Autograd8WaveguideBend.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1535d9e4-b444-46e3-9a39-9d5a6ce1f724",
   "metadata": {},
   "source": [
    "# Adjoint-based shape optimization of a waveguide bend\n",
    "\n",
    "> Note: native autograd support is an experimental feature in tidy3d 2.7. To see the original implementation of this notebook using jax and the adjoint plugin, refer to [this notebook](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin8WaveguideBend/).\n",
    "\n",
    "In this notebook, we will apply the adjoint method to the optimization of a low-loss waveguide bend. We start with a 90 degree bend in a SiN waveguide, parameterized using a `td.PolySlab`. \n",
    "\n",
    "We define an objective function that seeks to maximize the transmission of the TE0 output mode amplitude with respect to the position of the polygon vertices defining the bend. A penalty is applied to keep the local radii of curvature larger than a pre-defined value.\n",
    "\n",
    "The resulting device demonstrates low loss and exhibits a smooth geometry.\n",
    "\n",
    "> To install the `autograd` package required for this feature, we recommend running `pip install autograd`.\n",
    "\n",
    "<img src=\"img/adjoint_8.png\" width=400 alt=\"Schematic of the waveguide bend\">\n",
    "\n",
    "If you are unfamiliar with inverse design, we also recommend our [intro to inverse design tutorials](https://www.flexcompute.com/tidy3d/learning-center/inverse-design/) and our [primer on automatic differentiation with tidy3d](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd1Intro/).\n",
    "\n",
    "## Setup\n",
    "\n",
    "First, we import `tidy3d`. We will also use `numpy`, `matplotlib` and `autograd`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "49188672-e1c3-43f1-92c0-a575810aa0ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tidy3d as td\n",
    "import tidy3d.web as web\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "import autograd as ag\n",
    "import autograd.numpy as anp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba08a4ef-c31c-41bc-bff7-922bf83a7b1e",
   "metadata": {},
   "source": [
    "Next, we define all the global parameters for our device and optimization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "330887eb-6c9a-4173-8b73-b7583c8c5869",
   "metadata": {},
   "outputs": [],
   "source": [
    "wavelength = 1.5\n",
    "freq0 = td.C_0 / wavelength\n",
    "\n",
    "# frequency of measurement and source\n",
    "# note: we only optimize results at the central frequency for now.\n",
    "fwidth = freq0 / 10\n",
    "num_freqs = 1\n",
    "freqs = np.linspace(freq0 - fwidth / 2, freq0 + fwidth / 2, num_freqs)\n",
    "freqs = np.array([freq0])\n",
    "\n",
    "# define the discretization of the bend polygon in angle\n",
    "num_pts = 60\n",
    "angles = np.linspace(0, np.pi / 2, num_pts + 2)[1:-1]\n",
    "\n",
    "# refractive indices of waveguide and substrate (air above)\n",
    "n_wg = 2.0\n",
    "n_sub = 1.5\n",
    "\n",
    "# min space between waveguide and PML\n",
    "spc = 1 * wavelength\n",
    "\n",
    "# length of input and output straight waveguide sections\n",
    "t = 1 * wavelength\n",
    "\n",
    "# distance between PML and the mode source / mode monitor\n",
    "mode_spc = t / 2.0\n",
    "\n",
    "# height of waveguide core\n",
    "h = 0.7\n",
    "\n",
    "# minimum, starting, and maximum allowed thicknesses for the bend geometry\n",
    "wmin = 0.5\n",
    "wmid = 1.5\n",
    "wmax = 2.5\n",
    "\n",
    "# average radius of curvature of the bend\n",
    "radius = 6\n",
    "\n",
    "# minimum allowed radius of curvature of the polygon\n",
    "min_radius = 150e-3\n",
    "\n",
    "# name of the monitor measuring the transmission amplitudes for optimization\n",
    "monitor_name = \"mode\"\n",
    "\n",
    "# how many grid points per wavelength in the waveguide core material\n",
    "min_steps_per_wvl = 20\n",
    "\n",
    "# how many mode outputs to measure\n",
    "num_modes = 3\n",
    "mode_spec = td.ModeSpec(num_modes=num_modes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "491189f1-8d54-45d2-bd14-f0e46163cdc5",
   "metadata": {},
   "source": [
    "Using all of these parameters, we can define the total simulation size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "099d588f-6745-4db4-8ca0-452a498e6d2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "Lx = Ly = t + radius + abs(wmax - wmid) + spc\n",
    "Lz = spc + h + spc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89afa716-793b-40ff-b762-2dfccf05c867",
   "metadata": {},
   "source": [
    "### Define parameterization\n",
    "\n",
    "Next we describe how the geometry looks as a function of our design parameters.\n",
    "\n",
    "At each angle on our bend discretization, we define a parameter that can range between `-inf` and `+inf` to control the thickness of that section. If that parameter is `-inf`, `0`, and `+inf`, the thickness of that section is `wmin`, `wmid`, and `wmax`, respectively.\n",
    "\n",
    "This gives us a smooth way to constrain our measurable parameter without needing to worry about it in the optimization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "104ab125-3ac9-4569-ab70-873acd41f0fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def thickness(param: float) -> float:\n",
    "    \"\"\"thickness of a bend section as a function of a parameter in (-inf, +inf).\"\"\"\n",
    "    param_01 = (anp.tanh(param) + 1.0) / 2.0\n",
    "    return wmax * param_01 + wmin * (1 - param_01)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7261fdc3-9520-4995-9610-1586d330ed13",
   "metadata": {},
   "source": [
    "Next we write a function to generate all of our bend polygon vertices given our array of design parameters. Note that we add extra vertices at the beginning and end of the bend that are **independent** of the parameters (static) and are only there to make it easier to connect the bend to the input and output waveguide sections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f04524c0-9ac4-4990-a142-c3596323606d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_vertices(params: np.ndarray) -> list:\n",
    "    \"\"\"Make bend polygon vertices as a function of design parameters.\"\"\"\n",
    "    vertices = []\n",
    "    vertices.append((-Lx / 2 + 1e-2, -Ly / 2 + t + radius))\n",
    "    vertices.append((-Lx / 2 + t, -Ly / 2 + t + radius + wmid / 2))\n",
    "    for angle, param in zip(angles, params):\n",
    "        thickness_i = thickness(param)\n",
    "        radius_i = radius + thickness_i / 2.0\n",
    "        x = radius_i * np.sin(angle) - Lx / 2 + t\n",
    "        y = radius_i * np.cos(angle) - Ly / 2 + t\n",
    "        vertices.append((x, y))\n",
    "    vertices.append((-Lx / 2 + t + radius + wmid / 2, -Ly / 2 + t))\n",
    "    vertices.append((-Lx / 2 + t + radius, -Ly / 2 + 1e-2))\n",
    "    vertices.append((-Lx / 2 + t + radius - wmid / 2, -Ly / 2 + t))\n",
    "    for angle, param in zip(angles[::-1], params[::-1]):\n",
    "        thickness_i = thickness(param)\n",
    "        radius_i = radius - thickness_i / 2.0\n",
    "        x = radius_i * np.sin(angle) - Lx / 2 + t\n",
    "        y = radius_i * np.cos(angle) - Ly / 2 + t\n",
    "        vertices.append((x, y))\n",
    "    vertices.append((-Lx / 2 + t, -Ly / 2 + t + radius - wmid / 2))\n",
    "    return vertices"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e5f676f-0e63-4e41-9df9-514afd08eb7e",
   "metadata": {},
   "source": [
    "Let's try out our `make_vertices` function on a set of all `0` parameters, which should give the starting waveguide width of `wmid` across the bend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "580d21d7-6423-4299-b889-676294966565",
   "metadata": {},
   "outputs": [],
   "source": [
    "params = np.zeros(num_pts)\n",
    "vertices = make_vertices(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cea18164-b71b-4fca-9d0e-801390531f9c",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(*np.array(vertices).T)\n",
    "ax = plt.gca()\n",
    "ax.set_aspect(\"equal\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b4df320-a42a-463c-9a9a-bcd84c7f7dd3",
   "metadata": {},
   "source": [
    "Looks good, note again that the extra points on the ends are just to ensure a solid overlap with the in and out waveguides. At this time, the autograd differentiation does not handle polygons that extend outside of the simulation domain so we need to also ensure that all points are inside of the domain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "044f2377-62e8-4be8-9815-87cc1368d3ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_polyslab(params: np.ndarray) -> td.PolySlab:\n",
    "    \"\"\"Make a `tidy3d.PolySlab` for the bend given the design parameters.\"\"\"\n",
    "    vertices = make_vertices(params)\n",
    "    return td.PolySlab(vertices=vertices, slab_bounds=(-h / 2, h / 2), axis=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b57aa022-e979-4a19-9826-016ec22574ca",
   "metadata": {},
   "source": [
    "Let's visualize this as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ea0df1fd-0c81-4185-9455-81c1a2af2a4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "polyslab = make_polyslab(params)\n",
    "ax = polyslab.plot(z=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc3b3303-3942-4004-a49b-f66fc07a0eb3",
   "metadata": {},
   "source": [
    "Keeping with this theme, we add a function to generate the structure itself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8db1381a-dd4a-4e99-a207-6f1c1363d173",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_structure(params) -> td.Structure:\n",
    "    polyslab = make_polyslab(params)\n",
    "    medium = td.Medium(permittivity=n_wg**2)\n",
    "    return td.Structure(geometry=polyslab, medium=medium)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2ab62467-2ac4-4908-8344-2c342944f8e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ring = make_structure(params)\n",
    "ax = ring.plot(z=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92d145cd-20e9-4762-ba3b-227d667b3fcd",
   "metadata": {},
   "source": [
    "Next, we define the other geometries, such as the input waveguide section, output waveguide section, and substrate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0e756863-4009-421a-9b86-20509d1d807b",
   "metadata": {},
   "outputs": [],
   "source": [
    "box_in = td.Box.from_bounds(\n",
    "    rmin=(-Lx / 2 - 1, -Ly / 2 + t + radius - wmid / 2, -h / 2),\n",
    "    rmax=(-Lx / 2 + t + 1e-3, -Ly / 2 + t + radius + wmid / 2, +h / 2),\n",
    ")\n",
    "box_out = td.Box.from_bounds(\n",
    "    rmin=(-Lx / 2 + t + radius - wmid / 2, -Ly / 2 - 1, -h / 2),\n",
    "    rmax=(-Lx / 2 + t + radius + wmid / 2, -Ly / 2 + t, +h / 2),\n",
    ")\n",
    "\n",
    "geo_sub = td.Box.from_bounds(\n",
    "    rmin=(-td.inf, -td.inf, -10000),\n",
    "    rmax=(+td.inf, +td.inf, -h / 2),\n",
    ")\n",
    "\n",
    "wg_in = td.Structure(geometry=box_in, medium=td.Medium(permittivity=n_wg**2))\n",
    "wg_out = td.Structure(geometry=box_out, medium=td.Medium(permittivity=n_wg**2))\n",
