{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2519a2a0",
   "metadata": {},
   "source": [
    "# Bi-layer SiN edge coupler in the visible frequency"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12cce5cd",
   "metadata": {},
   "source": [
    "**Note: the cost of running the entire notebook is around 20 FlexCredits.**\n",
    "\n",
    "Photonic integrated circuits that operate at visible wavelengths (400-700nm) are gaining interest for emerging applications beyond traditional telecommunications. Compared to PICs designed for infrared wavelengths, visible light PICs can potentially enable new applications in spectroscopy, bio-medical imaging, quantum computing, augmented reality, and more. However, efficiently interfacing visible light between optical fibers and nanophotonic components on a chip remains challenging. Due to the shorter wavelengths, photonic waveguides must be smaller to confine light, leading to significant mode mismatch with optical fibers. Efficient coupling techniques like inverse tapers reach the limits of fabrication capabilities at visible wavelengths. Novel solutions are needed to address these challenges in fiber-to-chip coupling for visible light PICs.\n",
    "\n",
    "In this notebook, we demonstrate a SiN bi-layer edge coupler design for efficient and broadband fiber-to-chip coupling in visible frequency. The design uses a thin top SiN layer for mode field diameter expansion at the facet to improve fiber coupling and a thicker bottom SiN layer for tight mode confinement and routing. The design achieves a coupling loss ≤4 dB over a broad wavelength range, which is 3-5 dB lower compared to conventional single-layer edge couplers. The example is adapted from `Yiding Lin, Jason C. C. Mak, Hong Chen, Xin Mu, Andrei Stalmashonak, Youngho Jung, Xianshu Luo, Patrick G.-Q. Lo, Wesley D. Sacher, and Joyce K. S. Poon, \"Low-loss broadband bi-layer edge couplers for visible light,\" Opt. Express 29, 34565-34576 (2021)` [DOI:10.1364/OE.435669](https://doi.org/10.1364/OE.435669).\n",
    "\n",
    "<img src=\"img/bilayer_edge_coupler.png\" width=\"500\" alt=\"Schematic of the edge coupler\">\n",
    "\n",
    "In another case study, we investigate [inverse taper edge couplers](https://www.flexcompute.com/tidy3d/examples/notebooks/EdgeCoupler/) at the telecom frequency. In addition, different grating coupler designs including the [uniform grating coupler](https://www.flexcompute.com/tidy3d/examples/notebooks/GratingCoupler/), the [focusing apodized grating coupler](https://www.flexcompute.com/tidy3d/examples/notebooks/FocusedApodGC/), and the [inverse designed compact grating coupler](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd6GratingCoupler/) are also explored in various case studies. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "078e5bb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e2a66e4",
   "metadata": {},
   "source": [
    "## Simulation Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46d57530",
   "metadata": {},
   "source": [
    "The wavelength range of interest for this edge coupler is 400 nm to 640 nm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "daf4ef1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "lda0 = 0.52  # central wavelength\n",
    "freq0 = td.C_0 / lda0  # central frequency\n",
    "ldas = np.linspace(0.4, 0.64, 51)  # wavelength range\n",
    "freqs = td.C_0 / ldas  # frequency range\n",
    "fwidth = 0.5 * (np.max(freqs) - np.min(freqs))  # width of the source frequency"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31924aee",
   "metadata": {},
   "source": [
    "The bi-layer structure is surrounded by a 2 $\\mu m$ top SiO$_2$ cladding and a 3$\\mu m$ bottom SiO$_2$ layer. We do not model the air region above the top cladding and the silicon region below the SiO$_2$ layer since the mode diameter of the single mode fiber is only about 3.7 $\\mu m$. In this case, we first define two materials: SiN and SiO$_2$, both of which are described by a constant refractive index given in the [reference](https://doi.org/10.1364/OE.435669). Using non-dispersive media also helps to reduce the computational cost. \n",
    "\n",
    "Based on the [reference](https://doi.org/10.1364/OE.435669), the optical fiber used is a Nufern S405-XP single-mode fiber, which has a core diameter of 3 $\\mu m$ and a numerical aperture (NA) of 0.12. Here we assume The cladding material is the same SiO$_2$ and calculate the core refractive index based on the NA and $n_{SiO_2}$. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ff6e1104",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_SiN = 1.82  # silicon nitride refractive index\n",
    "SiN = td.Medium(permittivity=n_SiN**2)\n",
    "\n",
    "n_SiO2 = 1.46  # silicon oxide refractive index\n",
    "SiO2 = td.Medium(permittivity=n_SiO2**2)\n",
    "\n",
    "NA = 0.12  # numerical aperture of the optical fiber\n",
    "n_core = np.sqrt(NA**2 + n_SiO2**2)  # fiber core refractive index\n",
    "core_mat = td.Medium(permittivity=n_core**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e903b22a",
   "metadata": {},
   "source": [
    "Next we define the geometric parameters of the edge coupler. The physical meanings of the parameters are schematically shown in Fig.1 of the [publication](https://doi.org/10.1364/OE.435669). We assume a 500 nm air gap between the optical fiber facet to the beginning of the edge coupler."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b3757b55",
   "metadata": {},
   "outputs": [],
   "source": [
    "W_EC = 0.15  # width of the tip of the edge coupler\n",
    "W_base = 1  # width of the base\n",
    "W_tip1 = 0.15  # width of the tip of the bottom SiN\n",
    "W_tip2 = 0.15  # width of the tip of the top SiN\n",
    "W_wg = 0.38  # width of the straight SiN waveguide\n",
    "L_EC = 200  # length of the edge coupler\n",
    "L_tran = 75  # length of the transition region\n",
    "t_1 = 0.15  # thickness of the bottom SiN\n",
    "t_2 = 0.075  # thickness of the top SiN\n",
    "t_space = 0.1  # spacing between the two SiN layers\n",
    "buffer = 3  # buffer spacing\n",
    "r_core = 1.5  # radius of the fiber core\n",
    "l_fiber = 10  # length of the fiber\n",
    "gap = 0.5  # air gap size between the fiber and edge coupler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "721f4adf",
   "metadata": {},
   "source": [
    "Given the shapes of the SiN layers, the most convenient way to define them is by using [PolySlab](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PolySlab.html). The vertices can be easily calculated from the geometric parameters defined above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fde9e903",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define vertices for the bottom SiN\n",
    "vertices = [\n",
    "    (L_EC, -W_tip1 / 2),\n",
    "    (L_EC + L_tran, -W_wg / 2),\n",
    "    (L_EC + L_tran + 2 * buffer, -W_wg / 2),\n",
    "    (L_EC + L_tran + 2 * buffer, W_wg / 2),\n",
    "    (L_EC + L_tran, W_wg / 2),\n",
    "    (L_EC, W_tip1 / 2),\n",
    "]\n",
    "# define the bottom SiN structure\n",
    "bottom_SiN = td.Structure(\n",
    "    geometry=td.PolySlab(vertices=vertices, axis=2, slab_bounds=(-t_1 / 2, t_1 / 2)), medium=SiN\n",
    ")\n",
    "\n",
    "# define vertices for the top SiN\n",
    "vertices = [\n",
    "    (0, W_EC / 2),\n",
    "    (L_EC, W_base / 2),\n",
    "    (L_EC + L_tran, W_tip2 / 2),\n",
    "    (L_EC + L_tran, -W_tip2 / 2),\n",
    "    (L_EC, -W_base / 2),\n",
    "    (0, -W_EC / 2),\n",
    "]\n",
    "\n",
    "# define the top SiN structure\n",
    "top_SiN = td.Structure(\n",
    "    geometry=td.PolySlab(\n",
    "        vertices=vertices, axis=2, slab_bounds=(t_1 / 2 + t_space, t_1 / 2 + t_space + t_2)\n",
    "    ),\n",
    "    medium=SiN,\n",
    ")\n",
    "\n",
    "# define the SiO2 cladding structure\n",
    "cladding_SiO2 = td.Structure(\n",
    "    geometry=td.Box.from_bounds(\n",
    "        rmin=(0, -4 * r_core, -4 * r_core),\n",
    "        rmax=(L_EC + L_tran + 2 * r_core + buffer, 4 * r_core, 4 * r_core),\n",
    "    ),\n",
    "    medium=SiO2,\n",
    ")\n",
    "\n",
    "# define the optical fiber core\n",
    "core_fiber = td.Structure(\n",
