{
 "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 Tidy3D 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. Summary of the three common definitions for the impedance of transmission lines.\n",
    "\n",
    "5. Example simulation of a microstrip terminated by a matched load.\n",
    "\n",
    "5. Example of calculating the impedance of a lossy microstrip.\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 matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# tidy3D imports\n",
    "import tidy3d as td\n",
    "import tidy3d.rf as rf\n",
    "from skrf import Frequency\n",
    "from skrf.media.mline import MLine\n",
    "from tidy3d import web\n",
    "from tidy3d.plugins import microwave as mw\n",
    "from tidy3d.plugins.dispersion import FastDispersionFitter\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.\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 = 10e9\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, 50)\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 = 30 / fwidth\n",
    "\n",
    "gaussian = td.GaussianPulse.from_frequency_range(freq_start, freq_stop, remove_dc_component=True)\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 = 0.035 * mm\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",
    "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=pec,\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 = thickness\n",
    "mesh_overrides = [\n",
    "    td.MeshOverrideStructure(\n",
    "        geometry=td.Box(\n",
    "            center=strip_center,\n",
    "            size=[length + 2 * width, 2 * width, 2 * height],\n",
    "        ),\n",
    "        dl=[0.1 * mm, 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 is to use a 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",
    "    colocate=False,\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",
    "    colocate=False,\n",
    ")\n",
    "\n",
    "# Mode solver monitor, which will be used as an alternate approach for computing impedance.\n",
    "mode_spec = td.ModeSpec(num_modes=1, target_neff=np.sqrt(4.4))\n",
    "mode_yz = td.ModeSolverMonitor(\n",
    "    center=[0, 0, 2 * height],\n",
    "    size=[0, td.inf, td.inf],\n",
    "    freqs=freqs,\n",
    "    name=\"mode_yz\",\n",
    "    colocate=False,\n",
    "    mode_spec=mode_spec,\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",
    "    colocate=False,\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",
    "    colocate=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, the simulation is set up using the created components. Before running the simulation, we choose to plot the created structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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7pgRBQGBgIBRFQX5+Pmw2G2w2G5xOJ5xOpyVjAAC73a6/dgBuY7JK8ddedEysB+vBerAeGn+oR0BAACIiIgC4fo8V/Z32j3/8A8ePH0eTJk0sGw8RERFReRiCGKh27dqw2WyQ7IEAvLkSRIUIFWLBB0cAUCBAkgIMH2N5BCgIkCTUj2wEFQJkyY5gQar4QCPHoMqQ5Hz93ZNFGxTR2vcBqgpJyUeY6gqlFEGELNoRYvHVPaLigKS4mikVYD1YD9ajCH+vhwAZtYUMt/0vqhFQYfAY/aAeAhSIzssQAP3VKQBEyfURo3bt2paNhYiIiKgiDEEMVHgLjACI3n3QVRQZIgo+xEL0+ngjqIrrwyxEEapog2oLsn4MCIAqqBAU11UwSkAw4INbixRFhOjMdf27LQgQrf9PRVFtkBzZAABVlFgP1oP1KDoGP6+HCBE2wf0WEFENgGx0CAI/qIeqAE4RKPgdpkJwjaHgfeHtoURERORPeJOuP1AUPQAB4Pp3RSnnABOohWNQBQmiIkOUrbucWiPK+RAVGUpBCGRz5gAWL2AkKDJszlyogghVEGFz5upNjmVU1fXaASgi68F6uLAeBVgPnb/UQygSgAhQISjW3RJERERE5A2GIL6mKBBR8C2iIEEpuJxbhGxdEKIqEFXXGFQIkG1BkCU7JDnf0sZClPMhyfmQJTtkWzCctmAIqmJpY+FqKHKgCiKcBWNwNRY51jUWBQ2eoCpw2oIh24JZD9aD9dCwHjp/qYckF1yFgoKrCLUgBBaH+UREREQeYAjiS8UCEAgiIIjWBiFFAhAFrucHAEWyW9pYFG0oFMnuGpooWdpYFG8oIAiAIFjbWBRr8NSCb5hZD9aD9QDrUYR/1UN13QJT8PujMAghIiIi8j8MQXyltABEY1UQ4haAFBsDrGssSmso9CFa1FiU2lDoD1rUWJTR4GlYD+1B1kMfIuuh87d6iM4803IQf6uHLAWW+P3hCkKIiIiI/A9DEF8oLwDRmB2EFA9AxNL/KpjdWJTXUOhDNbnRK7eh0HcyubGooMHTsB7aTqyHPlTWQ+dv9XA4RcODkCupHio/YhAREZEf4icUq3kSgGjMCkI8DED0IZvUWHjSUOhDNqnR86ih0Hc2qbHwsKHQsB7azqyHPmTWQ+cv9ZBtgVBVwdAg5EqsBxEREZG/YQhiJW8CEI3RQYiXAYjG6MbCm4ZCY3Sj51VDoR9kcGNRyYaC9dAOYj00rEchf6gHRAkBNtmwIORKrgcRERGRP2EIYpXKBCAao4KQSgYgGqMai0o1FAWMavQq1VDoBxvUWFSxoWA9tINZDw3rUcgf6iGKcAtCanI9iIiIiPwFQxArVCUA0VQ1CKliAKKpamNRlYZCU9VGr0oNhX6SKjYWBjUUrId2EtZDw3oU8od6FA1CRGdeja4HERERkT9gCGI2IwIQTWWDEIMCEE1lGwsjGgpNZRs9QxoK/WSVbCwMbihYD+1krIeG9SjkD/XQghDWg4iIiMj3GIKYycgARONtEGJwAKLxtrEwsqHQeNvoGdpQ6Cf1srEwqaFgPbSTsh4a1qOQP9RDFAHZFsh6EBEREfkYQxCzmBGAaDwNQkwKQDSeNhZmNBQaTxs9UxoK/eQeNhYmNxSsh3Zy1kPDehTyh3qA9SAiIiLyOYYgpjAxANFUFISYHIBoKmosTG0oClTU6JnaUOhPUkFjYVFDwXpoT8J6aFiPQqyH9iT+UQ8iIiIiX2AIYjBBEPQ31bQARH+yMoIQiwIQTVmNhRUNhaasxsKShkJTVmNhcUPBehRgPXSsRyHWo4Cf1IOIiIjIagxBDCYWBA6mByCaEkGIbGkAoineWFjZUGiKNxaC4rSuodCUaCycPmkoWI8CrIeO9SjEehTwk3oQERERWcnm6wFURwoEawIQjSBCASCqMlz/Zm0AotGaB6ng21UrGwqN1ljYnDmwOXOtbSg0BY2FNgYAPmkoWI8CrIeO9SjEehTwk3oQERERWYVXghARERERERFRjcAQxAQiVECtYOlaI7nNASIWjMGD5XMNVvSScm+WozSS+z31QR4vR2kot3vqgzxfjtJgrEcB1kPHehRiPQr4ST2IiIiIrMIQxGBKQfAgqrI1QUiJSVAlz5bPNVjxe+o9XY7SSMUnFVRFm0fLURqqxKSCNs+WozQY61GA9dCxHoVYjwJ+Ug8iIiIiKzEEMZiqqtBiB9ODkLJWgalo+VyDlTWpoJWNRVmrKlS0HKWhylpVoaLlKA3GehRgPXSsRyHWo4Cf1IOIiIjIagxBTCG6QgmYGIRUtAyuRUFIRasqWNFYVLSspCWNRUXLSlrUWLAe2pOwHhrWoxDroT2Jf9SDiIiIyBcYgphFNDEIqSgA0ZgchHi6rKSZjUVFDYXG1MaiooZCH6y5jQXroZ2c9dCwHoX8oR5gPYiIiIh8jiGImcwIQjwNQDQmBSGeNhQaMxoLTxs8jSmNhacNhT5ocxoL1kM7KeuhYT0K+UM9FAWQnHmsBxEREZGPMQQxm5FBiLcBiMbgIMTbhkJjZGPhbYOnMbSx8Lah0AdvbGPBemgnYz00rEchf6iHogAOp8R6EBEREfkBhiBWMCIIqWwAojEoCKlsQ6ExorGobIOnMaSxqGxDoTGosWA9tJOwHhrWo5A/1EMLQARBhWILrNH1ICIiIvIHDEGsUpUgpKoBiKaKQUhVGwpNVRqLqjZ4mio1FlVtKDRVbCxYD+1g1kPDehTyh3oUDUACbEqNrgcRERGRv2AIYqXKBCFGBSCaSgYhRjUUmso0FkY1eJpKNRZGNRSaSjYWrId2EOuhYT0K+UM9oMhuAUgVy3FF14OIiIjInzAEsZo3QYjRAYjGyyDE6IZC401jYXSDp/GqsTC6odB42ViwHtrOrIc+ZNZD5y/1kJx5hgUgmiuxHkRERET+hiGIL3gShJgVgGg8DELMaig0njQWZjV4Go8aC7MaCo2HjQXroe3EeuhDZT10/lYPIwMQzZVUDwHGLstOREREZASGIL5SXhBidgCiKR6EFAtjzG4oNOU1FmY3eJpyGwuzGwpNBY0F66E9yHroQ2Q9dP5WD8UWaFY5/K4ekpxX4veHoDhh0ssnIiIiqhKGIL5UWhBiVQCicQtCFP2DrFUNhaa0xsKqBk9TamNhVUOhKaPRYz1YD9YDrEcR/lUPAQJU/feHKwBRUYUFfYmIiIhMY/P1AGo8UYSiuK7E0MIPwKIARCOIUOAKYgSokJy5EFTFsoZCoz2XJOdDUGWIimxZQ6HRGgubM8fVWADWNRSagsZCG4MiShAVmfVgPQCwHqxHIX+phywFuQIhKFAVtSAAEaDyWhAiIiLyQ7wSxB+IIpQipVAgWheAaITCMQiqDEWULG0oNIpk15saAJY2FBpXYxEEQVUKGoog6xoKTUGjBwCiwnqwHi6sRwHWQ+cv9VC13x9aACLyOxYiIiLyTwxB/IGquG5FKVD0thQrCUUuXhYV2Sez/guKrDcUrnE4LB8DVBVSkXv9JTnfs+UoDVb0tbMerAfAehTFehTwk3qgyO8PAarHy68TERERWY0hiK8VmwPE4+VzDVZ4D7cAp62WT5Y/LHpPvSMgxOPlKA1V7J56j5ejNFjROQ4cASGsB+vBehTBehTwo3povz/cJtvm6jBERETkhxiC+FJpk6B6snyuwYoGIBBEj5c/NHYMJScV9GQ5SkOVMqmgR8tRGqzEJI+sB+vBeuhYjwJ+VA9RcbhugRGkYpNtExEREfkffkbxlfJWgbEwCCkagKhCkfvILWwsyltVwbLGopxVFaxsLMpc5YL1KHyI9dAfYz0K+GE9BNnEW2P8rB6KGOAK0DVFghAiIiIif8MQxBc8WQbXgiDELQApbRI7CxoLT5aVNL3R82BZSSsaiwqX+WQ9CndhPXSsRwE/q4coO+B0mjBJ6hVTDxEKV4chIiIiP8QQxGqeBCAaE4OQCgMQfUfzGgtPGgqNaY2eBw2FvquJjUWFDYWG9SjclfXQsR4F/KgeihQApywaG4RcafVgCEJERER+iCGIlbwJQDQmBCEeByD6AcY3Ft40FBrDGz0vGgr9EBMaC88bigKsR+EhrIeO9SjgJ/VQpQDYJMW4IORKrQcRERGRn/G7ECQlJQVdunRB7dq10bBhQyQlJeHQoUNu++Tm5mLcuHGoV68eQkNDMXDgQKSnp5d7XlVVMWPGDDRq1AjBwcFISEjA4cOH3fY5f/487r//foSFhSEiIgLJycm4dOmSMS+sMgGIxsAgxOsARD/QuMaiMg2FxrBGrxINhX6ogY1FpRsK1qPwUNZDx3oU8JN62GyqMUHIlV4PIiIiIj/idyHI1q1bMW7cOHz//ffYsGEDHA4Hbr31VmRnZ+v7PPbYY/jiiy+wevVqbN26FSdPnsSAAQPKPe9LL72EhQsXYsmSJdi1axdCQkKQmJiI3NxcfZ/7778fv/zyCzZs2IC1a9di27ZtGD16dNVfVFUCEI0BQUilAxD9BFVvLKrSUGiq3OhVoaHQT2FAY1HlhoL1KDwF66FjPQr4ST2KBiGVmiy1utSDiIiIyE8IqmrBGnpVcObMGTRs2BBbt27FTTfdhMzMTDRo0AArVqzA3XffDQA4ePAg2rRpg9TUVHTr1q3EOVRVRXR0NB5//HE88cQTAIDMzExERkZi2bJlGDJkCH799Ve0bdsWu3fvRufOnQEA69evx2233Ya//voL0dHRJc6bl5eHvLw8/eesrCwsXrwYkj0I0D6oGhGAFKUoEFFwvoLlCD3hUQCiqhCgwmEPhVreeSv5odyIhqKoSn0oN6ChKKqyr8nQhoL10LEehViPAj6qhwQZ4cI5/WenU0CuHASnFFjt6iGoCgLyLxUss17knIqM52bNQGZmJsLCwqo0diIiIiKj+N2VIMVlZmYCAOrWrQsA2Lt3LxwOBxISEvR9WrdujWbNmiE1NbXUcxw5cgRpaWlux4SHhyMuLk4/JjU1FREREXoAAgAJCQkQRRG7du0q9bwpKSkIDw/X/zRt2tR9B6MDEKBSV4RU+QqQEif0/htWoxs8oBLfeBvcUACV+4bV8G9UWY/CU7IeOtajgJ/Uw2ZToUgBrAcRERGRj/l1CKIoCiZNmoQbb7wR7du3BwCkpaXBbrcjIiLCbd/IyEikpaWVeh5te2RkZJnHpKWloWHDhm6P22w21K1bt8zzTp06FZmZmfqf48ePFz5oRgCi8SIIMTwA0U/seWNhRkOh8bjRM6Gh0E/tRWNhWkPBehSemvXQsR4F/KQeqhTAehARERH5mIFdsfHGjRuHn3/+GTt27PD1UEoVGBiIwMDAUh5RIRYEEwpE14doo+86EgQoqggRrrBFUdUSt8YIqhMCABWAKkgVj0FVXXurMgQPxytLdkjOPNgcl+G0BZW8PUdVYHPmQhVEyJIdgqq4BmQgVZAgizZIzjxAVaCKAcX3gOTMK2goggDA0KUzNU4pEDZnLmyOy5BtgSi+PKSgOCDJDshSAFRBMmUMrEch1qPgKVgPnbX1UKAUz09UpZrWQzH+dxwRERGRSfw2BBk/frw+OWmTJk307VFRUcjPz0dGRobb1SDp6emIiooq9Vza9vT0dDRq1MjtmNjYWH2f06dPux3ndDpx/vz5Ms9bGlVVIRZ8alYhQhC0GMIEggBVFSFAgQjF9RlU+9ZSVQoCEAEQRAgejcG1j6jIgOD5mFUpAFBkSIrDFbboY1AhqDJU0QZVlPRgyBSCCEUKgKgqUBWHW3MjKLIrNBLtrtqoxjcUGkWyQ1BlSLLD/dtbVYGgKlCkAAiCCMHEMbAehViPAqxH4VNZVA8RMhRVKLbNCUCqfvUoeK88+z1DRERE5Ft+F4KoqooJEybg008/xZYtWxATE+P2eKdOnRAQEICNGzdi4MCBAIBDhw7h2LFjiI+PL/WcMTExiIqKwsaNG/XQIysrC7t27cLYsWMBAPHx8cjIyMDevXvRqVMnAMCmTZugKAri4uI8Hn92djakwGDIAbU8nrS0yop+e2kLhKA4IckOOKWAUr5lLP88oiojq87VUGylXeFCRESeCEI2rhJ/cNv2s9IeuQjx0YjMIzrzEHbh95KTdctO3w2KiIiIqAx+F4KMGzcOK1aswGeffYbatWvr83GEh4cjODgY4eHhSE5OxuTJk1G3bl2EhYVhwoQJiI+Pd1sZpnXr1khJScFdd90FQRAwadIkzJ07Fy1btkRMTAymT5+O6OhoJCUlAQDatGmDPn36YNSoUViyZAkcDgfGjx+PIUOGlLoyTFkEQYAsBbq+bbSMBGfBve42p2vJX9nmxQoEBQQFAFQotkDItmDjh0lEVEOocCJAcr+aRJUDIaOa/r9VEAFBdL+iRDHx6iYiIiKiSvK7EGTx4sUAgJtvvtlt+9KlSzFixAgAwPz58yGKIgYOHIi8vDwkJiZi0aJFbvsfOnRIX1kGAKZMmYLs7GyMHj0aGRkZ6N69O9avX4+goCB9n+XLl2P8+PHo3bu3fv6FCxd6NX5Fcd3zbTVVlKCIkutWFgCKN1eAEBEREREREdUAgqpyNjOjZGVlYcaMGQipFwXFXsvS59Zm8deCkMqsbCAoMkRVRkb91rwShIioCmrhIq6Xtrpt2yf3xGXU9tGIzCM5cxBx9iAUQXK/EsTpwAvTnkBmZibCwsJ8N0AiIiKiIvx6idwrkdPphKQ4y1/+0GBFlzGUbcEeL39IREREREREVJMwBDGY0+l0LX8o51sShBQNQLQ5QFRRYhBCREREREREVAxDEBMoYgBkyW56EFJaAKJhEEJERERERETkjiGISRTJbmoQUl4AomEQQkRERERERFSIIYiJzApCPAlANAxCiIiIiIiIiFwYgpjM6CDEmwBEwyCEiIiIiIiIiCGIJYwKQioTgGgYhBAREREREVFNxxDEIlUNQqoSgGgYhBAREREREVFNxhDEQpUNQowIQDQMQoiIiIiIiKimYghiMW+DECMDEA2DECIiIiIiIqqJGIL4gKdBiBkBiIZBCBERmUlQZV8PgYiIiKgEhiA+UlEQYmYAoikRhIBBCBERVZ2gyJAMXBqeiIiIyCgMQXyorCDEigBEUzQIkZx5pj4XERFVT4IiQwvSBUWGzZkDVeBHDCIiIvI/Nl8PoKbTQo6i35hZFYBotCDE5rgMKLx8mYiIvKVCcuZBtgE2Zy5UQYQs8CMGERER+R9+QvEDxYMQKwMQjSsICYKkOCx9XiIiuvKpggRRydcDEKctGJCdvh4WERERUQm8VpWIiIiIiPzeli1bIAgCtmzZ4uuhkAl++OEH2O12HD161NdD0a1fvx6hoaE4c+aMr4dCBmII4geKzgHizfK5RnLdw50LQLD0eYmI6MonqHLBFSBBXHWMiKps0aJFWLZsma+HUSkrVqzAggULfD0MN++88w7atGmDoKAgtGzZEq+//rrHx+bl5eGpp55CdHQ0goODERcXhw0bNpS6786dO9G9e3fUqlULUVFRePTRR3Hp0iWPn+vZZ5/Fvffei+bNm3t8jNn69OmDa665BikpKb4eChmIIYiPFZ8E1dPlc41UdBI7VZQseU4iIqpOBMi2QKiirXCybYWrwxBR5ZQVgtx0003IycnBTTfdZP2gPORvIchbb72Fhx56CO3atcPrr7+O+Ph4PProo3jxxRc9On7EiBGYN28e7r//frz22muQJAm33XYbduzY4bbf/v370bt3b1y+fBnz5s3DQw89hLfffhuDBg3y6Hn279+Pb7/9FmPGjPH6NZrt4YcfxltvvYWLFy/6eihkEM4J4kNlrQJTfI4QM+cHKRqAyJIdoqqY9lxERFQ9uQJ0Qf93py0YUn62bwdFdIXLzs5GSEiIr4fhV0RRRFBQkK+HccXIycnBs88+i379+uHjjz8GAIwaNQqKouC5557D6NGjUadOnTKP/+GHH7By5Uq8/PLLeOKJJwAAw4YNQ/v27TFlyhTs3LlT3/eZZ55BnTp1sGXLFoSFhQEAWrRogVGjRuGbb77BrbfeWu5Yly5dimbNmqFbt25VfdmGGzhwICZMmIDVq1fjwQcf9PVwyAC8EsRHKloG14orQooGIE5bMHgrDBERGUEVJcgWT/BN5M9OnDiB5ORkREdHIzAwEDExMRg7dizy812f8ZYtWwZBELB161Y88sgjaNiwIZo0aaIfv2jRIrRr1w6BgYGIjo7GuHHjkJGR4fYchw8fxsCBAxEVFYWgoCA0adIEQ4YMQWZmpr7Phg0b0L17d0RERCA0NBStWrXCM888U+H4PTkuLy8PM2fOxDXXXIPAwEA0bdoUU6ZMQV5eXonzffjhh+jatStq1aqFOnXq4KabbsI333wDwNU4//LLL9i6dSsEQYAgCLj55psBlD0nyOrVq9GpUycEBwejfv36GDp0KE6cOOG2z4gRIxAaGooTJ04gKSkJoaGhaNCgAZ544gnIcsWrI3722Wfo16+fXsOrr74azz33nNuxN998M9atW4ejR4/qY2/RokWZ51y6dCkEQcC7777rtv3555+HIAj48ssvKxxXeTZv3oxz587hkUcecds+btw4ZGdnY926deUe//HHH0OSJIwePVrfFhQUhOTkZKSmpuL48eMAgKysLGzYsAFDhw7VAxDAFZiEhobio48+qnCsa9asQa9evSAI7v1IixYtcPvtt2PLli3o3LkzgoOD0aFDB/3vwCeffIIOHTogKCgInTp1wo8//uh2vFb3Y8eO4fbbb0doaCgaN26MN998EwDw008/oVevXggJCUHz5s2xYsWKEmNr2LAhrr32Wnz22WcVvg66MvBKEB+oKADRmHlFSIkARBAA3r5NREQGUQXeXkkEACdPnkTXrl2RkZGB0aNHo3Xr1jhx4gQ+/vhjXL58GXZ74ee7Rx55BA0aNMCMGTOQne26mmrWrFmYPXs2EhISMHbsWBw6dAiLFy/G7t278d133yEgIAD5+flITExEXl4eJkyYgKioKJw4cQJr165FRkYGwsPD8csvv+D222/Htddeizlz5iAwMBC//fYbvvvuu3LH78lxiqKgf//+2LFjB0aPHo02bdrgp59+wvz58/G///0Pa9as0fedPXs2Zs2ahRtuuAFz5syB3W7Hrl27sGnTJtx6661YsGABJkyYgNDQUDz77LMAgMjIyDLHt2zZMowcORJdunRBSkoK0tPT8dprr+G7777Djz/+iIiICH1fWZaRmJiIuLg4vPLKK/j222/x6quv4uqrr8bYsWPLfR+WLVuG0NBQTJ48GaGhodi0aRNmzJiBrKwsvPzyywBcc1pkZmbir7/+wvz58wEAoaGhZZ5z5MiR+OSTTzB58mTccsstaNq0KX766SfMnj0bycnJuO222/R9L1y44FFYU6tWLdSqVQsA9ECgc+fObvt06tQJoijixx9/xNChQ8s8148//oi//e1vbsEGAHTt2hWA6xYWbcxOp7PE89jtdsTGxpYIJoo7ceIEjh07huuvv77Ux3/77Tfcd999ePjhhzF06FC88soruOOOO7BkyRI888wzesiTkpKCe+65B4cOHYIoFn7XL8sy+vbti5tuugkvvfQSli9fjvHjxyMkJATPPvss7r//fgwYMABLlizBsGHDEB8fj5iYmBLvWdG/x3RlYwhiMU8DEI0ZQUipAQgRERERGW7q1KlIS0vDrl273JrEOXPmQC02gXDdunWxceNGSJIrRDxz5gxSUlJw66234quvvtIbu9atW2P8+PH48MMPMXLkSBw4cABHjhzB6tWrcffdd+vnmzFjhv7vGzZsQH5+Pr766ivUr1/f4/F7ctyKFSvw7bffYuvWrejevbu+vX379hgzZgx27tyJG264Ab/99hvmzJmDu+66Cx9//LFbo6q9F0lJSZg2bZp+RUd5HA4HnnrqKbRv3x7btm3Tb5Xp3r07br/9dsyfPx+zZ8/W98/NzcXgwYMxffp0AMCYMWNw/fXX45133qkwBFmxYgWCg4P1n8eMGYMxY8Zg0aJFmDt3LgIDA3HLLbegcePGuHDhQoVj1/zzn/9Eu3btkJycjLVr12L48OGIiorCvHnz3Pa77rrrPFo1ZebMmZg1axYA4NSpU5AkCQ0bNnTbx263o169ejh58mS55zp16hQaNWpUYru2TTv+1KlTbtuL77t9+/Zyn+fgwYMAUCJ40Bw6dAg7d+5EfHw8AKBt27ZITEzEqFGjcPDgQTRr1gwAUKdOHTz88MPYtm2bfvUQ4Kr70KFDMXXqVADAfffdh+joaDz44IP497//jcGDBwMAbrnlFrRu3Rrvvfee/h5qrrrqKpw9exanT58u8X7SlYe3w1jI2wBEY+StMQxAiIiIiKyhKArWrFmDO+64o8S35ABKXPo/atQoPQABgG+//Rb5+fmYNGmSW2AwatQohIWF6bczhIeHAwC+/vprXL58udSxaFdEfPbZZ1AUz+eA8+S41atXo02bNmjdujXOnj2r/+nVqxcA120ZgOuWB0VRMGPGDLfXA5R8LzyxZ88enD59Go888ojbXCH9+vVD69atS73do/jEmz169MAff/xR4XMVDUAuXryIs2fPokePHrh8+bLexFdGVFQU3nzzTWzYsAE9evTA/v378e6775a4+mL58uXYsGFDhX+GDRumH5OTk+N2pVFRQUFByMnJKXdsOTk5CAwMLPVY7fGi/yxr34qe59y5cwBQ5vwkbdu21QMQAIiLiwMA9OrVSw9Aim4vrZ4PPfSQ/u8RERFo1aoVQkJCcM899+jbW7VqhYiIiFKP18Z29uzZcl8LXRl4JYhFKhuAaIy4IoQBCBEREZF1zpw5g6ysLLRv396j/Yt/E65989+qVSu37Xa7HVdddZX+eExMDCZPnox58+Zh+fLl6NGjB/r374+hQ4fqAcngwYPxr3/9Cw899BCefvpp9O7dGwMGDMDdd99dIpAoypPjDh8+jF9//RUNGjQo9RynT58GAPz+++8QRRFt27b16P2oSFnvD+C6Wqb4CiZBQUElxlinTh1cuHChwuf65ZdfMG3aNGzatAlZWVlujxWdd6UyhgwZgg8//BDr1q3D6NGj0bt37xL73HjjjV6fNzg4WJ93prjc3Fy3YKes40ub0yU3N1d/vOg/y9q3oufRFL8ySlM06AAKQ7+mTZuWur14PUure3h4OJo0aVIifAsPDy/174M2tsqEdeR/GIJYoKoBiKYqQQgDECIiIiL/5mmzWJpXX30VI0aMwGeffYZvvvkGjz76KFJSUvD999+jSZMmCA4OxrZt27B582asW7cO69evx6pVq9CrVy988803blegFB9TRccpioIOHTqUuIVDU7xZ9ZWyXmNFMjIy0LNnT4SFhWHOnDm4+uqrERQUhH379uGpp57y6sqa0pw7dw579uwBABw4cACKopQIps6cOePRnCChoaH6PCSNGjWCLMslbuHIz8/HuXPnEB0dXe65GjVqVGKCWaDw9hfteO02GG178X0rep569eoBKBleaMqqW1nbi4cpVT2+6Ni8uZWM/BdvhzGZUQGIpjK3xjAAISIiIrJegwYNEBYWhp9//rlSxzdv3hyAa06EovLz83HkyBH9cU2HDh0wbdo0bNu2Ddu3b8eJEyewZMkS/XFRFNG7d2/MmzcPBw4cwD/+8Q9s2rRJv12lLBUdd/XVV+P8+fPo3bs3EhISSvzRrtS4+uqroSgKDhw4UO7zefpte1nvj7at+PtTWVu2bMG5c+ewbNkyTJw4EbfffjsSEhJKvX2jMlcKjBs3DhcvXkRKSgp27NiBBQsWlNinS5cuaNSoUYV/XnnlFf2Y2NhYANADFs2ePXugKIr+eFliY2Pxv//9r8SVL7t27XI7f/v27WGz2Uo8T35+Pvbv31/h87Ru3RoAcOTIkXL386UjR46gfv36ZV7tRFcWhiAmMjoA0XgThDAAISIiIvINURSRlJSEL774okSDCJR9+b8mISEBdrsdCxcudNv3nXfeQWZmJvr16wfAtUSp0+l0O7ZDhw4QRVG/ReH8+fMlzq81p6XdxqDx5Lh77rkHJ06cwD//+c8S++bk5Ogr3SQlJUEURcyZM6fE1RNFX19ISEiJJYBL07lzZzRs2BBLlixxew1fffUVfv31V/39qSrtioGiY8zPz8eiRYtK7BsSEuLV7TEff/wxVq1ahRdeeAFPP/00hgwZgmnTpuF///uf236VmROkV69eqFu3LhYvXux2rsWLF6NWrVpu78/Zs2dx8OBBtzll7r77bsiyjLffflvflpeXh6VLlyIuLk6/wic8PBwJCQn48MMPcfHiRX3fDz74AJcuXcKgQYPKfQ8aN26Mpk2blvrfiL/Yu3ev27wkdGXj7TAmMSsA0XhyawwDECIiIiLfev755/HNN9+gZ8+e+vKxp06dwurVq7Fjxw63JVyLa9CgAaZOnYrZs2ejT58+6N+/Pw4dOoRFixahS5cu+gokmzZtwvjx4zFo0CD87W9/g9PpxAcffABJkjBw4EAArtVotm3bhn79+qF58+Y4ffo0Fi1ahCZNmrit6FKcJ8c98MAD+OijjzBmzBhs3rwZN954I2RZxsGDB/HRRx/h66+/RufOnXHNNdfg2WefxXPPPYcePXpgwIABCAwMxO7duxEdHY2UlBQAruVIFy9ejLlz5+Kaa65Bw4YN9UlWiwoICMCLL76IkSNHomfPnrj33nv1JXJbtGiBxx57rLJlc3PDDTegTp06GD58OB599FEIgoAPPvig1BCrU6dOWLVqFSZPnowuXbogNDQUd9xxR6nnPX36NMaOHYu///3vGD9+PADgjTfewObNmzFixAjs2LFDvy2msnOCPPfccxg3bhwGDRqExMREbN++HR9++CH+8Y9/oG7duvq+b7zxBmbPno3NmzfrK6vExcVh0KBBmDp1Kk6fPo1rrrkG7733Hv7880+88847bs/1j3/8AzfccIP+9/yvv/7Cq6++iltvvRV9+vSpcKx33nknPv30U6iq6nfzbpw+fRr//e9/MW7cOF8PhQzCEMQEouKABMW0AERTXhDCAISIiIjI9xo3boxdu3Zh+vTpWL58ObKystC4cWP07dsXtWrVqvD4WbNmoUGDBnjjjTfw2GOPoW7duhg9ejSef/55BAQEAAA6duyIxMREfPHFFzhx4gRq1aqFjh074quvvkK3bt0AAP3798eff/6Jd999F2fPnkX9+vXRs2dPzJ49W59QsjSeHCeKItasWYP58+fj/fffx6effopatWrhqquuwsSJE/G3v/1NP9+cOXMQExOD119/Hc8++yxq1aqFa6+9Fg888IC+z4wZM3D06FG89NJLuHjxInr27FlqCAIAI0aMQK1atfDCCy/gqaeeQkhICO666y68+OKL5QZM3qhXrx7Wrl2Lxx9/HNOmTUOdOnUwdOhQ9O7dG4mJiW77PvLII9i/fz+WLl2K+fPno3nz5mWGIGPHjtWvrNAa/3r16uHtt9/GnXfeiVdeeQVTpkyp0tgfeeQRBAQE4NVXX8Xnn3+Opk2bYv78+Zg4caJHx7///vuYPn06PvjgA1y4cAHXXnst1q5di5tuusltv+uvvx7ffvstnnrqKTz22GOoXbs2kpOT9WCrIg8++CDeeOMNfPfdd+WGcr7wySefIDAw0G0lGbqyCWpF1+GRx7KysvDMM8+gboMoyAFBpgYgRRW/6qSyAYigyBBVGRn1W0O2VX5iLiKimq4WLuJ6aavbtn1yT1xGbR+NyDySMwcRZw9CESSoYpFJ5pwOvDDtCWRmZpZY6pGIiKi43r17Izo6Gh988IGvh+Lmuuuuw80334z58+f7eihkEM4JYjCbzQZZtFkWgADuc4RIzhxeAUJERERERFeU559/HqtWrdKXPvYH69evx+HDhzF16lRfD4UMxNthDKYoChQxwPrnlewQVBmi4lo6iwEIERERERFdKeLi4pCf79nql1bp06cPLl265OthkMF4JYjBRFGEoFa8hrfRBKUwAAFc85IQERFR1S1evBjXXnstwsLCEBYWhvj4eHz11VflHrN69Wq0bt0aQUFB6NChA7788ku3x1VVxYwZM9CoUSMEBwcjISEBhw8fNvNlEBERERiCGE5RFEhyPgTFuiCk6BwgjoAQj5fPJSIiooo1adIEL7zwAvbu3Ys9e/agV69euPPOO/HLL7+Uuv/OnTtx7733Ijk5GT/++COSkpKQlJSEn3/+Wd/npZdewsKFC7FkyRLs2rULISEhSExMRG5urlUvi4iIqEbixKgGysrKwpQpU1A/uhlEwXVLitskcSYoaxLUyizRy4lRiYiMwYlRUe0nRq1bty5efvllJCcnl3hs8ODByM7Oxtq1a/Vt3bp1Q2xsLJYsWQJVVREdHY3HH38cTzzxBAAgMzMTkZGRWLZsGYYMGWLZ6yAiIqppOCeICWTRDgFO2Jw5pgYh5a0CU97yuURERFQ5sixj9erVyM7ORnx8fKn7pKamYvLkyW7bEhMTsWbNGgDAkSNHkJaWhoSEBP3x8PBwxMXFITU1tcwQJC8vD3l5efrPiqLg/PnzqFevnr68JhERUU2lqiouXryI6OhoiGLZN70wBDGDIMApBcNWsFKLGUGIJ8vgMgghIiIyxk8//YT4+Hjk5uYiNDQUn376Kdq2bVvqvmlpaYiMjHTbFhkZibS0NP1xbVtZ+5QmJSUFs2fPrsrLICIiqvaOHz+OJk2alPk4QxCzCAKcNnOCEE8CEA2DECIioqpr1aoV9u/fj8zMTHz88ccYPnw4tm7dWmYQYoapU6e6XWGSmZmJZs2a4findyEsxPqV6YiIiPxJVrYDTe/6FLVrl3/7MUMQM5kQhHgTgGgYhBAREVWN3W7HNddcAwDo1KkTdu/ejddeew1vvfVWiX2joqKQnp7uti09PR1RUVH649q2Ro0aue0TGxtb5hgCAwMRGBhYYntYSABDECIiogIV3SLK1WHMVhCEqIIImzOnSqvGVCYA0SiSnavGEBERGURRFLf5OYqKj4/Hxo0b3bZt2LBBn0MkJiYGUVFRbvtkZWVh165dZc4zQkRERMbglSBWMOCKkKoEIBpeEUJEROS9qVOnom/fvmjWrBkuXryIFStWYMuWLfj6668BAMOGDUPjxo2RkpICAJg4cSJ69uyJV199Ff369cPKlSuxZ88evP322wBc31BNmjQJc+fORcuWLRETE4Pp06cjOjoaSUlJvnqZRERENQJDEKtUIQgxIgDRMAghIiLyzunTpzFs2DCcOnUK4eHhuPbaa/H111/jlltuAQAcO3bMbRb6G264AStWrMC0adPwzDPPoGXLllizZg3at2+v7zNlyhRkZ2dj9OjRyMjIQPfu3bF+/XoEBQVZ/vqIiIhqEoYgVqpEEGJkAKJhEEJEROS5d955p9zHt2zZUmLboEGDMGjQoDKPEQQBc+bMwZw5c6o6PCIiIvIC5wSxmhdzhJgRgGg4RwgREZlKVX09AiIiIqISGIL4ggdBiJkBiIZBCBERmUJVISn8vUJERET+hyGIr5QThFgRgGiKBiGC4jDteYiIqBpTlSL/rrp+rxXdRkREROQn/C4E2bZtG+644w5ER0dDEASsWbPG7XFVVTFjxgw0atQIwcHBSEhIwOHDhys875tvvokWLVogKCgIcXFx+OGHH9wez83Nxbhx41CvXj2EhoZi4MCBSE9PN/KllVRKEGJlAKIpDEIc7h9kiYiIPCCoiitILxKAyJxvioiIiPyQ34Ug2dnZ6NixI958881SH3/ppZewcOFCLFmyBLt27UJISAgSExORm5tb5jlXrVqFyZMnY+bMmdi3bx86duyIxMREnD59Wt/nsccewxdffIHVq1dj69atOHnyJAYMGGD46yuhWBBidQCicQUhAfzmjoiIvKYKIiTZgQBHNgRVKfi95t1S8ERERERW8LvVYfr27Yu+ffuW+piqqliwYAGmTZuGO++8EwDw/vvvIzIyEmvWrMGQIUNKPW7evHkYNWoURo4cCQBYsmQJ1q1bh3fffRdPP/00MjMz8c4772DFihXo1asXAGDp0qVo06YNvv/+e3Tr1q3U8+bl5SEvL0//OSsrq3IvWhAgS3bYnK4gR5bslgYgGlW0AZwbhIiIvCUUfqeiiJJr5TOFoToRERH5H7+7EqQ8R44cQVpaGhISEvRt4eHhiIuLQ2pqaqnH5OfnY+/evW7HiKKIhIQE/Zi9e/fC4XC47dO6dWs0a9aszPMCQEpKCsLDw/U/TZs2rdTrct0CkwtVEAuuCMktd9UYU6gqJGdexfsREREVo/3OUkQJoiJzsm0iIiLyW1dUCJKWlgYAiIyMdNseGRmpP1bc2bNnIctyucekpaXBbrcjIiLC4/MCwNSpU5GZman/OX78uLcvqcQcIJ4un2uoIvdw8/JlIiLyngqnLQiyLbhw1TFOtk1ERER+yO9uh7mSBAYGIjAwsNLHlzUJqtMWrM8P4rQFuy4rNkuRAMRpC4II1bznIiKiakkVJP2WGKVgQlTJUfZcXURERES+ckVdCRIVFQUAJVZtSU9P1x8rrn79+pAkqdxjoqKikJ+fj4yMDI/PW1XlrgJTzvK5hnILQILd7ukmIiLyWLF5rBTJDlnk9yxERETkf66orjcmJgZRUVHYuHGjvi0rKwu7du1CfHx8qcfY7XZ06tTJ7RhFUbBx40b9mE6dOiEgIMBtn0OHDuHYsWNlnrcqPFoG1+wgpFgAYurVJkREVOMoYoCvh0BERERUgt99TXPp0iX89ttv+s9HjhzB/v37UbduXTRr1gyTJk3C3Llz0bJlS8TExGD69OmIjo5GUlKSfkzv3r1x1113Yfz48QCAyZMnY/jw4ejcuTO6du2KBQsWIDs7W18tJjw8HMnJyZg8eTLq1q2LsLAwTJgwAfHx8WWuDFNZHgUg+s6CObfGMAAhIiIiIiKiGsjvQpA9e/bg73//u/7z5MmTAQDDhw/HsmXLMGXKFGRnZ2P06NHIyMhA9+7dsX79egQFBenH/P777zh79qz+8+DBg3HmzBnMmDEDaWlpiI2Nxfr1690mS50/fz5EUcTAgQORl5eHxMRELFq0yNDX5lUAoh9kcBDCAISIiIiIiIhqKEFVVc6EaZCsrCxMmTIFdZq2BGzulwFXKgApyojwooJzCIoMUZWRUb81ZFuw9+cnIiIAQC1cxPXSVrdt++SeuIzaPhqReSRnDiLOHoQiSO6/V5wOvDDtCWRmZiIsLMx3A6zGsrKyEB4ejsxv7kFYCG8/IiKimi0r24HwWz+q8LPHFTUnyJWqygEIUPU5QngFCBEREREREdVwDEFMZkgAop+skkEIAxAiIiIiIiIihiBmMjQA0U/qZRDCAISIiIiIiIgIAEMQ05gSgOgn9zAIYQBCREREREREpGMIYgJBNTEA0Z+kgiCEAQgRERERERGRG4YgBhMEAZKcb24AUvhkpQchDECIiIiIiIiISmAIYrDAwEBrAhBNiSDEyQCEiIiIiIiIqBQMQQymqipk0W5NAKJxC0JyGYAQERERERERlYIhCBERERERERHVCAxBDCYIAiQlH1BV657UbQ6QIM+XzyUiIiIiIiKqQRiCGCwvLw+CqsDmzLEmCCkxCarNs+VziYiIiIiIiGoYhiAGU1UVsmS3JggpaxWYipbPJSIiIiIiIqqBGIKYQBUkOG3B5gYhFS2DyyCEiIiIiIiIyA1DEJOooolBSEUBiIZBCBEREREREZGOIYiJTAlCPA1ANAxCiIiIiIiIiAAANl8PoLrTghCbMwc2Zw6ctmBAECp5Mi8DEE1BEFJ0DB4fS0REXgvGJVwj/OS27Vd0wmXU9tGIiIiIiAjglSCWMOSKkMoGIBpeEUJEREREREQ1HEMQi1QpCKlqAKJhEEJEREREREQ1GEMQC1UqCDEqANEwCCEiIiIiIqIaiiGIxbwKQowOQDQMQoiIiIiIiKgGYgjiAx4FIWYFIBoGIUREZCJRcfh6CEREREQlMATxkXKDELMDEE2xIASqYs7zEBFR9VYszBflfEiK00eDMV5KSgq6dOmC2rVro2HDhkhKSsKhQ4cqPG716tVo3bo1goKC0KFDB3z55Zduj6uqihkzZqBRo0YIDg5GQkICDh8+bNbLICIiIjAE8alSgxCrAhCNWxCSW7mVa4iIqEYTVFkP0kU5H5KcD1m0+XhUxtm6dSvGjRuH77//Hhs2bIDD4cCtt96K7OzsMo/ZuXMn7r33XiQnJ+PHH39EUlISkpKS8PPPP+v7vPTSS1i4cCGWLFmCXbt2ISQkBImJicjNzbXiZREREdVI1ecTyhVKC0JszhxXEAJYF4BoCoIQm+Oy64MsERGRVwTYnLlQVCdERYYs2aGogq8HZZj169e7/bxs2TI0bNgQe/fuxU033VTqMa+99hr69OmDJ598EgDw3HPPYcOGDXjjjTewZMkSqKqKBQsWYNq0abjzzjsBAO+//z4iIyOxZs0aDBkypMQ58/LykJeXp/+clZVl1EskIiKqMXgliB9wBSFBEFSlIAAJsi4A0QgCZFugtc9JRETVgvY7S1RkKKIERbL7eETmyszMBADUrVu3zH1SU1ORkJDgti0xMRGpqakAgCNHjiAtLc1tn/DwcMTFxen7FJeSkoLw8HD9T9OmTav6UoiIiGochiD+QFUhyfn6j5Kc75PbUoRqdP82ERFZqMicUqIiV+vJthVFwaRJk3DjjTeiffv2Ze6XlpaGyMhIt22RkZFIS0vTH9e2lbVPcVOnTkVmZqb+5/jx41V5KURERDVSpW6HOXbsGI4ePYrLly+jQYMGaNeuHQIDeRVBpRSbAwSAfmuM0xYMCNZcTuy6h9sBRQqw5PmIiKj6EFQFshQARQos/B0mVM87bseNG4eff/4ZO3bssPy5AwMD+XmLiIioijz+hPLnn39i8eLFWLlyJf766y+oRa5UsNvt6NGjB0aPHo2BAwdCFHmBiUfKmAS16BwhVgQh+iR2UgAEgbUjIiLvqIIIVQwonGPKmeN2haOv5OXlYdeuXW5f3Fx33XWIiYmp1PnGjx+PtWvXYtu2bWjSpEm5+0ZFRSE9Pd1tW3p6OqKiovTHtW2NGjVy2yc2NrZS4yMiIqKKedTxPvroo+jYsSOOHDmCuXPn4sCBA8jMzER+fj7S0tLw5Zdfonv37pgxYwauvfZa7N692+xxX/nKWQWm3OVzDVYYgNhdH2CJiIi8VTRAL7LqmK989913uOeeexAREYFevXph0qRJeO655zB06FBcc801aNmyJV5++WVcvHjRo/Opqorx48fj008/xaZNmzwKUeLj47Fx40a3bRs2bEB8fDwAICYmBlFRUW77ZGVlYdeuXfo+REREZDyPrgQJCQnBH3/8gXr16pV4rGHDhujVqxd69eqFmTNnYv369Th+/Di6dOli+GCrDQ+WwS2+aowZV4QUDUAUyV6t7+EmIiILCQJk0TeTo/bv3x/79u3Dfffdh2+++QadO3dGcHCw/vgff/yB7du349///jfmzZuH999/H7fccku55xw3bhxWrFiBzz77DLVr19bn7AgPD9fPPWzYMDRu3BgpKSkAgIkTJ6Jnz5549dVX0a9fP6xcuRJ79uzB22+/DQAQBAGTJk3C3Llz0bJlS8TExGD69OmIjo5GUlKSCe8MERERAR6GINovdE/06dOn0oOpETwIQPRdTQxCigcgREREhrJoTqvi+vXrh//7v/9DQEDpVzdeddVVuOqqqzB8+HAcOHAAp06dqvCcixcvBgDcfPPNbtuXLl2KESNGAHDNl1b0duAbbrgBK1aswLRp0/DMM8+gZcuWWLNmjdtkqlOmTEF2djZGjx6NjIwMdO/eHevXr0dQUJCXr5qIiIg8VT1nLfNXXgQg+iEmBCEMQIiIqLp6+OGHPd63bdu2aNu2bYX7qR7clrply5YS2wYNGoRBgwaVeYwgCJgzZw7mzJlT4fmJiIjIGF6HIOfOncOMGTOwefNmnD59GoqiuD1+/vx5wwZXrVQiANEPNTAIYQBCREQ10aVLl0p8ZgkLC/PRaIiIiMhXvA5BHnjgAfz2229ITk5GZGQkBB9d7npFqUIAop/CgCCEAQgREdUkR44cwfjx47Flyxbk5ubq21VVhSAIkGXOhUVERFTTeB2CbN++HTt27EDHjh3NGE/1Y0AAop+qCkEIAxAiIqpphg4dClVV8e677/KLGyIiIgJQiRCkdevWyMnJMWMs1Y+BAYh+ykoEIQxAiIioJvrPf/6DvXv3olWrVr4eChEREfkJseJd3C1atAjPPvsstm7dinPnziErK8vtDxUwIQDRT10QhAiqApszByhnwjYGIEREVFN16dIFx48f9/UwiIiIyI94fSVIREQEsrKy0KtXL7ftvL+2CBMDEP0pPLgihAEIERHVZP/6178wZswYnDhxAu3bty+xbO61117ro5ERERGRr3gdgtx///0ICAjAihUreH9tGSQlH4IA0wIQTXlBCAMQIiKq6c6cOYPff/8dI0eO1LcJgsAvboiIiGowr0OQn3/+GT/++CPvry2D3W53XQESEGJqAKIpLQgRFQcDECIiHwpCNhoIJ0psI2s9+OCDuO666/Dvf/+bX9wQERERgEqEIJ07d8bx48cZgpRBFEXIkt2SAERTNAgJcLg+ZDMAIbryXTyXjtxLnGvpSlTLfgzOyFy3bRfSj+JMftlzOF2p7HAiVMiDEwJUFIYMguL7qyyOHj2Kzz//HNdcc42vh0JERER+wusQZMKECZg4cSKefPJJdOjQgffXFqMoClTBugBEo4oSFFGCWPChUxEDKjiCiPyRU7qM8xH/Re6li9i7ZymcDhkZ32ZAvuj7hpI816EpMCjZfduGd1LwUzWco7NOnTq45557kJOTA4fDoW8v/vnAF3r16oX//Oc/DEGIiIhI53UIMnjwYACuS0w1vL+2kCiKEBUHFFj74U+U8yEqsh6EeLp8LhH5J0deDpwOGckDgdsfAEIUX4+oZhNlG8IyIlH7Yn2cjvwdecGXyt0/SAaaFft1uPIVILecjFxQBDQ+3gF5gZeQUecUHIFXxnL02XnAnuNAgATYiqw5d+Gi78akueOOO/DYY4/hp59+KvWLm/79+/toZEREROQrXocgR44cMWMc1YbT6YSkOAE537LbUYpPgqoUhCAMQoiufI0aAB2bAmH8z9hyqgogozaUo42A41FAruv/6fUj0yA0LT8EQTaAYld9tG0KIKSc58sJgPJbPeBiFKKzmgOR5yC2OAlEnYMg+u9tNOcvAQfOAEF2wF7kU0Weo+xjrDJmzBgAwJw5c0o8xi9uiIiIaiavQ5DmzZubMY5qw+l0QhZtkOR8ADA9CCltFRhPls8lIqLSqU4RONkQypHGwLlwQBYBSQaC8oDcQO9OJgNQAQgF//REYL5r3xMNoZxsAITmQIg5AaFZGoTgPO+ev4ZTFF5CRURERO68DkEA4OTJk9ixYwdOnz5d4gPGo48+asjAKvLmm2/i5ZdfRlpaGjp27IjXX38dXbt2LXP/1atXY/r06fjzzz/RsmVLvPjii7jtttv0x1VVxcyZM/HPf/4TGRkZuPHGG7F48WK0bNnS67EpYgAgqKYHIeUtg8sghIjIO2pWLahHG0E9Gu0KO1QAAQ7Anu8WYqjnwgG1ov+fnodwOb/w3IoA/NgAqCcCoWU8v0P7lawCdhmwO10BzKVgqP9tCfXXGCD6NMQWp4AGF/i/dCIiIqJK8DoEWbZsGR5++GHY7XbUq1fPbbk5QRAsCUFWrVqFyZMnY8mSJYiLi8OCBQuQmJiIQ4cOoWHDhiX237lzJ+69916kpKTg9ttvx4oVK5CUlIR9+/ahffv2AICXXnoJCxcuxHvvvYeYmBhMnz4diYmJOHDgAIKCgrweoxZKmBWElBeAaBiEEBGVT5UFIK0BlCONgDN1AacEiAoQmAeUdguKKkA93Lz8izpUFUKd3yDU+7NwW244lBPtgOM2QAIglnUsXFecaCQFCM5zbXcEAEejoRxvBIRfgtDiBISm6RAC/eC+Ez+2e/dubN68udQvbubNm+ejUREREZGveB2CTJ8+HTNmzMDUqVMhimV9ijPXvHnzMGrUKIwcORIAsGTJEqxbtw7vvvsunn766RL7v/baa+jTpw+efPJJAMBzzz2HDRs24I033sCSJUugqioWLFiAadOm4c477wQAvP/++4iMjMSaNWswZMiQSo3TrCDEkwBEwyCEiKgkNTsI6rEoqH82BrKDXFd2BDiB4FygrP9FCgBCPJisNCuo9CtFVBGw5brClWAvBywAsDtcV6bIInChNtQLraEeuBpokgax+Smgbhb/917M888/j2nTpqFVq1aIjIws8cUNERER1TxehyCXL1/GkCFDfBaA5OfnY+/evZg6daq+TRRFJCQkIDU1tdRjUlNTMXnyZLdtiYmJWLNmDQDXZK9paWlISEjQHw8PD0dcXBxSU1PLDEHy8vKQl1f4jV1WVlaJfYwOQrwJQDQMQoiIXNR8G5QfWwMnG7iu+hBU1+0ukkETj3oyBYVasF9lfo0KAGyKK0xRAeQHAL83hfJnY6BOFsTYQxDq+MGyLH7itddew7vvvosRI0b4eihERETkJ7z+CJacnIzVq1ebMRaPnD17FrIsIzIy0m17ZGQk0tLSSj0mLS2t3P21f3pzTgBISUlBeHi4/qdp06al7qdIdsiSHZKcD1HOL3UfT1QmANFoQYigKrA5cwqWPSAiqmEUEcgOBhQBgOC69cXIlVc8PZVRTykWpC6KCOQEAo5KTfVVbYmiiBtvvNHXwyAiIiI/4vWnJW1ejfXr16NDhw4ICAhwe7wm3V87depUtytMsrKyMHfu3FL3reoVIVUJQDS8IoSIajohKB/i33cD5yKg/NkIOBEJ5AQVXBHicM3BUaUnMHi/0iiC6woQRXSNN+ocxBYngOizEKo6/mrmsccew5tvvokFCxb4eihERETkJyoVgnz99ddo1aoVAFh+f239+vUhSRLS09PdtqenpyMqKqrUY6KiosrdX/tneno6GjVq5LZPbGxsmWMJDAxEYKDnyyVWNggxIgDRMAghoppOEADUz4BUPwNq+9+hHo+EeqQxcDEEyBMAm9M1P0jx/zWqKFgit6L/Z5bxuKACzkDXw2WtdKtNjFr86hQVgCy5wg8ACM6D0OwUhOanIIRnVzCemuuJJ55Av379cPXVV6Nt27Ylvrj55JNPfDQyIiIi8hWvQ5BXX33Vp/fX2u12dOrUCRs3bkRSUhIAQFEUbNy4EePHjy/1mPj4eGzcuBGTJk3St23YsAHx8fEAgJiYGERFRWHjxo166JGVlYVdu3Zh7Nixho7f2yDEyABEwyCEiMhFCMqH0PI41KuPA2fqQvkz2jVfiH51SLH5QlQBQvNTQO3LZZ80R4WQf9p9W1AWhPq/AaoE1AVQq5TjnBLU35q6Jj4V5YLngyv4kCXXVR/1MyDEnIDQ+DSEALmKr776e/TRR7F582b8/e9/L7GiHREREdVMXocggYGBPr+/dvLkyRg+fDg6d+6Mrl27YsGCBcjOztZXixk2bBgaN26MlJQUAMDEiRPRs2dPvPrqq+jXrx9WrlyJPXv24O233wbguoJl0qRJmDt3Llq2bKkvkRsdHa0HLUbyNAgxIwDRlBqEEBHVUIIIIPI8pMjzZa8cY3O69m10FkLT9PJPmAbgZNHzKxAaHAOaAahT+iFqjt0VggCAUwTyC/6/H+gAWpy84laAkRXfz0/y3nvv4f/+7//Qr18/Xw+FiIiI/ITXn1AmTpyI119/HQsXLjRjPB4ZPHgwzpw5gxkzZiAtLQ2xsbFYv369PrHpsWPH3FavueGGG7BixQpMmzYNzzzzDFq2bIk1a9agffv2+j5TpkxBdnY2Ro8ejYyMDHTv3h3r169HUFCQKa+hoiDEzABEUzwIkU16HiKiK4kQkguhzZ9Q/3YUSKsP5Ug0cKau6+oQT9WG62oPGa6rOQQAzQGEenBsXiBgk4GIixCuOgGhSTqEQEclXol1is/zmu8MQK6zjLTHQnXr1sXVV1/t62EQ+TdVBVTOJ0R+QhB5hTqZzusQ5IcffsCmTZuwdu1atGvXzmf3144fP77M21+2bNlSYtugQYMwaNCgMs8nCALmzJmDOXPmGDXECpUVhFgRgGiKBiGSMw+qFFDxQURENYAgqUDjM5Aan4GaVQvqsUZQTzQEgnM9P4lU9IQV7BvgBEIvA+EXIbY4BTS4cMV8Dsx32BEU4Hpf8p0BOJtVH4Lg+9t1Zs2ahZkzZ2Lp0qWoVau0e5CICKoCZP/h61EQuYRcBQhSxfsRVYHXIUhERAQGDBhgxlhqpOJBiPbvVgQgGj0IcVwGFN9/aCUi8jdC2GUI7X8H2v9u3nPYFEi37DLt/GZSIeLCpTqoE5qBcxfrIkByQlIzfT0sLFy4EL///jsiIyPRokWLEl/c7Nu3z0cjIyIiIl/xOgRZunSpGeOo0YoHIVYGIBpXEBIESfHvS66JappjJ4H/ZAMhvFL5ihIkA82KZcrHfgdyq+GXW9l5gOzMQ66jNnLz60MSHahlO4uMPN/PCWLGvF5E1Vru6Yr3ITJDUENfj4BqEN9/QiEiIp1NroWG57ohJOMczn26EPM/Beb7elDktQ5NgbeT3beNfgf46bhvxmOmOnWAwYOB2rVdUws4HMCZM4DNDz5hzJw509dDICIiIj/j0UeUPn36YNasWejWrVu5+128eBGLFi1CaGgoxo0bZ8gAa4Kic4AAni+fayRBkWFz5kIV/eBTKxEhJKIeHnh1FS5nnvf1UKgSGtjOIK/+WrdtN466HX9zNvDRiMxjhxN1hAsAVDggIRQKakXUQ76Prl5SVZVL4RJVlb1uwbJZRCZSFSCfn3PIeh51vIMGDcLAgQMRHh6OO+64A507d0Z0dDSCgoJw4cIFHDhwADt27MCXX36Jfv364eWXXzZ73NVGWZOgWhmEuAKQHKiCCFWshtdqE12hQiLqISSinq+HQZVQD6EIstV23xYYA6CRbwZkIsmZA+l0NmQpADbRBqXgd4roo/6pXbt2mDFjBgYMGAC7vezfoYcPH8a8efPQvHlzPP300xaOkOgKIIgMQYio2vIoBElOTsbQoUOxevVqrFq1Cm+//TYyM10TngmCgLZt2yIxMRG7d+9GmzZtTB1wdVJWAFLR8rlGKhqAyJIdIpdIIyIiL7kCdEH/d6ctGFJ+tk/G8vrrr+Opp57CI488gltuuaXML25++eUXjB8/HmPHjvXJOImIiMg3PL73ITAwEEOHDsXQoUMBAJmZmcjJyUG9evVKzLZOFatoGVwrgpCiAYjTFgyBAQgRERlAFSX9Fk+r9e7dG3v27MGOHTuwatUqLF++HEePHkVOTg7q16+P6667DsOGDcP999+POnXq+GSMRERE5DuVngAiPDwc4eHhRo6lxqgoANGYGYQUD0AgCIBq2OmJiKiGUwXf3l7ZvXt3dO/e3adjICIiIv/DWTAt5mkAojEjCCk1ACEiIiIiIiKq5hiCWMjbAERjZBDCAISIiIiIiIhqKk77bJHKBiAaRbJDluyQ5HyIBWGItxiAEBEReW/btm244447EB0dDUEQsGbNmgqP2bJlC66//noEBgbimmuuwbJly0rs8+abb6JFixYICgpCXFwcfvjhB+MHT0RERG54JYgFqhqAaKpyRQgDECIiosrJzs5Gx44d8eCDD2LAgAEV7n/kyBH069cPY8aMwfLly7Fx40Y89NBDaNSoERITEwEAq1atwuTJk7FkyRLExcVhwYIFSExMxKFDh9CwYUOzXxJRleXm5cPpdPh6GOSnbLYABAX6ZoJsoop4HYIMHz4cycnJuOmmm8wYT7VjVACiqUwQwgCEiIio8vr27Yu+fft6vP+SJUsQExODV199FQDQpk0b7NixA/Pnz9dDkHnz5mHUqFEYOXKkfsy6devw7rvv4umnnzb+RRAZKDcvH//9z3+gKJxVn0onigKu7diRQQj5Ja9DkMzMTCQkJKB58+YYOXIkhg8fjsaNG5sxtiue0QGIxpsghAEIERHVVL169ULPnj0xc+ZMt+0XLlzAwIEDsWnTJlOeNzU1FQkJCW7bEhMTMWnSJABAfn4+9u7di6lTp+qPi6KIhIQEpKamlnnevLw85OXl6T9nZWUZO3AiDzmdDiiKiqgGQHCgr0dD/ibfAZxIV11XCjEEIT/kdQiyZs0anDlzBh988AHee+89zJw5EwkJCUhOTsadd96JgIAAM8Z5xTErANF4EoQwACEioppsy5Yt+Omnn/Djjz9i+fLlCAkJAeAKIbZu3Wra86alpSEyMtJtW2RkJLKyspCTk4MLFy5AluVS9zl48GCZ501JScHs2bNLbN/zRzpCgnmHM1WOABn1BVegVlvIBQBcysqCWs7UgYrTCQBIO2P++PyZJAL1Qt23nbsEyIpvxuNPBBE4lZkLKVsuex8oCC34O3exINQ9q6ZBhW+XWKcrV3aO06P9KvUbs0GDBpg8eTImT56Mffv2YenSpXjggQcQGhqKoUOH4pFHHkHLli0rc+pqQVQckKCYFoBoygtCGIAQEREB3377LR5++GF069YNX3zxBVq0aOHrIVXa1KlTMXnyZP3nrKwsNG3a1IcjoppKtNlQv9k1UGTPGo7qSoSCECnbbZtSOwQK156AIEmQbPxynPxTlb42OHXqFDZs2IANGzZAkiTcdttt+Omnn9C2bVu89NJLeOyxx4wa5xXDZrNBUpyQA4JMDUA0pQUhDECIiIhcGjVqhK1bt2LkyJHo0qULVq9ejTZt2pj6nFFRUUhPT3fblp6ejrCwMAQHB0OSJEiSVOo+UVFRZZ43MDAQgYG894D8g2izQbTV7CuQRMgQBfdVG222QCi8koHIr3kdUzocDvzf//0fbr/9djRv3hyrV6/GpEmTcPLkSbz33nv49ttv8dFHH2HOnDlmjNfv2Ww2yKLNkgBEU3T5XMmZwwCEiIgIgFDwOzAwMBArVqzAxIkT0adPHyxatMjU542Pj8fGjRvdtm3YsAHx8fEAALvdjk6dOrntoygKNm7cqO9DRERE5vA6vm3UqBEURcG9996LH374AbGxsSX2+fvf/46IiAgDhnflURQFimj9pV+KZIegyhAV1313DECIiKimU1X3lSumTZuGNm3aYPjw4V6d59KlS/jtt9/0n48cOYL9+/ejbt26aNasGaZOnYoTJ07g/fffBwCMGTMGb7zxBqZMmYIHH3wQmzZtwkcffYR169bp55g8eTKGDx+Ozp07o2vXrliwYAGys7P11WKIiIjIHF6HIPPnz8egQYMQFBRU5j4RERE4cuRIlQZ2pRJFEYIqQ4W1QYigFAYggGteEiuvRiEiIvI3R44cQYMGDdy2DRw4EK1bt8aePXs8Ps+ePXvw97//Xf9Zm5dj+PDhWLZsGU6dOoVjx47pj8fExGDdunV47LHH8Nprr6FJkyb417/+pS+PCwCDBw/GmTNnMGPGDKSlpSE2Nhbr168vMVkqERERGcvrEOSBBx4wYxzVhqIoBavCBEAVrbkfsPgcIKLi8Gj5XCIiouqsefPmpW5v164d2rVr5/F5br755hJXlRS1bNmyUo/58ccfyz3v+PHjMX78eI/HQURERFXHqYsNlp+fD1UQYXPmQFDKXhLKKKVNglp0jhBRzq/4JEREREREREQ1QM2e0tkksmiHACdszhw4bcGmXRFS3iow5S2fS0RERERUFgEqAMXXw/B7AhQAaoltAjgvnycElH2FHZGZGIKYQRDglIJhK1ipxYwgxJNlcBmEEBEREZG3QoSLvh7CFUGAgkDkum8TVKi82J7Ir/G/ULMIgiv8MOHWGE8CEA1vjSEiIiIiIiJy4ZUgZioIQoy8IsSbAETDK0KIiIiIqCIX1QhfD+GKIkKGKrh/p5yt1oYCaxZHqE7OqI19PQSqQXgliNkMvCKkMgGIhleEEBERERERUU3HK0GsYMAVIVUJQDS8IoSIyBo5CMVvaocS24iI/I0KEWfVKF8P44okwQm12CSo59WGkNliEfk1/hdqlSoEIUYEIBoGIURERERUSIDK2zcqRYVSYhJUFSLfTyI/x9thrFSJW2OMDEA0vDWGiIiIiIiIaiKGIFbzIggxIwDRMAghIiJTqaqvR0BERERUAkMQX/AgCDEzANEwCCEiIlOoKiSFv1eIiIjI/zAE8ZVyghArAhBN0SBEUBymPQ8REVVjqlLk31XX77Wi24iIiIj8BCdG9aVSJksFYFkAotEnS3XmQZECTH8+IiKqXgRVgaA4CoN9VYGTE29bRoST0zAS+YAEJ0TIJbYB5n9+J6KSRDg92o8hiK8VC0IAWBqAaBTJDqgKRH5zR0REXlIFEZLsgKS4Pnw4bcFQFf4+sUod4TRqC4xBiKwmQkaIcLHEdoWxJJFPBAgVLzwC8HYY/yAIkIt8YyZLdksDEI0qMhMjIqJKEAo/Tiii5PES8ERERERWYwjiB1xzgORCFcSCS4lzPVo+11CqCsmZZ+1zEhFRtaD9zlJECaIic7JtIiIi8lsMQXys+CSoni6fa6gik9ipvJyWiIi8psJpC4JsCy5cdYyTbRMREZEf4v0PPlTWKjDFJ0s19bLiIgGI0xYEEap5z0VERNWSKkj6LTH6ZNuOXF8OqUa5oDaEQ+VHOiKrSSgZ9l5QG0AGFxog8oVLqhPA4Qr3429MHyl3GdxSVo0xJQhxC0CCC7ZZfBsOERFd+YrNY6VIdkD2bIZ2qjoFNsj8SEfkA2qJSVBl/vdI5DOeTsnO22F8oNwARN9JMPfWmGIBCCexIyIiIykivwklIiIi/8MQxGIeBSD6ziYFIQxAiIiIiIiIqAZiCGIhrwIQ/SCDgxAGIERERERERFRDMQSxSKUCEP1gg4IQBiBERERERERUgzEEsUCVAhD9JFUMQhiAEBERERERUQ3HEMRkhgQg+skqGYQwACEiIiIiIiLyrxDkk08+wa233op69epBEATs37+/xD65ubkYN24c6tWrh9DQUAwcOBDp6enlnldVVcyYMQONGjVCcHAwEhIScPiw+/rB58+fx/3334+wsDBEREQgOTkZly5dqtLrMTQA0U/qZRDCAISIiIiIiIgIgJ+FINnZ2ejevTtefPHFMvd57LHH8MUXX2D16tXYunUrTp48iQEDBpR73pdeegkLFy7EkiVLsGvXLoSEhCAxMRG5ubn6Pvfffz9++eUXbNiwAWvXrsW2bdswevToSr8WUwIQ/eQeBiEMQIiIiIiIiIh0Nl8PoKgHHngAAPDnn3+W+nhmZibeeecdrFixAr169QIALF26FG3atMH333+Pbt26lThGVVUsWLAA06ZNw5133gkAeP/99xEZGYk1a9ZgyJAh+PXXX7F+/Xrs3r0bnTt3BgC8/vrruO222/DKK68gOjraq9chqDJszjxzAhD9SVxBiM2ZA5szp2TIwQCEiIiIiIiIyI1fXQlSkb1798LhcCAhIUHf1rp1azRr1gypqamlHnPkyBGkpaW5HRMeHo64uDj9mNTUVEREROgBCAAkJCRAFEXs2rWrzPHk5eUhKyvL7Y8gCJDkfHMDEE1ZV4QwACEiIiIiIiIq4YoKQdLS0mC32xEREeG2PTIyEmlpaWUeo+1T1jFpaWlo2LCh2+M2mw1169Yt87wAkJKSgvDwcP1P06ZNERgYaE0AoikRhDgZgBARERERERGVwmchyPLlyxEaGqr/2b59u6+GUmlTp05FZmam/uf48eNQVRWyaLcmANG4BSG5DECIiIiIiIiISuGzOUH69++PuLg4/efGjRtXeExUVBTy8/ORkZHhdjVIeno6oqKiyjxG26dRo0Zux8TGxur7nD592u04p9OJ8+fPl3leAAgMDERgYGCF4yYiIiIiIiIi3/PZlSC1a9fGNddco/8JDg6u8JhOnTohICAAGzdu1LcdOnQIx44dQ3x8fKnHxMTEICoqyu2YrKws7Nq1Sz8mPj4eGRkZ2Lt3r77Ppk2boCiKW1DjCUEQICn5gKp6dVyVuM0BEuT58rlERERERERENYhfrQ5z/vx5HDt2DCdPngTgCjgA15UaUVFRCA8PR3JyMiZPnoy6desiLCwMEyZMQHx8vNvKMK1bt0ZKSgruuusuCIKASZMmYe7cuWjZsiViYmIwffp0REdHIykpCQDQpk0b9OnTB6NGjcKSJUvgcDgwfvx4DBkyxOuVYfLy8lBbVfQVW0y/LaaUSVCdglT2qjFERERERERENZRfhSCff/45Ro4cqf88ZMgQAMDMmTMxa9YsAMD8+fMhiiIGDhyIvLw8JCYmYtGiRW7nOXToEDIzM/Wfp0yZguzsbIwePRoZGRno3r071q9fj6CgIH2f5cuXY/z48ejdu7d+/oULF3r9GlRVhSzZYVOd5gchZa0CU9HyuUREREREREQ1kKCqVt63Ub1lZWVhypQpqNO0JQTRdUuKaSvFeLIMrpdL5QqKDFGVkVG/NWRbxbcnERFR6WrhIq6Xtrpt2yf3xGXU9tGIzCM5cxBx9iAUQXL/PeN04IVpTyAzMxNhYWG+G6CB3nzzTbz88stIS0tDx44d8frrr6Nr165l7r969WpMnz4df/75J1q2bIkXX3wRt912m/64qqqYOXMm/vnPfyIjIwM33ngjFi9ejJYtW3o0nqysLISHh2Pjku4IDfar77WIagQJDtQVzrhtO682gIwAH42IqGa7lONE7zE7Kvzswd+YJlFFqcTVGIYFIZ6GG7wihIiIyBCrVq3C5MmTsWTJEsTFxWHBggVITEzEoUOH0LBhwxL779y5E/feey9SUlJw++23Y8WKFUhKSsK+ffvQvn17AMBLL72EhQsX4r333tNv101MTMSBAwfcrlatSD0hHbUF/n4nspoIGSHCRbdtAlQo4H+PRL4QKHg2JyavBDFQ0StBYHMlwIIiG3tFiJdXd3hzDK8EISIyBq8EQbW7EiQuLg5dunTBG2+8AQBQFAVNmzbFhAkT8PTTT5fYf/DgwcjOzsbatWv1bd26dUNsbCyWLFkCVVURHR2Nxx9/HE888QQAIDMzE5GRkVi2bJl+S3BReXl5yMvL03/OyspC06ZN8e9nm6JWIJsuIquJkBEsXHbblqPWYghC5COX82Tc+4/jvBLE1wy9IqQyAQjAK0KIiIiqID8/H3v37sXUqVP1baIoIiEhAampqaUek5qaismTJ7ttS0xMxJo1awAAR44cQVpaGhISEvTHw8PDERcXh9TU1FJDkJSUFMyePdttmygC9/7jeGVfGhEZ7pyvB0BEFWAIYgFDgpDKBiAaBiFERESVcvbsWciyjMjISLftkZGROHjwYKnHpKWllbp/Wlqa/ri2rax9ips6dapbsKJdCfLhS0Cbq7x7TURERNXNr38AQ6dUvB9DEItUKQipagCiYRBCRER0xQoMDERgYGCJ7W2uAq5v54MBERERXYEYglioUkGIUQGIhkEIERGRV+rXrw9JkpCenu62PT09HVFRUaUeExUVVe7+2j/T09PRqFEjt31iY2M9Gpc2rduvf3i0OxERUbWm/T6saNpThiAW8yoIMToA0TAIISIi8pjdbkenTp2wceNGJCUlAXBNjLpx40aMHz++1GPi4+OxceNGTJo0Sd+2YcMGxMfHAwBiYmIQFRWFjRs36qFHVlYWdu3ahbFjx3o0rosXXatSeHLpLxERUU1x8eJFhIeHl/k4QxAf8CgIMSsA0ZQShBARERlFVBy+HoKhJk+ejOHDh6Nz587o2rUrFixYgOzsbIwcORIAMGzYMDRu3BgpKSkAgIkTJ6Jnz5549dVX0a9fP6xcuRJ79uzB22+/DQAQBAGTJk3C3Llz0bJlS32J3OjoaD1oqUh0dDSOHz+O2rVrQ6jq6nMW0uYyOX78eLVYOchf8X22Dt9r6/C9ts6V+F6rqoqLFy8iOjq63P0YgvhIuUGI2QGIpngQIpW8z5iIiKhCqgoU6cFFOR+S4vTdeEwwePBgnDlzBjNmzEBaWhpiY2Oxfv16fWLTY8eOQRRFff8bbrgBK1aswLRp0/DMM8+gZcuWWLNmDdq3b6/vM2XKFGRnZ2P06NHIyMhA9+7dsX79egQFBXk0JlEU0aRJE2NfqIXCwsKumA/WVzK+z9bhe20dvtfWudLe6/KuANEwBPGhUoMQwJoAROMWhORCkezmPh8REVU7gioDqghAcgUgcj5ksfp9xBg/fnyZt79s2bKlxLZBgwZh0KBBZZ5PEATMmTMHc+bMMWqIREREVIHq9wnlClM8CAFgXQCi0YIQx2XXB1kiIiKvCK4gXXVCVGTIkh2KeuXcnkFEREQ1h1jxLmQ2VxASBEFVCgKQIOsnKhUEyDbeDkNERN7TfmeJigxFlHhVIZUrMDAQM2fOLHW5XzIO32fr8L22Dt9r61Tn91pQK1o/hjyWlZWFKVOmoE7TloAtwPMDi8wBAgCqIHq2fK7BRGcuRFXBhYbtIXOiVCKiSquFi7he2uq2bZ/cE5dR20cjMo/kzEHEmQMQZYf+e8tpC4aqKHhh2hPIzMy8ou4lJiIiouqNV4L4WrFJUJ22YAiq4ro1xsJ8ynUPtwOqwL8SRETkHUFVIEsBcASEQBXEgt9rvL2SiIiI/A87Xl8qZRUYbY4QK4MQfRI7KQBgCEJERF5SBRGqGKDPMaUKIiQ539fDIiIiIiqBHa+vlLMMrpVBSGEAYnd9gCUiIvJW0QC9SBBCRERE5G/4CcUXyglA9F0sCEKKBiCcxI6IiAwjCJBF/l4hIiIi/8MQxGoeBCD6riYGIQxAiIjIVBZP7k1ERETkCYYgVvIiANEPMSEIYQBCREREZhkzZgwEQcCCBQsq3PfNN99EixYtEBQUhLi4OPzwww9uj+fm5mLcuHGoV68eQkNDMXDgQKSnp5s0cv/mcDjw1FNPoUOHDggJCUF0dDSGDRuGkydPVngs32fvVfSeFbd69Wq0bt0aQUFB6NChA7788ku3x1VVxYwZM9CoUSMEBwcjISEBhw8fNvMl+L2UlBR06dIFtWvXRsOGDZGUlIRDhw5VeBzf66p54YUXIAgCJk2aVO5+1fl9ZghilUoEIPqhBgYhDECIiIjILJ9++im+//57REdHV7jvqlWrMHnyZMycORP79u1Dx44dkZiYiNOnT+v7PPbYY/jiiy+wevVqbN26FSdPnsSAAQPMfAl+6/Lly9i3bx+mT5+Offv24ZNPPsGhQ4fQv3//co/j++w9T96zonbu3Il7770XycnJ+PHHH5GUlISkpCT8/PPP+j4vvfQSFi5ciCVLlmDXrl0ICQlBYmIicnNzrXpZfmfr1q0YN24cvv/+e2zYsAEOhwO33norsrOzyzyG73XV7N69G2+99Rauvfbacver7u+zoKoWrsNazWVlZWHKlCmo07QlYCsyyWgVApCiBEWGzZkDVRDhtAV7falxRQGIoMgQVRkZ9VtDtgVXaoxERATUwkVcL21127ZP7onLqO2jEZlHcuYg4uxBKILk/vvN6cAL055AZmYmwsLCfDdAssyJEycQFxeHr7/+Gv369cOkSZPK/aYxLi4OXbp0wRtvvAEAUBQFTZs2xYQJE/D0008jMzMTDRo0wIoVK3D33XcDAA4ePIg2bdogNTUV3bp1s+Jl+bXdu3eja9euOHr0KJo1a1bqPnyfvVfRe1bc4MGDkZ2djbVr1+rbunXrhtjYWCxZsgSqqiI6OhqPP/44nnjiCQBAZmYmIiMjsWzZMgwZMsSaF+bnzpw5g4YNG2Lr1q246aabSt2H73XlXbp0Cddffz0WLVqEuXPnIjY2tswr9qr7+8wrQcxmUAACVO2KEF4BQkRERGZRFAUPPPAAnnzySbRr167C/fPz87F3714kJCTo20RRREJCAlJTUwEAe/fuhcPhcNundevWaNasmb5PTZeZmQlBEBAREVHq43yfvefJe1Zcamqq2/4AkJiYqO9/5MgRpKWlue0THh6OuLi4GvkelyUzMxMAULdu3TL34XtdeePGjUO/fv1KvH+lqe7vs83XA6jWDAxA9FMWBCE2Zw5szhyPrghhAEJERERmevHFF2Gz2fDoo496tP/Zs2chyzIiIyPdtkdGRuLgwYMAgLS0NNjt9hINfmRkJNLS0gwZ95UsNzcXTz31FO69994yr7bi++w9T96z4tLS0krdX3v/tH+Wt09NpygKJk2ahBtvvBHt27cvcz++15WzcuVK7Nu3D7t37/Zo/+r+PvNKELOYEIDop/biihAGIERERGSk5cuXIzQ0VP+zdetWvPbaa1i2bBkErgpkmOLv8/bt2/XHHA4H7rnnHqiqisWLF/twlETGGDduHH7++WesXLnS10Opdo4fP46JEydi+fLlCAoK8vVw/AJDEDOYGIDoT+FBEMIAhIiIiIzWv39/7N+/X/+zc+dOnD59Gs2aNYPNZoPNZsPRo0fx+OOPo0WLFqWeo379+pAkqcQKJOnp6YiKigIAREVFIT8/HxkZGWXuU50Vf587d+4MoDAAOXr0KDZs2FDunDt8n73nyXtWXFRUVIXvsbbN03PWJOPHj8fatWuxefNmNGnSpNx9+V57b+/evTh9+jSuv/56/f/RW7duxcKFC2Gz2SDLcoljqvv7zBDEBJKSb2oAoikvCGEAQkRERGaoXbs2rrnmGv3P6NGj8d///tetYY+OjsaTTz6Jr7/+utRz2O12dOrUCRs3btS3KYqCjRs3Ij4+HgDQqVMnBAQEuO1z6NAhHDt2TN+nOiv+PgcHB+sByOHDh/Htt9+iXr165Z6D77P3PHnPiouPj3fbHwA2bNig7x8TE4OoqCi3fbKysrBr164a+R5rVFXF+PHj8emnn2LTpk2IiYmp8Bi+197r3bs3fvrppxKh6v3334/9+/dDkkr2q9X9feacIAaz2+2uACQgxNQARFPaHCGi4mAAQkRERJaoV69eiWY8ICAAUVFRaNWqlb6td+/euOuuuzB+/HgAwOTJkzF8+HB07twZXbt2xYIFC5CdnY2RI0cCcE2yl5ycjMmTJ6Nu3boICwvDhAkTEB8fXyNXLHE4HLj77ruxb98+rF27FrIs6/fe161bF3a76zMf3+eqq+g9GzZsGBo3boyUlBQAwMSJE9GzZ0+8+uqr6NevH1auXIk9e/bg7bffBgAIgoBJkyZh7ty5aNmyJWJiYjB9+nRER0cjKSnJVy/T58aNG4cVK1bgs88+Q+3atfW/z+Hh4QgOdq1Uyfe66mrXrl1inpWQkBDUq1dP317T3meGIAYTRRGyZLckANEUDUICHK51tRmAEBERkT/5/fffcfbsWf3nwYMH48yZM5gxYwbS0tIQGxuL9evXu020N3/+fIiiiIEDByIvLw+JiYlYtGiRL4bvcydOnMDnn38OAIiNjXV7bPPmzbj55psB8H02QkXv2bFjxyCKhRfU33DDDVixYgWmTZuGZ555Bi1btsSaNWvcGs8pU6YgOzsbo0ePRkZGBrp3747169fX6DkatPlstL+7mqVLl2LEiBEA+F5bpaa9z4KqerHOKpUrKysL06ZNQ0hkc8AWYPnzS84ciIrrni5HQEiFq8YUJygyRFVGRv3WkG3BZgyRiKhGqIWLuF7a6rZtn9wTl1HbRyMyj+TMQcTZg1AEyf0LAKcDL0x7ApmZmeXOWUBERERkJc4JYjBRFCEqDuufV86HqMhQCj6AVrRqDBEREREREVFNwxDEYE6nE5LihCjnW/acRSdBlW3BHi+fS0RERERERFSTMAQxmNPphCzaIMn5lgQhpa0C48nyuUREREREREQ1DUMQEyhiAGTJbnoQUt4yuAxCiIiIiIiIiNwxBDGJItlNDULKC0A0DEKIiIiIiIiICjEEMZFZQYgnAYiGQQgRERERERGRC0MQkxkdhHgTgGgYhBARERERERExBLGEUUFIZQIQDYMQIiIiIiLvvPPOO7j11lt98txPP/00JkyY4JPnJqrOGIJYpKpBSFUCEA2DECIiIiIiz+Tm5mL69OmYOXOmT57/iSeewHvvvYc//vjDJ89PVF0xBLFQZYMQIwIQDYMQIiIiIqKKffzxxwgLC8ONN97ok+evX78+EhMTsXjxYp88P1F1xRDEYt4GIUYGIBoGIURERERUU7z//vuoV68e8vLy3LYnJSXhgQceKPO4lStX4o477nDbNmLECCQlJeH5559HZGQkIiIiMGfOHDidTjz55JOoW7cumjRpgqVLl+rH/PnnnxAEAR999BF69OiB4OBgdOnSBf/73/+we/dudO7cGaGhoejbty/OnDnj9nx33HEHVq5cacC7QEQahiA+4GkQYkYAomEQQkREZhJU2ddDICICAAwaNAiyLOPzzz/Xt50+fRrr1q3Dgw8+WOZxO3bsQOfOnUts37RpE06ePIlt27Zh3rx5mDlzJm6//XbUqVMHu3btwpgxY/Dwww/jr7/+cjtu5syZmDZtGvbt2webzYb77rsPU6ZMwWuvvYbt27fjt99+w4wZM9yO6dq1K/766y/8+eefVXsTiEjHEMRHKgpCzAxANCWCEDAIISKiqhMUGZKBS8MTEVVFcHAw7rvvPrerMz788EM0a9YMN998c6nHZGRkIDMzE9HR0SUeq1u3LhYuXIhWrVrhwQcfRKtWrXD58mU888wzaNmyJaZOnQq73Y4dO3a4HffEE08gMTERbdq0wcSJE7F3715Mnz4dN954I6677jokJydj8+bNbsdoz3/06NEqvgtEpGEI4kNlBSFWBCCaokGI5Myr+AAiIqJiBEWGFqQLigybMweqwI8YROQ/Ro0ahW+++QYnTpwAACxbtgwjRoyAIAil7p+TkwMACAoKKvFYu3btIIqF/4+LjIxEhw4d9J8lSUK9evVw+vRpt+OuvfZat2MAuB0XGRlZ4pjg4GAAwOXLlyt+kUTkEZuvB1DTaSFH0W/MrApANFoQYnNcBhRevkxERN5SITnzINsAmzMXqiBCFvgRg4j8x3XXXYeOHTvi/fffx6233opffvkF69atK3P/evXqQRAEXLhwocRjAQEBbj8LglDqNkVRyjxOC1+Kbyt+zPnz5wEADRo0KO/lEZEX+DWNHyh6RYjVAYjGFYQEgbfEEBGRt1RBKri10hWAOG3BQBnfrhIR+cpDDz2EZcuWYenSpUhISEDTpk3L3Ndut6Nt27Y4cOCAhSMs6eeff0ZAQADatWvn03EQVScMQYiIiIiIqNq777778Ndff+Gf//xnuROiahITE0vM62G17du36yvKEJEx/OZaVYfDgWnTpuHLL7/EH3/8gfDwcCQkJOCFF15wm5Do/PnzmDBhAr744guIooiBAwfitddeQ2hoaJnnzs3NxeOPP46VK1ciLy8PiYmJWLRokX4vHgAcO3YMY8eOxebNmxEaGorhw4cjJSUFNpt3b5EoihBUxavbSgTFAUl2QJZcl8NJzjxAVaCKARUcaSBVgc2RA1WUIHJuECKiKhGQB4fi/j2DoORB8p9fu4YRnXkQFCdUCJAlO2zOPNgclyFD8vXQiIjchIeHY+DAgVi3bh2SkpIq3D85ORmdO3dGZmYmwsPDzR9gKVauXIlZs2b55LmJqitBVf1jbdTMzEzcfffdGDVqFDp27IgLFy5g4sSJkGUZe/bs0ffr27cvTp06hbfeegsOhwMjR45Ely5dsGLFijLPPXbsWKxbtw7Lli1DeHg4xo8fD1EU8d133wEAZFlGbGwsoqKi8PLLL+PUqVMYNmwYRo0aheeff97j15CVlYUXXngBAYFeXAasKhCgQoUAaJPIlbbNTKoKAa77D1UIkG1BAISC5Q0FqKI1H2S1ifVUQdLfv9K2mUpVIKiKa0K/ovUovs3UMagl3/vStpmM9dDGwHroWA9dRfUQBQXBuOR2TA5CoRgZDPhJPUTFCVHJhwrR9V4U+Z0ye9YsZGZmIiwszPyxEBF5oHfv3mjXrh0WLlzo0f6DBg3C9ddfj6lTp5o8spK++uorPP744/jvf//r9RezRFQ2vwlBSrN792507doVR48eRbNmzfDrr7+ibdu22L17t75m9/r163Hbbbfhr7/+KnUJq8zMTDRo0AArVqzA3XffDQA4ePAg2rRpg9TUVHTr1g1fffUVbr/9dpw8eVK/OmTJkiV46qmncObMGdjtns3PkZWVhRdffBGSPcijD5+C6oQA1ywcarEJ5Mp7zFCqArHgw6oCAQIEOOy1AIiuq0O0Ce5sgQDMarJcE+oJquKal8TtvSvvMWMVvSKn+FU45T1mqPLec9bDo8cMxXroWA/9iTyqh2ILQG0xy+3Ii2odKAbdhepf9RAgKs7CEKTgMREKQxAi8hsXLlzAli1bcPfdd+PAgQNo1aqVR8f9+eef+OKLLzBhwgSTR1jSxx9/jKZNmyIuLs7y5yaqzvw6UszMzIQgCIiIiAAApKamIiIiQg9AACAhIQGiKGLXrl246667Spxj7969cDgcSEhI0Le1bt0azZo100OQ1NRUdOjQwe32mMTERIwdOxa//PILrrvuulLHl5eXh7y8wltHsrKKfOCt4FtZQdFCDgGqWLIMqhAAKE4IUAFVLnWfKlOKBCCCBGixiyAVLG0owSmIsDlzIMn55kx0p6qwOXMgQIUzoFap3+I6AyTYnDmwyXlw2oJN+aZXlPMhKU7ItsBSJ6VVRQkQRNfEtYJoysS1giLDJufpq/WUfK9ZD32YrIeO9dD4Vz0k2QGIgOiWkYhQDbgSxN/qIUuBEB3ZAITC91yQSqxwQETkS9dddx0uXLiAF1980eMABABatGjhkwAEgP4FLhEZy28nRs3NzcVTTz2Fe++9V/8GKS0tDQ0bNnTbz2azoW7dukhLSyv1PGlpabDb7XqQoomMjNSPSUtLcwtAtMe1x8qSkpKC8PBw/U95M0wXJRSEG2UFIBpVtEGFAAEqBMXp0bk9pigQ4Zq3RBGkMr9B1hoO16z/OYCRFw5pDYWqlN+8CYLr8YIGRzB4GV/Rw1V5iq7iIxZZ0tgIgiLD5sypcFUF1qMQ66ENlvXQ+Fs9HE4JRucAV1I9/PgjBhHVQH/++ScyMzPxxBNP+HooRORjPvuEsnz5coSGhup/tm/frj/mcDhwzz33QFVVLF682FdDrNDUqVORmZmp/zl+/HiFx3gagGhMCUI8DEAKx2BCY+FpQ6ExqdHztKHQmNFYeN5QuLAehVgPbdCsh8Zf6qHYAiEIqqFByJVYDyIiIiJ/47PbYfr37+92f1vjxo0BFAYgR48exaZNm9zuI46KisLp06fdzuN0OnH+/HlERUWV+jxRUVHIz89HRkaG29Ug6enp+jFRUVH44Ycf3I5LT0/XHytLYGAgAgMDPXi1Lt4GIBpVtBXeGqM4q3ZrjJcBSOEYXI2FzZkDmzOnah9+vW0oNAWNXtExVOXSf28bCo22r1TQVFTlUvPKNhSsRyHWQxs866Hxl3oE2BQ4nK4rQgJscpWmKbmS60FERETkT3x2JUjt2rVxzTXX6H+Cg4P1AOTw4cP49ttvUa9ePbdj4uPjkZGRgb179+rbNm3aBEVRypwwqFOnTggICMDGjRv1bYcOHcKxY8cQHx+vn/enn35yC1g2bNiAsLAwtG3b1pDXW9kARGPIFSGVDEAKx2DAN6yVbSg0Bn3jXdmGQmPEN6xVbShYj0KsRwHWQ+cP9RAEIMCm6FeEeLN0elHVoR5ERERE/sJvbth1OBy4++67sWfPHixfvhyyLCMtLQ1paWnIz3d9aGvTpg369OmDUaNG4YcffsB3332H8ePHY8iQIfrKMCdOnEDr1q31KzvCw8ORnJyMyZMnY/Pmzdi7dy9GjhyJ+Ph4dOvWDQBw6623om3btnjggQfwn//8B19//TWmTZuGcePGeXWlR1mqGoBoqhSEVDEAKRxDFRqLqjZ4mio2elVtKDRVaSyMaihYj0KsRwHWQ+cP9SgahEjOvBpdDyIiIiJ/4DchyIkTJ/D555/jr7/+QmxsLBo1aqT/2blzp77f8uXL0bp1a/Tu3Ru33XYbunfvjrffflt/3OFw4NChQ7h8+bK+bf78+bj99tsxcOBA3HTTTYiKisInn3yiPy5JEtauXQtJkhAfH4+hQ4di2LBhmDNnTpVfl1EBiKZSQYhBAUjhGCrRWBjV4Gkq2egZ1VBoKtNYGN1QsB6FWI8CrIfOH+qhBSGsBxEREZHvCapq5PT5NVtWVhZefPFFSPYgQJQMD0CK8vjc3gQgqgoBKhz20IIlcisag4cfjo1u8Cp5bqMbisqc28yGgvXw/tysRyHWo4BB9ZAgI1w457YtU6kLwZlf7eohqAoC8i9BLbpELgAoMp6bNQOZmZlu83sRERER+ZLfXAlS3ZgZgAAeXhFi8BUgJcfgwTesZjZ4gMffeJvZUACefcNq9jeqrEch1qMA66FjPQr5Qz2IiIiIfIUhiAkEKKYGIJpygxCTA5DCMZTTWJjdUGgqaCzMbig05TUWVjUUrEch1qMA66FjPQr5Qz2IiIiIfIEhiMFEUYQAmB6AaEoNQiwKQArHUEpjYVVDoSmjsbCqodCU1lhY3VCwHoVYjwKsh471KOQP9SAiIiKymvldeg0jCAJUwJIARKOKNqDg9hv9n7AmACkcg6uxsDlzXI0FYF1DoSloLLQxKKIEUZEtayg02nNJcj4EVYaoyJY3FKxHIdajAOuhYz0K+UM9iIiIiKzEK0FMoPrgbS16RQhgbQBSOAYJTlsQBFUpaCiCrGsoNAWNBQCIigxFlCxtKDSKZNebGgA+aShYj0KsRwHWQ8d6FPKHehARERFZhSGIKRQfPKWiByCA61tNy6kqpCL3lktyvmfLURpMVBxF/l32eDlKIwmKrDcUxcdkGdZDx3oUYj0KsB46v6gHERERkUUYgphABADFwhCi2BwgFa4aY4Zi99RXuAqDSYreU+8ICKlwFQYzFL2n3hEQUuEqDKZgPXSsRyHWowDrofOLehARERFZiCGIwWS5YII7yNYEIaVMgurR8rlGKmVSQY+WozRYiUkFPVyO0kilTSroyXKUhmI9dKxHIdajAOuh84t6EBEREVmMIYgJFLjupTY9CClnFRjLgpByVlWwsrEoc1UFCxuL8lZVsKyxYD0Kn4r10LEeBViPwqfyh3oQERER+QBDEFMIrlACJgYhHiyDa3oQ4sGyklY0FhUuK2lBY+HJspKmNxasR+FTsB461qMA61H4FP5QDyIiIiIfYQhiFkE0LwjxIADRmBaEeNBQFI7BvMaiwoZCY2Jj4UlDoTGtsWA9Ck/NeuhYjwJ+Ug9BdrAeRERERD7GEMRMZgQhXgQgGsODEC8aisIxGN9YeNzgaUxoLLxpKDSGNxasR+EpWQ8d61HAT+rhdAoQZQfrQURERORjDEHMZmQQUokARGNYEFKJhqJwDMY1Fl43eBoDG4vKNBQawxoL1qPwVKyHjvUo4Cf1cDoFOGURihRQs+tBRERE5AcYgljBiCCkCgGIpspBSBUaisIxVL2xqHSDpzGgsahKQ6GpcmPBehSegvXQsR4F/KQeWgBikxSoUoDXx1ebehARERH5CYYgVqlKEGJAAKKpdBBiQENROIbKNxZVbvA0VWgsjGgoNJVuLFiPwkNZDx3rUcBP6lE0ALHZqnBLzZVeDyIiIiI/whDESpUJQgwMQDReByEGNhSFY/C+sTCswdNUorEwsqHQeN1YsB6Fh7AeOtajgJ/UQ5AdxgQg+gmv0HoQERER+RmGIFbzJggxIQDReByEmNBQFI7B88bC8AZP40VjYUZDofG4sWA9CndlPXSsRwE/qocoO4wLQDRXWj1g/BLDRERERFXFEMQXPAlCTAxANBUGISY2FIVjqLixMK3B03jQWJjZUGgqbCxYj8JdWA8d61HAz+qhSAHGBiCaK6YeCkSGIEREROSHGIL4SnlBiAUBiMYtCFGLfJi2oKEoHEPZjYXpDZ6mnMbCioZCU2ZjwXoUPsR66I+xHgX8sB6VmgTVU35WD1FxAGqR32GqAlH1fgJXIiIiIiswBPGl0oIQCwMQTdEgBKpiaUNROIaSjYVlDZ6mlMbCyoZCU6LRYz1YD9ZDx3oU8KN6KGJAYZBeJACpwoLwRERERKax+XoANZ4gQgEgqrIefgDWBSAaVbQBihMCVNiclwEIljUUhWNwNRY2Zw4CHNkAYF1DoSloLGzOHFdzA1jaUGi01ywVNFYAWA/Wg/UowHoU8KN6qM5ctysKFUjgnCBERETkj3gliD8QRChFSqFAtDQA0ago/NCsiJKlDYU+BlGCUuR5FdHES8rLIgiQizQysmS3tKHQFH3trAfrAbAeRbEeBfykHijy+0OFAIj8eEFERET+iZ9S/IGiQCxy4bAIxbPlc42kFo5BFSSIiuyT5Q9FOR+iIuuNhafLURrJdUl5LlRBLLjUPNej5SgNVXCJP+Bq8FgP1gNgPXSsh85f6iFovz+8WX6diIiIyAcYgvhasTlAPF4+10hF7uFWIUC2BXm4/KGxit5TL9uCPV6O0kjF76n3dDlKQxWb40C2BbMerAfroWE9dP5SD0nOBeC6itFtsm3OCkJERER+iCGIL5U2Caony+cayW0Su8LbcCpc/tBgpU0q6MlylEYqdVJBD5ajNFQZkzyyHqwH6wHWowj/qofqugWm4PdHYRBCRERE5H8YgvhKeavAWBWEuAUgJSditaqxKG9VBasai3JXVbCqsahglQvWQ3uQ9dCHyHro/K0eojPPtBzE3+ohS4Elfn+4ghAiIiIi/8MQxBc8WQbX7CCkeABSxiR2ZjcWniwraXaj59GykmY3Fh4u88l6aDuxHvpQWQ+dv9XD4RQND0KupHqo/IhBREREfoifUKzmSQCiMSsI8TAA0YdsUmPhSUOhD9mkRs+jhkLf2aTGwsOGQsN6aDuzHvqQWQ+dv9RDtgVCVQVDg5ArsR5ERERE/oYhiJW8CUA0RgchXgYgGqMbC28aCo3RjZ5XDYV+kMGNRSUbCtZDO4j10LAehfyhHhAlBNhkw4KQK7keRERERP6EIYhVKhOAaIwKQioZgGiMaiwq1VAUMKrRq1RDoR9sUGNRxYaC9dAOZj00rEchf6iHKMItCKnJ9SAiIiLyFwxBrFCVAERT1SCkigGIpqqNRVUaCk1VG70qNRT6SarYWBjUULAe2klYDw3rUcgf6lE0CBGdeTW6HkRERET+gCGI2YwIQDSVDUIMCkA0lW0sjGgoNJVt9AxpKPSTVbKxMLihYD20k7EeGtajkD/UQwtCWA8iIiIi32MIYiYjAxCNt0GIwQGIxtvGwsiGQuNto2doQ6Gf1MvGwqSGgvXQTsp6aFiPQv5QD1EEZFsg60FERETkYwxBzGJGAKLxNAgxKQDReNpYmNFQaDxt9ExpKPSTe9hYmNxQsB7ayVkPDetRyB/qAdaDiIiIyOcYgpjCxABEU1EQYnIAoqmosTC1oShQUaNnakOhP0kFjYVFDQXroT0J66FhPQqxHtqT+Ec9iIiIiHyBIYjBBEHQ31TTAhD9ycoIQiwKQDRlNRZWNBSashoLSxoKTVmNhcUNBetRgPXQsR6FWI8CflIPIiIiIqsxBDGYWBA4mB6AaEoEIbKlAYimeGNhZUOhKd5YCIrTuoZCU6KxcPqkoWA9CrAeOtajEOtRwE/qQURERGQlm68HUB0pEKwJQDSCCAWAqMpw/Zu1AYhGax6kgm9XrWwoNFpjYXPmwObMtbah0BQ0FtoYAPikoWA9CrAeOtajEOtRwE/qQURERGQVhiAGUvV7u1XA0+UHjXv2Yj/LgOLZMowlziE7UOmLhBRnkflJnIBq4Yd5jVrk1iBBBZwOa5sKwHV5uyIX3u8vOzxf0thIrEfBGFgPHetRyMR6KJDhFNxfk6I6ABSfv6k61EMBVO12zKJjVwtO7+3vIiIiIiLzCCo/nRjmr7/+QtOmTX09DCIiIr9x/PhxNGnSxNfDICIiIgLAEMRQiqLg5MmTqF27NgSrv8kzQFZWFpo2bYrjx48jLCzM18MxBV9j9cDXWD3wNVYPZb1GVVVx8eJFREdH6/NlEREREfkab4cxkCiK1eLbrrCwsGr7YV3D11g98DVWD3yN1UNprzE8PNxHoyEiIiIqHb+aISIiIiIiIqIagSEIEREREREREdUIDEFIFxgYiJkzZyIwMNDXQzENX2P1wNdYPfA1Vg814TUSERFR9cGJUYmIiIiIiIioRuCVIPT/7d1/TFX1Gwfw94XLBYT4IQhIBspQSSRMEaTmXMoAp6LWUilNjVTKamtG6jSx2gy/On/kGs1EnM5F0kg3xUpQBPWKgJAi/sJER4ooBGKCCPf5/uHuHRcveLtewQvv13b/uJ8f5zzP55ydcZ4d7iEiIiIiIiLqFVgEISIiIiIiIqJegUUQIiIiIiIiIuoVWAQhIiIiIiIiol6BRZAe6Pz584iJiYGzszMcHBwwevRoXL9+Xdff1NSExYsXw83NDY6Ojnjrrbdw69atTrcpIli1ahX69+8Pe3t7RERE4PLly3pjamtr8e6778LJyQkuLi6Ii4vDvXv3zJ7fvHnzoFAo9D7R0dFPHYsx63L9+nVMmjQJffr0gYeHBxISEtDS0mL2HNuKj4+HQqHApk2b9NotOcfVq1cjICAADg4OcHV1RUREBPLz83tMfg8fPsTSpUsRFBQEBwcHeHt747333sONGzd6TI4AkJGRgcjISLi5uUGhUKCkpMSkeNt7nq437X3//fcYOHAg7OzsEBYWhlOnTnU6Pj09HQEBAbCzs0NQUBAyMzP1+o3J9VnJzc3FlClT4O3tDYVCgb1795oltietkSnnBBEREZHZCPUo5eXl0rdvX0lISJDTp09LeXm57Nu3T27duqUbEx8fLy+99JJkZ2dLYWGhjBkzRl577bVOt5uUlCTOzs6yd+9e+fPPPyUmJkYGDRokjY2NujHR0dESHBwsJ0+elLy8PPH395fY2Fiz5zh37lyJjo6Wmzdv6j61tbV6Y0yJ5Unr0tLSIsOHD5eIiAgpLi6WzMxMcXd3l+XLl5s9R62MjAwJDg4Wb29v2bhxo16fJee4e/duOXTokFy5ckVKS0slLi5OnJycpLq6ukfkV1dXJxEREfLzzz/LhQsXRK1WS2hoqIwaNUpvnCXnKCKyc+dO+eqrr+THH38UAFJcXPyf4zXkebretJWWliYqlUq2b98u586dkwULFoiLi4ve9bWt48ePi7W1tfzvf/+TsrIyWblypdjY2MjZs2f/U67PSmZmpqxYsUIyMjIEgPz66696/abEZswamXJOEBEREZkLiyA9zMyZM2X27Nkd9tfV1YmNjY2kp6fr2s6fPy8ARK1WG5yj0WjEy8tL1q1bp7cdW1tb+emnn0REpKysTABIQUGBbszBgwdFoVDI33///bRp6Zk7d65MnTq1w35TYjFmXTIzM8XKykqqqqp0Y5KTk8XJyUkePHjwlFk9rrKyUl588UUpLS0VX19fvSJIT8lRq76+XgBIVlaWiPS8/ERETp06JQDk2rVrItKzcrx69arBIkhPuN60FRoaKosXL9Z9b21tFW9vb/n2228Njp8xY4ZMmjRJry0sLEwWLVokIsbl2lXaF0FMje1Ja2TKOUFERERkTvx3mB5Eo9HgwIEDGDJkCKKiouDh4YGwsDC9R5yLiorw8OFDRERE6NoCAgLg4+MDtVptcLtXr15FVVWV3hxnZ2eEhYXp5qjVari4uCAkJEQ3JiIiAlZWVo/9m4M55OTkwMPDA0OHDsWHH36ImpoaXZ8psRizLmq1GkFBQfD09NSNiYqKwt27d3Hu3Dmz5qfRaDBnzhwkJCQgMDDwsf6ekKNWc3Mztm7dCmdnZwQHB+vi6Cn5adXX10OhUMDFxUUXS0/L0ZR423serzfAo/O0qKhILy4rKytERER0mItardYbDzw6FtrxxuTaXUyJzZg1MuWcICIiIjInFkF6kOrqaty7dw9JSUmIjo7GH3/8genTp+PNN9/E0aNHAQBVVVVQqVS6GzEtT09PVFVVGdyutr3tTVX7OVVVVfDw8NDrVyqV6Nu3b4fbNVV0dDR27tyJ7OxsrF27FkePHsXEiRPR2tpqcizGrEtVVZXBNdD2mdPatWuhVCrx6aefdhivpee4f/9+ODo6ws7ODhs3bsShQ4fg7u6u25el59dWU1MTli5ditjYWDg5Oen215NyNKQnXG+07ty5g9bW1k7jaq+jY9E2D22bsdvsKqbEZswamXJOEBEREZkTiyAWbPfu3XB0dNR9Ll68CACYOnUqPvvsM4wYMQLLli3D5MmT8cMPP3RztKZpn2NeXh5mzZqFmJgYBAUFYdq0adi/fz8KCgqQk5PT3eGapH2OR48exebNm7Fjxw4oFIruDu+pGTqGAPDGG2+gpKQEJ06cQHR0NGbMmIHq6upujtY0HeUIPPqR1BkzZkBEkJyc3I1RPp3OciQiIiIishQsgliwmJgYlJSU6D4jRoyAUqnEsGHD9Ma9/PLLurfDeHl5obm5GXV1dXpjbt26BS8vL4P70ba3//X+tnO8vLweu4FtaWlBbW1th9s1Jce2j79r+fn5wd3dHeXl5SbHYsy6eHl5GVwDbZ+p2ud44sQJVFdXw8fHB0qlEkqlEteuXcOSJUswcOBAi8uxo2Po4OAAf39/jBkzBikpKVAqlUhJSbG4/DrLUVsAuXbtGg4dOqR7CqQn5dgZS7vedMbd3R3W1tadxtVeR8eibR7aNmO32VVMic2YNTLlnCAiIiIyJxZBLNgLL7wAf39/3cfZ2RmjR4/WPRGidenSJfj6+gIARo0aBRsbG2RnZ+v6L168iOvXryM8PNzgfgYNGgQvLy+9OXfv3kV+fr5uTnh4OOrq6lBUVKQbc/jwYWg0GoSFhZktR3t7+8fGVFZWoqamBv379zc5FmPWJTw8HGfPntW7+dLe2LYvPD1NjgsXLsSZM2f0bji9vb2RkJCA33//3eJyNOYYAo9+B+XBgwcWl19HOWoLIJcvX0ZWVhbc3Nz05vSEHJ/E0q43nVGpVBg1apReXBqNBtnZ2R3mEh4erjceeHQstOONybW7mBKbMWtkyjlBREREZFbd/cusZF4ZGRliY2MjW7dulcuXL8uWLVvE2tpa8vLydGPi4+PFx8dHDh8+LIWFhRIeHi7h4eF62xk6dKhkZGToviclJYmLi4vs27dPzpw5I1OnTjX4yspXX31V8vPz5dixYzJ48GCzv7KyoaFBPv/8c1Gr1XL16lXJysqSkSNHyuDBg6WpqcnoWCorK2Xo0KGSn59v9LpoXz0aGRkpJSUl8ttvv0m/fv2e6Stytdq/HcaSc7x3754sX75c1Gq1VFRUSGFhocyfP19sbW2ltLTU4vMTEWlubpaYmBgZMGCAlJSU6L3Oue0bWiw5RxGRmpoaKS4ulgMHDggASUtLk+LiYrl586bR8Yo8v9eb9tLS0sTW1lZ27NghZWVlsnDhQnFxcdG9iWfOnDmybNky3fjjx4+LUqmU9evXy/nz5yUxMdHgK3KflOuz0tDQIMXFxVJcXCwAZMOGDVJcXKx7g5ExsY0fP162bNmi+/6kNRIx7pwgIiIielZYBOmBUlJSxN/fX+zs7CQ4OFj27t2r19/Y2CgfffSRuLq6Sp8+fWT69Ol6Ny0ij16XmJqaqvuu0Wjkyy+/FE9PT7G1tZUJEybIxYsX9ebU1NRIbGysODo6ipOTk8yfP18aGhrMmtv9+/clMjJS+vXrJzY2NuLr6ysLFizQ+wPbmFi0r/Q8cuSIrs2YdamoqJCJEyeKvb29uLu7y5IlS+Thw4dmzdEQQ0UQS82xsbFRpk+fLt7e3qJSqaR///4SExMjp06d6hH5tY3N0KdtvJaco4hIamqqwRwTExP/U7zP6/XGkC1btoiPj4+oVCoJDQ2VkydP6vrGjRsnc+fO1Ru/Z88eGTJkiKhUKgkMDJQDBw7o9RuT67Ny5MgRg8dPm4Mxsfn6+uodb5HO10jEuHOCiIiI6FlRiIh03XMnRERERERERETdg78JQkRERERERES9AosgRERERERERNQrsAhCRERERERERL0CiyBERERERERE1CuwCEJEREREREREvQKLIERERERERETUK7AIQkRERERERES9AosgRERERERERNQrsAhCRBYjJSUFkZGR3bLvZcuW4ZNPPumWfRMRERERkXkoRES6OwgioidpamqCn58f0tPT8frrr3f5/u/cuQM/Pz+UlJTAz8+vy/dPRERERERPj0+CEJFF+OWXX+Dk5NQtBRAAcHd3R1RUFJKTk7tl/0RERERE9PRYBCGiLnX79m14eXlhzZo1urYTJ05ApVIhOzu7w3lpaWmYMmWKXtu8efMwbdo0rFmzBp6ennBxccHXX3+NlpYWJCQkoG/fvhgwYABSU1N1cyoqKqBQKLBnzx6MHTsW9vb2GD16NC5duoSCggKEhITA0dEREydOxO3bt/X2N2XKFKSlpZlpJYiIiIiIqKuxCEJEXapfv37Yvn07Vq9ejcLCQjQ0NGDOnDn4+OOPMWHChA7nHTt2DCEhIY+1Hz58GDdu3EBubi42bNiAxMRETJ48Ga6ursjPz0d8fDwWLVqEyspKvXmJiYlYuXIlTp8+DaVSiXfeeQdffPEFNm/ejLy8PJSXl2PVqlV6c0JDQ1FZWYmKigqzrAUREREREXUt/iYIEXWLxYsXIysrCyEhITh79iwKCgpga2trcGxdXR1cXV2Rm5uLsWPH6trnzZuHnJwc/PXXX7CyelTTDQgIgIeHB3JzcwEAra2tcHZ2xrZt2zBr1ixUVFRg0KBB2LZtG+Li4gA8esokNjYW2dnZGD9+PAAgKSkJO3bswIULF3T7u3v3LpydnZGTk4Nx48Y9k3UhIiIiIqJnR9ndARBR77R+/XoMHz4c6enpKCoq6rAAAgCNjY0AADs7u8f6AgMDdQUQAPD09MTw4cN1362treHm5obq6mq9ea+88oreHAAICgrSa2s/x97eHgBw//79J+ZHRERERETPH/47DBF1iytXruDGjRvQaDRP/PcSNzc3KBQK/PPPP4/12djY6H1XKBQG2zQaTYfzFAqFwbb2c2prawE8+pceIiIiIiKyPCyCEFGXa25uxuzZszFz5kx88803+OCDDx576qItlUqFYcOGoaysrAujfFxpaSlsbGwQGBjYrXEQEREREZFpWAQhoi63YsUK1NfX47vvvsPSpUsxZMgQvP/++53OiYqKwrFjx7ooQsPy8vJ0b5QhIiIiIiLLwyIIEXWpnJwcbNq0Cbt27YKTkxOsrKywa9cu5OXlITk5ucN5cXFxyMzMRH19fRdGqy8tLQ0LFizotv0TEREREdHT4dthiMhivP322xg5ciSWL1/e5fs+ePAglixZgjNnzkCp5G9KExERERFZIj4JQkQWY926dXB0dOyWff/7779ITU1lAYSIiIiIyILxSRAiIiIiIiIi6hX4JAgRERERERER9QosghARERERERFRr8AiCBERERERERH1CiyCEBEREREREVGvwCIIEREREREREfUKLIIQERERERERUa/AIggRERERERER9QosghARERERERFRr8AiCBERERERERH1Cv8HMP2eJHEN9egAAAAASUVORK5CYII=",
      "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",
    "        snapping_points=(strip.geometry.bounds[0], strip.geometry.bounds[1]),\n",
    "    ),\n",
    "    structures=[substrate, strip],\n",
    "    sources=[uniform_current],\n",
    "    monitors=[propagate_mon, freq_yz, mode_yz, time_voltage_yz, time_current_yz],\n",
    "    run_time=run_time,\n",
    "    boundary_spec=boundary_spec,\n",
    "    plot_length_units=\"mm\",\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",
    "ax1.set_title(\"Top view of microstrip\")\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\">13:15:32 EST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'impedance_calc'</span> with resource_id                     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-267d2128-4d89-4555-8dbb-7ccbd5d73786'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:15:32 EST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'impedance_calc'\u001b[0m with resource_id                     \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-267d2128-4d89-4555-8dbb-7ccbd5d73786'\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-267d2128-4d89-4555-8dbb-7ccbd5d73786\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-267d2128-4d8</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-267d2128-4d89-4555-8dbb-7ccbd5d73786\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">9-4555-8dbb-7ccbd5d73786'</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=41409;https://tidy3d.simulation.cloud/workbench?taskId=fdve-267d2128-4d89-4555-8dbb-7ccbd5d73786\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=495693;https://tidy3d.simulation.cloud/workbench?taskId=fdve-267d2128-4d89-4555-8dbb-7ccbd5d73786\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=41409;https://tidy3d.simulation.cloud/workbench?taskId=fdve-267d2128-4d89-4555-8dbb-7ccbd5d73786\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=597105;https://tidy3d.simulation.cloud/workbench?taskId=fdve-267d2128-4d89-4555-8dbb-7ccbd5d73786\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=41409;https://tidy3d.simulation.cloud/workbench?taskId=fdve-267d2128-4d89-4555-8dbb-7ccbd5d73786\u001b\\\u001b[32m-267d2128-4d8\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=41409;https://tidy3d.simulation.cloud/workbench?taskId=fdve-267d2128-4d89-4555-8dbb-7ccbd5d73786\u001b\\\u001b[32m9-4555-8dbb-7ccbd5d73786'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/f89aec3e-3357-4624-9c24-096a87582f12\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=993403;https://tidy3d.simulation.cloud/folders/f89aec3e-3357-4624-9c24-096a87582f12\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c9b9a3cfde224f8d871e1e081c359ad5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:15:42 EST </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.225</span>. Minimum cost depends on task     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:15:42 EST\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m0.225\u001b[0m. Minimum cost depends on task     \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:15:43 EST </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:15:43 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c4929b95d0504cebbdd5362bd46a5cec",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:31:28 EST </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:31:28 EST\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2d489d6dacc146e1a1d6fba62faaeae8",
       "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\">13:31:46 EST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">28</span>%, exiting.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:31:46 EST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m28\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\"><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": "f93ab729ef13450a9f7fac05b270514e",
       "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\">13:31:51 EST </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:31:51 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:31:53 EST </span>View simulation result at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-267d2128-4d89-4555-8dbb-7ccbd5d73786\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-267d2128-4d8</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-267d2128-4d89-4555-8dbb-7ccbd5d73786\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">9-4555-8dbb-7ccbd5d73786'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:31:53 EST\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=375720;https://tidy3d.simulation.cloud/workbench?taskId=fdve-267d2128-4d89-4555-8dbb-7ccbd5d73786\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=10820;https://tidy3d.simulation.cloud/workbench?taskId=fdve-267d2128-4d89-4555-8dbb-7ccbd5d73786\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=375720;https://tidy3d.simulation.cloud/workbench?taskId=fdve-267d2128-4d89-4555-8dbb-7ccbd5d73786\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=156604;https://tidy3d.simulation.cloud/workbench?taskId=fdve-267d2128-4d89-4555-8dbb-7ccbd5d73786\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=375720;https://tidy3d.simulation.cloud/workbench?taskId=fdve-267d2128-4d89-4555-8dbb-7ccbd5d73786\u001b\\\u001b[4;34m-267d2128-4d8\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=375720;https://tidy3d.simulation.cloud/workbench?taskId=fdve-267d2128-4d89-4555-8dbb-7ccbd5d73786\u001b\\\u001b[4;34m9-4555-8dbb-7ccbd5d73786'\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": "f925e90ff6864d8a94af7cea1b9644e3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:31:59 EST </span>Loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:31:59 EST\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",
    "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",
    "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 [AxisAlignedVoltageIntegral](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.AxisAlignedVoltageIntegral.html) and [AxisAlignedCurrentIntegral](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.AxisAlignedCurrentIntegral.html) tools 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 = rf.AxisAlignedVoltageIntegral(\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 = rf.AxisAlignedCurrentIntegral(\n",
    "    center=strip_center,\n",
    "    size=(0, width + 2 * dl, thickness + 2 * dl),\n",
    "    snap_contour_to_grid=True,\n",
    "    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 `AxisAlignedVoltageIntegral` 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 near 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=4e-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 $t=4 \\times 10^{-10}$ 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",
    "ax1.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The discontinuity at the interface between the substrate and 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=[3e-10, 5e-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.ImpedanceCalculator.html). 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 three possible methods. In this step, we will take the usual approach of computing impedance using $Z_0 = \\frac{V}{I}$ [1]. Later, we will demonstrate the two other methods, which are more useful definitions at higher frequencies.\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. We also include results from `scikit-rf`, which uses the Hammerstad-Jensen dispersion model to account for how the characteristic impedance changes with frequency. This model captures physical effects like field distribution changes at higher frequencies that a simple quasi-static model cannot represent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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 = rf.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, _) = mw.models.microstrip.compute_line_params(4.4, width, height, thickness)\n",
    "\n",
    "# Also compare to results from scikit-rf\n",
    "frequency = Frequency.from_f(freqs, unit=\"Hz\")\n",
    "microstrip = MLine(\n",
    "    frequency=frequency,\n",
    "    w=width * 1e-6,\n",
    "    h=height * 1e-6,\n",
    "    ep_r=4.4,\n",
    "    t=thickness * 1e-6,\n",
    ")\n",
    "skrf_z0 = np.real(microstrip.z0)\n",
    "\n",
    "f, ax1 = plt.subplots(1, 1, tight_layout=True, figsize=(10, 3))\n",
    "ax1.plot(freqs / 1e9, np.real(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.plot(freqs / 1e9, skrf_z0, label=\"scikit-rf\")\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 of these dimensions. Tidy3D shows excellent agreement at low frequencies with reference models: within 0.25% of both quasi-static and `scikit-rf` models at 100 MHz. At 10 GHz, the relative error grows to 6% when compared to `scikit-rf`, which is mainly a result of the chosen definition of impedance. Later, we will describe the different definitions and show how to achieve a much closer match to the `scikit-rf` model."