    "substrate = td.Structure(geometry=geo_sub, medium=td.Medium(permittivity=n_sub**2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36c7b8fa-51fb-4852-9e62-f231410d8e0e",
   "metadata": {},
   "source": [
    "## Fabrication Constraints\n",
    "\n",
    "With the current parameterization, it is possible to generate structures with wildly varying radii of curvature which may be difficult to fabricate. To alleviate this, we introduce a minimum radius of curvature penalty transformation using the tidy3d `autograd` utilities. The penalty will take a set of vertices, compute the local radius of curvature using a quadratic Bezier curve, and return an average penalty function that depends on how much smaller the local radii are compared to a desired minimum radius."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a4e9cb73-c8a9-41ec-a4e2-af2eac220c04",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tidy3d.plugins.autograd import make_curvature_penalty\n",
    "\n",
    "penalty = make_curvature_penalty(min_radius)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "557d4e5b-8441-433f-8965-e81c2f4b6231",
   "metadata": {},
   "source": [
    "We then wrap this penalty to look at only the inner and outer vertices independently and average the penalty from each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4e4bc5d9-2c57-412c-8cc2-cc026b39857b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_penalty(params):\n",
    "    \"\"\"Evaluate penalty on a set of params looking at radius of curvature.\"\"\"\n",
    "    vertices = make_vertices(params)\n",
    "    _vertices = anp.array(vertices)\n",
    "    vertices_top = _vertices[1 : num_pts + 3]  # select outer set of points along bend\n",
    "    vertices_bot = _vertices[num_pts + 4 :]  # select inner set of points along bend\n",
    "    penalty_top = penalty(vertices_top)\n",
    "    penalty_bot = penalty(vertices_bot)\n",
    "    return (penalty_top + penalty_bot) / 2.0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9881812c-1795-4f83-a24f-0b01833638e7",
   "metadata": {},
   "source": [
    "Let's try this out on our starting parameters. We see we get an autograd traced float that seems reasonably low given our smooth starting structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d13993be-d625-4537-af2a-f7700c199612",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.57973410373255e-23"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_penalty(params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28462443-1777-4b35-8c03-32490755f475",
   "metadata": {},
   "source": [
    "## Define Simulation\n",
    "\n",
    "Now we define our sources, monitors, and simulation.\n",
    "\n",
    "We first define a mode source injected at the input waveguide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "96cf199b-0a3b-4ace-9e1e-ba6c62d165f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "mode_width = wmid + 2 * spc\n",
    "mode_height = Lz\n",
    "\n",
    "mode_src = td.ModeSource(\n",
    "    size=(0, mode_width, mode_height),\n",
    "    center=(-Lx / 2 + t / 2, -Ly / 2 + t + radius, 0),\n",
    "    direction=\"+\",\n",
    "    source_time=td.GaussianPulse(\n",
    "        freq0=freq0,\n",
    "        fwidth=fwidth,\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79c7db54-87c6-4ba4-9da7-68ae176a222f",
   "metadata": {},
   "source": [
    "Next, we define monitors for storing:\n",
    "\n",
    "- The output mode amplitude at the central frequency.\n",
    "\n",
    "- The flux on the output plane (for reference).\n",
    "\n",
    "- The output mode amplitude across a frequency range (for examining the transmission spectrum of our final device).\n",
    "\n",
    "- A field monitor to measure fields directly in the z-normal plane intersecting the waveguide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "731f5f2f-0c38-4e86-add9-7ba8eea6c951",
   "metadata": {},
   "outputs": [],
   "source": [
    "mode_mnt = td.ModeMonitor(\n",
    "    size=(mode_width, 0, mode_height),\n",
    "    center=(-Lx / 2 + t + radius, -Ly / 2 + t / 2, 0),\n",
    "    name=monitor_name,\n",
    "    freqs=[freq0],\n",
    "    mode_spec=mode_spec,\n",
    ")\n",
    "\n",
    "flux_mnt = td.FluxMonitor(\n",
    "    size=(mode_width, 0, mode_height),\n",
    "    center=(-Lx / 2 + t + radius, -Ly / 2 + t / 2, 0),\n",
    "    name=\"flux\",\n",
    "    freqs=[freq0],\n",
    ")\n",
    "\n",
    "mode_mnt_bb = td.ModeMonitor(\n",
    "    size=(mode_width, 0, mode_height),\n",
    "    center=(-Lx / 2 + t + radius, -Ly / 2 + t / 2, 0),\n",
    "    name=\"mode_bb\",\n",
    "    freqs=freqs.tolist(),\n",
    "    mode_spec=mode_spec,\n",
    ")\n",
    "\n",
    "fld_mnt = td.FieldMonitor(\n",
    "    size=(td.inf, td.inf, 0),\n",
    "    freqs=[freq0],\n",
    "    name=\"field\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e47e54a-65f3-463f-b790-fd873597659e",
   "metadata": {},
   "source": [
    "Next we put everything together into a function that returns a `td.Simulation` given our parameters and an optional boolean specifying whether to include the field monitor (to save data when fields are not required)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f33bcf5e-2f31-4969-acb7-42bc06b1347e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_sim(params, use_fld_mnt: bool = True) -> td.Simulation:\n",
    "    monitors = [mode_mnt_bb, flux_mnt]\n",
    "    if use_fld_mnt:\n",
    "        monitors += [fld_mnt]\n",
    "    ring = make_structure(params)\n",
    "    return td.Simulation(\n",
    "        size=(Lx, Ly, Lz),\n",
    "        structures=[substrate, wg_in, wg_out, ring],\n",
    "        sources=[mode_src],\n",
    "        grid_spec=td.GridSpec.auto(min_steps_per_wvl=min_steps_per_wvl),\n",
    "        boundary_spec=td.BoundarySpec.pml(x=True, y=True, z=True),\n",
    "        monitors=monitors + [mode_mnt],\n",
    "        run_time=10 / fwidth,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7d38368-afd9-4a29-864a-4c6c6385acea",
   "metadata": {},
   "source": [
    "Let's try it out and plot our simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "42fcce5a-11e9-4de2-b03e-97ca75af1628",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">inaccurate results or divergence, please increase gap between any  </span>\n",
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       "\u001b[2;36m11:17:02 EST\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Structure at structures\u001b[0m\u001b[1;31m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;31m]\u001b[0m\u001b[31m was detected as being less than\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mhalf of a central wavelength from a PML on side x-min. To avoid    \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31minaccurate results or divergence, please increase gap between any  \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mstructures and PML or fully extend structure through the pml.      \u001b[0m\n"
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     "data": {
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       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Suppressed \u001b[0m\u001b[1;36m1\u001b[0m\u001b[31m WARNING message.                             \u001b[0m\n"
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     "data": {
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nnCFKS0sFgNBWL5235tGtXLkydH8VFRXi4osvFj/88EPUdS677DJRXFzc5fndcccdIp5fuXfeeUf86Ec/EkVFRWLQoEHipptuEv/617+61NPS0iL+8z//U/Tr108AiLlNz+TJk3vcZueOO+7otabeBAIBce+994qhQ4cKq9UqjjzySPHMM890uZ6+bdHOnTujLm9sbBRXXHGFqKysFA6HQ0yePLnbf6P6a5ip50FERERE+UMSopfzT4mIiIiIiIhyDOfMEhERERERUd5hmCUiIiIiIqK8wzBLREREREREeYdhloiIiIiIiPIOwywRERERERHlHYZZIiIiIiIiyjtmowvIJk3TsGfPHpSWlkKSJKPLISKKmxACHo8HgwYNgizzc0giIiKiggqze/bswcMPPwyzOfy0JUmKGhgKIaBpWtT3TU1NyPR2vFarNVSHpmlQFCWjj9cds9kc9dr4fL6MP+/OJEmCzWYLfa+qKlRVzWoNAPuhYz/CUu2HJEkoLy9P6YO0e++9F7t378bgwYOTvg8iIiKivqKgwmxpaSnMZjMcDkdUgI0cGFsslqjBphACw4YNg8lkymhtiqLA5XIBAMrKymC1WjP6eN3RNA0NDQ0AguGhrKws6zUAgMvlCvWksrLSkKNQ7EcY+xGUaj8CgQDq6+uTDrP6h2ylpaVJ3Z6IiIiorymoMKsPImVZhsViARA8uqJfJoRAIBCAzWaDLMvQNA2apsFms2V08KwoCpqbm1FUVAQAaG1thcPhyOqAXR+oWywW2Gw2tLe3IxAIZH3g7PF4oGkaSkpK4PP50NbWlvUAxX6EsR9B6eiHoiiQZTn0lSi/3w8AnCJBRERE1KGgJ175fD6oqgqz2YyioiJYrVYIIeDz+aJONc4kRVFQX18Pi8WCqqoqVFVVwWKxoL6+PmunUuoDdb/fj6qqKlRWVsLpdMLtdsPj8WSlBiAYnNxuN5xOJyorK1FVVQW/34+Ghgb2g/0o+H4YcXo3ERERUS4r2DAbGWT1OYFmszkq0GZ6fmLkQF0/2iXLMiorK7M2YO88UNePdpWWlmZ1wB4ZnPSjXVarNasBiv0IYz+CcqUf2fyAjYiIiChfFGSY1RexiQyyus6BNlMDyO4G6rpsDdh7GqjrsjVg7y446bIVoNiPMPYjKFf6oX/wxhWMiYiIiKIV3OhInwvbXZDVRQbaQCCQ9gF7rIF6ZJ2ZHLD3NlDXZXrAHis46TIdoNiPMPYjKFf6EXkGSeRKykRERERUgGFW34qnpyCriwy0zc3NaRuwxzNQ12VqwB7vQF2XqQF7PMFJl6kAxX6EsR9BudKP7qZCEBEREVFYwYVZIUTcRzhMJhPMZjNUVU3LgD2Rgbou3QP2RAfqunQP2BMJTrp0Byj2I4z9CMqVfjDIEhEREfWu4MJsogNuWZZRXl6e8oA9mYF6ZA3pGLAnO1DXpWvAnkxw0qUrQLEfYexHUK70g0GWiIiIKD4FF2aToW8LkuyAPZWBui7VAXuqA3VdqgP2VIKTLtUAxX6EsR9BudIPBlkiIiKi+DHMxinZAXs6Buq6ZAfs6Rqo65IdsKcjOOnYjzD2Iyjf+8EgS0RERJQYhtkEJDpgT+dAXZfogD3dA3VdogP2dAYnHfsRxn4E5Ws/GGSJiIiIEscwm6B4B+yZGKjr4h2wZ2qgrot3wJ6J4KRjP8LYj6B86weDLBEREVFyGGaT0NuAPZMDdV1vA/ZMD9R1vQ3YMxmcdOxHGPsRlIv98Hq9XX7OIEtERESUPIbZJPU0YM/GQF3X04A9WwN1XU8BKhvBScd+hLEfQbnWj5aWFqiqGrqcQZaIKGzDhg2QJAkbNmwwuhQiyiPxbbjaxwgh4lqgRtM0CCHg9/t7vE5ZWRmamppQV1eHkpISNDc3w2w2w+l0Rg1cM8npdIZq6NevX2jQXF5eDgAp770ZD5vNBofDgebm5tDzbmlpQUlJCWw2W1ZqANgPHfsRliv9KCoqQltbG3w+HyRJQiAQgMlkgsViievvkRAi43USEWXasmXL4HA4MHv2bKNLSdizzz6Luro6XH/99UaXErcVK1bgkksuQXFxMVpaWnq9/ptvvokVK1bg7bffxg8//IABAwbglFNOwd13342BAwdGXXfKlCnYuHFjl/uYMWMGXnvttbQ9B6JYJFFAIyS3243f/OY3KCkpieuIkB5m+/XrB7O559yvaRp8Ph+A4NEgq9UKSZLSVnc8hBBQFCU0KLbZbBk96tUTVVVD4d9iscR83TKF/QhjP4JyoR+qqqKxsRGapkGSJMiynFA/NE3DbbfdBpfLBafTmcFKiYgyZ8yYMaiqqupyBFbTNCiKAqvVasj7ZTzOPPNMbN26Fbt27TK6lLi0tLTgsMMOg8vlCn3fmwkTJqCxsRHnn38+DjnkEHz77bd45JFH4HA4sGXLFgwYMCB03SlTpuCbb77BkiVLou5j0KBBOOWUU9L7ZIh6UHBHZpubmzF06FDY7fZer+v3++Hz+XD66aejrKwsC9URUV/lcrnw8ssvo7W1FbIso6SkBMXFxXHfvr29PYPVEVE2eb3ehH7/C4Esy3GNzSh+99xzD0pLSzF16lS89NJLcd3mgQcewIknnhj1gcLMmTMxefJkPPLII7jnnnuirl9WVoZLLrkknWUTJaTgwqymaTCbzXHPkwsEAigrK0NFRUWGKyOivk7TNDidTpjNZrjdbpjN5rjnL2frtGwiSkxtbS0WLVqEV199FQ0NDRg0aBBmzpyJP/zhD7BarXjqqacwZ84cbNiwAStXrsSqVavg9/vR1NQEIHja7R//+Efs2LEDlZWVOPvss/HrX/8a/fr1Cz3G119/jVtuuQXvvPMOmpubUVVVhRNPPBGPP/546MP2tWvXYvHixdi6dStUVcVBBx2Ec889F/fee2/M+uO5nc/nw7333osVK1Zg9+7dqKmpwUUXXYS77767y5z/Z555Bg899BC2bt0Km82GsWPH4vbbb8epp56KYcOG4bvvvgOA0Bk6kydPxoYNG7BhwwZMnToV69evx5QpU0L398ILL+A3v/kNvvjiCxQXF2PmzJm47777cNBBB4WuM3v2bKxatQrbt2/HvHnz8MYbb6CoqAiXXXYZ7rvvPphMppivwerVq/GnP/0Jn3zyCRoaGjB48GDMnj0bt956a+i2kafU6rUPHTq0x6O0y5cvx+WXX44///nPuPzyy0OX33vvvbjtttuwZs0anH766THrSsXXX3+NpUuX4sUXX8Tzzz8f9+1OPvnkbi+rqKjAl19+2e1tVFVFe3s7SkpKkq6XKFkFF2aJiIxisVhQXFwc+jDN7XYDQMYX5CKizNizZw+OO+44NDc348orr8To0aNRW1uLVatWobW1NeqD82uuuQbV1dVYtGhRaHXzO++8E4sXL8b06dNx9dVXY/v27Xj00Ufx4Ycf4p133oHFYoGiKJgxYwZ8Ph+uvfZaDBgwALW1tXjllVfQ3NyMsrIyfP755zjzzDNx1FFH4a677oLNZsOOHTvwzjvvxKw/nttpmoazzjoLb7/9Nq688kocfvjh+Oyzz7B06VJ89dVXUUf8Fi9ejDvvvBOTJk3CXXfdBavVivfffx/r1q3DqaeeigcffBDXXnstSkpKcNtttwEA+vfv32N9+gcBEydOxJIlS7B//3784Q9/wDvvvINPPvkkKvAHAgHMmDEDxx9/PO6//3688cYb+P3vf4+RI0fi6quvjvk6PPXUUygpKcGCBQtQUlKCdevWYdGiRXC73fjd734HAKFpHj/88AOWLl0KADHD25w5c/CPf/wDCxYswI9//GMMGTIEn332GRYvXowrrrgiKsg2NTUhEAjErBEAHA4HHA5Hr9cDgOuvvx5Tp07F6aefnlCY7U5LSwtaWlpQVVXV5WdfffUViouLoSgK+vfvj7lz52LRokWwWCwpPSZRvBhmiYiyJHKOrB5gGWiJ8tfChQuxb98+vP/++5gwYULo8rvuuqvLom0VFRV48803Q0f6Dhw4gCVLluDUU0/Fq6++Gjqtc/To0Zg/fz6eeeYZzJkzB1988QV27tyJF154Aeedd17o/hYtWhT6/7Vr10JRFLz66qvdBo6exHO7Z599Fm+88QY2btyIE088MXT5mDFjcNVVV+Hdd9/FpEmTsGPHDtx11104++yzsWrVqqjTVPXXYtasWbj99ttRVVXV66mpfr8fN998M8aMGYNNmzaFTkE+8cQTceaZZ2Lp0qVYvHhx6Prt7e248MIL8f/+3/8DAFx11VU49thj8ec//7nXMPvss8+iqKgo9P1VV12Fq666CsuWLcM999wDm82GH//4xzjooIPQ1NQU92m1TzzxBI488khcccUVeOWVV3DZZZdhwIABeOCBB6Kud8wxx4SOWMdyxx134M477+z1emvWrMHrr7+OTz/9NK46e/Pggw9CURRceOGFUZePHDkSU6dOxdixY+H1erFq1Srcc889+Oqrr7By5cq0PDZRbxhmiYgMwkBLlL80TcNLL72En/zkJ1FBVtd5obu5c+dGne76xhtvQFEUXH/99VHBb+7cubj11luxZs0azJkzJ3Qa8b/+9S+cfvrp3R6Z049Qrl69GnPmzIl7AaV4bvfCCy/g8MMPx+jRo1FfXx+6XF/gZ/369Zg0aRJeeuklaJqGRYsWdbmfZBb9++ijj1BXV4c777wzai7tGWecgdGjR2PNmjVRYRYIhtBIJ510Ep5++uleHysyyHo8Hvh8Ppx00kl4/PHHsW3bNhx99NEJ1w8AAwYMwB//+EdcdNFFOOmkk7BlyxasXbu2yyJ+K1asQFtbW6/3N2LEiF6voygKbrjhBlx11VU44ogjkqo70qZNm7B48WJccMEFXRZ1+vOf/xz1/aWXXoorr7wSTzzxBG644Qb86Ec/SvnxiXrDMEtEZCAGWqL8dODAAbjdbowZMyau6w8fPjzqe/1I3GGHHRZ1udVqxYgRI0I/Hz58OBYsWIAHHngAK1aswEknnYSzzjoLl1xySSjoXnjhhfif//kf/PznP8ctt9yCadOm4ZxzzsF5550XM9jGc7uvv/4aX375Jaqrq7u9j7q6OgDAN998A1mW0xKggJ5fHyB49Prtt9+Ousxut3epsby8PDQ3OZbPP/8ct99+O9atWxf6W6zTVwJO1s9+9jM888wzWLNmDa688kpMmzaty3VOOOGEhO933759Ud+XlZWhqKgIS5cuRX19fZegn4xt27bh7LPPxpgxY/A///M/cd3mxhtvxBNPPIE33niDYZaygmE2S9yqG280vxF12fR+0+E0c4sNokLHQEvU90Ue/UvU73//e8yePRurV6/G66+/jl/+8pdYsmQJ/v3vf2Pw4MEoKirCpk2bsH79eqxZswavvfYaVq5ciVNOOQWvv/56jwsgxXM7TdMwduzYLqfG6oYMGZL080qn3hZ56klzczMmT54Mp9OJu+66CyNHjoTdbsfmzZtx8803x7UPeCwNDQ346KOPAABffPEFNE3r8gHDgQMH4pozW1JSEpqn23nP1+XLl+Pss8/GPffcg2uuuQZutzv0ntLS0gIhBHbt2gWHw4GamppeH2v37t049dRTUVZWhn/+859xvy/p/x4aGxvjuj5RqhhmiYhyAAMtUX6prq6G0+nE1q1bk7r90KFDAQDbt2+POn1UURTs3LkT06dPj7r+2LFjQysDv/vuuzjhhBPw2GOPhbZKkWUZ06ZNw7Rp0/DAAw+EVs1dv359l/uK1NvtRo4ciU8//RTTpk2LebrwyJEjoWkavvjiC4wbN67H68V7ynHk69P59Nbt27eHfp6qDRs2oKGhAf/4xz+iVvLduXNnl+smc7r0vHnz4PF4sGTJEixcuBAPPvggFixYEHWdiRMnJjxndu3atVE/O/LII9HU1ISWlhb89re/xW9/+9sutx8+fDh++tOf9rpNT0NDA0499VT4fD68+eabXYJzLN9++y0A9HgknyjdGGaJiHIEAy1R/pBlGbNmzcIzzzyDjz76qMu8WSFEzPAzffp0WK1WPPTQQ5g5c2boun/+85/hcrlwxhlnAAj+PXA4HFELyI0dOxayLMPn8wEIHgXrvIWgHij163QnnttdcMEF+Oc//4knnngCV155ZdR129raoGkaiouLMWvWLNx888246667ul0ASn9+xcXFaG5u7rEm3YQJE1BTU4PHHnsMl19+eWgLoFdffRVffvll1AJYqdCP6EYu2KUoCpYtW9blusXFxQmddrxq1SqsXLkSDz30EK699lp8+umnuP3223HmmWfi0EMPDV0vmTmz3X1A0draihdffLHL5Q899BDee+89/O1vf4sKpnv37oXL5cLIkSNDqw97vV6cfvrpqK2txfr163HIIYd0W4vb7YbNZovamkkIEfpwZcaMGb0+H6J0KLgwG/lmEI9UTy8hIkoEAy1R/rj33nvx+uuvY/LkyaFta/bu3YsXXngBb7/9dtTWMZ1VV1dj4cKFWLx4MWbOnImzzjoL27dvx7JlyzBx4sTQirnr1q3D/Pnzcf755+PQQw+Fqqp4+umnYTKZcO655wIIrp68adMmnHHGGRg6dCjq6uqwbNkyDB48OGoF4s7iud2ll16K559/HldddRXWr1+PE044AYFAANu2bcPzzz+Pf/3rX5gwYQJGjRqF2267DXfffTdOOukknHPOObDZbPjwww8xaNAgLFmyBAAwfvx4PProo7jnnnswatQo1NTUdDnyCgS3MrvvvvswZ84cTJ48GRdddFFoa55hw4bhhhtuSLZtUSZNmoTy8nJcdtll+OUvfwlJkvD00093WY1ar33lypVYsGABJk6ciJKSEvzkJz/p9n7r6upw9dVXY+rUqZg/fz4A4JFHHsH69esxe/ZsvP3226HAn8yc2e44HA7MmjWry+UvvfQSPvjggy4/W7hwIf7yl79g586dGDZsGADg4osvxgcffIDLL78cX375ZdTesiUlJaH72Lx5My666CJcdNFFGDVqFNra2vDiiy/inXfewZVXXoljjz02Lc+JqDcFGWZbW1vj2qfL7/dDUZQsVEVEFMZAS5QfDjroILz//vv4f//v/2HFihVwu9046KCDcNppp8U1zrjzzjtRXV2NRx55BDfccAMqKipw5ZVX4t577w0dKTv66KMxY8YMvPzyy6itrYXD4cDRRx+NV199NbTAzllnnYVdu3bhySefRH19PaqqqjB58mQsXrw4tEhUd+K5nSzLeOmll7B06VL89a9/xYsvvgiHw4ERI0bguuuuizrCeNddd2H48OF4+OGHcdttt8HhcOCoo47CpZdeGrrOokWL8N133+G3v/0tPB4PJk+e3G2YBYDZs2fD4XDgN7/5DW6++WYUFxfj7LPPxn333Rfzg4JEVFZW4pVXXsGNN96I22+/HeXl5bjkkkswbdq0LkcXr7nmGmzZsgXLly/H0qVLMXTo0B7D7NVXXw2fz4fly5eHjkpXVlbiT3/6E37605/i/vvvx0033ZSW55BOW7ZsAQA8+eSTePLJJ6N+NnTo0FCYHTp0KE466SS8+OKL2LdvH2RZxuGHH47HHnusyxF8okySRHcfPfVRbrcbt956KwYPHoyKioqYA0RFUVBXVwebzYZLLrmky2k4CT82F4AiKmiNjY144YUXYLfbYbVa47qNx+OB2+2G0+mEyWTCz3/+c7hcri7bOhAREREVooI7MquqKoqLi2Me8VAUBfX19TCbzXEPOomI0i3yCG3kvCQiIiIiKsAwCwTnFNhstm4DrR5kLRYLnE5nzIUTiIgyTf/7xG0OiIiIiKIVZJgFup+TFhlkKysroaqqkSUSEQEI/n3iB2tERERE0Qo2zALRgVZRFPh8vlCQ7byhNRGRkeJZTIaIiIiokBR0mAXCR2Tb29sBgEGWiIiIiIgoDxR8atOPyOq8Xq+B1RAREREREVE8CvrIbOc5sl6vl/s6ElFO4hx+IiIiomgFG2Y7B1lZlrssCsWtMIgoFyiKApfLZXQZfYqmadizZw9KS0shSZLR5RAREVEHIQQ8Hg8GDRrU6/TPggyzqqqiubm528WeIgOtw+GAyWQyqkwiotAHb/xblF579uzBww8/DLM5sbdBSZJC7xmBQCDqZ/qbr8lkgqqqGT2abrVaIcsyfD4fhBBdarTZbNA0DYqiZKwGs9kMs9nc43ONVWO69PZce6sxXdiPIPYjjP0IYz+C+kI/JElCeXl5yh8Cy7IMSZIghICmaV1+bjKZcPfdd2P37t0YPHhwzPsquDArSRJcLhfsdnuPiz3pgba5uRnFxcXZLpGICED0GSRFRUVGl9OnlJaWwmw2w+FwxL3on6ZpoQGGxWLp8mYuhMCwYcPg8/ng9XpRXFyc9lWoNU2D2+1GIBBAWVlZj2FcVVW4XC6YTCY4nc60L2zY2tra63OMt9Zkxfsc46k1WexHGPsRxn4EsR9hfaUfgUAA9fX1KYVZVVWhaRpkWY5ZAxDftM+8DbO/+c1vsHDhQlx33XV48MEH476dzWaDyWTqddXi0tJSqKqa0U+JiIh60nkqhL7iOqWH/kYsyzIsFkuv11dVFYFAALIsw2azdXn/0DQNmqbBZrOhtLQUNpsNbrc79H06aJqGhoYGAED//v1htVpjXt9ut6O+vh5tbW1pXanf4/HA5/OhoqKi1+fmcDjQ0NCAlpYWVFVV9VpzvBRFQXNzc8wPpiNrYD+C2I8w9iNcA/sRxH6E9dQPRVEgy3LoK1E+nw+apsFsNsc1nTOe0JyXqxl/+OGHePzxx3HUUUclfFtN0+L+1Ke4uDiuQQ4RUTp1N6efjKN/sKmffhVPP0pLS+F0OuF2u+HxeFKuQR+I+P3+uAdZVqsVVVVV8Pv9aGho6PZUrkR5PB643W44nc64BlmyLKOyshIWiwX19fVp+YA4md8P9iOI/QhjP8LYjyD2IywT/fD5fFBVNe4gG6+8GyG1tLTg4osvxhNPPIHy8vKEb69/ohCvdJ9mQEQUC4NsbkkmyOrSNSBJZiCiS+eAJNGBoS6dA8RUfj/YjyD2I4z9CGM/gtiPsHT2I1NBFsjDMDtv3jycccYZmD59eq/X9fl8cLvdUV9ERLmKQTa3pBJkdakOSFIZiOjSMSBJdmCoS8cAMR2/H+xHEPsRxn6EsR9B7EdYOvqRySAL5FmYfe6557B582YsWbIkrusvWbIEZWVloa8hQ4ZkuEIiouQwyOaWdARZXbIDknQMRHSpDEhSHRjqUhkgpvP3g/0IYj/C2I8w9iOI/QiL7Edzc3NC/ch0kAXyKMzu3r0b1113HVasWAG73R7XbRYuXAiXyxX62r17d4arJCJKHINsbklnkNUlOiBJ50BEl8wAMV0DQ10yA8RM/H6wH0HsRxj7EcZ+BLEfYXo/9MUQ49m+KBtBFsijMPvxxx+jrq4Oxx57bGgfpo0bN+Khhx6C2Wzust8fEFy52Ol0Rn0REeUSBtnckokgq4t3QJKJgYgukQFiugeGukQGiJn8/WA/gtiPMPYjjP0IYj/CrFYrysvLIYQIrUrck2wFWSCPwuy0adPw2WefYcuWLaGvCRMm4OKLL8aWLVtgMpmMLpGIKCEMsrklk0FW19uAJJMDEV08A8RMDQx18QwQs/H7wX4EsR9h7EcY+xHEfoRZLBaYzeaYgTabQRbIozBbWlqKMWPGRH0VFxejsrISY8aMMbo8IqJeRf7RZ5A1XuT+ddkIsrqeBiTZGIjoYg0QMz0w1MUaIGbz94P9CGI/wtiPMPYjiP0Ik2UZVqu120Cb7SAL5FGYJSLKd4qiwO/3M8jmCFmWoWlaVoOsrvOAJJsDEV13A8RsDQx13Q0Qjfj9YD+C2I8w9iOM/QhiP8JMJlOXQGtEkAWAvN5EdcOGDUaXQEQUN0mS0NjYGPpUk0HWeKqqQlVVyLKctSCr0wdf+tZxkiRlbSCi0weI9fX12Lt3LwBkbWCo0weIDQ0NOHDgQKiubP9+sB9B7EcY+xHGfgSxH2FmczBGKoqCtra20GXZDLIAj8wSEWWNxWIJ/b/T6WSQNVjkqVEWi8WQfhQXF4f+32azZXUgorNarVGDj8iaskWW5ahFGo36/WA/gtiPMPYjjP0IYj/CzGZz1JSdyHFOtnAkRUSUJYqiwGw2w2KxoKGhIamN2Cl99MGHJElQFAWqqmb18fVTwyRJgt1uR3t7e0L7CKaLx+NBe3s77HY7JElKeF/HdFAUBQ0NDbBYLIb9frAfYexHGPsRxH6EsR9h+inGsixDkqReVznOBIZZIqIskSQJFRUVqKqqSmojdko/i8USGhRlM9B2nuNUWVmZ0D6C6RI556yysjLhfR3TIXLOWVVVlSG/H+xHGPsRxn4EsR9h7EdY5BzZoqKiHheFyjSGWSKiLLFarZAkKamN2Cn9AoFAqB82my1rgbanxTri3UcwXbpbPCWRfR3TobvFU7L9+8F+hLEfYexHEPsRxn6E6e+VkXNkzWazIYGWYZaIKEsi55Uw0OaWbAXa3ladzNaAJNYqoNkaIMZaBTRbvx/sRxj7EcZ+BLEfYexHmKqqCAQC3S72ZESgZZglIjIIA21uyXSgjXf7hEwPSOLZziLTA8R4trPI9O8H+xHGfoSxH0HsRxj7Eeb1eqFpGkwmU4+rFmc70DLMEhEZiIE2t2Qq0Ca6D2CmBiSJ7MuYqQFiIvsyZur3g/0IYz/C2I8g9iOM/QjzeDxoaWkJbS8YSzYDLcMsEZHBGGhzS7oDbbIb2qd7QJLIwFCX7gFiIgNDXbp/P9iPMPYjjP0IYj/C2I8wvR8lJSWh/WV7k61AyzBLRJQDGGhzS7oCbbIDEV26BiTJDAx16RogJjMw1KXr94P9CGM/wtiPIPYjjP0Ii+xHovv6ZiPQFlyYjVyAJR5CiAxVQkQUjYE2t6QaaFMdiOhSHZCkMjDUpTpATGVgqEv194P9CGM/wtiPIPYjjP0IS0c/Mh1oCy7M2my2uAckQggOJokoqxhoc0uygTZdAxFdsgOSdAxEdMkOENMxMNQl+/vBfoSxH2HsRxD7EcZ+hKWzH5kMtAUXZjVNg8vl6vUflKZpaGpq4pFZIso6BtrckmigTfdARJfogCSdAxFdogPEdA4MdYn+frAfYexHGPsRxH6EsR9hmehHpgJtwYVZRVFgMpli/oPS/yGpqpq2f0RERIno/AaYqX1PKT7xBtpMDUR08Q5IMjEQ0cU7QMzEwFAX7wCR/QhjP8LYjyD2I4z9CMtkP+INtIkE3YILswDgdDp7/AcV+Q+pvLw8rf+YM0X1tMLf3AK/ywvV0wq1tR0Bnx9C41FlonwW+QbocrmMLqfg9RZoNU1Dc3NzxgYiut4GJJkciOh6GyBmcmCo622AmOmBoY79CGI/wtiPMPYjiP0I6y3Qqqqa0Af48a2t3Me0fbsHFskEn0nDXpcXJQEZZiFBQKDFpCEgASUBGe0NbVBlgW13LEdxILV/2F6rD7uHfx912dadX6FY6X7D4XSRLCbIVgtkixmy3QpTkQ0mhw0mhx2mIhvMJUUwOx2wOIthLnXAXOaApawEsqUg/2kQ5Rz9DXDfvn1Gl0IIB1qfzxcajJjNZgghEAgEoKoqampqMn5Wjz7IcLvdUd9nYyCi0weI9fX1aGhoCA0CszEw1Om/Hw0NDaivrw8NArM1MNSxH0HsRxj7EcZ+BLEfYfr2PoqiwOfzwWazQZZlqKqa8NSqgk0sEiSUBGS0mDS0mDQUB2S0y+EgaxYSVOT/kU3hDyDgDyCQ4O3MTgesFU5YykthrXDCWl0GW00/2KrLYSkvgZQHR6yJ+gpZluF0Oo0uI6csWbIE//jHP7Bt2zYUFRVh0qRJuO+++3DYYYdl/LG7C7T6f8vLy7M2PaXzgET//2wMRHSdB4hOpxMNDQ1ZGRjqOg8QKysr4Xa7szYw1LEfQexHGPsRxn4EsR9hnQOtxWKBoiiQJCnuvWyBAg6zQDjQeszBQAsApR1BttCp7lao7lZgV9ejQZLZBFtNP9gHVsI+sBK2gZUoGlQFa3UZQy5RhuTDlIds2rhxI+bNm4eJEydCVVXceuutOPXUU/HFF18kvA9eMvRA297eDp/PBwCwWCywWCwZf+xInQck2RyI6PQB4oEDB7J2hKMzfYBYX1+P+vp6AEB1dXXW171gP4LYjzD2I4z9CGI/wvTQ6vP54PP5IEkSbDYbAoH4D8MVdJil5Ag1gPY9DWjf0xB1uWyzwD64Go4hNSg6uAZFB/eHfUAFAy4Rpd1rr70W9f1TTz2FmpoafPzxxzj55JMNqoqIiIiyqaDDrD5HVkPHHFk5eIS2hEdnk6L5/Gj9Zg9av9kTuky2W+EY2h+OEYNQPHwgHCMGwuywG1glUX5K9ybjfY2+QFZFRUVWHk/TtNARWZvNFloQyu/3Z/XT9cg5TkDXOVDZoM85s1qtodP2IuekZUPkLgRVVVVwu91Rc9Kyhf0IYj/C2I8w9iOI/QjT58jKshw6zdjn88FkMsV9HwUbZjsv9mQWEswRc2hLAjL6wJRZw2ntClq270bL9t3BCySgaHANig8ZjJJDB6N41EEMt0S90DQtal4LRdM0Dddffz1OOOEEjBkzptvr6Kcw6VJ5PfUgK4SA1WqF2WwOrXDc1NQEi8WSlQFJT4t1ZHNA0t3iKd0tspJJ3S2e0t0iK5nGfgSxH2HsRxj7EcR+hOlBVj+1WH/tFUWB3++P+34K8vxPAXQJskB4Dq1J6D9nmk07AbTtrkP9us3Y9dj/4vP/fhRf3fcs9r78Llp21EIEePSJKJL+xpfI/JFCM2/ePGzduhXPPfdcj9dZsmQJysrKQl9DhgxJ6rG6C7IAIEkSTCYTzGZzzH0E06WngUi8+wimQ0+rgMa7r2M69LQKaLz7OqYL+xHEfoSxH2HsRxD7EdZTkNW37UlEQYbZNhO6BFldZKBtNWk8OJtpAmj7bj/qXn0f3zzwPLb+96PY9aeX0fjvz6G2tBldHZGhIt/4ysrKjC4nJ82fPx+vvPIK1q9fj8GDB/d4vYULF8LlcoW+du/enfBj9RRkdbIso1+/fhkfkPS2fUI2BiS9bWeRjQFib9tZZGuAyH4EsR9h7EcY+xHEfoT1FGR1ZrM5odWMCy7MWq1WaD0EWZ0eaGUBqDw6m1VauwLXlh3Y/dfX8fnNj2PH71eibu1H8B1oNro0oqzq/MaXyB/2QiCEwPz58/Hiiy9i3bp1GD58eMzr22w2OJ3OqK9E9BZkdZkekMS7D2AmByTx7suYyQFivPsysh9h7EcY+xHEfoSxH2GZ7EdvQVaXyOneBTc6kmUZRQHAjNgLPEmQ4AjI8JkYZg0jBLzf7IH3mz3Y++JbKBpSg7JjD0G/Yw+Frbqf0dURZUx3b3yqqhpdVk6ZN28enn32WaxevRqlpaXYty+4jVhZWRmKiorS+ljxBlld530E0zUHKtEN7Ttvu5COOVDxDgx1nfd1TMectHgHhjr2I6wQ+yGEQHCpz2iOEgc0ocHldkETGkpKS1KuQVEUNDY0wmKxoF9FPwhJICB6niJisphQXlmOxoZG1DfUo7yiPC39aGpsgt/vR0VlBUwWU8waIAH9KvqhqbEJB+oPoKKyIi39aPG0wOPxoLS0FI4SR+wawH6EGNwPGTIkScrI36t4g2yiCi7M+nw+mIqAXrIsgGCgZZbNHW2769C2uw77Vr+DoiE1KD/ucPSbcBgsZZnfU5IoWxIdGBaqRx99FAAwZcqUqMuXL1+O2bNnp+1xEg2yunQP2BMNTrp0DkgSDU66dAaoZH8/2I+wQuuHBg11/rrub2gHtICGelc9XAEXLMXJ7xOt+TX4mnyQzBKkUgn1gXognqUOJABlQEtTC7wHvLD1s0GSk9tRQ2gCvmYfhCpgK7ehWWoG4lxHR5QKKM0K9h7YC1u5DbIl+aDh9/rhb/HDUmJBq70Vrf7W+G7IfoRva1A/aiw1MCG4knA6/15lKsgCBRhmhUgsnXKDntykB9s9/9iE0sMPRvlxh6Ns3CjI1uT/8BEZjUE2fon+LY91Pz2dTqZpGhRFCQVZWZZjXlcI0WUFRqfTiaamJtTV1aG8vBwWS+J/o7xeL1paWlBSUhLaBigRNpsNDocDzc3NUFUVxcWJfwDo9/vR1NQEs9kMp9OZ1JkCZWVlUa+FJCX2DiuEQFNTE1RVRXl5OQAk/FqwH2GF0g9NaNDUnk8ZNVlN0Io0KG4FWkCDpSjxGjRVg+JSIJklWIotEAEBEUjsb5Sl1ALFpaC9sR3WUmvCAUpoAopHgVAFrGXB9w7Nn9ipspZiCxSPgvaGdljLrJDNiQcOf5sfqleFudgcfG0TrIH9iKjBgH74hR8BKZz6O/+9slqtMd83uxMIBEJBVv8d7+32ibzHF1yYpT5GCHi++A6eL76DbLeifOJoVJwwBo6D+xtdGVFCGGSzT3+z7O5NVQgRCggWiwWSJMV889XDrM/n67LydFFRERRFgdfrDYXieOl71xYXF8NkMqG9vT3u20YymUwoLi4OhfNE5mDrod5ms8FqtUZtcZSokpISKIoCt9sNq9Uad4ASQoT2IiwpKUEgEEh6hW/2I6wQ+iGECNXWqsU4QmgDAr4A2tX2xEbHGiD5JcACCLOA4kthnqM9eF/+Fj+EWcR/REUAkioBAhD2jg/V4t/ZJJoluDq72qJCWERiq+uogBSQIGwCKlS0tyX3+wGA/dBlqR8O2QEA8Km+Ln8HIv9eqaoKTdPi/luhaRpUVYUsy6G/c+leYIthNke1QcF2Sy0O9w+GDTzaGA+tXUHDW/+Hhrf+D0VDalBxwhiUH3c4THaGAsptDLLGaG5uxtChQ2G3R+913fmIUzxHi/x+P3w+H04//XSuPE2UQxRNwXrXegDA597PDa6GKDcdXHwwAGBq2VRY5Z7HIC6XCy+//DIcDkev7436GSRFRUUJn/2RyAeFDLM56n3bV7i/ZDVKhB2n+MZisu9IHKoOgsQTn+PStrsOtc+tw96X3kbFj45A5eSjYe9fYXRZRF0wyBpH07Que9rp/dA0DTU1NQn1IxAIoKysDBUV/FtDlCt8mg822ILfBKcC4gjHETBLHAJTYVOFii9avwAA2EqCvyPlFeWwybaYt9PPDok1RUJRFLhcLthstqTm5ScydYK/yTkqAA2tkg8BKYCVRW/jxaL3MULtjx+3H40TlcNRpSW2rUSh0toV1G/YgvoNW1By+FBUn3IMSo8YlvDcIKJMYJDNLewHUWEwS2aGWaIkWSwWtLS0wGw2d7soVLKL0yWLv8k5rlwrgYBAu+THdnMttpXU4knxJiYoo3CKbywmKqN4GnKcWr78Di1ffgf7wEpUTTsW5RNHQ7bwV4CyJ3JBAwan3MJ+EBER9c5sNqOkpKTbVY6zHWQBhlnDtGo+3FiyHF7R/cINqhSAJgUHvhIkFAkrioQVGjR4JR822j7HW7YvUKmV8jTkBLXvbcAPz6zFvv99B1VTjkHV5KNhKop9SgVROiiKArvdzuCUI6xWKzRNYz+IiIgSUFxcDLPZHBVojQiyAMOsYbyiDbvkOmjQYBKmbq/j1Iq6XCZDRqkoQqkogh8qXLKXpyEnSXW3Yt//voMDaz9C1dRxqJp6LMzF9t5vSJQkIQQaGxthMpmgqiqDk8FkWYbb7UZrayv7QURElIDIfWgVRYHP58t6kAUYZg1XqhUlfZqwBeZeT0M+XjkEFrY5pkCbD/v/+T4OvLkZlZOPRs30CTCXdP0ggShVVqsVHo8HmqYxOOUARVHg9/shhGA/iIiIEqQfkdVXH852kAUS262IcpR+GnKNVoZqrRStkg+v2Tfjt6Uv4vmid40uL29oPj8OvP4Rvlz0JPa98i4CrSnsj0bUDb8/vNGc2+1O+15rlJjIvT3ZDyIiosToR2R1Xq836zXwkF0f4YcKj9wOPwKwCjOOVQdjevvRmOYba3RpeUdrV7D/n++jfsMWVE+fgKqpx8Bk4yJblDohBCoqKmCxWFBfX4+GhgZDPsWkIFmWUV5eDrvdzn4QEREloPMcWa/X2+2iUJnGMGuwVskHBd3vpWQRJtjR82lv+mJQrZICGRKqNCemtY/FycqROEQdyMWgUhRo9WHf/76D+o1bMODMSaj4jyMgcZBLKbBarbBYLLBaraiqqmKAMpjP5wvtM8t+EBERxcfv98PlckXNkY2cQwtkL9AyzBqkSLKhTDjQBqXH6zTKXgzSosOsPj/WI7VDQKBY2DDVNwan+MZigjIKVrY07VSXFz+sWIv6dZsx8OyTUHok96ml5EQGJAYo40VulcR+EBER9U7TNLjdbthsti7vlUYEWiYfg5TIRfiT5xo4lO6PvK6zbcV9pf8Ifd/5NOLR6kGY3n40TvYdjgqRvUP5hax9bwN2LnsJJaMPxkHnT4F9YKXRJVGeY4DKLewHERFRbIqiwGw29/geme1AyzBrIDNMPa40bO5Ym8sjtfE04hzTsu17bP/106iaPA4DzvgRTA5u50PJY4DKLewHERFRzyRJQnl5ecz3xmwGWobZHCUBsAkLJEg8jTgXaQL16z9B04fbMHDWiaj40ZGQZH64QMlhgMot7AcREVH3rFZrXNPtshVomYxy1ATlEPyq5ac4RhnO04hzWKClDT88sxZN732OwRdNg31QldElUZ5igMot7AcREVFXiawbk41Ay3fmHOUURZjmO4pBNk94v9mD7feuwN6X3oam+Hu/AVE39ADl9/vR0NDAfU8Nxn4QERGlprS0FE6nE263Gx6PJ+33X3Bh1mq1QvR+tZAAzxyleGka6l7/ENvveRot23cbXQ3lKQao3MJ+EBERpSaTgTZvwuySJUswceJElJaWoqamBrNmzcL27dsTvh9ZltFmCm5x0xufrCEgJRJ9iQCl3oVv/rAKPzz7BgJtPqPLoTzEAJVb2A8iIqLUZCrQ5k2Y3bhxI+bNm4d///vfWLt2Lfx+P0499VR4vd6E7sfn80GTgBaTFjPQtssafLKASfDQLCWn4e3PsP3uv8L9+S6jS6E8xACVW9gPIiKi1MQbaBN5j82bBaBee+21qO+feuop1NTU4OOPP8bJJ58c9/0IIVAUAHzmYKAtCchdtrhplzW0yQI2TYIpLdVTofI3t2DnH19E5UlHYeA5J8NksxhdEuWRyEWI/H7OxTYaF4UiIiJKTW+LQmmaFvpZPPL2XdjlcgEAKioqeryOz+eD2+2O+gIAkwBKAjIC3Ryh1YNskSbBpuXty0M5puGt/8NXS56Bd+deo0uhPKMHqEAgYHQphNhHaFVVNbAyIiKi/NDTEVpN09DQ0JDQmCcv05qmabj++utxwgknYMyYMT1eb8mSJSgrKwt9DRkyJPQzs5C6BNrIIGtnkKU0U+qaseP+ldj3ynsQAZ6iSPGzWq0oKyszugzq0F2g9Xq9PHpOREQUp86BVg+yfr8/oTFPXia2efPmYevWrXjuuediXm/hwoVwuVyhr927o1eYjQy0zWYGWcoCIbD/n//GN39YBaUx/cuTU99lNufNrJCCEBlo9+7di5aWFlgsnEZAREQUr8hAu3fvXvj9flRVVSU05sm71DZ//ny88sorWL9+PQYPHhzzujabDU6nM+qrM7OQYI5Y5MmmccEnyjzvjlp8de/TcH36jdGlEFGSrFYrbDZb6HuTiassEBERJaK4uDj0/zabDVarNaHb502YFUJg/vz5ePHFF7Fu3ToMHz48LffbLmvwSwIWEVwGqrdVjonSJdDqw67H/xe1L2yApnI+JFG+8Xg8aG9vh91uhyRJUBTF6JKIiIjyhn5qsSRJsNvtaG9vT3jbnrw5b23evHl49tlnsXr1apSWlmLfvn0AgLKyMhQVFSV1n53nyKqSQItJC61yTJQN9es/QeuufRj68zNgLS/t/QZUkFpbW40ugSJ4PB643W44nU6UlpbC6/UmvFUcERFRoYqcI1tVVQWr1Rp6b40866k3eZPYHn30UbhcLkyZMgUDBw4Mfa1cuTKp++tusafuFoUiyobWnXvx9ZIV8Gz73uhSKAd5PB4GpRzSOcgCgMViSfjUKCIiokLUXZAFwnNoExnz5M2RWSHSFywVGfD3sNiTHmhbTBpaTRqsIm/yPuU5taUN3z78DwycdSKqp4+HJHH+NoWDU+ScEjJOd0FWxz1niYiIYuspyOpKS0vh8/nivr+8CbPpYjaboZbaUFlR0WUgEqlUUVBXVweTzYbRCy6JuZ9tPNyqG/ua34i6bEy/6XCauy5KlQghBNp210FrVyA0AaFpEGoAwq9CU4JfgXYfAm0+BFp9CHjboba0QXW3QnV7EWiL/x8LZYEQ2PviW2j74QCGXPxjyNaC+xWlCJHBiYsLGS9WkCUiIqLYeguyOofDEfd9FtxI2Ww2o7i4uNeBiNVqRXl5ec6f2idJEhwH90/69pqiwt/sgdLkgb/RA6XBBV9dc/DrQDM0hl1DNH+4Db79jRj2i7M4j7ZAdQ5OnDNrLAZZIiKi5MUbZBNVcGFWVdW4034hzIGSrWbYasphqynv8jMhBFS3F+17GtC+twHttfVo++EA2vfUQwQ0A6otLG3f1+Hr+57F8Kt+CsewAUaXQ1nE4JRb2A8iIqLkZSrIAgUaZhNRyHOgJEmCpawElrISlB4+NHS55lfRvrcBrbv2oXXnXrTu3AdfXZOBlfZdqrsVO5a+gKFzTkPZuFFGl0NZwOCUW9gPIiKi5GUyyAIFGGYpdbLFDMfB/YOnN598NIDg4kXeHbVo+Wo3Wr7+Ae219QZX2XcIv4pdT7yMgWefjOppx3JhqD6MwSm3sB9ERETJy3SQBRhmKU3MJUUoGzcqdPRQ9bTCs+17eL78Dp4vdkF1c75fSgSw9x+boDS4cND5UyAV8BkDfRWDU2I2bdqE3/3ud/j444+xd+9evPjii5g1a1ba7p/9ICIi6krT4ptqmI0gCzDMUoaYSx0onzga5RNHh1Zcdv/ft3B/9i3adtcZXV7eatj4KVR3Kw6ePROyhb++fQWDU+K8Xi+OPvpoXH755TjnnHPSet/sBxERUfcURel1XaFsBVmAYZayQF9x2XFwfww48z+gNLjR/MnXcG3+Cq279hldXt5xffI1vm1pw/CrzoKpyGZ0OZQiBqfknHbaaTjttNPSfr/sBxERUc8kSUJTU1OPgTabQRZgmCUDWCudqJk+HjXTxweD7cfb0fT+l2jf22B0aXnD+/UP2PHA8xgx/xxYyoqNLoeSxOCUPT6fL2oTdrfb3eU67AcREVFsVqsVgUAA9fX1XcJqtoMsAHDiHRnKWulEzakTcejtl+LQhRej6pRjYSq2G11WXmivrceOpc9Daew6KKfcFLmaOoNTdi1ZsgRlZWWhryFDhsBsDn+ey34QERH1TpIklJeXw2KxoL6+HoqiADAmyAIMs5QjJElC0ZAaHHTeZBxx71wMveJ0lIw+2Oiycp5S14wdDzwPX12z0aVQHPx+P7xeL4OTARYuXAiXyxX62r17N8xmM1pbW9kPIiKiBEiShMrKylCg9fl8hgRZgKcZUw6SLWb0G38Y+o0/DL66JtRv+hSN730Brc3X+40LkL/Rgx0PrMTIX54L+6Aqo8uhGCwWC1paWiDLMoNTltlsNths0XPMVVWF1+uFz+djP4iIiBIgyzIqKytRX1+P+vrglpzV1dVZDbIAj8xSjrPVlOOg86bgiF//HAf97BRYa/oZXVJOUt2t+ObBVWjbw/19iYiIiKgwMMxSXjDZrag6+WiMXnQZhl35EzhGDjK6pJyjtrThWwbanOb3+1FSUgKn0wm32w2Px2N0SXmrpaUFW7ZswZYtWwAAO3fuxJYtW/D999/HfR9msxnFxcXsBxERUYL0ObKqqoZOLY6cQ5stDLOUVyRZRtm4UTjkxgsx8obzUXL4UKNLyikMtLnNYrGguLgYpaWlDFAp+uijj3DMMcfgmGOOAQAsWLAAxxxzDBYtWhT3faiqCofDwX4QERElQAgRNUfWZrNFzaHNZqDlnFnKWyWHDEbJIYPRumsf9q35Nzyf7zS6pJygtrTh2z/8HSMXnA97/wqjy6EIkavn6vMz9S1iOF8zMVOmTIEQIqX7iFxdmv0gIiLqnRACTU1N0DQtarEnfQ5tQ0NDt9v2ZAqPzFLecwwbgBHzZmHUry5EyWFDjC4nJ6ieVnz7h79DaXAZXQrFwCOCuYX9ICIiik1RlKhTiyPpgTabR2gZZqnPKB4xCCOvOw8jrjsXRUNqjC7HcP7mFnzzh7/D39xidCkUAwNUbmE/iIiIeiaEQHl5eY9HXbMdaBlmqc8pPexgHHLzf+LgOafBUuE0uhxDKfUufPPw36G2tBldCsXAAJVb2A8iIqLuWa1WWCyWmNfJZqBlmKU+SZIllE8cjdF3XIYBZ50A2Vq408N9exux89HV0BS/0aVQDAxQuYX9ICIi6kqW44uP2Qq0DLPUp8kWM/rPPA6H3TEb/SaONrocw7Tu3Ivv/mcNREAzuhSKgQEqt7AfREREyctGoC24MBu5mmg8NI2D/77AWl6KoXNOw8jrzoOtf7nR5RjCvXUnfnj2jZRXgKXMYoDKLewHERFR8jIdaAsyzLa2tsZ1Xb/fn/WNfymzSg4bgkNvvQQDfjIJktlkdDlZ1/je59i/5j2jy6BeMEDlFvaDiIgoeZkMtAUXZlVVhdfr7XVAoigKmpqaIElSliqjbJEtZvQ/7XgcetslcIwYaHQ5Wbf/n++j8f0vjC6DesEAlVvYDyIiouRlKtAWZJgtLi6OOSBRFAX19fUwm81Z2eyXjGHvX4FRCy7AoPOnFNwCUT88sxYtX/9gdBnUi8gAFe8ZJZQ5DLRERETJizfQJjLmKbgwCwAOh6PHAYkeZC0WC8rLy3lkto+TZBnVU4/BobdeCsfwwjlKKwIadv3pZfj2NxldCvVCD1Ber9foUgixAy3XWCAiIoqtt0Dr8XgSGvMUZJgFuh+QRAbZyspKBtkCYqvph1ELLsCAs04A4lxyPN8FvO3Y+dj/ItDmM7oU6kVpaSmKi4uNLoM6dPf+wTUWiIiI4tNToPV4PHC73QmNeQpj1N6DyAFJQ0NDVJCNdw8l6jskk4z+M4/DIf99IazV/YwuJyt8+xvx3fJXIXhEKec5HA6jS6AInd8/uMYCERFR/DoH2oaGBrjdbjidzoTGPAWf2EpLS2G329He3g4hBIMswTF0AA5deDHKjyuMfWk9W3di38tc4ZgoUZ3fP7jGAhERUfz0QCuEQHt7O+x2O0pLSxO7jwzVljcURYHPFz7NkvPSCABMdisOnn0ahlx6KiRL39/Cp+5fH6D54+1Gl0GUVzq/fwQCAQOrISIiyj+R2cvn8yU8Zaegw2zkHNmBAwdylUrqouI/jsQhv/oZrNVlRpeScbufWYv2fY1Gl0E9UFXV6BIoQuf3j5KSEvj9fqPLIiIiyhv6HFmn04mBAweGTjlOZMxTsGG282JPsixz2wXqVtGQGhx683/COXaE0aVklObzY9cTLyPQzkVsco2iKHC5XEaXQR26e/8oLi6GxWIxujQiIqK8EBlkS0tLo+bQJjLmKcgwq6pqj4s9RQZannJMOpPDjmG/OAs1px1vdCkZ5dvbiB+efQNCCKNLoQ56cDKZ+v7p7vmguyCrM5sLa79qIiKiZHQOsjo90CYy5im4MCtJElwuV8xVi/VA29LSwlP7KESSJQz8ySQM/fkZkCx9d9Da/NF2NLz1f0aXQYgOTk6n0+hyCl6sIEtERES96ynI6mRZTmjMU3DvxDabDSaTqdeBSGlpKedAUbf6HXsoRt14AczOvrtVyp5VG9FWW290GQWNwSm3sB9ERESp6S3I6hJ5jy24d2NN0+B0OuN6kTgHinriOLg/DrnpItgHVRldSkYINYDv/rwGmsIPc4zA4JRb2A8iIqLUxBtkE1Vw78iKoiQ0EOEcKOqJtcKJUTdegJLRBxtdSkb49jWidtVGo8soOAxOuYX9ICIiSk2mgixQgGGWKJ1MRTYMv2YW+k04zOhSMqLx7c/g2rLD6DIKBoNTbmE/iIiIUpPJIAsAPOxIlCLZbMLBs0+D2VmM+nWbjS4n7XY/+wYcwwfCUlZsdCl9GoNTbmE/iKiv2r3iDaNLyBlDLp5udAl5J5EdLzIdZAEemSVKC0mWMOjck9H/zP8wupS0C7S04YcVa7ldTwYxOOUW9oOIiKh7iqLENSbMRpAFGGaJ0kaSJAw4/UcYdO7JRpeSdu6tO9H47lajy+iTGJxyC/tBRETUMyEEmpqaoGlaj9fJVpAFGGaJ0q562ngc9LNTjC4j7fas2gilwW10GX0Kg1NuYT+IiIhis1qtUFUVDQ0N3QbabAZZgHNms8ZpduKcqnOMLoOypOrkoyFJEn7425tGl5I2ms+P3c++gRHzz4YkSUaXk5ci/+gzOBkv8t8x+0FERNQ7WZZRXl4Ol8uFhoaGqPfMbAdZgEdmiTKm8qSjMPiiaUaXkVYtX36Hpn9/YXQZeUtRFPj9fganHGGz2aCqKvtBRESUAIvFgqqqKvj9/tARWiOCLJBEmL3sssuwadOmTNQSlz/+8Y8YNmwY7HY7jj/+eHzwwQeG1ULUm8qTjsKg86cYXUZa7Vm1EX5Xi9Fl5CVJktDY2IgDBw70meB0yimnYPHixV0ub2pqwimn5Pbp9pqmoampqU/1g4iIKBusVmso0O7du9eQIAskEWZdLhemT5+OQw45BPfeey9qa2szUVe3Vq5ciQULFuCOO+7A5s2bcfTRR2PGjBmoq6vLWg1EiaqeegwGnHWC0WWkTaDNh9rn1xtdRl6yWCyh/3c6nX0iOG3YsAGPPPIIZs2aBa/XG7pcURRs3LjRwMp6p6pq6P/7Sj+IiIiyxWq1wmazhb4vLs7+No4Jv3O/9NJLqK2txdVXX42VK1di2LBhOO2007Bq1Sr4/f5M1BjywAMPYO7cuZgzZw6OOOIIPPbYY3A4HHjyyScz+rhEqeo/8zjUnDrR6DLSxvXJDri37jS6jLyjKArMZjMsFgsaGhqgKIrRJaXFG2+8gX379uFHP/oRdu3aZXQ5cbNarbBYLH2uH0RERNng8XjQ3t4Ou90OSZJ6XBQqk5L6GLq6uhoLFizAp59+ivfffx+jRo3CpZdeikGDBuGGG27A119/ne46oSgKPv74Y0yfHt7cWJZlTJ8+He+99163t/H5fHC73VFfREYZ8NMTUPEfRxpdRtrUrlwHTcnsB1h9jSRJqKioQFVVFSwWC+rr6/tEgBo4cCA2btyIsWPHYuLEidiwYYPRJcVF0zQ4nc4+1w8iIqJMi5wjW1lZ2WUObbakdE7V3r17sXbtWqxduxYmkwmnn346PvvsMxxxxBFYunRpumoEANTX1yMQCKB///5Rl/fv3x/79u3r9jZLlixBWVlZ6GvIkCFprYkoEZIkYfB/TodzzHCjS0kLpcGN/a++b3QZecVqtUKSJMiyjMrKyj4RoPQVgW02G5599llcd911mDlzJpYtW2ZwZb1TFAWyLPepfhAREWWa1+vtMkc2cg5tNgNtwmHW7/fj73//O84880wMHToUL7zwAq6//nrs2bMHf/nLX/DGG2/g+eefx1133ZWJehOycOFCuFyu0Nfu3buNLokKnGSSMfTnZ8AxbIDRpaRF3dqP0b6v0egy8kbkVjB9JUAJIaK+v/3227FixQr8/ve/z8rjp2tRwL7SDyIiokxSVRUtLS3dLvZkRKBNeJ/ZgQMHQtM0XHTRRfjggw8wbty4LteZOnUq+vXrl4bywqqqqmAymbB///6oy/fv348BA7oPBjabLWpSMlEukK0WDLvqLOz43XNQGvL81HdNw55VGzF83izuPZsEPUA1NDSgvr4eVVVVsFqtRpeVkJ07d6K6ujrqsnPPPRejR4/GRx99lNHH1hcFfOyxx3D88cfjwQcfxIwZM7B9+3bU1NTEdydCBbRgcJUBVJaXoqGxGfX1daiqKIfVaol9+9D9+AERAPwuQOHvAlHO0Hyw+IMr8NvVNgCAxd8Cs5TwELjPsEk+o0vIGfq/jUIkCTXqdwIAoDQBcozs5HfBr/hQ4ihCabEt9P4ZyWoGqirKUN/YhIb6OlRW9Et8gUWh9n6dDgn/Ji9duhTnn38+7HZ7j9fp168fdu5M7+IwVqsV48ePx5tvvolZs2YBCM53evPNNzF//vy0PhZRplmcxRh+zSx8/bvnoLXn9xEgzxe74Nm6E86xI4wuJS/le6AdOnRot5cfeeSROPLIzM4Rj1wUEAAee+wxrFmzBk8++SRuueWW+O6k/QAi3wplAJUOgQZ3APUH9qKq1ASrJY43YVUAigbseRXwFO4gmSjXyJofB7V+BgAItH8PABhs80CWCnf1coftG6NLyBnl++P8wLIP0oQGyfcDAOCgluBuBLLHA8gxXhOPCgtaUWw2AW3eHq9mBVDl0FDvaUdDnReVThPkRA56tMcfZhP+Tb700ktjBtlMWrBgAZ544gn85S9/wZdffomrr74aXq83NJAhyif2gZUY9vMzgT5wRLN21UZo/vj/8FA0nuKauEQXBUxkQUBZklDpNMFiklDvCUDxZ3dlRiIiolxlNsU3brVaZFSVmuAPBD8g1jpNS0qXvPpY6sILL8T999+PRYsWYdy4cdiyZQtee+21LotCEeWL0iOGYtA5JxldRsqUA82o37DF6DLyGgNtYhJdFDDRBQEZaImIiFKTjUCbV2EWAObPn4/vvvsOPp8P77//Po4//viEbp/ovL7Oi5sQpVvVKcei/LjDjS4jZXWvfQDV2250GXmNgTZzklkQkIGWiIgoNZkOtAU3scdms0FV4zsdUgjBwSRlnCRJGHzxdLTvbUDb7jqjy0laoM2H/a++j4POm2x0KXkt3+fQZkuiiwL2uCCgvRoo6nnqjAyg0q4FF4VqVXteFMrvB6AAg04Dyvsl9mSIKGM0zYfapuCKq1u9xQAA4TiyoBeAqvVxj3jdQf0nGV2CYVSh4vPWzwEApuKjAQBHlp8aewGopmbA+ipgtwKW+OcbW4uAKrs/uChUqzmORaHiPzhScL/JmqbB5XLBbrfHHCBqmoampqbEV98iSoJsMWPo3DPw9ZJnEWjL31UGGzZuQdWUcbBVlRldSl7rLtBStLQtCiiZATn2hwWyDFRW1QT70ejq/gMGCYAUACxlgLU8sSdDRJmj+eC3lAAA2s1FAAC/pQSigMOsT3CnD53+b6MQqUKN+p0AEHz/ihVmLQKQTIBk6fW9szOr3YqqquCZZw1NHlRWVvacs6QMLgCV7xRFgclkinkKn6ZpaGhogKqqPCJCWWOr6oeDZ880uoyUiICGff/7jtFl9AmdTzmO94ySQpLNRQF5CjgREVFq4t2HNpExT8GFWQBwOp09Dkj0IOv3+1FeXs4js5RVzrEjUD19vNFlpKT5o+1o++GA0WX0CZEByuVyGV1Ozsn2ooC9BVqusUBERBRbb4FWUZSExjwFmdR6GpBEBtmqqipYEjgXnChdBpx1AoqG5vcK3fvWdN0ahZKj/70ymUxGl5KTUl0UMFE9vX9wjQUiIqL49BRoFUVBfX19QmOeggyzQNcBic/niwqyPL2YjCKbTRh6xemQ7fn7b9D96Tdo/a7r9iiUHFmW4XQ6jS6DOnT3/tHU1MQjs0RERHHqHGh9Ph/q6+thsVgSGvMUbJgFwgMSs9kc+oSdQZZyga2qHw66cKrRZaRk38s8OptOnPKQWzq/f/j9fr53EBERJUAPtPoRWbPZHHthqG5wdESUo8qPOxxlx4wyuoykeb7YhdZdPDpLRERERJlR0GE2ctVi/YgsV6mkXCFJEgZfNB1mp8PoUpK2/7X3jS6hz+hpxT8yRuf3D4vFwvcOIiKiBOhHZPUjtKqqxlzluDsFG2Y7L/Zks9m47QLlHHNJEQb/53Sjy0ia+/++5crGaaBpGtxut9FlUIfu3j/Ky8shSZLRpREREeUFPchaLBZUVlbCZrOF5tAmMuYpyDDbeSCiz3PqvKiH3+83uFIioOyokeg3cbTRZSRt/2sfGF1CXtP/XgUCAaNLIfT8/iFJEufMEhERxaFzkNXnyOpHaBMZ8xRkmHW73T2uWhwZaJuamnhqH+WEg86fAnNJkdFlJMX1yVfw1TUZXUZeigxOZWVlRpdT8HoKsjoemSUiIoqtpyCrs1qtCY15Ci7MWq1WBAKBmKsWR65SydONKReYS4ow6PwpRpeRHAEceHOz0VXknc7ByWw2G11SQestyBIREVFsvQVZXSJjnoILs7Iso6ysrNeBiCzLnANFOaXfhMNQMvpgo8tISuO/v4Da0mZ0GXmDwSm3sB9ERESpiTfIJqrgPur3+Xxxp33OgaJcIkkSBv9sGrbf81cINb/mTwq/ioZNn6L/6T8yupScx+CUXTv2NQNmb89XEAJmtQ2S0KCai1C/z9Xt1SQtAFkEsGjVuwiY83NKAFFfpEl+1Fd8CQDwFAe3i/vca4UMk5FlGWvYQUZXkDve+dLoCgyjIQBvx+/El97g6/CvRgdkYenxNia1Df0aPNCkVgg5/t8hSQvArLZBSDJUvxl72xpi30CNf92igjsyK4RI6Po8Mku5xFbTDzUzJhpdRlLqN34Kza8aXUZOY5DNMZ2CbCJv3ERERNQpyJqLgDRnq4ILs0T5rubHE2GpcBpdRsJUTytcm782uoycxSCbYxhkiYiIUpLpIAswzBLlHdlqxqBzTza6jKTUb9xidAk5iUE2xzDIEhERdU/Et9NLNoIswDBLlJfKxo1CyaFDjC4jYa279qF11z6jy8gpDLI5hkGWiIioR5LQIGmx57RmK8gCDLNEeUmSJAzk0dm8xyCbYxhkiYiIYhKSDFPADznQ/fal2QyyAMMsUd5yDKlB+XGjjS4jYc2bv4La2m50GYaIXICOQTbHMMgSERH1TpIRMFlgCihdAm22gyzAMEuU1wb85ARI5vwadAt/AM0fbDO6DEMoigIhBINsjrBarYAQDLJEREQJELIFAZM1KtAaEWQBhlmivGatdKLy5KONLiNhDe98lvA2WX2BEAKNjY2or69nkM0BsizDrPkYZImIiBKkmayhQGtS2wwJsgDDLFHeq5kxEbLVbHQZCWmvrUfb9/uNLiPrrFYrVFWF3+9HZWUlg6zBFEWBJERHkLUzyBIRESVAM1mhySbIWgAAsh5kAYZZorxnKXWgasoxRpeRsMZ3Pze6hKzz+8Or/7ndbmhafMvbU2aYzeEPgUwBJXjKMREREcVF0gKhIAsAci+rHGcCwyxRH1D94wmQbRajy0hI88fboflVo8vIKiEEKioqUF1dDb/fj4aGBgZaA8myDNVkhWougiQ0mNU2BloiIqI4RM6R9VuKu8yhzRaGWaI+wFxsz7u5s4FWHzyf7zK6jKyyWq2wWCywWq2oqqpioDWYz+eDkEwQsomBloiIKF4d75eRc2Qj59BmM9AyzBL1EdXTjoVkya85f03vf2l0CVkly+E/uQy0xotchIyBloiIKA5CwKy2d7vYkxGBlmGWqI+wOItRecJYo8tIiHvrt1C9hbnnLMBAm2sYaImIiGKTRCDmqsXZDrQMs0R9SPW0Y7O+ilwqRECD69MdRpdhKAba3MJAS0REFIuEgNkWc7yZzUDLMEvUh1gry9Dv2EOMLiMhrs1fGV2C4RhocwsDLRERUfeC29j1fuAkW4GWYZaoj6mePt7oEhLi2ba7oE811jHQ5hYGWiIiotRkI9AyzBL1MY6hA+AYOcjoMuKn8VRjHQNtbmGgJSIiSk2mA23BhVmr1ZrQAFFVC2sfTOobqiaPM7qEhLg++droEnIGA21uYaAlIiJKTSYDbcGFWVmW4Xa74xoger1e+P3+LFRFlF5l40bB7HQYXUbcWrbvRqA9u5ts5zIG2tzCQEtERJSaTAXagguzPp8PgUCg1wGix+NBS0sLLBZLFqsjSg/ZbELlifmzTY9QA/B8+Z3RZeQUBtrcwkBLRESUmrgDbQLvsQUXZoUQKCsrizlA9Hg8cLvdKCkpgdlsNqBKotRV/MeYeBabyxnu//vG6BJyTmSgdbvdRpdT8BhoiYiIUtNroBUCJi3+I7cFF2YBwGw293jEQw+yTqcTxcXFBlZJlBprpROlhw81uoy4ubfuhAjw6GNneqANBAJGl0LoJdAK/vslIiLqTY+BVgiY1TZICbyfFmSYBbo/hS8yyJaWlhpdIlHKKiaNMbqEuAW87Wjdtc/oMnKS1WpFWVmZ0WXklF//+teYNGkSHA4H+vXrl9XH7i7QSpo/oTdfIiKiQtYl0EYE2YDJGvf9FGyYBaID7d69exlkqc9xjh0Bk8NmdBlx83y5y+gSchanPERTFAXnn38+rr76akMePzLQWvxemAJ+CKmg31KJiIgSEhloLX4vJKFBNRdBSKa476Pg33mtVitstvBgn6cWU18iW8zoN/4wo8uIm+cLLgJF8Vm8eDFuuOEGjB1r3EJnQjZBkyPecBlmiYiIEqLJloj/N0HI8QdZgGEWHo8H7e3tsNvtkCSJq4ZSn1N+3GijS4hb63f7oXrbjS6DKC5yQIGsBUKBVtI4r5mIiChuHacWA8EgK2uBhLftKegwGzlHtrKykttgUJ/kGDEI1kqn0WXERwi0bP/e6CpyUmtrq9El5DWfzwe32x31lQo5oMAUUBAwWREwF0E12wFwdWMiIqK4RMyRVc1FCJiLwnNoNX/cd1OwYba7xZ46LwoluO0C9QGSJKHs2EONLiNuLdt3G11CzvF4PPB6vUaXkXG33HILJEmK+bVt27ak7nvJkiUoKysLfQ0ZMiTpOiODrKYvUiHJCc3xISIiKlidgqx+anFoDq2mxn1XeRFmd+3ahSuuuALDhw9HUVERRo4ciTvuuAOKkthhaF1ra2uPiz1FBtqmpiYGWuoT+h17iNElxK3l6x+MLiGn6B+8FcJ8/htvvBFffvllzK8RI0Ykdd8LFy6Ey+UKfe3endyHJt0GWZ2URxs7ExERGaGHIKvTTFYE5PgXvcyL5TG3bdsGTdPw+OOPY9SoUdi6dSvmzp0Lr9eL+++/P6H7MpvN8Hq9qKio6HHVYj3Q1tXVJR2YiXJJ0cH9Ya10QmlI7dTKbPDta4Tf0wpLqcPoUgwXeQaJydT3j/pVV1ejuro6I/dts9miFvtLRswgS0RERLH1EmR1kYtC9SYvwuzMmTMxc+bM0PcjRozA9u3b8eijjyYVZouLi3vdfsdqtaK8vLwgTu2jvk+SJJSNG4UDb242upS4eL/+Af3y6NToTOg8FYJzZqN9//33aGxsxPfff49AIIAtW7YAAEaNGoWSkpK0Px6DLBERUQriDLKJyovTjLvjcrlQUVGR8O1UVYXDEd8RH4vFAquVgxbqG5xjRxpdQtwK/VTj7ub0U7RFixbhmGOOwR133IGWlhYcc8wxOOaYY/DRRx+l/bEYZImIiFKQoSAL5GmY3bFjBx5++GH84he/iHm97lavVNX4JxQDgCzn5UtE1EXxyEEwOVI7zTJbWnfuNboEwzDIxuepp56CEKLL15QpU9L6OAyyREREKchgkAUMDrPJrFxZW1uLmTNn4vzzz8fcuXNj3n86V68kyneSSUbpkcONLiMubT/UQ1PiX5a9r2CQzS0MskRERCnIcJAFDA6zia5cuWfPHkydOhWTJk3Cn/70p17vP12rVxL1Fc4jhhldQnw0Da3f7Te6iqxikM0tDLJERETdiHenlywEWcDgBaASWbmytrYWU6dOxfjx47F8+fK4Tv9Nx+qVRH1JyRFDjS4hbq0796LkkMFGl5EVDLK5hUGWiIioe5IIAEIGECOcZinIAnmymnFtbS2mTJmCoUOH4v7778eBAwdCPxswYICBlRHlF0upA0VDatC2u87oUnrV+t0+o0vICgbZ3MIgS0REFIsEs9oOVZK7D6lZDLJAnoTZtWvXYseOHdixYwcGD44+UiPiPdRNRACAktEH50WYbfs+92tMFYNsbmGQJSIiik3IJkBoMKttXcNqloMskCerGc+ePbvbVSsZZIkSly+n7ioNbqit7UaXkVaRq6kzyBrPbA5/nssgS0REFJ+A2QYhycHgqgWCFxoQZIE8CbNElD7FIwcBkmR0GXHJhyPIifD7/fB6vQyyOcJsNkPW/AyyRERECZGCgTUUaFVDgizAMEtUcExFNhQdXGN0GXHpa6caWywWtLS0MMjmCFVVYdJUBlkiIqJESZGBtt2QIAswzBIVpOIRg4wuIS7te+qNLoGIiIiIchTDLFEBcgwfaHQJcWnf22B0CWnl9/tRUlICp9MJt9sNj8djdEkFzWw2IyCbETBZYQookAOK0SURERHlh6g5svauc2izJC9WMyai9CoekR9h1revEUITkOT8mOPbG4vFguLiYlitwdNZ3W43APB0Y4OoqgpNtgAmCwDA1BFmeboxERFRLF0Xe1IlE8xqW/erHGcQj8wSFSBLeSnMZcVGl9ErTVGhNLqMLiNtIlfPLS0t5RFag0WuLq2ZrDxCS0REFAeT6us6R1bqvChUdo7QMswSFSBJkuAYkh+LQPn2NhpdQsYw0OYWBloiIqLYJC3Q82JPBgRahlmiApUvKxr7DjQbXUJGMdDmFgZaIiKiWERwjmxPpxFnOdByzixRgSoa0t/oEuLiq2s2uoSM0+fMcg5tbtDnzHIOLRERUTQhmQCpl+OhHYE2G3NoGWaJCpT9oCqjS4hLXz8yq2OgzS0MtERERN2Q4lyUM0uBlmGWqEBZK5yQrWZoitr7lQ2k1DUZXULWMNDmFgZaIiKiFGQh0BbcnNnI1UTjoWlahiohMpYkS7ANrDS6jF4pjR6IQOH8HnIObW7hHFoiIqIUZHgObUGG2dbW1riu6/f7oSgcvFDfZc+DMAsh4Hd5ja4iqxhocwsDLRERUQoyGGgLLsyqqgqv19vrAFFRFDQ1NUGK97xwojxkqyk3uoS4+JvcRpeQdQy0uYWBloiIKAUZCrQFGWaLi4tjDhAVRUF9fT3MZjOsVs6Ror7LVtPP6BLiojQWZpiLDLTxnlFCmcNAS0RElII4A62s+eO+y4JcAMrhcMBms3W7yIoeZC0WC5xOJ3w+n1FlEmWcrbqf0SXExd9UmGEWCP99amxsNLgSAnpZFEoII0oiIiLKH70sCiUHFJi0+BcnLcgwC3S/amhkkK2srISq5vYqr0SpsuZLmC2wObOdlZaW8oO1HNJtoBUaJJHZjeGJiIj6hB4CrRxQYAooCMjxR9SCDbNAdKBVFAU+ny8UZGW54M7ApgJkslthctgQaM3toKS6CzvMAsEzSih3RAZaSQQgB1SIBN58iYiIClqnQKvJJshaAAGTFZqIf82igk9spaWlsNvtaG9vhxCCQZYKjqU89/cy9bs5X5Ryj2ayht58AWRkM3giIqI+qyPQAoCsBaDJpoT3dC/41KYfkdV5vTwCRIXFmgdhVvUwzFLukbRAKMgCAETh7IdMRESUDpGLPclaIOFVjgv6nKjOc2S9Xm+3i0IR9WWWfiVGl9ArhllwDn+OkbQAzGobhCRDNRdBDvggM8wSERHFLTRH1mSFJlvCc2glzpntVecgK8tyl0WhbDabkSUSZYXZmftzMQOtPghNQJILc99nRVHgcrmMLoM6dA6ykCQI2QKRwFYCREREhSwqyHacWqzPoTUlsP1dQYZZVVXR3Nzc7WJPkYHW4XDAZOIcKOrbzKXFRpfQOyGg+RSYigrvAyb9gzf+LcoN3QXZ8A8LfuYOUV4QUgAad9KiAick41bg7y7IAgjNoTVp8U/7LLgwK0kSXC4X7HZ7j4s96YG2ubkZxcV5MNAnSkE+HJkFgkdnCy3MRp5BUlRUZHQ5BS9mkCWivNHqqDW6BKKC1WOQ1UkSAnL8i0AVXJi12WwwmUy9rlpcWloKVVWhKPEf5ibKR+Ziu9ElxCXQltvbB6Vb56kQ7e3tRpfU58iyDEloQDyLTQgNZrUdQpIRMFmDtxPRP4fQIKuF9e+UKNdJUGH1BX9Z7VxxnKhb+u+Iyd8OGT2v0SGrvo7FDiVISSwTIWl+mAJ+BEwWCMkUY7Gn+E+dKLgwq2kanE5nXNvvFBcXQwieh0J9m8mRJ2E2x/fCTafu5vRT+vXr1w+WQDug9XKEVQhI0Dr+V4bs7+bNt+O9wtn0Tfh0Y6FBEhqEJKflFOTgm76AkEzxHxUWApIIAJDSs3VQks8pqdp7rCHJ58R+hBRSPwQEnM367+ygTjUAcrB0aHLwv6nVAMha8L5EvE9JBG8DAWim1GtI9jklVXusGpJ5TuxHSLb7YVb1seCOiNt187stNJhUH0xA4n87hAYJAgISzAEBxJoXm0D+KrgwqyhKQgNDs7ngXiIqMPly6q7mL4zFdRhks0eSpI7PfmO8IQsNckeQ1RB70C1BQJNNCO16J5miP4WWLUlWKmBSfZCEBtVsT2zgLwEQMsxqOyA0BMw2JDs6S+m5mGSYVB9kTUn8OUQSGswBX/AIudmKhJ4L+xFWUP0QUGNkbFkCrAoAASgJPoVIZj9gUgHVDKiJPhURrMHsD9agJfk5hxwArP5gAFIsSOy5mIKPb072Oeg6nousJfdc2I8OWe6HHOjmB939vZIkmAAISEjkCUlCDd4dEPzAKZ7C48SkRlTgTI70h1l78Q9QfP2gqenb9kdT+v7WNAyy8dm1axfuvvturFu3Dvv27cOgQYNwySWX4LbbboPVmthm6wB6/nRZiwiykim+Ab9kCh5p6iDk4O1MAQUBSU54M3gIAbPaBgkCqsWR5NE8E1RJDq0Qmcx8XzmgwKSpCJhtiT+HDqrFFNx2IeCDai5K+LlIWiAYnGRT0nOW2Y+wQuqHRe0X8240UwBmtR2mdrkjECfRj445gJJkhSWJtyshC0haO+y+YChPqh8dUyGEbIclkEQKlAAhK7D6FZi0HuYzxiIEzGp76IMFkzDBlMRrwX7od5K9foged4uI/nsVMNk6+iHF3RdJ04OsBCHHGT0TODGWYZaowMnW9P4ZkOV2jBr7R3jdw7Dzi1+k7X41pW8fmWWQjd+2bdugaRoef/xxjBo1Clu3bsXcuXPh9Xpx//33p+dBNA0ygqcmxh1ke7qrjgGIvtVA3AMSfaAutKTCRtRddQSO0B5+CYSPXhfriFfHKpWRNcT7nNK5+Bb70aGg+hG7PiGbI16L9iT64UfAlPwHC8ESpS41JNaP9jT1I3i2QLAfUoL9aE/T7wf7ocuNfoT/XpkC7cFTgBMKsiKxIJsghlmiAieZTJBMMkQgiZn83agc+A5kk4Lism8hy+3QtPTMyRV9+Mgsg2xiZs6ciZkzZ4a+HzFiBLZv345HH300PWE2jUE2dJeJDtjTGJxCd5lEgEpbcNIlEaAysYo0+9GB/QjfJfsRwn50YD/CdxnRDwlacEprL6cLZyPIAqGJPURUyCRL+v7IlFd/DEBAllVUDnwnbfer+ftmmGWQTQ+Xy4WKiorU7ygDQTZ01yYrAiYrTAEFci8LX6R7IBK6644BiSQ0mNW2mItspH1gqOsYIIqOU9d6Xs0yMwNDHfvRgf0I3zX7EcJ+dGA/wnctm4KnGQPBKTii54Mg2QqywVqIqODJaQqzVvt+2BwHIIQMQHQE2zTpgyuLM8imx44dO/Dwww/jF7/o+bR2n88Ht9sd9dVFBoNs6CF6G5BkcCASeog4BogZGxjq4hggZmNfX/ajA/sRfgj2I4T96MB+hB9CNnUs/gTIItBtoM1mkAUYZokICC4fmAY1B22EJGkQQoYQMmyOA7Da96flvtN1GrSRNC38HBhku7rlllsgSVLMr23btkXdpra2FjNnzsT555+PuXPn9njfS5YsQVlZWehryJAhkCIHG1kIsqGH6mlAkoWBSOihYgwQMz4w1MUYIGZjYKhjPzqwH+GHYj9C2I8O7EeYJAdX90fXQJvtIAtwziwRAZDSEqQ0OCu3IrgEnRTcj1MOoOagjfjhmwtSvneh5f+RWUVRYLEE19dnkO3qxhtvxOzZs2NeZ8SIEaH/37NnD6ZOnYpJkybhT3/6U8zbLVy4EAsWLAh973a7O26jAZqUtSCr6zIHSrZkbyDSobs5abLmz87AUNfNnDQAWRsY6tiPDuxHCPsRxn50YD/CJBmaCL53yiIADYAUsY9stoIswDBLRACkNByZLa34AiZzK0RoE7PgQuzOyq3AN+ch5RNB+sBpxpIkobGxEbIsw2q1Msh2Ul1djerq6riuW1tbi6lTp2L8+PFYvnx5r6+jzWaDzdZ1G6rgrbIbZHWRAxJ9UJK1gUiHyAGixe8FgOwNDHWdBogAsjow1LEfHdiPEPYjjP3owH6EyTI0DaFACyS4/U66ysjqoxFRTlIaupk/mKCqgW8HTzGO2AJBaCaYzK0orfgi5fsvOWxIyvdhNP2oLAA4nU4G2STV1tZiypQpOPjgg3H//ffjwIED2LdvH/bt25fQ/USe9q1BzmqQDT2ubIn4f1N2ByIdhGyCFvG4kTVljSQhEDEgDZisWR0Y6tiPDuxHCPsRxn50YD/CZDl63GfA+yhHUkSUMlluR7FzJ4To2Ei7g4AESdJQNfDtlB+jeMSglO/DaIqiwGw2w2KxoKGhAYoSY0VC6tHatWuxY8cOvPnmmxg8eDAGDhwY+kpE5IcJMjRAy/K87I45TkBwICJrgdirVGaIHFAga4HQALG3VUMzIXJfxuCctPaYq4ZmBPsRwn6EsR8d2I8Q9iMsco4s0POiUJnEMEtEKasc+A5kWQVE5z8pEoSQUOzcCVluN6S2XCJJEioqKlBVVQWLxYL6+noG2iTMnj0bQohuvxKlQQqeXozgqVJZC7SdFusImIvi23YhzSIXTwmYi+LeBiOdOi+eEu82GGnFfoSwH2HsRwf2I4T9COu82JOGjvfSLAdahlkiSpm+BY/o7k+KkNO+52y+slqtkCQJsiyjsrKSgdZggUAAgBRcyCKbgbaHVSfj3kcwTbpbBTSRfR3TodtVQBPY1zEt2I8Q9iOM/ejAfoSwH2GSCHRd7EmWDQm0DLNElJLw3rLdzxcJBtw07zmbpyK3gmGgzTHZCrS9bJ+QrQFJrO0ssjVAjLmdRbYGiOxHCPsRxn50YD9C2I/IOmKsWmxAoGWYJaKURO4t25N07znbVzDQ5phMB9o49wHM9IAknn0ZMz1AjGtfxkwPENmPEPYjjP3owH6EsB9hckDpffudLAdahlkiSkH03rI9EUKGJGmoOWhj1irLFwy0OSZTgTbBDe0zNSCJZ2AYKjlDA8S4BoahK2dogMh+hLAfYexHB/YjhP0ICy6+5Q8GWamXlZOzGGgZZokoaV33lu1JxJ6zyPKKsXmAgTbHpDvQJjgQ0aV7QJLIwFCX7gFiQgPD0I3SPEBkP0LYjzD2owP7EcJ+hOn90GRL/NvYZSnQMswSUdK621u2J+ncc7YvYqDNMekKtEkORHTpGpAkMzDUpWuAmNTAMHTjNA0Q2Y8Q9iOM/ejAfoSwH2Gp9CMbgTbvwqzP58O4ceMgSRK2bNmS8O2lRP4xAklt9UBUEGQFxc6dAABJCkCSVEhdjrqK4OWSCkkOBPecHcBVjXvCQJtjUg20KQ5EdKkOSFIaiHRIdYCY0sAwdCcpDhDZjxD2I4z96MB+hLAfYenoR6YDbd6F2ZtuugmDBg1K+vY2mw2qqsZ1XSEEB5NEPdEAX1s1FF85/Eo/aJoVktz1j7UkAQG1GIqvHIqvHL62KgOKzR8MtDkm2UCbpoGILtkBSVoGIh2SHSCmZWAYurMkB4jsRwj7EcZ+6MWzHzr2Iyyd/chkoO1hGarc9Oqrr+L111/H3//+d7z66qtJ3YemaXC5XLDb7bBae26MpmloamqCLOdd3ifKEiu+3vKr0HejjvoDHKXfdXvNfd/NQOP+E7JVWN7TA21DQwPq6+tRVcUPAAwlydAQfAOWEQjm2VjvDWkeiOj0wYSpYzDS2+AirQORDvoA0ay2way29TrYS+vAMHSnUpcaYr7G7EcI+xHGfuhFsx869iMsE/2ALEPTgh8MyyIQPJcv3vm3se425XvIkv3792Pu3Ll4+umn4XA4kr4fRVFgMpliHvHQNA0NDQ1QVTVm4CUiypTOR2jjPaOEMiTeI7QZGojo4v2EPSMDkQ7xHvHIyMAwdOdxHvFgP0LYjzD2Qy+W/dCxH2GZ7Ef8R2jjP3KbF2FWCIHZs2fjqquuwoQJE+K+nc/ng9vtjvoCAKfT2eMpfHqQ9fv9KC8v55FZIjJMZKB1uVxGl0O9BVohYAq0Z2wgouttQJLRgUiH3gaIGR0Yhh6klwFihgeGOvZDfxD2Q8d+hLEf+oOwH+EH6SXQalpCAdXQpHbLLbdAkqSYX9u2bcPDDz8Mj8eDhQsXJnT/S5YsQVlZWehryJAhAHqekxYZZKuqqmCxWNL+nImIEqH/vTKZMvPGRgnqKdAKDRI0SEJkdCCi62lAkpWBSIeeBohZGRjqehogZmlgqGM/OrAfIexHGPvRgf0I6ynQalrwvTUBhs6ZvfHGGzF79uyY1xkxYgTWrVuH9957DzabLepnEyZMwMUXX4y//OUv3d524cKFWLBgQeh7t9uNe+65B0DXOWmVlZVwu92hIGu1WrnwChHlBFmW4XQ6jS6jTwmvVC+AJLZN0CBBhuiYQxsIfTKsSiYITUt9b9o4awBkmPztQCB4GrpJUxGQzdCEBKj+jNcgAKiSGaaAApPmhSYH/1+TZAQkc6iuTFNhhkkoMCleBExWyJoKCA2qycp+sB/sB/sBgP2IlHw/tHDwFKkHfw3BI6uyCEAT4fdS/RWIZ1cZQ8NsdXU1qqure73eQw89FAqhALBnzx7MmDEDK1euxPHHH9/j7Ww2W5cAHEkPtPX19aivrw/VxHmyRJRrOOUhvTweD+69916jyyAiIqIeeDwelJWVxbxOXqxmfPDBB0d9X1JSAgAYOXIkBg8ebERJRNQDWYr4ZFNC8KNQZPC0HaIkDBo0CLt370ZpaWnC+4/3FW63G0OGDMHu3bt55D8D+PpmHl/jzOLrm3l8jbsnhIDH44lrO9a8CLOZErlqcVVVFdxud2gbDB6dpbRp3GJ0Bb2yFf2Q8n00HxgDLdB1nocQZrS1VqT+GHnwOgIAKsZl5G61LJx2VEhkWeaHoR2cTicHURnE1zfz+BpnFl/fzONr3FVvR2R1eRlmhw0bFtc51LF0XuzJarVyX0fKjA9/YXQFvRpyyL4M3ruKg4a/lvrdfPh26veRDTPeT/tdapoWWo2diIiIiIIKchJWd0EW6LrKsd+f+cnoRESx6H+vAoHEFykiIiIi6ssKMsx2XrU4UmSgbWpq4ql9RGSYyA/e4j3dhiheNpsNd9xxR8yFEil5fH0zj69xZvH1zTy+xqmTRKrn6+YRt9uN22+/HYMHD0b//v1jzovVNA11dXWQZRmXXnopKioqslgp9Sn/6nnF7Vzh3ZmO04xVmCxtXS8WMgJqEVL97Kx4+ICUbp81PZxm3NjYiBdeeAF2uz2uOfmdzyBRVRU///nP4XK5OK+GiIiICHk6ZzYVsiyjrKys18GkLMsoLy/nPDVK3cTHja6gV7tffzrl+xhyyPOw2Zu7XC6EhH3fTYC78ciU7n/0BZemdPt80t1UCFXNzv53RERERPmi4MKsz+eD2Rzf05YkiasaU+oytLptOvna1qfhXkwABDQt/PslQUCSNQTUEvjaUlw5Ng9ex3ToaU4/EREREUUruDCb6FnVhbr/IFHywr8zAtxhNhEMskRERETxK8gFoIiIcg2DLBEREVFiGGaJiAzGIEu5wufzYdy4cZAkCVu2bDG6nD5h165duOKKKzB8+HAUFRVh5MiRuOOOO6AoitGl5bU//vGPGDZsGOx2O44//nh88MEHRpfUZyxZsgQTJ05EaWkpampqMGvWLGzfvt3osvqs3/zmN5AkCddff73RpeQlhlkiIgMxyFIuuemmmzBo0CCjy+hTtm3bBk3T8Pjjj+Pzzz/H0qVL8dhjj+HWW281urS8tXLlSixYsAB33HEHNm/ejKOPPhozZsxAXV2d0aX1CRs3bsS8efPw73//G2vXroXf78epp54Kr9drdGl9zocffojHH38cRx11lNGl5C2GWSIigzDIUi559dVX8frrr+P+++83upQ+ZebMmVi+fDlOPfVUjBgxAmeddRZ+9atf4R//+IfRpeWtBx54AHPnzsWcOXNwxBFH4LHHHoPD4cCTTz5pdGl9wmuvvYbZs2fjyCOPxNFHH42nnnoK33//PT7++GOjS+tTWlpacPHFF+OJJ55AeXm50eXkLYZZIkqShoHDXsKQQ1ZgyCErYLE293jN8pqPQ9crq/okeyXmmMgF6BhkKZfs378fc+fOxdNPPw2Hw2F0OX2ey+Xi/vVJUhQFH3/8MaZPnx66TJZlTJ8+He+9956BlfVdLpcLAPhvNs3mzZuHM844I+rfMiWu4FYzJqL0kOV2VA16F5KkQl+zWIju1y4udn6HYud3AASs9nq46o/JXqE5RFEU2O12BlnKKUIIzJ49G1dddRUmTJiAXbt2GV1Sn7Zjxw48/PDDPAKepPr6egQCAfTv3z/q8v79+2Pbtm0GVdV3aZqG66+/HieccALGjBljdDl9xnPPPYfNmzfjww8/NLqUvMcjs0SUFE1zoL21BoAETTND08wQovPnYxE/02QAElwNRxtQbW4QQqCxsRH19fUMspRxt9xyCyRJivm1bds2PPzww/B4PFi4cKHRJeeVeF/fSLW1tZg5cybOP/98zJ0716DKieI3b948bN26Fc8995zRpfQZu3fvxnXXXYcVK1bAbrcbXU7e45FZIkpaU90EFA1/GRI0iN4+G5M0aAEL6vdNyk5xOchqtcLj8UDTNAZZyrgbb7wRs2fPjnmdESNGYN26dXjvvfdgs9mifjZhwgRcfPHF+Mtf/pLBKvNXvK+vbs+ePZg6dSomTZqEP/3pTxmuru+qqqqCyWTC/v37oy7fv38/BgwYYFBVfdP8+fPxyiuvYNOmTRg8eLDR5fQZH3/8Merq6nDssceGLgsEAti0aRMeeeQR+Hw+mEwmAyvMLwyzRJS0+n2TMODg1yDJfkDECrMCkgR4XCMArXADnN/vD/2/2+1GZWUlZJknyFBmVFdXo7q6utfrPfTQQ7jnnntC3+/ZswczZszAypUrcfzxx2eyxLwW7+sLBI/ITp06FePHj8fy5cv5e58Cq9WK8ePH480338SsWbMABE+FffPNNzF//nxji+sjhBC49tpr8eKLL2LDhg0YPny40SX1KdOmTcNnn30WddmcOXMwevRo3HzzzQyyCWKYJaLkaVa0uEbAWbG9Y3Gj7ufMShAQQkLD3pOyW1+OEUKgoqICFosF9fX1aGhoYKAlwx188MFR35eUlAAARo4cyaMxaVBbW4spU6Zg6NChuP/++3HgwIHQz3gkMTkLFizAZZddhgkTJuC4447Dgw8+CK/Xizlz5hhdWp8wb948PPvss1i9ejVKS0uxb98+AEBZWRmKiooMri7/lZaWdpl/XFxcjMrKSs5LTgLDLBGlpGHvSSgt/yoYWHsKs3IAfsUJT/NhWa4ut1itVlgsFlitVlRVVTHQEhWAtWvXYseOHdixY0eXDwciVzin+F144YU4cOAAFi1ahH379mHcuHF47bXXuiwKRcl59NFHAQBTpkyJunz58uW9nlpPlG0Ms0SUEk/zYVD9JbBY3R2LPHUWPGLrahiDQl9zLjKwMtBSrho2bBhDVhrNnj2bASAD5s+fz9OKM4S//9m3YcMGo0vIWxw5EVGK5I6gKiEYXKNJkgYhZNTXTs56ZblOD7R+vx8NDQ3QNM3okoiIiIjyBsMsEaWsvnYyhJAhSYEuP5MkgfbW/lB88S2UUmgYaImIiIiSU3CnGcuyDFVVoShKr9f1+/1QVRUulysLlREZx2tKLUB51UrsdY+C3XEAmhZehU+CgCRrqNs/KeXHaGxsTOn2RnO5XFBVNWpF40hlZWVoampCXV0dysvLIUnR849VVc1GmURERER5o+DCbL9+/dDQ0BDX3DRN0yCEwD//+U8AwXBrsVhgNif2sgkhoCgKhBCwWq1Jz4vTNA2KokCSJFit1i6D3d7oA+lknoMu1eeS6nPQpfJc2I8w/Tn4+3lhElIPyzf1LiAB7x04B3aLGyZJRXhV4+B82XbfQKCstfvnACAgCQgA5hg1fPbCCzFryPV+qKqK5uZmSJLUY580TUNbWxtaWlpgNpujngOP2BIRERFFK7gwqw8O4x30CyFCA8ySkhIUFxcn9bh2ux1NTU1oaWlBeXk5LBZLQrf3+/1wu90wm83dHrWJl9frRUtLS1LPRQiBpqYmqKqa1HPQWSwWNDU1IRAIJPVcvF4vvF4v+5HGfhxoaEYAAo6AjEQjrU/W4JMEbH4bim1eQNIQnMEgAAgEVAfUbheGCjMDaDVpaJeDNZhE1xrsdnuPt8+Hfvj9fsiyHDPM6j9XFAV+vz8qEDPMEhEREUUruDCrb0Qcb5hVFAVtbW3o168fSktLU3rsmpoaNDQ0wOVyoaqqClarNe4aXC4XbDZbyiueWq1WmM3m0MA/3uekaVpoPl9NTU3ctfdUg77PptvtTug5eTwetLa2sh9p7kd7wIQWk4Z2k0BJQIo70LbLGhQZcGgy7JoMWbPAbGlB5H6zrb4ymHu9PwnOgIQWk4a2jhrMnQJtT88xn/qhB9lYNXYOtDabLXQZEREREYUV5AJQfr8/rqMciqJA0zSUlJSkHJyA4CC1srIyFOTimberKArq6+thsVjStnVHaWkpnE4n3G43PB5Pr9fXB+p+vz+h0BdLMoveeDweuN1uOJ1O9iPN/TALCSUBGQEJaDFpEN2sStxZu6yhTRYo0iTYO468Ku0ViDzFWGgmqGpJXDVICNZgEsEaVKn3GvpsP8xmWK1WCCHg8/l4VJaIiIioGwUXZvVBYW8DRJ/Ph0AgAFmWkz6VtTuJBKhMDNR18Q7YMzFQ1yUSaNMdZHXsR1gigba7IAsAqloCoZmgb9Hj9zuBBE5bTiTQ9vl+dAq03PePiIiIKFrBhVkhBMxmc8wjHj6fD6qqwmQyJb0QTCzxBKhMDtR1vQ3YMzlQ18UTaDMVZHXsR1g8gbanIBskdQTY4P8Hj9QmJp5AWzD9iAi0Pa2CTERERFSoCi7MAsHw0tMpfHqQ1QeRmayhpwCVjYG6rqcBezYG6rpYgTbTQVbHfoTFCrSxg2yQfqqxFrBB02xJ1RAr0BZcPzL8t4iIiIgoXxVkmAW6n5MWGWRttuQG4YnoLkBlc6Cu6zxgz+ZAXdddoM1WkNWxH2HdBdp4giwAaJoNvrZqtLcOSKmGzoG2oPthNmfkLBEiIiKifFawYRaIDrRtbW1ZDbK6yAB14MABHDhwIKsDdV3kgH3v3r1ZHajrIgPt3r17sxpkdexHWGSgbTbHF2R1vvZqqGrqc80jA22h9yObz5eIKB/89a9/RWVlJXw+X9Tls2bNwqWXXmpQVUSUTQU/OjKbzVFbXiS7V2cqZFmG0+kMfe90Og0ZuEYudGWz2Qw5tdFqtUZ9mJDOxbfixX6EmUX0Fjk2Lfvbw0iIDtCF3A8iIgo7//zzEQgE8L//+7+hy+rq6rBmzRpcfvnlBlZGRNlS8GFWP8VY38fRiG0wFEVBQ0MDLBYLLBYLGhoa4tomJp30UyclSYLdbkd7e3tc25Kkm8fjQXt7O+x2OyRJinvbnnRiP8LaZQ1+ScAigjvPxrttTzqpkoDXpLEfREQUpaioCP/5n/+J5cuXhy575plncPDBB2PKlCnGFUZEWVPQYTZyjmxRUZEh+zpGzgGsqqpCVVVVQvuepkPnOYCVlZUJ7bOZLpFzZCsrKxPehzYd2I+wyDmyJQE54X1o00GVBFpMGkwCBd8PVVWz9lhERPli7ty5eP3111FbWwsAeOqppzB79uyos+6IqO8qqBVF9H0aNU1DW1tb6IisLMuhbS9MJhNUVUVbW1voFGQ99KabqqpwuVwwmUwoKipCe3s7gOAnjX6/H/v370dZWVlGF37RNA1utxuBQABlZWVQVTW0LZHNZkNjYyN8Ph8cDkfGagCA1tZWeL1eFBcXw2QyobW1FQBQUlICl8uFffv2Zfz00kLuhyKi/30rMuCTAVsAkDUBBRogAJsA2kyASw6gKJDIDrKJC0hAmwzIGmANIO/7EQgEoGkahBAIBAIJ1aGqaugDHe43S0QUdswxx+Doo4/GX//6V5x66qn4/PPPsWbNGqPLIqIsKagw6/F4cO+99xpdBhFR0jweD8rKyowug4goZ/z85z/Hgw8+iNraWkyfPh1DhgwxuiQiyhJJFNDH/JqmYc+ePSgtLc3J00/cbjeGDBmC3bt3Ry1A1BfwueUnPrfcIYSAx+PBoEGDuLIxEVEEl8uFQYMGQVVV/PWvf8WFF15odElElCUFdWRWlmUMHjzY6DJ65XQ682JwnQw+t/zE55YbeESWiKirsrIynHvuuVizZg1mzZpldDlElEX8eJ+IiIiI8lptbS0uvvjiqO39iKjvK6gjs0RERETUdzQ1NWHDhg3YsGEDli1bZnQ5RJRlDLM5xGaz4Y477uiTnyryueUnPjciIsplxxxzDJqamnDffffhsMMOM7ocIsqygloAioiIiIiIiPoGzpklIiIiIiKivMMwS0RERERERHmHYZaIiIiIiIjyDsNsjvP5fBg3bhwkScKWLVuMLidlu3btwhVXXIHhw4ejqKgII0eOxB133AFFUYwuLSl//OMfMWzYMNjtdhx//PH44IMPjC4pLZYsWYKJEyeitLQUNTU1mDVrFrZv3250WWn3m9/8BpIk4frrrze6FCIiIiJKEMNsjrvpppswaNAgo8tIm23btkHTNDz++OP4/PPPsXTpUjz22GO49dZbjS4tYStXrsSCBQtwxx13YPPmzTj66KMxY8YM1NXVGV1ayjZu3Ih58+bh3//+N9auXQu/349TTz0VXq/X6NLS5sMPP8Tjjz+Oo446yuhSiIiIiCgJXM04h7366qtYsGAB/v73v+PII4/EJ598gnHjxhldVtr97ne/w6OPPopvv/3W6FIScvzxx2PixIl45JFHAACapmHIkCG49tprccsttxhcXXodOHAANTU12LhxI04++WSjy0lZS0sLjj32WCxbtgz33HMPxo0bhwcffNDosoiIiIgoATwym6P279+PuXPn4umnn4bD4TC6nIxyuVyoqKgwuoyEKIqCjz/+GNOnTw9dJssypk+fjvfee8/AyjLD5XIBQN71qSfz5s3DGWecEdU/IiIiIsovZqMLoK6EEJg9ezauuuoqTJgwAbt27TK6pIzZsWMHHn74Ydx///1Gl5KQ+vp6BAIB9O/fP+ry/v37Y9u2bQZVlRmapuH666/HCSecgDFjxhhdTsqee+45bN68GR9++KHRpRARERFRCnhkNotuueUWSJIU82vbtm14+OGH4fF4sHDhQqNLjlu8zy1SbW0tZs6cifPPPx9z5841qHLqzbx587B161Y899xzRpeSst27d+O6667DihUrYLfbjS6HiIiIiFLAObNZdODAATQ0NMS8zogRI3DBBRfg5ZdfhiRJocsDgQBMJhMuvvhi/OUvf8l0qQmL97lZrVYAwJ49ezBlyhT86Ec/wlNPPQVZzq/PVRRFgcPhwKpVqzBr1qzQ5Zdddhmam5uxevVq44pLo/nz52P16tXYtGkThg8fbnQ5KXvppZdw9tlnw2QyhS4LBAKQJAmyLMPn80X9jIiIiIhyF8NsDvr+++/hdrtD3+/ZswczZszAqlWrcPzxx2Pw4MEGVpe62tpaTJ06FePHj8czzzyTt+Hh+OOPx3HHHYeHH34YQPB03IMPPhjz58/P+wWghBC49tpr8eKLL2LDhg045JBDjC4pLTweD7777ruoy+bMmYPRo0fj5ptv7hOnURMREREVCs6ZzUEHH3xw1PclJSUAgJEjR/aJIDtlyhQMHToU999/Pw4cOBD62YABAwysLHELFizAZZddhgkTJuC4447Dgw8+CK/Xizlz5hhdWsrmzZuHZ599FqtXr0ZpaSn27dsHACgrK0NRUZHB1SWvtLS0S2AtLi5GZWUlgywRERFRnmGYpaxau3YtduzYgR07dnQJ5vl2ksCFF16IAwcOYNGiRdi3bx/GjRuH1157rcuiUPno0UcfBQBMmTIl6vLly5dj9uzZ2S+IiIiIiKgTnmZMREREREREeSe/Vt0hIiIiIiIiAsMsERERERER5SGGWSIiIiIiIso7DLNERERERESUdxhmiYiIiIiIKO8wzBIREREREVHeYZglIiIiIiKivMMwS0RERERERHmHYZaIiIiIiIjyDsMsERERERER5R2GWSIiIiIiIso7DLNEKThw4AAGDBiAe++9N3TZu+++C6vVijfffNPAyoiIiIiI+jZJCCGMLoIon/3zn//ErFmz8O677+Kwww7DuHHj8NOf/hQPPPCA0aUREREREfVZDLNEaTBv3jy88cYbmDBhAj777DN8+OGHsNlsRpdFRERERNRnMcwSpUFbWxvGjBmD3bt34+OPP8bYsWONLomIiIiIqE/jnFmiNPjmm2+wZ88eaJqGXbt2GV0OEREREVGfxyOzRClSFAXHHXccxo0bh8MOOwwPPvggPvvsM9TU1BhdGhERERFRn8UwS5Si//7v/8aqVavw6aefoqSkBJMnT0ZZWRleeeUVo0sjIiIiIuqzeJoxUQo2bNiABx98EE8//TScTidkWcbTTz+Nt956C48++qjR5RERERER9Vk8MktERERERER5h0dmiYiIiIiIKO8wzBIREREREVHeYZglIiIiIiKivMMwS0RERERERHmHYZaIiIiIiIjyDsMsERERERER5R2GWSIiIiIiIso7DLNERERERESUdxhmiYjo/7dfByQAAAAAgv6/bkegLwQA2JFZAAAAdmQWAACAHZkFAABgJ3kUCwPRUSlMAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim = make_sim(params)\n",
    "\n",
    "f, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(10, 4))\n",
    "ax = sim.plot(z=0.01, ax=ax1)\n",
    "ax = sim.plot(x=-Lx / 2 + t / 2, ax=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fa5b961a-1b10-416d-9b68-c4d1d24d087a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's turn off warnings because we know this ring is intersecting with our waveguide ports.\n",
    "td.config.logging_level = \"ERROR\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b11fca49-0b3f-4f96-9b2e-933b0c8ea6ac",
   "metadata": {},
   "source": [
    "## Select the desired waveguide mode\n",
    "\n",
    "Next, we use the [ModeSolver](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.mode.ModeSolver.html) to solve and select the `mode_index` that gives us the proper injected and measured modes. We plot all of the fields for the first 3 modes and see that the TE0 mode is `mode_index=0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8480cca1-a8df-4c43-81d0-18b7ffa5076f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Effective index of computed modes:  [[1.7968912 1.7528987 1.6045505]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x1000 with 18 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tidy3d.plugins.mode import ModeSolver\n",
    "\n",
    "ms = ModeSolver(\n",
    "    simulation=sim, plane=mode_src, mode_spec=mode_spec, freqs=mode_mnt.freqs\n",
    ")\n",
    "data = ms.solve()\n",
    "\n",
    "print(\"Effective index of computed modes: \", np.array(data.n_eff))\n",
    "\n",
    "fig, axs = plt.subplots(num_modes, 3, figsize=(14, 10), tight_layout=True)\n",
    "for mode_ind in range(num_modes):\n",
    "    for field_ind, field_name in enumerate((\"Ex\", \"Ey\", \"Ez\")):\n",
    "        field = data.field_components[field_name].sel(mode_index=mode_ind)\n",
    "        ax = axs[mode_ind, field_ind]\n",
    "        field.real.plot(x=\"y\", y=\"z\", ax=ax, cmap=\"RdBu\")\n",
    "        ax.set_title(f\"{field_name}, mode_ind={mode_ind}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9fe1aaf-5e75-445a-80a5-e7a75197a8ee",
   "metadata": {},
   "source": [
    "Since this is already the default mode index, we can leave the original `make_sim()` function as is. However, to generate a new mode source with a different `mode_index`, we could do the following and rewrite that function with the returned `mode_src`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5e1974b0-28c8-4d65-9d79-976cb012b870",
   "metadata": {},
   "outputs": [],
   "source": [
    "# select the mode index\n",
    "mode_index = 0\n",
    "\n",
    "# make the mode source with appropriate mode index\n",
    "mode_src = ms.to_source(\n",
    "    mode_index=mode_index,\n",
    "    source_time=mode_src.source_time,\n",
    "    direction=mode_src.direction,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab0e3e8c-3336-4fa3-9eca-6e89e0c50812",
   "metadata": {},
   "source": [
    "## Defining objective function\n",
    "\n",
    "Now we can define our objective function to maximize. The objective function first generates a simulation given the parameters, runs the simulation using the builtin `web.run()` function, measures the power transmitted into the TE0 output mode at our desired polarization, and then subtracts the radius of curvature penalty that we defined earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9f02432c-1de7-45da-ac8d-dc730093acff",
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective(params: np.ndarray, use_fld_mnt: bool = True) -> float:\n",
    "    sim = make_sim(params, use_fld_mnt=use_fld_mnt)\n",
    "    sim_data = web.run(sim, task_name=\"bend\", verbose=False)\n",
    "\n",
    "    # be sure to take .values here before summing the transmission\n",
    "    amps = sim_data[monitor_name].amps.sel(direction=\"-\", mode_index=mode_index).values\n",
    "\n",
    "    transmission = anp.abs(anp.array(amps)) ** 2\n",
    "    J = anp.sum(transmission) - eval_penalty(params)\n",
    "    return J"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e82ef72-d130-48dd-b88d-bdc5e5ccde0f",
   "metadata": {},
   "source": [
    "Next, we use [autograd.value_and_grad](https://ag.readthedocs.io/en/latest/_autosummary/ag.value_and_grad.html) to transform this objective function into a function that returns the \n",
    "\n",
