{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a65a6737",
   "metadata": {},
   "source": [
    "# Distributed Bragg reflector and cavity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47e433db",
   "metadata": {},
   "source": [
    "A [distributed Bragg reflector](https://en.wikipedia.org/wiki/Distributed_Bragg_reflector) (DBR) is a multilayer structure consisting of alternating layers of high refractive index and low refractive index. When the thickness of each layer is close to a quarter of the medium wavelength, nearly perfect reflection occurs due to constructive interference of the reflected waves at each layer. DBR is commonly used at optical and UV wavelengths due to the fact that metallic reflectors have a high loss at high frequencies. Besides free space optics, similar concepts have also been applied to integrated photonics and fiber optics. Furthermore, high-Q cavities based on DBR structures are widely popular in lasers, filters, and sensors.\n",
    "\n",
    "Although Tidy3D uses highly optimized algorithms and hardware designed to perform large 3D simulations at ease, the computational efficiency can be improved exponentially if we can reduce the dimension of the model. The DBR structure has translational symmetry along the two in-plane directions. Thus, simulating a DBR is effectively a 1D problem. \n",
    "\n",
    "<img src=\"img/dbr_schematic.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "67c41dde",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T23:58:21.080158Z",
     "iopub.status.busy": "2023-03-27T23:58:21.079762Z",
     "iopub.status.idle": "2023-03-27T23:58:22.520800Z",
     "shell.execute_reply": "2023-03-27T23:58:22.520114Z"
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0c8e122",
   "metadata": {},
   "source": [
    "## Simulating the Stopband of a DBR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f150734b",
   "metadata": {},
   "source": [
    "### Simulation Setup "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86d2bbb5",
   "metadata": {},
   "source": [
    "The most common DBR is made of alternating layers of titanium dioxide ($TiO_2$) and silica ($SiO_2$). The target stopband of the DBR is around 630 nm. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3c528785",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T23:58:22.524277Z",
     "iopub.status.busy": "2023-03-27T23:58:22.523663Z",
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     "shell.execute_reply": "2023-03-27T23:58:22.553198Z"
    }
   },
   "outputs": [],
   "source": [
    "lda0 = 0.63  # central wavelength\n",
    "freq0 = td.C_0 / lda0  # central frequency\n",
    "freqs = freq0 * np.linspace(0.5, 1.5, 1001)  # frequency range of interest\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a80a24bf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T23:58:22.556427Z",
     "iopub.status.busy": "2023-03-27T23:58:22.556167Z",
     "iopub.status.idle": "2023-03-27T23:58:22.584708Z",
     "shell.execute_reply": "2023-03-27T23:58:22.584096Z"
    }
   },
   "outputs": [],
   "source": [
    "n_tio2 = 2.5  # refractive index of TiO2\n",
    "n_sio2 = 1.5  # refractive index of SiO2\n",
    "n_s = 1.5  # refractive index of the substrate material. It's set to SiO2 in this case\n",
    "inf_eff = 10  # effective infinity in this model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c710663",
   "metadata": {},
   "source": [
    "The bandwidth of the stopband is given by \n",
    "\n",
    "$$\n",
    "\\frac{\\Delta f}{f_0} = \\frac{4}{\\pi} arcsin(\\frac{n_1-n_2}{n_1+n_2}),\n",
    "$$\n",
    "\n",
    "where $f_0$ is the central frequency, $n_1$ is the refractive index of the high index material, and $n_2$ is the refractive index of the low index material. We use the above equation to estimate the bandwidth of the DBR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2a4913bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T23:58:22.587490Z",
     "iopub.status.busy": "2023-03-27T23:58:22.587234Z",
     "iopub.status.idle": "2023-03-27T23:58:22.612575Z",
     "shell.execute_reply": "2023-03-27T23:58:22.612030Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The normalized bandwidth of the reflection band is 0.32\n"
     ]
    }
   ],
   "source": [
    "df = 4 * np.arcsin((n_tio2 - n_sio2) / (n_tio2 + n_sio2)) / np.pi\n",
    "print(f\"The normalized bandwidth of the reflection band is {df:1.2f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7be52b05",
   "metadata": {},
   "source": [
    "Next, we construct a function that builds the DBR layers given four parameters: the refractive indices of the materials, the number of layer pairs, and the starting position of the lowest layer. This function will be handy for constructing the DBR as well as the cavity device in the next section. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8c34ee9c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T23:58:22.615099Z",
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     "shell.execute_reply": "2023-03-27T23:58:22.636000Z"
    }
   },
   "outputs": [],
   "source": [
    "def build_layers(n_1, n_2, N, z_0):\n",
    "    # n_1 and n_2 are the refractive indices of the two materials\n",
