{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using lumped elements in Tidy3D simulations\n",
    "\n",
    "This notebook presents a demonstration of `Tidy3D`s [LinearLumpedElement](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.LinearLumpedElement.html) feature, designed for modeling various linear lumped circuit elements within `Tidy3D` simulations, including resistors, inductors, and capacitors.\n",
    "\n",
    "We will perform a simulation of a terminated parallel-strip transmission line, employing the [LinearLumpedElement](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.LinearLumpedElement.html) to represent the load. This example is adapted from [1]. While not essential for this tutorial, gaining some familiarity with the [TerminalComponentModeler](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.smatrix.TerminalComponentModeler.html), could be beneficial, as it is utilized here for extracting scattering parameters.\n",
    "\n",
    "As an additional optional component of this tutorial, we will explore the creation of an admittance transfer function and a circuit diagram using the [lcapy](https://lcapy.readthedocs.io/en/latest/index.html) Python package. Should this be of interest, please refer to the provided link for installation details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "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.plugins.mode as mode\n",
    "import tidy3d.rf as rf\n",
    "import tidy3d.web as web\n",
    "from tidy3d.plugins.mode import web as web_mode\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": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Introduce a scaling factor to easily convert from mills to microns\n",
    "mill = 25.4  # one millionth of an inch is 25.4 microns\n",
    "\n",
    "# Media present\n",
    "substrate_med = td.Medium(permittivity=2.2)\n",
    "pec = td.PEC\n",
    "\n",
    "# Simulation is from 1 to 50 GHz\n",
    "fstart = 1e9\n",
    "fstop = 50e9\n",
    "freqs = np.linspace(fstart, fstop, 1001)\n",
    "fwidth = fstop - fstart\n",
    "# Choose 25 GHz as a rough central wavelength for determining padding for PMLs\n",
    "freq0 = 25e9\n",
    "wavelength0 = td.C_0 / freq0\n",
    "# Parameterize the parallel-strip transmission line using parameters from [1].\n",
    "strip_height = 16 * mill\n",
    "strip_width = 8 * mill\n",
    "strip_thickness = 4 * mill\n",
    "tline_length = 240 * mill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we create the main transmission line structure along with a substrate structure that is sandwiched between the two strips.\n",
    "strip = td.Structure(\n",
    "    geometry=td.Box(\n",
    "        center=(0, strip_height + strip_thickness / 2, 0),\n",
    "        size=(tline_length, strip_thickness, strip_width),\n",
    "    ),\n",
    "    medium=pec,\n",
    "    name=\"strip\",\n",
    ")\n",
    "\n",
    "strip2 = td.Structure(\n",
    "    geometry=td.Box(\n",
    "        center=(0, -strip_thickness / 2, 0), size=(tline_length, strip_thickness, strip_width)\n",
    "    ),\n",
    "    medium=pec,\n",
    "    name=\"strip2\",\n",
    ")\n",
    "\n",
    "substrate = td.Structure(\n",
    "    geometry=td.Box(\n",
    "        center=(0, strip_height / 2, 0), size=(tline_length, strip_height, strip_width)\n",
    "    ),\n",
    "    medium=substrate_med,\n",
    "    name=\"substrate\",\n",
    ")\n",
    "\n",
    "# PML wavelength at 25 GHz\n",
    "wl_pml = wavelength0\n",
    "\n",
    "# Quarter wavelength (at 25 GHz) padding on each side\n",
    "simx = tline_length + wl_pml / 2\n",
    "simy = strip_height + wl_pml / 2\n",
    "simz = strip_width + wl_pml / 2\n",
    "\n",
    "boundary_spec = td.BoundarySpec(\n",
    "    x=td.Boundary.pml(),\n",
    "    y=td.Boundary.pml(),\n",
    "    z=td.Boundary.pml(),\n",
    ")\n",
    "# This monitor will be used to record the fields along the propagation axis of the transmission line.\n",
    "prop_mon = td.FieldMonitor(\n",
    "    center=(0, strip_height / 2, 0), size=(simx, 0, simz), freqs=[fstart, freq0, fstop], name=\"prop\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RL Series Network\n",
    "\n",
    "Before we can create the simulation object, we need to define the lumped element. Each lumped element is associated with a `Box` and a network. The network describes the intended voltage-current relationship, and the box describes the location and size of the lumped element in the `Tidy3D` simulation.\n",
    "\n",
    "Let's start by using a simple RL network composed of a resistor in series with an inductor.\n",
    "\n",
    "<img src=\"img/RL_series_schematic.png\" width=\"300\" alt=\"Series RL network\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First example is a simple RL series network\n",
    "Rval = 75\n",
    "Lval = 1e-9\n",
    "\n",
    "# The RLCNetwork can model either a single resistor, inductor, or capacitor, as well as,\n",
    "# simple series or parallel combinations of these circuit elements.\n",
    "RLC = rf.RLCNetwork(\n",
    "    resistance=Rval,  # units are Ohms\n",
    "    inductance=Lval,  # units are Henries\n",
    "    network_topology=\"series\",\n",
    ")\n",
    "# The LinearLumpedElement models the network as a thin sheet with an equivalent medium that\n",
    "# results in the same voltage-current relationship as the desired network.\n",
    "# In this case, we are using the element to terminate a transmission line, so we ensure that it\n",
    "# is located at the end of the transmission line, with the proper width and height\n",
    "lumped_element = rf.LinearLumpedElement(\n",
    "    network=RLC,\n",
    "    center=(tline_length / 2, strip_height / 2, 0),\n",
    "    size=(0, strip_height, strip_width),\n",
    "    name=\"network\",\n",
    "    voltage_axis=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will create a simulation object incorporating the structures we've constructed, as well as the specified boundary conditions, monitors, and the lumped element. Beyond plotting the scattering parameters, we aim to plot the impedance of the lumped element to ensure it is functioning as expected, utilizing data derived from the FDTD simulation. To facilitate this computation, we will employ the convenience method `to_monitor` to establish a monitor around the lumped element. This monitor will enable precise computation of both voltage and current."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now we create the simulation and include the definition of the lumped element.\n",
    "sim = td.Simulation(\n",
    "    center=(0, strip_height / 2, 0),\n",
    "    size=(simx, simy, simz),\n",
    "    boundary_spec=boundary_spec,\n",
    "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=80, wavelength=3000),\n",
    "    lumped_elements=[lumped_element],\n",
    "    structures=[substrate, strip, strip2],\n",
    "    # Add monitor around lumped element to enable computation of voltage, current, and impedance.\n",
    "    monitors=[prop_mon, lumped_element.to_monitor(freqs=freqs)],\n",
    "    # The source will be added by the TerminalComponentModeler\n",
    "    sources=[],\n",
    "    run_time=50 * (tline_length / td.C_0),\n",
    "    plot_length_units=\"mm\",  # This option will make plots default to units of millimeters.\n",
    ")\n",
    "# The next step is to setup a LumpedPort for the TerminalComponentModeler, which is needed for extracting scattering parameters\n",
    "# as well as providing a source for the simulation.\n",
    "port1 = rf.LumpedPort(\n",
    "    voltage_axis=1,\n",
    "    size=(0, strip_height, strip_width),\n",
    "    center=(-tline_length / 2, strip_height / 2, 0),\n",
    "    num_grid_cells=5,\n",
    "    name=\"port_1\",\n",
    ")\n",
    "# Finally, we combine the base simulation with the LumpedPort in the TerminalComponentModeler, which will enable use\n",
    "# to compute scattering parameters as well as retrieve any other desired data from the simulation.\n",
    "modeler = rf.TerminalComponentModeler(\n",
    "    simulation=sim,\n",
    "    freqs=freqs,\n",
    "    ports=[port1],\n",
    "    remove_dc_component=False,  # Include DC component for more accuracy at low frequencies\n",
    ")\n",
    "# Before running the solver, we plot the created structure along its propagation direction. We also plot the cross-section\n",
    "# of the structure at the terminated end of the transmission line, which includes the location of the lumped element.\n",
    "sim_temp = list(modeler.sim_dict.values())[0]\n",
    "f, (ax1, ax2) = plt.subplots(2, 1, tight_layout=True, figsize=(15, 12))\n",
    "sim_temp.plot(z=0 * mill, ax=ax1)\n",
    "sim_temp.plot(x=tline_length / 2, ax=ax2)\n",
    "sim_temp.plot_grid(\n",
    "    x=tline_length / 2,\n",
    "    ax=ax2,\n",
    "    vlim=[-2 * strip_width, 2 * strip_width],\n",
    "    hlim=[-strip_height, 2 * strip_height],\n",
    ")\n",
    "# Add arrows that indicate locations of the Port and the load\n",
    "ax1.annotate(\n",
    "    \"Port 1\",\n",
    "    xy=(-tline_length / 2 - 10 * mill, strip_height + 10 * mill),\n",
    "    xytext=(-tline_length / 1.2, strip_width * 10),\n",
    "    arrowprops=dict(facecolor=\"black\", shrink=0.05),\n",
    ")\n",
    "ax1.annotate(\n",
    "    \"Load\",\n",
    "    xy=(tline_length / 2 + 10 * mill, strip_height + 10 * mill),\n",
    "    xytext=(tline_length / 1.5, strip_width * 10),\n",
    "    arrowprops=dict(facecolor=\"black\", shrink=0.05),\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run Tidy3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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\">14:02:10 UTC </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'rl_series'</span> with resource_id                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'sid-dedde843-1a7b-49cf-a399-89934d9cceee'</span> and task_type           \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'TERMINAL_CM'</span>.                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:02:10 UTC\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'rl_series'\u001b[0m with resource_id                          \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'sid-dedde843-1a7b-49cf-a399-89934d9cceee'\u001b[0m and task_type           \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'TERMINAL_CM'\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/rf?taskId=pa-15a94805-bc09-4bcc-b67e-f25ba2b24614\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/rf?taskId=pa-15a94805-bc09-4bcc-b6</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/rf?taskId=pa-15a94805-bc09-4bcc-b67e-f25ba2b24614\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">7e-f25ba2b24614'</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=717680;https://tidy3d.simulation.cloud/rf?taskId=pa-15a94805-bc09-4bcc-b67e-f25ba2b24614\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/rf?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=559383;https://tidy3d.simulation.cloud/rf?taskId=pa-15a94805-bc09-4bcc-b67e-f25ba2b24614\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=717680;https://tidy3d.simulation.cloud/rf?taskId=pa-15a94805-bc09-4bcc-b67e-f25ba2b24614\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=853550;https://tidy3d.simulation.cloud/rf?taskId=pa-15a94805-bc09-4bcc-b67e-f25ba2b24614\u001b\\\u001b[32mpa\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=717680;https://tidy3d.simulation.cloud/rf?taskId=pa-15a94805-bc09-4bcc-b67e-f25ba2b24614\u001b\\\u001b[32m-15a94805-bc09-4bcc-b6\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=717680;https://tidy3d.simulation.cloud/rf?taskId=pa-15a94805-bc09-4bcc-b67e-f25ba2b24614\u001b\\\u001b[32m7e-f25ba2b24614'\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/9b36e144-ddb6-41f8-8dd8-30b62b26a870\" 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=82053;https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "82df734978b6479f93125a53536d7a88",
       "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\">14:02:19 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</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> after run.         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:02:19 UTC\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\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 after 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\">14:02:20 UTC </span>Subtasks status - rl_series                                        \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Group ID: <span style=\"color: #008000; text-decoration-color: #008000\">'pa-15a94805-bc09-4bcc-b67e-f25ba2b24614'</span>                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:02:20 UTC\u001b[0m\u001b[2;36m \u001b[0mSubtasks status - rl_series                                        \n",
       "\u001b[2;36m             \u001b[0mGroup ID: \u001b[32m'pa-15a94805-bc09-4bcc-b67e-f25ba2b24614'\u001b[0m                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "125d89240bf24f828f0c25644f0ad260",
       "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\">             </span>Batch status = preprocess                                          \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mBatch status = 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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:02:29 UTC </span>Batch status = running                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:02:29 UTC\u001b[0m\u001b[2;36m \u001b[0mBatch status = running                                             \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\">14:03:11 UTC </span>Batch status = postprocess                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:03:11 UTC\u001b[0m\u001b[2;36m \u001b[0mBatch status = postprocess                                         \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\">14:03:33 UTC </span>Batch status = success                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:03:33 UTC\u001b[0m\u001b[2;36m \u001b[0mBatch status = 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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:03:36 UTC </span>Modeler has finished running successfully.                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:03:36 UTC\u001b[0m\u001b[2;36m \u001b[0mModeler has finished running successfully.                         \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>Billed flex credit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>.                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.025\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\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "765914145843445a80969fa7d20eb7fe",
       "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\">14:03:39 UTC </span>Loading results from cm_data.hdf5                                  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:03:39 UTC\u001b[0m\u001b[2;36m \u001b[0mLoading results from cm_data.hdf5                                  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "modeler_data = web.run(modeler, task_name=\"rl_series\")\n",
    "s_matrix = modeler_data.smatrix()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we plot the dominant components of the electric and magnetic field within the transmission line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data = modeler_data.data[\"port_1\"]\n",
    "\n",
    "f, (ax1, ax2) = plt.subplots(2, 1, tight_layout=True, figsize=(6, 6))\n",
    "sim_data.plot_field(field_monitor_name=\"prop\", field_name=\"Ey\", val=\"abs\", f=freq0, ax=ax1)\n",
    "sim_data.plot_field(field_monitor_name=\"prop\", field_name=\"Hz\", val=\"abs\", f=freq0, ax=ax2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plots of the fields we confirm that the transmission line is correctly guiding the the electromagnetic field. In addition, we observe a standing wave at this frequency which indicates that the load is not perfectly matched to the characteristic impedance of the transmission line. This is expected, since the transmission line would need to be terminated by a load with a real impedance to avoid reflections.\n",
    "\n",
    "### Computing the Reflection Coefficient Using Transmission Line Theory\n",
    "\n",
    "The next step will be plotting the scattering parameter associated with the reflection coefficient. In order to validate this result, we will first use transmission line theory to obtain a reference value. To compute the reflection coefficient using transmission line theory, we need the characteristic impedance and the phase constant $\\beta$ of the transmission line mode. Both quantities can be computed from the eigenmode of the transmission line. The phase constant $\\beta$ is found from the computed effective index. The characteristic impedance can be computed using the [ImpedanceCalculator](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.microwave.ImpedanceCalculator.html#tidy3d.plugins.microwave.ImpedanceCalculator), see the associated [tutorial notebook](https://docs.flexcompute.com/projects/tidy3d/en/latest/notebooks/CharacteristicImpedanceCalculator.html) for a detailed description of its usage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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\">14:03:41 UTC </span>Mode solver created with                                           \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #808000; text-decoration-color: #808000\">task_id</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'fdve-bf0c05c2-1154-445b-b93d-2dd7ba84ff78'</span>,               \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #808000; text-decoration-color: #808000\">solver_id</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'mo-4ce825f5-8c19-457a-9120-268a7bab9725'</span>.               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:03:41 UTC\u001b[0m\u001b[2;36m \u001b[0mMode solver created with                                           \n",
       "\u001b[2;36m             \u001b[0m\u001b[33mtask_id\u001b[0m=\u001b[32m'fdve-bf0c05c2-1154-445b-b93d-2dd7ba84ff78'\u001b[0m,               \n",
       "\u001b[2;36m             \u001b[0m\u001b[33msolver_id\u001b[0m=\u001b[32m'mo-4ce825f5-8c19-457a-9120-268a7bab9725'\u001b[0m.               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a985d62f8aa948c1b4be2596ffcb34fb",
       "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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8058379be7b845419e00c755c6b7a626",
       "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\">14:03:44 UTC </span>Mode solver status: queued                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:03:44 UTC\u001b[0m\u001b[2;36m \u001b[0mMode solver status: 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\">14:04:10 UTC </span>Mode solver status: running                                        \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:04:10 UTC\u001b[0m\u001b[2;36m \u001b[0mMode solver status: running                                        \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\">14:04:12 UTC </span>Mode solver status: success                                        \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:04:12 UTC\u001b[0m\u001b[2;36m \u001b[0mMode solver status: success                                        \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "75c3582bd0a1416f8b1baa6221ed6b3f",
       "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": [
    "# The eigenmode associated with the transmission line is computed at the central frequency 25 GHz\n",
    "mode_spec = td.ModeSpec(num_modes=1, target_neff=2.2)\n",
    "mode_plane = td.Box(center=sim.center, size=(0, simy, simz))\n",
    "\n",
    "mode_solver = mode.ModeSolver(\n",
    "    simulation=sim_temp, plane=mode_plane, mode_spec=mode_spec, freqs=[freq0]\n",
    ")\n",
    "mode_data = web_mode.run(mode_solver, task_name=\"mode_rl_series\")\n",
    "\n",
    "# Convert the effective refractive index into an effective permittivity\n",
    "er_eff = (np.real(mode_data.n_eff.values[0])) ** 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we define path integrals in the mode plane that will be used to compute voltage, current, and, ultimately, the characteristic impedance of the transmission line mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a path integral along the y axis that will be used to compute voltage.\n",
    "V_integral = td.AxisAlignedVoltageIntegral(\n",
    "    center=(0, strip_height / 2, 0),\n",
    "    size=(0, strip_height, 0),\n",
    "    snap_path_to_grid=True,\n",
    "    extrapolate_to_endpoints=True,\n",
    "    sign=\"+\",\n",
    ")\n",
    "# Define a path integral around the upper signal conductor that will be used to compute current.\n",
    "I_integral = td.AxisAlignedCurrentIntegral(\n",
    "    center=(0, strip_height + strip_thickness / 2, 0),\n",
    "    size=(0, strip_thickness * 3, strip_width * 3),\n",
    "    snap_contour_to_grid=True,\n",
    "    extrapolate_to_endpoints=True,\n",
    "    sign=\"+\",\n",
    ")\n",
    "# Pass these definitions to the ImpedanceCalculator\n",
    "impedance_calc = rf.ImpedanceCalculator(voltage_integral=V_integral, current_integral=I_integral)\n",
    "characteristic_impedance_data = impedance_calc.compute_impedance(mode_data)\n",
    "# Select the real part of the only value in the array\n",
    "characteristic_impedance = np.real(characteristic_impedance_data.values[0])\n",
    "\n",
    "\n",
    "# Define a helper function that computes the reflection coefficient using transmission line theory.\n",
    "def compute_Gamma_from_transmission_line_theory(ZL, Z0, er_eff):\n",
    "    beta = 2 * np.pi * freqs / td.C_0 * np.sqrt(er_eff)  # Phase constant of the transmission line.\n",
    "    # Calculate impedance seen at the input of the transmission line\n",
    "    # Equation 2.44 from [2]\n",
    "    Zin = (\n",
    "        Z0\n",
    "        * (ZL + complex(0, 1) * Z0 * np.tan(beta * tline_length))\n",
    "        / (Z0 + complex(0, 1) * ZL * np.tan(beta * tline_length))\n",
    "    )\n",
    "    # Calculate the reflection coefficient using the reference impedance associated with the port.\n",
    "    Zport = port1.impedance\n",
    "    # Equation 2.72 from [2] to account for generator and load mismatch.\n",
    "    Gamma = (Zin - Zport) / (Zin + Zport)\n",
