{
  "cells": [
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      "metadata": {},
      "source": [
        "# Nanobeam cavity\n",
        "\n",
        "1D photonic crystal cavities, also known as nanobeam cavities, are\n",
        "widely used in photonics and quantum optics due to their high-quality\n",
        "factors (Q-factors) and low mode volumes. These attributes make them\n",
        "ideal for exploiting Purcell enhancement, which is crucial for\n",
        "applications involving quantum emitters. The cavities consist of a\n",
        "one-dimensional array of unit cells acting as Bragg mirrors surrounding\n",
        "a tapered region. In this region, the photonic bandgap is gently\n",
        "shifted, allowing the accommodation of an optical mode with minimal\n",
        "scattering and a high-quality factor. In other dimensions, the light\n",
        "confinement is due to total internal reflection.\n",
        "\n",
        "In this notebook, we will implement the basic simulation for such\n",
        "cavities, and go through analysis procedures to calculate mode volume\n",
        "and Q-factors, without the need to run the simulation until the total\n",
        "decay of the fields, which is not practical for high-Q cavities. The\n",
        "Q-factor is a figure of merit related to the photon lifetime in the\n",
        "cavity; hence, a high Q-factor means that the fields in the cavity will\n",
        "decay slowly over time. The mode volume is a metric to evaluate the\n",
        "volumetric distribution of the mode; a low mode volume indicates a\n",
        "highly concentrated field. To explore Purcell enhancement, a cavity with\n",
        "a high Q-factor and low mode volume is needed.\n",
        "\n",
        "As a reference, we will reproduce the results of the work of\n",
        "`Parag B. Deotare, Murray W. McCutcheon, Ian W. Frank, Mughees Khan,and Marko Lončar, \"High quality factor photonic crystal nanobeam cavities\", Applied Physics Letters 94(12), (2009).`\n",
        "[DOI:\n",
        "https://doi.org/10.1063/1.3107263](https://doi.org/10.1063/1.3107263).\n",
        "\n",
        "The design consists of two stacks of unit cells that will act as\n",
        "mirrors, and a tapered region, where the parameters of the unit cell are\n",
        "modified given a polynomial function, to reduce abrupt mode mismatch and\n",
        "avoid scattering. It is possible to explore asymmetries in the cavities,\n",
        "reducing the number of unit cells in one direction and adding a tapered\n",
        "region at its end, allowing efficient light extraction with tapered\n",
        "fiber optics.\n",
        "\n",
        "<img src=\"img/NanobeamCavity_1.png\" width=\"400\" alt=\"Schematic of the experiment\">\n",
        "\n",
        "For more information, please refer to our [learning\n",
        "center](https://www.flexcompute.com/tidy3d/examples/), where you can\n",
        "find tutorials and examples on how to carry out [band structure\n",
        "calculation](https://www.flexcompute.com/tidy3d/examples/notebooks/Bandstructure/),\n",
        "use the [resonance finder\n",
        "plugin](https://www.flexcompute.com/tidy3d/examples/notebooks/ResonanceFinder/)\n",
        "and how to calculate [Q-factor and mode volume for a 2D photonic crystal\n",
        "cavity](https://www.flexcompute.com/tidy3d/examples/notebooks/CavityFOM/)"
      ],
      "id": "40d7d827-f86d-40f5-8342-a247e123552c"
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "from typing import Callable\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import tidy3d as td\n",
        "from tidy3d import web\n",
        "\n",
        "# defining a random seed for reproducibility\n",
        "np.random.seed(12)"
      ],
      "id": "e100e666"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Auxiliary Functions\n",
        "\n",
        "We will start defining some auxiliary functions to help us set up the\n",
        "simulation.\n",
        "\n",
        "Since for using the [resonance finder\n",
        "plugin](https://www.flexcompute.com/tidy3d/examples/notebooks/ResonanceFinder/)\n",
        "it is necessary to analyze the fields after the source decays, we will\n",
        "define a simple function to estimate the decay time of the source, given\n",
        "the relation of the temporal width ($\\Delta t$) and the spectral\n",
        "linewidth ($\\Delta  \\nu$) of a gaussian pulse\n",
        "$\\Delta t \\Delta \\nu \\approx 0.44$, and the `offset` attribute of the\n",
        "`source_time`object."
      ],
      "id": "4a5b2d49-563b-4345-ad2d-bc36388f2693"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "def find_source_decay(source):\n",
        "    # source bandwidth\n",
        "    fwidth = source.source_time.fwidth\n",
        "\n",
        "    # time offset to start the source\n",
        "    time_offset = source.source_time.offset / (2 * np.pi * fwidth)\n",
        "\n",
        "    # time width for a gaussian pulse\n",
        "    pulse_time = 0.44 / fwidth\n",
        "\n",
        "    decay_time = time_offset + pulse_time\n",
        "    return decay_time"
      ],
      "id": "eef93fce"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Next, we will define the function to return the tapered parameter as a\n",
        "function of the unit cell position. A given parameter in the tapered\n",
        "region will follow the definition:\n",
        "\n",
        "$P_{tapered}(x) = P[1 - M_d(2x^3 -3x^2 + 1)]$\n",
        "\n",
        "Where\n",
        "$x \\equiv \\frac{\\text{unit cell index}}{\\text{number of tapered unit cells}}$,\n",
        "and the unit cell at the center is indexed as 0. Hence $x$ ranges from 0\n",
        "to 1, so the center unit cell would have a given parameter as\n",
        "$P(1- M_d)$.\n",
        "\n",
        "The $M_d$ parameter is a number ranging from $[0,1)$ and is interpreted\n",
        "as the maximum reduction of a given parameter, in proportion to its\n",
        "original value. For example, $M_d = 0.3$ means that the smallest value\n",
        "of this parameter will be 30% less than its original value."
      ],
      "id": "fcd46364-8af3-457d-8703-b39f797abb6d"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "def taper_factor(\n",
        "    max_dip,\n",
        "    structure_index,\n",
        "    n_tapered_structures,\n",
        "    cubic_factor=2,\n",
        "    quadratic_factor=-3,\n",
        "    linear_factor=0,\n",
        "    offsetFactor=1,\n",
        "):\n",
        "    cubic = cubic_factor\n",
        "    quadratic = quadratic_factor\n",
        "    linear = linear_factor\n",
        "    offset = offsetFactor\n",
        "\n",
        "    \"\"\"\n",
        "    Compute a taper factor using a cubic polynomial.\n",
        "\n",
        "    Parameters:\n",
        "    - max_dip: float, maximum dip in taper factor.\n",
        "    - structure_index: int, index of the current structure.\n",
        "    - n_tapered_structures: int, total number of tapered structures.\n",
        "    - cubic_factor: float, coefficient for cubic term (default 2).\n",
        "    - quadratic_factor: float, coefficient for quadratic term (default -3).\n",
        "    - linear_factor: float, coefficient for linear term (default 0).\n",
        "    - offsetFactor: float, constant offset (default 1).\n",
        "\n",
        "    Returns:\n",
        "    - float, the taper factor for the given structure.\n",
        "    \"\"\"\n",
        "\n",
        "    # to avoid division for 0 in a case where there is no tapered region\n",
        "    if n_tapered_structures == 0:\n",
        "        x = 1\n",
        "    else:\n",
        "        x = structure_index / np.ceil(n_tapered_structures / 2)\n",
        "\n",
        "    # factor is 1 if the structure is not in the tapered region\n",
        "    factor = 1 if x >= 1 else 1 - max_dip * (cubic * x**3 + quadratic * x**2 + linear * x + offset)\n",
        "\n",
        "    return factor"
      ],
      "id": "f2211417"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "To reduce the computational cost, we can exploit mirror symmetries of\n",
        "the problem to reduce the computational size up to 8 times. However, to\n",
        "properly set the symmetries parity, it is necessary to consider the\n",
        "polarization and vectorial nature of the fields. For more information on\n",
        "how to set up symmetries and their relation with source polarization,\n",
        "please refer to our [symmetries\n",
        "tutorial](https://www.flexcompute.com/tidy3d/examples/notebooks/Symmetry/).\n",
        "\n",
        "The function defined below will generate the right symmetry parity for a\n",
        "given polarization."
      ],
      "id": "c83cbc22-30ce-48d4-828c-7fa224c3846f"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "def get_symmetry(polarization):\n",
        "    if polarization == \"Ex\":\n",
        "        sym = [-1, 1, 1]\n",
        "    elif polarization == \"Ey\":\n",
        "        sym = [1, -1, 1]\n",
        "    elif polarization == \"Ez\":\n",
        "        sym = [1, 1, -1]\n",
        "    elif polarization == \"Hz\":\n",
        "        sym = [-1, -1, 1]\n",
        "    elif polarization == \"Hy\":\n",
        "        sym = [-1, 1, -1]\n",
        "    elif polarization == \"Hx\":\n",
        "        sym = [1, -1, -1]\n",
        "    return sym"
      ],
      "id": "4fc19761"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Simulation Setup\n",
        "\n",
        "Now, we will start creating functions to set up the simulation.\n",
        "\n",
        "First, we will define a function that creates a stack of unit cells on\n",
        "one side of the nanobeam, positioning them correctly and defining the\n",
        "appropriate tapered parameter. Since unit cell assembly is consistent\n",
        "across different types, the function will accept a `unit_cell_function`\n",
        "argument, which returns the unit cell geometry. Additionally, it\n",
        "requires a list of original parameter arguments (`original_params`) and\n",
        "a list of the $Md$ parameters (`max_dip_params`).\n",
        "\n",
        "Note that `n_tapered_structures` refers to the total number of tapered\n",
        "unit cells on both sides of the nanobeam. Hence, if there is an odd\n",
        "number of unit cells, the first one is exactly in the middle. Otherwise,\n",
        "it is separated by a lattice constant from its twin on the other side,\n",
        "plus a gap (`central_gap` argument). The definition of\n",
        "`n_constant_structures` refers to the mirror cells on each side."
      ],
      "id": "3d1d4ee9-ad0e-43e8-bd35-30113bb06f5e"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "def assemble_unit_cells(\n",
        "    unit_cell_function: Callable,\n",
        "    lattice_constant: float,\n",
        "    max_dip_lattice: float,\n",
        "    n_constant_structures: int,\n",
        "    n_tapered_structures: int,\n",
        "    original_params: list,\n",
        "    max_dip_params: list,\n",
        "    central_gap: float = 0,\n",
        "):\n",
        "    \"\"\"\n",
        "    Assemble unit cells into a structure with tapered lattice constants and parameters.\n",
        "\n",
        "    Parameters:\n",
        "    - unit_cell_function: callable, function to generate a unit cell given position and parameters.\n",
        "    - lattice_constant: float, base lattice constant for the unit cells.\n",
        "    - max_dip_lattice: float, maximum dip for tapering the lattice constant.\n",
        "    - n_constant_structures: int, number of constant lattice structures on either side.\n",
        "    - n_tapered_structures: int, total number of tapered structures.\n",
        "    - original_params: list of floats, original parameters to be tapered (default empty list).\n",
        "    - max_dip_params: list of floats, maximum dips for each parameter to be tapered (default empty list).\n",
        "    - central_gap: float, gap between central unit cells (only valid for an odd number of tapered structures).\n",
        "\n",
        "    Returns:\n",
        "    - unit_cells: combined unit cells as generated by the unit_cell_function.\n",
        "    - pos_x: final position of the last unit cell in the x-direction.\n",
        "    \"\"\"\n",
        "\n",
        "    unit_cells = None\n",
        "\n",
        "    # start position\n",
        "    pos_x = 0\n",
        "\n",
        "    # defining the number of unit cells for each side\n",
        "    positions = range(\n",
        "        n_constant_structures + int(np.floor(n_tapered_structures / 2)) + n_constant_structures % 2\n",
        "    )\n",
        "\n",
        "    # iterating over the unit cells\n",
        "    previous_lattice_constant = 0\n",
        "    for i in positions:\n",
        "        # defining the current value of the unit cell given the position\n",
        "        current_lattice_constant = lattice_constant * taper_factor(\n",
        "            max_dip_lattice, i, n_tapered_structures\n",
        "        )\n",
        "\n",
        "        # condition for odd or even number of unit cells\n",
        "        if i == 0:\n",
        "            if n_tapered_structures % 2 != 0:\n",
        "                pos_x = 0\n",
        "            else:\n",
        "                pos_x = central_gap / 2 + current_lattice_constant / 2\n",
        "        else:\n",
        "            pos_x += current_lattice_constant / 2 + previous_lattice_constant / 2\n",
        "\n",
        "        # now we create a list of parameters to input to the unit_cell_function\n",
        "        parameters = []\n",
        "        for idx in range(len(original_params)):\n",
        "            original = original_params[idx]\n",
        "            max_dip = max_dip_params[idx]\n",
        "            tapered_param = original * taper_factor(max_dip, i, n_tapered_structures)\n",
        "            parameters.append(tapered_param)\n",
        "\n",
        "        # defining the center\n",
        "        center = (pos_x, 0, 0)\n",
        "\n",
        "        # creating the unit cell calling the unit_cell_function\n",
        "        new_unit_cell = unit_cell_function(center, *parameters)\n",
        "\n",
        "        # adding the unit cell to the geometry (or initializing the variable if is the first unit cell)\n",
        "        unit_cells = new_unit_cell if unit_cells is None else unit_cells + new_unit_cell\n",
        "\n",
        "        # the separation to the next unit cell is the mean value of the lattice constants of the current and next unit cells\n",
        "        previous_lattice_constant = current_lattice_constant\n",
        "\n",
        "    # return the geometry and the position of the last unit cell\n",
        "    return unit_cells, pos_x"
      ],
      "id": "f6350ad5"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Unit Cell Functions\n",
        "\n",
        "As an example, we will create 3 different unit cell functions. The first\n",
        "two are simple functions to return elliptical and square unit cells. The\n",
        "third one is an example of more complex geometry using the\n",
        "[PolySlab](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PolySlab.html).\n",
        "\n",
        "Given the definition `assemble_unit_cells` function, the first argument\n",
        "must be the center of the geometry object, and non-taper parameters must\n",
        "be passed as default arguments."
      ],
      "id": "8fdb686a-1e96-4e5c-afff-867850a5d013"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "# function for creating ellipse unit cells\n",
        "def ellipse_uc(center, x_axis, y_axis, height=0.5):\n",
        "    theta = np.linspace(0, np.pi * 2, 1001)\n",
        "    x = x_axis * np.cos(theta) + center[0]\n",
        "    y = y_axis * np.sin(theta) + center[1]\n",
        "\n",
        "    geometry = td.PolySlab(\n",
        "        vertices=np.array([x, y]).T,\n",
        "        slab_bounds=(-height / 2 + center[2], height / 2 + center[2]),\n",
        "        axis=2,\n",
        "    )\n",
        "    return geometry\n",
        "\n",
        "\n",
        "# function for creating ellipse unit cells\n",
        "def square_uc(center, x_size, y_size, height=0.5):\n",
        "    geometry = td.Box(center=center, size=(x_size, y_size, height))\n",
        "    return geometry\n",
        "\n",
        "\n",
        "# function for creating \"sawfish\" unit cells\n",
        "def sawfish_uc(center, x, y, height=0.5, width=0.1):\n",
        "    e = 6\n",
        "\n",
        "    yArray = []\n",
        "    numPoints = 501\n",
        "\n",
        "    # defining the vertices for the PolySlab\n",
        "    xaxis = np.linspace(center[0] - x / 2, center[0] + x / 2, numPoints)\n",
        "    xArray = np.linspace(-x / 2, x / 2, numPoints)\n",
        "    yArray = y * ((np.cos((np.pi / x) * xArray)) ** e) + width / 2\n",
        "    yArray2 = -y * ((np.cos((np.pi / x) * np.flip(xArray))) ** e) - width / 2\n",
        "\n",
        "    X = np.concatenate([xaxis, np.flip(xaxis)])\n",
        "    Y = np.concatenate([yArray, yArray2])\n",
        "    arr = np.array([X, Y]).T\n",
        "\n",
        "    geometry = td.PolySlab(\n",
        "        vertices=arr,\n",
        "        slab_bounds=(-height / 2, height / 2),\n",
        "        axis=2,\n",
        "    )\n",
        "\n",
        "    return geometry"
      ],
      "id": "e8bdeb9a"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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aEHUiIiIiIiIiIiI1YcBSAb1794ZerwcAbNq0qdjt7N/r37+/28pz9OhR+bpO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\nnq5+EGRPOAAAAABJRU5ErkJggg==\n"
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
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          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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hUGDRokXIyMjAL7/8gjfffBP3338/u/jPyMjA8uXLER0djd27d1c4hhDHqCn1Hcvaybuu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\nhnZGAAAAAElFTkSuQmCC\n"
          }
        }
      ],
      "source": [
        "# plot the geometries\n",
        "\n",
        "args = dict(\n",
        "    lattice_constant=1,\n",
        "    max_dip_lattice=0.5,\n",
        "    n_constant_structures=15,\n",
        "    n_tapered_structures=8,\n",
        ")\n",
        "\n",
        "ellipse_geometry, _ = assemble_unit_cells(\n",
        "    unit_cell_function=ellipse_uc, original_params=[0.2, 0.3], max_dip_params=[0.4, 0.3], **args\n",
        ")\n",
        "ellipse_geometry.plot(z=0)\n",
        "\n",
        "square_geometry, _ = assemble_unit_cells(\n",
        "    unit_cell_function=square_uc, original_params=[0.4, 0.3], max_dip_params=[0.4, 0.3], **args\n",
        ")\n",
        "square_geometry.plot(z=0)\n",
        "\n",
        "sawfish_geometry, _ = assemble_unit_cells(\n",
        "    unit_cell_function=sawfish_uc, original_params=[1, 0.5], max_dip_params=[0.4, 0.5], **args\n",
        ")\n",
        "sawfish_geometry.plot(z=0)\n",
        "\n",
        "plt.show()"
      ],
      "id": "26ed24c4"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Source and Monitors\n",
        "\n",
        "Auxiliary function for creating the [point dipole\n",
        "source](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PointDipole.html)"
      ],
      "id": "3b63a619-3eaa-4411-adb9-855789fae043"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [],
      "source": [
        "def create_source(\n",
        "    polarization=\"Ey\",\n",
        "    wl1=0.7,\n",
        "    wl2=1.0,\n",
        "    center=(0, 0, 0),\n",
        "):\n",
        "    # defining the frequencies based on the given wavelengths\n",
        "    freq1 = td.C_0 / wl1\n",
        "    freq2 = td.C_0 / wl2\n",
        "    freq0 = (freq1 - freq2) / 2 + freq2\n",
        "    fwidth = (freq1 - freq2) / 2\n",
        "\n",
        "    # defining the source\n",
        "    source = td.PointDipole(\n",
        "        center=center,\n",
        "        name=\"pointDipole\",\n",
        "        polarization=polarization,\n",
        "        source_time=td.GaussianPulse(\n",
        "            freq0=freq0, fwidth=fwidth, phase=2 * np.pi * np.random.random()\n",
        "        ),\n",
        "    )\n",
        "\n",
        "    return source"
      ],
      "id": "52594414"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Function for creating the monitors.\n",
        "\n",
        "For this simulation we will have distinct types of monitors:\n",
        "\n",
        "1.  Some point\n",
        "    [`FieldTimeMonitor`](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldTimeMonitor.html#tidy3d.FieldTimeMonitor),\n",
        "    that will record the fields as a function of time in different\n",
        "    positions of the cavity, and will be used for Q-factor and resonant\n",
        "    frequency calculation. We will record fields during the entire\n",
        "    simulation and select the portion we want to analyze during the data\n",
        "    analysis process.\n",
        "\n",
        "2.  Six\n",
        "    [`FluxTimeMonitor`](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FluxTimeMonitor.html)\n",
        "    surrounding the simulation domain, that will be used for\n",
        "    discriminating the directions of losses, and calculation of\n",
        "    directional Q-factors.\n",
        "\n",
        "3.  One 3D `FieldTimeMonitor` for calculating the total energy at the\n",
        "    end of the simulation. To reduce memory requirements, we will set\n",
        "    the `interval` as `10`, which means that we will record fields at\n",
        "    each 10 time steps. For this example, we will not apply spatial\n",
        "    downsampling, although it is possible to further reduce memory usage\n",
        "    by adjusting the `interval_space` parameter.\n",
        "\n",
        "4.  One planar `FieldTimeMonitor`, also recorded at the end of the\n",
        "    simulation, to show the field profile.\n",
        "\n",
        "Note that, in principle, it would be possible to use a frequency-domain\n",
        "monitor to visualize the resonant mode profile. However, the exact\n",
        "resonance frequency is often not known beforehand, therefore it is\n",
        "difficult the set the exact frequency to visualize. On the other hand,\n",
        "setting the time field monitors to record only a fell oscillation cycle\n",
        "at the end of the simulation ensures that only the resonant modes are\n",
        "being recorded, as the other frequencies have already decayed."
