CharacteristicImpedanceCalculator.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Computing the characteristic impedance of transmission lines using Tidy3D\n",
    "\n",
    "In this tutorial notebook, we explore the use of Tidy3D to compute the characteristic impedance of transmission lines. The characteristic impedance is a fundamental parameter in the design of transmission lines that affects signal integrity and power distribution. This notebook also serves to introduce [LumpedElements](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/lumped_elements.html) and path integrals, which are components that are used to set up [LumpedPorts](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.smatrix.LumpedPort.html#tidy3d.plugins.smatrix.LumpedPort) in the [smatrix](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/plugins/smatrix.html) plugin.\n",
    "\n",
    "The microstrip, a type of transmission line with a conductor strip separated from a ground plane by a dielectric layer, will serve as our example for the study. We will demonstrate how to use the post processing tools available in the `microwave` plugin to determine the voltage and current distribution along the microstrip both in the time and frequency domains.\n",
    "\n",
    "The notebook is structured as follows:\n",
    "\n",
    "1. Set up structures and simulation parameters for a microstrip transmission line.\n",
    "\n",
    "2. Computation of the voltage and current using path integrals.\n",
    "\n",
    "3. Calculation of the characteristic impedance.\n",
    "\n",
    "4. Final simulation of a microstrip terminated by a matched load.\n",
    "\n",
    "By the end of this notebook, you will have a clear understanding of how to use Tidy3D for simulating and analyzing the characteristic impedance of transmission lines, as well as many of the building blocks used within microwave simulations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# standard python imports\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "# tidy3D imports\n",
    "import tidy3d as td\n",
    "from tidy3d import web\n",
    "import tidy3d.plugins.microwave as microwave\n",
    "\n",
    "# We set the logging level to \"ERROR\". Otherwise there are numerous warnings due to the proximity of the structure to PML boundaries.\n",
    "td.config.logging_level = \"ERROR\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up a Microstrip Simulation\n",
    "\n",
    "The microstrip transmission line that we wish to characterize is presented below. It is characterized by a `width`, `height`, `thickness`, and substrate electric permittivity $\\epsilon_r$. For simplicity, conductors are assumed to be perfect electric conductors, and the microstrip will be modeled with an infinitesimally small `thickness`, which we will implement using a `Medium2D`.\n",
    "\n",
    "<img src=\"img/microstrip_geometry.svg\" width=800 alt=\"Schematic of the microstrip\">\n",
    "\n",
    "We can also simplify the simulation by using a PEC boundary condition at the $z$ minimum boundary, which will model the effect of the ground plane."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Frequency range of interest is from 0.1 GHz to 10 GHz\n",
    "freq_start = 1e8\n",
    "freq_stop = 100e8\n",
    "freq0 = (freq_stop) / 2\n",
    "fwidth = freq_stop - freq_start\n",
    "wavelength0 = td.C_0 / freq0\n",
    "freqs = np.linspace(freq_start, freq_stop, 100)\n",
    "\n",
    "# Fields should decay rapidly in this notebook, since we are using PMLs.\n",
    "# Later we will use a matched load that also quickly dissipates power.\n",
    "run_time = 60 / fwidth\n",
    "\n",
    "gaussian = td.GaussianPulse(freq0=freq0, fwidth=fwidth)\n",
    "\n",
    "# Default units in Tidy3D are microns\n",
    "mm = 1e3\n",
    "# Microstrip parameters (thickness is assumed 0)\n",
    "length = 40 * mm\n",
    "width = 3 * mm\n",
    "height = 1 * mm\n",
    "thickness = 0.0\n",
    "\n",
    "# Size and offset of simulation, so that the microstrip will terminate inside the PML\n",
    "sim_size = (length + wavelength0 / 2, 3 * width + wavelength0 / 2, height + wavelength0 / 2)\n",
    "sim_center = (-sim_size[0] / 2 + length / 4, 0, sim_size[2] / 2)\n",
    "\n",
    "# Conductors are assumed to be perfect electric conductors\n",
    "# Substrate has permittivity of 4.4\n",
    "air = td.Medium()\n",
    "pec = td.PECMedium()\n",
    "pec2d = td.Medium2D(ss=pec, tt=pec)\n",
    "diel = td.Medium(permittivity=4.4)\n",
    "\n",
    "# Create a structure representing a microstrip along the x axis\n",
    "strip_center = (0, 0, height+thickness/2)\n",
    "strip = td.Structure(\n",
    "    geometry=td.Box(\n",
    "        center=strip_center,\n",
    "        size=[length, width, thickness],\n",
    "    ),\n",
    "    medium=pec2d,\n",
    ")\n",
    "\n",
    "# Create a structure representing the substrate\n",
    "substrate = td.Structure(\n",
    "    geometry=td.Box(\n",
    "        center=[0, 0, 0],\n",
    "        size=[td.inf, td.inf, 2*height],\n",
    "    ),\n",
    "    medium=diel,\n",
    ")\n",
    "\n",
    "# Use a refined mesh near the microstrip, where the fields are mostly contained\n",
    "dl = width / 50\n",
    "mesh_overrides = [\n",
    "    td.MeshOverrideStructure(\n",
    "        geometry=td.Box(\n",
    "            center=strip_center,\n",
    "            size=[length + 2 * width, 4 * width, 4 * height],\n",
    "        ),\n",
    "        dl=[dl, dl, dl],\n",
    "    )\n",
    "]\n",
    "\n",
    "# Place an excitation at the left (x min) side of the microstrip\n",
    "uniform_current = td.UniformCurrentSource(\n",
    "    center=(-length / 2, 0, height / 2),\n",
    "    size=(0, width, height),\n",
    "    source_time=gaussian,\n",
    "    polarization=\"Ez\",\n",
    "    name=\"current\",\n",
    "    interpolate=True,\n",
    "    confine_to_bounds=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize monitors and finalize simulation\n",
    "\n",
    "The voltage and current along the microstrip can be computed by exciting the transmission line using a `UniformCurrentSource`. The source will excite the transmission line mode, which we can then analyze using field monitors in the transverse plane.\n",
    "\n",
    "In the transverse plane (yz), the fields of the microstrip line are quasi-TEM, which allows for unique definitions of voltage and current [1].\n",
    "\n",
    "The voltage can be computed by defining a path from the ground to the strip and applying the equation\n",
    "$$\n",
    "V_{ab} = V_{a} - V_{b} = - \\int^a_b  \\mathbf{E} \\cdot \\mathbf{dl}.\n",
    "$$\n",
    "\n",
    "Similarly, to determine the current flowing in the strip along the x axis, we may use Ampère's circuital law\n",
    "$$\n",
    "I = \\oint_C  \\mathbf{H} \\cdot \\mathbf{dl},\n",
    "$$\n",
    "where the path is a closed contour surrounding the strip.\n",
    "\n",
    "The integration paths are shown in the figure below:\n",
    "\n",
    "<img src=\"img/microstrip_paths.svg\" width=800 alt=\"Schematic of the microstrip with path integrals\">\n",
    "\n",
    "In order to perform these computations, we need to set up the `Tidy3D` simulation to record the electric and magnetic fields at these locations. One option would be to use one monitor that is large enough to encompass both integration paths. We can also choose two monitors that will be independently used for the voltage and current computations.\n",
    "\n",
    "In the next cell, monitors for recording the time domain and frequency domain fields are created. An additional monitor is added for plotting the field along the direction of propagation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define all monitors in the simulation\n",
    "\n",
    "# Monitor for plotting the field along the axis of propagation\n",
    "propagate_mon = td.FieldMonitor(\n",
    "    center=[0, 0, height / 2],\n",
    "    size=[length + 4 * width, 4 * width, 0],\n",
    "    freqs=[freq0],\n",
    "    name=\"propagate\",\n",
    ")\n",
    "\n",
    "# Frequency domain monitor for computing both voltage and current\n",
    "freq_yz = td.FieldMonitor(\n",
    "    center=[0, 0, 2 * height],\n",
    "    size=[0, 4 * width, 4 * height],\n",
    "    freqs=freqs,\n",
    "    name=\"freq_yz\",\n",
    ")\n",
    "\n",
    "# Time monitor for computing voltage\n",
    "time_voltage_yz = td.FieldTimeMonitor(\n",
    "    center=[0, 0, height / 2],\n",
    "    size=[0, 0, height],\n",
    "    fields=[\"Ez\"],\n",
    "    interval=40,\n",
    "    name=\"time_voltage_yz\",\n",
    ")\n",
    "\n",
    "# Time monitor for computing current\n",
    "time_current_yz = td.FieldTimeMonitor(\n",
    "    center=strip_center,\n",
    "    size=[0, width + 500, thickness + 500],\n",
    "    fields=[\"Hy\", \"Hz\"],\n",
    "    interval=40,\n",
    "    name=\"time_current_yz\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, the simulation is set up using the created components. Before running the simulation we also plot the structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1100x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using PMLs on every boundary, except for the minus z boundary\n",
    "boundary_spec = td.BoundarySpec(\n",
    "    x=td.Boundary.pml(),\n",
    "    y=td.Boundary.pml(),\n",
    "    z=td.Boundary(plus=td.PML(), minus=td.PECBoundary()),\n",
    ")\n",
    "\n",
    "# Simulation\n",
    "sim = td.Simulation(\n",
    "    center=sim_center,\n",
    "    size=sim_size,\n",
    "    grid_spec=td.GridSpec.auto(\n",
    "        min_steps_per_wvl=20,\n",
    "        wavelength=td.C_0 / freq_stop,\n",
    "        override_structures=mesh_overrides,\n",
    "    ),\n",
    "    structures=[substrate, strip],\n",
    "    sources=[uniform_current],\n",
    "    monitors=[propagate_mon, freq_yz, time_voltage_yz, time_current_yz],\n",
    "    run_time=run_time,\n",
    "    boundary_spec=boundary_spec,\n",
    ")\n",
    "\n",
    "# Formatter to aid plotting in units of millimeters\n",
    "formatter = ticker.FuncFormatter(lambda y, _: f\"{(1e-3) * y}\")\n",
    "\n",
    "\n",
    "def update_axis_with_format(axis, format, xlbl, ylbl):\n",
    "    axis.set_xlabel(xlbl)\n",
    "    axis.set_ylabel(ylbl)\n",
    "    axis.xaxis.set_major_formatter(format)\n",
    "    axis.yaxis.set_major_formatter(format)\n",
    "\n",
    "\n",
    "# Plot the current simulation setup and scale to millimeters\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(11, 4))\n",
    "\n",
    "sim.plot(z=height, ax=ax1)\n",
    "sim.plot(x=0, ax=ax2, vlim=[0, 3 * mm], hlim=[-5 * mm, 5 * mm])\n",
    "update_axis_with_format(ax1, formatter, \"x (mm)\", \"y (mm)\")\n",
    "ax1.set_title(\"Top view of microstrip\")\n",
    "update_axis_with_format(ax2, formatter, \"y (mm)\", \"z (mm)\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running the simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:23:11 EDT </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'impedance_calc'</span> with task_id                         \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-ab7b2552-41e4-4b43-b1d8-1a7d1ee21bed'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:23:11 EDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'impedance_calc'\u001b[0m with task_id                         \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-ab7b2552-41e4-4b43-b1d8-1a7d1ee21bed'\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-ab7b2552-41e4-4b43-b1d8-1a7d1ee21bed\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab7b2552-41e</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab7b2552-41e4-4b43-b1d8-1a7d1ee21bed\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">4-4b43-b1d8-1a7d1ee21bed'</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=978657;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab7b2552-41e4-4b43-b1d8-1a7d1ee21bed\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=374674;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab7b2552-41e4-4b43-b1d8-1a7d1ee21bed\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=978657;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab7b2552-41e4-4b43-b1d8-1a7d1ee21bed\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=380278;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab7b2552-41e4-4b43-b1d8-1a7d1ee21bed\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=978657;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab7b2552-41e4-4b43-b1d8-1a7d1ee21bed\u001b\\\u001b[32m-ab7b2552-41e\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=978657;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab7b2552-41e4-4b43-b1d8-1a7d1ee21bed\u001b\\\u001b[32m4-4b43-b1d8-1a7d1ee21bed'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5b2a2b2361a4488ca29656791597df84",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:23:13 EDT </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:23:13 EDT\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\">11:23:16 EDT </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:23:16 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:23:17 EDT </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.252</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;36m11:23:17 EDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.252\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": "df2b94114216438dbec0456f5c06b685",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:23:27 EDT </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8</span>%, exiting.                