOjwwolyy3bGSJwYShqANKH0XBgKyEhxWK5vmBJuj4EUlw023fpu2jK3D3abhmSn9Y93pdeA35CMkSAxPF7LVYiIiIiIiBodi1ARUNKAJhRsGmAqCaUUbJr1rS6mkoDSoAsJXVj30aSCTUjYQrQzRuLEUFBQyg+lAVKJkMWGGjZdC9lH0wRsWuht2DQBs452xmhaMXTN+tZQIiIiIiKixsYiFBEREREREVEd5syZE+sUTihWn9eECRNikAnFGz4dj4iIiIiIiIiIoo5FKCIiIiIiIiIiijoWoYiIiIiIiIiIKOpYhCIiIiIiIiIioqjjxOQRsLkyUKXS4fVLSOgA/Dhkplj2lbAD8KACqdDgD9HHDxM6fAi1DcZIlBhKAUIIaH4FoSkcrPBary+rn6pX5vZBC/HUMykVqnwmvIascxuMkRgxDMOwXIeIiIiIiKixsQgVASX90JUXupDwCxcAwKYqLPtKAZhwQYcHmrI+uaxvG4yRODEUJIQCakoITpv1IEVDKvgNCbtNgy1EQaLKZ9a5jfraGaNpxRDKeh0iIiIiIqLGxiJUBExfJRyaHQ67gKZ88CsXkjTrUQamAtwqDS5RBl2E6CM12IUHzhDtjJE4MZQ0oEwvlF3ArwSSXXbL9Q1ToszwIdlpg023Llp4DQmnTQu5jZo+jJEYMQSsR2ARERERERE1Ns4JRUREREREREREUcciFBERERERERERRR2LUEREREREREREFHUsQhERERERERERUdSxCEVERERERERERFHHp+NFQHckwyedMP0SfuEAIFFlWn+EUjgB+OGBE5qy3p4UEl44IJX1NhgjcWIopUHAAelXEJpCpcdvub4hqzdc6TVg04R1DKlQ5TOtEwACbYyRGDF8/tCxiYiIiIiIGhOLUBEQmh2mcEJBQqnqEzsDTsu+UtkBmDBgDzncrL5tMEbixFAAhBCB9byGtI5xuCDhNyTMEAWJGqG2UV87YzStGIYZoiJKRERERETUyFiEioDhKUNSKuCwC3hVGvzKhVSt1LKvqWxwq5ZIEfugC8OyT4XMgl144BRuy3bGSJwYShpQphfK3h5+JdAs1brQZZgSZW4fMlIcsOnWpa6DFV44bRqSXXbL9kqPH15DMkaCxKjyWK7S5Nx///146623sHHjRjgcDpSWltbqs337dowdOxYffvghUlNTMWrUKMyePRs2Gw+FRERE9Js5c+bEOoUmyepznTBhQgwyoVjinFBERHTC8/l8GDFiBMaOHWvZbpomhg4dCp/Ph88++wxLly7F888/j6lTpzZypkREFEv3338/zjnnHCQnJyMzM9Oyz/bt2zF06FAkJyejVatWuOeee2AY1hcYiYgoMixCERHRCW/69Om46667cPrpp1u2v/fee/j+++/x0ksvoXfv3hgyZAhmzpyJBQsWwOfzNXK2REQUK7xoQUQUW3FVhFJKYerUqWjdujWSkpIwcOBA/PTTT/Wut2DBAnTs2BEulwt5eXn44osvAm3btm2DEMLy9eqrr0Zzd4iIKE6sW7cOp59+OrKzswPL8vPzUV5eju+++y7kel6vF+Xl5UEvIiI6cfGiBRFRbMXVRBgPP/wwnnjiCSxduhSdOnXClClTkJ+fj++//x4ul8tynRUrVqCgoACLFi1CXl4e5s2bh/z8fPzwww9o1aoV2rVrh927dwet88wzz+CRRx7BkCFDGmO3iIgoxoqLi4MKUAAC74uLi0OuN3v2bEyfPj2quVH0cW6P6Av3M+bcHxTvQl20GDt2LL777jucccYZtdbxer3wer2B97xgQUQUWtyMhFJKYd68eZg8eTIuvfRS9OzZEy+88AJ27dqFVatWhVxv7ty5uPnmmzF69Gh069YNixYtQnJyMhYvXgwA0HUdOTk5Qa+VK1fiiiuuQGpqaiPtHRERRWrSpEkhR7LWvDZt2hTVHAoLC1FWVhZ47dixI6rxiIgoto7losXs2bORkZEReLVr1y7qeRIRnajiZiTU1q1bUVxcjIEDBwaWZWRkIC8vD+vWrcPIkSNrrePz+bBhwwYUFhYGlmmahoEDB2LdunWWcTZs2ICNGzdiwYIFdeZjdUVDaDZIJWBIAQMaJKr/b0VCAyBhKg1KheojYCgNeoh2xkicGEoKQNmhJCCVgmFK6xwOLw/VDgBSKhiyjm1IVd2HMRIihilVyPXi3YQJE3DDDTfU2eekk04Ka1s5OTlBt2oDQElJSaAtFKfTCafT+smFREQUHyZNmoSHHnqozj7//e9/ceqpp0YlfmFhIQoKCgLvy8vLWYgiIgohbopQNVcWrK48hLrqsG/fPpimablOqKvjzz33HE477TScc845deZjdQvGynfeQRUyoAVO9LxwI72OrXhRhSQASSHbDQBGPdtgjASIoRQgNNhMBaEBZe665xxwe+p+QovfkCgz6t4GYyRGDMM8cZ/mk5WVhaysrAbZVr9+/XD//fdjz549aNWqFQBgzZo1SE9PR7du3RokBhERxUasL1rwggURUfhiVoRatmwZbrnllsD7t956K+oxq6qq8PLLL2PKlCn19rW6ovH5l58hI0nCpgt4kQ4TTiRjr+X6EnZ4kAkXSqHBb9mnElnQ4YUT1veNM0bixFDSAKQJpefCUEBGisNyfcOUcHsMpLhssOnWd9OWuX2w2zQkO61/vCu9BvyGZIwEieHxWqzQBG3fvh0HDhzA9u3bYZomNm7cCADo0qULUlNTMWjQIHTr1g3XXXcdHn74YRQXF2Py5Mn4y1/+whMHIqITHC9aEBGdOGJWhBo2bBjy8vIC72tufSspKUHr1q0Dy0tKStC7d2/LbbRs2RK6rgeuThy5jtWVitdeew2VlZW4/vrr683P6oqGkgY0oWDTAFNJKKVg06xvdTGVBJQGXUjowrqPJhVsQsIWop0xEieGgoJSfigNkEqELDbUsOlayD6aJmDTQm/DpgmYdbQzRtOKoWvWt4Y2NVOnTsXSpUsD72smjv3www8xYMAA6LqON998E2PHjkW/fv2QkpKCUaNGYcaMGbFKmYiIYoAXLYiIYitmRai0tDSkpaUF3iulkJOTg6KiokDRqby8HOvXr8fYsWMtt+FwONCnTx8UFRVh+PDhAAApJYqKijBu3Lha/Z977jkMGzaswa6UEBFRfHj++efx/PPP19mnQ4cOePvttxsnISIiiku8aEFH4xNUY8vq8+eTVJu2uJkTSgiB8ePHY9asWejatSs6deqEKVOmIDc3N1BgAoCLLroIl112WaDIVFBQgFGjRuGss85C3759MW/ePLjdbowePTpo+5s3b8bHH3/MExAiIiIiogTFixZERLEVN0UoAJg4cSLcbjfGjBmD0tJSnHvuuVi9ejVcLlegz5YtW7Bv377A+yuvvBJ79+7F1KlTUVxcjN69e2P16tW1JitfvHgx2rZti0GDBjXa/hARERERERERUbW4KkIJITBjxow6h7tu27at1rJx48ZZ3n53pAceeAAPPPDA8aZIRERERERERETHoO6ZcImIiIiIiIiIiBpAXI2Einc2VwaqVDq8fgkJHYAfh8wUy74SdgAeVCAVGvwh+vhhQocPobbBGIkSQ6