{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Group delay calculation from FDTD and mode solver\n",
        "\n",
        "**Group delay** is a measure of the time it takes for the envelope of a\n",
        "modulated signal (or a pulse) to propagate through a device.\n",
        "Mathematically, group delay is defined as the derivative of the phase\n",
        "response with respect to angular frequency:\n",
        "\n",
        "$$\\tau_g(\\omega) = \\frac{d\\phi(\\omega)}{d\\omega}$$\n",
        "\n",
        "where $\\phi(\\omega)$ is the phase of the S-parameter at angular\n",
        "frequency $\\omega$. Group delay provides insight into the dispersion and\n",
        "temporal distortion experienced by signals, and is particularly\n",
        "important in high-speed communication systems and photonics, where\n",
        "preserving the shape and timing of signals is crucial.\n",
        "\n",
        "This notebook demonstrates the group delay calculation using both FDTD\n",
        "and mode solver on the example of a waveguide bend. Both methods yield a\n",
        "consistent result. FDTD has the advantage of being more general as this\n",
        "approach works for the group delay calculation of any device. The mode\n",
        "solver-based approach only works for certain devices where the\n",
        "propagating mode is well defined."
      ],
      "id": "85f51b5e-fbc1-43fa-83e4-d98e91197464"
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "import gdstk\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import tidy3d as td\n",
        "import tidy3d.web as web"
      ],
      "id": "d6ff6fc8"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## FDTD Approach\n",
        "\n",
        "### Simulation Setup\n",
        "\n",
        "First we will use FDTD to extract the group delay on the waveguide bend.\n",
        "To start, we define the relevant wavelength and frequency ranges.\n",
        "Although it’s technically not necessary, it’s more convenient to define\n",
        "the frequency array in an equal spacing and increasing order."
      ],
      "id": "0fa251c2-1fe2-485d-b1bf-83841b30b7dc"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "# define wavelength and frequency ranges\n",
        "lda0 = 1.55\n",
        "lda_min = 1.5\n",
        "lda_max = 1.6\n",
        "\n",
        "freqs = np.linspace(td.C_0 / lda_max, td.C_0 / lda_min, 101)\n",
        "ldas = td.C_0 / freqs\n",
        "fwidth = 0.5 * (np.max(freqs) - np.min(freqs))"
      ],
      "id": "fecc6463"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We will focus on the case of a single-mode silicon waveguide with oxide\n",
        "cladding. For simplicity we will use the materials from the built-in\n",
        "[material\n",
        "library](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/material_library.html)."
      ],
      "id": "0736f938-e43f-4248-b6b9-2571c3ccf3f1"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "si = td.material_library[\"cSi\"][\"Palik_LowLoss\"]\n",
        "sio2 = td.material_library[\"SiO2\"][\"Palik_LowLoss\"]"
      ],
      "id": "d9763577"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Define the geometry parameters. We will define our ports on the straight\n",
        "waveguide, leaving some spacing between the ports and the bend. The bend\n",
        "is a circular bend with a radius of 5 µm."
      ],
      "id": "df52b762-dea4-4155-b7c3-d18bba215236"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "l_straight = 2  # length of the straight waveguide\n",
        "r = 5  # bend radius\n",
        "w = 0.5  # waveguide width\n",
        "t = 0.22  # waveguide thickness\n",
        "inf_eff = 1e3  # effective infinity"
      ],
      "id": "277a4d12"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Use [gdstk](https://heitzmann.github.io/gdstk/) to create a simple\n",
        "waveguide circular bend."
      ],
      "id": "39a73f67-19f9-4f2e-b5e6-e4ca82f81e15"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "# create a waveguide bend structure using gdstk\n",
        "cell = gdstk.Cell(\"BEND\")\n",
        "path = gdstk.RobustPath(initial_point=(-inf_eff, 0), width=0.5, layer=1, datatype=0)\n",
        "path.segment(xy=(0, 0))\n",
        "path.arc(radius=r, initial_angle=-np.pi / 2, final_angle=0)\n",
        "path.segment(xy=(r, inf_eff))\n",
        "cell.add(path)\n",
        "\n",
        "bend = td.Structure(\n",
        "    geometry=td.PolySlab.from_gds(\n",
        "        cell,\n",
        "        gds_layer=1,\n",
        "        axis=2,\n",
        "        slab_bounds=(-t / 2, t / 2),\n",
        "    )[0],\n",
        "    medium=si,\n",
        ")"
      ],
      "id": "8ccf3769"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Add the\n",
        "[ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.html)\n",
        "and\n",
        "[ModeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeMonitor.html)\n",
        "at the port positions."
      ],
      "id": "fb20bc9f-686d-4af0-a019-666e56a99825"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "# define a mode source\n",
        "mode_source = td.ModeSource(\n",
        "    center=(-l_straight, 0, 0),\n",
        "    size=(0, 4 * w, 6 * t),\n",
        "    source_time=td.GaussianPulse(freq0=td.C_0 / lda0, fwidth=fwidth),\n",
        "    mode_spec=td.ModeSpec(),\n",
        "    mode_index=0,\n",
        "    direction=\"+\",\n",
        "    num_freqs=7,\n",
        ")\n",
        "\n",
        "# define a mode monitor\n",
        "mode_monitor = td.ModeMonitor(\n",
        "    center=(r, r + l_straight, 0),\n",
        "    size=(4 * w, 0, 6 * t),\n",
        "    freqs=freqs,\n",
        "    mode_spec=td.ModeSpec(),\n",
        "    name=\"s21\",\n",
        ")"
      ],
      "id": "1fa521a9"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Put together a\n",
        "[Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html)\n",
        "and visualize it."
      ],
      "id": "0386c429-c751-4a29-9bac-21048d045663"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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Ky5Yt08yZM/Xmm2+qoqJCoVBIffv21ZFHHqnTTjstof0nJ0yYEFugKpFVnO06\nb6raS8Tll1+uDz74QKFQSC+88IKuuOKKhF53yimnaMWKFXrkkUe0ZMkSORwO7b///rrsssti21i9\n/vrr+t3vfqcdO3bo+OOP13e/+93Y65P9mTzqqKNivyPjhdtRo0bF2u7fv3+bx1VWVsYWSTvllFPU\nu3fvuHUA2cJhWYZ3vAYAAAASEAgENHjwYG3ZskWHHHJIs31Qm3ryySd10UUXSZJ+8Ytf6De/+U06\ny0yJO+64I7YK/dtvv50xK3cDdmD4LgAAALKC1+vV9ddfL0n65JNP9PHHHxuuKD1CoZD+9re/Sdqz\n8BuBFLmGUAoAAICscfXVV8dWUf7DH/5guJr0eO6557Ru3TpJ0q9//WvD1QD2I5QCAAAgaxQXF+t3\nv/udJOmll17S559/brii1AqFQrEgeuqpp+r44483XBFgPxY6AgAAQFb53ve+F1vo7I033tABBxxg\nuqSU+fjjjzVkyBANHTo0tvczkGsIpQAAAMgqDodDjzzyiOky0uKII47Q9OnTTZcBpBShFAAAADml\nX79+sW1WRo0aZbgaAPGwJQwAAAAAwBgWOgIAAAAAGEMoBQAAAAAYQygFAAAAABhDKAUAAAAAGEMo\nBQAAAAAYQygFAAAAABiTt/uURiIRffrpp1q4cKF27Nghj8ejvn376tBDD9WwYcNsPdfOnTs1c+ZM\nVVRUyOv1auTIkTruuOPkdif+7bejjWSEQiFt2LBBklRSUiKnk79fAAAAANiTpWpqaiRJAwcO7Hwm\nsfLQU089ZQ0aNMiS1OrH0UcfbS1YsKDT52loaLB++tOfWgUFBXudo1evXtazzz6bljY6YvXq1W1+\nf/jggw8++OCDDz744IMPPiRZq1ev7nT2yLue0rvvvls33HCDJMnlcmnq1KkaOXKk6urqNG/ePH3+\n+ed67733dMQRR2jWrFk66KCDOnSeSCSic889V6+88ookacKECTrmmGPk9/v13//+V1u3btV5552n\n6upqXXnllSlro7N+9rOfyeVyJf06y7IUCoVif0ExyeVyyePxxD4PBoMKh8NJH5OtuH6un+vn+qO4\nfq6f6+f6kz0mW+Xy9ZeUlMjtdsvhcLR5jNPpbPa8ZVmKRCJJHxPP7373u6SOb4vDsizLlpayQGVl\npQYPHqzGxkb16tVL7777rvbZZ59mxzz66KO69NJLJUmHH364Pvjggw6d67777tOPf/xjSdIvfvEL\n/eY3v4k9V1NTo1NOOUWzZ8+W2+3WkiVLNGrUqJS00VE7duxQjx49dPPNN6ugoKDdN31LlmXJ6XTK\n5XKpsLDQtpo6IxQKqbGxMfZ5QUFBbJhBe8/lCq6f6+f6uf4orp/r5/rz9/oDEYesr+7pHJYlp/V1\nAIs4XLHnTHBIKvPZe9+Yq//+DQ0NCofDikQi7d6jtxbzose391yiwuGwbr/9dm3fvl3du3dP6rV7\nnTufQunTTz+tCy+8UJL04IMP6qqrrmr1uJNOOknTp0+XJO3atUslJSVJnaexsVEDBgzQtm3bNHHi\nRM2ZM2evf+QNGzZoxIgRamxs1IUXXqgnn3zS9jY6Y+fOnSovL9fNN98sr9er4uLihF8bCARigbR3\n79621dRZfr9f1dXVsc9LS0slaa+v+Xy+9BaWJlw/18/1V8c+5/pLJXH9XH/zr3H9uX39EctSbUNQ\nYZdXkuQKB2LHhF1eRVyetppIC6fDofEDe9jebi7++2/ZsiUWTL1eb7vHNjY2KhgMxj6P9gy3/FpB\nQUFSNUQiEdXW1urXv/61qqqqVFZWltTrW8qr1Wvq6upij8eOHdvmcePGjYs9rq+vT/o8M2fO1LZt\n2yRJV199dat/dRg4cKBOP/10SdLLL7+shoYG29tAcz6fL/aLSNrzyyibfyEli+vn+rn+0tjnXD/X\nz/VXxz7n+vPr+l3hQMYF0lTK93//goKCvYYodzaQpkJehdKmYXP58uVtHhd9rnv37h3q6XvzzTdj\nj0844YQ2j4s+5/f79eGHH9reBvbW8hdTVK7/Qori+rl+rr90r69z/Vw/18/15zqfz6eSkm57fT3X\nA2lUvv/7twymUZkSSKU8C6WHHXaYjj32WEnSbbfdprVr1+51zPPPP69p06ZJkn7+85936DxLliyR\nJPXs2VN9+vRp87j99ttvr9fY2QYAAAAAZLrsmM1roxdeeEEXXHCBZsyYodGjR+ub3/ymRowYobq6\nOn366af64IMP5Ha7dfPNN+snP/lJh84RDbsDBw5s97imz69Zs8b2NuLZuXNnm881HdaQS1rOK4iK\nfi3X/1rG9XP9XH/1Xl/n+vd8jevn+nMZ1+9XTc2uvb4eHcab672l+f7v33JeaVT0a5nQW5p3obS8\nvFyvvfaafv/73+tXv/qV/vOf/zR7vmfPnnrqqafaHTIbT3QblK5du7Z7XNPnd+/ebXsb8ZSXlyd1\nfLaLN9E9138xcf1cP9dfHfuc6y+VxPVz/dXN/sv158f1t1zoKNeDab7/+8db6ChTgmnehdLVq1fr\ntNNO09KlS+XxeHTyySdr9OjRamxs1Jw5c/TJJ59oypQpuvDCC/XII4906B8oujhSvNc2XS2r5YJK\ndrSBr7X2C6npL59c/8XE9XP9XH917HOun+vn+rn+qHy7/pZzSHM9mOb7v39rgbRptsikYJpXoXTr\n1q064ogjVFlZqeHDh+v111/XyJEjmx3z4osv6rzzztNTTz2l+vp6vfDCC0mfp6ioSDU1Nc32PmpN\nIPD1ymdFRUW2txFPVVVVm89VV1dr2LBhSbWXqeL9Qoo+ztVfTFw/18/1V8c+5/q5fq6f64/Kt