    "    # N is the number of repeated pairs of low/high refractive index material\n",
    "    # z_0 is the z coordinate of the lowest layer\n",
    "\n",
    "    material_1 = td.Medium(permittivity=n_1**2)  # define the first material\n",
    "    material_2 = td.Medium(permittivity=n_2**2)  # define the second material\n",
    "    t_1 = lda0 / (4 * n_1)  # thickness of the first material\n",
    "    t_2 = lda0 / (4 * n_2)  # thicness of the second material\n",
    "    layers = []  # holder for all the layers\n",
    "\n",
    "    # building layers alternatively\n",
    "    for i in range(2 * N):\n",
    "        if i % 2 == 0:\n",
    "            layers.append(\n",
    "                td.Structure(\n",
    "                    geometry=td.Box.from_bounds(\n",
    "                        rmin=(-inf_eff, -inf_eff, z_0),\n",
    "                        rmax=(inf_eff, inf_eff, z_0 + t_1),\n",
    "                    ),\n",
    "                    medium=material_1,\n",
    "                )\n",
    "            )\n",
    "            z_0 = z_0 + t_1\n",
    "        else:\n",
    "            layers.append(\n",
    "                td.Structure(\n",
    "                    geometry=td.Box.from_bounds(\n",
    "                        rmin=(-inf_eff, -inf_eff, z_0),\n",
    "                        rmax=(inf_eff, inf_eff, z_0 + t_2),\n",
    "                    ),\n",
    "                    medium=material_2,\n",
    "                )\n",
    "            )\n",
    "            z_0 = z_0 + t_2\n",
    "\n",
    "    return layers\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00772287",
   "metadata": {},
   "source": [
    "We plan to perform a parameter sweep of $N$, the number of layer pairs. In order to do so, we define another function that takes $N$ as an argument and builds the simulation including structures, source, monitor, and so on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "426ca532",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T23:58:22.639269Z",
     "iopub.status.busy": "2023-03-27T23:58:22.639107Z",
     "iopub.status.idle": "2023-03-27T23:58:22.660929Z",
     "shell.execute_reply": "2023-03-27T23:58:22.660365Z"
    }
   },
   "outputs": [],
   "source": [
    "def make_DBR(N):\n",
    "\n",
    "    # build the DBR layers using the previously defined function\n",
    "    DBR = build_layers(n_tio2, n_sio2, N, 0)\n",
    "\n",
    "    thickness = N * (\n",
    "        lda0 / (4 * n_tio2) + lda0 / (4 * n_sio2)\n",
    "    )  # total thickness of the DBR layers\n",
    "\n",
    "    # build the substrate structure\n",
    "    sub = td.Structure(\n",
    "        geometry=td.Box.from_bounds(\n",
    "            rmin=(-inf_eff, -inf_eff, -inf_eff), rmax=(inf_eff, inf_eff, 0)\n",
    "        ),\n",
    "        medium=td.Medium(permittivity=n_s**2),\n",
    "    )\n",
    "\n",
    "    # the entire DBR structure includes the layers and the substrate\n",
    "    DBR.append(sub)\n",
    "\n",
    "    # create a plane wave excitation source\n",
    "    fwidth = 0.5 * freq0  # width of the frequency distribution\n",
    "    plane_wave = td.PlaneWave(\n",
    "        source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
    "        size=(td.inf, td.inf, 0),\n",
    "        center=(0, 0, thickness + lda0 / 4),\n",
    "        direction=\"-\",\n",
    "        pol_angle=0,\n",
    "    )\n",
    "\n",
    "    # create a flux monitor to measure the reflectance\n",
    "    flux_monitor = td.FluxMonitor(\n",
    "        center=(0, 0, thickness + lda0 / 2),\n",
    "        size=(td.inf, td.inf, 0),\n",
    "        freqs=freqs,\n",
    "        name=\"R\",\n",
    "    )\n",
    "\n",
    "    Lz = thickness + 2 * lda0  # simulation domain size in z direction\n",
    "    run_time = 100 / fwidth  # simulation run time\n",
    "\n",
    "    sim = td.Simulation(\n",
    "        size=(0, 0, Lz),  # simulation domain sizes in x and y directions are set to 0\n",
    "        center=(0, 0, thickness / 2),\n",
    "        grid_spec=td.GridSpec.auto(min_steps_per_wvl=60, wavelength=lda0),\n",
    "        structures=DBR,\n",
    "        sources=[plane_wave],\n",
    "        monitors=[flux_monitor],\n",
    "        run_time=run_time,\n",
    "        boundary_spec=td.BoundarySpec(\n",
    "            x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n",
    "        ),  # pml is applied in the z direction\n",
    "        shutoff=1e-7,\n",
    "    )  # early shutoff level is decreased to 1e-7 to increase the simulation accuracy\n",
    "    return sim\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "318abb79",
   "metadata": {},
   "source": [
    "To visualize the relationship between reflectance and the number of repeated pairs, we perform a parameter sweep. N is swept from 2 to 10 in a total of 5 simulations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c42cfd32",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T23:58:22.663620Z",
     "iopub.status.busy": "2023-03-27T23:58:22.663458Z",
     "iopub.status.idle": "2023-03-27T23:58:22.729026Z",
