{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c5a03881-944c-416c-9f42-7b2b4fab62e9",
   "metadata": {},
   "source": [
    "# Designing a polarization splitter/rotator on thin-film lithium niobate"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "de15eadf-2e49-4308-9e19-37c7b49b3128",
   "metadata": {},
   "source": [
    "Note: the cost of running the total notebook is almost 27.5 FlexCredits. The cost of running the FDTD validation is just over 26 FlexCredits.\n",
    "\n",
    "Here we design a polarization splitter/rotator (PSR) on the lithium niobate on insulator (LNOI) platform based on `Xuanhao Wang, An Pan, Tingan Li, Cheng Zeng, and Jinsong Xia, \"Efficient polarization splitter-rotator on thin-film lithium niobate,\" Opt. Express 29, 38044-38052 (2021)` [DOI: 10.1364/OE.443798](https://doi.org/10.1364/OE.443798) and follow the same design process. This will operate in a bandwidth of 1.5 μm to 1.6 μm.\n",
    "\n",
    "<center><img src=\"img/PSR.png\" width=\"600\" alt=\"Schematic of the PSR\"></center>\n",
    "\n",
    "PSRs are useful devices that control optical input. By splitting and rotating its input, a PSR can isolate different modes and transform them according to our needs. This PSR consists of an adiabatic taper to convert the TM0 mode to TE1 mode and an asymmetric Y-junction that separates TE0 and TE1 modes. In this way, our PSR is able to split TE0 and TM0 modes, and rotate the TM0 mode into a TE0 mode. Devices like these allow us to modulate inputs and interface with other optical devices in a controlled way.\n",
    "\n",
    "In this design process, we will optimize the taper and Y-junction separately using a combination of `Tidy3D`'s mode solver and eigenmode expansion (EME) solver. Finally we validate our optimized designs with FDTD to further ensure the performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0951be40-7ab5-4009-a977-e5563a457c96",
   "metadata": {},
   "outputs": [
    {
     "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\">11:08:00 EST </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Configuration found in legacy location </span><span style=\"color: #008000; text-decoration-color: #008000\">'~/.tidy3d'</span><span style=\"color: #800000; text-decoration-color: #800000\">.       </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">Consider running </span><span style=\"color: #008000; text-decoration-color: #008000\">'tidy3d config migrate'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                          </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:08:00 EST\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Configuration found in legacy location \u001b[0m\u001b[32m'~/.tidy3d'\u001b[0m\u001b[31m.       \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mConsider running \u001b[0m\u001b[32m'tidy3d config migrate'\u001b[0m\u001b[31m.                          \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web\n",
    "from matplotlib import pyplot as plt\n",
    "from tidy3d import material_library\n",
    "from tidy3d.plugins.mode import ModeSolver\n",
    "from tidy3d.plugins.mode.web import run_batch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40782527-706f-4285-8d76-9b6c731b88c3",
   "metadata": {},
   "source": [
    "## Simulation Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3c5df5f-53b8-46e9-bef5-b7f91e5f8610",
   "metadata": {},
   "source": [
    "First we define some values in our bandwidth and LNOI setup. Our LNOI setup consists of a lithium niobate (LN) structure with a sidewall angle of 20 degree on top of a thin film that is also LN. All of our LNOI structures will be defined in this way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1aaf6271-694a-4030-8425-274e2d9a5a27",
   "metadata": {},
   "outputs": [],
   "source": [
    "wvl0 = 1.55  # central wavelength for device\n",
    "freq0 = td.C_0 / wvl0  # central frequency\n",
    "wvls = np.linspace(1.5, 1.6, 41)  # bandwidth we'll consider for device\n",
    "freqs = td.C_0 / wvls  # corresponding frequency range\n",
    "\n",
    "sidewall_angle = np.deg2rad(20)\n",
    "etch_depth = 0.26\n",
    "film_thickness = 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ed7f504-86df-4442-af56-4128864c2159",
   "metadata": {},
   "source": [
    "LN is an anisotropic medium, with different refractive indices in the ordinary and extraordinary directions. We incorporate this information using Tidy3D's [AnisotropicMedium](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.AnisotropicMedium.html) object. Since the propagation direction of the PSR will be defined along the x-direction, this will have the ordinary refractive index.\n",
    "\n",
    "<i>NOTE: Tidy3D offers lithium niobate in its [material library](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/material_library.html#lithium-niobate-linbo3). However, the values there differ slightly from the values used in the paper on which this notebook is based, so we will use the latter.</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8f8887e1-97a7-4954-921c-cf90d90cd348",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_e = 2.169  # extraordinary refractive index of LN\n",
    "n_o = 2.234  # ordinary refractive index of LN\n",
    "n_SiO2 = 1.46  # refractive index of SiO2\n",
    "\n",
    "# define LN medium\n",
    "LN_e = td.Medium(permittivity=n_e**2)\n",
    "LN_o = td.Medium(permittivity=n_o**2)\n",
    "LN = td.AnisotropicMedium(xx=LN_o, yy=LN_e, zz=LN_o)\n",
    "\n",
    "# define SiO2 medium\n",
    "SiO2 = td.Medium(permittivity=n_SiO2**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9be7448-0521-40dd-8524-fa60dcbe9b4b",
   "metadata": {},
   "source": [
    "Next we define a function that takes in a set of points (like a polyslab) and produces the corresponding LNOI structure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "06ddfac3-26aa-4de4-8c88-48c827ae6e8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_ridge_waveguide(pts):\n",
    "    \"\"\"\n",
    "    Define the sidewall angled waveguide that sits on top of a Lithium Niobate film:\n",
    "    \"\"\"\n",
    "    LN_slab_thickness = film_thickness - etch_depth\n",
    "    waveguide_geo = td.PolySlab(\n",
    "        sidewall_angle=sidewall_angle,\n",
    "        vertices=pts,\n",
    "        reference_plane=\"bottom\",\n",
    "        slab_bounds=[-0.005, etch_depth],\n",
    "    )\n",
    "    waveguide = td.Structure(geometry=waveguide_geo, medium=LN)\n",
    "\n",
    "    \"\"\"\n",
    "    Define the film that the waveguide sits on:\n",
    "    \"\"\"\n",
    "    LN_slab_geo = td.Box(\n",
    "        center=(0, 0, -LN_slab_thickness / 2),  # ensure overlap\n",
    "        size=(td.inf, td.inf, LN_slab_thickness),\n",
    "    )\n",
    "    LN_slab = td.Structure(geometry=LN_slab_geo, medium=LN)\n",
    "\n",
    "    return [LN_slab, waveguide]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84d0b642-c689-4676-bc0a-9f0f7af9ab18",
   "metadata": {},
   "source": [
    "## Mode Analysis\n",
    "To design our adiabatic taper, we want to determine the LNOI cross section at which a TM0 mode hybridizes with the TE1 mode. To do this, we will use Tidy3D's mode solver for cross sections at increasing LNOI taper widths. We will do this by creating a single taper structure on LNOI and placing mode solver planes along the taper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fb2d5755-5194-4390-b7f2-0fd0197b441a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We will define the taper by a (somewhat arbitrary) difference in widths over a length.\n",
    "\n",
    "width_test_w0 = 2  # starting width of taper\n",
    "width_test_w1 = 3  # ending width of taper\n",
    "width_test_length = 10  # length of taper\n",
    "\n",
    "\"\"\"\n",
    "Define points that take the shape of a taper. Note that we extend the inital and final\n",
    "widths past the boundaries to avoid the sidewall angles from interfering with the ends\n",
    "of the taper.\n",
    "\"\"\"\n",
    "width_test_pts = [\n",
    "    (-1, -width_test_w0 / 2),\n",
    "    (0, -width_test_w0 / 2),\n",
    "    (width_test_length, -width_test_w1 / 2),\n",
    "    (width_test_length + 1, -width_test_w1 / 2),\n",
    "    (width_test_length + 1, width_test_w1 / 2),\n",
    "    (width_test_length, width_test_w1 / 2),\n",
    "    (0, width_test_w0 / 2),\n",
    "    (-1, width_test_w0 / 2),\n",
    "]\n",
    "width_test_structures = make_ridge_waveguide(width_test_pts)\n",
    "\n",
    "# create simulation environment of the taper\n",
    "width_test_sim = td.Simulation(\n",
    "    center=(width_test_length / 2, 0, 0),\n",
    "    size=(width_test_length + 0.2, 2 * width_test_w0, 3 * film_thickness),\n",
    "    structures=width_test_structures,\n",
    "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=30, wavelength=wvl0),\n",
    "    medium=SiO2,\n",
    "    run_time=1e-15,\n",
    ")\n",
    "\n",
    "# plot the simulation to check the taper and LNOI geometry\n",
    "_, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
    "width_test_sim.plot(z=etch_depth / 2, ax=ax[0])\n",
    "width_test_sim.plot(x=0, ax=ax[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7bfffb5-eb2a-4c0f-9c28-01296b2256ef",
   "metadata": {},
   "source": [
    "To find the relationship between modes and width, we will place mode solver planes along the taper (along the x direction). These planes will solve for modes at each cross-section, which will be increasing in thickness as we go along the taper. This will tell us at which thickness a mode conversion will occur.<br>\n",
    "\n",
    "<i>NOTE: Since this cell runs the remote mode solver in series, it may take a couple minutes to run.</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bf5e7595-6832-49ab-9938-0012bf0d903c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "plane_xs = np.linspace(0, width_test_length, 31)  # x values of the mode solver planes\n",
    "\n",
    "# modespec for the mode solver planes\n",
    "width_test_mode_spec = td.ModeSpec(num_modes=3, target_neff=n_o)\n",
    "\n",
    "mode_solvers = []\n",
    "\n",
    "for i, m in enumerate(plane_xs):\n",
    "    # define plane at x coordinate\n",
    "    plane = td.Box(center=(m, 0, 0), size=(0, td.inf, td.inf))\n",
    "\n",
    "    # create mode solver from plane\n",
    "    mode_solver = ModeSolver(\n",
    "        simulation=width_test_sim,\n",
    "        plane=plane,\n",
    "        mode_spec=width_test_mode_spec,\n",
    "        freqs=[freq0],\n",
    "    )\n",
    "\n",
    "    # add modesolver for this mode solver plane\n",
    "    mode_solvers.append(mode_solver)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "baf8caa5-63d7-430f-8dd6-71f3cd5a8077",
   "metadata": {},
   "outputs": [
    {
     "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\">11:08:01 EST </span><span style=\"color: #008080; text-decoration-color: #008080\">Running a batch of </span><span style=\"color: #af005f; text-decoration-color: #af005f; font-weight: bold\">31</span><span style=\"color: #af005f; text-decoration-color: #af005f\"> mode solvers.</span>                                \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:08:01 EST\u001b[0m\u001b[2;36m \u001b[0m\u001b[36mRunning a batch of \u001b[0m\u001b[1;38;5;125m31\u001b[0m\u001b[38;5;125m mode solvers.\u001b[0m                                \n",
       "\u001b[2;36m             \u001b[0m                                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "af1218911b7d49f6bcd33bd83b9769f1",
       "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\">11:08:38 EST </span><span style=\"color: #008000; text-decoration-color: #008000\">A batch of `ModeSolver` tasks completed successfully!</span>              \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:08:38 EST\u001b[0m\u001b[2;36m \u001b[0m\u001b[32mA batch of `ModeSolver` tasks completed successfully!\u001b[0m              \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></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": [
    "# run mode solvers in parallel\n",
    "mode_batch = run_batch(mode_solvers=mode_solvers, task_name=\"mode_conversion\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1ce5dd6c-006d-423b-be8e-bc644c139b3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# collect relevant data from batch solve\n",
    "modes = np.reshape([mode_data.n_eff.data[0] for mode_data in mode_batch], (len(plane_xs), 3))\n",
    "te_fracs = np.reshape(\n",
    "    [mode_data.pol_fraction.te.data[0] for mode_data in mode_batch], (len(plane_xs), 3)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "015037cf-a0ea-4895-93a6-85ccda608cd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.colors as mcolors\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Ellipse\n",
    "\n",
    "ridge_waveguide_widths = (\n",
    "    width_test_w1 - width_test_w0\n",
    ") / width_test_length * plane_xs + width_test_w0\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(7, 4))\n",
    "\n",
    "conversion = [2.52, 2.7]\n",
    "\n",
    "cmap = plt.cm.cool\n",
    "norm = mcolors.Normalize(vmin=0, vmax=1)\n",
    "\n",
    "# TE0\n",
    "ax.plot(ridge_waveguide_widths, modes[:, 0], label=\"TE0\")\n",
    "ax.scatter(\n",
    "    ridge_waveguide_widths,\n",
    "    modes[:, 0],\n",
    "    c=te_fracs[:, 0],\n",
    "    cmap=cmap,\n",
    "    norm=norm,\n",
    ")\n",
    "\n",
    "# TM0\n",
    "ax.plot(ridge_waveguide_widths, modes[:, 1], label=\"TM0\")\n",
    "ax.scatter(\n",
    "    ridge_waveguide_widths,\n",
    "    modes[:, 1],\n",
    "    c=te_fracs[:, 1],\n",
    "    cmap=cmap,\n",
    "    norm=norm,\n",
    ")\n",
    "\n",
    "# TE1\n",
    "ax.plot(ridge_waveguide_widths, modes[:, 2], label=\"TE1\")\n",
    "ax.scatter(\n",
    "    ridge_waveguide_widths,\n",
    "    modes[:, 2],\n",
    "    c=te_fracs[:, 2],\n",
    "    cmap=cmap,\n",
    "    norm=norm,\n",
    ")\n",
    "\n",
    "circle = Ellipse((2.61, 1.887), width=0.1, height=0.013, ec=\"blue\", fill=False, ls=\"--\")\n",
    "ax.add_patch(circle)\n",
    "\n",
    "ax.axvline(x=conversion[0], color=\"gray\", ls=\"--\")\n",
    "ax.axvline(x=conversion[1], color=\"gray\", ls=\"--\")\n",
    "ax.axvspan(conversion[0], conversion[1], color=\"gray\", alpha=0.2)\n",
    "\n",
    "ax.set_xlabel(\"Ridge waveguide width (μm)\")\n",
    "ax.set_ylabel(\"Effective refractive indices\")\n",
    "\n",
    "# Use the same norm + cmap for the colorbar\n",
    "sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)\n",
    "sm.set_array([])\n",
    "plt.colorbar(sm, ax=ax, label=\"TE fraction\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5219ac63-107e-4370-b2ee-52963960ffee",
   "metadata": {},
   "source": [
    "To show the TM0 to TE1 hybridization, we will plot the effective indices vs taper width, as well as the TE fraction as the color of the curves. We can see that around a taper width of 2.55 μm is when the modes cross."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61777742-f3de-41ea-b4c7-648666b596d6",
   "metadata": {},
   "source": [
    "This mode conversion behavior differs from what is shown in Wang et. al. This is likely due to sub-pixel averaging. When the mode solving is done locally (and thus without subpixel averaging) the result is very similar to what is given in Wang et. al."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8545e43-2f90-4870-a42a-346e42bd9777",
   "metadata": {},
   "source": [
    "## Adiabatic Taper Design\n",
    "\n",
    "Efficient mode conversion can only happen if the taper changes gradually. Therefore, we will set our conversion taper to be made up of 3 tapers. The first taper will start with a width of 1 μm and end with a width of 2.52 μm. This will be for the single-mode input. The next taper is from 2.52 μm to 2.7 μm for mode conversion. The final taper from 2.7 μm to 3 μm is the connection to the asymmetric Y-junction.\n",
    "\n",
    "Now that we've established the widths of the tapers, the next question is how long the tapers should be. According to the paper this notebook is based on, the choice of lengths of the first and third tapers do not have a significant effect (as the length of the second taper can be tuned for these choices), and thus will be chosen as 100 μm for each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9bc919bf-98f0-4bdc-bd84-49c60b6dc436",
   "metadata": {},
   "outputs": [],
   "source": [
    "l1 = 100  # length of first taper\n",
    "l3 = 100  # length of third taper\n",
    "\n",
    "w0 = 1  # starting width of first taper\n",
    "w1 = conversion[0]  # starting width of second taper\n",
    "w2 = conversion[1]  # starting width of third taper\n",
    "w3 = 3  # ending width of third taper"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61c3c4a9-fcba-4956-bcea-a17a7e21e66e",
   "metadata": {},
   "source": [
    "To determine the length of the second taper ($L_2$) that is optimal for mode conversion efficiency, we will run an EME parameter sweep for different simulations with different $L_2$ values. To do this efficiently, we will use the EMELengthSweep feature in Tidy3D's EME simulations, requiring us to only specify the scale factors we'll assign to the EME cells we'd like to vary.\n",
    "\n",
    "EME is an excellent choice for investigations of this kind. This type of simulation is mainly about solving for modes at different cross sections, and seeing how these modes change from one cross section to another (in this case, along the taper). Since these changes are adiabatic, it is necessary to check for very long lengths in the propagation direction. For this reason, the device can get very large, making FDTD solving very slow and expensive. On the other hand, mode solving in the device's cross section is impacted a lot less by extending the length normal to the cross-section. Since this is mainly what EME relies on, EME will be a much more efficient option."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9f900683-24ee-41f0-a373-10fa3b0a170c",
   "metadata": {},
   "outputs": [],
   "source": [
    "l2_sweep = 1  # value to construct EME sweep\n",
    "\n",
    "l2_test_pts = [\n",
    "    (-10, -w0 / 2),  # extend taper into -x boundary\n",
    "    (0, -w0 / 2),\n",
    "    (l1, -w1 / 2),\n",
    "    (l1 + l2_sweep, -w2 / 2),\n",
    "    (l1 + l2_sweep + l3, -w3 / 2),\n",
    "    (l1 + l2_sweep + l3 + 10, -w3 / 2),  # extend taper into +x boundary\n",
    "    (l1 + l2_sweep + l3 + 10, w3 / 2),\n",
    "    (l1 + l2_sweep + l3, w3 / 2),\n",
    "    (l1 + l2_sweep, w2 / 2),\n",
    "    (l1, w1 / 2),\n",
    "    (0, w0 / 2),\n",
    "    (-10, w0 / 2),\n",
    "]\n",
    "l2_test_structures = make_ridge_waveguide(l2_test_pts)\n",
    "\n",
    "\"\"\"\n",
    "Here we define the cells we'll use in our EME simulation. We will segment our cells into the three\n",
    "tapers. Since we care most about our L2 taper, we will divide this into more cells than the other tapers.\n",
    "\"\"\"\n",
    "num_modes = 4\n",
    "l1_grid_spec = td.EMEUniformGrid(num_cells=8, mode_spec=td.EMEModeSpec(num_modes=num_modes))\n",
    "l2_grid_spec = td.EMEUniformGrid(num_cells=15, mode_spec=td.EMEModeSpec(num_modes=num_modes))\n",
    "l2_total_grid = td.EMECompositeGrid(\n",
    "    subgrids=[l1_grid_spec, l2_grid_spec, l1_grid_spec],\n",
    "    subgrid_boundaries=[l1, l1 + l2_sweep],\n",
    ")\n",
    "\n",
    "# define regular mesh grid. This is required for mode solving\n",
    "auto_x = td.AutoGrid(min_steps_per_wvl=10)\n",
    "auto_yz = td.AutoGrid(min_steps_per_wvl=30)\n",
    "\n",
    "l2_scales = np.linspace(1 / l2_sweep, 500 / l2_sweep, 51)\n",
    "l2_scale_factors = np.ones((len(l2_scales), 2 * l1_grid_spec.num_cells + l2_grid_spec.num_cells))\n",
    "l2_scale_factors[:, l1_grid_spec.num_cells : l1_grid_spec.num_cells + l2_grid_spec.num_cells] = (\n",
    "    l2_scales[:, None]\n",
    ")\n",
    "\n",
    "# define EME simulation\n",
    "l2_sweep_sim = td.EMESimulation(\n",
    "    center=((l1 + l2_sweep + l3) / 2, 0, 0),\n",
    "    size=(l1 + l2_sweep + l3, w3 + 3 * w0, 5 * film_thickness),\n",
    "    structures=l2_test_structures,\n",
    "    grid_spec=td.GridSpec(grid_x=auto_x, grid_y=auto_yz, grid_z=auto_yz, wavelength=wvl0),\n",
    "    medium=SiO2,\n",
    "    eme_grid_spec=l2_total_grid,\n",
    "    freqs=[freq0],\n",
    "    axis=0,  # EME will be solved along the x axis\n",
    "    sweep_spec=td.EMELengthSweep(scale_factors=l2_scale_factors),\n",
    "    symmetry=(0, 1, 0),  # the adiabatic taper has symmetry in y\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce3577f0-ef7f-4e80-958b-527a602a89f7",
   "metadata": {},
   "source": [
    "We will plot this simulation just to make sure everything looks correct:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3a13b27d-aa8d-4d39-9f63-4e893c08a40d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
    "l2_sweep_sim.plot(z=0.005, ax=ax[0])\n",
    "l2_sweep_sim.plot(x=l2_sweep, ax=ax[1])\n",
    "ax[0].set_aspect(6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d11d952-1de2-451f-8fee-fec57859e9b0",
   "metadata": {},
   "source": [
    "Now we will run the EME simulation (sweep):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4bdcb6cb-6fac-4c5e-bcf9-e97816f26812",
   "metadata": {},
   "outputs": [
    {
     "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>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'L2 EME sweep'</span> with resource_id                       \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'eme-a96d46e9-2abb-4f7f-b870-48d93ccd605c'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'EME'</span>.    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'L2 EME sweep'\u001b[0m with resource_id                       \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'eme-a96d46e9-2abb-4f7f-b870-48d93ccd605c'\u001b[0m and task_type \u001b[32m'EME'\u001b[0m.    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Tidy3D's EME solver is currently in the beta stage. Cost of EME    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>simulations is subject to change in the future.                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTidy3D's EME solver is currently in the beta stage. Cost of EME    \n",
       "\u001b[2;36m             \u001b[0msimulations is subject to change in the future.                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "68af373aff774e5d9f4d21553036dee3",
       "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\">11:08:43 EST </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.176</span>. Minimum cost depends on task     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:08:43 EST\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m0.176\u001b[0m. Minimum cost depends on task     \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \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\">11:08:44 EST </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:08:44 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \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>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8bb7b4a9e96540b68f50560863f19dc2",
       "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:08:49 EST </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:08:49 EST\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \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\">11:08:50 EST </span>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:08:50 EST\u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e71e03754c3844f1becfe9caa1a18ee4",
       "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\">11:10:05 EST </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:10:05 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a9fd8c1006a64386b2abbae49e1d669e",
       "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\">11:10:17 EST </span>Loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:10:17 EST\u001b[0m\u001b[2;36m \u001b[0mLoading simulation from simulation_data.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "l2_EME_data = web.run(l2_sweep_sim, task_name=\"L2 EME sweep\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3afbd5dd-ea4f-400d-91b0-44fc16c994a9",
   "metadata": {},
   "source": [
    "Plotting the mode conversion efficiency vs $L_2$, we can determine the optimal $L_2$ value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fd8b6b73-c47c-4bfe-b58c-2e59e9487ac6",
   "metadata": {},
   "outputs": [],
   "source": [
    "l2_TM0_TE1 = l2_EME_data.smatrix.S21.sel(f=freq0, mode_index_in=0, mode_index_out=0).abs.data ** 2\n",
    "\n",
    "l2_TM0_TM0 = l2_EME_data.smatrix.S21.sel(f=freq0, mode_index_in=0, mode_index_out=1).abs.data ** 2\n",
    "\n",
    "arg_min = np.argmin(l2_TM0_TM0[:25])\n",
    "l2 = l2_scales[arg_min] * l2_sweep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "23685239-8578-44b5-9d4b-d503e31663b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SkpJo3bo1586dY9u2bc9siRXiCbllJnK1AgUKMHfuXMaMGZPhD90nWrZsiV6vNxlWDjB9+nQ0Gg0tWrQAMP759ddfmxw3Y8YMk+darZZ27dqxbt26DD/sMhq2/E9t27bFysqKzz//3GQYO/z9G32TJk2wtbXl66+/Nvkt/7vvviM2NpZWrVo98xpP4+rqSvPmzVm9ejU//vgjtra2tG3b1uSYDh06EB4ezubNm9O9/uHDh6Smpv7nde7du2fy3NbWlkqVKqEoCikpKRm+Jqve87+98cYbaLVaxo4dm64FRVGUdNlfRLt27YiMjOTnn39Ot+9prTeenp40bNiQb7/9llu3bqXb/1//vjLSrFkzChYsyMSJE9MNnf93jhYtWuDu7s7kyZPZtWvXc7UOPdGuXTu0Wi0rV65kzZo1vPrqqzg5ORn3x8XFpfv3ExAQgJWVFcnJyWa/r3/T6/V07NiR8PBw1qxZQ0hIyAufU+QP0kIkcr3n6SjZunVrGjVqxCeffEJUVBSBgYFs2bKFX375hcGDBxt/ew4KCuKtt95izpw5xMbGUqdOHbZv386FCxfSnXPSpEns2LGDWrVq0bdvXypVqsT9+/c5evQo27Zt4/79+0/N4+fnxyeffMK4ceOoX78+b7zxBnZ2dhw6dIiiRYsyceJEPDw8CA0NZezYsbzyyiu0adOGs2fPMmfOHGrUqGHWh9S/dezYka5duzJnzhyaN2+e7lbjhx9+yIYNG3j11Vfp2bMnwcHBxMfHc+LECdauXUtUVJTJLbaMNGvWDG9vb+rWrYuXlxenT59m1qxZtGrVKl1friey8j3/k6+vL+PHjyc0NJSoqCjatm1LwYIFuXz5Mj///DPvvPMOw4cPt8i1PvzwQ9auXUv79u3p3bs3wcHB3L9/nw0bNjBv3jwCAwMzfN3s2bOpV68eAQEB9O3blzJlyhATE0N4eDjXr18nMjLSrBzOzs5Mnz6dPn36UKNGDTp37oybmxuRkZEkJCSwdOlS47E2NjZ06tSJWbNmodVqeeutt577Op6enjRq1IivvvqKR48emXTWh7QZvgcMGED79u0pV64cqampLF++3PhLxn9JSUnJsBWpUKFC9OvXj2HDhrFhwwZat27N/fv3003EaKl/QyIPyu5hbUK8iH8Ou3+Wfw+7V5S0+WCGDBmiFC1aVLGxsVHKli2rTJ061WTIsaIoSmJiojJo0CClcOHCipOTk9K6dWvl2rVr6YbdK4qixMTEKP3791d8fHwUGxsbxdvbW3n55ZeV+fPnP9f7WbRokVK1alXFzs5OcXNzUxo0aKBs3brV5JhZs2YpFSpUUGxsbBQvLy/l/fffN86T80SDBg0yHOLeo0cPpWTJkum2x8XFKQ4ODgqgfP/99xlme/TokRIaGqr4+fkptra2iru7u1KnTh1l2rRpxvmAngwXnzp1arrXf/vtt8pLL72kFC5cWLGzs1N8fX2VDz/8UImNjTUek9E8RFn1njOybt06pV69eoqTk5Pi5OSkVKhQQenfv79y9uxZ4zEvOuxeURTl3r17yoABA5RixYoptra2SvHixZUePXoYp2zIaNi9oijKxYsXle7duyve3t6KjY2NUqxYMeXVV19V1q5dazzmaf8nduzYkeFQ+Q0bNih16tRRHBwcFGdnZ6VmzZrKypUr072PJ8PlMzN3z4IFCxRAKViwYLoh/pcuXVJ69+6t+Pr6Kvb29kqhQoWURo0aKdu2bfvP8/bo0UMBMnz4+voqivL3dAxPewjxNBpFMbPHnRBCiDwvMjKSoKAgli1bRrdu3dSOI0SWkz5EQggh0lmwYAEFChQwmY5BiLxM+hAJIYQw+vXXXzl16hTz589nwIABJh2ihcjL5JaZEEIIo1KlShETE0Pz5s1Zvnz5UzvAC5HXqHrLbPfu3bRu3ZqiRYui0WhYv379f75m586dVKtWzbj69r9XhxZCCJF5UVFRJCYmsn79eimGRL6iakEUHx9PYGAgs2fPfq7jL1++TKtWrWjUqBEREREMHjyYPn36ZDhXihBCCCHE88oxt8w0Gg0///xzugni/umjjz5i48aNJhPhderUiYcPH7Jp06ZsSCmEEEKIvChXdaoODw9PN4198+bN061E/k/Jyckms58aDAbu379P4cKF/3OhQiGEEELkDIqi8OjRI4oWLYqVleVvcOWqgig6OhovLy+TbV5eXsTFxZGYmJjhApMTJ0602EKSQgghhFDXtWvXLLYI8D/lqoIoM0JDQxk6dKjxeWxsLCVKlODatWs4OzurmEyIjOl0Or788ksAhg0bhq2trcqJhBBCfXFxcfj4+GRZZ/9cVRB5e3sTExNjsi0mJgZnZ+cMW4cA7OzssLOzS7fd2dlZCiKRI+l0OuNK8M7OzlIQCSHEP2RVd5dcNVN1SEgI27dvN9m2detWWc1YCCGEEC9E1YLo8ePHREREEBERAaQNq4+IiODq1atA2u2u7t27G49/7733uHTpEiNGjODMmTPMmTOH1atXM2TIEDXiCyGEECKPUPWW2eHDh2nUqJHx+ZO+Pj169GDJkiXcunXLWBwBlC5dmo0bNzJkyBBmzpxJ8eLFWbhwIc2bN8/27EJkFSsrKwIDA41fCyGEyHo5Zh6i7BIXF4eLiwuxsbHSh0gIIYTIJbL681t+/RRCCCFEvperRpkJkR/E340n+q9oHAo54O7rjq2jjDITQoisJgWRECpKTU4lOiKaGwdupD0O3uD+lfvwyf8P+AIKuBfArbQbrqVdcS3tiltpN9zKpD13Lu6MlVYaeoUQ4kVJQSRENjHoDdy/cJ+bh25y/cB1bhy4QXRENIYUg+mBNqZPH996zONbj7kWdi3dOa1srPAJ8aFMszL4NfejSLUiaKxkSRohspuiKCTHJZN4P5GkB0kk3k9MezxINH79ZLshxYBtAVtsCthgW8D2qY8C3gVwr+COtZ18VGcH6VQthIUpisKjG4+4ffK2yePOqTukJqY+87VaOy1eNby42eQmAEW3FSXuQhyPox8/17Ud3R0p07QMvs188W3mS8GiWTOjqxD5WUpiCnf+ukN0ZDQxkTHEHE97JD1Isvi1NFoNhcsVxrOy59+PAE/cyrjlu9bhrP78loJIiBeUHJfMyVUnuXX0FndO3uH2ydskPXy+H4zuFdwpVrMYxWqlPbwCvNCjZ+LEiUDaXFy2trakJKTwMOohDy4/4OHl//956SExJ2J4cPHBU8/vGeCJbzNf/F7xo1TDUlhZ568foEK8qIR7CVzff/3vwicyhnvn7qEY1P3otLa3xqOSB56VPSlaoyi+zXwpVLZQnl60PKs/v6UdTohMSopN4uA3Bwn/Kvy/fzPUQCG/Qnj6e1IkuEhaAVSjGPau9ukO1ev06bbZONrgUckDj0oe6fY9uPSAC5svcGnLJS5tv4Tukc647/aJ29w+cZvwL8Mp4F2AwB6BBPUKwr28u/lvWIh8wKA3cPPQTS5susCFPy5w49ANeI7ap2CxgrhXcMfR3RF7N3scCjmkPdwcjF8/2a610aKL16F7/I/HI9PnyY+SiY2KNbYu//vnQmpSKreO3uLW0VtELosEwLWUq/H2eemXS2Pvkv7ni3g6aSESwkxJD5PYP3M/B2YcyLAlyNnH2bR5u7In7hXdsXGwyeBs6el0unQtRM9Ln6Ln+v7rXNxykYubL3Lz8M0Mf5j71PWh6ttV8W/vj20BGcUm8rfH0Y+5sDmtALq45eIzf8HR2mnx9PfEq4oXXoFeaX9W8cLR3THL8hlS0/of/vs2/P3z95/aUqXRaiheu3ja7fPmvhStXjTX32KTW2YWJgWRyKzEB4nsn7GfAzMPkBybbNyusdJQpWsVqvapileAV4atPuZ4kYLo3xLuJXBp2yVOrT7F2Q1nMaSaduC2LWCLf0d/qvauSvGQ4nm6uV2IJxRF4ebhm5z+6TQXN10kOiL6qcd6BnhSpmkZilYvilcVL9zLu+eYW8+pSanc/us2l/+8zMXNF7m652qGLcwA9m72lG9Tnmp9quFT1ydX/l+XgsjCpCAS5kq8n0j49HAOfn2Q5Lh/FEJaDYHdAqn/SX0K+RWy2PVSU1P5+eefAXj99dextrbMne342/Ec//44x747xp1Td9Ltd6/gTlDvIKq9XQ2HQg4WuaYQOUnc9TiOf3+cyGWR3D19N8Nj7Fzs8G3qi+8rvvg198O5eO75nNDF67iy+woXN6e1EN89k/F7dK/gTtU+VQnsHoiTh1M2p8w8KYgsTAoi8bxSElLYM2EPB74+YNIvx8raiirdq1D/4/oU8rVcIZRdFEXhxsEbHFt0jJMrT5q8NwAbJxuq9alG7SG1cS3pqk5IISxEF6/j9E+nOb7sOJe2X8rwFnKR4CL4veKHXws/itcqnmNagF5U7NVY4+3zi1svmrRsQ9q0HRXaVqBa32qUeblMjp+yQwoiC5OCSDyPu2fvsubNNdw+edu4zcraiqBeQdQLrYdbaTcV01mOLl7H6XWnObboGFd2XTHZp9FqqNypMnU+rIN3oLdKCYUwn2JQiNoVReTSSE6tPUVKfEq6Y0rUL0Fg90DKtymPk2fuaSXJrJTEFE7/dJqjC46m+78O4FralapvVyWoZxDOxXLmZ6MURBYmBZH4Lyd+OMGv7/xq/CFqZWNF1d5VqRdaL0+3mNw7f48DMw9wbNGxdPMl+Tb3pe6IupRqVCpX9j0Q+UP87XgOf3uYYwuPEXs1Nt1+tzJuVOlehcBugbiVyRu/1GTGvXP3OLrwKBFLIki4k2CyT2OloeIbFan3cT2KVC2iUsKMSUFkYVIQiadJTUpl0+BNHPn2iHGbRyUP2q9pn+Fw96xiyU7VmRF/J55Dsw9xcNZBEu8lmuwrUq0IdUbUoVK7SnnmtoLI/W4du8XBrw9y4ocT6ToV2znb4d/Rn8Dugbm2M3FW0ev0nP31LEcXHOXilovpbieWbVmW+p/Ux6eOjzoB/0UKIguTgkhk5P6F+6xpv8ZktElg90BazmmJrVP2FiRqF0RPpCSkcGzxMcK/DOfh5Ycm+1xLu1L/k/oEdg9Ea6NVJZ/I3wypBs78coYDMw9wdc9Vk30aKw2+zX0J7JF2S+x5p7zIzx5GPeTY4mMcnX803cz4pRqWov4n9Sn9cmlVC0opiCxMCiLxb6fWnuKX3r8YOxdb21vTcnZLgnoFqfKfP6cURE8YUg2cWneKsClh3Dp6y2SfWxk3XvrsJap0rSItRiJbJD5I5OjCoxyadSjdbTF7V3uq9qlKzf41cS3lqk7AXC41KZVji46xb/K+dN/fYjWLUf/T+pR7tZwqPxulILIwKYjEE6nJqWz9cCsHvzlo3Fa4XGHar2mPVxUv1XLltILoCUVRuPznZfZN3selrZdM9hXyK8RLo14ioHNArp/8TeRM987fI/yrcI4vO05KgmknafcK7tQcVJPA7oHZ3qKbV+lT9JxYcYK9E/dy79w9k31eVbyo93E9Kr1ZKVv/v0tBZGFSEAmAB5cfsLbjWm4eumncVvmtyrz67avYFbRTMVnOLYj+6VrYNXaO3smlbaaFUeHyhWkwqgH+Hf2lMBIWER0Zzd6Jezm15lS6WZnLtixLzUE18W3qm+OHjOdWBr2BU2tPsXfCXmKOx5js8wzwpPn05pR5uUy2ZJGCyMKkIBLX919nRYsVxmU3tHZaXpn5CsHvBOeIDpe5oSB64sqeK+wcvZOoHVEm290rutNgdAP82/vLB5XIlGvh19g7YS/nfjtnst3GyYagXkHUGliLwuUKq5Qu/1EUhXO/nWPPF3u4ceCGyb4KbSvQdGpTi05QmxEpiCxMCqL87cbBGyxvutw447Sbrxvt17TPUcNLc1NB9ETUzih2jt7Jld2m85t4Vvak4diGVHi9Qo4oNkXOpigKl7dfZs+EPemKbEcPR0KGhlD9veovvDyOyLwnt863j9yetlbi/2lttdQaXIuXPnkJO+esaWWXgsjCpCDKv24eucmyl5cZZ2st/XJpOqzrkONWhE5NTWX16tUAdOjQwWJLd2Q1RVGI2hHFjlE7uLbvmsm+IsFFaDy+Mb7NfaUwEukoBoWzv55l74S93Dho2vrg7ONMnQ/rUO3tatg4ymixnEIxKEQuj2R76HYe3/p7VJqTlxONv2hMUM8gi982l4LIwqQgyp9uHbvFspeXGVexLtWwFJ03dpYfsFlAURQubbvEzlE7ub7/usm+EvVK0PiLxpR8qaRK6UROYtAb+Gv1X+ydsNdkVniAQmULUW9kPap0rYLWVqZ2yKl0j3XsmbiH8C/D0Sf/PQeUd1VvXpn5CiXrW+7/uhREFiYFUf4THRnNssbLSLyfNslgifol6PJHFxmNksUUReH8xvP8+emfxESadsYs07QMjb9oTLEaxVRKJ9SkT9Fz/Pvj7J24l/vn75vs8wr0ov7H9anYrqJ0zM9FHlx+wLYR2zi19pTJ9krtK9Hsy2a4+Li88DWkILIwKYjyl9snb7O00VIS7qZNT+9T14cuf3RRfSRZfqIYFE6tO8XOUTvTrb5d/rXyNBrXCK8A9aY5ENknNSmVY4v/P8fNFdM5boqHFKf+J/Up27Ks3FbNxaJ2RbHpg00mvwTZu9nz2qLXqNC2wgudWwoiC5OCKP+4c+oOSxouMa7VU7x2cbpu7pplHf4sRafTMW3aNACGDx+eKzpVPw9DqoHjK46za8wuHkY9/HuHBip3rMxLo17Co2L2LZEiso8uXseR+UcImxpm0t8EoHTj0tT/tD6lGso6eXmFQW/g2KJj/PnJnyZrpdUcWJOmU5tibZe5fpFSEFmYFET5w90zd1nScAnxMfEAFK1RlG5bu+W4DtQZyY2jzMyh1+k5+t1R9ozfw6Obj/7eoYGAtwJ46bOXcK/grl5AYTHJcckcnH2Q/dP3p1tENKetkyUsL+lhEr/2/dXkNpp3VW/eXPUmhcuaP2WCFEQWJgVR3nfv/D2WNFhi/E20SHARum/rnmuG6ub1guiJlMQUDs89zN6Je423NCFtHarKb1VOK4zKS2GUG8XfjufA1wc4NPuQcb6vJyq+UZH6n9SnSLWcM9WFyDqKonB43mE2D9ls7HRtW8CWVvNaUaVLFbPOJQWRhUlBlLfdv3ifJQ2W8OhGWsuDd5A33bd3x6GQg8rJnl9+KYie0D3WcWjOIcKmhklhlMs9uPSAsGlhRCyOIDUp1bhdY6WhcqfK1Auth2dlTxUTCrVER0aztuNa7p39exmQoF5BtPimxXMPcJGCyMKkIMq7Ht18xMLaC4m7FgekrbfT/c/uOBZ2VDmZefJbQfSE7rGOg7MPEjY1jMR7icbtGisNAZ3TbqXJzMQ5061jt9g3eV+65TWsbKyo0rUK9ULrZeoWichbdI91/D7gdyKXRhq3uVd0581Vbz7XwAopiCxMCqK8Sa/Ts7TRUq6FpU0I6FnZk+5/dsfJw0nlZObLrwXRE88qjCq/VZm6I+qquviuSPOsxX5tC9gS/G4wtQfXxrm4/JwVpiKXR7Lx/Y2kxKct0mttb03zGc3/c/kkKYgsTAqivGljv40cnnsYAJcSLvQ52IcCXgVUTpU5+b0geiL5UTKHZh8ibJppYQTg29yXuiPqUqqRjEzKbga9gdM/nWbf5H3cOnLLZJ+jhyO1PqhFjX41cHDLPbepRfa7e/YuazuuNRmeX75NeVrNa0XBIgUzfI0URBYmBVHec2zxMTb03gCkLdTae19vigYXVTlV5qWkpLBixQoAunTpgo1N/p5N+1mFUZHgItT5sA6V2lXCylom8ctK+hQ9J344wd6Je036gQC4lXEjZHgIQT2DsHHI3/9exfNLTUply/AtHJp9yLjN3tWe5jOaE9g9MN0vO1IQWZgURHnLzcM3WVRvkXH0wmuLXyOoZ5C6oUSW0MXriFgcQfiX4abzGAGupV0JGRpCUK8gmYHcwp5Mphg2JSzd9927qjd1P6orBal4IWfWn+G3d38j/na8cVvZlmV59dtXTW65SkFkYVIQ5R3xd+KZHzzf2Im6er/qtJrdSuVUIqsZUg2cWneKsClh3DpqesvGobADNfrXoGb/mjh55r7+YzmJLl7HkW+PEDYt/WSKJRuUpP7H9SnTtIzcshQWkXAvgU0fbOLEihPGbXbOdjT7shlV366KRqORgsjSpCDKGwypBr5v/j2X/7wMgE8dH3rs6CGLQOYjTzr1hk0N4+Lmiyb7rGysqNC2AtX6VqPMy2XQWMmH9vNKephknEzx37co/V7xo/4n9SlRr4RK6URed3bDWX577zeTIrxM0zK0XtAaKzcrKYgsSQqivGHL8C2EfxkOQAHvArxz5B0KFs24I15uo9PpmDlzJgAffPBBvu1UbY7oyGjCp4VzYuUJFL3pjzSXki5UfbsqVXtVlRFPz3D37F0ilkRweM5hkuOSTfZVfKMi9T6ul6v75oncI/FBIpuHbDYZnm9bwJbaY2vTeFhjKYgsRQqi3O/kjydZ99Y6AKysreixswcl6uad31hllFnmxV6N5eCsg0QsiUi3VITGSoPfK35U7VOVcq+WQ2sjrYmPYx5z8seTnPj+BDcP3zTZ92Sag3qh9fD0l8kURfY7/8d5fnvnN+Kup3WLSCKJSUzKss/vzK2wJoRKYo7HsOHtDcbnr8x8JU8VQ+LFuJRwoemUpjQe35hzv53j6MKjXNh0ARRQDArnfz/P+d/P4+TlRGCPQKp0rYJnZc981Q9G91jHmfVnOP79cS5tvWQykSKk3W4M7BFIvY/qUcivkEophYCyLcry/sn32TJ8C8cWHsvy60lBJHKNxAeJrHpjFSkJaZN5BfYIpPr71VVOJXIira2Wim9UpOIbFYm9GsuxxceIWBRB7NVYAOJj4gmbEkbYlDBcS7lSrnU5yrcpT8mXSubJfmiGVAMXt17kxIoTnPn5jPH/0D8VqVaEgK4BVO5U+anzwAiR3exd7GmzoA3+HfxZ03sNXM+6a8ktM5ErKAaFla1Xcv7380DaD+9ee3vlyTlP5JZZ1jDoDVzadoljC49xZv0ZDKmGdMfYOdvh94of5dqUo2yLsrlqDbx/e3TrERc3X+TCpgtc2nqJxPuJ6Y5xKelCQJcAqnSpgkclDxVSCvH87ty4g2dxT7llJvK3nWN2Goshh8IOdPipQ54shkTWsdJa4dfcD7/mfsTfjufkjyc59+s5onZGGYuj5Lhk/lr9F3+t/guNVkOJeiUo17ocJV8qiVeAF9b2OfdHpl6n51rYNS5susCFTRdMZgD+J3s3e/w7+FOlaxV86vjICDyRa9gVtMvS8+fc/91C/N/lHZfZPW43kNbR880f38S1pKu6oUSu5uTpRK1Btag1qBZJsUlc3HyRc7+e49zGcyQ9SAJA0Stc2XWFK7uuAGkd+D38PShSrUjaI7gIXlW8VJsIUhev4/6F+1wLu8bFTRe5/OdldI91GR5r52KHX3M/KneujN8rfljbyY9+If5N/leIHE33WGfSibrxhMaUaVJGxURZT6PRULRoUePXImvZu6S1mPh38MeQauBa2DXObjjL2Q1nuX/+vvE4Q6qBmMgYYiJjiFgcAaQV6O4V3ClSrQjeVb1xKeFCAe8CxodtgRcrlpLjkrl/4X6Gj39PlvhvRasXxfcVX/xe8aN4reIyk7QQ/0H6EIkc7fcBvxvXuSlRvwQ9d/aUJn6Rbe6evcvFLRe5deQWt47e4s6pO+nmOXoWG0cbkwLJydsJh0IO6JP1pCSkkJKYQmpCKimJKaQkpJCamGrcnnA3Id3UAc/i5OmEb3PftEdTX5mpW+Q5Wf35LS1EIse6vOOysRiydrDmtUWvSTEkspV7eXfcy7sbn6ckphBzPIZbR9MKpFtHbnH75G0MKek7aAOkJKTw4NIDHlx6YNFcTl5OFPIrRCG/QrhXdMe3qS/eQd7y/0OIFyAFkciR/n2rrMmkJjInilCdjYMNxWsVp3it4sZtqcmp3PnrDjEnYngc/ZjH0Y+Jj4k3fv04+rGxX9Lz0Gg12DjaYOdsRyHfQrj5uRmLnyePrO5cKkR+JAWRyJG2jdzGw8sPgbRbZTUH1FQ3UDZKSUlh9uzZAPTv3x8bGxlNl5NZ21kbO1o/TWpyKvG3443FkbW9NTaONlg7pP1p42BjfC4zaAuhDimIRI6T32+VKYpCbGys8WuR+1nbWePi44KLj4vaUYQQTyHDDkSOIrfKhBBCqEEKIpGj5OdbZUIIIdQjBZHIMfL7rTIhhBDqkYJI5Ahyq0wIIYSapCASOcI/b5WVfKmk3CoTQgiRrWSUmVDdv2+VtVnUJl/fKtNoNHh4eBi/FkIIkfWkIBKqyvBWmW/+vlVmY2NDv3791I4hhBD5itwyE6qSW2VCCCFyAimIhGpuHLwht8qEEELkCHLLTKhCMSj8MfAP4/PGXzTO97fKnkhJSWHBggUA9O3bV5buEEKIbCAFkVBF5LJIbhy8AYCHv4fcKvsHRVG4c+eO8WshhBBZT26ZiWyXHJfMtpHbjM9fmfmKLGgphBBCVVIQiWy3a9wu4mPiAaj4RkXKvFxG5URCCCHyOymIRLa6e/YuB2YcAMDa3ppmXzZTOZEQQgghBZHIRoqisHnwZgypBgDqfFgH11Ku6oYSQgghyAEF0ezZsylVqhT29vbUqlWLgwcPPvP4GTNmUL58eRwcHPDx8WHIkCEkJSVlU1rxIs5vPM+FTRcAcPZxpt7IeionEkIIIdKoWhCtWrWKoUOHMnr0aI4ePUpgYCDNmzfn9u3bGR7/ww8/MHLkSEaPHs3p06f57rvvWLVqFR9//HE2JxfmSk1OZdPgTcbnzaY1w8ZRhpNnRKPR4OLigouLiyzdIYQQ2USjqDiut1atWtSoUYNZs2YBYDAY8PHxYeDAgYwcOTLd8QMGDOD06dNs377duG3YsGEcOHCAvXv3Ptc14+LicHFxITY2FmdnZ8u8EfGf9k7ay/bQtL+3kg1K0mNHD/mwF0II8dyy+vNbtRYinU7HkSNHaNKkyd9hrKxo0qQJ4eHhGb6mTp06HDlyxHhb7dKlS/z++++0bNnyqddJTk4mLi7O5CGy16Obj9g9fjcAGisNr8x8RYohIYQQOYpqEzPevXsXvV6Pl5eXyXYvLy/OnDmT4Ws6d+7M3bt3qVevHoqikJqaynvvvffMW2YTJ05k7NixFs0uzLPto22kxKcAEPxeMN6B3ionEkIIIUyp3qnaHDt37mTChAnMmTOHo0eP8tNPP7Fx40bGjRv31NeEhoYSGxtrfFy7di0bE4trYdc4/v1xAOzd7Gn0eSOVE+V8T5buWLBgASkpKWrHEUKIfEG1FiJ3d3e0Wi0xMTEm22NiYvD2zrgF4bPPPqNbt2706dMHgICAAOLj43nnnXf45JNPsLJKX9/Z2dlhZ2dn+Tcg/pNBb+CPQf9Yr2x8YxwLO6qYKHdQFIWbN28avxZCCJH1VGshsrW1JTg42KSDtMFgYPv27YSEhGT4moSEhHRFj1abtuSDfHDkPBGLI7h15BYAXlW8CH4nWOVEQgghRMZUXdx16NCh9OjRg+rVq1OzZk1mzJhBfHw8vXr1AqB79+4UK1aMiRMnAtC6dWu++uorqlatSq1atbhw4QKfffYZrVu3NhZGImdIephkHFUG8MrXr2Blnavu0AohhMhHVC2IOnbsyJ07dxg1ahTR0dEEBQWxadMmY0frq1evmrQIffrpp2g0Gj799FNu3LiBh4cHrVu35osvvlDrLYin2DlmJwl3EwDw7+BPqQal1A0khBBCPIOq8xCpQeYhynr3L9xndsXZGFINWDtYM+DMAFxKuKgdK9fQ6XTGVtHQ0FBsbW1VTiSEEOrLs/MQibxrx6gdJuuVSTEkhBAip1P1lpnIe6Ijojm58iQAju6O1BlWR+VEuZOjo4zGE0KI7CQFkbCoPz/50/h1vY/rYecsUx6Yy9bWlg8//FDtGEIIka/ILTNhMVf2XOH87+eBtNXsa7xfQ+VEQgghxPORgkhYhKIoJsPsG45tiLW9NEAKIYTIHeQTS1jE+Y3nubYvbVkU9wruBHYLVDlR7pWSksKKFSsA6NKlCzY2NionEkKIvE8KIvHCFIPC9o//bh1q/EVjmYTxBSiKwpUrV4xfCyGEyHryqSVe2ImVJ7h94jYARWsUpcLrFVROJIQQQphHCiLxQvQ6PTs+22F8/vLEl9FoNComEkIIIcwnBZF4IUcXHuXh5YcAlGlShjIvl1E3kBBCCJEJUhCJTNPF69j1+S7j88YTGquYRgghhMg8swuiHj16sHv37qzIInKZA18fID4mHoCK7SpSrEYxlRMJIYQQmWN2QRQbG0uTJk0oW7YsEyZM4MaNG1mRS+RwifcT2Td5HwAaKw2Nx0vrkCXZ2NjIcHshhMhGZhdE69ev58aNG7z//vusWrWKUqVK0aJFC9auXUtKSkpWZBQ50N7Je0mOTQYgsGcg7hXcVU6Ud9ja2vLxxx/z8ccfy0r3QgiRTTLVh8jDw4OhQ4cSGRnJgQMH8PPzo1u3bhQtWpQhQ4Zw/vx5S+cUOUjcjTgOfn0QAK2dloajG6obSAghhHhBL9Sp+tatW2zdupWtW7ei1Wpp2bIlJ06coFKlSkyfPt1SGUUOs3vcblKTUgGo0b8GLiVcVE4khBBCvBizC6KUlBTWrVvHq6++SsmSJVmzZg2DBw/m5s2bLF26lG3btrF69Wo+//zzrMgrVHbv/D2OLjwKgG1BW+qH1lc5Ud6TmprKDz/8wA8//EBqaqracYQQIl8we+mOIkWKYDAYeOuttzh48CBBQUHpjmnUqBGurq4WiCdymp2jdqLo05aTqDO8Do7ujionynsMBoPxtrPBYFA5jRBC5A9mF0TTp0+nffv22NvbP/UYV1dXLl++/ELBRM5z59QdTq46CYCjhyO1h9RWOZEQQghhGWbfMmvTpg0JCQnptt+/f5+4uDiLhBI50+7xu+H/a43WHVEXu4J26gYSQgghLMTsgqhTp078+OOP6bavXr2aTp06WSSUyHnunrnLyR//3zrk7kj196urnEgIIYSwHLMLogMHDtCoUaN02xs2bMiBAwcsEkrkPHsm7DG2DoUMD8HWSebHEUIIkXeYXRAlJydnOPIlJSWFxMREi4QSOcv9C/c5seIEAA6FHKjRr4bKiYQQQgjLMrsgqlmzJvPnz0+3fd68eQQHB1sklMhZ9nyxB8WQ1jwUMixE+g4JIYTIc8weZTZ+/HiaNGlCZGQkL7/8MgDbt2/n0KFDbNmyxeIBhboeXHpA5PJIAOxd7ak5oKbKifI+W1tbRo8erXYMIYTIV8xuIapbty7h4eH4+PiwevVqfv31V/z8/Dh+/Dj168skfXnNnol7jPMO1R5SGztnaR0SQgiR95jdQgQQFBTEihUrLJ1F5DAPox4SuSStdcjOxY5ag2qpnEgIIYTIGpkqiAwGAxcuXOD27dvpZtJ96aWXLBJMqG/vpL0YUtP+fmt9UAt716dPxiksJzU1lZ9//hmA119/HWvrTP03FUIIYQazf9Lu37+fzp07c+XKFRRFMdmn0WjQ6/UWCyfUE3stlmOLjgFpa5bV/kBmpc4uBoOBU6dOAfDaa6+pnEYIIfIHswui9957j+rVq7Nx40aKFCmCRqPJilxCZfsm78OQ8v/WoUG1cCjkoHIiIYQQIuuYXRCdP3+etWvX4ufnlxV5RA4QdyOOowvSVrS3cbKRNcuEEELkeWaPMqtVqxYXLlzIiiwih9g3ZR96Xdqtz5oDauJYWFa0F0IIkbeZ3UI0cOBAhg0bRnR0NAEBAdjY2Jjsr1KlisXCiez36NYjjs7/f+uQow0hw0JUTiSEEEJkPbMLonbt2gHQu3dv4zaNRoOiKNKpOg8ImxpGalLa0izV+1XHycNJ5URCCCFE1jO7ILp8+XJW5BA5wOOYxxyedxgAa3tr6gyvo3IiIYQQInuYXRCVLFkyK3KIHCD8y3BSE9Nah4LfC6aAVwGVE+VPNjY2hIaGGr8WQgiR9czuVA2wfPly6tatS9GiRbly5QoAM2bM4JdffrFoOJF94u/Ec2j2IQC0dlrqjqircqL8S6PRYGtri62trUxrIYQQ2cTsgmju3LkMHTqUli1b8vDhQ2OfIVdXV2bMmGHpfCKbhH8VTkpCCgDB7wRTsEhBlRMJIYQQ2cfsguibb75hwYIFfPLJJ2i1WuP26tWrc+LECYuGy0ojto4gOTVZ7Rg5QtLDpL9bh2yldUhtqamprF+/nvXr15Oamqp2HCGEyBfMLoguX75M1apV0223s7MjPj7eIqGyw7eHv+XzXZ+rHSNHODTnELpHOgACewbiXNxZ5UT5m8FgIDIyksjIyHRrBQohhMgaZhdEpUuXJiIiIt32TZs2UbFiRUtkyjaT9k3i0I1DasdQVUpiCvtn7AdAY6WR1iEhhBD5ktmjzIYOHUr//v1JSkpCURQOHjzIypUrmThxIgsXLsyKjFnGoBjo+UtPjrxzBHvr/LmS+7FFx0i4kwCAfwd/CvkWUjmREEIIkf3MLoj69OmDg4MDn376KQkJCXTu3JmiRYsyc+ZMOnXqlBUZs0SgdyCRDyM5decUY3aOYVKTSWpHynb6FD1hU8OMz+t+JK1DQggh8qdMDbvv0qUL58+f5/Hjx0RHR3P9+nXefvttS2fLUnNbzcXGKm2Ol6lhUzlw/YDKibLfX6v+IvZKLAB+LfzwDvJWOZEQQgihjkwVRE84Ojri6elpqSzZyt/TnzENxwB/3zpLSk1SN1Q2UgwKeyftNT6vN7KeimmEEEIIdT3XLbNq1aqxfft23NzcqFq16jMnizt69KjFwmW1EXVH8POZnzl88zBn7p5h1I5RTGk6Re1Y2eLcxnPc+esOAD51fChRv4TKiYQQQgj1PFdB9Nprr2FnZwdA27ZtszJPtrK2smbJa0uoNr8aOr2OaWHTeL3C64T45O0V3hVFYe/Ef7QOhdaTGZFzEBsbG4YPH278WgghRNbTKIqiqB0iO8XFxeHi4kJsbCzOzmnz7UzaO4nQ7WlrR5UrXI6IdyNwsHFQM2aWurL7CksaLAHAs7In70W+h8ZKCiIhhBA5V0af35Zkdh+iQ4cOceBA+g7IBw4c4PDhwxYJld2G1xlOzWI1ATh37xyf/vmpyomy1j9bh+qOrCvFkBBCiHzP7IKof//+XLt2Ld32Gzdu0L9/f4uEym5Pbp3ZadNuC07fP519V/epnCpr3Dp2iwubLgDgWsqVyh0rq5xI/FtqaiobN25k48aNsnSHEEJkE7MLolOnTlGtWrV026tWrcqpU6csEkoNFT0qMq7ROAAUFHr+0pOElASVU1nevsl/F3p1PqyDlfULDTQUWcBgMHD48GEOHz4sS3cIIUQ2MfvT0M7OjpiYmHTbb926hbW12fM85ihDQ4ZSu3htAC7cv8An2z9ROZFl3b9wn1Nr0opWJ08ngnoFqRtICCGEyCHMLoiaNWtGaGgosbGxxm0PHz7k448/pmnTphYNl920VlqWvLbEuIzHzAMz2XNlj8qpLGff1H0ohrQ+9LUG18LGQUYwCSGEEJCJgmjatGlcu3aNkiVL0qhRIxo1akTp0qWJjo7myy+/zIqM2aq8e3nGNxoPpN066/VLL+J18SqnenGPbj4ickkkALYFbanxfg2VEwkhhBA5h9kFUbFixTh+/DhTpkyhUqVKBAcHM3PmTE6cOIGPj09WZMx2g2sPpo5PHQAuPrjIF3u+UDnRiwufHo5epwegRr8a2Lvmz8VshRBCiIxkqtOPk5MT77zzjqWz5BhaKy2LX1tMwNwAdHodM/bPYGDNgRQpWETtaJmS+CCRI/OOAKC101J7cG2VEwkhhBA5y3MVRBs2bKBFixbY2NiwYcOGZx7bpk0biwRTW7nC5ehXvR8zDswgMTWRz3d9ztxX56odK1MOzT6E7rEOgKBeQRTwLqByIiGEECJnea6Zqq2srIiOjsbT0xMrq6ffZdNoNOj1eosGtDRzZrq8E38H3699eaR7hFaj5XT/05QtXDabklpGSkIKM0rOIOFuAhorDQPPD8StjJvascQzKIpiHLTg4uIiy6oIIQQ5ZKZqg8FgXNXeYDA89ZHTiyFzeTh5MLxO2ppSekXPqJ2jVE5kvqPfHSXhbtp8Sv4d/aUYygU0Gg2urq64urpKMSSEENnkuQqiQoUKcffuXQB69+7No0ePsjRUTjKk9hA8HD0A+PHkjxy9dVTlRM/PkGog/Mtw4/N6I+upmEYIIYTIuZ6rINLpdMTFxQGwdOlSkpKSsjRUTlLQriCfvvT32mYfb/9YxTTm+WvNX8ReSbv14veKH15VvFROJJ6HXq9ny5YtbNmyJc+1ugohRE71XJ2qQ0JCaNu2LcHBwSiKwqBBg3BwyHg1+EWLFlk0YE7wbvC7TN8/naiHUWy+uJkdl3fQqHQjtWM9k6IohE0JMz6v+1FdFdMIc+j1esLD01r2GjZsiFarVTmREELkfc/VQvT999/TsmVLHj9+DEBsbCwPHjzI8JEX2Vnb8XnDz43PQ7eH8hx90VV1adsloiOiAShaoyglG5RUOZEQQgiRcz1XQeTl5cWkSZNYs2YNJUuWZPny5fz8888ZPsw1e/ZsSpUqhb29PbVq1eLgwYPPPP7hw4f079+fIkWKYGdnR7ly5fj999/Nvq65Ogd0prJn2srwB24cYP2Z9Vl+zRfxz9ahOh/Wkc65QgghxDOY3am6UaNG2NraWuTiq1atYujQoYwePZqjR48SGBhI8+bNuX37dobH63Q6mjZtSlRUFGvXruXs2bMsWLCAYsWKWSTPs2ittExoPMH4/JM/PyHVkJrl182MW8ducWnbJQDcyrhR8Y2KKicSQgghcjZVO1V/9dVX9O3bl169elGpUiXmzZuHo6PjU/shLVq0iPv377N+/Xrq1q1LqVKlaNCgAYGBgRbJ819eLfcqdX3S+uKcvnua5ZHLs+W65gqb+nfrUMjwEKy0Zq/QIoQQQuQrqnWq1ul0HDlyhNDQUOM2KysrmjRpYuxQ+m8bNmwgJCSE/v3788svv+Dh4UHnzp356KOPntrxNDk5meTkZOPzJ4VdZmg0GiY1mUT9xfUBGL1zNG8FvIW9dc5ZF+zB5Qf8tfovABw9HAnqGaRuICGEECIXMLtTtUajsUin6rt376LX6/HyMh0K7uXlRXR0dIavuXTpEmvXrkWv1/P777/z2Wef8eWXXzJ+/PinXmfixIm4uLgYHy+6AG29EvVoVbYVANfirjHn0JwXOp+l7Z++H0Wf1uG75oCa2DjYqJxICCGEyPmea+mOfypdujSHDx+mcOHCL3ThmzdvUqxYMcLCwggJCTFuHzFiBLt27eLAgQPpXlOuXDmSkpK4fPmysUXoq6++YurUqdy6dSvD62TUQuTj4/NCU38fjzlO0LwgFBQKOxTm4qCLuNi7ZOpclpRwL4EZJWaQkpCCjaMNg68OxrGwo9qxhJkUReHOnTsAeHh4SId4IYQghyzd8U+XL182FkMv0pfI3d0drVZLTEyMyfaYmBi8vb0zfE2RIkUoV66cye2xihUrEh0djU6ny/A1dnZ2ODs7mzxeVBWvKnSp0gWAe4n3+DL8yxc+pyUcmnOIlIQUAKq+XVWKoVxKo9Hg6emJp6enFENCCJFNzC6IDAYD48aNo1ixYhQoUIBLl9JGM3322Wd89913z30eW1tbgoOD2b59u8m5t2/fbtJi9E9169blwoULGAwG47Zz585RpEgRi418e15jG47FxirtdtRX4V8R8zjmP16RtVISUzj4ddqUBRqthpChGX8PhRBCCJGe2QXR+PHjWbJkCVOmTDEpQipXrszChQvNOtfQoUNZsGABS5cu5fTp07z//vvEx8fTq1cvALp3727S6fr999/n/v37fPDBB5w7d46NGzcyYcIE+vfvb+7beGFl3MrwbvC7AMSnxDN+99P7MWWHiCURfy/i2sEf11KuquYRmafX69m5cyc7d+6UpTuEECKbmF0QLVu2jPnz59OlSxeTW1eBgYGcOXPGrHN17NiRadOmMWrUKIKCgoiIiGDTpk3GjtZXr1416Rvk4+PD5s2bOXToEFWqVGHQoEF88MEHjBw50ty3YRGfvvQpTjZOAHx75FsuPbikSg6D3nQR1zof1lElh7AMvV7Prl272LVrlxREQgiRTZ5r2P0/3bhxAz8/v3TbDQYDKSkpZgcYMGAAAwYMyHDfzp07020LCQlh//79Zl8nK3gV8GJoyFDG7R5HiiGFL3Z/wXevPf9tQ0s58/MZHlxMG+FXpkkZilQtku0ZhBBCiNzM7BaiSpUqsWfPnnTb165dS9WqVS0SKjcZFjIMV3tXAJYdX8blB5ez9fqKorBvyj7j8zojpHVICCGEMJfZLUSjRo2iR48e3LhxA4PBwE8//cTZs2dZtmwZv/32W1ZkzNFc7F34oNYHjN01llRDKpP2TuLb1t9m2/Wv7LrCzUM3AfAO8qZMkzLZdm0hhBAirzC7hei1117j119/Zdu2bTg5OTFq1ChOnz7Nr7/+StOmTbMiY473Qa0PKGhbEIDFEYu5Gns12679z2U6ZBFXIYQQInMytchV/fr12bp1K7dv3yYhIYG9e/fSrFkzS2fLNdwc3BhUaxAAKYYUJu+dnC3XjTkRw/nfzwPgUtKFSu0rZct1hRBCiLxGVv20kCG1hxhHnC08tpAbcTey/Jrh0/4eWRYyNAStTcbruQkhhBDi2aQgspDCjoUZUDNttJxOr2Nq2NQsvV7stVhO/HACAIdCDlR9O/91aM+rrK2t6dOnD3369MHa2uxufkIIITJBftpa0NCQoXxz8BsSUhL49si3jKw3Eu8CGS9D8qIOzDyAITVtxu4a/Wtg65S9M3WLrGNlZUWxYsXUjiGEqvR6faamchG5m62tLVZW6rTVSEFkQZ5Onrxf/X2+DP+SpNQkpoVNY1qzaRa/TtLDJI58ewQArZ2WmgNqWvwaQgihBkVRiI6O5uHDh2pHESqwsrKidOnS2b4cF0hBZHHD6wxn9qHZJKUmMffwXEbUHYGnk6dFr3F43mF0j9MWsw3qFYSTp5NFzy/UpdfrjZOP1q5d22RGeCHyuifFkKenJ46OjjJyNh8xGAzcvHmTW7duUaJEiWz/uze7INLr9SxZsoTt27dz+/Ztk4VWAf7880+LhcuNvAt48061d/j64NckpCTwVfhXTGoyyWLnT01O5cDMA2lPNFBnmEzEmNfo9Xq2bdsGQI0aNaQgEvmGXq83FkOFCxdWO45QgYeHBzdv3iQ1NRUbG5tsvbbZN+o++OADPvjgA/R6PZUrVyYwMNDkIWBE3RHYatOa+2YdnMXdhLsWO/fx74/zOPoxABXfqEghv0IWO7cQQqjpSZ8hR0dHlZMItTy5VabGOo5mtxD9+OOPrF69mpYtW2ZFnjyhmHMx+lTtw5zDc4hPiWfG/hmMbzz+hc+rGJR0EzEKIUReI7fJ8i81/+7NbiGytbXNcHFXYeqjeh9hY5XW3Pf1ga95kPjghc959tez3Dt7D4CSDUpSvFbxFz6nEEIIITJREA0bNoyZM2eiKEpW5MkzSriUoFdQLwAe6R4x88DMFz5n2JS/W4fqjqj7wucTQgghRBqzC6K9e/eyYsUKfH19ad26NW+88YbJQ/wttH4o1lZpdyVn7J9BbFJsps91dd9VroVdA8DD3wO/FtJKJ4QQOYFGo3nmY8yYMURFRaHRaNBqtdy4YbqSwa1bt7C2tkaj0RAVFWXcfvXqVVq1aoWjoyOenp58+OGHpKamZsl7WLJkyX++j6ioKMaMGZPhvgoVKhjP9dNPP9GsWTMKFy6MRqMhIiIiSzJbmtkFkaurK6+//joNGjTA3d0dFxcXk4f4WynXUnSv0h2A2ORYvjn4TabPJYu4CiFEznTr1i3jY8aMGTg7O5tsGz58uPHYYsWKsWzZMpPXL126NN1krHq9nlatWqHT6QgLC2Pp0qUsWbKEUaNGZcl76Nixo0nmkJAQ+vbta7LNx8cHAH9/f5Ptt27dYu/evcZzxcfHU69ePSZPzp51PS3F7E7VixcvzoocedbH9T9maeRS9Iqer8K/4oNaH1DQrqBZ57h75i5nfzkLQMFiBQl4KyArooocwtramh49ehi/FkLkbN7ef69I4OLigkajMdkGcPdu2mjjHj16sHjxYkJDQ437Fi9eTI8ePRg3bpxx25YtWzh16hTbtm3Dy8uLoKAgxo0bx0cffcSYMWPMnrjw8uXLeHl5PXUEn4ODAw4ODsbntra2ODo6pnsfkPZzKaPtT3Tr1g3ApLUrN8j0/Nh37txh79697N27lzt37lgyU57iW8iXLlW6APAg6QGzD802+xxhX/7dOlR7cG20tjIvTV5mZWVFqVKlKFWqlGpT2AshskabNm148OCBsUVl7969PHjwgNatW5scFx4eTkBAAF5eXsZtzZs3Jy4ujr/++svs6w4bNoxXX32VxMTEF3sDeZjZv37Gx8czcOBAli1bZpyUUavV0r17d7755huZPyIDH9f7mO+Pf49BMfBl+JcMqDmAArYFnuu1j2494viy4wDYOdsR/E5wVkYVQogcZ371+cb517JTAe8CvHP4HYue08bGhq5du7Jo0SLq1avHokWL6Nq1a7pJCKOjo02KIcD4PDo62uzrLl68mCZNmtC6dWt+/fVXk9Ygc504cYICBUw/w7p27cq8efMyfc6cwOyCaOjQoezatYtff/2VunXTRjrt3buXQYMGMWzYMObOnWvxkLldeffydPTvyMqTK7mbcJf5R+YzNGToc732wNcH0OvSJqiq/n517JztsjKqyAH0ej1HjqStVRccHCwzVYt873H0Yx7deKR2DIvp3bs3derUYcKECaxZs4bw8HCLdJbu2bMnS5cu/c/jpk2bxmeffZbp65QvX54NGzaYbHN2ds70+XIKswuidevWsXbtWho2bGjc1rJlSxwcHOjQoYMURE/xSf1PWHlyJQBfhX/FgJoDjLNZP03yo2QOzz0MgNZWS61BtbI8p1CfXq/njz/+ACAoKEgKIpHvFfB+vhb13HLdgIAAKlSowFtvvUXFihWpXLlyupFY3t7eHDx40GRbTEyMcV9GJk6cyMiRIzPcl5qaSu/evXn8+DHvvPNirV55dT5CswuihISEdM14AJ6eniQkJFgkVF7k7+lPm/Jt2HB2Azce3eD749/Tu2rvZ77m6IKjJMcmAxDQNYCCRc3rjC2EEHmBpW9b5QS9e/emX79+T21ECAkJ4YsvvuD27dt4eqYtEL5161acnZ2pVKlShq8pUqQIRYoUyXBfjx49iIuLY+fOnRl+hotMdKoOCQlh9OjRJCUlGbclJiYyduxYQkJCLBourwmt9/eogin7pqA3PH2tFr1Oz/7p+43P6wyXZTqEECKv6Nu3L3fu3KFPnz4Z7m/WrBmVKlWiW7duREZGsnnzZj799FP69++PnZ35XSfeffddduzY8czRYc8rNTWV6Ohok8eT1iuA+/fvExERwalTpwA4e/YsERERmer7lJ3MbiGaOXMmzZs3p3jx4sbFXCMjI7G3t2fz5s0WD5iX1C5emwYlG7Dryi7O3jvLL2d/4Y2KGU9mefLHk8RdjwOgfJvyeFT0yM6oQgghspC1tTXu7u5P3a/Vavntt994//33CQkJwcnJiR49evD5559n6np16ljul+q//vorXUuUnZ2dsaFkw4YN9OrVy7ivU6dOAIwePZoxY8ZYLIelaZRMrMGRkJDAihUrOHPmDAAVK1akS5cuL9RrPbvExcXh4uJCbGysKp3ANl3YRIsVLQCoXrQ6B/scTDfJoqIozKsyj9snbwPQa08vStQrke1ZhTp0Oh0TJ04EIDQ01Oz5RoTIrZKSkrh8+TKlS5fG3t5e7ThCBc/6N5DVn9+ZmvXN0dGRvn37WjpLvtDctzlB3kFEREdw+OZh/rz8Jy+XednkmAubLhiLoeIhxfGp66NGVCGEECLfeK6CaMOGDbRo0QIbG5t0Q+3+rU2bNhYJlldpNBpG1h1Jp3VpTYiT9k1KVxD9exFXWaZDCCGEyFrPVRC1bduW6OhoPD09adu27VOP02g06PVP7ygs0rSr1A7fP325+OAi2y5t4/DNw1QvWh2AG4duELUzCoDC5QpTvk15FZMKNVhbW/PWW28ZvxZCCJH1nmuUmcFgMA77MxgMT31IMfR8rK2sGVF3hPH5pL2TjF/vm7zP+HXI8BA0VtI6lN9YWVlRrlw5ypUrJ0t3CCFENrHIT9uHDx9a4jT5SvfA7ngXSBv++NPpnzh79yx3z97l9E+ngbQJwQK7BaoZUQghhMg3zC6IJk+ezKpVq4zP27dvT6FChShWrBiRkZEWDZeX2VvbM6T2EAAUFKbsm0LY1DD4/5i/2kNqY20vt0vyI71eT0REBBEREdLqKoQQ2cTsgmjevHn4+KSNetq6dSvbtm1j06ZNtGjRgg8//NDiAfOy96q/h4udCwDLjy9n98+7AbBzsaP6e9XVjCZUpNfr+eWXX/jll1+kIBJCiGxidkEUHR1tLIh+++03OnToQLNmzRgxYgSHDh2yeMC8zNnOmf41+gOQYkghrHra6LIa/WvIIq5CCCFENjK7IHJzc+PatWsAbNq0iSZNmgBpkwnKb7Pm+6D2B9hr0yafOlz9MDpXnSziKoQQQmQzswuiN954g86dO9O0aVPu3btHixZpsy4fO3YsT65+m9U8nTx5Rf8KACm2KVzpc4UCXuqs7CyEEELkV2YXRNOnT2fgwIFUqlSJrVu3UqBA2of3rVu36Nevn8UD5nW6eB3ll5ZHY0gbXv9H4T+I18WrnEoIIcTz0mg0z3yMGTOGqKgoNBoNWq2WGzdumLz+1q1bWFtbo9FoiIqKMm6/evUqrVq1wtHREU9PTz788ENSU1Oz5D0sWbLkP99HVFQUY8aMQaPR8Morr6Q7x9SpU9FoNDRs2NBk+5o1a6hQoQL29vYEBATw+++/Z8l7eFFmFUQpKSm8++67vPHGG8ycOZOqVasa9w0ZMuSpq/aKpzv23TEcLjtQ+WRlAO4n3+e7Y9+pnEoIIcTzunXrlvExY8YMnJ2dTbYNHz7ceGyxYsVYtmyZyeuXLl1KsWLFTLbp9XpatWqFTqcjLCyMpUuXsmTJEkaNGpUl76Fjx44mmUNCQujbt6/Jtif9h4sUKcKOHTu4fv26yTkWLVpEiRKm626GhYXx1ltv8fbbb3Ps2DHatm1L27ZtOXnyZJa8jxdhVkFkY2PDunXrsipLvqNP0RM2La0jdb299Yzbp4VNQ6fXqRVLCCGEGby9vY0PFxcXNBqNybYnd1IAevToweLFi01ev3jxYnr06GGybcuWLZw6dYrvv/+eoKAgWrRowbhx45g9ezY6nfmfD5cvXyYhIeGp+x0cHEwy29ra4ujoaLJNq9UC4OnpSbNmzVi6dKnx9WFhYdy9e5dWrVqZnHfmzJm88sorfPjhh1SsWJFx48ZRrVo1Zs2aZfZ7yGpm3zJr27Yt69evz4Io+c/JlSeJuxYHQL0a9Xi13KsAXIu7xsoTK9WMJlRkbW3Nm2++yZtvvilLdwiRx7Rp04YHDx6wd+9eAPbu3cuDBw9o3bq1yXHh4eEEBATg5eVl3Na8eXPi4uL466+/zL7usGHDePXVV0lMTHyxN/B/vXv3ZsmSJcbnixYtokuXLtja2pocFx4ebhx89UTz5s0JDw+3SA5LMvunbdmyZfn888/Zt28fwcHBODk5mewfNGiQxcLlZYpBMVmmo97IepQsUZLfzv0GwOR9k+kW2A0rjSzdkN9YWVnh7++vdgwhcozq86sT/Tg626/rXcCbw+8ctug5bWxs6Nq1K4sWLaJevXosWrSIrl27YmNjY3JcdHS0STEEGJ9HR5v/vVi8eDFNmjShdevW/Prrrzg4OGT+TQCvvvoq7733Hrt37yY4OJjVq1ezd+9eFi1aZHLc095HZt5DVjO7IPruu+9wdXXlyJEjHDlyxGSfRqORgug5nf31LHdO3QGgRL0SaQ9KUL9EffZc3cPpu6f59eyvvFbhNZWTCiGEuqIfR3Pj0Y3/PjCX6N27N3Xq1GHChAmsWbOG8PBwi3SW7tmzp8ltrKeZNm0an3322Qtd60lht3jxYi5dukS5cuWoUqXKC51TbWYXRJcvX86KHPmKoijsnbjX+LzuyLrGr0fWG8meH/YAMCVsihRE+ZDBYOD06bQ17SpWrCgLvIp878m6j3nlugEBAVSoUIG33nqLihUrUrlyZSIiIkyv7e3NwYMHTbbFxMQY92Vk4sSJjBw5MsN9qamp9O7dm8ePH/POO++8+JsgrbCrVasWJ0+epHfv3hke4+3tbcz9RExMzFPfg5oy3UFBp9Nx+fJlfH19pZ+Dma7svsKNA2m/7XhW9qRsy7LGfS38WlDZszInb58k7FoY+67uo26Juk87lciDUlNTWbt2LQChoaHp7skLkd9Y+rZVTtC7d2/69evH3LlzM9wfEhLCF198we3bt/H09ATSlstydnamUqVKGb6mSJEiFClSJMN9PXr0IC4ujp07d6a7hZVZ/v7++Pv7c/z4cTp37vzU97F9+3YGDx5s3LZ161ZCQkIsksGSzP7VMyEhgbfffhtHR0f8/f25evUqAAMHDmTSpEkWD5gX7Zv0d9+huiProtFojM81Gg0f1vl7TbgpYVOyNZsQQois17dvX+7cufPU6WqaNWtGpUqV6NatG5GRkWzevJlPP/2U/v37Y2dn/tJO7777Ljt27LB4y8yff/7JrVu3cHV1zXD/Bx98wKZNm/jyyy85c+YMY8aM4fDhwwwYMMCiOSzB7IIoNDSUyMhIdu7cib29vXF7kyZNWLVqlUXD5UXREdFc2HQBANdSrlTuWDndMZ0qd6K4c3EANpzdwKk7p7I1oxBCiKxlbW2Nu7v7U++waLVafvvtN7RaLSEhIXTt2pXu3bvz+eefZ+p6derUeWrr0YtwcnJ6ajH05Lo//PAD8+fPJzAwkLVr17J+/XoqV07/2ac2jaIoijkvKFmyJKtWraJ27doULFiQyMhIypQpw4ULF6hWrRpxcXFZldUi4uLicHFxITY2Fmdn52y//tpOa/lrVdqQyZazW1KjX40Mj5sePp2hW4YC0CuoF4teW5ThcSLv0el0TJw4EZBbZiJ/SUpK4vLly5QuXdrkF26Rfzzr30BWf36b3UJ0584d4/3Mf4qPjze59SPSu3/hPqfWpLX2OHo4EtQr6KnH9qnWB1d7VwC+P/49N+LyzggLIYQQIqcxuyCqXr06GzduND5/UgQtXLgwR3aSyknCpoWhGNIa5GoPro2Ng81Tjy1oV5B+1dPWhksxpDDzwMxsySiEEELkR2YPD5swYQItWrTg1KlTpKamMnPmTE6dOkVYWBi7du3Kiox5wuPox0QsiQDAtqDtU2+V/dPAWgP5MvxLkvXJzDs8j0/qf4KLvUsWJxVCCCHyH7NbiOrVq0dERASpqakEBASwZcsWPD09CQ8PJzg4OCsy5glh08LQJ+sBqP5+dexd//v+uHcBb3oEpq1v80j3iHmH52VpRpEzaLVaXnvtNV577TXj2kFCCCGyltmdqnM7NTpVx9+OZ2bpmaQkpGBtb82gS4MoWKTgc732/L3zlJ9VHgUF7wLeRH0QhZ21+UMuhRAip3vSobZUqVIvvLSEyJ0SExOJiorKHZ2qmzRpwpIlS3L8aLKcJPyrcFISUgCo9k615y6GAMoWLssbFd8A0qav//7491mSUQgh1PZkPa9nrcou8jadTgegSuu42X2I/P39CQ0NpV+/frRq1YquXbvSsmXLdAvTiTQJdxM4OCtt+nWtrZa6I8yfdfrDOh+y7vQ6AKaGTaVX1V6y6GseZjAYuHAhba4qPz8/WbpD5BtarRZXV1du374NgKOjo4xezkcMBgN37tzB0dFRlRUwzL7izJkzmT59Otu2beOHH36ge/fuaLVa3nzzTbp06UKDBg2yImeuFT49nJT4/7cO9a2GczHzm/lqFa9Fg5IN2HVlF2fvnZVFX/O41NRUVq5cCcg8RCL/eTKT8pOiSOQvVlZWlChRQpVCOFMlmJWVFc2aNaNZs2bMmzePX3/9lS+++ILvvvsOvV5v6Yy5VuL9RA5+k9Y6ZGVjRd2PMr8m2Yi6I9h1JW0U3+R9k2lTvo385iSEyHM0Gg1FihTB09OTlJQUteOIbGZra6taq/gLtUlFR0fz448/8v3333P8+HFq1qxpqVx5wv6Z+9E9SrsfWrV3VVx8Mj9k/p+LvoZfD2fftX3UK1HPUlGFECJH0Wq1MspSZCuzy7C4uDgWL15M06ZN8fHxYe7cubRp04bz58+zf//+rMiYKyU9TOLAzAMAWFlbUS/0xYoXjUbDiDojjM+n7JNFX4UQQghLMbuFyMvLCzc3Nzp27MjEiROpXr16VuTK9Q58fYDk2GQAAnsG4lrS9YXP2alyJz758xOuxV3j13O/curOKSp5VHrh8wohhBD5ndktRBs2bOD69etMnz5diqGnSI5LZv/0tNYyjVZD/dD6FjmvjdaGIbWHGJ9PDZtqkfMKIYQQ+Z3ZBVHTpk1lGPB/OPDNAZIeJgEQ2D0QtzJuFjv3Pxd9XXF8Bdfjrlvs3EIIIUR+ZXZlExMTQ7du3ShatCjW1tbGjm/SAS5N8qNk9n/1/9YhKw31P7ZM69ATBe0K0r9Gf+D/i77ul0Vf8xqtVkuLFi1o0aKF/J8SQohsYnYfop49e3L16lU+++wzihQpIkO//+XQnEMk3k8EIKBLAIX8Cln8GgNrDmRa2DSS9cl8e+RbPnnpE2Orkcj9tFqtjNgUQohsZnZBtHfvXvbs2UNQUFAWxMnddPE6wqeFA/9vHfrEsq1DT3gV8KJnUE++PfKtcdHXkfVGZsm1hBBCiPzA7FtmPj4+WHo92NmzZ1OqVCns7e2pVasWBw8efK7X/fjjj2g0Gtq2bWvRPJl1eO5hEu6mrcFTuVNl3Mu7Z9m1hoUMQ0Na69zMAzNJSk3KsmuJ7GUwGIiKiiIqKgqDwaB2HCGEyBfMLohmzJjByJEjiYqKskiAVatWMXToUEaPHs3Ro0cJDAykefPm/zlte1RUFMOHD6d+/axphTFXSkIKYVPD0p5oyLLWoSfKFi5Lu0rtAFn0Na9JTU1l6dKlLF26lNTUVLXjCCFEvmB2QdSxY0d27tyJr68vBQsWpFChQiYPc3311Vf07duXXr16UalSJebNm4ejoyOLFi166mv0ej1dunRh7NixlClTxuxrZoUj848QfzseAP/2/nhU8sjya/5zosapYVPRG2TZFCGEECIzzO5DNGPGDItdXKfTceTIEUJDQ43brKysaNKkCeHh4U993eeff46npydvv/02e/bseeY1kpOTSU5ONj6Pi4t78eD/kpKYwr7J+4zPX/rsJYtfIyM1itWgYamG7Izaybl759hwdgOvV3w9W64thBBC5CVmF0Q9evSw2MXv3r2LXq/Hy8vLZLuXlxdnzpzJ8DV79+7lu+++IyIi4rmuMXHiRMaOHfuiUZ/p6MKjPI5+DEDFdhXxrOyZpdf7p4/qfsTOqJ1A2qKvbSu0lZF/QgghhJkyNcOiXq9n3bp1jB8/nvHjx/Pzzz9nyyr3jx49olu3bixYsAB39+frsBwaGkpsbKzxce3aNYtmSk1KZd+k7G8deqK5b3OqeFUB4MCNA+y5+uwWMyGEEEKkZ3YL0YULF2jZsiU3btygfPnyQForjI+PDxs3bsTX1/e5z+Xu7o5WqyUmJsZke0xMDN7e3umOv3jxIlFRUbRu3dq47ckoHGtra86ePZvu+nZ2dtjZ2T13JnMd/vYwj24+AqBC2wp4B6bPnZWeLPra9eeuQNqiry+VzN6iTAghhMjtzG4hGjRoEL6+vly7do2jR49y9OhRrl69SunSpRk0aJBZ57K1tSU4OJjt27cbtxkMBrZv305ISEi64ytUqMCJEyeIiIgwPtq0aUOjRo2IiIjAx8fH3LfzQpLjktkz/u8WmZdGqVOIdPDvQAmXEgBsPL+Rk7dPqpJDCCGEyK3MbiHatWsX+/fvNxlRVrhwYSZNmkTdunXNDjB06FB69OhB9erVqVmzJjNmzCA+Pp5evXoB0L17d4oVK8bEiROxt7encuXKJq93dXUFSLc9O4R/Ff73vENvVaZI1SLZngHSFn0dWnsogzcPBtJGnC1tu1SVLOLFabVamjRpYvxaCCFE1jO7hcjOzo5Hjx6l2/748WNsbW3NDtCxY0emTZvGqFGjCAoKIiIigk2bNhk7Wl+9epVbt26Zfd6sFn87nvAv00bCWVlb0WhcI1Xz9KnWh0IOaUXqDyd+4GrsVVXziMzTarXUrVuXunXrSkEkhBDZRKOYOe109+7dOXr0KN99951xvaUDBw7Qt29fgoODWbJkSVbktJi4uDhcXFyIjY3F2dk50+f5Y9AfHPwmbUbt6v2q02p2K0tFzLRRO0Yxbvc4AIbUHsJXzb9SOZEQQghhGZb6/H4as1uIvv76a3x9fQkJCcHe3h57e3vq1q2Ln58fM2fmj5XXH1x6wOF5hwGwcbShwWcNVE6UZmDNgdhb2wMw/8h8HiQ+UDmRyAyDwcCNGze4ceOGLN0hhBDZxOyCyNXVlV9++YVz586xdu1a1q5dy9mzZ/n5559xcXHJiow5zo5ROzCkpH1Q1R5amwLeBVROlMbDyYPeQb0BiE+JZ86hOSonEpmRmprKwoULWbhwoSzdIYQQ2SRT8xAB+Pn50bp1a1q3bo2fn58lM+Vo0ZHRnPjhBAAOhR2oM7yOyolMDaszDCtN2l/r1we/JjElUeVEQgghRM5ndkHUrl07Jk+enG77lClTaN++vUVC5WR/fvwn/L/XVf2P62PvYq9uoH8p41aG9pXS/h5ux99maaSMNhNCCCH+i9kF0e7du2nZsmW67S1atGD37t0WCZVTXdl9hfO/nwfA2ceZGv1qqJwoYx/W+dD49bSwabLoqxBCCPEfzC6Inja83sbGJksWTs0pFEVh20fbjM8bfd4Ia3uzp3HKFsFFg3m59MsAXHxwkZ/P/KxyIiGEECJnM7sgCggIYNWqVem2//jjj1SqVMkioXKisxvOcn3/dQA8KnlQpVsVlRM920d1PzJ+PXnfZMycXUEIIYTIV8xu4vjss8944403uHjxIo0bNwZg+/btrFy5kjVr1lg8YE5g0BvS+g79X+MJjbHSZro/erZoUqYJVb2rciz6GIdvHmZn1E4alVZ38kghhBAipzL7U71169asX7+eCxcu0K9fP4YNG8b169fZtm0bbdu2zYKI6otcFsmdU3cA8KnjQ/k25VVO9N80Gg0j6o4wPp+8L31HeJEzabVaGjRoQIMGDWSmaiGEyCZmz1Sd25k702VqUirflPuGuGtp/aN67u5JyfolszqmRaQaUin7TVmiHkYBcPSdo1QtUlXdUEIIIUQm5LiZqvObg7MPGouhsq3K5ppiCMDayprhIcONzyftm6RiGiGEECLnkoLoGZJik9g7YW/aEw28POFldQNlQu+qvfF08gRg7am1nL93XuVE4r8oisLt27e5ffu2dIYXQohsIgXRM4RNDSPxftpMz1W6VMGripfKicznYOPA4FqDATAoBqbsm6JuIPGfUlJSmDt3LnPnziUlJUXtOEIIkS9IQfQUcTfi2D99PwBWNlY0/LyhuoFeQL8a/XC2S7vfujRyKTfibqicSAghhMhZMl0Q6XQ6zp49m2cXn9w6fCspCWm/nVd/vzpupd1UTpR5LvYu9K/RH4AUQwpfhX+lciIhhBAiZzG7IEpISODtt9/G0dERf39/rl69CsDAgQOZNClvdNq9/OdlTv54EkhbwLXh6IbqBrKAwbUHY2+dtu7at0e+5V7CPZUTCSGEEDmH2QVRaGgokZGR7Ny5E3v7vxc2bdKkSYYzWOc2ep2e3wf8bnzeZHITHAo5qJjIMjydPHm76tsAxKfE883Bb1ROJIQQQuQcZhdE69evZ9asWdSrVw+NRmPc7u/vz8WLFy0aTg37Z+zn7um7ABSrVYyqvfLOvD3D6wxHq0mb6O/rA1/zWPdY5URCCCFEzmB2QXTnzh08PT3TbY+PjzcpkHKj2Gux7Pp8FwAaKw2t5rRCY5W739M/lXItReeAzgA8SHrA/CPzVU4khBBC5AxmF0TVq1dn48aNxudPiqCFCxcSEhJiuWQq2DJsCynxf3ekLlKtiMqJLO+fi75+Gf4lyanJKqYRGdFqtYSEhBASEiJLdwghRDYxe3HXCRMm0KJFC06dOkVqaiozZ87k1KlThIWFsWvXrqzImC0ubr3IqTWnAHD0cKTRuLy5EKq/pz9tK7Rl/Zn13Hx0k+XHl9OnWh+1Y4l/0Gq1NGvWTO0YQgiRr5jdQlSvXj0iIiJITU0lICCALVu24OnpSXh4OMHBwVmRMculJqfyx4A/jM+bTmmKg1vu70j9NKH1Qo1fT9k3Bb1Br2IaIYQQQn1mtxAB+Pr6smDBAktnUc3+6fu5dy5tGLpPHR8CuweqnChr1SxWk8alG/Pn5T85f/88606vo4N/B7Vjif9TFIXY2FgAXFxccn3fPCGEyA2eq4UoLi7uuR+5TezVWHaP2w2kdaRuObtlnupI/TT/bCWasGeCrJmVg6SkpDBz5kxmzpwpS3cIIUQ2ea4WIldX1+f+LVWvz123XzYP2WyckbpG/xp4B3mrnCh7vFz6ZaoXrc7hm4eJjIlk04VNtCjbQu1YQgghhCqeqyDasWOH8euoqChGjhxJz549jaPKwsPDWbp0KRMnTsyalFnkwqYLnP7pNABOXk40+jxvdqTOiEajIbReKO1WtwNg4t6JUhAJIYTIt56rIGrQoIHx688//5yvvvqKt956y7itTZs2BAQEMH/+fHr06GH5lFkgNTmVPwb+oyP11KbYu9o/4xV5T9sKbangXoEzd8+w5+oe9l3dR90SddWOJYQQQmQ7s0eZhYeHU7169XTbq1evzsGDBy0SKjsc+PoA9y/cB6BEvRJU6VpF5UTZz0pjxci6I43PJ+7NXS18QgghhKWYXRD5+PhkOMJs4cKF+Pj4WCRUdgibFgaARvv/jtT5dCRP54DOlHApAcDG8xs5HnNc5URCCCFE9jN72P306dNp164df/zxB7Vq1QLg4MGDnD9/nnXr1lk8YFZJTUrFGmtqDqyJVxUvteOoxkZrw/CQ4QzaNAhIayVa2W6lyqmEEEKI7GV2C1HLli05f/48rVu35v79+9y/f5/WrVtz7tw5WrZsmRUZs0wB7wI0Gpt/OlI/zdvV3sbD0QOA1X+t5ty9cyonyt+srKyoXr061atXx8rK7P+iQgghMkGj5LMJaOLi4nBxcWEkI+m8ojMBnQPUjpQjTNo7idDtaXMTdavSjWWvL1M5kRBCCPG3J5/fsbGxODs7W/z8mSqIHj58yHfffcfp02lD1v39/enduzcuLi4WD2hpT76hR1YdoWr7qvm279C/PUp+RKmZpbifeB8rjRVnB5zFr5Cf2rGEEEIIIOsLIrPb4w8fPoyvry/Tp0833jL76quv8PX15ejRoxYPmFX8XvGTYugfCtoVZGjtoQAYFANf7PlC5UT5l6IoxMfHEx8fLzOICyFENjG7hah+/fr4+fmxYMECrK3T+mSnpqbSp08fLl26xO7du7MkqKVkdYWZm8Ulx1FyRkkeJj1Eq9FydsBZfAv5qh0r39HpdMZJTkNDQ7G1tVU5kRBCqC9HthB99NFHxmIIwNramhEjRnD48GGLhhPZy9nOmSG1hwCgV/QyL5EQQoh8w+yCyNnZmatXr6bbfu3aNQoWLGiRUEI9g2oNwsUurS/Y0silRD2MUjeQEEIIkQ3MLog6duzI22+/zapVq7h27RrXrl3jxx9/pE+fPibLeYjcydXelcG1BwOQakhlwp4J6gYSQgghsoHZEzNOmzYNjUZD9+7dSU1NBcDGxob333+fSZMmWTygyH4f1PqA6funE5ccx5KIJXxS/xNKupZUO5YQQgiRZcxuIbK1tWXmzJk8ePCAiIgIIiIiuH//PtOnT8fOzi4rMops5ubgxqCaaTNXpxhSmLRXCl0hhBB5W6anwXV0dCQgIICAgAAcHR0tmUnkAENChlDAtgAA3x37jmux11ROJIQQQmSd575l1rt37+c6btGiRZkOI3KOQg6FGFhzIBP3TiTFkMLkfZOZ1XKW2rHyBSsrKwIDA41fCyGEyHrPPQ+RlZUVJUuWpGrVqs+cLO7nn3+2WLisIPMQPb+7CXcpNaMU8Snx2GptuTToEsWci6kdSwghRD6U1Z/fz91C9P7777Ny5UouX75Mr1696Nq1K4UKFbJ4IJFzuDu6M6DmACbvm4xOr2Pyvsl83eJrtWMJIYQQFvfc7fGzZ8/m1q1bjBgxgl9//RUfHx86dOjA5s2bZXmBPGxYyDAcbdL6iM0/Mp9bj26pnCjvUxQFnU6HTqeT/1tCCJFNzOqgYGdnx1tvvcXWrVs5deoU/v7+9OvXj1KlSvH48eOsyihU5OHkQb/q/QBI1iczZd8UlRPlfSkpKUycOJGJEyeSkpKidhwhhMgXMt1j08rKCo1Gg6Io6PV6S2YSOczwOsNxsHYAYN6ReUQ/jlY5kRBCCGFZZhVEycnJrFy5kqZNm1KuXDlOnDjBrFmzuHr1KgUKFMiqjEJlXgW8eK/6ewAkpSYxLWyayomEEEIIy3rugqhfv34UKVKESZMm8eqrr3Lt2jXWrFlDy5YtZWhwPjCi7gjsre0BmHNoDrfjb6ucSAghhLCc5x5lNm/ePEqUKEGZMmXYtWsXu3btyvC4n376yWLhRM7hXcCbd4PfZeaBmSSmJjItbBpTmkp/IiGEEHnDcxdE3bt3R6PRZGUWkcONqDuCeYfnkaxPZtbBWQypPYQiBYuoHUsIIYR4Yc9dEC1ZsiQLY4jcoGjBorxf/X1mHJhBYmoi43aPY06rOWrHEkIIIV6YdP4RZvm4/sfGNc4WHF3AhfsXVE6U91hZWVGpUiUqVaok/fOEECKbyE9bYRYPJw+GhwwHINWQyqgdo1ROlPdYW1vTvn172rdvj7X1czfiCiGEeAFSEAmzDQ0ZirujOwArT64kIjpC3UBCCCHEC5KCSJitoF1BPqn/ifH5J39+8oyjhRBCiJxPCiKRKe9Vf48SLiUA+P387+y+slvlRHmHTqdj7NixjB07Fp1Op3YcIYTIF6QgEplib23P2IZjjc9Dt4fKQqRCCCFyLSmIRKZ1q9KNiu4VAQi7FsZv535TOZEQQgiROVIQiUzTWmn5ovEXxucf//kxeoMs9CuEECL3kYJIvJC2FdpSs1hNAE7ePsnKkytVTiSEEEKYTwoi8UI0Gg2TXp5kfP7Zjs/Q6aUjsBBCiNxFCiLxwhqVbkQz32YARD2MYv6R+SonEkIIIcyTIwqi2bNnU6pUKezt7alVqxYHDx586rELFiygfv36uLm54ebmRpMmTZ55vMgeExpPMH49bvc4Huseq5gmd7OysqJs2bKULVtWlu4QQohsovpP21WrVjF06FBGjx7N0aNHCQwMpHnz5ty+fTvD43fu3Mlbb73Fjh07CA8Px8fHh2bNmnHjxo1sTi7+KbhoMO0rtQfgdvxtZuyfoW6gXMza2prOnTvTuXNnWbpDCCGyiUZRefKYWrVqUaNGDWbNmgWAwWDAx8eHgQMHMnLkyP98vV6vx83NjVmzZtG9e/f/PD4uLg4XFxdiY2NxdnZ+4fzib2fvnsV/jj96RY+znTOXBl2isGNhtWMJIYTIA7L681vVFiKdTseRI0do0qSJcZuVlRVNmjQhPDz8uc6RkJBASkoKhQoVynB/cnIycXFxJg+RNcq7l6d31d4AxCXHMWnvpP94hRBCCJEzqFoQ3b17F71ej5eXl8l2Ly8voqOjn+scH330EUWLFjUpqv5p4sSJuLi4GB8+Pj4vnFs83agGo7DT2gHwzcFvuB53XeVEuY9Op2PChAlMmDBBlu4QQohsonofohcxadIkfvzxR37++Wfs7e0zPCY0NJTY2Fjj49q1a9mcMn8p7lycgTUHApCsT2bUjlEqJ8qdUlJSSElJUTuGEELkG6oWRO7u7mi1WmJiYky2x8TE4O3t/czXTps2jUmTJrFlyxaqVKny1OPs7OxwdnY2eYisNbLeSJzt0r7PSyKWcPjmYZUTCSGEEM+makFka2tLcHAw27dvN24zGAxs376dkJCQp75uypQpjBs3jk2bNlG9evXsiCrMUNixMKMbjAZAQWHQH4MwKAaVUwkhhBBPp/ots6FDh7JgwQKWLl3K6dOnef/994mPj6dXr14AdO/endDQUOPxkydP5rPPPmPRokWUKlWK6OhooqOjefxY5r3JSQbUHEAF9woAhF8PZ8XxFSonEkIIIZ5O9YKoY8eOTJs2jVGjRhEUFERERASbNm0ydrS+evUqt27dMh4/d+5cdDodb775JkWKFDE+pk2bptZbEBmw1doy85WZxucfbfuIR8mPVEwkhBBCPJ3q8xBlN5mHKHu99uNrbDi7AYCP6n7EpCYyFP+/6HQ6Jk6cCKQNCrC1tVU5kRBCqC9Pz0Mk8r6vmn2FrTbtA336/umcv3de5UQ5n0ajoWTJkpQsWRKNRqN2HCGEyBekIBJZyreQL8NDhgOg0+sYumWoyolyPhsbG3r27EnPnj2xsbFRO44QQuQLUhCJLBdaP5RiBYsB8Nu53/jj/B8qJxJCCCFMSUEkslwB2wJMaTrF+Hzw5sHo9DIDsxBCiJxDCiKRLd6q/BZ1feoCcO7eOb4+8LXKiXIunU7H1KlTmTp1qizdIYQQ2UQKIpEtNBoN37T4Bg1pnYQ/3/U50Y+fb726/CghIYGEhAS1YwghRL4hBZHINlWLVKVvtb4APNI9InR76H+8QgghhMgeUhCJbDW+8Xhc7V2BtHXODlw/oG4gIYQQAimIRDbzcPLg84afG58P/GOgrHMmhBBCdVIQiWz3fo338ffwB+DQzUMsi1ymciIhhBD5nRREIttZW1nzdYu/R5mN3DaS2KRYFRMJIYTI76QgEqpoXLox7Sq2AyAmPoZRO0apnCjn0Gg0FC1alKJFi8rSHUIIkU1kcVehmqiHUVScXZGk1CQ0aNjVcxf1S9ZXO5YQQogcSBZ3FXlWKddSfNH4CwAUFHpv6E1Cisy9I4QQIvtJQSRU9UGtD6jjUweAC/cv8PH2j1VOJIQQIj+SgkioSmulZfFri7G3tgfg6wNfs+fKHpVTqSslJYUZM2YwY8YMUlJS1I4jhBD5ghREQnXlCpeTW2f/oCgKsbGxxMbGks+6+AkhhGqkIBI5gtw6E0IIoSYpiESOILfOhBBCqEkKIpFjyK0zIYQQapGCSOQocutMCCGEGqQgEjmK3DoTQgihBimIRI6T32+daTQaPDw88PDwkKU7hBAim8jSHSJH0hv0vLTkJcKuhQFpt9JmvDJD3VBCPKfk1GQeJj3kYdJDHiQ9+PvrxAfEp8TjaONIQduCFLAtQAHbAhS0+8fX/99uq7WVgliIf8jqz28piESOde7eOQLnBcpaZyLH0Rv0XHpwieMxxzlx+wTHY45z7t457ife52HSQxJTE1/4Gg7WDpQrXI5KHpWo6F6Rih4VqehekbKFy2KrtbXAuxAid5GCyMKkIMpdvgr/imFbhgHgV8iPyPcicbRxVDmVyE/uJtxNK3xiThgLoJO3T1qk6MkMrUaLXyE/Y4Hk7+FPjWI1KFuorLQoiTxNCiILk4Iod/n3rbPeQb1Z2GZhnv7Bn5KSwoIFCwDo27cvNjY2KifKX1L0Key7to8/zv/BHxf+4MTtE8/1OjutHe6O7rg5uOFq74qbfdqf//zazcENRxtHElMSeax7zCPdIx7rHps8nmyLfhzNxfsX0Sv657q+m70bNYrVoGbRmtQqXosaRWvgVcDrRb4VQuQoWf35bW3xMwphQU9GnQXNCyIxNZFFEYuoWawm71Z/V+1oWUZRFO7cuWP8WmS963HXjQXQtkvbeKR79NRjNWjwLeRLgGcAVbyqEOAZQIBXAL5uvmittBbNpdPrOH/vPKfvnub0ndNpf949zZm7Z0hKTTI59kHSA7Zc3MKWi1uM20q6lKRmsZrUKlaL+iXrU6NojTz9y4QQL0IKIpHjlStcjoVtFtLlpy4ADPxjIIHegdQuXlvlZCK30hv07Lm6h9/P/84fF/7g5O2TGR6nQUP1otUJKR5CgFdaAeTv4Y+TrVO25LTV2uLv6Y+/p7/Jdr1Bz5XYK5y+c5qI6AgO3jzIgesHiImPMTnuSuwVrsReYc2pNQBU8qjEu8Hv0q1KN9wc3LLlPQiRW8gtM5FrDNk0hBkHZgBQtGBRjrxzBO8C3uqGygI6nY6JEycCEBoaiq2tdKC1lPP3zrMkYglLI5dy49GNDI9xd3SnuW9zWvi1oJlvMzycPLI5ZeYoisK1uGscvJFWHB28eZDDNw9nOGWFg7UDHSt35N3gd6lVrJa0GolcITY2FldXV7llJsSUplM4Fn2MXVd2cfPRTTqs6cD27tux0UofG/F0j3WPWfPXGhZHLGbP1fSTfGrQULNYTVr4taBF2RYEFwm2+K2v7KDRaCjhUoISLiV4s9KbAKQaUjl95zRh18L4/sT37L26F4DE1ESWRCxhScQSqnhV4b3g9+hSpQvOdvJLosiZ7ifep+u6rll6DWkhErlKzOMYgucHG3+7H1RzEDNbzFQ5lWVJC9GLUxSFvVf3sjhiMav/Wk18SrzJfq1GS8uyLelUuRPNfJvh7uiuUtLs9dftv/j2yLcsi1xGbHKsyT4nGyfeqvwW71Z/l+AiwdJqJHKMfVf38da6t7h2+xpMQkaZWYoURLnfgesHeGnJS+j0OgCWv76crlWy9jeH7CQFUebdTbjLgiMLWByxmPP3z6fbX9G9Ir2r9qZrla558nbr80pISWD1X6v59si37L++P93+IO8g+lTtQ+eAztLXSKjGoBiYvHcyn+34LG20ZRJSEFmSFER5w/wj83n3t7SRZg7WDoS9HUaQd5C6oSwkJSWF2bNnA9C/f38Zdv8cLj+4zFfhX/Hdse/SzQ/kbOdMJ/9O9K7am5rFakrLx79ERkfy7ZFv+f749+lG19lb29OuYjv6VOtDg5IN5Hsnsk3M4xi6/dyNrZe2GrfV8axDWL8wKYgsRQqivKPvhr4sPLYQgFKupTjyzhEKORRSOZXITkduHmFq2FTWnFqDQTGY7GtcujG9g3rzesXXZTLP5/BY95iVJ1by3bHvOHDjQLr9foX8eLvq2/QI7EGRgkVUSCjyi22XttH1p67GUZMaNHz20md8UPUDCrsVloLIUqQgyjuSUpN4afFLHLp5CIBmvs34vfPvubJDrHh+iqKw9dJWpuybwvbL2032Odo40rdaXwbVGkQZtzIqJcz9TsSc4Ltj37H8+HLuJ9432afVaGlVrhXvBr/LK36vYKWRNcKFZaQaUhmzcwwT9kxAIa00KVKgCCveWEGj0o1kpmpLk4Iob7kWe43g+cHcSUibyPDjeh/zxctfqJxKZIUUfQprTq1hyr4pRMZEmuzzcPRgUK1BvF/9fQo7FlYpYd6TlJrEL2d+YeGxhWy7tC3d/oruFRkWMowuVbpgb22vQkKRV1yPu85b694yjoQEaO7bnGWvL8PTyROQpTssTgqivGdn1E6aLGtiXOLgpw4/8XrF11VOlXkpKSksWbIEgJ49e+b7PkQp+hSWRi5l/O7xXIm9YrLPr5Afw0OG0z2wOw42DiolzB8uP7jMomOLWByxON0cTl5OXgyqNYj3qr8nt62F2X4+/TN9fu1jbI3UarRMeHkCw+sMN2mBlILIwqQgypumh09n6JahABSwLcDeXnsJ9A5UOVXmyCizNHqDnh9O/MDYXWO5+OCiyb6axWoyos4I2lZoK7dIs5neoGfj+Y18Gf4lu6/sNtnnaOPI21XfZkjtIZR2K61SQpFbXI29yqA/BvHL2V+M20q4lODHdj8S4hOS7vis/vyWm78iTxhcezCdKncC0jqHNlne5KnLMYiczaAYWP3XagLmBtB9fXeTYugVv1fY1XMX+9/eT7tK7aQYUoHWSkub8m2Mfw/tK7U3/hafkJLANwe/we8bPzqu7cihG4dUTityolRDKl+GfUml2ZVMiqG2FdoS8W5EhsVQdpCCSOQJGo2Gha0XGtc3u5twl5eXvczpO6dVTiael6IobDi7garfVqXj2o6cvvv3312TMk0I6x3GH13+4KWSL8nw7xyiVvFarG6/mvMDzzOgxgDjaL4nRW3NhTVptLQR2y5tk4WKBQD7r++n+vzqDN863DhhqncBb35s9yM/dfhJ1XmvpCASeYaTrRObumyiRtEaANyOv03jZY05e/esysnEsyiKwuYLm6m1sBav/fgax2OOG/fVK1GPHT12sLXbVtV+axT/rYxbGb5p+Q1XB19lfKPxxk6wkNbHr+nypoR8F8KvZ3+VwiifepD4gPd/e58639UxDorQoKFf9X6c7n+ajpU7qv6LjhREIk9xsXdhS7ctVCtSDYDox9E0XtaYC/cvqJxMZGTf1X28tOQlXlnxinH6BIAaRWuwqcsmdvfcTcNSDdULKMxS2LEwn7z0CVcGX2FB6wWUK1zOuO/AjQO0+bENVb+typq/1qA36FVMKrKLoij8cOIHKsyuwLwj84zD6YO8g9jfZz+zW83G1d5V3ZD/JwWRyHNc7V3Z2m2rcebqm49u0mhpIy49uKRuMGF08vZJ2qxsQ73F9UyG2VbxqsIvnX7hQJ8DNPdrrvpvjCJz7K3t6VOtD6f6nWLVm6uo4lXFuC8yJpIOazvgP8efZZHLSNGnqJhUZKXz987T7PtmdPmpC7fjbwNpg16mN5/Oob6HqFmspsoJTUlBJPKkQg6F2NptKwGeAUDaHBeNljYi6mGUusGek6OjI46OeW925auxV+m5vidV5lbh13O/GreXL1yeVW+u4ti7x2hTvo0UQnmE1kpLB/8ORLwbwYZOG4y3swHO3jtLj/U9KD+rPN8e/pbk1GQVkwpLSk5NZtyucQTMDTCZv+qNim9wuv9pBtcejLWVtYoJMybD7kWedjv+No2WNuLUnVMAlHYtzc6eOynhUkLlZPnLvYR7TNgzgdmHZpOs//uDr1jBYoxpOIaeQT1z5A9IYVmKorDt0jbG7xmfbsh+0YJFGR4ynHeC38HJ1kmlhOJF7YzayXu/vcfZe3/33SzhUoLZLWfzarlXX+jcMg+RhUlBlP/EPI6h4dKGnLl7BgBfN1929txJcefi6gbLB+J18czYP4MpYVOIS44zbne1dyW0XigDaw6UCRXzqT1X9vDFni/YfHGzyfbCDoUZXHsw/Wv0V3XEkTDP3YS7DN8ynKWRS43btBotQ0OGMrrBaIsUuVIQWZgURPnTrUe3aLCkAefvnwegbKGy7Oy5k6IFi6qcLG/S6XUsOraIsbvGEv042rjd3tqeD2p9wEd1P5IPOwHAoRuHmLB3AuvPrDfZXtC2IP1q9GNI7SF4FfBSJ5z4T4qisCRiCR9u/ZB7ifeM22sXr828VvMsOkGuFEQWJgVR/nUj7gYNljQwTvRXvnB5fuv8G36F/FROZiolJYUVK1YA0KVLl1y1dMeT2aVH7xzN5YeXjdutNFb0DurN6IajpWVOZOiv238xad8kVp5YaVyGB/7fQbtqHz6s+6Hc6s5hztw9w3u/vceuK7uM21zsXJjUZBLvBL9j8YV/ZaZqISykmHMxdvTYQWnXtCUFzt47S/D8YH4+/bPKyUwpisKVK1e4cuVKrpmzRVEU1p1aR5V5Vei+vrtJMfRGxTf4q99fLGizQIoh8VT+nv4sf3055wae493gd7HVpi1Zk5SaxKxDs/D92pfev/SWyVZzgMSUREbtGEWVuVVMiqFOlTtxZsAZ3qv+nsWLoeyQ+xIL8QJ8XHz4s8eflC9cHoC45DjeWP0Gw7cMl+G/maAoCpsubKLGghq8ueZNY+d1SJtdev/b+1nXYR0V3CuomFLkJmXcyjDv1Xlc/uAyQ2sPNc5+nWpIZXHEYirNqUSLFS3YfGFzrvmFIa9ISk3imwPf4Pu1L+N2jyPFkPYzs4xbGTZ12cTKdivxLuCtcsrMk4JI5DulXEtxqO8hOvp3NG77MvxLGi1txI24G894pfinPVf20GBJA1qsaMGRW0eM2+v41DHOLl2reC0VE4rcrGjBonzZ/EuuDr7KqJdGmUzet+nCJl5Z8Qr+c/z59vC3JKQkqBc0H0hOTWbOoTn4fe3HoE2DuPX4FgDWVtZ8XO9jTr5/kuZ+zVVO+eKkD5HItxRFYfah2QzdPNT4m46Howcr263k5TIvq5Yrp692f/jmYT7989N0o4OCvIMY32g8Lcu2lHmEhMXFJcex6Ngivj7wtcktWUibd+zd4HfpX6M/xZyLqZQw79HpdSw+tpgv9nzBtbhrJvter/A64xuPp5JHpWzLI52qLUwKIvFvB64foMPaDlyNvQqkra/zeaPP+bj+x6rcB8+JBZGiKPx5+U8m7ZtkMtEapHVO/7zR57xZ6c1c2W9A5C56g54NZzcw48CMdHMZWVtZ075SewbXHpzjZkHOTVL0KSyLXMa43eO4EnvFZF+b8m0Y02AMVYtUzfZcUhBZmBREIiP3Eu7R7edu/HHhD+O2Fn4tWP76cgo7Fs7WLDmpINIb9Kw/s55J+yZx+OZhk30lXUoypuEYulbpKpMqClUcvXWUmQdmsvLESmMr7xPVilSjZ2BPOgd0zvb/w7mVTq/jhxM/MG73uHRLHbUq24oxDcdQvWh1ldJJQWRxUhCJpzEoBibsmcCoHaOMCxCWcCnB6jdXZ2tfGJ1Ox7Rp0wAYPny4KgVRcmoy3x//nilhUzh375zJvjJuZfiwzof0CuqFnbVdtmcT4t9uPbrF3MNzmXt4LncT7prss7GyoU35NvQK6kVzv+ZSvGfg/L3zLDy6kMURi7mTcMdk3yt+rzCmwZgc0R9QCiILk4JI/Jdtl7bReV1n4w8GDRo6B3RmTMMxOW7OIkt7lPyI+Ufm89X+r7j56KbJviDvIEbWHUm7Su3kQ0XkSEmpSaw4voJvj3zLoZuH0u33LuBNtyrd6BnUM1v7vuREyanJ/HzmZ+Yfmc+OqB3p9jct05SxDccS4hOiQrqMSUFkYVIQiedxI+4GHdd2ZN+1fcZtWo2Wt6u+zWcNPstz8+mcuXuGxccWM//ofB4mPTTZ17BUQ0bWHUkz32bSWVrkGn/d/oslEUtYfnw5MfEx6fbXKFqDnkE9eb3C6xQpWESFhOo4e/csC44uYEnEEpOZpSGtNe31iq8zsOZA6pWop1LCp5OCyMKkIBLPK0WfwtcHvmbi3okmPzjstHa8X/19QuuH4unkqWLCFxObFMuPJ39kSeQS9l/fn25/2wpt+ajuR9QuXluFdEJYRqohlU0XNrE4YjG/nv01XV8jgOAiwbQq24pW5VpRvWj1PDc44H7ifTae28jCYwvTdUSHtKWM3gl+h+6B3XP0zzQpiCxMCiJhrrjkOGbsn8G0sGk80j0ybneyceKDWh8wvM5wi67LlZqayurVqwHo0KED1taWuz1lUAz8eflPFkcs5qfTP5GUmmSy39rKmq5VujKizggqelS02HWFyAnuJtzlhxM/sCRiCceij2V4jKeTJy3LtqRV2VY0LdMUF3uXbE754vQGPYduHmLThU1svriZgzcOYlAMJsfYam1pV7Ed7wS/Q4OSDXJF668URBYmBZHIrHsJ95iybwrfHPyGxNRE43ZXe1c+rPMhA2sOpKBdwRe+TlaMMrt4/yJLIpawNHJpuvlEAAI8A+gV1IsuVbrk6N8QhbCUiOgI1p1ax2/nfyMiOiLDY6ytrKlfoj4t/FpQq3gtgrz/1969B0V1nn8A/y7CIgi7RBfYxXBRQMCgXANZ06iRrZAoDZ2OJcqoJSY2rcRbmqnGEi9JipmY1CTNxGTSSJoZpTVVar0GURAMotwUMKIQuYzusiBhuSOwz+8P6vl1ZVVE2EX2+Thndjnve877nn1cfTjvOe8JhsR2dP6/cb3lOo5XHcexymM48eMJ/NT1k9F6/jJ/rAxdiaVBSyGzl5m4lw+HE6JhxgkRe1jqVjX+nPNnfF74ucHpd/E4MZ7xeAYxPjGI9o5GoEvgkH7rGo6EqLmrGadrTuPUtVM4VX0KF+ovDKgz0W4ilgQuQWJIIkLkIY/Eb4iMjYTrLddx5OoRHL56GBk/Ztxz5mvfib4IUYQgVB6KUEUoQhQhJk0siAiNHY34ofEHXG68jHJtOU5Wn0SZtuyu2zzh/ASivaMR5x+Hn3n87JH9rltEQvTpp5/i/fffh0ajQVBQED755BNERNx9Uq19+/YhOTkZ1dXV8PX1xXvvvYfnn39+UG1xQsSGS3VzNbZlb8PXF74ecDoa6H/0wHzv+Yj2jsbPp/580HOhDCUhau1uRU5tjpAAFWuKjfbJSmSFGJ8YJAYnInZaLN82z9gdunq7kF2djcNXD+Pw1cMD5uMxxl3ijlBFKAJdAiF3kMPZ3hkuE1zgPKH/dZLdJIyzGjfoPhARevQ9qNPVCYnP5cbLwvumzqZ7bu803gmqqSrEeMdgvvd8uEvdB932aDbmE6J//OMfWLZsGXbt2oXIyEjs3LkT+/btQ0VFBVxcBp66//777zF79mykpKRg4cKF2LNnD9577z0UFRUhMDDwvu1xQsSGW0VjBXae3YmjlUcHzOp6mwgihLuFC2eO5A5yuDq4Qu4gh9RWavAb290SIj3p0dDeAHWbGupWNdRtaly5eQVZ1VkouFGAPuq7a9shihDEPxGPpTOXWtQdNYw9DCJCxc0KnK45jWJ1MYo0RbhYf3HAtXf3I4IIk+wn9SdJ9s6wt7FHZ28nOns6DV67eruE98Z+obnX/iMmRyDaOxrRPtGImBwxJqfGGPMJUWRkJJ588kn89a9/BQDo9Xq4u7vjtddew4YNGwbUj4+PR3t7Ow4dOiSse+qppxAcHIxdu3bdtz1OiNhIISJcuXlFGMfPqs4yuNbobmzH2QrJkesEVyjsFHC74AYAKPYpxvWO61C3qqFp09w16bnTDJcZeNbrWTw75VnM9pyNiXYTH+rYGGP9evW9uNx4GUXqImEp1hSj7VabSdqf7DgZAc4B8J/kD39Z/xIsD7aI2bhH+v9vs6aQt27dQmFhITZu3Ciss7KygkqlQl5entFt8vLysH79eoN10dHRSE9PH8muMnZfIpEIfjI/+Mn8sDpyNbp6u5Bbm4vjlcdxvOo4SrWlRrfr7utGra5WeJaaDWywCZsAAEcqj6AHA28TvlOALEBIgOZ4zoHzBOfhOzDGmMDayhqBLoEIdAnEsqBlAPrP3lY2VaKqqQoNHQ1oaG+Atl2Lho47Xtsb0N7TbrC/8dbjYWdt1/9qYwc7azvhVWYvQ4AsAP4yfwQ4B8Bvkt+w3LjBjDNrQtTY2Ii+vj64uroarHd1dcXly5eNbqPRaIzW12g0Rut3d3eju7tb+Fmn0wHozzQZG2kRsghEyCKQ/FQy1K1qnKk7A3WrGvVt9dB2aKFt1wrvG9v7HzlAIHShS3gvEongPMEZ8gn9w2wKB4VwNslN4oZQeSjkjvL/b7SP/34zZmpyGznkrvL71mu/1Y7uvm7YWdvB1tr2geY8om5CS7flfrdv/7s2UgNbY2+Q8Q4pKSnYunXrgPXu7mPjIjM29vSiF9ux3WCd9r9/GGPM0t28eRNS6fDPD2XWhEgmk2HcuHGorzecVr2+vh5yufFMWy6XP1D9jRs3GgyxNTc3w9PTE7W1tSPygbLBa2lpgbu7O+rq6vh6rlGA4zF6cCxGD47F6KHT6eDh4YGJE0fmmkizJkRisRhhYWHIzMxEXFwcgP6LqjMzM5GUlGR0G6VSiczMTKxdu1ZYl5GRAaXS+APobG1tYWs78NZiqVTKf7lHCYlEwrEYRTgeowfHYvTgWIweVlYj82gVsw+ZrV+/HsuXL0d4eDgiIiKwc+dOtLe3IzExEQCwbNkyTJ48WbgNec2aNZgzZw4++OADLFiwAGlpaSgoKMAXX3xhzsNgjDHG2CPM7AlRfHw8Ghoa8NZbb0Gj0SA4OBjHjh0TLpyura01yAZnzZqFPXv24E9/+hPefPNN+Pr6Ij09fVBzEDHGGGOMGWP2hAgAkpKS7jpElpWVNWDdokWLsGjRoiG1ZWtri82bNxsdRmOmxbEYXTgeowfHYvTgWIweIx0Ls0/MyBhjjDFmbiNzZRJjjDHG2COEEyLGGGOMWTxOiBhjjDFm8TghYowxxpjFs7iE6NNPP4WXlxfGjx+PyMhInDt3ztxdGnNOnz6N2NhYuLm5QSQSDXjwLhHhrbfegkKhgJ2dHVQqFa5evWpQp6mpCQkJCZBIJHBycsKKFSvQ1maap0mPJSkpKXjyySfh6OgIFxcXxMXFoaKiwqBOV1cXVq1ahUmTJsHBwQG/+tWvBswGX1tbiwULFsDe3h4uLi5444030Nvba8pDeeR99tlnmDlzpjDBn1KpxNGjR4VyjoP5bN++HSKRyGDCX46HaWzZsgUikchg8ff3F8pNGgeyIGlpaSQWi+mrr76i8vJyeuWVV8jJyYnq6+vN3bUx5ciRI7Rp0ybav38/AaADBw4YlG/fvp2kUimlp6fThQsX6Be/+AVNmTKFOjs7hToxMTEUFBREZ8+epZycHPLx8aHFixeb+EgefdHR0bR7924qKyujkpISev7558nDw4Pa2tqEOq+++iq5u7tTZmYmFRQU0FNPPUWzZs0Synt7eykwMJBUKhUVFxfTkSNHSCaT0caNG81xSI+sgwcP0uHDh+nKlStUUVFBb775JtnY2FBZWRkRcRzM5dy5c+Tl5UUzZ86kNWvWCOs5HqaxefNmeuKJJ0itVgtLQ0ODUG7KOFhUQhQREUGrVq0Sfu7r6yM3NzdKSUkxY6/GtjsTIr1eT3K5nN5//31hXXNzM9na2tLevXuJiOjSpUsEgM6fPy/UOXr0KIlEIrp+/brJ+j4WabVaAkDZ2dlE1P/Z29jY0L59+4Q6P/zwAwGgvLw8IupPcK2srEij0Qh1PvvsM5JIJNTd3W3aAxhjHnvsMfryyy85DmbS2tpKvr6+lJGRQXPmzBESIo6H6WzevJmCgoKMlpk6DhYzZHbr1i0UFhZCpVIJ66ysrKBSqZCXl2fGnlmWa9euQaPRGMRBKpUiMjJSiENeXh6cnJwQHh4u1FGpVLCyskJ+fr7J+zyW6HQ6ABAejlhYWIienh6DePj7+8PDw8MgHjNmzBBmjweA6OhotLS0oLy83IS9Hzv6+vqQlpaG9vZ2KJVKjoOZrFq1CgsWLDD43AH+Xpja1atX4ebmhqlTpyIhIQG1tbUATB+HUTFTtSk0Njair6/P4EMDAFdXV1y+fNlMvbI8Go0GAIzG4XaZRqOBi4uLQbm1tTUmTpwo1GEPTq/XY+3atXj66aeFR91oNBqIxWI4OTkZ1L0zHsbidbuMDV5paSmUSiW6urrg4OCAAwcOYPr06SgpKeE4mFhaWhqKiopw/vz5AWX8vTCdyMhIpKamws/PD2q1Glu3bsUzzzyDsrIyk8fBYhIixizdqlWrUFZWhtzcXHN3xWL5+fmhpKQEOp0O3377LZYvX47s7Gxzd8vi1NXVYc2aNcjIyMD48ePN3R2L9txzzwnvZ86cicjISHh6euKf//wn7OzsTNoXixkyk8lkGDdu3ICr0+vr6yGXy83UK8tz+7O+Vxzkcjm0Wq1BeW9vL5qamjhWQ5SUlIRDhw7h1KlTePzxx4X1crkct27dQnNzs0H9O+NhLF63y9jgicVi+Pj4ICwsDCkpKQgKCsJHH33EcTCxwsJCaLVahIaGwtraGtbW1sjOzsbHH38Ma2truLq6cjzMxMnJCdOmTUNlZaXJvxcWkxCJxWKEhYUhMzNTWKfX65GZmQmlUmnGnlmWKVOmQC6XG8ShpaUF+fn5QhyUSiWam5tRWFgo1Dl58iT0ej0iIyNN3udHGREhKSkJBw4cwMmTJzFlyhSD8rCwMNjY2BjEo6KiArW1tQbxKC0tNUhSMzIyIJFIMH36dNMcyBil1+vR3d3NcTCxqKgolJaWoqSkRFjCw8ORkJAgvOd4mEdbWxuqqqqgUChM/7144EvCH2FpaWlka2tLqampdOnSJVq5ciU5OTkZXJ3OHl5raysVFxdTcXExAaAPP/yQiouLqaamhoj6b7t3cnKif//733Tx4kV64YUXjN52HxISQvn5+ZSbm0u+vr582/0Q/O53vyOpVEpZWVkGt7V2dHQIdV599VXy8PCgkydPUkFBASmVSlIqlUL57dta58+fTyUlJXTs2DFydnbm24sf0IYNGyg7O5uuXbtGFy9epA0bNpBIJKLvvvuOiDgO5va/d5kRcTxM5fXXX6esrCy6du0anTlzhlQqFclkMtJqtURk2jhYVEJERPTJJ5+Qh4cHicViioiIoLNnz5q7S2POqVOnCMCAZfny5UTUf+t9cnIyubq6kq2tLUVFRVFFRYXBPm7evEmLFy8mBwcHkkgklJiYSK2trWY4mkebsTgAoN27dwt1Ojs76fe//z099thjZG9vT7/85S9JrVYb7Ke6upqee+45srOzI5lMRq+//jr19PSY+GgebS+99BJ5enqSWCwmZ2dnioqKEpIhIo6Dud2ZEHE8TCM+Pp4UCgWJxWKaPHkyxcfHU2VlpVBuyjiIiIiGfG6LMcYYY2wMsJhriBhjjDHG7oYTIsYYY4xZPE6IGGOMMWbxOCFijDHGmMXjhIgxxhhjFo8TIsYYY4xZPE6IGGOMMWbxOCFijI0YLy8v7Ny509zdAACkpqYOeGr2YCUnJ2PlypXD2yEjdu3ahdjY2BFvhzE2ECdEjDEAwG9+8xvExcUZLWtqasJrr70GPz8/2NnZwcPDA6tXr4ZOpzNtJwdpOBMxjUaDjz76CJs2bRqW/d3LSy+9hKKiIuTk5Ix4W4wxQ5wQMcbu68aNG7hx4wZ27NiBsrIypKam4tixY1ixYoW5uzbivvzyS8yaNQuenp4j3pZYLMaSJUvw8ccfj3hbjDFDnBAxxu4rMDAQ//rXvxAbGwtvb2/MmzcP7777Lv7zn/+gt7d30Ptpbm7Gyy+/DGdnZ0gkEsybNw8XLlwQyrds2YLg4GB888038PLyglQqxYsvvojW1lahTmtrKxISEjBhwgQoFAr85S9/wdy5c7F27VoAwNy5c1FTU4N169ZBJBJBJBIZ9OH48eMICAiAg4MDYmJioFar79nntLS0AcNYxs5ABQcHY8uWLcLPIpEIn3/+ORYuXAh7e3sEBAQgLy8PlZWVmDt3LiZMmIBZs2ahqqrKYD+xsbE4ePAgOjs77/dxMsaGESdEjLEh0el0kEgksLa2HvQ2ixYtglarxdGjR1FYWIjQ0FBERUWhqalJqFNVVYX09HQcOnQIhw4dQnZ2NrZv3y6Ur1+/HmfOnMHBgweRkZGBnJwcFBUVCeX79+/H448/jm3btkGtVhskPB0dHdixYwe++eYbnD59GrW1tfjDH/5w1/42NTXh0qVLCA8PH/Qx/q+3334by5YtQ0lJCfz9/bFkyRL89re/xcaNG1FQUAAiQlJSksE24eHh6O3tRX5+/pDaZIwNzeD/JWOMsf9qbGzE22+//UAXGufm5uLcuXPQarWwtbUFAOzYsQPp6en49ttvhX3p9XqkpqbC0dERALB06VJkZmbi3XffRWtrK77++mvs2bMHUVFRAIDdu3fDzc1NaGfixIkYN24cHB0dIZfLDfrQ09ODXbt2wdvbGwCQlJSEbdu23bXPtbW1ICKD/T+IxMRE/PrXvwYA/PGPf4RSqURycjKio6MBAGvWrEFiYqLBNvb29pBKpaipqRlSm4yxoeGEiDH2QFpaWrBgwQJMnz7dYIjofi5cuIC2tjZMmjTJYH1nZ6fBsJGXl5eQDAGAQqGAVqsFAPz444/o6elBRESEUC6VSuHn5zeoPtjb2wvJ0J37Nub2sNX48eMHtf87zZw5U3jv6uoKAJgxY4bBuq6uLrS0tEAikQjr7ezs0NHRMaQ2GWNDwwkRY2zQWltbERMTA0dHRxw4cAA2NjaD3ratrQ0KhQJZWVkDyv73dvg79ykSiaDX64faZQPG9k1Ed60vk8kAAD/99BOcnZ3vue++vr57tnf7WiZj6+48vqampvu2xxgbXnwNEWNsUFpaWjB//nyIxWIcPHjwgc+ahIaGQqPRwNraGj4+PgbL7cTjfqZOnQobGxucP39eWKfT6XDlyhWDemKx2GiC8qC8vb0hkUhw6dKlAWX19fXC+56eHtTV1T10e0D/NVRdXV0ICQkZlv0xxgaHEyLGmECn06GkpMRgqaurE5Kh9vZ2/O1vf0NLSws0Gg00Gs2gEw+VSgWlUom4uDh89913qK6uxvfff49NmzahoKBgUPtwdHTE8uXL8cYbb+DUqVMoLy/HihUrYGVlZXA3mZeXF06fPo3r16+jsbFxSJ8FAFhZWUGlUiE3N3dA2VdffYUTJ07g6tWrWLduHXQ6HaqqqgwSpaHIycnB1KlTDYb2GGMjjxMixpggKysLISEhBsvWrVtRVFSE/Px8lJaWwsfHBwqFQlgGe2ZEJBLhyJEjmD17NhITEzFt2jS8+OKLqKmpEa6vGYwPP/wQSqUSCxcuhEqlwtNPP42AgACDM1bbtm1DdXU1vL29H3ro6eWXX0ZaWtqAYa3Y2FisXr0aM2bMQFNTE9555x3s378fJ06ceKj29u7di1deeeWh9sEYe3AiutcAOmOMjXLt7e2YPHkyPvjggxGZKJKIEBkZiXXr1mHx4sUA+s9ArV27Vpj7aLiUl5dj3rx5uHLlCqRS6bDumzF2b3yGiDH2SCkuLsbevXtRVVWFoqIiJCQkAABeeOGFEWlPJBLhiy++eKAJKIdKrVbj73//OydDjJkB32XGGHvk7NixAxUVFRCLxQgLC0NOTs6gL8weiuDgYAQHB4/Y/m9TqVQj3gZjzDgeMmOMMcaYxeMhM8YYY4xZPE6IGGOMMWbxOCFijDHGmMXjhIgxxhhjFo8TIsYYY4xZPE6IGGOMMWbxOCFijDHGmMXjhIgxxhhjFo8TIsYYY4xZvP8DEZKw6yQ6AggAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal length: 170.66\n"
