{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "60244b2c",
   "metadata": {},
   "source": [
    "# Band structure calculation of a photonic crystal slab\n",
    "\n",
    "In this notebook, we simulate a photonic crystal slab consisting of a square lattice of air holes in a dielectric slab. Our goal is to compute the band structure of this photonic crystal slab, as found in:\n",
    "\n",
    "> Shanhui Fan and J. D. Joannopoulos, \"Analysis of guided resonances in photonic crystal slabs,\" Phys. Rev. B 65, 235112 (2002).\n",
    "\n",
    "To this end, we excite the structure with several `PointDipole` sources, and we measure the response with several `FieldTimeMonitor` monitors. We excite modes with a fixed Bloch wavevector by using Bloch boundary conditions. We then use the `ResonanceFinder` to find the resonant frequencies. By sweeping the Bloch wavevector, we obtain the full band structure of the photonic crystal slab.\n",
    "\n",
    "See also the api reference for `ResonanceFinder` [here](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.resonance.ResonanceFinder.html).\n",
    "\n",
    "If you are new to the finite-difference time-domain (FDTD) method, we highly recommend going through our [FDTD101](https://www.flexcompute.com/fdtd101/) tutorials. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "193cb096",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:06:12.631685Z",
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     "shell.execute_reply": "2023-08-18T17:06:13.969348Z"
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   },
   "outputs": [],
   "source": [
    "# standard python imports\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tidy3d as td\n",
    "import xarray as xr\n",
    "from tidy3d import web\n",
    "from tidy3d.plugins.resonance import ResonanceFinder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6022fa5a",
   "metadata": {},
   "source": [
    "We will randomly position our sources and monitors. Here, we seed the random number generator to guarantee reproducible results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "be1a51fc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:06:13.972640Z",
     "iopub.status.busy": "2023-08-18T17:06:13.972373Z",
     "iopub.status.idle": "2023-08-18T17:06:13.990585Z",
     "shell.execute_reply": "2023-08-18T17:06:13.990058Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "rng = np.random.default_rng(12345)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b3da68c",
   "metadata": {},
   "source": [
    "Now we define the parameters for the simulation, structure, sources, and monitors.\n",
    "\n",
    "We take the dipole polarization to be `Hz` and the symmetry to be `(0,0,1)` in order to excite only modes which are even with respect to the xy mirror plane."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "da1f5f85",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:06:13.992634Z",
     "iopub.status.busy": "2023-08-18T17:06:13.992460Z",
     "iopub.status.idle": "2023-08-18T17:06:14.012757Z",
     "shell.execute_reply": "2023-08-18T17:06:14.012192Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total runtime = 6.74 ps\n"
     ]
    }
   ],
   "source": [
    "# Simulation parameters\n",
    "runtime_fwidth = 200.0  # in units of 1/frequency bandwidth of the source\n",
    "t_start_fwidth = (\n",
    "    5.0  # time to start monitoring after source has decayed, units of 1/frequency bandwidth\n",
    ")\n",
    "dPML = 1.0  # space between PhC slabs and PML, in unit of longest wavelength of interest\n",
    "\n",
    "# Structure parameters (um)\n",
    "a_lattice = 1  # lattice constant \"a\"\n",
    "r_hole = 0.2 * a_lattice  # radius of the air holes\n",
    "t_slab = 0.5 * a_lattice  # slab thickness\n",
    "ep_slab = 12  # dielectric constant of the slab\n",
    "ep_hole = 1  # dielectric constant of the holes\n",
    "\n",
    "# Frequency range of interest (Hz)\n",
    "freq_range_unitless = np.array((0.1, 0.43))  # in units of c/a\n",
    "freq_scale = td.constants.C_0 / a_lattice  # frequency scale determined by the lattice constant\n",
    "freq_range = freq_range_unitless * freq_scale\n",
    "lambda_range = (td.constants.C_0 / freq_range[1], td.constants.C_0 / freq_range[0])\n",
    "\n",
    "# Gaussian pulse parameters\n",
    "freq0 = np.sum(freq_range) / 2  # central frequency\n",
    "freqw = 0.3 * (freq_range[1] - freq_range[0])  # pulse width\n",
    "\n",
    "# Runtime\n",
    "run_time = runtime_fwidth / freqw\n",
    "print(f\"Total runtime = {(run_time * 1e12):.2f} ps\")\n",
    "t_start = t_start_fwidth / freqw\n",
    "\n",
    "# Simulation size\n",
    "spacing = dPML * lambda_range[-1]  # space between PhC slabs and PML\n",
    "sim_size = Lx, Ly, Lz = (a_lattice, a_lattice, 2 * spacing + t_slab)\n",
    "\n",
    "# Number of k values to sample, per edge of the irreducible Brillouin zone\n",
    "Nk = 4\n",
    "\n",
    "# Number of dipoles and monitors\n",
    "num_dipoles = 7\n",
    "num_monitors = 2\n",
    "\n",
    "# Dipole polarization and symmetry\n",
    "polarization = \"Hz\"\n",
    "symmetry = (0, 0, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27c57eee",
   "metadata": {},
   "source": [
    "We define the materials and structures in terms of the above parameters. We take the photonic crystal slab to be lying in the xy plane. Because the photonic crystal slab is periodic in the x and y directions, we only need to simulate a single unit cell, containing the dielectric slab and a single air hole."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cbcc09f9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:06:14.015238Z",
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     "shell.execute_reply": "2023-08-18T17:06:14.035928Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "mat_slab = td.Medium(permittivity=ep_slab, name=\"mat_slab\")\n",
    "mat_hole = td.Medium(permittivity=ep_hole, name=\"mat_hole\")\n",
    "\n",
    "slab = td.Structure(\n",
    "    geometry=td.Box(\n",
    "        center=(0, 0, 0),\n",
    "        size=(td.inf, td.inf, t_slab),\n",
    "    ),\n",
    "    medium=mat_slab,\n",
    "    name=\"slab\",\n",
    ")\n",
    "\n",
    "hole = td.Structure(\n",
    "    geometry=td.Cylinder(\n",
    "        center=(0, 0, 0),\n",
    "        axis=2,\n",
    "        radius=r_hole,\n",
    "        length=t_slab,\n",
    "    ),\n",
    "    medium=mat_hole,\n",
    "    name=\"hole\",\n",
    ")\n",
    "\n",
    "structures = [slab, hole]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbda5f33",
   "metadata": {},
   "source": [
    "We will excite the photonic crystal slab with several `PointDipole` sources. Each dipole will have a random position and phase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7cd6c4ed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:06:14.038940Z",
     "iopub.status.busy": "2023-08-18T17:06:14.038764Z",
     "iopub.status.idle": "2023-08-18T17:06:14.065919Z",
     "shell.execute_reply": "2023-08-18T17:06:14.065387Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dipole_positions = rng.uniform([-Lx / 2, -Ly / 2, 0], [Lx / 2, Ly / 2, 0], [num_dipoles, 3])\n",
    "\n",
    "dipole_phases = rng.uniform(0, 2 * np.pi, num_dipoles)\n",
    "\n",
    "pulses = []\n",
    "dipoles = []\n",
    "for i in range(num_dipoles):\n",
    "    pulse = td.GaussianPulse(freq0=freq0, fwidth=freqw, phase=dipole_phases[i])\n",
    "    pulses.append(pulse)\n",
    "    dipoles.append(\n",
    "        td.PointDipole(\n",
    "            source_time=pulse,\n",
    "            center=tuple(dipole_positions[i]),\n",
    "            polarization=polarization,\n",
    "            name=\"dipole_\" + str(i),\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "700880a7",
   "metadata": {},
   "source": [
    "We create `FieldTimeMonitors` to record the field as a function of time at several random locations within the photonic crystal slab. Crucially, we start the monitors after the source pulse has decayed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bc6dca60",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:06:14.068466Z",
     "iopub.status.busy": "2023-08-18T17:06:14.068331Z",
     "iopub.status.idle": "2023-08-18T17:06:14.241613Z",
     "shell.execute_reply": "2023-08-18T17:06:14.241062Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "monitor_positions = rng.uniform([-Lx / 2, -Ly / 2, 0], [Lx / 2, Ly / 2, 0], [num_monitors, 3])\n",
    "\n",
    "monitors_time = []\n",
    "for i in range(num_monitors):\n",
    "    monitors_time.append(\n",
    "        td.FieldTimeMonitor(\n",
    "            fields=[\"Hz\"],\n",
    "            center=tuple(monitor_positions[i]),\n",
    "            size=(0, 0, 0),\n",
    "            start=t_start,\n",
    "            name=\"monitor_time_\" + str(i),\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6939eae",
   "metadata": {},
   "source": [
    "We will perform `3*Nk` different simulations, each with different Bloch boundary conditions, as we sweep the Bloch wavevector over the boundary of the irreducible Brillouin zone. We sweep over three lines, namely $\\Gamma X$, $XM$, and $M\\Gamma$. We use a PML in the z direction.\n",
    "\n",
    "Here, we simply define all of the boundary conditions we will use and put them into a single array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "84ec0547",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:06:14.244042Z",
     "iopub.status.busy": "2023-08-18T17:06:14.243862Z",
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     "shell.execute_reply": "2023-08-18T17:06:14.265543Z"
    },
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   },