    "    return Gamma"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Next, we plot the extracted scattering parameter, which is associated with the reflection coefficient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fvj3W1tZYWVnh4OCg/cEbGhqaaN0CBQokmYvU1tY2UcD4zTffYGFhQY0aNShRogRDhw5NMrZMo1ChQokeW1tbY2Jiou2mmXB5SkEpyIH57Nmz2bVrF3nz5qVBgwbMmTOHZ8+eaddp2LAhHTt2ZOrUqeTJk4e2bdvi7e1NdHR0ivt9+fIlERERlCpVKslzrq6uqNXqJOMcP3xNmmAqtfKnV1rHuHnzJgBubm44ODgkuu3du5cXL1581HHTu18DAwMKFCig02vQBHQfdqfVLNel/nSp++DgYEaOHEnevHkxNTXFwcGBokWLAknP9+TcvHkTSZIoUaJEkjoIDAzU1oHmQkKJEiUSbW9oaJjoQkhaPtweoGTJkkRERGjHOZcuXZrq1auzevVq7TqrV6+mVq1aFC9eXKfjTJo0CV9fX3x8fOjdu7e2K2pGSetz4eXLl7x+/Zply5YlqVfNRbcPzy/N/y01mnO2T58+Sfb7559/Eh0drf2/z5kzh8uXL1OwYEFq1KjBlClTdLpooqHL+1KXcyc1VapUwczMTBuoaoLXBg0acPbsWaKiorTPacaqas7FlD7HNMF8QinVbVRUFK1ataJy5cps2LAhx2d1FgRBd2LMqyAIWSq5ForXr1/TsGFDrKysmDZtGi4uLpiYmODv788333yTZDoSlUqV7L6lBGOiXF1duX79Otu3b2f37t1s2rSJRYsWMWnSJKZOnZrm/nQ5RnJGjRqFp6cnW7ZsYc+ePXz//ffMnDmTAwcOULlyZRQKBRs3buTkyZNs27aNPXv24OXlxbx58zh58mSGzV35seXPyGNo/m8rV65MdpqTj50KKL37NTY2TjEASuk1fEr96bJtly5dOH78OOPGjaNSpUpYWFigVqtp0aKFTtPvqNVqFAoFu3btSvZ4+poDtXfv3owcOZJHjx4RHR3NyZMn+e2333Tevnz58jRt2hSAdu3aERERwVdffUW9evV0Hp+bmrQ+FzR136tXL/r06ZPsPipUqJDosS5jLDX7/emnn6hUqVKy62j+Z126dKF+/fr4+Piwd+9efvrpJ2bPns3mzZu1Y/pTo8v78lPPHUNDQ2rWrMnhw4e5desWz549o379+uTNm5fY2FhOnTrFkSNHKF26tHaM9sdIqW6NjY1p2bIl//33H7t376Z169YffQxBEHIWEbwKgqB3Bw8e5NWrV2zevDlRMqS7d+9+0n7Nzc3p2rUrXbt2JSYmhg4dOjBjxgwmTpyYqdNYuLi4MGbMGMaMGcPNmzepVKkS8+bNY9WqVdp1atWqRa1atZgxYwZr1qyhZ8+erFu3LlEXZg0HBwfMzMy4fv16kueuXbuGUqnMkB/2Gc3FxQUAR0dHbUCSHh+2rmfUfvUtJCSE/fv3M3XqVCZNmqRdrmmdSyi1OpAkiaJFi1KyZMkUj1W4cGHtvt3c3LTLY2NjuXv3LhUrVtSpzMmV7caNG5iZmSUKTrp168bo0aNZu3atdg7krl276nSM5MyaNQsfHx9mzJjBkiVLPno/CaX2ueDg4IClpSXx8fEZem5pzlkrKyud9psvXz6GDBnCkCFDePHiBVWqVGHGjBk6Ba+6lEWXcwdSPv9A7jo8e/Zs9u3bR548eShdujQKhYKyZcty5MgRjhw5kiio1JyLKX2O5cmTR+epcBQKBatXr6Zt27Z07tyZXbt20ahRI522FQQhZxPdhgVB0DvN1f+ELVMxMTEsWrToo/eZcMoRkMfFlilTBkmSdB5rml4RERFERUUlWubi4oKlpaW2W3BISEiS1jtNS0xKXYdVKhXNmzfnv//+SzRNyvPnz1mzZg316tXDysoq415IBnF3d8fKyooff/wx2TpPa+oezQ/ZhFNvZMR+9S258x1gwYIFSdZNqQ46dOiASqVi6tSpSfYjSZL2/K9WrRoODg4sWbKEmJgY7TorVqxIss/UnDhxItFYz4cPH/Lff//RvHnzRK13efLkwcPDg1WrVrF69WpatGiRpPt9eri4uNCxY0dWrFiRqPv9x0rrc0GlUtGxY0c2bdrE5cuXk2z/sedW1apVcXFxYe7cuclO5aLZb3x8fJJu446Ojjg7O6c6tCA9dD13QD7/UurGXr9+faKjo1mwYAH16tXTBrr169dn5cqVPHnyRDveFeSAvFKlSvz999+Jzr3Lly+zd+9eWrZsma7XYWRkxObNm6levTqenp6cPn06XdsLgpAziZZXQRD0rk6dOtja2tKnTx9GjBiBQqFg5cqVn9TFtXnz5jg5OVG3bl3y5s1LYGAgv/32G61atUqUiCcj3bhxgyZNmtClSxfKlCmDgYEBPj4+PH/+nG7dugHw999/s2jRItq3b4+Liwtv3rzhjz/+wMrKKtUfb9OnT8fX15d69eoxZMgQDAwMWLp0KdHR0cnOcZsdWFlZsXjxYr744guqVKlCt27dcHBw4MGDB+zYsYO6deum2qW0UqVKqFQqZs+eTWhoKMbGxri5ueHo6PhJ+9U3Kysr7Xjo2NhY8ufPz969e5PtaaBJkvTtt9/SrVs3DA0N8fT0xMXFhenTpzNx4kTu3btHu3btsLS05O7du/j4+DBgwADGjh2LoaEh06dPZ+DAgbi5udG1a1fu3r2Lt7d3usa8litXDnd390RT5QBJuuCD3HW4U6dOgDyNzKcaN24cGzZsYMGCBcyaNUu7PDY2lunTpydZ387OjiFDhiS7L10+F2bNmoWfnx81a9bkq6++okyZMgQHB+Pv78++ffsIDg5O92tQKpX8+eefeHh4ULZsWfr160f+/Pl5/Pgxfn5+WFlZsW3bNt68eUOBAgXo1KkTFStWxMLCgn379nHmzJkk80x/LF3PHZDPv/Xr1zN69GiqV6+OhYUFnp6egJzEy8DAgOvXrzNgwADt/hs0aMDixYsBEgWvIHeb9vDwoHbt2vTv3187VY61tXWSea11YWpqyvbt23Fzc8PDw4NDhw5Rrly5j6wZQRByhKxLbCwIwuckpalyUpqm4tixY1KtWrUkU1NTydnZWRo/fry0Z8+eJFOlpLSPD6ctWbp0qdSgQQPJ3t5eMjY2llxcXKRx48ZJoaGh2nU00zG8fPkyyb6Sm4bjw2N/OFVOUFCQNHToUKl06dKSubm5ZG1tLdWsWVPasGGDdht/f3+pe/fuUqFChSRjY2PJ0dFRat26daJpSCQp6VQ5mm3d3d0lCwsLyczMTGrcuLF0/PjxROtoprH5cCqeD6fuSEl6psrR9Rh+fn6Su7u7ZG1tLZmYmEguLi5S3759k7zm5Pzxxx9SsWLFJJVKlWTfuuw3tSlVgCRTkmj+px9Od6R5bf/++2+ifSc3VU5yUyV9+P989OiR1L59e8nGxkaytraWOnfuLD158iTZ//sPP/wg5c+fX1IqlUmmzdm0aZNUr149ydzcXDI3N5dKly4tDR06VLp+/XqifSxatEgqWrSoZGxsLFWrVk06fPiw1LBhQ52nyhk6dKi0atUqqUSJEpKxsbFUuXLlFM+l6OhoydbWVrK2tpYiIyPT3L8kJV+/CTVq1EiysrLSTsOjmQIpuZuLi0uKx9Hlc0GSJOn58+fS0KFDpYIFC0qGhoaSk5OT1KRJE2nZsmU6lTml98L58+elDh06aI9fuHBhqUuXLtL+/fu1dTdu3DipYsWKkqWlpWRubi5VrFhRWrRoUZp1mNLnmeb9+uF0S7qcO+Hh4VKPHj0kGxsbCUgybU716tUlQDp16pR22aNHjyRAKliwYLLl3Ldvn1S3bl3J1NRUsrKykjw9PaWrV6/q9FokKfn3dFBQkFSmTBnJyclJunnzZop1JAhCzqeQpAzM3iEIgiAIwmctLi4OZ2dnPD09Wb58ub6LIwiCIOQiYsyrIAiCIAgZZsuWLbx8+ZLevXvruyiCIAhCLiNaXgVBEARB+GSnTp3i4sWL/PDDD+TJkydRgidBEARByAii5VUQBEEQhE+2ePFiBg8ejKOjI//884++iyMIgiDkQqLlVRAEQRAEQRAEQcj2RMurIAiCIAiCIAiCkO2J4FUQBEEQBEEQBEHI9kTwKgiCIAiCIAiCIGR7IngVBEEQBEEQBEEQsj0RvAqCIAiCIAiCIAjZngheBUEQBEEQBEEQhGxPBK+CIAiCIAiCIAhCtieCV0EQBEEQBEEQBCHbE8GrIAiCIAiCIAiCkO2J4FUQBEEQBEEQBEHI9gz0XYDsTq1W8+TJEywtLVEoFPoujiAIgqAHkiTx5s0bnJ2dUSrFdV9die9QQRAEISO/Q0XwmoYnT55QsGBBfRdDEARByAYePnxIgQIF9F2MHEN8hwqCIAgaGfEdKoLXNFhaWgJw9+5d7Ozs9FyanCE2Npa9e/fSvHlzDA0N9V2cHEPUW/qJOvs4ot7SLzg4mKJFi2q/EwTdiO/Q9BPvz48j6i39RJ19HFFv6ZeR36GfRfD6+++/89NPP/Hs2TMqVqzIwoULqVGjhk7baro5WVpaYmVllZnFzDViY2MxMzPDyspKvKnTQdRb+ok6+zii3tIvNjYWQHR9TSfxHZp+4v35cUS9pZ+os48j6i39MvI7NNcP3Fm/fj2jR49m8uTJ+Pv7U7FiRdzd3Xnx4oW+iyYIgiAIgiAIgiDoKNcHr/Pnz+err76iX79+lClThiVLlmBmZsZff/2l76IJgiAIgiAIgiAIOsrVwWtMTAznzp2jadOm2mVKpZKmTZty4sQJPZZMEARBEARBEARBSI9cPeY1KCiI+Ph48ubNm2h53rx5uXbtWrLbREdHEx0drX0cFhYGyH21Nf21hdRp6knUV/qIeks/UWcfR9Rb+om6yhySJBEXF0d8fLy+i5JtxMbGYmBgQFRUlKiXdBD1lpRKpcLAwECM1RdylVwdvH6MmTNnMnXq1CTL/fz8MDMz00OJci5fX199FyFHEvWWfqLOPo6oN91FRETouwi5TkxMDE+fPhV1+wFJknBycuLhw4ci6EgHUW/JMzMzI1++fBgZGem7KIKQIXJ18JonTx5UKhXPnz9PtPz58+c4OTklu83EiRMZPXq09nFYWBgFCxakcePG2NvbZ2p5c4vY2Fh8fX1p1qyZyMKWDqLe0k/U2ccR9ZZ+r1690ncRchW1Ws3du3dRqVQ4OztjZGQkAo531Go14eHhWFhYoFTm6tFdGUrUW2KSJBETE8PLly+5e/cuJUqUEPUi5Aq5Ong1MjKiatWq7N+/n3bt2gHyh9v+/fsZNmxYstsYGxtjbGycZLmhoaH4kZdOos4+jqi39BN19nGyst7i4uJ4/fo1ISEhBAcHExISQkhICGFhYURERKR4i46OJi4uTtu19MO/Nd0DVSoVSqUSlUqlvSV8bGRkhJmZGaamppiamib529LSEltbW+zs7LC1tdXerKysUCqVn+X5tXjxYhYvXsy9e/cAKFu2LJMmTcLDw+OT9x0TE4NaraZgwYKiV9MH1Go1MTExmJiYiGAjHUS9JWVqaoqhoSH379/X1k1uERgYyKhRo5g0aRJ169bVd3GELJSrg1eA0aNH06dPH6pVq0aNGjVYsGABb9++pV+/fvoumiAIQo4jSaBpIIuLgz/+gMePg7l79xbPn9/mzZsnvH37hPDwJyiVTzEweMLz58+1+QNyGqVSSZ48eciTJ4++i5LlChQowKxZsyhRogSSJPH333/Ttm1bzp8/T9myZTPkGCLIEITMlVPeY3fvwqVLUKgQVKqU9vo+Pj7s3bsXW1tbEbx+ZnJ98Nq1a1devnzJpEmTePbsGZUqVWL37t1JkjgJgiAIoFbDnTtw/z7cuyffNH/fvv0MV9cAmjW7wMWLF7l58yZnztwCQtJxBCvA9t3N7t1jc/LmNaNbNzNti+icOWaEh5sBxshfVQaACjCgSBEVCxcaYGBggEqlYvhwiagoNY6O8Tg4xOPgoMbePp48eeR7c/N4YmJiiIiIIDIyMtn7sLAwbWuwpmU4KioKtVrNixcvPsu5wT09PRM9njFjBosXL+bkyZMZFrwKgiAALFoEc+fCqFG6Ba+aXjfXr1/P1HIJ2U+uD14Bhg0blmI3YUEQdBMTA5GREB0NUVFqXr0Kw9AwhOjoYN68ecOLF5E8fhxFfHwksbGRxMdHYWgYh7Gx3FKnGc+mUChQKBSJum6amZlpb3Z2duTJkwdzc3MxBi6TSBI8f27G9u0K7tyBPHmgTx/5ObUaSpeG+Pg44AJwBDgMHAee8+QJ7N+fdJ8WFs7Y2blga1sQa2tnbGzy4eLiTLt2zuTNmxd7e3t8fGyQJAMUClAq5Zvmb0dHaNHi/f7KloWoKPl8e/MGwsIgNBRevIACBaB16/frPnkir3P/ftJyVagAFy68fzx1KhgbQ6lSUKMGFCnyviX5Q1FRUQQHB/Py5Utu3rxJ586d01PNuUp8fDz//vsvb9++pXbt2imup2vG/tjYWCRJQq1Wo1arM6/getCvXz9ev36Nj49Piuu4ublRsWJFfv755yTPSZKkvc9tdZOZRL0lT61WI0kSsbGxqFSqRM9lp8zzBQsqARV37qiJjU07W3RcXBwgB6/R0dFZ2sKcneotp8jIuvosgldBEJIKC4Nbt+DRI3j6FJ49k++HDIlArb7BvXv32LTpEZs3PyIq6iFq9SPgKRCM3NKWuT8OjI2NyZMnD/b29jg6OlKoUCEKFy6svRUrVizFxGtCYpIEAQGwezccPQqnTxsQFNRM+3zdunLw+vr1a3bu3Im5+VbCw3ehVifu6qtQKChevBRVq1aiYsWKlC5dmuLFi1OsWDGdxi1+9ZXuZe7QQffXdvr0+9bhD1uMixZNvO7cuRAe/n5ZgQLQuDG0by8Hz6am758zMTHB2dkZZ2dnChQooHvhc5FLly5Ru3ZtoqKisLCwwMfHhzJlyqS4vq4Z+w0MDHByciI8PJyYmJhMKXtmsLW1TfX5b775hmnTpgGk2lU+Li6OmJiYVNd58+aN9u9t27Yxf/587ty5Q1xcHMWKFWPo0KF069ZNu07r1q05duwYIOf8sLe3p0KFCvTs2TNJK3pulrDeBHl8eWRkJIcPH9YGfB/KDpnnnz93Ampy8eIbdu48mOb6N2/eBCAyMpK///5bLz0qs0O95RQZmVVeBK+CkMtFRICBAWiy5K9YAePHw8uX8UAgcBY4D1wDrrN0aTLNVykwMjLFwcEOS0tLoqNNefTIFEmSb2q1CZKkSXIjUbcuFCggXxm/dy+eU6eigAggEohAoYjAwOAt8fGvUKvl1pvHjx/z+PHjFI9vaWmJs7Mz27Zto0KFClStWpWqVavmqqQUGUGSwN0dXr7ULFFgYKCmTBkFpUrFYWq6ndatl7Nnz55EP26srKyoV68eDRo0oF69elSqVAlzc3O9vIaUKBRyS3Hp0sk/n7ABJi4Ovv5aDmxv3IBz5+SLNytXyremTUH8FkmsVKlSBAQEEBoaysaNG+nTpw+HDh1KMYDVNWN/VFQUDx8+xMLCIke9XxN+Hm3YsIHJkycTGBioXWZhYYGFhUWa+zEwMMDIyAgrK6skz0mSxJs3b7C0tNT2PsmfPz/fffcdpUuXxsjIiB07djBs2DAKFy6Mu7u7dp9ffvklU6dOJS4ujkePHrFlyxb69+9Pnz59WLp06ae+/GwtuXoT5PeaqakpDRo0SPJey06Z5ydNkkOSe/esadmyZZrrnzt3Tvu3s7Oz9n2QFbJTveUUGZmxXwSvgpDLvHoFBw/CgQNw+DBcvQo7d0LDhlEcP36cLVv28fLlUcAfeJvsPuzs7HBxcSFv3oJYWRWgcOECFCtWgMKFnXFyssfeXs7IapqwmSoZkZEQEgLBwXILl42NvPzQIVi8WA4cHj6U79VqkHuVSKxfH0HNmkEEBQWxZ08QK1Y8w9HxPlZWD4iOvs/Dh/e5e/cub9684fr164nGvGiyjNetW5fmzZvToEGDZDOI51aRkXIgtm0bbN36vltux45y99omTaBatTgCA9dx+/ZVli9fnmg8p6urK23btqVNmzbUqFEjSTeznCZhTzJDQ3jXKAbIF3ZOnpTfHxs3QsLGqago2LFDbpHNIflOMoWRkRHFixcHoGrVqpw5c4ZffvklxUBI14z98fHxKBQKlEpljkkoA/KPZA0bGxsUCkWiZQB9+/bl9evXbNmyBYC3b98yePBgNm/ejKWlJWPHjgXQvv5p06axYcMGLl++DKDt8lq1alU8PT354YcfcHNzS3SMUaNG8c8//3D8+PFE2Z/Nzc215SlUqBB16tTB1dUVLy8vunbtStOmTTO2QrIRTb1p6lWQKZVKFApFqtnls0PG/iJF4OJF+e+ICEOsrVNfP+F3061bt2idcCxJFskO9ZZTZGQ9ieBVEHKBx49h+XLYskXuHvpu6A9yN99NjBixlQcPjhAVFZVoO3Nzc21rZZkyZShdujSlS5fOsMyqpqby7YPfdjRsKN80oqPlTIM3b8KNGwrq1jUnf35zChcuzLZt8vJ3PYQwM4PmzWHq1BhcXK7y77+rMTQ05MqVK5w8eZIXL15w4sQJTpw4wdy5c7GwsKBp06a0a9eOjh076tQqkhO9fQu//AILFrxvYT1xAurUkf9evFi+f/78OTNmzGDp0qXa7pp58+alT58+9OvXj9IpNWHmQmZm4OYm3376SXPxRLZ8OQwbJo+9nTRJ7losyAFCwjGtmeFt8tfUAFCpIGHjUWrrKpWJu4GntG5mdyYYN24chw4d4r///sPR0ZH//e9/+Pv7U+ldVhovLy+mTp3KmTNnqF69OgAXL17k4sWLbN68Ocn+JEniwIEDXL9+ndmzZ6d5/D59+jBmzBg2b96cq4NXIWdL+D68f1/OV5Aa6f0PHa5du5ZJpRKyIxG8ClqxsXJClNevwcpKTqACciKUw4flv83NwdJSvtnYyIlexAVO/Ug4ZcnTpzB5suaZMPLlW41CsZanT48iSRI3bsjP5MuXj6ZNm9KoUSNq1qxJ6dKls0XLmrFxyl0/+/SRxy36+cGePfLY3C1bYMsWI6ytKzJlSghDh9bD0NAQSZK4c+cOx44d4+DBg+zatYtnz56xZcsWtmzZwpAhQ+jYsSP9+/enQYMGuaJrWVycHGhNmSLXDUDhwnL32HLl3q8XExPDr7/+yrRp07RjwqpWrcqECRNo27btZ3/1WKF437Ue5PeXtTVcuQJdu0LFivp/n2S1iRMn4uHhQaFChXjz5g1r1qzh4MGD7NmzJ1OPm9r1pZYt5RZxDUdHuQU9OQ0byr1QNIoUgaCgpOsl+A2c4cLDw1m+fDmrVq2iSZMmAPz999+JxlAXKFAAd3d3vL29tcHr6tWradiwIcWKFdOuFxoaSv78+YmOjkalUrFo0SKaNWtGWpRKJSVLltTO1ysI2VHC9+G9e2kHrwmJ4PXzIoLXz0jCYOf2bZgzBx48iOPu3Qc8efKEN2+eIrfUPaNGjVBKlw4nPDycZ8/COX488t1eFO9uSsAEpdKKChWsadrUCmtra/LmdebevXwoFK+oWTMMJyf75IoifCRJkgO4hQvlH2K//y4vr1oVPD0vEBr6O2fPruHp0/dNDLVr16ZTp060aNECV1fXHBewFS0q3/r0kV//hQuwfj2sWiUHG4UKvU94Eh2twMXFBRcXF3r37o1areb8+fNs27aN1atXc+vWLVauXMnKlSupWrUqY8eOpVOnThgY5MyPwkeP5Ky7mmy6xYrJ3WK7dpXHOWucP3+eL774gitXrgBQpUoVPD09+d///odRwohN0Bo2DHr1gl9/lZM8XbqUs943GeHFixf07t2bp0+fYm1tTYUKFdizZ49OAZMgu337NjExMdSsWVO7zM7OjlKlSiVa76uvvsLLy4v58+cDsHHjRu3fGpaWlgQEBBAeHs7+/fsZPXo0xYoVo1GjRmmWQ5KkHPfZL3ye6tSBd9dwUiVaXj9fOfMXm5CmsDA4c0Yez3XyJFy4ING7930qVDjN+fPnOX36GgcOXAduA0kzPZ4+Ld/SolbL3VQDApI+N2NGfwwMHHF0LEvp0mWoV68snp41qVSpQo4NFvRFrQYfH/jxR/D3l5dZWcG8eXD58lmmTZvGtm3btOu7urry5Zdf0rlzZwoWLKinUmc8hUKe/61SJZgxA+7fj9OOkYmKgvLloVkzuRXS0VFucdB0i548eTInT57E29ubVatWce7cObp3787UqVOZPXs2np6eOe7HXd68ckBvZye/5oEDE7ceqtVq5syZw6RJk4iNjcXR0ZHZs2fTvXt3du/eneNeb1azsZG7DA8aBFu3xqcrW3JusHz5cr0cN2E26A992FEktel3P+wVlJ0bHj09PTE2NsbHxwcDAwNiY2Pp1KlTonWUSqV2/HGlSpUIDAxk5syZaQav8fHx3Lx5U9uqKwjZkSYW7dIF8uVL37bPnz8nJCQkzWzgQu4gIohcJCQEli6Vu1QdOyYhSYHAXmA/cIoZM14mu52hoTF58+bH2TkfBQvmw9k5H7a2ttqsiRYWFtrEPJIkaedQe/s2igcPQomODiMuLpSQkBCuXXvC+fOPiIx8BLwmLu4FT5684MkTPw4ckFuFzMzMqFmzJnXr1qVly5a5IilMZjp9GkaOlC9CgDxOb+BAaNPmPr16jWHTpk2A/MOmY8eODB06NNd0iU2NUikngdIErzt2yFP/3LoFa9bIgb2X1/veBgqFgtq1a1O7dm1+/PFHFi9ezC+//MK1a9do27YtDRs2ZMmSJdl+zOetW3JLtEolJyBavx7s7cHBIfF64eHh9O3bV3t+tG/fnqVLl+Lg4CDmpksnR0do31767IJXfUnPGNTMWjejuLi4YGhoyKlTpyhUqBAAISEh3Lhxg4YJBv4bGBjQp08fvL29MTQ0pEOHDmkmxNN1/PHff/9NSEgIHTt2/LQXIwhZ4GN/uly7di3VOaiF3EMErzlcfPz7K9FqdTzffnsEtXo9sA1IPMWIoaEhFSpUoHr16pQpU4ZSpUpRsmRJChUqlKGZ+WJjY9m+fSdFi9Zn7947HDx4lUuXrvLkyQXMzE4SHv4aPz8//Pz8mD59OlZWefD0bEn37l1wd3cXrbIJLFwII0bIf5uby+MYBw2K5q+/5uDh8SNRUVEolUp69uzJt99+m6Qr2uekY0d5fNvo0XLr9Jdfwrp18tRA+fMnXjdPnjx8//33jBgxglmzZrFgwQIOHTpEpUqV+OGHHxg9enS2vKCydi307y9PdTRlirwsuVj75cuXuLu7c/78eQwNDfn999/58ssvc/0FDUHIbiwsLOjfvz/jxo3Tzln97bffJvud++WXX+Lq6grA7t27Ez03c+ZMqlWrhouLC9HR0ezcuZOVK1eyWJOJ7Z2IiAiePXumnSrHx8eHn3/+mcGDB9NYZB0TsrGqVeW8DZcuyRege/RIfX3pg8HqInj9fIgoIYe6eFHOjvnsGSxZcpslS5awevVq1Oqn2nVMTExo0KABzZo1o379+lSsWDHL5tNTKqFsWUsqVarG+PHVAJATm6q5desax44dY86c/dy6tZuwsCBWr/6H1av/IW9eJ774ohcDBgygRIkSWVLW7KxDB5g6VR7X+OOPEBJyhVatenLh3SDHRo0asXDhQsolzMzzGWvYUG6pXrAAvvsO9u2DypXlL8LkkmxaW1szc+ZMBg8ezMCBA9m9ezfjx49n9+7drF+/PsOyLn8qSZK7SX//vfz41KnEF64Sevr0KU2bNuXq1as4ODjg4+ND3bp1s7bAgiBo/fTTT4SHh+Pp6YmlpSVjxowhNDQ0yXolSpSgTp06BAcHU61atUTPvX37liFDhvDo0SNMTU0pXbo0q1atomvXronW++OPP/jjjz8wMjLC3t6eqlWrsn79etq3b5+pr1EQPtW4cXJCtnLlYNOmtIPXD4lxr58RSUhVaGioBEhBQUH6LookSZJ0/rwkeXhIEqgl2COBh6RQKCRAAiQbGxvJy8tL2rVrlxQREaGXMsbExEhbtmyRYmJiUl3v6lVJGjMmRrKz85NghAR5tK9DoVBI7du3l06cOJFFpda/mJgYycdni7RvX2yi5SEh8v2yZcskExMTCZDy5MkjrV27VlKr1Vlf0GwktXPt+nVJqlRJkkCSmjSRpLSqSq1WS8uXL5fMzc0lQCpcuLDk7++fSSXXnVotSSNHyq8DJGnMGEmKj09+3VevXkmurq4SIOXPn1+6du1asuvp+h4V3gsKCpIAKTQ0VN9FyVFS+g6NjIyUrl69KkVGRuqpZNmPWq2WXFxcpLlz50ohISFSfEpvdCFZ8fHxot6Skdp7Lbt9F7x58/677vXr1Nf97rvvJEAyNDSUAKlNmzZZU0gp+9VbTpCR36FikpMc4tEjOdtq5coSu3btBuoA7sAuJEmiRYsWbNmyhefPn7N8+XJatGiR5ngZfXN1hblzDXn2rBF///0Lrq6PgS1AKyRJwsfHh9q1a9O6dWuuXr2q59JmvogI+OmnajRtasCGDe+XW1jEMWLECAYMGEBUVBQeHh5cunSJbt26iW6gqShZEo4fhzFj5MzEaVWVQqHAy8uLU6dOUbx4ce7fv0+DBg04mHCujSwmSTB2rDx/K8Bvv8mZb5Pr5R8VFUXbtm0JDAykQIECHD58+LPuRi4IOcnLly/57bffePbsGX379tV3cQRBLyws5CkYQZ7rVReaJGaBgYGZVCohuxHBaw5w/Lgc6P3zz1WgGeABnMTU1JSRI0dy69Ytdu3aRdu2bXPktBeGhtC7N1y5YsT69W0pUmQ7Y8ZcwcvLCwMDA3bs2EH58uUZPHhwsl2tcoPQUPDwUHH8eH4MDSXevpvpJiIiAk9PTxYuXAjA9OnT2bFjB05OTnosbc5haioHewmra98+OXtzSsqWLcvp06dxc3MjPDycFi1asHPnzswvbDLmzAHNbBlLl8LQocmvJ0kSXl5eHD16FGtra3bt2pVofkhBELI3R0dHpk2bxrJly0TGVOGz1LOnPOe7Zi7mu3dTX196N+ZVM2zq1q1bRKQ06bOQq4jgNQcoWTIClWocCkVFYD/GxsaMHj2aO3fusGDBAlxcXPRdxAyhUMgp0gMD4ccfy7B8+XKuXLmCm1t71Go1S5YsoWzZsuxIOEN9LhASIk/vcuKEEnPzGPbsiadfPzlbbMuWLdm9ezdmZmZs3ryZb7/9VrS2foJffpHreuDA1ANYW1tbduzYQZs2bYiOjqZjx44cPXo06wr6jqWlPK51wQIYMCDl9ZYtW8batWsxMDBgy5YtYgy0IOQwkiTx8uVLeqR3oJ8g5BIxMZrcKLJbt3TbzsnJCUdHRyRJ0s5lLuRuInjNpo4ckbsMnjt3jvr1qxIaOhdJiqNt27ZcvXqVefPm5drWNxOT93NV5slTkrt3N6NQHCRPnuI8fvyY1q1bM2rUKGJiks5Pm9NERkKrVvKcvHnySPzwwzHq1ZOIjIykZcuWHDp0CCsrK3x9fUXCjQzg5CR3uf3zT5g8OfV1TUxM2LhxI56enkRFReHp6cnly5ezpqDvDBkCly/LUyWl5MKFC4x8t4Iucz4KgiAIQnZlby/f37iR+npSgmzDFSpUAOCiZu48IVcTwWs2o1bLGdcaNJBo3/5XatWqxbVr18iXLx/bt29ny5Ytn1V3QFNTaNAAJKkhQUEXKF9+NAC//PILjRs35smTJ3ou4ceLj5ez6Z04ATY2sHdvHMWKhREfH0+vXr04cuQI1tbW7Nu3jzp16ui7uLlC167wxx/y39Onk2hscXIMDQ1Zt24dderU4fXr17Rt25bXr19nahnj4uSLGhqpTTsbGxtL7969iY6OplWrVowePTpTyyYIgiAImUETi5YsKd+nFbxqKBQKypcvD4jg9XMhgtdsJDJS7jY7d24U0I///htJXFwcnTp14tKlS7Rq1UrfRcxypqbg7Q2//goKhRmXLs2jfv3/sLa25vjx49SpU4cbun7CZTMKhfwhbWwMW7fK6eEBJkyYwObNmzEyMmLLli1Ur15dvwXNZby85CROAH37yi2bqTEzM2Pbtm0UKVKEO3fu0KdPH9Sp9Tn+RLNny/PdnTuX9rrz58/n4sWL2NnZ8ddff2XofM2CIAiCkNUaNZLnNP/119TXS67l9dKlS5lYMiG7EL90somwMHks3qZNoSgUzYC/UalU/Pzzz2zYsAF7TT+Kz5BCAcOHyx9mBgZw5EgbatY8S4kSJbh//z716tXjnC6/9LMZpVIOVK5cgfr15WVHjx7ll3epZf/55x/RBTSTzJ4NzZvLF4x69YLo6NTXt7OzY+PGjRgZGbF161YWLFiQKeW6cwd++EEe951W4sTbt28zZcoUQA5iHR0dM6VMgiAIgpDZNLFowYLQrRu8a0xNk0KhSNRtOGFQK+ROInjNBsLD5YmZjx17gUrVGEmSM4bu2bOHUaNGiQQ973TtCv/9J4+H3bu3OG3bHqVKlSq8fPmSJk2acOHCBX0XUSehoYmTBWnybV2/fp3ffvsNkFtfP5x8Xsg4KhX8/beckv/CBfDxSXubqlWrai8sfPvtt5nS4j96tBxIN2kiZ15Mzbhx44iKisLNzY3evXtneFkEQRAEIavp+pM3YZBapkwZlEolr1694unTp5lUMiG7EMGrnsXGQuvWcOxYEEplI+Ljz+Po6MihQ4do0qSJvouX7bRsKbfA1q0LEyY44ufnR926dQkNDcXd3Z1buqan0xNJkq8oNmqUOJNeXFwcffr0ISoqioYNG/LDDz/orYyfCycnuUv6hg3y/0QXAwcOpFmzZkRFRfHll19maPfh/fvlizMGBppu8imve+rUKXx8fFAqlfz666/iApcgCIKQo5UvL1+4zZ8fTp6UZwc4ezbt7RQKBSYmJtp5zcW419xPBK96ZmgIbdqEo1K1Qq0OpECBAhw5coSKFSvqu2jZVocOcPiwnJHOysqK7du3U7FiRZ4/f46HhwchISH6LmKKVq+G3bvh9Gk5YZPGvHnz8Pf3x9zcnL///hsDAwP9FfIz0ro1dO6s+/oKhYJly5Zhbm7OkSNH+PPPPzOkHJL0PvvxoEFQpkxq60pMmDABgD59+lC2bNkMKYMgCJ+XKVOmUKlSpU/ez4oVK7Cxsfnk/aSlSJEiiYZsKBQKtmzZkunHTY4+j51bTZkiz8Pu6SknVhw1CtIzM6Lmd7O/v3+mlE/IPkTwqmexsbHs2dOR+PjT2NnZsXfvXkpqUq0JKUqYl2b7dhsWL95D4cKFuXXrFj179iQ+YWSYTQQFvZ/yZNIkeHeRkGvXrjH5XeTSv39/nJ2d9VTCz1tISNrJm0D+ATVjxgwAvvvuO0JDQz/52Pv2wbFj8jRR//tf6uv6+vpy8OBBjI2NtWNeBUHIGgqFItVbTnpPjh07lv3793/yfrp27aqXxIlPnz7Fw8MjU4+RUQG+kD6aa7KpfSd/OLa1WrVqAJw5cyaziiVkE7k6eC1SpEiSL5ZZs2bpu1iAPK9nWBiMGTOGvXv3YmZmxs6dO3F1ddV30XKUhQvhiy9g1Ki8/PuvD6ampuzatYtp06bpu2hJTJsGwcFQoYI8HRLIH74jRowgOjqaFi1a0LhxY/0W8jN18CAULSp3H9bluseQIUMoVaoUL1++ZObMmZ98/FWr5PtBgyBfvtTXnT17NgCDBw+mUKFCn3xsQRB09/TpU+1twYIFWFlZJVo2duxY7bqSJBEXF6fH0qbOwsIiQ5JBmpqa6iVhnJOTE8bGxll+XH3J7udTRtLMvqBL8mDNsBnNzAwieM39cnXwCjBt2rREXyzDhw/Xd5F4/Bg8PKB48ZUsXLgQgLVr11KzZk09lyznad8erK3lbrj79lXmj3eTeE6fPp2TJ0/quXTv3boFixfLf//8s9xdHGD37t34+vpiZGTEggULxNhFPdH00r9yBf79N+31DQ0NmTt3LgALFizg8ePHn3T8FStg71745pvU1zt//jwHDhxApVLx9ddff9IxBUFIPycnJ+3N2toahUKhfXzt2jUsLS3ZtWsXVatWxdjYmKNHj3L79m3atm1L3rx5sbCwoHr16uzbty/RfosUKcKPP/6Il5cX1tbWlCtXjmXLlmmfj4mJYdiwYeTLlw8TExMKFy6c6MKZQqFg6dKltG7dGjMzM1xdXTlx4gS3bt2iUaNGmJubU6dOHW7fvq3d5sNWxYMHD1KjRg3Mzc2xsbGhbt263L9/H4ALFy7QuHFjLC0tsbKyomrVqpx9NyAxuW7DixcvxsXFBSMjI0qVKsXKlSsTPa9QKPjzzz9p3749ZmZmlChRgq1bt6brf5Gw6+69e/dQqVRs27aNJk2aYGZmRsWKFTlx4kSibY4ePUr9+vUxNTWlYMGCjBgxgrdv3ya7/xUrVjB16lQuXLigbQBZsWKF9vmgoKBUy3/58mU8PDywsLAgb968fPHFFwQFBWmfj46OZsSIETg6OmJiYkK9evUSBV4HDx5EoVAkOp9WrVqFUqnU1r3GggULKFy4cKZO45bZvvgC7Ozk70NNpuGbNxPPe57Qhy2vVapUQalU8vjxY5G0KZfL9cGrpaVloi8bc3NzvZZHrZbfoK9eXSUoaAAAkyZNok2bNnotV05VoMD7ucAmT4YKFXrSq1cv1Go1vXv3TvFLKav9738QFydftHBzk5fFxcVpr9IPHz6cYsWK6bGEnzdb2/dzv06bljgbdEpatWpFvXr1iI6OZs6cOZ90fIVCnirLySn19ebNmwfI3fREq6uQ20iSxNu3b/Vyy8jpNSZMmMCsWbMIDAykQoUKhIeH07JlS/bv38/58+dp0aIFnp6ePHjwINF28+bNo1q1apw7d47+/fszdOhQrl+/DsCvv/7K1q1b2bBhA9evX2f16tUUKVIk0fY//PADvXv3JiAggNKlS9OjRw8GDhzIxIkTOXv2LJIkMWzYsGTLHBcXR7t27WjYsCEXL17kxIkTDBgwQHtBtWfPnhQoUIAzZ85w7tw5JkyYgKHmKuwHfHx8GDlyJGPGjOHy5csMHDiQfv364efnl2i9qVOn0qVLFy5evEjLli3p2bMnwcHBH1PlWtOnT2f06NEEBARQsmRJunfvrm2tvH37Ni1atKBjx45cvHiR9evXc/To0RTrpGvXrowZM4ayZctqG0ASzgKQWvlfv36Nm5sblStX5uzZs+zevZvnz5/TpUsX7fbjx49n06ZN/P333/j7+1O8eHHc3d2T1EHC86lNmzY0bdoUb2/vROt4e3vTt2/fHD3X95s38vCdmBj5u9DeXv4uvnYt9e0056iFhQVl3iWMEK2vuZyUixUuXFjKmzevZGdnJ1WqVEmaM2eOFBsbm659hIaGSoAUFBSUIWVavFiSIFpSKitLgOTu7i7Fx8dnyL6zi5iYGGnLli1STExMlhxPrZakNm0kCSSpYUNJCg4OkfLnzy8B0qhRo7KkDKkJDZWkkiUlSaGQpAsX3i//888/JUCys7OTgoODs7zecoOMrLPQUEmytJTPo927ddvG19dXAiQTExPpyZMn6T7mmzeS9Patbus+ePBAUqlUEiCdO3cu3cdKSJxr6RcUFCQBUmhoqL6LkqOk9B0aGRkpXb16VYqMjNQuCw8PlwC93MLDw9P92ry9vSVra2vtYz8/PwmQtmzZkua2ZcuWlRYuXKh9XLhwYalXr16SJElSfHy8FBwcLDk6OkqLFy+WJEmShg8fLrm5uUlqtTrZ/QHSd999p3184sQJCZCWL1+uXbZ27VrJxMRE+3jy5MlSxYoVJUmSpFevXkmAdPDgwWT3b2lpKa1YsSLZ5z6shzp16khfffVVonU6d+4stWzZMsXyav73u3btSvYYkiTX0c8//5xoHz4+PpIkSdLdu3clQPr111+1v6muXLkiAVJgYKAkSZLUv39/acCAAYn2eeTIEUmpVCY6DxNKWEcJpVX+H374QWrevHmibR4+fCgB0vXr16Xw8HDJ0NBQWr16tfb5mJgYydnZWZozZ44kSSmfT+vXr5dsbW2lqKgoSZIk6dy5c5JCoZDu3r2b7GtI7r2W8JjZ5bugbVv5+3fpUvlxw4by47//Tn79cePGSYA0evRo7bJ+/fol+d9khuxUbzlFRn6H5uqUpiNGjKBKlSrY2dlx/PhxJk6cyNOnT5k/f36K20RHRxMdHa19HBYWBsiJlWJjYz+pPE+fwoQJBsBU1Orz2Nvbs2zZMuLj47NlgqGPpamnT62v9Jg3D3x9DTh0SMGuXRYsWbIET09PFi5cSN++fbVX4/TB1FSeS/TsWQWurhKxsfJV7h9//BGAb775BgsLC73UW06XkXVmagp9+ij57TcVCxaocXNL+z3ZoEEDatWqxcmTJ5k7d266x9QvXqxk1iwl06apGTAg9eZezWdFgwYNKF++/Ce9ZnGupZ+oK0FXmsQxGuHh4UyZMoUdO3bw9OlT4uLiiIyMTNLyWqFCBe3fmu7IL168AKBv3740a9aMUqVK0aJFC1q3bk3z5s1T3D5v3rwAlNf0v3y3LCoqirCwMKysrBJta2dnR9++fXF3d6dZs2Y0bdqULl26kO/dIPzRo0fz5ZdfsnLlSpo2bUrnzp1x0UxS/oHAwEAGDBiQaFndunW182QnV15zc3OsrKy0r/djJcy+rin7ixcvKF26NBcuXODixYusXr1au44kSajVau7evZvunCOplf/ChQv4+flhYWGRZLvbt28TFRVFbGwsdevW1S43NDSkRo0aBAYGJlr/w/OpXbt2DB06FB8fH7p168aKFSto3Lhxkpb4nEbT+UEzeqp8eTh0KO1xrwmHW1WvXh1vb2/R8prL5bjgdcKECdqEJSkJDAykdOnSjB49WrusQoUKGBkZMXDgQGbOnJniIP+ZM2cyderUJMv9/PwwMzP7pLL/9FM1QkNfAvIP3P79+3P+/HnOnz//SfvNrnx9fbP0eO3alWTtWldGjoxh8WKJmjVrcurUKXr37s3UqVOzxXjSnTvl+0OHDnHnzh2srKwoXLgwOzVPkPX1lhtkVJ2VLWuOQtGE3buV/PGHH/nzh6e5jZubGydPnmTp0qXUqFEDExMTnY+3aFEjgoOtuXz5Mjt33ktxPbVazdKlSwH5h0zC8+VTiHNNdxEREfouQq5nZmZGeHja77nMOnZG+XB40tixY/H19WXu3LkUL14cU1NTOnXqRExMTKL1PuyGq1AotGMYq1Spwt27d9m1axf79u2jS5cuNG3alI0bNya7veb7LrllKY2L9Pb2ZsSIEezevZv169fz3Xff4evrS61atZgyZQo9evRgx44d7Nq1i8mTJ7Nu3Trat2+f3urR6fVmxD4/fL3h4eEMHDiQESNGJNnuY4ZhpFb+8PBwPD09k/29mi9fvkRjj9Py4flkZGRE79698fb2pkOHDqxZsybJhYGcTPNTbdAg6NnzfU6KD0nJdPXXJG06ffo0arU6R3ejFlKW44LXMWPG0Ldv31TXSWnsYM2aNYmLi+PevXvayYw/NHHixERBb1hYGAULFqRx48aflJXv+HEFx44pgU6Amo4dOzJ9+vSP3l92Fhsbi6+vL82aNUtxTExmaNwYQkLUfPmlEZ6eLShfviQVK1bk4sWLxMXF0bZt2ywri8bZswrKl5dIeK1EkiT+924+lDFjxtChQwdAf/WWk2VGnW3bJrF7NygUDWnZMu1xcC1atGD9+vXcvn2boKCgJC0OKbl4Ee7dM8TISGLKlDLY2aXcO2Dfvn28fPkSGxsbJk+ejKmpqc6vJzniXEu/V69e6bsIuZ5CodB7XorMcOzYMfr27asN9MLDw7l3716692NlZUXXrl3p2rUrnTp1okWLFgQHB2NnZ5dhZa1cuTKVK1dm4sSJ1K5dmzVr1lCrVi0ASpYsScmSJfn666/p3r073t7eyQavrq6uHDt2jD59+miXHTt2TK89oEC+AHD16lWKFy+u8zZGRkYf1TOuSpUqbNq0iSJFiiQ7b7smmdWxY8coXLgwIH8unzlzhlGjRqW5/y+//JJy5cqxaNEi4uLitL8jcrIPY1FdpzBP2DBRsWJFzM3NCQkJ4erVq5TTpC0WcpUcF7w6ODjg4ODwUdsGBASgVCpTTelubGycbKusoaHhJ/3IK1gQqldfzpkzJ7GwsOCXX37J9T8aP7XO0n882LULNHnISpUqxdixY5kxYwZTpkyhQ4cOWXoV7s0baN5cnrvz9Gl5KhaA/fv3c/XqVSwsLBgxYkSSOsrqessNMrLO5s+HZcsgf37dPx5HjBjByJEj+e233xgyZIhOrfzr1sn3rVsryJs39bL/888/gJw05cPufp9CnGu6+xzraebMmWzevJlr165hampKnTp1mD17dooXf4XklShRgs2bN+Pp6YlCoeD7779Pdwvj/PnzyZcvH5UrV0apVPLvv//i5OSUJMvvx7p79y7Lli2jTZs2ODs7c/36dW7evEnv3r2JjIxk3LhxdOrUiaJFi/Lo0SPOnDlDx44dk93XuHHj6NKlC5UrV6Zp06Zs27aNzZs3J8mwnNW++eYbatWqxbBhw/jyyy8xNzfn6tWr+Pr68ttvvyW7TZEiRbh79y4BAQEUKFAAS0tLnabnGTp0KH/88Qfdu3dn/Pjx2NnZcevWLdatW8eff/6Jubk5gwcPZty4cdjZ2VGoUCHmzJlDREQE/fv3T3P/rq6u1KpVi2+++QYvL69PvqCZnejaSS65lldDQ0Nq167Nvn37OHz4sAhec6lc255+4sQJFixYwIULF7hz5w6rV6/m66+/plevXtja2mZ5efLkCePu3QmAnKEuf/78WV6Gz9HYsWOxtrbmypUr/KvLHCgZaO1aCA+XU78nHIry+++/A9C7d+8M++EhZJxSpSC9b8++fftiYWHBtWvXOHr0aJrrS9L74PWLL1JfNyQkBB8fHwC8vLzSVzBB+ASHDh1i6NChnDx5El9fX2JjY2nevHm2yeKeU8yfPx9bW1vq1KmDp6cn7u7uVKlSJV37sLS0ZM6cOVSrVo3q1atz7949du7cmWEXZM3MzLh27RodO3akZMmSDBgwgKFDhzJw4EBUKhWvXr2id+/elCxZki5duuDh4ZHsECuQx2T+8ssvzJ07l7Jly7J06VK8vb1p1KhRhpT1Y1WoUIFDhw5x48YN6tevT+XKlZk0aRLOzs4pbtOxY0ftHOwODg6sXbtWp2M5Oztz7Ngx4uPjad68OeXLl2fUqFHY2Nho/2ezZs2iY8eOfPHFF1SpUoVbt26xZ88enX+j9u/fn5iYmFzzvVCqFNSqBQnbp3buhCFDYPfulLf78GJxgwYNADh8+HBmFFPIDj455VM2de7cOalmzZqStbW1ZGJiIrm6uko//vijNjubrjIq2/CkSZMkQCpVqlSuz06m7yxsb95I0owZklSvniTFxUnS1KlTJUBydXXNsszOarUkVa4sZ8qbN+/98vv370tKpVICpCtXriTaRt/1lhNldp29fq37ul5eXhIg9evXL811z5yRzw1zc0lKIcmlliYrdfny5VPMNJpe4lxLP5FtWJJevHghAdKhQ4d03iY92YYFWXx8vBQSEpLrZiLIbJ9bvU2bNk0qX758muvllGzDyRkxQv6uHDky6XOjR4+WAGncuHGJlh88eFACJGdn5wz7zvxQdq+37Cgjv0NzbctrlSpVOHnyJK9fvyYyMpKrV68yceJEnbp7ZKTt26FHjxfMnStnOJ4+ffpn2f0sKymVcvbho0dh2zYYOXIk1tbWBAYGZliim7ScPy/fjI0hwbAfli9fjlqtpnHjxnof/yOkLDRUnne1QAHQNU9Pv379ANiwYUOaSWe2bJHvPTzkbuWp0fQY6NatW7ZIOiZ8vkJDQwEydIylIAjpEx4ezuXLl/ntt98YPny4vouTqd7lX+LkyZTX+fB7sUaNGhgZGfHkyRPu3LmTiaUT9CXHjXnNSSQJZsyAkyd/BMKpWrVqimNEhIxjZgYDB8LMmfDzz9CunTVfffUVc+fOZcGCBbRu3TrTy7BmjXzfpo080TbI4zNWrlwJyMkWhOzLygpu3pS7ffv6gi65vurWrUvx4sW5desWmzZtSpSs5EOaee7r1El9n8HBwezfvx+ATp066Vp8QchwarWaUaNGUbdu3VTHkek63VxsbKx2mpJPzTCb20jvxvJp6kfQzedSb0OHDmXdunW0bduWvn37pvla1Wo1kiQRGxuLSqVK9Fx2nzatZk0AQ86dk3j9Oo6E+dw0rzs+Pj5R+Q0MDKhWrRrHjx/nwIEDH5VJOi3Zvd6yo4ysKxG8ZqIjR+DkyefAEkBOfiFaTrLG0KEwZw4cPgyBgTB8+HB+/vln9u/fz8WLFxPNz5bR1Or34xl79Hi//NixY9y9excLCwvatWuXaccXPp1CAe3bw4IF4OOjW/CqUCjo27cv3333Hd7e3qkGr+XLy7e0bNmyhbi4OCpUqEDJkiV1fwGCkMGGDh3K5cuX0xzTret0cwYGBjg5OREeHp5kyhhB9ubNG30XIUfK7fX2yy+/aKfG0WX8eUxMDJGRkRw+fJi4uLhk18kO06b98ktlLl50oF+/y9Sr90S73MGhGS9fmvHLL2eoVOmldrmmVfXu3btJetUVKFAAkJMdppak9VNlh3rLKTJyujkRvGain34C+A2IpkaNGjRt2lTPJfp85M8PLVvK3YZXrIDZswvRsWNHNmzYwO+//66dMzMznDkDjx+DtbXcLVRD0+rasWPHDJ1TUMgc7drJweu2bRAXB8nMdpBE7969+e677zh06BCPHz/+5MRsmjkcO3fu/En7EYRPMWzYMLZv387hw4e1PwpTout0c1FRUTx8+BALC4t0zY38OZAkiTdv3mBpaSkueKeDqLfkRUVFYWpqSoMGDZK817LTtGlLl6p49UpJqVKVadmyknZ58+YqVq+GqKiatGz5vpXZz88PkKfHbNmyZaJ92djYsGHDBq5evYq7u3uSFudPlZ3qLafIyOnmRPCaSe7fh+3bwwE5s+w333wjPkyzWL9+cuDxzz9y9+1BgwaxYcMG1q1bx88//5xpAWTNmnDlCly/jnZ+15iYGDZs2ADAF2mllxWyhXr1IE8eCAqSW/Dd3NLepmDBgtSpU4fjx4+zefPmZMcjLV4M+fJB06ZgYZHyvl6/fq2dWkIEr4I+SJLE8OHD8fHx4eDBgxTVzPeVCl2nm4uPj0ehUKBUKrN0CrOcQNMdUlM/gm5EvSVPqVSiUChSnRotO02bplIZkLAojRvD6tVw9KgKQ8P3QajmN7VKpUpS9rp162JtbU1wcDAXL16kRo0amVLW7FRv2V1G1pN4d2eSP/8E+BMIoUSJErTVpd+hkKFatZKDj2fP5DTrDRs2pGjRooSFhbF58+ZMPXaZMnK3U41Dhw7x+vVr8ubNq/fpAgTdqFTg6Sn/nZ48X5pAM7mpmWJiYNw4+dy4dSv1/ezdu5fY2FjKlCkj5tUU9GLo0KGsWrWKNWvWYGlpybNnz3j27BmRkZEZdgwpmbkaBUHIODntPfZhO0/DhvL969fysKyk6ydtGDIwMND2dtyd2jw7Qo4kgtdMEBsLf/yhBn4FYMyYMRneZUFIm5ERDBsG/fvL3YiVSqU2I+xff/2VpWXZ8i69bNu2bcW5kIM0by7fp2dYiyYp29GjR3n69Gmi506dgrdv5Xns0hp2vWPHDgBatWql+8EFIQMtXryY0NBQGjVqRL58+bS39evXf/K+NVfhM3IclCAISWneYzmlhfDDWNTFBZ48gUuX5NkkNNIKyt3d3QERvOZGottwJoiOhiZNfFmz5i7W1taim6geTZ6c+HGfPn2YPHkyfn5+PHjwIMOz0M2bJ495HTIE3s2TjVqt5r///gMQiZpymCZN5LGvzZvL2cN16flfsGBBatWqxcmTJ9m8eTNDhw7VPqcJgps0Sfwl/CG1Ws2uXbsAkozlEYSskpktNiqVChsbG168eAGAmZmZGFrzjlqtJiYmhqioKNH9NR1EvSUmSRIRERG8ePECGxubbH/hPKWPG4VCHmqTkpQ+N1q0aAHAqVOnePHiRaYmbhKylgheM4GFBUREyBmGe/fuLZLzZCOFChWiXr16HDlyhM2bNzNq1KgM3f+aNeDvDy1avA9ez507x+PHj7GwsMBNl4GTQrbh4CBnG06vzp07c/LkSf79999Eweu7Iaw0a5b69ufOnePly5dYWlpSt27d9BdAEHIAJycnAG0AK8gkSSIyMhJTU1MR0KeDqLfk2djYaN9rOUFq/7qoqPdzo6d1ca1gwYJUrVqVc+fOsXXrVjFFYS4igtdM8OTJE7Zt2wbAwIED9VwaQZLg9Gl48AA6d5bnyzxy5AgbN27M0OD1yRM5cFUoEmcZ3rp1KwAeHh7JJjIRcp8OHTowZswYjh49SnBwMHZ2drx9K7fKQ9rJnzRp/5s3b55junoJQnopFAry5cuHo6OjmC8xgdjYWA4fPkyDBg3E+z8dRL0lZWhomO1bXDWKFJGnkLOxSfqcWg2dOsn5Sy5dkrsSa6R2oaJDhw6cO3eOzZs3i+A1FxHBawbbvx/++ms58fHx1KtXj7Jly+q7SJ+9w4ehUSOwt4cOHeQPs5EjR3Ls2LEMmc5EQ9OqVrUq5M37frlmHjDR/TPnunMHDh2Cvn116zpcpEgRypUrx+XLl9mzZw/du3fn9Gl5yp38+aFw4dS31wSv4pwRPgcqlSrH/MDOCiqViri4OExMTEQQlg6i3nK2xYtTfk6plBM2RUbCjh0wYoRuwxo6dOjAt99+y759+wgNDcXa2jrjCizojRgUkMGmTpVYs+ZvAAYMGKDn0ggAdevKV/JevYKzZ+XJq2vXrg2QoVmH3005lqhVLSQkhDPvmtvEPL85U1QUuLqClxfcvav7dq1btwZg+/btAJw7Jy+vWzf1APjFixfac0YzZkcQBEEQPmea3IXvOjZqpdbyWrp0aVxdXYmNjdX2iBRyPhG8ZqCHD+HIkTPAbUxNzWifcK4UQW8MDOQEOQB798r3HTp0AN4HFhlBE7w2bvx+2cGDB1Gr1ZQuXZoCBQpk2LGErGNiAtWqyX8fPar7dprgddeuXcTFxTFmDNy+DVOnpr7dnj17kCSJypUr4+zs/JGlFgRBEITcQzPjpJ8fvHype0K5Tp06AbB69erMKpqQxUTwmoHk2QPkN0e7dm2xsLDQa3mE9zRTnmiCV013zMOHD2fInIV378L9+3KgXK/e++X73vUlFq2uOZvmf5qe4LVWrVrY2dkREhLCiRMnUCigWDEoXTr17TRT5Iguw4IgCMLnwssLSpWCd5MzJFG8OFSpAvHxkLDTXFrJuTQzfuzduzfJ9HVCziSC1wy0eXMcIM9/17NnT/0WRkhEk931xAkICwNXV1cKFChAVFQUhw4d+uT9BwXJY11r1ZKzTWtoxrs2Syu9rJCtfUzwqlKp8HiXuUvXFv74+Hj2vrvC4pEw65cgCIIg5GKPHsGNG/DmTcrrdOsm369bp/t+S5QoQe3atVGr1aL1NZcQwWsGefECTpw4ADzH1tae5pqmPiFbKFoUSpSQr9j5+clX6jTjCTNiAuvq1eXxtJquwwCPHz/m5s2bKJVKGjZs+MnHEPSnTh35PjBQvlChK03X4dWrt9OhQ+Krxck5c+YMISEh2NjYULNmzY8srSAIgiDkLLr0Au7SRb4/dAjCw+UNdJkWqU+fPgD8/fffmTp/tZA1RPCaQeSGFbnVtWvXziLTXTakuZ6wf798n5HBq4ZBgvzdJ06cAKBChQoiw10OZ28vJ20COH5c9+3c3d1RqVQ8fnwVH5872qRNKdGci82aNcPAQCSDFwRBED4vqcWihQvLWf9//hnS8zO7S5cuGBsbc/nyZc6fP//JZRT0SwSvGeTUqXhAns+zc+fO+i2MkKxhw+TA46ef5MdNmjRBpVJx/fp1Hjx48NH7jYmRM9J+6Pi7KKeOptlOyNHq1pXv0xO82traUk87CHo7lSunvr4meBVZhgVBEAQhKW9vGDkSDA11b3m1tbWlXbt2ACxZsiQziydkARG8ZpBevY4DQVhb21C/fn19F0dIRunSULs2GBvLj21sbKj8Lpo4mp7BjB/Ytw8sLeHDaxYieM1dBg6ErVth9Oj0bdeypee7v7ZRpUrK67169YrTp08DcoutIAiCIHwuNL15dZlL/WMMGTIEgFWrVhEcHJw5BxGyhAheM8h/79KjeXq2Fl2GcxDNhYYjR4589D5On4a4ODA1fb8sKioKf39/QASvuUW1auDpCY6O6duudOk27/46iJ1daIrr+fr6IkkSFSpUIH/+/B9fUEEQBEHIoXQJXt++hcDA9I1drV+/PhUqVCAyMpK//vrrI0snZAcieM0AarXEli1bALTdEoTs6ehRGDwY/vhDfqzp0vkpLa+nTsn3CfPrnDt3jtjYWPLmzUuRIkU+et9CzhccXAIoDcSxZ0/K46tFl2FBEAThc+XsDC4uiWdsSMm1a+8TZEZG6tZUq1AoGDFiBAC///478fHxH1tUQc9E8JoBKlS4yu3btzEyMhbd/bK5CxdgyRLYuFF+rAleL1++/FHdSCRJbnkFqFHj/fKEXYZ1GY8h5AxHj8LkyXKmQ13JDfBy6+vWrVuTXUetVovgVRAEQfhs/fMP3Lol93BKS9WqkCeP3PJ65ozux+jRowd2dnbcu3cPHx+fjyypoG8ieP1Ed+/ClSvyD9LGjZtiocslI0FvNEl3Tp6Up81xdHSkVKlSwPuAMz1u34bgYDAygooV3y8/e/YsgJjuJJdZvx6mTZPHvuoqKgoMDdsCsHPnTmJjY5Osc+bMGZ4/f46FhQV1NSepIAiCIAjJ0vzmOn1aQWSkbtuYmpoydOhQAGbMmCGmzcmhcmzwOmPGDOrUqYOZmRk2NjbJrvPgwQNatWqFmZkZjo6OjBs3jri4uAwth68vgNxi0rZtqwzdt5DxypcHc3MIC4ObN+VlmtbXjxn3+m5YKxUrygGsRkBAAIA2IZSQO1SrJt+/uzahk2XLICysJg4ODrx+/TrZ82zTpk2APC+sUcITSRAEQRCEJIoVkwPPiAi51VZXI0eOxMLCgoCAAHbs2JFJpRMyU44NXmNiYujcuTODBw9O9vn4+HhatWpFTEwMx48f5++//2bFihVMmjQpQ8uxc2cYILfYiS7D2Z9K9f5qnSbw1CRU0mR6TY+LF+X7hK2u4eHh3HwXGVeqVOljiypkQ5rg1d9fbrnXlYmJijZt5K7Da9euTfScJEna4LVjx44ZUk5BEITs7uTJk3To0IFWrVrh7e2NWq3Wd5EEPRowAKpUgT17dFtfqY1gFMybp/t3sr29vTbz8A8//CBaX3OgHBu8Tp06la+//pry5csn+/zevXu5evUqq1atolKlSnh4ePDDDz/w+++/ExMTkyFliI+Hffv8gDgKFChOsWLFMmS/QubSTFeiCV6rvYtI/P390/3lWakSdO0Kbm7vl126dAlJknB2dsYxvalphWytdGkwM4PwcLhxI33b9urVC4ANGzYQmaCPk7+/P3fu3MHExESMdxUE4bNw+/ZtmjRpgo+PDzt37sTLy4tGjRrx9OlTfRdN0JObN+H8eXj9Wrf1NUGniYm87bp1uh9rzJgxmJqacvr0abZv357+wgp6ZaDvAmSWEydOUL58efLmzatd5u7uzuDBg7ly5UqK3Tmjo6OJjo7WPg4LCwMgNjY2yVi1s2cVvH0rXyJq1apZsmPZPkeaesiu9VGxogIw4Nw5NbGx8ZQoUQITExPCwsIIDAykZMmSOu+rbVv5BqB5uefOnXt3nIrpqoPsXm/ZkT7qrHJlFceOKTlxIo7ixVO/YjtpkpIdO5SMGhVPz561KVy4MPfv3+fff/+le/fuAPzxLvW1p6cnxsbGWfJaxLmWfqKuBCHjzJkzh4iICGrVqkWrVq2YPXs2R44coXbt2uzevZvSpUvru4hCFvvYBtB69RTExUE6frrh6OjIiBEjmD17NuPHj8fDwwMDg1wbEuU6ufY/9ezZs0SBK6B9/OzZsxS3mzlzJlOnTk2y3M/PDzMzs0TL/v23BCAHr/b2tuzcufMTS527+MoDgrOd8HAroDH37r1h586DABQuXJjr16+zfPlyGjZs+En737ZtGwDm5uYfdU5k13rLzrKyzmxtywEu+Pjcx97+cqrr7tpVm0uXHDl79jL29vepVasW9+/fZ9q0aVhZWfHmzRv+eTdYp1y5cln+GSLONd1FRETouwiCkCuo1Wo2vkv5P2PGDNzc3OjevTseHh7cvHmTBg0acOjQIVxdXfVcUkEf0jtBQ716MGlS+rebOHEif/75J9euXWP58uUMHDgwfTsQ9CZbBa8TJkxg9uzZqa4TGBiYqVfkJk6cyOjRo7WPw8LCKFiwII0bN8be3v6DstwG7qBSGTJ27FiRafid2NhYfH19adasGYaGhvouThJxcdCtWyz29mZAS0DuZn79+nUkSaJly5Y67efVK7l7S9GiCcdewPTp0wHo0KGDzvuC7F9v2ZE+6iw4WMH27RARUZSWLQuluJ4kQf/+8kds797lqFq1LNWrV2fbtm3cvn2b169fc/r0aSIjI6lYsSLffPMNSmXWjOQQ51r6vXr1St9FEIRc4fr16wQHB2Nqakr9+vUBcHFx4fjx47i7u+Pv70+TJk04fPgwxYsX13NphexK021YpVKkO3AFsLa2ZtKkSYwcOZLJkyfTrVs3rK2tM7iUQmbIVsHrmDFj6Nu3b6rr6Dqu1MnJKUkCnufPn2ufS4mxsTHGxsZJlhsaGib5kWdldQCAevXqYGtrq1O5PifJ1Vl2YGgIpqaJl1WvXh2A8+fP61zmrVth4EBo3RreNbYSFxfH5ctya1y1atU+6vVn13rLzrKyztq1g9q1oUQJJSpVysHmw4fyBQ6VCipVMsDQEJydnZkwYQKTJk2iX79+2nVnzZqV7OdOZhPnmu5EPQnCp4mLk7PC3rhxHpCz8Sd8X+XJk4e9e/fSuHFjLl26hJubG4cPH6ZIkSJ6KrGQlTTdhj8mEAUIDYWpU8HGRm6J1cWgQYP4/fffuXHjBt9++y2//fbbxx1cyFLZKmGTg4MDpUuXTvWm6zQStWvX5tKlS7x48UK7zNfXFysrK8qUKZMh5d3zLiWayDKc81V8ly5Yk2xJF5cuyfcJOwLcuXOHqKgozMzMcHFxyehiCtmAra38P1epUl9PkxCsbFk5oYTG//73P9pqBkojd5sTiZoEQcjN1Gro3h369wd//9sAlCxZEkki0Ryd9vb27Nu3j9KlS/Pw4UOaNGnCkydP9FRqQR90DV41v9UU7zY4cAB+/hl+/BHu3tVtH0ZGRixevBiARYsWfdSsE0LWy1bBa3o8ePCAgIAAHjx4QHx8PAEBAQQEBBAeHg5A8+bNKVOmDF988QUXLlxgz549fPfddwwdOjRDWjgkSSIqKgqlUimC1xxo3z5o2hRGjpQfu7q6olQqCQ4O1jnb4bsGVhImvA4MDASgVKlSWdYFVMiezsuNC3yYG06lUuHj48PZs2e5fv06//vf/7K+cIKgg8OHD+Pp6YmzszMKhYItW7bou0hCDrVqFWzcKPd8UqvvAHJX4aVL5az97746ATmZzv79+ylWrBh37tyhWbNmBAUF6afgQpaxswMnp8QXe9OjXTto0gSio+Hrr3VPAOXm5sYXX3yBJEkMHDiQuLi4jyuAkGVy7K/rSZMmUblyZSZPnkx4eDiVK1emcuXKnD17FpB/IG7fvh2VSkXt2rXp1asXvXv3Ztq0aRlyfIVCwZ49ewgKChJzeeZAERGwfz8cPiw/NjExoUSJEgDabr9puXZNvk/YkH/t3UKRaCJ3O3AAevaEuXNTXkfT8qqZmikhhUJB1apV05XZWhCy2tu3b6lYsSK///67vosi5GCSJLeIAUyeDErlYwCcnQsxb5487VjdunDkyPttnJ2d2b9/P/nz5+fq1au4u7sTGhqqh9ILWcXHB54+lYdi6eLDlleFAn75BQwM4L//YMMG3Y89d+5cbG1tCQgIYNasWektupDFcmzwumLFCiRJSnJr1KiRdp3ChQuzc+dOIiIiePnyJXPnzs3wVNi2traihS0H0gSc1669n9haM2fwJU1/4FSEhYEmafW7mPfd/uTgVaT5z93u34c1a2DHjpTXKVYMSpWCqlWzrlxC7hMbG8vDhw+1SW6ykoeHB9OnT6d9+/ZZelwhd7lxAwICwNgYBg2Cly9fApAvnwPHj0OtWhASIveG+vff99sVKVKEffv24eDggL+/P61ateLt27f6eRFCjlC2LHz7rfz30KGQYORgqhwdHfnll18AmDJliug+nM1lq4RNgpBVihaVu6ZERcljI4oXl6cq2bhxo04tr7duyfeOjpAwOZ2m27Boec3dNAGpv788liu561cLFmRpkYRc5M2bN6xatYp169Zx+vRpYmJikCQJhUJBgQIFaN68OQMGDNAmmstO0jNXupC83DYP86FD8tzqNWqosbKK13YBtrW1xcYmlt27oXdvFVu3KunaVSIoKJ4vv5Rb1VxcXNi5cyfNmjXj2LFjtGvXjs2bN2OSTN/S3FZvWSEn11n8u5aH+Pj4ROUfNw58fAy4eFHBoEFq1q2L12kcbdeuXdm2bRv//vsvPXv25PTp0ynOIpKT601fMrKuRPAqfJZUKnB1lcclXrnyPngF3Vpeb9yQ7xP2+pQkSbS8fibKlJEvfoSFwe3biVvfBeFTzJ8/nxkzZuDi4oKnpyf/+9//cHZ2xtTUlODgYC5fvsyRI0do3rw5NWvWZOHChdohD9lBeuZKF1KXW+Zh3rChElAYR8db7NhxVTvzw8WLF7V/9+kD0dEV2LOnKEOGGHD8+FU6dbqp3cfEiROZPHky+/bto2nTpowfPz7FnnS5pd6yUnaosyVLKnD/vhU9ewZSrlzaU5M9fPgQgBs3biSZI71vXyvGjWvIgQOxrF59EDu7KJ3K0LZtW/z8/Lh16xbt27dn1KhR2m7JyckO9ZZTZORc6SJ4FT5bpUrJwevNd9+PmuA1MDBQ28qREldX+O47yJ///bLnz58TGhqKUqnMVj8mhYxnYAAVKsDp03Lr64f/7tBQsLRMvkVWEFJz5swZDh8+TNmyZZN9vkaNGnh5ebFkyRK8vb05cuRItvq8Sc9c6ULycts8zGvWqHBwkPjii2I0aOCgbYHp3Llzopat1q1h0qR4Zs9W4eJSipYt35/XLVu2pFKlSrRp04bTp0+zceNGvL29USVI+57b6i0rZKc6mzNHRWCgkpIla9GyZdrZlnx8fAA5QWbLli2TPG9vr6ZBAyX587ulqxxOTk64u7tz6NAh2rVrx9ChQ5Osk53qLafIyLnSRfAqfLY0v/c0wWvRokVRqVRERETw5MkT8ieMTD9QsaJ8S+j69eva/ehjzk4ha1WtKgev585B166Jn/vqK9i1CxYvhl699FM+IWdau3atTusZGxszaNCgTC5N+qVnrnQhdbmlztav1/xlwJMn8rw4SqUSGxubJBeJZ82C5s2hcWMVCkXi+ciaN2/Opk2baNeuHevWrcPU1JQ//vgjUQALuafeslJ2qjMDA3le9LRo8s3I6yfdoHfvxI8lSbdpeJo0acKcOXMYM2YMY8eOpXLlyjRs2DDZdbNTvWV3GVlPol1A+GwVLy6PWdX8zjIyMqJo0aIA3Lx5M5Utk3fnzp13+y2eYWUUsi/NuNdz55I+d/YshIeDs3PWlkkQBCE70yRcMjc3T7F3k5vb+yAjPBy+/17OTwHQqlUrVq9ejVKpxNvbmy+++EKMO8xl0jvPqy42bZKn0tH1VPn666/p3r078fHxdOzYkRuasWJCtvBJLa+xsbE8e/aMiIgIHBwcsLOzy6hyCUKm++KLpFfmSpYsya1bt7hx40aizNUfOnQIChWCwoXfdw29fVueeL1YsWKZVGIhO6lSRR47/eGUcK9evZ8gPblpcgQhNQm73KZl/vz5mVgSCA8P55YmOx1w9+5dAgICsLOzo1ChQpl6bCF3Cg8PB0gxEc6HevSAbdvg+HHYskUejtGlSxeUSiXdu3dn7dq1REREsH79ejHzw2cqtSFeIH8n9+sHb97AwIGwfHnaAbJCoeDPP//k5s2bnD17Fnd3d06cOIGTk1MGllz4WOkOXnNyFkRBSCi5Dy/N2LHUrrKFhoImrn3zBjTfwZqWVxG8fh4qVJD//6amiZdrWmJLlAAbmywvlpDDnT9/PtFjf39/4uLiKFWqFCB/NqlUKqpmwRxMZ8+epXHjxtrHmsC6T58+rFixItOPL+R8mzfDhAnyeNb58xO3vOpi1Cjw85Pn1m7SBHbuhDx5oFOnTpiamtKxY0f+++8/2rRpw/r3/ZOFHCgdDanv1tdtA3t7WLcOPD3B2xsKFIBp09LezszMjB07dlCnTh1u376Nh4cHhw4dwsrKKn0FFTJcui5TzZ8/nyJFiuDt7U3Tpk3ZsmULAQEB3LhxgxMnTjB58mTi4uJo3rw5LVq0+Kiul4KgTyXfpQ9O7dy9f1++z5PnfeAKInj93KhUSQNXkLsMA1SrlrXlEXIHPz8/7c3T05OGDRvy6NEj/P398ff35+HDhzRu3JhWrVplelkaNWqU7HzqInAVdHXrlpxXQjPfZnqDVzc3OXC1t4czZ6BBA3j0SH6uVatW7Ny5E3Nzc/bu3Uvz5s15/fp1JrwKISvp2m34/fppb9CyJfz+u/z3Dz/IN104OjqyZ88eHB0dCQgIwMPDQzv9l6A/6QpeNVkQT58+zffff4+7uzvly5enePHi2gyI3t7ePHv2jHbt2nHkyJHMKrcgZIjhw6FYMdi+XX6sCV5Ta3m9d0++L1Ik8XIRvH6+El4APnNGvhfBq/Cp5s2bx8yZM7G1tdUus7W1Zfr06cybN0+PJRME3Wi+LwsXlu/T220YoHp1OHJEzu4fGAh1676frs7NzQ1fX1/s7e05c+YM33zzjTZ5opCzmJnJ3cJTmAHpkw0aBLNny39PmgQzZui2nYuLC7t27cLGxobjx4/TokULEcDqWbqC17Vr16aYvj8hTRZELy+vjy6YIGSFly/l8Yma7zpN8Hr79m3iPhzM+E5yweubN294+fIlgDbpk5D7HT0KNWtC27byY7UaDh+W/65VS3/lEnKHsLAw7edKQi9fvuTNmzd6KJEgpI+mxTVfPvk+vS2vGq6ucOyYPBzjwQPo2FH+vAWoXbs2J06cwMXFhefPn9OgQQMOaz6IhRxj3z557nRdO5Voug3r0vKqMX48zJwp//3dd7B/v27bValShX379mFra8uJEydo2bKl+AzWIzG6XfisaeJMTVfgAgUKYGRkRGxsLE+ePEl2m+SCV02rq729PdbW1plTWCHbsbaWp8vZt0/OhhkdDV9/DR4ecmuBIHyK9u3b069fPzZv3syjR4949OgRmzZton///nTo0EHfxROENGl68Wo6D3xs8Apy6+3Ro3LOiX/+STyPdokSJTh8+DClSpUiJCSEJk2a8Pvvv6crI63weZgwQQ5gv/pK7pauq6pVq7J//37s7Ow4ffo0EydO5J7mB6GQpT46eE042ezDhw+ZNGkS48aNE12FhRxFkzDzwQP5XqlUUrBgQYAUP5Q+7AYFosvw56pcOXk6nMhIuVubqal8NXfnTnSap04QUrNkyRI8PDzo0aMHhQsXplChQvTo0YMWLVqwaNEifRdPENIUEiLfa4LXiIgI4OOCV5CntztwACpXfr/s1Ck567uDgwPTpk2jc+fOxMXFMWzYMPr27UtkZOSnvAQhm/qYlleNCRNg6dL342vfvpWTcaalcuXKHDp0iAIFCvDo0SMaNGiAv79/uo8vfJp0B6+XLl2iSJEiODo6Urp0aQICAqhevTo///wzy5Yto3HjxmzZsiUTiioIGU8TvGpaXgGKvGtSvZ9wYQLJtbw+eBf9Fk4Y0Qq5nkIB7u7y35s26bcsQu5jZmbGokWLePXqFefPnycgIIDg4GAWLVr00T/+s6u3byVWrQrkf//bxpQpW/j7bz9evAjSd7GET/Rh8BoTEwPIw8s+VsJY5cwZaNhQ/hx++VLe76pVq5g3bx4qlYp//vmHOnXqaC8wC9nXyJHy//Hkyaw5nuY8iouDrl3l3lJXrqS9Xbly5Thy5AhFihTh2bNnNGjQgI0bN2ZuYYVE0h28jh8/nvLly3P48GEaNWpE69atadWqFaGhoYSEhDBw4EBmzZqVGWUVhAz3YcsrvA9eU2p5HTMGvv1WnipF4+HDhwDaVlvh86GZK3jpUpg7Vx6zIwgZ6f79+zx58oR79+6xf/9+tm7dytatW/VdrAxx5040tWr9goVFEb74ogwzZ7Zh6tT29O3rRt68DhQtWpThw4fj5+dHfHy8vosrpFP+/PLUJPb28mNN8GpkZJQh+3/2TE7wc+AAVK1qQECAAwqFgtGjR7Nv3z4cHBwICAigUqVKrFq1KkOOKWSOkydh714I0vGa1ae0vCb08CFcvChnxa5ZE/79N+1t8ufPz48//kjTpk15+/YtnTt3Zvz48SnmShEyVrqD1zNnzjBjxgzq1q3L3LlzefLkCUOGDEGpVKJUKhk+fDjXrl3LjLIKQobTNJQGB8O7JIja1tOUgteePWH69PeBL8Cjd7n7RfD6+WnY8H1m4XHj5HkJBSEj3Llzh4oVK1KuXDlatWpFu3btaNeuHe3bt6d9+/b6Lt4nO3HiMaVKNeDUqVHAAxQKU8zNq2JqWhuFojggfw7/9ttvuLm54eBQnMmTZ/FCkwVIyPaOHJGDg3e5ELXBq2EGjavw9JS7DZcpA8+eKZgypQ4TJiiJiZGnevL396du3bq8efOGL774gh49eojpdLIpfQ1PLlpUnp/dzU3uPtylC4wdC+9O1RSZmZmxdetWxo0bB8BPP/1E8+bNU8yXImScdAevwcHBODk5AXKqc3Nz8yRp/EUGLiGnsLKSv1Rr1Hg/3iGtbsPJ0QSvBQoUyOgiCtmcQgGrV0OLFtCrl9z6KggZYeTIkRQtWpQXL15gZmbG5cuXOXz4MNWqVePgwYP6Lt4niYiIoEuXWsTFnUalsmX8+GWEh78iPPwsERHHiYq6ydOnoWzduhUvLy9MTW0JCbnHtGkTcXYuSPfu/cVc8jlQRre8ApQtK3cfHjBAbpmfP19F3bpw9ar8nXzw4EGmTZuGSqVi7dq1VKxYkf26ppkVspyuDakZmYzLwQH27JEvQAPMmwe1a7+fiSIlBgYGzJkzh3///RcLCwv8/PwoX748mzdvzrCyCUl9VMKmD5voP7XJXhD06fp1+cpt/vzy49RaXp89k6dC+TCu1XQbFsHr56lkSdi1C1auBDs7fZdGyC1OnDjBtGnTyJMnD0qlEpVKRb169Zg5cyYjRozQd/E+iZmZGePHj6ds2fJcunSW2bO/wszMVPu8kRE4OVnh6enJ8uXL8fZ+TIEC3kB14uNjWLfuL0qVKo2nZ1cCAgL09jqE9MmM4BXkOUJ/+03NhAmnsbOTOHtWzgIPcoDx/fffc/ToUYoVK8aDBw9o2rQpXl5eBAcHZ2g5hI/3sbFoRsUgBgYwZw5s2SJ3c/f3l8fC6lKuTp06cebMGSpXrkxwcDAdO3bEy8tLNOZlko8KXvv27UuHDh3o0KEDUVFRDBo0SPtYzO0q5HSaltcHDx6g1kwk946vr9xNtH//98vi4+O13UREt2FBEDJKfHw8lpaWAOTJk0f7OVO4cGGup9UkkAMMGzaMs2dP4+qadpb2rl1NefCgLzt2nKZ8+WNAayRJzfbtG6hcuTKtWrXixIkTmV9oQWcXL0Lp0tCu3ftlmRW8atSq9ZRz5+KYMAGGDXu/PDoaatWqRUBAAEOHDgXA29sbV1dXNmzYIKbUyYEy63/Wtq187rZoAcuW6d4SXLp0aU6ePMmECRNQKBR4e3tTpkyZXJOfIDtJd/Dap08fHB0dsba2xtraml69euHs7Kx97OjoSG9NBhNByIGcnZ1RqVTExsby9OnTRM9phjJoWmkBXrx4QVxcHEqlUtulXhAE4VOVK1eOCxcuAFCzZk3mzJnDsWPHmDZtWq6YlkuhUGBiYpKO9aFlS7hwoQ4HDmyjWrULQHcUCiU7d+6kTp06NGnSBD8/PxGMZAPPn8s9m27ffr8ss4NXkL+fZ858Pw9sRIScYHHYMIiNteS3337j6NGjuLq68uLFC7p27UrLli1FvhY907xl09uQmhm9P52d5d5UNWq8X/brr3Jixg/aNBIxMjJi5syZHDx4kKJFi/Lo0SPatm1Lx44defz4cYaX83OV7uDV29tbp5sg5BRr10Lx4jBggPzYwMCA/O+iU013YA3NZ4+z8/tlmvGu+fLlw8DAINPLKwjC5+G7777T9v6YNm0ad+/epX79+uzcuZNff/1Vz6XTH4UCGjeGM2cqcOTIGvbtu86XX36JoaEhBw4cwM3NjapV67Fr1y4RxOrRh9PkQNYErx/avBlu3IDff5eHeCxbBrVq1eX8+fNMnjwZQ0NDdu/eTfny5fn6669FQic9Uankmz7GvKblzh0YPx4GDYImTSAwMPX1GzRowOXLl/nmm29QqVRs3rwZV1dX5s2bR3R0dNYUOhf7qG7DgpCbxMbKV4YTDnF1fhedptTymjB4FdPkCIKQGdzd3enQoQMAxYsX59q1awQFBfHixQvc3Nz0XLrsoV49cHMrzh9//MGtW7coWXIYYMz588dp2bIlZctWw8fHJ8kQECHzaZIgWlu/XxYbGwtkbfDaq5c8lU7ZsvDqFQwcKLeoHTtmzJQpU7hy5Qpt2rQhLi6OBQsWUKJECZYsWaItq5A1Tp2S51z18EjfdlmRd6dwYZg1Sx5bffAgVKliwJ9/ltNeoEmOmZkZs2bNwt/fn5o1a/LmzRvGjh1L2bJl2bx5s7iw9gnSFbyOHj1a55sg5BT58sn3CeNUTfD6Ycrz5LoNi0zDgiBktNjYWJo0aZIko66dnZ1IkpiCQoUKsWbNQtzd7wJjAXMCA/3p0KEDJUtWYO3atWKu2CykmX7u3bBtQD8tryC31J8/DwsWyLMM+PvLLWju7lCoUAn+++8/9uzZQ5kyZQgKCmLw4MGUKVOGtWvXigsfAiqVPA3e5cvymNj4eAXbt7tQpowBixfLjSApqVChAseOHeOvv/7CycmJ27dv07FjRxo1asTp06ez7DXkJukKXs+fP5/otnz5cpYuXcrBgwc5ePAgy5YtY/ny5VmS+W/GjBnUqVMHMzMzbGxskl1HoVAkua1bty7TyybkLJphqgmD13zvIlpdWl414xicEy4UBEH4BIaGhly8eFHfxchxqlaF3bvzERDwE56e94DvACtu375Cjx49cHV1xdvbW7SqZYG3b+V7c/P3yzJ6ntf0MDSEkSPh5k15/KuhIVhYgLGx/Hzz5s0JCAjg119/xcHBgVu3btGjRw8qVarE1q1bRUtZNqP5f2TlxbyiReVsxDt3xlGwYBivXikYM0Zu0U+NSqWiX79+3Lx5k++//x4TExMOHz5MzZo18fT0xN/fP0vKn1ukK3j18/PT3jw9PWnYsCGPHj3C398ff39/Hj58SOPGjWnVqlVmlVcrJiaGzp07M3jw4FTX8/b25unTp9pbu4Rp7wSB9y2vr169n5Q6uZZXSUo+eH3+/DmASNYkCEKG6tWrF8uXL9d3MXKkihVh69Y8XL78Ax073gemY2Zmz82bN/Hy8qJ48eIsWrSIyMhIfRc119IErxYW75fpq+U1IUdHWLgQrl2T5/PUuHsX+vUzxM1tOHfu3GH69OlYW1tz6dIl2rZtS61atdi6datoic0kY8dChw5yC3l217SpxIIFB/nll3i+//59IwjA3r0pt8RaWFgwbdo0bty4Qd++fVEqlWzfvp2qVavSvn17ccFSRx895nXevHnMnDkT2wQj8W1tbZk+fTrzEn4aZJKpU6fy9ddfU758+VTXs7GxwcnJSXtLT2ZD4fNgZydfgQU5OyK8b3lNGLzGxclfeJMmJf6gevHiBQB58+bNkvIKgvB5iIuLY/HixVSrVo2BAweK4TkfoWxZ2LjRhmvXvuXmzXvMnTuXvHnz8uDBA4YOHUq+fAX5/vvvk/SyET6duTkULAgODu+XZYfgVaNYMXg3Mx4gj2lcvRrKlYM+fSzw8PiWu3fvMnHiRMzMzDh9+jRt27alQoUKrF69mri4OL2VPTc6cAB8fODdT6o06aPlNSGVSmLwYDUTJ75fdvy43BW9WDH5wkhYWPLbFixYEG9vbwIDA+nZsycKhYItW7ZQsWJF2rVrx/Hjx7PmReRQH50aNSwsjJcvXyZZ/vLly2w1Ke/QoUP58ssvKVasGIMGDaJfv36pnujR0dGJMoGFvTvzYmNjRTcjHWnqKSfVV968Bjx6pODhwzicnCQcHR0BOXhN+DoSTmOsWfzs2TMA7O3tP+k158R60zdRZx9H1Fv66aOuLl++TJUqVQC4ceNGoufEuNf0KVUKwIIxY8YwZMgQypRZzr178wgNvcf06dOZOXM23bt3Z8yYr6lUqZKeS5s7fPedfEsoOwWvHxo8GIKDYdMmOUPx5s3g4WHL2LE/MmLESH75ZQGLFi3iypUr9OrVi++//55x48bRr18/0TCSAXJDr+zHjyFvXnj0SG5JnjZNzlA8YkTiXCkaJUuWZNWqVXz77bdMnTqVDRs28N9///Hff/9Rt25dxo8fT+vWrVEqRX7dhD46eG3fvj39+vVj3rx51Hg3EdKpU6cYN26cNjuivk2bNg03NzfMzMzYu3cvQ4YMITw8nBEjRqS4zcyZM5k6dWqS5X5+fpiZmWVmcXMdX19ffRdBZ/ny1cDU1IRjxy7x8mUI996lHr5//z47d+5MddsHDx4AcOvWrTTX1UVOqrfsQtTZxxH1pruIiIgsP6afn1+WH/NzYGpqysGDw/jxx0H89dd/xMX9THz8MVat+odVq/6hfv3GjBv3Na1atRI/GjNYdg5eK1WCf/+Fq1fhxx/lafR27ZJvTZrkxdd3Jt988w2LFi1iwYIF3L17lyFDhjB58mQGDRrE4MGDtb22hI+X3qlystOFvM6dwdNTbsGfO1fumj5nDsyfDx07wqJFcm+/D7m6urJu3TqmTJnC3LlzWblyJceOHaNt27a4uroyduxYevToIS6SvKOQPnIEekREBGPHjuWvv/7SXpE2MDCgf//+/PTTT5gnHKGvowkTJjB79uxU1wkMDKR06dLaxytWrGDUqFE6zcs1adIkvL29k8zdmVByLa8FCxbk6dOn2Nvbp/0iBGJjY/H19aVZs2Z6ScqQEYKCgrTjXsPDwzEyMuLBA7h3T0GhQpK2q5EkSVhYWBAbG8utW7coVKjQRx8zN9RbVhN19nFEvaXfq1evyJcvH6GhoVhZWem7ODlGWFgY1tbWBAUFZcvv0GfP5B+Uv/xymrCwn4F/ATkjcfHixRk8eDB9+/bFLrlfnJkkNjaWnTt30rJly1z3/qxQoQKXLl3C19eXpk2bZui+M7rebt2Cn38Gb2/4+muYMUNeLknw9GkEmzYtZ+7cudoL2IaGhnTp0oWRI0dSvXr1Tz5+VshO51qlSnDhAuzZA82bp71+165d2bBhA7/++ivDhw/P9PIlpEu9qdWwY4ccxB4+LHdRv3VLzlwM8nmUUtz95MkTfv31VxYvXqztAWpvb89XX33FoEGDKFy4cCa8qsz16tUr8uTJkzHfodInCg8Ply5cuCBduHBBCg8P/6R9vXjxQgoMDEz1Fh0dnWgbb29vydraWqf9b9++XQKkqKgoncsUGhoqAVJQUFB6XspnLSYmRtqyZYsUExOj76J8NLVaLRkaGkqAdP/+fUmSJOnnnyUJJKlr1/frBQcHS4AESJGRkZ90zNxQb1lN1NnHEfWWfkFBQRIghYaGZupxNJ83unr06FEmlSRj5JTv0IgISVq6VJJcXB5IbdqMl6ytrbWf7UZGJlLfvn2l06dPZ0lZcsv7s18/SapZU5L8/N4vK1WqlARIhw4dyvDjZVa9BQVJUnDw+8e7d0uSubkkDR0qSRcuxEobNmyQ6tatqz1fAKl27drSunXrsv3/MDudaxUryr+x9uzRbf3OnTtLgLRw4cJMLVdy0ltv58/L541GdLT8er//XpIeP055u9DQUGnOnDlSwYIFteeWUqmU2rVrJ+3bt09Sq9Wf9DqyUkZ+h6arP4zm6lJC5ubmVKhQgQoVKiRpbdVMIaIrBwcHSpcunertU7qaBAQEYGtri7EmL7ogpEChUCSZLkczxDth8glNpmFra2vRnUMQhE9WvXp1Bg4cyJkzZ1JcJzQ0lD/++INy5cqxadOmLCxd7mVqCgMGwI0bBdm8eTaPHz9m2bJl5MtXiZiYKFasWEGNGjWoUqU63t7eeulGntNcvAinTkHCqsrO3YZTYm8PCXKTsmWLnEn599+hYkUDFi/uzKhRRzlx4gy9evXC0NCQEydO0K1bNwoXLsykSZOS/f0sJKbpB5qNegFnmEqV5EROGlu2yK3MP/wAhQtDjx5w8mTScb9WVlaMGzeOO3fu4OPjQ5MmTVCr1WzZsoWmTZtStmxZfvvtN516n+Ym6Qpes9OX6oMHDwgICODBgwfEx8cTEBBAQEAA4e9mxd62bRt//vknly9f5tatWyxevJgff/wxy7sWCDnDli3g4gLdu79f9mHGYU0GvOSCV5FpWBCEjHD16lXMzc1p1qwZTk5OtGrViq+++orhw4fTq1cvqlSpgqOjI3/99Rdz5sxJNYeDkH5Kpdytz9zcnK+++ooBA/wxNj4OfAEYcf78Wby8vMiXrwBjxozh5s2b+i5ytvXu51iieV41w8z03UX1UyxaBL6+0K6dfL74+cljHTt2rEbx4iu5efM+kyZNwtHRkadPn/LDDz9QtGhRPD092bFjB/Hx8fp+CbmClA3HvOqqfXvYsAHq1ZNnsli7FmrXhho1YOVKSDB6EZCHZbZr1459+/Zx5coVhg4dioWFBYGBgQwfPhxnZ2f69OnD0aNHP4v5iNMVvGanL9VJkyZRuXJlJk+eTHh4OJUrV6Zy5cqcPXsWkD8Yf//9d2rXrk2lSpVYunQp8+fPZ/LkyZlWJiFnu3NHnudNQ5NxOCgoCEi+5VVMkyMIQkayt7dn/vz5PH36lN9++40SJUoQFBSkDZJ69uzJuXPnOHHiBC1btsySMv3+++8UKVIEExMTatasyenTp7PkuNnBlCkKnj+vzcKF/1Cy5CNgNlCUsLAQ5s+fT8mSJWnatCkbN24U2bs/kF3nef1UCgU0bSpP63LvnpxROW9eeR74TZugUKF8TJ06lYcPH7J+/XoaN26MWq1m+/bttG7dmmLFijFjxgwxPdMHzpyBqChwc9N3STKfoaF8wePIEfD3h379wNgYzp6F3r3h+vWUty1Tpgy//fYbjx8/5tdff6Vs2bJERkbyzz//UL9+fcqWLcv8+fO1v11zo3QFr9npS3XFihVIkpTk1qhRIwBatGjB+fPnefPmDeHh4QQEBDBw4ECROVBIVp488v2rVwmXyQs/DF7fxbTA+5ZXx4QLBUEQPpGpqSmdOnViwYIF+Pj4sHv3blatWsWYMWMoV65clpVj/fr1jB49msmTJ+Pv70/FihVxd3fXXrj7HFhbw7BhcO2aA4cOjadLl5uoVDtwdm6FQqFg//79dO7cmfz5C/H999+LLqLvJNfymhuC14QKFpS7fj54ILeezZjxvttrdLQRP/3UBU/PA5w8eY3Ro0djZ2fHgwcP+O677yhUqBCdOnXC19cXtVqt3xeSDRgZyQGcJqFRWnJLC2PlyvDXX/DwoXz+9OgBFSq8f37mTPnciopKvJ2VlRXDhw/n0qVLHD9+HC8vL8zMzAgMDGTMmDHkz5+fbt26sX///lx3fn1UJJddvlQFIaNogteEF6pSCl5Ft2FBED4X8+fP56uvvqJfv36UKVOGJUuWYGZmxl9//aXvomU5hQIaNID161U8edKS48e3c/fuXb777jvy5HHi5ctnTJ8+nSJFitKypegimltbXpNjZATdusnTpGisXSu3pI0eDQ0bluLly3ls3PiYf/5ZSd26dYmLi2PTpk00b96ckiVLMnv2bO1vCkF3ObHbcHIcHOB//5On2dF49QqmTJEDWmdneb7YixcTb6dQKKhduzbLly/n6dOnLFmyhKpVqxITE8P69etp2rQpJUuWZObMmbmmtf+j53kVhNxEM4PD69fy+AMDg/fB66t3zbGi27AgCJ+TmJgYzp07x8SJE7XLlEolTZs25cSJE8luk9x0cyCPdcxN3WptbTVJfJyZNGkS+fL9j+HDt6NWL0WS9rNr13Z27dpOvnyFGDSoP/369cPJyUmnfWvqKSfXV0wMxMbK41qNjGLRvBRN8KpQKDL89WW3euvUCeLilCxbpuTCBQUrV8LKlSaUK9eTAQO6M3v2Rdas+ZPVq1dz+/ZtJkyYwPfff0+bNm346quvaNSoUab3FsxOdfbtt0oePVIwfnw8Zcumvb6mNTE+Pj7Ly59V9RYXB+PHK/n7byUPHypYuBAWLoSqVdX06yfRpYsaG5v365uamuLl5YWXlxfnz5/nr7/+Yu3atdy+fZv//e9/fP/997Rq1Yr+/fvTvHlzVLo2c2eAjKwrEbwKAvKPEIVCzvQWHCx3DU7Y8ipJcteNFy8gf/732718F9E6JIxoBUEQcoGgoCDi4+OTXJzLmzcv165dS3abmTNnMnXq1CTL/fz8MDMzy5RyZgf588Off1qyf/9Cdu6MISRkJeDN06cPmDx5MtOmTaNmzZq4u7tToUIFnVqLfH19M7/gmSQiwgB7ezciIw04fHgXhoYSarWauLg4AA4fPoy1tXWmHDs71Vv+/HLL2c2bNuzZU4QjR/Jz+bIBI0cqWbr0Jc2bN6dhw4YcPXqUPXv2cOPGDTZt2sSmTZvIly8fzZs3x83NLdPqSiM71Nn69Y158MAKV9cT3L+f9nhNTSvilStX2LlzZ2YXL1lZUW/Vqsldiy9ccGTfvkKcPp2Pc+eUnDsHZ88G0q7d7RS39fDwoHHjxhw7dgxfX1+uXbvG1q1b2bp1K/b29jRt2pQmTZpkydC3jMzQLoJXQUBuabWxgZAQuevwh8GrQgGDBiXdLiQkBCBLJ7AXBEHIriZOnMjo0aO1j8PCwv7f3n2HNXW9ARz/hr0EVFBQEXFi3atuBSfVatU6qnWgVlurddtqh6O2Vau1aq2rA2zrqnV0qZUq4Kj6c+FEKxS3uEVRRoD7+yMmEJlBIAHez/PkIbn35N6TQ+DkzTn3PXh4eODr60tp7RSXImzgQEhOhqCguaxY8THbt2/CwmIFavU//POP5la1ajVGjhzBoEGDMmwTtVpNUFAQHTt2LNRZeXv31t57CUBvRL5Lly55HpCZeruNHw/37yusWZPMf/+Bv7+vbt+hQ68yebKCt3cYa9d+x5o1a7hx4warV69m7dq19OjRgxEjRtC2bds8nSZrSm32/vuakKRp06b4+mZ/PWtgYCAAtWvXLrDkdVrGaLdu3TTJwW7fTmbNGoU1a8z45JMalClTA4A//lBx5oyKQYNSKFdO/7m9evUCNIF+YGAgP/30E3fv3mXDhg38/PPPdOrUiWHDhvHyyy/n2+u5mzapzHOS4FWIpxo0gEePQHtdu/ZDRVYZ2+7duwdAybSLwAkhRBHg4uKCubl5uuvwbt68mekUWGtr6wzXUre0tDT6h+OCYmmp+aDZrZsF164N4ty5Qbi6nmTlypX8+OOPRERc4N133+WDDz6ib9++vP32WzRv3jxdUFLU2iw+TcYZe3v7fHttptxuZcrAhAnaR5opm5cuwZdfamZ+OTk1ZtCgxvz99wJOnVrPypUrOXz4MBs3bmTjxo1Uq1aNkSNH4u/vr/uCPS+YQptp3/6WlhbkpCravxcLCwuj1d0Y7VauHEyZorlB6rkXLYI9e2DmTHO6d4dx4zTX6Kf9t1K/fn0WLVrEvHnz2LJlC9988w27d+/mr7/+4q+//qJMmTL4+/vzxhtvUK1atTytd162k6TeFeKpXbvgf/8Dbc6xtCOvN29CaChEPjM7QzvyKsGrEMIYDh06lG/HtrKyolGjRuzatUu3LSUlhV27dtG8efN8O29RUr48tG8PdevW5euvv+aPP64Dq4AGqNUJrFmjSd5Tu3Y9li1bprtGuCjSXu8KhXud17zm5ATz5mnWmo+JgaVLoWlTewIDhzNu3P84cOAYb775Jg4ODly4cIEpU6ZQvnx5BgwYQGhoaJHJultEXoZRKAoMG6ZZNzY5WbOMk48PNGwIAQHpMxVbW1vrMhFfuHCBadOm4ebmxq1bt/j888+pXr06vr6+rF27Vu9LJ1MhwasQmdAGrzExMQQFqfHxgTfe0C8j04aFEMbUp0+ffD3+xIkT+eabb1i9ejXh4eGMGjWKx48fM3To0Hw9b1HVpo0Dly6NYNq0ozg5/Q8YCthy9uwpRo8ejbt7Od5++23+++8/Y1f1uRw6BC1awJtvpm5Lm6ypIBPFmDpnZ80o2r//ws6d0KuXZrmYffs009CPHWvAihUruHHjBqtWrdJlkl23bh0+Pj7UrFmThQsX5um0TGPK6axobdBeVLINPw+VCoYM0awbe+aM5u/O1hbCwjRBbVbdRNWqVfnss8+4fPkyW7ZsoUuXLpiZmRESEsLrr79OuXLlGDduHGfOnCmw15Mdg6YNe3l55epNMn78eMaOHWvw84QwJmdnZ8zMzEhJSeHKlbuAG2ljVLVaTezThexk5FUIkV/69u2b4XZFUXSXLuSXfv36cfv2baZPn050dDT169dnx44dkmH9OVSsCJ99pmL69CasX9+EhQu/4NSpH4EVPHkSzrfffgvAunXrGDVqFP369St0ya5u3IADB/RH07TZRq2srCTgyICZGXTsqLldv65Z+3PjRhg8WLPfwcGBmjVHEBg4goSEo6xcuZK1a9dy/vx5Jk2axAcffMDgwYMZP348NWvWNO6LyQUZec0bL7wAK1bAZ5/Bt99qRvIHDUrdHxOj+fv09tZ/nqWlJT169KBHjx5cuXKFgIAAvvvuOy5fvsySJUtYsmQJ7du3Z+zYsXTt2tWoX0AZFLxqL442VKVKlXL1PCEK0uLFmtvAgfDxx2Bubk6pUqW4c+cO0dF3ADe9lOTaUVeVSpXvmQCFEMXX33//zY8//ohD2gUz0QSve/bsyffzjxkzhjFjxuT7eYobGxvw94chQ0ryzz9j+fXXd+jadS/Lly9j06ZNHD58mMOHDzNhwkRGjhzBuHHjKJ823b0Je/RI87NEidRtRXWN1/xQrpwmOc+HH6ZuUxR4+204dQo6dWrEpEmrmD9/AevXr2PFihWEhYWxatUqVq1ahZ+fHxMmTKBjx46F7osCGXnNG6VKwbvvatYZTttEy5fDtGnQvbtmf8uW6Z/r4eHB9OnT+eCDDwgKCmLVqlX8+uuv7Nq1i127dlG5cmXGjBnD0KFDcU77wbiAGBS8tm3bNr/qIYTRxcVBVBRcuZK6zcXF5ek1r5qkTWkHWLUjHk5OTjIFSgiRb3x8fChRogRt2rRJt69u3bpGqJHISyqV5gNky5YqoA0tWjTHx6cb48ZdJTFxFTEx/zF//nwWLVrEwIEDmTx5Mi+88IKxq52lp5OSJHjNQ48eQY0ammmhO3dqbrVqOTJx4pscODCS//1vL19++SW//vorO3bsYMeOHdSqVYvx48czcOBAbGxsjP0SsvTPP5qEmWnfM+L5WTwT6V28qPn522+aW4sWmiC2WzfN6H9a5ubm+Pn54efnx6VLl1i2bBnffPMN//33HxMnTuSjjz5iyJAhjBs3jurVqxfI6wG55lUIHW3ivrSXjaRN2gRkOPIqU4aFEPlp8+bNGQauYBrrM4q85+LiyLJlk6hX7wLwO9AGtVpNQEAAtWrVolu3buzdu9dkk/XIyGvec3TUTCOOiNAsu+PgoAlkhw+HatVU3LnThi1btnDhwgXGjh2Lg4MDZ86cYcSIEVSpUoVFixbx+PFjY7+MTDk7a0YLc5rLS0Zec2fFCggP1+RwsbLSfGnQo4dmunFWE2w9PT2ZN28eV69eZdWqVdSqVYvHjx+zbNkyvL296du3L8ePHy+Q15CvwWt+ZkEUIq9pl9tLuzKONni9dy/9yKskaxJCGJv0s0WTpWUKgwcrHD9uxu7dL9OxYyhwEHgVUPHHH3/Qpk0b2rRpw+7du00uiM0oeNWu8yqZhp+Pl5dmaZ2rV2H+fM0U46tXNdPQAapUqcLixYu5cuUKCxYsoEKFCly/fp0JEybg5eXFnDlzinRWa5E9b2/45hvNKOy0aZqM1+fPa1bdyI6dnR0jRozg1KlT7Nq1i5dffhlFUdi4cSMNGzbEz88v37Ng52vwmt9ZEIXISxkFr9q1XmNiNMOxaUdeZY1XIYSxST9btKlU4OurmSL6v/81pWfPX7C0PM/rr7+JtbU1+/bto3379vj4+BASEmLs6upkFbya+vTVwsLJCSZP1izht2YNvPRS6r6vv4b1650ZN24SkZGRfPPNN1SuXJnbt2/z/vvv4+npyccff6xLOmkKZszQZMm9cCFn5WXk9fm5u2sSO125Al98oZk+rPXff7BgATx5kvFzVSoV7dq14/fff+fkyZMMGDAAMzMz/vrrL3x8fGjVqhVBQUH5EsQadM1rRoyZBVGIvKQNXtO+bbWJmGrXjmHoUGjQIHWfjLwKIQqC9LMCoEkT2LwZoqOr4ea2gnnzPmLu3LksW7aKPXv24Ovri4+PD7Nnz6ZVq1ZGrau5uSZwTZvLUBu8WltbG6lWRZONDQwYkPr44UP46CO4fx+WLIH5860YPvwN/P39Wb9+PZ9++innzp1jxowZfP3117zyyit06NDB6CPiGzZoRv8GDoRq1YxalWKnRAlNYqe0Zs6EH3+EhQs1icNGjkx//axWnTp1WLNmDR9//DHz588nICCAf/75h06dOuHr68tnn31GtTz8pT538GrsLIhC5BVtJxsTo8nqp1Khy6JWtuwDvax/ICOvQoiCIf2sSMvNTfOzfPnyjBr1FUuXvgfMAb4hJCSE1q1b06NHD+bNm1egSVTS+uILzS2t+Ph4QEZe85uNDcyapbmFh8PLL0P79rB0qQUDBw5kwIABbNy4kQ8//JCIiAi++eYb/v77b2bPnk3//v0xezZrTwExsZnvxV6HDpp1Yy9ehNGjNVmKv/oKfHwyf06VKlVYsWIFM2bMYO7cuaxYsYLg4GCaN29O586d86xuz/0O1WZBbNu2rd7Nx8dHsiCKQsXZGapWhXr14GleCd3Ia0xMTLrykrBJCFEQpJ8VmXnhBdi1qwJNmnwNRAAjADO2bt1KrVq1GDt2rC7hoLHJyGvBsLKCd97RJHaaMkXzeNcuqFtXMzU3MdGMfv36cfbsWZYuXUrJkiWJiopi4MCBtGrVimPHjhm1/rJUjmkYPFgzEr50qSaR1unTmksYXntNsw5xVtzd3Vm8eDH//vsvw4YN000nzivPHbxKFkRRVDg4aK61OHIEtH2rduQ1KuoBJ09CcnJqee3Iq0wbFkLkJ+lnRVbatYODB2H16oq4ua0CTgFdSUpK4quvvqJq1aosXryYpKQko9ZTrnktWM7O8PnncO4cdOkCajV8+in8+69mv6WlJSNHjmT58uXMmjULe3t7Dhw4QOPGjRk1alSBX5IgI6+mx8pKM+r6778wapRmKZ0NG2DRopw939PTk++++44zZ87QvXv3PKuXZBsWIgva4PXw4Rjq1dP889eSkVchRH44c+YMERERxq6GKETMzFJHSqZMeQFLyz9wcfmbOnXqExMTw/jx42nSpAkHDx4skPr4+4OfH5w4kbpNO21YRl4LlpcX/PGHZpmdTz7RjMBqKYrmy4Rp06Zx/vx5+vfvj6IorFixgurVq7N69eoCz2QtI6+mp3RpWLZMM7jTr5/melhDeHt78/333+dZfSTbsBBZcNJlm3iAtXVqKnqQ4FUIkT8mTpzIsmXL9Lb9+eefvP7660yYMIGL2lXmhXiGo6NmtO30adi6tT1hYUdZuXIlJUuWJCwsjBYtWvDmm2/m+6ja3r3w11/6mUpl2rDxqFTQuzdMnZq67exZaN7cnP/+03zOKV++PGvXriUkJITatWtz9+5d/P396datG9ezmyeaB2Tk1fQ1aADr14OdneZxSormfZWHM4Jz5LmD1759+2Z469Onj2RBFIVO//5QuTIEB2seO+vWxnnAszGq9jpY57Tr5wghxHM6ceIEr776qu5xeHg4PXv2JDQ0lJ9++okXX3yxQD5MisKrenVo2RLMzMwYOXIk06efA4agKAqrVq2idu3a/Pnnn/l2fm2aCEfH1G0ybdi0TJ4Mx46Z8e67bViwwIyUFM32tm3bcuzYMebMmYOVlRV//vkntWrVKrBRWBl5LTx++AE2bdIs0/Thh1BQVyY8d/D6999/M2TIEEaPHp3uZm9vnxd1FKLAXL8OUVFw+7bmcerIawzPxqiPni5kVyLtQnZCCPGcYmJi8PDw0D3+4YcfqFy5MpcuXeLq1avUq1ePuXPnGrGGorD5998yQCAQipVVDW7cuMHLL7/MsGHDMkxI+DxSUjTLtEDqEnQg04ZNzQ8/QI8eKSQlmfH+++b07AkPHmj2WVpaMnXqVI4dO0bjxo158OAB/v7+DBgwgIcPH+ZLffbu1aw32qhRvhxe5IPXXoO33tKMmn/6qSZDcXR0/p9Xsg0LkYY2VtX+A08dVY3HySlBr6z2H7gEr0KIvFShQgVu3Lihe7xr1y769OmDubk51tbWTJs2jZ07dxqxhqKwWbYMfvsNypRpQ2LicczMJqJSqQgICKBOnTqEhITk2bkePEA3ipc2n6GMvJoWFxfYsCGZUaPCsLJS+O03zVrCJ0+mlqlVqxYHDhzg008/xcLCgvXr19O4cWNOpL2YOY+4uUGFCqkJM7MjI6/GZ2OjWUJn7VpN0tPQUHjxRQgLy9/zFspswxcvXmT48OF4eXlha2tLlSpVmDFjBona9U2eOnnyJK1bt8bGxgYPDw8+//zzfKmPKDq0sar2i+i0gamDQ+q304qiyMirECJfdOjQgYULFwJw6dIljh07RqdOnXT7q1SpwpUrV4xVPVFIdesGp05Bt262pKR8gaKEYmureS+1b9+e2bNnk5w2pX4u3b2r+VmihCZbqZZc82p6VCro3PkSISHJVKyoWV6nWTP43/9Sy1hYWPD++++zZ88ePDw8uHDhAk2bNs3TBDyicOvfX5PMqXp1zeh5y5aaJGH5xeDg1RSyIJ47d46UlBRWrlzJmTNn+PLLL1mxYgXvv/++rszDhw/p1KkTnp6eHD16lPnz5zNz5kxWrVplxJoLU6cdedUGr+bm5tjYaC7asbV9oCuXkJCgW3bAMe1FPUII8Zw+/PBDgoODqVy5Ms2bN8fDw4NWrVrp9t+8eRMHBwcj1lAUVmXKwK+/wsqVYGfXmvj4E3TuPJSUlBSmT59O586duXnz5nOdQxu8pp0yDKnThmXk1fQ0bqxw7Bh06qQJXuvXT1+mefPmHD9+nK5du5KQkMDw4cOZMmVKnnzhAZpppxMnQk7z0cnIq2mpUUOzZFenTpppxG5u+Xcug4NXU8iC6OfnR0BAAJ06daJy5cp0796dyZMns3nzZl2ZNWvWkJiYyPfff0+tWrV47bXXGDt2rO7bbCEy8mzwqtmm2di69QPdNu2oKyAfIoUQeap8+fIcPnyYnj178tJLL7F582a9D2i7d++mevXqRqyhKMxUKhg5Eo4dg2++sWfHju8JDAzEzs6OXbt2Ub9+ffbv35/r48fFafrSzIJXGXk1TaVLw59/ar7c0I6YK4r++valS5fmt99+Y+bTtVIWLFhAr169iI2Nfe7zf/cdfPllwVwzKfJHyZKa99DevdC4cf6dx8LQJ5w4cYLp06frHmuzIJYpU4aEhATWrFlDWFgY5cqVy9OKZicmJoZSaS6uOHDgAG3atMEqzZyVzp07M2/ePO7fv5/p8iYJCQm6qS2Qel2jWq1GnXaRT5EpbTsVxvZycDADzLl/PwW1WvMfu3TpEty8CfXrP9C9prtPv1q2t7cnOTk5T755LMztZizSZrkj7Wa4gm4rT09Pvvjiiwz3nT17lt69exdofUTRU6OG5gYwZMgQypRpwuuv9yU6+gy+vr6sXLmSoUOHGnxcX1/Nda/PXMmlC3DkC1/TZWGhme4NmsB1yhS4dk2T3MnSUrPdzMyMGTNmUKNGDfz9/fntt9/w8fFhx44duLi4FFhdZeTVNFlY6CfdOnYMVq0yfG3YLM9h6BMyy4J45swZkpKSePnll5k7dy5LlizJu1pmIyIigq+++ooFCxbotkVHR+Pl5aVXrmzZsrp9mQWvc+bMYdasWem2BwcHY6dd2EjkSH5d85yfbt6sgLt7DR49imbbtjMAuunBoaGhxMXFAfDff/8BYGVlxbZt2/K0DoWx3YxN2ix3pN1y7knaBSuN7IcffjB2FUQRk5ICH3zwAvfvH6JUqSHcu7eJYcOGcfr0aT7//HPMzc0NPmba611BMvQXNmfPwpIloFZDQoJmfc+0v9PXXnuNSpUq0b17d44ePUrbtm35+++/cXd3z9X5tKvwSCxadMTFaa61v34d/vvP8P8hmTE4eNVmQaxYsSKgnwXR3NycadOm8fbbb+eqMlOnTmXevHlZlgkPD8fb21v3+Nq1a/j5+dGnTx9GjBiRq/OmNW3aNCZOnKh7/PDhQzw8PPD19aX0s3NgRIbUajVBQUF07NgRS+1XdYVEly6aBd7B8+kN5sxZAvyLh0cNunTpAsC+ffsAcHFx0W17XoW53YxF2ix3pN0Mp51tIURRZGammbLZs6c99+79jLPzLB48+JiFCxcSHh7Ohg0bnjvolOC1cKlVC7ZsgVdf1fzs3VuzpmfaLqNZs2bs2bOHDh06cPbsWVq3bs2uXbvw9PQssHrKyKvpsrWFb7/VvIeCgp47R7COwcGrNgvihg0bdFkQ005tep4siJMmTcLf3z/LMpUrV9bdv379Or6+vrRo0SJdIiY3N7d0SQe0j92yuIrY2to6w+sxLC0t5UOegYpKm0VFaTraAwfieestzevRjsI4Ojrm+WssKu1WkKTNckfaLeeknURR17YtHDgAXbqY8d9/s7Czq01y8hC2b99Ou3bt2LZtG66urtkeZ+RIuHoVPvoImjdP3S7Ba+HTtatmiaVXXoHff4dhw2D1as2XHVre3t7s3buX9u3bExkZSYcOHdi3b59utmNOGTryqp02LEzbSy9proP97LMU/v47b45pcBicn1kQXV1d8fb2zvKmvYb12rVr+Pj40KhRIwICAjAz038pzZs3Z8+ePXrXKQUFBVGjRo1MpwwLkRFF0b6fU5M0SScshBCiqNFmDG3RAp486UNSUgglSrhw5MgRWrZsSVRUVLbHCA6G7dvTX/Mqa6MXTp06aUZczc3hp58018E+Gzd6eXmxZ88eKlWqREREBJ06deL+/fvGqbAwOb6+sH593mSlhlwEr6aQBVEbuFasWJEFCxZw+/ZtoqOjiU6TomzAgAFYWVkxfPhwzpw5w4YNG1i8eLHelGAhnnX5MjRsqLlppaQ4PP2Zmk1PglchhBBFkasr7NoFffpAcvKLeHruw9PTkwsXLtCiRQtOnDiR6XPV6tSlTqpW1d8n/Wbh1aULBARo7i9cqL8OrFaFChUICgrCzc2NkydP0rVr11zlCjB05FWmDRc/Bk8bhuyzIL766qvPVansBAUFERERQUREBBUqVNDbp30zOzk5sXPnTkaPHk2jRo1wcXFh+vTpjBw5Ml/rJgo3Cws4flzzDaOiaP6JJiVpgtfk5PTBq6zxKoQoaD///DPjx4/n+vXrxq6KKKJsbDQJeurVgzfeqEFy8j/4+flx6tQpfHx82LlzJ02aNEn3vHPnIClJk7H22bw9ErwWboMGwZ074OEBTZtmXKZq1aoEBQXRpk0bDhw4wNChQ1m/fn2OAsxduzTvnacpdYTIVN5dPfvUDz/8wPjx4/P6sHr8/f1RFCXDW1p169Zl7969xMfHc/XqVd577718rZco/LTrvCYnw+PHmvtqtaajTUpKDV5l+pMQIj/ExcWRkpKSbZlnczrkh08//ZQWLVpgZ2eHs7Nzvp9PmBYzM/jgAyhbFsqVK8eePXuoUaMFDx48oEOHDhw4cCDdc44d0/ysX1//uki1Wq0LXuXSrcJrwgRN4qas1K5dm61bt2JpacnPP//Mxx9/nKNjV6mimbZua5uzusjIa/FlUPDq5eVF5cqVDb4V5LI5QjwPO7vUDvdpP0tiombkVa2WacNCiPx19uxZGjVqxJEjR4xdFRITE+nTpw+jRo0ydlWECfj1V2fOn/+LEiXa8PDhQzp16qTLvK8VGqr52ayZ/nNv3boFgLm5uazcUERcu6a5/jWjZe7btGnD8uXLAZg5cyabN28u4NqJosygacOBgYG5OkmlSpVy9TwhCppKBQ4O8PChJnh1dU2dNpyYmBq8aq/jsLe3N0o9hRBFU8WKFbG0tKR58+aMHj2aTz75JNdJEJ+Xds3z3Pb9omipWhVKlXLg3r1t2Np2JzZ2N507d+bPP//Ex8eHpCTQLnveoYP+c7U5ScqUKZMuwaYofNRqaNVKc31zyZLw/vvpywwfPpzTp0+zaNEihg0bRsOGDbOMB+bPh5gYGDUKypfPvg4y8lp8GRS8tm3bNr/qIYTJKFEiNXhNSYFXX3Vg0yZISEgfvNrZ2RmrmkKIIsjV1ZVDhw6xePFipk+fzubNm1m6dCndu3c3dtVyJCEhgYSEBN1j7SUWarVaL/u/yJy2nUytvV58UTOy2q2bHRcv/o6lZU+ePNlJly5d2Lx5Mw0atKdFC3P271fRqlUSaat/7do1AMqWLZtvr8tU282UPU+bffSRiuHDLZgxQ8HXN5nGjdMvXfPpp59y4MABDh06xGuvvcbu3bszXXZsyRILrl5V0a2bmjJlsj+/NnhNSkoq8N+5vNcMl5dtlauETUIUZdqZwI8egZUVDBqkCV5jY1OXypHgVQiRX1QqFePHj6dXr16MHj2anj170rNnT7766ivcn82CY2LmzJmjG7FNKzg4WP5fGigoKMjYVcjQzJnWfPJJUyIifgVeJS5uG926dWfq1PcYMqQxr75qSVCQ/gfVHTt2AGBhYcE27fBsPjHVdjNluWmzUqWgVatG7NtXgQEDHrNgQSgWFukD2GHDhnH69GkOHTrE4MGDGTRoUIbHi4vrBNiyb99+oqNjsj3/7du3AThx4gRO2oQlBUzeazmXm8zTmZHgVYhnVKqkvz6ddspebKyMvAohCk7FihX5/fffddmFa9asyWeffYZtTjOaZGDq1KnMmzcvyzLh4eF4e3vn6vjTpk3TW5Lu4cOHeHh44OvrK9c65pBarSYoKIiOHTtmOkplbL17w7x5ZsyZs4mkpNdISvqVefPmsWbNGvr27ZKu/K5duwBo3bo1Xbqk358XCkO7mZrnbbMXX4S6dRUuXnTi33+7MnlyxsnmnJ2d6devH1u2bGHy5Mk0atQoXRkbG01I0rp1Sxo0yP7cixcvBqB+/fr59p7KjLzXDHf37t08O5YEr0I8Y/v21PuPHkF0tGYoVoJXIYQx9O3bl86dOzNlyhTeeeed58r8O2nSJPz9/bMsU7ly5Vwf39raGmtr63TbLS0t5UOegUy5zSwt4eOPYdQoc376aSMHDrzOli0b6d+/P2vWrKFfv3565c+ePQtAzZo18/01mXK7marctlm5crBgAQwdCrNnm/Paa+Z4