      ],
      "id": "dbae5aed-85da-4290-a3ef-aa5a55025f51"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [],
      "source": [
        "def create_monitors(\n",
        "    simulation,\n",
        "    n_point_monitors=5,\n",
        "    center=(0, 0, 0),\n",
        "    deviation=(0.2, 0, 0),\n",
        "):\n",
        "    simulation_size = simulation.size\n",
        "\n",
        "    freq0 = simulation.sources[0].source_time.freq0\n",
        "\n",
        "    # creating random positions around the center of the cavity\n",
        "    positions = np.random.random((n_point_monitors, 3)) * np.array(deviation) + center\n",
        "\n",
        "    # creating the randomly positioned monitors\n",
        "    point_monitors = [\n",
        "        td.FieldTimeMonitor(\n",
        "            center=tuple(positions[i]),\n",
        "            name=f\"pointMon{i}\",\n",
        "            start=0,\n",
        "            size=(0, 0, 0),\n",
        "            interval=1,\n",
        "        )\n",
        "        for i in range(n_point_monitors)\n",
        "    ]\n",
        "\n",
        "    # defining size for the 3D field monitor\n",
        "    size = tuple(np.array(simulation_size) * 0.9)\n",
        "\n",
        "    # defining the start time of the monitors to record only the last two oscillations of the field\n",
        "    start = simulation.run_time - 1 / freq0\n",
        "\n",
        "    # 3D field monitor for energy density calculation\n",
        "    field_monitor = td.FieldTimeMonitor(\n",
        "        center=(0, 0, 0),\n",
        "        size=size,\n",
        "        start=start,\n",
        "        name=\"fieldTimeMon\",\n",
        "        interval_space=(1, 1, 1),\n",
        "        interval=10,\n",
        "    )\n",
        "\n",
        "    # 2D field monitor for visualizing the resonant mode profile\n",
        "    field_profile_monitor = td.FieldTimeMonitor(\n",
        "        center=(0, 0, 0),\n",
        "        size=(td.inf, td.inf, 0),\n",
        "        start=start,\n",
        "        name=\"fieldProfileMon\",\n",
        "    )\n",
        "\n",
        "    # setup planar flux monitors surrounding the simulation volume\n",
        "    flux_monitors = []\n",
        "    for i in [-1, 1]:\n",
        "        for j in [(0.5, 0, 0), (0, 0.5, 0), (0, 0, 0.5)]:\n",
        "            j = np.array(j)\n",
        "            # defining the center\n",
        "            mon_center = tuple(np.array(size) * j * i + -1 * i * j * (0.95, 0.95, 0.95))\n",
        "\n",
        "            # defining the size\n",
        "            j2 = np.array([0 if l == 0.5 else 1 for l in j])\n",
        "            mon_size = tuple(np.array(size) * j2 * 2)\n",
        "\n",
        "            # defining the name\n",
        "            mon_name = np.array([\"x\", \"y\", \"z\"])[np.ceil(j).astype(bool)][0]\n",
        "            mon_name = (\"-\" + mon_name) if i == -1 else mon_name\n",
        "\n",
        "            # creating the monitors\n",
        "            flux_monitors.append(\n",
        "                td.FluxTimeMonitor(center=mon_center, size=mon_size, start=start, name=mon_name)\n",
        "            )\n",
        "\n",
        "    return (\n",
        "        point_monitors\n",
        "        + flux_monitors\n",
        "        + [\n",
        "            field_monitor,\n",
        "            field_profile_monitor,\n",
        "        ]\n",
        "    )"
      ],
      "id": "13ab5bbd"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Function for creating simulation object. Since many simulation\n",
        "parameters such as symmetry and source positions depend on the final\n",
        "nanobeam structure, it is convenient to wrap everything in a single\n",
        "function."
      ],
      "id": "fabacf0f-e729-446d-bcc8-8827929ff548"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [],
      "source": [
        "def create_simulation(\n",
        "    wl1: float,\n",
        "    wl2: float,\n",
        "    width: float,\n",
        "    height: float,\n",
        "    unit_cell_function: Callable,\n",
        "    lattice_constant: float,\n",
        "    n_constant_structures_right: int,\n",
        "    n_constant_structures_left: int,\n",
        "    n_tapered_structures: int,\n",
        "    n_tapered_output: int,\n",
        "    max_dip_lattice: float,\n",
        "    original_params: list,\n",
        "    max_dip_params: list,\n",
        "    polarization: str,\n",
        "    unit_cell_index: int,\n",
        "    waveguide_index: float,\n",
        "    run_time: float = 2e-12,\n",
        "    central_gap: float = 0,\n",
        "    delta_override: float = 0.01,\n",
        "    substrate_index: float = 1,\n",
        "    medium_index: float = 1,\n",
        "    sidewall_angle: float = 0,\n",
        "):\n",
        "    \"\"\"\n",
        "    create a simulation with specified parameters and unit cells.\n",
        "\n",
        "    Parameters:\n",
        "    - wl1: float, wavelength 2.\n",
        "    - wl2: float, wavelength 2 for the simulation.\n",
        "    - width: float, width of the waveguide structure.\n",
        "    - height: float, height of the structure.\n",
        "    - unit_cell_function: callable, function to generate unit cells.\n",
        "    - delta_override: float, spacing for mesh override.\n",
        "    - lattice_constant: float, lattice constant.\n",
        "    - n_constant_structures_right: int, number of constant structures on the right side of the nanobeam.\n",
        "    - n_constant_structures_left: int, number of constant structures on the left side of the nanobeam.\n",
        "    - n_tapered_structures: int, number of tapered structures at the cavity region.\n",
        "    - n_tapered_waveguide: int, number of tapered unit cells at the right end of the nanobeam.\n",
        "    - max_dip_lattice: float, maximum dip for the lattice constant tapering.\n",
        "    - original_params: list, list with the unit cell params.\n",
        "    - max_dip_params: list, list with the tapering params\n",
        "    - polarization: str, polarization of the source.\n",
        "    - unit_cell_index: int, index for the unit cell structure.\n",
        "    - waveguide_index: float, index for the waveguide.\n",
        "    - run_time: float, total run time for the simulation.\n",
        "    - central_gap: float, central gap between structures (if n_tapered_structures is odd).\n",
        "    - substrate_index: float, index for the substrate material.\n",
        "    - medium_index: float, index for the surrounding medium.\n",
        "    - sidewall_angle: float, waveguide sidewall angle.\n",
        "\n",
        "    Returns:\n",
        "    - sim: the simulation object with defined structures, sources, and monitors.\n",
        "    \"\"\"\n",
        "\n",
        "    # define the mesh override at the cavity region\n",
        "    mesh_override = td.MeshOverrideStructure(\n",
        "        geometry=td.Box(center=(0, 0, 0), size=(4, width, height)),\n",
        "        dl=(delta_override,) * 3,\n",
        "    )\n",
        "\n",
        "    # define grid specification for the simulation\n",
        "    grid_spec = td.GridSpec.auto(\n",
        "        min_steps_per_wvl=15,\n",
        "        override_structures=[mesh_override],\n",
        "    )\n",
        "\n",
        "    kwargs = dict(\n",
        "        unit_cell_function=unit_cell_function,\n",
        "        lattice_constant=lattice_constant,\n",
        "        max_dip_lattice=max_dip_lattice,\n",
        "        original_params=original_params,\n",
        "        max_dip_params=max_dip_params,\n",
        "        central_gap=central_gap,\n",
        "    )\n",
        "\n",
        "    # assemble unit cells for the right side of the nanobeam\n",
        "    unit_cells_right, pos_x_right = assemble_unit_cells(\n",
        "        n_constant_structures=n_constant_structures_right,\n",
        "        n_tapered_structures=n_tapered_structures,\n",
        "        **kwargs,\n",
        "    )\n",
        "\n",
        "    # assemble unit cells for the left side of the nanobeam\n",
        "    unit_cells_left, pos_x_left = assemble_unit_cells(\n",
        "        n_constant_structures=n_constant_structures_left,\n",
        "        n_tapered_structures=n_tapered_structures,\n",
        "        **kwargs,\n",
        "    )\n",
        "\n",
        "    # rotate and adjust the position of the left unit cells\n",
        "    unit_cells_left = unit_cells_left.rotated(np.pi, axis=2)\n",
        "    pos_x_left *= -1\n",
        "\n",
        "    # assemble and position the tapered output if applicable\n",
        "    if n_tapered_output > 0:\n",
        "        waveguide_tapered, pos_tapered = assemble_unit_cells(\n",
        "            n_constant_structures=0, n_tapered_structures=n_tapered_output * 2, **kwargs\n",
        "        )\n",
        "\n",
        "        # rotate and position the tapered output\n",
        "        waveguide_tapered = waveguide_tapered.rotated(np.pi, axis=2)\n",
        "        waveguide_tapered = waveguide_tapered.translated(\n",
        "            x=pos_x_right + lattice_constant + pos_tapered, y=0, z=0\n",
        "        )\n",
        "\n",
        "        # combine the left, right, and tapered waveguide geometries\n",
        "        nanobeam_geometry = unit_cells_left + unit_cells_right + waveguide_tapered\n",
        "\n",
        "    else:\n",
        "        pos_tapered = 0\n",
        "        nanobeam_geometry = unit_cells_left + unit_cells_right\n",
        "\n",
        "    # calculate sizes and defining the center of the nanobeam geometry\n",
        "    size_left = abs(pos_x_left) + lattice_constant + wl2 / 2\n",
        "    size_right = abs(pos_x_right + pos_tapered) + lattice_constant + wl2 / 2\n",
        "    nanobeam_size = size_left + size_right\n",
        "    nanobeam_center = size_left - nanobeam_size / 2\n",
        "\n",
        "    # recenter the geometry to optimize the simulation space\n",
        "    nanobeam_geometry = nanobeam_geometry.translated(x=nanobeam_center, y=0, z=0)\n",
        "\n",
        "    # define mediums for the substrate and nanobeam materials\n",
        "    nanobeam_medium = td.Medium(permittivity=unit_cell_index**2)\n",
        "\n",
        "    # create the nanobeam structure\n",
        "    nanobeam_structure = td.Structure(\n",
        "        geometry=nanobeam_geometry,\n",
        "        medium=nanobeam_medium,\n",
        "    )\n",
        "\n",
        "    # define the waveguide geometry\n",
        "    cell_size = (\n",
        "        nanobeam_size,\n",
        "        width + 2 * wl2,\n",
        "        height + 2 * wl2,\n",
        "    )\n",
        "\n",
        "    # defining the waveguide. Use a PolySlab for the sidewall angle\n",
        "    geometry_wvg = td.PolySlab(\n",
        "        vertices=[\n",
        "            [-2 * cell_size[0], width / 2],\n",
        "            [2 * cell_size[0], width / 2],\n",
        "            [2 * cell_size[0], -width / 2],\n",
        "            [-2 * cell_size[0], -width / 2],\n",
        "        ],\n",
        "        axis=2,\n",
        "        slab_bounds=(-height / 2, height / 2),\n",
        "        sidewall_angle=sidewall_angle,\n",
        "    )\n",
        "    waveguide = td.Structure(\n",
        "        geometry=geometry_wvg,\n",
        "        medium=td.Medium(permittivity=waveguide_index**2),\n",
        "    )\n",
        "\n",
        "    # substrate\n",
        "    substrate = td.Structure(\n",
        "        geometry=td.Box.from_bounds(rmin=(-999, -999, -999), rmax=(999, 999, -height / 2)),\n",
        "        medium=td.Medium(permittivity=substrate_index**2),\n",
        "    )\n",
        "\n",
        "    # defining the source\n",
        "    sources = [\n",
        "        create_source(\n",
        "            polarization=polarization,\n",
        "            wl1=wl1,\n",
        "            wl2=wl2,\n",
        "            center=(nanobeam_center, 0, 0),\n",
        "        )\n",
        "    ]\n",
        "\n",
        "    # boundary conditions\n",
        "    boundary_spec = td.BoundarySpec(x=td.Boundary.pml(), y=td.Boundary.pml(), z=td.Boundary.pml())\n",
        "\n",
        "    # determine symmetry based on structure configuration\n",
        "    if (n_constant_structures_left == n_constant_structures_right) and (n_tapered_output == 0):\n",
        "        symmetry = get_symmetry(polarization=polarization)\n",
        "    else:\n",
        "        symmetry = [0] + get_symmetry(polarization=polarization)[1:]\n",
        "\n",
        "    # create the simulation object\n",
        "    sim = td.Simulation(\n",
        "        size=cell_size,\n",
        "        structures=[substrate, waveguide, nanobeam_structure],\n",
        "        sources=sources,\n",
        "        monitors=[],\n",
        "        run_time=run_time,\n",
        "        boundary_spec=boundary_spec,\n",
        "        grid_spec=grid_spec,\n",
        "        symmetry=symmetry,\n",
        "        medium=td.Medium(permittivity=medium_index**2),\n",
        "    )\n",
        "\n",
        "    # add monitors to the simulation\n",
        "    monitors = create_monitors(\n",
        "        sim, n_point_monitors=5, center=(nanobeam_center, 0, 0), deviation=(0.5, 0, 0)\n",
        "    )\n",
        "    return sim.updated_copy(monitors=monitors)"
      ],
      "id": "2561bbb1"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Running the simulation\n",
        "\n",
        "Finally, we will set the parameters as reported in the reference\n",
        "[paper](https://doi.org/10.1063/1.3107263) and run the simulation."
      ],
      "id": "cd8a6bfc-bc5b-4224-91de-ad1db950ce34"
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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8EQAkSH1TC/xYEp4jQYIqV3e61Uw6iUwynp3wOYglbXIwDADOkrJM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          }
        }
      ],
      "source": [
        "height = 0.22\n",
        "width = 0.5\n",
        "lattice_constant = 0.430\n",
        "max_dip_lattice = 1 - 0.330 / lattice_constant\n",
        "radius_x = 0.28 * lattice_constant\n",
        "radius_y = radius_x\n",
        "max_dip_x = 0\n",
        "max_dip_y = 0\n",
        "n_constant_cells = 12  # each side\n",
        "n_tapered_cells = 12  # total\n",
        "central_gap = 0\n",
        "wl1 = 1.4\n",
        "wl2 = 1.6\n",
        "run_time = 5e-12\n",
        "polarization = \"Ey\"\n",
        "substrate_index = 1.44\n",
        "unit_cell_index = 1\n",
        "waveguide_index = 3.5\n",
        "sidewall_angle = 0\n",
        "\n",
        "# defining a unit cell function depending only on the taper parameters\n",
        "unit_cell_function = lambda center, x, y: ellipse_uc(center, x_axis=x, y_axis=y, height=height)\n",
        "\n",
        "\n",
        "sim = create_simulation(\n",
        "    unit_cell_function=unit_cell_function,\n",
        "    wl1=wl1,\n",
        "    wl2=wl2,\n",
        "    width=width,\n",
        "    height=height,\n",
        "    run_time=run_time,\n",
        "    lattice_constant=lattice_constant,\n",
        "    n_constant_structures_right=n_constant_cells,\n",
        "    n_constant_structures_left=n_constant_cells,\n",
        "    n_tapered_output=0,\n",
        "    n_tapered_structures=n_tapered_cells,\n",
        "    max_dip_lattice=max_dip_lattice,\n",
        "    polarization=polarization,\n",
        "    substrate_index=substrate_index,\n",
        "    unit_cell_index=unit_cell_index,\n",
        "    waveguide_index=waveguide_index,\n",
        "    sidewall_angle=sidewall_angle,\n",
        "    original_params=[radius_x, radius_y],\n",
        "    max_dip_params=[max_dip_lattice, max_dip_lattice],\n",
        ")\n",
        "\n",
        "\n",
        "sim.plot(z=0, monitor_alpha=0)\n",
        "plt.show()"
      ],
      "id": "af298c7c"
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">08:02:59 UTC </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'nanobeam_test'</span> with resource_id                      \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-39559841-385a-4931-8eca-6a821d57177e'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-39559841-385a-4931-8eca-6a821d57177e\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-39559841-385</span></a>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-39559841-385a-4931-8eca-6a821d57177e\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">a-4931-8eca-6a821d57177e'</span></a>.                                         \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"0c029f91f0404026b8db3dd36ab690ef\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">08:03:02 UTC </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.100</span>. This assumes the FDTD solver runs\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>for the full simulation time; if early shutoff is reached, the     \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>billed cost can be lower. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the  \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>billed FlexCredit cost after a simulation run.                     \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">08:03:03 UTC </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.100</span>. This assumes the FDTD solver runs\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>for the full simulation time; if early shutoff is reached, the     \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>billed cost can be lower. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the  \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>billed FlexCredit cost after a simulation run.                     \n",
              "</pre>"
            ]
          }
        }
      ],
      "source": [
        "# estimating the cost\n",
        "task_id = web.upload(sim, \"nanobeam_test\")\n",
        "cost = web.estimate_cost(task_id)"
      ],
      "id": "a16d4419"
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">08:03:04 UTC </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'ellipse_nanobeam'</span> with resource_id                   \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-8c73a129-18bc-4d36-97eb-e546e6f938d8'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-8c73a129-18bc-4d36-97eb-e546e6f938d8\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-8c73a129-18b</span></a>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-8c73a129-18bc-4d36-97eb-e546e6f938d8\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">c-4d36-97eb-e546e6f938d8'</span></a>.                                         \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"005da59d264c4b6da3117cc72f177070\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">08:03:07 UTC </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.100</span>. This assumes the FDTD solver runs\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>for the full simulation time; if early shutoff is reached, the     \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>billed cost can be lower. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the  \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>billed FlexCredit cost after a simulation run.                     \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>status = queued                                                    \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"c8c28bedf298478fb3764365e078c811\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">08:03:20 UTC </span>status = preprocess                                                \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">08:03:24 UTC </span>starting up solver                                                 \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>running solver                                                     \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"94aab64e693a438b9f152ebe880a683a\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">08:04:48 UTC </span>status = postprocess                                               \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"33a10e3517b947308fd436f3bc143115\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">08:05:09 UTC </span>status = success                                                   \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">08:05:11 UTC </span>View simulation result at                                          \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-8c73a129-18bc-4d36-97eb-e546e6f938d8\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-8c73a129-18b</span></a>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-8c73a129-18bc-4d36-97eb-e546e6f938d8\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">c-4d36-97eb-e546e6f938d8'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"44d991347f864b4d8271b957ab0782b4\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">08:06:07 UTC </span>Loading results from simulation_data.hdf5                          \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">08:06:08 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Simulation final field decay value of </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.284</span><span style=\"color: #800000; text-decoration-color: #800000\"> is greater    </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">than the simulation shutoff threshold of </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1e-05</span><span style=\"color: #800000; text-decoration-color: #800000\">. Consider running   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">the simulation again with a larger </span><span style=\"color: #008000; text-decoration-color: #008000\">'run_time'</span><span style=\"color: #800000; text-decoration-color: #800000\"> duration for more    </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">accurate results.                                                  </span>\n",
              "</pre>"
            ]
          }
        }
      ],
      "source": [
        "# running the simulation\n",
        "sim_data = web.run(simulation=sim, task_name=\"ellipse_nanobeam\")"
      ],
      "id": "31338bf2"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "It is possible to notice a warning at the end of the simulation, stating\n",
        "that the fields haven’t completely decayed. This is expected for high\n",
        "Q-factor cavities, and the suggestion to run with a larger `run_time`\n",
        "can be ignored in this case.\n",
        "\n",
        "## Data Analysis\n",
        "\n",
        "First, we will plot the electric field profile at the end of the\n",
        "simulation, where it is possible to observe a tightly confined mode."
      ],
      "id": "2e62273c-5b68-4378-b047-9ce1a3570aa7"
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
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KkFdrg7bqIpZWRqJa5qvV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\nE5M04d544w1ceOGFUErhtNNOw6OPPoolS5agpqYGdXV1OPzww/HMM8/g0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+y3336oqKiA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\nAAAAAElFTkSuQmCC\n"
          }
        }
      ],
      "source": [
        "sim_data.plot_field(\n",
        "    \"fieldProfileMon\",\n",
        "    \"Ey\",\n",
        "    val=\"abs\",\n",
        "    z=0,\n",
        "    t=sim_data[\"fieldProfileMon\"].Ez.t[-1],\n",
        ")\n",
        "plt.show()"
      ],
      "id": "68e2f3fc"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Resonance and Q-factor Analysis\n",
        "\n",
        "Next, we will collect the data for the point field monitors and use the\n",
        "`ResonanceFinder` plugin that implements the harmonic-inversion\n",
        "algorithm, which is a convenient technique to analyze exponential\n",
        "decaying fields, allowing us to get Q-factor information with a\n",
        "relatively short simulation time.\n",
        "\n",
        "For high Q-factor cavities (greater than $10^6$) a larger run time may\n",
        "be needed, as well as fine-tuning of the `freq_window` parameters, which\n",
        "is the frequency window where the resonances will be analyzed.\n",
        "\n",
        "Since the harmonic-inversion technique is valid for exponential decaying\n",
        "fields, we will only analyze the data after the sources decay. For high\n",
        "Q-factors, we usually want to analyze as much data as possible.\n",
        "Therefore, we apply a time filter to the fields signal now, allowing\n",
        "more flexibility to adjust the portion of the fields we want to analyze.\n",
        "An alternative approach is to set the `start` argument while defining\n",
        "the `FieldTimeMonitor` as show in other\n",
        "[example](https://www.flexcompute.com/tidy3d/examples/notebooks/ResonanceFinder/)."