   "outputs": [],
   "source": [
    "bspecs_gammax = []\n",
    "bspecs_xm = []\n",
    "bspecs_mgamma = []\n",
    "for i in range(Nk):\n",
    "    bspecs_gammax.append(\n",
    "        td.BoundarySpec(\n",
    "            x=td.Boundary.bloch((1 / 2) * i / Nk),\n",
    "            y=td.Boundary.periodic(),\n",
    "            z=td.Boundary.pml(),\n",
    "        )\n",
    "    )\n",
    "    bspecs_xm.append(\n",
    "        td.BoundarySpec(\n",
    "            x=td.Boundary.bloch(1 / 2),\n",
    "            y=td.Boundary.bloch((1 / 2) * i / Nk),\n",
    "            z=td.Boundary.pml(),\n",
    "        )\n",
    "    )\n",
    "    bspecs_mgamma.append(\n",
    "        td.BoundarySpec(\n",
    "            x=td.Boundary.bloch((1 / 2) * (1 - i / Nk)),\n",
    "            y=td.Boundary.bloch((1 / 2) * (1 - i / Nk)),\n",
    "            z=td.Boundary.pml(),\n",
    "        )\n",
    "    )\n",
    "bspecs = bspecs_gammax + bspecs_xm + bspecs_mgamma"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e80d5995",
   "metadata": {},
   "source": [
    "Now we define the simulations we want to run. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0c0a8a8a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:06:14.268679Z",
     "iopub.status.busy": "2023-08-18T17:06:14.268498Z",
     "iopub.status.idle": "2023-08-18T17:06:14.328855Z",
     "shell.execute_reply": "2023-08-18T17:06:14.328277Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "sims = {}\n",
    "for i in range(3 * Nk):\n",
    "    sims[f\"sim_{i}\"] = td.Simulation(\n",
    "        center=(0, 0, 0),\n",
    "        size=sim_size,\n",
    "        grid_spec=td.GridSpec.auto(),\n",
    "        structures=structures,\n",
    "        sources=dipoles,\n",
    "        monitors=monitors_time,\n",
    "        run_time=run_time,\n",
    "        shutoff=0,\n",
    "        boundary_spec=bspecs[i],\n",
    "        normalize_index=None,\n",
    "        symmetry=symmetry,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22ff9910",
   "metadata": {},
   "source": [
    "Let's check that the structure and source look correct. The source spectrum must fill the entire frequency range of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "443a41b7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:06:14.331418Z",
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     "shell.execute_reply": "2023-08-18T17:06:15.090363Z"
    },
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   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, tight_layout=True, figsize=(10, 4))\n",
    "sims[\"sim_0\"].plot(z=0.0, ax=ax[0])\n",
    "sims[\"sim_0\"].plot(x=0, freq=freq0, ax=ax[1])\n",
    "plt.show()\n",
    "\n",
    "f, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(8, 4))\n",
    "plot_time = 5 / freqw\n",
    "ax1 = (\n",
    "    sims[\"sim_0\"]\n",
    "    .sources[0]\n",
    "    .source_time.plot(times=np.linspace(0, plot_time, 1001), val=\"abs\", ax=ax1)\n",
    ")\n",
    "ax1.set_xlim(0, plot_time)\n",
    "ax2 = (\n",
    "    sims[\"sim_0\"]\n",
    "    .sources[0]\n",
    "    .source_time.plot_spectrum(\n",
    "        times=np.linspace(0, sims[\"sim_0\"].run_time, 10001), val=\"abs\", ax=ax2\n",
    "    )\n",
    ")\n",
    "ax2.hlines(1.5e-15, freq_range[0], freq_range[1], linewidth=10, color=\"g\", alpha=0.4)\n",
    "ax2.legend((\"source spectrum\", \"measurement\"))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7983aad",
   "metadata": {},
   "source": [
    "Now we run the simulations as a `Batch`.\n",
    "\n",
    "We set `verbose=True` to keep track of the status of the jobs in the `Batch`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "70058b88",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:06:15.093374Z",
     "iopub.status.busy": "2023-08-18T17:06:15.093227Z",
     "iopub.status.idle": "2023-08-18T17:08:27.948645Z",
     "shell.execute_reply": "2023-08-18T17:08:27.947959Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f49ca4377d884a1fbe50dc42f831eb3e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">19:23:39 CEST </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12</span> tasks.                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m19:23:39 CEST\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m12\u001b[0m tasks.                     \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\">19:23:55 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.300</span> for the whole batch.               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m19:23:55 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.300\u001b[0m for the whole batch.               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after   \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>the Batch has completed.                                          \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after   \n",
       "\u001b[2;36m              \u001b[0mthe Batch has completed.                                          \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "02e5aaf8ba674855850a71999727f9be",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">19:25:05 CEST </span>Batch complete.                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m19:25:05 CEST\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                   \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\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "36002925249042da8d6ff99c0d9fa5bd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# initialize a batch and run them all\n",
    "batch = td.web.Batch(simulations=sims, verbose=True)\n",
    "\n",
    "# run the batch and store all of the data in the `data/` dir.\n",
    "batch_data = batch.run(path_dir=\"data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c865c155",
   "metadata": {},
   "source": [
    "Now that the simulations are complete, we can analyze the data. Let's first look at one of the FieldTimeMonitors to make sure the source has decayed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4a97628f-c2d1-4e2f-82f2-ba97d88fe48e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:08:28.235611Z",
     "iopub.status.busy": "2023-08-18T17:08:28.235460Z",
     "iopub.status.idle": "2023-08-18T17:08:29.381759Z",
     "shell.execute_reply": "2023-08-18T17:08:29.381250Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(\n",
    "    batch_data[\"sim_1\"].monitor_data[\"monitor_time_0\"].Hz.t,\n",
    "    np.real(batch_data[\"sim_1\"].monitor_data[\"monitor_time_0\"].Hz.squeeze()),\n",
    ")\n",
    "plt.title(\"FieldTimeMonitor data\")\n",
    "plt.xlabel(\"t\")\n",
    "plt.ylabel(\"Hz\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21bc59dc-0163-432b-9cf0-5c2d2617ef35",
   "metadata": {},
   "source": [
    "We see that the source has mostly decayed by the time we switch on the monitors, and the remaining data shows decay and oscillation due to the resonances inside the system.\n",
    "\n",
    "Looking at the Fourier transform of this data, we can see resonances at the band frequencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e1b95adb-1ea6-40cb-ba63-ef5ac60de470",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:08:29.392606Z",
     "iopub.status.busy": "2023-08-18T17:08:29.392364Z",
     "iopub.status.idle": "2023-08-18T17:08:29.689950Z",
     "shell.execute_reply": "2023-08-18T17:08:29.689484Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5BvN1zDnPyP9+U6lUWLduHTZt2oTXX3/d4P4yAQEB8Pf3x9y5c5GamvrE9xAcHAxXV1ds2LABGzZsQPPmzQ2GAD08PNC+fXt88803Rn80FNxf3759ce7cOfz8889G7XTvr6jvlaZNm8LDwwPLli0z+D7YuXMnLl26ZPLVdPTiYs8Oyd6YMWOQnp6O3r17o06dOsjOzsaRI0ewYcMGVKtWTZpno1O/fn2EhIQYXHqO/Pvg6HzxxRfYt28fWrRogeHDh6Nu3bpISEjA6dOn8b///Q8JCQlA/qTgH374ARMmTMCJEycQFBSEtLQ0/O9//8OoUaPQs2dP2NnZoW7dutiwYQNq1aoFV1dX1K9f/4nzG9555x1s3rwZXbp0Qb9+/RAdHY2ffvpJ+uu9LNWpUwf+/v748MMPce/ePTg5OWHLli0G93LR0U32Hjt2LEJCQqBSqdC/f/9C97t69WosWbIEvXv3hr+/P1JSUvDdd9/ByckJr7322lPXHRAQgL59+2L+/Pl49OiRdOn5lStXgCf0Qjk5OWHp0qUYNGgQmjRpgv79+8Pd3R23b9/G77//jtatWxcaavXpgszly5cNhoPatm2LnTt3Qq1WS/eagZnnGfnh49VXX8VXX32FlJQUg14Y5Aer5cuXo2vXrqhXrx6GDBmCSpUq4d69e9i3bx+cnJzw22+/Se2tra3Rp08frF+/HmlpaZg7d67RMRcvXow2bdqgQYMGGD58OKpXr47Y2FgcPXoUd+/exblz5wAAEydOxObNm/Hmm29i6NChCAgIQEJCArZt24Zly5ahYcOG8Pf3h4uLC5YtWwZHR0fY29ujRYsW8PPzw5dffokhQ4agXbt2GDBggHTpebVq1TB+/PhizzsRLz0n2du5c6cYOnSoqFOnjnBwcBA2NjaiRo0aYsyYMSI2NtagLQARHh4ufvrpJ1GzZk2hVqtF48aNxb59+4z2GxsbK8LDw4Wvr6+wtrYWXl5eomPHjuLbb781aJeeni7+/e9/Cz8/P6ndG2+8IaKjo6U2R44cEQEBAcLGxsbgEujQ0FBhb29f5HubN2+eqFSpklCr1aJ169bi1KlTRV56vmnTJoNtdZdWF7zMW3cZ98OHD4s9rxcvXhTBwcHCwcFBuLm5ieHDh4tz584ZXTqcm5srxowZI9zd3YVCoSj2MvTTp0+LAQMGiCpVqgi1Wi08PDzE66+/bnArAFHMpecFay54+bgQQqSlpYnw8HDh6uoqHBwcRK9evcTly5cFAPHFF18Uu63ufIaEhAhnZ2dha2sr/P39RVhYmFGNRfHw8BAADL73Dh06JACIoKAgo/amnmed7777TgAQjo6OBpdp6ztz5ozo06ePqFixolCr1aJq1aqiX79+Yu/evUZtIyIiBAChUCjEnTt3Ct1fdHS0GDx4sPDy8hLW1taiUqVK4vXXXxebN282aPfo0SMxevRoUalSJWFjYyMqV64sQkNDDW7h8Ouvv4q6desKKysro/e4YcMG0bhxY6FWq4Wrq6sYOHCguHv3rsExnvQzQy8mhSiNmXtEMqFQKBAeHv7Ev9BJXs6ePYvGjRvjp59+4t12iWSIc3aI6IWi/5gFnfnz50OpVKJt27YWqYmIyhbn7BDRC2XOnDmIiorCq6++CisrK+zcuRM7d+7EiBEjjO45RETywLBDRC+UVq1aISIiAp9++ilSU1NRpUoVTJ8+Hf/+978tXRoRlRHO2SEiIiJZ45wdIiIikjWGHSIiIpI1ztnJf77L/fv34ejoWOJb9xMREdGzJYRASkoKfHx8in0eGsMOgPv37/MqDCIioufUnTt3in3SPcNO/kMdkX+ynJycLF0OERERmSA5ORm+vr7S7/GiMOzoPQ/HycmJYYeIiOg586QpKJygTERERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0REspKenWnpEqicYdghIiLZmP3LUlQf2x6HLkdZuhQqRxh2iIhINv67azUAYPrm/1q6FCpHGHaIiIhI1hh2iIiISNYYdoiISHaEEJYugcoRhh0iIiKSNYYdIiIikjWGHSIikh0OY5E+hh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIikh0BXo1F/2DYISIiIllj2CEiIiJZY9ghIiIiWWPYISIi2eENlEkfww4RERHJGsMOERHJjkJh6QqoPGHYISIi2eEwFumzaNjRaDT45JNP4OfnBzs7O/j7++PTTz81eFqtEAJTp06Ft7c37OzsEBwcjKtXrxrsJyEhAQMHDoSTkxNcXFwwbNgwpKamWuAdERERUXlj0bDz5ZdfYunSpVi0aBEuXbqEL7/8EnPmzMHChQulNnPmzMGCBQuwbNkyHD9+HPb29ggJCUFmZqbUZuDAgfjrr78QERGB7du3IzIyEiNGjLDQuyIiIqLyxMqSBz9y5Ah69uyJbt26AQCqVauGdevW4cSJE0B+r878+fPx8ccfo2fPngCAH374AZ6envjll1/Qv39/XLp0Cbt27cLJkyfRtGlTAMDChQvx2muvYe7cufDx8bHgOyQiIkvgHZRJn0V7dlq1aoW9e/fiypUrAIBz587h0KFD6Nq1KwDgxo0biImJQXBwsLSNs7MzWrRogaNHjwIAjh49ChcXFynoAEBwcDCUSiWOHz9e6HGzsrKQnJxs8EFERETyZNGencmTJyM5ORl16tSBSqWCRqPB559/joEDBwIAYmJiAACenp4G23l6ekrrYmJi4OHhYbDeysoKrq6uUpuCZs+ejRkzZpTRuyIiIqLyxKI9Oxs3bsSaNWuwdu1anD59GqtXr8bcuXOxevXqMj3ulClTkJSUJH3cuXOnTI9HRETPluDlWKTHoj07EydOxOTJk9G/f38AQIMGDXDr1i3Mnj0boaGh8PLyAgDExsbC29tb2i42NhaNGjUCAHh5eSEuLs5gv7m5uUhISJC2L0itVkOtVpfhOyMiIqLywqI9O+np6VAqDUtQqVTQarUAAD8/P3h5eWHv3r3S+uTkZBw/fhyBgYEAgMDAQCQmJiIqKkpq88cff0Cr1aJFixbP7L0QEVH5oeBdBUmPRXt2unfvjs8//xxVqlRBvXr1cObMGXz11VcYOnQokP/NOm7cOHz22WeoWbMm/Pz88Mknn8DHxwe9evUCALz88svo0qULhg8fjmXLliEnJwejR49G//79eSUWEdELisNYpM+iYWfhwoX45JNPMGrUKMTFxcHHxwfvvvsupk6dKrX56KOPkJaWhhEjRiAxMRFt2rTBrl27YGtrK7VZs2YNRo8ejY4dO0KpVKJv375YsGCBhd4VERERlScKwfiL5ORkODs7IykpCU5OTpYuh4iISsjrvZYAgFrefoicts7S5VAZM/X3N5+NRURERLLGsENERLLDQQvSx7BDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERDLEq7HoHww7REREJGsMO0RERCRrDDtERCQ7vKcg6WPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIi2RG8gzLpYdghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiItkRfBIo6WHYISIiIllj2CEiIiJZY9ghIiLZ4TAW6WPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiLZ4bVYpI9hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIZId3UCZ9DDtEREQkaww7REQkOwqFwtIlUDnCsENERLLDYSzSx7BDREREssawQ0RERLLGsENERLIjeA9l0sOwQ0RERLLGsENERESyxrBDRESyw6uxSB/DDhEREckaww4REckObypI+hh2iIhIdjiMRfoYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiISHY4Z4f0MewQERGRrDHsEBERkawx7BAREZGsMewQERGRrDHsEBERkawx7BARkezwWizSx7BDREREssawQ0RERLLGsENERPLDmwqSHoYdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1i4ede/fu4V//+hcqVqwIOzs7NGjQAKdOnZLWCyEwdepUeHt7w87ODsHBwbh69arBPhISEjBw4EA4OTnBxcUFw4YNQ2pqqgXeDREREZU3Fg07jx8/RuvWrWFtbY2dO3fi4sWLmDdvHl566SWpzZw5c7BgwQIsW7YMx48fh729PUJCQpCZmSm1GThwIP766y9ERERg+/btiIyMxIgRIyz0roiIyNIE76FMehRCWO5mBJMnT8bhw4dx8ODBQtcLIeDj44MPPvgAH374IQAgKSkJnp6eWLVqFfr3749Lly6hbt26OHnyJJo2bQoA2LVrF1577TXcvXsXPj4+T6wjOTkZzs7OSEpKgpOTUym/SyIiela83msJAPB5yQOnZ2+zdDlUxkz9/W3Rnp1t27ahadOmePPNN+Hh4YHGjRvju+++k9bfuHEDMTExCA4OlpY5OzujRYsWOHr0KADg6NGjcHFxkYIOAAQHB0OpVOL48eOFHjcrKwvJyckGH0RERCRPFg07169fx9KlS1GzZk3s3r0bI0eOxNixY7F69WoAQExMDADA09PTYDtPT09pXUxMDDw8PAzWW1lZwdXVVWpT0OzZs+Hs7Cx9+Pr6ltE7JCIiS7DgoAWVQxYNO1qtFk2aNMGsWbPQuHFjjBgxAsOHD8eyZcvK9LhTpkxBUlKS9HHnzp0yPR4RERFZjkXDjre3N+rWrWuw7OWXX8bt27cBAF5eXgCA2NhYgzaxsbHSOi8vL8TFxRmsz83NRUJCgtSmILVaDScnJ4MPIiKSD4VCYekSqByxaNhp3bo1Ll++bLDsypUrqFq1KgDAz88PXl5e2Lt3r7Q+OTkZx48fR2BgIAAgMDAQiYmJiIqKktr88ccf0Gq1aNGixTN7L0REVH5wGIv0WVny4OPHj0erVq0wa9Ys9OvXDydOnMC3336Lb7/9FshP5uPGjcNnn32GmjVrws/PD5988gl8fHzQq1cvIL8nqEuXLtLwV05ODkaPHo3+/fubdCUWERERyZtFw06zZs3w888/Y8qUKZg5cyb8/Pwwf/58DBw4UGrz0UcfIS0tDSNGjEBiYiLatGmDXbt2wdbWVmqzZs0ajB49Gh07doRSqUTfvn2xYMECC70rIiIiKk8sep+d8oL32SEikgfdfXa8nN1x9svfLF0OlbHn4j47RERERGWNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIZEfghb+rCulh2CEiIiJZY9ghIiIiWWPYISIi2eHDAUgfww4RERHJGsMOERHJAntzqCgMO0RERCRrDDtERCQL7NmhojDsEBGRLPDeOlQUhh0iIpIdBh/Sx7BDRESywGEsKgrDDhERyQKjDhWFYYeIiGSHvTykj2GHiIhkgQGHisKwQ0REsqAfdhQKhUVrofKFYYeIiGRB/wos9vKQPoYdIiIikjWGHSIikgX25lBRGHaIiEgWGHWoKAw7REQkOww+pK9EYScxMRHLly/HlClTkJCQAAA4ffo07t27V9r1ERERmYTDWFQUK3M3+PPPPxEcHAxnZ2fcvHkTw4cPh6urK7Zu3Yrbt2/jhx9+KJtKiYiIisOwQ0Uwu2dnwoQJCAsLw9WrV2Fraystf+211xAZGVna9RERERE9FbPDzsmTJ/Huu+8aLa9UqRJiYmJKqy4iIiKz8EnnVBSzw45arUZycrLR8itXrsDd3b206iIiIjILR7GoKGaHnR49emDmzJnIyckB8m/Jffv2bUyaNAl9+/YtixqJiIjMwsnKpM/ssDNv3jykpqbCw8MDGRkZaNeuHWrUqAFHR0d8/vnnZVMlERHRE3AYi4pi9tVYzs7OiIiIwKFDh/Dnn38iNTUVTZo0QXBwcNlUSEREZAL25lBRzA47Om3atEGbNm1KtxoiIqLSwOBDekwKOwsWLDB5h2PHjn2aeoiIiEqEPTtUFJPCztdff23w+uHDh0hPT4eLiwuQf0flChUqwMPDg2GHiIgsgnN2qCgmTVC+ceOG9PH555+jUaNGuHTpEhISEpCQkIBLly6hSZMm+PTTT8u+YiIiIiIzmH011ieffIKFCxeidu3a0rLatWvj66+/xscff1za9REREZmEo1hUFLPDzoMHD5Cbm2u0XKPRIDY2trTqIiIiMguHsagoZoedjh074t1338Xp06elZVFRURg5ciQvPyciIovhBGUqitlh5/vvv4eXlxeaNm0KtVoNtVqN5s2bw9PTE8uXLy+bKomIiMzAXh7SZ/Z9dtzd3bFjxw5cuXIFf//9NwCgTp06qFWrVlnUR0REZBL9nh128pC+Et9UsFatWgw4RERUjuiFHfbskB6zw87QoUOLXf/9998/TT1ERERPjfN3SJ/ZYefx48cGr3NycnDhwgUkJiaiQ4cOpVkbERGRyZhvqChmh52ff/7ZaJlWq8XIkSPh7+9fWnURERGZRX/oij07pM/sq7EK3YlSiQkTJhg9VoKIiMgSOGeH9JVK2AGA6OjoQm82SERE9CzwaiwqitnDWBMmTDB4LYTAgwcP8PvvvyM0NLQ0ayMiIjKZQdhhzw7pMTvsnDlzxuC1UqmEu7s75s2b98QrtYiIiJ4FztkhfWaHnX379pVNJURERE/BIN4w7JAes+fsdOjQAYmJiUbLk5OTeek5ERFZjOEwFtE/zA47+/fvR3Z2ttHyzMxMHDx4sLTqIiIiKjEOY5E+k4ex/vzzT+nzixcvIiYmRnqt0Wiwa9cuVKpUqfQrJCIiMgEnJVNRTA47jRo1gkKhgEKhKHS4ys7ODgsXLizt+oiIiEzCq7GoKCaHnRs3bkAIgerVq+PEiRNwd3eX1tnY2MDDwwMqlaqs6iQiIiqW4X12GHboHyaHnapVqwL5j4YgIiIqzxh2SJ9JYWfbtm3o2rUrrK2tsW3btmLb9ujRo7RqIyIiMpko4nMik8JOr169EBMTAw8PD/Tq1avIdgqFAhqNpjTrIyIiMg2HsagIJoUd/aErDmMREVF5xwnKpK/UHgRKRERkSfoBhz07pM+knp0FCxaYvMOxY8c+TT1EREQlwoBDRTEp7Hz99dcm7UyhUDDsEBERUbliUti5ceNG2VdCRET0FAr27AghoFAoLFYPlR9PNWdHCMFuQyIiKhcK/jbi7yfSKVHYWbFiBerXrw9bW1vY2tqifv36WL58eelXR0REVEK8Iot0zA47U6dOxfvvv4/u3btj06ZN2LRpE7p3747x48dj6tSpJS7kiy++gEKhwLhx46RlmZmZCA8PR8WKFeHg4IC+ffsiNjbWYLvbt2+jW7duqFChAjw8PDBx4kTk5uaWuA4iIno+FTaMRQRzHhehs3TpUnz33XcYMGCAtKxHjx545ZVXMGbMGMycOdPsIk6ePIlvvvkGr7zyisHy8ePH4/fff8emTZvg7OyM0aNHo0+fPjh8+DCQ/7T1bt26wcvLC0eOHMGDBw8wePBgWFtbY9asWWbXQUREz6+CPTmMOqRjds9OTk4OmjZtarQ8ICCgRD0qqampGDhwIL777ju89NJL0vKkpCSsWLECX331FTp06ICAgACsXLkSR44cwbFjxwAAe/bswcWLF/HTTz+hUaNG6Nq1Kz799FMsXrwY2dnZZtdCRETywZ4d0jE77AwaNAhLly41Wv7tt99i4MCBZhcQHh6Obt26ITg42GB5VFQUcnJyDJbXqVMHVapUwdGjRwEAR48eRYMGDeDp6Sm1CQkJQXJyMv76668ij5mVlYXk5GSDDyIier5xGIuKYvYwFvInKO/ZswctW7YEABw/fhy3b9/G4MGDMWHCBKndV199Vex+1q9fj9OnT+PkyZNG62JiYmBjYwMXFxeD5Z6enoiJiZHa6Acd3XrduqLMnj0bM2bMMOm9EhHR84HZhopidti5cOECmjRpAgCIjo4GALi5ucHNzQ0XLlyQ2j3p3gZ37tzB+++/j4iICNja2ppf+VOYMmWKQShLTk6Gr6/vM62BiIhKl/GcHaYfymN22Nm3b1+pHDgqKgpxcXFScEL+hOPIyEgsWrQIu3fvRnZ2NhITEw16d2JjY+Hl5QUA8PLywokTJwz2q7taS9emMGq1Gmq1ulTeBxERlU8cxiIdiz0ItGPHjjh//jzOnj0rfTRt2hQDBw6UPre2tsbevXulbS5fvozbt28jMDAQABAYGIjz588jLi5OahMREQEnJyfUrVvXIu+LiIgsw2jOjsUqofLG7J6dzMxMLFy4EPv27UNcXBy0Wq3B+tOnT5u0H0dHR9SvX99gmb29PSpWrCgtHzZsGCZMmABXV1c4OTlhzJgxCAwMlOYKde7cGXXr1sWgQYMwZ84cxMTE4OOPP0Z4eDh7boiIXjicoEyFMzvsDBs2DHv27MEbb7yB5s2bl+lzR77++msolUr07dsXWVlZCAkJwZIlS6T1KpUK27dvx8iRIxEYGAh7e3uEhoaW6F4/REQkMww7lE8hzIy+zs7O2LFjB1q3bl12VT1jycnJcHZ2RlJSEpycnCxdDhERlcDFu1fR4bNB0utr8/fCwdbeojVR2TL197fZc3YqVaoER0fHp62PiIioVBk/CNRChVC5Y3bYmTdvHiZNmoRbt26VTUVEREREpcjsOTtNmzZFZmYmqlevjgoVKsDa2tpgfUJCQmnWR0REZBLjq7HYtUN5zA47AwYMwL179zBr1ix4enqW6QRlIiIiU/FxEVQUs8POkSNHcPToUTRs2LBsKiIiIioFDDukY/acnTp16iAjI6NsqiEiIiohPi6CimJ22Pniiy/wwQcfYP/+/Xj06BGfHk5EROVCwY4cduyQjtnDWF26dAHyH/egTwgBhUIBjUZTetURERGVEHt2SKdUHwR6/vz5p62HiIioRIyGsdi1Q/nMDjvt2rUzeJ2SkoJ169Zh+fLliIqKwujRo0uzPiIiIpPwaiwqSomfeh4ZGYnQ0FB4e3tj7ty56NChA44dO1a61REREZmI4YaKYlbPTkxMDFatWoUVK1YgOTkZ/fr1Q1ZWFn755RfUrVu37KokIiIyG8MP5TG5Z6d79+6oXbs2/vzzT8yfPx/379/HwoULy7Y6IiIikxUcxrJYIVTOmNyzs3PnTowdOxYjR45EzZo1y7YqIiIiMxldes6eHcpncs/OoUOHkJKSgoCAALRo0QKLFi1CfHx82VZHRERUQpzDQzomh52WLVviu+++w4MHD/Duu+9i/fr18PHxgVarRUREBFJSUsq2UiIiomIU7MnRMuxQPrOvxrK3t8fQoUNx6NAhnD9/Hh988AG++OILeHh4oEePHmVTJRER0RMU7MnRCq3FaqHypcSXngNA7dq1MWfOHNy9exfr1q0rvaqIiIiekkbLsEN5nirs6KhUKvTq1Qvbtm0rjd0RERGZzfimggw7lKdUwg4REZGlGc3ZYc8O5WPYISIiWdKwZ4fyMewQEZEsFLz4Sqvl1ViUh2GHiIhkwfjSc/bsUB6GHSIikiVOUCYdhh0iIpKFgldj8dJz0mHYISIiWTC+qSDn7FAehh0iIpIlztkhHYYdIiKShYL9OLzPDukw7BARkTzw2VhUBIYdIiKSBV56TkVh2CEiIlni1Vikw7BDRESywKuxqCgMO0REJAt86jkVhWGHiIhkicNYpMOwQ0REsmB06Tl7digfww4REcmC0ZwdPvWc8jHsEBGRLLFnh3QYdoiISBaM7rPDOTuUj2GHiIhkwfjSc4YdysOwQ0REssSwQzoMO0REJAsF7yHIS89Jh2GHiIhkoeCcnYLDWvTiYtghIiJZ4jAW6TDsEBGRLBTsyeEwFukw7BARkUzwaiwqHMMOERHJAp96TkVh2CEiIlnSajWWLoHKCYYdIiKShYIdOezZIR2GHSIikgWjx0Vwzg7lY9ghIiJZ4rOxSIdhh4iIZMHo0nP27FA+hh0iIpIFozsoazlnh/Iw7BARkSxxzg7pMOwQEZEs8A7KVBSGHSIikgXjS88ZdigPww4REckSn3pOOgw7REQkCwUnKGt4B2XKx7BDRESywGdjUVEYdoiISJY4Z4d0GHaIiEgWjIexGHYoD8MOERHJQ4FRK8GeHcrHsENERLJg/CBQztmhPAw7REQkS7wai3QYdoiISBZ4NRYVhWGHiIhkwTjscM4O5WHYISIiWeJTz0mHYYeIiGTB6NJzwTk7lMeiYWf27Nlo1qwZHB0d4eHhgV69euHy5csGbTIzMxEeHo6KFSvCwcEBffv2RWxsrEGb27dvo1u3bqhQoQI8PDwwceJE5ObmPuN3Q0RElmT0IFD27FA+i4adAwcOIDw8HMeOHUNERARycnLQuXNnpKWlSW3Gjx+P3377DZs2bcKBAwdw//599OnTR1qv0WjQrVs3ZGdn48iRI1i9ejVWrVqFqVOnWuhdERFRecA5O6RjZcmD79q1y+D1qlWr4OHhgaioKLRt2xZJSUlYsWIF1q5diw4dOgAAVq5ciZdffhnHjh1Dy5YtsWfPHly8eBH/+9//4OnpiUaNGuHTTz/FpEmTMH36dNjY2Fjo3RER0bNkfJ8dhh3KU67m7CQlJQEAXF1dAQBRUVHIyclBcHCw1KZOnTqoUqUKjh49CgA4evQoGjRoAE9PT6lNSEgIkpOT8ddffxV6nKysLCQnJxt8EBHR883oaiw+LoLylZuwo9VqMW7cOLRu3Rr169cHAMTExMDGxgYuLi4GbT09PRETEyO10Q86uvW6dYWZPXs2nJ2dpQ9fX98yeldERGQpvM8O6ZSbsBMeHo4LFy5g/fr1ZX6sKVOmICkpSfq4c+dOmR+TiIjKVsEHf/IOyqRj0Tk7OqNHj8b27dsRGRmJypUrS8u9vLyQnZ2NxMREg96d2NhYeHl5SW1OnDhhsD/d1Vq6NgWp1Wqo1eoyejdERGQJuRrDq3DZs0M6Fu3ZEUJg9OjR+Pnnn/HHH3/Az8/PYH1AQACsra2xd+9eadnly5dx+/ZtBAYGAgACAwNx/vx5xMXFSW0iIiLg5OSEunXrPsN3Q0RElpRTIOzwqeekY9GenfDwcKxduxa//vorHB0dpTk2zs7OsLOzg7OzM4YNG4YJEybA1dUVTk5OGDNmDAIDA9GyZUsAQOfOnVG3bl0MGjQIc+bMQUxMDD7++GOEh4ez94aI6AVSsGen4LAWvbgsGnaWLl0KAGjfvr3B8pUrVyIsLAwA8PXXX0OpVKJv377IyspCSEgIlixZIrVVqVTYvn07Ro4cicDAQNjb2yM0NBQzZ858xu+GiIgsKVuTY/Cal56TjkXDTsHLBAtja2uLxYsXY/HixUW2qVq1Knbs2FHK1RER0fOEc3aoKOXmaiwiIqKnkaPJu/pKqcj71cY5O6TDsENERLKg69lRW+fdOZ9zdkiHYYeIiGQhOzdvzo5tftjhnB3SYdghIiJZ0PXs2Fjpwg7n7FAehh0iIpKFHKNhLN5BmfIw7BARkSxIc3asrAETr/ilFwPDDhERyUJOwWEsTlCmfAw7REQkC0bDWJygTPkYdoiISBaksMOeHSqAYYeIiGSh4H12OGeHdBh2iIhIFv6Zs5M3QZlXY5EOww4REclCTv5NBdVWaoD32SE9DDtERCQLBSco8w7KpMOwQ0REsqCbs2PLmwpSAQw7REQkC7qnntur7QAAWfnDWkQMO0REJAs5mrxw42BrDwDIysm2cEVUXjDsEBGRLOjm7Dja5YWdzOwsC1dE5QXDDhERyYJuzo6jrmcnl2GH8jDsEBGRLOh6duxtKwDs2SE9DDtERCQLObmGPTuZudm8izIBDDtERCQXOVrDsCOEQDavyCKGHSIikovcAhOUASArl1dkEcMOERHJhG7OTgUbOygUCoDzdigfww4REcmCrmfHSmUFW6u8uyhn5jDsEMMOERHJgP78