     ]
    }
   ],
   "source": [
    "plt.plot(l2_scales * l2_sweep, l2_TM0_TE1, c=\"purple\", linewidth=2, label=\"TM0 → TE1\")\n",
    "plt.plot(l2_scales * l2_sweep, l2_TM0_TM0, c=\"green\", linewidth=2, label=\"TM0 → TM0\")\n",
    "plt.xlim(0, 500)\n",
    "plt.ylim(0, 1)\n",
    "plt.xlabel(\"L2 length (μm)\")\n",
    "plt.ylabel(\"Mode conversion efficiency\")\n",
    "plt.title(\"Mode conversion efficiency vs L2\")\n",
    "plt.axvline(x=l2, color=\"gray\", ls=\"--\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(f\"Optimal length: {l2}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b70d5daf-3c9c-441f-826b-a06d3155a3f2",
   "metadata": {},
   "source": [
    "To verify our results, we will run FDTD validations of $L_2=170.66$ and $L_2=250$:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adc70fbf-2f57-4503-826d-d46f61d5cd64",
   "metadata": {},
   "source": [
    "## FDTD Validation of EME L2 Sweep\n",
    "\n",
    "The FDTD method, while much slower and more expensive than the EME method, is more robust. It is always a good idea to double-check our simulation results with multiple methods. Thus we will define a function that creates an FDTD simulation with a given $L_2$ value. This way we can easily create multiple FDTD simulations with multiple $L_2$ values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bed3637c-77ed-4441-9406-fce0dd7366fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_l2_FDTD_sim(l2_sweep):\n",
    "    # create taper structure\n",
    "    l2_test_pts = [\n",
    "        (-10, -w0 / 2),\n",
    "        (0, -w0 / 2),\n",
    "        (l1, -w1 / 2),\n",
    "        (l1 + l2_sweep, -w2 / 2),\n",
    "        (l1 + l2_sweep + l3, -w3 / 2),\n",
    "        (l1 + l2_sweep + l3 + 10, -w3 / 2),\n",
    "        (l1 + l2_sweep + l3 + 10, w3 / 2),\n",
    "        (l1 + l2_sweep + l3, w3 / 2),\n",
    "        (l1 + l2_sweep, w2 / 2),\n",
    "        (l1, w1 / 2),\n",
    "        (0, w0 / 2),\n",
    "        (-10, w0 / 2),\n",
    "    ]\n",
    "    l2_test_structures = make_ridge_waveguide(l2_test_pts)\n",
    "\n",
    "    # add mode monitor\n",
    "    l2_mode_monitor = td.ModeMonitor(\n",
    "        name=\"l2 mode\",\n",
    "        size=(0, td.inf, td.inf),\n",
    "        center=(l1 + l2_sweep + l3 * 0.95, 0, 0),\n",
    "        freqs=[freq0],\n",
    "        mode_spec=td.ModeSpec(num_modes=2, target_neff=n_o),\n",
    "    )\n",
    "\n",
    "    # specify FDTD grid\n",
    "    auto_x = td.AutoGrid(min_steps_per_wvl=10)\n",
    "    auto_yz = td.AutoGrid(min_steps_per_wvl=30)\n",
    "\n",
    "    # create simulation with these specs\n",
    "    l2_sweep_sim = td.Simulation(\n",
    "        center=((l1 + l2_sweep + l3) / 2, 0, 0),\n",
    "        size=(l1 + l2_sweep + l3, w3 + 2 * w0, 5 * film_thickness),\n",
    "        sources=[],\n",
    "        structures=l2_test_structures,\n",
    "        grid_spec=td.GridSpec(grid_x=auto_x, grid_y=auto_yz, grid_z=auto_yz, wavelength=wvl0),\n",
    "        medium=SiO2,\n",
    "        monitors=[l2_mode_monitor],\n",
    "        symmetry=(0, 1, 0),\n",
    "        run_time=5e-12,\n",
    "    )\n",
    "\n",
    "    \"\"\"\n",
    "    To inject the right mode into the FDTD simulation, we will need to solve for this correct\n",
    "    mode in FDTD. We will convert this to a source and add it to the simulation.\n",
    "    \"\"\"\n",
    "    fdtd_mode_solver = ModeSolver(\n",
    "        simulation=l2_sweep_sim,\n",
    "        plane=td.Box(center=(1, 0, 0), size=(0, td.inf, td.inf)),\n",
    "        mode_spec=td.ModeSpec(num_modes=3, target_neff=n_o),\n",
    "        freqs=[freq0],\n",
    "    )\n",
    "    fdtd_source = fdtd_mode_solver.to_source(\n",
    "        mode_index=0,\n",
    "        direction=\"+\",\n",
    "        source_time=td.GaussianPulse(freq0=freq0, fwidth=freq0 / 10),\n",
    "    )\n",
    "    return l2_sweep_sim.updated_copy(sources=[fdtd_source])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a319f49a-f9d2-421f-8f9c-16bf37712b14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create FDTD simulations where L2 = l2, 250 μm\n",
    "l2_FDTD_opt_sim = make_l2_FDTD_sim(l2)\n",
    "l2_FDTD_250_sim = make_l2_FDTD_sim(250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bf828237-c8be-4b8a-bebf-5a7d251b1dc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# validate our FDTD-creating function\n",
    "_, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
    "l2_FDTD_opt_sim.plot(z=0.005, ax=ax[0])\n",
    "l2_FDTD_opt_sim.plot(x=l1 + 125 + l3, ax=ax[1])\n",
    "ax[0].set_aspect(6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8d21546b-e523-4fae-9803-13fd49b6bcd3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'L2 FDTD opt'</span> with resource_id                        \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-1c32874f-b975-4ddc-9eab-59b16e8b065c'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'L2 FDTD opt'\u001b[0m with resource_id                        \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-1c32874f-b975-4ddc-9eab-59b16e8b065c'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c32874f-b975-4ddc-9eab-59b16e8b065c\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c32874f-b97</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c32874f-b975-4ddc-9eab-59b16e8b065c\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">5-4ddc-9eab-59b16e8b065c'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=636724;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c32874f-b975-4ddc-9eab-59b16e8b065c\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=915007;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c32874f-b975-4ddc-9eab-59b16e8b065c\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=636724;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c32874f-b975-4ddc-9eab-59b16e8b065c\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=787162;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c32874f-b975-4ddc-9eab-59b16e8b065c\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=636724;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c32874f-b975-4ddc-9eab-59b16e8b065c\u001b\\\u001b[32m-1c32874f-b97\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=636724;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c32874f-b975-4ddc-9eab-59b16e8b065c\u001b\\\u001b[32m5-4ddc-9eab-59b16e8b065c'\u001b[0m\u001b]8;;\u001b\\.                                         \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>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/folder-86acd7be-dbf5-477e-9c86-3e20787acc03\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=669362;https://tidy3d.simulation.cloud/folders/folder-86acd7be-dbf5-477e-9c86-3e20787acc03\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d76a0fe3b91843b381915909ce11b2c7",
       "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\">11:10:19 EST </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.081</span>. Minimum cost depends on task     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:10:19 EST\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m2.081\u001b[0m. Minimum cost depends on task     \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \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\">11:10:20 EST </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:10:20 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \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>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "562692648e3f427bbe901b4c7290e7f5",
       "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\">11:10:26 EST </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:10:26 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:10:30 EST </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:10:30 EST\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \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\">11:10:31 EST </span>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:10:31 EST\u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "565b494b092f476f924be621ddbf2037",
       "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\">11:13:08 EST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">68</span>%, exiting.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:13:08 EST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m68\u001b[0m%, exiting.                            \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": {
      "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>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View simulation result at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c32874f-b975-4ddc-9eab-59b16e8b065c\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c32874f-b97</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c32874f-b975-4ddc-9eab-59b16e8b065c\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">5-4ddc-9eab-59b16e8b065c'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=732994;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c32874f-b975-4ddc-9eab-59b16e8b065c\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=884429;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c32874f-b975-4ddc-9eab-59b16e8b065c\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=732994;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c32874f-b975-4ddc-9eab-59b16e8b065c\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=60832;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c32874f-b975-4ddc-9eab-59b16e8b065c\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=732994;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c32874f-b975-4ddc-9eab-59b16e8b065c\u001b\\\u001b[4;34m-1c32874f-b97\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=732994;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c32874f-b975-4ddc-9eab-59b16e8b065c\u001b\\\u001b[4;34m5-4ddc-9eab-59b16e8b065c'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7b6ca0f56aa0499ebc5427c98db1c2b8",
       "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\">11:13:10 EST </span>Loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:13:10 EST\u001b[0m\u001b[2;36m \u001b[0mLoading simulation from simulation_data.hdf5                       \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>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'L2 FDTD 250'</span> with resource_id                        \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-68290d95-4196-4b01-b470-ae287db380d2'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'L2 FDTD 250'\u001b[0m with resource_id                        \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-68290d95-4196-4b01-b470-ae287db380d2'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-68290d95-4196-4b01-b470-ae287db380d2\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-68290d95-419</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-68290d95-4196-4b01-b470-ae287db380d2\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">6-4b01-b470-ae287db380d2'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=928854;https://tidy3d.simulation.cloud/workbench?taskId=fdve-68290d95-4196-4b01-b470-ae287db380d2\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=114248;https://tidy3d.simulation.cloud/workbench?taskId=fdve-68290d95-4196-4b01-b470-ae287db380d2\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=928854;https://tidy3d.simulation.cloud/workbench?taskId=fdve-68290d95-4196-4b01-b470-ae287db380d2\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=740497;https://tidy3d.simulation.cloud/workbench?taskId=fdve-68290d95-4196-4b01-b470-ae287db380d2\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=928854;https://tidy3d.simulation.cloud/workbench?taskId=fdve-68290d95-4196-4b01-b470-ae287db380d2\u001b\\\u001b[32m-68290d95-419\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=928854;https://tidy3d.simulation.cloud/workbench?taskId=fdve-68290d95-4196-4b01-b470-ae287db380d2\u001b\\\u001b[32m6-4b01-b470-ae287db380d2'\u001b[0m\u001b]8;;\u001b\\.                                         \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>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/folder-86acd7be-dbf5-477e-9c86-3e20787acc03\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=408590;https://tidy3d.simulation.cloud/folders/folder-86acd7be-dbf5-477e-9c86-3e20787acc03\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ed5fbcf9a3c242a49d0391b1f1622355",
       "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\">11:13:12 EST </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.511</span>. Minimum cost depends on task     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:13:12 EST\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m2.511\u001b[0m. Minimum cost depends on task     \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \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\">11:13:13 EST </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:13:13 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \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>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4a7e77b76dde4c2f9ab9b9ed05e92c83",
       "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\">11:13:23 EST </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:13:23 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:13:28 EST </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:13:28 EST\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \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>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4625902d8f704dd1952781db55b722e4",
       "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\">11:14:49 EST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">80</span>%, exiting.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:14:49 EST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m80\u001b[0m%, exiting.                            \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": {
      "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\">11:14:50 EST </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:14:50 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View simulation result at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-68290d95-4196-4b01-b470-ae287db380d2\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-68290d95-419</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-68290d95-4196-4b01-b470-ae287db380d2\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">6-4b01-b470-ae287db380d2'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=113034;https://tidy3d.simulation.cloud/workbench?taskId=fdve-68290d95-4196-4b01-b470-ae287db380d2\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=642461;https://tidy3d.simulation.cloud/workbench?taskId=fdve-68290d95-4196-4b01-b470-ae287db380d2\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=113034;https://tidy3d.simulation.cloud/workbench?taskId=fdve-68290d95-4196-4b01-b470-ae287db380d2\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=619608;https://tidy3d.simulation.cloud/workbench?taskId=fdve-68290d95-4196-4b01-b470-ae287db380d2\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=113034;https://tidy3d.simulation.cloud/workbench?taskId=fdve-68290d95-4196-4b01-b470-ae287db380d2\u001b\\\u001b[4;34m-68290d95-419\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=113034;https://tidy3d.simulation.cloud/workbench?taskId=fdve-68290d95-4196-4b01-b470-ae287db380d2\u001b\\\u001b[4;34m6-4b01-b470-ae287db380d2'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8c1e032f37aa4d59b206d481ba249567",
       "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\">11:14:51 EST </span>Loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:14:51 EST\u001b[0m\u001b[2;36m \u001b[0mLoading simulation from simulation_data.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "l2_FDTD_opt_data = web.run(simulation=l2_FDTD_opt_sim, task_name=\"L2 FDTD opt\")\n",
    "l2_FDTD_250_data = web.run(simulation=l2_FDTD_250_sim, task_name=\"L2 FDTD 250\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82560256-d839-41a0-aa60-a2f9091c7645",
   "metadata": {},
   "source": [
    "Now that we have run our equivalent FDTD simulations, we will check these values against our EME values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "803ed994-4798-43fa-960b-e4fa0f9914c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "TM0_TM0_opt_FDTD = (\n",
    "    l2_FDTD_opt_data[\"l2 mode\"].amps.sel(direction=\"+\", mode_index=1).abs.data[0] ** 2\n",
    ")\n",
    "TM0_TE1_opt_FDTD = (\n",
    "    l2_FDTD_opt_data[\"l2 mode\"].amps.sel(direction=\"+\", mode_index=0).abs.data[0] ** 2\n",
    ")\n",
    "\n",
    "TM0_TM0_opt_EME = l2_TM0_TM0[arg_min]\n",
    "TM0_TE1_opt_EME = l2_TM0_TE1[arg_min]\n",
    "\n",
    "TM0_TM0_250_FDTD = (\n",
    "    l2_FDTD_250_data[\"l2 mode\"].amps.sel(direction=\"+\", mode_index=1).abs.data[0] ** 2\n",
    ")\n",
    "TM0_TE1_250_FDTD = (\n",
    "    l2_FDTD_250_data[\"l2 mode\"].amps.sel(direction=\"+\", mode_index=0).abs.data[0] ** 2\n",
    ")\n",
    "TM0_TM0_250_EME = l2_TM0_TM0[25]\n",
    "TM0_TE1_250_EME = l2_TM0_TE1[25]\n",
    "\n",
    "lengths_l2 = np.array([l2, 250])\n",
    "TM0_FDTD = np.array([TM0_TM0_opt_FDTD, TM0_TM0_250_FDTD])\n",
    "TM0_EME = np.array([TM0_TM0_opt_FDTD, TM0_TM0_250_EME])\n",
    "TE1_FDTD = np.array([TM0_TE1_opt_FDTD, TM0_TE1_250_FDTD])\n",
    "TE1_EME = np.array([TM0_TE1_opt_EME, TM0_TE1_250_EME])\n",
    "\n",
    "_, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
    "\n",
    "ax[0].scatter(lengths_l2, TM0_FDTD, marker=\"s\", c=\"blue\", label=\"FDTD\")\n",
    "ax[0].scatter(lengths_l2, TM0_EME, marker=\"^\", c=\"red\", label=\"EME\")\n",
    "ax[0].legend()\n",
    "ax[0].set_ylim(0, 1)\n",
    "ax[0].set_xticks(lengths_l2, lengths_l2)\n",
    "ax[0].margins(0.25)\n",
    "ax[0].set_xlabel(\"L2 (μm)\")\n",
    "ax[0].set_ylabel(\"Conversion Efficiency\")\n",
    "ax[0].set_title(\"TM0 to TM0\")\n",
    "\n",
    "ax[1].scatter(lengths_l2, TE1_FDTD, marker=\"s\", c=\"blue\", label=\"FDTD\")\n",
    "ax[1].scatter(lengths_l2, TE1_EME, marker=\"^\", c=\"red\", label=\"EME\")\n",
    "ax[1].legend()\n",
    "ax[1].set_ylim(0, 1)\n",
    "ax[1].set_xticks(lengths_l2, lengths_l2)\n",
    "ax[1].margins(0.25)\n",
    "ax[1].set_xlabel(\"L2 (μm)\")\n",
    "ax[1].set_title(\"TM0 to TE1\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85e1b542-8a9a-4ab7-8e20-36931e2ab87f",
   "metadata": {},
   "source": [
    "These results coincide nicely, verifying the accuracy of our EME sweep. Now let's check the field profile from our EME simulation where $L_2$ = 170.66 μm, as this is where maximum efficiency occurs. We will do this by adding a field monitor to this simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ce38d94c-24f0-4185-ab72-d09ac7d40039",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create taper structure\n",
    "l2_ideal_pts = [\n",
    "    (-10, -w0 / 2),\n",
    "    (0, -w0 / 2),\n",
    "    (l1, -w1 / 2),\n",
    "    (l1 + l2, -w2 / 2),\n",
    "    (l1 + l2 + l3, -w3 / 2),\n",
    "    (l1 + l2 + l3 + 10, -w3 / 2),\n",
    "    (l1 + l2 + l3 + 10, w3 / 2),\n",
    "    (l1 + l2 + l3, w3 / 2),\n",
    "    (l1 + l2, w2 / 2),\n",
    "    (l1, w1 / 2),\n",
    "    (0, w0 / 2),\n",
    "    (-10, w0 / 2),\n",
    "]\n",
    "l2_ideal_structures = make_ridge_waveguide(l2_ideal_pts)\n",
    "\n",
    "# create field monitor\n",
    "l2_field_monitor = td.EMEFieldMonitor(\n",
    "    name=\"l2 field\",\n",
    "    size=(td.inf, td.inf, 0),\n",
    "    center=(l1 + l2 + l3 * 0.95, 0, 0),\n",
    "    freqs=[freq0],\n",
    "    fields=[\"Ex\", \"Ey\", \"Ez\"],\n",
    "    num_modes=4,\n",
    ")\n",
    "\n",
    "# add field monitor to EME simulation\n",
    "optimal_l2_sim = l2_sweep_sim.updated_copy(\n",
    "    structures=l2_ideal_structures,\n",
    "    monitors=[l2_field_monitor],\n",
    "    symmetry=(0, 0, 0),\n",
    "    sweep_spec=None,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e7213694-a050-4dd3-b62b-163c0e69209e",
   "metadata": {},
   "outputs": [
    {
     "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\">11:14:52 EST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'Ideal L2 field validation'</span> with resource_id          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'eme-42b45ee9-bce5-4f12-a3a6-2c2448e074d5'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'EME'</span>.    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:14:52 EST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'Ideal L2 field validation'\u001b[0m with resource_id          \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'eme-42b45ee9-bce5-4f12-a3a6-2c2448e074d5'\u001b[0m and task_type \u001b[32m'EME'\u001b[0m.    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Tidy3D's EME solver is currently in the beta stage. Cost of EME    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>simulations is subject to change in the future.                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTidy3D's EME solver is currently in the beta stage. Cost of EME    \n",
       "\u001b[2;36m             \u001b[0msimulations is subject to change in the future.                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "a39e03d2015c4c668c50eb8a98f8d4f4",
       "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\">11:14:56 EST </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.387</span>. Minimum cost depends on task     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:14:56 EST\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m0.387\u001b[0m. Minimum cost depends on task     \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \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\">11:14:57 EST </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:14:57 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \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>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "model_id": "a3def66feda647a3a9335678647f0d56",
       "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:15:02 EST </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:15:02 EST\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \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\">11:15:03 EST </span>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:15:03 EST\u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "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\">11:16:33 EST </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:16:33 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \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"
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      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:18:24 EST </span>Loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:18:24 EST\u001b[0m\u001b[2;36m \u001b[0mLoading simulation from simulation_data.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ideal_l2_data = web.run(simulation=optimal_l2_sim, task_name=\"Ideal L2 field validation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cf66bf20-5f77-4400-8c9b-02a15943ed13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
    "\n",
    "ideal_l2_data.plot_field(\"l2 field\", \"E\", \"abs\", f=freq0, eme_port_index=0, mode_index=0, ax=ax[0])\n",
    "ideal_l2_data.plot_field(\"l2 field\", \"E\", \"abs\", f=freq0, eme_port_index=1, mode_index=1, ax=ax[1])\n",
    "ax[0].set_aspect(10)\n",
    "ax[1].set_aspect(10)\n",
    "ax[0].set_title(\"TE0 to TE0\")\n",
    "ax[1].set_title(\"TM0 to TE1\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aa603c1-8689-48ae-82ab-c90610d55917",
   "metadata": {},
   "source": [
    "## Design of Asymmetric Y-Junction\n",
    "\n",
    "We now move forward with the design of the Y-junction. The purpose of this junction is to separate TE0 and TE1 modes - in the wider arm of the junction, a TE0 mode will stay a\n",
    "TE0 mode since the effective refractive indices of the modes will be closest, and, in the thinner arm, a TE1 mode will evolve to a TE0 mode for the same reason. The widths we will choose in order to enable this will be 1.7 μm and 1.3 μm, respectively. We will choose the final distance between the arms to be 1 μm.\n",
    "\n",
    "This mode conversion will only happen adiabatically, so the angle splitting the two arms must be very small. The central question of our design, therefore, is how long the Y-junction will be. To find the optimal length (which we'll denote as $L_4$), we will use Tidy3D's EME sweep feature to do screen various $L_4$ lengths. Tidy3D's EME sweep feature is a fast and inexpensive way to run sweeps where the entire simulation is scaled, which is ideal for testing this geometry.\n",
    "\n",
    "We will measure the TE0 and TM0 conversion efficiencies in both arms. The upper, wider arm will be labeled 'port 1' and the lower, thinner arm will be labeled 'port 2.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5b3250ce-bb12-4825-9e25-19f5e9d5bb66",
   "metadata": {},
   "outputs": [],
   "source": [
    "w4 = 1.7  # width of upper arm\n",
    "w5 = 1.3  # width of lower arm\n",
    "split = 1  # ending distance between two arms\n",
    "\n",
    "# the starting length for the Y-junction. This will change as we change the scale\n",
    "l4_sweep = 100\n",
    "\n",
    "# create Y-junction geometry and structure\n",
    "l4_test_pts = [\n",
    "    (-2 * l3, -w2 / 2),  # extend beyond boundary\n",
    "    (-l3, -w2 / 2),\n",
    "    (0, -w3 / 2),\n",
    "    (l4_sweep * 2, 2 * (-split / 2) - w5),  # extend beyond boundary\n",
    "    (l4_sweep * 2, 2 * (-split / 2)),  # extend beyond boundary\n",
    "    (0, -w3 / 2 + w5),\n",
    "    (l4_sweep * 2, 2 * (split / 2)),  # extend beyond boundary\n",
    "    (l4_sweep * 2, 2 * (split / 2) + w4),  # extend beyond boundary\n",
    "    (0, w3 / 2),\n",
    "    (-l3, w2 / 2),\n",
    "    (-2 * l3, w2 / 2),  # extend beyond boundary\n",
    "]\n",
    "l4_test_structures = make_ridge_waveguide(l4_test_pts)\n",
    "\n",
    "num_modes = 5\n",
    "l4_port1 = td.ModeSolverMonitor(\n",
    "    name=\"l4 port 1\",\n",
    "    size=(0, w4 + 1.5 * split, td.inf),\n",
    "    center=(l4_sweep - 0.1, (-w3 / 2 + w5) + split / 2 + w4 / 2, 0),\n",
    "    freqs=[freq0],\n",
    "    mode_spec=td.ModeSpec(\n",
    "        num_modes=num_modes,\n",
    "        target_neff=n_o,\n",
    "        angle_theta=np.arctan((split / 2 - (-w3 / 2 + w5)) / l4_sweep),\n",
    "    ),\n",
    ")\n",
    "\n",
    "l4_port2 = td.ModeSolverMonitor(\n",
    "    name=\"l4 port 2\",\n",
    "    size=(0, w5 + 1.5 * split, td.inf),\n",
    "    center=(l4_sweep - 0.1, -split / 2 - w5 / 2, 0),\n",
    "    freqs=[freq0],\n",
    "    mode_spec=td.ModeSpec(\n",
    "        num_modes=num_modes,\n",
    "        target_neff=n_o,\n",
    "        angle_theta=np.arctan((-split / 2 - w5 / 2 + w3 / 4) / l4_sweep),\n",
    "    ),\n",
    ")\n",
    "\n",
    "# again owing to the adiabatic nature of this problem, we need only use a unifrom grid throughout the simulation\n",
    "l3_grid_spec = td.EMEUniformGrid(num_cells=16, mode_spec=td.EMEModeSpec(num_modes=num_modes))\n",
    "l4_grid_spec = td.EMEUniformGrid(num_cells=41, mode_spec=td.EMEModeSpec(num_modes=num_modes))\n",
    "l4_total_grid = td.EMECompositeGrid(subgrids=[l3_grid_spec, l4_grid_spec], subgrid_boundaries=[0])\n",
    "\n",
    "auto_x = td.AutoGrid(min_steps_per_wvl=31)\n",
    "auto_yz = td.AutoGrid(min_steps_per_wvl=41)\n",
    "refine_box = td.MeshOverrideStructure(\n",
    "    geometry=td.Box(center=(0, -w3 / 2 + w5, 0), size=(l4_sweep + l3, split / 2, 2.5 * etch_depth)),\n",
    "    dl=[0.1, 0.01, 0.01],\n",
    ")\n",
    "\n",
    "l4_scales = np.linspace(1 / l4_sweep, 200 / l4_sweep, 61)\n",
    "l4_scale_factors = np.ones((len(l4_scales), l3_grid_spec.num_cells + l4_grid_spec.num_cells))\n",
    "l4_scale_factors[:, l3_grid_spec.num_cells : l3_grid_spec.num_cells + l4_grid_spec.num_cells] = (\n",
    "    l4_scales[:, None]\n",
    ")\n",
    "\n",
    "l4_EME = td.EMESimulation(\n",
    "    center=((l4_sweep - l3) / 2, 0, 0),\n",
    "    size=(l4_sweep + l3, 3 * split + w4 + w5, 5 * film_thickness),\n",
    "    structures=l4_test_structures,\n",
    "    grid_spec=td.GridSpec(grid_x=auto_x, grid_y=auto_yz, grid_z=auto_yz, wavelength=wvl0),\n",
    "    medium=SiO2,\n",
    "    monitors=[l4_port1, l4_port2],\n",
    "    eme_grid_spec=l4_total_grid,\n",
    "    freqs=[freq0],\n",
    "    sweep_spec=td.EMELengthSweep(scale_factors=list(l4_scale_factors)),\n",
    "    axis=0,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7a8d5d8c-1707-4fef-a469-706faee00868",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1gre3t8nvEQBltjf8/fffUVBQUCdb0p0+fRrOzs7o1q1brbdNNcep+ERERETUID744APs3r0bQ4YMkbaCS05Oxvbt23HkyBGj7dju5+bmhjlz5mDhwoV4/PHHMXLkSERHR2P16tXo37+/tFXZvn37EBwcjLFjx6Jjx44oLi7Gxo0boVKpMGbMGABAeHg4Dh06hBEjRsDHxwepqalYvXo1WrdujUGDBpUbgzn1XnrpJXz33Xd47bXXsH//fgwcOBB6vR5XrlzBd999hz/++AP9+vVD+/bt8e677+K9997D4MGD8cwzz0Cj0eDkyZPw8vJCREQEAMOWY2vWrMGiRYvQvn17tGzZUlqIrzRra2t8+OGHmDx5MoYMGYJx48ZJ2935+vrirbfequ7bZuShhx6Cq6srJk6ciDfeeAMKhQIbN240ObDs27cvtm3bhtDQUPTv3x8ODg546qmnTLabmpqK6dOn45FHHkFwcDAAYNWqVdi/fz8mTZqEI0eOSFPyq/uM/XvvvYeZM2di7NixCAoKwuHDh7Fp0ya8//77aNasmVR21apVWLhwIfbv34+hQ4cCMCw8N3bsWMyZMwepqalo3749vv76a8TGxuKrr74yOtb777+Phx56SPqc37x5Ex9//DGGDx+Oxx9/vNJYR40ahR9//BGiKJaZ9XD58mU88cQTGDlyJJKSkvDJJ5/A2dkZR48exbfffotx48ZV+dyUZ8+ePXjqqaf4jL1cNcxi/EREREREohgXFydOmDBBdHNzEzUajdi2bVtx5syZYmFhoSiK97bqMrUlnigatrfr3LmzaG1tLbq7u4vTp08Xb9++LeVfv35dfPnll8V27dqJNjY2YrNmzcRHHnlE3Lt3r1QmMjJSHDVqlOjl5SWq1WrRy8tLHDdunPjPP/9UGLu59XQ6nfjhhx+K3bp1EzUajejq6ir27dtXXLhwYZmt29atWyf26dNHKjdkyBBxz549Ur5WqxVHjBghOjo6igCkbcnu3+6uxLZt26T2mjVrJr744ovizZs3jcpMnDhRtLe3L9O/sLAw0Zzhwp9//ik++OCDoq2trejl5SXOmjVL/OOPP8rEc+fOHfHf//636OLiIgKocOu7Z555RnR0dBRjY2ON0n/66ScRgPjhhx9WGpc5Pv/8c7FTp06iWq0W27VrJy5fvtxoSzhRvHce7j+3+fn54ttvvy16eHiIGo1G7N+/v7hr1y6Txzl8+LD40EMPiTY2NqKbm5s4c+ZMMTs726wYz5w5IwIos2UiADE0NFQcO3asaGtrK3p6eoqrVq0S165dK9rZ2YmvvPKKKIrlfw+V9Ov+7RpNfR4uX74sAjD6viF5UYhiHczTICIiIiIioloxbNgweHl5YePGjVKaQqFAWFgYFixYUOfHDwkJwaFDh3D69GnesZcpPmNPREREREQkYx988AG2bdsmbWNYnzIyMvDll19i0aJFHNTLGJ+xJyIiIiIikjF/f3/odLoGOXbz5s1x586dBjk2mY937ImIiIiIiIgsGO/YExERERERWRgulUal8Y49ERERERERkQXjwJ6IiIiIiIjIgnEqPhEREREAQRCQlJQER0dHrvxMRESyIIoicnJy4OXlBaWy/PvyHNgTERERAUhKSoK3t3dDh0FERFRGQkICWrduXW4+B/ZEREREABwdHQEACd9+Cyc7O8DUID8hwfBvbebVVbv1nSe3eNgPecXDfsgrHvZDXvFUkJcdHQ3vceOk31Hl4cCeiIiICJCm3zt16gQnR0egffuyhUr+sKrNvLpqt77z5BYP+yGveNgPecXDfsgrnspiBSp9RIyL5xERERERERFZMA7siYiIqEF89tln8PX1hY2NDfz9/XHixIlyy/7www/o168fXFxcYG9vj969e2Pjxo1GZURRxPz58+Hp6QlbW1sEBgbi6tWrdd0NIiKiBseBPREREdW7bdu2ITQ0FGFhYThz5gx69eqFoKAgpKammizfrFkzvPvuuzh27Bj+/vtvTJ48GZMnT8Yff/whlVmyZAlWrlyJtWvXIioqCvb29ggKCkJBQUF9dYuIiKhBcGBPRERE9W7ZsmWYOnUqJk+ejK5du2Lt2rWws7PDunXrTJYfOnQonn76aXTp0gXt2rXDm2++iZ49e+LIkSMADHfrV6xYgblz52LUqFHo2bMnvvnmGyQlJWHHjh312DMiIqL6x4E9ERER1SudTofTp08jMDBQSlMqlQgMDMSxY8cqrS+KIiIjIxEdHY2HH34YAHDjxg1otVqjNp2dneHv719um4WFhcjOzjZ6ERERWSIO7ImIiKhepaenQ6/Xw93d3Sjd3d0dWq223HpZWVlwcHCAWq3GiBEj8Omnn+Kxxx4DAKleVdqMiIiAs7Oz9OIe9kREZKk4sCciIiKL4OjoiHPnzuHkyZN4//33ERoaigMHDlS7vTlz5iArK0t6JZTsI0xERGRhuI89ERER1asWLVpApVIhJSXFKD0lJQUeHh7l1lMqlWh/d4/f3r174/Lly4iIiMDQoUOleikpKfD09DRqs3fv3ibb02g00Gg0ZTMSEgB7e9NBxMWV37Hq5tVVu/WdJ7d42A95xcN+yCse9kNe8VSUZ+ZFZ96xJyIionqlVqvRt29fREZGSmmCICAyMhIBAQFmtyMIAgoLCwEAfn5+8PDwMGozOzsbUVFRVWqTiIjIEvGOPREREdW70NBQTJw4Ef369cOAAQOwYsUK5ObmYvLkyQCACRMmoFWrVoiIiABgeB6+X79+aNeuHQoLC/Hbb79h48aNWLNmDQBAoVAgJCQEixYtQocOHeDn54d58+bBy8sLo0ePrlpw3t6AoyNwd3aASXWRV1ftsh/Vy5NbPOyHvOJhP+QVT2PuR05O+eVL4cCeiIiI6t3zzz+PtLQ0zJ8/H1qtFr1798auXbukxe/i4+OhVN6bWJibm4sZM2bg5s2bsLW1RefOnbFp0yY8//zzUplZs2YhNzcX06ZNQ2ZmJgYNGoRdu3bBxsam3vtHRERUnziwJyIiogYRHByM4OBgk3n3L4q3aNEiLFq0qML2FAoFwsPDER4eXlshEhERWQQ+Y09ERERERERkwTiwJyIiIiIiIrJgnIpPREREVBq3u6tentziYT/kFQ/7Ia942A95xcPt7oiIiIiIiIiaNt6xJyIiIiqN293VLE9u8bAf8oqH/ZBXPOyHvOKpwXZ3vGNPREREREREZME4sCciIiIiIiKyYBzYExEREREREVkwDuyJiIiIiIiILBgXzyMiIiIqjdvdVS9PbvGwH/KKh/2QVzzsh7ziqYXt7jiwJyIiIipl45HLsNXYINMtvUyeS5oWAGo1r67are88ucXTmPvh7myHFwd2MRk7madYL2Dr0StIvcjPVVXy5BZPU+iH5masyfjvx4E9ERERUSnxti7Q2NgizaFFmTy33EIAqNW8umq3vvPkFk9j7sdVPfBYSy+0LEmw9O28zMmr5Xb/SczAPxpnfq7YD1nEU1Gek22myfjvx2fsiYiIiIgszIkYbUOHYNH+iktr6BCIahUH9kREREREFibqmhaiKDZ0GBYpt7AI0cm3GzoMolrFgT0RERERkYVJz8lHfEZOQ4dhkU7fSIEg8KIINS4c2BMRERERWSBOJ6+eKD7GQI0QF88jIiIiKsX1VipsNDYm85plpJRbr7p5ddVufefJLZ6m0I/E7DQUuSphXV4BOW3ZJZNtyTJy8pFz/hI/V+yHrOKpKM/uVmq5eaXxjj0RERERkQUqLBIQm5bV0GFYlL/iOcuBGifesSciIiIq5Xazlobt7txblVumLvLqql32o3H342SxBh243Z1ZeaIo4sDZVKNzyc8V+yGneEzlORXkl1u+NN6xJyIiIiKyUP9oM3GnoKihw7AI11OzkJZj3iCJyNJwYE9EREREZKEEQcTpGxU/S0wGUde4aB41XhzYExERERFZsKiY5IYOQfaK9AJOX+cFEGq8OLAnIiIiIrJgN9KykZqV19BhyNrFhHTk6YobOgyiOsPF84iIiIhK4XZ37Iec4jE37/yB4xjWrY1xATltW9fA291dPnoFbim3pK/5uWI/5BQPt7sjIiIiIiL8FZcGURQbOgxZyi0sQnTy7YYOg6hO8Y49ERERUSnc7q5meXKLp6n0Iw3ANUc3tPdwKVtATtvWNcB2d6eKbJDi5mUyj58r9kNO8XC7OyIiIiKiJi7qGhfRM+UEV8OnJoADeyIiIiKiRuD0jVQUFesbOgxZycjJx/XUrIYOg6jOcWBPRERERNQI5OuKcf5mRkOHISt/xac1dAhE9YIDeyIiIiKiRoJ72t8jiiL+ik9v6DCI6gUXzyMiIiIqhdvdsR9yiqeqeSmpScj1tIa9Ri2vbesaYLu7pHOXoIiLg1vzgjJ5/FyxH3KKh9vdERERERGRRBBFXIjndHwAuJJ0q/JCRI0E79gTERFRg/jss8+wdOlSaLVa9OrVC59++ikGDBhgsuwXX3yBb775BhcuXAAA9O3bFx988IFReVEUERYWhi+++AKZmZkYOHAg1qxZgw4dOlQpLm53V7M8ucXTFPtxoNgG/qW3f5PTtnX1tN1dkV5AVHEUCpq7N/j70dDHZD8sux/c7o6IiIhka9u2bQgNDUVYWBjOnDmDXr16ISgoCKmppqccHjhwAOPGjcP+/ftx7NgxeHt7Y/jw4UhMTJTKLFmyBCtXrsTatWsRFRUFe3t7BAUFoaCg7DRcosYsNi0bKVl5DR1Gg7qQkI4CHXcIoKaDA3siIiKqd8uWLcPUqVMxefJkdO3aFWvXroWdnR3WrVtnsvzmzZsxY8YM9O7dG507d8aXX34JQRAQGRkJwHC3fsWKFZg7dy5GjRqFnj174ptvvkFSUhJ27Nhhss3CwkJkZ2cbvYgaixNNfE/7qBjuXU9NCwf2REREVK90Oh1Onz6NwMBAKU2pVCIwMBDHjh0zq428vDwUFRWhWbNmAIAbN25Aq9Uatens7Ax/f/9y24yIiICzs7P08vb2rkGviOQlKkYLQRQbOowGcaegCBducjV8alo4sCciIqJ6lZ6eDr1eD3d3d6N0d3d3aLXm3WWbPXs2vLy8pIF8Sb2qtDlnzhxkZWVJr4SEhKp2hUi2Mu4UID69ac5COX0jBXqhaV7UoKaLi+cRERGRRVm8eDG2bt2KAwcOwMbG9LZ05tBoNNBoNGXSud0d+yGneGrSj5jsOPj28DGdKact7Wp5u7voQ3/D7dYd2b0fcoqH/ZBXPNzujoiIiCxOixYtoFKpkJJi/IdMSkoKPDw8Kqz70UcfYfHixdi9ezd69uwppZfUq06bRI3V1ZTbKCpuWgvIZeTk4+atOw0dBlG94x17IiIiqldqtRp9+/ZFZGQkRo8eDQDSQnjBwcHl1luyZAnef/99/PHHH+jXr59Rnp+fHzw8PBAZGYnevXsDALKzsxEVFYXp06dXKT5ud1ezPLnF09T78Y/aCd3ktG1dHW93d/TM9TLnQ07vh9ziYT/kFU9NtrvjwJ6IiIjqXWhoKCZOnIh+/fphwIABWLFiBXJzczF58mQAwIQJE9CqVStEREQAAD788EPMnz8fW7Zsga+vr/TcvIODAxwcHKBQKBASEoJFixahQ4cO8PPzw7x58+Dl5SVdPCBqis7GpaFbQwdRT0RRRFRM094NgJouDuyJiIio3j3//PNIS0vD/PnzodVq0bt3b+zatUta/C4+Ph5K5b0nBtesWQOdTodnn33WqJ2wsDAsWLAAADBr1izk5uZi2rRpyMzMxKBBg7Br164aPYdPZOlitJnIydfB0Vbd0KHUuWspWci4U9DQYRA1CA7siYiIqEEEBweXO/X+wIEDRl/HxsZW2p5CoUB4eDjCw8NrITqixkEQRZy+kYKhXRv/do5R13i3npouLp5HRERERNSIRcWYt42kJSsq1uP0DfNWDydqjHjHnoiIiKgUbnfHfsgpntrox62LQJq3Bm6Odvcy5bSlXS1sd/fPzQw4JMTBoVSWXN8PS86TWzxNoR/c7o6IJAcOHIBCoSgztZUs34kTJ6BWqxFX2R9FdSAjIwP29vb47bff6v3YRERUNX/HpTV0CHXqrzjeraemjXfsiRqR1atXw87ODpMmTWroUKpsy5YtSE1NRUhISEOHAkEQ8NFHH2HNmjVITk5Gx44dMWfOHIwbN86s+pmZmZg1axZ+/PFH5OXlYcCAAfj444/xwAMPGJXbtm0bdu7ciaioKMTExGDIkCFVvvjy7rvvYty4cfDx8alSvdrQvHlzvPLKK5g3bx6eeOKJej8+UV3hdnc1y5NbPOyHIX+fzgaPtGsHpUJhnCmnLe2qud1dbqEOf8ZrIbjbmcyX4/th6Xlyi6cx98Pc7e54x56oEVm9ejU2bNhQJv3hhx9Gfn4+Hn744foPykxbtmzBihUrGjoMAIbB8uzZs/HYY4/h008/RZs2bfDvf/8bW7durbSuIAgYMWIEtmzZguDgYCxZsgSpqakYOnQorl69alR2zZo1+Omnn+Dt7Q1XV9cqx3nu3Dns3bsXr732WpXr1pbXXnsNZ86cwb59+xosBiIiqtytOwW4lpLZ0GHUiQvxGRBEsaHDIGpQHNhTo5Obm9vQIciOUqmEjY2N0dZRZFpiYiI+/vhjzJw5E59//jmmTp2KnTt3YvDgwfjPf/4DvV5fYf3vv/8eR48exYYNGxAWFoaZM2fiwIEDUKlUCAsLMyq7ceNGZGVlYd++ffDy8qpyrOvXr0ebNm3w4IMPVrlubenSpQu6d+9u8oISERHJS2NdRO9cfON+zIDIHPwrn2QtMTERU6ZMgZeXFzQaDfz8/DB9+nTodDoAwIYNG6BQKHDw4EHMmDEDLVu2ROvWraX6q1evRrdu3aDRaODl5YWZM2ciMzPT6BhXr17FmDFj4OHhARsbG7Ru3RovvPACsrKypDJ79uzBoEGD4OLiAgcHB3Tq1An//e9/K43fnHqFhYUICwtD+/btodFo4O3tjVmzZqGwsLBMe5s2bcKAAQNgZ2cHV1dXPPzww9i9ezcAwNfXFxcvXsTBgwehUCigUCgwdOhQAOU/Y799+3b07dsXtra2aNGiBcaPH4/ExESjMpMmTYKDgwMSExMxevRoODg4wM3NDW+//Xalg1wA+OmnnzBixAjpPWzXrh3ee+89o7pDhw7Fr7/+iri4OCl2X1/fctucNGmSVO7+V8l+1tX1008/oaioCDNmzJDSFAoFpk+fjps3b+LYsWMV1v/+++/h7u6OZ555Rkpzc3PDc889h59++snoffX29q7RxZYdO3bg0UcfheK+aZXlnQdfX1+jxzRKvn+OHDmCN954A25ubnBxccGrr74KnU6HzMxMTJgwAa6urnB1dcWsWbMgmrgj8thjj2Hnzp0m84iISD7OxKaiqLjy392WJD0nH4m37jR0GEQNjs/Yk2wlJSVhwIAByMzMxLRp09C5c2ckJibi+++/R15eHtRqtVR2xowZcHNzw/z586U79gsWLMDChQsRGBiI6dOnIzo6GmvWrMHJkyfx559/wtraGjqdDkFBQSgsLMTrr78ODw8PJCYm4pdffkFmZiacnZ1x8eJFPPnkk+jZsyfCw8Oh0WgQExODP//8s8L4zaknCAJGjhyJI0eOYNq0aejSpQvOnz+P5cuX459//sGOHTuksgsXLsSCBQvw0EMPITw8HGq1GlFRUdi3bx+GDx+OFStW4PXXX4eDgwPeffddAIC7u3u58W3YsAGTJ09G//79ERERgZSUFHzyySf4888/cfbsWbi4uEhl9Xo9goKC4O/vj48++gh79+7Fxx9/jHbt2mH69OkVnocNGzbAwcEBoaGhcHBwwL59+zB//nxkZ2dj6dKlAAxT37OysnDz5k0sX74cAODg4FBum6+++ioCAwON0nbt2oXNmzejZcuWUlp6enqFsZVwdHSERqMBAJw9exb29vbo0qWLUZkBAwZI+YMGDSq3rbNnz+KBBx4oM2AfMGAAPv/8c/zzzz/o0aOHWXFVJDExEfHx8WWe26+Oks/+woULcfz4cXz++edwcXHB0aNH0aZNG3zwwQf47bffsHTpUnTv3h0TJkwwqt+3b18sX74cFy9eRPfu3WscDxER1Y18XTH+TkhHX7/y/z6wNH/Fc9E8IoADe5KxOXPmQKvVIioqCv369ZPSw8PDy9wZbNasGSIjI6FSqQAAaWlpiIiIwPDhw/H7779Lg6zOnTsjODgYmzZtwuTJk3Hp0iXcuHED27dvx7PPPiu1N3/+fOn/e/bsgU6nw++//44WLVqYHb859bZs2YK9e/fi4MGDRoPF7t2747XXXsPRo0fx0EMPISYmBuHh4Xj66afx/fffGw0aS87F6NGjMXfuXOnOe0WKioowe/ZsdO/eHYcOHYKNjWFbp0GDBuHJJ5/E8uXLsXDhQql8QUEBnn/+ecybNw+A4bnqBx54AF999VWlA/stW7bA1tZW+vq1117Da6+9htWrV2PRokXQaDR47LHH0KpVK9y+fbvS2AEgICAAAQEB0tcxMTEIDg7GY489hldffVVKd3Nzq7QtwDClveROdnJyMtzd3cvcBff09ARguOBUkeTkZJNrGZSuXxsD+ytXrgAA/Pz8atyWu7s7fvvtNygUCsyYMQMxMTFYunQpXn31VaxZswYAMG3aNPj6+mLdunVlBvZt27YFAFy6dIkDe2oUuN0d+yGneGq7H5cP5qGvvou8trSr5nZ3gigi7tQFNMvXmcy3hPfD0vLkFk9T6Ae3uyOLJggCduzYgaeeespoUF/i/kHX1KlTpUE9AOzduxc6nQ4hISFGg+CpU6fCyckJv/76KwDA2dkZAPDHH38gLy/PZCwld65/+uknCIJgdh/Mqbd9+3Z06dIFnTt3Rnp6uvR69NFHAQD79+8HYJhyLQgC5s+fX+ZO8P3nwhynTp1CamoqZsyYIQ3qAWDEiBHo3LmzdH5Ku3+BtsGDB+P69euVHqv0oD4nJwfp6ekYPHgw8vLypMFpTeTm5uLpp5+Gq6srvv32W6PPwZ49e8x6BQUFSXXy8/Olu/ellZyn/PyKVyataX1zZWRkAEC1Ft2735QpU4w+R/7+/hBFEVOmTJHSVCoV+vXrZ/I9L4nB3BkSRETUcGK0mbhTYHogbGni07ORXc6gnqip4R17kqW0tDRkZ2ebfffv/ruWJXt6d+rUyShdrVajbdu2Ur6fnx9CQ0OxbNkybN68GYMHD8bIkSMxfvx4adD//PPP48svv8Qrr7yCd955B8OGDcMzzzyDZ599tsLno82pd/XqVVy+fLncO8upqYYrdNeuXYNSqUTXrl3NOh+VKe/8AIZZDUeOHDFKs7GxKROjq6srbt++XemxLl68iLlz52Lfvn3Izs42yiu9jkF1TZ06FdeuXcPRo0fRvHlzo7z7p+ubw9bW1uT6BgUFBVJ+Xdavqtp4rr1NmzZGX5d89r29vcukm3rPS2KozkUmIjnidnc1y5NbPOxH2fyTSkc84nP3kUY5bWlXxe3u/tRexq3mhscK5HTOm+rnyhLz5BZPTba748CeGoWaDJY+/vhjTJo0CT/99BN2796NN954AxERETh+/Dhat24NW1tbHDp0CPv378evv/6KXbt2Ydu2bXj00Uexe/duozvE98dUWT1BENCjRw8sW7bMZBv3D6waSnl9rExmZiaGDBkCJycnhIeHo127drCxscGZM2cwe/bsKs2AMOWTTz7Bt99+i02bNqF3795l8rVa81b/dXZ2lj5Dnp6e2L9/P0RRNBqoJicnA0Clq9d7enpKZUszt765Si5imHNxpUR5FwHKe39NpZtqoySGqjyqQkREDScqRotHutZ8xldDKirW43RsKspfkYeoaeFUfJIlNzc3ODk54cKFC9Wq7+PjAwCIjo42StfpdLhx44aUX6JHjx6YO3cuDh06hMOHDyMxMRFr166V8pVKJYYNG4Zly5bh0qVLeP/997Fv3z5pqnx5KqvXrl073Lp1C8OGDUNgYGCZV8kd9Xbt2kEQBFy6dKnC45l7x7S881OSdv/5qa4DBw4gIyMDGzZswJtvvoknn3wSgYGBJqePV/Vu7+HDh/H2228jJCQEL774oskynp6eZr22bdsm1enduzfy8vJw+fJlo7aioqKk/Ir07t0bZ86cKXPRIioqCnZ2dujYsWOV+lmezp07AwBu3LhhMj8nJ6dMWskMkNpWEsP9Cw4SEZE8xaVnIy3H9COIluJ8QjrydcUNHQaRbHBgT7KkVCoxevRo7Ny5E6dOnSqTX9n048DAQKjVaqxcudKo7FdffYWsrCyMGDECAJCdnY3iYuNfCj169IBSqZSmU9+6datM+yWDO1NTrkuYU++5555DYmIivvjiizJl8/PzpRX+R48eDaVSifDw8DIDxtL9s7e3L7Odnyn9+vVDy5YtsXbtWqM+/P7777h8+bJ0fmqq5I5v6Rh1Oh1Wr15dpqy9vb3ZU/OTk5Px3HPPYdCgQdLK+qZU5xn7UaNGwdra2ihGURSxdu1atGrVCg899JBRHFeuXEFRUZGU9uyzzyIlJQU//PCDlJaeno7t27fjqaeeMvn8fXW0atUK3t7eJr8/AJTZ2vD3339HQUFBnWxJd/r0aTg7O6Nbt2613jYREdWNv+Ise+/3qGvmzcojaio4FZ9k64MPPsDu3bsxZMgQaSu45ORkbN++HUeOHDHaju1+bm5umDNnDhYuXIjHH38cI0eORHR0NFavXo3+/ftLK6/v27cPwcHBGDt2LDp27Iji4mJs3LgRKpUKY8aMAWBYhf/QoUMYMWIEfHx8kJqaitWrV6N169YVbntmTr2XXnoJ3333HV577TXs378fAwcOhF6vx5UrV/Ddd9/hjz/+QL9+/dC+fXu8++67eO+99zB48GA888wz0Gg0OHnyJLy8vBAREQHAsO3YmjVrsGjRIrRv3x4tW7aUFuIrzdraGh9++CEmT56MIUOGYNy4cdJ2d76+vnjrrbeq+7YZeeihh+Dq6oqJEyfijTfegEKhwMaNG00OLvv27Ytt27YhNDQU/fv3h4ODA5566imT7b7xxhtIS0vDrFmzsHXrVqO8nj17omfPngCq94x969atERISgqVLl6KoqAj9+/fHjh07cPjwYWzevNloevqcOXPw9ddf48aNG/D19QVgGNg/+OCD0q4LLVq0wOrVq6HX6412GgCAQ4cO4dChQwAM60rk5uZi0aJFAICHH37Y5Or6pY0aNQo//vhjmccGAODy5ct44oknMHLkSCQlJeGTTz6Bs7Mzjh49im+//Rbjxo2r8rkpz549e/DUU0/xGXsiIgvyV1w6Hu3WxiLv8uXk63AhIaOhwyCSFQ7sSbZatWqFqKgozJs3D5s3b0Z2djZatWqFf/3rX7Czs6u0/oIFC+Dm5oZVq1bhrbfeQrNmzTBt2jR88MEHsLa2BgD06tULQUFB2LlzJxITE2FnZ4devXrh999/x4MPPggAGDlyJGJjY7Fu3Tqkp6ejRYsWGDJkCBYuXCgtMmaKOfWUSiV27NiB5cuX45tvvsGPP/4IOzs7tG3bFm+++abRtO3w8HD4+fnh008/xbvvvgs7Ozv07NkTL730klRm/vz5iIuLw5IlS5CTk4MhQ4aYHNgDwKRJk2BnZ4fFixdj9uzZsLe3x9NPP40PP/ywwosmVdG8eXP88ssv+L//+z/MnTsXrq6uGD9+PIYNG2Z0lxwAZsyYgXPnzmH9+vVYvnw5fHx8yh3Yp6WlQa/XIzQ0tExeWFiYNLCvrsWLF8PV1RX/+9//sGHDBnTo0AGbNm3Cv//970rrqlQq/Pbbb/jPf/6DlStXIj8/H/3798eGDRvKLFa4b9++MoP9ki0Fw8LCKh3Yv/zyy1i1ahX+/PPPMheZpk+fjoSEBISGhsLFxQUffPABrKysEBoain379tXawP7KlSu4cOECVqxYUSvtEckBt7tjP+QUT131Q5WRgsSzGnibuigr8+3uLl1NRnPtTQDyOuf8XFlOntziqY3t7hRiXczLJCKiejFs2DB4eXlh48aNUppCoUBYWBgWLFhQ58cPCQnBoUOHcPr0ad6xJ4uXnZ0NZ2dnRASHw0ZjI622XVrJH1+1mVdX7dZ3ntziYT8qr9utdXM89qSJi8glA2lTa+7Ud56J/P9F/o3EW3cAyOuc83NlOXlyi6eiPLukOMxZNR9ZWVlwcnIy2ReAd+yJiCzaBx98gMGDB2PRokW1tuihuTIyMvDll1/iu+++46CeGhVud1ezPLnFw36Un3+iWIUhvn5QW5Wz+40Mt7vTZuXinHUc4G48a1JO57ypf64sKU9u8XC7OyKiJsrf3x86na5Bjt28eXPcuXOnQY5NREQ1V1ikx/n4dPRta/qOphydiOGieUSmWOJ6GUREREREVAssaXV5QRRxwoLiJapPvGNPRNTIcOkUIiIy18WbGcjO18HJVt3QoVTqWkomMu4UNHQYRLLEO/ZERERERE2UIIo4db3i1cPlIorT8InKxTv2RERERKVwuzv2Q07x1Ec//jmSiUc1hfcyZbjdXVGxHvEX0uCm0xtlyemc83NlOXlyi6c2trvjwN5MgiAgKSkJjo6OXP2ZiIhkQRRF5OTkwMvLC0olJ+ERUfUk3cpFWnYe3JzsGjqUct1Iy0LBfYN6IrqHA3szJSUlwcfHG4LQ0JEQEREZS0hIQOvWrRs6jEaD293VLE9u8bAf5tU9KthjVPt2xpky2u7uREIe0tyblZsvp3POz5Xl5MktHm53Vw8cHR0hCMDKlSvRq1cvs+rcvHkTAKr0x1Z91anPY8m5Tn0ei326V2fTpk145513KmynvLbNLVvbaQ15bLnF05DHlls8DX3sPXv2YNGiRXB0dAQRUU2cuKbFU33bQinDmal3CnSI0WYCLeU7o4CooXFgb6aS6fe9evXCww8/bFadmJgYAED7iq5MNlCd+jyWnOvU57HYp3t19u7da/R9ZKqd8to2t2xtpzXkseUWT0MeW27xNPSxSwb8fESMiGrqVm4BYrSZ6Ojp2tChlHEhIQMCd3whqpCsHsiLiIhA//794ejoiJYtW2L06NGIjo6usM6GDRugUCiMXjY2xgveiKKI+fPnw9PTE7a2tggMDMTVq1frsitERER1Q3cbiPnc8EqJbOhoiKgRiYpJbugQTDoXn9bQIRDJnqwG9gcPHsTMmTNx/Phx7NmzB0VFRRg+fDhyc3MrrOfk5ITk5GTpFXffyppLlizBypUrsXbtWkRFRcHe3h5BQUEoKOA+mEREREREAHAmNhW6YnktUKfNykXSrTsNHQaR7MlqKv6uXbuMvt6wYQNatmyJ06dPVzj9XaFQwMPDw2SeKIpYsWIF5s6di1GjRgEAvvnmG7i7u2PHjh144YUXaq8DREREZPG43R37Iad46rsf0YdOoodYwU21et7u7sKFOFmdV36u5BVPU+iHudvdyeqO/f2ysrIAAM2alb8CJgDcuXMHPj4+8Pb2xqhRo3Dx4kUp78aNG9BqtQgMDJTSnJ2d4e/vj2PHjpXbZmFhIbKzs41eRERERESN2V/x5g0i6oMgivgrPr2hwyCyCLK6Y1+aIAgICQnBwIED0b1793LLderUCevWrUPPnj2RlZWFjz76CA899BAuXryI1q1bQ6vVAgDc3d2N6rm7u0t5pkRERGDhwoW10xkiIiIy8tlnn2Hp0qXQarXo1asXPv30UwwYMMBk2YsXL2L+/Pk4ffo04uLisHz5coSEhNSozYpwu7ua5cktHvajanUzRAVGuXvA0Ubd4NvdxSTfRoxDC7g1LzQZa2mWkie3eNgPecVTk+3uZHvHfubMmbhw4QK2bt1aYbmAgABMmDABvXv3xpAhQ/DDDz/Azc0N//vf/2p0/Dlz5iArK0t6JSQk1Kg9IiIiMti2bRtCQ0MRFhaGM2fOoFevXggKCkJqquk7hXl5eWjbti0WL15c7qN3VW2TiEwTRBEXEuRxlzzqWvk34YjImCwH9sHBwfjll1+wf//+Ku/fbW1tjT59+khbA5X8AZCSYvzcQkpKSrl/HACARqOBk5OT0YuIiIhqbtmyZZg6dSomT56Mrl27Yu3atbCzs8O6detMlu/fvz+WLl2KF154ARqNplbaJKLy/RXX8KvQ64r1OHOj4mekiegeWQ3sRVFEcHAwfvzxR+zbtw9+fn5VbkOv1+P8+fPw9PQEAPj5+cHDwwORkfe2BMrOzkZUVBQCAgJqLXYiIiKqnE6nw+nTp43WvlEqlQgMDKxw7Zu6aJPr6RCZlnQ7F6nZeQ0aw/n4dBQUyWuFfiI5k9XAfubMmdi0aRO2bNkCR0dHaLVaaLVa5Offe65gwoQJmDNnjvR1eHg4du/ejevXr+PMmTMYP3484uLi8MorrwAwrJgfEhKCRYsW4eeff8b58+cxYcIEeHl5YfTo0fXdRSIiIrPoBQH5umJk5xfi1p0CpGblIi49G9dTspB8+w4SMnKQV1jc0GFWWXp6OvR6fZXXvqmLNiMiIuDs7Cy9vL29q3V8osaooe/acxo+UdXIavG8NWvWAACGDh1qlL5+/XpMmjQJABAfHw+l8t71iNu3b2Pq1KnQarVwdXVF3759cfToUXTt2lUqM2vWLOTm5mLatGnIzMzEoEGDsGvXLtjYmN7KhoiI6H6CIKJIr0dOvg46vR5FxQKK9ALi0rNRrBeQp05DUbEAnV7AjevJKNaLuHpHBZ1eQFGxgIS46ygqFtAsIR+6u3WT42NRJAhw+ivtbjk90pISUFQswLHlve2fslISAQDO7umwQw4eUBnWfUm/Y96COmTanDlzEBoaKn2dnZ0Nb29vbnfHfsgqnobqR9zpWxC6t4FSoTDOrIft7nIKdEg9/TfcRNGsWC0hT27xsB/yiqc2truT1cBevPvNW5EDBw4Yfb18+XIsX768wjoKhQLh4eEIDw+vSXhERCQjgiiiWC9AVywgK68QRXoBNhk5hgGyXo+Y5FsoLhaQAfu7A2k9Yq/fRJGgx1+3RBTdHUgnxMWiWK+Hy7Ucw8C8WA/tzTgU6QU4uiWjSG9Iu5V8EwDg7J5oFMe9QXdW2TRtUak0wy9t5wL1vbSMHABAnvrelNfCRj71tEWLFlCpVFVe+6Yu2tRoNOU+s0/U1N0pKEJsWhbatnSp92NfSMiAYMa4gIjukdXAnoiILJd4d6CdW1gkDYa1mbko1gsQ7G9Ladfj0lCk1yNBp4Gu2HDnOz42DkV6PZpriwxpegGJ8bEo1gtwunQbRXfvkKckxqNIr4d9iwQU6wXp2PcG16ll027klk3LQKk0w+rPznq7e2nZhjvhooOuls8SqdVq9O3bF5GRkdIjcYIgIDIyEsHBwbJok9vd1SxPbvGwH9Wve0Rvh7blbU9Xh9vdHbx0y2Rccjqv/FzJK57G3A9zt7vjwJ6IqJESRRF6QUSBrhhFegFp2fmGAbJeQGxaFor0Au5Yp6Ho7kD6xrVk6AQB0TnKu4PwkunjIprF50nTzIuK9UhOiEOxXoBjSy10d+9yZyQlQATg7H5TiuHegPtW2bSbhWXTclSl0jINaUpHKS0n3zDQ1pQa1JPlCQ0NxcSJE9GvXz8MGDAAK1asQG5uLiZPngzAsJ5Oq1atEBERAcCwON6lS5ek/ycmJuLcuXNwcHBA+7sDgcraJKKqOxubihcCOkFtpaq8cC3RZuYiLj2n3o5H1FhwYE9EVI/0giANmm/nFqBIL0Cdni2lxSRloLhYRJpoJz2HHXvdcHf6XIZhYF1ylzsxPhZFegEuVzKlu9wpNw13tB1a3IROr4colh5cJ0tx3EvLLpuWUlwqzXAH3Lnw3vRxAMjKMkwdL7a/NzjnpEky1/PPP4+0tDTMnz8fWq0WvXv3xq5du6TF7+5fTycpKQl9+vSRvv7oo4/w0UcfYciQIdIjepW1SURVV1Ckx1/xaejftnqPyVRHVAwXzSOqDg7siahJE0QRxcUC7hToDHee9Xok376DIkFEsd2tu3epDQPpG9eTUSSIuJprJd3ljo+9jmK9ANeE/LvPbAtISjAMuJ3+TpMWWEtNjK9gQbS0smmxJqaP3zaOPSvFkOCscLiXlmcYaFsXN+7ntBuzpwsvQgERMQUqxGvcId6/cFUjERwcXO40+fvX0/H19TVrHZ6K2iSi6jkRo623gb0gijjB1fCJqqXKA/sbN27g8OHDiIuLQ15eHtzc3NCnTx8EBARwlXkiqjFRNCxqlqcrQnGxgNSsPKMVyGOSb6FILyID9tJd7rjrN6ETBPx9W5SmiyfE3UBxsQDXazlSueSEWBQLIhxbJEtTyjOkBdEqnj5ulJ5sYkG0/FILoqU3vQXRqPZ016eglZCNISkJyLByxl/27REl2EJb6pGEuiQIAg4ePGjyd31gYCC3hCNqYi4l3kJ2fiGcbOt+ockYbSZu5RbU+XGIGiOzB/abN2/GJ598glOnTsHd3R1eXl6wtbXFrVu3cO3aNdjY2ODFF1/E7Nmz4ePjU5cxEzUYURQhwjCdWhABXbFhqrMgilKeKIp3vy75v+HfjJwCACIcsnJL5ZUtJ8KwrZYIEbGpWRBEEUW2GaWOAwgQjY973/8T4lIgiIBWb2vUtnQMUYQA47TEuHgIAC5lKe61B7FMrKVjTr4ZB1EU4ZZUWLZPECEIkNooiS81MR4XEtLx4c6T0t3w1Jv3FkQruvvs9L3BtfGV+3vpd8qmZYil0koWRLO9l3Z3QTTBnguikXwVKlTIV1ihSKFGi6JMDM88iYcFIF7pgks53XHDtgVQB4+75ufn4+OPP8aaNWtw69Yt9O7dW/pdHxMTgx07dmDq1KkYPnw45s+fjwcffLD2g5AJbnfHfsgpHjn048KBKDzUwcvwRR1ud3cp9SrcUtLKZMvpvMrh/WjIPLnF0xT6Uavb3fXp0wdqtRqTJk3C//t//6/M1frCwkIcO3YMW7duRb9+/bB69WqMHTvWrACaIrH0wOe+wVC+rgiCaFggqnR+mcEVRIiCYXCWnHkHEAHr9GzTgytTg0cRiE3KgCgA2VZOZg0yRRG4GZsMASLiCtTGA0aYGqga/p+UEAtBADwyhDIxSOUAo4Fjys04iCLQIi6vkn7c+39aUgIEUUTzS7eMjlEyQDU1yBRF4FayoZ6LR1KZ+ITSx7hbFyg9uEww+303tWq32XWisyopWU69BPOvepsaHFde5+7z1/nqSkqWrpOF7HwdYtPuPdudfXdBNDUXRCOCDlZQAChSWqFIaQWIIlRCAdrpb6FDxhHoFFbIthORaG+PnILau7PVsWNHBAQE4IsvvsBjjz0Ga2vrMmXi4uKwZcsWvPDCC3j33XcxderUWjs+EcnXX3Fp9wb2dURXrMfFxFuVFyQik8wa2C9evBhBQUHl5ms0GgwdOhRDhw7F+++/j9jY2NqKT3bWHbiIvalWdwe75Q8yRQC3tTchiCKcWyaUySvPvcFfktkx3auTUUnJcupdv1NJSRN1Sk1FrryOoS/O2eY/J5qVYuiLc5H5j3eU7Ad9x7pqK6nmFBj6YlVgfp+IiOpKoUIFRenfFAoFChVWKFRYwVplBVt9PlrfyYNPdg7sb1ftol9Fdu/ejS5dulRYxsfHB3PmzMHbb7+N+Pj4Wju23HC7u5rlyS0e9qPm7aYBSG7uAU/Xe2u61PZ2d9Hx6bjZrOLpSHI6r/xcySuextyPWt3urqJB/f2aN2+O5s2bm13e0tzOK4Bttnknt+SZ2iLehSQiIjMVwAoKE1eAFaIIW6EQDkIBrEQBCtTuTgSVDepLs7a2Rrt27Wrx6EQkd1HXtBjdr4IBew39FZ8KwL7O2idq7Kq9Kn5qaipSU1MhCMaD1p49e9Y4KCIioqZKByso7xuyW4t6uIm5sC42/M4VAYgKRa0O7O9XUFCAv//+2+Tv+pEjR9bhkYlIjk5c02Jk33ZQVl60ynIKdIjRZgEtObAnqq4qD+xPnz6NiRMn4vLly9LWMwqFAqIoQqFQQK/nys9ERETVpVOoygzY9VCgAFZQKoqhunu3Xlr0ow7s2rULEyZMQHp6epk8/q4nappu5xbin+Tb6FwHbV9ISIdQhz/TiJqCKl90e/nll9GxY0ccPXoU169fx40bN4z+JSIiourTKVRl9q4XFErcUtohxdoVt60cUKxQQIFq/BI30+uvv46xY8ciOTkZgiAYvTioJ2q66mqP+b/iyq6ET0RVU+U79tevX8f/+3//D+0rWhSDiIiIqqWovL3sRBHWoh5qsRh6lRKZVtZItC2skxhSUlIQGhoKd3f3Omlf7rjdHfshp3jk1I/4DC10Pd2gtirn51Q1trtLzc5DQcx1NCu/pqzOq5zej4bIk1s8TaEf5m53V+WL/cOGDcNff/1V1WpERERkBt39A3tRhK1YhGbIh7VYjOs2nohyd8d+71aIdnGpkxieffZZHDhwoE7aJiLLpSvW43pK7e3GAfBuPVFtqfId+y+//BITJ07EhQsX0L179zL73HJBHSIiouorUhgG9iWr4KvFYuRDgZOq1jjjMQAZals8YHWoTmNYtWoVxo4di8OHD6NHjx5lfte/8cYbdXr8hsbt7mqWJ7d42I/abfekPhedfXxqZbs7QRSx/7QWVs3dK42lOrHKMU9u8bAf8oqnzre7K+3YsWP4888/8fvvv5fJ44I6RERENaODCipRhLM+F3lKDU46dMZeXXOkKh3grHGHHXLqPIZvv/0Wu3fvho2NDQ4cOABFqWf+FQpFox/YE1H5YlIykZOvg2MttHVVexu3cwvhVgttETV1VZ6K//rrr2P8+PFcUIeIiKgOpCvtkKx0xG6XfvjE61n82vwhpCod6jWGd999FwsXLkRWVhZiY2Nx48YN6cWFcomaNlEEzieU3TGjOqJi6mYxPqKmqMp37DMyMvDWW2812QV1iIiI6tJVVQsssnsEzi4VTx2sSzqdDs8//zyUyrpad5+ILNlf8Wl4qIZt6Ir1OBtr3qJgRFS5Kv/GfuaZZ7B///66iIWIiIhkYOLEidi2bVtDh0FEMpV8OxdJt+/UqI2/4tNQUMTZvkS1pcp37Dt27Ig5c+bgyJEjTXJBHSIiInMpFIBapYK1lRKw08BapYR7MweorVSwVimRqcyDtUqJVj4eUrkMV8BKpYSvX1tYW6mgtlJCm2APKysF2rdrD42QA+eMOKiUSvycmlkncev1eixZsgR//PEHevbsWeZ3/bJly+rkuHLB7e7YDznFI9d+XNgfBa+ePsaZVdjuLvrwJbilZFZ6vNqIVQ55couH/ZBXPLWx3V21VsV3cHDAwYMHcfDgQaO82lpQ57PPPsPSpUuh1WrRq1cvfPrppxgwYEC55bdv34558+YhNjYWHTp0wIcffognnnhCyhdFEWFhYfjiiy+QmZmJgQMHYs2aNejQoUONYyUiIstipVLCXmMlDa5tCuxgpVLCy8PFkGalxC2NDtYqBbx9W0vlUpOsoFYq4deuHaxVSsOgW6VEUoILrFVKdOjQAdYqw0A8PvY6rFUqdOrYQVp4LiYmBgDQvtSK0DExTibSlHfT7v3BHKPIM6S1ag7olEChraEvVnUzVf78+fPo06cPAODChQt1cgwismx/x6chsHsbKJWKygvfJ6dAh5ha3jaPqKmr8sD+xo0bdRGHZNu2bQgNDcXatWvh7++PFStWICgoCNHR0WjZsmWZ8kePHsW4ceMQERGBJ598Elu2bMHo0aNx5swZdO/eHQCwZMkSrFy5El9//TX8/Pwwb948BAUF4dKlS7CxMX1FnoiI6oeVSgkbtQrWSiXcHG1hbaWEtUqFbCEHViolWrVxkwbStxz1sFap0MbXTyqntlIi5aZhcN6uXTuo797lvhnXAtYqJTp17CANwmNvXIdCobhvIG1qwG1XNs1RuJvmZRS/uuA2AMDL9d4Cd7Zqwx3u0qvJ1xpdJpB6d7u7rLr5ndzUH7njdnc1y5NbPOxHHR3TyQ3/ODRHZ69mZTMr2e7uxMV4pLT0KpPF98Ny8uQWT2PuR51td1ee5ORkbNy4EbNmzapRO8uWLcPUqVMxefJkAMDatWvx66+/Yt26dXjnnXfKlP/kk0/w+OOP4z//+Q8A4L333sOePXuwatUqrF27FqIoYsWKFZg7dy5GjRoFAPjmm2/g7u6OHTt24IUXXqhRvEREjY1KqYC1Sgm1lQoKe8P0cY/mjrC2MqSpVUrcUhfCWqVAa59W0kA6LVEFKysl/PzaSne5k286wkppuJutvjs4T4i9AWsrBbp06gQrlRJKhaKcwbWriTTru2ltjWKOURl+6bX3biGlFWfZAwBc7O9dwK2TgXYTEhcXh6VLl2LVqlUNHQoRyUBUjNb0wN6MekRUu6o8sH/55ZdNpsfFxeHEiRM1GtjrdDqcPn0ac+bMkdKUSiUCAwNx7Ngxk3WOHTuG0NBQo7SgoCDs2LEDgGGGgVarRWBgoJTv7OwMf39/HDt2rNyBfWFhIQoLC6Wvs7Ozq9stIqIaUSoUsFIp4WBjLQ2sra1UcCo2DLa9vFtAfffudYZDMayVKrTx85XKpdxUQ22lQtu27aS73EkJrlCXTB+/m5Zwd/p4x473HlMyNeA2pNuXSY9xEu+mtb6Xpjfsud62pbOUlmOvAQCorVS1eZqoFj3yyCMmL4IkJycjOTmZA3siAgCcjU3FuIc6VennefLtO0jIyKnDqIiapioP7G/fvm30tV6vx/Xr13H58mWsXr26RsGkp6dDr9eX2UrP3d0dV65cMVlHq9WaLK/VaqX8krTyypgSERGBhQsXVrkPRNT4KQBpMCzaqWGtVMGjmYM0XTxLmQtrlQqt2nhId7nTXQFrlRI+fm2lu+Ham3awVinRrn17KS0xrgWs7j6bXXIn/MbdfcPLDq5N3eVW301rdy/NRmdIa3vv56B1/i0AgIeLvZSmsa61SVxk4Xr37m30dcnv+piYGGzYsKFBYiIi+Sks1uNcXBoGtPMwu07UNd6tJ6oLVf4r7scffzSZ/v7772PHjh149dVXaxyUHMyZM8doJkB2dja8vb0bMCIiqoiVSgk7tZW0+FnJgmieJQuiqZS4XWpBNOu7K5CnJVrBWnVvQTS1SonkBCdYlbqbfW8K+XVYqVToXFsLoilLLYh2V1GmYaDtbKepi9NEZJbly5ebTP/yyy+xatUqvPjii/UcERHJVVSM1uyBvSCKOHGt4pXKiah6au32zLhx47Bo0aIatdGiRQuoVCqkpBh/w6ekpMDDw/QPDA8PjwrLl/ybkpICT09PozL335EoTaPRQKPhH9ZEtc1wF1oFTxd72KmtYKu2QpamELbWKvi19YGNtRXUVkqkJmpgpVKibbv2dwfWSqhVKtyMbw713eniJQunxd64DmUtLojmUGx49MbXzcko9vS6XBCNyAIMGzYMr7/+ekOHUee43R37Iad45N6P9NRE5Hip4WirrnS7u4T0bFhdz4FbFY5Xm7E2ZJ7c4mE/5BVPg2x3V56//vpL2hqnutRqNfr27YvIyEiMHj0aACAIAiIjIxEcHGyyTkBAACIjIxESEiKl7dmzBwEBAQAAPz8/eHh4IDIyUhrIZ2dnIyoqCtOnT69RvERUdbpiPXTFeiRn5kppWSnpAIB/7hh+JKmUCtxJS4ZKCbRI1cNKqYRKpYCVUonMlJtQKZXwSNJBpTQ8e34rOQEqpRJeiYVSWnpiPJRKJbwzFVLdlJuJsFIqkVxsCyuVAiqlEskJ6VCqFCi0cYXV3XI3M3KgUipgn5kLK6WhnJVKiXxdEVRKJfSCAKVCwQE+NTn79u3DI4880tBhEJGMiCLwd0I6BnYsu8r9/aKTbgGwrvugiJqgKg/s71+oDjDc/f7pp58wYsQIo/xly5ZVOaDQ0FBMnDgR/fr1w4ABA7BixQrk5uZKq+RPmDABrVq1QkREBADgzTffxJAhQ/Dxxx9jxIgR2Lp1K06dOoXPP/8cgOHOWkhICBYtWoQOHTpI2915eXlJFw+ISF70gghdsR4AkJ2vM8rLyjRMX79jnXkvLcWw9sfNIptSaYZn+JyzFKXSEg1pybqyaTF3yqadzzA+dkm6+00oAKhUStxJS4JKqUALL610ESAr1XDxwf3ybViplLBSKnFLa7j44JmQf/dChRLpSfGwUirR+jakCxIpNw11E4tsDGlKJZIS0qBSKpGvdpEuUliplEi8lQOVUgmHrFxDmlKB3EIdVAolior1UN1dcZ6oqp555pkyaSkpKYiKisIjjzxilP/DDz/UZ2j1gtvd1SxPbvGwH3V/zAM6DQaWXgfGxHZ3umI9TpzRIs29/Gn7Dd2P+siTWzzsh7ziqdft7s6ePWsyvX///khNTUVqqmGqQHXvZD3//PNIS0vD/PnzodVq0bt3b+zatUta/C4+3nAXrsRDDz2ELVu2YO7cufjvf/+LDh06YMeOHdIe9gAwa9Ys5ObmYtq0acjMzMSgQYOwa9cu7mFPRNUmAijWCygsMlyAyMq7t4tGVpbhB3Ce+t5uGlkpmQCApGLbUmmGaVfO2cpSaXcvHmiLyqZdv3fxwSj97/Syae6Gf5UKBXLSkmClUqCFZxKsVErDxYeURKiUCrhfuiXNSLitTYBSoYRXfJ4hTXn34oNKiVYZojTLwUqpgDYxEVZKBZKKbQwXKpQKJMcbZj8UaFzutqnAzYwcWCkVsLt9R5p5kZOvg0qlQEFRsVSX5MXZ2dlkWseOHRsgGiKSu5u37iDx9h1UNHy6knhLumhPRLWvygP7/fv310UcRoKDg8uden/gwIEyaWPHjsXYsWPLbU+hUCA8PBzh4eG1FSIRkUUQRBHFegHFeiCnoNTFghzDxYcCm3tbDmWlZAEAUgS7UmmGi7XOOcZbGd2b/WDiAsS1imc/3Lv4kCSl5aQlwUqpRAuvROmiQlaq4eJDywsZ0iyFW9qbsFIq4BmbK12QUCkVSE9OgJVCgVa3ROkCQurNm1ApFbip00gXGpLi0wwzG9TOd9tU4GZ6NlQKBWxcc6BSGS5cZOYWQqVSIK+wSDqGKIryePxC7QK0fNjwf2cNgKO1foj169fXeptE1LidiNHiadfy8/+KT6u/YIiaIO5tREREDU4QROgEPXILi6W0rDsFAACdbakLBamGiw+por1RfWn2Q6kLENIFhJTismk3csumXbxVNs09WUrLTkmEUqVEC8+b0swHK6UCWSlJUKmAlhdvweruIxW3tYaLCp5xedIjFenJCVApFWidIUgzFVITDWlxBWrpAkLajXg4FmYjR+UAK5UVVCoFElOzoFIpYe2SDWv9HdjnF0KpUKBYL1T5XJdHNhcuiMgiRV3TYtQD7lCamIWVlVeImLsz14iobpg1sH/88cexYMECPPjggxWWy8nJwerVq+Hg4ICZM2fWSoBERERyIALQ6wUUFOmBonvTSbPuGGY/FJqc/ZBWKs0w+6FkkUhD2t0LCKn32nvg5iH466KRemEfjjp2xUU7P6Sn3b1wcfEW7JCDB1TXAQBJpRahrKlu3bph/vz5eOaZZ6BWq8std/XqVSxbtgw+Pj545513au34RGTZsvIKcT01C+09XMrknbqeAlGs/5iImhKzBvZjx47FmDFj4OzsjKeeegr9+vWDl5cXbGxscPv2bVy6dAlHjhzBb7/9hhEjRmDp0qV1HTcREVGjpBH1sBGL0aYwBW0KUxFodQqHRE9EWXnX6XE//fRTzJ49GzNmzMBjjz1W7u/6ixcvIjg4uFHvLMPt7tgPOcVjSf2IyYxF+16+ZdL/OfKXRfWjsbwf7Efj6Eetbnc3ZcoUjB8/Htu3b8e2bdvw+eefIyvLcDdCoVCga9euCAoKwsmTJ9GlSxezDkxERERlqSBAUCiQY+UAlaiHc3EuRuovI7DoGi5kJOOCgzdgV3k7VTVs2DCcOnUKR44cwbZt27B582bExcUhPz8fLVq0QJ8+fTBhwgS8+OKLcHWt4EFaImqyrqVkorBID02ptJSsXCTfzkWzBouKqGkw+xl7jUaD8ePHY/z48QCArKws5Ofno3nz5rC25n6UREREtUGFe/NV9QoVcqzsoBd0sEMRHsy5hH53riDbFoh1ckKSvvZXmB40aBAGDRpU6+2a8tlnn2Hp0qXQarXo1asXPv30UwwYMKDc8tu3b8e8efMQGxuLDh064MMPP8QTTzwh5YuiiLCwMHzxxRfIzMzEwIEDsWbNGnTo0KFKcXG7u5rlyS0e9qN+j3nF2hG9Sm13d/RkjFFZS+lHXeXJLR72Q17x1GS7O2XlRUxzdnaGh4cHB/VERES1SCWWXRBPVCiQq1AjU2WPYijhkZuHgUlJGJRquatMb9u2DaGhoQgLC8OZM2fQq1cvBAUFSdvm3u/o0aMYN24cpkyZgrNnz2L06NEYPXo0Lly4IJVZsmQJVq5cibVr1yIqKgr29vYICgpCQUFBfXWLqMn7K/7e97AgiDh5XduA0RA1HdUe2BMREVHts4IAhYlFplSiAHuhAE76fFgLAlQioCpbzGIsW7YMU6dOxeTJk9G1a1esXbsWdnZ2WLduncnyn3zyCR5//HH85z//QZcuXfDee+/hgQcewKpVqwAY7tavWLECc+fOxahRo9CzZ0988803SEpKwo4dO+qxZ0RN27WULGTlFQIA/tHexu3cwgaOiKhp4MCeiIhIRgxT8Y1H9hqxGJ5iDlyK78BaLIYIQLj7skQ6nQ6nT59GYGCglKZUKhEYGIhjx46ZrHPs2DGj8gAQFBQklb9x4wa0Wq1RGWdnZ/j7+5fbZmFhIbKzs41eRFQzogicvGa4Sx8Vw7v1RPWFA3siIiIZUYlln5vXQYUcqFGsMNyjt/Rf3unp6dDr9XB3dzdKd3d3h1ZreiCg1WorLF/yb1XajIiIgLOzs/Ty9q7bnQeImor/dzIG09dF4nhMckOHQtRkmL14HhEREdU9KxP34UWFAlkKW+RZWcFOnw8nIRcqUbT4AX5DmzNnDkJDQ6Wvs7Oz4e3tze3u2A9ZxcN+yCse9kNe8TSFftTqdnelTZw4EVOmTMHDDz9c1aqNwpN92qJP/+4QRBGiKEIUYfg/DM/3CXfTRFFEQpwaoijCy9sHAgBREA3/3ldO+j9EJDkb2nVv3fpe26XL4V5aSQwp1gUQRBFuXm4QcX/buBtnqf/fbSOtKBOiCDRr5lCqHyIEwRBL6WOUtFessYIIwF5jVarte+UE8V5dIiKqOisIEKEok64UBdgKhVCjGDqlEoUqFbKs6ub6/KOPPoohQ4YgLCzMKP327dsYM2YM9u3bV6P2W7RoAZVKhZQU4z9kUlJS4OHhYbKOh4dHheVL/k1JSYGnp6dRmd69e5tsU6PRQKPRmMwjIiKyJFX+iyArKwuBgYHw8fHB5MmTMXHiRLRqVfEWAo1JJ09X9GvrXnlBADFqw2Ih7dv7mN1+jKvibp32lZQsVSfGtsp1DPVcq3GsGLPqlFxAEEUR/1yNgSiKaNeu/b0LBTCslFpyEaD0hQdBBK5fi4EAEb5+7cpeRCl9gaJUGzeuO0MURbTxbXvv4gdEowsq91+sEEQRCXE2hgswbXzLXmwRRZMXZRLjFYAowr21j3RRxlTbpdvT2uogiEDLVh6my5m4KJMq3oEoimju7mJ0Ucb4ok3ZizLKXBsIoghXe02ZC0Mm64oilAoFRF6RIWpw1hAglozrRREasQhOQgGgAPIUdrhg74Mip1vIsLFBQlHdLJ934MABnD9/HmfPnsXmzZthb28PwPBs/MGDB2vcvlqtRt++fREZGYnRo0cDAARBQGRkJIKDg03WCQgIQGRkJEJCQqS0PXv2ICAgAADg5+cHDw8PREZGSgP57OxsREVFYfr06VWKj9vd1SxPbvGwH/KKh/2QVzzsh7ziqcl2d1Ue2O/YsQNpaWnYuHEjvv76a4SFhSEwMBBTpkzBqFGjuP0dAQAUCoXhfpNCAWuVYbKoxtr8P0CzHA0XKzxd7M2uo8x1AQC0b93c7DoAEKPMM9Rr39r8Oo7C3TpVuSiirkYdxyrXMdQz7wKMqTrt2rWr8AJA6Qsq12KuQRBF+LVtZ3xxpoILHTeuN8Nnf7VAyL/6QK8XUSwIuHHdAXq9AM82PijWi9ALAuJirSHoBbRs3QbFgohivQC9IOCmQzEEvYDmXh7QCwKK9SKSFbkoFkQ0c3dBsSBAL4hQ59tCEEQ42dvcTROQb6WCXrDU5caoqVBBgEoU4VCcBysI0Cms8I+qBU5YtUasZx8oVTo8oKr54Loye/fuxauvvooHH3wQO3fuhK+vb622HxoaiokTJ6Jfv34YMGAAVqxYgdzcXEyePBkAMGHCBLRq1QoREREAgDfffBNDhgzBxx9/jBEjRmDr1q04deoUPv/8cwCG3zshISFYtGgROnToAD8/P8ybNw9eXl7SxQMiIqLGqlpz+Nzc3BAaGorQ0FCcOXMG69evx0svvQQHBweMHz8eM2bMQIcOHWo7ViKqB6UvylR2KcbR1nCxopmD6WdRTSnOcoSTnRqdPJtJaXa6LABA+/ZeUtq9GS9tjerHtFDdTb930SImxs5EWjMTafcuXgiiiCK9gH+uXoVeL8LHr63hAoBeQExMDIoFEa19fKGXLiqIuHHdHnpBhKe3D4rvXlSIj7WCXhDh3qqNlJboqEexIMDNy1Oqm6TKg14Q4OrhCr1eQLEgIrXwNooFES6OtnfrCihUq6AXDDMoBM6gaJJUogi9QoFsK3ucte+A8/ZtcfW24Wq9s0oDO+jqJQ5PT08cPHgQkydPRv/+/bF9+3Z06dKl1tp//vnnkZaWhvnz50Or1aJ3797YtWuXtPhdfHw8lMp7qwg89NBD2LJlC+bOnYv//ve/6NChA3bs2IHu3btLZWbNmoXc3FxMmzYNmZmZGDRoEHbt2gUbG/N/RhEREVmiGj2cl5ycjD179mDPnj1QqVR44okncP78eXTt2hVLlizBW2+9VVtxEhHVGoVCAZVCAZVSCTu1YZZR6YsTua4OAID2Hq5G9eyLTFyA0JS9ABHTvOwjNaYemTH1OEzp2RaCKEIviIiO/gd6UYCPXzvpokCxIODa1asoFoHWbXwNsxcEETeuOUAviPBq4yPNdIiLtYZeL8K9dRtplsNNJ+HuxQevuxc0RCRZ5UMviGjm2Uy6IJGiy4ReEOHsbCelFQsCCqwNFyAUuH9jNqqpX9WdYCMWI9mrF/SKkstrifUag0Jh+AxrNBps2bIFixYtwuOPP47Zs2fX6nGCg4PLnXp/4MCBMmljx47F2LFjy21PoVAgPDwc4eHhtRUiERGRRajywL6oqAg///wz1q9fj927d6Nnz54ICQnBv//9bzg5OQEAfvzxR7z88ssc2BMR1YBSoYBSpYCN2vCj2sXOeJGv/GZ3H9XwvHcBwrYw05DW/t7iYTHWBXfT/O6l3a1ifFHBxkSaS5k0Q3qpCxB3B/v//HMVRYIAX7+20iMVV2OuQhBEtPbxky4gXL/uAL0AeHkbLj7oBQFx19UoFkV4lMx8EETcjBOh14to0aqVdEEjWV2AYr2I5p7NpQsXxYKAVF0mivUCXJxsDW3qBejUKugFwEqpQLFgOZcfrqkMjxM5K+rm+Xlz3L/exty5c9GlSxdMnDixgSIiIiKiilR5YO/p6QlBEDBu3DicOHHC5EqzjzzyCFxcXGohPCIikjulUgG1UgUbtRVsALja35v9cMfFMPuhnbuLlKYuuA0AaN/u3urnHirDVPP27X2ltBhn8W5a6QsNmjJphvSKZz+ULCAZHX0VxaIAH9+20uyDmKtXoRdEePv6ofjuug/Xrznenflw73GMuBtq6AXA/e66D/q7FxVuxhkuTrh5tZIeqUi2LkCxIKCZR/O7sykEaIuyIAgCnF3spPUl9DbWKBYEqK2UKNaLsnn84saNG3BzczNKGzNmDDp37oxTp041UFT1h9vdsR9yiof9kFc87Ie84mkK/aiz7e6WL1+OsWPHVvi8mouLC27cuFHVpomIiOpEyeMXamsV1FDBudTsh2xnwyKdvm7OUppV3t01Gvzu7YLSUlpo03ink7uTGqo1++H+hS4FUUT0P1chCHcfvbh7oeBqTAyKBQFtfPwgFN6GrfYGBAH4VXu76ifDDD4+pndz6datG7p161YnxyQiIqLqq/LA/qWXXqqLOIiIiJo8ZclOIiolHGzu7TJz++5OIW1aOAE6PZBvuBhhp2646fqNGbe7q1me3OJhP+QVD/shr3jYD3nFU5Pt7pSVFyEiIiIiIiIiueLAnoiIiIiIiMiCyWZgX1RUhNmzZ6NHjx6wt7eHl5cXJkyYgKSkpArrLViwwLDvdqlX586djcoUFBRg5syZaN68ORwcHDBmzBikpFS8mAIRERERERGRJZDNwD4vLw9nzpzBvHnzcObMGfzwww+Ijo7GyJEjK63brVs3JCcnS68jR44Y5b/11lvYuXMntm/fjoMHDyIpKQnPPPNMXXWFiIiIiIiIqN5UefG8uuLs7Iw9e/YYpa1atQoDBgxAfHw82rRpU25dKysreHh4mMzLysrCV199hS1btuDRRx8FAKxfvx5dunTB8ePH8eCDD9ZeJ4iIiMjicbs79kNO8bAf8oqH/ZBXPE2hH+ZudyebO/amZGVlQaFQwMXFpcJyV69ehZeXF9q2bYsXX3wR8fHxUt7p06dRVFSEwMBAKa1z585o06YNjh07Vm6bhYWFyM7ONnoRERERERERyY1s7tjfr6CgALNnz8a4cePg5ORUbjl/f39s2LABnTp1QnJyMhYuXIjBgwfjwoULcHR0hFarhVqtLnNxwN3dHVqtttx2IyIisHDhwtrqDhEREVkIbndXszy5xcN+yCse9kNe8bAf8orHIre727x5MxwcHKTX4cOHpbyioiI899xzEEURa9asqbCdf/3rXxg7dix69uyJoKAg/Pbbb8jMzMR3331Xo/jmzJmDrKws6ZWQkFCj9oiIiGqF2hVoP83wch/W0NEQERGRDDTYHfuRI0fC399f+rpVK8PViZJBfVxcHPbt21fh3XpTXFxc0LFjR8TExAAAPDw8oNPpkJmZaXTXPiUlpdzn8gFAo9FAo9FU6dhERERERERE9a3B7tg7Ojqiffv20svW1lYa1F+9ehV79+5F8+bNq9zunTt3cO3aNXh6egIA+vbtC2tra0RGRkploqOjER8fj4CAgFrrDxEREREREVFDkM3ieUVFRXj22Wdx6tQpbN68GXq9HlqtFlqtFjqdTio3bNgwrFq1Svr67bffxsGDBxEbG4ujR4/i6aefhkqlwrhx4wAYVtufMmUKQkNDsX//fpw+fRqTJ09GQEAAV8QnIiIiIiIiiyebxfMSExPx888/AwB69+5tlLd//34MHToUAHDt2jWkp6dLeTdv3sS4ceOQkZEBNzc3DBo0CMePH4ebm5tUZvny5VAqlRgzZgwKCwsRFBSE1atXVyk+URQBAH/99ZfZdW7evAkASEpKkl2d+jyWnOvU57HYp3t1tFotDh06VGE75bVtbtnaTmvIY8stnoY8ttziaehjX758GcC931FUO7jdHfshp3jYD3nFw37IK56m0A9zt7uTzcDe19fXrD9MYmNjjb7eunVrpXVsbGzw2Wef4bPPPqtueMjIyAAAvPHGG9Vug4ju+f333xs6BKJGIyMjA87Ozg0dBhERETUQ2Qzs5a5Zs2YAgPj4+Ebzx1N2dja8vb2RkJBQ5UUK5aix9QdgnywF+2QZGmOfsrKy0KZNG+l3FNUObndXszy5xcN+yCse9kNe8bAf8oqnJtvdcWBvJqXSsByBs7Nzo/mDsISTk1Oj6lNj6w/APlkK9skyNMY+lfyOIiIioqaJfwkQERERERERWTAO7ImIiIiIiIgsGAf2ZtJoNAgLC4NGo2noUGpNY+tTY+sPwD5ZCvbJMrBPRERE1FjxGXszaTQaLFiwoKHDqFWNrU+NrT8A+2Qp2CfLwD4RERFRY8WBPREREVEp3Mee/ZBTPOyHvOJhP+QVT1Poh7n72HMqPhEREREREZEF4x17IiIiolK4j33N8uQWD/shr3jYD3nFw37IK56a7GPPO/ZEREREREREFowDeyIiIiIiIiIL1mgH9j/88AOGDx+O5s2bQ6FQ4Ny5c2XKFBQUYObMmWjevDkcHBwwZswYpKQYL1wQHx+PESNGwM7ODi1btsR//vMfFBcXV3jsW7du4cUXX4STkxNcXFwwZcoU3Llzx6jM33//jcGDB8PGxgbe3t5YsmRJlfuoUChMvpYuXSqV8fX1LZO/ePHiCtutq/NijkmTJpWJ9/HHHzcqY875lUufioqKMHv2bPTo0QP29vbw8vLChAkTkJSUZFTO0t6n+3322Wfw9fWFjY0N/P39ceLEiQrLb9++HZ07d4aNjQ169OiB3377zShfFEXMnz8fnp6esLW1RWBgIK5evVrrcUdERKB///5wdHREy5YtMXr0aERHRxuVGTp0aJn35rXXXquwXXPir87n2BwLFiwoE2/nzp2lfHM+N3LqD2D6+0OhUGDmzJkALOM9OnToEJ566il4eXlBoVBgx44d9RKPnH5OEBERUd1ptAP73NxcDBo0CB9++GG5Zd566y3s3LkT27dvx8GDB5GUlIRnnnlGytfr9RgxYgR0Oh2OHj2Kr7/+Ghs2bMD8+fMrPPaLL76IixcvYs+ePfjll19w6NAhTJs2TcrPzs7G8OHD4ePjg9OnT2Pp0qVYsGABPv/88yr1MTk52ei1bt06KBQKjBkzxqhceHi4UbnXX3+9wnbr6ryY6/HHHzeK99tvvzXKr+z8yqlPeXl5OHPmDObNm4czZ87ghx9+QHR0NEaOHFmmrKW9TyW2bduG0NBQhIWF4cyZM+jVqxeCgoKQmmp6Bc+jR49i3LhxmDJlCs6ePYvRo0dj9OjRuHDhglRmyZIlWLlyJdauXYuoqCjY29sjKCgIBQUFtRr7wYMHMXPmTBw/fhx79uxBUVERhg8fjtzcXKNyU6dONXpvKrsQZ0781fkcm6tbt25G8R45ckTKq+xzI8f+nDx50qg/e/bsAQCMHTtWKiP39yg3Nxe9evXCZ599Vq/xyOXnBBEREdUthSiKYkMHUZdiY2Ph5+eHs2fPonfv3lJ6VlYW3NzcsGXLFjz77LMAgCtXrqBLly44duwYHnzwQfz+++948sknkZSUBHd3dwDA2rVrMXv2bKSlpUGtVpc53uXLl9G1a1ecPHkS/fr1AwDs2rULTzzxBG7evAkvLy+sWbMG7777LrRardTGO++8gx07duDKlSvV7uvo0aORk5ODyMhIKc3X1xchISEICQkxq426Oi/mmjRpEjIzM8vczSphzvmVW5/ud/LkSQwYMABxcXFo06YNAMt7n0rz9/dH//79sWrVKgCAIAjw9vbG66+/jnfeeadM+eeffx65ubn45ZdfpLQHH3wQvXv3xtq1ayGKIry8vPB///d/ePvtt6X+uru7Y8OGDXjhhRdqJW5T0tLS0LJlSxw8eBAPP/wwAMPd4N69e2PFihVmtWFO/NX5HJtrwYIF2LFjh8lZSuZ8buTWH1NCQkLwyy+/4OrVq1AoFBb3HikUCvz4448YPXp0ncYjp58TliI7OxvOzs6ICA6HjcYGt5q7lylTsiVRbebVVbv1nSe3eNgPecXDfsgrHvZDXvFUlGeXFIc5q+YjKysLTk5OJvsCNOI79pU5ffo0ioqKEBgYKKV17twZbdq0wbFjxwAAx44dQ48ePaQ/dgAgKCgI2dnZuHjxosl2jx07BhcXF+kPLwAIDAyEUqlEVFSUVObhhx82+oMpKCgI0dHRuH37drX6k5KSgl9//RVTpkwpk7d48WI0b94cffr0wdKlSyucYllX56UqDhw4gJYtW6JTp06YPn06MjIypDxzzq8c+1RaVlYWFAoFXFxcjNIt7X0CAJ1Oh9OnTxvFoVQqERgYKMVxv2PHjhmVL4mrpPyNGzeg1WqNyjg7O8Pf37/cNmtLVlYWAKBZs2ZG6Zs3b0aLFi3QvXt3zJkzB3l5eeW2YU781fkcV8XVq1fh5eWFtm3b4sUXX0R8fDwA8z43cuxPaTqdDps2bcLLL78MhUIhpVvae1Qf8cjl58T9qvOoTWWPMlS3XSIiosaiyW53V3K3/P7Blbu7O7RarVSm9B87JfkleeW127JlS6M0KysrNGvWzKhdPz+/ctt1dXWtcn++/vprODo6lplS+8Ybb+CBBx5As2bNcPToUcyZMwfJyclYtmxZufHXxXkx1+OPP45nnnkGfn5+uHbtGv773//iX//6F44dOwaVSmXW+ZVbn0orKCjA7NmzMW7cOKMrbpb2PpVIT0+HXq83eZzyZp+UF1fpuEvHaqpMXRAEASEhIRg4cCC6d+8upf/73/+Gj48PvLy88Pfff2P27NmIjo7GDz/8YLIdc+KvzufYXP7+/tiwYQM6deqE5ORkLFy4EIMHD8aFCxfM+tzIrT/327FjBzIzMzFp0iQpzdLeo/qKRy4/J+5X8tjB119/DT8/P8ybNw9BQUG4dOkSbGxsTNYpeZTh5ZdfLvfRkeq0Wx5ud1ezPLnFw37IKx72Q17xsB/yiqcm2901ioH95s2b8eqrr0pf//777xg8eHADRlT7KuvjunXr8OKLL5b54yU0NFT6f8+ePaFWq/Hqq68iIiICGo2m7gOvgKk+lZ5m3aNHD/Ts2RPt2rXDgQMHMGzYsIYIs0oqep+Kiorw3HPPQRRFrFmzxqienN+npmLmzJm4cOGC0fPoAIyeYe7Rowc8PT0xbNgwXLt2De3atavvMCv1r3/9S/p/z5494e/vDx8fH3z33XewtbVtwMhqx1dffYV//etfRlPPLe09aspEUcSKFSswd+5cjBo1CgDwzTffwN3dHTt27Cj3UZt//etfRp/t2mqXiIiosWgUU/FHjhyJc+fOSa/SUxXL4+HhAZ1Oh8zMTKP0lJQUeHh4SGXuXz245OuSMqbavX/RsOLiYty6datG7VbUx8OHDyM6OhqvvPJKRV0GYLibV1xcjNjY2HLjr4vzUtU+lWjbti1atGiBmJgYqf3Kzq8c+1QyqI+Li8OePXsqfD4GkNf7VJEWLVpApVKZPE5F70dF5Uv+rUqbNRUcHIxffvkF+/fvR+vWrSss6+/vDwDSZ/J+5sRfnc9xdbm4uKBjx46IiYkx63NzPzn1Jy4uDnv37q30Z52lvUd1FY9cfk6UVleP2lS33cLCQmRnZxu9iIiILFGjGNg7Ojqiffv20sucu1J9+/aFtbW10UJz0dHRiI+PR0BAAAAgICAA58+fN/pjqmRQ1rVrV5PtBgQEIDMzE6dPn5bS9u3bB0EQpD82AwICcOjQIRQVFRm126lTp3Kn4VfUx6+++gp9+/ZFr169Ku33uXPnoFQqy0zpLFFX56WqfSpx8+ZNZGRkwNPTUzp2ZedXbn0qGdRfvXoVe/fuRfPmzSttR07vU0XUajX69u1rFIcgCIiMjJTiuF9AQIBR+ZK4Ssr7+fnBw8PDqEx2djaioqLKbbO6RFFEcHAwfvzxR+zbt6/MIzKmlCxKV/KZvJ858Vfnc1xdd+7cwbVr1+Dp6WnW50bO/Vm/fj1atmyJESNGVFjO0t6juopHLj8nSqurR22q225ERAScnZ2ll7e3d7VjICIiakiNYmBvyq1bt3Du3DlcunQJgOGPmXPnzkm/4J2dnTFlyhSEhoZi//79OH36NCZPnoyAgABpZejhw4eja9eueOmll/DXX3/hjz/+wNy5czFz5kxpevSJEyfQuXNnJCYmAgC6dOmCxx9/HFOnTsWJEyfw559/Ijg4GC+88II0dfTf//431Go1pkyZgosXL2Lbtm345JNPjKZjmys7Oxvbt283eQfr2LFjWLFiBf766y9cv34dmzdvxltvvYXx48dLFxASExPRuXNnad/x2jov1XHnzh385z//wfHjxxEbG4vIyEiMGjUK7du3R1BQEADzzq+c+lRUVIRnn30Wp06dwubNm6HX66HVaqHVaqHT6QBY3vt0v9DQUHzxxRf4+uuvcfnyZUyfPh25ubmYPHkyAGDChAmYM2eOVP7NN9/Erl278PHHH+PKlStYsGABTp06heDgYACGFcNDQkKwaNEi/Pzzzzh//jwmTJgALy8vaRXx2jJz5kxs2rQJW7ZsgaOjo/Te5OcbnmW6du0a3nvvPZw+fRqxsbH4+eefMWHCBDz88MPo2bOn1E7nzp3x448/mh2/OZ/j6nr77bdx8OBBxMbG4ujRo3j66aehUqkwbtw4sz43cutPCUEQsH79ekycOBFWVveeIrOU9+jOnTvSTB7AcIf53LlziI+Pr7V45PhzYvPmzXBwcJBepS9oy8GcOXOQlZUlvRISEho6JCIiomppFM/Ym/Lzzz9LAwsA0vN1YWFhWLBgAQBg+fLlUCqVGDNmDAoLCxEUFITVq1dLdVQqFX755RdMnz4dAQEBsLe3x8SJExEeHi6VycvLQ3R0tNEfK5s3b0ZwcDCGDRsmtb9y5Uop39nZGbt378bMmTPRt29ftGjRAvPnz6/W/shbt26FKIoYN25cmTyNRoOtW7diwYIFKCwshJ+fH9566y2jCwhFRUWIjo42WkG6Ns5LdahUKvz999/4+uuvkZmZCS8vLwwfPhzvvfee0R+YlZ1fOfUpMTERP//8MwAYbbcIAPv378fQoUMt7n263/PPP4+0tDTMnz8fWq0WvXv3xq5du6Q7Z/Hx8VAq711DfOihh7BlyxbMnTsX//3vf9GhQwfs2LHDaMG6WbNmITc3F9OmTUNmZiYGDRqEXbt2VXkBrMqUrHUwdOhQo/T169dj0qRJUKvV2Lt3L1asWIHc3Fx4e3tjzJgxmDt3rlH56OhoaUV9c+Ov7HNcXTdv3sS4ceOQkZEBNzc3DBo0CMePH4ebmxuAyj83cutPib179yI+Ph4vv/yyUbqlvEenTp3CI488In1d8v09ceJEbNiwoVbikePPiZEjRxrNKCgsLARgmO5fekZFSkpKmZ+RVVH6cYaqtKvRaExewHC9lQobjemfNyVbEtVmXl21W995couH/ZBXPOyHvOJhP+QVT0V5drdSy80rrdHvY09ERETyIIoivLy88Pbbb+P//u//ABhmnrVs2RIbNmwwa5E7hUKBH3/80WgGT220W1KH+9izH3KLh/2QVzzsh7ziaQr9MHcf+0Z7x56IiIjkpfRjBx06dJC2pbv/UZthw4bh6aeflh7RuXPnjtFiiCWPMjRr1gxt2rQxu11zcbu7muXJLR72Q17xsB/yiof9kFc8TX67OyIiIrIM5jx2cO3aNaSnp0tfV/Yog7ntEhERNVYc2BMREVG9USgUCA8Pr/A5/vu3+hw6dCgqe3LQnHaJiIgaq0a7Kj4RERERERFRU8CBPREREREREZEF41R8IiIiolK43R37Iad42A95xcN+yCueptAPc7e74x17IiIiIiIiIgvGO/ZEVOu++uorbNu2Dbt3767T4+zatQvvvPMOzpw5A6WS1ymJqHZwu7ua5cktHvZDXvGwH/KKh/2QVzw12e6OfwkTUa0qKCjAvHnzEBYWVufHevzxx2FtbY3NmzfX+bGIiIiIiOSKA3siqlXff/89nJycMHDgwHo53qRJk7By5cp6ORYRERERkRxxYE9EJqWlpcHDwwMffPCBlHb06FGo1WpERkaWW2/r1q146qmnjNKGDh2KkJAQo7TRo0dj0qRJ0te+vr5YtGgRJkyYAAcHB/j4+ODnn39GWloaRo0aBQcHB/Ts2ROnTp0yauepp57CqVOncO3atep3loiIiIjIgnFgT0Qmubm5Yd26dViwYAFOnTqFnJwcvPTSSwgODsawYcPKrXfkyBH069evWsdcvnw5Bg4ciLNnz2LEiBF46aWXMGHCBIwfPx5nzpxBu3btMGHCBIiiKNVp06YN3N3dcfjw4Wodk4iIiIjI0nHxPCIq1xNPPIGpU6fixRdfRL9+/WBvb4+IiIhyy2dmZiIrKwteXl7VPt6rr74KAJg/fz7WrFmD/v37Y+zYsQCA2bNnIyAgACkpKfDw8JDqeXl5IS4urlrHJCK6H7e7Yz/kFA/7Ia942A95xdMU+sHt7oioVnz00UcoLi7G9u3bsXnzZmg0mnLL5ucbVu20sTH9B3FlevbsKf3f3d0dANCjR48yaampxj/gbG1tkZeXV61jEhERERFZOt6xJ6IKXbt2DUlJSRAEAbGxsUYD7fs1b94cCoUCt2/frrRdvV5fJs3a2lr6v0KhKDdNEASjerdu3YKbm1ulxyQiMge3u6tZntziYT/kFQ/7Ia942A95xcPt7oioTuh0OowfPx7PP/883nvvPbzyyitl7paXplar0bVrV1y6dKlMXkqK8RSj69ev10qMBQUFuHbtGvr06VMr7RERERERWRoO7ImoXO+++y6ysrKwcuVKzJ49Gx07dsTLL79cYZ2goCAcOXKkTPpPP/2EH374AdeuXcP777+PS5cuIS4uDomJiTWK8fjx49BoNAgICKhRO0RERERElooDeyIy6cCBA1ixYgU2btwIJycnKJVKbNy4EYcPH8aaNWvKrTdlyhT89ttvyMrKMkofMWIElixZgq5du+LQoUNYvXo1Tpw4gY0bN9Yozm+//RYvvvgi7OzsatQOEREREZGl4jP2RGTS0KFDUVRUZJTm6+tbZsB+v65du2LEiBFYvXo15syZI6W3atUK27dvNyo7ffp06f+xsbFl2iq9rV3J8Uunpaen4/vvvy+ztz0RERERUVPCgT0R1bqlS5di586ddX6c2NhYrF69Gn5+fnV+LCJqOrjdHfshp3jYD3nFw37IK56m0A9zt7vjwJ6Iap2vry9ef/31Oj9Ov3790K9fvzo/DhERERGRnHFgT0R17sCBAw0dAhGR2bjdXc3y5BYP+yGveNgPecXDfsgrHm53R0RERERERNREcWBPREREREREZME4sCciIiIiIiKyYBzYExEREREREVkwLp5HREREVAq3u2M/5BQP+yGveNgPecXTFPph7nZ3vGNPREREREREZMF4x56IiIioFG53V7M8ucXDfsgrHvZDXvGwH/KKh9vdERERERERETVRHNgTERERERERWTAO7ImIiIiIiIgsGAf2RERERERERBaMi+cRERERlcLt7tgPOcXDfsgrHvZDXvE0hX5wuzsiIiIiIiKiJoB37ImIiKjeiKKIsLAwfPHFF8jMzMTAgQOxZs0adOjQodw6ERER+OGHH3DlyhXY2trioYcewocffohOnTpJZQoKCvB///d/2Lp1KwoLCxEUFITVq1fD3d29yjFyu7ua5cktHvZDXvGwH/KKh/2QVzzc7o6IiIgswpIlS7By5UqsXbsWUVFRsLe3R1BQEAoKCsqtc/DgQcycORPHjx/Hnj17UFRUhOHDhyM3N1cq89Zbb2Hnzp3Yvn07Dh48iKSkJDzzzDP10SUiIqIGxzv2REREVC9EUcSKFSswd+5cjBo1CgDwzTffwN3dHTt27MALL7xgst6uXbuMvt6wYQNatmyJ06dP4+GHH0ZWVha++uorbNmyBY8++igAYP369ejSpQuOHz+OBx98sG47RkRE1MB4x56IiIjqxY0bN6DVahEYGCilOTs7w9/fH8eOHTO7naysLABAs2bNAACnT59GUVGRUbudO3dGmzZtKmy3sLAQ2dnZRi8iIiJLxIE9ERER1QutVgsAZZ57d3d3l/IqIwgCQkJCMHDgQHTv3l1qV61Ww8XFpUrtRkREwNnZWXp5e3tXoTdERETywan4REREVCc2b96MV199Vfr6119/rXGbM2fOxIULF3DkyJEatzVnzhyEhoZKX2dnZ8Pb25vb3bEfsoqH/ZBXPOyHvOJpCv0wd7s7DuyJiIioTowcORL+/v7S14WFhQCAlJQUeHp6SukpKSno3bt3pe0FBwfjl19+waFDh9C6dWsp3cPDAzqdDpmZmUZ37VNSUuDh4VFuexqNBhqNpgo9IiIikicO7ImIiKhOODo6wtHRUfpaFEV4eHggMjJSGshnZ2cjKioK06dPL7cdURTx+uuv48cff8SBAwfg5+dnlN+3b19YW1sjMjISY8aMAQBER0cjPj4eAQEBVY6b293VLE9u8bAf8oqH/ZBXPOyHvOKpyXZ3HNgTERFRvVAoFAgJCcGiRYvQoUMH+Pn5Yd68efDy8sLo0aOlcsOGDcPTTz+N4OBgAIbp91u2bMFPP/0ER0dH6bl5Z2dn2NrawtnZGVOmTEFoaCiaNWsGJycnvP766wgICOCK+ERE1CRwYE9ERET1ZtasWcjNzcW0adOQmZmJQYMGYdeuXbCxufdM+7Vr15Ceni59vWbNGgDA0KFDjdpav349Jk2aBABYvnw5lEolxowZg8LCQgQFBWH16tV13h8iIiI54MCeiIiI6o1CoUB4eDjCw8PLLRMbG2v0tSiKlbZrY2ODzz77DJ999llNQyQiIrI43O6OiIiIiIiIyILxjj0RERFRKdzujv2QUzzsh7ziYT/kFU9T6Ie5293xjj0RERERERGRBeMdeyIiIqJSuN1dzfLkFg/7Ia942A95xcN+yCuemmx3xzv2RERERERERBaMA3siIiIiIiIiC8aBPREREREREZEF48CeiIiIiIiIyIJx8TwiIiKiUrjdHfshp3jYD3nFw37IK56m0A9zt7vjwJ6IiIiolBmP9YKTvT3g41M2My7O8G9t5tVVu/WdJ7d42A95xcN+yCse9kNe8VSQl33JBXNWla1yPw7siYiIiErz9gYcHYH27csvUxd5ddUu+1G9PLnFw37IKx72Q17xNOZ+5OSUX74UPmNPREREREREZME4sCciIiIiIiKyYJyKT0RERARAFEUAQPadO4aE7OyyhUqmRNZmXl21W995couH/ZBXPOyHvOJhP+QVTwV5Jb+TSn5HlYcDeyIiIiIAOXf/sPJ++OEGjoSIiMhYTk4OnJ2dy81XiJUN/YmIiIiaAEEQkJSUBEdHRygUioYOp1zZ2dnw9vZGQkICnJycGjqcRoHntHbxfNY+ntPaZynnVBRF5OTkwMvLC0pl+U/S8449EREREQClUonWrVs3dBhmc3JykvUfo5aI57R28XzWPp7T2mcJ57SiO/UluHgeERERERERkQXjwJ6IiIiIiIjIgnFgT0RERGRBNBoNwsLCoNFoGjqURoPntHbxfNY+ntPa19jOKRfPIyIiIiIiIrJgvGNPREREREREZME4sCciIiIiIiKyYBzYExEREREREVkwDuyJiIiIiIiILBgH9kREREQNTBRFzJ8/H56enrC1tUVgYCCuXr1aab3ExESMHz8ezZs3h62tLXr06IFTp07VuN3GoKZ9X7x4MRQKBUJCQozSCwoKMHPmTDRv3hwODg4YM2YMUlJSajl6+anO+YyIiED//v3h6OiIli1bYvTo0YiOjjYq01TPJwB89tln8PX1hY2NDfz9/XHixIkKy2/fvh2dO3eGjY0NevTogd9++80ovyl/vwNVO59ffPEFBg8eDFdXV7i6uiIwMLBMeUs7nxzYExERETWwJUuWYOXKlVi7di2ioqJgb2+PoKAgFBQUlFvn9u3bGDhwIKytrfH777/j0qVL+Pjjj+Hq6lqjdhuLmvT95MmT+N///oeePXuWyXvrrbewc+dObN++HQcPHkRSUhKeeeaZuuiCrFTnfB48eBAzZ87E8ePHsWfPHhQVFWH48OHIzc2VyjTV87lt2zaEhoYiLCwMZ86cQa9evRAUFITU1FST5Y8ePYpx48ZhypQpOHv2LEaPHo3Ro0fjwoULUpmm/P1e1fN54MABjBs3Dvv378exY8fg7e2N4cOHIzExUSpjcedTJCIiIqIGIwiC6OHhIS5dulRKy8zMFDUajfjtt9+WW2/27NnioEGDar3dxqAmfc/JyRE7dOgg7tmzRxwyZIj45ptvGrVhbW0tbt++XUq7fPmyCEA8duxYrfdDLmrrs5SamioCEA8ePCi10RTPpyiK4oABA8SZM2dKX+v1etHLy0uMiIgwWf65554TR4wYYZTm7+8vvvrqq6IoNu3vd1Gs+vm8X3Fxsejo6Ch+/fXXoiha5vnkHXsiIiKiBnTjxg1otVoEBgZKac7OzvD398exY8fKrffzzz+jX79+GDt2LFq2bIk+ffrgiy++qHG7jUFN+j5z5kyMGDHCqG6J06dPo6ioyCivc+fOaNOmTaM+p7X1WcrKygIANGvWDEDTPZ86nQ6nT5826rdSqURgYGC5/T527FiZz2RQUJBUvil/v1fnfN4vLy8PRUVF0mfTEs8nB/ZEREREDUir1QIA3N3djdLd3d2lPFOuX7+ONWvWoEOHDvjjjz8wffp0vPHGG/j6669r1G5jUN2+b926FWfOnEFERES57arVari4uFSpXUtXG58lQRAQEhKCgQMHonv37lK7TfF8pqenQ6/XV+l8arXaCss35e/36pzP+82ePRteXl7SQN4SzycH9kRERET1aPPmzXBwcJBeRUVF1WpHEAQ88MAD+OCDD9CnTx9MmzYNU6dOxdq1a2s5YvmrjXOakJCAN998E5s3b4aNjU0dRGk5auszWtrMmTNx4cIFbN26tRYiJKo9ixcvxtatW/Hjjz9a9Pc+B/ZERERE9WjkyJE4d+6c9GrRogUAlFkJPCUlBR4eHuW24+npia5duxqldenSBfHx8QAg1a1qu5aoNs7p6dOnkZqaigceeABWVlawsrLCwYMHsXLlSlhZWUGv18PDwwM6nQ6ZmZlmt2uJauszWiI4OBi//PIL9u/fj9atW0vpTeV83q9FixZQqVRVOp8eHh4Vlm9K3+/3q875LPHRRx9h8eLF2L17t9FimZZ4PjmwJyIiIqpHjo6OaN++vfTq2rUrPDw8EBkZKZXJzs5GVFQUAgICym1n4MCBZbYO++eff+Dj4wMA8PPzq1a7lqg2zumwYcNw/vx5owFtv3798OKLL+LcuXNQqVTo27cvrK2tjdqNjo5GfHx8ozqntfUZFUURwcHB+PHHH7Fv3z74+fkZ5TeV83k/tVqNvn37GvVbEARERkaW2++AgACj8gCwZ88eqXxT+n6/X3XOJ2BY9f69997Drl270K9fP6M8izyfDb16HxEREVFTt3jxYtHFxUX86aefxL///lscNWqU6OfnJ+bn50tlHn30UfHTTz+Vvj5x4oRoZWUlvv/+++LVq1fFzZs3i3Z2duKmTZuq1G5jVZ1zer/7V8UXRVF87bXXxDZt2oj79u0TT506JQYEBIgBAQF11Q3ZqM75nD59uujs7CweOHBATE5Oll55eXlSmaZ6Prdu3SpqNBpxw4YN4qVLl8Rp06aJLi4uolarFUVRFF966SXxnXfekcr/+eefopWVlfjRRx+Jly9fFsPCwkRra2vx/PnzUpmm/P1e1fO5ePFiUa1Wi99//73RZzMnJ8eojCWdTw7siYiIiBqYIAjivHnzRHd3d1Gj0YjDhg0To6Ojjcr4+PiIYWFhRmk7d+4Uu3fvLmo0GrFz587i559/XuV2G6vqntPSTA3s8/PzxRkzZoiurq6inZ2d+PTTT4vJycl10AN5qc75BGDytX79eqlMUz2foiiKn376qdimTRtRrVaLAwYMEI8fPy7lDRkyRJw4caJR+e+++07s2LGjqFarxW7duom//vqrUX5T/n4XxaqdTx8fH5OfzdKfX0s7nwpRFMUGmixARERERERERDXEZ+yJiIiIiIiILBgH9kREREREREQWjAN7IiIiIiIiIgvGgT0RERERERGRBePAnoiIiIiIiMiCcWBPREREREREZME4sCciIiIiIiKyYBzYExEREREREVkwDuyJiIiIiKjKvvrqKwwfPrzOj7Nr1y707t0bgiDU+bGILBUH9kREREREVCUFBQWYN28ewsLC6vxYjz/+OKytrbF58+Y6PxaRpeLAnoiIiIiIquT777+Hk5MTBg4cWC/HmzRpElauXFkvxyKyRBzYExERERE1Ud988w2aN2+OwsJCo/TRo0fjpZdeKrfe1q1b8dRTTxmlDR06FCEhIWXamTRpkvS1r68vFi1ahAkTJsDBwQE+Pj74+eefkZaWhlGjRsHBwQE9e/bEqVOnjNp56qmncOrUKVy7dq16HSVq5DiwJyIiIiJqosaOHQu9Xo+ff/5ZSktNTcWvv/6Kl19+udx6R44cQb9+/ap1zOXLl2PgwIE4e/YsRowYgZdeegkTJkzA+PHjcebMGbRr1w4TJkyAKIpSnTZt2sDd3R2HDx+u1jGJGjsO7ImIiIiImihbW1v8+9//xvr166W0TZs2oU2bNhg6dKjJOpmZmcjKyoKXl1e1jvnEE0/g1VdfRYcOHTB//nxkZ2ejf//+GDt2LDp27IjZs2fj8uXLSElJMarn5eWFuLi4ah2TqLHjwJ6IiIiIqAmbOnUqdu/ejcTERADAhg0bMGnSJCgUCpPl8/PzAQA2NjbVOl7Pnj2l/7u7uwMAevToUSYtNTXVqJ6trS3y8vKqdUyixs6qoQMgIiIiIqKG06dPH/Tq1QvffPMNhg8fjosXL+LXX38tt3zz5s2hUChw+/btStvW6/Vl0qytraX/l1w8MJV2//Z2t27dgpubW6XHJGqKeMeeiIjo/7d3hyyRRWEYgN8tCjLgLxA0DYzFkSlmBYNFEMsYtYk/YH6AQS1imGJQMNgUi1nEIIN52oiCURhsNjfILrsr68o6hjvzPPHcc7771Zf7cS7AgFtbW8vh4WEODg4yNzeXsbGxv+4dGhpKpVJJu91+8+zP8fnb29ue9Pf8/JxOp5NqtdqTetBvBHsAABhw9Xo9Dw8P2d/ff/fSvB/m5+dzdXX1Zv3s7CwnJyfpdDrZ3NxMu93O/f39zzH//3V9fZ3h4eHMzMx8qg70K8EeAAAG3OjoaJaWllIqlbK4uPjP/aurqzk/P8/T09Nv6wsLC9ne3k6lUsnl5WWazWZarVaOjo4+1d/x8XFWVlYyMjLyqTrQr769/PofCQAAYCDNzs5mcnIye3t7H9q/vLyc6enpNBqNJK//sZ+amsru7m5P+3p8fEy5XM7NzU0mJiZ6Whv6hS/2AAAwwLrdbk5PT3NxcZH19fUPn9vZ2UmpVPrCzl7d3d2l2WwK9fAOt+IDAMAAq1ar6Xa72draSrlc/vC58fHxbGxsfGFnr2q1Wmq12pe/B4rMKD4AAAAUmFF8AAAAKDDBHgAAAApMsAcAAIACE+wBAACgwAR7AAAAKDDBHgAAAApMsAcAAIACE+wBAACgwL4DEXxEGooyWHQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_plot = 0\n",
