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    "# Adjoint gradient calculation\n",
    "\n",
    "Run this notebook in your browser using [Binder](https://mybinder.org/v2/gh/flexcompute-readthedocs/tidy3d-docs/readthedocs?labpath=docs%2Fsource%2Fnotebooks%2FAdjointOptimize.ipynb).\n",
    "\n",
    "This demo will get one started performing gradient-based optimization of a photonic device using the adjoint method. The adjoint method offers an efficient way to compute gradients with respect to any number of design parameters using only two simulations.\n",
    "\n",
    "The goal of this notebook is to show how to compute this gradient by wrapping Tidy3d. The approach shown here can be used to implement a gradient-based optimization, which will be elucidated in future tutorials.\n",
    "\n",
    "In the future, Tidy3d will provide a higher-level API for implementing gradient calculations based on this method.  For more details, this [paper](https://pubs.acs.org/doi/10.1021/acsphotonics.9b01238) provides an overview of the derivation of the adjoint method in FDTD ([arxiv preprint](https://arxiv.org/abs/1908.10507))."
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   "source": [
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## Overview\n",
    "\n",
    "We will look at the transmission of light through 4 dieletric `td.Box()` objects in the x-y plane.\n",
    "\n",
    "There is a waveguide extending through the x axis.  On one side of the boxes is a modal source and on the other is a modal monitor.\n",
    "\n",
    "We will measure the mode amplitudes at the mode monitor and compute the total power transmitted, which will server as our objective function.\n",
    "\n",
    "Then, we will set up an adjoint simulation where several modal sources are located at the measurement position and the phase and amplitude of each of the soucess is dependent on the measured amplitudes.\n",
    "\n",
    "With both the original (forward) and adjoint simulation fields, we can compute the gradient of the measured intensity with respect to the permittivity of each box by summing the product of the electric fields over each of the box volumes.\n",
    "\n",
    "Then, we will compute this gradient through a brute force perturbation of each of the box permittivity values, showing that the two gradients match with good accuracy.\n",
    "\n",
    "### Parameters\n",
    "\n",
    "First, let's set up some of the parameters of the system."
   ]
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    "# wavelength and frequency\n",
    "wavelength = 1.0\n",
    "freq0 = td.C_0 / wavelength\n",
    "\n",
    "# resolution control\n",
    "dl = 0.02\n",
    "\n",
    "# space between boxes and PML\n",
    "buffer = 1.5 * wavelength\n",
    "\n",
    "# initial size of boxes and waveguide\n",
    "lx0, ly0, lz0 = 1., 1., 8 * dl\n",
    "wg_width = .7\n",
    "\n",
    "# position of source and monitor (constant for all)\n",
    "source_x = -lx0 - 1\n",
    "meas_x = lx0 + 1\n",
    "\n",
    "# total size\n",
    "Lx = 2 * lx0 + 2 * buffer\n",
    "Ly = 2 * ly0 + 2 * buffer\n",
    "Lz = lz0 + 2 * buffer\n",
    "\n",
    "# simulation parameters\n",
    "subpixel = False\n",
    "boundary_spec = td.BoundarySpec.all_sides(boundary=td.PML())\n",
    "shutoff = 1e-8\n",
    "courant = 0.9\n",
    "\n",
    "# permittivity at each quadrant of box\n",
    "quadrants = [x + y for x in \"+-\" for y in \"+-\"]\n",
    "permittivities = [2.0, 2.5, 3.0, 3.5]\n",
    "\n",
    "wg_eps = 2.75\n",
    "eps_boxes = {quad: eps for (quad, eps) in zip(quadrants, permittivities)}\n",
    "\n",
    "# frequency width and run time\n",
    "freqw = freq0 / 10\n",
    "run_time = 10 / freqw\n",
    "\n",
    "# polarization of initial source\n",
    "pol = \"Ey\"\n",
    "\n",
    "# monitor for plotting\n",
    "monitor_field = td.FieldMonitor(\n",
    "    center=[0, 0, 0],\n",
    "    size=[td.inf, td.inf, 0],\n",
    "    freqs=[freq0],\n",
    "    name=\"field_pattern\",\n",
    ")\n",
    "\n",
    "# default box center and sizes\n",
    "center = np.array([-1e-5, -1e-5, -1e-5])\n",
    "\n",
    "size = np.array([lx0, ly0, lz0])\n",
    "ds = -0.0"
   ]
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   "source": [
    "### Structures\n",
    "\n",
    "Next, we'll construct the waveguide and each of the boxes.\n",
    "\n",
    "We'll give each of these these structures a `.name` representing what quadrant of the x,y plane it is in."