HBsrK9ja5D0MlMNYBIYdIiKSA/3nYFmprKQnn3MYi8CwQ0REcqCbrwMA1ir27JAhhh0iInru5WoK9OzkP/mcPTsEhh0iIpID/YnINipr2FrnD2PxYaDEsENERHKQmpkGALBXV4BSqYStrmcnJ9PClVF5wLBDRETPvdTMdACAQ/5zsXQ9O1ns2SGGHSIikoPUrLywo3tUhDRnh/fZIYYdIiKSg5SM/GGsAj07nLNDYNghIiI5SMsyHMZSS2GHPTvEsENERCW0OnIrjl49Y+kyAL05O7phLLv8++ykZ2VYtC4qH6wsXQARET1/Dl+OwqS1cwAAMcuOWbocaRhL17PjUsEJAJCckWrRuqh8YM8OERGZLTr2tqVLMKCboGyvNgw7j9OSLFoXlQ8MO0RE9FSEEJYuAan5PTuOdnnDWC72urCTbNG6qHxg2CEiIrMJ/BNwysPzp1Kz8oex8nt2XO2dAQCJDDvEsENERCWh1Wqlz9PKwSRg6aaCRj07HMYihh0iIiqBDL0HbKblBw1LSsk07NnRhR327BAYdoiIqCR0E4JRTnp2UgrM2ZGGsdJTDHqh6MXEsENERGZLy+9JQTm5l01c8iMAgLujKwDAuYIjAEArtLz8nBh2iIjIfKmZ+j07lh3GEkIgNjEeAODp4g7kPxurgtoOAJCYzqGsFx3DDhERma08DWMlpadIV4R5OleUlrs5vgQAiE2Kt1htVD4w7BARkdnKU89ObFLeEJZzBUfpAaAAUNnVCwBw91GMxWqj8oFhh4iIzKbfm5Nq4aux4pLzem48nCoaLPet6A0AuJvAsPOiY9ghIiKz6U9QtvQwVoxuvo6zm8FyXdi58+iBReqi8oNhh4iIzGY4jGXpsPMQAODlwrBDhWPYISIisyXpXc5t6Tk712JvAQD8PHwNluvm7NyKv2+Ruqj8YNghIiKzaLQagzsTJ6db9j42V2NuAgBqelUzWF7LO+/1rfh75eIuz2Q5DDtERGSWpPRUaMU/dyW25POnhBC4FpPXs1PTq6rBOnenivB0doMQAhfvXbNQhWUrOzdHuns0FY1hh4iIzPIo9bHBa0uGnbjkR0jOSIVSoTQaxgKABlVqAwDO37ligerKXt+vRiHg/3rygadPwLBDRERmSUhNLPb1s3T6xl8AgBpeVQ3usaPTwLd2frsLz7y2spajycXJ6+eRnJGKPX8esnQ55RrDDhERmSUhNa8XwT7/CeOPUi3Xq3Ai+k8AQIsaDQtd37p2AADgwKUTEEKUeT1CCHzw4yzUntAJX+/4vkyPFZd/M0UAeJRiucD5PGDYISIiszxKyRvGqpE/RyYxLdliTxY/fu0sAKC5f+Fhp1n1BrCzscXD5AT8dfdqmddzPe4O1hzehqT0FKw+8HOZBqwHiXHS51dibpTZceSAYYeIiMySkD8/pIZnFSD/yeJJGSnPvI64pEc4c/MioNeDU5Da2gbtXm4OAPjlVESZ17Tv4jHp85ikh2V6j58Hjx9Kn1+6F11mx5EDhh0iIjKLrmfH09kNjrb2gN7Q1rO089wBCCHQuFpd+LzkUWS7N1t2BQBsPrYL2bk5ZVrTvr+OGbw+mT/MVhb0e3ZuPrxbZseRA4YdIiIyy/W4OwAAH1dPuDq4ABaYpCyEwE8HfwEAdG/Ssdi2wfVbw9PZDTFJD7H5+M4yqykzJwtHrpwGAATVaQrozSkqC/cf/xN2ktJTeEVWMRh2iIjILBfzh0zqVa4JT+e8h28+60cyHL4chfN3rsDWWo3+rV4vtq3a2gYjO70NAPhy27dldl+aE9fOISM7E57Obhgc1Aco456dguecd4ouGsMOERGZLCk9BffynyL+so8/6lTyB57xnJFcTS5mbl0EABjQqjtcHZyfuE1Yu77wc6+M2KR4jFk1o0wmVG85sRsA0LF+oHR12KX70UjOKJs7TOsek6HDoayiMewQEZHJdDfnq+TqBRd7J9StVAMAnukdir/esRJ/3v4bjrb2mNBtiEnb2FqrsXjoDKitbLDrXCSmbppfqoEnPuUxtp36H5AfwDycK6KqWyUIIRB1vfTv8aPRanAjfzixZY1GAIBbD++V+nHkgmGHiIhMtvPsfgBAYM3GAPBP2Ll79Zncx2bdke2Y9/sKAMDn/T+Au1NFk7dt4lcP//nXZADA8n0bMeybKYhPefzE7Uwx+5elyMjJwitV6qBp9QYAgGb+rwBlNG/nzqMHyM7Nga21Gh3qBwIAzt68VOrHkQuGHSIiMklGdia2Re0FAPRsGgzkz9tRW9ngQeJDXCrD3h2tVouvfv8e43/4DAAwJmQw+rV8zez99Gv5GhYPmQ4bK2vsPHcAbaa9hYW7fkBSeskunRdCYNn/1mLN4W0AgBlvvg+FQgHo3ehQdy+g0nT5/nUAgJ9HZQTVaQYAOHwlCrma3FI/lhww7BARkUmWRKzBw+QEVHrJU7p3jb1tBXSol9ezoJuzUtpORv+J1/8zHHN++xYA8M6r/fB/vUaWeH99W3TBtonfol7lmkhMT8bnvyxBg49ew+AlH2LNoV9x+f6NJw5x5Wpysf/icbwxfzSmb14AAJjYfbjU4wW9K7KOXj2D26U8efiP/Evcm1V/Ba9UqQ3nCo5IzkjF0atnSvU4cmFl6QKIiKh8E0Jgw9HfMW973vDR//UaCRsra2n9my27Yue5A1h5YAuGvdqv2HvemCrveU8HserAVpy6fh4A4GBbAZ/2m4ABT7j6yhSNqr6M3VNWYuvJPVgasQaX7kVjz5+HpGdM2Vqr4VvRC5UresPZzhHWVtZQKZVISktBbHI8Lt2LRkZ2ptR2Uo8ReC/4bYNjVHOvjLZ1miHy75NYErEGXwyY+NR1Iz9o7fnzIAAgpGEQVEoVejYNxg+RP2PBrtVoU7up1LtEeRTiWQyylnPJyclwdnZGUlISnJycLF0OEVG5oNFqcPDvU1gasQYHLp0AAAwK6oU5b08y+GUqhMDrc4Yj6sYF1K1UAyvf+xJV3SuZdayk9BScv3MFx6+dxfGrZ3Hs2lnpBoA2Vtbo27wLJvUYAS8X91J+l3n+vn8dv0XtxZErp3H21iUpyBTH1cEF3Zt0wKjO/0JVN59C2xy6HIU3vg6HUqHExnEL0aaIOz2b44fIn/HR2i/h6uCC07N/ha21Grce3kOb6W8hR5OL8a8NxUfdh78QgcfU39+yCTuLFy/Gf/7zH8TExKBhw4ZYuHAhmjdvbtK2DDtE9CITQiApPQU34+/heuxtXI+7g3O3LuH4tXPSZdNqKxu83zUM47qGQak0ngFx6+E9dJvzDuJTHkOlVKFro3ZoVasJKr3kAVcHFwghkK3JRXpWBh4mP0Js0iPEJD3Ejbg7uPLgJmKT4o32WdOrKno27YTBQb3h4Wz6ROSnlavJxb2EWNx59AC3Hz1AWmY6sjU5yNXkwrmCI9wcX0Itbz/4e1aBSql64v7GrJqJTcd2wM7GFvP+NQW9m3UucRA5+PdJDF78ITJysvDpm+MxvONb0rqV+zdjyvq5QP5k7LEhoXi1XkuorW1KdKznwQsVdjZs2IDBgwdj2bJlaNGiBebPn49Nmzbh8uXL8PB4cncqww4RlWdarRY5mlzkaHKQnav7Nwc5uTnI1uQiJzfHYF1WTg7SszOQlpWBtKx0ZGRlIi0rHWlZGUjPykBqVjoepybhUWoiHqUk4lHqY+QUMbHVpYITejXrhPeCB6Cae+Vi67wVfx8frflC6gUyV6WXPNHM/xW0qNEQrWsHoJa3X4n2U95kZGdiyLJJ2H/xOACguf8rGNy2NzrUa2XSPYIysjNx7OpZrD38G347nTdB/NV6LfHjqLmwUhnORlkduRUztixEelYGkD/E1sC3FhpUqY2qbpVQxc0HXi7ucLV3hou9E5zsHJ7rHqAXKuy0aNECzZo1w6JFeTeZ0mq18PX1xZgxYzB58uQnbl9WYScm8aH0H4j+aTY64QbrRIFVovDPjXZh6j5gUruCRyjq2AW/fYqvo/BjF7cP4zrM30dp1GHq18ycfRRVo/E+Cq/XuO3T76NgHULkLRNC5K3Tf52/TBgt022rNXxdWBsIoJDjaLWihMfVFnoMU46rFdpC369WaKHRaqHRaqTPpX+1GsPXIq9d3udCWp/3WgOtEP+s19uusH1rtBop0ORqNXgWPJwqorqHL/w8fFHbxw8tazZG/co1jX6hPsmft//Gnj8P4dytS3iY/BgJaYlQKZSwtrKGrbUN3J0qwtO5Ijyd3VDVrRJqeVdDDa9qcLJzKLP3ZmkarQZzt6/A4j0/Gjyfq4qbD2p7+8HbxQMv2TvBSmUFrRBISk9GXHIComNvITr2tsE2A1v3wOf9P4CttbrQY8UmxWNpxFr8eioCDxIfFtpGR6VUwcXeCQ7qCrCzsYWdjTr/X70PazWsraxgpbKCjcoaViorWKusYG2V/6/KOv+1NaxVKqN2VvnLGlZ9GRVsbEvxrL5AYSc7OxsVKlTA5s2b0atXL2l5aGgoEhMT8euvvxptk5WVhaysLOl1cnIyfH19Sz3stJn2ltEdLomInpZKqYKN7peLlbX0uY3KOv8XkDUqqO1gn//xz+cV8l7b2MHF3gkVHV1Q0eGl/H9dYFfKv4jIWEziQ6yO/Bk7zu6XLh83hZezOzo3bIPQtn1Qr3JNk7YRQuB63B2cvvEXLj+4jlsP7+P2o/uIT05AQlqSSfOSStOh6RtQw6tqqe7T1LDz3F+NFR8fD41GA09PT4Plnp6e+PvvvwvdZvbs2ZgxY0aZ16a2toGdfvIu0FWo33WoQMF1KHRdwe7Gkuyj4EqjfRax/yfWYrCu4OGKqNOcYxf7XvXXFXNsM2pGCc670fs2eR9F11xwrclfc7O+dkXsX6HI206Rt7VCoZCWKRTI/1dRYLnucxgu022jUABQQKlQSq//afPPMVBgmdQehseD/jK9YygVSuk1ijiG/rK8eSjGbVQKJZRKJZQKJVRKJVRKleFrhRJKpQpKhQIqpQoqZV57lUIFpVKR/29eW902yvx2hvvO31ahgFKpgpVSZRhkrPL+eraxsjZpngiVT14u7pjUYwQm9RiBxLRkXLhzBdfj7iA2KR6J6cnQaLVQQAEXe0e8ZO+M6h5VUNO7GqpU9DZ7uEmhUMDfswr8PasUuj4zJwuJacl4nJaE9KxMZGRnIj07799/PrKQkZOZP1Sai5zcXORocpGrzUV2bt48puzcXORqcpGjzdVrl4McjQa52lzkajTQaHMtOnfouQ87JTFlyhRMmDBBeq3r2Sltez/+sdT3SURE8uBi74Q2dZqiTf79eJ41W2s1vFzcy+wKt/LkuQ87bm5uUKlUiI2NNVgeGxsLLy+vQrdRq9VQqwsf6yQiIiJ5ee7voGxjY4OAgADs3btXWqbVarF3714EBgZatDYiIiKyvOe+ZwcAJkyYgNDQUDRt2hTNmzfH/PnzkZaWhiFDTHsaLhEREcmXLMLOW2+9hYcPH2Lq1KmIiYlBo0aNsGvXLqNJy0RERPTiee4vPS8NvKkgERHR88fU39/P/ZwdIiIiouIw7BAREZGsMewQERGRrDHsEBERkawx7BAREZGsMewQERGRrDHsEBERkawx7BAREZGsMewQERGRrMnicRFPS3cT6eTkZEuXQkRERCbS/d5+0sMgGHYApKSkAAB8fX0tXQoRERGZKSUlBc7OzkWu57OxAGi1Wty/fx+Ojo5QKBSWLsfikpOT4evrizt37vBZYWWM5/rZ4vl+dniun50X+VwLIZCSkgIfHx8olUXPzGHPDgClUonKlStbuoxyx8nJ6YX7wbEUnutni+f72eG5fnZe1HNdXI+ODicoExERkawx7BAREZGsMeyQEbVajWnTpkGtVlu6FNnjuX62eL6fHZ7rZ4fn+sk4QZmIiIhkjT07REREJGsMO0RERCRrDDtEREQkaww7REREJGsMOy+oxYsXo1q1arC1tUWLFi1w4sQJk7Zbv349FAoFevXqVeY1yoW55zoxMRHh4eHw9vaGWq1GrVq1sGPHjmdW7/PM3HM9f/581K5dG3Z2dvD19cX48eORmZn5zOp9XkVGRqJ79+7w8fGBQqHAL7/88sRt9u/fjyZNmkCtVqNGjRpYtWrVM6lVDsw931u3bkWnTp3g7u4OJycnBAYGYvfu3c+s3vKIYecFtGHDBkyYMAHTpk3D6dOn0bBhQ4SEhCAuLq7Y7W7evIkPP/wQQUFBz6zW55255zo7OxudOnXCzZs3sXnzZly+fBnfffcdKlWq9Mxrf96Ye67Xrl2LyZMnY9q0abh06RJWrFiBDRs24P/+7/+eee3Pm7S0NDRs2BCLFy82qf2NGzfQrVs3vPrqqzh79izGjRuHd95554X/BWwqc893ZGQkOnXqhB07diAqKgqvvvoqunfvjjNnzpR5reWWoBdO8+bNRXh4uPRao9EIHx8fMXv27CK3yc3NFa1atRLLly8XoaGhomfPns+o2uebued66dKlonr16iI7O/sZVikP5p7r8PBw0aFDB4NlEyZMEK1bty7zWuUEgPj555+LbfPRRx+JevXqGSx76623REhISBlXJz+mnO/C1K1bV8yYMaNManoesGfnBZOdnY2oqCgEBwdLy5RKJYKDg3H06NEit5s5cyY8PDwwbNiwZ1Tp868k53rbtm0IDAxEeHg4PD09Ub9+fcyaNQsajeYZVv78Kcm5btWqFaKioqShruvXr2PHjh147bXXnlndL4qjR48afG0AICQkpNj/c6j0aLVapKSkwNXV1dKlWAwfBPqCiY+Ph0ajgaenp8FyT09P/P3334Vuc+jQIaxYsQJnz559RlXKQ0nO9fXr1/HHH39g4MCB2LFjB65du4ZRo0YhJycH06ZNe0aVP39Kcq7ffvttxMfHo02bNhBCIDc3F++99x6HscpATExMoV+b5ORkZGRkwM7OzmK1vQjmzp2L1NRU9OvXz9KlWAx7dqhYKSkpGDRoEL777ju4ublZuhzZ02q18PDwwLfffouAgAC89dZb+Pe//41ly5ZZujTZ2b9/P2bNmoUlS5bg9OnT2Lp1K37//Xd8+umnli6NqNSsXbsWM2bMwMaNG+Hh4WHpciyGPTsvGDc3N6hUKsTGxhosj42NhZeXl1H76Oho3Lx5E927d5eWabVaAICVlRUuX74Mf3//Z1D588fccw0A3t7esLa2hkqlkpa9/PLLiImJQXZ2NmxsbMq87udRSc71J598gkGDBuGdd94BADRo0ABpaWkYMWIE/v3vf0Op5N+CpcXLy6vQr42TkxN7dcrQ+vXr8c4772DTpk1Gw4gvGv40v2BsbGwQEBCAvXv3Ssu0Wi327t2LwMBAo/Z16tTB+fPncfbsWemjR48e0lUVvr6+z/gdPD/MPdcA0Lp1a1y7dk0KlABw5coVeHt7M+gUoyTnOj093SjQ6EImHxlYugIDAw2+NgAQERFR5NeGnt66deswZMgQrFu3Dt26dbN0OZZn6RnS9OytX79eqNVqsWrVKnHx4kUxYsQI4eLiImJiYoQQQgwaNEhMnjy5yO15NZbpzD3Xt2/fFo6OjmL06NHi8uXLYvv27cLDw0N89tlnFnwXzwdzz/W0adOEo6OjWLdunbh+/brYs2eP8Pf3F/369bPgu3g+pKSkiDNnzogzZ84IAOKrr74SZ86cEbdu3RJCCDF58mQxaNAgqf3169dFhQoVxMSJE8WlS5fE4sWLhUqlErt27bLgu3h+mHu+16xZI6ysrMTixYvFgwcPpI/ExEQLvgvLYth5QS1cuFBUqVJF2NjYiObNm4tjx45J69q1aydCQ0OL3JZhxzzmnusjR46IFi1aCLVaLapXry4+//xzkZuba4HKnz/mnOucnBwxffp04e/vL2xtbYWvr68YNWqUePz4sYWqf37s27dPADD60J3f0NBQ0a5dO6NtGjVqJGxsbET16tXFypUrLVT988fc892uXbti27+IFIL9tURERCRjnLNDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RUCrKzs1GjRg0cOXKkVPe7a9cuNGrUyOARIkTlQWRkJLp37w4fHx8oFAr88ssvZm2fmZmJsLAwNGjQAFZWVujVq1ex7Q8fPgwrKys0atTI7FoZdojISFhYGBQKhdHHtWvXLF1aubVs2TL4+fmhVatW0rKifgGEhYU98T92nS5dusDa2hpr1qwp1XqJnlZaWhoaNmyIxYsXl2h7jUYDOzs7jB079okPKk1MTMTgwYPRsWPHEh2LYYeICtWlSxc8ePDA4MPPz8+oXXZ2tkXqK0+EEFi0aBGGDRtWJvsPCwvDggULymTfRCXVtWtXfPbZZ+jdu3eh67OysvDhhx+iUqVKsLe3R4sWLbB//35pvb29PZYuXYrhw4fDy8ur2GO99957ePvtt0v88FiGHSIqlFqthpeXl8GHSqVC+/btMXr0aIwbNw5ubm4ICQkBAFy4cAFdu3aFg4MDPD09MWjQIMTHx0v7S0tLw+DBg+Hg4ABvb2/MmzcP7du3x7hx46Q2hfWEuLi4YNWqVdLrO3fuoF+/fnBxcYGrqyt69uyJmzdvSut1vSZz586Ft7c3KlasiPDwcOTk5EhtsrKyMGnSJPj6+kKtVqNGjRpYsWIFhBCoUaMG5s6da1DD2bNni+3ZioqKQnR0dImeLn3z5s1Ce9Hat28vtenevTtOnTqF6Ohos/dPZCmjR4/G0aNHsX79evz5559488030aVLF1y9etWs/axcuRLXr1/HtGnTSlwLww4RmW316tWwsbHB4cOHsWzZMiQmJqJDhw5o3LgxTp06hV27diE2Nhb9+vWTtpk4cSIOHDiAX3/9FXv27MH+/ftx+vRps46bk5ODkJAQODo64uDBgzh8+DAcHBzQpUsXgx6mffv2ITo6Gvv27cPq1auxatUqg8A0ePBgrFu3DgsWLMClS5fwzTffwMHBAQqFAkOHDsXKlSsNjrty5Uq0bdsWNWrUKLSugwcPolatWnB0dDTr/QCAr6+vQe/ZmTNnULFiRbRt21ZqU6VKFXh6euLgwYNm75/IEm7fvo2VK1di06ZNCAoKgr+/Pz788EO0adPG6OerOFevXsXkyZPx008/wcrKqsT1lHxLIpK17du3w8HBQXrdtWtXbNq0CQBQs2ZNzJkzR1r32WefoXHjxpg1a5a07Pvvv4evry+uXLkCHx8frFixAj/99JM05r569WpUrlzZrJo2bNgArVaL5cuXQ6FQAPlBxMXFBfv370fnzp0BAC+99BIWLVoElUqFOnXqoFu3bti7dy+GDx+OK1euYOPGjYiIiJDmCVSvXl06RlhYGKZOnYoTJ06gefPmyMnJwdq1a416e/TdunULPj4+ha4bMGAAVCqVwbKsrCypF0ilUkld+JmZmejVqxcCAwMxffp0g218fHxw69Yts84XkaWcP38eGo0GtWrVMlielZWFihUrmrQPjUaDt99+GzNmzDDaj7kYdoioUK+++iqWLl0qvba3t5c+DwgIMGh77tw57Nu3zyAc6URHRyMjIwPZ2dlo0aKFtNzV1RW1a9c2q6Zz587h2rVrRj0omZmZBkM89erVMwgY3t7eOH/+PJA/JKVSqdCuXbtCj+Hj44Nu3brh+++/R/PmzfHbb78hKysLb775ZpF1ZWRkwNbWttB1X3/9tdHky0mTJkGj0Ri1HTp0KFJSUhAREQGl0rDj3c7ODunp6UXWQFSepKamQqVSISoqyijsF/b/RGFSUlJw6tQpnDlzBqNHjwYAaLVaCCFgZWWFPXv2oEOHDibti2GHiAplb29f5LCNfvBB/n9s3bt3x5dffmnU1tvb2+SruBQKBYQQBsv059qkpqYiICCg0CuT3N3dpc+tra2N9qu7dNvOzu6JdbzzzjsYNGgQvv76a6xcuRJvvfUWKlSoUGR7Nzc3KUwV5OXlZXQeHR0dkZiYaLDss88+w+7du3HixIlCh8MSEhIM3iNReda4cWNoNBrExcUhKCioRPtwcnIy+rlasmQJ/vjjD2zevLnQCyaKwrBDRE+tSZMm2LJlC6pVq1bouLq/vz+sra1x/PhxVKlSBQDw+PFjXLlyxaCHxd3dHQ8ePJBeX7161aA3o0mTJtiwYQM8PDzg5ORUolobNGgArVaLAwcOFHm562uvvSZdKbJr1y5ERkYWu8/GjRtj6dKlEEJIw2vm2LJlC2bOnImdO3fC39/faL2u56px48Zm75uorKSmphr8IXPjxg2cPXsWrq6uqFWrFgYOHIjBgwdj3rx5aNy4MR4+fIi9e/filVdekYZxL168iOzsbCQkJCAlJQVnz54FADRq1AhKpRL169c3OKaHhwdsbW2Nlj8JJygT0VMLDw9HQkICBgwYgJMnTyI6Ohq7d+/GkCFDoNFo4ODggGHDhmHixIn4448/cOHCBYSFhRkN1XTo0AGLFi3CmTNncOrUKbz33nsGvTQDBw6Em5sbevbsiYMHD+LGjRvYv38/xo4di7t375pUa7Vq1RAaGoqhQ4fil19+kfaxceNGqY1KpUJYWBimTJmCmjVrPvFy11dffRWpqan466+/zD53Fy5cwODBgzFp0iTUq1cPMTExiImJQUJCgtTm2LFjUKvVJb7slqgsnDp1Co0bN5ZC+IQJE9C4cWNMnToVyJ9PN3jwYHzwwQeoXbs2evXqhZMnT0p/8CD/D4vGjRvjt99+w/79+w32V5oYdojoqfn4+ODw4cPQaDTo3LkzGjRogHHjxsHFxUUKNP/5z38QFBSE7t27Izg4GG3atDGa+zNv3jz4+voiKCgIb7/9Nj788EOD4aMKFSogMjISVapUQZ8+ffDyyy9j2LBhyMzMNKunZ+nSpXjjjTcwatQo1KlTB8OHD0daWppBm2HDhiE7OxtDhgx54v4qVqyI3r17l+jGf6dOnUJ6ejo+++wzeHt7Sx99+vSR2qxbtw4DBw4sdiiN6Flr3749hBBGH7orH62trTFjxgzcuHED2dnZuH//PrZu3YoGDRpI+7h582ah+yjK9OnTpd4fcyhEcXslIipD7du3R6NGjTB//nxLl2Lk4MGD6NixI+7cuQNPT88ntv/zzz/RqVMnREdHmzwB0xTx8fGoXbs2Tp06ZdYcBSL6B3t2iIj0ZGVl4e7du5g+fTrefPNNk4IOALzyyiv48ssvcePGjVKt5+bNm1iyZAmDDtFT4ARlIiI969atw7Bhw9CoUSP88MMPZm0bFhZW6vU0bdoUTZs2LfX9Er1IOIxFREREssZhLCIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikrX/B5f54VRg2m01AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "field = batch_data[\"sim_1\"].monitor_data[\"monitor_time_0\"].Hz.squeeze().real\n",
    "\n",
    "fmesh = np.fft.fftfreq(field.size, np.mean(np.diff(field.t)))\n",
    "spectrum = np.fft.fft(field)\n",
    "\n",
    "mask = (fmesh > freq_range[0]) & (fmesh < freq_range[1])\n",
    "\n",
    "plt.plot(\n",
    "    fmesh[mask],\n",
    "    np.abs(spectrum)[mask],\n",
    ")\n",
    "plt.title(\"Spectrum at single wavevector\")\n",
    "plt.xlabel(\"Frequency (Hz)\")\n",
    "plt.ylabel(\"Amplitude\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "663c96ba-8b16-4056-9983-bef38e7a5c9a",
   "metadata": {},
   "source": [
    "We use the ResonanceFinder plugin to find the band frequencies.\n",
    "\n",
    "We first construct a `ResonanceFinder` object storing our parameters, and then call `run()` on our list of `FieldTimeData` objects. This will add up the signals from all of the monitors before searching for resonances. The `ResonanceFinder` class has additional methods in case the signal takes another form; see the api reference [here](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.resonance.ResonanceFinder.html).\n",
    "\n",
    "The `run()` method returns an `xr.Dataset` containing the decay rate, Q factor, amplitude, phase, and estimation error for each resonance as a function of frequency.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7496ef10-068e-41ce-8907-418bbcd60e26",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:08:29.693707Z",
     "iopub.status.busy": "2023-08-18T17:08:29.693525Z",
     "iopub.status.idle": "2023-08-18T17:08:31.106128Z",
     "shell.execute_reply": "2023-08-18T17:08:31.105255Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>decay</th>\n",
       "      <th>Q</th>\n",
       "      <th>amplitude</th>\n",
       "      <th>phase</th>\n",
       "      <th>error</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2.555730e+13</th>\n",
       "      <td>3.195865e+07</td>\n",
       "      <td>2.512328e+06</td>\n",
       "      <td>0.001549</td>\n",
       "      <td>1.265575</td>\n",
       "      <td>0.000020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.847409e+13</th>\n",
       "      <td>7.187783e+11</td>\n",
       "      <td>4.304046e+02</td>\n",
       "      <td>0.009517</td>\n",
       "      <td>1.392777</td>\n",
       "      <td>0.000020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.067335e+14</th>\n",
       "      <td>1.085117e+11</td>\n",
       "      <td>3.090112e+03</td>\n",
       "      <td>0.027057</td>\n",
       "      <td>2.836346</td>\n",
       "      <td>0.000005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.139619e+14</th>\n",
       "      <td>1.869789e+12</td>\n",
       "      <td>1.914772e+02</td>\n",
       "      <td>0.036011</td>\n",
       "      <td>1.033897</td>\n",
       "      <td>0.000023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.198011e+14</th>\n",
       "      <td>9.581391e+11</td>\n",
       "      <td>3.928098e+02</td>\n",
       "      <td>0.021284</td>\n",
       "      <td>-2.684537</td>\n",
       "      <td>0.000017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.381860e+14</th>\n",
       "      <td>7.934120e+11</td>\n",
       "      <td>5.471610e+02</td>\n",
       "      <td>0.022406</td>\n",
       "      <td>-1.235989</td>\n",
       "      <td>0.000046</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     decay             Q  amplitude     phase     error\n",
       "freq                                                                   \n",
       "2.555730e+13  3.195865e+07  2.512328e+06   0.001549  1.265575  0.000020\n",
       "9.847409e+13  7.187783e+11  4.304046e+02   0.009517  1.392777  0.000020\n",
       "1.067335e+14  1.085117e+11  3.090112e+03   0.027057  2.836346  0.000005\n",
       "1.139619e+14  1.869789e+12  1.914772e+02   0.036011  1.033897  0.000023\n",
       "1.198011e+14  9.581391e+11  3.928098e+02   0.021284 -2.684537  0.000017\n",
       "1.381860e+14  7.934120e+11  5.471610e+02   0.022406 -1.235989  0.000046"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resonance_finder = ResonanceFinder(freq_window=tuple(freq_range))\n",
    "resonance_data = resonance_finder.run(signals=batch_data[\"sim_1\"].data)\n",
    "resonance_data.to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46db3d3c",
   "metadata": {},
   "source": [
    "We see the four resonances from the previous figure. All four have reasonable Q factors, amplitudes, and errors, so they are likely to represent physical resonances. Note that in order to accurately obtain the Q factor for high-Q modes, it may be necessary to run the simulation for a longer time.\n",
    "\n",
    "Now we are ready to compute the band structure. We run the resonance finder at every Bloch wavevector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4afe673e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:08:31.116954Z",
     "iopub.status.busy": "2023-08-18T17:08:31.116520Z",
     "iopub.status.idle": "2023-08-18T17:08:50.203875Z",
     "shell.execute_reply": "2023-08-18T17:08:50.202960Z"
    }
   },
   "outputs": [],
   "source": [
    "resonance_finder = ResonanceFinder(freq_window=tuple(freq_range))\n",
    "resonance_datas = []\n",
    "for i in range(3 * Nk):\n",
    "    sim_data = batch_data[f\"sim_{i}\"]\n",
    "    resonance_datas.append(resonance_finder.run(signals=sim_data.data))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f48f416-be79-4c85-9dd3-36a7101dfe70",
   "metadata": {},
   "source": [
    "We define a function to filter resonances based on their Q, amplitude, and error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "225f04aa-2390-456e-bcb0-395320ae4015",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:08:50.373773Z",
     "iopub.status.busy": "2023-08-18T17:08:50.373575Z",
     "iopub.status.idle": "2023-08-18T17:08:50.405541Z",
     "shell.execute_reply": "2023-08-18T17:08:50.404883Z"
    }
   },
   "outputs": [],
   "source": [
    "def filter_resonances(resonance_data, minQ, minamp, maxerr):\n",
    "    resonance_data = resonance_data.where(abs(resonance_data.Q) > minQ, drop=True)\n",
    "    resonance_data = resonance_data.where(resonance_data.amplitude > minamp, drop=True)\n",
    "    resonance_data = resonance_data.where(resonance_data.error < maxerr, drop=True)\n",
    "    return resonance_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9398f9cc-e31c-4fc8-a8af-4fb89cee9034",
   "metadata": {},
   "source": [
    "We plot the band structure with the light line overlaid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fc001a93",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:08:50.408852Z",
     "iopub.status.busy": "2023-08-18T17:08:50.408375Z",
     "iopub.status.idle": "2023-08-18T17:08:50.970373Z",