nqkYCaX0FoCgcrvNbry+qn6pW5fdBCPPVMSoUqnwmvIevcBmMkRgzDMCzXISIiIiIiamwsQkVAST905YUuJPyiep4qm6qw7CsFYMIFHR5oyvrksr5tMEbixFCQEAqoKSE4bdaDFA2p4Dck7DYNthAFiSqfWec26mtnjKYVQyjrdYiIiIiIiBobi1ARMH2VcGh2OOwCmvLBr1xI0qxHGZgKcKs0uEQZdBGij9RgFx44Q7QzRuLEUNKAMr1QdgG/Ekh22S3XN0yJMsOHZKcNNt26aOE1JJw2LeQ2avowRmLEELAegUVERERERNTYOCcUERERERERERFFHYtQREREREREREQUdSxCERERERERERFR1HFOKCIiIiIiImry5syZE+sUKAxWX6cJEybEIBOKBo6EIiIiIiIiIiKiqONIqAjojmT4pBOmX8IvHAAkqkzrj1AKJwA/PHBCU9bbk0LCCwekst4GYyRODKU0CDgg/QpCU6j0+C3XN2T1hiu9BmyasI4hFap8pnUCQKCNMRIjhs8fOjYREREREVFjYhEqAkKzwxROKEgoVX1iZ8Bp2VcqOwATBuwhh5vVtw3GSJwYCoAQIrCe15DWMQ4XJPyGhBmiIFEj1Dbqa2eMphXDMENURImaAN5WceIJ92vG2y6IiIiaJhahImB4ypCUCjjsAl6VBr9yIVUrtexrKhvcqiVSxD7owrDsUyGzYBceOIXbsp0xEieGkgaU6YWyt4dfCTRLtS50GaZEmduHjBQHbLp1qetghRdOm4Zkl92yvdLjh9eQjJEgMao8lqsQERERERE1Os4JRUREREREREREUcciFBERERERERERRR2LUEREREREREREFHUsQhERERERERERUdSxCEVERERERERERFHHp+NFQGg2SCVgSAEDGiSq/29FQgMgYSoNSoXqI2AoDXqIdsZInBhKCkDZoSQglYJhSuscDi8P1Q4AUioYso5tSFXdhzESIoYpVcj1iIiIiJqiOXPmxDoFamChvqYTJkxo5EzoeLEIFQGbKx1VyIAWONHzwo30OtbwogpJAJJCthsAjHq2wRgJEEMpQGiwmQpCA8rcvjpiAG6PUWe735AoM+reBmMkRgzDrHs9IiIiIiKixsIiVAQMTzkykiRsuoAX6TDhRDL2WvaVsMODTLhQCg1+yz6VyIIOL5wot2xnjMSJoaQBSBNKz4WhgIwUh+X6hinh9hhIcdlg063vpi1z+2C3aUh2Wv94V3oN+A3JGAkSw+O1XIWIiIiIiKjRsQgVASUNaELBpgGmklBKwaZZ3+piKgkoDbqQ0IV1H00q2ISELUQ7YyRODAUFpfxQGiCVCFlsqGHTtZB9NE3ApoXehk0TMOtoZ4ymFUPXrG8NJSIiIiIiamycmJyIiIiIiIiIiKKORSgiIiIiIiIiIoq6uCpCKaUwdepUtG7dGklJSRg4cCB++umnOtf5+OOP8Yc//AG5ubkQQmDVqlUNsl0iIiIiIiIiImo4cVWEevjhh/HEE09g0aJFWL9+PVJSUpCfnw+PxxNyHbfbjV69emHBggUNul0iIiIiIiIiImo4cTMxuVIK8+bNw+TJk3HppZcCAF544QVkZ2dj1apVGDlypOV6Q4YMwZAhQxp8u0RERERERERE1HDipgi1detWFBcXY+DAgYFlGRkZ+P/t3XlUlNf9P/D3DMIgIouCLLYiuMYFxRoItY1GPaLiQtPjkrqRGLdorCIutEUirhG3atT05CTiMR6tyTepMVJPrAfbKMSWCCZWmwoFsVEwxsimDMzM5/eHP6bizLAMs/N+ncM5Ptu9n3sZ4bmfe3memJgY5Obmmp0ssmS5HTx98Uh8oK7XQQc3APWo0nYyeq4O7gBqUQ1vKFFv4px6aOGGOpgqg3W0lzpEAIVCAWW9QKEU/FCtNn697vFb9Spq6qA08dYznU7wqE4LtUbXZBmso33UodFojF5DRERERERkaw6ThCorKwMABAUFNdofFBSkP2bLctVqNdTq/w0KKysrIbp6uIkabgod6hWeAIAOUm30ep0C0MITbqiFUowPLpsrg3W0nzoEOigEaEghqDoY/0tZjU5Qr9HBvYMSHUwkJB7VaZsso7njrMO16lCI8WuIiIiIiIhszW5JqKNHj2LRokX67dOnT9srFKO2bt2KDRs2NNp34v+OwkPpDg93BZRSh3rxREel8VUGWgFqpDM8FRVwU5g4R6eEu6IWKhPHWUf7qUN0GohWDXFXoF4U8PJ0N3q9RqtDhaYOXqoO6OBmPGmh1uig6qA0WUbDOayjfdShgPEVWERERERERLZmtweTT5kyBQUFBfqvgIAAAEB5eXmj88rLyxEcHGx2PQ3XtrbclJQUVFRU6L9u3bpldgxERERERERERO2d3VZCde7cGZ07d9ZviwiCg4Nx7tw5DB06FMDjP4G7dOkSlixZYnY94eHhZpWrUqmgUqnMrpeIiIjMs3PnTnuHQHbW0s/AqlWrrBwJERERWZLDPBNKoVBgxYoV2LRpE/r06YPw8HCkpqYiNDQUCQkJ+vPGjBmDX/ziF1i2bBkAoLq6GoWFhfrjxcXFKCgoQJcuXdCjR48Wl0tERERERERERNbjMEkoAFizZg1qamqwcOFCPHjwAD/72c9w5swZeHp66s8pKirCvXv39Nt5eXl44YUX9NtJSUkAgHnz5iEzM7PF5RIRERERERERkfU4VBJKoVAgPT0d6enpJs8pKSlptD1q1CiISJvLJSIiIiIiIufAP90moOnPAf9k2zE5VBLK0bl5eKFOp4K2Xod6hQcAHR5pjXehTqECUI9aqKA0kSPTKXRQwwM6MV4G62g/dYgooYAHdPUChVLwsLbe6PUa3eOCH6o16KBUGK9DJ3hUpzUeAKA/xjraRx119abrJiIiIiIisiUmoVpBoXSHVqGCQAeRxwM7DYw/vFwn7gC00MDd5CsImyuDdbSfOgSPV+w1XKfW6IzX8f8TEvUaHbQmEhINTJXR3HHW4Vp1aLRNrxQlIiIiIiKyFSahWkFTW4GO3oCHuwJq6Yx68YS38oHRc7XSATUSgE6Ke3BTaIyeU60LhLuiFipFjdHjrKP91CE6DUSrhrj3QL0o4O9tPNGl0epQUVMH304e6OBmPNX1Q7Uaqg5KeHm6Gz3+sLYeao2OdbSTOh7VGr3E5WzevBmnT59GQUEBPDw88ODBA4NzFArDJN6xY8cwc+ZMG0RIRERERERMQhERkdOrq6vDtGnTEBsbi3fffdfkeYcOHcL48eP1235+fjaIjoiIiIiIACahiIjIBWzYsAEA9G9FNcXPzw/BwcE2iIiIiIiIiJ7GJBQREbUbS5cuxauvvoqIiAgsXrwYL7/8stE/02ugVquhVqv12xUVFQAer7wi66msrLR3COQknvz/SZbX8LOuuTdRExERtRSTUERE1C6kp6dj9OjR8PLywmeffYbXXnsN1dXVWL58uclrtm7dql9l9aR33nnHmqG2e/v377d3CET0hKqqKvj6+to7DCIicgFMQrVQwwyQVqdAXb2gHjpoFYI6E2+e0kGgUwD1ItDC+DlahQCig8LEcdbRjuoQAcQdunoNNFCYfJi0VifQaDWoVQNuJt6UptFooBAFFDD+trW6ei00WmEd7aQOdV09AOecxV63bh3efPPNJs+5fv06+vfv36LyUlNT9f+OiopCTU0NMjIymkxCpaSkICkpSb+t0+lw//59dO3a1egKqsrKSvz4xz/GrVu34OPj06K4HBHb4VhcpR2A67SlvbRDRFBVVYXQ0FA7ROe8Gn7nctWsdXHVLDWHq2Wty9zVskxCtVBVVRWm/3KWvcMgIjKLM85ir1q1ComJiU2eExERYXb5MTEx2LhxI9RqNVQq428nVKlUBsda8jBzHx8fpx6YNmA7HIurtANwnba0h3Y42+8OR1BVVQWAq2atjatmiRxDa8cZTEK1UGhoKG7duoXOnTs3+fyQp7nKTJklsU8MsU8MsU8MmdMnzjyLHRgYiMDAQKuVX1BQAH9/f5MJKCIiInMYGze40n0N2+J4XKUdANviiEy1w9xxBpNQLaRUKvGjH/3I7OtdZabMktgnhtgnhtgnhlrbJ+1hFru0tBT3799HaWkptFotCgoKAAC9e/eGt7c3Tp06hfLycjz33HPw9PTE2bNnsWXLFiQnJ9s3cCIicjlNjRtc6b6GbXE8rtIOgG1xRMbaYc44g0koIiJyeuvXr8fhw4f121FRUQCA7OxsjBo1Cu7u7ti/fz9WrlwJEUHv3r2xa9cuLFiwwKJxqFQqpKWlOf3qKrbDsbhKOwDXaQvbQUREZB6FOOPTap1IZWUlfH19UVFR4RLZT0tgnxhinxhinxhinxARETknV/odzrY4HldpB8C2OCJLt0NpgZioCZxhMsQ+McQ+McQ+McQ+ISIick6u9DucbXE8rtIOgG1xRJZuB1dCERERERERERGR1XElFBERERERERERWR2TUEREREREREREZHVMQhEREVnA5s2b8dOf/hReXl7w8/Mzeo5CoTD4On78uG0DbUZL2lFaWor4+Hh4eXmhW7duWL16NTQajW0DNUPPnj0N+n/btm32DqtZ+/fvR8+ePeHp6YmYmBj8/e9/t3dIrfLGG28Y9Hv//v3tHVaz/va3v2Hy5MkIDQ2FQqHAn/70p0bHRQTr169HSEgIOnbsiLFjx+LGjRv2CbYJzbUjMTHR4Pszfvx4+wRLREQuj0koIiIiC6irq8O0adOwZMmSJs87dOgQ7ty5o/9KSEiwTYAt1Fw7tFot4uPjUVdXh5ycHBw+fBiZmZlYv369jSM1T3p6eqP+f/311+0dUpP++Mc/IikpCWlpabh8+TKGDBmCuLg43L17196htcrAgQMb9fuFCxfsHVKzampqMGTIEOzfv9/o8e3bt2Pv3r14++23cenSJXTq1AlxcXGora21caRNa64dADB+/PhG359jx47ZMEIiImpXhFpNp9NJamqqBAcHi6enp4wZM0b+/e9/N3nNX//6V5k0aZKEhIQIAPn4448tUq6jMDf2t956S8LCwkSlUkl0dLRcunSp0fGRI0cKgEZfixYtslYzLKa5dj3txIkT0q9fP1GpVDJo0CA5ffp0o+PO/NloYOk+mTdvnsFnIy4uzppNsLjW9MnVq1flxRdflLCwMAEgu3fvbnOZZB2HDh0SX19fo8dM/fx3RKbakZWVJUqlUsrKyvT7Dh48KD4+PqJWq20YYeuFhYWZ/L/jqKKjo2Xp0qX6ba1WK6GhobJ161Y7RtU6aWlpMmTIEHuH0SZP/9/V6XQSHBwsGRkZ+n0PHjwQlUolx44ds0OELWPsZ9C8efNk6tSpdomnvdi0aZPExsZKx44dm/z98PSXI36WWtKWmzdvysSJE6Vjx44SGBgoycnJUl9fb9tAzdBwj/Xkl7P8rHWF+7+0tDSD/u/Xr5+9w2qR5sb6zjKWa64dlhp/cSWUGcyZ+WrJLJSzzKgZY07sLZ3dXbBgQaPZue3bt1u7OW3S2lnrnJwcvPTSS5g/fz7y8/ORkJCAhIQEXL16VX+OM382AOv0CeDcM7et7ZOHDx8iIiIC27ZtQ3BwsEXKJPtYunQpAgICEB0djffeew/iZC+pzc3NxeDBgxEUFKTfFxcXh8rKSvzzn/+0Y2Qts23bNnTt2hVRUVHIyMhw6D8jrKurw5dffomxY8fq9ymVSowdOxa5ubl2jKz1bty4gdDQUERERGDWrFkoLS21d0htUlxcjLKyskbfG19fX8TExDjd9wYAzp8/j27duqFfv35YsmQJvv/+e3uH5FJcZaUswNWyjsiV7v+ccdUswJWzrWaZnFn7YYmZL5jIjjrjjJqI+bG3ZHZ35MiR8utf/9oqcVtLa2etp0+fLvHx8Y32xcTE6Fd8OfNno4Gl+0TE+Wdu27K6wdRqDldYMeEKmloJlZ6eLhcuXJDLly/Ltm3bRKVSye9//3vbBthCptqxYMECGTduXKN9NTU1AkCysrJsFJ15du7cKdnZ2XLlyhU5ePCg+Pn5ycqVK+0dlknffvutAJCcnJxG+1evXi3R0dF2iqr1srKy5MSJE3LlyhU5c+aMxMbGSo8ePaSystLeobXY0/duFy9eFABy+/btRudNmzZNpk+fbuPoWs7YPeixY8fk5MmT8tVXX8nHH38szzzzjDz77LOi0WjsE6QLc5WVsiJcLetIXOX+zxVWzYpw5WxLcCVUK1lr5suZZ9TMib01s7tHjx5FQEAABg0ahJSUFDx8+NA6DbEAc2atc3NzG50PPF5V0HC+M382AOv0SQNnnbm1xuoGV1ox4UjWrVtn9GHiT37961//anF5qampGDFiBKKiorB27VqsWbMGGRkZVmzBY5ZuhyNpTduSkpIwatQoREZGYvHixdi5cyf27dsHtVpt51a4tgkTJmDatGmIjIxEXFwcsrKy8ODBA5w4ccLeoRGAmTNnYsqUKRg8eDASEhLw6aef4h//+AfOnz9v79DaHWdfKQtwtaytudr9n6utmgWcfyz3NEuMvzpYIS6XVlZWBgCNfrA2bDccc6RybcGc2O/duwetVmv0micHQr/61a8QFhaG0NBQfPXVV1i7di2++eYbfPTRRxZuhWW0tF1PKisra7LvnPmzAVinT4DHS0FffPFFhIeHo6ioCL/5zW8wYcIE5Obmws3NzfINsSBz+sQeZRKwatUqJCYmNnlORESE2eXHxMRg48aNUKvVUKlUZpfTHEu2Izg42ODtbOXl5fpjttaWtsXExECj0aCkpAT9+vWzQnRtExAQADc3N33/NigvL7dLX1uKn58f+vbti8LCQnuHYraG/i8vL0dISIh+f3l5OYYOHWqnqCwjIiICAQEBKCwsxJgxY+wdTruRnp6O0aNHw8vLC5999hlee+01VFdXY/ny5fYOrVVM3cM1HHNky5cvx7Bhw9ClSxfk5OQgJSUFd+7cwa5du+wdmkmudP8XExODzMxM9OvXD3fu3MGGDRvw85//HFevXkXnzp3tHZ7ZnH0s9yRLjb+YhGrG0aNHsWjRIv326dOn7RiNY7BlnyxcuFD/78GDByMkJARjxoxBUVERevXqZbV6yfHNnDlT/+/BgwcjMjISvXr1wvnz53nTTBYTGBiIwMBAq5VfUFAAf39/qyagAMu2IzY2Fps3b8bdu3fRrVs3AMDZs2fh4+ODAQMGWKSO1mhL2woKCqBUKvXtcDQeHh74yU9+gnPnzumfDaPT6XDu3DksW7bMvsG1QXV1NYqKijBnzhx7h2K28PBwBAcH49y5c/qkU2VlJS5dutTsc38c3X//+198//33jZJrZGjdunV48803mzzn+vXr6N+/f4vKS01N1f87KioKNTU1yMjIsEkSytJtcSStaVtSUpJ+X2RkJDw8PLBo0SJs3brV6r+n6fGq2QaRkZGIiYlBWFgYTpw4gfnz59sxMmpgqfEXk1DNmDJlCmJiYvTbDUv2LT3z5UwzapboE3NndxvqLSwsdMgklDntCg4ObvJ8Z/psGGONPjHGmWZurbG6wVVXTDiT0tJS3L9/H6WlpdBqtSgoKAAA9O7dG97e3jh16hTKy8vx3HPPwdPTE2fPnsWWLVuQnJxs38Cf0lw7xo0bhwEDBmDOnDnYvn07ysrK8Lvf/Q5Lly516Jv03NxcXLp0CS+88AI6d+6M3NxcrFy5ErNnz4a/v7+9wzMpKSkJ8+bNw/DhwxEdHY09e/agpqYGL7/8sr1Da7Hk5GRMnjwZYWFhuH37NtLS0uDm5oaXXnrJ3qE1qbq6utFqreLiYhQUFKBLly7o0aMHVqxYgU2bNqFPnz4IDw9HamoqQkNDHe5h0k21o0uXLtiwYQN++ctfIjg4GEVFRVizZg169+6NuLg4O0bt+FxlpSzA1bLOuloWcO37P1dYNQs4/1iuKWaPv9r8VKl2puHBYjt27NDvq6iosNiDydtSrr2YG3t0dLQsW7ZMv63VaqV79+5NPkTvwoULAkCuXLlimeCtoLXtmj59ukyaNKnRvtjYWIMHkzvjZ6OBpfvEmFu3bolCoZCTJ09aJmgrM+fz36CpB5ObWya1nbHX1gKQ7OxsERH585//LEOHDhVvb2/p1KmTDBkyRN5++23RarX2DfwpzbVDRKSkpEQmTJggHTt2lICAAFm1apXDv377yy+/lJiYGPH19RVPT0955plnZMuWLVJbW2vv0Jq1b98+6dGjh3h4eEh0dLR88cUX9g6pVWbMmCEhISHi4eEh3bt3lxkzZkhhYaG9w2pWdna20f8L8+bNE5H/vXI7KChIVCqVjBkzRr755hv7Bm1EU+14+PChjBs3TgIDA8Xd3V3CwsJkwYIFjR4qTZbT1IPJn7Zp0ybx9/e3bkBt0NyDycvLy/X7/vCHP4iPj49T/Lx90vvvvy9KpVLu379v71Ca5Kr3f1VVVeLv7++wL3Ax5emxvrOO5YzlLJ5m7viLSSgzbNu2Tfz8/PRvEpk6daqEh4fLo0eP9OeMHj1a9u3bp9+uqqqS/Px8yc/PFwCya9cuyc/Pl5s3b7aqXEdlTp8cP35cVCqVZGZmyrVr12ThwoXi5+env/EpLCyU9PR0ycvLk+LiYjl58qRERETI888/b/P2tUZz7ZozZ46sW7dOf/7FixelQ4cOsmPHDrl+/bqkpaWJu7u7fP311/pznPmzIWL5PqmqqpLk5GTJzc2V4uJi+ctf/iLDhg2TPn36OM0NTmv7RK1W63+GhISESHJysuTn58uNGzdaXCYRERHZ3s2bNyU/P182bNgg3t7e+t/nVVVVIiLyySefyDvvvCNff/213LhxQw4cOCBeXl6yfv16O0duqLm2aDQaGTRokIwbN04KCgrkzJkzEhgYKCkpKXaOvGk5OTmye/duKSgokKKiInn//fclMDBQ5s6da+/QmuUq93+rVq2S8+fPS3FxsVy8eFHGjh0rAQEBcvfuXXuH1qzmxvrOMpZrqh2WHH8xCWWGlsx8hYWFSVpamn67udm0lpbrqMzpE5GmZ3dLS0vl+eefly5duohKpZLevXvL6tWrpaKiwhZNapOm2jVy5MhG33cRkRMnTkjfvn3Fw8NDBg4cKKdPn2503Jk/Gw0s2SeuMnPbmj4pLi42+jNk5MiRLS6TiIiIbM9VVsqKcLWso3KF+z9nXTUrwpWzraUQccJ3fxIRERERERERkVNR2jsAIiIiIiIiIiJyfUxCERERERERERGR1TEJRUREREREREREVsckFBERERERERERWR2TUEREREREREREZHVMQhERERERERERkdUxCUVERETkAN59912MGzfO6vWcOXMGQ4cOhU6ns3pdRERERE9iEorIRji4ICIiU2pra5Gamoq0tDSr1zV+/Hi4u7vj6NGjVq+LiIiI6ElMQhHZAAcXRETUlA8//BA+Pj4YMWKETepLTEzE3r17bVIXERG1HSe0yVUwCUVkAxxcEBG1D9999x2Cg4OxZcsW/b6cnBx4eHjg3LlzJq87fvw4Jk+e3GhfYmIiEhISsGPHDoSEhKBr165YunQp6uvr9ef07NkTmzZtwty5c+Ht7Y2wsDB88skn+O677zB16lR4e3sjMjISeXl5jcqePHky8vLyUFRUZKGWExGRtXBCm1wJk1BErWDJwcWoUaOwYsWKRvsSEhKQmJio3+bggojIuQQGBuK9997DG2+8gby8PFRVVWHOnDlYtmwZxowZY/K6CxcuYPjw4Qb7s7OzUVRUhOzsbBw+fBiZmZnIzMxsdM7u3bsxYsQI5OfnIz4+HnPmzMHcuXMxe/ZsXL58Gb169cLcuXMhIvprevTogaCgIHz++ecWazsREVkHJ7TJlTAJRdQKlh5ctAQHF0REzmXixIlYsGABZs2ahcWLF6NTp07YunWryfMfPHiAiooKhIaGGhzz9/fHW2+9hf79+2PSpEmIj483mPSYOHEiFi1ahD59+mD9+vWorKzEs88+i2nTpqFv375Yu3Ytrl+/jvLy8kbXhYaG4ubNm5ZpNBERNYurZYmYhCJqNUsOLlpaHwcXRETOZceOHdBoNPjggw9w9OhRqFQqk+c+evQIAODp6WlwbODAgXBzc9Nvh4SE4O7du43OiYyM1P87KCgIADB48GCDfU9f17FjRzx8+LClTSIiojbialkiJqGIzGKpwUVLcHBBROR8ioqKcPv2beh0OpSUlDR5bteuXaFQKPDDDz8YHHN3d2+0rVAoDB4W++Q5CoXC5L6nr7t//z4CAwObbwwREVkMV8tSe8ckFJEZLDW4eJpWqzXYx8EFEZFzqaurw+zZszFjxgxs3LgRr776qsFEwZM8PDwwYMAAXLt2zWYx1tbWoqioCFFRUTark4iIHuNqWWrPmIQiaiVLDi6ennH4z3/+Y5EYObggIrKf3/72t6ioqMDevXuxdu1a9O3bF6+88kqT18TFxeHChQs2ihD44osvoFKpEBsba7M6iYjoMa6WpfaMSSiiVrLk4OLkyZP46KOPUFRUhM2bN+PatWu4efMmvv322zbFyMEFEZF9nD9/Hnv27MGRI0fg4+MDpVKJI0eO4PPPP8fBgwdNXjd//nxkZWWhoqLCJnEeO3YMs2bNgpeXl03qIyKix7haltq7DvYOgMiZNAwusrOz4ePjAwA4cuQIhgwZgoMHD2LJkiVGr5s/fz6GDx+OiooK+Pr66vfHx8dj+/btyM/Px6hRo3DgwAEkJyfjyJEjWLdundlxcnBBRGQfo0aNavRmIuDx24maSy4NGDAA8fHxOHDgAFJSUgDA4OGyALBnz55G28Zm0J98uGxD/U/uu3fvHj788EODtyEREZH1PTmh7e3tjaysLLzyyiv49NNPTV7TMKG9YsUKm8TICW2yJiahiFrBkoMLAOjevTs++OCDRuc+mcji4IKIqP3IyMjAqVOnrF5PSUkJDhw4gPDwcKvXRURE/2PpCW1r4YQ2WZNCnh7REpFVlJSU4NSpU3j99dcBPE5oDR061GBWu63y8vJQVFSEGTNmWLRcIiIiIiKyj2nTpmHYsGGNJrSt4d69e+jXrx/y8vI4WUFWwWdCEdlIz5499Qkoaxo+fDgTUERERERELiQjIwPe3t5Wr4erZcnauBKKiIiIiIiIiIisjiuhiIiIiIiIiIjI6piEIiIiIiIiIiIiq2MSioiIiIiIiIiIrI5JKCIiIiIiIiIisjomoYiIiIiIiIiIyOqYhCIiIiIiIiIiIqtjEoqIiIiIiIiIiKyOSSgiIiIiIiIiIrI6JqGIiIiIiIiIiMjq/h9nxlpEGBjfawAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x380 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(12, 3.8))\n",
    "sim = sims[\"dl = 0.5 nm\"]\n",
    "sim.plot(z=0, ax=axes[0])\n",
    "axes[0].set_title(\"unit cell, z = 0\")\n",
    "\n",
    "zoom = td.Box(center=(0, 0, 0), size=(30 * nm, 30 * nm, 0))\n",
    "for ax, (name, s) in zip(axes[1:], sims.items()):\n",
    "    eps = s.epsilon(box=zoom, coord_key=\"centers\", freq=td.C_0 / (520 * nm))\n",
    "    ex = eps.real.squeeze()\n",
    "    ax.pcolormesh(\n",
    "        ex.x / nm,\n",
    "        ex.y / nm,\n",
    "        (ex < 0).T,\n",
    "        cmap=\"Greys\",\n",
    "        shading=\"nearest\",\n",
    "        vmin=0,\n",
    "        vmax=1.6,\n",
    "    )\n",
    "    ax.set_aspect(\"equal\")\n",
    "    ax.set_xlabel(\"x (nm)\")\n",
    "    ax.set_ylabel(\"y (nm)\")\n",
    "    ax.set_title(f\"metal cells near the gap, {name}\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6fa5808",
   "metadata": {},
   "source": [
    "## Running the Simulations\n",
    "\n",
    "Always check the cost before running. `web.estimate_cost` returns an upper bound; because the field decays below\n",
    "the `shutoff` level before `run_time` is reached, the billed cost is usually lower. In our runs the estimates were\n",
    "0.039 FlexCredit (1 nm) and 0.137 FlexCredit (0.5 nm), and the billed costs were 0.025 and 0.077 FlexCredit.\n",
    "\n",
    "The helper below reuses saved results when they exist, so the notebook can be re-executed without\n",
    "running the simulations again. Set `CHECK_COST = True` to print the estimates first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eb340cc6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-02T02:17:02.208893Z",
     "iopub.status.busy": "2026-10-02T02:17:02.208893Z",
     "iopub.status.idle": "2026-10-02T02:17:02.364843Z",
     "shell.execute_reply": "2026-10-02T02:17:02.364339Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded: ['dl = 1 nm', 'dl = 0.5 nm']\n"
     ]
    }
   ],
   "source": [
    "CHECK_COST = False\n",
    "os.makedirs(\"data\", exist_ok=True)\n",
    "files = {\n",
    "    \"dl = 1 nm\": \"data/g5_dl1_stair.hdf5\",\n",
    "    \"dl = 0.5 nm\": \"data/g5_dl0.5_stair.hdf5\",\n",
    "}\n",
    "\n",
    "if CHECK_COST:\n",
    "    for name, s in sims.items():\n",
    "        task_id = web.upload(s, task_name=f\"ag_dimer_{name}\")\n",
    "        print(name, web.estimate_cost(task_id))\n",
    "\n",
    "data = {}\n",
    "for name, s in sims.items():\n",
    "    if os.path.exists(files[name]):\n",
    "        data[name] = td.SimulationData.from_file(files[name])\n",
    "        # make sure the stored result belongs to exactly this simulation\n",
    "        assert data[name].simulation == s, (\n",
    "            \"saved data does not match the current simulation\"\n",
    "        )\n",
    "    else:\n",
    "        data[name] = web.run(s, task_name=f\"ag_dimer_{name}\", path=files[name])\n",
    "print(\"loaded:\", list(data))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf5aba6b",