eaUv17dvX7Zu3cq6desYOXIkR44cwcrKKsNjWlhYYkhVjPn7lvdazuVlO8lV80Jk4eBBGDhQRl6FEMbl5OTEqlWrCAkJoUxOLgjLhKurK97e3lneMvtQKcSz3N1hyhRLfv55LQMHDiQ5OZkBAwawcuVKXZnExETdsjrNnk1DLAq9IUPA1xfi4uCzzzIvt3jxYlxcXDh16hRz585Nt1+72qXkXxLZkeBViCxoVsRxeHpfrnkVQhhX69atOXnyJDNmzMj3c12+fJmwsDAuX75McnIyYWFhhIWF6X2RJwRormUNDAzkjTfeICUlhbfeeosRI0Zw//59Nm/ezJMnT3B3d6dWrVrGrqrIYyoVLFoE06fDwoWZl3N1ddUtnfnpp58SGRmZR+eXaLe4keBViGcsWgQNG8LixdrgVXN9WWJiIslPFzST4FUIYSyWlpZMnz49388zffp0GjRowIwZM4iNjaVBgwY0aNDAJNagFabH3NyclStX8umnn6JSqfj222/x8PBg6NChALz55puyTE4RVbcuzJqVmvAyM6+99hqdOnUiMTGRKVOmZFgmp7GoNtuwKH7kv4gQz4iOhuPHISpKs2SONngFiI+PByR4FUIUfYGBgSiKku7m4+Nj7KoJE2VmZsb7779PUFAQtWrV4vHjx8THx9OmTRveffddY1dPFABFgcxy86hUKr788kvMzc3ZsmULwcHBun07dsDRo1C9umHnk5HX4keCVyGe4eio+fnokf7IK0BcXBwgwasQQgiRmfbt23Py5EmOHTvGwYMHCQ4Ofq4s2aJwOH1ak4H4pZdSr2F91gsvvMCoUaMAmDBhAikpmgzFdepoZr3l9GOVjLwWXxK8CvGMtOu8aoJXc8zMNFnSJHgVQgghsmdmZkaDBg1o2rSpTBcuJsqWhbNn4fBh2Lkz83IzZ87E0dGREydOsHnz5oKroCgS5L+JEM9IH7yCpaXmG+O4uDjUajVJSUmABK9CCCGEEACurvDWW5r7s2dnPvpaunRpJkyYAMCMGTNITk5myRKYMwdu3crZubQjrzJtuPiR4FWIZ6QNXjt0gEmTwM4uNXjVjrqCBK9CCCGEEFqTJ4O1NezfD09XSMrQ+PHjcXZ25uzZs2zcuJHPPoP339fkHREiKxK8CvGMtMFrjx6aBbidnNIHr2ZmZrIeohBCCCHEU+7u8Prrmvtff515OWdnZyZOnAjAJ598YvA1rDLyWnxJ8CrEM5ydwcVF81NLm2gibfBqZ2cn/zSFEEIIIdIYPVrzc+PGrEdSx44di4ODA2fOnCEhIQjI+VI5oviS4FWIZ7z4Ity+DaGh8N9/cOkS2Npqpgc/G7wKIYQQQohUDRtCixagVsOaNZmXc3JyYtiwYQDExX1p0Dlk5LX4kuBViCy8/DJUqgRqdfqRV3t7eyPWTAghhBDCNH3yCfz2G4wfn3W5sWPHolKpSEzcAYTLyKvIlgSvQmRBm204o4RNMvIqhBBCCJGery906wbm5lmXq1KlCq+88srTR4tzfHwZeS2+JHgV4hkpKeDjA40awdWrmm329umDV1lwXQghhBAia9nlYho7duzTe2uJi3uc7/URhZsEr0I8w8wMDh2CY8dStzk4pAavcXFxgASvQgghhBCZSUiAadOgZk14+DDzcj4+PlSoUAV4xLFjGw06h4y8Fj8SvAqRAe1yOamPU4PXhIQEAKytrQu6WkIIIYQQhYKVFWzdCufPwy+/ZF5OpVIxapQmcdNPP32Xo2MburSOKDoKZfB68eJFhg8fjpeXF7a2tlSpUoUZM2aQmJioV0alUqW7HTx40Ig1F4VF2uBVpZLgVQghhBDCECoVDByoub8xmwFVf39/zMzM2LdvH//++68B55CR1+KmUAav586dIyUlhZUrV3LmzBm+/PJLVqxYwfvvv5+u7N9//82NGzd0t0aNGhmhxqKwSRu8OjjoJ2yKj48HwMbGxhhVE0IIIYQoFF59VfNz166spw7/8Uc5atToBMCGDRuyPa6MvBZfhTJ49fPzIyAggE6dOlG5cmW6d+/O5MmT2bx5c7qypUuXxs3NTXeztLQ0Qo1FYePoqPnp6Qlvvpl6fauMvAohhBBC5Iy3N9SooVnzddu2zMtNmwbh4f0AWL9+fY6PLyOvxY+FsSuQV2JiYihVqlS67d27dyc+Pp7q1avz7rvv0r179yyPk5CQoAtOAB4+/ZpIrVajVqvzttJFlLadCnN7OTiYA2Z8+GESQ4YozJljBUBsbKwu27ClpWWevsai0G4FTdosd6TdDCdtJYQQudOzJ8ydC1u2wGuvZVxGM5DaA0vLNzl79iynT5+mdu3amR5TRl6LryIRvEZERPDVV1+xYMEC3TYHBwe++OILWrZsiZmZGZs2baJHjx5s3bo1ywB2zpw5zJo1K9324OBgWdfTQEFBQcauQq7FxTWkRIkynDx5lm3bLnPx4kUAIiMjdcHrzZs32ZbV14i5VJjbzVikzXJH2i3ntH/3QgghDNOjhyZ43bZNk4E484lrzrRq1Zng4N9Zv349n3zySQHWUhQWJhW8Tp06lXnz5mVZJjw8HG9vb93ja9eu4efnR58+fRgxYoRuu4uLCxMnTtQ9btKkCdevX2f+/PlZBq/Tpk3Te97Dhw/x8PDA19eX0qVL5+ZlFTtqtZqgoCA6duxYaKdpd+kCMTEQE1ObkiVrc+XKFQICAihZsiQVK1YEoHr16nTp0iXPzlkU2q2gSZvljrSb4e7evWvsKgghRKHUpAk0bQp16sCjRxkHr9qB1C5d+hEc/Dtbt27NMnjVjrzKtOHix6SC10mTJuHv759lmcqVK+vuX79+HV9fX1q0aMGqVauyPX7Tpk2zHWmwtrbO8FpGS0tL+ZBnoMLeZhs2wJgx0KsXdOvmAGimlSclJQGa62Dz4/UV9nYzBmmz3JF2yzlpJyGEyB0zM8jpYh+tW7+Eubk5Z86cISoqCi8vr/ytnCh0TCp4dXV1xdXVNUdlr127hq+vL40aNSIgIAAzs+xzT4WFheHu7v681RTFxK1bmp9ly6YmbIqPj5eETUIIIYQQeUg78lqyZClatmzJnj17+PPPPxkzZkwm5WXktbgqlNmGr127ho+PDxUrVmTBggXcvn2b6OhooqOjdWVWr17NunXrOHfuHOfOneOzzz7j+++/55133jFizUVhERoKH3+suV+mDFhZaRI2JSYmylI5QgghhBAGSk6G//0PYmOzLvfyyy8D8OeffxZArURhY1IjrzkVFBREREQEERERVKhQQW9f2uxjs2fP5tKlS1hYWODt7c2GDRvo3bt3QVdXFEJxcan3y5ZNHWVNm41aRl6FEEIIIXKmRQtN8Lp1K7zyiv6+TZsgKQnKl9cEr++++y67d+8mNjYWBweHdMeSkdfiq1AGr/7+/tleGztkyBCGDBlSMBUSRU65cqn3PT1NJ3hNSUkhMTGxwM9rqtRqNRYWFsTHx5OcnGzs6hQa0m7pWVpaYm5ubuxqCCFEkdW4sSZ4/fvv9MFr+/ap9729vfHy8iIqKoqQkBDdSKwQUEiDVyHyW61amnXJXFygc2f455/004YLOnhNTEwkKiqKlJSUAj2vKVMUBTc3N65cuSLfvhpA2i1jzs7OuLm5SZsAFy9eZPbs2ezevZvo6GjKlSvHwIED+eCDD3SXUQghhCHatoVly+Cff7Iup1Kp6NChA9988w3BwcFZBq/y/7r4keBViAyYm8PmzamPMxp5LchrXhVF4caNG5ibm+Ph4ZGjBGXFQUpKim5KkbRJzkm76VMUhSdPnnDraZY2SewH586dIyUlhZUrV1K1alVOnz7NiBEjePz4sd6a6kIIkVPNm2t+njgBjx+DvX3qvtWrQa2Gvn3B0RF8fX11wWtG0l4mKIoXCV6FyAHtSIOxpg0nJSXx5MkTypUrh52dXYGd19Rpp1Hb2NhIEGYAabf0tBnFb926RZkyZYr9FGI/Pz/8/Px0jytXrsz58+dZvny5BK9CiFzx8NBc03rtGhw5ohmJ1XrnHc0asL6+qcEraFYKuXfvHqVKlcrwmDLyWvxI8CpEDmgD1cTERKMEr9rrEmW6nhD5R/vFkFqtLvbBa0ZiYmIy/QCplfYLPoCHDx8CmjZVq9X5Wr+iQttO0l6GkXYznDHarFkzczZtMmPv3mRatEi9DEpRLADV0/8VULp0aby9vTl37hy7du2iR48eesfRXkKVlJRU4L9zea8ZLi/bSoJXIXIg7bRhYy6VI98wCpF/5O8rcxEREXz11VfZjrrOmTOHWbNmpdseHBwss0YMFBQUZOwqFErSboYryDZzcqoM1OG3325Rp87/dNuTkroCFoSEhHD+/BMAvLy8OHfuHKtXr0735X1MTAwAR44cMdoUYnmv5dyTJ0/y7FgSvAqRA8aeNiyEEHlh6tSpzJs3L8sy4eHheHt76x5fu3YNPz8/+vTpw4gRI7J87rRp05g4caLu8cOHD/Hw8MDX15fSpUs/X+WLCbVaTVBQEB07dsTS0tLY1Sk0pN0MZ4w2q1wZKldOplkzV9q06aLbrp3t4uvrQ+XKmm1xcXFs376dy5cv06VLF73jaL8ka9Kkid4lDgVB3muGu3v3bp4dS4JXIXJAG6gmJSUR93QRWAlen5+/vz8PHjxg69atmZbx8fGhfv36LFq0qMDqJURRNWnSpGyXmqus/eQIXL9+HV9fX1q0aMGqVauyPb61tXWG/xstLS3lQ56BpM1yR9rNcAXZZnXqaG5Z10Vzv02bNgCcOXOGhIQEvfVetTNljPn7lvdazuVlO0nwKkQOpP0w9ujRo3TbRHrZTcGcMWMGixcvzvPpPps3b+azzz4jIiICtVpNtWrVmDRpEoMGDdKV8fHxITQ0FNCMqru4uNCwYUOGDh1Kr1698rQ+QpgSV1dXXF1dc1T22rVr+Pr60qhRIwICAiS5lxAi32g/CqT96FCuXDk8PDy4cuUKR44cwcfHJ015yTZcXElPJEQOpL3WQpuAxBjXvBYmN27c0N0WLVqEo6Oj3rbJkyfj5OSEs7Nznp63VKlSfPDBBxw4cICTJ08ydOhQhg4dyl9//aVXbsSIEdy4cYPIyEg2bdrECy+8wGuvvcbIkSPztD5CFEbXrl3Dx8eHihUrsmDBAm7fvk10dDTR0dHGrpoQopC7dg1++QX27s2+bLNmzQA4dOhQhvslV0HxIyOvQuRA2uA1KSkJMI2R18ePM99nbg5p4+usypqZwdOVQrIsm3ZNtuy4ubnp7js5OaFSqfS2Qfppw48fP2bUqFFs3ryZEiVKMHnyZL3yH3/8MT///DOnT5/W296wYUO6devG7Nmz9b6ZBRg3bhyrV69m3759dO7cWbfdzs5OV58KFSrQrFkzvL29GTZsGH379qVDhw45f7FCFDFBQUFEREQQERFBhQoV9PbJiIcQ4nn88AO8/z689hq0bq3ZtnYtJCdDmTL6ZZs1a8bGjRs5ePCg3nb5P1R8ycirEDlgZmaGhYX+dz2mELw6OGR+e/VV/bJlymRe9qWX9MtWqpRxufw2ZcoUQkND+fXXX9m5cychISEcO3ZMt3/YsGGEh4dz+PBh3baTJ0/qRlifpSgKu3bt4vz587prZ7IyZMgQSpYsyebNm/PmBQlRSPn7+6MoSoY3IYR4Hg0aaH4eP5667ZVXoFev9F+SN23aFICDBw/K/x8ByMirEDlmbW2tG3UFmTac12JjY/nuu+/46aefaN++PQCrV6/WG/WpUKECnTt3JiAggCZNmgCwZs0a2rZtq5dkJiYmhvLly5OQkIC5uTnLli2jY8eO2dbBzMyM6tWrc/Hixbx9cUIIIYQAoG5dzc8LFyAhAbIaC2jYsCEWFhZER0dz+fJlPD09gdSRV5k2XPxI8CpEDllbW/M4zXxaUxh5jY3NfN/TrPM6t25lXvbZPCzGiN0iIyNJTEzUfcsKmutXa9SooVduxIgRDBs2jIULFwLwyy+/6O5rlShRgrCwMGJjY9m1axcTJ06kcuXK6aYUZ0RRFOkMhRBCiHzi7g6OjvDwoSaArV0bfv5ZM224e3f90VdbW1vq1avH0aNHOXz4sC54FcWXBK9C5NCzC2SbQvBqyDWo+VW2oHXr1g1ra2u2bNmChYUFarWa3r1765UxMzOjatWqANSvX5/w8HDmzJmTbfCanJzMhQsXdKO6QgghhMhbKhXUrAmHDkF4uCZ4HTIE4uPh0qX0n0EaNmzI0aNHCQsLS9ffy5fNxY9c8ypEDqUNVlUqVbprYMXzqVKlCpaWlnoZBe/fv8+///6rV87CwoIhQ4YQEBBAYGAgvXr1wjZttqkMpKSkkJCQkG0dVq9ezf3793n12QuGhRBCCJFnXnhB8zM8XPMzq8tZ69evD0BYWJhum1z/WnzJp28hciht8GptbS3f9uUxBwcHhg8fzpQpUyhdujRlypThgw8+yHBtyTfeeIOaNWsCsGPHDr19c+bMoXHjxlSpUoWEhAS2bdvGjz/+yPLly/XKPXnyhOjoaJKSkrh69Spbtmzhyy+/ZNSoUfj6+ubfCxVCCCGKuadduC54zUq9evUA/eBVSz6LFT8SvAqRQ2mnDZvClOGiaP78+cTGxtKtWzdKlCjBpEmTiImJSVeuWrVqtGjRgnv37tG4cWO9fY8fP+btt9/m6tWr2Nra4u3tzU8//US/fv30yn3zzTd88803WFlZUbp0aRo1asSGDRvo2bNnvr5GIYQQorjr0QOqVIGncalu5DWjWLTu0wxP165d4/bt27i6usrIazEmwasQOZQ2YH32+leRNX9/f/z9/dNtDwwM1Hvs4ODAjz/+yI8//qjbNmXKlHTPUxSF69evM2rUqHT7PvnkEz755JMs6xMSEpKjegshhBAi71WrprnlRIkSJahatSoRERGcOHFCbx12GXktfuSaVyFySEZeTcPt27dZunQp0dHRGQbEQgghhChcshp5hfTXvcrIa/ElI69C5NCz17wK4yhTpgwuLi6sWrWKkiVL8vDhQ2NXSQghhBAGCgqC48ehW7fsy9avX59ffvkl3XWvMvJa/EjwKkQOSfBqGtJ+25qSkmLEmgghhBAitxYtgm3boGRJCAyElBTN/YxokzadOHECkJHX4kyCVyFyKO20YbnmVQghhBAi96pU0fyMiIB587IuW6tWLQDOnz9PUlKSbruMvBY/hfaa1+7du1OxYkVsbGxwd3dn0KBBXL9+Xa/MyZMnad26NTY2Nnh4ePD5558bqbaiKJCRVyGEEEKIvKENXiMjsy/r6emJra0tarWa//77T0Zei7FCG7z6+vry888/c/78eTZt2kRkZCS9e/fW7X/48CGdOnXC09OTo0ePMn/+fGbOnMmqVauMWGtRmEnwKoQQQgiRN9IGr7//Dr/9BvHxGZc1MzPTre9+9uxZ3XYZeS1+Cu204QkTJujue3p6MnXqVHr06IFarcbS0pI1a9aQmJjI999/j5WVFbVq1SIsLIyFCxcycuRII9ZcFFaSbVgIIYQQIm9Urar5GRGhWfc1JQWuXwd394zL16xZk2PHjhEeHi4jr8VYoQ1e07p37x5r1qyhRYsWWFpaAnDgwAHatGmjF3B07tyZefPmcf/+fUpmckV4QkICCQkJusfaTKZqtRq1Wp2Pr6Lo0LZTUWsv7XtLez+vX19W7aZWq1EUhZSUFElSlIa289K2jcgZabeMpaSkoCgKarUac3NzvX1F7f+ZEEIYm5eXZmmc2NiclX/hhRcA/ZFXUfwU6uD1vffeY+nSpTx58oRmzZrxxx9/6PZFR0fj5eWlV75s2bK6fZkFr3PmzGHWrFnptgcHB2NnZ5eHtS/6goKCjF2FPJX2mup79+6xbdu2fDlPRu1mYWGBm5sbsbGxJCYm5st5C7NHjx7l6/Hnzp3Ln3/+yd69e5/rOGvXrmXatGlcunQpj2qWsbp16zJq1ChGjRoFQMmSJfnpp5/o2rWrXrn8breszm2KEhMTiYuLY8+ePXoJQQCePHlipFoJIUTRZG0N5cvD1aup27KaBZxR8CrThosfkwpep06dyrxs0o2Fh4fj7e0NwJQpUxg+fDiXLl1i1qxZDB48mD/++OO53sjTpk1j4sSJuscPHz7Ew8MDX19fSpcunevjFidqtZqgoCA6duyoN1pZ2O3Zs0d339PTky5duuTp8bNqt/j4eK5cuYKDgwM2NjZ5et788uzI1bOmT5/OjBkznusciqLw6NEjSpQoka8d2Pvvv8+kSZNwdHR8ruMMGTKEXr16PfdxsmNmZoaNjY3uPNeuXaNkyZK66e750W6zZs3i119/5dixY+n22dra5vtrzgvx8fHY2trSpk2bdH9nd+/eNVKthBCi6Fq7FkqUgAYNsi+rveb13LlzeHh45HPNhKkyqeB10qRJ+Pv7Z1mmcuXKuvsuLi64uLhQvXp1atasiYeHBwcPHqR58+a4ublx8+ZNvedqH7u5uWV6fGtr6wyvZ7S0tCxSgVhBKGptlnbk3dbWNt9eW0btlpycjEqlwszMDDOzwpFn7caNG7r7GzZsYPr06Zw/f163zcHBQfdaFEUhOTkZCwvD/iVpp7xq2ya/5FXgZW9vj729fZ4cKztp26RcuXJ6+/Kj3bRBcEbHK+j3bW7fT2ZmZqhUqgz/BovS/zIhhDAVrVtrfqpUoChZj7xWqVIFS0tLnjx5opvBJCOvxY9JfQp2dXXF29s7y1tm62tqP4xpr1dt3rw5e/bs0btOKSgoiBo1amQ6ZViIrEjCJsO4ubnpbk5OTqhUKt3jc+fOUaJECbZv306jRo2wtrZm3759REZG8sorr1C2bFkcHBxo0qQJf//9t95xK1WqxGeffcawYcNwcnKidu3aelnEExMTGTNmDO7u7tjY2ODp6cmcOXN0+1UqFStXruTll1/Gzs6OmjVrcuDAASIiIvDx8cHe3p4WLVoQmSZ3/8yZM6lfv77ucUhICC+++CL29vY4OzvTsmVLXUd64sQJfH19KVGiBI6OjjRq1IgjR44AEBgYiLOzs97rWb58OVWqVMHKyooaNWrw448/6u1XqVR8++239OzZEzs7O6pVq8Zvv/1m0O9CpVKxdetWAC5evIi5uTm///477du3x87Ojnr16nHgwAG95+zbt4/WrVtja2uLh4cHY8eO5fHjxxkePzAwkFmzZnHixAlUKhUqlYrAwEDd/jt37mRZ/9OnT/PSSy/h4OBA2bJlGTRoEHfu3NHtT0hIYOzYsZQpUwYbGxtatWrF4cOHdftDQkJQqVR676effvoJMzMzXdtrLVq0CE9PT7nWVwghTEhO8i9ZWFhQvXp1QDNTRhRPJhW85tShQ4dYunQpYWFhXLp0id27d9O/f3+qVKlC8+bNARgwYABWVlYMHz6cM2fOsGHDBhYvXqw3JVgIQ6QNWDP7EqWgKIrC48ePjXLLywx/U6dOZe7cuYSHh1O3bl1iY2Pp0qULu3bt4vjx4/j5+dGtWzcuX76s97wvvviCxo0bc/ToUYYPH87o0aN1o7pLlizht99+0y2ltWbNGipVqqT3/NmzZzN48GDCwsLw9vZmwIABvPnmm0ybNo0jR46gKApjxozJsM5JSUn06NGDtm3bcvLkSQ4cOMDIkSN13/6+/vrrVKhQgcOHD3P06FGmTp2a6ajdli1bGDduHJMmTeL06dO8+eabDB06lODgYL1ys2bNom/fvpw8eZIuXbrw+uuvc+/evdw0uc4nn3zCxIkTCQsLo3r16vTv3193nWdkZCR+fn68+uqrnDx5kg0bNrBv375M26Rfv35MmjSJWrVqcePGDW7cuEG/fv1yVP8HDx7Qrl07GjRowJEjR9ixYwc3b96kb9++uue/++67bNq0idWrV3Ps2DGqVq1K586d07VB2vdT9+7d6dChAwEBAXplAgIC8Pf3LzQzGIQQoiiLjIT581MfZzeQqr3uNbW8jLwWO0ohdPLkScXX11cpVaqUYm1trVSqVEl56623lKtXr+qVO3HihNKqVSvF2tpaKV++vDJ37lyDzxUTE6MAyp07d/Kq+kVeYmKisnXrViUxMdHYVclTixYtUgAFUCZPnpznx8+q3eLi4pSzZ88qcXFxiqIoSmxsrK4uBX2LjY01+LUFBAQoTk5OusfBwcEKoGzdujXb59aqVUv56quvdI89PT2VgQMHKoqiKMnJycq9e/eUMmXKKMuXL1cURVHeeecdpV27dkpKSkqGxwOUDz/8UPf4wIEDCqB89913um3r1q1TbGxsdI9nzJih1KtXT1EURbl7964CKCEhIRkev0SJEkpgYGCG+55thxYtWigjRozQK9OnTx+lS5cumdZX+7vfvn17hudQFE0bffnll3rH2LJli6IoihIVFaUAypIlS5Tk5GRFURTlzJkzCqCEh4criqIow4cPV0aOHKl3zL179ypmZma69+Cz0rZRWtnVf/bs2UqnTp30nnPlyhUFUM6fP6/ExsYqlpaWypo1a3T7ExMTlXLlyimff/65oiiZv582bNiglCxZUomPj1cURVGOHj2qqFQqJSoqKsPX8OzfWVp37txRACUmJibD54qMSR9quKLah+Y3aTfDmUKbbdumKJpxV0VZsUJRnjzJuvyMGTP0PpPs2bOnYCqahim0W2GTl31oofzquU6dOuzevZu7d+8SHx9PVFQUy5cvp3z58nrl6taty969e4mPj+fq1au89957RqqxKApk2nDea9y4sd7j2NhYJk+eTM2aNXF2dsbBwYHw8PB0I69169bV3ddOR7516xYA/v7+hIWFUaNGDcaOHcvOnTvTnTft87VZyOvUqaO3LT4+XrdUVlqlSpXC39+fzp07061bNxYvXqx3fe/EiRN544036NChA3PnztWbfvys8PBwWrZsqbetZcuWhIeHZ1pfe3t7HB0dda83t2rVqqW77/50UT3tMU+cOEFgYCAODg66W+fOnUlJSSEqKsrgc2VV/xMnThAcHKx3Lm1SvsjISCIjI1Gr1XrtZGlpyYsvvpiunZ59P/Xo0QNzc3O2bNkCaKY3+/r6phuJF0IIYRyenpqfTk7w5ptga5t1eW3/oCUjr8WPSSVsEsKUpQ1YjR282tnZEZvThdHy4dx55dnkRZMnTyYoKIgFCxZQtWpVbG1t6d27d7rlgZ6dhqtSqXTXMDZs2JCoqCi2b9/O33//Td++fenQoQO//PJLhs/XdnwZbcvsusiAgADGjh3Ljh072LBhAx9++CFBQUE0a9aMmTNnMmDAAP7880+2b9/OjBkzWL9+PT179jS0eXL0evPimM++3tjYWN58803Gjh2b7nkVK1Z8rnNpz5f2XN26dcsw07y7u3uWwf+znn0/WVlZMXjwYAICAujVqxdr165l8eLFBtdfCCFE/tAGrzExmpuTU9bla9Sokf+VEiZNglchcsiUgleVSlVgWWsL0v79+/H399cFerGxsVy8eNHg4zg6OtKvXz/69etH79698fPz4969e5QqVSrP6tqgQQMaNGjAtGnTaN68OWvXrqVZs2YAVK9enerVqzNhwgT69+9PQEBAhsFrzZo12b9/P0OGDNFt279/f7pregpaw4YNOXv2LFWrVs3xc6ysrEhOTs7VuTZt2kSlSpUyzA6sTWa1f/9+PJ9+ylGr1Rw+fJjx48dne/w33niD2rVrs2zZMpKSkujVq5fBdSyuunfvTlhYGLdu3aJkyZJ06NCBefPmpcteLYQQuWVvD6VLw9278N13MGYMZJVWpFq1anqPZeS1+CmU04aFMIa004aNnbCpqKpWrRqbN28mLCyMEydOMGDAAINHGBcuXMi6des4d+4c//77Lxs3bsTNzS1dlt/cioqKYtq0aRw4cIBLly6xc+dOLly4QM2aNYmLi2PMmDGEhIRw6dIl9u/fz+HDh3Vr0z1rypQpBAYGsnz5ci5cuMDChQvZvHkzkydPzpO65tZ7773HP//8w5gxYwgLC+PChQv8+uuvmSZsAk0W6KioKMLCwrhz544u83t2Ro8ezb179+jfvz+HDx8mMjKSv/76i6FDh5KcnIy9vT2jRo1iypQp7Nixg7NnzzJixAiePHnC8OHDsz1+zZo1adasGe+99x79+/fHNrs5aULH19dXl/hs06ZNREZG0rt3b2NXSwhRxGgn9EyaBBlcraPHwcEh3WWConiRkVchcsiURl6LqoULFzJs2DBatGiBi4sL7733XobXnWalRIkSfP7551y4cAFzc3OaNGnCtm3b8iy7rJ2dHefOnWP16tXcvXsXd3d3Ro8ezZtvvklSUhJ3795l8ODB3Lx5ExcXF3r16sWsWbMyPFaPHj1YvHgxCxYsYNy4cXh5eREQEICPj0+e1DW36tatS2hoKB988AGtW7dGURSqVKmil0H4Wa+++iqbN2/G19eXBw8e6LL6ZqdcuXLs37+f9957j06dOpGQkICnpyd+fn6639ncuXNJSUlh0KBBPHr0iMaNG/PXX3/leNmz4cOH888//zBs2LAclRcaEyZM0N339PRk6tSp9OjRA7VaLeveCiHyTMWKcPx4zsvXqFGDa9euATLyWhypFCUP170ogh4+fIiTkxN37tyhdOnSxq5OoaBWq9m2bRtdunQpUh9wduzYwUsvvQTAd999l+cfhLNqN21iMi8vL2xsbPL0vIVZSkoKDx8+xNHRUZY+MUBxa7fZs2ezceNGTp48mWW5rP7O7t69i4uLCzExMTg6OuZndU3SvXv3GDVqFNeuXWPfvn2ZlktISNAbdX/48CEeHh7cuHFD+tAcUqvVBAUF0bFjxyLVh+Y3aTfDmUqbTZhgxtdfmwNw/boaF5esy48ZM0a3vntoaKhumcyCYirtVphov+zPiz5URl6FyCEZeRWicNFeM7106VI++eQTY1enUHrvvfdYunQpT548oVmzZvzxxx9Zlp8zZ06GMw2Cg4PzNNlbcRAUFGTsKhRK0m6GM3ab1aplD3QA4O+/g3B0VGdZXrsmOcCBAwe4f/9+flYvU8Zut8LkyZMneXYsCV6FyKG0Aatc8yqE6RszZgzr1q2jR48eMmX4qalTp2aY2Tmt8PBw3XIUU6ZMYfjw4Vy6dIlZs2YxePBg/vjjj0yn6k2bNo2JEyfqHmtHXn19fWXkNYdkVCd3pN0MZyptlpwMb7+tud+xY0ey+1ehUqn4/vvvAWjRooUuWWJBMZV2K0zu3r2bZ8eS4FWIHEqbqVb+WQlh+gIDAwkMDDR2NUzKpEmTsr0WuXLlyrr7Li4uuLi4UL16dWrWrImHhwcHDx7MdJqetbV1hjNTLC0t5f+mgaTNckfazXDGbrO034VZWVmSXVXSrlNuZmZmtLobu90Kk7xsJwlehcihtMuGXL9+3Yg1EUKI3HF1dcXV1TVXz9Vm/s5pJmkhhMgJdZpZwmlmBGdKu2wawNWrV/OhRsKUSfAqRA5ZWFhQtWpVIiIiaNeunbGrI4QQ+ebQoUMcPnyYVq1aUbJkSSIjI/noo4+oUqVKgSdHEUIUbTY24OcHjx5BTr5bs7CwwNXVldu3b9O0adP8r6AwKRK8CmGA48ePc/PmTapUqWKU80tycCHyj/x9pbKzs2Pz5s3MmDGDx48f4+7ujp+fHx9++KEkrBNC5CmVCrZvB0XRn0KclYiICO7evUulSpXytW7C9EjwKoQBHBwccHBwKPDzmptrUsgnJiZia2tb4OcXojjQZkOUa5igTp067N6929jVEEIUI4Ys2ero6Fgsly0TErwKUShYWFhgZ2fH7du3sbS0LBZrc+ZESkoKiYmJxMfHS5sYQNpNn6IoPHnyhFu3buHs7Kz7skgIIYQQpkWCVyEKAZVKhbu7O1FRUVy6dMnY1TEZiqIQFxeHra1tpkt3iPSk3TLm7OyMm5ubsashhBBCiExI8CpEIWFlZUW1atVITEw0dlVMhlqtZs+ePbRp00amehpA2i09S0tLGXEVQgghTJwEr0IUImZmZtjY2Bi7GibD3NycpKQkbGxsJAgzgLSbEEIIIQojudhJCCGEEEIIIYTJk+BVCCGEEEIIIYTJk+BVCCGEEEIIIYTJk2tes6FdtP7Ro0dybVgOqdVqnjx5wsOHD6XNDCDtZjhps9yRdjPco0ePgNQ+QeSM9KGGk7/P3JF2M5y0We5IuxkuL/tQCV6zcffuXQC8vLyMXBMhhBDGdvfuXZycnIxdjUJD+lAhhBBaedGHSvCajVKlSgFw+fJl+cCSQw8fPsTDw4MrV67g6Oho7OoUGtJuhpM2yx1pN8PFxMRQsWJFXZ8gckb6UMPJ32fuSLsZTtosd6TdDJeXfagEr9kwM9NcFuzk5CRvUAM5OjpKm+WCtJvhpM1yR9rNcNo+QeSM9KG5J3+fuSPtZjhps9yRdjNcXvSh0gsLIYQQQgghhDB5ErwKIYQQQgghhDB5Erxmw9ramhkzZmBtbW3sqhQa0ma5I+1mOGmz3JF2M5y0We5IuxlO2ix3pN0MJ22WO9JuhsvLNlMpkvdfCCGEEEIIIYSJk5FXIYQQQgghhBAmT4JXIYQQQgghhBAmT4JXIYQQQgghhBAmT4JXIYQQQgghhBAmT4LXLHz99ddUqlQJGxsbmjZtyv/+9z9jV8mk7Nmzh27dulGuXDlUKhVbt27V268oCtOnT8fd3R1bW1s6dOjAhQsXjFNZEzFnzhyaNGlCiRIlKFOmDD169OD8+fN6ZeLj4xk9ejSlS5fGwcGBV199lZs3bxqpxsa3fPly6tatq1sMvHnz5mzfvl23X9ore3PnzkWlUjF+/HjdNmm39GbOnIlKpdK7eXt76/ZLmxlG+tCsSR9qOOlDDSd96POTPjRnCqoPleA1Exs2bGDixInMmDGDY8eOUa9ePTp37sytW7eMXTWT8fjxY+rVq8fXX3+d4f7PP/+cJUuWsGLFCg4dOoS9vT2dO3cmPj6+gGtqOkJDQxk9ejQHDx4kKCgItVpNp06dePz4sa7MhAkT+P3339m4cSOhoaFcv36dXr16GbHWxlWhQgXmzp3L0aNHOXLkCO3ateOVV17hzJkzgLRXdg4fPszKlSupW7eu3nZpt4zVqlWLGzdu6G779u3T7ZM2yznpQ7MnfajhpA81nPShz0f6UMMUSB+qiAy9+OKLyujRo3WPk5OTlXLlyilz5swxYq1MF6Bs2bJF9zglJUVxc3NT5s+fr9v24MEDxdraWlm3bp0Ramiabt26pQBKaGiooiiaNrK0tFQ2btyoKxMeHq4AyoEDB4xVTZNTsmRJ5dtvv5X2ysajR4+UatWqKUFBQUrbtm2VcePGKYoi77PMzJgxQ6lXr16G+6TNDCN9qGGkD80d6UNzR/rQnJE+1DAF1YfKyGsGEhMTOXr0KB06dNBtMzMzo0OHDhw4cMCINSs8oqKiiI6O1mtDJycnmjZtKm2YRkxMDAClSpUC4OjRo6jVar128/b2pmLFitJuQHJyMuvXr+fx48c0b95c2isbo0ePpmvXrnrtA/I+y8qFCxcoV64clStX5vXXX+fy5cuAtJkhpA99ftKH5oz0oYaRPtQw0ocariD6UIs8rXERcefOHZKTkylbtqze9rJly3Lu3Dkj1apwiY6OBsiwDbX7iruUlBTGjx9Py5YtqV27NqBpNysrK5ydnfXKFvd2O3XqFM2bNyc+Ph4HBwe2bNnCCy+8QFhYmLRXJtavX8+xY8c4fPhwun3yPstY06ZNCQwMpEaNGty4cYNZs2bRunVrTp8+LW1mAOlDn5/0odmTPjTnpA81nPShhiuoPlSCVyGMZPTo0Zw+fVrvegCRsRo1ahAWFkZMTAy//PILQ4YMITQ01NjVMllXrlxh3LhxBAUFYWNjY+zqFBovvfSS7n7dunVp2rQpnp6e/Pzzz9ja2hqxZkKIZ0kfmnPShxpG+tDcKag+VKYNZ8DFxQVzc/N0GbBu3ryJm5ubkWpVuGjbSdowY2PGjOGPP/4gODiYChUq6La7ubmRmJjIgwcP9MoX93azsrKiatWqNGrUiDlz5lCvXj0WL14s7ZWJo0ePcuvWLRo2bIiFhQUWFhaEhoayZMkSLCwsKFu2rLRbDjg7O1O9enUiIiLkvWYA6UOfn/ShWZM+1DDShxpG+tC8kV99qASvGbCysqJRo0bs2rVLty0lJYVdu3bRvHlzI9as8PDy8sLNzU2vDR8+fMihQ4eKdRsqisKYMWPYsmULu3fvxsvLS29/o0aNsLS01Gu38+fPc/ny5WLdbs9KSUkhISFB2isT7du359SpU4SFhelujRs35vXXX9fdl3bLXmxsLJGRkbi7u8t7zQDShz4/6UMzJn1o3pA+NGvSh+aNfOtDc59Tqmhbv369Ym1trQQGBipnz55VRo4cqTg7OyvR0dHGrprJePTokXL8+HHl+PHjCqAsXLhQOX78uHLp0iVFURRl7ty5irOzs/Lrr78qJ0+eVF555RXFy8tLiYuLM3LNjWfUqFGKk5OTEhISoty4cUN3e/Lkia7MW2+9pVSsWFHZvXu3cuTIEaV58+ZK8+bNjVhr45o6daoSGhqqREVFKSdPnlSmTp2qqFQqZefOnYqiSHvlVNpMiYoi7ZaRSZMmKSEhIUpUVJSyf/9+pUOHDoqLi4ty69YtRVGkzQwhfWj2pA81nPShhpM+NG9IH5q9gupDJXjNwldffaVUrFhRsbKyUl588UXl4MGDxq6SSQkODlaAdLchQ4YoiqJJ9f/RRx8pZcuWVaytrZX27dsr58+fN26ljSyj9gKUgIAAXZm4uDjl7bffVkqWLKnY2dkpPXv2VG7cuGG8ShvZsGHDFE9PT8XKykpxdXVV2rdvr+t0FUXaK6ee7Xil3dLr16+f4u7urlhZWSnly5dX+vXrp0REROj2S5sZRvrQrEkfajjpQw0nfWjekD40ewXVh6oURVFyORoshBBCCCGEEEIUCLnmVQghhBBCCCGEyZPgVQghhBBCCCGEyZPgVQghhBBCCCGEyZPgVQghhBBCCCGEyZPgVQghhBBCCCGEyZPgVQghhBBCCCGEyZPgVQghhBBCCCGEyZPgVQhhFImJiVStWpV//vnHaHWYOnUq77zzjtHOL4QQQuSG9KGiuJLgVYg84O/vj0qlSneLiIgwdtVM1ooVK/Dy8qJFixZ624ODg3n55ZdxdXXFxsaGKlWq0K9fP/bs2aMrExISgkql4sGDB+mOW6lSJRYtWpSjOkyePJnVq1fz33//Pc9LEUII8RykDzWc9KGiuJLgVYg84ufnx40bN/RuXl5e6colJiYaoXamRVEUli5dyvDhw/W2L1u2jPbt21O6dGk2bNjA+fPn2bJlCy1atGDChAl5Xg8XFxc6d+7M8uXL8/zYQgghck760JyTPlQUZxK8CpFHrK2tcXNz07uZm5vj4+PDmDFjGD9+vO4fPcDp06d56aWXcHBwoGzZsgwaNIg7d+7ojvf48WMGDx6Mg4MD7u7ufPHFF/j4+DB+/HhdGZVKxdatW/Xq4ezsTGBgoO7xlStX6Nu3L87OzpQqVYpXXnmFixcv6vb7+/vTo0cPFixYgLu7O6VLl2b06NGo1WpdmYSEBN577z08PDywtramatWqfPfddyiKQtWqVVmwYIFeHcLCwrL81vzo0aNERkbStWtX3bbLly8zfvx4xo8fz+rVq2nXrh2enp7UrVuXcePGceTIkZz+KnQCAwMz/DZ/5syZujLdunVj/fr1Bh9bCCFE3pE+NJX0oUJkToJXIQrA6tWrsbKyYv/+/axYsYIHDx7Qrl07GjRowJEjR9ixYwc3b96kb9++uudMmTKF0NBQfv31V3bu3ElISAjHjh0z6LxqtZrOnTtTokQJ9u7dy/79+3FwcMDPz0/v2+vg4GAiIyMJDg5m9erVBAYG6nXegwcPZt26dSxZsoTw8HBWrlyJg4MDKpWKYcOGERAQoHfegIAA2rRpQ9WqVTOs1969e6levTolSpTQbdu0aRNqtZp33303w+eoVCqDXjtAv3799L7FX7duHRYWFrRs2VJX5sUXX+Tq1at6H0aEEEKYDulD9UkfKoo1RQjx3IYMGaKYm5sr9vb2ulvv3r0VRVGUtm3bKg0aNNArP3v2bKVTp056265cuaIAyvnz55VHjx4pVlZWys8//6zbf/fuXcXW1lYZN26cbhugbNmyRe84Tk5OSkBAgKIoivLjjz8qNWrUUFJSUnT7ExISFFtbW+Wvv/7S1d3T01NJSkrSlenTp4/Sr18/RVEU5fz58wqgBAUFZfjar127ppibmyuHDh1SFEVREhMTFRcXFyUwMDDT9ho3bpzSrl07vW1vvfWW4ujoqLftl19+0WvTkydPKoqiKMHBwQqgt097U6lUypdffpnunBEREUqpUqWUzz//XG97TEyMAighISGZ1lcIIUT+kT5U+lAhcsrCWEGzEEWNr6+v3nUf9vb2uvuNGjXSK3vixAmCg4NxcHBId5zIyEji4uJITEykadOmuu2lSpWiRo0aBtXpxIkTRERE6H07CxAfH09kZKTuca1atTA3N9c9dnd359SpU4Bm+pK5uTlt27bN8BzlypWja9eufP/997z44ov8/vvvJCQk0KdPn0zrFRcXh42NTbrtz34z3LlzZ8LCwrh27Ro+Pj4kJyfr7d+7d2+61+bj45PuuDExMbz88st07dqVKVOm6O2ztbUF4MmTJ5nWVwghRP6SPlT6UCFyQoJXIfKIvb19plN80nbCALGxsXTr1o158+alK+vu7p7jDIsqlQpFUfS2pb3OJjY2lkaNGrFmzZp0z3V1ddXdt7S0THfclJQUILVjysobb7zBoEGD+PLLLwkICKBfv37Y2dllWt7FxUXXsWtVq1aNmJgYoqOjcXNzA8DBwYGqVatiYZHxvyovLy+cnZ31tj1bNjk5mX79+uHo6MiqVavSHePevXuAfnsIIYQoWNKHSh8qRE7INa9CGEHDhg05c+YMlSpVomrVqno3e3t7qlSpgqWlJYcOHdI95/79+/z77796x3F1deXGjRu6xxcuXND79rNhw4ZcuHCBMmXKpDuPk5NTjupap04dUlJSCA0NzbRMly5dsLe3Z/ny5ezYsYNhw4ZlecwGDRpw7tw5vQ8NvXv3xtLSMsMPI89jwoQJnDp1iq1bt2b4TfXp06extLSkVq1aeXpeIYQQ+UP6UOlDRfElwasQRjB69Gju3btH//79OXz4MJGRkfz1118MHTqU5ORkHBwcGD58OFOmTGH37t2cPn0af39/zMz0/2TbtWvH0qVLOX78OEeOHOGtt97S+wb49ddfx8XFhVdeeYW9e/cSFRVFSEgIY8eO5erVqzmqa6VKlRgyZAjDhg1j69atumP8/PPPujLm5ub4+/szbdo0qlWrRvPmzbM8pq+vL7GxsZw5c0a3rWLFinzxxRcsXryYIUOGEBwczMWLFzl27BhLlizRnccQAQEBLFu2jBUrVqBSqYiOjiY6OprY2Fhdmb1799K6descfTsuhBDC+KQPlT5UFF8SvAphBOXKlWP//v0kJyfTqVMn6tSpw/jx43F2dtZ1rvPnz6d169Z069aNDh060KpVq3TX/XzxxRd4eHjQunVrBgwYwOTJk/WmGtnZ2bFnzx4qVqxIr169qFmzJsOHDyc+Ph5HR8cc13f58uX07t2bt99+G29vb0aMGMHjx4/1ygwfPpzExESGDh2a7fFKly5Nz549003Feuedd9i5cye3b9+md+/eVKtWjS5duhAVFcWOHTuoU6dOjusMEBoaSnJyMt27d8fd3V13S7sswfr16xkxYoRBxxVCCGE80odKHyqKL5Xy7GR/IYTJ8vHxoX79+ixatMjYVUln7969tG/fnitXrlC2bNlsy588eZKOHTsSGRmZYdKNgrB9+3YmTZrEyZMnM70mSAghRNEgfWjekj5UGIOMvAohnktCQgJXr15l5syZ9OnTJ0edLkDdunWZN28eUVFR+VzDzD1+/JiAgADpdIUQQhiF9KFCGEbebUKI57Ju3TqGDx9O/fr1+eGHHwx6rr+/f/5UKod69+5t1PMLIYQo3qQPFcIwMm1YCCGEEEIIIYTJk2nDQgghhBBCCCFMngSvQgghhBBCCCFMngSvQgghhBBCCCFMngSvQgghhBBCCCFMngSvQgghhBBCCCFMngSvQgghhBBCCCFMngSvQgghhBBCCCFMngSvQgghhBBCCCFMngSvQgghhBBCCCFM3v8BN7zzCXVK0GUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1100x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Tidy3D uses the physics convention for time-harmonic fields,\n",
    "# so we take the conjugate in order to switch to the engineering convention.\n",
    "load_impedance = np.conj(lumped_element.impedance(freqs))\n",
    "Gamma = compute_Gamma_from_transmission_line_theory(\n",
    "    load_impedance, characteristic_impedance, er_eff\n",
    ")\n",
    "\n",
    "# Get S11 data\n",
    "freq = s_matrix.data.f / 1e9\n",
    "S11_RL_term = s_matrix.data.isel(port_out=0, port_in=0).values.flatten()\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 3))\n",
    "fig.suptitle(\"Transmission line terminated by RL series network\")\n",
    "ax1.plot(s_matrix.data.f / 1e9, 20 * np.log10(np.abs(S11_RL_term)), \"--b\", label=\"Tidy3D\")\n",
    "ax1.plot(freqs / 1e9, 20 * np.log10(np.abs(Gamma)), \"-k\", label=\"Transmission line theory\")\n",
    "ax1.set_xlabel(\"Frequency (GHz)\")\n",
    "ax1.set_ylabel(r\"$|S_{11}|$ (dB)\")\n",
    "ax1.set_xlim([0, 50])\n",
    "ax1.grid()\n",
    "ax1.legend()\n",
    "\n",
    "ax2.plot(\n",
    "    s_matrix.data.f / 1e9,\n",
    "    -np.angle(S11_RL_term),  # Follow engineering convention exp(j omega t)\n",
    "    \"--b\",\n",
    "    label=\"Tidy3D\",\n",
    ")\n",
    "ax2.plot(freqs / 1e9, np.angle(Gamma), \"-k\", label=\"Transmission line theory\")\n",
    "ax2.set_xlabel(\"Frequency (GHz)\")\n",
    "ax2.set_ylabel(r\"$\\angle~S_{11}$ (rad)\")\n",
    "ax2.set_xlim([0, 50])\n",
    "ax2.grid()\n",
    "ax2.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe good agreement with the simple transmission line model, especially at low frequencies.\n",
    "\n",
    "As an additional validation step, we calculate the impedance of the lumped element and relate it back to the intended impedance of the RL series network. The impedance is calculated via Ohm's Law by computing the voltage across the lumped element along with the current flowing through the element using the path integral tools from the `microwave` plugin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the monitor data associated the lumped element\n",
    "vi_mon_data = sim_data[lumped_element.monitor_name]\n",
    "# Setup voltage and current path integrals automatically around the lumped element.\n",
    "voltage_integral, current_integral = rf.path_integrals_from_lumped_element(\n",
    "    lumped_element, sim_data.simulation.grid\n",
    ")\n",
    "# Compute the voltage and current using the path integral tools and the frequency-domain field data.\n",
    "V = voltage_integral.compute_voltage(vi_mon_data)\n",
    "I = current_integral.compute_current(vi_mon_data)\n",
    "# Apply Ohm's law to retrieve impedance\n",
    "impedance = V / I\n",
    "resistance = np.real(impedance).values\n",
    "reactance = -np.imag(\n",
    "    impedance\n",
    ").values  # Change of sign, since Tidy3D uses the physics convention for time-harmonic fields (exp(-j omega t))\n",
    "# Here we use the convenience methods from the lumped element to compute the exact impedance of the modeled network.\n",
    "ideal_impedance = lumped_element.impedance(freqs=freqs)\n",
    "ideal_resistance = np.real(ideal_impedance)\n",
    "ideal_reactance = -np.imag(\n",
    "    ideal_impedance\n",
    ")  # Change of sign, since Tidy3D uses the physics convention for time-harmonic fields (exp(-j omega t))\n",
    "\n",
    "# Plot the results for visual comparison\n",
    "f, (ax1, ax2) = plt.subplots(2, 1, tight_layout=True, figsize=(8, 6))\n",
    "f.suptitle(\"RL series network\")\n",
    "ax1.plot(freqs / 1e9, resistance, label=\"Computed\")\n",
    "ax1.plot(freqs / 1e9, ideal_resistance, \"--\", label=\"Desired\")\n",
    "ax1.set_ylabel(r\"Resistance ($\\Omega$)\")\n",
    "ax1.set_xlabel(\"Frequency (GHz)\")\n",
    "ax1.set_xlim(0, 50)\n",
    "ax1.set_ylim(0, 100)\n",
    "ax1.legend(loc=\"lower right\")\n",
    "ax2.plot(freqs / 1e9, reactance, label=\"Computed\")\n",
    "ax2.plot(freqs / 1e9, ideal_reactance, \"--\", label=\"Desired\")\n",
    "ax2.set_ylabel(r\"Reactance ($\\Omega$)\")\n",
    "ax2.set_xlabel(\"Frequency (GHz)\")\n",
    "ax2.set_xlim(0, 50)\n",
    "ax2.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At low frequencies, we obtain remarkable agreement with the initially targeted impedance. However, at high frequencies, the computed impedance deviates from the intended value. This discrepancy arises because the path integral used for the current calculation does not include the effect of the displacement current, which becomes stronger as the frequency increases. This error can be reduced by increasing the grid resolution, since the path integral will enclose a smaller area, while still enclosing the same current flowing through the lumped element."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seven Element Network\n",
    "\n",
    "Now, let's switch to a more complicated network consisting of seven circuit elements, as detailed in Chapter 15 of [1]. For this example, we will need to describe the network using its admittance transfer function in the Laplace domain. The transfer function is a rational expression, which is described by the monomial coefficients of its numerator and denominator, see [AdmittanceNetwork](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.AdmittanceNetwork.html#tidy3d.AdmittanceNetwork).\n",
