{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "23dcf333",
   "metadata": {},
   "source": [
    "# Suppressing artificial reflections with absorber and PML boundaries\n",
    "\n",
    "In an FDTD simulation, Maxwell's equations are discretized on a finite grid. Simply truncating the computational domain at a boundary can cause outgoing electromagnetic waves to reflect back, contaminating the solution. To prevent these artificial reflections, absorbing boundary conditions are introduced. These boundaries are artificial regions placed at the edge of the computational domain to simulate \"open\" or unbounded space. Their purpose is to absorb outgoing waves as if the domain extended to infinity, while minimizing reflections back into the interior. Tidy3D offers two primary types of absorbing boundaries: **perfectly matched layers (PML)** and **adiabatic absorbers**.\n",
    "\n",
    "PML uses a mathematically derived \"coordinate stretching\" or anisotropic material approach to achieve a perfect impedance match at the boundary. By design, PML ideally causes zero reflection at the interface and then exponentially attenuates the wave within the absorbing layer. In contrast, an adiabatic absorber employs a smooth spatial transition in material parameters (e.g., conductivity, permittivity) from the physical domain into an absorbing medium. It aims to minimize, rather than strictly eliminate, reflections by gradually grading the absorption profile so the wave encounters no abrupt transitions.\n",
    "\n",
    "Although PML is more effective at eliminating artificial reflections, it can lead to simulation stability issues, particularly when the structure in the PML is not translationally invariant, the medium is dispersive, or when structures are too close to PML such that evanescent fields leak into it. The adiabatic absorber, on the other hand, is very similar to a physical absorbing region that is purely passive and always stable. Therefore, the choice of absorbing boundary condition depends on the specific simulation setup. If a simulation is diverging with PML, switching to the adiabatic absorber can often resolve the issue. This [waveguide to ring coupling example](https://www.flexcompute.com/tidy3d/examples/notebooks/WaveguideToRingCoupling/) is a canonical example.\n",
    "\n",
    "Many users are concerned that the higher artificial reflections from the adiabatic absorber can result in inaccurate simulation results. However, using a larger number of layers to create a more gradual ramp-up of absorption can effectively mitigate this, albeit at a higher computational cost due to the increased number of FDTD cells required. Thus, achieving the right balance between accuracy and computational cost is crucial. This notebook aims to benchmark the undesired reflections from absorbing boundaries and guide users in selecting the most appropriate settings for their simulations and objectives."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a9af69b4-6e08-4837-ad9c-5d89209783fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f0d5e52",
   "metadata": {},
   "source": [
    "## Simulation Setup\n",
    "\n",
    "In this benchmark, we will focus on the telecommunications wavelength and examine the reflection of a basic waveguide mode at the boundary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "88e63fbb-f22d-413d-8a8b-e93f1ce29e37",
   "metadata": {},
   "outputs": [],
   "source": [
    "lda0 = 1.55  # Central wavelength\n",
    "freq0 = td.C_0 / lda0  # Calculate the frequency corresponding to the central wavelength\n",
    "\n",
    "# Create an array of 21 evenly spaced wavelengths between 1.5 and 1.6\n",
    "ldas = np.linspace(1.5, 1.6, 21)\n",
    "\n",
    "# Calculate the frequencies corresponding to each wavelength in the ldas array\n",
    "freqs = td.C_0 / ldas\n",
    "\n",
    "fwidth = 0.5 * (np.max(freqs) - np.min(freqs))  # Calculate the source frequency width"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9147e335",
   "metadata": {},
   "source": [
    "We use a standard single-mode silicon waveguide with a width of 500 nm and a thickness of 220 nm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4a641367-df95-41b4-9afb-fcef8472427d",
   "metadata": {},
   "outputs": [],
   "source": [
    "w_wg = 0.5  # Width of the waveguide\n",
    "t_wg = 0.22  # Thickness of the waveguide"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "665c3dd7",
   "metadata": {},
   "source": [
    "Define silicon and oxide as simple nondispersive media."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6067005e-48a4-4d59-a321-a24cefee0402",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_oxide = 1.44  # Refractive index of oxide\n",
    "\n",
    "oxide = td.Medium(permittivity=n_oxide**2)\n",
    "\n",
    "n_si = 3.47  # Refractive index of silicon\n",
    "si = td.Medium(permittivity=n_si**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03ed2e3d",
   "metadata": {},
   "source": [
    "Create a basic waveguide structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bf443a62-0dba-43ef-9143-23d77cce387d",
   "metadata": {},
   "outputs": [],
   "source": [
    "waveguide = td.Structure(geometry=td.Box(size=[td.inf, w_wg, t_wg]), medium=si)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28b916bd",
   "metadata": {},
   "source": [
    "Next, we define the size of the simulation domain. For this benchmark, the domain can be relatively small.\n",
    "\n",
    "Afterward, we create a [ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.html) to excite the fundamental mode in the waveguide. To measure the reflection from the boundary, we set up a [ModeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeMonitor.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e940f023-67f8-47c4-b903-29ee02d81ce6",
   "metadata": {},
   "outputs": [],
   "source": [
    "l_x = 3 * lda0  # Calculate the simulation domain size in the x-direction\n",
    "l_y = w_wg + 2 * lda0  # Calculate the simulation domain size in the y-direction\n",
    "l_z = t_wg + 2 * lda0  # Calculate the simulation domain size in the z-direction\n",
    "\n",
    "buffer = lda0 / 2  # Define a buffer spacing\n",
    "\n",
    "# Create a mode source for excitation\n",
    "modesource = td.ModeSource(\n",
    "    center=[-l_x / 2 + buffer, 0, 0],\n",
    "    size=[0, 3 * w_wg, 5 * t_wg],\n",
    "    source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
    "    direction=\"+\",\n",
    "    mode_spec=td.ModeSpec(num_modes=1, target_neff=n_si),\n",
    ")\n",
    "\n",
    "# Create a mode monitor to measure reflection at the boundary\n",
    "modemonitor = td.ModeMonitor(\n",
    "    name=\"modemonitor\",\n",
    "    center=[l_x / 2 - buffer, 0, 0],\n",
    "    size=modesource.size,\n",
    "    freqs=freqs,\n",
    "    mode_spec=modesource.mode_spec,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08d7e3f4",
   "metadata": {},
   "source": [
    "## Adiabatic Absorber\n",
    "\n",
    "We start by defining a function called `make_sim`, which is intended to create a simulation based on three parameters: `num_layers`,  `use_absorber`, and `grid_resolution`. The `num_layers` parameter specifies the number of absorbing boundary layers in the simulation, while the `use_absorber` parameter is a boolean that determines whether to use adiabatic absorbers or PML. `grid_resolution` specifies the number of grid points per material wavelength.\n",
    "\n",
    "This function will facilitate a parameter sweep to quantitatively analyze reflection as it relates to the number of boundary layers and grid resolution. In general, using a larger number of layers allows for more gradual absorption and less reflection. For an adiabatic absorber, the reflection also depends on the grid resolution since it affects the layer thickness. For simulations with a finer grid resolution, usually a larger number of absorbers is needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "95895116-be87-44d7-8f62-032b50fd278e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_sim(num_layers, use_absorber=True, grid_resolution=25):\n",
    "    # Create a simulation object using the tidy3d library\n",
    "    # Parameters:\n",
    "    # num_layers: Number of layers for the absorbing boundary\n",
    "    # use_absorber: Boolean to determine if absorbing or PML boundaries are used in the x direction\n",
    "    # grid_resolution: Minimum number of grid points per material wavelength\n",
    "\n",
    "    sim = td.Simulation(\n",
    "        size=[l_x, l_y, l_z],\n",
    "        symmetry=[0, -1, 1],\n",