   "metadata": {},
   "source": [
    "## Results\n",
    "\n",
    "### Far Field: Transmission and Reflection\n",
    "\n",
    "Fluxes are normalised to the source power, so $T$ and $R$ can be read directly; absorption is $A = 1 - T - R$.\n",
    "We locate the bonding-mode resonance with a Lorentzian fit to the transmission dip (±30 nm window)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6e6035ae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-02T02:17:02.365847Z",
     "iopub.status.busy": "2026-10-02T02:17:02.365847Z",
     "iopub.status.idle": "2026-10-02T02:17:02.688202Z",
     "shell.execute_reply": "2026-10-02T02:17:02.688202Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dl = 1 nm   : bonding-mode dip at  514.7 nm, FWHM 48.1 nm\n",
      "dl = 0.5 nm : bonding-mode dip at  516.5 nm, FWHM 44.5 nm\n"
     ]
    }
   ],
   "source": [
    "def spectra(sim_data):\n",
    "    f = sim_data[\"T\"].flux.f.values\n",
    "    order = np.argsort(td.C_0 / f)\n",
    "    wl = (td.C_0 / f)[order] / nm\n",
    "    T = sim_data[\"T\"].flux.values[order]\n",
    "    R = -sim_data[\"R\"].flux.values[order]\n",
    "    return wl, T, R\n",
    "\n",
    "\n",
    "def lorentz_dip(x, a, x0, w, c, s):\n",
    "    return c + s * (x - x0) - a * w**2 / ((x - x0) ** 2 + w**2)\n",
    "\n",
    "\n",
    "def fit_dip(wl, T, lo=470, hi=650):\n",
    "    sel = (wl > lo) & (wl < hi)\n",
    "    x0 = wl[sel][np.argmin(T[sel])]\n",
    "    win = (wl > x0 - 30) & (wl < x0 + 30)\n",
    "    p, _ = curve_fit(lorentz_dip, wl[win], T[win], p0=[0.8, x0, 20, 0.9, 0])\n",
    "    return p[1], 2 * abs(p[2])\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(10, 3.6))\n",
    "res = {}\n",
    "for (name, d), ls in zip(data.items(), [\"--\", \"-\"]):\n",
    "    wl, trans, refl = spectra(d)\n",
    "    res[name] = fit_dip(wl, trans)\n",
    "    ax[0].plot(wl, trans, ls, color=\"C0\", label=f\"T, {name}\")\n",
    "    ax[0].plot(wl, refl, ls, color=\"C3\", label=f\"R, {name}\")\n",
    "    ax[1].plot(wl, 1 - trans - refl, ls, color=\"k\", label=name)\n",
    "ax[0].set_ylabel(\"T, R\")\n",
    "ax[1].set_ylabel(\"absorption 1 - T - R\")\n",
    "for a in ax:\n",
    "    a.set_xlabel(\"wavelength (nm)\")\n",
    "    a.set_xlim(380, 750)\n",
    "    a.legend(frameon=False, fontsize=8)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "for name, (x0, fw) in res.items():\n",
    "    print(f\"{name:12s}: bonding-mode dip at {x0:6.1f} nm, FWHM {fw:4.1f} nm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06ff3430",
   "metadata": {},
   "source": [
    "The main transmission dip near 515 nm is the bonding mode of the dimer array (the shorter-wavelength dip\n",
    "near 410 nm is not analysed here). Halving the gap mesh moves it by about 2 nm, which is small compared with its ~45 nm\n",
    "width. The 1 nm spectrum also shows a narrow extra feature near 465 nm that is absent at 0.5 nm, so we attribute\n",
    "it to the coarse staircase rather than to a physical mode. A narrow feature should survive a mesh refinement\n",
    "before it is interpreted.\n",
    "\n",
    "### Near Field: Enhancement in the Gap\n",
    "\n",
    "The plane wave is normalised to unit power through the unit cell, so the incident intensity in the medium is\n",
    "$|E_0|^2 = 2 Z_0 / (n A)$, where $A$ is the unit-cell area and $Z_0$ the vacuum impedance. (A run of the same cell\n",
    "without particles reproduced this value within 1 %.) We plot the enhancement $|E|^2/|E_0|^2$ at the centre of\n",