   ]
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   "source": [
    "waveguide = td.Structure(\n",
    "    geometry=td.Box(size=(td.inf, wg_width, lz0)),\n",
    "    medium=td.Medium(permittivity=wg_eps)\n",
    ")\n",
    "\n",
    "boxes_quad = []\n",
    "\n",
    "for i, (quad, eps) in enumerate(eps_boxes.items()):\n",
    "\n",
    "    x, y = quad\n",
    "    xsign = 1 if x == \"+\" else -1\n",
    "    ysign = 1 if y == \"+\" else -1\n",
    "\n",
    "    center_quad = center.tolist()\n",
    "    center_quad[0] += xsign * lx0 / 2\n",
    "    center_quad[1] += ysign * ly0 / 2\n",
    "    size_quad = size.tolist()\n",
    "    size_quad[0] += i * ds\n",
    "    size_quad[1] += i * ds\n",
    "\n",
    "    box_quad = td.Structure(\n",
    "        geometry=td.Box(center=center_quad, size=size_quad),\n",
    "        medium=td.Medium(permittivity=eps),\n",
    "        name=quad,\n",
    "    )\n",
    "    boxes_quad.append(box_quad)\n",
    "\n",
    "    grad_mon = td.FieldMonitor(\n",
    "        center=center_quad,\n",
    "        size=size_quad,\n",
    "        freqs=[freq0],\n",
    "        name=quad,\n",
    "    )"
   ]
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   "source": [
    "### Construct Gradient Monitors\n",
    "As discussed, We'll need the fields within each box for both forward and adjoint simulations in order to compute the gradient.\n",
    "\n",
    "Here we'll construct those and assign each the same name as the corresponding structure."
   ]
  },
  {
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   "source": [
    "#The adjoint gradient is computed by summing up the forward * adjoint fields in the whole volume of each Box.\n",
    "grad_monitors = []\n",
    "for structure in boxes_quad:\n",
    "    grad_monitors.append(\n",
    "        td.FieldMonitor(\n",
    "            center=structure.geometry.center,\n",
    "            size=structure.geometry.size,\n",
    "            freqs=[freq0],\n",
    "            name=structure.name,\n",
    "        )\n",
    "    )"
   ]
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  {
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   "source": [
    "### Construct Base Simulation\n",
    "\n",
    "With this information, we can create a simulation that contains both the boxes and their gradient monitors.\n",
    "\n",
    "We'll copy this simulation and use it to construct both the forward and adjoint simuilations in a bit."
   ]
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    "sim_base = td.Simulation(\n",
    "    size=[Lx, Ly, Lz],\n",
    "    grid_spec=td.GridSpec.uniform(dl=dl),\n",
    "    # grid_spec=td.GridSpec.auto(wavelength=wavelength),\n",
    "    structures=[waveguide] + boxes_quad,\n",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(1, 3, tight_layout=True, figsize=(10, 5))\n",