    "\n",
    "_, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
    "l4_EME.plot(z=0.005, ax=ax[0])\n",
    "\n",
    "l4_EME.plot(x=x_plot, ax=ax[1])\n",
    "l4_EME.plot_grid(x=x_plot, ax=ax[1], lw=0.4, colors=\"r\")\n",
    "ax[0].set_aspect(6)\n",
    "ax[1].set_xlim(-0.7, 0.3)\n",
    "ax[1].set_ylim(-0.25, 0.32)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f7d689ae-0e40-42d7-9bb8-04250df9a78b",
   "metadata": {},
   "outputs": [
    {
     "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\">11:18:27 EST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'L4 EME sweep'</span> with resource_id                       \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'eme-3eaa01a6-3f98-44d8-97f8-c1d6390cf5d4'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'EME'</span>.    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:18:27 EST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'L4 EME sweep'\u001b[0m with resource_id                       \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'eme-3eaa01a6-3f98-44d8-97f8-c1d6390cf5d4'\u001b[0m and task_type \u001b[32m'EME'\u001b[0m.    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Tidy3D's EME solver is currently in the beta stage. Cost of EME    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>simulations is subject to change in the future.                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTidy3D's EME solver is currently in the beta stage. Cost of EME    \n",
       "\u001b[2;36m             \u001b[0msimulations is subject to change in the future.                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b20f7d17896448d0b6f3e63b34d8ec73",
       "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\">11:18:35 EST </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.513</span>. Minimum cost depends on task     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:18:35 EST\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m0.513\u001b[0m. Minimum cost depends on task     \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \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\">11:18:36 EST </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:18:36 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \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>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dacd4f66441447468a9c2b8d490c47b7",
       "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:18:41 EST </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:18:41 EST\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \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\">11:18:42 EST </span>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:18:42 EST\u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "447b4e331c5a4ce3a37a27a0cc5c6bbf",
       "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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "88bc8853a1f641958b8fc12d8aecd627",
       "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\">11:22:38 EST </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:22:38 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d24f409b2f6345c3867a71fa465d07fa",
       "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\">11:23:00 EST </span>Loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:23:00 EST\u001b[0m\u001b[2;36m \u001b[0mLoading simulation from simulation_data.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "l4_EME_data = web.run(l4_EME, task_name=\"L4 EME sweep\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "890cd7c7-9b60-4e0b-aaba-36f19da836d2",
   "metadata": {},
   "source": [
    "We want to examine the scattering matrix in the bases of modes in each junction arm. The `EMESimulationData.smatrix_in_basis` data is thus what we want to examine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1ba03ebd-c827-4771-8004-c24db8aeb0f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "TE0_TE0_port1 = (\n",
    "    l4_EME_data.smatrix_in_basis(modes2=l4_EME_data[\"l4 port 1\"])\n",
    "    .S21.sel(f=freq0, mode_index_in=0, mode_index_out=0)\n",
    "    .abs.data\n",
    "    ** 2\n",
    ")\n",
    "TE0_TE0_port2 = (\n",
    "    l4_EME_data.smatrix_in_basis(modes2=l4_EME_data[\"l4 port 2\"])\n",
    "    .S21.sel(f=freq0, mode_index_in=0, mode_index_out=0)\n",
    "    .abs.data\n",
    "    ** 2\n",
    ")\n",
    "TE0_TM0_port1 = (\n",
    "    l4_EME_data.smatrix_in_basis(modes2=l4_EME_data[\"l4 port 1\"])\n",
    "    .S21.sel(f=freq0, mode_index_in=0, mode_index_out=1)\n",
    "    .abs.data\n",
    "    ** 2\n",
    ")\n",
    "TE0_TM0_port2 = (\n",
    "    l4_EME_data.smatrix_in_basis(modes2=l4_EME_data[\"l4 port 2\"])\n",
    "    .S21.sel(f=freq0, mode_index_in=0, mode_index_out=1)\n",
    "    .abs.data\n",
    "    ** 2\n",
    ")\n",
    "\n",
    "TE1_TE0_port1 = (\n",
    "    l4_EME_data.smatrix_in_basis(modes2=l4_EME_data[\"l4 port 1\"])\n",
    "    .S21.sel(f=freq0, mode_index_in=1, mode_index_out=0)\n",
    "    .abs.data\n",
    "    ** 2\n",
    ")\n",
    "TE1_TE0_port2 = (\n",
    "    l4_EME_data.smatrix_in_basis(modes2=l4_EME_data[\"l4 port 2\"])\n",
    "    .S21.sel(f=freq0, mode_index_in=1, mode_index_out=0)\n",
    "    .abs.data\n",
    "    ** 2\n",
    ")\n",
    "TE1_TM0_port1 = (\n",
    "    l4_EME_data.smatrix_in_basis(modes2=l4_EME_data[\"l4 port 1\"])\n",
    "    .S21.sel(f=freq0, mode_index_in=1, mode_index_out=1)\n",
    "    .abs.data\n",
    "    ** 2\n",
    ")\n",
    "TE1_TM0_port2 = (\n",
    "    l4_EME_data.smatrix_in_basis(modes2=l4_EME_data[\"l4 port 2\"])\n",
    "    .S21.sel(f=freq0, mode_index_in=1, mode_index_out=1)\n",
    "    .abs.data\n",
    "    ** 2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2fcfcea7-e48d-411d-bdfd-f85face1d9a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal L4: 117.08333333333331\n"
     ]
    }
   ],
   "source": [
    "_, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
    "ax[0].plot(l4_scales * l4_sweep, TE0_TE0_port1, c=\"red\", label=\"TE0 in port1\")\n",
    "ax[0].plot(l4_scales * l4_sweep, TE0_TE0_port2, c=\"blue\", label=\"TE0 in port2\")\n",
    "ax[0].plot(l4_scales * l4_sweep, TE0_TM0_port1, c=\"purple\", label=\"TM0 in port1\")\n",
    "ax[0].plot(l4_scales * l4_sweep, TE0_TM0_port2, c=\"green\", label=\"TM0 in port2\")\n",
    "ax[0].set_xlim((0, 200))\n",
    "ax[0].set_ylim((0, 1))\n",
    "ax[0].legend()\n",
    "ax[0].set_xlabel(\"L4 (μm)\")\n",
    "ax[0].set_ylabel(\"Mode conversion efficiency\")\n",
    "ax[0].set_title(\"TE0 Input\")\n",
    "\n",
    "ax[1].plot(l4_scales * l4_sweep, TE1_TE0_port1, c=\"red\", label=\"TE0 in port1\")\n",
    "ax[1].plot(l4_scales * l4_sweep, TE1_TE0_port2, c=\"blue\", label=\"TE0 in port2\")\n",
    "ax[1].plot(l4_scales * l4_sweep, TE1_TM0_port1, c=\"purple\", label=\"TM0 in port1\")\n",
    "ax[1].plot(l4_scales * l4_sweep, TE1_TM0_port2, c=\"green\", label=\"TM0 in port2\")\n",
    "ax[1].set_xlim((0, 200))\n",
    "ax[1].set_ylim((0, 1))\n",
    "ax[1].legend()\n",
    "ax[1].set_xlabel(\"L4 (μm)\")\n",
    "ax[1].set_ylabel(\"Mode conversion efficiency\")\n",
    "ax[1].set_title(\"TE1 Input\")\n",
    "\n",
    "plt.show()\n",
    "\n",
    "l4 = l4_sweep * l4_scales[np.argmin(TE0_TE0_port2[:40])]\n",
    "print(f\"Optimal L4: {l4}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "76cd3805-55eb-40e6-857a-5c60b791885c",
   "metadata": {},
   "source": [
    "## Full FDTD Device Simulation\n",
    "\n",
    "Now we will combine our optimized taper and Y-junction to create a full polarizer/rotator in FDTD and examine the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "46d91b15-cebd-4f42-a59e-69e73fff797a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add buffer to extend structure into the boundary to avoid sidewall angle trouble\n",
    "buffer = 10\n",
    "\n",
    "# Create full polarization rotator/splitter structure in LNOI\n",
    "full_pts = [\n",
    "    (-buffer, -w0 / 2),\n",
    "    (0, -w0 / 2),\n",
    "    (l1, -w1 / 2),\n",
    "    (l1 + l2, -w2 / 2),\n",
    "    (l1 + l2 + l3, -w3 / 2),\n",
    "    (l1 + l2 + l3 + 2 * l4, 2 * (-split / 2) - w5),\n",
    "    (l1 + l2 + l3 + 2 * l4, 2 * (-split / 2)),\n",
    "    (l1 + l2 + l3, -w3 / 2 + w5),\n",
    "    (l1 + l2 + l3 + 2 * l4, 2 * (split / 2)),\n",
    "    (l1 + l2 + l3 + 2 * l4, 2 * (split / 2) + w4),\n",
    "    (l1 + l2 + l3, w3 / 2),\n",
    "    (l1 + l2, w2 / 2),\n",
    "    (l1, w1 / 2),\n",
    "    (0, w0 / 2),\n",
    "    (-buffer, w0 / 2),\n",
    "]\n",
    "full_structure = make_ridge_waveguide(full_pts)\n",
    "\n",
    "# Create mode monitors\n",
    "full_port1 = td.ModeMonitor(\n",
    "    name=\"Full Port 1\",\n",
    "    size=(0, w4 + 1.5 * split, td.inf),\n",
    "    center=(l1 + l2 + l3 + l4 - 1, (-w3 / 2 + w5) + split / 2 + w4 / 2, 0),\n",
    "    freqs=freqs[::2],\n",
    "    mode_spec=td.ModeSpec(\n",
    "        num_modes=num_modes,\n",
    "        target_neff=n_o,\n",
    "        angle_theta=np.arctan((split / 2 - (-w3 / 2 + w5)) / l4),\n",
    "    ),\n",
    ")\n",
    "full_port2 = td.ModeMonitor(\n",
    "    name=\"Full Port 2\",\n",
    "    size=(0, w5 + 1.5 * split, td.inf),\n",
    "    center=(l1 + l2 + l3 + l4 - 1, -split / 2 - w5 / 2, 0),\n",
    "    freqs=freqs[::2],\n",
    "    mode_spec=td.ModeSpec(\n",
    "        num_modes=num_modes,\n",
    "        target_neff=n_o,\n",
    "        angle_theta=np.arctan((-split / 2 - w5 / 2 + w3 / 4) / l4),\n",
    "    ),\n",
    ")\n",
    "full_field = td.FieldMonitor(name=\"Full Field\", size=(td.inf, td.inf, 0), freqs=[freq0])\n",
    "\n",
    "auto_x = td.AutoGrid(min_steps_per_wvl=11)\n",
    "\n",
    "# Create simulation\n",
    "full_sim = td.Simulation(\n",
    "    center=((l1 + l2 + l3 + l4) / 2, 0, 0),\n",
    "    size=(l1 + l2 + l3 + l4, 4 * split + w4 + w5, 5 * film_thickness),\n",
    "    structures=full_structure,\n",
    "    grid_spec=td.GridSpec(grid_x=auto_x, grid_y=auto_yz, grid_z=auto_yz, wavelength=wvl0),\n",
    "    medium=SiO2,\n",
    "    sources=[],\n",
    "    monitors=[full_port1, full_port2, full_field],\n",
    "    run_time=5e-12,\n",
    ")\n",
    "\n",
    "\"\"\"\n",
    "Solve for modes to be able to launch them as a source. We will create two simulations, one with the TE0 mode as a source, and one\n",
    "with a TM0 mode as a source.\n",
    "\"\"\"\n",
    "full_mode_solver = ModeSolver(\n",
    "    simulation=full_sim,\n",
    "    plane=td.Box(center=(1, 0, 0), size=(0, td.inf, td.inf)),\n",
    "    mode_spec=td.ModeSpec(num_modes=3, target_neff=n_o),\n",
    "    freqs=[freq0],\n",
    ")\n",
    "full_source_TE0 = full_mode_solver.to_source(\n",
    "    mode_index=0,\n",
    "    direction=\"+\",\n",
    "    source_time=td.GaussianPulse(freq0=freq0, fwidth=freq0 / 10),\n",
    ")\n",
    "full_source_TM0 = full_source_TE0.updated_copy(mode_index=1)\n",
    "\n",
    "full_sim_TE0 = full_sim.updated_copy(sources=[full_source_TE0])\n",
    "full_sim_TM0 = full_sim.updated_copy(sources=[full_source_TM0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b1990f6-19ff-44ba-a0cc-7da47e34932e",
   "metadata": {},
   "source": [
    "Before we run we'll check that we've constructed the simulation correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9f63c703-fa7e-491a-a60c-2e11f880ac18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
    "full_sim_TE0.plot(z=0.005, ax=ax[0])\n",
    "full_sim_TE0.plot(x=l1 + l2 + l3 + l4, ax=ax[1])\n",
    "ax[0].set_aspect(6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c7ae0ee5-c935-4eed-b6df-8aa1578954d9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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\">11:23:12 EST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'Full FDTD pol/rot TE0'</span> with resource_id              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-c0a5fadb-afa3-4efc-b601-2ac7b7045cd4'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:23:12 EST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'Full FDTD pol/rot TE0'\u001b[0m with resource_id              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-c0a5fadb-afa3-4efc-b601-2ac7b7045cd4'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-c0a5fadb-afa3-4efc-b601-2ac7b7045cd4\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-c0a5fadb-afa</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-c0a5fadb-afa3-4efc-b601-2ac7b7045cd4\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">3-4efc-b601-2ac7b7045cd4'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=611951;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c0a5fadb-afa3-4efc-b601-2ac7b7045cd4\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=744122;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c0a5fadb-afa3-4efc-b601-2ac7b7045cd4\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=611951;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c0a5fadb-afa3-4efc-b601-2ac7b7045cd4\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=650554;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c0a5fadb-afa3-4efc-b601-2ac7b7045cd4\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=611951;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c0a5fadb-afa3-4efc-b601-2ac7b7045cd4\u001b\\\u001b[32m-c0a5fadb-afa\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=611951;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c0a5fadb-afa3-4efc-b601-2ac7b7045cd4\u001b\\\u001b[32m3-4efc-b601-2ac7b7045cd4'\u001b[0m\u001b]8;;\u001b\\.                                         \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>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/folder-86acd7be-dbf5-477e-9c86-3e20787acc03\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=140381;https://tidy3d.simulation.cloud/folders/folder-86acd7be-dbf5-477e-9c86-3e20787acc03\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "47cb5cbdc7654e199de69fdb775e8772",
       "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\">11:23:14 EST </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">16.718</span>. Minimum cost depends on task    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:23:14 EST\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m16.718\u001b[0m. Minimum cost depends on task    \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \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\">11:23:15 EST </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:23:15 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \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>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9b9e04a642e94a4b9c633265c248bfdc",
       "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\">11:23:23 EST </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:23:23 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:23:56 EST </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:23:56 EST\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \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\">11:23:57 EST </span>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:23:57 EST\u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2fb8e2d7bfcd489eb64b5d5391db15ba",
       "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\">11:29:29 EST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">84</span>%, exiting.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:29:29 EST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m84\u001b[0m%, exiting.                            \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": {
      "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>status = postprocess                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d3c3a2d149ef425781979c52e905adf7",
       "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\">11:29:37 EST </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:29:37 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:29:39 EST </span>View simulation result at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-c0a5fadb-afa3-4efc-b601-2ac7b7045cd4\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-c0a5fadb-afa</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-c0a5fadb-afa3-4efc-b601-2ac7b7045cd4\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">3-4efc-b601-2ac7b7045cd4'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:29:39 EST\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=857220;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c0a5fadb-afa3-4efc-b601-2ac7b7045cd4\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=253842;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c0a5fadb-afa3-4efc-b601-2ac7b7045cd4\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=857220;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c0a5fadb-afa3-4efc-b601-2ac7b7045cd4\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=380874;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c0a5fadb-afa3-4efc-b601-2ac7b7045cd4\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=857220;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c0a5fadb-afa3-4efc-b601-2ac7b7045cd4\u001b\\\u001b[4;34m-c0a5fadb-afa\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=857220;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c0a5fadb-afa3-4efc-b601-2ac7b7045cd4\u001b\\\u001b[4;34m3-4efc-b601-2ac7b7045cd4'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "68cd575614014622a14eace6de5e4541",
       "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\">11:30:01 EST </span>Loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:30:01 EST\u001b[0m\u001b[2;36m \u001b[0mLoading simulation from simulation_data.hdf5                       \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>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'Full FDTD pol/rot TM0'</span> with resource_id              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-cbc2932d-094b-4eda-9d28-99f8951c31fc'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'Full FDTD pol/rot TM0'\u001b[0m with resource_id              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-cbc2932d-094b-4eda-9d28-99f8951c31fc'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-cbc2932d-094b-4eda-9d28-99f8951c31fc\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-cbc2932d-094</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-cbc2932d-094b-4eda-9d28-99f8951c31fc\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">b-4eda-9d28-99f8951c31fc'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=840887;https://tidy3d.simulation.cloud/workbench?taskId=fdve-cbc2932d-094b-4eda-9d28-99f8951c31fc\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=913226;https://tidy3d.simulation.cloud/workbench?taskId=fdve-cbc2932d-094b-4eda-9d28-99f8951c31fc\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=840887;https://tidy3d.simulation.cloud/workbench?taskId=fdve-cbc2932d-094b-4eda-9d28-99f8951c31fc\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=204246;https://tidy3d.simulation.cloud/workbench?taskId=fdve-cbc2932d-094b-4eda-9d28-99f8951c31fc\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=840887;https://tidy3d.simulation.cloud/workbench?taskId=fdve-cbc2932d-094b-4eda-9d28-99f8951c31fc\u001b\\\u001b[32m-cbc2932d-094\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=840887;https://tidy3d.simulation.cloud/workbench?taskId=fdve-cbc2932d-094b-4eda-9d28-99f8951c31fc\u001b\\\u001b[32mb-4eda-9d28-99f8951c31fc'\u001b[0m\u001b]8;;\u001b\\.                                         \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>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/folder-86acd7be-dbf5-477e-9c86-3e20787acc03\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=240854;https://tidy3d.simulation.cloud/folders/folder-86acd7be-dbf5-477e-9c86-3e20787acc03\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8dd24420c8084091a7f06bc702801b73",
       "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\">11:30:03 EST </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">16.718</span>. Minimum cost depends on task    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:30:03 EST\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m16.718\u001b[0m. Minimum cost depends on task    \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \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\">11:30:04 EST </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:30:04 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \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>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "model_id": "d5d59fa092d7462a91bd35ce42c240c5",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:30:12 EST </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:30:12 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:30:43 EST </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:30:43 EST\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \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>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:36:15 EST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">84</span>%, exiting.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:36:15 EST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m84\u001b[0m%, exiting.                            \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": {
      "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\">11:36:16 EST </span>status = postprocess                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:36:16 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f62692f116884fe6bbcc7546d4e7eac6",
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      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:36:23 EST </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:36:23 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \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": {},
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    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:36:25 EST </span>View simulation result at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-cbc2932d-094b-4eda-9d28-99f8951c31fc\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-cbc2932d-094</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-cbc2932d-094b-4eda-9d28-99f8951c31fc\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">b-4eda-9d28-99f8951c31fc'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:36:25 EST\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=979117;https://tidy3d.simulation.cloud/workbench?taskId=fdve-cbc2932d-094b-4eda-9d28-99f8951c31fc\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=557924;https://tidy3d.simulation.cloud/workbench?taskId=fdve-cbc2932d-094b-4eda-9d28-99f8951c31fc\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=979117;https://tidy3d.simulation.cloud/workbench?taskId=fdve-cbc2932d-094b-4eda-9d28-99f8951c31fc\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=781565;https://tidy3d.simulation.cloud/workbench?taskId=fdve-cbc2932d-094b-4eda-9d28-99f8951c31fc\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=979117;https://tidy3d.simulation.cloud/workbench?taskId=fdve-cbc2932d-094b-4eda-9d28-99f8951c31fc\u001b\\\u001b[4;34m-cbc2932d-094\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=979117;https://tidy3d.simulation.cloud/workbench?taskId=fdve-cbc2932d-094b-4eda-9d28-99f8951c31fc\u001b\\\u001b[4;34mb-4eda-9d28-99f8951c31fc'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
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     },
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       "Output()"
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     "metadata": {},
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    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></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\">11:36:44 EST </span>Loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:36:44 EST\u001b[0m\u001b[2;36m \u001b[0mLoading simulation from simulation_data.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_TE0_data = web.run(simulation=full_sim_TE0, task_name=\"Full FDTD pol/rot TE0\")\n",
    "full_TM0_data = web.run(simulation=full_sim_TM0, task_name=\"Full FDTD pol/rot TM0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d1eed22-0521-4f95-869b-bfeb67115a3c",
   "metadata": {},
   "source": [
    "## Full Device Results\n",
    "\n",
    "Plotting our results, we can see that we get high contrast and low insertion loss for both TE0 and TM0 inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ae399dbc-4d36-468b-83d1-45a9d09a3e94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9ezYeffRRPP7441i7di06dOiAoUOHora21t5m5MiR+Pjjj7F8+XIsWbIE77zzDsaOHdtiVyIZHN5PRETpjcP7iYiIMltSsd7f9g888ACOOuoozJs3z17Ws2dPx+EkHnnkEUyZMgWXX345AOCZZ55BYWEhXnnlFVx77bXYsmULli1bhvfffx8DBw4EAPzpT3/CJZdcgoceegjFxcX+3kMLYU8/ERGlN+l3uB+H9xMREaUd38P7/cX6v//97xg4cCCuvvpqdO3aFf3798df/vIXe/22bdtQWVmJ0tJSe1lBQQEGDRqE8vJyAEB5eTk6duxoJ/wAUFpaCkVRsHbt2mZegOQx6SciovQmweH9REREmS7JWF9TU+N61dXVeR7+iy++wNy5c9GrVy+8/vrrGDduHH7xi1/g6aefBgBUVlYCAAoLC137FRYW2usqKyvRtWtX1/pAIIBOnTrZ27QGJv1ERJTmzJ57vy8iIiJKH0nE+oMHD6KgoMD1mjlzpufhdV3HaaedhhkzZqB///4YO3YsbrnlFjz++OOH+I22PN7TT0RE6Y339BMREWU+v7Fel8jJyYnrYc/KyvLcvFu3bujbt69rWZ8+ffC3v/0NAFBUVAQA2LVrF7p162Zvs2vXLvTr18/eZvfu3a5jRCIR7Nmzx96/NbCnn4iI0h/v6SciIspsSd7Tn5+f73olSvrPOussbN261bXsk08+QY8ePQAYk/oVFRVhxYoV9vqamhqsXbsWJSUlAICSkhJUVVWhoqLC3uatt96CrusYNGhQi14OP9jTT0RE6U1K34/lgZSASE1xiIiIKAWSifU+3H777TjzzDMxY8YMXHPNNVi3bh2efPJJPPnkkwAAIQQmTpyI6dOno1evXujZsyfuvvtuFBcX44orrgBgjAy46KKL7NsCwuEwxo8fj2uvvbbVZu4H2NNPRETpzhre76un3/9pvv32W1x//fXo3LkzcnJycPLJJ2P9+vXRYkiJe+65B926dUNOTg5KS0vx6aefuo6xZ88ejBw5Evn5+ejYsSPGjBmDffv2NfMCEBERZbhkYr3P2wFOP/10vPzyy1i4cCFOOukk3HfffXjkkUcwcuRIe5s777wTEyZMwNixY3H66adj3759WLZsGbKzs+1tnn/+eZxwwgm44IILcMkll+Dss8+2Gw5aC3v6iYgozfkP7H5b/3/44QecddZZOO+88/CPf/wDXbp0waefforDDz/c3mb27Nl49NFH8fTTT9ut/0OHDsXmzZvtysDIkSOxc+dOLF++HOFwGDfddBPGjh2LBQsW+Cs/ERFRe5PiWA8Al156KS699NKE64UQmDZtGqZNm5Zwm06dOrW5uM6kn4iI0tshmMjvgQcewFFHHYV58+bZy3r27Bk9nJR45JFHMGXKFFx++eUAgGeeeQaFhYV45ZVXcO2112LLli1YtmwZ3n//ffv5vX/6059wySWX4KGHHmrVYX9ERERtWxIN/H63z2Ac3k9ERGlNSkDq0tfLb+v/3//+dwwcOBBXX301unbtiv79++Mvf/mLvX7btm2orKxEaWmpvaygoACDBg1CeXk5AKC8vBwdO3a0E34AKC0thaIoWLt2bTOvAhERUWbzG+slk34bk34iIkpz/p/bCwlomoaamhrXq66uzvMMX3zxBebOnYtevXrh9ddfx7hx4/CLX/wCTz/9NADYjwMqLCx07VdYWGivq6ysRNeuXV3rA4EAOnXqFPc4ISIiIorhN9aTjUk/ERGlt6Qm8pPYsmULCgoKXK+ZM2d6nkLXdZx22mmYMWMG+vfvj7Fjx9oz8xIREdEhkOKJ/DIZ7+knIqL0l8TkPn369MF7773nWpzo2b3dunVD3759Xcv69OmDv/3tbwCAoqIiAMCuXbvQrVs3e5tdu3ahX79+9ja7d+92HSMSiWDPnj32/kRERJQA7+lPGnv6iYgovckkWv4loKoq8vPzXa9ESf9ZZ52FrVu3upZ98skn6NGjBwBjUr+ioiKsWLHCXl9TU4O1a9eipKQEAFBSUoKqqipUVFTY27z11lvQdR2DBg1q6atCRESUOZIc1UcG9vQTEVF6S2r2fn/b33777TjzzDMxY8YMXHPNNVi3bh2efPJJ+7m7QghMnDgR06dPR69evexH9hUXF+OKK64AYIwMuOiii+zbAsLhMMaPH49rr72WM/cTERE1hj39SWPST0RE6c2cvd/XLj7rAaeffjpefvllTJ48GdOmTUPPnj3xyCOPYOTIkfY2d955J/bv34+xY8eiqqoKZ599NpYtW4bs7Gx7m+effx7jx4/HBRdcAEVRMHz4cDz66KP+CkNERNQO+Y71eooKkoaY9BMRUZpLYrKeJIb8XXrppbj00ksTrhdCYNq0aZg2bVrCbTp16oQFCxb4PjcREVG7dwhifaZi0k9EROktqeH9KSkJERERpQqH9yeNST8REaU3eWh6+omIiKgVMdYnjUk/ERGlP7+BnfUAIiKi9OI71jPYW5j0ExFRepNJTNbDegAREVH6SCLWcyK/KCb9RESU/jjkj4iIKLPxnv6kMeknIqL0ltQ9/akpChEREaUIY33SmPQTEVFak8kM+WNFgIiIKK1weH/ymPQTEVH64/B+IiKiDMYn9TQHk34iIkpvEgAn8iMiIspsfmM9e/ptTPqJiCi9SUD6bP1n4z8REVF68RvrOZFfFJN+IiJKb+zpJyIiymyM9c3CpJ+IiNKf38DOigAREVF6YaxPGpN+IiJKa1JK/0P+OL6fiIgorfi+lY/D+21M+omIKP1xyB8REVFmY6xPGpN+IiJKbzKJZ/eyIkBERJRWfMd6zt5vU1q7AERERM1iTe7j58Wkn4iIKL34jfU+k/57770XQgjX64QTTrDX19bWoqysDJ07d0ZeXh6GDx+OXbt2uY6xfft2DBs2DLm5uejatSvuuOMORCKRZN9xi2FPPxERpT3frflM+omIiNLKoRjVd+KJJ+LNN9+0vw8Eouny7bffjtdeew2LFy9GQUEBxo8fj6uuugrvvfceAEDTNAwbNgxFRUVYvXo1du7ciRtuuAHBYBAzZszwX5gWxKSfiIjSGx/jQ0RElNmSifVJDO8PBAIoKiqKW15dXY2nnnoKCxYswPnnnw8AmDdvHvr06YM1a9Zg8ODBeOONN7B582a8+eabKCwsRL9+/XDffffhrrvuwr333otQKOS/QC2Ew/uJiCitSRit+X5fRERElD4ORaz/9NNPUVxcjGOOOQYjR47E9u3bAQAVFRUIh8MoLS21tz3hhBPQvXt3lJeXAwDKy8tx8skno7Cw0N5m6NChqKmpwccff9y8N99M7OknIqL0lsREfuzpJyIiSi/JxvqamhrX4qysLGRlZcVtPmjQIMyfPx/HH388du7cid/97nf40Y9+hE2bNqGyshKhUAgdO3Z07VNYWIjKykoAQGVlpSvht9Zb61oTe/qJfIid3CPRa+XKlfjyyy8b3GbWrFmuY2/ZsgUXXXQR8vLy0KlTJ4waNQr/+c9/mlyu8ePHp+ItJ2XBggV45JFHWrsY1F5wIj8iaiHJxvnp06d7Hm/kyJEQQiAvLy9uHeM+kU9JTOR38OBBFBQUuF4zZ870PPzFF1+Mq6++GqeccgqGDh2KpUuXoqqqCi+++OIheHOpxZ5+Ih+effZZ1/fPPPMMli9fHre8T58+OHjwIABgxIgRuOSSS+KO1b9/f/vrb775Bj/+8Y9RUFCAGTNmYN++fXjooYewceNGrFu3rlXvAUrGggULsGnTJkycOLG1i0LtBB/ZR0QtIZk4n52djYULF2LKlCmubfbv349XX30V2dnZcedh3CfyRyK5WJ+TkxPXy+7Vy++lY8eO6N27Nz777DNceOGFqK+vR1VVlau3f9euXfYcAEVFRVi3bp3rGNbs/l7zBBxKTPqJfLj++utd369ZswbLly+PWw4AX375JQDgtNNO81zvNGPGDOzfvx8VFRXo3r07AOCMM87AhRdeiPnz52Ps2LEt8waIMlBS9+0x6SciD8nE+UsuuQQvvfQSPvroI5x66qn2+ldffRX19fW46KKL8NZbb7n2Zdwn8s9vrLe2z8/PT+p8+/btw+eff45Ro0ZhwIABCAaDWLFiBYYPHw4A2Lp1K7Zv346SkhIAQElJCe6//37s3r0bXbt2BQAsX74c+fn56Nu3b1JlaCkc3k/UBvztb3/DpZdeagd+ACgtLUXv3r2TGlK0cuVKCCHw4osv4v7778eRRx6J7OxsXHDBBfjss89c25577rk46aSTUFFRgTPPPBM5OTno2bMnHn/8cdd28+fPhxDCruTEnmvlypX28V577TV89dVX9rDHo48+2vd7IGoyKQDd50sKX6fI5Gf3ElHzlJSUoGfPnliwYIFr+fPPP4+LLroInTp1ituHcZ8oCX5jve4v1v/qV7/CqlWr8OWXX2L16tW48soroaoqRowYgYKCAowZMwaTJk3C22+/jYqKCtx0000oKSnB4MGDAQBDhgxB3759MWrUKHz00Ud4/fXXMWXKFJSVlTV5dEGqsKefKMUOHDiA7777Lm55x44dEQgE8O2332L37t0YOHBg3DZnnHEGli5dmvS5Z82aBUVR8Ktf/QrV1dWYPXs2Ro4cibVr17q2++GHH3DJJZfgmmuuwYgRI/Diiy9i3LhxCIVCuPnmm32d87e//S2qq6vxzTff4OGHHwYAz3sZiVoSn91LRK1pxIgReO655zBr1iwIIfDdd9/hjTfewLPPPotly5a5tmXcJ0qO71jvc/tvvvkGI0aMwPfff48uXbrg7LPPxpo1a9ClSxcAwMMPPwxFUTB8+HDU1dVh6NCheOyxx+z9VVXFkiVLMG7cOJSUlKBDhw4YPXo0pk2b5q8gKcCknyjFpk6diqlTp8YtLy8vx+DBg7Fz504AQLdu3eK26datG/bs2YO6urqkWghra2uxYcMG+97Aww8/HLfddhs2bdqEk046yd5ux44d+P3vf49JkyYBAG699VYMGjQIkydPxqhRoxAMBpt8zgsvvBBHHHEEfvjhh0ZvayBqERKQPnvuk5Gpz+4loua77rrrMGPGDLz33ns4++yz8eKLLyI7Oxs/+clP4pJ+xn2i5PiN9RL+tl+0aFGD67OzszFnzhzMmTMn4TY9evRoVsNdqnB4P1GKjR07FsuXL497Wff2WBMBeQV3a/Ifaxu/brrpJlcy8aMf/QgA8MUXX7i2CwQCuPXWW+3vQ6EQbr31VuzevRsVFRVJnZvoULEm9/H1kkbve01NjetVV1eX8DyZ+uxeImq+E088EaeccgoWLlwIwJjY7vLLL0dubm7ctoz7REmQ/mM9/D7iL4Mx6SdKsV69eqG0tDTuZU0qkpOTAwCeyUZtba1rG7+c9woCRos/YAzrcyouLkaHDh1cy3r37g0AcffyEbU5SVYEtmzZ0uTH+FjP7l22bBnmzp2Lbdu24Uc/+hH27t2b9s/uJaKWcd1112Hx4sX47LPPsHr1alx33XWe2zHuEyXHdwM/k34bh/cTtTJreJ813M9p586d6NSpU9KTf6iq6rlcJnFDsxDeQ6Q0TfN9LKKWlsyQvz59+tj33FsS/a1dfPHF9tennHIKBg0ahB49euDFF19MunJORJllxIgRmDx5Mm655RZ07twZQ4YM8dyOcZ8oGSLlw/szGXv6iVrZEUccgS5dumD9+vVx69atW4d+/fqlvAw7duzA/v37Xcs++eQTALBn4LV6C6qqqlzbffXVV3HHS1RRIEoFKQWk7vMljcpxfn6+65XMs3uLiorsZ/c6xT67N3Y2/7by7F4iahndu3fHWWedhZUrV+Lqq692TfbpxLhPlBzfsZ49/TYm/URtwPDhw7FkyRJ8/fXX9rIVK1bgk08+wdVXX53y80ciETzxxBP29/X19XjiiSfQpUsXDBgwAABw7LHHAgDeeecdeztN0/Dkk0/GHa9Dhw6orq5OcamJoqT090ISs/c7Wc/u7datm+vZvRavZ/du3LgRu3fvtrdpK8/uJaKWM336dEydOhUTJkxocDvGfSL//Mb6ZJ7Uk6k4vJ8oxT744AM899xzccuPPfZYOyH4zW9+g8WLF+O8887Dbbfdhn379uHBBx/EySefjJtuuinlZSwuLsYDDzyAL7/8Er1798YLL7yADRs24Mknn7Rn8D3xxBMxePBgTJ48GXv27EGnTp2waNEiz+eMDxgwAC+88AImTZqE008/HXl5ebjssstS/j6onUpi9n6/Q/5+9atf4bLLLkOPHj2wY8cOTJ061fPZvZ06dUJ+fj4mTJiQ8Nm9s2fPRmVlZZt5di8RtZxzzjkH55xzTqPbMe4T+ec71h+CJ/ukCyb9RCm2cOFCezZfp9GjR9tJ/1FHHYVVq1Zh0qRJ+PWvf41QKIRhw4bh97///SFJCA4//HA8/fTTmDBhAv7yl7+gsLAQf/7zn3HLLbe4tnv++edx6623YtasWejYsSPGjBmD8847DxdeeKFru5///OfYsGED5s2bh4cffhg9evRg8KeUMWbv9xnYfbb+Z/Kze4no0GPcJ/LPf6xn0m8RMpmZPYgoY5x77rn47rvvsGnTptYuCpFvs2fPRvmfHsfjg87ztd+ft/4Ln510nGeDHBFRJmPcp3SzYcMGnD9oMLZcNtLXfpuqvsc1H/4T33//fYpKlj7Y009ERGnO/4y+4Iy+REREaYXD+5PHpJ+IiNKalIDuc8gfx7gRERGlj2Rivd/tMxmTfiIiSnt+k3gm/UREROmFsTt5TPqJ2rmVK1e2dhGImkUmM7yfQ/6IqJ1i3Kd0xeH9yWPST0RE6S2pR/YRERFROvGf9KeoIGmIST8REaU1CUBn6z8REVFG8xvr/W6fyZj0N5Gu69ixYwcOO+wwCMFfICIiv6SU2Lt3L4qLi6EoSgseWPh+di9b/8kLYz0RUfOkLNYDvmM9b+WLYtLfRDt27MBRRx3V2sUgIkp7X3/9NY488sgWO54Ek3hqGYz1REQto6VjPcBJe5uDSX8THXbYYQCAk07qB1VVW7k0RETpR9M0bNq0wf48bTGSw/upZTDWExE1T8piPTi8vzmY9DeRNcxPVVVWBIiImqGlh00bPf1+J/JjRYDiMdYTEbWMVNwixdn7k8ekn4iI0pxIoqc/RUUhIiKilGCsT17Lzq7QTDNnzsTpp5+Oww47DF27dsUVV1yBrVu3NrjP/PnzIYRwvbKzs13bSClxzz33oFu3bsjJyUFpaSk+/fTTVL4VIiI6RKT5yD4/L7Cnv1Ux3hMRkR/WqD5fL8Z6m++e/m3btuGf//wnvvrqKxw4cABdunRB//79UVJSEhd8/Vq1ahXKyspw+umnIxKJ4De/+Q2GDBmCzZs3o0OHDgn3y8/Pd1UWYoeTzJ49G48++iiefvpp9OzZE3fffTeGDh2KzZs3N7vMRETU+nSf27Pxv2GpjPUA4z0REfnnN9b73T6TNTnpf/755/HHP/4R69evR2FhIYqLi5GTk4M9e/bg888/R3Z2NkaOHIm77roLPXr0SKowy5Ytc30/f/58dO3aFRUVFfjxj3+ccD8hBIqKijzXSSnxyCOPYMqUKbj88ssBAM888wwKCwvxyiuv4Nprr02qrERE1DYkdU8/s35PhyLWA4z3RETkl0gi1rOn39Kk4f39+/fHo48+ihtvvBFfffUVdu7ciYqKCrz77rvYvHkzampq8Oqrr0LXdQwcOBCLFy9ukcJVV1cDADp16tTgdvv27UOPHj1w1FFH4fLLL8fHH39sr9u2bRsqKytRWlpqLysoKMCgQYNQXl6e8Jh1dXWoqalxvYiIqC0y7un38+KQv3itFeuB1ov3jPVERGnCfFKPr1jPpN/WpKR/1qxZWLt2LX7+8597Pr82KysL5557Lh5//HH8+9//xjHHHNPsgum6jokTJ+Kss87CSSedlHC7448/Hv/7v/+LV199Fc899xx0XceZZ56Jb775BgBQWVkJACgsLHTtV1hYaK/zMnPmTBQUFNgvPreXiMib8PFKhWTu6WdPf7zWiPVA68Z7xnoiovThP9Yz6bc0KekfOnRokw/YuXNnDBgwIOkCWcrKyrBp0yYsWrSowe1KSkpwww03oF+/fjjnnHPw0ksvoUuXLnjiiSeadf7Jkyejurrafn399dfNOh4RUVvhJ0mPnTjN6wWh2i8hAolfSuoeGKNLfy8m/fFaI9YDrRvvGeuJiNKH31ivNzPWz5o1C0IITJw40V5WW1uLsrIydO7cGXl5eRg+fDh27drl2m/79u0YNmwYcnNz0bVrV9xxxx2IRCLNK0wzJV0D2717N3bv3g1dd0+RcMoppzS7UOPHj8eSJUvwzjvv4Mgjj/S1bzAYRP/+/fHZZ58BgH3v365du9CtWzd7u127dqFfv34Jj5OVlYWsrCz/hSciakG+26ib9FzcaHuvSHQG+ziK+W3sdqpjUwFAgYBiHFG49xFQAKFA01IT8JK5p5+z9zdNKmM90PrxnrGeiKhlpTK6+o71zUj633//fTzxxBNx8e7222/Ha6+9hsWLF6OgoADjx4/HVVddhffeew8AoGkahg0bhqKiIqxevRo7d+7EDTfcgGAwiBkzZiRfoGbynfRXVFRg9OjR2LJlC6TZVSKEgJQSQghompZ0YaSUmDBhAl5++WWsXLkSPXv29H0MTdOwceNGXHLJJQCAnj17oqioCCtWrLCDfk1NDdauXYtx48YlXVYiosY0OTT5TNTd54jZNyZZd6+y1ql2om4cw0jWo9spRi8+FAhFhYACRQQgIKAoATuJFxBQEUBAyYaKEELIQRDZUMxjRaChXhyALiMQQkUkUt+E95kMAd1nNYMd/Q1LZawHGO+JiFpTYxEzUYx07ZeoviFSF2H9xnq/21v27duHkSNH4i9/+QumT59uL6+ursZTTz2FBQsW4PzzzwcAzJs3D3369MGaNWswePBgvPHGG9i8eTPefPNNFBYWol+/frjvvvtw11134d5770UoFEqqTM3lO+m/+eab0bt3bzz11FMoLCz06P1JXllZGRYsWIBXX30Vhx12mH0PXkFBAXJycgAAN9xwA4444gjMnDkTADBt2jQMHjwYxx13HKqqqvDggw/iq6++ws9+9jMAsIdkTJ8+Hb169bIf4VNcXIwrrriixcpORO1Hg596rs/ERhL1BAHT/bmqOpZZvenuUjSUpBvrjP2EUKGKEAIihACyEEIuAogGnzBqEUateUwVKgJQpIoAQlClChWqcUwpoMBK+hUEpIqgUBBUFASEgCIAVQgIYbxXVQCqAMKBWrzW0LVLkkxiuD6H9zcslbEeYLwnSiWOYzp0UhFKWvznFztyL8EZpOPdiIQPu4s5hkedp4XDhYvvWG/+HztJa2OjvMrKyjBs2DCUlpa6kv6KigqEw2HXhLEnnHACunfvjvLycgwePBjl5eU4+eSTXfPLDB06FOPGjcPHH3+M/v37+3sTLcR30v/FF1/gb3/7G4477rgWL8zcuXMBAOeee65r+bx583DjjTcCMO6RUJRoBfmHH37ALbfcgsrKShx++OEYMGAAVq9ejb59+9rb3Hnnndi/fz/Gjh2LqqoqnH322Vi2bBmf2UtEcZJJ6L2SeCNJcifsxrbRIfCxibqRmCuuZF0RQbs3XYGKgMhGSBqfXTp0SOgIoxYS0uiJFwGo0uiNVxCAYp5XgYBiJu4BPYAAVISEgoBQoQgjMbfu4VcEEFAEgkIgqAqEFCCoAAEzeRfmtlYyn6VKhIRElioRFBIBRUIVxvz4CiQUIRFQgNrwQdzbnB9OA3S/j/FhtbhBqYz1AOM9HXpt9i8+lRlSm9Sk6cRSzorb0lfaHp8I2z89v9lowp+7UQeAs95gjrKyy2B+HS27V4KeqI6S6JY9471