    "* Objective function evaluated at the passed parameters.\n",
    "\n",
    "* Gradient of the objective function with respect to the passed parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d8390c85-0506-4a4f-aa85-fa9311a969bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "val_grad = ag.value_and_grad(objective)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1cad86d-800f-40c8-8966-d2483cdd254e",
   "metadata": {},
   "source": [
    "Let's run this function and take a look at the outputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "99393577-a86b-4d20-98b2-8e45b4b88058",
   "metadata": {},
   "outputs": [],
   "source": [
    "val, grad = val_grad(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0adcb4f4-8b8d-4526-bfcd-5be2caf810c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5687327977286296\n",
      "[-0.02197132 -0.03548033 -0.04649846 -0.06245152 -0.07582057 -0.08028191\n",
      " -0.09750328 -0.11971508 -0.12886769 -0.12268846 -0.11572924 -0.12323597\n",
      " -0.11574597 -0.10234237 -0.09314738 -0.07098229 -0.04850389 -0.02870289\n",
      " -0.00922439  0.00967577  0.02947844  0.04920563  0.06457968  0.07670694\n",
      "  0.08849218  0.10035768  0.10583492  0.11091066  0.11417485  0.11342399\n",
      "  0.11342389  0.11417456  0.11091014  0.10583414  0.10035673  0.08849117\n",
      "  0.07670596  0.06457884  0.04920507  0.02947839  0.00967649 -0.00922272\n",
      " -0.02870013 -0.04849996 -0.07097718 -0.09314163 -0.10233739 -0.11574306\n",
      " -0.12323595 -0.11573347 -0.12269917 -0.12888588 -0.11974001 -0.09753152\n",
      " -0.08031322 -0.07585848 -0.0624907  -0.0465358  -0.03551789 -0.02200616]\n"
     ]
    }
   ],
   "source": [
    "print(val)\n",
    "print(grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aea9708-fe5c-4f9e-aae3-0d4aa769fa17",
   "metadata": {},
   "source": [
    "These seem reasonable and can now be used for plugging into our optimization algorithm.\n",
    "\n",
    "## Optimization Procedure\n",
    "\n",
    "With our gradients defined, we write a simple optimization loop using the [optax](https://optax.readthedocs.io/en/latest/) package. We use the `adam` method with a tunable number of steps and learning rate. The intermediate values, parameters, and data are stored for visualization later.\n",
    "\n",
    "> Note: this will take several minutes. While not shown here, it is good practice to checkpoint your optimization results by saving to file on every iteration, or ensure you have a stable internet connection. See [this notebook](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin6GratingCoupler/) for more details. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f0a6b1e7-6d87-483e-a54e-ac8ca19a03fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 1\n",
      "\tJ = 5.6873e-01\n",
      "\tgrad_norm = 6.7184e-01\n",
      "step = 2\n",
      "\tJ = 6.0577e-01\n",
      "\tgrad_norm = 6.4348e-01\n",
      "step = 3\n",
      "\tJ = 6.4249e-01\n",
      "\tgrad_norm = 6.1895e-01\n",
      "step = 4\n",
      "\tJ = 6.7740e-01\n",
      "\tgrad_norm = 6.0413e-01\n",
      "step = 5\n",
      "\tJ = 7.1108e-01\n",
      "\tgrad_norm = 5.8883e-01\n",
      "step = 6\n",
      "\tJ = 7.4236e-01\n",
      "\tgrad_norm = 5.7051e-01\n",
      "step = 7\n",
      "\tJ = 7.7213e-01\n",
      "\tgrad_norm = 5.4820e-01\n",
      "step = 8\n",
      "\tJ = 7.9956e-01\n",
      "\tgrad_norm = 5.1796e-01\n",
      "step = 9\n",
      "\tJ = 8.2538e-01\n",
      "\tgrad_norm = 4.7583e-01\n",
      "step = 10\n",
      "\tJ = 8.4834e-01\n",
      "\tgrad_norm = 4.3081e-01\n",
      "step = 11\n",
      "\tJ = 8.6843e-01\n",
      "\tgrad_norm = 3.9428e-01\n",
      "step = 12\n",
      "\tJ = 8.8618e-01\n",
      "\tgrad_norm = 3.6297e-01\n",
      "step = 13\n",
      "\tJ = 9.0170e-01\n",
      "\tgrad_norm = 3.3193e-01\n",
      "step = 14\n",
      "\tJ = 9.1500e-01\n",
      "\tgrad_norm = 3.0084e-01\n",
      "step = 15\n",
      "\tJ = 9.2617e-01\n",
      "\tgrad_norm = 2.6857e-01\n",
      "step = 16\n",
      "\tJ = 9.3513e-01\n",
      "\tgrad_norm = 2.3627e-01\n",
      "step = 17\n",
      "\tJ = 9.4245e-01\n",
      "\tgrad_norm = 2.0232e-01\n",
      "step = 18\n",
      "\tJ = 9.4798e-01\n",
      "\tgrad_norm = 1.7353e-01\n",
      "step = 19\n",
      "\tJ = 9.5265e-01\n",
      "\tgrad_norm = 1.5200e-01\n",
      "step = 20\n",
      "\tJ = 9.5592e-01\n",
      "\tgrad_norm = 1.3729e-01\n",
      "step = 21\n",
      "\tJ = 9.5863e-01\n",
      "\tgrad_norm = 1.2807e-01\n",
      "step = 22\n",
      "\tJ = 9.6034e-01\n",
      "\tgrad_norm = 1.2406e-01\n",
      "step = 23\n",
      "\tJ = 9.6170e-01\n",
      "\tgrad_norm = 1.2231e-01\n",
      "step = 24\n",
      "\tJ = 9.6279e-01\n",
      "\tgrad_norm = 1.2270e-01\n",
      "step = 25\n",
      "\tJ = 9.6354e-01\n",
      "\tgrad_norm = 1.2513e-01\n",
      "step = 26\n",
      "\tJ = 9.6410e-01\n",
      "\tgrad_norm = 1.2935e-01\n",
      "step = 27\n",
      "\tJ = 9.6477e-01\n",
      "\tgrad_norm = 1.3198e-01\n",
      "step = 28\n",
      "\tJ = 9.6537e-01\n",
      "\tgrad_norm = 1.3056e-01\n",
      "step = 29\n",
      "\tJ = 9.6595e-01\n",
      "\tgrad_norm = 1.2694e-01\n",
      "step = 30\n",
      "\tJ = 9.6655e-01\n",
      "\tgrad_norm = 1.2271e-01\n",
      "step = 31\n",
      "\tJ = 9.6717e-01\n",
      "\tgrad_norm = 1.1853e-01\n",
      "step = 32\n",
      "\tJ = 9.6773e-01\n",
      "\tgrad_norm = 1.1403e-01\n",
      "step = 33\n",
      "\tJ = 9.6835e-01\n",
      "\tgrad_norm = 1.0872e-01\n",
      "step = 34\n",
      "\tJ = 9.6876e-01\n",
      "\tgrad_norm = 1.0259e-01\n",
      "step = 35\n",
      "\tJ = 9.6898e-01\n",
      "\tgrad_norm = 9.6108e-02\n",
      "step = 36\n",
      "\tJ = 9.6922e-01\n",
      "\tgrad_norm = 8.9905e-02\n",
      "step = 37\n",
      "\tJ = 9.6952e-01\n",
      "\tgrad_norm = 8.4388e-02\n",
      "step = 38\n",
      "\tJ = 9.6981e-01\n",
      "\tgrad_norm = 7.9492e-02\n",
      "step = 39\n",
      "\tJ = 9.7001e-01\n",
      "\tgrad_norm = 7.4915e-02\n",
      "step = 40\n",
      "\tJ = 9.7018e-01\n",
      "\tgrad_norm = 7.0923e-02\n"
     ]
    }
   ],
   "source": [
    "import optax\n",
    "\n",
    "# hyperparameters\n",
    "num_steps = 40\n",
    "learning_rate = 0.01\n",
    "\n",
    "# initialize adam optimizer with starting parameters\n",
    "params = np.array(params).copy()\n",
    "optimizer = optax.adam(learning_rate=learning_rate)\n",
    "opt_state = optimizer.init(params)\n",
    "\n",
    "# store history\n",
    "objective_history = []\n",
    "param_history = [params]\n",
    "data_history = []\n",
    "\n",
    "for i in range(num_steps):\n",
    "    # compute gradient and current objective funciton value\n",
    "    value, gradient = val_grad(params)\n",
    "\n",
    "    # outputs\n",
    "    print(f\"step = {i + 1}\")\n",
    "    print(f\"\\tJ = {value:.4e}\")\n",
    "    print(f\"\\tgrad_norm = {np.linalg.norm(gradient):.4e}\")\n",
    "\n",
    "    # compute and apply updates to the optimizer based on gradient\n",
    "    updates, opt_state = optimizer.update(-gradient, opt_state, params)\n",
    "    params[:] = optax.apply_updates(params, updates)\n",
    "\n",
    "    # save history\n",
    "    objective_history.append(value)\n",
    "    param_history.append(params)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9760808f-512b-4806-85b6-809ba15e389c",
   "metadata": {},
   "source": [
    "## Analyzing results\n",
    "\n",
    "After the optimization is finished, let's look at the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "09aac45c-d7d8-4fd1-a599-0a7888257120",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.plot(objective_history)\n",
    "ax = plt.gca()\n",
    "ax.set_xlabel(\"iteration number\")\n",
    "ax.set_ylabel(\"objective function\")\n",
    "ax.set_title(\"optimization progress\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdbff561-b56c-4e83-8841-3c1ea7d5ba9e",
   "metadata": {},
   "source": [
    "Next, we can grab our initial and final device from the history lists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d782b925-2af0-4bb8-bf87-88526a3f359f",
   "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\">11:48:41 EST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'start'</span> with task_id                                  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-6b302bc3-53ac-4251-80ae-f0b53f5f97db'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:48:41 EST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'start'\u001b[0m with task_id                                  \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-6b302bc3-53ac-4251-80ae-f0b53f5f97db'\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-6b302bc3-53ac-4251-80ae-f0b53f5f97db\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-6b302bc3-53a</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-6b302bc3-53ac-4251-80ae-f0b53f5f97db\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">c-4251-80ae-f0b53f5f97db'</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=161025;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6b302bc3-53ac-4251-80ae-f0b53f5f97db\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=22989;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6b302bc3-53ac-4251-80ae-f0b53f5f97db\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=161025;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6b302bc3-53ac-4251-80ae-f0b53f5f97db\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=544917;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6b302bc3-53ac-4251-80ae-f0b53f5f97db\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=161025;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6b302bc3-53ac-4251-80ae-f0b53f5f97db\u001b\\\u001b[32m-6b302bc3-53a\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=161025;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6b302bc3-53ac-4251-80ae-f0b53f5f97db\u001b\\\u001b[32mc-4251-80ae-f0b53f5f97db'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "af4261c147714f79bfe22978fb518c65",
       "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\">11:48:43 EST </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:48:43 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": "",
       "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\">11:48:48 EST </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:48:48 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\">11:48:50 EST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.029</span>. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>the billed FlexCredit cost after a simulation run.                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:48:50 EST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.029\u001b[0m. 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\n",
       "\u001b[2;36m             \u001b[0mthe billed FlexCredit 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\">             </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \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": "86ed0966b0c14849b2d412ce8827f830",
