Bandstructure.ipynb

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    "# Band structure calculation of a photonic crystal slab\n",
    "\n",
    "In this notebook, we simulate a photonic crystal slab consisting of a square lattice of air holes in a dielectric slab. Our goal is to compute the band structure of this photonic crystal slab, as found in:\n",
    "\n",
    "> Shanhui Fan and J. D. Joannopoulos, \"Analysis of guided resonances in photonic crystal slabs,\" Phys. Rev. B 65, 235112 (2002).\n",
    "\n",
    "To this end, we excite the structure with several `PointDipole` sources, and we measure the response with several `FieldTimeMonitor` monitors. We excite modes with a fixed Bloch wavevector by using Bloch boundary conditions. We then use the `ResonanceFinder` to find the resonant frequencies. By sweeping the Bloch wavevector, we obtain the full band structure of the photonic crystal slab.\n",
    "\n",
    "See also the api reference for `ResonanceFinder` [here](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.resonance.ResonanceFinder.html).\n",
    "\n",
    "If you are new to the finite-difference time-domain (FDTD) method, we highly recommend going through our [FDTD101](https://www.flexcompute.com/fdtd101/) tutorials. "
   ]
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   "source": [
    "# standard python imports\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tidy3d as td\n",
    "import xarray as xr\n",
    "from tidy3d import web\n",
    "from tidy3d.plugins.resonance import ResonanceFinder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6022fa5a",
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   "source": [
    "We will randomly position our sources and monitors. Here, we seed the random number generator to guarantee reproducible results."
   ]
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   "source": [
    "rng = np.random.default_rng(12345)"
   ]
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   "source": [
    "Now we define the parameters for the simulation, structure, sources, and monitors.\n",
    "\n",
    "We take the dipole polarization to be `Hz` and the symmetry to be `(0,0,1)` in order to excite only modes which are even with respect to the xy mirror plane."
   ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total runtime = 6.74 ps\n"
     ]
    }
   ],
   "source": [
    "# Simulation parameters\n",
    "runtime_fwidth = 200.0  # in units of 1/frequency bandwidth of the source\n",
    "t_start_fwidth = (\n",
    "    5.0  # time to start monitoring after source has decayed, units of 1/frequency bandwidth\n",
    ")\n",
    "dPML = 1.0  # space between PhC slabs and PML, in unit of longest wavelength of interest\n",
    "\n",
    "# Structure parameters (um)\n",
    "a_lattice = 1  # lattice constant \"a\"\n",
    "r_hole = 0.2 * a_lattice  # radius of the air holes\n",
    "t_slab = 0.5 * a_lattice  # slab thickness\n",
    "ep_slab = 12  # dielectric constant of the slab\n",
    "ep_hole = 1  # dielectric constant of the holes\n",
    "\n",
    "# Frequency range of interest (Hz)\n",
    "freq_range_unitless = np.array((0.1, 0.43))  # in units of c/a\n",
    "freq_scale = td.constants.C_0 / a_lattice  # frequency scale determined by the lattice constant\n",
    "freq_range = freq_range_unitless * freq_scale\n",
    "lambda_range = (td.constants.C_0 / freq_range[1], td.constants.C_0 / freq_range[0])\n",
    "\n",
    "# Gaussian pulse parameters\n",
    "freq0 = np.sum(freq_range) / 2  # central frequency\n",
    "freqw = 0.3 * (freq_range[1] - freq_range[0])  # pulse width\n",
    "\n",
    "# Runtime\n",
    "run_time = runtime_fwidth / freqw\n",
    "print(f\"Total runtime = {(run_time * 1e12):.2f} ps\")\n",
    "t_start = t_start_fwidth / freqw\n",
    "\n",
    "# Simulation size\n",
    "spacing = dPML * lambda_range[-1]  # space between PhC slabs and PML\n",
    "sim_size = Lx, Ly, Lz = (a_lattice, a_lattice, 2 * spacing + t_slab)\n",
    "\n",
    "# Number of k values to sample, per edge of the irreducible Brillouin zone\n",
    "Nk = 4\n",
    "\n",
    "# Number of dipoles and monitors\n",
    "num_dipoles = 7\n",
    "num_monitors = 2\n",
    "\n",
    "# Dipole polarization and symmetry\n",
    "polarization = \"Hz\"\n",
    "symmetry = (0, 0, 1)"
   ]
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   "id": "27c57eee",
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   "source": [
    "We define the materials and structures in terms of the above parameters. We take the photonic crystal slab to be lying in the xy plane. Because the photonic crystal slab is periodic in the x and y directions, we only need to simulate a single unit cell, containing the dielectric slab and a single air hole."
   ]
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   "source": [
    "mat_slab = td.Medium(permittivity=ep_slab, name=\"mat_slab\")\n",
    "mat_hole = td.Medium(permittivity=ep_hole, name=\"mat_hole\")\n",
    "\n",
    "slab = td.Structure(\n",
    "    geometry=td.Box(\n",
    "        center=(0, 0, 0),\n",
    "        size=(td.inf, td.inf, t_slab),\n",
    "    ),\n",
    "    medium=mat_slab,\n",
    "    name=\"slab\",\n",
    ")\n",
    "\n",
    "hole = td.Structure(\n",
    "    geometry=td.Cylinder(\n",
    "        center=(0, 0, 0),\n",
    "        axis=2,\n",
    "        radius=r_hole,\n",
    "        length=t_slab,\n",
    "    ),\n",
    "    medium=mat_hole,\n",
    "    name=\"hole\",\n",
    ")\n",
    "\n",
    "structures = [slab, hole]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbda5f33",
   "metadata": {},
   "source": [
    "We will excite the photonic crystal slab with several `PointDipole` sources. Each dipole will have a random position and phase."
   ]
  },
  {
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   "execution_count": 5,
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   "outputs": [],
   "source": [
    "dipole_positions = rng.uniform([-Lx / 2, -Ly / 2, 0], [Lx / 2, Ly / 2, 0], [num_dipoles, 3])\n",
    "\n",
    "dipole_phases = rng.uniform(0, 2 * np.pi, num_dipoles)\n",
    "\n",
    "pulses = []\n",
    "dipoles = []\n",
    "for i in range(num_dipoles):\n",
    "    pulse = td.GaussianPulse(freq0=freq0, fwidth=freqw, phase=dipole_phases[i])\n",
    "    pulses.append(pulse)\n",
    "    dipoles.append(\n",
    "        td.PointDipole(\n",
    "            source_time=pulse,\n",
    "            center=tuple(dipole_positions[i]),\n",
    "            polarization=polarization,\n",
    "            name=\"dipole_\" + str(i),\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "700880a7",
   "metadata": {},
   "source": [
    "We create `FieldTimeMonitors` to record the field as a function of time at several random locations within the photonic crystal slab. Crucially, we start the monitors after the source pulse has decayed."
   ]
  },
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   "source": [
    "monitor_positions = rng.uniform([-Lx / 2, -Ly / 2, 0], [Lx / 2, Ly / 2, 0], [num_monitors, 3])\n",
    "\n",
    "monitors_time = []\n",
    "for i in range(num_monitors):\n",
    "    monitors_time.append(\n",
    "        td.FieldTimeMonitor(\n",
    "            fields=[\"Hz\"],\n",
    "            center=tuple(monitor_positions[i]),\n",
    "            size=(0, 0, 0),\n",
    "            start=t_start,\n",
    "            name=\"monitor_time_\" + str(i),\n",
    "        )\n",
    "    )"
   ]
  },
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   "cell_type": "markdown",
   "id": "e6939eae",
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   "source": [
    "We will perform `3*Nk` different simulations, each with different Bloch boundary conditions, as we sweep the Bloch wavevector over the boundary of the irreducible Brillouin zone. We sweep over three lines, namely $\\Gamma X$, $XM$, and $M\\Gamma$. We use a PML in the z direction.\n",
    "\n",
    "Here, we simply define all of the boundary conditions we will use and put them into a single array."
   ]
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   "source": [
    "bspecs_gammax = []\n",
    "bspecs_xm = []\n",
    "bspecs_mgamma = []\n",
    "for i in range(Nk):\n",
    "    bspecs_gammax.append(\n",
    "        td.BoundarySpec(\n",
    "            x=td.Boundary.bloch((1 / 2) * i / Nk),\n",
    "            y=td.Boundary.periodic(),\n",
    "            z=td.Boundary.pml(),\n",
    "        )\n",
    "    )\n",
    "    bspecs_xm.append(\n",
    "        td.BoundarySpec(\n",
    "            x=td.Boundary.bloch(1 / 2),\n",
    "            y=td.Boundary.bloch((1 / 2) * i / Nk),\n",