    "\n",
    "<img src=\"img/fourth_order_network_schematic.png\" width=\"750\" alt=\"Fourth Order network\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping steps involving 'lcapy' package ...\n"
     ]
    }
   ],
   "source": [
    "# Example network from Sec 15.9.6 [1]\n",
    "L1 = 1e-9\n",
    "L2 = 1.5e-9\n",
    "C1 = 0.2e-12\n",
    "C2 = 0.2e-12\n",
    "R1 = 10\n",
    "R2 = 250\n",
    "R3 = 50\n",
    "\n",
    "# If you have installed lcapy, the transfer function will be automatically generated using a simple description of the network.\n",
    "# Otherwise, the precomputed transfer function coefficients will be used.\n",
    "try:\n",
    "    from lcapy import C, L, R, s\n",
    "\n",
    "    print(\"Generating transfer function using 'lcapy' ...\")\n",
    "    # Create the 4th order network using 'lcapy'\n",
    "    # The network is described by adding each of the elements in series and parallel\n",
    "    network = L(L1) + (C(C1) | (R(R1) + L(L2) + (R(R2) | C(C2)))) + R(R3)\n",
    "    # Can also get `lcapy` to draw the network\n",
    "    network.draw(layout=\"horizontal\", scale=0.4)\n",
    "    lca_a = network.Y(s).N.coeffs()\n",
    "    lca_b = network.Y(s).D.coeffs()\n",
    "    # Reverse order of coefficients\n",
    "    a = tuple(np.flip(lca_a.fval))\n",
    "    b = tuple(np.flip(lca_b.fval))\n",
    "\n",
    "except ModuleNotFoundError:\n",
    "    print(\"Skipping steps involving 'lcapy' package ...\")\n",
    "    # Manually derived admittance transfer function for network\n",
    "    a = [\n",
    "        1,\n",
    "        C1 * R1 + C1 * R2 + C2 * R2,\n",
    "        C1 * C2 * R1 * R2 + C1 * L2,\n",
    "        C1 * C2 * L2 * R2,\n",
    "        0,\n",
    "    ]\n",
    "    b = [\n",
    "        R1 + R2 + R3,\n",
    "        C1 * R1 * R3 + C1 * R2 * R3 + C2 * R1 * R2 + C2 * R2 * R3 + L1 + L2,\n",
    "        C1 * C2 * R1 * R2 * R3\n",
    "        + C1 * L1 * R1\n",
    "        + C1 * L1 * R2\n",
    "        + C1 * L2 * R3\n",
    "        + C2 * L1 * R2\n",
    "        + C2 * L2 * R2,\n",
    "        C1 * C2 * L1 * R1 * R2 + C1 * C2 * L2 * R2 * R3 + C1 * L1 * L2,\n",
    "        C1 * C2 * L1 * L2 * R2,\n",
    "    ]\n",
    "\n",
    "# With the admittance transfer function in hand, we can construct the AdmittanceNetwork\n",
    "fourth_order_network = rf.AdmittanceNetwork(a=a, b=b)\n",
    "# We create an updated copy of the lumped element from the previous simulation.\n",
    "lumped_element = lumped_element.updated_copy(network=fourth_order_network)\n",
    "# Instead of repeating all of the previous steps to build the Simulation and TerminalComponentModeler,\n",
    "# we create an updated copy of the modeler which includes the newly created lumped element.\n",
    "lumped_simulation = modeler.simulation.updated_copy(lumped_elements=[lumped_element])\n",
    "modeler = modeler.updated_copy(simulation=lumped_simulation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run Tidy3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:04:16 UTC </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'seven_element_modeler'</span> with resource_id              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'sid-dff6b214-c1f7-46f5-b9be-1def21baa5a1'</span> and task_type           \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'TERMINAL_CM'</span>.                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:04:16 UTC\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'seven_element_modeler'\u001b[0m with resource_id              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'sid-dff6b214-c1f7-46f5-b9be-1def21baa5a1'\u001b[0m and task_type           \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'TERMINAL_CM'\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/rf?taskId=pa-c35539c9-cd48-48a1-9998-a92db14cd1d6\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/rf?taskId=pa-c35539c9-cd48-48a1-99</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/rf?taskId=pa-c35539c9-cd48-48a1-9998-a92db14cd1d6\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">98-a92db14cd1d6'</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=467422;https://tidy3d.simulation.cloud/rf?taskId=pa-c35539c9-cd48-48a1-9998-a92db14cd1d6\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/rf?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=228222;https://tidy3d.simulation.cloud/rf?taskId=pa-c35539c9-cd48-48a1-9998-a92db14cd1d6\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=467422;https://tidy3d.simulation.cloud/rf?taskId=pa-c35539c9-cd48-48a1-9998-a92db14cd1d6\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=467880;https://tidy3d.simulation.cloud/rf?taskId=pa-c35539c9-cd48-48a1-9998-a92db14cd1d6\u001b\\\u001b[32mpa\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=467422;https://tidy3d.simulation.cloud/rf?taskId=pa-c35539c9-cd48-48a1-9998-a92db14cd1d6\u001b\\\u001b[32m-c35539c9-cd48-48a1-99\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=467422;https://tidy3d.simulation.cloud/rf?taskId=pa-c35539c9-cd48-48a1-9998-a92db14cd1d6\u001b\\\u001b[32m98-a92db14cd1d6'\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/9b36e144-ddb6-41f8-8dd8-30b62b26a870\" 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=74398;https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b4fed87e492f44ebb66382b16983cb75",
       "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\">14:04:24 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</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> after run.         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:04:24 UTC\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\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 after 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\">14:04:25 UTC </span>Subtasks status - seven_element_modeler                            \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Group ID: <span style=\"color: #008000; text-decoration-color: #008000\">'pa-c35539c9-cd48-48a1-9998-a92db14cd1d6'</span>                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:04:25 UTC\u001b[0m\u001b[2;36m \u001b[0mSubtasks status - seven_element_modeler                            \n",
       "\u001b[2;36m             \u001b[0mGroup ID: \u001b[32m'pa-c35539c9-cd48-48a1-9998-a92db14cd1d6'\u001b[0m                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4d24a3ca126d4dd9845bab94cd2966e5",
       "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\">             </span>Batch status = preprocess                                          \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mBatch status = 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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:04:32 UTC </span>Batch status = running                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:04:32 UTC\u001b[0m\u001b[2;36m \u001b[0mBatch status = running                                             \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\">14:05:12 UTC </span>Batch status = postprocess                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:05:12 UTC\u001b[0m\u001b[2;36m \u001b[0mBatch status = postprocess                                         \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\">14:05:39 UTC </span>Batch status = success                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:05:39 UTC\u001b[0m\u001b[2;36m \u001b[0mBatch status = 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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:05:41 UTC </span>Modeler has finished running successfully.                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:05:41 UTC\u001b[0m\u001b[2;36m \u001b[0mModeler has finished running successfully.                         \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>Billed flex credit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>.                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.025\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\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3fc40fc749b246e880f5c97e760a7c45",
       "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\">14:05:44 UTC </span>Loading results from cm_data.hdf5                                  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:05:44 UTC\u001b[0m\u001b[2;36m \u001b[0mLoading results from cm_data.hdf5                                  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "modeler_data = web.run(modeler, task_name=\"seven_element_modeler\")\n",
    "s_matrix = modeler_data.smatrix()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We again plot the scattering parameter for the transmission line terminated by this new load, along with the value predicted by transmission line theory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Tidy3D uses the physics convention for time-harmonic fields,\n",
    "# so we take the conjugate in order to switch to the engineering convention.\n",
    "load_impedance = np.conj(lumped_element.impedance(freqs))\n",
    "Gamma = compute_Gamma_from_transmission_line_theory(\n",
    "    load_impedance, characteristic_impedance, er_eff\n",
    ")\n",
    "\n",
    "# Get the S11 data\n",
    "freq = s_matrix.data.f / 1e9\n",
    "S11_ntwk = s_matrix.data.isel(port_out=0, port_in=0).values.flatten()\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 3))\n",
    "fig.suptitle(\"Transmission line terminated by the seven-element 4th-order network\")\n",
    "ax1.plot(s_matrix.data.f / 1e9, 20 * np.log10(np.abs(S11_ntwk)), \"--b\", label=\"Tidy3D\")\n",
    "ax1.plot(freqs / 1e9, 20 * np.log10(np.abs(Gamma)), \"-k\", label=\"Transmission line theory\")\n",
    "ax1.set_xlabel(\"Frequency (GHz)\")\n",
    "ax1.set_ylabel(r\"$|S_{11}|$ (dB)\")\n",
    "ax1.set_xlim([0, 50])\n",
    "ax1.grid()\n",
    "ax1.legend()\n",
    "ax2.plot(\n",
    "    s_matrix.data.f / 1e9,\n",
    "    -np.angle(S11_ntwk),  # Follow engineering convention exp(j omega t)\n",
    "    \"--b\",\n",
    "    label=\"Tidy3D\",\n",
    ")\n",
    "ax2.plot(freqs / 1e9, np.angle(Gamma), \"-k\", label=\"Transmission line theory\")\n",
    "ax2.set_xlabel(\"Frequency (GHz)\")\n",
    "ax2.set_ylabel(r\"$\\angle~S_{11}$ (rad)\")\n",
    "ax2.set_xlim([0, 50])\n",
    "ax2.grid()\n",
    "ax2.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We again observe good agreement between the full-wave simulation and the transmission line model.\n",
    "\n",
    "As a last step, we validate that the lumped element is operating as expected by directly computing its impedance and comparing it to the intended impedance, which is associated with the original network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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77be73p/6Bj82NhaAbdu2ndZrs23bNtf6vxIbG4vT6WTXrl1uvRPbtm1zK3dqZiiHw1Hmn+/ZnDpHvby8/jaGvzrmp5KFOXPmsHLlSh5//HEAOnbsyKRJk6hWrRr+/v60bNnStU1sbCzr16/H6XS6JQFbt251rT9X1113Hd27d2fAgAEEBga6zTAlIhcXfaUgcpGaN2+e21SUp5y6leLURdkNN9yA1WplzJgxp5U3TZNjx465LevTpw/5+fm8//77zJ49m5tvvtltfY8ePQgKCuLFF1+ksLDwtPbPZ/rRUy699FK6devmep26YLv55ps5cOAA77777mnb5Obmkp2dfdY6b775ZpYuXcoPP/xw2rr09HSKiorOO84zKYvj4efnBxTH+XciIiKYMWPGaa/OnTvj4+PDjBkzGDlypKu8v7//OdV7NomJiW6f1amL3VatWhEREcHkyZPdpvudNWsWW7ZsoWfPnn9b95VXXgngNp0qwKuvvur23mq1cuONN/LFF1+wcePG0+r5J8f8fEVERHDZZZfx9ttvuxL4s8Vw6vkbZzru8fHxVK9enQkTJlBYWMill14KFCcbu3btYvr06bRr186tx+uqq64iJSWFTz/91LWsqKiI119/nYCAADp16nRe+9KvXz8mTpzI5MmTeeyxx85rWxG5cKinQuQi9cADD5CTk8P1119P/fr1KSgoYMmSJa7ehYEDBwLF36Q///zzjBw5kj179nDdddcRGBjI7t27mTFjBvfccw8jRoxw1duiRQsSEhJ48sknyc/Pd7v1CYrHCEyaNIk77riDFi1acMsttxAeHk5ycjLfffcdl156KW+88Uap7OMdd9zBZ599xr333su8efO49NJLcTgcbN26lc8++4wffvjB1TPzZ4888ghff/01V199NQMGDKBly5ZkZ2ezYcMGpk+fzp49ewgLCytxjGVxPHx9fUlMTOTTTz+lbt26hIaG0qhRIxo1anRaWT8/P6677rrTls+cOZMVK1actq5ly5ZMmjSJ559/noSEBCIiIs46HuR8eHl5MW7cOAYOHEinTp249dZbOXz4MK+99hpxcXEMGzbsb+to1qwZt956K2+99RYZGRlccsklzJ07l507d55W9qWXXmLevHm0bduWu+++m8TERNLS0lizZg0//fTTGZ8lUtrefPNN2rdvT+PGjbn77rupVasWhw8fZunSpezfv59169a59stqtTJu3DgyMjLw9vamS5curlvSOnTowLRp02jcuLGr56dFixb4+/uzfft2t/EUUDyY/u2332bAgAGsXr2auLg4pk+fzuLFi3n11VcJDAw8730ZMmQImZmZPPnkkwQHB/PEE0+U8OiISKXjmUmnRMTTZs2aZd55551m/fr1zYCAANNut5sJCQnmAw88YB4+fPi08l988YXZvn1709/f3/T39zfr169vDh482Ny2bdtpZZ988kkTMBMSEs7a/rx588wePXqYwcHBpo+Pj1m7dm1zwIAB5qpVq1xlzjat6ahRo8xz/fNVUFBgjhs3zmzYsKHp7e1tVqlSxWzZsqU5ZswYMyMjw1Xuz1PKmmbxdK8jR440ExISTLvdboaFhZmXXHKJ+corr7im3j017ea//vWv0/aPP8zjf8qpqVdXrlxZpsdjyZIlZsuWLU273X7e08v+VVspKSlmz549zcDAQBNwTS/7V/vFGaZDPZtPP/3UbN68uent7W2Ghoaaffv2Nffv339OsZmmaebm5poPPvigWbVqVdPf39+85pprzH379p3xGBw+fNgcPHiwGRMTY3p5eZlRUVFm165dzXfeeee0+P/8OZ7N2c4H0zTPGMOuXbvMfv36mVFRUaaXl5dZvXp18+qrrzanT5/uVu7dd981a9WqZVqt1tOO55tvvmkC5n333ee2Tbdu3UzAnDt37mmxHD582Bw4cKAZFhZm2u12s3HjxuaUKVPOeV/OdlweffRREzDfeOONMx0eEbmAGaZ5hvsfREREREREzpHGVIiIiIiISIkoqRARERERkRJRUiEiIiIiIiWipEJEREREREpESYWIiIiIiJSIkgoRERERESkRJRUiIiIiIlIiSipERERERKRElFSIiIiIiEiJKKkQEREREZESUVIhIiIiIiIloqRCRERERERKREmFiIiIiIiUiJIKEREREREpESUVIiIiIiJSIkoqRERERESkRJRUiIiIiIhIiSipEBERERGRElFSISIiIiIiJaKkQkRERERESkRJhYiIiIiIlIiSChERERERKZFKl1SMHTuW1q1bExgYSEREBNdddx3btm1zK3PZZZdhGIbb695773Urk5ycTM+ePfHz8yMiIoJHHnmEoqKi8twVEREREZELgs3TAZyvBQsWMHjwYFq3bk1RURFPPPEE3bt3Z/Pmzfj7+7vK3X333Tz77LOu935+fq6fHQ4HPXv2JCoqiiVLlnDo0CH69euHl5cXL774Yrnuj4iIiIhIZWeYpml6OoiSOHLkCBERESxYsICOHTsCxT0VzZo149VXXz3jNrNmzeLqq6/m4MGDREZGAjB58mQee+wxjhw5gt1uL6/wRUREREQqvUp3+9OfZWRkABAaGuq2/KOPPiIsLIxGjRoxcuRIcnJyXOuWLl1K48aNXQkFQI8ePcjMzGTTpk3lE7iIiIiIyAWi0t3+9EdOp5OhQ4dy6aWX0qhRI9fy2267jdjYWKpVq8b69et57LHH2LZtG19++SUAKSkpbgkF4HqfkpJyxrby8/PJz893azstLY2qVatiGEZp75qIiIiISJkyTZMTJ05QrVo1LJaS9TVU6qRi8ODBbNy4kV9++cVt+T333OP6uXHjxkRHR9O1a1d27dpF7dq1/1FbY8eOZcyYMSWKV0RERESkotm3bx81atQoUR2VNqkYMmQI3377LQsXLvzbg9C2bVsAdu7cSe3atYmKimLFihVuZQ4fPgxAVFTUGesYOXIkw4cPd73PyMigZs2a7Nu3j6CgoJLsioiIiIhIucvMzCQmJobAwMAS11XpkgrTNHnggQeYMWMG8+fPJz4+/m+3Wbt2LQDR0dEAJCUl8cILL5CamkpERAQAc+bMISgoiMTExDPW4e3tjbe392nLg4KClFSIiIiISKVVGrfyV7qkYvDgwXz88cd89dVXBAYGusZABAcH4+vry65du/j444+56qqrqFq1KuvXr2fYsGF07NiRJk2aANC9e3cSExO54447ePnll0lJSeGpp55i8ODBZ0wcRERERETk7CrdlLJny6SmTJnCgAED2LdvH7fffjsbN24kOzubmJgYrr/+ep566im3HoW9e/dy3333MX/+fPz9/enfvz8vvfQSNtu55VmZmZkEBweTkZGhngoRERERqXRK83q20iUVFcU/+RAcTgfr9m5lR8oeere9AqvFWsZRioiIiPw9h8NBYWGhp8OQMmC32886s1NpJhWV7vanysw0Ta7/933kFxXQLqEZseHVPR2SiIiIXMRM0yQlJYX09HRPhyJlxGKxEB8fX+YPd1ZSUY5sVhu1ImPYcmAX21P2KKkQERERjzqVUERERODn56dnb11gnE4nBw8e5NChQ9SsWbNMP18lFeVsjNdvRIfsZO22edD4Uk+HIyIiIhcph8PhSiiqVq3q6XCkjISHh3Pw4EGKiorw8vIqs3ZK9ug8OW81rYXUsRVQeGiDp0MRERGRi9ipMRR+fn4ejkTK0qnbnhwOR5m2o6SinDmq1gHAJ22XhyMRERERKZ1nFEjFVV6fr5KKcuZXoykAobmH0cRbIiIiInIhUFJRzqrWSgKgtpHD4YyjHo5GRERERCqzqVOnEhIS4ukwlFSUN3u1RgDEWAvZtW+Lh6MRERERqZxSUlJ44IEHqFWrFt7e3sTExHDNNdcwd+5cT4f2typKIlCaNPtTefOrSrrFlxBnLkd+WwaNO3o6IhEREZFKZc+ePVx66aWEhITwr3/9i8aNG1NYWMgPP/zA4MGD2bp1q6dDvOiop8IDjgbUZH2RD4dSkz0dioiIiEilc//992MYBitWrODGG2+kbt26NGzYkOHDh7Ns2TIAkpOT6dWrFwEBAQQFBXHzzTdz+PBhVx2jR4+mWbNmvPfee9SsWZOAgADuv/9+HA4HL7/8MlFRUURERPDCCy+4tW0YBpMmTeLKK6/E19eXWrVqMX36dNf6+fPnYxiG2wMF165di2EY7Nmzh/nz5zNw4EAyMjIwDAPDMBg9ejQA+fn5jBgxgurVq+Pv70/btm2ZP3++W/tTp06lZs2a+Pn5cf3113Ps2LHSPbj/kJIKD1jTcQzd02sx54TT06GIiIiIuJimSXZ+brm/zmfymrS0NGbPns3gwYPx9/c/bX1ISAhOp5NevXqRlpbGggULmDNnDr/99ht9+vRxK7tr1y5mzZrF7Nmz+eSTT/jvf/9Lz5492b9/PwsWLGDcuHE89dRTLF++3G27p59+mhtvvJF169bRt29fbrnlFrZsObfb2i+55BJeffVVgoKCOHToEIcOHWLEiBEADBkyhKVLlzJt2jTWr1/PTTfdxBVXXMGOHTsAWL58OYMGDWLIkCGsXbuWzp078/zzz5/zsStLuv3JA+pExQGwI2WPR+MQERER+aOcgjxqP9S53Nvd9do8/L19z6nszp07MU2T+vXrn7XM3Llz2bBhA7t37yYmJgaADz74gIYNG7Jy5Upat24NFD9x+r333iMwMJDExEQ6d+7Mtm3b+P7777FYLNSrV49x48Yxb9482rZt66r/pptu4q677gLgueeeY86cObz++uu89dZbfxu/3W4nODgYwzCIiopyLU9OTmbKlCkkJydTrVo1AEaMGMHs2bOZMmUKL774Iq+99hpXXHEFjz76KAB169ZlyZIlzJ49+5yOXVlST4UH1ImKBeBY5jGOZ2d4OBoRERGRyuNcejW2bNlCTEyMK6EASExMJCQkxK1HIS4ujsDAQNf7yMhIEhMTsVgsbstSU1Pd6k9KSjrt/bn2VJzNhg0bcDgc1K1bl4CAANdrwYIF7Nq1y7Vff0xuzhSLp6inwgMCDJNFVfdSnRw27dtG6/ptPB2SiIiICH52H3a9Ns8j7Z6rOnXqYBhGqQzG9vLycntvGMYZlzmd537L+qmE5I/Jz6mnl/+VrKwsrFYrq1evxmq1uq0LCAg45/Y9RUmFJ9j9ibQU4WeaxTNAKakQERGRCsAwjHO+DclTQkND6dGjB2+++SYPPvjgaeMq0tPTadCgAfv27WPfvn2u3orNmzeTnp5OYmJiiWNYtmwZ/fr1c3vfvHlzAMLDwwE4dOgQVapUAYoHav+R3W7H4XC4LWvevDkOh4PU1FQ6dOhwxnYbNGhw2viOUwPTPU23P3mCYZDmV3wPXc7+tZ6NRURERKSSefPNN3E4HLRp04YvvviCHTt2sGXLFiZOnEhSUhLdunWjcePG9O3blzVr1rBixQr69etHp06daNWqVYnb//zzz3nvvffYvn07o0aNYsWKFQwZMgSAhIQEYmJiGD16NDt27OC7777j3//+t9v2cXFxZGVlMXfuXI4ePUpOTg5169alb9++9OvXjy+//JLdu3ezYsUKxo4dy3fffQfAgw8+yOzZs3nllVfYsWMHb7zxRoUYTwFKKjymoEotAKzHdng4EhEREZHKpVatWqxZs4bOnTvz8MMP06hRIy6//HLmzp3LpEmTMAyDr776iipVqtCxY0e6detGrVq1+PTTT0ul/TFjxjBt2jSaNGnCBx98wCeffOLqAfHy8uKTTz5h69atNGnShHHjxp02Q9Mll1zCvffeS58+fQgPD+fll18GYMqUKfTr14+HH36YevXqcd1117Fy5Upq1qwJQLt27Xj33Xd57bXXaNq0KT/++CNPPfVUqexTSRnm+czhJS6ZmZkEBweTkZFBUFDQeW+/5+uniFsxkZ/NcLq8sKsMIhQRERE5u7y8PHbv3k18fDw+Puc+puFiZxgGM2bM4LrrrvN0KOfkrz7nkl7P/pF6KjwktFY7AGIcGWTn5Xg4GhERERGRf05JhYcE1WwJQLy1gF0Hd3o4GhERERGRf06zP3lKYBTbrFXZllMI+7fQpFYTT0ckIiIiIn9DIwfOTD0VnmIY/LfevdxzogYbjh3zdDQiIiIiIv+YkgoPqhddPAPU1oMaqC0iIiIilZeSCg9KrJGAgcmhAyV7rLuIiIiIiCdpTIUHNeIEO0K3kWrayMzNIsi34j+CXURERETkz9RT4UGB1RIJsDiJsxSwfc8GT4cjIiIiIvKPKKnwJP8wjlt8sRhweMcvno5GREREROQfUVLhYWn+NQDI27faw5GIiIiIyClTp04lJCSkTOoePXo0zZo1K5O6PUVJhYcVRdQHwPvYDg9HIhcV04SsI+Ao8nQkIiIi523AgAEYhoFhGHh5eREZGcnll1/Oe++9h9PpLJU2+vTpw/bt20ulrouBkgoPC4htDUBEbooepiLl4/BmHK+3g5dqkz82AceW7z0dkYiIyHm74oorOHToEHv27GHWrFl07tyZhx56iKuvvpqiopJ/aebr60tERMRZ1xcUFJS4jQuJkgoPi6jbCYC6RjYH0lI8HI1c8DIPkf/ulVhTi6cxLsxJZ8QXkzmSmebhwERERM6Pt7c3UVFRVK9enRYtWvDEE0/w1VdfMWvWLKZOnQpAeno6d911F+Hh4QQFBdGlSxfWrVvnqmPdunV07tyZwMBAgoKCaNmyJatWrQJOv/3p1C1L//nPf4iPj8fHx+ec2gB46aWXiIyMJDAwkEGDBpGXl1e2B8cDlFR4mFdUIouNSKbmhbJ131ZPhyMXuPTP/g/vvONsLvLmKvNSbsprwCf7Uxkw6VGKdCuUiIicUpB99ldh3nmUzf37sqWoS5cuNG3alC+//BKAm266idTUVGbNmsXq1atp0aIFXbt2JS2t+Mu0vn37UqNGDVauXMnq1at5/PHH8fLyOmv9O3fu5IsvvuDLL79k7dq159TGZ599xujRo3nxxRdZtWoV0dHRvPXWW6W63xVBpXtOxdixY/nyyy/ZunUrvr6+XHLJJYwbN4569eq5yuTl5fHwww8zbdo08vPz6dGjB2+99RaRkZGuMsnJydx3333MmzePgIAA+vfvz9ixY7HZyvmQ2Lz5X9ytzFj5I0+m7KNb+bYuF5HC3UsI2TOfIhM+iLqarx54l+SjB7lq3CBW797Ie/Onc0/XWzwdpoiIVATPRp99Xd3u0G/67+/H1obCnDOXjWsPd/3hNttXGkHOMfcyz2f+8zjPoH79+qxfv55ffvmFFStWkJqaire3d3Hzr7zCzJkzmT59Ovfccw/Jyck88sgj1K9fPMa1Tp06f1l3QUEBH3zwAeHh4QDn1Marr77KoEGDGDRoUPHuPv88P/300wXXW1HpeioWLFjA4MGDWbZsGXPmzKGwsJDu3buTnf17pjts2DC++eYbPv/8cxYsWMDBgwe54YYbXOsdDgc9e/akoKCAJUuW8P777zN16lSeeeYZT+wSDarXBmDzgZ0eaV8uDvu+fgKAb5wRPHb3q3hZbdSOrMnoa+7idu/jVPnpabLzc/+mFhERkYrNNE0Mw2DdunVkZWVRtWpVAgICXK/du3eza9cuAIYPH85dd91Ft27deOmll1zLzyY2NtaVUADn1MaWLVto27atWz1JSUmlvNeeV+l6KmbPnu32furUqURERLB69Wo6duxIRkYG//3vf/n444/p0qULAFOmTKFBgwYsW7aMdu3a8eOPP7J582Z++uknIiMjadasGc899xyPPfYYo0ePxm63l+s+1Y+uRbhRRN6+X8u1Xbl4FBQVMj6lgGuKArBe9iBV/INd625q3p6b5qZgw+Sbn//LNVcO8WCkIiJSITxz6OzrDKv7+5F/cSFu/On76xEb/3lM52jLli3Ex8eTlZVFdHQ08+fPP63MqbESo0eP5rbbbuO7775j1qxZjBo1imnTpnH99defsW5/f3+39+fSxsWi0iUVf5aRkQFAaGgoAKtXr6awsJBu3X6/kah+/frUrFmTpUuX0q5dO5YuXUrjxo3dbofq0aMH9913H5s2baJ58+antZOfn09+fr7rfWZm6XXVNS86yIaq29mat5eCokLstrPfyyfyT8xcNYfpx50sDGrOyu73uq2zhVRnd2gj4tM2kLnqY1BSISIidv+/L1PWZf+Bn3/+mQ0bNjBs2DBq1KhBSkoKNpuNuLi4s25Tt25d6taty7Bhw7j11luZMmXKWZOKP2vRosXfttGgQQOWL19Ov379XMuWLVt2PrtVKVS625/+yOl0MnToUC699FIaNWoEQEpKCna7/bTsMDIykpSUFFeZPyYUp9afWncmY8eOJTg42PWKiYkptf0Iq30JALWt+ew6oPmQpXSZpsnkOR8DcHeXPnh7nd4TF9rmdgAaZ+/kt8PJ5RqfiIjIP5Gfn09KSgoHDhxgzZo1vPjii/Tq1Yurr76afv360a1bN5KSkrjuuuv48ccf2bNnD0uWLOHJJ59k1apV5ObmMmTIEObPn8/evXtZvHgxK1eupEGDBuccw9+1AfDQQw/x3nvvMWXKFLZv386oUaPYtGlTWR0Wj6nUScXgwYPZuHEj06ZNK/O2Ro4cSUZGhuu1b9++UqvbCK5BluGFlwH7ti4otXpFALatm02PtKU0sju5o8N1ZywT3PwmnBg0seXxw8KPyzdAERGRf2D27NlER0cTFxfHFVdcwbx585g4cSJfffUVVqsVwzD4/vvv6dixIwMHDqRu3brccsst7N27l8jISKxWK8eOHaNfv37UrVuXm2++mSuvvJIxY8accwx/1wYUP0Tv6aef5tFHH6Vly5bs3buX++67r6wOi8cYZiV94tqQIUP46quvWLhwIfHx8a7lP//8M127duX48eNuvRWxsbEMHTqUYcOG8cwzz/D111+7pgID2L17N7Vq1WLNmjVnvP3pzzIzMwkODiYjI4OgoKAS78/usU2Iz97DlzWu44Z7PyhxfSKn/DzxarqkLmSNX21aPHH2cTtHxrclPG0LE22NeHD0knKMUEREPCEvL4/du3e7PXNBLjx/9TmX5vVspeupME2TIUOGMGPGDH7++We3hAKgZcuWeHl5MXfuXNeybdu2kZyc7Bppn5SUxIYNG0hNTXWVmTNnDkFBQSQmJpbPjvxJQURxu96pmz3SvlyYCooKqZmyEgB7kxv/sqx/0+L1jXJ2cyDtcJnHJiIiIheOSpdUDB48mP/97398/PHHBAYGkpKSQkpKCrm5xVNhBgcHM2jQIIYPH868efNYvXo1AwcOJCkpiXbt2gHQvXt3EhMTueOOO1i3bh0//PADTz31FIMHD3bNMVzeAmoVj6uIyj2A0+n0SAxy4Vmx7AsSLLkUmgb1L/vrrla/xCspxIIDg3mblpZThCIiInIhqHRJxaRJk8jIyOCyyy4jOjra9fr0009dZSZMmMDVV1/NjTfeSMeOHYmKinI9WRHAarXy7bffYrVaSUpK4vbbb6dfv348++yzntglACLrF09/28CSw+7Dez0Wh1xYDq8u/r3YGxiPLaDqXxeObMikNi9xR2ZN5m5UUiEiIiLnrtJNKXsuQ0B8fHx48803efPNN89aJjY2lu+///6s68ubLbIBM7zqMC8tm67JW6gdHf/3G4n8hSJHEcEpa8AKtjrn8Kx2i4VOTS/jxe+nsnDrSgodRXhZK92fCBEREfGAStdTccGyWFlWpw+f5Yewbv9fP81R5Fws27aa1kbxc1xqtL75nLZpHFOXEL8gzPwsNiRvK8vwRERE5AKipKICaRpbH4AN+3QxJyW3fOVMvAyTXIs3tuotzmkbS3YqPwRtZ23odlbsWF3GEYqISEVQSScClXNUXp+vkooKpEl0HK1tOVQ7sFS/4FJiX+zaSb1j9VnZbQKc621M/uFEmzkEWZwc2LqwbAMUERGP8vLyAiAnJ8fDkUhZKigoAIrHFJcl3TBdgdSzO/kmZA+ZTgv7jh6gZngNT4ckldS+Y4f4LXUfVouNZq2vO/cNLVbyIhKxH1qDeWA1pmliGEaZxSkiIp5jtVoJCQlxTbHv5+env/kXGKfTyZEjR/Dz88NmK9vLfiUVFYhXdEMKsBBkcbJy8wJqdurr6ZCkklq4ZQUAzeMSCfINOK9t/Wu1h0NrqOc4xo6UPdTVpAEiIhesqKgoALdnd8mFxWKxULNmzTJPGJVUVCRWL1J8oqiZd5DjO+aDkgr5hw78OpOFITs5GlDtvLe11mwDi6G5LZeVu9YrqRARuYAZhkF0dDQREREUFhZ6OhwpA3a7HYul7Ec8KKmoYPIiG8Heg9gPrfV0KFJJOZ1O/PYvp65XAVXt/+BBijVaAlDfms8nu9dD+16lHKGIiFQ0Vqu1zO+5lwubBmpXMIF1OgFQPXs/RY4iD0cjldGGfdtpSRoAwY2vPv8KgqqR51MFmwE5e5aVcnQiIiJyIVJSUcFENrwCgIbWHLYmb/VwNFIZLdn0Cy1txTN52BI6n38FhkFh/av5PC+YLUdSyCvML+UIRURE5EJTrklFYWEh+/btY9u2baSlpZVn05WGJSyBE4Y3PobJno2zPR2OVEJHN36P3YAs71AIrfWP6gi48Q1GmYmsLfBiywE9jFFERET+WpknFSdOnGDSpEl06tSJoKAg4uLiaNCgAeHh4cTGxnL33XezcuXKsg6j8jAMfqrdh6vT4/j56AlPRyOVTG5BHqGp6wFwxreHfzjTg2EYNI1rAMDaPZtLLT4RERG5MJVpUjF+/Hji4uKYMmUK3bp1Y+bMmaxdu5bt27ezdOlSRo0aRVFREd27d+eKK65gx44dZRlOpeHf4hZWFfmxcq9uf5Lzs3znOpKsmQAEJl5Vorqa16xLPWseW/asL43QRERE5AJWprM/rVy5koULF9KwYcMzrm/Tpg133nknkyZNYurUqSxatIg6deqUZUiVQov44uO1I2UvGTknCPYL9HBEUlks2LycuCJfavr5EFarY4nqum/7ZB6tsocRezVYW0RERP5amSYVn3zyyd+WMU2T1NRU7r333rIMpVIJCwjh3jCD2NyDbNi2jPbNL/d0SFJJLNy6kknZ0QTd8izXh5Tsiey2yETI2ENI+m6y83Px9/YtpShFRETkQlNuA7WnTJnCFVdcQYMGDWjbti0jRozgwIEDpKamEh+vh2u5MQzu8zrAQN/jHNo4y9PRSCVxJPMYm/YX30LYvl6rEtfnG9sagERrLpv2bS9xfSIiInLhKvOkwuFw0KtXL+699178/Py49tpradq0KZ9//jkNGjRg9mzNcHQmJ8IbAWAk69YTOTeLtq4i0ZpH4+q1CQ8KLXmF0Y0BaGTLY72mNxYREZG/UOZP1J4wYQIrV65k/fr11KtXz7Xc6XQyfvx47rnnnrIOoVIKqNcVDv5CzIk9FBQVYrd5eTokqeBWb5jHz1V+I6/oIORngXdAySqMbgpAgrWA/yZrBigRERE5uzLvqZg6dSovv/yyW0IBYLFYGDFiBM8//zymaZZ1GJVOZONrAGhqzWbDbs2+I3/NNE0Kd84DoCiwWskTCoDASPK8q2AxIG/f6pLXJyIiIhesMk8qdu3aRdu2bc+6/pFHHsHpdJZ1GJWOEV6HDIsvPobJ7l+/8XQ4UsHtSNlDw4IUAHzqld7AfmdU8UxkAcd/o8hRVGr1ioiIyIWlzJMKf39/jhw5ctb1a9eu5c477yzrMCofw+BYaH0AinYv8nAwUtEt3LKC9vZsAGx1upRavT6t+vFqfjWW5dvZeTi51OoVERGRC0uZJxWdOnVi8uTJZ1yXkpLCLbfcwvvvv1/WYVRK9oROAFiP71FvjvylDRt/pra1ACcWiLu01Oq1NL+FueEd2OTwYfN+PZxSREREzqzMk4pRo0bxxRdf0L9/fzZu3EheXh4HDx7k7bffpnXr1oSFhZV1CJVWVKcHaJvVhAfSw9l6cJenw5EKqtBRhH3vUgByIxLBJ6hU60+sUfxAyk1KKkREROQsyjypaNKkCbNmzWLx4sU0bdoUf39/YmJiePDBB7n11lv55JNPNFD7LGyB4cTFtwBgyfY1Ho5GKqrVv22ktXEcAL963Uu9/tZhVeluP8HBvZowQERERM6szKeUBejYsSPbt29nxYoV7N69m6CgIJKSkggNDSU7O5tRo0aVRxiVUvv6rViwZQWLtq7iri59PB2OVEALtqxgXl4oPlH1ubbRtaVef49tU+kdtI8nDiuxFRERkTMrl6QCiqeQbdeuHe3atXNb7u/vr6TiL/QI9adh0F6y96ZQ5BiLzVpuH5lUEgu3rGBtkS9ZSQ9A9RalXr9PzVZweB01C49yJPMY4UFVS70NERERqdzK9Pan5OTzmy3mwIEDZRRJ5VU7KpYu9mw6WdNZu3uDp8ORCiYj5wS/7il+MF3HBm3KpA2vGi0BaGzNY9P+nWXShoiIiFRuZZpUtG7dmv/7v/9j5cqVZy2TkZHBu+++S6NGjfjiiy/KMpxKyVq9OdkWb4IsTnasmuHpcKSCWbxtNX29jzEowk4Nf/+yaaRaEwAa2vLYtG972bQhIiIilVqZ3kuzefNmXnjhBS6//HJ8fHxo2bIl1apVw8fHh+PHj7N582Y2bdpEixYtePnll7nqqqvKMpzKyWLlaFhj/FNX4dz5M/CspyOSCmTh5mU85XeYYOchOLIdTvYqlKrw+jgMKyEWByl7VgN3lH4bIiIiUqmVaU9F1apVGT9+PIcOHeKNN96gTp06HD16lB07iqem7Nu3L6tXr2bp0qVKKP5CQMPiYxN/YhfZeTkejkYqkiNbfiLY4qTQyx+qNSubRmx2soNjATAPaQYoEREROV25jPr19fWld+/e9O7duzyau+BUbdoL5j1LS1sOi7cso0vz0ntislReyUcPkpC9G/yBWp3AYi2ztqzVm0P6b4SdSCa/sABvL3uZtSUiIiKVT5k/p0JKQdUEjtuC8DZM9q76zNPRSAWxcMsKOnplA+BVt1uZtuWXdA8j8hP4X24Q2w/tLtO2REREpPJRUlEZGAZZse1ZUODPsj1b9bBAAWDppiW08sotflP7sjJty4hL4rfodux32vVkbRERETlNpUwqFi5cyDXXXEO1atUwDIOZM2e6rR8wYACGYbi9rrjiCrcyaWlp9O3bl6CgIEJCQhg0aBBZWVnluBfnJ/TW9+iXU4evjuWx/dAeT4cjHuZwOsjfOR9vwyTfPxKq1i7zNhvVqAugpEJEREROUymTiuzsbJo2bcqbb7551jJXXHEFhw4dcr0++eQTt/V9+/Zl06ZNzJkzh2+//ZaFCxdyzz33lHXo/5i/jx+X1iue2WfOhl88HI142to9W6jtOAacvPXJMMq8zfYBcK/vMbL3rCjztkRERKRyqZSPZ77yyiu58sor/7KMt7c3UVFRZ1y3ZcsWZs+ezcqVK2nVqhUAr7/+OldddRWvvPIK1apVK/WYS8PljS9l4+aFbFv7HfTQtJ4Xs3mbl/FabjjHE67g5cseLpc22xz+he7+h5lwdD2maWKUQyIjIiIilUO59lQsWrSI22+/naSkJNfTsz/88EN++aX0v3mfP38+ERER1KtXj/vuu49jx4651i1dupSQkBBXQgHQrVs3LBYLy5cvP2N9+fn5ZGZmur3K27XWI6yvuoOb0xZwPDuj3NuXimPepmUANG3aHarWKpc2A2pdCkAdM50Dxw+XS5siIiJSOZRbUvHFF1/Qo0cPfH19+fXXX8nPzweKn6j94osvlmpbV1xxBR988AFz585l3LhxLFiwgCuvvBKHwwFASkoKERERbtvYbDZCQ0NJSUk5Y51jx44lODjY9YqJiSnVmM9F1QbFM/y0tWWzaN28cm9fKobj2Rn8umczAJcltiu3dm0xxbffNbXlsTFZT9YWERGR35VbUvH8888zefJk3n33Xby8vFzLL730UtasWVOqbd1yyy1ce+21NG7cmOuuu45vv/2WlStXMn/+/H9c58iRI8nIyHC99u3bV3oBn6uwOqR5V8VuwL7lH5d/+1IhLNyyktf9k/ki/CjVcw+WX8PVmgIQYy1k5+5fy69dERERqfDKLanYtm0bHTt2PG15cHAw6enpZdp2rVq1CAsLY+fOnQBERUWRmprqVqaoqIi0tLSzjsPw9vYmKCjI7eUJzro9AKh2aDnZ+bkeiUE8a9HGxVzpfYJLzVQwyvEORp9gMnyLe/hy95z5NkERERG5OJXbFUlUVJTrov6PfvnlF2rVKtt7wvfv38+xY8eIjo4GICkpifT0dFavXu0q8/PPP+N0Omnbtm2ZxlJSVdsNAKCrLYP56xZ6Nhgpd6Zpkr31R/wMk3zfUIhuUq7tF0Q0BMD36JZybVdEREQqtnJLKu6++24eeughli9fjmEYHDx4kI8++ogRI0Zw3333nVddWVlZrF27lrVr1wKwe/du1q5dS3JyMllZWTzyyCMsW7aMPXv2MHfuXHr16kVCQgI9ehR/y9+gQQOuuOIK7r77blasWMHixYsZMmQIt9xyS4Wd+ekUI6