+sv\nKemmndVf79QQDaC5Gkzz/d8/XiCNPs6UYJpXCx3dfffdqqyslCQ98cQTewVSSTrrrLN04403StoT\nUOfMmZP0eaJDamtra9s9rulw25bDdO1oI56ysrI2P1pboSwbxfuFFNXacuF+vz8NFaYW18/1c/3V\nsc+5fq6f6+f6W8qn6y9qZc/5iMsTG84r7QmmzvDecw+zTb7/+8cLpFGtbRcTr0MsVfIqlL7xxhuS\npP79++uwww5r87hzzz039rjp1iyJGjJkiCRpw4YN7R7X9Pnoa+xsww5OZ/a+RRL9hRSVa7+YuH6u\nn+uvjn3O9XP9XD/X35Z8v/5cC6b5/u+faCCNypRgmr2JowO2b98uSe1usdLy+ehrkhHdD3Xr1q3a\nunVrm8ctXrw49njs2LG2t2EHh8Mhy7JsbzfVkv2FFJUrv5i4fq6f66+Ofc71c/1cP9cfT75ff64E\n03z/9082kEZlQjDNqzmlZWVlqqio0JYtW9o9runzHRnGevzxx+vee++VJM2cOVMXXHBBq8fNnDlT\nklRcXKzDDz/c9jbsVF9fn3CvaTgcViQSkWVZcb/XqRAKhZrNtfV6vaqtrY07FLqlaBtbtmyR1+uV\n250dPy5cP9fP9XP9UVw/18/1c/3R649YltzBQFsv/4olRyQiSXJH6mSFnbIcrlSV3IxD0pYt4Q6/\nPp/+/evr6xUKhWRZVqzeSCSiyFf/dtKe0Y5Nn0+E0+mMtREMBhUOh9u8/7csSw6HoxNX0VzmfZdT\n6KCDDtKiRYtUUVGhTz75RIccckirxzXdJubggw9O+jwnnHCCunfvrh07dujBBx9sNVBu2rRJL730\nkiTpm9/8popbjPO3ow07RHtJo/91ueL/YopEInI6nXK5XEkvvtRZoVBIkmK/QDweT4d/mRQVFe31\n16ZM/MXUFNfP9UtcP9fP9UtcP9fP9besORSOyKpvP/RZckvOiBzW1+HGcjglRxoGVzqSX7QzKt/+\n/RsbG2Mh1OVyKRwONwuJLpcroXv2lqJthcN73ift3f+3DMGdlVnf4RS77LLL9Nhjj0mSLrroIs2Y\nMUODBw9udsy0adN05513SpIGDBigKVOm7NXOX//6V23fvl2FhYW64YYb9nre6/Xq5ptv1vXXX68P\nPvhAv/nNb/TLX/4y9rzf79d3vvMdBQIBuVwu/d///V9K2rBDY2OjPB5P7M3odrvj/mBaliWXy6Uu\nXbpowIABKakLAAAAiWsIhrS+rtJ0GW1yOh0aMKC/6TKyQjgcVm1trcLhsNxut8LhcOxe3Y7e3Za9\nzq3d/0cikWYLrnaWw8rGCYOd8JOf/ER/+tOfJEmFhYU67bTTmu1T+v7770va81eR1157Tccff/xe\nbeyzzz5avny5unXr1mzcelPhcFinnXaaXn/9dUnSpEmTdOyxx8rv9+vFF1/Uxo0bJUn33nuvfvzj\nH6esjY7auXOnysvLdeWVV2rYsGHNuu7jjc/fsmWLioqKNGDAAJ1yyim21gUAAIDk7dhdr1/++0PT\nZbSpwO3UvRcfZ7qMrDBt2jRVVFTsFQoTnUObiHjzcxsbG/X555/r3nvvVVVVlcrKyjp1vrzqKZWk\ne+65RyNHjtTtt9+uyspK/fvf/97rmEMOOUT333+/Dj300A6fx+Vy6YUXXtCNN96ohx56SB999JE+\n+uij2PNlZWX605/+pO9+97spbcMObrdbJSUlsTdm9L+5so8TAAAAkE2ivZle754FquwMpFL693HN\nu1AqSVdddZUuv/xyzZkzRwsXLlRVVZXcbrf69u2riRMnxl3F9qqrrtK2bdvijnsvKirSfffdp1/9\n6leaMWOGNmzYIK/Xq1GjRmnKlCmxN1Gq27BDrm8wDAAAAGSLpvNe7Q6kUem8/8/LUCrtGZ579NFH\n6+ijj076tddee21Sx/fq1UsXXXRR0uexu43OIpgCAAAAmSNVgTSqrfv/plvI2CGv9ilF5+XKPk4A\nAABANvN6vWnpHGrt/r++vt7Wc+RtTyk6jh5TAAAAwBy7eyrjaXn/X1NTY2sN9JSiQ+gxBQAAAMww\nsXdqy/t/O9e2oacUHdZWjykAAACA3BO9/6+qqrK1XXpK0Smt9ZiGQiFzBQEAAABIGZ/Pp5KSElvb\nJJSi01oG00AgQDAFAAAAclRRUZECgYBt7RFKYYuWwbTp3kkAAAAAcoud9/uEUtimZTAFAAAAgHgI\npbCVz+ezdSUuAAAAALmNUArbud3utO+dBAAAACA7EUqREib2TgIAAACQfQilAAAAAABjCKUAAAAA\nAGMIpQAAAAAAYwilAAAAAABjWI0GAAAAQMJKQ7t17rZ3VOf06rmex6nRWWC6JGQ5ekoBAAAAJGxS\nzRKVh3apX+N27e9fZboc5ABCKQAAAICEFEYCGlO/Tr5wgwojAU3wfymHZZkuC1mOUAoAAAAgIeP9\na1QQCakw0qjiSEBlwd0aFKg0XRayHKEUAAAAQHyWpQn+L1UYaZRDltxWWB4rpANqvzRdGbIcoRQA\nAABAXIMCW1UerFFxpEENzgKFHC4VRxo0oqFCXUJ1pstDFiOUAgAAAIhrgn+lPFZIbiusOmeh6pyF\nKow0ymVFtH8dCx6h4wilAAAAANrlC9drVP0GFUcCCjlcCjpcanAWyJJDxZGA9vOvksOKmC4TWYpQ\nCgAAAKBd+/pXy2VFVBhpVJ3TKzkcshwO1TsLVBRpUNdQnUY0bDRdJrIUoRQpEQqFTJcAAAAAGzi+\nWuCoKBKQJanB6Y09V+cqlMuKqMAKagILHqGDCKWwXSgUUjAYNF0GAAAAbDCsYaNKQn4VRwKqdxbI\ncjhiz4UdLgUdbhVHGjQ0sFmlod0GK0W2IpTCVn6/X4FAwHQZAAAAsMkB/i9VYAXlssKqd3n3er7O\n5ZU3EpTTitBbig4hlMI2fr9f1dXVpssAAACATbqFajW0YbOKIwEFHW6FHO69jmlwFCjy1YJH+9at\nlssKG6gU2YxQClu0DKQej8dcMQAAALDF/v4v5bLC8kYXOGqNw6F6l1dF4YCKwgGNrluf3iJhhJ33\n+4RSdFrLQOr1euV27/1XNAAAAGQPlxXWfv7VXy1w5FCDs6DNY+udXjkVkddq1AH+lWmsEibU19fL\n623jjxQdQChFp7QMpKWlpQRSAACAHDCyvkLF4QYVRwKxbWDaEna41Oj0qDgcUP/G7erZuDONlSKd\n/H6/ampqbG2TUIoOay2Q+nw+cwUBAADANgfUrpTXapTTirS6wFFLdU5vbEGkCX4WPMpFqVpDhlCK\nDiGQAgAA5K7uwV0a2LhVxeGAGh0ehR2uuK8JODyKOJwqDgc0rm6tPBG2CEyVUCiU9nO2vP+3c8cN\nQimSRiAFAADIbQfUrpTLiqjACqougV5SSZLDoTqnV0WRgAoijRpXtzalNeazYDCY1mDa8v6/pKRE\nwaB9f3QglCIpBFIAAIDc5omENK5ujYrCDYo4nAo4El9ltd7plUOWCiONe4bwWlYKK81vgUBAfr8/\n5edp7f6/qKjI1nMQSpEwAikAAEDuG1O/Vt5IMKEFjlqKOJxqcBaoOBJQr+BO9WvcnsJKUV1dndJg\nmq77f0IpEkIgBQAAyAOWpQm1X6owEpBDlurb2pu0HfVOrzxWSO5IWAew4FFKNN0jNFXBNJ33/4RS\nxBUKhQikAAAAeaBPsEp9glUqjgQUcBYo4kg+LjQ63Ao7XCqONGh03XoVhu1bEAd7uN3uZvuE2h1M\n090hRShFu1wulxobG2OfE0gBAABy14TalXJHwvJYoT1DdzviqwWPCiON8lgh7Vu32t4iIWlPMC0t\nLY19blcwNTFCklCKdjUdGkAgBQAAyF2FkYDG1K9TcaRBYYdLjQ53h9uqdxbI8VWbE/xfysGCRynh\n8/lsDaampuwRSpEQAikAAEBuG+9fo4JISIWRxqQXOGrJcjhV/9WCR2XB3RoUqLSxUjRlVzA1uYYM\noRRxFRQUEEgBAABymWVpgv9LFUYav1rgqKDTTdY5vXJbe4YCT2DBo5TqbDA1vagpoRTtCgaDcrs7\nPnQDAAAAmW9QYKvKgzUqjjSowVkgqwMLHLUUcrgU+mrBo5H1FeoSqrOhUrSlo8HUdCCVCKWIIxwO\nmy4BAAAAKTbBv3LPNi5WWHXOQnsadThU5yxUYaRRLiui/etW2dMu2pRsMM2EQCpJdIEBAAAAecwX\nrteo+g0qjuzZuqVLuF5q0i9hOaQ6Z6EanZ42WtijMBxQkdUoNVnTyPHVJ8WRgPbzr9KHXcfZ0guL\ntkVDZTRsRv/bMmxmSiCV6CkFAAAA8trAwFY5LUsROVTv9CrscDb7cFqWSsLtD711WJa6hf2ypGav\nDTlcqnd6JVnqGq5T91BNWq4p38XrMc2kQCrRUwoAAADktfXe3qosKFcgtHdPqMcKyReuV9dwfZxW\n9vSI+p1FanAWKORw7XVEhbenqtxd7SgZCWirx7TlY9OBVCKUAgAAAHmtzlWof/U+sdXnzt86U6Pq\n1yfV3gLfcM0sO8SO0tBJ7QVTKTMCqcTwXQAAAADIWS2H8kZlSiCVCKUAAAAAAIMIpQAAAACQo1ou\nahSV6D6m6UAoBQAAAIAc1Noqu8nsY5ouLHQEAAAAADkm3rYv8fYxTSd6SgEAAAAgh8QLpPH2MU03\nQikAAAAA5Ih4gTQqk4IpoRTtcrn23vgYAAAAQOZJNJBGZUowJZSiXR6PR6FQyHQZAAAAANqRbCCN\nyoRgSihFXI2NjRmxKhcAAACAvXU0kEaZDqaEUiTE9ORnAAAAAHvrbCCNMhlMCaVoVzAYjD0mmAIA\nAACZw65AGmUqmBJK0a5wOKyCgoLY5wRTAAAAwLxQKGRrII0yEUwJpYjL7XYbn/wMAAAAs5xWuM0P\nlxUxXV5eCYVCCgQCsc/tCqRR6Q6m7pS1jJwSfZNH/xoT/a+db34AAABkJocs9QzuMl0GvtJ0ip3d\ngTQqnff/hFIkjGAKAACQX3a5fQo4vdrhSCw2hB0u7XJ3SXFViEpVII1q6/7f4/HYeh5CKZJCMAUA\nAMgfs7odoCp3iQojgfgHS9rt8mmhb3iKq4Ikeb3etNyDt3b/36WLvX94IJQiaQRTAACQE778u+kK\n0sbb0KgjXauSf6FLUoEUSvDwIkkTtTL50zic0pfJvy6rjbiiwy+1u6cynpb3/zU1NbbWwEJH6BDT\nG+wCAAAA+crtTn/fYsv7f6/Xa1vb9JSiw9rqMQUAAACQe6L3/1VVVba2S08pOqW1HtNQKNEBHgAA\nAACyic/nU0lJia1tEkrRaS2DaSAQIJgCAAAAOaqoqKjZPqmdRSiFLVoG06Z7JwEAAADILXbe7xNK\nYZuWwRQAAAAA4mGhI9jK5/PZuhIXAAAAzHFFIupTVydPJJLQ8Q0ut7YUF8lyOFJcGXIJoRS2c7vd\nad87CQAAIGmd2Ccy2wR212v23A+Tft2UnXM1pHalHLISOj6ikFaVjdbnXUYldZ4Ch1PnjTgu6fqQ\nGwilSAkTeycBAADAXt2DNSqMBFQSrkvo+Cp3iboHd6W4KuQakgMAAACAdllyaKe7a5vPO2SpLLQ7\njRUhlxBKAQAAAMQVdLYdHRxWYnNOgdaw+i4AAAAAwBhCKQAAAADAGEIpAAAAAMAYQikAAAAAwBhC\nKQAAAADAGEIpAAAAAMAYQikAAAAAwBhCKQAAAADAGEIpAAAAAMAYQikAAAAAwBhCKQAAAADAGEIp\nUiIUCpkuAQAAAEAWcJsuALmHQAoAAAAgUYRS2Mrv9ysQCMjt5q0FAACQDbyRRp1a9aHKQrv3eq4k\nVJd0e+Pq1mpIoHKvr1cU9NSMsomyHI4O1YncRXKAbfx+v6qrq02XAQAAgCQMbdis4fWb5IvUq7W4\n6ImEFIkbJPc8XxwJyGsF1T1U0+zZsBwqD+3WvK77aLun1Ja6kTsIpbBFy0Dq8XjMFQMAAICEbSro\nIcvhkMcKyxtpVMjhava8JYd2u4rbbcNy7DmmKBKQ2/r66w5JLiusWleR6pxe7XR3TcEVwAQ77/cJ\npei0loHU6/UyfBcAACBL1Lh9Wl3YV2PqGuWNNGqXy6eQM/l7uTpXoepchc2+VhgJqFvIr3qnV4uK\nhyncIvAiO9XX18vr9drWHqvvolNaBtLS0lICKQAAQJb53DdSjQ6Pwg6XiiMB29otDgcUcBYo7HBp\nfpeRtrULc/x+v2pqauIfmARCKTqstUDq8/nMFQQAAIAOWVPYT7vcPtU5vSqMNMphWfFfFIfbCslj\nhVTn9GpNYV/tcnexoVKYlKo1ZAil6BACKQAAQO6wHA4t8I1QvdMrh/YMu+2sonBAYYdTjQ6PPveN\n6HyRiDGxBWPL+/9AwL4edUIpkkYgBQAAyD0LfcMVcrjU4CzYM4S3E72lDstSUaRRdc5C7XYXa3Vh\nPxsrRTAYTGswbXn/X1JSomAwaFv7hFIkhUAKAACQm+pchVpRNFB1Tq/cVlgeK9zhtgojATkk1Tu9\nmu8bIctB7LBbIBCQ3+9P+Xlau/8vKiqy9Ry8O5AwAikAAEBu+7zLSAUdboUcLhVHGjrWiGWpOBJQ\ng7NAYYdTC33D7S0SMdXV1SkNpum6/yeUIiEEUgAAgNxXUdBT2z3dVOcs/GrBo0jSbXissNxWWHVO\nr1YWDZDfZW+vGprvEZqqYJrO+39CKeIKhUIEUgAAgHzw1YJHDc4CWXJ0aHuY4kiDQg6Xgg635vvY\nBiYV3G53s31C7Q6m6e6QIpSiXS6XS42NjbHPCaQAAAC5bbFvqBqdHtU7vSpKcsEjhxVR4VcLHFV5\numqdt3cKK81vbrdbpaWlsc/tCqYmRkgSStGupkMDCKQAAAC5L+As0LKiwap3eeWyIiqwEl/ltSjS\nKEsONTgL9vSSOhwprBQ+n8/WYGpqyh6hFAkhkAIAAOSPz7uMjA3BTXjBoyYLHAUdbi32DU1tkZBk\nXzA1uYYMoRRxFRQUEEgBAADyyJaCcm32dFedq1DeSFDOBBY8KrBCcllh1TkL9UXxIDU4vXFfA3t0\nNpiaXtSUUIp2BYNBud1u02UAAAAgzeZ3GaEGR4Eicu6ZWxpHcSSwZzsZp0ufs8BR2nU0mJoOpBKh\nFHGEwx3fNBkAAADZa1nRYDU4C1TvKlBxuP0Fj5xWRN5Io+qcXm3xlGlzQfc0VoqoZINpJgRSiVAK\nAAAAoBUh5555ofXOQjkVkdcKtnlsUSTw1QJH3j29pCxwZEyiwTRTAqlEKAUAAADQhgW+EQo7nGp0\nevb0lrbmqwWO6pxeNTo9WlY8OL1FYi/xgmkmBVJJYrIgAAAAgFbt8HTTem8veSNBlYZ2y2WFFXa4\nmh3jtfYshFTv9mpx8VAFnZ42WkM6RUNmNHw2DaGZFEglekoBAAAAtGO+b6QCDo8iDmervaXFkYAa\nHR6FHS7N7zLCQIVoS2s9ppkWSCVCKQAAAIB2rCgaIL+rUHVO755VeJsseOSywiqIBFXn8qqioKe2\ne0rNFYpWtQymUZkSSCVCKQAAAIB2RBwuLfQNV73TK4csFUYaY88VhQOKyKmAo0Cfd2EbGHQMoRQA\nAABAu/YseORSwFmg4uiepdEFjlxe1bkKtaJooNki0aqWixpFJbqPaToQSgEAAAC0q8bt06rCfqpz\neuWxQnJHQiq0GuWQpXqnV4t8w/ZaAAnmtbbKbjL7mKYLoRQAAABAXPN9I2ILGhVHAioOBxRwFuxZ\n4MjHAkeZpq1tXxLdxzSdCKUAAAAA4lpT2E+73L7YgkceK6Q6p1drCvtql7uL6fLQRLx9SDMtmBJK\nAQAAAMRlORxa4BuheqdXkkNhh1ONDo8+p5c0o8QLpFGZFEwJpWiXy8XcAAAAAOyx0DdcIYdLDc4C\n1TkLVeMu1urC/qbLwlcSDaRRmRJMCaVol8fjUSgUMl0GAAAAMkB0lV2/s1D1Tq8W+EbIcjhMlwUl\nH0ijMiGYEkoRV2Njo/HJzwAAAMgMc0rGye8q1E53VxY4yhAdDaRRpoOpO21nQlaLvsmTeXMDAAAg\n92z3lOr+fmfJkhRhGxjjOhtIo6KvibaVzvt/QinaFQwGY48JpgAAAJDEnqQZwq5AGmUqmDJ8F+0K\nh8MqKCiIfW56uWgAAAAAUigUsjWQRpkYyksoRVxut9v45GcAAAAAe4RCIQUCgdjndgXSqHQHU0Ip\nEmJ68jMAAACAPZpOsbM7kEal8/6fOaVImMnJzwAAAACaS1UgjWrr/t/j8dh6HnpKkRR6TAEAAADz\nvF5vWjqHWrv/r6+vt/Uc9JQiafSYAgAAAObY3VMZT8v7/5qaGltroKcUHUKPKQAAAGCG253+vsWW\n9/9er9e2tukpRYe11WMKAAAAIPdE7/+rqqpsbZeeUnRKaz2moVDIXEEAAAAAUsbn86mkpMTWNgml\n6LSWwTQQCBBMAQAAgBxVVFTUbJ/UziKUwhYtg2nTvZMAAAAA5BY77/cJpbBNy2AKAAAAAPEQSmEr\nn89n60pcAAAAAHIboRS2c7vdad87CQAAAEB2IpRKsixLO3bs0Lp161I2FzIYDGrDhg3aunWrLMsy\n1ka6mNg7CQAAAED2yetQOm/ePF1wwQUqLS1Vjx49NGTIEBUVFWncuHG6/fbbVVFR0elzfPjhhzr5\n5JNVXFysQYMGqXfv3iovL9eVV16pLVu2pK0NAAAAAMhEeRtKb775Zk2cOFHPPPOMampqVFZWpmHD\nhqlr165aunSpbrnlFj355JOdOsff/vY3HX300Xr99dcVDofVv39/lZWVqbq6Wn//+981fvx4LV68\nOOVtAAAAAECmystQ+tOf/lR33HGHIpGIvvWtb2nRokWqqqrSqlWrtHPnTi1fvly33nqrBg4c2OFz\nvP/++7r66qsVDoc1adIkLV++XBUVFaqqqtL//vc/lZeXa/v27TrllFPk9/tT1gYAAAAAZLK8C6Vv\nv/227r77bknStddeq+eff