GDSImport.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "# Importing GDS files\n",
    "\n",
    "In Tidy3D, complex structures can be defined or imported from GDSII files via the third-party [gdstk](https://heitzmann.github.io/gdstk/) package. In this tutorial, we will first illustrate how to use the package to define a structure. Then we will save this to file, and then we will read that file and import the structures in a simulation.\n",
    "\n",
    "<img src=\"img/splitter.png\" alt=\"Schematic of the directional coupler\" width=\"400\"/>\n",
    "\n",
    "Note that this tutorial requires gdstk, so grab it with `pip install gdstk` before running the tutorial or uncomment the cell line below.\n",
    "\n",
    "We also provide a comprehensive list of other tutorials such as [how to define boundary conditions](https://www.flexcompute.com/tidy3d/examples/notebooks/BoundaryConditions/), [how to compute the S-matrix of a device](https://www.flexcompute.com/tidy3d/examples/notebooks/SMatrix/), [how to interact with tidy3d's web API](https://www.flexcompute.com/tidy3d/examples/notebooks/WebAPI/), and [how to define self-intersecting polygons](https://www.flexcompute.com/tidy3d/examples/notebooks/SelfIntersectingPolyslab/).\n",
    "\n",
    "If you are new to the finite-difference time-domain (FDTD) method, we highly recommend going through our [FDTD101](https://www.flexcompute.com/fdtd101/) tutorials. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# standard python imports\n",
    "import os\n",
    "\n",
    "import gdstk\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# tidy3d import\n",
    "import tidy3d as td\n",
    "from tidy3d import web"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the geometry with gdstk\n",
    "\n",
    "In this section, we will construct an integrated adiabatic coupler as in the title image in this notebook using `gdstk`. If you are only interested in importing an already existing GDSII file, just jump ahead to the next section.\n",
    "\n",
    "We create a function that generates the coupler and returns the corresponding cell. The geometry is built by defining the cross-section of the device in the x-y plane. The cross section can be supplied at the `top`, `bottom`, or the `middle` of the device, specified by the parameter `reference_plane`. Here we choose to define the cross section on the base. There, the two arms of the device start at a distance `wg_spacing_in` apart, then come together at a coupling distance `wg_spacing_coup` for a certain length `coup_length`, and then split again into separate ports. In the coupling region, the field overlap results in energy exchange between the two waveguides. The cell also includes a rectangle around the whole device, which we will use as substrate later. The waveguides use GDS layer 1 and the rectangle layer 0.\n",
    "\n",
    "Here, we will only see how to define, export, and import such a device using `gdstk`. When importing the device, we can optionally dilate or erode its cross section via `dilation`. In a later example we will simulate the device and study the frequency dependence of the transmission into each of the ports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def make_coupler(\n",
    "    length: float,\n",
    "    wg_spacing_in: float,\n",
    "    wg_width: float,\n",
    "    wg_spacing_coup: float,\n",
    "    wg_spacing_side: float,\n",
    "    coup_length: float,\n",
    "    bend_length: float,\n",
    ") -> gdstk.Cell:\n",
    "    \"\"\"Make an integrated coupler using the gdstk RobustPath object.\"\"\"\n",
    "    input_length = length / 2 - coup_length / 2 - bend_length\n",
    "    if input_length <= 0:\n",
    "        raise ValueError(\"Device length must be larger than coupling length plus bending regions.\")\n",
    "\n",
    "    delta = (wg_spacing_in - (wg_spacing_coup + wg_width)) / 2\n",
    "    turn_angle = 2 * np.arctan(delta / bend_length)\n",
    "    bend_radius = bend_length / 2 / np.sin(turn_angle)\n",
    "\n",
    "    coup = gdstk.RobustPath(\n",
    "        (-0.5 * length, wg_spacing_in / 2),\n",
    "        wg_width,\n",
    "        simple_path=True,\n",
    "        layer=1,\n",
    "    )\n",
    "    coup.segment((input_length, 0), relative=True)\n",
    "    coup.turn(bend_radius, -turn_angle)\n",
    "    coup.turn(bend_radius, turn_angle)\n",
    "    coup.segment((coup_length, 0), relative=True)\n",
    "    coup.turn(bend_radius, turn_angle)\n",
    "    coup.turn(bend_radius, -turn_angle)\n",
    "    coup.segment((0.5 * length, wg_spacing_in / 2))\n",
    "\n",
    "    arm_cell = gdstk.Cell(\"COUPLER_ARM\")\n",
    "    arm_cell.add(coup)\n",
    "\n",
    "    cell = gdstk.Cell(\"COUPLER\")\n",
    "    cell.add(gdstk.Reference(arm_cell))\n",
    "    cell.add(gdstk.Reference(arm_cell, x_reflection=True))\n",
    "\n",
    "    substrate = gdstk.rectangle(\n",
    "        (-device_length / 2, -(wg_spacing_in + wg_width) / 2 - wg_spacing_side),\n",
    "        (device_length / 2, (wg_spacing_in + wg_width) / 2 + wg_spacing_side),\n",
    "        layer=0,\n",
    "    )\n",
    "    cell.add(substrate)\n",
    "    return cell"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define some structural parameters. We consider the sidewall of the device to be slanted, deviating from the vertical sidewall by `sidewall_angle`. `sidewall_angle > 0` corresponds to a typical fabrication scenario where the base of the device is larger than the top."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "### Lengths in micrometers\n",
    "\n",
    "# Waveguide width\n",
    "wg_width = 0.45\n",
    "\n",
    "# Waveguide separation in the beginning/end\n",
    "wg_spacing_in = 4\n",
    "\n",
    "# Length of the coupling region\n",
    "coup_length = 4\n",
    "\n",
    "# Angle of the sidewall deviating from the vertical ones, positive values for the base larger than the top\n",
    "sidewall_angle = np.pi / 6\n",
    "\n",
    "# Reference plane where the cross section of the device is defined\n",
    "reference_plane = \"bottom\"\n",
    "\n",
    "# Length of the bend region\n",
    "bend_length = 6\n",
    "\n",
    "# Waveguide separation in the coupling region\n",
    "wg_spacing_coup = 0.10\n",
    "\n",
    "# Waveguide separation in the coupling region\n",
    "wg_spacing_side = 4.0\n",
    "\n",
    "# Total device length along propagation direction\n",
    "device_length = 25"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "### Saving the geometry to a GDS file\n",
    "\n",
    "Next, we construct the coupler and add the cell (including its dependencies) to a GDS library. The library is saved to a file, so that we can demonstrate how to load the geometry straight from a gds file. Alternatively, we could use the created cell directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create a gds cell to add our structures to\n",
    "coup_cell = make_coupler(\n",
    "    device_length,\n",
    "    wg_spacing_in,\n",
    "    wg_width,\n",
    "    wg_spacing_coup,\n",
    "    wg_spacing_side,\n",
    "    coup_length,\n",
    "    bend_length,\n",
    ")\n",
    "\n",
    "# Create a library for the cell and save it, just so that we can demosntrate loading\n",
    "# geometry from a gds file\n",
    "gds_path = \"misc/coupler.gds\"\n",
    "\n",
    "if os.path.exists(gds_path):\n",
    "    os.remove(gds_path)\n",
    "\n",
    "lib = gdstk.Library()\n",
    "lib.add(coup_cell, *coup_cell.dependencies(True))\n",
    "lib.write_gds(gds_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Loading a GDS file into Tidy3D\n",
    "\n",