    "        # Define boundary specifications based on whether absorption is enabled\n",
    "        boundary_spec=(\n",
    "            td.BoundarySpec(\n",
    "                x=td.Boundary.absorber(\n",
    "                    num_layers\n",
    "                ),  # Use adiabatic absorber boundary with the specific number of layers in the x direction if use_absorber is True\n",
    "                y=td.Boundary.pml(),\n",
    "                z=td.Boundary.pml(),\n",
    "            )\n",
    "            if use_absorber\n",
    "            else td.BoundarySpec(\n",
    "                x=td.Boundary.pml(\n",
    "                    num_layers\n",
    "                ),  # Use PML with the specified number of layers in the x direction if use_absorber is False\n",
    "                y=td.Boundary.pml(),\n",
    "                z=td.Boundary.pml(),\n",
    "            )\n",
    "        ),\n",
    "        grid_spec=td.GridSpec.auto(min_steps_per_wvl=grid_resolution, wavelength=lda0),\n",
    "        run_time=1e-12,\n",
    "        medium=oxide,\n",
    "        sources=[modesource],\n",
    "        monitors=[modemonitor],\n",
    "        structures=[waveguide],\n",
    "    )\n",
    "\n",
    "    return sim"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd5ea431",
   "metadata": {},
   "source": [
    "Create a sample simulation and visualize the setup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d9c349a4-8385-4c26-aba9-2be47d126276",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a simulation object with 40 layers of absorber\n",
    "sim = make_sim(num_layers=40)\n",
    "\n",
    "# Plot the simulation results at z=0 to visualize the simulation\n",
    "sim.plot(z=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b96cbbbe",
   "metadata": {},
   "source": [
    "### Reflection as a Function of Number of Layers\n",
    "\n",
    "By default, the adiabatic absorber contains 40 layers. Here, we keep the grid resolution the same and generate a list of absorber layer counts ranging from 10 to 100, with increments of 10. This list will enable us to explore how varying numbers of absorber layers influence reflection. Simultaneously, we will monitor the number of FDTD cells for each simulation, as this could directly affect both the FlexCredit cost and the simulation time.\n",
    "\n",
    "To conduct the parameter sweep, we will create a dictionary of simulations and then use a [Batch](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.container.Batch.html) to execute them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4be08323-c191-41c2-86c8-e8e96d00c870",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3e01477f0b104094b4baf0e85c5128f7",
       "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\">16:39:52 CEST </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span> tasks.                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:39:52 CEST\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m10\u001b[0m tasks.                     \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\">16:40:02 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.251</span> for the whole batch.               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:40:02 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.251\u001b[0m for the whole batch.               \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>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after   \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>the Batch has completed.                                          \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after   \n",
       "\u001b[2;36m              \u001b[0mthe Batch has completed.                                          \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "61d95b1ba06e494bbdad7f5b90ea99e6",
       "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\">16:42:03 CEST </span>Batch complete.                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:42:03 CEST\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                   \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": "ae316821234c40be9d977cc522738147",
       "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": [
    "num_layers_list = list(\n",
    "    range(10, 101, 10)\n",
    ")  # Create a list of absorber layer counts from 10 to 100 in increments of 10\n",
    "num_cells_list = []  # Initialize an empty list to store the number of cells for each simulation\n",
    "\n",
    "# Initialize an empty dictionary to store simulation objects with their respective layer counts as keys\n",
    "sims = {}\n",
    "\n",
    "# Loop over each number of absorber layers to create simulations\n",
    "for num_layers in num_layers_list:\n",
    "    sim = make_sim(num_layers)\n",
    "    # Append the number of cells (converted to millions) to the num_cells_list\n",
    "    num_cells_list.append(sim.num_cells / 1e6)\n",
    "    sims[f\"{num_layers} layers\"] = sim\n",
    "\n",
    "# Create a batch of simulations\n",
    "batch = web.Batch(simulations=sims, verbose=True)\n",
    "\n",
    "# Run the batch of simulations and save results to the specified directory\n",
    "batch_results = batch.run(path_dir=\"data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad733e65",
   "metadata": {},
   "source": [
    "After completing the simulations, we can extract and plot the reflection spectrum. The results clearly show that increasing the number of adiabatic absorber layers reduces the reflection. Notably, the default setting of **40 layers** results in a reflection of **-25 dB**, while increasing to **80 layers** reduces the reflection to approximately **-60 dB**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f7da396f-71d1-4cc1-be69-fa18a43eb027",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as mcolors\n",
    "\n",
    "# Normalize the colormap based on the minimum and maximum values in the num_layers_list\n",
    "norm = mcolors.Normalize(vmin=min(num_layers_list), vmax=max(num_layers_list))\n",
    "colormap = cm.viridis  # Use the 'viridis' colormap for plotting\n",
    "\n",
    "# Iterate over each number of absorber layers in the list\n",
    "for num_layers in num_layers_list:\n",
    "    # Calculate the reflection values by taking the absolute square of the mode amplitude values from the simulation results\n",
    "    reflection = (\n",
    "        np.abs(batch_results[f\"{num_layers} layers\"][\"modemonitor\"].amps.sel(direction=\"-\").values)\n",
    "        ** 2\n",
    "    )\n",
    "    # Get the color corresponding to the current number of layers using the normalized colormap\n",
    "    color = colormap(norm(num_layers))\n",
    "    # Plot the reflection values in dB against the wavelengths, using the specified color and label\n",
    "    plt.plot(ldas, 10 * np.log10(reflection), color=color, label=f\"{num_layers} layers\")\n",
    "\n",
    "\n",
    "plt.ylim(-100, 0)\n",
    "plt.xlabel(\"Wavelength (μm)\")\n",
    "plt.ylabel(\"Absorber reflection (dB)\")\n",
    "plt.legend(loc=\"upper left\", bbox_to_anchor=(1.05, 1), borderaxespad=0.0)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53cf4c52",
   "metadata": {},
   "source": [
    "As discussed, increasing the number of layers can help reduce unwanted reflections, but it may also significantly raise the cost. In the accompanying plot, we show the reflection at the central wavelength (1550 nm) alongside the number of FDTD cells. In very small simulations, such as this benchmark model, using more absorber layers might not increase the cost since all simulations remain below the minimal cost threshold. However, for larger simulations, the cost difference between using 100 layers and 40 layers can be substantial. Users must balance accuracy and cost based on their objectives and tolerance