{
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   "source": [
    "# Microring resonator add-drop filter\n",
    "\n",
    "Microring resonators (MRRs) are compact, ring-shaped optical waveguides that can confine and recirculate light through total internal reflection. When light of a specific wavelength enters the resonator, constructive interference causes the light to resonate within the ring, creating sharp resonance peaks. These devices are widely used in integrated photonics for applications such as filtering, sensing, modulation, and wavelength division multiplexing (WDM), owing to their small footprint and high quality factor (Q-factor).\n",
    "\n",
    "<img src=\"img/ring_resonator.png\" width=\"400\" alt=\"Schematic of the ring resonator\">\n",
    "\n",
    "The resonance condition depends on the optical path length of the ring and the wavelength of the input light, and is given by:\n",
    "\n",
    "$$\n",
    "m \\lambda = n_{\\text{eff}} L\n",
    "$$\n",
    "\n",
    "where $m$ is an integer (resonance order), $\\lambda$ is the resonant wavelength, $n_{\\text{eff}}$ is the effective refractive index, and $L$ is the circumference of the ring. By carefully engineering the coupling between the ring and a nearby waveguide, microring resonators can achieve precise spectral control, making them essential components in modern photonic circuits.\n",
    "\n",
    "In this notebook, we demonstrate how to model an MRR add-drop filter consisting of a ring and two bus waveguides. Due to the high Q-factor of the ring, 3D FDTD simulations are traditionally considered very challenging. Tidy3D's speed makes this type of simulation easy to do. To avoid running long 3D FDTD, which can be expensive, one can consider one of the following alternative strategies:\n",
    "\n",
    "1. Model only the [waveguide-to-ring coupling](https://www.flexcompute.com/tidy3d/examples/notebooks/WaveguideToRingCoupling/) region and analyze the ring response analytically or using a circuit simulation like in [PhotonForge](https://www.flexcompute.com/photonforge).\n",
    "\n",
    "2. Use the 2D effective index method to approximate the 3D simulation with a 2D one as demonstrated in the tutorial [here](https://www.flexcompute.com/tidy3d/examples/notebooks/EffectiveIndexApproximation/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
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   },
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   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulation Setup\n",
    "\n",
    "First we define the wavelength range of interest. To resolve the narrow peaks in the spectrum, we need to sample a lot of wavelength points. In this case, we use 2001 wavelength points with a 0.1 nm resolution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "lda0 = 1.55  # central wavelength\n",
    "freq0 = td.C_0 / lda0  # central frequency\n",
    "ldas = np.linspace(1.50, 1.6, 1001)  # wavelength range\n",
    "freqs = td.C_0 / ldas  # frequency range\n",
    "fwidth = 0.5 * (np.max(freqs) - np.min(freqs))  # width of the source frequency range"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define geometry parameters used in the simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2025-05-15T10:48:26.500170Z"
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   },
   "outputs": [],
   "source": [
    "# define geometry parameters\n",
    "wg_height = 0.22  # waveguide thickness\n",
    "wg_width = 0.43  # waveguide width\n",
    "gap = 0.1  # coupling region gap size\n",
    "ring_radius = 5  # ring radius\n",
    "buffer = 0.7 * lda0  # buffer spacing between structures and simulation domain boundaries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the materials used in the simulation. In this model, the waveguide is made of silicon. The top cladding is made of silicon oxide. We will directly use the silicon and oxide media from Tidy3D's material library. More specifically, we use the data from the widely used Handbook of Optical Constants of Solids by Palik."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
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   "source": [
    "si = td.material_library[\"cSi\"][\"Palik_Lossless\"]\n",
    "\n",
    "sio2 = td.material_library[\"SiO2\"][\"Palik_Lossless\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we define the ring resonator [Structures](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Structure.html). The ring is created from the Boolean operation of two [Cylinders](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Cylinder.html) geometries. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:48:26.503985Z",
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     "shell.execute_reply": "2025-05-15T10:48:26.508148Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# create the waveguide structures\n",
    "wg_center_y = ring_radius + wg_width + gap\n",
    "waveguide_top = td.Structure(\n",
    "    geometry=td.Box(\n",
    "        center=(0, wg_center_y, 0),\n",
    "        size=(td.inf, wg_width, wg_height),\n",
    "    ),\n",
    "    medium=si,\n",
    ")\n",
    "\n",
    "waveguide_bottom = td.Structure(\n",
    "    geometry=td.Box(\n",
    "        center=(0, -wg_center_y, 0),\n",
    "        size=(td.inf, wg_width, wg_height),\n",
    "    ),\n",
    "    medium=si,\n",
    ")\n",
    "\n",
    "# outer cylinder\n",
    "outer = td.Cylinder(\n",
    "    center=(0, 0, 0),\n",
    "    axis=2,\n",
    "    radius=ring_radius + wg_width / 2,\n",
    "    length=wg_height,\n",
    ")\n",
    "\n",
    "# inner cylinder\n",
    "inner = td.Cylinder(\n",
    "    center=(0, 0, 0),\n",
    "    axis=2,\n",
    "    radius=ring_radius - wg_width / 2,\n",
    "    length=wg_height,\n",
    ")\n",
    "\n",
    "# create the ring structure\n",
    "ring = td.Structure(\n",
    "    geometry=outer\n",
    "    - inner,  # boolean operation to calculate the difference of the two cylinders to form a ring\n",
    "    medium=si,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use a [ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.htm) to excite the waveguide using the fundamental TE mode.\n",
    "\n",
    "A [ModeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeMonitor.html) is placed on the other side of the waveguide to measure the transmission. A [FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html) is added to the $xy$ plane to visualize the field distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:48:26.509454Z",
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     "shell.execute_reply": "2025-05-15T10:48:26.512251Z"
    },
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   },
   "outputs": [],
   "source": [
    "# add a mode source as excitation that launches the fundamental TE mode into the waveguide\n",
    "mode_spec = td.ModeSpec(num_modes=1, target_neff=3.47)\n",
    "mode_source = td.ModeSource(\n",
    "    size=(0, 4 * wg_width, 6 * wg_height),\n",
    "    center=(-ring_radius, wg_center_y, 0),\n",
    "    source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
    "    direction=\"+\",\n",
    "    mode_spec=mode_spec,\n",
    "    mode_index=0,\n",
    "    num_freqs=7,  # using 7 (Chebyshev) points to approximate frequency dependence of the mode profile\n",
    ")\n",
    "\n",
    "# add a field monitor to visualize the field distribution\n",
    "field_monitor = td.FieldMonitor(\n",
    "    center=(0, 0, 0), size=(td.inf, td.inf, 0), freqs=[freq0], name=\"field\"\n",
    ")\n",
    "\n",
    "# add a mode monitor to measure through transmission\n",
    "mode_through = td.ModeMonitor(\n",
    "    size=mode_source.size,\n",
    "    center=(ring_radius, wg_center_y, 0),\n",
    "    freqs=freqs,\n",
    "    mode_spec=mode_spec,\n",
    "    name=\"through\",\n",
    ")\n",
    "\n",
    "# add a mode monitor to measure drop transmission\n",
    "mode_drop = td.ModeMonitor(\n",
    "    size=mode_source.size,\n",
    "    center=(-ring_radius, -wg_center_y, 0),\n",
    "    freqs=freqs,\n",
    "    mode_spec=mode_spec,\n",