    "\n",
    "for dim, ax in zip('xyz', axes):\n",
    "    sim_base.plot(**{dim:0}, ax=ax)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd810a95-b554-42de-84bb-94208dcec6fb",
   "metadata": {},
   "source": [
    "## Forward Simulation\n",
    "\n",
    "The forward simulation corresponds to the system we want to compute the gradient for.\n",
    "\n",
    "It will contain a point source and a `FieldMonitor`, which will be used to compute the intensity from the objective function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a7379ae0-1c3f-4132-b106-49c9452ee8b8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:35:04.432368Z",
     "iopub.status.busy": "2022-07-21T20:35:04.432205Z",
     "iopub.status.idle": "2022-07-21T20:35:04.445739Z",
     "shell.execute_reply": "2022-07-21T20:35:04.445341Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING </span> No sources in simulation.                               <a href=\"file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/components/simulation.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">simulation.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/components/simulation.py#406\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">406</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m No sources in simulation.                               \u001b]8;id=590402;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/components/simulation.py\u001b\\\u001b[2msimulation.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=163100;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/components/simulation.py#406\u001b\\\u001b[2m406\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_forward = sim_base.copy(deep=True)\n",
    "\n",
    "mode_size = (0,4,3)\n",
    "\n",
    "# source seeding the simulation\n",
    "forward_source = td.ModeSource(\n",
    "        source_time=td.GaussianPulse(freq0=freq0, fwidth=freqw),\n",
    "        center=[source_x, 0, 0],\n",
    "        size=mode_size,\n",
    "        mode_index=0,\n",
    "        direction=\"+\"\n",
    "    )\n",
    "\n",
    "sim_forward = sim_base.copy(update={'sources': list(sim_forward.sources) + [forward_source]})\n",
    "\n",
    "# we'll refer to the measurement monitor by this name often\n",
    "measurement_monitor_name = 'measurement'\n",
    "\n",
    "num_modes = 3\n",
    "\n",
    "# monitor where we compute the objective function from\n",
    "measurement_monitor = td.ModeMonitor(\n",
    "    center=[meas_x, 0, 0],\n",
    "    size=mode_size,\n",
    "    freqs=[freq0],\n",
    "    mode_spec=td.ModeSpec(num_modes=num_modes),\n",
    "    name=measurement_monitor_name,\n",
    ")\n",
    "\n",
    "sim_forward = sim_forward.copy(update={'monitors': list(sim_forward.monitors) + [measurement_monitor]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bbe028d3-edc8-49a8-9b1a-419a90808a86",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:35:04.447274Z",