levels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "286cb826-e679-4f87-b76f-3cb7925db316",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6NbELF4Aff5Q1Tnrh4YC3t+Hr9I9T2xFj4MCBiIiIiD/CwsKs1GuydcWKyfKSAweAd99V248dAxo0kBuCAwe07CEROZ24OFm0W726GsBlzw6sXi3tDOCcmqZB3IAByW9DlvDQF5zWu3lTLqgtW8rm52/K3d3dIIslS5Ysb35SsmuVKgFbtkh5krJl1fbt24F33gE++CDp7yURkcX99x/QtKks3n35UtqqVpXF9I0ba907sgGaBnHBwcCZM6kfhQurr791S0ZIqlRJmrDg4yPbMCWkf+zjkwZfDDkUnU6mTw8fltqZb72lPrdihewM0bUrcOOGlr0kIoe1e7fcRa5dq7YNGCDrhFjYkl7RNIjLlUumsFI79CPFN29KYeTy5WWrs8RblQUFATt3GmYUhoYCRYsat58tUXJcXGQZSliYZKzqbwji4oD//U+Cu379ZN0lEdEbi4uTRbi1aql3iTlzyiLwkBApgEn0il2sidMHcAUKyBKAe/dknVvCtW5t2kjA16WLlCFZskSyrfv00bLn5CjSpwe+/FLWY44ZwxpzRGQFd+8CDRtK/azYWGmrWRM4cQKoX1/r3pENsosgLjRULp5btshG5nnyqIeelxewaZOUzSlfXqZqhw6VfYCJLCVTJikddPGijMDpSzJFRLDGHBG9ge3bZWP1TZvksU4HDBkCbN4M5M2rde/IRtltiRFrccYUZTLfjRtSVHrOHJkF0StSRAoKf/xx0ql/IqJ4sbHA6NHA99+rbyLe3sD8+UDdulr3zq444/WblxeiN5A/v+wzffq0ZK3qXbwItG4t+ytv3ChVOYmIDISHSwbVsGFqAFenjmSfMoAjIzCII7KAYsWkSHBKNebq1GGNOSJKYPNmmT7dulUeu7gAI0bIXR9LKpCRGMQRWVBKNea2bZMacx9+yBpzRE7t5UtZ61avnloHK29eCeaGDAHSpdO6h2RHzArirl0Ddu2SC9XRo5KhR0QiYY25RYtkfZze8uWsMUfktG7elGH5UaPUNRb168v0ac2aWveO7JDRQdyVK8C33wJ+fkChQvL71rChrPnx8gLeew9YtsxwcTeRM3NxAT75RIpW//ILa8wRObUNG2T6dOdOeZwundR9W7dOiqYSmcGoIK5nT6B0aSnfMWqUFD6NiABevJB1mevWAdWqSUmPUqWAQ4es33Eie5E+PdCtm5TJGT0a8PSU9oQ15kJCWGOOyCHFxMhOCw0byjZaeJURtWOHtDN9nd6AUSVGBg4E+vYFcuR4/Qk3bACePjXM1LMnzpiiTGnr/n1g7Fjgxx8NlyL4+EiSWpcuLMpO5BCuX5fh+L171bbGjYG5c427oJJJnPH6bdQtQEiI8b9vDRrYbwBHlBZy5AAmTADOn5eATX8jHh4uI3aBgbLjCJcmENmx1atl+lQfwLm6ytD7qlUM4Mhi3ngc98ULICrKMp0hcia+vlJj7tQpwxufCxfk5r1iRSnezhpzRHbkxQvZMqhpU3XBq5+fZAMGB0vmE5GFmBTEzZkD9OgBLFggjwcOBLJkURMb7t+3Ui+JHFhAgNSY279f9gjWO3pUEtfq1AEOHtSyh0RklCtXgOrVgcmT1bbmzaVg5DvvaNkzclBGB3GjRwPdu0uNq549Zdpn7lypTTh2rLQPHmzdzhI5ssqVpVTUhg1Ja8xVrgx89BFrzBHZrBUr5A9Xf8eVPj0wdarUFcqWTevekYMyOoibO1fKIoSGSn24mTOBn36SsiP9+snjdeus21kiR6fTyehbcjXm/vpLasx99hlrzBHZjOhoGdn44APg0SNpK1xY1sL17MnpU7Iqo4O4a9ekjAggteFcXYESJdTnS5UCbt+2RheJnI++xlxYmNSY8/aW9rg4WUfn7w/0788ac0SaungRqFpVUs31WraUtRAVKmjZM3ISRgdxMTGAu7v62M3NsAyCqysQG2vp7hE5Nzc3Wbpw8aJhjbnnzyXDVV9j7tkzrXtK5GSWLQPKlQOOHJHH7u5yx7VkiSwUJ0oDJiU2hIUBJ0/KoSiyPkf/+PRp63WSyNllygQMGgRcuiQJbvobqogIaS9TRhIjiMjKnj8HvvoK+PhjIDJS2vz95Q+wWzdOn1KaMqrYL15N7+h0yZc70LfrdPY/GueMxQLJ/ly/DgwfLmtV9fXkXFxkferw4YCHh9Y9JHJA//4rwduJE2pbmzbA9OlSqoE05YzXb6NH4i5fllGAy5eTHvr2S5es21kiEr6+kmh08iRQqZK0xcUB48YB5ctLYgQRWdDChfLHpQ/gPDyAWbOA+fMZwJFmXI19oZ+fdTtCRKYrXhzYs0cKwQ8bJnVGw8KkJNXAgcCQIbKujojM9PQp0KuXZBTpFSsGLF0KlCypZc+IjAviTp40/oSlSr1Bb4jIZK6uso9248ZAhw6SGBcbC4waJTv8zJsna+aIyERnzsj06alTaluHDsDPP8tCVSKNGRXElSljuO4tNfa+Jo7IXpUoIWurQ0KAkSOBly/lBqxiRWDoUAn0EmaUE1Eq5s2TBIanT+VxxoySfdqhg9Y9I4pn1Jq4hOve/voLKFRIfpePHZPjl1+kKOlff1m/w0SUsvTpJWA7eFCd6Xn5UtqCggwHFIgoGU+eAB07yqEP4EqUAA4dYgBHNseokbiE6+FatgSmTQPef19tK1VKFloPGSLbxBGRtsqWleQG/bZ4sbFSzqp8eeD774G+fWUalogS+OcfmT5NuL9d166yfVbGjFr2jOxdXBywYwewaxdw9arcIOTKJW/WdetKEGUGk+rE4dXveKFCSdsLFZIF1URkG9zcZF3cvn1AQIC0vXghCQ/VqnEfVqJ4iiKJC5UqqX8YmTMDCxZIBioDODLXs2fyRuzrK6Nf69fL9mzp0gEXLkhGWqFC8pwZxT5NDuICAmTNzYsXatuLF9Kmv1AQke2oWFGSHfr3l1pyAHDggKx1nTSJ61jJyT1+DHz6qWxK/Py5tJUuLUPXbdpo3Tuyd2+/LYuTZ82S4tD79snas/nzZcP5a9dkS57q1WWvxVmzTDq90cV+9Q4eBJo0kRsXfSbqyZOS8LB6tVqzyl45Y7FAch779smynvPn1baqVYE5c6ToPJFTOX5cpk8T/kF06wZMnsyK2XbIJq/fZ84YP8IVEyNBXZEiRp/e5JG4SpUkyWHUKAniSpWSPR0vXbL/AI7I0QUFyXXrm2/UTPM9e2Tg4ccf1d0fiByaogC//ioFFfUBnKen7Hv6yy8M4MhyTJmiTJ/epAAO5ozEOTqbjOSJrGDnTqBTJ8OdVmrVAn77Lfl1r0QOISJCpk6XLVPbypeXAM7ECyjZFru4fj96JFOad+8mvWtu397k0xmVn7Z/v9ywGOPpUylFUry4yX0hojRUo4Yshfj2W6ldCgDbt0tpkokTgS++4F7e5GAOHwZatTK8c+nZExg/HnB317Jn5AxWrwbatgWiomTkN+EbrE5nVhBn1HRqu3ZA/fpy4/LkSfKvCQsDBg2SG5kjR0zuBxFpIFMm4KefgC1b1FJCT57IsqB69WR5BpHdUxSpjVWlihrAZc0KLF8u5UMYwFFaCA4GOneWIO7RI+DhQ/V48MCsUxoVxIWFAY0aAYMHy+998eLAe+9JgkO1akDOnEC5cjICt2mTWcEkEWmodm0Zlfv8c7Vt82YZlfvtN7kGEtmlhw+BDz+U/U9jYqStUiWpVN+ihda9I2dy86aM/FqwZI1RQVz69PJ5z52T7LbPPpMC1vnyyRqaGTOAW7eARYu4HzCRvfL0lL/lDRvkbxuQjPguXWRf1ps3te4hkYkOHJBiqitWqG3BwVJwtWBBLXtGzqh+fZnStyCTa7ZXqCAHETmm+vVle67evYG5c6Vt3Tq5cZs2TUpqca0c2TRFkTIhAwbIvnMAkD27/EI3aaJ178hZNWoE9Osn05slSybdzLppU5NPyezUROwiu4UojaxeLVOs4eFqW7NmMmLn7a1lz4hScP++7Hu6Zo3aVqUKsHix2VsbkX2w+eu3SyqTnzqdWZXXTa4TR0TOo0kT4PRpw8L1f/8t62KXLNGyZ0TJ2LNHpk8TBnADBkjaNQM40lpcXMqHmVvnMIgjolRlzy5bSP71l+zXjFeDHZ98IsXu793Tuofk9OLigLFjgZo1gevXpS1nTtmnMiQk6bQVkYOwmyCuaVOgQAEppJ0nj5Q9uXXL8DUnT8r2Yx4ectM1frxWvSVyPB98IKNyH32kti1bJqNyy5dr2TNyavfuyVqjgQPV0YwaNWRrkgYNtO4dkaEdO2SK46235GjaVBJtzGRyEJewRmJaevddYOlSyZD96y/ZLzbhxSQyUupa+flJnboJE4Dhw4GZM7XpL5EjypVLArfFi2WEDq+uoR9+KDUszSx1RGSeHTuAMmUkpRqv1hUNGSKFD/Up1kS2Yv58oG5dKTHSs6ccGTIAdeoACxeadUqTExtcXGTEuksXCaK02mJu1SqgeXMgOlpGyn/9FfjuO1mA7eYmrxkwAFi5Ejh7NuXzREdHIzo6Ov7xzZs3ERgYaLsLI4lsRHg48OWXskZOz8cHmDVLSpIQWU1sLDBmjNyp67cu8vZWL5LklGw+sSEgQDLFevc2bJ88Wd44z5wx+ZQmj8QdPSqb3vfpI2/YX3wh24ClpQcPZI1OlSrqUod9+2QEXR/A4VWphHPnpNZjSkJCQuDl5RV/BAYGWv8LIHIAPj5SfuuPP6QIOF4Fdk2aSHLgo0da95AcUni4vLkPHaoGcLVry/QpAziyZZcuJV/ipmlT2S3BDCYHcWXKyC4lt25JJffbt2XXhhIlJJi05iLnb7+VbYJy5JDtgBKOAISHJy15oH+csDxCYgMHDkRERET8ERYWZqXeEzkenU7qxp0+Dbz/vto+b568J+hnuYgsYssWuQht2SKPXVyAESNkqyAfH617R5Q6X1/1dzehzZvNzp42O7HB1VUWOi9bBowbB1y4APTtK/1o316Cu9cZMEAuAqkdCadC+/WTnVI2bQLSpZPP86ZV7tzd3eHp6Rl/ZMmS5c1OSOSE8uaVqg6//SY7P+DVDjMNG8rsQWSk1j0kuxYbCwwbJvs93rkjbXnyyAVxyBC5IBDZuuBgWQfXrZtMYfzxh6xJ+eYbCaDMYHax38OH5Q178WIZHevQQdbJ3bgBfP+9vGm/bpr13j0pVZCawoUNp0j1btyQgHHvXiAoSAK6yEhZA6e3bZuMsj94AGTLZtzXZfNz6kQ27to1oGtXIDRUbStQQN4v6tTRsmdkl27dkkKFO3aobfXrA7//DuTOrWXPyMbYxfV7xQpg0iR1/VtAgIxQNWtm1ulM3nZr8mRgzhxZa/b++/J39P77aiHiQoVkZxNjtqXLlUutO2Uq/VIIfU5CUJAkNsTEqOvkQkOBokWND+CI6M0VKABs3CiZ4cHBwJMnEtjVrQt89ZWM3GfOrHUvyS5s3Cjz9f/9J4/TpQNGjQL690+9+j2RrWrRQg4LMfmv4Ndf5abo6lUZ9WrcOOnfUu7cwP/+Z7E+4sAB4KefZN3q1avA1q1A69ZAkSISvAHSJzc3GQ08fVqqyU+dKgkYRJS2dDpJevrnH6BWLbX9l1+A0qWBnTu17B3ZvJcvpe5bgwZqAJc/v+y8MGAAAziiV+xi79R//gF69QJOnJC7+jx55G978GDDUkAnTwLduwOHDkmx7h49JBnCFHYxHEtkR+LigJ9/lr/FZ8+kTaeTpSFjxkjJJKJ416/LXfqePWpbo0aSLZMjh5Y9Ixtnk9fv7NmBf/+VoCRbNnnzS4kZhTZNDuLmzJGpkJYtDduXLQOePpW1cfbMJn8JiBzAhQtSeiThtdnfX5ZfVKmiZc/IZqxdKwuc9RczV1fZTqt3b46+0WvZ5PV73jzZo9DdXT5OjRkBlMl/FSEhElAmlju33FUTESXnrbdkbfqkSfJ+BgDnz8tWef37A8+fa91D0kxMjGTnNW6sBnB+frIdUXAwAziyjps3Zc1ljhyyc0LJkpK1mZrt24Fy5eRN7K235C40NR06qG94HTqkfpjB5L+Ma9ckeSExPz95jogoJenSyTrV48eBSpWkLS5OtskrV05KCJGTuXJFIvlJk9S25s3ll+Gdd7TsGTmyhw+BqlUlE3L9eiAsTH4HU8uEvHxZpvbffVfexL75RlLxN25M+f9ERhp/mMHk7NTcuWXtWeLs0xMnuFyBiIxTrJhMq06cKOW/XryQjPuqVSXjPeG+yOTAVq4EOnVSt/dIn15+KXr0SH3tENGbGjdO6pTNmaO2JTdCldD06fIa/Q1HQACwezfwww9S9iY5WbO+/ndZUeQ1sbGmfhWmB3GtW8uC5CxZZJsrvNqDuFcvmfYlIjKGq6skGjZuLDMJR49K4kPLlsDIkVIyiNdxBxUdLXPo06apbYULS1mBChW07Bk5gMePHyMywciWu7s73PVTmnqrVkng1bKlBDH58kkNpM8+S/nE+/Yl3dqtfn0ZkUvJtm1mfx3GMDmIGzlSRr/r1JE3YbyaDmnfnmviiMh0JUpI0e7PP5dROECK8J85I6WKPDy07iFZ1MWLQKtWwJEjattHHwGzZwNeXlr2jBxE4j3Qhw0bhuHDhxu+6NIlqZnWpw8waJCUtejZU2qVpbQ+LaX9PSMj5Q40Q4ak/6dmzTf+elJjchDn5iY3SyNHyhSqfi2gn591OkhEjs/dXdYHBwZKeTBFARYulOv9ypXcFtNhLFsma4j0oyTu7jIV9eWXHHYliwkLC0O+BPXHkozC4dXoU4UK6uhT2bLAqVMyZWrJMhsnTxr/2lKlTD69yUGc3ttvy0FEZAk6ndSSK1oUaNtWShYdOCAJEKtXS5FgslPPn8uIx6+/qm3+/sDSpbKhPZEFZcmSBZ76TZxTkieP3DUmFBAA/PVXyv/Hx0fdu1fvzh3ZMDq5UThAfr91utdv9J5Wa+JiY+WOecsW4O5ddfsrva1bTe4DEVG85s1lrXDTprJH8vXrkvCwYIHZ2wuSls6fBz7+WLL59Fq3BmbMkMXVRFqoWlX2D03o339Tn1YMCgLWrTNsCw1Vt45KzuXLb9jR1JkcxPXqJUFco0ayloUj4ERkaWXLAgcPSkB38KDs1NKihdR97deP7zt2Y9EiWewYFSWPPTyAH3+U/RH5QyQt9e4tVcbHjJGbjIMHZcPnmTPV1wwcKLXk9It1v/xS9gDt3x/o3FlGrZYulSLVKbHyWjOTg7jFi6XP779vnQ4REeHVbMf27fJeuXixzEZ8+60kPEyfrtbPJBv07JksEp89W20rVkwuHiVLatkzIlGxIrBihQRqI0ZI6ZApU2Qth97t24YFcAsVkoCtd2/ZnD1/fvkdT6m8CF5lwTZsKOVzVq1KvU9Nm5r8ZZi87VbevPLG6qjr4Wxy2w4iJ6Yokkg1bJjaVq0asHw5kCuXlj2jZJ05IyMbp06pbR06yAa6mTJp2TNycDZ5/XZxkazW3LlT33nEzDVxJu/YEBwsAahpoR8RkXl0OmDoUMmK15cb2b1bEh4SxglkA+bNk4w//Q8mY0ZZfzN3LgM4ck5xcRLA6T9O6TAjgIM506m7d0vtuvXrgeLFZYQwoeXLzeoHEVGqPv5YZjOaNZNZjitXZEnL4sVc3qG5J0+A7t0NN/guXlymTxNnABKRxZgcxGXNKguMiYjSWsWKUpOzaVPZ4eHxY6BJE9kFp1cvrpXXxKlTEmGfOaO2de0qUzYZM2rZMyLbc+iQjIQlV95j8mSTT2dyEJdwmzEiorSWLx+wc6css/rrL3kf7N1b9q/+6ScpSE5pQFGA334Dvv5a6sABQObMUjqkTRute0dke8aMAQYPlmKY3t6Gd51m3oGaVez35UtJbrh4Uf5Ws2QBbt2SeneZM5vVDyIio2XKJDN1Q4cCo0dL26xZUpLszz+BHDm07qGDe/wY6NZNivfplS4tPxRHzXojelNTp8qNT8eOFjulyYkNV69KhnizZrIE4t49aR83Dujb12L9IiJKlYsLMGoUMH++Wm5k+3bgnXeAs2e17p0DO3FCkhcSBnBffgns388Ajig1Li5SZNiSpzT1P/TqJX+/Dx8a7jLRooXs4kBElJbatpUlJvoEsAsXJJALDdW6Zw5GUaRAX+XKUtkekGmYJUtkOy196jARJa93bym1Y0EmT6fu2gXs3Zt03UnBglLYmIgorQUFScH1pk1lv+mICKmvOW0a8NVXWvfOAUREyM4LS5eqbeXLSwBXpIiWPSOyH337ynZXRYpI1rYFynuYPBKXUjmTGze4DR4RacfPD9izRy16HhsrSz6+/lrW8ZKZjhwBypUzDOB69JBvNgM4IuP17CnTBm+/LQt3vbwMDzOYPBJXr57sTKHfXkynk23xhg1jrSYi0lbmzHIzO3AgMGGCtP38s8z+LV0qJZLISIoi6b59+wIvXkhb1qyyMJt1pohMN2+epNQ3amSxU5o8EjdpktyABQZKVnmbNupU6rhxFusXEZFZ0qUDxo+XWEM/WxEaKuvkLlzQund24uFD4MMPZeRAH8BVqgQcO8YAjshc2bNbfPTa5CAuf35JTho0SNbolS0LjB0rf9v6hcVERFrr1EmSrfTlRs6dkzX527dr3TMbd+CATJ+uWKG2BQfLguiCBbXsGZF9Gz5cpi2fPrXYKc2qE+fqCnz6qcX6QERkFdWrS8JDkyZSDPjBA+C99ySZsmtXrXtnYxQF+OEH4Ntv1UWE2bPLvqdNmmjdOyL7N22aFNj19pYbosSJDUePmnxKk4O4339P/fn27U3uAxGR1RQuDOzbB3zyiez5/PIl8NlnEtRNmCDTr07v/n0pQLpmjdpWpQqwaBFQoICWPSNyHM2bW/yUOkVRFFP+Q7Zsho9jYmRk0M1Ntsl78MDCPUxjN27cgK+vL65fv478+fNr3R0ispCXL4F+/SQxS+/99yVO8fTUsmca27tXItzr19W2b78FRo5MOlJAZMOc8fpt8pq4hw8Nj6goWWtSrZq8GRIR2SJXV5ktnD5dPgaAdetkwOnyZa17p4G4OMlGq1FDDeBy5pThyrFjGcARWYJp42QmMzmIS46/v/zN9+plibMREVnPF18AGzeqswqnT0vi5e7dWvcsDd27J2UOBgxQC3/WqAEcPw40aKB174gcR/HiwOLFapZ3Ss6fl/2Ix4416fQWCeLw6i731i1LnY2IyHpq15YkTP1Wn//9B9SpI2WcHN7OnUCZMsCGDfJYpwMGD5ZU3nz5tO4dkWP58Udg4kTAxwdo1UoW4i5YIPXiZs8G+vSRu8gyZWRdR7duJp3e5MSGVasMHysKcPu21IS08L6uRERW4+8ve7Z//DGwebPcKHfsCJw5A4wZI3tVO5TYWCAkREocxMVJm7c3MH8+ULeu1r0jckx16gCHD8tQ/5IlEsBdvQo8eybLF8qWlYzQtm2TJh0YweQgLnFyhU4H5Mold7aTJpn8+YmINJMtm6yL69VLyo4Askzs7FmJbTJn1rqHFnLnjlwktmxR22rXlguKj4+WPSNyDtWqyWFhZu2dmvCIjQXCw4GFC4E8eSzePyIiq0qfHvjlF5n10I++/f23vN9eu6Z17yxgyxagdGk1gHNxAb7/Hti0iQEckZ1ztAkDIiKzfP21JGbq96E+cUKWquzfr3XPzBQbK1On770nI3GA3Glv2QIMHcoCeUQOwOTp1D59jH/t5Mmmnp2ISDv16klh4CZNpLD6nTtArVrAnDlA69Za984Et27J9GnCPcbq1QP++IP7IxI5EJODuGPH5IiJAYoWlbZ//5WbunLl1NfpdJbsJhFR2ggIkMzVDz8EduwAoqOBNm0k4WH4cDtIeNi4EWjXTsqIAPLmPHKkFPC1+c4TkSlM/otu0kTKCd24Idt8HT0qdSLffRdo3BjYtk2OrVut0+HoaMnE1emkpFFCJ0/KXokeHoCvLzB+vHX6QESOLUcOWTLWpYvaNnKkVAiw4N7VlvXyJTBokNR50wdw+fLJaNzAgQzgiByQyX/VkyZJlnrCTNhs2YBRo9ImO7V/fyBv3qTtkZEyW+DnBxw5IqVYhg8HZs60fp+IyPG4uQGzZsmyEP3Mwp9/yk3szZta9y6RGzfkTjokRG1r1EjudK2QEUdEZjh6FPjnH/Xx339LyY9Bg15fDDgFJgdxkZHqTV5C9+4Bjx+b1QejrV8vd8cTJyZ9bsEC+R789psUSP7kE6Bnz9evy4uOjkZkZGT88djaXwQR2Q2dDujdG1i9Wi03cuSIJDwcOaJ1715Zu1amJ/RbTri6yl3sqlVSh4qIbMMXX8j6MwC4dEkClYwZgWXLZITKDCYHcS1aAJ06AcuXy83fjRtSeLhLF+CDD8zqg1Hu3AE++0zW5WbMmPT5ffvkDtnNTW2rX1/2dX34MOXzhoSEwMvLK/4IDAy0zhdARHarUSN5jylYUB7fuiVLN/78U8NOxcQA/frJOpb796WtQAFg1y6gb19OnxLZmn//lRsuQAK3GjWkPtvcuRJImcHkv/Lp04GGDWWhr5+fHG3ayDKMX34xqw+vpShSSf3LL4EKFZJ/TXi4FB9PSP84PDzlcw8cOBARERHxR1hYmAV7TkSOokQJSXjQ70zz7BnQsqUsJbHyHtdJXb0qF4CE0xLNmknW2TvvpHFniMgoiqLulrJ5M/D++/Kxr6/s/WcGk4O4jBklWLt/X81UffBA2jJlMu1cAwbIdEVqx9mzUoTz8WNZm2tp7u7u8PT0jD+yZMli+U9CRA4hd24ps9a+vdo2ZAjw6afA8+dp1Im//5a7eX0Bu/TpgSlTgBUrgOzZ06gTRGSyChXkru+PPyT1vVEjab98OekolJFMLjGid/u2HDVqABkySIBpalmR4GAZYUtN4cKS6bpvH+DubvhchQpSCmnePCk8rq9nqad/zKLkRGQp7u4y+xEYKDeWiiIzIhcvAitXWvH95sULWTczdaraVqiQ7MdYsaKVPikRWcyUKRK0rFwJfPcd8NZb0v7nn0CVKmadUqcopk0E3L8vG0Zv2yZB2/nzEmh17ixZqtbIUL12TRIq9G7dkvVuf/4JVK4M5M8v+x5+950EbunTy+sGDZK1e2fPGv+5bty4AV9fX1y/fh358+e3/BdDRA5j5Up5T9aXHfH1lSSI0qUt/IkuXZL6JocPq20ffgjMng1kzWrhT0Zkn+z2+v38udRz1AcvJjB5OrV3b/k8164ZJhi0agVs2GDy5zdKgQKyHkV/vP22tBcpIgEcIOvy3NwkweL0abk5nTrVtB0miIhM0bw5sGeP+j50/bqsmfv7bwt+kj//BMqWVQM4Nzfg559lYTQDOCL75+FhVgAHc6ZTN22SguCJg1x/f1lrqxUvL+lb9+5A+fKSWT90KPD559r1iYgcX5kywMGDEtAdPAg8eSJZ/GPGyCYJZu9e8/y5rDlJmDH21lvA0qUS1BGR7cuWzfg3gQcPTD69yUHckyfJl/h48CDpmjVrKVgw+WywUqUku56IKC3lySMbI3TuDCxeLO9PAwcCYWFScNzDw8QTnj8v61YSbkvTujUwYwbA5Csi+zFlilVPb3IQV7068PvvsgUNXhXDjIuTLa7efdcKPSQisgMZMkiCQ2CgzAIAkoR24YIkjhqdfLZokUwhREXJYw8PYNo0oGtXbkpNZG86dLDq6U0O4saPB+rUkeUZ+mSp06dlJG7PHut0kojIHuh0UnIkMFDKkDx9Kpn1FSvKBgr6Op/JevYM6NVL9vrSK1pUpk9LlUqL7hORpSXMynwdT0+TT29yYkOJElJ0uFo1qS355Ins1HDsmCQaEBE5uw8/lKUd+fLJY33Cw8qVKfyHs2cl1T5hANeundwtM4Ajsl9Zs8q6uNQO/WvMYNJIXEyM7MwwfbqU8yAiouSVKwccOqQmPDx9qiY86AudA5D1Kd26qXVKMmSQZIbXFdEkItu3bZtVT29SEJc+PXDypPU6Q0TkSPQJD126yFI3vKpfefo0MHvqE3j0/VoqB+sVLy7Tp9zDmcgx1Kxp1dObPJ366afA//5nnc4QETmaDBmABQtktx29YwtO45ZvJcMArnNnGbJjAEfkuHbtkkCqShXg5k1p++MPYPdus05ncmLDy5fAb7/J3q3lyyfdL3XyZLP6QUTksHQ6WYISUExBaJs5mPTia2R89gwAEJshE9LNnC5v7ETkuP76S9a6tm0LHD0KREdLe0SErLNYt87kU5ocxJ06JWs9AElwSIjZ70REKYiKwgcrvsQHLxbEN51AKXRQlmJoxqL4QNPOEZHVjRolSQXt20tBSb2qVQ2H6k1gVBB38qRkpbq4WH2NHhGR4zlxQor3JrjzXZ77C7S9+wOeP8+ADz+U9/BBg3gzTOSwzp0DatRI2u7lBTx6ZNYpjVoTV7Ys8N9/8nHhwsD9+2Z9LiIi56IosstC5cpqAJclC7B4Md6/Oh0fts0Q/9LBg2VG9dUsKxE5Gh8fqf6d2O7dElyZwaggLmtW4PJl+fjKFdmhgYiIUhEZCXzyCfDll+ral3LlZC1Mq1bw8JD1zGPGqP9l4UKgVi3g9m3Nek1E1vLZZ1LQ+8ABGXK/dUuynvr2lTJDZjBqOvXDDyVLNk8e+bwVKgDp0iX/2kuXzOoHEZHjOHpUpk8vXlTbvv4amDjRYJNpnU72WC1WTEbhnj6VBNVKlWSHB+5zT+RABgyQUbA6deSPvUYNeT/o2xfo0cOsU+oUJbmt5JPasEFGAXv2BEaMSHkP5l69zOqHzbhx4wZ8fX1x/fp15M+fX+vuEJE9URTg55+B4GDZlxCv1rv8739yN5yK48eBpk1ldwcAyJhR6gC/5r8R0St2c/1+8UICqqgoKSmUObPZpzI6O7VBA/n3yBEJ1FIK4oiInNKjR1LVd/lyta1iRWDJEqBQodf+9zJlZIeHFi1kv9WnT4GPPpKb5sGDmfBAZPciIoDYWCB7dsN6kA8eAK6uabN36pw5DOCIiAwcPChznwkDuN69ZcGyEQGcnrc3sHWrlJLSGzoUaNOGCQ9Edu+TTwxLi+gtXSrPmcHkII6IiF5RFOCHH4Bq1STrC5CNrP/+Wyqfu7mZfEoPD2DePCAkRB19W7xY1iXfumXh/hNR2jlwAHj33aTttWrJc2ZgEEdEZI4HD4BmzYA+fYCYGGkLClIXt70BnU7WQK9Yoe6Kc+iQJDwcPWqBvhNR2ouOlm2vEouJMXuonUEcEZGp9u6VRWyrV6tt/fsDO3YABQpY7NM0awbs2aOe8uZNGfT780+LfQoiSiuVKgEzZyZtnz5d9jE1A4M4IiJjxcUB48dLaQB9GmmOHMDatcC4cUD69Bb/lKVLy5K7KlXk8bNnQMuWkvBgXG0BIkpi+HAZ8k54FCuW8uvnzk36eg8P0z7nqFHA7Nny/vH993LUqCEb0icsGGkCo7JTV60y/oRvOItARGSb7t0DOnQA1q9X26pXlwq9Vi5noE94+PxzKTsCAMOGAWFhkmyWIcPrzkBESRQvDmzerD52fU1I5OkpW2fpmZoyXrWqpJ5PmCDJDBkyAKVKSQkif38TO/+qy8a8qHlz406m00n2LBGRQ9m1S7LH9JkFOp1sdDp8+Ovf+C3E3V0GA4oXl/VyiiLVSy5elDyKvHnTpBtEjsPVVbbCMpZOZ9rrk1OmjOzSYCFGTafGxRl3MIAjIocSFweMHi3ZY/oALnduYONGmRpJowBOT6eTpXcrV6r1QQ8flnJ0hw+naVeIbNbjx48RGRkZf0Trt71L7Px5ufspXBho2xa4di31E0dFAX5+gK+vLFg9fdoq/TcF18QRESXnzh2pcj54sLph9LvvSvbpe+9p2rWmTSXhwc9PHt+6JUtrli7VtFtENiEwMBBeXl7xR0hISNIXVa4sQ9sbNgC//iobxFevDjx+nPxJixaVtWt//w3Mny/vCVWqADduWP3rSY3R224l9OSJJGFdu6buLKPXs6cFe6cBu9m2g4isZ+tWuTMPD5fHOp0sQhs8OOWNozVw9y7wwQcS0OkNGyYHd3ggZ6O/foeFhSFfvnzx7e7u7nBPsGdxsh49kruiyZNl55XXiYkBAgKA1q2BkSMt0HvzmDwXcOwY8P77siXMkyeye8R//8k+f7lz238QR0ROLDZW3pATpn76+EjyQnJFOjWWOzewZQvwxRdSIBiQhLczZyThIWNGrXtIlPayZMkCT1O3sMqaFXj7bdnT1Bjp08suLca+3kpMnk7t3Rto0gR4+FASK/bvB65elRInEydap5NERFZ3+zZQt65EQfoA7r33ZPrUBgM4PXd3CdgmTFBH35YulenVmze17h2RnYiKkiyhPHmMe31sLPDPP8a/Xk9RZOTr/n2zupmYyUHc8eNAcDDg4iKzCtHRssZv/HhJ1iIisjubNklBtu3b5bGLiyQ0bNgg9T1snE4H9O0r5aD0CQ9HjkjCw6FDWveOyAb17Svrwq5ckeLdLVpIUNO6tTzfvj0wcKD6+hEj5H3i0iXZNuXTT2UEq2tX4z5feLicM1s2eU/JnVs+7txZ1t+ayeTp1PTp5f0Nr4byr12TaWEvL7X2JRGRXXj5UhaQhYSoo2/58gGLFskiZzvTuLFcj5o2lWvT7dsyIjd3LtCqlda9I7IhN25IwHb/PpArl2yFsn+/fAxIcOOSYJzr4UPgs88kGMuWTaYf9+4FAgNf/7kiIyUJIioK6NRJigorihR6XLQI2L1bAkP9HZgJTA7iypaVOzt/f9mQeehQGRn84w+gRAmTPz8RkTZu3ADatJEacHoNG0o13Zw5tezZGylZUnZ4+OADuTY8fy4l7sLCJF51YU0CImDx4tSf14/K6/3wgxzmmDpVRvlOn1aDRL3Bg6UI8LRpZk1nmvznPGaMOgU8erQEpN26STHz5LYEIyKyOevWSdFNfQCXLp2sCVmzxq4DOL1cuaQQfadOatuIETIa9/Splj0jckJr10qAljiAw6spzYEDDfdhNoFJQZyiyOcLClI/94YNMlJ45IgsKSEislkxMVItt1EjdWFxgQISzPXr51DDVO7uspvPxIlqwsOff8osscalrYicy7//qpsfJ6dKFcPtvExgchD31ltc+0ZEdujaNVkDMmGC2ta0qdRN0t+ZOhidThLRVq8GsmSRtqNHJeHh4EGte0fkJCIjpYRJSrJmldeYwaQgzsVF1sJZKDOWiChtrFol06f79snj9OllfcvKlVLs0sE1aiRfeqFC8jg8XOLZRYu07hmRE1CU1Ef5dTo1scpEJs8djB0rsw6nTpn1+YiI0s6LF1LcslkzyS4DgIIFZYuDb75xqm0NiheX0Td90u3z55LXMWSIuqsYEVmBokgh4ezZkz+KFTP71CZnp7ZvLwtjS5cG3Nyk4G9CDx6Y3ZdUFSwoJVkSCgkBBgxQH588CXTvLtmzuXIBPXrI8hcickKXL8tK/oSF0j74QBaKpTa14cBy5pSEh6++km8DAIwaJTs8zJsHZMqkdQ+JHNCcOVY7tclB3A8/aHfzOmKElGnR06/xwKsp53r1pOD69OlSSLlzZ3mv/vxzTbpLRFpZvlzeACIi5LGbm+yJ+NVXTjX6lhw3N2DWLBmZ69tXRuH++ktqmP79txRvJyILKlRIkhdcTQ65XsvkM3bsaPE+GC1LFtnGMDkLFsjMyW+/yZtU8eKyu8TkyQziiJzG8+ey3uOnn9S2IkVkH6py5bTsmU3R6WSWuWhRqSH3+LHkd1SqJMsEK1fWuodEDuTdd6Xydu7cFj+1yWvi0qUD7t5N2n7/vjxnTWPHAjlySMHhCROk2Lrevn1SmdzNTW2rX1+ydvVLYZITHR2NyMjI+OPx48fW/SKIyDouXJC73YQBXKtWko7JAC5Z778vReoLF5bH+oSHhQu17hmRAzEzacEYJgdxKfUlOtowgLK0nj2lwPK2bcAXX0jR4YTr3cLDk25xqH8cHp7yeUNCQuDl5RV/BBqzhQYR2ZYlSyRQO3ZMHru7AzNmSPqlp6fWvbNpgYHAgQMSvOHVe3nbtsB33zHhgchirLSMw+jp1GnT1H7Mnm24xVdsLLBzp+kJFgMGAOPGpf6aM2fkvH36qG2lSknA+MUXktzg7m7a501o4MCB6JPg5Ddv3mQgR2Qvnj2TecEZM9S2t98Gli2TNwoySs6csrd39+7y/o5Xu/OcOCFrjPPn17qHRHauY8fXByvLl5t8WqODOP2WYYoif9QJp07d3CR7dPp00z55cPDr19jph/kTq1xZplOvXJF1HT4+wJ07hq/RP05pHR0AuLu7wz3BNzbSzIJ7RJTGzp0DPv5Y0tL1Pv0U+PVXszaSdnZubrJ1YvHi8t4cFye7BQUGys3yl19af8kMkcPKkiVpOQ8LMDqIu3xZ/n33XQkWs2V780+eK1fyW4kZ4/hxqZ2nXycYFCTD/zExUscTAEJDJcCzRF+JyIbMny9RxZMn8jhDBuDnn+Wu0MmzT9+ETifl84oVAzp0kPXPjx8DX38N/PGHZLWWLKl1L4ns0LRptpHYsG1b2gdF+/YBU6bI0P6lS5KJ2ru33HTr+9KmjdxJdukCnD4tS2SmTjWchiUiO/f0qZQOaddODeACA6UWXKdODOAspEEDWcrStavaduCALDscNEhmsYnISFZ8X9IpimlpE7GxwNy5wJYtcpeWeOHr1q0W7uGrvf6++go4e1YW3RYqJO/hffoYTjEnLPabM6cU+/32W9M+140bN+Dr64vr168jPxeCENmO06dl+jQsTG3r1An48UdWqbWiHTtk/XHC/bnfekuWz9Spo2XPiAzZ7PXbxUUyLK0wEmdynbhevSSIa9QIKFEibW58y5WTNPjXKVUK2LXL+v0hojSkKPKm0727OgSUKZOsfWvXTuveObyaNWX5SkiIHDExUs2lbl2Zcp04UW6aiSgF27ZZbY9mk0ficuYEfv9d6gs5IpuN5ImcUVSUDMP/8YfaVrKkFO99g/0GyTxhYVI8fc8etS1nTkl8a9uWs9mkLZu+fsfFyc3o8uWSkanTybTiRx/JzaiZfzwmr4lzc5OhdCIiqzp5EqhQwTCA+/xzWZzFAE4TgYFSTmr6dMDLS9r++0+uQfXry5plIkpEUYCmTWWR6c2bciNavLhsCN+xI9CihdmnNjmICw6WhAErFiAmImemKFLronJldSFW5sxSuHfGDKuk6ZPxXFxkjdyZM0DLlmp7aKgssRk/XqZcieiVuXPl7mfLFilIvmiR7F5w4gSwebMkE/z+u1mnNnk6tUULdXq3eHG1nIeeGbXqbIpND8cSObrISIkQFi9W28qWlXRzf38te0YpWL1alitev662lS4t5UgqVtSyZ+RsbPb6Xa8eULu27HCQnDFjJINo40aTT23ySFzWrBLI1awpayG8vAwPIiKzHDsGlC9vGMB17w7s3csAzoY1aSKJw716qct6TpyQgdRevaTOHJFTO3lS6vakpGFD+aMxg8kjcY7OZiN5IkelKMAvv0jNoBcvpM3TE/jf/2TRL9mNgwdl2WLC61H+/PLjbdJEy56RM7DZ67ebm6x/y5Mn+edv3ZIkh+hok09t8kgcINtdbd4sy1P0d1m3bkkiGRGR0R49ktpvX3+tBnAVKsioHAM4u1OpktTpHDdOXbp444as6W7ZErh9W+seEmkgNhZwTaWiW7p0EliZweQ6cVevyqjgtWsSNL73nmwJNm6cPDZ1/1QiclKHDgGtWql7+gGy59O4cXLnSnYpfXqgf3+Jwb/8UhIeAODPP+XjceOAzz6TBAkip6AokoWacHeChMwYgdMz+c+oVy+5UX740DBJrEULSbwgIkqVosg+elWrqgFc1qzAypVScIwBnEMoXFjWac+frxYDjoiQwK5GDcONN4gcWvv2sltD4iQC/ZE7t7zGDCaPxO3aJeuME7/PFiwo5U+IiFL04IFslbVqldr2zjuSzODnp2XPyAp0OikCXL8+0LcvMG+etO/ZA5QpAwwcKIeHh9Y9JbKiuXOtdmqTR+Li4mR6N7EbN2RalYgoWfv2SbmQhAFcv35SP4kBnEPLmVOuY5s3q8XiY2KAESMkmNuxQ+seElnRpUtWK65rchBXr57MhOjpdJLQMGyY427FRURvIC4OmDBB5tCuXZO2HDmANWukMmziYpPksOrUkWoLgwap67zPnQNq1ZJ1cg8fat1DIivw9wfu3VMft2oF3LljkVObHMRNmiRD4YGBwPPnQJs26lTquHEW6RMROYr//pPaEv37q9lX1arJjuqNGmndO9JAhgzA6NHA0aNSS05v9mzZTW3JEu4IRA4m8S/0unXAkycWObXJQVz+/FID6LvvgN69ZXZk7FipCJA7t0X6RESOYNcumStbt04e63QyBLNtm7yRkFMrWVIGBH76SV2Kc/cu8MknQOPGUgmBiFJnVpK3q6ssVh0/Xoo4du3K7QyJ6JW4ONlG5t131WynXLmADRtkCCa1eknkVNKlk005wsKA5s3V9nXrZLZn8mSzy2cR2Q6dTt3OJGGbBZj8bhoSAnh7A507G7b/9ptM+X77rUX6RUT26O5doF07YNMmta1WLWDBAiBvXi17RjYsf35gxQo5vv5aisc/fQoEB8uvzqxZQLlyWveSyEyJ68Q9fy61djJlMnydGZvPmzwSN2OGrFtIrHhxFvolcmrbtsnO5/oATqeTjKfNmxnAkVFatJBRua++Ugcqjh4FKlaUEiUWWkZElLY6dDCsE/fpp/KeaIHN503eO9XDAzhzRrb5SujSJTXZwZ7Z7N5rRLYqNhYYNUrqRcTFSZuPjwyh1K6tde/ITu3bJxmrp0+rbX5+wK+/yn7hRIk54/Xb5JE4X19ZjJrYnj282SZyOrdvS92h4cPVAK5uXck+ZQBHbyAoSEbhRo9WZ6GuXpVSVm3aWKxCA5FdMzmI++wz2d5wzhz5g7p6VdbD9e4tzxGRkwgNlezTrVvlsYuLjMht3CgLZ4nekJubJDT/84/kyegtWgQEBMi1h+VIyJmZHMT16wd06SJrFgoXlqNHD6BnT9k+hYgc3MuXwODBspfS3bvSljevrIn77jvubE4W5+8ve3PPmQNkzy5tDx/Ktah2beDff7XuIZE2TH631emkqO+9e8D+/VIz7sEDYOhQ63SQiGzIzZty1Rw9Wh0CadBApk9r1NC6d+TAdDpJ8DtzRkpc6W3fDpQqJYPAL15o2UOitGf2LXPmzJIxVKKEul6BiBzY+vUyfbprlzxOl07u6NaulTpwRGkgd25g/nwpO6hPsIuOBoYMkeLzya3ZJtJMuXLqfnIjRkjtHAvivAcRpS4mRgpAvv++bKOFVxlOO3fKdlqcPiUN1K8PnDolv4Lp0klbWJjs6tatGxARoXUPiSBDx/raON9/L5vNWxBLpxNRyq5dk32Q9u1T25o0AebOVRcnEWkkY0YZDG7dWhLrDh+W9unTgb//Bn78EfjgA4sVxycyXZkyQKdOcnehKMDEiTKVmRwz1qWZXCfO0TljnRmiZK1aJYuQ9FMB6dPLFfObb3hVJJsTGyv7sH73nWFR4KZNpd3XV8veUVqwyev3uXNS9PziRamZExiY/NaDOp08byKTg7idO4EqVZL24eVLYO9e+1/bbJO/BERp6cULYMAA4Icf1LaCBYElS4BKlbTsGdFrXbsm1RPWrlXbsmUD1qyRaxc5Lpu/fru4AOHhsrDTUqc09T+8+65koyYWEWFYx4eI7NDly0D16oYB3AcfAMeOMYAju1CgALB6NbB0qVqu8OFDqUG9erXWvSOnFhdn0QAO5gRxipL8TMr9+0n3ciUiO7J8uaT3HTwoj93cZFHRn38CWbNq3Tsio+l0QMuWsqa8bl1pe/ZM9madM0fr3pFTu3hRiuvWrStHz57SZiajExs++ED+1dfqSVhWJDYWOHmSQ9VEdik6WnYX/+knta1IEZk+LV9ey54RvZFs2WRatUMHYPFiuVZ17iwzWgMGcGknpbGNG2WRZpkyQNWq0rZnD1C8uAwTv/eeyac0Oojz8pJ/FQXIkgXIkEF9zs0NeOcdbrtFZHcuXABatTJcUNuqFTBzJuDpqWXPiCzCzQ1YsECmVqdOlbZBgySQ++EHVsihNDRggOxROnZs0vZvv7VuEKcfgi5YUG7aOXVKZOeWLgW6dgUeP5bH7u5ylfv8cw5RkENxcZGALU8euV4CwLRpwJ07wLx5LFhPaeTMGXnfTaxzZ2DKFLNOafI9yLBhDOCI7NqzZ8CXX8qImz6Ae/tt4MAB4IsvGMCRQ9LpZLBjzhy1OPCSJUCjRkBkpNa9I6eQK5dsUZjY8eNmJzyYVez3zz8lmLx2LeledWaUOSGitHLuHPDxx7KIVe/TT4Fff025ACWRA+nYUa6lLVvK/cyWLUCtWrKrnD6blcgqPvtMZjouXVKTCPbskfqbffqYdUqTR+KmTZPiw97eatWBHDmkTw0bmtUHIkoL8+dLooI+gMuQAfjf/4Dff2cAR06lUSMJ3vSbjhw7JuvM3yBJkOj1hgyRXRl+/BGoWVOOn34Chg8HBg8265QmB3G//CJrnn/8URaM9u8PhIZKlqy196pbuxaoXFmuPdmyAc2bGz5/7Zr8cWbMKCOT/fpJEWIip/b0KdClC9CunVrKPiAAOHRI1mJw+pScUFAQsHu3upPDxYsyOMLZJLIanU4SG27ckIApIkI+7tXL7Pdhk4O4a9fUUcAMGdQlNe3aAYsWmdUHo/z1l3yOTp2AEydkBLJNG/X52FgJ4F68kJ0j5s2T7R3N2IqMyHGEhclw+W+/qW2dOkkAV7y4lj0j0lxAgFwv9H8Kd+/K4MiWLVr3jBxelixyvCGTgzgfH3XHhgIFgP375ePLl6X8iDW8fCmB6oQJsh777bdl+7GPP1Zfs2mTXK/mz5cSLA0bAiNHAj//nHTdHpFTmDsXqFABOH1aHmfMKFOnv/3G7CSiV/LnB3btUst2RUXJ9WPJEq17RlY1fLiMfiU8ihVL/f8sWyav8fAASpYE1q1Lq96myOQgrnZt2Rcbr27oe/eW0iatWkk1bGs4ehS4eVPSxMuWlTTxhg2BU6fU1+zbJ9/ThAtT69eXrCP9NSw50dHRiIyMjD8e64cWiexVVJRUN+3USVZuA/LHceSIDGcTkYFs2WRZUNOm8jgmBmjdWpYNkQMrXhy4fVs9du9O+bV798ovRZcusoiyeXM5EgYiGjA5iJs5E/juO/m4e3e5qQ8IAEaMkAQ3a7h0Sf7Vr/1bs0b+6GrVUkcFw8OTZhbpH4eHp3zukJAQeHl5xR+BgYHW+SKI0sLJk0DFijLipvf551I+5HV3mUROLEMGWbajL1qvKLLWe9Ag680ykcZcXWV6UX/kzJnya6dOBRo0kMX2AQEy1VeunOFONxowOYhzcZGvW++TTyRjtUcPSXQwhX7bk9SOs2dlz1hAgscPP5QEuzlz5Plly0z9CgwNHDgQERER8UdYWNibnZBIC4oid1iVK8sfDSAZp4sWATNmGG6xQkTJcnWVP5chQ9S2kBAZfGGSnP14/PixwQxbdHR08i88fx7ImxcoXBho21YW/adk3z51I169+vWl3RgxMUCdOvI5LcisOnGWEhwsNXtSU7iwjHICsg5Oz91dntN/z3181H279e7cUZ9Libu7O9wTlOuOZNVHsjeRkVKkd/Fita1MGSnm6O+vZc+I7I5OJzNLPj7A11/L/dGcOcC9e7JOLmNGrXtIr5N4Rm3YsGEYPny44YsqV5Z1w0WLSpDx/fdA9eoyPZpcwkFK032pTfUllD69YX1OC9E0iMuVS47XKV9egrZz54Bq1aQtJga4cgXw85PHQUHA6NGSXaQvfBwaKts/coaUHNaxY5Lhc+GC2ta9OzBxoiy+JSKzfPWVXEvatpXkuDVrZCBm9WqpjUq2KywsDPny5Yt/7J7cvmoJC9uWKiVBnZ+f3Px26WKdjn36qdTmTLx36hvQNIgzlqenZKUOGyY1ffz8JFMVkKrbAFCvngRr7doB48dLcDx4sFzPuC8eORxFkUWovXur6deenvIG8dFHWveOyCF89JEEbM2by4D3vn0yWLNxo1pfjmxPlixZ4Onpadp/yppVSl8kvCFOyMdHnd7Tu3Mn9am+xF6+lESCzZtldCpxlYDJk03rszlr4rQyYYKsv2vXTtZtX70KbN0qCQ6A7IW3Zo38GxQkAW/79jIsTuRQIiJk9K17dzWAq1BBRuUYwBFZ1LvvAjt2qNfqM2fkGpNa1QOyQ1FRUvE5T57knw8KSlpAMDRU2o116pQkQ2TJAvz7r7xn64/k9lQ1gk5RmHeT0I0bN+Dr64vr168jf/78WneHyNChQ1LP5/Jlte2bb2R4nkPORFZz+bLM+OgHarJmlYEDfX050p5J1+++fYEmTWRq79Ytmeo7flwKzubKJaNA+fJJZgtelRipWVPeaxs1kjXIY8ZIDbQSJdLk60uOySNxd+7IaFjevJLJky6d4UFEVqAowJQpcsXQB3BZswIrVwI//MAAjsjKChWSnYIqVJDHjx7JGjl93VSyMzduSN23okVlZiNHDtm9QL9Q/9o1NasSkK2qFi6UKgClSwN//invv+YEcBcuyJy8vo7nG4ylmTwS17ChfG1ffy2jjom3+2rWzOy+2ASOxJHNefBA9jj9+2+17Z135E5Qn9lDRGni8WMpdRUaKo9dXOS6bq218GQ8m79+378vAeO2bRI8nT8vZTY6d5a1YZMmmXxKkxMbdu+WLUrKlDH5cxGRqfbvl+nThPWL+vWTVOz06bXsGZFTypJFplE7dZKBmbg4oGtXSaYbNMjsfczJGfTuLe/b165JwWC9Vq2APn3MCuJMnk719WX1aiKri4uTbJ7q1dUALkcOuXqMH88AjkhDbm7AH3/INVlv8GDZ4SE2VsuekU3btAkYN0427E3I31+yNc1gchA3ZYrstHDlilmfj4he57//ZBPH/v3VMvHVqsmi20aNtO4dEb2aRp00Se6p9H76SZZZpbRBADm5J0+Srxb94IHZ65pNDuJatQK2bweKFJFh5ezZDQ8iegO7d8tahbVr1baBA2UNhS2u8SByYjqdrG6YN09N7Fu2DHj/fakrR2SgenXDfa11Opl1GT9eatmYweQ1cVOmmPV5iCg1cXEyzD5kiDofkyuXzNnUr69174goFe3by97pLVsCT59KDdOaNYH1602rBUsObvx42T/18GGp8dm/vxQcfPBAUp/NYHIQ16GDWZ+HiFJy967U7dm0SW2rVQtYsEBq+RCRzXv/fakF26iRXJOPH5eqFJs2AW+9pXXvyCaUKCFFfn/6SaYyo6KADz6Qwu0pFRl+DbO23YqNlfIoZ87I4+LFZQkP68QRmWj7dqBNG7UekU4no3FDh/IPisjOvPOODKjUry/5SJcvSyC3fr3sskQELy/gu+8sdjqTg7gLF+SO4+ZNqZEHSEFjX19ZxlOkiMX6RuS4YmOBUaNkX7i4OGnz9paaBbVra907IjJTsWJS3L9BA9ll6d49GVhfvhx47z2te0eae/hQ9rjWj4IFBkq9GjOTCkxObOjZUwK169dlt4mjR+WOo1AheY6IXiM8XPbvGT5cDeDq1gVOnGAAR+QA8uUDdu6Udex4tS1no0bAokVa94w0tXMnULAgMG2aBHMPH8rHhQrJc2YwOYjbsUPW5iUMGnPkkO3Eduwwqw9EzmPzZtmyZetWeeziAowcCWzYICNxROQQsmWTnZWaN5fHMTGycoLJgU6se3d17+vly+W4dAn45BN5zgwmB3Hu7rLtSGJRUVIAkYiS8fKlVAOtV08SGQBJWti6Vdq5/o3I4WTIIFtsfv652ta7t9RaZdF8J3ThAhAcbPh+ny6d7NZw4YJZpzQ5iGvcWH4hDxyQX0JFkZ2BvvxSkhuIKJGbNyWtfPRo9Z27QQNJX6tZU+veEZEVpUsHTJ8ODBumto0bJ8ugYmK07BmluXLl1LVwCZ05IzM0ZjA5sWHaNCkzEhSk7vzz8qUEcFOnmtUHIse1YYOUD/nvP3mcLp0Ec/36yVQqETk8nU6WwPr4AF99Jfdy8+ZJ0sPSpUCmTFr3kKzm5En14549gV69ZNTtnXekbf9+4OefZU2aGXSKYt6g7vnzwNmz8nFAgOPUwblx4wZ8fX1x/fp15GeFfDJXTIyUChk3Tm3Lnx9YvBioWlXLnhGRhv76S9bGvXghjytXlsoOOXJo3TP7Z5PXbxcXieJfF2rpdGZtvGtWnTi82q/V39/c/03kwK5dkw0U9+5V2xo3BubO5Ts1kZP78EMpANy0qWzNdeCAbI28cSNQoIDWvSOLu3zZqqc3Kojr00cS6DJlko9TM3myhXpGZI9Wr5b1Bg8fymNXVxmN691b7rSIyOnVrCkVJRo0kIpDZ8/KEqWNG6WoPzkQPz+rnt6oIO7YMXUB5rFjVu0PkX168UI2qk94F+PnByxZIvMlREQJlC4tg/X168vypFu3pK7cqlVqfTlyQLduAbt3S5UCfZ1QPTOK7Zq9Js5R2eScOtm2y5elzs/Bg2pbixZSlTtbNi17RkQ27t49KQR86JA89vCQpbPNmmndM/tj89fvuXOBL76Qemw5chjOzuh0UjPORCanx3XunHyduCdP5Dkip7J8OVC2rBrAublJCvdffzGAI6LXypVLykXWry+Pnz+XPdFnzdK6Z2Rx+n2xIyKAK1dkAEB/mBHAwZwgbt484NmzpO3PngG//25WH4jsT3Q00KOHrFKOiJC2woVlfqRHD65/IyKjZc4s06iffiqP4+KkHuvIkSwK7FCePpVZGwuWlzL6TJGRcq1SFBmJi4xUj4cPgXXrgNy5LdYvItt14QJQpQrw009q28cfy0bC5ctr2TMislNubjJIEhystg0dCnz9tVmVJ8gWdekCLFtm0VMaXWIka1YZXNDpgLffTvq8Tgd8/71F+0Zke5YuBbp2VdcUuLvLZohffMHRNyJ6Iy4uwMSJUhS4Xz9p++UXWQP/xx+yXo7sWEiIlJvasAEoWVLdMUHPjPIeRgdx27bJKFzt2rLcJ3t29Tk3N0nEy5vX5M9PZB+ePZMyITNmqG3+/hLUlSmjZc+IyMH07Qt4e8s685cvZf/V//4DVq4EvLy07h2ZLSRE6sgULSqPEyc2mMHoIE6/xePly1KQkIMO5DTOnZPp0oTbp7RpIxsiZsmiZc+IyEG1aydJDx9+KEuptm8HatUCdu2SNXRkhyZNAn77DejY0WKnNHl13datcleQ2LJlMp9P5FAWLJB1bvoAzsMDmD0bmD+fARwRWVWDBnLN1W/0cvw4MGqU1r0is7m7W3zbRZODuJAQIGfOpO25cwNjxlioV0Rae/pU1r59+qnUz8GrTYIPHZLFqRyKJqI0ULkysGOHXP/xajBHv2852ZlevYAff7ToKU3eO/XaNaBQoaTtfn7yHJHdCwuT6dPTp9W2Dh2An3+WveeIiNJQ8eJA//5ScuTlS6litGkT7yXtzsGDMrS6Zo38UBMnNixfbvIpTR6Jy53bcGmQ3okT3NubHMDcuUDFimoAlzGjtM2dywCOiDQzYABQsKB8vHlz8suayMZlzSqVnGvWlClNLy/Dwwwmj8S1bi3be2XJAtSoIW07dsgo4SefmNUHIu1FRQHduxtWrC5RQrJPAwK07BkRETJmlGpGzZvL4969gYYNmeRgV+bMsfgpTR6JGzlS5ujr1AEyZJCjXj0pPcI1cWSX/vlHRt8SBnBduwIHDjCAIyKb0bQp8P778vHNm0xyIECnKOZt6vHvvzKFmiGD1Kzz87N857Rg8xvokuUoimSa9uwpGxbi1f43M2ZICREiIhtz8aIsp4qOBlxd5R60WDGte2UbbP76XahQ6gsZzdg/1eTpVL2CBeUaWKSI/CIR2ZXISNllYfFita10aZk+TW5LEiIiG1CkCJMc7NY33xg+jokBjh2THRz0W3SYyOTw6+lT+aXR14T791/Z97tHDyBfPll8SWTTjh0DWrUCzp9X27p1ky1PuK8NEdm4AQNkG64rV9Qkh5Ytte4VvVavXsm3//wzcPiwWac0eU3cwIEyjbp9u+H1rm5dYMkSs/rwWtu3q/u2Jj4OHVJfd/IkUL269MvXFxg/3jr9ITulKLIR4TvvqAGcp6f84v7yCwM4IrILGTMCU6eqj3v3ltwsslMNG8p+pmYwOYhbuRL46SegWjXD4dvixWWu3hqqVAFu3zY8unaV6eUKFeQ1kZGSYOHnBxw5AkyYAAwfDsycaZ0+kZ2JiJDab927Ay9eSFv58sDRo9JORGRHmjRhkoPD+PNPww3pTWDydOq9e1IrLrEnT6w3J+/mBvj4qI9jYoC//5YpXP3nXLBArs2//SavL15ctiiZPBn4/HPr9IvsxOHDEqhdvqy29ewpQ7X6MuhERHZEpwOmTQO2bJEkh0mTpCY5E+ptWNmyhoGSogDh4RJY/fKLWac0OYirUAFYu1YCKEDtz+zZQFCQWX0w2apVwP37QKdOatu+fVK3zs1NbatfHxg3Dnj4EMiWLflzRUdHIzo6Ov7x48ePrdl1SkuKIu9y/fpJ5I9XxRbnzFGLLRER2akiRYBvvwVGjFCTHEJDmeRgsxJfd1xcgFy5gFq1zE4xNjmIGzNGpm/DwuSXZupU+XjvXin6mxb+9z8J0BJmEIeHJ90OzNtbfS6lIC4kJATff/+9FXtLmnj4EOjcWeb/9SpXlmxUfdlzIiI7N2CAlLi8ckVG5ZjkYMOGDbP4KU1eE1etmkxTvnwp9eE2bZLp1X37ZImRKQYMSDlhQX8k3uj3xg1g40bZg9wSBg4ciIiIiPgjLCzMMicm7ezfL8PWCQO44GBg504GcETkUDJkYJKDMzOrwluRIsCsWW/+yYODgY4dU39N4cKGj+fMkT1amzY1bPfxAe7cMWzTP064ni4xd3d3uCdYFxUZGWlk78nmxMXJIsiBA+UuA5DFovPmAY0ba907IiKraNIEaNRIljrdvCk15MaN07pXFM/F5fVz3Dqdet0ygVFBnClxjaen8a/NlUsOYymKBHHt2wPp0xs+FxQEfPedLH3SPxcaChQtmvJUKjmQ+/dlVe/atWpb1arAokVSb4aIyEHpdDIat3mzJDlMniwDJExysBErVqT83L59snY7Ls6sUxsVxGXN+vogUlHkNbGxZvXDKFu3SoJh165Jn2vTBvj+e5lm/fZb4NQp+aX+4Qfr9YdsxO7dQOvWMteuN3Cg/EIkjvaJiBwQkxxsWLNmSdvOnZM1ZatXA23byg/ODEYFcdu2mXVui/vf/6RmXHJJHF5esj6ve3dZm5czJzB0KMuLOLS4OJkzGDJEvXvImROYP18yX4iInEjiJIdly1gG0+bcuiUJDvPmyXXq+HGgRAmzT2dUEDd1KjB3rkyV/v677FikRXmthQtTf75UKWDXrrTqDWnq7l2gXTuJ3PVq1pRfkrx5tewZEZEm9EkO+oGfPn2kIHDmzFr3jBARIeU9fvwRKFNGouzq1d/4tEZlp65ZI8V8AanNFhHxxp+XyHzbt8sfgT6A0+lkNG7zZgZwROTU9EkOgJrkQBobP16yNNeskXXae/daJIADAJ2iKMrrXlSqFFCuHPDuuxLETZuWcgJD+/YW6Zdmbty4AV9fX1y/fh35ExaiI+3FxgKjR8taN/0iUG9vmT6tW1fr3hER2YSLF2XXouhowNVV9hV3hiQHm71+u7jIMGndukC6dCm/bvlyk09t1HTq9OkyLLt2rQx6DB6c/GJJnc7+gziyUeHhsvhz61a1rU4dCeBSqyFDRORkmORgY9q3t9o336iRuIRcXOR6mtz+qY7AZiN5Z7Z5M/Dpp2rhPxcXYPhwYNCg1O9qiIic1LNnQGCgJDkAwJIljp/k4IzXb5N3bLh82bTabkRme/lS1rrVq6cGcHnyyGjckCEM4IiIUpB4J4c+fbiTgyMyOYjz85OyXJ9+KgV2b96U9j/+kHYii7h5U6ZLR42SIoSAmo5ds6bWvSMisnlMcnB8Jgdxf/0l19IMGYBjx2ThJBJkzxK9sQ0bJPt05055nC4dEBICrFvnuPP4REQWpt/JQV8SbPJk4MwZrXtFlmRyEDdqlCQ6zJplWAy/alXg6FEL946cS0yM7LTQsCHw33/Slj8/sGOHVLF0MfnXlYjIqemTHAA1ycG0lfBky0y+Kp47B9SokbTdywt49MhCvSLnc/06UKsWMHas2ta4sUyfVq2qZc+IiOzagAFAwYLysX4nB3IMJgdxPj7AhQtJ23fvllp2RCZbs0amT/fulceursDEicCqVUCOHFr3jojIrmXIIPVd9ZjkkIyxY2X++ZtvUn7N3LnymoSHh0da9jIJk4O4zz4DevUCDhyQ/t+6BSxYAPTtC3TrZp1OkoN68QIIDpbVtw8eSJufn+ydFhzMokZERBbCJIdUHDoEzJghOxu8jqcncPu2ely9mhY9TJHJQdyAAUCbNpI4GBUlU6tduwJffCFz7URGuXJFfnkmT1bbmjeXbJl33tGyZ0REDolJDsmIipJC8rNmAdmyvf71Op1MSeoPb++06GWKTA7idDrgu+9k4OTUKWD/fuDePYnqnz2zTifJwaxcCZQtK8O5gGTITJ0qW44Y80dEREQmK1JEBmLwKsnh668dM8nh8ePHiIyMjD+i9WU0ktO9uwxRGrt1Y1SUzBj5+gLNmgGnT1us3+YwO93PzU2qQVeqJNfgyZOBQoUs2zlyMNHRMhffooWaBVO4sKyF69mT06dERFb27bfqtXrrVsdMcggMDISXl1f8ERISkvwLFy+WshopPZ9Y0aLAb78Bf/8tWz7GxQFVqgA3bli0/6Ywau9UvLr+Dh8u+6+5uQH9+8vs15w5MjKXLh3Qu7d1O0t27OJFoFUr4MgRta1lSxnC9vLSsmdERE5Dv5ND06byuHdvqeqUJYvWPbOcsLAw5MuXL/6xu34OOaHr12VQITTU+OSEoCA59KpUAQICZD2dRosMjR6JGzoU+PVXSVO+ckWuv59/Dvzwg4zCXbmi1qIhMrBsGVCunBrAubsDv/wim/kxgCMiSlNNmkgFJ0CSEx0tySFLlizw9PSMP5IN4o4cAe7elWuTq6scO3ZIGq+rKxAb+/pPlD69LA1KrmRHGjE6iFu2DPj9d+DPP4FNm+Tre/kSOHEC+OQTbmNJyXj+HPjqK9l1OTJS2vz9ZSFlt26cPiUi0kjCJIcffnDCJIc6dYB//pFapPqjQgVJcjh+3LigJjZWzpEnT1r0OFlGB3E3bgDly8vHJUrID793b16HKQX//itZpr/+qra1aSN3P2XKaNkzIiKnV7iwcyQ5pChLFglmEh6ZMklt0hIl5DXt28suQnojRsgo1qVLspbu00+lxEjXrpp9GUYHcbGxshZOz9UVyJzZSr0i+7ZwoUT8J07IYw8PWfs2f75jLbwgIrJjiZMcli7Vukc25to1qQWn9/ChFMsNCADef19mmPbulSxPjegUxbjY28VFFj/qh19XrwZq15bANaHly63RzbRz48YN+Pr64vr168ifP7/W3bEvT5/KQtHZs9W2YsXknaFkSS17RkREyVi9Wk1yyJsXOHvWfu+1nfH6bfRIXIcOQO7csg7dy0tGEfPmVR/rD3JSZ84AlSsbBnAdOgCHDzOAIyKyUY6e5ODojB6JcxbOGMm/sXnzJIHh6VN5nDGjZJ926KB1z4iI6DUuXZIZwehoWSp18qTMGNobZ7x+m13slwhPngAdO8qhD+BKlJB96BjAERHZBadPcrBjDOLIPKdOSTr2vHlqW9euspWWhos8iYjIdExysE8M4sg0iiLr3ipWlBWwgKQpL1ggGagZM2rdQyIiMpF+Jwe9Pn2Ax4+17BEZg0EcGe/xY8lo+ewzKeQLAKVLS+23Nm207h0REb0BJjnYHwZxZBx9NeuFC9W2bt1k94W339ayZ0REZCGJd3IIC9O6R5QaBnGUOkWRXRfeeUd2YcCrStdLlkgGqrEbBxMRkc1LnOTQoweTHGwZgzhKWUSEbIz71VeSew7ITgzHjsl+qERE5HCY5GA/GMRR8g4fBsqVM/zr7dkT2LMHKFJEy54REZEVZcgATJumPmaSg+1iEEeGFEX+eqtUkQqQAJA1q+ynlnCxBBEROazGjZnkYA8YxJHq4UPgww9l/9OYGGmrVEmmT1u00Lp3RESUhpjkYPsYxJE4cAAoWxZYsUJtCw4Gdu0CChbUsmdERKSBwoWBgQPlY+7kYJsYxDk7RQEmTQKqVQOuXpW27NmBVauAiRMBNzete0hERBrp319Ncti2jUkOtoZBnDO7fx9o2hTo21duswBZC3f8uFR9JCIip8YkB9tmN0Hcv/8CzZoBOXMCnp4ycLRtm+Frrl0DGjWSnZ9y5wb69VNjE0pkzx6gTBlgzRq1bcAAYPt2wNdXy54REZENSZzkMGKE1j0iPbsJ4ho3loBs61bZ5al0aWkLD5fnY2MlgHvxAti7V/ZlnzsXGDpU657bmLg4YOxYoGZN4MYNacuZE1i/HggJAdKn17qHRERkYxImOUyZwiQHW2EXQdx//wHnz8tAUalSgL+/xCFPnwKnTslrNm2SX6r582WAqWFDSYn++WcJ7AjAvXsS6Q4cKFEvANSoIdOnDRpo3TsiIrJRTHKwTXYRxOXIARQtCvz+O/DkifwCzZghU6bly8tr9u0DSpYEvL3V/1e/PhAZCZw+nfK5o6OjERkZGX88dtTJ/h07JLrdsEEe63TA4MHAli1Avnxa946IiGwckxxsj10EcTodsHmzlCvLkkW265w8WeKRbNnkNeHhhgEcoD7WT7kmJyQkBF5eXvFHYGCgFb8SDcTGypBk7dqymAGvvjGbNkm7q6vWPSQiIjvAJAfbo2kQN2CABGipHWfPypBt9+4y8rZrF3DwINC8uSRQ3r79Zn0YOHAgIiIi4o8wR5roDw+X4cihQ2UtHCDB3PHjQN26WveOiIjsTOPGavECJjloT9NhmOBgoGPH1F9TuLAkM6xZIxsKeHpK+y+/AKGhksAwYADg4yPBXUJ37si/Pj4pn9/d3R3uCbaSioyMNP8LsiVbtgBt26rfBBcXYPhwYNAgIF06rXtHRER2asoUmcyJjpaPO3UCHG0Sy15oGsTlyiXH6zx9Kv+6JBo3dHFRB5iCgoDRo4G7d2XEDpAgz9PTyX65YmOB778HRo1SV53myQMsXAjUqqV174iIyM7pkxyGD1eTHLZskdkzSlt2sSYuKEjWvnXoAJw4ITXj+vUDLl+WZEsAqFdPgrV27eQ1GzfKuv3u3Z1oz/Zbt4A6dWStmz6Aq1dPpk8ZwBERkYX07y/BHF4lOSxZonWPnJNdBHE5c0oSQ1SULOmqUAHYvRv4+2+pFwfIDOGaNfJvUBDw6adA+/ZONF+/caN8M3bskMfp0kndt/Xr1aFJIiIiC8iQQWrH6QUHM8lBC3aTmlihgsQpqfHzA9atS6se2YiXL4EhQ6Rwnl7+/MCiRbKtBRERkRXokxxWr1aTHCZM0LpXzsUuRuIoBdevyzRpwgCuUSOpxcIAjoiIrGzqVCn7Be7koAkGcfZq7Vop3rtnjzx2dZVboFWrZP6ZiIjIygoVkgoR4E4OmmAQZ29evAD69pVx7AcPpM3PTwro9e2bNIWXiIjIipjkoB1e8e3JlSuy1+mkSWpbs2YyffrOO1r2jIiInFTinRyY5JB2GMTZi5UrgbJlgQMH5HH69LIAYcUKde8xIiIiDTRqxJ0ctMAgztZFRwO9egEtWgCPHklb4cLA3r3SzuqKRERkAxInOZw+rXWPHB+DOFt28SJQtarhOPVHHwFHj0rNFSIiIhvBJIe0xyDOVi1bBpQrBxw5Io/d3WXD2KVLAS8vrXtHRESURMIkh+3bmeRgbQzibM3z58BXXwEffwxERkqbvz+wfz/QrRunT4mIyGYxySFtMYizJf/+K1mmv/6qtrVuLaNxZcpo2TMiIiKjMMkh7TCIsxULFwLlywMnTshjDw9g5kxgwQIgSxate0dERGQ0JjmkDQZxWnv6FPjsM6BtWyAqStqKFQMOHpR2Tp8SEZGdKVQIGDhQPmaSg/UwiNPSmTNA5crA7NlqW/v2wKFDQMmSWvaMiIjojTDJwfoYxGll3jwpE3LqlDzOmBGYM0faM2fWundERERvxMPDMMmhTx8mOVgag7i09uQJ0LGjHE+fSlvx4jL61rGj1r0jIiKymEaNgKZN5ePbt4Hvv9e6R46FQVxaOnVKRt/mzVPbunSR9W+BgVr2jIiIyCqmTFGTHKZOZZKDJTGISwuKIuveKlYEzp6VtkyZgPnzpT1jRq17SEREZBVMcrAeBnFpIThYMk2fP5fHpUpJ7be2bbXuGRERkdUlTnJYvFjrHjkGBnFpoVkzwOXVt/rLL2X3haJFte4VERFRmkic5HD0qJa9cRyuWnfAKdSsCYwfD/j6ynZaRERETqZRI6BvX0l0qF5d6944BgZxaSU4WOseEBERaWrCBK174Fg4nUpERERkhxjEEREREdkhBnFEREREdohBHBEREZEdYhBHREREZIcYxBERERHZIQZxRERE5NzGjgV0OuCbb1J/3bJlQLFiUr24ZElg3bq06mGyGMQRERGR8zp0CJgxQ7bETM3evUDr1kCXLsCxY0Dz5nKcOpVWPU2CQRwRERE5p6go2cd81iwgW7bUXzt1KtCgAdCvHxAQAIwcCZQrB/z0U1r1NgkGcUREROQwHj9+jMjIyPgjOjo65Rd37y77gdWt+/oT79uX9HX160u7RhjEERERkcMIDAyEl5dX/BESEpL8CxcvBo4eBVJ6PrHwcMDb27DN21vaNcK9U4mIiMhhhIWFIV++fPGP3d3dk77o+nWgVy8gNFSSFOwUgzgiIiJyGFmyZIGnp2fqLzpyBLh7V9a06cXGAjt3yhq36GggXTrD/+PjA9y5Y9h25460a4RBXCJxcXEAgNu3b2vdFSIiIjKS/rqtv46nqk4d4J9/DNs6dZLyId9+mzSAA4CgIGDLFsMyJKGh0q4RBnGJ3HkVZVeqVEnrrhAREZGJ7ty5gwIFCqT+oixZgBIlDNsyZQJy5FDb27cH8uVT18z16gXUrAlMmiTJEIsXA4cPAzNnWukreT0GcYmULVsWBw8ehLe3N1xcmPeR2OPHjxEYGIiwsDBkyZJF6+44Pf48bA9/JraFPw/bYs2fR1xcHO7cuYOyZcta5oTXrgEJ44AqVYCFC4HBg4FBgwB/f2DlyqTBYBrSKYqiaPbZye5ERkbCy8sLERERr19zQFbHn4ft4c/EtvDnYVv487AsDjURERER2SEGcURERER2iEEcmcTd3R3Dhg1Lvu4OpTn+PGwPfya2hT8P28Kfh2VxTRwRERGRHeJIHBEREZEdYhBHREREZIcYxBERERHZIQZxRERERHaIQRwlERISgooVKyJLlizInTs3mjdvjnPnzhm85vnz5+jevTty5MiBzJkz48MPP4zfsoysa+zYsdDpdPgmwf59/HmkvZs3b+LTTz9Fjhw5kCFDBpQsWRKHDx+Of15RFAwdOhR58uRBhgwZULduXZw/f17TPjuq2NhYDBkyBIUKFUKGDBlQpEgRjBw5Egnz9vjzsJ6dO3eiSZMmyJs3L3Q6HVauXGnwvDHf+wcPHqBt27bw9PRE1qxZ0aVLF0RFRaXxV2J/GMRREjt27ED37t2xf/9+hIaGIiYmBvXq1cOTJ0/iX9O7d2+sXr0ay5Ytw44dO3Dr1i188MEHmvbbGRw6dAgzZsxAqVKlDNr580hbDx8+RNWqVZE+fXqsX78eYWFhmDRpErJlyxb/mvHjx2PatGmYPn06Dhw4gEyZMqF+/fp4/vy5pn13ROPGjcOvv/6Kn376CWfOnMG4ceMwfvx4/Pjjj/Gv4c/Dep48eYLSpUvj559/TvZ5Y773bdu2xenTpxEaGoo1a9Zg586d+Pzzz9Pwq7BTCtFr3L17VwGg7NixQ1EURXn06JGSPn16ZdmyZfGvOXPmjAJA2bdvn4Y9dWyPHz9W/P39ldDQUKVmzZpKr169FIU/D018++23SrVq1VJ8Pi4uTvHx8VEmTJgQ3/bo0SPF3d1dWbRoURr10nk0atRI6dy5s0HbBx98oLRt21ZR+PNIUwCUFStWxD825nsfFhamAFAOHToU/5r169crOp1OuXnzZhp/BfaFI3H0WhEREQCA7NmzAwCOHDmCmJgY1K1bN/41xYoVQ4ECBbBv3z7N+unounfvjkaNGhl838GfhyZWrVqFChUqoGXLlsidOzfKli2LWbNmxT9/+fJlhIeHG/xMvLy8ULlyZf5MrKBKlSrYsmUL/v33XwDAiRMnsHv3bjRs2BDgz0NTxnzv9+3bh6xZs6JChQrxr6lbty5cXFxw4MABTfptL1y17gDZtri4OHzzzTeoWrUqSpQoAQAIDw+Hm5sbsmbNavBab29vhIeHa9RTx7Z48WIcPXoUhw4dSvIcfx5p79KlS/j111/Rp08fDBo0CIcOHULPnj3h5uaGDh06xH/fvb29Df4ffybWMWDAAERGRqJYsWJIly4dYmNjMXr0aLRt2xZ49TcC/jw0Ycz3Pjw8HLlz5zZ43tXVFdmzZ+fP5zUYxFGqunfvjlOnTmH37t1ad8VpXb9+Hb169UJoaCg8PDy07g69urmpUKECxowZAwAoW7YsTp06henTp6NDhw5ad8/pLF26FAsWLMDChQtRvHhxHD9+HN988w3y5s3Lnwc5NE6nUoq+/vprrFmzBtu2bUP+/Pnj2318fPDixQs8evTI4PV37tyBj4+PBj11bEeOHMHdu3dRrlw5uLq6wtXVFTt27MC0adPg6uoKb29v/jzSWJ48eRAYGGjQFhAQgGvXrgGv/kbw6meQEH8m1tGvXz8MGDAAn3zyCUqWLIl27dqhd+/eCAkJAfjz0JQx33sfHx/cvXvX4PmXL1/iwYMH/Pm8BoM4SkJRFHz99ddYsWIFtm7dikKFChk8X758eaRPnx5btmyJbzt37hyuXbuGoKAgDXrs2OrUqYN//vkHx48fjz8qVKiAtm3bxn/Mn0faqlq1apKyO//++y/8/PwAAIUKFYKPj4/BzyQyMhIHDhzgz8QKnj59ChcXw8tZunTpEBcXB/DnoSljvvdBQUF49OgRjhw5Ev+arVu3Ii4uDpUrV9ak33ZD68wKsj3dunVTvLy8lO3btyu3b9+OP54+fRr/mi+//FIpUKCAsnXrVuXw4cNKUFCQEhQUpGm/nUnC7FSFP480d/DgQcXV1VUZPXq0cv78eWXBggVKxowZlfnz58e/ZuzYsUrWrFmVv//+Wzl58qTSrFkzpVChQsqzZ8807bsj6tChg5IvXz5lzZo1yuXLl5Xly5crOXPmVPr37x//Gv48rOfx48fKsWPHlGPHjikAlMmTJyvHjh1Trl69qihGfu8bNGiglC1bVjlw4ICye/duxd/fX2ndurWGX5V9YBBHSQBI9pgzZ078a549e6Z89dVXSrZs2ZSMGTMqLVq0UG7fvq1pv