17jx49vdsyoUaN0yy236MILL+zweW644QaFw2H17t1b06ZN08iRI2PP\nnXrqqXr66aclSevXr9c999yTsjYAAAAAIJPlXSi96aabZFmWhg8frj/84Q8pOceCBQs0d+5cSdI1\n11yj8vLyvY6ZOnWqDj30UEnSP/7xj5S0AQAAAACZLq9C6fz58/XJJ59Ikn7wgx+ooKAgJef53//+\nF3t8+umnt3ncGWecIWlPT+eCBQtsbwMAAAAAMl1ehdI333wz9vikk06KPa6urtaaNWu0a9cuW84z\nf/58SZLP59O4cePaPC7ay9n0NXa2AQAAAACZLq9C6bx58yRJRUVFGjNmjP75z39q3LhxsZV3S0tL\nNXr0aP3+979XfX19h8+zfPlySdKgQYPkcDjaPG7IkCF7vcbONgAAAAAg07lNF5BOmzZtkiT17dtX\n3/ve9/TEE09Iknr27Kni4mJVVFRoxYoVuummm/Tvf/9bM2bMUPfu3ZM+z86dOyUp7mubPh99jZ1t\nJFpna6qrq5NqCwAAAAA6Iq96SqPDc9esWaMnnnhC48eP19y5c7V161atXbtWlZWVuvzyyyVJn376\nqS666KIOnSe6PUthYWG7xxUVFcUe19bW2t5GPOXl5W1+DBs2LKm2AAAAAKAj8iqUOp17LteyLJWV\nlenNN9/UIYccEnu+R48eevjhh3XmmWdKkl5//XV9+OGHSZ/H5XJJkiKRSLvHhcPh2GO3u3mntR1t\nAAAAAECmy6tQ2qVLl9jj733ve+rTp0+rx/3sZz+LPf7vf//b4fPEm5daV1fXam12tRFPVVVVmx+r\nV69Oqi0AAAAA6Ii86lprGkKbrlrb0kEHHSSn06lIJNKhxYP69OmjdevWafPmze0e1/T53r17295G\nPGVlZUkdDwAAAAB2y6ue0qZbq7TXq+h2u2NzNaNzO5MxZswYSVJFRUW7PZ1ffvll7PHYsWNtb8Ok\nUChkugQAAAAAWSCvQukRRxwRe7xly5Y2j9u9e3csjPbo0SPp8xx22GGS9gSzjz/+uM3jZs+eLUly\nOByaOHGi7W2YEgqFFAwGTZcBAAAAIAvkVSg97rjjVF5eLkmaPn16m8fNmDEj9njSpElJn+f000+P\nLar05JNPtnpMKBTSs88+K2nPUOL+/fvb3oYJfr9fgUDAdBkAAAAAskRehVKPx6Of/OQnkqQXX3xR\nb7311l7HVFdX6xe/+IUkyefz6fzzz9/rmDVr1uiLL77QypUrWz1P7969Y9vJPP744632dP7mN7+J\n7Zv605/+NCVtpJvf72d/UwAAAABJyatQKkk/+clPdOCBByoSiei0007TL3/5S82ZM0cLFizQo48+\nqkMPPVQrVqyQJP3xj39Uz54992rjpJNO0pgxY5ptJ9PS7373O/Xt21ehUEhTpkzR3XffrU8//VTv\nvfeevv/97+u2226TJJ166qk6++yzU9ZGurQMpB6Px1wxAAAAAFLKzvv9vFp9V5KKior02muv6dvf\n/rbee+89/fa3v9Vvf/vbZsd4vV798Y9/1FVXXdXh8/Tr10/Tp0/XWWedpVWrVumGG27Y65jTTjtN\nzzzzTErbSIeWgdTr9bJnKgAAAJCj6uvr5fV6bWsvL5ND7969NWvWLL3yyit68cUXtXLlStXV1al3\n79468sgjdemll2rAgAFtvn7YsGGSpJKSknbPs99++2nBggV68skn9dprr2nDhg3yer0aNWqUzj33\nXJ188slxa7WjjVRqGUhLS0tVW1trriAAAAAAKeP3+1VTU2Nrm3kZSqU9q9WeccYZOuOMM5J+7Wuv\nvZbwsT6fT1deeaWuvPLKpM9jZxup0Fog9fl8hFIAAAAgB6VqDZm8m1MKe7QVSAEAAACkVigUSvs5\nW97/27njBqEUSSOQAgAAAOYEg8G0BtOW9/8lJSUKBoO2tU8oRVIIpAAAAIB5gUBAfr8/5edp7f6/\nqKjI1nMQSpEwAikAAACQOaqrq1MaTNN1/08oRUIIpAAAAEBmaLpHaKqCaTrv/wmliCsUChFIAQAA\ngAzhdrub7RNqdzBNd4cUoRTtcrlcamxsjH1OIAUAAADMc7vdKi0tjX1uVzA1MUKSUIp2NR0aQCAF\nAAAAMofP57M1mJqaskcoRUIIpAAAAEDmsSuYmlxDhlCKuAoKCgikAAAAQIbqbDA1vagpoRTtCgaD\ncrvdpssAAAAA0I6OBlPTgVQilCKOcDhsugQAAAAACUg2mGZCIJUIpQAAAACQMxINppkSSCVCKQAA\nAADklHjBNJMCqSQxWRAAAAAAckw0ZEbDZ9MQmkmBVCKUAgAAAEBOai+YSpkRSCWG7wIAAABAzmo5\nlDcqUwKpRCgFAAAAABhEKAUAAACAHNVyUaOoRPcxTQdCKQAAAADkoNZW2U1mH9N0YaEjAAAAAMgx\n8bZ9abn4kcn5pfSUAgAAAEAOiRdI4+1jmm6EUgAAAADIEfECaVQmBVNCKdrlcrlMlwAAAAAgAYkG\n0qhMCaaEUrTL4/EoFAqZLgMAAABAO5INpFGZEEwJpYirsbExI1blAgAAALC3jgbSKNPBlFCKhJie\n/AwAAABgb50NpFEmgymhFO0KBoOxxwRTAAAAIHPYFUijTAVTQinaFQ6HVVBQEPucYAoAAACYFwqF\nbA2kUSaCKaEUcbndbuOTnwEAAADsEQqFFAgEYp/bFUij0h1MCaVIiOnJzwAAAAD2aDrFzu5AGpXO\n+393SlpFToq+2aPDBKL/TcUPAQAAAID2pSqQRrV1/+/xeGw9Dz2lSAo9pgAAAIB5Xq83LZ1Drd3/\n19fX23oOekqRNHpMAQAAAHPs7qmMp+X9f01Nja010FOKDqHHFAAAADDD7U5/32LL+3+v12tb2/SU\nosPa6jEFAAAAkHui9/9VVVW2tktPKTqltR7TUChkriAAAAAAKePz+VRSUmJrm4RSdFrLYBoIBAim\nAAAAQI4qKipqtk9qZxFKYYuWwbTp3kkAAAAAcoud9/uEUtimZTAFAAAAgHgIpbCVz+ezdSUuAAAA\nALmNUArbud3utO+dBAAAACA7EUqREib2TgIAAACQfQilAAAAAABjCKUAAAAAAGMIpQAAAAAAYwil\nAAAAAABjCKUAAAAAAGMIpQAAAAAAYwilAAAAAABjCKUAAAAAAGMIpQAAAAAAYwilAAAAAABjCKUA\nAAAAAGMIpQAAAAAAYwilAAAAAABjCKVIiVAoZLoEAAAAAFnAbboA5B4CKQAAAIBE0VMKW/n9fgUC\nAdNlAAAAAMgShFLYxu/3q7q62nQZAAAAALIIoRS2aBlIPR6PuWIAAAAApJSd9/uEUnRay0Dq9Xrl\ndjNdGQAAAMhF9fX18nq9trVHKEWntAykpaWlBFIAAAAgR/n9ftXU1NjaJqEUHdZaIPX5fOYKAgAA\nAJAyqVpDhlCKDiGQAgAAAGaY2IKx5f2/nTtuEEqRNAIpAAAAYE4wGExrMG15/19SUqJgMGhb+4RS\nJIVACgAAAJgXCATk9/tTfp7W7v+LiopsPQehFAkjkAIAAACZo7q6OqXBNF33/4RSJIRACgAAkL0s\n0wXAVk33CE1VME3n/T+hFHGFQiECKQAAQBYLhiOmS2iXx0UsSYbb7W62T6jdwTTdHVL866NdLpdL\njY2Nsc8JpAAAANknEAybLqFdXg/73CfL7XartLQ09rldwdTECElCKdrVdGgAgRQAACA7NQTTv4VI\nMrxul+kSspLP57M1mJqaskcoRUIIpAAAANkrEMrsntJCD6G0o+wKpibXkCGUIq6CggICKQAAQBZj\n+G5u62wwNb2oKaEU7QoGg3K7+SUBAACQzTJ9+C49pZ3X0WBqOpBKhFLEEQ5n9l/VAAAAEF/m95QS\nSu2QbDDNhEAqEUoBAACAnNeQ6aGUhY5sk2gwzZRAKhFKAQAAgJyX6T2lhcwptVW8YJpJgVSS+NcH\nAAAAclwgw+eUMnzXftGQGQ2fTUNoJgVSiVAKAAAA5LwGtoTJS+0FUykzAqnE8F0AAAAg52X68F22\nhEmdlkN5ozIlkEqEUgAAACDnZfrw3UIWOsprhFIAAAAgx2X86rsM302ZlosaRSW6j2k6EEoBAACA\nHEcozU+trbKbzD6m6cLgbQAAACDHBTJ+oSNiid3ibfvScvEjk/NL6SkFAAAAclymzymlp9Re8QJp\nvH1M041QCgAAAOQwy7IyfvVdtoSxT7xAGpVJwZRQina5XPyCAAAAyGaNoYgs00XEwfBdeyQaSKMy\nJZgSStEuj8ejUCizh3sAAACgbYEMv5dzOCSPi1jSWckG0qhMCKb86yOuxsbGjFiVCwAAAMnL+JV3\n3S45HA7TZWS1jgbSKNPBlFCKhJie/AwAAICOyfT5pF6G7nZKZwNplMlgSihFu4LBYOwxwRQAACD7\nNGT4yrssctRxdgXSKFPBlFCKdoXDYRUUFMQ+J5gCAABkl4zvKXUTSjsiFArZGkijTARTQinicrvd\nxic/AwAAoGMyPZTSU5q8UCikQCAQ+9yuQBqV7mBKKEVCTE9+BgAAQMc0hDI7lDKnNHlNp9jZHUij\n0nn/zzsACYu+2aPDBKL/TcUPAQAAAOwRyPA5pV56SjssVYE0qq37f4/HY+t56ClFUugxBQAAyC6Z\nviUMw3c7xuv1pqVzqLX7//r6elvPQU8pkkaPKQAAQPbI9DmlXjeRJFl291TG0/L+v6amxtYa6ClF\nh9BjCgAAkB3YEib3uA0E+Zb3/16v17a2+bMEOqytHlMAAABkjkzvKSWUZo/o/X9VVZWt7dJTik5p\nrcc0FMrsv8YBAADkk0DGr75LKM0mPp9PJSUltrZJKEWntQymgUCAYAoAAJAhMn2hI7aEyT5FRUXN\n9kntLEIpbNEymDbdOwkAAADmsCUMUsHO+31CKWzTMpgCAADAvEzvKWVOKQilsJXP57N1JS4AAAB0\nTsbPKWVLmLxHKIXt3G532vdOAgAAQOsyffguPaUglCIlTOydBAAAgOZC4YjqGgmlyGyEUgAAACBH\nba+tl2WZrqJ9rL4LQikAAACQo7buqjNdQlxeNz2l+Y5QCgAAAOSoTA+lHpdTTqfDdBkwjFAKAAAA\n5KgtNZkdSrsWFZguARmAUAoAAADkqEzvKe3drdh0CcgAhFIAAAAgR22pqTddQrt6lxBKQSgFAAAA\nclJDMKRddQHTZbSrFz2lEKEUAAAAyEnbMryXVKKnFHsQSgEAAIActCXD55NK9JRiD0IpAAAAkIO2\nZvjKu26nQ+W+QtNlIAMQSgEAAIAclOk9pT1KitijFJIIpQAAAEBOyvjtYJhPiq8QSgEAAIAcY1mW\ntmT48F3mkyKKUIqUCIVCpksAAADIW7UNQdU3Zvb9WC96SvEVt+kCkHsIpAAAAGZl+iJHktSbnlJ8\nhZ5S2Mrv9ysQyOxNmgEAAHJdpi9yJNFTiq8RSmEbv9+v6upq02UAAADkvUzvKS30uFRSVGC6DGQI\nQils0TKQejwec8UAAADkuUzvKe1VUiyHg+1gspmd9/uEUnRay0Dq9XrldjNdGQAAwJRM7yll5d3s\nVl9fL6/Xa1t7hFJ0SstAWlpaSiAFAAAwKGJZ2lpTb7qMdrFHafby+/2qqamxtU1CKTqstUDq8/nM\nFQQAAADt9DcoFI6YLqNd9JRmp1StIUMoRYcQSAEAADLT1gyfTyqxHUxnmdiCseX9v507bhBKkTQC\nKQAAQObK9EWOJLaD6axgMJjWYNry/r+kpETBYNC29gmlSAqBFAAAILNl+iJHJUUFKipgDZLOCgQC\n8vv9KT9Pa/f/RUVFtp6DUIqEEUgBAAAy35Zdmb3IEb2k9qmurk5pME3X/T+hFAkhkAIAAGSHTO8p\nZZGjzmu6R2iqgmk67/8JpYgrFAoRSAEAALJAMBzRjtrM7illO5jOc7vdzfYJtTuYprtDilCKdrlc\nLjU2NsY+J5ACAABkru2762VZpqtoHz2l9nC73SotLY19blcwNTFCklCKdjUdGkAgBQAAyGxZsR1M\nib2L5OQzn89nazA1NWWPUIqEEEgBAAAy35YMn0/qkNSD4bu2siuYmlxDhlCKuAoKCgikAAAAWSDT\ne0rLuxTK4yKC2K2zwdT0oqa8I9CuYDAot5t9pAAAALJBpq+825v5pCnT0WBqOpBKEmnjKw888IAe\neuih2OdvvPGG+vXr1+l26+vr9cwzz2jatGmqqKiQ1+vVyJEjdd555+mEE05IWxsdFQ6HU9o+AAAA\n7JPpPaUscpRa0TAZDZnR/7YVMjMhkEqEUknSypUr9dOf/lT19V8vn910xdmOWrx4sc4++2ytWLGi\n2dfff/99PfLIIzrzzDP15JNPqri47R9OO9oAAABA7msIhrSrvvP3sKnEdjCpl2gwzZRAKjF8V5Zl\n6bLLLlN9fb26d+9uW7uVlZU68cQTtWLFCnXp0kV33nmn5syZo5kzZ+q73/2uJOmll17SBRdckNI2\nAAAAkB8yvZdUoqc0XeIN5c2kQCrRU6r7779f77//vsaPH68pU6bonnvusaXdX/ziF9q4caNcLpdm\nzJihww8/PPbc5MmTNWDAAP32t7/VK6+8opdffllnnHFGStoAAABAfsj0lXclekrTqa0e05aPTQdS\nKc97StesWaOf//zncjgc+vvf/95sT87O2Lp1qx5//HFJ0sUXX9wsTEbdcsst6tu3ryTpD3/4Q0ra\nAAAAQP6o2FFruoR2uV1OlfkKTZeRV1rrMc20QCrlcSi1LEvf//735ff79YMf/ECHHXaYbW2/8sor\nsQWCLrroolaP8Xg8Ou+88yRJH330kTZt2mR7GwAAAMgfKyt3mi6hXT27FsnpdJguI++0DKZRmRJI\npTwOpX//+9/11ltvqV+/frrzzjttbfvDDz+UJLndbk2aNKnN44488khJewLyRx99ZHsbAAAAyA8N\nwZDWbd9tuox2sR0M2pKXoXTDhg268cYbJUl/+ctfVFJSYmv7y5YtkyT1799fRUVFbR43cuTIvV5j\nZxsAAADID6u27FLEskyX0a5ezCc1ouWiRlGJ7mOaDnm50NEVV1yhmpoanXbaaTr77LNtb7+yslKS\n4u5zGp0P2vQ1drYRz86dbQ/xaO2NCwAAgMy0YnNmD92VWHnXhNZW2ZUS38c0XfIulD722GOaPn26\nunTpogceeCAl56it3TPJvL0eTknN9haNvsbONuIpLy9P6ngAAABkphUZPp9UknqVtH9fC3vF2/Yl\nk4JpXg3f3bx5s6677jpJ0m9/+1sNHDgwJeeJLlDkcrnaPa7p86FQyPY2AAAAkPsagiGtz/D5pBJz\nStMpXiCNt49puuVVT+kPfvADVVdX6+CDD9aPfvSjlJ3H5/Opurpa9fX17R7X9PkuXbrY3kY8VVVV\nbT5XXV2tYcOGJdUeAAAA0i8b5pMWelzqWlhguoy8EC+QRrW1j6mJHtO8CaWvvvqq/vvf/8rtduvh\nhx+W05m6TuKysjJt3Lix3dAnNQ+FLZdptqONROqMJ15PLQAAAMzKhvmkg3uUyOFgO5hUSzSQRmVK\nMM2bULp27VpJe7ZY+c53vtPqMVu2bIk9njp1qjwej8rKyvT+++8nda5Ro0Zp8eLFWrdunSzLavMH\nMFqTJI0ePdr2Nuzg8XgYFgwAAJDBsmE+6ai+8TtD0DnJBtKoTAimeRNKoxoaGrRkyZK4x61YsUKS\n1L1796TPMWHCBL344ovy+/1aunSpxo0b1+pxc+fObfYau9uwS2Njo/x+v/FVuQAAANBctswnHdmn\n1HQJOa2jgTTKdDDNm4WOLrjgAi1atKjdj8suuyx2/IwZM7Ro0SLNnj076XOddtppscevvPJKm8dF\nnxs4cOBegdKONuxkevIzAAAA9vZlZXXGzyf1uJwa0rOb6TJyVmcDaZTJxY/ypqe0vLw87hYoPXr0\niD0eNWqUhgwZ0qFzTZgwQQcffLDmzZun++67T1ddddVe8zfffPNNffTRR5LULAzb2YYdgsFg7HEm\nLBcNAACAr62srDZdQlzDenWTx5U3fWFpZVcgjTLVY8q7owO++c1vavz48Tr88MPbPOauu+6S0+lU\nZWWlTjvtNK1evTr23IwZM3TBBRdIkgYMGKDrr78+ZW10VjgcVkHB1yul0WMKAACQObJhkSPmk6ZG\nKBSyNZBGmegxJZR2wIoVK7RkyRItXbq0zWOOOeYY/eUvf5HT6dQHH3ygkSNHaujQoerVq5dOPPFE\nbd++XeXl5Xr11Vfb3MrFjjbs4Ha7M2ofIwAAAEj1jSGt35H580lH9SGU2i0UCikQCMQ+tyuQRqU7\nmBJKU+jqq6/WrFmzdPzxx8vhcGjt2rXatm2bunbtqksuuUSLFy/W/vvvn/I27JBpG+wCAADku1Vb\nsmM+6eCeJabLyDlNp9jZHUij0nn/nzdzShNx3XXXxbaL6d+/f5vH/e9//1MgEEhoD8+jjjpKb775\nphoaGrRp0yZ5vV716dMnqf0/7WjDDqZX5QIAAMDXmE+KVAXSqLbu/z0ej63nIZQ20bt3b/Xu3Tvu\ncSNHjky67cLCQg0bNqwjZdnaRmcRTAEAADID80nzm9frTcs9eGv3/3ZPHSSUImkEUwAAALOYT5rf\n7O6pjKfl/X9NTY2tNdCXjg5hjikAAIA5zCfNb253+vsWW97/e71e29qmpxQd1laPKQAAAFJrRWXm\nD91lPmnuid7/V1VV2dou7xJ0Sms9pqFQyFxBAAAAeWDF5mrTJcQ1mvmkOcnn86mkxN4ecEIpOq1l\nMA0EAgRTAACAFNkzn7TGdBlxschR7ioqKmq2T2pnGRm+GwgEtG3bNjU0NKhnz57q1q2biTJgo5ZD\neZvunQQAAAD7rNpSrQyfTrpnPmkP5pPmMjvv91MeSi3L0ocffqi3335bs2bN0rx581RT0/wvOx6P\nRyNHjtQxxxyjY445RlOmTFFZGX9ZyTbRYLplyxbDlQAAAOSubJhPOrx3qdzMJ0WCUhZKd+zYoUcf\nfVR//etftWrVqnaPDQaDWrp0qZYuXaqHHnpIRUVFOvfcc3XVVVfp0EMPTVWJSAGfz2frSlwAAABo\nLhvmk47qU2q6BGQR20NpbW2tfv/73+vuu+9WfX197OuFhYU64IADNH78eJWXl6t79+7yer3asWOH\nqqqqtH79en388cfasmWL6uvr9dhjj+mxxx7Tcccdp3vuuUcTJkywu1SkiNvtTvveSQAAAPmA+aTI\nRbaG0qefflrXX3+9KisrJUkDBgzQhRdeqDPPPFMHHHCACgoK4raxZs0avfXWW3ryySf13nvv6Z13\n3tFBBx2k733ve/rTn/5k+0pPSA0TeycBAADkui+ZT4ocZOtA73vuuUeVlZU64YQTNHPmTK1bt053\n3nmnJk6cmFAglaShQ4fq8ssv16xZs7R27VrdfPPNKi4u1iOPPKIVK1bYWS4AAACQVVZsZj4pco+t\n75Zjjz1Ws2fP1htvvKHJkyfL6exc84MGDdJvf/tbrV27VjfeeKOKiopsqhQAAADIPiuzYJEj5pMi\nWbaOsbzrrrvsbC6me/fu+v3vf5+StgEAAIBssGc+6W7TZcTFfFIki351AAAAIAtkw3zSAjfzSZE8\nQikAAACQBbJiPmkv5pMiebxjAAAAgCyQDfNJRzJ0Fx2Q1n07Kioq9M9//lOzZs3Sl19+qZqaGoXD\n4XZf8/777+uAAw5IU4UAAABA5sma+aQscoQOSFsoffrpp/X9739fdXV1Sb0uXmgFAAAAch3zSZHL\n0hJK58+fr4svvljhcFgul0tHH3209t9/f5WVlcXdNqZ///7pKBEAAADIWMs27jBdQlzMJ0VHpSWU\nPvTQQwqHwxowYICmT5+ucePGpeO0AAAAQNaLRCx9umar6TLiYj4pOiotf8pYunSpJOm2224jkAIA\nAABJWL65SjX1jabLiGs0oRQdlJZQ2rVrV0nSmDFj0nE6AAAAIGd8vKrSdAlx7ZlP2tV0GchSaQml\nEyZMkCRt3LgxHacDAAAAckJjKKz5a7eZLiOu4b1L5YqzVgzQlrS8c6666ip5vV498sgj6TgdMkAo\nFDJdAgAAQNZbsH6bAqHM341iVB+G7qLj0hJKBw4cqKeeekpvvPGGrr32WgUCgXScFoaEQiEFg0HT\nZQAAAGS9uVkwdFeSRjGfFJ2Qtn1Kzz77bL377ru64oorNGTIEJ1++ukaNWqUiouL476uZ8+eaaoS\nneX3+xUIBOR2p+2tBQAAkJN21zdqaUWV6TLiYj4pOittySESiWjevHmqra1VZWWl/va3vyX0uoMP\nPphQmiX8fr+qq6tNlwEAAJATPl2zRRHLMl1GXMwnRWelJZRalqVvf/vbeuGFF9JxOhjQMpB6PB5z\nxQAAAOSArBm6y3zSvGTn/X5aQumzzz4bC6Snn366rrzySu23334qKyuTM85fVbxebzpKRCe0DKRe\nr5fhuwAAAJ2wraZOa7bVmC4jIcwnzT/19fW25rS0JIeXXnpJknT++efr6aefTscpkSYtA2lpaalq\na2vNFQQAAJADsqWX1Ot2MZ80z/j9ftXU2PsHk7QM/t68ebMk6Xvf+146Toc0aS2Q+nw+cwUBAADk\nAMuysiaUjh1QznzSPJKqNWTS8g7q16+fJKmoqCgdp0MaEEgBAABSY9323dpaU2+6jIQcOryv6RLy\nUigUSvs5W97/27nNZ1pC6emnny5J+vjjj9NxOqQYgRQAACB15q7abLqEhBQVuDVuQHfTZeSlYDCY\n1mDa8v6/pKREwWDQtvbTEkq//e1v65hjjtEf//hHrV+/Ph2nRIoQSAEAAFInHIlo3uotpstIyEFD\ne8njYuiuKYFAQH6/P+Xnae3+3+4RsGl5F7ndbr388ss68sgjNWnSJD322GMshpOFCKQAAACp9cWm\nndrdYF8PVCodOryP6RLyXnV1dUqDabru/9Oy+u4555yjxYsXS5K2bt2qSy65RJdffrn69eun4uLi\ndl/7wgsvaNy4cekoE+0gkAIAAKRetixwVO4r1PDepabLyFtN9wiN3qPbfW+ezvv/tITStWvXavny\n5c2+Fg6HtWHDhrivra/PjkneuSwUChFIAQAAUqwhGNL8dVtNl5GQQ4b3ltPhMF1G3nK73c32CbU7\nmKa7QyotofTss8/WwQcf3KHX9urVy+ZqkAyXy6XGxkYVFhZKIpACAACkysL129UYipguIyEM3TXP\n7XarS5cusfBoVzA1MUIyLaH05z//eTpOgxRoOjSAQAoAAJA62TJ0d0B5F/Ur62K6DOjrAGpXMDU1\nZY/lspAQAikAAEDq1NQHtGxjlekyEjKRXtKM4vP5VFpaGvu8o4sfmVxDJmND6eOPP65nn33WdBmQ\nVFBQQCAFAABIoXmrtypiWabLiMsh6eBhvU2XgRY6G0xNL2qasaH0D3/4gy655JK07L2DtgWDQbnd\naRnlDQAAkLc+yZKhu6P6lqnUV2i6DLSio8HUdCCV0jSn9De/+Y3Wrl2b8PEbN27U0qVLJUnr1q3T\n2LFjU1QZ4gmHw6ZLAAAAyGlbd9Vp7fYa02UkhAWOMluyc0wzIZBKaQqlL7/8sj799NOkX9ezZ08N\nGTLE/oIAAACADJEtCxx5XE4dMISdMTJdosE0UwKplKZQOnr0aIVCoXaPCQQCWr9+verq6iRJU6dO\n1b333qvi4uJ0lAgAAACknWVZWRNK9xvUQ0UFTOvKBvGCaSYFUilNofSpp55K6LhQKKTnn39eP/zh\nDzV37lwFg8EUVwYAAACYs2ZbjbbtrjddRkIOYehuVmkrmLZ8bDqQShm20JHb7dYFF1ygf/7zn9q5\nc6d++MMfmi4JAAAASJlsWeDI53VrXP/upstAklpb/CjTAqmUYaE06qyzzlJpaanef/99bdu2zXQ5\nAAAAgO3CkYjmrdliuoyEHDi0t9yujIwOiKNlMI3KlEAqZWgodTgcGjhwoCSpsjI7/noEAAAAJGPZ\nxirVNmTHdLWJDN1FCmVkKJWkrVu3SpJ69OhhuBIAAADAfh9nydDd7l0KNaxXN9NloINaLmoUleg+\npumQkaF02bJl2rJli/bdd1/17dvXdDkAAACArRqCIS1Ylx3T1A4Z3kcOh8N0GeiA1lbZbTnHNBOC\nacat6bx+/XpdcsklKioq0gMPPGC6HAAAAMB2C9ZtUzAcMV1GQg5l6G5WirftS7x9TNMpLaH0Zz/7\nmVatWtXuMZZlaf369Vq8eLEaGho0fvx43Xvvvbr33ntbPf4Pf/iDhg0blopyAQAAgJTKlqG7A7t3\nVd/SzFgMB4mLF0jj7WOabmkJpW+99ZY+/fTTpF6zePFiLV68uM3nb7rpJkIpAAAAsk61v0FfbKoy\nXUZCWOAo+8QLpFGZFEzTEkoPOuggdenSxdY2u3btamt7aJ3L5TJdAgAAQE55a8kGWZbpKuJzOKSD\nh/U2XQaSkGggjcqUYJqWUPq3v/0tHadBCng8HoVCIdNlAAAA5AR/IKj3v9houoyE7NO3XN2KvabL\nQIKSDaRRmRBMM3L1XWSWxsbGjFiVCwAAINu9u6xCgVDYdBkJYYGj7NHRQBrl8/mMrspLKEVCMmW5\naAAAgGwVCIb19pINpstIiMfl1IQhPU2XgQR0NpBGmQymhFK0KxgMxh4TTAEAADrugxUb5Q8E4x+Y\nAfYf1FOFnozbPRIt2BVIo0wFU1tD6aZNm+xsrpna2lrV1NSkrH20LhwOq6CgIPY5wRQAACB5oXBE\nMxevN11Gwg4dwdDdTBcKhWwNpFEmgqmtofSMM87Queeeq2XLltnWZl1dnf74xz9q6NChWrFihW3t\nInFut9voGHMAAIBsN3dVpXb6A6bLSEiXQo/G9i83XQbaEQqFFAh8/X6yK5BGpTuY2hpKi4qK9Pzz\nz2v8+PE688wz9dJLL6mxsbFDbS1atEg33nijhg4dqhtvvFFVVVUqKiqys1wkwfTkZwAAgGwViVia\nsXCd6TISdtDQ3nI5meWXyZpOsbM7kEal8/7f1oHi06ZN05133ql77rlHL7/8sl5++WWVl5fr5JNP\n1qRJkzRp0iSNHTt2r3AZDodVUVGhuXPn6uOPP9bMmTO1YMGC2PPf+MY3dM8992jcuHF2loskZcJy\n0QAAANlm/rqt2lpTZ7qMhLHqbvZIVSCNauv+3+Px2HoeW0Nply5d9Jvf/EZXXnmlfv3rX+upp55S\nVVWVnnzyST355JOx44qKilReXi6v16uqqirt2rVLVis7CB9xxBG66aabdOqpp9pZJjqBYAoAAJA4\ny7I0PYt6SXt0LdLQniWmy0ACvF5vWu7BW7v/79Kli63nSEm//MCBA/X3v/9dmzZt0r333quDDjpI\nziZDAOrr67Vx40atXr1a1dXVzQLpgAEDdPXVV2vRokWaPXs2gTQDMZQXAAAgMcs2VWnDjt2my0jY\nocP7yOFwmC4DcXg8Hrnd6VsdueX9f01Nja29pSm9km7duunHP/6xfvzjH2vXrl2aPXu2Pv30U23Z\nskXbtm1TY2OjevTooZ49e2rkyJE65phjNHz48FSWBJvQYwoAABDfjAVrTZeQFIbuZge3291sXmk6\ntLz/93q9trWdtnjdrVs3nXLKKTrllFPSdUqkWFvBFAAAANLqrbu0orLadBkJG9yjRL27FZsuAxks\nev9fVVVla7ssq4VOaW0obygUMlcQAABAhpixcK3pEpJy6PDepktAFvD5fCopsXfeMaEUndYymAYC\nAYIpAADIaxt31mrh+u2my0iYw7FnKxggEUVFRc32Se0sQils0TKYpnuMOwAAQCZ5I4tW3JWkg4b0\nVrdi++YIIvfZeb9PKIVtWgZTAACAfLR9d73mrd5iuoykTN1vsOkSkMcIpbCVz+ezdSUuAACAbPPm\nonWKNNnyMNONH9BdA7p3NV0G8hihFLZzu9227lsEAACQLXbVBfThys2my0jK1P2GmC4BeY5QipRI\n52a+AAAAmeLtJRsUCkdMl5GwEb1LNaJPqekykOcIpQAAAIAN6gJBvfdFhekyksJcUmQCQikAAABg\ng3e/2KiGYNh0GQkbUN5F4wZ0N10GQCgFAAAAOqsxFNbbS9abLiMpU/cbIofDYboMID2hlD0rAQAA\nkMs+XLFJtQ3Zc8/bs6RIBw7pZboMQFKaQukJJ5ygSy+9VB988EE6TgcAAACkTTgS0ZuLsquXdMq+\ng+V00kuKzJCWUFpbW6tHH31URx55pMaMGaO77rpLW7duTcepAQAAgJT6ZNUWVfkbTJeRsG7FXk0c\n0dd0GUBMWkLp2Wefre7d90yi/uKLL/TTn/5UAwYM0Nlnn63XXntN4XD2TAgHAAAAoiKWpRmL1pou\nIynHjxsoj4ulZZA50vJu/PnPf66NGzfqmWee0Te+8Q05HA4Fg0G9+OKLOuWUUzRkyBD96le/0po1\na9JRDgAAAGCLheu3q7K6znQZCSsucOvIffqbLgNoJm1/IvF6vTrvvPP01ltvaeXKlbrpppvUp08f\nSVJFRYV+85vfaPjw4Tr++OP1zDPPqKEhe4ZAAAAAIP9YlqUZC9aaLiMpx44dqEKP23QZQDNG+u2H\nDx+uO+64Qxs2bNBLL72kk08+WU6nU5Zl6a233tIFF1ygfv366cc//rEWLFhgokQAAACgXcs379Ta\n7TWmy0iY1+3ScWMHmi4D2IvRweRut1tnnHGGpk2bprVr1+rWW2/VoEGDJEk7d+7UfffdpwkTJujg\ngw/WP//5T9XX15ssFwAAAIiZsXCd6RKScuTofupS6DFdBrCXjJnh3L17dw0ZMkQDBgzY67lPP/1U\nl19+uYYPH64nnnjCQHUAAADA19Ztr9EXm6pMl5Ewl9OhyeMHmS4DaJXxAeWff/65Hn74YT399NPa\ntWtX7OuDBg3S5Zdfri5duuiRRx7R4sWLtXnzZl188cXauXOnfvzjHxusGgAAAPlsepbNJZ04oq/K\nfIWmywBaZaSntKamRn/961918MEH68ADD9RDDz2kXbt2yeVy6bTTTtOrr76qNWvW6Fe/+pWuu+46\nLVq0SB988IEOPfRQSdKtt96qUChkonQkiH8fAACQqyp27NaCddtMl5Ewh6Qp+w42XQbQprT2lH74\n4Yd6+OGH9fzzz6uu7uuls/v376/LLrtM3//+91sdvitJhx9+uF5//XUNGjRIO3fu1Lp16zR8+PB0\nlY4kEEgBAECusixLz360XJbpQpJwwJBe6t2t2HQZQJvSEkofeeQR3XXXXVq2bFnsa06nU1OnTtWV\nV16pU089VS6XK2475eXlGjFihBYsWKCdO3emsmR0kN/vVyAQkNttfGQ4AACA7eauqtSqLbviH5hB\nTtx/iOkSgHalJTk8+OCDsUDap08fXXrppbriiis0eHDywwj69OmjyspKeTysHJZp/H6/qqurTZcB\nAACQEvWNIb34yZemy0jK2P7lGti9q+kygHalJZQ6nU6dcMIJuvLKK3X66ad3qhdt+vTpNlYGu7QM\npPzRAAAA5Jr/fbZaNfWNpstIytT9hpguATnKzvv9tITS119/Xd27d0/HqWBAy0Dq9XoZvgsAAHJK\nRdVuzVq2wXQZSRnWq5tG9ik1XQZyUH19vbxer23tpWX1XQJp7moZSEtLSwmkAAAgp1iWpWfnLJeV\nTasbSTpxvyFyOBymy0CO8fv9qqmpsbVNI1vCIDe0Fkh9Pp+5ggAAAFIgGxc36l/WReMH0jEEe6Vq\nDRlCKTqEQAoAAPJBNi5uJElT9htML2kOM7EFY8v7/0AgYFvbhFIkjUAKAADyRTYubtSja5EOGtrL\ndBlIoWAwmNZg2vL+v6SkRMFg0Lb2CaVICoEUAADki41VtXp3WYXpMpJ2wr6D5HJym5/rAoGA/H5/\nys/T2v1/UVGRrefg3YqEEUgBAEC+iC5uFMmy1Y1Kigp02Ii+pstAmlRXV6c0mKbr/p9QioQQSAEA\nQD6Zu6pSX26pNl1G0iaPGySP22W6DKRY0z1CUxVM03n/TyhFXKFQiEAKAADyRrYublRU4NZR+/Q3\nXQbSwO12N9sn1O5gmu4OKUIp2uVyudTY+PXkfgIpAADIda9+nn2LG0nSMWMGqKiA/eLzhdvtVmlp\naexzu4KpiRGShFK0q+nQAAIpAADIdRurajVrafYtbuRxOfWNsQNNl4E08/l8tgZTU1P2CKVICIEU\nAADkumxd3EiSjhzdT12LCkyXAQPsCqYm15AhlCKugoICAikAAMh5n6zekpWLGzkdDh0/frDpMmBQ\nZ4Op6UVNCaVoVzAYlNvN3AQAAJDb6htDemHuStNldMjEEX1U3qXQdBkwrKPB1HQglQiliCMcDpsu\nAQAAIOWmZeniRm6nQyfuN8R0GcgQyQbTTAikEqEUAAAAeW7jzlq9k4WLG0nS8eMHq1e3YtNlIIMk\nGkwzJZBKhFIAAADkMcuy9FyWLm5U7ivUifsPMV0GMlC8YJpJgVSSmCwIAACAvPXJ6i1aWVltuowO\nOWfiSHk9LtNlIENFQ2Y0fDYNoZkUSCVCKQAAAPJUNi9uNKZ/uSYM7mm6DGS49oKplBmBVGL4LgAA\nAPLUtM/XZOXiRi6nQ+dOGi2Hw2G6FGSBlkN5ozIlkEqEUgAAAOShPYsbbTBdRoccP36QerO4EXII\noRQAAAB5JZsXNyrzeXXS/kNNl4Es0nJRo6hE9zFNB0IpAAAA8kp2L240isWNkLDWVtlNZh/TdGGh\nIwAAAOSN+saQXszixY0OYHEjJCjeti8tFz8yOb+UnlIAAADkjdfmr9EuFjdCjosXSOPtY5puhFIA\nAADkhU07a/X2EhY3Qm6LF0ijMimYEkrRLpeLOQsAACD7sbgR8kGigTQqU4IpoRTt8ng8CoVCpssA\nAADolI++rNSKbF3c6NCRLG6EuJINpFGZEEwJpYirsbExI1blAgAA6Igtu+r03JzlpsvokDH9ynXA\nkF6my0CG62ggjTIdTAmlSIjpyc8AAAAdEQyF9Y93FikQCpsuJWkup0PfPmwUixuhXZ0NpFEmgymh\nFO0KBoOxxwRTAACQbf4zd6UqqmpNl9Ehk8cNUp9u5rbpQOazK5BGmQqmhFK0KxwOq6CgIPY5wRQA\nAGSLz9Zs0XtfbDRdRoeU+bw6acIQ02Ugg4VCIVsDaZSJYEooRVxut9v45GcAAIBkbKup1xOzl5ku\no8POPnSkCj1u02UgQ4VCIQUCgdjndgXSqHQHU0IpEmJ68jMAAECiQuGI/jlrsRqC2TePVJL26Veu\nA1ncCO1oOsXO7kAalc77f/78goRF3+zRYQLR/6bihwAAAKCjXvl0ldZtrzFdRoe4nA6dO4nFjZCY\nVAXSqLbu/z0ej63noacUSaHHFAAAZLJF67dr5uL1psvosMnjBqlPKX/wR3xerzctnUOt3f/X19fb\neg56SpE0ekwBAEAm2ulv0OPvLzFdRoexuBESZXdPZTwt7/9rampsrYGeUnQIPaYAACCThCN75pH6\nAyHTpXTY2YewuBES43an/33S8v7f6/Xa1jbvenRYWz2mAAAA6fbq52u0assu02V02Oi+ZTpwKIsb\nIbNF7/+rqqpsbZeeUnRKaz2moVD2/oUSAABkn2Ubd2jGgrWmy+gwp8Ohcw8bzeJGyAo+n08lJSW2\ntkkoRae1DKaBQIBgCgAA0mJXXUCPvrtElulCOuH48YPUl8WNkEWKioqa7ZPaWYRS2KJlMG26dxIA\nAEAqRCKWHn13iXY3ZO99R2kxixshO9l5v08ohW1aBlMAAIBUmrFwrZZv3mm6jE4551AWNwIIpbCV\nz+ezdSUuAACA1qys3Kn/fb7adBmdwuJGwB6EUtjO7Xanfe8kAACQP2obGvXIrCWysngiKYsbAV8j\nlCIlTOydBAAAcl/EsvT4+0tVXWffIismTB4/kMWNgK/kZXKora3VvHnztHHjRm3evFmWZalPnz