     "iopub.status.busy": "2022-07-21T20:35:04.447118Z",
     "iopub.status.idle": "2022-07-21T20:35:04.673262Z",
     "shell.execute_reply": "2022-07-21T20:35:04.672863Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">&lt;</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff; font-weight: bold\">Figure</span><span style=\"color: #000000; text-decoration-color: #000000\"> size 72</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0x360</span><span style=\"color: #000000; text-decoration-color: #000000\"> with </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span><span style=\"color: #000000; text-decoration-color: #000000\"> Axes</span><span style=\"font-weight: bold\">&gt;</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 72\u001b[0m\u001b[1;36m0x360\u001b[0m\u001b[39m with \u001b[0m\u001b[1;36m3\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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0syfGcyNn+nlivOsGopGz5H7HA2Xgty2OtQE4zWzaGfCnxbcfVFb8+wYkS2qvfdJe3X0f8CngvxONzv+/ZJvaa+bqwF+4+3rgMcCrzGx9p4WVRwJKRUUG6W+1ejhtWkU6IHdyJUm94iM3E31F+g9mNmZmS8zs8a12dPcGUafwDjNbHn91+udEZ1tjZs83szXx7g8RdTShmZ0Vj9qMEM0D3Ef0Ne00ZnaEmT07nutYJRoZSfb7MHCZRWeDY9EJSc+Pt32RKFS+zszKcd3Oibc9CBxr8clKLXwCeL2ZHWdmyzgwJ3KmkbrZLAe2ufs+MzsbeHFq25b4scwUDjCzc4nmi76M6CSc95vZagB3/248N3Kmn+/GxVwNPCvupMeAtwPXJtM2mnwbaAB/Fv/dXh3f/s34348Cf25mq83saOAvgKva/JtIb6m99kl7jX2UaI70+aRCstprttz9fne/Jf59J9HAw+rZ7zW7QIlB+lypGOyfQ99tQAaFZGB/R/osopNn7gY2EX2FOpPXEHWcdxLNYfw4kKzAcBbwQzPbRTRC89p47uA40QjTQ0Rfj24F/rFF2QFRJ34f0dezTwJeGdfzOuCdwDVmNgncCjw93raT6OvOZxF91fo7ohN7AD4d/7vVzG5pccwriTq3G4G7iALBa2Z5/LP5U+DtZrYTeAtRQCGu4x6iUeLvxV9BPyZ9x/jr7I8Cr3b3e+NO9CPAvzaNHM3K3TcQfW18NbCZKAj8aeo4XzazN8X71oiWjHoZsJ3oxMHnxLdDtDTVF4BfEv29vxTfJhlRe+2P9pra73tEYfoWd9/Yap/ZqL0uPDM7FngU85/3LpJLQRBdEGdissHEZHcBGcCGcKqWiIj0kJl9E/i4u/9L1nWR6eJvG74DvMPdr22x/WKi9aVZdciKR3/gf79xxrIm9zS45BmHdVyXW3Yd+Mx3xrIzOi5HBtDmGw/8fvi5M+83h8uv30J5xJiYjE7Sq4xPD8kv/pPLfuLuZ863PI0ki4hIx8zsLOAM4JNZ10Wmi6cLfRa4ulVABnD3K9z9THc/c9myscWtoEiPhWG0EktlvEBlvPvlCxWSRUSkI2b2b0TrPb9uhjnEkpF4itpHgNvc/d29KLPVygEi/aRWD/dPsejFOt9aJ1lERDri7i/Pug4yo8cTrTjySzP7WXzbm9z9+k4Kq06FWgJO+l6pGEybXpFe53tVq5XX56CQLCIiMmDiNebnfcLzbJJVAnQxEel3rVZgSQfltsvrQZ1ERERkAKWX0dIScJJXydSLduVqJHlsbMxXrlw543Yzo1CI/gjuznxXDWtn307uo/JVfqLRaDDbijL33XffhLt3fvp4H2mnvTbL6/Or8ger/LnaKwxWm23WvM5sdar9kTiRftHJUnC5CskrV67k9a9//Yzb6/U669ZFF6KqVqts27aNVatWUS6XW+4/n31movJVfiflb9y4kWKxSL1ep1arUSqVKBYPNMNLL7207XVm+9XKlSu55JJLDnqMiXR7bZbX51flD1b5SXttlm6/l1122cC02bReXIhBJO8G9pVfLpdZtWoV27Zto1qtHrS9mzdgla/yuy2/WCxSKpWo1WrU651cKC0fZgrIc8n786vyB7v8dPsdRArIIpGBfvXP9EbZ7Rukylf5vSh/GIJyJwE5kffnV+UPdvlJ+x00CsgiBwx8C2h+o+zVG6TKV/m9KH8YgnI38v78qvzBLr+bD4F9yVFAFkkZsBbeWvJGOTExAUClUunJG6TKV/m9kHS0rb4Wlvw/vyp/sMsfJCEoIIukqCWIiIiIiDQZipHk5Cu2SqUC0NOv21S+yu9Wcqa8Rrday/vzq/IHu/xBEnDgymQaTRbJWUg2s1nnbTYaDSYnJ6fdFoYhtVqN0dHR/V9nj46OsmPHDkqlEkGXq6OrfJXfjkZj+jqjYRhSr9cHb24jnbXXZnl7flX+YJXf3F5blT9QLJpuoaAsEslVz1woFGZcVxVgcnKSSy65ZBFrJNKeyy+/nPHxcaB3Z9n3qyAIOPLII2d8bGqv0u/S7bVZ0n4HTfoSvgrKMuz06hfJwKAHZIhGkmdap1Ykz9LtdxAll/DdtrNBdWrARstF2pCrkWSRQTAMATmRLL+1EI/12olr9/9+QeWCnpYtA+D2Kw78fuLFPSt2WNqvRpRFNJIssqjCMByKDjYx15XPRPJkWAJyQiPKMkg6eQ3nKiS7+6wd7cCdRCEDp1arDU0Hm1BQlkEwbAE5kQ7K6mIlr5IrSbYrVyF5tjmO1WqVWq2WQa1E5q9UKg1VB5tQUJY8G9aAnEiCcq2ulCz9rdUHufSl1tuVq5AMtOxokzewUqmUYc1E5tbtklZ5pqAseTTsATlRHgkoFYf3/UvyoVYPp02rSAfkTubV5+4V39zRpt/AhjmAiORBuv1qepT0u2E7h2Au6mKl35WKwf459N0GZMjp6hZJRzsxMQFApVKhXC5rdEokB5L2u3379qyrIjKrarWqgCySI0EQXRBnYjKaf1wZ7zwgQw5HkkVEREREFlouQ3IyxaJSqVCpVDTHUSRHkvar0Tnpd+VyWf2LSI6EYbS2d2W8QGW8++ULcxeSm0+i0BxHkfzQOQSSJ0EQ6GTTFHWx0u9q9XD/HORerPOdu16q1UkUSVDWEnDS74b5g5xWCZA80qoskepUqCXgpO+VisG0OcjdBuVchWR3n7GDLZfLWgJO+l6tVhvKjlYBWfJs2INyskqAloCTftfqC8p0UG67vB7UadGY2awdrL6+lX5XKpWGrqNVQJZBMKxBOb2MlrpYyaskKLcr05e8mV1pZpvN7NYs6yGyWPI8x7GT9qqALIMkb0G52z62F+vMivSLPF5M5CrgvIzrILKo8tbRplxFm+1VAVkGTc7a71V02McqIItkfDERd7/RzI7Nsg4iWUh3tHkJke2219nOIRDJs3T77Wed9rEDH5A335h1DWDn7w78vvyk7OqRdvi5Wdeg7/T9FffM7GLgYoBVq1ZlXBuR3sljUJ5Lc3sdhMck0krSfvNuWps9ZMXgB+TYxJe+QVBs/0SuXimv2IstWwbAVHUys3oArDh9PRRHM61Dv+r7kOzuVwBXAKxbt84zro5ITzUH5bxTe5VhMggfAqe12bWrfRgCMkBQbFBcOpXd8UsNLJjCzPGRPZnVA4DGXoXkGfR9SBYZdElQnpiYyLoqIjLEQhiKgJy2b+tYJscdGTswn72697BM6gBQHt2S2bHzYHhagoiIiIjIPGW9BNwngB8ADzOzTWb2x1nWRyQLyTJplUol66rMSu1VJF/abbMBdHUJX5FBk/XqFi/K8vgiWcvTOsJqryLT9fsScG23WWP/lclWLe9sXVmRQaIWIJKRPAXkTrh734cIkU4l7XfQpC/hqxFlGXYKySIZGPSADNFl5HNywQWRtqTb7yBSUBaJKCSLLLJhCMiJHF2ZTGRehqX9KiiLKCSLLKowDIeig03k7BK+IrMaloCcUFCWQdLJazhXIXmuOY5hqEYs/a1Wqw1NB5tQUJZBMGwBOZEOyupiJa+SK0m2K1chebY5jtVqlVqtlkGtROavVCoNVQebUFCWPBvWgJxIgnKtrpQs/a3VB7n0pdbblauQDK3nOCZvYKVSKcOaicwtCHLX5HpGQVnyaNgDcqI8ElAqDu/7l+RDrR5Om1aRDsidLGmYu1d8c0ebfgMb5gAikgfp9qvpUdLvhu0cgrmoi5V+VyoG++fQdxuQIeOLiXQq6WgnJiYAqFQqlMtljU6J5EDSfrdv3551VURmVa1WFZBFciQIogviTExG848r450HZMjhSLKIiIiIyELLZUhOplhUKhUqlYrmOIrkSNJ+NTon/a5cLqt/EcmRMIRtOxtUxgtUxrtfvjB3Ibn5JArNcRTJD51DIHkSBIFONk1RFyv9rlYP989B7sU637nrpVqdRJEEZS0BJ/1umD/IaZUAySOtyhKpToVaAk76XqkYTJuD3G1QzlVIdvcZO9hyuawl4KTv1Wq1oexoFZAlz4Y9KCerBGgJOOl3rb6gTAfltsvrQZ0WjZnN2sHq61vpd6VSaeg6WgVkGQTDGpTTy2ipi5W8SoJyu/SSF1lEwzbHUQFZBsmwBeVerDMr0i86eQ3ncp1kkTxLd7SDHh6H4THKcGluv4Ok0XA2bdsFRCfpTdULjBQbbNkZba/Xjau/d1vH5e8ce2D/77ft7rycbp1oD3BSZkfvP7feM8Fuatz+u2yfk0Q39di2axmT+7wXVQI0kiySiWEYkZrtHAKRPEu330GUDsiaYiHDTC9/kYwMelCe6xwCkTxL2u+gUUAWOUBNQCRDgx6URQbZoH0IdBSQRdLUDEQylgTliYmJrKsiIkNOAVnkADUFEREREZEmCskiGUuWSatUKllXZWDVGjV+teVXhK4rhuVW2IDtG6DXz6GHUblhvbfl5tRUvdDXl5/eVt/FZGNP1tWQ/ZxxthGQffspsY/xwo6elqkl4EQypHWEF8cnb/0kb/zGGzl6+dFc8IgLeOZJz+TUw08lMI0T9LWwAdt+DPd+HjZ9AaZ2wGOvgiP+oHfH2HITfP+lMDIOq58Fa86HVWdCMHf3OGjnERjRdIt+npf8oS1fYUt9knWlwzhn7GROHV3LeGFp1tUaMlEwXh3cxVq7nTHbyS8b53C7