       "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\">11:48:55 EST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">36</span>%, exiting.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:48:55 EST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m36\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": "",
       "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\">11:48:57 EST </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:48:57 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-6b302bc3-53ac-4251-80ae-f0b53f5f97db\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-6b302bc3-53a</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-6b302bc3-53ac-4251-80ae-f0b53f5f97db\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">c-4251-80ae-f0b53f5f97db'</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=648621;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6b302bc3-53ac-4251-80ae-f0b53f5f97db\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=275063;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6b302bc3-53ac-4251-80ae-f0b53f5f97db\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=648621;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6b302bc3-53ac-4251-80ae-f0b53f5f97db\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=708966;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6b302bc3-53ac-4251-80ae-f0b53f5f97db\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=648621;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6b302bc3-53ac-4251-80ae-f0b53f5f97db\u001b\\\u001b[4;34m-6b302bc3-53a\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=648621;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6b302bc3-53ac-4251-80ae-f0b53f5f97db\u001b\\\u001b[4;34mc-4251-80ae-f0b53f5f97db'\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": "5351ebacbe9f468496999ec89377ffb0",
       "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\">11:49:01 EST </span>loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:49:01 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\">'final'</span> with task_id                                  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-94a3fd3f-200c-40bf-a9e0-433ebf5c3338'</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'final'\u001b[0m with task_id                                  \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-94a3fd3f-200c-40bf-a9e0-433ebf5c3338'\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-94a3fd3f-200c-40bf-a9e0-433ebf5c3338\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-94a3fd3f-200</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-94a3fd3f-200c-40bf-a9e0-433ebf5c3338\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">c-40bf-a9e0-433ebf5c3338'</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=577198;https://tidy3d.simulation.cloud/workbench?taskId=fdve-94a3fd3f-200c-40bf-a9e0-433ebf5c3338\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=331252;https://tidy3d.simulation.cloud/workbench?taskId=fdve-94a3fd3f-200c-40bf-a9e0-433ebf5c3338\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=577198;https://tidy3d.simulation.cloud/workbench?taskId=fdve-94a3fd3f-200c-40bf-a9e0-433ebf5c3338\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=481899;https://tidy3d.simulation.cloud/workbench?taskId=fdve-94a3fd3f-200c-40bf-a9e0-433ebf5c3338\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=577198;https://tidy3d.simulation.cloud/workbench?taskId=fdve-94a3fd3f-200c-40bf-a9e0-433ebf5c3338\u001b\\\u001b[32m-94a3fd3f-200\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=577198;https://tidy3d.simulation.cloud/workbench?taskId=fdve-94a3fd3f-200c-40bf-a9e0-433ebf5c3338\u001b\\\u001b[32mc-40bf-a9e0-433ebf5c3338'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "model_id": "91bfcce3b3ab4e85ba92001144601a06",
       "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\">11:49:03 EST </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:49:03 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": {
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       "model_id": "",
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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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:49:07 EST </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:49:07 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\">11:49:10 EST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.029</span>. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>the billed FlexCredit cost after a simulation run.                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:49:10 EST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.029\u001b[0m. 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\n",
       "\u001b[2;36m             \u001b[0mthe billed FlexCredit 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\">             </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \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": {
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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\">11:49:15 EST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">32</span>%, exiting.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:49:15 EST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m32\u001b[0m%, exiting.                            \n"
      ]
     },
     "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\">             </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": {
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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\">11:49:17 EST </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:49:17 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-94a3fd3f-200c-40bf-a9e0-433ebf5c3338\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-94a3fd3f-200</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-94a3fd3f-200c-40bf-a9e0-433ebf5c3338\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">c-40bf-a9e0-433ebf5c3338'</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=265781;https://tidy3d.simulation.cloud/workbench?taskId=fdve-94a3fd3f-200c-40bf-a9e0-433ebf5c3338\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=365438;https://tidy3d.simulation.cloud/workbench?taskId=fdve-94a3fd3f-200c-40bf-a9e0-433ebf5c3338\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=265781;https://tidy3d.simulation.cloud/workbench?taskId=fdve-94a3fd3f-200c-40bf-a9e0-433ebf5c3338\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=111731;https://tidy3d.simulation.cloud/workbench?taskId=fdve-94a3fd3f-200c-40bf-a9e0-433ebf5c3338\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=265781;https://tidy3d.simulation.cloud/workbench?taskId=fdve-94a3fd3f-200c-40bf-a9e0-433ebf5c3338\u001b\\\u001b[4;34m-94a3fd3f-200\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=265781;https://tidy3d.simulation.cloud/workbench?taskId=fdve-94a3fd3f-200c-40bf-a9e0-433ebf5c3338\u001b\\\u001b[4;34mc-40bf-a9e0-433ebf5c3338'\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": "ee06ed95fe214317bbe15eba6dd6fdc4",
       "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\">11:49:22 EST </span>loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:49:22 EST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tidy3d.web as web\n",
    "\n",
    "sim_start = make_sim(param_history[0])\n",
    "data_start = web.run(sim_start, task_name=\"start\")\n",
    "\n",
    "sim_final = make_sim(param_history[-1])\n",
    "data_final = web.run(sim_final, task_name=\"final\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63a876ae-1d19-4f38-a958-170c9576b21a",
   "metadata": {},
   "source": [
    "Let's take a look at the final structure. We see that it has a smooth design which is symmetric about the 45 degree angle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7ec2b20e-74de-4b82-949f-591696437315",
   "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_final.plot(z=0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95b503b9-a29e-455c-aef3-e4a97a0c59b2",
   "metadata": {},
   "source": [
    "Now let's inspect the difference between the initial and final intensity patterns. We notice that the final device is quite effective at coupling light into the output waveguide! This is especially evident when compared to the starting device."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a2dddafd-8cee-42fd-a1c7-ddc94f4ef9e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, tight_layout=True, figsize=(10, 6))\n",
    "\n",
    "_ = data_start.plot_field(\"field\", \"E\", \"abs^2\", ax=ax1)\n",
    "_ = sim_start.plot(z=0, ax=ax2)\n",
    "ax1.set_title(\"starting device\")\n",
    "ax2.set_title(\"starting device\")\n",
    "\n",
    "_ = data_final.plot_field(\"field\", \"E\", \"abs^2\", ax=ax3)\n",
    "_ = sim_final.plot(z=0, ax=ax4)\n",
    "ax3.set_title(\"final device\")\n",
    "ax4.set_title(\"final device\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38997828-6ea8-489c-96b0-3cb4aae502c2",
   "metadata": {},
   "source": [
    "Let's view the transmission now, both in linear and dB scale.\n",
    "\n",
    "The mode amplitudes are simply an [xarray.DataArray](https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html) that can be selected, post processed, and plotted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9075ae74-7b0a-4336-98a6-0b89a650d0d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "amps = data_final[\"mode_bb\"].amps.sel(direction=\"-\", mode_index=mode_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "93a9b8e8-7581-4fb3-a3f4-f718a80082d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "transmission of 97.041 %\n"
     ]
    }
   ],
   "source": [
    "transmission = abs(amps) ** 2\n",
    "transmission_percent = 100 * transmission\n",
    "\n",
    "print(f\"transmission of {transmission_percent.values[0]:.3f} %\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b2b6145",
   "metadata": {},
   "source": [
    "## Export to GDS\n",
    "The `Simulation` object has the [.to_gds_file](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html#tidy3d.Simulation.to_gds_file) convenience function to export the final design to a `GDS` file. In addition to a file name, it is necessary to set a cross-sectional plane (`z = 0` in this case) on which to evaluate the geometry. See the [GDS export](https://www.flexcompute.com/tidy3d/examples/notebooks/GDSExport/) notebook for a detailed example on using `.to_gds_file` and other GDS related functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e6dc501b-54ef-4ef5-b54d-a45ce55af44e",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_final.to_gds_file(\n",
    "    fname=\"./misc/inverse_des_wg_bend.gds\",\n",
    "    z=0,\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "applications": [
   "Passive photonic integrated circuit components"
  ],
  "description": "This notebook demonstrates the adjoint optimization of a waveguide bend in Tidy3D using autograd.",
  "feature_image": "./img/adjoint_8.png",
  "features": [
   "Adjoint inverse design"
  ],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "inverse design, bend, design optimization, adjoint, Tidy3D, FDTD",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  },
  "title": "Adjoint Optimization of a Waveguide Bend in Tidy3D | Flexcompute"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}