6a\nNGmSRo4cafv5duzYoTfeeEMVFRXyer0aOXKkJk+erIKCgrS2AQAAkO3eXrxeizfsMF1Gp3Qr9urk\nCUNNlwFkjLwKpU8++aQeeOABzZs3r80tS4488kjdd999mjBhQqfP19DQoJtvvln333//XqtT9ejR\nQ/fee68uuOCClLcBAACQC9Zs3aWX5q0yXUansbgR0Fxe/TTMnDlTH330kSSpW7duOvbYYzVixAg5\nHA7NnTtX7733nmbPnq1Jkybp1Vdf1fHHH9/hc4XDYX3rW9/Sq6++Kkk6+OCDdcwxx8jv9+vll19W\nZWWlLrzwQlVXV+uHP/xhytoAAADIBXWBoP45a7Ei2TyRVHsWNzqIxY2AZvIqlErSmDFjdNNNN+m8\n887ba+jrG2+8oTPPPFN1dXW68MILtXLlSpWUlHToPPfdd18sTN5yyy269dZbY8/94Q9/0Kmnnqr3\n3ntP1157rSZPnqzRo0enpA0AAIBsZ1mWnvxgmXbUNpgupVPcTofOnTSKxY2AFvJqoaNrrrlGCxcu\n1MUXX9zqXMwpU6bot7/9rSRp69ateuGFFzp0nsbGxlg7kyZN0i233NLs+a5du+qpp56S1+tVKBTS\n7bffnpI2AAAAcsF7X2zU52u3mS6j0846dKT6lnUxXQaQcfIqlB500EFxV4U988wzY48/++yzDp3n\nzTff1Pbt2yVJP/rRj1r9a9iAAQN0xhlnSJJeeeUV1dfX294GAABAttuwY7f+8/EK02V02v6DeurY\nMQNMlwFkpLwKpYnwer2xxw0NHRsiMnPmzNjj9ualRp/z+/368MMPbW8DAAAgmzUEQ/rHO4sUimT3\nPNJyX6EuOmoMw3aBNhBKW4guhCRJw4YN61AbixcvliT17NlTvXv3bvO4fffdN/Z4yZIltrcBAACQ\nrSzL0tMffKGtNdk9EszpcOiy48bL5/WYLgXIWHm30FF7LMvS3XffLUlyOBz65je/2aF21q5dK0ka\nOHBgu8c1fT76GjvbiGfnzp1tPlddXZ1UWwAAAHaas3KzPlm9xXQZnXb6QcM1rFc302UAGY1Q2sT9\n99+v2bNnS5LOO+88jRs3rkPt7N69W9KexYja0/T56GvsbCOe8vLypI4HAABIh807a/XsnOWmy+i0\nsf276/h9B5kuA8h4DN/9ynvvvafrr79ektS/f3/dd999HW4ruuBQayv8NtV0/mrLRYrsaAMAACDb\nNIbCevidxQqGI6ZL6ZRuxV597+ixcjKPFIiLnlLtmYt5xhlnKBgMyufz6cUXX1T37t073F5RUZFq\namrU2NjY7nGBQKDZa+xuI56qqqo2n6uuru7wnFoAAICOsCxLT3/4hTZX+02X0ikOh3TpMePUtaj9\nzgUAe+R9KF29erVOOOEE7dy5U16vV6+88ooOPfTQTrXZtWtXbdmyJe5w2qbPtxyma0cb8ZSVlSV1\nPAAAQCq98ukqffxlpekyOu2UCUM1qi/3WUCi8nr4bkVFhSZPnqzNmzfL7Xbr+eef1+TJkzvd7pAh\nQyRJGzZsaPe4ps9HX2NnGyaFQiHTJQAAgCzy9pL1mrFwnekyOm1U3zKdtP9Q02UAWSVvQ+mWLVt0\n/PHHa+3atXK5XHrqqac6vNpuS+PHj5ckbdu2TVu2tL1q3MKFC2OPWy6qZEcbpoRCIQWDQdNlAACA\nLPHJ6kr9++OVpsvotC6FHl16zDg5ncwjBZKRl6G0qqpKJ5xwgpYvXy6Hw6FHHnlE3/72t21r//jj\nj489njlzZpvHvfXWW5Ikn8+nww8/3PY2TPD7/c3muQIAALRn2cYdevy9pabLsMUlR49Tt2Jv/AMB\nNJN3obSmpkZTp07VokWL5HA49Le//U0XX3yxrec4/vjj1aNHD0nSAw88IMuy9jqmoqJCL7/8siTp\n9NNP32uRIjvaSDe/38/+pgAAIGHrttfob28tUjiy931Otpm632CNHdDxhTKBfJZXobSurk6nnHKK\n5s2bJ0n6y1/+ou9///tJt3P//ffr1ltv1Z133tnq816vV7/4xS8kSXPmzNFtt93W7Pna2lp95zvf\nUSAQkMvl0v/93/+lpI10ahlIPR6PuWIAAEDG27qrTve/MV+BUNh0KZ02rFc3nXYguxYgv9h5v59X\nq+9eddVVmj17tiRp1KhR2r59u2699dY2jx82bFirvaj333+/li9frm7duummm25q9bXXXHONZs6c\nqWnTpum2227TtGnTdOyxx8rv9+ull15SZeWeleXuvfdejR49OmVtpEPLQOr1euV259VbCwAAJGFX\nXUB/mfG5ahuyfw2K4gK3Ljt2vFzOvOrrQZ6rr6+X12vfUPW8Sg7r1n29otuKFSv26n1safLkyR0e\n2utyufSf//xHP//5z/XAAw9o3rx5sR5aSerRo4f+/Oc/68ILL0xpG6nWMpCWlpaqtrbWWD0AACCz\n1TeGdP8b87WjtsF0Kba4+OixKu9SaLoMIG38fr9qampsbTOvQun3vvc9HXvssQkfP2xY68MwfvSj\nH2n79u0qLGz/F1BhYaH+9Kc/6Ze//KXeeOMNbdiwQV6vV6NGjdLkyZNVUBB/Q2U72kiV1gKpz+cj\nlAIAgFYFQ2H9deYCVVTlxr3CN8YN1P6DepouA0ibVK0hk3eh1A4/+tGPkjq+e/fuOv/88zt1Tjva\nsFNbgRQAAKA1kYilR99bohWV1aZLscWg7l11xsEjTJeBPBUKhdJ+zpb3/3buuMHgdySNQAoAAJJh\nWZae/Wi5Pl+7zXQptij0uHT5cePlcXErDTOCwWBag2nL+/+SkhIFg/bNCecnCUkhkAIAgGRNm79G\n73+x0XQZtvnOEWPUs6TYdBnIc4FAQH6/P+Xnae3+3+6tKAmlSBiBFAAAJOu9Lyo07fM1psuwzVGj\n++ugYb1NlwFIkqqrq1MaTNN1/08oRUIIpAAAIFmfrd2qZz9cbroM2/Qv66JzJo40XQbQbI/QVAXT\ndN7/E0oRVygUIpACAICkrNi8U4/OWizLdCE2KXA7dflx41XgdpkuBZDb7W62T6jdwTTdHVKEUrTL\n5XKpsbEx9jmBFAAAxFOxY7cemrlAoUiuRFLp/MP2UZ9S7oGQOdxut0pLS2Of2xVMTYyQJJSiXU2H\nBhBIAQBAPNt31+u+N+arIRg2XYptJo3oq0kj+5ouA9iLz+ezNZiamrJHKEVCCKQAACCemvpG/WXG\n56qpb4x/cJbo3a1Y5x42ynQZQJvsCqYm15AhlCKugoICAikAAGhXQzCkB9+cr2019aZLsY3btWce\naaHHbboUoF2dDaamFzUllKJdwWBQbje/iAEAQNtC4Yj+/tYirdu+23Qptvr2xJEaUN7VdBlAQjoa\nTE0HUolQijjC4dyZDwIAAOwXsSw9/v5SLdtUZboUWx00tJeOHN3fdBlAUpINppkQSCVCKQAAADrI\nsiz95+OVmrd6i+lSbNWja5EuPGKMHA6H6VKApCUaTDMlkEqEUgAAAHTQG4vW6Z2lG0yXYSuX06HL\njxuvogKmLyF7xQummRRIJYmfNgAAACTtwxWb9PK8VabLsN1Zh4zQ4B4lpssAOi0aMqPhs2kIzaRA\nKhFKAQAAkKRF67frqQ++MF2G7fYb1EPHjR1ougzANu0FUykzAqnE8F0AAAAkYdWWaj38ziJFLMt0\nKbYa1L2rLjlmHPNIkXNaDuWNypRAKhFKAQAAkKDNO2v14JsLFAxHTJdiq54lRbp6ygT2IwUM4ScP\nAAAAcVVU7db9M+arrjFkuhRblRQV6JopB6ikqMB0KUBKtFzUKCr6tUzoLSWUAgAAoF0rK3fqoZkL\nVZ9jgbTQ49KPpkxQz5Ii06UAKdHaKrvS3nNMTQdTQikAAADaNH/tVv3z3SUK5diQXbfToR8cv58G\ndu9quhQgJeJt+5JJwZRQCgAAgFa9/8VGPTPnC+XYmkZySLrkmHEa3bfcdClASsQLpG2tymsqmBJK\nAQAA0IxlWXpt/hq9+vka06WkxLmHjdaBQ3ubLgNIiXiBNCqTgimhFO1yuVymSwAAAGkUiVh67qPl\neu+LjaZLSYmTJwzVMWMGmC4DSIlEA2lUpgRTQina5fF4FArl1qIGAACgdcFQWI+9t1Sfrd1qupSU\nOHJ0P516wFDTZQApkWwgjcqEYMo+pYirsbFRfr/fdBkAACCF6htDuv+N+TkbSCcM7qnzD9tHDofD\ndCmA7ToaSKN8Pl9sZV5pTzBN5/0/PaVIiOnJzwAAIHV21QV0/xvzVVFVa7qUlBjZp1SXHjNOTieB\nFLmns4E0ymSPKaEU7QoGg7HHBFMAAHLP1po6/WX659pR22C6lJToX9ZFP5i8nzxu1slA7rErkEaZ\nCqaEUrQrHA6roKAg9jnBFACA3LFue43uf2O+ahuC8Q/OQuVdCvWjqRNU7PWYLgWwXSgUsjWQRpkI\npswpRVxut9voGHMAAGC/ZRur9KfXPsvZQNql0KMfTz1ApcVe06UAtguFQgoEArHP7QqkUemeY0pP\nKRKSCatyAQAAe8xbvUWPvbdE4YhlupSU8LpduvqE/dW7W7HpUoCUaDrFzu5AGpXO+39CKRJGMAUA\nIPu9s3SD/v3RCuVmHJWcDoeu+Ma+GtKzm+lSgJRLVSCNauv+3+Oxd0g8oRRJIZgCAJCdLMvSfz9d\nrekL15ouJaW+e/RYjR3Q3XQZQMp5vd603IO3dv/fpUsXW89BKEXSCKYAAGSXcCSipz/4Qh+u3Gy6\nlJQ659CROnR4H9NlAClnd09lPC3v/2tqamytgVCKDiGYAgCQHRpDYf3jncVatGG76VJSasq+gzV5\n/CDTZQBp4Xa7m80rTYeW9/9er32LiBFK0WFtBVMAAJAZ/IGgHnpzgVZt3WW6lJSaNKKvzjh4uOky\ngJwXvf+vqqqytV1CKTqFYAoAQGba6W/QfTPma3N1bm/jNn5gd33nyH3kcDhMlwLkBZ/Pp1AopJqa\nGtvaJJSi01oG00AgoKKiIoMVAQCQ3zZX+3XfjM+10x+If3AWG9qzRN8/bl+5nE7TpQB5paioqNk+\nqZ1FKIUtWgbTdI9xBwAAe6zeuksPvjlf/kDIdCkp1ae0WD88YYIK3C7TpQB5yc77fUIpbBMNplu2\nbDFcCQAA+Wnxhu36+9uLFAxHTJeSUmU+r66ZcoC6FKZ3BVIAqUEoha18Pp+tK3EBAIDEfLRys56Y\nvUwRyzJdSkoVF7j1oykTVN6l0HQpAGxCKIXt3G532vdOAgAgn72xaJ1e+uRL02WknMfl1A9P2F/9\nyrqYLgWAjQilSAm3m7cWAACpFrEsvTj3S721ZL3pUlLO6XDo8uPGa3jvUtOlALAZyQEAACAL1QWC\nevKDZfp87TbTpaTFhUfso/0G9TRdBoAUIJQCAABkmbXbdukf7yzWjtoG06WkxRkHDdfho/qZLgNA\nihBKAQAAsoRlWXpryQa99MmXOb+gUdRxYwdqyn6DTZcBIIUIpQAAAFmgtiGof72/VIs2bDddStpM\nGtFX50wcKYfDYboUAClEKAUAAMhwX1ZW65F3F2unP2C6lLQ5fvwgnXnICDkJpEDOI5QCAABkqIhl\nacbCdXr1s9V5M1xXks4+ZISO35chu0C+IJQCAABkoJr6gB57d6mWbaoyXUraOB0OXXzUGE0c0dd0\nKQDSiFAKAACQYb7YVKVH312imvpG06WkTYHbqSu+sZ/GDehuuhQAaUYoBQAAyBDhSESvzV+j1+ev\nVf4M1pV8Xo+uPmF/De3VzXQpAAwglAIAAGSAnf4GPTJrib7cUm26lLQq9xXqmqkT1KfUZ7oUAIYQ\nSgEAAAxbtGG7Hn9vqfyBoOlS0qpfmU/XTJmgUl+h6VIAGEQoBQAAMCQUjuiVT1dp5uL1pktJu+G9\nu+mq4/eXz+sxXQoAwwilAAAABuzYXa9/zFqstdtqTJeSdvsP6qFLjx2vArfLdCkAMgChFCkRCoVM\nlwAAQMb6fO1WPTF7meob8+//l0eM6qfzDx8tl9NpuhQAGYJQCtsRSAEAaF0wFNaLn3ypWcsqTJdi\nxEn7D9FpBw6Tw+EwXQqADEIoha38fr8CgYDcbt5aAAA0tXVXnf4xa7E27NhtupS0c0j69qRROnbs\nQNOlAMhAJAfYxu/3q7q62nQZAABknLmrKvX0B18oEAqbLiXt3E6Hvnf0OB00rLfpUgBkKEIpbNEy\nkHo8rKQHAEBjKKznPlqhD1dsMl2KEYUel66cvJ/26VduuhQANrPzfp9Qik5rGUi9Xi/DdwEAeW/T\nzlr9453F2lztN12KEV0LPfrRlAka1KPEdCkAbFZfXy+v12tbeyQHdErLQFpaWqra2lpzBQEAYJhl\nWZqzcrOenbNcwXDEdDlG9OhapB9PnaCeJcWmSwFgM7/fr5oae7eyIpSiw1oLpD6fj1AKAMhbDcGQ\nnv7gC32yeovpUowZ2L2rfjRlf5UU2deLAiAzpGoNGUIpOqStQAoAQL7asGO3/vHOIm2tqTddijGj\n+5bpysn7qaiAW0wglUxswdjy/j8QCNjWNr8xkDQCKQAAXwtHInpnaYVemfelQhHLdDnGHDS0l757\n9Dh5XE7TpQA5LxgMpjWYtrz/Lykp0aZN9i3gRihFUgikAAB8bWXlTj07Z7k27czPxYyijh0zQN+a\nNEpOh8N0KUDeCAQC8vv9Kb8Xb+3+3+6dNgilSBiBFACAPXbVBfTiJ19q7qpK06UY980Dh+nE/YfI\nQSAF0i56b56qe/K27v8bGxttPQ+hFAkhkAIAsGeo7qylFXr189VqCIZNl2OUwyFdcPg+OnJ0f9Ol\nAHmnaU9lqoJpOu//CaWIKxQKEUgBAHmPobpf87icuuzY8dp/cE/TpQB5ye12N9sn1O5gmu4OKUIp\n2uVyudTY2KjCwkJJBFI7vLj9xXafP6vHWWmqBACQiOq6gF6cuzKvt3lpqqjArauO308j+5SZLgXI\na263W126dImFR7uCqYkRkoRStKvp0AACKQAgn0RX1X31s9UKhPJ7qG5Ut2KvrpkyQf3Lu5guBYC+\nDqB2BVNTU/YIpUgIgRQAkE9WbN4zVHdzNUN1o3p3K9Y1Uyeoe5ci06UAaMKuYGpyDRlCKeIqKCgg\nkAIA8kJ1XUAvzF2peQzVbWZIjxJdPWV/dSksMF0KgFZ0NpiaXtSUUIp2BYNBud28TQAAuS0cieid\nJRv06udrGKrbwqHD++iCw/eR1+MyXQqAdnQ0mJoOpBKhFHGEw/yPGQCQ2xiq27oCt1PnHTZak0b0\nZQ9SIEskG0wzIZBKhFIAAJCnGKrbtn5lPl1+7Hj1LWNBIyDbJBpMMyWQSoRSAACQZxiq274jR/XT\ntyaNUoGb4bpAtooXTDMpkEqEUgAAkEeWb67Sc3NWMFS3FYUely44Yh8dMqyP6VIA2KCtYNryselA\nKhFKAQBAHqj2N+iFuV9q3hqG6rZmYPeuuvy48epVUmy6FAA2ai+YSpkRSCVCKQAAyGHhSERvL9mg\naQzVbdNxYwfozENGyuNymi4FQAq0DKZRmRJIJUIpAADIUV9sqtJzHy1XZXWd6VIyUlGBWxcfNVYT\nBvc0XQqAPEcoBQAAOaXa36D/zF2pT9dsNV1Kxhras0SXHTde3bsUmS4FQIq1XNQoKtF9TNOBUAoA\nAHJCKBzR20s36DWG6rZryr6D9c2DhsnlZLgukOtaW2VXSnwf03QhlAIAgKxmWZaWbazSv+euYKhu\nO7oUevTdo8Zq/MAepksBkAbxtn3JpGBKKAUAAFkpYlmav26bZixYq/U7dpsuJ6ON7FOqS48dr9Ji\nr+lSAKRBvEAabx/TdCOUAgCArBIKRzR3VaXeWLROW3bRM9oeh6STJwzVyROGyul0mC4HQBrEC6RR\nmRRMCaVol8vlMl0CAACSpEAwrA9WbNTMxeu10x8wXU7GKykq0CXHjNM+/cpNlwIgTRINpFGZEkwJ\npWiXx+NRKBQyXQYAII/5A0G9u6xCby/ZIH8gaLqcrDCmf7m+d/Q4lRQVmC4FQJokG0ijMiGYEkoR\nV2Njo/x+v/FVuQAA+WVXXUAzF6/X+19sZDXdBDkdDn3zwGE6Yb/BcjoYrgvki44G0ijTwZRQioSY\nnvwMAMgf22rq9Oai9ZqzcpNCEct0OVmjzOfVZceO1/DepaZLAZBGnQ2kUSaDKaEU7QoGvx4mRTAF\nAKRSRdVuzVi4Tp+u2SKLLJqU/Qb10MVHjZXP6zFdCoA0siuQRpkKpoRStCscDqug4Ov5KARTAIDd\nVm2p1vSFa7V4ww7TpWQdl9Ohsw4ZoePGDpSD4bpAXgmFQrYG0igTwZRQirjcbrdKSkqMr8oFAMgd\nlmVpScUOzVi4Tl9uqTZdTlbq0bVIlx83XoN7lJguBUCahUIhBQIBeb179h62K5BGpTuYEkqRENOT\nnwEAuSESsfTZ2q2asXCtKqpqTZeTtQ4a2ksXHjFGRQXcygH5qOkUO7sDaVQ67//5TYaEEUwBAB0V\nDEf08Zeb9caiddpWU2+6nKzldjn17YkjdeTo/gzXBZCyQBrV1v2/x2Pv/HVCKZJCMAUAJKMhGNLs\n5Zs0c/F67aoLmC4nq/X5/+3deXxU9b3/8fdsmSSTZZIACfuODQgqVgRbCwIKLqhFrbWbdWnxtrVX\nvbbuVq1trctDq219XPcidbntrVVvXWr7w4U+wBWxKohGdpKwhOzJrN/fH3GOScieM3Mmyev5ePDI\nTM7J9/v9MGfOnPecLT9bFxx3qMYU5jo9FABpwO/3p2QbvKPt/5ycHFv7IJSi1wimAIDu1DdH9PKH\nO/Tyxh1qCEWdHs6AN3fKSJ09b5oyfWy6AbB/T2V32m//19bW2joG1mzoE4IpAKAjBxqa9c/3t2vN\nR7sVisacHs6A5/d6dPa8QzRv6kinhwIgjXi93jbnlaZC++3/xEWW7EAoRZ91FkwBAENPZU2j/v7v\nbXr9k3LF4txk1A5fnFSsM46