    "To load the geometry from a GDSII file, we use gdstk to load the library and select the cell with the geometry we want.\n",
    "It is usually easier to create a dictionary of all the cells in the library to verify that we can find the correct one by name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cell names: COUPLER, COUPLER_ARM\n"
     ]
    }
   ],
   "source": [
    "# Load a GDSII library from the file we just created\n",
    "lib_loaded = gdstk.read_gds(gds_path)\n",
    "\n",
    "# Create a cell dictionary with all the cells in the file\n",
    "all_cells = {c.name: c for c in lib_loaded.cells}\n",
    "\n",
    "print(\"Cell names: \" + \", \".join(all_cells.keys()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, we only have the single cell that we created previously, called “COUPLER”, but usually GDSII files hold tens to hundreds of cells, so it is important to find them by name. Getting to the cell we're interested in now is as simple as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cell 'COUPLER' with 1 polygons, 0 flexpaths, 0 robustpaths, 2 references, and 0 labels\n"
     ]
    }
   ],
   "source": [
    "coup_cell_loaded = all_cells[\"COUPLER\"]\n",
    "\n",
    "print(coup_cell_loaded)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up Geometries\n",
    "\n",
    "Then we can construct tidy3d geometries from the GDS cell we just loaded, along with other information such as the axis, sidewall angle, and bounds of the \"slab\". When loading GDS cell as the cross section of the device, we can tune ``reference_plane`` to set the cross section to lie at ``bottom``, ``middle``, or ``top`` of the generated [Polyslab](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PolySlab.html) with respect to the axis. E.g. if ``axis=1``, ``bottom`` refers to the negative side of the y-axis, and ``top`` refers to the positive side of the y-axis. Additionally, we can optionally dilate or erode the cross section by setting `dilation`. A negative `dilation` corresponds to erosion.\n",
    "\n",
    "Note, we have to keep track of the `gds_layer` and `gds_dtype` used to defined the GDS cell earlier, so we can load the right components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define waveguide height\n",
    "wg_height = 0.22\n",
    "dilation = 0.02\n",
    "\n",
    "substrate_geo = td.Geometry.from_gds(\n",
    "    coup_cell_loaded,\n",
    "    gds_layer=0,\n",
    "    gds_dtype=0,\n",
    "    axis=2,\n",
    "    slab_bounds=(-4, 0),\n",
    "    reference_plane=reference_plane,\n",
    ")\n",
    "\n",
    "arms_geo = td.Geometry.from_gds(\n",
    "    coup_cell_loaded,\n",
    "    gds_layer=1,\n",
    "    gds_dtype=0,\n",
    "    axis=2,\n",
    "    slab_bounds=(0, wg_height),\n",
    "    sidewall_angle=sidewall_angle,\n",
    "    dilation=dilation,\n",
    "    reference_plane=reference_plane,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the base and the top of the coupler waveguide arms to make sure it looks ok. The base of the device should be larger than the top due to a positive `sidewall_angle`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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58uWO/VWtWlUMHjxYJCYmFmgzfPhw4ebmVuj4ZsyYIYrzK3X37t2iRYsWQqPRCH9/f/Haa6+JjRs3FqonNzdX/Pvf/xZVqlQRAO46BEb79u2LHMJixowZ96zpXmw2m3j//fdFSEiIUCqVon79+uKHH34o1O7OkCCXL18usD4jI0M888wzwtvbW2i1WtG+ffsiv0fXrFkjYmNjhUqlEoGBgWLatGnCbDaX+BiIHnWcOomIiIhIYuxDRkRERCQxBjIiIiIiiTGQEREREUmMgYyIiIhIYgxkRERERBJjICMiIiKSWKUaGNZutyM5ORnu7u73PS0JERER0f0SQiAnJwf+/v6Qy4s+D1apAllycvIjMzkxERERVR7Xr19HYGBgkY9XqkDm7u4OIP9F8fDwkLgaIiIiquiys7MRFBTkyCBFqVSB7M5lSg8PDwYyIiIiemju1VWKnfqJiIiIJMZARkRERCQxBjIiIiIiiTGQEREREUmMgYyIiIhIYgxkRERERBJjICMiIiKSGAMZERERkcQYyIiIiIgkxkBGREREJDEGMiIiIiKJMZARERERSYyBjIiIiEhiDGREREREEiu3geyDDz6ATCbDpEmTpC6FiIiIqETKZSA7ePAgvv76a8TExEhdChEREVGJlbtAlpubi8GDB+Obb76Bl5eX1OUQERERlVi5C2Tjx49Hjx490KVLF6lLISIiIioVrlIXcD9++uknHDlyBAcPHixWe5PJBJPJ5Pg6Ozu7rEojIiIiemDl5gzZ9evX8eKLL2Lp0qVQq9XF2mb27Nnw9PR0LEFBQWVcJREREdH9kwkhhNRFFMfatWvRu3dvuLi4ONbZbDbIZDLI5XKYTKYCjwHOz5AFBQUhKysLHh4eD612IiIiqpyys7Ph6el5z+xRbi5Zdu7cGQkJCQXWjRw5EpGRkXj99dcLhTEAUKlUUKlUD6tEIiIiogdSbgKZu7s7oqKiCqxzc3ODt7d3ofVERERE5Um56UNGREREVFGVmzNkzsTFxUldAhEREVGJ8QwZERERkcQYyIiIiIgkxkBGREREJDEGMiIiIiKJMZARERERSYyBjIiIiEhiDGREREREEmMgIyIiIpIYAxkRERGRxBjIiIiIiCTGQEZEREQkMQYyIiIiIokxkBERERFJjIGMiIiISGIMZEREREQSYyAjIiIikhgDGREREZHEGMiIiIiIJMZARkRERCQxBjIiIiIiiTGQEREREUmMgYyIiIhIYgxkRERERBJjICMiIiKSGAMZERERkcQYyIiIiIgkxkBGREREJDEGMiIiIiKJMZARERERSYyBjIiIiEhirlIX8Cg4dOgQmjZtCrmLi9SlVFqurgqo1WqoVGqoNWpoNBpoNRpotVq4aTXQ6XSoXr06AgICHIu/vz8CAgLg6ekJmUwm9SEQEZWY1WrFjRs3kJSUhOTkZCQlJTmWjIwM5On1yNMboNcbYDDoYTAYYTQaYDQYYDabYLfbpT6ESsvbxxcH9+9DSEjIA21fbgLZ7Nmz8fPPP+PMmTPQaDRo1aoVPvzwQ0RERJR43++99x4AoEqX50u8L3oAQkDYbRBWE8wWM0xWMzKtJgirGSLTDJGWB1jTgb3HYc3NgDk3s8Dmao0W1WvUQM2wUDSIiUF0dDRiYmJQr149aDQaaY6JiKgIQgikpqYiISEB8fHxSEhIQMLJU0hKTMKttBsFQpXcxRVKDx/IdVUhVDrIXFWQubpBpvCCrIoSMh8V5K5KqFyVULsqATkvfEnBbshB2o7/4ezZsxU/kG3fvh3jx49H06ZNYbVa8eabb6Jr1644deoU3NzcSrx/Ta2mcI/tXgqVUlkTVgusuemw5WbAlpMOW246MnLScfN6KvYeXwFD+lxACMjlcoSE1ULjhg0cIa158+aoUaOG1IdARJWEzWbD8ePHcfToUSQkJODo8eOIj09AZkY6AMBFqYbKLxQyr0C41qqNKrHecHH3hqvOGy66qpBrPSCTMWQ96qzZacjc8b8S7aPcBLINGzYU+Hrx4sXw8/PD4cOH0a5dO4mqIinIXBVQVKkORZXqTh+3m42w3LoKc9oV3Eq7gt8PnsMvf2yGOS8LAFArvA66dO6IDh06oH379gxoRFRqbDYbjh07hri4OGzdtg07duxEbk42IJNB4x0AmXcwFPUeg69vCBS+YXCtUo2BiwCUo0D2T1lZ+X9cq1atKnEl9KiRK9VQ+UdA5f//l7OFELDlpsOUeBo3rifgf2v+wNdffw2gYEDr0KEDqld3HvSIiP7Jbrfj6NGjhQKYi1IFlX9dKGKfQrWgKCirh0OuUEtdLj3CymUgs9vtmDRpElq3bo2oqKgi25lMJphMJsfX2dnZD6M8egTJZDK4uvvAtW5buNVtCwCw5d6G8fqJQgGtWfMWGNC/H/r27YvQ0FAJqyaiR5HVasWOHTuwevVqrFz9M9JupBYMYMHRUFWvA5mrQupSqRwpl4Fs/PjxOHHiBHbt2nXXdrNnz8bMmTMfUlVU3rjovOD2j4BmuHIEJ8/twWtvvIlXXnkFDRo2woB+fdG3b99SuYGEiMons9mMLVu2YPXq1fh5zVrczkiHqko1KMNboFqnFlD5RzKAUYnIhBBC6iLux4QJE7Bu3Trs2LEDYWFhd23r7AxZUFAQsrKy4OHh4Vjfu3dvbExIgl+/GWVWN5UvdpMehkuHYDi3B6bLh2E1GRBRtx6eHtAfw4YNQ82aNaUukYjKmM1mw8aNG7Fs2TKsXfcLcnOyofYOgDK8JbQRraCsXptD7hCA/E79SQtGYuPGjejatWuBx7Kzs+Hp6Vkoe/xTuTlDJoTAxIkTsWbNGsTFxd0zjAGASqWCSqV6CNVRRSNXaeFWtx3c6raD3WKC8fIRJJ7bg/c+/BizZs3Cv7p2xbixY9GjRw+4upabHyMiKobU1FR89913mL/wayRdvwaNXwiU0T1QI6IVFL6hDGFUJsrNX5Lx48fjxx9/xLp16+Du7o7U1FQAgKenJ8eaojIlV6igrdMS2jotYbcYoT+zCzuP/4FNvXqhWg1/jH1uDJ599lkEBARIXSoRPSAhBOLi4jB//nysWbsWkLtAE9EW1TtO4pkweijKzSXLon4YFi1ahBEjRhRrH0WdNuzduzd+3bobmrBGpVEqPQCZi+tfAx4q/1pUkCn+//8uWg+46LzhovOGXPlo3KlkSr2A3GMbYDyzHXarGU8++STGjR2LLl26QM7BGYnKhYyMDHz//feYN38BLl04D7VvMDQxj8EtqhNc1Dqpy4MQdtj12fnjLuamw27SQ/w1cLbdYnb8/84CwZH6pWA3G6E/vb1ElyzLTSArDUW9KL/99htmzJwFVJpX4tEihIDJbILBkD8diNFogMlohMlogLNvT4VGB4W7N4SbF+Ru+YMoKqoGQOkbCoV3EGSuyodav92Uh7yTcTDE/wHDjSuoVz8KM6a/hb59+8KF03ERPZJu3LiBTz75BPO+mg+TyQRNnVbQxXaHKijqoZ8Ns+Vlwpx2BZa0q7Bm3YAtNx3Iy4A97zbM2bdgt1kLbeOqUPw11ZwGarUGGo0aWo2WXSgk5FfND8uW/oAqVaoUWM9A5kRxXxR6NAghYDabodfrkZaWVmBOtztzvF29nojE64lITUkCAMjkLlD7BEDmHQKlTygUfqFQ+obBxcO3zH/JCiFgSjyJ3H0rkHfpCGrXicBb06Zi0KBB/CVJ9IhISkrCRx99hIVf/wd2mRza2B7waPIUXNy8yvy5hdUC862rsKRdgTntCmy3rsB66yrMObcBAEqVGkHBwQgOCkRQYGCBOXvv/L9KlSrQaDT8sFeOMJA5wUBWcWVnZ+PkyZOOeeGOHY9HfHw8crLzBxBWefjANbA+VEHRUAdHw9XLv0wDmin5LHL2LkfehQMICauJt6a+iaFDh0KpfLhn74go39WrV/HBBx/gv//9DnBVQtvoKbg3eapML0sKqxmm5LMwXkuAJfEEjElnYLeaIZPJEBQShkaxDRATE43o6PwlPDycQasCYiBzgoGschFCICkpCceOHcPOnTvx59ZtOHbkMOx2e8GAFhIDhZd/mdRgvnER2XuXI+/sHgQEBmHqm1PwzDPPMJgRPSSXLl3Cu+++i/8tWQK5yg1ujXvCvdETkKu0pf5cwmqBKflMoQDm4VkFHTu0R8eOHdGiRQtERUWVyhzMVD4wkDnBQEbZ2dnYvXs34uLiCgQ0jV+oY2yhsrit3Zx2Fdn7VkB/eidqhofjy8/nont3TmZPVFZyc3Px3nvv4ZNPPoVM7Qa3Jr2hi3281G8Kyh8W5zD05/bAdPEgrMa8AgGsQ4cOiI6O5o0+lRgDmRMMZPRP2dnZ2Lp1K37++WesWbuuzAd+NN+8jKyt30B/NR7dH38cn8+di9q1a5fa/okqO7vdjqVLl+KVV19DesZt6Jr2gUeLvqU6j6TdpIfh4kEYzu+B8dJh2MxGRNarj4