{ "cells": [ { "cell_type": "markdown", "id": "73ecbe60", "metadata": {}, "source": [ "# Bullseye grating for quantum emitter collection\n", "\n", "**Author: Abhishek Das, North Carolina State University**\n", "\n", "In this notebook, we simulate a bullseye grating on a GaAs slab using Tidy3D. A point dipole source excites the structure, and the far-field radiation pattern is computed to evaluate Gaussian mode overlap and power collection efficiency within a target numerical aperture. Resonance analysis is also performed to identify the high-Q modes of the structure.\n", "\n", "The bullseye geometry is defined using GDS (gdstk) and imported into Tidy3D.\n", "\n", "\"diagram\"" ] }, { "cell_type": "code", "execution_count": 1, "id": "002476ec-c5c9-4277-8ba2-73a272849f47", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import tidy3d as td\n", "import tidy3d.web as web\n", "from tidy3d.plugins.resonance import ResonanceFinder\n", "\n", "import gdstk" ] }, { "cell_type": "markdown", "id": "fddb44a4", "metadata": {}, "source": [ "## Set Up the Simulation" ] }, { "cell_type": "code", "execution_count": 2, "id": "50c0fc64-eea4-44c5-97e9-ed60e4e43f9b", "metadata": {}, "outputs": [], "source": [ "lambda_min = 0.8\n", "lambda_max = 1.1\n", "\n", "monitor_lambda = np.linspace(lambda_min, lambda_max, 401)\n", "monitor_freq = td.constants.C_0 / monitor_lambda" ] }, { "cell_type": "code", "execution_count": 3, "id": "cbe717bd-bcef-457f-9da6-71a191ac0e62", "metadata": {}, "outputs": [], "source": [ "automesh_per_wvl = 40\n", "sim_size = Lx, Ly, Lz = (5.5, 5.5, 2)\n", "offset_monitor = 0.4" ] }, { "cell_type": "code", "execution_count": 4, "id": "05381ab7-90eb-4fae-ad2c-21a11d6c6c2f", "metadata": {}, "outputs": [], "source": [ "vacuum = td.Medium(permittivity = 1, name = 'vacuum')\n", "GaAs_permittivity = 3.55**2\n", "GaAs = td.Medium(permittivity = GaAs_permittivity, name = 'GaAs')\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "1d416224-dc93-4365-bb83-06d08ef149ab", "metadata": {}, "outputs": [], "source": [ "freq0 = 302.675e12\n", "fwidth = 144.131e12\n", "\n", "dipole_source = td.PointDipole(\n", " center = (0, 0, 0),\n", " source_time = td.GaussianPulse(\n", " freq0 = freq0,\n", " fwidth = fwidth\n", " ),\n", " polarization = 'Ex',\n", " name = 'dipole_source'\n", ")\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "a4fc3316-1b73-4031-b739-9e8eebb06dff", "metadata": {}, "outputs": [], "source": [ "t_start = 10/fwidth\n", "t_stop = 30e-12" ] }, { "cell_type": "markdown", "id": "8f590e22", "metadata": {}, "source": [ "## Define Structures" ] }, { "cell_type": "code", "execution_count": 7, "id": "3c7e3ddc-c7c6-41ab-801e-0c357c293184", "metadata": {}, "outputs": [], "source": [ "slab_side_length = 5\n", "slab_height = 0.2\n", "slab = td.Structure(\n", " geometry = td.Box(\n", " center = (0, 0, 0),\n", " size = (slab_side_length, slab_side_length, slab_height),\n", " ),\n", " medium = GaAs\n", ")" ] }, { "cell_type": "code", "execution_count": 8, "id": "2d2c738c-1f89-40cf-b773-3eb53984fc64", "metadata": {}, "outputs": [], "source": [ "lib = gdstk.Library()\n", "cell = lib.new_cell(\"BULLSEYE\")\n", "\n", "central_inner_radius = 0.355 + 0.014\n", "ring_width = 0.07 + np.array([-0.036, -0.015, +0.011, -0.009, -0.011, +0.012, +0.005])\n", "ring_height = 0.2\n", "ring_period = 0.1075\n", "\n", "angle = 3\n", "quadrants = 4\n", "theta1 = angle * np.pi / 180\n", "theta2 = (90 - angle) * np.pi / 180\n", "\n", "ring_numbers = np.size(ring_width)\n", "\n", "x_origin = 0\n", "y_origin = 0\n", "\n", "inner_radius = central_inner_radius\n", "\n", "for i in range(ring_numbers):\n", "\n", " outer_radius = inner_radius + ring_width[i]\n", "\n", " for j in range(quadrants):\n", "\n", " gc_slice = gdstk.ellipse(\n", " (x_origin, y_origin),\n", " inner_radius,\n", " outer_radius,\n", " theta1 + j*np.pi/2,\n", " theta2 + j*np.pi/2,\n", " layer = 1,\n", " datatype = 1,\n", " tolerance = 0.0001\n", " )\n", " cell.add(gc_slice)\n", "\n", " inner_radius = outer_radius + ring_period\n", "\n", "gc_etch = td.Geometry.from_gds(\n", " cell,\n", " gds_layer = 1,\n", " axis = 2,\n", " slab_bounds = (-ring_height/2, ring_height/2)\n", " )" ] }, { "cell_type": "code", "execution_count": 9, "id": "6bed53e0-1947-4ab6-a57c-34b3bd8d66e3", "metadata": {}, "outputs": [], "source": [ "etch = td.Structure(\n", " geometry = gc_etch,\n", " medium = vacuum,\n", " name = \"etch\"\n", ")" ] }, { "cell_type": "markdown", "id": "263fdb48", "metadata": {}, "source": [ "## Define Monitor" ] }, { "cell_type": "code", "execution_count": 10, "id": "c45d14e4-13bb-46f7-becd-89892e774d8e", "metadata": {}, "outputs": [], "source": [ "time_monitor_1 = td.FieldTimeMonitor(\n", " name = \"time_monitor_1\",\n", " size = [0.0, 0.0, 0.0],\n", " center = [0.1, 0.1, 0.0],\n", " start = t_start\n", ")\n", "\n", "time_monitor_2 = td.FieldTimeMonitor(\n", " name = \"time_monitor_2\",\n", " size = [0.0, 0.0, 0.0],\n", " center = [0.1, 0.2, 0.0],\n", " start = t_start\n", ")\n", "\n", "time_monitor_3 = td.FieldTimeMonitor(\n", " name = \"time_monitor_3\",\n", " size = [0.0, 0.0, 0.0],\n", " center = [0.2, 0.2, 0.0],\n", " start = t_start\n", ")\n", "\n", "time_monitor_4 = td.FieldTimeMonitor(\n", " name = \"time_monitor_4\",\n", " size = [0.0, 0.0, 0.0],\n", " center = [0.2, 0.1, 0.0],\n", " start = t_start\n", ")\n", "\n", "time_monitors = [time_monitor_1, time_monitor_2, time_monitor_3, time_monitor_4]" ] }, { "cell_type": "code", "execution_count": 11, "id": "ed22ea91-3a3e-4f28-9f01-f6edebbafb34", "metadata": {}, "outputs": [], "source": [ "far_field_monitor = td.FieldProjectionAngleMonitor(\n", " center = (0.0, 0.0, offset_monitor),\n", " size = (td.inf, td.inf, 0),\n", " name = 'far_field_monitor',\n", " freqs = monitor_freq,\n", " normal_dir = '+',\n", " phi = np.linspace(0, 2*np.pi, 361),\n", " theta = np.linspace(0, np.pi, 181)\n", ")" ] }, { "cell_type": "markdown", "id": "4c50c3cf", "metadata": {}, "source": [ "## Create the Simulation" ] }, { "cell_type": "code", "execution_count": 12, "id": "4540f2e4-9b1f-47de-9cde-0ea8c25b0db3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
17:18:20 EDT WARNING: Structure: simulation.structures[0] (no `name` was        \n",
       "             specified) was detected as being less than half of a central       \n",
       "             wavelength from a PML on side x-min. To avoid inaccurate results or\n",
       "             divergence, please increase gap between any structures and PML or  \n",
       "             fully extend structure through the pml.                            \n",
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             WARNING: Field projection monitor 'far_field_monitor' has          \n",
       "             observation points set up such that the monitor is projecting      \n",
       "             backwards with respect to its 'normal_dir'. If this was not        \n",
       "             intentional, please take a look at the documentation associated    \n",
       "             with this type of projection monitor to check how the observation  \n",
       "             point coordinate system is defined.                                \n",