51J4iCOP4+0t3r1aqVEiRKKu7u7UqxYMWXmzJkGz8fFxSlDhgxRvL29FXd3d6VOnTrKuXPnNOuvI4uMjFR69eqlFChQQPHw8FAKFy6sfPfdd0p0dHT8a/jzsJ5t27Yle83o0KGDohj5vb9//77SunVrJXPmzIqnp6fSqVMn5fHjxxp9RfZDpyQsaU1EREREdoFr4oiIiIjsEIM4IiIiIjvEII6IiIjIDjGIIyIiIrJDDOKIiIiI7BCDOCIiIiI7xCCOiIiIyA4xiCMiIiKyQwziiJzYlStXoNPpcPz4ca27Eu/s2bN455134OHhgTJlyph1Di2/roIFC2LKlClp8rnmzp2LrFmzpsnnIiLbwyCOSEMdO3aETqfD2LFjDdpXrlwJnU6nWb+0NGzYMGTKlAnnzp0z2DSbiIgMMYgj0piHhwfGjRuHhw8fat0Vi3nx4oXZ//fixYuoVq0a/Pz8kCNHDov2y5re5GvW8tyWEBMTo3UXiJwSgzgijdWtWxc+Pj4ICQlJ8TXDhw9PMrU4ZcoUFCxYMP5xx44d0bx5c4wZMwbe3t7ImjUrRowYgZcvX6Jfv37Inj078ufPjzlz5iQ5/9mzZ1GlShV4eHigRIkS2LFjh8Hzp06dQsOGDZE5c2Z4e3ujXbt2+O+//+Kfr1WrFr7++mt88803yJkzJ+rXr5/s1xEXF4cRI0Ygf/78cHd3R5kyZbBhw4b453U6HY4cOYIRI0ZAp9Nh+PDhyZ5nw4YNqFatGrJmzYocOXKgcePGuHjxoklf18OHD9G2bVvkypULGTJkgL+/v8H35p9//kHt2rWRIUMG5MiRA59//jmioqKSfL9Hjx6NvHnzomjRovHPPX78GK1bt0amTJmQL18+/Pzzzwb9evToEbp27YpcuXLB09MTtWvXxokTJ+Kf1/+8Z8+ejUKFCsHDwyPZ70NiFy9eRLNmzeDt7Y3MmTOjYsWK2Lx5c/zzI0aMQIkSJZL8vzJlymDIkCHxj2fPno2AgAB4eHigWLFi+OWXX+Kf009VL1myBDVr1oSHhwcWLFiAq1evokmTJsiWLRsyZcqE4sWLY926dUb1m4jMwyCOSGPp0qXDmDFj8OOPP+LGjRtvdK6tW7fi1q1b2LlzJyZPnoxhw4ahcePGyJYtGw4cOIAvv/wSX3zxRZLP069fPwQHB+PYsWMICgpCkyZNcP/+feBVwFG7dm2ULVsWhw8fxoYNG3Dnzh18/PHHBueYN28e3NzcsGfPHkyfPj3Z/k2dOhWTJk3CxIkTcfLkSdSvXx9NmzbF+fPnAQC3b99G8eLFERwcjNu3b6Nv377JnufJkyfo06cPDh8+jC1btsDFxQUtWrRAXFyc0V/XkCFDEBYWhvXr1+PMmTP49ddfkTNnzvjz169fH9myZcOhQ4ewbNkybN68GV9//bXB+bds2YJz584hNDQUa9asiW+fMGECSpcujWPHjmHAgAHo1asXQkND459v2bIl7t69i/Xr1+PIkSMoV64c6tSpgwcPHsS/5sKFC/jrr7+wfPlyo9f2RUVF4f3338eWLVtw7NgxNGjQAE2aNMG1a9cAAJ07d8aZM2dw6NCh+P9z7NgxnDx5Ep06dQIALFiwAEOHDsXo0aNx5swZjBkzBkOGDMG8efMMPpf+6zpz5gzq16+P7t27Izo6Gjt37sQ///yDcePGIXPmzEb1m4jMpBCRZjp06KA0a9ZMURRFeeedd5TOnTsriqIoK1asUBL+eQ4bNkwpXbq0wf/94YcfFD8/P4Nz+fn5KbGxsfFtRYsWVapXrx7/+OXLl0qmTJmURYsWKYqiKJcvX1YAKGPHjo1/TUxMjJI/f35l3LhxiqIoysiRI5V69eoZfO7r168rAJRz584piqIoNWvWVMqWLfvarzdv3rzK6NGjDdoqVqyofPXVV/GPS5curQwbNuy150ro3r17CgDln3/+MfrratKkidKpU6dkzzdz5kwlW7ZsSlRUVHzb2rVrFRcXFyU8PFxRXn2/vb29lejoaIP/6+fnpzRo0MCgrVWrVkrDhg0VRVGUXbt2KZ6ensrz588NXlOkSBFlxowZivLq550+fXrl7t27qX7dc+bMUby8vFJ9TfHixZUff/wx/nHDhg2Vbt26xT/u0aOHUqtWLYN+LFy40OAcI0eOVIKCghQlwfd2ypQpBq8pWbKkMnz48FT7QkSWxZE4Ihsxbtw4zJs3D2fOnDH7HMWLF4eLi/pn7e3tjZIlS8Y/TpcuHXLkyIG7d+8a/L+goKD4j11dXVGhQoX4fpw4cQLbtm1D5syZ449ixYoBr6bv9MqXL59q3yIjI3Hr1i1UrVrVoL1q1aomf83nz59H69atUbhwYXh6esZPK+tHnIz5urp164bFixejTJky6N+/P/bu3Rv/2jNnzqB06dLIlCmTQT/j4uJw7ty5+LaSJUvCzc0tSf8Sfl7944Tfz6ioKOTIkcPge3r58mWD76efnx9y5cpl0vclKioKffv2RUBAALJmzYrMmTPjzJkzBt+Xzz77DIsWLcLz58/x4sULLFy4EJ07dwZejUBevHgRXbp0MejbqFGjkkxXV6hQweBxz549MWrUKFStWhXDhg3DyZMnTeo7EZnOVesOEJGoUaMG6tevj4EDB6Jjx44Gz7m4uEBRFIO25BaTp0+f3uCxTqdLti3xtGNqoqKi0KRJE4wbNy7Jc3ny5In/OGHAY21NmjSBn58fZs2ahbx58yIuLg4lSpQwKQGgYcOGuHr1KtatW4fQ0FDUqVMH3bt3x8SJE40+hzlfc1RUFPLkyYPt27cneS5huRBzzt23b1+EhoZi4sSJeOutt5AhQwZ89NFHBt+XJk2awN3dHStWrICbmxtiYmLw0UcfxfcNAGbNmoXKlSsbnDtdunQGjxP3r2vXrqhfvz7Wrl2LTZs2ISQkBJMmTUKPHj1M/jqIyDgciSOyIWPHjsXq1auxb98+g/ZcuXIhPDzcIJCzZA20/fv3x3/88uVLHDlyBAEBAQCAcuXK4fTp0yhYsCDeeustg8OUQMPT0xN58+bFnj17DNr37NmDwMBAo89z//59nDt3DoMHD0adOnUQEBCQYmZval8XXn1fO3TogPnz52PKlCmYOXMmACAgIAAnTpzAkydPDPrp4uJikMCQkoSfV/844fczPDwcrq6uSb6f+jV55tqzZw86duyIFi1aoGTJkvDx8cGVK1cMXuPq6ooOHTpgzpw5mDNnDj755BNkyJABeDVymzdvXly6dClJ3woVKvTaz+/r64svv/wSy5cvR3BwMGbNmvVGXw8RpY4jcUQ2pGTJkmjbti2mTZtm0F6rVi3cu3cP48ePx0cffYQNGzZg/fr18PT0tMjn/fnnn+Hv74+AgAD88MMPePjwYfwUW/fu3TFr1iy0bt0a/fv3R/bs2XHhwgUsXrwYs2fPTjJCk5p+/fph2LBhKFKkCMqUKYM5c+bg+PHjWLBggdHnyJYtG3LkyIGZM2ciT548uHbtGgYMGGDy1zV06FCUL18exYsXR3R0NNasWRMfaLVt2xbDhg1Dhw4dMHz4cNy7dw89evRAu3bt4O3t/do+7tmzB+PHj0fz5s0RGhqKZcuWYe3atcCrbOSgoCA0b94c48ePx9tvv41bt25h7dq1aNGiRZJpSlP4+/tj+fLlaNKkCXQ6HYYMGZLsqGvXrl3jv9bEQfX333+Pnj17wsvLCw0aNEB0dDQOHz6Mhw8fok+fPil+7m+++QYNGzbE22+/jYcPH2Lbtm0GATMRWR5H4ohszIgRI5JceAMCAvDLL7/g559/RunSpXHw4MEUMzfNMXbsWIwdOxalS5fG7t27sWrVqvhRIf3oWWxsLOrVq4eSJUvim2++QdasWQ3W3xmjZ8+e6NOnD4KDg1GyZEls2LABq1atgr+/v9HncHFxweLFi3HkyBGUKFECvXv3xoQJE0z+utzc3DBw4ECUKlUKNWrUQLp06bB48WIAQMaMGbFx40Y8ePAAFStWxEcffYQ6dergp59+MqqPwcHBOHz4MMqWLYtRo0Zh8uTJ8WVXdDod1q1bhxo1aqBTp054++238cknn+Dq1atGBYipmTx5MrJly4YqVaqgSZMmqF+/PsqVK5fkdf7+/qhSpQqKFSuWZNq0a9eumD17NubMmYOSJUuiZs2amDt37mtH4mJjY9G9e3cEBASgQYMGePvttw1KkxCR5emUxAttiIjIoSmKAn9/f3z11Vepjq4RkW3jdCoRkRO5d+8eFi9ejPDw8PjacERknxjEERE5kdy5cyNnzpyYOXMmsmXLpnV3iOgNMIgjInIiXEFD5DiY2EBERERkhxjEEREREdkhBnFEREREdohBHBEREZEdYhBHREREZIcYxBERERHZIQZxRERERHaIQRwRERGRHfo/xAulksVH3kYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the reflection values at the central frequency for different numbers of absorber layers\n",
    "reflection_freq0 = [\n",
    "    np.abs(\n",
    "        batch_results[f\"{num_layers} layers\"][\"modemonitor\"].amps.sel(direction=\"-\", f=freq0).values\n",
    "    )\n",
    "    ** 2\n",
    "    for num_layers in num_layers_list\n",
    "]\n",
    "\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "# Plot the reflection values in dB against the number of absorber layers\n",
    "ax1.plot(num_layers_list, 10 * np.log10(reflection_freq0), color=\"b\", linewidth=2)\n",
    "ax1.set_xlabel(\"Number of absorber layers\")\n",
    "ax1.set_ylabel(\"Reflection at central frequency (dB)\", color=\"b\")\n",
    "ax1.tick_params(axis=\"y\", labelcolor=\"b\")\n",
    "\n",
    "# Create a second y-axis sharing the same x-axis for additional data visualization\n",
    "ax2 = ax1.twinx()\n",
    "# Plot the number of FDTD cells (in millions) against the number of absorber layers\n",
    "ax2.plot(num_layers_list, num_cells_list, color=\"r\", linewidth=2)\n",
    "ax2.set_ylabel(\"Number of FDTD cells (million)\", color=\"r\")\n",
    "ax2.tick_params(axis=\"y\", labelcolor=\"r\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff86e278-ec82-47a1-8f24-288839516a52",
   "metadata": {},
   "source": [
    "### Reflection as a Function of Grid Resolution\n",
    "\n",
    "Next we are going to test how the reflection depends on the grid resolution and thus the physical layer thickness of the absorber region. Similarly we perform a parameter sweep from 10 to 30 minimum steps per wavelength. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "49c82b7c-4f50-444d-9e85-24daa1703df0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7f37376354034e998e275a04e8d2203c",
       "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\">16:42:40 CEST </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span> tasks.                      \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:42:40 CEST\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m5\u001b[0m tasks.                      \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\">16:42:45 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.133</span> for the whole batch.               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:42:45 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.133\u001b[0m for the whole batch.               \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>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after   \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>the Batch has completed.                                          \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after   \n",
       "\u001b[2;36m              \u001b[0mthe Batch has completed.                                          \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8ca1cb5e561b4fcd9db34cfe1f957b9c",
       "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\">16:43:03 CEST </span>Batch complete.                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:43:03 CEST\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                   \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": "b28bee86cf734a9390f9a18e2da59ec0",
       "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": [
    "grid_resolution_list = list(\n",
    "    range(10, 31, 5)\n",
    ")  # Create a list of absorber layer counts from 10 to 100 in increments of 10\n",
    "\n",
    "# Initialize an empty dictionary to store simulation objects with their respective layer counts as keys\n",
    "sims = {}\n",
    "\n",
    "# Loop over each number of absorber layers to create simulations\n",
    "for grid_resolution in grid_resolution_list:\n",
    "    sim = make_sim(num_layers=60, grid_resolution=grid_resolution)\n",
    "    # Append the number of cells (converted to millions) to the num_cells_list\n",
    "    sims[f\"{grid_resolution} steps per wavelength\"] = sim\n",
    "\n",
    "# Create a batch of simulations\n",
    "batch = web.Batch(simulations=sims, verbose=True)\n",
    "\n",
    "# Run the batch of simulations and save results to the specified directory\n",