    "    geometry=td.Cylinder(\n",
    "        axis=0,\n",
    "        radius=r_core,\n",
    "        length=l_fiber,\n",
    "        center=(-l_fiber / 2 - gap, 0, t_1 / 2 + t_space + t_2 / 2),\n",
    "    ),\n",
    "    medium=core_mat,\n",
    ")\n",
    "\n",
    "# define the optical fiber cladding\n",
    "cladding_fiber = td.Structure(\n",
    "    geometry=td.Box.from_bounds(\n",
    "        rmin=(-2 * buffer, -4 * r_core, -4 * r_core),\n",
    "        rmax=(-gap, 4 * r_core, 4 * r_core),\n",
    "    ),\n",
    "    medium=SiO2,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15bbf739",
   "metadata": {},
   "source": [
    "Since we already know the core radius and the refractive indices of the fiber core and cladding materials, we model the fiber explicitly and then use a [ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.html) to launch the fiber mode. Alternatively, we can use a [GaussianBeam](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.GaussianBeam.html) with the same beam size to mimic the fiber mode, as demonstrated in the previous [inverse taper edge couplers](https://www.flexcompute.com/tidy3d/examples/notebooks/EdgeCoupler/) example. We will run two separate simulations to investigate the coupling efficiencies of TE and TM modes. The two polarizations correspond to two different symmetry conditions.\n",
    "\n",
    "To compute the coupling efficiency, we add a [ModeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeMonitor.html) at the straight SiN waveguide. Although not necessary, it's still quite helpful to add a [FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html) to visualize the field distribution. Since the simulation domain is quite large, we don't need to store the field at each grid point to have a good field distribution plot. Therefore, we set `interval_space=(3,1,1)` to downsample the stored field by a factor of 3, which helps reduce the size of the downloaded data significantly. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "80af6e47",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add a mode source as excitation\n",
    "mode_spec = td.ModeSpec(num_modes=1, target_neff=n_core)\n",
    "fiber_mode = td.ModeSource(\n",
    "    center=(-gap - lda0, 0, t_1 / 2 + t_space + t_2 / 2),\n",
    "    size=(0, 5 * r_core, 5 * r_core),\n",
    "    source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
    "    direction=\"+\",\n",
    "    mode_spec=mode_spec,\n",
    "    mode_index=0,\n",
    ")\n",
    "\n",
    "# define a mode monitor at the straight SiN waveguide to compute coupling efficiency\n",
    "mode_spec = td.ModeSpec(num_modes=1, target_neff=n_SiN)\n",
    "mode_monitor = td.ModeMonitor(\n",
    "    center=(L_EC + L_tran + buffer / 2, 0, 0),\n",
    "    size=(0, 4 * W_wg, 6 * t_1),\n",
    "    freqs=freqs,\n",
    "    mode_spec=mode_spec,\n",
    "    name=\"mode\",\n",
    ")\n",
    "\n",
    "# add a field monitor to visualize field distribution at y=0\n",
    "field_monitor = td.FieldMonitor(\n",
    "    center=(0, 0, 0),\n",
    "    size=(td.inf, 0, 3 * r_core),\n",
    "    freqs=[freq0],\n",
    "    interval_space=(3, 1, 1),\n",
    "    name=\"field\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb918025",
   "metadata": {},
   "source": [
    "First we define the simulation with TE polarization. This polarization corresponds to `symmetry=(0,-1,0)`. To learn how to properly utilize symmetry to reduce computational cost, please refer to our [symmetry tutorial](https://www.flexcompute.com/tidy3d/examples/notebooks/Symmetry/). The simulation domain sizes in the $y$ and $z$ directions need to be sufficiently large to ensure the optical fiber mode as well as the waveguide mode field at the tip of the edge coupler decays to zero at the boundaries. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3e6ad3e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define simulation domain size\n",
    "Lx = L_EC + L_tran + 2 * buffer\n",
    "Ly = 6 * r_core\n",
    "Lz = 6 * r_core\n",
    "sim_size = (Lx, Ly, Lz)\n",
    "\n",
    "run_time = 3e-12  # simulation run time\n",
    "\n",
    "# construct simulation\n",
    "sim_te = td.Simulation(\n",
    "    center=((L_EC + L_tran) / 2, 0, 0),\n",
    "    size=sim_size,\n",
    "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=12, wavelength=lda0),\n",
    "    structures=[cladding_SiO2, top_SiN, bottom_SiN, cladding_fiber, core_fiber],\n",
    "    sources=[fiber_mode],\n",
    "    monitors=[mode_monitor, field_monitor],\n",
    "    run_time=run_time,\n",
    "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
    "    symmetry=(0, -1, 0),\n",
    ")\n",
    "\n",
    "# plot a few cross sections of the simulation to examine the setup\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(10, 3), tight_layout=True)\n",
    "sim_te.plot(y=0, ax=ax1)\n",
    "ax1.set_ylim(-r_core / 2, r_core / 2)\n",
    "ax1.set_aspect(\"auto\")\n",
    "\n",
    "sim_te.plot(z=0, ax=ax2)\n",
    "ax2.set_aspect(\"auto\")\n",
    "ax2.set_ylim(-r_core / 2, r_core / 2)\n",
    "\n",
    "sim_te.plot(z=t_1 / 2 + t_space + t_2 / 2, ax=ax3)\n",
    "ax3.set_aspect(\"auto\")\n",
    "ax3.set_ylim(-r_core / 2, r_core / 2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f48ddd0",
   "metadata": {},
   "source": [
    "In addition, we zoom in to the optical fiber region to ensure it is set up correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c184a52a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sim_te.plot(z=t_1 / 2 + t_space + t_2 / 2)\n",
    "ax.set_aspect(\"auto\")\n",
    "ax.set_xlim(-gap - lda0, lda0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd606e84",
   "metadata": {},
   "source": [
    "The TM polarization simulation can be defined by copying the TE simulation and updating the symmetry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3708ec54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The simulation contains 1.347661944 billion grid points.\n"
     ]
    }
   ],
   "source": [
    "sim_tm = sim_te.copy(update={\"symmetry\": (0, 1, 0)})\n",
    "print(f\"The simulation contains {sim_tm.num_cells / 1e9} billion grid points.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "748d8565",
   "metadata": {},
   "source": [
    "Each simulation contains over **1 billion** grid points since the length of the coupler is over 500 free space wavelengths. For `Tidy3D`, such large simulations can be handled easily."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c24da3b8",
   "metadata": {},
   "source": [
    "## Batch Sweeping Different Polarizations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61539ff5",
   "metadata": {},
   "source": [
    "We can run the two simulations sequentially. However, for maximum efficiency, we will run them concurrently by making a simulation [Batch](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.container.Batch.html). To do so, we first put simulations in a Python dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "796ba26c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sims = {\n",
    "    \"TE\": sim_te,\n",
    "    \"TM\": sim_tm,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54ef9361",
   "metadata": {},
   "source": [
    "Then we submit the simulation as a [Batch](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.container.Batch.html) to run on the server."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "91d3fbf5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7693cc798e91425196a9a36785e96bac",
       "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\">19:23:50 CEST </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span> tasks.                      \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m19:23:50 CEST\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m2\u001b[0m tasks.                      \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">19:23:53 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">26.339</span> for the whole batch.              \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m19:23:53 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m26.339\u001b[0m for the whole batch.              \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after   \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>the Batch has completed.                                          \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after   \n",