YNmV5BBFmc/LbsA0+HI+Vs68HfaF5YPMmBtf6V5TKV7B8F1LoEgNiCVDJyTpRr2yIiIlJxlduUso8//jhOp5OuXbuSk5NDx44d8fb2ZsSIETzwwAPnVdeqVavo3Lmz6/3w4cMB6N+/P5MmTWL9+vW8//77pKenU61aNbp3785zzz2Ht7e3a5uPPvqIIUOG0LVrVywWCzfeeCMTJ04snZ0tSxYL+XW6w+bpRB9YQm5BHr52H09HJeXk541L6Gkvvpi3Nbiq3Nv3bdmXAT//wPwTWXy8fweX1G1R7jGIiIhIxVNuSYVhGDz55JM88sgj7Ny5k6ysLBITEwkICDjvui677DJM0zzr+h9++OFv6wgNDeXjjyvnYOewzsN4euMmPs104rdpGVc1v8zTIUk52bH2W+KshRRZvLAldCn/AEJqYNRoTd6xBWzap6RCREREipXb7U9jx47lvffew263k5iYSJs2bQgICOC9995j3Lhx5RXGBcGIboy1RV8yTStfrvzR0+FIOUnLyiD28CoACuI7gd3fI3E0jKkDwMb9mlZWREREipVbUvH2229Tv37905Y3bNiQyZMnl1cYF4zeba8AYM76X0jPLv8H8Un5m7txCdsddtYbVfFr3sdjcXS05zLO/xBRe+Z6LAYRERGpWMotqUhJSXHNvvRH4eHhHDp0qLzCuGA0jIrl6Uj43H8bs1bM8nQ4Ug5+WL+IGfnBzGo9Epp5Lqmo60yjv+9xGmXvoqCo0GNxiIiISMVRbklFTEwMixcvPm354sWLK/yMSxWRYbNzu9cR2njlcmjpfz0djpSx/MIC5m1aBkCPph08GktwQvHzZppYc9h+aLdHYxEREZGKoVynlB06dChTpkxh79697N27l/fee49hw4Zx9913l1cYFw7DwNL8VgCap29g79FyfLKylLsl29fQzTxEveAQmsTU82gsRo0WOIFYayE7dq70aCwiIiJSMZTb7E+PPPIIx44d4/7776egoAAAHx8fHnvsMUaOHFleYVxQAtvdCUsn0tErm3cWfMx9N47wdEhSRlYu/5K3gw5QYBzGUpAFPp55ojsAPsEc8YkgMi+VEzvmQ+e+notFREREKoRy66kwDINx48Zx5MgRli1bxrp160hLS+OZZ54prxAuPFVrcTS0HlYDClb/jyJHkacjkjJQ5CgiYPu3AGRFNfdsQnFSbkQjAOwp6zwciYiIiFQE5ZZUnBIQEEDr1q1p1KiR28Po5J8JvrT4aeTXmvv4eePpY1ak8luybTVXGocBCL7kLg9HU8z/5LiKmOz9GqwtIiIi5Xf7E8DcuXOZO3cuqampOJ1Ot3XvvfdeeYZywfBq3oe87x8nnjw+/elNujft5OmQpJStXfwhHa2FFBhe2BOv8XQ4AIQl9oCfRxNkFLH1wE6axDbwdEgiIiLiQeXWUzFmzBi6d+/O3LlzOXr0KMePH3d7yT9k9yevcR++yQ9k7u7t7Dum6XkvJEWOIkJ3FT/gMD22A3if/xPoy4IRUZ9BoX24PL0Wa/du9XQ4IiIi4mHl1lMxefJkpk6dyh133FFeTV40Qm6cyAf7TTZsXcWHi2byxHX3eTokKSVLNi/lSssRAKpe+n8ejuYPLFYSarWA7RtYu3cz/bje0xGJiIiIB5VbT0VBQQGXXHJJeTV3cTEMBnbqDcCHi2aQnZ/r4YCktKxe/BGBhoN0WyDWupd7Ohw3zeISAVi7Z4uHIxERERFPK7ek4q677uLjjz8ur+YuOj2aduCSsFAeNLfz5S9fejocKQXZeTm8vmUbLdLqcuDysWAt1yFQf6tlkC/TgvbySvYccgryPB2OiIiIeFC5XaXk5eXxzjvv8NNPP9GkSRO8vLzc1o8fP768QrkgWQ0LU3x3EEwar8x7DWfnW7FYyn1yLylF3/46j5z8XCLD40i8pOLdNhgRlUCEPRuANduW0aLxZZ4NSERERDym3JKK9evX06xZMwA2btzots4wjPIK48JlGPh0fBB+eJLejl38uG4BVzTv7OmopAS++eULAPok9ayYvyN+oeyzhRJTlMbRDd+CkgoREZGLVrklFfPmzSuvpi5a3m3vJOenF4gjh4++G0uPZpdVzItR+VvJh3bx6vEZbA/2Jr5hC0+Hc1bHwhsRc2ghtuRlng5FREREPEj3x1xI7P6YbQYBcF3Wr8zbtMTDAck/tfm7FwizOEjwNoiu0dDT4ZyVb92uAFQ/sQvTND0cjYiIiHhKuY/83Lx5M8nJyRQUFLgtv/baa8s7lAuSf+eHyVv+Dg1t+Xw181k6N5yt3opKJr8gj7q/fQcWOJx4MxEVbID2H9VseSMsGEUdstm/fzMxMRU3ARIREZGyU25XK7/99hvXX389GzZswDAM17eapy54HQ5HeYVyYfMLxdH2/2DpRG7MWsPCTUvp1EhT+VYmK2e9QntLLidMK/WvHe3pcP6Sb2hN9lkCiXGeYM+q6UoqRERELlLldvvTQw89RHx8PKmpqfj5+bFp0yYWLlxIq1atmD9/fnmFcVHw7zKCdK9gvisI5F9fT8bpdHo6JDlHpmkSsPo9ALbVuAwvvyoejujv7Q9ryoICfzYf3u/pUERERMRDyi2pWLp0Kc8++yxhYWFYLBYsFgvt27dn7NixPPjgg+UVxsXBN4TCB1fypiOOVcnb+Xr1XE9HJOdo65L/0cx5lALToNa1z3s6nHNy4rIn6JMZy/9SMj0dioiIiHhIuSUVDoeDwMBAAMLCwjh48CAAsbGxbNu2rbzCuGiEV4licPfbAXjxq0nkFxb8zRZSERxf8BoAK0OaEFq9ctxK1Lp2YwB2pOzl6InjHo5GREREPKHckopGjRqxbt06ANq2bcvLL7/M4sWLefbZZ6lVq1Z5hXFR+b9ut3JliI0JBUuYNuc9T4cjf2Pjvu3cnGxlWFY1Iq99ydPhnLMq/sHUq1aLCKOQjet/9HQ4IiIi4gHlllQ89dRTrnv7n332WXbv3k2HDh34/vvvmThxYnmFcVHxt/vwStUsLrXn4LfgZQ5nHPV0SPIXxn/3X4owyG10Ewn1LvV0OOflkSq5rK+6g5Blb3o6FBEREfGAcksqGjRowHXXXQdAQkICW7du5ejRo6SmppKQkFBeYVxcDIMqN00C4Cavo3z04aMeDkjOZvuGH5m7dh6GYTDsqjs9Hc55q5LQAYCY41tAz6sQERG56JRbUhEfH8/Ro+7flIeGhpKWlkZ8fHx5hXHRscQlcazB9QBcs/9rftm02MMRyZ+ZhXkETR/Iwiq7GNykGfWqVb7fh8RLbifHNKhKPke2L/R0OCIiIlLOyi2pONvTdrOysvDx8SmvMC5KVW94jRO2AOrYCtj+yf1k5+V4OiT5g20zHifKcQJfw6Tv1ZVzJrQqwRFstEUBcGDFRx6ORkRERMpbmT/8bvjw4UDxQ+6eeeYZ/Pz8XOscDgfLly+nWbNmZR3Gxc03BNu1E+DLu+ln7ubt/z3O4Ls0jqUiyEvbS4317wOwPP46rolp4OGI/rmMGm1h70x89v7i6VBERESknJV5UvHrr78CxT0VGzZswG63u9bZ7XaaNm3KiBEjyjqMi55viz6krplGxJ65+G35ivmb+3JZYltPh3XRS55yG3VxsMEMpMvtlTvRi2jZB/bOpHbuARzZx7D6V/V0SCIiIlJOyjypmDdvHgADBw7ktddeIygoqKyblLOI6DuF6VOHMHLdDsKnjmHukx8SEawLP0/57ec3qHt8A4UmHOkyhsa+AZ4OqUQaNu3B9s99qWvNZc+SqcRd/rCnQxIREZFyUm5jKt566y1stt9zmL179/Lqq6/y44+a177c+IZw1aD/UL9aAkcy07jvvWdwOB2ejuqilH0smeCfRwPwQ0hrOncZ5NmASoHNamNueHvuP1Gd73K8PR2OiIiIlKNySyp69erFBx98AEB6ejpt2rTh3//+N7169WLSpEnlFcZFz8/uw7v3vEC4t53bD37FRx8/4emQLjqmafLczEmszLfzm+lH+3s+wTAMT4dVKgJb38GX+cF8vXm1p0MRERGRclRuScWaNWvo0KF4Lvvp06cTFRXF3r17+eCDD8774XcLFy7kmmuuoVq1ahiGwcyZM93Wm6bJM888Q3R0NL6+vnTr1o0dO3a4lUlLS6Nv374EBQUREhLCoEGDyMrKKtE+VhZ1ouL4snlNrvfOpPumd/l2gWbrKU9vzfmIqSvncWdWLKk3fURIcISnQyo1PZp2xDAM1u3dwv60FE+HIyIiIuWk3JKKnJwcAgMDAfjxxx+54YYbsFgstGvXjr17955XXdnZ2TRt2pQ33zzz03tffvllJk6cyOTJk1m+fDn+/v706NGDvLw8V5m+ffuyadMm5syZw7fffsvChQu55557/vkOVjJ1bp1Mqk8kUdYias0exspNizwd0kVh3tIveX7GGwA8e9NQ2jXr6uGISld4UChX1KrLEN+jHP5SYypEREQuFuWWVCQkJDBz5kz27dvHDz/8QPfu3QFITU0978HbV155Jc8//zzXX3/9aetM0+TVV1/lqaeeolevXjRp0oQPPviAgwcPuno0tmzZwuzZs/nPf/5D27Ztad++Pa+//jrTpk3j4MGDJd7XSsE7gLD7fiDd4keiNQ/zf7ew8bf1no7qgrZ45XckfnMXb/jv5872VzOo882eDqlMXFe7Lk/5p1J/z2wozPV0OCIiIlIOyi2peOaZZxgxYgRxcXG0bduWpKQkoLjXonnz5qXWzu7du0lJSaFbt26uZcHBwbRt25alS5cCsHTpUkJCQmjVqpWrTLdu3bBYLCxfvvyM9ebn55OZmen2quwsVWvhfdc3ZONFG+sJUt69lm37tns6rAvSwvUL8PtiEJGWItoGWBlzw+ALZhzFn7XqdCf7HV74U0jaqmmeDkdERETKQbklFb179yY5OZlVq1Yxe/Zs1/KuXbsyYcKEUmsnJaX4Pu7IyEi35ZGRka51KSkpRES438dus9kIDQ11lfmzsWPHEhwc7HrFxMSUWsye5FuzNdz+CQVY6GZNY8NbPVmfvNXTYV1Qvlz6LUUf3UZzWw5ZhjcR9/+El18VT4dVZqpXjWaJf10AMpe87eFoREREpDyUW1IBEBUVRfPmzbFYfm+2TZs21K9fvzzD+EdGjhxJRkaG67Vv3z5Ph1Rq/Ot3J7/3exzFhzcz/blh/P38snWVp8Oq9JxOJ69+9x+cX9xHF68TFBg2vAfOxB5Rx9OhlTmf1v0BiDm+GTNTA7ZFREQudGX68Lvhw4fz3HPP4e/vz/Dhw/+y7Pjx40ulzaioKAAOHz5MdHS0a/nhw4dp1qyZq0xqaqrbdkVFRaSlpbm2/zNvb2+8vS/cufcDm90AtToS+t9RbNm2mtveGMaEvo9xY9LVng6tUsrMzWLolNFc+dun9PbJwIEFW9+PsdS61NOhlYtOHfqyet5oWtpyOLjwTapd/ZynQxIREZEyVKY9Fb/++iuFhYWun8/2Wrt2bam1GR8fT1RUFHPnznUty8zMZPny5a5xHElJSaSnp7N69e9z6f/88884nU7atm1barFUNoFBYXw0ZDw9m3emJek0/moA46c8SkFRoadDq1S2HNhJjxcHsGfTT1zjnYnTsGDt8x6W+ld4OrRyE+wXyJbI4t8l49ePwTQ9HJGIiIiUJcM0K9//9llZWezcuROA5s2bM378eDp37kxoaCg1a9Zk3LhxvPTSS7z//vvEx8fz9NNPs379ejZv3oyPjw9QPIPU4cOHmTx5MoWFhQwcOJBWrVrx8ccfn1MMmZmZBAcHk5GRcd6zV1V0DkcRqS83JTp7H5lOC+P9k+h/9zvER1wY40jKimma/O+Xr3jmswnkFuZTPTSKT6+8hoSqkZB48fX4LN+4gHof9+KnohC6P7WCoKAL53kcIiIiF4LSvJ4tt6QiNzcX0zTx8/MDYO/evcyYMYPExETX9LLnav78+XTu3Pm05f3792fq1KmYpsmoUaN45513SE9Pp3379rz11lvUrVvXVTYtLY0hQ4bwzTffYLFYuPHGG5k4cSIBAQHnFMOFnFQAkH2U4+9eQ5WjmwD4b344dB/DwK63uY2JkWLHstJ5+MMX2bFxDv44qVK3E28NepaqASGeDs1jTNOk+5g+bEhJ5vk+w7nrAp1CV0REpLKqlElF9+7dueGGG7j33ntJT0+nXr162O12jh49yvjx47nvvvvKI4xSc8EnFQBF+Zz4+hEC10wFYF2RD+8Gd+Ku256lWWwDz8ZWgczfvJwHpz5L+/zfGBdwCIt3AL5DFmMJjfV0aB733vzpPDHtFWqGVWPJmM+wWct0GJeIiIich9K8ni23r5zXrFlDhw4dAJg+fTpRUVHs3buXDz74gIkTJ5ZXGHI+bN4E3jARZ99p5HkF0NSWx/isHxj8cl8eev85Dmcc9XSEHpVXmM+oz19l0OtDeNK5nrcCDxBoOPGPboTF7ufp8CqEPkk9qRpYBf/jv7H86xc8HY6IiIiUkXJLKnJycggMDASKH3h3ww03YLFYaNeuHXv37i2vMOQfsDS4Cp9hq8lNuJwNAXXZ5bDz6dLvSHrmJp6f8SZHTxz3dIjlbvVvG7n8hf7sWfQuC0J20ccnA9OwQJeRcOe3EBDu6RArBH9vX55r2ZJ5VX6j4eqJOPIq/0MjRURE5HTlllQkJCQwc+ZM9u3bxw8//OAaR5Gamnrh3j50IQmKxrf/dFqO+IXvHv0PLeIbElyYyaVLn+eup7sz5ovXOZJ5zNNRlrmcgjxGTX+Na/51F49lL+L9oH3UsBZBlTiMO78rTip0i4+by695lL1Ob0IoZOvMJz0djoiIiJSBcksqnnnmGUaMGEFcXBxt27Z1Te/6448/0rx58/IKQ0rCMMDLh5a1GvHdo//hm5bxdLZnMzNgG+2Xj2XIM9155H9j2XrwN09HWiYWbllBl+du5+2fPsFpQtWwmpgWG3R8GB5YBvEXxzMozlegfxCbE64DoNrGTyjMzfBsQCIiIlLqynVK2ZSUFA4dOkTTpk1dMwitWLGCoKCgSvFU7T+6KAZq/530/Zhzn4e10zBMJwCrCn15NzeUE/GdGdD1Nro1uqTSzxa1O3Ufk6Y9R8O9P/BhXghpQbG8fNvjdIuNg9x0iEz0dIgVXmbWcTLG1iPGyGNprRtJunOKp0MSERG56FXK2Z8uNEoq/uDYb5i/TMRc/T8szgIAthV50ym9FrFhNbj1kqu5qd1VVA+N9HCg52dvym7mzxhD9d1z6OJ1AosB64IbUWvIHAJ9/T0dXqWz7NOHabfhXY45bZjD1hIWXtPTIYmIiFzUKm1SsWjRIt5++2127drF9OnTqV69Oh9++CHx8fG0b9++vMIoFUoqzuDEYVj+DkUr32eFf10G7ikkI+cENkwe9DvK0eqXkNThdi5v0p4An4p5Ue50ONj0y1SOLH+fpukbqGpxuNZlxXYk4PInIO4SD0ZYeTkK80h5Np7qZjY/VWlLt4fneDokERGRi1ppXs+W24jSL774gjvuuIO+ffvy66+/kp+fD0BGRgYvvvgi33//fXmFImUlMBK6PY2t80guKcplDTa+XfMzmxe8y6NZW+D4V6TM+I4fpwdwJKwJkc2vp1XLntQIjfJczKbJkYNb+fXYUeZtWs6PG37hY+diutjywQLHLb7kNLyR6l2HExCW4Lk4LwBWLx+yuzzNsTlP8l1yMuaGxVzeWONQRERELgTl1lPRvHlzhg0bRr9+/QgMDGTdunXUqlWLX3/9lSuvvJKUlJTyCKPUqKfiPOxbSfZPL2L/bQFeZpHbqsNOG8/TELNOVxpUTyAxojpxkbFEhEbj7+37l9U6nU7yigrILcgjryC/+N9C939zC/J/X1aQhzXrMAHpv+Gftouq2fuJz08hjHzqHatP7sl5C4YHZtI5IpSwpAHEJw3QbE6lyTR54dOXeX3+DCKCqjL/mY8JDQj2dFQiIiIXpUrZU7Ft2zY6dux42vLg4GDS09PLKwzxhJjW+A+cAYV5mMnLSVv/NQXb5hCRtZdISxFbj2eyYflsAO7yOcZo/8Psd3qx2vTlmOFLusWHTMOHY9iZVxjIkUInuQV5BDhyiLQUYcPEapjYgADDSbDhIMTiYEZ+MBmmFYCHfI/wgO8xAixO99gMKDTh8siqhNS9jG6NL6F9/db42X3K+SBdJAyD4Tc8xOyta9iRspdH/jeW//zfWAzD8HRkIiIiUgLlllRERUWxc+dO4uLi3Jb/8ssv1KpVq7zCEE/y8sGo3YmqtTsVvy/IJnfvKp7Ks7I6eTtbD+6i6b5vsDkhzlpIHIWA+8PSumTVIt1RfMF/n+9xHvc/ctbmssIast83Cl+7N7XythCQcQQHBke9w0kPqklBWH3ssa2p0fQa3gkMK6u9lj/xtfswccAoXp/Qh4f3/Jf3Z8Uz4Kr/83RYIiIiUgLlllTcfffdPPTQQ7z33nsYhsHBgwdZunQpI0aM4Omnny6vMKQisfvjW6cTnYBOjU8O1Defx8w8RG7KZrIPbsR54jCW7CNYco7hlXuct24bgzU0Fh8vb6qs/g/ONR9gWG0YFi+wWMDLH3yrgG8Ib3R7GiLqFdeblQp5GVhDahJp86ZyzUN14WkeU5dXoh2EZuWza97zLK7Vkkvrt/J0WCIiIvIPlduYCtM0efHFFxk7diw5OTkAeHt7M2LECJ577rnyCKFUaUyFSMmYB37FOakzVpw8UxBPv+FfkxAV6+mwRERELhqVdkpZgIKCAnbu3ElWVhaJiYkEBASQm5uLr+9fD8qtaJRUiJRcwbx/YZ/7HPmmwT204uVHPyUyWLeiiYiIlIfSvJ4t90cd2+12EhMTadOmDV5eXowfP574+PjyDkNEKgD7ZSPIr9MDb8PkBedahrx+Pydysz0dloiIiJynMk8q8vPzGTlyJK1ateKSSy5h5syZAEyZMoX4+HgmTJjAsGHDyjoMEamIDAPvPv+hMCSWGtZCns6az6DXh5CVp8RCRESkMinzpOKZZ55h0qRJxMXFsWfPHm666SbuueceJkyYwPjx49mzZw+PPfZYWYchIhWVTzBeA2dS6FOF9WYQv/y2mdteH6bEQkREpBIp86Ti888/54MPPmD69On8+OOPOBwOioqKWLduHbfccgtWq7WsQxCRiq5qbbweXEaj+74m0C+IFbvWc+vEoWTknPB0ZCIiInIOyjyp2L9/Py1btgSgUaNGeHt7M2zYMD3sSkTcBUXTLC6Rzx56nTA/fzqnzOW2fw3i4PFUT0cmIiIif6PMkwqHw4Hdbne9t9lsBAQElHWzIlJJNY2tzy/NIhjud5R/587jnpf7svXgb54OS0RERP5CmT/8zjRNBgwYgLe3NwB5eXnce++9+Pv7u5X78ssvyzoUEakkQro9RtGBldTPTmWqcxWP/Ptmbuk3nh5NO3o6NBERETmDMn9OxcCBA8+p3JQpU8oyjFKn51SIlLHMgxR9cBO2lA0ATMoJJafTYwy75v+wWMp9NmwREZELTqV++N2FQkmFSDkozMUx60msK/4DwMYib/4TdiWPDnqFalUiPByciIhI5VapH34nInLOvHyxXjse+n5CvlcANSxFzNm1mcuevY0vls9G34mIiIhUDEoqRKTia9AT7xHryb7hXWrGNiEzN4vBU0bxn5e6s+23tZ6OTkRE5KKn25/+Id3+JOIZRY4iXv/hA9b/+BpTAnaT7rTwa3hb6vZ6nuq1Wns6PBERkUpDYyoqACUVIp51eM0XFH09nOpFxwFwmLDeP4GADoNJSOqHYfPycIQiIiIVm5KKCkBJhUgF4HSwdd4kCn95ncaFh1yLj2Fnequn6di6J/Wr1dbDNkVERM5ASUUFoKRCpGLZtvY7Dv/4Eo3TN3LYaeOy9FqAQXRIOE9HWaharQHhLXpTN74ZNmuZP6JHRESkwlNSUQEoqRCpmLKyjzN/yZd8vHUjS7avwSzMZUvVbfgZJk4Ttjp92eEdTVpofcyabYiObUGd6Hjiwqsr2RARkYuKkooKQEmFSMWXW5DHqo0LsS+ZSPUj66jhyDitzGd5wTyYVR2bxUpMaBQdQwPwiW5ATGQ88RExxIfXICasGl5KOERE5AJTmtez+l9SRC5YvnYfOrToDi26A+DMOEjqxu/J2zEf30O/Epa9n5zAavgW+JBbkEdR2m7GsRPSZrB/vRe7HHYWOOzsdvqQ7x9BblhdAqIaUCsihrjwGGpFKOEQERGBC7SnYvTo0YwZM8ZtWb169di6dSsAeXl5PPzww0ybNo38/Hx69OjBW2+9RWRk5Dm3oZ4KkQtAYR44CnDaA0jJOMrRDd9Q7+cn8S7KOWPxV3LCeCWn+EneNS0FvBp4kENOO/neQVj8w/EKjsY/tCa+VarjE9mAkIjahAeFEuIXiMWixwKJiEjFop6Kc9CwYUN++ukn13ub7fddHTZsGN999x2ff/45wcHBDBkyhBtuuIHFixd7IlQR8RQvH/DywQJUqxJBtY6DoMOdkJMGx3bC0Z2YR3aQd3gzRWl7aVe9C4Ot1dlzZD9VUn7lksKdQA6QDtnJkA0cLK76pexwXs0NB6C+LZ+pQfvJwotci508qw+5Fm9yLXZyLd5sskez07s6FouFABzUcRzDYfXB9PLFsPuB3R+Ltz+Glz92b18CfPwJ8QskxD+IEL9Agv2CCPYLJNgvUL0mIiLiERfs/z42m42oqKjTlmdkZPDf//6Xjz/+mC5dugAwZcoUGjRowLJly2jXrl15hyoiFYlhgH/V4lfNthiA78lV7U++ADhxGHPXAk6k7uDEsb3kpR/AeSIVW146fkVZOAOjCDH8Sc/JJNQoIs6SD+QXb+s4+TrphaMpzMvdB0Bjay5vV9l91vAm5ITxxB96Sz4JTqbANDhgGvyGgdOw4bDYcFq8WGaPYWlAA/y9fQm3Wbg2cyVFhpUiw1pcBgtFGDiAZFsYW7yr4TQdWBwFtMzeSaEJRaZJkUnxzxT/m2bxI80/mgBvf4J8fElwZuLj64+P3R8fH198vP3w8fLF19sXn4BQ7EFR2G12TNOJJecYJhachoHDNClwOChyOihwOChwmhRgUFhUSIGjkMLCQgqdDgodhRQWFVLoKKLQUYSBgZ+3L/7evgT4+OHv7Ye/ty/+Pn742X3w8/bF1+6Dn93nLwffm6ZZ3HbRqfoLKSgqOvlvcXsFRYUUnfz31HuLYcHP28cVg5/dFz9vH/y9/bD/w+ejOJwO8osKKSgsoKCokPyiAgqLCouXFRXiNJ3YbV54WW0n//XCbrNht9mx27zwttlLrTfMNE0cTgeFjiLXv0UOBxaLBS+rDZvVVvyvxarpmkXE5YJNKnbs2EG1atXw8fEhKSmJsWPHUrNmTVavXk1hYSHdunVzla1fvz41a9Zk6dKlZ00q8vPzyc/Pd73PzMws830QkQosMBKj2c0EAWfqMH7i5Cu/sIAT6Qc4uH8dhdnHKMg+hiM7DUt+FpbCE1gLsukU1YY6YY1wOJ0EHd/F8Q2TsRblY3Xk4+UowOYswELxnap1YxrQ068x6TknCM3aT+38nWcNcW26gyUHcwGItRQwPvTsZf+bW4VvsqMBCDOKeLfq9rOWnZYXzNA91QHww8lvYVvPWnZmfhD3nqgBgAWTg2Fbzlp2TkEAd2TWdL3fU3ULPoaJwwQn4MTACZjAkkJ/bv9D2V9CdhJsceDEwGHCCYpfGAabnf7cn1cHp2liYvKp71YijEKcmJgYnLoH2DRhl8NO/xO/1/tBUDI1LYWYJ9vl5L8mcNDpRb8/xPB6wAHq2AqKL7QNAwsWMAwMwyADOw8bzYsv1B0OHrVsow7ZOE0Th1lco2kW15uDxa3ex/1SaWrL5Y/3KpsUX8w7wXXMbBYrQ/2P0cqWg8WwYLFYsBgGhmE5uX8mTxjNyTuZNNxi7qEFxzFN8+TLiePkzwCDs6qTbVoB6OeTxmVe2W6flwkn6zcYVVCLE1ZfrBYL11uP0MmShmEYGBSvP/UzmEy0NuSwacfhdNDZeZDLzMO/x4BZ/DmdjOGVghokO70xMelmPc7VtjRX20DxUThZ9+sF0ezBH4thob01netsR0+WMU4VdP38IbH8ZgRjGAYtOM61HHDVWBwrOE9+Lp9Tk00E4XA6SDTTuZH9xefAyXhNs/hni2Ewk+pssYVht3rRwJLDjeZeLIaB1WIp/kxOfh4GsCygPjt8Y7BarEQXptElfTUO04nT6cThdBSfG04nTtPJXDOcJWYoRY4iIh1ZDLTsxWk6cTqLj4TFYpys38IvlkhW2qLx9vKmmqWI24u2YrVYsBpWrFbr7z9bLOytkshvYc0odBThlXucTgd/xGk6i9t1OnA4HRSdjGc1ocw2I8grzMdWkM2Dxk6cpnnyXDOwus45C9u9IlnsXw9fuzfBNhu3n1iGzVLcvs1iw2a1Fr+3WMmoksCW6PbkFeaTX5BHhx2fUOQoosjpoOjkFwlFjuKft5l+THNEk52fg2maPGnbjZel+DOzWazFCbfNC7vVRoZ/NBtqdCXQxx8/b18u/e0LfHBgO5kQmyd/6U1Mcvwi2VzrarLysjmRm03T7Z9hyT9BoaMQx8kvPk7Fk2p68YGlTnE9Viu3F2yhqlGA3WrH7uWF3eqF3Vb8Mv2qsrfpgOIvPbz9iNn0Cb65xzj5q3Dy/DEpcjjIs3ixpWE/svKzyc7LodaOr/HLPkTRqc/BUfyv0+mkyOLFN5FdsRoWvGxeXJa5nmpFx3//0sFWHIeX1YbN5kX25c/ia/fGz+6Lfe1HGCkbXeewI+f3a9uSuiCTirZt2zJ16lTq1avHoUOHGDNmDB06dGDjxo2kpKRgt9sJCQlx2yYyMpKUlJSz1jl27NjTxmmIiPwdby873uHxEB5/1jKnren5kPt70wRHARRk09Nqp6d3QPHygmw4uA4cBTgKcsnJySA3N4P8vGwK8rNo61+DtwNqkp2fQ1HmYVYn/4jVWYTFWYTVLALTidV0YsVJ7dBGvFC9PVaLBd+iHA5tmIzFdLpeBr//3KJ+G16rfR0n8rIpzDpG5poXwHRiOB2uspjFl+te3v6EG6HkFxbgZfz1ED5/H3+aVmmA3WrDy+aFNW0rYGI1oPjS9vftqwWG0DmmHTn5uWTl5RCet53gP3b//EGKI58Teb9fEFf3y6eateiMZQsNy8keBxteVi/qeu0hzjjzf7rBpkGIXxDZ+TkUOoqoZ8uniS3vjGVTnVb2pO13va8dnEFrr9wzlj3htOB9sgfC7mWnjdchLrFkn7Gs4w+HtMjpoAEnuMx64veFf8yGgN1H9pJPcZJRM+AonX1OnxHtFK+s339uaM3nKu8TZy37xIks0pzFx6ma/xG6+qadNYYnjvmx0+ENQG+/VC73O3ryov90r2aFkO0o/lxr+mZxrT39rDH8JzuQzKLihiJ9jnO199Gzlv0ww5u9hcU7mOR9nCsCf394Jn86TT/LtLCnoPirgxb2DK4NOnzWeuee8CI5vwCAWvYTXBG076xlv0lN5+u84uS9vVcWzwQnn7Xsiqw89uQVj/WKsOXQO+TsMezILmJHbnEMDmse3av8dtayS/fv4d85q4HiLx4e+YsvHvblHmd7dvF5GG4UcUvVI2cuaMLHmSdYdrA43gDDweSq285a78xdq3n0RPHt5xZMHviLLx4s+QHsOfH77/n1VQ/hc+rvihMoOPkCFh3bxcSNv//ObQndShWL84z1ri705Z65S39/X2U71c/yN2JzkTePp/9+wr4WspM6toIzlt3j8GLg0t/358eQ3/7yb8Tt89e63s8M3k3Tv/gb0XfX7+u6B+2lif3sfyPq/fx7ve8F7uMq7xMYFP/aWfNLb2j1BTlQ+8/S09OJjY1l/Pjx+Pr6MnDgQLdeB4A2bdrQuXNnxo0bd8Y6ztRTERMTo4HaIiLnyzTBdJ7+Mizg5ft7ueyj7uudf/jZ5g1B0b+XPbIdnEXgdGA6iyhwFJJfkE9eQT75hpWiqgmub8vtRzZjdRbhdfLbUpvVhpfFitUwisewVG/+e737VkBBLq4rzZPfXmOaxbHGXQJAoaOI/J0LKMg6SlFRIYXOIoqKiihyFN82ZVq9yIq5xPUtaVDKWuwFGa5vbb0sVmwWS/G3tzY7RuPrf49h13zIPPR7u67DaOIwneQ1utF165Rl9yLM9OTib3pPfrtb5HRgABaLhWMJV2K12bGejMEv6wCWk/tuMSzF32JbLFgsVpxNbsbm7Y+X1YY1eTnmka04Tn5r7XA6cDicOMzib07TE66k0OaNw+nAfmAN3se2n/wmvfibdtPpxGE6MQyDY3FdMX1DsFqsBB/biv+xba5v8g3DKP42/2S/RnbdK3D6VcUwDLwPb8L70K8Axb1BJ4/FqW9cs2p3oyggsvhWsdTN+BxY5erxME3nyZ6F4rLpsR3JD4jGaTrxTttB8IGVrvWcLGucjCejZnsKq8RhMQx8M5IJSf7l5DpOxgwGFpymk+PV25IVHEt+USHWtN1U2bvg9+N18ltm0yzujTkQ2pDUwBhMp4lfzmHqpq462ZNQfF5aTp0PVisnoltREN0Uq8WKb14aVXd87zpmwMneBQcOp5PMiCYcC0skv7AA88RhonZ97/qWu8jhoMh58txwOtjtE802nxi8rDaCKKJd2kqsJ9v2Ovm7YbMW/5sTlkhO7KX4ennjZziI2PQZVsOC42TbzpPtFzkcpAXGsC+8GbkFeeTlZlJ/17cUOYtO3sp4qveh+N9kI4j1gfXw9rLj62XnyvQV2P5wm5+XzVb8rbvNhiMkjux6V+Hn7YvFMKiy8l1MZxFO01n8O1hYUPwqKiDVEsBS/3pk5eeQnZdL57RlUJTnupXyjz1oxyy+/OKfSICvP4E+/lyVvZFAi/Pk7YW/3/LnZfXC9K9KSt1erh6E6tu+wsg9Tn5Rcdt5hfmun9OK4AtrPNn5uWTl53BV4W5CHNm/n8Mn//Wy2iiy+fCNb0P8vf0I8PHj8oLfiCT35K2GNtdnYbVYKcLCipjuOBzFt03WPrSEgOxDxbdOFhacvG2ywHXb5risqhQ5i5Ox6+wZ1LX9fj2bl+/kybf26jkV56N169Z069aNyy+/nK5du3L8+HG33orY2FiGDh3KsGHDzqk+zf4kIiIiIpVBoaOI3II8cgvyXLfhWS0Wsk5kUTO6Rqlcz14UcxxmZWWxa9cuoqOjadmyJV5eXsydO9e1ftu2bSQnJ5OUlOTBKEVERERESp+X1UaQbwCRwWGEB1UlLLAKVfyDCfYLLLU2LsgxFSNGjOCaa64hNjaWgwcPMmrUKKxWK7feeivBwcEMGjSI4cOHExoaSlBQEA888ABJSUma+UlERERE5B+4IJOK/fv3c+utt3Ls2DHCw8Np3749y5YtIzy8eM74CRMmYLFYuPHGG90eficiIiIiIufvohlTUdo0pkJEREREKrPSvJ69KMZUiIiIiIhI2VFSISIiIiIiJaKkQkRERERESkRJhYiIiIiIlIiSChERERERKRElFSIiIiIiUiJKKkREREREpESUVIiIiIiISIkoqRARERERkRJRUiEiIiIiIiWipEJEREREREpESYWIiIiIiJSIkgoRERERESkRJRUiIiIiIlIiSipERERERKRElFSIiIiIiEiJKKkQEREREZESUVIhIiIiIiIloqRCRERERERKREmFiIiIiIiUiJIKEREREREpESUVIiIiIiJSIkoqRERERESkRJRUiIiIiIhIiSipEBERERGRElFSISIiIiIiJaKkQkRERERESkRJhYiIiIiIlIiSChERERERKRElFSIiIiIiUiJKKkREREREpESUVIiIiIiISIlc9EnFm2++SVxcHD4+PrRt25YVK1Z4OiQRERERkUrlok4qPv30U4YPH86oUaNYs2YNTZs2pUePHqSmpno6NBERERGRSuOiTirGjx/P3XffzcCBA0lMTGTy5Mn4+fnx3nvveTo0EREREZFKw+bpADyloKCA1atXM3LkSNcyi8VCt27dWLp06Wnl8/Pzyc/Pd73PyMgAIDMzs+yDFREREREpZaeuY03TLHFdF21ScfToURwOB5GRkW7LIyMj2bp162nlx44dy5gxY05bHhMTU2YxioiIiIiUtWPHjhEcHFyiOi7apOJ8jRw5kuHDh7vep6enExsbS3Jycok/BLmwZWZmEhMTw759+wgKCvJ0OFJB6TyRc6HzRM6VzhU5FxkZGdSsWZPQ0NAS13XRJhVhYWFYrVYOHz7stvzw4cNERUWdVt7b2xtvb+/TlgcHB+uXVc5JUFCQzhX5WzpP5FzoPJFzpXNFzoXFUvJh1hftQG273U7Lli2ZO3eua5nT6WTu3LkkJSV5MDIRERERkcrlou2pABg+fDj9+/enVatWtGnThldffZXs7GwGDhzo6dBERERERCqNizqp6NOnD0eOHOGZZ54hJSWFZs2aMXv27NMGb5+Jt7c3o0aNOuMtUSJ/pHNFzoXOEzkXOk/kXOlckXNRmueJYZbGHFIiIiIiInLRumjHVIiIiIiISOlQUiEiIiIiIiWipEJEREREREpESYWIiIiIiJSIkop/6M033yQuLg4fHx/atm3LihUrPB2SeNDChQu55pprqFatGoZhMHPmTLf1pmnyzDPPEB0dja+vL926dWPHjh2eCVY8ZuzYsbRu3ZrAwEAiIiK47rrr2LZtm1uZvLw8Bg8eTNWqVQkICODGG2887SGdcuGbNGkSTZo0cT24LCkpiVmzZrnW6zyRM3nppZcwDIOhQ4e6lulcEYDRo0djGIbbq379+q71pXGeKKn4Bz799FOGDx/OqFGjWLNmDU2bNqVHjx6kpqZ6OjTxkOzsbJo2bcqbb755xvUvv/wyEydOZPLkySxfvhx/f3969OhBXl5eOUcqnrRgwQIGDx7MsmXLmDNnDoWFhXTv3p3s7GxXmWHDhvHNN9/w+eefs2DBAg4ePMgNN9zgwajFE2rUqMFLL73E6tWrWbVqFV26dKFXr15s2rQJ0Hkip1u5ciVvv/02TZo0cVuuc0VOadiwIYcOHXK9fvnlF9e6UjlPTDlvbdq0MQcPHux673A4zGrVqpljx471YFRSUQDmjBkzXO+dTqcZFRVl/utf/3ItS09PN729vc1PPvnEAxFKRZGammoC5oIFC0zTLD4vvLy8zM8//9xVZsuWLSZgLl261FNhSgVRpUoV8z//+Y/OEznNiRMnzDp16phz5swxO3XqZD700EOmaepvivxu1KhRZtOmTc+4rrTOE/VUnKeCggJWr15Nt27dXMssFgvdunVj6dKlHoxMKqrdu3eTkpLids4EBwfTtm1bnTMXuYyMDABCQ0MBWL16NYWFhW7nSv369alZs6bOlYuYw+Fg2rRpZGdnk5SUpPNETjN48GB69uzpdk6A/qaIux07dlCtWjVq1apF3759SU5OBkrvPLmon6j9Txw9ehSHw3HaU7cjIyPZunWrh6KSiiwlJQXgjOfMqXVy8XE6nQwdOpRLL72URo0aAcXnit1uJyQkxK2szpWL04YNG0hKSiIvL4+AgABmzJhBYmIia9eu1XkiLtOmTWPNmjWsXLnytHX6myKntG3blqlTp1KvXj0OHTrEmDFj6NChAxs3biy180RJhYiIBwwePJiNGze63dMq8kf16tVj7dq1ZGRkMH36dPr378+CBQs8HZZUIPv27eOhhx5izpw5+Pj4eDocqcCuvPJK189NmjShbdu2xMbG8tlnn+Hr61sqbej2p/MUFhaG1Wo9bUT84cOHiYqK8lBUUpGdOi90zsgpQ4YM4dtvv2XevHnUqFHDtTwqKoqCggLS09PdyutcuTjZ7XYSEhJo2bIlY8eOpWnTprz22ms6T8Rl9erVpKam0qJFC2w2GzabjQULFjBx4kRsNhuRkZE6V+SMQkJCqFu3Ljt37iy1vylKKs6T3W6nZcuWzJ0717XM6XQyd+5ckpKSPBiZVFTx8fFERUW5nTOZmZksX75c58xFxjRNhgwZwowZM/j555+Jj493W9+yZUu8vLzczpVt27aRnJysc0VwOp3k5+frPBGXrl27smHDBtauXet6tWrVir59+7p+1rkiZ5KVlcWuXbuIjo4utb8puv3pHxg+fDj9+/enVatWtGnThldffZXs7GwGDhzo6dDEQ7Kysti5c6fr/e7du1m7di2hoaHUrFmToUOH8vzzz1OnTh3i4+N5+umnqVatGtddd53ngpZyN3jwYD7++GO++uorAgMDXfeqBgcH4+vrS3BwMIMGDWL48OGEhoYSFBTEAw88QFJSEu3atfNw9FKeRo4cyZVXXknNmjU5ceIEH3/8MfPnz+eHH37QeSIugYGBrjFZp/j7+1O1alXXcp0rAjBixAiuueYaYmNjOXjwIKNGjcJqtXLrrbeW3t+UEs5QddF6/fXXzZo1a5p2u91s06aNuWzZMk+HJB40b948Ezjt1b9/f9M0i6eVffrpp83IyEjT29vb7Nq1q7lt2zbPBi3l7kznCGBOmTLFVSY3N9e8//77zSpVqph+fn7m9ddfbx46dMhzQYtH3HnnnWZsbKxpt9vN8PBws2vXruaPP/7oWq/zRM7mj1PKmqbOFSnWp08fMzo62rTb7Wb16tXNPn36mDt37nStL43zxDBN0yzlZEhERERERC4iGlMhIiIiIiIloqRCRERERERKREmFiIiIiIiUiJIKEREREREpESUVIiIiIiJSIkoqRERERESkRJRUiIiIiIhIiSipEBERERGRElFSISIiF5WCggISEhJYsmSJx2J4/PHHeeCBBzzWvohIaVNSISJSiQ0YMADDME577dy509OhVViTJ08mPj6eSy65xG35vHnzuPrqqwkPD8fHx4fatWvTp08fFi5c6Cozf/58DMMgPT39tHrj4uJ49dVXzymGESNG8P777/Pbb7+VZFdERCoMJRUiIpXcFVdcwaFDh9xe8fHxp5UrKCjwQHQVi2mavPHGGwwaNMht+VtvvUXXrl2pWrUqn376Kdu2bWPGjBlccsklDBs2rNTjCAsLo0ePHkyaNKnU6xYR8QQlFSIilZy3tzdRUVFuL6vVymWXXcaQIUMYOnSo6yIWYOPGjVx55ZUEBAQQGRnJHXfcwdGjR131ZWdn069fPwICAoiOjubf//43l112GUOHDnWVMQyDmTNnusUREhLC1KlTXe/37dvHzTffTEhICKGhofTq1Ys9e/a41g8YMIDrrruOV155hejoaKpWrcrgwYMpLCx0lcnPz+exxx4jJiYGb29vEhIS+O9//4tpmiQkJPDKK6+4xbB27dq/7KlZvXo1u3btomfPnq5lycnJDB06lKFDh/L+++/TpUsXYmNjadKkCQ899BCrVq0614/CZerUqWfsQRo9erSrzDXXXMO0adPOu24RkYpISYWIyAXs/fffx263s3jxYiZPnkx6ejpdunShefPmrFq1itmzZ3P48GFuvvlm1zaPPPIICxYs4KuvvuLHH39k/vz5rFmz5rzaLSwspEePHgQGBrJo0SIWL15MQEAAV1xxhVuPybx589i1axfz5s3j/fffZ+rUqW6JSb9+/fjkk0+YOHEiW7Zs4e233yYgIADDMLjzzjuZMmWKW7tTpkyhY8eOJCQknDGuRYsWUbduXQIDA13LvvjiCwoLC3n00UfPuI1hGOe17wB9+vRx6zn65JNPsNlsXHrppa4ybdq0Yf/+/W6JlohIpWWKiEil1b9/f9NqtZr+/v6uV+/evU3TNM1OnTqZzZs3dyv/3HPPmd27d3dbtm/fPhMwt23bZp44ccK02+3mZ5995lp/7Ngx09fX13zooYdcywBzxowZbvUEBwebU6ZMMU3TND/88EOzXr16ptPpdK3Pz883fX19zR9++MEVe2xsrFlUVOQqc9NNN5l9+vQxTdM0t23bZgLmnDlzzrjvBw4cMK1Wq7l8+XLTNE2zoKDADAsLM6dOnXrW4/XQQw+ZXbp0cVt27733mkFBQW7Lpk+f7nZM169fb5qmac6bN88E3NadehmGYU6YMOG0Nnfu3GmGhoaaL7/8stvyjIwMEzDnz59/1nhFRCoLm0czGhERKbHOnTu73Zvv7+/v+rlly5ZuZdetW8e8efMICAg4rZ5du3aRm5tLQUEBbdu2dS0PDQ2lXr165xXTunXr2Llzp1uPAEBeXh67du1yvW/YsCFWq9X1Pjo6mg0bNgDFtzJZrVY6dep0xjaqVatGz549ee+992jTpg3ffPMN+fn53HTTTWeNKzc3Fx8fn9OW/7k3okePHqxdu5YDBw5w2WWX4XA43NYvWrTotH277LLLTqs3IyODq6++mp49e/LII4+4rfP19QUgJyfnrPGKiFQWSipERCo5f3//s97u88cEAyArK4trrrmGcePGnVY2Ojr6nGeNMgwD0zTdlv1xLERWVhYtW7bko48+Om3b8PBw189eXl6n1et0OoHfL7r/yl133cUdd9zBhAkTmDJlCn369MHPz++s5cPCwlxJyyl16tQhIyODlJQUoqKiAAgICCAhIQGb7cz/TcbHxxMSEuK27M9lHQ4Hffr0ISgoiHfeeee0OtLS0gD34yEiUllpTIWIyEWkRYsWbNq0ibi4OBISEtxe/v7+1K5dGy8vL5YvX+7a5vjx42zfvt2tnvDwcA4dOuR6v2PHDrdv3Fu0aMGOHTuIiIg4rZ3g4OBzirVx48Y4nU4WLFhw1jJXXXUV/v7+TJo0idmzZ3PnnXf+ZZ3Nmzdn69atbglR79698fLyOmOiVRLDhg1jw4YNzJw584y9Ixs3bsTLy4uGDRuWarsiIp6gpEJE5CIyePBg0tLSuPXWW1m5ciW7du3ihx9+YODAgTgcDgICAhg0aBCPPPIIP//8Mxs3bmTAgAFYLO7/XXTp0oU33niDX3/9lVWrVnHvvfe69Tr07duXsLAwevXqxaJFi9i9ezfz58/nwQcfZP/+/ecUa1xcHP379+fOO+9k5syZrjo+++wzVxmr1cqAAQMYOXIkderUISkp6S/r7Ny5M1lZWWzatMm1rGbNmvz73//mtddeo3///sybN489e/awZs0aJk6c6GrnfEyZMoW33nqLyZMnYxgGKSkppKSkkJWV5SqzaNEiOnTocE49MiIiFZ2SChGRi0i1atVYvHgxDoeD7t2707hxY4YOHUpISIgrcfjXv/5Fhw4duOaaa+jWrRvt27c/bWzGv//9b2JiYujQoQO33XYbI0aMcLvtyM/Pj4ULF1KzZk1uuOEGGjRowKBBg8jLyyMoKOic4500aRK9e/fm/vvvp379+tx9991kZ2e7lRk0aBAFBQUMHDjwb+urWrUq119//Wm3ZT3wwAP8+OOPHDlyhN69e1OnTh2uuuoqdu/ezezZs2ncuPE5xwywYMECHA4H1157LdHR0a7XH6fAnTZtGnffffd51SsiUlEZ5p9vihUREfmTyy67jGbNmp3zE6PL06JFi+jatSv79u0jMjLyb8uvX7+eyy+/nF27dp1xwHp5mDVrFg8//DDr168/67gNEZHKRD0VIiJSKeXn57N//35Gjx7NTTfddE4JBUCTJk0YN24cu3fvLuMIzy47O5spU6YooRCRC4b+momISKX0ySefMGjQIJo1a8YHH3xwXtsOGDCgbII6R7179/Zo+yIipU23P4mIiIiISIno9icRERERESkRJRUiIiIiIlIiSipERERERKRElFSIiIiIiEiJKKkQEREREZESUVIhIiIiIiIloqRCRERERERKREmFiIiIiIiUiJIKEREREREpESUVIiIiIiJSIkoqRERERESkRJRUiIiIiIhIiSipEBERERGRErF5OoDKyul0cvDgQQIDAzEMw9PhiIiIiIicF9M0OXHiBNWqVcNiKVlfg5KKf+jgwYPExMR4OgwRERERkRLZt28fNWrUKFEdSir+ocDAQKD4QwgKCvJwNCIiIiIi5yczM5OYmBjXdW1JKKn4h07d8hQUFKSkQkREREQqrdK4lV8DtUVEREREpEQqXVIxadIkmjRp4uohSEpKYtasWa71eXl5DB48mKpVqxIQEMCNN97I4cOH3epITk6mZ8+e+Pn5ERERwSOPPEJRUVF574qIiIiIyAWh0iUVNWrU4KWXXmL16tWsWrWKLl260KtXLzZt2gTAsGHD+Oabb/j8889ZsGABBw8e5IYbbnBt73A46NmzJwUFBSxZsoT333+fqVOn8swzz3hql0REREREKjXDNE3T00GUVGhoKP/617/o3bs34eHhfPzxx/Tu3RuArVu30qBBA5YuXUq7du2YNWsWV199NQcPHiQyMhKAyZMn89hjj3HkyBHsdvs5tZmZmUlwcDAZGRkaUyEiIiIilU5pXs9Wup6KP3I4HEybNo3s7GySkpJYvXo1hYWFdOvWzVWmfv361KxZk6VLlwKwdOlSGjdu7EooAHr06EFmZqart+NM8vPzyczMdHuJiIiIiEglTSo2bNhAQEAA3t7e3HvvvcyYMYPExERSUlKw2+2EhIS4lY+MjCQlJQWAlJQUt4Ti1PpT685m7NixBAcHu156RoWIiIiIVEqmCU5nqVZZKaeUrVevHmvXriUjI4Pp06fTv39/FixYUKZtjhw5kuHDh7ven5rXV0RERESkQivMgwO/QvIySF4Oycsx+0xhpyO01JqolEmF3W4nISEBgJYtW7Jy5Upee+01+vTpQ0FBAenp6W69FYcPHyYqKgqAqKgoVqxY4VbfqdmhTpU5E29vb7y9vUt5T0REREREysCx32DFf4sTiYNrwVHotvr1d4fwfKpPqTVXKW9/+jOn00l+fj4tW7bEy8uLuXPnutZt27aN5ORkkpKSAEhKSmLDhg2kpqa6ysyZM4egoCASExPLPXYRERERkX/M6YTDW2DlFNj9i2uxI/c4LH4d9q0ERyFHTS++zQ9kdHYkV6XH8a90f3y8Su8L80rXUzFy5EiuvPJKatasyYkTJ/j444+ZP38+P/zwA8HBwQwaNIjhw4cTGhpKUFAQDzzwAElJSbRr1w6A7t27k5iYyB133MHLL79MSkoKTz31FIMHD1ZPhIiIiIhUbAXZsH/1yduYlkHySshLByClVg8+27aLZTvWsmbXOh6zVmF1kS8rC/3Y6/TCx8uHVrUa061uC56p24LaoTWIeCe8VMKqdElFamoq/fr149ChQwQHB9OkSRN++OEHLr/8cgAmTJiAxWLhxhtvJD8/nx49evDWW2+5trdarXz77bfcd999JCUl4e/vT//+/Xn22Wc9tUsiIiIiImdWkAN2v+Kf80/Ai3Gn3cqUi4XVBT58v34d763Y51r+ok8CrROacHud5rSr04ymsQ2w27xc60tzNtML4jkVnqDnVIiIiIhIqXI64PAm2LvsZC/ECoqCY1je8XnW7d3K+uStPLbnP3g78llZ5MeKwuJeiM0OH4owiAuvQZOa9Whdqwnt6jQjsUYCVov1rM2V5vWskop/SEmFiIiIiJSKxW/A9jmY+1ZgFGS7rcp0WqifVg8nBgCBhoMTppWaYdVoUrM+TWPr0yy2AY1j6hHif37XpKV5PVvpbn8SEREREal0TBPSk4t7IVK3QvdR5BcWsHbvZiKW/o/49M0YFCcRp8ZBrCzyZU2RL9VCo2kaW5+msQ1oUrM+TWrWJzQg2NN75EZJhYiIiIhIaXMUwqF1xQOq954cVH3i9wct37N+L3P27iK3MJ/L7SeItkSxstCPIz4RtEpoRrPYBtwXW5xAhAVW8eCOnBslFSIiIiIiJZWTBt6BYD05EHr2k7B0sluRQhM2FPmwosiPlWnryHV6UTWwCt51ulCvTnMG1m1Bveh4LJbK99QHJRUiIiIiIufDNCFtd3Hvw6lB1albOX7b56x2BLJ851rsm9dyl9PKylO3MhX6srbIl8CgMJLqN+ehui1IqtOCutFxGIbh6T0qMQ3U/oc0UFtERETkInPgV1g4HvYuhazU01Y/lhXF+3mhAFgxcQJRIREk1Wn+/+3dd3hUVf7H8fek94RASAESAil0CD006aiIgoqoKF1XBaQq8rOAroq9YAF1F7CDDV1XaQKhhi5NakIglIRAIAnpk5n7+wOZ3SyokMKkfF7Pk2edc+/c+xlyN7nfnHPuoWNkDLFRMUQEhlWYIkITtUVEREREykt+JiRvudgDUb8z6UGt2X3sAGd+W8pdv/0AQKEBu37vhdhS5M42swfpOBMRGErr8KbERrWmU2QMobVCKkwRUZ5UVIiIiIhI9WUYkHH896FM8ViPbcJ0eh8mLg7m+db4nLHpfgA4Y3DIvTZbze7sKnInpHYYLaMa0yE0mgd/f6yrt7unHT+M/aioEBEREZHqw1IEeefBKwCAgsxTuL7ezLb50hTpoxZntpg9WFp4saVB7Xq0CG1EjdBoHgttRPPQaHw9vK93+gpLRYWIiIiIVF0F2XB8q21StXF8C1m1GjOv/n1sPLSDbYl7WObpQo7hcPGpTGYPDrsGExnZgTbhTRkd2oi3QqPxcfey9yep0FRUiIiIiEjVs+I5OPwLRsoeTIbF1mwCso/v5OXdOb+/gjstbekQ2YZOUTE8FtWmyjyR6XpSUSEiIiIilZPVCmn7L/ZCnE2gqN/fOXDqCDuSfqP91m9olHsUE3Dc4swWsztbijzYYvbgrEcQA1pfLCI6qYgoEyoqRERERKRyKMyFkzvgWDzGsU0YyZtwKLhg2xy7dA3HC60A9HC24mOqw5YiDxz96tE6uimxkTGMUhFRLlRUiIiIiEjFlH0G3GuAoxNZednkfPUgwYf/DVwcuGQCcgwT28webC1yJ89ciLebLzH1m9AyvCkx9ZvwfHhTAnxq2vVjVAcqKkRERETE/gwDzh6+uLDcsU2Yk9bjnHGMN+vdx3cp50g4fYw7XM7zpIfT78OY3Nlu8YagprQMb05M/SZ8F96UiMAwHBwc/vp8UqZUVIiIiIiI/RzfihH3GtZj8TjmZ9ianQGrAcf2reJwgR8AW70aMbP+HbQOb8Zt9ZvwVGg07i5udoktxamoEBEREZHyl5MOyZsvzoeI6EmSd0M2HtpB2u4fmZyyBEcg1zCxs8idzWYPdli8KAhuRYu2HfikYQti6jfRMKYKTEWFiIiIiJQtw4D0RDi2CZI3YRyLx3T2sG3zwjVfM+m8HwCuWMl0C2SH1RvnujG0i2pLbFRrHmzQHE83Dzt9ALlWKipEREREpHT+Z5XqzNOH8H23nW3zpecsHSpyYUuRB8sKHHFxcqZ1/abERrWmU1QM0xo0x0NDmSotFRUiIiIicm0KsuHENjgWj/XoRozkLaT4RfFqjb7sSNrL4dRjrPNz4ZzhyBazB1uKPNht+NIwvBWxkTE8FNWaD8Obaj5EFaKiQkRERESuzornMB9cjuPpvTgYF9eDuPScpaLT+1h0oJBL/RLDnW4gpkEzWoc3Y0qDZjSpE4GLk7N9cku5U1EhIiIiIv9hGHAu6eKE6rOJ7G9yDxsP/8rWxN08kPQJbU0ZAJywOLH5916IPQ418a7XhkmxzWkd3oyY+k2o5V3Dvp9DrisVFSIiIiLVmdUCqXvh2EasR+OxJK3HOfcscLHP4Y7F/+K8cfGWMc/FG2+TF+k1G1G3QTtahzdjVHgzIoO0NkR1p6JCREREpDox54GjCzg4YrYUkfHFSAIO/gBcHMrkABQasLPInS1mD7xd3WnVoA0dI1vRpkFzWoU1wsvN064fQSoeFRUiIiIiVVluOhzb/Puk6g1waidfNJvIjyln2XpkD7dximc9HdhW5M4mswd7HWriFh5Lm6h2dIqM4aHQaJwcdcsof05XiIiIiEhVk7oXNn1E0dENOJ09ZGu+NEBp18bPWZN/cc7DSs96ZNa9lY7RbRgQGcO0uhE4OjjaIbRUZioqRERERCorqwXS9sPRjViCW3LIqSbbjuwlY+9PjD+5yHajd6jIhc1FHmwxe3DIJZjQxrG8GN2a2MgYooPDNR9CSk1FhYiIiEhlYc6HkzvgWDzmI+sgeTPO5hwAPikMZHpWTQA8sOLk4c+mIk/O1YikYVRb2oY349HIVkQEhmEymf7sLCLXTEWFiIiISEVltYCDI4ZhcOLYLkLm9cTRWgTApRUfsq0X50PsLnDAw9Wd1vWb0q5hcyLDm3N3g6bU8PS1X36pNlRUiIiIiFQUGSfg2EaMY/EUJqwl1cmHlzy7sunwr6RkpLGjBjiZHNls9mCz2YPjXuH4RnSkdcOWjG7QnFdDGmhStdiFrjoRERERe9rxOdbE1RQlrsMlOwW4uD6EK+BpdWTxuVzAhLOjM4/79ic6sj3tGrZkQoOmBPjUtGdyERsVFSIiIiLXg9UCqXsg7QDm5ney+9gB4g//Sp9NzxFtPo0LUGTA3iI3NhV5sNPqS2HdtkztFEvHyBjahDfF3cXN3p9C5IpUVIiIiIiUh6ICOPkrHN2AJWk9xrFNOJlzsAJt5r1HWqEZgERXB+o51mKXqQYOobG0jm5Px8hWjAprjIuT85+fQ6SCUFEhIiIiUsZylz2H64bZOFoLAbi06kOW1YGtRR64FeVQw7MWHSNiaBTZio6RMTxaN0LzIaTS0pUrIiIiUhK56XBsE8bRDRQmrGF1o2H8cvocmxN20jFjJ696FXLW6sgmswfxZg8SXOvg1zCWjlFt+FjrQ0gVo6JCRERE5GrkpsPhVRhHN1KQEIfb+UTgP5OqVye+zmf5/gCcNXlz0jeSOo060z6iFQ9GtCS0ZrDWh5AqS0WFiIiIyP8yDDh3BBxdKPIOZu+Jw5zY9Bm37HkHE3BpuvShIhfizZ5ss3pxPiiGRyI70CGiJW0btqCml58dP4DI9VXpiopZs2bx3XffceDAAdzd3enUqRMvv/wy0dHRtn3y8/OZMmUKCxcupKCggH79+vH+++8TGBho2yc5OZmHH36Y1atX4+XlxfDhw5k1axZOTpXun0RERERKy2qFtH1wdCOWpHVYjqzDJe8c//JozqTTruQU5OJpslDXx43NRR78ih9FddvTNLojHSJacVf9Jnoyk1Rrle4Oes2aNYwdO5Z27dpRVFTE//3f/9G3b1/27duHp6cnAJMmTeKnn37i66+/xtfXl3HjxnH77bezYcMGACwWC/379ycoKIiNGzeSkpLCsGHDcHZ25sUXX7TnxxMREZHrKT+Loq9GYxzdiHPhBeDipGpHoMAwcT79BDkFwfi4e9EhoiUbImOIjYxhVGg0zppULWJjMgzDsHeI0jhz5gy1a9dmzZo1dOvWjczMTAICAvjiiy+48847AThw4ACNGzcmPj6ejh07smTJEm655RZOnTpl672YO3cu06ZN48yZM7i4uPzlebOysvD19SUzMxMfH59y/YwiIiJSSoW5cHwrHNtIvsXChuCexB/+lU2HfmV+5ncEOBSRY5jYavZgk9mDfc6BeIR3pm10W2KjWtMopAGODo5/fR6RSqQs72crfYmdmZkJgL//xYlR27dvx2w207t3b9s+jRo1IjQ01FZUxMfH07x582LDofr168fDDz/Mb7/9RkxMzGXnKSgooKCgwPY6KyurvD6SiIiIlFZ+FiRvgqQNmI+sxfHULhyMIgAyrU4MPfcjF6dYwxSXYCwetagR0ZUO0W25I7IV0wLDNKla5BpU6qLCarUyceJEOnfuTLNmzQBITU3FxcUFPz+/YvsGBgaSmppq2+e/C4pL2y9tu5JZs2bx7LPPlvEnEBERkTJRmAMuF4dBp2acweWfN+J//jAAl5aPO2VxYpPZg81FHjSsVYd2Ua2JjYyhY2SMnswkUkqVuqgYO3Yse/fuZf369eV+runTpzN58mTb66ysLOrVq1fu5xUREZEryE2HoxsxktZjTojDMf0w/9dgPGsTfyPpzAlmeKRzk6sz8WZPNpk9SK0RTVjjWDpFtWFiREterlHb3p9ApEqptEXFuHHj+Pe//83atWupW7eurT0oKIjCwkIyMjKK9VacPn2aoKAg2z5btmwpdrzTp0/btl2Jq6srrq6uZfwpRERE5Kolb8bY9RWFh1fjei4BuDiA6dJMyIPbviOpyBOTycQP/l04FdWG2MgYno5oSS3vGnaLLVIdlKqoMJvNpKamkpubS0BAgG1eQ3kyDIPx48ezePFi4uLiCA8PL7a9TZs2ODs7s3LlSu644w4ADh48SHJyMrGxsQDExsbywgsvkJaWRu3aF/9SsWLFCnx8fGjSpEm5fwYRERH5C1kpkLQea1gnDmTnEn94J+47FnDv+XVc+hPfwSJX4s0ebLZ4cSGwFW3bdGJcRAztGjbH18PbrvFFqptrLiouXLjAZ599xsKFC9myZQuFhYUYhoHJZKJu3br07duXBx98kHbt2pVHXsaOHcsXX3zBDz/8gLe3t20OhK+vL+7u7vj6+jJ69GgmT56Mv78/Pj4+jB8/ntjYWDp27AhA3759adKkCffffz+vvPIKqampPPXUU4wdO1a9ESIiIvaQcfxiEZG0jsKEONyyTgDwTEED/nHh4voPDR0LyHXzZ5vVh7yQNjSJjqVTVAyDw5vh6epuz/Qi1d41PVL2jTfe4IUXXqBhw4YMGDCA9u3bExISgru7O+fOnWPv3r2sW7eO77//ng4dOvDOO+8QGRlZtoH/YBLV/PnzGTFiBPCfxe++/PLLYovf/ffQpmPHjvHwww8TFxeHp6cnw4cP56WXXrrqxe/0SFkREZHSKzy+Hevn9+CWXfxBKRYD9lrc+CCvJksJon3DFnSMaEVsVAytwprg6vzXj38XkT9Xlvez11RU3HPPPTz11FM0bdr0T/fLz89nwYIFuLi4MGrUqFIFrKhUVIiIiFwlw4CzCXB0PUWJazjmGsS3Tg2JP7SDhKO72OWzFyuwq8idTWYPdjnUxFS/Ey2jOhAbFUPzelE4aaE5kTJnt6LiahiGwfHjxwkNDS3Lw1Y4KipERET+gGHAmYOQtA5zQhzWpPW45p+3bY43ezAos77tdR8fR3waxBIT3Z5OkTE0CmmIg4ODHYKLVC8VYvG7+fPns2jRIo4dO4aPjw9du3Zl0qRJODk5ER4ejsViKVUwERERqSQMA7JPg3cQ6dkZbD60g+4/3IOHJc+2RkS+YeLXInfizR7sc63LHe172NaIaBgYqjUiRCq5ay4qLBYLt99+O0uXLqV///7ceuutnD9/nq+//poPP/yQd955pzxyioiISEVhGHDmECStI+/gLzgcXU+uAbcZXTiUehSA972cCXAwEW/25KhnGJ6RN9CuUTvujGhFPS00J1LlXHNR8eabb7J161Z2795NdHS0rd1qtfLGG2/w4IMPlmlAERERqRiMvT+Qu+NLHI5txL0gA4BLz1yyGCbOn0sAnIgOacDmiNuJjYphWEQrgvwC7BVZRK6Tay4qFixYwCuvvFKsoABwcHBg6tSpGIbBtGnTyiygiIiIXGeGAeeSMI6sJTE4lvik/Ww6/CtdEr7iHoeTAOQZJraZ3Ykv8iKlRjR+kT14pVE72ke0pKaXn33zi8h1d80Ttd3d3dm9e3eZPyq2stFEbRERqVLOHcV6ZA1Z+5bidGwjXgUXJ1YPyggjvsgTgA5OOXRzyeNMrab4RvegfXR72jVsgY+7lz2Ti0gJ2XWitqenJ2fOnPnDomLnzp3Mnj2befPmlSqYiIiIlC/DMDi56TN8Vz6Dd346DoDf79sKDdhe5IGbkzOdGrQmNjKG2MgYWjdohoeLmx1Ti0hFdM09FXfccQeenp588sknl21LTU2le/fuHD58uMo//Uk9FSIiUqlknoSkdeTsX852tzC+yjBYd2AbgdnHWV4jCbMBvxa5s9XqR0ZgC/wa9aFdow60DGusheZEqii79lTMmDGD2NhYTCYTjz32GBEREZw7d44ff/yR559/nrCwMA4fPlyqUCIiIlJKWSmQtI6CQyspSojDMycFAE/gZL4f32SHAHDB2ZcXfPtQq+mNtG/Smb9poTkRKYESLX63du1aRo0aRVJSkq3NycmJCRMmMH78eMLCwrBarWUatKJRT4WIiFQoliJwdCKnII9d+9bT6ds7im82YHeRG/FFXhz1b0aNpjfRtXF72jZohpuzq51Ci4g92X3xu27dunHo0CG2bNlCUlISPj4+xMbG4u/vT05ODjNmzChVKBEREfkLBRfg6EbMh1eSf3AFZywOTKA1O4/tx2wpYpWfK2ZMbCz0IMkrHM/oXrRv2pWhUa3x9fC2d3oRqWJK1FMh6qkQERE7OLaJwv1LyDuwDK/0Azga/xkVkGuYiE5vhBkTITVq0zWyFZ2bdKRrdFuCa9S2Y2gRqajs1lORnJxMaGjoVe9/8uRJ6tSpc82hREREqj1LEaTtI6dGBFsSdxN/+Ff6/fombSypXJo2fdTizDqzJ/tcQjA1uIGXb+5C56jWhNYK0YrVInJdXVNR0a5dOwYOHMiYMWNo167dFffJzMzkq6++4u233+bBBx/k0UcfLZOgIiIiVZphQNp+Cg6uIOu3n/FO/RU3Sz7dzjfipMUBgHRXE0edfdjrEoIR3pXopj3oFtWa+2oGq4gQEbu6pqJi3759vPDCC/Tp0wc3NzfatGlDSEgIbm5unD9/nn379vHbb7/RunVrXnnlFW6++ebyyi0iIlIlWI+sJ3PNW7gmx+NhvoArEPD7tgyrA2EOeTj4RRAbGUO7qNa0i2rNHbVC7BlZROQyJZpTkZeXx08//cT69es5duwYeXl51KpVi5iYGPr160ezZs3KI2uFojkVIiJyzS6chiNrSPOuz8rUNNbs24zXoZ95zeXio9hzDRObzR7sdQqiIKwToc1vpGNUG0JVRIhIOSjL+1lN1C4hFRUiIvKXCi5A0nrMh36h4MAyvLKSAXghpzbv5NUCoJapiL95X+BCSDvqtOhPt2ZdqB9QV8OZRKTc2f2RsiIiIvLHrOePk/f5UNxO78bRsOIMOANWA/ZY3DhvOBJTvwndm3Sge5MOtA5vhrMWnBORSkw/wURERErKMCA9ARJWcz4/j+WuEaw7sJUN+zex3nkXjiaDJIszawu92OsSglt0b9o1786TjdpSw9PX3ulFRMqMigoREZFrkXMWEuPI278EI2EVHnnpAKQXuTAhI8K22ziPBviFtaVp897c0KQDwwLDNKRJRKosFRUiIiJX4eyF85g/GUxwyhYA3H9vLzBMbDW7s67Im7b1m9CpUTtuaNyetg2a4+rs8scHFBGpQlRUiIiI/DerFVJ3k7d/KVkHVvJezb6sO7yT/ScTedXrFPe7wW9Frqw1e5HsF41XdG/aN45lXEQrprt72ju9iIhdlKqoWLduHR988AGJiYl888031KlTh08//ZTw8HC6dOlSVhlFRETKV9YprIdWkLn7h4vrRRTl4M7F3oht+0+zv8gDgH/7tOFEgxhaNbuBuyNbaV6EiMjvSlxUfPvtt9x///0MHTqUX3/9lYKCAuDiitovvvgiP//8c5mFFBERKQ8ZOVkk//sZWuyZhwNQ4/f2bKsDG4s82OdWj9j2/Xio6Q10impNLe8af3Y4EZFqq8RFxfPPP8/cuXMZNmwYCxcutLV37tyZ559/vkzCiYiIlAnDgLMJWA+vIHvPv1jl2Yx5KVlsO7KXGMdsfvSFnUVubLDW4EKd9oTF3Eb35l3p6x9o7+QiIpVCiYuKgwcP0q1bt8vafX19ycjIKE0mERGR0svPgiNrKNi/BMvBZXjknsEB8AEy8/azJScYgNyAprzWaCidWvXmbw1b4OLkbNfYIiKVUYmLiqCgIBISEqhfv36x9vXr19OgQYPS5hIRESkRwzA4eCieyM9uxtGw4vp7e4FhYrPZgw1WPzJDO/NKTH96NO1IvZrBds0rIlIVlLioeOCBB5gwYQLz5s3DZDJx6tQp4uPjmTp1Kk8//XRZZhQREbmy7DQ4vJKCA0tJvZDFW25tWf3bJlIzz7ChhhOGAXFmLw55huPT9Ga6tbiByRGt9KhXEZEyVuKi4oknnsBqtdKrVy9yc3Pp1q0brq6uTJ06lfHjx5dlRhERkYusFjixDcuBpeTv/Ree5w4D4ArUtDrwzblTmDHh7uLGyyH30KlFd/o2jWVMrRD75hYRqeJMhmEYpTlAYWEhCQkJZGdn06RJE7y8vMoqW4WWlZWFr68vmZmZ+Pj42DuOiEiVZhgGCaeP4fblUOql7ym2bVeRG3GFniT6RFGjcT96NO9Mx8hWuDm7/sHRREQEyvZ+tsQ9FbNmzSIwMJBRo0bRpEkTW/u8efM4c+YM06ZNK1UwERGppgwDUnZzYdd3FOz7mVf9erP88F5SMs4w3v0sY90diDN7sclUG2tED1o368FdjdsTXKO2vZOLiFRbJS4qPvjgA7744ovL2ps2bcrdd9+tokJERK5ewQUKDy7nzNaFeB/fgE9RNt6AN5B5LJuUQl9cnVzYH9qLTxt14Iamnbi1bhQODg72Ti4iIpSiqEhNTSU4+PInZgQEBJCSklKqUCIiUj1k5WWzd8Vs2m95BRes1Pm9Pccwsc7sxQGvCBp1uZmvW/SkbYPmuLu42TWviIhcWYmLinr16rFhwwbCw8OLtW/YsIGQEE2IExGR/1FUCEc3kLPrW7abXXk/rYgNB7dT08hjp7+VRIsL8aYA8up3o27rO+jYuAM3evraO7WIiFyFUj1SduLEiZjNZnr27AnAypUrefzxx5kyZUqZBRQRkUos7zwcWsGFnd/gciQOV0s+noCl0JO4rDAAfIKjeK/RHXTuOIihoY0wmUz2zSwiItesxEXFY489Rnp6Oo888giFhYUAuLm5MW3aNKZPn15mAUVEpPKxFhVx4aOb8D65FQeseP/enmZ1ZEWhN4dqNOepXndzU6sbaBgYatesIiJSeqV+pGx2djb79+/H3d2dyMhIXF3L/xF+a9eu5dVXX2X79u2kpKSwePFiBg4caNtuGAYzZszgo48+IiMjg86dOzNnzhwiIyNt+5w7d47x48fz448/4uDgwB133MHbb7991Y/E1SNlRUR+9/vaEUXJW1lbow3Ldq1j+e51vGndRneXHPYXufKL2ZfTIe2JbHs7N7bqTqBvLXunFhGp9irEI2Uv8fLyol27dqU9zDXJycmhZcuWjBo1ittvv/2y7a+88gqzZ8/m448/Jjw8nKeffpp+/fqxb98+3NwuTvIbOnQoKSkprFixArPZzMiRI3nwwQev+EQrERH5H4U5kLCa/D2L4eAy3AqzcAImnYvktNUZgFfdw1hWvy3t293K8Gad8HGvHusYiYhUR6XqqVi5ciUrV64kLS0Nq9VabNu8efNKHe5qmEymYj0VhmEQEhLClClTmDp1KgCZmZkEBgayYMEC7r77bvbv30+TJk3YunUrbdu2BWDp0qXcfPPNnDhx4qommqunQkSqI+PIWrJXvYZ78gacrGZbe6bVgZWFXsx3bETjFn3o06ILXRu11QJ0IiIVWIXoqXj22Wd57rnnaNu2LcHBwRVmYl1SUhKpqan07t3b1ubr60uHDh2Ij4/n7rvvJj4+Hj8/P1tBAdC7d28cHBzYvHkzgwYNskd0EZGKJyuFfMNgw/Gj/LJnI057v+Xvpt8ASLY4s7TQm8O+jQlseRt9WnXnX6HRFeb3gYiIXD8lLirmzp3LggULuP/++8syT6mlpqYCEBgYWKw9MDDQti01NZXatYuvvOrk5IS/v79tn/9VUFBAQUGB7XVWVlZZxhYRqTjSj5C5/UsKdn1L7cwE3soL4a0cPwB8TBb8PQNJDYmlcetb6d+iK3X8A//8eCIiUuWVuKgoLCykU6dOZZmlQps1axbPPvusvWOIiJQ9w4DT+8jY9jlFuxdTK/ck/706RB1TLsF+kfRu3pnezTrRpVE7PF3d7RZXREQqnhIXFWPGjOGLL77g6aefLss8pRYUFATA6dOni634ffr0aVq1amXbJy0trdj7ioqKOHfunO39/2v69OlMnjzZ9jorK4t69eqVcXoRkevrTFY6/96yhHvixuOHBYAiA+LNnuz1ica52UBi2/ZnR50IDWsSEZE/VOKiIj8/nw8//JBffvmFFi1a4OzsXGz7G2+8UepwJREeHk5QUBArV660FRFZWVls3ryZhx9+GIDY2FgyMjLYvn07bdq0AWDVqlVYrVY6dOhwxeO6urpel8flioiUG8OA07+Rv2MhZxI2MMXajPUHt2M1rHh5eeHtYCWhRnNqtx9Krw4D6OrlZ+/EIiJSSZS4qNi9e7ftpn3v3r3FtpX3X7Oys7NJSEiwvU5KSmLnzp34+/sTGhrKxIkTef7554mMjLQ9UjYkJMT2hKjGjRtz44038sADDzB37lzMZjPjxo3j7rvvvqonP4lcbxarhf0nEzmUkkT2hXPUdHUiMqwFkXWj9Ndj+WtpBzDvXET+joV4Z5/EDagHpJ7PwGq4ElO/CefbTqBr297c6Bdg77QiIlIJlXrxO3uIi4ujR48el7UPHz6cBQsW2Ba/+/DDD8nIyKBLly68//77REVF2fY9d+4c48aNK7b43ezZs7X4nVQoR8+c4MvVC/ls60rSL5wHYJpHGpM8zgKQhCf54d2JGjQLR//69gsqFZJ57w8U/PwUXlnHbG35holVhV5s9YigZtt7uKXjLdQPqGvHlCIiYi9leT9bKYuKikBFhZSn05lnmf/tS4Tt+4rbXDO5LaM+R5xq0jw0mjuNZO7LWFdsfzMO5HedjHefJ8HB0U6pxe7OH8Pi4Mym1BS+37aC7J3fMsf1IIUGrC70YoNLKD6th9C/4wAa14mwd1oREbGzClVU7Nu3j+TkZAoLC4u133rrraUKVtGpqJDyYBgGP62cj3nF37nVKR3H30c2JTS5h/pD3sPJ0QksRWAykXPuBOuXz8Zvz5d0cMwGILdBLzyGLQInFzt+Crmucs9h7P2enC0L8ErdyUeWUJ4+f7HH1QUr9/tZcG9xOzd1vI3W4U01XE5ERGwqxOJ3R44cYdCgQezZsweTycSl2uTSLyyLxVKqYCLVTVbmaTZ+eA99Mrbj6nzx/0/n63aixo0ziAjrCJduBh0v/t/Ws1YY/e59ncTUR5n1/r1MtOxldeJvtL1wnsAaWjegSjPnw6FlXNg0D4+ja3E0LHgBVgN8zJn4egTTP6Y7g9r1pVNUaxzVeyUiIuWsxD0VAwYMwNHRkX/84x+Eh4ezZcsW0tPTmTJlCq+99hpdu3Yt66wVinoqpCxtT9yN7/x+RJADwFG/RtQb8j6O9dr+xTsvOp15limvDmXV2Qw6RLbm64nvXOzVkCon+cwJfOd2xbcg3db2W5Er/zLXJDPyRnrE3k73Jh1xdVZvlYiI/LkKMfypVq1arFq1ihYtWuDr68uWLVuIjo5m1apVTJkyhV9//bVUwSo6FRVSFixWC+8u+5RXfvyIES5neMQzg9w+zxJ5w0P/6Zm4Somnk+n74ghyCnKZ0n80j/W5C9x8//qNUrGdPUz2jkUsdIrk+22/sO3IHl7xPEUvl2y+N9fgRN1utOt0N/1adMXTzcPeaUVEpBKpEMOfLBYL3t7ewMUC49SpU0RHRxMWFsbBgwdLFUqkyjMMMjZ+yBsblvPhkZMAnG86GO8hE6njU7JHejYMDOXVodN4dsH/0XLdsxQkfIDro5tsw6WkEinIpuDXRWRt/ICAcwfwAr7LqM+OIg9MJhPLQ/rh0O4m7m3TixqeKhxFRMT+Sny30axZM3bt2kV4eDgdOnTglVdewcXFhQ8//JAGDRqUZUaRquVCKmmf3EftlC08YHFmsWtTnhzyOENi+5d6Eu2gdn1ZvvlftEv5HNf0Qxhb/okp9m9lFFzKlWFgPRpPWtzb1DjyC66GmQDAYsAqsxcNgxsysOMQbm3TiyCtJSEiIhVMiYc/LVu2jJycHG6//XYSEhK45ZZbOHToEDVr1mTRokX07NmzrLNWKBr+JNfMMCjY8TmWf03Gw5JPoQGfuTThhge/oGFw2RXix86e4qNZvXne/QQFrn64PnEQnN3L7PhS9o6dOcm6pW9x38EPbG2JFheWOYbh2OY++nUZrLUkRESkzFWI4U+NGzembt2Lv+QiIiI4cOAA586do0aNGhw/frxUoUSqnAunyVw4Gt9jawHYZXZjQ7MHGH33jDKfUBtWKwS32AdJ3v53QgsyMLZ9jCn2oTI9h5SS1Yrl8Ar2HtzES0fPs3rfJkwYtPVzZa/hTVrEzbTr8QAPN2yhR8CKiEilUOKeCkdHR1JSUqhdu3ax9vT0dGrXrl3lHymrngq5Wpbzxyh8uwPuRbkUGvChNYxm986he/Mu5XbO9OwM3pzZhefdkslzr4X7tANau6IiyDnLhQ1zsWz+J34F6Zy1OtL6XCSFONC9SQfu7XQLfVt2w83Z1d5JRUSkGqgQPRV/VItkZ2fj5uZW4kAiVcn+k4k8/tksHspxpJ6jG9/VHcijI17F36t8J9fW9PLDp9ODnN42k8C8s3DgJ2g2qFzPKX/AMODYJs6tfgPvxBV4YwUg0+rAEmttxvcczOAe92h4k4iIVGrXXFRMnjwZuLjI3TPPPIOHx38eYWixWNi8eTOtWrUqs4AilY5hkPfrImYfOsrstT9isVo47taQJwdNZUbsgOs2nGV4r3v5csNsJrqnkbHxI/xUVFx3FquFhIXjiN73Of6/t+0yu7HWpwWhPcZzV7ubtJ6EiIhUCddcVFxaf8IwDPbs2YOLy39+Ibq4uNCyZUumTp1adglFKhHjwmlSPr2fkFObiMz3wWKty82tbuC5uyZR1z/oumYJ9K3FucaDmLDnRyw0593revZqLOsUublZfLF/Nx+tXITb+USW+Jn4V6EfyQ37c3P/iYyvF2XvlCIiImXqmouK1atXAzBy5EjefvttzScQATAMUtd9gMeKpwkxCjAbcM6tFp8Pf41e5Th34q8M7juGPtvW4bxrA89mZ1DTy89uWaq8E9vIjXsT1wM/sbKoBk9lBALg51Gb91uP4L5eQ/UoWBERqbJKPKdi/vz5ZZlDpNLKPXeM4/PuJjrjNwB+K3JnW8wj3Hf7E3afcNs8NJrm9aLYc/wQi7csY0yPu655pW75E1YL7PsXOatfw/P0Hi4NBvWx5hMRUJcxve9hcMeb8XTVI31FRKRqK3FRMWvWLAIDAxk1alSx9nnz5nHmzBmmTZtW6nAiFZlhGGxe9SGNVk0n2lREoQE/eLeh3Yh5DA8Kt3c8m7s7DaDdD0/Sc/UUaFAP6neyd6Qqwdj1NXlLnsYj+xSeQIFh4vsCH7bU7ky/G8eytllnHBwc7B1TRETkuijxb7wPPviARo0aXdbetGlT5s6dW6pQIhXd8fQUhs95jGHf/INcKxwwvNjSZzZ3TltF/QpUUADc3r4vLZ0LaGBkkb7hI3vHqfSKLEV8v3UF/1z8Oh7ZpzhndeTNvNrMCB1Do/EreP3xhfRt0VUFhYiIVCsl7qlITU0lODj4svaAgABSUlJKFUqkojIXmfn5mxlM3LiBvMICnB1d+aHFBIbdNolGHhVzflENT19Ohd4Ap7/H7dCSi0N2HBztHatyyTmLef27xGUV8dSu/Rw7exJvk4WTnnVxaDOMkf1GXPeJ+CIiIhVJiYuKevXqsWHDBsLDi/9VdsOGDYSEhJQ6mEhFs3PXcnK+GcttxmlWm0I4GtmDl+95nOiQitUzcSUx3R8k48t/4UcuRUfjcWpgv8njlUpuOvmrX8Nh80e4WAsJMLtxLDMcf08/Rve8i5E33Fnua46IiIhUBiUuKh544AEmTpyI2WymZ8+eAKxcuZLHH3+cKVOmlFlAEXvLybtA3LxRdD+1HE+TgdkwMbx9L1rd++51W3OitLo06cgSw58BnCUlfj71VFT8ubzz5Kx6FafNH+JmLQQuri/xuWMEL9w1kXu63IqHixb5FBERuaTERcVjjz1Geno6jzzyCIWFhRiGgbu7O9OmTeOJJ54oy4widvPb5q9w/HES/bkAJjjiVoeaQz8mJry9vaNdEydHJ87X6wKnvsctceXFVZ4rSUF0vWWtex+XFTPwtBYAsKfIjYVuLWh9yxRebNcHZ8cS/9gUERGpskyGYRilOUB2djb79+/H3d2dyMhIXF3t+wjN6yUrKwtfX18yMzO1VkcVlG8uYM0/htP7xM84miDLcOJU+3E0GjATKukE3PW7VtPuq4G4mgyKxsXjFNTU3pEqlNSMM7y3/DNOxy/gQ88k9hW58q1XG2JvfYpezTtXml4pERGRq1WW97Ol/pNbcnIy6enpFBYWcuTIEVv7rbfeWtpDi9jFrmMHGL/gWXzO7KOPL2zzakzUqC9pVLuBvaOVSodmXVn+pT8FRWbCju6jjYoKsFrJ3f4Fy3euZsKuwxQUFQJuhNTqyg23PclTTWNVTIiIiFyFEhcVR44cYdCgQezZsweTycSlDo9Lv4AtFkvZJBS5TsznjvHvH19j3OYdWKwWAnzqsvHGaXTpOtTe0cqEs6MTK5uO4YsNPzIs8QhtOto7kX0VHl5NxnePUvvCMbpaHXG1RNCiQSum3jKGbo3bq5gQERG5BiUexzFhwgTCw8NJS0vDw8ODvXv3snbtWtq2bUtcXFwZRhQpZ1YLp5e+QOEbMdx06BPqkcdtbXsT98wXVaaguGRA614A/LwzDqvVauc09mGcP8bJ9/ri8vFt1L5wjGyrA4udGjLnwZf412MfckOTDiooRERErlGJeyri4+NZtWoVtWrVwsHBAUdHR7p06cKsWbN49NFH+fXXX8syp0i5sJzcydnP7ifwwjEAfrV68fzgR+ndY4R9g5WTztFt8HbzoFZuCvt3LaFpTH97R7p+zPmcWfIsPlvmUgcLFgO+sQbj3OcpRtxwD06agC0iIlJiJf4tarFY8Pb2BqBWrVqcOnWK6OhowsLCOHjwYJkFFCkXBdlk/vQkXjsWEIhBptWBb/1i6f/gx8T41bZ3unLj4uTMGyEmBmQdYdea16GaFBU5+bl8/s3zjNn/Hg4m2FzkxYHWD3PnbVPxdHW3dzwREZFKr8RFRbNmzdi1axfh4eF06NCBV155BRcXFz788EMaNKjcE1qlarOaC7jwemt8c1MB+Mlcg/w+zzGy17BqMezFr3Ef2LyVkLN7q/6jZc35xB3exZTPZnHyXCrZHrVwC2lB/+Hv0SGgjr3TiYiIVBklLiqeeuopcnJyAHjuuee45ZZb6Nq1KzVr1mTRokVlFlCkLO1OPsD/LXydbmcs3OPmzALfzgwbM5ewWtVnFfimXUeSt2kWAaZ8Ug+tJSj6BntHKntWK/kb52Je8RxPng3mpMWVejWDaTbkVfq26GrvdCIiIlVOiYuKfv362f47IiKCAwcOcO7cOWrUqFEt/torlYjVQs7695h3KIEXt2/FMAwSXOtSs/vTPNlnGA6VdN2JkvL3C2S7cxBtilI4Fv9p1Ssqzhwm84sR+J7Zgxswyu0cR9o/yv8NfERDnURERMpJmc5M9Pf3L8vDiZSa5dROzn0+koDMRHoWufKy0YAB7frxzO3jCK5RdedO/JW80M5w5Bvcj623d5SyY7VQtG42xi9/x9coIscwMccUSeyY9xnduHKtgC4iIlLZlKqoWLduHR988AGJiYl888031KlTh08//ZTw8HC6dOlSVhlFrl1hDqmLpxKw5wsCMLhgdWCFayRfTXybTo3a2Tud3dXreD8c+YaowhSyL5zByzvA3pFKJ+M4eV8Ox/3kNgBWFXqyuclIHr37KTzdPOwcTkREpOor8biPb7/9ln79+uHu7s6vv/5KQUEBAJmZmbz44otlFlDkWmXu/Jb0FxsRtOdzHDFYWlSDH7u+zLhn1qqg+F1Y4+6k4oabyeC3dQvsHafU9v74LO4nt5FjmHi6sAGF9yxk+ogXVVCIiIhcJyUuKp5//nnmzp3LRx99hLOzs629c+fO7Nixo0zCiVyLIksRP3/7LL7fjKRmUSYnLM58GDyINv+3g3tv+pvWIfhvJhNrw27l7sxQFqVm2TtNieXk5zJ+wXPcuGEPH+fVYIrvAB6evpwbY6rYPBEREZEKrsR3WQcPHqRbt26Xtfv6+pKRkVGaTCLXLP7wr/zfwtfYfzKBr308SPUIoeH9c3gwuoO9o1VYdbo9TNz2vfjv28qrVguODo72jnT1UnaTtfwFbjtiZf/p4ziYHDnT42neu3lk5focIiIiVUSJi4qgoCASEhKoX79+sfb169drnQq5bs4eXs/xbx7lvmRHcgxH/Dx8Ser/LkO7DtLN5V9oH9ESXw9vzmVnsP3IXtpHtLR3pKuz/VMsP0zCx1rIgNxanPdtypwxzxEbGWPvZCIiItVWiYuKBx54gAkTJjBv3jxMJhOnTp0iPj6eqVOn8vTTT5dlRpHLmPMvsPvjUbRIXk4tk8FUj5oktX6Iabf9jZpefvaOVyk4OzoxIqIBfgnLSF47l/YRc+wd6c8V5mL9cTIOv36BI/BLoRe76vRkxQOvEOCjJ8+JiIjYU4mLiieeeAKr1UqvXr3Izc2lW7duuLq6MnXqVMaPH1+WGUWKObD2n3gvf5I25IIJNjqF0G3UXB5u2t3e0SqdW32hqcc51iX9Yu8of+78McyfDMb5zAEsBryUWxuj6wT+edvDmisjIiJSAZgMwzBKc4DCwkISEhLIzs6mSZMmeHl5kZeXh7t75Vhk6r333uPVV18lNTWVli1b8s4779C+/V8/0z4rKwtfX18yMzPx8fG5Dknl/JmjHJ5/L+2z9gKQarhwqM1YugycUe0WsCsrF47E4z2vHzmGibPjdhIWHG7vSJc7voXCBXfiUpDBGasjkwsiuPf+17iplSZji4iIlEZZ3s+W+k7MxcWFJk2a0L59e5ydnXnjjTcID6+ANyZXsGjRIiZPnsyMGTPYsWMHLVu2pF+/fqSlpdk7mvwXwzBYFP8Tv7zem/ZZe7EasN6vNS6Td9Lt9mdVUJSCd3hHzpnc8TQZ7F2/wN5xLmMYBp9tW0tuXha7i9wY69aDZx//VgWFiIhIBXPNd2MFBQVMnz6dtm3b0qlTJ77//nsA5s+fT3h4OG+++SaTJk0q65zl4o033uCBBx5g5MiRNGnShLlz5+Lh4cG8efPsHU1+dyYrnRFzHmfCx3/nuUw/dpn8OdB/Dl2mxuFfs66941V+JhOnAy9OcDYOLrVzmOJy8nMZ8+H/MXXZNwzODGNB5IMseOILGgSG2juaiIiI/I9rHoz8zDPP8MEHH9C7d282btzI4MGDGTlyJJs2beKNN95g8ODBODpW/KfuFBYWsn37dqZPn25rc3BwoHfv3sTHx1+2f0FBgW2BP7jYXSTlyGLmwNeTObBrCcvO18LZ0YkHBjxMkz5DcdYY+jLlHzMYlmykUc4RsvKy8XH3sm+gogKyF43h6YQ0fkrJxNnRifuGzGBY10GYTCb7ZhMREZEruua7s6+//ppPPvmEW2+9lb1799KiRQuKiorYtWtXpfqFf/bsWSwWC4GBgcXaAwMDOXDgwGX7z5o1i2efffZ6xavWshM3kPH5MBoVnqGRI6wKjuBvo9+had1Ie0erkgLb3EnRkslEOBawYsv39LnhPvuFyc8i4x+34pe6g6esjmzwac97f3uVdg1b2C+TiIiI/KVrHv504sQJ2rRpA0CzZs1wdXVl0qRJlaqgKInp06eTmZlp+zp+/Li9I1U95nyOf/kg7vNuom7hGc5ZHflX2J28Ov0HFRTlyc2X4171SbU4sX/vKrvFMLJSOftWLH6pO8i2OvCaRyzfT/9UBYWIiEglcM09FRaLBRcXl/8cwMkJLy87D5cogVq1auHo6Mjp06eLtZ8+fZqgoKDL9nd1dcXV1fV6xat28o+s58Lnw6hXcBZMsJxAat7zIbc272HvaNXC2f5vM+C9adQoPMEjlqLr/pjWorSDZM7pQy3zxSc8zQu9m2dGvYm7i9t1zSEiIiIlc813DoZhMGLECNsNdn5+Pg899BCenp7F9vvuu+/KJmE5cXFxoU2bNqxcuZKBAwcCYLVaWblyJePGjbNvuGpmZ9IeAufdQTB5pFkdWRI6iDtHvIunm4e9o1UbMU264Ofhy/mcLLYd2UvHyFbX7dy5x3dg/ugmalrzSLK4sDH2KR4fMKHK936KiIhUJddcVAwfPrzY6/vus+P461KaPHkyw4cPp23btrRv35633nqLnJwcRo4cae9o1YLZUsTbSxbw5s/zucGxFvd6F+B1+3sMb9vP3tGqHSdHJ3o1i2XxlqWs27H0uhUVZ7LS2frPEdxszeM3izspt85laOyg63JuERERKTvXXFTMnz+/PHLYxZAhQzhz5gzPPPMMqamptGrViqVLl142eVvKWGEO5394jHn79/PaqTwAvGIG0umex/H38rVzuOpreIArM/0PsWv3PzDumlbuPQXH01O4881xnDrrzjO+IbQd8U96N+5crucUERGR8lHqFbWrK62oXTLWY1u48Nm9+OalkWl1oFd+DE/d838MbNfH3tGqvbxjW3D/qDe5hokDw1bQOvqvV5YvqeOHNjBo/sucOJ9GaK0QFo5/S+tPiIiIXGcVakVtkatiMZP17+kYH/XBNy+NkxYn3q/Rix+fXqSCooJwD23HWScfPEwGh+LmlNt5kn/9Hr+Pb2Fs4S4iaofyw5S5KihEREQqOa0iJuXOSDvEuY+HUDMzEYDvC2uQ2/d5nuh1nybjViQmE1mRN1Fr/yLqHFtFUTk8BSp527fUXDwaT5OVGA8HFk94k4Aatcv0HCIiInL9qadCytXZlEMUvNORmpmJZFgdeMmtIy2mrOPe3veroKiA6vaeAkAn03m27VxepsdO/fU7An4vKLY7BFBv8kYCatYp03OIiIiIfainQsrN0p1rmfr5LB6x+NLMuYDDnaYx9Zax130NBLl6LoGNOOJelwZ5Jziz9n1oc3OZHPf8rsX4fTsKN5OVzaYAIidtwL/G5evBiIiIfVgsFsxms71jSDlwcXHBwaH8+xF0dydlLv+3n3htUxzvbl0HwLfBHek2cgajQxvZOZlcldZDYcPLtDqzicycTHw9S/dErgu7FuPx9UhcsbKOWkROWKOCQkSkgjAMg9TUVDIyMuwdRcqJg4MD4eHhxRavLg8qKqTsWMykfjOBoD2fcZPZnQ9M4TzQeyjTbv0brs7leyFL2QnvNYlv4r/irfMOjNi8lDE9h5T4WNn5Obz94xyeMKysNGoRMWEVQbXqlmFaEREpjUsFRe3atfHw8NDQ5CrGarVy6tQpUlJSCA0NLdfvr4oKKROW9COkfXQbwdnHAEhw9GfR+Dfp1CTWzsnkWplcPLjQ82kSFr7Gx2u/Y1T3wSXqNs03FzBizjTWn8jioHdTnnr0Y8IC65d9YBERKRGLxWIrKGrWrGnvOFJOAgICOHXqFEVFRTg7O5fbeTRRW0rtbPw88t9sR3D2MTKsDswJuIl+T25WQVGJ3dnhJrzcPDiceoxV25dd8/uLtn/G03MfZf3BbXi6ejBl3Dyi60WVQ1IRESmpS3MoPDw87JxEytOlYU8Wi6Vcz6OeCim5okKOfHwfDZKWArDD4smpPi/xUI9h6j6t5LzdPRnbpT91N71Fi3+NwGiRiMnV66/faBgYv/wdpzWv8bciF352iubDR16lVVjj8g8tIiIlot/ZVdv1+v6qp0JKpMBcyFOLXiPncBwAX7k0puaEeG7pOVw/nKqIYb2GEuuSR20jj+RPh//1GwpzML5+ANOa1wD4t9mXNx94ic7Rbco5qYiISPW1YMEC/Pz87B1DRYVcu+Szp7j1tb/xj3X/YnRWXRZFjOT2JzcQFlTf3tGkDNWsEcSGxsOxGhB2dAV56975451TdmO8fwOm3V9RZMCk7BDC7p5D35Zdr19gERGpVlJTUxk/fjwNGjTA1dWVevXqMWDAAFauXGnvaH+pohQCZUnDn+TqGQYHv57ET9uWsyvLhxqePrw4Yia9m3eydzIpJ7cOeYEPn1vNQyTgvuxJjOwUTD2mgdt/PWb25+kYm+ZislpIsTgxLrsOtw1+jkHt+tovuIiIVGlHjx6lc+fO+Pn58eqrr9K8eXPMZjPLli1j7NixHDhwwN4Rqx31VMhVseZlcvCNrkTvnsdE5xPcEVqHFU9+ooKiinN3caP5fR/yZm4AAKYN72K8HAUnd9j2yU1LwGS18O8Cb/pkRXLvfa8yrNsge0UWEZFq4JFHHsFkMrFlyxbuuOMOoqKiaNq0KZMnT2bTpk0AJCcnc9ttt+Hl5YWPjw933XUXp0+fth1j5syZtGrVinnz5hEaGoqXlxePPPIIFouFV155haCgIGrXrs0LL7xQ7Nwmk4k5c+Zw00034e7uToMGDfjmm29s2+Pi4jCZTMXW/ti5cycmk4mjR48SFxfHyJEjyczMxGQyYTKZmDlzJgAFBQVMnTqVOnXq4OnpSYcOHYiLiyt2/gULFhAaGoqHhweDBg0iPT29bP9xS0g9FfKXclP2c/6DG4kuOk+hAUvq3MybD3yCi9aeqBY6N2pL4sDXGPnNk0zzOEMj8lh34jiF5/NZ/dsm9u1MwmQJY7djbd5/6Dn6NO9s78giIlJChmGQW5h/3c/r4eJ21XMyz507x9KlS3nhhRfw9PS8bLufnx9Wq9VWUKxZs4aioiLGjh3LkCFDit2kJyYmsmTJEpYuXUpiYiJ33nknR44cISoqijVr1rBx40ZGjRpF79696dChg+19Tz/9NC+99BJvv/02n376KXfffTd79uyhceO/fjBJp06deOutt3jmmWc4ePAgAF5eFx+GMm7cOPbt28fChQsJCQlh8eLF3HjjjezZs4fIyEg2b97M6NGjmTVrFgMHDmTp0qXMmDHjqv7dypuKCvlTabt+wPXr0dShkFSrE3s6P81t/SfZO5ZcZ8O6DaKwqJDe37xNhCmXg/NfxODSD38nOka245dhTxEWUMeuOUVEpHRyC/NpOKHHdT9v4tur8XR1v6p9ExISMAyDRo0a/eE+K1euZM+ePSQlJVGvXj0APvnkE5o2bcrWrVtp164dcHFxuHnz5uHt7U2TJk3o0aMHBw8e5Oeff8bBwYHo6GhefvllVq9eXayoGDx4MGPGjAHg73//OytWrOCdd97h/fff/8v8Li4u+Pr6YjKZCAoKsrUnJyczf/58kpOTCQkJAWDq1KksXbqU+fPn8+KLL/L2229z44038vjjjwMQFRXFxo0bWbp06VX925UnFRXyh44seYF661/B2WSw1+pF0T2f0ad5T3vHEjsZ03MIXRq148OVX2I9shfDsBJTvym3te1Nz6axeuqXiIhcF4Zh/OU++/fvp169eraCAqBJkyb4+fmxf/9+W1FRv359vL29bfsEBgbi6OhYbNHXwMBA0tLSih0/Njb2stc7d+4sycex2bNnDxaLhaio4us6FRQU2BYn3L9/P4MGFR9iHBsbq6JCKq7vtixj8/LPeNnTIM6xDlHjlxCi1ZCrvUYhDXjj/iftHUNERMqJh4sbiW+vtst5r1ZkZCQmk6lMJmP/7wrTJpPpim1Wq/Wqj3mpIPnv4ufSQoN/Jjs7G0dHR7Zv346jo2OxbZeGR1VkKiqkGMMweHf5p7yw+H3Al6CwGB586B94umm1TRERkarOZDJd9TAke/H396dfv3689957PProo5fNq8jIyKBx48YcP36c48eP23or9u3bR0ZGBk2aNCl1hk2bNjFs2LBir2NiYgAICLj4cJOUlBRq1KgBcFkvhouLy2UrXMfExGCxWEhLS6Nr1ys/kr1x48Zs3rz5siwVgZ7+JDaWzJPseTWW97+fDcDfet3NhEc/U0EhIiIiFcp7772HxWKhffv2fPvttxw+fJj9+/cze/ZsYmNj6d27N82bN2fo0KHs2LGDLVu2MGzYMG644Qbatm1b6vN//fXXzJs3j0OHDjFjxgy2bNnCuHHjAIiIiKBevXrMnDmTw4cP89NPP/H6668Xe3/9+vXJzs5m5cqVnD17ltzcXKKiohg6dCjDhg3ju+++IykpiS1btjBr1ix++uknAB599FGWLl3Ka6+9xuHDh3n33XcrxNAnUFEhv8s/uZvzb7SjRdY+XvdK4dk7J/Ds4InFxhSKiIiIVAQNGjRgx44d9OjRgylTptCsWTP69OnDypUrmTNnDiaTiR9++IEaNWrQrVs3evfuTYMGDVi0aFGZnP/ZZ59l4cKFtGjRgk8++YQvv/zS1gPi7OzMl19+yYEDB2jRogUvv/wyzz//fLH3d+rUiYceeoghQ4YQEBDAK6+8AsD8+fMZNmwYU6ZMITo6moEDB7J161ZCQ0MB6NixIx999BFvv/02LVu2ZPny5Tz11FNl8plKy2RczWwXuUxWVha+vr5kZmbi4+Nj7zilkn3gF/hsCF6YOWJx5chNb9G721B7xxIREZFylJ+fT1JSEuHh4bi5Xf2churOZDKxePFiBg4caO8oV+XPvs9leT+rORXV3IVtn+H6/ThcsLLD4oX13i/o3by7vWOJiIiISCWioqIay1r1Ol6rnsUB+MXiT8iD/6JJeAt7xxIRERGRSkZFRTV1IiUBx1Uv4wMstIbQZvxPRIY0tHcsERERkQpNMweuTEVFNZR4OpnBsyfjklmXe/3htkk/aiVkERERESkxPdqnOjEMjuz8NwNff4hT59Nwrh3J4ClLVVCIiIiISKmop6K6sFpI/3w49Q78SMu8uqTWa8OX498iwMff3slEREREpJJTUVEdWMycXTCEWkm/YAFaBQYzZuK7+HlW7kfhioiIiEjFoKKiqjPnc/afA6l1YiNmA97y7srDE7/Cy83zr98rIiIiInIVVFRUZYW5nJ17I7XSdpJvmJhdozfjH/0cdxctcCMiIiIiZUcTtauqwlzOvteDWmk7yTFMvFP7ViZOXKiCQkREROQqLFiwAD8/v3I59syZM2nVqlW5HNteVFRUUZ9vXsbykylkWh2YW/ceJo2bj4uTs71jiYiIiJTaiBEjMJlMmEwmnJ2dCQwMpE+fPsybNw+r1Vom5xgyZAiHDh0qk2NVByoqqqCPVi5iyucvMzU7mA+iHmLS397HyVEj3URERKTquPHGG0lJSeHo0aMsWbKEHj16MGHCBG655RaKiopKfXx3d3dq1679h9sLCwtLfY6qREVFFWJkHGf7ewOY+fUbAPytz308NnwWDg76NouIiEjV4urqSlBQEHXq1KF169b83//9Hz/88ANLlixhwYIFAGRkZDBmzBgCAgLw8fGhZ8+e7Nq1y3aMXbt20aNHD7y9vfHx8aFNmzZs27YNuHz406UhS//4xz8IDw/Hzc3tqs4B8NJLLxEYGIi3tzejR48mPz+/fP9x7EB3m1WEkZ5I5tudaZOyhqc8TvPYgAd45vZxmEwme0cTERGRyqYw54+/zPnXsG/eX+9bhnr27EnLli357rvvABg8eDBpaWksWbKE7du307p1a3r16sW5c+cAGDp0KHXr1mXr1q1s376dJ554AmfnPx4unpCQwLfffst3333Hzp07r+ocX331FTNnzuTFF19k27ZtBAcH8/7775fp564INCamCrCk/kb23D74FWWTaHHBt9fjPNx/tL1jiYiISGX1XPAfb4vqC8O++c/rWQ3BnHvlfet3gTE//+f1a80gN734Ps9nlTznFTRq1Ijdu3ezfv16tmzZQlpaGq6urhdP/9prfP/993zzzTc8+OCDJCcn89hjj9GoUSMAIiMj//TYhYWFfPLJJwQEBABc1TneeustRo8ezejRF+/Nnn/+eX755Zcq11tR6XoqXnjhBTp16oSHh8cfzshPTk6mf//+eHh4ULt2bR577LHLxtbFxcXRunVrXF1diYiIsHWTVTZFyVvJe78HvkXZHChyZVefN7m3/yP2jiUiIiJiF4ZhYDKZ2LVrF9nZ2dSsWRMvLy/bV1JSEomJiQBMnjyZMWPG0Lt3b1566SVb+x8JCwuzFRTAVZ1j//79dOjQodhxYmNjy/hT21+l66koLCxk8ODBxMbG8s9//vOy7RaLhf79+xMUFMTGjRtJSUlh2LBhODs78+KLLwKQlJRE//79eeihh/j8889ZuXIlY8aMITg4mH79+l3vj1RiBYdWYf10MF6GmV+L3Em9dS63dxpk71giIiJS2T2T8sfbTI7FX0//kxtx0//8/Xrq3pJnukr79+8nPDyc7OxsgoODiYuLu2yfS3+YnjlzJvfeey8//fQTS5YsYcaMGSxcuJBBg658P+XpWXzx4Ks5R3VR6YqKZ599FuAPexaWL1/Ovn37+OWXXwgMDKRVq1b8/e9/Z9q0acycORMXFxfmzp1LeHg4r7/+OgCNGzdm/fr1vPnmm5WmqEhPP4HTp3fha5jZWORF4ZBPuCmmt71jiYiISFXg4vnX+5T3viWwatUq9uzZw6RJk6hbty6pqak4OTlRv379P3xPVFQUUVFRTJo0iXvuuYf58+f/YVHxv1q3bv2X52jcuDGbN29m2LBhtrZNmzZdy8eqFCrd8Ke/Eh8fT/PmzQkMDLS19evXj6ysLH777TfbPr17F78B79evH/Hx8dc1a0kdO3OSW2dP5m8ZQSwt8sdxxGK6q6AQERGRaqSgoIDU1FROnjzJjh07ePHFF7ntttu45ZZbGDZsGL179yY2NpaBAweyfPlyjh49ysaNG3nyySfZtm0beXl5jBs3jri4OI4dO8aGDRvYunUrjRs3vuoMf3UOgAkTJjBv3jzmz5/PoUOHmDFjhu2etCqpdD0VfyU1NbVYQQHYXqempv7pPllZWeTl5eHu7n7ZcQsKCigoKLC9zsoq20lFV8VqZe/eldz96RucvXCefP8IGox/k6jg8OufRURERMSOli5dSnBwME5OTtSoUYOWLVsye/Zshg8fbnuc/s8//8yTTz7JyJEjOXPmDEFBQXTr1o3AwEAcHR1JT09n2LBhnD59mlq1anH77bfbRsVcDZPJ9KfngIuL6CUmJvL444+Tn5/PHXfcwcMPP8yyZcvK5d/FXkyGYRj2DvHEE0/w8ssv/+k++/fvt83Mh4vDnyZOnEhGRkax/R588EGOHTtW7BuVm5uLp6cnP//8MzfddBNRUVGMHDmS6dOn2/b5+eef6d+/P7m5uVcsKmbOnHnFiywzMxMfH5+r/ahYrVbSL5wnwLfmVb8HgIILHPtoEN6ntnNzRjhedZrx2djXCfIL+Ov3ioiIiPyP/Px8kpKSiq25IFXPn32fs7Ky8PX1veb72SupED0VU6ZMYcSIEX+6T4MGDa7qWEFBQWzZsqVY2+nTp23bLv3vpbb/3sfHx+eKBQXA9OnTmTx5su11VlYW9erVu6pMl+QU5PH4/Ke558RiPPtPxyN2zFW9r+DEr2TOu52wwnQKTTA0ogEjHp6Dt3v5jksUEREREbkaFaKoCAgIKPZ4rtKIjY3lhRdeIC0tzba0+ooVK/Dx8aFJkya2fX7++edi71uxYsWfPt7L1dXV9vzhksotyCMseTVdHM5Q9NNULP6hOEb3/eM3FBWS8uOT1Nz+EbWxcsrixNqWYxl717NaJVtEREREKoxKd2eanJzMzp07SU5OxmKxsHPnTnbu3El2djYAffv2pUmTJtx///3s2rWLZcuW8dRTTzF27FhbUfDQQw9x5MgRHn/8cQ4cOMD777/PV199xaRJk8o1e4CPP/0f/oIlZj+csMKngylY/RoUFV62b+a2zznzYjTB2z/ABStrrDVIuPNz7r777yooRERERKRCqRBzKq7FiBEj+Pjjjy9rX716Nd27dwfg2LFjPPzww8TFxeHp6cnw4cN56aWXcHL6T8dMXFwckyZNYt++fdStW5enn376L4dg/bfSjEFbseMXsheOYpBLBgA5rn4UhXUht/lg9jgGsGzXWkJ2f8IU11OcsTryXUAv7hzzETW9a1zTeURERET+iOZUVA/Xa05FpSsqKorSfhO2Je5myUcjedAhiUCHi6t9T8sO4uN8fwC8TRamBznT+PaXiG3erUyzi4iIiKioqB6q1UTt6qhtwxY0eW4Dn69eyInt3+J3/jAHTb5EBtWnQ0RLbm/fj9jIGEwmk72jioiISBWmvy9Xbdfr+6uiwo48XNx4oN8I6DcCgPKd0SEiIiLyH87OzgB/+Dh9qRoKCy/O3XV0dCzX86ioEBEREamGHB0d8fPzIy0tDQAPDw+NkKhirFYrZ86cwcPDo9jc4vKgokJERESkmrq0htelwkKqHgcHB0JDQ8u9YFRRISIiIlJNmUwmgoODqV27Nmaz2d5xpBy4uLhcl+UIVFSIiIiIVHOOjo7lPuZeqjatoiYiIiIiIqWiokJEREREREpFRYWIiIiIiJSK5lSU0KWFRLKysuycRERERETk2l26jy2LBfJUVJRQeno6APXq1bNzEhERERGRkktPT8fX17dUx1BRUUL+/v4AJCcnl/qbIFVbVlYW9erV4/jx4/j4+Ng7jlRQuk7kaug6kaula0WuRmZmJqGhobb72tJQUVFCl5736+vrq/+zylXx8fHRtSJ/SdeJXA1dJ3K1dK3I1SiLdSw0UVtEREREREpFRYWIiIiIiJSKiooScnV1ZcaMGbi6uto7ilRwulbkaug6kauh60Sulq4VuRpleZ2YjLJ4hpSIiIiIiFRb6qkQEREREZFSUVEhIiIiIiKloqJCRERERERKRUVFCb333nvUr18fNzc3OnTowJYtW+wdSexo7dq1DBgwgJCQEEwmE99//32x7YZh8MwzzxAcHIy7uzu9e/fm8OHD9gkrdjNr1izatWuHt7c3tWvXZuDAgRw8eLDYPvn5+YwdO5aaNWvi5eXFHXfcwenTp+2UWOxlzpw5tGjRwrbGQGxsLEuWLLFt13UiV/LSSy9hMpmYOHGirU3XigDMnDkTk8lU7KtRo0a27WVxnaioKIFFixYxefJkZsyYwY4dO2jZsiX9+vUjLS3N3tHETnJycmjZsiXvvffeFbe/8sorzJ49m7lz57J582Y8PT3p168f+fn51zmp2NOaNWsYO3YsmzZtYsWKFZjNZvr27UtOTo5tn0mTJvHjjz/y9ddfs2bNGk6dOsXtt99ux9RiD3Xr1uWll15i+/btbNu2jZ49e3Lbbbfx22+/AbpO5HJbt27lgw8+oEWLFsXada3IJU2bNiUlJcX2tX79etu2MrlODLlm7du3N8aOHWt7bbFYjJCQEGPWrFl2TCUVBWAsXrzY9tpqtRpBQUHGq6++