    "batch_results = batch.run(path_dir=\"data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dec1c00-76d1-4849-8f1c-8cf3ffdb756a",
   "metadata": {},
   "source": [
    "Plotting the results in a similar way, we can clearly see that the reflection increases with grid resolution since, for the same number of layers, the physical thickness of the absorber region decreases with resolution. Therefore, for simulations with a very fine grid, a larger number of absorber layers is needed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1d66cd54-eb84-4d44-8efd-71d281a639f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Normalize the colormap based on the minimum and maximum values in the grid_resolution_list\n",
    "norm = mcolors.Normalize(vmin=min(grid_resolution_list), vmax=max(grid_resolution_list))\n",
    "colormap = cm.viridis  # Use the 'viridis' colormap for plotting\n",
    "\n",
    "# Iterate over each grid_resolution in the list\n",
    "for grid_resolution in grid_resolution_list:\n",
    "    # Calculate the reflection values by taking the absolute square of the mode amplitude values from the simulation results\n",
    "    reflection = (\n",
    "        np.abs(\n",
    "            batch_results[f\"{grid_resolution} steps per wavelength\"][\"modemonitor\"]\n",
    "            .amps.sel(direction=\"-\")\n",
    "            .values\n",
    "        )\n",
    "        ** 2\n",
    "    )\n",
    "    # Get the color corresponding to the current grid_resolution using the normalized colormap\n",
    "    color = colormap(norm(grid_resolution))\n",
    "    # Plot the reflection values in dB against the wavelengths, using the specified color and label\n",
    "    plt.plot(\n",
    "        ldas,\n",
    "        10 * np.log10(reflection),\n",
    "        color=color,\n",
    "        label=f\"{grid_resolution} steps per wavelength\",\n",
    "    )\n",
    "\n",
    "\n",
    "plt.ylim(-100, 0)\n",
    "plt.xlabel(\"Wavelength (μm)\")\n",
    "plt.ylabel(\"Absorber reflection (dB)\")\n",
    "plt.legend(loc=\"upper left\", bbox_to_anchor=(1.05, 1), borderaxespad=0.0)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3b208b8",
   "metadata": {},
   "source": [
    "## PML\n",
    "\n",
    "For comparison, we conducted the same simulation using the PML boundary condition. In Tidy3D, the default number of PML layers is 12, which typically performs very well. Increasing the number of layers generally does not provide additional benefits. In our study, we varied the number of PML layers from 10 to 20, increasing in increments of 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "19e474bb-e1ba-42c9-a75d-5ab9b2fa9c4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "326ffc26fdcd4c6bbedf425d17ced86f",
       "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\">16:43:16 CEST </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6</span> tasks.                      \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:43:16 CEST\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m6\u001b[0m tasks.                      \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\">16:43:22 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.150</span> for the whole batch.               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:43:22 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.150\u001b[0m for the whole batch.               \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>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after   \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>the Batch has completed.                                          \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after   \n",
       "\u001b[2;36m              \u001b[0mthe Batch has completed.                                          \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6b8826c3fcb9446285be216427b70cbf",
       "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\">16:43:38 CEST </span>Batch complete.                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:43:38 CEST\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                   \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": "2f5d4788a89a4817820ca07c11cfab6a",
       "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": [
    "# Create a list of even numbers from 10 to 20, representing the number of PML layers for simulations\n",
    "num_layers_list = list(range(10, 21, 2))\n",
    "\n",
    "\n",
    "sims = {\n",
    "    f\"{num_layers} layers\": make_sim(num_layers, use_absorber=False)\n",
    "    for num_layers in num_layers_list\n",
    "}\n",
    "\n",
    "\n",
    "batch = web.Batch(simulations=sims, verbose=True)\n",
    "\n",
    "batch_results = batch.run(path_dir=\"data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86014885",
   "metadata": {},
   "source": [
    "The plot clearly demonstrates that PML with 12 layers outperforms the adiabatic absorber with 100 layers, highlighting the superiority of PML. The reflection is already at the level of numerical noise, so users can typically rely on the default settings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "645545b8-b3bf-4dc0-ae60-22d9552bf441",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "norm = mcolors.Normalize(vmin=min(num_layers_list), vmax=max(num_layers_list))\n",
    "colormap = cm.viridis  # Use the 'viridis' colormap for plotting\n",
    "\n",
    "for num_layers in num_layers_list:\n",
    "    reflection = (\n",
    "        np.abs(batch_results[f\"{num_layers} layers\"][\"modemonitor\"].amps.sel(direction=\"-\").values)\n",
    "        ** 2\n",
    "    )\n",
    "    color = colormap(norm(num_layers))\n",
    "    plt.plot(ldas, 10 * np.log10(reflection), color=color, label=f\"{num_layers} layers\")\n",
    "\n",
    "plt.ylim(-130, 0)\n",
    "plt.xlabel(\"Wavelength (μm)\")\n",
    "plt.ylabel(\"PML reflection (dB)\")\n",
    "plt.legend(loc=\"upper left\", bbox_to_anchor=(1.05, 1), borderaxespad=0.0)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc3f7c92-c409-437e-8285-bda71af615f3",
   "metadata": {},
   "source": [
    "For PML, the reflection shouldn't depend noticeably on the grid resolution so we can skip the corresponding test here."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23a60fcf",
   "metadata": {},
   "source": [
    "## Final Remarks\n",
    "\n",
    "This notebook illustrates the differences in reflection between adiabatic absorbers and PML, emphasizing how the number of layers influences performance. Reflection from the absorber can arise from two main sources. The first, and more common, issue is that the conductivity does not increase gradually enough. This can be addressed by increasing the number of absorber layers, which is set to 40 by default. The second issue occurs when the absorption is insufficient, allowing light to reach the Perfect Electric Conductor (PEC) boundary at the end of the absorber and reflect back without being fully attenuated before re-entering the simulation region. This would manifest itself as the reflection level not decreasing when more layers are added. In such cases, increasing the maximum conductivity (refer to the [API documentation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.AbsorberParams.html)) can be beneficial. In some cases, it could be possible to decrease `sigma_max` instead of increasing `num_layers`, if the absorption stays strong enough while becoming more gradual due to the lower maximum value. Additionally, altering the conductivity scaling order (`sigma_order`) might have an impact, though this is an advanced setting that we typically do not recommend adjusting. \n",
    "\n",
    "In summary, PML is highly preferred as the boundary condition when the simulation does not diverge. When absorber boundaries need to be used, users are encouraged to experiment and benchmark the reflection for the particular simulation setting they are interested in by following a similar procedure outlined in this notebook to achieve a balance between accuracy and cost. "
   ]
  }
 ],
 "metadata": {
  "description": "This notebook benchmarks the performance of absorbing boundaries like PML and adiabatic absorbers in Tidy3D FDTD.",
  "feature_image": "",
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "keywords": "FDTD, absorbing boundaries, PML, adiabatic absorber, Tidy3D, reflections, simulation, waveguide",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  },
  "title": "Suppressing Artificial Reflections with Absorbing Boundaries in FDTD Simulations"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}