     "shell.execute_reply": "2023-08-18T17:08:50.969788Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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s3y+uTL32WnFXezUym4GQkPoecnuuuQYoKVHO6057Xr9l7SGqqanBnj17kJiYWN8gLy8kJiYiLy+v2ce99NJL6NmzJ+bMmdPka/n5+SguLra5ZlBQEOLj45u9ZnV1NUwmk81NaRiiKR8GuRKRM6QYD6i8lyg3t+ViCFenE+TmdlSL3EvWgujcuXMwm80ICQmxOR4SEoLi4mK7j/nhhx/wwQcfYM2aNXa/Lj3OmWump6cjKCjIeouMjGzjT+Q+DNGUB4NciagtPGHYzNFChwWRDC5duoS//OUvWLNmDYJd+OqUlpYGo9FovZ06dcpl13YVhmjKQwlBrlpY3UHkaRjjoT6ybswYHBwMb29vlJSU2BwvKSlBqJ1kvOPHj6OgoAB333239Zjl6l9ap06dcOTIEevjSkpKENagOigpKUFsbKzdduj1euj1epf9XO7AEE15yD2ZOjNTzF1rOFwaESFuG8BIDCLl8vMTV6TW1IjL79W4a/XYscCSJY6d5wlk7SHy9fXF8OHDkZ2dbT1msViQnZ2NUaNGNTl/0KBB+PXXX7F//37r7Z577kFCQgL279+PyMhIREdHIzQ01OaaJpMJO3futHtNtWCIZseTO8iVqwqJ1E3tw2Zjx7a+79o117AgcpnU1FSsWbMG69evx6FDhzB37lxUVlYiJSUFADBz5kykpaUBAPz8/DB48GCbW9euXREYGIjBgwfD19cXOp0OCxYswJIlS7Blyxb8+uuvmDlzJsLDw5vsV6Q2DNHsWHIGuZrNYs+Qvd5AQRBvCxZw+IxIyaSCSK0bNHp7A++/3/I577/vOW/EZc8ymzp1KsrKyrBo0SIUFxcjNjYWWVlZ1knRhYWF8PJyrm575plnUFlZiUceeQTl5eW45ZZbkJWVBT8/Pzf9FB2HIZodQ+4g19ZWFQL1qwo95d0ZkaeRVppdvgzU1gI+PnK3yHnJycAXXwD/8R9i77TEE4fuZd+HSImUuA8RdawLF8Q9nfR6YPDgjv/+n3wCPPhg6+d9/DHwwAMd0SIiaovDh4HKSnEfOTXH/pjN6ngj3p7Xb9l7iIjk1Nx/cmm4TK4nsLIy155HRPIwGMSCyGSy/3yilkLD29vze6NZEJFmNbeCa/lyICZG/FyugqhHD9eeR0TyMBjEYsfexGquIlUW2SdVE8mhpRVc06YB330nb5Br44nz7T2PiOQRECCuVK2rE1euSriKVHk4h8gOziFyDaV2BbeWCweI+T2//Sbf/kM1NeJmkC2tIvP2Fp9g5SraiMgxx44BRqPY+xMSwmxKd1Jtlhl5rsxM8T98QgIwY4Z4HxWljHc9jqzgKikBfv21o1rU1E8/tb6k3mwWzyMiZZNel41G8Z7ZlMrEgkiFlB7loPSuYEfz3pqJvusQzK4j8hyNYzz4/1uZWBC1YPt2ZRYbSu15gQMbCgLybyiohlw4NbSRiBwjxXgIglgU8f+3MrEgasFddymr2FB6zwtU0hUs5cI1jkBpKDJS3ly41tqo08nfxsaU3nNJJKeGMR5q/P+tBSyIWqGUYkMNPS9QyVBPS7lwErlz4dSWXaf0nksiuTUsiNT2/1srWBC1QinFhhp6XqCioZ7mcuF69gQ+/VQZe4CoJbtODT2XRHJrHOOhlv/fWsKNGR3QsNiQa6dONfS8oMFQT1GR/d4saTmpUrqCG7dRp1PWuzKlZ9e11nOp04lvJiZNUk6bieTQqZO4lUZVVf2u1Ur//601LIicIGexoZaeF6kreMoU8cWw4QulkrqCpV6Nxi/kJSXicSW9Q1PylvnO9Fwq9Wcg6igGg21BBIX//9YaDpk5Qc5iQ02T8JTeFdxSr4ZE7iFStVBLzyWREkjziC5dkrslZA8LIgcoodhQ2yS85GSgoADIyQEyMsT7/Hz5iyGoaD6WGqil55JICbp0EWM8amvFuUSkLCyIWqGkYkPpPS+NSV3B06eL93L//iTs1XAdNfVcEslNp6ufXG0v7JXkxYKoFUorNpTc86IWPXu69jwtU1vPJZHcGi6/J2XhpOoWfPklMHGi8p7MOQmPlETquZw/33YoMiJCLIZYrBPVaxzj4cVuCcVgQdQCLn/0TKWlrj2PuHyYyFF+foCPjziPqKKivkAi+bEgIs3hRGD3YM8lkWMMBuD8eXHYjAWRcrCzjjRn9OimE9Mb4kRgInInziNSJhZE5DZKDfv09gZeesn+1zgRmIjcTSqIpBgPUgYWROQWSg/7/NOfgGXLmg6LKW1VoZootQAmUhopxgPcpFFROIeIXK65WAwp7FPugqOyUnxnlpgoroz66SdOBG6vzEz7q8zefJPFJZE9DWM8uneXuzUEADpBaCnAQJtMJhOCgoJgNBph4Iw3p5jNYk9QcztBS+Gu+fnyFR6FhUBZmfgkFB0tTxs8SXMFsDT8KHcBTKREly4Bf/whrjgbOlTu1niO9rx+c8iMXErpsRgWC3DhgvhxcLA8bfAkraXdg7lwRHYxxkN5WBCRSyk9FuPiRfHF2de3fgt9ajulF8BESqXTiUURuNpMMVgQtWD7dmW+s1Xy5FWl7/Fz/rx4z94h11B6AUykZFx+rywsiFpw113KWhkFFazeUnLYZ3V1/YqOa67p+O/viZReABMpWeMYD5IXC6JWSCujlFBwSJNXGw9RKKmNSg77lHqHDAZxyIzaT8kFMJHS+fuLk6otFnH1K8mLBVErlDIxVE2TV6Wwz8a7Qcu5x48gAOfOiR9zuMx1lFwAE6mB1EtkNMrdEmJB5AAlTAxV2+TV5GSgoADIyQEyMsT7/Hz5ll+bTOJqjk6dgK5d5WmDp1JiAUykFlJBxA0a5ceNGZ0g58RQNU5eVVLYpzRc1r1788M71HZqSbs3m5XfRtIWabVrVRVQVye+aSN5KKKHaOXKlYiKioKfnx/i4+Oxa9euZs/NzMxEXFwcunbtioCAAMTGxuKjjz6yOWfWrFnQ6XQ2t4kTJ7a7nXJODOXk1barqwPKy8WPOVzmPlIBPH26eK+0QkPpCxJIm3x86mM8uNpMXrIXRJs2bUJqaioWL16MvXv3YtiwYUhKSkJpaand87t3747nnnsOeXl5OHDgAFJSUpCSkoJt27bZnDdx4kScPXvWetuwYUOb26iEiaGcvNp258+LQ4qdO4uTGEl71LAggbRL6iViQSQv2QuiN954Aw8//DBSUlJw3XXXYfXq1ejcuTM+/PBDu+ePHTsW9957L6699lr069cP8+fPx9ChQ/HDDz/YnKfX6xEaGmq9devWrc1tFAT5J4Zy8mrbce8hbVPTggTSJu5HpAyyFkQ1NTXYs2cPEhMT6xvk5YXExETk5eW1+nhBEJCdnY0jR47g1ltvtflabm4uevbsiYEDB2Lu3Lk4L70q2lFdXQ2TyWRzUyJOXnWeFOTq5cUARa1S24IE0h7GeCiDrNO3zp07B7PZjJCQEJvjISEhOHz4cLOPMxqN6NWrF6qrq+Ht7Y13330Xt912m/XrEydORHJyMqKjo3H8+HE8++yzuP3225GXlwdvO10o6enpePHFF5v9fjqd+A5y0iT5e2DUMnlVKaQ6uGtX/o60So0LEkhbvLzEoshkElebcWhfHqqczx4YGIj9+/ejoqIC2dnZSE1NRd++fTH26pKmadOmWc8dMmQIhg4din79+iE3Nxfjx49vcr20tDSkpqZaPzeZTIiMjLR+3vAdpBJWTSlp9ZaSMciVwAUJpBIGg1gQmUxAz55yt0abZC2IgoOD4e3tjZKSEpvjJSUlCA0NbfZxXl5eiImJAQDExsbi0KFDSE9PtxZEjfXt2xfBwcE4duyY3YJIr9dDr9e32l6+g1QXKchVr2eQq5ZJCxKKiuzPI9LpxK9zQQLJqeF+RILA7UHkIOscIl9fXwwfPhzZ2dnWYxaLBdnZ2Rg1apTD17FYLKiurm7266dPn8b58+cR1s63gHwHqS7ScBlzy7SNCxK0S8lB2I01jPGoqJC7Ndok+yqz1NRUrFmzBuvXr8ehQ4cwd+5cVFZWIiUlBQAwc+ZMpKWlWc9PT0/HN998gxMnTuDQoUN4/fXX8dFHH+HBBx8EAFRUVODpp5/Gjh07UFBQgOzsbEyaNAkxMTFISkpqUxu5pF19GORKDXFBgvaocd8pJS+/V1Nx2VayzyGaOnUqysrKsGjRIhQXFyM2NhZZWVnWidaFhYXw8qqv2yorK/HYY4/h9OnT8Pf3x6BBg/Dxxx9j6tSpAABvb28cOHAA69evR3l5OcLDwzFhwgS8/PLLDg2LNcZ3kOok5ZYxyJUkXJCgHdK+U42HSKV9p5RaBBsM4rxHk6lp8S6nzExx64qGqzUjIsSeVyX+HttKJwj2RtW1zWQyISgoCIARkZEGrFjhWf/onk4QgF9/FZew9u0LtGMLKvIwjO7wfGaz2BPU3FYL0pyx/Hzl/dvX1gIHDogfDxumjBiP5opLqbNAacWl9PptNBphkCZmOUj2ITMl+/JLeQNJqW0Y5Er2qHEIhZyn5n2nfHzql9wrIexVa5uasiBqAd89qhODXKkxRndoh9r3nVLSrtVqLi7bggUReRQGuVJjWnuXq3Vq33dKSQWR2otLZ7EgIo/CIFdqTGvvcrVO7UHYXbqIbaypAa5ckbctai8uncWCiDwKg1ypMa29y9U6te875eWlnOX3ai8uncWCiDwGg1zJHq29yyX17zullGEztReXzmJBRB5D2nuIQa7UkNbe5ZIoORkoKABycoCMDPFeLauGpR4iKcZDTmovLp2hgF0OyFncS6Upi0XMLgOHy6gR6V3ulCli8dPwBcYT3+VSPbUGYXfuLG4bUlcnxnjIncWolU1N2UOkMtxLxT4GuVJLtPQulzyDUobNJFJxOX26eO9pxRDYQ6Quat2OviMwyJVao5V3ueQZpBgPJWzQqBUsiFSitb1UdDpxL5VJk7T3BM8gV3KUWodQSHukHqLKSnHoTO4YDy1M1eCQmUpwL5XmMciViDyNkmI8tDJVgwWRSnAvFfsEgXsPEZFnUsI8Ii3F3rAgUgnupWIfg1zJ05jNQG4usGGDeM9IEe2SuyDSWuwNCyKV4F4q9jHIlTyJVoYmqF5LBbDcMR5am6rBgkgltLZjqCMY5EqeREtDEyRqrQD28hKLIsjUS6S1qRosiFSEe6nYYpAreQqtDU2Q4wWwnMNmWpuqoRMEuTcGVx6TyYSgoCAYjUYYpL9GBdHC8kdH/P67mF3WuzfQo4fcrSFqu9xcsXegNTk53DbAE5jNYk9Qc8NROp34Rjc/X9xW5NAhsbcoNrZjpwZI7Swqsl+sN2ynUl6D2vP6zX2IVIh7qTDIlTyL1oYmtM6ZuTljx9bHeFRW1g+hdQStxd5wyIxUSZpMzSBX8gRaG5rQOmcLYDmHzbQ0VYM9RKQ6Fou4pT04mZo8xOjR4i7rUqFvzzXXaG8VqadytgCWYjxMJiA83K1Ns0srsTcsiEh1GORKRGombaPS2twcqQCWnucqK8XnPjkKES1M1eCQGakOg1zJ02zf3nLvEK7+3XvKfi9a5+w2Kr6+gJ+f+LGcu1Z7OhZEpCoMciVPxEnV2uPs3By5d63WAg6ZkaowyJU8ESdVu4fStyhxZm6OwQCUlrIgcicWRKQaDHIlT+XsnBJqXWamuNllw+XtERHiUJWSVkY5OjcnMLA+xqO6WpxDSa7FITNSDQa5kqdiNI9reWIMitwxHlrAgohUQxouY5AreSK17ffSUiipnDw5BoXziNyLQ2akCnV1gNEofszhMvJUatnvRcnDUc7uAq0mBoPYy3Xpkvhz8I2ha7EgIlVgkCtphdL3e5GGoxr3wEjDUXL3Znnyij1/f/liPLSAQ2akCtJwGXuHiOSjhuEoT16xp9PVb9LIYTPXY0FEildZCVy5wiBXIrk5MxwlF2nFXnPDSTodEBmp3hV7nEfkPoooiFauXImoqCj4+fkhPj4eu3btavbczMxMxMXFoWvXrggICEBsbCw++ugjm3MEQcCiRYsQFhYGf39/JCYm4ujRox3wk5A7SL1DDHIlkpcahqM8fcWeVBBJMR7kOrIXRJs2bUJqaioWL16MvXv3YtiwYUhKSkJpaand87t3747nnnsOeXl5OHDgAFJSUpCSkoJt27ZZz1m2bBneeustrF69Gjt37kRAQACSkpJw5cqVDvzJyBUsFjG7DBwuI5KdWoaj1LZizxkNYzykXfvJNXSCYG80uOPEx8djxIgReOeddwAAFosFkZGRmDdvHhYuXOjQNW688UbceeedePnllyEIAsLDw/Hkk0/iqaeeAgAYjUaEhIRg3bp1mDZtWqvXM5lMCAoKgtFohEEqx0kW588DBQXiJmSDB8vdGiJtM5uBqKjWN5DMz1dGD4zSd6puq1OnxF2re/QAeveWuzXK0p7Xb1l7iGpqarBnzx4kJibWN8jLC4mJicjLy2v18YIgIDs7G0eOHMGtt94KAMjPz0dxcbHNNYOCghAfH9/sNaurq2EymWxu1H6u2KdEGi5jbhmR/NQ2HCWt2Js+XbxXSrsaasvzJOcRuYesBdG5c+dgNpsREhJiczwkJATFxcXNPs5oNKJLly7w9fXFnXfeibfffhu33XYbAFgf58w109PTERQUZL1FRka64KfTtsxM8Z1kQgIwY4Z4HxXl3A6x1dVARYX4MQsiImXw5OGojtbW50kpxqO6WryRa8g+h6gtAgMDsX//fvz888945ZVXkJqaitzc3DZfLy0tDUaj0Xo7deqUS9urNa7aNp9BrkTKlJwsDmXn5AAZGeJ9fj6LIWe053nSywsICBA/Zi+R68i6MWNwcDC8vb1RUlJic7ykpAShoaHNPs7LywsxMTEAgNjYWBw6dAjp6ekYO3as9XElJSUIazCzr6SkBLGxsXavp9froWdSnku0tk+JTifuUzJpUsvd1wxyJVI2pW8gqWSueJ40GMQedJNJnEtE7SdrD5Gvry+GDx+O7Oxs6zGLxYLs7GyMGjXK4etYLBZUX+03jI6ORmhoqM01TSYTdu7c6dQ1qW1ctU8Jg1yJyFO54nlSmkckxXhQ+8ke3ZGamoqHHnoIcXFxGDlyJFasWIHKykqkpKQAAGbOnIlevXohPT0duDrfJy4uDv369UN1dTW++uorfPTRR1i1ahUAQKfTYcGCBViyZAn69++P6OhoPP/88wgPD8fkyZNl/Vm1wFX7lDDIlYg8lSueJzt3FnuPzGagqqp+CI3aTvaCaOrUqSgrK8OiRYtQXFyM2NhYZGVlWSdFFxYWwsurviOrsrISjz32GE6fPg1/f38MGjQIH3/8MaZOnWo955lnnkFlZSUeeeQRlJeX45ZbbkFWVhb8pM0byG1csU8Jg1yJyJO54nlSpxN7iS5eFHvUWRC1n+z7ECkR9yFqO1fsU1JSInYnd+4MXHut25tMRNShXLWf07lzwMmTYsjrwIFubbJqqHYfIvI8rtinhEGuROTJXLWfE2M8XIsFEblce/YpYZArEWmBK/ZzkmI8BIExHq7AITM7OGTmGm3ZNv/kSbGHqHt3IDq6o1pKRCSP9saLFBYCZWWM8ZC05/Vb9knV5Lmc3aeEQa5EpDXt3c/JYBALIm7Q2H4cMiPFuHhRfLek14tb0xMRUcsY4+E6LIhIMRjkSkTkHG/v+iX3nEfUPiyISBEY5EpE1DbSVBkOm7UPCyJSBAa5EhG1TcOCiMuk2o4FEcmOQa5ERG3XOMaD2oarzEh2DHIlIndo75J2tWCMh2uwh4hkxyBXInK1zEwxHiMhAZgxQ7yPihKPeyJpZS7nEbUdCyKSFYNcicjVMjOBKVPETMSGiorE455YFDHGo/1YEJGszp8X5xAFBAD+/nK3hojUzmwG5s+3P7lYOrZggecVDXq9eGOMR9uxICJZce8hInKl7dub9gw1JAjAqVPieZ5G6iViQdQ2Tk2qtlgs+P7777F9+3acPHkSVVVV6NGjB2644QYkJiYiMjLSfS0lj8MgVyJytbNnXXuemjDGo30c6iG6fPkylixZgsjISNxxxx3YunUrysvL4e3tjWPHjmHx4sWIjo7GHXfcgR07dri/1eQRpN6hbt08c+UHEXW8sDDXnqcmUozHlStATY3crVEfh3qIBgwYgFGjRmHNmjW47bbb4OPj0+SckydPIiMjA9OmTcNzzz2Hhx9+2B3tJQ/RMMiVw2VE5CqjRwMREeIEanvziHQ68eujR8vROveSYjwqKsReIi5UcY5OEFrf1/LQoUO49tprHbpgbW0tCgsL0a9fP1e0TxYmkwlBQUEwGo0wSIOy5FLnzwMFBeIkwMGD5W4NEXkSaZUZYFsUSdt6fP45kJwsT9vc7exZ4MwZsee9b1+5W9Px2vP67dCQmaPFEAD4+PiouhiijsHJ1ETqZjYDubnAhg3ivZJWbSUni0VPr162xyMiPLsYQoP9iC5dYoyHs9q8U3VVVRUKCwtR02igcujQoa5oF3kwBrkSqVtmpri0veFqrogI4M03lVNsJCcDkyZpY6fqhgICxJ+xrk6M8eCu1Y5zuiAqKytDSkoKtm7davfrZiW9TSBFknqHgoIY5EqkNtJwVOPeB2nTQyX1wHh7A2PHyt2KjqXTib1E5eWM8XCW0/sQLViwAOXl5di5cyf8/f2RlZWF9evXo3///tiyZYt7Wkkeo2GQqxJ6h5Tc7U/apOS/Sa1ueqg23I+obZzuIfruu+/wP//zP4iLi4OXlxf69OmD2267DQaDAenp6bjzzjvd01LyCEoKclVDtz9pi9L/Jp3Z9FBrPTNKIhVEFRVicerpw4Su4nQPUWVlJXr27AkA6NatG8rKygAAQ4YMwd69e13fQvIoDSdTyxnkqsWsI1I2NfxNannTQzVpGOMhzdek1jldEA0cOBBHjhwBAAwbNgzvvfceioqKsHr1aoR54k5X5DINg1zlHC5jtz8pjVr+JrW86aHaSL1E3LXacU4XRPPnz8fZq+X/4sWLsXXrVvTu3RtvvfUW/vnPf7qjjeQhlBLkquWsI1ImtfxNSpseNte7q9MBkZGeuemh2rAgcp7Tc4gefPBB68fDhw/HyZMncfjwYfTu3RvB3BaTWiANl8n9Z8Juf1IatfxNenuL85mmTBGLH3ubHq5YwTkrSiDtRyTFeHBFb+sc7iEaPXo0li9fjj/++MPmeOfOnXHjjTeyGKIWNQxy7dZN3raw25+URk1/k1re9FBNpBgPsJfIYQ4XRA8//DDy8vIwfPhwXHvttfjHP/6BH3/8EQ4kfxApKsiV3f6kNGr7m0xOFqN3cnKAjAzxPj+fxZDScNjMOQ4XRDNnzsQXX3yBc+fO4fXXX0d5eTnuv/9+hIaGYvbs2di8eTMuX77s3taSKiktyFXq9geavgCx25/koMa/SWnTw+nTxXsltY1E3I/IOU5Pqtbr9bjjjjvw3nvv4cyZM9iyZQvCwsLw/PPP45prrsFdd92FH3/80T2tJVW6eFFcHaPX149ry43d/qQ0/JskV2sc40Etcyjt3lHHjx/Hli1bEBkZiSlS1LAKMe3etY4cEffC6NULCA2VuzW2zGbtZR2RstXUAO++Cxw/DvTrBzz2GCfEUtsdPy7GeCjx+dcd3J5239DPP/+MnTt3Njm+c+dOXLx4EX//+9+dLoZWrlyJqKgo+Pn5IT4+Hrt27Wr23DVr1mD06NHo1q0bunXrhsTExCbnz5o1CzqdzuY2ceJEp9pErqH0IFd2+2uLkmMxcHVzxn79gL//HXjnHfG+Xz9lbMpI6sR5RI5zuiB6/PHHcerUqSbHi4qK8PjjjzvdgE2bNiE1NRWLFy/G3r17MWzYMCQlJaG0tNTu+bm5uZg+fTpycnKQl5eHyMhITJgwAUVFRTbnTZw4EWfPnrXeNmzY4HTbqP0aBrn6+MjdGtKyzEwgKgpISABmzBDvo6KUU2yoYadqUp+GMR4Wi9ytUTanh8y6dOmCAwcOoG/fvjbH8/PzMXToUFxycvZWfHw8RowYgXfeeQcAYLFYEBkZiXnz5mHhwoWtPt5sNqNbt2545513MHPmTOBqD1F5eTk2b97sVFskHDJzDUEAfv1VzC7r21f+5fakXc0ltEsTluWeo2M2i8VZc5sz6nTiXKL8fPZikvN+/VUcio2JEd+cerIOHTLT6/UoKSlpcvzs2bPo1Mm5fR5ramqwZ88eJCYm1jfIywuJiYnIy8tz6BpVVVWora1F9+7dbY7n5uaiZ8+eGDhwIObOnYvzUsS6HdXV1TCZTDY3aj8lBbmSdqkhFkMtO1WTOnHYzDFOF0QTJkxAWloajFIoFYDy8nI8++yzuO2225y61rlz52A2mxESEmJzPCQkBMXFxQ5d4x//+AfCw8NtiqqJEyfi3//+N7Kzs/Hqq6/i+++/x+233w5zM8946enpCAoKst4iIyOd+jnIPqUEuZK2qaHYUMtO1aROLIgc43R0x/Lly3HrrbeiT58+uOGGGwAA+/fvR0hICD766CN3tLFZS5cuxcaNG5Gbmws/Pz/r8WnTplk/HjJkCIYOHYp+/fohNzcX48ePb3KdtLQ0pKamWj83mUwsitpJKUGuRGooNtS0UzWpD2M8HON0QdSrVy8cOHAAn3zyCX755Rf4+/sjJSUF06dPh4+Ts2aDg4Ph7e3dZAiupKQEoa2sD1y+fDmWLl2Kb7/9FkOHDm3x3L59+yI4OBjHjh2zWxDp9Xro9Xqn2k4tU0qQK5Eaig1pp+qiIvtDe9IcIqXsVE3q0qmT+FxcWSlu0sg3qfY5XRABQEBAAB555JF2f3NfX18MHz4c2dnZmDx5MnB1UnV2djaeeOKJZh+3bNkyvPLKK9i2bRvi4uJa/T6nT5/G+fPnEca3Vx2m4XAZaYNS93RSQ7HB0FRyN4NBLIhMJj4vN8ehOUQ7duxw+IJVVVU4ePCgw+enpqZizZo1WL9+PQ4dOoS5c+eisrISKSkpwNXIkLS0NOv5r776Kp5//nl8+OGHiIqKQnFxMYqLi1FxdbObiooKPP3009ixYwcKCgqQnZ2NSZMmISYmBklJSQ63i9quYZBro7nu5KGUvKRdLbEY3Kma3InziBwgOCAmJkaYMGGC8OmnnwoVFRV2zzl48KCQlpYmhIaGCuvXr3fkslZvv/220Lt3b8HX11cYOXKksGPHDuvXxowZIzz00EPWz/v06SMAaHJbvHixIAiCUFVVJUyYMEHo0aOH4OPjI/Tp00d4+OGHheLiYofbYzQaBQCC0Wh06ucgUUGBIOzeLQj5+XK3hDrCF18Igk4nCGK/Rv1NpxNvX3whdwtFX3whCBERtm2MjFRO+yR1dYKQkyMIGRnifV2d3C0iT2CxCMLeveJzc2Wl3K1xn/a8fju0D1FtbS1WrVqFlStX4sSJExgwYADCw8Ph5+eHixcv4vDhw6ioqMC9996LZ599FkOGDOmYas5NuA9R21kswIED4vDJgAHKyS5TM6UORUGF++co+XdJ5G7HjomLXTw5xqM9r99Ob8y4e/du/PDDDzh58iQuX76M4OBg3HDDDUhISGiyF5BasSBqu/PngYICMch18GC5W6N+mZniHjoNC46ICHEISAlDKLm54vBYa3JyxGgUIpJPaam4xURgoPiG1RO15/Xb6UnVcXFxDk1kJm2SJlMHB8vdEvVrbndlKcpBCfNK1LCknYhEjWM8vJzeidCz8ddBLnPlirKDXNVEDbsrQyVL2olI5Ocn7kEkCPXP1VSPBRG5jJSOwiDX9lPD7sposKS9uZ3IdTogMpL75xApBVebNY8FEbmEINQXROwdaj+1DEWpZUk7EYlYEDWPBRG5BINcXUtNQ1HcP4dIPaSVv5cvi8/ZVM/pSdUnTpxA37593dMaUi0GubqWGnZXbig5GZg0iUvaiZSuUyegc2egqoq7VjfmdA9RTEwMEhIS8PHHH+PKlSvuaRWpCoNcXU+NQ1He3uLS+unTxXsltY20yWwWt4bYsEG8l3sRglJw2Mw+pwuivXv3YujQoUhNTUVoaCgeffRR7Nq1yz2tI1VgkKt7cCjK9fgCqR1KjpORGwsi+5zemFFSV1eHLVu2YN26dcjKysKAAQMwe/Zs/OUvf0GPHj1c39IOxI0ZnXPwoLjkvk8f7j/kDtxd2TWUvskluU5ze3hJvataf0MhCMD+/eJeRNdeKw6heYoO3am6serqarz77rtIS0tDTU0NfH198ec//xmvvvqqatPlWRA5rrISOHxY3OBr6FC+UJMy8QVSO9QWJyMXT43xaM/rd5tXme3evRuPPfYYwsLC8MYbb+Cpp57C8ePH8c033+DMmTOYNGlSWy9NKiJNpu7WTdtPLqRcatnkklxDLXt4yU2qFS5dkrslyuH0KrM33ngDa9euxZEjR3DHHXfg3//+N+644w54Xd0DPDo6GuvWrUNUVJQ72ksKYrEAFy+KH3MyNSmVMy+QzFtTP7Xs4SW3hgURYzxEThdEq1atwuzZszFr1qxmh8R69uyJDz74wBXtIwW7eFF8V63XM9WelIsvkNqipj285CTFeNTUiDEenB3ShoLo6NGjrZ7j6+uLhx56qK1tIpVgkCupAV8gtUVte3jJKTBQXCVsMrEgQlvmEK1duxafffZZk+OfffYZ1q9f76p2kcIxyJXUgnlr2qLGPbzkwuX3tpwuiNLT0xFsp0ugZ8+e+Oc//+mqdpHCMciV1IIvkNrDPbwcIxVEjPEQOV0QFRYWIjo6usnxPn36oLCw0FXtIgVjkCupDV8gtSc5GSgoAHJygIwM8T4/n//WDUkxHmAvEdCWOUQ9e/bEgQMHmqwi++WXX3ANXx01gUGupEbMW9MeKU6GmmcwiLlmly7xDa7TBdH06dPxH//xHwgMDMStt94KAPj+++8xf/58TJs2zR1tJIVhkCupFV8giWwZDEBxMXuI0JaC6OWXX0ZBQQHGjx+PTp3Eh1ssFsycOZNziDSAQa5ERJ6jSxdxD6LaWnEukZbzKJ0uiHx9fbFp0ya8/PLL+OWXX+Dv748hQ4agT58+7mkhKQqDXImIPIdOJy6/NxrFXiItP687XRBJBgwYgAEDBri2NaR43HuImsMQWiJ1algQhYTI3Rr5OF0Qmc1mrFu3DtnZ2SgtLYXFYrH5+nfffefK9pGCVFaK+w95eYnZZUQSJskTqZe0/L6iQtsxHk4XRPPnz8e6detw5513YvDgwdBxVq1mMMiV7GkuSb6oSDzOZe1EyubvL+4nV1ur7RgPpwuijRs34tNPP8Udd9zhnhaRIlkswIUL4secTE2S1pLkdToxSX7SJBbRREpmMDDGw+mOMV9fX8TExLinNaRYFy+KRRGDXKkhZ5LkiUi5pCLo0iW5WyIfpwuiJ598Em+++SYEe28JyWNxMjXZwyR5Is8gFURVVdqN8XB6yOyHH35ATk4Otm7diuuvvx4+jYKsMjMzXdk+UgAGucpLyau3mCRP5BmkGA9p1+ru3eVuUcdzuiDq2rUr7r33Xve0hhSJQa7yUfrqLSlJvqjI/jwinU78OpPkiZRPivEwmVgQOWTt2rXuaQkpEoNc5aOG1VtSkvyUKWLx07CtTJInUhetx3i0abeBuro6fPvtt3jvvfdw6eoMrDNnzqBCGlchj8EgV3m0tnoLEFdvmc0d3rQmmCRP5BkCAmxjPLTG6YLo5MmTGDJkCCZNmoTHH38cZWVlAIBXX30VTz31VJsasXLlSkRFRcHPzw/x8fHYtWtXs+euWbMGo0ePRrdu3dCtWzckJiY2OV8QBCxatAhhYWHw9/dHYmIijh492qa2aR2DXOWhttVbyclAQQGQkwNkZIj3+fkshojUxMtLzDYDtNlL5HRBNH/+fMTFxeHixYvwbxB6cu+99yI7O9vpBmzatAmpqalYvHgx9u7di2HDhiEpKQmlpaV2z8/NzcX06dORk5ODvLw8REZGYsKECSgqKrKes2zZMrz11ltYvXo1du7ciYCAACQlJeHKlStOt0/LamsZ5CoXNa7ekpLkp08X7zlMRqQ+0mozLRZEEJzUvXt34fDhw4IgCEKXLl2E48ePC4IgCPn5+YK/v7+zlxNGjhwpPP7449bPzWazEB4eLqSnpzv0+Lq6OiEwMFBYv369IAiCYLFYhNDQUOG1116znlNeXi7o9Xphw4YNDl3TaDQKAASj0ej0z+NJiosFYfduQTh0SO6WaE9OjiCI/UAt33Jy5G4pEXmSqirxeX/vXkGwWORujfPa8/rtdA+RxWKB2c7EhdOnTyPQyR37ampqsGfPHiQmJlqPeXl5ITExEXl5eQ5do6qqCrW1teh+dUp8fn4+iouLba4ZFBSE+Pj4Zq9ZXV0Nk8lkcyPuPSQnafVWc8OUOh0QGcnVW0TkWlKMh8VSv92KVjhdEE2YMAErVqywfq7T6VBRUYHFixc7Hedx7tw5mM1mhDSK1w0JCUFxcbFD1/jHP/6B8PBwawEkPc6Za6anpyMoKMh6i4yMdOrn8EQMcpWXtHoLaFoUcfUWEbmTVofNnC6IXn/9dfz444+47rrrcOXKFcyYMQNRUVEoKirCq6++6p5WNmPp0qXYuHEj/vu//xt+fn5tvk5aWhqMRqP1durUKZe2U40Y5Co/rt4iIjlotSByeh+iiIgI/PLLL9i4cSMOHDiAiooKzJkzBw888IDNJGtHBAcHw9vbGyUlJTbHS0pKEBoa2uJjly9fjqVLl+Lbb7/F0KFDrcelx5WUlCCswfa4JSUliI2NtXstvV4PvV7vVNs9GYNclSM5WQxGVepO1UTkeaTZL1VVQF2duO2KFrTpx+zUqRMefPDBdn9zX19fDB8+HNnZ2Zg8eTJwdY5SdnY2nnjiiWYft2zZMrzyyivYtm0b4uLibL4WHR2N0NBQZGdnWwsgk8mEnTt3Yu7cue1usxYwyFVZpNVbREQdwcdHnEt0+bK2dq12uiD697//3eLXZ86c6dT1UlNT8dBDDyEuLg4jR47EihUrUFlZiZSUFOv1evXqhfT0dODqfkeLFi1CRkYGoqKirPOCunTpgi5dukCn02HBggVYsmQJ+vfvj+joaDz//PMIDw+3Fl3UMk6mJiLSNoOBBVGr5s+fb/N5bW0tqqqq4Ovri86dOztdEE2dOhVlZWVYtGgRiouLERsbi6ysLOuk6MLCQnh51U91WrVqFWpqajBlyhSb6yxevBgvvPACAOCZZ55BZWUlHnnkEZSXl+OWW25BVlZWu+YZaQWDXImIyGAASkq0NY9IJwj2wgGcc/ToUcydOxdPP/00kpKSXNMyGZlMJgQFBcFoNMIgzS7TiKIiMcsmKAiIiZG7NUREJAeLBfjlF/H++usBtfQntOf1u01ZZo31798fS5cubdJ7ROrCIFciIoJGYzxcUhDh6kTrM2fOuOpyJAMGuRIRkURry++dnkO0ZcsWm88FQcDZs2fxzjvv4Oabb3Zl26iDMciViIgkUkF06ZI4guDprwtOF0SNV2rpdDr06NED48aNw+uvv+7KtlEHYpArERE1JMV41NaKi208fRsWpwsii8XinpaQrC5cEN8BBASI/wmIiIgCA8XXB5PJ8wsil80hInXj3kNERNSYluYROd1DlJqa6vC5b7zxhrOXJxkwyJWIiOyRCiItxHg4/aPt27cP+/btQ21tLQYOHAgA+OOPP+Dt7Y0bb7zRep7O02dfeRAtB7mazcwJIyJqTsMYj0uXPPtNs9MF0d13343AwECsX78e3a7+Zi5evIiUlBSMHj0aTz75pDvaSW7SMMhVa8NlmZnA/PnA6dP1xyIigDffZJI8EZGkYYyHJxdETu9U3atXL3z99de4/vrrbY7/9ttvmDBhgkfsRaSlnarPnwcKCsQg18GD5W5Nx8nMBKZMESeSNyR1bH7+OYsiIiJcnT909Cjg6wsMGSJ3a1rWoTtVm0wmlJWVNTleVlaGS5cuOXs5kpkWJ1ObzWLPkL23AtKxBQvE84iItK5LF/HNYk2NON/UUzldEN17771ISUlBZmYmTp8+jdOnT+OLL77AnDlzkMy31Kqi1SDX7dtth8kaEwTg1CnxPCIirdNKjIfTc4hWr16Np556CjNmzEBtba14kU6dMGfOHLz22mvuaCO5iZRbFhQkTpzTirNnXXseEVFbqWVhh8EgTqo2mYCePeVujXs4XRB17twZ7777Ll577TUcP34cANCvXz8EBAS4o33kJg2DXLU0XAaITzquPI+IqC3UtLDDYACKijw7xqPNGzOePXsWZ8+eRf/+/REQEAAn52aTzBoGuQYFyd2ajjV6tPik09x/aJ0OiIwUzyMicgdpYUfj4fuiIvF4ZqZcLbOvc2fx9cJiEfeu80ROF0Tnz5/H+PHjMWDAANxxxx04e3VcYc6cOVxyryJaDnL19hbfgQFNf3bp8xUrlNltTUTqp9aFHZ6+a7XTBdHf//53+Pj4oLCwEJ07d7Yenzp1KrKyslzdPnIDBrmK3dGffw706mV7PCKCS+6JyL3UurDD0wsip+cQff3119i2bRsiIiJsjvfv3x8nT550ZdvITRjkKkpOBiZNUseERiLyHGpd2CEVRJWVnhnj4fSPU1lZadMzJLlw4QL0er2r2kVupMW9h5rj7Q2MHSt3K4hIS9S6sMPHB/DzE7ds8cQYD6eHzEaPHo1///vf1s91Oh0sFguWLVuGhIQEV7ePXIxBrkRE8lLzwg5PHjZzuodo2bJlGD9+PHbv3o2amho888wzOHjwIC5cuIAff/zRPa0kl9FykCsRkRJICzumTBGLn4aTq5W+sMNgAEpLPbMgcrqHaPDgwfjjjz9wyy23YNKkSaisrERycjL27duHfv36uaeV5BJaDnIlIlIStS7sCAz03BgPp3qIamtrMXHiRKxevRrPPfec+1pFbnHxolgU6fX127ATEZE81LiwQ4rxkHat9vOTu0Wu41RB5OPjgwMHDrivNeRWnExNRKQsalzYIcV4XLrkWTEeTg+ZPfjgg/jggw/c0xpyG60GuRIRkWtJE6ulGA9P4fSk6rq6Onz44Yf49ttvMXz48CYZZm+88YYr20cuotUgVyIici0pxqOuTly57ClTMJwuiH777TfceOONAIA//vjD5ms6rWVAqISWg1yJiMj1AgPFeakmkwYLohMnTiA6Oho5OTnubRG5nJaDXImIyPUMhvqCKDxc7ta4hsNziPr374+ysjLr51OnTkVJSYm72kUupOUgVyIicr2GMR5KC6FtK4cLIqHRzKmvvvoKlZWV7mgTuVDDIFcOlxERkSv4+tYvufeUTRqdXmVG6tIwyNWT9osgIiJ5NVxt5gkcLoh0Ol2TSdOcRK183HuIiIjcQSqIpFEItXNqyGzWrFlITk5GcnIyrly5gr/97W/Wz6Wbs1auXImoqCj4+fkhPj4eu3btavbcgwcP4r777kNUVBR0Oh1WrFjR5JwXXnjBWrxJt0GDBjndLk/AIFciInKXhjEe1dVyt6b9HF5l9tBDD9l8/uCDD7b7m2/atAmpqalYvXo14uPjsWLFCiQlJeHIkSPoaWf7y6qqKvTt2xf3338//v73vzd73euvvx7ffvut9fNOnZzeXcAjMMiViIjcpXGMR48ecreofRyuFNauXevyb/7GG2/g4YcfRkpKCgBg9erV+H//7//hww8/xMKFC5ucP2LECIwYMQIA7H5d0qlTJ4SGhrq8vWrCIFciInI3KcbDEwoi2SZV19TUYM+ePUhMTKxvjJcXEhMTkZeX165rHz16FOHh4ejbty8eeOABFBYWtnh+dXU1TCaTzU3tGORKRETuFhgo3ntCjIdsBdG5c+dgNpsREhJiczwkJATFxcVtvm58fDzWrVuHrKwsrFq1Cvn5+Rg9ejQutTANPj09HUFBQdZbZGRkm7+/UnAyNRERuZsU42E2i/NW1czjlt3ffvvtuP/++zF06FAkJSXhq6++Qnl5OT799NNmH5OWlgaj0Wi9nTp1qkPb7GoMciUioo6g09X3Eql9cEW22cbBwcHw9vZustt1SUmJS+f/dO3aFQMGDMCxY8eaPUev10Ov17vse8qNQa5ERNRRpBgPte9HJFsPka+vL4YPH47s7GzrMYvFguzsbIwaNcpl36eiogLHjx9HWFiYy66pZAxyJSKijuQpMR6yrkdPTU3FQw89hLi4OIwcORIrVqxAZWWlddXZzJkz0atXL6SnpwNXJ2L//vvv1o+Lioqwf/9+dOnSBTExMQCAp556CnfffTf69OmDM2fOYPHixfD29sb06dNl/Ek7DoNciYioI0kxHleuiL1EXbvK3aK2kbUgmjp1KsrKyrBo0SIUFxcjNjYWWVlZ1onWhYWF8PKq78Q6c+YMbrjhBuvny5cvx/LlyzFmzBjk5uYCAE6fPo3p06fj/Pnz6NGjB2655Rbs2LEDPdS+HtBBDHIlIqKOZjCIBZHJpN6CSCc0Tm0lmEwmBAUFwWg0wiD1BapAbS3w66/isNn11zO7jIiIOobRCBw7Jm71MniwfO1oz+u3Nrdw9lBKC3I1m4Ht24GzZ4GwMGD0aO6YTUTkibp0EUclqqvFmxrXKXncsnstU9LeQ5mZQFQUkJAAzJgh3kdFiceJiMizeHuLb8ah4uX3LIg8REWFcoJcMzOBKVOA06dtjxcVicdZFBEReR5phEqty+9ZEHkIaam93EGuZjMwf779LdylYwsWqHtpJhERNSUVRCaTOmM8WBB5ACUFuW7f3rRnqCFBAE6dEs8jIiLP0bmz+IbcbAaqquRujfNYEHkAJQW5nj3r2vOIiEgddDrbXiK1YUHkAZQ0mdrRDcE1snE4EZGmsCAi2SgtyHX0aCAiovlNIXU6IDJSPI+IiDyLFPSqxhgPFkQqp7QgV29v4M03xY8bF0XS5ytWcD8iIiJPpNeLN0FQ32ozFkQqptQg1+Rk4PPPgV69bI9HRIjHk5PlahkREbmbWofNuFO1ihmNyg1yTU4GJk3iTtVERFpjMABlZSyIqANJvUNKDXL19gbGjpW7FURE1JECA+tjPGpqAF9fuVvkGA6ZqVRtrdhDBIUNlxERkbapNcaDBZFKKS3IlYiISKLGeUQsiFRKSXsPERERNaTGGA8WRCqkpCBXIiKixtQY48GCSIWUEuRKRERkj05Xv0mjWobNWBCpjJKCXImIiJqjtnlELIhU5sIF5QS5EhERNUcqiNQS48GCSGWUuDM1ERFRYw1jPKTMTSVjQaQiUpCrTqeMIFciIqKWqGnYjAWRiki9QwaDMoJciYiIWsKCiFxOqUGuREREzZFWml25IsZ4KBkLIpVQcpArERGRPWqK8WC4q0ooPciViIg8l9kMbN8OnD0LhIUBo0c7vg+ewSCuNDOZlD3CwR4iFWCQKxERySUzE4iKAhISgBkzxPuoKPG4I6R5RJcuKTvGgwWRCpw/zyBXIiLqeJmZwJQpwOnTtseLisTjjhRFAQFib1JdHXD5stua2m4siFSAk6mJiKijmc3A/Pn2e3WkYwsWtL7polpiPFgQKRyDXImISA7btzftGWpIEIBTp8TzWqOG5fcsiBSOQa5ERCSHs2ddd55UEFVUiPFTSsSCSMHMZga5EhGRPMLCXHdewxiPS5fa3TS3kL0gWrlyJaKiouDn54f4+Hjs2rWr2XMPHjyI++67D1FRUdDpdFixYkW7r6lkFy8yyJWIiOQxejQQEdH8Vi86HRAZKZ7nCKXPI5K1INq0aRNSU1OxePFi7N27F8OGDUNSUhJKS0vtnl9VVYW+ffti6dKlCA0Ndck1lYyTqYmISC7e3sCbb4ofNy6KpM9XrHBuPyKwILLvjTfewMMPP4yUlBRcd911WL16NTp37owPP/zQ7vkjRozAa6+9hmnTpkGv17vkmkrFIFciIpJbcjLw+edAr162xyMixOPJyY5fSyqIlBrjIVtBVFNTgz179iAxMbG+MV5eSExMRF5enmKuKRcGuRIRkRIkJwMFBUBODpCRId7n5ztXDEEFMR6yRXecO3cOZrMZISEhNsdDQkJw+PDhDr1mdXU1qqurrZ+bZP6XYpArEREpibc3MHZs+68jxXhcuqS81zfZJ1UrQXp6OoKCgqy3yMhIWdvDIFciIvJESp5HJFtBFBwcDG9vb5SUlNgcLykpaXbCtLuumZaWBqPRaL2dOnWqTd/fVRjkSkREniggQNxouK4OqKqSuzW2ZCuIfH19MXz4cGRnZ1uPWSwWZGdnY9SoUR16Tb1eD4PBYHOTC4NciYjIUyk5xkO2OUQAkJqaioceeghxcXEYOXIkVqxYgcrKSqSkpAAAZs6ciV69eiE9PR24Omn6999/t35cVFSE/fv3o0uXLoiJiXHomkrHIFciIvJkBoP4xt9kAto4IOQWshZEU6dORVlZGRYtWoTi4mLExsYiKyvLOim6sLAQXl71nVhnzpzBDTfcYP18+fLlWL58OcaMGYPc3FyHrql0nExNRESerHGMh5dCZjPrBMFejq22mUwmBAUFwWg0dujwWUUFcOSI+McxdCizy4iIyDP9+qu4F1FMjGsXD7Xn9VshdRmBQa5ERKQRUq2ipFwzWYfMqJ4zQa5mM7B9u5gwHBYm5siwgCIiIrUwGIBz55Q1sZoFkUI4GuSamQnMnw+cPl1/LCJCzJtxdtdQIiIiOUgrzS5fFldXKyGRgUNmCuHIZOrMTGDKFNtiCACKisTjmZnubSMREZErdOqkvBgPFkQK4EiQq9ks9gzZmwIvHVuwQDyPiIhI6ZS2HxELIgU4d068bynIdfv2pj1DDQkCcOqUeB4REZHSKS3GgwWRzATBscnUZ886dj1HzyMiIpJTly7KivFgQSQzR4Ncw8Icu56j5xEREclJaTEeLIhk5miQ6+jR4mqy5s7R6YDISPE8IiIiNVDSfkQsiGTkTJCrt7e4tB5oWhRJn69Ywf2IiIhIPRoWRBaLvG1hQSQjZ4Nck5OBzz8HevWyPR4RIR7nPkRERKQmfn6Ar6/4WlhRIW9buDGjjNoS5JqcDEyaxJ2qiYjIMzTctboD40ObYEEkk4oKcf8hLy8xu8wZ3t7A2LHuahkREVHHUUqMB4fMZCLtPcQgVyIi0rLGMR5yYUEkA7NZzC6Dk8NlREREnqZTJ6BzZ/FjOXuJWBDJQApy9fNrOciViIhIC5SwazULIhk03HuIiIhI65SwHxELog7mSJArERGRlkgxHrW14lwiObAg6mBS71BLQa5ERERaooQYDxZEHUgQ2rb3EBERkaeTex4RC6IOJAW5+vi0HORKRESkNVJBVFEhT4wHC6IOJPUOde/ecpArERGR1vj5iR0GFos8MR4siDqIM0GuREREWiTnsBkLog7ibJArERGR1rAg0gBOpiYiImqZVBDJEePBgqgDtCfIlYiISCsaxnh09CaNLIg6AINciYiIHCPXsBkLIjdjkCsREZHjWBB5KAa5EhEROS4gQJ4YDxZEbsYgVyIiIsd5edV3IHRkLxELIjdikCsREZHz5Bg2Y0HkRtJkaga5EhEROU6OGA9FFEQrV65EVFQU/Pz8EB8fj127drV4/meffYZBgwbBz88PQ4YMwVdffWXz9VmzZkGn09ncJk6c6OafwpYgABcuiB9zMjUREZHj/P3rYzwqKzvme8peEG3atAmpqalYvHgx9u7di2HDhiEpKQmlpaV2z//pp58wffp0zJkzB/v27cPkyZMxefJk/PbbbzbnTZw4EWfPnrXeNmzY0EE/kYhBrkRERG3X0cNmOkEQhI75VvbFx8djxIgReOeddwAAFosFkZGRmDdvHhYuXNjk/KlTp6KyshJffvml9dif/vQnxMbGYvXq1cDVHqLy8nJs3ry5TW0ymUwICgqC0WiEQfoXcdLx40B5ORASAkREtOkSREREmnXhApCfL27UeO21jj2mPa/fsvYQ1dTUYM+ePUhMTKxvkJcXEhMTkZeXZ/cxeXl5NucDQFJSUpPzc3Nz0bNnTwwcOBBz587FeWm5VwdgkCsREVH7BAaK91VVQF2d+79fJ/d/i+adO3cOZrMZISEhNsdDQkJw+PBhu48pLi62e35xcbH184kTJyI5ORnR0dE4fvw4nn32Wdx+++3Iy8uDt52toqurq1FdXW393NTO/jkpyLVLFwa5EhERtYWPjziX6PJlcdise3f3fj9ZCyJ3mTZtmvXjIUOGYOjQoejXrx9yc3Mxfvz4Juenp6fjxRdfdNn3595DRERE7WcwdFxBJOuQWXBwMLy9vVFSUmJzvKSkBKGhoXYfExoa6tT5ANC3b18EBwfj2LFjdr+elpYGo9FovZ06dapNPw8Y5EpEROQyHTmxWtaCyNfXF8OHD0d2drb1mMViQXZ2NkaNGmX3MaNGjbI5HwC++eabZs8HgNOnT+P8+fMICwuz+3W9Xg+DwWBzaysGuRIREblGly71MR5Xrrj3e8m+7D41NRVr1qzB+vXrcejQIcydOxeVlZVISUkBAMycORNpaWnW8+fPn4+srCy8/vrrOHz4MF544QXs3r0bTzzxBACgoqICTz/9NHbs2IGCggJkZ2dj0qRJiImJQVJSklt/Fga5EhERuU5HxnjIPodo6tSpKCsrw6JFi1BcXIzY2FhkZWVZJ04XFhbCy6u+brvpppuQkZGB//zP/8Szzz6L/v37Y/PmzRg8eDAAwNvbGwcOHMD69etRXl6O8PBwTJgwAS+//DL0er1bfxYGuRIREbmWwSAWQyYT0LOn+76P7PsQKVFb9zE4fFjcUbNXL6CFKU1ERETkoMuXgd9/F3uLYmPFfNDmqHYfIk9y5YpYDDHIlYiIyHUaxnhUVLjv+7AgchFpMnVQEINciYiIXKkjVpuxIHIBQeDeQ0RERO4i7VrNgkjhjEZxW3EGuRIREbme1EPkzhgPFkQuIA2Xde/e8mQvIiIicp4U4wE39hKxIGqn2tr6fxzuPUREROQeUi/RpUvuuT4LonaSglwDAhjkSkRE5C7unljNgqidpMnU7B0iIiJyny5dxGkpNTXuifFgQdQODHIlIiLqGF5e7l1txoKoHRjkSkRE1HHcOWzGgqiNGORKRETUsaQeokuXxPm7rsSCqI0Y5EpERNSxOncGOnVyT4wHC6I2kobLuDM1ERFRx3HX8nsWRG3AIFciIiJ5uGseEQuiNmCQKxERkTykgqiy0rUxHiyInMQgVyIiIvk0jPFw5bAZCyInMciViIhIXu4YNmNB5CQGuRIREcmLBZHMGORKREQkP3fEeLAgcoIU5NqlC4NciYiI5OLlVb8HoKt6iVgQOYGTqYmIiJTB1fsRsSByEINciYiIlKNhQeSKGA8WRA5qOJmaQa5ERETykmI8zGZxT6L2YkHkgIZBrhwuIyIiUgZXrjZjQeQABrkSEREpDwuiDsYgVyIiIuUJDBTvKyvF0Zz2YEHUCga5EhERKZOvb/02OO3tJWJB1AoGuRIRESmXq4bNWBC1gEGuREREysaCqAMwyJWIiEjZAgPrYzyqq9t+HRZELWjYO8QgVyIiIuVpGOPRnl2rWRC1QPrFcriMiIhIuVwxbKaIgmjlypWIioqCn58f4uPjsWvXrhbP/+yzzzBo0CD4+flhyJAh+Oqrr2y+LggCFi1ahLCwMPj7+yMxMRFHjx51ul0MciUiIlI+qSCqqGj7NWQviDZt2oTU1FQsXrwYe/fuxbBhw5CUlITS0lK75//000+YPn065syZg3379mHy5MmYPHkyfvvtN+s5y5Ytw1tvvYXVq1dj586dCAgIQFJSEq5cueJ0+9g7REREpGz+/vUxHm2lEwRXRKK1XXx8PEaMGIF33nkHAGCxWBAZGYl58+Zh4cKFTc6fOnUqKisr8eWXX1qP/elPf0JsbCxWr14NQRAQHh6OJ598Ek899RQAwGg0IiQkBOvWrcO0adNabZPJZEJQUBD+7/+MuOkmA7PLiIiIFO7ECeDUKRPGjg2C0WiEQeo2cpCsPUQ1NTXYs2cPEhMT6xvk5YXExETk5eXZfUxeXp7N+QCQlJRkPT8/Px/FxcU25wQFBSE+Pr7ZazanWzcGuRIREamBk/VPE51c1ZC2OHfuHMxmM0JCQmyOh4SE4PDhw3YfU1xcbPf84uJi69elY82d01h1dTWqG6zVMxqNAAAfH5NL8lGIiIjI/SorxRfttgx+yVoQKUV6ejpefPHFJscHDYqUpT1ERETUdufPn0eQkxsIyloQBQcHw9vbGyUlJTbHS0pKEBoaavcxoaGhLZ4v3ZeUlCAsLMzmnNjYWLvXTEtLQ2pqqvXz8vJy9OnTB4WFhU7/QoncwWQyITIyEqdOnXJ6XJzIXfh3SUpjNBrRu3dvdO/e3enHyloQ+fr6Yvjw4cjOzsbkyZOBq5Oqs7Oz8cQTT9h9zKhRo5CdnY0FCxZYj33zzTcYNWoUACA6OhqhoaHIzs62FkAmkwk7d+7E3Llz7V5Tr9dDr9c3OR4UFMT/5KQoBoOBf5OkOPy7JKXx8nJ+irTsQ2apqal46KGHEBcXh5EjR2LFihWorKxESkoKAGDmzJno1asX0tPTAQDz58/HmDFj8Prrr+POO+/Exo0bsXv3brz//vsAAJ1OhwULFmDJkiXo378/oqOj8fzzzyM8PNxadBERERE1JHtBNHXqVJSVlWHRokUoLi5GbGwssrKyrJOiCwsLbSq9m266CRkZGfjP//xPPPvss+jfvz82b96MwYMHW8955plnUFlZiUceeQTl5eW45ZZbkJWVBT/usEhERER2yL4PkRJVV1cjPT0daWlpdofSiDoa/yZJifh3SUrTnr9JFkRERESkebJHdxARERHJjQURERERaR4LIiIiItI8FkRERESkeSyIGpg1axZ0Ol2T21//+le5m0YaZDabcdNNNyE5OdnmuNFoRGRkJJ577jnZ2kbaJD1H/u1vf2vytccffxw6nQ6zZs2SpW2kba54/ZZ9HyKlmThxItauXWtzrHPnzrK1h7TL29sb69atQ2xsLD755BM88MADAIB58+ahe/fuWLx4sdxNJA2KjIzExo0b8a9//Qv+/v4AgCtXriAjIwO9e/eWu3mkYe19/WZB1Iher282R42oow0YMABLly7FvHnzMG7cOOzatQsbN27Ezz//DF9fX7mbRxp044034vjx48jMzLQW6ZmZmejduzeio6Plbh5pWHtfvzlkRqRw8+bNw7Bhw/CXv/wFjzzyCBYtWoRhw4bJ3SzSsNmzZ9u8E//www+tcUtEasWCiEjhdDodVq1ahezsbISEhGDhwoVyN4k07sEHH8QPP/yAkydP4uTJk/jxxx/x4IMPyt0sonbhkBmRCnz44Yfo3Lkz8vPzcfr0aURFRcndJNKwHj164M4778S6desgCALuvPNOBAcHy90sonZhDxGRwv3000/417/+hS+//BIjR47EnDlzwMQdktvs2bOxbt06rF+/HrNnz5a7OUTtxoKISMGqqqowa9YszJ07FwkJCfjggw+wa9curF69Wu6mkcZNnDgRNTU1qK2tRVJSktzNIWo3FkRECpaWlgZBELB06VIAQFRUFJYvX45nnnkGBQUFcjePNMzb2xuHDh3C77//Dm9vb7mbQ9RuLIiIFOr777/HypUrsXbtWpu9NB599FHcdNNNHDoj2RkMBhgMBrmbQeQSOoHPqERERKRx7CEiIiIizWNBRERERJrHgoiIiIg0jwURERERaR4LIiIiItI8FkRERESkeSyIiIiISPNYEBEREZHmsSAiIpdavXo1AgMDUVdXZz1WUVEBHx8fjB071ubc3Nxc6HQ6HD9+XIaWulZBQQF0Oh32798vd1OIqA1YEBGRSyUkJKCiogK7d++2Htu+fTtCQ0Oxc+dOXLlyxXo8JycHvXv3Rr9+/WRqrTLV1tbK3QQizWFBREQuNXDgQISFhSE3N9d6LDc3F5MmTUJ0dDR27NhhczwhIQEfffQR4uLiEBgYiNDQUMyYMQOlpaUAAIvFgoiICKxatcrm++zbtw9eXl44efIkAKC8vBx//etf0aNHDxgMBowbNw6//PILAOCPP/6ATqfD4cOHba7xr3/9y6YY++2333D77bejS5cuCAkJwV/+8hecO3fO+nWLxYJly5YhJiYGer0evXv3xiuvvAIAiI6OBgDccMMN0Ol01t4wi8WCl156CREREdDr9YiNjUVWVpb1mlLP0qZNmzBmzBj4+fnhk08+ccm/BRE5jgUREblcQkICcnJyrJ/n5ORg7NixGDNmjPX45cuXsXPnTiQkJKC2thYvv/wyfvnlF2zevBkFBQWYNWsWAMDLywvTp09HRkaGzff45JNPcPPNN6NPnz4AgPvvvx+lpaXYunUr9uzZgxtvvBHjx4/HhQsXMGDAAMTFxTUpND755BPMmDEDuFpQjRs3DjfccAN2796NrKwslJSU4M9//rP1/LS0NCxduhTPP/88fv/9d2RkZCAkJAQAsGvXLgDAt99+i7NnzyIzMxMA8Oabb+L111/H8uXLceDAASQlJeGee+7B0aNHbdqycOFCzJ8/H4cOHUJSUpIL/zWIyCECEZGLrVmzRggICBBqa2sFk8kkdOrUSSgtLRUyMjKEW2+9VRAEQcjOzhYACCdPnmzy+J9//lkAIFy6dEkQBEHYt2+foNPprOeazWahV69ewqpVqwRBEITt27cLBoNBuHLlis11+vXrJ7z33nuCIAjCv/71L6Ffv37Wrx05ckQAIBw6dEgQBEF4+eWXhQkTJtg8/tSpUwIA4ciRI4LJZBL0er2wZs0auz9zfn6+AEDYt2+fzfHw8HDhlVdesTk2YsQI4bHHHrN53IoVKxz87RKRO7CHiIhcbuzYsaisrMTPP/+M7du3Y8CAAejRowfGjBljnUeUm5uLvn37onfv3tizZw/uvvtu9O7dG4GBgRgzZgwAoLCwEAAQGxuLa6+91tpL9P3336O0tBT3338/AOCXX35BRUUFrrnmGnTp0sV6y8/Pt07YnjZtGgoKCqxDdp988gluvPFGDBo0yHqNnJwcm8dLXzt+/DgOHTqE6upqjB8/3uHfg8lkwpkzZ3DzzTfbHL/55ptx6NAhm2NxcXFt/n0TUft1krsBROR5YmJiEBERgZycHFy8eNFa4ISHhyMyMhI//fQTcnJyMG7cOFRWViIpKQlJSUn45JNP0KNHDxQWFiIpKQk1NTXWaz7wwAPIyMjAwoULkZGRgYkTJ+Kaa64Brq5iazxvSdK1a1cAQGhoKMaNG4eMjAz86U9/QkZGBubOnWs9r6KiAnfffTdeffXVJtcICwvDiRMn3PK7kgQEBLj1+kTUMvYQEZFbJCQkIDc3F7m5uTbL7W+99VZs3boVu3btQkJCAg4fPozz589j6dKlGD16NAYNGmSdUN3QjBkz8Ntvv2HPnj34/PPP8cADD1i/duONN6K4uBidOnVCTEyMzS04ONh63gMPPIBNmzYhLy8PJ06cwLRp02yucfDgQURFRTW5RkBAAPr37w9/f39kZ2fb/Xl9fX0BAGaz2XrMYDAgPDwcP/74o825P/74I6677ro2/26JyA3kHrMjIs/04YcfCv7+/kKnTp2E4uJi6/H169cLgYGBAgDhzJkzQmlpqeDr6ys8/fTTwvHjx4X/+Z//EQYMGGB3Ps7NN98sDBs2TAgMDBSqqqqsxy0Wi3DLLbcIw4YNE7Zt2ybk5+cLP/74o/Dss88KP//8s/U8k8kk+Pv7C8OGDRPGjx9vc+2ioiKhR48ewpQpU4Rdu3YJx44dE7KysoRZs2YJdXV1giAIwgsvvCB069ZNWL9+vXDs2DEhLy9P+K//+i9BEAShtrZW8Pf3F5YsWSIUFxcL5eXlgnB17pLBYBA2btwoHD58WPjHP/4h+Pj4CH/88YcgtDD3iIg6FgsiInIL6YV+0KBBNscLCgoEAMLAgQOtxzIyMoSoqChBr9cLo0aNErZs2WK3SHj33XcFAMLMmTObfD+TySTMmzdPCA8PF3x8fITIyEjhgQceEAoLC23O+/Of/ywAED788MMm1/jjjz+Ee++9V+jatavg7+8vDBo0SFiwYIFgsVgE4epk7iVLlgh9+vQRfHx8hN69ewv//Oc/rY9fs2aNEBkZKXh5eQljxoyxPuaFF14QevXqJfj4+AjDhg0Ttm7d2uT3xIKISF46QRAEuXupiIiIiOTEOURERESkeSyIiIiISPNYEBEREZHmsSAiIiIizWNBRERERJrHgoiIiIg0jwURERERaR4LIiIiItI8FkRERESkeSyIiIiISPNYEBEREZHmsSAiIiIizfv/A/xQO1nXhjwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(3 * Nk):\n",
    "    resonance_data = resonance_datas[i]\n",
    "    resonance_data = filter_resonances(\n",
    "        resonance_data=resonance_data, minQ=0, minamp=0.001, maxerr=100\n",
    "    )\n",
    "    freqs = resonance_data.freq.to_numpy()\n",
    "    Qs = resonance_data.Q.to_numpy()\n",
    "    plt.scatter(np.full(len(freqs), (1 / 2) * i / Nk), freqs / 3e14, color=\"blue\")\n",
    "\n",
    "lightx = np.linspace(0, 0.5, 100)\n",
    "lighty1 = lightx\n",
    "lighty3 = (0.5 - lightx) * np.sqrt(2)\n",
    "\n",
    "plt.plot(lightx, lighty1, color=\"blue\", alpha=0.2)\n",
    "plt.plot(1 + lightx, lighty3, color=\"blue\", alpha=0.2)\n",
    "\n",
    "plt.ylim(0, freq_range_unitless[1])\n",
    "\n",
    "plt.title(\"Band diagram\")\n",
    "plt.ylabel(\"Frequency (c/a)\")\n",
    "plt.xlabel(\"Wavevector\")\n",
    "plt.xticks([0, 0.5, 1, 1.5], [r\"$\\Gamma$\", \"X\", \"M\", r\"$\\Gamma$\"])\n",
    "plt.xlim(0, 1.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c9ac653-3d76-4588-946a-71b2db764030",
   "metadata": {},
   "source": [
    "The bandstructure we obtained matches the expected result from the paper. If we were seeing too many resonances, we could change the parameters to our filter_resonances function to eliminate the spurious ones. If we were seeing too few resonances even before filtering, we might have to change the parameters of the ResonanceFinder, for example decreasing `rcond` or increasing `init_num_freqs`. If the ResonanceFinder takes too long, we can decrease `init_num_freqs`. There can also be resonances on the light line associated with Wood's anomaly; we filter those out here based on their small amplitude."
   ]
  }
 ],
 "metadata": {
  "applications": [
   "Photonic crystals"
  ],
  "description": "This notebook demonstrates how to calculate the band structure of a photonic crystal in Tidy3D FDTD.",
  "feature_image": "./img/band_structure.png",
  "features": [
   "Resonance finder"
  ],
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "keywords": "photonic crystal, band structure, 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": "3.13.5"
  },
  "title": "Photonic Crystal Band Structure Calculation | Flexcompute",
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        {
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