     "shell.execute_reply": "2023-03-27T23:58:22.728386Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "Ns = np.array([2, 3, 4, 5, 10])  # collection of N for the parameter sweep\n",
    "sims = {\n",
    "    f\"N={N:.2f}\": make_DBR(N) for N in Ns\n",
    "}  # construct simulations for each N from Ns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6568d3d7",
   "metadata": {},
   "source": [
    "Submit the batch to the server. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "20157b64",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2023-03-27T23:58:22.731179Z",
     "iopub.status.idle": "2023-03-27T23:58:45.423438Z",
     "shell.execute_reply": "2023-03-27T23:58:45.422881Z"
    }
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   ],
   "source": [
    "batch = web.Batch(simulations=sims, verbose=True)\n",
    "batch_results = batch.run(path_dir=\"data\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5fb6368",
   "metadata": {},
   "source": [
    "### Result Visualization "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5624996",
   "metadata": {},
   "source": [
    "Once the batch of simulations is complete, we can plot the reflectance spectra.\n",
    "\n",
    "Analytically, the reflectance at the central frequency is given by\n",
    "\n",
    "$$\n",
    "R = [\\frac{n_0(n_1)^{2N}-n_s(n_2)^{2N}}{n_0(n_1)^{2N}+n_s(n_2)^{2N}}]^2,\n",
    "$$\n",
    "\n",
    "where $n_0=1$ is the refractive index of the superstrate, $n_s=n_{SiO_2}$ is the refractive index of the substrate. We will use this analytical solution to validate the accuracy of our simulations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "75083244",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T23:58:45.727344Z",
     "iopub.status.busy": "2023-03-27T23:58:45.727049Z",
     "iopub.status.idle": "2023-03-27T23:58:48.547023Z",
     "shell.execute_reply": "2023-03-27T23:58:48.546610Z"
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i, N in enumerate(Ns):\n",
    "    sim_data = batch_results[f\"N={N:.2f}\"]\n",
    "    R = sim_data[\"R\"].flux  # extract reflectance data from the flux monitor\n",
    "    plt.plot(freqs / freq0, R, label=f\"N={N}\")  # plot reflectance spectrum\n",
    "\n",
    "    # plot the analytically calculated reflectance at the central frequency with a star marker\n",
    "    plt.scatter(\n",
    "        1,\n",
    "        (\n",
    "            ((n_tio2) ** (2 * N) - (n_sio2) ** (2 * N + 1))\n",
    "            / ((n_tio2) ** (2 * N) + (n_sio2) ** (2 * N + 1))\n",
    "        )\n",
    "        ** 2,\n",
    "        marker=\"*\",\n",
    "        s=50,\n",
    "    )\n",
    "\n",
    "plt.title(\"Reflectance of DBR of different number of periods\")\n",
    "plt.xlabel(\"Normalized frequency\")\n",
    "plt.ylabel(\"Reflectance\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bd979cb",
   "metadata": {},
   "source": [
    "As the number of pairs increases, the reflectance at the stopband increases to unity. The width of the stopband agrees with the analytical expression discussed above. The analytical solution of the reflectance at the central frequency (stars) also agrees well with Tidy3D simulation results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5ad1b17",
   "metadata": {},
   "source": [
    "## Modeling a High-Q DBR Microcavity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4702ac74",
   "metadata": {},
   "source": [
    "Similar to a defect in a photonic crystal, an optical microcavity is formed when one layer of a DBR has an anomalous thickness. For example, if a high index layer in the middle of a DBR has a thickness of half a material wavelength instead of the usual quarter material wavelength, the DBR will show greatly suppressed reflection at the central frequency. The quality factor of this cavity mode can be very high if the total number of layers is large. DBR-based microcavities are widely used in lasers, filters, and sensing applications. \n",
    "\n",
    "To obtain the reflectance spectrum of a high-Q DBR cavity, a long simulation run time is required since we need to run the time stepping until the energy in the simulation domain dissipates. Fortunately, for 1D or 2D simulations, this is still an easy task. \n",
    "\n",
    "On the other hand, for a large 3D cavity with a very high Q value, this kind of simulation can become computationally impractical. In this case, the [ResonanceFinder](../_autosummary/tidy3d.plugins.ResonanceFinder.html) plugin of Tidy3D is a handy tool. It can be used to extract the resonance frequencies and Q values without running the time stepping until the resonance modes fully decay, as demonstrated in the [photonic crystal cavity example](../notebooks/OptimizedL3.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33303304",
   "metadata": {},
   "source": [
    "### Simulation Setup "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "930a6064",
   "metadata": {},
   "source": [
    "To resolve the cavity resonance in the spectrum, we narrow the frequency range a bit compared to the previous simulations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c0eb2030",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T23:58:48.581408Z",
     "iopub.status.busy": "2023-03-27T23:58:48.581188Z",