n5ZprR4VfJfDjrqHh+qH8ps9D+eu6gnsbKzoqkyFZJGMDHpAdneq1WpfPLaGNygVSuyb2sc//+Sf+def/isrlqxQYO5HzcE43BeN8o4sg8IoePuXlp2VN6A4ChbAxmvgns9CUILVz+LwoEHVfw+3g6/UlbTfQRME/R2U6zRYEhS5b2orn9n+fT6zHdaOVBSYF9z0YFy2fZiH1CkSeoCZR2d+ZiggpB4WWVaY5OzlP+Ds5f/BjsbKrgJzpiHZzM4D3gsUgH9x93/Isj594/Yrsq5BZPONB34//Nzs6pE48eKsa9AzeQ3I7bRZM+u7x1gulikXo7r0LDCrvc5svm12tmA8Mn5gv0ZtYeoJEIxAOV7SLZyCjdfwB0t2AyXu9UdzL2ewjeNxK0xrv/2s0z6234OyYSwJSkD0YVyBeaHMHIynfITouweAqSwreZC6j1BnBPBUYP4BOxqH8IY2y8osJJtZAfgg8DRgE/AjM/u8u/8qqzr1k2s+fRNLC7szrcO6sbs4NtyLAXt3PzDn/gvliPP/EEorMzt+r+U4ILfVZmv1kImqsf2e+6kXl+DB9NG4QqPGK6/8Rsf12bTiFm6641+ZauzjFeFrZt3XPQSM3bt3Nm0ZAWDr5AR//+D7eOd3PkjJxjhu9ImsGz2HVSPHYnME5icUbuPx3/wqxXK2HcXSo7ZTqByaeXsF+N5Rx7GXZdx048zPr9Fgnf2W3wt+wGmFHzBCjYAGVZbSoEj03FTjn8gSdhN8/WX4/s55ZitsKzXKgLH3P942Sz2ckIB9Bw2DjVCsl7BGyJF2E4fzfRpe5La96/ndzpPY1jiBLQ9OzlmPrHTbxy5EUL7ugQ38dPL+qHzvLH5MeYNlwZL9/zczlpgCc+/MNxgfEBKwPvgJjwhu6fioZfbgBDgB6/lxR2UEhOxppPtUOygwt2ter1Iz+wbwLne/PnXbFe7ezdDe2cDt7n5nXN41wLOBmRtwWIfdG2cusdron1Gdbmy+kUeM/4qxYnOHvrjGR3ZQDkPMnMJIhidKPFg6MMKTc+HeCbbtCFm1vEC5/gALca7DArVXaLPNFmhwSGErocFUfR8jNr2jrWM8ovDFjiuzsbqBmxs7WG4FCjZHQI3f1822z7odQqZ8K3ft+TR37/kso0GZs5aewBOXreeQ4rKWdz3Rfsn4sVsoLs02JI+MVbElfdBegZNtkt2MR+OXTZbzEMcFv2KN3UmBBkbIFCN4vPMou2Ypef7f545QZYoiYBTmGOkKgGUt9qlRwPA4HIB5ndNHf8LpS39KgwK37z2Jbr/+NLPXAB9z94e6LKpZ+31sk+ag3K3Ntd0EBiUrUAhLHZUxalEwbmU+gfmxYydzWHAv5RV7CUoNRsaymVc+MlaDoAEGY+N3ZVKHxCFmLOchRqw672CcNkWp66kWS2w3xO8FNe/stQFQ92I09eMgxlQ40nZ58/0odxzwv8zsLHdPPpKf2fbRplsN3JP6/ybgnOadzOxi4GKAQ1auoDoVUh5p/ZG2n8/I7dSD+47K7NjjIwfOEq3uPSyTOpRHt2Ry3IVSq8cBeYbXcI8sRHuFebTZdHtduSL6mnwhv7q16JgzdpqdGLECAcY+r7GjsZsbd/2KE8pHzRiS0/ZtHetZPdqV7uyzaq8wd5s91O7nuOA3GE7Dg/2jvfPTyfNsHd5vOncI4/mXZatRtiqPWLqh63KBI4hGeW8BrgRucPdezO5su49N2mxauv0GQW862V632ZmOUWaEKRpUwynuqD3A7nAfjxk7GYDwqKOBfQtah9m4TzA11SD0ArtpP7z1tC5E4fKEwgaKTBF6wBQlfN4LoPXmufT9ZfX+teHOvL6BajbfkLwdeArwPjP7AvDf2z5Sh9z9CuAKgHVrV/u2nQ1WLeegkFGdCqnVBzAly0ApFYOFDsjQJ+11zeqj93f0CxWU6+6EjTrBPCJF0QqUbOa3vIaH7PMaAQGGcdrocZy19EROKh9FaR4rHcj8/KevZ1P9BI6wTRxjt1MJ7gMM3KMRqVk6sgJ1AuY7omkUvQGEhMw+WhgS0JglqLg7JZvCLOpm762t4bd7H8491XXA/5lnfWYs+y/N7K+APwReAXzAzD4FfMTd7+iq8Pkdv2WbTUvab73eXcM1g6kwJPT5tdlWHFhqpRlDtrszRYOa1wkwlhdGedKyUzh99DiOHjlk2v2MLDOD06CAE2CWbXbZ52Ns5Shurj+Vit3PGruDo4ONGHXMwzkDc0CDQpdfi1p8hKLX52yvs5liCc3vIUlAtg6Gu+f7zm/uXgf+1MwuBG4CDmn7aNPdC6xN/X9NfNvMgiKrDl8bzeccPTCfs1qtsm1yG6NjowNzctfG3Xs5tJztKOrk1AqWNqawTt/NeqC69zDGVj0KxtYNxHMbjF4OYweP1vTYQrRXaLPNNiiwzQ9P1Qqs0KA4tY96cQkBDW5q/H7HlXlwxaE8bO1RNMIa4ztPnn3f6q+4a+9NLAmm/+1DbzDle7B49YI15cdz/NIncET5FIpW4iHgZodZc1kBVt9/N6WV2U5xmNpdJgiWZdpeIWqz94yexDYO56bGH825/xJ2c2LwC84IbuSEIBqZdYwqowd1zGX2cHPjqTzgx8xZ7iOD77KPaB7qr8NHz7jfEbaJcwpfo8rotNuNkKW2PXrd4mysHc8v9z6Su6onUfUlrQvrkLu7mT0APEA0CesQ4DNm9jV3b/dco0T7fewsgoCuX1vPPnw99+6L5oWOVg+fY+/WPrf9ZhqEFFNzeVoG46WtgzHAFl/NeNCg6NvZGxzX+QPqworGj9hTn8Is5Hc7H55JHQCOWHI/+4rRN2AhRTb7Wjb7Wn4W1ucdmEeoscmPZ7Ov6bgea+