H9+6Fnz55o0KABAxg5MJA5wUBGd2M2m7F161asWrUKq39eg8zbGVBVqQZVRBvoGnQrtcuaQgjoz+5Gzo5FsOVk4KWXJmHatGn8niQqoYMHD2L8hIk4eGA/dJFt4NlhFFw9/Upl38Jug+HSIegTNsNw6QjsVjMaNGyEgX/Ne1unTp1SeR6qeBjInGAgo+L6++TBPyz9EdlZmdDWbAS3Bo9BU6sZZC4lv2vSbjEh+8DPyD2wCl6enpjz0YcYNmwYP1kT3afU1FRMmTIFixcvhqZaTXh0ehbq4JhS2bc1Jx258ZtgPLEZpsybiG3YCEOHDEafPn0QGhpaKs9BFRsDmRMMZPQgDAYDVq5ciXlfzcfBA/uh8vCBOupf0DXoClcP3xLv35p9E1lxi5F7egcaNW6Cr+Z9iRYtWpRC5UQVm9lsxhdffIG3Z86E2S6He5sh0DXoBpm8ZHcqCmGH8Wo88o79Af35fVCpVBj870F4/vnn0aRJk1KqnioLBjInGMiopI4dO4avv/4a/1uyBAa9AdrwZtA16wt1YN0S79t4/QSyt34DQ+pFDB48BB9++AGnYyJyQgiB9evX48VJL+Hy5cvQxT4GzzZD4KJxL9F+7RYTcuM3wXD0NxjTkxBRtx4mjh+HIUOGwNPTs5Sqp8qGgcwJBjIqLTk5Ofjxxx/x2dzPcfbMaWhDG8C95UCogqJLdBOAsNuQG78Zubt/gIvdgmlT38TkyZOhVj8a00URSe306dN4cdIkbN60CdrQWHh2ehZK39AS7dNuNiDn6B/QH14Dqz4bA/oPwPjx49C6dWvOYUklxkDmBAMZlTa73Y41a9Zg5qx3kBB/HNqg+tC1GAB1WKMS/SK3G3ORuecn5B35FQGBQfj8s0/Rq1cv/nGgSiszMxMzZ87El/PmwdXDFx7tR0FTu0XJfs5Mecg5sh55h9fBbsrD8GHD8Oabb6JWrVqlWDlVdgxkTjCQUVm5cwnl7ZmzcPjQQWgCIqBrMSD/BoAS/MGwpCcic9u30F88hFat22Dm2zPQuXNnBjOqNHJycjB//nx8+NEc5OTpoWsxAB5NepZozlqbIQc5h36B/uivgNWM0aOfxWuvvYaQkJBSrJwoHwOZEwxkVNaEENi8eTPenjkLe/fshjaoPjw7j4GyWsk+cRsuHkLOnh9hSD6Hps2a4+0Z09G9e3cGM6qwMjMz8cUXX+CTTz9Dbl4etFGd4dlqEFzdvR94n8JuQ87R35G750fI7VaMff45vPrqq/D3L5uZOogABjKnGMjoYdq8eTMmvjgJZ8+chi6mK6q0GwYX7YN3DBZCwHj5CHL3LYf++ik0iG2IGdPfQs+ePTlUBlUYt27dwty5czH3iy9gNJqhjekKj2Z94erhU6L9Gq4cQ/a2b2FKu4pRo0bhvffeQ7Vq1UqpaqKiMZA5wUBGD5vVasWCBQswddpbMFhscG85CO6NepRoHDMhBIzX4pG7bwX0V46jbr36eGvaVPTt25dzZFK5df36dXzxxRf4av58WGwC2gbd4dG0N1x0XiXaryUzFVnb/ou8c3vRomUrzPvyCzRu3LiUqia6NwYyJxjISCppaWmYNm0avvnmG6h8guDR8VlowhqVeL/GxFPI2bsc+kuH4e3jizGjn8Xo0aMRFhZWClUTlS2bzYZNmzZh/oIF+H39esiVGmgbPgGPJk+V6GwyANjNRmTtW4ncg2vg5+uDTz6eg0GDBvEyPz10DGROMJCR1I4dO4bxEyZiz+5dcKvdAp4dR5XKlEzmW9eQe+wPGE5tg82Yh67dumHc2LF4/PHH4epa8lkFiErTjRs38N1332H+wq+ReO0qNNVrQdOgO9zqtoNcpS3RvoUQ0J/ejpwdi2E35OD1117FG2+8ATc3t1Kqnuj+MJA5wUBGjwIhBFasWIHJL7+C1NRU6Jr0hGfLgSX+QwQAdosR+tM7oI/fAEPSOVT3D8DY58Zg8ODBvJWfJGWxWLBt2zZ8++1/sWbNGgiZHJrIttDFdoeyRp1SOXNlSjmP7K3fQJ94Cr1698YnH3+MmjVrlkL1RA+OgcwJBjJ6lOj1esyZMwezP/gQQqGBe9thcIvqBJmsdDrom1Iv5J81O70dNrMRUTENHBMh161b8pkFiO7FZDJh8+bNWL16NdasXYeszNtQ+wZDE9MNblGd4aLWlcrz2HJvI3PH98g9sQV169bDvC+/QKdOnUpl30QlxUDmBAMZPYquXbuGV199FStWrIDGvw48O42GKqD0ApPdbITh0iEYzu2B6dIhWE161I6IdISzBg0asF8NlRq9Xo8NGzZg1arVWPfrL9Dn5kLtEwRl7ZbQ1mkFZbVapfb9JqwWZB9eh9x9K+CmVuH9997FmDFjeJmeHikMZE4wkNGjbOfOnZgw8QXEHz8Gt7rt4NHqaSh9gkv1OYTVDMPlo9Cf2w3zxQOwGHIRGByCrl06o0OHDujQoQOCgoJK9TmpYrNYLDh8+DDi4uKwdds27Ny5C0aDHprqNaEMzw9hCp/gUg39wm5D3ukdyNv7E8yZqRg/bhzefvttVK1atdSeg6i0MJA5wUBGjzqbzYZFixZh+oy3kZKSDLeIVvBoORBKv9LvByNsFhivxsNw6RCsSSdhSL0EAAgOCUOXzh3RoUMHtG/fHsHBpRsKqXz7ZwDbtWsXDHo9XFVaKAPqQhkcDW3tllBUDSj15xY2K/JObkXegdUwpifhse7dMeejjxAVFVXqz0VUWhjInGAgo/LCZDLh+++/x7vvvY/r167CrXaL/MnLa9Qus+e06bNgSjwJ47WEAgGtWg1/xDaIQWyDBoiOjkZ0dDQiIyM55lklkJ6ejoSEBMTHxyMhIQFHjh3HqZMnYTT8LYAFRUMdHJ1/KbIE4+vdjbBakJuwGXkHV8N0+wZ69uqFt6ZN43hiVC4wkDnBQEbljcViwY8//ohZ776HSxfOw61WY+haPA11YNl3yrcZsmG6dgKm1POw3roCe/o1GG/fAAC4uLoivHYEGjdsgPDwcAQEBBRYvL29OXtAOWAymZCSkoKkpCTHkpiYiIQTJ3DseDzSbqQCAOSuCqh9QyDzDobSNxSqwPplGsDusFtMyD2+EfpDP8OcnY7+/ftj2rRpiI6OLtPnJSpNDGROMJBReWWz2bBy5UrMnPUOzpw+BW1QfWhju0NbpzVkroqHVofdmAvzrauwpF2F+eZl2NOvwpZ9E+bsDAhhd7RzVSjgV60GAgMD4FO1KrRaDTQa54tCoeBNBaVICAGj0QiDwQC9Xg+DwVBgyc3LQ3JKKpKTk5GZkV5gWxelGkoPH8i8AuDqEwqlbwiUvmFwreoPmdzloR2DNesGco5vhDFhM6yGbAz+92BMnfomIiIiHloNRKWFgcwJBjIq7+x2O9atW4e5n3+BHdvjoHTzhDqqC3QNHoPCq4ZkdQm7Dba827DlZsCWkw5bbjqsf/1rN+khs5oB21+L1QxhNcNuMcFuMUHYbZLVXVHJXZWQK5SQuaogU6ggc1FCuCohXBSQuSghd/OCq64qXNy94aLzhou7N1x1VSFTuUkWjoXdBsOlw8g79gf0lw7BTafDiGHD8NJLL3EMPSrXGMicYCCjiuTMmTP4+uuv8d/vFiEnOwvamo3g1uAxaMKbP9SzGUQlYc3NQG78JhgTNsGUeRMNYhtiwvhxGDRoEEfXpwqBgcwJBjKqiAwGA1asWIF5X83HoYMHoPLwgapeR2gjWpfqmE9EpcUxNt6ZHTBc2A+FQonB/x6EsWPHokmTJlKXR1SqGMicYCCjiu7o0aP4+uuvseyn5cjOyoS6ag0oa7XID2f+dUptFgCi+2U35UF/4QCM5/bAeOUIbGYT6kfH4LnRz2Lo0KGoUqWK1CUSlQkGMicYyKiyuDNv4OrVq7H65zVIv5UGlaePY6BOVWA9XtakMmczZMNwfh8M5/bCePUY7FYLGjdpigH9+6FPnz4IDw+XukSiMsdA5gQDGVVGNpsNu3btwurVq7Fi1WrcSEmGUucJRWA0VEHRUAVHQ+EdxEubVGJ2sxGm5DP5Y9klJsCQdBYQdrRs1doRwjgTBFU2DGROMJBRZWe323HgwAH8+uuv2LJ1Gw4dOgib1QqlrgoUgVEMaHRf/hnAjMnnYLdZ4eXtg84dO6JLl87o2bMnqlevLnWpRJKpsIHsq6++wpw5c5CamooGDRrgyy+/RLNmzYq1LQMZUUF5eXnYs2cP4uLiCgU01+p14OobCqVPCBR+YVBUDeBlzkrMbsyFOe0KLGlXYE67AvutqzCmnC8QwDp2zJ8PtW7dugz0RH8ps0A2fPhwPPPMM2jXrl2Ji7xfy5cvx7Bhw7Bw4UI0b94cc+fOxcqVK3H27Fn4+fndc3sGMqK7+3tAO3T4MI4fT8CN1GQAd0ZrD4asajAUvmFQ+obAxd0Hru7eko5fRaXHMZ5cTjqsmSkwp12BNe2vWRoybwLIH/S3du0INIyNQevWrRnAiO6hzAJZr1698PvvvyMkJAQjR47E8OHDERBQ+pPIOtO8eXM0bdoU8+bNA5B/+SUoKAgTJ07EG2+8cc/tGciI7l9GRobT+QwN+jxHG8cI725egFtVuP412KiL1jN/cFJXZf4Apa7Kv5a/reOdn6VO2CwQVhOEJX8QXmE1wf7XgLzCYoIwG2DLuw1rTjrsuemAPgO23IxCMy7U8A9EbINoNPhrHtOYmBjUqVOH85gS3YcyvWSZlpaGJUuW4Pvvv8epU6fQpUsXPPPMM+jZsycUirKZxsVsNkOr1WLVqlXo1auXY/3w4cORmZmJdevWFdrGZDLBZDI5vs7OzkZQUBADGVEJ2e12XLt2rcAciMnJyUhKSsK164lITEpGakoyTEaD1KVSEapU9Ya/vz+CAwMQGBiIgIAA+Pv7O+YjDQ0N5VAURKWguIHsgWaG9fX1xeTJkzF58mQcOXIEixYtwtChQ6HT6TBkyBCMGzcOtWvXfuDinbl16xZsNhuqVatWYH21atVw5swZp9vMnj0bM2fOLNU6iAiQy+UIDQ1FaGioY53NZsPNmzcd4SwxMRGpqanIyclBTk4OcnNzkZeXh7y8POj1eugNBhgMRtjt9qKfiB6IUqmEVqOGVqt1LDqdDm5ubnB3d4eXl1ehEMYPqUTSeqBAdkdKSgo2b96MzZs3w8XFBY8//jgSEhJQr149fPTRR3jppZdKq84HMmXKFEyePNnx9Z0zZET0YLKzs5GQkICEhAScOnUKiYmJuHo9EUmJSUhLuwG77f/npZTJXaBw84CLQg2ZqxK4s7goIVyUkLl6Qqbyg0zOS5alSQgB2K0QuSaILCNkthzgr7lE71yytJkMsBhyCmyndXNDter+CA4KQFBgIMLCwhAdHY3o6GiEh4fD1bVEfy6I6B7u+yfMYrHgl19+waJFi7Bp0ybExMRg0qRJ+Pe//+34hLVmzRqMGjWqVAOZj48PXFxccOPGjQLrb9y4UeQt1SqVCiqVqtRqIKosrFYrzp075+g7Fh8fj6PH4pGUeA0AIHNxgdo7ADKdL2RuVeFasxaqxPz/ZNWuOm/ItR68K/MRZreY8ieDz013TAh/KzcDN1LTcfDSUdh++R2m7AwAgFKlRmTdumgU28AR0mJiYgpdsSCiB3ffgaxGjRqw2+0YNGgQDhw4gNjY2EJtOnbsWOp9D5RKJRo3bowtW7Y4+pDZ7XZs2bIFEyZMKNXnIqpsLBYLDh8+jLi4OGzdtg27du12dNpXefrAxTsErv5N4B3bH0rfECiqBkHmWjb9RenhkCtUkHvVgMKrRpFtbPosx1AXF29ewYXNe7Fk6TLYLPl9c4NDwtClc0d06JA/3AWvQBA9uPvu1L9kyRL0798farW6rGoq0vLlyzF8+HB8/fXXaNasGebOnYsVK1bgzJkzxfqkxrssifIVFcBcVRooA+pBGRQNlX8dKHxD4aLhzwr9P2G3wZqZCvPNyzAlnoQ16SQMqZcAMKAROVNhB4adN2+eY2DY2NhYfPHFF2jevHmxtmUgo8osJycH69evx6pVq/HHhj+gzysYwNTB0VBWqwWZC/sK0f2x6bNgSjyZP2L/3wJaWK1wDOjXF3379kWTJk04VhlVShU2kJUEAxlVNrdv38avv/6KlatWYePGTbCYTdD414aqVguoQ2MZwKhM2PRZMF0/CcOlQzBd3A9LXhb8A4Mc4axVq1aQ82YOqiQYyJxgIKPKICMjA6tXr8bKVauwdetW2KxWaIPqQRXeEtqIVnD1ZEdseniE3Qbj9RPQn90Dy8V9MGWnw8evGvr37YN+/fqhQ4cODGdUoTGQOcFARhWVEAJ79+7FggULsXzFClgtFmhCoqGu3RKa2i3h6u4tdYlEEMIOU9IZ6M/uhvnCPpgybyA4NAzjxz6PkSNHwtfXV+oSiUodA5kTDGRU0WRnZ2Pp0qWYN38BTp1IgLpqDahjukEX1Rkubl5Sl0dUJCEETElnkHf8D+jP7IKLDOjbty/Gjn0ebdu2ZX8zqjAYyJxgIKOK4tixY1i4cCH+98MPMBqM0IY3g1tsd6hDYx+ZuSGFELAbc2A35uXPq/j3uRSt/z/HouBI/aVO5qL4a67QO/OGqiBXKB3zh8o1npArHp0xGm2GbOQlbIEhfgOM6UmoE1kXE8aNxdChQzl9E5V7DGROMJBReSaEwKZNmzBz1jvYu2c3VJ4+UEd1ha5BV7i6+zz0euxmAyy3rsGamQpbbjqsOemw5WYA+gyI3AyYstNht5rvuR+eCSldxf2VrnTzgKvOG3Crmj+4r+6vgX3dvaHwDoJrleoPPdwLIWC8Fo+8Y39Af34vlAolnn9uDF599VUEBAQ81FqISgsDmRMMZFQeCSHw66+/Yuasd3Dk8CFoAiKha9YHmvDmD2UkfGG3wXo7xTFAqOXWVYj0qzCkJzvaaN3cUL2GP4IC86fd+fsciV5eXtBoNEUuLi4czb80CSFgsVhgMBig1+thMBgKLHq9Hjdu3CgwIfzV64lISkpG2s1Ux/RXLko1VL4hkHuHQOEbAqVvGBS+IXDRej6U47Dl3kbOsd+hP/IrYDXjmWdG4Y033kBISMhDeX6i0sJA5gQDGZUndrsdP//8M2bOegcnEuKhDY6GruVAqEMalOlZJbvFCFPSmfwxpRITYEq96BiZ3dvXDw1iohHboIFj+pzw8HD+PFUQNpsNqampOHXqFOLj45GQkICjx+Nx5tQpmM353wMqTx+41qgLVXA01EHRcPUOLNvvR5MeOUfXI+/QWthNeRg2dCjefPNNhIeHl9lzEpUmBjInGMioPLBarVi+fDlmvfMuzp09A21YQ7i3HAh1UFSZPJ/dbIQp+f8DmDH5HOw2K7yqeqNTx45o3boVYmJiEB0dDT8/vzKpgR5tVqsVFy5cyJ/T9OhRbIvbjkOHDsJmtUKp84IisD5UQdFQBUdD4R1UJgHNbjYi99gfyDu0Bta8TDz99CBMmzYVdevWLfXnIipNDGROMJDRo27r1q2YMPEFnD51Em7hTeHeYiBUAZGl/jy2vNvQn98H47k9MF5LyA9g3j7o3LEjOnbMn/ambt267N9FRcrLy8OePXsQFxeHLVu3/X9Ac68KZa3m0Ea0hjooqtQHHrZbTMiN3wT9wZ9hyUnHs88+i3fffZdDZtAji4HMCQYyelRdunQJk19+GevWroU2sB48Oj0LVY06