     "iopub.status.idle": "2023-03-27T23:58:48.600200Z",
     "shell.execute_reply": "2023-03-27T23:58:48.599624Z"
    }
   },
   "outputs": [],
   "source": [
    "lda0 = 0.63  # central wavelength\n",
    "freq0 = td.C_0 / lda0  # central frequency\n",
    "freqs = freq0 * np.linspace(\n",
    "    0.9, 1.1, 1001\n",
    ")  # frequency range of interest. The range is narrowed compared to previous simulations\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dfb9aab",
   "metadata": {},
   "source": [
    "To create a DBR cavity, we will use the previously defined `build_layers` function twice to construct the regular DBRs on the top and bottom of the cavity. Then a single layer of $TiO_2$ with thickness $\\lambda_0/2n_{TiO_2}$ is added between them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e3c9ded3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T23:58:48.602728Z",
     "iopub.status.busy": "2023-03-27T23:58:48.602471Z",
     "iopub.status.idle": "2023-03-27T23:58:48.817584Z",
     "shell.execute_reply": "2023-03-27T23:58:48.817080Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N_bottom = 6  # number of layer pairs for the bottom DBR\n",
    "thickness_bottom = N_bottom * (\n",
    "    lda0 / (4 * n_tio2) + lda0 / (4 * n_sio2)\n",
    ")  # thickness of the bottom DBR\n",
    "bottom_DBR = build_layers(n_tio2, n_sio2, N_bottom, 0)  # construct the bottom DBR\n",
    "\n",
    "thickness_cavity = lda0 / (2 * n_tio2)  # thickness of the cavity layer\n",
    "# construct the cavity layer\n",
    "cavity = td.Structure(\n",
    "    geometry=td.Box.from_bounds(\n",
    "        rmin=(-inf_eff, -inf_eff, thickness_bottom),\n",
    "        rmax=(inf_eff, inf_eff, thickness_bottom + thickness_cavity),\n",
    "    ),\n",
    "    medium=td.Medium(permittivity=n_tio2**2),\n",
    ")\n",
    "\n",
    "N_top = 6  # number of layer pairs for the top DBR\n",
    "# construct the bottom DBR\n",
    "top_DBR = build_layers(n_sio2, n_tio2, N_top, thickness_bottom + thickness_cavity)\n",
    "thickness_top = N_top = N_bottom * (\n",
    "    lda0 / (4 * n_tio2) + lda0 / (4 * n_sio2)\n",
    ")  # thickness of the bottom DBR\n",
    "\n",
    "# construct the substrate\n",
    "sub = td.Structure(\n",
    "    geometry=td.Box.from_bounds(\n",
    "        rmin=(-inf_eff, -inf_eff, -inf_eff), rmax=(inf_eff, inf_eff, 0)\n",
    "    ),\n",
    "    medium=td.Medium(permittivity=n_s**2),\n",
    ")\n",
    "\n",
    "# combining the top DBR, the bottom DBR, the cavity layer, and the substrate\n",
    "DBR_cavity = bottom_DBR + top_DBR\n",
    "DBR_cavity.append(cavity)\n",
    "DBR_cavity.append(sub)\n",
    "\n",
    "thickness = (\n",
    "    thickness_bottom + thickness_cavity + thickness_top\n",
    ")  # total thickness of the device\n",
    "\n",
    "fwidth = 0.1 * freq0  # width of the frequency range\n",
    "\n",
    "# add a plane wave source\n",
    "plane_wave = td.PlaneWave(\n",
    "    source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
    "    size=(td.inf, td.inf, 0),\n",
    "    center=(0, 0, thickness + lda0 / 4),\n",
    "    direction=\"-\",\n",
    "    pol_angle=0,\n",
    ")\n",
    "\n",
    "# add a flux monitor to measure reflectance\n",
    "flux_monitor = td.FluxMonitor(\n",
    "    center=(0, 0, thickness + lda0 / 2), size=(td.inf, td.inf, 0), freqs=freqs, name=\"R\"\n",
    ")\n",
    "\n",
    "# add a field monitor to measure the field distribution\n",
    "field_monitor = td.FieldMonitor(\n",
    "    center=(0, 0, 0), size=(td.inf, td.inf, td.inf), freqs=freqs, name=\"field\"\n",
    ")\n",
    "\n",
    "# simulation domain size in z direction\n",
    "Lz = thickness + 2 * lda0\n",
    "\n",
    "# run time needs to be sufficiently long to ensure the field decays away in the end\n",
    "run_time = 500 / fwidth\n",
    "\n",
    "sim = td.Simulation(\n",
    "    size=(0, 0, Lz),\n",
    "    center=(0, 0, thickness / 2),\n",
    "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=60, wavelength=lda0),\n",
    "    structures=DBR_cavity,\n",
    "    sources=[plane_wave],\n",
    "    monitors=[flux_monitor, field_monitor],\n",
    "    run_time=run_time,\n",
    "    boundary_spec=td.BoundarySpec(\n",
    "        x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n",
    "    ),\n",
    "    shutoff=1e-7,\n",
    ")  # early shutoff level is decreased to 1e-7 to increase the simulation accuracy\n",
    "\n",
    "# visulize the simulation setup\n",
    "ax = sim.plot(y=0)\n",
    "ax.get_xaxis().set_visible(False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9eb096c3",
   "metadata": {},
   "source": [
    "Submit the simulation job to the server."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "20b4d1f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T23:58:48.819577Z",
     "iopub.status.busy": "2023-03-27T23:58:48.819430Z",
     "iopub.status.idle": "2023-03-28T00:00:24.726459Z",
     "shell.execute_reply": "2023-03-28T00:00:24.725862Z"
    }
   },