wOwnhO8v2+rqMyTghuZYntpOYlkqfcPZqrXAqqbYyMHzDfkPzh5Bd3v8rMfgm8qu2jTfcj4CQzO46o4b4QePFcd0ou4Zs+szj5fZDWrDz/j86J1gTN0rafRv+OHsnYaHbTPhhZAatm7uTkIAvRXqHNNlsqBpx2zMFf+ycnLo6OjvG3Fz2l48pcO7EDWA/ABZULZt33qp9dxdu+82MOHV1OPayzs7qTYqGIUeRpxz+P5z7iuTxu7eNYUuwg/Nx+B5x1lNprytNHVsCRT+El826350f/TE3Cg9+Buz8DE9+HZKRvZHm0RNuUcf5ZL4Ejnzp3kanzU86d7QP2g9+CH30PisuiC4tMHTgX5Dfbjue2Xaewe+wsguUrKS2Hh83zEc2Xmb0WeBkwAfwLcKm7T1l0xujvoO0T8hMd9bGz1rWbOwNHlJdxRDmatrR8d2fh9MuTt7AvrM0YjB85ehxHtQjG0p5WgXmt3cFRMwTmSVZxj5/U8fHKqakvnZazxu9kCTtxjMDrlIIaIQXqFNmw5zTu3HcS0Yym+ZtXSHb3y5v+/xPgoraOdHCZdTN7NXAD0ekdV7r7vCZ4JUF5YmICgEqlQrlcHqiQTGll9is67N4YjeCOdfaprmdWPVohuQ0L0V7jcjpus2lJ+92+fXu3VWrLntoeSoUShvGMk57RXTBupvY6XadtdmQc1jwr+jkoMBMtDbcQ6nuiZegAKo+FY/4rHPH7fPmDV7Jy5cqFXoVmFXCBu087K93dQzObeyh+Br1qr/1od1hlxAoKxoskHZiDFoG5yFTmayQnSkENc6cWlvj5zkfyn9UT2NI4kk4/4mU60S4++/76OXccNv0yraBf6iF9I69t9syjz+RFv/cizn/Y+b0Lxol+aSf9Uo9eaRWYH/gajPf4q+nlD4M1z4lGp4/4/enrMi8Cd//rWbbd1mXZuWyvs3nM0pNpECoYZ2R6YG7sn5IxER6ZddXY5Mdx784Kd+w7iXv2rAaM0kh358Dk8myU5OvaSqUCkItF3UUkcmC6xejcO/fIqYefynvOe8+iHU96LB2Ye23p0XDm+1puKpfLuVzTfJD9lxWPyroKEgspsNnXdDUXuZfu8ZPZuGMZYRhQGom+Ger2ZPE+u47O3JovxJCeoxwO4hpwIgMk3X6DfruMl0iTIAj29y8DNZ2vQ33yjbrIjDy0/aE4vapSp/Ewd71Uq0/1SVCu1RbwkqUiPTDMH+TyeqVBGW7pgZhhDsphGAUQkX5mgU8bNe42KOcqJLv7jB1suVymVOr8Ki0ii6FWqw1lR6uALHk27EE5DKOvrbNeXlBkLq0+xqWDcrtyFZLNbNYOVl/fSr8rlUpD19EqIMsgGNagnATkkWJjAa6DJrI4kqDc9v0WoC4iMoNhm+OogCyDZNiCcjogawxK8q6T13AuV7cQybPmC+IMcngchscow6XVBa0GRaFgrFkVXeyjOhWybWeDIw4tUB6JLxixp8FLHr92tiJmdcuuvft/P2PZI7qrbDc2b2Hino4vPjhwTl1bgfIqzjk82+ck0U09Lr9+C+NL259WMRN9NhTJwDCMSM12DoFInqXb7yBKAvKq5QcCssgw0qtfJCODHpTnOodAJM+S9jtoFJBFDlALEMnQoAdlkUE2cB8CHQVkkRS1ApGMJUF5YmIi66qIyBALQQFZJEUtQURERESkiUKySMaSZdIqlUrWVRGRIRYQTbeoTg3vlUFF0hSSRTKkdYRF8mvgziOwaLqFgrJIRCFZJCODHpDdffBChEgsab+DpjwSKCiLxBSSRTIw6AEZoiXgtGqHDKJ0+x1ECsoiEYVkkUU2DAE5oeXtZNAMS/tVUBZRSBZZVGEYDkUHm9A60DJIhiUgJxSUZZB08hrOVUiea45jGKoRS3+r1WpD08EmFJRlEAxbQE6kg7K6WMmr5EqS7cpVSJ5tjmO1WqVWq2VQK5H5K5VKQ9XBJhSUJc+GNSAnkqBcqyslS39r9UEufan1duUqJEPrOY7JG1ipVMqwZiJzC4LcNbmeUVCWPBr2gJwojwSUisP7/iX5UKuH06ZVpANyJ1eSzN0rvrmjTb+BDXMAEcmDdPvV9Cjpd8N2DsFc1MVKvysVg/1z6LsNyADFHtdvUSQd7cTEBACVSoVyuazRKZEcSNrv9u3bs66KyKyq1aoCskiOBEF0QZyJyWj+cWW884AMORxJFhERERFZaLkMyckUi0qlQqVS0RxHkRxJ2q9G56Tflctl9S8iORKGsG1ng8p4gcp498sXZhKSzez5ZrbBzEIzO7Od+zafRKE5jiILr5s2m6ZzCCRPgiDI5cmmvWqvzdTFSr+r1cP9c5B7sc53Vr3UrcAFwI3t3rHVSRRJUNYScNLvcvxBruM2m9AqAZJHOV2Vpev22qw6FWoJOOl7pWIwbQ5yt0E5kxP33P02iNY9bvN+M3aw5XJZS8BJ36vValSr1dyFxE7bbEIBWfIsHZTz8Brutr02S1YJGC0N3rc/o+FdWVeBI5bcn3UVBkarLyijoExHFxPp+9UtzOxi4GJgzjcnfX0r/a5UKuWmo+1Ec3sFBWQZDHkLyvM1rc0esuKg7elltKpTvtjVWzCVo1ZBYzS7CuyZYuk4MLKCw0cO/rsvqsIojK3Ltg4LLAnK7VqwkGxmXweObLHpze7+ufmW4+5XAFcArFu3bnBaqAyl9BzHfutoe9Fmm9urArIMkn4KygvSxx6zelof27zObHWq/ZG4vlUcjX6yUtsKpVVQPjS7OiTG1g18SAY6WgpuwUKyuz91ocoWybN+6mjTFqLN9ttjFOlWc/vNykL3sb24EEPfOvzcrGvQH3WQOQ3YK18kH3J6MlBbZjuHQCTP0u13EA10