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:23:27 EDT\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m8\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\">11:23:29 EDT </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:23:29 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:23:30 EDT </span>View simulation result at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab7b2552-41e4-4b43-b1d8-1a7d1ee21bed\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab7b2552-41e</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab7b2552-41e4-4b43-b1d8-1a7d1ee21bed\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">4-4b43-b1d8-1a7d1ee21bed'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:23:30 EDT\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=513813;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab7b2552-41e4-4b43-b1d8-1a7d1ee21bed\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=328339;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab7b2552-41e4-4b43-b1d8-1a7d1ee21bed\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=513813;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab7b2552-41e4-4b43-b1d8-1a7d1ee21bed\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=636013;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab7b2552-41e4-4b43-b1d8-1a7d1ee21bed\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=513813;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab7b2552-41e4-4b43-b1d8-1a7d1ee21bed\u001b\\\u001b[4;34m-ab7b2552-41e\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=513813;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ab7b2552-41e4-4b43-b1d8-1a7d1ee21bed\u001b\\\u001b[4;34m4-4b43-b1d8-1a7d1ee21bed'\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": "bcf3a798d31e4d05ad942630cabf91aa",
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       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:23:32 EDT </span>loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:23:32 EDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data = web.run(sim, \"impedance_calc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we plot the electric field along the microstrip demonstrating that the transmission line mode has been excited properly. The fields in the transverse plane are presented in the second set of plots. Although the longitudinal field $\\mathrm{E}_x$ is not zero, it is two orders of magnitude smaller than the dominant transverse field, which validates the quasi-TEM approximation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Ez field in the xy plane\n",
    "f, ax1 = plt.subplots(1, 1, tight_layout=True, figsize=(8, 3))\n",
    "sim_data.plot_field(field_monitor_name=\"propagate\", field_name=\"Ez\", val=\"abs\", f=freq0, ax=ax1)\n",
    "update_axis_with_format(ax1, formatter, \"x (mm)\", \"y (mm)\")\n",
    "ax1.set_title(\"Cross section at z = 0.5 mm\")\n",
    "\n",
    "# Plot both the Ex and Ez fields in the xz plane\n",
    "f, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(12, 3))\n",
    "sim_data.plot_field(field_monitor_name=\"freq_yz\", field_name=\"Ex\", val=\"abs\", f=freq0, ax=ax1)\n",
    "sim_data.plot_field(field_monitor_name=\"freq_yz\", field_name=\"Ez\", val=\"abs\", f=freq0, ax=ax2)\n",
    "update_axis_with_format(ax1, formatter, \"y (mm)\", \"z (mm)\")\n",
    "update_axis_with_format(ax2, formatter, \"y (mm)\", \"z (mm)\")\n",
    "ax1.set_title(\"Cross section at x = 0.0 mm\")\n",
    "ax2.set_title(\"Cross section at x = 0.0 mm\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Domain Voltage and Current\n",
    "\n",
    "Next, we use the [VoltageIntegralAxisAligned](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.microwave.VoltageIntegralAxisAligned.html#tidy3d.plugins.microwave.VoltageIntegralAxisAligned) and [CurrentIntegralAxisAligned](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.microwave.CurrentIntegralAxisAligned.html) tools from the `microwave` plugin to compute and plot the voltage and current in the time domain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Retrieve monitor data from the completed simulation\n",
    "voltage_time_data = sim_data[\"time_voltage_yz\"]\n",
    "current_time_data = sim_data[\"time_current_yz\"]\n",
    "\n",
    "# The path for the voltage integral is defined by its center and size along a desired axis.\n",
    "voltage_path = microwave.VoltageIntegralAxisAligned(\n",
    "    center=(0, 0, height / 2),\n",
    "    size=(0, 0, height),\n",
    "    extrapolate_to_endpoints=True,  # We will explain this in the next step.\n",
    "    snap_path_to_grid=True,  # If true, the path will be snapped to the Yee grid.\n",
    "    sign=\"+\",\n",
    ")\n",
    "# Compute voltage\n",
    "voltage_time_domain = voltage_path.compute_voltage(voltage_time_data)\n",
    "\n",
    "# The path for the current integral is defined by its center and a size.\n",
    "# The contour will follow the perimeter of the rectangle defined by size.\n",
    "current_path = microwave.CurrentIntegralAxisAligned(\n",
    "    center=strip_center, size=(0, width + 400, 400), snap_contour_to_grid=True, sign=\"+\"\n",
    ")\n",
    "# Compute current\n",
    "current_time_domain = current_path.compute_current(current_time_data)\n",
    "\n",
    "# Plot the time domain voltage and current that is observed at the x=0 position of the microstrip\n",
    "f, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(10, 3))\n",
    "max_voltage = max(abs(voltage_time_domain))\n",
    "max_current = max(abs(current_time_domain))\n",
    "(voltage_time_domain / max_voltage).plot(ax=ax1)\n",
    "ax1.set_title(\"Voltage in normalized units\")\n",
    "(current_time_domain / max_current).plot(ax=ax2)\n",
    "ax2.set_title(\"Current in normalized units\")\n",
    "ax1.set_ylabel(\"Voltage (normalized units)\")\n",
    "ax2.set_ylabel(\"Current (normalized units)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the options for the `VoltageIntegralAxisAligned` is whether to `extrapolate_to_endpoints`. The reason for this option is to avoid problems with interpolation of the normal $\\mathrm{E}$ component close to metallic interfaces, where the field is discontinuous. As an example, let's first plot the electric field between the ground and microstrip close to the peak of the time signal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax1 = plt.subplots(1, 1, tight_layout=True, figsize=(10, 3))\n",
    "Ez_time_slice = voltage_time_data.Ez.sel(t=2e-10, x=0.0, y=0.0, method=\"nearest\")\n",
    "Ez_time_slice.plot(ax=ax1)\n",
    "ax1.set_title(r\"$\\mathrm{E}_z$ field at $t=2 \\times 10^{-10}$ s\")\n",
    "# Add the locations of the PEC interfaces\n",
    "ax1.axvline(x=0, color=\"k\", linestyle=\":\", label=\"PEC boundary\")\n",
    "ax1.axvline(x=height, color=\"k\", linestyle=\":\")\n",
    "update_axis_with_format(ax1, formatter, \"z (mm)\", \"field value\")\n",
    "ax1.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The discontinuity at the interface between the substrate and 2D PEC material is located at $z = 1$ mm. An accurate integration of $\\mathrm{E}_z$ should extrapolate from $z < 1$ mm, since the field should remain constant up to the PEC interface.\n",
    "\n",
    "As an example, we will be compute the voltage with `extrapolate_to_endpoints=False` and compute the relative error when compared to the original result. The error is quite small in this case, but can be worse when there are fewer Yee cells along the integration path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define the new voltage path object and compute voltage\n",
    "voltage_path_no_extrapolate = voltage_path.updated_copy(extrapolate_to_endpoints=False)\n",
    "voltage_time_domain_no_extrapolate = voltage_path_no_extrapolate.compute_voltage(voltage_time_data)\n",
    "\n",
    "# Plot the relative error between the original voltage and the new voltage where interpolation is used near PEC interface\n",
    "f, ax1 = plt.subplots(1, 1, tight_layout=True, figsize=(10, 3))\n",
    "ax1.set(xlim=[1.8e-10, 2.6e-10])\n",
    "voltage_max = max(abs(voltage_time_domain))\n",
    "error = 100 * (abs(voltage_time_domain - voltage_time_domain_no_extrapolate) / voltage_max)\n",
    "error.plot(ax=ax1)\n",
    "ax1.set_title(\"Relative error in voltage when using interpolation at PEC boundary\")\n",
    "ax1.set_ylabel(\"Relative error (%)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing the Characteristic Impedance\n",
    "\n",
    "Now, we return to the original goal of computing the characteristic impedance of the microstrip. It is more common to compute characteristic impedance in the frequency domain, which we may easily do using the [ImpedanceCalculator](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.microwave.ImpedanceCalculator.html) in the [microwave](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/plugins/microwave.html) plugin. We simply reuse the definitions of the voltage and current path integrals to create an `ImpedanceCalculator` object. Then, we pass the field data associated with the frequency domain monitor identified by its name \"freq_yz\". The output of the `compute_impedance` function will be a data array with impedance values as a function of frequency.\n",
    "\n",
    "Internally, the `ImpedanceCalculator` computes impedance using $Z_0 = \\frac{V}{I}$ [1].\n",
    "\n",
    "We plot the computed characteristic impedance below as a function of frequency, where we observe that the impedance increases with frequency. The propagation constant and characteristic impedance of microstrip transmission lines are known to depend on frequency, which is due to their quasi-TEM nature [1]. As a sanity check, we include the computed characteristic impedance resulting from a quasi-static model approximation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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58iQSExMRExODH374AeHh4fj7778xadIk/P7779i5cye8vb1vSS0uLi5wcXG5Jc/VEGq12t4lEBEREdFtrrSiGjtPFl/o0S5CdmGF1XkHqQQdgj3RI0qFxAgfdAzxhLOjzE7V0q3GP6k0ooqKClRUVEAQBPFYdXU1KioqYDQa621rNl/ab6+mpgYVFRUwGAzXbWurMWPGQC6X448//kDv3r0REhKCQYMG4a+//sK5c+fw1ltviW0lEkmdocmenp5YsmSJeP+NN95ATEwMXF1dERERgalTp6Km5tK8lIMHD6Jv375wd3eHh4cHOnfujL179wKw9Dx7enpes960tDR069YNbm5u8PT0RM+ePXHq1CksWbIEM2fOxMGDByGRSCCRSMS6PvnkE7Rr1w5ubm4IDg7GSy+9hPLycvF6o0aNgk6nEx83Y8YMAJah5nPnzhWfW6vV4oUXXoCfnx+cnZ1x11134ddff71qrRKJBF988QUeeOABuLq6onXr1tixYwdOnDiBPn36wM3NDT169EB2drbV41JTUxEZGQm5XI7Y2Fj8+9//tjqflZWFe+65B87OzmjTpg3+/PPPOs995swZDBs2DJ6envD29saQIUOQm5t7zX9bIiIiIrp5FcZabMzQ4N3/HcV9n21Bp1l/4sXv9+PfO08hu7ACEgkQH6TEC70jsPSZbjg4fSBWvdgDEwbEIDHSh6H7DsPg3YgUCgUUCgWKiorEY3PmzIFCocDYsWOt2vr6+kKhUOD06dPisc8//xwKhQKjR4+2ahsWFgaFQoFjx47dUF0lJSVYt24dXnrppTo9zf7+/njqqaewfPlyqz8YXI+7uzuWLFmCo0eP4rPPPsOXX36JTz/9VDz/1FNPISgoCHv27MG+ffswefJkODo6NujatbW1GDp0KHr37o1Dhw5hx44deP755yGRSDB8+HC89tpraNu2LfLy8pCXl4fhw4cDAKRSKebNm4e///4bS5cuxYYNG/D6668DAHr06IG5c+fCw8NDfNzEiRPrPLfZbMagQYOwbds2fPfddzh69Cjef/99yGTX/mB85513MGLECKSnpyMuLg5PPvkkXnjhBUyZMgV79+6FIAhW74HVq1fj1VdfxWuvvYYjR47ghRdewKhRo7Bx40axjocffhhyuRy7du3CwoUL8cYbb1g9Z01NDZKTk+Hu7o4tW7Zg27ZtUCgUuPfee1FdXd2gf2siIiIiahhjrQk7Txbjkz8z8WjqdrSf+QdGLd6DL7fk4GieHoIAxPgpMLJHGL54ujPSpw7Ef8f2wpRBrdE7Rg03Jw42vpPxp38HyMrKgiAIaN26db3nW7dujdLSUhQWFsLX17dB1/znP/8pfh8WFoaJEyfixx9/FIPu6dOnMWnSJMTFxQEAoqOjG1yvXq+HTqfDAw88gMjISLHGixQKBRwcHODv72/1uMsXRwsLC8OsWbPwj3/8AwsWLIBcLodSqYREIqnzuMv99ddf2L17N44dO4aYmBgAQERExHVrHjVqFIYNGwbAMhogMTERU6dORXJyMgDg1VdfxahRo8T2H330EUaOHImXXnoJADBhwgTs3LkTH330Efr27Yu//voLx48fx7p16xAYGAgAeO+99zBo0CDxGsuXL4fZbMZXX30lbiWxePFieHp6Ii0tDQMHDrxu3URERERUP5NZwNHzemzLLsK2E0XYk1sCQ431CNRgbxf0iFChR5RlQTRfd2c7VUvNHYN3I7o4rNnV1VU8NmnSJIwbNw4ODtb/1BqNBgCseqDHjBmD5557rk7v6sWhwzc7L/p6PdpyubzB11q+fDnmzZuH7OxslJeXo7a2Fh4eHuL5CRMm4Nlnn8W///1vJCUl4bHHHhND9OVOnz6NNm3aiPfffPNNvPnmmxg5ciSSk5MxYMAAJCUlYdiwYQgICLhmTX/99Rdmz56N48ePQ6/Xo7a2FgaDAZWVlVY/k2tJT09HUFCQGLobKj4+Xvzez88PANCuXTurYwaDAXq9Hh4eHjh27Bief/55q2v07NkTn332GQDg2LFjCA4OFkM3ACQmJlq1