aomAg0+mhAICkz7f/q6qqbG2XUIp+IZgCwNBljNGne2r0/z7YofVb\n94goao+RwYDOnjdNh4wsdHooAHCQQCCgaDSq2tpa29oklKLf2gfTUCikrKwsB0cEAEimiuoGvVFW\noTc/rdS+Oq6kaxe/16NTjpio42aMlcftdno4ANCprKwshUL2XbyOUApbtA+mqT7GHQCQXDWNIb21\npVJvllVo2746p4cz6HxxUrHOmDNVwWz7ztECgGSyc3ufUArbJIJpZWWlwyMBANihORLVhm179XpZ\nhTbtrpLh+FzbjQwG9PV5h2jayAKnhwIAjiGUwlaBQMDWK3EBAFIrFo9r464qvV5Wofe271U4Gnd6\nSINSps+jk4+YpOOmj+FQXQBDHqEUtvN6vSm/dxIAoO+MMdq6t1ZvlFXorS2Vqm/mFIxkOmpSsZZz\nqC4AWAilSAqvl0ULANLdnppGvVFWoTfKKrSXCxYlHYfqAkDHSA4AAAwhtU1hvb2lUm98UqGt++y7\nnD86l+nz6JQjJmkBh+oCQIcIpQAADHKhSEwbtu/VG2UV2rirSnGuWJQycyaXaPlRU5TPoboA0ClC\nKQAAg1AsHtem3Qf0RlmFNmzbq1A05vSQhpRRBQGdPZdDdQGgJwilAAAMEsYYbdtXpzfKyvXWp5Wq\n44JFKZfp8+iU2ZO0oJRDdQGgpwilAAAMcHtrExcsqtSe2kanhzNkcaguAPQNoRQAgAGo7rMLFr35\naaU+3VPj9HCGtNEFOTp73jRNLeFQXQDoC0IpAAADQCwe1/Z9ddpccUAflR/QR7sPcMEih2X6PFo2\ne5Lmc6guAPQLoRQAgDQUi8e1Y3+dPio/oM3l1SqrrOZiRWnk6Mkl+iqH6gKALQilAACkgUQI3Vxe\nrc0VB/RJBSE0HXGoLgDYj1AKAIADWkJovTaXH9DmigMqq6xWc4QQmq44VBcAkodQCgBAClghtOKA\nPi4/oE8IoQMGh+oCQHIRSgEASAJC6MDHoboAkBqEUgAAbBCLx7Wz6vPDcT+pIIQOVC2H6k7W/NLR\nHKoLAClAKEVSRKNRp4cAAEkVjxvtqKojhA4i2RleLZg+VsdNH6OczAynhwMAQwahFLYjkAIYjBIh\n9OPyA9pcUa2PKw4QQgeJ/KwMLT50nL78hdHK9LFpBACpxpoXtmpoaFAoFJLXy6IFYGAyxqi2Kaw9\ntY2qrGnUntpGlR9o4JzQQWh4bpZOmDVeR08ZKZ+Hw3QBwCkkB9imoaFB1dXVTg8DAHqkKRz9PHjW\nNKqytuXnntpGwucgN6YwR0tmTdDsCSPkdrucHg4ADHmEUtiifSD1+XzODQYAPhOJxbWvtlGVtU0H\nBc/aprDTw0OKTS7O19JZEzRjTJFcLsIoAPSHndv7hFL0W/tA6vf7OXwXQMrE40YHGpqtwGn9rGlU\nVUOzjHF6hHDaoWOKtGTWBE0pCTo9FAAYFJqamuT323fvZpID+qV9IA0Gg6qvr3duQAAGJWOM6poj\n1l7OylY/99Y1KRqLOz1EpBmXSzpyQrGWzBqvMUW5Tg8HAAaNhoYG1dbW2tomoRR91lEgDQQChFIA\nvWKMUTgaV3MkqlA0psZQVHvrEofZNlkBtCnMlb3RPa/bpblTR+r4meM1Ii/b6eEAwKCSrGvIEErR\nJ50FUgCDXyweVygSUygaU3MkplAk1hIord9FP/tdy7RQJNryuP20aMu0UCQmjrBFf/m9Hh37hdFa\ndOg4BbPtO6QMANKRE7dgbL/9HwqFbGubUIpuhaMxHWho/vx5c5OaGuqs51mBXIXlUfizeULRmEwo\noj21jXqjrCLl4013W8I1XU5/o4b/M6ROImA2J4JlNBEk2wbI1tMiHCqLNBLw+3Tc9DFaMH2sAn4u\nsgdgaIhEIikNpu0DaV5ennbv3m1b+4RSdKs+FFHN/pYQ6o5F5Il9/q1IzONXbUNUahVSvZGITNio\nIlylN175IOXjTXd7irp+A3+yn/8zAOhOQcCvRYeO05enjZbf53F6OACQcqFQSA0NDUk/WrGjIyTt\nvtMGoRQ91lEgjXv4VhoAkDoj8rK1ZNZ4zZlcIq/H7fRwAMBRibCYrGDa2Sl74bC9t1UjlKJHCKQA\nACeNLcrV0lnjdfj4EXK7uccogKGt9Z7KZAXTVF5DhlCKbrmMkZtACgBwwLSSoJYcNkGlowrlchFG\nAUCSvF5vm/uE2h1MU31RU0IpuuTxeOQ2MRlXy6JCIAUApMKsccO0ZNYETRqR7/RQACAteb1e5eTk\nWOHRrmDqxF02CKXoUutDAwikAIBkcrtc+uKkYp0wa7xGF+Q4PRwASHuJsGhXMHXqto+EUvQIgXRg\naY40avO+95Tty9G4gqnK8HDPPgDpy+tx65ipI3X8zPEalpvl9HAAYECxK5g6FUglQil6IO7yEEgH\nmM37NuijPRvkkvR+xesaG5yiiYWlKswe4fTQAMCSk+nTMVNHaeGMscrP5sszAOir/gZTJwOpRChF\nNyKRiAwXlhhwQtFmReMRNYbrlOH1q2zfB9patUkFWcM1qWi6xgQny+vmiwYAqZfhdeuwccM1Z3KJ\nSkcXyuPmti4AYIe+BlOnA6lEKFVDQ4N2794tv9+vkSNH2n4jWDv7SdVYW4vFYknvA8ljZBSJhRWK\nNsvr9ioUC6mqaa82lK/V+IJpmlQ4XXmZBU4PE8Ag53JJpaMKNWdyiQ4bP1yZviG/+QEASdHbYJoO\ngVSShuzXky+//LIWLlyovLw8TZs2TePHj1dhYaHOPfdc7dq1K636SdVYMTjl+QuUk5Enl8utxnCd\napr3q7b5gDbvfU8vbf4fvVL2rHZUlykW5wsIAPYaPyxPZx09Vb86+8u6eMkROnrKSAIpACRZIBBQ\nMBi0nldXV6uhoeGg+dIlkEpDdE/p3XffrUsuuUTGGHk8Hk2ePFkNDQ2qqKjQypUr9eyzz+qf//yn\njjjiCMf7SdVYMbj5PBnyeTIUNzGFoiGFYs0KRZvkdfsUjoW1t2G3Mr1ZmlD4BU0sLFUgI9fpIQMY\noIblZmnO5GIdNblEJfnObNwAwFDX3R7TdAqk0hDcU7p69WpdeumlMsbo2GOPVVlZmT755BOVl5fr\npZde0vDhw3XgwAGdcsopqqurc7SfVI0VQ4fb5VGWL1v5/gIreDaEa1XbXKXqpv3aWPm2Xtj0uP61\n5XmV126TMXGHRwxgIAj4fZpfOkY/OeWLuunMeVo2ezKBFAAc1tke03QLpNIQ3FN6+eWXKx6Pa+TI\nkXr22WeVn//5TbkXL16sJ554QosWLdLu3bt1++2368Ybb3Ssn1SNFenDGKN/bX1BlXU7+t1OV1wu\nlzI8fmV4/IrFY9ae0+Zoo3yeDO2oDqu8brsCGbmaWFiqCQWHKNOX3a8xARhcfJ7PL1g0fQwXLAKA\ndNTZHtOEdAik0hDbU7p+/Xq98847kqQf//jHbUJewsKFC3XMMcdIkh566KFuN+6T1U+qxor0Ulff\noIrabWoI16o+VNPnfw3hWoWiTXKp+ysne9weZfsCys8sVLYvR8bEVR+uUW3zAVU17tG/y1/Xc5v+\nqNe3/0N763eznAFDWOKCReceO123fuNYXXDcoZo5bhiBFADSWPs9pgnpEkilIRZKn332Wevxqaee\n2ul8iWk7d+7Uu+++60g/qRor0ksi8IVjYcXiURlj+vzP5XIpOyNHrh7e0sflcsnvzVSuP6hcf1A+\nt0/NkUbVNO9XfahGW6s+0iufPquXNv9Jn+z7t8KxUDL/KwCkkbFFuTpzTssFi3689AjNncoFiwAA\n9hlSnyiJ0BYIBFRaWtrpfEcddVSbv+ntRYTs6CdVY0X6yvBmKtOb5UjfXrdX3owcZZmAwrFmhaLN\nqgtVy+P2KhxtVm1zld6veENjg1M0sbBUhdkjHBkngOQpysnUUZNLNGdyiUYG0+ObdABA77U/hzSh\np/cxTYUhFUo//vhjSdL48eO73Hs0YcIE6/HmzZsd6SdVYwW60rL3NEsZnkzFTFShaLMaI/VqijQo\nw+tX2b4PtLVqkwqyhmtS0XSNCU6W1538++cCSI6A36vZE4t19OQSTRqR3+MjLQAA6amjixpJPb+P\naaoMqVB64MABSVJRUVGX8xUWFlqPO/pWIRX9pGKsiT460pe6MXi5XC55XT55M3wte0+jzZ9dHKlZ\nXrdXoVhIVU17taF8raYOm6nSEbPlcg2pswOAAcvncWvm2GGaM6VEM0YXyevhvQsAg0F3V9lNp2A6\npEJpfX29JCkzM7PL+bKyPj9kMvE3qe4nFWNtHWiBnnK73Mr0ZcvvzVI0HlFjpF6N4To1ueqV6c3W\nh5Vva1hgpEbkjHZ6qAA64ZI0bWSB5kwu0RETRigrY0htDgDAoNddIO3uPqapNqQ+hbzelnJjsViX\n87WenvibVPeTqrECfRE3cYWizQrHmhU3cXndPvm9WfJ5MpSTka88f4HTQwTQgTGFOZozuURHTSpW\nMND1l54AgIGpp/chTadgOqRSTE5Ojvbv36/GxsYu52s9PScnx5F+UjHWqqqqTqdVV1dr0qRJ8ng8\nvWoTg5cxRtF4RKFYsyKxsFxyKcObKb83Ux6XR0WBkZpcNF2j8ibI4x5SqxYgbbkkjSnK1fTRhZoz\nuUSjCnr/mQYAGDh6GkgT0iWYDqktx5EjR2rbtm0qLy/vcr7du3dbj0tKShzpJxVjLSjofm+Wz+eT\ni/tSDmlxE1c4FlIo2qS4icvj9io7I1cZHr987gyNK5iqSUXTlZ/J4eCA0xIhdFpJgaaNDGpKcVDZ\nfi4+BgBDQW8DaUI6BNMhFUpLS0u1bt067dy5U01NTW3Ox2wtceVbSZo+fboj/aRqrD3hNjG5YxHF\nPWzYpFIsHu3XvUBdcsnr9vXp6pnGGOtquy1jcCnD45ffmymv26v8rGGaXNhytV2fJ6PPYwTQP5+H\n0KCmjSzQ5OKgAoRQABhy+hpIE5wOpkMqlB5zzDF6+OGHFYvFtHbtWi1cuLDD+dasWSOp5Yqjc+fO\ndaSfVI21pzyfhSOCaeqEY6F+hVJJ8rg8yvUHexxMjTHWXtGYicnj8ijLlyO/xy+P26uxwSmaVDRd\nBVnDuVUE4IDWIXTqyAJNIYQCwJDX30Ca4GQwHVKh9LTTTtNFF12kWCymlStXdhj0IpGInnjiCUnS\nvHnzNHLkSEf6SdVYuxOJRKzHBNPky87KktvtteVQ2EgsrIZwbY/mjcU/3ytqZJTh8SvbmyuP26tc\nf1CTCks1vmCaMrxcGAVIJZek0YU5mjayQNMIoQCAduwKpAlOBdMhFUqHDx+u8847Tw888IBWrVql\nCy64QMcee2ybea6//npVVFRIkq644ooO29m0aZOam5vl8Xg0c+bMpPRj11j7KxaLKe7yKLFPjGCa\nXD6fV0umfU37Gir61c6nVR+qvHZ7l/MYYxSJhRSKNSsaj1q3esnwZMrj9mhU3gRNKpqu4YFR7BUF\nUqRNCC0p0JQSQigAoGPRaNTWQJrgRDAdUqFUkm6++Wa9+OKL2rFjh0488URdddVVWrBggRoaGvTo\no49q1apVkqTly5fr1FNP7bCN008/XR999JHy8/PbLAh292NHG3YwLpfiHr8VSAmmyZWdkatxGbn9\naqOyfmen02LxmMKxZoWizTIy1m1cvB6fsn05mlhYqgmFhyjL59wNlIGhZExhjqaWFOiQkYRQAEDP\nRKNRhUIh+f1+SfYF0oRUB9MhF0qLi4v1wgsv6Mwzz9TGjRt17bXXHjTPWWedpT/84Q+O95OqsfZE\nIoASTAcmY4wi8bDC0WZF4hG5XG75vVnyezPldnlUnDtGk4qmqyR3nNwut9PDBQa1RAhNHI6bk8l6\nFADQO61PsbM7kCakMpgOuVAqtVyl9p133tGTTz6p5557Tjt27JDf79e0adN09tlnd3pRoYTS0lJl\nZmYqN7frvVn97ceuNuxCMB2YmqONCsdCipu4vG6fAhl58nky5PdmakLBFzSxsFQ5/jynhwkMWoRQ\nAECyJCuQJnQWTH0+ez/LXMZwE0oc7MCBAyosLNSKFSs0bPQ4xTI+v+G6OxaxAqkkxTz+NsHUG2mU\ncXsVychVbeGUlI57INhTtK7L6SP29/8qym/uWK1P9r2vhnCtXHIpw5spvzdTHpdHRYGRmlw0XaPy\nJsjjHpLfSwFJNbogR9NGBj87J7SAEAoAsNXf/vY3ffzxx4pGoxo9enRK+mx/QaWcnBxt2rRJv/nN\nb1RVVaWCgoJ+tc8WKXqNPabpz+1yy+1yKzsjVxkev3zuDI0rmKpJRdNtubIvgBb52X4V52W1XJyI\nEAoASBG791R2p/0e09raWlvHQChFnxBM09vY4BTtrtmq7IxcTSos1ZjgZPk8GU4PCxiQsjK8Ks7L\n1oj8bBXnZ2tEXsvP4XlZyvTxMQoASD2v19vmvNJUaB9MExdZsgOfpuizzoIpnDciZ7SWzTjX6WEA\nA4bX49aIvCwrcLb+mZPp47ZIAADo82BaVVVla7uEUvRLx8HUyLBoAUgzLpc0LCdLIz4LmyPyslqC\nZ362CgKZchM8AQDoViAQUDQaVW1trW1tkhzQb+2DqSsel9xxJ4cEYAjLz8poOdT2s0NuE4+LcrPk\n83DLIwAA+isrK0uhkH1HSRJKYYuDgqkhlAJInkyfR8X5gc8Osc1qE0I5zxMAgOSz85xWPrlhm0Qw\n9cYbHR4JgIHI5ZL8Xo8yfV75fR5l+jzy+7zKzvC2OcdzRH62cjnPEwCAQYNQClvFPT6ZGIfHAUOB\n1+2Sv3WAbBUoW37n/ex3rZ63m6/1NJ/HTdAEAGAIIpSiWz63W1mZPb+dSCzuk8vtUXYgU6NGcU/M\n9tyZgS6nT/Pzf4bU8bhdVqD0+7xtA6T3872V7QOk3+uRl/MzAQCADQil6FZuVoZGjsjv8fyVlc3K\nysrSmDHDdPLSI5I4soHpL/u2dDl9+TD+zwAAADB08DU3AAAAAMAxhFIAAAAAgGMIpQAAAAAAxxBK\nAQAAAACOIZQCAAAAABxDKAUAAAAAOIZQCgAAAABwDKEUAAAAAOAYQikAAAAAwDGEUgAAAACAYwil\nAAAAAADHEEoBAAAAAI4hlAIAAAAAHEMoRVJEo1GnhwAAAABgAPA6PQAMPgRSAAAAAD3FnlLYqqGh\nQaFQyOlhAAAAABggCKWwTUNDg6qrq50eBgAAAIABhFAKW7QPpD6fz7nBAAAAAEgqO7f3CaXot/aB\n1O/3y+vldGUAAABgMGpqapLf77etPUIp+qV9IA0GgwRSAAAAYJBqaGhQbW2trW0SStFnHQXSQCDg\n3IAAAAAAJE2yriFDKEWfEEgBAAAAZzhxC8b22/923nGDUIpeI5ACAAAAzolEIikNpu23//Py8hSJ\nRGxrn1CKXiGQAgAAAM4LhUJqaGhIej8dbf9nZWXZ2gehFD1GIAUAAADSR3V1dVKDaaq2/wml6BEC\nKQAAAJAeWt8jNFnBNJXb/4RSdCsajRJIAQAAgDTh9Xrb3CfU7mCa6h1ShFJ0yePxKBwOW88JpAAA\nAIDzvF6vgsGg9dyuYOrEEZKEUnSp9aEBBFIAAAAgfQQCAVuDqVOn7BFK0SMEUgAAACD92BVMnbyG\nDKEU3crIyCCQAgAAAGmqv8HU6YuaEkrRpUgkIq/X6/QwAAAAAHShr8HU6UAqEUrRjVgs5vQQAAAA\nAPRAb4NpOgRSiVAKAAAAAINGT4NpugRSiVAKAAAAAINKd8E0nQKpJHGyIAAAAAAMMomQmQifrUNo\nOgVSiVAKAAAAAINSV8FUSo9AKnH4LgAAAAAMWu0P5U1Il0AqEUoBAAAAAA4ilAIAAADAINX+okYJ\nPb2PaSoQSgEAAABgEOroKru9uY9pqnChIwAAAAAYZLq77Uv7ix85eX4pe0oBAAAAYBDpLpB2dx/T\nVCOUAgAAAMAg0V0gTUinYEooRZc8Ho/TQwAAAADQAz0NpAnpEkwJpeiSz+dTNBp1ehgAAAAAutDb\nQJqQDsGUUIpuhcPhtLgqFwAAAICD9TWQJjgdTAml6BGnT34GAAAAcLD+BtIEJ4MpoRRdikQi1mOC\nKQAAAJA+7AqkCU4FU0IpuhSLxZSRkWE9J5gCAAAAzotGo7YG0gQngimhFN3yer2On/wMAAAAoEU0\nGlUoFLKe2xVIE1IdTAml6BGnT34GAAAA0KL1KXZ2B9KEVG7/e5PSKgalxMKeOEwg8TMZbwIAAAAA\nXUtWIE3obPvf5/PZ2g97StEr7DEFAAAAnOf3+1Oyc6ij7f+mpiZb+2BPKXqNPaYAAACAc+zeU9md\n9tv/tbW1to6BPaXoE/aYAgAAAM7welO/b7H99r/f77etbfaUos8622MKAAAAYPBJbP9XVVXZ2i57\nStEvHe0xjUajzg0IAAAAQNIEAgHl5eXZ2iahFP3WPpiGQiGCKQAAADBIZWVltblPan9x+C5s0f5Q\n3tb3TkJby4ctd3oIAAAAQL/Yub3PnlLYpv0eUwAAAADoDqEUtgoEArZeiQsAAADA4EYohe28Xm/K\n750EAAAAYGAilCIpnLh3EgAAAICBh1AKAAAAAHAMoRQAAAAA4BhCKQAAAADAMYRSAAAAAIBjCKUA\nAAAAAMcQSgEAAAAAjiGUAgAAAAAcQygFAAAAADjG6/QAkN7y8vLU3NysysrKHv9NU1OTwuGwYrGY\n/va3vyVxdAAAAMDQsm3bNtXX1ysWi/VqG91O8XhcWVlZtrVHKEWXvF6vYrGYmpube/w30WhU8Xhc\n9fX12rlzp/W7SCRizePz+eT1erudNlhQP/VTP/UnUD/1Uz/1dzdtsKD+5NRfX1+vSCSieDzeq210\nO8XjcVtfr8H1ysN2LpdL8XhcsVisx39jjLH+pqmpSdFoVKFQyJru9/slqc0bsaN5BsuKifqpn/qp\nP4H6qV+ifuqn/q7mof7u64/FYorH4zLG9Gob3U7GGLnd9p0JOjhedSSVy+Wy3kg9EQqF5PF4lJmZ\nqZycHFVXV1t/HwwGFQgEOvy7hoYGVVdXW89zcnI6nXegSNRE/dQvUT/1Uz/1U39X8yZQP/VTf9f1\nV1ZWqrm5WbFYrFfb6HaKx+MKh8O2tceFjpA0kUikzZusqzekJAUCAQWDQet5dXW1GhoakjjC5Gq/\nkqF+6qd+6u8M9VM/9Qet59RP/dQ/cOvvK0IpkiIajbb59qS7N2TCYHlj9naFlED91E/9Qes59VM/\n9VP/QEP91D+U6+8PQilsF4/HFY/Hrec9fUMmDPQ3Zl9XSAnUT/3UH7SeUz/1Uz/1DxTUT/1Duf7+\nIpTCVuFwuF+BNGGgvjH7u0JKoH7qp/6g9Zz6qZ/6qT/dUT/1D+X67UAohW3C4XCbK4plZGT060T1\ngfbGtGuFlED91E/9Qes59VM/9VN/uqJ+6h/K9duFUApbtA+kbrdbPp+v3+0OlDem3SukBOqnfuoP\nWs+pn/qpn/rTDfVT/1Cu3+Vy2dYWoRT91lEgtfMeU+n+xkzWCimB+qmf+oPWc+qnfuqn/nRB/dQ/\nlOuPRCK2hlLuU4p+aR9IfT6fjDG295N4kyfe/Imfdr75+yLZK6QE6qd+ifqpv7rNT+qnfidRP/VT\nf7X1PNX1Oy0cDisajdraJntK0WcdBdKMjIyk9Zdu3xilaoWUQP3UT/1B6zn1Uz/1U79TqJ/6na6/\n9fZ3qrXf/rcLe0rRJ6kOpAnp8o1ZqldICdRP/RL1U391m5/UT/2pRP3UT/3V1nOn6g+HwzLG2Hr4\nbE+03/638+hI9pSi15wKpAlOf2Pm1Aopgfqpn/qD1nPqp37qp/5UoX7qT6f64/F4m9swJlv77X+v\n10sohXOcDqQJTq2YnF4hJVA/9VN/0HpO/dRP/dSfbNRP/elYfzweVzgcTnq/HW3/23GXjdYIpeix\ndAmkCaleMaXLCimB+qmf+oPWc+qnfuqn/mShfupPt/pbb39HIpGkBtNUbf8TStEj6RZIE1K1Ykq3\nFVIC9VM/9Qet59RP/dRP/XajfupPx/p9Pp/c7s9jXLKCaSq3/wml6JYxJi0DaUKyV0zpukJKoH7q\np/6g9Zz6qZ/6qd8u1E/96Vy/1+tNajBN9Q4pQim61Hphl9IvkCYka8WU7iukBOqnfuoPWs+pn/qp\nn/r7i/qpfyDU73a725zbaVcwdeIISUIputT6UtPpGkgT7F4xDZQVUgL1Uz/1B63n1E/91E/9fUX9\n1D+Q6s/IyLA1mDp1yh6hFD2S7oE0wa4V00BbISVQP/VTf9B6Tv3UT/3U31vUT/0DsX67gqmT15Ah\nlKJHBkIgTejvimmgrpASqJ/6qT9oPad+6qd+6u8p6qf+gVx/f4Op0xc1JZSiS8aYNofwDhR9XTEN\n9BVSAvVTP/UHrefUT/3UT/3doX7qHwz19zWYOh1IJUIpuhGPx50eQp/1dsU0WFZICdRP/dQftJ5T\nP/VTP/V3hvqpfzDV39tgmg6BVCKUYpDr6YppsK2QEqif+qk/aD2nfuqnfupvj/qpfzDW39Ngmi6B\nVCKUYgjobsU0WFdICdRP/dQftJ5TP/VTP/UnUD/1D+b6uwum6RRIJcnrWM9ACiVWMomVT+uV0GBe\nISVQP/VL1E/91W1+tn9M/dRP/dQ/2Az1+hMhMxE+W4fQdAqkEqEUQ0hXKyZp8K6QEqif+iXqp/7q\nNj8TqJ/6qZ/6B6uhXn9XwVRKj0Aqcfguhpj2h3IkDPYVUgL1Uz/1Bw/6PfVTP/VT/2BH/UO7/vaH\n8iakSyCVCKUAAAAAAAcRSjGktD+pPaH9ye+DFfVTP/VXH/R76qd+6qf+wY76h3b97S9qlNDT+5im\nAqEUQ0ZHV1nr6qpsgw31Uz/1V1vPqZ/6qT9oPad+6qf+wVt/R1fZ7c19TFOFCx2hQ/F4vMvnXTHG\nyBijeDyeFgu5JDU1Nam2ttZ6npeXZ70hc3JyrGlVVVWKRqPKyspyZJzJQv3UT/3Un0D91E/91C9R\n/0CuPx6Pt9ne7kwkElE0GrWee71eeb0t8c8YY02LRCIyxnR43mlnjDFtxtNfLtO6ReAzW7Zs0aRJ\nk3T11VfL6/WqN4uJy+VSPB5XLBZTY2NjEkfZMz6fT36/33oeCoU6vPJYd/MMVNRP/dRP/QnUT/3U\nT/29nWegGsz1Z2dny+PxyO12d7qN7nK55HK5rOeJENvbebricrl000036dNPP9XEiRN7WUVb7ClF\nl375y186PQQAAAAAgxh7StGhaDSqHTt2SGo51MHt5vRju1VXV2vSpEmSpE8//bTDS5UDycLyByex\n/MFpLINw0mBY/uLxuHX489ixY63DgvuKPaXokNfr7fduePRcMBhUQUGB08PAEMXyByex/MFpLINw\n0kBe/oqKimxri91fAAAAAADHEEoBAAAAAI4hlAIAAAAAHEMoBQAAAAA4hlAKAAAAAHAMoRQAAAAA\n4BjuUwoAAAAAcAx7SgEAAAAAjiGUAgAAAAAcQygFAAAAADiGUAoAAAAAcAyhFAAAAADgGEIpAAAA\nAMAxhFIAAAAAgGMIpQAAAAAAxxBKAQAAAACO8To9AGCg2Ldvn1555RVt2rRJ5eXlqqqqUm5urqZP\nn66lS5fqkEMOsbW/qqoq/fnPf9aGDRtUU1Oj4uJiLViwQEuXLpXP50tZG3BONBrV3//+d61evVrl\n5eXKy8vTzJkzdeaZZ2r48OFp1U+qxorU2Lhxo9asWaPt27ervLxczc3NGj58uL74xS9q2bJlysvL\ns7W/999/X0899ZS2bNkiSZowYYK++tWvaubMmSltA+krFArpoosuUl1dnSRp8eLFuuiii2xpe//+\n/frzn/+s9957TzU1NSopKdFxxx2nJUuWyOvt2aayHW0g/RhjtG7dOr344ovauXOnYrGYxo8frzlz\n5uj444+3ZVsqGo3qxRdf1OrVq1VRUaH8/HzNmjVLZ555poqKilLWhuMMgC7t2bPHHHroocblchlJ\nHf5zuVzmrLPOMnv37rWlz0ceecTk5uZ22Fdpaal59913U9IGnPP++++bmTNndvj6BQIBc99996VN\nP6kaK5LviSeeMMXFxZ2u6ySZnJwcc8cdd9jSX0NDg/nud7/baV/f/va3TX19fdLbQPq76qqr2ryu\nF1xwgS3tPvjggyYnJ6fDZWfGjBnmvffeS0kbSD/r1q0zhx9+eKfrlqKiIvPWW2/1q4/33nvPzJgx\no9N17YMPPpiSNtIBoRToxpYtW6w3dzAYNOecc465++67zWOPPWbuuOMOM2fOnDYfPtXV1f3qb+XK\nlVZ7U6ZMMb/+9a/NypUrzWWXXWZ96A0bNsx8/PHHSW0DztmyZYsVDAKBgLnkkkvMypUrzW233WYO\nOeQQ67W9//77He8nVWNFavzqV7+yXrNDDjnEXHbZZeb+++83q1atMldccYUZMWKENf3yyy/vV1+x\nWMwsW7bMau+UU04x9913n7n//vvb/P6kk04y0Wg0aW0g/b399tvG6/WazMxMW0PpQw89ZLU3bdo0\nc9ttt5mVK1eaSy+91AQCASPJDB8+3JSVlSW1DaSfZ5991mRkZBhJJj8/36xYscLcd9995rHHHjO3\n3367Oe2000xGRoZ5/vnn+9xHWVmZGT58uBUeL7vsMrNy5Upz6623mqlTp1rL1UMPPZTUNtIFoRTo\nxpYtW8yIESPMPffc0+G37fF43PzkJz+x3viXXHJJn/vas2ePtXfzi1/8oqmtrW0zff369SY7O9tI\nMosXL05aG3DWySefbCSZzMxM8+abb7aZVl9fb+bNm2ckmezsbLN7925H+0nVWJEav/rVr8wxxxxj\n/vGPf3Q4fe/evW32HKxdu7bPfT388MNWOz/72c8Omn7TTTdZ0x944IGktYH0Fg6HzaxZs4wk84tf\n/MK2UFpRUWGFxqOPPvqgz/e3337bZGVlGUlm6dKlSWsD6WfTpk3WFyDz5s0zFRUVHc63f/9+s2/f\nvj73s2TJEiPJZGVlmXfeeafNtLq6OmunRyAQ6HQMdrSRLgilQDcaGxtNTU1Nl/PEYjEzbdo0aw9k\nLBbrU1/XXHON9YHbfuWScOONN1rzrFu3LiltwDnr16+3Xpurr766w3k++OCDfu+tsqOfVI0VqdOT\njZY333zTek1/8IMf9LmvKVOmGKnldIKO1pmxWMw6JG3ChAlJawPp7YYbbjCSzFFHHWVqampsC6VX\nXHGF1daGDRs6nOe6666z5unoME072kD6mT9/vrWHe//+/Unpo/V69Prrr+9wng0bNljzXHnllUlp\nI50QSgGbrFixwnrj9/XbqMThjrNnz+50np07d1r9/Nd//VdS2oBzrr76auu1+fTTTzudL7EHsq8b\n2nb0k6qxIv0Eg0EjySxYsKBPf//OO+9Yy86tt97a6Xx33HGHNV/7PfF2tIH09t577xmfz2e8Xq95\n9913TV1dnW2hdPLkyUaSmTNnTqfzbNu2zerviiuuSEobSC+tv2y97bbbktbPT3/6U6uf7du3dzrf\nUUcdZSSZyZMnJ6WNdMItYQCbRCIR67Exptd/X1lZqY8++kiStGDBgk7nGz16tKZOnSpJeuWVV2xv\nA8569dVXJUnjxo3TxIkTO50v8fpu3bpV27Ztc6SfVI0V6SccDkvq27pO+nzZkbpeV7We1n5dZUcb\nSF+xWEznnXeeIpGILrvsMh122GG2tb1r1y6VlZVJ6nrZab1ua7/s2NEG0s/jjz9uPf7GN76RtH4S\n669JkyZp7Nixnc6XWLbKysq0a9cu29tIJ4RSwAbGGK1evVqSVFhYqBEjRvS6jQ8++MB6XFpa2uW8\nX/jCFyS13Lah9UahHW3AWR9++KGknr9+rf8m1f2kaqxIL+vWrVNjY6Ok7l/7zrReV7VePtrratmx\now2kr1tvvVVvv/22Jk6cqJ/97Ge2tt3Xz0q720D6WbdunSRp4sSJGjVqlKqqqnTvvfdqxYoV+ta3\nvqXLL79cTz/9tKLRaL/64TP4YNw4CbDBypUrrXvjnX322XK7e/99T+tvr0aPHt3lvInpDQ0Nqq6u\nVkFBgW1twDlNTU2qqqqS1PPXT5J27tyZ8n5SNVaknxtvvNF6fM455/SpjcS6Ki8vT7m5uZ3Ol52d\nrWAwqOrq6oOWHTvaQHrauHGjtZzde++9ys7OtrX9vnxW1tTUqK6uzlrW7GgD6SfxxcG0adP0P//z\nP1qxYoWqq6vbzHPHHXdo4sSJWrVqlY455phe91FXV6fa2lpJff/8tKONdMOeUqCftmzZossuu0yS\nlJ+fr2uvvbZP7SRuCC5JgUCgy3lbT6+vr7e1DTgnVa8fyxr66r777tMLL7wgSTrllFP0la98pU/t\nJJaf7pad1vO0X3bsaAPpJx6P6/zzz1coFNI3vvENLVmyxPY+WAeiM4kAunnzZn3jG99QQ0ODLrjg\nAj3yyCN66KGHdO6558rr9WrLli1atGiR3nrrrV73wfLXMfaUAv1QX1+v008/3dpjdN9992nUqFF9\naitxjpYkeb1dvzUzMjKsx6FQyNY24JxUvX4sa+iLNWvW6OKLL5YkFRcX68EHH+xzW4nlp7tlR/p8\n+Wm/7NjRBtLPXXfdpXXr1qmwsFB33nlnUvpgHYiORKNR6/ogW7ZsUUZGhl566aU2X76dd955+ta3\nvqUTTzxRzc3NuvDCC7V+/Xq5XK4e98Py1zH2lAJ9FA6H9dWvflXvvfeeJOm6667T1772tT631/rw\npO5WGk1NTdbj1t+A2dEGnJOq149lDb31/vvv69RTT1U4HFZOTo6eeeaZPp07n5BYfnqygZRYftov\nO3a0gfTyySefWEcb3Xbbbf1axrrCOhAd8Xq98vl81vMf/ehHHR4NsnjxYn3ve9+TJG3YsEGvv/56\nr/ph+esYe0oxZJ1//vnW8fjt3X777ZowYUKnfxuLxfT1r39d//jHPyRJF198sW666aZ+jSc/P996\n3P78hfZqamqsx3l5eba2Aefk5ubK5XLJGJPU18+OflI1VjivrKxMJ5xwgg4cOCC/36+nn35ac+bM\n6VebiXVVTU2NjDFd7mVILD/tlx072kB6+f73v6+mpibNnz9f5513XtL64fMWncnPz9e+ffskScuX\nL+90vuXLl+vee++VJK1du1Zz5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ppr+tXH/PnzJUmvv/56j//mX//6V4e/D4VCev755/s1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\nAAAAADjm/wNNWQ+wMcofXwAAAABJRU5ErkJggg==\n"
          }
        }
      ],
      "source": [
        "buffer = 0.6 * lda0  # buffer spacing\n",
        "\n",
        "# define a simulation domain box\n",
        "sim_box = td.Box.from_bounds(\n",
        "    rmin=(-l_straight - buffer, -w / 2 - buffer, -w / 2 - buffer),\n",
        "    rmax=(r + w / 2 + buffer, r + l_straight + buffer, w / 2 + buffer),\n",
        ")\n",
        "\n",
        "# define a simulation\n",
        "sim = td.Simulation(\n",
        "    center=sim_box.center,\n",
        "    size=sim_box.size,\n",
        "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=30),\n",
        "    run_time=5e-13,\n",
        "    structures=[bend],\n",
        "    sources=[mode_source],\n",
        "    monitors=[mode_monitor],\n",
        "    symmetry=(0, 0, 1),\n",
        "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
        "    medium=sio2,\n",
        ")\n",
        "\n",
        "sim.plot(z=0)\n",
        "plt.show()"
      ],
      "id": "cb153f01"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Run the simulation in the cloud."
      ],
      "id": "a7c81bef-05c5-4f6c-b30f-8a4420ded34a"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "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\">04:48:34 UTC </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'waveguide_bend'</span> with resource_id                     \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-8bf27202-144c-4c7b-baaf-1a180cdca9a6'</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-8bf27202-144c-4c7b-baaf-1a180cdca9a6\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-8bf27202-144</span></a>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-8bf27202-144c-4c7b-baaf-1a180cdca9a6\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">c-4c7b-baaf-1a180cdca9a6'</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\":\"f86fe30a1ede4f12af33101dd7917c2c\",\"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\">04:49:05 UTC </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.278</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\">04:49:06 UTC </span>status = success                                                   \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"73f905bc3a514e149bf1cf1ac661c144\",\"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\">             </span>Loading results from simulation_data.hdf5                          \n",
              "</pre>"
            ]
          }
        }
      ],
      "source": [
        "sim_data = web.run(simulation=sim, task_name=\"waveguide_bend\")"
      ],
      "id": "484f510d"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Postprocessing\n",