   "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\">[16:32:12] </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'dbr_cavity'</span> with task_id <span style=\"color: #008000; text-decoration-color: #008000\">'fdve-cfd2823d-834c-4a37-b625-e30dc1549f2fv1'</span>.      <a href=\"file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#139\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">139</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[16:32:12]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'dbr_cavity'\u001b[0m with task_id \u001b[32m'fdve-cfd2823d-834c-4a37-b625-e30dc1549f2fv1'\u001b[0m.      \u001b]8;id=221840;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=821157;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#139\u001b\\\u001b[2m139\u001b[0m\u001b]8;;\u001b\\\n"
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       "Output()"
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       "<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": []
     },
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     "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[16:32:13] </span>status = queued                                                                            <a href=\"file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#269\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">269</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[16:32:13]\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                                            \u001b]8;id=935431;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=139028;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#269\u001b\\\u001b[2m269\u001b[0m\u001b]8;;\u001b\\\n"
      ]
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       "Output()"
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[16:32:16] </span>status = preprocess                                                                        <a href=\"file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#263\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">263</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[16:32:16]\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                                        \u001b]8;id=423821;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=521518;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#263\u001b\\\u001b[2m263\u001b[0m\u001b]8;;\u001b\\\n"
      ]
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    {
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      "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"
      ],
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     },
     "metadata": {},
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[16:32:21] </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.029</span>. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed FlexCredit cost <a href=\"file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#286\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">286</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>after a simulation run.                                                                    <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[16:32:21]\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.029\u001b[0m. 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 FlexCredit cost \u001b]8;id=557024;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=483966;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#286\u001b\\\u001b[2m286\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0mafter a simulation run.                                                                    \u001b[2m             \u001b[0m\n"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>starting up solver                                                                         <a href=\"file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#290\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">290</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                                         \u001b]8;id=319712;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=744660;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#290\u001b\\\u001b[2m290\u001b[0m\u001b]8;;\u001b\\\n"
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       "</pre>\n"
      ],
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       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                                             \u001b]8;id=892410;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=460348;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#300\u001b\\\u001b[2m300\u001b[0m\u001b]8;;\u001b\\\n"
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       "Output()"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[16:33:39] </span>early shutoff detected, exiting.                                                           <a href=\"file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#313\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">313</span></a>\n",
       "</pre>\n"
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       "\u001b[2;36m[16:33:39]\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected, exiting.                                                           \u001b]8;id=999710;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=198707;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#313\u001b\\\u001b[2m313\u001b[0m\u001b]8;;\u001b\\\n"