    "the gap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "842dae10",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-02T02:17:02.690206Z",
     "iopub.status.busy": "2026-10-02T02:17:02.690206Z",
     "iopub.status.idle": "2026-10-02T02:17:02.986933Z",
     "shell.execute_reply": "2026-10-02T02:17:02.986933Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dl = 1 nm   : |E|^2/|E0|^2 at the gap centre, at the dip wavelength =    4737\n",
      "dl = 0.5 nm : |E|^2/|E0|^2 at the gap centre, at the dip wavelength =   11158\n",
      "ratio 0.5 nm / 1 nm = 2.36\n"
     ]
    }
   ],
   "source": [
    "E0_sq = 2 * td.ETA_0 / (n_bg * period**2)\n",
    "\n",
    "\n",
    "def intensity(field_data):\n",
    "    return sum(np.abs(field_data.field_components[c]) ** 2 for c in (\"Ex\", \"Ey\", \"Ez\"))\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5, 3.6))\n",
    "enh = {}\n",
    "for (name, d), ls in zip(data.items(), [\"--\", \"-\"]):\n",
    "    I = intensity(d[\"gap_center\"]).squeeze()\n",
    "    wl_c = td.C_0 / I.f.values / nm\n",
    "    o = np.argsort(wl_c)\n",
    "    ax.semilogy(wl_c[o], I.values[o] / E0_sq, ls, color=\"k\", label=name)\n",
    "    enh[name] = np.interp(res[name][0], wl_c[o], I.values[o] / E0_sq)\n",
    "ax.set_xlabel(\"wavelength (nm)\")\n",
    "ax.set_ylabel(r\"$|E|^2/|E_0|^2$ at gap centre\")\n",
    "ax.set_xlim(380, 750)\n",
    "ax.legend(frameon=False)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "for name in data:\n",
    "    print(\n",
    "        f\"{name:12s}: |E|^2/|E0|^2 at the gap centre, at the dip wavelength = {enh[name]:7.0f}\"\n",
    "    )\n",
    "print(f\"ratio 0.5 nm / 1 nm = {enh['dl = 0.5 nm'] / enh['dl = 1 nm']:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22eac53f",
   "metadata": {},
   "source": [
    "The spectrum barely moved, but the gap-centre enhancement changed by a factor of about 2.4. With 1 nm cells the\n",
    "5 nm gap is resolved by only a handful of cells, and the staircase changes the local surface-to-surface\n",
    "distance, to which the gap field is far more sensitive than the far-field resonance is.\n",
    "\n",
    "### Field Map of the Gap Mode\n",
    "\n",
    "Finally, we plot the field map in the $z = 0$ plane at the monitor frequency closest to the transmission dip\n",
    "(0.5 nm mesh). The field is concentrated in the gap and is largest next to the metal surface."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d3cebdd8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-02T02:17:02.988936Z",
     "iopub.status.busy": "2026-10-02T02:17:02.988936Z",
     "iopub.status.idle": "2026-10-02T02:17:03.150989Z",
     "shell.execute_reply": "2026-10-02T02:17:03.150989Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 460x380 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max |E|^2/|E0|^2 in this plane: 7.46e+04\n"
     ]
    }
   ],
   "source": [
    "name = \"dl = 0.5 nm\"\n",
    "I = intensity(data[name][\"gap_plane\"]).squeeze()\n",
    "wl_p = td.C_0 / I.f.values / nm\n",
    "k = np.argmin(np.abs(wl_p - res[name][0]))\n",
    "Ik = I.isel(f=k)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4.6, 3.8))\n",
    "im = ax.pcolormesh(\n",
    "    Ik.x / nm,\n",
    "    Ik.y / nm,\n",
    "    np.log10(Ik.values.T / E0_sq),\n",
    "    cmap=\"magma\",\n",
    "    shading=\"auto\",\n",
    "    vmin=0,\n",
    "    vmax=5.5,\n",
    ")\n",
    "for s in (-1, 1):  # nominal sphere outlines\n",
    "    ax.add_patch(\n",
    "        plt.Circle(\n",
    "            (s * (R + gap / 2) / nm, 0), R / nm, fill=False, color=\"w\", lw=0.6, ls=\"--\"\n",