        "\n",
        "After the simulation is completed, we first extract the complex S21 and\n",
        "calculate the derivative of the unwrapped phase with respect to\n",
        "frequency. The final group delay is then given by the derivative divided\n",
        "by 2$\\pi$."
      ],
      "id": "23d2491c-4a15-4b9a-8706-edb60776a04b"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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KhF5bbbVVkuTKK69s/ll9fX1uvPHGJMmee+65xDVNyxqbZnwBAAAAQHtVJPQ6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\n/PJccsklSZJ99903a665ZiVKBAAAAKBAKhJ67b/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RmZkJExMT9O7d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\nREQ65/8AlNpcJkwa57wAAAAASUVORK5CYII=\n"
          }
        }
      ],
      "source": [
        "s21 = sim_data[\"s21\"].amps.sel(direction=\"+\").squeeze(drop=True).values  # extract the s-parameter\n",
        "dphi_df = np.gradient(np.unwrap(np.angle(s21)), freqs)  # calculate gradient\n",
        "group_delay_fdtd = dphi_df / (2 * np.pi)  # calculate group delay\n",
        "\n",
        "# plot group delay as a function of wavelength\n",
        "plt.plot(ldas, group_delay_fdtd * 1e12, c=\"red\", linewidth=2)\n",
        "plt.xlabel(\"Wavelength (µm)\")\n",
        "plt.ylabel(\"Group delay (ps)\")\n",
        "plt.ylim(0.16, 0.17)\n",
        "plt.show()"
      ],
      "id": "c4fc5738"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Mode Solver Approach\n",
        "\n",
        "### Group Index Calculation\n",
        "\n",
        "Next we demonstrate how to calculate the same group delay only using\n",
        "mode solving. To do so, we need to run mode solving on the same\n",
        "waveguide structure and calculate the group index of the mode. A\n",
        "convenient way to do so is by using the [waveguide\n",
        "plugin](https://www.flexcompute.com/tidy3d/examples/notebooks/WaveguidePluginDemonstration/)."
      ],
      "id": "4e644a46-6791-4cc1-95d7-c314aebbb8a5"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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5ZcydOzcaNWoU7dq1i86dO8dBBx1U4WWEdt9991i9enVERDRt2rTa8+6yyy7x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\nRU5ErkJggg==\n"
          }
        }
      ],
      "source": [
        "from tidy3d.plugins import waveguide\n",
        "\n",
        "# define the straight waveguide\n",
        "wg_straight = waveguide.RectangularDielectric(\n",
        "    wavelength=ldas,\n",
        "    core_width=w,\n",
        "    core_thickness=t,\n",
        "    core_medium=si,\n",
        "    clad_medium=sio2,\n",
        "    grid_resolution=30,\n",
        "    mode_spec=td.ModeSpec(num_modes=1, precision=\"double\", group_index_step=0.005),\n",
        ")\n",
        "\n",
        "wg_straight.plot_structures(x=0)\n",
        "plt.show()"
      ],
      "id": "455b3a50"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Run mode solving on the straight waveguide and calculate the group\n",
        "velocity."
      ],
      "id": "73769a75-bb92-4fec-8b28-e3c3f8e678b0"
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "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\">04:49:07 UTC </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'straight waveguide'</span> with resource_id                 \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'mo-85b5cc5d-2891-4848-9571-c1c9a2b5ac64'</span> and task_type            \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'MODE_SOLVER'</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=mo-85b5cc5d-2891-4848-9571-c1c9a2b5ac64\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=mo-85b5cc5d-2891-</span></a>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=mo-85b5cc5d-2891-4848-9571-c1c9a2b5ac64\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">4848-9571-c1c9a2b5ac64'</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\":\"adc01ca242644f839125c822af243966\",\"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\">04:49:09 UTC </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.109</span>. For this solver type, the        \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>estimate is the final billed cost.                                 \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\">04:49:10 UTC </span>status = success                                                   \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"d5dd023560fa4e0c992779715c86e7f4\",\"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\">04:49:31 UTC </span>Loading results from simulation_data.hdf5                          \n",
              "</pre>"
            ]
          }
        }
      ],
      "source": [
        "straight_data = web.run(simulation=wg_straight.mode_solver, task_name=\"straight waveguide\")\n",
        "vg_straight = (\n",
        "    td.C_0 / straight_data.n_group\n",
        ")  # calculate the group velocity of the straight waveguide mode"
      ],
      "id": "ca934a3a"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Similarly, we need to do so for the bend waveguide since the group\n",
        "velocity is potentially slightly different. This can be done by copying\n",
        "the straight waveguide case and updating the mode specification to\n",
        "include a bend radius."
      ],
      "id": "f9ceeeb1-61c0-4bba-9369-853e418cfc2b"
    },
    {
      "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\">04:49:32 UTC </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'bend waveguide'</span> with resource_id                     \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'mo-08f8d289-c287-4aa5-9ccb-11cd342b3668'</span> and task_type            \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'MODE_SOLVER'</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=mo-08f8d289-c287-4aa5-9ccb-11cd342b3668\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=mo-08f8d289-c287-</span></a>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=mo-08f8d289-c287-4aa5-9ccb-11cd342b3668\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">4aa5-9ccb-11cd342b3668'</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\":\"c94460110c054b3c9c7a86a626eccaac\",\"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\">04:49:34 UTC </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.109</span>. For this solver type, the        \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>estimate is the final billed cost.                                 \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\">04:49:35 UTC </span>status = success                                                   \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"f93ccb23bad642078f72b96c700b2e59\",\"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\">04:51:19 UTC </span>Loading results from simulation_data.hdf5                          \n",
              "</pre>"
            ]
          }
        }
      ],
      "source": [
        "wg_bend = wg_straight.updated_copy(\n",
        "    mode_spec=td.ModeSpec(\n",
        "        num_modes=1,\n",
        "        bend_radius=r,\n",
        "        bend_axis=1,\n",
        "        num_pml=(12, 12),\n",
        "        precision=\"double\",\n",
        "        group_index_step=0.005,\n",
        "    ),\n",
        ")\n",
        "\n",
        "bend_data = web.run(simulation=wg_bend.mode_solver, task_name=\"bend waveguide\")\n",
        "vg_bend = td.C_0 / bend_data.n_group  # calculate the group velocity of the bend waveguide mode"
      ],
      "id": "43e1040b"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Finally, the group delay can be calculated by dividing the mode\n",
        "propagation length by the group velocity. We compare the obtained group\n",
        "velocity from FDTD and mode solving. The results are very consistent as\n",
        "expected."
      ],
      "id": "95590926-b28c-421e-adc8-d6e0a53d9052"
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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kNQpYtcrl+VvUXZ9qhD20clzOV7bvlOPPJXL+u/+vRmu0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\noZycHFndNFQdN26cEhISfFghAAC1UEFB+bfmOb4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          }
        }
      ],
      "source": [
        "group_delay_mode = 2 * l_straight / vg_straight + 2 * np.pi * r / (\n",
        "    4 * vg_bend\n",
        ")  # calculate the total group delay\n",
        "\n",
        "plt.plot(ldas, group_delay_fdtd * 1e12, c=\"red\", linewidth=2, label=\"FDTD\")\n",
        "plt.plot(ldas, group_delay_mode * 1e12, \"--\", c=\"blue\", linewidth=2, label=\"Mode\")\n",
        "plt.xlabel(\"Wavelength (µm)\")\n",
        "plt.ylabel(\"Group delay (ps)\")\n",
        "plt.ylim(0.16, 0.17)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "id": "c43e4dd0"
    }
  ],
  "nbformat": 4,
  "nbformat_minor": 5,
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      "display_name": "Python 3",
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