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\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Field projection monitor \u001b[0m\u001b[32m'far_field_monitor'\u001b[0m\u001b[31m has \u001b[0m\n", "\u001b[2;36m \u001b[0m\u001b[31mobservation points set up such that the monitor is projecting \u001b[0m\n", "\u001b[2;36m \u001b[0m\u001b[31mbackwards with respect to its \u001b[0m\u001b[32m'normal_dir'\u001b[0m\u001b[31m. 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Gaussian term\n", " gauss = np.exp(-(np.sin(theta_grid) / NA)**2)\n", "\n", " # Debye apodization with safe clipping\n", " apod = np.sqrt(np.clip(np.cos(theta_grid), 0, None))\n", "\n", " return gauss * apod\n" ] }, { "cell_type": "code", "execution_count": 14, "id": "e44e1b84", "metadata": {}, "outputs": [], "source": [ "def compute_gaussian_overlap_without_pol(\n", " phi, theta, vals, \n", " NA = 0.68, \n", " n = 1.0,\n", " theta_max_deg = None,\n", " gauss_func = gaussian_far_field_no_polarization_sin_debye_apod,\n", "):\n", " \"\"\"\n", " Compute the mode-overlap between the simulated far-field intensity (vals)\n", " and an ideal Gaussian far-field WITHOUT polarization effects.\n", "\n", " Overlap is computed as:\n", " | ∫ E_sim E_gauss dΩ|^2 / ( ∫|E_sim|^2 dΩ ∫|E_gauss|^2 dΩ )\n", "\n", " Added FEATURE:\n", " If theta_max_deg is provided (in degrees), the integrals are evaluated\n", " ONLY for theta <= theta_max_deg.\n", "\n", " Parameters\n", " ----------\n", " phi, theta : 1D arrays (rad)\n", " Angular sampling coordinates of the simulated far-field.\n", " vals : 2D array\n", " Simulated intensity map, shape = (len(theta), len(phi)).\n", " NA, n : floats\n", " Gaussian beam NA and refractive index.\n", " theta_max_deg : float or None\n", " Upper limit of theta (in degrees) for restricting the overlap region.\n", " If None → use entire theta-domain.\n", " gauss_func : callable\n", " Function that generates the ideal Gaussian amplitude:\n", " gauss_func(theta, phi, NA, n) -> 2D array (len(theta), len(phi))\n", "\n", " Returns\n", " -------\n", " percentage_overlap : float\n", " Mode overlap (0–100%).\n", " \"\"\"\n", "\n", " # Convert inputs to arrays\n", " phi = np.asarray(phi)\n", " theta = np.asarray(theta)\n", " vals = np.asarray(vals)\n", "\n", " assert vals.shape == (len(theta), len(phi)), \\\n", " f\"vals has shape {vals.shape}, but expected {(len(theta), len(phi))}\"\n", "\n", " # --- Choose which Gaussian model to use via gauss_func ---\n", " E_gauss = gauss_func(theta, phi, NA, n)\n", " I_gauss = E_gauss**2\n", "\n", " # Sim amplitude = sqrt(intensity)\n", " I_sim = vals\n", " E_sim = np.sqrt(I_sim)\n", "\n", " # Meshgrid for solid-angle element\n", " theta_grid, phi_grid = np.meshgrid(theta, phi, indexing='ij')\n", "\n", " # Solid angle element (assume uniform grid)\n", " dtheta = float(np.mean(np.diff(theta)))\n", " dphi = float(np.mean(np.diff(phi)))\n", " dOmega = np.sin(theta_grid) * dtheta * dphi\n", "\n", " # --------------------------------------\n", " # Mask to restrict region if requested\n", " # --------------------------------------\n", " if theta_max_deg is not None:\n", " theta_max_rad = np.radians(theta_max_deg)\n", " mask = theta_grid <= theta_max_rad\n", " else:\n", " mask = np.ones_like(theta_grid, dtype=bool)\n", "\n", " # Apply mask\n", " E_sim_m = E_sim[mask]\n", " E_gauss_m = E_gauss[mask]\n", " I_sim_m = I_sim[mask]\n", " I_gauss_m = I_gauss[mask]\n", " dOmega_m = dOmega[mask]\n", "\n", " # --------------------------\n", " # Mode overlap numerator\n", " # --------------------------\n", " overlap = np.sum(E_sim_m * E_gauss_m * dOmega_m)\n", " numerator = np.abs(overlap)**2\n", "\n", " # --------------------------\n", " # Normalization denominator\n", " # --------------------------\n", " norm_sim = np.sum(I_sim_m * dOmega_m)\n", " norm_gauss = np.sum(I_gauss_m * dOmega_m)\n", " denominator = norm_sim * norm_gauss\n", "\n", " eta = numerator / denominator if denominator > 0 else 0.0\n", " return float(100 * eta)\n" ] }, { "cell_type": "code", "execution_count": 15, "id": "29535c18", "metadata": {}, "outputs": [], "source": [ "def compute_power_fraction_and_ratio(\n", " phi, theta, vals,\n", " NA_target,\n", " NA_gauss=0.68,\n", " n=1.0,\n", " theta_max_deg=None,\n", " gauss_func=gaussian_far_field_no_polarization_sin_debye_apod,\n", "):\n", " r\"\"\"\n", " Compute the percentage of power captured within a target NA cone for:\n", " (1) simulated far-field intensity vals(θ,φ)\n", " (2) ideal Gaussian (amplitude) generated by gauss_func\n", "\n", " Then compute the ratio:\n", " ratio = (percent_sim / percent_gauss)\n", "\n", " Power is integrated over solid angle:\n", " P \\propto \\iint I(θ,φ) sinθ dθ dφ\n", "\n", " Optional FEATURE:\n", " If theta_max_deg is provided, both total-power integrals are computed\n", " only over θ <= theta_max_deg (same as your overlap style).\n", "\n", " Parameters\n", " ----------\n", " phi, theta : 1D arrays (rad)\n", " vals : 2D array\n", " Simulated intensity map, shape = (len(theta), len(phi)).\n", " NA_target : float\n", " NA defining the capture cone for the fraction calculation.\n", " NA_gauss : float\n", " NA used to *define the Gaussian model* in gauss_func (beam divergence).\n", " n : float\n", " Refractive index of surrounding medium for θ = asin(NA/n).\n", " theta_max_deg : float or None\n", " If provided, restrict all integrals to θ <= theta_max_deg.\n", " gauss_func : callable\n", " Gaussian amplitude model:\n", " gauss_func(theta, phi, NA_gauss, n) -> 2D amplitude array\n", "\n", " Returns\n", " -------\n", " percent_sim : float\n", " 0–100 (%) power fraction within NA_target for simulated intensity.\n", " percent_gauss : float\n", " 0–100 (%) power fraction within NA_target for Gaussian intensity |E|^2.\n", " ratio : float\n", " (percent_sim / percent_gauss); np.inf if percent_gauss == 0.\n", " \"\"\"\n", "\n", " if gauss_func is None:\n", " raise ValueError(\"gauss_func must be provided (it should return Gaussian amplitude).\")\n", "\n", " # Convert inputs to arrays\n", " phi = np.asarray(phi)\n", " theta = np.asarray(theta)\n", " vals = np.asarray(vals)\n", "\n", " assert vals.shape == (len(theta), len(phi)), \\\n", " f\"vals has shape {vals.shape}, but expected {(len(theta), len(phi))}\"\n", "\n", " # --- Build Gaussian intensity from amplitude ---\n", " E_gauss = gauss_func(theta, phi, NA_gauss, n)\n", " I_gauss = np.abs(E_gauss)**2\n", "\n", " # Simulated intensity\n", " I_sim = vals\n", "\n", " # Meshgrid for solid-angle element (assume uniform grid like your function)\n", " theta_grid, phi_grid = np.meshgrid(theta, phi, indexing='ij')\n", "\n", " dtheta = float(np.mean(np.diff(theta)))\n", " dphi = float(np.mean(np.diff(phi)))\n", " dOmega = np.sin(theta_grid) * dtheta * dphi\n", "\n", " # --------------------------------------\n", " # Masks: (A) optional theta restriction\n", " # --------------------------------------\n", " if theta_max_deg is not None:\n", " theta_max_rad = np.radians(theta_max_deg)\n", " mask_theta = theta_grid <= theta_max_rad\n", " else:\n", " mask_theta = np.ones_like(theta_grid, dtype=bool)\n", "\n", " # --------------------------------------\n", " # Masks: (B) NA capture cone restriction\n", " # --------------------------------------\n", " theta_cap = np.arcsin(np.clip(NA_target / float(n), -1.0, 1.0))\n", " mask_cap = theta_grid <= theta_cap\n", "\n", " # Combined masks\n", " mask_total = mask_theta\n", " mask_in = mask_theta & mask_cap\n", "\n", " # --------------------------\n", " # Simulated power fractions\n", " # --------------------------\n", " P_sim_total = np.sum(I_sim[mask_total] * dOmega[mask_total])\n", " P_sim_in = np.sum(I_sim[mask_in] * dOmega[mask_in])\n", "\n", " frac_sim = (P_sim_in / P_sim_total) if P_sim_total > 0 else 0.0\n", "\n", " # --------------------------\n", " # Gaussian power fractions\n", " # --------------------------\n", " P_g_total = np.sum(I_gauss[mask_total] * dOmega[mask_total])\n", " P_g_in = np.sum(I_gauss[mask_in] * dOmega[mask_in])\n", "\n", " frac_gauss = (P_g_in / P_g_total) if P_g_total > 0 else 0.0\n", "\n", " percent_sim = 100.0 * float(frac_sim)\n", " percent_gauss = 100.0 * float(frac_gauss)\n", " ratio = np.inf if percent_gauss == 0 else (percent_sim / percent_gauss)\n", "\n", " return percent_sim, percent_gauss, float(ratio)\n" ] }, { "cell_type": "code", "execution_count": 16, "id": "ba040ad7", "metadata": {}, "outputs": [], "source": [ "def make_far_field_plot_without_pol(phi, theta, vals, NA = 0.68, n=1.0, \n", " gauss_func = gaussian_far_field_no_polarization_sin_debye_apod,\n", " calculate_overlap=True):\n", " \"\"\"\n", " Plots the far-field intensity distribution, optionally saving the figure and calculating the mode overlap and mentioning it on the plot.\n", " Parameters:\n", " theta, phi : 1D arrays (rad)\n", " Input angular coordinates\n", " vals : 2D array\n", " Far-field intensity values\n", " NA, n : float \n", " Numerical aperture and refractive index (of air)\n", " calculate_overlap : bool\n", " Whether to calculate and display the mode overlap (Ideally when plotting the Gaussian beam, this should be False)\n", " \"\"\"\n", "\n", " phi = np.asarray(phi)\n", " theta = np.asarray(theta)\n", " vals = np.asarray(vals)\n", " assert vals.shape == (len(theta), len(phi))\n", "\n", " if calculate_overlap:\n", " overlap = compute_gaussian_overlap_without_pol(\n", " phi, theta, vals,\n", " NA=NA, n=n,\n", " gauss_func=gauss_func\n", " )\n", "\n", "\n", " I = vals\n", " I_norm = I / (I.max() if I.max()>0 else 1.0)\n", "\n", " PHI, THETA = np.meshgrid(phi, theta, indexing = 'xy')\n", "\n", " theta_NA_deg = np.degrees(np.arcsin(min(NA/float(n), 1.0)))\n", " phi_circle = np.linspace(0, 2.0 * np.pi, 721)\n", " theta_circle = np.full_like(phi_circle, theta_NA_deg)\n", "\n", " fig1, ax1 = plt.subplots(subplot_kw={'projection': 'polar'}, figsize = (6.5,5.5))\n", " ax1.tick_params(axis='y', colors='white')\n", " pcm1 = ax1.pcolormesh(PHI, np.degrees(THETA), I_norm, shading='auto', cmap='magma')\n", " cbar1 = fig1.colorbar(pcm1, ax=ax1, label=r\"$|E|^2$\")\n", " ax1.set_ylim(0, 90)\n", " #ax1.set_title('Far_field: Normalized Intensity')\n", " ax1.plot(phi_circle, theta_circle, 'w--', lw = 2, label=f'NA={NA:.2f}, n = {n}')\n", " ax1.legend(loc='upper right')\n", " if calculate_overlap:\n", " ax1.text(0.02, 0.98, f'Mode overlap: {overlap:.2f}%', transform = ax1.transAxes, va = 'top', ha = 'left',\n", " bbox = dict(boxstyle = 'round, pad = 0.3', facecolor = 'black', alpha = 0.55), color = 'white')\n", " \n", " fig1.tight_layout()\n", " plt.show()\n", " return" ] }, { "cell_type": "code", "execution_count": 17, "id": "77fad1b1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