I1/tIfFznyMHY46Vq8jyJJGK9FDh48CAKCgpcy6dOnYp7773Xc59Fixbhgw8+wPvvvx+3rrKyEqFQCB07dnQtd04YW1lZ6TmhrLWutfhO+i+44AJ89NFHKakIyCb8waxcudL1/cMPP4yHH364wX2EEJg2bRqmTZvWnOIR2ayPHK/f2NiPI9nAOuf69hb2m63FK0oew+E9k/mAo9ddjSbvIgBFqFBFEKoSQkBkQRUhBEUOAiLamqxDgybDxtFEwO5NtxJ0BQJCWr3oKlSpIgAVAakiAIGAUKAo0QH5Qhhfq4pA0H4ZSboijHAshPm1MBL3kApkCYmQqiNLkQgIaSbzRiocEDoCCpClRJClasgKaAgFNaiqBkUxtxOAUHQEghJKUIeaDag5AsgSUEKK2TIg7EsrgipqwvXAcy38Y4NZEeDw/haVylgPMN63FymLa836/E8+6Uw4B0qTdm7Jq5HaxLn5I3vUxjdp1nkbf/8i4TaJziEdX3klu9Fl0c8vLfq9MJLxRI0Irt+duPfpSNDNDgPhqHe4d9HtcwpHGeLFzrtjHMtrtGCUBITz/TeQ9CcYdWjslbrfz2SG9+fk5MQl24l6+b/++mvcdtttWL58ecY1FvtO+v/nf/4Ho0ePxqZNm3DSSSchGAy61v/kJz9pscLRoZXUR3y7a6F2a8q7j98mZii360PV64NSb2DdodesSg/g43emCUG90WN5Bb3E53IGMSuZt5YLRTW3NJJ7RTFmpLe+VxEwEn6RhaDMQghZCMqQ3auuCgUqYPesBxWBkOroTReAYibqVlt8QDES9ZAChBSJoACCVi+6AASk2ZMOBBQdWUJHSNURUjWEFB2qokNVpFluCUXREQzoCIU0BIIaAtk6lCxACQJCdZw4KCACCkS2CpEdhMgNAaEAEFABVYn+DAMKEAgAoSCQkw0ZDBlfB83QYlYApFAgVRXyQG2jP9Nk+R6u3wIz+k6ePBm33XYbHnnkEQDGjL6//OUvsWjRItTV1WHo0KF47LHHXC3+27dvx7hx4/D2228jLy8Po0ePxsyZMxEIpO7JBslgrG9/mvTJnHTMb3r8ajDGNHr+BLdUee7nnZgmjhWJbtdq6L15H8tKjpp23sbOAfuztjGNxczY83id11l2r5+Va71IcLwExxCw4qyI2cYasaY4jqnY88fY+5m3tUVHuylo+HdPt0fMaSICTYahIYKIfgARhAGpQ5cR6FKDLiOQ0CGlhJQR838dgIQUOqTUAAEIzyS8obpI7Pt0jwR0XS9pJvyOxN/4P1qPbOh3y+v62xzHdjbKJjq2833E/p4oItGtAc2X7K18+fn5Tdq+oqICu3fvxmmnnWYv0zQN77zzDv785z/j9ddfR319Paqqqly9/bt27bInky0qKsK6detcx7Vm97e2aQ2+axnl5eV477338I9//CNuXUtM7kPeWiS1bkLS4z5nzPYe9x/7SboMWgPrms7rvM7JphoX+351xH/werf2Nhp8G/lpxX4gN/Zh3ZLrndu4l8eOR/Bu8U703iV0j3Xu47iXN8zrfcRv04TjOMvkPKbzvTuH2ccMr1dEwPM++CyZgyyZhSACCAgFIaEaSbyiIEsVyAkI5KhATgDm0Hhj2Lsqol9nqRqyFR3ZqtGbHgzoCAgdEEYSryhGkh4I6EaCniWhZEkoAUCogPutRXvYRVYAIidodPWrAaMVAYgm7AEVyA4BWaFooh5QALNRA4oCqApkIAiEsiBDwZbrFddSUxGQUhzS4f2ZNqOvF8b69NLk32bPz83YxCzR7Upe4of2xh7bO26YSYiIP7YzMfFKLIQj8XMljPZTQ8xzxmxjzEBiJJPOMkUTyfgk0tpfCNVOJp1lUezbqKIjtKzl9r6OOVCsK+I6dsxyHTJajgR1Hfd1cayL2zq6jw5p/7POAwAaNEiRuIc6kYYaZ4R0X4dE+yoex7CviUdxFHN5ovcPGf1tFh7XMPZ8OqSRvENCk4AmjUYADTo0oUETGiIiAh0RREQYEVkHHRHo0BCR9WZjQD10GYGmh6HDaAyA1BOMFoh9r/F1FKs+YjVgGMvc9TbrZ+WsxzV0vsbqrRI6IFSzLqe6R2LF1JcSnsP5M1FSl/T7jfV+t7/ggguwceNG17KbbroJJ5xwAu666y4cddRRCAaDWLFiBYYPHw4A2Lp1K7Zv346SkhIAxuNl77//fuzevRtdu3YFACxfvhz5+fmu29EONd9J/4QJE3D99dfj7rvvjrtfoT0QOBQJeCIN/LElTNDd+ya+B8f9uC1FqPZ9xoDZ8qc7Knki9niJJUqgYz+EmpJcJtq/sWM7t7ErDp7Jb8OJc2xFw/nB61UJcW7jXCeEEvNhLeM+3JMuWwP7ex3Dq9HA69o22kLsUcGSHi3edsUrhlUBsdc1kvg7K13u4+iu5VZlzrmFmwJj4Hz0fngVAQQQhCJVhGQIWTKIbBFAlqogN6AgWxXICQDZKpClGPe0ZysSuaqODqqGvGAYHUIR5ITCyMqKIBDUoIYklKCEEgKULAGRq0DkBKDkhoDsIJCVBaiq2dVvvpw96aEQEAxCBh2JvH0xFEBVIYMhyGAAMhQyjtVOSPhP4pNtyMjEGX29tPdYnyotP6IuQcLu2s8Zr2MnBlWhKKp9exLgbsSNbQg19lVccceZYMeWxZlou9cprmTb/Y6M/ZzJdnSdCiGFvb/z/cXeHuWMDa7kW4r4xNtcZiRc7itrJaHG+vjrLATM2lO0TqRED+7ez/njQXRba54U13UQcJXHeT7XOTwosdtYbfAifhvnJs7jx3fzeG8XexyvbbzWee1nrU/YhNTIH5Drfdtfx3/a20PEJRCRgGb+H9GBsPl/vQ5oukSdLhHRjP/DUkNY6qhHBGERRq1Si4ioQ1jWIiJrocl6RBA2RwLo0KU1/N9d74irV1nvPWZOn0R/W2hisu9VB4yte1rXwSqPhG7/3KzGgIa4RwWk/sa5VM/Hc9hhh+Gkk05yLevQoQM6d+5sLx8zZgwmTZqETp06IT8/HxMmTEBJSQkGDx4MABgyZAj69u2LUaNGYfbs2aisrMSUKVNQVlbWqo+I9Z30f//997j99tvbbSVACCM4xraIJjXkuSkt7gln8Y5u6zUk2fqgUIQ1k7cKVQnZHx66brRQxiaazg+Z2IAdG6StwGy9fzsBNhM9K2g7xSbAwqpkWB+M5nrnOntfGU0gYysCEtJeH7vO67yKoxVfR/TcznVCCkgh7fXGFY+uAwAppOsYzgqJPfzK8egSa120XNFtrGM21hjirMjoMR/28UPZdNc2XuutrZwVKOtrr+N7tZRH1xsVLqOsIu7vxFru1fofl6ybFTEgeg1dvx8y/jjx7xVxFTwd0WNF+/cVBIQCVQoEzBnoA0IgoApkKQI5ZpLfIQB0UCXyAhHkBTTkBoz73XMCEeRk1SMrO4KsPA2BjgLKYUEo+VlAbi6Qm2UMjw8GjR72rBBkVhaQkw09JxcIhhqvzVCD9CSG/GmallYz+n7wwQdYvHgx7rzzThx++OGYMmWKqwwtqd3HeqQiQbf4GVkX3d670d67wV5BwE4grM+62ETCiOCBmPgesGO7/ZhOqOa8I84kO3rbWWyy7WzEVs2YqDh6wWGXWoEiRVxiqwBQhGJuY9VRHOsUY7LR2GTaiq6KcCfMrn2FYz2c+0bnP1GFe7m1rXDsb/9UhJFUCmHubx/TilWw50uxkntjubSP4T6Po75gNSaIaJqjuOoy0fM4w0d88h7/4agIGbd99HvpuZ3XDtZvRHxTultjNzE2VD6vnlrPcjk1lvBL47i6BCJSQURXENYF6nQFdbpAva6gVheo0wRqNYE6HajTgDpNRa0mUa8FUatnoVbPRh3qUa/UoU4cRL04iADqo6MAELEbANzJtndjnfU3aiyLH60S3UF11IcSNwoI4aiLxjU8uEcLJBrZ6rXcaySAe1HqRoL5jfV+t2+Khx9+GIqiYPjw4a5b+SyqqmLJkiUYN24cSkpK0KFDB4wePbrJc82kKtb7TvqvuuoqvP322zj22GObffJ0pKi5UNSGL5v7D9DZGuwO3LFD2BINL4Ywho8Z9wsHEEAWAiLLHF5Ua08KFj1HNCGPHZZmJ42KggBUV4u4Iu3QDSvlFxBQhRF2zWpFdL2wkvPou5UyNnF3Xxvnh4IV0ABAF4nXAWbAMLdxtqrbxzWPAXivs4/hWO+cDTV2Mj33LKTStd6aOM06pvPDz7nOeU7ncDxnCzSkRzCMKbzXegUimmzHjMyPS3AbW28dw75+0Z9bbKNtbG+Ftd763+qgjm2QcR3DqqLGHCP2fTb0u2OfC9HKjh5zOZy9CbE/T2tba11AmDPTK8b/1kR4WQqQrerIUTXkBzR0CERwWFYYHbLrkJ0TRqiDDjUPUHIVKPlZEHl5wGG5QEEecFgeZG4OZG4HICcnvZN6TQMiESCiuYf66RIiEgHCdRB1YaCuDqgPG9taQ/mlbvxwIxEo3+9NSfGSmtEXAlu2bEmrGX3Hjh2LK6+8EldeeSVeffVVvPXWW43uk6z2HuutBn6PFR5bJ9tYH99Q7xzaa8wdEoLiqKrFN8IHzJqDeSsSAhDSTObNbVRp1gGsBNxI46MxHo4YbybhqhB2DLc+j1Vnd7Tjg9b5eE7nFbGTaSU++QbiE3BnD7MKc/oQuD/rrSRaEc54KKPngpEIG9vojnPJmKTfSsLdTxmJlsf43kj2neus5eY5zeVGUm+ss27Rsn5dFEU3zmMdT3E0Cpj7QkSPZ18Hs7Jib2v370T3dW7nuviO9XFZvUMTpwLwlOgOwYSTv3skX85jJOwkthr/Y3LPps4Qb10/a3upGefSdQFdU6BpApFIAPURFXURFbURFXW6igOaigMRBQc1BQc0gVoFqFUFghGBg0JAaGajlA7XHaJhGL+MEdSb9/kHAETMMuienSFWQ5krMRfON+3xg2riCAA7qTfPLc1bD5zJfJN76JswpwAAaDLStOMlwffw/ob+AJoodlLZ7OxszJkzB3PmzEm4T48ePbB06dKkzpeqWO876e/duzcmT56Md999FyeffHLc5D6/+MUvWqRgbVVuqAsCAeM9u5J2q5UupmdcFUbgVRFwzdQdQAgBGUBIhpAjQlBg3O2+HwcRMT8cvFrKVekI1GYyrsIdpK35uKwA7QzIiVrBrY+OgCNIOAOxVSEwV7mGdcUl39K9TjgS+tg7x52JX6J1setjKwcSiRNCeKyPHUbnXO9cpwjjvcR+FArHutj35FwPxO/vrLg4r5Vz1J0zN4xd39j1ts7htd7apin7x9brXNc+Zv+G1ltLvRLxWLHlbGh9om28xG7nDBhWxVER0kz2jZnsg4pEliKRrWrIUjTkBiPokBVGTk49Qh0iCB4GKPkqlLwciNwgkJcN5OUCHXIhO+QAuR0gO+QaQ/ZbQ9hKvDXrTQMAhNTN5D0MUVsP1NdHE3TdmczrQFgzEvjaeqAuDBnWgLBuH0tKYzykHtEha3XIOh3aQUCrE9AjArrZCmfc3qhAiwh8vz9VFQGRxPB+gT59+tj321va8oy+2dnZ+O1vf4shQ4ZgzJgxKR1K2d5jvaLmQVWj79mz0T7mHlxno72iBMzvzURcBMyneWSb+xvHsUaHqTAnBUX00ZrRZN1M0oUxts7uBTdju2qWwfgsE67GV3t+zgQx35U8O+KDM+47l3kNCbd6x2P/Aq3zOs9hvOf4JN0ZGwSiibuAjPa828eT9ue2Yv7l28m3tdyVhEc/5yEA1Ry+7EzYjTqTe04VO1GPSdYVJT5JdyboxtfxyTsUs+5ibe9MzhXzd8u+EB4X3d7ecaWdrQvWuQDXMl9iPlM8Roc7Wuql61tjGZy9HY6vnbu7ezO8Ggmkaz8Z3SZmuVcZrc6h2GMLSEAKIwnUYP+dOFkNPHHlinlLVoePZiXSwpwzQcjoBH92kh2BrkdcCbcXO6eQ7l7y+MkQRcJ1Rhnj7/u3knxrvgFjvZ4whsTeuhs/sZ/VYWa9F3cDgkxh0u9/eH8SfwetLFWxPqnZ+/Py8rBq1SqsWrXKtU4I0SIVgTlz5uDBBx9EZWUlTj31VPzpT3/CGWeckXD7xYsX4+6778aXX36JXr164YEHHsAll1xir5dSYurUqfjLX/6CqqoqnHXWWZg7dy569erlu2z9goPRIZiLgBBQFYEs1eglzAkAhwWMW3DrdaCqDjioSVei4mztVmAm2MLdaq2I7Oj39jLn9+5hZNHAaAYjWMFaOgKrUQjn0DFn0HcOQ7PEDiHzYu3n5Gx5bWi917rG1nutcybfDWlKuWKPC7TMsWN5JvUx52+J4zvLnsx1jT1GIl6t7c5jOY/f1Pd5qFiVPuNxdrr5qDqJgGLOgB/UEApFEMrWEMjRoXYARI4CJTsAkReEyM0CckJAbjbQIQfIChq1Li0CcfAAZDhsDOkPBBq+x143A7FzojupAxENImL0oIvaOiOZr6+PJulSwhynDtRHgNowUFcPWRcB6jRITbdqJ4CU0DUJWSchw0aCrtcJaPUCmiYAKSClMCo1moCmKwiHVdRpKuoiAYT1ECJSgS7N4koBDQIRKVCvGcMi63WBeikQ0aMNXhLm/ZI6UFN/MCU/RymTG96vqmpazeibk5MDADj99NNx8cUXY9y4cU0qezIORawH2m68L8w9CdmBDq6EPCCNVwhBBIVqN7hbI7lUYSTiKozGdlUxv7fjvHDFd+tr57ByZwLtlTQrjhgen5S7k2dn9dgeBu54j14Jj5fG8kirnLHHcPbsx9Yz7PfhLJtwvpfo8Zw97sZ20tGAEP0+ekxHwu1I7O3zORI72Ouj20d74aXrmgLG556uWwm60T8LIe0kEhDmfo647egxSdRrb53Ptc44Y/QiWxL0zjeY48euc9UPHMs9eua9kmrjC2Gvc8VvM5xJM6Z47mf1usf8yknnMR3LXOvtfeOPERcHHPvq0mgclgA03YhxEV1BRBcISxVhTSAsFdRqArXm8P6DEWNof61mDO+v1SRqIzpqdR21uoZaWY86UYewqEO9MO7rD8s6RGQtdBmGrkccw/zdCbdTQ/NYed7/7zGvVqLJ96JPHIhN1hPNBaDE92YZVzDmPFpMMupM/FOT9Md2ZDVFKob3p1qqYr3vpH/btm0tcuJEXnjhBUyaNAmPP/44Bg0ahEceeQRDhw7F1q1b7RkQnVavXo0RI0Zg5syZuPTSS7FgwQJcccUV+OCDD+wJF2bPno1HH30UTz/9NHr27Im7774bQ4cOxebNm3332FxSrCM3aN0rY7YOQ0JVpN3CrJpDvxSz19AeDiai2xnrdTvYxA7/AqKtys5gZHwRDfpOTUkILV4JVlxDUgMfrK4P4ZjPjZgnizRLbA9x7HJ4rE/133eyyamV9CZz/ET7xv7M/R6/KQ0gifZt6FyxIysSrUtEd1QOjIPE1gz8/QwS3hPorGyaf4eKokNVJdSADjUoEcgyH2sXEkDIvPVB1yFrI8bw9nAEqAsDB+sA1ZxtWjEDsq4bXyswkn6ra0FKI3GvDxuvcMToWdei2avUjUxZ1kcgD0YgayW0gxIyDOgaIDWz8iON77WIivp61U7Uw1oIEWn0uFsVHauCUycVO1EP6wIRAJp1LPP6R6TRsV9ntD2g3iyapks7kZeQiOgSmjXBka4hIiU0c55oa7ZoHTp0oeOglprh/UAyrf/+tIUZfadPn45IJIJAIIAxY8agc+fOLfwuo1Id64G2He8v7Xgs8kI5CIjoSCBVSAQUo6E9YA8hN9YZvcq6vayhnmYFMi4xBdwNwok+gwUaj3HSleh4L4+OvnNsa30trX2FuRyu5XZBXCdNsNyDc0aA2OXRsiReF0uX9vRjcZ/1sfd+u0d6xV9Ja32j94wD0ffapJjm47ge+yVc7/ja6xo1WA2L+TlHFwvn6oa3sX+PnNs66o1x5XEvj+0UsMoc1xhg7uc1wjJ2W3e7hft3XjfPaYTaaOyL6NHJ/MJmvKvXgDpNIiwl6jWJel1Hna6hXkZQJ+pRL+oRFrUIoxYRhBHRjYn8dBk2ZvI3H/Xnfryf11WxxP9CNToqIIHYHnjvRL3h3x6rQcEzsbdvjT20Sb9xTn+xPtV1g1RIVawXsoXGDOzcuRPPPvss7rzzzmYdZ9CgQTj99NPx5z//GQCg6zqOOuooTJgwAb/+9a/jtv/pT3+K/fv3Y8mSJfaywYMHo1+/fnj88cchpURxcTF++ctf4le/+hUAY5blwsJCzJ8/H9dee22TylVTU4OCggJUrX8K+fl5sIdcWRmu9cfhitpKNPN1fC3cUdj8X3d8mkmjF0+3+skQ/d5aZ/XwWT2EOjyWy2jPoW5mBoDxqWadz2w2k9a5dBn9xNVltKfQLIN0rpNwD+VSYv73akiMjepeN/LHDg1LdIN3opvA7U9267he53V8H+16sNe5HokT06IQ+ydjD8lzrnNck7iW2Jhyuz5YdY+E2Xk9zbdnXZfY9XGf5Yr70ln7On8tYz/rnZ/VwvkztfZz/JrG3TMoHeVzUqK5rvP625P+x15j673EJP2uIXse9/lZwyqNFR69EBJxvS1Wj4R9DHN4plAkhApj4tpE788c4SsCjrJpgB6BkZxHBKQuXGV03ksYDquoqw8grCsI66qx3DyPJhVEpIKwFp+ka7D+VI1EPWK+6jRj5uF6zfg+NknXpZGgR6REWJeolxoi5iOKnAm6FBIaoo8q0hCBJiJ2gBcQCMlsBGAMf45Ag4Z61GGf8ZxjWW8OJozAenSRrkcQjtRj9dpXUF1d3eQe9sbMnj0b/5g9H7/pdbmv/V7csQb7zizCwoULkz73ueeei379+uGRRx4BAIwbNw5Lly7F/Pnz7Rl9ASNZBYyRAf369UNxcbE9o++oUaPws5/9rM09si+Rlor1QNuM93asX/IA8g/rYCz0GtfuFBvTdT0aY3TpuHXG0arWhBhsxF9HTJbSHYM1OOI17BhkLZf2MvN7LfrZaX/umVUDa5SP1Qurm59bEtY6AV2PnbtARkcHOT+v7bcebXCMbSS2EkPdGm7tZCZkuowmm86Ku2YdV7ovM8zzaDHndFYpjH2jsTL6kxGu9a5jWtcAcIYvxzHcDaZx7xP2j9k+HhzHcK1z/iolOJ9rfaIafMz7iy1TIq6EuYHtHKeIX+7YUZdet9jF7+M5QjHh8b2Xx75XZ0IqY66vLs2+bsf/mpTQpTEtsybNyCciiJgz9WuoR0SEock6I84hAk0a9+1bCb4uI9D1sJns64g+wcidfCd68pW7fql5LGuM97CMRMl5VOJbCOLKaP9mus9hrDNomoaPPqpo0Vi/YcMG/Pj0Evxt4ERf+32+fxd+++2r+P7771ukHOnMd0//zTff7Ln8q6++wrp165pVEaivr0dFRQUmT55sL1MUBaWlpSgvL/fcp7y8HJMmTXItGzp0KF555RUARm9FZWWla0blgoICDBo0COXl5U1O+i36scdAz8/ztU8sP3++TaZLQIsYlQtzsi1hTb6l60ZUj+jmct0YDqxpdmOAsIYL6+Y6sxFB6Hp0KLFmVV5ktOFAk5ARszEjdkpcV2UmZr2EuZ91NUR0fWwFCAAUJWa97o4O1nogus7ZGKEI2Cd2VqKshgZnVIpId1RRo+d1NogYxxZwNVbYDSKOD3bV8WGqmfdQmZFQxFQmXZU8s1z2NtZxI46yBYS7UcE5nNtZ9tj3JqPX1X7/1nmd93bH3vTmbPhxbGOP7tCs9+8+hDOixzUWwXqv1jnMl+ao0VnX2Jn1a45gaDeCOMprVoCNxpDY7RBtyJCAjJiVaQ1225iVrOu1AnrE6EmPRATC4QDqNRURTTErbCKafOsC9bqKek1BWBqzAEfMXgWr0mj0JghzW+N2oLBuJOu61X5n/ig0XRqPENIlwrqOiDReutTtyqA0e9Y16HYFRRPGcEId7vsHBRSEkGVeCgldRFCvGEMSdWjQpDHTsFERikDX6u2KjPN+QO9JfCSsIYPxlRTjokYiYaSC8VHjs/U/BbeTpHJG33feeSepMh199NHo3r277/1SGeuBth/v9bPOaHas9xQOG3/o4bARozXdiN3mHBzO+CyseK5p0dgcseKx+YpYcVya+5nx0fysl5r5tfXZrelGo4D1We1oPLAbBczPQWs0kXPiM6krcbdv6bow+xxieoPNhgKjChGNgxLGiClNt9poRTRJlcIMQ8bnqiatz9loQ4Fm9uhr1uevlVhLQEO0F9f5uWt9Xlq9urCOZy+3GknN7xGTkJuhV3cstyaFjU0cnQm38flsVQ2M0U8wl1ufmPaxzIZXK0GLHSllLDM/e63jCN0+ZuzTdgA9bplr8rYEow6cTyxy8nrqkXNZIqKBz1pnj7XXk4m8nvTT2DbO9xzbU+584pL11Cfnk6d0Yfy2SLspXDcTePNlNmA778/XpYZo47YG46djDudPcC2ljL1uWsx662dj91yYZYz9mXkl8PZBEq+LJWJ/T/wdKyV5TQK+J/JrhVtHmyOVsd530v/DDz+4vtc0DV988QW2bNniqtwk47vvvoOmaZ6zG//73//23CfRbMjO2ZKtZYm28VJXV4e6ujr7+9jHOrU5igAUc9Ihcx4q1x+hLo1KhdQhYysbZoOA0MxI72wAsCoc1qgBV+OA8b+wGgMCqtmrbn4Amw0Fwkoinevt/awPOhFdL83RELrmSNzN9eZ7EboWTX7tmx4dQ6qtCpBQ3OvtBgFzvSLMZ6M7rlVYcyf1zvURc79oW0X02eqAXemyR3Oown0vt6ZBaEZFy27EUKMfSMLRiAKYDSWORgdrX6sAQlFcDSmwGlKcjSUB6+dh7mtllo7GEFeDidUw4GoQMU8QkRBWRRKINkhYkwiZjRrC2TChAMJs+JDSvDZaTMOK1fhgvXVdtxsQ7J+Ds3HBqnlp0tW4E9vAACmACCC8RpTAOJ+ImJWtevNXLqwgEo72xIfDKg6GA9gfCWJ/JIADEQUHNAX1UiCsC4R19/N96zUjka/TJMKajnop7cqgUQE0vo9AQxgRI1E3e9RjK2ghZCEgg3Y/vHXfoAajl0HKCHShQ0qjN17q7h4HV3IeM3mPVYGxLoRXop6o4hGr8V4Ea/dUDvlL7fZeDuWMvqNHj/a9jxACEydOTOr++1TGeqDtxPtDHuutCRGzsjx/B6VjEk4RMW/90TUgHIk2CFjLND06YaejMUCEzVgekWasNxoCRESaSb/ZYK9J4zPbbgSA3QggIsbX0KTdAKDogK5p0dsSFOM0QhdQdMXVXgwAuj3Rm2KPwNJl9KF+gJG0Wz37gNXubCT8EV2xE3hrNICVzFuNqVYjgXPItgZjvRXqreTeCikRHfZoKSupt6sFEojE3MJkhJpogm+NmtIR/UyXACJmAqlZCaOQ5vcapDB6jnWYDaiKcT2Mz3ENuoiYyXv00W5xSaf1G+NqeNXhnBHe/Vi4+AZa9y9bAwljY2Inl/PoubYSemdi73dSuoaWe50L8Ej0Yx9V5zHRnRf72sVeQ48Z7KMN3xHHtY5N+KN1wfjrFXK/B6HDeAy1GYcFAOgQ0hlvWyDhb+pkj84Zub1WO0/dtCMmrTVi/aGUyljvO+l/+eWXPZfff//9eOWVV3Drrbf6PWSbNHPmTPzud79r7WI0jyPRt3sVpB6tMJiJr1fPP7x6/q2GAqsBwNrWSvB0aSaJVnKuRUcECCW63pFAxj7+y5VcO295UNXo17DKYn5vJXrWNuYIBGN7PZo0W2O5zchuJ97OxNKxDjCTbk1GP9Ek4kcoyOhbihuFoCtG44ZzBIOZdEsroY4YSbedzDsSZqmYx1CEewSD1eqryviefOdkcKqZyDt78q3E3JGwS+u6a44hpY2NRIAZfJwjJRwJvT10X5plh1luKeMTel2PS+ilM6GX0j2vQezQVsDo+RcCUKxrb65z/rgAWJMuWT9PPSKh1wN6nUCkTiBiJvrOZH9vOIg99Sr2RhTsCwMHNOBgRCIsdUR04752ASAroJh/HhL1UmJ/JIz94qBx7x9qEVHMoYEwhsBHEIbUI/ZkP3GVNGvWXUelwih2fI+OvY91MVzfu7ezfxbWzzBOzPGbWIFobCtfoxR9sIYJ+9onNUVJmUNxj70TY/0hYo3Scyb6mg5EwtHGeCuxtxriIxHjjymsReOyFROtuBvb628l+0ZWDWkm/eZNzUZs04zPVesF83BSM25Tct0GAKMx1bhFwH07gB0uYyZyS/g3Kox5Dox78wHYk75aVQJpJjvWwZ3TBZqPOoMxR4Iuo3d46QB0YTYqAFDN8OQI9UZIErBHWGm6+eQj+7IYEzVqdqOC1ZtvNTY4b59S7dncY3vwjWHjjsYAKe3GXKtBAFbPvDATdiET9kgb7y/aSODZuy2jowCcpCMZ9f7898eVuHv0/NuPCU6U1DfQm6/ENBgkStC91sePfPC+NjK2lz1B44rzf+NRm9YTO1RjHzteRxtuEl3fuBn1HeeTUkIYs4WZb0zCu4G+geH/IvF1ih+KmWBd7G6NzkdhXhuPw7aYJGJ9uvX0pzLW+076ExkxYgSmT5/erGP813/9F1RVtWcztjhnP45VVFTU4PbW/7t27UK3bt1c2/Tr1y9hWSZPnuwaRlhTU4OjjjrK1/tpNueQfTv5lhB6xEzezMAv9fih+1JG93MO0bcSdnsf63tze68h/GZzuHT2ItvDuGU0CXQORwfc6wB3cghEk9vYXmUrGdRiPpiccaGhnmIrsdQdMUZx7OxYD1j3gTsTW8exrd5lx7ByGVMue711TK/h/85zO4a/2z3TVitqoqHxsesd19Trnn17fcBjvbOzNfaefmtfVwERc/sCnG0envMlOIOQ56NfdEdZlQRxR4f7Z2SVA451Mb8GwjGrs70uLul3ngiu3n0tYiT79WHjeb11mvHM3gOawEFNwf4IsC8C7I9I1Gk6Dmoa6nUNmnXBI8b97fZsvmot6uVBRGQtIrIOum4Ml9dkvdkrHx0y6A7mFq+g3kjvjEfQbm5inw4Jst8+q3R4T21RS8R6oO3Ee9+x3mpYtu7b1yLRBnTrCRzSisN6NHGXViLuaFy3RpzZo9Mc8dt1W52j4d3ZMG2N2rJuG7OH9Dv+1+HqxYduJvXW56cjubfv9bdubdLN++Z1ozFT1+Ifv2oktsYw/vjh/eZb1a2pC53rjAYBYTUTCBiT3FmZvdAhdAldWJV8K0ET5qWK3rtvHQ/mW9WsXn67jMIOXbHD/oFoiLAmM3X2Y1j7W3ebWXMNWO/PGWKs0QL2hUG0MUF3bG99njtvSzCOIe3jOv/XIe2Gi+j1s66Hq208uj7mWLG8GgOcx3Uew3t/t8b74v1xVhsdBUp4/tj17t8183rbv0PWbRPR2yeAaFJrv2+r+iMVKEJBACqCQkWOGkCHgIosVRiDUM3qqTH5rUStJs1b8qQxuS2MR/vpMBp+NKHZt+FFUA8JHZqImKPzJDREAOjG7XZmZUc318TeYmC8P0fjREwjQvR6uDsGvBtPpGtbryvtNedA/Do9dUl/XIkax1gf1WJJ/0cffYT+/fs36xihUAgDBgzAihUrcMUVVwAAdF3HihUrMH78eM99SkpKsGLFCkycONFetnz5cnu25J49e6KoqAgrVqywg35NTQ3Wrl3b4CMQsrKyPJ/XXD31DSAnCzCf1aqYk3wpqjQm/lIAWJN0q3DdP2xP+GYlibGJDGKSnwQTB9m/wI6e74Q5gVc0cCRBznudnSN5nZOMxo7wNRs0o4d1BnpnS2DCVn3pWudKzmPEJX0xy6M017yJRtnMIedCxq1vaAZ7a3KjuPfj2si93tULLWM2tCsy8WU33pd07dTQeud1T/Sh2lAjbmPrpbSujff1kVKxt2v0+F6/F/aTIIS9bVPeR2x5o+U0v/f4PYl97I/3OYzKqq4LRDTFmFBPM+7Jr9NV1GpGwn8gotp3PVhBPiyAgFAQETrqpY46c+h9nTiIsDyAiG4k+hG9zjPJdw3dc/0BOsqXoPRxk+w4Z3S0V0V/kVx/lvYxFTQWPtMlWFqJh6990qz1HwBefPFFXHHFFQiFQgCAb775BsXFxVDMRs0DBw7gz3/+c4tMspdIS8R6oO3E+0Sx/rOJq1GQFUJANWbgVxUNqgL7STuKkEbcF1Zjo8dz2wH779FrNv7YmOUU+wg0ICbuwv05anwR/Xx1PtbM+VkYt87Rc6+b8dw5eZ6d1DvOmeipMbFxwS5ngknBrLlQYilmHUFRovfAu08I6AnrR4kePGgk7LGzzjtHLVuJu+dxXY0EwvVYY72BczY0I71VXjR03gTHheN43umbO1ZbYn82Tu5GBfd7cpZPOrbxLJfH8WLXe5/fXYdKtH+ia+W13lm70h0/Z6sBxsl6N3ZV3fzf+jqgmE/wAOwndHid23gqgDDnjwhEB1lK2LeXWIMyIxKOESRGuTRIsz0vWmDdLENQURAy8wnnhNOaLlGvS9RrOsJSIqIbN5JEpGbP96NBgy40+zYTXWiukSTGLYYSUjrmA3I2OJiV/4ZuLbFGL4TD9Q3/kJrBd6xPUTlSKVWx3nfSHzuJDmC0or/66qsYNmyYa/0f/vAHv4fHpEmTMHr0aAwcOBBnnHEGHnnkEezfvx833XQTAOCGG27AEUccgZkzZwIAbrvtNpxzzjn4/e9/j2HDhmHRokVYv349nnzySQDR+xymT5+OXr162Y/wKS4utisafqz9tiu65oRwWFBDraZgb0TFrlrV/mOO/bCwn8kL60PCuKvH+loxH/sjIO1n+VpfOx8JaD9rFtY5mvjsWsDVs+kMVInDlLleeAfG5nAG/0TntwbrNbSvc/9EFYpE6xvbLxViKwktedymSnT+Ro/h3M3vJWvgLTc05EoR0rU+9vu4Y/kslouMVgaNIZwCmm48r7dej1aAAwLIDlhvSUARivF4Ti0ARQMUKaJDEc0cXNclVMXojdOUeghddzdWxCT8jQ219E72G7gKjf7ROhJ/j9qgszevrWsPrf8jRozAzp077cfZ9e3bFxs2bMAxxxwDANi7dy8mT57cIkl/qmO9dY62Gu/nf56FvFAuQoqCLFUgIqPt80IAAUXYsT2gmAkBnMlBdPCa9dg/Z13Aqh8IIe1+ACPWR//uFDPWOwdcxT7v3lge/zfrpSmPYk3Ebudu5LixubhwbWctc5dDiXlPzvWx9RjrDkFX0mWvi6/f2I0yrvNFy+0qX+x5E+yX6Lj2zybm/1iJrnb0YVCJP5389qD6HcltN+DENJo7G4qs/70eAWmxGpCMr90n9mpMch/bXT9z1VEc28ceV0+wTUN1PasubUz7JBEQOgKqjoDQoSgSqqpDVXSoAYlAUIMaklCC0nhijzWSUoE9L5J1futWGT0C18SYekRA04Q5MbACTVMQ0RTjaT2aMXFlRBfGk3t0xR7REjEb4yK6hnpzImCjcUGaI2qsbZTok3us/2GNuHE3MjjnurDmrjBGnRgXMKAIZKsKAtbfl/m/8VhDacxbpBtTOoZ13bjdURqNCgfEPqzDPxJe9+bwG+ubVT9sJamK9b6T/g8//NBz+emnn47du3dj9+7dAJDwcRSN+elPf4r//Oc/uOeee1BZWYl+/fph2bJl9sQ827dvt1s6AODMM8/EggULMGXKFPzmN79Br1698Morr9jP7AWAO++8E/v378fYsWNRVVWFs88+G8uWLfP1zF7LvF3/RiiQCwEBIVQICChQoUgVChQoCECVKlSoCMiAPRwoS1GRrSoIKgo0XaJW13FAc09qZST4xvB4VZhHthsPjC9UIaAIYVcmjFf03je7UqFEGwisJ47Zt3AjcUumxTqOVTDFEUCtsgKOShDMVnLH+4np6HCtj11nrY+9HkiwvqF11norcMa2WDvXxa63Kx2OfZ3Hj93XuY19fLMi4QyW0uPYXvvHVkKcx4it7Hm1tje2v7My6Wz1dpXfY3+v9c5jOPl5PE+ibZSY37PGyuJc7zyWVwXU6/fIqngDVo9MzHN8zZn2rTs7FAGoikBAB0JCQBMqAlIiLBUERBCajECDClUEoSMCo93fuP8v4Wy+nv0jjvsZne/W/nx13u/o8ZkrrIaF2Pfs7DJU3ecXMWWQ3r9TbYlE470/cfu0xTfSiNifZQs9cddTqmM90Lbj/Qf1KxHUs+xJxIyYH72PF0KBgICKACAUqAhAiAACCCIgjAk4rUdaAoCQ1vaqWUcwagyqUMzGAiUmvluNCooZz0W0PqCIaEyHI157xXjEx38418d81sbWBYD4+oBrnaOhwjkgLxonZMx5zU4KNN7QYTVyCOs85veKXXbvzg+rQwUCUKQxH4CVpFvrrGQP1vHM9a4XjETcHr2RaESHiL4363GvgHudPerT2se6CHB/nAvXxXBeUMcPwefoUJfG/l6tbN9rJKke8791XwIQFxicjyeOHYka++hf9zERN6IldmSo+zyO9+NsQPA4t3MCSuORvMZoXREAlBAgQsJ45QQgsgMQWQEgKwhkh4wRvjlZQE42ZE62MRFnMMuYmDMU/TtvEk0D6uuNyTkjEWP+Dk2DCJtze4TNCTzrrYk8zduGwhFjTo/6CGR9BAi7J+WU5mzCMiLNRwbDbnDQI4r9FCItIhDRzEcEmw0Mmi6g6QrqdQWabjwu2HkbjdWoYIxSsDpIAE2q5iMOA9ERDBLYWxfEIn9XpUkY65tHyFTWGjKI9eze0wdeiEDA+AOPm3zEqhxYM5nHzFwqzEpC7AQgAioUoZpJfggqAgiKbARkECGZbQysERoOir3RU5mNDUIaXynmzdkBqK7KhTB7H1VYyxQIIewArQjFWCPclQohRLQSYa0X1nk9KgzmUCOv5M15j39sQhb3DHvr/Ql3n6b0sd5a5xoK7vwxwR33nOudFaJE652xN3bIXuzUA42tt7axeN3l4HlvWyP7J0rIrXX2vo0k5F4/z0bLL2LKkKBVwfM8ZuuIs5IIx6rYw8TNV4CGf9ZeSb+zAc3r98ae2dn82pqtv16TxkvXUatpqJUR1Io61Ipa1IsDiEjrfv46aJp1T3/YvBfPmHlfms/CknFNZk7RpMP4wl0jjP4NNf7oJGeDg+c8AZ4jDtwNAIk01igDGDPAb2jhZ/fOnj0bf3/gaUw65ipf+72yczXCP+qKhQsXtkg5DgVFUVBZWWm3/h922GH46KOP7Nb/Xbt2obi4GJrm3bBEjbNiff/+JVDVgEejRqIZuN3xHmast7azGwrMrxURMOKpEm0sUEXI3D6a3dmR29xelQGjocER71Woxn3HjlhvxHNjCjWrM8GK5VbDQmysV4VIGOdj46r7XRujGeIaBByfq9ZVs0ctONfFJP3O46l20m+VNzpKQrUSb0hXeVXhHi1pNRioinQ1Fih24h8dgWE3CChWY0C0UUExb+tM1EDQ6O0e1vcK7PWAo3HA0TBgL3c2DFiNAvYvoOMHkOgHE/u1Q6P5f4IwEHMfQPyOrvXWZjJufWzCLz0aEJo8nY1HMeJvlYTxM1ABEQSUgDAee5wlILJUiNwgRHYIyAkCHbKB3BwgNxcyNwcytwPQIbeBArRRzid1WY0M4Trvp4HYjQph88ldevSRoOEIZFgHIjpkRDMbHDwaGuoAPSJQtb8eR85+oEVj/YYNG/CjgWfi6f6/9LXftgOVmFH5Er7//vsWKcehkKpY32L39LcXEa0GMuFlS5SWGbx7RLwqEM7evWhTsTArDPZyodiNBcL8WhEBKCIEVQQhrWlDpOPeGqGYDQVG1QBm76MijWNARisZiUYzmH0PxnZmAwMQTRSkOWuoc53FmUxYzRUAohOp2IOnHUOlTc6ZWK1SOvdxPrNVQEBIET8xi3ls53NjrcfqeK2z1tv7mutinycrhXSts2YT9iq3dZ7YsjvfkzXbrTXRjPuaRcvnXO/c133+6P7WOtf7g3EXlnVcEbN/3LXzKIPr+PbvqHOkQXyDjbPhBI5toxU8YW8vZfx1sNY5r5XXuayKrv1+Yxth4nrWGq4JWbM3G5P2aAhLHXUIo07UGffzizqEZS0ishaarLcn75O6Zr4PazZ+GZ/we96AqcOotsq4n52Xhnpenc8GFsLZAGDVLK2/XXM5JFw/qUQXEvEJ/iElk2j9T01JKENIWW/es9qUrRPHfs+/WY/GAmOxuwFPiOgnpVUXsOoBQgTsmK8q7p5Gu4HBivXCaBwwYkwAir08YHYMuON7os6D2M9+6/1Zy2Mb463PcVUo0QTX/uw2RyGan7vG9tF1ioh2Qjg7F6zOCSDaMOAcraDYx40+DdfZeOEaJWnu5BytYOwr7e2thgbXrZfW+zOXRUdcSDhHJMBxroZuyVQcXxvXwYrp0YYD5y0J1nt1HQdAotsRPAeBNXALgnNZ7KhHP5qyr/sWgaafo6FbIBJtbzcYBSQU1RiyrwR1qFmAkq1BHIhAyQ5D5AWNXvewkfgKeyZ+pF/ir6rmY6ODQI477jU3Bjrap+Lk1uwDZj/QzDN48xvr/W6fyZqU9F900UW49957MXjw4Aa327t3Lx577DHk5eWhrKysRQrY1hi9c8nu29DaRhoMGq04uI/hXXkwtnE2JCiKalcQVBG0exisZ307E4jo8GS4Ri1Y33vNBup8vmrseq9nr0qpR4/peD+xjyjzesSLtY29TijmEICYcsXNCujYxjGU01gl4z4ZPRsypHud9dga61EwzoQt9pE2zsfFRBtjDF6TGDW0Pu7ROPbzfmX8OrsA8c3h1jX0uu6xjTGxhLN7wjy7+5Ipnom19V6sqqZxvmhtLvZ+d6tRKpZ1Pa11VqOVY0fo0OxlQjeG6CoIxDVkWaV1lQVWQ4kxOU5E1BsvWQtNhqHpxqP4Ymfq1/QwjNl3682E33oEU/wkfuaAh+hy4Uj87dYRc5k9mY8GQI1r9DAO09QPLQV2ci+jj8KKrgPi5gCwC+3VYBEjRS0DRt+cv4Mfyjk9WtLrr7+OgoICALAnvtu0aRMAoKqqqlnHZqyPMtrkmvp34/wMjzmO58ETnLPBrlf356737T7R7Zx1AKvxwB5dKAQUJWjHdGnOOmPsFzMi0Rxh4IpNzhkIzVGLitn5EF9qYzQjpLvMVseCIqMjFqP7xHcuON+3YnZUOBvKgWijtnNEo/OqCLNhQhXRxm975AHMpN8xvD7aCGGNgDBKFm3AMLcTsY0XjvM6GxecPzLrf6tBAvG/O163ZMTu7zUqLnZ9Iq5bOmN2jr2lM+HxG1jX2JwGzif9xpapob8E5+147uXOczg7I6LrjXm2JALCuH8/qEiEFA0hVUdQ1ZEViCAUDCOUfRDBnD0I5AFKngLRIQiRHYTIDQE5ISA3G+iQC9khB8jrAJmb53+YPyXFf+xmrLc0Kem/+uqrMXz4cBQUFOCyyy7DwIEDUVxcjOzsbPzwww/YvHkz3n33XSxduhTDhg3Dgw8+mFRh0oJEk4a4+v8Va3iIRoMVB49P5fgixn9ECgi4RoY0qeJgrBNQ7dEF8cVyzBoec96mrotdH/9c8phzxjyGpKHGhuZq7Nh+1sdu41Vp8l8+Z8u5TLjOa5um3J/b0DN1nQ0KVg99ovIl+vnG3hbTYFmacL2ilTvF81rH3qMb2/DibAiJG+EhdejmrLdSRqDrEWgybPTm27P167AetQPrf2cPvz2k3s31WSIlrF54q7pj1Kc0QFizbyuwnscYTfYTX5f4n01ThvE38LfUhN+dVGoP9/kBwOjRo13f33rrra7vm3OPPWN98zXr16qBX0oR9yzxBk5qfyY4NaXRQImL+QIBAMLoIBDxVUbrdgVno4HzHM7PWff53Z+57pIKuzEh/l2Yx1MSffZ7Nyo7G5JdjcrS2ivaCBx73ujnvnejt7NhIrZTwNloHDuKMFpa779Z5+i7+PcjXNvErXeMuHOSMcti341zFF2i48eO2ohdbh3XszHCPq7XSmtfd4NM7Dmsn09c2RM0iljnFeY2CszJNRWjESAgjMk3g+b/IUUiS5HIVnRkqxLZqoaQoiNL1RBUI8hS6xAMViErK4JAlo5Arg41B1ByFWM+gNyQ0TiQmwVkB4FAwPFSgWAQMhgAQiEgEIC0euPVAKAqZs88JdJeevpTEeublPSPGTMG119/PRYvXowXXngBTz75JKqrq+2T9u3bF0OHDsX777+PPn36+C5EOpFoWlA/pL9jTai9xlYYAI8yNqniYGl+ctoU7p7G+HUWr6Hfrcp5Y3iidZbGnrHX4mJ/do3NKuDW+AdNwwHLOWQ14TYNjOSIaqnGHPfwWbsMsZVUEV8ZjA7VN2YmMsroeE5uzCP5XJP1JUj2Y3n3+lv7KfZyZ8+GlGja71KTHhPYyHVu7PPnEPxOy3YyvF/XUzsPMWN92+Xr99Xzb9I9GiFRo4H9Zezfrdb0BlbvlYnWNXBrRJNvifQ+nnfDcWyi6jVCMsH+PrZ1fh/bEBJXorhYk/hYnmXxuu4y2tARKy6Rjxnhp8AYJemcW8K5n5COBhTzFlAAUKzljrmmvEbQqYhuB8B1i6V1XGs0nTWnlfE4OSv2Ne1zsLHGG1WqcN7CYkzBbcyLERACAaFCVQRCIghVMSbZNBoIjFtMslSjoSCkAiHFaDDIEhIhVTcaDVQdQVGLoCKNeSYUHQHzCQFBVYeqaAgGdCiKDkWB+Rhw87aDgPkY8KDx4xEBGHMQhARESDFfAWNOgoBqtF4EVaNhIWQ2MFjfq4q5PAQZCBgjEoIByEDQaGBQzPVp0tjQHpL+VMX6Jt/Tn5WVheuvvx7XX389AKC6uhoHDx5E586dEQxySEtbl9TvfILKvPExfWgmimqo3InWtYm/72QK3ti6lpRMo0QTeo9bTss1KjWpESjBPbbR1dHeL+ey+N5y83YOz0fxxU6W13TObYXrnJpd/vhGPL/XsIEg05wfuvTKKFqe/+H95IWxPvM16Xc/bs6OxmN+g8f1WtlIg2BzOx18dQA0qXGy6Z+pTe+F85doJT+Sx39MbcpoO+/+fMfaRkbjNfR+Ep7fo0G+oX1jR6G4bl9xjDjxfBKHzEJADyIYyULAfDpXFoIIKSqCiuKal0KBQERK6OYvbsCaZ0sxzqkA9pN/oo/ydM77YPw2qIq5nYjOQWE99jMggKBi3Z4gEVCM5aqQCEAajQpKPQKiFgFFQjUfPRhQJIKqBlXVEQjoUFUdiqpDCQCKajYyWE8xCAggS0AEFAhVAEHFaFgIKBBBBcgKOBoTVEdjQ9AYvRAMQhyoa/Dn3hzt5Va+VEh6Ir+CggL7XgNqX1hZzgANj/v2v0+La7yC2dSP8aZVcBtZ3dSKls/Z7ZMRNyAn4XlbtmGuOeVPdciVSG54fzpVBXr27JlUhX/ixIn4xS9+kfR5GesJSFHcTyKm+Ol08Dc6wnkSR6N4kkm2v7d2aEZOJqtNjJ4EWmDUWNMb9GNHAHpNpO28tUURqqshIfbJXNE5L4xGBUUYT+EwRhhE5xRSpPG4bxUqAlChCgVB83GeASGQpSrIVgWCMbe3RCRQp0mEdd0eHqha5RXGEzycDQ7OWxysyS8V8/aGgNXgIKzGB2P6ckWYjQpm44MqJAKKBhURBJRaBBUdAXM0Q234QDN/Voll+q18qYz1nL2fiNJSW7uFprW03ZJFyZj/U3F8v4PhYu9tbevmz5+f1H5HH310i5aDqDUdks871+ikptw+2Vxt+xGbrRZj4kYktvwndnQ+nHD86a3z+Zg0273Ma14KKxGPaUSwV8cvF3aDQXSUgiIC9ggFY7lqz4VhjFQwHuBpT5gpArCe0hFACKpU7Ud9Wrc1BISKoFAQVIzbG0KqQJYijIYCR9kBo4GhXjeeZAREH/dpNS6EtdSkl8nE+tTeFNfyUhnr20zSHw6HMWXKFCxduhRffPEFCgoKUFpailmzZqG4uDjhfvfeey9+97vfuZYdf/zx+Pe//21/X1tbi1/+8pdYtGgR6urqMHToUDz22GMoLCxM2fshIqJDRSTxSKl0SvmBc845p7WL0CIY6ynTpEPDa1qKbXBp4cb3aATwbnSRzi8S9Ly6i+Q9YXbiAjQUgxoalQA0PLeF12TI0cYG5z6up13BPXeRV2NDwomPzeWqCABQoEdS15DlN9an2/D+VMb6NpP0HzhwAB988AHuvvtunHrqqfjhhx9w22234Sc/+QnWr1/f4L4nnngi3nzzTfv7QMD9tm6//Xa89tprWLx4MQoKCjB+/HhcddVVeO+991LyXoiI6NBJtqefDj3GeiJqC5o7OWZ8KtmECbMbOULjRbAS+fiRCcYR/TYyNNa4ADSlgcE4t2J/FdEiicvRTIz1yWszSX9BQQGWL1/uWvbnP/8ZZ5xxBrZv347u3bsn3DcQCKCoqMhzXXV1NZ566iksWLAA559/PgBg3rx56NOnD9asWdPo84iJiKiNS2L2fl36nUKLWgJjPRFlgmYnk42MXPBO35N4vHfsyqQm0PT39A49lUl/O5i9P1V8zx4yevRovPPOO6koS5zq6moIIdCxY8cGt/v0009RXFyMY445BiNHjsT27dvtdRUVFQiHwygtLbWXnXDCCejevTvKy8sTHrOurg41NTWuFxERtT0yiRc1jLGeiKj1JBPXmvSSstEX4l5aoy8pI45XCof3J/Eig++kv7q6GqWlpejVqxdmzJiBb7/9NhXlQm1tLe666y6MGDEC+fn5CbcbNGgQ5s+fj2XLlmHu3LnYtm0bfvSjH2Hv3r0AgMrKSoRCobjKRGFhISorKxMed+bMmfasxQUFBTjqqKNa5H0REVHL06Xw9WJFoGGM9URE7VNLNC6kiu9Yz2Bv8530v/LKK/j2228xbtw4vPDCCzj66KNx8cUX469//SvCYe97TLw8//zzyMvLs1///Oc/7XXhcBjXXHMNpJSYO3dug8e5+OKLcfXVV+OUU07B0KFDsXTpUlRVVeHFF1/0+9ZcJk+ejOrqavv19ddfN+t4RESUGm2tUpIJGOuJiKitSXWsnzt3Lk455RTk5+cjPz8fJSUl+Mc//mGvr62tRVlZGTp37oy8vDwMHz4cu3btch1j+/btGDZsGHJzc9G1a1fccccdiERSd8tDUyX1cNAuXbpg0qRJ+Oijj7B27Vocd9xxGDVqFIqLi3H77bfj008/bfQYP/nJT7Bhwwb7NXDgQADRSsBXX32F5cuXN9jy76Vjx47o3bs3PvvsMwBAUVER6uvrUVVV5dpu165dCe8NBICsrCz7B269iIiobdKlvxdb/xvHWE9ERG2J31jv957+I488ErNmzUJFRQXWr1+P888/H5dffjk+/vhjAMaEsf/3f/+HxYsXY9WqVdixYweuuuoqe39N0zBs2DDU19dj9erVePrppzF//nzcc889LXkZkpJU0m/ZuXMnli9fjuXLl0NVVVxyySXYuHEj+vbti4cffrjBfQ877DAcd9xx9isnJ8euBHz66ad488030blzZ99l2rdvHz7//HN069YNADBgwAAEg0GsWLHC3mbr1q3Yvn07SkpKfB+fiIjaFgljRl8/L+b8TcdYT0REre1QxPrLLrsMl1xyCXr16oXevXvj/vvvR15eHtasWWNPGPuHP/wB559/PgYMGIB58+Zh9erVWLNmDQDgjTfewObNm/Hcc8+hX79+uPjii3Hfffdhzpw5qK+vb5HrkCzfSX84HMbf/vY3XHrppejRowcWL16MiRMnYseOHXj66afx5ptv4sUXX8S0adN8H/f//b//h/Xr1+P555+HpmmorKxEZWWl6yJdcMEF+POf/2x//6tf/QqrVq3Cl19+idWrV+PKK6+EqqoYMWIEAGOm4DFjxmDSpEl4++23UVFRgZtuugklJSWczZeIKAN4zjvUyIsaxlhPRERtTbLxPnbC1rq6ukbPpWkaFi1ahP3796OkpKRJE8aWl5fj5JNPRmFhob3N0KFDUVNTY48WaC2+H9nXrVs36LqOESNGYN26dejXr1/cNuedd16js/DG+vbbb/H3v/8dAOKO+fbbb+Pcc88FAHz++ef47rvv7HXffPMNRowYge+//x5dunTB2WefjTVr1qBLly72Ng8//DAURcHw4cNRV1eHoUOH4rHHHvNVPiIiart0v8889rl9e8NYT0REbY3fWK9D4ODBgygoKHAtnzp1Ku69917PfTZu3IiSkhLU1tYiLy8PL7/8Mvr27YsNGzY0OmFsZWWlK+G31lvrWpPvpP/hhx/G1Vdfjezs7ITbdOzYEdu2bfN13KOPPtp4TEQjvvzyS9f3ixYtanSf7OxszJkzB3PmzPFVJiIiavuMxxD53Mfn9nPnzsXcuXPtGHTiiSfinnvuwcUXXwzAmNznl7/8JRYtWuRKOJ3Bf/v27Rg3bhzefvtt5OXlYfTo0Zg5cyYCAd+hOOUY64mIqK3xHesB5OTkxCXcWVlZCfc5/vjjsWHDBlRXV+Ovf/0rRo8ejVWrViVR2rbFd01j1KhRqShHm2dVUjQtdc+eJCLKZNbnZ1OSPr90n9v7LYE1uU+vXr0gpcTTTz+Nyy+/HB9++CFOPPFE3H777XjttdewePFiFBQUYPz48bjqqqvw3nvvAYhO7lNUVITVq1dj586duOGGGxAMBjFjxgyfpUk9xnrGeiKiZLTFWO9nktZQKITjjjsOgDFfzPvvv48//vGP+OlPf2pPGOvs7XdOGFtUVIR169a5jmfN7t/QpLKHQtvrXmijvv/+ewDApk0bWrcgRERpbu/evXFD7ZpDwv8MvclM7uN0//33Y+7cuVizZg2OPPJIPPXUU1iwYAHOP/98AMC8efPQp08frFmzBoMHD7Yn93nzzTdRWFiIfv364b777sNdd92Fe++9F6FQyGeJKBX27t0LgLGeiKi5WjrWA/5jvd/tPY+h66irq3NNGDt8+HAA8RPGlpSU4P7778fu3bvRtWtXALCfUNO3b9/mF6YZmPQ3UadOnQAYwzNb+hc4XdXU1OCoo47C119/zcccOfC6xOM1idcer4mUEnv37kVxcXHLHzuJ7TVNQ01NjWt5VlZWg8P+YO63ePHiJk/uM3jw4IST+4wbNw4ff/wx+vfv7/MdUCoUFxdj8+bN6Nu3b7v622xMe/y8agyvSTxek3jt8Zq0tVjvx+TJk3HxxReje/fu2Lt3LxYsWICVK1fi9ddfd00Y26lTJ+Tn52PChAmuCWOHDBmCvn37YtSoUZg9ezYqKysxZcoUlJWVNVq3SDUm/U2kKMaDDgoKCtrNH21T8dnG3nhd4vGaxGtv1yQVjaYyiWfxSgls2bKFk/uQi6IoOOKIIwC0v7/NpuA1icdrEo/XJF57uyYp6SBNMtb7sXv3btxwww3YuXMnCgoKcMopp+D111/HhRdeCKDxCWNVVcWSJUswbtw4lJSUoEOHDhg9erTvJ92kApN+IiJKe75vHZRAnz597HvuLe1xch8iIqJ0kOpJe5966qkG1zdlwtgePXpg6dKl/k58CDDpJyKitCaR3OQ+qqpych8iIqI0kEys97t9JlNauwDpIisrC1OnTm31+zHaEl4Tb7wu8XhN4vGatBxrIj8/r5aYU9hrch+L1+Q+GzduxO7du+1t2srkPuTGv814vCbxeE3i8ZrE4zVpWb5jfcs/QCBtCZmK5ykQEREdArNnz8Zz05/FNd2u9rXfP/e8i06lnbFw4cImbe81uc8DDzxg3+s3btw4LF26FPPnz7cn9wGA1atXAzAm/+vXrx+Ki4vtyX1GjRqFn/3sZ23ykX1ERERtxYYNG3DmgDPxm+N+7Wu/nbU7sWjfi/ZT2NozDu8nIqK0luxEfn5k8uQ+RERE6cD3I/tSU4y0xKSfiIjSnICE8LWH3yFumTy5DxERUTrwH+v9bZ/JmPQTEVFas+7p97sPERERpY9Uj+rLZEz6iYgorSWV9LMiQERElFZ8D+9nrLe169n758yZg6OPPhrZ2dkYNGhQ3OOUYi1evBgnnHACsrOzcfLJJ8cN05RS4p577kG3bt2Qk5OD0tJSfPrpp6l8C032zjvv4LLLLkNxcTGEEHjllVdc65tS9j179mDkyJHIz89Hx44dMWbMGOzbt6/B89bW1qKsrAydO3dGXl4ehg8fbj+myrJ9+3YMGzYMubm56Nq1K+644w5EIpEWed+JzJw5E6effjoOO+wwdO3aFVdccQW2bt16SMrelOv4r3/9Cz/60Y+QnZ2No446CrNnz26ZN96IuXPn4pRTTkF+fj7y8/NRUlKCf/zjH/b69nhNnGbNmgUhBCZOnGgva+/XpK2QPl/UfjDWR7W3WA8w3nthrG8c433b5DfWM95Htduk/4UXXsCkSZMwdepUfPDBBzj11FMxdOhQ1+OUnFavXo0RI0ZgzJgx+PDDD3HFFVfgiiuuwKZNm+xtZs+ejUcffRSPP/441q5diw4dOmDo0KGora09VG8rof379+PUU09NeL9pU8o+cuRIfPzxx1i+fDmWLFmCd955B2PHjm3wvLfffjv+7//+D4sXL8aqVauwY8cOXHXVVfZ6TdMwbNgw1NfXY/Xq1Xj66acxf/583HPPPS3zxhNYtWoVysrKsGbNGixfvhzhcBhDhgzB/v37U172xq5jTU0NhgwZgh49eqCiogIPPvgg7r33Xjz55JMtfyFiHHnkkZg1axYqKiqwfv16nH/++bj88svx8ccfA2if18Ty/vvv44knnsApp5ziWt6er0lbYU3kd6gf2UdtH2O9W3uL9QDjvRfG+oYx3rdNST2el8E+SrZTZ5xxhiwrK7O/1zRNFhcXy5kzZ3puf80118hhw4a5lg0aNEjeeuutUkopdV2XRUVF8sEHH7TXV1VVyaysLLlw4cIUvIPkAZAvv/yy/X1Tyr5582YJQL7//vv2Nv/4xz+kEEJ+++23nuepqqqSwWBQLl682F62ZcsWCUCWl5dLKaVcunSpVBRFVlZW2tvMnTtX5ufny7q6uhZ5v02xe/duCUCuWrUqpWVvynV87LHH5OGHH+46xl133SWPP/74lnvDPhx++OHyf/7nf9r1Ndm7d6/s1auXXL58uTznnHPkbbfdJqXk70lb8MADD8g+HU6Svz7md75e53a6UF577bWtXXxKMcb6l+3vGesNjPfeGOsNjPdt04cffiizlRzfsf6mI/5bdurUqbWL3ya0y57++vp6VFRUoLS01F6mKApKS0tRXl7uuU95eblrewAYOnSovf22bdtQWVnp2qagoACDBg1KeMy2oillLy8vR8eOHTFw4EB7m9LSUiiKgrVr13oet6KiAuFw2HXcE044Ad27d3cd9+STT0ZhYaG9zdChQ1FTU2O3OB8K1dXVAIBOnTqltOxNuY7l5eX48Y9/jFAo5Dru1q1b8cMPP7TQO26cpmlYtGgR9u/fj5KSknZ9TcrKyjBs2LC4z4D2fE3aEglAk/5evM8v8zHWuzHWGxjv3Rjr3Rjv2za/sV5jrLe1y4n8vvvuO2ia5vqjBIDCwkL8+9//9tynsrLSc/vKykp7vbUs0TZtVVPKXllZia5du7rWBwIBdOrUKeH7q6ysRCgUQseOHRs8rtd5neVKNV3XMXHiRJx11lk46aST7HOnouxNuY6VlZXo2bNnwuMefvjhSbzLptu4cSNKSkpQW1uLvLw8vPzyy+jbty82bNjQLq/JokWL8MEHH+D999+PW9eef0/aEs7eT14Y693ae6wHGO+dGOvjMd63fZy0N3ntMuknciorK8OmTZvw7rvvtnZR2oTjjz8eGzZsQHV1Nf76179i9OjRWLVqVWsXq1V8/fXXuO2227B8+XJkZ2e3dnEokWTu22NFgKjdYbyPYqx3Y7xPD35jPUN9VLsc3v9f//VfUFU1bsbNXbt2oaioyHOfoqKiBre3/vdzzLaiKWUvKiqKm/goEolgz549DV6z+vp6VFVVNXhcr/M6y5VK48ePx5IlS/D222/jyCOPtJenquxNuY6tfU1CoRCOO+44DBgwADNnzsSpp56KP/7xj+3ymlRUVGD37t047bTTEAgEEAgEsGrVKjz66KMIBAIoLCxsd9ekLZIAdJ8vVgQyH2O9W3uO9QDjfSzGejfG+/TgN9brrVPMNqldJv2hUAgDBgzAihUr7GW6rmPFihUoKSnx3KekpMS1PQAsX77c3r5nz54oKipybVNTU4O1a9cmPGZb0ZSyl5SUoKqqChUVFfY2b731FnRdx6BBgzyPO2DAAASDQddxt27diu3bt7uOu3HjRtcH3vLly5Gfn4++ffu26Pt0klJi/PjxePnll/HWW2/FDZtKVdmbch1LSkrwzjvvIBwOu457/PHHt8oQLl3XUVdX1y6vyQUXXICNGzdiw4YN9mvgwIEYOXKk/XV7uyZtFWfvp1iM9W7tMdYDjPdN1Z5jPcB4ny44e38ztPZMgq1l0aJFMisrS86fP19u3rxZjh07Vnbs2NGecXPUqFHy17/+tb39e++9JwOBgHzooYfkli1b5NSpU2UwGJQbN260t5k1a5bs2LGjfPXVV+W//vUvefnll8uePXvKgwcPHvL3F2vv3r3yww8/lB9++KEEIP/whz/IDz/8UH711VdSyqaV/aKLLpL9+/eXa9eule+++67s1auXHDFihL3+m2++kccff7xcu3atvey///u/Zffu3eVbb70l169fL0tKSmRJSYm9PhKJyJNOOkkOGTJEbtiwQS5btkx26dJFTp48OaXXY9y4cbKgoECuXLlS7ty5034dOHCgRcu+du1aefzxx8tvvvnGXtbYdayqqpKFhYVy1KhRctOmTXLRokUyNzdXPvHEEym9JlJK+etf/1quWrVKbtu2Tf7rX/+Sv/71r6UQQr7xxhvt9prEcs7mKyWvSWt74IEHZK/ck+SEo37n63VmAWfvbw8Y69t3rJeS8d4LY33TMN63Hdbs/X5j/bWFnL3f0m6Tfiml/NOf/iS7d+8uQ6GQPOOMM+SaNWvsdeecc44cPXq0a/sXX3xR9u7dW4ZCIXniiSfK1157zbVe13V59913y8LCQpmVlSUvuOACuXXr1kPxVhr19ttvSxidW66X9R6bUvbvv/9ejhgxQubl5cn8/Hx50003yb1799rrt23bJgHIt99+21528OBB+fOf/1wefvjhMjc3V1555ZVy586druN++eWX8uKLL5Y5OTnyv/7rv+Qvf/lLGQ6HU3YtpJSe1wKAnDdvXouW3bru27Zts5c1dh2llPKjjz6SZ599tszKypJHHHGEnDVrVkquQ6ybb75Z9ujRQ4ZCIdmlSxd5wQUX2JUAKdvnNYkVWwngNWldVtJfdtTvfL1KmPS3G4z17TfWS8l474WxvmkY79uODz/8UGYpOb5j/TVM+m1CSg58ICKi9DR79mz85d5nMaTz1b72+3Dvu+hxcWcsXLgwRSUjIiKilrBhwwYMHnAmxhT/2td+39XvxJuRF/H999+nqGTpg7P3ExFR2vM7WQ8n9yEiIkovjPXJY9JPRERpTcKYrMvfThzkRkRElDak/1jPAe1RTPqJiCjt6cz5iYiIMprvWJ+aYqQlJv1ERJTWrJm5/O5DRERE6YOxPnlM+omIKK1JydZ/IiKiTOc31vvdPpMx6SciorTH4f1ERESZjUl/8pTWLgAREVFzGMP7/f0jIiKi9JLqWD9z5kycfvrpOOyww9C1a1dcccUV2Lp1q2ub2tpalJWVoXPnzsjLy8Pw4cOxa9cu1zbbt2/HsGHDkJubi65du+KOO+5AJBJp1ntvLib9RESU3szh/X5eTPuJiIjSh4T/WO+3p3/VqlUoKyvDmjVrsHz5coTDYQwZMgT79++3t7n99tvxf//3f1i8eDFWrVqFHTt24KqrrrLXa5qGYcOGob6+HqtXr8bTTz+N+fPn45577mmhK5EcJv1EaeSpp57CkCFDUn6eZcuWoV+/ftB1PuGU2j7jkX3+X0REbRFjPZG3VMf6ZcuW4cYbb8SJJ56IU089FfPnz8f27dtRUVEBAKiursZTTz2FP/zhDzj//PMxYMAAzJs3D6tXr8aaNWsAAG+88QY2b96M5557Dv369cPFF1+M++67D3PmzEF9fX1LX5ImY9JPlCZqa2tx9913Y+rUqSk/10UXXYRgMIjnn38+5eciaj4J3efL77C/TB7yR0RtB2M9UWLJxvqamhrXq66urknnq66uBgB06tQJAFBRUYFwOIzS0lJ7mxNOOAHdu3dHeXk5AKC8vBwnn3wyCgsL7W2GDh2KmpoafPzxxy1yHZLBpJ8oTfz1r39Ffn4+zjrrrENyvhtvvBGPPvroITkXUXMk1dPv8xyZPOSPiNoOxnqixJKJ9QcPHkRBQYHrNXPmzEbPpes6Jk6ciLPOOgsnnXQSAKCyshKhUAgdO3Z0bVtYWIjKykp7G2fCb6231rUWJv1Eh9h//vMfFBUVYcaMGfay1atXIxQKYcWKFQn3W7RoES677DLXsnPPPRcTJ050Lbviiitw44032t8fffTRmD59Om644Qbk5eWhR48e+Pvf/47//Oc/uPzyy5GXl4dTTjkF69evdx3nsssuw/r16/H5558n/2aJDhHd58tv0p/JQ/6IqOUx1hO1PL+xXgeQk5OD6upq12vy5MmNnqusrAybNm3CokWLUvNmDjEm/USHWJcuXfC///u/uPfee7F+/Xrs3bsXo0aNwvjx43HBBRck3O/dd9/FwIEDkzrnww8/jLPOOgsffvghhg0bhlGjRuGGG27A9ddfjw8++ADHHnssbrjhBkjHzU/du3dHYWEh/vnPfyZ1TqJDxWjRl75ekEbPO4f8EVEqMNYTtTy/sd76Xc/Pz3e9srKyGjzP+PHjsWTJErz99ts48sgj7eVFRUWor69HVVWVa/tdu3ahqKjI3ib21j7re2ub1sCkn6gVXHLJJbjlllswcuRI/Pd//zc6dOjQ4FCjqqoqVFdXo7i4OOnz3XrrrejVqxfuuece1NTU4PTTT8fVV1+N3r1746677sKWLVviPqSKi4vx1VdfJXVOokMpmdn7t2zZwiF/RJQyjPVELct3rPc5rE9KifHjx+Pll1/GW2+9hZ49e7rWDxgwAMFg0DVaZ+vWrdi+fTtKSkoAACUlJdi4cSN2795tb7N8+XLk5+ejb9++yb/5Zgq02pmJ2rmHHnoIJ510EhYvXoyKiooGWx0PHjwIAMjOzk7qXKeccor9tZVknHzyyXHLdu/e7WqFzMnJwYEDB5I6J9GhIs0Je/zu06dPH7z33nuu5Y21/gPRIX/vvvuur3MSUfvDWE/UcvzGer/bl5WVYcGCBXj11Vdx2GGH2Q3yBQUFyMnJQUFBAcaMGYNJkyahU6dOyM/Px4QJE1BSUoLBgwcDAIYMGYK+ffti1KhRmD17NiorKzFlyhSUlZU1qY6RKuzpJ2oln3/+OXbs2AFd1/Hll182uG3nzp0hhMAPP/zQ6HE1TYtbFgwG7a+FEAmXxT62Z8+ePejSpUuj5yRqbck8xkdVVQ75I6KUYqwnahmH4vG8c+fORXV1Nc4991x069bNfr3wwgv2Ng8//DAuvfRSDB8+HD/+8Y9RVFSEl156yV6vqiqWLFkCVVVRUlKC66+/HjfccAOmTZvWQlciOezpJ2oF9fX1uP766/HTn/4Uxx9/PH72s59h48aN6Nq1q+f2oVAIffv2xebNm+Oe3RubRHzxxRfo3Llzs8tYW1uLzz//HP3792/2sYhSScJ/a77fifyklJgwYQJefvllrFy5ssEhf8OHDwfgPeTv/vvvx+7du+2/9bYw5I+IUoOxnqhlJTOqz9f2TWglyM7Oxpw5czBnzpyE2/To0QNLly71de5UY08/USv47W9/i+rqajz66KO466670Lt3b9x8880N7jN06FDP4cSvvvoqXnrpJXz++ee4//77sXnzZnz11Vf49ttvm1XGNWvWICsry05YiNqsJFr+/Sb9ZWVleO6557BgwQJ7yF9lZaU9HNc55O/tt99GRUUFbrrppoRD/j766CO8/vrrbWLIHxGlBmM9UctKdU9/JmPST3SIrVy5Eo888gieffZZ5OfnQ1EUPPvss/jnP/+JuXPnJtxvzJgxWLp0qT1ruGXYsGGYPXs2+vbti3feeQePPfYY1q1bh2effbZZ5Vy4cCFGjhyJ3NzcZh2HKNWsnn4/L7+t/5k85I+IWh5jPVHL8xvr9cYP2W4I2ZRxDETUJlx99dU47bTT7OeLnnvuuejXrx8eeeSRFj3Pd999h+OPPx7r16+PG8ZM1JbMnj0bj0yZj/4drvC13+e1a9D/ikIsXLgwNQUjIkoSYz2R24YNG3DGaSW4sOB2X/vVRHZhc+gVfP/99ykqWfpgTz9RGnnwwQeRl5eX8vN8+eWXeOyxx1gJoLQhff7zP8CfiOjQYKwn8uY31vsd1ZfJOJEfURo5+uijMWHChJSfZ+DAgRg4cGDKz0PUEozh/f73ISJqixjribz5jfUc3h/FpJ8oja1cubK1i0DU6g7F7P1ERK2FsZ7IkOrZ+zMZk34iIkpzskmP2YnZI0VlISIiolRgrE8ek34iIkpryfX0syJARESULpKJ9X63z2RM+omIKO1xeD8REVFm85/0k4VJPxERpTVjfl5/oZ09/UREROnFb6xn2h/FpJ+IiNIeh/cTERFlNg7vTx6TfiIiSmtGPz+H9xMREWUyJv3JY9JPRERpzZjch8P7iYiIMhljffKY9BMRUZqTkMLvfXusCBAREaUP/7He/xwAmYtJPxERpTU+so+IiCiz8ZF9zcOkn4iI0pz0PeSPFQEiIqL0wuH9yWPST0REaS2ZR/ZxeD8REVF68f94Xg7vtzDpJyKitCYB6H7v8xNM+omIiNKJ31ivM9bbmPQTEVGa8z+8n0P+iIiI0ov/WM+efguTfiIiSmuSST8REVHG8z9/D5N+C5N+IiJKc/7v6WfST0RElF4Y65PHpJ+IiNKakfJrPvdh6z8REVH6SCbW+9s+kzHpJyKitMfZ+4mIiDIbe/qTx6SfiIjSmhTS/4y+rAgQERGljWSe1ON3+0ymtHYBiIiImsMa3u/nxeH9RERE6eVQxPp33nkHl112GYqLiyGEwCuvvOJaL6XEPffcg27duiEnJwelpaX49NNPXdvs2bMHI0eORH5+Pjp27IgxY8Zg3759zXnrzcakn4iI0pyR9vt5+R3en6mVACIionThN9Ynk/Tv378fp556KubMmeO5fvbs2Xj00Ufx+OOPY+3atejQoQOGDh2K2tpae5uRI0fi448/xvLly7FkyRK88847GDt2bNLvuyUw6SciorQmpYQuNV8vv/f5ZWolgIiIKD34j/W69D+R38UXX4zp06fjyiuvjC+BlHjkkUcwZcoUXH755TjllFPwzDPPYMeOHXZnwJYtW7Bs2TL8z//8DwYNGoSzzz4bf/rTn7Bo0SLs2LGjuRchaUz6iYgo7aW69T9TKwFERETpwn+sNxr4a2pqXK+6urqkzr9t2zZUVlaitLTUXlZQUIBBgwahvLwcAFBeXo6OHTti4MCB9jalpaVQFAVr165txrtvHib9RESU5iQkNF+vlpy9P50rAUREROnCb6yX0HDw4EEUFBS4XjNnzkzq/JWVlQCAwsJC1/LCwkJ7XWVlJbp27epaHwgE0KlTJ3ub1sDZ+4mIKK0Zd/T7f4yPpmmoqalxLc/KykJWVpavY6VzJYCIiChdJBPrc3Jy4uKs3zifCdjTT0REac7/RH4SElu2bGmx1n8iIiJKrWRv5cvPz3e9kk36i4qKAAC7du1yLd+1a5e9rqioCLt373atj0Qi2LNnj71Na2DST0REaU1CQkrN1wtSR58+fVBdXe16TZ482ff507kSQERElC78xnopW/bxvD179kRRURFWrFhhL6upqcHatWtRUlICACgpKUFVVRUqKirsbd566y3ouo5Bgwa1aHn84PB+IiJKc/6H9+uQUFUV+fn5zT67sxLQr18/ANFKwLhx4wC4KwEDBgwA0DYqAUREROkguVv5/Cf9+/btw2effWZ/v23bNmzYsAGdOnVC9+7dMXHiREyfPh29evVCz549cffdd6O4uBhXXHEFAKBPnz646KKLcMstt+Dxxx9HOBzG+PHjce2116K4uNh3eVoKk34iIkpr0pzIzx9/FYFMrQQQERGlC7+xPpmkf/369TjvvPPs7ydNmgQAGD16NObPn48777wT+/fvx9ixY1FVVYWzzz4by5YtQ3Z2tr3P888/j/Hjx+OCCy6AoigYPnw4Hn30Ud9laUlM+omIKO35HcJnPcanqTK1EkBERJQu/Md6/0n/ueeeCykT1xGEEJg2bRqmTZuWcJtOnTphwYIFvs+dSkz6iYgorSU7e78fmVoJICIiShf+b+XTIVJUlnTDpJ+IiNKbNCby87sPERERpQ//sb5lJ/JLZ0z6iYgorUmzr9/vPkRERJQukon17Om3MOknIqK05/uxPGz9JyIiSh8yiXv6OarPxqSfiIjSnJ7EjL6sCBAREaUT/7He75N9MheTfiIiSmsSSfT0M+knIiJKK+zpTx6TfiIiSm9SsiJARESU4fzHet7KZ2HST0REae1QPLKPiIiIWpf/WM+k38Kkn4iI0p7v1n8m/URERGkjmVv5GOujmPQTEVGak3x2LxERUUbzH+t91w0yGJN+IiJKazKpZ/ey9Z+IiCidMNYnj0k/ERGltyQm8uPs/UREROnFd6znqD4bk34iIkp7nL2fiIgos3H2/uQx6SciorSWzPB+9vQTERGlFw7vTx6TfiIiSnP+h/ezIkBERJRe2NOfPCb9RESU3iTv8yMiIspsbOBvDib9RESU5iTA4f1EREQZzm+sZwO/hUk/ERGlNcnWfyIiooyX3Kg+kZKypBsm/URElOY4kR8REVGmS24iPyb9AJN+IiLKAHxkHxERUWZLbiI/JTWFSTNM+omIKM1JAFoS+xAREVH68BvreU+/hUk/ERGlNSn939PPigAREVE6SWL+Hj6px8akn4iI0hxn7yciIsp8bOBPFpN+IiJKf75n9GXST0RElFYY65PGpJ+IiNKc9P0IPlYDiIiI0ov/WM9ob2HST0REGYDD+4mIiDIbh/cni0k/ERGlOZnEED4m/URERGmFw/WTxqSfiIjSHofwERERZTYJHYDwtQcZhJRsMiEiovT01ltv4YILLgCgoukVAQnjWb8KpPT7zF8iIiI6lKqrq9GxY0cAivlqKiPG89F9TPqJiCjNCWFVANQm7qED0HHw4EFkZ2enqFRERETUUoRQYcTvpjbyGw38GzZswKmnnprSsqUDP00lREREbc6//vURjODelDZsCUDH888/z4SfiIgoTdTWHjC/amp/tXErABN+A3v6iYgo7TW9t9/o5dc0DYrCdm8iIqJ08cILL+Daa69F4739Ri//119/jSOPPPLQFK6NY9JPRERp79tvvzUDe0MVAaMS8NZbb+G88847dIUjIiKiZpNSmg32Dd3bb83bI3gvvwOTfiIiyghGb79E4sSfE/oQERGls3feeQfnnHMOEsd6Y0RfVVUVCgoKDm3h2jCObSQiooxQXV1lfuXVlm3c8//xx5sOXYGIiIioRf34xz+Gkex7NeAb8/Y8+uijTPhjMOknIqKMkJ+fjzlz5sCoCMQm/saEPn379j30BSMiIqIWs2XLZnhP4Gt8f+uttx7qIrV5HN5PREQZIxwOIxQKwX2/n3F/344dO9CtW7fWKxwRERG1iPgJfI1Y/9e//hXDhw9vpVK1XUz6iYgoo7zyyiu48sorEa0IcEIfIiKiTLJr1y4UFRUhem+/cS+/rusQoqGZ/dsnJv1ERJRRorP7ClgVgb179yIvL6+VS0ZEREQtRQgVRrKvAtDw3nvv4cwzz2zlUrVNvKefiIgyihAC5eXlsCb0efzxx5nwExERZZh9+2rMr4wRfUz4E2NPPxERZSTrEX7hcBiBQKC1i0NEREQt7KmnnsLPfvYzfPLJJ+jVq1drF6fNYtJPRERERERElKE4vJ+IiIiIiIgoQzHpJyIiIiIiIspQTPqJiIiIiIiIMhSTfiIiIiIiIqIMxaSfiIiIiIiIKEMx6SciIiIiIiLKUEz6iYiIiIiIiDIUk34iIiIiIiKiDMWkn4iIiIiIiChDMeknIiIiIiIiylBM+omIiIiIiIgyFJN+IiIiIiIiogzFpJ+IiIiIiIgoQzHpJyIiIiIiIspQTPqJiIiIiIiIMhSTfiIiIiIiIqIMxaSfiIiIiIiIKEMx6SciIiIiIiLKUEz6iYiIiIiIiDIUk34iIiIiIiKiDMWkn4iIiIiIiChDMeknIiIiIiIiylBM+omIiIiIiIgyFJN+IiIiIiIiogzFpJ+IiIiIiIgoQzHpJyIiIiIiIspQTPqJiIiIiIiIMhSTfiIiIiIiIqIMxaSfiIiIiIiIKEMx6SciIiIiIiLKUEz6iYiIiIiIiDIUk34iIiIiIiKiDMWkn4iIiIiIiChDMeknIiIiIiIiylBM+okylBCiSa+VK1fiyy+/bHCbWbNmuY69ZcsWXHTRRcjLy0OnTp0watQo/Oc//2lyucaPH5+Kt5yUBQsW4JFHHmntYhARETVZsjF++vTpnscbOXIkhBDIy8uLW8eYT5T+Aq1dACJKjWeffdb1/TPPPIPly5fHLe/Tpw8OHjwIABgxYgQuueSSuGP179/f/vqbb77Bj3/8YxQUFGDGjBnYt28fHnroIWzcuBHr1q1DKBRKwbtJnQULFmDTpk2YOHFiaxeFiIioSZKJ8dnZ2Vi4cCGmTJni2mb//v149dVXkZ2dHXcexnyizMCknyhDXX/99a7v16xZg+XLl8ctB4Avv/wSAHDaaad5rneaMWMG9u/fj4qKCnTv3h0AcMYZZ+DCCy/E/PnzMXbs2JZ5A0REROQpmRh/ySWX4KWXXsJHH32EU0891V7/6quvor6+HhdddBHeeust176M+USZgcP7iciXv/3tb7j00kvt4A8ApaWl6N27N1588UXfx1u5ciWEEHjxxRdx//3348gjj0R2djYuuOACfPbZZ65tzz33XJx00kmoqKjAmWeeiZycHPTs2ROPP/64a7v58+dDCGFXdGLPtXLlSvt4r732Gr766it76OPRRx/t+z0QERG1dSUlJejZsycWLFjgWv7888/joosuQqdOneL2Ycwnygzs6Sci24EDB/Ddd9/FLe/YsSMCgQC+/fZb7N69GwMHDozb5owzzsDSpUuTPvesWbOgKAp+9atfobq6GrNnz8bIkSOxdu1a13Y//PADLrnkElxzzTUYMWIEXnzxRYwbNw6hUAg333yzr3P+9re/RXV1Nb755hs8/PDDAOB5PyMREVEmGDFiBJ577jnMmjULQgh89913eOONN/Dss89i2bJlrm0Z84kyB5N+IrJNnToVU6dOjVteXl6OwYMHY+fOnQCAbt26xW3TrVs37NmzB3V1dcjKyvJ97traWmzYsMG+P/Dwww/Hbbfdhk2bNuGkk06yt9uxYwd+//vfY9KkSQCAW2+9FYMGDcLkyZMxatQoBIPBJp/zwgsvxBFHHIEffvih0dsaiIiI0t11112HGTNm4L333sPZZ5+NF198EdnZ2fjJT34Sl/Qz5hNlDg7vJyLb2LFjsXz58rhX3759AcCeDMgrwFsTAFnb+HXTTTe5JgT60Y9+BAD44osvXNsFAgHceuut9vehUAi33nordu/ejYqKiqTOTURE1B6ceOKJOOWUU7Bw4UIAxsR2l19+OXJzc+O2Zcwnyhzs6SciW69evVBaWppwfU5ODgCgrq4ubl1tba1rG7+c9wsCRqs/YAztcyouLkaHDh1cy3r37g3AmKxo8ODBSZ2fiIioPbjuuuvw+9//HrfffjtWr16N3/zmN57bMeYTZQ729BNRk1lD/Kwhf047d+5Ep06dkhrmBwCqqnoul1L6PpYQwnO5pmm+j0VERJRJRowYge+++w633HILOnfujCFDhnhux5hPlDmY9BNRkx1xxBHo0qUL1q9fH7du3bp16NevX8rLsGPHDuzfv9+17JNPPgEAexZeq8egqqrKtd1XX30Vd7xElQUiIqJM1L17d5x11llYuXIlrr76agQC3gN/GfOJMgeTfiLyZfjw4ViyZAm+/vpre9mKFSvwySef4Oqrr075+SORCJ544gn7+/r6ejzxxBPo0qULBgwYAAA49thjAQDvvPOOvZ2maXjyySfjjtehQwdUV1enuNRERERtx/Tp0zF16lRMmDChwe0Y84kyA+/pJyLbBx98gOeeey5u+bHHHouSkhIAwG9+8xssXrwY5513Hm677Tbs27cPDz74IE4++WTcdNNNKS9jcXExHnjgAXz55Zfo3bs3XnjhBWzYsAFPPvmkPYvviSeeiMGDB2Py5MnYs2cPOnXqhEWLFiESicQdb8CAAXjhhRcwadIknH766cjLy8Nll12W8vdBRETUWs455xycc845jW7HmE+UGZj0E5Ft4cKF9oy+TqNHj7aT/qOOOgqrVv1/9u48Pqrqbvz4586afSZ7AgQIBCFsgqwBF1AERUVbq7VU2ay7VMUiuJRCLUVaFLG20McFaOuvj/ZRUXFjt8ruwiaLAmHPvk3WWe/vj5lMMtkIMMlMku/79ZrX3HvuveeeCSFnvuee5QtmzZrF3LlzMRgM3HTTTbz44osXPbbvQkRHR7N69WpmzpzJa6+9RmJiIq+++ir33Xefz3lvvfUWDzzwAC+88AJms5l7772XsWPHcv311/uc9/DDD7Nnzx5WrlzJ0qVL6datm3wBEEIIIZA6X4j2QlEvZsYMIYQIgDFjxpCfn8+BAwcCXRQhhBBCtCCp84XwHxnTL4QQQgghhBBCtFMS9AshhBBCCCGEEO2UBP1CCCGEEEIIIUQ7JWP6hRBCCCGEEEKIdkqe9AshhBBCCCGEEO2UBP1CCCGEEEIIIUQ7pQt0AdoKl8vFuXPniIyMRFGUQBdHCCGEQFVVSktL6dSpExqNtONfKqnrhRBCBBt/1PUS9DfTuXPnSElJCXQxhBBCiHpOnz5Nly5dAl2MNk/qeiGEEMHqUup6CfqbKTIyEnD/sKOiogJcGiGEEAIsFgspKSneOkpcGqnrhRBCBBt/1PUS9DdTdTe/qKgo+SIghBAiqEhXdP+Qul4IIUSwupS6XgYACiGEEEIIIYQQ7ZQE/UIIIYQQQgghRDslQb8QQgghhBBCCNFOyZh+IYRoRaqq4nA4cDqdgS6KaAO0Wi06nU7G7AshhBDioknQL4QQrcRms5GVlUVFRUWgiyLakLCwMJKTkzEYDIEuihBCCCHaoA4V9P/1r3/lz3/+M9nZ2Vx++eX85S9/Yfjw4YEulhCiA3C5XGRmZqLVaunUqRMGg0Ge3oomqaqKzWYjLy+PzMxMevXqhUYjo/LOR+p6IYQQwleHCfrffvttZs2axYoVKxgxYgQvv/wyEyZM4MiRIyQkJAS6eEKIds5ms+FyuUhJSSEsLCzQxRFtRGhoKHq9npMnT2Kz2QgJCQl0kYKa1PVCCCFEfR3mkcFLL73Efffdx/Tp0+nbty8rVqwgLCyMN998M9BFE0J0IPKkVlwo+Z1pPqnrhRBCiPo6xJN+m83GN998w9NPP+1N02g0jBs3ju3btzd4jdVqxWq1evctFotfyvLRN5t46ZM3znueqjY/T5Xzn6w2M8Pmnkez7tncnFq//M05q/l5BWf5m/sP4N/yN+s0/97Tr3n5T917JkfG8cz1v8IeAhqd1o93Eg1pzu/FBWboV1FhEXSL6+zfTDu4YKrrRRNUFewVYK8EWznYKsDuebeVg8MKThs4bO53pw2cdk+atWb7fOkOK7jsoLrc91Rd9V8uZ9PHfV6ePwKKAijud0VTs41nv8HtWq/qYxotaHQNv7R1jzV0bq00rd7zMoDOWLOtNTS+rWvsHE8eOqM7bxmGJkS70CGC/vz8fJxOJ4mJiT7piYmJHD58uMFrFi1axIIFC/xeluIKC4fOHvN7vkKI4BamNeJ0uXC5XKiuQJembfjgP+/z59+/wFf7dwa6KH6nuvzciiCCqq5vV1TVHYxXlbhflSVQVezZt7jfrWW1AvfqIL6ygbQKd8Av2gZFAV0IaI2gM7i3q9+9jQMNpRlrHWvgXR/qm6ave04I6D3v0vAghF90iKD/Yjz99NPMmjXLu2+xWEhJSbnkfLUaLaGG5o3JvJBJvhTOf25zs2tOXu78mnPPZuYVgPI3J8Pml785t/Pfz6K5+TU/L//c70Lu2ayff/Ny8uuEeP79na05JzEiBp1Wh16nb1NP+vt2SWvy+MNPzOSnd97OuIwxDR7/9wf/YdCQwQDs2raDF37/R47+8CPJyck8+Ngj/OTO2xvNe9Jtt3Ld9eMw6i5y1voW/p64a9sOpt5xNzsPfkuUKarR86xVVubP/S3f7z/A8R+PMWbcWFb9v3+2bOFEs7RUXR90VBWsFijPd78qCqGyqFYQXyuA9wb3xZ6A3uJ+It5WabSep+4NvaqfyDd2jgIaTc0TfQDUWr0DPC9qv7uasa26f6YuR80rGKmqu0eGvTJwZVA0dRoI6jQKeLdDazUoNPBetzFBF+I+Vi8fT7rWII0Nol3pEEF/XFwcWq2WnJwcn/ScnBySkpIavMZoNGI0Gv1elsmjb2Hy6Fv8nq8QIrhVVVWRmZlJamLXNjUZW1ZWlnf77bffZt68eRw5csSbFhERQX5+PgAbNmygX79+PtfHxsai1+vJzMzkoWn38+CDD/Krd/6PjRs38vjjj3NF38uZMGFC63wYP7Lb7WTHnQbgsuRUzGZzo+eWl5eTFJvAxFm/4d133yUkJIQuMQ3XPeLiBVNd3+JU1R20lxd4gvj8moC+9quiAMry3O9OW2DKagj3BFfhYAhzv6q39WENpIX6djXX6t1PkrXGBrqnG327p+sMvtdqtG0jcKseXlC7EcDlqGkYcNbZdzncQxdqH/MZ7lBn+ENT247a6Z6hEg4bOK2ebat72+55d9jAUeXevpCxqBf9s3F5hoO0cg+R6l4OOqNvg0JDjQ36Rhof6jYw+KSH1lzjbZgIBW2HCM1EAHSI3yyDwcCQIUPYuHEjt912G+BePmvjxo08+uijgS2cEEIEsdrBkslkQlGUegFUddAfGxvbaHC1YsUKUlNTefHFFwFIT0/nq6++YunSpY0G/atWreLxxx+nuLgYgPnz57NmzRqefPJJfvvb31JUVMSNN97Ia6+9RmRkZJN5rFq1itmzZ3P69GmuueYaXn/9dZ8nusuXL2fJkiWcPn2a1NRUnnvuOe655x7vcUVR+Nvf/sann37Kxo0bueOOO1i9ejUA0dHRAEydOpVVq1bVK0N4eDjLly8HYOvWrd7PI/yrXdT19ioozfa8ssCS5d62ZEFpDpTn1QTzLfl0WKOFEJPnZYaQqJr9UFOtY56XMbJOEB/u3taHto2gO9AUxd3bQKMF2kgjlKp6Gg9qNQTUbiRwWN2/z7W3HVbPeZ5te2WttDrv9qqmz7VX0qJj5Xx6ORS33H3q0ujqNyjUbhTw7ofUaTSo07Dg05Oh9nWh9a/RtJ3eh+LidYigH2DWrFlMnTqVoUOHMnz4cF5++WXKy8uZPn16oIsmhBDt3vbt2xk3bpxP2oQJE3j88ccvKJ9jx46xZs0a1q5dS1FREXfeeScvvPACCxcubPSaiooKFi5cyD/+8Q8MBgMPP/wwd911F1u3bgXg/fff57HHHuPll19m3LhxrF27lunTp9OlSxfGjh3rzWf+/Pm88MILvPzyy2i1WiZNmsTtt9/OkSNHiIqKIjQ09II+i/C/oK3rnXYoy/UE8nUC+uptS5b76b0/aXQQHud+hcVBeGzNfmi0J2iPcgf2tYN5Q7gE66JpiuIZy28ITDuFqrobvqobA7yNApW+jQZ13x1Vvg0I9so6DQyec2vnZ6/OtxUaG1wOsJW5X61Fq6/VY6G6USDMd7hD9TFDqPuYrtY5Pu+hTbzC3PcSAdFhgv6f//zn5OXlMW/ePLKzsxk0aBCfffZZvQl/hBCiNY3/4zTyLAWtes/4qFjWPbPK7/mOGjWq3vJyZWXuLy7Z2dkNTrBmsViorKxsdsDscrlYtWqV98n+Pffcw8aNG5sM+u12O6+++iojRowAYPXq1aSnp7Nr1y6GDx/OkiVLmDZtGg8//DDgDhx37NjBkiVLfIL+yZMn+wSPmZmZACQkJDTZvV+0nqCp6/OPwqfPeJ7Sn3M/ofdHV2id0RO81wngvWl1joWYJXgX7ZOi1Kxa0Jqqezg46jYq1G5sqKzT2FCrwaDJ9AaOV++35PAcp939srbC6iUaba3GAc97dS8hfWit4UC1t+seC6sZClS3d5EhXHouNKLDBP0Ajz76aNvp4ieE6BDyLAVkFecFuhh+8fbbb5Oent6i9+jevbtPV/7k5GRyc3ObvEan0zFs2DDvfp8+fTCbzRw6dIjhw4dz6NAh7r//fp9rRo8ezbJly3zShg4d6odPIFpa0NT1Rz5r/rk6I0QmQ2QSRNV5j0yGqE4QmejuRi9BvBCBU7uHA41P4Op3LmcjjQyV9Xsx1G4wqHfeBRxviQk8XU6wlrpfLUUX4jtPiCHc82ogzdtoEFHrPRyM4Z7tiJrtNt5LoUMF/UIIEWzio2LbzT1TUlJIS2t4tv+kpKQGJ1i70G7xer1vpasoCi5X66yBGB4e3ir3Ee1ApGduC40WIpIgqjp4rx3E1wrsQ6MlmBdCNE6jrQlUW4vTXjOvgU/jQKVncsUq33dHlWdZzkp3o4HN826v+/JcYyuv2fen6gYQCv2br87o+TeIAGNEA9vhnvlN6mwPuD0o/r5L0C+EEAHUEt3sg1FGRgaffPKJT9r69evJyMho8Xs7HA6+/vprhg8fDsCRI0coLi729kpIT09n69atTJ061XvN1q1b6du3b5P5GgzupQSdzja8nJloGcYImHsUwmKlq6kQom2qHj4R0sI9GlS1VoOBp9GgdoNAg+m1jtnK629by2pdW+6f4VXVE1VWXEBjglYPA3926ff2Awn6hRBC+EVBQQHZ2dk+aWazmZCQEB588EFeffVVnnrqKWbMmMGmTZt45513+Pjjj1u8XHq9npkzZ/LKK6+g0+l49NFHGTlypLcRYPbs2dx5550MHjyYcePG8dFHH/Hee++xYcOGJvPt1q0biqKwdu1aJk6cSGhoKBEREQ2ee/DgQWw2G4WFhZSWlrJnzx4ABg0a5M+PKoJJREKgSyCEEMFPUWrG8dMCPRG9jQrlnpenIaC6kaB2mrV6v8z9bi1rfLs5PRQMDX8nCAQJ+oUQQvhF3dn5Af79739z1113kZqayscff8wTTzzBsmXL6NKlC6+//nqjy/X5U1hYGHPmzGHy5MmcPXuWq666ijfeeMN7/LbbbmPZsmUsWbKExx57jNTUVFauXMmYMWOazLdz584sWLCAuXPnMn36dKZMmdLgkn0AEydO5OTJk979wYMHA6C2xjrXQgghREdVu1EhPM5/+bqcNb0KGmsYCCKKKt84msVisWAymSgpKSEqqhUn7hBCtAtVVVVkZmaSmppKSEhIoIvTYaxatYrHH3+c4uLiQBflojX1uyN1k3/Jz1MIIUSw8UfdpDn/KUIIIYQQQgghhGiLJOgXQgghhBBCCCHaKQn6hRBCtFvTpk1r0137hRBCCCEulQT9QgghhBBCCCFEOyVBvxBCCCGEEEII0U5J0C+EEEIIIYQQQrRTEvQLIYQQQgghhBDtlAT9QgghhBBCCCFEOyVBvxBCCCGEEEII0U5J0C+EECIorVq1CrPZHOhiCCGEEEK0aRL0CyGEaJSiKE2+5s+fz4kTJxo9vmPHDm9eW7Zs4YorrsBoNJKWlsaqVauavPfPf/5zfvjhhxb+hBdvy5YtKIpCcXHxec+79dZbSU5OJjw8nEGDBvHWW2+1TiGFEEII0eHpAl0AIYQQwSsrK8u7/fbbbzNv3jyOHDniTYuIiCA/Px+ADRs20K9fP5/rY2NjAcjMzOSmm27iwQcf5K233mLjxo386le/Ijk5mQkTJjR479DQUEJDQ/39kfzCbrc3+9xt27YxcOBA5syZQ2JiImvXrmXKlCmYTCZuvvnmFiylEEIIIYQ86RdCCNGEpKQk78tkMqEoik9aRESE99zY2FifY0lJSej1egBWrFhBamoqL774Iunp6Tz66KP87Gc/Y+nSpY3eu273/vnz5zNo0CD++c9/0r17d0wmE3fddRelpaXnzWPNmjX06tWLkJAQJkyYwOnTp33OW758OT179sRgMNC7d2/++c9/+hxXFIXly5czadIkwsPDue+++xg7diwA0dHRKIrCtGnTGizDM888w/PPP8+oUaPo2bMnjz32GDfccAPvvfdeo+UWQgghhPAXCfqFEEK0uO3btzNu3DiftAkTJrB9+/YLyufYsWOsWbOGtWvXsnbtWr744gteeOGFJq+pqKhg4cKF/OMf/2Dr1q0UFxdz1113eY+///77PPbYYzz55JMcOHCABx54gOnTp7N582affObPn89PfvIT9u/fz4IFC3j33XcBOHLkCFlZWSxbtqzZn6OkpISYmJgL+ORCCCGEEBdHuvcLIUQg/e0aKMtp3XtGJMLDX/g921GjRqHR+LYll5WVAZCdnU1iYqLPscTERCwWC5WVlc3uxu9yuVi1ahWRkZEA3HPPPWzcuJGFCxc2eo3dbufVV19lxIgRAKxevZr09HR27drF8OHDWbJkCdOmTePhhx8GYNasWezYsYMlS5Z4n+YDTJ48menTp3v3MzMzAUhISLigCQffeecddu/ezd///vdmXyOEEEIIcbEk6BdCiEAqywHLuUCXwi/efvtt0tPTW/Qe3bt39wb8AMnJyeTm5jZ5jU6nY9iwYd79Pn36YDabOXToEMOHD+fQoUPcf//9PteMHj263pP7oUOHXnL5N2/ezPTp03nttdfqzX8ghBBCiLbL6XJRXl5JeXkFZWWVuFQXvXt1D3SxAAn6hRAisCISz39OG7lnSkoKaWlpDR5LSkoiJ8e3R0NOTg5RUVEXNFlf9RwB1RRFweVyXXhhL0J4ePglXf/FF19wyy23sHTpUqZMmeKnUgkhhBCipblcKpWVVZSVV1BWXklZWQVl5RWUl3n2yyuoqKjyuSYsLESCfiGEELRIN/tglJGRwSeffOKTtn79ejIyMlr83g6Hg6+//prhw4cD7jH4xcXF3l4J6enpbN26lalTp3qv2bp1K3379m0yX4PBAIDT6TxvGbZs2cLNN9/M4sWL6/UqEEIIIUTgqKpKldVWK5Cv9AT37if2ZeUVVJRX4lLVC8q3oqIKp9OFVhv4afQk6BdCCOEXBQUFZGdn+6SZzWZCQkJ48MEHefXVV3nqqaeYMWMGmzZt4p133uHjjz9u8XLp9XpmzpzJK6+8gk6n49FHH2XkyJHeRoDZs2dz5513MnjwYMaNG8dHH33Ee++9x4YNG5rMt1u3biiKwtq1a5k4cSKhoaE+qxlU27x5MzfffDOPPfYYt99+u/dnZDAYZDI/IYQQooW5PN3uy8orKC2toLSsOqAvp7TUve1wnL8BvylhYSFEhIcRER5KeESYdztYSNAvhBDCL+rOzg/w73//m7vuuovU1FQ+/vhjnnjiCZYtW0aXLl14/fXXmTBhQouXKywsjDlz5jB58mTOnj3LVVddxRtvvOE9ftttt7Fs2TKWLFnCY489RmpqKitXrmTMmDFN5tu5c2cWLFjA3LlzmT59OlOmTGHVqlX1zlu9ejUVFRUsWrSIRYsWedOvueYatmzZ4qdPKYQQQnRMdrvDHciXeQJ5z3Z1cF9eXol6gU/pawsxGjyBfKg7mI8II7z2dlhoUDzNb4qiXspPoAOxWCyYTCZKSkqIiooKdHGEEG1MVVUVmZmZpKamEhISEujidBirVq3i8ccfp7i4ONBFuWhN/e5I3eRf8vMUQojg43A4KS0tx1JWTmlpzctSWk5ZWQVVVttF563TaomICPO+IiPCCPc8pa8O7vW6wD4n90fdJE/6hRBCCCGEEEIEhNPlorysAkutYL7U0/W+tLScisqq82fSiJAQAxHhYURGhBMRGebZDiMyMpyI8DBCQgwoiuLHTxOcJOgXQgghhPCzb/ccIsRooFdaN/R6+bolhOi4VFWlqspGiaUMi6WsXnBfXl7BxfQ9VxQI9wTx1V3tIyPDffbl76+b/BSEEEK0W9OmTWPatGmBLoboYKqqbHz73SEcTic7du+nz2Xd6dc3DVNU/YkehRCiPVBVlfKKSkpK3EG9xVLmDfJLLGXY7Y6LyjcsNMQdyEeGE+UJ6Ku3wyPC0GqCeyx9sJCgXwghhBDCj06dzsLhWcrRZrOz78CP7DvwI11TkhnQL40unRM7RHdSIUT74nS5KCurqBfQWyzlWErLcDpdF5yn0WggMiLMHdD7BPfubZ1O2wKfpOORoF8IIYQQwo8u69WNmOgo9h88ytFjp7xfhE+dzuLU6SxMpgj6p6fR+7LuGAz6AJdWCCFquJ/YV1FSUkpRsYXiklJKSkopsZRRWlpxwbPgK4riDuqjIoiKisAUFeET4Bvlb2CraPNB/8KFC/n444/Zs2cPBoOhwRmaT506xUMPPcTmzZuJiIhg6tSpLFq0CF2AZ2IUQgghRPsUFxfN2KuHMXL4QA4fyeT7Q8coK6sAoKSkjK079rDrmwNc1qsb/fumEW2W1QKEEK3H4XC6A3tPUF9U7H4vLim94K74Wq2GqMgIoqLC3UG9N7iPICJSuuAHgzYf9dpsNu644w4yMjJ81l2u5nQ6uemmm0hKSmLbtm1kZWUxZcoU9Ho9f/zjHwNQYiGEEEJ0FKEhRgZf3ofLB1zGyVNZ7P/+R85l5QHutaW/P3iM7w8eo0vnRPr3TaNrSjIajXT9F0JcOlVVqaiooriklOJid0BfXGKhuLiUUk8jZHPp9TrfgN7zxN5kiiA8LFSGLAW5Nh/0L1iwAHCvxdyQdevWcfDgQTZs2EBiYiKDBg3i+eefZ86cOcyfPx+DwdCKpRVCCCFER6TRaEjt3pnU7p0pLCzhwMGj/HD0JA6He+z/mbM5nDmbQ2RkOP3Se5LeOxWjUb6jCCHOT1VVKiqrKCwsobCwhIKiEoqKLBf81F5RIDIyHLMpErMpCrM50rMdSWioUQL7NqzNB/3ns337dgYMGEBiYqI3bcKECTz00EN8//33DB48OIClE0IIIURHExNj4uorhzBi2AAO/3CC7w8exVJaDkBpaTk7du3j62++p1daV/r360VsjCnAJRZCBAubzU5hkYXCIneAX/1eZbU1Ow+DXofZHOUO6KsDe3MkpqgItFqZOK89avdBf3Z2tk/AD3j3s7OzG73OarVitVq9+xaLpWUKKIQQ4rxOnDhBamoq3333HYMGDQp0cYTwC6PRwOUDLmNg/16cOp3NgYM/cvpMDgAOp5NDRzI5dCST5KR4+vdLI7VbJzQyNlaIDsHpclFSUup+cl8ruL+Qbvnep/bmSKJNkZhMkUSbo+SpfQcUlEH/3LlzWbx4cZPnHDp0iD59+rRYGRYtWuQdOiCEEB3V+b4U/O53v2PatGmkpqai0Wg4deoUnTt39h7PysoiJSUFp9NJZmYm3bt3By58gtWUlBSysrKIi4vz22fzN0VReP/997ntttuaPK85E9CKjkVRFLp1TaZb12SKi0s5cPAoR3484e2Wm5WdR1Z2HmZTJCOGDaB7t07yhV2IdqSqykpefhH5BcXuAL+whOISCy5X82bKDwsNISbGREy0iZgYE7ExJszmSPQyabnwCMrfhCeffJJp06Y1eU6PHj2alVdSUhK7du3yScvJyfEea8zTTz/NrFmzvPsWi4WUlJRm3VMIIdqLrKws7/bbb7/NvHnzOHLkiDctIiKC/Px8ADp37sw//vEPnn76ae/x1atX07lzZ06dOuVNu5gJVrVabZN/swPJZrNd0Pww55uAVnRsZnMkV44azPCh/fnhx5McOHiU4pJSAIpLSvl8wzYSE2IZOXwgyUnB2wgmhGiYzWYnL7/I/corJC+/yDu853z0eh0x0VHe4L76PTTE2MKlFm1dUAb98fHxxMfH+yWvjIwMFi5cSG5uLgkJCQCsX7+eqKgo+vbt2+h1RqMRo1H+AwkhOrbagbbJZEJRlHrBd3XQP3XqVFauXOkT9K9cuZKpU6fy/PPPe9MuZoLVut37t2zZwtixY9mwYQNz5szh4MGDDBo0iJUrV9K7d+8GP0t1Hv/+97955ZVX+Pbbb0lLS+Ovf/0r11xzjfe8L774gtmzZ7N3715iYmKYOnUqf/jDH7y9EMaMGUP//v3R6XT861//YsCAAWRmZgLwk5/8BIBu3bpx4sSJBstxvglohQAwGPT075dGv749OXM2h6+/PUhObgEAObkFfLB2M927dmL4sAHERMtyf0IEI4fDSUFhMbl5heTlFZGXX0hRcel5r9MoCiZzJLF1gvvIiDDp5SMuSlAG/Rfi1KlTFBYWcurUKZxOJ3v27AEgLS2NiIgIxo8fT9++fbnnnnv405/+RHZ2Ns899xyPPPKIBPVCCOFHkyZNYsWKFXz11VdceeWVfPXVVxQVFXHLLbf4BP3+nGD12Wef5cUXXyQ+Pp4HH3yQGTNmsHXr1iavmT17Ni+//DJ9+/blpZde4pZbbiEzM5PY2FjOnj3LxIkTmTZtGv/4xz84fPgw9913HyEhIcyfP9+bx+rVq3nooYe894qJiSEhIYGVK1dyww03yERIwm8URSGlSxJdOidy4tQ5du7a733yf+LUOU6ePkfvXqkMHdKPiPDQAJdWiI7L6XJRVFhCbn6RO8jPL6KosASX2nQXfZ1WS1ycmfj4GOJjoz1d86PQamX+DuE/bT7onzdvHqtXr/buV39Z3Lx5M2PGjEGr1bJ27VoeeughMjIyCA8PZ+rUqfz+978PVJGFEMLr3TUbqKioatV7hoWFcPtt4/yer16v5+677+bNN9/kyiuv5M033+Tuu+9Gr9f7nHexE6w2ZOHChd6n9HPnzuWmm26iqqqKkJCQRq959NFHuf322wFYvnw5n332GW+88QZPPfUUf/vb30hJSeHVV19FURT69OnDuXPnmDNnDvPmzfNOotarVy/+9Kc/1cvbbDYH7TAE0bYpikJqt850S0nmyA8n2P3t91RUVKGqcPiHTH48dpKB/S9j0MDestSfEK2grLyCc+fyyM0rJDevkILCYpxOV5PXaDQKsTFm4uOiSYiPIT4+hmhzpEzQKVpcmw/6V61add4ukt26deOTTz5pnQIJIcQFqKiooryiMtDF8JsZM2YwatQo/vjHP/Kf//yH7du343A0f43gCzVw4EDvdnJyMgC5ubl07dq10WsyMjK82zqdjqFDh3Lo0CHAPUlsRkaGT/fJ0aNHU1ZWxpkzZ7z5DhkyxK+fQ4jm0mg0pPfpQVpaV/Yf+JE9ew9jsztwOl18t/cwBw8f54pB6fTv21N6nAjhR2VlFZzLynO/snOxWJoeh68oEG2Ocj/B9wT5sTEm+X8pAqLNB/1CCNGWhYU1/kS6Ld5zwIAB9OnTh1/84hekp6fTv39/77Crahc7wWpDavciqA7UXa6mn7T4Q3h4eIvfQ4im6HU6rhiUTt8+Pfh2zyEOHDyGy+XCarWxfede9n//I8OH9KdXWlcZAyzERagJ8nM5l5V33sn2TFERxMdHEx8XQ0J8DHGxZvR6CbVEcJDfRCGECKCW6GYfaDNmzODhhx9m+fLlDR6/2AlW/WXHjh1cffXVADgcDr755hseffRRANLT03n33XdRVdUbKG3dupXIyEi6dOnSZL56vR6n09myhReijpAQI6NGDqJ/v17s/uYAPx51r5RRVlbBpi92sXf/EUYMG0hKl0QJ/oVoQmlZhTfAzzpPkK/RaEhMiKFTcjxJifEkxEfLsBoR1CToF0II4Vf33Xcfd9xxB2azucHjgZ5g9a9//Su9evUiPT2dpUuXUlRUxIwZMwB4+OGHefnll5k5cyaPPvooR44c4Xe/+x2zZs0675jL7t27s3HjRkaPHo3RaCQ6OrrB8843Aa1o2MKFC/n444/Zs2cPBoOB4uLieuecOnWKhx56iM2bNxMREcHUqVNZtGiRd+WF9iwqMpzrxozg8gG92bl7P6fPuOfHKCgs4ZPPv6RTcjwjhw8kIT4mwCUVIjiUlpZzLjvP22W/tFlBfgKdkuNJTIhFp5Nu+qLtaP+1oBBCiFal0+mIi2t8/fBAT7D6wgsv8MILL7Bnzx7S0tL48MMPveXt3Lkzn3zyCbNnz+byyy8nJiaGe++9l+eee+68+b744ovMmjWL1157jc6dOze6ZN/5JqAVDbPZbNxxxx1kZGTwxhtv1DvudDq56aabSEpKYtu2bWRlZTFlyhT0ej1//OMfA1DiwIiLNXPTDVdx9lwuO3btIy+/CIBzWXm898FGeqZ2YfjQAZhM0sAkOha7w8GZszmcPHmOs80K8mPplBwvQb5oFxRVPc86EgIAi8WCyWSipKSEqChZD1cIcWGqqqrIzMwkNTW1yZnlRcs5ceIEqampfPfddwwaNCjQxWm2pn53OmLdtGrVKh5//PF6T/o//fRTbr75Zs6dO+ddDWLFihXMmTOHvLw8DIbzd71tbz9PVVU5dvwMu77e79NVWaMo9O/Xi+FD+0sgI9q1qiorJ09lkXnyLGfO5OBoZAiWVqshIV6CfBGc/FE3yZN+IYQQQrR527dvZ8CAAT7LQU6YMIGHHnqI77//3tujoiNRFIW0nimkdu/MocPH+fq7g1RVWXGpKvsO/MDps9lcN2YEcbHmQBdVCL8pLS3nxMlzZJ48S1Z2Pg0939Rqaz/JTyAhPkaCfNGuSdAvhBBCiDYvOzvbJ+AHvPvZ2dkNXmO1WrFard59i8XScgUMIK1WQ/9+aVzWqxt79//Ann2HcTpdFBVZeO+DjQwf2p/LB1wmE/2JNklVVQoLS8g8eY4TJ8+SX1Dc4HmhIUa6d+tE9+6d6ZycIEG+6FAk6BdCCNEhdO/evcEnPiJw5s6dy+LFi5s859ChQ/Tp06dF7r9o0SIWLFjQInkHI4NBz7Ah/ejZowsbN++koLAEl8vFjl37OHU6i2uvGU5ERFigiynEeblcKtk5+Zw4eZbMk+caHZ8fFRVOarfOpHbvTEJ8LBqNNGyJjkmCfiGEEEIExJNPPsm0adOaPKdHjx7NyispKYldu3b5pOXk5HiPNeTpp59m1qxZ3n2LxUJKSkqz7teWxUSb+Omt17H7m+/Zs+8I4J7o75331nH16CtI69k1wCUUoj6Hw8mZszmcOHmWE6fOUVVla/C8+LhounfrTGr3TkSbo6QHixBI0C+EEEKIAImPjyc+Pt4veWVkZLBw4UJyc3NJSEgAYP369URFRdG3b98GrzEaja2yTGQw0mq1jBw+kJQuSWz+Yhdl5ZXYbHY2bN7JiVNZXDVqsKw7LgLO5VI5dTqLIz+e4PSZbByO+hPxaRSF5OR4Urt3pnvXTtJbRYgGSNAvhBBCiKB36tQpCgsLOXXqFE6nkz179gCQlpZGREQE48ePp2/fvtxzzz386U9/Ijs7m+eee45HHnmkwwb2zdG5UwJ3/HQ8X277jqPHTgFw9NgpsrPzGHvNcDp3SghwCUVHVF5eyeEfMjl0+Dhl5ZX1jut0Wrp2SaJ7t85065osDVRCnIcE/UIIIYQIevPmzWP16tXe/erZ+Ddv3syYMWPQarWsXbuWhx56iIyMDMLDw5k6dSq///3vA1XkNsNoNDBu7Ai6dU3my63fYrPZKSuv5KNPvmDQwN4MG9IPrVYmPRMtS1VVzp7L5eChY2SePFdvDpaQECPdu3YitXsnOndKlIn4hLgAEvQLIYQQIuitWrWKVatWNXlOt27d+OSTT1qnQO1Qr55dSUqMY/MXuziXlQfAnn1HOH02h+vGDCcm2hTgEor2qLLKypEfTnDw8HEslrJ6x7ulJNM3vScpXZJkIj4hLpIE/UIIIYQQAoDIiDBumXgNe/f/wK6v9+NyqRQUFPPumg2MHDaQ/v3SZGI0cclUVSU7p4CDh45xLPMMLpfL53hYaAh9eqeS3juVyMjwAJVSiPZDgn4hhBBB78SJE6SmpvLdd98xaNCgQBdHiHZNURQGDexNl86JbNyyk6IiC06ni6079nDydBZjrx5GeHhooIsp2iCrzc6PR09y8NAxCoss9Y537pRAv/SedOvWCa1GE4ASCtE+yf8mIYQQjVIUpcnX/PnzOXHiBIqioNVqOXv2rM/1WVlZ6HQ6FEXhxIkT3vRTp05x0003ERYWRkJCArNnz8bhcDRajpSUFLKysujfv39LfdRLpigKa9asafKcEydOcO+995KamkpoaCg9e/bkd7/7HTZbw0tPCRFIcbFmbr91HAP69fKmnTmbwzvvreN45pkAlky0NXn5RWz58mv++f8+4qtt3/kE/EajgcsHXMZdd9zALROvoUdqFwn4hfAzedIvhBCiUVlZWd7tt99+m3nz5nHkyBFvWkREBPn5+QB07tyZf/zjHzz99NPe46tXr6Zz586cOnXKm+Z0OrnppptISkpi27ZtZGVlMWXKFPR6PX/84x8bLIdWq210rfVAs9lsGAzNmzn68OHDuFwu/v73v5OWlsaBAwe47777KC8vZ8mSJS1cUiEunE6nZXTGILqmJLH5v7upqKjCarWxbuN2el/WndEjB2Ew6ANdTBGE7HYHx46f5vvDx8jLK6p3PDEhln7pPemR2kUm5ROihUkzmhBCiEYlJSV5XyaTCUVRfNIiIiK8506dOpWVK1f6XL9y5UqmTp3qk7Zu3ToOHjzIv/71LwYNGsSNN97I888/z1//+tdGn3hX9yaoXqZty5YtKIrCxo0bGTp0KGFhYYwaNcqnQaKxPP73f/+XUaNGERISQv/+/fniiy98zvviiy8YPnw4RqOR5ORk5s6d69MLYcyYMTz66KM8/vjjxMXFMWHCBLp37w7AT37yExRF8e7XdcMNN7By5UrGjx9Pjx49mDRpEr/5zW947733Gi23EMEgpUsSd/50PD26d/amHfnhBP/3/nqyc/IDWDIRbKw2Ozt37+ef/17Lli+/9gn49Xod/dJ7csdPrucnk67lsl7dJOAXohVI0C+EEMIvJk2aRFFREV999RUAX331FUVFRdxyyy0+523fvp0BAwaQmJjoTZswYQIWi4Xvv//+gu757LPP8uKLL/L111+j0+mYMWPGea+ZPXs2Tz75JN999x0ZGRnccsstFBQUAHD27FkmTpzIsGHD2Lt3L8uXL+eNN97gD3/4g08eq1evxmAwsHXrVlasWMHu3bsBdyNHVlaWd785SkpKiImJuYBPLURghIQYuf66DMZePQy93t1Z1FJazgdrt/DjsVPnuVq0d06XiwPfH+Xf73zCd3sPY7PZvcfiYs1cfeUQpky+hatGX0FsrDlwBRWiA2qx7v1WqxWj0dhS2QshRLtQOWsxanH9yYxakmKOIvSlOX7PV6/Xc/fdd/Pmm29y5ZVX8uabb3L33Xej1/t2/c3OzvYJ+AHvfnZ29gXdc+HChVxzzTUAzJ07l5tuuomqqipCQkIavebRRx/l9ttvB2D58uV89tlnvPHGGzz11FP87W9/IyUlhVdffRVFUejTpw/nzp1jzpw5zJs3D41nnGmvXr3405/+VC9vs9l8QcMQjh49yl/+8pc227Vf6vqOR1EUel/WneSkODZu2UVObgGqqrJx807sdgd9+/QIdBFFK1NVlRMnz7Fj1z5Kai25p9Fo6JXWlX7pPYmPi5ZVH4QIIL8F/Z9++in/+7//y5dffsnp06dxuVyEh4czePBgxo8fz/Tp0+nUqZO/bieEEO2CWmxBLSgOdDH8ZsaMGYwaNYo//vGP/Oc//2H79u1NTtB3qQYOHOjdTk5OBiA3N5euXbs2ek1GRoZ3W6fTMXToUA4dOgTAoUOHyMjI8PlyOnr0aMrKyjhz5ow33yFDhlxy2c+ePcsNN9zAHXfcwX333XfJ+bUGqetFtaioCG69eQxfbvuOQ4ePA/Dfr77Bbndw+YDLAlw60VpycgvYvnMv2TkFPulpPVMYPnQAUbLcnhBB4ZKD/vfff585c+ZQWlrKxIkTmTNnDp06dSI0NJTCwkIOHDjAhg0beP7555k2bRrPP/888fHx/ii7EEK0eYo5ql3dc8CAAfTp04df/OIXpKen079/f+84/GpJSUns2rXLJy0nJ8d77ELU7kVQHajXXe+5JYSHX9oX2XPnzjF27FhGjRrF//zP//ipVC1H6nrREI1Gw9Wjr8Cg17F3/w8AbN+5F7vdwZDB6fJktx2zWMrYuXs/x+qs4pCcFEfGiMtJiJchS0IEk0sO+v/0pz+xdOlSbrzxRm+3x9ruvPNOwP1E4y9/+Qv/+te/eOKJJy71tkII0S60RDf7QJsxYwYPP/wwy5cvb/B4RkYGCxcuJDc3l4SEBADWr19PVFQUffv2bfHy7dixg6uvvhoAh8PBN998w6OPPgpAeno67777LqqqegOWrVu3EhkZSZcuXZrMV6/X43Q6z3v/s2fPMnbsWIYMGcLKlSsbrDuDjdT1ojGKojBy+ED0eh1ff3sQgK+//R673c7I4QMl8G9nqqqsfLPnEN8fPIrLpXrTzaZIRg4fSLeuyfJvLkQQuuSgf/v27c06r3PnzrzwwguXejshhBBB7r777uOOO+7AbDY3eHz8+PH07duXe+65hz/96U9kZ2fz3HPP8cgjj7TK+PC//vWv9OrVi/T0dJYuXUpRUZF3AsCHH36Yl19+mZkzZ/Loo49y5MgRfve73zFr1qzzBufdu3dn48aNjB49GqPRSHR0dL1zzp49y5gxY+jWrRtLliwhLy/PeyxYlyQEqetF0xRFYegV/dDr9WzfuReAvft/wG53cNXoKyQIbAccDicHDh7l2z2HfCboCw0xMvSKfvTpk4q2DTRgCtFRtdhEfkIIITomnU5HXFxco8e1Wi1r167loYceIiMjg/DwcKZOncrvf//7VinfCy+8wAsvvMCePXtIS0vjww8/9Ja3c+fOfPLJJ8yePZvLL7+cmJgY7r33Xp577rnz5vviiy8ya9YsXnvtNTp37syJEyfqnbN+/XqOHj3K0aNH6/UcUFW13vlCtCWXD7gMvV7Hf7/6BoCDh49jtzsYe82wNtGjRdSnqipHj59m5+79lJVVeNN1Wi0DB1zGoIG9MRj0TeQghAgGiuqnbxk//vgj+/bt44orriA1NZWPP/6YxYsXU1lZyW233cYzzzzTplt6LRYLJpOJkpISoqJafwyuEKJtq6qqIjMzk9TU1CZnlhct58SJE6SmpvLdd98xaNCgQBen2Zr63WntuknqetEcPx49xaYvdnkbsrp368T1145Eq5X12NuSc1l5bN+5l7z8Ip/03pd1Z9iQfkSEhwWoZEJ0LP6om/zypP/999/nzjvvRKPRoCgK//M//8MDDzzAmDFjiIqKYv78+eh0OubMaX9jV4UQQoiOQOp60Vy90rqi02tZv3EHLpeLEyfP8em6rUwYNwq9XjqZBruiYgs7d+3nxKlzPuldOicycvhA4mLNgSmYEOKi+aWv1cKFC3nqqaeoqqpi+fLlPPjggyxatIhPP/2UtWvX8te//pVVq1b541ZCCCGECACp68WFSO3WmRvHX4lO5366f+ZsDh9/9iXWWuPBRXCprKziv1u/5Z131/kE/LExJm664SpuvvFqCfiFaKP80r0/MjKSPXv20LNnT1wuFwaDgT179tC/f3/A3aWyb9++VFRUnCen4CVd/oQQl0K694uLFSzd+6WuFxcjKzufTz//EpvdAUB8XDQTb7iK0JCWn7RTNN/pM9ls2LwTq9XmTQsLC2H40AFcltYNjabtDtsRoq3zR93klyf95eXlREZGujPUaAgNDSUsrGacT2hoKFar1R+3EkIIIUQASF0vLkZyUhy33DSGEKMBgLz8Ij5cu4XyisoAl0yAe6K+b7476O6F4Qn49Xodw4b05xd33kify7pLwC9EO+CXoF9RFJ+Je+ruCyGEEKJtk7peXKz4uGgm3TyWsDB3T5WiYgsfrN2MpbQ8wCXr2KxWG5+t38rub773pnXv2olf3HEjQwano9fJ/AtCtBd++d+sqiqXXXaZt/IvKytj8ODB3uVZZBkiIYRwk7+H4kIFy++M1PXiUsRER3HbzWP56JMvKC2rwGIp54O1m7nlxmswmyMDXbwOp7CwhM82bMNiKfOmDRvSnysG9ZHGPCHaIb8E/StXrvRHNhfsxIkTPP/882zatIns7Gw6derE3XffzbPPPovBYPCet2/fPh555BF2795NfHw8M2fO5KmnngpImYUQHZNe717HuKKigtDQ0ACXRrQl1WPkq3+HAiVQdb1oP6KiIrj1lrGs/eS/FJeUUl5eyQdrN3OTTBDXqo4eO8WWL7/G4XACYDQaGDd2BCldkgJcMiFES/FL0D916lR/ZHPBDh8+jMvl4u9//ztpaWkcOHCA++67j/LycpYsWQK4Jz4YP34848aNY8WKFezfv58ZM2ZgNpu5//77A1JuIUTHo9VqMZvN5ObmAhAWFiZPU0STVFWloqKC3NxczGZzwNc4D1RdL9qXiPAwbr15DGs//S8FhSVUVln58OMt3HTDVSQmxAa6eO2a0+Vix6597D/wozctLtbM+HGjiIoMD2DJhBAtzS+z9weTP//5zyxfvpzjx48DsHz5cp599lmys7O9T//nzp3LmjVrOHz4cLPz9duMvj+shy+XgT4EdCGgM4I+1PddF+K7rQvxnG8EXSjoq9OrrwupOU+rBwkkhAhKqqqSnZ1NcXFxoIsi2hCz2UxSUlKDjUQy27x/yc+z9VitNj75/EtycgsB0Om03Dj+Sjp3Sghwydqniooq1m/aTlZ2vjetd6/uXDX6Cu+yikKI4OSPuumSn/RHR0c3+2lVYWHhpd7uvEpKSoiJifHub9++nauvvtqnu/+ECRNYvHgxRUVFREdHN5iP1Wr1mYXYYrH4qYBnIfO//smrIYqmpjHB21gQUme7bgNDrXRvo0Lthojqcxo4Vjt/aWwQokmKopCcnExCQgJ2u6xVLc5Pr9cH/Ak/BF9dL9o+o9HAzTdew2frt3L2XC4Oh5NPPv+S8deNolvX5EAXr13Jzilg3cZtVFRUAaDRKIzOGEzfPj2kx5kQHcQlB/0vv/yyd7ugoIA//OEPTJgwgYyMDMAddH/++ef89re/vdRbndfRo0f5y1/+4u3aD5CdnU1qaqrPeYmJid5jjQX9ixYtYsGCBf4vpKPK/3nWprrAXuF+tTZdEw0C3v1a6bX3qxsXqtO9jQ2hDZ+nD3PfRyor0QZptdqgCOSEaK5gqutF+6HX67hx/JWs37idk6ezcDpdfL5+KzdOuIqULomBLl6bp6oq3x86xrYde3C53B17w8NCGT8uQ4ZSCNHB+LV7/+23387YsWN59NFHfdJfffVVNmzYwJo1a5qVz9y5c1m8eHGT5xw6dIg+ffp498+ePcs111zDmDFjeP31173p48ePJzU1lb///e/etIMHD9KvXz8OHjxIenp6g/k39KQ/JSXl0rv8uVzuwN9RCQ4r2Ks8+1Vgt9akO6p8jzmsYG/kmL0551S6GwTaE0WpNRSiulEgrKaRwFArve6+PszToFD7FeZ7zBAmDQxCiKAWiO7o/qrrg5F07w8Mp8vFpi27OHb8NAAhIQZ+9pPxRITLpKcXy+Fw8t+t3/DDjye9aclJ8Vx/7Ujv0olCiLbBH3WTX4P+iIgI9uzZQ1pamk/60aNHGTRoEGVlZY1c6SsvL4+CgoImz+nRo4e3y/65c+cYM2YMI0eOZNWqVd7lgwCmTJmCxWLx+RKyefNmrr32WgoLCxt90l9Xu/gi4HTUaWyobLyhoG6DQWPHGkqve63LEehPfukUxRP8exoBDKG19j0NBN6Gg1DfhgOf9FDf6+teI3MyCCEuQCDqJn/V9cGoXdT1bZTLpfLZ+q2cOp0FQHJSHLdMvMbnO51oHktpOZ9v2EZBQbE3bWD/yxg5fID8PIVog4JiTH9tsbGxfPDBBzz55JM+6R988AGxsc3vRhQfH098fHyzzj179ixjx45lyJAhrFy5st4fs4yMDJ599lnsdrt3uaP169fTu3fvZgf87YZWB9pIMLbyerjVjQ32Wg0I1Q0C1Y0PtY/VTq9ulPCeV1mzb6+odX6da/xNVcFW7n7RdIPUJdFofRsBDHW3azUeGMLrNDDU3Q9r+JjOcP5yCCFEI/xV1wtRm0ajcO01w/i/99dTVl5JVnY+u7/5nhHDBgS6aG3KqdPZbNyyA6vVPW+MTqdlzFXDSOuZEuCSCSECya9B/4IFC/jVr37Fli1bGDFiBAA7d+7ks88+47XXXvPnrQB3wD9mzBi6devGkiVLyMvL8x5LSnKvNTp58mQWLFjAvffey5w5czhw4ADLli1j6dKlfi+PaERrNza4nDU9DqobBHwaDOq8HFVgq6g5z1ZZq0Ghwnffe57nmNPPk7G5nGAtdb9aikbXdMNAY40FPumNnOPtseDXPy1CiCDS2nW96DhCQoxcf10GH3y0GZeq8t3ewyQnxdE1RSb2Ox9VVfl2zyF2f/O9N80UFcGE60cRE20KYMmEEMHA70v27dy5k1deeYVDhw4BkJ6ezq9//WvvFwN/WrVqFdOnT2/wWO2PtW/fPh555BF2795NXFwcM2fOZM6cORd0L+nyJxrkdNRvELB73m3lvvv2yvrn1H631b62wvdYW6M1XEJPhdrDIsLrD4UwhLnnZpCGBSECVje1Zl3fmqSuDw579//A9p17AQgxGvjZT64nIiIswKUKXlarjU1f7OLkqSxvWvdunRh7zXCMBn0ASyaE8IegG9PfnskXAREwqlqrUaFuI0F5/cYE73kV7t4O3saH8loNC3Wut1cG+lNeuOqGBX1o43MknO9Yo/MshNZMECnjH0UQk7rJv+TnGRxUVeXz9ds4ceocAIkJsUy6eQxa+XtcT0FhCZ9v2IbFUjOXxvCh/Rl8eR9Zjk+IdiIoxvSXl5cTHh7eYucL0eEpSk0QSguNl3W5aoZAVDcS1O6pULvBwWe77jkNnF+977T5t8xOm/tVVezffOvyLkFZa3WH2hM66mr1TvAuM9nAq/oafYhvo0Lt4/KFVgQpqetFa1IUhbHXDOM/76+nrKyCnNwCdu0+QMaIgYEuWlDJys7n48/+i8PhBMBoNDBu7AhSuiQFuGRCiGBzyUF/Wloajz32GFOnTiU5ueExV6qqsmHDBl566SWuvvpqnn766Uu9rRDCnzQad/d6QziEx7XMPaondLRVuBsLfOZMqNtjoaHhERUND4+ofY2jyv/ldljdr8pi/+ddl85YqyGgbuNASK3Gg1pLUXobJUIaOc+zrQupf0wr3T5F80hdL1qb0Wjg+msz+GDtJlwulb37j9ApOY5uXTsFumhBoaSkjM/Xb/UG/HGxZsaPG0VUpDS2CSHqu+Tu/UeOHOGZZ57h448/5vLLL2fo0KF06tSJkJAQioqKOHjwINu3b0en0/H000/zwAMPoNVq/VX+ViNd/oRoA7yTODYxf0KD6VU1DQjVjRH2uu+VLdu4EAgabU2DgE+jgLGmAaE6XRcKemOdhoZa51an6Yyea6rTQ+ofk6Up/aa16iap60Wg7DvwI9t27AHAaNTzs9uuJ7KDB7ZVVVbe/3ATJZ4u/Z07JXDj+CvR6dre/zkhxPkF1Zj+U6dO8Z///Icvv/ySkydPUllZSVxcHIMHD2bChAnceOONbfILQDX5IiCE8HK5PEtPVq/20NAKEdUNBnUaC2qn19335lMr3VHpntehPVE0DTcG6Gs1QFSn+TQi1E6vc47+PMe0xlqNDu1nEsjWrpukrhetTVVV1m3YTubJswAkxMdw681j0Wo75nAoh8PJ2k//S3ZOPgDR0VHcdsu1MmGfEO1YUAX97Z18ERBCBISqeoYYVNZpLKisaRSwV9UsU9nQec0537tf1X56MjTG28PBWNMYoA/xTavXyGD0Pb/2q6E0XQjoDJ5Ghkau8cMcDlI3+Zf8PIOT1Wrj/9ZsoLS0HICB/S9j1MjLA1yq1qeqKhs37+To8dMAhIWG8JNJ13b4ng9CtHdBMZGfEEKIFqQonm73IRDaSves29Dg00BQN73Sc27tRgNrrXNrNSTUblRwWH2vrd5XXS3/+VxO95wRtvKWv1djek+Ae/4TuPsL0Ya4x/ePZM1Hm3G5XOw78APJyXGkdusc6KK1ql1fH/AG/DqdlhsnXCkBvxCiWSToF0II4SsQDQ3gbmxwOWo1AtRuRGjg3d7UsUpw2GrSnNZajRCe94bSXI7W+ay6kNa5jxDtREJ8DKNGXM5X278DYPMXu4n9ibnDTFx3+Egm3+097N0fN3Yk8XHRASyREKItkaBfCCFEcFAU9yR/Wj0YIwNTBpezgYYEm7vngsNa05DgrNWgUPflbCDNm+7JLyE9MJ9PiDasX9+enMvO43jmGWw2O+s3bee2m69t9+P7z5zN4b9ffePdHz1yEN27ySoGQojmk6BfCCGEqKbRgiHM/RJCBBVFUbjmqqHkFxRhsZSTl1fEjl37GJ0xKNBFazGFhSWs27ANl2cKrv790hjQv1eASyWEaGvad9OoEEIIIYRoN4wGPddfm4HGMxHm/u9/5HjmmQCXqmWUV1TyyedfYrO7hx1179qJUSMGBbZQQog2ye9P+ouLi9m1axe5ubm4XL4TMk2ZMsXftxNCCCFEK5O6XgRSfFw0o0cO4stt3wKw5cuviYs1ExUVEeCS+Y/d7uCzdVspK68E3J/5urEj0GiUAJdMCNEW+TXo/+ijj/jlL39JWVkZUVFRKErNHyZFUeSLgBBCCNHGSV0vgkHf9B6cy87j2PHTnvH9O7jtlrFotdpAF+2SuVwqGzbvIC+/CICIiDBuHH8ler2MyhVCXBy/du9/8sknmTFjBmVlZRQXF1NUVOR9FRYW+vNWQgghhAgAqetFMFAUhWuuHILJ83Q/L7+IbTv3BrhU/rFt5x5OnsoCwKDXMXHClYSFyYofQoiL59eg/+zZs/z6178mLEwmQBJCCCHaI6nrRbAwGPRcf12Gd/b+7w8e45hnHfu2at+BHznw/VEANIrC+HGjiIk2BbhUQoi2zq9B/4QJE/j666/9maUQQgghgojU9SKYxMWaGZ0x2Lu/5cuvKSkpC2CJLl7mybNs27HHu3/1VUPo0jkxcAUSQrQbfh0cdNNNNzF79mwOHjzIgAED0Ov1PscnTZrkz9sJIYQQopUFoq4/ceIEzz//PJs2bSI7O5tOnTpx99138+yzz2IwGLzn7du3j0ceeYTdu3cTHx/PzJkzeeqpp/xeHhFc0nunci4rj6PHTmG3O1i3aTs/ueVadLq2M74/N6+QjZt3evevGJROn8tSA1giIUR74teg/7777gPg97//fb1jiqLgdDr9eTshhBBCtLJA1PWHDx/G5XLx97//nbS0NA4cOMB9991HeXk5S5YsAcBisTB+/HjGjRvHihUr2L9/PzNmzMBsNnP//ff7vUwieFSP78/PL6K4pJSCgmK27djD1VcOCXTRmqW0tJxP132Fw+H+v5PWM4VhQ/oFuFRCiPbEr0F/3WV7hBBCCNG+BKKuv+GGG7jhhhu8+z169ODIkSMsX77cG/S/9dZb2Gw23nzzTQwGA/369WPPnj289NJLEvR3AHq9juuvy+C9DzbgdLo4ePg4ycnx9OrZNdBFa5LVauOTdV9RWWkFIDkpjrFXD/NZFUMIIS6VX8f0CyGEEEK0hpKSEmJiYrz727dv5+qrr/bp7j9hwgSOHDlCUVFRg3lYrVYsFovPS7RdsTEmrhx1hXf/v199Q3FxaQBL1DSn08W6jdspKnL/3pmiIpgwblS7WHZQCBFc/B70f/HFF9xyyy2kpaWRlpbGpEmT+PLLL/19GyGEEEIESKDr+qNHj/KXv/yFBx54wJuWnZ1NYqLvpGfV+9nZ2Q3ms2jRIkwmk/eVkpLScoUWraLPZd25LK0bAHa7g8/Wb8VqtQW4VPWpqsp/t37D2XO5AISEGJg44SpCQowBLpkQoj3ya/f+f/3rX0yfPp2f/vSn/PrXvwZg69atXHfddaxatYrJkyf783ZCCCGEaGX+rOvnzp3L4sWLmzzn0KFD9OnTx7t/9uxZbrjhBu644w7v/AIX6+mnn2bWrFnefYvFIoF/HS6XC7vTgcPpwOa0Y3dUv9uxOx3YnQ6cLieq6j5fxb2hqr7veNObPk9RFLQaLXqtDp1Wh05ba1ujRafVufc92zqtFq2m5sm4oihcNfoK8vKLKCq2UFxSyrqN25l4w1VoNcHTwfXbPYc58sMJALRaDTdcPxqTKSKwhRJCtFuKWvPX+JKlp6dz//3388QTT/ikv/TSS7z22mscOnTIX7dqdRaLBZPJRElJCVFRUYEujhBCCBGQusmfdX1eXh4FBQVNntOjRw9vl/1z584xZswYRo4cyapVq9DUCuKmTJmCxWJhzZo13rTNmzdz7bXXUlhYSHR09HnL0xbqepfLRYWtkgprlee9knKr+73CVuV9L7dWNHBOzb7NG7Q3HMjbHO59hyv4J2FWFAW9VufTWBBjNHF715sJ1YYAcLLqNEcdxwnRGwk1hNS8G4yENpBWvV19LNRgJDwknIiQMCJDwjHo9OcpVeN+PHqKjVtqZuofd+1I0npIY5MQomH+qJv8+qT/+PHj3HLLLfXSJ02axDPPPOPPWwkhhBAiAPxZ18fHxxMfH9+sc8+ePcvYsWMZMmQIK1eu9An4ATIyMnj22Wex2+3eZQTXr19P7969mxXwtxabw05JRSklFaVYKkspqShz71eWYqkoo7jCgqWyjOLy2sctWKrKqaiqoNJuDfRHCDqqqmJz2AE7lZ60wrJi/p/1faZedgc6jY5uISkcOn2UL3O2+uWeRp2BiNBwIoxhRIaGExESTqSnQSCiunEgNJzIkHDCax1TK+HI3tPefEYMGyABvxCixfk16E9JSWHjxo2kpaX5pG/YsEG6ywkhhBDtQCDq+rNnzzJmzBi6devGkiVLyMvL8x5LSkoCYPLkySxYsIB7772XOXPmcODAAZYtW8bSpUtbpExNOVOYzQsfrKC4ohRLRannvYySylIqbVWtXp7G6DRa9Do9eq0OvU6PwfuuR69zd6PXa/X1jum0Ogw6nfdan+71uGedr558vnoW+pr0uvvVF7o3VJeKw+XA4XTicDmwO504ne53h9NR75jDWbPv8OzbnQ4cLidWh5V1575gYpfrAJjQZQyFVcX8UHL8kn92VocNa6mNgtKGJ4lsSGpkCpPTfoJe626U+iZvH4tffxVTWBTm8CjMYZGYw6Mwed7NYZGYvcfc6dHhUZjCojCFRfj83IUQoil+DfqffPJJfv3rX7Nnzx5GjRoFuMf5rVq1imXLlvnzVkIIIYQIgEDU9evXr+fo0aMcPXqULl26+ByrHqVoMplYt24djzzyCEOGDCEuLo558+YFZLk+m93G/+38zC95KYqCKTTS8zQ5jDBDKGHGEMIMoYQbQwkzhtak1d03uPfD655jCMGoM9TrLdFe7f7mAN98dwhFUbgn/WdkXDUAQ5iOKruVSlsVVTarZ9tKZa20SnvNseohEqVV5ZRVlVNaWU5pVTnlVRVYKsvOOwyiR2RXftHrNvQad8B/pPgYH5/aiEt1UVmSR3ZJXpPXNyQqNAJzeBTR4SZiIkzERkQTG2kmLiLaZz8mwkxshBlTWKQsBShEB+XXMf0A77//Pi+++KJ3TF96ejqzZ8/m1ltv9edtWl1bGOcnhBCiYwlU3SR1fdMKyorp95sbvPuhhhDMYZFEhUW630MjMIVFYgqLJCo00nvMVCu9+hVhDOswwXlLUVWVDZt3cuy4u1t9eHgoP731OsLDQv2Wv9Vho7SynHJrhbdBoKyqnLKqCooLyrCec6Ko7oC7VCnloO0IReUlFFeUUlxuoaTC0uJDN3QaLdG1GgNiI2oaBGIj3Q0FcZExxEVGEx8VQ3RYlPzuCREE/FE3+T3ob68k6BdCCBFspG7yL3/9PF0uF5l5Z7zB/aVM+ib8w+Fw8tEnW8jJLQQgPi6aSTePQa/za6fXek6dzubzDVtxOl0AdO/WieuvzUCrrR9MV9mtlFSUUuRpBCgut3gbBWo3DhRVWCipTvccc6kuv5ddp9ES62kAiI+MIS4qhvjqfU9a9XZMhFmGGwjRQoJuIj8hhBBCiI5Oo9HQM7FroIshatHptEwYN5r3PtxIWVkFeflFbNqyi/HXZbRYl/eTp7L4fMM2XC53QJ7avTPjrh3Z6NKBIXojISYjiaa4C7qPy+WiuKKUgrIiCkqLKSwrpqCsmILSIgrLStzpZZ70Uvexqmb0KnC4nOSU5JNTkn/eczWKhpgIk09jQFxUDIlRcSSYYkiIiiXBFEdCVCzR4VEyzECIVnbJQX9MTAw//PADcXFxREdHN/mfuLCw8FJvJ4QQQohWJnW9aA/CwkKYOP5K3v9oE3a7g8wTZ9n19QFGDBvg93udOHmOdRu34XK5O9T2SO3CdWNHNBrwXwqNxh1wx0SY6JV0/vNVVaXCVkVBaa3GAE8jQX5pEXmWQvJKC8m3FJJnKSK/tPC8cxa4VBf5nusPcazJc/VanacRILbee3UjQXyUO82oN1zIj0II0YhLDvqXLl1KZGSkd1ta7oQQQoj2Rep60V7ExJi4/toMPl33JaoK3+09jMkUSZ/LuvvtHpknz7J+43ZvwN+zRwrXjRkeNOPjFUUh3DPBY9e4Tuc9v7ongW9jgHu79nu+Z9u9fGLj7E4HZ4tyOFuUc957m8OiahoFomJIMMWRaIol0RTnsx0ZEi5/l4RogozpbyYZNymEECLYSN3kX/Lz7DgOfH+Ur7Z/B4BGo3DzjdfQKTn+kvM9nnmGDZt24PJ8vU7r2ZVrrxkWNAF/S1NVFUtlGXmWQnItBe73kgJyLQXklOR703NK8ikoK8ZfYUio3uhtBPBpGIiqbiBwNxzERpg7zL+FaD+Cbkz/t99+i16vZ8AAdzepDz74gJUrV9K3b1/mz5+PweD/LjqTJk1iz5495ObmEh0dzbhx41i8eDGdOtW0XO7bt49HHnmE3bt3Ex8fz8yZM3nqqaf8XhYhhBCivQtEXS+Ev/Xvl0ZxSSkHDh7F5VL5fMM2fjLpWsymyIvO85gn4K8OZC9L68aYq4eh0XScJ9CKonhXnkhL6tbkuQ6ng/zSIk8jQAF5lgJyS9zbuZYCckvyybUUkluSf96VDSrtVk7mn+Vk/tkmz9NptMR7egwkRMWQaIojPirW3VjgaSBwH48lRG+84M8vRLDya9D/wAMPMHfuXAYMGMDx48f5+c9/zk9/+lP+85//UFFRwcsvv+zP2wEwduxYnnnmGZKTkzl79iy/+c1v+NnPfsa2bdsAd8vI+PHjGTduHCtWrGD//v3MmDEDs9kckLV7hRBCiLYsEHW9EC1h1MjLKbGUcfpMNlarjU/XfcVPbrmOkJALb7g6euw0G7fs9Ab8vXt155qrhnaogP9C6bQ6kszxJJmb7mGhqiplVRXeSQWrewrklLgbBnJKanoRFFdYmszL4XKSVZxHVnHeectnCov0DimoPbwgwdNIEO9pJJCJCUVb4Nfu/SaTiW+//ZaePXuyePFiNm3axOeff87WrVu56667OH36tL9u1agPP/yQ2267DavVil6vZ/ny5Tz77LNkZ2d7nz7MnTuXNWvWcPjw4WbnK13+hBBCBJtA1E3BUNe3FKnrOx6rzc6ajzZRVOQOFjslx3PTDVc3uKReY348dopNW3ZS/Y26z2XugF8CwdZXZbf6DCeo2zBQnZ5fWuS3oQXVvQfio2KIq7WMYUJUrM9yhwlRsZjDImV4gbhgQde9X1VV77IkGzZs4OabbwYgJSWF/PzzL/dxqQoLC3nrrbcYNWoUer17Tdzt27dz9dVX+3Q3nDBhAosXL6aoqIjo6OgG87JarVitNV2JLJamWw6FEEKIjiDQdb0Q/mQ06Llx/JW8/8FGKqusnMvK48ut33LNVUOaFbT/8ONJNv93lzfgT++dytVXNu9a4X8heiNd4zqdd4JCh9NBYVlJrYYAz/CCWtvVDQWVtqqm87qA3gM6jdbTMBDtbQiovcyhu+EgmrjIaKLDTei0srq68A+//iYNHTqUP/zhD4wbN44vvviC5cuXA5CZmUliYqI/b+Vjzpw5vPrqq1RUVDBy5EjWrl3rPZadnU1qaqrP+dVlyc7ObjToX7RoEQsWLGixMgshhBBtUaDqeiFaSlRkOBOuH81Hn2zB6XRx+IdMzOZIBg3s3eR1R348weYvdnv3+/bpwVWjr5CAvw3QaXXuyf1Msec9t9wztCDXUugZRlDg3c4tKahZvaC0COd5ljZ0uJxkl+SRXXL+BgJFUYgOiyI2MppYT0NAbITZ2yhQnVa9HR0WJb0IRKP8GvS//PLL/PKXv2TNmjU8++yzpKWlAfB///d/jBo1qtn5zJ07l8WLFzd5zqFDh+jTpw8As2fP5t577+XkyZMsWLCAKVOmsHbt2kv6o/v0008za9Ys777FYiElJeWi8xNCCCHaA3/V9UIEk6TEWMZePYwNm3cCsGPXPkxREaR279zg+Yd/yGTLf7/27vdL78mVowZLwN8OhYeE0SOkKz0SuzZ5nsvlorC8xNMAUOjTIJBXZ5nD5jQQqKpKYXkJheUl/Jh94rzl1CgaYiJMxEXGeBoCzMSEm4mJMBETYSY2omY7xrMtkxV2HK2yZF9VVRVardbb5f588vLyKCgoaPKcHj16NDhD8JkzZ0hJSWHbtm1kZGQwZcoULBYLa9as8Z6zefNmrr32WgoLCxt90l+XjPMTQggRbIKpbrrQuj4YBdPPUwTG198e5OtvvwdAp9Ny681jiY/z/a546EgmX3xZE/D375vG6IxBEvCLZqvbQFB7icOCsmIKSovILy2ioKyY/NIiKqyVLVKOMGMoMeENNQq436O9x0xEh7tfRr2s0NLagm5M/+nTp1EUhS5dugCwa9cu/t//+3/07dv3gmbKj4+PJz7+4tZKrR5nWD0ePyMjg2effRa73e79IrJ+/Xp69+7d7IBfCCGEEG7+quuFCEZDBqdTUlLKj8dO4XA4+XTdV/z01nFEhIcCcPDwcf771Tfe8wf078WoEZdLwC8uiEaj8XbNh57nPb/CVlXTEFBaTH5pobdBoG4DQUFpEVXnWeLQm6+1kgprJWcKs5td9jBjqKcBIMrzMnkaB2oaBszhUcRUnxNhwhQqExgGml+f9F911VXcf//93HPPPWRnZ9O7d2/69evHjz/+yMyZM5k3b56/bgXAzp072b17N1deeSXR0dEcO3aM3/72t+Tk5PD9999jNBopKSmhd+/ejB8/njlz5nDgwAFmzJjB0qVLL+jLibT+CyGECDaBqJtau65vTVLXCwCHw8lHn3xBTq6712lcrJlbbx7LDz+e5Mtt33rPu3zAZYwcPlACfhFUVFWlwlpJfmmRe3hAWTGFZdXvxd60glppReWW8w43uBQaRYM5LBJzdSNBdQNBWJQnLcrbkGD2NiiYiAgJk/9f+Kdu8mvQHx0dzY4dO+jduzevvPIKb7/9Nlu3bmXdunU8+OCDHD9+3F+3AmD//v089thj7N27l/LycpKTk7nhhht47rnn6Ny5ZgzWvn37eOSRR9i9ezdxcXHMnDmTOXPmXNC95IuAEEKIYBOIuqm16/rWJHW9qFZZaeW9DzdSWloOQEx0FIVFNSs5DRrYmxHDBkhAItoFl8uFpbKs0UaBwrISisrdr8KyEoorLBSVleBowYYCcK92ULtRwFz7Pczdi8AcFuXTyyA6wkSYIaRFy9Xagq57v91ux2h0TwixYcMGJk2aBECfPn3Iysry560AGDBgAJs2bTrveQMHDuTLL7/0+/2FEEKIjqa163ohAiE01MiN469kzYcbsdkdPgH/4Mv7MHxofwn4Rbuh0Wgwh7ufuvdo5jWqqlJWVUGhpzGgyNMYULuBoKishKJyi+flTrNUljW7XA6Xk3zP8IULEaI31vQe8DQIxHgbDaLqDUdwNx5EotVoL+g+bYlfg/5+/fqxYsUKbrrpJtavX8/zzz8PwLlz54iNPf+SGEIIIYQIblLXi44iJjqK66/L4JPPv6K6Y+wVg9IZNqSfBPyiw1MUhcjQcCJDw+kW16nZ19mdDkoqSikqL6HY2xhg8dmud6zCQllVRbPvUWW3klWcR1bx+ZdGrP15TKGRnlUPfFc5qN6OrTPhYVRoRJv5W+DXoH/x4sX85Cc/4c9//jNTp07l8ssvB+DDDz9k+PDh/ryVEEIIIQJA6nrRkaR0SeK6McP5bu9hLuvVnYH9e7WZL/lCBCO9VldrEsPmsznslFRYKCyzUFxRQlGZhSLPMANvzwLP8IPaPQtsDnuz8ldVleIKdwPDsWaWSafREu2zJKK7QSA2oma5xFuHjkOn9WvIfVH8vmSf0+nEYrH4zIx/4sQJwsLCSEhI8OetWpWM8xNCCBFsAlU3SV0vhBAi2KmqSoWtqtZQgxL3cIQ6DQPVQxKq3y9kCEJTNIqGM3/96pJXLgi6Mf0AWq223lJ43bt39/dthBBCCBEgUtcLIYQIdoqiEG4MJdwYSpeYpGZfZ3PYKS63UFBWTIF3MsP6qyAUlBZRWFZCQVlxg8skRkeYgmapwksO+q+44go2btxIdHQ0gwcPbrLL07ffftvoMSGEEEIEJ6nrhRBCdBQGnZ4EUywJpubPU1Nhq/JpHCgoLWrx1Q0uxCUH/bfeeqt3Ft/bbrvtUrMTQgghRJCRul4IIYRoXJghhLCYpAvqUdCa/D6mv72ScX5CCCGCjdRN/iU/TyGEEMEmKMf0VysrK8PlcvmkSQUqhBBCtB9S1wshhBDBz68zC2RmZnLTTTcRHh6OyWQiOjqa6OhozGZzvQl/hBBCCNH2SF0vhBBCtC1+fdJ/9913o6oqb775JomJibKOqRBCCNHOSF0vhBBCtC1+Dfr37t3LN998Q+/evf2ZrRBCCCGChNT1QgghRNvi1+79w4YN4/Tp0/7MUgghhBBBROp6IYQQom3x65P+119/nQcffJCzZ8/Sv39/9Hq9z/GBAwf683ZCCCGEaGVS1wshhBBti1+D/ry8PI4dO8b06dO9aYqioKoqiqLgdDr9eTshhBBCtDKp64UQQoi2xa9B/4wZMxg8eDD//ve/ZXIfIYQQoh2Sul4IIYRoW/wa9J88eZIPP/yQtLQ0f2YrhBBCiCAhdb0QQgjRtvh1Ir9rr72WvXv3+jNLIYQQQgQRqeuFEEKItsWvT/pvueUWnnjiCfbv38+AAQPqTe4zadIkf95OCCGEEK1M6nohhBCibVFUVVX9lZlG03jHgbY+uY/FYsFkMlFSUkJUVFSgiyOEEEIEpG6Sul4IIYRoPf6om/z6pN/lcvkzOyGEEEIEGanrhRBCiLbFr2P6G1JcXNzStxBCCCFEAEldL4QQQgQvvwb9ixcv5u233/bu33HHHcTExNC5c2eZ9EcIIYRoB6SuF0IIIdoWvwb9K1asICUlBYD169ezYcMGPvvsM2688UZmz57tz1sJIYQQIgCkrhdCCCHaFr+O6c/OzvZ+EVi7di133nkn48ePp3v37owYMcKftxJCCCFEAEhdL4QQQrQtfn3SHx0dzenTpwH47LPPGDduHACqqrbp2XyFEEII4SZ1vRBCCNG2+PVJ/09/+lMmT55Mr169KCgo4MYbbwTgu+++Iy0tzZ+3EkIIIUQASF0vhBBCtC1+DfqXLl1K9+7dOX36NH/605+IiIgAICsri4cfftiftxJCCCFEAEhdL4QQQrQtiqqqaqAL0RZYLBZMJhMlJSVERUUFujhCCCGE1E1+Jj9PIYQQwcYfdZNfn/QD/Pjjj2zevJnc3FxcLpfPsXnz5vn7dkIIIYRoZYGo6ydNmsSePXvIzc0lOjqacePGsXjxYjp16uQ9Z9++fTzyyCPs3r2b+Ph4Zs6cyVNPPdUi5RFCCCHaCr8G/a+99hoPPfQQcXFxJCUloSiK95iiKBL0CyGEEG1coOr6sWPH8swzz5CcnMzZs2f5zW9+w89+9jO2bdsGuJ+EjB8/nnHjxrFixQr279/PjBkzMJvN3H///S1SJiGEEKIt8Gv3/m7duvHwww8zZ84cf2UZNKTLnxBCiGATiLopWOr6Dz/8kNtuuw2r1Yper2f58uU8++yzZGdnYzAYAJg7dy5r1qzh8OHDzcpT6nohhBDBxh91k1+X7CsqKuKOO+7wZ5bNZrVaGTRoEIqisGfPHp9j+/bt46qrriIkJISUlBT+9Kc/BaSMQgghRFsXyLq+WmFhIW+99RajRo1Cr9cDsH37dq6++mpvwA8wYcIEjhw5QlFRUaCKKoQQQgScX4P+O+64g3Xr1vkzy2Z76qmnfMb1Vavu7tetWze++eYb/vznPzN//nz+53/+JwClFEIIIdq2QNb1c+bMITw8nNjYWE6dOsUHH3zgPZadnU1iYqLP+dX72dnZDeZntVqxWCw+LyGEEKK98euY/rS0NH7729+yY8cOBgwY4G19r/brX//an7fz+vTTT1m3bh3vvvsun376qc+xt956C5vNxptvvonBYKBfv37s2bOHl156Scb4CSGEEBfIn3X93LlzWbx4cZPnHDp0iD59+gAwe/Zs7r33Xk6ePMmCBQuYMmUKa9eu9ZlX4EIsWrSIBQsWXNS1QgghRFvh1zH9qampjd9IUTh+/Li/buWVk5PDkCFDWLNmDXFxcaSmpvLdd98xaNAgAKZMmYLFYmHNmjXeazZv3sy1115LYWEh0dHRDeZrtVqxWq3efYvFQkpKiozzE0IIETQCMQbdn3V9Xl4eBQUFTZ7To0cPny771c6cOUNKSgrbtm0jIyPjoup7qeuFEEIEu6Bbsi8zM9Of2Z2XqqpMmzaNBx98kKFDh3LixIl652RnZ9f7glK7u19jQb+0/gshhBD1+bOuj4+PJz4+/qKurV4qsDpoz8jI4Nlnn8Vut3t7H6xfv57evXs3WtcbjUaMRuNF3V8IIYRoK/w6pt9f5s6di6IoTb4OHz7MX/7yF0pLS3n66af9Xoann36akpIS7+v06dN+v4cQQgghzm/nzp28+uqr7Nmzh5MnT7Jp0yZ+8Ytf0LNnTzIyMgCYPHkyBoOBe++9l++//563336bZcuWMWvWrACXXgghhAgsvz7pB3d3uw8//JBTp05hs9l8jr300kvNyuPJJ59k2rRpTZ7To0cPNm3axPbt2+u10g8dOpRf/vKXrF69mqSkJHJycnyOV+8nJSU1mr+0/gshhBAN80ddfyHCwsJ47733+N3vfkd5eTnJycnccMMNPPfcc9662mQysW7dOh555BGGDBlCXFwc8+bNk/l7hBBCdHh+Dfo3btzIpEmT6NGjB4cPH6Z///6cOHECVVW54oormp1Pc7v7vfLKK/zhD3/w7p87d44JEybw9ttvM2LECODiuvsJIYQQomH+qusvxIABA9i0adN5zxs4cCBffvlli5ShNqfTid1ub/H7iLbPYDCg0QRlx1ohRAfi16D/6aef5je/+Q0LFiwgMjKSd999l4SEBH75y19yww03+PNWAHTt2tVnPyIiAoCePXvSpUsXwN3db8GCBdx7773MmTOHAwcOsGzZMpYuXer38gghhBDtXWvX9cFEVVWys7MpLi4OdFFEG6HRaEhNTW1wMkohhGgtfg36Dx06xL///W93xjodlZWVRERE8Pvf/55bb72Vhx56yJ+3axbp7ieEEEL4TzDW9a2lOuBPSEggLCysyaUCXWXl4FJRIsMveklB0ba5XC7OnTtHVlYWXbt2ld8DIUTA+DXoDw8P947tS05O5tixY/Tr1w+A/Px8f96qQd27d6ehFQhbq7ufEEII0d4Fuq4PFKfT6Q34Y2NjmzxXdThxWSrA5UKpsqMkxKCEyDxBHVF8fDznzp3D4XB4h5kKIURr82vQP3LkSL766ivS09OZOHEiTz75JPv37+e9995j5MiR/ryVEEIIIQKgo9b11WP4w8LCznuuWlEJniUFVZsN9Uw2SmQESpwZRatt0XKK4FLdrd/pdErQL4QIGL8G/S+99BJlZWUALFiwgLKyMt5++2169erVIrP5CiGEEKJ1dfS6vjldtDVREah6PWpeIaqnV4RaWoZaXokSa0aJki7/HYX8OwshgoHfgn6n08mZM2cYOHAg4O7+t2LFCn9lL4QQQogAk7q++ZRQI6QkQUkpakEJqC5wOVHzCqC0DOJjUIwyuZsQQoiW57c1RLRaLePHj6eoqMhfWQohhBAiiEhdf2EURUFjjkLTLRklItybrlZZcZ3OxpVXiOp0BbCELWfVqlWYzeZAF0MIIQR+DPoB+vfvz/Hjx/2ZpRBCCCGCiNT1F07R6dAkxaHplAjecd0qakkprlPnUEvLG5yI+JLvqyhNvubPn8+JEycaPb5jxw5vXlu2bOGKK67AaDSSlpbGqlWrmrz3z3/+c3744Qe/fyZ/2bJlC4qinHf5xaqqKqZNm8aAAQPQ6XTcdtttrVI+IYTwJ7+O6f/DH/7Ab37zG55//nmGDBlCeHi4z/GoqCh/3k4IIYQQrUzq+ounhIWgSUlGLbagFpWAqoLTiSsnH8USAnHRfu3yn5WV5d1+++23mTdvHkeOHPGmRUREeFdc2LBhg3cVhmrVqxRkZmZy00038eCDD/LWW2+xceNGfvWrX7e3eUoAAF8WSURBVJGcnMyECRMavHdoaCihoaF++yz+VD0pY3M4nU5CQ0P59a9/zbvvvtuCpRJCiJbjlyf9v//97ykvL2fixIns3buXSZMm0aVLF6Kjo4mOjsZsNhMdHe2PWwkhhBAiAKSu9w9Fo6CJMaHp2gklvGYlALWyyt3lP78I1eWfLv9JSUnel8lkQlEUn7SIiAjvubGxsT7HkpKSvLPNr1ixgtTUVF588UXS09N59NFH+dnPfsbSpUsbvXfd7v3z589n0KBB/POf/6R79+6YTCbuuusuSktLz5vHmjVr6NWrFyEhIUyYMIHTp0/7nLd8+XJ69uyJwWCgd+/e/POf//Q5rigKy5cvZ9KkSYSHh3PfffcxduxYAKKjo1EUhWnTpjVYhvDwcJYvX859991HUlJSo2UVQohg5pcn/QsWLODBBx9k8+bN/shOCCGEEEFG6nr/UvQ6lOR41PJKXHmF4HAAqrsXQFkFmrhoCA8Nitnft2/fzrhx43zSJkyYwOOPP35B+Rw7dow1a9awdu1aioqKuPPOO3nhhRdYuHBho9dUVFSwcOFC/vGPf2AwGHj44Ye566672Lp1KwDvv/8+jz32GC+//DLjxo1j7dq1TJ8+nS5dungDe3A3Orzwwgu8/PLLaLVaJk2axO23386RI0eIiooK2l4JQgjhD34J+qvHoV1zzTX+yE4IIYQQQUbq+oaN/+M08iwFl56RS633hF9RFNBooE7cHx8Vy7pnVl36PWsZNWoUGo1vB9DqpRmzs7NJTEz0OZaYmIjFYqGysrLZAbPL5WLVqlVERkYCcM8997Bx48Ymg3673c6rr77KiBEjAFi9ejXp6ens2rWL4cOHs2TJEqZNm8bDDz8MwKxZs9ixYwdLlizxCfonT57M9OnTvfuZmZkAJCQkyISDQoh2z29j+oOhJVoIIYQQLUfq+vryLAVkFecFuhiX7O233yY9Pb1F79G9e3dvwA+QnJxMbm5uk9fodDqGDRvm3e/Tpw9ms5lDhw4xfPhwDh06xP333+9zzejRo1m2bJlP2tChQ/3wCYQQom3yW9B/2WWXnffLQGFhob9uJ4QQoo1SVRXKK1GLS1FLar0s5WCzg8uF6nSCwwkuFzhd4HS605w1+02lq04nitGAYopAiYxAMUVAVARKlHtbqbVNaIgEs80kdX198VGx/s9U9Tz1rz2hvwKKRgOK0iL3TElJIS0trcFjSUlJ5OTk+KTl5ORccLd4vXflAjdFUXD5af6C86k74aQQQnQkfgv6FyxYgMlk8ld2Qggh2hDVavME72Xu9zoBPcVlvvutsDZ5sxdA0+lQosI9DQOR7u2oCBRTre1YM5quyShhHXvcr9T19fm7m3011elCLSpBLS6l5rdZQYmOQolp3X+DjIwMPvnkE5+09evXk5GR0eL3djgcfP311wwfPhyAI0eOUFxc7O2VkJ6eztatW5k6dar3mq1bt9K3b98m8zUY3KskOJ3OFiq5EEIED78F/XfddRcJCQn+yk4IIUQQUa021Ox8XFl5qFl5uLJy3du5he6gpMoa6CLW0Gnd46BtzVyWy+FALSxBLSw576lKQiyabp3QdO/sfu/WCaVzAopWe4mFbhukrm89ilaDEheNGhmOmleIWmUFVPdSf5VVkBiHovffyssFBQVkZ2f7pJnNZkJCQnjwwQd59dVXeeqpp5gxYwabNm3inXfe4eOPP/bb/Ruj1+uZOXMmr7zyCjqdjkcffZSRI0d6GwFmz57NnXfeyeDBgxk3bhwfffQR7733Hhs2bGgy327duqEoCmvXrmXixImEhob6rGZQ28GDB7HZbBQWFlJaWsqePXsAGDRokD8/qhBCtBi/1BbSLVIIIdo+taIKV3Z1UJ/vfvfsqwXFl34Dndb99NwU6e5ib4oEc639qAj3GuVaDWi07ned1t2lWevZ975r3IF2rX00WhRtzURkqsOJWloGlnJ3D4TSMve7xT2UwL1dBhb3u2opcw8paOpnlFuAM7cA5+79NYl6HZouSe4GgFoNAkqMqV3Vj+3ps7QlitEAnROhyOJpmFJRq6yop7PRJMSgRISdN4/mqDs7P8C///1v7rrrLlJTU/n444954oknWLZsGV26dOH1119nwoQJfrl3U8LCwpgzZw6TJ0/m7NmzXHXVVbzxxhve47fddhvLli1jyZIlPPbYY6SmprJy5UrGjBnTZL6dO3dmwYIFzJ07l+nTpzNlyhRWrVrV4LkTJ07k5MmT3v3BgwcDNZNbCiFEsFNUP/zF0mg0ZGdnt+vWf4vFgslkoqSkhKioqEAXRwghLopqteE6ne0J7PN8A/siy4VnGB6KEm1CMdcE8t5XnbRgWX6sMaqqQkWVtwFAtZR5GwbU7DxcJ87hOnmu+b0aIsO9vQG8PQO6JqOEhvitzK1ZN3X0ur6qqorMzExSU1MJCfHfv+GFUCutuHLyPcv7uSmmSJRYs7txrJ1ZtWoVjz/+OMXFxYEuykULht8bIUTb5o+63i9P+ltrEhYhhBAXxpVfhOvwcZyHM3EdPo7r+OkLH09vikCTFI8mOR4luc57ZPuZHEtRFHfDRHgoJMc3eI7qcqHmFXoaAM56GwLUc7nuSQdrKy3HdeBHXAd+9L1PUhzaQX0wPvSLlvooLULq+sBTQo1oUpLcw2rKKwDcc2RUWd3d/Q368+QghBCiI/LfYDAhhBABpTqduE6cxXXoOM7Dx3EdPo6aV9Ssa5XoKHcgn1QroO/k2Q/v2JPX1aZoNCiJcWgS42DEQG+6arPjOpON68Q51NqNAUX15wlQs/P9M1xCdEiKVgtJce5hKflF7pn+rbaa7v7tqCFOCCGEf0jQL4QQbZRaVoHzSGZNkP/DCbDamrxGSUlC26s7SpfEmif2SXF+7XLeESkGPdoeKWh7pPikq5YyXCfPuRtjTp7zvjTdOgWopKI9UBQFxRSJGmLElZ0PdjuoLlw5+SgVVSjx0e2iu/+0adOYNm1aoIshhBAXRHW6oLwCtbLK/ZAgCEjQL4QQbYCqqqjnct3BvSfIV09nN32R0YDmsu5o+/RA0ycVbZ8efpv0SzSPEhWBdsBlaAdc5k1TXS6wO5q4SojmUYwGd3f/vELU0nIA94SVVVZ3Y57REOASCiFE26Y6XVBWjlpa7pnvp2YbS5ln23Os1H2MsgpQVZRYM2FvLgz0RwAk6BdCiKClOl049xzCsXE7zn0/gOdLfWOUuGg0fXqgTe+Bpk8PNN07o+g6xlJybYmi0YAEY8JPqoecuEJD3MN5VBfY3cNNlLho96oYQTyBphBCBIpaVoHrXC7quVz3pMa5hY0G8BeVv6UMVVWD4m+wBP1CCBFkXHlFODZuw7Fhe+Nj8jUaND261AT5vXugiY9u3YIKIYKGJioCNcTonjPCZnOP9c8rhMoqiI9xzwUghBAdjDewz8rDlZXrXrnonHsFo/M9TLlg4aEokeHuV1SEe44VpwuC4AGMBP1CCBEEVIcT59cHcKzbivO7g+Cq06ocEebTTV/TqxtKiDEwhRVCBCXFoIcuiVBQ7J7VH/cXXrXK5u7uL38zhBDtkFpW4Qnm3UG9Kyv30gP76gDeE7wrUREQVSug96Z7jkWEB3XvSgn6hWgnVFXF6XRht9ux2R3Y7Q6cTpd7LHitl8uloqrudJeqorrqHlNrjtVK12gUtBoNWq0WrU6LTqtBo9Gi03nStFq0Wve2Tqvx7ms0mqDo1hSsXNn5ONZvxbFxB2qRxfegRkE7uC+68aPRDu0f1JWJECI4KBoNSnwMamgIrtwC91KSDgeuMzkosWYUc6T8TRZCtFlqcal7EuMfMt3LEZ/KAkvZBeejxEW7VylKjkeTnODeToxDMUcGfQB/MSToFyLAVFXF4XBSWWWlqsqK3e7AZrO73+12n333qzqorwnuq4+rFznmqKVpazUCaLVa9DotRqPB5xViNGA01E7Te9MNBgMaTfv5kqra7Th37MO+biuufUfqHVfiotFdn4HuulHSZV8IcVGUiDA0RgNqTj5qlRVQUQuK3N39E2Lb3RdaIUT7ozqcuE6exXU40x3oH8lEzc5v9vVKrBmlUwKaTvEoSfFoqrcTO95EpxL0C+Fnqqpis9m9QXxllZWqSqvvfpWVysqafafTFehityin03XJn9FgcDcCGA01jQHVDQThYaGEhYUQHh5KWGgIYWGhaLXBt1yV60w29nVbcWzeVb9VWqNBO3yA+6n+oHSUICy/EKJtUfQ66JwIhSWenkQqakUl6uks9xOtsJZbqnPVqlU8/vjjFBcXt9g9hBDti1pscT+9P+IJ8n88CTZ7k9coMSZPYJ/gXoY42R3cK7KCiQ8J+oVoBlVVsdkdVJRXUlFZRXn1e0UllRVV9QJ6V93x2C1Ep9Ni0OvR63Welx6DXofe4E7TajQoGgWNorjXdfa8NJo6+4riOc/dFV/R1D+mulScTqcngHfi8LzXTnM6XTgaSHOfX7NttztwOJwX9FltNjs2m53SZp4fElLdGBBKeJi7ISAsLKQmLTyE0NAQtC28lrVqteHY+h2O9VtxHTxW77iSFIfu+tHorhuBJtrUomURQnQs5+vGP2/u00x/4H5SU1MbPL59+3ZGjhwJwJYtW5g1axbff/89KSkpPPfcc0ybNq3RvH/+858zceLEiy57S9uyZQtjx46lqKgIs9nc5HlLly5l165dWCwWevXqxezZs/nlL3/ZeoUVoh1SHU5cmWdqAvzDmai5BU1fpNehSeuKtneqe5Wiy7qjiTW3SnnbOgn6RYfWUDBfXuHerqgV2FdUVF1wkHo+iqIQEmIgNMRISIjR+27wBOze91pBvUGvR29wB/d6na5Nd3l3Op1YrXasVhtVVhs2m/vdarX5pFtrv2zu9+aMYqiqslFVZaOgsKTJ80JDjISF1zQMREaEERkRTmSk+xUeFnJR419dJ85i//wrHF/shvJK34M6HdqMy9GPH42mfy/3Em5CCOFnWVlZ3u23336befPmcWjLV6hVVQBEhIVTUOyeS2TDhg3069fP5/rY2FgAMjMzuemmm3jwwQd566232LhxI7/61a9ITk5mwoQJDd47NDSU0NDQlvhYl8xub/rJYW3btm1j4MCBzJkzh8TERNauXcuUKVMwmUzcfPPNLVhKIdoX1eXC9cMJnLv24zx0DNfRU+d/ip8Qi6Z395ogv3tndw8mccHkpybaLYfD6QnYK93BvCegL6+ocqdVVFJRXoXD6Z9gXqPRuAP3UGO9QD60gTSjUd+hJ1PSarWEhWkJu8DupaqqYrc7vA0CVVVWKipqGmd83yvP2+ui0tNDo6CRxmWNRuNuCPA0AkRGhBHl3Q4nNNTo8++oVlZhe/M9HOu21stL6ZKEfvxodGOHu2d6FUKIFpSUlOTdNplMKIpC8qD+qPlF3tn988+cBiAmJsbn/NpWrFhBamoqL774IgDp6el89dVXLF26tNGgv273/vnz57NmzRqefPJJfvvb31JUVMSNN97Ia6+9RmRkZJN5rFq1itmzZ3P69GmuueYaXn/9dVJSUrznLV++nCVLlnD69GlSU1N57rnnuOeee7zHFUXhb3/7G59++ikbN27kjjvuYPXq1QBER7vnTZk6dSqrVq2qV4ZnnnnGZ/+xxx5j3bp1vPfeexL0C3Eeqt2Oc98POHfuxblrf/0Ji2sz6N1P8auf4PdORRMjPSD9RYJ+0eaoqkplpdUdwJdXegN4b1Bf7g74rFabX+5nMOhrxox73n26jIeGEBpqRK/XdeggvrUoioLBoMdg0ENkeJPnqqpKldVGRbmnsafS/fvhbfTxNABVVFThaqT7gMvlosRSRkkjM8PqtFoiPI0CSSWl9Pj0Swy1KzWDHt2VV6AbPxpNnx7yOyJEe/O3a6Asp3XvGZEID39xUZcqigJx0aDXoeYXedPVvCJUp6vB+US2b9/OuHHjfNImTJjA448/fkH3PnbsGGvWrGHt2rUUFRVx55138sILL7Bw4cJGr6moqGDhwoX84x//wGAw8PDDD3PXXXexdau7YfX999/nscce4+WXX2bcuHGsXbuW6dOn06VLF8aOHevNZ/78+bzwwgu8/PLLaLVaJk2axO23386RI0eIioq6oF4JJSUlpKenX9BnF6KjUMsqcH57EMeOvTi/+R6qrA2epyTGoumdirZPKprenqf4MsFoi5GgPwBchSUo0VHy5b8Oh8NJRWUVlZVVnnf3E9zq/QpvkN94gHYhzhfMV7/r5A9Qm6UoCqGe3hWeXqoNUlWVqiorZeWVlJVVYCktp7S0nNIy97ultLzR4R0OpxNLYQmpO/bQ++hpqv9X27Ua9vZO5UyvboTHx2DOK8Js/wGTKQKzKZKoyIignGxQCHGBynLAci7QpbggiqKgmKNQtVo4cwaAK2+9CY1GA7W+m5SVuRs7s7OzSUxM9MkjMTERi8VCZWVlswNml8vFqlWrvE/277nnHjZu3Nhk0G+323n11VcZMWIEAKtXryY9PZ1du3YxfPhwlixZwrRp03j44YcBmDVrFjt27GDJkiU+Qf/kyZOZPn26dz8zMxOAhISEJsf01/XOO++we/du/v73vzf7GiHaO1dBMc6d+3Ds3Itr/w/Q0OTNBj3awelohw9EO6SvzGPUyiTob2WWnAJcT7+EKzEW2/SfYIiP8S5T1h6DS6fTVSuIr3I/Wa20+qZVVlFZUYXN7vDLPbVajSdoDyXcM1a7ejvMmybBvKihKAqhoe6J/eLj6i+RV91jwN0QUOF+9zQGKKezGPjVt0TX6gmQGx3F1sHplIWHgstFaU4+2Tm+S8woikJUZDgmUyRmUyRmcySmqAjM5kjCQi9uHgEhRABEJJ7/nCC9pxIZjiYpDoD/9/KrpKelgVaLJiEGxeD/Wa+7d+/u05U/OTmZ3NzcJq/R6XQMGzbMu9+nTx/MZjOHDh1i+PDhHDp0iPvvv9/nmtGjR7Ns2TKftKFDh15y+Tdv3sz06dN57bXX6s1/IERHoqoq6ulsHDv34ty5zz3LfkMiw9EN6492xOVoB/VBCTG2bkGFV5sP+rt3787Jk76/aIsWLWLu3Lne/X379vHII4+we/du4uPjmTlzJk899VRrFxWAqv95h/CCYigoxvXcMr4clE5WQgzgDla9a5QbPOuTG/U+S5PVrGOu96YZPN3Kq4MEfwQLLpeK3eHAbrNjs9ux2Tzrwtvca8PXrBtfN819rq06/TwTdFyomknXaoL3cM9SbdVpRqNBAibhV7V7DCTEu/+/qk4X9g82Yv9sKzjcDVaqVkvpDVdSPKw/KWUVlFjKKC4ppaKiql6eqqp6hw2cOp3lc8yg12EyRdY0CJgiiTZHYjZHSe8AIYLNRXazDxbVX8JTUlJI69bdnajRoEmK956TlJRETo7vEIacnJwL7hav1+t9760ouFyts2RteHjTw8HO54svvuCWW25h6dKlTJkyxU+lEqLtUJ2eifh27sWxcx/quYYb7JSEGLQjLkc3YiCavj1RtPKQLRi0+aAf4Pe//z333Xefd792K7LFYmH8+PGMGzeOFStWsH//fmbMmIHZbK7XMtwaSvr3QrP/CKFWO6FWO+N27uNgjy5816cHTvCMMa4fIFwoRQEFBRSl1ja1lmkDqDmmuDcAPMG8f566N4dBryM0NISwMPeT1uox8mG10qrTJeARwcCVk4/15X/4LMGndOtEyONTiOiRQnKd8202O8UlpZSUuBsBql8lJaUNDhuw2R3k5ReRV2u8LYBGUTCbo4iNMRETY/K8my96hQEhhKimSYhBMRpQrTZwuXCdy0WTGIsSGU5GRgaffPKJz/nr168nIyOjxcvlcDj4+uuvGT58OABHjhyhuLjYO6Y+PT2drVu3MnXqVO81W7dupW/fvk3ma/D0ZHA2YzLfLVu2cPPNN7N48eKAfHcUIlBUVcX1wwkcG7bj3LUPtbjhhZM1qV3QjhiIdsRANKld5DtJEGoXQX9kZGSjM86+9dZb2Gw23nzzTQwGA/369WPPnj289NJLAfnDHXvNMHI6JxLzzqdEeLrC9D1+hi4lZXx75RUUhhqxWm2XvDycqoKKSrPWNvMzrVbjWVpO734yXx3Eh1UH9LWDeSN6Xbv4NRQdgKqqODZsx/b6/9VMTKMo6G+9Fv0vb0Ex6Bu8zmDQkxAf4+0lUDu/8opKiotrNwS4GwZKS8vr5eNSVQqLSigsKoGa9gaMRkOthgAzsTEmoqOj5P+WEKLZCktKyOnUCbWsFLXSvcyo2VpJaKckHnjgAV599VWeeuopZsyYwaZNm3jnnXf4+OOPW7xcer2emTNn8sorr6DT6Xj00UcZOXKktxFg9uzZ3HnnnQwePJhx48bx0Ucf8d5777Fhw4Ym8+3WrRuKorB27VomTpxIaGgoERH1V1XZvHkzN998M4899hi333472dnZgLvRICYmpt75QrQHqtWG48uvcXzyX1zHTtc/QaOg6ZuGbuTlaIcPRJPYxMRJIii0i2+EL7zwAs8//zxdu3Zl8uTJPPHEE+g8X3a3b9/O1Vdf7W3RBfeMs4sXL6aoqMi7VEtriYkxETN8AOqw/jjWbsG2ag04HEQVFDPms68w3HcHunEZuFyuRtYwt9dbt9xud7iDfFUF1FrbnjE3qurTCFC97T7dc5yaawx69zrwBoPevW3QY9DrMRgaSq+TptfL03jRLqnFFqyv/j+cu/d705SEWIyP34O2X6+LylNRFCLCw4gID6NLZ98xug6HE4ulpmdAYZGFwsJiiotL601kabXaOJeVx7msPJ90U1SEtzdArKdnQGRkuLTACyHqqTs7P8BbS1/h5zdPonuUmbVr1zJr1iyWLVtGly5deP311xtdrs+fwsLCmDNnDpMnT+bs2bNcddVVvPHGG97jt912G8uWLWPJkiU89thjpKamsnLlSsaMGdNkvp07d2bBggXMnTuX6dOnM2XKlAaX7Fu9ejUVFRUsWrSIRYsWedOvueYatmzZ4qdPKURwcGXlYf/sSxwbtkNZhe9Bgx7t4L5oRw5EN7S/LD3cxiiqGoBHwX700ksvccUVVxATE8O2bdt4+umnmT59Oi+99BIA48ePJzU11WeW1YMHD9KvXz8OHjzY6JIrVqsVq7VmiQmLxUJKSgolJSVERUX5rfzOzDNYl7yJeqZmrJx29GCMD09GiQjz232EEBfPsX0P1r/9G2pN1qcbNwrDvT9FCWv+eFZ/cDqdFBeXUlBYQkFhCYVFxRQUljR7WJBeryMm2kR8XLS394HJFCENAW2UxWLBZDL5vW7qqJr6eVZVVZGZmUlqaiohISEBKmHrUFUVtciCWljsTVPCQ1ES41A0rdewv2rVKh5//HGKi4vPe26w6ki/N6JtUp0unN8dxPHJFzi/PVSvl7CmZ1d0E69Gd+UVMhFfgPijrg/KJ/1z585l8eLFTZ5z6NAh+vTpw6xZs7xpAwcOxGAw8MADD7Bo0SKMxov/xVy0aBELFiy46OubS5vahdCX5mJ74/9wfO5ec9a59TsqfziBcdY0tH3TWrwMQoiGqeWV2F7/D45NO2sSTZEYH5mMbsTAgJRJq9USG2smNtbsk15ZZaWwuiGgsNjTIFCCs86yOXa7g5zcAnJyC7xpBoOe+Lhon4aA8PBQaQgQooNSFAUlxoRLp0XNLQRU1PJKOJsLyfGylrYQ7YBqKcO+YTuOz75EzSnwPajTobvyCnQTr0ZzWXf5PtAOBGXQ/+STTzJt2rQmz+nRo0eD6SNGjMDhcHDixAl69+7d6IyzQKPzAAA8/fTTPg0K1U/6W4JiNGB8eDLawX2xvvoWlFWg5hVR9ezL6O+8Ef2dN8jMl0K0Muf+H7Au+wdqXs1ketoRl2N85BcopsgmrgyM0BAjnTsl0LlTgjfN5VKxWMooKKppCCgoKKa0Tpc9m83O2XO5nK01E29oqJGEuBgSEmKIj4shIT6aEGnhF6JD0URFoOq0uLLyQXWhWq2oZ7PRJCc0OoeJECK4OX88ieOT/+L46huos8qWEh+N7oar0V+fEZTfdcTFC8qgPz4+nvj4+POf2IA9e/ag0WhISHB/8c3IyODZZ5/Fbrd7l4pZv349vXv3bnI8v9FovKSeAhdDlzEITVo3rC+vxnXgR3Cp2P/3E5x7D2OcNQ1NgkySIURLU212bP/8EMeHm2oSQ0Mw3H8HurEj2lRrt0ajYDZHYjZH0jO1izfdarWRm1dEbl4hefmF5OYV1hseUFlp5eTpLE7WWk4wMjKcBE9vgPj4GOLjotHrg7IaEUL4iRIWiqZLIq6sPPfypHYHrjM5aJLjUUJb9nvStGnTzvsQSAhxfqrNjuOrb9wT8/14st5x7aB0dBOvRju0P4rMzdUutelva9u3b2fnzp2MHTuWyMhItm/fzhNPPMHdd9/tDegnT57MggULuPfee5kzZw4HDhxg2bJlLF26NMClb5gmPpqQ3/8a+7ufY//3J+5lcw4dp/LxRRgf/gW6K4cEuohCtFvOY6ewLl2Nejrbm6bp3wvjY/e0q0Y3o9FASpdEUrrUTB5YVl5JXl6hpyHA3SBgq/MEoLS0nNLSco5lngHcS4NGm6NITIglKTGOpMQ4oqJkokAh2hvFaEDT2RP422zgcuI6l4MmMU7mHxIiiLlyCnB89iX29dug7qpA4aHorh2J/sar0NSZTFi0P2066Dcajfzv//4v8+fPx2q1kpqayhNPPOHTLd9kMrFu3ToeeeQRhgwZQlxcHPPmzQvqdVYVrQbDnTeiHdgb64urUHMLoLwS65/fxPndIQy/+hlKqEwGI4Q/2ddtxbbif6F6DLxeh+GeSehuGduqE1cFSkR4KBHhnUnt3hlwT+RlsZSTm1dIbn4heXmF5OcX46i1prWq4l5VoMjCoSOZgHuYQWJiTSNAfFy0rOghRDug6HVoOieiZuehVlaBquLKzkeJM6MxyySSQgQT58Gj2N9bj/Pr7+tPzJfaGd3Ea9BdPVQm5utA2vzs/a0lUDMkq+WVWFf8L87/fu1NUzolYPzNdLQ9u7ZaOYRozxxfH8C6cAW43H8ONT1SMD4xBU3XTgEuWXBxuVwUFlncPQLyi8jLK6SgsISmqhGtVkN8XAxJ3oaAWJkbwI9k9n7/ktn7z091qah5Bai1nhoqpiiUOLP08mmA/N6I1uTMPIP9nx/i/OZ73wM6LdpRg9FPvAZNn1T5v9rGtNvZ+0UNJTwU46xpOAb3xfb3t6HKinoul6qnlrifQk66tkM8hRSipTiPn8b65ze8Ab/upmswTP8pioxVr0ej0RAXayYu1kz1Yqd2u4PcvEKyc/LJznGvClB7WIDT6fIcyweOAGA2RbobAJLcDQGmKFkyUDSf1WplxIgR7N27l++++45BgwZ5j+3bt49HHnmE3bt3Ex8fz8yZM3nqqacCV9h2SNEokBALOh1qUQkAaokFXC5IiJH/y0IEgCsrF9v/+9jnISGAEmtGd8NV6MePQpEeOR2afKttAxRFQX/tCLTpqViXrMR19BQ4nNhWvo/zu8MYHr8HTbQp0MUUos1xFRRj/cMKqLIBoL1yiHv4jDSkNZter/NZNUBVVQqLLN5APyenAEudcYTFJaUUl5Ry+Af3kICQEGOtngAyJEA07amnnqJTp07s3bvXJ91isTB+/HjGjRvHihUr2L9/PzNmzMBsNgf1kL62SFEUlFize0m/vCJARS0tc3cjToyVwF+IVuIqKMb+zqc41m+rGZ4IKHHR6H8x0T0BsawAJpCgv03RJCcQ8sKT2P/fWuzvrQfAuecQlb/+I4afTUB3/SiUMOk6JkRzqJVVWP+wArWgGABN71SMv75bAv5LpCgKsTEmYmNM9EvvCUB5RSU5OQXe3gD5+UW4ag0JqKqycuLkOU6cPAeATqslISGG5KQ4kpPiSUyIlVUCBACffvop69at49133+XTTz/1OfbWW29hs9l48803MRgM9OvXjz179vDSSy9J0N9CNKZIVK0GV3YBoKKWldcE/vK3VIgWo5aWY39vPfa1W3yX3YuKwHDHBHQ3XCXLagof8i2qjVH0OgxTb0N7eR+sL//D3bXOUobtzXexvf0J+huvRnfzNW3+yb9aUYUrOw81Kw9XVj5qTj6EGtF07YSmWyc0XZNRjIZAF1O0UarThfWlVbiOnwZASYgl5JkH5HeqhYSHhdIjtQs9PMsG2h0O8vKKvL0BsnN8hwQ4nE7OZeVxLisPOISiKMTFmUlOjCc5KY6kpDhCZV6ADicnJ4f77ruPNWvWEBZWf8b47du3c/XVV2Mw1Pw/njBhAosXL6aoqKjBZXqtVitWq9W7b7FYWqbw7ZgSEY4mWcGVlQ+oqOUVkK1CUpw38D9x4gSpqan1hmMIIS6MWmXF/tFm7O9vgPLKmgOhIehvuxb9pOvkAaBokAT9bZR2UB9Clz2N9e9v49z6nTuxvBL7/32Ofc1GdNeOQH/rdWi6BO8SHGp5Ja6s6sDe8+4J9NWi83zxUhSUxFg03Tqj6ZbsaQjohNIpAUUn3ZhE02yr3se5a797JzyUkN8+hGKODGyhOhC9Tken5Hg6JccD7iEBRcUWsrPzycrOJysnn7KyCu/5qqqS9//bu+/wKKr98ePvmS0pkATSQwoJhF4VRANK8aqgiKJebKAgyE8QvCrKBb8gqAgoKnr1IlgR7xUsV0UsqEhRmopoUOktRFJJCCSBlN2d8/tjw5KFELKQZJPweT3PPmTOzM6cOWxy9nPmlEN5HDqUx+9/7gKgaZMAIiPDiIoIJSoylICARl65F1E7lFKMGDGCMWPG0L17d1JSUk47JjMzk4SEBLe0iIgI176Kgv7Zs2fz5JNP1kie64KzdbOfPn06I0aMICEhAV3XSU1NJTo62rU/IyOD2NhYHA4H+/fvJz4+HoDU1FTGjh3L6tWrady4McOHD2fW49PQD+WBUqjjRZBxCCLD0Ew6sbGxZGRkEBoaWpO3e140TePTTz9l8ODBlR43c+ZMvvzyS5KTk7FarRw5cqRW8icubMpmx/7temwfLkcdKTi5w2LGfG1vrH+/Bi1IvseIM5Ogvx7TggLw/ee9GH9lYlv6HfY1P4PdAXbnHwb7ig2YenTGcvNVmNq2qPX8KaWg4JhbQO/8Oce51m9+4fmcHJWZgyMzB8dP5cZ1ms3oMRFocVFuDQJamEwuJJxsy3/AvmyVc8Ok4/vPe9HjorybqQucpmkENw0iuGkQ7cuGBBQUHicj85CrISDviHtDYN6RAvKOFLB9xz7AueRgZGSYa0hA0yYB8jtfD0yePJlnn3220mO2b9/Ot99+S0FBAY899li1Xv+xxx5zW+Y3Pz+f2NjYar2GN2VkZLh+/uCDD5g2bRo7d+50pTVu3JicnBwAoqOjeffdd93KeNGiRURHR5OamupKczgcDBw4kMjISDZs2EBGRgZ33303FouFmY9Pw0g/BMpwLuuXkQ1RYZhMJiIjI2vhjj1XWlrq1jukKscPGTKEpKQk3nrrrRrMmRDOnomOtb9QuvgLVFbuyR26hvnKJCy3X4cednqDphCnkiX7qqg+LItk5B7B/sUabF+vhePFbvv09i2x3HQ1pu4damycnTIMjJQ0jD924/hjF44d++CUCbyqQmsahBYVih4VhhYV5vw3IhSOHcc4kIFxIA0jNQMjNQNKSqt2Uj9f9LiyHgHNm6EnRKMnNpfu3BcY+6/bKJkx3znLNGAddyeWa3p5OVeiKoqKS5xDAcoaAQ7l5FW6VKCvj7VshYATkwM2wdQAJzOqD3VTZQ4dOkRubm6lx7Ro0YJbb72Vzz//3K0hx+FwYDKZGDp0KIsWLeLuu+8mPz+fpUuXuo5ZvXo1V155JYcPH67wSf+pGvKSfe+88w4PPfTQaU+mT3S9nzp1Kh988AG7du1y7WvTpg233XYbM2bMcD3pX758Oddffz3p6emunhQLFixg0qRJHDp0CIuhMNKzXX9nNR8rB0qP0yIx0dW9f82aNfTr14/vvvuOSZMmsW3bNrp27crChQtp06ZNhfk/kc8lS5bw8ssv8+uvv5KYmMi8efPo06eP67jvv/+eiRMnsmXLFoKDgxk+fDhPP/00ZrPzOVffvn3p2LEjZrOZ//73v3Tq1In9+/dz4MAB1zmaN29eYW+SqpTnqer750Z4h1IKx89/UPre56iy+W5OMPW8COvQ69Fj6mZDmqh+smSfcKOHNME6fDCWIf2xf7Me27JVqMPO5XSMbXsp2bYXLSYCy01XYe5zCZrl/Cb4UEqhUjOcAf4fu3Bs3VPlIF8LaXIyoC//b2Qomt+ZK0VT13aun5VhoLJyMVLTMQ6kuxoEVNrJLxsuRcUYO/dj7Nx/Ms1sRk+Mw9S+JXqHRExtW6A1Pn2cqGgYjJQ0Sua85fpsWG66SgL+esTP14eE5tEkNHd2PbbZ7GRl55KReXKVALvD4Tq+uKSUlNR0UlKdX5ZMJp3wsGDXCgGRESH4SKOf14WFhREWFnbW415++WWefvpp13Z6ejr9+/fngw8+4NJLLwUgKSmJKVOmYLPZsJTVbytWrKBNmzZVCvjP1cdLv+P4KQ3tNc3f35dbBl9Vree84YYbWLBgAevWrePyyy9n3bp15OXlMWjQIGbMmOE6buPGjXTq1MkV8INz7oSxY8eydetWLrroIvToCGfg73CgSkoxsnIqvOaUKVN44YUXCAsLY8yYMYwcOZL169dXms+JEyfy0ksv0b59e+bOncugQYPYv38/ISEhpKWlcd111zFixAjeffddduzYwejRo/H19eWJJ55wnWPRokWMHTvWda3g4GDCw8NZuHAhAwYMaJANhKL+cPy5m9J3P3P/zorzO7DlrhswJcZ5KWeiPpOgvwHS/P2cgf31fbGv/QXbJytQf2UCoA5mUfrKe9j++wXmQX2x9L+8yoGuUgqVlnUyyP9zNxytpIt+YGP0hBj0Cp7aV8cTdk3XXefk0i4n82mzodKyyxoC0l2NAir7sPsJ7HaMHfswduyDT1aApjl7AbRvial9Inr7lughTc47n8L7jLyjFD89H4qcX8xNSV2x3H2jl3MlzofFYiYmOoKYaGfg4TAMcnLynI0AZfMClJTrCeRwGM75AjJPBh9NmwYSdaIRIDKUgMb+MiSgjoqLc/+S27hxYwBatmxJTIxzgsg777yTJ598klGjRjFp0iT+/PNP/vWvf/Hiiy/WaN6OHy/m2PGisx9Yx1ksFoYNG8bbb7/N5Zdfzttvv82wYcNcDSgnZGZmugX84D53Ajif7uvRERhpWeBwgM05Uaey2d3eN3PmTNdT+smTJzNw4ECKi4srfSI+fvx4brnlFgDmz5/P119/zVtvvcU///lPXn31VWJjY/n3v/+Npmm0bduW9PR0Jk2axLRp09DLejq2atWKOXPmnHbuJk2a1NlhCKLhc+z7C9u7n+H4bbtbut46HutdN2Lq3NpLORMNgQT9DZhmMWO58jLMfXvg+HUbtk9WYGzdA4DKO4rt3c+wffQNlv69MA/qhx7q/iREKYXKPISjrLu+8ceuyifYa+yPqWMrTJ1aY+rUGi0uyitfoDWLBS0+Gj0+2i1dHS/C+CvTOQRhVwqObXtR6dnlDlDOfSlp2L/6wXmuiBBXA4CpfSJadLgEBfWMKimlZOZrZWtJg54Yh8/Dw2U5qQbGpOtEhIcQER4CnduUTQ5YcHKFgMwc8k/piZSXl09eXj7byuYF8Pf3dfUEiIoMJSQ4yBUkiLovKCiIb7/9lnHjxtGtWzdCQ0OZNm1ajS/X5++FmbJr6pojR46kZ8+ezJo1i48++oiNGzdit9vP/sYKaFbLySf+ZYxDh1G2kyt1dO7c2fVzVJRzbpXs7OzTGnnKS0pKcv1sNpvp3r0727c7g6Tt27eTlJTkVk/36tWLwsJCDh486Dpvt27dzumehKgJ6lgRpe99jn35D2CcHLamxUZiHXYDpks7y3dPcd4k6L8AaLqOuXtHzN074ti5H9un3+H4cYtzLd2iYmxLV2L7fDXmPpdgvronRvohjLKn+SfWMK+Qvy+mDs4gX+/UCj0+uk4HUpq/H6Y2CZjaJED/ywHnE2Bj214c2/ZibNuLkXLQ7Q+uysrFnpULq39yJgQFYGrf0jkkoH0iekI0mnQDrLOUYVDy4iKM3c6xmlpoU3ymjJG5HC4AzskBAwluGkj7solMjx0vIjMr19UIkJN7xG1egOPHi9m3/yD79h8EwGw2EREe4moIiAgPxirrHtcJ8fHxFc7p0LlzZ9auXVureanubvbe1KlTJ9q2bcsdd9xBu3bt6NixI8nJyW7HREZG8vPPP7ulZWVlufaVdyLw56Dzdwq7HeNgluuJf/leBCeCGuPU4Xk1oFEjWe1DeJ9SCsfazZS+/bHbQzUtPBjLHdc7h+Ka6u73alG/SNB/gTG1ScA0eTRGeja2pSuxr/oRbHZwGNhX/YR91U9nfrOvD6YOLdE7Op/k6y1i6/0fI71pEHqvizH3uhhw9gZw7NiHsXUvjm17nMFi+e6IRwtwbEzGsTHZue3rg94iBswmKP/989Qvo+W3K5p8rHyaplX40jQNdA00QNPL0st+1stagHWtbB9g0jG1isfct8cFO1eB7T/LTv5f+fni+/hY9OAgr+ZJeE8jfz9aJsTQMsHZHdxms5N96PDJeQGyc7GV+3232x2kpWeTVvakUtMguGmQ27wAjWVIgGhgRo4cyf3338/8+fMr3J+UlMTMmTPJzs4mPDwccM6dEBgYSPv27U87XrOY0SNDTiY4HBg5eeecvx9//JHevXsDYLfb2bx5M+PHjwegXbt2fPzxxyilXL+X69evJyAgwDUM5EwsFguOcvOCCFGTjLQsSl77EGPLjpOJPlYst12L5YZ+5z3vlhCnkqD/AqU3C8fn/juw3jkQ25ffY/vqByi3LjYAVouzW3tZd329ZRyauWE/1db8/TBf3AEu7gA45wcwdh/AsW0fxrY9zhUJjpUbu1lcgrFtr5dye3aONZsoffczzFd0x3ztFRfU5C+2b9dj+2SFc0PX8Jk48rQhH+LCZrGYiW4WTnQzZ+BiGIrDeUddcwJkZua4jdVWCnIPHyX38FG2bnf+3pcfEhAZEUJISBNMdbjHkxBnM3r0aIYMGUKTJk0q3H/NNdfQvn177rrrLubMmUNmZiZTp05l3Lhx+Pj4VPgerWzmfO1ETxnDGVyr4hKP8zdv3jxatWpFu3btePHFF8nLy2PkyJEA3H///bz00ks88MADjB8/np07dzJ9+nQmTJhw1qE68fHxrFy5kl69euHj43PGyR9TU1M5fPgwqampOBwOV0+IxMRE11wTQpyJKinF9r9vnd9Pyg2dMV3aGeu9f0cPD6nk3UKcOwn6L3Bak0CsQwdhuflq7N9txLH7AHp0BKZOrdBbxaNZLuyPiGaxYGqfiKl9InANymE4JwbctgfHVueQAJV31NvZrFxJKfbvNmD/bgN6q+aYB1yB+YpuDbqLu2PLDkoXvO/ato6+FXO3Dl7MkagPdF0jNKQJoSFN6NghEaUUhYXHyShbHSAzK4fcw+6/7xUNCTixSkBERAiR4bJKgKhfzGYzoaGhZ9xvMpn44osvGDt2LElJSTRq1Ijhw4fz1FNPnfXcWlgwmu/JhgEj4xAqKKjSVXtO9cwzz/DMM8+QnJxMYmIiy5Ytc+U3Ojqar776iokTJ9KlSxeCg4MZNWoUU6dOPet5X3jhBSZMmMAbb7xBdHT0GZfsmzZtGosWLXJtX3TRRYBzeci+fftW+T7Ehce+eSulr3+IKjehrBYe7PyO0qOTF3MmLgSaqmyhY+FS39dCFjVDKQWltop3nujy6+r5q1Wyr/yGcs4roMq9DFVxulKoCtJQCpV/DPvqn7Cv/glOXU6qkR/mKy/DMuAK9Bj3WZjrOyM1g6LJL7h6ZJgH9cPn3r97OVeioSgttZGVneuaGyD70GG3IQEVado00NkToGx+gMDARtU2JEDqpupVWXnKeuvVQzkMVOYhVNlqKmg6elQomr9fpe9LSUkhISGB3377ja5du9Z8RquJfG6EkZNH6Zv/OzncEMCkYxl8FZZbB7g1hAlRkeqo6y/sx7hCnCdN06AmnuJ5MIrijKFDRCimVs2x3nUD9rWbsS9fi7HvL+e+Y0XYP1+N/fPV6J1bYxlwBaZLu9T74RvqSIFzab6ygN90SSes99zs5VyJhsRqtRAbE0lsjHPCMsMwOJyXf3KVgKxcCk8ZKnVilYDtZasE+Pn5EBcbRb/el9R6/oXwNs2kQ1QYZOagjheBMjAyDqFHhqE1qjzwF6I+UXYH9i9WU7rkSyg+uYSs3rEVPvfdhh4X5cXciQuNBP1CNHCany+Wa3o5V2bYlYL967XY1/3q6qFg/L6Lkt93oTUNwnx1T8zX9EIPq3gsY12mSm0Uz3oNlZULgN4iFp9HRtT7ySZF3abr+skhAe0TASg8dtzVEyArK/e0VQKKikooKio+0ymFaPA0XYfIUMjKRR077lwyN+MQemToBTvxrGhYHNv3UjL/fdSB9JOJQY2x3nOzc4JlmQBW1DIJ+oW4QGia5lqy0DryFuyrfsT29TpU2czkKu8otg+XY/vf15gu6YR5wBWYurat08swnqAMg5J//Qdj534AtOAgfKaO8WicqBDVpXEjfxJb+JPYIhY4uUrAiZ4AWdm5REacecy0EBcCt8C/8BigMDJz0CNC0AJOX1LvTMs0ClGXqPxCShd9hv27DScTNQ1z/15Y77pRGrWE10jQL8QFSAtohOXGv2Ee1A/j913Yvl6L46ffwTDAUDh++h3HT7+jRYZi7n85lquS0ALr7qzEtiVf4li32bnha8Xn8bHoIU28michTjh1lQClFA5Hza9FLkRdp2kaRISApqEKCgGFkZWLVmpDCw6Sp6Gi3lCGgX3lj5QuWgoFx1zpestYrGNux9Q63mt5EwIk6BfigqbpOqaubTF1bYuRewT7ivXYv1mPKpuhXGXmYFu0FNviLzD3uxTryJvr3NNz26ofsX34tXND1/B5ZCSmsiesQtRFmqZhrufzZwhRXTRNg/Bg0DXU0QJAOVfFOV4MESEnl/kToo4yUtIomf8+Rtm8LQD4+2IdOgjztb1lmKGoEyToF0IAoIc0wXr7QCxDBuD4+Q9sX6/FSN7h3GmzY/92PcbuA/hMHYMeWjfG/Dv+3E3pvMWubevIW2TZGyGEqGc0TYPQpmA2oXKPAgpVUoL6KxMtrClaQPWteCFEdVGFxyn9cDn2z9c4e0qWMfXujvWem9GDg7yWNyFOJUG/EMKNZjJhTuqKOakrRno2tm/WYf9mPRQVY+w/SPHE5/CZMgZTYpxX82n/fhMl/34P7A4AzNf2xnx9X6/mSQghxLnRNA2taRDKzxcjKxdsNlAGKjsXjhdBWDCaSXrICO9Tdgf2r9dS+v5Xbl35tWbh+Nx3G6aubb2YOyEqJkG/EOKM9Gbh+NxzM5arelL89HxUZg7q8FGK/+9FfB4ZgfnSLrWeJ2WzU7rwE+xffu9KM3Vrj3X03+VJkBBC1HOarw96bCTqUF7ZOH/nE1VVXIoeHoLmX7eGmIkLh1IKx8+/U/rOUtckyABYzFiGDMBy81VoFhmOIuomGWQihDgrPTYSvzmPordt4UwoKaVk9hvYln5Xq7MpG7lHKH78X24Bv/mqJHwmjZYnQEIIUUelpKSgaRrJyclVOl7TdfSIEPTIMDixgozdjpGejZGThzJkFn9Ruxx7Uime+i9KZr3uFvCbenfH79VpWG+7VgJ+UadJ0C+EqBItKADfGf/A1Lu7M0EpShd+Sun891FlXexrkmPrboonPIOxvWyiHLMZ67g78XlgGJqPtcavL4QQ9Y2maZW+nnjiCVdAbjKZSEtLc3t/RkYGZrMZTdNISUlxpaempjJw4ED8/f0JDw9n4sSJ2O32M+YjNjaWjIwMOnbs6Fn+G/ujx0aVm0BWoY7ko9IyUaU2j8511mtpGkuXLq30mJSUFEaNGkVCQgJ+fn60bNmS6dOnU1paWq15EXWHcSiPkhcXUfzIsxh/7nal6+1b4vvcRHwfuQc9PMSLORSiaqR7vxCiyjSrBZ8JI7BFh2Nb8hUA9m/WYWTl4DtxVI2sP6uUwr5sNaXvfOqaKEcLbYrP5NGYWjWv9usJIURDkZGR4fr5gw8+YNq0aezcudOV1rhxY3JycgCIjo7m3Xff5bHHHnPtX7RoEdHR0aSmprrSHA4HAwcOJDIykg0bNpCRkcHdd9+NxWJh1qxZFebDZDIRGRl5TvegWczQLByOFKByj+Cc5K/UOclfaFO0wPOb5K+0tBSrtWoNxzt27MAwDF577TUSExP5888/GT16NMeOHeP5558/5zyIukcdL8b2yQpsn62Ecg1MWlQY1uGDMV3WRYYUinpFnvQLITyiaRrW2wfi8/BwMDvbDY3kHRRNegEjK6dar6WKiil5fiGlb3/sCvj1zm3wmztJAn4hhDiLyMhI1ysoyLnuffm0xo0bu44dPnw4CxcudHv/woULGT58uFvat99+y7Zt2/jvf/9L165dufbaa5kxYwbz5s074xPvU7v3r1mzBk3TWLlyJd27d8ff35+ePXu6NUiUp2kaqUcPY27VnA+Wf8Xlt95Mo/aJdL6kG2s+XYZynOxt9v3339OjRw98fHyIiopi8uTJbr0Q+vbty/jx43nooYcIDQ2lf//+xMfHA3DTTTehaZpr+1QDBgxg4cKFXHPNNbRo0YIbbriBRx99lE8++aTC40X9oxwObN+so2jsE9g++vpkwN/YH+u9f8fvlamYk7pKwC/qHQn6hRDnxNy3B74zHoBA55dGdTCToonP4Si/Tu15MNKyKPrn8zjWbXalWf5+Db5PjEcLCqiWawghhHC64YYbyMvLY926dQCsW7eOvLw8Bg0a5Hbcxo0b6dSpExEREa60/v37k5+fz9atWz265pQpU3jhhRf45ZdfMJvNjBw58qzvmTRnFhPGP8Avn33FZRddzI13D+PQlm2o40WkpaVx3XXXcckll7Blyxbmz5/PW2+9xdNPP+12jkWLFmG1Wlm/fj0LFixg06ZNgLORIyMjw7VdFUePHiU4ONij+z4fSilUfiFGShr2X7di+24Dtu82YP/pdxw79mFkZKOOFdXqfDsNhf3XrRQ9NJvSV5egjhQ4E80mzDdeif9rT2IZ1M/Z80SIekg+uUKIc2Zqn4jfnEcpnjEflZYFRwspnvovfP5xF+YTY//PgX1jMiX/+g8UFTsT/H3xefBuzJfV/moBQghRmaIJz6KO5NfqNbUmgfjNnVSt57RYLAwbNoy3336byy+/nLfffpthw4ZhOWVysszMTLeAH3BtZ2ZmenTNmTNn0qdPHwAmT57MwIEDKS4uxtf3zDP0jx8/niEjR6AKjzOvRUu++eF73n5/MRP/3xjmzXuZ2NhY/v3vf6NpGm3btiU9PZ1JkyYxbdo09LJJAVu1asWcOXNOO3eTJk08GoawZ88eXnnllWrp2q+UgsLjqMNHMQ4fRR0+gsrLRx0+Wu7lTKMq8+iYzc6hD0GNITAALagxWmDjk/+6fnbuo7E/mn5hPgs0UtIoXfgpjuTtbummnhdhvftG9KgwL+VMiOojQb8Q4rzoUWHOwP/ZNzB+3wU2OyUvLMTIPIRlyACPusAphwPbe19g+/hbV5oWF4Xv5NHo0RGVvFMIIbxDHckvG2te/40cOZKePXsya9YsPvroIzZu3FjpBH3nq3Pnzq6fo6KiAMjOziYuLu6M70lKSgKck/xZW8TQrUtXduzdA8D2rVu5rHMXsNnB6mys6NWrF4WFhRw8eNB13m7dup133tPS0hgwYABDhgxh9OjRVXqPKjiGse8vjP0HMQ7luQf0eUed+a4udrvr3FWiaxDQCD24CXrLWPTE5uitmqM3b9Zgn24bh49iW/wF9pUbodyKEHrreKwjb8bUrqUXcydE9WqYv8VCiFqlNfbHd/p4Sue/j/27DQDY3vsClZaNdfydVVrGRh0toPj5hRi/nxzTabq8Gz7j7yw3c7MQQtQtWpPABnPNTp060bZtW+644w7atWtHx44dT1tmLzIykp9//tktLSsry7XPE+V7EZxoIDbK5m+pCs1sRvPzAV8foKyB2WFg/JVRNslf4wrf16hRI4/year09HT69etHz549ef311ys8RtkdUFKKUVCIOlpA0eMv49h14LyuS1Bj9KZBaMHlXk2DQNNQBYWoo4WofOe/5Be4tqvUM8BQcLQQ42ghxv6D8N1GZ7rFjB4f7WwAaNUcU2JztOgINFP97RWgikuwLV2J7dMVUHxyHgotPBjrXTdiuqKbjNkXDY4E/UKIaqGZTc4APzoc26KlANjX/IyRnYvvY//vjF++ABy7Uih59k1UTp4zwaRjvedmzNf3lYpXCFGnVXc3e28bOXIk999/P/Pnz69wf1JSEjNnziQ7O5vw8HAAVqxYQWBgIO3bt6/x/P3444/07t0bALvdzubNmxk/fjx6bCRtW7fm0+VfogwDDh2GY0Ws+24VAQEBRIeGoSp5km6xWHA4zh4cp6Wl0a9fP7p168bChQvRNM153pJSVEmp899SG5T1kFAOO6rEVnlvkIBGaMFBpwf05V9NAs/pibtSCo4Xo040ApRrGFD5BXDKtso96po4FwCbHWP3AYzd5RosfH3QE+MwJca5GgO08JA6W18rw0D9lYlj214c2/fiSN4BRwtOHuDvi2XIACzX90Wznv0hhRD1UYMI+r/88kueeuopfv/9d3x9fenTp4/bWqupqamMHTuW1atX07hxY4YPH87s2bMxmxvE7QtRZ2iahvXmq9GjwiiZ+w6U2jC27aVo4nP4Pn4/eox7F32lFPZv11P6+keuL0ha00B8Jo7C1CHRC3cghBAXttGjRzNkyBCaNGlS4f5rrrmG9u3bc9dddzFnzhwyMzOZOnUq48aNw8fHp8bzN2/ePFq1akW7du148cUXycvLY+TIkWg+VsZNmsjLC9/iwaemc/+wu9m1fx9PzHyah0aMhPRsDJyrwqijBTgOpKOZTWAygUknPi6O75Z/Tc+LLsbHz4+mYaFgMrkFsgcPHqRfv340j4lhzpTHyfpjO6q0FBwOIsPCK8+4jwW9XQv0lnHoLWLQm0WUPakPrNFAU9M0aOSH1sgPos6SR0CVlDqHIOw+gGP3AYw9qaj0bPeDiksw/tzttm49gY2djQAnhgW0ikNvGlTNd1M1ymbD2JOKY9tejO17cWzfB4XHTz9Q1zEPuBzr7dfJBMGiwav3Ue/HH3/M6NGjmTVrFldeeSV2u50///zTtf9c1pMVQpwfc1JXtFkPUzJzgXMioswciiY9j+/k0Zg6tQacXyxKX/sA+8ofXe/T27XA55/3ogd754uCEEJc6MxmM6GhoWfcbzKZ+OKLLxg7dixJSUk0atSI4cOH89RTT9VK/p555hmeeeYZkpOTSUxMZNmyZa78xsTG8tVXXzHxkUe4+IbrCA4K4p4htzLl/gfcT6IU2Gwo28n11+dMfIyJs5/mzUXvEB0Rwd416wENdB3MJjRd49vFH7Jnzx727NlDXKcObqe07045uaHraD5W8LGiawqttAi/5yfh18i/hkql+mg+VkztWmJq15ITTRGq8DjG3lQcu1Mx9jif+rt65p2QX4jj1204ft128lwhTdCiwtAjQtAiQsv+DXH2CmgaWG0TB6rC486VC7bvdQb6uw9UPj+CjxXTxe2xDhuEHuPZkBQh6itN1eM1Pex2O/Hx8Tz55JOMGjWqwmOWL1/O9ddfT3p6umt22QULFjBp0iQOHTqE1Wqt0rXy8/MJCgri6NGjBAbW/vg9Ieoj41AeJU/Px0hJcyaYdKz334mpU2tKnn0DY+9frmPN1/fFes/NzicvQogqkbqpelVWnsXFxezfv5+EhIRKZ5cXNSMlJYWEhAR+++03unbtetbjlcOA4mKU3QCHwzmu3eF8KYcD7Aaoqs8fcEa6yRXga74WsFrBYnb1EGionxt1JN/ZE2D3AYw9B3DsToX8wqqfwGpBCwt2awgo3zhAY/8zDhcwcvIwtu11Pck3DqQ7G3LOJLCxsyGjQ0v0di3RW8TKdw1Rr1RHXV+vn/T/+uuvpKWloes6F110EZmZmXTt2pXnnnuOjh07AmdeT3bs2LFs3bqViy66qMJzl5SUUFJS4trOz6/d5XiEaAj0sKb4zp5AyfNv49i8FRwGpa/8F3ytJyfP8bHiM37oeS3xJ4QQQpSnmXRo5E9lo8yVYZQ1BhjlGgNONg5gN5xpDoczqDSVC/DL/sVsqrNj2WuS1iQQ8yWd4JJOgHO4nso+7OoJ4Nh9wNngX1G3eoBSGyotC0daVsX7/X2djQDhoc5GgaaBGClpGNv3orIPV563yFBM7Z0Bvql9Ilp0+AX5fyREefU66N+3bx8ATzzxBHPnziU+Pp4XXniBvn37smvXLoKDg895PdnZs2fz5JNP1lzmhbhAaP6++Ey5j9K3P8H+xRpnYlnAr0WFOZfji4/2XgaFEEJckDRdB+vJLuZnCguVUqDUBbuOfVVomoYW4XxaT6+LXenqWBFGVg4qKxeVnYuRlYvKynWmZR+GktKKT3i8GGN/GuxPq/zCuoaeEFMW4Jc9yZchgkKcpk4G/ZMnT+bZZ5+t9Jjt27e7lnWZMmUKt9xyCwALFy4kJiaGjz76iPvuu++c8/DYY48xYcIE13Z+fj6xsbHnfD4hLmSayYTP6CHozcIoffN/YChMPTrh89Bw5+RCQgghRCXi4+Px1ohUTdNAnhSfE62RH6YWsdDi9O/QSik4WuDeEJBV1jCQnYs6dNjZC6M8qwW9dTym9ono7VtiahOP5i/fI4Q4mzoZ9D/yyCOMGDGi0mNatGhBRkYGgNsSMT4+PrRo0YLU1FTg3NeT9fHxqZVZaIW4kFgG9sXUtR3q8FH0Dony1EQIIYS4QGmaBk0CMTUJhDYJp+1XDgOVm+fsJXD4KFpkqHM8/jksXSjEha5O/taEhYURFhZ21uO6deuGj48PO3fu5PLLLwfAZrORkpJC8+bNAe+vJyuEcKdHR0B0xNkPFEIIIcQFSzPpaOEhEB7i7awIUe/VyaC/qgIDAxkzZgzTp08nNjaW5s2b89xzzwEwZMgQwPvryQohhBCi4ajHix4JL5DPixCiLqjXQT/Ac889h9ls5q677qKoqIhLL72UVatW0bRpU8D768kKIYQQov6zWJyrlh8/fhw/PxlDLKqmtNQ5UZ3JJEvECSG8R1PSBFklshayEEKIukbqpup1tvLMyMjgyJEjhIeH4+9/5nXEhQAwDIP09HQsFgtxcXHyeRFCnJPqqOvr/ZN+IYQQQojacGIC4OzsbC/nRNQXuq5LwC+E8DoJ+oUQQgghqkDTNKKioggPD8dms3k7O6IesFqt6LJSjRDCyyToF0IIIYTwgMlkkjHaQggh6g1pehRCCCGEEEIIIRooCfqFEEIIIYQQQogGSoJ+IYQQQgghhBCigZIx/VV0YmXD/Px8L+dECCGEcDpRJ8nqu9VD6nohhBB1TXXU9RL0V1FBQQEAsbGxXs6JEEII4a6goICgoCBvZ6Pek7peCCFEXXU+db2m5PFAlRiGQXp6OgEBAee91mp+fj6xsbH89ddfBAYGVlMOGzYpM89JmXlOyswzUl6eq+4yU0pRUFBAs2bNZFmwaiB1vXdJmXlOysxzUmaekzLzXHWWWXXU9fKkv4p0XScmJqZazxkYGCi/OB6SMvOclJnnpMw8I+XlueosM3nCX32krq8bpMw8J2XmOSkzz0mZea66yux863p5LCCEEEIIIYQQQjRQEvQLIYQQQgghhBANlAT9XuDj48P06dPx8fHxdlbqDSkzz0mZeU7KzDNSXp6TMrtwyP+156TMPCdl5jkpM89JmXmurpWZTOQnhBBCCCGEEEI0UPKkXwghhBBCCCGEaKAk6BdCCCGEEEIIIRooCfqFEEIIIYQQQogGSoJ+IYQQQgghhBCigZKg/zz98MMPDBo0iGbNmqFpGkuXLq30+DVr1qBp2mmvzMxMt+PmzZtHfHw8vr6+XHrppfz88881eBe1qybKbPbs2VxyySUEBAQQHh7O4MGD2blzZw3fSe2pqc/ZCc888wyapvHQQw9Vf+a9pKbKLC0tjWHDhhESEoKfnx+dOnXil19+qcE7qT01UWYOh4PHH3+chIQE/Pz8aNmyJTNmzKAhzCHraXkBlJSUMGXKFJo3b46Pjw/x8fG8/fbbbsd89NFHtG3bFl9fXzp16sRXX31VQ3cgqkrqes9JXe85qes9J3W956Su91xDqO8l6D9Px44do0uXLsybN8+j9+3cuZOMjAzXKzw83LXvgw8+YMKECUyfPp1ff/2VLl260L9/f7Kzs6s7+15RE2X2/fffM27cOH788UdWrFiBzWbjmmuu4dixY9Wdfa+oiTI7YdOmTbz22mt07ty5urJbJ9REmeXl5dGrVy8sFgvLly9n27ZtvPDCCzRt2rS6s+8VNVFmzz77LPPnz+ff//4327dv59lnn2XOnDm88sor1Z39Wncu5XXrrbeycuVK3nrrLXbu3MmSJUto06aNa/+GDRu44447GDVqFL/99huDBw9m8ODB/PnnnzVxC6KKpK73nNT1npO63nNS13tO6nrPNYj6XolqA6hPP/200mNWr16tAJWXl3fGY3r06KHGjRvn2nY4HKpZs2Zq9uzZ1ZTTuqO6yuxU2dnZClDff//9+WWwDqrOMisoKFCtWrVSK1asUH369FEPPvhgteWzLqmuMps0aZK6/PLLqzdzdVR1ldnAgQPVyJEj3dJuvvlmNXTo0GrIZd1RlfJavny5CgoKUrm5uWc85tZbb1UDBw50S7v00kvVfffdVx3ZFNVA6nrPSV3vOanrPSd1veekrvdcfa3v5Um/l3Tt2pWoqCiuvvpq1q9f70ovLS1l8+bNXHXVVa40Xde56qqr2LhxozeyWmecqcwqcvToUQCCg4NrI2t11tnKbNy4cQwcONDt83ahq6zMli1bRvfu3RkyZAjh4eFcdNFFvPHGG17Kad1RWZn17NmTlStXsmvXLgC2bNnCunXruPbaa72RVa868fmZM2cO0dHRtG7dmkcffZSioiLXMRs3bjzt97F///4X/N//+krqes9JXe85qes9J3W956Sur7q6WN+ba+Ss4oyioqJYsGAB3bt3p6SkhDfffJO+ffvy008/cfHFF5OTk4PD4SAiIsLtfREREezYscNLufaus5XZqQzD4KGHHqJXr1507NjRCzn2vqqU2fvvv8+vv/7Kpk2bvJzbuqEqZbZv3z7mz5/PhAkT+L//+z82bdrEP/7xD6xWK8OHD/fyHdS+qpTZ5MmTyc/Pp23btphMJhwOBzNnzmTo0KFezn3t27dvH+vWrcPX15dPP/2UnJwc7r//fnJzc1m4cCEAmZmZFf79P9MYXVE3SV3vOanrPSd1veekrvec1PWeq5P1fY30H7hAUYXuHhXp3bu3GjZsmFJKqbS0NAWoDRs2uB0zceJE1aNHj+rIZp1SHWV2qjFjxqjmzZurv/766zxzVzdVR5mlpqaq8PBwtWXLFtf+C73LX0VO/ZxZLBaVlJTkdswDDzygLrvssvPNYp1TXWW2ZMkSFRMTo5YsWaJ+//139e6776rg4GD1zjvvVGNuva8q5XX11VcrX19fdeTIEVfaxx9/rDRNU8ePH1dKOT9jixcvdnvfvHnzVHh4eLXnWZwbqes9J3W956Su95zU9Z6Tut5z9bW+l+79dUCPHj3Ys2cPAKGhoZhMJrKystyOycrKIjIy0hvZq5PKl1l548eP54svvmD16tXExMR4IWd1V/ky27x5M9nZ2Vx88cWYzWbMZjPff/89L7/8MmazGYfD4eXc1g2nfs6ioqJo37692zHt2rUjNTW1trNWZ51aZhMnTmTy5MncfvvtdOrUibvuuouHH36Y2bNnezGX3hEVFUV0dDRBQUGutHbt2qGU4uDBgwBERkbK3/8GSup6z0ld7zmp6z0ndb3npK6vXF2s7yXorwOSk5OJiooCwGq10q1bN1auXOnabxgGK1euJCkpyVtZrHPKlxmAUorx48fz6aefsmrVKhISEryYu7qpfJn97W9/448//iA5Odn16t69O0OHDiU5ORmTyeTl3NYNp37OevXqddryULt27aJ58+a1nbU669QyO378OLruXtWYTCYMw6jtrHldr169SE9Pp7Cw0JW2a9cudF13BS5JSUluf/8BVqxYIX//GwCp6z0ndb3npK73nNT1npO6vnJ1sb6XMf3nqbCw0K2la//+/SQnJxMcHExcXByPPfYYaWlpvPvuuwC89NJLJCQk0KFDB4qLi3nzzTdZtWoV3377rescEyZMYPjw4XTv3p0ePXrw0ksvcezYMe65555av7+aUBNlNm7cOBYvXsxnn31GQECAazxMUFAQfn5+tXuDNaC6yywgIOC0MZCNGjUiJCSkwYyNrInP2cMPP0zPnj2ZNWsWt956Kz///DOvv/46r7/+eq3fX02oiTIbNGgQM2fOJC4ujg4dOvDbb78xd+5cRo4cWev3V908La8777yTGTNmcM899/Dkk0+Sk5PDxIkTGTlypOvv1IMPPkifPn144YUXGDhwIO+//z6//PJLg/mM1VdS13tO6nrPSV3vOanrPSd1vecaRH1fI4MGLiAnlrE49TV8+HCllFLDhw9Xffr0cR3/7LPPqpYtWypfX18VHBys+vbtq1atWnXaeV955RUVFxenrFar6tGjh/rxxx9r6Y5qXk2UWUXnA9TChQtr78ZqUE19zspraOP8aqrMPv/8c9WxY0fl4+Oj2rZtq15//fVauqOaVxNllp+frx588EEVFxenfH19VYsWLdSUKVNUSUlJLd5ZzfC0vJRSavv27eqqq65Sfn5+KiYmRk2YMME1vu+EDz/8ULVu3VpZrVbVoUMH9eWXX9bSHYkzkbrec1LXe07qes9JXe85qes91xDqe00ppc69yUAIIYQQQgghhBB1lYzpF0IIIYQQQgghGigJ+oUQQgghhBBCiAZKgn4hhBBCCCGEEKKBkqBfCCGEEEIIIYRooCToF0IIIYQQQgghGigJ+oUQQgghhBBCiAZKgn4hhBBCCCGEEKKBkqBfCFGpJ554gq5du3o7Gy6aprF06VKP37dz504iIyMpKCio/kyVk5OTQ3h4OAcPHqzR6wghhBDVRep6z0hdL+obCfqFqAMWLFhAQEAAdrvdlVZYWIjFYqFv375ux65ZswZN09i7d28t57J2VfcXkMcee4wHHniAgICAajtnRUJDQ7n77ruZPn16jV5HCCFE/SJ1/emkrheidkjQL0Qd0K9fPwoLC/nll19caWvXriUyMpKffvqJ4uJiV/rq1auJi4ujZcuW3shqvZSamsoXX3zBiBEjauV699xzD++99x6HDx+ulesJIYSo+6Sur1lS1wtxZhL0C1EHtGnThqioKNasWeNKW7NmDTfeeCMJCQn8+OOPbun9+vUD4D//+Q/du3cnICCAyMhI7rzzTrKzswEwDIOYmBjmz5/vdq3ffvsNXdc5cOAAAEeOHOHee+8lLCyMwMBArrzySrZs2VJpft98803atWuHr68vbdu25dVXX3XtS0lJQdM0PvnkE/r164e/vz9dunRh48aNbud44403iI2Nxd/fn5tuuom5c+fSpEkTAN555x2efPJJtmzZgqZpaJrGO++843pvTk4ON910E/7+/rRq1Yply5ZVmt8PP/yQLl26EB0d7Uqr6OnCSy+9RHx8vGt7xIgRDB48mFmzZhEREUGTJk146qmnsNvtTJw4keDgYGJiYli4cKHbeTp06ECzZs349NNPK82XEEKIC4fU9VLXC+EtEvQLUUf069eP1atXu7ZXr15N37596dOnjyu9qKiIn376yfVFwGazMWPGDLZs2cLSpUtJSUlxtXDrus4dd9zB4sWL3a7z3nvv0atXL5o3bw7AkCFDyM7OZvny5WzevJmLL76Yv/3tb2dsuX7vvfeYNm0aM2fOZPv27cyaNYvHH3+cRYsWuR03ZcoUHn30UZKTk2ndujV33HGHq0vj+vXrGTNmDA8++CDJyclcffXVzJw50/Xe2267jUceeYQOHTqQkZFBRkYGt912m2v/k08+ya233srvv//Oddddx9ChQyttaV+7di3du3evtPzPZNWqVaSnp/PDDz8wd+5cpk+fzvXXX0/Tpk356aefGDNmDPfdd99p4/p69OjB2rVrz+maQgghGiap66WuF8IrlBCiTnjjjTdUo0aNlM1mU/n5+cpsNqvs7Gy1ePFi1bt3b6WUUitXrlSAOnDgQIXn2LRpkwJUQUGBUkqp3377TWma5jre4XCo6OhoNX/+fKWUUmvXrlWBgYGquLjY7TwtW7ZUr732mlJKqenTp6suXbq47Vu8eLHb8TNmzFBJSUlKKaX279+vAPXmm2+69m/dulUBavv27UoppW677TY1cOBAt3MMHTpUBQUFubZPve4JgJo6dapru7CwUAFq+fLlFZaJUkp16dJFPfXUU25pFZ3/xRdfVM2bN3dtDx8+XDVv3lw5HA5XWps2bdQVV1zh2rbb7apRo0ZqyZIlbud6+OGHVd++fc+YJyGEEBceqeulrhfCG+RJvxB1RN++fTl27BibNm1i7dq1tG7dmrCwMPr06eMa67dmzRpatGhBXFwcAJs3b2bQoEHExcUREBBAnz59AOe4NoCuXbvSrl071xOA77//nuzsbIYMGQLAli1bKCwsJCQkhMaNG7te+/fvr3DyoGPHjrF3715GjRrldvzTTz992vGdO3d2/RwVFQXg6o64c+dOevTo4Xb8qduVKX/uRo0aERgY6Dp3RYqKivD19a3y+cvr0KEDun7yT2VERASdOnVybZtMJkJCQk67vp+fH8ePHz+nawohhGiYpK6Xul4IbzB7OwNCCKfExERiYmJYvXo1eXl5rkq9WbNmxMbGsmHDBlavXs2VV14JOCvl/v37079/f9577z3CwsJITU2lf//+lJaWus47dOhQFi9ezOTJk1m8eDEDBgwgJCQEcM4afOr4whNOjLkrr7CwEHCO0bv00kvd9plMJrdti8Xi+lnTNMA59rA6lD/3ifNXdu7Q0FDy8vLOel6Hw1Gla1Xl+ocPHyYsLOys1xRCCHHhkLq+6qSuF6L6SNAvRB3Sr18/1qxZQ15eHhMnTnSl9+7dm+XLl/Pzzz8zduxYAHbs2EFubi7PPPMMsbGxAG4zAp9w5513MnXqVDZv3sz//vc/FixY4Np38cUXk5mZidlsdpvU5kwiIiJo1qwZ+/btY+jQoed8n23atGHTpk1uaaduW63WCivmc3HRRRexbdu209KzsrLctvft21ct1wP4888/T1uCSQghhJC6/iSp64WoHdK9X4g6pF+/fqxbt47k5GRX6z9Anz59eO211ygtLXVN7BMXF4fVauWVV15h3759LFu2jBkzZpx2zvj4eHr27MmoUaNwOBzccMMNrn1XXXUVSUlJDB48mG+//ZaUlBQ2bNjAlClTKvxSAc6JdWbPns3LL7/Mrl27+OOPP1i4cCFz586t8n0+8MADfPXVV8ydO5fdu3fz2muvsXz5ctdTghP53r9/P8nJyeTk5FBSUlLl85+qf//+bNy48bQvFpmZmTz11FPs27ePjz/+mP/85z/k5eWxY8eOc74WwPHjx9m8eTPXXHPNeZ1HCCFEwyN1vdT1QtQ2CfqFqEP69etHUVERiYmJREREuNL79OlDQUGBa7kfgLCwMN555x0++ugj2rdvzzPPPMPzzz9f4XmHDh3Kli1buOmmm/Dz83Ola5rGV199Re/evbnnnnto3bo1t99+OwcOHHC7fnn33nsvb775JgsXLqRTp0706dOHd955h4SEhCrfZ69evViwYAFz586lS5cufP311zz88MNuY/FuueUWBgwYQL9+/QgLC2PJkiVVPv+prr32WsxmM999951beseOHdm1axcdOnTg8ccf580338RqtfLoo4+e87UAPvvsM+Li4rjiiivO6zxCCCEaHqnrpa4XorZpSinl7UwIIcTo0aPZsWNHjS19M2/ePJYtW8Y333wDONfuXbp0KcnJydV+rcsuu4x//OMf3HnnndV+biGEEKK+krpeCO+QMf1CCK94/vnnufrqq2nUqBHLly9n0aJFvPrqqzV2vfvuu48jR45QUFBAQEBAjV0nJyeHm2++mTvuuKPGriGEEELUB1LXC1E3yJN+IYRX3HrrraxZs4aCggJatGjBAw88wJgxY2rt+jXZ+i+EEEIIqeuFqCsk6BdCCCGEEEIIIRoomchPCCGEEEIIIYRooCToF0IIIYQQQgghGigJ+oUQQgghhBBCiAZKgn4hhBBCCCGEEKKBkqBfCCGEEEIIIYRooCToF0IIIYQQQgghGigJ+oUQQgghhBBCiAZKgn4hhBBCCCGEEKKBkqBfCCGEEEIIIYRooP4/sfW6TolcRxQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_TE0_TE0_port1 = np.abs(full_TE0_data[\"Full Port 1\"].amps.sel(mode_index=0, direction=\"+\")) ** 2\n",
    "full_TE0_TE0_port2 = np.abs(full_TE0_data[\"Full Port 2\"].amps.sel(mode_index=0, direction=\"+\")) ** 2\n",
    "full_TE0_TM0_port1 = np.abs(full_TE0_data[\"Full Port 1\"].amps.sel(mode_index=1, direction=\"+\")) ** 2\n",
    "full_TE0_TM0_port2 = np.abs(full_TE0_data[\"Full Port 2\"].amps.sel(mode_index=1, direction=\"+\")) ** 2\n",
    "\n",
    "full_TM0_TE0_port1 = np.abs(full_TM0_data[\"Full Port 1\"].amps.sel(mode_index=0, direction=\"+\")) ** 2\n",
    "full_TM0_TE0_port2 = np.abs(full_TM0_data[\"Full Port 2\"].amps.sel(mode_index=0, direction=\"+\")) ** 2\n",
    "full_TM0_TM0_port1 = np.abs(full_TM0_data[\"Full Port 1\"].amps.sel(mode_index=1, direction=\"+\")) ** 2\n",
    "full_TM0_TM0_port2 = np.abs(full_TM0_data[\"Full Port 2\"].amps.sel(mode_index=1, direction=\"+\")) ** 2\n",
    "\n",
    "_, ax = plt.subplots(2, 2, figsize=(12, 8))\n",
    "full_TE0_data.plot_field(\"Full Field\", \"E\", \"abs^2\", ax=ax[0][0])\n",
    "full_TM0_data.plot_field(\"Full Field\", \"E\", \"abs^2\", ax=ax[0][1])\n",
    "ax[0][0].set_aspect(10)\n",
    "ax[0][1].set_aspect(10)\n",
    "ax[0][0].set_title(\"TE0 Input\")\n",
    "ax[0][1].set_title(\"TM0 Input\")\n",
    "\n",
    "ax[1][0].plot(wvls[::2], 10 * np.log10(full_TE0_TE0_port1), linewidth=2, label=\"TE0 in port 1\")\n",
    "ax[1][0].plot(wvls[::2], 10 * np.log10(full_TE0_TE0_port2), linewidth=2, label=\"TE0 in port 2\")\n",
    "ax[1][0].plot(wvls[::2], 10 * np.log10(full_TE0_TM0_port1), linewidth=2, label=\"TM0 in port 1\")\n",
    "ax[1][0].plot(wvls[::2], 10 * np.log10(full_TE0_TM0_port2), linewidth=2, label=\"TM0 in port 2\")\n",
    "ax[1][0].set_title(\"TE0 Input\")\n",
    "ax[1][0].set_xlabel(\"Wavelength (μm)\")\n",
    "ax[1][0].set_ylabel(\"Transmission (dB)\")\n",
    "ax[1][0].legend()\n",
    "\n",
    "ax[1][1].plot(wvls[::2], 10 * np.log10(full_TM0_TE0_port1), linewidth=2, label=\"TE0 in port 1\")\n",
    "ax[1][1].plot(wvls[::2], 10 * np.log10(full_TM0_TE0_port2), linewidth=2, label=\"TE0 in port 2\")\n",
    "ax[1][1].plot(wvls[::2], 10 * np.log10(full_TM0_TM0_port1), linewidth=2, label=\"TM0 in port 1\")\n",
    "ax[1][1].plot(wvls[::2], 10 * np.log10(full_TM0_TM0_port2), linewidth=2, label=\"TM0 in port 2\")\n",
    "ax[1][1].set_title(\"TM0 Input\")\n",
    "ax[1][1].set_xlabel(\"Wavelength (μm)\")\n",
    "ax[1][1].set_ylabel(\"Transmission (dB)\")\n",
    "ax[1][1].legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5668f46-b658-4884-b93a-cbc03590c5d7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "applications": [
   "Passive photonic integrated circuit components"
  ],
  "description": "PSRs are useful devices that control optical input. By splitting and rotating its input, a PSR can isolate different modes and transform them according to our needs. This PSR consists of an adiabatic taper to convert the TM0 mode to TE1 mode and an asymmetric Y-junction that separates TE0 and TE1 modes. In this way, our PSR is able to split TE0 and TM0 modes, and rotate the TM0 mode into a TE0 mode. Devices like these allow us to modulate inputs and interface with other optical devices in a controlled way.",
  "feature_image": "./img/PSR.png",
  "features": [
   "Anisotropic material",
   "Eigenmode expansion (EME)",
   "Mode analysis"
  ],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "EME, eigenmode, expansion, splitter, rotator, Tidy3D, FDTD, mode, conversion",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  },
  "title": "Designing a polarization splitter/rotator on thin-film lithium niobate"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}