    "            z=td.Boundary.pml(),\n",
    "        )\n",
    "    )\n",
    "    bspecs_mgamma.append(\n",
    "        td.BoundarySpec(\n",
    "            x=td.Boundary.bloch((1 / 2) * (1 - i / Nk)),\n",
    "            y=td.Boundary.bloch((1 / 2) * (1 - i / Nk)),\n",
    "            z=td.Boundary.pml(),\n",
    "        )\n",
    "    )\n",
    "bspecs = bspecs_gammax + bspecs_xm + bspecs_mgamma"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e80d5995",
   "metadata": {},
   "source": [
    "Now we define the simulations we want to run. "
   ]
  },
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   "source": [
    "sims = {}\n",
    "for i in range(3 * Nk):\n",
    "    sims[f\"sim_{i}\"] = td.Simulation(\n",
    "        center=(0, 0, 0),\n",
    "        size=sim_size,\n",
    "        grid_spec=td.GridSpec.auto(),\n",
    "        structures=structures,\n",
    "        sources=dipoles,\n",
    "        monitors=monitors_time,\n",
    "        run_time=run_time,\n",
    "        shutoff=0,\n",
    "        boundary_spec=bspecs[i],\n",
    "        normalize_index=None,\n",
    "        symmetry=symmetry,\n",
    "    )"
   ]
  },
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   "id": "22ff9910",
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   "source": [
    "Let's check that the structure and source look correct. The source spectrum must fill the entire frequency range of interest."
   ]
  },
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    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BcHd3R48ePdC7d2/UqVMHY8aMwbvvvgtzc3OsXLkSbm5uGg9cNzc3vPPOO5g7dy769++Pp556CqdOncKOHTtKtJF99913sWXLFvTv3x+jRo1CSEgIsrOzcfbsWaxfvx43btwosQ2RviUkJGDVqlVISEiAt7c3AOCdd95BVFQUVq1ahTlz5ujluH369MEzzzwDf39/xMXF4f3330ffvn1x9OhRrZrBmKrq+KyYNWsWDhw4gH79+sHX1xcpKSn4+uuvUbt2bXTq1EmjbI0aNdCpUyeMHj0aycnJWLJkCQICAjBu3DgAj5KF7777Dn379kWTJk0wevRo1KpVC7dv30Z0dDQcHR2xdetWAMCcOXOwa9cudO3aFa+88goaNWqExMRErFu3DocOHYKzszNatGgBc3NzzJ8/H+np6VAqldLcS48zduxYzJs3D2PHjkWrVq1w4MABqeacSGL4AaeoKsjLyxPvvvuuCAoKEg4ODsLOzk4EBQWJr7/+ukTZtWvXiuDgYKFUKkWNGjXECy+8IP75558S5X766SdRt25dYWVlJVq0aCF27txZ5jCBn332WalxnTt3TgwaNEg4OzsLa2trERgYKD766CONMsnJyWLChAnCx8dHWFpaCk9PT9GzZ0/x7bffPvG8t2zZIpo3by6sra2Fn5+fmD9/vli5cmWJIVx9fX1Fv379SmwPoMTQiqWd08iRI4WdnZ2Ii4sTvXv3Fra2tsLDw0NMnz5dFBUVldjnk4aUTUpKEv369RMODg4CgMaQjCdOnBBt27YVVlZWok6dOmLRokWl7qOoqEjMnDlTeHl5CRsbG9GtWzdx7tw54evrW2JIwMzMTDF16lQREBAgrKyshKurq+jQoYNYsGCByM/Pf+J1JqosAGLjxo3Sa/XwpHZ2dhqLhYWFGDJkiBBCiIsXLwoAj10mT55cruOVJS4uTgAQe/bs0cVpGr3q+KzYu3evGDBggPD29hZWVlbC29tbDBs2TGOYbfWQsr/++quYOnWqcHd3FzY2NqJfv34lhroVQohTp06JZ555RtSsWVMolUrh6+srhgwZIvbu3atR7ubNm2LEiBHCzc1NKJVKUbduXTFhwgSRl5cnlVmxYoWoW7euMDc31xhetqznlhCPhrAdM2aMcHJyEg4ODmLIkCEiJSWlzCFl/zucrvqZ9l9du3YVTZo0eez1JNOhEII9ZIiMzahRo7B+/fpSq7uJ6MkUCgU2btyIgQMHAgDWrl2LF154AefPny9RQ2Bvbw9PT0/k5+c/cXjLmjVrllpT+N/jPY6bmxtmz56NV199tdznQ1VLTEwMunfvjnXr1uG5556TOxwinWDzJyIiqvKCg4NRVFSElJQUdO7cudQyVlZWaNiwoV7j+Oeff3D//n14eXnp9ThERIbGpIKIiKqErKwsXLt2TXodHx+P2NhY1KhRAw0aNMALL7yAESNGYOHChQgODsbdu3exd+9eNG/eXKMzsC6OV6dOHWRlZWHmzJl49tln4enpibi4OLz33nsICAjQyQRjRETGhEkFERFVCcePH0f37t2l15MmTQLwaOKwyMhIrFq1CrNnz8b//vc/3L59G66urmjXrh369++vl+OZm5vjzJkzWL16NdLS0uDt7Y3evXvj448/rnZzVRBR1cc+FUREREREVCmcp4KIiIiIiCqFSQUREREREVWKSfSpUKlUuHPnDhwcHCo0xTwREZVNCIHMzEx4e3vDzMy0fmvi84GISH+0eT6YRFJx584d+Pj4yB0GEVGVduvWLdSuXVvuMLTC5wMRkf6V5/lgEkmFg4MDgEcn5OjoKHM0RERVS0ZGBnx8fKR7rSnh84GISH+0eT6YRFKhrtJ2dHTkQ4OISE9MsfkQnw9ERPpXnueDaTWeJSIiIiIio8OkgoiIiIiIKoVJBRERERERVYpJ9KkgInoSlUqF/Px8ucMwWlZWViY3XCxRVcZ7FhkLS0tLmJubV3o/TCqIyOTl5+cjPj4eKpVK7lCMlpmZGfz9/WFlZSV3KETVHu9ZZGycnZ3h6elZqQE7mFQQkUkTQiAxMRHm5ubw8fHhr/GlUE8Ql5iYiDp16pjkKE9EVQXvWWRMhBDIyclBSkoKAMDLy6vC+2JSQUQmrbCwEDk5OfD29oatra3c4RgtNzc33LlzB4WFhbC0tJQ7HKJqi/csMjY2NjYAgJSUFLi7u1e4KZTW6fGBAwcQHh4Ob29vKBQKbNq06YnbxMTEoGXLllAqlQgICEBkZGQFQiUiKqmoqAgA2KznCdTXR329iEgevGeRMVInuAUFBRXeh9ZJRXZ2NoKCgrB06dJylY+Pj0e/fv3QvXt3xMbGYuLEiRg7dix27typdbBERGVhk57H4/UhMi78P0nGRBf/HrVu/tS3b1/07du33OWXL18Of39/LFy4EADQqFEjHDp0CIsXL0ZYWJi2hyciIiIiIiOj995BR48eRWhoqMa6sLAwHD16tMxt8vLykJGRobFURZs2bUKrVq0wduxYPHz4UO5wiMiI3LhxAwqFArGxsXKHQlTtZWVlISoqClOnTkWHDh0QEhKCl19+GV988QVOnTold3hERkHvHbWTkpLg4eGhsc7DwwMZGRnIzc2VOocUN3fuXMycOVPfocnq+vXrGDp0KPLz83HixAm4u7tjzpw5codFRERE/08IgR9++AFvvfUW0tPTNd47efKk9Pfhw4dj0aJFJb7vEP1XTEwMunfvjtTUVDg7O8sdjk4Z5ThmU6dORXp6urTcunVL7pB0btmyZRqT3ixfvpy1FUREREYiOTkZgwYNwqhRo5Ceno46depg1KhRiIyMxIYNGzBt2jT069cPZmZm+OWXXxAYGIjly5dDCCF36FVCUVFRtZ7HwxQnRtR7UuHp6Ynk5GSNdcnJyXB0dCy1lgIAlEolHB0dNZaqRKVS4ddffwUArF+/Hj4+PkhNTcWWLVtkjoyIDCkqKgqdOnWCs7Mzatasif79+yMuLk6jzKVLl9ChQwdYW1ujadOm2L9/v/ReamoqXnjhBbi5ucHGxgb169fHqlWrDH0aRFVObGwsmjVrhs2bN8PS0hJz5sxBXFwcVq1ahZEjR+KZZ57BzJkz8ccff+DYsWMICQlBeno6xo8fj7fffrtKJhbr169Hs2bNYGNjg5o1ayI0NBTZ2dkAHn2vmTVrFmrXrg2lUokWLVogKipK2jYmJgYKhQJpaWnSutjYWCgUCty4cQMAEBkZCWdnZ2zZsgWNGzeGUqlEQkIC8vLyMHnyZPj4+EijiH7//ffSfs6dO4e+ffvC3t4eHh4eeOmll3Dv3r0yz+PmzZsIDw+Hi4sL7Ozs0KRJE2zfvl0jzm3btqF58+awtrZGu3btcO7cOY19HDp0CJ07d4aNjQ18fHzw5ptvStcCQJkx37hxA927dwcAuLi4QKFQYNSoUQCAbt26ISIiAhMnToSrqyvCwsJKbQablpYGhUKBmJgYjZh37tyJ4OBg2NjYoEePHkhJScGOHTvQqFEjODo6Yvjw4cjJySnfh11Bem/+1L59e+nDUtu9ezfat2+v70MbrdjYWNy+fRsODg7o378//vzzTyxYsAA7d+7EkCFD5A6PyKSpJ/KRg62trVYjaGRnZ2PSpElo3rw5srKyMG3aNAwaNEjjAfLuu+9iyZIlaNy4MRYtWoTw8HDEx8ejZs2a+Oijj3DhwgXs2LEDrq6uuHbtGnJzc/VwZkTVR3x8PPr27Yu7d++iefPm+PHHH9G8efMyy7dq1QrHjh3D4sWL8e677+Lzzz+HjY0N5syZU677gSncsxITEzFs2DB8+umnGDRoEDIzM3Hw4EEpefr888+xcOFCfPPNNwgODsbKlSvx9NNP4/z586hfv36548nJycH8+fPx3XffoWbNmnB3d8eIESNw9OhRfPHFFwgKCkJ8fLyUNKSlpaFHjx4YO3YsFi9ejNzcXEyePBlDhgzBvn37Sj3GhAkTkJ+fjwMHDsDOzg4XLlyAvb29Rhn15+jp6Yn3338f4eHhuHLlCiwtLREXF4c+ffpg9uzZWLlyJe7evYuIiAhERERIP+qUFbOPjw82bNiAZ599FpcvXy7xA/vq1asxfvx4HD58uNzXTG3GjBn46quvYGtriyFDhmDIkCFQKpX45ZdfkJWVhUGDBuHLL7/E5MmTtd53uQktZWZmilOnTolTp04JAGLRokXi1KlT4ubNm0IIIaZMmSJeeuklqfz169eFra2tePfdd8XFixfF0qVLhbm5uYiKiir3MdPT0wUAkZ6erm24Rmnx4sUCgHjqqaeEEEJs375dABD+/v4yR0ZkenJzc8WFCxdEbm6uEEKIrKwsAUCWJSsrq1LncvfuXQFAnD17VsTHxwsAYt68edL7BQUFonbt2mL+/PlCCCHCw8PF6NGjK3SdijPle6wpx07GJyUlRdSvX18AEM2bNxdpaWlabf/1119L94OPP/641DKmeM86ceKEACBu3LhR6vve3t7ik08+0VjXunVr8frrrwshhIiOjhYARGpqqvS++ntkfHy8EEKIVatWCQAiNjZWKnP58mUBQOzevbvU43788ceid+/eGutu3bolAIjLly+Xuk2zZs3EjBkzSn1PHeeaNWukdffv3xc2NjZi7dq1QgghxowZI1555RWN7Q4ePCjMzMxEbm7uE2Mu7VoIIUTXrl1FcHCwxjr1c+DUqVPSutTUVAFAREdHa+xvz549Upm5c+cKACIuLk5a9+qrr4qwsLBSYxKi7GeENvdYrZs/HT9+HMHBwQgODgYATJo0CcHBwZg2bRqAR9lsQkKCVN7f3x/btm3D7t27ERQUhIULF+K7776r1sPJHjhwAADQpUsXAECnTp1gbm6O+Pj4Ktl/hIhKd/XqVQwbNgx169aFo6Mj/Pz8AEDjHlq8VtfCwgKtWrXCxYsXAQDjx4/HmjVr0KJFC7z33ns4cuSIQeMnqkqysrLQr18/XL16Fb6+vtixYwecnJy02sf48eOlIfQ/+ugjrFy5Uh+hGlxQUBB69uyJZs2aYfDgwVixYgVSU1MBABkZGbhz5w46duyosU3Hjh2le1V5WVlZadQKxcbGwtzcHF27di21/OnTpxEdHQ17e3tpadiwIQCUaEqq9uabb2L27Nno2LEjpk+fjjNnzpQoU/y+W6NGDQQGBkrncvr0aURGRmocMywsDCqVCvHx8U+M+XFCQkK03kat+HXz8PCAra0t6tatq7EuJSWlwvsvD62bP3Xr1u2xbQVLmy27W7duHHKtmL/++gsApP+ADg4OaNKkCc6cOYOTJ0/Cx8dHzvCITJqtrS2ysrJkO7Y2wsPD4evrixUrVsDb2xsqlQpNmzYtdwe9vn374ubNm9i+fTt2796Nnj17YsKECViwYEFFwieq1iZNmoS///4brq6u2LVrF7y9vSu8n4yMDMycORNvvvkmunbtinr16pVZ3hTuWebm5ti9ezeOHDmCXbt24csvv8QHH3yAY8eOoWbNmk/c3szs0W/Yxb8/ljZzs42NjUZzrLL63qplZWUhPDwc8+fPL/Gel5dXqduMHTsWYWFh2LZtG3bt2oW5c+di4cKFeOONN554Hupjvvrqq3jzzTdLvFenTh1cu3atXPspjZ2dncbr8l43ALC0tJT+rlAoNF6r1+m747tRjv5Uld27dw+3b98G8CjzV1PX/DD5IqochUIBOzs7WRZt+lPcv38fly9fxocffoiePXuiUaNG0i9/xf3555/S3wsLC3HixAk0atRIWufm5oaRI0fip59+wpIlS/Dtt99W7gISVUN79uzBihUrAADr1q1DgwYNKrW/adOmoVu3bsjOzsaIESNQVFRUZllTuWcpFAp07NgRM2fOxKlTp2BlZYWNGzfC0dER3t7eJfoBHD58GI0bNwbw6D4FPGrNolaeOXiaNWsGlUqlMUBFcS1btsT58+fh5+eHgIAAjeW/X9CL8/HxwWuvvYbff/8d//vf/6TPXq34fTc1NRVXrlyR7rstW7bEhQsXShwvICAAVlZWT4zZysoKAB77b0KtotdNLkwqDOz06dMAgHr16sHBwUFaz6SCqHpxcXFBzZo18e233+LatWvYt28fJk2aVKLc0qVLsXHjRly6dAkTJkxAamoqXn75ZQCPvrhs3rwZ165dw/nz5/HHH39oJBxE9GRZWVkYN24cgEedeLt161bpfZqZmSEyMhKOjo44cuQIPv3000rvU07Hjh3DnDlzcPz4cSQkJOD333/H3bt3pfvNu+++i/nz52Pt2rW4fPkypkyZgtjYWLz11lsAgICAAPj4+GDGjBm4evUqtm3bJjUTexw/Pz+MHDkSL7/8MjZt2oT4+HjExMTgt99+A/Do83rw4AGGDRuGv//+G3Fxcdi5cydGjx5d5pf2iRMnYufOnYiPj8fJkycRHR1d4r45a9Ys7N27F+fOncOoUaPg6uqKgQMHAgAmT56MI0eOICIiArGxsbh69So2b96MiIiIcsXs6+sLhUKBP/74A3fv3n1sLZWNjQ3atWuHefPm4eLFi9i/fz8+/PDDJ1432Tyx14URqEod8RYuXCgAiGeffVZjfUxMDDtrE1XA4zogG7vdu3eLRo0aCaVSKZo3by7dBzZu3Ch10Pvll19EmzZthJWVlWjcuLHYt2+ftP3HH38sGjVqJGxsbESNGjXEgAEDxPXr10s9FjtqE5UuIiJCABC+vr4iMzNTp/tevXq1ACAsLCzEyZMnhRCmec+6cOGCCAsLE25ubkKpVIoGDRqIL7/8Unq/qKhIzJgxQ9SqVUtYWlqKoKAgsWPHDo19HDp0SDRr1kxYW1uLzp07i3Xr1pXoqO3k5FTi2Lm5ueLtt98WXl5ewsrKSgQEBIiVK1dK71+5ckUMGjRIODs7CxsbG9GwYUMxceJEoVKpSj2XiIgIUa9ePaFUKoWbm5t46aWXxL1794QQ/3Z63rp1q2jSpImwsrISbdq0EadPn9bYx19//SV69eol7O3thZ2dnWjevLlGR/UnxTxr1izh6ekpFAqFGDlypBDiUUftt956q9Rr3759e2FjYyNatGghdu3aVWpH7eIdv0u7ltOnTxdBQUGlXhN1zJXtqK0QwvgHU87IyICTkxPS09NNfs6KV199Fd9++y0++ugjzJo1S1qflJQELy8vmJmZIScnB0qlUsYoiUzHw4cPER8fD39/f1hbW8sdjtF63HUy5XusKcdO8jt48KA0aMru3bsRGhqq0/0LIfDcc8/h999/R9u2bXH06FHk5eXxnmWkqvJs109S1jNCm3ssmz8Z2NWrVwGgxLjNHh4ecHBwgEqlwvXr1+UIjYiIqNoQQuDtt98GAIwZM0bnCQXwqB/C0qVLYWdnh2PHjmHDhg06PwaRsWBSYWBlJRUKhULqGHblyhWDx0VERFSdbNy4ESdOnIC9vT3mzp2rt+N4enrinXfeAQBMnTq13KO7EZkaJhUGlJOTg3/++QdAyaQCAJMKIiIiAygqKpI6vL799tvSKDv68s4778DDwwPXrl2TOuyS8VFPm1Ddmj7pCpMKA1KPXawe9eW/mFQQERHp388//4yLFy/CxcUF//vf//R+PHt7e8ycORPAoxHd9D1fAJEcmFQYUFlNn9SYVBAREelXfn4+pk+fDgCYMmWK1rNmV9SYMWPQsGFDpKamIiMjwyDHJDIkJhUG9KSkIiAgAAAqNRsjUXVlAgPZyYrXh+iR7777Djdu3ICnp6c0t4AhWFhYYN68eQAejahTWFhosGMTGYKF3AFUJzdu3AAA1K1bt9T3fX19ATyaObGgoKDEFOtEVJKlpSUUCgXu3r0LNzc3rWaIrS6EELh79y4UCgXvK1StFRYWShPRffjhh7C1tTXo8Z9++mksXboUQgikpqbC3t7eoMcn0icmFQak7qTt4+NT6vtubm5QKpXIy8vDP//8A39/f0OGR2SSzM3NUbt2bfzzzz9S4k4lKRQK1K5dG+bm5nKHQiSbzZs34+bNm3B1dZVmpjckhUKB0aNHAwDu37+PWrVqwcyMjUaoamBSYUC3bt0CUHZSYWZmhjp16uDq1atISEhgUkFUTvb29qhfvz4KCgrkDsVoWVpaMqGgam/JkiUAgNdeew02NjayxNCvXz8cO3YMhYWFePDgAVxdXQEAq06tMmgco4NHG/R4VPUxqTCgJyUVADSSCiIqP3Nzc35pJqIyHT9+HIcOHYKlpSVef/112eKwsrKSZiZOSkpCzZo1oVAoUKDijyJk2ljnZiDZ2dlITU0F8OSkAgBu3rxpkLiIiIiqA3UtxfPPPw8vLy9ZY7G3t4eZmRkePnyI9PR0WWMh4zJjxgy0aNFC7jAqhEmFgahrKRwcHKRfKEqj7qzNmgoiIiLduHPnDtauXQsAeOutt2SO5lFz5xo1agB4VFtBhiGE4KhbesSkwkDK0/QJ+LemgkkFERGRbixduhSFhYXo3LkzQkJC5A4HAFCjRg0oFApkZWUhNzdX7nCeqFu3bnjjjTcwceJEuLi4wMPDAytWrEB2djZGjx4NBwcHBAQEYMeOHdI2586dQ9++fWFvbw8PDw+89NJLuHfvnvR+VFQUOnXqBGdnZ9SsWRP9+/dHXFyc9H5+fj4iIiLg5eUFa2tr+Pr6Yu7cuQAejaipUCgQGxsrlU9LS4NCoUBMTAwAICYmBgqFAjt27EBISAiUSiUOHToElUqFuXPnwt/fHzY2NggKCsL69eul/ai327lzJ4KDg2FjY4MePXogJSUFO3bsQKNGjeDo6Ijhw4cjJydH2q68+927dy9atWoFW1tbdOjQAZcvXwYAREZGYubMmTh9+jQUCgUUCgUiIyN18vkZApMKA3nSyE9qbP5ERESkO/n5+VixYgUAYOLEifIGU4yVlZU08d7du3dljqZ8Vq9eDVdXV/z111944403MH78eAwePBgdOnTAyZMn0bt3b7z00kvIyclBWloaevTogeDgYBw/fhxRUVFITk7GkCFDpP1lZ2dj0qRJOH78OPbu3QszMzMMGjRImnH8iy++wJYtW/Dbb7/h8uXL+Pnnn+Hn56d13FOmTMG8efNw8eJFNG/eHHPnzsUPP/yA5cuX4/z583j77bfx4osvYv/+/RrbzZgxA1999RWOHDmCW7duYciQIViyZAl++eUXbNu2Dbt27cKXX34plS/vfj/44AMsXLgQx48fh4WFhTQS2dChQ/G///0PTZo0QWJiIhITEzF06FCtz1cu7KhtIOWtqahduzYA4Pbt23qPiYiIqKrbtm0b7t69Cy8vLzz99NNyh6PBzc0NaWlpuH//PlRCBTOFcf/WGxQUhA8//BAAMHXqVMybNw+urq4YN24cAGDatGlYtmwZzpw5gz179iA4OBhz5syRtl+5ciV8fHxw5coVNGjQAM8++6zG/leuXAk3NzdcuHABTZs2RUJCAurXr49OnTpBoVBITcS1NWvWLPTq1QsAkJeXhzlz5mDPnj1o3749gEfzhx06dAjffPMNunbtKm03e/ZsdOzYEcCjGdGnTp2KuLg4ab6x5557DtHR0Zg8ebJW+/3kk0+k11OmTEG/fv3w8OFD2NjYwN7eHhYWFvD09KzQucqJSYWBlDepUHcey8zMRHZ2Nuzs7PQeGxERUVW1cuVKAMDIkSNhYWFcX3scHR1hZWWF/Px85GTnGP1keM2bN5f+bm5ujpo1a6JZs2bSOg8PDwBASkoKTp8+jejo6FLPKS4uDg0aNMDVq1cxbdo0HDt2DPfu3ZNqKBISEtC0aVOMGjUKvXr1QmBgIPr06YP+/fujd+/eWsfdqlUr6e/Xrl1DTk6OlGSo5efnIzg4uMzz9fDwgK2trcYExh4eHvjrr78qtV/1976UlBSptYqpMq7/XVVYeZMKBwcH2NraIicnB4mJiQgICDBEeERERFXOnTt3sH37dgCQJp0zJgqFAm5ubrh9+zaysrKMPqmwtLTUeK1QKDTWKRQKAI/6FmRlZSE8PBzz588vsR/1F+nw8HD4+vpixYoV8Pb2hkqlQtOmTZGfnw8AaNmyJeLj47Fjxw7s2bMHQ4YMQWhoKNavXy9NGiiEkPZb1lxFxX+gzcrKAvCoBqtWrVoa5ZRKZZnn+99zVa9TJ0KV2S8AaT+mjEmFgZQ3qVAoFPDy8kJcXByTCiIiokr48ccfoVKp0KlTJzRo0EDucEpVs2ZN3L59Gw/zHiI/Px9WVlZyh6QTLVu2xIYNG+Dn51dqDdH9+/dx+fJlrFixAp07dwYAHDp0qEQ5R0dHDB06FEOHDsVzzz2HPn364MGDB3BzcwMAJCYmSjUBxTttl6Vx48ZQKpVISEjQaJJUWbrar5WVFYqKinQWlyExqTAQdR+J/2avpfH29paSCiIiItKeEEJq+qTuCGuMrKys4OzsDCQ8+rVbPdSsqZswYQJWrFiBYcOG4b333kONGjVw7do1rFmzBt999x1cXFxQs2ZNfPvtt/Dy8kJCQgKmTJmisY9FixbBy8sLwcHBMDMzw7p16+Dp6QlnZ2eYmZmhXbt2mDdvHvz9/ZGSkiL193gcBwcHvPPOO3j77belhDM9PR2HDx+Go6MjRo4cWaHz1dV+/fz8EB8fj9jYWNSuXRsODg4lajqMlXH3CKoiik9uU54Jd9RlmFQQkamYO3cuWrduDQcHB7i7u2PgwIHSMIlliYyMlIZNVC/W1tYGipiquiNHjuDKlSuws7PD4MGD5Q7nsdS/umdlZUElTL8ZDPDoB9LDhw+jqKgIvXv3RrNmzTBx4kQpITAzM8OaNWtw4sQJNG3aFG+//TY+++wzjX04ODjg008/RatWrdC6dWvcuHED27dvl5o+rVy5EoWFhQgJCcHEiRMxe/bscsX28ccf46OPPsLcuXPRqFEj9OnTB9u2bYO/v3+lzlkX+3322WfRp08fdO/eHW5ubvj1118rFZMhKUTxxmhGKiMjA05OTkhPT3/sxHHG6ubNm/Dz84NSqURubq7Ufq4sEydOxOeff47Jkydj3rx5BoqSiKorXdxj+/Tpg+effx6tW7dGYWEh3n//fZw7dw4XLlwoc8CJyMhIvPXWWxrJh0KhkDp7Gip2qprGjBmDlStXYvTo0VKNhTF4+PAh4uPj4e/vLyXRQghM3zIdhUWFcHN1M8ggLa+EvKL3Y5DpKO3fJaDdPZbNnwxAPVumh4fHExMKgDUVRGR6oqKiNF5HRkbC3d0dJ06cQJcuXcrcTqFQmOTQiWTccnJy8NtvvwEw7qZPagqFAk4Oj7645eXmwdnBWe6QiLTGpMIA1ElFeR+cTCqIyNSpm3w+qX14VlYWfH19oVKp0LJlS8yZMwdNmjQps3xeXh7y8vKk1xkZGboJmKqUbdu2ISsrC/7+/tI8A8bu9fav4/z581AoFAhqFmR0w98SPQn7VBhAcnIyACYVRFQ9qFQqTJw4ER07dkTTpk3LLBcYGIiVK1di8+bN+Omnn6BSqdChQwf8888/ZW4zd+5cODk5ScuTRtSj6mnNmjUAgOeff75cLQSMgY2NDWxsbCCEQGpqqtzhEGmNSYUBFG/+VB7qpOLOnTt6i4mISF8mTJiAc+fOSV/sytK+fXuMGDECLVq0QNeuXfH777/Dzc0N33zzTZnbTJ06Fenp6dKiHq6bSC0jIwPbtm0DAAwdOlTmaLRTs2ZNAI+GWyUyNaxbM4CKNn968OAB8vLyTGYoMSKiiIgI/PHHHzhw4ABq166t1baWlpYIDg7GtWvXyiyjVCp5T6TH2rx5M/Ly8tCwYUONmYtNQY0aNfDPP/8gKyuLz38yOaypMABtk4oaNWpIk9+otyUiMmZCCERERGDjxo3Yt29fhYZmLCoqwtmzZ8s19DZRWUyl6VNpg29aWVnBwcEBwKMfFokMRRczerOmwgC0TSrUo6EkJCQgMTERvr6++gyPiKjSJkyYgF9++QWbN2+Gg4ODdN9zcnKCjY0NAGDEiBGoVasW5s6dCwCYNWsW2rVrh4CAAKSlpeGzzz7DzZs3MXbsWNnOg0zb/fv3sWvXLgDG2/TJ0tISCoUCd+/ehZubW4nEx9HREZmZmbh79y6cnZ2NOjEi0yeEQH5+Pu7evQszM7NKzejOpMIA1B21tRl7XZ1UqLclIjJmy5YtAwB069ZNY/2qVaswatQoAEBCQoI0aRUApKamYty4cUhKSoKLiwtCQkJw5MgRNG7c2FBhUxWzceNGFBYWIigoCA0bNpQ7nFKZm5ujdu3a+Oeff3Djxo0S76tUKty/fx9CCAghKvUlj6i8bG1tUadOHY17tLaYVOiZEELrmgoAcHd3BwDcvXtXL3EREelSeeZRjYmJ0Xi9ePFiLF68WE8RUXVUvOmTMbO3t0f9+vVRUFBQ6vuff/45du3ahVdffRVvv/22gaOj6sbc3BwWFhaVrhVjUqFnWVlZyMnJAaBdTYWbmxsAICUlRS9xERERVSXJycmIjo4GYLxNn4ozNzeHubl5qe91794dK1aswKpVqzBlyhQ2gSKTwI7aeqaupbCzs4O9vX25t2NNBRERUflt2bIFKpUKrVq1qtBAAcakf//+UCqVuHr1Ks6ePSt3OETlwqRCz7Sd+E5NnVSwpoKIiOjJNm7cCAAYNGiQzJFUnoODA/r06QMAWL9+vczREJUPkwo9UycV6iShvNj8iYiIqHwyMjKwd+9eAMDAgQPlDUZHnnvuOQBMKsh0MKnQM3XzJXWSUF5s/kRERFQ+O3bsQH5+Pho0aIBGjRrJHY5OhIeHw9LSEhcvXsSFCxfkDofoiZhU6Nm9e/cAaJ9UsKaCiIiofDZt2gTgUdOnqtKp2cnJCb179wYArFu3TuZoiJ6MSYWeqWsaXF1dtdqueE1FeYZqJCIiqo7y8vKwbds2AFWn6ZMam0CRKWFSoWeVrakoLCxEWlqarsMiIiKqEvbt24fMzEx4eXmhTZs2coejUwMGDICFhQXOnTuHS5cuyR0O0WMxqdCzitZUKJVKODo6auyDiIiINKmbPg0YMKBSswEbIxcXF/Ts2RPAv+dJZKyq1v8+I1TRmgqAw8oSERE9TlFRETZv3gygagwlWxp1ky71eRIZqwolFUuXLoWfnx+sra3Rtm1b/PXXX48tv2TJEgQGBsLGxgY+Pj54++238fDhwwoFbGoqWlMB/JuIsKaCiIiopGPHjiE5ORlOTk7o1q2b3OHoRXh4OIBH56qeUJfIGGmdVKxduxaTJk3C9OnTcfLkSQQFBSEsLKzMX9N/+eUXTJkyBdOnT8fFixfx/fffY+3atXj//fcrHbyxE0KwpoKIiEhP/vjjDwBA3759YWVlJXM0+lGrVi20atUKQghs3bpV7nCIyqR1UrFo0SKMGzcOo0ePRuPGjbF8+XLY2tpi5cqVpZY/cuQIOnbsiOHDh8PPzw+9e/fGsGHDnli7URXk5ORINTIVqalgUkFERFQ29ahP/fv3lzkS/RowYAAAYMuWLTJHQlQ2rZKK/Px8nDhxAqGhof/uwMwMoaGhOHr0aKnbdOjQASdOnJCSiOvXr2P79u146qmnKhG2aVA3W1IqlbC3t9d6ezZ/IiIiKt2tW7dw5swZmJmZoU+fPnKHo1fqpGLPnj3Izs6WORqi0lloU/jevXsoKiqCh4eHxnoPD48yhzobPnw47t27h06dOkEIgcLCQrz22muPbf6Ul5eHvLw86XVGRoY2YRoNddMnV1fXCk3Gw5oKIiKi0qlrKdq1a4eaNWvKHI1+NW3aFP7+/oiPj8euXbuqbKd0Mm16H/0pJiYGc+bMwddff42TJ0/i999/x7Zt2/Dxxx+Xuc3cuXPh5OQkLT4+PvoOUy/UNQwV6U8BQLpJ3r9/X2cxERERVQXVpekTACgUCjz99NMA2ASKjJdWSYWrqyvMzc2RnJyssT45ORmenp6lbvPRRx/hpZdewtixY9GsWTMMGjQIc+bMwdy5c6FSqUrdZurUqUhPT5eWW7duaROm0ahMJ22ASQUREVFpcnNzsXfvXgBAv379ZI7GMNRNoP744w8UFRXJHA1RSVolFVZWVggJCZH+IwOASqXC3r170b59+1K3ycnJKTEZjbm5OYBHoyOVRj3xW/HFFFVmOFmASQUREVFpYmJikJubi9q1a6NZs2Zyh2MQnTt3houLC+7du4cjR47IHQ5RCVo3f5o0aRJWrFiB1atX4+LFixg/fjyys7MxevRoAMCIESMwdepUqXx4eDiWLVuGNWvWID4+Hrt378ZHH32E8PBwKbmoqlhTQUREpHvqoWT79etXoT6LpsjCwkKqleHQsmSMtOqoDQBDhw7F3bt3MW3aNCQlJaFFixaIioqSOm8nJCRo1Ex8+OGHUCgU+PDDD3H79m24ubkhPDwcn3zyie7OwkjpqqYiOzsbDx8+hLW1tc5iIyIiMkVCiGrVn6K4/v3746effsK2bdvw6aefyh0OkQaFKKsNkhHJyMiAk5MT0tPTTaop1KBBg7Bp0yYsW7YMr732mtbbCyFgaWmJoqIi/PPPP6hVq5YeoiSi6s5U77GAacdOFXP+/Hk0bdoU1tbWuH//PmxtbeUOyWBSU1Ph5uaGoqIixMfHw8/PT+6QqIrT5h6r99GfqrPK1lQoFAo2gSIiIipm+/btAIDu3btXq4QCAFxcXNChQwcA/45+RWQsmFToUWX7VADsV0FERFRcVFQUAKBv374yRyIPdb8KJhVkbJhU6FFlayoAJhVERERqWVlZOHToEABU+Vm0y6JOKqKjo5GTkyNzNET/YlKhJ0VFRUhNTQXApIKIiEgXYmJikJ+fD39/fwQEBMgdjiyaNGmCOnXq4OHDh4iOjpY7HCIJkwo9SU1NlebhUCcGFaHeVt2UioiIqLrauXMnACAsLKzaDCX7XwqFgk2gyCgxqdCTBw8eAAAcHR1hYaH1yL0S1lQQERE9ou5PUV2bPqk99dRTAB4lFSYwiCdVE0wq9ESdBNSoUaNS+1E3nWJSQURE1VlcXByuXbsGCwsLdO/eXe5wZNWjRw9YW1sjISEB58+flzscIgBMKvRGXVNRmaZPxbdnUkFERNWZuulTx44dq/2cJLa2tlJixSZQZCyYVOiJOqmobE0FkwoiIiLN/hT07yhQO3bskDkSokeYVOiJrpMKdtQmIqLqKj8/H/v27QPA/hRq6utw+PBhZGRkyBwNEZMKvVHXLLD5ExERUeUcOXIEWVlZcHd3R1BQkNzhGIV69eohICAAhYWFUsJFJCcmFXqi65qKtLQ0FBUVVTouIiIiU1O86ZOZGb+6qKlnFVePikUkJ/7P1BNdJRXq7YUQ0mR6RERE1cmuXbsAAL1795Y5EuOibgIVFRXFoWVJdkwq9ERXQ8paWlrCyclJY59ERETVxb1793Dq1CkAQGhoqMzRGJeuXbtCqVTi5s2buHz5stzhUDXHpEJPdDWkbPF9MKkgIqLqZt++fRBCoGnTpvD09JQ7HKNiZ2eHLl26AGATKJIfkwo90VXzJ4AjQBERUfW1e/duAECvXr1kjsQ4qZtAcWhZkhuTCj3RVfMngDUVRGT85s6di9atW8PBwQHu7u4YOHBguZpjrFu3Dg0bNoS1tTWaNWuG7du3GyBaMhVCCCYVT6BOKvbv34+cnByZo6HqjEmFHhQWFiI9PR0Amz8RUfWwf/9+TJgwAX/++Sd2796NgoIC9O7dG9nZ2WVuc+TIEQwbNgxjxozBqVOnMHDgQAwcOBDnzp0zYORkzK5du4abN2/CyspKauZDmho1agQfHx/k5eVh//79codD1RiTCj1IS0uT/u7i4lLp/TGpICJjFxUVhVGjRqFJkyYICgpCZGQkEhIScOLEiTK3+fzzz9GnTx+8++67aNSoET7++GO0bNkSX331lQEjJ2OmrqXo0KED7OzsZI7GOCkUCg4tS0aBSYUeqPtTODo6wsLCotL7c3V1BcCkgohMh7q29nFNQI8ePVpiNJ+wsDAcPXpUr7GR6dizZw8ANn16krCwMABMKkhelf/GSyXoajZtNdZUEJEpUalUmDhxIjp27IimTZuWWS4pKQkeHh4a6zw8PJCUlFTmNnl5ecjLy5NeZ2RkVD5gMkrFZ4pmUvF4PXv2hLm5Oa5cuYKbN2/C19dX7pCoGmJNhR7ocuQngKM/EZFpmTBhAs6dO4c1a9bofN9z586Fk5OTtPj4+Oj8GGQcjh8/jvT0dLi4uKBly5Zyh2PUnJyc0K5dOwD/ThRIZGhMKvRA10mFej/q/RIRGauIiAj88ccfiI6ORu3atR9b1tPTE8nJyRrrkpOTHzsXwdSpU5Geni4tt27d0kncZHzU/SnUv8LT46lnG2dSQXJhUqEHuhxOFvi3poJJBREZKyEEIiIisHHjRuzbtw/+/v5P3KZ9+/bYu3evxrrdu3ejffv2ZW6jVCrh6OiosVDVpO5PwVm0y0edVOzZsweFhYUyR0PVEZMKPdDlbNrAv8nJ/fv3IYTQyT6JiHRpwoQJ+Omnn/DLL7/AwcEBSUlJSEpKQm5urlRmxIgRmDp1qvT6rbfeQlRUFBYuXIhLly5hxowZOH78OCIiIuQ4BTIi2dnZUod9JhXl07p1azg7OyMtLQ3Hjx+XOxyqhphU6IG++lTk5eVpPKCJiIzFsmXLkJ6ejm7dusHLy0ta1q5dK5VJSEhAYmKi9LpDhw745Zdf8O233yIoKAjr16/Hpk2bHtu5m6qHgwcPoqCgAL6+vqhbt67c4ZgEc3NzKQHbuXOnzNFQdcTRn/RA10mFvb09LC0tUVBQgPv378PW1lYn+yUi0pXy1KLGxMSUWDd48GAMHjxYDxGRKVM3i+vZsycUCoXM0ZiOsLAwrF+/Hrt27cL06dPlDoeqGdZU6IGuh5RVKBTsrE1ERNVG8aSCyk899O6xY8c0JuIlMgQmFXqg65oKgHNVEBFR9XDv3j3ExsYCAHr06CFvMCbG19cXgYGBKCoqkub4IDIUJhV6oI+kgjUVRERUHURHR0MIgSZNmjx2eGEqnXp2bQ4tS4bGpEIPdN38qfi+WFNBRERVGZs+VY56aNmdO3dyxEgyKCYVOlZYWIj09HQArKkgIiLSFpOKyunWrRssLS1x48YNxMXFyR0OVSNMKnSseMcoFxcXne2XNRVERFTVJSQk4Nq1azAzM0PXrl3lDsck2dnZoUOHDgD+nZWcyBCYVOiY+ku/o6MjLCx0N2Jv8QnwiIiIqiJ1LUWbNm3g5OQkczSmS90Eiv0qyJCYVOiYrmfTVlPvj82fiIioqmLTJ91QDy27b98+FBYWyhwNVRdMKnRMHyM/Fd8fayqIiKgqEkJISQWHkq2cli1bokaNGsjIyMBff/0ldzhUTTCp0DF9JRWsqSAioqrs0qVLSEpKgrW1tdQngCrG3Nxcqu1hvwoyFCYVOqaP4WQB1lQQEVHVpq6l6NixI6ytrWWOxvSxXwUZGpMKHTNETQXHnSYioqpGPQM0mz7phrpfxbFjx6Sh7on0iUmFjum7T0VhYSEyMzN1um8iIiI5FRUVISYmBgCTCl3x9fVF/fr1UVRUhOjoaLnDoWqASYWO6av5k62trVQdzH4VRERUlZw+fRqpqalwcHBAq1at5A6nylA3gWK/CjIEJhU6pq+aCoAT4BERUdWkbvrUtWtXnc7xVN2pm0CxXwUZApMKHdNnUqHeJ2sqiIioKuFQsvrRvXt3mJub49q1a7hx44bc4VAVx6RCx/TV/Kn4PllTQUREVUV+fj4OHjwIgEmFrjk6OqJdu3YA2ASK9K9CScXSpUvh5+cHa2trtG3b9okTq6SlpWHChAnw8vKCUqlEgwYNsH379goFbOxYU0FERFR+f//9N7Kzs+Hq6opmzZrJHU6VwyZQZChaJxVr167FpEmTMH36dJw8eRJBQUEICwtDSkpKqeXz8/PRq1cv3LhxA+vXr8fly5exYsUK1KpVq9LBG5vCwkJp2Db2qSAiInoydX+K7t27w8yMDSh0TZ1U7N27F0VFRTJHQ1WZ1v97Fy1ahHHjxmH06NFo3Lgxli9fDltbW6xcubLU8itXrsSDBw+wadMmdOzYEX5+fujatSuCgoIqHbyxSUtLk/7u4uKi8/2zpoKIiKoa9qfQrzZt2sDR0RGpqak4efKk3OFQFaZVUpGfn48TJ04gNDT03x2YmSE0NBRHjx4tdZstW7agffv2mDBhAjw8PNC0aVPMmTOnSmbL6hoER0dHvYxewZoKIiKqSnJycqTvD927d5c5mqrJwsJCStjYr4L0Sauk4t69eygqKoKHh4fGeg8PDyQlJZW6zfXr17F+/XoUFRVh+/bt+Oijj7Bw4ULMnj27zOPk5eUhIyNDYzEF6hoEfXTSBlhTQUREVcuRI0eQn5+PWrVqoUGDBnKHU2Wpm0AxqSB90nvjRZVKBXd3d3z77bcICQnB0KFD8cEHH2D58uVlbjN37lw4OTlJi4+Pj77D1Al9dtIGWFNBRERVi7o/RY8ePaBQKGSOpupSJxWHDx9GVlaWzNFQVaVVUuHq6gpzc3MkJydrrE9OToanp2ep23h5eaFBgwYwNzeX1jVq1AhJSUnIz88vdZupU6ciPT1dWm7duqVNmLLRd1LBmgoiIqpK1ElFz549ZY6kagsICICvry8KCgpw4MABucOhKkqrpMLKygohISFSpyrgUU3E3r170b59+1K36dixI65duwaVSiWtu3LlCry8vGBlZVXqNkqlEo6OjhqLKWBNBRERUfmkp6fj77//BsD+FPqmUCjYBIr0TuvmT5MmTcKKFSuwevVqXLx4EePHj0d2djZGjx4NABgxYgSmTp0qlR8/fjwePHiAt956C1euXMG2bdswZ84cTJgwQXdnYSQMVVORmpqqkaQRERGZmoMHD0KlUiEgIAB16tSRO5wqr3fv3gCYVJD+aD1E0dChQ3H37l1MmzYNSUlJaNGiBaKioqTO2wkJCRrjTPv4+GDnzp14++230bx5c9SqVQtvvfUWJk+erLuzMBKG6qitUqmQnp6ul2FriYiIDKF4fwrSP3W/lfPnz+POnTvw9vaWOySqYio07mlERAQiIiJKfS8mJqbEuvbt2+PPP/+syKFMir5rKpRKJezs7JCdnY379+8zqSAiIpPFpMKwatasiZCQEBw/fhy7d+/GyJEj5Q6JqhhOXalD6r4O+koqgH9rQdhZm4iITNW9e/dw+vRpAEC3bt3kDaYaYRMo0icmFTqk75oKgJ21iYjI9KlbNTRt2rTE3FekP8WTCvbNJF1jUqFDhkgqOKwsERGZOjZ9kkf79u1hZ2eHlJQUnDlzRu5wqIphUqFDrKkgIiJ6MiYV8rCyspKam7EJFOkakwodKSoqQlpaGgDWVBAREZXl9u3buHz5MszMzNC1a1e5w6l21PNV7Nq1S+ZIqKphUqEj6enpEEIAgF5HZWJNBRERmTJ1LUXLli3h7OwsbzDVkLpfxcGDB5GbmytzNFSVMKnQEXXNgYODQ5kzhesCayqIiMiU7d27FwDQs2dPmSOpnho2bIjatWsjLy8PBw8elDscqkIqNE8FlWSI4WQB1lQQEdG/Vp1aJXcIWhEQ2HJrC9ACKGxaaHLxVxV+A/3wz6F/sGjfItx2u63VtqODR+spKjJ1TCp0xBCdtIvvnzUVRERUoCqQOwStJKckIzU9FeZW5vCr52dy8VcVDZs0xKGjh3Du4jk8o3pG7nCoimDzJx0xVFLBmgoiIjJVly9dBgDUrVtXr02F6fEaNWoE4FGn+fT0dJmjoaqCSYWOsKaCiKq7AwcOIDw8HN7e3lAoFNi0adNjy8fExEChUJRYkpKSDBMwGdylS5cAPGrXT/Kxt7dHnTp1AAAXLlyQORqqKphU6IihayrS0tJQWFio12MREWkjOzsbQUFBWLp0qVbbXb58GYmJidLi7u6upwhJTiqhwuXLj2oqmFTIr3HjxgCYVJDusE+FjhgqqSg+XG1aWhpcXV31ejwiovLq27cv+vbtq/V27u7uHFq0Grh9+zaysrJgZWUFPz8/ucOp9ho3boyoqChcuHABKqGCmYK/M1Pl8F+QjhgqqbCwsICTkxMA9qsgoqqhRYsW8PLyQq9evXD48OHHls3Ly0NGRobGQqZB3Z+ifv36sLDgb5pyq1evHpRKJbKysvDPrX/kDoeqACYVOqJOKtTNk/SJ/SqIqCrw8vLC8uXLsWHDBmzYsAE+Pj7o1q0bTp48WeY2c+fOhZOTk7T4+PgYMGKqDPanMC4WFhYIDAwEwCZQpBtMKnTEUPNUABwBioiqhsDAQLz66qsICQlBhw4dsHLlSnTo0AGLFy8uc5upU6ciPT1dWm7dumXAiKmiioqKcOXKFQBMKoyJul/F+fPnZY6EqgLWP+qIoZo/Af8mFaypIKKqpk2bNjh06FCZ7yuVSiiVSgNGRLpw4+YN5OXlwdbWFrV9assdDv2/xk0eJRVxcXF4mPcQ1kprmSMiU8aaCh0xZFKhPgZrKoioqomNjYWXl5fcYZCOXbr4qOlTYGAgOwQbEXc3d9SsWfNRTdLlK3KHQyaONRU6oFKpkJqaCoDNn4io+srKysK1a9ek1/Hx8YiNjUWNGjVQp04dTJ06Fbdv38YPP/wAAFiyZAn8/f3RpEkTPHz4EN999x327duHXbt2yXUKpCcXL14E8O+ka2QcFAoFmjRpggMHDuDChQto3ry53CGRCWNSoQMZGRlQqVQANId81Rd21CYiY3T8+HF0795dej1p0iQAwMiRIxEZGYnExEQkJCRI7+fn5+N///sfbt++DVtbWzRv3hx79uzR2AeZvry8PFy/fh0Akwpj1LhxYxw4cID9KqjSmFTogPrLva2tLayt9d8ekTUVRGSMunXrBiFEme9HRkZqvH7vvffw3nvv6Tmqqs3SzFLuEJ7oUtwlFOUXoUbNGvD29IYCCrlDomKaNm4KhVAgJTEF6Q/SOf8VVRiTCh0wZH+K4sdhTQURUfU2Oni03CE80bu/vAvEAgNfHoiXg1+WOxwqxRrbNTh06BAcrztidC/j/zdFxom9pXTAkHNUFD8OayqIiMjY7dmzBwDQs2dPmSOhsoSFhQEAoqKiZI6ETBmTCh0w5BwVxY/DmgoiIjJmd+/eRWxsLAAmFcasb9++AIC9e/ciPz9f5mjIVDGp0AFDN39iTQUREZmC6OhoAECzZs3g4eEhczRUluDgYLi5uSEzMxNHjx6VOxwyUUwqdECuPhVZWVn8RYGIiIwWmz6ZBjMzM6kJ1I4dO2SOhkwVkwodMHRS4ezsDIVCoXFsIiIiY6NOKkJDQ2WOhJ5E3QSK/SqoophU6IChkwozMzNpPgwmFUREZIyuX7+O+Ph4WFhYoEuXLnKHQ0/Qq1cvKBQKnD59Gnfu3JE7HDJBTCp0wNBJBcB+FUREZNz27t0LAGjTpg0cHBxkjoaexM3NDa1atQIA7Ny5U+ZoyBQxqdABOZIKjgBFRETGbPfu3QAe/QJOpkHdBIr9KqgimFTogLq2wFDzVBQ/FmsqiIjI2BQVFUn9KXr37i1zNFReffr0AfAoISwsLJQ5GjI1TCp0QM7mT6ypICIiY3Py5EmkpqbC0dERbdq0kTscKqc2bdrAxcUFaWlpOHbsmNzhkIlhUlFJQghZmz+xpoKIiIyNuulTjx49YGFhIXM0VF7m5uZSzRKbQJG2mFRUUmZmJoqKigCwpoKIiAgAdu3aBYD9KUwR+1VQRTGpqCT1l3pra2vY2NgY7LisqSAiImOUlZWFI0eOAGB/ClOk7ldx8uRJJCYmyhwNmRImFZUkR9MngDUVRERknPbv34+CggL4+fmhXr16codDWvLw8EDr1q0BANu3b5c5GjIlTCoqSV1TYOikgjUVRERkjNT9KXr37g2FQiFzNFQR/fr1A8CkgrTDpKKS7t27B+DRpDGGxCFliYjIGHF+CtP31FNPAXj0Webn58scDZkKJhWVpE4qDDlHBcDJ74iIyPj8888/uHDhAhQKBXr06CF3OFRBISEh8PDwQGZmJg4ePCh3OGQimFRUkjqpcHV1Nehx1UlMbm4ucnNzDXpsIiKi0qgnvGvdurXBmwWT7piZmUmjQG3btk3maMhUMKmoJHXzI0MnFQ4ODtLY36ytICIiY8ChZKsO9qsgbTGpqCS5aioUCgU7axMRkdEoKiqSkgr1sKRkunr16gULCwtcvnwZcXFxcodDJoBJRSXJlVQA7FdBRETG48SJE7h//z6cnJzQrl07ucOhSnJyckKnTp0AsAkUlQ+TikqSM6ngCFBERGQsoqKiAAChoaFS81wybeomUEwqqDyYVFSSXKM/AaypICIi46FOKtj0qero378/ACA6OhoZGRkyR0PGrkJJxdKlS+Hn5wdra2u0bdsWf/31V7m2W7NmDRQKBQYOHFiRwxodIQRrKoiIqNp78OABjh07BgAICwuTORrSlcDAQNSvXx8FBQVSfxmismidVKxduxaTJk3C9OnTcfLkSQQFBSEsLAwpKSmP3e7GjRt455130Llz5woHa2xycnKQl5cHQN6kgjUVREQkpz179kClUqFx48bw8fGROxzSEYVCgaeffhoAsGXLFpmjIWOndVKxaNEijBs3DqNHj0bjxo2xfPly2NraYuXKlWVuU1RUhBdeeAEzZ85E3bp1KxWwMVHXUiiVStjZ2Rn8+Bz9iYiIjMHOnTsBsOlTVRQeHg7g0dCyhYWFMkdDxkyrpCI/Px8nTpxAaGjovzswM0NoaCiOHj1a5nazZs2Cu7s7xowZU67j5OXlISMjQ2MxRsWbPikUCoMfnzUVREQkNyEE+1NUYR07doSLiwvu37//2O96RFolFffu3UNRURE8PDw01nt4eCApKanUbQ4dOoTvv/8eK1asKPdx5s6dCycnJ2kx1qpUOftTAKypICIi+Z07dw537tyBjY1NlWriTI9YWFhIo0CxCRQ9jl5Hf8rMzMRLL72EFStWaPXFe+rUqUhPT5eWW7du6THKipNz5Kfix2VNBRERyUVdS9G9e3dYW1vLHA3pA/tVUHloNZC0q6srzM3NkZycrLE+OTkZnp6eJcrHxcXhxo0bUns8AFCpVI8O/P+zNNarV6/EdkqlEkqlUpvQZMGaCiIiqu527NgBgKM+VWVhYWGwtLTElStXcPnyZQQGBsodEhkhrWoqrKysEBISgr1790rrVCoV9u7di/bt25co37BhQ5w9exaxsbHS8vTTT6N79+6IjY012mZN5aX+Mi9XUlG8pkIIIUsMRERUfaWnp+PgwYMAgKeeekrmaEhfHB0d0a1bNwDA1q1b5Q2GjJbWzZ8mTZqEFStWYPXq1bh48SLGjx+P7OxsjB49GgAwYsQITJ06FQBgbW2Npk2baizOzs5wcHBA06ZNYWVlpduzMTBjqanIz89Hdna2LDEQEVH1tXv3bhQWFqJBgwYICAiQOxzSI3UTqM2bN8scCRkrrZOKoUOHYsGCBZg2bRpatGiB2NhYREVFSZ23ExISkJiYqPNAjZHcSYWtra3UTIz9KoiIyNC2bdsGAFJHXqq61EnF4cOHnzg3GVVPWvWpUIuIiEBERESp78XExDx228jIyIoc0ijJ3VFboVCgRo0aSExMxP3791GnTh1Z4iAioupHpVJh+/btAJhUVAd16tRBy5YtcfLkSWzZsgVjx46VOyQyMnod/amqk7umAuAIUEREJI8TJ04gJSUFDg4OHEq2mnjmmWcAABs3bpQ5EjJGTCoqwRiSCo4ARUREclA3ferVq5fJ95Gk8hk0aBAAYM+ePUY7MTHJh0lFBQkhZB/9Cfi3poJJBRHJ7cCBAwgPD4e3tzcUCgU2bdr0xG1iYmLQsmVLKJVKBAQEVKkmslUd+1NUP40aNUJgYCDy8/Olpm9EakwqKigrKwv5+fkAjKOmgs2fiEhu2dnZCAoKwtKlS8tVPj4+Hv369ZOGGZ84cSLGjh2LnTt36jlSqqykpCQcP34cAIeSrU4UCoVUW/H777/LHA0Zmwp11KZ/mz7Z2NjA1tZWtjhYU0FExqJv377o27dvucsvX74c/v7+WLhwIYBHv4IeOnQIixcv5kRqRk494V1ISEipk99S1fXMM89g3rx52L59Ox4+fMhZ1EnCmooKknvkJzUmFURkqo4ePYrQ0FCNdWFhYTh69KhMEVF5selT9dWqVSvUrl0b2dnZ2L17t9zhkBFhUlFBxtBJGwDc3NwAAHfv3pU1DiIibSUlJUlzHKl5eHggIyMDubm5pW6Tl5eHjIwMjYUMKy8vT2qixqSi+ineBIqjQFFxTCoqSP0lXv2lXi7u7u4AwIloiKhamDt3LpycnKTFx8dH7pCqnejoaGRlZcHLywutWrWSOxySgTqp2LJlCwoLC2WOhowFk4oKSk5OBoASv7IZGpMKIjJVnp6e0r1ULTk5GY6OjrCxsSl1m6lTpyI9PV1abt26ZYhQqZjNmzcDAMLDw2Fmxq8R1VHnzp3h6uqK+/fvP3HSY6o+eDeoIPWXePWXerkUTyqEELLGQkSkjfbt22Pv3r0a63bv3o327duXuY1SqYSjo6PGQoYjhMCWLVsAAAMGDJA5GpKLhYUFnn32WQDAb7/9JnM0ZCyYVFSQsSUVeXl5yMzMlDUWIqresrKyEBsbi9jYWACPhoyNjY1FQkICgEe1DCNGjJDKv/baa7h+/Tree+89XLp0CV9//TV+++03vP3223KET+Vw4sQJ3LlzB3Z2dujRo4fc4ZCMhgwZAuDR0LIFBQUyR0PGgElFBRlL8ycbGxs4ODgAQIlmBEREhnT8+HEEBwcjODgYADBp0iQEBwdj2rRpAIDExEQpwQAAf39/bNu2Dbt370ZQUBAWLlyI7777jsPJGjF106c+ffpwKNFqrkuXLnBzc8P9+/cRHR0tdzhkBDhPRQUZS02FOobMzEykpKSgfv36codDRNVUt27dHtsMs7TZsrt164ZTp07pMSrSJXVSwaZPpG4CtXz5cvz222/o3bu33CGRzFhTUUHGllQA7KxNRET6Ex8fj7Nnz8Lc3JyzaBOAf5tAbdy4kU2giElFRQghmFQQEVG1oq6l6NSpk+wTv5Jx6NKlC9zd3fHgwQPs27dP7nBIZkwqKiAtLU3KyJlUEBFRdcCmT/Rf5ubmeO655wBwFChiUlEh6i/vjo6ORtFRjUkFERHp071793Dw4EEATCpI0+DBgwE8agKVn58vczQkJyYVFWBMTZ8AJhVERKRfmzdvRlFREVq0aIG6devKHQ4Zkc6dO8PT0xOpqanYtWuX3OGQjJhUVICxDCerxqSCiIj0acOGDQAgTXhGpGZubo6hQ4cCAH7++WeZoyE5MamoAGOtqeA8FUREpGtpaWnYs2cPAEjt54mKe/HFFwE8qtHiRLzVF5OKCjDWpII1FUREpGtbt25FQUEBmjRpgoYNG8odDhmhkJAQNGjQALm5udi4caPc4ZBMmFRUgPrLu7E0f1LHcf/+fRQWFsocDRERVSXr168HwKZPVDaFQoEXXngBAJtAVWdMKipA3czIWGoqatSoATOzRx/lvXv3ZI6GiIiqiszMTOzcuRMAmz7R4w0fPhwAsGfPHiQlJckcDcmBSUUFGFvzJ3Nzc7i6ugJgEygiItKdbdu2IS8vDw0aNEDTpk3lDoeMWEBAANq1aweVSoU1a9bIHQ7JgElFBRhb8yeA/SqIiEj3ijd9UigUMkdDxo5NoKo3JhUVYGzNnwCOAEVERLqVnZ2NHTt2AGDTJyqfIUOGwNzcHMePH8elS5fkDocMjEmFlvLy8pCeng7AuJIKLy8vAEBiYqLMkRARUVWwdetW5OTkoG7duggODpY7HDIB7u7uCAsLAwD88MMPMkdDhsakQkvqzkdWVlZwcXGROZp/MakgIiJd+vXXXwEAw4YNY9MnKrfRo0cDAFavXo2ioiKZoyFDYlKhJfWXdi8vL6O6yaqTijt37sgcCRERmboHDx5ITZ+GDRsmczRkSsLDw1GzZk3cuXMHu3btkjscMiAmFVoqnlQYE29vbwCsqSAiosr7/fffUVBQgObNm6NJkyZyh0MmRKlUSh22V65cKXM0ZEhMKrSkrgkwtqSCNRVERKQrv/zyCwDWUlDFvPzyywCAzZs3c/6saoRJhZZYU0FERFXZnTt3EBMTAwB4/vnn5Q2GTFJQUBCCg4NRUFAgJahU9TGp0JL6S7v6S7yxUCc5WVlZyMzMlDkaIiIyVWvXroUQAh06dICfn5/c4ZCJUtdWrFq1SuZIyFCYVGjJWGsq7O3t4eDgAIC1FUREVHHFR30iqqjhw4fDysoKsbGxOHXqlNzhkAEwqdCSsfapANivgoiIKufq1av4+++/YW5ujsGDB8sdDpmwGjVqYODAgQCAFStWyBsMGQSTCi0Za/MngP0qiIioctQTloWGhsLDw0PmaMjUvfrqqwCAH3/8kU2zqwEmFVooKCjA3bt3ARh3TQWTCiIi0pZKpcLq1asBAKNGjZI3GKoSunfvjsDAQGRlZeHnn3+WOxzSMyYVWkhOToYQAhYWFnB1dZU7nBLUNRVs/kRERNqKjo7GrVu34OTkJDVbIaoMhUKB1157DQCwbNkyCCFkjoj0iUmFFtQ1AB4eHjAzM75Lx5oKIiKqqMjISACPOmhbW1vLGwxVGSNHjoSNjQ3OnDmDI0eOyB0O6ZHxfTM2YsbcnwJgTQUREVVMeno6NmzYAIBNn0i3XFxcpPlOli1bJnM0pE9MKrRgrMPJqrGmgoiIKmLdunXIzc1Fo0aN0KZNG7nDoSpm/PjxAB79O1P3TaWqh0mFFm7dugUAqF27tsyRlI41FUREVBHqpk+jRo2CQqGQNxiqclq3bo2QkBDk5+dj5cqVcodDesKkQgvGnlSoayoyMzORlZUlczRERGQKrl69isOHD8PMzAwvvfSS3OFQFTVhwgQAwNKlS1FQUCBzNKQPTCq0oE4qfHx8ZI6kdA4ODtKs2rdv35Y5GiIiMgXfffcdAKBPnz5G27yXTN+wYcPg7u6OW7duSf13qGqpUFKxdOlS+Pn5wdraGm3btsVff/1VZtkVK1agc+fOcHFxgYuLC0JDQx9b3pgZe1IBAHXq1AEAJCQkyBwJEREZu7y8PKk5yiuvvCJzNFSVWVtb4/XXXwcALFy4kMPLVkFaJxVr167FpEmTMH36dJw8eRJBQUEICwtDSkpKqeVjYmIwbNgwREdH4+jRo/Dx8UHv3r1N7pd0IQT++ecfAMadVKhjUydAREREZdmwYQPu3buH2rVro1+/fnKHQ1Xc+PHjoVQqcfz4cRw+fFjucEjHtE4qFi1ahHHjxmH06NFo3Lgxli9fDltb2zI73vz88894/fXX0aJFCzRs2BDfffcdVCoV9u7dW+ngDenevXt4+PAhAKBWrVoyR1M21lQQEVF5qYf4fOWVV2BhYSFzNFTVubu7S/12Fi9eLHM0pGtaJRX5+fk4ceIEQkND/92BmRlCQ0Nx9OjRcu0jJycHBQUFqFGjhnaRykz9y7+HhweUSqXM0ZSNSQUREZXHuXPncOjQIZibm2PMmDFyh0PVxMSJEwEAGzduRFxcnLzBkE5plVTcu3cPRUVF8PDw0Fjv4eGBpKSkcu1j8uTJ8Pb21khM/isvLw8ZGRkai9xMoT8FwOZPRERUPsuXLwcADBw40GgndaWqp0mTJujTpw+EEPj888/lDod0yKCjP82bNw9r1qzBxo0bYW1tXWa5uXPnwsnJSVqM4Yu8qSQVrKkgIqInycrKwg8//AAAeO2112SOhqqb//3vfwAejTxWVp9cMj1aJRWurq4wNzdHcnKyxvrk5GR4eno+dtsFCxZg3rx52LVrF5o3b/7YslOnTkV6erq0GMOv7qaYVHBkBSIiKs2vv/6KzMxM1K9fHz169JA7HKpmevbsidatWyM3N5d9K6oQrZIKKysrhISEaHSyVne6bt++fZnbffrpp/j4448RFRWFVq1aPfE4SqUSjo6OGovcTCWpUHcif/jwIe7fvy9zNERU3Wgz5HhkZCQUCoXG8rhabNINIQSWLFkC4FEthZkZp6wiw1IoFPjwww8BAF999RUePHggc0SkC1rfSSZNmoQVK1Zg9erVuHjxIsaPH4/s7GyMHj0aADBixAhMnTpVKj9//nx89NFHWLlyJfz8/JCUlISkpCSTm/HZ2GfTVlMqlVKtEZtAEZEhaTvkOAA4OjoiMTFRWm7evGnAiKunqKgoXLhwAQ4ODuygTbLp378/mjdvjqysLHz55Zdyh0M6oHVSMXToUCxYsADTpk1DixYtEBsbi6ioKKnzdkJCAhITE6Xyy5YtQ35+Pp577jl4eXlJy4IFC3R3FgagftD5+vrKHMmTqWtTmFQQkSFpO+Q48OgXS09PT2n570AgpHuLFi0CAIwdOxZOTk4yR0PVlZmZGT744AMAwOeff24Ug/JQ5VSozjMiIgI3b95EXl4ejh07hrZt20rvxcTEIDIyUnp948YNCCFKLDNmzKhs7AaTl5cnTXxXt25dmaN5MnbWJiJDq+iQ41lZWfD19YWPjw8GDBiA8+fPP/Y4xjg6oCk5ffo09uzZAzMzM7z11ltyh0PV3LPPPovAwECkpqZKc6aQ6WJDynK4efMmhBCwtbWFu7u73OE8kZ+fHwAgPj5e3kCIqNqoyJDjgYGBWLlyJTZv3oyffvoJKpUKHTp0kH7EKY0xjg5oStSdYp977jmTqHmnqs3c3Bzvv/8+gEcD+mRmZsocEVUGk4pyUH85r1u3LhQKhczRPFm9evUAANevX5c5EiKisrVv3x4jRoxAixYt0LVrV/z+++9wc3PDN998U+Y2xjg6oKm4c+cOfvnlFwD/DulJJLfhw4ejQYMGuHfvntQ0j0wTk4pyUH85N4WmT8C/cXKmSiIylMoMOa5maWmJ4OBgXLt2rcwyxjg6oKn46quvUFBQgI4dO6JNmzZyh0MEALCwsMDs2bMBPKqtuHv3rswRUUUxqSgHdVLh7+8vcyTlU7ymgnNVEJEhVHTI8eKKiopw9uxZeHl56SvMais1NRVLly4FALzzzjsyR0Ok6dlnn0VISAiysrIwZ84cucOhCmJSUQ6mVlPh6+sLc3Nz5ObmltmWmYhI17QdcnzWrFnYtWsXrl+/jpMnT+LFF1/EzZs3MXbsWLlOocpSj67TrFkzPP3003KHQ6TBzMwMc+fOBQB8/fXXHFraRDGpKIfifSpMgaWlpTQCFJtAEZGhaDvkeGpqKsaNG4dGjRrhqaeeQkZGBo4cOYLGjRvLdQpVUnp6ujTZ3UcffcTJ7sgohYaGokePHsjPzzepEULpXwphAu1jMjIy4OTkhPT0dIO3nxVCwNnZGRkZGTh//rzJPOxCQ0Oxd+9eREZGYuTIkXKHQ0RGTM57bGWZcuyGMnv2bHz00Udo3Lgxzp49y6SCjNZff/2Ftm3bQqFQ4MSJEwgODpY7pGpPm3ss7yxPkJqaKo2Drh6q1RSo+1WwpoKIqPrKzMyURtT58MMPmVCQUWvTpg2ef/55CCHw5ptvsl+oieHd5QnUX8o9PT1ha2srczTlx2FliYho6dKlSE1NRWBgIIYMGSJ3OERP9Omnn8LW1haHDh3CmjVr5A6HtMCk4gkuXboEAGjYsKHMkWiHNRVERNVbamoqPv30UwDABx98AHNzc5kjInoyHx8faUK8d999F1lZWTJHROXFpOIJTDWpCAgIAABcvnyZ1YdERNXQvHnzkJqaiiZNmmD48OFyh0NUbv/73//g7++P27dvS6NCkfFjUvEEpppUNGjQAAqFAqmpqUhJSZE7HCIiMqCEhAR8/vnnAB41J2EtBZkSa2trLF68GMCjCfEuX74sc0RUHkwqnsBUkwobGxtpCNyLFy/KHA0RERnShx9+iLy8PHTv3h19+/aVOxwirT399NPo06cP8vPzMXbsWKhUKrlDoidgUvEYhYWFuHr1KgDTSyoAoFGjRgCACxcuyBwJEREZyqlTp/DTTz8BeFRLoVAoZI6ISHsKhQLLly+HnZ0dDh06hGXLlskdEj0Bk4rHiI+PR0FBAWxsbODj4yN3OFpTz6nBmgoioupBCIH33nsPQggMGzYMrVq1kjskogrz9fXFvHnzAABTpkzhTNtGjknFY6ibPgUGBprk2N6sqSAiql42bNiAPXv2wMrKCp988onc4RBV2uuvv46OHTsiKysLr732GgefMWKm903ZgNRJhfrLualRx82aCiKiqi8zMxNvvfUWgEe/6vr7+8scEVHlmZmZ4fvvv4dSqURUVBRWrFghd0hUBiYVj3Hu3DkAptmfAvg3qUhMTERaWpq8wRARkV5NmzYNd+7cQb169TB16lS5wyHSmcDAQMyePRsAMHHiRLbAMFJMKh7j1KlTAIAWLVrIG0gFOTo6onbt2gCA8+fPyxwNERHpS2xsLL744gsAj2bRtra2ljkiIt2aNGkSevXqhdzcXDz//PN4+PCh3CHRfzCpKMPDhw+lZkPBwcEyR1NxQUFBAP5NkIiIqGopKirC+PHjoVKpMHjwYISFhckdEpHOmZmZ4YcffoCbmxvOnj2L9957T+6Q6D+YVJTh/PnzKCwsRI0aNaRf+01Ry5YtAQAnT56UORIiItKHRYsW4c8//4S9vb00YRhRVeTp6YnVq1cDAL788kts3LhR5oioOCYVZVD/sh8cHGzSY3yHhIQAYFJBRFQVnTlzBh9++CEAYMmSJahVq5bMERHpV9++fTFp0iQAwIgRI6T+ryQ/JhVlKJ5UmDJ1TcX58+fZ/pCIqArJy8vDiy++iPz8fISHh+Pll1+WOyQig5g3bx66d++OrKwsDBgwAA8ePJA7JAKTijLFxsYCMN1O2mq1a9eGq6srCgsLcfbsWbnDISIiHZk2bRrOnj0LNzc3rFixwqRr1Ym0YWlpiXXr1sHf3x/Xr1/H0KFDUVhYKHdY1R6TilIUFhbi9OnTAEy/pkKhUEhNoE6cOCFzNEREpAt79uzBZ599BgBYsWIFPDw8ZI6IyLBq1qyJzZs3w87ODnv27MEbb7zBifFkxqSiFGfOnEF2djYcHR1Ndo6K4tRNoP7++2+ZIyEiosq6efMmnn/+eQghMHbsWAwYMEDukIhk0axZM/z4449QKBRYvnw5ZsyYIXdI1RqTilIcOXIEANC+fXuYmZn+JerQoQMA4NChQzJHQkRElZGbm4tnnnkG9+/fR6tWrfDll1/KHRKRrAYNGoSvv/4aADBr1ixpvhYyPNP/xqwHhw8fBgB07NhR5kh0o1OnTlAoFLhy5QoSExPlDoeIiCpACIHx48fj5MmTcHV1xYYNGzjJHRGA1157DbNmzQIAvPXWW/jxxx9ljqh6YlJRCnVNhfoXflPn7OwsTYJ38OBBmaMhIqKKWLRoEVavXg0zMzOsWbMGderUkTskIqPx4Ycf4s033wQAjBw5Et9//73MEVU/TCr+Iy4uDgkJCbCwsEDbtm3lDkdnunTpAgA4cOCAzJEQEZG2fvrpJ7zzzjsAgM8++ww9e/aUOSIi46JQKLB48WKMHz9e6m/01VdfyR1WtcKk4j92794N4FF/Cnt7e5mj0Z2uXbsCAPbv3y9zJEREpI2oqCiMHj0aADBp0iS8/fbbMkdEZJzMzMywdOlSaXK8N954A3PmzOGoUAbCpOI/1ElFr169ZI5Etzp37gwAOHfuHPtVEBGZiKNHj+LZZ59FYWEhXnjhBXz22Wecj4LoMRQKBRYsWCDNNP/BBx9gzJgxyM/Plzmyqo9JRTGFhYXYt28fgKqXVLi5uaFNmzYAgG3btskcDRERPUlMTAx69eqFnJwc9O7dGytXrqwSIxIS6ZtCocDHH3+ML774AmZmZli1ahV69eqFe/fuyR1alca7UzH79+9HWloaatasiVatWskdjs71798fAPDHH3/IHAkRET3O9u3b0bdvX2RnZ6NXr174/fffYWVlJXdYRCbljTfewLZt2+Do6IgDBw6gdevWOHbsmNxhVVlMKorZsGEDAGDgwIGwsLCQORrdUycVu3fvRnZ2tszREBFRadasWYOBAwfi4cOHePrpp7FlyxbY2dnJHRaRSerTpw+OHj2KunXr4saNG+jUqRM+/fRTqFQquUOrcphU/L+ioiJs3LgRAPDss8/KHI1+tGjRAv7+/sjJycGWLVvkDoeIiIpRqVT44IMPMGzYMBQUFGDo0KFYv34956IgqqTGjRvjxIkTGDJkCAoLCzF58mT06dMHN2/elDu0KoVJxf/bvXs3kpKS4OLiUmWH6lMoFHjxxRcBPBqekIiIjENGRgYGDhyIOXPmAADeffdd/Pzzz7C0tJQ5MqKqwdnZGWvWrMGKFStgY2OD3bt3o0mTJliyZAmKiorkDq9KYFLx/7777jsAwEsvvVSl262+8MILAICdO3fi9u3bMkdDRERHjx5Fy5YtsXXrViiVSvz444/49NNPYW5uLndoRFWKQqHA2LFjcfLkSXTu3BnZ2dl4++230a5dOxw+fFju8EwekwoAd+7cwebNmwEAY8aMkTka/QoMDETnzp1RVFSEr7/+Wu5wiIiqrfz8fHzwwQfo1KkT4uLi4OPjg4MHD0o1ykSkHw0bNkRMTAy++eYbODk54fjx4+jUqROeeeYZXL58We7wTBaTCgALFy5EYWEhOnfujObNm8sdjt5NnDgRAPDNN9+wwzYRkQxiYmLQqlUrzJkzByqVCi+++CLOnDmD1q1byx0aUbVgZmaGV155BZcuXcK4ceNgZmaGjRs3okmTJnjppZdw7tw5uUM0OdU+qUhKSsLy5csBAO+//77M0RjGgAEDUK9ePdy/fx9LliyROxwiomrjxo0bGDx4MLp3746zZ8+iRo0aWLduHX788Uc4OzvLHR5RtePp6Ylvv/0WZ8+eRf/+/VFUVISffvoJzZo1w9NPP409e/ZwpKhyqvZJxZQpU5CTk4M2bdogLCxM7nAMwtzcHB9//DEAYP78+Zxhm4hIz65fv45XX30VDRo0wPr162FmZobXX38dV65cwXPPPSd3eETVXuPGjbF161b8/fffeO6556BQKLB161b06tULDRo0wPz583Hnzh25wzRqCiGEkDuIJ8nIyICTkxPS09Ph6Oios/3u3r0bvXv3BgD8+eefaNu2rc72bexUKhXatWuHv//+G+Hh4di8eTMUCoXcYRGRDPR1jzUEY45dCIE///wTS5cuxZo1a6QRZnr06IHFixdXi+a2RKbqypUr+Pzzz/HTTz8hIyMDwKOO3l26dMGQIUMwaNAgeHl5yRyl/mlzj622ScXNmzfRtm1bJCcnY/z48dWy0/LZs2cREhKCgoICfPbZZ3jnnXfkDomIZGDMX8yfxBhjv3PnDtatW4cVK1bg/Pnz0vo+ffpIHbOJyDRkZ2dj7dq1+P7773HkyBGN94KDg/HUU0+hd+/eaNOmTZWcU0abe2yFmj8tXboUfn5+sLa2Rtu2bfHXX389tvy6devQsGFDWFtbo1mzZti+fXtFDqszN2/eRO/evZGcnIxmzZph4cKFssYjl+Ln/t5771XLxIqIdMvUnw8VIYTAhQsXsHjxYnTq1Am1a9fGxIkTcf78edjY2GDUqFE4fvw4duzYwYSCyMTY2dnh5ZdfxuHDh3Hz5k189tlnaNOmDQDg1KlT+OSTT9C1a1c4OzujS5cueP/99/H777/j5s2bMIHf7XVLaGnNmjXCyspKrFy5Upw/f16MGzdOODs7i+Tk5FLLHz58WJibm4tPP/1UXLhwQXz44YfC0tJSnD17ttzHTE9PFwBEenq6tuGWsHXrVuHq6ioAiDp16ohbt25Vep+mTKVSiYkTJwoAAoB47bXXREZGhtxhEZEB6eoea+rPh/LKz88XJ06cEMuWLRMvvvii8PLyku6h6qVdu3biq6++EqmpqQaLi4gMJykpSaxevVoMHTpUeHh4lLgHABA1a9YUXbp0EePHjxdffvmliIqKElevXhX5+flyh19u2txjtW7+1LZtW7Ru3RpfffUVgEdt8318fPDGG29gypQpJcoPHToU2dnZ+OOPP6R17dq1Q4sWLaRRl56kstXbWVlZ2LVrF5YuXYp9+/YBAFq2bIlNmzbBx8dH6/1VNUIIzJo1CzNmzAAAuLu7IyIiAs8//zzq168vb3BEpHe6akJkis+HxyksLMSNGzdw9epVXLx4EefOncPZs2dx7tw5PHz4UKOstbU1OnfujKeffhqDBg1CrVq1dBoLERkvIQSuXbuGAwcO4NixYzh+/DjOnj2LwsLCUsubm5vD29sbPj4+8PHxgbe3Nzw9PeHp6Qk3Nze4urqiZs2aqFGjBhwdHWFmJt+4StrcYy202XF+fj5OnDiBqVOnSuvMzMwQGhqKo0ePlrrN0aNHMWnSJI11YWFh2LRpkzaHBgCcO3cONjY2KCoqKnUpLCxERkYG7t+/j/v37yMuLg4XLlzAyZMnUVBQAACwtLTExIkTMWPGDNja2modQ1WkUCgwffp0dOrUCa+88gquX7+OadOmYdq0afDz80OLFi0QGBgIDw8PuLu7w9HREdbW1lAqlbC2toaVlZXUybv4n6WtK+1PIpJXVlZWpfch9/OhIh4+fIhbt24hISEBt27dwq1bt3Dz5k3cuHEDN27cwM2bN8v8UuDk5IQ2bdqgbdu26NGjB9q3b18l21MT0ZMpFArUr18f9evXlyZRfvjwIS5evIjz58/j3LlzuHjxIuLi4hAXFyfde27dulWufTs4OMDJyQn29vZwcHCAvb097OzsYGtrC1tbW1hbW8PGxkb6TqZUKmFlZQVLS0vpz5o1a2LAgAF6vQ5aJRX37t1DUVERPDw8NNZ7eHjg0qVLpW6TlJRUavmkpKQyj5OXl4e8vDzptbrXfceOHbUJV4Ovry+ef/55vPbaa/Dz86vwfqqynj174tKlS1i7di0iIyOxf/9+6eFKRPQ4cj8fKuL7779HRETEY8tYW1sjICAAgYGBaNq0KZo1a4ZmzZohICBA1l8Pici4WVtbIzg4GMHBwRrrVSoVkpKSNH7QSEpKkpZ79+5JS25uLoQQyMjIqNS9DgAaNWpkXEmFocydOxczZ84ssd7NzQ2WlpYwNzcvc3F0dJSqjHx9fdGwYUO0bNkS9erV4y/j5WBpaYkXX3wRL774ItLT03Hy5EmcOXMG169fR0pKCpKTk5GVlSU92B8+fIj8/HypM1LxP8v6e/E/iUh+QgikpaXJHUa5lPV8qAgfHx/Y2dlJTRB8fHzg6+sLPz8/+Pn5oW7duvD29mbyQEQ6Y2ZmBm9vb3h7ez9xKoO8vDykp6cjLS0N6enpyMzMRFZWFjIzM5Gbm4ucnBxkZ2cjLy8Pubm5ePjwIfLy8pCfn4+8vDwUFBRIS+3atfV+blolFa6urjA3N0dycrLG+uTkZHh6epa6jaenp1blAWDq1KkaVeIZGRnw8fHBtWvXjGbIwOrAyckJ3bt3R/fu3eUOhYj0SN1mtjLkfj5URP/+/ZGZmckfnIjIKCmVSri7u8Pd3V3uUMpFq59frKysEBISgr1790rrVCoV9u7di/bt25e6Tfv27TXKA48mnSurPPDoIjo6OmosRERkvEzx+WBmZsaEgohIR7Ru/jRp0iSMHDkSrVq1Qps2bbBkyRJkZ2dj9OjRAIARI0agVq1amDt3LgDgrbfeQteuXbFw4UL069cPa9aswfHjx/Htt9/q9kyIiEhWfD4QEVVfWicVQ4cOxd27dzFt2jQkJSWhRYsWiIqKkjrbJSQkaLQ/7dChA3755Rd8+OGHeP/991G/fn1s2rQJTZs21d1ZEBGR7Ph8ICKqvrSep0IO+hyHnIioujPle6wpx05EZOy0ucdySAsiIiIiIqoUJhVERERERFQpTCqIiIiIiKhSmFQQEREREVGlMKkgIiIiIqJKYVJBRERERESVwqSCiIiIiIgqRevJ7+SgnkojIyND5kiIiKoe9b3VBKYtKoHPByIi/dHm+WASScX9+/cBAD4+PjJHQkRUdWVmZsLJyUnuMLSSmZkJgM8HIiJ9Ks/zwSRm1E5LS4OLiwsSEhJM7oH3XxkZGfDx8cGtW7dMfvbXqnQuQNU6H56LcTLWcxFCIDMzE97e3jAzM61WsSqVCnfu3IGDgwMUCoXW2xvrZ2IKeO0qjteu4njtKkfb66fN88EkairUJ+Hk5FRl/gE5OjryXIxUVTofnotxMsZzMdUfbMzMzFC7du1K78cYPxNTwWtXcbx2FcdrVznaXL/yPh9M6ycpIiIiIiIyOkwqiIiIiIioUkwiqVAqlZg+fTqUSqXcoVQaz8V4VaXz4bkYp6p0LlUFP5OK47WrOF67iuO1qxx9Xj+T6KhNRERERETGyyRqKoiIiIiIyHgxqSAiIiIiokphUkFERERERJUiS1KxdOlS+Pn5wdraGm3btsVff/312PLr1q1Dw4YNYW1tjWbNmmH79u0a7wshMG3aNHh5ecHGxgahoaG4evWqPk9Bgzbns2LFCnTu3BkuLi5wcXFBaGhoifKjRo2CQqHQWPr06aPv0wCg3blERkaWiNPa2lqjjJyfjTbn0q1btxLnolAo0K9fP6mMXJ/LgQMHEB4eDm9vbygUCmzatOmJ28TExKBly5ZQKpUICAhAZGRkiTLa/j/UBW3P5ffff0evXr3g5uYGR0dHtG/fHjt37tQoM2PGjBKfS8OGDfV4Fv/S9nxiYmJK/XeWlJSkUU6Oz6Yq0/UzpzrR9TOhutDXfbs60Nd9tTqYO3cuWrduDQcHB7i7u2PgwIG4fPnyE7fT1T3P4EnF2rVrMWnSJEyfPh0nT55EUFAQwsLCkJKSUmr5I0eOYNiwYRgzZgxOnTqFgQMHYuDAgTh37pxU5tNPP8UXX3yB5cuX49ixY7Czs0NYWBgePnxodOcTExODYcOGITo6GkePHoWPjw969+6N27dva5Tr06cPEhMTpeXXX381unMBHk2eUjzOmzdvarwv12ej7bn8/vvvGudx7tw5mJubY/DgwRrl5PhcsrOzERQUhKVLl5arfHx8PPr164fu3bsjNjYWEydOxNixYzW+jFfks9YFbc/lwIED6NWrF7Zv344TJ06ge/fuCA8Px6lTpzTKNWnSRONzOXTokD7CL0Hb81G7fPmyRrzu7u7Se3J9NlWVPp451YU+ngnVhT7u29WFPu6r1cX+/fsxYcIE/Pnnn9i9ezcKCgrQu3dvZGdnl7mNTu95wsDatGkjJkyYIL0uKioS3t7eYu7cuaWWHzJkiOjXr5/GurZt24pXX31VCCGESqUSnp6e4rPPPpPeT0tLE0qlUvz66696OANN2p7PfxUWFgoHBwexevVqad3IkSPFgAEDdB3qE2l7LqtWrRJOTk5l7k/Oz6ayn8vixYuFg4ODyMrKktbJ9bkUB0Bs3LjxsWXee+890aRJE411Q4cOFWFhYdLryl4fXSjPuZSmcePGYubMmdLr6dOni6CgIN0FVkHlOZ/o6GgBQKSmppZZxhg+m6pE18+c6kTXz4TqSlf37epIV/fV6iolJUUAEPv37y+zjC7veQatqcjPz8eJEycQGhoqrTMzM0NoaCiOHj1a6jZHjx7VKA8AYWFhUvn4+HgkJSVplHFyckLbtm3L3KeuVOR8/isnJwcFBQWoUaOGxvqYmBi4u7sjMDAQ48ePx/3793Ua+39V9FyysrLg6+sLHx8fDBgwAOfPn5fek+uz0cXn8v333+P555+HnZ2dxnpDfy4V8aT/M7q4PnJRqVTIzMws8f/l6tWr8Pb2Rt26dfHCCy8gISFBpgjLp0WLFvDy8kKvXr1w+PBhab0pfzbGSB/PnOpCH88EKhv/3VVeWffV6iw9PR0ASjwzi9Plvz2DJhX37t1DUVERPDw8NNZ7eHiU2fYtKSnpseXVf2qzT12pyPn81+TJk+Ht7a3xgfbp0wc//PAD9u7di/nz52P//v3o27cvioqKdBp/cRU5l8DAQKxcuRKbN2/GTz/9BJVKhQ4dOuCff/4BIN9nU9nP5a+//sK5c+cwduxYjfVyfC4VUdb/mYyMDOTm5urk361cFixYgKysLAwZMkRa17ZtW0RGRiIqKgrLli1DfHw8OnfujMzMTBkjLZ2XlxeWL1+ODRs2YMOGDfDx8UG3bt1w8uRJALq5p9C/9PHMqS708Uygsj3pvk1le9J9tbpSqVSYOHEiOnbsiKZNm5ZZTpf3PAuttyCdmTdvHtasWYOYmBiNzmzPP/+89PdmzZqhefPmqFevHmJiYtCzZ085Qi1V+/bt0b59e+l1hw4d0KhRI3zzzTf4+OOPZYyscr7//ns0a9YMbdq00VhvKp9LVfXLL79g5syZ2Lx5s0Zb2b59+0p/b968Odq2bQtfX1/89ttvGDNmjByhlikwMBCBgYHS6w4dOiAuLg6LFy/Gjz/+KGNkRJVXVZ8JZNx4Xy3dhAkTcO7cOYP1MQQMXFPh6uoKc3NzJCcna6xPTk6Gp6dnqdt4eno+trz6T232qSsVOR+1BQsWYN68edi1axeaN2/+2LJ169aFq6srrl27VumYy1KZc1GztLREcHCwFKdcn01lziU7Oxtr1qwp15dRQ3wuFVHW/xlHR0fY2Njo5LM2tDVr1mDs2LH47bffSlTT/pezszMaNGhgdJ9LWdq0aSPFaoqfjTHTxzOnutDHM4HK9qT7Nmmn+H21OoqIiMAff/yB6Oho1K5d+7FldXnPM2hSYWVlhZCQEOzdu1dap1KpsHfvXo1fN4pr3769RnkA2L17t1Te398fnp6eGmUyMjJw7NixMvepKxU5H+DRiEgff/wxoqKi0KpVqyce559//sH9+/fh5eWlk7hLU9FzKa6oqAhnz56V4pTrs6nMuaxbtw55eXl48cUXn3gcQ3wuFfGk/zO6+KwN6ddff8Xo0aPx66+/agzxW5asrCzExcUZ3edSltjYWClWU/tsjJ0+njnVhT6eCVQ2/rvTreL31epECIGIiAhs3LgR+/btg7+//xO30em/Pa27dlfSmjVrhFKpFJGRkeLChQvilVdeEc7OziIpKUkIIcRLL70kpkyZIpU/fPiwsLCwEAsWLBAXL14U06dPF5aWluLs2bNSmXnz5glnZ2exefNmcebMGTFgwADh7+8vcnNzje585s2bJ6ysrMT69etFYmKitGRmZgohhMjMzBTvvPOOOHr0qIiPjxd79uwRLVu2FPXr1xcPHz40qnOZOXOm2Llzp4iLixMnTpwQzz//vLC2thbnz5/XOF85Phttz0WtU6dOYujQoSXWy/m5ZGZmilOnTolTp04JAGLRokXi1KlT4ubNm0IIIaZMmSJeeuklqfz169eFra2tePfdd8XFixfF0qVLhbm5uYiKipLKPOn6GMu5/Pzzz8LCwkIsXbpU4/9LWlqaVOZ///ufiImJEfHx8eLw4cMiNDRUuLq6ipSUFL2eS0XOZ/HixWLTpk3i6tWr4uzZs+Ktt94SZmZmYs+ePVIZuT6bqkofz5zqQh/PhOpCH/ft6kIf99XqYvz48cLJyUnExMRoPDNzcnKkMvq85xk8qRBCiC+//FLUqVNHWFlZiTZt2og///xTeq9r165i5MiRGuV/++030aBBA2FlZSWaNGkitm3bpvG+SqUSH330kfDw8BBKpVL07NlTXL582RCnIoTQ7nx8fX0FgBLL9OnThRBC5OTkiN69ews3NzdhaWkpfH19xbhx4wz2hUKbc5k4caJU1sPDQzz11FPi5MmTGvuT87PR9t/ZpUuXBACxa9euEvuS83NRD5f330Ud/8iRI0XXrl1LbNOiRQthZWUl6tatK1atWlViv4+7PsZyLl27dn1seSEeDbvo5eUlrKysRK1atcTQoUPFtWvX9H4uFTmf+fPni3r16glra2tRo0YN0a1bN7Fv374S+5Xjs6nKdP3MqU50/UyoLvR1364O9HVfrQ5Ku24ANP4t6fOep/j/IIiIiIiIiCrE4DNqExERERFR1cKkgoiIiIiIKoVJBRERERERVQqTCiIiIiIiqhQmFUREREREVClMKoiIiIiIqFKYVBARERERUaUwqSAiIiIiokphUkEEICYmBgqFAmlpabIcf+/evWjUqBGKioqeWDYqKgotWrSASqUyQGRUEQcOHEB4eDi8vb2hUCiwadMm2Y83Y8YMNGzYEHZ2dnBxcUFoaCiOHTum17iIdEkIgVdeeQU1atSAQqFAbGys3CEZrfz8fAQEBODIkSM63S+fP4ZX2efJw4cPMWrUKDRr1gwWFhYYOHDgY8sfPnwYFhYWaNGihdaxMqmgaqdbt26YOHGixroOHTogMTERTk5OssT03nvv4cMPP4S5ufkTy/bp0weWlpb4+eefDRAZVUR2djaCgoKwdOlSozlegwYN8NVXX+Hs2bM4dOgQ/Pz80Lt3b9y9e9cgMRJVVlRUFCIjI/HHH38gMTERTZs2lTsko7V8+XL4+/ujQ4cO0rqyvpCOGjXqiV801fj8MbzKPk+KiopgY2ODN998E6GhoY8tm5aWhhEjRqBnz54VOhaTCiIAVlZW8PT0hEKhMPixDx06hLi4ODz77LPl3mbUqFH44osv9BgVVUbfvn0xe/ZsDBo0qNT38/Ly8M4776BWrVqws7ND27ZtERMTo7fjAcDw4cMRGhqKunXrokmTJli0aBEyMjJw5syZCh+XyJDi4uLg5eWFDh06wNPTExYWFiXK5OfnyxCZcRFC4KuvvsKYMWP0sn8+fwyrss8TOzs7LFu2DOPGjYOnp+djj/Xaa69h+PDhaN++fYViZVJB1cqoUaOwf/9+fP7551AoFFAoFLhx40aJ5k+RkZFwdnbGH3/8gcDAQNja2uK5555DTk4OVq9eDT8/P7i4uODNN9/UaLJUkS+La9asQa9evWBtbS2tO336NLp37w4HBwc4OjoiJCQEx48fl94PDw/H8ePHERcXp9PrQ4YRERGBo0ePYs2aNThz5gwGDx6MPn364OrVqwY5fn5+Pr799ls4OTkhKCjIIMckqoxRo0bhjTfeQEJCAhQKBfz8/AA8qnmOiIjAxIkT4erqirCwMADAuXPn0LdvX9jb28PDwwMvvfQS7t27J+0vOzsbI0aMgL29Pby8vLBw4cIStdil/bLv7OyMyMhI6fWtW7cwZMgQODs7o0aNGhgwYABu3LihEffAgQOxYMECeHl5oWbNmpgwYQIKCgqkMnl5eZg8eTJ8fHygVCoREBCA77//HkIIBAQEYMGCBRoxxMbGQqFQ4Nq1a6VeqxMnTiAuLg79+vXT4go/cuPGDenZWHzp1q2bVIbPH+Oiq+fJqlWrcP36dUyfPr3CsTCpoGrl888/R/v27TFu3DgkJiYiMTERPj4+pZbNycnBF198gTVr1iAqKgoxMTEYNGgQtm/fju3bt+PHH3/EN998g/Xr10vbVOQ/98GDB9GqVSuNdS+88AJq166Nv//+GydOnMCUKVNgaWkpvV+nTh14eHjg4MGDlbwiZGgJCQlYtWoV1q1bh86dO6NevXp455130KlTJ6xatUqvx/7jjz9gb28Pa2trLF68GLt374arq6tej0mkC59//jlmzZqF2rVrIzExEX///bf03urVq2FlZYXDhw9j+fLlSEtLQ48ePRAcHIzjx48jKioKycnJGDJkiLTNu+++i/3792Pz5s3YtWsXYmJicPLkSa1iKigoQFhYGBwcHHDw4EEcPnwY9vb26NOnj0aNSXR0NOLi4hAdHY3Vq1cjMjJSIzEZMWIEfv31V3zxxRe4ePEivvnmG9jb20OhUODll18ucV9YtWoVunTpgoCAgFLjOnjwIBo0aAAHBwetzgcAfHx8pGdjYmIiTp06hZo1a6JLly5SGT5/jIeunidXr17FlClT8NNPP5VaA1heFd+SyAQ5OTnBysoKtra2T6wGLCgowLJly1CvXj0AwHPPPYcff/wRycnJsLe3R+PGjdG9e3dER0dj6NCh0n/uhIQEeHt7AwDeeecdREVFYdWqVZgzZ06px7l586ZUXi0hIQHvvvsuGjZsCACoX79+ie28vb1x8+ZNra8Byevs2bMoKipCgwYNNNbn5eWhZs2aAIBLly6hUaNGj93P5MmTMW/ePK2O3b17d8TGxuLevXtYsWIFhgwZgmPHjsHd3V27kyAyMCcnJzg4OMDc3LzEvbt+/fr49NNPpdezZ89GcHCwxj135cqV8PHxwZUrV+Dt7Y3vv/8eP/30k9R2fPXq1ahdu7ZWMa1duxYqlQrfffed1HR21apVcHZ2RkxMDHr37g0AcHFxwVdffQVzc3M0bNgQ/fr1w969ezFu3DhcuXIFv/32G3bv3i21d69bt650jFGjRmHatGn466+/0KZNGxQUFOCXX34pUXtRXGnPFLVhw4aV6LuXl5cn1WoUv74PHz7EwIED0b59e8yYMUNjGz5/jEN5nidPUlRUhOHDh2PmzJkl9qMtJhVEZbC1tZUSCgDw8PCAn58f7O3tNdalpKQAqPh/7tzcXI2mTwAwadIkjB07Fj/++CNCQ0MxePBgjVgAwMbGBjk5ORU+P5JHVlYWzM3NceLEiRIPd/W/rbp16+LixYuP3U95HxjF2dnZISAgAAEBAWjXrh3q16+P77//HlOnTtV6X0TGIiQkROP16dOnER0drXGvVouLi0Nubi7y8/PRtm1baX2NGjUQGBio1XFPnz6Na9eulagRePjwoUbToCZNmmj8X/fy8sLZs2cBPGrKZG5ujq5du5Z6DG9vb/Tr1w8rV65EmzZtsHXrVuTl5WHw4MFlxlXaM0Vt8eLFJTrrTp48udSRB19++WVkZmZi9+7dMDPTbNjC549xKM/z5EkyMzNx/PhxnDp1ChEREQAAlUoFIQQsLCywa9cu9OjRo1z7YlJBVIbizY2AR+1rS1unHlqvov+5XV1dkZqaqrFuxowZGD58OLZt24YdO3Zg+vTpWLNmjUZHrQcPHsDNza1C50byCQ4ORlFREVJSUtC5c+dSy1hZWf1fe3cX0tQfxgH8a+J0WhqxtBXpenEGku3YTXf2QgWCmUQvYk3L3iDJLKQisIgiFMIuyuimdVtkRdGLF9lqBekGhZiYMWakKDQn5WJMaM//Iv4H11Knm9X/7/cDXpzD75zz28GdZ8/5Ped31FGqqRQIBOD3+6f8OERTKSkpKWjZ6/WioKAAtbW1IW31ev2ozyL8LCYmBiIStG7ksxBerxcrV6785UxII6/NY8UNrVY7bj/27t2LXbt2ob6+HhaLBdu3b0diYuKo7XU6nZq0/GzevHkhZVOzZs0KmU793LlzaGpqQmtr6y/LqBh//g7hxJPxJCcnh/y/NDQ0oLm5Gbdv38aiRYvC3heTCpp2NBpNWO+DmKjJfrkVRUFHR0fIeqPRCKPRiKqqKhQXF8NisahJxb93whRFiVr/KXq8Xm/QDxeXy4W3b99izpw5MBqNKCkpgdlsxsWLF6EoCj5//oynT58iJydnUg9XjnW89PR0fPv2DefPn8emTZug1+vhdrtx5coV9Pb2jnnHk+i/KDc3F42NjTAYDL+sD1+yZAni4uLQ0tKC9PR0AMDg4CC6urqCRgzmzp2Lvr4+dfnDhw9Bd+dzc3Nx8+ZNpKamIjk5eVJ9Xb58OQKBAJ4/fz7qdJ/5+fnqDD5PnjzBixcvxtynoii4evUqRGRSMxo2Njbi7NmzePz4ccgIOcD487tFI550dHRgeHgYHo8HQ0ND6jteTCYTZsyYETI9c2pqKhISEiY8bTMf1KZpx2AwoKWlBd3d3XC73VF7ic/IL/edO3fgcrnQ2tqKCxcu4OHDh6Nut3HjRrx8+VJd9vl8qKiogNVqxcePH/Hq1SvY7fagGvvXr18jPj5+0tO+0dRyOBxQFEUNukePHoWiKKipqQHwo+7abDbj2LFjyMrKwubNm2G329UfONE+XmxsLDo7O7FlyxYYjUYUFBRgYGAANpsN2dnZUfjERH+PQ4cOwePxoLi4GHa7HU6nE01NTdi9eze+f/+OmTNnory8HNXV1WhubkZ7ezvKyspCSnzWrl2Ly5cv482bN3A4HDh48GDQqENJSQl0Oh0KCwths9ngcrlgtVpx+PBh9PT0hNVXg8GA0tJS7NmzB/fu3VP3cevWLbVNbGwsysrKcPLkSWRmZo573V+zZg28Xi/evXs3gbP2Q3t7O8xmM44fP47s7Gz09/ejv78fHo9HbcP483tFI57k5+dDURQ8ePAAVqs1aH9RJUTTzPv372XVqlWi1WoFgLhcLnn27JkAkMHBQRERsVgskpKSErTd6dOnZcWKFUHrSktLpbCwUF0eHh6WmpoaMRgMEhcXJ3q9XoqKiqStrW3U/gwMDEhCQoJ0dnaKiIjf75cdO3bIwoULRaPRyPz586WiokJ8Pp+6zf79++XAgQMRnQciov+S+vp6ycjICFqXl5cnlZWVIW27urqkqKhIZs+eLVqtVpYtWyZHjhyRQCAgIiJDQ0Oyc+dOSUxMlLS0NKmrqwvZV29vr2zYsEGSkpIkMzNTHj16JCkpKWKxWNQ2fX19YjabRafTSXx8vCxevFj27dsnX758EZHQGCEiUllZKXl5eeqyz+eTqqoq0ev1otFoZOnSpXL9+vWgbZxOpwCQurq6sM7Vtm3b5MSJE0HrAMjdu3dD2o7so8ViEQAhfyP7y/hDo4kR+algkIh+u+rqanz9+hXXrl0bt63b7UZWVhYcDseEah2JiGh0q1evhslkwqVLl/50V0LYbDasW7cOnz59Qlpa2rjt29rasH79ejidzrAf2A0H4w+NheVPRH+BU6dOISMjI6xSrO7ubjQ0NPCCTkT0P+f3+9HT04MzZ85g69atYSUUAJCTk4Pa2lq4XK6o9ofxh8bCkQoiIiKa9v7GkYobN26gvLwcJpMJ9+/fx4IFC/50l4hGxaSCiIiIiIgiwvInIiIiIiKKCJMKIiIiIiKKCJMKIiIiIiKKCJMKIiIiIiKKCJMKIiIiIiKKCJMKIiIiIiKKCJMKIiIiIiKKCJMKIiIiIiKKCJMKIiIiIiKKyD8eMByMgSBERAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, tight_layout=True, figsize=(10, 4))\n",
    "sims[\"sim_0\"].plot(z=0.0, ax=ax[0])\n",
    "sims[\"sim_0\"].plot(x=0, freq=freq0, ax=ax[1])\n",
    "plt.show()\n",
    "\n",
    "f, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(8, 4))\n",
    "plot_time = 5 / freqw\n",
    "ax1 = (\n",
    "    sims[\"sim_0\"]\n",
    "    .sources[0]\n",
    "    .source_time.plot(times=np.linspace(0, plot_time, 1001), val=\"abs\", ax=ax1)\n",
    ")\n",
    "ax1.set_xlim(0, plot_time)\n",
    "ax2 = (\n",
    "    sims[\"sim_0\"]\n",
    "    .sources[0]\n",
    "    .source_time.plot_spectrum(\n",
    "        times=np.linspace(0, sims[\"sim_0\"].run_time, 10001), val=\"abs\", ax=ax2\n",
    "    )\n",
    ")\n",
    "ax2.hlines(1.5e-15, freq_range[0], freq_range[1], linewidth=10, color=\"g\", alpha=0.4)\n",
    "ax2.legend((\"source spectrum\", \"measurement\"))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7983aad",
   "metadata": {},
   "source": [
    "Now we run the simulations as a `Batch`.\n",
    "\n",
    "We set `verbose=True` to keep track of the status of the jobs in the `Batch`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "70058b88",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:06:15.093374Z",
     "iopub.status.busy": "2023-08-18T17:06:15.093227Z",
     "iopub.status.idle": "2023-08-18T17:08:27.948645Z",
     "shell.execute_reply": "2023-08-18T17:08:27.947959Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "52e5d90efd6741638ccab28fa5bf64ca",
       "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\"></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\">14:04:45 -03 </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12</span> tasks.                      \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:04:45 -03\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m12\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\">14:04:56 -03 </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.300</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:04:56 -03\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.300\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 the\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Batch has completed.                                               \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 the\n",
       "\u001b[2;36m             \u001b[0mBatch has completed.                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5ff218f0187248c1b7d240538ea6cc59",
       "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\">14:05:23 -03 </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:05:23 -03\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \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\"></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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7be9495d0f0a4fefa1f7270259db71ff",
       "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\"></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"
    }
   ],
   "source": [
    "# initialize a batch and run them all\n",
    "batch = td.web.Batch(simulations=sims, verbose=True)\n",
    "\n",
    "# run the batch and store all of the data in the `data/` dir.\n",
    "batch_data = batch.run(path_dir=\"data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c865c155",
   "metadata": {},
   "source": [
    "Now that the simulations are complete, we can analyze the data. Let's first look at one of the FieldTimeMonitors to make sure the source has decayed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4a97628f-c2d1-4e2f-82f2-ba97d88fe48e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:08:28.235611Z",
     "iopub.status.busy": "2023-08-18T17:08:28.235460Z",
     "iopub.status.idle": "2023-08-18T17:08:29.381759Z",
     "shell.execute_reply": "2023-08-18T17:08:29.381250Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(\n",
    "    batch_data[\"sim_1\"].monitor_data[\"monitor_time_0\"].Hz.t,\n",
    "    np.real(batch_data[\"sim_1\"].monitor_data[\"monitor_time_0\"].Hz.squeeze()),\n",
    ")\n",
    "plt.title(\"FieldTimeMonitor data\")\n",
    "plt.xlabel(\"t\")\n",
    "plt.ylabel(\"Hz\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21bc59dc-0163-432b-9cf0-5c2d2617ef35",
   "metadata": {},
   "source": [
    "We see that the source has mostly decayed by the time we switch on the monitors, and the remaining data shows decay and oscillation due to the resonances inside the system.\n",
    "\n",
    "Looking at the Fourier transform of this data, we can see resonances at the band frequencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e1b95adb-1ea6-40cb-ba63-ef5ac60de470",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:08:29.392606Z",
     "iopub.status.busy": "2023-08-18T17:08:29.392364Z",
     "iopub.status.idle": "2023-08-18T17:08:29.689950Z",
     "shell.execute_reply": "2023-08-18T17:08:29.689484Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "field = batch_data[\"sim_1\"].monitor_data[\"monitor_time_0\"].Hz.squeeze().real\n",
    "\n",
    "fmesh = np.fft.fftfreq(field.size, np.mean(np.diff(field.t)))\n",
    "spectrum = np.fft.fft(field)\n",
    "\n",
    "mask = (fmesh > freq_range[0]) & (fmesh < freq_range[1])\n",
    "\n",
    "plt.plot(\n",
    "    fmesh[mask],\n",
    "    np.abs(spectrum)[mask],\n",
    ")\n",
    "plt.title(\"Spectrum at single wavevector\")\n",
    "plt.xlabel(\"Frequency (Hz)\")\n",
    "plt.ylabel(\"Amplitude\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "663c96ba-8b16-4056-9983-bef38e7a5c9a",