             WARNING: Simulation has 2.36e+06 time steps. The 'run_time' may be \n",
       "             unnecessarily large, unless there are very long-lived resonances.  \n",
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17:18:21 EDT Created task                                                       \n",
       "             'Inverse_Designed_19th_Obj_6th_Stage_From_Epoch=5_Epoch=15_Rounded_\n",
       "             UP_APW=40_T=30' with resource_id                                   \n",
       "             'fdve-054f38e1-2068-451e-b2a4-266ac88da4c4' and task_type 'FDTD'.  \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m17:18:21 EDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \n", "\u001b[2;36m \u001b[0m\u001b[32m'\u001b[0m\u001b[32mInverse_Designed_19th_Obj_6th_Stage_From_Epoch\u001b[0m\u001b[32m=\u001b[0m\u001b[32m5_Epoch\u001b[0m\u001b[32m=\u001b[0m\u001b[32m15_Rounded_\u001b[0m\n", "\u001b[2;36m \u001b[0m\u001b[32mUP_APW\u001b[0m\u001b[32m=\u001b[0m\u001b[32m40_T\u001b[0m\u001b[32m=30'\u001b[0m with resource_id \n", "\u001b[2;36m \u001b[0m\u001b[32m'fdve-054f38e1-2068-451e-b2a4-266ac88da4c4'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
             View task using web UI at                                          \n",
       "             'https://tidy3d.simulation.cloud/workbench?taskId=fdve-054f38e1-206\n",
       "             8-451e-b2a4-266ac88da4c4'.                                         \n",
       "
\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=221116;https://tidy3d.simulation.cloud/workbench?taskId=fdve-054f38e1-2068-451e-b2a4-266ac88da4c4\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=791227;https://tidy3d.simulation.cloud/workbench?taskId=fdve-054f38e1-2068-451e-b2a4-266ac88da4c4\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=221116;https://tidy3d.simulation.cloud/workbench?taskId=fdve-054f38e1-2068-451e-b2a4-266ac88da4c4\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=519391;https://tidy3d.simulation.cloud/workbench?taskId=fdve-054f38e1-2068-451e-b2a4-266ac88da4c4\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=221116;https://tidy3d.simulation.cloud/workbench?taskId=fdve-054f38e1-2068-451e-b2a4-266ac88da4c4\u001b\\\u001b[32m-054f38e1-206\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0m\u001b]8;id=221116;https://tidy3d.simulation.cloud/workbench?taskId=fdve-054f38e1-2068-451e-b2a4-266ac88da4c4\u001b\\\u001b[32m8-451e-b2a4-266ac88da4c4'\u001b[0m\u001b]8;;\u001b\\. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
             Task folder: 'default'.                                            \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=690616;https://tidy3d.simulation.cloud/folders/folder-86acd7be-dbf5-477e-9c86-3e20787acc03\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "15c9d0062f7b452ab072c1928511291f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "
\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
17:18:23 EDT Estimated FlexCredit cost: 24.324. Minimum cost depends on task    \n",
       "             execution details. Use 'web.real_cost(task_id)' to get the billed  \n",
       "             FlexCredit cost after a simulation run.                            \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:18:23 EDT\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m24.324\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": [
    "task_id = web.upload(sim, task_name = \"Inverse_Designed_19th_Obj_6th_Stage_From_Epoch=5_Epoch=15_Rounded_UP_APW=40_T=30\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45943b6a",
   "metadata": {},
   "source": [
    "## Run the Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b8e260f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
17:18:24 EDT status = success                                                   \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:18:24 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "web.start(task_id)\n",
    "web.monitor(task_id, verbose = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dd893632",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
17:18:58 EDT WARNING: Structure: simulation.structures[0] (no `name` was        \n",
       "             specified) was detected as being less than half of a central       \n",
       "             wavelength from a PML on side x-min. To avoid inaccurate results or\n",
       "             divergence, please increase gap between any structures and PML or  \n",
       "             fully extend structure through the pml.                            \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:18:58 EDT\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Structure: simulation.structures\u001b[0m\u001b[1;31m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;31m]\u001b[0m\u001b[31m \u001b[0m\u001b[1;31m(\u001b[0m\u001b[31mno `name` was        \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mspecified\u001b[0m\u001b[1;31m)\u001b[0m\u001b[31m was detected as being less than half of a central       \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mwavelength from a PML on side x-min. To avoid inaccurate results or\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mdivergence, please increase gap between any structures and PML or  \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mfully extend structure through the pml.                            \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             WARNING: Suppressed 3 WARNING messages.                            \n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Suppressed \u001b[0m\u001b[1;36m3\u001b[0m\u001b[31m WARNING messages.                            \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             WARNING: Field projection monitor 'far_field_monitor' has          \n",
       "             observation points set up such that the monitor is projecting      \n",
       "             backwards with respect to its 'normal_dir'. If this was not        \n",
       "             intentional, please take a look at the documentation associated    \n",
       "             with this type of projection monitor to check how the observation  \n",
       "             point coordinate system is defined.                                \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Field projection monitor \u001b[0m\u001b[32m'far_field_monitor'\u001b[0m\u001b[31m has          \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mobservation points set up such that the monitor is projecting      \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mbackwards with respect to its \u001b[0m\u001b[32m'normal_dir'\u001b[0m\u001b[31m. If this was not        \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mintentional, please take a look at the documentation associated    \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mwith this type of projection monitor to check how the observation  \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mpoint coordinate system is defined.                                \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             WARNING: Simulation final field decay value of 4.6e-07 is greater  \n",