P3jwIE6cOAF3d3er4waDoc6wdiIiIiK6NkEQkFNUgW0nirDtRDF2nCyGrsp6iy+VQo4ekSr0iPRBzygVgr0b9jsmEYN3I6pvZWy5XF5voK2vraOjY73DsW92xe2oqChIJBIcO3YMDz30UJ3zx44dg1qtFuddSySSOiH98vnbO3bswFNPPYWZM2ciOTkZSqUSP/74Iz7++GOxzYwZM/Dkk0/if//7H37//XdMnz4dP/74Y53nDwwMtFqd/OICb4sXL8Yrr7yCtWvXYvny5fjnP/+JP//8E927d6/3Nebm5uKBBx7Aiy++iHfffRfe3t7YunUrRo8ejerq6gYH7xv948blP7eLvc/1HbuZefpXKi8vR+fOnfH999/XOccF44iIiIiur0BvEIP29uwi5Oms11pSODmge4Q3ekSq0DNKhRg/hfh7HZEt7Bq8U1NTkZqaKvbotm3bFtOmTROH02ZnZ2PixInYunUrjEYj7r33XvzrX/8SexTrM2PGDMycOdPqWGxsLI4fP95kr6O58/HxwYABA7BgwQKMHz/eKlzm5+fj+++/x5gxY8RjarUaeXl54v2srCxUVlaK97dv347Q0FCrBdlOnTpV53ljYmIQExOD8ePH44knnsDixYvrBG8HBwdERUXVW3fHjh3RsWNHTJkyBYmJiVi2bBm6d+8OuVwOk8lk1Xbfvn0wm834+OOPIZVali5YsWKFVZv6Hnel+Ph4nD17FpmZmTb3etuidevW2LZtG1JSUsRj27ZtE3v/W7dujTNnziAvL0/s6d+5c6fVNTp16oTly5fD19fXarQBEREREdVPb6jBrpMlF8J2EbI05Vbn5TIpOoV6omekCj2jVYhvpYQDt/iiRmDX4B0UFIT3338f0dHREAQBS5cuxZAhQ3DgwAGEhYVh4MCBaN++PTZs2AAAmDp1KgYPHoydO3eK4ao+bdu2xV9//SXev3KY951o/vz56NGjB5KTkzFr1iyr7cRiYmIwbdo0sW2/fv0wf/58JCYmwmQy4Y033rDqvY2Ojsbp06fx448/omvXrvjf//6H1atXi+erqqowadIkPProowgPD8fZs2exZ88ePPLIIw2qNScnB4sWLcKDDz6IwMBAZGRkICsrCyNGjABgmb+dk5MjDgt3d3dHVFQUampq8K9//QuDBw/Gtm3bsHDhQqvrhoWFoby8HOvXr0f79u3h6upapye8d+/euOeee/DII4/gk08+QVRUFI4fPw6JRIJ7773X5n/3q5k0aRKGDRuGjh07IikpCb/88gt+/vln8X2blJSEmJgYpKSkYM6cOdDr9VZ/6AAsC9jNmTMHQ4YMwdtvv42goCCcOnUKP//8M15//XUEBQU1Wr1EREREtyNjrQkHTmux/UQRtp4owsGzOpjMl0Z2SiTAXYFK9IjyQa8oFbqEesNFztXGqQkIzYyXl5fw1VdfCevWrROkUqmg0+nEc1qtVpBIJMKff/551cdPnz5daN++vU3PaTAYBJ1OJ97OnDkjALB67ouqqqqEo0ePClVVVTY9R3OQk5MjpKSkCH5+foJEIhEACA8//LBQUVFh1e7cuXPCwIEDBTc3NyE6Olr47bffBKVSKSxevFhsM2nSJMHHx0dQKBTC8OHDhU8//VRQKpWCIAiC0WgUHn/8cSE4OFiQy+VCYGCgMHbsWPHfbPHixWLb+uTn5wtDhw4VAgICBLlcLoSGhgrTpk0TTCaTIAiWn9cjjzwieHp6CgDEuj755BMhICBAcHFxEZKTk4Vvv/1WACCUlpaK1/7HP/4h+Pj4CACE6dOnC4IgCKGhocKnn34qtikuLhZGjRol+Pj4CM7OzsJdd90l/Prrr1etF4CwevVqq39nAMKBAwfEYxs3bqxTy4IFC4SIiAjB0dFRiImJEb799lur62ZkZAi9evUS5HK5EBMTI6xdu7bOc+Xl5QkjRowQVCqV4OTkJERERAjPPfec+N5NSUkRhgwZctXab+f3MxEREdGVTCazcPS8Tvhyc7Yw4utdQtw/fxdC3/jV6tZnzkbhzZ8PCb8dOi+UVhjtXTI1Uzqd7qqZ8EZIBMGGPaSakMlkwsqVK5GSkoIDBw4gOzsbDz30ECoqKuDk5AQAMBqNcHNzwz//+U9xD+YrzZgxA3PmzIFSqYSzszMSExMxe/ZshISEXPW56xueDgA6na7OEF6DwYCcnByEh4fD2fn2XrVw+vTp+OSTT645d5patpb0fiYiIqI70zltFbZlWXq0t2cXoajceltVlUKOnlEq9Iy0rD4e5MUF0ej69Ho9lEplvZnwRth9DPbhw4eRmJgIg8EAhUKB1atXo02bNlCr1XBzc8Mbb7yB9957D4IgYPLkyTCZTFbzj6+UkJCAJUuWIDY2Fnl5eZg5cybuvvtuHDlypM7qzxdNmTIFEyZMEO/r9XoEBwc3+mttbmbOnImwsDDs3LkT3bp1u+bwfSIiIiKi5kBXVYOdJ4uxNcsyT/tkUYXVeRdHGRIivNEryrIgWpy/OxdEI7uze/COjY1Feno6dDodVq1ahZSUFGzatAlt2rTBypUr8eKLL2LevHmQSqV44okn0KlTp2sGxMv3OY6Pj0dCQgJCQ0OxYsUKjB49ut7HODk5ib3qd5rL95YmIiIiImpuakxmHDitxdasQmw5UYSDZ7S4bJo2pBKgfbAnekWp0CtKhY4hXpA7sEOJmhe7B2+5XC6uat25c2fs2bMHn332Gb744gsMHDgQ2dnZKCoqgoODAzw9PeHv74+IiIgGX9/T0xMxMTE4ceJEU70EIiIiIiJqJIIg4ISmHFsu9GjvPFmMimrrnWkiVG7oGaVCr2gVukf4QOlSd0teoubE7sH7SmazGUaj0eqYSqUCAGzYsAEajQYPPvhgg69XXl6O7OxsPP30041aZzOZGk90U/g+JiIiouagqNyIbSeKsCWrCFuzipCvt95P29tNjh6RPrg7WoVe0Wq08nS5ypWImie7Bu8pU6Zg0KBBCAkJQVlZGZYtW4a0tDSsW7cOALB48WK0bt0aarUaO3bswKuvvorx48cjNjZWvEb//v3x0EMPYezYsQCAiRMnYvDgwQgNDcX58+cxffp0yGQyPPHEE41S88VttSorK632wya6HV3cn/3y7eKIiIiImpqhxoQ9uSXYmmUJ20fz9Fbn5Q5SdAvzRq9oy/DxNgEekEo5T5tuX3YN3hqNBiNGjEBeXh6USiXi4+Oxbt06DBgwAACQkZGBKVOmoKSkBGFhYXjrrbcwfvx4q2tcHIp+0dmzZ/HEE0+guLgYarUavXr1ws6dO6FWqxulZplMBk9PT2g0GgCAq6srF2ug244gCKisrIRGo4GnpydkMu5XSURERE1HEAQczy/DlqxCbMkqwu6cEhhrzVZt2gR4XOjRVqFrmDecHfn7CbUcNm8nZjQasWvXLpw6dQqVlZVQq9Xo2LEjwsPDm6rGW+56S8cLgoD8/HxotdpbXxxRI7q4bgL/eERERESNTVNmEHu0t2QVoajcejqpv4czekWrcHe0ZfVxleLOXOyYmie7bSe2bds2fPbZZ/jll19QU1MDpVIJFxcXlJSUwGg0IiIiAs8//zz+8Y9/XHXbrpZCIpEgICAAvr6+qKmpsXc5RDfE0dGRPd1ERETUaAw1JuzOKRF7tY/nl1mdd3GUITHSB72iLGE7ylfBP/7THaNBwfvBBx/E/v378eSTT+KPP/5Aly5drOY3nzx5Elu2bMEPP/yATz75BN9++604XLwlk8lkDC5EREREdEcSBAEZBWXYklmEzVmF2JVTgurLho9LJMBdgUrcHa3C3dFqdAr1hJMDf3emO1ODgvf999+Pn3766aoLMEVERCAiIgIpKSk4evQo8vLyGrVIIiIiIiKyv4urj2/OLMKWrEJoyqyHjwconcWg3TNKBW83uZ0qJWpebJ7jfSdo7PH8RERERES3o+paM/adKsXmrEJsySrEkXPWq487O0rRPcIH90SrcU+MCpFqDh+nlsFuc7zrU15eDrPZejVCBlUiIiIiotuTIAjILa7E5sxCbM4sxI6TxaisNlm1aRPggbtjVLgnWo0uYV4cPk7UADYH75ycHIwdOxZpaWkwGC5tbC8IAiQSCUwm0zUeTUREREREzYneUIPtJ4qxOcsSts+WVlmdVynkuDtaLW715evubKdKiW5fNgfv//u//4MgCPjmm2/g5+fHoSRERERERLcRs1nAkfM6bMooxOasQuw/rYXJfGn2qaNMgi6h3rgnxhK22wR4QCrl7/xEN8Pm4H3w4EHs27cPsbGxTVEPERERERE1Mo3egM1ZRdicWYitJ4pQUlFtdT5c5YZ7olW4J0aN7hE+cHO6qRmpRHQFm/+L6tq1K86cOcPgTURERETUTFXXmrH3VAk2ZRZic2YRjuVZL4qmcHJAj0gf3BOjRu8YNYK9Xe1UKdGdwebg/dVXX+Ef//gHzp07h7vuuqvOFmPx8fGNVhwRERERETXM6eJKbMrUYFNmIbZn110UrV0rJe6JUaF3jC86hnjCUSa1U6VEdx6bg3dhYSGys7MxatQo8ZhEIuHiakREREREt1BldS12nizGpoxCbMosRG5xpdV5lUJ+YZsvy1xtH4WTnSolIpuD9zPPPIOOHTvihx9+4OJqRERERES3iCAIyNKUi0F7d04Jqk2XtvZ1kErQKdQLvS8MH+eiaETNh83B+9SpU/jvf/+LqKiopqiHiIiIiIgusGz1VYRNmYXYlFGI8zqD1flWni7oHWsJ2j0ifeDu7HiVKxGRPdkcvPv164eDBw8yeBMRERERNTKzWcDRPL0YtPedLrXa6svJQYruET6WXu1YNSJUbhyBSnQbsDl4Dx48GOPHj8fhw4fRrl27OourPfjgg41WHBERERFRS6etrMbmrCJxCHlRudHqfITaTRw+3j3CB86OMjtVSkQ3SiIIgnD9ZpdIpVdf/bClLK6m1+uhVCqh0+ng4eFh73KIiIiIqAUxmwUcOqfDpoxCpGVqcPCMFpd1asNVLkOPSBV6x6rRh1t9EdlFY2dCm3u8zWbz9RsREREREZGouNyIzVmFSMsoxJasIpRUVFudj/FToE+sL/rEqNE5zAtODuzVJmpJbA7eRERERER0bSazgINntUjLKMSmDA0OndPh8nGmCicH9IpSiQujBXq62K9YImpyNxS8169fj/Xr10Oj0dTpAf/mm28apTAiIiIiotvJ5b3amzMLUVpZY3W+dYAH+lwI2p1DveAou/oUTiJqWWwO3jNnzsTbb7+NLl26ICAggKsoEhEREdEdyWQWcOisFhuv0qvt7uyAu6NV6BPji96xavh5ONuvWCKyK5uD98KFC7FkyRI8/fTTTVEPEREREVGzVVJRjc2ZhUjL0GBTPb3abQMtvdp9Yn3RMdgTDuzVJiLcQPCurq5Gjx49mqIWIiIiIqJmxWwWcPicDhszNEjLKMTBs9o6vdr3RKvFFch92atNRPWwOXg/++yzWLZsGaZOndoU9RARERER2VVpRTU2ZxWK+2oXX7ECeZuAS73anULYq01E19eg4D1hwgTxe7PZjEWLFuGvv/5CfHw8HB0drdp+8sknDX7y1NRUpKamIjc3FwDQtm1bTJs2DYMGDQIAZGdnY+LEidi6dSuMRiPuvfde/Otf/4Kfn981r/v5559jzpw5yM/PR/v27fGvf/0L3bp1a3BdRERERHTnMJsFHM3TY+NxDdIyC3HgdKnVvtoKJ8tc7b6xnKtNRDemQcH7wIEDVvc7dOgAADhy5IjVcVsXWgsKCsL777+P6OhoCIKApUuXYsiQIThw4ADCwsIwcOBAtG/fHhs2bAAATJ06FYMHD8bOnTshldb/l8Xly5djwoQJWLhwIRISEjB37lwkJycjIyMDvr6+NtVHRERERC2T3lCDrVlFYtguLDNanY/1c0efODX6xPiiSxhXICeimyMRhMtnqdift7c35syZg+DgYAwaNAilpaXw8PAAAOh0Onh5eeGPP/5AUlJSvY9PSEhA165dMX/+fACWHvrg4GC8/PLLmDx5cr2PMRqNMBovfdjq9XoEBwdDp9OJz01EREREty9BEJBZUI6NGRpsPK7BvlOlqL2sW9tVLkOPSBX6xfmiTyz31Sa60+n1eiiVykbLhDe0j7dWq8WJEycAAFFRUfD09LzpQkwmE1auXImKigokJiYiOzsbEokETk5OYhtnZ2dIpVJs3bq13uBdXV2Nffv2YcqUKeIxqVSKpKQk7Nix46rPPXv2bMycOfOmXwMRERERNR+V1bXYdqLYsjDacQ3O6wxW5yPUbugb64u+sb7oGu4FJweZnSolopbOpuCdm5uLMWPGYN26dbjYUS6RSHDvvfdi/vz5CAsLs7mAw4cPIzExEQaDAQqFAqtXr0abNm2gVqvh5uaGN954A++99x4EQcDkyZNhMpmQl5dX77WKiopgMpnqzAH38/PD8ePHr1rDlClTrOaxX+zxJiIiIqLbS25RBTYc12Bjhga7Tpag2mQWzzk5SJEY6YO+sZZe7VAfNztWSkR3kgYH7zNnzqB79+5wdHTEO++8g9atWwMAjh49itTUVCQmJmLPnj0ICgqyqYDY2Fikp6dDp9Nh1apVSElJwaZNm9CmTRusXLkSL774IubNmwepVIonnngCnTp1uur87hvl5ORk1bNORERERLcHY60Ju3NKsOG4ZbuvnKIKq/NBXi7i8PHECBVc5OzVJqJbr8HBe8aMGYiNjcW6devg7HxpJcehQ4di/PjxuPfeezFjxgx89dVXNhUgl8sRFRUFAOjcuTP27NmDzz77DF988QUGDhyI7OxsFBUVwcHBAZ6envD390dERES911KpVJDJZCgoKLA6XlBQAH9/f5vqIiIiIqLmKU9XhY3HC7ExQ4NtJ4pQWW0SzzlIJegW7m0ZQh6nRqRaYfMCwEREja3BwXvt2rVYvny5Vei+yMXFBe+88w4ef/zxmy7IbDZbLXQGWAI1AGzYsAEajQYPPvhgvY+Vy+Xo3Lkz1q9fj6FDh4rXW79+PcaOHXvTtRERERHRrVdrMuPAGS02Htdgw3ENjueXWZ33dXcSg3bPKBXcnR2vciUiIvtocPAuKiq65hzuiIgIlJSU2PTkU6ZMwaBBgxASEoKysjIsW7YMaWlpWLduHQBg8eLFaN26NdRqNXbs2IFXX30V48ePR2xsrHiN/v3746GHHhKD9YQJE5CSkoIuXbqgW7dumDt3LioqKjBq1CibaiMiIiIi+ympqMamTA02HC/E5sxC6KpqxHMSCdAx2PPCEHJftA30YK82ETVrDQ7eAQEBOHr06FXncB85csTm4dwajQYjRoxAXl4elEol4uPjsW7dOgwYMAAAkJGRgSlTpqCkpARhYWF46623MH78eKtrXByKftHw4cNRWFiIadOmIT8/Hx06dMDatWvrLLhGRERERM2HIAj4+7ze0qudoUH6GS0u3/RW6eKI3jFq9IvzxT0xani7ye1XLBGRjRq8j/e4ceOwYcMGrF+/Hmq12uqcRqPBgAED0LdvX8ydO7cp6rylGnvPNiIiIiKqq9xYi61ZRUjLsKxCXqC3nm7YOsADfWMtYbtDsCccZI27wC4R0dU0diZscPAuLS1FQkIC8vPz8X//93+Ii4uDIAg4duwYli1bBn9/f+zcuRPe3t43XZS9MXgTERERNY2ci9t9HddgV04xakyXfhV1cZShZ5QK/eIs87UDlC52rJSI7mSNnQkbPNTcy8sLu3btwptvvokff/wRWq0WAODp6Yknn3wS7733XosI3URERETUeKprzeJ2XxszNHW2+wr1cUXfWF/0i/NFt3BvODtyuy8ianka3ON9OUEQUFhYCABQq9UtbjEL9ngTERER3TiN3oCNGZYVyLdmFaGinu2+LL3avohQubW43yWJ6PZntx7vy0kkEvj6+t70kxMRERHR7c9sFnDwrFZcGO3IOb3VeZXCSZyr3Sua230R0Z2nQcH73nvvxYwZM9C9e/drtisrK8OCBQugUCgwZsyYRimQiIiIiJofvaEGWzKLsOG4BmkZGhRXVFudbx+kRN84yxDyuwKVkErZq01Ed64GBe/HHnsMjzzyCJRKJQYPHowuXbogMDAQzs7OKC0txdGjR7F161b89ttvuP/++zFnzpymrpuIiIiIbiFBEJBdWI4Nxy1DyPfmlqLWfGnGoruTA+6OUaFvrGVvbbW7kx2rJSJqXho8x9toNGLlypVYvnw5tm7dCp1OZ7mARII2bdogOTkZo0ePRuvWrZu04FuBc7yJiIiIAEONCbtySixDyI9rcLqk0up8pNpNnKvdJdQbcgdu90VELYPdthO7kk6nQ1VVFXx8fODo2LLm6TB4ExER0Z2qQG/AxuMarD+uwbYTRai8bGE0uUyKhAjLwmj94nwR6uNmx0qJiJpOs1hcDQCUSiWUSuVNF0BERERE9nP5wmjrj2vw93nrhdF83Z3EXu1eUSq4Od3wr49ERHcsfnISERER3WH0hhpszSrC+mMabMrUoKj80sJoEgnQPshT7NVuG+jB7b6IiG4SgzcRERHRHeDkZQuj7c4pqXdhtH5xfugdo+bCaEREjYzBm4iIiKgFqq41Y09uCdYf02DD8QLkFlsvjBahchN7tbuEcWE0IqKmxOBNRERE1EIUlhmRlmHp1d6SVYRyY614zlEmQbdwb/SL80O/OF+Eq7gwGhHRrXJDwVur1WLVqlXIzs7GpEmT4O3tjf3798PPzw+tWrVq7BqJiIiIqB6CIODv83psuLAw2sEzWqvzKoUT+saq0b+1L3pFq6HgwmhERHZh86fvoUOHkJSUBKVSidzcXDz33HPw9vbGzz//jNOnT+Pbb79tijqJiIiICECFsRbbThRhw3ENNmZoUKA3Wp1v10opDiFv10oJqZQLoxER2ZvNwXvChAkYOXIkPvzwQ7i7u4vH77vvPjz55JONWhwRERERAWdKKsVe7Z3Zxag2mcVzrnIZekWpxLDt6+Fsx0qJiKg+NgfvPXv24IsvvqhzvFWrVsjPz2+UooiIiIjuZLUmM/adKhVXIc/SlFudD/Z2Qf8Lc7UTIrzh5CCzU6VERNQQNgdvJycn6PX6OsczMzOhVqsbpSgiIiKiO01pRTU2ZRZi/XENNmVooDdcWhhNJpWgS6gX+sX5on9rX0SqFdxbm4joNmJz8H7wwQfx9ttvY8WKFQAAiUSC06dP44033sAjjzzS6AUSERERtUSCICCjoAzrj2mw8bgG+0+X4rKtteHp6oi+sb7oG+eL3tFqKF0d7VcsERHdFIkgCML1m12i0+nw6KOPYu/evSgrK0NgYCDy8/ORmJiI3377DW5ut//WFHq9HkqlEjqdDh4eHvYuh4iIiFoIQ40JO7KLsf54ATYeL8Q5bZXV+Th/d7FXu0OwF2RcGI2IyC4aOxPa3OOtVCrx559/Ytu2bTh48CDKy8vRqVMnJCUl3XQxRERERC1Nnq7KMlf7mAbbsotgqLm0MJqTgxQ9In3Qr7VlvnYrTxc7VkpERE3F5h7vOwF7vImIiOhGmcwCDp7VYsMxyyrkx/Ks18YJVDqj74Ve7cQIFVzkXBiNiKi5sXuP9yuvvIKoqCi88sorVsfnz5+PEydOYO7cuTddFBEREdHtRG+owebMQmw4rkFaRiFKKqrFcxIJ0CnES9zuK87fnQujERHdYaS2PuCnn35Cz5496xzv0aMHVq1aZdO1UlNTER8fDw8PD3h4eCAxMRG///67eD4/Px9PP/00/P394ebmhk6dOuGnn3665jVnzJgBiURidYuLi7OpLiIiIqJrEQQB2YXl+HLzSTy+aAc6vf0nxi47gJ/3n0NJRTXcnR3wQHwAPhnWHvv+OQA/vdgDY/pGoXWAB0M3EdEdyOYe7+LiYiiVyjrHPTw8UFRUZNO1goKC8P777yM6OhqCIGDp0qUYMmQIDhw4gLZt22LEiBHQarX473//C5VKhWXLlmHYsGHYu3cvOnbseNXrtm3bFn/99Zd438HB5pdJREREZMVYa8LunBJxb+1TxZVW56N8FWKvdudQLzjKbO7fICKiFsrmRBoVFYW1a9di7NixVsd///13RERE2HStwYMHW91/9913kZqaip07d6Jt27bYvn07UlNT0a1bNwDAP//5T3z66afYt2/fNYO3g4MD/P39G1yH0WiE0WgU79e3TzkRERHdeTR6AzZmWIL21qwiVFSbxHNymRQJEd7oH+eLfnF+CPFxtWOlRETUnNkcvCdMmICxY8eisLAQ/fr1AwCsX78eH3/88U3N7zaZTFi5ciUqKiqQmJgIwDJ8ffny5bj//vvh6emJFStWwGAwoE+fPte8VlZWFgIDA+Hs7IzExETMnj0bISEhV20/e/ZszJw584ZrJyIiopbBbBZw+JxO7NU+fE5ndd7X3UncW/vuaBXcnDiqjoiIru+GVjVPTU3Fu+++i/PnzwMAwsLCMGPGDIwYMcLmAg4fPozExEQYDAYoFAosW7YM9913HwBAq9Vi+PDh+OOPP+Dg4ABXV1esXLkSAwcOvOr1fv/9d5SXlyM2NhZ5eXmYOXMmzp07hyNHjsDd3b3ex9TX4x0cHMxVzYmIiO4AZYYabM0qwobjGmzMKERRudHqfPtgT/SLtaxC3ibAA1LurU1E1OI19qrmN7WdWGFhIVxcXKBQKG64gOrqapw+fRo6nQ6rVq3CV199hU2bNqFNmzZ4+eWXsXv3brz33ntQqVRYs2YNPv30U2zZsgXt2rVr0PW1Wi1CQ0PxySefYPTo0Q16DLcTIyIiatlOFpZfCNoa7M4pQY3p0q9DCicH3B2tQr84X/SJ9YXa3cmOlRIRkT00q+DdFJKSkhAZGYnXX38dUVFROHLkCNq2bWt1PioqCgsXLmzwNbt27YqkpCTMnj27Qe0ZvImIiFqW6lqzuDDaxgwNcooqrM5HqNzQ98LCaF3DvCF34MJoRER3Mrvv411QUICJEydi/fr10Gg0uDK3m0ymqzyyYcxmM4xGIyorLSuFSqXW/+OTyWQwm80Nvl55eTmys7Px9NNP31RdREREdHvRlBmQdrwQ648X1FkYzVEmQUK4jxi2w1VudqyUiIhaOpuD98iRI3H69GlMnToVAQEBN7UX5ZQpUzBo0CCEhISgrKwMy5YtQ1paGtatW4e4uDhERUXhhRdewEcffQQfHx+sWbMGf/75J3799VfxGv3798dDDz0krrI+ceJEDB48GKGhoTh//jymT58OmUyGJ5544obrJCIioubPbBZw6MLCaBvrWRhNpXBC31g1+rf2Ra9oNRRcGI2IiG4Rm/+Ps3XrVmzZsgUdOnS46SfXaDQYMWIE8vLyoFQqER8fj3Xr1mHAgAEAgN9++w2TJ0/G4MGDUV5ejqioKCxdulRcfA0AsrOzrfYPP3v2LJ544gkUFxdDrVajV69e2LlzJ9Rq9U3XS0RERM2L3lCDLZmWhdE2ZWpQVF5tdb59kFLs1b4rUMmF0YiIyC5snuPdpk0bfP/999fcR/t2xzneREREzZMgCMi+sDDahuMa7M0tRa257sJofeN80SdWDV93ZztWS0REtyu7z/GeO3cuJk+ejC+++AJhYWE3XQARERHRtRhqTNh5shgbj2uwIUODMyVVVucj1G7oF2vp1e7ChdGIiKgZsjl4Dx8+HJWVlYiMjISrqyscHR2tzpeUlDRacURERHRnOqetwsYLc7W3ZRfBUHNpYVW5TIqECG/0uzCEPNSHC6MREVHzdkM93kRERESNqdZkxv7TWmw4rkFahgbH88uszvt7OItztXtG+cBVzoXRiIjo9mHz/7VSUlKaog4iIiK6wxSXG7E5qxAbjhdic2YhdFU14jmpBOgU4oW+cb7oG+uL1gHuN7WTChERkT3d1J+LDQYDqqutVw/lYmRERERUH7NZwN/n9diYocHGDA3Sz2hx+RKvnq6O6B2jRr84X9wTrYaXm9x+xRIRETUim4N3RUUF3njjDaxYsQLFxcV1zptMpkYpjIiIiG5/ekMNtmVZtvtKyyxEYZnR6nybAA/0ibWE7Y4hXpBxuy8iImqBbA7er7/+OjZu3IjU1FQ8/fTT+Pzzz3Hu3Dl88cUXeP/995uiRiIiIrpNCIKAE5pyS6/28ULsyS2x2u7LVS5DryiVOITcX8ntvoiIqOWzOXj/8ssv+Pbbb9GnTx+MGjUKd999N6KiohAaGorvv/8eTz31VFPUSURERM1UVbUJ27OLxLB9Tmu93Ve4yg19Y33RN06NbuHecHKQ2alSIiIi+7A5eJeUlCAiIgKAZT73xe3DevXqhRdffLFxqyMiIqJmKbeoAmkZGmzMKMSOk8Worr1suy8HKbpH+KBvrBp9Yn0RruJ2X0REdGezOXhHREQgJycHISEhiIuLw4oVK9CtWzf88ssv8PT0bIISiYiIyN4MNSbszinBxgwN0jIKkVNUYXW+lacL+sap0TfWF4mR3O6LiIjocjb/X3HUqFE4ePAgevfujcmTJ2Pw4MGYP38+ampq8MknnzRFjURERGQHZ0srsTGjEJsyNNh2ohhVNZcWUHWQStA51Av94nzRN84X0b4KbvdFRER0FRJBuHwjD9udOnUK+/btQ1RUFOLj4xurLrvS6/VQKpXQ6XTcHo2IiO4Y1bVm7M291KudpSm3Ou/r7iTO1e4ZpYK7s6OdKiUiImpajZ0Jb3ocWGhoKEJDQ2+6ECIiIrr1zmurkJZRiLQMDbadKEJF9aVebakE6BzqhT6xlhXIWwe4s1ebiIjoBjQoeM+bN6/BF3zllVduuBgiIiJqWjUmM/bmliItU4O044XIKCizOq9SOKF3jBp9YtW4J1oNpSt7tYmIiG5Wg4aah4eHW90vLCxEZWWluJiaVquFq6srfH19cfLkySYp9FbiUHMiImpJ8nSX92oXo9xYK56TSoAOwZ7oG+uLPrG+aBvoAamUvdpERHRns8tQ85ycHPH7ZcuWYcGCBfj6668RGxsLAMjIyMBzzz2HF1544aYLIiIioptTXWvG3lMl2JRRiLSMur3a3m5y9IlRo/eFXm0vN7mdKiUiIroz2Ly4WmRkJFatWoWOHTtaHd+3bx8effRRq5B+u2KPNxER3W4un6u9Pdu6V1tyoVe7T4wv+sSq0a6Vkr3aRERE12D3xdXy8vJQW1tb57jJZEJBQcFNF0RERETXZ6w1WeZqX2UFch83OXqzV5uIiKhZsDl49+/fHy+88AK++uordOrUCYClt/vFF19EUlJSoxdIREREFmdKKpGWocGmzEJszy5G5RUrkHcM8RIXRrsrkL3aREREzYXNwfubb75BSkoKunTpAkdHy0qntbW1SE5OxldffdXoBRIREd2pqqpN2JlTjE0ZhdicWYiTRRVW59Xul1Yg7xWlgqcre7WJiIiaI5uDt1qtxm+//YbMzEwcP34cABAXF4eYmJhGL46IiOhOIggCsgvLkZZRiE2ZhdiVU4LqWrN4XiaVXNhXW43eMWq0CfDgvtpERES3AZuD90VhYWEQBAGRkZFwcLjhyxAREd3Rygw12HaiGJuzCrEpoxDntFVW5wOVzugd64veMWr0iPKBhzP31SYiIrrd2JyYKysr8fLLL2Pp0qUAgMzMTERERODll19Gq1atMHny5EYvkoiIqKUwmwX8fV4vBu39p0tRa760wYhcJkVChLdlYbQYNaJ8FezVJiIius1JbX3AlClTcPDgQaSlpcHZ2Vk8npSUhOXLl9t0rdTUVMTHx8PDwwMeHh5ITEzE77//Lp7Pz8/H008/DX9/f7i5uaFTp0746aefrnvdzz//HGFhYXB2dkZCQgJ2795tU11ERESNqajciNUHzmL88nR0ffcvDJ6/FXPWZWB3bglqzQLCVW5ISQzF4pFdkT59AP49OgHP3h2BaD93hm4iIqIWwOYe7zVr1mD58uXo3r271S8Dbdu2RXZ2tk3XCgoKwvvvv4/o6GgIgoClS5diyJAhOHDgANq2bYsRI0ZAq9Xiv//9L1QqFZYtW4Zhw4Zh7969dfYRv2j58uWYMGECFi5ciISEBMydOxfJycnIyMiAr6+vrS+XiIjIZtW1Zuw7VYrNWYXYklWII+f0Vufd5DL0iFKJvdrB3q52qpSIiIhuBYkgCML1m13i6uqKI0eOICIiAu7u7jh48CAiIiJw8OBB3HPPPdDpdDdVkLe3N+bMmYPRo0dDoVAgNTUVTz/9tHjex8cHH3zwAZ599tl6H5+QkICuXbti/vz5AACz2Yzg4GC8/PLLVx0GbzQaYTQaxft6vR7BwcGNtlk6ERG1fLlFFdicZVl9fEd2MSou2+oLANoGeqB3jBr3xKjRKcQLcgebB50RERHRLaLX66FUKhstE9rc492lSxf873//w8svvwwAYq/3V199hcTExBsuxGQyYeXKlaioqBCv06NHDyxfvhz3338/PD09sWLFChgMBvTp06fea1RXV2Pfvn2YMmWKeEwqlSIpKQk7duy46nPPnj0bM2fOvOHaiYjozlNmqMGO7OILYbsIp0sqrc6rFHLcHa3GPTEq9IpSQ+3uZKdKiYiIyN5sDt7vvfceBg0ahKNHj6K2thafffYZjh49iu3bt2PTpk02F3D48GEkJibCYDBAoVBg9erVaNOmDQBgxYoVGD58OHx8fODg4ABXV1esXr0aUVFR9V6rqKgIJpMJfn5+Vsf9/PzErc/qM2XKFEyYMEG8f7HHm4iI6CKTWcDhczpsySzE5qxC7D+themyRdEcZZatvu6JUeOeaMtWX1Ip52cTERHRDQTvXr16IT09He+//z7atWuHP/74A506dcKOHTvQrl07mwuIjY1Feno6dDodVq1ahZSUFGzatAlt2rTB1KlTodVq8ddff0GlUmHNmjUYNmwYtmzZckPPdTVOTk5wcmJPBBERWTuvrcKWrEJszirCthNF0FbWWJ0PV7nh7mgV7olWo3ukDxRO3F6TiIiI6rJ5jndTS0pKQmRkJF5//XVERUXhyJEjaNu2rdX5qKgoLFy4sM5jq6ur4erqilWrVmHo0KHi8ZSUFGi1WvznP/9pUA2NPZ6fiIhuDxXGWuzKKcbmzCJsPVGEE5pyq/Puzg7oGanC3TGWsM1F0YiIiFomu8/xBizzsVevXo1jx44BANq0aYMhQ4bAweHm/9JvNpthNBpRWWmZKyeVWi8+I5PJYDab632sXC5H586dsX79ejF4m81mrF+/HmPHjr3p2oiIqGUxmQX8fV6HLVlF2Jxp2VO7xnTp79FSCdAh2FOcq90+yBMOMi6KRkRERLaxOSn//fffePDBB5Gfn4/Y2FgAwAcffAC1Wo1ffvkFd911V4OvNWXKFAwaNAghISEoKyvDsmXLkJaWhnXr1iEuLg5RUVF44YUX8NFHH8HHxwdr1qzBn3/+iV9//VW8Rv/+/fHQQw+JwXrChAlISUlBly5d0K1bN8ydOxcVFRUYNWqUrS+ViIhaoLOlldiaVYQtJ4qw/UQRSq8YPh7k5XJhnrYKiREqKF0d7VQpERERtRQ2B+9nn30Wbdu2xd69e+Hl5QUAKC0txciRI/H8889j+/btDb6WRqPBiBEjkJeXB6VSifj4eKxbtw4DBgwAAPz222+YPHkyBg8ejPLyckRFRWHp0qW47777xGtkZ2ejqKhIvD98+HAUFhZi2rRpyM/PR4cOHbB27do6C64REdGdQX9h9fGtWZbh4zlFFVbn3Z0ckBjpg7tj1Lg7SoVQH1dxxw4iIiKixmDzHG8XFxfs3bvXat41ABw5cgRdu3ZFVVVVoxZoD5zjTUR0+6oxmXHwjBZbsoqwJasQB8/qrFYfl0klaB+kRK9oS692+2BPOHL4OBEREV3G7nO8Y2JiUFBQUCd4azSaq27zRURE1FQEQcAJTTm2nrCsPL7zZAnKjbVWbcJVbugVpUKvaBUSI33g4czh40RERHTr2By8Z8+ejVdeeQUzZsxA9+7dAQA7d+7E22+/jQ8++AB6vV5sy95iIiJqChq9Aduyi7DlwjZfBXqj1XlPV0f0jLQE7V5RKq4+TkRERHZl81Dzy1cZvzgH7uIlLr8vkUhgMpkaq85bikPNiYialzJDDXbnlIi92pkF1tt8yR2k6BbmjZ5RlqDdNtADUinnaRMREdGNsftQ840bN970kxIREV1Lda0ZB06XYtuJImzLLkb6Ga3VPG2JBLgrUCkG7S5hXnB2lNmxYiIiIqKrszl49+7duynqICKiO5jZLOBYvh7bTxRj64ki7M4pQVWN9aipUB9X9IhUoWeUD3pEquDtJrdTtURERES2sTl4A4DBYMChQ4eg0WhgNputzj344IONUhgREbVcgiAgt7gS204UYXt2EXZkF9fZT9vHTY4eUSr0uhC0OU+biIiIblc2B++1a9dixIgRVntnX3Q7z+smIqKmVaA3YHt2EbadKMb2E0U4rzNYnXeVy5AQbpmn3TNKhVg/d87TJiIiohbB5uD98ssv47HHHsO0adPg5+fXFDUREVELUFpRjZ0ni7E9uxjbs4uQXVhhdV4uk6JjiKc4fJz7aRMREVFLZXPwLigowIQJExi6iYjISpmhBntyS7D9hCVsH8vX4/J9MyQSoF0rJRIjfdAzUoWuYd5wkXNBNCIiImr5bA7ejz76KNLS0hAZGdkU9RAR0W2iqtqEfadKseNkEbZnF+PQWZ3VyuMAEOOnQI9IFRIjfdA93AdKV0c7VUtERERkPzbv411ZWYnHHnsMarUa7dq1g6Oj9S9Rr7zySqMWaA/cx5uIqC5DjQkHTmux42QxdmYX48CZUtSYrP8XYll53AeJkSp0j/CGr7uznaolIiIiunF238f7hx9+wB9//AFnZ2ekpaVBIrm08I1EImkRwZuIiCx7aR86q8WO7GLsOFmMfadKYay13skiQOmMxAgfdI/0QY9IHwR5ceVxIiIioivZHLzfeustzJw5E5MnT4ZUykVwiIhaiotBe+fJYuw8WYK9p0pgqLEO2mp3JyRG+CAx0geJET4I9XG1+gMsEREREdVlc/Curq7G8OHDGbqJiG5z1bVmHD6nxc6TJdh5shh7c0tRVWO9JaSPmxwJEd5i2I5UKxi0iYiIiGxkc/BOSUnB8uXL8eabbzZFPURE1ESMtSYcOqvDzuxi7Mopwb5TdYO2t5sc3SO80T3CB90jfBDty6BNREREdLNsDt4mkwkffvgh1q1bh/j4+DqLq33yySeNVhwREd04Q40J6We02HWhR3v/6bpztL1cHcWQfTFoS6UM2kRERESNyebgffjwYXTs2BEAcOTIEatz7BUhIrKfyupa7D+lxa4cS492+hktqq8I2j5ucnSP8EFChDcSwhm0iYiIiG4Fm4P3xo0bm6IOIiKyka6qBvtOlWBXTgl2nSzBkXM61F6xj7ba3QkJ4d5IiPBBYoQ352gTERER2YHNwZuIiOyjsMyIPbkl2J1Tgj25JTiap4dgnbMRqHRGQoQPuoV7o1u4NyJUbgzaRERERHbW4OD98MMPN6jdzz//fMPFEBGRhSAIOFtahV05JdhzIWifLKqo0y7MxxUJ4ZeCdrA399EmIiIiam4aHLyVSmVT1kFEdEczmwVkFJRhb24JdueWYk9OCfL1Bqs2EgkQ6+eOrmHe6BrujYRwb/h5ONupYiIiIiJqqAYH78WLFzdlHUREdxRDjQmHz+mwO6cEe3NLsPdUKcoMtVZtHKQStAtSWnqzw7zRJdQbSlfHq1yRiIiIiJorzvEmIroFSiuqse9UKfaeKsXe3BIcOqtDtcl6xXE3uQydQr3QOdQL3cK90THYCy5ymZ0qJiIiIqLGYtfgnZqaitTUVOTm5gIA2rZti2nTpmHQoEHIzc1FeHh4vY9bsWIFHnvssXrPjRw5EkuXLrU6lpycjLVr1zZq7UREVyMIAnKLK7E3t0QM2yc05XXaqRRO6BbuhS6h3uga5o3WAe5wkEntUDERERERNSW7Bu+goCC8//77iI6OhiAIWLp0KYYMGYIDBw4gLi4OeXl5Vu0XLVqEOXPmYNCgQde87r333ms1NN7JyalJ6iciAgBjrQlHzumx71QJ9uaWYv/pUhSVV9dpF6l2Q5dQb3QJ80LXMG+E+rhyxXEiIiKiO4Bdg/fgwYOt7r/77rtITU3Fzp070bZtW/j7+1udX716NYYNGwaFQnHN6zo5OdV57LUYjUYYjUbxvl6vb/BjiejOoykzYP+pUuw7VYr9p7U4XM+wcblMivggJTqHWXq0O4d6wdtNbqeKiYiIiMiems0cb5PJhJUrV6KiogKJiYl1zu/btw/p6en4/PPPr3uttLQ0+Pr6wsvLC/369cOsWbPg4+Nz1fazZ8/GzJkz6xyvqKiAu7u72CNVXV2NmpoaODg4WPWiV1RYtvhxcXGBVGoZJlpTU4Pq6mrIZDI4OzvfUNvKykoIggBnZ2fIZJZ5nrW1tTAajZBKpXBxcbmhtlVVVTCbzXBycoKDg+UtYDKZYDAYbGorkUjg6npp6yKDwQCTyQS5XA5HR0eb25rNZlRVVQEA3NzcxLZGoxG1tbVwdHSEXC63ua0gCKisrAQAuLq61vl52tK2IT/7xnif1PfzbIz3ycWf582+T678ed7s++RqP8+bfZ9c/vO80fdJZZUBR86W4Mj5MhzKq8S+06U4U1IFc7VlxXGJoxMkEgl83ORo30qBDoEKdI1QoVOEL5wcZJf97GtgNjvwM4KfEfyMaGGfEdf7ed7o+4SfEfyM4GcEPyOubMvPiFv7GXHx37LRCHZ26NAhwc3NTZDJZIJSqRT+97//1dvuxRdfFFq3bn3d6/3www/Cf/7zH+HQoUPC6tWrhdatWwtdu3YVamtrr/oYg8Eg6HQ68XbmzBkBgABA0Gg0YrtZs2YJAIRnn33W6vGurq4CACEnJ0c89umnnwoAhCeffNKqrUqlEgAIR44cEY8tWrRIACAMGTLEqm1oaKgAQNi9e7d47LvvvhMACElJSVZt27RpIwAQNm7cKB5bvXq1AEDo0aOHVdsuXboIAIRff/1VPPbHH38IAIT27dtbte3du7cAQFixYoV4bOvWrQIAISoqyqrtfffdJwAQFi9eLB47cOCAAEAIDAy0avvoo48KAIT58+eLxzIzMwUAglKptGqbkpIiABA+/PBD8djZs2cFAIKDg4NV25deekkAIEyfPl08VlpaKv48q6urxeMTJ04UAAgTJ04Uj1VXV4ttS0tLxePTp08XAAgvvfSS1fM5ODgIAISzZ8+Kxz788EMBgJCSkmLVVqlUCgCEzMxM8dj8+fMFAMKjjz5q1TYwMFAAIBw4cEA8tnjxYgGAcN9991m1jYqKEgAIW7duFY+tWLFCACD07t3bqm379u0FAMIff/whHvv1118FAEKXLl2s2vbo0UMAIKxevVo8tnHjRgGA0KZNG6u2SUlJAgDhu+++E4/t3r1bACCEhoZatR0yZIgAQFi0aJF47MiRIwIAQaVSWbV98sknBQDCp59+Kh7LyckRAAiurq5WbZ999lkBgDBr1izxmEajEX+el3v11VcFAMKbb74pHisvLxfb/mdPtvD+78eE4V9sF3x6DhcACO6dHxRC3/hVCH3jVyFs8q9i22/+ShdyCssFs9nMz4gL+BlxCT8jLFraZ0R5ebl4/M033xQACK+++qrVNfh7hAU/Iyz4GWHBz4hL+Blh0dw/IwAIOp1OaAx27/GOjY1Feno6dDodVq1ahZSUFGzatAlt2rQR21RVVWHZsmWYOnXqda/3+OOPi9+3a9cO8fHxiIyMRFpaGvr371/vY5ycnDgPnOgOZDILOHxWhwNnSrEr47x4fOyyA5DKLX8RrjELAIBWXi54ISkanUO90CHYEx7vW9o+EB8ItcqtzrWJiIiIiC6SCIIg2LuIyyUlJSEyMhJffPGFeOzf//43Ro8ejXPnzkGtVtt8TbVajVmzZuGFF15oUHu9Xg+lUonz58/D39+fwz+a6fAPDhG78Z/9nThEzNXVFed1BqSf1mJPdgHSTxXjaEEFqgWZ2Faosaz1ENPKB51DvdEp1BN3+bshxNMJcrkjh4jxM4KfES34M4LDSPkZcbW2/IzgZ4StbfkZ0TI+IwoKChAYGAidTgcPDw/crGYXvPv164eQkBAsWbJEPNanTx+oVCqsWrXK5uudPXsWISEhWLNmDR588MEGPeZi8G6sf2QiuvXKjbU4dFaL9DNapJ/W4sAZLQrLjHXaeTg7oEOIFzqFeKJTiBfaB3tC6eJoh4qJiIiIqLlo7Exo16HmU6ZMwaBBgxASEoKysjIsW7YMaWlpWLdundjmxIkT2Lx5M3777bd6rxEXF4fZs2fjoYceQnl5OWbOnIlHHnkE/v7+yM7Oxuuvv46oqCgkJyffqpdFRLdYjcmMjPwypJ/R4uAZLQ6e1SJLU44r/6wok0rQOsAdHYI90THYCx1CPBHu4waplFt6EREREVHTsWvw1mg0GDFiBPLy8qBUKhEfH49169ZhwIABYptvvvkGQUFBGDhwYL3XyMjIgE6nAwDIZDIcOnQIS5cuhVarRWBgIAYOHIh33nmHc7iJWghBEJBbXIlDZ7U4dFaH9DNaHDmng7HWXKdtK08XtA9WWoJ2iBfuClTCRS6zQ9VEREREdCdrdkPNmwMONSdqHgRBQJ7OgENntTh4VofDZ3U4dFYLvaG2Tlt3Zwd0CPZE+yBPdAj2RHywEr7uzvVclYiIiIjo2lrUUHMiostp9AYcPqfDobM68WtRed152XIHKdoEeKB9kBLtgy1BO4xDxomIiIiomWLwJiK7uBiyD5/T4ciFkK2pZ/EzmVSCWD93tA9Wol0rT8QHKRHr7w5HmdQOVRMRERER2Y7Bm4ialCAIyNcbcOScHkfO6fD3eUvYLtDXDdlSCRDlq0C7Vp5o18oD8cGeaBPgAWdHzssmIiIiotsXgzcRNRpBEHC6pNISss9berKPntejuKK6TluJBIhSK9CulRLtgpRo10qJNoEecJXzY4mIiIiIWhb+hktEN6S61ozMgjIczdPj6HnL7VieHmXGugufyaQSRPsq0DZQibtaeeCuVkq0CfCAmxM/goiIiIio5eNvvUR0XdrKahzLK8OxPD3+Pq/H0Tw9TmjKUGOquymC3EGK1v7uaHMxZAda5mRzuDgRERER3akYvIlIZDILyCmqwLE8PY7n68Wwnacz1Nte6eKINgEeaBvogTYXbpFqBRc+IyIiIiK6DIM30R2quNyIjPwyHMsvQ0a+Hhn5ZcgoKIOhxlxv+2BvF8T5XwjZAR5o20qJQKUzJBJu4UVEREREdC0M3kQtXFW1CVmaMhzPL7OE63zL9/Xtjw0ALo4yxPq7o3WAO1oHeKB1gAdi/d3h4ex4iysnIiIiImoZGLyJWghjrQknCyuQWVCGzIIyZOSXI0tThtMllRDqTsWGRAKEeLsi1s8dcf7uiPX3QOsAd4T6uEEmZS82EREREVFjYfAmus0YakzIKapAlqYcJwrKkKUpR0ZBGU4VV8JkridhA/B2kyPWzx2x/hdDtjti/Ny5qjgRERER0S3A37qJmqlyYy1OFpYjq6DcErI15ThxoQf7KvkaHs4OiPFzR4y/O2J8FeL3KoXTrS2eiIiIiIhEDN5EdmQ2C8jXG5BdWI5sTTmyCytwsqgc2ZoK5OvrX0kcsATsaD93RPsqEHUhYMf6u8PX3YmLnRERERERNTMM3kS3gK6qBrlFFcgpqsDJi18Ly3GysAJVNaarPk6lkCPKV4FoX3dE+ykQpVYgyk8BtYIBm4iIiIjodsHgTdRIKoy1OFVciVPFFcgprkBOoSVg5xRVoLii+qqPc5BKEOrjigi1ApFqBSLVboj0VSBSpYDSlSuJExERERHd7hi8iWygq6rBmZJK5BZXILeoArkXgnZucSUKy+rfnusiX3cnhKncEKFyQ/iFW6SvAiHernCUSW/RKyAiIiIioluNwZvoMjUmM/K0BpwuqRRvZy77XldVc83He7k6ItTHDWE+rghXKRCutgTtMJUbFFxBnIiIiIjojsQkQHeUWpMZeToDzpZW4Wxp5YWvl77P1xuuuiXXRSqFHGE+bmLADlVd+OrtxqHhRERERERUB4M3tSh6Qw3ytAac11bhnLYK57VVyNMZcE5bhXMNDNZODlIEe7si5MLN+nsXuMr5nw0RERERETUcEwTdFgRBgL6qFnn6KuTrDJab3vI178L989oqlBlrr3stuUyKIC8XtPJyQZCXC4K8XC98tXyvVjhBKuWK4URERERE1DgYvMmuBEFAubEWmjIjCvQGFF74qtEbxWOaMiPydFUw1JgbdE1PV0cEKl0Q6OmCVp7OCPR0QcCF74O9XKFisCYiIiIioluIwZsa3cUwXVxejaJyI4rKjSgsr0ZRmVG8X3ThXGGZEZXVV9/H+kpero7w83BGgNIZ/koX+F/43k/pjFaeLgj0dOZQcCIiIiIialbsmlBSU1ORmpqK3NxcAEDbtm0xbdo0DBo0CLm5uQgPD6/3cStWrMBjjz1W7zlBEDB9+nR8+eWX0Gq16NmzJ1JTUxEdHd1UL6PFq6o2QVtVjdKKGmirqqGtrEFpZTVKyqtRXFGNknpu1aaG9U5f5O7kALWHE3zdneDn4Qxfdyf4ujvD18Py1RK0neHsKGuiV0lERERERNQ07Bq8g4KC8P777yM6OhqCIGDp0qUYMmQIDhw4gLi4OOTl5Vm1X7RoEebMmYNBgwZd9Zoffvgh5s2bh6VLlyI8PBxTp05FcnIyjh49Cmdn56Z+Sc1SjcmMCmMtyo21KDPUQl9VA734tQb6qtoLXy33dVU10FbWiAHbWGtbiL7I2VEKtbsTVIpLN7VCfunYha9+Hk7spSYiIiIiohZLIgjCtZd4vsW8vb0xZ84cjB49us65jh07olOnTvj666/rfawgCAgMDMRrr72GiRMnAgB0Oh38/PywZMkSPP744w2qQa/XQ6lUQqfTwcPD48ZfjA1qTGYYakww1lq+Gmou3TfWmFBVY0JltQmV1bUXvppQVX3lMUuwrqiuRbnBErTLjbUNnht9LQ5SCTxdHeHpKoeni+Wrj5sc3grLVy9X6+99FHKGaSIiIiIiui01diZsNsnIZDJh5cqVqKioQGJiYp3z+/btQ3p6Oj7//POrXiMnJwf5+flISkoSjymVSiQkJGDHjh1XDd5GoxFGo1G8r9frr1mr2Szg9Z8OwWwWUGsWYBIE8XvxqyCg1iSg1mxGtUlATa0ZNSbLrbr2wrEL94215utucdUY5A5SuDs5QOniCHcXR3g4O8DDxREezo7wcHGAh7MjlC6O8HBxhJerIzxd5BfCtiMUTg6QSLggGRERERERka3sHrwPHz6MxMREGAwGKBQKrF69Gm3atKnT7uuvv0br1q3Ro0ePq14rPz8fAODn52d13M/PTzxXn9mzZ2PmzJkNrlkiAVbtO9vg9rZycpDCyUEKZ0fZhZvle1e5DK5yB7jIZXC9eN/JAa6OMrjIZXBzcoDCyQEK5wtfL7u5OTlA7iBtspqJiIiIiIiofnYP3rGxsUhPT4dOp8OqVauQkpKCTZs2WYXvqqoqLFu2DFOnTm2SGqZMmYIJEyaI9/V6PYKDg6/aXiKR4I174yCTAjKpFDIJIJNJIZNI4CCVQCq1/iqXSeHoIIVcJoXcQQJHmRSOMinkF445yqRiuJbLpNzqioiIiIiIqAWxe/CWy+WIiooCAHTu3Bl79uzBZ599hi+++EJss2rVKlRWVmLEiBHXvJa/vz8AoKCgAAEBAeLxgoICdOjQ4aqPc3JygpOTk011v9gn0qb2REREREREdGdqdmOPzWaz1XxrwDLM/MEHH4Rarb7mY8PDw+Hv74/169eLx/R6PXbt2lXvvHEiIiIiIiKipmbXHu8pU6Zg0KBBCAkJQVlZGZYtW4a0tDSsW7dObHPixAls3rwZv/32W73XiIuLw+zZs/HQQw9BIpFg3LhxmDVrFqKjo8XtxAIDAzF06NBb9KqIiIiIiIiILrFr8NZoNBgxYgTy8vKgVCoRHx+PdevWYcCAAWKbb775BkFBQRg4cGC918jIyIBOpxPvv/7666ioqMDzzz8PrVaLXr16Ye3atXfsHt5ERERERERkX81uH+/mwB77eBMREREREVHz0NiZsNnN8SYiIiIiIiJqSRi8iYiIiIiIiJoQgzcRERERERFRE7L7Pt7N0cVp73q93s6VEBERERER0a12MQs21pJoDN71KC4uBgAEBwfbuRIiIiIiIiKyl+LiYiiVypu+DoN3Pby9vQEAp0+fbpR/ZCJ70uv1CA4OxpkzZ7hKP932+H6mlobvaWpJ+H6mlkSn0yEkJETMhjeLwbseUqll6rtSqeSHBrUYHh4efD9Ti8H3M7U0fE9TS8L3M7UkF7PhTV+nUa5CRERERERERPVi8CYiIiIiIiJqQgze9XBycsL06dPh5ORk71KIbhrfz9SS8P1MLQ3f09SS8P1MLUljv58lQmOtj05EREREREREdbDHm4iIiIiIiKgJMXgTERERERERNSEGbyIiIiIiIqImxOBNRERERERE1IQYvOvx+eefIywsDM7OzkhISMDu3bvtXRKRzWbPno2uXbvC3d0dvr6+GDp0KDIyMuxdFlGjeP/99yGRSDBu3Dh7l0J0Q86dO4f/+7//g4+PD1xcXNCuXTvs3bvX3mUR3RCTyYSpU6ciPDwcLi4uiIyMxDvvvAOu4Uy3g82bN2Pw4MEIDAyERCLBmjVrrM4LgoBp06YhICAALi4uSEpKQlZWls3Pw+B9heXLl2PChAmYPn069u/fj/bt2yM5ORkajcbepRHZZNOmTRgzZgx27tyJP//8EzU1NRg4cCAqKirsXRrRTdmzZw+++OILxMfH27sUohtSWlqKnj17wtHREb///juOHj2Kjz/+GF5eXvYujeiGfPDBB0hNTcX8+fNx7NgxfPDBB/jwww/xr3/9y96lEV1XRUUF2rdvj88//7ze8x9++CHmzZuHhQsXYteuXXBzc0NycjIMBoNNz8PtxK6QkJCArl27Yv78+QAAs9mM4OBgvPzyy5g8ebKdqyO6cYWFhfD19cWmTZtwzz332LscohtSXl6OTp06YcGCBZg1axY6dOiAuXPn2rssIptMnjwZ27Ztw5YtW+xdClGjeOCBB+Dn54evv/5aPPbII4/AxcUF3333nR0rI7KNRCLB6tWrMXToUACW3u7AwEC89tprmDhxIgBAp9PBz88PS5YsweOPP97ga7PH+zLV1dXYt28fkpKSxGNSqRRJSUnYsWOHHSsjunk6nQ4A4O3tbedKiG7cmDFjcP/991t9ThPdbv773/+iS5cueOyxx+Dr64uOHTviyy+/tHdZRDesR48eWL9+PTIzMwEABw8exNatWzFo0CA7V0Z0c3JycpCfn2/1e4dSqURCQoLN+dChsYu7nRUVFcFkMsHPz8/quJ+fH44fP26nqohuntlsxrhx49CzZ0/cdddd9i6H6Ib8+OOP2L9/P/bs2WPvUohuysmTJ5GamooJEybgzTffxJ49e/DKK69ALpcjJSXF3uUR2Wzy5MnQ6/WIi4uDTCaDyWTCu+++i6eeesrepRHdlPz8fACoNx9ePNdQDN5Ed4AxY8bgyJEj2Lp1q71LIbohZ86cwauvvoo///wTzs7O9i6H6KaYzWZ06dIF7733HgCgY8eOOHLkCBYuXMjgTbelFStW4Pvvv8eyZcvQtm1bpKenY9y4cQgMDOR7mugCDjW/jEqlgkwmQ0FBgdXxgoIC+Pv726kqopszduxY/Prrr9i4cSOCgoLsXQ7RDdm3bx80Gg06deoEBwcHODg4YNOmTZg3bx4cHBxgMpnsXSJRgwUEBKBNmzZWx1q3bo3Tp0/bqSKimzNp0iRMnjwZjz/+ONq1a4enn34a48ePx+zZs+1dGtFNuZgBGyMfMnhfRi6Xo3Pnzli/fr14zGw2Y/369UhMTLRjZUS2EwQBY8eOxerVq7FhwwaEh4fbuySiG9a/f38cPnwY6enp4q1Lly546qmnkJ6eDplMZu8SiRqsZ8+edbZ3zMzMRGhoqJ0qIro5lZWVkEqtY4VMJoPZbLZTRUSNIzw8HP7+/lb5UK/XY9euXTbnQw41v8KECROQkpKCLl26oFu3bpg7dy4qKiowatQoe5dGZJMxY8Zg2bJl+M9//gN3d3dxHopSqYSLi4udqyOyjbu7e531Cdzc3ODj48N1C+i2M378ePTo0QPvvfcehg0bht27d2PRokVYtGiRvUsjuiGDBw/Gu+++i5CQELRt2xYHDhzAJ598gmeeecbepRFdV3l5OU6cOCHez8nJQXp6Ory9vRESEoJx48Zh1qxZiI6ORnh4OKZOnYrAwEBx5fOG4nZi9Zg/fz7mzJmD/Px8dOjQAfPmzUNCQoK9yyKyiUQiqff44sWLMXLkyFtbDFET6NOnD7cTo9vWr7/+iilTpiArKwvh4eGYMGECnnvuOXuXRXRDysrKMHXqVKxevRoajQaBgYF44oknMG3aNMjlcnuXR3RNaWlp6Nu3b53jKSkpWLJkCQRBwPTp07Fo0SJotVr06tULCxYsQExMjE3Pw+BNRERERERE1IQ4x5uIiIiIiIioCTF4ExERERERETUhBm8iIiIiIiKiJsTgTURERERERNSEGLyJiIiIiIiImhCDNxEREREREVETYvAmIiIiIiIiakIM3kRERERERERNiMGbiIiIbonq6mpERUVh+/btdqth8uTJePnll+32/EREdGdi8CYiIroBI0eOhEQiqXM7ceKEvUtrthYuXIjw8HD06NHD6vjGjRvxwAMPQK1Ww9nZGZGRkRg+fDg2b94stklLS4NEIoFWq61z3bCwMMydO7dBNUycOBFLly7FyZMnb+alEBER2YTBm4iI6Abde++9yMvLs7qFh4fXaVddXW2H6poXQRAwf/58jB492ur4ggUL0L9/f/j4+GD58uXIyMjA6tWr0aNHD4wfP77R61CpVEhOTkZqamqjX5uIiOhqGLyJiIhukJOTE/z9/a1uMpkMffr0wdixYzFu3Dgx6AHAkSNHMGjQICgUCvj5+eHpp59GUVGReL2KigqMGDECCoUCAQEB+Pjjj9GnTx+MGzdObCORSLBmzRqrOjw9PbFkyRLx/pkzZzBs2DB4enrC29sbQ4YMQW5urnh+5MiRGDp0KD766CMEBATAx8cHY8aMQU1NjdjGaDTijTfeQHBwMJycnBAVFYWvv/4agiAgKioKH330kVUN6enp1+zx37dvH7Kzs3H//feLx06fPo1x48Zh3LhxWLp0Kfr164fQ0FDEx8fj1Vdfxd69exv6oxAtWbKk3pEIM2bMENsMHjwYP/74o83XJiIiulEM3kRERE1g6dKlkMvl2LZtGxYuXAitVot+/fqhY8eO2Lt3L9auXYuCggIMGzZMfMykSZOwadMm/Oc//8Eff/yBtLQ07N+/36bnrampQXJyMtzd3bFlyxZs27YNCoUC9957r1XP+8aNG5GdnY2NGzdi6dKlWLJkiVV4HzFiBH744QfMmzcPx44dwxdffAGFQgGJRIJnnnkGixcvtnrexYsX45577kFUVFS9dW3ZsgUxMTFwd3cXj/3000+oqanB66+/Xu9jJBKJTa8dAIYPH241AuGHH36Ag4MDevbsKbbp1q0bzp49a/XHCCIioqbkYO8CiIiIble//vorFAqFeH/QoEFYuXIlACA6OhoffviheG7WrFno2LEj3nvvPfHYN998g+DgYGRmZiIwMBBff/01vvvuO/Tv3x+AJbwHBQXZVNPy5cthNpvx1VdficF18eLF8PT0RFpaGgYOHAgA8PLywvz58yGTyRAXF4f7778f69evx3PPPYfMzEysWLECf/75J5KSkgAAERER4nOMHDkS06ZNw+7du9GtWzfU1NRg2bJldXrBL3fq1CkEBgZaHcvMzISHhwf8/f3FYz/99BNSUlLE+zt27EC7du3E+/X9e1RWVorfu7i4wMXFBQCQnZ2NMWPG4L333sOAAQPENhfrOHXqFMLCwq5aMxERUWNh8CYiIrpBffv2tZor7ObmJn7fuXNnq7YHDx7Exo0brYL6RdnZ2aiqqkJ1dTUSEhLE497e3oiNjbWppoMHD+LEiRNWPcsAYDAYkJ2dLd5v27YtZDKZeD8gIACHDx8GYBk2LpPJ0Lt373qfIzAwEPfffz+++eYbdOvWDb/88guMRiMee+yxq9ZVVVUFZ2fnOsev7NVOTk5Geno6zp07hz59+sBkMlmd37JlS53X1qdPnzrX1el0eOCBB3D//fdj0qRJVucuBvPLAzsREVFTYvAmIiK6QW5ublcdWn15CAeA8vJyDB48GB988EGdtgEBAQ1eDV0ikUAQBKtjl8/NLi8vR+fOnfH999/XeaxarRa/d3R0rHNds9kM4FIwvZZnn30WTz/9ND799FMsXrwYw4cPh6ur61Xbq1QqMdhfFB0dDZ1Oh/z8fLHXW6FQICoqCg4O9f+KEh4eDk9PT6tjV7Y1mUwYPnw4PDw8sGjRojrXKCkpAWD970FERNSUOMebiIjoFujUqRP+/vtvhIWFISoqyurm5uaGyMhIODo6YteuXeJjSktLkZmZaXUdtVqNvLw88X5WVpZVz22nTp2QlZUFX1/fOs+jVCobVGu7du1gNpuxadOmq7a577774ObmhtTUVKxduxbPPPPMNa/ZsWNHHD9+3OqPBo8++igcHR3r/WPEzRg/fjwOHz6MNWvW1NvLfuTIETg6OqJt27aN+rxERERXw+BNRER0C4wZMwYlJSV44oknsGfPHmRnZ2PdunUYNWoUTCYTFAoFRo8ejUmTJmHDhg04cuQIRo4cCanU+n/V/fr1w/z583HgwAHs3bsX//jHP6x6r5966imoVCoMGTIEW7ZsQU5ODtLS0vDKK6/g7NmzDao1LCwMKSkpeOaZZ7BmzRrxGitWrBDbyGQyjBw5ElOmTEF0dDQSExOvec2+ffuivLwcf//9t3gsJCQEH3/8MT777DOkpKRg48aNyM3Nxf79+zFv3jzxeWyxePFiLFiwAAsXLoREIkF+fj7y8/NRXl4uttmyZQvuvvvuBvXsExERNQYGbyIiolsgMDAQ27Ztg8lkwsCBA9GuXTuMGzcOnp6eYrieM2cO7r77bgwePBhJSUno1atXnbniH3/8MYKDg3H33XfjySefxMSJE62GeLu6umLz5s0ICQnBww8/jNatW2P06NEwGAzw8PBocL2pqal49NFH8dJLLyEuLg7PPfccKioqrNqMHj0a1dXVGDVq1HWv5+Pjg4ceeqjOEPiXX34Zf/zxBwoLC/Hoo48iOjoa9913H3JycrB27VqrhdUaYtOmTTCZTHjwwQcREBAg3i5f+O3HH3/Ec889Z9N1iYiIboZEuHKiGBERETUbffr0QYcOHTB37lx7l1LHli1b0L9/f5w5cwZ+fn7XbX/o0CEMGDAA2dnZ9S4ydyv8/vvveO2113Do0KGrziMnIiJqbOzxJiIiIpsYjUacPXsWM2bMwGOPPdag0A0A8fHx+OCDD5CTk9PEFV5dRUUFFi9ezNBNRES3FP+vQ0RERDb54YcfMHr0aHTo0AHffvutTY8dOXJk0xTVQI8++qhdn5+IiO5MHGpORERERERE1IQ41JyIiIiIiIioCTF4ExERERERETUhBm8iIiIiIiKiJsTgTURERERERNSEGLyJiIiIiIiImhCDNxEREREREVETYvAmIiIiIiIiakIM3kRERERERERN6P8BsV0iQ7bX0FAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pass the voltage and current integral definitions to the impedance calculator.\n",
    "impedance_calculator = microwave.ImpedanceCalculator(\n",
    "    voltage_integral=voltage_path, current_integral=current_path\n",
    ")\n",
    "# Compute impedance from frequency domain field data.\n",
    "impedance = impedance_calculator.compute_impedance(sim_data[\"freq_yz\"])\n",
    "\n",
    "# Use a quasi-static model of the microstrip to compute an approximate value for the characteristic impedance as a sanity check.\n",
    "(Zo, _) = microwave.models.microstrip.compute_line_params(4.4, width, height, thickness)\n",
    "\n",
    "f, ax1 = plt.subplots(1, 1, tight_layout=True, figsize=(10, 3))\n",
    "ax1.plot(freqs / 1e9, abs(impedance).values, label=\"Tidy3D\")\n",
    "ax1.set_ylabel(\"Impedance (Ohm)\")\n",
    "ax1.set_xlabel(\"Frequency (GHz)\")\n",
    "ax1.set_xlim(0, 10)\n",
    "ax1.axhline(y=Zo, color=\"k\", linestyle=\":\", label=\"Quasi-static model\")\n",
    "ax1.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The quasi-static model for impedance is computed following the equations in [2], which is known to be accurate to within 0.2% for microstrips with these dimensions. As a result, the relative error of the Tidy3D simulation is approximately 1% when using the quasi-static model as a reference in the DC limit. However, the Tidy3D simulation will capture the effect of dispersion at higher frequencies, which the quasi-static model does not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining a custom path integral\n",
    "\n",
    "Using a path integral that must be defined along an axis, as in `CurrentIntegralAxisAligned`, can be limiting in some circumstances, e.g., computing the current within the inner core of a coaxial cable. In this next part, we demonstrate the usage of `CustomCurrentIntegral2D`, which can accept any user-defined path to perform the integration. We also support `CustomVoltageIntegral2D` which can be used in the same manner.\n",
    "\n",
    "When using a `CustomCurrentIntegral2D` or `CustomVoltageIntegral2D`, the electromagnetic fields are interpolated from the Yee grid. Therefore, to achieve good accuracy the spacing between vertices on the path should be roughly the same as, or even slightly less than, the grid spacing. On the other hand, choosing too many vertices may lead to a more expensive integration step. Finally, interpolating from fields directly on PEC or other metallic interfaces should be avoided. Hence, below we define a path that includes a small buffer distance from the strip."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4888/MXv2bPz66684fPgwPDw87kgtDg4OcHBwuCPP1RAKhcLaJRARERFRG3O5pMLUo30woxD5aq3ZeQdbCXqEeKBPmBx9wuSI9ZNBwqDdZomtXQDdGVOnToVUKsVvv/2GgQMHIjAwEAkJCfj9999x5coVvPbaa6a2IpGoztBkNzc3rFu3znR/7ty5iIyMhKOjI0JDQ7FgwQJUV//1V7szZ85g8ODBcHFxgaurK7p164bjx48DMPY8u7m53bLepKQk9OzZE05OTnBzc0Pfvn1x8eJFrFu3DosXL8aZM2cgEokgEolMdS1fvhwdOnSAk5MTAgIC8MILL0Cj0ZiuN3nyZKhUKtPjXn/9dQDGoeYrVqwwPXdpaSmee+45eHt7w97eHvfccw9+/vnnm9YqEonw2Wef4YEHHoCjoyPat2+PQ4cOIT09HYMGDYKTkxP69OmDjIwMs8clJiYiLCwMUqkUUVFR+Ne//mV2Pi0tDQMGDIC9vT1iYmKwY8eOOs996dIljBs3Dm5ubvDw8MDo0aORnZ19y39bIiIiImp6yrIq/Of0Fcz79x8Y8N5u9Ht3N+b88Ae2nLqCfLUWUhsxeod6YOawSPzwjzicWXQvvn6qJ/4xMAwd/d0Yuts49ng3ofLycgCAo+NfKwrqdDpUV1fDxsYGdnZ2ddo6ODhALDb+/aO6uho6nQ4SiQT29va3bGuJ4uJibN++HW+99VadnmYfHx888cQT2LRpE1atWtXgeSMuLi5Yt24d/Pz8cPbsWTzzzDNwcXHBnDlzAABPPPEEunTpgsTEREgkEpw+fRq2trYNunZNTQ3GjBmDZ555Bt999x10Oh2OHj0KkUiE8ePH49y5c9i2bRt+//13AIBMJgMAiMVifPzxxwgJCUFmZiZeeOEFzJkzB6tWrUKfPn2wYsUKLFy4ECkpKQAAZ2fnOs9tMBiQkJCAsrIyfPPNNwgLC8P58+chkUhuWfObb76J5cuXY/ny5Zg7dy4ef/xxhIaGYv78+QgMDMRTTz2FadOm4ddffwUAbNmyBS+//DJWrFiB+Ph4/Pzzz5g8eTL8/f0xePBgGAwGPPTQQ/D29saRI0egUqnqzEGvrq7G8OHDERcXh3379sHGxgZLlizBiBEj8Mcff0AqlTbo35uIiIiILKeqrMaRzCIczCjCgfRCpCk1ZuclYhE6+cvQJ8wTfcLk6BrkDnvbW/9OSW0Xg3cTuhbklEqlafjysmXL8M9//hNPP/00Pv/8c1NbLy8vVFRUICsrC8HBwQCATz/9FDNmzMDjjz+Ob7/91tQ2ODgYhYWFOHfuHGJjYy2uKy0tDYIgoH379vWeb9++PUpKSlBQUAAvL68GXfOf//ynWX2zZs3Cxo0bTcE7JycHs2fPRnR0NAAgIiKiwfWq1WqoVCo88MADCAsLM9V4jbOzM2xsbODj42P2uOuDaXBwMJYsWYJ//OMfWLVqFaRSKWQyGUQiUZ3HXe/333/H0aNHceHCBURGRgIAQkND/7bmyZMnY9y4cQCMowHi4uKwYMECDB8+HADw8ssvY/Lkyab277//PiZNmoQXXngBADBz5kwcPnwY77//PgYPHozff/8dycnJ2L59O/z8/AAAb7/9NhISEkzX2LRpEwwGA7744gvTH0zWrl0LNzc3JCUl4d577/3buomIiIioYSp1ehy/WIyDGUU4mF6Is1dUMAh/nReJgBhf19qh457oEeIBZzvGLTLiO+EuIgjCLc9b0kO6adMmfPzxx8jIyIBGo0FNTQ1cXV1N52fOnImnn34a//rXvxAfH49HHnnEFKKvl5OTg5iYGNP9V199Fa+++iomTZqE4cOHY9iwYYiPj8e4cePg6+t7y5p+//13LF26FMnJyVCr1aipqUFVVRUqKirg6OjYoNd1+vRp+Pv7m0J3Q3Xs2NH0vbe3NwCgQ4cOZseqqqqgVqvh6uqKCxcu4NlnnzW7Rt++ffHRRx8BAC5cuICAgABT6AaAuLg4s/ZnzpxBeno6XFxczI5XVVXVGdZORERERJap0Rtw5rIKB9MLcSCjECcvlkKnN99LO1ThhD5hcvQN80TvUDncnTjikOrH4N2Ers0nvj7kzZ49G9OnT4eNjfk/tVKpBACzod9Tp07FM888U2dY87U5u41dkCw8PBwikQgXLlzAgw8+WOf8hQsXoFAoTPOuRSJRnZB+/fztQ4cO4YknnsDixYsxfPhwyGQybNy4ER988IGpzeuvv47HH38c//vf//Drr79i0aJF2LhxY53n9/PzM1ud/NoCb2vXrsVLL72Ebdu2YdOmTfjnP/+JHTt2oHfv3vW+xuzsbDzwwAN4/vnn8dZbb8HDwwP79+/HlClToNPpGhy8G/tvfP0w+mu9z/UdMxjMP6xvh0ajQbdu3cxGR1zDBeOIiIiILCMIAlLzNTiQXogD6YU4klUMjdZ8iy9fmb1p6HifcDl8ZS1nwWBq2Ri8m1B9W1JJpdJ6e5Lra2tra1vvPOjb3epKLpdj2LBhWLVqFWbMmGEWLvPy8vDtt99i6tSppmMKhQK5ubmm+2lpaaioqDDdP3jwIIKCgswWZLt48WKd542MjERkZCRmzJiBxx57DGvXrq0TvG1sbBAeHl5v3V26dEGXLl0wf/58xMXFYcOGDejduzekUin0er1Z2xMnTsBgMOCDDz4wzYPfvHmzWZv6Hnejjh074vLly0hNTbW419sS7du3x4EDBzBx4kTTsQMHDph6/9u3b49Lly4hNzfX1NN/+PBhs2t07doVmzZtgpeXl9loAyIiIiJqmMslFTiYXoT96YU4mFGEQo35yuNujraIC5WjT7gn+obJEeLpxL20qVEYvO8SK1euRJ8+fTB8+HAsWbLEbDuxyMhILFy40NR2yJAhWLlyJeLi4qDX6zF37lyzPwhEREQgJycHGzduRI8ePfC///0PW7ZsMZ2vrKzE7Nmz8fDDDyMkJASXL1/GsWPHMHbs2AbVmpWVhTVr1mDUqFHw8/NDSkoK0tLSMGHCBADG+dtZWVmmYeEuLi4IDw9HdXU1PvnkE4wcORIHDhzA6tWrza4bHBwMjUaDnTt3olOnTnB0dKzTEz5w4EAMGDAAY8eOxfLlyxEeHo7k5GSIRCKMGDHC4n/3m5k9ezbGjRuHLl26ID4+Hj/99BN+/PFH04Jx8fHxiIyMxMSJE7Fs2TKo1WqzP3QAxgXsli1bhtGjR+ONN96Av78/Ll68iB9//BFz5syBv79/k9VLRERE1BaUlOtwKLM2aKcXIruowuz8tS2++obJ0TfcEzG+rtxLm5oEtxO7S0RERODYsWMIDQ3FuHHjEBQUhISEBERGRuLAgQNmK3x/8MEHCAgIQP/+/fH4449j1qxZZgF11KhRmDFjBqZNm4bOnTvj4MGDWLBggem8RCJBUVERJkyYgMjISIwbNw4JCQlYvHhxg2p1dHREcnIyxo4di8jISDz77LOYOnUqnnvuOQDA2LFjMWLECAwePBgKhQLfffcdOnXqhOXLl+Pdd9/FPffcg2+//RZLly41u26fPn3wj3/8A+PHj4dCocB7771X7/P/+9//Ro8ePfDYY48hJiYGc+bM+dueckuNGTMGH330Ed5//33Exsbis88+w9q1azFo0CAAxhXat2zZgsrKSvTs2RNPP/003nrrrTr/Tnv37kVgYCAeeughtG/fHlOmTEFVVRV7wImIiIhgXBBtX1oBlv56AQ98sg9dl+zAC9+exIYjOcguqoBELEK3IHe8NCQcG5/tjdOLhuHrp3riuYFhuKedjKGbmoxI+LsVt+5CarUaMpkMKpWqToCpqqpCVlYWQkJCzLb8ao0WLVqE5cuX33LuNLVtben9TERERKQ3CDh3RYX96YXYn1aIExdL6iyIFuXtgj7hcvQL90TPEA+42Ddsy1u6u9wqEzYGh5rfxRYvXozg4GAcPnwYPXv2bNQe4URERERE1iIIAi4WVZiC9sGMQqir6i6I1i/cE33DjYuiebmys4HuPKsG78TERCQmJppW7Y6NjcXChQtNexVnZGRg1qxZ2L9/P7RaLUaMGIFPPvnEtF1TfV5//fU6Q5qjoqKQnJzcbK+jNbt+b2kiIiIiopauSKPFwYwi7E8rxP70QlwprTQ772Jvgz5hxh7tPuGeCOWCaNQCWDV4+/v745133kFERAQEQcD69esxevRonDp1CsHBwbj33nvRqVMn7Nq1CwCwYMECjBw5EocPH75l72xsbKxpkSoAdbbyIiIiIiKi1qGqWo9j2cWmXu0/r6rNzttKjPO0r/Vqd2gng42EIzmpZbFqIh05cqTZ/bfeeguJiYk4fPgwrly5guzsbJw6dco0pn79+vVwd3fHrl27EB8ff9Pr2tjYwMfHp1lrJyIiIiKipmcwCDifqzYF7WPZxdDWmM/TjvZxQb9wT/SLMM7TdpSyo41athbzDtXr9fj+++9RXl6OuLg4ZGRkQCQSwc7OztTG3t4eYrEY+/fvv2XwTktLg5+fH+zt7REXF4elS5ciMDDwpu21Wi202r/27FOr1Tdtew3XpKO2gO9jIiIiagmullZif1oh9qUX4kB6IYrLdWbnfVzt0S/C09SrrXCxu8mViFomqwfvs2fPIi4uDlVVVXB2dsaWLVsQExMDhUIBJycnzJ07F2+//TYEQcC8efOg1+uRm5t70+v16tUL69atQ1RUFHJzc7F48WL0798f586dg4uLS72PWbp0aYO3urq2n3VFRQUcHBwsf8FELUhFhXHvyuv3aSciIiJqbhptDY5kFmFfWiH2pRUgo6Dc7LyTVILeoXL0i/BE/whPhCmcOU+bWjWLtxPTarU4cuQILl68iIqKCigUCnTp0gUhISGNKkCn0yEnJwcqlQo//PADvvjiC+zZswcxMTH47bff8PzzzyMrKwtisRiPPfYYzp8/j549eyIxMbFB1y8tLUVQUBCWL1+OKVOm3PQ13djjHRAQcNOl43Nzc1FaWgovLy84OjryQ4BaHUEQUFFRAaVSCTc3N/j6+lq7JCIiImrD9AYBZ6+osC+1APvSCnEypwQ1hr9iiFgEdPR3w4AIT/SLUKBLoBtsOU+brMhq24kdOHAAH330EX766SdUV1dDJpPBwcEBxcXF0Gq1CA0NxbPPPot//OMfN+1Zro9UKkV4eDgAoFu3bjh27Bg++ugjfPbZZ7j33nuRkZGBwsJC2NjYwM3NDT4+PggNDW3w9d3c3BAZGYn09PSbtrGzszMb0v53rs0fVyqVDX4MUUt07b8pIiIioqZ2qdi4zde+tAIcSC+CqrLa7HyghyP6RXhiQIQn4kI9IXPkCDxquxoUvEeNGoWTJ0/i8ccfx2+//Ybu3bubDbPOzMzEvn378N1332H58uX4+uuvMWzYsEYVZDAYzHqfAcDT0xMAsGvXLiiVSowaNarB19NoNMjIyMCTTz7ZqHrqIxKJ4OvrCy8vL1RXV//9A4haIFtbW0gkEmuXQURERG2ERluDQxlF2Jdm7NXOKjQfPu5ib4O+YZ6m4eNBcicrVUp05zUoeN9///3497//fdN5oKGhoQgNDcXEiRNx/vz5W87Bvt78+fORkJCAwMBAlJWVYcOGDUhKSsL27dsBAGvXrkX79u2hUChw6NAhvPzyy5gxYwaioqJM1xg6dCgefPBBTJs2DQAwa9YsjBw5EkFBQbh69SoWLVoEiUSCxx57rEE1WUIikTC4EBEREdFdSW8QcO6KCvvSCrA3te7wcYlYhC4BbugfoUC/CE908uc2X3T3alDwfu655xp8wZiYGMTExDSorVKpxIQJE5CbmwuZTIaOHTti+/btpt7ylJQUzJ8/H8XFxQgODsZrr72GGTNmmF3j2lD0ay5fvozHHnsMRUVFUCgU6NevHw4fPgyFQtHg10BERERERHXlqiqxL7UQe9MKsD+9EKUV5qM/g+SOGBChQP8IT/QOk8PVnsPHiYBGLK52PY1GA4PBfE+9pph4bm1NPZGeiIiIiKg1qtTpcSSrCHtTjXO105Qas/MudjboEy7HgEgF+ocrECh3tFKlRE3LaourXZOVlYVp06YhKSkJVVVVpuOCIEAkEkGv1992UUREREREdOcJgoDkvDLT8PGj2cXQ1fzV0SYWAZ1qh48PjPREJ383Dh8nagCLg/f//d//QRAEfPXVV/D29uZWWkRERERErViRRov96YWmXm1lmflCx34yewyIVGBApAJ9w7j6OFFjWBy8z5w5gxMnTpgtcEZERERERK1Dtd6AUzml2JtagD2pBTh3VYXrJ5/a24rRO1SOARHGsB2mcGJnG9Ftsjh49+jRA5cuXWLwJiIiIiJqJS4VV2BvWgH2phbgYHoRyrQ1ZuejfVwwsLZXu3uwO+xsuHMPUVOyOHh/8cUX+Mc//oErV67gnnvuqbPFWMeOHZusOCIiIiIislylTo/DWUXYk1KAvWkFyCww31Pb3dEW/Wt7tAdEeMLL1d5KlRLdHSwO3gUFBcjIyMDkyZNNx0QiERdXIyIiIiKyEkEQkK7UYE/t8PEjWeaLoknEInQNdMOACAUGRilwj58MYjGHjxPdKRYH76eeegpdunTBd999x8XViIiIiIisRFVZjYPphaawnauqMjvfzs0BAyKNq4/3CffkntpEVmRx8L548SL++9//Ijw8vDnqISIiIiKiehgMAv68qsaeVCX2pBbgZE4p9Ia/VkWzsxGjV6gcAyMVGMhF0YhaFIuD95AhQ3DmzBkGbyIiIiKiZlak0WJfmrFXe29qAYrKdWbnwxROGBjphYFRCvQK8YC9LRdFI2qJLA7eI0eOxIwZM3D27Fl06NChzuJqo0aNarLiiIiIiIjuJjV6A85cLsWelAIkpRbg7BXzrb6c7WzQN1yOgZFeGBDpCX93R+sVS0QNJhKE6/9T/ntisfjmF2sji6up1WrIZDKoVCq4urpauxwiIiIiasOU6iok1c7T3p9WCFVltdn5GF9XDIwyDh/vGugOqc3Nfx8noqbR1JnQ4h5vg8Hw942IiIiIiKhe1XoDTlwswZ7UAiSlFOBCrtrsvMzBFv0jPE1ztbnVF1HrZ3HwJiIiIiIiy+SqKpGUUoCkFCUOpBdBo60xnROJgI7tZBgY5YWBkQp0DnCDhFt9EbUpjQreO3fuxM6dO6FUKuv0gH/11VdNUhgRERERUWulqzHg+MVi41ztlAKk5JeZnZc7SWu3+lKgf4Qn5M52VqqUiO4Ei4P34sWL8cYbb6B79+7w9fXlFgVERERERACull7fq12Ict1fax+JREDnADcMivTCoCgFOrSTQcxebaK7hsXBe/Xq1Vi3bh2efPLJ5qiHiIiIiKhVuL5Xe3eKEqn5GrPzns5/9WoPiFDA3UlqpUqJyNosDt46nQ59+vRpjlqIiIiIiFq0W83VFouALoHuGBSpwKAoL8T6ubJXm4gANCJ4P/3009iwYQMWLFjQHPUQEREREbUY11Yg352ixJ6UAiTn1Z2rPTDKGLQHRHjCzZG92kRUV4OC98yZM03fGwwGrFmzBr///js6duwIW1tbs7bLly9v2gqJiIiIiO6gfHWVafj4/rRClN2wAnnnADcMjjLO1b7Hj3O1iejvNSh4nzp1yux+586dAQDnzp0zO86F1oiIiIiotanRG3D6Uil2pyixO7kA52/YV9vDSYqBkQoMiuJcbSJqnAYF7927dzd3HUREREREd0yRRos9qQXYnVKAvakFUFVWm86JREBHfzcMilRgcLQXOrSTcV9tIrotjdrHu7S0FOnp6QCA8PBwuLm5NWVNRERERERNymAQcPaKytirnVKAPy6XQhD+Oi9zsMWASAUGRykwIFIBT+6rTURNyKLgnZ2djalTp2L79u0Qaj+pRCIRRowYgZUrVyI4OLg5aiQiIiIispiqohp70wpMC6MVlevMzsf6uWJwlBcGRyvQyd8NNhKxlSolorauwcH70qVL6N27N2xtbfHmm2+iffv2AIDz588jMTERcXFxOHbsGPz9/Rv85ImJiUhMTER2djYAIDY2FgsXLkRCQgIAICMjA7NmzcL+/fuh1WoxYsQIfPLJJ/D29r7ldT/99FMsW7YMeXl56NSpEz755BP07NmzwXURERERUesjCAKS88qwO0WJpOQCnMgpgd7wV7e2s50N+kd4YnCUFwZGKeDtam/FaonobiIShOsH2dzclClTkJ6eju3bt8Pe3vxDqrKyEiNGjEBERAS++OKLBj/5Tz/9BIlEgoiICAiCgPXr12PZsmU4deoUgoOD0bFjR3Tq1AmLFy8GACxYsABXr17F4cOHIRbX/xfJTZs2YcKECVi9ejV69eqFFStW4Pvvv0dKSgq8vLwaVJdarYZMJoNKpYKrq2uDXw8RERER3Vnl2hocSC/E7tq9tXNVVWbnI72dTUG7e5AHpDbs1Saiv9fUmbDBwbtdu3bYtGkT+vXrV+/5vXv34tFHH8XVq1dvqyAPDw8sW7YMAQEBSEhIQElJiemFqlQquLu747fffkN8fHy9j+/Vqxd69OiBlStXAjBufxYQEIAXX3wR8+bNa1ANDN5ERERELVdWYTl2JSuxO1mJo1nF0OkNpnP2tmL0DfPEoGgvDIpUIMDD0YqVElFr1dSZsMFDzQsLC285hzs0NBTFxcWNLkSv1+P7779HeXk54uLikJGRAZFIBDu7vxa2sLe3h1gsxv79++sN3jqdDidOnMD8+fNNx8RiMeLj43Ho0KGbPrdWq4VWqzXdV6vVN21LRERERHeWtkaPI5nFtdt9KZFdVGF2PtDDEUOijftq9w6Vw95WYqVKiYjq1+Dg7evri/Pnz990Dve5c+fg4+NjcQFnz55FXFwcqqqq4OzsjC1btiAmJgYKhQJOTk6YO3cu3n77bQiCgHnz5kGv1yM3N7feaxUWFkKv19eZA+7t7Y3k5OSb1rB06VLTcHYiIiIisr5cVSV2JxdgV7ISBzMKUaHTm87ZSkToGeJRuzCaF0I9nSAScbsvImq5Ghy8x4wZg1mzZmHnzp1QKBRm55RKJebOnYsxY8ZYXEBUVBROnz4NlUqFH374ARMnTsSePXsQExOD77//Hs8//zw+/vhjiMViPPbYY+jatetN53c31vz58zFz5kzTfbVajYCAgCZ9DiIiIiK6Ob1BwKmcEuxKVmJXshLJeWVm571c7ExBu1+EJ5ztGrUrLhGRVTT4E2vRokX45ZdfEBYWhv/7v/9DdHQ0BEHAhQsXsGHDBvj4+GDhwoUWFyCVShEeHg4A6NatG44dO4aPPvoIn332Ge69915kZGSgsLAQNjY2cHNzg4+PD0JDQ+u9lqenJyQSCfLz882O5+fn37I33s7OzmxIOxERERE1v5JyHfak1m73lVqA0opq0zmRCOgS4GYK27F+ruzVJqJWq8HB293dHUeOHMGrr76KjRs3orS0FADg5uaGxx9/HG+//TY8PDxuuyCDwWA23xowBmoA2LVrF5RKJUaNGlXvY6VSKbp164adO3eaet8NBgN27tyJadOm3XZtRERERNR4giDgQq5xu69dyUqcyinBdbt9wdXeBgOjvDAkWoGBkV7wcJJar1gioiZk0Rgdd3d3JCYmYtWqVSgoKAAAKBSKRv/1cf78+UhISEBgYCDKysqwYcMGJCUlYfv27QCAtWvXon379lAoFDh06BBefvllzJgxA1FRUaZrDB06FA8++KApWM+cORMTJ05E9+7d0bNnT6xYsQLl5eWYPHlyo2okIiIiosar0NXgQHoRdiUr693uK9rHBYOjvTAk2gtdAtxgI+F2X0TU9jRqcoxIJGrwnti3olQqMWHCBOTm5kImk6Fjx47Yvn07hg0bBgBISUnB/PnzUVxcjODgYLz22muYMWOG2TWuDUW/Zvz48SgoKMDChQuRl5eHzp07Y9u2bXUWXCMiIiKi5pFTVIFdyfnYlVKAw5lF0NXU3e5rcLRxCHk7NwcrVkpEdGc0aB/vESNG4PXXX0fv3r1v2a6srAyrVq2Cs7Mzpk6d2mRF3mncx5uIiIio4ar1BhzPLjGG7WQlMgrKzc77uztgSG3QjuN2X0TUClhlH+9HHnkEY8eOhUwmw8iRI9G9e3f4+fnB3t4eJSUlOH/+PPbv349ffvkF999/P5YtW3bbhRERERFRy1Wo0SIppQC7k5XYm1aAsqoa0zmJWITuQe4YUjuEPNzLmQujEdFdrUE93gCg1Wrx/fffY9OmTdi/fz9UKpXxAiIRYmJiMHz4cEyZMgXt27dv1oLvBPZ4ExEREZkTBAF/XlWbtvs6c7kU1/8WKXeSYmCUAkOivdA/QgGZg631iiUiuk1NnQkbHLxvpFKpUFlZCblcDlvbtvXByuBNREREBJRra7A/vRC7k5XYnaJEvtp855lYP1cMrR1C3tHfDRIxe7WJqG2wylDz+shkMshkstsugIiIiIhajotF5aZe7SOZxdDp/1oYzVEqQb9wT9N8bW9XeytWSkTUejQ6eBMRERFR63f9wmg7k5XIvGFhtEAPR9Nc7V6hHrCz4cJoRESWYvAmIiIiussU1S6MtitFib2p5guj2YhF6BHsYerVDlM4cWE0IqLbxOBNRERE1MYJgoDzuWrsTlZiZ7ISpy/VvzDa0Ghv9Ivw5MJoRERNjMGbiIiIqA2q1OlxIL0Qu1KU2J2sRK6qyux8rJ+raQg5F0YjImpejQrepaWl+OGHH5CRkYHZs2fDw8MDJ0+ehLe3N9q1a9fUNRIRERFRA1wuqTD1ah/KKIK25q+F0RxsJegbLseQaG8MjlbAV+ZgxUqJiO4uFgfvP/74A/Hx8ZDJZMjOzsYzzzwDDw8P/Pjjj8jJycHXX3/dHHUSERER0Q1q9AaculRqXIX8ghIp+WVm5/3dHUy92r1D5bC35cJoRETWYHHwnjlzJiZNmoT33nsPLi4upuP33XcfHn/88SYtjoiIiIjMlVbosCe1ALuSldiTWoDSimrTObEI6B7kgSHtjWE7wsuZC6MREbUAFgfvY8eO4bPPPqtzvF27dsjLy2uSooiIiIjISBAEpCk12HnBOFf7+MViGK5bGE3mYItBUQoMifbCwEgF3Byl1iuWiIjqZXHwtrOzg1qtrnM8NTUVCoWiSYoiIiIiuptVVetxOLPIOIQ8WYnLJZVm56O8XTA42gtD23uhS4AbbCRiK1VKREQNYXHwHjVqFN544w1s3rwZACASiZCTk4O5c+di7NixTV4gERER0d0gT1WF3SlK7LygxIH0QlRW603npDZi9AmTG/fWjvJCgIejFSslIiJLiQTh+l0c/55KpcLDDz+M48ePo6ysDH5+fsjLy0NcXBx++eUXODk5NVetd4xarYZMJoNKpYKrq6u1yyEiIqI2yGAQcOZyqalX+8+r5iMKvV3tMCTaG0OivdA3XA5HKXeBJSK6U5o6E1r8CS6TybBjxw4cOHAAZ86cgUajQdeuXREfH3/bxRARERG1ZWVV1diXVoidF5TYk6pEoUZnOicSAZ383TA02gtD2nshxteVC6MREbURFvd43w3Y401ERERNJauwHDsv5GNXshJHs4pRc93KaC52Nugf6Ykh0d4YFKWAp7OdFSslIqJrrN7j/dJLLyE8PBwvvfSS2fGVK1ciPT0dK1asuO2iiIiIiForXY0Bx7KLTUPIswrLzc6HejqZ9tbuHuwBqQ0XRiMiauss7vFu164d/vvf/6Jbt25mx0+ePIlRo0bh8uXLTVqgNbDHm4iIiCxRqNFid7ISu1OU2JtaCI22xnTOViJCrxA5BteG7RDP1r8eDhFRW2f1Hu+ioiLIZLI6x11dXVFYWHjbBRERERG1dIIg4M+rauxKVmJnshJ/XC7F9V0Zns5SDI4yBu1+EZ5wsbe1XrFERGR1Fgfv8PBwbNu2DdOmTTM7/uuvvyI0NLTJCiMiIiJqScq1NTiQXohdtT3b+Wqt2fkO7WTGvbWjvdChnQxiMRdGIyIiI4uD98yZMzFt2jQUFBRgyJAhAICdO3figw8+4PxuIiIialNyiiqwKzkfu1IKcDijCDq9wXTOUSpBv3BPDG3vhUFRXvB2tbdipURE1JJZHLyfeuopaLVavPXWW3jzzTcBAMHBwUhMTMSECROavEAiIiKiO6Vab8CJiyXYXTuEPF2pMTsf6OGIIdFeGBzthd6hHrCzkVipUiIiak1uazuxgoICODg4wNnZuVGPT0xMRGJiIrKzswEAsbGxWLhwIRISEgAAeXl5mD17Nnbs2IGysjJERUXhtddew9ixY296zddffx2LFy82OxYVFYXk5OQG18XF1YiIiO4exeU6JKUYVyDfk1qAsqq/FkaTiEXoEexeuwq5N8IUTtxbm4joLmD1xdWup1AobuvJ/f398c477yAiIgKCIGD9+vUYPXo0Tp06hdjYWEyYMAGlpaX473//C09PT2zYsAHjxo3D8ePH0aVLl5teNzY2Fr///rvpvo3Nbb1MIiIiakMEQcD5XDV21273deqS+cJo7o62xoXR2nuhf4QCMgcujEZERLfH4kSan5+PWbNmYefOnVAqlbixw1yv1zf4WiNHjjS7/9ZbbyExMRGHDx9GbGwsDh48iMTERPTs2RMA8M9//hMffvghTpw4ccvgbWNjAx8fHwteFREREbVlFboaHEgvwq5kJZJSlMhVVZmdj/F1NQ0h7xzgBgkXRiMioiZkcfCeNGkScnJysGDBAvj6+jbZcCu9Xo/vv/8e5eXliIuLAwD06dMHmzZtwv333w83Nzds3rwZVVVVGDRo0C2vlZaWBj8/P9jb2yMuLg5Lly5FYGDgTdtrtVpotX+tTKpWq5vkNREREZH1XCquMG33dTizCLqavxZGs7cVo1+4J4ZEe2NwtAK+MgcrVkpERG2dxcF7//792LdvHzp37twkBZw9exZxcXGoqqqCs7MztmzZgpiYGADA5s2bMX78eMjlctjY2MDR0RFbtmxBeHj4Ta/Xq1cvrFu3DlFRUcjNzcXixYvRv39/nDt3Di4uLvU+ZunSpXXmhRMREVHrUq034Hh2CXbXzte+cWE0f3cHU692XKgc9rZcGI2IiO4MixdXi4mJwbfffnvLod6W0Ol0yMnJgUqlwg8//IAvvvgCe/bsQUxMDF588UUcPXoUb7/9Njw9PbF161Z8+OGH2LdvHzp06NCg65eWliIoKAjLly/HlClT6m1TX493QEAAF1cjIiJq4Qo1WiSlFGB3shJ70+oujNYtyLgw2tBoL4R7OXNhNCIiapCmXlzN4uD922+/4YMPPsBnn32G4ODg2y7gRvHx8QgLC8OcOXMQHh6Oc+fOITY21ux8eHg4Vq9e3eBr9ujRA/Hx8Vi6dGmD2nNVcyIiopbJYBDw51U1diUrsStFiT8umy+M5uEkxaBIBQZHe2FAhAIyRy6MRkRElrP6qubjx49HRUUFwsLC4OjoCFtb8/+hFRcX31ZBBoMBWq0WFRUVAACxWGx2XiKRwGAw1PfQemk0GmRkZODJJ5+8rbqIiIjIOjTaGuxPK8TuZCV2pyihLNOanY/1+2thtE7+XBiNiIhaHouD94oVK5rsyefPn4+EhAQEBgairKwMGzZsQFJSErZv347o6GiEh4fjueeew/vvvw+5XI6tW7dix44d+Pnnn03XGDp0KB588EFMmzYNADBr1iyMHDkSQUFBuHr1KhYtWgSJRILHHnusyeomIiKi5pVZoMGu2qB9NKsY1fq/urUdpZLahdGMYdvb1d6KlRIREf09i4P3xIkTm+zJlUolJkyYgNzcXMhkMnTs2BHbt2/HsGHDAAC//PIL5s2bh5EjR0Kj0SA8PBzr16/HfffdZ7pGRkYGCgsLTfcvX76Mxx57DEVFRVAoFOjXrx8OHz5823uOExERUfPR1uhxJLMYu1OU2J2sRHZRhdn5ILkjhkR7YUi0F3qGeMDOhgujERFR62HxHO/rVVVVQafTmR1rC3OiOcebiIio+eWqKpGUUoBdyUocSC9EhU5vOmcrEaFniAcGRxnDdqjC2YqVEhHR3cbqc7zLy8sxd+5cbN68GUVFRXXO6/X6eh5FREREd7savQGnLpXWztUuwIVctdl5Lxc7DI4yDh/vF+EJZzuLf00hIiJqkSz+P9qcOXOwe/duJCYm4sknn8Snn36KK1eu4LPPPsM777zTHDUSERFRK1VcrsOeVCV2JRdgb2oBVJXVpnMiEdAlwA2Danu1Y/1cud0XERG1SRYH759++glff/01Bg0ahMmTJ6N///4IDw9HUFAQvv32WzzxxBPNUScRERG1Ate2+9qdosSuZCXO3LDdl8zBFgMjFRgS7YUBkQp4OEmtVywREdEdYnHwLi4uRmhoKADjfO5r24f169cPzz//fNNWR0RERC2eqrLauN1XihJJKQUo1Jhv99Xe1xVDohUYHOWFzgFusJGIb3IlIiKitsni4B0aGoqsrCwEBgYiOjoamzdvRs+ePfHTTz/Bzc2tGUokIiKilkQQBKTkl5kWRjtxsQR6w1/d2k5SCfpFeGJwlBcGRXnBR8btvoiI6O5mcfCePHkyzpw5g4EDB5q2+lq5ciWqq6uxfPny5qiRiIiIrKxcW4MD6YXYnVKApBQlclVVZufDFE6mhdF6BHtAasNebSIiomtuazsxALh48SJOnDiB8PBwdOzYsanqsipuJ0ZERHc7QRCQUVCOpNrh40eziqHTG0zn7WzE6B0qx5BoLwyO8kKg3NGK1RIRETUtq28ndqOgoCAEBQXddiFERERkXZU6PQ5lFmJ3cgGSUpW4VFxpdj7QwxGDoxQYFO2FuFA57G0lVqqUiIiodWlQ8P74448bfMGXXnqp0cUQERHRnZVVWI7dyUokpRbgcGYRdDV/9WpLJWL0CvXAoCgvDIpSINTTidt9ERERNUKDhpqHhISY3S8oKEBFRYVpMbXS0lI4OjrCy8sLmZmZzVLoncSh5kRE1FZVVetxKLMIe1IKsDtFiYtFFWbn27k5YFCUAoOivNAnTA4nu9seHEdERNTqWGWoeVZWlun7DRs2YNWqVfjyyy8RFRUFAEhJScEzzzyD55577rYLIiIioqaVVfjXXO3DmUXQXterbSsRoUewR+0K5AqEezmzV5uIiKiJWby4WlhYGH744Qd06dLF7PiJEyfw8MMPm4X01oo93kRE1JpV6vQ4nFlkDNupBXV6tX1l9qbh433DPeHMXm0iIiIzVl9cLTc3FzU1NXWO6/V65Ofn33ZBREREZBlBEGp7tQuQlFqAI/X0ancP8jANIY/0Zq82ERHRnWRx8B46dCiee+45fPHFF+jatSsAY2/3888/j/j4+CYvkIiIiOqq0NXgUEZRbdiuuwK5n8weA9mrTURE1CJY/H/hr776ChMnTkT37t1ha2sLAKipqcHw4cPxxRdfNHmBREREZOzVTldqkJRSgD2pdffVtpWI0DPEAwMjjb3aEZyrTURE1GJYHLwVCgV++eUXpKamIjk5GQAQHR2NyMjIJi+OiIjoblZWVY0D6YXYk1qAPSkFuKqqMjvv7167AnmkF+K4AjkREVGL1ej/QwcHB0MQBISFhcHGhv+jJyIiul0Gg4DzuWpj0E4twMmLJagx/LUGqtRGjN6hcgyKVGAg99UmIiJqNSxOzBUVFXjxxRexfv16AEBqaipCQ0Px4osvol27dpg3b16TF0lERNRWFZfrsC+tAHtTjT3bhRqt2flQhRMGRiowMFKBXiFyOEglVqqUiIiIGsvi4D1//nycOXMGSUlJGDFihOl4fHw8Xn/9dQZvIiKiW6jRG3D6Uin2pBZgb2oB/riiwvUbezpKJegT5omBUQoMjFAgUO5ovWKJiIioSVgcvLdu3YpNmzahd+/eZsPbYmNjkZGR0aTFERERtQVXSiuxtzZo708vRFmV+bac0T4uxl7tKAW6B3lAaiO2UqVERETUHCwO3gUFBfDy8qpzvLy8nPPMiIiIAFTq9DiSVYS9qYXYm1aAdKXG7Lyboy36RygwIMITAyIV8Ha1t1KlREREdCdYHLy7d++O//3vf3jxxRcBwBS2v/jiC8TFxTVtdURERK2AIAhIzdcYe7XTCnAkqxi6mr+2+hKLgM4BbhgY6YUBkZ7o6O8GiZh/rCYiIrpbWBy83377bSQkJOD8+fOoqanBRx99hPPnz+PgwYPYs2ePRddKTExEYmIisrOzARiHqy9cuBAJCQkAgLy8PMyePRs7duxAWVkZoqKi8Nprr2Hs2LG3vO6nn36KZcuWIS8vD506dcInn3yCnj17WvpSiYiIbqqkXIf96YXYm1qAfWmFyFObb/XlJ7PHgEgFBkQq0DfMEzJHWytVSkRERNZmcfDu168fTp8+jXfeeQcdOnTAb7/9hq5du+LQoUPo0KGDRdfy9/fHO++8g4iICAiCgPXr12P06NE4deoUYmNjMWHCBJSWluK///0vPD09sWHDBowbNw7Hjx9Hly5d6r3mpk2bMHPmTKxevRq9evXCihUrMHz4cKSkpNQ7RJ6IiKghqvUGnLxYgn1pxuHjZ29YFM2udquvAZEKDIz0RJjCmVOwiIiICAAgEoTrf22wPg8PDyxbtgxTpkyBs7MzEhMT8eSTT5rOy+VyvPvuu3j66afrfXyvXr3Qo0cPrFy5EgBgMBgQEBCAF198scErrqvVashkMqhUKri6ut7+iyIiolZHEARcLKrA3tqtvg5lFKJcpzdrE+3jgv6187R7BHvA3pZbfREREbUFTZ0JLe7xBgC9Xo8tW7bgwoULAICYmBiMHj0aNjaNupzpmt9//z3Ky8tNc8X79OmDTZs24f7774ebmxs2b96MqqoqDBo0qN5r6HQ6nDhxAvPnzzcdE4vFiI+Px6FDhxpdGxER3R1UFdU4mFGIvWmF2JdWgMsllWbn5U5S9IvwRP8IBfpHeHJRNCIiImoQi5Pyn3/+iVGjRiEvLw9RUVEAgHfffRcKhQI//fQT7rnnHouud/bsWcTFxaGqqgrOzs7YsmULYmJiAACbN2/G+PHjIZfLYWNjA0dHR2zZsgXh4eH1XquwsBB6vR7e3t5mx729vZGcnHzTGrRaLbRarem+Wq226DUQEVHrVF27p/a+1ALsTSvEH5dLYbhuHJitRITuQR7oH+mJAREKxPi6QsxF0YiIiMhCFgfvp59+GrGxsTh+/Djc3d0BACUlJZg0aRKeffZZHDx40KLrRUVF4fTp01CpVPjhhx8wceJE7NmzBzExMViwYAFKS0vx+++/w9PTE1u3bsW4ceOwb98+i+eT38rSpUuxePHiJrseERG1TIIgIKuwHPvSCrEvrRCHM4ug0ZrvqR3u5WwcPh6hQM8QDzjZNX40FxERERHQiDneDg4OOH78OGJjY82Onzt3Dj169EBlZeVNHtkw8fHxCAsLw5w5cxAeHo5z586ZPVd8fDzCw8OxevXqOo/V6XRwdHTEDz/8gDFjxpiOT5w4EaWlpfjPf/5T73PW1+MdEBDAOd5ERG1AcbkOB9ILsT+tEPvTC3Gl1Pz/U+6OtuhXO3S8f4QnfGUOVqqUiIiIWgqrz/GOjIxEfn5+neCtVCpvOgTcEgaDAVqtFhUVFQCMc7SvJ5FIYDAY6nsopFIpunXrhp07d5qCt8FgwM6dOzFt2rSbPqednR3s7Oxuu3YiIrK+qmq9cfXxdOM87T+vqs1WH5dKxOgW5I5+tb3asX4cPk5ERETNy+LgvXTpUrz00kt4/fXX0bt3bwDA4cOH8cYbb+Ddd981mx/9d38ZmD9/PhISEhAYGIiysjJs2LABSUlJ2L59O6KjoxEeHo7nnnsO77//PuRyObZu3YodO3bg559/Nl1j6NChePDBB03BeubMmZg4cSK6d++Onj17YsWKFSgvL8fkyZMtfalERNQKGAwCzueqjb3a6YU4ll2MqmrzP9BG+7igX7gn+kV4omeIBxylHD5OREREd47Fv3k88MADAIBx48aZ9ie9Nlp95MiRpvsikQh6vb7+i9RSKpWYMGECcnNzIZPJ0LFjR2zfvh3Dhg0DAPzyyy+YN28eRo4cCY1Gg/DwcKxfvx733Xef6RoZGRkoLCw03R8/fjwKCgqwcOFC5OXloXPnzti2bVudBdeIiKj1ulxSgQPpxnnaBzOKUFyuMzuvcLFD/9qg3S/cE15cfZyIiIisyOI53nv27Glw24EDB1pcUEvAfbyJiFqW0godDmUUYX96IQ6kFyK7qMLsvJNUgt6hcvStDdsRXs6mPw4TERERWcrqc7xba5gmIqLWo6paj2PZxTiQXoQD6YU4d1VlNk9bIhahS4CbKWh3DnCDrUR88wsSERERWVGjJrlVVVXhjz/+gFKprLPQ2ahRo5qkMCIiunvU6A04e0WFgxlF2J9WiBM5JdDVmP//JdLbGX3CPNE33BO9Qz3gYm9rpWqJiIiILGNx8N62bRsmTJhgNq/6mobM6yYiIhIEAan5GhxIL8TBjEIcySxG2Q37afvK7NE33BN9w+XoG8Z52kRERNR6WRy8X3zxRTzyyCNYuHAhFywjIqIGu1RcgYMZhTiQXoSDGUUo1GjNzrva2yAuTF4btj0R6unEedpERETUJlgcvPPz8zFz5kyGbiIiuiWlugqHMotwML0IBzMLcam40uy8va0YPYI9aoePyxHrJ4OE+2kTERFRG2Rx8H744YeRlJSEsLCw5qiHiIhaqdIKHQ5nGnuzD2YUIV2pMTsvEYvQOcANfcLk6BPmia5BbrCzkVipWiIiIqI7x+LtxCoqKvDII49AoVCgQ4cOsLU1X9zmpZdeatICrYHbiRER/b2yqmocyy7GodqgfT5XbbbyuEgExPq5ok+YJ+LC5OgR7AFnu0at6UlERER0R1l9O7HvvvsOv/32G+zt7ZGUlGQ2/04kErWJ4E1ERHVV6GpwLLsEhzKKcCizCOeuqKA3mP/tNsLLGX3C5IgLM6487uYotVK1RERERC2HxcH7tddew+LFizFv3jyIxdwzlYioraqq1uPkxRIcyizCoYwinLlcimq9edAOkjsiLlSOuDA54kLlXHmciIiIqB4WB2+dTofx48czdBMRtTFV1XqczCnB4YwiHM4sxulLpdDpzffSbufmYArZcWFy+Lk5WKlaIiIiotbD4uA9ceJEbNq0Ca+++mpz1ENERHdIVbUep3JKcTjTOHT8dE7doO3jao/eoR61YdsTAR4O3OKLiIiIyEIWB2+9Xo/33nsP27dvR8eOHessrrZ8+fImK46IiJpOpc7Yo30kswiHs4rrDdpeLnaIC5Ojd6ixVztI7sigTURERHSbLA7eZ8+eRZcuXQAA586dMzvHX86IiFqOCl0NTlwsweHMIhzJLK53jrbCxQ5xocag3TvUAyGeTvwsJyIiImpiFgfv3bt3N0cdRER0m9RV1TiRXYIjWcU4klWEs5dVqLlh1XEfV3v0CvVA71A5eoUwaBMRERHdCdxQlYiolSou1+FoVrHxll2E81fVuCFno52bA3qFeJjCdqAHh44TERER3WkNDt4PPfRQg9r9+OOPjS6GiIhuLldViaNZxTiWbQzbqfmaOm2C5Y7oGeKBniHGHm1/dy6GRkRERGRtDQ7eMpmsOesgIqLrCIKAzMJyHDP1aBfjckllnXYRXs7oFWoM2j2DPeAj4z7aRERERC1Ng4P32rVrm7MOIqK7Wo3egPO5ahzLLsGxrGIcv1iMQo3OrI1YBMT6ydAj2AM9Q9zRI9gDcmc7K1VMRERERA3FOd5ERFZQoavBqZxSHMsuxvHsEpzMKUGFTm/WRmojRucAN/QK8UCPYA90DXKHsx0/tomIiIhaG/4GR0R0BxSUaXHiYjGOZZfgeHYxzl1VQ3/DSmiu9jboHuyBbkHu6BXigQ7+MtjZSKxUMRERERE1FQZvIqImZjAISC/Q4Hh2CY5fLMaJiyW4WFRRp52vzB49gj3QI8QDPYLdEenlArGYC6ERERERtTUM3kREt6lSp8cfl0tx/GIJTtTeVJXVZm1EIiDK2wXdgtxNYbudm4OVKiYiIiKiO4nBm4jIQldLK3HionFe9smLJfjzqho1Nwwbt7c1zs/uHuSBbsHu6BroDpmDrZUqJiIiIiJrsmrwTkxMRGJiIrKzswEAsbGxWLhwIRISEpCdnY2QkJB6H7d582Y88sgj9Z6bNGkS1q9fb3Zs+PDh2LZtW5PWTkR3B12NcbXxkxdLcKI2aOeqquq083KxQ/dgd3QL8kD3IHfE+LnCViK2QsVERERE1NJYNXj7+/vjnXfeQUREBARBwPr16zF69GicOnUK0dHRyM3NNWu/Zs0aLFu2DAkJCbe87ogRI8y2P7Oz43Y7RNQw+eoqnKztzT6VU4qzV1TQ1hjM2kjEIrT3dUG3QHd0DXJHtyB3tHNzgEjE+dlEREREVJdVg/fIkSPN7r/11ltITEzE4cOHERsbCx8fH7PzW7Zswbhx4+Ds7HzL69rZ2dV5LBHRjbQ1evx5VY1TOaU4mVOC0zmluFJaWaedm6MtugS4oVuQMWh38neDE7f1IiIiIqIGajG/Oer1enz//fcoLy9HXFxcnfMnTpzA6dOn8emnn/7ttZKSkuDl5QV3d3cMGTIES5YsgVwub46yiaiVEAQBl4orceqSsSf71KVSnL+qQrXefG62WARE+biiS6Abuga6o2ugG0I8ndibTURERESNZvXgffbsWcTFxaGqqgrOzs7YsmULYmJi6rT78ssv0b59e/Tp0+eW1xsxYgQeeughhISEICMjA6+++ioSEhJw6NAhSCT174er1Wqh1WpN99Vq9e29KCKyOlVlNf64XIrTOaU4fckYtIvLdXXayZ2k6Bzghq5B7ugS4IaOAW5wZm82ERERETUhkSAIwt83az46nQ45OTlQqVT44Ycf8MUXX2DPnj1m4buyshK+vr5YsGABXnnlFYuun5mZibCwMPz+++8YOnRovW1ef/11LF68uM7xq1evwsfHx9TTpdPpUF1dDRsbG7N54+Xl5QAABwcHiMXGxZSqq6uh0+kgkUhgb2/fqLYVFRUQBAH29vamPxrU1NRAq9VCLBbDwcGhUW0rKythMBhgZ2cHGxtjwNDr9aiqqrKorUgkgqOjo6ltVVUV9Ho9pFIpbG1tLW5rMBhQWWkc5uvk5GRqq9VqUVNTA1tbW0ilUovbCoKAigrjHsqOjo51fp6WtG3Iz74p3if1/Tyb4n1y7ed5u++TG3+et/s+udnPs6FttTV6/JFdiFMXi/BnXjnO5pUjs6AcgiBAqDb+UU0stYetRIQYPxk6+Diig58zugUrEOYjg0gkavTPvrHvE35G8DOCnxF37jOiIT/7232f8DOCnxENacvPCH5G3Pjz5GdEy/2MyM/Ph5+fH1QqFVxdXXHbhBZm6NChwrPPPmt27OuvvxZsbW0FpVLZqGt6enoKq1evvun5qqoqQaVSmW6XLl0SAAgAzJ5zyZIlAgDh6aefNnu8o6OjAEDIysoyHfvwww8FAMLjjz9epxYAwrlz50zH1qxZIwAQRo8ebdY2KChIACAcPXrUdOybb74RAAjx8fFmbWNiYgQAwu7du03HtmzZIgAQ+vTpY9a2e/fuAgDh559/Nh377bffBABCp06dzNoOHDhQACBs3rzZdGz//v0CACE8PNys7X333ScAENauXWs6durUKQGA4OfnZ9b24YcfFgAIK1euNB1LTU0VAAgymcys7cSJEwUAwnvvvWc6dvnyZQGAYGNjY9b2hRdeEAAIixYtMh0rKSkx/Tx1Op3p+KxZswQAwqxZs0zHdDqdqW1JSYnp+KJFiwQAwgsvvGD2fDY2NgIA4fLly6Zj7733ngBAmDhxollbmUwmABBSU1NNx1auXCkAEB5++GGztn5+fgIA4dSpU6Zja9euFQAI9913n1nb8PBwAYCwf/9+07HNmzcLAISBAweate3UqZMAQPjtt99Mx37++WcBgNC9e3eztn369BEACFu2bDEd2717twBAiImJMWsbHx8vABC++eYb07GjR48KAISgoCCztqNHjxYACGvWrDEdO3funABA8PT0NGv7+OOPCwCEDz/80HQsIyNTACDY2TsIi/5zThi1cr8Q8eovgnPHewUAglv/J4WguT8LQXN/Fnot+NH08zxxsVio1NUIgiAIL7/8sgBAePXVV03X1Wg0prYajcZ0/NVXXxUACC+//LJZbfyMMOJnhBE/I4xawmdEVlaWAEBwdHQ0a/v0008LAIQlS5aYjimVStPP83r8jDDiZ4QRPyP+ws8II35GGN0NnxEABJVKJTSFFjee0mAwmA37BozDzEeNGgWFQmHx9S5fvoyioiL4+vretI2dnR1XPidqwf68qsLSXy7gzOVSnPwzFQCg0xuw7mC2qY3UxvhX3bgwOWZP7oFO/m7QV6jg9abxfNdA9ztdNhERERERACsPNZ8/fz4SEhIQGBiIsrIybNiwAe+++y62b9+OYcOGAQDS09MRGRmJX375BSNGjKhzjejoaCxduhQPPvggNBoNFi9ejLFjx8LHxwcZGRmYM2cOysrKcPbs2QaHa7VaDZlMxqHmf9PW2sM/OESs8T/7ljhErKKiAnmqKmSU1uDcFRX+uKzC6SwlSsq1EElsIJLY1P6MDLBDDWJ8XdE1zBedAmToEuAOLycx9Ho9h4jxM8LitvyMaB2fERxGanlbfkbc/OfJzwh+Rlj6s+dnxN33GdHUQ82tGrynTJmCnTt3Ijc3FzKZDB07dsTcuXNNoRsAXn31VXzzzTfIzs42vXmuJxKJsHbtWkyaNAmVlZUYM2YMTp06hdLSUvj5+eHee+/Fm2++CW9v7wbXdS14N9l4fiIyIwgC8tRVOHtZZQzZV4xfCzV1Fz+zlYgQ7eOKjv4ydPJ3Q8cAGcIVzrCR1P08ICIiIiJqCk2dCa2+uFpLxOBN1HQEQcDlkkr8eVWFc1fU+POqCmevqFGo0dZpKxGLEOHljA7tZOjgL0NHfze093WBnU39OxIQERERETWHps6ELW6ONxG1XgaDgKyicvx5VY0/r6hwrjZsqyqr67S9MWR3aCdDe19X2NsyZBMRERFR28LgTUSNUlWtR2p+Gf68qsb5q2qcz1XjQq4aFTp9nba2EhEivV1wj58M97RzRWw7GWIYsomIiIjoLsHgTUR/q7hchwu1wfpa0E4v0EBvqDtTxd5WjPa+rn+FbD8ZIr1dTKuOExERERHdbRi8icikRm9AZmF5bcguM4VtZVnd+dgA4O5oi1g/GWL9XBHj54oYX1eEeDpx4TMiIiIiousweBPdhQRBQIFGi5S8MqTklSE5rwzJeWqk5mugqzHU+5hguSOifVxNITvWTwZvVzvTFhhERERERFQ/Bm+iNq5CV4PUfA1S8tRIvi5oF5fX3boLAJykEkT7uiLaxwXtfV3RvvZ7Jzt+XBARERERNQZ/kyZqI6qq9cgo0CA1vwyp+Rqk5pUhVVmGS8WV9bYXi4BguROifFwQ5eNiCtoB7o4Qi9mLTURERETUVBi8iVqZawE7XalBWr4GaUpj0L5YVI561joDAHg62yG6NmBfC9kRXi5wkHJVcSIiIiKi5sbgTdRCqauqkVlQjrT8MqQra4O2UoNLJRUQbhKw3RxtEentgkhvZ0R5uyDC2wWR3i7wcJLe2eKJiIiIiMiEwZvIigwGAbnqKmQoNcgoqL0py5FRoLnpSuKAMWBHeDkj3MsFEV7OxrDt4wyFMxc7IyIiIiJqaRi8ie4AVUU1sorKkVWoQVZBOTILy5FZUI7MQg2qqutfRRwAFC52iPByNoZsbxeEK5wR4e0MuZOUAZuIiIiIqJVg8CZqIhptDS4WleNiUQWyCsvNbjdbQRwAbCUiBMudEKpwQpjC2Xjzckaowgmu9rZ38BUQEREREVFzYPAmsoCqohoXi8uRXVSBi4W1X4uMXws1Nx8aDgDernYI8XRCiKczQj2dEOLphDAvZwS4O8BGIr5Dr4CIiIiIiO40Bm+i6+hqDLhaWomc4grkFFfgUu3Xa7eyqppbPl7uJEWg3NHYg+3phODagB3i6cR9sImIiIiI7lJMAnRXqdYbkKeqwqWSClwuqay9Gb+/UlKJXFXlTbfkukbhYocQuROC5I4I9qz9KndCoNyRQ8OJiIiIiKgOBm9qMwRBgLqqBrmqSlwtrcSV0ipcLa1EbmklrpZW4Uppw4K1va0YgR6OCPRwREDt12s3f3dH7n1NREREREQWYfCmVkEQBKgqq5GrqkKeugp5qirkqqqQr6pCrroKeSpjuNZobz0UHACkNmL4uzugnZsD/N0d4e/uUHtzRIC7AxQu3JKLiIiIiIiaDoM3WZUgCCjT1kCp1kKproKyTIv82q+m79XGkK2tufm2W9dzd7SFn5sD/NyM4drPzR6+MuP9AA8HeDrZQSxmsCYiIiIiojuDwZuanCAI0GhrUKTRoVCjRaFGiwKNDoVlWhRotCgs09Ye16GgTIvKan2Dr+3hJIW3qz18ZfbwkdnDx/Wvr+3cHeAnc+BQcCIiIiIialEYvOmWBEFAVbUBpZU6lJRXo7RSh9KKapRU6FCs0aGoXIfiem46fcN6p69xsbeBl4sdvFzs4e1qBy9Xe+P92q9+Mgd4udrB3pahmoiIiIiIWhcG77tAtd6Acm0NNNoalFXVQF1ZDbXpazVUldVQV9ZAXVUNdaXxfmmFMWSXVFRD18Ah3jdysJVA4WIHT2cpPJ3t4OliB09nOyhc7KC4dszZDt6u9uylJiIiIiKiNovBu4Wo1htQVa2Htsb4tar6r/vaaj0qq/Wo0OlRoaup/apHpe7GY8ZgXa6rgabKGLQ12hpUVTcuOF/PRiyCm6MUbo62cHOwhZujFHInKTyca7/ecJM72TFMExERERERgcG7UQwGAXP+/QcMBgE1BgF6QTB9b/oqCKjRC6gxGKDTC6iuMaBab7zpamqP1d7X1hig/7s9rpqA1EYMFzsbyBxs4eJgC5mDLVztbeDqYAtXe1u4OtjA1d543L02ZMscbOHuJIWTVMKVvomIiIiIiBrBqsE7MTERiYmJyM7OBgDExsZi4cKFSEhIQHZ2NkJCQup93ObNm/HII4/Ue04QBCxatAiff/45SktL0bdvXyQmJiIiIqLJ6haJgB9OXG6y693IzkYMOxsx7G0ltTfj945SCRylNnCQSuB47b6dDRxtJXCQSuBkZwNnOxs429d+ve7mZGcDqY242WomIiIiIiKi+lk1ePv7++Odd95BREQEBEHA+vXrMXr0aJw6dQrR0dHIzc01a79mzRosW7YMCQkJN73me++9h48//hjr169HSEgIFixYgOHDh+P8+fOwt7dvkrpFIhHmjoiGRAxIxGJIRIBEIoZEJIKNWASx2PyrVCKGrY0YUokYUhsRbCVi2ErEkNYes5WITeFaKhFzqysiIiIiIqI2RCQIQvOPcbaAh4cHli1bhilTptQ516VLF3Tt2hVffvllvY8VBAF+fn545ZVXMGvWLACASqWCt7c31q1bh0cffbRBNajVashkMqhUKri6ujb+xRAREREREVGr09SZsMWMPdbr9di4cSPKy8sRFxdX5/yJEydw+vTpegP5NVlZWcjLy0N8fLzpmEwmQ69evXDo0KFmqZuIiIiIiIjoVqy+uNrZs2cRFxeHqqoqODs7Y8uWLYiJianT7ssvv0T79u3Rp0+fm14rLy8PAODt7W123Nvb23SuPlqtFlqt1nRfrVZb+jKIiIiIiIiI6mX1Hu+oqCicPn0aR44cwfPPP4+JEyfi/PnzZm0qKyuxYcOGW/Z2346lS5dCJpOZbgEBAc3yPERERERERHT3sXrwlkqlCA8PR7du3bB06VJ06tQJH330kVmbH374ARUVFZgwYcItr+Xj4wMAyM/PNzuen59vOlef+fPnQ6VSmW6XLl1q5KshIiIiIiIiMmf14H0jg8FgNuwbMA4zHzVqFBQKxS0fGxISAh8fH+zcudN0TK1W48iRI/XOG7/Gzs4Orq6uZjciIiIiIiKipmDVOd7z589HQkICAgMDUVZWhg0bNiApKQnbt283tUlPT8fevXvxyy+/1HuN6OhoLF26FA8++CBEIhGmT5+OJUuWICIiwrSdmJ+fH8aMGXOHXhURERERERHRX6wavJVKJSZMmIDc3FzIZDJ07NgR27dvx7Bhw0xtvvrqK/j7++Pee++t9xopKSlQqVSm+3PmzEF5eTmeffZZlJaWol+/fti2bVuT7eFNREREREREZIkWt493S6BSqeDm5oZLly5x2DkREREREdFdRq1WIyAgAKWlpZDJZLd9PatvJ9YSFRUVAQBXNyciIiIiIrqLFRUVMXg3Fw8PDwBATk5Ok/wjE1nTtb/WcQQHtQV8P1Nbw/c0tSV8P1NbolKpEBgYaMqGt4vBux5isXGxd5lMxg8NajO4Yj+1JXw/U1vD9zS1JXw/U1tyLRve9nWa5CpEREREREREVC8GbyIiIiIiIqJmxOBdDzs7OyxatAh2dnbWLoXotvH9TG0J38/U1vA9TW0J38/UljT1+5nbiRERERERERE1I/Z4ExERERERETUjBm8iIiIiIiKiZsTgTURERERERNSMGLzr8emnnyI4OBj29vbo1asXjh49au2SiCy2dOlS9OjRAy4uLvDy8sKYMWOQkpJi7bKImsQ777wDkUiE6dOnW7sUoka5cuUK/u///g9yuRwODg7o0KEDjh8/bu2yiBpFr9djwYIFCAkJgYODA8LCwvDmm2+CS0lRa7B3716MHDkSfn5+EIlE2Lp1q9l5QRCwcOFC+Pr6wsHBAfHx8UhLS7P4eRi8b7Bp0ybMnDkTixYtwsmTJ9GpUycMHz4cSqXS2qURWWTPnj2YOnUqDh8+jB07dqC6uhr33nsvysvLrV0a0W05duwYPvvsM3Ts2NHapRA1SklJCfr27QtbW1v8+uuvOH/+PD744AO4u7tbuzSiRnn33XeRmJiIlStX4sKFC3j33Xfx3nvv4ZNPPrF2aUR/q7y8HJ06dcKnn35a7/n33nsPH3/8MVavXo0jR47AyckJw4cPR1VVlUXPw1XNb9CrVy/06NEDK1euBAAYDAYEBATgxRdfxLx586xcHVHjFRQUwMvLC3v27MGAAQOsXQ5Ro2g0GnTt2hWrVq3CkiVL0LlzZ6xYscLaZRFZZN68eThw4AD27dtn7VKImsQDDzwAb29vfPnll6ZjY8eOhYODA7755hsrVkZkGZFIhC1btmDMmDEAjL3dfn5+eOWVVzBr1iwAgEqlgre3N9atW4dHH320wddmj/d1dDodTpw4gfj4eNMxsViM+Ph4HDp0yIqVEd0+lUoFAPDw8LByJUSNN3XqVNx///1mn9NErc1///tfdO/eHY888gi8vLzQpUsXfP7559Yui6jR+vTpg507dyI1NRUAcObMGezfvx8JCQlWrozo9mRlZSEvL8/s9w6ZTIZevXpZnA9tmrq41qywsBB6vR7e3t5mx729vZGcnGylqohun8FgwPTp09G3b1/cc8891i6HqFE2btyIkydP4tixY9Yuhei2ZGZmIjExETNnzsSrr76KY8eO4aWXXoJUKsXEiROtXR6RxebNmwe1Wo3o6GhIJBLo9Xq89dZbeOKJJ6xdGtFtycvLA4B68+G1cw3F4E10F5g6dSrOnTuH/fv3W7sUoka5dOkSXn75ZezYsQP29vbWLofothgMBnTv3h1vv/02AKBLly44d+4cVq9ezeBNrdLmzZvx7bffYsOGDYiNjcXp06cxffp0+Pn58T1NVItDza/j6ekJiUSC/Px8s+P5+fnw8fGxUlVEt2fatGn4+eefsXv3bvj7+1u7HKJGOXHiBJRKJbp27QobGxvY2Nhgz549+Pjjj2FjYwO9Xm/tEokazNfXFzExMWbH2rdvj5ycHCtVRHR7Zs+ejXnz5uHRRx9Fhw4d8OSTT2LGjBlYunSptUsjui3XMmBT5EMG7+tIpVJ069YNO3fuNB0zGAzYuXMn4uLirFgZkeUEQcC0adOwZcsW7Nq1CyEhIdYuiajRhg4dirNnz+L06dOmW/fu3fHEE0/g9OnTkEgk1i6RqMH69u1bZ3vH1NRUBAUFWakiottTUVEBsdg8VkgkEhgMBitVRNQ0QkJC4OPjY5YP1Wo1jhw5YnE+5FDzG8ycORMTJ05E9+7d0bNnT6xYsQLl5eWYPHmytUsjssjUqVOxYcMG/Oc//4GLi4tpHopMJoODg4OVqyOyjIuLS531CZycnCCXy7luAbU6M2bMQJ8+ffD2229j3LhxOHr0KNasWYM1a9ZYuzSiRhk5ciTeeustBAYGIjY2FqdOncLy5cvx1FNPWbs0or+l0WiQnp5uup+VlYXTp0/Dw8MDgYGBmD59OpYsWYKIiAiEhIRgwYIF8PPzM6183lDcTqweK1euxLJly5CXl4fOnTvj448/Rq9evaxdFpFFRCJRvcfXrl2LSZMm3dliiJrBoEGDuJ0YtVo///wz5s+fj7S0NISEhGDmzJl45plnrF0WUaOUlZVhwYIF2LJlC5RKJfz8/PDYY49h4cKFkEql1i6P6JaSkpIwePDgOscnTpyIdevWQRAELFq0CGvWrEFpaSn69euHVatWITIy0qLnYfAmIiIiIiIiakac401ERERERETUjBi8iYiIiIiIiJoRgzcRERERERFRM2LwJiIiIiIiImpGDN5EREREREREzYjBm4iIiIiIiKgZMXgTERERERERNSMGbyIiIiIiIqJmxOBNREREd4ROp0N4eDgOHjxotRrmzZuHF1980WrPT0REdycGbyIiokaYNGkSRCJRnVt6erq1S2uxVq9ejZCQEPTp08fs+O7du/HAAw9AoVDA3t4eYWFhGD9+PPbu3Wtqk5SUBJFIhNLS0jrXDQ4OxooVKxpUw6xZs7B+/XpkZmbezkshIiKyCIM3ERFRI40YMQK5ublmt5CQkDrtdDqdFaprWQRBwMqVKzFlyhSz46tWrcLQoUMhl8uxadMmpKSkYMuWLejTpw9mzJjR5HV4enpi+PDhSExMbPJrExER3QyDNxERUSPZ2dnBx8fH7CaRSDBo0CBMmzYN06dPNwU9ADh37hwSEhLg7OwMb29vPPnkkygsLDRdr7y8HBMmTICzszN8fX3xwQcfYNCgQZg+fbqpjUgkwtatW83qcHNzw7p160z3L126hHHjxsHNzQ0eHh4YPXo0srOzTecnTZqEMWPG4P3334evry/kcjmmTp2K6upqUxutVou5c+ciICAAdnZ2CA8Px5dffglBEBAeHo7333/frIbTp0/fssf/xIkTyMjIwP333286lpOTg+nTp2P69OlYv349hgwZgqCgIHTs2BEvv/wyjh8/3tAfhcm6devqHYnw+uuvm9qMHDkSGzdutPjaREREjcXgTURE1AzWr18PqVSKAwcOYPXq1SgtLcWQIUPQpUsXHD9+HNu2bUN+fj7GjRtneszs2bOxZ88e/Oc//8Fvv/2GpKQknDx50qLnra6uxvDhw+Hi4oJ9+/bhwIEDcHZ2xogRI8x63nfv3o2MjAzs3r0b69evx7p168zC+4QJE/Ddd9/h448/xoULF/DZZ5/B2dkZIpEITz31FNauXWv2vGvXrsWAAQMQHh5eb1379u1DZGQkXFxcTMf+/e9/o7q6GnPmzKn3MSKRyKLXDgDjx483G4Hw3XffwcbGBn379jW16dmzJy5fvmz2xwgiIqLmZGPtAoiIiFqrn3/+Gc7Ozqb7CQkJ+P777wEAEREReO+990znlixZgi5duuDtt982Hfvqq68QEBCA1NRU+Pn54csvv8Q333yDoUOHAjCGd39/f4tq2rRpEwwGA7744gtTcF27di3c3NyQlJSEe++9FwDg7u6OlStXQiKRIDo6Gvfffz927tyJZ555Bqmpqdi8eTN27NiB+Ph4AEBoaKjpOSZNmoSFCxfi6NGj6NmzJ6qrq7Fhw4Y6veDXu3jxIvz8/MyOpaamwtXVFT4+PqZj//73vzFx4kTT/UOHDqFDhw6m+/X9e1RUVJi+d3BwgIODAwAgIyMDU6dOxdtvv41hw4aZ2lyr4+LFiwgODr5pzURERE2FwZuIiKiRBg8ebDZX2MnJyfR9t27dzNqeOXMGu3fvNgvq12RkZKCyshI6nQ69evUyHffw8EBUVJRFNZ05cwbp6elmPcsAUFVVhYyMDNP92NhYSCQS031fX1+cPXsWgHHYuEQiwcCBA+t9Dj8/P9x///346quv0LNnT/z000/QarV45JFHblpXZWUl7O3t6xy/sVd7+PDhOH36NK5cuYJBgwZBr9ebnd+3b1+d1zZo0KA611WpVHjggQdw//33Y/bs2WbnrgXz6wM7ERFRc2LwJiIiaiQnJ6ebDq2+PoQDgEajwciRI/Huu+/Waevr69vg1dBFIhEEQTA7dv3cbI1Gg27duuHbb7+t81iFQmH63tbWts51DQYDgL+C6a08/fTTePLJJ/Hhhx9i7dq1GD9+PBwdHW/a3tPT0xTsr4mIiIBKpUJeXp6p19vZ2Rnh4eGwsan/V5SQkBC4ubmZHbuxrV6vx/jx4+Hq6oo1a9bUuUZxcTEA838PIiKi5sQ53kRERHdA165d8eeffyI4OBjh4eFmNycnJ4SFhcHW1hZHjhwxPaakpASpqalm11EoFMjNzTXdT0tLM+u57dq1K9LS0uDl5VXneWQyWYNq7dChAwwGA/bs2XPTNvfddx+cnJyQmJiIbdu24amnnrrlNbt06YLk5GSzPxo8/PDDsLW1rfePEbdjxowZOHv2LLZu3VpvL/u5c+dga2uL2NjYJn1eIiKim2HwJiIiugOmTp2K4uJiPPbYYzh27BgyMjKwfft2TJ48GXq9Hs7OzpgyZQpmz56NXbt24dy5c5g0aRLEYvP/VQ8ZMgQrV67EqVOncPz4cfzjH/8w671+4okn4OnpidGjR2Pfvn3IyspCUlISXnrpJVy+fLlBtQYHB2PixIl46qmnsHXrVtM1Nm/ebGojkUgwadIkzJ8/HxEREYiLi7vlNQcPHgyNRoM///zTdCwwMBAffPABPvroI0ycOBG7d+9GdnY2Tp48iY8//tj0PJZYu3YtVq1ahdWrV0MkEiEvLw95eXnQaDSmNvv27UP//v0b1LNPRETUFBi8iYiI7gA/Pz8cOHAAer0e9957Lzp06IDp06fDzc3NFK6XLVuG/v37Y+TIkYiPj0e/fv3qzBX/4IMPEBAQgP79++Pxxx/HrFmzzIZ4Ozo6Yu/evQgMDMRDDz2E9u3bY8qUKaiqqoKrq2uD601MTMTDDz+MF154AdHR0XjmmWdQXl5u1mbKlCnQ6XSYPHny315PLpfjwQcfrDME/sUXX8Rvv/2GgoICPPzww4iIiMB9992HrKwsbNu2zWxhtYbYs2cP9Ho9Ro0aBV9fX9Pt+oXfNm7ciGeeecai6xIREd0OkXDjRDEiIiJqMQYNGoTOnTtjxYoV1i6ljn379mHo0KG4dOkSvL29/7b9H3/8gWHDhiEjI6PeRebuhF9//RWvvPIK/vjjj5vOIyciImpq7PEmIiIii2i1Wly+fBmvv/46HnnkkQaFbgDo2LEj3n33XWRlZTVzhTdXXl6OtWvXMnQTEdEdxf/rEBERkUW+++47TJkyBZ07d8bXX39t0WMnTZrUPEU10MMPP2zV5yciorsTh5oTERERERERNSMONSciIiIiIiJqRgzeRERERERERM2IwZuIiIiIiIioGTF4ExERERERETUjBm8iIiIiIiKiZsTgTURERERERNSMGLyJiIiIiIiImhGDNxEREREREVEzYvAmIiIiIiIiakb/D71KGbE/S3MYAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setup a path along a rectangular perimeter\n",