       "\u001b[2;36m              \u001b[0mthe Batch has completed.                                          \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "33dd637bd344493fae7a2608c5c8b33d",
       "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\">19:29:02 CEST </span>Batch complete.                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m19:29:02 CEST\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f0b2994c18c4e758ab00a698daca638",
       "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"
    }
   ],
   "source": [
    "batch = web.Batch(simulations=sims, verbose=True)\n",
    "batch_results = batch.run(path_dir=\"data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba663685",
   "metadata": {},
   "source": [
    "## Result Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e6e2a25",
   "metadata": {},
   "source": [
    "After the simulations are complete, we can calculate and plot the coupling efficiencies. For both polarizations, the coupling losses are below 4 dB in a broad wavelength range, which is a great improvement compared to the simple single-layer edge coupler."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c980d35c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "amp_te = batch_results[\"TE\"][\"mode\"].amps.sel(mode_index=0, direction=\"+\")\n",
    "T_te = np.abs(amp_te) ** 2\n",
    "\n",
    "amp_tm = batch_results[\"TM\"][\"mode\"].amps.sel(mode_index=0, direction=\"+\")\n",
    "T_tm = np.abs(amp_tm) ** 2\n",
    "\n",
    "plt.plot(ldas * 1e3, -10 * np.log10(T_te), label=\"TE\")\n",
    "plt.plot(ldas * 1e3, -10 * np.log10(T_tm), label=\"TM\")\n",
    "plt.xlabel(\"Wavelength (nm)\")\n",
    "plt.ylabel(\"Coupling loss (dB)\")\n",
    "plt.ylim(0, 10)\n",
    "plt.xlim(np.min(ldas * 1e3), np.max(ldas * 1e3))\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff63554f",
   "metadata": {},
   "source": [
    "Lastly, we can plot the field norm distribution to visualize the coupling process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ae17ae3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IMv4AR6UaV3W+XtCPPkpR4fmuoiC0bTNNaWyvXoNe73rqVi3IKakD56Fo90xNxDNHZPIHEVe4KMN05Q8DlQOxcuVKrF69Gt/+9rcxf/78NP50ZGQk3ZTjzDPPxAte8II0GeX9738/TjjhBHzmM5/BSSedhJtuugnr16/HF7/4RcBa+AMPPBBLly7FxRdf3NPCc1bTEuOypGoge7OL41bLUVoUS21+0g8MFOGehhaHAYfx6zJmCbz9WO828deTXbp1oplYKwKarex7h+/PUUcdhW9961u46KKLcNlll2Hp0qW48sorccYZZ6Rt/uZv/gY7duzAu971LmzevBnHH388br31VgwNDfXzTqY1pgWPYKZBSfEHIuHMadCPqRw6MxfCBuWuc05Dz8crQ1kIUT+Ub7DBNkCV3aO22ZgCWX8PmPjcFmW/hK3wFXu2AqNqpInucnP6gi7lmp6fzliY/KHVmQdiuvKHgVIgrr32WgDAq171KuP49ddfj3e+850AgMcffzzd9AMAXvnKV2L16tX42Mc+ho985CM48MADccsttxhJdf1eeJkDwYzv3aKUyE+UkD9QysMAwMUo/RolcDEDXnKuz8L8oPwOcSQ/Cl3M6w1veAPe8IY3FJ5njOGyyy7DZZdd1u0sZzymA4/QvQ4EkfKHTClQ5+K00EY7ZcAawRJ55HuY8ZIy4U6hNwFv6sOT2oRiFgj9Uz3rMgyMMpKj312GxWGCIygGCUQmf+jQA4Fpyh8GSoGoYi1Zu3Zt7tipp56KU089tfCafi88ZzWw1GLErX0QzHG7k39KXiJD2J2cCjoTaz2aQoJhlw0cNFdsj0jvJr3PfjwrPfZR9kJMxPr3q88ePRAe/cF04BEMHDzZ80GGLunJqZoSoQR+S4nQvRA2sr0iqlRpUigyAtj7FWU99xumF6J47Kx9Eew1aZ/T1U+PQeW3Xqc704ZWmLk1UhEuUx6KFNXy53EQvVE9P/PC8kBEnSsQ0xEDpUBMCzAGnoQuISGMitCrTeWKXNXlsB64aUN0PAYROYJoKJ0enYJFEVhLYxDCr6OHAyzjB0po1vkDKoQeocQzrXsxTHTjhegEekiWRV0GQli2lBJH6NGkYRoYomwf1rTEABn9mBAGf2AdJlFPV3gFogtID4Ss+cu5XEKdqHdrFXIxDF1bTwl3L16IKSGq+n1OYt5+GYHxClqCKr8HnzRvV1/RTwYTR2ZlDf/8eBRAeiCC5G8zxJXIFOoNLwQApuXQuZJT8/kRuhfCViLsfYnaC9XmrsSOjcMMb4rj3SoJBe09F6LKRnsdKvb9FkCL1qRSUnoHw/SgAFQZR/c+tN99ul/osABMlWNVwFjvzwEJkz/E3gPhUQBuJcUB2VtpM4jyzeRcBC/7bhNbAlWIPbU3CZtqTPEcBshKMVXoT2ztBCsPrt+oCkNox5z79fs3fQiTRzWY5bx5V8nORdfkk6zL4PZCZIJ8wn+Sd6hYeejh3e+bJ2AKjRfaPXQtuHezDl14dnqmdIx1t8v5bIegnpKopyu8AtEFOKuBMwFi2g6jHew43dZD0WYzNwZW0QtRHB/q9Gz0ANWHqfRMoPLQTjDshWlNE+FwOiQGTgn6vUkQkqQ43ao0PR4RjykA54FZZpVx6VlgAKOEZ1jV+ux8CBSUarVzJKQSUcULYSsLDiUifyfGt5RPdCpUGgK45YXQlJccPetKeO3AC5Hr317vifNmtFVE7L66VcRYatksnQs6kAMqV/eaKPTT+9DrtXofOn+YJSGuXoHoAulGcpQniPJ7e3SeIzFxqObZUFBz7tYi1IdQmGki4PcDVbwHRW3MVSpTMCXcSmAfMVG/22RYyXJJ1IPx7noMKJQikBTZKHrylZKgKwYoqurnSLRWvbiUCAndC9FGiTBQoDzkUEbLy3IS8mNWTZx2zaWMZjmVkzZj5Dvpgyelah9F9My6viMlxDF2+fWB5n3osixrm5DswUKPMkkuidp7IDwKwJHkQLAALCXQHNIx4HA7gwNMEvbsfCcvkplD0JnAjxwB6M9L7FYEeopz1WN1q7bvNvSlDFOZgOfAZJT3M8oST3bd9n7+ho7yjX1Zu9h7IDyqQVZgYiDGwREa5Vp1GMqDphi4YOdJZG2LErQ5GBNpQjVjcfK3CrkVaV9yDhUUho7DmiwPeJn3w37fDeG1ZE7WuaJ+i4tKlN2LOzHbOYtSb4Zbkaqm2DjGKVAi2uWvFCkR6bnkeWLOtZnovR0q9j0R3uV+QHgPhEdF8FRx6A2S2KPDahkuwb2i9uzcTGwAE2NnUfLzZIQhZX1PUTL7TEArlnkQCjPsOfToL5hm+WcIQJDPjgw7shKplfJQ4pVWQl0aluQo+eqq8lSYUO3wRhTfTLGwXarwGNe0yQPUQ23aJW93glLlBBXoYDeCs4s/FysRxXMrgEOJqNyHK6TJtd5WLoRHGwhh8oeWT6L2KECAGjhYalWKYSZLFyW45S1GOmFXVqLEW2FYajqzzutE3Y5VlP3pcxNtvRrZPFwJ37ycuFed90QKZP1MpLaIdzsFoBcFoZcV6eRa1+9XzQsxeVYWpydhMhP8ohbgy7h6VABjITgxyV4TviDt1kpRYPkwpaovLBOpt4BRdkzCViLUfASIVEUdpIqNzIvQry9DZ4m1eR5U5Pkg01NheR5KS8baKArLKrymYAzVnbFvBfKKTq7vMoHb7Y0x0LYvkW9b2odj3NL55fd+0L1kRcrUpOVDDLLRRgiTP/gQJo8icFKEh4MYwCizMCkwjWASuOF2rvTCOQRVN5krtwh1nfBUFsbTjUDetRBflQBODAoF10EmZm0xQJaliQhB07vvV0cRAZH2DE7nn99j4lFafc+RO2dX9XNAeSmgQpMcG9BlSgTLhTMhFfakdyTjDdXogclbqnrgYzcPyoUzoTzXoQPLvKlEoIB3lCsPcPxG5UpJ1XDe6opN8fGC3JLSOTkUGEDzhOSvLXsWJz2RetD5LcHkD/GAz7dP8ApEx+AIUAMYBwdHrL3MLC3v6nBfqUocgJEPYYYxaV4I1c61mRCyeMX0mJO4x+k52acew20SHWeFDZegrL73rBDo3/X8jGpCX0fJxUVu9uTfXvsxUKHqxWSi8ryd1/YxF6Lf69FG8TCej354KlqRD2HyqIQAQZITp+c4BCBGmUWXhMO63kaQ1x85pRA4vBGMBSAiSyFRSgYSehsmoU/oIIRWs0xXfKcyz4fl9dDugRW9ShWEdBOZh8Dt4dC6rqKcWKFdeaWkfd6IfaxcsSnP93B6RHLzrxKS5biWsdT7YP62bVO1K8NdobFCiNh0oLV2CJPfidqjCCx193EwWzB3tXVYjOQxrV1BrKqq4JG90g5iWGgZKtiNlKh6cmkfrO2dKAXFyAiNu12PSV6ztN51TnGcDsS6IvqafB4LK4l65qyTR//BNTqtU+DMW2DScadCoZ8H5bwUetK1rkjkvRFIqzRBN5ykXolq3oRu9gSwd8YuzMNAWbhrOc/L9YeMb7XNneggX6Cyt8Rxrd6uqI/CvqzjbkWijZKUE+BtJca+H94b9Sylj21K2E82L2asdx3Jl3H1qIoQIUSiRCBx9pFWqi+NcW1jMWKMDC+EJOjKMqTnQ2RVnHTvQ5HiYBLXAiXCBUflC/Wy66UIM8GMOwVQnUgWGpcqWberhtrkK0X0rGS0U5zaxbAWXd9OUC+o0lF5Xk7kXdm97/3RBwJZ1YpZhZVNtEDvPRAelZAZlzJ6wwGKJc1j5aEhOszE6ETJYIGkm2SXgJX/kgo9oeSYlXwt+U2QvFW6V6I6qs4/S+RGidcj0HbKdq2Ba3KB1aaov85yOwqh9dOxt8Qxh8I+CvvS4FCKqIymW3w5113yjJreB6k85HM5O6f5nVeMnDz01cAkyCdRe1QDJ56U6GOmaK52HbWSqc1NhYThaciHMmVJb1KJUMK2nqzsVh5cxFYlaANR996EKsJ01bYaoZuwcqHOOVQtRzfR5eoSlK1TO+XDPmyftzZm6ie6+s1mgrDdEkBTtzBN5WQ8BhlcMy5lxzJeYQvgLo+EgvM4ZRuYuvgLlGCon2OmpZlKNqurBpsW6QGhygCST8DNqknpBq92wlYVD0kWtuvO7SiuQFfmWTH7Kdpwz91v/pg7/0CbSEnfjjZF+QuufhzXAZlskQ9dUpcVhGSnXam+ivd3qqxEVOUTE5KD2CNBj8nkD9HsYBBegegCTGMQMg/C1YY53c7ypJ5YHWtXZC7mrHIGybjWTLvQRimKSXV4EBjL3jnGwFJXoe7VsK+FJVCXh2u5YAqyJclhTIBRXjTtfHOzhFF1JUBPcHJxOwZR1RTYIwHtpLJJz0pep/N05vxMPSgWoFhnCjNAKfKYELDEuARLLFGlXUlXCtokTqfNEiVDeiG0BG2jL30wYeztoPiH8lSoPAloykRvsOPm9W9m6JSpSJClIMROhaFK+JQdKmUeb6c02GPGRrtihaRdSLEdYqVd77ynKjyoXRK2qy9HBadsgsac889ju3Hcz8/gbhpno3ejIREZ/IF8CJNHETg4ArVBEBLPA2Ubyimrke52RqqJSyYgWARKbFW2mxmUuBFZlPxEcaKMqBfZZS1QY9rWLaWMIA22IvCUJtgJ2S5CrZeW1ZUPVzK37EQkypFUOBgyomUqKypmV7eIwFRs0uOJpSRRNFDQrqivrA8yFBZYbbK1zEIH3G1EthaO36HS+TLSnMbw6uOb542+HV4gdf/Vw8XKiV5bK5IRv9qmP9si1ony1AYTwrZaseWBmC7M0WOywcC1fYKyHaI5GISdl2bwhvbWbaZCNNP8Bb0ABUuVBiIBsCyMCCzI/iazLzWHrJ/2wk/ZnhVGX7nQKZh0NBfi4+An2piF4xihWKbyUEQyypWS0Jh/1jQLlTJR7CUxh8lfT5YA3x6BMbf8eOVhX/Z1srmtYHFNebL76reX3uqvjB9UpP9l/jE4/u4ZwvJAtLwC4VEAyQhY8lK2cueL3NHKakSKGGqeCHmhXc+ba+FMqm+3pyAjrnnrjyT0WRiTEtpzwrymoGSItb6SPAiH0mBvdKSd0FbN7VpNlQ1XzGpXyb08V8GqatiUrWQUj8sdVh23EmLchzGYqwSf5S53KiBlmzKVXWspcBMWWuR4/ifE7Tx5oJhAmlt6Gt+KxyTBrPQjvQaZEmEj23TOjaSKkbYbNVSIiVEJUCkPmUHJ3mBOeSFS5SWX6Gt6SIx7cLUr82CoSkuaxwNGCFUAU+Uvs8CXeUqz6wxHTGkeRHtrv319UcJ5p1WpMsRp/kFniEvGLHuGglzIGBG3FEJZkck81rtAnNvXarKTpScSZPGH2CsQHgUIKIRgIWJqpZWYOASEKtVnGIZ1a0tCuIUUmImEtCQl1iGmWRaIIhDJ+MPU7VygJNjj6MK8tPyEeW+G1bY4jyJMSHIMQpx4WtTFgeP6LPlbrkNgKSsu4VL3WKg7tNrrbXS+7PBoALpCYodowaGwmMng+T7gVIA6PZ/OV0t8c503NkJyKoyuBD2H8pJpnW0kXp6GuCkCnz5PlfYC6a9FymUtKh1/MqR52wPhNQiPAjCLFkjuIAVwSspvGCCRbTpXBFIhUVnCMRk7XSeWdtvzYHspUiOWzqSKw5ioSiIycwusafla24pPmvJizFMz+HQhXGaVCwPtfvS5GapFxV5NBYexvIemozyS3Hp2qjhkoxajaD7qGlPsk96GwHgGpIFTGTdVCV6Xp0Q4lAEV6lwQ5lVCO8nx2xfxg7ZJ0BWMVpRw7p5heyB8DoRHR2AcPNlxNLUSOeL3GQGchxAiMpKujYSzpBoTECVVJTLlojjZTlnYbfewy5uhWyBsxcEV05mFQLkrWwSa0uLeUbsoTCqNLdXzPFBg0U+VCC15zG5neA1soZ5roUWO6hVOgbugjTOu1UW0LKVI+9uuUa57SQBTobKvd9U3N583PVSKW3GznQr8xQlypVa8gs2jJhV9snJRTCB9cyCvP3gUQO4HlAiGiZEJZUnLjFsGIlenmvKRCnqSfat9rglxYowiwzug8ww79Ckr+GGHMZmJ0O3gUkCYVeXJCKcirf90DpWGcuzinV2fVX4qD/MxuivMWbCGMbwm3SHvcSlvXdxWT1DXryj7vYqusataJcpDmjdJjjVx50P2b4M5M9S3LcoKj5TMJ/3ley1VL2Dyh1kS4uoViC4QgiNGgDgJX1IWJmKJdyKpmeRCzCIAHAHnpVYigRjEAghHBYSicKHcLtQIct4MqQBYBMMgOokQa5RwU5sixUYMp650mMxCEb44sW6R4aY150mpa9XcmdXl1lXeE83qb3hAYs3jYVrn9XZyTw677nqm3MjzSYxwztKeLVN6HpbCw5AkcpeHKLnPa0PkFCp9eIswMuY8R+k9KSVAMQYOQgQGpq11bCkLSglEongl5KLNPiJ2Gd9ia2J1Raa8n6J59A/UEqCmFafr4eGA8kBQwgcEOAJms9qiZ79IANQEfuvBzmhdTctzUMK63oN6J+P0eGE4Z5vE6nb5Dy6vR3YtZeNb99NWkXKNZYcZWXMrE2jdOX/drUlVVMkdqdQPkVOZaXdP9jWc10AUpboKY1oIU8qEFN+v5/JfVIRBEX0uDpvllrHMXaAlVaMMY6GeR+Qw/Ln4qd6+3/RbkMEfqDU7+ENHCoQQAnfeeSfuvvtuPPbYY9i5cyf23ntvHHHEEVi+fDmWLFkycTMdWHAwBnBCqkRkgUZ2QrNIFQxisWV9zh4+gUiyFwK4K3S+pH63GQua92YoV7jLAZgmfytvhr2jqbFPBUsVBzWysq5kidtKYYDRr3Hf1t4XGfKJ4oZ3w1IysnK1MHfzdigjcpygwGUemLumMmEoF+Z8HCFKqXJXEKLU5rz5gxSFKMFRxSOfyA69cgiR9nMn+TDQv9t9VGfiKsGdVMJ8JZgxy4XXDUjuBLVMBuHLuObh+UMGjgBx8o7xlLpz7bykSUIT4MtDKWTbvAdaINATp5O/hcNTHKTCa+AUhvW5UJvSqswy+OTPa8JULo/ATPzOz7Iz6J7aXMXDNmFYKS/VrPNFdgrq0IDh7IOoYwXJ3VFn+3e42iolgLMAwnoeWMrbXeFLKj8iTmizbnTKys+78hz0/aRck8wXHVHXVW9bCf3mK7YC4UOYMuzatQuf+cxncO211+L555/H4YcfjsWLF2N4eBi//e1vccstt+Dcc8/FiSeeiEsuuQTHHnvsxM98iiDF5IzIKQsTY1ySeG0PCJfQJ4mVMK1F0N4QEhCsDoEIAYlSawWRPV4egmK5wVCSZ5FVehLOmFunOzeZB1ESTpUqBDy1VEAjSKnSweCMOzVc6iQkG2EA6UJ+Lt8ja8NyeQNKOM8YJHMI32ZCuUjnlldszPP5NqrvYg8QY25LVtamKvEqqvoBy2LjKEPIlMUnSGM9GQuTOSReifQ7T6xQlFqgpJIVJYwgcLqVU+uSHtKlQsQMIp+3HJnMRFOotApSediW2wJXun6+pI55ZcTWMFOv0wwMPH8wIfPiWOKllO8l1yy/0lobgCg2fb9Mp3/58FdOcXqdam/U6GflpTNVzoQAFWjsxV4Ds1X+XI5P6fkOlnJiehxi7e8eXqqcl6OcbxYWOmlz7z2XvG3zG1VFP0rvKsUpDBoYpyxngbEQnIVgYBCpoSnhq4msI9fQpt9BZshjcfJD81wuYyoHaJ4JMy/R8kw48gzTcR0hzDmeo44V5eyxEqWm+mKa/GF27CNXTYE46KCDsGzZMnzpS1/CX/zFX6BWq+XaPPbYY1i9ejXe9ra34aMf/SjOPffciZjvQCKr+Z2VcOWpRT4DIYZIFA0UEQEGABE4MQhGBoE1+0rCdGxGkCa/mlYeI2kbKEx8c1lviDGApKqkrDDZBkWaZV0rD5grTWsL2KkXRfMEkC6A2xZ75QkJLMWMmcyAlMPTjitW8852YTV2GDXa5c+bbXRi6FZmMkuW24uQPwe4FE7zFbUFeFdYhDyve39YpvFpO40Gjuv0UKGSsoTQCDFj1jHtBtOfnhthTXb73CNs35PtTXHMOxtzYiR7ahFEU7PQ9jDMJz/5SVx00UV4//vfjyuvvBIAMDY2hg9+8IO46aabMD4+jhUrVuCaa67BwoUL+zH9CYXnD+VgibdBKQ5prhMLc+Vbecl7JxiT5/VwKBZCOHiE3m/23snreIFCTRU8EMIw86B4DwAthEo46k5lAr7KFeleILYVIukJded0wLJLpPs1afTJabdwhIRVRTtlJptXXqkpVBQ0/t3OiFjcj+IRWclaZTziSf+MEqNb6sFmmsHJysFTUQlMJOcyfmmWhFX801Is9I3xDMUC5nHS+bOZo6iHOhk8J2ck5JYxqzeQgMkfotLmbTFdeEQlBeKHP/whXvKSl5S2edGLXoSLLroIH/rQh/D444/3a34DCSlKx5IhaEuYiqAJk3AxA5HE8cuG+b6JYoRJiJO02hQTaMFMD4URvpQIv9IDkYxZ0Upjh0EpTweBcsTKLFWYEWoq8Z7kwqws7wU0xcHl3bA3UconZTsYkiPxrmjt7LmVztuab66PksoivSablTGO1D3NA8SiKZ9JVgdDDYzx1Ioon9UsDCxjAtLaJMPymom3KdT6zgQipJZEplX0UEQ+I/ySoKvfLmEeVj6Ky+KkQpxs65ObQSg+p/bC6IN1KWEIBlPostOf/exn+Kd/+iccdthhxvEPfOAD+Pd//3fcfPPNGBkZwapVq/CWt7wFP/7xj3ub+CTA84cMLKH8ilYoSy4S5YARKwxhce1gXQZV+6/7Nu7jensXDSctOculvNhtkPC1nMdEF+yLwoYqmHKF82VsL7Qruqk8EqVWfeb2vLRD23wHu0xujm+1H8N9/9XBmfIcRGCsAY4QnNflOYoTYx+BsxCcBzJnQsRJTmWQFIapgUEZDjgYy0KbMq+3dWssMHYQBxIDoFbd0aj8qHgGAxhpGw7qtJ/Mqo96JUhYSgjllIoeIEz+0IvTezrxiEoKRDvmoKNWq+GAAw7oZU4DDz1HwDhewCi4ikuFnbjrQg0xi0AISy1FxGJNNHLF0ctjMSJpM0qS1VzExibSuvWGEEtmUKAQuIXqpDRtAUHOhzBpigeKhWJ1npyeg/RAWuPcTsgzkr/0jZUK7sUsQWjHcgbOTYpsZaYsTpUxBwMxbrZACaLsHopASWgZUwpEYjniPFEMUgtRAM5lPyKWygRjYbIWKpkusKxIckNCGAxAeQh4lkeiPEqGm9n+F6Ynw1UdS7cWKc+G5vkw3eixNl5seUK6h4gAoW350o3ut337dpxxxhn40pe+hMsvvzw9vmXLFnz5y1/G6tWr8ZrXvAYAcP311+MlL3kJ7rnnnoEP+fH8oRjKkMQoM4gUKQryTDl/YJbVNyjwWBTxmXbhMzptdfEKAaG9TzIU19nGGDNpR8i1txUNs5/Y2X/Wr7w2590okd4IjvAtZv1rtM/60sfRC6XYf7eHI3S56LsDNl+tkjWifhOXQsktHslZAJ5slGtWBuQZb+BSWJdhTIo/qAIdBKZyLlMjlYo4CDXDmV29EdqGuZkHnaArI0qxCNJj5vol1RxTZSORJ1Krk3Zcf5d6zG8hMvmD6NIDMd14RFdVmMbGxvDLX/4SzzzzDIS1Zfeb3vSmfs1t4KEzh4wxAABHiACMXHsD5MM49BdYJHkKxEQhMZIEQMi2ZZYWRhAsTjwHxbGmZVYeoSkpsjpURoiK5+awvLQRho0EPocLWp9nUSiMca5MMNfOl1X9KFOYDGVJnbPG6TVOtZfrlRUm4HXEcRMIhhDwIYR8ODkvIMQ4OKsj4A0AgBAxBEdyrAYwjjgGGI/AWB2c1UAkwDlAFCbKRQiAgRgDUZJjgVASeK67xUPD48J5LRkz84Rk81Yuc+VGlxXJkFrKdAUq84YAkO2UYmHHx/YAivMeiFarha1btxrtGo0GGo2Gs4+VK1fipJNOwvLlyw3mcN9996HVamH58uXpsYMPPhj77bcf1q1bN/AKhI3Zzh+4pgAHFCa8IUwVBJsv8JzCjFThsNvY7VwKh10uWodhibUgvdRmOx0mjda9EOVeijKUeWHzikhJfkeb/SpEWhGrfF7tvQztabKAMIyG7ZAazSru7g2GZL8pVnmdladIBlCb/F5QBK7C23iIMBhGnQ/LKIKAwOMQhCGEfAg1PpSWoSdOIBoCGBDwekrTCQKIAUp4gzJQIeExnKvnPci8QBCS7zNhRHWonEfJR/RCKMJK7E7aU5QoYdZmuEYzVXFQf6e6L80rb8ziD7F8tjvhD5iGPKJjBeLWW2/FmWeeieeeey53jjGGOJ752SPc4WmwGUSgQjwsRqBeeEbMyThIinYAFRNh1UaUxKkCSCoriAKLkDknncBk/WQCO1Gcm09+XwL1d6z1Ue6iViVPUSIw6+7lygS5rUGhCiNwr7/OiPRY4VzcbZtKJkZbKxa650S7JEmeIwRnAYhq4LwmFQMAMbXS7wGrSRc1DwBRS93U8malu5ohkP0wBiFU2FES6pQeAxhCSeiJJbHalCkaTGhxpyr8LIvDzSpxBYYlihIlQoVQMbWjelKOVnpDkrXT9x2hxCLWh+oposUgWkagNb7zne/g7LPPNtpdeuml+PjHP567/qabbsL999+Pn/3sZ7lzo6OjqNfr2G233YzjCxcuxOjoaM9zn0x4/pCBJ0mnHKFhUOLW/jRFvIA7FIHC83YIXxvVuVCBSFBEHVULJfgX0ciq4Znt6Jzdv52vkLYj07BVxid0HpwLtapAd9vxIHf+SUn71ErOK23cJzRFryqbKAtji1hLeh14wiNYHQHqIBaDUwjOa4ngX0PA6wCTZegFxSCeVBrjtSzxmuJEuWDZhnSJZ0Jel4UepwY5VVo8CSdLy6mT8nwgDZFNq40xh/Evfaz1fEEzx0NCq0yZeq27BwkY/EHEwKZNmzAyMmK0K+IPmKY8omMF4rzzzsOpp56KSy65ZFok+E0EeMIcOAUIUZcRrxRIpYFklaZAtgCc4S+yPGsaVmIR8zIrCRElCoQ00roIdVbvm1ICpo7ZrtZUYSkaj1VsZyTg5duV3VPar6ZM5MCstoXzqKBgWDG8RbkqWZ/UluEWjlUxGLIThUF5aOx/1TkdHCFawXbUAiAIhhAy0/pRD+agzucmm1ExxLyFkDfSGNg4HgPj0qUd8BoExRA8gqAYjDEELGnHm1LRSNoKRCBR00LSQoAEYiHnmSoyyXfOs5LBgPTAqTAqyVxCcJZ4LXicuL+VdYppFqdIc4FHiXci6LkshogYYo1BkJDW9BtuuMFo57IuPfHEE3j/+9+P2267DUNDQz3NY9Dh+UMWZqT4Q0ABQoQpb+BJpgRSL7YKiWVO4V+vsS8rqenHkR6HyhViVh8O2chUPvIheU56TeqcdqiAbLkOs5JzLhgeD+d0OqCZJRMyxzFDVpXQWUUhcnttJOzacdaF5fO0m1dUzqqEUxEI2/l21IJ5iGgcjXAEw3w3NGiurMDEARE0EbKG9EywOYmxJwJjAQIhaTLndYRJuFmMCIJaECICYxxcy50TIjK8YHoeiqoYqVd6Ii3KQvf+u/bAUnte2W3TNWNWW0hjWRTvAnfVy+8AJEz+ICKG3XffHY888ojRrsj7MF15RMcKxMaNG3HBBRfMWuagQ3kcOAUIKTQUB87SKHJDgVAMQBF/ZYvSdYySvVCk0qAKwlIxgU0VBMoUgCJ3tigg6kSkMY1ibwgs4ikKCFwRwc95SErcsu0tVsJo67JWGQS7guu76njl0NspdpJnK3asqh3e1g6uvI4wkGFLdTYHgSaEEwmEfA5CPifpPZIWJ1ZDyCQRYwEDEzxhBDXpZBfN1CMUJIoGEwyCx4klK4QQAWLeyhhCkvwmG/PE66GFfyUKhMq1kRavJDdDyFwSzhNGJOqp90Mm8sEQubJKU3HigejRPQ1AxAwi1nNkGGq1GhYsWND22vvuuw/PPPMM/uzP/iw9Fscx7rrrLlx11VX4wQ9+gGazic2bNxsWpo0bN2LRokU9z30y4flDBg6OIDEsyX9Vbb4slls9qbaSAI3+64pCYCgNUA1MxYIpxQRWP9rc2shLgvINMu+D1q7gehedLjPCuC32zBirTG4uMpA5w5bSeHj5t5sSF9xXG5m8WgZEmzyUtmNka1Ya1lVRvxqnOmqsgTiYiwabiwbNxTANI0IEwWM0+S5wVkedD6PG5hjlXVsJjQ5YLTUmBRQhRhNxEKmnPR0rDqLMqMa48btzh/Cvl6qHFo3gkhFUuXr7WLYeZCiGIAHGAzAxBlBveyoTweAPQsh3vAp/wDTmER2v2lvf+lasXbt2ViXC2WCJ0lCjBmpUQy1xUYeMgTOOkHEEjIGrTeU0Aq+Ie8CAICmxyW2GkMA+rl4FoTatS+NVM6Su5QLioTMOoV2v+uqEIUCfg0PR0AmH4XbV4BqvjPDpp8o9NUXXu5WKKszJPU53lqn8GKqthIu/6+EDVXdQjSEgghicAtTRQEg1CAjs4tvR5LtQxzCGaC5iFqMezIWgSFqbSFpKmnwnIj4mdz9hNQiKEPFWqgAFkG7rKBhP3c8cAUQQI6ImBMXyOUgqjETJ7u0haokS3ExumGv7jUhrlColGAupiKhQK1mnXIaHxNTK9jRJrlOu8VhI7wznDQC72v4Gpb9PzBFHerng6te+9rWvxa9+9Svj2Nlnn42DDz4YF154IZYsWYJarYY1a9bglFNOAQBs2LABjz/+OJYtW9bTvCcbnj9kCFFL+UONhQhUXgRLNh9N3uEgNSZlL76tEASp4SnxXrPM8KSeylBdyzNPRJZnp+nvzOQDqgxBRhnztEXnJy6+g0KTiLofNVZOfk//Tvu3+syOV6PZzNqQ2G6Xmzc5TurukoquE6H1xVkxD26HdgqNbWB0e2fsazLPiu1liXYOY5zti0Y4H3PFCBaIeRjiISKKsTMeQsgbABNS3hE1cHBELEKTNdFiY9JIlXjaACBmMSLWdHoOYhZBJN4LWIqjsGqfElyVKHmaj4mcwVBIHqDluxTuM5LwGs5qEKIlX5oeQMQM/iDizjwa05VHdKxAXHXVVTj11FNx991349BDD83V/H7f+97Xz/kNLGSatPwvRIAa4wi5VB44g1QmeGYJ4iwj/EqxCFme2OuENlAlkjUCD6VAkC506p4I9UKa0L0cugCdejRcRFSDK9VYV2iqQJQQfXvo0uS6CgQTTq+MhIs4d2vJMa1xGeHKWb6KXOhtFDfXWFSgYLiGaQmBMTGCkMLkieVJLfcA42wIDWqgQXUQEUIWQrAYoaghTCwyTVZHkzWlNVUEkjkgYw4BkhAmNBHzKE0aFSxGhHEIpnbjDUGMENBYsuliHTEixCQZEmMcMUXS+5CUHk434UqYEVcKBBg4l+MwIS1ScjfVGAyRsUdJHI/lqqV1AyEYhNBCmDoQDubPn4+XvexlxrG5c+dizz33TI+fc845uOCCC7DHHntgwYIFOO+887Bs2bJpl0Dt+YMEB0/DWsNEzQ6Y9EwHifAfMNO4pLzWSPlBnmcYRimW0XVVRoAxIGCkXU9JCeRMabAVjwxkbIBpC98pvylSICgbQxekFcpEKpcwbCgSbZSAIprICtra8y46VwXmnK1wZGsd2vHKovsvQxWjn5IL5G9qerjmBDUsiOajwRoYogbmhjXUOYMgDhYxcLEAAoQ6QoRJOe4IdbREHU3WACOGWpIDKvMlCU3Wcub1xRRLrz+ZFcWICUSINfUJaf6m9CxQmk+qisPk7zFO+U22NpT9pXkyVAn8ACHGeC0rFdsliGDyB1H2tOcxXXlExwrEjTfeiB/+8IcYGhrC2rVrrd132axgEJwx1KiGBtVRZyEaPECdc9QDhpAx1Lj0MOgKAmdSHAo5IeTJOQaECbEPAARJOI08Rxmz0CwattCevhLEzO/6fAGAUWqhshFRdr2rD2691jp0q4sgBl5Wlo+q1eQni1nZxva8Rcl9Yy6vit1fxgyLX3ibwTDrX9Laqd+pyNOjQ+9HFMxDX7FYEVDtHmxLl7022yPCvNZu6TPJGYMgwlg8hJYg1DhDnctRxuJhgJJnNbmBVizk86HNNyKRjsOZFH5aQoAEpTk9kYgRkbQhQVNam2iBE08VmUgRfALiZL+SmMeJi1w+cU22EwAQUh2CxWjxsTSutkU7kzvliKkpk8NZkHo7xrAZIW9gbPzZwt+3CqKYI9IsTOQI8egFn/vc58A5xymnnGJsEjTd4PkDkt2BahimIQyxEHUeosZZalQKIBWCgFuKATOFfKVQ8EQp4InXOkiUg4ApbwIhAKVtuEbreSq8yvNIr6MkfIqsuUsUGd5Fwb4q9vug+AXT+IEaTyT1hlzj6uMJQ/gsHsO2Txv9MnJeZ3ynzPPiUnxsFHsZWE5B0I1WrmMufq3zEVcfzu/EkhrYbj5edF8cgKAAI9FctOI5aIQMc0KgxhgiAsbjALsiaSQKEx4CADEBLUGIkqIV6rlUxs2IyOBTRCrs2u1BoUTYNxUo+T0mYTyEWQK/+WzGZBaMEVoOaMxiQ9mIEaeVLlvhOIKeQ5iYwR/iDj0QVTCIPKLjVfvoRz+Kv/3bv8WHP/zhtBzXbESYhC3VmVQeGgFHnUMyCg75AUPAEwKfEPWQATVOCNV3TgmRl22ClAEIhFwKkLpQLoVwlr502XH5r00sWcpUZF+B1pf6KyaVyGQeT/tI+8qvgyIYRePq7ShRIIQlJNvMRDEpV59UQCDLw550wm7bpDKvThHaFvajcqVLEXhXG4WoZPxsnMy7pFaBMzKO293UWwyNgGMoyHZMiImjKQgtAdS4ZBYEYFzIPRM4BwKmFAgGtcEmTxiHII44ORYmP3IkCDEhZTARBYhExjCUNbNF9YThMMREiEikoW4RSXUjEsLYM2WcyRyOkEJELEKLNWVeBhjGWCMVSSKuFIhQhmKhiYjvSnNAeoEQDLHoH71bu3at8X1oaAhXX301rr766r6NMRXw/EEipJr0TPMADc4QBgy1RDHnLDMgKaU+0EKLWOIJCJjkDQFDygvC5DgsQ1No8Y8ABJZ8ZxrtZyAEnFIvBSwjhR7CZHu+1d+GV6JAkaZUCcoa28YlYwcXp8GFmWOXjOsaD0UJ4pRtTklWv4oXdrL9pD7HODcvcy1dx+x7LPO86IqF+jvzKJAzAkFX+vTZKWVuj1YdcwJ57zVOmBMAAReIBcO4AMZCuSYhp4y+C6AluFQgWKYUE8lyFYIofx9UzOcEbENYwjtS5UNfU8rWKlWyZOM4fXaAWMv/VAqMnIsskUKC0ESE7XwegrjunlhFEJn8QVDvtG868IiOFYhms4nTTjttVjMHJMKM9DZIz8NQwNDgQC1gqHNJ5Os8sxpJhUEeq3Mg5CL5LpI2IlEaSP4dyH8DLnIWc0UkdCJKFsmTNfkJHJT2wTmBc2ESGmLpB9ZLqZDmbjAyLEpEMqxDzUn3QDDlRmdZn6qNy8qurksVpCoKhJoKsVLrvxqviJgT5RlSTjB3eFkYA2KR9W17J7Ix4LzvgIl0DPVvbM2fawww1pQwpilpuvJo0+ehIMRYzDHEVcyoshAxjAuGGifUk3k0Sa4FT55VAGgKhqbI5hIn12YeCPlpCY5YZGF3kZBWqFiYilRTBKn1NCapeKTeNQoRg6QyIjKFdTwh7kHAEAvCOMVpzaUxMZQyhhZrIWItGdbEBMaxC+PBdtTZHPSKifZAzBR4/iARUog6z4xLtYAlBiPFD6ShSb1rulcBqUEpCXVNDE66osDU35pCEDIh+0t4CE9ofhkfSI1DKupPo7E2rQdkulEVD4TiP6oPloxheCT08CbXWJZhSo2dCf4sxxP0ezLmo9FkYQncMuGVDD6o5qPGcN1ndk3G54QVuqLGso2A+jHFG5TyZvNxF1zhYtlFeaOYzvv0vwU4tkdBQn8ZQq7kFsn/JP3P5huyjH+0BEt3U+AaD4+J5RQFkRjQlMERVlgXpXwymy8lvDu2LG9khE9TdozM/tNKlYlCkioXmodkWxRgG/ZA2CM9F5PggRhEdKxAnHXWWfjGN76Bj3zkIxMzo2mCIVbDnKCGuTWOOSHD3JBhbkhoBAJ1TVEIEqWgwQUaPEYjFGiEERphjDCMEIYEHgiENQKvCfA6wIcA3uBAg4NlZip3LAyz3zztJGNAjYPVAiBgYGGY9JU86GqTJ5ElQqS5B5qfkSmpUL9WXR8TKE42/oJ2DeMZN0zmnLYTwqTonFm+TqFM3dn1DhMVZVK9y6mQrVWsSrlly5Ke1imPbaZR3zVfKQmSv0nSjhRVimGU/GOcpfdIiYuCFOVTzDrIKKFoWS4ElrVRoZsUJeuY5AxrucnpfIVWrZQxYOsfh0DEMDTUQhxzxDFLmWYr4gi4QBgKwwXLGKU/cyviaAmeushjwSUzSJSVkMsJR4IhFlK5YolC0RJcHteKzY7HPA2zUAxKKRcRyX4i4oiSsAJBDGOq1CuApgBaop6+DjsSTiUA7IoIcVKxKRaEXbEA4xxDNAejyNfW7gRxHCCKtR3avQLhhOcPEvP5EEbqIeaFHMMhQ4MThgLpNQg4IYTkEVI5UJ4E5SWQu5+EiSAXMoF6IJWDWiBSw1IQSEUgCAhhGIMHBB4SgjpJ2sIBXktK5nMGVks0kprFV7SE2pz1ApYkqnhELinBlhhFvn89RgvIYl50+q/3pdPkhEYbZVXtWB8g41X6PVhzpViY5+Isbp5EZhyqCoq1ecRk0PhKdVw1mk+xkdyYLIeaE3N/VzxIGyPlNekckyE0fsI4IMaBPf+4A2FN8UgGzkXCt6SBMI6ZoQASyWNxHECIJEw7URaVUdFWpGLiCStmRpgVjHDsLBJCUBY1EOu5EoYnPovEUApQrIyFxBBpfQgtAkDxGQDYsK2O2tg+QK1Z8ONUAxEz+IMqTz7T0bECEccxPvWpT+EHP/gBDjvssFyS3Gc/+9l+zm9gccCS/bCgFmBejWFBDVhQI8wLBYYDgSEeS0WCC9R4jFooMFSL0GhEqA3FqM0VCOYwsCEG1gjBGgFQC8CGasBwDZg7BAwNAUMNIAxBAc/t3AzA3GGZBJgw3QcUcKBeB+o1UFgDajUgDC2pMxGwSUiinyoiGpUJA/lvwIFAezEEAVEkCbDQJFueKBrqo8ZQ7ezNpILAZFZxDEYChunBZdBU54WQknNmRjMZWkvF2cs1Iu0emJqLiE2zjr5GsdCUrYRiCpLrEsWJ5J7MV40d8Kw/db1a37S8lnZTrZYk+Go+ao5BMg8BudbqN1aMkvNsbvo8kz7oyU1AIwSb1wDGWqCxKAuMHo+zOAoAaAmTmXOAmrE8riw9LZEyIwggqQoLapqMk5oE0ZRKj0j26SHBEI0n1WQCQERZ6TupwEimFEUcrYQACwJ2RZJMhYwwFgcYjzkCJpnAtihILFsM22OOVqKQjAtgewSs/U2M4aCGXzsen04QEzPCE6oHOMwueP4gUd93Lmo0D4yHYAGHCBhajEEkIUwxA+KEVIZp8rOZPK1e0zDxaMs8OUrizSn1UgQMCIkQxgJcEIKWHhIrkkRt6fVW4VFhkAWSmqFEugFK8QLd65v3pKLgfWDG31LZ0WEkWDsEdsPynBgtzDHzFylvtu4p1i3+rvkLzZPhso7beQN2m1gwwxtRVs0q7UPLA1G/BAcZ9yissNfyHIh8dK4haKfXmB6IpgjAhwn1WgQOQiQkDSXIxEy1WjznhWHJvibZuXR4YslOJ5SOzxNBP3A8KO3CrshaB1fSutI1sxAmlv6t+og1ZUKxsDHxNOaEYaZhdQmZq6GHsvXU3bRBxwrEr371KxxxxBEAgAcffHAi5oSrr74an/70pzE6OoqXv/zl+MIXvoCjjz66sP3NN9+Miy++GI8++igOPPBA/P3f/z1e//rXp+eJCJdeeim+9KUvYfPmzTjuuONw7bXX4sADD+x6jrEgmUQkGJoxMMZZunGcIIaIBCIuZGUmIWQIRMzQiBjiVozamABvCASNJljIgAYDbwRgwzWwbWPAUA0YqgNhABZa0rMro1kkwq3uOeAcqAVArSa9D7VQCpUBN5UNEplgqisRUBl8iYDPA5BGTVmqQMR5qhoEprIRx1o7++1imdIhNGFbCcTSxJE1Vz5jJWzbcVd2SY84ztYHkOuSrpvIri8qmZGOkxwLmEmwhbZuafKDFsykPBxxbHpe9PWJ5NpQqgAwaSXU5kJRnPllmSZhZKYa87cLOGg8Ah+uAZyDIgFqRmD1RGnl3BxvOMhxThYGiVVO9s308UlLerAoJrViBDFJ61gs0nnWW+q5UvPN1lZEBIoSpSOSjIgEEDUzz0jUkgoGT7whu5q1lPHvaoWIEmtVS3DsjANs2mMIQwFw26bePAaRYGjp+0DMEgbRKSaDP2Aa8Ii5NAdzwxqGA445IUeNA/VAMtw0R45lHgmWbH3ItUTogClPhAqBFYlXIgt5lQqG9Eik4a+hAOfSY8GDJJQ08VQwDjBOYAGBK/LDNK+oKfUni6M973a9afXVYV1XXlIg8YIwU6vQ7V/qvCEcKseA7hTX4yEdQrO9J6grB8K+NjVwUEZz5NwpS042blYZmchoT0IaSdLrLTucvU7pNZpnXIUE6/Mxb4jy41shWVkQgRn+pY+p/o5jhpFF46i9oA5wBtrRQrxNgFrSyMMbDKgx6bmgZG/OmLSkE+uhiUmr2avx4ZRVmyXIjWqLusdE86CQ9czZydnq91TrD0agmBmVkKR9VIXhqdBrhv9qbsLmsQbG4x7LfJPJH/qZLzfI6FiBuOOOOyZmJgm+8Y1v4IILLsB1112HY445BldeeSVWrFiBDRs2YJ999sm1/8lPfoLTTz8dV1xxBd7whjdg9erVOPnkk3H//fen5a8+9alP4fOf/zy++tWvYunSpbj44ouxYsUKPPTQQ13v+vfok09gnjOEidqGMA2FEeq9hDDBCuosCmEKZBgRqwVAyKV7txaYVFUJvnYIE7I3lQU8M4vllAI9hCkjGoxZ8zZCmCyCbLuwY2EGNRaVxNDd6TozsUOYEuWoUgiTHlaVCuYVQpj0MKcuQpgoTghlJs9nbRICqBgqxdlxO4QpDXcipAJCbW8ONhxCbG0h3kEIhhjY/EDeX0uZhmRIAQsSBhQJKeUoJmybVLSEE5tJp+1Ts5C23io4lptrBmQKBEi7VwGIllowQtxKGEPi0Wg2gzR2ebwVpla3KGZoigBLhodRD4pqiFVHTNxypfsQJhcmmj9gmvCITaPPQtRDRCFH5AxhQuUQJuIxiBNiRhBJCJPgAiIJYYqTEKa4RmA8SaBWIUxBEurI5LvtCmEy9pSpGsJkHy9yQeghTLohjJk0xBnCFOZDmLImfQxh4lq/RSFMTk3EnG4awqQpZLoCZXxXx3QFiCUJwhYPyIUsQVn8WcZfYPZVGMKkC+TJv9GfvAB04AuBIADbvBl4dpP0iIcBMH8u0Ghk3vYoygyNykiovO2UGMl077lCK8oMhEFgyh8uCM14qP8L21Whed8B06ioGzZ1I2Es5H0AWDi2DQs2j2FnPOaeR0WQFWplJ9PPVDCqui96Gzz22GP49Kc/jauuuqqnfo455hgcddRRaT9CCCxZsgTnnXcePvzhD+fan3baadixYwe++93vpseOPfZYHH744bjuuutARFi8eDE++MEP4kMf+hAAYMuWLVi4cCFuuOEGvO1tb6s0r61bt2JkZASLFu6HI5tvR7znOOawGoaDGoZChuGAO5KoswgRVxJ1XbMohUykykbABGqhMBLfdPplJ5fpbln9x2SJmzLgIokmEkZiW5qMLbiRuKagNP1sQ6N8ErWMecwnlzEHT1AWAFflDt1FquIl1RjMsgAVJVJnE7DK2ZFZ0cNVEUPFXbqS3fTRdetQmjinuYqztadcYpyeLOe8Z0cahmHE0SxLak3Kkqg5Iyyauwt7L9wOXiOMbw0wNlbD0HALw3vH4CFDvJMQN4GgAQTDMk6aWgTRJPC6DLNDLSOMSvBgtcD02AQsCbdKlE5bgQQkE7IRpA8X0nIbqXJB5oOfZSpm4V+tjElQpGsqUgkS25pgdY6j//FWjG58HFu2bKm8O6hCvV7HjYefiRcOZzuAvvvnN+FD1/w9zjjjjI76mgjcddddXV23//77Y7/99uv7fFzoF3/AgPIInT/8ychyLBC7YT4fwnAQYDhJotb5gazIpIck9SeJOkiUEo7qSdS6J8CV5KzzG3LIcLBosrMvANyKXylKokbKW8y+des9WXQwTdq258Dz3hH92nZJ1EWwx4Ymoxp9O0qbu5Ko1Xed98GxrnaSux12ZRcWye45z/sAYO/dd2DkvS8HvfCF2Rh/+APYjp3A8DDEwn2kApFNXi5oMENi/FsR2KbnsW0swiHHvacr/vCJT3wCd//jv+DjB2cezfWbHseVux7Aww8/PAGT7gwTyR869kC8+tWvdu6C+/TTT+Ppp5/uiUE0m03cd999uOiii9JjnHMsX74c69atc16zbt06XHDBBcaxFStW4JZbbgEAPPLIIxgdHcXy5cvT8yMjIzjmmGOwbt26ygqEjRZrYZw4WMwgIJOJmpyhJmBU21Al+gLOUmZR40DIeFqSz67dHSYuar1eNzSirWICUwKbK0VqmuBV/6ouN6vQl+rPiFN1mJliyvzS+ZxnU50RjnKnRdArKbhqaMvJMudxO18tV6UiN1amKNjOC/suiuZTpobrSooO2/Pbrk2WEGbWiyfrvAJnwGEH7IEX7L8VPAR2/THA2K4a5o2MY/iAGlgjgNg6DrFTgM/h4LsPgdVD0FgEGovAhkKw3YaAOUPZZOo1+RkeMnM66nVQvZbkhkjTJzWGgEY9sxLV6xnjKfMsTQD2vflXGN34eNfXx0mSuMIgeSDOOuusjq9hjOH888/v+74ME8kfMI14xDgbR6jl8YQCqHGeKgu6Y5kb+Q8sMUxLBUPG9DNt3yCkhQqC1JCfeTKQ5Aq5aL4q7aqX9YZlyChy4ioU5kAU0D/9UVD7HOljZX8nwri194NdabBoPH3ORfsGpRUBrXs1HSwsPVcV+lRi68qyila2AgKH3cQ2SsGhDKgy5a59KbL7kv/qTh9BwKa5L8Yb7n8SIasB9RB4+o+Ifr8V0TZCOG8rgoVbwHYflq6sZgQak9U+WBjIMOtGTU42irN8wFoSLh1q4qXyTHDI47YXS8+btPL4jInrbfW1FnHmcjd+GZZd41oMAOAhtqLXHAiTPxSVN54KTCR/6FiBOPzww43vcRzj97//PX7729/ihhtu6HiiOp577jnEcYyFCxcaxxcuXIj//M//dF4zOjrqbD86OpqeV8eK2rgwPj6O8fHx9PvWrVuN803WBCcORgwUE4jCtLRZoDaS4yypvy2vURvLycQ5ru0RobwFirADAQvS+t12SL+whH44hEjbkq36dXmKY+sau08XU4FFfMmaj0oG1PuxCaILdiRWGTNDaWELnWAXj2cyRzedKcuYtRUVm0A7POil/bjCrPT7sLvSfxvS3N2AVF63NeuIdzGIgDC2q4btY3U0GhGGIgHWCEAtIN4FsJCMSVAkwGIhew7DxN3OpUIwPASaO5zldTAODA+DGg35XTEPK4HWwCQqD9Dc/91CvttmNZBBwSOPPDLVU0gxkfwBA8Qj2vGHFmuhSS0wISsGhcQRCV1hYMZGcVyFGSX0Xt9AVFcwZL19lioATPVlt9X6yXhL5rVUm4uiYnCfy3Bj014XnTMiotSYegy/fs4xTieGoG6Q2xMiOa7zrjL+Y/TlVGyKvd5F+0Do/VWBi7/l+KTLSwFgLjHw3evAwr1AQw2wZgT+3E6EFIGPhGB7zgP2mC/p+dgY2K6mFPDrNWlYGk6MS1GU5Sw26lnBlnQCUsCnIJD8AchyL+HImVRICsgQ42AiSt1lFAS5IjLO65WnWv1r2jTlPOMYYvO2wvWtApHk3SlEA6RATCR/6FiB+NznPuc8/n/+z//BVVddNRAu/X7giiuuwN/+7d86zwkiCESIWAROHETSAxERR8g4AqEsRizbQ0FZllwWKCDNI1ZgqRAuH8osZJyM0mXqhahCaFOFW2svHNejwMKDAmKqNn0pg962DFk4f3E7J8EsaC7KSH4BsbaZWVUFpAjCUmaKlAM47pvpzEUxHEtRsyN91JgBY3hyVwN7PTcHYIRndszFtihEKw4QProF4ZxdGNscYOeOORgebmF4204Ew0C8A4h2cYRDYwi3NBGM7EgWhslqYfPqYHOG5MMTxfKhmDsEphSIQCbu09xhYHjY8ECQiidvtYB6QzKiaYCYeJqgjTbPxGyG5w/qPRVoMbnrOkgW3YiZfCfVzr2GssCyzRsVfQhUdR5NwVBeBcDkHboHI4Bqmwmp6hx0Y1LFeyXH3za9r/I6MIchucgRaRvCUCKc58chI1Q0X3XJHN9liOtagShsYXsm9LKsSQvmNhIV1fYoNE5Zx8oUkZExhtYfxtF4aiNYvQZ66o9oPtnC+LYAje0t1LEZfLwpi6/sbEoPhCBZiGNeQ3ohWJLjECXhTbVQeib0kFWSO5SykLs9EHaFR4VAeiWYZcZkdj6mXpnQjMmTp+2+1WImChDb1psCIcvGzr4ciN7279bw2te+Fuedd15Pfey1114IggAbN240jm/cuBGLFi1yXrNo0aLS9urfjRs3Yt999zXa2NYyHRdddJHh9t66dSuWLFmSfo+VAgEOQSIRogNETCAAQ8B4qkBwjZjZDMNWLmwLtB02pCz9sbbtu01YU4HVRVwst4C+2YoLwiG02nAJtsX9lLMa1VcZ0SvrwdV/mRJRpqhUOV+qoFTsQ/bT3t5Errhch5dEjccFx50bNuKZBdI9+9x4E9taDHs0BF70aBNDgcDmVg3bW4Q5ocDu9SZqXGAsDrEr5mhwgQX1FobDzSkTrgUx6rUItXqc1vxmDAhrMYKGAEVcVngJCXw4qeKRJEuzOgMaKndBgNWT0jQq+VHPr1BJe5xJixYSK1M9YUzqd1YKiKCkQEAS9Mylt4TmzwNrNvHUU0+2Xd8yRIKltcMxYCFMAPCv//qvOPnkk1Gvy7X6wx/+gMWLF6cbuu3cuRNXXXUV/uZv/mZK5tcP/oAB4hHt+IOAQMxiRIhkUQAKIIhSrwODzBPiyHiAzFXQQ2hMBSNta/EMQCkQUsjiLO9VyB1jbsEdZZZ+y9g0ER6BIrioYzkvcak9rm9mP3YYbpV7ND3A7dvDCoXNGe+q8BS7ElFBG3PM7PlS31uPPYVH9xzF/D13AY0a4seex/YNhO3bG5g7t4m5WyIEe9ZlKeKxCLRTVgJkQwx8Xh0YCuW+HK0Y1JJ8htUDsHoo6XE6mEq6ZplioWujtcBMuoaytgZZkr0uHKVhTBrC0EpS52Y/urUtuZ6GhoB6Hdu2bG+75mUQ2t4SAIxwpkHARPGHvikQt99+O1796lf31Ee9XscrXvEKrFmzBieffDKQJMitWbMGq1atcl6zbNkyrFmzBueff3567LbbbsOyZcsAAEuXLsWiRYuwZs2alBls3boV9957L97znvcUzqXRaKChJw9pIBCIEQRFiBhLKjoTWhQhoAABOAKSCgQsF7GyAjHGUmtUUcwmCuI5CYSYsmoYLoKjF7gpI7RC3o1xb+3a5M9nJNAp5Fr9lgnLpZ6HJIbWFto5WHZMWWkqOYDL21QS6rsV/AvH7DEWU1sbBoZHtg0hYEMgAH8cE9jeijG/HuD58WHUAsK2FrCjRWgEIebXaqhxwnjMMC5k+cg5gSyDqpRMzpDsXk1GnfJ6kuipYj9V4mfIlcdMWk/ryW8ovXUi3Ygu87hl9etjqoOBUA9kxYyYGEI+jpAJLS87UTgpK4epGHrIYwwPtxBFHOSqM9kBImJoDfA+EKeffjqefvrptArRIYccgp///Of4kz/5EwDAtm3bcNFFF02ZAtEP/oAB4hFl/EEhQhOcMQgICAoRIABPa+Qz+TeT1dpYWpRHi3lP+UfGI7h+LG2n8ZhE0VDHjURlS+ktCl1qJ9C2NVz1CwUeYnseHXTjhO0hVigpvGT1n5XLgCbcV1cmEoOPzsfS+VTrRM0BJfIAHPl7Y7HA75/eAwetfw68AWwdrePxTbthczPEgu0R9tm2C/PnjoHzCM1mgLHxIRAY6mGMRqOFMNmALY4CWRpVAGGtiSAcAw+zcDUirXxwmP0oaUnZIKssqENVE9Nlf/mdZS9NcjMsZJYCkbm9WMDyCgSD3IdrZAhs845K61wEAZM/RANmYJoo/tCxAvGWt7wld2zjxo2499578epXv9o4/81vfrPT7nHBBRfgrLPOwpFHHomjjz4aV155JXbs2IGzzz4bAHDmmWfiBS94Aa644goAwPvf/36ccMIJ+MxnPoOTTjoJN910E9avX48vfvGLgJYMcvnll+PAAw9MS/QtXrw4ZUDdQCBOEqZkTXqQzFuQIUYBYggElJF5nTjwhIFEeslk7eVvZ30nolSoLxK41XklBCshlqkXWiVwaW3c4yXtmGjLILJ+3P2Vj1OshDBi6bGqAnZbz0KFjWMqWYIqzEfoeQkq4bxi8FNJCnkFcDyPbXh+vAECYXOzia3YhWh8GAEaqAUM21sxdkQxGjzAWLJBW1MQxmOBkDEMhww1nuXcyIpiMs+HEsMSknyfENkGPUGiRCgrJ0FdizSZniOr7KiXDldhGnFC6xWRomQjrZBlKxGy5MlJqp3oYREcwLwwQpOKRKXqyG0kN2AahC1o9Km4XseYaP6AacAjiAkwCiADXePkeZVGpoCCxHsg3ymWJu0yQ1EAAKbTjdQ+knkuoAxMZCseticjO+4KoXRZwgut2iUCt9nOfUIXtNvBDsnMn68uXJfBuO90nub30v713bFzfRcrB93MVW/Tbi2rjLcrbuJ32+ej8fjuqHGBZ3YN4YlddWyLGOYGIba2api3YxicAWNxgKbg0mHABYa4QC0x4LTibBfokAnUkhLE0BKKeVoNLB+xwJKCMjY4o1wUBmcyf8b2oJnFALLx7aqQejXJgEcYnr8J23a00AvIClsapCRqTCB/6FiBGBkZcR476KCD+jKh0047Dc8++ywuueQSjI6O4vDDD8ett96aJrg9/vjjqdsFAF75yldi9erV+NjHPoaPfOQjOPDAA3HLLbek9b0B4G/+5m+wY8cOvOtd78LmzZtx/PHH49Zbb+16DwhFcGIm3dMCHIJxtMDlf4yBEUeQ7GpTZBlgyW6Ouf5TS7tbYCRGICaZU9kspcgvSoXvzGqSnWOJKUAdk6pI7LT8Z6Pp5xKlpaLVN1Vy0k0RsnMqByTtq0KfVS1hLuGfITCO2xZ983rNjMIKhNSiHZYq3IeuVDFNkaji9VBosh2YOzYHMQQ28z9iO/0RW/gIdoztgxABdrCd2MW3IxQ1zNk1DzWEGGdNWSQADEPNYYQIkl9fWrlCBAgYB5F8KgAgZPJpVyvHkhjuLM6XUq8bAMREaSKpHbanR7wiKT6ARKFQlc0EEqWdm/3Znu45YQOtmHreaTQioGWEMHm4MNH8AdOERyCh0zE1AYQQiVAfJU83197n1CtNTOZMWMd1cItnFLVBB4UDmPU8twsDbeexttu70E1YZ5knt72SUI1mtjU8MRc9z/6tynsERPoMqLlx8L54zl1jFaHFxvHz5+dhc2s+QgZsaQEbd0mj0hAPsFsjwHAos3HGY2A8qbgS8kBWk0we10jLh1bFYvTnKt3uI/Gi2UVOVGiffnd6nSVXeHdWiTDzAZkeN7Naoe6d1pWP3WsCW5rNjtbUhrD4Q9Sbw3vaoGMF4vrrr5+YmWhYtWpVoTt67dq1uWOnnnoqTj311ML+GGO47LLLcNlll/VlfgYxZVK4JpKb04uEEHAWgiDA9MRLLQQnsxCZgqey9JdZ88sUA44g/S69ITGIKCcsuwT+bBLWV8qUkbYgd5hRYfMCYV0hp3xRpmwYa2cJ5a65ylAzrY0lxDPGAbSMc+pf5lAQXAqSakfGXDKLkXnvvYmh+hj6/PQ5x2hhR7gTxAR2YRuaYhs4OLbzIYSoYSfbhnGxDQGrQXABjhAxmmhil/SS8RY4QghEKdPjCBFQAOKZElujWmoRIybAKQBHUqUseZ45cfCkUoVgAlxIJdu1PrrFTu3wTiBwcASMI07uMUjuOyYBnlSoUR4fzhh2tELDA9Qtch6IAXNRDwomgz9gGvAIJHSZswCCBIBIvj2JIqEL6VlycyZAuhQDaB4LbvENpyLRoRW0SEkw25ge7aL2/RDYbWG9jKe0H68zY1bhnDoU3quEryp+rfPujsboko8QxYiohedbTcwbH0KdAVtahM2tFnZgF4aiBggN7Irlc9SMCU0hknxlhrryRBMQUVbcRS8gI7f20SIvtEiiXNVGx+PqVpCza/RVsFuqwgOU5B7pY6k+YgJ2NQJsa/VGzwkmfxCzhD9UUiDs7cdnOxTxFxRJBYLi1GqP1B0dgBEzBTuH0M+twD8h06Ol4I+84J+1kxUPXEIwpUJ8IvSTSOzHZl9lbmZYQjtBjuUSqG2hOf23hLCZMaOWB8KhWOnnq7Szd++EpUC4rGx62/R8GqTp8DAUeRes8zllx3FPrnuxmVXqjSlgYnkFiSBoHFtqMoF0V/xH7GpuQhy2IEKBgIUYj7ejGW1HwGto8h1gLERMLcQkd+YcY8NgLARRhBgRGAJwFsixSKT3yFiYWtCUMq0LOUr5YDx7VuTf3LI5qb/MkD9oijdHmCruAWuAKIZgam4hRLKNNWchatSQz4NzxaojEsz0QAygC+IHP/hB6gFQeQEPPvggAGDz5s0TNq7nDybksx4kFDeCYHHKD5QKnpcvVLW98nA73uE6KwOW03JuzJm0v3MFWh3tLe+Ao02R8GwLydWShssF61JFpAOhvD2l6FCBKNpp2ehR5GhlR2N0HNqqXyzwB/4o2I79UWcBtotxPM+fw07ahBofxjaxO4bGhoGkdH2Tjcn5ihB1qqcGoBgxYibXWeaBBum92AYil6el3XOvw1YqdI+c3jdPy9dQGiJo9xFDYPfxYexs9eaBoJwHgjne8anFRPCHSgrES1/6UlxyySV4y1vekmZxu/Dwww/js5/9LF70ohc5dwSdSZDWgigV9KH+VcIxczNVpRwoCET584ilIlEQ5mJ4A3JtuHE8RpTMT5Ra33XY1myiJKNC60MxJsa4ER6iKwNlgm52v5ZAbwnsOaHeEsoJcaHQDk3QS9tq49pKBlGJkkFx7vdMk9hJpGvGrGuzdY5zXoy88keF5/Q+zHPKKatinM1zQoyjSbvAwBGLFgS1EIlxtGgXYgoRi3HEYhwCERjjYAghRBORGJe/UwAwSAVCiKQN4+lvqAvryguUKkyO38UIj2PSm9EulIwlzIgQpwqMUoo525XMI5YMIpmDPBegxYrpVScY9CRqODYM+uu//mvj+0QJ+Z4/FEM+54llmUm+oRRst/GgXJCyxa62Aq3jJ6+Si1ZF+C8vtR1nibIF7VzvfWeqvkNpKeGZ3aIjAb1y2K55n536HRQt7EQ5cvdDGKNt2EFjaFENO9ku7MI2jNMOxIjAeYiIyzEiNo4m7YAAIUQNEW+Aq1wf1krlGs7DVGEmRrkIiWwN3MfbIRf2nYYp5d8dpVBIBYUbx5HIX0HEsZN2Vh7fBTuJOqbBUyAmgj9UUiC+8IUv4MILL8R73/te/MVf/AWOPPJILF68GENDQ9i0aRMeeugh/OhHP8Kvf/1rrFq1qrS60UxAWpJUs+rHWbSf/Ie4ofGa1+Y9B9DdwxSlHgMl/Ocs4OQW0I2QqWQcQXEq/LcjhkVWcBKxU+h2CYp6O7fHIm/pl+10ospy16k2qRKjpaDrM4ZF9M12+vnst7Lv3TgPSOJDthdAV8iyNhksK51qo53T1zNX51wPc7BqhrvOZ9VR1PEYQrTQjLeBsxCRGIcQuxABaDJprY/iXYjETkDwxNUbQIgWYtEEwBCLcTAegEQMkShRxm+uFAYWZomEBc8rGDdyIsz7otK/dYFLhVSpvwEgplaSXBqmz7k6Z3v5ukFM2YaLGEAFQlSwdE4UPH/IoEpXZN5SAiVKNgNLFPXAmQBrhvApC27vxR5s630Vgb447M+mae0FdifPsa7rxUdYybNe5i1W9KpE+O/Fyq8MTEVrpRugis73CjuXTzd2jtMWPF+fjzrmYBw7MBZvwli0VYa1hjHqfBhEAi0xjpjGQCTAWQjO6qmME6GV0fh0NxJkMkxBCK8rRKnoeDb/rG/9uwvKk23IYpa80gzG0OxRgSCLP/Sm1vUfE8UfKikQr33ta7F+/Xr86Ec/wje+8Q18/etfx2OPPYZdu3Zhr732whFHHIEzzzwTZ5xxBnbfffcJmeggwSZ2SimA8bILCKZblbLzemiRq2+ZUxGnCoC8MD+Pshh8dZ4gy82mQn0bYuTeayBTQlIBPAnPkpYQM2wmm5cmrKcMKS8om7WnRCpkyzAZ02uAHGPS2mp9GN4DJizLnu1hkIJx1p9+PqsrlLWB87zqx+47Q2B91z036jwqnreYJuXPKeE7Fi2p21CUeJSkMkBQv2sEIJCKq/qtEwFdEAcTIvld46SKS5B6ndQzwVhWicSlNNrHFNPUq5fYSkPhcWTKLCVuc6Xc2OcIAgEvL7dZBdMhhGmq4PlDe0hhR9W/z8xNZkWdzEMX257pkhAeM6m5XHQRtqHLGENo7boQpgv4mXltG2FfNz50IDg7Q6jaXU8Ff7fpo1KuXu4im16L0vNVUZTrp87Zni5X2ygaQ6u2C2BARGOIRRNCtAAmENMYmsnlMY0hiselcYbFCLhIqosJCBFl+w+xIKXzQpjPsStHsOy4c85W26L8ROUtz7Uj01g4LrahSbvajlsGO4k6tkWOGYqOkqiPP/54HH/88RM3m2kAMoTdROBPLEwy/jt5YZhtjU7aU2xo5TaRbOc1KKvGw3IW8KS/xHsgRFxAKfUEI/fLKAm/cFjMueH1yOYlDAGbVEWq1PtQJKgrSPesSyDPW+Z1V26stVdvMUv7NPvIYu/1nUHN8XRlqOx8bIxl9g+tDx29nFeKparLGOe8GyACYyEisUv7/QWImojjcTDGIagFEhHAYsQiyW1AJBlIkrMjhRrt2abAKlsYO44lQpElaTN9fai6B8L8m6fPl7AqhmVzleeI4uS+e0OMwfVALF26tCv38/nnn4/3ve99fZuH5w8SREJW6EsE5szA1P6p4RTljuWt88XCskvwN/gEmbwr37bD/ISKVntbMHQVmegFuf6LKhi2yZ9r10ffyiP3wauQdtXhPSmlQv0bi50YE1sQsyYitNASY4jFmKStEUfIZWGRmJqI46b0QPAAkQg1o1FkeIuRCvnZ81wku3RaYdDOBSyKmnCuhUO+EUGEKO4xB8LiD/EAMYiJ5A9920hu9iIj3ARtrwQVk220NMOSDA9DgnZeg5yHQMsZ0AVupEKaMJQHZ2hOgjQx1hzArRAkFnHG8v6YdGxD2I8SYqMUCVGgFEihnzE1Tib0Z/3qjClrC1iCPRHABCjN17D6SBMkRKJEFAjoSRsyFCXrfK6N+7wh5NvMyE50LDtvxjIlSoQ5ZlrcjnSlVxHfOGmXJLklHgYw9dwka8rixGsUaUI6cuFSjMWOECqXwmUrhPr38r+hPXe2IkmG4JWdI4oAVkOvyOdADE6A6w033NDVdfvvv3/f5+LhhrSIFp9DYrzJ3vD87gztPARFeXHC4aGoEuZTaZPMdh7tgoIapgLRH2E67x1u176CYcHpgSi2+leZm3HcEabadkol91blnrJnIIkqoBhRPA4Wyvw3ec9xUikpgkh4MAkVGRFBCJIbPRM35YsCD3Vyh8YssmOx47je3j4WW8dd6yb5hh06ZodSExEiNp7m8nULAZg5ELOEP3gFogvIgpZ6InQWkqR7F3Ikw6pmpBKT09Np3KztNTDhjJdUIRy2fkFkeRCi9HhGsBIhnZlCpro7UxkQmsCqhLt86E1eSFdzI2OeuXbq+nSbSf3Flu2MmEqXYK+1ASUb0dhrU9DGed7VxjifKD0EM+yo0vlszXO6Ycl5+5ycn+1Gl+MKaoEJloi9TFMW1POglL4o/e3S/omlv1EWTmeuQ/qP65iFtF2q5Fh/q7XV2tnXgMXaNUr5i9IwkGyuypvX2yZBSOp6twr0t6nGCSecMNVT8NBASdQ1tPBWURCyanoHigVRl3JQqhiUKAN2tTyzXbGg364IRNGcbeHZLE6R53/thPGysZxjOOZblnNQdI0+Wh5VBEY30Ui9AR3QlKpKV7sQJkAAjCESu8BiDkERYpEkRFOEmAWprCBEBEFjyXcCicyLIY1LcbL7T2R4iaGF7+U81878BddxPVS47Jh53PaCG7kVyeFWHzZtkCFM2fdB2gdiIvmDVyB6hL7Bmdr9ucg96VIe7BClYq+BRJYX4CJYRXsVUPoy6xb8vGU+SJSKvCVHF/QJJIXZRHiX1zvCg3RhPxUwbYFfXx/tRSe3xV4XnEkJ1ZZlPidcIy/0FrbJoV0bkf5L6RZothBfdh658/lr8+uTnwO3zgvj79QLoQvv6fOg1kckv6PQlLrkuOH5CQwvh4JScMy47vyc5cbtlLsmdz0V/21cwzIrJ0M21+yn6kMI04AnUXsMDjKBKvnuKFOdnbTecacxVXm58woDHEpDUeiQyxvgViKKrOXVpKJCYb7A65DbJddVla2Ndd70Rua96Pn2RTy0zXUFa1Om9BQL8PZ6VFWcqkun7RQNuW4cQsQQPEra67RdySzquSYjhwfMNjBGllExO+4u9BEVhA5HjvDlouPutrZX3GVglQ15zxFlZIUtiVnCIGZBmkf/EbPIcHlRWu04zoR/ioyPTEyKIJIEVkGR3JyLYlnhRrQgRCtxGcq4benNiEDUTD6xjFlHlJyzPqQ+6qVXCkiUximmgnjiukTC7DIiEKWCpX4sE+BjI+zF9FBEDuUhsQSrkBnKjsl2ca6t3X+uP62ftF3y0b0KRhvtWFE/pBHOsjbu87CuheO8qHQ+d84K2zIE/Nz5/DmVPK1DeRskMuWISLfuIwtv0tZQednUs6D/ZrnfXP+tHcfVs2v/7WpnjhOnpZPTZznNG8rWRFqgelcglAdCfTr1QFxxxRU46qijMH/+fOyzzz44+eSTsWHDBqPN2NgYVq5ciT333BPz5s3DKaecgo0bN/Y8d4/JBRnvsemhzuh/8kn2ikg/1My1UeeE+iS8IOUHFCV8I+MzsWjlPoLiZI+XpvZp5T6KF3X7IRGnn5SnJeMLiuVcRJx8IuNe1CfjZck5EWmf2PqYfWQ8sZV8ooJPy81HofFe7eOalzluwTgF16jfLfsUtbM/5Li26FPeNisgov1+mhFGfm+l8gWSHE7Y8xdJf/a9iUijzdkH2ic9rrVFQdui487r1XORHCfhWGMRQYjxnsvhKg+E+kSzhD94BaILZKKS+6GzrT665Uf3OujeBiWwCxE7vQaZAqAnAOsf3dpAmhVBHYvVhDLLAizh1XAtxjnLrSGIF7qMLUFXE5DN6+1+88J42rbjeBG1HtlcdeXAHNu8n5wA32YMY545Id8+b11vuPL19dMtU/aYtleiZE7OlvYr3870UhS3o4Uz2XMv+s3s42V/l7YrWWd7vD4hTnYaVZ9Oe77zzjuxcuVK3HPPPbjtttvQarVw4oknYseOHWmbD3zgA/i3f/s33Hzzzbjzzjvx1FNP4S1veUvf7sFjcmGGQGb5cXlB1f7PfR4kMn6R8pAkTE9EmWc74TNpHh1EqnzovIZEnPWpC4MQ/fukvMs0aiG9T9U2MvhZWZ8Zf1P96Oe0Pgze6f64DBxF15t8Mf8pHkOhvH27/quM5R5TewydbWWYkkgNiTDCebN1csgQFj3OGf50o5pl6Msd19rqx02jHXLH02P29Y7jhtFN3QfFFfhgu3fd5A+iw13gpyt/6DiE6TWveQ1OOOEEXHrppcbxTZs24ZRTTsHtt9/ez/kNJJRFKE2S1nIf9MToVGi1NzdLiZOyEOhuQy3kRVn0jVKmRbkHdilSZC+wRiwNYZ0JLQyoLLxIE3ZJ9sCIZIUecif/Gm7ypH06nhH3braDq22FdlVgKAdOB7XrGnMsY/MmynrUN7E316HkvN5/mznI8e0cDRjfDaKv/0VxshNtMrbLNcyYEUZkjk/GmqVz0TwVrEhhcN5QBSVC+57r3z7OtLnp80yb9+ifduVAdHj9rbfeany/4YYbsM8+++C+++7Dn//5n2PLli348pe/jNWrV+M1r3kNAOD666/HS17yEtxzzz049thje76HyYDnD3JzxSwsQ98TwpHA7AwhyiPtQzNI6X0V8Rh7DNJ4Aiq9G1UoVPvrzVCaovAlu+S1iTRMxSHsFfVTlnBcGNJS2jccwqbiP1UTcYtCxOz+ql9b3FdVxFpYl07o4jR/kCgTzWWoKEvDW1NBnLFUptD5BgCLT7uO27zRKjjClOHGCmtOK/PZ/SIvC7nKuQPpXlDdIpcD0eH6T1f+0PGqrV27FldddRVOPvlkQztqNpu48847+z2/AYZI/l9ADDTCTgYTce3HoBPZzPqhj2No0EZyUkEFIotRmJOjvHXe6UGwv3cuiFV7j4qt/c6Qn3Z9VZqYqx9h/dtD/72iF+u5y1NQ+rtal6c5DN3Pzy0EmcfL/i7qvZNV6Z//AYiJjE+vzo0tW7YAAPbYYw8AwH333YdWq4Xly5enbQ4++GDst99+WLduXW+DTSI8f5BQVF//jtRCK7KP4gltLfki781WHm3bK6HzmJzHwvQI2Nb8Iut+u3bFH9MoZlv2XVZ1ibwXvL3FPWtvHnd5GOw2eeTP5z3A5cddbSyPLpkW+OL5Osawr3f2lbupwrZ6/oO9Dtm8HP05zplKskMBq+SldqyXfrzot3GtJ2WyU65tH0AWfyjeiLEapgt/6Ert+o//+A+Mjo7i2GOPxaOPPtr/WQ04IhYhpii/S6idIG0R79RFaIQtKTdupBHXKLMIqLwDla+QUyLgJJx5wlrhZXESXKVwuAVu2wVZ3FYbxnYz9huFykFVQt/PcQvOd2Kxt89b7cjVxjqmngczGTGvkBX9zoVTcsxBP2aKEu62rj6KxrGvkQesdSXpp2Pp/fSu9MVCeiHUhwC0Wi1s3brV+IyPj7ftSwiB888/H8cddxxe9rKXAQBGR0dRr9ex2267GW0XLlyI0dHRnuc/mZjd/CGpuJQKy3Z4h80TIiMMSX10hUF9F0nOj9ojSIUoqdAkxV9kuc3smJ4vkMZ+F8T5258spEg/3kkYk+JrscHbzHw7Zf0WWls9TCcyrmsf1qPlXpGWe2cIzcIaz1ZKrPwtq59UATP6ded8GQKsfr31X6lC4PiU/VemWOTapnKMpnwChvXeSE4GcuMjteRT7r7zx+FuT3Y/qqHGlSwe6Gpr/l1C+3WZpUeBn2Dyh1hIWWym84euFIh9990Xd955Jw499FAcddRRWLt2bf9nNsCwvQ5Ce4l0uKormcqDpbUTGYpAvsMy6wAKqs30nkBqo9Ay7Zhfb9WQqwp+VTwHHaJXy0S311exIFU4NzhVqN3oZnVc11Tqpw9WpshKkiMA3/nOdzAyMmJ8rrjiirZ9rVy5Eg8++CBuuummnuc1iJjt/EEpDnqFPh05b4IVluRs4whVsr0Kef6i8xjbo5D3BlTLGUAbC7nb0i9R5B1Ib7rUAl0U5+/sr6olXvvNSr0E6Sra4bkW76/gESgynFUxqlU1vrna5I+5ohSKPAku77/LO1ABJW1zhqFODG2aUmH3kx4r9NL0Jju4kqg3bdo04/lDxzkQyoLZaDSwevVqXH755Xjd616HCy+8cCLmN5CI0QKHQEyRqSTAwQisZGlYLyxphDy3oRqZ2j1Ljme5AcLYAI2lOxKbsBOdwFguZp20PQLsWHsDzNGmDezt9Ir01vIeRZt2eQLQbobG+ZJ7ch7tRUGYJKSWIkZgRm1t7liv3usp9OPO7D6q9um6Tr0v/UCujCsBb3rTm3Kb9DQajdJ+Vq1ahe9+97u466678MIXvjA9vmjRIjSbTWzevNmwMm3cuBGLFi3qyz1MBjx/kDQ9TuLhs11zY4sHWJ4J9be1V4GTn9h8RFcqlBBVkFdnXFchV8AunyqbuWr3l8E2iuUFT1vYNXKx7Bw7wNr40y3IVvFwu3K+bOT7ydNOvQ2zOEZnXl27HHcR2tE19dy1G1spTIHzuZCdlPF7+/l05Ovpx7XvRv8VQBYvdvWR66lN33af3YIcZVx33313PPLII0a7mcYfOlYgbELzsY99DC95yUtw1lln9XNeA4ysJF+HifYGzB0a82NU6CD/kuZCVEpQ1o7MJNmJRZWQoqkrFtaRElJwfvJUhmLIZ6NDb1QHSuJEoFvlwTzZn0C5SBBawhROarUaFixYUOl6IsJ5552Hb33rW1i7di2WLl1qnH/FK16BWq2GNWvW4JRTTgEAbNiwAY8//jiWLVvWhzuYHHj+oEw8cPIIfR8g+7gNl/KQncuUh7LE6MzbYF3n9HTnaa39e5pGqnaKRFF+XntBPzVowc3r2vENu2BGHpmAbQv87n7K+jLXrT3FKZt7O35Xkodgh6dWqbhHyjNFuSU29vHpRhbok/wwKfyzx7lKD0TWR5TIYjOdP3SsQDzyyCPYe++9jWOnnHIKDj74YKxfv76fcxtYRKwFBgJPLT+xWemipFQrCpiFWZ5VCT5VQlIs4qV5I1xWJUkUdAt0AbEqe6FSSlOBCFf1WJQqLH0MTeoFVe6jwzCuKiJ6t0oMGedFF5bDzucyaOgX8+l1I7mVK1di9erV+Pa3v4358+encasjIyMYHh7GyMgIzjnnHFxwwQXYY489sGDBApx33nlYtmzZtKnABM8fAEDuC5D8zbXyrfl2xQpCrq0rHNbpdYDlebBz5bQ+AIdAn7QpoHPZ5qNub3e+vUMAz3kd3PetKuboVftSlHjJnf3mhGyRs9IXb/JZ0Jcxlzb81L7HIl5nbeRaCrsP5z22v46S2o7FO0Nn7SqjS69Cu+P9RD8VE5cHohNMV/7QsQLxohe9yHn8pS99KV760pf2Y04DD0XMixhD6bUWkYfLfex6+Tq0BrdPCjLDWKpZYVyhLxXQT0t2Fc/IpHlPJh79JqLFSuXMWC+Ffq9bzgPR4XJde+21AIBXvepVxvHrr78e73znOwEAn/vc58A5xymnnILx8XGsWLEC11xzTT+mP2nw/AGaAB9DJAIZJfkH6d8FykOpEtE2ZEkf24rlL/I4tIknT2EJ3mbYbMlc9bG0fp30pkDIdxqoCuadUx5cvNWhRBTOSe+raNySPnPX9wvt8js64rn556S07TThr5OhhCjkPRCdXT9d+UPHCoQH5G6daYxrJnwV1dTOJ00XEfkq4A5hv2KYT+7F739oUE+CaKf5FVMcYjPV6OjuK1THmqnox1OiyvMpdLqSVap8DA0N4eqrr8bVV1/dxQw9BgVEAsTUngD2Rl52eJHj71LhsDPlwZlblw2UtNfPmTzBCCNC3tperXpNBc+D3k9uLJ7zErjQVnko6b/t3K3+TO+uS4komqRt/S/rp/31zj6K+ilbj6IAgjLj0qSGOltDW393SuPtGffKG8niD52WcZ2u/MErEF0g3YGaBMCUhamqItDNg9qroG/OrTeLM6+usHSDqVIKZqgyMjPvqjr6df8xEaIePBAeswcEIZOnSYCY2jizGn8oFCSKFA+n8tCmL5QJ82YoTk5wb2tt19FlKE5Z04Jw2fZViSSKC3AUbKDqmGO/Xv1CIbhIiShRHnJ92O2LPEV6cRbihaHPxTfhCSGSddT5Q9xpDNM0hVcgukCMKKlNLXJkp4pgXl3ZyCMlnpUFXvdYuhJRZtVxI09s3YR9Yqr9VLr3dt6MaeKG7QUs/TfZZXqKE9InAzoTTf/ug3LYEoQW04WImf3seHQPKdQnHgjSYu31PYK0akqwhf0ST7bevlh5MPMe7Mp+WX/u466YfoNXVLW2m4uSHxPFgrmqIOfyEpS/e45kdOvvjr0GJagk+OvnHXNq17Zs7NL5dNgfKlrCi+AqGDLVVLL6+L2FmQkyQ1zjGS5bKHgFogtQspGPdFWLtFRf39CxcFsxccseps/26YETqnoRHGeKgjFDPStlmIjYV0HoKYTJY/ZAL4bBmBLAqwrabk9Dvv+sldm2rFKQfpUjjCgboL2XoZNQ0y6F4YFBp4J/Re9Brskke4yNGZWEMXXT71QlRldFv6sjkqU0xAP9QPcPXoHoAjFaEKSS0gSkUSa/B4Rs0K5kWwbpPsys9oVeAoNATULCVk+oUPFpqjBTlIQ2MErxOWuqd49BKVPrQr+8DwDQohgtal8338NDlvlOKvqQAOdhIX9weR5cioP9vLlzKYTV1lHPv8QLYIT5FCgR7curmmMZh3qiFFW8p22SnV3oOOHY0UW7/IMJoBW99ui+vgMPdQ+8s5OrOh2hE37UT94liAz+EFP/N/AdRHgFolNYO0g7mUIlIb6aINdvL8HUCM0DpjjomIUW+mpwhJ/NEoXLhRiESK+cNqWz8RhYKP6Q5D64Ki65r8srD0VKqquSX/F0enhSu/FETCh9mEYhmFOYYDwIKLrriVQeekKPvxMBBn+YHeqDVyC6QkwtCCIwcJBG0nRmQUYcahUE5mNXRHymQOCd2DKfXZaGnQzYvwFjxecGFZprmkBy51XHM9TLbzzIXoh+WBgBICJheiB67tFjpkJ6IGDY+XVlImdgsku6lngb9FGADrwPfUQu361SeI7eJn8/Og0pf1snqUyqQq8K2BSgm1Ch7DfV1tfJ4/rPrycztMkeq1/jCpgeiGhQZZo+wysQXYASlqCqbfRfuK76knZpjZkuwu8gwK+VB4A4+U9h4PJ9PAYGlIag8kIe4TIw2YpFJc+F0b7omazq7Z5axbj6+J2tS9t++2RkmPYwktYzzMR9gvoNgjD4g5glPgivQHQBQS1QsnQEAU7ZMnZaYckm+kYZtYkUXgdKMB5gLwSmd5iTtLgIMARWLkSX91Tw3Ey18GGDtDn149eLWIQWa2n9D9LdegwSKMk1Sjccpai40IYVupTf2yFtmL+0MHHatWlcNRjvSgmNKCqnWtS2q/H7iH7166Jz05c7DA76SU31KnyTNbYAGfwh8gqERxFkQhylwr5diamXMq2lqCT0DbgwPpMwUEpYCewwpsosb4Y8S334jSK00IKuQMyAdfGYGGj7AwHavhBFCdKlz9Ik0JcJpGPTWtGeDrR9KjHg/G8yjVoCwuAPkSrjPMPhFYguEFMEUABKNwvqzgZRbiHi2bbxE4UOys21c2MqobT3DeqmGaYJEc390hMw50H0QvQLEYsRsIwpEBukO/UYJBBERsqSMq6CRaVKBJzeh4IkauP6PuU+6HSsIs9pZ4yoxAsGhX524JWtnq8xeGjrkdXXYRB+lwLYvKYXTzMDupbh0vGZQKTxh5jF0yXdvyd4BaIXqIQ44gATacwreqx+UW03yNnweE4TTAOC21cMCtOfRERogmM8/T6tLasekwelDLDA8ETkmrV5nybF49VHY9VMfz8GzVjSO0RSyMWjG0gPRMYfYrRmhYTmFYguIEQLhBqQEv5ILiWzlIcOk+DymAKrfIlwWOSF6F+p2UHf06INBlGwzs1JgMD7Xx5YDTdgjLVf84lZhFj3QAzUXXoMElQOhAEBMB60NTAV7y7tQvG5MoUko+O985fOQiIVrHEnouJgN7S4w9yw6eZ9aIf0t+zzbtRTjcmYE0EY/CFmcSIhzmx4BaILqAoazCI2g7O51DQNB8phGt7HACoRvTG6zn+DQWQivSKiJhiNpd99DoRHEaQSII0hypssyQKZ3uWeDExdXNtTWc7q9tSulOtBo5sTPJepTMamGUKjB+keBGK0NP4QU3NK5zNZ8ApEF1C7iqZ8gADO4XRPdytoZIxmsL0QtvXJl3wbMGaYWNWUdUnOqg9eiDb32GkljEGHQAsCvgqTRxXI/R4Y47LIBom0uj5ZPEIv51petrWKt6Hbyi9lPKa94tCdF8KBQah2N4l0u5dciokSnqv9ltrzMsm8rizXYTL3k8iPTQZ/ED6J2sMFSgn9VD6umF67cvaEaeiF8EgxSFaiXhBTE0wv19xzeKLHTIfzGUl2qS73PlQJX+oNeUOPi8525nXwxqPOMQAqk4RWqW8mwXVLE5EATxQjIi0Hglql7WcKvALREzJnoIp7dZVzHZzQpv5g8hmFVyImAn2zHLbBVCsRfdkHgpoAmaU5PTyKQYYfjpJqfQxu5aF370M/0JtByisP0xO2KXSy+EK36MZ0O9E8SECYCgRmhwIxMCbsVquFCy+8EIceeijmzp2LxYsX48wzz8RTTz3V9tqrr74a+++/P4aGhnDMMcfgpz/9qXF+bGwMK1euxJ577ol58+bhlFNOwcaNG7ueK0GkH+W8UntDmOc6Qd79nOVYTMHP1MadzJL/Oj3XPQbmUa0GxszPlEM9j9z43hPTnwYJh/1iGgIRYmqln5nhV5lemA48gjT+oL6pM4o/wGjj6qHNGETpp2MMBC2aJMzwey26u7K7ZhXbdcQXrHUe5FUvnFuPijhBGPxB0OwIYRoYqWznzp24//77cfHFF+P+++/HN7/5TWzYsAFvetObSq/7xje+gQsuuACXXnop7r//frz85S/HihUr8Mwzz6RtPvCBD+Df/u3fcPPNN+POO+/EU089hbe85S1dz9VtLVJxrG7CXm5hmh27Fs5qTGtmNjBkYsoQUxMxjacf74GYfEwnHoGcoSk5pikR7a7NHauoNFRVLAbZyuzRHfwvWg39Xiei2OAPYpaEMDEa4Pian/3sZzj66KPx2GOPYb/99nO2OeaYY3DUUUfhqquuAgAIIbBkyRKcd955+PCHP4wtW7Zg7733xurVq/HWt74VAPCf//mfeMlLXoJ169bh2GOPrTSXrVu3YmRkBPvssxibNwH7LFyQnOFpPoIMX2JgzJFMneZNKOYgKiW/mT9PpzGqPQo5fXg0JsatPc2Ft8l+5ZJEN8bC5PfQ16+qclCy5h3cz1QQGwYgFgLPPPMUtmzZggULFlS4KkO9XsdQuBQBb6THtu36Hb76z1/EGWecMQEz9qiKQeEROn+o1+3nK6nIhACMcaN6n5lAnb0dmQIhcm3diEvaWe+udX5qQo8mmYYPrpjTN3QS2qNXYkqvYUEH4cntnqmJR1FYUrska9c6CSGwsUv+8IlPfAIfv+TTmDu0JD3Wirdj3xcGePjhhzvqa7phoE2LW7ZsAWMMu+22m/N8s9nEfffdh+XLl6fHOOdYvnw51q1bBwC477770Gq1jDYHH3ww9ttvv7RNf6AT+mku4PYR3srlwGR7I8hHJ/eKWDQRifH041d0MDC9eARloa6aN8HmF0XeB48eMK09wIOIctFxKle7mzel17eLEBv8IRazwwMxsEnUY2NjuPDCC3H66acXaoTPPfcc4jjGwoULjeMLFy7Ef/7nfwIARkdHUa/Xcwxm4cKFGB0dLRx/fHwc4+NZUszWrVu1s5n3gDHNk6AlUfdLiSjflXqCKzENUjlSAz6puhuYpVz7iA6ek6lOqO4WgmJAj2sdyPdidmEqeUQZf7DpNVMbjEKW+lYlXk10Wnmph7BX632dmupJg1WefCagG6HdvKaH3ahn+Nq2AxEZeQ9EsyMsfco8EF//+tcxb9689HP33Xen51qtFv7yL/8SRIRrr712SuZ3xRVXYGRkJP0sWbLEaiEfEDvEyMdG5zExXoiBdp61xyRaxPJkXV+7yX9eJ9s61Q+2JqiFWDTTj3/PJx6DzCPa8wcXTC+1+lRpb2J2CCcTAu+JAGZIrsRUeBqK+xUGf5gtORBTJoW96U1vws9//vP0c+SRRwIaY3jsscdw2223lcaj7bXXXgiCIFctY+PGjVi0aBEAYNGiRWg2m9i8eXNhGxcuuugibNmyJf088cQT6bmM6McaMY8TZcKuwKF/TAuTvZN1e7h+rtksyPDpr0hMEpxPWj8tRjOcMQvRND7drF27SkAeJgaZR5TxB50n6IamLIHa/ewUKaWmkapT5aEaf5hVoaYDUxlvgNCP9ZhG1Zhs9DpXotjiEd1VYZpuPGLKpK/58+fjT//0T9PP8PBwyhgefvhh/Md//Af23HPP0j7q9Tpe8YpXYM2aNekxIQTWrFmDZcuWAQBe8YpXoFarGW02bNiAxx9/PG3jQqPRwIIFC4yPAhkJ0LAYRtZm6jE4wvXEMqjBuc9BBIMi7kXP5CA8q4MNQTEERemn05CPKpWAPEwMMo8o4w8SRUnNRQYmG7PhnZxiuu0VCRPtku+nGYoo9MSE0ZLFHzr3Ek5HHjEwkler1cJb3/pWrF+/Hl//+tcRxzFGR0cxOjqKZrOZtnvta1+bVtMAgAsuuABf+tKX8NWvfhW/+c1v8J73vAc7duzA2WefDQAYGRnBOeecgwsuuAB33HEH7rvvPpx99tlYtmxZ5QpMRTAT4fTEuKzWt9G+yxeyvadiAl90T2AnDlOQTI2JVOamwb4Q3YKoCaLx9NPpO/fZz34W5557Ls4++2wccsghuO666zBnzhx85StfmbA5zzRMFx6h84QMscNLXYTuQ5cqJ1s73tVZ5YXQ4XlcivwmcmXPaXvxcZBXtp9KBFFs8AfqIoRpOvKIgUmifvLJJ/Gd73wHAHD44Ycb5+644w686lWvAgD87ne/w3PPPZeeO+200/Dss8/ikksuwejoKA4//HDceuutRtLc5z73OXDOccopp2B8fBwrVqzANddc0/Vc2+3pQBQkiXN8AjwRPoHYo48g0rwTA2NPGDjIpDid5VRnP6oS0EUXXZQesysBebTHdOIRJtyCP0EruuEo29oppmelpgHhZ7M8Cbj83j1vaA8yEqc7TaKerjxiYBSI/fffvxIBfPTRR3PHVq1ahVWrVhVeMzQ0hKuvvhpXX3111/NTcxNChi8JIazdfbO/GRNgLEgYR3negu6xqDK+fb15rIMciVRwrIA+EtaJrfYxAIyoU0wG00qYIxGBsTirxKQU4X56uDq8n8li2fJ97U7IkqEzW3O3FoahVZ1NhrY0Gg3jWJVKQB7tMcg8QucP6llTUB5k+f4lewYxltBrW9CwvNYldL/as9z5/i2TX5FpgOj2bFYiIHlBVpVL/106kC9cG+n2d5aVYXM2coQwsT7wB1mZc8w4PjQ0VIk/YBrziIFRIAYd27ZtAwA895ws6zc6ummKZ+Th4dEptm3bhpGRkY6uuffee/HYY48Zx2q1Gu6880687W1vM45feuml+PjHP96XuXpMH9j8wcPDY/qhG/6wcuVKHHLIIbnj3//+93N9zTT+4BWIili8eDGeeOIJzJ8/H9u2bcOSJUvwxBNPdLxroYcbW7du9WvaZ/g1zUBE2LZtGxYvXtzxtQcffDAOPvjg3PHjjjsOH/zgB41jLutSlUpAHtMbnj9MLDwt6z/8mmbohT/U63WsWLEid/xVr3oVLrvsMuOYiz9gGvMIr0BUBOccL3zhCwHNJe2uvuHRC/ya9h9+TSU6tSy1Q5E72oZeCejkk08GtEpAZWE1HtMHnj9MDvya9h9+TSWmij9gGvMIr0B4eHh4TDAuuOACnHXWWTjyyCNx9NFH48orrzQqAXl4eHh4zF5MRx7hFQgPDw+PCUaVSkAeHh4eHrMT05FHeAWiCzQaDVx66aWV3VMe7eHXtP/wazpYaFcJyGNmwL93/Ydf0/7Dr+ngYbrxCEbTs3i0h4eHh4eHh4eHh8cUwO8O4uHh4eHh4eHh4eFRGV6B8PDw8PDw8PDw8PCoDK9AeHh4eHh4eHh4eHhUhlcgAFx99dXYf//9MTQ0hGOOOQY//elPS9vffPPNOPjggzE0NIRDDz0U3/ve94zzRIRLLrkE++67L4aHh7F8+XI8/PDDE3wXk4O77roLb3zjG7F48WIwxnDLLbcY56vc+/PPP48zzjgDCxYswG677YZzzjkH27dvLx13bGwMK1euxJ577ol58+bhlFNOyW268vjjj+Okk07CnDlzsM8+++B//a//hSiK+nj3E4MrrrgCRx11FObPn4999tkHJ598MjZs2GC0maj7r/Jb/PKXv8T/9//9fxgaGsKSJUvwqU99qo937+Ex+PA8ojo8j+gvPH/wGFjQLMdNN91E9XqdvvKVr9Cvf/1rOvfcc2m33XajjRs3Otv/+Mc/piAI6FOf+hQ99NBD9LGPfYxqtRr96le/Stt88pOfpJGREbrlllvoF7/4Bb3pTW+ipUuX0q5duybxziYG3/ve9+ijH/0offOb3yQA9K1vfcs4X+XeX/e619HLX/5yuueee+juu++mP/3TP6XTTz+9dNx3v/vdtGTJElqzZg2tX7+ejj32WHrlK1+Zno+iiF72spfR8uXL6YEHHqDvfe97tNdee9FFF100AavQX6xYsYKuv/56evDBB+nnP/85vf71r6f99tuPtm/fnraZqPtv91ts2bKFFi5cSGeccQY9+OCDdOONN9Lw8DD90z/90wSthofHYMHziM7geUR/4fmDx6Bi1isQRx99NK1cuTL9HscxLV68mK644gpn+7/8y7+kk046yTh2zDHH0F//9V8TEZEQghYtWkSf/vSn0/ObN2+mRqNBN95444Tdx1TAZg5V7v2hhx4iAPSzn/0sbfP973+fGGP05JNPOsfZvHkz1Wo1uvnmm9Njv/nNbwgArVu3jihhWpxzGh0dTdtce+21tGDBAhofH+/znU8snnnmGQJAd955J9EE3n+V3+Kaa66h3Xff3ejjwgsvpBe/+MUTcOceHoMHzyO6h+cR/YfnDx6DglkdwtRsNnHfffdh+fLl6THOOZYvX45169Y5r1m3bp3RHgBWrFiRtn/kkUcwOjpqtBkZGcExxxxT2OdMQZV7X7duHXbbbTcceeSRaZvly5eDc457773X2e99992HVqtl9HvwwQdjv/32M/o99NBDjU1XVqxYga1bt+LXv/71hNzvRGHLli0AgD322AOYwPuv8lusW7cOf/7nf456vW70u2HDBmzatKnv9+7hMUjwPKK/8Dyid3j+4DEomNUKxHPPPYc4jnM7/S1cuBCjo6POa0ZHR0vbq3876XOmoMq9j46OYp999jHOh2GIPfbYo3TN6/U6dtttt9J+XePq85oOEELg/PPPx3HHHYeXvexlwATef5XfYqasq4dHN/A8or/wPKI3eP7gMUjwO1F7eAwQVq5ciQcffBA/+tGPpnoqHh4eHh4DBM8fPAYJs9oDsddeeyEIgly1go0bN2LRokXOaxYtWlTaXv3bSZ8zBVXufdGiRXjmmWeM81EU4fnnny9d82azic2bN5f26xpXn9egY9WqVfjud7+LO+64Ay984QvT4xN1/1V+i5mwrh4e3cLziP7C84ju4fmDx6BhVisQ9Xodr3jFK7BmzZr0mBACa9aswbJly5zXLFu2zGgPALfddlvafunSpVi0aJHRZuvWrbj33nsL+5wpqHLvy5Ytw+bNm3HfffelbW6//XYIIXDMMcc4+33FK16BWq1m9LthwwY8/vjjRr+/+tWvDIJ32223YcGCBTjkkEMm5H77BSLCqlWr8K1vfQu33347li5dapyfqPuv8lssW7YMd911F1qtltHvi1/8Yuy+++59XAUPj8GD5xH9hecRncPzB4+BxVRncU81brrpJmo0GnTDDTfQQw89RO9617tot912S6sVvOMd76APf/jDafsf//jHFIYh/cM//AP95je/oUsvvdRZom+33Xajb3/72/TLX/6S3vzmN8+YEn3btm2jBx54gB544AECQJ/97GfpgQceoMcee4yo4r2/7nWvoyOOOILuvfde+tGPfkQHHnigURruD3/4A734xS+me++9Nz327ne/m/bbbz+6/fbbaf369bRs2TJatmxZel6VqTvxxBPp5z//Od1666209957D3yJPiKi97znPTQyMkJr166lp59+Ov3s3LkzbdOP+7/33nvpxS9+Mf3hD39Ij7X7LTZv3kwLFy6kd7zjHfTggw/STTfdRHPmzPFl+jxmDTyP6AyeR/QXnj94DCpmvQJBRPSFL3yB9ttvP6rX63T00UfTPffck5474YQT6KyzzjLa/+u//isddNBBVK/X6aUvfSn9+7//u3FeCEEXX3wxLVy4kBqNBr32ta+lDRs2TNr9TCTuuOMOApD7qDWqcu9//OMf6fTTT6d58+bRggUL6Oyzz6Zt27al5x955BECQHfccUd6bNeuXfTe976Xdt99d5ozZw799//+3+npp582+n300Ufpv/23/0bDw8O011570Qc/+EFqtVoTvia9wrWeAOj6669P2/Tj/tVv98gjj6TH2v0WRES/+MUv6Pjjj6dGo0EveMEL6JOf/OSEroeHx6DB84jq8Dyiv/D8wWNQwUg+oB4eHh4eHh4eHh4eHm0xq3MgPDw8PDw8PDw8PDw6g1cgPDw8PDw8PDw8PDwqwysQHh4eHh4eHh4eHh6V4RUIDw8PDw8PDw8PD4/K8AqEh4eHh4eHh4eHh0dleAXCw8PDw8PDw8PDw6MyvALh4eHh4eHh4eHh4VEZXoHw8PDw8PDw8PDw8KgMr0B4zBp8+ctfxoknnjjh49x66604/PDDIYSY8LE8PDw8PHqH5w8eHp3BKxAeswJjY2O4+OKLcemll074WK973etQq9Xw9a9/fcLH8vDw8PDoDZ4/eHh0Dq9AeMwK/N//+3+xYMECHHfccZMy3jvf+U58/vOfn5SxPDw8PDy6h+cPHh6dwysQHtMKzz77LBYtWoRPfOIT6bGf/OQnqNfrWLNmTeF1N910E974xjcax171qlfh/PPPN46dfPLJeOc735l+33///XH55ZfjzDPPxLx58/CiF70I3/nOd/Dss8/izW9+M+bNm4fDDjsM69evN/p54xvfiPXr1+N3v/tdH+7aw8PDw6MdPH/w8Jg8eAXCY1ph7733xle+8hV8/OMfx/r167Ft2za84x3vwKpVq/Da17628Lof/ehHOPLII7sa83Of+xyOO+44PPDAAzjppJPwjne8A2eeeSbe/va34/7778cBBxyAM888E0SUXrPffvth4cKFuPvuu7sa08PDw8OjM3j+4OExefAKhMe0w+tf/3qce+65OOOMM/Dud78bc+fOxRVXXFHYfvPmzdiyZQsWL17c9Xh//dd/jQMPPBCXXHIJtm7diqOOOgqnnnoqDjroIFx44YX4zW9+g40bNxrXLV68GI899lhXY3p4eHh4dA7PHzw8JgdegfCYlviHf/gHRFGEm2++GV//+tfRaDQK2+7atQsAMDQ01NVYhx12WPr3woULAQCHHnpo7tgzzzxjXDc8PIydO3d2NaaHh4eHR3fw/MHDY+LhFQiPaYnf/e53eOqppyCEwKOPPlrads899wRjDJs2bWrbbxzHuWO1Wi39mzFWeMwuy/f8889j7733rnA3Hh4eHh79gucPHh4TD69AeEw7NJtNvP3tb8dpp52Gv/u7v8P//J//M2fd0VGv13HIIYfgoYceyp2z3cq///3v+zLHsbEx/O53v8MRRxzRl/48PDw8PNrD8wcPj8mBVyA8ph0++tGPYsuWLfj85z+PCy+8EAcddBD+x//4H6XXrFixAj/60Y9yx7/97W/jm9/8Jn73u9/hf//v/42HHnoIjz32GJ588sme5njPPfeg0Whg2bJlPfXj4eHh4VEdnj94eEwOvALhMa2wdu1aXHnllfja176GBQsWgHOOr33ta7j77rtx7bXXFl53zjnn4Hvf+x62bNliHD/ppJPwqU99CocccgjuuusuXHPNNfjpT3+Kr33taz3N88Ybb8QZZ5yBOXPm9NSPh4eHh0c1eP7g4TF5YKTXFvPwmME49dRT8Wd/9me46KKLgKTO9+GHH44rr7yyr+M899xzePGLX4z169dj6dKlfe3bw8PDw6P/8PzBw6MzeA+Ex6zBpz/9acybN2/Cx3n00UdxzTXXeObg4eHhMU3g+YOHR2fwHgiPWYuJsjB5eHh4eExveP7g4VEOr0B4eHh4eHh4eHh4eFSGD2Hy8PDw8PDw8PDw8KgMr0B4eHh4eHh4eHh4eFSGVyA8PDw8PDw8PDw8PCrDKxAeHh4eHh4eHh4eHpXhFQgPDw8PDw8PDw8Pj8rwCoSHh4eHh4eHh4eHR2V4BcLDw8PDw8PDw8PDozK8AuHh4eHh4eHh4eHhURlegfDw8PDw8PDw8PDwqIz/H3OuFJkRj3qsAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 2), tight_layout=True)\n",
    "batch_results[\"TE\"].plot_field(\n",
    "    field_monitor_name=\"field\", field_name=\"E\", val=\"abs\", ax=ax1, vmin=0, vmax=60\n",
    ")\n",
    "ax1.set_aspect(\"auto\")\n",
    "ax1.set_title(\"TE\")\n",
    "\n",
    "batch_results[\"TM\"].plot_field(\n",
    "    field_monitor_name=\"field\", field_name=\"E\", val=\"abs\", ax=ax2, vmin=0, vmax=60\n",
    ")\n",
    "ax2.set_aspect(\"auto\")\n",
    "ax2.set_title(\"TM\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "applications": [
   "Passive photonic integrated circuit components"
  ],
  "description": "This notebook demonstrates a SiN bi-layer edge coupler design for efficient and broadband fiber-to-chip coupling in visible frequency.",
  "feature_image": "./img/bilayer_edge_coupler.png",
  "features": [
   "Parameter sweep"
  ],
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "keywords": "integrated photonics, silicon nitride, edge coupler, Tidy3D, FDTD",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  },
  "title": "Visible Light Bi-layer SiN Edge Coupler | Flexcompute"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}