   "metadata": {},
   "source": [
    "We use the ResonanceFinder plugin to find the band frequencies.\n",
    "\n",
    "We first construct a `ResonanceFinder` object storing our parameters, and then call `run()` on our list of `FieldTimeData` objects. This will add up the signals from all of the monitors before searching for resonances. The `ResonanceFinder` class has additional methods in case the signal takes another form; see the api reference [here](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.resonance.ResonanceFinder.html).\n",
    "\n",
    "The `run()` method returns an `xr.Dataset` containing the decay rate, Q factor, amplitude, phase, and estimation error for each resonance as a function of frequency.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7496ef10-068e-41ce-8907-418bbcd60e26",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:08:29.693707Z",
     "iopub.status.busy": "2023-08-18T17:08:29.693525Z",
     "iopub.status.idle": "2023-08-18T17:08:31.106128Z",
     "shell.execute_reply": "2023-08-18T17:08:31.105255Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>decay</th>\n",
       "      <th>Q</th>\n",
       "      <th>amplitude</th>\n",
       "      <th>phase</th>\n",
       "      <th>error</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2.555730e+13</th>\n",
       "      <td>2.905852e+07</td>\n",
       "      <td>2.763066e+06</td>\n",
       "      <td>0.001549</td>\n",
       "      <td>1.265584</td>\n",
       "      <td>0.000020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.847409e+13</th>\n",
       "      <td>7.187784e+11</td>\n",
       "      <td>4.304045e+02</td>\n",
       "      <td>0.009517</td>\n",
       "      <td>1.392776</td>\n",
       "      <td>0.000020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.067335e+14</th>\n",
       "      <td>1.085117e+11</td>\n",
       "      <td>3.090112e+03</td>\n",
       "      <td>0.027057</td>\n",
       "      <td>2.836346</td>\n",
       "      <td>0.000005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.139619e+14</th>\n",
       "      <td>1.869788e+12</td>\n",
       "      <td>1.914773e+02</td>\n",
       "      <td>0.036011</td>\n",
       "      <td>1.033897</td>\n",
       "      <td>0.000023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.198011e+14</th>\n",
       "      <td>9.581388e+11</td>\n",
       "      <td>3.928099e+02</td>\n",
       "      <td>0.021284</td>\n",
       "      <td>-2.684537</td>\n",
       "      <td>0.000017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.381860e+14</th>\n",
       "      <td>7.934254e+11</td>\n",
       "      <td>5.471517e+02</td>\n",
       "      <td>0.022407</td>\n",
       "      <td>-1.235991</td>\n",
       "      <td>0.000046</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     decay             Q  amplitude     phase     error\n",
       "freq                                                                   \n",
       "2.555730e+13  2.905852e+07  2.763066e+06   0.001549  1.265584  0.000020\n",
       "9.847409e+13  7.187784e+11  4.304045e+02   0.009517  1.392776  0.000020\n",
       "1.067335e+14  1.085117e+11  3.090112e+03   0.027057  2.836346  0.000005\n",
       "1.139619e+14  1.869788e+12  1.914773e+02   0.036011  1.033897  0.000023\n",
       "1.198011e+14  9.581388e+11  3.928099e+02   0.021284 -2.684537  0.000017\n",
       "1.381860e+14  7.934254e+11  5.471517e+02   0.022407 -1.235991  0.000046"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resonance_finder = ResonanceFinder(freq_window=tuple(freq_range))\n",
    "resonance_data = resonance_finder.run(signals=batch_data[\"sim_1\"].data)\n",
    "resonance_data.to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46db3d3c",
   "metadata": {},
   "source": [
    "We see the four resonances from the previous figure. All four have reasonable Q factors, amplitudes, and errors, so they are likely to represent physical resonances. Note that in order to accurately obtain the Q factor for high-Q modes, it may be necessary to run the simulation for a longer time.\n",
    "\n",
    "Now we are ready to compute the band structure. We run the resonance finder at every Bloch wavevector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4afe673e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:08:31.116954Z",
     "iopub.status.busy": "2023-08-18T17:08:31.116520Z",
     "iopub.status.idle": "2023-08-18T17:08:50.203875Z",
     "shell.execute_reply": "2023-08-18T17:08:50.202960Z"
    }
   },
   "outputs": [],
   "source": [
    "resonance_finder = ResonanceFinder(freq_window=tuple(freq_range))\n",
    "resonance_datas = []\n",
    "for i in range(3 * Nk):\n",
    "    sim_data = batch_data[f\"sim_{i}\"]\n",
    "    resonance_datas.append(resonance_finder.run(signals=sim_data.data))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f48f416-be79-4c85-9dd3-36a7101dfe70",
   "metadata": {},
   "source": [
    "We define a function to filter resonances based on their Q, amplitude, and error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "225f04aa-2390-456e-bcb0-395320ae4015",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:08:50.373773Z",
     "iopub.status.busy": "2023-08-18T17:08:50.373575Z",
     "iopub.status.idle": "2023-08-18T17:08:50.405541Z",
     "shell.execute_reply": "2023-08-18T17:08:50.404883Z"
    }
   },
   "outputs": [],
   "source": [
    "def filter_resonances(resonance_data, minQ, minamp, maxerr):\n",
    "    resonance_data = resonance_data.where(abs(resonance_data.Q) > minQ, drop=True)\n",
    "    resonance_data = resonance_data.where(resonance_data.amplitude > minamp, drop=True)\n",
    "    resonance_data = resonance_data.where(resonance_data.error < maxerr, drop=True)\n",
    "    return resonance_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9398f9cc-e31c-4fc8-a8af-4fb89cee9034",
   "metadata": {},
   "source": [
    "We plot the band structure with the light line overlaid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fc001a93",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:08:50.408852Z",
     "iopub.status.busy": "2023-08-18T17:08:50.408375Z",
     "iopub.status.idle": "2023-08-18T17:08:50.970373Z",
     "shell.execute_reply": "2023-08-18T17:08:50.969788Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(3 * Nk):\n",
    "    resonance_data = resonance_datas[i]\n",
    "    resonance_data = filter_resonances(\n",
    "        resonance_data=resonance_data, minQ=0, minamp=0.001, maxerr=100\n",
    "    )\n",
    "    freqs = resonance_data.freq.to_numpy()\n",
    "    Qs = resonance_data.Q.to_numpy()\n",
    "    plt.scatter(np.full(len(freqs), (1 / 2) * i / Nk), freqs / 3e14, color=\"blue\")\n",
    "\n",
    "lightx = np.linspace(0, 0.5, 100)\n",
    "lighty1 = lightx\n",
    "lighty3 = (0.5 - lightx) * np.sqrt(2)\n",
    "\n",
    "plt.plot(lightx, lighty1, color=\"blue\", alpha=0.2)\n",
    "plt.plot(1 + lightx, lighty3, color=\"blue\", alpha=0.2)\n",
    "\n",
    "plt.ylim(0, freq_range_unitless[1])\n",
    "\n",
    "plt.title(\"Band diagram\")\n",
    "plt.ylabel(\"Frequency (c/a)\")\n",
    "plt.xlabel(\"Wavevector\")\n",
    "plt.xticks([0, 0.5, 1, 1.5], [r\"$\\Gamma$\", \"X\", \"M\", r\"$\\Gamma$\"])\n",
    "plt.xlim(0, 1.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c9ac653-3d76-4588-946a-71b2db764030",
   "metadata": {},
   "source": [
    "The bandstructure we obtained matches the expected result from the paper. If we were seeing too many resonances, we could change the parameters to our filter_resonances function to eliminate the spurious ones. If we were seeing too few resonances even before filtering, we might have to change the parameters of the ResonanceFinder, for example decreasing `rcond` or increasing `init_num_freqs`. If the ResonanceFinder takes too long, we can decrease `init_num_freqs`. There can also be resonances on the light line associated with Wood's anomaly; we filter those out here based on their small amplitude."
   ]
  }
 ],
 "metadata": {
  "applications": [
   "Photonic crystals"
  ],
  "description": "This notebook demonstrates how to calculate the band structure of a photonic crystal in Tidy3D FDTD.",
  "feature_image": "./img/band_structure.png",
  "features": [
   "Resonance finder"
  ],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "photonic crystal, band structure, 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": "3.11.7"
  },
  "title": "Photonic Crystal Band Structure Calculation | Flexcompute",
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        {
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