   ]
  },
  {
   "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 `AxisAlignedCurrentIntegral`, 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 `Custom2DCurrentIntegral`, which can accept any user-defined path to perform the integration. We also support `Custom2DVoltageIntegral` which can be used in the same manner.\n",
    "\n",
    "When using a `Custom2DCurrentIntegral` or `Custom2DVoltageIntegral`, 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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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w9vbGvn37Wg3eBQUFKC4uNnqNUqlEbGws9u3bx+BNRERERES3vNKKi9ibcxB7cnRhO7/klFG7VCJFT68gw9Tx2MDesLe+8WtNU9cxafBeu3YtDh48iNTU1BZtxcXFUCgUsLe3Nzru6uqK4uLiVs/XdPzKNeDXeg0AqFQqqFSXLwhfWVnZ1o9ARERERER0Q85XXjIE7X05B5FbbDx1XCKRIMKrB/r36KsP2r2gtLI1UbXUESYL3qdPn8bLL7+MrVu3mnzt9eLFi7Fw4UKT1nA3WbVqFWbMmIHy8nJTlwIA8PX1xYwZMzBjxgxTl3JdEokEGzduxAMPPNCm/pMnT0Z5eTk2bdrUpXURERERUdtdqCrT7TiuD9s5RQVG7U1Txwf06IP+PfogLiiKQfs2Z7LgnZaWhtLSUvTp08dwTKPRYOfOnVi+fDm2bNmChoYGlJeXG416l5SUwM3NrdVzNh0vKSmBu7u70Wt69+591VrmzZuHWbNmGZ5XVlbCy8urg5/s1nX69GksWLAAmzdvxoULF+Du7o4HHngA8+fPh6PjzdvVcMKECbjvvvs6/Pq33noLmzZtQnp6erted7XAn5qaCmtr6w7XQ0RERER0LZeqK5Ccewh7ctKwJ/sgss7lt+gT1j0Q/Xv0wYDgvogL6o1u1lyjfScxWfAePnw4jh49anRsypQpCAkJwWuvvQYvLy+YmZlh27ZtGD9+PAAgOzsbp06dQnx8fKvn9PPzg5ubG7Zt22YI2pWVlUhJScELL7xw1VrMzc1hbm7eOR/sFnXixAnEx8ejR48e+OGHH+Dn54fjx49jzpw5+PXXX5GcnAwHB4ebUoulpSUsLS1vynu1hbOzs6lLICIiIqI7SFVdDZLz0rE7+wD2ZKfh+JlcCCGM+oR4BKB/jyh90I6Co429aYqlm0Jqqje2tbVFz549jW7W1tZwdHREz549oVQqMXXqVMyaNQvbt29HWloapkyZgvj4eKON1UJCQrBx40YAuikZM2bMwKJFi/Cf//wHR48excSJE+Hh4dHmqbl3qmnTpkGhUOC3337DkCFD4O3tjVGjRuH333/H2bNn8cYbbxj6SiSSFlOT7e3tsWrVKsPz1157DT169ICVlRX8/f3x5ptvQq1WG9oPHz6MoUOHwtbWFnZ2dujbty8OHDgAQDfyfOXa/SslJSUhJiYG1tbWsLe3x4ABA3Dy5EmsWrUKCxcuxOHDhyGRSCCRSAx1LVmyBBEREbC2toaXlxf+/Oc/o7q62nC+KVOmoKKiwvC6t956C4BuqnnTteMBoLy8HM899xxcXV1hYWGBnj174ueff75qrRKJBF9++SXuv/9+WFlZITQ0FPv27UNeXh4SEhJgbW2N/v37Iz/f+C+bK1asQEBAABQKBYKDg/GPf/zDqD03NxeDBw+GhYUFwsLCsHXr1hbvffr0aTz66KOwt7eHg4MDxo0bh8LCwmt+bYmIiIioc9U21GNn5n68t2kF7vtgKkJeuRdP/e0VfPn7Dzh2OgdCCAS5+WLykPH46tl3cfTDX5A0/59477HZGB01lKH7LmDyXc2v5ZNPPoFUKsX48eOhUqkwYsQIfP7550Z9srOzUVFRYXj+6quvoqamBn/6059QXl6OgQMHYvPmzTdlHXlNTQ0AwMrKChKJBADQ0NAAtVoNuVxuNKre1NfS0hJSqe7vH2q1Gg0NDZDJZEb1tta3PS5duoQtW7bg3XffbTHS7ObmhieffBLr1q3D559/bqj7emxtbbFq1Sp4eHjg6NGjePbZZ2Fra4tXX30VAPDkk08iKioKK1asgEwmQ3p6OszMzNp07sbGRjzwwAN49tln8cMPP6ChoQH79++HRCLBhAkTcOzYMWzevBm///47AN3O9QAglUqxbNky+Pn54cSJE/jzn/+MV199FZ9//jn69++PpUuXYv78+cjOzgYA2NjYtHhvrVaLUaNGoaqqCt9//z0CAgKQkZEBmUx2zZrfeecdLFmyBEuWLMFrr72GJ554Av7+/pg3bx68vb3x9NNPY/r06fj1118BABs3bsTLL7+MpUuXIjExET///DOmTJkCT09PDB06FFqtFg899BBcXV2RkpKCioqKFmvQ1Wo1RowYgfj4eOzatQtyuRyLFi3CyJEjceTIESgUijZ9vYmIiIiofRoa1ThYcBy7sw9gd3YaDhYcQ0Oj2qiPr7MnBgb3xcDgfujfow9clDdvaSfdggS1UFFRIQCIioqKFm11dXUiIyND1NXVtWgDIACI0tJSw7FFixYJAOKZZ54x6mtlZSUAiIKCAsOxTz75RAAQTzzxhFFfJycnAUAcO3asQ58nOTlZABAbN25stX3JkiUCgCgpKTF8jiv7KpVKsXLlyqu+x0cffST69u1reG5raytWrVrVat+VK1cKpVJ51XNdvHhRABBJSUmtti9YsED06tXrqq9vsmHDBuHo6Hjd9/Xx8RGffPKJEEKILVu2CKlUKrKzs697/iYAxF/+8hfD83379gkA4ttvvzUc++GHH4SFhYXhef/+/cWzzz5rdJ5HHnlE3HfffYY65HK5OHv2rKH9119/Nfq3+cc//iGCg4OFVqs19FGpVMLS0lJs2bJFCCHEpEmTxLhx465a+7W+n4mIiIhIR92oFmknjoplv64Wjy59UfhOHyxcn4s1ukXNHSOmr1wo1u79WZy+WGTqkukGXSsTdsQtPeJNnUtcsa7kSu0ZIV23bh2WLVuG/Px8VFdXo7GxEXZ2l68dOGvWLDzzzDP4xz/+gcTERDzyyCMICAhocZ5Tp04hLCzM8Pz111/H66+/jsmTJ2PEiBG45557kJiYiEcffdRow7zW/P7771i8eDGysrJQWVmJxsZG1NfXo7a2FlZWVm36XOnp6fD09ESPHj3a+JXQiYyMNDxuupxdRESE0bH6+npUVlbCzs4OmZmZ+NOf/mR0jgEDBuDTTz8FAGRmZsLLywseHh6G9iv3Njh8+DDy8vJga2u8w2V9fX2Lae1ERERE1HZarRaZ5/KxO+sAdmcfwL7cQ6iurzXq42jbzTCiPTC4L3ydPds8e5TuPgzenahpPXHzkDdnzhzMmDEDcrnxl7q0tBQAjKZ+T5s2Dc8++2yLac1Na3Y7uiFZYGAgJBIJMjMz8eCDD7Zoz8zMhLOzs2HdtUQiaRHSm6/f3rdvH5588kksXLgQI0aMgFKpxNq1a/Hxxx8b+rz11lt44okn8L///Q+//vorFixYgLVr17Z4fw8PD6PdyZs2eFu5ciVeeuklbN68GevWrcNf/vIXbN261Wh9f3OFhYW4//778cILL+Ddd9+Fg4MDdu/ejalTp6KhoaHNwbujX+Pm0+ibfuC2dkyr1Xbo/K2prq5G37598c9//rNFGzeMIyIiImo7IQQKSk9jd3YadmUdwJ6cNFyqLjfqo7SyRf+gPhgQogvbwe5+DNrUZgzenai1S1IpFIpWR5Jb62tmZtbqOugbvdSVo6Mj7rnnHnz++eeYOXOmUbgsLi7GP//5T0ybNs1wzNnZGUVFRYbnubm5qK29/Be+vXv3wsfHx2hDtpMnT7Z43x49eqBHjx6YOXMmHn/8caxcubJF8JbL5QgMDGy17qioKERFRWHevHmIj4/HmjVrEBcXB4VCAY1GY9Q3LS0NWq0WH3/8sWEd/Pr16436tPa6K0VGRuLMmTPIyclp96h3e4SGhmLPnj2YNGmS4diePXsMo/+hoaE4ffo0ioqKDCP9ycnJRufo06cP1q1bBxcXF6PZBkRERER0fUVlpfqgnYo92Wk4W1Zi1G5lbom4wN66Ee2Qvgj3DIJMeu19f4iuhsH7LrF8+XL0798fI0aMwKJFi4wuJ9ajRw/Mnz/f0HfYsGFYvnw54uPjodFo8Nprrxn9QSAoKAinTp3C2rVrER0djf/973+GneUBoK6uDnPmzMHDDz8MPz8/nDlzBqmpqYbLwl1PQUEBvvrqK4wdOxYeHh7Izs5Gbm4uJk6cCEC3C3lBQYFhWritrS0CAwOhVqvx2WefYcyYMdizZw+++OILo/P6+vqiuroa27ZtQ69evWBlZdViJHzIkCEYPHgwxo8fjyVLliAwMBBZWVmQSCQYOXJku7/uVzNnzhw8+uijiIqKQmJiIv773//ip59+MmwYl5iYiB49emDSpEn46KOPUFlZafSHDkC3gd1HH32EcePG4e2334anpydOnjyJn376Ca+++io8PT07rV4iIiKi292l6grszdGPaGenIa/EeOBIITdDX7+eGBQSjYHBfRHlFw4zGeMSdQ5+J90lgoKCkJqairfeeguPPvooSktLIYTAQw89hH/84x9GAfTjjz/GlClTMGjQIHh4eODTTz9FWlqaoX3s2LGYOXMmpk+fDpVKhdGjR+PNN980XJ5LJpPh4sWLmDhxIkpKSuDk5ISHHnoICxcubFOtVlZWyMrKwurVq3Hx4kW4u7tj2rRpeO655wAA48ePx08//YShQ4eivLwcK1euxOTJk7FkyRJ88MEHmDdvHgYPHozFixcbwjoA9O/fH88//zwmTJiAixcvYsGCBYaam/vXv/6F2bNn4/HHH0dNTQ0CAwPx/vvvd+CrfnUPPPAAPv30U/z1r3/Fyy+/DD8/P6xcuRIJCQkAdDu0b9y4EVOnTkVMTAx8fX2xbNkyo/BvZWWFnTt34rXXXsNDDz2EqqoqdO/eHcOHD+cIOBEREd31auprkZyXrg/aB3DsimtpSyVSRHqHYFCIbo12dGAvWCm6/kpIdHeSiOvtuHUXqqyshFKpREVFRYsAU19fj4KCAvj5+d2US5R1pQULFmDJkiXXXDtNd7Y76fuZiIiI7m4NjWqkFRzDrqwD2J2VioMFx9GoNV5mGOzhj4HBfTEoOBrxPaKgtLK9ytnobnetTNgRHPG+iy1cuBC+vr5ITk5GTExMh64RTkRERERkClqtFhln87ArKxU7s1KRnJuOuoZ6oz7eTh66oB0SjQE9+vJa2mQyDN53uSlTppi6BCIiIiKiNjl5/ix2ZqViV1Yqdme33Hnc0bYbBgX3000fD4mGj5NH6yciuskYvImIiIiI6JZ0vvIS9uh3Ht+ZlYrTF4uM2q3MLREfFIXBIdEYFNIPIR4BnMVJtyQGbyIiIiIiuiXU1NdiX246dmXtx66sA8g4m2fULpfK0Ndft/P4oJB+iPINh0Le8nK8RLcaBm8iIiIiIjIJtaYR6YUZuunjmak4cOJoiw3Rwj2DMDCkn25DtKDesLawusrZiG5d7Q7eKpUKKSkpOHnyJGpra+Hs7IyoqCj4+fl1RX1ERERERHSHEEIgt7jQELT35KShur7WqI+Xo7tu6niobkM0ZzsHE1VL1HnaHLz37NmDTz/9FP/973+hVquhVCphaWmJS5cuQaVSwd/fH3/605/w/PPPw9aW2/ITERERERFQUnFBt0Y7U7cpWlH5eaP2btZ2GBgcjcGhuunjvs6eJqqUqOu0KXiPHTsWBw8exBNPPIHffvsN/fr1g6WlpaH9xIkT2LVrF3744QcsWbIEf//733HPPfd0WdFERERERHRrqqmvxd7cQ9iVqdsQLetcvlG7uVyB2MBeGBQajcEhMejpFQSZVGaiaolujjYF79GjR+Nf//oXzMxa37jA398f/v7+mDRpEjIyMlBUVNRqPyIiIiIiurM0ahqRfjITOzL3t7pOWyKRIMIrGINDozE4JBrRAZGwVFiYsGKim69Nwfu5555r8wnDwsIQFhbW4YLo1iORSLBx40Y88MADKCwshJ+fHw4dOoTevXtft39nKC4uxlNPPYW9e/fCzMwM5eXlnXJeIiIiImo/IQROlJ7Gjsz92Jm5H3uy01BVX2PUx9vJA0NCYzAoJBoDgvvC0cbeNMUS3SJuaFfz6upqaLVao2N2dnY3VBDdeoqKitCtW7cO9W9LUL+eTz75BEVFRUhPT4dSqezQOYiIiIio4y5UlWF31gFd2M5KxdlLxUbt9lZ2GBjSD0NCYzA4JBo+zt1NVCnRrandwbugoADTp09HUlIS6uvrDceFEJBIJNBoNNd4Nd2O3NzcurT/1TQ0NEChUCA/Px99+/ZFUFBQp5yXiIiIiK6tXq3C/rzDhlHto6dzjNoVcjNEB0TqgnZoDCK8enCdNtE1tDt4/9///R+EEPjuu+/g6uoKiUTSFXVRF/jxxx+xcOFC5OXlwcrKClFRUfj3v/8Na2trfPfdd/j444+Rl5cHBwcHjB8/HsuXLwdw7anjGo0Gzz77LPbu3YvffvsN3t7eRv2bLjMXFRUFABgyZAiSkpJarS8hIQE9e/aEXC7H999/j4iICBQUFODkyZMAgL///e+YNGkSVq1a1elfGyIiIqK7mVarRcbZPEPQTsk7jHq1yqhPaPcADA6NwZDQGMQFRcGK67SJ2qzdwfvw4cNIS0tDcHBwV9RzexICUNdev19nM7MC2viHj6KiIjz++OP48MMP8eCDD6Kqqgq7du2CEAIrVqzArFmz8P7772PUqFGoqKjAnj17rntOlUqFxx9/HIWFhdi1axecnZ1b9Nm/fz9iYmLw+++/Izw8HAqF4prnXL16NV544QXD+zs4OGDixImws7PDp59+arSbPhERERF13NlLJboN0bJ0u49frCozandTOmNwaLRhrbaL0tFElRLd/todvKOjo3H69OlOCd4rVqzAihUrUFhYCAAIDw/H/PnzMWrUKABAfn4+Zs+ejd27d0OlUmHkyJH47LPP4OrqetVzvvXWW1i4cKHRseDgYGRlZd1wvVelrgXedu+681/N/CJAYd2mrkVFRWhsbMRDDz0EHx8fAEBERAQAYNGiRXjllVfw8ssvG/pHR0df83zV1dUYPXo0VCoVtm/fftW1101h3NHRsU1T0IOCgvDhhx8aHTM3N4elpWWnTWEnIiIiuhtV19dgb84h7MhMwc7M/cgtPmnUbmVuif49+mBIiG76eA93X85uJeok7Q7e33zzDZ5//nmcPXsWPXv2bHGJscjIyDafy9PTE++//z6CgoIghMDq1asxbtw4HDp0CL6+vrj33nvRq1cv/PHHHwCAN998E2PGjEFycjKkUulVzxseHo7ff//d8Fwuv6E95O4IvXr1wvDhwxEREYERI0bg3nvvxcMPPwy1Wo1z585h+PDh7Trf448/Dk9PT/zxxx/tHoXetWuX4Y8rAPDll1/iySefBAD07du3XeciIiIiotZptBocPpmFHZkp2JGZigP5R4wu8yWVSNHbN9SwTruvX08o5K1fPpiIbky7E+n58+eRn5+PKVOmGI5JJJIOba42ZswYo+fvvvsuVqxYgeTkZJw9exaFhYU4dOiQYaf01atXo1u3bvjjjz+QmJh41fPK5fKbOzpqZqUbfb7ZzKza3FUmk2Hr1q2GtdifffYZ3njjDWzbtq1Db33ffffh+++/x759+zBs2LB2vbZfv35IT083PG8+g8Haum0j+ERERETU0skL57Azcz+SMlKwJzsN5bWVRu0+Tt0xJDQGQ8JiMKBHX9hb84pERDdDu4P3008/jaioKPzwww+durmaRqPBhg0bUFNTg/j4eOTn50MikcDc3NzQx8LCAlKpFLt3775m8M7NzYWHhwcsLCwQHx+PxYsXw9vbu1PqbJVE0uYp36YkkUgwYMAADBgwAPPnz4ePjw+2bt0KX19fbNu2DUOHDm3zuV544QX07NkTY8eOxf/+9z8MGTKk1X5Na7qb/0HG0tISgYGBN/ZhiIiIiAiVddWXL/OVuR8F588YtdtZ2mBQSD/Dpmi+zp4mqpTo7tbu4H3y5En85z//6bTgdPToUcTHx6O+vh42NjbYuHEjwsLC4OzsDGtra7z22mt47733IITA3LlzodFoUFR09dHl2NhYrFq1CsHBwSgqKsLChQsxaNAgHDt2DLa2tq2+RqVSQaW6vGtjZWVlq/1uZykpKdi2bRvuvfdeuLi4ICUlBefPn0doaCjeeustPP/883BxccGoUaNQVVWFPXv24MUXX7zmOV988UVoNBrcf//9+PXXXzFw4MAWfVxcXGBpaYnNmzfD09MTFhYWvBY3ERERUQc1ahpxqDADSRkp2JG5H4cKM6BpNn1cLpWhr39PDAmNxZDQGPTyCYFcxmWXRKbW7v8Khw0bhsOHD3da8A4ODkZ6ejoqKirw448/YtKkSdixYwfCwsKwYcMGvPDCC1i2bBmkUikef/xx9OnT55rru5uvHY6MjERsbCx8fHywfv16TJ06tdXXLF68uMWGbHcaOzs77Ny5E0uXLkVlZSV8fHzw8ccfG75e9fX1+OSTTzB79mw4OTnh4YcfbtN5Z8yYAa1Wi/vuuw+bN29G//79jdrlcjmWLVuGt99+G/Pnz8egQYOuejkxIiIiImqp8PwZXdDO2I/d2QdQVV9j1B7o6oPBodFICItF/x59YGNx68/EJLrbSIQQoj0v+Oqrr7Bo0SI8/fTTiIiIaLG52tixY2+ooMTERAQEBODLL780HLtw4QLkcjns7e3h5uaGV155BXPmzGnzOaOjo5GYmIjFixe32t7aiLeXlxcqKioM68ub1NfXo6CgAH5+frCw4LUL6fbG72ciIqJbT9P08aRMXdg+eeGsUXs3azvD1PHBoTHwdOCVX4g6W2VlJZRKZauZsCPaPeL9/PPPAwDefvvtFm3t3VytNVqt1igEA4CTkxMA4I8//kBpaWm7wn11dTXy8/Px1FNPXbWPubm50VpyIiIiIqKbpVHTiPSTmfpR7RQcbGX6eHRAJBLCYjEkLBYRXj0gk8pMWDERtVe7g7dWq+20N583bx5GjRoFb29vVFVVYc2aNUhKSsKWLVsAACtXrkRoaCicnZ2xb98+vPzyy5g5c6bRNcSHDx+OBx98ENOnTwcAzJ49G2PGjIGPjw/OnTuHBQsWQCaT4fHHH++0uomIiIiIbsTJC+ewIyMFOzJTsCvrACrrqo3aA119MCQsBkNCY9G/RxSnjxPd5ky600JpaSkmTpyIoqIiKJVKREZGYsuWLbjnnnsAANnZ2Zg3bx4uXboEX19fvPHGG5g5c6bROfLz83HhwgXD8zNnzuDxxx/HxYsX4ezsjIEDByI5ORnOzs439bMRERERETWpqqvBnuw0/fTxlBa7j9tb2WFQSDQSwnTTx70c3U1UKRF1hXav8QaAbdu2Ydu2bSgtLW0xAv7dd991WnGmcq35/FwTS3cSfj8TERF1DY1Wg8Mns7AjMwVJGSk4cOJYi+nj/fwjDKPavXxCOH2c6BZi8jXeCxcuxNtvv41+/frB3d29067jfbvpwN8riG45/D4mIiLqPGcvlRiC9q6sVJTVGF+i1s/ZEwlhcUgIi0H/Hn1ha8np40R3i3YH7y+++AKrVq265mZld7KmXdxra2thaWlp4mqIbkxtbS0AtLg6AREREV1fjaoOybmHsCNjP7ZnJCO3uNCo3dbCGoNC+iEhLA5DQmPg49zdNIUSkcm1O3g3NDS0uFbz3UQmk8He3h6lpaUAACsrq7t21J9uX0II1NbWorS0FPb29pDJOLWNiIjoeoQQyDibh+3Hk7EjMwUpeYfR0Kg2tEslUkT5hmFIWCyGhsUiyjcMcplJt1QioltEu38SPPPMM1izZg3efPPNrqjntuDmprtWYlP4Jrpd2dvbG76fiYiIqKXzlRexI3O/7lJfmftxvvKSUXt3BzcMDYvFkNBYDArpB3vrG18LSkR3njYF71mzZhkea7VafPXVV/j9998RGRnZYorqkiVLOrfCW5BEIoG7uztcXFygVquv/wKiW5CZmRlHuomIiK6gUjcgNf8IkvRrtY+dzjFqt1RYYEBwXySExSIhNBYBrt6c/UhE19Wm4H3o0CGj57179wYAHDt2zOj43fZDRyaTMbgQERER3caEEMgvOYWkjGQkZe7Hnuw01DXUG/WJ8OqhW6cdFoto/wiYmylMVC0R3a7aFLy3b9/e1XUQEREREd0UFbVV2JWViqQM3aj2mUvFRu0udo4YEhaLhFDdNbWd7RxMVCkR3Sk6tNtDeXk58vLyAACBgYGwt7fvzJqIiIiIiDqNRqtBemEmtmckY0dGCg4WZhhdU1shN0NsYC/9pb5iEdY98K6byUlEXatdwbuwsBDTpk3Dli1bDNf/lUgkGDlyJJYvXw5fX9+uqJGIiIiIqF3OXipBUkYytuuvqV1RW2XUHuTmo7/MVyzie0TB2pyXiSWirtPm4H369GnExcXBzMwM77zzDkJDQwEAGRkZWLFiBeLj45GamgpPT88uK5aIiIiIqDW1DfVIzj2EpOMprV5TW2lli0Eh0bodyMNi4enAq3oQ0c0jEU1D19cxdepU5OXlYcuWLbCwsDBqq6urw8iRIxEUFIRvvvmmSwq9mSorK6FUKlFRUQE7O14SgoiIiOhWI4RA1rkTuk3RMlKQnJsOVWODoV0qkaKPXzgSwmIxNCwOvXxCeE1tImqzzs6Ebf7ps3nzZqxbt65F6AYAS0tLvPPOO3jsscduuCAiIiIiotZcqq7Aziz9NbUzUlBUft6ovXs3V91lvsLieE1tIrqltDl4X7hw4ZpruP39/XHp0qXOqImIiIiICI2aRhwsOI7tGSlIykhG+slMNJ+saWFmjvgeURiq3xQtyM2Xm6IR0S2pzcHb3d0dGRkZV13DfezYMbi5ca0MEREREXXcmUvFSDp+eVO0yrpqo/ZgD38MDYvD0LBYxAb1hoWZuYkqJSJquzYH7wceeACzZ8/Gtm3b4OzsbNRWWlqK1157DQ888EBn10dEREREd7Dahnrsyzmov6Z2MnKLTxq121vZYXBotOFSXx7dXExUKRFRx7V5c7WysjLExsaiuLgY//d//4eQkBAIIZCZmYk1a9bAzc0NycnJcHBw6Oqauxw3VyMiIiLqGm3ZFK2vXzgSwnWj2r18QiGTykxYMRHdjUy2uVq3bt2QkpKC119/HWvXrkV5eTkAwN7eHk888QTee++9OyJ0ExEREVHnKqupwM7MVMOo9tU2RRsaHodBIdFQWtmaqFIioq7R5hHv5oQQOH9e9wPT2dn5jtvEgiPeRERERB3XqGlE+slMbD+ejO0ZyUgvzIRWaA3tFmbmiA+KwtBwbopGRLcmk414NyeRSODiwvU1RERERKRzrqwUSRnJ+ON4MnZlpaKitsqoPdjD33BN7djAXrBUtLxELRHRnapNwXvkyJF46623EBcXd81+VVVV+Pzzz2FjY4Np06Z1SoFEREREdOupV6uQnJuO7RnJ2H48GTlFBUbtSitbDA6JwdDwWAwJjUV3B1cTVUpEZHptCt6PPPIIxo8fD6VSiTFjxqBfv37w8PCAhYUFysrKkJGRgd27d+OXX37B6NGj8dFHH7XpzVesWIEVK1agsLAQABAeHo758+dj1KhRAID8/HzMnj0bu3fvhkqlwsiRI/HZZ5/B1fXaP7j/9re/4aOPPkJxcTF69eqFzz77DDExMW2qiYiIiIhaEkIgr+Qkko6n4I+MfUjOOYQ6tcrQLpVIEeUbZlir3dsnFHJZhyZXEhHdcdq8xlulUmHDhg1Yt24ddu/ejYqKCt0JJBKEhYVhxIgRmDp1KkJDQ9v85v/9738hk8kQFBQEIQRWr16Njz76CIcOHYKvry8iIyPRq1cvLFy4EADw5ptv4ty5c0hOToZUKm31nOvWrcPEiRPxxRdfIDY2FkuXLsWGDRuQnZ3d5unxXONNREREBFTWVWN31gHDqPaZS8VG7W5KZ8M67cGh0ehmrTRRpUREnauzM2GHNlcDgIqKCtTV1cHR0RFmZmY3XEgTBwcHfPTRR/Dy8sKoUaNQVlZm+KAVFRXo1q0bfvvtNyQmJrb6+tjYWERHR2P58uUAAK1WCy8vL7z44ouYO3dum2pg8CYiIqK7kVarxZHT2UjSb4p24MQxaLQaQ7tCboa4wN5ICIvD0PA4hHj4c1M0Iroj3RKbqwGAUqmEUtl5f9XUaDTYsGEDampqEB8fj/z8fEgkEpibmxv6WFhYQCqVYvfu3a0G74aGBqSlpWHevHmGY1KpFImJidi3b1+n1UpERER0pzhfeRFJGfux/fg+7MhKxcWqMqP2AFdvDA2LQ0JYHOJ7RMHa3NJElRIR3b5MvvDm6NGjiI+PR319PWxsbLBx40aEhYXB2dkZ1tbWeO211/Dee+9BCIG5c+dCo9GgqKio1XNduHABGo2mxRpwV1dXZGVlXbUGlUoFleryGqXKysrO+XBEREREtxi1phGp+UeQlJGC7cf34ejpHKN2GwsrDAqORkJ4LBLC4uDj5GGiSomI7hwmD97BwcFIT09HRUUFfvzxR0yaNAk7duxAWFgYNmzYgBdeeAHLli2DVCrF448/jj59+lx1fXdHLV682LCOnIiIiOhOc/LCOSTp12nvzj6A6vpao/ZI72DD9PF+/hEw46ZoRESdyuQ/VRUKBQIDAwEAffv2RWpqKj799FN8+eWXuPfee5Gfn48LFy5ALpfD3t4ebm5u8Pf3b/VcTk5OkMlkKCkpMTpeUlICNze3q9Ywb948zJo1y/C8srISXl5enfDpiIiIiG6+2oZ67Ms5iO36tdr5JaeM2h1tuyEhNAZDw+MwJDQGznaOJqqUiOjuYPLgfSWtVms07RvQBWoA+OOPP1BaWoqxY8e2+lqFQoG+ffti27ZteOCBBwzn27ZtG6ZPn37V9zQ3NzdaS05ERER0OxFCILuoANuP70NSRgqSc9OhamwwtMukMkT7R+h3II9DhFePTp9BSEREV9eh4F1eXo4ff/wR+fn5mDNnDhwcHHDw4EG4urqie/fubT7PvHnzMGrUKHh7e6Oqqgpr1qxBUlIStmzZAgBYuXIlQkND4ezsjH379uHll1/GzJkzERwcbDjH8OHD8eCDDxqC9axZszBp0iT069cPMTExWLp0KWpqajBlypSOfFQiIiKiW1J5TSV2ZR3A9gxd2D5XVmrU3t3BDcPC4zA0LA4DQ/rBztLGRJUSEVG7g/eRI0eQmJgIpVKJwsJCPPvss3BwcMBPP/2EU6dO4e9//3ubz1VaWoqJEyeiqKgISqUSkZGR2LJlC+655x4AQHZ2NubNm4dLly7B19cXb7zxBmbOnGl0jqap6E0mTJiA8+fPY/78+SguLkbv3r2xefPmFhuuEREREd1ONFoNDp/MMmyKllZwHFqhNbRbmJkjvkcUhurXage6+vBSX0REt4h2X8c7MTERffr0wYcffghbW1scPnwY/v7+2Lt3L5544gkUFhZ2Uak3D6/jTURERLeCkooL+qCdjJ2Z+3GppsKovYe7H4aGxWJoeDxiA3vBUmFhokqJiO4sJr+Od2pqKr788ssWx7t3747i4uIbLoiIiIjobtXQqMb+/COGtdrHz+QatdtZ2mBQSDSGhsUiITwOng5X3zyWiIhuHe0O3ubm5q1e5zonJwfOzs6dUhQRERHR3aLw/BnD7uO7s9NQq6oztEkkEkR6hxjWavfxC4ecl/oiIrrttPsn99ixY/H2229j/fr1AHT/Qzh16hRee+01jB8/vtMLJCIiIrqT1NTXYk/OQcN1tQvOnzFqd7Zz0F1TOywWg0Nj4GTbzUSVEhFRZ2n3Gu+Kigo8/PDDOHDgAKqqquDh4YHi4mLEx8fjl19+gbW1dVfVetNwjTcRERF1FiEEMs/mYbt+rfb+/MNoaFQb2uVSGaIDIjEsPB4JYbEI9wzipb6IiEzM5Gu8lUoltm7dij179uDw4cOorq5Gnz59kJiYeMPFEBEREd0JLlVXYGfWfmw/nowdGftRXHHeqN3L0R1Dw+MwLCweA4L7wtby9h+4ICKiq2v3iPfdgCPeRERE1B4arQaHCjMMa7UPFWag+a9Ylmbm6B/cV7cpWlgcAly9eakvIqJbmMlHvF966SUEBgbipZdeMjq+fPly5OXlYenSpTdcFBEREdGtrqis1DB9fFdWKsprjTefDfEIQEJYLIaFxyEmsBcszMxNVCkREZlau0e8u3fvjv/85z/o27ev0fGDBw9i7NixOHPmzFVeefvgiDcRERFdSaVuQEpeuiFsZ53LN2pXWtlicEgMhobHISEsFh7dXExUKRER3SiTj3hfvHgRSqWyxXE7OztcuHDhhgsiIiIiuhUIIXCi9DS263cf35tzEHUN9YZ2iUSCKN8wJITFYVh4HHr7hPJSX0RE1Kp2/98hMDAQmzdvxvTp042O//rrr/D39++0woiIiIhutqq6GuzOPmAI26cvFhm1u9g5Ymh4HIaGx2FwSAwcbFoORhAREV2p3cF71qxZmD59Os6fP49hw4YBALZt24aPP/6Y67uJiIjotqLVanHsTI5+U7QUHMg/gkatxtBuJpMjNrA3EsJiMTQ8DmHdA7kpGhERtVu7g/fTTz8NlUqFd999F++88w4AwNfXFytWrMDEiRM7vUAiIiKiznS+8hJ2ZKYgKUN3u1BVZtTu5+ypX6cdhwE9+sDawspElRIR0Z3ihi4ndv78eVhaWsLGxqYzazI5bq5GRER051BrGnHgxFFsP56MpIxkHDmVbdRubW6FgcF9dVPIw+Lg49zdRJUSEdGtwuSbqzXn7Ox8wwUQERERdbaTF84hSb9Oe3f2AVTX1xq1R3j1QEKYbvfx6IBIKORmJqqUiIjuBu0O3iUlJZg9eza2bduG0tJSXDlgrtForvJKIiIioq5RU1+LPTkHkZSRjKSMFJwoPW3U7mBjj4TQGAwNj8eQ0Bi4KB1NVCkREd2N2h28J0+ejFOnTuHNN9+Eu7s7NxghIiKim04IgeNncrFdH7T35x2GWtNoaJdLZejnH4GEsFgkhMch0isYUqnUhBUTEdHdrN3Be/fu3di1axd69+7dBeUQERERte585SXszNyP7Rkp2JGZgvOVl4zavZ08dLuPh8VhYHA/2Fpam6hSIiIiY+0O3l5eXi2mlxMRERF1NrWmEan5R/S7j7fcFM1SYYEBwX2REBaLYWFx8HPx4kw8IiK6JbU7eC9duhRz587Fl19+CV9f3y4oiYiIiO5WhefPGC7z1dqmaOGeQYZR7eiASJibKUxUKRERUdu1O3hPmDABtbW1CAgIgJWVFczMjHcBvXTp0lVeSURERGSsur4Gu7PSkKS/rnbh+TNG7U2boiWExyEhNJabohER0W2pQyPenWXFihVYsWIFCgsLAQDh4eGYP38+Ro0aBQAoLi7GnDlzsHXrVlRVVSE4OBhvvPEGxo8ff9VzvvXWW1i4cKHRseDgYGRlZXVa3URERNQxWq0WR05nI+l4MpIy9+NA/hE0ai9fEUUulaFfQCSGhsUiISwOEV49uCkaERHd9todvCdNmtRpb+7p6Yn3338fQUFBEEJg9erVGDduHA4dOoTw8HBMnDgR5eXl+M9//gMnJyesWbMGjz76KA4cOICoqKirnjc8PBy///674blcfkOXKyciIqIbUFx+HkkZKdiRuR87MvfjUnW5UbufsyeGhMViaFgsBgT3hY0FN0UjIqI7yw0l0vr6ejQ0NBgds7Oza/Prx4wZY/T83XffxYoVK5CcnIzw8HDs3bsXK1asQExMDADgL3/5Cz755BOkpaVdM3jL5XK4ubm145MQERFRZ6lXq5CSm46kzP1IykhG5tl8o3YbCysMCo7GkLAYDA2Lg49zdxNVSkREdHO0O3jX1NTgtddew/r163Hx4sUW7RqNppVXXZ9Go8GGDRtQU1OD+Ph4AED//v2xbt06jB49Gvb29li/fj3q6+uRkJBwzXPl5ubCw8MDFhYWiI+Px+LFi+Ht7d2huoiIiOjahBDILirAjowUJGWmIDnnEOrUKkO7RCJBL+9QDA2PxZDQWPT17wkzGWejERHR3aPd/9d79dVXsX37dqxYsQJPPfUU/va3v+Hs2bP48ssv8f7777e7gKNHjyI+Ph719fWwsbHBxo0bERYWBgBYv349JkyYAEdHR8jlclhZWWHjxo0IDAy86vliY2OxatUqBAcHo6ioCAsXLsSgQYNw7Ngx2NratvoalUoFleryLwiVlZXt/hxERER3k4vV5diVmYrtGcnYkbEfxRXnjdrdlM5ICI9FQlgsBoVEw9HG3jSFEhER3QIkop0X5fb29sbf//53JCQkwM7ODgcPHkRgYCD+8Y9/4IcffsAvv/zSrgIaGhpw6tQpVFRU4Mcff8Q333yDHTt2ICwsDC+++CL279+P9957D05OTti0aRM++eQT7Nq1CxEREW06f3l5OXx8fLBkyRJMnTq11T6tbcgGABUVFe2aOk9ERHSnamhUIzX/CHZk7kdSRgqOns5G818hLMzMER8UhcGh0UgIi0OIhz+vqU1ERLetyspKKJXKTsuE7Q7eNjY2yMjIgLe3Nzw9PfHTTz8hJiYGBQUFiIiIQHV19Q0VlJiYiICAALz66qsIDAzEsWPHEB4ebtQeGBiIL774os3njI6ORmJiIhYvXtxqe2sj3l5eXgzeRER01xJCIK/kpH76+H7szTmIWlWdUZ9wzyAMCY1BQlgsYgJ7wcLM3ETVEhERda7ODt7tnmru7++PgoICeHt7IyQkBOvXr0dMTAz++9//wt7e/oYL0mq1UKlUqK2tBYAWlxCRyWTQarVtPl91dTXy8/Px1FNPXbWPubk5zM35ywIREd3dymoqsCvrAJL008fPlpUYtTvZdkNCmG6d9uDQaLgqnUxUKRER0e2l3cF7ypQpOHz4MIYMGYK5c+dizJgxWL58OdRqNZYsWdKuc82bNw+jRo2Ct7c3qqqqsGbNGiQlJWHLli0ICQlBYGAgnnvuOfz1r3+Fo6MjNm3ahK1bt+Lnn382nGP48OF48MEHMX36dADA7NmzMWbMGPj4+ODcuXNYsGABZDIZHn/88fZ+VCIiojtaQ6MaaQXHsEN/qa/0k5lG08cVcjPEBvZCQlgcEsJiEeoRwGtqExERdUC7g/fMmTMNjxMTE5GVlYW0tDQEBgYiMjKyXecqLS3FxIkTUVRUBKVSicjISGzZsgX33HMPAOCXX34xhPvq6moEBgZi9erVuO+++wznyM/Px4ULFwzPz5w5g8cffxwXL16Es7MzBg4ciOTkZDg7O7f3oxIREd1RhBDILS7ETv31tPe0Mn082MMfCaExGBIWi7igKFgpLExULRER0Z2j3Wu87wadPZ+fiIjIVC5UlWFXVip2ZO7Hzsz9OFdWatTuYGOPIaExGBwag4TQGLh3czFRpURERLcOk6zxXrZsWZtP+NJLL3W4GCIiIrox9WoVUvOPICkjBTsz9+Po6RyjdnO5AjGBkRgSGoshoTEI9wzi9HEiIqIu1qYRbz8/P6Pn58+fR21trWEztfLyclhZWcHFxQUnTpzokkJvJo54ExHR7UIIgcyzefoR7VQk5x5CnVpl1Cese6BuRFu/+zinjxMREV2bSUa8CwoKDI/XrFmDzz//HN9++y2Cg4MBANnZ2Xj22Wfx3HPP3XBBREREdG3F5eexMysVO/Vhu7TyolG7q9IJg0NjMCQ0GoNDYuCidDRRpURERAR0YI13QEAAfvzxR0RFRRkdT0tLw8MPP2wU0m9XHPEmIqJbSXV9DfbmHNIF7axU5BQZ/7/W0swc8T36GNZqh3j4QyKRmKhaIiKi25/Jr+NdVFSExsbGFsc1Gg1KSkpaeQURERG1h1rTiPTCDOzI3I9dWalIO3EMjVqNoV0ikSDSO0QXtEOiER0QCXMzhQkrJiIiomtpd/AePnw4nnvuOXzzzTfo06cPAN1o9wsvvIDExMROL5CIiOhOJ4RAXslJXdDOTMWenDRU19ca9fFx6o4hoTEYFBKNgSF90c1aaaJqiYiIqL3aHby/++47TJo0Cf369YOZmRkAoLGxESNGjMA333zT6QUSERHdic5XXsTOzFTszErFrqzUFpf56mZth4HB0RgcGo3BIdHwce5uokqJiIjoRrU7eDs7O+OXX35BTk4OsrKyAAAhISHo0aNHpxdHRER0p6ipr8W+3HTsytKt0848m2/U3nSZr8EhunXaPb2CIJPKTFQtERERdaZ2B+8mvr6+EEIgICAAcnmHT0NERHRHUmsacbDgGHZmpmJ39oEW67QBoKdXDwwOicaQ0BhE8zJfREREd6x2J+ba2lq8+OKLWL16NQAgJycH/v7+ePHFF9G9e3fMnTu304skIiK61Wm1WmSdy9dPHT+AfbmHUKuqM+rj7eSBQcH99Ou0+8HJtpuJqiUiIqKbqd3Be968eTh8+DCSkpIwcuRIw/HExES89dZbDN5ERHTXOHXhHHbpg/au7AO4WFVm1O5gY49BIf0MYZvrtImIiO5O7Q7emzZtwrp16xAXF2d0jdDw8HDk5+df45VERES3t4vV5diddQC7sw9gZ2YqTl44a9RuqbBAfFAUBoX0w+DQGIR6BEAqlZqoWiIiIrpVtDt4nz9/Hi4uLi2O19TUGAVxIiKi2111fQ325aZjd/YB7MlOw7HTOUbtMqkMff3CMTBEt/N4H79wKORmJqqWiIiIblXtDt79+vXD//73P7z44osAYAjb33zzDeLj4zu3OiIiopuoXq3CgRPHsDsrFbuz03CoMAOaKzZEC+0egEHB0RgUGo34oN6wsbA2UbVERER0u2h38H7vvfcwatQoZGRkoLGxEZ9++ikyMjKwd+9e7NixoytqJCIi6hKNmkYcPpmF3dm66eOp+UdRr1YZ9fFx6o5BIf0wMLgfBgT3gbOdo4mqJSIiottVu4P3wIEDkZ6ejvfffx8RERH47bff0KdPH+zbtw8RERFdUSMREVGnaNp5fJd+nfa+3EOorq816uOqdMLA4L4YGBKNAT36wNvJw0TVEhER0Z1CIoQQpi7iVlNZWQmlUomKigrY2dmZuhwiIuogIQROlJ7Gnuw07Mo6gD05abhUXW7Ux97KDgOC+2BAcD8MCumHQFcf7llCRER0l+vsTNjuEW8A0Gg02LhxIzIzMwEAYWFhGDduHOTyDp2OiIio05y8cA57stOwNycNe7LTUFR+3qjdytwScYG9MTC4HwaG9EW4ZxBkUpmJqiUiIqK7QbuT8vHjxzF27FgUFxcjODgYAPDBBx/A2dkZ//3vf9GzZ89OL5KIiOhqzl4qwR59yN6TnYYzl4qN2hVyM/T164lBIdEYGNwXvX3DuPM4ERER3VTtDt7PPPMMwsPDceDAAXTr1g0AUFZWhsmTJ+NPf/oT9u7d2+lFEhERNSmpuIC9OQexO+sA9uQcROH5M0btcqkMffzCMSC4Lwb06Iu+/j1hqbAwUbVEREREHQje6enpRqEbALp164Z3330X0dHR7TrXihUrsGLFChQWFgIAwsPDMX/+fIwaNQoAUFxcjDlz5mDr1q2oqqpCcHAw3njjDYwfP/6a5/3b3/6Gjz76CMXFxejVqxc+++wzxMTEtO+DEhHRLeFCVRn25hw0TB/PLT5p1C6VSNHLJ8QQtGMCImFtYWWiaomIiIhaanfw7tGjB0pKShAeHm50vLS0FIGBge06l6enJ95//30EBQVBCIHVq1dj3LhxOHToEMLDwzFx4kSUl5fjP//5D5ycnLBmzRo8+uijOHDgAKKiolo957p16zBr1ix88cUXiI2NxdKlSzFixAhkZ2fDxcWlvR+XiIhusovV5UjOPaQP2weRdS7fqF0ikSDCqwf69+iLAcF9ERfYG7aWvJY2ERER3bravav5L7/8gldffRVvvfUW4uLiAADJycl4++238f7772PgwIGGvh3Z/c3BwQEfffQRpk6dChsbG6xYsQJPPfWUod3R0REffPABnnnmmVZfHxsbi+joaCxfvhyA7tIxXl5eePHFFzF37tw21cBdzYmIbp4LVWXYl3MI+3IPYm/OoRZBGwBCuwdggD5oxwdFwd6aP5uJiIio65h8V/P7778fAPDoo48aLrfSlN3HjBljeC6RSKDRaNp8Xo1Ggw0bNqCmpgbx8fEAgP79+2PdunUYPXo07O3tsX79etTX1yMhIaHVczQ0NCAtLQ3z5s0zHJNKpUhMTMS+ffva+1GJiKgLnK+8hH25h7Av5xD25h5E9rkTLfoEe/ijf1Af9O/RB/E9ouBk262VMxERERHdHtodvLdv396pBRw9ehTx8fGor6+HjY0NNm7ciLCwMADA+vXrMWHCBDg6OkIul8PKygobN2686pT2CxcuQKPRwNXV1ei4q6srsrKyrlqDSqWCSqUyPK+srOyET0ZERABwvvIi9ubopo7vyz2EnKKCFn1CuwcgPigK/Xv0QVwQgzYRERHdWdodvIcMGdKpBQQHByM9PR0VFRX48ccfMWnSJOzYsQNhYWF48803UV5ejt9//x1OTk7YtGkTHn30UezatQsRERGdVsPixYuxcOHCTjsfEdHdrLTiIvbmHtQF7ZyDLTZDA4Cw7oGG0ey4oCg42tjf/EKJiIiIbpJ2r/EGgPr6ehw5cgSlpaXQarVGbWPHjr2hghITExEQEIBXX30VgYGBOHbsmNFGbomJiQgMDMQXX3zR4rUNDQ2wsrLCjz/+iAceeMBwfNKkSSgvL8e///3vVt+ztRFvLy8vrvEmImqD0xeLkJybjuTcQ0jOS0d+ySmjdolEcjloB+mCtoON0kTVEhEREV2fydd4b968GRMnTsSFCxdatLV3XXdrtFotVCoVamtrAejWaDcnk8lahP0mCoUCffv2xbZt2wzBW6vVYtu2bZg+ffpV39Pc3Bzm5uY3VDcR0d1ACIETpaeRnHsI+3LTsS/3EM5eKjbqI5FIEO4ZZFijHRvUC92sGbSJiIjo7tXu4P3iiy/ikUcewfz581uspW6vefPmYdSoUfD29kZVVRXWrFmDpKQkbNmyBSEhIQgMDMRzzz2Hv/71r3B0dMSmTZuwdetW/Pzzz4ZzDB8+HA8++KAhWM+aNQuTJk1Cv379EBMTg6VLl6KmpgZTpky5oVqJiO5GWq0W2UUndCE75xCS8w7hfOUloz4yqQy9fEIQF9gbcUFRiA3sBaWVrYkqJiIiIrr1tDt4l5SUYNasWTccugHdtb8nTpyIoqIiKJVKREZGYsuWLbjnnnsA6C5dNnfuXIwZMwbV1dUIDAzE6tWrcd999xnOkZ+fbzT6PmHCBJw/fx7z589HcXExevfujc2bN3dKvUREd7pGTSOOns7RTRvPTUdK3mGU1xpvOGkuVyDKLxzxQb0RF9gb/fwjYG1hZaKKiYiIiLpAo+r6fdqh3Wu8n376aQwYMABTp07t1EJuJbyONxHdLeoa6nGoMAP78w4jJe8w9ucfQY2q1qiPlbklov0jEBekG9GO8g2DhRmX5xAREdFtTKsFqoqAspNAWSFwqVD/WPe88sI5KD+qMt0a7+XLl+ORRx4x7CxuZmZm1P7SSy/dcFFERNQ1ymoqkJp/BCn6oH34ZCbUmkajPnaWNogN7IW4oCjEB0UhwjsYZrJ2/++CiIiIyLTqynWhuuykPlgXXg7a5aevPard/j3Ir6ndI97ffvstnn/+eVhYWMDR0RESieTyySQSnDhxolMLNAWOeBPRneL0xSLDaHZK/mFkn2v5M9rFzhGxgb0RGxiJ+B59EOLhD5lUZoJqiYiIiNqhoRYoP6UP1KeajVjrb/Xl1369VAYovYBuvkA3H93NQfe4UuYIZfeATsuE7Q7ebm5ueOmllzB37twWO47fKRi8ieh2pNsIrQApebq12fvzDuNsWUmLfoGuPogJjNSH7V7wcepu9EdUIiIioltCo0o3Ml12Uh+wrwjWNeevfw5rZ0OY1gVs38sB2647cJVZfSa/nFhDQwMmTJhwx4ZuIqLbRb1ahcMnM7E//wj25x1Gav7RFhuhyaQyRHgFI1YftKMDIuFs52CiiomIiIia0aiBirP6UK0fuW4esKuKrj/l29zu8mh1Nx+gm7fxCLbC+mZ8kutqd/CeNGkS1q1bh9dff70r6iEioqs4X3kJqflHsD//CA6cOIojp7LQ0Kg26mOpsEA//56ICeiFuKDe6OMbzh3HiYiIyDSuDNblJ/X3+ueVZwGhvfY5zKyaBWofwP6KkG3Z7eZ8lhvU7uCt0Wjw4YcfYsuWLYiMjGyxudqSJUs6rTgioruVVqtFTnHh5aCdfwQF58+06Odk2w0xAZGICeyF2MDe6OnVgxuhERER0c3R2KALz+WnOx6s5eaAvTdgr19rbe8NOPhcDtnWTsAdsCSu3b+dHT16FFFRUQCAY8eOGbVxjSARUcfUNtTjUMFxQ9BOKziGitoqoz4SiQTB7v6IDohAdEAkYgIiuT6biIiIuk5DLVBxptmI9Wnd46b7tkwFbwrW3bx1QbrpcdNza2fgLljG3O7gvX379q6og4jorlJUVorUE0cNQfv46Rw0ajVGfSwVFujjF45of13Q7ucfAaWVrYkqJiIiojtOfUUrgfq0buS6/DRQc+H652CwbhPORyQi6mIqdQOOnc7BgRNHceDEMaQVHMW5stIW/dztnQ0j2f0CIhHuGcRp40RERNQxWi1QXXw5TDeNXJef0T8+rQve12Nue3kquL1Xs2nh3rrH1s53xFTwrtbm3+geeuihNvX76aefOlwMEdGd4FxZKQ6cOIo0fdA+ejq7xSZoUokUYZ6BiPaPRExgJKIDItG9myunjRMREVHbNNRe3riseahuCtmVZ3Wbm12PleMVYdrLOGhb2DNYd4I2B2+lUtmVdRAR3Zbq1Srj0ewTR1FU3vKakg429ujn3xP9/CPQ168nevuEcrdxIiIiap1WC9SUXg7RFWebjVqfASraOA1cKtNdq1rp2WzE2sv4+S1yua07XZuD98qVK7uyDiKiW54QAmfLSpB24hjSThzDgYKjOHY6p8VotkwqQ1j3APT1jzCEbW6CRkRERAb1lfpAfeby1O/mj9s6Wq2wuSJQewH2nvp7L8DWDeCytVsC/xWIiK6isq4ah09m4mDBcRwsOI5DhRkorbzYop+jbTf089OPZvv3RC+fUFibW5qgYiIiIjI5db0uOFecvTxaXXlOP2KtP9aWtdUSKWDnASibjVgru18O1faenAZ+G2HwJiICoNY0IvNsniFgHyw4jrySkxBXXCJDLpUhzDMQff0uj2Z7O3lwNJuIiOhuoFHrQnTzUF1xxvhYW6aAA4ClfbMRan2gVnrqQ7YnYOvO0eo7CP8lieiuI4TAqYtFOFR4HIf0o9lHT+egXq1q0dfL0R19/MLRxzccUb5hiPAOhqXCwgRVExERUZfSqIHKIl2IrjwLVJxrOXJdXXL961YDgJmlLkzbNYXq7pcDtVI/cm1u0/WfiW4ZDN5EdMcrq6nA4ZNZ+tHs4zhYmIGLVWUt+imtbBHlG6a/6YK2s52DCSomIiKiTtWouhyqm6Z+Nx+prjzX9lAtU+ingHteDtTNw7XSA7B04BRwMsLgTUR3lKq6Ghw5lYXDJ7OQfjIDh09m4eSFsy36mcnkCPMMQh/fcPTx04VsfxcvSKVSE1RNREREHaaqNh6pNjw+d3nUurq0beeSmel2Abfz0AVou+4tw7WVE8DfF6idGLyJ6LZV21CPY6eycfhkFg6fykR6YSbySk622tfX2RNRvmGGoB3uFQQLM/ObXDERERG1mVYL1F68HKINtyLdKHVVke5xWzYqAwC5+eWR6qZNy4zuPXXXtGaopi7A4E1EtwWVugEZZ/Nw+GQm0k9m4vDJTGSfK4BWaFv09XRwQy+fUPT2CUVv31BEeAXD3trOBFUTERFRq9R1+pFp/eh0VfHlUN10X3WubZfUAgBzW8DOXT9a7d5KsO4OWHH6N5kOgzcR3XLq1SpknTuBo6eycfRUNtJPZiDzbD7UmsYWfV3sHNHbNxS9fcLQyycEkd4hXJdNRERkKlqNblp302i0UZAuunxfV972c9q46AJ0U6BucXMHLPgHdrq1MXgTkUnV1Nfi+JlcHDmdbQjaOUUFaNRqWvR1sFail69uJLuXt240283e2QRVExER3WW0WqDu0uXgXFVyxWP9qHV1iS58t4WZpS4027ob3zeNUtu6A7ZugFzRtZ+N6CYwafBesWIFVqxYgcLCQgBAeHg45s+fj1GjRqGwsBB+fn6tvm79+vV45JFHWm2bPHkyVq9ebXRsxIgR2Lx5c6fWTkTtV15TiWOnc3DkVDaO6oN2fumpFtfKBnQhO8I7GD29ehhGs70c3Xm9bCIios4kBFB7qdlodIk+TDcP18W6m7blzLNWSWWAjasuNNt5AHZugG3TiHWzgG2h5NRvumuYNHh7enri/fffR1BQEIQQWL16NcaNG4dDhw4hJCQERUVFRv2/+uorfPTRRxg1atQ1zzty5EisXLnS8NzcnBsoEd1s5ysv6gK2PmQfOZWN0xeLWu3rbu+Mnl49EOEdgkivYER4B8OjmwtDNhERUUdpGoGa85dDc1XJ5RFpw/MS3XNNQ9vPa+2sD89ul29Nz5tGq62ddeGbiAxMGrzHjBlj9Pzdd9/FihUrkJycjPDwcLi5uRm1b9y4EY8++ihsbK59sXlzc/MWryWirqHWNCKv+CQyzuTi+JlcZJzNw/EzuThfeanV/j5O3dHTqwcivXUBO8KrB5ztHG9y1URERLcpdd3lwHxlqG7+uPZC265J3cTKsVmgdtePUjd/7K5bay0z67rPRnQHu2XWeGs0GmzYsAE1NTWIj49v0Z6Wlob09HT87W9/u+65kpKS4OLigm7dumHYsGFYtGgRHB2v/ou9SqWCSqUyPK+srOzYhyC6w5XVVOD4mbzLIftMLrKLCtDQ2HLHUYlEgkBXH0R49dAFbO8Q9PQM4u7iREREV9JqgJoL+jBdot+crKTZ86bHpYCqHb+nSqS6sGzrBti6Ajb6+6bnhmNcR03U1UwevI8ePYr4+HjU19fDxsYGGzduRFhYWIt+3377LUJDQ9G/f/9rnm/kyJF46KGH4Ofnh/z8fLz++usYNWoU9u3bB5ms9SkvixcvxsKFCzvl8xDdCTRaDQpKzxjC9fGzurB9rqy01f7W5lYI6x6AMM8ghOtvId0DYG1ueZMrJyIiukUIAdSV6UL0lTdDmC7VjU7XXABauTzmVcnNWwnRbi0fWzlyyjfRLUIiWtvV6CZqaGjAqVOnUFFRgR9//BHffPMNduzYYRS+6+rq4O7ujjfffBOvvPJKu85/4sQJBAQE4Pfff8fw4cNb7dPaiLeXlxcqKipgZ8fRObpzCSFwoeoSss6dQNbZE8g6l4+Ms3nIOncCdQ31rb7Gy9Ed4Z5B+pAdiHDPIHg7ekAqld7k6omIiG4yIXQ7e1efbxakS654rr/VnG/7NagB3SZj1s76TclcLm9OZuOiH5l2vdxmbsdNyYi6WGVlJZRKZadlQpOPeCsUCgQGBgIA+vbti9TUVHz66af48ssvDX1+/PFH1NbWYuLEie0+v7+/P5ycnJCXl3fV4G1ubs4N2OiOV1lXjexzJ5B5Nh9Z5/J1YfvcCVyqLm+1v6WZOYI9AhDuGWgYyQ7zDISd5bX3WCAiIrqtaNS6Eeea85cDtOF56RXH2xmmAcDCXheWrV30Idrl8mh181Bt5QjITP6rORF1kVvuv26tVms0+gzoppmPHTsWzs7tv17vmTNncPHiRbi7u3dWiUS3tLqGeuQWF+pHsXUBO/vcCZwtK2m1v0Qiga9Td4R4BCCkuz+CPfwR7hkEfxcvyDg9jYiIbjdC6NZB11zQr5tuFpoNIfo8UFOqu68ra/97WNrrQrN1U5BuFqxtnHVh2lr/WM7BHSIycfCeN28eRo0aBW9vb1RVVWHNmjVISkrCli1bDH3y8vKwc+dO/PLLL62eIyQkBIsXL8aDDz6I6upqLFy4EOPHj4ebmxvy8/Px6quvIjAwECNGjLhZH4vopqhR1SG/5CRyiwqRV3IS2foR7ILzZ1q9LjYAeHRzQbCHvy5k6++D3H1hpbC4ydUTERG1kRBAQ40+OF8wDtS1ze8vXO7TnstjAbp10FZOuqBs7Xz53tpZH6admoVrhmkiaj+TBu/S0lJMnDgRRUVFUCqViIyMxJYtW3DPPfcY+nz33Xfw9PTEvffe2+o5srOzUVFRAQCQyWQ4cuQIVq9ejfLycnh4eODee+/FO++8w6nkdNu6UFWG3OJC5BXrQnZucSFyigtx9lLxVV/jYK1ESPfL4TrEQzeSrbSyvYmVExERtUIIoL5CF5BrLwI1Fy+H6Vr9Y0OQ1ofpxtb3HbkmhQ1g3TxMu1x+buPS7JgzYNkN4F4lRNSFTL652q2osxfSE12PVqvF6UvFyCvWBeumUezcokJcqqm46uscbOzRw80XQe6+CHLzRag+bDvZOkDCTVeIiOhmaGzQbTjWFKBrrwzSVxyvvQhoG9v/PnILfVh20o9GN79vCtjNjpnxyhpE1HF33OZqRHeTspoKFJSexonS0zhRegYnSk4hr+Qk8otPok6tavU1EokEng5uCGoK2K4+CHL3RaCbLxxt7G/uByAiojubRq1b81xzUReQm4Jz7aXLz42OXWrfdaWbU9joNhSzdtQFZatm9zbNAnVTkFZYcydvIrptMXgTdbLKumqcKD1tCNjN78tqrv7LiUJuBn8XbwS5+SLQzQc99KPY/q7eXINNRETtIwSgrr0cjmsv6UalDY/LjNtq9UG6/uqzrK5JItVN124eoK2dACuHZo8djR+b8f9tRHT3YPAm6oCa+loUnD/TasC+UHXt3VHd7Z3h5+IFfxcv+Ll4IcjNB4GuPvB28oCclxEhIqIrNar0QblMd294fOmK8NwUrvVtja3PpLouiUQXoq0cdcHZyvHyyHTTYysH/XN9u4U910gTEV0Df8snakWjphFny0px+sI5nLxwFqcunMOpi0U4pX9+vXDtZNsNAa7elwO2sxf8Xb3g6+wJa3OuOSMiuus07cxdVw7UlxuH6KYg3TQSbRSuy3Qj1x0lMwMsHfQBWX9r/tyy2+Xw3BSsLe11u3wTEVGnYfCmu5IQAheqLuHUhaJmwfqc4fm5slJotJprnqObtR38XS6H66YRbH8XL9haWt+kT0JERDeNEIC6Thec65rfmsJyebO2MuM+9eW69dMdJZHqArFlt8th2dK+ZZA2CtfddOuouS6aiMjkGLzpjqTWNKKorBTnykpx9lKx7r6sBGcvFePkhXM4fbEIdQ3XvjSJQm4GL0d3eDt5wNvRw/jeyR3drJU36dMQEVGn0aiB+kpdEK6v0IfiCqCuotlz/bEW7eUdn77dRCq/IjB3uxymLbsZP2/+2NyOU7mJiG5jDN5029FqtSitvIhzZSU4e6kUZ8uKce6SLlifKyvBubJSlFZexPWulCeRSOBu7wxvp+7wbgrYTh7w0QdsV6UTpPwlh4jo1tE04qyqbBaeK/Uh+cr7Kx43BeuG6huvQyrTrWm2bH7rpru3aPbYcG9/uT935iYiuisxeNMtQwiBqvoalFZcREnFBZRW6u8rLqKo/Lw+aJeguPw8Gq8zDRzQjVi727vAo5sLuju4oXs3F3g4uMJHH7S7O7jB3ExxEz4ZERFBqwXUNbowrKrShWBVVbPnlZcDddPzpuCs0gdnVeWNTdduTmEDWCj1oVgJWCr14Vipe26h1AVnw2N9O6dvExFRBzB4U5fTaDW4VF2OEn2gLqm4iPOVVz6+iNKKC1e9lvWVpBIp3Oyd4NHN1ThYd3OFh4MrundzgaNNN45YExHdKI1aF4RV1br7hurL4VhVrQvDTW2qKv3zKwO1/vh1ZiK1mUSqm3ptoQQsmt9feazZ46aAbaG/51UkiIjoJuL/dajdGhrVKKupwKXqclysKsfF6nJcqi7HJf2xS9VN9+UorbyEC1Vl192orDlbC2u4KB3hYucIV6WT7t7eCd27uaK7gys8urnCVenIS28REbWmaffshqagXNMsGFcbtzU9NwRq/b2q8vJjdV3n1ieVGYdmc9sr7vU3C9tmwdn+cpulkiPORER022FyuUsJIVCnVqGqrhoVtVWoqqtBZV214VZWU6EP1JdDdNPjqvqadr+fRCKBk203uNg5wkV5OVC7KB3haucEF6UDXJVOcLZz5OW2iOjuodXogq+6tlko1gfjhhpA1fS49vKxFu01QEPT6HO1bjp3Z40sNyc314Vjhc3loGxuo7+3a/bYBjC/MlTbXX5uZsnQTEREdx0G79uIVqtFnVqFuoZ61DXUo1ZVjzp1veF5TX0dquqbwnONcaiur0aV/nhlbRUq66rbtE76aqQSKRxslHCwsYeDjT0cmx5bXz7WzVoJF6UDXOwc4WTbjSPURHT7EUI31Vpdqxv5bQrJDbXNHtfonhs91t8bjl95TB+YO3s0uTmJBFDY6jbzMrfR3RS2gLm1LjybN29rCtS2+se2usdNxxS2gJx7YhAREXUUk1AHaLVaHCrMgFrTiEZNI9T6W2Mr9w1XOd78vqFRbQjPdQ0q1DbUGYfrprY2rn9uD6lECjtLG9haWuvvbWBnad0iRDs2C9kONkooLW25fpqITEerBRrrgcY6QF2vC7DqupbP1XW6fobntZcfN9RePtYUkFv0q9WNSnc1iVQXchXWl4OxwhpQWDV7bG3cx0zfbgjUTeFaf29mxZFlIiKiWwSDdwcICIz+8BmT1mBhZg5LhTmsFJawVFjAUmEOS4UFbC1toDQEaP3NSndva6EL1XZWtvo2a1ibW0HCX8yIqCOEALSNuusaN6oAjUoXejUN+lCsuhx8DY+b3TfW6/o31l/xWKUP0KpmQbpW3153ud/NJpHqQq+ZlT4QNz3WB+CrPrZu1t/y8jHzZoFabsGQTEREdAdj8O4AmVQGH6fukMtkkMvkMJPJIZfKYdb8+RX3ZnKzVtt1NzNYmVvoA7QFrBSXH+ueWxqCtZW5JSzNzDnaTHQ3EUI36qpp0N/UgFYNNOofaxp0oVej1gfgBn1bw+XnhsfqyyG5RX/V5XujAH3FvUZ1OWwLram/OoDMDJBbAmYWunuFZbPnFrqwa2ahD8CWulBsZnk5CDc9b3rcvE/ze5kZwzERERF1CIP3NdTU1MDW1tYwItzQ0AC1Wg25XI6UcWN0ox8SCVQNakAigZnCHFKpDJBI0ajVolGjhVQmh0Kh0PeVol6lAiCFwsJCF54lUjRqtFBrtJDJ5FCYW+r6SqWoV9VAqGuhkFlC1iAH1FI0VgnUqxshlclhYWlpOG9dvQoCEphbWkEmNzOcV6Xva2lppT+vDHX1KmgFYG5hCbmZApBIoNFoUF9fD6lUCkvLy5ub1dXVQavVwtzcHHK57tulqa9EIoGVlZWhb319PTQaDRQKBczMzNrdV6vVoq5Ot97R2tra0FelUqGxsRFmZma6r2U7+wohUFtbCwCwsrJq8e/Znr5yuRzm5uZG3yMAYGlpafhjSHv6qtVqNDQ0QCaTwcLCwtC3trYWQghYWFhAJpO1u29jYyNUKtVV/z3b07ct//Y3+n1ytX/PNvcVAqr6OjSqVTCTSaGQSwGtBkKrQW1NFSRaDawszHWjs9pGNDT1lQJmMgmgbYTQqFFfWwOJthHmCjkkWg2gbURjQz0a1SrIoIWZVKI/hxqqOl1fMykg0Z9Xo1ZBq1ZBCg1kEIaA3KiqB7RqyCRaXV9NI7SNDRCNDZBo1ZCKy6Fa26gCNI2QaNWQ6EO10KghQRdsltXZpHJAbgEhV0DIzAG5BaQKS92mXHJLaKRmEDJzSM2tIDWzBOQW0MrN0Qg5JHJzmFnZ6ftaoAEyaKUKyC2sIbfQbcillSlQr5UCcgtY2TkagraqUdO1PyNkZlDI+TPitv4Z0YZ/+xv9Prnav2dHv0+u9u95o98nV/v37Mj3SWv/nvw9gj8jOvJvz58R/BlxK/6MaPpadhpBLVRUVAgAAoAoLS01HF+0aJEAIJ55ZqoQb9jeObe/2AnNG0qhmmsj6ubaCfGWqxAL3YV4u7uofM1enJ9pLeoWeAixOECI94NE/du+4uR0a3F6hlKIv0YIsaSXEJ9EiVOvuIhjf7ISlxb2EOKzeCGWDxQ1f+0n9k+xEml/UgrxxTAhvkwU4qt7xfGZ3mL7/1mK0wt7CfHtGCG+Gyuq/3aP+OUxS7H5SVsh/v6IEP+YIMT3j4vUWcFiw0MWImtBjBA/TBJi7SRRs/Ix8f04C/HPByyF2PCsEBv+JMSPz4u9s3uLr0ebiwN/iRdi44tCbHpZ1K9/QSwfYS4+G2EuGjfNFOK/s4X4eY7Y+Vq8WJJoLna81l+IX+YJ8cvrovHneeKDYebi/aEKUffvV4XYPF+ILQvEzr8MF4sSFOKXWfFCbH1biK3vCPH7IvF2goV4a7BCVGycK8Tv7wrx+7ti99ujxfxBCvHT9Bghti023BYNtxN/GagQFzbMEeKPD4T44wOxb/FD4o0BCrHmT1FC/PG+4fbhKEfx5kCFOPfPl4XY9p4Q294TBz9+VCwYpBDfPx2uf69FQvy+SCwb5yYWDlaIU9/9SYjfFgrx20KR8ekE8V6CQvxzYqAQm98U4te/CPHrG2LVY57ir8PNReHnjwvxv9eE+PlVceKzR8Sn95qLtf/nJcR/Zgnxn5lC/HuG2DjZR3x1n7ko+OR+If71ghA/PieKPhsr/j7WQvx3oqsQayfr/j1+mCh2vOAt/jXeQpxaPFD37/b3R0TZskTx62OWYseUbkJ8e78Q39wnxFcjxPEZnmLvJCtRsjBciM8HC7F8oKj9qI848qyVyHzBTohP+gixpLcQf40QJa+6iJPTrUXVm/rvv3d9RONCD1Ex20bUvGojxHxHId60N/1/Q6a4zXcQ1a/aikuzbETD215CfBgqxMeRomxhD3FwqpXIeNlNiK9HCvHdWCH+/rD4bYqLWPOAhShaPlb338Z/XxG5yx4R7yYoxDdPBAiR9Fchdi0TYt+X4u1xgeLJnnJxYPVfhMj8RYic38X+9Z+IGA+peGRgiBCl2UJcKhSiskiMHjZAWMoh1q9ba/hZuXv3bgFABAYGGv1sve+++wQAsXLlSsOxQ4cOCQDCw8PDqO/DDz8sAIjly5cbjuXk5AgAQqlUGvWdNGmSACA+/PBDw7EzZ84IAEIulxv1/fOf/ywAiAULFhiOlZWVGX7mNzQ0GI7Pnj1bABCzZ882HGtoaDD0LSsrMxxfsGCBACD+/Oc/G72fXC4XAMSZM2cMxz788EMBQEyaNMmor1KpFABETk6O4djy5csFAPHwww8b9fXw8BAAxKFDhwzHVq5cKQCI++67z6hvYGCgACB2795tOLZ+/XoBQAwZMsSob69evQQA8dtvvxmO/fzzzwKA6Nevn1Hf/v37CwBi48aNhmPbt28XAERYWJhR38TERAFAfP/994Zj+/fvFwCEj4+PUd9x48YJAOKrr74yHDt27JgAIJycnIz6PvHEEwKA+OSTTwzHCgoKBABhZWVl1PeZZ54RAMSiRYsMx0pLSw3/ns29/PLLAoB4/fXXDceqq6sNfaurqw3HX3/9dQFAvPzyy0bnuPbvEc8Y9bWyshIAREFBgeHYJ598IgCIJ554wqivk5OTACCOHTtmOPbVV18JAGLcuHFGfX18fAQAsX//fsOx77//XgAQiYmJRn3DwsIEALF9+3bDsY0bNwoAon///kZ9+/XrJwCIn3/+2XDst99+EwBEr169jPoOGTJEABDr1683HOPPiMv4M0KHPyN0+DNC51b6GQFAVFRUiM7AEe8OkABAyH0ABCC02LJ5M7RaDRIGD4alhTkgBE6fPom83Fy4ubogNCQYELq+Bw6kQtPYiMiInoa+Fy+U4uyZM7BX2sHbqzsgtIDQ4mRhIYSmEe7ubjA3kwNCi7raWlRXlsNcoYCdrY2hb11tDaTQwszMDFKJaN9mQEJACgGFTKL7TOpaQ5OtHLCVS4HGKqC6CgBgDsBbKQWgBcoKDX29LABYyICGIqC4CABgBSDaQ6brezrV0DfMCgjzkQMNJ4ATJwAA1gBGBei/JbM3G/r2swT6hZoBjZnAsUzDeZ/sqfvrFdLXGvrGmwPxvRWAOAYcOGaod1o//W68qd8Y+g6SA4NiFQCOAnuOAgBkAF6N1/fdv+JyXwCDBpgDOAYkHTMcf3OAvoYDfzMcGwBgwGBzAJnAH5mG42/E6atJ/8JwLA5AXII5gDxg27uG43P66PtmfAdk6I5FAYgabA7gFLD9fUPfF3vq++b/AOTrjoUCCB1gDqAE2LXU0HdSAIAABXD2Z+Cs7pgfgJeiFQDKgZSvDX0f6A6guwK4sAO4oDvmBuCpCDMAtcDRfxn6DnYA4GAGVB8Gsg4DAOwBjAyQA2gETuww9A2zBmAtAxpOAWdPAQAsAUS4yAAI4EKuoa+LGQClFNBe/v6TAbAz10/31TSgLRq1AnIzc91UYakc1XX1qK6pg6W1LZTdHAGpDFpIkZGVA7UWiOzdBzIzBSCV40ThKeQXnISntw9CwyN0o7oyM/xz7XqoNcCEJ56EpbUtIJUj+cBBJO3cjZ6RUbh/7AO695OZYc7c11FT34C3F70HJxd3QCrH/7ZsxTcrV6P/wCGYM/d1QKYAZGZIHHEfSi+V41+b/oOgHqGATIHv167HizNfwYhRo7F2/U+GKc/hvr44efIk9u//A9HR0QCA//3zn/i/Bf+HxMT+2Lr0V8PXYMb74cjIKMX2l2fCLSEBAHBs0ya8kbQB/fu7YuqQVwx9/3P2axw41ojHHeOAkFEAgPJCLfaf00LlbA449zD0rdbIUdcI3YwaIiIiImqVRIiuuNjn7a2yshJKpRLnzp2Dm5vb7Tv9Q62Gqr4OUglgaa64HNLraqFtVMNcYQa5TAoILTTqBkNfC4UZAF14r6+vg1bTCIWZHHKpvq+mEQ31tZBAwMJcofujglYLlaoeWk0jzGQS3Xm1Gmi0GqhVKgACForLNTQ0qKDVNEIuk0IulQBCQGvoq4W5mZmhr1rdAK2mETKp1FCvVquFWq0CBGCuaOor0KhugFargVSir0EICKGFuqEBEFqYmcl1fzgRAhpNIzQaDWRSie5rpu/bqFYDEJDL5brpvUJAo9VCq2mEpNl5df92jZf7StCsrxbSpvPq30/dqAYAyGUySHRVQKvVQKPV6s8rN6wfVTc2AgBkMpnh+6TpvBKpFHK5/ryQ6PoKQCaX65cvSKDVCv15pZCbmQGQABIJ1I0aCAByuVy3LAISaIVAo0YDiUSqm4ojkQASmaGvzEwBmb42rQDU+r4KhblhuUWDWn9eQ18ptEJA3agFZDKYm1sA+mUYDY0aaAUgNzPX1SaRQQsJVOpGQCLTLYuQynTLONQaaIWAXGEBM4WFPiBLUN+g62tlbaPrK5VDpW5Eo1bAzNxCv2RDBiGRorauHpBIOEXsLpwixmmkHf+35zRS/oy4Vl/+jODPCP6M4M+Ia/W9U35GlJSUwMPDAxUVFbCzs8ONYvBuRVPw7qwvMhEREREREd0+OjsTmnRu4IoVKxAZGQk7OzvY2dkhPj4ev/6qmxpZWFgIiUTS6m3Dhg1XPacQAvPnz4e7uzssLS2RmJiI3Nzcq/YnIiIiIiIi6komDd6enp54//33kZaWhgMHDmDYsGEYN24cjh8/Di8vLxQVFRndFi5cCBsbG4waNeqq5/zwww+xbNkyfPHFF0hJSYG1tTVGjBiB+noTXPOViIiIiIiI7nq33FRzBwcHfPTRR5g6dWqLtqioKPTp0wfffvttq68VQsDDwwOvvPIKZs+eDQCoqKiAq6srVq1ahccee6xNNXCqORERERER0d3rjppq3pxGo8HatWtRU1OD+Pj4Fu1paWlIT09vNZA3KSgoQHFxMRITEw3HlEolYmNjsW/fvqu+TqVSobKy0uhGRERERERE1BlMHryPHj0KGxsbmJub4/nnn8fGjRsRFhbWot+3336L0NBQ9O/f/6rnKi4uBgC4uroaHXd1dTW0tWbx4sVQKpWGm5eXVwc/DREREREREZExkwfv4OBgpKenIyUlBS+88AImTZqEjIwMoz51dXVYs2bNNUe7b8S8efNQUVFhuJ0+fbpL3oeIiIiIiIjuPnJTF6BQKBAYGAgA6Nu3L1JTU/Hpp5/iyy+/NPT58ccfUVtbi4kTJ17zXG5ubgCAkpISuLu7G46XlJSgd+/eV32dubm50bXyiIiIiIiIiDqLyYP3lbRaLVQqldGxb7/9FmPHjoWzs/M1X+vn5wc3Nzds27bNELQrKysNo+lt1bTfHNd6ExERERER3X2asmBn7UVu0uA9b948jBo1Ct7e3qiqqsKaNWuQlJSELVu2GPrk5eVh586d+OWXX1o9R0hICBYvXowHH3wQEokEM2bMwKJFixAUFAQ/Pz+8+eab8PDwwAMPPNDmui5evAgAXOtNRERERER0F7t48SKUSuUNn8ekwbu0tBQTJ05EUVERlEolIiMjsWXLFtxzzz2GPt999x08PT1x7733tnqO7OxsVFRUGJ6/+uqrqKmpwZ/+9CeUl5dj4MCB2Lx5MywsLNpcl4ODAwDg1KlTnfJFJjKlyspKeHl54fTp07w8Ht32+P1Mdxp+T9OdhN/PdCepqKiAt7e3IRveqFvuOt63Al7Hm+4k/H6mOwm/n+lOw+9pupPw+5nuJHfsdbyJiIiIiIiI7kQM3kRERERERERdiMG7Febm5liwYAEvMUZ3BH4/052E3890p+H3NN1J+P1Md5LO/n7mGm8iIiIiIiKiLsQRbyIiIiIiIqIuxOBNRERERERE1IUYvImIiIiIiIi6EIM3ERERERERURdi8G7F3/72N/j6+sLCwgKxsbHYv3+/qUsiarfFixcjOjoatra2cHFxwQMPPIDs7GxTl0XUKd5//31IJBLMmDHD1KUQdcjZs2fxf//3f3B0dISlpSUiIiJw4MABU5dF1CEajQZvvvkm/Pz8YGlpiYCAALzzzjvgHs50O9i5cyfGjBkDDw8PSCQSbNq0yahdCIH58+fD3d0dlpaWSExMRG5ubrvfh8H7CuvWrcOsWbOwYMECHDx4EL169cKIESNQWlpq6tKI2mXHjh2YNm0akpOTsXXrVqjVatx7772oqakxdWlENyQ1NRVffvklIiMjTV0KUYeUlZVhwIABMDMzw6+//oqMjAx8/PHH6Natm6lLI+qQDz74ACtWrMDy5cuRmZmJDz74AB9++CE+++wzU5dGdF01NTXo1asX/va3v7Xa/uGHH2LZsmX44osvkJKSAmtra4wYMQL19fXteh9eTuwKsbGxiI6OxvLlywEAWq0WXl5eePHFFzF37lwTV0fUcefPn4eLiwt27NiBwYMHm7ocog6prq5Gnz598Pnnn2PRokXo3bs3li5dauqyiNpl7ty52LNnD3bt2mXqUog6xf333w9XV1d8++23hmPjx4+HpaUlvv/+exNWRtQ+EokEGzduxAMPPABAN9rt4eGBV155BbNnzwYAVFRUwNXVFatWrcJjjz3W5nNzxLuZhoYGpKWlITEx0XBMKpUiMTER+/btM2FlRDeuoqICAODg4GDiSog6btq0aRg9erTRz2mi281//vMf9OvXD4888ghcXFwQFRWFr7/+2tRlEXVY//79sW3bNuTk5AAADh8+jN27d2PUqFEmrozoxhQUFKC4uNjo9w6lUonY2Nh250N5Zxd3O7tw4QI0Gg1cXV2Njru6uiIrK8tEVRHdOK1WixkzZmDAgAHo2bOnqcsh6pC1a9fi4MGDSE1NNXUpRDfkxIkTWLFiBWbNmoXXX38dqampeOmll6BQKDBp0iRTl0fUbnPnzkVlZSVCQkIgk8mg0Wjw7rvv4sknnzR1aUQ3pLi4GABazYdNbW3F4E10F5g2bRqOHTuG3bt3m7oUog45ffo0Xn75ZWzduhUWFhamLofohmi1WvTr1w/vvfceACAqKgrHjh3DF198weBNt6X169fjn//8J9asWYPw8HCkp6djxowZ8PDw4Pc0kR6nmjfj5OQEmUyGkpISo+MlJSVwc3MzUVVEN2b69On4+eefsX37dnh6epq6HKIOSUtLQ2lpKfr06QO5XA65XI4dO3Zg2bJlkMvl0Gg0pi6RqM3c3d0RFhZmdCw0NBSnTp0yUUVEN2bOnDmYO3cuHnvsMUREROCpp57CzJkzsXjxYlOXRnRDmjJgZ+RDBu9mFAoF+vbti23bthmOabVabNu2DfHx8SasjKj9hBCYPn06Nm7ciD/++AN+fn6mLomow4YPH46jR48iPT3dcOvXrx+efPJJpKenQyaTmbpEojYbMGBAi8s75uTkwMfHx0QVEd2Y2tpaSKXGsUImk0Gr1ZqoIqLO4efnBzc3N6N8WFlZiZSUlHbnQ041v8KsWbMwadIk9OvXDzExMVi6dClqamowZcoUU5dG1C7Tpk3DmjVr8O9//xu2traGdShKpRKWlpYmro6ofWxtbVvsT2BtbQ1HR0fuW0C3nZkzZ6J///5477338Oijj2L//v346quv8NVXX5m6NKIOGTNmDN599114e3sjPDwchw4dwpIlS/D000+bujSi66qurkZeXp7heUFBAdLT0+Hg4ABvb2/MmDEDixYtQlBQEPz8/PDmm2/Cw8PDsPN5W/FyYq1Yvnw5PvroIxQXF6N3795YtmwZYmNjTV0WUbtIJJJWj69cuRKTJ0++ucUQdYGEhAReToxuWz///DPmzZuH3Nxc+Pn5YdasWXj22WdNXRZRh1RVVeHNN9/Exo0bUVpaCg8PDzz++OOYP38+FAqFqcsjuqakpCQMHTq0xfFJkyZh1apVEEJgwYIF+Oqrr1BeXo6BAwfi888/R48ePdr1PgzeRERERERERF2Ia7yJiIiIiIiIuhCDNxEREREREVEXYvAmIiIiIiIi6kIM3kRERERERERdiMGbiIiIiIiIqAsxeBMRERERERF1IQZvIiIiIiIioi7E4E1EREQ3RUNDAwIDA7F3716T1TB37ly8+OKLJnt/IiK6OzF4ExERdcDkyZMhkUha3PLy8kxd2i3riy++gJ+fH/r37290fPv27bj//vvh7OwMCwsLBAQEYMKECdi5c6ehT1JSEiQSCcrLy1uc19fXF0uXLm1TDbNnz8bq1atx4sSJG/koRERE7cLgTURE1EEjR45EUVGR0c3Pz69Fv4aGBhNUd2sRQmD58uWYOnWq0fHPP/8cw4cPh6OjI9atW4fs7Gxs3LgR/fv3x8yZMzu9DicnJ4wYMQIrVqzo9HMTERFdDYM3ERFRB5mbm8PNzc3oJpPJkJCQgOnTp2PGjBmGoAcAx44dw6hRo2BjYwNXV1c89dRTuHDhguF8NTU1mDhxImxsbODu7o6PP/4YCQkJmDFjhqGPRCLBpk2bjOqwt7fHqlWrDM9Pnz6NRx99FPb29nBwcMC4ceNQWFhoaJ88eTIeeOAB/PWvf4W7uzscHR0xbdo0qNVqQx+VSoXXXnsNXl5eMDc3R2BgIL799lsIIRAYGIi//vWvRjWkp6dfc8Q/LS0N+fn5GD16tOHYqVOnMGPGDMyYMQOrV6/GsGHD4OPjg8jISLz88ss4cOBAW/8pDFatWtXqTIS33nrL0GfMmDFYu3Ztu89NRETUUQzeREREXWD16tVQKBTYs2cPvvjiC5SXl2PYsGGIiorCgQMHsHnzZpSUlODRRx81vGbOnDnYsWMH/v3vf+O3335DUlISDh482K73VavVGDFiBGxtbbFr1y7s2bMHNjY2GDlypNHI+/bt25Gfn4/t27dj9erVWLVqlVF4nzhxIn744QcsW7YMmZmZ+PLLL2FjYwOJRIKnn34aK1euNHrflStXYvDgwQgMDGy1rl27dqFHjx6wtbU1HPvXv/4FtVqNV199tdXXSCSSdn12AJgwYYLRDIQffvgBcrkcAwYMMPSJiYnBmTNnjP4YQURE1JXkpi6AiIjodvXzzz/DxsbG8HzUqFHYsGEDACAoKAgffvihoW3RokWIiorCe++9Zzj23XffwcvLCzk5OfDw8MC3336L77//HsOHDwegC++enp7tqmndunXQarX45ptvDMF15cqVsLe3R1JSEu69914AQLdu3bB8+XLIZDKEhIRg9OjR2LZtG5599lnk5ORg/fr12Lp1KxITEwEA/v7+hveYPHky5s+fj/379yMmJgZqtRpr1qxpMQre3MmTJ+Hh4WF0LCcnB3Z2dnBzczMc+9e//oVJkyYZnu/btw8RERGG5619PWpraw2PLS0tYWlpCQDIz8/HtGnT8N577+Gee+4x9Gmq4+TJk/D19b1qzURERJ2FwZuIiKiDhg4darRW2Nra2vC4b9++Rn0PHz6M7du3GwX1Jvn5+airq0NDQwNiY2MNxx0cHBAcHNyumg4fPoy8vDyjkWUAqK+vR35+vuF5eHg4ZDKZ4bm7uzuOHj0KQDdtXCaTYciQIa2+h4eHB0aPHo3vvvsOMTEx+O9//wuVSoVHHnnkqnXV1dXBwsKixfErR7VHjBiB9PR0nD17FgkJCdBoNEbtu3btavHZEhISWpy3oqIC999/P0aPHo05c+YYtTUF8+aBnYiIqCsxeBMREXWQtbX1VadWNw/hAFBdXY0xY8bggw8+aNHX3d29zbuhSyQSCCGMjjVfm11dXY2+ffvin//8Z4vXOjs7Gx6bmZm1OK9WqwVwOZheyzPPPIOnnnoKn3zyCVauXIkJEybAysrqqv2dnJwMwb5JUFAQKioqUFxcbBj1trGxQWBgIOTy1n9F8fPzg729vdGxK/tqNBpMmDABdnZ2+Oqrr1qc49KlSwCMvx5ERERdiWu8iYiIboI+ffrg+PHj8PX1RWBgoNHN2toaAQEBMDMzQ0pKiuE1ZWVlyMnJMTqPs7MzioqKDM9zc3ONRm779OmD3NxcuLi4tHgfpVLZplojIiKg1WqxY8eOq/a57777YG1tjRUrVmDz5s14+umnr3nOqKgoZGVlGf3R4OGHH4aZmVmrf4y4ETNnzsTRo0exadOmVkfZjx07BjMzM4SHh3fq+xIREV0NgzcREdFNMG3aNFy6dAmPP/44UlNTkZ+fjy1btmDKlCnQaDSwsbHB1KlTMWfOHPzxxx84duwYJk+eDKnU+H/Vw4YNw/Lly3Ho0CEcOHAAzz//vNHo9ZNPPgknJyeMGzcOu3btQkFBAZKSkvDSSy/hzJkzbarV19cXkyZNwtNPP41NmzYZzrF+/XpDH5lMhsmTJ2PevHkICgpCfHz8Nc85dOhQVFdX4/jx44Zj3t7e+Pjjj/Hpp59i0qRJ2L59OwoLC3Hw4EEsW7bM8D7tsXLlSnz++ef44osvIJFIUFxcjOLiYlRXVxv67Nq1C4MGDWrTyD4REVFnYPAmIiK6CTw8PLBnzx5oNBrce++9iIiIwIwZM2Bvb28I1x999BEGDRqEMWPGIDExEQMHDmyxVvzjjz+Gl5cXBg0ahCeeeAKzZ882muJtZWWFnTt3wtvbGw899BBCQ0MxdepU1NfXw87Ors31rlixAg8//DD+/Oc/IyQkBM8++yxqamqM+kydOhUNDQ2YMmXKdc/n6OiIBx98sMUU+BdffBG//fYbzp8/j4cffhhBQUG47777UFBQgM2bNxttrNYWO3bsgEajwdixY+Hu7m64Nd/4be3atXj22WfbdV4iIqIbIRFXLhQjIiKiW0ZCQgJ69+6NpUuXmrqUFnbt2oXhw4fj9OnTcHV1vW7/I0eO4J577kF+fn6rm8zdDL/++iteeeUVHDly5KrryImIiDobR7yJiIioXVQqFc6cOYO33noLjzzySJtCNwBERkbigw8+QEFBQRdXeHU1NTVYuXIlQzcREd1U/L8OERERtcsPP/yAqVOnonfv3vj73//ertdOnjy5a4pqo4cfftik709ERHcnTjUnIiIiIiIi6kKcak5ERERERETUhRi8iYiIiIiIiLoQgzcRERERERFRF2LwJiIiIiIiIupCDN5EREREREREXYjBm4iIiIiIiKgLMXgTERERERERdSEGbyIiIiIiIqIuxOBNRERERERE1IX+Hx+mBkBCNM2VAAAAAElFTkSuQmCC",
      "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 Custom2DCurrentIntegral with the created vertices, along with the normal axis and position of the plane at x=0\n",
    "custom_path = rf.Custom2DCurrentIntegral(axis=0, position=0.0, vertices=vertices)\n",
    "# Setup  the impedance calculator using the previously defined voltage_integral\n",
    "impedance_calculator = rf.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.plot(freqs / 1e9, skrf_z0, label=\"scikit-rf\")\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 `AxisAlignedCurrentIntegral` was used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Definitions of Characteristic Impedance\n",
    "\n",
    "In transmission lines supporting quasi-TEM modes, such as microstrips, the electric and magnetic fields do not allow for a unique or consistent definition of impedance [3]. As a result, there are three commonly used definitions:\n",
    "\n",
    "1. **Voltage–Current Definition:**\n",
    "   This is the definition used at DC, where impedance is defined as the ratio of voltage (V) to current (I)\n",
    "   $$\n",
    "   Z_{VI}=\\frac{V}{I}.\n",
    "   $$\n",
    "\n",
    "2. **Power–Current Definition:**  \n",
    "   Here, the impedance is defined using the total power flow (or flux) and the current\n",
    "   $$ \n",
    "   Z_{PI}=\\frac{2P}{|I|^2}.\n",
    "   $$\n",
    "\n",
    "3. **Power–Voltage Definition:**  \n",
    "   In this case, impedance is calculated based on the total power flow (flux) and the voltage\n",
    "   $$\n",
    "   Z_{PV}=\\frac{|V|^2}{2P^*}.\n",
    "   $$\n",
    "\n",