       "             than the simulation shutoff threshold of 1e-07. Consider running   \n",
       "             the simulation again with a larger 'run_time' duration for more    \n",
       "             accurate results.                                                  \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Simulation final field decay value of \u001b[0m\u001b[1;36m4.6e-07\u001b[0m\u001b[31m is greater  \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mthan the simulation shutoff threshold of \u001b[0m\u001b[1;36m1e-07\u001b[0m\u001b[31m. Consider running   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mthe simulation again with a larger \u001b[0m\u001b[32m'run_time'\u001b[0m\u001b[31m duration for more    \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31maccurate results.                                                  \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             WARNING: Warning messages were found in the solver log. For more   \n",
       "             information, check 'SimulationData.log' or use                     \n",
       "             'web.download_log(task_id)'.                                       \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Warning messages were found in the solver log. For more   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31minformation, check \u001b[0m\u001b[32m'SimulationData.log'\u001b[0m\u001b[31m or use                     \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.download_log\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m.                                       \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18:45:02] WARNING: Structure: simulation.structures[0] (no `name` was          \n",
      "           specified) was detected as being less than half of a central         \n",
      "           wavelength from a PML on side x-min. To avoid inaccurate results or  \n",
      "           divergence, please increase gap between any structures and PML or    \n",
      "           fully extend structure through the pml.                              \n",
      "           WARNING: Suppressed 3 WARNING messages.                              \n",
      "           WARNING: Field projection monitor 'far_field_monitor' has observation\n",
      "           points set up such that the monitor is projecting backwards with     \n",
      "           respect to its 'normal_dir'. If this was not intentional, please take\n",
      "           a look at the documentation associated with this type of projection  \n",
      "           monitor to check how the observation point coordinate system is      \n",
      "           defined.                                                             \n",
      "           USER: Simulation domain Nx, Ny, Nz: [800, 800, 134]                  \n",
      "           USER: Applied symmetries: (-1, 1, 0)                                 \n",
      "           USER: Number of computational grid points: 2.1978e+07.               \n",
      "           USER: Subpixel averaging method: SubpixelSpec(attrs={},              \n",
      "           dielectric=PolarizedAveraging(attrs={}, type='PolarizedAveraging'),  \n",
      "           metal=Staircasing(attrs={}, type='Staircasing'),                     \n",
      "           pec=PECConformal(attrs={}, type='PECConformal',                      \n",
      "           timestep_reduction=0.3, edge_singularity_correction=True),           \n",
      "           pmc=Staircasing(attrs={}, type='Staircasing'),                       \n",
      "           lossy_metal=SurfaceImpedance(attrs={}, type='SurfaceImpedance',      \n",
      "           timestep_reduction=0.0, edge_singularity_correction=True),           \n",
      "           type='SubpixelSpec')                                                 \n",
      "           USER: Number of time steps: 2.3613e+06                               \n",
      "           USER: Automatic shutoff factor: 1.00e-07                             \n",
      "           USER: Time step (s): 1.2705e-17                                      \n",
      "           USER:                                                                \n",
      "                                                                                \n",
      "           USER: Compute source modes time (s):     0.1242                      \n",
      "[18:45:03] USER: Rest of setup time (s):            0.9249                      \n",
      "[18:45:31] USER: Compute monitor modes time (s):    0.0001                      \n",
      "[20:15:58] USER: Solver time (s):                   5450.9610                   \n",
      "           USER: Time-stepping speed (cells/s):     1.00e+10                    \n",
      "[20:16:07] USER: Post-processing time (s):          8.4307                      \n",
      "\n",
      " ====== SOLVER LOG ====== \n",
      "\n",
      "Processing grid and structures...\n",
      "Building FDTD update coefficients...\n",
      "Solver setup time (s):             25.1779\n",
      "\n",
      "Running solver for 2361336 time steps...\n",
      "- Time step    435 / time 5.53e-15s (  0 % done), field decay: 1.00e+00\n",
      "- Time step  94453 / time 1.20e-12s (  4 % done), field decay: 2.82e-03\n",
      "- Time step 188906 / time 2.40e-12s (  8 % done), field decay: 7.30e-05\n",
      "- Time step 283360 / time 3.60e-12s ( 12 % done), field decay: 1.63e-03\n",
      "- Time step 377813 / time 4.80e-12s ( 16 % done), field decay: 4.25e-04\n",
      "- Time step 472267 / time 6.00e-12s ( 20 % done), field decay: 3.74e-04\n",
      "- Time step 566720 / time 7.20e-12s ( 24 % done), field decay: 7.02e-04\n",
      "- Time step 661174 / time 8.40e-12s ( 28 % done), field decay: 8.19e-06\n",
      "- Time step 755627 / time 9.60e-12s ( 32 % done), field decay: 3.99e-04\n",
      "- Time step 850080 / time 1.08e-11s ( 36 % done), field decay: 1.34e-04\n",
      "- Time step 944534 / time 1.20e-11s ( 40 % done), field decay: 7.57e-05\n",
      "- Time step 1038987 / time 1.32e-11s ( 44 % done), field decay: 1.88e-04\n",
      "- Time step 1133441 / time 1.44e-11s ( 48 % done), field decay: 3.73e-06\n",
      "- Time step 1227894 / time 1.56e-11s ( 52 % done), field decay: 9.55e-05\n",
      "- Time step 1322348 / time 1.68e-11s ( 56 % done), field decay: 4.35e-05\n",
      "- Time step 1416801 / time 1.80e-11s ( 60 % done), field decay: 1.47e-05\n",
      "- Time step 1511255 / time 1.92e-11s ( 64 % done), field decay: 4.99e-05\n",
      "- Time step 1605708 / time 2.04e-11s ( 68 % done), field decay: 1.88e-06\n",
      "- Time step 1700161 / time 2.16e-11s ( 72 % done), field decay: 2.25e-05\n",
      "- Time step 1794615 / time 2.28e-11s ( 76 % done), field decay: 1.29e-05\n",
      "- Time step 1889068 / time 2.40e-11s ( 80 % done), field decay: 2.70e-06\n",
      "- Time step 1983522 / time 2.52e-11s ( 84 % done), field decay: 1.29e-05\n",
      "- Time step 2077975 / time 2.64e-11s ( 88 % done), field decay: 8.37e-07\n",
      "- Time step 2172429 / time 2.76e-11s ( 92 % done), field decay: 5.02e-06\n",
      "- Time step 2266882 / time 2.88e-11s ( 96 % done), field decay: 3.75e-06\n",
      "- Time step 2361335 / time 3.00e-11s (100 % done), field decay: 4.60e-07\n",
      "Time-stepping time (s):            5186.8286\n",
      "Field projection time (s):         232.2404\n",
      "Data write time (s):               6.7131\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sim_data = web.load(task_id, verbose=False, path = \"Inverse_Designed_19th_Obj_6th_Stage_From_Epoch=5_Epoch=15_Rounded_UP_APW=40_T=30.hdf5\")\n",
    "print(sim_data.log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8126b0e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
17:18:59 EDT WARNING: Structure: simulation.structures[0] (no `name` was        \n",
       "             specified) was detected as being less than half of a central       \n",
       "             wavelength from a PML on side x-min. To avoid inaccurate results or\n",
       "             divergence, please increase gap between any structures and PML or  \n",