      ],
      "id": "f54bfeff-a3a8-4095-9532-29fb991139cd"
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [],
      "source": [
        "def analyse_resonance_monitors(sim_data, start_time=1e-12, freq_window=None):\n",
        "    # importing needed libraries\n",
        "    from tidy3d.plugins.resonance import ResonanceFinder\n",
        "\n",
        "    combinedSignal = 0\n",
        "    polarization = sim_data.simulation.sources[0].polarization\n",
        "\n",
        "    # iterate through all monitors and combine the signal\n",
        "    i = 0\n",
        "    name = f\"pointMon{i}\"\n",
        "    while name in sim_data.monitor_data:\n",
        "        combinedSignal += sim_data[\"pointMon0\"].field_components[polarization].squeeze()\n",
        "        i += 1\n",
        "        name = f\"pointMon{i}\"\n",
        "\n",
        "    # create the ResonanceFinder instance\n",
        "    rf = ResonanceFinder(freq_window=freq_window)\n",
        "\n",
        "    # boolean mask to set the data after source decay\n",
        "    bm = combinedSignal.t >= start_time\n",
        "\n",
        "    # data containing the resonance information\n",
        "    data = rf.run_raw_signal(combinedSignal[bm], sim_data.simulation.dt)\n",
        "\n",
        "    # creating a DataFrame\n",
        "    df = data.to_dataframe()\n",
        "    df[\"wl\"] = (td.C_0 / df.index) * 10**3\n",
        "\n",
        "    return df, combinedSignal"
      ],
      "id": "3b2009ec"
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "\n",
              "</div>"
            ]
          }
        }
      ],
      "source": [
        "# defining the frequency window\n",
        "freq0 = sim_data.simulation.sources[0].source_time.freq0\n",
        "fwidth = sim_data.simulation.sources[0].source_time.fwidth\n",
        "freq_window = (freq0 - fwidth / 2, freq0 + fwidth / 2)\n",
        "\n",
        "# start time to analyze the exponential decaying fields after source decay\n",
        "start_time = 2 * find_source_decay(\n",
        "    sim.sources[0],\n",
        ")\n",
        "\n",
        "df, signal = analyse_resonance_monitors(sim_data, start_time, freq_window)\n",
        "df.head(10)"
      ],
      "id": "c52a2cc9"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We note that the Q-factor is close of the one reported by the\n",
        "[paper](https://doi.org/10.1063/1.3107263). The resonance frequency is a\n",
        "few nanometers different, although this is due to the different taper\n",
        "function used in this notebook. In the paper, the tapering function is\n",
        "linear.\n",
        "\n",
        "### Mode Volume Calculation\n",
        "\n",
        "The mode volume will be calculated by the definition:\n",
        "\n",
        "$V_{eff} = \\frac{\\int\\varepsilon(r)|E(r)|^{2}d^{3}r}{max[\\varepsilon(r)|E(r)|^{2}(r)]}$,\n",
        "\n",
        "where $\\varepsilon(r)$ is the cavity permittivity distribution, which\n",
        "can be obtained from the simulation object.\n",
        "\n",
        "First, we will define a function to compute the energy density from the\n",
        "time field monitor, defining it as the temporal mean of the last two\n",
        "field oscillations. This approach ensures that we can estimate the mode\n",
        "volume without prior knowledge of the resonant frequency."
      ],
      "id": "68fc3b34-8cec-4f4e-92a6-59173092f158"
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [],
      "source": [
        "def get_energy_density(sim_data):\n",
        "    # retrieving monitor data\n",
        "    Efield = sim_data[\"fieldTimeMon\"]\n",
        "\n",
        "    # retrieving permittivity data and interpolating it at the same grid points of the monitor data\n",
        "    eps = abs(sim.epsilon(box=td.Box(center=(0, 0, 0), size=(td.inf, td.inf, td.inf))))\n",
        "    eps = eps.interp(coords=dict(x=Efield.Ex.x, y=Efield.Ex.y, z=Efield.Ex.z))\n",
        "\n",
        "    # calculating the energy density\n",
        "    energy_density = np.abs(Efield.Ex**2 + Efield.Ey**2 + Efield.Ez**2) * eps\n",
        "\n",
        "    # calculating the temporal mean value\n",
        "    delta_t = energy_density.t[-1] - energy_density.t[0]  # signal duration\n",
        "    energy_density_temporal_mean = energy_density.integrate(coord=(\"t\")) / delta_t\n",
        "\n",
        "    return energy_density_temporal_mean, eps\n",
        "\n",
        "\n",
        "energy_density, eps = get_energy_density(sim_data)"
      ],
      "id": "641e543c"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now we will calculate the mode volume. As we can note, the value is\n",
        "close to $0.39 (\\frac{\\lambda}{n})^3$, as reported in the paper.\n",
        "\n",
        "It is important to note that mode volume values $<< 1$ are most like due\n",
        "to the absence of a resonance, hence the field integral would be near to\n",
        "zero."
      ],
      "id": "0a52b7de-5ad9-446e-898e-b784c4808713"
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mode volume: 0.38 (lambda/n)^3"
          ]
        }
      ],
      "source": [
        "def mode_volume(energy_density, eps):\n",
        "    # volume integral of the energy density\n",
        "    Integrated = np.abs(energy_density).integrate(coord=(\"x\", \"y\", \"z\"))\n",
        "\n",
        "    # scaling factor to present in common units of (wl/n)^3\n",
        "    wl = abs(df.sort_values(\"Q\").iloc[-1][\"wl\"] / 1000)  # um\n",
        "    index = np.sqrt(abs(eps.max()))\n",
        "\n",
        "    Vmode = Integrated / np.max(energy_density) / (wl / index) ** 3\n",
        "\n",
        "    return Vmode\n",
        "\n",
        "\n",
        "Vmode = mode_volume(energy_density, eps)\n",
        "print(f\"Mode volume: {Vmode:.2f} (lambda/n)^3\")"
      ],
      "id": "dd7d14d8"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Reducing Memory Usage for Mode Volume Calculation\n",
        "\n",
        "When calling `Efield = sim_data[\"fieldTimeMon\"]` in the\n",
        "`get_energy_density` function, a copy of the raw field monitor is\n",
        "created and expanded to symmetric coordinates, which increases memory\n",
        "usage.\n",
        "\n",
        "To reduce memory usage during mode volume calculation, one can use the\n",
        "raw fields without symmetry expansion and multiply the result by 8,\n",
        "since the integration is performed over only 1/8 of the total volume.\n",
        "The results may differ slightly due to the central coordinate being\n",
        "included in the reduced domain, which causes the central point to be\n",
        "overcounted in the integration. However, this does not substantially\n",
        "affect the results."
      ],
      "id": "0436b4d1-f363-47f5-a866-1dcbc5502853"
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [],
      "source": [
        "def get_energy_density_raw_data(sim_data):\n",
        "    # retrieving monitor data\n",
        "    Efield = sim_data.monitor_data[\"fieldTimeMon\"]\n",
        "\n",
        "    # retrieving permittivity data and interpolating it at the same grid points of the monitor data\n",
        "    eps = abs(sim.epsilon(box=td.Box(center=(0, 0, 0), size=(td.inf, td.inf, td.inf))))\n",
        "    eps = eps.interp(coords=dict(x=Efield.Ex.x, y=Efield.Ex.y, z=Efield.Ex.z))\n",
        "\n",
        "    # calculating the energy density\n",
        "    energy_density = np.abs(Efield.Ex**2 + Efield.Ey**2 + Efield.Ez**2) * eps\n",
        "\n",
        "    # calculating the temporal mean value\n",
        "    delta_t = energy_density.t[-1] - energy_density.t[0]  # signal duration\n",
        "    energy_density_temporal_mean = energy_density.integrate(coord=(\"t\")) / delta_t\n",
        "\n",
        "    return energy_density_temporal_mean, eps\n",
        "\n",
        "\n",
        "energy_density2, eps2 = get_energy_density_raw_data(sim_data)"
      ],
      "id": "0070e9f4"
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mode volume: 0.38 (lambda/n)^3"
          ]
        }
      ],
      "source": [
        "Vmode2 = 8 * mode_volume(energy_density2, eps2)\n",
        "print(f\"Mode volume: {Vmode2:.2f} (lambda/n)^3\")"
      ],
      "id": "5bb5d8e3"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Directional Q-factor Calculation\n",
        "\n",
        "We can decompose the total Q-factor into its directional contributions:\n",
        "\n",
        "$Q^{-1} = Q_x^{-1} + Q_{-x}^{-1} +Q_y^{-1} + Q_{-y}^{-1} +Q_z^{-1} + Q_{-z}^{-1}$\n",
        "\n",
        "This provides useful information for understanding the contribution of\n",
        "scattered losses ($z$ and $y$ directions) and waveguide losses ($x$\n",
        "direction), which are due to fields that penetrate the Bragg mirror and\n",
        "propagate into the nanobeam slab. If the cavity is to be coupled to an\n",
        "optical fiber, it is desirable that the main direction of losses be\n",
        "waveguide losses in the direction of the fiber.\n",
        "\n",
        "To illustrate the directional Q-factor calculation we will run the\n",
        "simulation again using an asymmetric number of unit cells, and an output\n",
        "tapering region."
      ],
      "id": "ad3cac7f-9f32-4c90-9302-92b8f4cba4c8"
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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+/p6TbltUDs/1NPTA04EU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hmfLfGkGmOSVHj9YprikmWl/enhbelmWVYy9NPDA8nvptQUy8iy2tXqDdrp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\nkJ/85Cd/UMhPfvKT30N+8pM/P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          }
        }
      ],
      "source": [
        "n_constant_structures_right = 2\n",
        "n_tapered_output = 5\n",
        "\n",
        "asymmetric_sim = create_simulation(\n",
        "    unit_cell_function=unit_cell_function,\n",
        "    wl1=wl1,\n",
        "    wl2=wl2,\n",
        "    width=width,\n",
        "    height=height,\n",
        "    run_time=run_time,\n",
        "    lattice_constant=lattice_constant,\n",
        "    n_constant_structures_right=n_constant_structures_right,\n",
        "    n_constant_structures_left=n_constant_cells,\n",
        "    n_tapered_output=5,\n",
        "    n_tapered_structures=n_tapered_cells,\n",
        "    max_dip_lattice=max_dip_lattice,\n",
        "    polarization=polarization,\n",
        "    substrate_index=substrate_index,\n",
        "    unit_cell_index=unit_cell_index,\n",
        "    waveguide_index=waveguide_index,\n",
        "    sidewall_angle=sidewall_angle,\n",
        "    original_params=[radius_x, radius_y],\n",
        "    max_dip_params=[max_dip_lattice, max_dip_lattice],\n",
        ")\n",
        "\n",
        "asymmetric_sim.plot(z=0)\n",
        "plt.show()"
      ],
      "id": "9ea2d120"