amvLyMgwXF1djS+//NIOCaWiSEtLMwBjzZo1hmFcvC6cnZ2Nr7/+2rbP/v37DcCIj4+3V0ypIGrUqGH84x//0HUil7lw4YIRGRlprFixwrjhhhuMCRMmGIahnynyHzNmzDBatmx5xW1ldZ2op+IaFRYWsn37dnr37m1rc3BwoHfv3sTHx9sxmVRUSUlJpKamFrtmfH196dChg66Zai4zMxMAf39/ALZv347ZbC52rTRq1IjQ0FBdK9WYxWJh4cKF5OTkEBsbq+tELjN27Fj69+9f7JoA/UyR4g4fPkxISAgNGjRg6NChJCcnA2V3nTiVeeIq7uzZs1gsFgIDA4u1BwYGcuDAATulkoosNTUV4IrXzKVtUv1YrVYmTpxI586dadasGXDxWnFxccHPz6/YvrpWqqc9e/YQGxtLfn4+Xl5eLF68mCZNmrBz505dJ2KzcOFCduzYwdatWy/bpp8pckmHDh1YsGAB0dHRpKSk8Oyzz9K1a1f27t1bZteJigoRETsYO3Yse/fuLTamVeS/RUdHs3PnTjIzM/nmm28YPnw4a9assXcsqUCOHz/OhAkTWLFiBW5ubvaOIxXYTTfdZPvvFi1a0KFDB8LCwvjqq69wd3cvk3No+NM1qlWrFo6OjpfNiD99+jRBQUF2SiUV2aXrQteMXDJu3Dj+/e9/s3r1aurWrWtrDwoKorCwkIyMjGL761qpnlxcXIiIiKBNmzbMmjWLli1b8vbbb+s6EZvt27eTlpZG69atcXJywsnJiTVr1jB79mycnJwIDAzUtSJX5OfnR1RUFAkJCWX2M0VFxTVycXGhTZs2rFy50tZmtVpZuXIlsbGxdkwmFVV4eDhBQUHFrpmsrCw2b96sa6aaMQyDcePGsXjxYlatWkV4eHix7W3atMHZ2bnYtXLw4EGSk5N1rQhWq5WCggJdJ2LTq1cv9uzZw86dO21fbdu2ZejQobb/1rUiV5KdnU1iYiLBwcFl9jNFw59KYPLkyQwfPpy2bdvSvn173nrrLXJychg5cqS9o4mdZGdnk5CQYHudlJTEzp078ff3JzQ0lIkTJ/L8888TGRlJeHg4Tz/9NCEhIQwcONB+oeW6Gzt2LF988QU//PAD3t7etrGqvr6+uLu74+vry+jRo5k8eTL+/v74+Pgwfvx4YmNj6dixo53Ty/U0ffp0brrpJkJDQ7lw4QJffPEFcXFxLFu2TNeJ2Hh7e9vmZF3i6elJzZo1be26VgRg6tSpDBgwgLCwME6dOsWMGTNwdHTknnvuKbufKaV8QlW19c477xihoaGGi4uL0b59e2PTpk32jiR2tHr1agO47Gv48OGGYVx8rOzTTz9tBAYGGq6urkavXr2MgwcP2je0XHdXukYAY/78+bZ98vLyjEceecSoUaOG4eHhYQwaNMhISUmxX2ixi1GjRhlhYWGGi4uLERAQYPTq1ctYvny5bbuuE/kj//1IWcPQtSIXDRkyxAgODjZcXFyMOnXqGEOGDDESEhJs28viOjEZhmGUcTEkIiIiIiLViOZUiIiIiIhIqaioEBERERGRUlFRISIiIiIipaKiQkRERERESkVFhYiIiIiIlIqKChERERERKRUVFSIiIiIiUioqKkREREREpFRUVIiISLVSWFhIREQEGzdutFuGJ554gvHjx9vt/CIiZU1FhYhIJTZixAhMJtNlXwkJCfaOVmHNnTuX8PBwOnXqVKx99erV3HLLLQQEBODm5kbDhg0ZMmQIa9eute0TFxeHyWQiIyPjsuPWr1+ft95666oyTJ06lY8//pgjR46U5qOIiFQYKipERCq5G2+8kZSUlGJf4eHhl+1XWFhoh3QVi2EYvPvuu4wePbpY+/vvv0+vXr2oWbMmixYt4uDBgyxevJhOnToxadKkMs9Rq1Yt+vXrx5w5c8r82CIi9qCiQkSkknN1dSUoKKjYl6OjI927d2fcuHFMnDjRdhMLsHfvXm666Sa8vLwIDAzk/vvv5+zZs7bj5eTkMGzYMLy8vAgODub111+ne/fuTJw40baPyWTi+++/L5bDz8+PBQsW2F4fP36cu+66Cz8/P/z9/bnttts4evSobfuIESMYOHAgr732GsHBwdSsWZOxY8diNptt+xQUFDBt2jTq1auHq6srERER/POf/8QwDCIiInjttdeKZdi5c+ef9tRs376dxMRE+vfvb2tLTk5m4sSJTJw4kY8//piePXsSFhZGixYtmDBhAtu2bbvab4XNggULrtiDNHPmTNs+AwYMYOHChdd8bBGRikhFhYhIFfbxxx/j4uLChg0bmDt3LhkZGfTs2ZOYmBi2bdvG0qVLOX36NHfddZftPY899hhr1qzhhx9+YPny5cTFxbFjx45rOq/ZbKZfv354e3uzbt06NmzYgJeXFzfeeGOxHpPVq1eTmJjI6tWr+fjjj1mwYEGxwmTYsGF8+eWXzJ49m/379/PBBx/g5eWFyWRi1KhRzJ8/v9h558+fT7du3YiIiLhirnXr1hEVFYW3t7et7dtvv8VsNvP4449f8T0mk+maPjvAkCFDivUcffnllzg5OdG5c2fbPu3bt+fEiRPFCi0RkUrLEBGRSmv48OGGo6Oj4enpafu68847DcMwjBtuuMGIiYkptv/f//53o2/fvsXajh8/bgDGwYMHjQsXLhguLi7GV199Zduenp5uuLu7GxMmTLC1AcbixYuLHcfX19eYP3++YRiG8emnnxrR0dGG1Wq1bS8oKDDc3d2NZcuW2bKHhYUZRUVFtn0GDx5sDBkyxDAMwzh48KABGCtWrLjiZz958qTh6OhobN682TAMwygsLDRq1aplLFiw4A//vSZMmGD07NmzWNtDDz1k+Pj4FGv75ptviv2b7t692zAMw1i9erUBFNt26ctkMhlvvvnmZedMSEgw/P39jVdeeaVYe2ZmpgEYcXFxf5hXRKSycLJrRSMiIqXWo0ePYmPzPT09bf/dpk2bYvvu2rWL1atX4+XlddlxEhMTycvLo7CwkA4dOtja/f39iY6OvqZMu3btIiEhoViPAEB+fj6JiYm2102bNsXR0dH2Ojg4mD179gAXhzI5Ojpyww03XPEcISEh9O/fn3nz5tG+fXt+/PFHCgoKGDx48B/mysvLw83N7bL2/+2N6NevHzt37uTkyZN0794di8VSbPu6desu+2zdu3e/7LiZmZnccsst9O/fn8cee6zYNnd3dwByc3P/MK+ISGWhokJEpJLz9PT8w+E+/11gAGRnZzNgwABefvnly/YNDg6+6qdGmUwmDMMo1vbfcyGys7Np06YNn3/++WXvDQgIsP23s7PzZce1Wq3Af266/8yYMWO4//77efPNN5k/fz5DhgzBw8PjD/evVauWrWi5JDIykszMTFJTUwkKCgLAy8uLiIgInJyu/GsyPDwcPz+/Ym3/u6/FYmHIkCH4+Pjw4YcfXnaMc+fOAcX/PUREKivNqRARqUZat27Nb7/9Rv369YmIiCj25enpScOGDXF2dmbz5s2295w/f55Dhw4VO05AQAApKSm214cPHy72F/fWrVtz+PBhateufdl5fH19rypr8+bNsVqtrFmz5g/3ufnmm/H09GTOnDksXbqUUaNG/ekxY2JiOHDgQLGC6M4778TZ2fmKhVZpTJo0iT179vD9999fsXdk7969ODs707Rp0zI9r4iIPaioEBGpRsaOHcu5c+e455572Lp1K4mJiSxbtoyRI0disVjw8vJi9OjRPPbYY6xatYq9e/cyYsQIHByK/7ro2bMn7777Lr/++ivbtm3joYceKtbrMHToUGrVqsVtt93GunXrSEpKIi4ujkcffZQTJ05cVdb69eszfPhwRo0axffff287xldffWXbx9HRkREjRjB9+nQiIyOJjY3902P26NGD7OxsfvvtN1tbaGgor7/+Om+//TbDhw9n9erVHD16lB07djB79mzbea7F/Pnzef/995k7dy4mk4nU1FRSU1PJzs627bNu3Tq6du16VT0yIiIVnYoKEZFqJCQkhA0bNmCxWOjbty/Nmzdn4sSJ+Pn52QqHV199la5duzJgwAB69+5Nly5dLpub8frrr1OvXj26du3Kvffey9SpU4sNO/Lw8GDt2rWEhoZy++2307hxY0aPHk1+fj4+Pj5XnXfOnDnceeedPPLIIzRq1IgHHniAnJycYvuMHj2awsJCRo4c+ZfHq1mzJoMGDbpsWNb48eNZvnw5Z86c4c477yQyMpKbb76ZpKQkli5dSvPmza86M8CaNWuwWCzceuutBAcH277++xG4Cxcu5IEHHrim44qIVFQm438HxYqIiPyP7t2706pVq6teMfp6WrduHb169eL48eMEBgb+5f67d++mT58+JCYmXnHC+vWwZMkSpkyZwu7du/9w3oaISGWingoREamUCgoKOHHiBDNnzmTw4MFXVVAAtGjRgpdffpmkpKRyTvjHcnJymD9/vgoKEaky9NNMREQqpS+//JLRo0fTqlUrPvnkk2t674gRI8on1FW688477Xp+EZGypuFPIiIiIiJSKhr+JCIiIiIipaKiQkRERERESkVFhYiIiIiIlIqKChERERERKRUVFSIiIiIiUioqKkREREREpFRUVIiIiIiISKmoqBARERERkVJRUSEiIiIiIqXy/6DJP2WoM0s3AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Retrieve the simulation data\n",
    "sim_data = modeler_data.data[\"port_1\"]\n",
    "# Get the monitor data associated the lumped element\n",
    "vi_mon_data = sim_data[lumped_element.monitor_name]\n",
    "# Setup voltage and current path integrals automatically around the lumped element.\n",
    "voltage_integral, current_integral = rf.path_integrals_from_lumped_element(\n",
    "    lumped_element, sim_data.simulation.grid\n",
    ")\n",
    "# Compute the voltage and current using the path integral tools and the frequency-domain field data.\n",
    "V = voltage_integral.compute_voltage(vi_mon_data)\n",
    "I = current_integral.compute_current(vi_mon_data)\n",
    "# Apply Ohm's law to retrieve impedance\n",
    "impedance = V / I\n",
    "resistance = np.real(impedance).values\n",
    "reactance = -np.imag(\n",
    "    impedance\n",
    ").values  # Change of sign, since Tidy3D uses the physics convention for time-harmonic fields (exp(-j omega t))\n",
    "# Here we use the convenience methods from the lumped element to compute the intended impedance of the modeled network.\n",
    "ideal_impedance = lumped_element.impedance(freqs=freqs)\n",
    "ideal_resistance = np.real(ideal_impedance)\n",
    "ideal_reactance = -np.imag(\n",
    "    ideal_impedance\n",
    ")  # Change of sign, since Tidy3D uses the physics convention for time-harmonic fields (exp(-j omega t))\n",
    "\n",
    "# Plot the results for visual comparison\n",
    "f, (ax1, ax2) = plt.subplots(2, 1, tight_layout=True, figsize=(8, 6))\n",
    "f.suptitle(\"Seven-element 4th-order network\")\n",
    "ax1.plot(freqs / 1e9, resistance, label=\"Computed\")\n",
    "ax1.plot(freqs / 1e9, ideal_resistance, \"--\", label=\"Desired\")\n",
    "ax1.set_ylabel(r\"Resistance ($\\Omega$)\")\n",
    "ax1.set_xlabel(\"Frequency (GHz)\")\n",
    "ax1.set_xlim(0, 50)\n",
    "ax1.set_ylim(0, 250)\n",
    "ax1.legend(loc=\"upper right\")\n",
    "ax2.plot(freqs / 1e9, reactance, label=\"Computed\")\n",
    "ax2.plot(freqs / 1e9, ideal_reactance, \"--\", label=\"Desired\")\n",
    "ax2.set_ylabel(r\"Reactance ($\\Omega$)\")\n",
    "ax2.set_xlabel(\"Frequency (GHz)\")\n",
    "ax2.set_xlim(0, 50)\n",
    "ax2.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Advanced lumped element usage\n",
    "\n",
    "The default implementation of the lumped element in Tidy3D results in a distributed lumped element that spans one or more grid cells. This implementation is ideal for most cases, since it minimizes the parasitic inductance associated with the lumped element. However, since the distributed implementation assumes that the electric field is uniform throughout the element, at high frequencies and for networks with a high inductance, distributing the lumped element over multiple grid cells can give inaccurate results.\n",
    "\n",
    "As a result, we offer a different implementation for lumped elements that restricts the network portion of the lumped element to a single grid cell and uses PEC wires or sheets to connect the network portion to the edges of the lumped element. Below is a diagram which demonstrates how wires are used to connect the single grid cell to the edges of the lumped element when the width of the lumped element is set to 0.\n",
    "\n",
    "<img src=\"img/single-cell-lumped-element.png\" width=\"500\" alt=\"Single-cell lumped element\">\n",
    "\n",
    "Switching between these two implementations is done by setting the `dist_type` field to either `off` or `on`. By restricting the network portion to a single cell, there is no longer an issue at high frequencies stemming from the assumption that the electric field is uniform. However, the use of PEC wires results in a parasitic inductance that manifests as a small inductor, which acts in series with the intended network. There is also a parasitic shunt capacitance associated with the wire connections, but it is usually quite small.\n",
    "\n",
    "In certain cases, it is possible to accurately estimate the value of this parasitic inductance and account for it within the lumped element's network definition. As an example of this, we return to the original lumped element simulation involving a series RL network. Since the parasitic inductance acts in series, we can simply create a new lumped element where the value of the inductance is equal to the intended value of `1 nH` minus the value of the parasitic inductance, which can be estimated using a provided convenience method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The parasitic inductance of the wire connections is 0.18 nH, while the parasitic capacitance is 4.68 fF.\n"
     ]
    }
   ],
   "source": [
    "# We create an updated copy of the lumped element from the previous simulation,\n",
    "# where Distribute Type is turned 'off' and the width is set to an infinitesimal 1e-9\n",
    "lumped_element = lumped_element.updated_copy(\n",
    "    dist_type=\"off\", size=(1e-9, strip_height, 0), network=RLC\n",
    ")\n",
    "# Use a convenience method for estimating the parasitic inductance and capacitance\n",
    "# associated with the wire connections, which are used in the lumped element.\n",
    "Lp, Cp = lumped_element.estimate_parasitic_elements(grid=modeler.sim_dict[\"port_1\"].grid)\n",
    "print(\n",
    "    f\"The parasitic inductance of the wire connections is {Lp * 1e9:.2f} nH, \"\n",
    "    f\"while the parasitic capacitance is {Cp * 1e15:.2f} fF.\"\n",
    ")\n",
    "\n",
    "# We create an updated copy of the lumped element that compensates for the parasitic inductance by subtracting it.\n",
    "lumped_element_comp = lumped_element.updated_copy(path=\"network\", inductance=Lval - Lp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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\">14:05:45 UTC </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'modeler_not_comp_simulation'</span> with resource_id        \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'sid-d803a64f-ec3d-4781-a4c3-afe92f758007'</span> and task_type           \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'TERMINAL_CM'</span>.                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:05:45 UTC\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'modeler_not_comp_simulation'\u001b[0m with resource_id        \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'sid-d803a64f-ec3d-4781-a4c3-afe92f758007'\u001b[0m and task_type           \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'TERMINAL_CM'\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/rf?taskId=pa-41171a6f-56fc-4d90-8595-53a6a1884a9e\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/rf?taskId=pa-41171a6f-56fc-4d90-85</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/rf?taskId=pa-41171a6f-56fc-4d90-8595-53a6a1884a9e\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">95-53a6a1884a9e'</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=248701;https://tidy3d.simulation.cloud/rf?taskId=pa-41171a6f-56fc-4d90-8595-53a6a1884a9e\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/rf?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=568871;https://tidy3d.simulation.cloud/rf?taskId=pa-41171a6f-56fc-4d90-8595-53a6a1884a9e\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=248701;https://tidy3d.simulation.cloud/rf?taskId=pa-41171a6f-56fc-4d90-8595-53a6a1884a9e\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=413258;https://tidy3d.simulation.cloud/rf?taskId=pa-41171a6f-56fc-4d90-8595-53a6a1884a9e\u001b\\\u001b[32mpa\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=248701;https://tidy3d.simulation.cloud/rf?taskId=pa-41171a6f-56fc-4d90-8595-53a6a1884a9e\u001b\\\u001b[32m-41171a6f-56fc-4d90-85\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=248701;https://tidy3d.simulation.cloud/rf?taskId=pa-41171a6f-56fc-4d90-8595-53a6a1884a9e\u001b\\\u001b[32m95-53a6a1884a9e'\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/9b36e144-ddb6-41f8-8dd8-30b62b26a870\" 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=221869;https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1882627bffa649fa8adeac3e7ca3955f",
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       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:05:54 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</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> after run.         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:05:54 UTC\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\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 after 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\">14:05:55 UTC </span>Subtasks status - modeler_not_comp_simulation                      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Group ID: <span style=\"color: #008000; text-decoration-color: #008000\">'pa-41171a6f-56fc-4d90-8595-53a6a1884a9e'</span>                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:05:55 UTC\u001b[0m\u001b[2;36m \u001b[0mSubtasks status - modeler_not_comp_simulation                      \n",
       "\u001b[2;36m             \u001b[0mGroup ID: \u001b[32m'pa-41171a6f-56fc-4d90-8595-53a6a1884a9e'\u001b[0m                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "511a734ff94d4898a28a29bae86bb68e",
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       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Batch status = preprocess                                          \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mBatch status = preprocess                                          \n"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:06:04 UTC </span>Batch status = running                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:06:04 UTC\u001b[0m\u001b[2;36m \u001b[0mBatch status = running                                             \n"
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     },
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     "output_type": "display_data"
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:06:39 UTC </span>Batch status = postprocess                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:06:39 UTC\u001b[0m\u001b[2;36m \u001b[0mBatch status = postprocess                                         \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\">14:06:59 UTC </span>Batch status = success                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:06:59 UTC\u001b[0m\u001b[2;36m \u001b[0mBatch status = 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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:07:02 UTC </span>Modeler has finished running successfully.                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:07:02 UTC\u001b[0m\u001b[2;36m \u001b[0mModeler has finished running successfully.                         \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>Billed flex credit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>.                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.025\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\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4529b177609246799e36050de292146e",
       "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\">14:07:05 UTC </span>Loading results from cm_data.hdf5                                  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:07:05 UTC\u001b[0m\u001b[2;36m \u001b[0mLoading results from cm_data.hdf5                                  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lumped_element_not_comp_simulation = modeler.simulation.updated_copy(\n",
    "    lumped_elements=[lumped_element_comp]\n",
    ")\n",
    "modeler = modeler.updated_copy(simulation=lumped_element_not_comp_simulation)\n",
    "s_matrix_not_comp = web.run(modeler, task_name=\"modeler_not_comp_simulation\").smatrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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\">14:07:06 UTC </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'modeler_comp_simulation'</span> with resource_id            \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'sid-915b49a8-8c3b-4b8b-ae73-db68e40aeda1'</span> and task_type           \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'TERMINAL_CM'</span>.                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:07:06 UTC\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'modeler_comp_simulation'\u001b[0m with resource_id            \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'sid-915b49a8-8c3b-4b8b-ae73-db68e40aeda1'\u001b[0m and task_type           \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'TERMINAL_CM'\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/rf?taskId=pa-a4cba4b4-bab1-41f0-9de1-d7f2ff30f8a6\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/rf?taskId=pa-a4cba4b4-bab1-41f0-9d</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/rf?taskId=pa-a4cba4b4-bab1-41f0-9de1-d7f2ff30f8a6\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">e1-d7f2ff30f8a6'</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=213389;https://tidy3d.simulation.cloud/rf?taskId=pa-a4cba4b4-bab1-41f0-9de1-d7f2ff30f8a6\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/rf?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=340291;https://tidy3d.simulation.cloud/rf?taskId=pa-a4cba4b4-bab1-41f0-9de1-d7f2ff30f8a6\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=213389;https://tidy3d.simulation.cloud/rf?taskId=pa-a4cba4b4-bab1-41f0-9de1-d7f2ff30f8a6\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=513527;https://tidy3d.simulation.cloud/rf?taskId=pa-a4cba4b4-bab1-41f0-9de1-d7f2ff30f8a6\u001b\\\u001b[32mpa\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=213389;https://tidy3d.simulation.cloud/rf?taskId=pa-a4cba4b4-bab1-41f0-9de1-d7f2ff30f8a6\u001b\\\u001b[32m-a4cba4b4-bab1-41f0-9d\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=213389;https://tidy3d.simulation.cloud/rf?taskId=pa-a4cba4b4-bab1-41f0-9de1-d7f2ff30f8a6\u001b\\\u001b[32me1-d7f2ff30f8a6'\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/9b36e144-ddb6-41f8-8dd8-30b62b26a870\" 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=188258;https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8ba395ed273140cabf1f4027992a38d0",
       "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\">14:07:14 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</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> after run.         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:07:14 UTC\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\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 after 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\">14:07:16 UTC </span>Subtasks status - modeler_comp_simulation                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Group ID: <span style=\"color: #008000; text-decoration-color: #008000\">'pa-a4cba4b4-bab1-41f0-9de1-d7f2ff30f8a6'</span>                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:07:16 UTC\u001b[0m\u001b[2;36m \u001b[0mSubtasks status - modeler_comp_simulation                          \n",
       "\u001b[2;36m             \u001b[0mGroup ID: \u001b[32m'pa-a4cba4b4-bab1-41f0-9de1-d7f2ff30f8a6'\u001b[0m                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "92387bacf88d449187b438250f3b7b70",
       "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\">             </span>Batch status = preprocess                                          \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mBatch status = 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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:07:25 UTC </span>Batch status = running                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:07:25 UTC\u001b[0m\u001b[2;36m \u001b[0mBatch status = running                                             \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\">14:07:58 UTC </span>Batch status = postprocess                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:07:58 UTC\u001b[0m\u001b[2;36m \u001b[0mBatch status = postprocess                                         \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\">14:08:18 UTC </span>Batch status = success                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:08:18 UTC\u001b[0m\u001b[2;36m \u001b[0mBatch status = 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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:08:20 UTC </span>Modeler has finished running successfully.                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:08:20 UTC\u001b[0m\u001b[2;36m \u001b[0mModeler has finished running successfully.                         \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>Billed flex credit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>.                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.025\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\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8b08d59a45584015b8aad02243cfad82",
       "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\">14:08:28 UTC </span>Loading results from cm_data.hdf5                                  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:08:28 UTC\u001b[0m\u001b[2;36m \u001b[0mLoading results from cm_data.hdf5                                  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "modeler = modeler.updated_copy(lumped_elements=[lumped_element], path=\"simulation\")\n",
    "lumped_element_comp_simulation = modeler.simulation.updated_copy(\n",
    "    lumped_elements=[lumped_element_comp]\n",
    ")\n",
    "modeler = modeler.updated_copy(simulation=lumped_element_comp_simulation)\n",
    "s_matrix_comp = web.run(modeler, task_name=\"modeler_comp_simulation\").smatrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Tidy3D uses the physics convention for time-harmonic fields,\n",
    "# so we take the conjugate in order to switch to the engineering convention.\n",
    "# Get S11 data\n",
    "freq = s_matrix.data.f / 1e9\n",
    "S11_not_comp = s_matrix_not_comp.data.isel(port_out=0, port_in=0).values.flatten()\n",
    "S11_comp = s_matrix_comp.data.isel(port_out=0, port_in=0).values.flatten()\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 3))\n",
    "fig.suptitle(\"Transmission line terminated by RL series network\")\n",
    "ax1.plot(s_matrix.data.f / 1e9, 20 * np.log10(np.abs(S11_RL_term)), \"-b\", label=\"Distributed\")\n",
    "ax1.plot(s_matrix.data.f / 1e9, 20 * np.log10(np.abs(S11_not_comp)), \"--r\", label=\"Single-cell\")\n",
    "ax1.plot(\n",
    "    s_matrix.data.f / 1e9, 20 * np.log10(np.abs(S11_comp)), \"--g\", label=\"Compensated-single-cell\"\n",
    ")\n",
    "ax1.set_xlabel(\"Frequency (GHz)\")\n",
    "ax1.set_ylabel(r\"$|S_{11}|$ (dB)\")\n",
    "ax1.set_xlim([0, 50])\n",
    "ax1.grid()\n",
    "ax1.legend()\n",
    "\n",
    "ax2.plot(\n",
    "    s_matrix.data.f / 1e9,\n",
    "    -np.angle(S11_RL_term),  # Follow engineering convention exp(j omega t)\n",
    "    \"-b\",\n",
    "    label=\"Distributed\",\n",
    ")\n",
    "ax2.plot(\n",
    "    s_matrix.data.f / 1e9,\n",
    "    -np.angle(S11_not_comp),  # Follow engineering convention exp(j omega t)\n",
    "    \"--r\",\n",
    "    label=\"Single-cell\",\n",
    ")\n",
    "ax2.plot(\n",
    "    s_matrix.data.f / 1e9,\n",
    "    -np.angle(S11_comp),  # Follow engineering convention exp(j omega t)\n",
    "    \"--g\",\n",
    "    label=\"Compensated-single-cell\",\n",
    ")\n",
    "ax2.set_xlabel(\"Frequency (GHz)\")\n",
    "ax2.set_ylabel(r\"$\\angle~S_{11}$ (rad)\")\n",
    "ax2.set_xlim([0, 50])\n",
    "ax2.grid()\n",
    "ax2.legend()\n",
    "plt.show()"
   ]
  },
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   "metadata": {},
   "source": [
    "From the scattering parameter results, we see that the parasitic inductance slightly shifts the results produced by the unmodified single-cell version from the original blue curve. In addition, by compensating for the parasitic inductance in the network, we are able to almost completely undo the parasitic effects associated with the wire connections. Note that it is not possible to compensate for the parasitic capacitance in this network using this approach."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "[1] A. Taflove and S. C. Hagness, *Computational Electrodynamics The Finite-Difference Time-Domain Method*, Third Edition,\tArtech House, 2005.\n",
    "\n",
    "[2] David M. Pozar, \"Microwave Network Analysis\" in Microwave Engineering, 4th ed. 2011, ch. 2."
   ]
  },
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