    "    name=\"drop\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we define the Tidy3D [Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html) by putting the components we created earlier together. \n",
    "\n",
    "For an accurate simulation result, we use the automatic nonuniform grid with `min_steps_per_wvl=15`. That is, 15 grids per wavelength in material. To ensure field decay, we need to use a long `run_time` of 30 ps. The background medium is set to oxide since the ring is surrounded by the bottom oxide and oxide cladding. To save cost, we can make sure of the symmetry in the $z$ direction. Since we are launching the TE0 mode, the symmetry is even (PMC) so we can set `symmetry=(0,0,1)`. Since we are using the dispersive mediums for silicon and oxide, it's better to use the absorber boundary condition instead of default PML to avoid divergence of the simulation. For the absorbers in the $x$ direction, we increase the number of layers to 60 to minimize undesired artificial reflections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:48:26.513584Z",
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     "shell.execute_reply": "2025-05-15T10:48:26.526724Z"
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   "source": [
    "run_time = 3e-11  # simulation run time in seconds\n",
    "\n",
    "# simulation domain size in x, y, and z\n",
    "l_x = 2 * ring_radius + wg_width + 2 * buffer\n",
    "l_y = l_x + 2 * gap + 2 * wg_width\n",
    "l_z = wg_height + 2 * buffer\n",
    "\n",
    "# create simulation\n",
    "sim = td.Simulation(\n",
    "    center=(0, 0, 0),\n",
    "    size=(l_x, l_y, l_z),\n",
    "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=15, wavelength=lda0),\n",
    "    structures=[waveguide_top, waveguide_bottom, ring],\n",
    "    sources=[mode_source],\n",
    "    monitors=[mode_drop, mode_through, field_monitor],\n",
    "    run_time=run_time,\n",
    "    boundary_spec=td.BoundarySpec(\n",
    "        x=td.Boundary(plus=td.Absorber(num_layers=60), minus=td.Absorber(num_layers=60)),\n",
    "        y=td.Boundary.absorber(),\n",
    "        z=td.Boundary.absorber(),\n",
    "    ),\n",
    "    medium=sio2,\n",
    "    symmetry=(0, 0, 1),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize structure, source, and modes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"simulation-viewer\" data-width=\"800\" data-height=\"800\" 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y2kTspFB9jadsU2mgYS2YDRy8rnotbNoW0Al2Z9qDd2bdWYJq+GnY1bk1/F8cki1jjtTxBNvS1kxfx5TW5/zAx5DBw24gkNYjtdP8JRRLYS452n0L2lHhPyhvdGOu3Ck9lB7MQsqFxeQQNZ+JFefJo38uAHfxR9bRTqylMZ9zwR7ZdAQ/FvPiG5j66gU4TzlZA0+eQ+Csgka+SmzJJKRIyshMSxmhSgdqQpqkAuWFjkukUGQ5ujhWhSAjBQNqGSwrBifhftTgPeBWGyUimiXl3CmQApcjVSJWYbOPwQJYN+7E+p+2TAJsYz6eQNGiFpFWSR7fy2zKLMtSj7ZGGgn/AhvCdWOJEwVMAPYVndr0QJhZbZaCEf4woWr2URchqT2WRlZwxKxbYFywTLkcrg6Hg3b9G1kbyjbcJBsgkUBAFKCUC2GOYPxlZIRrLRghnyHpkSUsh4l/rnWkgoSO9Hn1fIpDcqnzBqO6P9YBcXpVtFMdd8KQk7F/iqLDFnyN1Bs/IH5qZieBWOVrcoSmyuEVWCeDu+RMBKIXKZNW0HdtEQOKBkGHsFlWkAPdoNd0SWQ1/L4MvTVbksagZSDJKuzuefLkP2S+SX6LoELxdWw0iucwTsNVTYCTsEBpyGx4rNBlIpsV82AyVbKEUTyJXh/ELjVU4Ia5uDhZJ2eRtgnKh4LyGaiWVDK3MYEtbExCa9FO15cq9bNs0igIMZjoq2TNJiIDCvrZKKC5fNsjRmDYJnBLay1OIAkZfoUfZ2ACM03PZnmtTqqU60VJgFdXv8lvYAJMv8SCQXpdf7W52KhGMqKmx+jQG+7fSQXmLhfLfCtDIJmaC0UnhSnyWiNBk4m1WYUx2EQkWnZpSiAbJJSXdiSLoOdjs0J8r3pG/GbLpFTPraKSSS1Q8hXR6kFUhZQZ+6Ru+/0a6n+y8whffBG5xZqyFkMzuigXyHmKZ3J3Lq7Q3glIrgbQp54zqBT8Shse30SgcCIoN7KQRVCIu4JpZ57Z/T2QCRoKvzZxUoIICDzmQ1k0B5BIL/SIS47CTRR8KAAAxxRw+Mpb9x7eLC5hBE4UXW7JcgJRajN3K07Ta7ERbQ2P+XJgdNyiU2x3cYiU/pqjMwQLJeX1PMZ+cq5OgkBVEXEih2vfkJrSygunNy0DRx1LJ1AqZurorSpGwoxgCoGag5DYUY0gONr3uFCgIZ87zd6ah4r7GpBYleG6AbBa9HworuscVNYNcMGWNxMWigIRazKX1V9TzMSlpxPgWrJGNJcpUATX3bD76k4jRpRzufBVYlUqAtwnWWmRV0WOgUQUK21rSCt8BhA5JZJ61jRDA7x40EslKNwrlK3fm+mvA5Ad5ErOzhMcJiSITZQYJGBggDmbWdiYmdDYbKYUYH+MWtJKZhKm+OvneGZMKpnmeVsi9AJDowPbCnd6YCJ7gcZc9+faEEU2i4D6H3C6pZOG35LuV1ZYODbLoY/8eYTkwXIbInL7EURBKGCf17GU/uRmJ95SP4AlRecs8K3KZNI9RbxUIcCFb38fkQ0Ojn7sb3KQqxcointQZHgiFMWlYgvWqlFEYpNM7VBxFkfoE6NSpQtWem6mfRMrXgUWZblT8xr6awyyr+IVda269WQKpFKya+ZFC1kBBrELA7LhUpp2Cm4zGFj1ap2KSqKxITz3kFEipWLOR990WQnWxSDFjcDUgFErkTkhs+c5EdnxXpGJjRC4Ke5lFbq+5uy2zkqgIgByoGQzFqJh1ojFXemqgGyVS+XnXgsJfGt3zcY/NC0VUKSlVVyeAcACK36lW2SbkeUhByvoF14si3qMrgz66ZRzJQQlZ4yOTQxxir0lPzVucTG26DZzScONYiH1Ah9Jojy6kAZRW1v98r5haUZfMqScOBL5rpIRLxcIUQwtOEnqKOMVhWWmzjCG8LpU82FCYjCafPJZvlbHUg4JMVCd91jpmVpSkjXtDwGzGFJBo8RWL/X0WA5nBPifsTXmqkuqKa4h0tYCjhLO6TJBGr1QTBHZdYsGkmQqogJnPishSrtMmzPYU+E82bis7nivQc8wsZnRYou3UWfkD/uzPUdMVrouSKd8EbpuDFsKJnv0us1SvcALIuo8FDHUgums+h8wRZQfAu2XnihRO1JT8CO37LhS/ycSRYinuMkkaECEWvIlLj5wDFVvaBy2dSnGiY3qzYmwTdrmGOPH3tqMhTq3gCqgK27mekmbxg8RT2/efcHcVYZbNWusgoyWSyGFmN5UK5guFY5Ixw4RdSHuW9fHpvvuEL5I1T6SrTHSSTLkY1gu1326zCmsBGYWQdkWJFMQUc0me4j7W2boQSBBWW5sgh0DrhJySx6+csJHrku70p41FPVGnOUuOheTUqMUUwHjf36VxINDQGnfuRAB8or60eUlAX9NDE3LBMRvrxNLg+N1JFWLosmCn4yhRLZHYqZRA93f1PwG6QZOGzyuvQ1qTZe+2IkQwpwJPUgI2pOc0ifxr9KpzBgLInAysnE2RSW6TOY6Y1LCG5Mhp7pjZj2V5M25fo5TiHZnm2R+TqaAOS+wh2DIDkmYEKq4h+TCRg9TBZvvPWkemuiT++GNa1aDsSBHDdIzUqYgrz0CFySbFgdEDogDLQjVaUNIEC6Sd7WuZdFIH63pHBHWPiC6Kmx6qU8MkXC0E2UbMTNhAdpJY3dQuk71twGTqOssYy2d3ajZJJnTzFdMSHUFIs+y2hurqd8By+xQCfIr9sv7JaX81kRsYM89q+qkHZGfmHM2TwkQqG80Jr8UNVM4LkA9t8SWTvELRaBcyVckNkZP7xToYQCZQl1cQPMjowBoCvsq89afiqayBCApVRRYjMkGIwk09mDwLo9fgIbXNYWEoJc2MyaPK7F+rcwOUZIalRGcfFdZoe2o7EBLGOQlDA8iWcAKtKdfQsWPLrSxN/kjOzktMciSYz0Jbl2f1cp25Ogomm+ewwQ5IMHgrmjhJMbUp1rg7XlJXGBAAlKd+MiqohTDHLtiFLNKQS5C/MNtGVp4EJXrgUO6bneCzU0HKSWWXVLQtLw+LB+g5W5+bWDGSr6JRYg01UgbknDePBDIpLepJuEGnOqJEXWQkz9WQZyLNLmiYvVst43jl6rSL2nzSQFKHYqofACWioIkFh+mmeGalSCRhkWyC1Fb/gwg6XIcT8YIibCrbpiUpUyLk2GP2+j9ljWLZaYfUUksStYk4v+xjYp4UIA4cwNq9gIkINP/DapMZWCJsTu+cFdjkNElTAEf2DJQcPfFI5FyD95KJHgHhRSI8hswyNhtckAHchU5cL31+oHpLXybKYsNDy3NLRDxFVBKo69gcuAnCZrHbMIEMBkG64C5dIqatE01Q/S3JM/NExaPNwlkv/mqiQYEXFfOthQRaq1B/kkY2FOaWBgVPkzgS8HhKOjJM5WQsKJqT/QrWv5fILtNf059qEIkId4ACaBtYLMTY0gojw06e0p4THKqF7DS9KWtIDhtQxz8mJMBDmT3OljQDHWQuVw0/7ZqUBsLGMV/iQpuV9hcSHaKVF2tIgs7+NdnPZPwSjcho03uwp2UUtG6z3Yyys2kwJ3hnaxbAbp9DEKE3ym7J6lViuyInipFAwvU1cpsAE+E99xuEyAQZMNxgEp5amk5OKLmA0ZGF6Z7/90knG+TUKv1wizk9zr5UrSQ7EKfsQxMHxUNTCJmgXDihjAU0cyCrKSNK0C9bbygkAVMllaFTTPexWVxL1COfnpOlk/ei8ci6umbNTzZWEJ4K7hqS1av0nSBz9pQQkkbaxJfrqVkwIAkrN+Y71QZCnggpZ/OQiXJmow6tTZiZS6SfKGi8Zg+kHWXqpxC/E1iSgP8m6+1BYiL1EMi/0eNq3K+c894o9O9anSgWaBTRscJCKqsJU+YaLVxDlAPjzKW6uIKEAr0Q+t7SuDQbicLsTdv8WwQvHcpvzZ+SXSkUYbRCUzipOM27srXFssCymA3bSoHV23sFxgvdcYmKoS2M6owiSZm5PemCALGd2UXpapkpsbEpuGAzZAjZ6EoI+2O0/MyyguPFRGRI1YcqqZuRxJbChH3FNppmSY0yjgzwWpsEOtWJPKT3xhZJCIuh4dKadBJ9PRud1xIwmzMBSCjKxeatFsT8wG0SZ05c+UAavunm2/k+G1dp75bRMbM6wR6NYWTFzA3KuBX0u1EZ258j1izE13nbn6MNsBNgR+/JEP8Ah4GupOSWihzwoLI0YfUawgfMVtD2NG2QINFwUuhQX6+ScFJYkVbX9loK4lZnQ0BxWxBotG1lNjUahEqBng/RNDwV0aVZCBT2anNwRC4UlI8wOl+DwLFoSe1q32wgQpUJw4ktJtJOIEZ3COpMnG00NIZX6ljDxhrFPmpt2bDMTFg2mo2olttTkvROw1Te4+9pvbV74YXhWUj9r83eo0ziyxYSZjM2JmfY9iPFLgAipDbKRVxGJUwlwtvWUOtzxqcLLuKsmhX6oqob/UhuKNJ8qT0vFlJIxfUQ1FsyA19HN9VMzZhsTmxCOpUOF5uApqrEBO2JRjSx0BnfCQxt950WC8nbjHltIZ0UCq0sisGNljTdYeEmaF9yoy2lifDrTLqvIXq5E/U4OalojyFxJFFadAOvHZEAoqFgdz8E9xEAtYVdI0CEeoTO673cK1hFuwO52uThMqnaiT7JuxrpGjCYjg9KNks0Iwmfjbx+T9ZAGynFFRp/enbSVWJ7CiAYyMXBSrKY7oaRzIyy1NnyDUIy80Uah2QBmQWv2ZPtpesW5R4+QW6zUXUW4mxXtCtqeZ3chfMLViW6eUO2eC9SHyETwNUctgPym6KP/iNaP7FQUZ3on9Khv6jpJdGdDlyz53TYKlSiVtld8RXYDDoAoxD4riHIJcYUtO3MJ09GPTTh33xMDk9CzE6sBSrSTq6AP06wZFTKZHZpRlQE5RJMeh+v2mf78PpcBteCF6r3MXOihe4PqXDwkiArJf5DVLwMJetOqzRhWt1XTKaYateGrVqzwkEm0Gc3l5GJr+mTklVzAggk6kMIaHOsPDWJUhmdLL4z+VbKXZHTCL48fWpW4mQOvFdW5B2zAlb3djw0hshYnPTqb5ypJkmn7JOb/tk9KuUXh7O38UT6p+ni0p5dJuJsVQAabzu3aUUPshRo2v5qRlsF+MGoRgAqvWLHDEzMMFOUonxKpWa3wmQdOxEq5z0WrNRjhUw9uc9ows4VMxAhUAewacVE+kzBKcMEJcj7EGbTDbrca5zxg7xawpIWm5R+XxK+4og/pp3JtbZQXPdJ9tKyiLOvi5i0OEXurkmc4lleKdDbx+cHoCSsoTa7+EWOauguUGmJYW7OK1yEm7SnBfKHtqkA7wh2qFeaWAf0hAiHnrplv6li0DtVZolmyRdNkfMMF63chj3lp6SaQiUyJVZRoFkVouGF3LyEMbNBQtTESv45zrXI9VM93p+TraWnq3OgaHErcHAk0kA5+vApAnUXUvn7WbKATs7DOHSxL7QcaHjlWECmBLle7bNxmh7outNJniSxilkdXHqosJRrh1aHjYm1jO/MH3HwyuNigoQx05Eyn/v38CVciFD2XpLQKPXSG0gxy77XZmsyZbPmnG106CVUfe//UgiMze5YHs8zB3rVZvegHKztjObWmXwlZzNsLHEuQtvr7qyREkAjp4N22hHq0kCqyMfTgqHOkxFpm61TtpJKcrrN/Kz1LNJ0mCivy257BjDUSrl09djsUwhBDaxLznH/HEfMaHknWvaxSEPYTCx55gmQQhoA/L8L46xsyOJVitWLdjMhgn/Ne3cG2pYLjfCAhBXgzFIwjYL6w/MH3D4VaeUFTvm0hSA9YS+D7xZTR92SKqnrJ6l4EsUisvlmsKViZRy2ZlhPUU0c1OfqzolMyg7Oxv3kZJhXYWW2HDxlFejwEhqaP74vDmxwDHN1xa89zCQQZ3i2NnwhaA9VRFlUMzFpnkVsxEeOOMiUBBkPynZm6QNxEJMIjnlkpa9zmIXIrbqDAY8TLo5EO+EyRdRFZe4rnsjWQaxChiYRe5pEJOw/CZk+vOod5mHEzJUZe8MF4TpNgp0DCrY4CVaedevunom2bMFxMULwxGx/iPNcAvFxCf6YEIh84Vyzq2ukn0smlX9c/bkSjbb1MJM2ayhMw0Szjh8dQcypwzValv1znH/An9B87XulXE0sK6S+Ww4J7sAJIU6+YsqYtDYCWlwkKNdwqE+fDfu7E60XqjReKQhMQi/ROnfHmjntVuqsFpDHn88NTrpV4Sz2tu3P9WmGME+raXvM++5Ec8oAlmkYHPjkfCYn9LaDvcnta5zvwIrHNWSFZ9oc1oIHpwIzXRR9BqHrVaodGpjtH1NOBkfbEpEJTR6LJuB+8ml0oyWjJyf2FOkqJJNqWvRGD36g57BwhG7xggk4ssZxh2gdfNTJFQ4TKPenaeUA57lQmtGn2A2sekTgyR7YxjpyEcint82/Njv/SHLou75Z2QIShZwMNEhELyyn8PCWm+nTAOfJQnP4Nrb9XSLmTEKvmXHSWMThcVi2mAEgPhCUoKucTkejQNtmo1jESy9BHjTGBBpWC90rJgFksOm81rqzsbFx5rKRdk9mFQjTCRI6HaXrXNCYjZgCLHT7JX9T9p9kGf7caSfZwgnMm4EOaw+VU0CZkmIf9jFJJi2y4lGxY930kclM6DX6YQ5rtThnjvdQpV7OCdNP3om7DetwcaK7miCZNjjfPb2VBXZzKKjaSsgmzy7utFNda+Wg6DyJ7t8rVAo7j9dmKy5dKhypqY1malJoRUYXwVP2YqXPBKifl28mhk4zc5PyqC45ZUbNNCy2ZnIohlITlIhtyZkKEiChKFnqvlwqLHkm/S0cpIKTyjwYQqOfETNzqCHRNZmiq3CmqxvQOuxUO5UpEAb57GqNWpxhLRhUqbGV5wcRc5snaIYdaxhk9vs8wkBC2daWaJ3o1OySMzrRclFhV/WPpXkklH4PRHBJDfiNcwz8z+gmf0bHYKFy5XRLlE7Jb0Tr1aadEchFqX0bLoN6BFRK539xcwBSmUmtOHZaziBJYCCQz7DnyEsoZpCpiyuLNgjyASaJxqcFJGjyrBMlSuGSmQhiOewwOU4jLwedCNWp7BqzIl3TiT6Ytps0oHAk/yAv7LynXWfQJdL7PqksC0eaZ2u3rzdMZ023Zt+1V/ZBLmue/w8u0ijqGCuEdXUIHDjvNPNYGXLg1OnXSbM0b2SajVeC+xzr3T9HEw/H+vFM63OY8MqFVzTer91iDzk7VTgAsWaYtoUO4pj8WCv+I3FcR7TzHr0xewdnC9Co63j5QJqkrtpGImVkj0kAKcIl+i+7fS5w2LLGRhiyaMwB65TnqTDLWQ+OeJDvoyFnJWlJsNHLLIteXH/nhRGZ+jQVIZuU8A5Fp6vILLUCK841zR64YpTj5MfgmEup1mzOme55SwS98C7WWDz8PMu11kiNcdkAFQQM/+K/rACGm3himPuSvtF7SB5/a74SgPFMtUcLtymF0Fkuj0nGey2kg6A5ZAsUNg6CK/o24yQT/jHzuHE2SjczLuQYwZBUzlb3F8fSKQtUqlrZt9DQGwqJ87DuYg0iTksX2bClJDN8mimI5ofQNEaHMnE7OXXjapsH7OdpajtHMjhTEjhHI9+0b4K8gjAuZ5QNRw/sO3mgRiuBCRNRBVWBVtzMkU3krBlN0i6t83D8POfhx23JMzRuUeAUUtwn5SiP7c9FrnJpAodmqXbYxgDplIdxdUbhYj4ikzFZTxVadmeWwz0LSUJuXxmz0Lc4WGZCnfgLfVtz6kuBErHQ6i4P1EQU+1UOiZjY4G1w5SR0TM7pyiExitb4DvKsP3M8bLNmoEHL2ODWXT3WnauZK3471Xxre9DQ7JjHnozhi8v0UA1sZBz+mFgQiAX6NkxWM6kyZGkrw3bKieIyGzLkfBc2pGefYICklzlCin4zA5VCTMZSznZovjbPqdqbqU03TY24+tISNUoam2MKzqupWZg5SL64MC8uoe+DXlB7iow4BqJ7mm2egacNIHG4zmedjSWQPO44Y5Arl2MSBPGzXAQo2hgnBrIV6unPGPM6BCnD6tqnwqE5AwmqsMtl5OhEApbH4Eojc6fQMddp0Gx19GN1IYi+57GnVeIUOUdqXEG4UYa6KSftnNGBc0ic3Ym5Ggfp/pbdCPOQumk+pywpHw0es1mpTsi34rF2VcVZkGgEIhr3ifdndwXnp9a+uCeAztJZ915f69zlMbtgNu9zHsu4K+wWSczb0uFa8uCczLZaPjRUqS5RzNisYjwLIvNqk3mtwLaGSByRVK/F0seDboKMwdkocvoYnTjb7Jlyxe8ELnSbcsmHbbVjNTn5m7BK61XyUOtioM1Oo2H1qS9NbORqIxhARB0nDY3XM+7G0nH8ynfBsXIJ46AZxmkyFBzjMukbONhQmD1qWCejMEltySEHHKP114957rpSFBp2sIF83zylysG44QvuuMc2Gx/7/qrQHcERi97HZoMPuClZ+YD2NpnjWjicPnwlXDXAQ3GsxCstS4OmQijSncKcDedwOKlWJ0mbp4DlaxG6RZHW8oTPwEGTiFlLoK9IJHBhajjGub3Nt0o4LR7SjxNtaZycpX9uK1ap1hBJ4ZQof1gmmqVS95tXEpi17ehBhM0t78JKWI6j4ZTJEn2pVseyUj1ZRW4N0WYcOP0WHN2K3LSxzOssunOhztIH9b9kyVMkkIt+JG5UJ9bQLFLOjt5mjrvPJCkp92ni11AnV5+hpJODSij1CmIFn7PMHXFPQDdbQOK0chURVz7597lOQ6Tl0gd/KmIv6PTJHq1zlqvMS2JiyfYmNlXM4w4oN5g0dHFtDFDDkVKnpWVwOG8WL+cFtHe3F5df+u2Yb94i93h+//h0Ld3x5uLpL34d6ZtX1j197+tXZJ6uE/2trhN9eLy9P569uDxeXTycvX4zKyv45qXVv/Eyvd/9jlu/Su/xM23h089Ot+mdbtM73aZ3uk3vdJve6Ta90216p9v0TrfpnW7TO92md7pN73Sb3uk2vdNteqfb9E636Z1u0zvdpne6Te90m97pNr3TbXqn2/ROt+mdbtM73aZ3uk3vdJve6Ta90216p9v0TrfpnW7TO92md7pN73Sb3uk2vdNteqfb9E636Z1u0zvdpne6Te90m97pNr3TbXqn2/ROt+mdbtM73aZ3uk3vdJve6Ta90216p9v0TrfpnW7TO92md7pN73Sb3uk2vdNteqfb9E636Z1u0zvdpne6Te90m97pNr3TbXqn2/ROt+mdbtM73aZ3uk3vdJve6Ta9f+zb9P6CF771Or25a//X6xfoPfvpzYtLvfCK51/7b3vML9ObS/n/u0rvzR7Gfy65W1Sce33/C7bw/tzr+1/y75/MkZ/MkZ98+ezjrz6e3Lq8+A5Jnb/64pvj+wIkSj/WM3zv+lzMfn5+xS+mSXx2fXz4bPLjO97+sYTvePGz9RS/uLg6u7682aVW/z3F5+HsTmL/q88R+9Ue8MavHi6vz4zD9C7vK3/1B7vyL/8QV/6r88+PV8ebT6eoyvzKmj+7/fx4r8XKFEpFnj++vJ9W76Opkzfnd3eXN5/KoEnN9uGr81f6/P1Runm8lkZP0dx/+7SPy4td5jWHGzeh7kOBNHdXt49nazVnL6XlfOHZf3kZ8ifPr/n91cvru+PFmRbMJE+LevnJ3eUXx6tvUvBCiiXDdH/5/Lup+8Ht1fm9iHLxQy3p/FNtbvkCWeer737rw8fzy/vn5w/++Leq4j7F+z/60e3Ni9v762VrHy/FcnFFFLt46XZTYIN3rm4fHl6d/abZX96/kG37KeQ4v5GR++6Phq+/tyj1ZMUvr19eTWN05s88Xl68ylNS724f5OG06ikaYVrPl/fnN4/zw1x1+uxm7knUW56DX/DVzyTv0zHG4w8CInX/UhNcTkOdjz+I8au3+Pm/fhBGV7i/AAA=\" ></div>\n",
       "    <script>\n",
       "        \n",
       "        /**\n",
       "        * Simulation Viewer Injector\n",
       "        *\n",
       "        * Monitors the document for elements being added in the form:\n",
       "        *\n",
       "        *    <div class=\"simulation-viewer\" data-width=\"800\" data-height=\"800\" data-simulation=\"{...}\" />\n",
       "        *\n",
       "        * This script will then inject an iframe to the viewer application, and pass it the simulation data\n",
       "        * via the postMessage API on request. The script may be safely included multiple times, with only the\n",
       "        * configuration of the first started script (e.g. viewer URL) applying.\n",
       "        *\n",
       "        */\n",
       "        (function() {\n",
       "            const TARGET_CLASS = \"simulation-viewer\";\n",
       "            const ACTIVE_CLASS = \"simulation-viewer-active\";\n",
       "            const VIEWER_URL = \"https://tidy3d.simulation.cloud/simulation-viewer\";\n",
       "\n",
       "            class SimulationViewerInjector {\n",
       "                constructor() {\n",
       "                    for (var node of document.getElementsByClassName(TARGET_CLASS)) {\n",
       "                        this.injectViewer(node);\n",
       "                    }\n",
       "\n",
       "                    // Monitor for newly added nodes to the DOM\n",
       "                    this.observer = new MutationObserver(this.onMutations.bind(this));\n",
       "                    this.observer.observe(document.body, {childList: true, subtree: true});\n",
       "                }\n",
       "\n",
       "                onMutations(mutations) {\n",
       "                    for (var mutation of mutations) {\n",
       "                        if (mutation.type === 'childList') {\n",
       "                            /**\n",
       "                            * Have found that adding the element does not reliably trigger the mutation observer.\n",
       "                            * It may be the case that setting content with innerHTML does not trigger.\n",
       "                            *\n",
       "                            * It seems to be sufficient to re-scan the document for un-activated viewers\n",
       "                            * whenever an event occurs, as Jupyter triggers multiple events on cell evaluation.\n",
       "                            */\n",
       "                            var viewers = document.getElementsByClassName(TARGET_CLASS);\n",
       "                            for (var node of viewers) {\n",
       "                                this.injectViewer(node);\n",
       "                            }\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                injectViewer(node) {\n",
       "                    // (re-)check that this is a valid simulation container and has not already been injected\n",
       "                    if (node.classList.contains(TARGET_CLASS) && !node.classList.contains(ACTIVE_CLASS)) {\n",
       "                        // Mark node as injected, to prevent re-runs\n",
       "                        node.classList.add(ACTIVE_CLASS);\n",
       "\n",
       "                        var uuid;\n",
       "                        if (window.crypto && window.crypto.randomUUID) {\n",
       "                            uuid = window.crypto.randomUUID();\n",
       "                        } else {\n",
       "                            uuid = \"\" + Math.random();\n",
       "                        }\n",
       "\n",
       "                        var frame = document.createElement(\"iframe\");\n",
       "                        frame.width = node.dataset.width || 800;\n",
       "                        frame.height = node.dataset.height || 800;\n",
       "                        frame.style.cssText = `width:${frame.width}px;height:${frame.height}px;max-width:none;border:0;display:block`\n",
       "                        frame.src = VIEWER_URL + \"?uuid=\" + uuid;\n",