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       "</pre>\n"
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       "\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>status = postprocess                                                                       <a href=\"file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#330\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">330</span></a>\n",
       "</pre>\n"
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       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                                                       \u001b]8;id=792199;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=568483;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#330\u001b\\\u001b[2m330\u001b[0m\u001b]8;;\u001b\\\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[16:33:53] </span>status = success                                                                           <a href=\"file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#337\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">337</span></a>\n",
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       "\u001b[2;36m[16:33:53]\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                                           \u001b]8;id=281227;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=242615;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#337\u001b\\\u001b[2m337\u001b[0m\u001b]8;;\u001b\\\n"
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       "</pre>\n"
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       "\u001b[2;36m[16:33:56]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from data/simulation.hdf5                                           \u001b]8;id=199703;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=855178;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#512\u001b\\\u001b[2m512\u001b[0m\u001b]8;;\u001b\\\n"
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   ],
   "source": [
    "sim_data = web.run(\n",
    "    sim, task_name=\"dbr_cavity\", path=\"data/simulation.hdf5\", verbose=True\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "293b98dc",
   "metadata": {},
   "source": [
    "### Result Visualization "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ff29699",
   "metadata": {},
   "source": [
    "Once the simulation is complete, we can plot the reflectance spectra. A sharp dip at the central frequency due to the cavity resonance mode is observed, in agreement with our expectation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7e966b70",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-28T00:00:26.389576Z",
     "iopub.status.busy": "2023-03-28T00:00:26.389346Z",
     "iopub.status.idle": "2023-03-28T00:00:26.625484Z",
     "shell.execute_reply": "2023-03-28T00:00:26.624863Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "R = sim_data[\"R\"].flux  # extracting reflection data from the flux monitor\n",
    "plt.plot(freqs / freq0, R)\n",
    "plt.xlabel(\"Normalized frequency\")\n",
    "plt.ylabel(\"Reflectance\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5da1c2f8",
   "metadata": {},
   "source": [
    "Finally, we visualize the frequency domain field distribution. Since the simulation is in 1D, we can not plot the field as a 2D false color image. Instead, we can plot it as a curve. The incident plane wave is polarized in the x direction, so we will look at the x component of the field. \n",
    "\n",
    "At the resonance frequency, the field distribution shows a strong localization at the cavity. On the contrary, when the frequency is slightly off-resonance, the field is exponentially decaying, leading to strong reflection. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5304a252",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-28T00:00:26.627960Z",
     "iopub.status.busy": "2023-03-28T00:00:26.627751Z",
     "iopub.status.idle": "2023-03-28T00:00:27.146433Z",
     "shell.execute_reply": "2023-03-28T00:00:27.145806Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(8, 4))\n",
    "\n",
    "# plot the field distribution at the resonance frequency\n",
    "np.squeeze(sim_data[\"field\"].Ex.sel(f=freq0)).abs.plot(ax=ax1)\n",
    "ax1.set_title(\"|Ex(x, y)| at $f_0$\")\n",
    "\n",
    "# plot the field distribution at the off-resonance frequency\n",
    "np.squeeze(sim_data[\"field\"].Ex.sel(f=0.9 * freq0)).abs.plot(ax=ax2)\n",
    "ax2.set_title(\"|Ex(x, y)| at $0.9f_0$\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bfb8b98d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  },
  "widgets": {
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          "text/plain": "N=2.00: status = success  \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nN=3.00: status = success  \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nN=4.00: status = success  \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nN=5.00: status = success  \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nN=10.00: status = success \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n"
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