    "        )\n",
    "    )\n",
    "ax.set_aspect(\"equal\")\n",
    "ax.set_xlim(-20, 20)\n",
    "ax.set_ylim(-20, 20)\n",
    "ax.set_xlabel(\"x (nm)\")\n",
    "ax.set_ylabel(\"y (nm)\")\n",
    "ax.set_title(f\"{wl_p[k]:.0f} nm, {name}\")\n",
    "plt.colorbar(im, ax=ax, label=r\"$\\log_{10}(|E|^2/|E_0|^2)$\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "print(f\"max |E|^2/|E0|^2 in this plane: {float(Ik.max()) / E0_sq:.3g}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff931fe7",
   "metadata": {},
   "source": [
    "The maximum of this map sits on the staircased metal surface, not in the middle of the gap. Its value depends\n",
    "on where the grid corners fall, so a maximum over the map is not a mesh-converged quantity; quote the field at\n",
    "the gap centre (or its mean over the gap volume) instead.\n",
    "\n",
    "## Takeaways\n",
    "\n",
    "* For a 5 nm gap, the far-field bonding resonance moved by about 2 nm when the gap mesh was halved from 1 nm to\n",
    "  0.5 nm, while the gap-centre intensity enhancement changed by a factor of about 2.4. The spectrum converges\n",
    "  at a coarser mesh than the near field.\n",
    "* In a separate check (not included here, about 0.35 FlexCredit) we refined the gap mesh further to 0.25 nm:\n",
    "  the resonance moved by less than 2 nm again and the gap-centre enhancement by about 6 %. For this geometry,\n",
    "  about 10 cells across the gap (0.5 nm for a 5 nm gap) were needed for the near field, and about 5 cells (1 nm)\n",
    "  for the spectrum.\n",
    "* Peak values of $|E|^2$ on a staircased metal surface do not converge with mesh refinement and should not be\n",
    "  reported as \"the enhancement\".\n",
    "* There is no universal cells-per-gap rule. In a similar check with a 2 nm gap, the fitted resonance still moved\n",
    "  from 584 nm to 575 nm between 0.5 nm and 0.25 nm cells, so 8 cells across the gap were not enough there. Always halve the\n",
    "  mesh at least once, and judge convergence on the quantity you will report.\n",
    "\n",
    "## References\n",
    "\n",
    "1. A. Calà Lesina, A. Vaccari, P. Berini, L. Ramunno, \"On the convergence and accuracy of the FDTD method for\n",
    "   nanoplasmonics,\" *Opt. Express* **23**, 10481 (2015). doi:10.1364/OE.23.010481\n",
    "2. I. Romero, J. Aizpurua, G. W. Bryant, F. J. García de Abajo, \"Plasmons in nearly touching metallic\n",
    "   nanoparticles: singular response in the limit of touching dimers,\" *Opt. Express* **14**, 9988 (2006).\n",
    "   doi:10.1364/OE.14.009988"
   ]
  }
 ],
 "metadata": {
  "applications": [
   "Nanophotonics",
   "Metamaterials, gratings, and other periodic structures"
  ],
  "description": "This notebook demonstrates how to simulate a periodic array of silver nanosphere dimers with a 5 nm gap in Tidy3D, and how to compare 1 nm and 0.5 nm local mesh overrides to show that the far-field transmission spectrum converges at a coarser mesh than the near-field gap enhancement.",
  "feature_image": "./img/silver_dimer_array.png",
  "features": [
   "Mesh override"
  ],
  "kernelspec": {
   "display_name": "flex",
   "language": "python",
   "name": "python3"
  },
  "keywords": "plasmonics, silver nanoparticle, nanoparticle dimer, gap plasmon, field enhancement, mesh convergence, 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": "3.14.7"
  },
  "title": "How to Check Mesh Convergence for a Silver Nanoparticle Dimer with a 5 nm Gap in Tidy3D | Flexcompute"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