       "\n",
       "                        var postMessageToViewer;\n",
       "                        postMessageToViewer = event => {\n",
       "                            if(event.data.type === 'viewer' && event.data.uuid===uuid){\n",
       "                                frame.contentWindow.postMessage({ type: 'jupyter', uuid, value: node.dataset.simulation, fileType: 'hdf5'}, '*');\n",
       "\n",
       "                                // Run once only\n",
       "                                window.removeEventListener('message', postMessageToViewer);\n",
       "                            }\n",
       "                        };\n",
       "                        window.addEventListener(\n",
       "                            'message',\n",
       "                            postMessageToViewer,\n",
       "                            false\n",
       "                        );\n",
       "\n",
       "                        node.appendChild(frame);\n",
       "                    }\n",
       "                }\n",
       "            }\n",
       "\n",
       "            if (!window.simulationViewerInjector) {\n",
       "                window.simulationViewerInjector = new SimulationViewerInjector();\n",
       "            }\n",
       "        })();\n",
       "    \n",
       "    </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the simulation\n",
    "sim.plot_3d()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we verified all the settings, we are ready to submit the simulation job to the server. Before running the simulation, we can get a cost estimation using `estimate_cost`. This prevents us from accidentally running large jobs that we set up by mistake. The estimated cost is the maximum cost corresponding to running all the time steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:48:26.554160Z",
     "iopub.status.busy": "2025-05-15T10:48:26.553929Z",
     "iopub.status.idle": "2025-05-15T10:48:30.655672Z",
     "shell.execute_reply": "2025-05-15T10:48:30.655205Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:48:27 CEST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'ring_resonator'</span> with task_id                        \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-75d3a8a1-1f16-406c-9e77-a538945ec30e'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>. \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:48:27 CEST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'ring_resonator'\u001b[0m with task_id                        \n",
       "\u001b[2;36m              \u001b[0m\u001b[32m'fdve-75d3a8a1-1f16-406c-9e77-a538945ec30e'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m. \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>View task using web UI at                                         \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-75d3a8a1-1f16-406c-9e77-a538945ec30e\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-75d3a8a1-1f</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-75d3a8a1-1f16-406c-9e77-a538945ec30e\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">16-406c-9e77-a538945ec30e'</span></a>.                                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                         \n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=411505;https://tidy3d.simulation.cloud/workbench?taskId=fdve-75d3a8a1-1f16-406c-9e77-a538945ec30e\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=197500;https://tidy3d.simulation.cloud/workbench?taskId=fdve-75d3a8a1-1f16-406c-9e77-a538945ec30e\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=411505;https://tidy3d.simulation.cloud/workbench?taskId=fdve-75d3a8a1-1f16-406c-9e77-a538945ec30e\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=349842;https://tidy3d.simulation.cloud/workbench?taskId=fdve-75d3a8a1-1f16-406c-9e77-a538945ec30e\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=411505;https://tidy3d.simulation.cloud/workbench?taskId=fdve-75d3a8a1-1f16-406c-9e77-a538945ec30e\u001b\\\u001b[32m-75d3a8a1-1f\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=411505;https://tidy3d.simulation.cloud/workbench?taskId=fdve-75d3a8a1-1f16-406c-9e77-a538945ec30e\u001b\\\u001b[32m16-406c-9e77-a538945ec30e'\u001b[0m\u001b]8;;\u001b\\.                                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/folder-bfe11006-2cae-4abd-bc02-04b6047e01be\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                           \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=501693;https://tidy3d.simulation.cloud/folders/folder-bfe11006-2cae-4abd-bc02-04b6047e01be\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                           \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d4430dba95fb4669ad4618252c34cb24",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:48:29 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6.896</span>. Minimum cost depends on task      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>FlexCredit cost after a simulation run.                           \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:48:29 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m6.896\u001b[0m. Minimum cost depends on task      \n",
       "\u001b[2;36m              \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed \n",
       "\u001b[2;36m              \u001b[0mFlexCredit cost after a simulation run.                           \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:48:30 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6.896</span>. Minimum cost depends on task      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>FlexCredit cost after a simulation run.                           \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:48:30 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m6.896\u001b[0m. Minimum cost depends on task      \n",
       "\u001b[2;36m              \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed \n",
       "\u001b[2;36m              \u001b[0mFlexCredit cost after a simulation run.                           \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "job = web.Job(simulation=sim, task_name=\"ring_resonator\")\n",
    "estimated_cost = web.estimate_cost(job.task_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cost is high due to the long `run_time` but that's expected for simulating high-Q devices. Now we are ready to run the simulation on the server. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:48:30.671946Z",
     "iopub.status.busy": "2025-05-15T10:48:30.671800Z",
     "iopub.status.idle": "2025-05-15T10:48:35.263267Z",
     "shell.execute_reply": "2025-05-15T10:48:35.262935Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:48:31 CEST </span>status = success                                                  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:48:31 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cf48057587fb4ca2bd8e843f5e52ac14",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:48:35 CEST </span>loading simulation from data/simulation_data.hdf5                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:48:35 CEST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from data/simulation_data.hdf5                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data = job.run(path=\"data/simulation_data.hdf5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Result Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After the simulation is finished, we can plot the monitor data. First, we can plot the field distribution at the central wavelength. We only recorded the field distribution at the central wavelength but in principle we can record at multiple wavelengths as needed, for example one at the dip of the through port transmission and one at the peak of the drop port transmission."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:48:35.277348Z",
     "iopub.status.busy": "2025-05-15T10:48:35.277287Z",
     "iopub.status.idle": "2025-05-15T10:48:35.750294Z",