    "Nx = 21\n",
    "Ny = 11\n",
    "# Add a buffer distance around the strip, to ensure that we do not interpolate from grid points on the PEC interface\n",
    "buffer = 200\n",
    "left = -width / 2 - buffer\n",
    "right = width / 2 + buffer\n",
    "bottom = height - buffer\n",
    "top = height + buffer\n",
    "y_bottom = np.full((1, Nx), bottom)\n",
    "y_top = np.full((1, Nx), top)\n",
    "x_left = np.full((1, Ny), left)\n",
    "x_right = np.full((1, Ny), right)\n",
    "x = np.linspace(left, right, Nx)\n",
    "y = np.linspace(bottom, top, Ny)\n",
    "\n",
    "# Concatenate the arrays of yz coordinates into a single array\n",
    "xy_bottom = np.vstack((x, y_bottom))\n",
    "xy_right = np.vstack((x_right, y))\n",
    "xy_top = np.vstack((np.flip(x), y_top))\n",
    "xy_left = np.vstack((x_left, np.flip(y)))\n",
    "vertices = np.transpose(np.hstack((xy_bottom, xy_right, xy_top, xy_left)))\n",
    "# Supply the CustomCurrentIntegral2D with the created vertices, along with the normal axis and position of the plane at x=0\n",
    "custom_path = microwave.CustomCurrentIntegral2D(axis=0, position=0.0, vertices=vertices)\n",
    "# Setup  the impedance calculator using the previously defined voltage_integral\n",
    "impedance_calculator = microwave.ImpedanceCalculator(\n",
    "    voltage_integral=voltage_path, current_integral=custom_path\n",
    ")\n",
    "# Compute the impedance again in the same manner\n",
    "impedance = impedance_calculator.compute_impedance(sim_data[\"freq_yz\"])\n",
    "\n",
    "# Plot the results for visual comparison\n",
    "f, ax1 = plt.subplots(1, 1, tight_layout=True, figsize=(10, 3))\n",
    "ax1.plot(freqs / 1e9, abs(impedance).values, label=\"Tidy3D\")\n",
    "ax1.set_ylabel(\"Impedance (Ohm)\")\n",
    "ax1.set_xlabel(\"Frequency (GHz)\")\n",
    "ax1.set_xlim(0, 10)\n",
    "ax1.axhline(y=Zo, color=\"k\", linestyle=\":\", label=\"Quasi-static model\")\n",
    "ax1.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot generated using the custom-defined path closely matches the plot from earlier, where the `CurrentIntegralAxisAligned` was used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding a Matched Load\n",
    "Now that we have determined the characteristic impedance of the transmission line, we can perform a final experiment where the microstrip will be terminated by a matched load ($Z_L = Z_0$). If the impedance was computed accurately, the resulting simulation should result in small reflections from the load.\n",
    "\n",
    "In order to model the load, the `LumpedResistor` element is be added to the simulation. We choose a resistance of 38 $\\Omega$, which is roughly equal to the characteristic impedance at the central frequency of 5 GHz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Shift the center of the simulation domain so that the microstrip does not reach the PML boundary\n",
    "new_center = list(sim_center)\n",
    "new_center[0] = 0\n",
    "# Define a resistor that will be added at the end of the microstrip.\n",
    "# The lumped resistor is implemented as a 2D conductive sheet, where the conductivity is computed from\n",
    "# the supplied dimensions, voltage_axis, and the desired resistance.\n",
    "load = td.LumpedResistor(\n",
    "    center=(length / 2, 0, height / 2),\n",
    "    size=(0, width, height),\n",
    "    num_grid_cells=None,\n",
    "    voltage_axis=2,\n",
    "    resistance=38,\n",
    "    name=\"load\",\n",
    ")\n",
    "sim_with_load = sim.updated_copy(center=new_center, lumped_elements=[load])\n",
    "\n",
    "\n",
    "# Plot the current simulation setup and scale to millimeters\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(11, 4))\n",
    "sim_with_load.plot(z=height, ax=ax1)\n",
    "sim_with_load.plot(x=length / 2, ax=ax2, vlim=[0, 3 * mm], hlim=[-5 * mm, 5 * mm])\n",
    "update_axis_with_format(ax1, formatter, \"x (mm)\", \"y (mm)\")\n",
    "ax1.set_title(\"Top view of microstrip\")\n",
    "update_axis_with_format(ax2, formatter, \"y (mm)\", \"z (mm)\")\n",
    "ax2.set_title(\"Terminated side of microstrip with lumped resistor visible\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running the simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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\">11:23:34 EDT </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'impedance_calc_with_load'</span> with task_id               \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-a6ceed8c-aabe-42b3-a246-3733bd36ab66'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:23:34 EDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'impedance_calc_with_load'\u001b[0m with task_id               \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-a6ceed8c-aabe-42b3-a246-3733bd36ab66'\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-a6ceed8c-aabe-42b3-a246-3733bd36ab66\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-a6ceed8c-aab</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-a6ceed8c-aabe-42b3-a246-3733bd36ab66\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">e-42b3-a246-3733bd36ab66'</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=668527;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a6ceed8c-aabe-42b3-a246-3733bd36ab66\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=349945;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a6ceed8c-aabe-42b3-a246-3733bd36ab66\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=668527;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a6ceed8c-aabe-42b3-a246-3733bd36ab66\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=286361;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a6ceed8c-aabe-42b3-a246-3733bd36ab66\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=668527;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a6ceed8c-aabe-42b3-a246-3733bd36ab66\u001b\\\u001b[32m-a6ceed8c-aab\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=668527;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a6ceed8c-aabe-42b3-a246-3733bd36ab66\u001b\\\u001b[32me-42b3-a246-3733bd36ab66'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "d36ca33bee7e4e0dbdafa419c6077617",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:23:35 EDT </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:23:35 EDT\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\">11:23:38 EDT </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:23:38 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:23:40 EDT </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.333</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;36m11:23:40 EDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.333\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": {
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       "model_id": "190ad27be8964c2096be644b6784bb9e",
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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\">11:24:08 EDT </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;36m11:24:08 EDT\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m32\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\">11:24:12 EDT </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:24:12 EDT\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-a6ceed8c-aabe-42b3-a246-3733bd36ab66\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-a6ceed8c-aab</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-a6ceed8c-aabe-42b3-a246-3733bd36ab66\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">e-42b3-a246-3733bd36ab66'</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=242148;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a6ceed8c-aabe-42b3-a246-3733bd36ab66\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=323630;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a6ceed8c-aabe-42b3-a246-3733bd36ab66\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=242148;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a6ceed8c-aabe-42b3-a246-3733bd36ab66\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=121517;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a6ceed8c-aabe-42b3-a246-3733bd36ab66\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=242148;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a6ceed8c-aabe-42b3-a246-3733bd36ab66\u001b\\\u001b[4;34m-a6ceed8c-aab\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=242148;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a6ceed8c-aabe-42b3-a246-3733bd36ab66\u001b\\\u001b[4;34me-42b3-a246-3733bd36ab66'\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": "84ca737505da4b109da9e743b65f2449",
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       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:24:23 EDT </span>loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:24:23 EDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data_with_load = web.run(sim_with_load, \"impedance_calc_with_load\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we plot the frequency domain fields at the central frequency. Large reflections will manifest as very apparent standing waves along the x axis. In the plot below, we observe only a slight variation of the electric field along x meaning reflections are small. This indicates that the load resistor was closely matched to the characteristic impedance of the transmission line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Ez field in the xy plane\n",
    "f, ax1 = plt.subplots(1, 1, tight_layout=True, figsize=(8, 3))\n",
    "sim_data_with_load.plot_field(field_monitor_name=\"propagate\", field_name=\"Ez\", val=\"abs\", f=freq0, ax=ax1)\n",
    "update_axis_with_format(ax1, formatter, \"x (mm)\", \"y (mm)\")\n",
    "ax1.set_title(\"Cross section at z = 0.5 mm\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we inspect the voltage at x = 0.0 in the time domain to get a clearer view of reflections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Retrieve monitor data from the completed simulation\n",
    "voltage_time_data = sim_data_with_load[\"time_voltage_yz\"]\n",
    "voltage_freq_data = sim_data_with_load[\"freq_yz\"]\n",
    "\n",
    "# The path for the voltage integral is defined by its center and size along a desired axis.\n",
    "voltage_path = microwave.VoltageIntegralAxisAligned(\n",
    "    center=(0, 0, height / 2),\n",
    "    size=(0, 0, height),\n",
    "    extrapolate_to_endpoints=True,\n",
    "    snap_path_to_grid=True,  # If true, the path will be snapped to the Yee grid.\n",
    "    sign=\"+\",\n",
    ")\n",
    "# Compute voltage\n",
    "voltage_time_domain = voltage_path.compute_voltage(voltage_time_data)\n",
    "\n",
    "voltage_freq_domain = voltage_path.compute_voltage(voltage_freq_data)\n",
    "\n",
    "# Plot the time domain voltage that is observed at the x = 0 position of the microstrip\n",
    "f, ax1 = plt.subplots(1, 1, tight_layout=True, figsize=(6, 3))\n",
    "max_voltage = max(abs(voltage_time_domain))\n",
    "(voltage_time_domain / max_voltage).plot(ax=ax1)\n",
    "ax1.set_title(\"Voltage in normalized units\")\n",
    "ax1.set_ylabel(\"Voltage (normalized units)\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the time domain, the reflected voltage waves are easily spotted, and as expected their amplitudes are much smaller than the incident wave. Also note that the left-hand side of the microstrip was left open-circuited, which results in large reflections for waves that propagate back to the source position. As a result, multiple reflections are observed in the plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "[1]   David M. Pozar, \"Microwave Network Analysis\" in Microwave Engineering, 4th ed. 2011, ch. 4.\n",
    "\n",
    "[2]   E. Hammerstad and O. Jensen, \"Accurate Models for Microstrip Computer-Aided Design,\"\n",
    "      1980 IEEE MTT-S International Microwave Symposium Digest,\n",
    "      Washington, DC, USA, 1980, pp. 407-409."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "description": "This notebook demonstrates how to use the tools within the microwave plugin to compute the voltage and current along a transmission line. The voltages and currents can be computed in either the time domain or the frequency domain and may be used to compute the characteristic impedance. We also introduce the LumpedResistor and demonstrate how they are added to a simulation.",
  "feature_image": "",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "microstrip, path integral, line integral, characteristic impedance, voltage, current, 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": "3.10.14"
  },
  "title": "Computing the characteristic impedance of transmission lines using Tidy3D"
 },
 "nbformat": 4,
 "nbformat_minor": 4
}