       "             fully extend structure through the pml.                            \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:18:59 EDT\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Structure: simulation.structures\u001b[0m\u001b[1;31m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;31m]\u001b[0m\u001b[31m \u001b[0m\u001b[1;31m(\u001b[0m\u001b[31mno `name` was        \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mspecified\u001b[0m\u001b[1;31m)\u001b[0m\u001b[31m was detected as being less than half of a central       \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mwavelength from a PML on side x-min. To avoid inaccurate results or\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mdivergence, please increase gap between any structures and PML or  \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mfully extend structure through the pml.                            \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             WARNING: Suppressed 3 WARNING messages.                            \n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Suppressed \u001b[0m\u001b[1;36m3\u001b[0m\u001b[31m WARNING messages.                            \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             WARNING: Field projection monitor 'far_field_monitor' has          \n",
       "             observation points set up such that the monitor is projecting      \n",
       "             backwards with respect to its 'normal_dir'. If this was not        \n",
       "             intentional, please take a look at the documentation associated    \n",
       "             with this type of projection monitor to check how the observation  \n",
       "             point coordinate system is defined.                                \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Field projection monitor \u001b[0m\u001b[32m'far_field_monitor'\u001b[0m\u001b[31m has          \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mobservation points set up such that the monitor is projecting      \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mbackwards with respect to its \u001b[0m\u001b[32m'normal_dir'\u001b[0m\u001b[31m. If this was not        \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mintentional, please take a look at the documentation associated    \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mwith this type of projection monitor to check how the observation  \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mpoint coordinate system is defined.                                \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data = td.SimulationData.from_file(fname = \"Inverse_Designed_19th_Obj_6th_Stage_From_Epoch=5_Epoch=15_Rounded_UP_APW=40_T=30.hdf5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dd55737",
   "metadata": {},
   "source": [
    "## Analyze Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1e40c4f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/qn/syhrzy8n7930sgqvxv2x65s40000gn/T/ipykernel_59250/3831151282.py:15: RuntimeWarning: divide by zero encountered in divide\n",
      "  wavelengths_Ex = td.constants.C_0 / freqs_Ex\n"
     ]
    },
    {
     "data": {
      "image/png": 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BBx4Q169fF0LYdx4RuQIWTrfcWTj9+OOPAoDw9fU1e3h4eIj+/fsLIYS4ePGiqF+/vnjjjTfEwYMHxS+//CJiYmJE586d7/qxZaKybkcAQKxYsUIUFhaK//u//zP7j6jYhx9+KACINWvWlHkcuYWTEEIUFBSI9957TzRp0kTodDpRtWpV0bJlSzFt2jRx48YNqd/NmzfFK6+8IqpXry58fX1F7969xV9//WVROAkhRGJiomjatKnw9PQUDRs2FF999ZXF7QiK/fe//xWPPPKI9O+vUaNGYsyYMeLEiRN3jd/arQ/y8vLEpEmTRO3atYVWqxUhISHi6aeflorUkl566SUBQKxevbrU3N2prFyvXLlS+nst9tlnn4mWLVsKb29vUblyZdGsWTPx5ptvSsXOwYMHxaBBg0TNmjWFTqcTQUFB4vHHHxf79++XxigunN5//33xwQcfiPDwcKHT6UT79u3F4cOHLeL46aefRLt27YS3t7fw8/MTvXv3FkePHjXrY2vhtHXrVtGnTx8RGhoqPD09RWhoqBg0aJA4efKk2X62nkdErkAlhIzVk25IpVJh/fr16Nu3LwBgzZo1GDJkCP7880+LBZKVKlVCSEgI3nnnHWzevBn79u2TXvv7778RHh6OXbt2oU2bNvfzLRA5HZVKhfj4eLO7h7uK8ePHY9myZUhPT3fqr585d+4cateujffffx+vv/66o8MhcnsVenF4WVq0aAGj0YhLly6hffv2Vvvk5eVJi8KLFRdZpd3ThIicX35+Pr766is89dRTTl00EdH9V6EXh+fk5CAlJQUpKSkAim4vkJKSgrS0NDRo0ABDhgzBsGHDsG7dOqSmpmLv3r2YPXs2Nm7cCKDo0zL79u3D9OnTcerUKRw8eBCxsbGoVasWWrRo4cB3RkRyXLp0CatXr8bgwYNx5coVjBs3ztEhEZGTqdCF0/79+9GiRQupyImLi0OLFi0wZcoUAMCKFSswbNgwvPbaa2jYsCH69u2Lffv2STeF69SpE1avXo0NGzagRYsWeOyxx6DT6bB582abPmJMRM7l6NGj0i0IPvroI+kTYkRExbjGiYiIiMhGFXrGiYiIiMgeLJyIiIiIbFThPlVnMpnwzz//oHLlyrz9PxEREUEIgezsbISGhlp8Wv5OFa5w+ueff/ilkkRERGThr7/+wgMPPFBmnwpXOFWuXBlAUXL8/PzKfXyDwYDExER069aN31dnJ+ZOHuZNPuZOHuZNPuZOPiVzl5WVhfDwcKlGKEuFK5yKL8/5+fkpVjj5+PjAz8+P/yjsxNzJw7zJx9zJw7zJx9zJdz9yZ8sSHi4OJyIiIrKRQwun7du3o3fv3ggNDYVKpcKGDRvuuo9er8ekSZNQq1Yt6HQ6REREYPny5coHS0RERBWeQy/V5ebmIjIyEiNHjkS/fv1s2qd///7IyMjAsmXLUK9ePVy8eJHfC0dERET3hUMLpx49eqBHjx4299+8eTN++eUXnD17FtWqVQMAREREKBQdERERkTmXWuP0ww8/oFWrVpgzZw7CwsLQoEEDvP7667h586ajQyMiIqIKwKU+VXf27Fn8+uuv8PLywvr165GZmYmXXnoJV65cwYoVK6zuo9frodfrpe2srCwARavzDQZDucdYPKYSY7s75k4e5k0+5k4e5k0+5k4+JXNnz5hO8yW/KpUK69evR9++fUvt061bN+zYsQPp6emoUqUKAGDdunV4+umnkZubC29vb4t9pk6dimnTplm0r169Gj4+PuUWPxEREbmmvLw8DB48GDdu3LjrrYpcasapRo0aCAsLk4omAGjcuDGEEPj7779Rv359i30mTpyIuLg4abv4JlfdunVT7D5OSUlJ6Nq1K+/RYSfmTh7mTT7mTh7mTT7mTj4lc1d8NcoWLlU4tWvXDmvXrkVOTg4qVaoEADh58iTUanWpt0jX6XTQ6XQW7VqtVtGTVunx3RlzJw/zJh9zJw/zJh9zJ58SubNnPIcuDs/JyUFKSgpSUlIAAKmpqUhJSUFaWhqAotmiYcOGSf0HDx6M6tWrIzY2FkePHsX27dvxxhtvYOTIkVYv0xERERGVJ4cWTvv370eLFi3QokULAEBcXBxatGiBKVOmAAAuXrwoFVEAUKlSJSQlJeH69eto1aoVhgwZgt69e+Ojjz5ySPxEZJu8gnz0X/Aylv68xtGhEBHdE4dequvQoQPKWpu+cuVKi7ZGjRohKSlJwaiIqLx9/st/sf34Pmw/vg+jOg1wdDhERLK51H2ciMg15ep5rzUicg8snIhIcU5y1xMionvGwomIiIjIRiyciIiIiGzEwomIiIjIRiyciIiIiGzEwomIFMel4UTkLlg4EREREdmIhRMRKU7l6ACIiMoJCyciUhwv1RGRu2DhRERERGQjFk5ERERENmLhRERERGQjFk5ERERENmLhRESK45f8EpG7YOFEREREZCMWTkSkOJWKd3IiIvfAwomIFMdLdUTkLlg4EREREdmIhRMRERGRjVg4EREREdmIhRMRERGRjVg4EdF9wMXhROQeWDgRERER2YiFExEREZGNHFo4bd++Hb1790ZoaChUKhU2bNhg8747d+6Eh4cHoqKiFIuPiIiIqCSHFk65ubmIjIzEokWL7Nrv+vXrGDZsGDp37qxQZERERESWPBx58B49eqBHjx527/fCCy9g8ODB0Gg0ds1SEREREd0LhxZOcqxYsQJnz57FV199hX//+9937a/X66HX66XtrKwsAIDBYIDBYCj3+IrHVGJsd8fcyeMKeTMaTdJzZ4rTFXLnjJg3+Zg7+ZTMnT1julThdOrUKUyYMAE7duyAh4dtoc+ePRvTpk2zaE9MTISPj095hyhJSkpSbGx3x9zJ48x5O336lPQ8ISHBgZFY58y5c2bMm3zMnXxK5C4vL8/mvi5TOBmNRgwePBjTpk1DgwYNbN5v4sSJiIuLk7azsrIQHh6Obt26wc/Pr9zjNBgMSEpKQteuXaHVast9fHfG3MnjCnn7w3QBOPMbAKBnz54OjuY2V8idM2Le5GPu5FMyd8VXo2zhMoVTdnY29u/fj0OHDmHs2LEAAJPJBCEEPDw8kJiYiE6dOlnsp9PpoNPpLNq1Wq2iJ63S47sz5k4eZ86bRn37cyjOGKMz586ZMW/yMXfyKZE7e8ZzmcLJz88PR44cMWv75JNP8PPPP+O7775D7dq1HRQZERERVRQOLZxycnJw+vRpaTs1NRUpKSmoVq0aatasiYkTJ+LChQv44osvoFar0bRpU7P9g4KC4OXlZdFOREREpASHFk779+9Hx44dpe3itUjDhw/HypUrcfHiRaSlpTkqPCIiIiIzDi2cOnToACFK//LPlStXlrn/1KlTMXXq1PINioiIiKgU/K46IiIiIhuxcCIixZU1s0xE5EpYOBERERHZiIUTERERkY1YOBERERHZiIUTERERkY1YOBERERHZiIUTERERkY1YOBERERHZiIUTESmOd3EiInfBwomIiIjIRiyciIiIiGzEwomIiIjIRiyciIiIiGzEwomIFMcv+SUid8HCiYiIiMhGLJyISHEqlcrRIRARlQsWTkSkOF6qIyJ3wcKJiIiIyEYsnIiIiIhsxMKJiIiIyEYsnIiIiIhsxMKJiBQn+DW/ROQmWDgRERER2cihhdP27dvRu3dvhIaGQqVSYcOGDWX2X7duHbp27YrAwED4+fmhbdu22LJly/0JlohkU4H3cSIi9+DQwik3NxeRkZFYtGiRTf23b9+Orl27IiEhAQcOHEDHjh3Ru3dvHDp0SOFIiehe8FIdEbkLD0cevEePHujRo4fN/RcsWGC2PWvWLHz//ff43//+hxYtWpRzdERERETmHFo43SuTyYTs7GxUq1at1D56vR56vV7azsrKAgAYDAYYDIZyj6l4TCXGdnfMnTyukDeT0SQ9d6Y4XSF3zoh5k4+5k0/J3NkzpksXTnPnzkVOTg769+9fap/Zs2dj2rRpFu2JiYnw8fFRLLakpCTFxnZ3zJ08zpy3M2fPSM8TEhIcGIl1zpw7Z8a8ycfcyadE7vLy8mzu67KF0+rVqzFt2jR8//33CAoKKrXfxIkTERcXJ21nZWUhPDwc3bp1g5+fX7nHZTAYkJSUhK5du0Kr1Zb7+O6MuZPHFfJ22JAGnN0NAOjZs6eDo7nNFXLnjJg3+Zg7+ZTMXfHVKFu4ZOH0zTffYNSoUVi7di26dOlSZl+dTgedTmfRrtVqFT1plR7fnTF38jhz3lTq25+qc8YYnTl3zox5k4+5k0+J3Nkznsvdx+nrr79GbGwsvv76a/Tq1cvR4RAREVEF4tAZp5ycHJw+fVraTk1NRUpKCqpVq4aaNWti4sSJuHDhAr744gsARZfnhg8fjg8//BDR0dFIT08HAHh7e6NKlSoOeQ9ERERUcTh0xmn//v1o0aKFdCuBuLg4tGjRAlOmTAEAXLx4EWlpaVL/zz77DIWFhRgzZgxq1KghPcaNG+eQ+ImIiKhiceiMU4cOHSBE6TfGW7lypdl2cnKysgERERERlcHl1jgREREROQoLJyIiIiIbsXAiIiIishELJyJSXFlrGYmIXAkLJyIiIiIbsXAiIiIishELJyIiIiIbsXAiIiIishELJyIiIiIbsXAiIiIishELJyJSHG9GQETugoUTERERkY1YOBERERHZiIUTERERkY1YOBERERHZiIUTESmO31VHRO6ChRMRERGRjVg4EZHiVCqVo0MgIioXLJyISHG8VEdE7oKFExEREZGNWDgRERER2YiFExEREZGNWDgRERER2YiFExEpTvBrfonITTi0cNq+fTt69+6N0NBQqFQqbNiw4a77JCcn46GHHoJOp0O9evWwcuVKxeMkIiIiAhxcOOXm5iIyMhKLFi2yqX9qaip69eqFjh07IiUlBa+++ipGjRqFLVu2KBwpEd0LFXgfJyJyDx6OPHiPHj3Qo0cPm/svXrwYtWvXxgcffAAAaNy4MX799VfMnz8f3bt3VypMIrpHvFRHRO7CpdY47dq1C126dDFr6969O3bt2uWgiIiIiKgiceiMk73S09MRHBxs1hYcHIysrCzcvHkT3t7eFvvo9Xro9XppOysrCwBgMBhgMBjKPcbiMZUY290xd/K4Qt5MRpP03JnidIXcOSPmTT7mTj4lc2fPmC5VOMkxe/ZsTJs2zaI9MTERPj4+ih03KSlJsbHdHXMnjzPnLTU1VXqekJDgwEisc+bcOTPmTT7mTj4lcpeXl2dzX5cqnEJCQpCRkWHWlpGRAT8/P6uzTQAwceJExMXFSdtZWVkIDw9Ht27d4OfnV+4xGgwGJCUloWvXrtBqteU+vjtj7uRxhbztzz8LnNsLAOjZs6eDo7nNFXLnjJg3+Zg7+ZTMXfHVKFu4VOHUtm1bi99Wk5KS0LZt21L30el00Ol0Fu1arVbRk1bp8d0ZcyePM+dNrb79qTpnjNGZc+fMmDf5mDv5lMidPeM5dHF4Tk4OUlJSkJKSAqBoOj8lJQVpaWkAimaLhg0bJvV/4YUXcPbsWbz55ps4fvw4PvnkE3z77bcYP368I8InIiKiCkZW4XT9+nUsXboUEydOxNWrVwEABw8exIULF+waZ//+/WjRogVatGgBAIiLi0OLFi0wZcoUAMDFixelIgoAateujY0bNyIpKQmRkZH44IMPsHTpUt6KgMjZqXgfJyJyD3Zfqvv999/RpUsXVKlSBefOncNzzz2HatWqYd26dUhLS8MXX3xh81gdOnSAEKXf38XaXcE7dOiAQ4cO2Rs2ETlSGf/OiYhcid0zTnFxcRgxYgROnToFLy8vqb1nz57Yvn17uQZHRERE5EzsLpz27duH0aNHW7SHhYUhPT29XIIiIiIickZ2F046nc7qx/ZOnjyJwMDAcgmKiIiIyBnZXTg98cQTmD59unSXTZVKhbS0NLz11lt46qmnyj1AIiIiImdhd+H0wQcfICcnB0FBQbh58yZiYmJQr149VK5cGTNnzlQiRiJycVwbTkTuwu5P1VWpUgVJSUn49ddf8fvvvyMnJwcPPfSQxZfvEhEREbkb2XcOf+SRR/DII4+UZyxERERETs2mwumjjz6yecBXXnlFdjBEREREzsymwmn+/Plm25cvX0ZeXh78/f0BFN1J3MfHB0FBQSyciIiIyG3ZtDg8NTVVesycORNRUVE4duwYrl69iqtXr+LYsWN46KGHMGPGDKXjJSIiInIYuz9V984772DhwoVo2LCh1NawYUPMnz8fkydPLtfgiIiIiJyJ3YXTxYsXUVhYaNFuNBqRkZFRLkERkXsR4P0IiMg92F04de7cGaNHj8bBgweltgMHDuDFF1/kLQmIiIjIrdldOC1fvhwhISFo1aoVdDoddDodWrdujeDgYCxdulSJGImIiIicgt33cQoMDERCQgJOnjyJ48ePAwAaNWqEBg0alHtwRERERM5E9g0wGzRowGKJiIiIKhS7C6eRI0eW+fry5ctlB0NERETkzOwunK5du2a2bTAY8Mcff+D69evo1KlTuQVGRO5D8Ft+ichN2F04rV+/3qLNZDLhxRdfRN26dcslKCIiIiJnZPen6qwOolYjLi7O4qtZiIiIiNxJuRROAHDmzBmrN8YkIiIichd2X6qLi4sz2xZC4OLFi9i4cSOGDx9eboERERERORu7C6dDhw6ZbavVagQGBuKDDz646yfuiIiIiFyZ3YXTtm3blIiDiIiIyOnZvcapU6dOuH79ukV7VlaW7NsRLFq0CBEREfDy8kJ0dDT27t1bZv8FCxagYcOG8Pb2Rnh4OMaPH4/8/HxZxyYi5fFLfonIXdhdOCUnJ6OgoMCiPT8/Hzt27LA7gDVr1iAuLg7x8fE4ePAgIiMj0b17d1y6dMlq/9WrV2PChAmIj4/HsWPHsGzZMqxZswZvv/223ccmIiIisofNl+p+//136fnRo0eRnp4ubRuNRmzevBlhYWF2BzBv3jw899xziI2NBQAsXrwYGzduxPLlyzFhwgSL/r/99hvatWuHwYMHAwAiIiIwaNAg7Nmzx+5jExEREdnD5sIpKioKKpUKKpXK6iU5b29vLFy40K6DFxQU4MCBA5g4caLUplar0aVLF+zatcvqPg8//DC++uor7N27F61bt8bZs2eRkJCAoUOH2nVsIiIiInvZXDilpqZCCIE6depg7969CAwMlF7z9PREUFAQNBqNXQfPzMyE0WhEcHCwWXtwcDCOHz9udZ/BgwcjMzMTjzzyCIQQKCwsxAsvvFDqpTq9Xg+9Xi9tZ2VlASj6qhiDwWBXvLYoHlOJsd0dcyePK+TNZDJJz50pTlfInTNi3uRj7uRTMnf2jGlz4VSrVi0A5j8AHSE5ORmzZs3CJ598gujoaJw+fRrjxo3DjBkz8M4771j0nz17NqZNm2bRnpiYCB8fH8XiTEpKUmxsd8fcyePMeTt37pz0PCEhwXGBlMKZc+fMmDf5mDv5lMhdXl6ezX1tKpx++OEH9OjRA1qtFj/88EOZfZ944gmbDx4QEACNRoOMjAyz9oyMDISEhFjd55133sHQoUMxatQoAECzZs2Qm5uL559/HpMmTYJabb7efeLEiWY37czKykJ4eDi6desGPz8/m2O1lcFgQFJSErp27QqtVlvu47sz5k4eV8jb7ryTwPkDAICePXs6OJrbXCF3zoh5k4+5k0/J3BVfjbKFTYVT3759kZ6ejqCgIPTt27fUfiqVCkaj0eaDe3p6omXLlti6das0rslkwtatWzF27Fir++Tl5VkUR8WXCK19A7tOp4NOp7No12q1ip60So/vzpg7eZw5byX/zTpjjM6cO2fGvMnH3MmnRO7sGc+mwqnk5bnyvlQXFxeH4cOHo1WrVmjdujUWLFiA3Nxc6VN2w4YNQ1hYGGbPng0A6N27N+bNm4cWLVpIl+reeecd9O7d2+41VkR0f1j7pYaIyBXZfefw8jZgwABcvnwZU6ZMQXp6OqKiorB582ZpwXhaWprZb6uTJ0+GSqXC5MmTceHCBQQGBqJ3796YOXOmo94CERERVRA2FU4fffSRzQO+8sordgcxduzYUi/NJScnm217eHggPj4e8fHxdh+HiIiI6F7YVDjNnz/fpsFUKpWswomIiIjIFdhUOKWmpiodBxEREZHTs/u76koSQnDRJxHdFX9MEJG7kFU4LVu2DE2bNoWXlxe8vLzQtGlTLF26tLxjIyIiInIqdn+qbsqUKZg3bx5efvlltG3bFgCwa9cujB8/HmlpaZg+fXq5B0lErk2lcnQERETlw+7C6dNPP8WSJUswaNAgqe2JJ55A8+bN8fLLL7NwIiILvFRHRO7C7kt1BoMBrVq1smhv2bIlCgsLyyUoIiIiImdkd+E0dOhQfPrppxbtn332GYYMGVIuQRERERE5I1l3Dl+2bBkSExPRpk0bAMCePXuQlpaGYcOGmX2h7rx588onSiIiIiInYHfh9Mcff+Chhx4CAJw5cwYAEBAQgICAAPzxxx9SPxVXgxIREZGbsbtw2rZtmxJxEJEbE+DqcCJyD/d0A0wiIiKiisTuGaf8/HwsXLgQ27Ztw6VLl2AymcxeP3jwYLkFR0TuQQVeuici92B34fTss88iMTERTz/9NFq3bs21TER0V/wxQUTuwu7C6ccff0RCQgLatWunRDxERERETsvuNU5hYWGoXLmyErEQEREROTW7C6cPPvgAb731Fs6fP69EPETk5gS/f4WIXJjdl+patWqF/Px81KlTBz4+PtBqtWavX716tdyCIyL3wLWQROQu7C6cBg0ahAsXLmDWrFkIDg7mD0QisosQgj83iMhl2V04/fbbb9i1axciIyOViIeI3BxvhklErszuNU6NGjXCzZs3lYiFiNwU7+NERO7C7sLp3XffxWuvvYbk5GRcuXIFWVlZZg8iorJwcTgRuTK7L9U99thjAIDOnTubtRevWzAajeUTGRG5JZZNROTKyvVLfo8cOXJPwRCReyq5GJwzTkTkyuwunGJiYsy2s7Oz8fXXX2Pp0qU4cOAAxo4dW27BERERETkTu9c4Fdu+fTuGDx+OGjVqYO7cuejUqRN2794ta6xFixYhIiICXl5eiI6Oxt69e8vsf/36dYwZMwY1atSATqdDgwYNkJCQIOvYRHR/ccaJiFyZXTNO6enpWLlyJZYtW4asrCz0798fer0eGzZswIMPPigrgDVr1iAuLg6LFy9GdHQ0FixYgO7du+PEiRMICgqy6F9QUICuXbsiKCgI3333HcLCwnD+/Hn4+/vLOj4R3V+8HQERuTKbZ5x69+6Nhg0b4vfff8eCBQvwzz//YOHChfccwLx58/Dcc88hNjYWDz74IBYvXgwfHx8sX77cav/ly5fj6tWr2LBhA9q1a4eIiAjExMTwvlJEToy3IyAid2HzjNOmTZvwyiuv4MUXX0T9+vXL5eAFBQU4cOAAJk6cKLWp1Wp06dIFu3btsrrPDz/8gLZt22LMmDH4/vvvERgYiMGDB+Ott96CRqOx6K/X66HX66Xt4lsmGAwGGAyGcnkfJRWPqcTY7o65k8cV8mY03f60bUGBARr5qwTKlSvkzhkxb/Ixd/IpmTt7xrS5cPr111+xbNkytGzZEo0bN8bQoUMxcOBAWQEWy8zMhNFoRHBwsFl7cHAwjh8/bnWfs2fP4ueff8aQIUOQkJCA06dP46WXXoLBYEB8fLxF/9mzZ2PatGkW7YmJifDx8bmn+MuSlJSk2NjujrmTx5nzdu7cOen5li2b4anRlt7ZAZw5d86MeZOPuZNPidzl5eXZ3NfmwqlNmzZo06YNFixYgDVr1mD58uWIi4uDyWRCUlISwsPDUblyZVkB28NkMiEoKAifffYZNBoNWrZsiQsXLuD999+3WjhNnDgRcXFx0nZWVhbCw8PRrVs3+Pn5lXt8BoMBSUlJ6Nq1q8UXIFPZmDt5XCFv+/LPAOf2AQC6desGH523gyMq4gq5c0bMm3zMnXxK5s6eG3jbfTsCX19fjBw5EiNHjsSJEyewbNkyvPvuu5gwYQK6du2KH374weaxAgICoNFokJGRYdaekZGBkJAQq/vUqFEDWq3W7LJc48aNkZ6ejoKCAnh6epr11+l00Ol0FuNotVpFT1qlx3dnzJ08zpy3kv9ePZwwTmfOnTNj3uRj7uRTInf2jHdPCw0aNmyIOXPm4O+//8bXX39t9/6enp5o2bIltm7dKrWZTCZs3boVbdu2tbpPu3btcPr0aZhMJqnt5MmTqFGjhkXRREROiLcjICIXVi4rNDUaDfr27WvXbFOxuLg4LFmyBJ9//jmOHTuGF198Ebm5uYiNjQUADBs2zGzx+IsvvoirV69i3LhxOHnyJDZu3IhZs2ZhzJgx5fFWiEhhvB0BEbkyuy/VlbcBAwbg8uXLmDJlCtLT0xEVFYXNmzdLC8bT0tKgVt+u78LDw7FlyxaMHz8ezZs3R1hYGMaNG4e33nrLUW+BiIiIKgiHF04AMHbs2FK/qiU5OdmirW3btrLvUk5E91/J+zjxSh0RuTLnuJkKEVUYvFRHRK6MhRMR3Vf8rjoicmUsnIhIcSoVv3KFiNwDCyciuq8440REroyFExEpruR8E9c4EZErY+FEREREZCMWTkR0X/FKHRG5MhZORKS4kovDeamOiFwZCyciuq+4OJyIXBkLJyIiIiIbsXAiovuKM05E5MpYOBHRfcbCiYhcFwsnIiIiIhuxcCIixZW8PMcrdUTkylg4EdF9xdsREJErY+FERIorWSpxcTgRuTIWTkREREQ2YuFERPcVZ5yIyJWxcCIixZktDndgHERE94qFExEREZGNWDgRkeJKfpKOl+qIyJWxcCIi5ZldqmPhRESui4UTESnOxFkmInITLJyISHHmdw5nEUVErsspCqdFixYhIiICXl5eiI6Oxt69e23a75tvvoFKpULfvn2VDZCI7olJmKTnLJyIyJU5vHBas2YN4uLiEB8fj4MHDyIyMhLdu3fHpUuXytzv3LlzeP3119G+ffv7FCkRyWXi7QiIyE04vHCaN28ennvuOcTGxuLBBx/E4sWL4ePjg+XLl5e6j9FoxJAhQzBt2jTUqVPnPkZLRHKUnHEiInJlHo48eEFBAQ4cOICJEydKbWq1Gl26dMGuXbtK3W/69OkICgrCs88+ix07dpR5DL1eD71eL21nZWUBAAwGAwwGwz2+A0vFYyoxtrtj7uRxhbwZjUbpuVL/9uRwhdw5I+ZNPuZOPiVzZ8+YDi2cMjMzYTQaERwcbNYeHByM48ePW93n119/xbJly5CSkmLTMWbPno1p06ZZtCcmJsLHx8fumG2VlJSk2NjujrmTx5nzdv78eel58rZtCPSt6sBoLDlz7pwZ8yYfcyefErnLy8uzua9DCyd7ZWdnY+jQoViyZAkCAgJs2mfixImIi4uTtrOyshAeHo5u3brBz8+v3GM0GAxISkpC165dodVqy318d8bcyeMKefv52u/A34cBAI92iEHtwAccHFERV8idM2Le5GPu5FMyd8VXo2zh0MIpICAAGo0GGRkZZu0ZGRkICQmx6H/mzBmcO3cOvXv3ltpMpqK1Ex4eHjhx4gTq1q1rto9Op4NOp7MYS6vVKnrSKj2+O2Pu5HHqvKluP9VoNE4Xp1Pnzokxb/Ixd/IpkTt7xnPo4nBPT0+0bNkSW7duldpMJhO2bt2Ktm3bWvRv1KgRjhw5gpSUFOnxxBNPoGPHjkhJSUF4ePj9DJ+IbFTyU3VcKE5Erszhl+ri4uIwfPhwtGrVCq1bt8aCBQuQm5uL2NhYAMCwYcMQFhaG2bNnw8vLC02bNjXb39/fHwAs2onIeYgSxZLJxBsSEJHrcnjhNGDAAFy+fBlTpkxBeno6oqKisHnzZmnBeFpaGtRqh981gYjuQfEldYDfVUdErs3hhRMAjB07FmPHjrX6WnJycpn7rly5svwDIqJyZXYDTF6qIyIXxqkcIlJcyXVN/MJfInJlLJyISHFmi8NNnHEiItfFwomIFFfy8hzXOBGRK2PhRESKK/lJOl6qIyJXxsKJiBRntsaJl+qIyIWxcCIixZUsnARnnIjIhbFwIiLF8c7hROQuWDgRkeI440RE7oKFExEpTgguDici98DCiYgUV3JBOC/VEZErY+FERIozccaJiNwECyciUpz5GifOOBGR62LhRESKM/+SX844EZHrYuFERIoT/JJfInITLJyISHFcHE5E7oKFExEpruQsk5FfuUJELoyFExEpzvy76owOjISI6N6wcCIixXHGiYjcBQsnIlJcycXhLJyIyJWxcCIixZnNOAleqiMi18XCiYgUV/JTdUYjCycicl0snIhIceYzTrxUR0Sui4UTESlOgIvDicg9sHAiIsWZX6ordGAkRET3xikKp0WLFiEiIgJeXl6Ijo7G3r17S+27ZMkStG/fHlWrVkXVqlXRpUuXMvsTkeMJXqojIjfh8MJpzZo1iIuLQ3x8PA4ePIjIyEh0794dly5dsto/OTkZgwYNwrZt27Br1y6Eh4ejW7duuHDhwn2OnIhsVVjippe8VEdErszhhdO8efPw3HPPITY2Fg8++CAWL14MHx8fLF++3Gr/VatW4aWXXkJUVBQaNWqEpUuXwmQyYevWrfc5ciKylaHQID3np+qIyJU5tHAqKCjAgQMH0KVLF6lNrVajS5cu2LVrl01j5OXlwWAwoFq1akqFSUT3qMB4u3AqNHGNExG5Lg9HHjwzMxNGoxHBwcFm7cHBwTh+/LhNY7z11lsIDQ01K75K0uv10Ov10nZWVhYAwGAwwGAwWN3nXhSPqcTY7o65k8cV8lZQYsbppj7faWJ1hdw5I+ZNPuZOPiVzZ8+YDi2c7tW7776Lb775BsnJyfDy8rLaZ/bs2Zg2bZpFe2JiInx8fBSLLSkpSbGx3R1zJ48z5y1fny89P3r8GBKMCQ6MxpIz586ZMW/yMXfyKZG7vLw8m/s6tHAKCAiARqNBRkaGWXtGRgZCQkLK3Hfu3Ll499138dNPP6F58+al9ps4cSLi4uKk7aysLGlBuZ+f3729ASsMBgOSkpLQtWtXaLXach/fnTF38rhC3sYmvi89r1W7Fnr27OnAaG5zhdw5I+ZNPuZOPiVzV3w1yhYOLZw8PT3RsmVLbN26FX379gUAaaH32LFjS91vzpw5mDlzJrZs2YJWrVqVeQydTgedTmfRrtVqFT1plR7fnTF38jhr3oQQMJS4d1OhMDldnM6aO2fHvMnH3MmnRO7sGc/hl+ri4uIwfPhwtGrVCq1bt8aCBQuQm5uL2NhYAMCwYcMQFhaG2bNnAwDee+89TJkyBatXr0ZERATS09MBAJUqVUKlSpUc9j6IyLqStyIAgAKu7SAiF+bwwmnAgAG4fPkypkyZgvT0dERFRWHz5s3SgvG0tDSo1bc//Pfpp5+ioKAATz/9tNk48fHxmDp16v0MnYhsUHJhOAAYjCyciMh1ObxwAoCxY8eWemkuOTnZbPvcuXPKB0RE5cZwR+GkL2ThRESuy+E3wCQi93bnDNOdhRQRkSth4UREiiooNL/hJS/VEZErY+FERIq6s1DipToicmUsnIhIUfmGArPtgju2iYhcCQsnIlJUbr75HXlvFuSX0pOIyPmxcCIiRWXn55pt5+pvOigSIqJ7x8KJiBSVc6tw0qg1Rdt6278TiojI2bBwIiJF5dy6VBdSJcBsm4jIFbFwIiJFFc8wBfsXFU55vFRHRC6MhRMRKSrnZtGluuIZp3yD3uxLf4mIXAkLJyJSVPGMU4h/oNR2Iy/bUeEQEd0TFk5EpKjiNU1VfPzg7+MHALiSfd2BERERycfCiYgUlZl9DQBQvVIVVK/sDwC4mnvdcQEREd0DFk5EpKiM65cBFF2qq17JHwCQmXXNgREREcnHwomIFHWxROEUWjUYAPD31YuODImISDYWTkSkGCEEMm5kAigqnGoGhAIA0jJZOBGRa2LhRESKSb9+GQZjITRqDYL8qqPWrcLpfOYFB0dGRCQPCyciUsyxf84AAOoEhcPTQ4vaQeEAgBMXUx0ZFhGRbCyciEgxR/8+DQBoFFoXANAsvCFUKhUuXE3H5ayrjgyNiEgWFk5EpJidJw8AAFpEPAgAqOzti/ohEQCAXacOOSosIiLZWDgRkSJuFuRj98mi4qhjk2ipvVOTtgCAhEPJjgiLiOiesHAiIkVs2P8Tbhr0CK9eQ7pUBwBP/l83AMDGQ9tw8dolR4VHRCQLCyciJ5OZfQ1Gk9HRYdyTvIJ8zE9YDgAY9uiTUKlU0muRtRohul4kDMZCvLN2AYQQjgqTiMhuLJyInMimlF/Q7M2eGP/FTEeHIluhsRDjv/g30jL/QVjVYIyMedqiz7/7x0Gj1uDHgz9j+rqPXb5QJKKKg4UTkZMQQmDGuo8hhMC3uxNw4WqGo0Oy27ELZ/D0gpfx/f6foNV44MPh78DXy8eiX7OaDTFzQBwA4NOkVegz9wXsOL6Ps09E5PSconBatGgRIiIi4OXlhejoaOzdu7fM/mvXrkWjRo3g5eWFZs2aISEh4T5FSqScnScO4Oylv6TtVTt/cGA0thFC4Nzlv7F65w8Y+NE4dPr3v7D71CH46nyw5LlZeKRRq1L3HRHzFBaOiIePzhv7zx7BMwteRqu3+2Li13Px3Z5NOJ1+njNRROR0PBwdwJo1axAXF4fFixcjOjoaCxYsQPfu3XHixAkEBQVZ9P/tt98waNAgzJ49G48//jhWr16Nvn374uDBg2jatKkD3gGRfCaTCeczLyD56B68/78lAICqvn64lpuF//z0NaLrNsejjVubrRG6G4OxEJezriL9+mX8cy0D5zP/Qcb1TKjValT29kU13yrw8vRCJZ0PWtR+EA9UCylzPL2hABk3MnEp6woybmTiwtUMnMlIw5mMNJy4eNbifky9H+qMSU++iIjAB+4a6zNteqBdw5ZYuPkLrNm9EReuZWDFL99hxS/fAQA81BqEVQtBzYAaCKsWgmq+VVDVtwqq+FSGv68f/H0qo4qPH7w9ddBpdfDW6qDTesJLq4Onh9auvBER2UIlHDw3Hh0djf/7v//Dxx9/DKDoP5Lw8HC8/PLLmDBhgkX/AQMGIDc3Fz/++KPU1qZNG0RFRWHx4sV3PV5WVhaqVKmCGzduwM/Pr/zeCACjyYiTF1Kxfft2PProo/DQmtelxakumXEBYfYaICz737Ft/trdxzT/KzbvL6wd746xrfW3HssdncuIxSyiW22GwkLs27cX/9fq/6Dx0Nz1uCjRJkRRH5MwSdsmYSrar/i5ELf6iFt9ip4Dt59LfUwmCBTthzvGK+5rMplgNBlRaDLCaDLCaDKh0Fh4+/mt9kKjEfkGPW4W5EuPXP1NZGZfw5XsaygsMavSvGZDrH31Y8Qufgu/nTwIoKiQCq9eA9Uq+UOlUhUd32SCSQgYTUbk6fORceUSVFoNcvV5yM7PteuSV62AMDQNrw9fnQ9y9Xm4kZeDrJtFj+u5Wbiel1Xm/lqNB5rXbIQuzR5G31ZdpbuD2+tmQT5+OboXO08ewKFzR/HHXyeRb9DLGgsAVCoVvDw8odPq4HWroNJpPaHVeECj1kCjVkOjUuPG9RsICgyE1kMLtUoND40GHmoNNGoPaNRF2xr1rTaNBhqVBh4aDdRqNdQqNVS3jqVSqe7YVhf9eWtbrVJDpQJUuN0Xt7al11Qqs21I26pSjnHrOGYxqKweQ3rt1utl5c1qe4l9Co2FOHDgAFq2bAkPjQdKq0/LLlxLOY4Nx7fcx/7jlzZe2fvYdxwhBIzCBKPJBJPJCJMQKDAUYP+BA2geGQmVGjCaTLceRrOfEXn6fOQX6uGt1aGSly98dN7Iyc/FlZzruJaThWu5N24/cm5A66FFtUr+qFapCqpV8kf1Sv63fsmohMrelVDFuzL8vH2h9dBazUHxeyj5Tkq+L7Pnxb3M2iz3s7pPid2sHb+0/VQqoLCwEMcO/oHB/QZAq739PsqDPbWBQwungoIC+Pj44LvvvkPfvn2l9uHDh+P69ev4/vvvLfapWbMm4uLi8Oqrr0pt8fHx2LBhAw4fPmzRX6/XQ6+//cM3KysL4eHhyMzMLPfC6XpeNpq91bNcxyT3p/PwxIMP1EOfll0wpN0T8NLqkKvPw6zvF2Ptnk24WZBv95geag2CqlRHSJWiL9at4R8IkzAhJz8PV3NuIN+gR2b2NRy9cNqmy2GeHloE+VVHkF91hPgHoHZgOOoEhaNucE08GFYf3p46OW+9TEaTERk3MvHXlXT8ffUiLly7JBVyN/KycT0vG9fzspB1Mwf5BXroCwuQb9BznRSRG3uyQQd88MIURQqngIAAmwonh16qy8zMhNFoRHBwsFl7cHAwjh8/bnWf9PR0q/3T09Ot9p89ezamTZtm0Z6YmAgfH8tFq/ciz5APX63XXXqVXdUXvaa6s/vtqtvaWKo7Opd4Zu03oTt/01Kpynqt9P1V5h1LicD6bxcW26X0KWt/szYVoIbq1m/ot3/DLn4Pxb9j3/6tqvg376IttZW+xb/tF80S3DqWSnXrOIAa6qJZB6igvjV7ob71W7761nPNredatQc8NVroNFp43npU9vRBZU8f+Ol8oVFrgDzg56St0ntrV6kRojvWx4XsS8jS5yLHcFOKvWS8Oo0WXhoddB5a6DSe8NF6oZKnj9THjCeAEj8T8hvpcerqX8i8eQP6wgJ4eRTt7+2hg7fWCz4eOvjpfOGr9bY8F64CGVfTkHEszfI45cwbQD0EAF4BgBeAatb7Ff+GbzAaYDAZUWA0oPDWnwZTYdEsoDDBVDwLIIpm7kzCJLUXt1ndLt4HAkaTCUXznLdnPItjELe2hDSjWRSfCaJoBrOo560ZT0gzmaJoAAiprfjV2+OaHUN63VoMt8e9s61UpRSdZZWipY5X1mHKHNG+/nLq5PsRc7GSs4DqEj8jSs4kqlVFPz88NR7w1HjCU+MBncYTHmoNDKZC5BfqoS80wMvDE76e3qik9UYlTx/4ar3h6+kNX60XjCYTcgw3kVOQh5yCW38a8nDToMfNwqJHniHf7Jy78z1be4/WrjyYXRmxfPH2VZJScmbtlxtrV0+sxeDtoUNSUpLVce9FXl6ezX0dvsZJaRMnTkRcXJy0XTzj1K1bt3KfcQKAPj17IykpCV27di33itjdGQwG5k4G5k0+5k4e5k0+5k4+JXOXlVX2koSSHFo4BQQEQKPRICPD/GPXGRkZCAmxvmA1JCTErv46nQ46neVlBK1Wq+hJq/T47oy5k4d5k4+5k4d5k4+5k0+J3NkznkNvR+Dp6YmWLVti69bblydMJhO2bt2Ktm3bWt2nbdu2Zv0BICkpqdT+REREROXF4Zfq4uLiMHz4cLRq1QqtW7fGggULkJubi9jYWADAsGHDEBYWhtmzZwMAxo0bh5iYGHzwwQfo1asXvvnmG+zfvx+fffaZI98GERERVQAOL5wGDBiAy5cvY8qUKUhPT0dUVBQ2b94sLQBPS0uDWn17Yuzhhx/G6tWrMXnyZLz99tuoX78+NmzYwHs4ERERkeIcXjgBwNixYzF27FirryUnJ1u0PfPMM3jmmWcUjoqIiIjInFN85QoRERGRK2DhRERERGQjFk5ERERENmLhRERERGQjFk5ERERENmLhRERERGQjp7gdwf1U/OWC9nwvjT0MBgPy8vKQlZXF2+nbibmTh3mTj7mTh3mTj7mTT8ncFdcE1r6A+E4VrnDKzs4GAISHhzs4EiIiInIm2dnZqFKlSpl9VMKW8sqNmEwm/PPPP6hcuTJUKlW5j5+VlYXw8HD89ddf8PPzK/fx3RlzJw/zJh9zJw/zJh9zJ5+SuRNCIDs7G6GhoWbfVmJNhZtxUqvVeOCBBxQ/jp+fH/9RyMTcycO8ycfcycO8ycfcyadU7u4201SMi8OJiIiIbMTCiYiIiMhGLJzKmU6nQ3x8PHQ6naNDcTnMnTzMm3zMnTzMm3zMnXzOkrsKtziciIiISC7OOBERERHZiIUTERERkY1YOBERERHZiIXTXSxatAgRERHw8vJCdHQ09u7dW2b/BQsWoGHDhvD29kZ4eDjGjx+P/Pz8exrTVZV37qZOnQqVSmX2aNSokdJvwyHsyZ3BYMD06dNRt25deHl5ITIyEps3b76nMV1Veeetopxz27dvR+/evREaGgqVSoUNGzbcdZ/k5GQ89NBD0Ol0qFevHlauXGnRx93POSXyxnPOuosXL2Lw4MFo0KAB1Go1Xn31Vav91q5di0aNGsHLywvNmjVDQkJC+QcvqFTffPON8PT0FMuXLxd//vmneO6554S/v7/IyMiw2n/VqlVCp9OJVatWidTUVLFlyxZRo0YNMX78eNljuiolchcfHy+aNGkiLl68KD0uX758v97SfWNv7t58800RGhoqNm7cKM6cOSM++eQT4eXlJQ4ePCh7TFekRN4qyjmXkJAgJk2aJNatWycAiPXr15fZ/+zZs8LHx0fExcWJo0ePioULFwqNRiM2b94s9akI55wSeeM5Z11qaqp45ZVXxOeffy6ioqLEuHHjLPrs3LlTaDQaMWfOHHH06FExefJkodVqxZEjR8o1dhZOZWjdurUYM2aMtG00GkVoaKiYPXu21f5jxowRnTp1MmuLi4sT7dq1kz2mq1Iid/Hx8SIyMlKReJ2JvbmrUaOG+Pjjj83a+vXrJ4YMGSJ7TFekRN4qyjlXki3/ib355puiSZMmZm0DBgwQ3bt3l7YrwjlXUnnljefc3cXExFgtnPr37y969epl1hYdHS1Gjx59jxGa46W6UhQUFODAgQPo0qWL1KZWq9GlSxfs2rXL6j4PP/wwDhw4IE1Hnz17FgkJCejZs6fsMV2RErkrdurUKYSGhqJOnToYMmQI0tLSlHsjDiAnd3q9Hl5eXmZt3t7e+PXXX2WP6WqUyFsxdz/n5Ni1a5dZrgGge/fuUq4rwjknx93yVoznnDy25vdesXAqRWZmJoxGI4KDg83ag4ODkZ6ebnWfwYMHY/r06XjkkUeg1WpRt25ddOjQAW+//bbsMV2RErkDgOjoaKxcuRKbN2/Gp59+itTUVLRv3x7Z2dmKvp/7SU7uunfvjnnz5uHUqVMwmUxISkrCunXrcPHiRdljuhol8gZUjHNOjvT0dKu5zsrKws2bNyvEOSfH3fIG8Jy7F6Xlt7zPORZO5Sg5ORmzZs3CJ598goMHD2LdunXYuHEjZsyY4ejQnJ4tuevRoweeeeYZNG/eHN27d0dCQgKuX7+Ob7/91oGRO96HH36I+vXro1GjRvD09MTYsWMRGxt712/4ruhsyRvPObrfeM45Pw9HB+CsAgICoNFokJGRYdaekZGBkJAQq/u88847GDp0KEaNGgUAaNasGXJzc/H8889j0qRJssZ0RUrkzloR4O/vjwYNGuD06dPl/yYcRE7uAgMDsWHDBuTn5+PKlSsIDQ3FhAkTUKdOHdljuhol8maNO55zcoSEhFjNtZ+fH7y9vaHRaNz+nJPjbnmzhuec7UrLb3mfc/yVtBSenp5o2bIltm7dKrWZTCZs3boVbdu2tbpPXl6exX/wGo0GACCEkDWmK1Iid9bk5OTgzJkzqFGjRjlF7nj3co54eXkhLCwMhYWF+O9//4s+ffrc85iuQom8WeOO55wcbdu2Ncs1ACQlJUm5rgjnnBx3y5s1POdsJye/spTrUnM388033widTidWrlwpjh49Kp5//nnh7+8v0tPThRBCDB06VEyYMEHqHx8fLypXriy+/vprcfbsWZGYmCjq1q0r+vfvb/OY7kKJ3L322msiOTlZpKamip07d4ouXbqIgIAAcenSpfv+/pRkb+52794t/vvf/4ozZ86I7du3i06dOonatWuLa9eu2TymO1AibxXlnMvOzhaHDh0Shw4dEgDEvHnzxKFDh8T58+eFEEJMmDBBDB06VOpf/LH6N954Qxw7dkwsWrTI6u0I3P2cUyJvPOes504IIfVv2bKlGDx4sDh06JD4888/pdd37twpPDw8xNy5c8WxY8dEfHw8b0fgCAsXLhQ1a9YUnp6eonXr1mL37t3SazExMWL48OHStsFgEFOnThV169YVXl5eIjw8XLz00ktmP4jvNqY7Ke/cDRgwQNSoUUN4enqKsLAwMWDAAHH69On7+I7uH3tyl5ycLBo3bix0Op2oXr26GDp0qLhw4YJdY7qL8s5bRTnntm3bJgBYPIrzNXz4cBETE2OxT1RUlPD09BR16tQRK1assBjX3c85JfLGc264EMJ67qz1r1Wrllmfb7/9VjRo0EB4enqKJk2aiI0bN5Z77KpbwRARERHRXXCNExEREZGNWDgRERER2YiFExEREZGNWDgRERER2YiFExEREZGNWDgRERER2YiFExEREZGNWDgRERER2YiFExG5jKlTpyIqKsrRYUhUKhU2bNhg934nTpxASEgIsrOzyz+oEjIzMxEUFIS///5b0eMQVSQsnIjIzOLFi1G5cmUUFhZKbTk5OdBqtejQoYNZ3+TkZKhUKpw5c+Y+R3l/lXfBNnHiRLz88suoXLlyuY1pTUBAAIYNG4b4+HhFj0NUkbBwIiIzHTt2RE5ODvbv3y+17dixAyEhIdizZw/y8/Ol9m3btqFmzZqoW7euI0J1SWlpafjxxx8xYsSI+3K82NhYrFq1ClevXr0vxyNydyyciMhMw4YNUaNGDSQnJ0ttycnJ6NOnD2rXro3du3ebtXfs2BEA8OWXX6JVq1aoXLkyQkJCMHjwYFy6dAkAYDKZ8MADD+DTTz81O9ahQ4egVqtx/vx5AMD169cxatQoBAYGws/PD506dcLhw4fLjHfp0qVo3LgxvLy80KhRI3zyySfSa+fOnYNKpcK6devQsWNH+Pj4IDIyErt27TIbY8mSJQgPD4ePjw+efPJJzJs3D/7+/gCAlStXYtq0aTh8+DBUKhVUKhVWrlwp7ZuZmYknn3wSPj4+qF+/Pn744Ycy4/32228RGRmJsLAwqc3ajNaCBQsQEREhbY8YMQJ9+/bFrFmzEBwcDH9/f0yfPh2FhYV44403UK1aNTzwwANYsWKF2ThNmjRBaGgo1q9fX2ZcRGQbFk5EZKFjx47Ytm2btL1t2zZ06NABMTExUvvNmzexZ88eqXAyGAyYMWMGDh8+jA0bNuDcuXPSrIparcagQYOwevVqs+OsWrUK7dq1Q61atQAAzzzzDC5duoRNmzbhwIEDeOihh9C5c+dSZ0tWrVqFKVOmYObMmTh27BhmzZqFd955B59//rlZv0mTJuH1119HSkoKGjRogEGDBkmXInfu3IkXXngB48aNQ0pKCrp27YqZM2dK+w4YMACvvfYamjRpgosXL+LixYsYMGCA9Pq0adPQv39//P777+jZsyeGDBlS5uzOjh070KpVqzLzX5qff/4Z//zzD7Zv34558+YhPj4ejz/+OKpWrYo9e/bghRdewOjRoy3WNLVu3Ro7duyQdUwiuoMgIrrDkiVLhK+vrzAYDCIrK0t4eHiIS5cuidWrV4tHH31UCCHE1q1bBQBx/vx5q2Ps27dPABDZ2dlCCCEOHTokVCqV1N9oNIqwsDDx6aefCiGE2LFjh/Dz8xP5+flm49StW1f85z//EUIIER8fLyIjI81eW716tVn/GTNmiLZt2wohhEhNTRUAxNKlS6XX//zzTwFAHDt2TAghxIABA0SvXr3MxhgyZIioUqWKtH3ncYsBEJMnT5a2c3JyBACxadMmqzkRQojIyEgxffp0szZr48+fP1/UqlVL2h4+fLioVauWMBqNUlvDhg1F+/btpe3CwkLh6+srvv76a7Oxxo8fLzp06FBqTERkO844EZGFDh06IDc3F/v27cOOHTvQoEEDBAYGIiYmRlrnlJycjDp16qBmzZoAgAMHDqB3796oWbMmKleujJiYGABFa3oAICoqCo0bN5ZmnX755RdcunQJzzzzDADg8OHDyMnJQfXq1VGpUiXpkZqaanXxeW5uLs6cOYNnn33WrP+///1vi/7NmzeXnteoUQMApMuIJ06cQOvWrc3637ldlpJj+/r6ws/PTxrbmps3b8LLy8vm8Utq0qQJ1OrbP7aDg4PRrFkzaVuj0aB69eoWx/f29kZeXp6sYxKROQ9HB0BEzqdevXp44IEHsG3bNly7dk0qgkJDQxEeHo7ffvsN27ZtQ6dOnQAUFTHdu3dH9+7dsWrVKgQGBiItLQ3du3dHQUGBNO6QIUOwevVqTJgwAatXr8Zjjz2G6tWrAyj65N6da6uKFa83KiknJwdA0fqk6Ohos9c0Go3ZtlarlZ6rVCoAReuuykPJsYvHL2vsgIAAXLt2zaxNrVZDCGHWZjAYbDqWLce/evUqAgMDS38TRGQzFk5EZFXHjh2RnJyMa9eu4Y033pDaH330UWzatAl79+7Fiy++CAA4fvw4rly5gnfffRfh4eEAYPapvGKDBw/G5MmTceDAAXz33XdYvHix9NpDDz2E9PR0eHh4mC2KLk1wcDBCQ0Nx9uxZDBkyRPb7bNiwIfbt22fWdue2p6cnjEaj7GOU1KJFCxw9etSsLTAwEOnp6RBCSIVdSkpKuRwPAP744w+LW0kQkTy8VEdEVnXs2BG//vorUlJSpBknAIiJicF//vMfFBQUSAvDa9asCU9PTyxcuBBnz57FDz/8gBkzZliMGRERgYcffhjPPvssjEYjnnjiCem1Ll26oG3btujbty8SExNx7tw5/Pbbb5g0aZLVIgwoWpg9e/ZsfPTRRzh58iSOHDmCFStWYN68eTa/z5dffhkJCQmYN28eTp06hf/85z/YtGmTVMAUx52amoqUlBRkZmZCr9fbPP6dunfvjl27dpkVYh06dMDly5cxZ84cnDlzBosWLcKmTZtkH6OkvLw8HDhwAN26dSuX8YgqOhZORGRVx44dcfPmTdSrVw/BwcFSe0xMDLKzs6XbFgBFMyYrV67E2rVr8eCDD+Ldd9/F3LlzrY47ZMgQHD58GE8++SS8vb2ldpVKhYSEBDz66KOIjY1FgwYNMHDgQJw/f97s+CWNGjUKS5cuxYoVK9CsWTPExMRg5cqVqF27ts3vs127dli8eDHmzZuHyMhIbN68GePHjzdbh/TUU0/hscceQ8eOHREYGIivv/7a5vHv1KNHD3h4eOCnn36S2ho3boxPPvkEixYtQmRkJPbu3YvXX39d9jFK+v7771GzZk20b9++XMYjquhU4s4L60REFdxzzz2H48ePK/YR/kWLFuGHH37Ali1bFBm/pDZt2uCVV17B4MGDFT8WUUXANU5EVOHNnTsXXbt2ha+vLzZt2oTPP//c7Eaa5W306NG4fv06srOzFf3alczMTPTr1w+DBg1S7BhEFQ1nnIiowuvfvz+Sk5ORnZ2NOnXq4OWXX8YLL7zg6LCIyAmxcCIiIiKyEReHExEREdmIhRMRERGRjVg4EREREdmIhRMRERGRjVg4EREREdmIhRMRERGRjVg4EREREdmIhRMRERGRjVg4EREREdno/wHZbGD712m3dAAAAABJRU5ErkJggg==",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, tight_layout = True, figsize = (6,4))\n",
    "\n",
    "time_monitor_names = [monitor.name for monitor in time_monitors]\n",
    "\n",
    "time_response_Ex = np.zeros_like(sim_data[\"time_monitor_1\"].Ex.squeeze())\n",
    "\n",
    "for monitor_name in time_monitor_names:\n",
    "  time_response_Ex += sim_data[monitor_name].Ex.squeeze()\n",
    "\n",
    "time_response_Ex /= len(time_monitor_names)\n",
    "\n",
    "freq_response_Ex = np.abs(np.fft.fft(time_response_Ex))\n",
    "\n",
    "freqs_Ex = np.linspace(0, 1/sim_data.simulation.dt, len(time_response_Ex))\n",
    "wavelengths_Ex = td.constants.C_0 / freqs_Ex\n",
    "\n",
    "plot_inds_Ex = np.where((monitor_freq[-1] < freqs_Ex) & (freqs_Ex < monitor_freq[0]))\n",
    "\n",
    "ax.plot(wavelengths_Ex[plot_inds_Ex], freq_response_Ex[plot_inds_Ex])\n",
    "ax.set_xlabel(\"Wavelength (um)\")\n",
    "ax.set_ylabel(\"Amplitude\")\n",
    "ax.set_title(\"Ex Frequency Response\")\n",
    "ax.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5b99526e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           freq  wavelength         decay            Q   amplitude     phase  \\\n",
      "0  2.699416e+14    1.110583  2.142817e+12   395.762551   30.476848  2.108739   \n",
      "1  2.746487e+14    1.091549  2.879579e+12   299.638982   41.040942 -2.805304   \n",
      "2  2.833892e+14    1.057883  1.830160e+12   486.456684   12.831113 -3.017307   \n",
      "3  3.235854e+14    0.926471  1.137087e+11  8940.158494  254.820183 -0.476974   \n",
      "4  3.298842e+14    0.908781  3.083029e+12   336.150431   88.478080 -0.707611   \n",
      "5  3.309785e+14    0.905776  3.699974e+12   281.028907   99.558649  0.086578   \n",
      "6  3.360605e+14    0.892079  9.866268e+11  1070.075330  160.566584  0.696290   \n",
      "7  3.525750e+14    0.850294  2.285641e+12   484.611064    8.606144  1.830012   \n",
      "8  4.125453e+14    0.726690  1.534290e+12   844.722658  418.070821 -2.465529   \n",
      "\n",
      "      error  \n",
      "0  0.004749  \n",
      "1  0.006332  \n",
      "2  0.006637  \n",
      "3  0.000090  \n",
      "4  0.004032  \n",
      "5  0.005352  \n",
      "6  0.001005  \n",
      "7  0.007987  \n",
      "8  0.002071  \n"
     ]
    }
   ],
   "source": [
    "resonance_finder = ResonanceFinder(freq_window = (monitor_freq[-1], monitor_freq[0]))\n",
    "resonance_data = resonance_finder.run(signals = sim_data.data)\n",
    "df_actual = resonance_data.to_dataframe().reset_index()\n",
    "df_actual['wavelength'] = td.constants.C_0 / df_actual['freq']\n",
    "print(df_actual[['freq', 'wavelength', 'decay', 'Q', 'amplitude', 'phase', 'error']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c379893a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index: 160, Wavelength: 0.920000 um\n",
      "Index: 161, Wavelength: 0.920750 um\n",
      "Index: 162, Wavelength: 0.921500 um\n",
      "Index: 163, Wavelength: 0.922250 um\n",
      "Index: 164, Wavelength: 0.923000 um\n",
      "Index: 165, Wavelength: 0.923750 um\n",
      "Index: 166, Wavelength: 0.924500 um\n",
      "Index: 167, Wavelength: 0.925250 um\n",
      "Index: 168, Wavelength: 0.926000 um\n",
      "Index: 169, Wavelength: 0.926750 um\n",
      "Index: 170, Wavelength: 0.927500 um\n",
      "Index: 171, Wavelength: 0.928250 um\n",
      "Index: 172, Wavelength: 0.929000 um\n",
      "Index: 173, Wavelength: 0.929750 um\n"
     ]
    }
   ],
   "source": [
    "indices = np.where((monitor_lambda >= 0.92) & (monitor_lambda <=0.93))[0]\n",
    "for idx in indices:\n",
    "    print(f\"Index: {idx}, Wavelength: {monitor_lambda[idx]:.6f} um\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf9fa226",
   "metadata": {},
   "source": [
    "## Far-Field Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "348249e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "f_index = 168\n",
    "far_field_data = sim_data[far_field_monitor.name] \n",
    "Etheta = far_field_data.Etheta.isel(f = f_index, r=0).values\n",
    "Ephi = far_field_data.Ephi.isel(f = f_index, r=0).values\n",
    "phi_vals = far_field_data.phi  \n",
    "theta_vals = far_field_data.theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "089b2204",
   "metadata": {},
   "outputs": [],
   "source": [
    "intensity = np.abs(Etheta)**2 + np.abs(Ephi)**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8b4a3087",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.88443254695612"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compute_gaussian_overlap_without_pol(\n",
    "    phi_vals, theta_vals, intensity,\n",
    "    NA = 0.68, n = 1.0,\n",
    "    gauss_func=gaussian_far_field_no_polarization_sin_debye_apod\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1fc3d3ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "76.16387279212572 87.2671390916859 0.8727669267592844\n"
     ]
    }
   ],
   "source": [
    "p_sim, p_g, r = compute_power_fraction_and_ratio(\n",
    "    phi_vals, theta_vals, vals=intensity,\n",
    "    NA_target=0.68,\n",
    "    NA_gauss=0.68,\n",
    "    n=1.0,\n",
    "    theta_max_deg=90,\n",
    "    gauss_func=gaussian_far_field_no_polarization_sin_debye_apod\n",
    ")\n",
    "\n",
    "print(p_sim, p_g, r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3c0777f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_far_field_plot_without_pol(\n",
    "    phi_vals, theta_vals, intensity,\n",
    "    NA = 0.68, n=1.0,\n",
    "    gauss_func=gaussian_far_field_no_polarization_sin_debye_apod,\n",
    "    calculate_overlap=False\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "applications": [
   "Nanophotonics",
   "Quantum emitters",
   "Optical scattering and far-field radiation"
  ],
  "description": "This notebook demonstrates the simulation of a bullseye grating structure on a GaAs slab using a point dipole source. The circular grating geometry is defined via GDS and imported into Tidy3D. Resonance finding is used to identify high-Q modes, and far-field projections are computed to evaluate Gaussian mode overlap and power collection efficiency within a target numerical aperture.",
  "feature_image": "./img/Bullseye.png",
  "features": [
   "Far field projection",
   "Resonance finder",
   "GDS component"
  ],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "circular grating, GaAs, far-field projection, mode overlap, resonance finder, GDS, Tidy3D, FDTD",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  },
  "title": "How to simulate a bullseye grating with far-field overlap in Tidy3D FDTD"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}