     "shell.execute_reply": "2025-05-15T10:48:35.750070Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data.plot_field(\"field\", \"Hz\", val=\"real\", vmin=-0.3, vmax=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we plot the through and drop port transmission spectra. From here we can determine the free spectral range of the MRR. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:48:35.751313Z",
     "iopub.status.busy": "2025-05-15T10:48:35.751233Z",
     "iopub.status.idle": "2025-05-15T10:48:35.802512Z",
     "shell.execute_reply": "2025-05-15T10:48:35.802173Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extract transmission of the mode from the monitors\n",
    "T_through = np.abs(sim_data[\"through\"].amps.sel(mode_index=0, direction=\"+\")) ** 2\n",
    "T_drop = np.abs(sim_data[\"drop\"].amps.sel(mode_index=0, direction=\"-\")) ** 2\n",
    "\n",
    "# plotting\n",
    "plt.plot(ldas, 10 * np.log10(T_through), label=\"Through port\", c=\"red\")\n",
    "plt.plot(ldas, 10 * np.log10(T_drop), label=\"Drop port\", c=\"blue\")\n",
    "plt.xlabel(\"Wavelength (μm)\")\n",
    "plt.ylabel(\"Transmission (dB)\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also zoom in to one of the peaks and determine the Q-factor from the width of the peak. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:48:35.803680Z",
     "iopub.status.busy": "2025-05-15T10:48:35.803602Z",
     "iopub.status.idle": "2025-05-15T10:48:35.849838Z",
     "shell.execute_reply": "2025-05-15T10:48:35.849439Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZMnbvHrBggdSpatZMNoRLT9ckvKtXJYSNG2Vq1+rV0itlxSOQZlOmDLBjh3Qu3r0rHTw//qhxUOfPyy8kNFR2Yb52Tc5PniyVUXfvBt58Uzb6JqICYyJFZCt8fYE33pAq0VFRwAsvAM7OshKwe3fpWbBwF8iZM7LB8MGD8jn8xx+y6a8jK1ZM1g88+yyQkiJ1sz75RINADh2SWhMVKwJz5kjyXauWlGo/dQoYN47DdkQqYCJFZGt0Oinm+eOPMin9vfekWuS+fXJ9164yBGhm//0nSdSpU7K3886dUsSdpGbWjz/KnDFFkdHY0aMt1Gm4b5+s+KxdW8rHp6YCrVoBv/8utcxefpkr7YhUxESKyJaVKgVMmSJDNIMGSQ/V2rWyz9+QIcCNG2Z52j17ZDjv8mV5qh072LnxMGdnGYmdOlUuf/ihTD9KSTHTE965I7/zxx+XpMnJSbrD9u0DtmyRyeWOON5KZGZMpIjsQcmSUi390CGZ9Z2WJiUVKlaUTe6SklR7qs2bpQj7rVtSmPLPP4GQENUe3q4YNmZevFgSq+XL5dcTH6/ikyiKlC+oWlV+54oiuyyfOAF8951soE1EZsNEisieVK8uRYy2bAHq1pVP7LFjgbAwua6QVq2S3Wzu3QPCw2UuULFiKsRt5yIipBSEt7e0WatWstdvoR0/Lr+IXr1kErnh9/ztt0CFCio8ARE9ChMpInvUqpUM6SxbBpQuLZvehYfLSq20NJMe8quvZE57SorMc//lF5maRfnTrp2sESheXH41Tz0FXLxo4oMlJQHjx8tmwFu2SAHN99+XWf+tWqkaNxHljYkUkb1ycpJVW8eOAf37y5DPhAkyEfn69QI91MyZwKuvymTpV1+VESN3dzPFbccaNpRFlqVLSwX0J5+UEbgC2bpVEqgpUySrbd9eZv6/+y5/KUQaYCJFZO+8vGQzuKVL5XxkpAz7bd/+yB9VFFklP2qUXB49WrZEcXY2c8x2rGpVmZxfubIsunzqKelIeiRFkToK4eGyVLJUKeCnn2QjQw7jEWmGiRSRo+jdG9i7V0pvX74MtGwJzJiR65p8RZE9895/Xy5Pmybz1rnwq/DKlpVkqk4dmdrUvLmUj8iVXi+1FIYNk99X375ATAzw/PP8hRBpjIkUkSOpUUOSqZdflrlSY8YAnTsDsbFZ7paWJtUU5syRy59+Kncl9ZQoIXOmnnxSqqCHh0vVgmzu3AE6dMjYf2fmTJmwVqSIpUMmohwwkSJyND4+Mgn9yy9lTs2vv8refvv3A5D6jX36yBCekxPw9ddSnojUV7SobK3Tvr3MH+/cGfjhh0x3OHVKdkDevFmW/K1ZI92E7IUishpMpIgckU4ns8Z37wYqVZL92Fq3RvLfh9C9u9Q7cnGR8kR9+2odrH3z8pL86MUXZQTvpZckx8Wff8q2P0ePyuz0HTsk0yIiq+JAe7MTUTZ16khPVIcOSNx5AM8/cQO/6wE3N5nH3KmT1gE6Bjc3SV79/WUE77XXgDtOv2FUeqws9Vu7FggO1jpMIsoBe6SIHJ2vL+JXrkcHnz/xu/5peOkS8etXV5lEWZizM/DF5wrGNN4GAHgnfTrerboKStQ2JlFEVoyJFJGDu30bCO9WFH8kNICvUzw2Km3QeuKTwJUrWofmcHTTp2Ha7paYjtEAgA+OPoch73hZZrNjIjIJEykiB3b9ulRB2LNHtnrZ8ksSnix/WSY5h4ertI8J5ctPP0lRTQCjPw01LtL74gupXKHXaxwfEeWIiRSRg7p0SeoXHTwoex5v2wY06FBCVoiFhEi17HbtgLg4rUO1f3//LdkSILWihgzBwIEZk/6XL5eSUffvaxsmEWXHRIrIAZ05IxW1jx4FQkOlyHnNmg9urFBBkqnixWUieseOQGKipvHatQsXZDVeUpLUi5o1y3hTjx6yos/DQ/Y2bN9e9qEmIuvBRIrIwRw9KknUmTNS+WD7dtmuJItq1YBNmwA/P7nDc88BycmaxGvXEhJkaeTVq8BjjwErV2bbf+eZZ6RQZ5Ei0mv49NPZ6qcSkYaYSBE5kOhooFkzGdarUUNKFZUtm8ud69aVYp1eXlI1sndv2TeG1JGWBvTqJWOrJUpIl5Ovb453bd5c9ioOCJBRQMPvkIi0x0SKyEHs3i0Ty2/ckELm27blY1X9E09IDSM3Nym5/c03lgjVMYwZA6xbJ9Xl16wBypXL8+4NGkjiGxICHDkCNG0KHD9ukUiJKA9MpIgcwLZtQOvWsm1b06bSu1G8eD5/uHVrYOJEOT9sGMsiqGHRItkzDwCWLJFtYPKhenXZ3LhyZSlG/8QTxp19iEgjTKSI7Nxvv8kk5Xv3ZH6NYepTgYwaBdSvL5nY4MEc4iuMqChpQ0AS1JdeKtCPlysnu8XUqyfVKVq0kMSYiLTBRIrIjq1cCXTpIsvmO3UC1q+XvW8LzMUFWLwYcHWVob7vvlM9Vodw6pTUMUhNlSV548eb9DAlSkg+1qqVzFdv3x5YtUrlWIkoX5hIEdmpefNkLnNqKtCzp3zQengU4gFr1gTee0/Ov/kmcO2aKnE6DEUB3nhDSsk3bgx8/bVU3DSRr6+sBXjuOSAlBeje/cFmx0RkUUykiOyMogBTpwKvv57x2f3NN9KZVGhjxwK1a8v6+yFDVHhAB7J+vax+dHOTX0ihslrh4SFrAAYMANLTZbPjDz7gyCuRJTGRIrIj6enAyJEZHUfjxgGffgo4qfWX7uoqQ3wuLrKlyU8/qfTAdi45GRg+XM4PHy4FvFTi7AwsWGDcXQbvvguMGAHuz0dkIUykiOxEairQvz8wZ45cnjMHmDy5UKNHOatbV3qmAOn24n58jzZnjsyPCg7OyHhUpNMB778PzJ0rl+fOBV5+mTVUiSyBiRSRHbh/H+jWTVbSOzsDS5cCb71lxid87z2pxH3jBjB0qBmfyA5cvixZDgDMmCElys1k2DDg22+lw3DlStkq8c4dsz0dEYGJFJHNi4+XbUTWrJHajqtWZex/azZubjLE5+wsn9hr1pj5CW3YmDFSe6JxY5n9b2a9egEbNmRsKfPkk7KdHxGZBxMpIht2/bosgd+6FfDxkQ/QLl0s9OQNGkh9KQAYNAi4dctCT2xDdu2SieU6HfDJJypOVstb69ayRWJICPDff1Lv89Ahizw1kcOxq0SqXLly0Ol0WY7p06drHRaRWZw8KVXK9+2TPdiiomQLGIuaMEE2OL52zcxjiTYoPT1j2LNvX6BhQ4s+fe3aksdVry778j31FLBli0VDIHIIdpVIAcDkyZNx5coV4/Hmm29qHRKR6vbulSTq1CmgfHngr7+kg8jiPDykHpKTk/S87NypQRBWaskSyXJ9faUmgQbKlJEq6M2bA3FxUrjz2281CYXIbtldIlWkSBEEBQUZD2+TyjgTWa9ff83YfLh+fel1qFJFw4AaN5YeFwCYPVvDQKzI3bsZKxsnTABKltQsFH9/KV/14ouAXg+88gowfTprTRGpxUXrANQ2ffp0TJkyBWXKlEHPnj0xfPhwuLjk/jKTk5ORnGmNcFxcHABAr9dDr9ebPV57ZWg7tmHhZW7LxYt1eP11Z6Sl6dCmTTq++y4NPj7yAampIUPg+tVXUNasQerx49JNZoUs9b50mjgRztevQ6lSBakDB2r+C3JykpWcpUo5YfZsZ4wdC5w+nYa5c9NNKtTKv2/1sC3Vo1Ub6hTFfr6XzJ49G/Xq1UOxYsXw119/YezYsejbty9m5/EteeLEiZg0aVK261esWAEvLy9zhkuUb4oCfP99GL77rioAoGXL83jjjWi4uFjPn2+TiRNRIjoapzp1wr/9+2sdjmZ8Ll5Ey2HD4JSWhl3jx+N6vXpah5TF+vUV8NVXj0FRdKhV6wbeeedv+PjwQ5xsX2JiInr27Im7d+/C19fXYs9r9YnUmDFjMGPGjDzvExMTg6pVq2a7/uuvv8bAgQORkJAAd3f3HH82px6p0NBQXLlyBQEBAYUL3oHp9XpERkYiPDwcrqrsTeK4kpL0eOGF64iMLAcAGDMmDZMmpatfaLOQdBs3wqVTJyhFiiD1zBmZG2RlzP6+VBQ4d+oEp02bkP7MM0j7+Wf1n0MF69fr8Morzrh3T4fKlRWsWZOKypXz//P8+1YP21I9sbGxCA4OtngiZfVDeyNHjkRERESe96lQoUKO1zdq1Aipqak4e/YswsLCcryPu7t7jkmWq6sr39QqYDsWTkIC0KOHMyIjy8HJScFnn+kweLAzAGetQ8vumWeAatWgi4mB67JlGVuiWCGzvS9/+QXYtAlwc4PT3LlwstL3/rPPyrqATp2AEyd0ePJJV6xeDbRoUbDH4d+3etiWhadV+1l9IhUYGIjAwECTfjY6OhpOTk4oUaKEylERmd/Fi/JBFx3tBDe3NCxfruCFF6z4T1ankxIIAwdKzaShQ6VgpyOZPFlOVd5Pzxxq15bVn127Anv2AOHhwLx5wKuvah0ZkW2xm1V7u3btwty5c3Hw4EGcPn0ay5cvx/Dhw/Hyyy/D399f6/CICmT/fqBRIyA6GggMVDB58k506WLVo/DilVekqNXZs45X7fzQISl34OoqO0fbgKAgqT/20kuyV+OAAcDbbwNpaVpHRmQ77CaRcnd3x3fffYfmzZujRo0amDp1KoYPH46FCxdqHRpRgaxZAzRrJlu0Va8O7NiRiqpVb2sdVv54ekqVcyBj92RHsXixnHbuDJjYi64FT09gxQpg4kS5PGuW9FLFx2sZFZHtsJtEql69eti9ezfu3LmDpKQkHDlyBGPHjs11kjmRtVEUYOZM4LnngMREoE0bKbRppZUEcvfGG9Irs3Mn8PffWkdjGSkpUpAUAPr10zYWE+h0Uu5q5Uqpsbp+PfDEE8Dp01pHRmT97CaRIrJlej3w2muydZ2iAIMHS+FNPz+tIzNBcLCMFQGO0yv1yy9AbKxsbtemjdbRmOyll2Sj46Ag4PBhqZYfGal1VETWjYkUkcZu35atOxYtkp6BuXOBzz8H8qgja/0MK/Z+/FFmzdu7r76S04gIG//Fydy8ffuAxx+X92a7dtJTat2Fcoi0w0SKSEOGjYe3bAG8vYF164Bhw2B1NaIKrG5d2eAtNRX47DOtozGvixdlDxYgY6scG1eqFPDHHzJKmZ4uPaW9esmQMxFlxUSKSCObNgENGwJHjwKlS8uUoo4dtY5KRYZeqYULgXv3tI3FnJYtk2yjWTOrL3lQEB4e0kv62WfSybZypcybOntW68iIrAsTKSILUxTZ27d9e+DOHdnzd+9eqetjVzp2BCpWlPGhpUu1jsY8FAX4+ms5b4OTzB9Fp5O1A1u2ACVKSDmOBg2ArVttvcuUSD1MpIgsKCkJ6NNHygylp8tI0LZtMj/b7jg7yzglIBO/0tM1Dccstm8HTp0CfHyAF17QOhqzadZM5k01aCBz6jt0cMbatRU5b4oITKSILObiRflA+uYbyTE++UTmKNt1hY6+fWXp4YkTwG+/aR2N+gy9US+9JJPc7FhoKPDnn/JFID1dh8WLH0PPns6Ii9M6MiJtMZEisoC//pJv8/v2SeHvTZuAN9+0g0nlj+LjI+WyAfsrhRAXJ6sSAaB/f21jsRBPT6k7OmdOGpyd07FqlRMaNJBSCUSOiokUkZktWiSbwV67BtSqJTUqW7XSOioLevNN6YLbuhU4eFDraNTz/feyjK1aNakZ4CBk3lQ6pk7dgdKlFZw4IS/fXqfBET0KEykiM0lOBl5/XTpk9Hrg+edlZZ7NVSovrDJlpFw7ACxZomkoqso8ydzuuxazq1r1NvbuTUXbtjL3LyJC3utJSVpHRmRZTKSIzODcOeCpp4B58+TylCkyCuTjo21cmunWTU4N9ZZs3ZEjwO7d0tP2yitaR6OZ4sVl6tvkyZJLLlokddFOndI6MiLLYSJFpLKNG4F69WQIr1gx+aB57z2H7LTI0Lo14OQExMQAFy5oHU3hGTYo7tgRKFlS21g05uQEjBsn8/4CA6VEQr16wM8/ax0ZkWUwkSJSSXo6MGmS1Ie6dQuoXx/Yv18uOzx/f9lzBLD9Xim9XopwAg4zyTw/WrcG/vlHinbGxclo7tChwP37WkdGZF5MpIhUcPMm0KEDMHGi1GgcOBDYsQMoV07ryKxI27ZyauuJ1K+/Atevy86+zJKzKFUKiIqSOmkA8OmnMhE9JkbbuIjMiYkUUSHt3Su9Txs3yvLwpUuB+fNliw3KxJBIbd4se/DZKsMk8969bX6DYnNwdZVNjn/9VYb6Dh2Sv4+FC7nxMdknJlJEJlIU4IsvgCefBM6fl23W9uyRz1fKQcOGQNGisi/O339rHY1prlzJKCxqJxsUm0uHDpJEhYfLSr6BA6X4+61bWkdGpC4mUkQmuH1bPhTeeEOmzDz7rBTbrFlT68ismIuLTKQBbHd475tvgLQ0WZpWtarW0Vi9oCDg99+Bjz6SnqrVq2VPyT//1DoyIvUwkSIqoJ07gTp15EPB1RWYNQtYtUp2QqFHaNdOTm01kTKs1rPDDYrNxckJePttqe5fqZJsldSyJTB+vG2P8BIZMJEiyqe0NOD994HmzTOG8nbtAkaMcPDSBgVhmCe1d69069mS8+eBo0eldpShLhblW4MGwIEDhr36pLbaU08Bx49rHRlR4TCRIsqHy5dlrse4cZJQvfyyfCjUr691ZDamdGmgenX5JN28WetoCmbHDjmtVw/w9dU2FhtVpIgUt1+xQppw927p3f30U3lLENkiJlJEj/DrrzKvIyoK8PaWVXnffCMfCmQCQ6/