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">08:06:37 UTC </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'asymmetric_ellipse_nanobeam'</span> with resource_id        \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-4ccd5679-b840-4d25-8993-037f09c587ef'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ccd5679-b840-4d25-8993-037f09c587ef\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ccd5679-b84</span></a>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ccd5679-b840-4d25-8993-037f09c587ef\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">0-4d25-8993-037f09c587ef'</span></a>.                                         \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"5c52f6300756430982fde5f8683bbc1d\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">08:06:40 UTC </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.179</span>. This assumes the FDTD solver runs\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>for the full simulation time; if early shutoff is reached, the     \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>billed cost can be lower. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the  \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>billed FlexCredit cost after a simulation run.                     \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">08:06:42 UTC </span>status = queued                                                    \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"de22a7db57954965b07918ba644e1535\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">08:07:28 UTC </span>status = preprocess                                                \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">08:07:33 UTC </span>starting up solver                                                 \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>running solver                                                     \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"090f7cdc3053496aba23eb55bf113201\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"776f8b2ffaee47d8ad885cbd5c89988e\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">08:10:27 UTC </span>status = postprocess                                               \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">08:11:10 UTC </span>status = success                                                   \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">08:11:12 UTC </span>View simulation result at                                          \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ccd5679-b840-4d25-8993-037f09c587ef\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ccd5679-b84</span></a>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ccd5679-b840-4d25-8993-037f09c587ef\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">0-4d25-8993-037f09c587ef'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"f5cf92beaf5e4971b5266bf619e40f31\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">08:12:07 UTC </span>Loading results from simulation_data.hdf5                          \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Simulation final field decay value of </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0693</span><span style=\"color: #800000; text-decoration-color: #800000\"> is greater   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">than the simulation shutoff threshold of </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1e-05</span><span style=\"color: #800000; text-decoration-color: #800000\">. Consider running   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">the simulation again with a larger </span><span style=\"color: #008000; text-decoration-color: #008000\">'run_time'</span><span style=\"color: #800000; text-decoration-color: #800000\"> duration for more    </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">accurate results.                                                  </span>\n",
              "</pre>"
            ]
          }
        }
      ],
      "source": [
        "asymmetric_sim_data = web.run(asymmetric_sim, task_name=\"asymmetric_ellipse_nanobeam\")"
      ],
      "id": "b59e67bb"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "First, we will plot the fields and recalculate the Q-factor and mode\n",
        "volume."
      ],
      "id": "6766c44a-9d26-44a2-be2a-c187d47162a1"
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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5TpY8MuSzLFmx\n73d8rjW2rYhjq9qp1RazQjVGtUzDq4hdJduJyHGqJcOQZQZlWX4FvP0UMPPlkVcB5cgjr7yOT5Zj\nJb5ntmErZIQkSdVmuSSpyixxP5fkRGpirx032WfFqgg9Vrb8seXb8aXeVqw5TDy2JEUcN3Yk6i6d\nDtvu59GooWj8VR3fLiD2HBqGR2Ys2RmvdDTjCcnk13Si0Uw09jG+5YIlx6mNnRM/ue59Fn8P1jWa\nsep9PbnZVaKpTDxZH28yJdU1eoj9fDCcxjVtjZOfUpu2zTcaVGC3s2ajvT9HJRKUQGv43RLouNra\nWs2ZM0dz5szRsGHDdMopp+iXv/ylttpqq0xPLSEtCck333xTAwYM0IUXXqgrrrhCDz/8sI4//niN\nGTNGWVktZ//33HPPxF8LAADoi7qi4qZRzLb+o99x2pScbJyMbKkiKjlZEK+ASlr+l5R8SB6zrnLR\njVHXydeSu/ejISPxTx2jUWddd2l4rL7OiUpG1K1edKSGe1a6yUgrkYg0Da+7jNuwZBnucm7D8Mhs\n8E8rR9FY9WJEUTssWxFFo17ZTk2scs2OLakxZRjuPnpeK8tNGprZ8pnZsgy//E62Ak5Ahgx5Ystl\nHTmKGlFFjahqzZAiRlghq0KhaLnC0WrVRGzZdrUcx5Zp+OW1spTlLZLPyFKeU6R8J1d+WcrxWvIY\nhvweQx7D/W5EHPc7URNxVBmJKmRHVWZUKmRWqcRYpagdVtSJyjQCyvYUKWDmq9gepizTp6KAV4V+\nQ35LyvU68kjymm6dXNSRqm1TEdur0tqANtVkqypia42Rr83eTQrbIUWj1fJYQeV6BylPAzTE6K+B\nOR7leKRCv9sNPMdjy2fGk2OGIo6hirCpsnBAVRFpfShLa0K5qjA3K+oNqdpxFFVUWd4iZVv9NdQe\nrhzTo6Jsjwr8kt+Usi1HHtORaTiyHVNRR6qKehSKSmW1AZWEslUZjWqdmasqs0xRO6xItEyG4VHA\nmy+/lat+5nD1twsUMD3K8VrymoYCliGPmXhBqNaRKsPu3DfVuqtMNpru8uhNXikadROItuFWM3os\nNwmZ4x2gHGugJCkrtpza7/jjr37Vxs6vNqtjH91kZ6W9SZIUUplsJ1YhqXgn7PgS6qA8HjfJGV+6\nnajGTHpNR2KVnfE5RhId3UN1GyrEvpb8Po0nIg0zXn3pix1rvut1fMm2o0iiytNIfK1x1+sWqzAb\nfK1O8h8+Giw5jv1hIvmxNNTuKszEWPFka+qrMOsN04HYbRmPRCoAdH9Tp07ViBEj9N1332nVqlWa\nOXOm/vjHP2q//fbT6aefrqOPPlp+v7/1QF0oLQnJ3/3ud1q0aFHi808++USffPJJm6794IMPNGXK\nlK6aGgAA3Vb6KxfbUUXU1l+oGy11jlcYNlUZJSWqo1RXHZXICzZsbJM0bze+WX/psyxZplcyzLoK\nxiSObDm2W7Vo2xE5siTVJh5eXT2ZGVv2bLh7UhqWLMvdc880PPKYQXfJrxFw97FMeoZtJ6KI3CXU\nYbNGtlOrWlUqEnXDRqORxGM25FZFeq2gPIZfQatQOXY/+W2/sg2fgpZHpmEkmpVI8WpDR9XRiMJ2\nVBVmULJMmYZPUbtGEdmKRqvd+VpBZZuFCto5Gmj0U1HAo6BlqJ9fCphStseW13RkyEkk+Sojpspq\nPaqKehSstrQlmqWIFVbIUyrbqZZpehS0CpVv91exL0t5PlPDsqWhgbCyPREVBkPyWVH5vG61X9Q2\nVVXjU3XYo/Uhv1ZVe1UWNuVsLpA36tVmz1rV1m6UZfrVzxmk/kaehmd7NDonqn7eqIbmVCroCysn\nv0ZW0HG3Mo1IkRpTlRU+ravIVkXEqxWVPklZCtR4VG7mq9aqlO2ElWMVqX+0WEOz/OrnMzQ8y9bg\nYK2yvWHlB2rk8URkWY6iUUORiEflIb+qwh6tC/m0ymupvNaSXdlP5fJpi7U+9pL2KeApUI7RX4Oc\n/irO8inoMVTokwKWo2xPVD7DnWvUkWptU5tq3ddioMr9RSBa5SYmt5ilqjTicd1z4lWRuWaxiqJu\nQjLf4yb0/FY8CS/VRt1Xa2XETSiWxBrORM3Yc2+GFDGq6r0H4suyLSsgvxVLcsaSo/EqTE+sEY2t\nqKKxhGaNscW9PrrZfb+YUTmxqksnsc9gYpCkish4M5vYknOzrtlM/P0Z3ycyHi9qGzKMeGKw/s+f\npjVsXNPSzxup7mdO/aXmbdZssrOFfRWTfo422MKy5aFaHD8eKPkPKW2P3eo4zRxP2R/FmtDZxGeq\ntx8B0sH9vzCVoei8o446SkcccYRefPFFzZo1S88884wikYgWLFigBQsWqLCwUCeeeKJOP/10TZw4\nMSNzZMM/AAC6ofT/U7Tlrtv1umzLVvNNbdo+TryKsd44TjxxEV+2qQ40G3CTG6ZpSYYpM7G02qyX\nIHAcW7YpyTHdhIdjxJKS0diYzc3bTaC4Hb198hg+WfLJawTkcbyS21LHXZ6riEyjVlGFE0tqLaNW\nUaNGhlM/eWnEE6exSkyv45ff8SsQS0YGPZY8huS1jESLn3DUUcRxZ1djm6q1AwpZQTlGRIZpSXZy\nMyF3Wbbf8SvoMZXtNZRlSf28tgKWo3xPRD7L/VUo6hiK2IZ8piXJI29UqopYciT57KAsw+tWi8qM\nxfQpy2sqxyMVeCMaEAgpx1erfoXV8gRsxYru5ISlrIpa1VR7ZEuqjLqd0jd4TFXbAZmKLxf2xOZp\nKc8r9fdGVBioUf+CSvnybHmLPTJyfG6X7ZqofDURedeF5DiGfNUBldV6lO0xFYpY8tgBd39MWfI5\nWQoYXuV4DeX7pCJfWMVZlcrKqlVWQVimTzJ9huxaR3ZtrfylYVVVexW2DZVHLEVtKWh5VBVxE9Ny\nHBmGJa/hl19BZXk8yvEayvVKhT5bQctWjiciv+W2mIk6hmqibnMgSarwxhKTscpBj7xJ74l4Z2r3\nXL+Cyool8LI97vsk6Kl7/dTEknZ2rILWH4l1sE40lKlL+tWxYmO4WwW457tLxH1yVzF5HPfrtmxF\nDLf6MRpLGkbMWKWlabXYJLpuybZZ72O8othM2q8yfixqun8cMJxo3fvWaX9HanfshkvE69T9GIvP\nrfX9KVscr4mfn80nJjv6c1TNV2Q20Ximo4nJNk2jh8XuyvkCQHdjmqYOPvhgHXzwwVq7dq0eeOAB\n/eMf/9A333yjkpIS3Xnnnbrzzjs1ZcoUnX766ZoxY4by8vLSNr+0JCTnzp2rUCjUoWuHDx+e4tl0\nzPfff69Vq1bJ7/drq6226rINdVMxTrrmCgBIo+Z++WzwS2fnms800wTGcRr8Qt10x+vWB6jba7iu\nirFBgiSxj5sR20Muvqdj7LG1+gt88r6ObldsQ4ZbkdVoyXZEtm3JVjTWyTcix7bcpIdhJz2ZyY81\nXinpJiO9RqwxihOQzw7I5/hkJT2mqGzVGDUKG24n77ATcpOUTs3/s/ffcZJk5Z3v/zkRka5sV7X3\nZrqHMTgNEsJIePsbYEESRkKAQLDAla602vtiMJJ2L0YYmSsWFmZ5gQQaEEYCJBCeZWEROwgzAwMj\nhukx3dO+u3xWpQtzzu+PiMzKqsqqyqquru6Z/r77la+uqow4ERkZ6Z58nvOkt8+YBXeawcc3eQqu\nRJ8pUPJ9NhQC+nKQ99LyYs+kjaBDa4gsTMcBjdhB2ENkB8FLS28TE5I0uwbjk8sCnH15n+F8Gozc\nUWrQl4sY7KlTKMRpGbv1iGPDVKXE2VqJ6dgntj45L2Ci0seIX8JEs4GyksmxIQebio69vTX27Jok\nPwzBvn7oK8JAb3rjGhGFsTK9U3VKd03ASejxi4yHAZHNUUjSbEDfy9Fr8gzmfbYVY/YPTzGwsU7p\nYQOwsR+3axtuYBA8g2k0MLUaheOn2fSzMwycqdE46jMa9hBZn3y9lJbWezn6bT+D+Rxbi46txZgD\nGycZPhjiDxcwOzZCKQ+FAjQaUAvJn5xgYKxG/t6E0PoEJmCk7lNLcvjk00eDyVFkgH7bz3CPz/aS\nYzBI2NlTpycf0d/TIJePMQasNdRrOQpTaRbiTJbNeM5PA2aBnS2damYPFrwsQ9L2M1RK37pvKqSP\no77c7HlTS0x27NJl6kk61hRpgHFuA5zsXGtmSJp0igCAEulUST02DUjmsyCxw9Ew2TyR2SkekzXl\nWTK7Og06p7epWSKeBUv9ufNWwmyp9uzsDUlrrspuzD6PtXfWbgYb52dIulaEymXZnyvXoYlWuuGF\n+9bq5L2ywOqqpthYZbDzQn8Zth5ftq2kukCBSRG5XGzbto03vOENvP71r+eb3/wmH/rQh/jsZz9L\no9Hghz/8IT/84Q/5z//5P/Mbv/EbvPKVr+RXf/VXL/g+rUtActeuXeuxmQviM5/5DG9+85vnlJj7\nvs/Tn/50/uqv/oqrrrrqktnOeu2riIikFvvQs+YfcJb68LmKD51zut52s722baw8W7ETn9lg5Nxt\nzXY4BmMsaWByufEss6WYCdDMCMt6JnvNBjQ+Hs1GKA5r0m2k4dU4zZTMyjsXbrO5jdkrvGxeysDl\nCciRd3kKWYZb81bFzmKdxWBICHEkWbZes4zUW/T+NRgCzyPneRR86A0gMGkZcHNP6llAMk6jtRRi\njyAJ8F3aqdvLsvjaxzSk4/T4jpJvGciH9BVD+gYbBL0O44GNE2wWe6rHPo48Jd+n5kPOpON73mxw\nxzceOR+KnqMvH5IfBn9rCXZugr5e3EAagCOMMMU8Xu8MhYkRBkZCanFAwQvI+wYvmR0zve1pKXlP\nb0huswfbh2HTMK7tvaXrSwN2xjqC8RmIZ+g7FZL3esh5Bp/0WGA8AnwKnkeP7+gPYnoHQvztPZiN\n/bBrK66nJw1I1muYWh3PWkx+hp5zdXomE4qRT+AZPOO1TgXP+OQpkDcexQD6Akt/kDBQrNNTiujZ\nEOH3pHe1i8AvO/qr6cEt+s1g4ew54Fr3VzOLMD0meeNR9NPlsrgkvX6zS7trLV/0Zo8fgGk2nsma\nIHXieUFrrsicawaa863twmzzIADPdWh4M8+cOSTnZ0ia9mDh3Hkqs7hq56Y6K9CeDTl/u619dJb5\nza9W9HTa6bHbcRqMVXyB07Ul5sVcK0t0QV8P513WLnI/Yl16EblQjDE86UlP4klPehJjY2N89KMf\n5YMf/CA/+9nPqFar3HTTTdx0001ceeWVvOpVr+J3f/d3L1iSm0q2l/Bf/+t/5S1veQsAPT09POQh\nD6FSqXD77bfzpS99iW9+85t89atfPe/I8VpsZ732VURElne+mYpzB1ufj1dLN36A+XNHpoG0xeZ0\nbAt6umZTmraSzGw+R7Og0y5ZN2qLwcuOYTP0tnB+NwezTSeMw7gEh48zFpNlSvpePi1VNkW8tsY3\nzlkSfBLPYF2ElyRpdbNdPBPVkeBMjLVgs3n5PLJgVDbPY8EP5oRZI+fwrUfkYmxWhu6bHJ4JsERt\nw1usizDWkLg6icvhcGmzGT8tAR7KWYq+pcdvNl4x1BND3aaB1JyBWuBTiPM0TDEN9pj0OFobkbg6\noRcSW0tgoDdIGMpHbBqaobghIb8rjzdYBN+HMMY1YnKnasAMhZkSE2FA5DyKno9v8xgTkCQNYhIS\nl45Z8i0DgzWCQ0OwYxP22qsXHsqdOzDjEwRxwvDIBMmIR+90kaLv4RG0yunTgCz0BzGlnRAcGMI+\n7MHp/nVg9+zGi2OC4gk23DtDadwReM0gk8UYQ9545H3DhnzExlKd0oEAc80+7Nat0AyaAgwOpufX\nwCDm7FmK43fRfzZkMkybFznnSFyYdUdPO2kX/ID+IC0D31iqs2l7lWCDwd/ei+krpGdFbAnGK2xu\nVADom+nLNmiy86XeyhBsBtH8bA7HkhfQnyUSbsynj8cN+awTN45SdlwaNiv/rjWD7+l4cdKYzT5s\nbXU2MJjLSrV7bJq12eunG8t7zceMw8+ihXVXmnvsbTTbhMYtVbud3aYs+7M5P2Vg8rQCg1m3budl\nwVZbYzZdsrn/Kwm6zT7XtJeGQzrfpbWLl2iv6Hm8w/PnbJAz+9uC59HVbmPuduZawyDoerz+XORg\np4jI5Wzjxo086lGP4qc//SmHDx8mjuPWdYcPH+Z1r3sdb3nLW7jhhht4/etfTy6XW2K0lbtkA5JP\necpTaDQa/Ou//utF2f4XvvCFVoDvWc96Fn/3d3/H8HA64fiPfvQjnvvc53Ls2DGe97zncfjw4dZ1\nF2M767WvcmlxzjExMcHk5CS1Wg1rLUEQ0NfXx6ZNmyiVSssP0qUkSRgbG2NqaopGIy0zzOfzDAwM\nsGnTpjV9YgrDkNHRUaanpwnDEGMMhUKBDRs2sHHjRjxvdZkSndRqNUZHR5mZmSGOYzzPo1QqMTQ0\ntObfApXLZcbHx6lWq8RxTBAE9Pb2Mjw8TH9///IDdMk5x/j4eOu8cM6Ry+Va50WxWFyzbSVJwujo\nKOVyuTUtR6FQYGBggI0bN17w86JYLLJhwwaGh4fX9LyoVCqMjY1RqVSIogjf9+np6WFoaIgNGza0\nlluLj2lTU1NMjE9QrVZJ2h7Dw0ND9PX1dlhjkUyYZbIknXOMj40zVZ6hVquBg1wuR39/Pxs3DVMo\ntN9Xc7OFmut3W6adxAmjYxPMzDQIwzSwWCwW6esvsXHjIEHrnYeZbTTTlrVksFmQIMmCkknH7QBE\nUcJ0OaFWi/EokvNzFEoe/QOGXN62gp6+yacXZudsdMZhCMFZ0lhs1IoMOOeyAOQsm/QTNQx5r49c\nUMD303n0LC6dQ5F0nsdikM5T2Txavs3mjbTQcDlik+Dh4VwzW822BW3B94p4JkdkK9SCGWCIwDP0\nB47hfELJT+jLRZCV/1bjgHrWtdozhqnIEBgf3/npbXPp3IWxrUIEU95p8KBue9IsyVxMz5aEYFsB\n78BW2NCfBvwaDUytQZAbob+aBs96Kr0UvNl5OI0xWFejyjhl10MtLmAd5Pss7NqMPbB/0fvODQ9h\n9u2g+PNxesohnoHEurSLs0uIbYOajaknAb5n8bcUYdfWRYORrfvpwH68KKJQmiQBGgnUmWl1gw6d\nxQEFz9JbbODt3IA9dHDJ/XR9ffiHT5LzLdbBTJhQdjXCeGY2wJ0V4RZ8GMw3GOyrkd+VwxsuwZ6t\n0N+XPk7jGG9knJ5zJ9JjeLzZiCa9/yvJCNbVs+Obn7MvgWfozR4/Q1kgcmMhvV2+seTCNLg3EaUL\nZT1uqNlJAArBILXw3Nzbx+wHjyDLjMxnWYvFrGFO8/85+5I0t5EGDwtuG/XaONY6GnaUvt5eLOPZ\n0rPPG3OnbGgGIiGgMBs0zM6vZgMd5xz5YJDy1DTVSp5GvUGUhOTzOXLFOgMDhY5zQ7bmjWxvamPm\nPb/ZdIl29YZjbGyCaqVBI6zieR7FAgwNbWBwQ/ev13MyNBfJvpyammZyYopKpUKcZO8Neops3LiR\n3o6vAatXq9WZmJigXq+n7xlzAf39/WzYsGHha2i3JeGw4EY556jVasRxjO/7lEql1vim0/orGHvV\n5k1BsiZDdvibwqgicn8xPj7ORz/6UT70