    "Of these, the current‐based approach (power–current definition) is generally favored because it exhibits the smallest dependence on frequency, at least for microstrips. Moreover, there is a unique method for defining current, namely by computing the path integral around the conductor. On the other hand, there are many different possible paths one could choose when computing the voltage, which will give slightly different answers since the electromagnetic field is non-conservative, unless the mode is truly a TEM mode.\n",
    "\n",
    "In the next section, we will demonstrate how the [ImpedanceCalculator](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ImpedanceCalculator.html) can be used to compute the characteristic impedance using these three different definitions. We also switch to computing the impedance using the electromagnetic mode that have been computed in the [ModeSolverMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSolverMonitor.html#tidy3d.ModeSolverMonitor). Finally, we compare the three different definitions of impedance to the model in `scikit-rf`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+xBNPZKhFRERERERdq6HBj4oqByor7aisdiIYDEmOF+TnJoaPW1FcZEYW99SmfYTBu5eqqqpCWVlZpptBRERERNRlwuEIqu01qKx0oLLKDrenXnI8K0uFkmILykqsKC0tRH5e915ImXovBu9eauvWrZluAhERERFRpxIEAbV13uY9te0uxGIx8bhMBljMhvjq46VWWMwGyOXcU5syr8PBOxgMYtWqVdixYwf8fj/MZjPGjBmD8vLyrmgfERERERHtx/z+ACqr4tt8VVY50NQUlBzPzc0We7RLiizQaLIy1FKitrU7eH/77bd4/PHH8eGHHyIcDqOgoABarRZ1dXUIBoPo378/rrjiClx55ZXIy8vryjYTEREREVEvFYlEYXe4UJEYPl5b55UcVyoVKC6yxBdFKylEQUEuF0Wjbq9dwfvkk0/GmjVrcN555+HTTz/FuHHjoNU2L6+/detWfP3113jzzTfx6KOP4l//+hcmTZrUZY0mIiIiIqLeQRAEuD314urjNlsNItGopI7JqBMXRSu0GqFQcE9t6lnaFbynT5+Od999FypV66v+9e/fH/3798fMmTPx+++/w2azdWojiYiIiIio9wgEgqiscsbDdpUDjY1NkuPZWg1KS5N7alug1Woy1FKiztGu4P3Xv/613RccNmwYhg0btscNIiIiIiKi3iUai8HhqBXnatfUuCXHFQo5igrNKC2J76lt0Odz+Dj1Knu1qnlDQ4NkFUEAyM/P36sGERERERFRzyYIAny+xviCaJUOVNmcCIcjkjoGfb64+nhhoQkqJTdcot6rw9/d27Ztw9VXX42VK1ciEAiI5YIgQCaTIdpiPgYREREREfV+wVAY1dVOVFTGh4/X1zdKjms0WSgtsaK0JD5XOzdH28aViHqfDgfvCy64AIIg4KWXXoLVauUQECIiIiKi/VAsFkONy42KSgcqKu1w1tRBEATxuFwuQ6HVJA4fNxl1zA603+pw8P7ll1/w888/Y/DgwZ3akAceeABz587F7NmzMX/+fADAc889h0WLFmHNmjWor6+H2+2GTqfr1NclIiIiIqL2qa9vREWVA5WJWygUlhzXFeQlgrYVxUUWqFQcPk4E7EHwPvjgg1FRUdGpwXv16tV49tlnMWrUKEm53+/HCSecgBNOOAFz587ttNcjIiIiIqLdC4cjqKp2iouieb0NkuNZWarE8HErykqsyMvLyVBLibq3DgfvF154AVdeeSWqqqowYsSItC3GWobn3WloaMD555+P559/HvPmzZMcu+666wAAK1eu7Ggze72PPvoI06dPb/P42WefjcWLF+/DFhERERFRTycIAmpc7njQrnTA4XQhFmsePi6TyWC1GMRF0cwmA+RyDh8n2p0OB++amhps2bIFF198sVgmk8n2eHG1WbNmYfr06Zg4cWJa8N4TwWAQwWBQfO7z+fb6mt3Rsccem7ZfejQaxcUXX4w1a9bg1ltvzVDLiIiIiKgnaWj0i0G7qsqBQDAkOZ6Xl4OyxDzt4mIL1FmqNq5ERG3pcPC+5JJLMGbMGLz55pt7vbjaW2+9hTVr1mD16tV7fI2W7r//ftx1112ddr3uSqvVQqttXgkyGo3i/PPPx5o1a7BixQoceOCBGWwdEREREXVX4XAENntNfK52pQNuj7SjSqVSoqTYgrKSQpSWWlGQn5uhlhL1Hh0O3jt27MAHH3yAgQMH7tULV1RUYPbs2Vi+fDk0Gs1eXSvV3LlzMWfOHPG5z+dDWVlZp12/O4pGo7jgggvw2WefMXQTERERkYQgCKit86Ki0o7KKgdsdhdisZh4XCYDzCYDykrjW31ZLAYo5PIMtpio9+lw8D7uuOPwyy+/7HXw/vnnn+F0OjF27FixLBqN4quvvsKCBQsQDAahUCg6fF21Wg21Wr1HbRIEAZFIZvYhVyoVezR6IBm6P/30U4ZuIiIiIgIANPqb4iuPV8ZXH28KBCXHc3O0KC0tRFlpIUqKLNBosjLUUqL9Q4eD90knnYTrr78eGzZswMiRI9MWVzv55JPbdZ3jjz8eGzZskJRdfPHFGDJkCG6++eY9Ct17KxKJ4sVXl+7z1wWAS2ee1uHtFqLRKC688EIxdI8ePbprGkdERERE3VokEoXNXiPO1a5zeyXHlUoFSoosKC2Nr0CuK8jjntpE+1CHg/eVV14JALj77rvTjnVkcbW8vDyMGDFCUpaTkwOj0SiW2+122O12bN68GQCwYcMG5OXloU+fPjAYDB1teq+SDN3Lli3DZ5991mroPu2006BWq7FlyxbU1dVh8eLFiEajmDdvHj788EMAwCeffII33ngDr7322j5+B0RERES0pwRBQF2dV9xT22avQTQak9Qxm/SJPbULYbUYoVBw+DhRpnQ4eKfOB+lqzzzzjGShtKOPPhoA8PLLL+Oiiy7q9NdTKhW4dOZpnX7d9r52e0WjUcyYMUMM3WPGjGm13vr16/HXv/4Vb731Ft544w088sgjePbZZ/Hnn3+Kde655x688sore9t8IiIiIupifn8gPnw8cfM3BSTHc3K0KCuJz9MuKbFAq9mz6ZdE1Pk6HLy7Usv9uu+8807ceeed++z1ZTJZh4d772uxWAwzZszA+++/j3feeQdFRUWw2+2SOmazGU1NTQgEArjhhhsAAEOHDsVrr72G/Px8hEIhhMNhLF++HIMGDcKgQYMy8VaIiIiIaBcikSjsDld8+HiVA7W1HslxpVKB4iJzfE/tEit0Og4fJ+qu9ihlrlixAitWrIDT6UzrAX/ppZc6pWHUutWrV2PRokUAgGnTpqUdl8lk8Hg8+O233zB8+HBxrvyaNWswcuRIAMCgQYOwefNmzJs3j0PMiYiIiLoJQRDgdvsSw8ftqLalDx83GXUoKy1EaYkVhVZjRtZFIqKO63Dwvuuuu3D33Xdj3LhxKCoq4qdq+9ihhx4KQRB2W2/9+vXYsWMHwuEwfD4fnnzySbz33nsA4r3fjzzyCIYOHYoBAwZ0dZOJiIiIqA1NTQFUVjlRURXf6svvlw4fz87WxPfTLokviqbVcvg4UU/U4eD9zDPP4JVXXsGFF17YFe2hTrJ+/XpMmzYNBx10EARBwIMPPiiG7KFDh2L27NmSud5ERERE1PV2O3xcoUBRkTmxKJoVel0+O7qIeoEOB+9QKITx48d3RVuoE61fvx6LFi3CY489lnbsqquuwlVXXZWBVhERERHtXwRBgNvjQ0ViP22brQaRFrsAmYw6yerjHVl0l4h6hg4H78suuwyLFi3Cbbfd1hXtoU5SVVWFsrKyTDeDiIiIaL/TseHjFmi1mgy1lIj2lXYF7zlz5oiPY7EYnnvuOXz22WcYNWoUVCqVpO6jjz7auS2kPbJ169ZMN4GIiIhov8Dh40S0O+0K3mvXrpU8Hz16NADg119/lZTzBwgRERER9XaCIKDO7Uvsp22HzeZqc/h4fPVxE4ePE+3n2hW8v/jii65uBxERERFRt+X3B+JBu9rB4eNE1GF7tI+3x+PB5s2bAQADBw6ETqfrzDYREREREWVUJBKFze5CZWKedm2dV3I8OXy8rDTeq83h40S0Kx0K3tu3b8esWbOwbNkycS9pmUyGE044AQsWLEC/fv26oo1ERERERF1KEATU1nkTw8cdsNlrEI3GJHVMRh3KSgsTw8eNUCg4fJyI2qfdwbuiogKHHXYYVCoV7rnnHgwdOhQA8Pvvv2PhwoU4/PDDsXr1apSWlnZZY4mIiIiIOktjY1N86HhlfAh5U1NQcjwnRxtfEK3EipJiK7RadYZaSkQ9nUxIdl3vxqWXXorNmzdj2bJl0Gikc1aamppwwgknYNCgQXjhhRe6pKF7yufzoaCgAF6vF/n5+ZJjgUAA27ZtQ3l5edp7oszhvwsRERF1hXAkAputefh4ndsnOa5UKlBcZBHDtk6Xx+HjRPupXeXIPdHuHu9PPvkEixcvbjUIabVa3HPPPfjLX/6y1w0iIiIiIuoMgiDAVeuJb/NVaYfdUYtYTDp83GzWo7TYirLSQlgtRigU8gy1loh6s3YHb5fLtcs53P3790ddXV1ntImIiIiIaI80NPjF/bSrqh0IBEKS47m52ShLbPNVUmyBRsPh40TU9dodvIuKivD777+3OYf7119/RWFhYac1jIiIiIhod0KhMKptNeKiaB5vveS4SqVESbFF3FO7ID+Xw8eJaJ9rd/A+9dRTceONN2LFihUwm82SY06nEzfffDNOPfXUzm4fdTKZTIalS5fi1FNPxfbt21FeXo61a9di9OjRu63fGex2Oy688EJ89913UKlU8Hg8nXJdIiIi2j/EYjHUuNxi0HY4ahFLWbJIJgMsZqMYtC0WAxRyDh8nosxqd/C+44478NFHH2HAgAG44IILMGTIEAiCgP/9739YtGgRCgsLcfvtt3dlW6kT2Gw26PX6ParfnqC+O4899hhsNhvWrVuHgoKCPboGERER7V+8vob4yuNVDlTZnAiFwpLj+fk5KC0pRFmJFcVFZqjVWRlqKRFR69odvPV6PVatWoVbb70Vb731lthTqdPpcN555+G+++6DwWDoqnZSJ+nodIDOmj4QCoWQlZWFLVu24KCDDsKgQYM65bpERETU+wQCIVTZnGKvdn19o+S4Wq1CSbFV7NXOz8vJUEuJiNqn3cEbiIfvhQsX4umnn0ZNTQ0AwGw2c55MBrzzzju46667sHnzZmRnZ2PMmDH497//jZycHLz00kt45JFHsHnzZhgMBpxxxhlYsGABgF0PHY9Go7j88svx3Xff4dNPP0WfPn0k9cvLywEAY8aMAQAcc8wxWLlyZavtmzBhAkaMGAGlUonXX38dI0eOxLZt27Bjxw4AwL/+9S/MnDkTr7zySqd/bYiIiKhniUZjcDhrxaBd46pD6oa3crkMVotJDNpmkx5yOf/+JKKeo0PBO0kmk8FisXR2W7qNxsb4p6rZ2dnihwqhUAjhcBhKpRJqtTqtrlarhTwxfygcDiMUCkGhUEi2X2urrkql6lD7bDYbzj33XDz44IM47bTTUF9fj6+//hqCIGDhwoWYM2cOHnjgAUydOhVerxfffvvtbq8ZDAZx7rnnYvv27fj666/T5vEDwI8//ohDDjkEn332GYYPH46srF0P43r11Vdx1VVXia9vMBgwY8YM5Ofn4/HHH4dWq+3Q+yYiIqLeQRAEuD0+MWhX22oQiUQldfS6fDFoFxeZoVLt0Z+tRETdQrt+gp1wwgm48847cdhhh+2yXn19PZ5++mnk5uZi1qxZndLATMjNzQUQXzQuGUAfeugh/N///R8uu+wyPP/882Jdi8UCv9+Pbdu2idutPfXUU7j++utx3nnn4Y033hDr9uvXDy6XC7/++iuGDx8OAHjllVdw+eWXd6h9NpsNkUgEp59+Ovr27QsAGDlyJABg3rx5uOGGGzB79myx/sEHH7zL6zU0NGD69OkIBoP44osv2px7nfxaGI3Gdg1BHzRoEB588EFJmVqthlar5Qr4RERE+xm/PxAP2tXxsO33ByTHtRo1Skqah4/n5vADeiLqPdoVvM866yycccYZKCgowEknnYRx48ahuLgYGo0Gbrcbv//+O7755ht89NFHmD59Oh566KGubvd+7cADD8Txxx+PkSNHYsqUKZg8eTLOPPNMhMNhVFdX4/jjj+/Q9c4991yUlpbi888/73Av9Ndff42pU6eKz5999lmcf/75AICDDjqoQ9ciIiKi3iMcjsBmr0FlVXyudp3bKzmuUMhRVGgWg7bRUMDpi0TUa7UreF966aW44IIL8Pbbb2Px4sV47rnn4PXGf3jKZDIMGzYMU6ZMwerVqzF06NAubfC+0NDQACA+1DzppptuwnXXXQelUvolczqdACAJrLNmzcLll18OhUIhqbt9+/a0uhdddFGH26dQKLB8+XJxLvaTTz6Jf/zjH1ixYkWHrwUA06ZNw+uvv47vv/8exx13XIfOHTduHNatWyc+t1qt4uOcHC50QkREtL+IxQS4apu3+bI7ahGLxSR1TEadGLQLrSYolYo2rkZE1Lu0e7KMWq3GBRdcgAsuuAAA4PV60dTUBKPR2OE5yq154IEHMHfuXMyePRvz588HAAQCAdxwww146623EAwGMWXKFDz99NOScNcVWguMWVlZrc5pbq2uSqVq9WvSVt09IZPJcMQRR+CII47A7bffjr59+2L58uXo168fVqxYgWOPPbbd17rqqqswYsQInHzyyfjvf/+LY445ptV6yfcfjTbPwdJqtRg4cOAevQciIiLq2Xz1jWLQrqp2IhgMSY7n5majNLH6eEmJBVqNuo0rERH1bnu8SkVBQUGn7cO8evVqPPvssxg1apSk/Prrr8d///tfvP322ygoKMDVV1+N008/vV2LhfVmq1atwooVKzB58mRYLBasWrUKNTU1GDp0KO68805ceeWVsFgsmDp1Kurr6/Htt9/immuu2eU1r7nmGkSjUZx44on4+OOPceSRR6bVsVgs0Gq1+OSTT1BaWgqNRsO9uImIiPYjwWAIVdVOca62zyfd5itLpURxsUXs1S7Iz+XwcSIi7EXw7iwNDQ04//zz8fzzz2PevHliudfrxYsvvohFixaJw59ffvllDB06FD/88MNuF3rrzfLz8/HVV19h/vz58Pl86Nu3Lx555BFxrnUgEMBjjz2GG2+8ESaTCWeeeWa7rnvdddchFoth2rRp+OSTTzB+/HjJcaVSiSeeeAJ33303br/9dhx11FFtbidGREREPV80GoXdUSsGbZfLLd3mSyaDxWIUg7bFrBd3biEiomYyQUj98bnvzZw5EwaDAY899hgmTJiA0aNHY/78+fj8889x/PHHw+12Q6fTifX79u2L6667Dtdff327ru/z+VBQUACv14v8/HzJsUAggG3btqG8vFyy7RdlFv9diIiIMkMQBNTVeRNB2wmbvbVtvvLiq48Xx7f5ysra+ymHRESZ5A8FsMW+A3/atmKjbTs22rbh9+0bsfrBf7eaI/dERnu833rrLaxZswarV69OO2a325GVlSUJ3UB88S673d7mNYPBIILBoPjc5/N1WnuJiIiIepuGRr+48nhVlQNNgaDkuFarlszTzs3JbuNKRETdW2OwCZvFgL0NG23b8Gf1NuysrUbL/uhYKNKpr52x4F1RUYHZs2dj+fLlndqref/99+Ouu+7qtOsRERER9SahUBjVthpxUTSPt15yXKlUSLb5MujzOU+biHqUxoAfG+3b8Wd1ImAnHlfU2to8R5+Tj8FF/XFAcTkOKCpHaZ4ZU1/u2I5Pu7JHwdvj8eCdd97Bli1bcNNNN8FgMGDNmjWwWq0oKSlp1zV+/vlnOJ1OjB07ViyLRqP46quvsGDBAixbtgyhUAgej0fS6+1wOFBYWNjmdefOnYs5c+aIz30+H8rKyjr+JomIiIh6gWgsBqezLrHyuAMOZ52kZ0cmA8wmgxi0rRZD2paoRETdka+pAZts2/Fnovd6o20b/rRtQ1Vd2yOkjXl6DC6Kh+sDisrFx6Y8veRDxs4eOd3h4L1+/XpMnDgRBQUF2L59Oy6//HIYDAa899572LlzJ/71r3+16zrHH388NmzYICm7+OKLMWTIENx8880oKyuDSqXCihUrcMYZZwAA/vzzT+zcuROHH354m9dVq9VQq7lVBREREe2fBEGA2+MTh4/b7DUIh6VDJvPzc+NBu9iCkmIL1Or0LVOJiLoLT6NPDNYbbdux0b4Nf1Zvhc1T0+Y5lnxjIlz3iwfs4v4YVNgPpjz9Pmx5sw4H7zlz5uCiiy7Cgw8+iLy8PLF82rRpOO+889p9nby8PIwYMUJSlpOTA6PRKJZfeumlmDNnDgwGA/Lz83HNNdfg8MMP369XNCciIiJqqaGxCVXVif20q5zwNwUkxzWaLJQUW1GS2OorPy8nQy0lImpbbYMnJWDH519vtG2D01fb5jlFOrPYe31AUTkGFcaDtiG3e2173OHgndxzu6WSkpJdLnq2Jx577DHI5XKcccYZCAaDmDJlCp5++ulOfQ0AaRPpKbP470FERLRrqfO0q6odcHuk87QVCrlknrbRUMB52kTULQiCAFd9Hf5MrB6eOkS8tt7d5nklhsLm3uuUkF2QndfmOd1Jh4O3Wq1udbz7xo0bYTab96oxLfeE1mg0eOqpp/DUU0/t1XXbolLFt7/w+/3QarVd8hrUcX6/H0Dzvw8REdH+LhqNwVlTKw4fd9bUpX1QbTbrxdXHrRYjlErO0yaizBEEAXZPjWR4eDJkuxvbnj/dx1Qc771O9Fwnh4vnanr2SJ0OB++TTz4Zd999N5YsWQIAkMlk2LlzJ26++WZxLnZPoVAooNPp4HQ6AQDZ2dn8NDiDBEGA3++H0+mETqfjwi5ERLTfEgQBdW5fokfbiWqbM20/7fz8XJQmho4XF1mg0XCeNhHte4IgoMrtaF5BPKUnuz7Q2Oo5MpkM/Uwl0iHiRf0wqLAfctS9s0NUJnRwXK/X68WZZ56Jn376CfX19SguLobdbsfhhx+Ojz76CDk53euTCJ/Ph4KCgjY3PhcEAXa7HR6PZ983jlql0+lQWFjID0GIiGi/Ut/gT8zTdqKq2oGmJul+2sl52qUl8bnanKdNRPtSNBZFRa1NHBa+ybYdG23bscm+HY1Bf6vnKOQK9LeUSgL2AUXl6G8pgzar87aU7gq7y5Ed1eHgnfTtt9/il19+QUNDA8aOHYuJEyfudWO6Qnu/YNFoFOFweB+2jFqjUqnY001ERPuFYDCEqmonKqudqKpywOtrkBxXKhQoKjLFw3axBUajjh9KE1GXi0Qj2F5TJZl/vdG+HZvtOxAIB1s9R6VQor+1Dw4ojA8LH1zcXwzYWcqeOX202wTvnqKzv2BEREREeyISicLucCXCtgMulxupf4XJZIDFbBB7tbmfNhF1pWA4hK3OirRturY6diIcjbR6jlqZhYGFfcV514MK4yG7n7kUKkWHZzF3a52dIzv81bn22msxcOBAXHvttZLyBQsWYPPmzZg/f/5eN4qIiIiop4vFBNTWeVBZFd/my+5wIRqNSeroCvLiQ8dLLCguNHM/bSLqdP5QAFvsO8SAvckeHyK+raYS0Vi01XO0WRrJwmbJhc76mIqhkPMDwT3R4R7vkpISfPDBBzjooIMk5WvWrMHJJ5+MysrKTm3g3mKPNxEREe0LgiDA62tAVWKOdpWtBsFgSFInO1uD0uJ40C4ptiI3p3cuIkRE+15DoFGysNlG+3Zssm3HztrqNrfrzdPkSAN24nGJ3gq5XL6P30H3kvEe79raWhQUpG9Gnp+fD5fLtdcNIiIiIuop/P4AKqsdYthuaGySHFeplCgpsqCkxILSYit0ujzO0yaiveJu9CYWNpNu01XtdrZ5jj4nH4OL+mNQUT8cUBhfQXxwUTkKdWb+TNpHOhy8Bw4ciE8++QRXX321pPzjjz9G//79O61hRERERN1NKBRGta0mHrarnXC7pXvRyuVyWC1GlCZ6tM1mPRT7ea8REXWcIAhw1dfhz0TA3pQSsGt8dW2eZ8k3SuZfJ3uwTXl6BuwM63DwnjNnDq6++mrU1NTguOOOAwCsWLECjzzyCOd3ExERUa8SjUZhd9Siqjreo+2scacN2TQZdYkF0SwoLDRBpexdCwwRUdcRBAHVbmeL+dfxnmyP39fmeSV6q2R4+KDCfhhU1A/6nPSRydQ9dPg3wyWXXIJgMIh7770X99xzDwCgX79+WLhwIWbMmNHpDSQiIiLaVwRBgKs2viBaVbUTdrsLkah08aH8/FyUFltQUmJFSZEZGo06Q60lop4idQ/sjSnDxHe1B7ZMJkNfU4nYe51cQXxQYV/kanL28TugvbVX24nV1NRAq9UiNze3M9vUqbi4GhEREbVFEAR4vQ3i0PHqVhZE02rV4l7aJcUW5OXxD14ial0oEsY2ZyU22ZuD9UbbNmxx7GxzD2ylXIFyS1naImf9LWXQZmn28TugpIwvrpbKbDbvdQOIiIiI9qWGxqb4quPVTlRVOdHoT18QrbjQjJKSeNjW6/M5N5KIJFK36EqG60327djmrESkjS26NCo1Blj7pM3BLreU9bo9sCldh/+FHQ4HbrzxRqxYsQJOpzNtnlM02vo3GhEREVEmBAIhVNucqKx2orraCY+3XnJcLpej0GpESTEXRCMiKV9TAzbZtuPPFgG7otbW5hZduZpsMVTH7+OPy4xF3AN7P9bh4H3RRRdh586duO2221BUVMRPgImIiKhbCYcjsDtcqExs8eWq9UiOy2SAyaSPb/NVbEVhoZELohHtx5IriKcODd9k345Nth2we2vaPM+QUyAOD08G7EFF5SjiFl3Uig7/lvnmm2/w9ddfY/To0V3QHCIiIqKOiUZjcNbUicPHHc5axGLSnii9Lg8lxVaUFFtQXGSGWp2VodYSUabEYjFUuR3N4ToRtDfZt8Pd2PYK4kU6s2Tl8ORjU55+H7aeeroOB++ysrI2h1UQERERdbVYTEBtnSexxZcTNnsNIhHpVLfcHK0YtEuKLcjJ0WaotUS0r0WiEWyvqWruubZvF3uzm0KBVs9JriA+qLBvyv7X/TCwsB/ytd13IWnqOTocvOfPn49bbrkFzz77LPr169cFTSIiIiJqJggCPN56MWhX25wIBsOSOhp1FoqLLSgttqC42IKC/FwO9STq5ZpCAWxx7JQE6422bdjmrEA4Gmn1HJVCif7WPs3hOtGLzRXEqat1OHifc8458Pv9GDBgALKzs6FSqSTH6+rqOq1xREREtH+qr29ElS2+6niVzQm/X9pLpVIpUVRoFnu0jYYCBm2iXsrT6Evrud7dAmfZai0GWvuKC5sl98HuZy6BkiuIUwbsUY83ERERUWdqagqIPdpVNid8vkbJcYVCjkKrCcVF8aDNlceJehdBEODwutLC9Sbbdjh9tW2ep8/JlwTrQYltukr0Vsj5M4K6kQ4H75kzZ3ZFO4iIiGg/EgyGYLO7UFntQHW1E3Vu6cJGMpkMFrNenKdttRihVHIbHqKeLhqLYqerOi1gb7bvgK+poc3zivWWtHCdXOCMo12oJ9ircRaBQAChUEhSlp+fv1cNIiIiot4nucVXslfbVetGyxGiRkOBuJd2UaEJWVmq1i9GRN1eIBwU51+nrh6+1VGBYCTU6jlymRz9zCVp4XpQYT/kaXP28Tsg6lwdDt6NjY24+eabsWTJEtTWpg/7iEajrZxFRERE+5NIJAqHsxbVtnjQdjrrEGuRtAsKchN7aVtQXGSBVqvOUGuJaE95/fWtDg/fWVvd5vxrjUqNAYkFzgYV9RPnYve39IFaxa3+qHfqcPD++9//ji+++AILFy7EhRdeiKeeegpVVVV49tln8cADD3RFG4mIiKibi8ZiqKlxi6uO2x0uRKMxSZ3c3Ox4j3ZRfOXxXG7xRdQjCIIAm6cGm+3bsTERrDc7dux2/rUuOz++PVdRPwy0JvbALuyHUmMhFHJOHaH9S4eD94cffoh//etfmDBhAi6++GIcddRRGDhwIPr27Ys33ngD559/fle0k4iIiLqRlntp2x0uhMPS7XuytRoUF1tQUhRffTwvL4dzMYm6sXA0gu01ldhs3yH2Ym+278Bmx3Y0BPxtnlest2Cgta9kePgBRf1gyjPw/zxRQoeDd11dHfr37w8gPp87uX3YkUceiauuuqpD11q4cCEWLlyI7du3AwCGDx+O22+/HVOnTgUAbNmyBTfeeCO++eYbBINBnHDCCXjyySdhtVo72mwiIiLaC4IgoM7tQ3Vi1XGbvSZtL221OgslReZE2LZAp8vjH91E3VBjwI9Njh3YLJl/vQPbnBWIxFqfNqqQK1BuLm0eHl7YNxGy+yJXw/nXRLvT4eDdv39/bNu2DX369MGQIUOwZMkSHHLIIfjwww+h0+k6dK3S0lI88MADGDRoEARBwKuvvopTTjkFa9euRb9+/TB58mQceOCB+PzzzwEAt912G0466ST88MMP3B6AiIioCwmCAI+3HtXVNaiyxYePBwLSBZFUKiWKCxNBm3tpE3UrgiCgxleHjfbt8YCdWDl8s30HqtyONs9L3f86tRe7n7kUWUoueEi0p2RCW6setOGxxx6DQqHAtddei88++wwnnXQSBEFAOBzGo48+itmzZ+9VgwwGAx566CGUlZVh6tSpcLvd4krpXq8Xer0en376KSZOnNiu6/l8PhQUFMDr9XLFdSIiojYIggCfr1EM2dW2Gvj9AUkdpUKBwkITihNDx80mPT8IJ8qwSDSCHa5qcf51Mlxvsm/f5fZcpjy9ZNXwZC92sc7C/9dE6Pwc2eEe7+uvv158PHHiRPzxxx/4+eefMXDgQIwaNWqPGxKNRvH222+jsbERhx9+OLZs2QKZTAa1unmFU41GA7lcjm+++abdwZuIiIhaV1/fiCpbTTxoVzvR0NgkOa5QyGG1GMVVxy1mAxQK/kFOlAmNAT82O3aKvdebEuF6m7MC4Wik1XPkMjn6mIoxqLAvBiaGhQ8q7IcB1r4w5Bbs43dAtH/bq328AaBv377o27fvHp+/YcMGHH744QgEAsjNzcXSpUsxbNgwmM1m5OTk4Oabb8Z9990HQRBwyy23IBqNwmaztXm9YDCIYDAoPvf5fHvcNiIiot6kobEJ1YlVx6tsNaivb5Qcl8tlsJiTQdsMq8UIpZIrDxPtK4IgwOmrxSb7jg4ND9eq1BiQCNWpc6/LLWXQqLhNH1F30K7g/cQTT7T7gtdee22HGjB48GCsW7cOXq8X77zzDmbOnIkvv/wSw4YNw9tvv42rrroKTzzxBORyOc4991yMHTt2l8Nf7r//ftx1110dagMREVFv1OhvQrWtJnFzwuuVDjuVyWSwmPUoLor3aBdajVCp9vozeSLajdTVw5PDwpMhuz7Q2OZ5RnF4uDRkl+itHB5O1M21a453eXm55HlNTQ38fr+4mJrH40F2djYsFgu2bt26Vw2aOHEiBgwYgGeffVYsc7lcUCqV0Ol0KCwsxA033ICbbrqp1fNb6/EuKyvjHG8iIur1/P6AGLKrbTXweOslx2UywGTUi3O0C60mZGVxsSSiruJrahCDdTJkb3ZsxzZnZZurh8tlcvQ1FWOgODw8HrQ5PJxo38rIHO9t27aJjxctWoSnn34aL774IgYPHgwA+PPPP3H55Zfjr3/96143KBaLSYIzAJhMJgDA559/DqfTiZNPPrnN89VqtWReOBERUW/V1BRIzNGugc3mhNtTn1bHZNQlerTNKCo0Qa3OykBLiXqvWCyGKrejuffasR1bHDux2b4DDq+rzfOSq4fH51/3xUBrfIGzcnMp1Cr+PyXqbTo8nuy2227DO++8I4ZuID5c/LHHHsOZZ56J888/v93Xmjt3LqZOnYo+ffqgvr4eixYtwsqVK7Fs2TIAwMsvv4yhQ4fCbDbj+++/x+zZs3H99ddLXpuIiGh/0dQURLW9JjFPuwZuT/o6JkajLt6jXWRh0CbqRP5QANucFS16r3dgi30HmsLBNs+z5BvFLbkGFvbFIGu8J7tYb+H2e0T7kQ4Hb5vNhkgkfeXEaDQKh6PtRR9a43Q6MWPGDNhsNhQUFGDUqFFYtmwZJk2aBCDekz537lzU1dWhX79++Mc//iFZVZ2IiKg3a2oKwmZvnqNd524laBsKUnq0zdBoGLSJ9lRycbNkqE6dg11ZZ2/zPJVCiXJLWbz32toXAxL3Awv7Il+buw/fARF1Vx3ex/ukk05CVVUVXnjhBYwdOxYA8PPPP+OKK65ASUkJPvjggy5p6J7iPt5ERNRTNDUFUG13odrmhM1W02rQNugLUFxkFm8aDadXEXVUMBzCtppKbHEkg/UO8fGuFjfT5+QnAnVyYbN4wO5jKoZSwYUJiXqTjO/j/dJLL2HmzJkYN24cVKr4giyRSARTpkzBCy+8sNcNIiIi2l/4/YGUHu3Wh47r9fkpQ8fN0GoZtInaQxAE1DZ4Er3W2+N7YCfC9U5XNWJCrNXz5DI5yoxFGFTUTzoHu7AfjLm6ffsmiKjX6HDwNpvN+Oijj7Bx40b88ccfAIAhQ4bggAMO6PTGERER9SbiquP2thdDS+3RZtAm2r3k1lxb7DslC5tttu+Ax5/+YVZSniYHAxLDweOrhvfBwEIubkZEXWOPx8T069cPgiBgwIABUCo5tIaIiKilRn8TbLbmHu2W23sB8TnaRUVmFBeaUVRkhpZDx4laFe+9jgfr5NDwLY6d2F5ThWgbW3PJZDKUGgoTq4bHQ/aARC+2Jd/Ixc2IaJ/pcGL2+/245ppr8OqrrwIANm7ciP79++Oaa65BSUkJbrnllk5vJBERUU/Q0OhPbO0V79X2ehvS6hiNOhQXmsXtvThHm6hZKBLGDldV84rhid7rLY4dcDe23Xsd35qrj9iDnRwiXm4pgzZLsw/fARFR6zocvOfOnYtffvkFK1euxAknnCCWT5w4EXfeeSeDNxER7RcEQUB9Q3PQttlr4KtPX5TJlNjeq6jIjCIrVx0nEgQBrno3tjh2xhc0S97bd2CHq7rN3msAYu/1AGsfsQd7oLUvCnVm9l4TUbfW4eD9/vvvY/HixTjssMMkP+CGDx+OLVu2dGrjiIiIugtBEOD1NcSDtj0ethsamyR1ZDLAZNSL87O5jzbtzwLhILY5K8Uh4cmAvcWxE15/+rSLpHjvdd94D3bKEPFySxmy2XtNRD1Uh4N3TU0NLBZLWnljYyM/aSQiol5DEAS4PT7YbK74Ymj2Gvj9AUkduUwGs9kgDhsvtJqQlaXKUIuJ9j1BEODwuqRDwxP3lbX2NlcOF+deW+O91wOsfTCosB8GWPuw95qIeqUOB+9x48bhv//9L6655hoAEH8wvvDCCzj88MM7t3VERET7SCwmoM7tFedn2+w1CARCkjpyuRxWiwFFiTnaVosRKhUXGKXerzHgx1ZnhRist4o92DvRGPS3eV6+NjcRrPumzMHug37mUs69JqL9Sof/WrjvvvswdepU/P7774hEInj88cfx+++/47vvvsOXX37ZFW0kIiLqdNFYDC6XOzF03AW7w4VQKCypo1QoYLUaxaBtMRugVCoy1GKirhWNRVFRa8Nm+05sdSYDdgW2OHbA5qlp8zyFXIE+xiIMSJt73QemPAN7r4mIsAfB+8gjj8S6devwwAMPYOTIkfj0008xduxYfP/99xg5cmRXtJGIiGivhSMROJ11sNldsNlr4HDWIhKRLuKkUilRmBK0zSYDFAp5hlpM1PkEQUBdoxdb7PFFzbY6d2KLPR6yd7iqEIqE2zzXmKfHAEsZBqQMDx9gjfdeZyk5xYKIaFdkgiAImW5EV/L5fCgoKIDX60V+fn6mm0NERPtIMBSGw+GCze5Cta0GNa46xGLSX3kadRYKC01i0DYaCiCXM2hTz+cPBbDdWYEtiR7rLc4KbE0E7V1ty6VRqVFuKY2Ha0tZvBfbEg/Yuhz+HUVE+4/OzpF7NDEtGo1i6dKl+N///gcAGDZsGE455RQolZznRkREmdHUFIyvNp7o0a6t86DlR8vZ2Zr4QmhWM4qKTNDr8jkMlnqsaCyKylo7tiR6rbc4d2JrYt51lduxy3NLDIWJOdd90D8xPHyAtQ9K9FZ++ERE1AU6nJR/++03nHzyybDb7Rg8eDAA4J///CfMZjM+/PBDjBgxotMbSURElCq5h7bd7oLN4YLdXgO3J317ovz8nMS2XmYUF5qQl5fDoE09SnLP663OnYl9r3diq6OiXUPDddn5KcG6D/pb+mCAtQzlljIubEZEtI91OHhfdtllGD58OH766Sfo9XoAgNvtxkUXXYQrrrgC3333Xac3koiI9m+CIMDt9sV7sx3xXu3GFntoA4BBny8G7aJCE3JytBloLVHH1Tc1YquzQgzY2xLDxLc6d8LX1NDmeWplFsotZRhgLUsE6+ZebGOubt+9ASIi2qUOB+9169ZJQjcA6PV63HvvvTj44IM7tXFERLR/ikZjcNW6xWHjdkctgsEWW3vJZDCZ9PH9swtNKLKaoNGoM9Riot0LhkPY4aqKrxSeHBaemHvt9NW2eZ5MJkOJ3oqBhX3R31ImDg3vbylDicEKhZwr7RMRdXcdDt4HHHAAHA4Hhg8fLil3Op0YOHBgpzWMiIj2H+FwBHZHLeyOeNB2OusQiUpXHFcqFbBajPGgbTVxD23qlqKxKKrqHIne64pEuI4PD6+otSEmxNo815Snj/dWW/qgv7UMAyzxnut+5hJoVPxQiYioJ+vwXyz3338/rr32Wtx555047LDDAAA//PAD7r77bvzzn/+Ez9e8UiZXESciotY0NQVgd9SKi6G5aj1oucmGuOK41YTCQjNMJh0UXPSJugFBEODwusRgLYZsZwW211Tuct51jjpbHBbe39oHAxI92P0tZSjIztuH74KIiPalDm8nlrrSZXKBmuQlUp/LZDJEW/RWZAK3EyMiyixBEOD1NTQvhOZwwetNn7Oam5uNopSgrdflcSE0yqi6Bi+2One2GrD9wfQ1BpKylCr0NZWIw8KT4XqAtQ8s+UZ+XxMR9QAZ307siy++2OsXJSKi3is5P9tud8V7tR0uBALBtHoGfT4KrSZxIbTc3OwMtJb2d76mBmx1VmBbIlBvE4eIV8Djb3u/a7lMjjJjEfpby+IB29JHDNqlnHdNREQtdDh4H3PMMV3RDiIi6qGCoTAcifnZdoer1fnZCoUcFrMBhdb4QmiFFiPU6qwMtZj2N40BP7bVVLYI1juxtaYStfXuXZ5brLfEVw239EG5pRQDrH1QbilDX1MJspSqffQOiIiop9ujVWkCgQDWr18Pp9OJWEy6SMjJJ5/cKQ0jIqLuqaHBLw4Zt9tdqK3zptVRq7MSQ8ZNKLQaYTbpoVCwB5C6TlMogO2JcN0csCuxzVkBh9e1y3PN+Qb0t8T3t+5vKUO5Ob49Vz9LGbK53zUREXWCDgfvTz75BDNmzIDLlf5LrLvM6yYios4Ri8VQW+dN9GbXwuFwoaGV/bPz83PivdnW+BxtHednUxeIh+sqMWBvc1ZgW008XFe7nbs815BTgPJEuB5glYbsPG3OPnoHRES0v+pw8L7mmmtw1lln4fbbb4fVau2KNhERUYYEgyE4nHWwO1xwOGvhcNYiEpF+oCqTyWAy6pqHjVuNyMnWZqjF1Nu0DNepIXt34bogOw/l5jL0t5Q2B+vEvS6HC6wSEVHmdDh4OxwOzJkzh6GbiKiHEwQBvvrGeMhOzNGuc6cvJpWVpYLVYkSh1YhCqwkWs4H7Z9Ne8YcC2NFKuN7urESV27HLc/O1uWKY7mcuTYTreNA25BRwpAUREXVLHf7L6cwzz8TKlSsxYMCAvX7xhQsXYuHChdi+fTsAYPjw4bj99tsxdepUAIDdbsdNN92E5cuXo76+HoMHD8Y//vEPnHHGGXv92kRE+5toNIoal6c5aDtdaGpKX21cHDZuiQdtvT6fYYY6rDHgx3ZXlTgcfLuzUhwWbvPU7PLcZLguN6f2XDNcExFRz9Xhfbz9fj/OOussmM1mjBw5EiqVdEXPa6+9tt3X+vDDD6FQKDBo0CAIgoBXX30VDz30ENauXYvhw4dj8uTJ8Hg8WLBgAUwmExYtWoQ77rgDP/30E8aMGdOu1+A+3kS0v/L7A3A4a8Vh4zUuN6JR6YKYcrkMZpNenJ9ttRiRnc3FpKh96psasa2mAtucldheUykJ2Ltb0KxluC63lIpDwxmuiYgo0zo7R3Y4eL/44ou48sorodFoYDQaJb8YZTIZtm7dulcNMhgMeOihh3DppZciNzcXCxcuxIUXXigeNxqN+Oc//4nLLrusXddj8Cai/UE0FkNdnTfRkx1fBK2+wZ9WT6PJQqHFBGti2LjZpIdSydXGqXWCIMDd6MP2mgpsr6lK9FhXYkciZLt2sxWXIacA/SylzeHaXIpySyn6mcugz+FICiIi6r46O0d2eKj5P/7xD9x111245ZZbIJfL97oBSdFoFG+//TYaGxtx+OGHAwDGjx+PxYsXY/r06dDpdFiyZAkCgQAmTJjQ5nWCwSCCweahkz5f+nxFIqKerqkpKC5+ZnfUosZVl7YIGgAY9AWwWo3xOdoWIwoKchl2SEIQBDi8LjFYb3dWJIaIx3uxfU0NuzzflKdHubkU/RLBur+lDP0spehnKuGCZkRERAkdDt6hUAjnnHNOp4XuDRs24PDDD0cgEEBubi6WLl2KYcOGAQCWLFmCc845B0ajEUqlEtnZ2Vi6dCkGDhzY5vXuv/9+3HXXXZ3SNiKi7iAWE1Dn9kqCts+XHobii6AZYLXEh4xbLAaos1StXJH2N5FoBFVup9hTvb2mShwSvsNVhaZQYJfnF+nM6GcuRb9Ej3VfU4m4uBm34iIiItq9Dg81v/7662E2m3Hrrbd2SgNCoRB27twJr9eLd955By+88AK+/PJLDBs2DNdccw1+/PFH3HfffTCZTHj//ffx2GOP4euvv8bIkSNbvV5rPd5lZWUcak5EPUZTIAins04M2s6aOoTDkbR6el0erJZ4b7bVaoKee2fv15Irhe+oqcJ2VzxcJ1cOr6i1IRJLHxGRJJfJUWYsQj9ziRiu+5lK0c9Sir6mYmizOO+fiIj2Lxmf433ttdfiX//6Fw488ECMGjUqbXG1Rx99dK8aNHHiRAwYMAB///vfMXDgQPz6668YPny45PjAgQPxzDPPtOt6nONNRN1Z6txsh7MWjppa+HyNafVUKiUsZgMKE8PGLWYjNJqsDLSYMqV5vnW8l3p7TWq4roLdu+uVwtXKLPQxFcWDtbkMfc0l8ZXCzaUoNRZBpeAWcUREtH/avHkzbDYbBg8eDIvFAgD4+eefMW7cuMzN8d6wYYO4ovivv/4qOdYZPS2xWAzBYBB+f3xRoJZD2hUKBWKxWGunEhF1ew2NfjicdXAmerNbW2kcAHQFeeJw8UKLEXp9AeRy9mb3dtFYFFV1Dux0VScCdnU8XCd6sHc337ogOw99TSViz3U/c0nieSmKdOZOXZuFiIiouwgEAmhsbITRaBTL3n//fWzatAmnnnoqBg0aBABYtWoVrrzySpSUlOA///mPWPfSSy/FV199hcWLF+Pss88GAHg8nk5tY4eD9xdffNFpLz537lxMnToVffr0QX19PRYtWoSVK1di2bJlGDJkCAYOHIi//vWvePjhh2E0GvH+++9j+fLlki8SEVF3FYlEUeNyx4eLO2vhcNah0d+UVk+tVsFiNibmZxthMRugVrM3u7dqDPgTPdZV4n0yaFfW2RGOpk8rSFVYYI4HanM8YCd7r/uZS6DPKdhH74KIiKjr1NXVYe3atVAqlTjmmGPE8rvuugu//PILbr31VowbNw4A8Mknn2Dq1KkYM2YM1qxZI9adP38+vvzyS/Tp00cM3gCwbt26tFBdXl4Om80GpbI5Hvfr169T31NGx5U5nU7MmDEDNpsNBQUFGDVqFJYtW4ZJkyYBAD766CPccsstOOmkk9DQ0ICBAwfi1VdfxbRp0zLZbCKiNIIgwOttgKMmPifb6axDba0HsRazeWQyGYyGAlgszUG7IJ8rjfcmsVgMDq8LO1zVKcG6OWjvbguuLKUKfYzF6GMqTumxLkFfcyn6mIqRzfnWRETUzdXU1MDlcqGsrAy5ubkAgD///BOLFi2CyWTCNddcI9Y95ZRT8P3332PRokWYOHEiAODHH3/E1KlTMXr0aKxdu1as+8UXX+DLL7/EOeecIwZvvV4PIL2HetKkSejTpw9KSkrEsqFDh+Ljjz+G1WqV1H3llVfS3oPZbN7zL0Ar2j3H+/TTT2/XBd977729alBn4xxvIuoKfn8gHrBTbqFQOK1etlYjDhm3Wowwm/RQqTiXtqdrDPixs7YaO2ri4Tp+q8ZOVxV2umwIRkK7PN+QU4A+yUBtKkZfc3wRs37mUhTqTFDIubc6ERFlTiQSQVNTE/Ly8sSyDz74ABUVFTj11FPFMPv111/jtttuw6BBg/D888+LdQ888ECsX78ey5Ytw+TJkwEAH3/8MaZNm5YWpidMmIAvv/wSb775Jv7yl78AiPdKX3DBBRg2bBiWLFki1n333XfhdDoxadIkcaercDgMv9+PvLy8Tp1SlbF9vAsKOHyNiPZP4UgELpc7vtJ4ImQ3NPjT6ikVCphMeljMBljMBlitRuTmaNmb3QNFY1HYPS5JoN5RUyX2Yu+u11ohV6BYbxHnWvcxFaOfqRR9zcXoaypBQXbeLs8nIiLqTHa7HWvXrkVubi6OOuoosfzmm2/Gpk2bcO+992Lo0KEAmrd0PvbYY/H555+LdW+77TasX78egwYNEoN3Y2MjvvzyS3i9XsnrmUwm6PV6yW5TAwcOxJVXXokBAwZI6j711FOIxWKSod2jR49OW08MAM4444y0MpVK1SOyaruD98svv9yV7SAi6hZiMQEej08M2E5nLercPrQ2OEivz48H7ETQ1hsKoODiVT2CIAjw+H3Y6bJhp6s63nvtqkKFy4adtdWoqLUhFEkfwZBKn5OPPqZEj3Xivk/icbHBylXCiYio0/z+++9wuVwYO3asOHR79erVeP311zFgwABce+21Yt3DDjsMGzZswBdffIFDDjkEQHyI9nnnnZcWpj/55BOsX78ef/3rX8XgnezddbulHzIff/zxGDhwIHQ6nVg2ZswYvPXWW5Lh3ADw2WefpXU8DBo0CAsXLkx7b6k7WPVm/KuAiPZbgiCgvsGPmpo6OGvcqHHVocblbnXP7OxsDazm+JBxi9kAs0mPrCxVK1el7qIpFEBFrQ07XTbscFXFA7WrGjtrbdhRU4X6QPq2balUCiXKjEXoaypBHzFUF4vP2WtNRETtUV9fj8rKSmRlZUl6ex977DHYbDbMmTMHhYWFAIB33nkHt9xyC4466ihJx+fUqVOxc+dOrFq1SgzTmzdvxhNPPIFjjz1WErwDgQD8fr9kznNJSQnGjBkjDs9Ouummm1BfXy+GbiA+9NvlcqX1Ire2bbTVasU555yTVs7RfukYvIlov5Gcl13jcifu6xAIpM/FVSoV4nBxi9kAiyU+ZJy6l0g0gmq3Eztr473WFbXV8d5rVzV2uKrh9NXu9hqWfKMYqPuYilGWsqhZkc7MudZERITq6mp4PB4MHDgQWVnxXUfWrFmDTz75BIMGDcJZZ50l1p04cSIqKirw0UcfiSH79ddfx9/+9jecdtppkvWw5s+fj507d+LMM88Ug3ckEsGWLVvQp08fSRsGDBgAtVot2VZ51KhRmDt3LgYPHiypu2TJEiiVShQXF4tlRx99tGTF76QLLrggrUyj0UCj4UKenY3Bm4h6pWAw1Bywa9xwuurQ2Ji+lZdcLoPRoIPZpIc5MWxcp8vnntndQHJ18OTQ750pw8B3uqpR7XYiGovu8hp5mpx4b7WxGH3MxeJq4X1NxSgzFkHLFcKJiHq1hoYGOBwOaDQayXDoxx9/HG63G3PmzBGHVi9atAiPPPIIJk+ejPvvv1+sO2TIENTX1+PPP//EAQccACC+H/Q//vEPnH766ZLgvWnTJuzcuRO1tbVi8DYYDNDr9Wlh9qKLLkJjYyNMJpNYdtxxx+Gbb74Rg3hS6vDwpOHDh+O+++5LK0+2kTogEgICHqDJAzS547ea6k59CQZvIurxwuEIXLUesTe7pqYOXl9Dq3X1unyYzfrEcHEDjIYCKJXs1cwEQRDgqnfHg3RtNSpcNvHxTlc1Kuvsu51nnaVUocxYhDJjkRiqU4eF67LzOdyNiKiHEgQB27Ztg9frxahRo6BQxH9ff/fdd/j8888xatQonHzyyWLdI488Em63G1999ZUYZp988knceuutuPjii/HSSy+J177ttttQX1+P8847TwzeHo8Ha9asSdu/2WQyISsrC01NzR/gjxw5Epdccom4pVXSq6++CrlcLhm6fc4557Q6HPuuu+5KK7NYLLBYLB35MlFSNJIIz+5EgE7cAh7A724RrFOON7mBcPqiuQi2a/OvdmPwJqIeJRyJoLbWC5fLHQ/ZLjfcHi9a2xgxLy8HlkRPtsVsgMmo47zsfSg1WKfeKuvs4uOmUGCX10iuDp7stS4zFqGPqUh8bC0wderWIURE1HlsNhsqKipgMpnQv39/AEAoFML9998Pn8+H+++/Xxy6vWDBAjz11FM499xzcfvttwOIj3xK9ho7HA4xkK5cuRK33XYbLrnkEjF4y2QybNiwAfX19XC73WLw1ul0yMnJSftdceGFFyIajSI7O1ssmzZtGv7zn/+kDfPesmVL2oe4Rx55JI488si09zxhwoQ9+lpRQjScEphbBmRPSnhu5Xmo9U6XdpPJAHUBoNXFb0IOgE/27popGLyJqNuKRKKorfMkerGTIbv1FcazszWwmAwwm/UwJ+61GnUGWr3/iAfrukQvdSJU1zaH6spaG5rCwV1eQyaTobDAJM6vjgfrYvRJ9GIX6y1QcnVwIqIuJwgCYrGY2KscCoXwzTffoKGhQQy3ALB06VJ8+umnmDRpEk4//XQA8cXDRo0aBZ/PB5vNJobp+fPn48EHH8ScOXPwyCOPAAAUCgXuvPNOAMAtt9wCs9kMIL5n8h9//IEdO3aIr6VQKGC1WiGXyyW9zQcddBAuu+yytOC7ZMkSZGVlSeY2X3nllbjqqqvS3u9TTz2VVtavX7+03m6AC4V1iCAAoUYg4E0EY29zr7JY5gGavC3uE7fWep47Sp0HaHRAtj5+nwzSWn3K45TnyTqaAiB1bRefD7i687Yp418zRNQtSEJ2Imi3FbK1WnV8TrZJD5MpHrS5+Fnni8aicHhrUZnopa6ss8eDdV08VFfW2tsdrJPDwVveivVWqFVZ++gdERH1PrFYDF6vF42NjSgtLRXLV61ahd9++w1jxozBmDFjAAC1tbW4+uqrEQgEsHTpUrHu9ddfjwULFuD222/HbbfdBiC+P/Pxxx8PIB7CVar4iLHvv/8ezzzzDHJycsTgnZ2dje3btwOIB+hkb7PVakWfPn3E7a+AeJi+5pproNVqoVQ2R5Fzzz0X48ePT+ttttvtae95ypQpmDJlSlr5CSeckFbG0LwHIiEg6EsJy6kh2SsN0GKwTnkcS98dpsM0BS1Csy79uViWrKuPn9dNP7Dvnq0iol4tHImgrs4rhmyXy93mXtlajTrRi50M2XrkZGv5i7QThCJh2NxOVNTZUZkyDDwZsKvdDoSju/7lKZPJUKQz7zJYZyk5vJ+IqC3btm1DdXU1+vXrJy7+5XK5sHDhQgiCIA67BoC7774bH3zwAa699lrMmDEDALBjxw70798f2dnZaGxs3ibxxRdfxPPPP4+7775bDN4A8NZbbwEAwuGwGKaVSiUikQi8Xq9YLy8vD8OGDUNBQQGamprEupMmTUJ2djaOOOIIsa5CocAPP/yAvLw8yR7Pc+bMwZw5c9Le8xNPPJFWVl5ejvLy8vZ/4aht0QgQ9EqDciD1uaf1QJ0Mz53R6yxXpoTjgpR7fYuwXCDtcW6t57mXYPAmoi4VDIbgqvUkbm64aj3weHytzsnWatQwmfSwmBmyO0NjwI/KOgcq62yoqnMkAnXzHGu719Xqhx2pknOsSw2FKDUWodRQiDJjoTjHukhvYbAmov2Cz+eDx+OBXq9HXl4egHhA/uijj6BQKHD++eeLdefPn4/vvvsOV1xxBSZOnAgA+O233zBhwgTk5uZi27ZtYt1bbrkFS5YswRNPPIFrrrkGAOD1enH77bcjOztbErwrKirw888/Y+fOnWJZcmGwWCyGaDQqDhUfPXo0pk+fLs6tBoCCggLMnz9fbH/SzTffjOuuuw56vV4sUyqV+O2339K+DpMmTcKkSZPSyg899NDdfQmpvSIhaWAO+Fo89rQI0i2C9d7OdU5S5zcHYU1Bi7CcCMnq/JQh2ynzo1XZ8TnTJGLwJqJO4/cHJAHb5XLDV9/Yat1kyI5v48WQ3VGxWAw19XWoqrOjotaOqjoHqtzxnuoqtwOVtXZ4/L7dXkejUidCdWH8Xnwc77Eu1Jm4lzUR9SixWAyNjY1Qq9XiXOPa2lr88MMPyMrKkoTGhQsX4rfffsPFF1+Mgw46CACwdu1azJw5E1arFcuXLxfrnnXWWfj000/x6quvir3NO3fuxMyZM1FcXCwJ3t9++y3eeecdHH300WLw1mg0cLlcCASki0qWlpZiwIAB0Gqbp0yZTCZcfvnlyM/PhyAI4u/G5F7QqStmGwwGBINB8b0m/e1vf8Pf/vY3SZlSqcTs2bPTvmap21nRXorF4sFXDMu+5t7noK/FfUpgTj0WTt/+dI9k5aYE5gJpgE4G5l0Fav7+71QM3kTUYYIgoKHBL4bsGlf83u9vfYXq3NxsmI16mEw6mIx6mIw6ZGdrGLJ3wR8KoLrOgWp3oqe6zoGqxDDwqkT57rbaAoCC7DyUGgpRYrCiRF+IMmORGLLLjIUw5Rn470BEGSEIAgKBAFQqlTjXt6amBuvXr4dWq8X48ePFugsXLsS2bdtw2WWXiXsUf//997jxxhvRv39/vPbaa2LdI444Aj/88AP+/e9/i4uCrV+/HieeeCKGDRsm6cVdunQpli9fjsMOO0wM3rFYDBs2bIDb7Za0Ny8vD2q1GqFQSCwzm82YPHkyrFarpO5FF12Eo48+GkcffbRYVlZWhl9//TWtt/mRRx4RFx5LKigowHPPPZf2NUudr50kk8nSQjftIUGID7MO+NIDc7Istec5NSynnrOb0WTtps4HNPkpoTm/RZDW7SJYd9+5zvsr/msQ0S5FYzF4PfVw1XpQW9c8ZDwYbD306QryJAHbZNRDo+EfBKmSi5ZV1cV7p6sSobra7YyX1TlQ1+jd7XXkMjmKdGaUGKyJcB3vrS7RF4pledqcffCOiKg3CwaDaGxsRH5+vhiQq6ursX79euh0Ohx22GFi3UcffRTV1dW49tprxUWyPvvsM9xxxx048MAD8fTTT4t1R4wYgd9//x0rV67EMcccAyDeU3zaaadh/Pjx+Pbbb8W6L7/8MlavXo2jjz5aDN5+vx/fffcdfD7p6J6cnPjPvfr6erHMZDLhoIMOEremSjr33HNx2GGHYcSIEWLZAQccgGXLlkmGXQPxFbNbbklVVlaGZcuWpX3Npk+fnlaWlZWF4cOHp5VTJ4nFgHBjSgBO6XEWn/ukoTngkx4P+oBYtHPao1C1EpxbBGgxSOsSPdApx9nj3OsweBORKBgKozYRsGsT87Lr3D7EYrG0unK5DHp9AcyJcG0y6WA06KBS7d8/VgRBgMfvk4ToZLiO9147YPfUINqOX+w56uz4/OqUYeDxQF2EEoMVhTozVPw0m4gSKisr4ff7UV5eLi6EtXHjRqxevRplZWWS3te5c+eitrYW99xzj9hb+8477+Cee+7BhAkT8Pjjj4t1y8vLYbPZsHbtWowePRoA8Omnn+Liiy/G1KlT8dFHH4l1n332WWzcuBGnnHKKGLy9Xi++++67tNE1yaHVDQ3N81HNZjNGjBghmZcMxAPy0Ucfjb59+4plo0aNwnvvvSduR5X03nvvQaVSQaPRiGUjR47ETz/9lPY1u/jii9PK8vLyMHny5LTylqGbOokgAJFgcxAO+oBAfRvP6+M9ysH6xLGU+6Cv83qaZfJEaE6G43xpWE7OfVa3PJZyXKXlHGeS4F9sRPshcah4ImDX1nrhqvOgvo352CqVEkaDDiajDkZDAUwmPQz6fHEBl/2FIAjwNTWg2u1AtduZuDla3DvRFGp9yH0qpVyBIr0FJXorSgxWFCfuSwyFKNFbUGIoRL42l8PAiXqwaDSKpqYmyGQysRc2Go3i22+/hd/vx8SJE8Ue5G+++QZfffUVxowZg6lTpwKI/8w5++yz4ff78cYbb4irRS9YsADz5s3DOeecIwnIBxxwAJqamrBt2zZxL+KPP/4Y1113Hf7yl79IgvdLL70Ep9OJa665RgzePp8P69evR1lZmeR9ZGdnA4BkxezCwkKMHj06LSBfdNFFcLvdKCoqEssOP/xwvPvuu5IyAFi2bBlUKpX4tQHiw8Q3bNiQ9rW8/vrr08rMZjNOO+20tPLkYmPUxQQBiATaDsHB+ubALDnWSr3o7qdOtZtc2Rx+U4Nzy5CszmtRnhKas3IYmqnTMXgT9XKRSBQejw+uOq/Yi11b50Eo1Povudzc7Hi4NurEsJ2Xl7NfBMD6psaUEN0yXDtQ7a5BY7B9W2wY8/Qo0VtRmhaq44/N+QYuWka0jyXnFAeDQcmWR1u2bEFlZSX69u0rBtaGhga8/PLLCIVCuOGGG8S6r7/+Oj7++GOccsopOPvsswHEA+v48ePR1NSEP/74Q+xtvuWWW/Dwww/jpptuwoMPPgggPn84Oay6rq5OHM68YsUK3HnnnbjyyivF4C2TyfDhhx8iGAyivr5ebHMoFILD4UBtba3k/el0OqjVagSDQbGsvLwcEydOlAylBoAbbrgBoVBIsqjW5MmTsWzZsrSAvHbtWmg0GvF9AfH9klvbM3nu3LlpZcXFxeJ+z6mMRmNaGe0D0QgQSobi3dySddqq3xn7NadS5yWCcOp9MhDnpdwXtHieEqaVGoZm6pYYvIl6CUEQ0NDYhLo6D2rrvKir86K2zguPt77VLaPkchn0unwYjToxZBsNul45H1sQBLgbfbB54kHa7qlBtdsJm8cJWyJc2zxONATaF6oNOQUoNlhRpDOLobpIZ0Gx3iIOAdeo1F38roh6h7q6Ovh8PhgMBrGn0uv14ttvv4VcLpeEu/feew+//fYbpk6dinHjxgEAqqqqcOONN0KtVuOVV14R6/7973/Hu+++i1tvvRWXXnopAGD79u2t7nd8//3348UXX8Q999yD//u//wMANDU14dprrwUQ721NDjP++eefsWjRIpSVlYnBOysrS1ywK3W/4+RQ6tTXUqlUGDZsGNRqNSKR5tBy0EEH4ZJLLpEsKAYATz75JJRKpeSDgvPPPx+TJk1KW4m6uro67et78skniwuMpfr73/+eVlZaWorS0tK08paLgVEGRELN4TfUAAQbEr3FDSlBuKH5PtSyLOVxZPejsjpEJgOyUkKwOq85OCefS3qXU8qSdTR58WtwOD/1YgzeRD1QKBRGndvXHLLd8ZDdVi+2Wq2KB2ujDqbEvV6XD4Wi5/+Ci8aiqK33oLpFiLa5nbB5asT7QDi4+4shvgp4sd6CYr01cR9/XKK3okhvQZHeguwsze4vRNQD1NTUoLGxEVarVQyJNTU1WLduHbKzs3HEEUeIdd9++21UVFTg1FNPFYcX/+9//8Pjjz8Oq9WKu+66S6x77bXX4scff8S8efPErZRWrVqFKVOmoE+fPli/fr1Y9/zzz8cnn3yCV155BTNnzgQAbN26FdOnT0dxcTGqqqrEum+++Sbeeecd6PV6MXg3NTXhrbfeQl5eniR4u1wubN26FTU1NWJZ8j02NTVJtmgqLS3F4MGDJeE2NzcXZ511FrRaLSKRiLhq9CmnnIKysjIccsghYl21Wo0VK1ZAq9VKtoSaO3cu5s6dK5lrDKDVvZFPPPFEnHjiiWnll19+eVqZ1WpNW0WbuplopDkgh1IDccrjXR0LNaYE6AYgGtr9a3aUUt1GAM6VhuiWNzFU58brZuUyMBO1A4M3UTcWiwnw1TckerCbe7Lb2htbLpNBp8uDwRCfi200FMBg0CGnh27d1Rhsgt1TA5unBnaPM3Ffk1JWA6e3FpF2rkBqzNOjWGdGkd6CQp0Zxbp4kC7WW1Cks6BIZ0aOJruL3xXtb5LDhFv2Wq5ZswZNTU0YO3asGNY2bdqEH3/8ESUlJZgwYYJY97HHHkNdXR1mzZqFwsJCAMBXX32FZ555BiNHjpQM7z399NOxdetWvPrqqzjwwAMBxHuKZ86ciSOOOAKffPKJWPe4447Dr7/+iuXLl4sB+YcffsDJJ5+Mgw8+GD/++KNY99FHH8UPP/yAAQMGiMHbbrfj2WefxbBhwyTB+48//sCqVavgcDjEMrlcDq/XC69XumJ/dnY2tFqtZBHHgoICjBs3DhaLRVJ30qRJMBgMGDZsmFhmsVgwf/58yTxhALj55ptx2WWXoby8XFLX6/VCq9VKfibeeeeduPPOOyXna7VaLFmyBC1NmDBB8m8DxIeEH3fccWl1U0M4dWOCEN83OdQYD7+hxkTwTQ3B9SlhOCU0i49bHOvsXuUklTYedNW5zeFXnRsPxFkpwTnteH5zPXVuvK6y941wI+rOGLyJugFBENDob0JdnQ917ngPttvtg9vtQyTaeqjMztbEg7W+OWTrekgvdjQWhaveHQ/P7hrYPE5JmLZ5auDwuuBratj9xRD/o9eab0KR3twcovXxIF2kt6BYZ4FVZ+Lw716ioaEBjY2NyM7OFofAhsNh/PbbbwiHwzj44IPFuhs2bMCmTZswePBgcRufUCiEZ599FqFQCLNnzxYXt/roo4/w2Wef4aijjhIXbIpGozjnnHMQCoXwxhtviK/3/PPP46mnnsLpp5+O22+/XXy9wsJCNDQ04I8//hCH7D755JO46aabcOGFF+Jf//qXWHfixIlwu9343//+hyFDhgCIz/O96qqrcPrpp0vC3aOPPorKykqceuqpYvDesWMH3nzzTdTW1kqC9//+9z/88ccf8Hg8aV+31O2VgHjo1Wg0iKb8nDEYDBg1ahQGDRokqTt58mQMGDBAMv93wIABuOuuu8Q2Jd1+++24+uqrMXbsWLFsxIgR+PPPP8WFupLeffddtNS/f3+sXr06rfyKK65IK8vPz8fs2bPTygcPHpxWJpfLufBWT5fcMirkbw7JwcZEWWNzCE4Ny6mBOtiYEphTjgnpu3d0CrmyOegmQ3BWSgDu6DGFavevSUTdEoM30T7W1BQUw3Wd2wd34r6tYeIKhRwGfXPvdfy+AFpN9wuRsVgMtQ0eOLwu2D0uOLw1sHtdcHhc8bLEY6evFrF2/pGTo85Gkc6MQp0ZhTpT4t4cD9U6Mwp1FljyDVDup9tqRaNRBAIByGQySaCx2+0IBAKwWCxiuc/nw6ZNm6BWqyULLa1atQoulwtjx44VQ5Xdbscnn3yCvLw8nHHGGWLdt99+G5s3b8ZJJ50kXmPnzp149NFHkZ+fj7vvvlus+8gjj+CHH37AFVdcgUmTJgGIz7GdMWMGcnNzJVsQ3XDDDXjvvffwf//3f+J83B07dmDw4MHIysqS7NN7/fXX44UXXsC8efPwj3/8AwDgdrsxZswYAPHvw2Rv5osvvojHH38ct956K+69914AQCQSEefuXnnllWLw/vbbb/HYY48hGo2KwVsul4vB0O/3i8Hb5XLhl19+EYc7JzU2NqKxsRGhUPOw0OQQ5XBY+n+8f//+8Hq9kp7XPn36YNKkSRg1apSk7syZM+H1eiWLUY0bNw6PPfaYpEcXAJ555hkEg0GMHDlSLJs8eTI2b96M3NxcSd1Vq1ahpSOOOAK//PJLWnlqj3Zqe1M/eEg68sgj08q0Wq24/zLtB6KR5jAcSgblRFgO+1PKEseTZanBubVbuH1rceyxrJz4TZWd0juck+hRzpEOr27rWGpQVna/39VElBn751+qrXjooYcQCoUwa9YscSjgzz//jBUrVmDYsGGSeVfPPPMMmpqacOGFF4oLm/z+++/44osvUF5ejmnTpol133rrLdTX1+OUU04Rh8xt27YNX331FYqLi8U/RoH4lh9erxfHHnusOHeruroaP/zwA4xGo7gKKhD/A9Hr9UqG4tXV1WHNmjXIy8vDoYceKtZdv349vF4vhgwZIu51WV9fj99//x1arVbyB97WrVvh8/nQp08fGAwGAEAgEMDWrVuhUqkkPSB2ux0NDQ0wm80oKCgAEP/D0mazQaFQoKSkRKzr8XgQCASQl5cnDgeMxWJwu90ApCubNjU1IRQKQaPRQK2O/8ISBAF+vx8ymUwyRDASiSAWi0GhUIhbWwmCIPbeKBQKsW7qAmP7Yth1MBQWQ3WyB7uuzoumQOtzjWUyGXQFudDrC2DQF8Cgz4dBX4D8/FzI5c3tTb6P1PmJsVhMHKaZDBFAvGdPEAQolUrx65Pc3kYul0uCWkNDA8LhMHJyciRBoa6uDgCgyM6C0+uCw1uLTTu3orrGBl+0Ce5APRw+F2y1TtiqqhARYlDmN89njDYEIYSikGtVkGvjn9QL0Rhi7gD0OTr0HzJQDNYKfxTKkAzDDxiCA4eNRJHOgiy5EitXrkQsFhNX+gXi39eb126EerAMxXqL2N7FixcjFovhvPPOE78Wq1atwpo1azBy5EgxEAiCgIcffhiCIODqq68WvxZffvklPvvsM4wbNw6nnHKK+Ho33ngjgsEg7rrrLvH/xrJly7Bo0SIcfvjhuPLKK8W6M2fOhMfjwVNPPSX2en7wwQd45JFHcOSRR4oBEACmTJmCqqoqLF68WOyRfe+993Dttdfi6KOPxqJFi8S6Y8eOxe+//47ly5fjqKOOAgC8//77OPPMM3HkkUfi66+/FutOmzYNa9euxccffywuTvXdd99h6tSpGDt2LH7++WfJe/vmm2/wzjvviCF706ZNuPjii3HAAQdIgveLL76IZcuWoaSkRAzeLpcLjz/+OEpLSyXB+7vvvsN7772HY489VvxZFwwG8fXXX0uGXCevsX37dvH7DYj//w0Gg2n7yCcXrkrtpVWr1SguLoZKpUIkEhHrDBgwAEcccYRke6SsrCycffbZyMrKkvwsOOaYYxCNRiXzmmUyGRYuXAiVSiVZYOrss8/GQQcdlLbt0po1a6BUKiULVM2aNQuzZs1K236vtT2Fp02bJvkdkjRv3ry0sqFDh2Lo0KFp5am/K5Jyc3PTQjft5wQhPm84GYCTwTc55Do1HEseNwKhpvTwLOmF9nfNnORUMlkiAOcAWdktHieDcE5KSM5JqZPTIjjnNodt7jZBRF1FyKCnn35aGDlypJCXlyfk5eUJhx12mPDRRx8JgiAI27ZtEwC0eluyZEm7X8Pr9QoABK/Xu8t62dnZAgBh69atYtljjz0mABDOPfdcSV2TySQAEDZs2CCWPffccwIA4eSTT5bU7du3rwBA+OGHH8SyN954QwAgHH/88ZK6w4YNEwAIn3/+uVi2dOlSAYBw2GGHSeoefPDBAgDhww8/FMs+/fRTAYAwatQoSd1jjjlGACAsXrxYLPvmm28EAMKAAQMkdadNmyYAEF566SWxbO3atQIAoaioSFL3zDPPFAAITz75pFi2ceNGAYCQn58vqTtz5kwBgPDPf/5TLKusrBQACEqlUlL3b3/7mwBAuP3228Uyj8cj/vsHg0Gx/KabbhIACDfeeKNYFgqFxLput1ssv+OOOwQAwpVXXil5PbVaLchkMqGiokIse/jhhwWFQiFcdNFFkromk0lQKpXCn3/+KZY9+eSTgkqlEqZOO1H47od1wn8+/kp4bdGHgsFoFhQKhXDrbQ8KC59fIix8fokw8+JZglyhEMaMPUT4eNk3wg8/rhc2bt4hDBp0gKBQKIRvvvlGvO6SJUsEmUwmTJgwQdKGAw88UAAgLFu2TCz773//KwAQxo0bJ6k7fvx4AYCwdOlSseyLL74QAAhDhw4VQpGwUFXnENZs+00Yc+hBAgDhL3MuFW58/X7hwqduEA696kQBgKDIzRKsfz1UvKn76gQAQt5R5WKZ8ayRAgBBplEKI26aKkycd6Fw/oI5wpDDRwkAhPOuvlhY9stXwrrt/xNWrftJACBkZ2dL2nvZZZcJAIR7771XLHM4HOK/Z6prr71WACDceuutYll9fb1Yt7GxUSyfO3euAECYPXu2WBaLxcS6TqdTLJ83b54AQLjsssskr6fVagUAwrZt28SyRx99NP7ezjtPUjf5M+LXX38Vy5599tld/oxYtWqVWPb666/v9c+I8ePHC1qtVvyZKgiC8NVXXwmlpaXCCSecIKl7+eWXCwcffLCwfPlysey3334Tpk6dKlxxxRWSuo8++qhwySWXSL5Xq6urhVtuuUXy/1sQBOGjjz4SFixYIKxfv14s8/l8wttvvy352SUI8Z8dP/zwg2Cz2cSycDgs7NixQ6iurpbUjUQiQiwWE4h6tWhEEAI+Qah3CELtNkGw/y4IFasFYctXgvDHJ4Kw4T1B+Pl1QfjheUH4+nFBWHG/IHxymyB8eIMgvPs3QXhrpiC8drYgvHiiIDxznCA8ebggPDJKEB4YJAh3lwjCbTpB+Ede199u0wnCPaWC8MABgvDoaEFYcKQgPDdZEF45VRAWXSAIb18hCP++ThA+/r/4e/j6CUFY9aIgrH1LEH77UBA2fS4IO1cJgu3X+NehoUYQQn5B4M8AIupi7c2R7ZXRHu/S0lI88MADGDRoEARBwKuvvopTTjkFa9euxZAhQ2Cz2ST1n3vuOTz00EOSXq/OctFFFyEUCkl6NIYNG4YZM2bgsMMOk9Q944wz4PP5JD025eXlOPPMMyVzC4H48D6HwyHu0wkARUVFOOGEEzB69GhJ3UMOOQQWi0VS12AwYPz48Wn7bw4dOhSxWCxtBdaRI0emzcsrKyvDoEGDJO9NrVajb9++aduGGAwGFBYWSnpCFQoFDAaDpF1AfNhgbm6u2Duaeu2WK7jKZDLx1l6pdYVWtsPaVXl7rwvEe4uFlN7jZFk0GpX0tDUFggiHw4hEIvhp7e/4c7MNbo8P336/DuFwGA6HC79s2CjWj0YjiEajsFr0OHDkAdDrCxBucuLVl6MotBpxwuQjUlohSHrvUt9fR9+jIAioDzSixleH+kB8Ebbl67/Bb0I1HF4Xfv05vprwFsdO9Ln6KPH67qpNAICP1q2EtvFXAEDY2ZBoXZwhVwdrgRGbdbWwVdVj0qjxOPHMU1GoM8Pv8uGij8+D2WzGhgebhxBf9etVqN1sw9EjDsXkUfFe2kpFJUpLS9MWHioqKsLgwYMl32sqlQqjR4+GTCaT/Dv1798f48ePl/Q4qlQqTJo0CXK5XPLvOWLECJxxxhmS0R0ymQwzZsyAXC6XfA+PGzcOV199ddqWPjfffDMikYhkfuhRRx2FBx98ULLQEwA8/PDDCIVCkrmwEydOxJIlSyQjQQDgtddeQzgcFuf4AvH9cX/66SdxJEnSp59+ilgsJo5cAeIrITc0NEhGOgDxUTEtHXXUUaioqEgrf+6559LKhg0bJhkKnnT99denlRUVFeH+++9PK2/tZ3VeXh7OPPPMtPKWP7eA+OiNPn36pJW37Dkm2qei4XhvbzjQ+n0kEO/xjbQ8nuwlbmp+3PI+9VhX9xinUqgAVbK3ODve86tKPE72BCd7k5OPVdqU3uPs+PnqnJTrJI4psrinMhERAJmwJ8mlCxkMBjz00EPiHL9UY8aMwdixY/Hiiy+2+3o+nw8FBQXwer1cUKWbEQRBDL2poSESiQdWhUIhlguJoeZAfDGgZKhKDkvPysoSQ5wgCOIQdp1OJ+692tjYCL/fD41GI/kQIrnqrslkglwuR1NTEJVVNlRV2eAPhBGJCHB7fAgEgvB63BAgIC+vQPzjPxAIQIYYzGYD+vYpg16XB72+AMFAI1QqJcxmsxjs/H4/PB4P1Gq1ZHi90+lENBqF0WgU6wYCAXg8HqhUKhiNRvhDAdR4a7G5YhucHhcaYwG4m+pR46uDrc4Jm8uBukYP6oL14tZZsXAUEATIFHLIEouuCTEBiMYAmQwypRxKuQLmfANMuQZYCwwo1Jlh1ZlhLTDBnGeAVWeKP843IEvJRV2IqBsRBCAWiYfVSCARWgNAJHEvKW+jTqt1k8+b4mE40tR8jVhk9+3qbKpsQKVpDrWq7ETwTQnIuyxLCcpKTXqg5oJdRERpOjtHdps53tFoFG+//TYaGxtx+OGHpx3/+eefsW7dOjz11FMZaB11BZlM1mrPlVKpTOu9k8lkaVvFAEjbMzVZNzkHN1VOTo5kfrmvvhEeTz083np4PPVwr/oNbk/bi5wBQGlpCfT6fOh1iZs+HzpdPtRZrf3RYkoryc7OFkcTCIKAhoAfrvo61PjqUFNfB9cmN2p8dXDVJ+/jx5y+WjQEOragTK4mGxaLEZZ8I0z5BlgLjLAWmGDJT9wXxI8Zc5s/nCAi2iOxaDy0RgJAJJgItMHm58lA27IsEmwuF4NvIOVagZQQHEwE4MR98pyuWo16d2QyQKmNB9q0WyIoK5NBWNscgiV1WjueLE88VmrYY0xE1AtkPHhv2LABhx9+OAKBAHJzc7F06dK0YZtAfEGfoUOHpg3/bCkYDCIYbF68KnUlXNr/BIMhMVg33/vg9TUgPsU3nUwG5OflisFaDNgFeVCpdv1fJhqLoq7BC1e9G6761ADtbg7XPjdq6utQW+8We6bbS6NSw5xvgCXfKL0vMMKcl7hPlGVnaXZ/QSLq2WIxIBqMh9BoKCXQhhLlKWXi8WCLAJzyPBpMBNtk/UD8ebRlYA5Jn0fb/sByn1JqmgOvShMPsMrEfWq5eDx7F/V3cVNq46tVMxATEVE7ZTx4Dx48GOvWrYPX68U777yDmTNn4ssvv5SE76amJixatAi33Xbbbq93//33t7rlCfVesZiAhkY/PB4f3J56eL31cCcCdlNT28FWqVCgQJcHXUHipsuDQZePgoI8KJWJFcDFIF2HP7duRW29G656N2obPKhN3Lvq3eJjd6O3w3Oys9VamPL0MOcZ4vf5BpjzDTAmypI90+Z8A/I0OftkRXYiSpEczhwNJW7h5qDb8nEy3Eoep94nArF4XiIcp9ZJ1kseT9ZN3sRzgt0n8KaSK+PhValOhFi19LnkmAZQqFNCrzoRgtUpIbnlMU3zuanlDMJERNSNdbs53hMnTsSAAQPw7LPPimWvvfYaLr30UlRVVUkWFWpNaz3eZWVlnOPdwwmCgKamIDzeenh9DfC2uI9G2x5qmJ2tgV6Xj4L8XGTnaiHPAiLyCBoijXA3euFu9KK2wYvaBjdq6z2JUF2H2noP3H7fHi3gps/JhykvHqCTYbo5XCfKE2U5au3uL0jUWwhCPCzGwon7SCKkJoKqGHATxyKhRN1QfF/gWDhRFpGGXUndcEqATXmcGp7bU556616/KtumVMeDrDJ5y5I+V6jjoVYs06TUTX3c2rGslOCc1XxcDL+JEK3I+Gf6REREe63XzvFOisVikuAMxIeZn3zyybsN3UB8Re3k3s/U8wQCQXi8DfD66uH1NkjCdTjc9oI2MhmgUMsRVcQQQAD1kQa4gnWwNzjhcrhR1+BBXYO3w0O7k+JBWg9jrh7GPF3iPv7YlKuPH0uU63MLoOIfnrQnBCE+XzUWic+ZjUVSHocTwTPluXg8AkRblkWbw6tYJ/k4GXpT64Slx5P1U4+L9cMpx1oE6N0di6Wv3t8jKdXx1ZpTb0pVInhmxRerUmQlAmoi6CpUKQE4UVcMxslrpFw3+Ti1TjLsJo+nBmqFij2+RERE3VRG08HcuXMxdepU9OnTB/X19Vi0aBFWrlyJZcuWiXU2b96Mr776qtVtbTrNjy/F/1iRyeM3yKTPZfJWnqeWtThXrgBkivi9XNF8LLVcpgDk8hbPd3VMmXjc8/+oCgZD8UCdCNWuOg88Xh8a6psQibT9R7kgCGiM+lEX9MDpd6GmqRa1ATdqg254gj4IaF+PVJZSBUOODobcAhhym+/1OQVigDYlgrUpL16uZJDuuGSIFGLxsCVEWzwWmh/HoimPY81lLe/bPJZSngydyfJYpJXrpD5P1o81n59annrN1GtJwnHL60WaX1tySz23ZbiOZGa15O5ArogHSbkq3luafKxUJcoSN7kqJdSm1EseTw284uPdlbc8pkwJv8nwrE6po+41P4uJiIho38lomnA6nZgxYwZsNhsKCgowatQoLFu2DJMmTRLrvPTSSygtLcXkyZO7riEfXNd11+5sMlk8hItBXdki1Cubw75CmR7s2/oQQCZrEfyT5fL0DwFafOgQgwyhaBTBaBjBSBSBSATeiBLeiBoNMS0aY1o0IRtB5CIiz4Mg3/WiX9GwB9FgLSKhWkSCLoRDrvjjUB0EIQoZACtksACQy+XQarXI1mUjR61Btjp+n6PORrZai+yUxzlZWuSotchSqlqfJy0IAOqAcC3gFoC6ZJAXmoeZCoL0efKxpDz1PtYcQNPqtjzW1vOUANvqrT11YilBN/k85XEs1kZ5tJVzoy2OxaTHUsup88iVifCpbP6/L1fGg6dcEf//Lj5vUUehan4uXiPlmHhclV4n+VihTD8unpssSwbaXdVNOZ58zlX1iYiIqJfrdnO8O9tux+YLAvDmBenBJRl8Yq2FoZahJ9bi/D3opUsNLD1ASKaBT2lCvcoMr9ICn9IMn8oMn9KMBqURMdmuP9PRRr0oCDtQEHaiIOKALuxAQdiB/IgTKiG0j94FZZxM1sqHQ22MBkl+YLSrupIPpeTS52mjSlp8UCX58ErZfH7yGorkh1wtQm1aWcoHYpKQq5CelxagW3ldSfvZw0pERES0r/T6Od77nEwGnPdGplvRTJzjKQ3p0UgYgWAj/IFGNAUaEQg1IRBoRCDoRyDUhGDQj2CoKX4LNiEYbkI41IRQOIhQKIBAqAmhYBOC4QCCKeUyIQq5DJBDgAKAAgLkAJSKLGSpCpCl0kOVVQClSgdllg4KlR6KLAPkytzdvI8olFEvsoR6aIV6ZKMRuTI/8uV+FCiCyFYLUOeokKVQIUuZD6VcDxkGp/ckt3rf4jg6MZDIZPHryQBxygFSH7e838UxcWRAskye/lic2pD6HNK6qSMV0qY6tDYFopV6qdeRXE/RfI208uToiJRryRUp10oJhC3DccvREmnlKSMniIiIiIh6OQZvAIIgIBqLIhKLIhqN30seRyPi8Ug02qJuRFIWiUYRioQQDIcQjIQQCIfE54FwCMFwEKFIWDwmPg+HEAgHEYyEEs+D8IeCaAo2wR8K7PGiYK2RAchRFUCXVYaCrDzo1AUwaQ3I1xQgPysPOYocZMlVu72OUqVATo4G+fm5MOgKoC/IR15eTnz18Gwt5HKGKiIiIiIiov0+eMdiMRT/bXymm9FuMpkM2Vnxucut3mdpka/ORa4yB1qFBhq5GipkQSUoIYvJEQsLiASj7doiS63OQm5uNvJys5GbE7/Py8tBfn4u8vJyoM7afTgnIiIiIiLa3+33wVsul0Mmk7UZRFUKJZRyBRQKBZRyZeJekVKmgEKugFJ8rIRGlQW1KgtZShXUKjU0ycdKNdSqrMTzeB21KgsaZRayVFlQK7MS56qRpVRBo1LHFwjL0kCbpYEsKkM0HIO/KQB/YwD+piY0+gPw+5vg9wfQ6G9CU1MQCCJ+SxAAhBAF0LxiuEwG5GRrkZubI4brvNxs5OZmx8tytMhisCYiIiIiItpr+33wBoD1//xvIjhLg7W8i1baFQQB4XAEwWAIgWAofh8INT/3BREIhuBuaoTfHw/XgUD7h5rLZDJotWrkZGuRna1Bdra2udc6L36fk6PtsvdHREREREREzRi8AZjzDe2qJwgCYjEBkUgEkUg0fotGm59Ho4i0DNTBEIKB5POgWB6LdXwxeblMJgbp5H1OyvMcbfyxRqPm/GoiIiIiIqJuYr8P3rFYDF99swaRaEqYjiQWTotEEI5EEU0J2J25+ZpCIYdanQWNOgtqtTp+r0k+z4JWq0a2tjlcazRZre8/TURERERERN3Wfh+8ZTIZ/ti4bY/OUyoV8ZtCAaVSKT5vDtNZ0GjUUCcfi2Xxe5Vyv//yExERERER9Xr7ffKTyWQ49OCRUCjkkgCtSAnUquRzZcpxzo8mIiIiIiKidtjvgzcAjDlwSKabQERERERERL0Uu22JiIiIiIiIuhCDNxEREREREVEXYvAmIiIiIiIi6kIM3kRERERERERdiMGbiIiIiIiIqAsxeBMRERERERF1IQZvIiIiIiIioi7U6/fxFgQBAODz+TLcEiIiIiIiIuoJkvkxmSf3Vq8P3rW1tQCAsrKyDLeEiIiIiIiIepLa2loUFBTs9XV6ffA2GAwAgJ07d3bKF4you/D5fCgrK0NFRQXy8/Mz3RyiTsPvbeqN+H1NvRW/t6m38nq96NOnj5gn91avD95yeXwae0FBAX8YUK+Un5/P723qlfi9Tb0Rv6+pt+L3NvVWyTy519fplKsQERERERERUasYvImIiIiIiIi6UK8P3mq1GnfccQfUanWmm0LUqfi9Tb0Vv7epN+L3NfVW/N6m3qqzv7dlQmetj05EREREREREaXp9jzcRERERERFRJjF4ExEREREREXUhBm8iIiIiIiKiLtSrg/dTTz2Ffv36QaPR4NBDD8WPP/6Y6SYR7ZX7778fBx98MPLy8mCxWHDqqafizz//zHSziDrdAw88AJlMhuuuuy7TTSHaa1VVVbjgggtgNBqh1WoxcuRI/PTTT5luFtFeiUajuO2221BeXg6tVosBAwbgnnvuAZePop7mq6++wkknnYTi4mLIZDK8//77kuOCIOD2229HUVERtFotJk6ciE2bNnX4dXpt8F68eDHmzJmDO+64A2vWrMGBBx6IKVOmwOl0ZrppRHvsyy+/xKxZs/DDDz9g+fLlCIfDmDx5MhobGzPdNKJOs3r1ajz77LMYNWpUpptCtNfcbjeOOOIIqFQqfPzxx/j999/xyCOPQK/XZ7ppRHvln//8JxYuXIgFCxbgf//7H/75z3/iwQcfxJNPPpnpphF1SGNjIw488EA89dRTrR5/8MEH8cQTT+CZZ57BqlWrkJOTgylTpiAQCHTodXrtquaHHnooDj74YCxYsAAAEIvFUFZWhmuuuQa33HJLhltH1DlqampgsVjw5Zdf4uijj850c4j2WkNDA8aOHYunn34a8+bNw+jRozF//vxMN4toj91yyy349ttv8fXXX2e6KUSd6sQTT4TVasWLL74olp1xxhnQarV4/fXXM9gyoj0nk8mwdOlSnHrqqQDivd3FxcW44YYbcOONNwIAvF4vrFYrXnnlFfzlL39p97V7ZY93KBTCzz//jIkTJ4plcrkcEydOxPfff5/BlhF1Lq/XCwAwGAwZbglR55g1axamT58u+flN1JN98MEHGDduHM466yxYLBaMGTMGzz//fKabRbTXxo8fjxUrVmDjxo0AgF9++QXffPMNpk6dmuGWEXWebdu2wW63S/4uKSgowKGHHtrhXKns7MZ1By6XC9FoFFarVVJutVrxxx9/ZKhVRJ0rFovhuuuuwxFHHIERI0ZkujlEe+2tt97CmjVrsHr16kw3hajTbN26FQsXLsScOXNw6623YvXq1bj22muRlZWFmTNnZrp5RHvslltugc/nw5AhQ6BQKBCNRnHvvffi/PPPz3TTiDqN3W4HgFZzZfJYe/XK4E20P5g1axZ+/fVXfPPNN5luCtFeq6iowOzZs7F8+XJoNJpMN4eo08RiMYwbNw733XcfAGDMmDH49ddf8cwzzzB4U4+2ZMkSvPHGG1i0aBGGDx+OdevW4brrrkNxcTG/t4la0SuHmptMJigUCjgcDkm5w+FAYWFhhlpF1Hmuvvpq/Oc//8EXX3yB0tLSTDeHaK/9/PPPcDqdGDt2LJRKJZRKJb788ks88cQTUCqViEajmW4i0R4pKirCsGHDJGVDhw7Fzp07M9Qios5x00034ZZbbsFf/vIXjBw5EhdeeCGuv/563H///ZluGlGnSWbHzsiVvTJ4Z2Vl4aCDDsKKFSvEslgshhUrVuDwww/PYMuI9o4gCLj66quxdOlSfP755ygvL890k4g6xfHHH48NGzZg3bp14m3cuHE4//zzsW7dOigUikw3kWiPHHHEEWnbPm7cuBF9+/bNUIuIOoff74dcLo0SCoUCsVgsQy0i6nzl5eUoLCyU5Eqfz4dVq1Z1OFf22qHmc+bMwcyZMzFu3DgccsghmD9/PhobG3HxxRdnumlEe2zWrFlYtGgR/v3vfyMvL0+cW1JQUACtVpvh1hHtuby8vLS1CnJycmA0GrmGAfVo119/PcaPH4/77rsPZ599Nn788Uc899xzeO655zLdNKK9ctJJJ+Hee+9Fnz59MHz4cKxduxaPPvooLrnkkkw3jahDGhoasHnzZvH5tm3bsG7dOhgMBvTp0wfXXXcd5s2bh0GDBqG8vBy33XYbiouLxZXP26vXbicGAAsWLMBDDz0Eu92O0aNH44knnsChhx6a6WYR7TGZTNZq+csvv4yLLrpo3zaGqItNmDCB24lRr/Cf//wHc+fOxaZNm1BeXo45c+bg8ssvz3SziPZKfX09brvtNixduhROpxPFxcU499xzcfvttyMrKyvTzSNqt5UrV+LYY49NK585cyZeeeUVCIKAO+64A8899xw8Hg+OPPJIPP300zjggAM69Dq9OngTERERERERZVqvnONNRERERERE1F0weBMRERERERF1IQZvIiIiIiIioi7E4E1ERERERETUhRi8iYiIiIiIiLoQgzcRERERERFRF2LwJiIiIiIiIupCDN5EREREREREXYjBm4iIiDpNKBTCwIED8d1332WsDbfccguuueaajL0+ERFRSwzeREREbbjooosgk8nSbps3b85007qtZ555BuXl5Rg/fryk/IsvvsCJJ54Is9kMjUaDAQMG4JxzzsFXX30l1lm5ciVkMhk8Hk/adfv164f58+e3qw033ngjXn31VWzdunVv3goREVGnYfAmIiLahRNOOAE2m01yKy8vT6sXCoUy0LruRRAELFiwAJdeeqmk/Omnn8bxxx8Po9GIxYsX488//8TSpUsxfvx4XH/99Z3eDpPJhClTpmDhwoWdfm0iIqI9weBNRES0C2q1GoWFhZKbQqHAhAkTcPXVV+O6664Tgx4A/Prrr5g6dSpyc3NhtVpx4YUXwuVyiddrbGzEjBkzkJubi6KiIjzyyCOYMGECrrvuOrGOTCbD+++/L2mHTqfDK6+8Ij6vqKjA2WefDZ1OB4PBgFNOOQXbt28Xj1900UU49dRT8fDDD6OoqAhGoxGzZs1COBwW6wSDQdx8880oKyuDWq3GwIED8eKLL0IQBAwcOBAPP/ywpA3r1q3bZY//zz//jC1btmD69Oli2c6dO3Hdddfhuuuuw6uvvorjjjsOffv2xahRozB79mz89NNP7f2nEL3yyiutjkS48847xTonnXQS3nrrrQ5fm4iIqCsweBMREe2hV199FVlZWfj222/xzDPPwOPx4LjjjsOYMWPw008/4ZNPPoHD4cDZZ58tnnPTTTfhyy+/xL///W98+umnWLlyJdasWdOh1w2Hw5gyZQry8vLw9ddf49tvv0Vubi5OOOEESc/7F198gS1btuCLL77Aq6++ildeeUUS3mfMmIE333wTTzzxBP73v//h2WefRW5uLmQyGS655BK8/PLLktd9+eWXcfTRR2PgwIGttuvrr7/GAQccgLy8PLHs3XffRTgcxt///vdWz5HJZB167wBwzjnnSEYgvPnmm1AqlTjiiCPEOocccggqKyslH0YQERFlijLTDSAiIurO/vOf/yA3N1d8PnXqVLz99tsAgEGDBuHBBx8Uj82bNw9jxozBfffdJ5a99NJLKCsrw8aNG1FcXIwXX3wRr7/+Oo4//ngA8fBeWlraoTYtXrwYsVgML7zwghhcX375Zeh0OqxcuRKTJ08GAOj1eixYsAAKhQJDhgzB9OnTsWLFClx++eXYuHEjlixZguXLl2PixIkAgP79+4uvcdFFF+H222/Hjz/+iEMOOQThcBiLFi1K6wVPtWPHDhQXF0vKNm7ciPz8fBQWFopl7777LmbOnCk+//777zFy5EjxeWtfD7/fLz7WarXQarUAgC1btmDWrFm47777MGnSJLFOsh07duxAv3792mwzERHRvsDgTUREtAvHHnusZK5wTk6O+Piggw6S1P3ll1/wxRdfSIJ60pYtW9DU1IRQKIRDDz1ULDcYDBg8eHCH2vTLL79g8+bNkp5lAAgEAtiyZYv4fPjw4VAoFOLzoqIibNiwAUB82LhCocAxxxzT6msUFxdj+vTpeOmll3DIIYfgww8/RDAYxFlnndVmu5qamqDRaNLKW/ZqT5kyBevWrUNVVRUmTJiAaDQqOf7111+nvbcJEyakXdfr9eLEE0/E9OnTcdNNN0mOJYN5amAnIiLKFAZvIiKiXcjJyWlzaHVqCAeAhoYGnHTSSfjnP/+ZVreoqKjdq6HLZDIIgiApS52b3dDQgIMOOghvvPFG2rlms1l8rFKp0q4bi8UANAfTXbnssstw4YUX4rHHHsPLL7+Mc845B9nZ2W3WN5lMYrBPGjRoELxeL+x2u9jrnZubi4EDB0KpbP3PkPLycuh0OknZ/7dzL6Hw7nEcxz/+mg1CuUWRQpFozE6Zcl8oRCNhYZiUiAgLO1tJISEbZUNWdi4Lt8lCRLmUSKMoSrEZhRrn7KYzhv9/nOPhLN6vehbz9Hu+v1+zefo8v8vbth6PR7W1tQoPD9f09LRfjfv7e0m+/wcAAD+FPd4AAHwRi8Wik5MTJScnKzU11ecKDQ1VSkqKTCaTdnZ2vM88PDzo7OzMp05MTIxubm68v8/Pz31mbi0Wi87PzxUbG+vXT0REREBjzcrK0uvrqzY3Nz9sU1ZWptDQUE1OTmp5eVnNzc2/rZmTk6PT01OfjwY2m00mk+ndjxH/RXd3t46OjrS4uPjuLPvx8bFMJpMyMzO/tF8AAP4NgjcAAF+kvb1d9/f3qqur0+7uri4uLrSysqKmpiZ5PB6FhYXJ4XCor69Pa2trOj4+lt1u169fvq/jwsJCjY+P6+DgQHt7e2ptbfWZvW5oaFB0dLQqKyvldDrlcrm0sbGhzs5OXV9fBzTW5ORkNTY2qrm5WYuLi94aCwsL3jbBwcGy2+3q7+9XWlqacnNzf1uzoKBAbrdbJycn3ntJSUkaHh7W6OioGhsbtb6+rsvLS+3v72tsbMzbz2fMzMxoYmJCU1NTCgoK0u3trW5vb+V2u71tnE6nrFZrQDP7AAAYjeANAMAXSUhI0Pb2tjwej0pLS5WVlaWuri5FRkZ6w/XQ0JCsVqvKy8tVXFysvLw8v73iw8PDSkxMlNVqVX19vXp7e32WeIeEhGhra0tJSUmqrq5WRkaGHA6Hnp6eFB4eHvB4JycnZbPZ1NbWpvT0dLW0tOjx8dGnjcPh0MvLi5qamv5YLyoqSlVVVX5L4Ds6OrS6uqq7uzvZbDalpaWprKxMLpdLy8vLPgerBWJzc1Mej0cVFRWKj4/3Xv88+G1+fl4tLS2fqgsAgFGC/nq7iQwAAHyr/Px8mc1mjYyM/PRQ/DidThUVFenq6kpxcXF/bH94eKiSkhJdXFy8e8jcd1haWlJPT48ODw8/3EcOAMB3YsYbAAD4eX5+1vX1tQYGBlRTUxNQ6Jak7OxsDQ4OyuVyGTzCjz0+PmpmZobQDQD43+CNBAAA/MzNzcnhcMhsNmt2dvZTz9rtdmMGFSCbzfaj/QMA8BZLzQEAAAAAMBBLzQEAAAAAMBDBGwAAAAAAAxG8AQAAAAAwEMEbAAAAAAADEbwBAAAAADAQwRsAAAAAAAMRvAEAAAAAMBDBGwAAAAAAAxG8AQAAAAAw0N+5A4erZ6w2KwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First we calculate the usual impedance using the same approach as before\n",