U779rG0dB7dwpp08+qW0cdqBHD9no+OmnZSL60KHyReXcOa0jIyo4JlJEubh/Hxg+XIpX37wJ1K0rvVBclVdImetJ2dJ6eEOPFBMpVZQpI9XQP/tMyoZs3SqLNRYvtq23BRETKaIcREfLav25c+XysGEyH6pKFS2jshPNmkmRrUuXZM86W3DnjnShAFK6m1Th5CQrXw8eBJo0AeLjZR5/ly7A1ataR0eUP0ykiDJJSwOmTZPdTP79FyhRAvjlF0mo3N21js5OeHpKMgXYzuq9Xbukm6RSJYffW88cKlcGtm8Hpk8H3Nzkb+6xx4AffmDvFFk/JlJED5w6JZ/v//uf1Ibq2lWSqY4dtY7MDtnadjEc1jM7Z2dg9Gipx1a7NhAbC7z4IvD881IHlchaMZEih6cowJdfyj/vv/6SSeSLF0udqMBAraOzU4ZE6s8/ZbaxteNEc4upWVOqY4wbJzVcf/4ZqFZNduZh7xRZIyZS5NCuXQM6dwZeew24d096pA4dAiIiWBvKrKpXl1II9+9bf5nrlBTZ+wdgImUhbm7A5MnA/v1SYuTuXaB/f1nZd/q01tERZcVEihzWjz/KPIz16+Uf98yZUuKgXDmtI3MAOp3tDO8dOCAJX/HiXG1gYbVqSa2pjz6S9QlbtkiP1Zw5Mp+RyBowkSKHc/Wq7JPXvbuUNahdW+ZljBwpq4jIQmwlkTLMj3riCXZTasDFRbaYOXwYaNECSEyU3QSaNpU5jERa48cGOQxFAb79FqhRQ/bGc3GR/b727uVmw5po3Voy1yNHgAsXtI4md5xobhUqVZIeqYULpSr63r1SZP7dd21jmh3ZLyZS5BAuXZK5UK+8Aty6JcU19+0DJk2SYT3SgL+/1JkApDKjNVIUTjS3Ik5OwIABknt37iyraz/4QIboba1QPtkPJlJk1xQF+OormdtsmAs1darMHa5dW+voyOq3izl+XMZ/PTyk+4OsQqlSwJo1srK2VCmZgN6+vZRLuHxZ6+jI0TCRIrt19qx8Tr/6KhAXBzRqBPzzj9SJcnXVOjoCkJFIbd4MpKZqG0tODMN6jRqx69LK6HTAs88CMTGylZOTkxTwrFZNtp3hZHSyFCZSZHdSU4FZs2QuVGSkdCbMmiUjNNWrax0dZdGwIVC0qGzB8vffWkeTHedHWb0iRYDZs2Wo/vHH5UvTm28CjRvLgksic2MiRXbl77/ls/ntt2V1j6Eu1IgRUjmZrIyLi0w6B6xz9V7mFXtk1erWlYK6X3wB+PlJYtWwoezld+uW1tGRPWMiRXYhPl42Fm7cWDYc9veXuVHbtsk+XmTF2rWTU2tLpK5dA06elDGkJk20jobywdkZGDwYOHoU6NEDSE+XxKpKFWD+fA73kXkwkSKbt2aNzIv45BP5x9mrl/wj7dePZX9sgmGe1N69wO3b2saSmWG1Xs2aMvxINiMoCFixAti6VVb0xcZKgtWgQUYnI5FamEiRzbp4USabPvuslDeoUEFW0X/7LVCihNbRUb6VLi2T19LTZdK5teD8KJvXsqUsMPn0U8mFo6OBp56SL1uXLmkdHdkLJlJkc1JSZMuIatWkN8rFBRg7Vqoch4drHR2ZxBqrnDORsgsuLsCQIVLJ4rXXpJd6xQogLAyYPh1ITtY6QrJ1TKTIpmzeLPWf3nkHSEiQqSsHDkhRPk9PraMjk7VqJaeGzYG1du9expIvTjS3C4GBwIIFMgm9aVP5FY8dK1/IfvhBas4RmYKJFNmECxeAbt2kx+noUfmnuHixdBpwexc7YKhLceKEdcwI3rNH4ggNBcqU0ToaUlG9evJ/45tvgOBg4MwZKeTZtGnGtDiigmAiRVYtORmYNg2oWhX46Scpujd0qHTTR0Rwk2G7UbYs4O4uv/Dz57WOhtvC2DmdDnj5ZcnbJ00CvL2B3bvl1/3CC7JYkyi/+DFEVmvjRult+t//pCbUk0/KxNGPP+YiKrvj7Cy70gLAsWPaxgJwfpSD8PaWjctPnJA9/JycZEPzatWknMrNm1pHSLbAZhKpqVOnomnTpvDy8kLRXD5Fz58/j2eeeQZeXl4oUaIERo0ahVRr3HaC8nT0KPDMM1Je6MQJWcr8zTfAn38CtWppHR2ZTViYnB49qm0cqalS2RFgIuUggoOBhQuleG+HDvIW+OQTye0//BBIStI6QrJmNpNIpaSkoFu3bhg8eHCOt6elpeGZZ55BSkoK/vrrLyxduhRLlizB+PHjLRwpmSo2VrZ2eOwx4LffZLXN8OHSQfHyy6wJZfcMiZTWPVKHD8tKBj8/2WeIHEaNGsCvv8rWUnXqAHfvAqNHS0K1YAGg12sdIVkjm0mkJk2ahOHDh6NmLjOLN23ahCNHjuDbb79FnTp10L59e0yZMgWff/45UlJSLBwtFURKCjBnjvyzMmw22rkz8N9/soeWr6/WEZJFWEsiZRjWa9KE+wo5qNatgf37gaVLZfre5cvAoEGyJmLlSil5RmTgonUAatm1axdq1qyJkiVLGq9r27YtBg8ejP/++w9169bN8eeSk5ORnKmQSFxcHABAr9dDz68fJjO0XV5tqCjAunU6jB3rjJMnpbupZk0FH32UhlatlAc/b/5YrV1+2tIe6CpWhAsA5dgxpJrpteanLZ23b4cTgLQmTZBu521uKkd5T/boATz3HPDll06YNs0JJ0/q0LMnMH26gsmT09C+vVLonnJHaUtL0KoNC51IJScnw93dXY1YCuXq1atZkigAxstXr17N9eemTZuGSZMmZbs+KioKXl5e6gbpgCIjI3O8/tQpPyxZUgOHDwcCAIoWvY9evWLQqtV53L8vQ3uUVW5taS9cExLQAYDu8mVsWrUKqWYsDJZrWyoK2mzZAk8Au5ydEcs3Yp7s/T1pUKEC8Mknzvjll4pYs6YSDh1yRdeuLqhWLRYvv3wENWoUfldkR2lLc0pMTNTkeQucSG3YsAHfffcdtm/fjgsXLiA9PR3e3t6oW7cu2rRpg759+yIkJCRfjzVmzBjMmDEjz/vExMSgatWqBQ0z38aOHYsRI0YYL8fFxSE0NBQtW7ZEQECA2Z7X3un1ekRGRiI8PByurq7G60+fBiZMcMb338uosru7grfeSsc77zijSJHHADymUcTWK7e2tEfK8OHQ3biBNuXLS8EflT2yLc+eheutW1BcXdFoyBCAX6Zy5Ejvycyef17mcn70URq++MIJMTEBePfdpxAeno7x49PRqFHBq3o6aluaQ2xsrCbPm+9E6ueff8bo0aMRHx+PDh06YPTo0QgJCYGnpydu3bqFf//9F5s3b8aUKVMQERGBKVOmIDAwMM/HHDlyJCIiIvK8T4UKFfIVX1BQEPbu3ZvlumvXrhlvy427u3uOPWqurq58U6vA0I7XrwPvvy87sBt6X3v2BD74QIeyZZ0BcC7KozjEezIsDLhxA66nTgGNGpntaXJtyweV1XX168PVz89sz28vHOI9+ZCgIGDWLGDECGDKFOCrr4DISCdERjqhfXupS9WwYcEf1xHbUm1atV++E6kPP/wQc+bMQfv27eGUQxXE7t27AwAuXbqETz/9FN9++y2GDx+e52MGBgY+MtnKryZNmmDq1Km4fv06SjzYsTYyMhK+vr6obqiaTBaXkCAbhn70kZwHZFu1adOAXKatkSMLC5PJ3lpNOGf9KMqnUqXki+Ho0fIlcelSYMMGOTp2lITKDJ2qZIXynUjt2rUrX/crVaoUpk+fbnJAuTl//jxu3bqF8+fPIy0tDdHR0QCASpUqwcfHB23atEH16tXxyiuv4MMPP8TVq1fx3nvv4Y033rCKOVyORq8HNmwoh4EDXfCgYxD16wMzZgBPP61tbGTFtF65Z6hozv31KJ/Kl5deqbFjJaH65htg/Xo5unQBJk6UUgpkv2ym/MH48eNRt25dTJgwAQkJCahbty7q1q2Lffv2AQCcnZ2xfv16ODs7o0mTJnj55ZfRu3dvTJ48WePIHUtaGvDtt0CtWi5YsKA2rl3ToWJF4LvvgL17mUTRI2iZSN2+Dfz7r5xnIkUFVKkSsGQJEBMjde+cnIC1a6Xn/dlnZbNksk8FTqROnDiBVatW4cyZMwCAX3/9Fc2aNUPDhg0xdepUKGbaQnvJkiVQFCXb0aJFC+N9ypYti99++w2JiYm4ceMGZs6cCRcXu6nwYNUUBVi9WiqPv/IKcOqUDn5+9/Hxx2k4ckQ2BeW+ePRIhkTq+HHLF+s5cEBOK1WSXbGJTFClivRK/feflE/Q6YA1a2TeVPv2GaPHZD8K9NH2888/o3r16ujZsyeqVauGZcuW4YUXXoC3tzdKliyJiRMn4sMPPzRXrGSFFEXmBDRoICtajhyRffCmTEnD/PmbMXhwOtzctI6SbEaFClLSPjERuHTJss997pycGvb8IyqEqlWBFSvkf2Lv3lLb9fffgaeeAlq0ADZvlv+fZPsKlEhNnToV77zzDu7fv4958+Zh0KBBmDZtGjZs2ID169fj888/x5IlS8wUKlmbbdvkn0KHDvJl3scHGDcOOHMGGD06HZ6eaVqHSLbG1RWoWFHOW3p47/x5OS1TxrLPS3atalWZiH78OPDaa/IW/+MPIDxciuf/+quOCZWNK1AidezYMfTr1w86nQ59+vRBSkoKWrdubby9TZs2OGf4Vkd2a8cO+SfQsqXMzfXwAN5+W2pETZ4sPVJEJtNqnpQhkSpb1rLPSw6hQgXZr+/UKWDoUPm/uWcP8OyzLhg+vAWWL9dxJwcbVaBE6t69eyhSpIj8oJMTPD09s1T/9vT0zLLdCtmXP/+UyeJPPSXd0q6uwOuvyz+Gjz7itBJSidaJFHukyIxCQ4GPPwbOngXeeQfw8VFw9qwf+vZ1QcWKsu9ofLzWUVJBFCiR0ul00GXaWOjhy2Sftm2T3qfmzYGtWyWBeu016ar+/HMgn4XsifKHiRQ5gJIlpRzMyZOp6NXrCEqWVHDhghT6LFMG+N//gDx2NyMrUqBESlEUVKlSBcWKFUOxYsWMZQgMl825lQtZlqJI0tS8uSRR27ZJAjVoEHDypHRRlyundZRkl7RIpBSFiRRpolgxoFu3EzhxIhULF8qqvzt3pGhx2bLAq68CR49qHSXlpUC1ARYvXmyuOMhKKIqsLPngg4xlum5u8sc8Zox0SxOZlSGROn8eSEoCzLh5sdGNG0BysqxVL1XK/M9H9BAPD2DAAKB/f+CXX4APPwT++kuKfX71lZROeOstmZ/KgSDrUqBEqk+fPuaKgzSWlgb89BMwfTrwoGg83N3lD3v0aKB0aU3DI0dSvDjg7y8FMk+ckOJk5mbojQoJka5XIo04OUlF9C5dJJH66CMp7GnYfqZ6dWDYMCn6yT21rQNLJDq45GRg4ULpBHjpJUmivL1lnP70adknj0kUWZROZ/nhPQ7rkRVq2hT4+Wf5PjFsGFCkiNSlGjhQRgfGjgUuXtQ6Ssp3IuXv72+cC/Wog6xffDwwc6bsEzVwoKy8CwiQjTbPnZPdzTmJnDTDRIrIqGJFYO5cSZrmzpVSCrduyQhCuXLyJfivv1jgUyv5HtqbO3eu8XxsbCzef/99tG3bFk2aNAEgmxpv3LgR48aNUz1IUs+VK8BnnwFffCETGgHpcRo5UobxvL01DY9IMJEiysbXV3qmhgyRTZHnzpWFQN9/L0fdunJbjx6WmVpIIt+JVOb5Uc8//zwmT56MIUOGGK8bOnQoPvvsM2zevBnDhw9XN0oqtH//lV6mFSuAlBS5LixM5j/16gVu40LWhYkUUa6cnTPmUUVHy5fj5cuBf/6RyeqjRsnp4MEy6kDmZdIcqY0bN6Jdu3bZrm/Xrh02b95c6KBIHYoihTPbtQNq1pSdyVNSZGP71atlU82+fZlEkRXKnEhZYrzCsCMDEymyMXXqAIsWybDfRx/JUN+tW3K+YkWgUydg40bL7wHuSExKpAICArB27dps169duxYBAQGFDooKJyVFdh+vU0eWym7cKCtBXngB2LVLyho8+6x8qyGySpUqyZs2Lg64ds38z8ceKbJxAQGyVdfJk1I+oU0b+Q6yfr18mQ4Lk+Tqxg2tI7U/BSp/YDBp0iS8+uqr2LZtGxo1agQA2LNnD37//Xd8+eWXqgZI+XfjhhTK/OILmQsFyPLY/v2l/kiFCpqGR5R/7u7y1fr0aemVCgoy33MlJQHXr8t5JlJk45ydgY4d5Th+XD4PFi+WBOudd4D33pMv1YMGAU8+yZpUajCpRyoiIgI7d+6Er68vVq9ejdWrV8PX1xc7duxARESEyiHSoxw6JMlSaCgwbpwkUUFBUlTzwgXgk0+YRJENstQ8KcP6cW9vqV9FZCeqVJEJ6Zcvy/Bf/foyYrFiBdCsGfDYY1LixrDwiExjUo8UADRq1AjLly9XMxYqgLQ04Ndf5Y8kKirj+gYNZFVH9+6c+0Q2LixMKhCaO5EyDOuVLcuv52SXvL3ly3b//sC+fTJysWKF1KQaOlQWHb34otz+xBP8MyiofPdI3bt3r0APXND7U/7cuSPJU5UqsmIjKkq6crt1A3buBPbulYq3TKLI5hl6pMy90RjnR5EDadAA+PJL6aX67DOgRg0Z3V6yBHjqKaBaNdmehhsm51++E6lKlSph+vTpuGKYfJMDRVEQGRmJ9u3b45NPPlElQBKHD8uYdqlSwPDhMnXE31++SZw+Dfzwg1TB5TcJshuWGtpjIkUOyM8PeOMN+WzZsUNWcHt5yZ+bYVuwLl2AdesAvV7raK1bvof2tm3bhv/973+YOHEiateujQYNGiAkJAQeHh64ffs2jhw5gl27dsHFxQVjx47FwIEDzRm3Q9DrZXuAzz4Dtm/PuP6xx6To2ssvs4Am2TFDInXmjOxl5O5unudhIkUOTKeT4bwnngA+/li+lH/1lazwXrdOjqAgoHdvoE8f2euPssp3IhUWFoZVq1bh/Pnz+PHHH7F9+3b89ddfSEpKQvHixVG3bl18+eWXaN++PZy5rr5QLl+W/e8WLsxYfefsDDz3nHyDaNaMPU/kAIKDAR8fICFB9jAy139wJlJEAGQvP8NcqiNHZLXfsmUyzPfhh3LUry9JVY8eQGCg1hFbhwJPNi9TpgxGjhyJkSNHmiMeh5WeLsUzFy6Unb5TU+X6oCDgtdfkKFVK2xiJLMqwefH+/TLewESKyGKqV5e6Ux98ILWoli6VBU7798sxciTQoYMkVR07mq/D2BaYVP6A1HP1KjBtmtQfbNsWWLVKkqgnnwS++04KLk+axCSKHJS550kpChMpojy4ukoB5zVrZLTkk09kwnpqqgz7vfCCdB4PHixzrRyxgjoTKQ2kpwObNskbMDQU+N//ZBqInx/w5ptSF2r7dlmOytV35NCqVpVTcyVSN24A9+9L7xe/rRDlKTBQPqP+/lu2GBszRv5sbt8G5s+XVX8VKgDvvitDg46CiZQFXbwITJ2avfepSRNZemrI9mvW1DpSIith7h4pQ29UcDC/tRAVQPXqMppy7hwQGSlDfD4+cvmDD6SsQt26wMyZwKVLWkdrXkykzCw5GfjxR6B9e6n39957Gb1PQ4ZI79Nff8lqCC8vraMlsjKWSqQ4rEdkEmdnoHVrmUN17ZpMSenUCXBxAaKjgVGjZOSlVSuprn7rltYRq4+JlJkcPCgVxkNCpMr477/LkF6zZhm9T59+yt4nojxVriynt24BN2+q//hMpIhU4+UlU1LWrZP5v/PmyXxfRZHi0QMGyAKqTp2ksnpCgtYRq8PkLWLu3LmDvXv34vr160h/aHZZ7969Cx2YLYqNlWx88WJZ1WAQEgJERMhh+Fwgonzw8pIk5/x56ZUqXlzdx2ciRWQWAQFSRHrQIODsWflsXLlSRmHWr5fD01OSqh49gHbtAA8PraM2jUmJ1C+//IJevXohISEBvr6+0GUqaqTT6RwqkUpJke3Ali6VN4ahAqyrq1SF7dcPaNNGuj+JyARhYRmJ1BNPqPvYTKSIzK5cOZmYPmaMTEI3JFUnT0oB0B9+AHx9ZXXgCy8A4eG2VU7BpKG9kSNHol+/fkhISMCdO3dw+/Zt43HLHgdAH6Io0uM0bJisWOjaVSqQ6/Uyuc6w27ZhbhSTKKJCMOc8KSZSRBZVvToweTJw/Lis/hs5Uj5H4+KkQ6JTJ6BECeCVV6Sm4v37Wkf8aCb1SF26dAlDhw6Fl4PNjr50ScZ1ly6VpZ8GQUFAr14yYZxznohUZolEqmxZ9R+biHKl00k9qgYNpGL6jh3S+bBqlezo8e23cvj4SHLVrZsM/3l6ah15diYlUm3btsW+fftQoUIFteOxOnFxwOrV8gvdulV6owDpduzaVZKn8HBZoUBEZmCuROr+fVlmBLBHikhDTk6yEKtZM9nvb9cu4Kef5Lh4UYYBV66UKZPt2slnb8eOgL+/1pELkz7+n3nmGYwaNQpHjhxBzZo14erqmuX2zp07qxKcljZu1GHduuxdi08+KV2O3bsDRYtqFh6R4zAkUqdOSeE1tb61XLwop97e1vMfmcjBOTllbKI8axawd29GUnXunHRsrF4tU2ZatJCkqksXKbGgFZP+Iw0YMAAAMHny5Gy36XQ6pKWlFS4qK9CrV0bThIVJ8tSzJ1C+vIZBETmi0qWlPz8pSYqwqbX0NfP8KO4CTmR1nJyAxo3l+Ogj4J9/ZKuaNWuAw4eBLVvkePNN2Uw5PFybik4mJVIPlzuwR4GBCnr1Al5+GahXj/9niTTj5ARUqSLF2Y4dUy+ROndOTjmsR2T1dDr5LK5XTyarnzwpI0Zr1gA7dxo2U9ZmZRcLcubi8OFUzJkjWS6TKCKNmWOeFFfsEdmsSpVkxd/27VL8c9EiIDxcm04ekxOpP/74A506dUKlSpVQqVIldO7cGdu3b1czNk1x8jiRFWEiRUS5KFEC6N8fWLlSm2lFJiVS3377LVq3bg0vLy8MHToUQ4cOhaenJ55++mmsWLFC7RiJyNExkSIiK2VSv8vUqVPx4YcfYvjw4cbrhg4ditmzZ2PKlCno2bOnagESETGRIiJrZVKP1OnTp9GpU6ds13fu3BlnzpwpdFBERFlUqiSn164BiYmFfzxFYSJFRKowKZEKDQ3Fli1bsl2/efNmhGpZzIGI7JOvb8Z5NbaMv3lTCsTpdLI/BRGRiUwa2hs5ciSGDh2K6OhoNG3aFACwc+dOLFmyBB9//LGqARIRwclJyhonJgL37hX+8S5ckNOgINvaHZWIrI5JidTgwYMRFBSEWbNm4YcffgAAVKtWDd9//z26dOmiaoBERABk063ERFV6pHQc1iMilZhc/uDZZ5/Fjh07EBsbi9jYWOzYscOsSdTUqVPRtGlTeHl5oWgue7PodLpsx3fffWe2mIjIgnx85FSNRMrQI8VEiogKyWaqJaWkpKBbt25o0qQJvvrqq1zvt3jxYrRr1854Obeki4hsjLe3nKo5tFe2bOEfi4gcWr4TqWLFiuH48eMoXrw4/P39ocuj3PetW7dUCS6zSZMmAQCWLFmS5/2KFi2KoKAg1Z+fiDSmZo8Ut4chIpXkO5GaM2cOihQpYjyfVyKlpTfeeAOvvvoqKlSogEGDBqFv3755xpqcnIzk5GTj5bi4OACAXq+HXq83e7z2ytB2bMPCY1sKZ29vOAFIvXMHioltYWhD5cEcqdSQEJMfy5HxPaketqV6tGrDfCdSffr0MZ6PiIgwRyyFNnnyZLRq1QpeXl7YtGkTXn/9dSQkJGDo0KG5/sy0adOMvV2ZRUVFwcvLy5zhOoTIyEitQ7Abjt6Wj8fHIxjAv3v34py/f6EeS3/qFJwB7Dh/Hnd/+02V+ByRo78n1cS2LLxENWrMmUCnKIpS0B86cOAAXF1dUbNmTQDA2rVrsXjxYlSvXh0TJ06Em5tbvh5nzJgxmDFjRp73iYmJQdWqVY2XlyxZgrfeegt37tx55OOPHz8eixcvxgXDfIgc5NQjFRoaiitXriAgIODRL4JypNfrERkZifDwcLi6umodjk1jWwrniAg4rViBtBkzkJ5pV4WC0Ov12PLrr+jUvbtcvnIF4N95gfE9qR62pXpiY2MRHByMu3fvwjdz7TkzM2my+cCBAzFmzBjUrFkTp0+fxosvvojnnnsOP/74IxITEzF37tx8Pc7IkSMf2btVoUIFU0IEADRq1AhTpkxBcnIy3HOpFePu7p7jba6urnxTq4DtqB6Hb8sH/xidk5LgXIh28IyNlTNeXnAtWVKKcpJJHP49qSK2ZeFp1X4mJVLHjx9HnTp1AAA//vgjmjdvjhUrVmDnzp146aWX8p1IBQYGIjAw0JQQ8iU6Ohr+/v65JlFEZENUWrXneeOGnClThkkUERWaSYmUoihIT08HINvCdOzYEYBsHXPz5k31osvk/PnzuHXrFs6fP4+0tDRER0cDACpVqgQfHx/88ssvuHbtGho3bgwPDw9ERkbigw8+wNtvv22WeIjIwlRatZclkSIiKiSTEqkGDRrg/fffR+vWrfHHH39g3rx5AIAzZ86gZMmSqgZoMH78eCxdutR4uW7dugBkUniLFi3g6uqKzz//HMOHD4eiKKhUqRJmz56NAQMGmCUeIrIwtRIpw5c9JlJEpAKTEqm5c+eiV69eWLNmDd59911UerAz+08//WTce09tS5YsybOGVLt27bIU4iQiO6PS0J4Xe6SISEUmJVK1atXC4cOHs13/0UcfwdnZudBBERFlw6E9IrJCJu21d+HCBVy8eNF4ee/evXjrrbewbNkyrjogIvPg0B4RWSGTEqmePXsiKioKAHD16lWEh4dj7969ePfddzF58mRVAyQiAqDO0J6isEeKiFRlUiL177//4vHHHwcA/PDDD3jsscfw119/Yfny5Y/cC4+IyCRq9EjdvAmXlBQ5X7p04WMiIodnUiKl1+uNtZk2b96Mzp07AwCqVq2KK1euqBcdEZGBGonUg10OlOBggPXliEgFJiVSNWrUwPz587F9+3ZERkYaV8tdvnyZ26oQkXmoMLSne7BZsRIaqkZERESmJVIzZszAggUL0KJFC/To0QO1a9cGAKxbt8445EdEpCpDj9S9e8CDgsAFpTPsu8lEiohUYlL5gxYtWuDmzZuIi4uDf6Zd2F977TV4eXmpFhwRkZEhkVIUICkpo4eqIAxDe5xoTkQqMSmRAgBnZ+csSRQAlCtXrrDxEBHlzNMz4/y9eyYlUoahPfZIEZFa8p1I1atXD1u2bIG/vz/q1q0LXR6bfR44cECV4IiIjJycJHm6d08mnJcoUfDHMPRIMZEiIpXkO5Hq0qWLcaVe165dzRUPEVHufHwyEikT6Di0R0Qqy3ciNWHChBzPExFZTGFW7iUnQ3f1qpxnjxQRqcTkOVIGCQkJSH9oBY2vr29hH5aIKLvC1JJ6sK1VqpsbwDItRKQSk8ofnDlzBs888wy8vb3h5+cHf39/+Pv7o2jRotkmoBMRqcbQI2VKIvVgonlSYCCQxxxPIqKCMKlH6uWXX4aiKPj6669RsmTJPCeeExGpJnMtqYI6dw4AkFS8ODxUDImIHJtJidTBgwexf/9+hIWFqR0PEVHuCjO092CieVLx4mC/ORGpxaShvYYNG+KCoUIwEZGlFGZo79YtAECKn5+KARGRozOpR2rRokUYNGgQLl26hMceewyurq5Zbq9Vq5YqwRERZVGYob24OACAPnNhTyKiQjIpkbpx4wZOnTqFvn37Gq/T6XRQFAU6nQ5paWmqBUhEZFSYob0HiVQqt7EiIhWZlEj169cPdevWxcqVKznZnIgspzBDe0ykiMgMTEqkzp07h3Xr1qFSpUpqx0NElDsO7RGRlTFpsnmrVq1w8OBBtWMhIsobh/aIyMqY1CPVqVMnDB8+HIcPH0bNmjWzTTbv3LmzKsEREWXBoT0isjImJVKDBg0CAEyePDnbbZxsTkRmw6E9IrIyJiVSD++tR0RkEaYO7aWnA/HxANgjRUTqMmmOVE7u3Lmj1kMREeXM1KG9e/cARQHARIqI1GVSIjVjxgx8//33xsvdunVDsWLFUKpUKU5CJyLzMXVo78GwnuLsjDQ3N5WDIiJHZlIiNX/+fISGhgIAIiMjsXnzZvz+++9o3749Ro0apWqARERGpg7tPUik4OsLsO4dEanIpDlSV69eNSZS69evR/fu3dGmTRuUK1cOjRo1UjVAIiIjw9BeYqLMe3LK53fBzIkUEZGKTOqR8vf3N25a/Pvvv6N169YAAEVRuGKPiMzH0COlKEBSUv5/zpBIFSmifkxE5NBM6pF67rnn0LNnT1SuXBmxsbFo3749AOCff/5htXMiMh9PTxmaUxQZ3jP0UD2KYY4Ue6SISGUmJVJz5sxBuXLlcOHCBXz44YfwefAt8cqVK3j99ddVDZCIyMjJCfDyksnmBZlwzqE9IjITkxIpV1dXvP3229muHz58eKEDIiLKk4+PJFEFmXDOoT0iMhOTEikAOHHiBKKionD9+vVsBTrHjx9f6MCIiHLk4wNcu2ZaIsUeKSJSmUmJ1JdffonBgwejePHiCAoKgi7TcmKdTsdEiojMxzAvyoShPc6RIiK1mZRIvf/++5g6dSpGjx6tdjxERHkzpZYUh/aIyExMKn9w+/ZtdOvWTe1YiIgerTCJFHukiEhlJiVS3bp1w6ZNm9SOhYjo0Ti0R0RWxKShvUqVKmHcuHHYvXs3atasCVdX1yy3Dx06VJXgiIiy4dAeEVkRkxKphQsXwsfHB3/88Qf++OOPLLfpdDomUkRkPoUd2tPr1Y+JiByWSYnUmTNn1I6DiCh/CjG0B19fIDZW/ZiIyGGZNEeKiEgzheiRUji0R0QqM7kg58WLF7Fu3TqcP38eKSkpWW6bPXt2oQMjIspRQRMpReGqPSIyG5N6pLZs2YKwsDDMmzcPs2bNQlRUFBYvXoyvv/4a0dHRKocInD17Fv3790f58uXh6emJihUrYsKECdkSuEOHDuGpp56Ch4cHQkND8eGHH6oeCxFprKBDe/fvA6mpcp6JFBGpzKREauzYsXj77bdx+PBheHh4YNWqVbhw4QKaN29ulvpSR48eRXp6OhYsWID//vsPc+bMwfz58/G///3PeJ+4uDi0adMGZcuWxf79+/HRRx9h4sSJWLhwoerxEJGGCtojZeiNyvyzREQqMWloLyYmBitXrpQHcHFBUlISfHx8MHnyZHTp0gWDBw9WNch27dqhXbt2xssVKlTAsWPHMG/ePMycORMAsHz5cqSkpODrr7+Gm5sbatSogejoaMyePRuvvfaaqvEQkYYMPVIFTaSKFAGcOC2UiNRlUiLl7e1tHFYLDg7GqVOnUKNGDQDAzZs31YsuD3fv3kWxYsWMl3ft2oVmzZrBzc3NeF3btm0xY8YM3L59G/7+/jk+TnJyMpKTk42X4x7809Xr9dBzmbTJDG3HNiw8tmVWOg8PuABQEhKQmp82uXULrpBinGxLdbAd1cO2VI9WbWhSItW4cWPs2LED1apVQ4cOHTBy5EgcPnwYq1evRuPGjdWOMZuTJ0/i008/NfZGAcDVq1dRvnz5LPcrWbKk8bbcEqlp06Zh0qRJ2a6PioqCl5eXilE7psjISK1DsBtsS+F/9CiaAUi8fh2bf/vtkfcvfvgwngCQoNNh64M2ZFuqg+2oHrZl4SUmJmryvCYlUrNnz0bCg271SZMmISEhAd9//z0qV65coBV7Y8aMwYwZM/K8T0xMDKpWrWq8fOnSJbRr1w7dunXDgAEDTAk/i7Fjx2LEiBHGy3FxcQgNDUXLli0REBBQ6Md3VHq9HpGRkQgPD89W+Z4Khm35kNKlAQBeioIOHTo88u66BxPNvUNCEB4ezrZUAd+T6mFbqidWoxpxBU6k0tLScPHiRdSqVQuADPPNnz/fpCcfOXIkIiIi8rxPhQoVjOcvX76Mli1bomnTptkmkQcFBeHatWtZrjNcDgoKyvXx3d3d4e7unu16V1dXvqlVwHZUD9vygQe9y7p79/LXHg++pTr5+Rnvz7ZUB9tRPWzLwtOq/QqcSDk7O6NNmzaIiYlB0aJFC/XkgYGBCAwMzNd9L126hJYtW6J+/fpYvHgxnB6aNNqkSRO8++670Ov1xsaMjIxEWFhYrsN6RGSDDCvv7t0D0tMfPYGcNaSIyIxMWsLy2GOP4fTp02rHkqtLly6hRYsWKFOmDGbOnIkbN27g6tWruHr1qvE+PXv2hJubG/r374///vsP33//PT7++OMsw3ZEZAcMq/YAY29TnphIEZEZmTRH6v3338fbb7+NKVOmoH79+vDO/I8NgK/K/7AiIyNx8uRJnDx5EqUfzI8wUBQFAODn54dNmzbhjTfeQP369VG8eHGMHz+epQ+I7I2nJ6DTScXye/ceXRuKiRQRmVGBEqnJkydj5MiRxgmenTt3hk6nM96uKAp0Oh3S0tJUDTIiIuKRc6kAoFatWti+fbuqz01EVsbJSXqlEhLkeLA6N1dMpIjIjAqUSE2aNAmDBg1CVFSUueIhInq0zInUozCRIiIzKlAiZRhGa968uVmCISLKFx8f4Nq1/O23x0SKiMyowJPNMw/lERFpoiD77TGRIiIzKvBk8ypVqjwymbp165bJARERPVJB9ttjIkVEZlTgRGrSpEnw8/MzRyxERPmTuZbUozCRIiIzKnAi9dJLL6FEiRLmiIWIKH84tEdEVqJAc6Q4P4qIrAKH9ojIShQokTKs2iMi0lR+h/ZSUoD79+U8EykiMoMCDe2lp6ebKw4iovzL79BefHzG+SJFzBcPETksk/baIyLSVH6H9gzDep6egEY7wxORfWMiRUS2J79De5wfRURmxkSKiGxPfof2mEgRkZkxkSIi21PQoT0mUkRkJkykiMj2cGiPiKwEEykisj0c2iMiK8FEiohsj2Fojz1SRKQxJlJEZHvYI0VEVoKJFBHZHiZSRGQlmEgRke0xDO0lJgJ57bjARIqIzIyJFBHZHkOPFCDJVG6YSBGRmTGRIiLb4+kJ6HRyPq/hPSZSRGRmTKSIyPbodPlbucdEiojMjIkUEdmm/Ew4ZyJFRGbGRIqIbFN+tolhIkVEZsZEiohsU362iWEiRURmxkSKiGzTo4b20tIybvPzs0xMRORwmEgRkW161NBe5uvZI0VEZsJEiohs06OG9gzDem5ugLu7ZWIiIofDRIqIbNOjhvY4P4qILICJFBHZpkcN7TGRIiILYCJFRLYpv0N7TKSIyIyYSBGRbeLQHhFZASZSRGSbOLRHRFaAiRQR2SYO7RGRFWAiRUS2iUN7RGQFmEgRkW3i0B4RWQEmUkRkmzi0R0RWgIkUEdkmDu0RkRVgIkVEtolDe0RkBZhIEZFt4tAeEVkBJlJEZJsMiVRiIpCWlv12JlJEZAFMpIjINhmG9gBJph7GRIqILICJFBHZJk9PQKeT8zkN7zGRIiILsIlE6uzZs+jfvz/Kly8PT09PVKxYERMmTEBKSkqW++h0umzH7t27NYyciMxGp8t95Z6iMJEiIotw0TqA/Dh69CjS09OxYMECVKpUCf/++y8GDBiAe/fuYebMmVnuu3nzZtSoUcN4OSAgwNLhEpGleHsD8fHZE6nERCA9Xc4zkSIiM7KJRKpdu3Zo166d8XKFChVw7NgxzJs3L1siFRAQgKCgIEuHSERayG3lnqE3yskJ8PKybExE5FBsIpHKyd27d1GsWLFs13fu3Bn3799HlSpV8M4776Bz5855Pk5ycjKSk5ONl+Me/APW6/XQ6/XqBu1ADG3HNiw8tmXuXLy9oQOQeucOlMztExsLVwCKry9SU1ONV7Mt1cF2VA/bUj1ataFNJlInT57Ep59+mqU3ysfHB7NmzcITTzwBJycnrFq1Cl27dsWaNWvyTKamTZuGSZMmZbs+KioKXvwmW2iRkZFah2A32JbZPZmSggAAB/78E1cyJUxFT5xAcwBJrq6I/O23bD/HtlQH21E9bMvCS8xp9a4F6BRFUTR5ZgBjxozBjBkz8rxPTEwMqlatarx86dIlNG/eHC1atMCiRYvy/NnevXvjzJkz2L59e673yalHKjQ0FFeuXOH8qkLQ6/WIjIxEeHg4XF1dtQ7HprEtc+fcsSOcNm1C6ldfQXnlFeP1uq1b4dKuHZQaNZD6zz/G69mW6mA7qodtqZ7Y2FgEBwfj7t278LXg3EhNe6RGjhyJiIiIPO9ToUIF4/nLly+jZcuWaNq0KRYuXPjIx2/UqNEjs3x3d3e4u7tnu97V1ZVvahWwHdXDtsxBkSIAAJf794HMbfPgm6nOzy/HNmNbqoPtqB62ZeFp1X6aJlKBgYEIDAzM130vXbqEli1bon79+li8eDGcnB5duSE6OhrBwcGFDZOIrFVu++2x9AERWYhNzJG6dOkSWrRogbJly2LmzJm4ceOG8TbDCr2lS5fCzc0NdevWBQCsXr0aX3/99SOH/4jIhj1q1R4TKSIyM5tIpCIjI3Hy5EmcPHkSpUuXznJb5ileU6ZMwblz5+Di4oKqVavi+++/xwsvvGDpcInIUtgjRUQas4lEKiIi4pFzqfr06YM+ffpYJiAisg65VTZnIkVEFmITW8QQEeWIQ3tEpDEmUkRkuzi0R0QaYyJFRLaLPVJEpDEmUkRkuzhHiog0xkSKiGwXh/aISGNMpIjIdnFoj4g0xkSKiGwXh/aISGNMpIjIdnFoj4g0xkSKiGyXoUcqKQlIS5PzyclASoqcZyJFRGbGRIqIbJchkQKAxEQ5NfRGPXw7EZEZMJEiItvl4QHodHLeMLxnSKR8fABnZ23iIiKHwUSKiGyXTpd95R7nRxGRBTGRIiLb9vDKPSZSRGRBTKSIyLY9vHKPiRQRWRATKSKybRzaIyINMZEiItvGoT0i0hATKSKybRzaIyINMZEiItvGoT0i0hATKSKybRzaIyINMZEiItvGoT0i0hATKSKybRzaIyINMZEiItvGoT0i0pCL1gHYorS0NOj1eq3DsGp6vR4uLi64f/8+0tLStA7H4lxdXeHMfd4sg0N7RKQhJlIFoCgKrl69ijt37mgditVTFAVBQUG4cOECdIZNZR1M0aJFERQU5LCv32I4tEdEGmIiVQCGJKpEiRLw8vLiB2Qe0tPTkZCQAB8fHzg5OdYIsqIoSExMxPXr1wEAwcHBGkdk5zi0R0QaYiKVT2lpacYkKiAgQOtwrF56ejpSUlLg4eHhcIkUAHh6egIArl+/jhIlSnCYz5w4tEdEGnK8TzgTGeZEeXl5aRwJ2QrDe4Xz6cws89BeaiqQmCiXmUgRkQUwkSogDudRfvG9YiGZh/bi4zOuL1JEm3iIyKEwkSIi25Z5aM8wrOfhAbi5aRcTETkMJlKEbdu2QafTWfVqxHLlymHu3Llah0HWKPPQHudHEZGFMZGyczqdLs9j4sSJWodokyIiItC1a1etwyAgo0cqKQm4fVvOM5EiIgvhqj07d+XKFeP577//HuPHj8exY8eM1/n4+GDfvn0FftyUlBS4OeDQSVpaGuc+WRtDjxQAGN7vTKSIyELYI1UYiiLDCVocipKvEIOCgoyHn58fdDpdlut8Mn0I7d+/Hw0aNICXlxeaNm2aJeGaOHEi6tSpg0WLFqF8+fLw8PAAAJw/fx5dunSBj48PfH190b17d1y7ds34c3379s3Wc/PWW2+hRYsWxsvx8fHo1asXvL29ERwcjDlz5qBFixZ46623svxcYmIi+vXrhyJFiqBMmTJYuHBhnq+9RYsWGDJkCIYMGQI/Pz8UL14c48aNg5Kp7W7fvo3evXvD398fXl5eaN++PU6cOGG8fcmSJShatCjWrVuH6tWrw93dHf369cPSpUuxdu1aY8/etm3bHvWrIHPx8AAMJTaYSBGRhTGRKozERPk2rMVhWOKtonfffRezZs3Cvn374OLign79+mW5/eTJk1i1ahVWr16N6OhopKeno0uXLrh16xb++OMPREZG4vTp03jxxRcL9LwjRozAzp07sW7dOkRGRmL79u04cOBAtvvNmjULDRo0wD///IPXX38dgwcPzpLs5WTp0qVwcXHB3r178fHHH2P27NlYtGiR8faIiAjs27cP69atw65du6AoCjp06JClZEFiYiJmzJiBRYsW4b///sMnn3yC7t27o127drhy5QquXLmCpk2bFug1k4p0uozhvcuX5ZSJFBFZCIf2yGjq1Klo3rw5AGDMmDF45plncP/+fWPvU0pKCpYtW4bAwEAAQGRkJA4fPowzZ84gNDQUALBs2TLUqFEDf//9N8LCwh75nPHx8Vi6dClWrFiBp59+GgCwePFihISEZLtvhw4d8PrrrwMARo8ejTlz5iAqKirP5wkNDcWcOXOg0+kQFhaGw4cPY86cORgwYABOnDiBdevWYefOncZEaPny5QgNDcWaNWvQrVs3AFIH6osvvkDt2rWNj+vp6Ynk5GQEBQU98jWSBfj4SOkDJlJEZGFMpArDyyujmrIWz62yWrVqGc8btjW5fv06ypQpAwAoW7asMYkCgJiYGISGhhqTKACoXr06ihYtipiYmHwlUqdPn4Zer8fjjz9uvM7Pzy/Hn80cn2GI0rANS24aN26cZU5TkyZNMGvWLKSlpSEmJgYuLi5o1KiR8faAgACEhYUhJibGeJ2bm1uW5yYrZBii5tAeEVkYE6nCyDykYAdcXV2N5w3JR3p6uvE6bxNeq5OTU5Y5SYDplb4zxwdIjJnjMxdPT09OMLd2HNojIo1wjhSZrFq1arhw4QIuXLhgvO7IkSO4c+cOqlevDgAIDAzMsnIQAKKjo43nK1SoAFdXV/z999/G6+7evYvjx4+rEuOePXuyXN69ezcqV64MZ2dnVKtWDampqVnuExsbi2PHjhnjz42bmxvS0tJUiZFUYOiRYiJFRBbGRIpM1rp1a9SsWRO9evXCgQMHsHfvXvTu3RvNmzdHgwYNAAAtW7bEvn37sGzZMpw4cQITJkzAv//+a3yMIkWKoE+fPhg1ahSioqLw33//oX///nByclKlF+j8+fMYMWIEjh07hpUrV+LTTz/FsGHDAACVK1dGly5dMGDAAOzYsQMHDx7Eyy+/jFKlSqFLly55Pm65cuVw6NAhHDt2DDdv3uR+elozJFIsyElEFsZEikym0+mwdu1a+Pv7o1mzZmjdujUqVKiA77//3niftm3bYty4cXjnnXfQsGFDxMfHo3fv3lkeZ/bs2WjSpAk6duyI1q1b44knnkC1atWMk9wLo3fv3khKSsLjjz+ON954A8OGDcNrr71mvH3x4sWoX78+OnbsiCZNmkBRFPz222/ZhhEfNmDAAISFhaFBgwYIDAzEzp07Cx0rFcLDw85MpIjIQnTKwxNYHFxcXBz8/Pxw8+ZNBAQEGK+/f/8+zpw5k6WGEuUuPT0dcXFx8PX1hZNTwfL1e/fuoVSpUpg1axb69+9vcgwtWrRAnTp1NNtaRq33jF6vx2+//YYOHTo8MsFzWBERwNKlGZfXrAFy6FVkW6qD7agetqV6YmNjUbx4cdy9exe+FvwyxcnmpLl//vkHR48exeOPP467d+9i8uTJAPDI4TUio8zVzQH2SBGRxTCRIqswc+ZMHDt2DG5ubqhfvz62b9+O4sWLax0W2QoO7RGRRmxmjlTnzp1RpkwZeHh4IDg4GK+88gouG1boPHDo0CE89dRT8PDwQGhoKD788EONoqWCqFu3Lvbv34+EhATcunULkZGRqFmzZqEfd9u2bZoN65GFsUeKiDRiM4lUy5Yt8cMPP+DYsWNYtWoVTp06hRdeeMF4e1xcHNq0aYOyZcti//79+OijjzBx4sRH7sdGRHaAiRQRacRmhvaGDx9uPF+2bFmMGTMGXbt2hV6vh6urK5YvX46UlBR8/fXXcHNzQ40aNRAdHY3Zs2dnWaVFRHaIQ3tEpBGbSaQyu3XrFpYvX46mTZsaVzns2rULzZo1g5ubm/F+bdu2xYwZM3D79m34+/vn+FjJyclITk42Xo57UIdGr9dnqQ2k1+uhKArS09MtUk3b1hkWgxrazBGlp6dDURTo9Xo4Ozub/DiG9yFrVeVO5+Fh/GemuLgg1dkZyKG92JbqYDuqh22pHq3a0KYSqdGjR+Ozzz5DYmIiGjdujPXr1xtvu3r1KsqXL5/l/iVLljTellsiNW3aNEyaNCnb9VFRUfDKtJ+di4sLgoKCkJCQgJSUFDVejkOIj4/XOgTNpKSkICkpCX/++SdSU1ML/XiRkZEqRGWfgo4dg2HHRL2nJzZs2JDn/dmW6mA7qodtWXiJiYmaPK+mdaTGjBmDGTNm5HmfmJgYVK1aFQBw8+ZN3Lp1C+fOncOkSZPg5+eH9evXQ6fToU2bNihfvjwWLFhg/NkjR46gRo0aOHLkCKpVq5bj4+fUIxUaGoorV65kqyN14cIFlCtXjnWk8kFRFMTHx6NIkSIOu0/d/fv3cfbsWYSGhha6jlRkZCTCw8NZZyYXuq1b4dKuHQBAKVcOqblsMcS2VAfbUT1sS/XExsYiODjYsepIjRw5EhEREXnep0KFCsbzxYsXR/HixVGlShVUq1YNoaGh2L17N5o0aYKgoCBcu3Yty88aLgcFBeX6+O7u7nB3d892vaura5Y3dVpaGnQ6HZycnApcYNIRGYbzDG3miAzb3Dz8XjKVWo9jl4oWNZ7V+fo+sp3YlupgO6qHbVl4WrWfpolUYGAgAgMDTfpZwwe1oTepSZMmePfdd42TzwHpKg0LC8t1WI+I7ETmVXucaE5EFmQTXQV79uzBZ599hujoaJw7dw5bt25Fjx49ULFiRTRp0gQA0LNnT7i5uaF///7477//8P333+Pjjz/GiBEjNI5eexEREdDpdMbekZIlSyI8PBxff/213U4Ej4iIQNeuXbUOgywl86o9JlJEZEE2kUh5eXlh9erVePrppxEWFob+/fujVq1a+OOPP4zDcn5+fti0aRPOnDmD+vXrY+TIkRg/fjxLHzzQrl07XLlyBWfPnsWGDRvQsmVLDBs2DB07dsxzIrStrSRJS0uz2+SQ8sAeKSLSiE0kUjVr1sTWrVsRGxtr3Ah23rx5KFWqVJb71apVC9u3b8f9+/dx8eJFjB492qxxKQpw7542R0GXCLi7uyMoKAilSpVCvXr18L///Q9r167Fhg0bsGTJEuP9dDod5s2bh86dO8Pb2xtTp04FAMybNw8VK1aEm5sbwsLC8M0332R5fMPPtW/fHp6enqhUqRLWrl2bZ0wtWrTAkCFDMGTIEPj5+aF48eIYN24cMq9/uH37Nnr37g1/f394eXmhffv2OHHihPH2JUuWoGjRoli3bh2qV68Od3d39OvXD0uXLsXatWuNPXHbtm0rWIORbWEiRUQasanyB9YmMTF7QWVLSUjIXoOwoFq1aoXatWtj9erVePXVV43XT5w4EdOnT8fcuXPh4uKCn3/+GcOGDcPcuXPRunVrrF+/Hn379kXp0qXRsmVL48+NGzcO06dPx8cff4xly5ahf//+aNCgAWrUqJFrDEuXLkX//v2xd+9e7Nu3D6+99hrKlCmDAQMGAJAhuhMnTmDdunXw9fXF6NGj0aFDBxw5csQ4Fy4xMREzZszAokWLEBAQgODgYCQlJSEuLg6LFy8GABQrVqxwjUXWzd0dcHIC0tOZSBGRRTGRcnBVq1bFoUOHslzXs2dP9O3b13i5R48eiIiIwOuvvw4AGDFiBHbv3o2ZM2dmSaS6detmTMgmT56MjRs34rPPPsO8efNyff7Q0FDMmTMHOp0OYWFhOHz4MObMmYMBAwYYE6idO3eiadOmAIDly5cjNDQUa9asQbdu3QDI8OMXX3yB2rVrGx/X09MTycnJea7YJDui08m3mrg4JlJEZFFMpArBy0t6hrR6bjUoipKtzlODBg2yXI6Jick21+yJJ57Axx9/nOU6w8R/g4YNGyImJibP52/cuHGW52/SpAlmzZqFtLQ0xMTEwMXFBY0aNTLeHhAQgLCwsCyP6+bmhlq1auX5POQAmEgRkQaYSBWCTlf44TWtxcTEZKsI721jL8rT09Nhi35SJob3LRMpIrIgm5hsTuaxdetWHD58GM8//3ye96tWrRp27tyZ5bqdO3eievXqWa7bvXt3lsv79u0zVqXPzZ49e7I9RuXKleHs7Ixq1aohNTU1y31iY2Nx7NixbM/9MDc3N6SlpeV5H7IzhgmLRYpoGwcRORT2SDmI5ORkXL16FWlpabh27Rp+//13TJs2DR07dkTv3r3z/NlRo0ahe/fuqFu3Llq3bo1ffvkFq1evxubNm7Pc78cff0SDBg3w5JNP4ttvv8X+/fvx9ddf5/nY58+fx4gRIzBw4EAcOHAAn376KWbNmgUAqFy5Mrp06YIBAwZgwYIFKFKkCMaMGYNSpUqhS5cueT5uuXLlsHHjRhw7dgwBAQHw8/Nj1WB716OHrAB58kmtIyEiB8JEykH8/vvvCA4OhouLC/z9/VG7dm188skn6NOnzyO3cOnatSs+/vhjzJw5E8OGDUP58uWxePFitGjRIsv9Jk2ahO+++w6vv/46goODsWjRokf2HPXu3RtJSUl4/PHH4ezsjGHDhmWZj7V48WJjvauUlBQ0a9YMv/322yOTogEDBmDbtm1o0KABEhISEBUVlS1esjOjRslBRGRBTKQcwJIlS7LUispLbntYDx48GIMHD87zZ0NCQrBp0yYAsoVPXFzcI5/P1dUVc+fOzXVln7+/P5YtW5brz0dEROS4X2NgYKAxFiIiInPhHCkiIiIiEzGRIiIiIjIRh/ZIFbkNCeaF27YQEZGtY48UERERkYmYSBWQKT0v5Jj4XiEisn9MpPIp8wa5RPlheK+wfhURkf3iHKl8cnZ2RtGiRXH9+nUAgJeXF7clyUN6ejpSUlJw//79R9apsjeKoiAxMRHXr19H0aJF4ezsrHVIRERkJkykCiAoKAgAjMkU5U5RFCQlJTn0PnhFixY1vmeIiMg+MZEqAJ1Oh+DgYJQoUQJ6vV7rcKyaXq/Hn3/+iWbNmjnk0Jarqyt7ooiIHAATKRM4OzvzQ/IRnJ2dkZqaCg8PD4dMpIiIyDE41uQVIiIiIhUxkSIiIiIyERMpIiIiIhNxjtRDDEUU4+PjObenEPR6PRITExEXF8d2LCS2pXrYlupgO6qHbame+Ph4AJYvhsxE6iGxsbEAgPLly2scCRERERVUbGws/Pz8LPZ8TKQeUqxYMQDA+fPnLfqLsDdxcXEIDQ3FhQsX4Ovrq3U4No1tqR62pTr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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ldas, 10 * np.log10(T_through), label=\"Through port\", c=\"red\")\n",
    "plt.plot(ldas, 10 * np.log10(T_drop), label=\"Drop port\", c=\"blue\")\n",
    "plt.xlim(1.58, 1.586)\n",
    "plt.xlabel(\"Wavelength (μm)\")\n",
    "plt.ylabel(\"Transmission (dB)\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Remarks\n",
    "\n",
    "In some cases, users might only be interested in determining the Q-factor of the ring. In that case, one can use the [ResonanceFinder](https://www.flexcompute.com/tidy3d/examples/notebooks/ResonanceFinder/) plugin and only run the simulation for a much shorter period of time. The plugin will analyze the time domain field profile and extract the Q-factor using harmonic inversion. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "applications": [
   "Passive photonic integrated circuit components"
  ],
  "description": "This notebook demonstrates how to model a microring resonator add-drop filter in Tidy3D FDTD.",
  "feature_image": "./img/ring_resonator.png",
  "features": [],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "ring resonator, Tidy3D, FDTD",
  "language_info": {
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   "name": "python",
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  "title": "Ring Resonator Add-drop Filter Modeling in Tidy3D | Flexcompute",
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