oQ9x++23t/5eKBT4jd/4DR71qEfxiU98gptvvpnp6Wn+\n9E//lJ/+9Kd86lOfWtP9WJeAZPPDfrdOnjzJj3/8Y8bGxjh27Bh79uy5gHu3kHOOG264AYB9+/bx\nqU99ip6entb1v/ALv8A//MM/8OhHP5qxsTHe+c538ud//ucXZTvrta9yaalWqxw9enTB3KxRFFGr\n1RgZGWHjxo3s3r37vAM1k5OT3HfffSTJ3KBAFEVUKhXOnDnDjh072Lp163ltB+DMmTOcPn26rVwt\nFYYh09PTnD59mj179swJCq2GtZbjx48zNja24LpGo8Hk5CSnT59m//795x3YDcOQo0ePMjMzM+fv\ncRxTr9cZGxtjYGCAffv2EQTn95RcqVQ4cuQIYRjO+XsURVSrVc6dO8fmzZvZtWvXIh/eujc+Ps7x\n48c7nhczMzOcPn2aXbt2sXnz5vPaDsCpU6c4c+bMgr+HYUi5XObUqVPs27fvvCdgTpKEY8eOMTEx\nMffvcULYCJmcmKRUKrFv/77zDuw2Gg2OHDlCrVqb/aMxrfNidHSUwcFB9u3dix80AzCdHsvLBwmn\np6e5775jRFEz6yrLxIoTqrWQs+dG2LplMzt2bG8LFswrozZmNijZnhU577E6OjrKqZNncQSkDWvS\nzL04dlSrEefOTrJ9xzCbN2+abbBhvHnBzyVuTBYkdM5x+vQYY6MzFHIbsqBjg9gPiGowNVZlYEOe\nrTtcOh8jaTlqswO2w2WZVlkw1DVLmT3Sj5S2VZY+NTnFxLjPoNlKiQHydpg8ATGWqaERNu0IMTmD\nbzwCYwiMwTezx8xhCKwhMR6e9dJSX8g6eyc4Z6nWalRn+hmu76Mn3MBQUKLQkyMa8Ig2W4yBnIGi\nn1AMYoq5GANYZ7AOHAE5zxHYdNsAzlisi7EuYaZSYci7lk2NXWzM9xAWoBqk503OS/D6PLz+Amzo\nxw0PpcekUcfka5iJafyeCrmyJfBcNnelI3INkjhienoSLz5LUOpjJt5A6AwmANffl5Y+L3V3DvRD\n0eB7ltim2aSxrdIIQwxVZnJVqnGexHqYfIDr7fL5uFQCA/XEUIkstWSGWq1C4OdoBCFRko7j+Q56\nlt5HAPI5TE+exCY0rEc5ChltjDI2fRZjGuRzCbGJSIt/HbnAkssnmP4SDPbCQD92w4Z0vss4wkQx\nXl9WBu3SO2wyrDAzVWN3eA3/Gt1HsZAnX6qTy+WwLsuCNGnGKKQBVYBi1r08MJaGbZZ2p+du7Cxx\nmLBrYh/Vcp1GPeaom6JQzDPBzWzZshmXjU1b6bKfPUc0s0vbu7rnmoGlOLtuZiNnTk2wqTZMPsuw\nHDNVZnDMcIJdu3aSz7HgdX3+PI+eCVql6c0vIZrZjI16wsTpkHrdEtp0zspGXKdSqRMnNY67Efbs\n2cNAluHaYebGtkzQuaXjzszul3M+x46doDyVBnmtDbEuARIajYipcpXCacf+/fspFAvNlRZssHNH\n7+ZrZbrdKGxw5MhRKtUGZPeBw2WvAVXGxscZGOhn7969i7w3WLwJ2dwGZAlxnHDfsWNMTU3Ny8x0\njI2NEQQBu3fvPu8vYpMk4cyZM4yMjGDt3CD00NAQO3bsoJDPLzHC8hqNBmNjY+mXeElCPp9ncHCQ\noaGhzu9rVvBeJ45jqtUq1lry+fycz1PnIwzD1hf55/veSy4P6Wu7zhVZe845vvWtb/HBD36wNXdk\n04Me9CD+43/8j7zsZS9j48aNAPz+7/8+d9xxB3/6p3/KZz7zGf7hH/6BG264gUc84hFrtk/rEpB8\nwhOewC233LKqded/2F0P3/ve97jjjjsA+IM/+IOOL0i//Mu/zBOe8AS++c1vctNNN/HOd75zxYGf\ntdjOeu2rXDoqlQqHDx9e8OZ+vrGxMer1OldeeeWq3wCNjo5y7NixJZdxznHy5EkajcZ5fXlw7Ngx\nRkdHl1wmjmPuvfde9u7d23qiXClrLXfddReVSmXJ5er1OnfeeSeHDh2it3d1WQphGHLnnXcSRUtP\nlF8ul/n5z3/Ogx70oFVnFU5PT3P33Xcve16MjIxQr9c5ePDgqs+Lc+fOceLEiSWXcc5x/PhxwjBk\n586dq9oOwJEjRxYECOeL45i77777vJp4JUnC4cOH0+zBJdRqNQ7feZgrr7ySUml1Qcl6vc7hOw8v\n+/o2NTXNnYfv5kEPOojvr+68mJqa4t57j7B4aV+aqXRuZIJGaDlwYO+cjrSzkrag5FzNv5w5M8KZ\nM+dIyyOzsdtKpAE8k2fkTB2PClu2FjFe1oCG2SBdWnqdzok3W/qZBgibwch77z1JrQq+P3sftG+n\nx9uALecYi2DPgRx+kCNninjOb3W/bs6ll2aI2VaemMOm2YsuYeRcjZnJIqVgI77JtW6swWOL38PG\ncAO5E4bqFkOx36cUeJQCQyt+49Is0Ng6LB6+TeewtFisjbAuZHSkTlge5oD7BfYWBhjo99nVAxvy\nloO9NUZrltHiFgZylqFCg558RKkUYYwjjjw8UyCIHDNxQGQNnjEkzhKaBjO1ce6+52c8mt/mYcMl\nthThcZvPcWDXGF7B55bJh9CzMSbY2Q+7tmD37YW25yDXaOCFIf7pMoWZiMA4rIPJuMq/3/1dpssx\nL9/427xo0zS7h0bpHT7HqN2Txni3b1/0nGuNv3kzXl8Oz7eM1iwTp8ocOTPBTDzII8wjePzeOls2\njTNWG4Aeg9vS3RdfdusW4obHRlvll8IyjRM7+cnEMP9z+kb2bHkktT15qltzeJ6DzV1WjQz0UInq\nXOWO8djdp5ieKvCV6lP40PGz/PS+r+Ht30r/UD9QopiLyQ9azJZB2LIRu2e2QaMDXE8fwbH0S5ad\npTrhWJkPPPkeAL7xtZ0MnXkK1OEDRz/Mtm2bGdw7BaSxlWIWiOzLp68rA73pF5N+YPFm0hO0t5o+\nLp7UF1I5NYkrAkX4WTkHk9dAA37qHNEpn/rwT+jt7cG6GC97HDbnnix4c+erTH9LryuFBcbOlNk1\nM8Aub4BTZvZDTY404Juz+xk54TG4ZYL+/rmB32ZzmebckYHLtzpuNwNsdaapzFSpHBsgsuNpZ/cF\nc1B6YPs5dt8E27Y32LRpUysgNzcbb25X7ybPgPN8rHXcd2SUanXp19B6I+HOw/dy8ODergJWs5mR\nzefUmEajweHDx4jjZoZ7s1S9+ZyUKpenufPOO9P3Biv4wrK9AVkYJdx552GiqNHcIbIdai0fx3Hr\ny8zFv1xeep7KRiPkrrvuWvCFaLqp2cqNg1ccpL+/b8Ey3Th16jRnzpyZM4dxpVJhYmKCU6dOceDA\nge6CiPOOQRzFnDhxYs57DUdaXbJz584VVZY1z7nEWs6cOcPY6FgrKacZmN2+Yzv58wzMioisRLO7\n9oc+9CHuueee1t/z+TzPe97zePWrX80Tn/jEjuteffXVfPrTn+bRj340//Zv/8att966pgHJSzoq\n9fKXv5z9+xcv97lQvvjFL7Z+ftaznrXoctdffz0AZ8+e5Yc//OFF2c567atcGpIk4Z577lk26NRU\nqVSWDRwtplqtLhuMbDc6Otox47AbY2NjywYj2913333LBo4Wc+LEiWWDkU3WWu69995VfzFy7733\nLhuMbGpmUq5GM1Db7XkxPT3NqVOnVrWtmZmZFZ1TZ8+eZXJyclXbOnfu3LLByHZHjx6d803fShw7\ndqzrc6r5OLSrmHE8DaZ1f07V63WOHj2+4u1Adk4dOdr5yg5NIqamZjh7doRWmWNbR+yFAUpo/0A6\nXZ7mzOmFWaytzZm53bTPnS0zM13Ha2YtGq+1jDFmQdZku5MnT1GphAuaU6RjzO6nZ3xytUFGT9bI\nm14CVyBPgYAcAfnZjEkzWzLeLKF2LmF0dIKJsdnzyeC1MrjSZjbppPOeg+2TdQILvbm0pLY3gJKf\nlu/mvTSrLDCmVYrqXExs65w8eQ+VyRxFM9i6Lt332Tdojxk+xe/s/D4DfshQf42BDXVKGyKKQzE9\ngxE9xQY9uYiil5D30wY9MQlT0Si3//ut+Mm2tHTcGIqeI+9bkthj4Ff7eML7drLtMQ3YvxN7xf45\nwUgACgXcpo14QyWCgTQLL44STh0e58zZeygGQyQOEmcoliKGXrGfKz7yq+Sfe/WypdWt29pfIKo7\nnuPdwS8GM+SSHpyz+MbwoL4KT7ziZzzhfbvTLLJ8l4H5QoENL9nDM198iiccuIu+ID2enimwNfB4\naDjG1d5hoshL547sxvAAD/ngdRz4+FMZuAaKhYiGNQT4+K6HYydOMHlyGusMhUKMP+DB8AZspyzx\nfA42DsDGAR783x/KdZ/4ZUzRwxQ9iv7s42pn7yPwp/dw6vgInvExGAqeo+A5eoKIniCi2J9eShsT\nBodrDA7X2JCL6QsrPIw7ecyOs+Q8R85zGLJzy0DOFTBRwD33nCYMPWI7e643M31zXnopeGln95IP\nA7n0si1x7GjYtnXav3jw8YyPdRFx0uC+o+M0GtW0i71zOOeyryGa3eoDAvLkXI6cy1GgRIESuaSf\n0WMhxpo5X1wAeNm/dsePn6JarUNzKoSMyZ5P0ovXavbkGT+dz9XLcerkCLVqo7WddFudPyZZa7n3\nnntxC14D5q4zv2Q7/d1w5Mix7irHnKPRaHD06NE0kD3vatNqUbXY6o577zlCHHVXpXby5EnK5XJX\ny7bfzsRa7rr77o7ByHbWWu6++27q9RW8Tpt0buNjx453rJRoCsOQuw7fRb1Wn7PechqNkJ/feScT\nHd6nRFHE0aNHOXny5NxdYukpW+I44fCdhzl75uyc+7kZmL3jZ3dQrVSX3TcRkfP1wx/+kOc973ns\n3r2bN77xja1g5BVXXMG73vUuTpw4wSc/+clFg5HtHvOYxwBpldxaWpcMybe+9a3LBioajQb33HMP\nn/zkJzly5AgvetGL+Ju/+Zv12L0FfvKTnwAwMDDAoUOHFl3ul37pl+as88hHPnLdt7Ne+yqXhrNn\nz65o+gNIM+K2bNlCYZnSufnmvwHrdp3h4eEVZd41MyxXs62DBxef96uTZinsSkRRxLlz59jeRbZP\nu+Z8kSsxPT3N1NQUg4ODK1rv9OnTKw6anj17li1btqw4I3M199WJEydWXGZvrV1x0NQ5x6lTp1b8\nRVa1Wl1R4BPSDz8jIyNs3bplReuNjo5Sr9eX/BA5X3mqzMzMDP19c+cum1uWt7Bs+8zpM2nZnDHZ\n9d6S2zXG4+yZCTZv3jKnRLA9M3J+9+nmjydWeF4Y4zF2rsrwhk1zAosW0o7Axs4t3XZpGXU6l2gZ\nzytkTUoWBgzSZjazwbD8xADhJktfT7FVrp2QgGl2L57fdTfGWsvZM9Pk/IXTAKRhkCxXLAsc9nmO\n7fEMLjdEPhsudukcfrFLO1pHZjZzKSGmVpvi7Nkphnq3LZgn02uObRzGQFJLePjVh7GFYfySSaND\nFmzVUmjEWOuR8xx54/A8iEzEyeNnCcMapSDCMhuMMsalpcqBh+vvh+c/AUcvi33EdsPDmMEevP4q\nRd/inRunJ/YxJod1WZmyIc02zF5n7P4dpCWqXQQlt25g62/61N56Bg/wsw7pHgbf2PRgFAvYJ/3i\n8mO1JNir9+P97GeYwOGbtAu4MR6+gcCDTQ+r4F3/CGxvd2WZ9tAeyJoQmZyXHkPS3N3ExRRMH145\nwsxUyJdivIE8bnjD3EY57eNd9zAghlr23JNlI/pm9rEcZ3NJnj17L16xRrLzEQRZiXEhm0Mylw3v\nDwX4Q+l119pz2JMnGZlOMxVN9kj1OtzFhWCYc6dr9PfOfnnW7PjdKg/P7sbAZBmeFjZOTzHc5/j3\nqSWeU5rZjOQ5ceI4BzrMKdrMkPSd3+ru7WUNYM6enqQvGqZqpmfHNIufU8bkOHnyHFcealZrzD6u\nZvelOS3FbIZlXG0wPbmyL7Oi2HJuZIytWzd1eOS07+Pc55eJiWnq9ZW9jyuXy0xPTy8/3/S8zL/x\nsXGqtZW9Dzl+/DiD1167onVOnz6zbDCyyTnHsWPHuPLKTp9Z2o/V7OOgXJ5mdGxs2bmSrbUcOXKE\na65Z2EirI2M4cvTosl8cnz17lr6+vq7fmx09enTJLzittdxzzz1ce+21+L63INAs0ukLCJHV+PSn\nP80///M/A+nc8c95znN49atfzVOe8pQVV8r19fXR29t7/2xq88xnPrPrZf/Lf/kv/MZv/Aaf/OQn\nue6663jd6153Afess7vvvhtg2fLT9uvvuuuui7Kd9dpXuTScTwbijh07ul4+iiKmp6eXX3CeOI4p\nl8srCqiVy+UVB1nb11vJvIur/UZnbGxsxQHJ87mvVhqQXO3tGh8fX9Hcn41Go+vs0nZhGDIzM0Nf\nX/dlWpOTk3PmoOrWxMQEe/bswe8yOwvO775aaUCy+23N/RA7PjaxICC5lGYmxko555icLLNp06bs\nL8vfB9VqNQuydnfMmwGFer1Box5R6im1SrOb8zi6Ttt1jrGx8Y77ZFrlmB0ClM7DG83j7W52uW3O\nKGcwLvt/ftBgfIrExgT+4re/Gd41WZBmYqLOzm0W53lzPkkY0zkQNHLuXMduxJ1ysoxxBFsNSQSm\n5GMCD6yFyOEF4Hu2Fbw0QK3eoDqdNexpH4c0yJluyGvLYoyARUoIfR8KeUzeg3qd4aSavYn12u43\nh/EcBO3nQAgsP+ej2zyIG9pGLvdz/Lb5/Ob0ICkUSO/3COjmjXAWHAn8Bcc/MOAbh+kvYLd3O/WH\ng7aO6AQexqSvWzbLqA1cDmNg08woft5gigGusNy0DiF08frnXMKZM2dh5+xt8bPSbRNkAbZCgMll\npdaDI2Bj/NPpMp3Ov9lbZpmpzFCt1iDb3eY52JyPtPl7YCDnQa1cZaYB7UFsY1jwCXp2WgTL5MQE\ncbSbQvM0m5/hnOUgN38GRzhuKboSNVPp+NiezxiParVGoxFSKCxdEtv+ZcbURA3fK8zJEu1G+hqw\nafkF56yz+vchXTfAyx48o6Mrf21rNBrMzFToa5VVL33cnXMr/qJ3plKhEYZdf1F+9uzZrhvR1Ot1\nyuVpBgaXn1N6aqrc9RfHp06d6uq9WaVS7SrLNI7j7MvubV1tfzmtWULWZDQReSDZt28fr3rVq3jF\nK17Btm2rf85585vfzJvf/OY13LPUJVeyXSwWufHGGzHG8Od//uerClScr2Z54XJzkbVfPzU1dVG2\nsx77OjExsehltaWYsnJRFHVd/jvfSoNIqwk6rXbd+c1eLuS2Vnu7wjBc8XPRare10vXq9fqqS8of\niPcVsOLM1NVuq95oYJ3retJ859yK961ppfvY7LC+GrVqe6bLwo7b853PfVWrxa15JtsDBPMDhM1s\n0GplbiOvrrdTaZDLirX9bB5J47yF28nKSWcqtba/LR+UNaRNZ2w9JG8cgecITBb8Ij1+vpktr3Q4\nytPlZcf3DBjPpXMylusE2wv4m3vwBgqYvjymaPBzFj9wBMbiZ8E3vxxg8NNc0KxMtrWvxmGMSxds\nfaGzzHNIXwlvsMjmYJS8Zwmy0tfmvnvNMed8EdDd85LbsxOsJQhs2pin7bty3zjmJsR1+1yXLZcP\nMMHcN7tpgyCHKQZZpK6bMect45m5TXux+OTIeYYeGuSGE8yGXljyS5g065dSKb3MDt1is38OS61e\nwyUW37j0EqQXUzTppZTD5ANMPiC3LU/uwCCFQpyWj2frzB07SS82xtqYmfYS0qwmtVn1GnjpJe85\nBgNHb9SgYaFhZx8zS0k7yjtmKjOtzu+tTbnmFwPenBLkRi0iZwMK5FpTJSwnnfvVMj0zvWhgZv5z\nDZAGY0m/LFkqA3MuS6NRW+b1d+5zmsGjVg3xzMrzQZZ8nu1Qnuyco1pb3fNl98/pHrVaY1VfHk5P\nt7+fWLw83jm34i/Iu12++/J0Wl3DlzNVLnf9nqD5eWy5EvDlmHk/n89YIvLA8gd/8Afcc889vOlN\nbzqvYOSFtC4Zkiu1e/du9u/fz7333svRo0dXXJZ5vpovxMt1UW3vvruaD+prsZ312NeVTOYsF875\nBOdXuu4DdVurDeg21+02G9M5t6o36HBpHz9tq/O63U5Ov5rAcfPDeZx0yApsdovu4HzP9ZVY7fEz\n+MTZtppZkcZ4WffrRfYtXnzf5mdoznbRtURxgodH2vPasPTca5Y46q78sMkBkQWT2DQLMd2BrHt3\nZ0mcsFQG6pwPmR6YgQIMlsDloBGmE1j6HsazmGaGZHY+JLHNmvAsEShK66zbbsES8nko5BjeMUPf\nvf3p2POPt0fbeE2O5T8eW8zMdBrQ7GBu7Kib59V53Zyz9b3snpmTLRiGUOomcD8v09QzaUYos1mA\nzVzbniDGlApQyC2dmtjcz+x1pcPsA7PbawZrEkvzeLaOl59d55t0jgCAQ9shCcl9NW16uFT2QXP/\n4w6P++beN9f3TFb+blf3JdhKniuS2NJgdc9j3Wyn/cuIKA4XTJuwkm0FweKvAe0Nu2ySLOw23hbY\nX247K9F8vZn9EqR7K9nWap//l18vPW5Jkj0Xd2jGs5ioy31a6WtdN+8DVzLm+bxOi4h0YyUVkhfL\nJRmQBFpp/BcjQ7JZF7/ctttfSFbTLW0ttrNe+yoX30pKUM933fvLtlbaLX69bldz8vrVZKjpvlrd\n8u0u1WO4mu00A2id4hqLBSNXu63Vrrva+8qR4Gcf7lqlnct8MA/8gFY5bofx5v6eBYqcR+CnnbXT\nv8/+68zDW2I7nRjSUlZrTDPvrRUBWHQrvpdOMLmIOVNoWnDlBmaqCrliGniyFhKbJZx5WGeymSLB\n8w2Ji5bO1LHZGN3cf2EIjYjxk73MRAGJc8zPGnSWdLw5usnVMbi+fpxbZA5LO3fZlWqub7N7Zk4f\nkny+632cO6bD2Sww2N4hHkc1DnDVBBpR1vVosfGzv2fv35Y69VtTGLSN1TpezS86krZtHT4FYZUo\nTB/LSz2qmvvf6XHfPFStXvcunUM2WWWB1UqeWzzfo9BVef7qttM+LUTg5whXGZBcblvtAWuvw7nQ\nTTASVv8autTrxFpsa7WvNcuvZ+fuywreT/ld7v+FeK+wkjFXMuWQXF6sM9hFXhNFLrT77ruP48eP\ns2/fPnbt2nXBt3fJlWzD7KTExWJx2bkRL4TmPGfLlda1X7+SudHWcjvrsa/j4+OLXu69994VjSWr\nl8/nV/3Gr6enu0n7V7t8u/Zs3Au9rfW6Xb7vrziQv9Lj0LTSfSwWiyuelLjpgXhfrWbd1d5XuVxu\nxYHq5bLZF7Netyldd2UNsFZ7XzlnKRU7b2v+HJLNLJ9iaeXHz8Ojv6eXJCt+7UZvl41O2jkH+Z48\nCVmsj9mEtbSsdW5woLe3F2BB+eiCca1Jg46Rxc5YXDXChQlEFpdk1ztwGKzLchJ7TRagzVrwmA6B\nLGgLIC5zHocRrhEzlWxIw3rZzWjue+uD05yAZLdvMQNMEmNtGlC1bfe9xcyLpnXzeGvlqLbuiPkh\nHwsQW6jXuxxz6WUMXlr+7ADfI6n5UGvAkpm2WXFlFKWX5r4tcormcjn8vE+aB5vd5y5NKE7Pg9lb\nmUzFJOcqJIlHknjpYVjm1J/zOJ7XUaG5fgLUrCEsFFrdume7Ry/NYOjt6V30epsFdJuPkUIp1+p3\nvVJLPSc1y7rblUqlZR+HnQRBsKKgUvoaUOg6CNlupc+znudRXGYezfPflqVUKqzqPUjz+W85nuet\n+PWs2885Xc/JSfoevJv3gf19fV0HT5vbP98GJpo3UkQA3vCGN2CM4Z3vfGfH6//5n/+Z17zmNXz1\nq19dcpwbb7yRX/3VX+VjH/vYhdjNBS7JgOQHPvAB6vU6L3vZy87rQ+lq7dy5E1i+m2z79atJh12L\n7azHvg4NDS16WWnnXDk/y80VulbrFQqFVQU0PM9bcUOWwcHBVb2ZLZVKK+4cvl7HD1Y/1cFKt2WM\nWfXjcKX72NPTs6oMa9/3GRhYfoL5dqu9TX19fSvOOljtfTU8NEQrKtCl7u/fuR9Yh4Y3dL9jrO6Y\nNw1u6Mu237ywZLbvSo+5y0qy8/k8xR4f6+JWkCANRyzyYd0Yhoc30OmtS/MDvu2wbmJiasMzxCZu\nXRISEpNgTZKFQbIspiy4MjTU35rzbbFAhc1CJ81D09ufo2x9GokhtIbQpgmQsWvGxdrmccSwefOW\njmO334JmEMk5g23kic7EJOfqJJN1kqkQW7XEDUMU+UR2NvCU7/MJil42J97sNtJgokmDknHSFghb\n4nGdJDBdxZZDwmI/p5MeEudwLm6N7TDYxIOovVqju+cK74c/xRy+l0YjoJHMBiSbDy2bAI0GWR5q\nV2O2tl0PscncYFxkIbQedqKGN9rtnNpp/++WMMFmGZJpwNcnMg1i6zgabCEqe9ipOmbZOeqWvj1e\n85/JsWXzFqxN46ixJb3PIx9Xs+ml3CAZqZCMVKidKVG509IIAxphQOIMiTNzjoOHj4eP7+XpKfXR\n3z8bIGo+8pvbap7HjcRQiSHuKbEh59iQmx2w01NE8zHkmYC+/n7yhXw2/4DXmkvSGZde0qLmVmAS\nA9FgyISZIiFqXbecQqFAb0/PsmHM9sBk/2CBOKnhXNJ6flqex/Dwcg1tZvfXZXOBbhjqX/w5bglL\nvnbMfx3Kfl/Ne5cgCBiY0xl+9nWgE8/zVvx6XSwU6Jnz5dLS29i8eXPHeTI7CYKADRu6ex86NLSh\n626x3TYAHBwc6OqLR2MMW7asrCneUty8i4jIfP/2b//GBz7wAX70ox9d7F2ZY10CkqdPn+bo0aNL\nXo4cOcL//t//mxtuuIE//MM/5JGPfCT/9//9fy+6fBiubH6nlbj66quBtKPaUvMt3nnnna2fr7nm\nmouynfXaV7k0bNu2bcXBu4GBga6/iW63miD71q1bV1UCs5JOz03NYPxK9PX1regbcUjfNK5mEuBN\nmzZ1/Ua3qVAorCo4ttIO4JB+uFlNtt5qzovVnLe5XG5Vb9ZXs3+reYx4nreq83bLli0rfowUS0U2\nbNgwp9R4bhle5w9y23e0nxfLl+85Zxka7iWfz88JQC72M2Q5XsawY4XnoHOWDZsKWJfMuaRBSbsw\nAJp1de7pKTEwUMz+tEgTBJI55duVvnF6NuSomzqRCYlNlAYjibEsnMfRmIBcLsfwxs6Pj2bIBGYz\nIcsR3Bn0U46gEkMtgUYCWSJjKyjZPP4+Af39G9mwIbst8zLwbHPsrETWz8OP7noQM+fy1M8Z4lFL\nPGmJpw2Nehp0iqwhdAZrIedy7N6zFc/L4Zk006yZ2eicwSYGYouZnsZ88ut4d94NtRqdmPFxmKpi\np2PqiUeyZZhpF+JclI3drP42WeAQvHuO0fWsQOcmOfuxaSIXpFml2byBFkfivPRg1Ot4//P7dF+y\nHeD9+13gebjYkDhIsgBU4tIg2+gPcoz8j2NQ6a7RlHf4eGv7LrLpMSSdm9I3AQ03Q6MAbOgnrAXY\ncoQZn4Ry5wYb3q234d16B3i5tGw+iyYnbva8DkyRwBTZvGEP+3YewvcMsUsvzWBjNA3RNMSjMfVj\nlvoxy78f38pt41eRyyXkckkrq7JTlmQtHGXT1iKeN/t6ZZ3DOtcKSDay87mepL87z2Ok1M89M4bY\nOuJF0i+bQbjE1tm5cxedPnYkxOnFJEQmIjIRda9G3atR2umY8uZ2cF4qaOhcxPbtm9qCVz7NQHJz\nX1rPoNn5YG1Msden0LPCkmjj2Lpl0yJnZMLslAZ2zmXTpg34/spCRsVisbv3BvMCk5u3bCIX5JaY\nL3eh7du3r/j1eufOHSsq8168BHDuF2FNmzZtpK+3t6sv//bu3dv1/htg/759yy4/MDCQBkW7dODA\ngWVf5/fu3Usun1PwUDqy96OLyPlal4Dks5/9bPbv37/k5cCBAzzhCU/gL/7iL4iiiO9///s8+MEP\nXnT5n/zkJxdsfx/3uMcBaen4d77znUWX+/a3vw2kAZVHP/rRF2U767WvcmnI5/Ps3bu36+VzudyK\nlm83ODi4ojdgfX19q+7etX379hVNJbBly5ZVZ3/t27dvRYHCvXv3rior0PM89u/f3/UbY8/zOHDg\nwKqyRYvFIrt37+56+UKhsOrpMIaHh9m4cWPXyw8MDKwqcAdpcHElWfIrPY/a7d+/f0VZfunyK59C\nwfd99u/fv8Ll9614O5BmtO7YuUiAtkPJYLFYZNeuHTQ/SC/fPXf2LcTGTRtXlCUzNDTIxuENWfDR\n0qmMcrFt792zh0J++WC6dQlB4LNlTx+xa2CJm6GPLBhpZwO0bnaeN7IS5507ts7JGpuvuXvOQX24\nHwo+oaV1mZMdmQV4Wkya9bZv7wEGBvsWdPuG9I2+A6zxuat+LZGfp1YPaNQC4rpHUjNEdY84Dois\nR+y8Vum0j8dgzzBXHNhJkDOtoGHiILFZh+hiDvuZ7zH1kzyMT2LGJzreTlOt4eoxtpEGSAulHJsP\nDdKTZdE3n7KS2IMwxvu326h/+q5l75/WcYwS8v0Bd/RcxZhXIjFx67haBxQLJH/zf6B3ZY/tymeO\n0/hZnThOMwSBrHESzPQN8HPvWnJFi6l02egvjPG+9G94//YjXGhJkrRNEqTB8eGNAwztTzP+k9jg\nGmlJuKkt0rG42W36szfDR77YFpCcfQ2wLqGnr8DeA9ta52sz2zGyXnq/17NLFRrVgEY1YDoOaPQP\n0ugZptrItQKL7ZrZi7t2DdHb62WNirLtMjvlQOJmz+XY0uquHQ/2MJPLtbJyO02H0NzGjp3D9PX1\nZx2u0yzkVngwCzBaYpyxOGOJiYiJMPmQ4R15/GUzY9PH7aaNQwxtWPq9wdxsbJd+EWITdu/e1noN\naAYvl/q4vXffXoLc/NeMuevM/zLHOYfnGfbt6/71t/XegJXPoOr7PvsP7Ot6xaGhoRW972vK53Jc\n0eX7l127ds3LwOzOFVdcseT7PmMM+/btY3AwW6bL6oW+vl6uPHSIwiLv87Zs2cIVV1wx52/LZSEW\niwWuvvrqjvtbLBY5eOggQ8Orq9gREXmg0Wy6HTzrWc8in88ThiEf/vCHecYznrFgmWq1yqc+9SkA\nHv/4x6/qBXwttrNe+yqXjua35MeOHVuyk3NPTw8HDhxYcZZeu927d+P7PmfOnFlyuQ0bNrCvi2+Z\nF2OM4eDBgxw9epTJyckll922bdt5dQzL5XI86EEP4p577qG2SEYQpB8C9u7du+oyb0iDtIcOHeLe\ne+9dsvFUPp/nwIED5zXv3+bNmzHGcPz48SWDSL29vV19e7+UvXv34vs+586dW3K54eHhVQfEIb0P\nDh06xJEjRygvU/q4c+fOVQc+Ib0PmudFvV5fdDnf9+d+6FmFgYEBrjh4BUeOHMF26J7dVCgUOHBg\nL8VinvRD7sq/Q9y6dSue8Thx8vSSy/X397Nvb5rl4pzDmGZ2j097gLKTrJk0+/bt4viJ04yNzS2D\nTQONaaDB4DE4VGTrjt60QNMlmOzvwGx2ZCs4ObfMGcAPfK44uIUTx6epVaM523EuARNgsZRKAaWd\n4/i5jcQ0MMbHJ8E4gzOOdM6/mMTF88qpvTRj0YPtu3KMjoS4GT8rKE2PRUJa6j1pLT3be8j3WvqS\nhGrs4ZyH3/ZUWE8ckXXEzrXmsfQJ8P081vWyYXOZcn6KqLoDLxnAOYd1aSOje7whvl3dzRABVw8m\nDNaKNOKY3ijCGEeSeFQaOWpxQDUx1K0hdhAYn7zrYcumq+gfGGekfg8z8XXUAsvRZJgT49s49feD\n7BlwXLVhmsHj4xjrIJ/DDQ+D70OliqlW4NwYyXidaNojyRosDZf6+aXrnsmZ0X/n58EkX27s5keT\nuyi8t8DmouH6R4KZmsItN4VHo4ErN7DW45zNc3bDINsfVMSbdEwXx/msu5ov3X6A501F/NJD6pjJ\nCdyG5Z+TzdgYNjZ89LYHc6JiOTpYZqb3GIcaVzCyGe7N9/FgPwZnMCNjuG6yscen+Pm/5Lh9spdy\n9VGUyyE/sjWmt93BnnyeK4avoI8CvueohwG95ZDc6GR61hZLc46FOXcOd0/6/Pkv/7IV2Iqr1QnL\ndSDiaBRSKOYINt9LfqCfUvDw1rphVipeCdPX9t5KOnWJX08oV4vZOZc+nj4zvZ9arcEV+TJxLZwT\nRJko/jvbtm9hs5d+keh5sx8JkizjMcr+r8VZNqiBSpT+7WRchp0+xdEeJs/NzAm42yyD0XpVdu/e\ngZezGDP3NaeZxWxJXxtjE2Gzx31swmwcy9DGAZw3wfTJ9Mv3Zrlze/l2nEyzffs2tm4dSudQnfdc\nmT43pPvnjG39Ld2P9HkoyHkcOLiDe++9j3jxlwCMcezbt5fBwe6Cap2eN/v7+zh4cDdHjhyl/a3B\n/Az2fD7PgSsOLFrN0Fx+qQzIvr4errzyIEeOHEmryxZ5Ht+6desylSdL50L19/dx9dVXc+zYsY4V\nW+kXXrtW/mVytr++73Hw4BVMTk4xMjpCpVLBWks+n2dwcJAtW7ak2f2sPHDb09vDNddew/T0NJVK\nhSRJKBQKDA4Orvg9dPPo5vI5rjh4BWEYUq1Wcc5RLBbP632eiMgD0boEJN/97ncvG2RYqUOHDq3p\neO2GhoZ4zWtew3ve8x7+8R//kZe+9KVcf/31c5b5f/6f/4exsTEA3vjGN3Yc57vf/S6VSoVcLsfj\nH//4C7KdtdpXuX8ZHh6mv7+fs2fPMjk5OWcKg97eXjZt2rSiLLal7Nixg6GhIc6ePcvU1BRJ1tHT\nGEN/f/95ZSu2a2YBlMtlzp07x/T0dOuNvO/7DA4OsnXr1jV5M5fP57nqqqsYGxtjbGyMSqUy57qh\noSG2bNlyXsHcpr6+Pq699lpGRkYYHx+fE+wqlUoMDw+zefPm8+oq3bRp0yYGBgZa50XU1iihr6+P\nTZs2rXq+xPl27drF8PAwZ8+epVwut84Lz/Na58VKy+M78X2fgwcPMjk5ycjICDMzM63zIgiC1nmx\n2mYx7QqFNKthdHSUsbGxVjMwhyOfzzM8PMyWLVsIgmBVH3raDQwM8OAHP5hz584xMT5BIyt1hfS8\n2Dg8zObNm+YF+TsFJZcvmNm8ZTMDgwOcO3uOyalp4jjGYDA4+vpKbNw0zNDQYFY2nG4jDUoaFgYj\n7dwPtMbM/m4Mu3fvYHhokHMjY8zMNHDOZt1lobfPZ+vWYXp7ezDGYW2SNnh2Hs60z3/WLNtuv21e\nui/GYJwjCAL27R+iXJ6hPJVQq4VYfJxp4JccPQMJA4MWP+gjcWFrDEuQfnjPyqcTQpyLszLv5lyS\nBoyPceCI2bQ5TzI8Tr1aJ4x8pswUuVyeWo+hf6CXujfAJreThguox2nQxWQhEQtE1hJaS2QTYhO3\nAikeAb6Xx3o5Bgch2nALP7MlcD3cnhsgX/AZyu3hoOkjcVCJPSbCAvUkIEp8MGmZdDUOqFuPapzO\nX9lspJNzOXJ+iSTopZG/j+/1jHGL10O/t5PBcDsP94c5YGJqcUB0eprAjePlc5jJqTQg2WhArYE7\nNUY8ZqlV8jSyBilFL6DEEBuHD1CObuGnvQmn4108khJ9QYwNwZw6jSsWYYm5fr0TJ4nLCXEc0BNA\nX86nx/bTUwqJimcIc1ezreBTCmq4mTrm7Gh3AcnRMYwHG/KOsYbHZP8ZbDjCcO82NphBBvI+vhcR\nRx6cm4BrlxkwjHATVQq+x2CQ8OOa4RQhR4d+SCmsU8zvotcNUPA9EmeohTlq5RyF09N4jRjj++lx\nNSbtqj0yTng6fS0o+un58LVyDshxW/BdpnceA2CDSQOlOZNmisfWUc2CjdNx+vqUz4KQvrFMhemx\nnsmWGavHkPP5lH+CJGepFKaZKNxJsVhkC2A5R+ClmafGBK3gYJgFFJuBzfbnnWo8t1z67o3/TjKY\nUJ82TFXGSZKExI7S19fLhmIBY8bTx62Z+5GjGUxrBvlj12gFLRPXyP5PX8NyvXW2XxEwOjqBnTHU\n6+njtlDI01v02D98gEKh0HpOXhigS+c8TbeXLZM9tzqbtJ5r/CDk4KHtjI+XGR0dp16bDVzmgiyD\ncMvw3Gz6Dl/ENp8vZ7/YaZeO19fXwzXXPIhz50YWvgYUi2zcuJGNmzYu8d5g9vVg8ak40ufr3p4S\n115zNePjE0xMTFCr1XDOEeQC+vv72bRp09q8hhYLHLryEPV6nZmZGeI4xvd9enp6Zpt4nec2BjcM\nMtjlHJHAnNenBX+bp7+/f03et7TrthmOyBzNuZ5FLgPrEpD8lV/5lfXYzJr6f//f/5evfOUrHD58\nmOc973n83u/9Hk94whOoVCp89KMf5Stf+QoAr3jFK3jKU57ScYyXv/zl3HnnnQwODi4akF2L7azF\nGHL/k8vl2LVrF7t27cJai7UW3/dXnaW4lFKpxL59+wBIkiR9I7vCxiHdGhgYaAU44zjGGHNe2XyL\nMcawadMmNm3ahHOOJEnwPG9NAoPz+b7Ptm3b2LZtG9ZakiQhCIILcl/l83l2797N7t27W/fVhTov\nenp6WuXHzQzQC3VebNiwoVUSfKHPi82bN7N582acc60PVJ3Oi/MNSvq+z/bt29m+fXvH86Lz2IsE\nIJcpTSsUCuzes5vdzD6Gc837asG5MRuU7Grb8/T29bK/L/3wmWTBiyAXpAHPOdtKSxgdCWZemWMa\nBFiiwU1mYKCPVrKq8dNAhPHxvALGeFlgMyEhxODhaGvE4mxr/jrrojkBUGPSOffSoGR6XvcPJGAs\nnlfB0CAX9OIT4GGITUTDedSSNOjpMXv/Rc5RtwmRi4m9iIQQi03LV7NS0+YcmUEuwPcgn/PIe32U\nbB/4aSBqOjYUQp+ib6glPp5JMynriaFuPaZjQzVOMzJjl5B4SauBiAMCr4dCboBBs50hu4Gil5bh\nVqOA6jmfYtggb8/gDU6mAckwxjVi4lM1psfyTM6UqCYeDTsbAHHO4ZkSPQwzYEqUgjSLLpzxKJ4Y\nwbMOe+3VHe87Mz4BR09RHzVUG3msA98zBOTA+ARegZIXUPQhsR7JuTrBwFk4eCDdv0V49x6BE2dp\n1AJ8oOBDkT4CUyB2DfIm7dvcsB6VeoHhk5N4d92N3boVOpSSmvEJzNmzxKNVomQAz0Bf3mcgLpEP\n+qg2muX+6VncSGAqLBDMOHpOlAlmYvwowfQV0rMittjxCtWzzfLg9EzpDdLzspfNVEzn7PPYOipZ\nRt1E2Fw/5eMoZwHx5jLNTN2StwE8qNlRenvTB0ySRNl+zz5nxyb9W2jTAepJOt5scyVHLfvyKc6C\nl77J4+cg7D1NXzH9EsfaHBDinFmQsdjKVMwChHHzCwMDZPNnNr9EaAZI0xLvGYY35bFDFijhmo2L\nXGmJ8tnsCxX82SAhzQzJbPXW5AjZbhgYHupheKgH62KiKFr0NWA5s1/sdM6UTF8DtrBt2+YL/t7A\nGMPGjcMMb+zyS8lOgbzFlpmnWCwukdXZ9tq21DZW0CyuK2s9HmoeIyKyFlSyvYihoSG+9rWv8cIX\nvpDvfe97vPvd7+bd7373nGWamYkXezvrta9y6bpQgbROLkQQaDEXKrg1nzFm3bal++r+sy1jzKqz\nZFf6QWXJ88K5rrqLdmux88LhMI5sW83gnMfigcjlA5SLzbPpnANj05JZMzf42V5Kmc1kN3dbbVmZ\npv1v2f/GmLkBkFaANSYxHsbNlofbbP66uc10Frn3sqAhWVdlL8v4sjhiEjwTEzqLSRJ847V2M3YJ\nsUuIiImJSMxsV/E5w2d5lcYEeCaHbwKMS8MnsUsb5NRsOk+kw2LSe4x61tW7YSF0aUZmWho+b3zP\nxzM5ci5HkAVlI+vRSDzqtTye1yAYj9KJAo3BxRZXt4SThkq9QCXOUbeGKIHEpQXsOIvn+QT4+NmY\n1kFYD7DjFbxCkJYn9/dDW3a7OXcOMzGJG6vQqAbUoiCdd9PONmwy2TG0Lt3PZNriT9YwZ87itm3t\nHJSMIhgZx03UCMMgDUe1Yh4euOxsco5G4lGLA5LJCG90Ij3TS0Vof8yHEWZkBMamsGWbdudujWfw\nXZCdATYt43eO0EIt8alEAfVyQMHGkKvhVaM0WhtZksmIWq0nuw/MnFM4ncuxOedlM0O5GQRMxweo\nZevlYi9bD6pZQDLMEvNa2YDNuUW93ILS6ZYsQA8QNf+3C59ro+y8TbxsI83j4fmY5m1p7X/n7aS3\nKSu9ds1pEGK8Zsafi+f972hlaTYP1AqfZFvPK/OeS9MvRTo/lxljyGfnQ1ebawXYZp8/l56HN1sc\n8Lt9b9BNoPD+5oF4m0REpCsKSC5h7969/J//83/4yle+wpe+9CWOHz9OoVDgyiuv5IUvfCEPfehD\nl1z/MY95DLt27Vq2ycL5bmetxhARkZVZtwyJpYKSq8j8WPJj35wPh3bBNuaUBy6x7bnBwvkftBPI\nOj/PH2M2OGC7aKwzO34aBPGgrWO1dQmJjTDE4C0seXcuxtq4rct33LnLN9BsdmOYzTi2WSlo6FVp\nHqvI5fHbOiUnWBqmQWQiGqZG5OqtJjtpMCYrg/cMxvitbYCHwxEmCT5QjtIu1HnPUIh9vCxYF9q0\nk/d0DI3YUYkT6qZOaKpYG2Jt1ArSmlbHYahbmIw8IEeh3EdfrcBgtU6hEIOxOOsRxz5TlRJnayWm\nY5/xhmEqgqoNiamT2Ag/686cOEsthqnYZ3S6l567JsmPjRDM1DF9RRjIGgQ1IhgrY6fq1O6KOD05\nxEi9yEwM1dhm2azp8Q2ThEYSMBYGTJ4uMhBWKRXvxGw8hdu1DTcwmB63RgNqNczx08Q/O0N4xnJu\nZhMzsaGWOBKyILBNaLiISpxjLPQJvCLDRwsMB6fxh8fxzo5BKZ+WmTcaUAuxJyewYyGTJwtMNApM\nxT5R4tL5DrNgepKE1PNVKkkPU2GOs42AepKGxXumI/qnGuTycRpgtYZ6rcjp6fR4TEVZVqDNpr3A\nx/Oy0mWbZizGpOW8lSRiKsuMzGXnYKOtsUotixFOZbN1hFk2Y/PB6Jlg4fyK2Xmb2JDIpWXkVS/r\nPJ49FPNJMyjnaGTzO4Ymm4PZzm0Ilf6SBSbnPFW4edvLMjRNPft70rZsMmcZFkzf0P5/547b7ds0\nZnaW2IVTb7vWDe0meNhhIwufUBcEJjtctybWocftBcgsXM22VrIX51u9IHKx3Z86WCtTWM6XApLL\n8H2f66+/fsG8jN3427/923XZzlqOISIil6j5QckLUoLmZudf6zD+bDCyy7fKi8yvZozJ/u/Umzcd\nPw0ONLOwuguCzu5nmn2Js1gXp8E4F4OjVbINtIKRaRAyzra5MAg7G/yYDXamWZVJWvbtIkIzu5yX\nBUhdtkRkGtlyDRIXtrY1P0ty/jFITEJkLb71qCUW33hEHsTebDOhRtZBuRY7GokltAmR1yB29SzI\nOq97ubHENqGROOqJYcbzKEcBkTVYDMUwxpB21I4Sj3KYpxz5TMcetRhqiSU0EdZGWRAplxW+O+oW\n6olhKswxPVGgNw4p5mbwSjVMfzZXb2RJxhok05bpyRJTYZ5ylI4dWtsq4XXOpmMmjkrsM1Ut4nmW\n3IkZ/EqISSymfxI8rxU8dKcmCc9YKpN5ylGOWgwN69Ks1KyjcmQiQuuoJobpyGeyUqLnbES+VidI\nxjHFAJMPcGGMq8fEZ0LCssdUpchM7FFPDJG1xNm5Ypr7akIiYmqxYyY2ePhMhHlC65FYQ8HPmrg4\nQzXOUY7St+D17BQP3dw5FdNjMLesuUFIPc7miZyXIWrMbECynjSD481sxizTsOPjtpmR7Fql0rGX\nBkAjMxuIbP4fZgHJuDnPI7NdWTp1i5/PtW5nNqejzW6HN7u+bWVGtmdLd2+22cvsNk2zmc28fV38\nC4iVaI7tZWOvQwmyiIhIB8ePH+ff/u3fFvz91KlTS14/f7n1sqYByb/4i7/g13/91zlw4MBaDkuj\n0eCDH/wgT3/60y9oMxsREZFLRccsj2U+1J7fR96stHnZUToF7BbboQ5BVGNxzsOYJBtifgnpwkY6\ny+/T/PXT7MtWE3PjYW3cMTvM2TQgaW3cCkx23mazfDzN2rTEJLaBMwl44LuAxEQ0vGBOQMK6tFTb\nuZjINbAuJEpqWUAvygKbtAJbaZftkNgaav40vvFp2AJxI089DvCMIefNjt/s4F1LYiKXUPammWGC\n0FaIkxqOiPR+tVgXUjdVjEkDjXkvYCYy1JMcRS9HbyNPznMY0vkpY2eoxB6TIVQTGKnFzCQx094k\nUVTDuRBHnoapMOOKjNfzxNYj8IqE1qd3KmZ4rE7eT8jn0my3xHpUG33UooBz9QInajkmIxitx4wn\nFSKXBi5j26Dsz5ALPU7W8hT8XjbUi+ysVyjlI/oGz+CXXFoaH0Pc8KiU85wtb6Qc57ivmuNc3THZ\niGgwTeJCrIsoe+Pk4hynq33UYg/oYzrK05uLGDzWIAhifD8iSQxxHDBV76UaBZyt5zlR85kKYSoK\nqZgasW2AMVgXUrNTTHkBY40CgZdnMjDMxHmKfo7eoEDepPuaOAitx1iYnosj9fQ8m46zzEOv1jbP\nYpLdx+kxmfYnGYvSuRNjl8/+lgXBDIRZR6NKnB7rskm7HYcuzWZsnuPQHqyLW8e7uZ1a9jiJTRp0\nDEwhewQkrUYzDTeT3Z+NbOxoicChbW3HZus3y7vjZlJlEs0GJLNgpcuyRq1NWsdiZXlDzczHuO0W\nN7OE2zMr5469suebhVrru9kveDo/n9B1oHI2kNr8Q9t687/46fhlUvdWmmG40qO14Las4djdbEOh\nYRF5IHv/+9/P+9///lVfv97WNCD5qU99ije96U38zu/8Dq9//es5ePDgeY1XqVT42Mc+xp/92Z9x\n/PhxfvCDH6zRnoqIiFz6uvngtpoPV4s3Fujiw/6CEusuljd2NnvIASStZh5pMKA9UDj3g7prK6lc\n/MN7Or7Bti3ig7EkdvYotmdHpsPNNs9JAx6zc0qmY7btkzPZklHaGCMha+rik9gQzwswpoLH/G7C\n6ZjWxSQ2SgOZSYh1DayNaD/mjgRrozRgmWW1JV4D3ysw43opJkUMhsA1G5o4EpOQkBCaOrEXUXdl\n6skUUVIjtnVsFjBKkpCIGhVvnMjUwUGj1k/B+EyEPoExFAKPwKT3RuzSPWuVgScJk1Somyrl5CxR\nXMXZGGtiask4zksohHmm4zyNJMdEI0fBz9FfKRIAOS89molL58KMLUyEMNawVGPLGTvBtDdGozGT\n3i7bYNKcwZKQq2ykkQT0BT4nazlKvqNv1JL3mvMQpsHTcuQxGXlUYzhXd5yuVyl709STKZIkxLmQ\nmWQU5zsKtd1MhQGVOOBso0jBK9Lr9xF4rtUwKHFQTTzqCUyGMF5PqCQJI2aSKpNESS07j0Lq8QTO\njznj5YmqQxS9gImcT84zFH2fwGvdyYQOKlG672NhGoic8MYBaNhyK/jWzBBsxGnwb9qcxflZ0DFJ\nG/AUsoxJD0OYNaWpZeXUNaYAqNv0/8jW2wJ7zcdAVh6d1Gk0pyLIgodhVrrttze+IWotD22Zkjaa\nHXv+47Qt29llWZjNLwtsFnSc/9hs3zdH3ApodhfAm9u4pjnfajZAx2W7H7uDJUq0Fx1xzjQYK9hU\n9v+c14Q1KnVear2FfcvP33oEBhV8lPsj62abiYk80K1pQPLFL34xd9xxBx/60If40Ic+xGMe8xhe\n8pKX8LznPY+tW7d2NUaj0eBf//Vf+ehHP8pnP/tZZmbSN2FPe9rTWl1+RURELicX4n3pgiyYpTJu\n5l/fNkbX488va5yz8sJ54BaUh7cClB32q9Xooj2ImWZFOZqdbr0FwZjZ0nDmBTyWauSTZnY6vHQ+\nOmNxnsUkUTZH39zAp3O2VRbezCJLu3qnmZit4+pcGgzFSzMvs07cWDCmgTXpXJUePp5rmyfTpGXj\nkavjbEzDVYiSWjqGS2aPiUuz2yJbwXmWipcDCzmboxEWCTDkYx+frKuxS/PdwiSh4kIik2YXhq5G\nlMyk2Z04HDGRq2FcwJRXJnQlvEYfkfMpeIZyaPA9CLL7KCFtuhJZmI4SpqKImguZ8Sep2+ksSJtm\n84W2wowXMBH34NdLzAQe9cQj5xtKvkc+O8yJywKdCUxHacnyRCOk7E1TYYI4aWTHPCG2Veomx6SZ\nohH3YOslGomfjWnwTDrTZtr8Ju2aHVnHdGSZikKqpk7VTFJ3061sQecska1jjEfVG8fzPAquQCMs\nkTMeBc/Da3tMxc5RTdLzbYppAKpMptclNVyrDDoruU7SoF/oV6iZdLlmGXbdNQOSXqt8OiQNSDZs\nOVu/3naOzw9INh+XaaAc2sqZmyXf7dMcNAOYzU7Y2f2VBt6bpeFLTPfQeh5pzm/ZDB56tJdRt9/+\n1mNljsWDf2ZegNDhtT3fNLe7cGqKxZ5zlrL4lzuLrbA2z+bdZDGu5euGYiMiIpe2Zz/72Wzbtm3N\nxvuVX/mVNRtrKcad/8Qpc5w6dYo3velN3HTTTXPmZNm3bx+PetSjuOaaa9i4cSPDw8MUCgXGx8cZ\nHx/n2LFjfP/73+fHP/4xYfZtMcA111zDX/7lX/LMZz5zLXdT1sjExATDw8MAPPSh161rp10REbkw\n1rrMreMH5+U6qnaZiTlnlNaYHeZyWyLI2ikQ0b4901rfmx3TzDa3odXoZrE59GbnxHTOgUtamZ+z\njTcMxgTpNkyQNaLJ4Xl+Ogem57eyL5sBz2Z3cKDVlCedFzPK5qmMwSVZ9/E8xgT4XhHP+PhegcAv\