pfoc1pxb0J/bC9P5PTBcOwmZDGjbrj369umNzp07M4BRidwJaBs3bsTylauQeO0qFFoPKGs1gzaiNTQhsaU6Ib2wWZBz9Hfk7lkGlUKOd2bOxPjx46FQcNJ7erQwkDnBQEaPmtzcXLz//vv4+ONPINd6wr3dCGjrtiu1YGTTZyHvxFYYz++GIfEMXF0V6NylM/r364eePXvCx+fh351JFZ8QAkeOHMHq1auxfOUqXLpwHq5qN6hqNoFbvQ5QhzUqtRtSbPosZO36AbnHNiK8dm188flcPPbYY6Wyb6LSwEDmBAMZPSrsdjt++OEHvPra60jPuA1d0z7waN4XcqW6xPsWQsCUeBK5x/6A4dweuMhkeKz7YxjQvz+eeOIJjutED5UQAidPnsTq1avx04qVOHPqJFRe1aCJ7gZd9L/goiudAYzNNy8ha+u30F+NR/fHH8fczz5DnTqle5aZ6EEwkDnBQEaPggMHDmD8hIk4dPAA3CLboEqHkaUyv6TdlIfcE1thjN8Iw80rCK1ZCxPGjcXw4cN5JoweCUIIHDx4EAsWLMCyn5bDbDFDW6cVdA26QxUcXeIzw0II6M/tQc7272DLycCLL76A6dOn8/c9SYqBzAkGMpJSSkoK3nhjCv73v++hqV4THh2fhTo4psT7NaddQc6hX2A4swPCZkGvnj0xbtw4dOzYkZM20yPr9u3b+N///od58xfgwrmzUPsEQRPzGHQNukKu1JRo33aLCdkH1yB3/ypU8XDHRx9+gBEjRvDngSTBQOYEAxlJwWw24/PPP8fbM2fCAle4tx4MXYNuJe5DY0q9gJy9y5F3bi+q+wdg/NjnMWrUKPj7+5dS5URlTwiBHTt2YMGChVi1ehXkKje4Ne4J90Y9IFe5lWjf1uxbyNy+CHmntqNho8b4at6XaNmyZSlVTlQ8DGROMJDRw7Z+/Xq88OIkXL58GbqGj8Oz9b/honEv0T5NSWeQvW859BcOIjSsJt6aNhVDhw7l3WVU7l29ehUfffQRvvnmW8BVCW3DJ+He5KkS/8wYE08ie+s3MKRcwODBQ/DRRx/ygws9NAxkTjCQ0cNy4MABvDV9OjZt3AhtaCw8Oz0LpW9oifZpvJaAnH0roL98FHUiIjH9rWkYOHAgXF1Ld5wnIqklJyfj448/xvwFC2ETMmhjH4dH015wcavywPsUdhty4zcjZ9cSuAorXn/tVbzwwgvw8iqdmwqIisJA5gQDGZW1Xbt2YeasWfhz82aofYLg3nYoNLVblqizsjntSv7dY1eOISo6BjOmv4U+ffqwPwxVeDdv3sSnn36KL+fNg8lig65p7/y7kRUPfjey3ZiLzD0/QX/sD6jVSrw4cSJeeukl3vhCZYaBzAkGMioLQghs27YNb8+chZ07tkPtFwpdiwHQRrQuUT8xmyEbWbuWIvfoHwirVQufzPkIPXv25OCtVOlkZGTgww8/xGefzYXcrUr+eH2RbUv0s2DLvY3sg2ugP/4HXOUyjB83Fq+88gqqV69eipUTMZA5xUBGpUkIgQ0bNuDtmbNwYP8+aGqEQ9diIDS1m0Mme/CzV8Ju+2sE8h+hdAFmzpiBiRMnQqlUlmL1ROXPhQsXMPnll/HrL79AGxwFz06joaxWq0T7tOmzkH3oF+iP/ga5sOG5MaPx6quvIigoqJSqpsqOgcwJBjIqDenp6fj+++/x1YKFuHThPDSBkXBvMRDqmk1KfPbKcOUYsrd9C1PaVYwaNQrvvfceqlUr+RhlRBXJ5s2bMfGFF3H27BnoYrqiStuhJepfBuRfysw+/Cv0R36BzaTHE088gXFjx+Jf//oXuwdQiTCQOcFARg9KCIF9+/ZhwYKF+Gn5cthsNmgiWkMX2x2qwPolDmKWzFRkbfsv8s7tRYuWrTDvyy/QuHHjUqqeqOKxWCxYuHAhpk57CwaLDe4tn4Z7oydKPJm53aRH3qk4GI7/AcONywgODcP4sc9j5MiRnMCcHggDmRMMZHS/srKy8OOPP+Kr+Qtw8kQCVFVr/DXlS5cSfyIHALvZgKy9K5B7aC2q+fnhk4/n4Omnn2Y/MaJiunXrFt566y385z//gdI7AB4dnoWmZsk/zAghYEo6g7xjv0N/djdcZEDfvn3x/PPPoW3btjxrRsXGQOYEAxkVR3p6OtatW4eVq1Zhy59bYLVaoa3dHG4NHoM6rGGJ+ofdIYRA3qk45Oz4HsKYgzdefw2vv/463NxKNhAmUWV1/PhxTJj4Anbt3AG32s3h2fEZKLxKZ6wxmyEbeQl/whC/Ecb0JFSr4Y8B/fqib9++aNOmDVxcSmeidKqYGMicYCCjoqSmpmLt2rVYsWIltu/YDrvdDm1gPahqt4I2ojVcPUrvlnhTynlkbf0PDImn0btPH3z6yScIDQ0ttf0TVVZCCKxatQovTX4ZKSkp0DXpCc+WAyFXaUtp/3aYEk9Bf3Y3zBf2wpR1C1V9fNGvT2/07dsXHTt25ADNVAgDmRMMZHSHyWTCwYMHERcXhz82bMTePbsBmRya4Gio67SCtnZLuOhKd8BIa9ZNZO1ZhtyEP1G3bj3M+/ILdOrUqVSfg4gAvV6POXPmYPYHH0IoNNC1HgxdVOcS9y/7OyHsMCefg/7cHpgv7IUxIwUenlXw1JNPoGPHjujQoQPCwsLY/YAYyJxhIKu8/h7Atm7dhj1798JkNECh0UEZWB/q8ObQ1G4BF03pf19Ybqcge99K6E9ugYeHJ959Zxaee+45jrBPVMauXbuGV199FStWrIDKqxrcmvaFLvpfkLmW7lksIQQsNy8h7+weWK8ehj7lIiAEagQEokun/HDGgFZ5MZA5wUBWOej1epw6dQrx8fFISEjA0WPHsG/ffkcAUwTWgyowGqrgaCj9wko8yXdRLOnXkb13BfJO74C3tzfeeP01PPfcc9DpdGXyfETkXEJCAt577z2sWLECCndvuDXtDV2DbiUa8f9u7MZcGBNPwngtAbakE46A5h8YhDatWiI6OhrR0dGIiYlBSEgIbxCo4BjInGAgqzjMZjNSUlKQlJSExMREnD59GgkJCTh89DiuXr4IIQQgk0Hj7Q9Z1WCoAupCFRxTpgHMUVvaFWTvXY68M7tQvXoNvDnlDTz77LPQaDRl+rxEdHdnz57Fe++9j6U/LoWrxgPaJr3g3vBxyJVl+7NZIKDdvAhL2hVY9DkAAK2bG+rXj0JsgxjExMSgZs2aCAgIQEBAALy9vXlGrQJgIHOiqBfFZDLh0KFDElZWuQkhYDKZYDAYoNfrYTAYCi03b95EYmISrl1PRFJyEm6n3yqwD6WbJ1x9Q+HiHQyFbxiUviFQ+IRAriybT8CFj8EO45XjyDu6Hnnn9yEwOATT3pyCESNGQKVSPZQaiKh4Ll26hNmzZ2Px4u8hU2qgbdAdutjH4OrxcMYZE0LAlpsOy80rMN+6AkvaVYj0qzCmXYPdZnW0UypV8KteA4EB/ggOCkRAQAA8PT2h0WiKXHhTgXSqVq2KunXrFlrPQOZEUS/KhAkT8NVXX0lYGf2TTCaHi1IFuUIFuasScq0nhKYKXNy94aLzhutf/975Wq7WSfJJ0qbPQm7CnzDGb4QxIxkRdevh9VdfwZAhQ/iLkegRd+3aNcyZMwffLVoEg94AbXhTuDXonj+8TRmfSXdG2Kyw5d2GLScd1tx02HIzYMtJhy03HSIvAyIvA3aTHnaLGTaLCXar+aHXSEWTu7jg8KFDiI2NLbC+QgWyK1eu4J133sHWrVuRmpoKf39/DBkyBFOnTr2v+f2KelF69+6NPw5fgHfX8WVRPhWHqwIyVyXkrkrIFCpA7vrInqrPHzDyFHKP/QHD2d1wkckwYMAAjB37PFq1avXI1k1EzuXk5GDZsmWY99V8JMQfh8qrOjTRXaGL+Rdc3Er3buvSJIQdwmrOXyxmCKsJsNulLqtSsuZl4OZPU7Fx40Z07dq1wGPFDWTl4javM2fOwG634+uvv0Z4eDhOnDiB0aNHIy8vDx9//HGpPIdcqYHCh5PJUtFsubeRd3YXjAkbYbhxBaE1a2H87PcxYsQI+PiU3jhlRPRwubu7Y8yYMRg9ejQOHDiAhQsX4sdlPyF794/Q1GkFt6jOUIc0KNVhM0qDTCaHTKEGFGqAXVQlJSuF7jGP1ndXER577DE89thjjq9r1qyJs2fPYsGCBaUWyIicsWbfhP7sXpjO74E+8RTkcjmeeuopjB/3DTp16sS7o4gqEJlMhubNm6N58+b49NNP8b///Q/z5i/AhZUzoNC6Q1mzGbQRraAJbQiZa/GvzhAVR7kIZM5kZWWhatWqUpdBFZDldgr053bDfH4v9ElnoVAo0eVfXdB/5st46qmn4O3tLXWJRFTGvLy88OKLL+KFF17A8ePHsXr1aixfuQrnV78DV5UWqppNoKnTCpqaTR7azUNUsZXLQHbhwgV8+eWX9zw7ZjKZYDKZHF9nZ2eXdWlUDtlNepiSTsN4LQGWq0dgSL0ElVqDx7s/hn79pqNHjx7w9PSUukwikoBMJkNsbCxiY2Pxzjvv4NSpU45wdnLdB3BRqqAKaQhVcAzUwdFQ+IaUyny3VPlIGsjeeOMNfPjhh3dtc/r0aURGRjq+TkpKwmOPPYb+/ftj9OjRd9129uzZmDlzZqnUShXH3wOYNekEDMnnIew2+PhVwxNdOqNPn4/w2GOPcaJvIiqkXr16qFevHt566y1cvHgRq1evxrpffsXBHYtx22KGQusBRWB9qIKiGdDovkh6l2VaWhrS09Pv2qZmzZqOOymTk5PRoUMHtGjRAosXL75n/x1nZ8iCgoKc3mW5MSEJfv1mlOBo6FEkrGZY0q/DnHYF5puXYUs5UyCA/X1akzp16vAOSSJ6IAaDAfv27UNcXBy2bN2GA/v3w/K3gKasXhsKv/wxEl3cffm7poKxZqchacHI8nuXpa+vL3x9izcQX1JSEjp27IjGjRtj0aJFxepMrVKpOChnJSHsNliz02BJuwpL2hWY065AZFyF8VYShN0GAAgMCkGbNi3RocMkBjAiKlUajQYdO3ZEx44dMXPmzAIBbeu2OBw9ugaZOfmj8ys0uvyBq31CoPQNhcI3FEqfYMhUbvydVImViz5kSUlJ6NChA0JCQvDxxx8jLS3N8Vj16tUlrIzKirDbICwmxxg7drMhf8DE3HTHQIm2nHTAcBu2nAyYczIcwcvDswoaRkejYZenHHPGRUVFwd3dXeKjIqLKomBAyx+/8Pr16445dhMSEnDk6HFc2LoJNmv+6PyuKg0UHj6QuVWFzK0qXHRVHQNgu+q8Ide4Q6ZQQeaqgsxVmb8wwFUY5SKQbd68GRcuXMCFCxcQGBhY4LHSuOLq7+8Pw9q1uPnd2BLvix6AEBA2K+xWE+wWE2wWE4TNVmRzzypeCPD3R1BYAIIC6znmfQsODkZMTAz8/f35S4qIHikymQzBwcEIDg7GE0884VhvMplw9uxZnD59GklJSY7l2vVEJCYdxo2UFJjNpiL366JQQa5QOmY1gQQzDFD+LAsASjQUUrkYqb+03O067muvvVYq4Y4ejFKpvOv8bG5ubqhevTr8/f05STcRVRpCCGRkZCApKQkZGRmO+X2LmvfXzpH6JePn54eXX365UCirUFMnlZbivihEREREpaG42YP34hIRERFJjIGMiIiISGIMZEREREQSYyAjIiIikhgDGREREZHEGMiIiIiIJMZARkRERCQxBjIiIiIiiTGQEREREUmMgYyIiIhIYgxkRERERBJjICMiIiKSGAMZERERkcQYyIiIiIgkxkBGREREJDEGMiIiIiKJMZARERERSYyBjIiIiEhiDGREREREEmMgIyIiIpIYAxkRERGRxBjIiIiIiCTGQEZEREQkMQYyIiIiIokxkBERERFJjIGMiIiISGIMZEREREQSYyAjIiIikhgDGREREZHEGMiIiIiIJMZARkRERCSxchfITCYTYmNjIZPJcOzYManLISIiIiqxchfIXnvtNfj7+0tdBhEREVGpKVeB7I8//sCmTZvw8ccfS10KERERUalxlbqA4rpx4wZGjx6NtWvXQqvVFmsbk8kEk8nk+Do7O7usyiMiIiJ6YOXiDJkQAiNGjMDzzz+PJk2aFHu72bNnw9PT07EEBQWVYZVERERED0bSQPbGG29AJpPddTlz5gy+/PJL5OTkYMqUKfe1/ylTpiArK8uxXL9+vYyOhIiIiOjByYQQQqonT0tLQ3p6+l3b1KxZEwMGDMCvv/4KmUzmWG+z2eDi4oLBgwfj+++/L9bzZWdnw9PTE1lZWfDw8ChR7URERET3UtzsIWkgK65r164V6P+VnJyMbt26YdWqVWjevDkCAwOLtR8GMiIiInqYips9ykWn/uDg4AJf63Q6AECtWrWKHcaIiIiIHlXlolM/ERERUUVWLs6Q/VNoaCjKwZVWIiIiomLhGTIiIiIiiTGQEREREUmMgYyIiIhIYgxkRERERBJjICMiIiKSGAMZERERkcQYyIiIiIgkxkBGREREJDEGMiIiIiKJMZARERERSYyBjIiIiEhiDGREREREEmMgIyIiIpKYq9QFPExCCABAdna2xJUQERFRZXAnc9zJIEWpVIEsJycHABAUFCRxJURERFSZ5OTkwNPTs8jHZeJeka0CsdvtSE5Ohru7O2QymdTllKns7GwEBQXh+vXr8PDwkLocKgLfp/KB71P5wPepfKhs75MQAjk5OfD394dcXnRPsUp1hkwulyMwMFDqMh4qDw+PSvENX97xfSof+D6VD3yfyofK9D7d7czYHezUT0RERCQxBjIiIiIiiTGQVVAqlQozZsyASqWSuhS6C75P5QPfp/KB71P5wPfJuUrVqZ+IiIjoUcQzZEREREQSYyAjIiIikhgDGREREZHEGMiIiIiIJMZAVgG99957aNWqFbRaLapUqeK0zbVr19CjRw9otVr4+fnh1VdfhdVqfbiFUiGhoaGQyWQFlg8++EDqsiq9r776CqGhoVCr1WjevDkOHDggdUn0N2+//Xahn5vIyEipy6r0duzYgSeffBL+/v6QyWRYu3ZtgceFEJg+fTpq1KgBjUaDLl264Pz589IU+whgIKuAzGYz+vfvj7Fjxzp93GazoUePHjCbzdizZw++//57LF68GNOnT3/IlZIzs2bNQkpKimOZOHGi1CVVasuXL8fkyZMxY8YMHDlyBA0aNEC3bt1w8+ZNqUujv6lfv36Bn5tdu3ZJXVKll5eXhwYNGuCrr75y+vhHH32EL774AgsXLsT+/fvh5uaGbt26wWg0PuRKHxGCKqxFixYJT0/PQut///13IZfLRWpqqmPdggULhIeHhzCZTA+xQvqnkJAQ8dlnn0ldBv1Ns2bNxPjx4x1f22w24e/vL2bPni1hVfR3M2bMEA0aNJC6DLoLAGLNmjWOr+12u6hevbqYM2eOY11mZqZQqVRi2bJlElQoPZ4hq4T27t2L6OhoVKtWzbGuW7duyM7OxsmTJyWsjADggw8+gLe3Nxo2bIg5c+bwUrKEzGYzDh8+jC5dujjWyeVydOnSBXv37pWwMvqn8+fPw9/fHzVr1sTgwYNx7do1qUuiu7h8+TJSU1ML/Gx5enqiefPmlfZnq1JNLk75UlNTC4QxAI6vU1NTpSiJ/vLCCy+gUaNGqFq1Kvbs2YMpU6YgJSUFn376qdSlVUq3bt2CzWZz+vNy5swZiaqif2revDkWL16MiIgIpKSkYObMmWjbti1OnDgBd3d3qcsjJ+78rXH2s1VZ/w7xDFk58cYbbxTqtPrPhX8gHk33895NnjwZHTp0QExMDJ5//nl88skn+PLLL2EymSQ+CqJHV/fu3dG/f3/ExMSgW7du+P3335GZmYkVK1ZIXRpRsfEMWTnx8ssvY8SIEXdtU7NmzWLtq3r16oXuErtx44bjMSpdJXnvmjdvDqvViitXriAiIqIMqqO78fHxgYuLi+Pn444bN27wZ+URVqVKFdSpUwcXLlyQuhQqwp2fnxs3bqBGjRqO9Tdu3EBsbKxEVUmLgayc8PX1ha+vb6nsq2XLlnjvvfdw8+ZN+Pn5AQA2b94MDw8P1KtXr1Seg/5fSd67Y8eOQS6XO94neriUSiUaN26MLVu2oFevXgAAu92OLVu2YMKECdIWR0XKzc3FxYsXMXToUKlLoSKEhYWhevXq2LJliyOAZWdnY//+/UWOEFDRMZBVQNeuXUNGRgauXbsGm82GY8eOAQDCw8Oh0+nQtWtX1KtXD0OHDsVHH32E1NRUTJs2DePHj4dKpZK2+Eps79692L9/Pzp27Ah3d3fs3bsXL730EoYMGQIvLy+py6u0Jk+ejOHDh6NJkyZo1qwZ5s6di7y8PIwcOVLq0ugvr7zyCp588kmEhIQgOTkZM2bMgIuLCwYNGiR1aZVabm5ugbOUly9fxrFjx1C1alUEBwdj0qRJePfdd1G7dm2EhYXhrbfegr+/v+PDT6Uj9W2eVPqGDx8uABRatm3b5mhz5coV0b17d6HRaISPj494+eWXhcVika5oEocPHxbNmzcXnp6eQq1Wi7p164r3339fGI1GqUur9L788ksRHBwslEqlaNasmdi3b5/UJdHfDBw4UNSoUUMolUoREBAgBg4cKC5cuCB1WZXetm3bnP4tGj58uBAif+iLt956S1SrVk2oVCrRuXNncfbsWWmLlpBMCCGkCoNERERExLssiYiIiCTHQEZEREQkMQYyIiIiIokxkBERERFJjIGMiIiISGIMZEREREQSYyAjIiIikhgDGREREZHEGMiIiIiIJMZARkRERCQxBjIiIiIiiTGQEREVIS0tDdWrV8f777/vWLdnzx4olUps2bJFwsqIqKLh5OJERHfx+++/o1evXtizZw8iIiIQGxuLnj174tNPP5W6NCKqQBjIiIjuYfz48fjzzz/RpEkTJCQk4ODBg1CpVFKXRUQVCAMZEdE9GAwGREVF4fr16zh8+DCio6OlLomIKhj2ISMiuoeLFy8iOTkZdrsdV65ckbocIqqAeIaMiOguzGYzmjVrhtjYWERERGDu3LlISEiAn5+f1KURUQXCQEZEdBevvvoqVq1ahePHj0On06F9+/bw9PTEb7/9JnVpRFSB8JIlEVER4uLiMHfuXCxZsgQeHh6Qy+VYsmQJdu7ciQULFkhdHhFVIDxDRkRERCQxniEjIiIikhgDGREREZHEGMiIiIiIJMZARkRERCQxBjIiIiIiiTGQEREREUmMgYyIiIhIYgxkRERERBJjICMiIiKSGAMZERERkcQYyIiIiIgkxkBGREREJDEGMiIiIiKJMZARERERSYyBjIiIiEhiDGREREREEmMgI6JKJy4uDjKZDHFxcVKXQkQEgIGMiCqw+fPnY/HixVKX8UB+/PFHzJ07V+oyAAB2ux0fffQRwsLCoFarERMTg2XLlhVr2y1btmDUqFGoU6cOtFotatasiWeffRYpKSkF2un1enz11Vfo2rUratSoAXd3dzRs2BALFiyAzWYri8MieqTIhBBC6iKIiMpCVFQUfHx8Cp0Js9vtMJvNUCqVkMsfzc+lTzzxBE6cOIErV65IXQqmTJmCDz74AKNHj0bTpk2xbt06rF+/HsuWLcPTTz99122bNGmCjIwM9O/fH7Vr18alS5cwb948aLVaHDt2DNWrVwcAnDhxAjExMejcuTO6du0KDw8PbNy4EWvWrMGwYcPw/fffP4xDJZIMAxlRBZeXlwc3Nzepy5BEUYGsPHhUAllSUhLCwsIwZswYzJs3DwAghED79u1x+fJlXLlyBS4uLkVuv2PHDrRp06ZA8N2xYwfat2+PqVOn4t133wUA3Lp1Czdu3ED9+vULbD9q1CgsWrQI58+fR3h4eBkcIdGj4dH8aEhETiUlJeGZZ56Bv78/VCoVwsLCMHbsWJjNZgDA4sWLIZPJsH37dowbNw5+fn4IDAx0bD9//nzUr18fKpUK/v7+GD9+PDIzMws8x/nz59G3b19Ur14darUagYGBePrpp5GVleVos3nzZrRp0wZVqlSBTqdDREQE3nzzzXvWX5ztTCYTZsyYgfDwcKhUKgQFBeG1116DyWQqtL8ffvgBzZo1g1arhZeXF9q1a4dNmzYBAEJDQ3Hy5Els374dMpkMMpkMHTp0AFB0H7KVK1eicePG0Gg08PHxwZAhQ5CUlFSgzYgRI6DT6ZCUlIRevXpBp9PB19cXr7zySrEura1btw49evRwvIe1atXCO++8U2DbDh06YP369bh69aqj9tDQ0CL3OWLECEe7fy5vv/32PWu6V70WiwXjxo1zrJPJZBg7diwSExOxd+/eu27frl27Qmch27Vrh6pVq+L06dOOdT4+PoXCGAD07t0bAAq0JaqIXKUugIiKJzk5Gc2aNUNmZibGjBmDyMhIJCUlYdWqVdDr9VAqlY6248aNg6+vL6ZPn468vDwAwNtvv42ZM2eiS5cuGDt2LM6ePYsFCxbg4MGD2L17NxQKBcxmM7p16waTyYSJEyeievXqSEpKwm+//YbMzEx4enri5MmTeOKJJxATE4NZs2ZBpVLhwoUL2L17913rL852drsdTz31FHbt2oUxY8agbt26SEhIwGeffYZz585h7dq1jrYzZ87E22+/jVatWmHWrFlQKpXYv38/tm7diq5du2Lu3LmYOHEidDodpk6dCgCoVq1akfUtXrwYI0eORNOmTTF79mzcuHEDn3/+OXbv3o2jR4+iSpUqjrY2mw3dunVD8+bN8fHHH+PPP//EJ598glq1amHs2LF3fR0WL14MnU6HyZMnQ6fTYevWrZg+fTqys7MxZ84cAMDUqVORlZWFxMREfPbZZwAAnU5X5D6fe+45dOnSpcC6DRs2YOnSpfDz83Osu3Xr1l1ru8Pd3R0qlQoAcPToUbi5uaFu3boF2jRr1szxeJs2bYq13ztyc3ORm5sLHx+fe7ZNTU0FgGK1JSrXBBGVC8OGDRNyuVwcPHiw0GN2u10IIcSiRYsEANGmTRthtVodj9+8eVMolUrRtWtXYbPZHOvnzZsnAIjvvvtOCCHE0aNHBQCxcuXKIuv47LPPBACRlpZ2X/UXZ7slS5YIuVwudu7cWWD9woULBQCxe/duIYQQ58+fF3K5XPTu3bvA8Qjx/6+FEELUr19ftG/fvtDzbNu2TQAQ27ZtE0IIYTabhZ+fn4iKihIGg8HR7rfffhMAxPTp0x3rhg8fLgCIWbNmFdhnw4YNRePGje/+Iggh9Hp9oXXPPfec0Gq1wmg0Otb16NFDhISE3HN/zpw/f154enqKf/3rXwW+DwAUa1m0aFGBOmrWrFnoOfLy8gQA8cYbb9x3fe+8844AILZs2XLXdiaTSdSrV0+EhYUJi8Vy389DVJ7wkiVROWC327F27Vo8+eSTaNKkSaHHZTJZga9Hjx5doF/Pn3/+CbPZjEmTJhW4fDR69Gh4eHhg/fr1AABPT08AwMaNG6HX653WcudM0bp162C324t9DMXZbuXKlahbty4iIyNx69Ytx9KpUycAwLZt2wAAa9euhd1ux/Tp0wtdDvvna1Echw4dws2bNzFu3Dio1WrH+h49eiAyMtLx+vzd888/X+Drtm3b4tKlS/d8Lo1G4/h/Tk4Obt26hbZt20Kv1+PMmTP3Xfs/5eXloXfv3vDy8sKyZcsKfB9s3ry5WEu3bt0c2xgMBsfZsr+78zoZDIb7qm/Hjh2YOXMmBgwY4HhfizJhwgScOnUK8+bNg6srL+hQxcbvcKJyIC0tDdnZ2YiKiipW+7CwsAJfX716FQAQERFRYL1SqUTNmjUdj4eFhWHy5Mn49NNPsXTpUrRt2xZPPfUUhgwZ4ghrAwcOxLfffotnn30Wb7zxBjp37ow+ffqgX79+d71jsTjbnT9/HqdPn4avr6/Tfdy8eRMAcPHiRcjlctSrV69Yr8e9FPX6AEBkZCR27dpVYJ1arS5Uo5eXF27fvn3P5zp58iSmTZuGrVu3Ijs7u8Bjf++n96BGjx6NixcvYs+ePfD29i7w2D8vaxaHRqNx2n/PaDQ6Hi+uM2fOoHfv3oiKisK3335717Zz5szBN998g3feeQePP/74/RVNVA4xkBFVQPfzR/KfPvnkE4wYMQLr1q3Dpk2b8MILL2D27NnYt28fAgMDodFosGPHDmzbtg3r16/Hhg0bsHz5cnTq1AmbNm0q8o674mxnt9sRHR2NTz/91Ok+goKCHvi4StPd7iq8m8zMTLRv3x4eHh6YNWsWatWqBbVajSNHjuD111+/rzOOznz++edYtmwZfvjhB8TGxhZ6/E5/rHvx9PR0fA/VqFED27ZtgxCiwNnHO+OI+fv7F2uf169fR9euXeHp6Ynff/8d7u7uRbZdvHgxXn/9dTz//POYNm1asfZPVO5Jfc2UiO7NZrMJDw8P0bNnz7u2u9OH7J/9zH788UcBQPz+++8F1ptMJuHp6Sn69u1b5D53794tAIipU6cW2ea9994TAMTmzZvvfTB32e7xxx8XAQEBBfqBOTNnzhwBQBw9evSu7aKioorVh2zPnj0CgJg/f36htnXr1i3QN2z48OHCzc2tULsZM2aIe/1KXbNmjQAgtm/fXmD9f/7znwL1CCHEE088cV99yHbs2CFcXV3FpEmTimyDB+hDdqef4cmTJwvsa+nSpQKA2LFjxz1ru3XrloiMjBR+fn7i3Llzd227du1a4eLiIvr27VuofyBRRcY+ZETlgFwuR69evfDrr7/i0KFDhR4X9xhOsEuXLlAqlfjiiy8KtP3vf/+LrKws9OjRAwCQnZ0Nq9VaYNvo6GjI5XLHZauMjIxC+79zNsbZpa07irPdgAEDkJSUhG+++aZQW4PB4LhjtFevXpDL5Zg1a1ahs0p/Pz43N7dCw3o406RJE/j5+WHhwoUFjuGPP/7A6dOnHa9PSd05s/b3Gs1mM+bPn1+orZubW7EvYaakpGDAgAFo06aN405NZx6kD1nPnj2hUCgK1CiEwMKFCxEQEIBWrVoVqOPMmTOwWCyOdXl5eXj88ceRlJSE33//HbVr1y6yvh07duDpp59Gu3btsHTp0kd20F6issBLlkTlxPvvv49Nmzahffv2jiEhUlJSsHLlSuzatavAsAz/5OvriylTpmDmzJl47LHH8NRTT+Hs2bOYP38+mjZtiiFDhgAAtm7digkTJqB///6oU6cOrFYrlixZAhcXF/Tt2xcAMGvWLOzYsQM9evRASEgIbt68ifnz5yMwMPCuwx8UZ7uhQ4dixYoVeP7557Ft2za0bt0aNpsNZ86cwYoVK7Bx40Y0adIE4eHhmDp1Kt555x20bdsWffr0gUqlwsGDB+Hv74/Zs2cDABo3bowFCxbg3XffRXh4OPz8/Jx2JFcoFPjwww8xcuRItG/fHoMGDXIMexEaGoqXXnrpQd+2Alq1agUvLy8MHz4cL7zwAmQyGZYsWeI0UDdu3BjLly/H5MmT0bRpU+h0Ojz55JNO9/vCCy8gLS0Nr732Gn766acCj8XExCAmJgbAg/UhCwwMxKRJkzBnzhxYLBY0bdoUa9euxc6dO7F06dICl2+nTJmC77//HpcvX3aMmzZ48GAcOHAAo0aNwunTpwuMJ6bT6dCrVy8A+f34nnrqKchkMvTr1w8rV64s8jiIKiQpT88R0f25evWqGDZsmPD19RUqlUrUrFlTjB8/XphMJiFE0Zcs75g3b56IjIwUCoVCVKtWTYwdO1bcvn3b8filS5fEqFGjRK1atYRarRZVq1YVHTt2FH/++aejzZYtW0TPnj2Fv7+/UCqVwt/fXwwaNOiel6KKu53ZbBYffvihqF+/vlCpVMLLy0s0btxYzJw5U2RlZRVo+91334mGDRs62rVv377AZdPU1FTRo0cP4e7uLgA4Ll/+85LlHcuXL3fsr2rVqmLw4MEiMTGxQJuSXLIUIv8ScIsWLYRGoxH+/v7itddeExs3bixUT25urvj3v/8tqlSpIgDc9fJl+/bti7z8OGPGjHvWdC82m028//77IiQkRCiVSlG/fn3xww8/FGp3Z0iQy5cvO9aFhIQUWdvfj+nOe1KWx0H0KOPUSUREREQS4wV6IiIiIokxkBERERFJjIGMiIiISGIMZEREREQSYyAjIiIikhgDGREREZHEGMiIiIiIJFapRuq32+1ITk6Gu7t7gUlyiYiIiMqCEAI5OTnw9/e/63RglSqQJScnIygoSOoyiIiIqJK5fv06AgMDi3y8UgUyd3d3APkvioeHh8TVEBERUUWXnZ2NoKAgRwYpSqUKZHcuU3p4eDCQERER0UNzr65S7NRPREREJDEGMiIiIiKJMZARERERSYyBjIiIiEhiDGREREREEmMgIyIiIpIYAxkRERGRxBjIiIiIiCTGQEZEREQkMQYyIiIiIokxkBERERFJjIGMiIiISGIMZEREREQSYyAjIiIikli5DWQffPABZDIZJk2aJHUpRERERCVSLgPZwYMH8fXXXyMmJkbqUoiIiIhKrNwFstzcXAwePBjffPMNvLy8pC6HiIiIqMTKXSAbP348evTogS5dutyzrclkQnZ2doGFiIiI6FHjKnUB9+Onn37CkSNHcPDgwWK1nz17NmbOnFnGVRERERGVTLk5Q3b9+nW8+OKLWLp0KdRqdbG2mTJlCrKyshzL9evXy7hKIiIiovsnE0IIqYsojrVr16J3795wcXFxrLPZbJDJZJDL5TCZTAUecyY7Oxuenp7IysqCh4dHWZdMRERElVxxs0e5uWTZuXNnJCQkFFg3cuRIREZG4vXXX79nGCMiIiJ6VJWbQObu7o6oqKgC69zc3ODt7V1oPREREVF5Um76kBERERFVVOXmDJkzcXFxUpdAREREVGI8Q0ZEREQkMQYyIiIiIokxkBERERFJjIGMiIiISGIMZEREREQSYyAjIiIikhgDGREREZHEGMiIiIiIJMZARkRERCQxBjIiIiIiiTGQEREREUmMgYyIiIhIYgxkRERERBJjICMiIiKSGAMZERERkcQYyIiIiIgkxkBGREREJDEGMiIiIiKJMZARERERSYyBjIiIiEhiDGREREREEmMgIyIiIpIYAxkRERGRxBjIiIiIiCTGQEZEREQkMQYyIiIiIokxkBERERFJjIGMiIiISGIMZEREREQSYyAjIiIikhgDGREREZHEyk0gmz17Npo2bQp3d3f4+fmhV69eOHv2rNRlEREREZWYq9QFFNf27dsxfvx4NG3aFFarFW+++Sa6du2KU6dOwc3NrdSe54cfl2HWrFnQarWltk+6OxkAhVIJrUYDN60WWq0GGk3Bxd3dHTVq1EBAQAACAgLg7++PKlWqQCaTSV0+EVGps1qtSE1NRXJyMpKSkpCUlIS0tDTo9XoYDIYCi16vR57eAIPBCJvdJnXplYpcJseXX8xF69atS7wvmRBClEJND11aWhr8/Pywfft2tGvXrljbZGdnw9PTE1lZWfDw8HDaRiaTATI5dA26lma5dDcCEHYrhNUMYTVDZjMDVhNgNQN/rbOZ9DDnZRXYTKXWoHoNfwQG+CM4KBC1a9dGTEwMoqOjUatWLbi4uEh0QERE95abm4sTJ04gPj4eJ06cwJUrV3EtMRHJyclIT7sJu93uaCt3cYVS5wWZQgWZqxJwVQIuSggXJWSu/79AXm4ufFUIxnN7MGbEEHz11VdFtilO9gDK0Rmyf8rKyv/jXLVq1SLbmEwmmEwmx9fZ2dn33K9MJoNX13Fwj32s5EVSqRJWC2x5GbDmpMOWkw5bbjpu52YgLT0dR68lwPrrHzDnZgLID2t169VDo9gGiI6ORkxMDBo0aABvb29pD4KIKh273Y7z588jPj4eCQkJOH48HkePx+P61csAAJlMDrVvIGQ6P8h1PnAJj0CVWG+4uHvDVZf/r1zjDpmMYetRk3bzQqntq1wGMrvdjkmTJqF169aIiooqst3s2bMxc+bMh1gZlSWZqwKuntXg6lmtyDa2vNsw37wCS9oVnE+7inMbduL7JUths+QH88h69dGlU0d06NAB7dq1g6+v78Mqn4gqCbvdjpMnTyIuLg7btsVhW1wcMm9nAABUHlXh4h0CV79YeNfvBYVvCBTeQZArVBJXTVIrl4Fs/PjxOHHiBHbt2nXXdlOmTMHkyZMdX2dnZyMoKKisyyMJubh5QRPmBU1YQ8c6YbfBejsZppTzSLx+At8u+xnz5s0DwIBGRCVXVACTuyqg9o+Aom5X+AXWh7JaTbhoPaUulx5R5a4P2YQJE7Bu3Trs2LEDYWFh97Vtca7juri4wNXDF65u/KF5mGRyVwgXJYSLAjJXFWSKO30i8vtLyNXucHWvCpe/Tt+76LxL9InSmp0G4/UTMF1LgDXpBIzpyZDJZGjWvAUG9O+Hvn37IiQkpBSPkIgqEqvVip07d2L16tVYufpn3ExN+SuARUIRGAV1cDSU/hEP/HtKCAG7IRu23PwuGtbcDNhyMyAsRgiLCcJqht2a/6/MagZsZsBmAYT93junUmO8eRVjxzzr+JDvTHH7kJWbQCaEwMSJE7FmzRrExcWhdu3a972P4rwoS5cuxc6dO0taLt0HIQTMZrPjjqE8vR55eXro9QboDXoYDEbcvp0OfW5uge0UGncoPLwBt6qQu/tB4RsCpW8oFL6hcNG431cN1uw0GK8eh/H8XhguH4XdakbDRo0d4exBvt+IqGIxm83YunUrVq9ejdU/r8HtjHSoqvhBWasFNLWbQ+UfeV8BTAg7rJmpsNy8AnPaFVjSrwN5GbDnpcOcnQ671eJoK5PJUNXHFzqdOzQaNbRa7V93pmvy/6/VQq1Ww9W1XF74KrdkMhmmTJmC4ODgIttUuEA2btw4/Pjjj1i3bh0iIiIc6z09PaHRaIq1j+K+KPRoysnJKXAL+N//f/bcBZw7ewYWixkAoPL0hYt3MFz/CmjKarWg8A4q1jAZdpMehosHYTi/B8ZLh2EzG1G3fhQG9u+HYcOG3feZWSIqv2w2GzZt2oSffvoJa9auQ052FtTe/lCGt4S2Tisoa9Qp3u8VswHmlPMwp12GJe0q7OlXYUq7CpvZCACoUtUb0VH1ERoSUmB4nzv/r1atGhQKRVkfLpWBChfIivqGX7RoEUaMGFGsfTCQVWwWiwXnzp1DQkLCX3cyHcex4wlISrwGAFC6eUIRFAVVUDRUQdFQ+ATf8xep3WKE8fIR6M/tgeniAdhMBvyra1eMGzsWPXr04KdRogoqNTUV3333HeYv/BpJ169B7RsMVe1W0Ea0gsI37N6/O8wGmJJOw3gtAdbEEzCknIOw2aBUqhBZrx4aNohx3AEeHR2NatWqcVzFCqrCBbLSwEBWOWVlZeHgwYPYvn07tmzdhoMHDsBqtRQIaOqQBlB43/2GD7vFCP3pndDHb4Ah6Syq+wdg7HNj8MwzzyAgIOAhHQ0RlRUhBOLi4jB//nysWbsWkLtAE9EWutju9zwTJqwWGK8nFApgVX180bljR3TsmH/jUEREBD/IVTIMZE4wkBEA6PV67N27F3FxcQUCmto3GKrwltBGtIbC7+6fgE2pF5B7bAOMZ7bDbjXjySefxPhx49ClSxd+yiUqZ27fvo3Fixdj3vwFuHThPNS+wdDEPAa3qE5wUeuK3M5uMcJ46Qj053bDdOkgrEZ9gQDWoUMHREZG8ndCJcdA5gQDGTmj1+uxZcsWrFq16v/7iFStkd9HJKL1XT8Z20165J2Kg+H4HzDcuIyo6BjMmP4W+vTpAzlHzCZ6pN28eROffvopvpw3D0ajCZqI1tDFdocqsP5df+YNFw/CcG4PjJfz+5jWi4rGwP790Lt3b0RFRTGAUQEMZE4wkNG9mM1mbNu2zXEXVUb6Lag8faGKaAtd7GNQePk73U4IAdP1E8jZtwL6y0dRJyIS09+ahoEDB/LyBNEjJjk5GXPmzMGChV/DJmTQNnwcHk16wsXNy2l7YbfBcOkQ9PGbYbh8GHarBY0aN8GA/v3Qp08f3oVNd8VA5gQDGd0Pm82GnTt3YtWqVVjyw1JkZ2VCW7MR3Bo8Bk14c8jkzufKNCWdQc6+5ci7cBChNWvhralvYujQobxDikhiV69exUcffYRvvv0v4KKAttGTcG/8VJHD5FhzM5AbvwnGhE0wZd5EbMNGGDZ0CPr06cNxCqnYGMicYCCjB2UwGLBy5UrM+2o+Dh7YD5WHD9RR/4KuQVe4ejgf3d+UegE5e5cj79xeBAYF461pUzFq1CieMSN6yK5du4aZM2fi+//9D3KVG9wa94R7ox6Qq9wKtRXCDuPVeOQd+wOGC/ugVKow+N+D8Pzzz6NJkyYSVE/lHQOZEwxkVBqOHTuGr7/+Gv9bsgQGvQHa2s2ga9oH6sB6Ttub064gZ+8K5J7ZiXr16mPel1+gY8eOD7lqosrHYDDgo48+wuwPPoRwVUPbpDfcG3aHXFl47Eq7xYjc45tgOLYexvQkRETWxYTx4zBkyBBUqVLl4RdPFQYDmRMMZFSacnJysHTpUsz9/AucPXMa2tAG0LUYAHVwjNNOvaaU88ja+h8YEk+jd58++PSTTxAaGvrwCyeq4IQQWLVqFV6a/DJSUlKga9ITni0HQq7SFmprNxuQc/R36A+thdWQjf79+mP8+HFo06YNO+dTqWAgc4KBjMqC3W7H2rVrMXPWO4g/fgzawHrQtRwIdVijQr/QhRDIOxWHnB3fA6ZcvP7aq3j99dfh5lb40gkR3b/4+HhMfOEF7Ni+HW7hzeDZ8RkoqhYeJ9BuykP24V+hP/wLhNmA4cOHYcqUKahVq5YEVVNFxkDmBAMZlSUhBH7//Xe8PXMWDh08AI1/HehaDMi/AeAfwcxuNiBr30rkHlyDan5++PSTjzFw4EB+Iid6QOnp6Zg2bRr+85//QFk1AB4dn4WmZuNC7WyGbOQc+gV5R3+DzGbBmNHP4rXXXrvrXIREJcFA5gQDGT0MQghs2bIFb8+chd27dkLjXweencZAFRBZqK0lMxVZ2/6LvHN70bJVa8z78gs0atRIgqqJyier1YqFCxdi6rS3oDdZ4N5qENwbPQGZS8GbZ4TNguxDvyB333K4yoBxY5/Hyy+/DH9/50PZEJUWBjInGMjoYYuLi8MLL05CQvxx6Op3hGf7EXB19y7UznDlGLK3fQtT2lWMGjUK77//Pvz8/CSomKj8+PPPPzHhhRdx9sxp6GL+hSpthzodS0x/8SBytn0Lc2Yqxo0di7feeos/X/TQMJA5wUBGUrDZbPjvf/+LN6a8iew8PdxbDIBHk16QuRYcl0zYbcg5+jty9/wIpQsw6+23MWHCBCiVSokqJ3o0Xbx4ES9Nnoxff/kF2qD68Og0Gqrq4YXaWdITkbntW+gvHkL7Dh0x78svEBUVJUHFVJkxkDnBQEZSun37NmbOnIkv582D0rMa3Ds8A014s0L9xmyGbGTtWorco3+gZq1a+OLzuXj88cclqpro0ZGTk4P3338fn3zyKeRuVeDebgS0kW0L99E05SFr90/IPfILAgICMfezT9G7d2/20SRJMJA5wUBGj4JTp07hhRdfxJY//4S2ZiN4dnwWSp/CHYrNaVeQtfUb6K8cR5u27fD2jOno1KkT/6hQpZOTk4MFCxbgozkfIzMrG7pmfeDRvC/kCnWBdkLYkZfwJ3J2LoHcZsLUN6fg5ZdfhkZTeNwxooeFgcwJBjJ6VAgh8Msvv+DFlybj2tWr0DV8HJ5tBsNFrSvUznDhAHL3LoMh5QKaNW+Bt2dMx2OPPcZgRhVeZmYmvvzyS3zy6WfIycmBNroLPFsOgKtH4f5fxsRTyN72DQzJ5/H004MwZ85HCAwMlKBqooIYyJxgIKNHjclkwty5czHrnXdggSvcWw+GrkG3QvNkCiFgvHQIOfuWw5B4Bg0bNcaM6W/hySefhFwul6h6orKRnp6OuXPn4rPPP4fBYIJbTFd4NO/rdJoya/YtZG5fhLxT2xHbsBHmffkFWrduLUHVRM4xkDnBQEaPqpSUFEyZ8ia+/34xNNVqwqPTs1AHxxRqJ4SA8epx5O5bDv3VBNSPisbUN6egb9++7PxP5d61a9fw1VdfYd5XX8FkscGtQXe4N+sNV13VQm3tFhOyD/yM3AOrUcXDHR99+AFGjBjBDyj0yGEgc4KBjB51Bw4cwISJL+Dggf1wi2gNj9b/htI3xGlb4/UTyNm7HPrLR+Ht44vRzz6DMWPGICws7CFXTfTgbDYbNm7ciPkLFuCP33+HXKmGNrYHPJr2govWs1B7Ybch79R25O5ZCltOBl56aRKmTZvG3+n0yGIgc4KBjMoDu92OH3/8Ea+/MQXJSYn5wazlQCir1XTa3nzrGnKP/QHDqW2wGfPQtVs3jBs7Fo8//jhcXV2dbkMktRs3buC7777D/IVfI/HaVWiq14KmQXe41W3ndM5JYbMg98Q26A+sgjEjGU8+9RQ++fhj1K5dW4LqiYqPgcwJBjIqT8xmM5YsWYJZ776Ha1cuw612c7i3GACVf4TT9naLEfrTO6CP3wBD0jlU9w/AuOefw5AhQ3jWjB4JVqsVcXFx+M9/vsGaNWsgZHJoIttCF9sdyhp1nN6oIqxm5MZvRt7B1TBl3kTvPn3w1rRpaNiwoQRHQHT/GMicYCCj8shqtWLZsmWY+c67uHj+HLQ1G8G9xQCog4oe4NKUeiH/rNnp7bCZjYhuEIuB/fuhb9++iIwsPIUTUVkxmUzYsmULVq9ejZ/XrEXm7QyofYOhiekGt6jOhe4svsNuMSL32EbkHVoDa24GBgwYiGnTpqJ+/foP+QiISoaBzAkGMirPbDYbVq9ejbdnvYPTJ09AG1QP2tjHoa3TutCo/3fYzQYYLh6C4fwemC4dgtVkQJ3Iunh6QH/07dsX0dHRHD6DSp3BYMCGDRuwevVqrP3lF+Tl5EDtEwhleEtoI1pDWa1Wkd931qwbyDm+EcaETbAacjB0yFC8+eYU1KlT5yEfBVHpYCBzgoGMKgK73Y5ffvkFn839HDu2x0Hh5glNVBfoGjwGhVeNorezmGC8chT6s7thvnQQFkMuQsJqonu3rujQoQPat2+P6tWrP8QjoYrCZrMhPj4ecXFx2LptG/78cwuMBj001UIdIUzhE1JkCBN2GwyXDiPv+B/QXzwEN50OI4cPx+TJk3m5nco9BjInGMioojlz5gy+/vpr/Pe7RcjJzoK2ZiO4NeiePyXTP8Yy+zths8B4NR768/tgSzoBQ9p1AEB47Qh06dyRAY3u6p8BbPv2HcjJzoKLQgVVQCSUwQ3yQ1jVgLvux5qbgdz4TTAmbIIp8yYaxDbEhPHjMGjQILi5uT2koyEqWwxkTjCQUUVlMBiwYsUKfDV/AQ4e2A+lhw/U9Tre8/LQHdbcDJiuJcB4PQG2pFMwpF0DANQKr4OmTRohOjoaMTExiI6ORnBwMC9zViJGoxGnTp1CQkICEhIScOz4cRw4cLBAAFMERkEdHA1VjYgiL5/fYTcbYbh0CIYzO2G4sA8KhRKD/z0IY8eORZMmTR7SURE9PAxkTjCQUWVw9OhRfP311/hp+QpkZd6Gyqs6VOEtoY1oBaV/BGSyew+cacu9DeP1BJgST8J26yosaVdhMeQAANzc3REdFY3YBv8f0AICAhAQEAAfHx8OzFkOGY1GpKSkICkpCUlJSTh//jwSEhJw+OhxXL50AXabDQCg9vaH3DsECr+axQ5gQP5k3/oLB2A8twfGy0dgs5gQFdMAY559BkOHDkWVKlXK+AiJpMNA5gQDGVUmFosF27dvx+rVq7Fy9c9IT7sJlacPlLVaQBvRCqrA+ne9rPl3QgjYcm7BknYF5r8WkX4VxlvXIf76Yw0ArgoFfP2qIzAwAMGB+SHNw8MDGo2m0KLVaqHRaKBQ3PsPOhWfEAJGoxEGg8Gx6PX6Al+npaUhMTER1xKTkZychKzbGQX2oXDzgMInBC4+oVD4hEDpGwqFbwjkyuJP0m0zZMNwfh8M5/bCeOUo7DYrmjRthgF/3e1bs6bzcfWIKhoGMicYyKiystls2L17N1avXo0Vq1YjNTkJCq07lIFRUAblX25S+IYW6+zZ3wm7Dba8TNhy02HLSYctNwPWv/4v8tKBvNuwW4ywW0ywW0ywmU0Qwl5GR0lFkbsq4KJQQa5QQu6qgkzjAaH1govOG67u3nDRecNFVxUu7vlfy5Ta+74sbTfpYUo8CeO1BFiTTsKQfB6AQKvWbTCgfz/07t0bQUFBZXOARI8wBjInGMiI8u/SPHjwIDZs2IAtW7dh3759sJhNUGjdoQioD1VQdH5A87v/gHYvQgjAboWwmiEsZtitJsBuu/eGdF9krsq/FhVkropinwm9H84CmBB2+FWvgS6dOqJjx4544okneGMIVXoMZE4wkBEVZjQasX//fsTFxRUMaBodFL5hcPEJhsI3DErfECh8QpxOa0MVV/7l6jSY067CcvMyzGlXITKuwnjzmiOA/atzJ3To0AEdOnRArVr3vomEqDJhIHOCgYzo3u4EtF27diE+Ph5HjsXj0oVzsNvzLzWqq9bI79jtEwJXrxr5l7tKcKmLpPfPS8/WnFuwpF+H/dZVWG5dhcWQCyD/ho6Y6BjENohBo0aNGMCIiqHCBrKvvvoKc+bMQWpqKho0aIAvv/wSzZo1K9a2DGRED8ZoNOL06dNISEhAfHw8jsfH43h8AtJupBZo56JUQ+nhA5lbVcjcqkLuVgVyhfr/L58p/n4pTQmZQlUml9MqNSEgbJa/Lgub8v+1mmC35P8rrGbYTXrYc9MB/W3YctNhzs4o0LfPVaFAzVrhaNwwtsCQJ0FBQQxfRPepQgay5cuXY9iwYVi4cCGaN2+OuXPnYuXKlTh79iz8/PzuuT0DGVHp+udwCcnJyY7/X7ueiJTUGzDo9TAYDTAZjTAZDShHv3IqDFeFAiqVGmqNBmq1Bh4eHggO9EdgYKBjyBJ/f38OX0JUBsoskA0fPhzPPPMM2rVrV+Ii71fz5s3RtGlTzJs3D0B+5+SgoCBMnDgRb7zxxj23ZyAjkpYQAmazucAQDDYbO/WXNpVKVWCIERcXnoUkkkpxs4fr/e44KysLXbp0QUhICEaOHInhw4cjIODu02OUBrPZjMOHD2PKlCmOdXK5HF26dMHevXvL/PmJqORkMhlUKhVUKhUHAyUi+pv7Pie9du1aJCUlYezYsVi+fDlCQ0PRvXt3rFq1ChaLpSxqBADcunULNpsN1apVK7C+WrVqSE1NdbqNyWRCdnZ2gYWIiIjoUfNAnQR8fX0xefJkHD9+HPv370d4eDiGDh0Kf39/vPTSSzh//nxp1/lAZs+eDU9PT8fCQQmJiIjoUVSiXpspKSnYvHkzNm/eDBcXFzz++ONISEhAvXr18Nlnn5VWjQAAHx8fuLi44MaNGwXW37hxo8iBB6dMmYKsrCzHcv369VKtiYiIiKg03HcfMovFgl9++QWLFi3Cpk2bEBMTg0mTJuHf//63o7PamjVrMGrUKLz00kulVqhSqUTjxo2xZcsW9OrVC0B+p/4tW7ZgwoQJTre501eFiB4em82GGzduICkpCTdu3Cg0j+LfF6PRyE79ZUCpVDqdP/TO4uHh4bir0sPDg0NZED0C7juQ1ahRA3a7HYMGDcKBAwcQGxtbqE3Hjh3LpMPu5MmTMXz4cDRp0gTNmjXD3LlzkZeXh5EjR5b6cxGRcxaLBWfPnkVCQgKuXLniGObi6vVEJCUm4VbaDccgsn8nk8nholRBrlBBfmcMMhcFwHHIStffpqfKn0PUDJvFBLvV7LS5RuuG6jVqICgwAEF/GwYjIiICMTExqF69OgMb0UNw34Hss88+Q//+/aFWq4tsU6VKFVy+fLlEhTkzcOBApKWlYfr06UhNTUVsbCw2bNhQqKM/EZWcEAJJSUmOwWATEhJw9Fg8zp07A+tfN/Ao3DygcPcGtFUh13nDpVY4qsT+bbJqNy/IlfkDw0Luyj/sEhLC/tcgsfkDwzomhM9Jx63cdNy4mYFDl49D5G2DKeuWI8BV8aqKmAYxiP1rcNiYmBjUr18fbm5uEh8RUcVSrgaGLSmOQ0ZUNJvNhuPHjyMuLg5bt23Drl27kZV5GwDgqtZC6RMCuU8IlL6hUPy1uKh1EldNZUEIO6yZN2BJuwJz2hVY0q5AZFyD8VYShLBDJpOhVngddOncER06dED79u05iThRESrkSP0lxUBG9P9sNhvi4+MdAWz79h3Iyc6Ci0IFVUAkFAH1oaxWC0q/ULh4+PHsFsFuMcGSfh2Wm5dhSjkHW9JJGNKuAQDCa0cwoBE5wUDmBAMZVXa5ubn4/fffsWrVamzYuLFgAAuMgjo4GqoaEZC5Kh74OYQQsBtz8i+H5WbAbjY45lAUf5tP8c4ci8JJfzMqGZmL4q95Q/+aM/Sv+UPlf80lKtd4wMXdB666qvmXk0vAmpsB07UEGK8n/BXQ8u9mrx0Rib69e6Fv375o3LgxAz1VWgxkTjCQUWWUmZmJX3/9FStXrcLGjZtgNhmhqREOZc1mUIfE3HcAE0LAnpcJc9plWG5dhzUnDbacdECfAZF3G+bsdNgspkLbyWQyqNQaqNRqqNUaaDRqaDRauLred1dWugshBEwm0193sRpgNOTPI2qxOO/U///9AL0gc/OGq7s3XKtUz78s7R0EueL+7lS/E9AMV47CfOkgLHlZCAgKxoB+fdG3b1+0bNmS82RSpcJA5gQDGVUW6enpWLNmDVauWoWtW7bCarVAExgJVXgraCNaQVGleJeT7BYjLLeu5fclunkFtvSrsN66AnNuFgBApdbAPyAQQUEBCA4MLDBBtb+/P/z9/eHp6QmNRgOlUsmzJBKy2WwwGo3Q6/W4detWocngk5OTHXfK3ryRAiEEZDI51D4BkHmHQHGn/6BfGFw9/SCT3TtUCbsNxmsJ0J/bA/OFfTDnZMC3WnX079sH/fr1Q/v27RnOqMJjIHOCgYwqMiEE9u7di/nzF2DFypWwWizQBEdBVbsltLVbwtXD5577sJv0MCWehPFaAqxJJ2BIvuDoxB0SVguNYhsgJib/Trvo6GjUrFmTf1AroNzcXJw8edJxh+2x4/GIj4933OSh0LpDGRgFZVD+ZW6Fb+g9A5qw22BKPgP92fxwZsq8gaCQUIwf+zxGjhwJPz+/h3FoRA8dA5kTDGRUEWVnZ+OHH37AvPkLcPrkCair+kMd0w26qE5wcfO667ZFBTC/6jXQpVNHtGvXDo0aNUL9+vWh1Wof0hHRo0gIgZSUFMTHx2P//v3YsnUb9u3bB4vZdN8BTQgBU9IZ5B7fAOPZXZAJO/r27YuxY59Hu3bteCaVKhQGMicYyKgiOXr0KBYuXIglP/wAo9EEbXgzuMV2hzo09q5/DG15t6E/vw/Gc3tguBYPYbM5AliHDh3QoUMHhIeH848i3ZPRaMT+/fsRFxdXIKAp3atCWasZtHVaQx0cDZlL0f0EbYYc5J3YAkP8BhhvJaJ2RCQmjBuLYcOGlckA40QPGwOZEwxkVN4JIbB582bMnPUO9uzeBZWnD9RRXaFr0BWu7kVfkrRmp0F/bg9M5/dBf/0E5HI52rVth379+qJr164MYFQqjEYj9u3bh99++w3LV65C4rWrUGg9oKzZDNqIVtCENizyBhIhBIzX4pF37A/oz++FUqHE2Oefw6uvvgp/f/+HfCREpYeBzAkGMiqvhBBYv3493p45C4cPHYTGvw50zftBE94csiKmHrLps5B3YgtM53ZDn3QWrq4KdO7SGf379UPPnj3h43PvPmVED0oIgaNHj2L16tVYvnIVLp4/B1e1FqqaTeFWrwPUYY2K/t7NvY2co78j7+ivkFnNePbZZ/D6668jJCTkIR8FUckxkDnBQEbljd1ux5o1azBz1jtIiD8ObVB96FoOhDq0odMzWkIImBJPIvfYHzCc2wMXmQyPP94d/fr1wxNPPMFLQCQJIQROnTqF1atXY9nyFThz6iRUXtWgie4GXfS/4KJz3tfRbspDzpH1yDu8DnZTHoYPG4Y333wTtWrVeshHQPTgGMicYCCj8sJms2H58uWY9c67OHvmNLRhsXBvMQCqoGinQcxuykPuia0wxm+E4eYVhNashQnjxmL48OE8E0aPFCEEDhw4gIULF+LHZT/BYrVAW6cVdA26QxVcxPe32YDcY38g79AaWPXZ+Pegf2PatKmIiIiQ4AiI7g8DmRMMZFQe7Nq1CxMmvoDjx45CW6sJPFoOhCqgrtO25rQryDn0CwxndkDYLOjVsyfGjh2LTp06cTgKeuTdvn0b//vf/zBv/gJcOHcWap9AaGK6QxfzL8hVhe/qtVtMyI3fBP3B1bDk3saE8ePx9ttvw8vr7ncTE0mJgcwJBjJ6lF2/fh2vvfYafvrpJ2j868Cj42ioA50HMVPqBeTs/Ql55/ahRkAgxj3/HEaNGsXOz1QuCSGwY8cOLFiwEKtXr4ZMqYG28VPwaPwk5Cq3wu2tFmQfXofcvcuh02rw/nvvYvTo0Zz1gR5JDGROMJDRo0iv1+Pjjz/G+7M/gFCo4d52ONyiOjkdusKUdBrZe5dDf/EQwmqFY/q0qRg8eDAUigefe5LoUXLt2jXMmTMHX//nGwgXBdxie8C9aU+4aAr/zrbl3kbmju+Rm/An6kdF48svPkfHjh0lqJqoaAxkTjCQ0aNECIGVK1di8suvICUlBbomPeHZcmChSzVCCJiuJyBn73LorxxHRGRdzJj+FgYMGAAXF+d3qRGVdykpKfj4448xf8FCWO2ANrY7PJr2hotblUJtTSnnkLX1PzAknkHvPn3wyccfIyws7OEXTeQEA5kTDGT0qDh+/DjGT5iI3bt2wq12c3h2GAVF1YBC7UypF5Ad91/oryYgOqYBZkx/C71792b/MKo00tLS8Nlnn+HzL7+EyWyBW6Mn4dligNMPLnmn4pCz43sIYw5ee/UVTJkyBW5uhS95Ej1MDGROMJCR1G7duoWpU6fi22+/hdI7AB4dnoWmZuNC7Wx5t5G5YwlyEzYjMrIuPvrwAzzxxBMcvJUqrYyMDHz22Wf4aM7HgMoN7m2Hwa1+x0KX9v+vvbuPqqrO9zj+OTycA8iDTyjyJJKmmSKVI8lMVyyLymvZNWuZudSZ66RZXseH0so0K61sGm/m2LrrTul4ZxrLmVzVWGmOLlNIUwdF8wkTURA0VA6PBzhn3z9MRuWAVMhP4P1aa6/Y5+yz/W52Gz789u/3257KchV9tVolX3+oTuEd9dvXF2nUqFFcOzCGQOYFgQymVFVVadmyZXpuzvMqr3IrZOAohdw8tNYjZSx3lYp3fqzi9FUKcvhrwcsv6bHHHqOzMvC9Y8eOaebMmfrggw8UGNVTYbf/Wo7I2tNfVJ3LV9HGP6j0ULpuHZispW8t0c0332ygYrR2BDIvCGQw4YsvvtATT07RwYMHFJxwl9reNsZrP5iyI1+reOP/qvJcvh6fNEnz5s1Thw4dmr5goBnYvHmznnhyijL37FbwjYMVNmic/EJqXy/l2RlybvxfuU4f0y9/+UstWLBAnTp1MlAxWisCmRcEMjSlHTt2aO68eVr7978rKKa3Qm//tRwR3WttV1V4XOc2/kFlR3ZoUMpgvbXkTfXp08dAxUDz4na79c477+jpWbNVXFqm4KSRCv3ZcNn87JdsZ3ncKv7nWpVs/ZMcfj6aOWO6pkyZwpMr0CQIZF4QyNAUtm7dqvkvvqh1n3+ugI7RCh44SkE3/FutPiweV6nObX1Ppbs+VlR0jP77d29o+PDh9HUBfqBz585p/vz5enPJEvmFhit00C8V2OPWWteSu6xIRWl/UenuzxUY6NDUKVM0depUnmaBq4pA5gWBDFeLZVnatGmT5s2fr82bNimgU5yCk0YqqNcvaj1A2bI8KtnzhUq2rJSP26U5zz2radOmKSAgwFD1QMuwf/9+/dfUqVq/bp2Cut2ksNsnyN4xttZ21SVnVLz9Q5Xu/lR2P189MflxTZ8+XZ07dzZQNVo6ApkXBDI0No/Ho3Xr1umF+S/qq/Q0BXbpruCkhxR4/a1eJ3atOLFfzo3/o/K8w3rkkdF67bVXFRVVe7oLAD+OZVn65JNPNOW/purYsWMKvulehf1itHwDgmtt6y4rkvPrNSrL+Lt8LI8mPvZrzZgxQzExMQYqR0tFIPOCQIbGUlhYqOXLl2vpsrd19EiWgqJ6qc3AhxUY39/rLcfqogIVfblSJfs2KfGmm7X0rSVKTk42UDnQOrhcLi1evFjzX3xRVfJTyM9HKzjhTtl8az/Vwl1RouIdH6ls10dyu8o09N//XZMff1x33nknc/7hJyOQeUEgw09hWZbS09O1bNnbWvX++3K73Qrs+XMFJ94jR/SNXoNY1dk8OdPfV9k3G9WuXXu9+spCjR8/nh/yQBM5efKknn56llau/KMcbTupzc9GnA9ml3X8lySPq0yl32xS+e5PVV5wVDFd4zR50kSNHz+ekZn40QhkXhDI8GM4nU796U9/0tLfL9O+vZkKaN9FAQmpCu4zxOv0FZJU9d1xOb9apdJvNqtjeLhmPf2UHnvsMWYNBwzZt2+fXnrpZb3//ir5BbdXm/4PKDgxVT7+tftuWpalyrwDKsn4VGUHtshHlh588EFNmjRRt912GwNv8IMQyLwgkKGhzpw5o48++kgfrF6t9evWq9rtVlD3AWqTeI8C4hK99g+TpMpT38qZvkqlB9PUpUuknpk9S7/61a8UGBjYxEcAwJtDhw5pwYKFWvl/K+UXGKqg/vcrJPHeWo9iusBd7lRp5gaV7/lMFYW56hIVrYceHKERI0YoOTmZ58niighkXhDIUJ+CggKtWbNGH6xerU2bNsntdiso+gY5eiQrqNcv5BfifWi8ZXlUkb1bpf/8RKWHtymma5zmPPuMxo4dK7u99m0RAOYdPXpUCxcu1LvvLpfNHqigxHsV3C9VfqHhXre3LEuu43tVdnCrKrO+ksv5nTqEd9LIEf+hESNGaNCgQfL3r90/DSCQeUEgw8Wqq6u1a9cubdq0SR9/8nelbd0iSzYFxvZRQI9kBV4/UH7B7ev8vLusSCWZG1SR+bkqCnPV84bemv30U3rkkUf4wQw0E8ePH9eiRYv0h3feUXlZ+fmW8H53K6DbTbWmrLnAsjyqzDuosoNpqsxKV8XZfIW1bacHht+vIUOGaNCgQYqOjm7iI8G1ikDmBYGsdbs4gP1j40Z9+eWXKistla89QI6YvgroMVBBPZLkGxRW5z4sy5Ir9xuVZHyq8oNb5WuzaeTIkXr88UlKTk6mbwnQTBUXF+vPf/6z3lr6e+3N3CNHuwgF9r1LwQl3yrdNuzo/Z1mWKguOqOzgVlUd/VrlBdmSpLhu12nIHYOVkpJCQGvlCGReEMhaD7fbrSNHjigzM1N79uzRtu3bLw1g0b1lj+6jgNi+skf0qPWQ78tVFxeq7OBWVexdp/KCbMXFX6fJkyZq3LhxzPINtCCWZWn79u16++239ef3/qLq6moFXj9QbfoMUUDXBK/TZlzMXVakiuN75crJVHXu3pqA1jUuXrcPHqTExET17dtXCQkJPKu2lSCQeUEga3kqKyuVl5d3Sfj65+492v/NN3JVlEuS7CHt5BceL3v0jQ0OYJJUXXRKZQe3ypWVrrIT++Xr66v77rtPj0+apNtvv52pK4AW7uzZs/rjH/+ot36/TFmHDso/MFj2+J8pqOfPFRB3k3z8HVfcx8UBzX3ygCpOH5OnukqSFN45Qon9EtQvIUF9+/ZVnz59FBMTo44dO9La3oK0qECWnZ2tF198Uf/4xz+Un5+vyMhIPfroo3r22Wd/UKfphnxTqqurVVlZ2VilowEsy5LL5VJ5eXmdS2FhoXJzc5Wbm6sTJ04o50Su8vLydLbwu5r9+Po75AiPla1DV9nD4+QfHid7eNd6bzdcrupMrsoOpcl1OF3leYfkb3forrvu1MgHH9SwYcPUvn3dfcoAtEyWZWnPnj3661//qlUfrNahA/vl5wiUI76/AnskK/C6/vKxN2wkteVxq/pMnipPH1Xl6WOq/i5bnsJjqjiTX7ONv79dnSK6KCY6SrEx0YqMjFRUVJQiIyMVEhKiwMDAOhc/vyv/sYnGFRTkfYTuBS0qkH322WdatWqVRo0ape7du2vv3r2aMGGCxowZo9dff73B+2nIN+WWnw3Qrh1fN1bpaCw2mxwh7eQb3EEKaief4A7yDekgvwv/Dessv7YRdXbCrYu7vFiuE/tUkZOp6uO7VV6QrYDAIA299149+OAIDR06VCEhIVfpoAA0RwcOHKgJZ5m7M87/MRibIHtM3/Ot8J3jf/DPIo+rTFWFx+UuLlR1SaHcJYVyFxfKKj0jq/SMqpyFqnaVXaUjwk+xfPlyjR07ts73W1Qg82bRokVatmyZvv322wZ/piHfFB8fH7Xpe6cCuiY0VqloCF9/+fjZZfNzyOZnl83/oq/97PJxtGnQbcYruSSA5e5Vef63kmUpMjpGd95xu+677z7dfffdV/yLBwAk6dtvv9Xf/vY3ffb559q6NU0V5WXyC2gje3Rv2aN/fEDzxlNZLquqQp4ql6zqyu8Xl6yqf30ty9MIR4WGcn65Uv/5yAgtXbq07m0aGMiabdtmUVHRFW8fuVwuuVyumnWn09mgfdu7XK82vVN+Snm4BrjLnao6na3K08dUdeqo3KeP1ASwqJhYDblrsFJSnlNKSori4uJMlwugGYqPj9eMGTM0Y8YMVVZWaseOHTUjubdu/YvObXpHfgFBskfeIL/wbrJ3Ot+dwr991BUHCFzOxx4o2QPFVLTXjrKdaxptX80ykGVlZWnJkiVXvF25cOFCvfDCC01UFUywPG65y4rkLi5UVeFxVZ3OVvV3x+QuPCZX0fn+Zf7+dvXsdYP6p96mQYMIYACuDrvdruTkZCUnJ+uZZ565JKBt2bJFGbu/0sltqyVJPr5+CgiPkdrHyh7eTfbwrvILi5BvSIc6nxqAls1oIJs1a5ZeffXVerfZv3+/evXqVbOem5uru+++WyNHjtSECRPq/ezs2bM1bdq0mnWn06mYmJh6P2NZlqxqlzxVFQ04AjQKS7I81bJqmuFdF319ft1TWS53caHcJWfkLimUSs/IXXJGlcVnZHncNbuKio5VYr++6tcvtWZoeY8ePZioFUCTuzigXXD27Fnt3btXe/bsUWZmpjJ271HmztU6V1JSs42fI0j+oR1kC2onW3AH+X7fV9Y3KEw2f8el3Tn8HN938TjfzUOM/m5ajdjry2gfstOnT6uwsLDebeLj42tGUubl5SklJUW33nqrli9f/oOnHWjIfdygNsEqLyv9QftF0wgNa6sukZGKjYlWdFRUzaijqO+/7t69u8LC6p7UFQCuRZZl6dixY8rJyVFu7vkR5BdGleccP6ETuXkqOJmnqipmALgWzZ49WwsWLKjz/WbRhyw8PFzh4d6fG3a53NxcDR48WLfccovefffdqzYH1KGDB7R58+arsm/UzW631wzbDgoKqjWUOzg4WAEBAabLBIBGZ7PZFBcXV29XCsuy5HQ665waqKysTOXl5fJ46NTflGw2mx544IHG2VdzGGWZm5urlJQUde3aVStWrJCv77+6NEZERDR4P0wMCwAAmlKzaCFrqPXr1ysrK0tZWVm1ngfWDPIkAABAvZpF779x48ad72zvZQEAAGjumkUgAwAAaMkIZAAAAIYRyAAAAAwjkAEAABhGIAMAADCMQAYAAGAYgQwAAMAwAhkAAIBhBDIAAADDCGQAAACGEcgAAAAMI5ABAAAYRiADAAAwjEAGAABgGIEMAADAMAIZAACAYQQyAAAAwwhkAAAAhhHIAAAADCOQAQAAGEYgAwAAMIxABgAAYBiBDAAAwDACGQAAgGEEMgAAAMMIZAAAAIYRyAAAAAwjkAEAABhGIAMAADCMQAYAAGAYgQwAAMCwZhfIXC6XEhMTZbPZlJGRYbocAACAn6zZBbKnnnpKkZGRpssAAABoNM0qkH366adat26dXn/9ddOlAAAANBo/0wU0VEFBgSZMmKA1a9YoKCjIdDkAAACNplkEMsuyNG7cOE2cOFH9+/dXdnZ2gz7ncrnkcrlq1p1O51WqEAAA4Mczesty1qxZstls9S4HDhzQkiVLVFxcrNmzZ/+g/S9cuFBhYWE1S0xMzFU6EgAAgB/PZlmWZeofP336tAoLC+vdJj4+Xg899JA+/vhj2Wy2mtfdbrd8fX01evRorVixwutnvbWQxcTEqKioSKGhoY1zEAAAAHVwOp0KCwu7YvYwGsgaKicn55LbjXl5eUpNTdXq1auVlJSk6OjoBu2nod8UAACAxtDQ7NEs+pDFxsZesh4cHCxJuu666xocxgAAAK5VzWraCwAAgJaoWbSQXS4uLk7N4E4rAABAg9BCBgAAYBiBDAAAwDACGQAAgGEEMgAAAMMIZAAAAIYRyAAAAAwjkAEAABhGIAMAADCMQAYAAGAYgQwAAMAwAhkAAIBhBDIAAADDCGQAAACGEcgAAAAM8zNdQFOyLEuS5HQ6DVcCAABagwuZ40IGqUurCmTFxcWSpJiYGMOVAACA1qS4uFhhYWF1vm+zrhTZWhCPx6O8vDyFhITIZrOZLueqcjqdiomJ0fHjxxUaGmq6HNSB89Q8cJ6aB85T89DazpNlWSouLlZkZKR8fOruKdaqWsh8fHwUHR1tuowmFRoa2ir+h2/uOE/NA+epeeA8NQ+t6TzV1zJ2AZ36AQAADCOQAQAAGEYga6EcDofmzp0rh8NhuhTUg/PUPHCemgfOU/PAefKuVXXqBwAAuBbRQgYAAGAYgQwAAMAwAhkAAIBhBLIW6OWXX1ZycrKCgoLUtm1br9vk5ORo6NChCgoKUqdOnTRz5kxVV1c3baGoJS4uTjab7ZLllVdeMV1Wq7d06VLFxcUpICBASUlJ2r59u+mScJF58+bVum569epluqxWb/PmzRo2bJgiIyNls9m0Zs2aS963LEvPP/+8unTposDAQA0ZMkSHDx82U+w1gEDWAlVWVmrkyJGaNGmS1/fdbreGDh2qyspKpaWlacWKFVq+fLmef/75Jq4U3syfP18nT56sWZ588knTJbVqq1at0rRp0zR37lzt2rVL/fr1U2pqqk6dOmW6NFzkxhtvvOS62bJli+mSWr3S0lL169dPS5cu9fr+a6+9pjfffFNvv/22tm3bpjZt2ig1NVUVFRVNXOk1wkKL9e6771phYWG1Xl+7dq3l4+Nj5efn17y2bNkyKzQ01HK5XE1YIS7XtWtX63e/+53pMnCRAQMGWJMnT65Zd7vdVmRkpLVw4UKDVeFic+fOtfr162e6DNRDkvXhhx/WrHs8HisiIsJatGhRzWvnzp2zHA6H9d577xmo0DxayFqh9PR09e3bV507d655LTU1VU6nU/v27TNYGSTplVdeUYcOHXTTTTdp0aJF3Eo2qLKyUjt37tSQIUNqXvPx8dGQIUOUnp5usDJc7vDhw4qMjFR8fLxGjx6tnJwc0yWhHkePHlV+fv4l11ZYWJiSkpJa7bXVqp5lifPy8/MvCWOSatbz8/NNlITvTZkyRTfffLPat2+vtLQ0zZ49WydPntQbb7xhurRW6bvvvpPb7fZ6vRw4cMBQVbhcUlKSli9frp49e+rkyZN64YUXdNttt2nv3r0KCQkxXR68uPC7xtu11Vp/D9FC1kzMmjWrVqfVyxd+QVybfsi5mzZtmlJSUpSQkKCJEyfqt7/9rZYsWSKXy2X4KIBr1z333KORI0cqISFBqampWrt2rc6dO6f333/fdGlAg9FC1kxMnz5d48aNq3eb+Pj4Bu0rIiKi1iixgoKCmvfQuH7KuUtKSlJ1dbWys7PVs2fPq1Ad6tOxY0f5+vrWXB8XFBQUcK1cw9q2bavrr79eWVlZpktBHS5cPwUFBerSpUvN6wUFBUpMTDRUlVkEsmYiPDxc4eHhjbKvgQMH6uWXX9apU6fUqVMnSdL69esVGhqq3r17N8q/gX/5KecuIyNDPj4+NecJTctut+uWW27Rhg0bNHz4cEmSx+PRhg0b9MQTT5gtDnUqKSnRkSNHNGbMGNOloA7dunVTRESENmzYUBPAnE6ntm3bVucMAS0dgawFysnJ0ZkzZ5STkyO3262MjAxJUvfu3RUcHKy77rpLvXv31pgxY/Taa68pPz9fzz33nCZPnszDXg1KT0/Xtm3bNHjwYIWEhCg9PV2/+c1v9Oijj6pdu3amy2u1pk2bprFjx6p///4aMGCAFi9erNLSUo0fP950afjejBkzNGzYMHXt2lV5eXmaO3eufH19NWrUKNOltWolJSWXtFIePXpUGRkZat++vWJjYzV16lS99NJL6tGjh7p166Y5c+YoMjKy5o+fVsf0ME80vrFjx1qSai0bN26s2SY7O9u65557rMDAQKtjx47W9OnTraqqKnNFw9q5c6eVlJRkhYWFWQEBAdYNN9xgLViwwKqoqDBdWqu3ZMkSKzY21rLb7daAAQOsr776ynRJuMjDDz9sdenSxbLb7VZUVJT18MMPW1lZWabLavU2btzo9XfR2LFjLcs6P/XFnDlzrM6dO1sOh8O64447rIMHD5ot2iCbZVmWqTAIAAAARlkCAAAYRyADAAAwjEAGAABgGIEMAADAMAIZAACAYQQyAAAAwwhkAAAAhhHIAAAADCOQAQAAGEYgAwAAMIxABgAAYBiBDADqcPr0aUVERGjBggU1r6Wlpclut2vDhg0GKwPQ0vBwcQCox9q1azV8+HClpaWpZ8+eSkxM1P3336833njDdGkAWhACGQBcweTJk/XFF1+of//+yszM1Ndffy2Hw2G6LAAtCIEMAK6gvLxcffr00fHjx7Vz50717dvXdEkAWhj6kAHAFRw5ckR5eXnyeDzKzs42XQ6AFogWMgCoR2VlpQYMGKDExET17NlTixcvVmZmpjp16mS6NAAtCIEMAOoxc+ZMrV69Wrt371ZwcLAGDRqksLAwffLJJ6ZLA9CCcMsSAOqwadMmLV68WCtXrlRoaKh8fHy0cuVKffnll1q2bJnp8gC0ILSQAQAAGEYLGQAAgGEEMgAAAMMIZAAAAIYRyAAAAAwjkAEAABhGIAMAADCMQAYAAGAYgQwAAMAwAhkAAIBhBDIAAADDCGQAAACGEcgAAAAM+39UA5v4an7SzQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(2, 1, figsize=(15, 6), tight_layout=True)\n",
    "arms_geo.plot(z=0.0, ax=ax[0])\n",
    "arms_geo.plot(z=wg_height, ax=ax[1])\n",
    "\n",
    "ax[0].set_ylim(-5, 5)\n",
    "_ = ax[1].set_ylim(-5, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up Structures\n",
    "\n",
    "To make use of these new geometries, we need to load them into a tidy3d.Simulation as td.Structures with material properties.\n",
    "\n",
    "We'll define the substrate and waveguide mediums and then link them up with the Polyslabs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Permittivity of waveguide and substrate\n",
    "wg_n = 3.48\n",
    "sub_n = 1.45\n",
    "medium_wg = td.Medium(permittivity=wg_n**2)\n",
    "medium_sub = td.Medium(permittivity=sub_n**2)\n",
    "\n",
    "# Substrate\n",
    "substrate = td.Structure(geometry=substrate_geo, medium=medium_sub)\n",
    "\n",
    "# Waveguides\n",
    "arms = td.Structure(geometry=arms_geo, medium=medium_wg)\n",
    "\n",
    "structures = [substrate, arms]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up Simulation\n",
    "\n",
    "Now let's set up the rest of the Simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Spacing between waveguides and PML\n",
    "pml_spacing = 1.0\n",
    "\n",
    "# Simulation size\n",
    "sim_size = list(arms_geo.bounding_box.size)\n",
    "sim_size[0] -= 4 * pml_spacing\n",
    "sim_size[1] += 2 * pml_spacing\n",
    "sim_size[2] = wg_height + 2 * pml_spacing\n",
    "\n",
    "sim_center = list(arms_geo.bounding_box.center)\n",
    "sim_center[2] = 0\n",
    "\n",
    "# grid size in each direction\n",
    "dl = 0.020\n",
    "\n",
    "### Initialize and visualize simulation ###\n",
    "sim = td.Simulation(\n",
    "    size=sim_size,\n",
    "    center=sim_center,\n",
    "    grid_spec=td.GridSpec.uniform(dl=dl),\n",
    "    structures=structures,\n",
    "    run_time=2e-12,\n",
    "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: `Tidy3D` is warning us that our Simulation does not contain sources. In this case, since we are using the simulation as a demonstration and are not running any simulations, we may safely ignore this warning throughout this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Simulation Geometry\n",
    "\n",
    "Let's take a look at the simulation all together with the PolySlabs added. Here the angle of the sidewall deviating from the vertical direction is 30 degree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(17, 5))\n",
    "\n",
    "sim.plot(z=wg_height / 2, lw=1, edgecolor=\"k\", ax=ax1)\n",
    "sim.plot(x=0.1, lw=1, edgecolor=\"k\", ax=ax2)\n",
    "\n",
    "ax2.set_xlim([-3, 3])\n",
    "_ = ax2.set_ylim([-1, 1])"
   ]
  }
 ],
 "metadata": {
  "description": "This notebook demonstrates how to import geometries from a gds file to Tidy3D for FDTD simulations.",
  "feature_imag": "",
  "feature_image": "N/A",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "gds, import, 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": "3.11.0"
  },
  "title": "GDS File Import in Tidy3D | Flexcompute"
 },
 "nbformat": 4,
 "nbformat_minor": 4
}