    "# the only difference is now it is computed from mode data.\n",
    "impedance_calculator = rf.ImpedanceCalculator(\n",
    "    voltage_integral=voltage_path, current_integral=current_path\n",
    ")\n",
    "Z_vi = impedance_calculator.compute_impedance(sim_data[\"mode_yz\"])\n",
    "mode_freqs = Z_vi.f.values\n",
    "# We select the first mode and convert to a numpy array for plotting\n",
    "Z_vi = Z_vi.sel(mode_index=0).values\n",
    "\n",
    "# Next we compute the power-current definition of impedance by simply\n",
    "# passing \"None\" in place of the \"voltage_path\".\n",
    "impedance_calculator = rf.ImpedanceCalculator(voltage_integral=None, current_integral=current_path)\n",
    "Z_pi = impedance_calculator.compute_impedance(sim_data[\"mode_yz\"])\n",
    "Z_pi = Z_pi.sel(mode_index=0).values\n",
    "# We compute the power-voltage definition using the same approach.\n",
    "impedance_calculator = rf.ImpedanceCalculator(voltage_integral=voltage_path, current_integral=None)\n",
    "Z_pv = impedance_calculator.compute_impedance(sim_data[\"mode_yz\"])\n",
    "Z_pv = Z_pv.sel(mode_index=0).values\n",
    "\n",
    "# Plot the data associated with the three definitions of impedance\n",
    "f, ax1 = plt.subplots(1, 1, tight_layout=True, figsize=(10, 3))\n",
    "ax1.plot(mode_freqs / 1e9, np.real(Z_vi), label=r\"$Z_{vi}$\")\n",
    "ax1.plot(mode_freqs / 1e9, np.real(Z_pi), label=r\"$Z_{pi}$\")\n",
    "ax1.plot(mode_freqs / 1e9, np.real(Z_pv), label=r\"$Z_{pv}$\")\n",
    "ax1.plot(freqs / 1e9, skrf_z0, \":k\", label=\"scikit-rf\")\n",
    "ax1.set_ylabel(\"Impedance (Ohm)\")\n",
    "ax1.set_xlabel(\"Frequency (GHz)\")\n",
    "ax1.set_xlim(0, 10)\n",
    "ax1.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above plot, we observe that the three definitions of impedance result in different frequency responses. The `scikit-rf` model is based on the work in [4], which also uses a power-current relationship for defining the impedance, which explains the discrepancies observed previously. When using the power-current relationship, the characteristic impedance calculated by Tidy3D is within 1% of the results produced by `scikit-rf`."
   ]
  },
  {
   "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 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": 15,
   "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 = rf.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",
    "# 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",
    "ax2.set_aspect(\"auto\")\n",
    "ax1.set_title(\"Top view of microstrip\")\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": 16,
   "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\">13:35:09 EST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'impedance_calc_with_load'</span> with resource_id           \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-2258019a-80aa-4da9-9c40-179d28b04d87'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:35:09 EST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'impedance_calc_with_load'\u001b[0m with resource_id           \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-2258019a-80aa-4da9-9c40-179d28b04d87'\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-2258019a-80aa-4da9-9c40-179d28b04d87\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-2258019a-80a</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-2258019a-80aa-4da9-9c40-179d28b04d87\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">a-4da9-9c40-179d28b04d87'</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=623335;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2258019a-80aa-4da9-9c40-179d28b04d87\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=126896;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2258019a-80aa-4da9-9c40-179d28b04d87\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=623335;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2258019a-80aa-4da9-9c40-179d28b04d87\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=26544;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2258019a-80aa-4da9-9c40-179d28b04d87\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=623335;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2258019a-80aa-4da9-9c40-179d28b04d87\u001b\\\u001b[32m-2258019a-80a\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=623335;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2258019a-80aa-4da9-9c40-179d28b04d87\u001b\\\u001b[32ma-4da9-9c40-179d28b04d87'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/f89aec3e-3357-4624-9c24-096a87582f12\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=883485;https://tidy3d.simulation.cloud/folders/f89aec3e-3357-4624-9c24-096a87582f12\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "908fa1fd47b442fc8acd534cc50f4352",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:35:17 EST </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.279</span>. Minimum cost depends on task     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:35:17 EST\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m0.279\u001b[0m. Minimum cost depends on task     \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:35:18 EST </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:35:18 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f07c016b84a240c3ab119662db5aee51",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:35:28 EST </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:35:28 EST\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3a31553e06cb478daeb6317596f9d570",
       "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\">13:35:53 EST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">44</span>%, exiting.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:35:53 EST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m44\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\"><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": "bd6604f98b7543549697ba2658c4abee",
       "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\">13:36:11 EST </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:36:11 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:36:13 EST </span>View simulation result at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-2258019a-80aa-4da9-9c40-179d28b04d87\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-2258019a-80a</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-2258019a-80aa-4da9-9c40-179d28b04d87\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">a-4da9-9c40-179d28b04d87'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:36:13 EST\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=868313;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2258019a-80aa-4da9-9c40-179d28b04d87\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=331824;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2258019a-80aa-4da9-9c40-179d28b04d87\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=868313;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2258019a-80aa-4da9-9c40-179d28b04d87\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=384865;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2258019a-80aa-4da9-9c40-179d28b04d87\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=868313;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2258019a-80aa-4da9-9c40-179d28b04d87\u001b\\\u001b[4;34m-2258019a-80a\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=868313;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2258019a-80aa-4da9-9c40-179d28b04d87\u001b\\\u001b[4;34ma-4da9-9c40-179d28b04d87'\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": "449f3a6861f442caa2c8056d8ffc8a9f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:36:19 EST </span>Loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:36:19 EST\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": 17,
   "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(\n",
    "    field_monitor_name=\"propagate\", field_name=\"Ez\", val=\"abs\", f=freq0, ax=ax1\n",
    ")\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": 18,
   "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",
    "\n",
    "# The path for the voltage integral is defined by its center and size along a desired axis.\n",
    "voltage_path = rf.AxisAlignedVoltageIntegral(\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",
    "# 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 barely visible, 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": [
    "## Calculating the Impedance of a Lossy Transmission Line\n",
    "\n",
    "Finally, we may also analyze lossy transmission, which uses the same steps as shown previously. We simply replace the substrate medium with a lossy dielectric defined by a constant loss tangent. We need to add one additional step, which is to use the `FastDispersionFitter` to create a complex conjugate pole-residue model that approximates the constant loss tangent model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fa6e88ac83a7477a98a6fbe9cda6c1c4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use FastDispersionFitter to create a material with an approximately correct loss tangent for the frequency range.\n",
    "diel = FastDispersionFitter.constant_loss_tangent_model(\n",
    "    4.4, 0.025, (freq_start, freq_stop), tolerance_rms=1e-6, number_sampling_frequency=100\n",
    ")\n",
    "\n",
    "# Now update the substrate with the lossy dielectric\n",
    "lossy_substrate = substrate.updated_copy(medium=diel)\n",
    "sim_with_loss = sim.updated_copy(structures=[lossy_substrate, strip])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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\">13:36:23 EST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'impedance_calc'</span> with resource_id                     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-2e3df301-9c1b-4dad-80a9-03ced2f6d20a'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:36:23 EST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'impedance_calc'\u001b[0m with resource_id                     \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-2e3df301-9c1b-4dad-80a9-03ced2f6d20a'\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-2e3df301-9c1b-4dad-80a9-03ced2f6d20a\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-2e3df301-9c1</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-2e3df301-9c1b-4dad-80a9-03ced2f6d20a\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">b-4dad-80a9-03ced2f6d20a'</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=470137;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2e3df301-9c1b-4dad-80a9-03ced2f6d20a\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=571030;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2e3df301-9c1b-4dad-80a9-03ced2f6d20a\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=470137;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2e3df301-9c1b-4dad-80a9-03ced2f6d20a\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=539140;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2e3df301-9c1b-4dad-80a9-03ced2f6d20a\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=470137;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2e3df301-9c1b-4dad-80a9-03ced2f6d20a\u001b\\\u001b[32m-2e3df301-9c1\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=470137;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2e3df301-9c1b-4dad-80a9-03ced2f6d20a\u001b\\\u001b[32mb-4dad-80a9-03ced2f6d20a'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/f89aec3e-3357-4624-9c24-096a87582f12\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=957632;https://tidy3d.simulation.cloud/folders/f89aec3e-3357-4624-9c24-096a87582f12\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "23fde1dd318c40dab34d466127698ab8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:36:33 EST </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.389</span>. Minimum cost depends on task     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:36:33 EST\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m0.389\u001b[0m. Minimum cost depends on task     \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:36:34 EST </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:36:34 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2d98fc6898f146238a8c9f10681be29e",
       "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\">13:36:46 EST </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:36:46 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:36:50 EST </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:36:50 EST\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f157b11b4ee84c3eadf723f38844af9e",
       "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\">13:37:09 EST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">28</span>%, exiting.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:37:09 EST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m28\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\"><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": "717b89dabcc04ba1bd7fb473d3300f05",
       "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\">13:37:14 EST </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:37:14 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:37:16 EST </span>View simulation result at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-2e3df301-9c1b-4dad-80a9-03ced2f6d20a\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-2e3df301-9c1</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-2e3df301-9c1b-4dad-80a9-03ced2f6d20a\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">b-4dad-80a9-03ced2f6d20a'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:37:16 EST\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=758418;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2e3df301-9c1b-4dad-80a9-03ced2f6d20a\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=750050;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2e3df301-9c1b-4dad-80a9-03ced2f6d20a\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=758418;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2e3df301-9c1b-4dad-80a9-03ced2f6d20a\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=817256;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2e3df301-9c1b-4dad-80a9-03ced2f6d20a\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=758418;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2e3df301-9c1b-4dad-80a9-03ced2f6d20a\u001b\\\u001b[4;34m-2e3df301-9c1\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=758418;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2e3df301-9c1b-4dad-80a9-03ced2f6d20a\u001b\\\u001b[4;34mb-4dad-80a9-03ced2f6d20a'\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": "259bb73eb2844d3394a061386df033b9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:37:23 EST </span>Loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:37:23 EST\u001b[0m\u001b[2;36m \u001b[0mLoading simulation from simulation_data.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data_with_loss = web.run(sim_with_loss, \"impedance_calc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After the simulation is complete, we compare the computed impedance to the model from `scikit-rf`, demonstrating a close agreement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Also compare to results from scikit-rf\n",
    "frequency = Frequency.from_f(freqs, unit=\"Hz\")\n",
    "microstrip = MLine(\n",
    "    frequency=frequency,\n",
    "    w=width * 1e-6,\n",
    "    h=height * 1e-6,\n",
    "    ep_r=4.4,\n",
    "    tand=0.025,  # Add an equal loss tangent to the microstrip\n",
    "    t=thickness * 1e-6,\n",
    ")\n",
    "skrf_z0 = microstrip.z0\n",
    "\n",
    "# First we calculate the usual impedance using the same approach as before\n",
    "# the only difference is now it is computed from mode data.\n",
    "impedance_calculator = rf.ImpedanceCalculator(\n",
    "    voltage_integral=voltage_path, current_integral=current_path\n",
    ")\n",
    "Z_vi = impedance_calculator.compute_impedance(sim_data_with_loss[\"mode_yz\"])\n",
    "mode_freqs = Z_vi.f.values\n",
    "# We select the first mode and convert to a numpy array for plotting\n",
    "Z_vi = Z_vi.sel(mode_index=0).values\n",
    "\n",
    "# Next we compute the power-current definition of impedance by simply\n",
    "# passing \"None\" in place of the \"voltage_path\".\n",
    "impedance_calculator = rf.ImpedanceCalculator(voltage_integral=None, current_integral=current_path)\n",
    "Z_pi = impedance_calculator.compute_impedance(sim_data_with_loss[\"mode_yz\"])\n",
    "Z_pi = Z_pi.sel(mode_index=0).values\n",
    "# We compute the power-voltage definition using the same approach.\n",
    "impedance_calculator = rf.ImpedanceCalculator(voltage_integral=voltage_path, current_integral=None)\n",
    "Z_pv = impedance_calculator.compute_impedance(sim_data_with_loss[\"mode_yz\"])\n",
    "Z_pv = Z_pv.sel(mode_index=0).values\n",
    "\n",
    "# Plot the imaginary part of the data associated with the three definitions of impedance\n",
    "# With physics convention, the imaginary part of the impedance is negative.\n",
    "f, ax1 = plt.subplots(1, 1, tight_layout=True, figsize=(10, 3))\n",
    "ax1.plot(mode_freqs / 1e9, np.imag(Z_vi), label=r\"$Z_{vi}$\")\n",
    "ax1.plot(mode_freqs / 1e9, np.imag(Z_pi), label=r\"$Z_{pi}$\")\n",
    "ax1.plot(mode_freqs / 1e9, np.imag(Z_pv), label=r\"$Z_{pv}$\")\n",
    "ax1.plot(freqs / 1e9, -np.imag(skrf_z0), \":k\", label=\"scikit-rf\")\n",
    "ax1.set_ylabel(r\"$\\mathrm{Im}(Z_0)$ (Ohm)\")\n",
    "ax1.set_xlabel(\"Frequency (GHz)\")\n",
    "ax1.set_xlim(0, 10)\n",
    "ax1.set_ylim(-1, 0)\n",
    "ax1.legend()\n",
    "plt.show()"
   ]
  },
  {
   "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,\" 1980 IEEE MTT-S International Microwave Symposium Digest, Washington, DC, USA, 1980, pp. 407-409.\n",
    "\n",
    "[3]   R. H. Jansen and N. H. L. Koster, \"New Aspects Concerning the Definition of Microstrip Characteristic Impedance As a Function of Frequency,\" 1982 IEEE MTT-S International Microwave Symposium Digest, Dallas, TX, USA, 1982, pp. 305-307.\n",
    "\n",
    "[4]   R. H. Jansen and M. Kirschning, “Arguments and an accurate Model for the Power-Current Formulation of Microstrip Characteristic Impedance”, Archiv für Elektronik und Übertragungstechnik (AEÜ), vol. 37, pp. 108-112, 1983.\n",
    "\n"
   ]
  }
 ],
 "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": "3.11.13"
  },
  "title": "Computing the characteristic impedance of transmission lines using Tidy3D"
 },
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 "nbformat_minor": 4
}