nYExAQA6Mh0+wIJiauHC25DwrNY9tlSSp4VyC5+XwvT58L0c+6MPz8uRMiYLpxTMBgcvj4eERtDp/\nJ4QkJiZ29bS03DVoJGkwMoqncbaB8Qrkgn58r0A+6Kfo9eOTp0AvHgH5bNzmXJ0WS2hCQlMlcREN\nN0MjniJ2DaJ4GmsbOBfj+/34Xp6834fvFymYXvJeHz4BgctBtrfpvWZb+xq6GrGtEhMRxtm+JhWs\nrYJzeH4RY/IEfg85r4jnBfgmO9YE2X3bPK5xK6AXZeXUYZKVqsc1kqx0uhns87LGQZ5XwvfSUm3f\nywKRc8bNzoXW/Iyzwd30HEmD1s19aGdMAFkH7ua+LtqRO9tK+zhzgt/z5n6dEzxb7DHa1eNzdruL\nfkHRfpvm/NJFgfB5ljp33O4ajCdyOYvjmJ/85FYAxsfHGRoaush7dGGUy2UGBwcB+MjD/oiBXM9F\n3qPu/PaP/pJq0uCWW27huuuuu9i7I/dDax492rFjBx/5yEd4wxvewI033shNN93E5OQkR48e5ejR\no12NYYzhaU97Gq95zWt49rOfje8v9YZILhV33nln24czERHpVscmCLDiD9GLzX228EO/t/A6gA5l\nk7ODt8+v1h64c/O24c/+bmYDaCZrkGHw2oJe87fXDHLYrIza4lwELm1K0gx+zI7n4XkFDB6el8f3\nchjj4XuFNKzkBXgmN2+bs+NbFxFnnajT8uaExFawtoZzNh3bFMgFveSDfgIvT8HvwzdFCqaPkusn\nIKBgSwT45PDxmO3AbXE0iGh4NRrUqDJJ6GaoRGPUGqMkyTSYgMDvp7+4nQ25PZRcPxvtRnq8HMOF\nHIM5j4IP/Tko+uCZ2aMWWqjEhnrimAwdldhyulHluHcvFTvC6PTPcS5iY/9D2O89gmH62N9XYjBv\n2NebsDvXoC+I2NxbI5+PKZUiPN/inCFsBIRhwOj0Ro5Ve5gIfe4se4zULbeF93F4+msktsaVg/8/\nfqFwiG0lj+uGQ7YFdQ7tHGPgqjL+cBGzMw+lHJSKEEXQCHGnqrjJGjM/i7nz6CZO14p8dyzPiUrM\nv0bfolw/w7aeh/HE0sPZ02t47MYKDz14ltL2GrlfKMHGIm7XdhjoxxkP0wihXsOcOI27fYT4dI3j\nt/Xz3bN7uK/q8+2RMke8OzhV/gG/2Peb7CsM8pjNHjtLEQ/ffo7NV43hbyrg7R2G3hz0ltL0yCiC\ns1XcVI3G4RlO39XPuZkefjTTx6ma4dbxCrfGX6NcvYctA9exxTvItYVdXLvBsDFv2d9bpT8fsXGg\nQqmnhuc7jAdJZChPph82j0xuB+DfZ0oA3DZhuaVxLwB3TX8dgJ7CFgAO5R7BlfnNAOzsTc+CjWlc\nkoLnSFx67s1kscbxNLGS01m3m5PhNKe8uwEYqf0cgGr9OAClwk42lPYAsMHsAmDApl84l8iCoMYj\naZaRZ0HPipdmeM4wTsWOAFCLJgCoh2kZepxMth7/gZ9+4M4HvXP+901hkSkVIHENomaJeFYm35y7\nMrE1nM2yNZvzU7aed3Kt/73W3zp99Jl93knHaQZrk7kBV0gD/DD3ea/ZVbw5H+6c29H8eV5GZ1uz\nqcWzwRc+ny8ZwF3Kgi94Fg+NLvp61L7dJcvEVzl2F7qZ3/N8tnG+84fK2lvj3CkRucRcsHS2q666\niv/23/4b73jHO/j85z/PN7/5Tb797W/z85//vOPyw8PDPO5xj+Pxj388z3nOc9a8MY5ceI1GnZVN\ngy0iIk1dfQhsWuQNevcBSVg0KLmIBVlK8z40z91GlklovPRn4wHNYEEzM8qb8/v8jrquVQ4bZ8EA\n21omDUgm2dgJxnh4Js4CkAGeiTDGxzdBtqzXChY4ZufnS1zWmMZZ4qSeZq7ZGs7WcTg8z2GMI/Y9\nYt8QeAUiHwIvJDaW2Dh8F5CjQeACfIJ5AUlLw9QJTY3I1am5CSJboxpO0oimsLaG55WIvToBVXJx\nmdg4ckmRkAKEljgXUPChHhjyfnqcPZPGy2LnqMZpM5OJRkI1iRljgilGqcXj1GszYDxqQY1yMI1v\nAybwSXIePRa8yKMvyFGvOQp+QinI4XsO5wzVOCBMPEYaeU7VYDKynC0njDZCxjlHtZbOSzjTmGEi\nbJBPAk77EBcCeumBaIri4CT5U2VMj4/JeZBYbN0SnUsIyx6nTg9ybCLHmbphpBwxWqszHY9TC2vM\nmEnGogalJOBE4LPpvhLDE1WG4uP4Qzm8E2ehv4TxDNQjqDWwp8pU74qojBe4b7TIqbLlXNUy2Zhm\nhklqtWnG8+P0NvKczJdwoWGIXvwkpGdghuK5GbweH9ObywKSCfFYRDwF46d6OD5WZKQRcG7aMl5z\nTNRnqMY16o2QcnWcIDfCucZGNpg81Tx4UY7+wGOmAv35CN+z6fmU+Ew20iDfyUrW3GYmPS8npyNm\nsoYxjXoWaIsnARgvjTLS6AMgiNP1G4X0fCt4pvUIrcXpT5Nh+n+5nj6Wpt0MlaxEvF5vZNvImtIk\n0/guvU/x0+BoMyxXcmnw1Hc+LiuDbmQNb+qk2ZwVpqlmWZ71KJ17shE1g4ghkN6WIEsyiPxsHD/d\nSuDl8bIAYlPzsRrbsC0Amc1BmQUo07lY0+vav7BI/28GJIM5X4i0L5OuN/+5p+05aH4Z+hIByYVZ\nnx2+4Jnf9X7J7M8lApLpjVg4/mLWKiC51Ca6DOatR8BwNdtQMPJSdfndL2lrOX2mlsvDBa+v7enp\n4UUvehEvetGLAKhWq5w9e5aRkRHCMGTTpk1s3ryZ4eFhZdeJiIh0smx37S7fsM/pep121W5fv9OH\nuI7dYZf4wDx3ZQum4zSPrTkSXXumkGt2sm7+bLMP8HbhMcjGnm2KkwYsLTHOZWObpFV2uiDwmTWa\nsa0y5zC9LsvGnD0WSTYPZEzifDzXwFkLXlrS65MjMgU84+PNmwfPGkvk6oR2hoSIRlImtmEa/LTh\nnNtubVoWbPCpehUSl+DFhsQ5csaj4nvkfQO4VkAyso5abImcZSppUDU1prxR6tEEjXga52KMKWJd\nRN1UmDF5JsMCiQ3IeR6J8yn6aVObnOco+a7VAKSWGEJrGA89ztVhJnKcrdeZYIaKG8PaBp7XQ8PN\nMGVDcnXDqXxANcnhmX4qUUDpbMyGk3UCPyEILHHsEyd5ytUilSjgRLXEPTM5xkI4V28wRtqMxzlL\n6GqMRw3yNY+j1YDixCBDlV52V8r09oT0bhgl6EnPCRtB0jDMTBY4M7mBcpjn7pkSJ6swUkuYNhNp\nAxoXM805xunlVLVAaA2B6aWe+PSPRgyfqVHIx+Tz6TyhSeJTrZWoRTnO1UocrRSYjDzO1RxjjYSq\nV8nOtYBGPE3FH2XUVeivBFSi9PiWfJ/hMKAvsJi2+246Ts/LM/X0fBltdttOIiJTnXPOxkn6+7Q7\nxyhp1qJfTzMN60n6Vr7ozwYkG1m37ZkofSxN2TSrsOpNp53B2x4HzQemdWGrbLzuZYHJ7CHQ7Lbt\nmxw2e3xG2d8il+5baGdmO263gnjtwbdm9mEzsDh/+oEEz5tbRj6bsRjNlp3buWO3P4Zaj9nWc0V7\noLE512Uzg7tTE53mWN139Ha42fkp2+asBDBu8fU7BiJXark5K1eZWTb3+e/ScCGDhQpEiohcPOs+\n4V9PTw/79+9n//79671pERERERERERERucjUgUREROQSsFSWYjfrdb/C/CzJJm+RsebPfdZNdqRN\nO9ti2rIkmxmZs2XX87tet7IiYe6clW2ZPvOPT7pOhCXNSDJtZeLWxJ0zoZrZWjZt/JLOV5k1gpnf\nhZe0FNS6uLVrvkmzMBMvTueqNLm0wUqWjelcgsVmna1jYltNMyCTSpppaes4kuy2+IAltg1CO4Pz\n0hL0ulckciGVJJ2fshDn8I3XdgQhcgk1GkQmouJN0aDKdHyGejRFFM+0jnPiYmpuCjwYTQpUbYHI\nFZiO0qzLnsAjMIZcdqisg4Z1RDbNjBxvxFSSmDNmhGnGqDZGWuWxDTvDuJkkCQcIZnoYz3lU4hyn\n6wFFz9E3ZfGNwzfpHIeRNUzHPnVrGKkbTlUt5TDhtBtj3JwmjGdwLiaMy4zkx7CNYYrTvYS2wEAu\nz6lagaJvGTwdk/cSPAOR9UicYSoKGGkEzMSGE1U4UYkYj+tMm3OESQVHwkwywjk/x/FqH+UwR5gE\njIU99AaOwXIfBd+R95pzFxrqiUctMWlmZB1mIjhdjZhKGkx7Y615C6N4hoo3ytnCcYKGR29YYDrK\nU/QNA3mPou/jG1oZkvUsi3EqK6s+V0uP5xhTNLKS7aY4y1ysxCOcy6Ul27FNz9Ppevp7oa0BTZQ9\ndmpZmXQ5y3iskk4ZkN7HzWzErIGPrRHG09lf0r8lQXYfm5nsvJ8tqU6yhjlxlvEY2ypxay7H5ryL\n87OfZ8uqbdbUJ84ebp4XYLL1O03jYOc145kdu1O37bmdvI2bzVqcXX6pDMnZ56AF8zt2ZW6mZDfL\nZjuw7NIdXydWkAnZ7evF/OW6y55f3T6s5djruQ2RC8WSvk6IdOvzn/88X/va19ZlW//hP/wHnvrU\np67ZeApIioiIiIiIiIiI3M/cfPPNvO9971uXbe3atUsBSRERkQeqC5G1sWBOsPaMmo7Zku0rL9yf\n7vbRdphLrTlv3dy5I9NBF+k629bJe/4+mLY5KiFK54dzWdMbt7B5RXpzZrOuWl28gdlO3nbOsWrO\nS2ls2JqfMjEenm3geWnGmN+hc2+6nsXaiMSGOCxJUse6GJd18U73LZ2jMrHpHH6JbZD4Mb4JqJkp\nAq+Ab3IELjcn29NhibwGsauTEFNPpkhcg3o0RRiXsbbW6ngeJzWqyRix18D6CQXXw3TUT09YImd8\nCsbHGPCNwbn0nousJcEx4+qUvSlCr8ZUcpLQzhDG5dYxCuMyY/kTVM0gUX0rxUae0XqevpxPzjMU\n/KDVGTzOkl1riaORWMpRzGhSoepVGOEI1XiMxNaBhEY8zVjuPup+haSyg8lGL0XfY7DgU/B8SkG+\nlXEYW0iycWciRz22jIUNzpgxZvxJKuFImv3nHLVoHOcsR3NF+uJBpqaGOF3Nk/cNvYFP4NHKFHUO\nGtnYlcgyFcbUXMw4k1S9aWbsSKvJSpxMUw89Jrz7SIKIIn2MN4YpuDw91Tx5L+3A7mePt9Cm52E1\nyzScMGkW47g5TSMuzz1XsyzEajjWavwSBWmm45Q3AEDOFfCy+RETk2VtZvM8NjMu68lUa57IVgOY\n7H50Lmpl1TY155sMvLSVd7NBVPu+Jdk4iW20joXN/p+di7H9cRFly2S/m+Y8kbPzr3Z6zHZsOAML\nmsTMWzHbRtL62SyVvdix4Ux380gCs3NJNpkusypX0HCm0zJLZdWv1evJ/X0OR2VDisgD2UMe8hB+\n/dd/fV22ddVVV63peApIioiIiIiIiIhcZOqyLSv14he/mBe/+MUXezdWpZvJTURERERERERERETW\nhDIkRURELgOLNs3psiFC1yVvzfGMYba5Q1OzFnZhKWc3zXPm7nvWOCdrhJOWYDYbXsx+3zq/xHTO\nttqbVnTap1ZJeERis0Y8STyncU77Ntq3097EI23ykaRlss7iXJzdFq9VEp7YOi62GDyipIYxPoGX\nT8tk8TuWsVoXkbi4VRZuXUycVLG2Ci7BmADnLHFSpxaNE3oVQn8Gz+SYNCVyXg8GD9/k8PDxrIc1\nFktCYiIsESE1wmSGxKbl4IkNiZNpMAbnIurxFFPmBBVvjIo/QUCBnmSQQlzEJyDn0hJjD0NMgjOW\nhmkQE1MzZapmgiipUovGiZIqztbAeMRJlanwJDV/kmowwTm7kcAW6A178V1AgVzaNskYnHNYHA0i\n6qZGaEIqZoKqHSO0M9SiCeKkisPRiCazEvqECa+HMW8DPfEQubhAoV7Edz4Bsw1iYhISk9Awdeqm\nQkSdmpsgThrUonGSpJ6WFNuQmClmGh5hUiHnF5nw+ghMgZzpIaDQOsYANivnDb0q0FZWHU9Qj9Ly\n7fnNYaJ4imZRdbOsO+eXAPC9QqvkeX55c5KVXke2RpzU54zdfj65rOFNlHWaSUy6bJhNSeB5nacm\naK3falgztwENtJeGt1oyZQNk62ePKVh8moVUh6ZXi5RVzymlNvOei5Z83usw3moazqyg2Uz7+iul\ncmQREbm/UkBSREREREREROQis05dtuXyoZJtERERERERERERWTfKkBQREbmMXKjyvu7KFTuVai8+\nVqe/m2Zb7awkvHMJZtLal+afF47ZLP1cWBo+Zz9cgsFmXblhYVmpP2+tJBu2rbN4qzR8tmt4a30X\npx24bYJ1MQYv6zTtEbZ1NZ6zX/PKZNN9i1pdw2dLZR0Gm44XWyKqRF4FYzw8E+B5OTy8jiXniYtx\nLt2nxIY4F5PYWro9G6bHPysRrzqLZwLq/hTGeAReHt+knZn9rCt0eiQsLrudjoTYhsRJjcSGJLaO\ntY1s330SW6MRTRDG0zTiMjP+GTyTw/cKeHh4Jt13mC1/ti4ito3s/5AorrTK2Judqm1SIXIRMzbG\n8wKq3gg5v4QxHr4pYIzfGre5zziysvgGiYtJbANr0zL7xNZaHdqxIVE8RWLrNEyA7xWXPNbN4wzp\n+ACxrZNkZdXNTtKz5c4Nojgt507PEQjjfOt87HSupGM3zwfb6oTdHLv9cds8b5xtdqdurped89Zj\n4fneuiVzz/l0oLm/t5ldtvm4M63ttGZ9aO92v+jzll22NDp9HM+fsqK756KOY63BMiIiIqKApIiI\niIiIiIjIRaeSbbmcrEvJ9j/8wz9w9uzZ9diUiIiIiIiIiIiIXMLWJSD553/+5+zatYvnPe95fOEL\nXyBJVlcmISIiIiIiIiIiIvdv6xKQNMYQxzH//M//zLOf/Wz27NnDH//xH3PPPfesx+ZFRERERERE\nRETkErEuc0h+9rOf5W//9m/527/9W44dO8apU6d4+9vfzjve8Q6e8IQn8Lu/+7v8+q//OsVicT12\nR0RERC6QTg0d2ptTLLdsN+O3GtvMkWSNbtqXbf9lkaY1i27IQdYsxc1ZP5nTLGf5MWebbsy5Lhvf\n4YFL0sY3JmZh05wmn/ZGHEs1zYHmMW824mkQJxHGpA1zjPEwCxqseLQ3H3Gu2cjHZg1QLM4l6f/Z\nfeBwWFvLrveJk2o2pjenyYrBw9HeiKf5c4x1MZDgbIjL/pmsSU+cVFr7HMYB4OF5QWvM+c14ZvfX\ndhw7vQ8SnG3gnMVYj9hUW2M399m0fV+/cL9t2pSneVyaDW2a969rYJMYa7y04Q1+h+Pdftxnxwey\nsaNs4zHt0mWazXmyZjgm/X3ufdlsPDO3Iqm5/+kvCxvOtBrcuGTObW/tr+v8OJ6//vzb1s06xnXY\nX7PE9lb6eO5imVbzIDWlEZGLyuAWed8k8kCzLhmSu3fv5r/+1//KkSNH+NKXvsTznvc8giDAOcc3\nv/lNfvu3f5vt27fz+7//+/z4xz9ej10SERERERERERGRi2BdApKtjXkez3zmM/nsZz/LiRMneOc7\n38nBgwcBmJyc5H3vex+/8Au/wCMe8Qje//73MzU1tZ67JyIiIiIiIiIiIhfYugYk223dupXXv/71\nHD58mP/1v/4Xv/mbv0mhUADg1ltv5fd+7/fYvn07L3nJS/jWt751sXZTREREREREROSCs+7+c9EM\nF3K+LlpAsskYwxOf+EQ+/vGPc/LkSV70ohe1rqvVanzsYx/jiU98IldffTV/8zd/QxzHS4wmIiIi\nIiIiIiIil7J1aWqznPHxcT72sY/xwQ9+kNtvv73191KpRBAETE9P8/Of/5xXvvKVvP/97+dzn/sc\nu3btuoh7LCIiIt1a6yYRi41nXHfLdTP+wsY5bQ1lOjXbWKTLzcoa5yQ0vyterGnOrGZzktkFF2yr\n2TjHWcBLf8WkSy1omtO+3vxGJ7Pbam3DOSDJGsmkjX6SOeMu9Z33bGOV+c14Fo7tYQixzXGT2XHn\nN7XpPDZzxnfONtvxzI69iv0GOu57uh8OMFkToHlNnRY77guOedv22hdrLTfbZCn9e7c5BvPGXKo5\nTKtBUrNpT5s1bjjT8fq2cdaj4Yya2YiIiKyvi5Yh6ZzjW9/6Fi9+8YvZsWMHf/iHf9gKRl599dX8\n9V//NSdPnuT06dN85CMf4dGPfjSQlnO/+tWvvli7LSIiIiIiIiKy5uz96CJyvtY9Q/Ls2bN85CMf\n4UMf+hB333136++FQoFf//Vf59WvfjWPe9zj5qzzspe9jJe97GV84AMf4DWveQ1f+tKXmJqaYnBw\ncL13X0RERERERERERM7DumRIWmv50pe+xK/92q+xa9cu3vCGN7SCkYcOHeIv/uIvOHHiBH//93+/\nIBjZ7tWvfjV79uwB4OjRo+ux6yIiIiIiIiIiIrKG1iVD8mlPexrf+MY3Wr/n83me+9zn8upXv5on\nPvGJmKXmoZln8+bNHDt2jCiKLsSuioiIiIiIiIisO+cMznUfH7mYNPOunK91CUhOTk4CcODAAV71\nqlfxile8gi1btqxqrP/0n/4TZ86cYffu3Wu4hyIiIiIiIiIiIrIe1iUgef311/P2t7+dpz71qSvK\nhuzkt3/7t9dor0RERERERERERGS9rUtA8s1vfvN6bEZEREQuY24Ni4eWGsu4899ec12zYIhkVeN0\n+rvBgGten47rml8Mu2W24+aOO387c/c/SX8zZvlxF9lG+/gdx2b+F9re/F1kQc/PbIFO+778fs+f\nZr1DP9Elxsc5DM3rs21Ad8dnmWPf/rfZ82dl581i43V73cLzdmVjrtRajiUicilTB2u5nKxLUxsR\nERERERERERERUEBSRERERERERERE1pECkiIiIiIiIiIiIrJu1mUOSRERERERERERWZx16UXkcqAM\nSREREREREREREVk3ypAUERERWYH7S/fg1XZHXmzd5Zbrduxux1967KW7Si83/vL7vXzX6pVso9k5\n2yzoFr7ycVe77FpS12sRERE5XwpIioiIiIiIiIhcZC67iFwOVLK9iGq1yne+8x3+6q/+ihe+8IXs\n37+fYrFIsVjkta997Zpv7/Tp07zpTW/iYQ97GBs3bmTHjh08/vGP58YbbySKonUbQ0RERERERERE\n5EJShuQinv70p/Od73yn43VrHdz7X//rf/H85z+f8fHxOX8/ffo03/72t/nQhz7El7/8ZbZs2XJB\nxxAREREREREREbnQlCG5iJ6eHh772MfyR3/0R3ziE5/gZz/72QXZzuHDh3ne857H+Pg4O3fu5NOf\n/jQjIyMcPXqUt771rQRBwK233spznvMckqTzfEZrMYaIiIiIiIiIXDzOgXXmfnEROV/KkFzEV7/6\n1Tm/1+v1C7Kd173udZTLZXp6evjmN7/JoUOHANi0aRN/8id/wqZNm3jta1/L9773PT784Q/zyle+\n8oKMISIiIiIiIiIish6UIXkRHT16lM9//vMA/Mf/+B9bgcR2r371qzl48CAA/+2//bcLMoaIiIhc\nHlyX/y7k2KsZfyVjr3T8lY59oY+RiIiIyOVAAcmL6HOf+1zr5xe96EUdlzHG8IIXvACA22+/nXvu\nuWfNxxARERERERGRi8vdjy4i50sByYvo+9//PgD5fJ7rrrtu0eUe85jHtH7+wQ9+sOZjiIiIiIiI\niIiIrBcFJC+in//85wDs2bOHXC636HIHDhxYsM5ajiEiIiIiIiIiIrJe1NTmIhoZGQFg69atSy7X\nfn1znbUcYzkTExOLXjc5ObmisURERERERERkIevSi8jlQAHJi2hmZgaAYrG45HKlUmnBOms5xnKG\nh4dXtLyIiIiIiIiIiMhiVLJ9CTDGdH29c52/LlmLMURERERERERERC40ZUheRH19fUxMTFCr1ZZc\nrv36/v7+NR9jOePj44teNzk5OWd+ShERERERERERkaVcNhmS/+N//A+KxWLHywte8IKLsk8bN24E\nlp/T8dy5cwvWWcsxljM0NLToZcOGDSsaS0REREREREQWsveji8j5umwCknEc02g0Ol7CMLwo+3TV\nVVcBcOzYMZIkWXS5I0eOLFhnLccQERERERERERFZL5dNQPK1r30ttVqt4+Uf//EfL8o+PeIRjwCg\nXq/z4x//eNHlvvvd7y5YZy3HEBERERERERERWS+XTUDS9/1FS7ZzudxF2afnPve5rZ+XCop++tOf\nBuBBD3oQD3rQg9Z8DBERERERERG5uJy7/1xEztdlE5C8FB08eJCnPe1pANx4440cP358wTIf+9jH\n+NnPfgbA7//+71+QMURERERERERERNaLApIX0MMe9jCKxSLbtm1bdJm//Mu/pFQqUS6XedKTnsQ3\nvvENoihiamqK973vfbzyla8E4KEPfSivfvWrL9gYIiIiIiIiIiIi6yG42DtwqfrIRz7Ca17zmkWv\n+9jHPtb6va+vj9HR0QXLNZvm1Ov1RbfzkIc8hE9+8pP81m/9FnfffTdPecpTMMbg2nKgr7zySr7w\nhS8sWlq+FmOIiIiIiIiIyMVjMVjMxd6NLt1f9lMuVcqQXESnrtxNSZLM+ftSAcduPOc5z+EnP/kJ\nr3nNa9izZw+e51EqlXjoQx/Kn/3Zn/GjH/2I3bt3X/AxRERERERERERELjTjnKYj7SRJEqIo6mpZ\nYwyFQmHB38MwxFq76PWLsdbieecXK16LMboxMTHB8PBw9puPviURERERERGR8+eABIDx8XGGhoYu\n7u5cIOVymcHBQQD+7EE30Bf0XuQ96s4b7ngHNVvnlltu4brrrrvYuyP3QyrZXoTv+/i+f15j5PP5\nVa23FoHE9QhGioiIiIiIiMjauD91sL6f7KZcwhS1EhERERERERERkXWjgKSIiIiIiIiIiIisGwUk\nRUREREREREREZN1oDkkRERERERERkYvMZpfLWZIk3HrrrXzrW9/ijjvu4PTp04yNjdHf389VV13F\nM57xDK6//vqu+2bccccd3HTTTfzoRz9icnKSLVu28LjHPY6XvvSlbNmy5X41xgONumzLeVGXbRER\nEREREVl7l1+X7bdcef/psv2mn7+D+hp32R4bG+PAgQOUy+Ull3voQx/KJz7xCa655poll3vb297G\nm9/8ZuI4XnDd8PAwH/7wh3nOc55zvxjjgUgl2yIiIiIiIiIiclFFUdQKRj7oQQ/ij//4j/niF7/I\n97//ff7lX/6Fl7zkJQD85Cc/4YlPfCL33XffomP9f//f/8ef/umfEscxj33sY/n0pz/NzTffzAc+\n8AH27t3L+Pg4z3/+8/nOd75zyY/xQKUMSTkvypAUERERERGRtXf5ZUi++dAN9N5PMiT/5M61z5A8\nc+YMT37yk3nXu97F9ddfjzEL4wsf/vCHecUrXgHAC1/4Qj75yU8uWObEiRMcOnSIer3Ok5/8ZL78\n5S+Ty+XmbOcXfuEXOHPmDA9+8IO57bbbFpSAXypjPJBdPrdUREREREREREQuSZs3b+a2227jWc96\nVsdgJMDLX/5yHvOYxwDwuc99jkajsWCZ9773vdTr9dbP7UFAgG3btvGWt7wFgNtvv50vfvGLl+wY\nD2QKSIqIiIiIiIiIyEXl+z5BsHzv5Uc/+tEA1Ot1Tp48ueD6z3zmMwD80i/9EldffXXHMX7zN3+z\nta3Pfvazl+wYD2QKSIqIiIiIiIiIXGTufnS5mKamplo/5/P5OdedO3eOe+65B4Bf+ZVfWXSMvr4+\nHv7whwNw8803X5JjPNApICkiIiIiIiIiIpe8JEn46le/CsCmTZvYsWPHnOvvuOOO1s9XXnnlkmMd\nOnQIgHvuuYcwDC+5MR7ols+FFRERERERERERmadcLjMxMbHo9WvdjOj9738/x48fB+ClL33pgiYw\np0+fbv28ffv2JcdqBjOTJGFkZISdO3deUmM80CkgKSIiIiIiIiJykVmXXu5PnvjEJy55vXNrd4N+\n8pOf8MY3vhGArVu38qY3vWnBMjMzM62fe3p6lhyv/fr29S6VMR7oVLItIiIiIiIiIiKXrHPnzvHc\n5z6XSqWC53ncdNNNbNy4ccFycRy3fvZ9f8kx269vX+9SGeOBThmSIiIiIiIiIiKyYt/85jd52MMe\ndkG3US6XeeYzn8mRI0cA+O///b/ztKc9reOyfX19rZ/r9fqS49ZqtY7rXSpjPNApICkiIiIiIiIi\ncpE5l17uTwYGBtZ8nsh2tVqNZz3rWdx6660AvP3tb+e1r33tosu378vY2NiSY4+Pj7d+3rBhwyU3\nxgOdSrZFREREREREROSS0mg0eO5zn8u//uu/AvDHf/zHrTkkF9PsWA1w9OjRJZdtXr9lyxYGBwcv\nuTEe6BSQFBERERERERGRS0Ycx7zwhS/ka1/7GgD/+T//Z972trctu94VV1xBf38/AD/4wQ8WXc45\nxy233ALAwx/+8EtyjAc6BSRFREREREREROSSYK3lJS95CZ/73OcAeO1rX8tf/dVfdbWu7/s885nP\nBNL5Laenpzsu953vfKdVKv3sZz/7khzjgU4BSRERERERERGRi8zejy4XinOOV73qVXzyk58E4BWv\neAXve9/7VjTGK1/5SgBmZmZ497vf3XGZZrZlX18fL3zhCy/ZMR7IFJAUEREREREREZGL7j/9p//E\n3/7t3wLw4he/mA9+8IMYY1Y0xlOf+lSe8YxnAPCWt7yFG2+8kSRJgLRj92tf+9pWKfgb3/hGNm/e\nfMmO8UBmnLu/9XCSS8nExATDw8PZbz6wsicKERERERERkYUckAZvxsfHL2gn54upXC63Gpn88RU3\n0Bv0XuQ96s5b7noHdVvnlltu4brrrluTMW+55RZ+8Rd/sfX7gx/8YHzfX3KdD3zgA/zyL//ygr9P\nTEzwuMc9jttvvx1Iu1dv3bqV++67j3q9DsCLXvQiPv7xjy8a8LxUxnigCi72DoiIiIiIiIiIXO4s\nYC/jlLEoiub83gziLWWxuRmHhob4/ve/z9ve9jY+/OEPc/r0aSYnJwG49tpr+cM//ENe9apXLTn2\npTLGA5UyJOW8KENSRERERERE1t7llyH5xituoNe/f2RIvu3utc+QrFarHD58eEXrHDx4kL6+viWX\ncc5x5swZJicn2bp1a1sMo3uXyhgPJMqQFBERERERERGRi6qnp4eHP/zhaz6uMYbt27ezffv2+/0Y\nDyQKSIqIiIiIiIiIXGQuu4hcDtRlW0RERERERERERNaNApIiIiIiIiIiIiKyblSyLSIiIiIiIiJy\nkVl3eXfZlsuLMiRFRERERERERERk3SggKSIiIiIiIiIiIutGJdsiIiIiIiIiIheZw+AwF3s3uqLK\ncjlfypAUERERERERERGRdaOApIiIiIiIiIiIiKwbBSRFRERERERERERk3WgOSRERERERERGRi8w5\nsJqcUS4TypAUERERERERERGRdaOApIiIiIiIiIiIiKwblWyLiIiIiIiIiFxkViXbchlRhqSIiIiI\niIiIiIisG2VILiJJEn784x/z3e9+l5MnT3L69Gmcc2zbto1HPepRXH/99eTz+TXd5s0338wXv/hF\nTpw4QaFQ4NChQzz/+c9n37596zqGiIiIiIiIiIjIhaKAZAfvete7eMc73sHU1NSiy2zdupX3vve9\nPP/5zz/v7Z05c4aXvvSlfP3rX19w3Rvf+EZuuOEG3va2t+F5iye0rsUYIiIiIiIiInJxuOwicjlQ\nQLKDO+64oxWM/MVf/EWe/vSnc/DgQYwxfP/73+fv/u7vOHv2LC94wQv44Ac/yCtf+cpVb2tmZoZn\nPOMZ3HbbbXiex0tf+lIe//jHU6lU+PjHP87NN9/MO97xDqrVKu9+97sv2BgiIiIiIiIiIiLrwTjn\nFICf5+UvfzmTk5P86Z/+Kdddd92C6++66y4e//jHc/r0aXp6erj77rvZvn37qrb1hje8gXe9610A\nfOITn+BFL3pR6zprLS972cv42Mc+BsB3vvMdHvvYx16QMVZrYmKC4eHh7DcfMGs2toiIiIiIiFyu\nHJAAMD4+ztDQ0MXdnQukXC4zODgIwB/tez09fu9F3qPu/OWRt9OwdW655ZaOcROR5ah+t4N3vvOd\n/NM//dOiD6pDhw7x53/+5wBUq1U+/elPr2o709PTvOc97wHgOc95zpxAIoDnebz//e9vPTm97W1v\nuyBjiIiIiIiIiMjF1eyyfX+4iJwvBSQ72Lp167LLPOUpT2n9/LOf/WxV2/nCF75ArVYDWLTsu7+/\nnxe84AUAfOMb32BiYmLNxxAREREREREREVkvCkiuUhRFrZ9XW/X+7W9/GwBjDI973OMWXe4JT3hC\na5s333zzmo8hIiIiIiIiIiKyXhSQXKVvfOMbrZ+vvvrqVY3x7//+7wDs2LGjVVLdyVVXXdX6eX42\n5lqMISIiIiIiIiIXl7sf/RM5X+qyvQphGPLOd74TgHw+z6/92q+tapyTJ08CsHPnziWXa7/+xIkT\naz7GcpYq8Z6cnFzRWCIiIiIiIiIicnlTQHIV3vCGN3DnnXcC8Ad/8Afs3r17VeNMT08D0Nu7dBet\n9utnZmbWfIzlzHbRFhEREREREREROT8q2V6hm266ib/+678G4OEPf/h5da0OwxCAIFg6LpzP51s/\nNxqNNR9DRERERERERERkvShDcgW++tWvtjpZ79q1i8997nMUCoVVj9fT08PU1NSyAcJmF21YmAm5\nFmMsZ3x8fNHrJicnOXDgwIrGExEREREREZG5rEsvIpeDyyYg+ZWvfIUPfehDHa975CMfyQ033LDk\n+t/5znf4tV/7NaIoYvPmzXz9619nz54957VPg4ODnD59etl5GKemplo/DwwMrPkYyxkaGlrR8iIi\nIiIiIiIiIou5bAKSd999N5/5zGc6XhfH8ZLr/vCHP+T666+nWq0yNDTE1772tTldq1friiuu4Oc/\n/znHjh1bcrn77ruv9fPBgwfXfAwREREREREREZH1ctkEJJ/5zGfyj//4jx2vW6pD9U9/+lOe/vSn\nUy6X6e/v5ytf+QoPf/jD12SfHvrQh/LFL36RyclJjhw5wv79+zsud+utt7Z+fshDHrLmY4iIiIiI\niIjIxeWyi8jl4LIJSF5xxRVcccUVK1rn8OHDPPWpT2V8fJze3l6+9KUv8chHPnLN9umZz3wm73jH\nOwD40pe+xO/93u91XO7LX/4yABs3buSXf/mX13wMERERERERERGR9aIu24s4evQoT37ykzl79izF\nYpHPf/7z/Mqv/MqabuOxj31sK0j6nve8p2Njmttuu42vf/3rAPzWb/0Wvu+v+RgiIiIiIiIiIiLr\nRQHJDk6dOsWTn/xkTpw4QT6f55/+6Z940pOetOJx/vAP/5Df+I3f4GUve1nH6z3P4+1vfzuQZmO+\n/OUvp1qttq6/++67ecELXoC1lv7+ft70pjddkDFERERERERE5OJqdtm+P1xUWy7nyzjndBrN8+xn\nP5svfOELAOzbt49HPOIRSy7/0Ic+lP/yX/7Lgr9fddVV3HnnnQwODi7ZBfuP/uiPePe73w3A8PAw\nj3zkI6lUKnz3u98ljmOCIOBf/uVfeMYznnFBx1iNiYkJhoeHs998wKzp+CIiIiIiInI5ckACwPj4\nOENDQxd3dy6QcrnM4OAgAK/dfQM9fu9F3qPu/Pf73kHD1bnlllu47rrrLvbuyP3QZTOH5EpMT0+3\nfj569ChHjx5dcvmlgo3d+Ou//mse/OAH85a3vIVjx47xla98pXXdox/9aN7znvfwi7/4ixd8DBER\nERERERERkQtNAckO3vzmNzMyMtL18lu3bu349/e85z2Uy2Xy+fyyY/zu7/4ur3jFK7j99ts5fvw4\nhUKBQ4cOsWfPnq73Yy3GEBEREREREZGLwIFqWOVyoYBkB49//OPXZJynPe1pK1reGMNDHvIQHvKQ\nh6x6m2sxhoiIiIiIiIiIyIWipjYiIiIiIiIiIiKybpQhKSIiIiIiIiJykdnscn+gynI5X8qQFBER\nERERERERkXWjgKSIiIiIiIiIiIisGwUkRUREREREREREZN1oDkkRERERERERkYvMuvQicjlQhqSI\niIiIiIiIiIisGwUkRUREREREREREZN2oZFtERERERERE5CJzLr2IXA6UISkiIiIiIiIiIiLrRgFJ\nERERERERERERWTcq2RYRERERERERuchsdhG5HChDUkRERERERERERNaNApIiIiIiIiIiIiKyblSy\nLSIiIiIiIiJykanLtlxOlCEpIiIiIiIiIiIi60YBSREREREREREREVk3KtkWEREREREREbnI1GVb\nLifKkBQREREREREREZF1o4CkiIiIiIiIiIiIrBsFJEVERERERERERGTdaA5JEREREREREZGLzDmH\nc+5i70aX7i/7KZcqZUiKiIiIiIiIiIjIulFAUkRERERERERERNaNSrZFRERERERERC4y58CqElou\nE8qQFBERERERERERkXWjgKSIiIiIiIiIiIisG5Vsi4iIiIiIiIhcZA71rpbLhzIkRURERERERERE\nZN0oICkiIiIiIiIiIiLrRiXbIiIiIiIiIiIXmUVdtuXyoQxJERERERERERERWTcKSIqIiIiIiIiI\niMi6Ucm2iIiIiIiIiMhFZt39p2T7frKbcglThqSIiIiIiIiIiIisGwUkRUREREREREREZN0oICki\nIiIiIiIiIiLrRnNIioiIiIiIiIhcZA5wmp1RLhPKkBQREREREREREZF1o4CkiIiIiIiIiIiIrBuV\nbC9jenqaI0eOcOrUKaamphgcHOTqq69m7969a76tJEm49dZbOXHiBIVCgUOHDnHo0KF1H0NERERE\nRERE1pd16UXkcqCAZAeNRoM/+7M/43/+z//JD37wA+I4XrDM1Vdfzete9zp+53d+B2PMeW3POceN\nN97IW9/6Vs6cOTPnuuuuu473vve9POYxj7ngY4iIiIiIiIiIiFxoxjmn+Ps8o6OjbN68GYChoSH2\n7dvHrl27ALjvvvv4yU9+0lr2pS99KX/3d393Xtv7v/6v/4sbb7wRgK1bt/KoRz2KSqXCt7/9bcIw\nxPd9PvvZz/Kc5zzngo6xGhMTEwwPD2e/+cD5BWdFRERERERE0hYvCQDj4+MMDQ1d3N25QMrlMoOD\ngwA8f/PrKHo9F3mPuvPJc+8kcg1uueUWrrvuuou9O3I/pDkkOygWi7zrXe/i9ttvZ2xsjFtvvZXP\nf/7zfP7zn+e2227jpz/9KY961KMAuOmmm/j4xz++6m39/d//fSuQ+LKXvYz77ruPf/7nf+brX/86\nhw8f5pprriFJEn7rt36LU6dOXbAxREREREREROTice7+cxE5XwpIdtDX18cNN9zAtdde27Ec+8EP\nfjCf+cxnCIK04v0Tn/jEqraTJAl/8id/AqQl4B/84AcpFAqt6/fu3cs//MM/4HkelUqFt73tbRdk\nDBERERERERERkfWigOQq7dixg3379gFw9uzZVY3xne98h6NHjwLwB3/wB+RyuQXLXHvttTzjGc8A\n4JOf/OSC+SzXYgwREREREREREZH1ooDkKsVxzMjICABXXHHFqsb48pe/3Pq5GTDspHndxMQE3/ve\n99Z8DBERERERERG5uCzufnMROV8KSK7SW9/6VqampoC0ocxq/PSnPwVmG+cspn2C2OY6azmGiIiI\niIiIiIjIegku9g5c6k6fPs03vvENAKrVKidOnOCf/umfuP3228nn87z3ve/lV3/1V1c19j333APA\nnj17llyu/frmOms5hoiIiIiIiIiIyHpRQHIZt912Gy95yUsW/P0xj3kMf//3f79kVuJyyuUyAIOD\ng0su1359c521HGM5ExMTi143OTm5orFEREREREREpAN1sJbLiAKSy9ixYwcvfvGLAajX69x77738\n+Mc/5uabb/7/t3ff8VFVeR/Hv5NCAiSEauhFmiJFigssShWkWRAiSHEBYfVxRQQVXJZHYPdBKbZl\ncWFx3cWlGJBVVECiYlBcEDCUaOg9oS2hhRSSkDnPHyFDApOQMrmTyXzevvJyhnvmdw5zc8PNb37n\nHLVt21Z///vfNWDAgELFTkpKkqQcu2I7ExgYeMtrXBnjdipXrlyg9gAAAAAAAEBuSEjeRsuWLbV0\n6dIcf3bgwAGNGjVKmzdv1sCBAxUeHq4nnniiwLGzkojp6el5tktLS7vlNa6M4Tp8lAMAAAAAcIUb\nv1/a7XY3jgNAcfCahOSBAwe0bds2p8fq1KmjLl265DtWkyZNFBERoWbNmik2NlYvvfSSBg4cKF9f\n3wKNKTg4WOfOnbttxWL240FBQS6PcTsXLlzI9djx48fVunXr68/4RwIAAAAA4FoJCQmqUqWKu4cB\nwIW8Jqp8IiIAADyXSURBVCH51Vdfady4cU6PPfroowVKSEqZSb0RI0bo9ddfV1xcnPbs2aMWLVoU\nKEbt2rV15MgRnTx5Ms92cXFxjsd16tRxeYzbqVSpUq7HWEMSAAAAAICis4syH3gPr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          }
        }
      ],
      "source": [
        "asymmetric_sim_data.plot_field(\n",
        "    \"fieldProfileMon\",\n",
        "    \"Ey\",\n",
        "    val=\"abs\",\n",
        "    z=0,\n",
        "    t=asymmetric_sim_data[\"fieldProfileMon\"].Ez.t[-1],\n",
        ")\n",
        "plt.show()"
      ],
      "id": "e26e3cdb"
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mode volume: 0.35 (lambda/n)^3"
          ]
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "\n",
              "</div>"
            ]
          }
        }
      ],
      "source": [
        "asymmetric_energy_density, asymmetric_eps = get_energy_density(asymmetric_sim_data)\n",
        "asymmetric_Vmode = mode_volume(asymmetric_energy_density, eps)\n",
        "\n",
        "print(f\"Mode volume: {asymmetric_Vmode:.2f} (lambda/n)^3\")\n",
        "\n",
        "asymmetric_df, asymmetric_signal = analyse_resonance_monitors(\n",
        "    asymmetric_sim_data, start_time, freq_window\n",
        ")\n",
        "asymmetric_df.head()"
      ],
      "id": "60cd4d26"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Direction Q-factor definition\n",
        "\n",
        "To calculate the directional Q-factors, we will use the definition:\n",
        "\n",
        "$Q^i = \\omega \\frac{U_{stored}}{U^i_{lost}}$\n",
        "\n",
        "Where $U_{stored}$ is the total energy in the resonator and $U^i_{lost}$\n",
        "is the energy leaking from the resonator in one oscillation of the field\n",
        "at coordinate $i$. The energy stored is calculated by integrating the\n",
        "energy density. The energy leaving in each direction is calculated as\n",
        "the temporal mean of the flux monitors around the simulation domain."
      ],
      "id": "57e8e6e4-8f54-4ff8-88b2-49c480b8f6aa"
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {},
      "outputs": [],
      "source": [
        "def get_directional_Q(sim_data, energy_density, resonant_frequency):\n",
        "    dict_fluxes = {}\n",
        "\n",
        "    omega = 2 * np.pi * resonant_frequency\n",
        "\n",
        "    # calculating the total energy\n",
        "    Energy = td.EPSILON_0 * np.abs(energy_density).integrate(coord=(\"x\", \"y\", \"z\"))\n",
        "\n",
        "    # iterating through all flux monitors and calculating the Q factor\n",
        "    for coord in [\"x\", \"y\", \"z\"]:\n",
        "        for direction in [\"\", \"-\"]:\n",
        "            delta_t = sim_data[direction + coord].flux.t[-1] - sim_data[direction + coord].flux.t[0]\n",
        "            flux = abs(sim_data[direction + coord].flux.integrate(coord=\"t\")) / delta_t\n",
        "            dict_fluxes[direction + coord] = (omega * Energy / flux).values\n",
        "\n",
        "    # estimating the total Q as the contributions of all directional Q factors\n",
        "    totalQ = sum([1 / i for i in dict_fluxes.values()]) ** -1\n",
        "    dict_fluxes[\"total Q\"] = totalQ\n",
        "\n",
        "    return dict_fluxes"
      ],
      "id": "62afcc6e"
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Q x = 1.73 MM\n",
            "Q -x = 82.40 MM\n",
            "Q y = 17.17 MM\n",
            "Q -y = 17.17 MM\n",
            "Q z = 18.46 MM\n",
            "Q -z = 18.46 MM\n",
            "Q total Q = 1.23 MM"
          ]
        }
      ],
      "source": [
        "resonant_frequency = abs(df.sort_values(\"Q\").index[0])\n",
        "directional_Q = get_directional_Q(\n",
        "    asymmetric_sim_data, asymmetric_energy_density, resonant_frequency\n",
        ")\n",
        "\n",
        "for i in directional_Q.keys():\n",
        "    print(f\"Q {i} = {directional_Q[i] * 10**-6:.2f} MM\")"
      ],
      "id": "869f3e6d"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We can note that the x+ directional Q is lower than all the others,\n",
        "indicating that this is a preferential loss path. Also, the total Q\n",
        "calculated as the sum of all directional components agrees well with the\n",
        "one obtained using `ResonanceFinder`."
      ],
      "id": "18155bff-80b1-4cd8-8a78-99b9655b7b68"
    }
  ],
  "nbformat": 4,
  "nbformat_minor": 5,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  }
}