QBZpQ1ZLwD3XzDYBjwW+ZGY3ZFEPkSzlKSh30mbNTAFZBlbSfvtRN32sArLIAVmtbnEdcF0WxxbpJ/3y1e1c1GZFDtavHwI7bq+OArJIilqBSMaSoDwxMZF1VURkiIWggCySopYgIiIiItJEIVkkY8kyaZVKJeuqiMgQC6CrS/iKDBqFZJEMaR1hkfzq9xNu22Z0dQlfkUGjkCySkUEPyO4+eCFCJJa030ETXZlMQVkEFJJFMjHoARmiJeDysLydSLvS7XcQKSiLRBSSRRbZMATkRF7WgRaZr2FpvwrKIgrJIosqDMOh6GATebpgishchiUgJxSUZZB08hrOVUiea45jGKoRS3+r1WpD08EmFJRlEAxbQE6kg7K6WMmr5EqS7cpVSJ5tjmO1WqVWq2VQK5H5K5VKQ9XBJhSUJc+GNSAnkqBcqyslS39r9UEufan1duUqJEPrOY7JG1ipVMqwZiJzC4LcNbmeUVCWPBr2gJwojwSUisP7/iX5UKuH06ZVpANyJ1eSzN0rvrmjTb+BDXMAEcmDdPvV9Cjpd8N2DsFc1MVKvysVg/1z6LsNyADFHtdvUSQd7cTEBACVSoVyuazRKZEcSNrv9u3buyrngsoFvamQDKYTL+66iGq1qoAskiNBEF0QZ2Iymn9cGe88IEMOR5JFRERERBZarkaSG40GGzduJAxD6vU6xWJU/bvvvptisYi7c/nll3d9nDAMqdVq++c4J7/3ajqHyh/e8icmJnjooYd6cpx+l7TXZkn7DYKAD33oQwP1/Kr8wSp/165dTE5OUiwWNZ1PJAfCELbtbFAZj07Si6ZbMBzTLdwdgHq9TqlU2h+SgyCgVqtRKBQYHx/v6hitTtLo5YkbKn+4yy8UCvtft4PO3Q96rPV6fX/7BVi5cuVAPb8qf7DK37FjB3AgiA9L252JTiOQflerh9PmIK9a3l1Qzt1H41ZvVsVikVKpRKPR6Gpe8kxvtr06K1/lq/xGo/11GgdFvV5v2X4H6flV+YNXftK/1Go16vV6x+XnXXUq1BJw0vdKxWBaGO72gji5C8kzfZovFosUCoWO3yjnGo3o9o1Y5av8VatW0Wg0hrKjnSkgJwbl+VX5g1n+sAflZJUALQEn/a7VrKh0UG67vB7UaVHN9nVXoVDo6I1yvl/XdfpGrPJVflJ+oVAYuo52roCcGITnV+UPbvnDGpTTy2hpWrbkVRKU2zVwL/l23yjbnc+m8lV+N+UXCoWh6mjnG5ATeX9+Vf5glz9sQbkX68yK9IuhmJM8H/N9o+z0hA+Vr/K7KX+YOtpOTnjK+/Or8ge7/GFpvwrIIgMakmHuN8pO3yBVvsrvRfnD0tF2uiJA3p9flT/Y5afb7yBSQBaJ5Go9m/vuu2/i0ksvPXjh1d6qABMLfIz5UD36qw6wOPVYt8DlL5r77rtv4rLLLlN7XVyqx3Rqs21SQBY5IFch2d0PW+hjmNmP3f3MhT6O6pGvOvRTPfJC7VX1UD1yxlFAFklRKxARERkgZvaPZvZrM/uFmV1nZivnc78QFJBFUtQSREREBsvXgFPd/TTgt8BlGddHJJcUkg92RdYViKkeB/RDHaB/6iEH9MtzonpMp3pkyN2/6u7JGcH/AayZz/0C6PjKZCKDSCG5ibv3xZuq6tFfdYD+qYcc0C/PieoxnerRVy4CvjyvPY2uLuErMmhydeKeiIiIgJl9HTiyxaY3u/vn4n3eDNSBq2cp52LgYoBVh6yIr0yWnMDX2QUYRAaFXv0tdHrSQ4/r8Hwz22BmoZkt+tnZZnaemf3GzG43szcu9vHjOlxpZpvN7NYsjp+qx1oz+5aZ/Sp+Tl6bZX1kun5or3E9Mmuz/dBe43pk3maHpb26+1Pd/dQWP0lAvhD4I+Al7u6zlHOFu5/p7mcuWzYGHLiEb69HlM9Ydsb+H5FpDj/3wE8fUUhurR9OergVuAC4cbEPbGYF4IPA04H1wIvMbP1i1wO4Cjgvg+M2qwN/4e7rgccAr8ro7yGt9UN7hYzabB+1V+iPNjv07dXMzgPeAJzv7ns6KWOhgrJInigkt9DpSQ89rsNt7v6bxT5u7Gzgdne/091rwDXAsxe7Eu5+I7BtsY/boh73u/st8e87gduA1dnWShL90F7jemTVZvuivUJ/tFm1VwA+ACwHvmZmPzOzD3dSiIKyDJJOXsOakzy3i4BPZl2JRbYauCf1/03AORnVpa+Y2bHAo4AfZlwVaU3tVe11v2Ftr+5+Yq/KSs9RHi1pXE3yKbmSZLuGNiT36qSHha6D9A8zWwZ8Fnidu09mXZ9h0g/tdb71kP6g9to7SVDesbv9kCGymMIWg8XpS623a2hDsrs/dbbtqZMenjLbSQ8LWYcM3QusTf1/TXzb0DKzEaIO92p3vzbr+gybfmiv86lHRtRem6i99l55JKBUXLCmJdITtXpIdcr2r8qSDsidrNSi705a6MVJDzn3I+AkMzvOzErAC4HPZ1ynzJiZAR8BbnP3d2ddH5lO7VXtNU3tdeEESgzS50rFYP8c+m4DMigkz6QnJz10w8yea2abgMcCXzKzGxbr2PFJUK8GbiA66eVT7r5hsY6fMLNPAD8AHmZmm8zsjxe7DrHHAy8Fnhy/Hn5mZs/IqC5ysMzbK2TXZvulvULftFm1V5EhFQTRBXEmJhtMTHYXkGGIp1vMppcnPXRRh+uA6zI8/vXA9VkdP67Di7I8fsLdbwIs63pIa/3QXiHbNtsP7TWuR+ZtVu1VRHpFIVlEREREci8Mo5VYKuPRSXrdXjlS0y1ERERkTq1WDhDpJ7V6uH+KRS/W+VZIFhERkVlVp0JqdaVk6W+lYjBt1LjboKyQLCIiIjNKVgkoFRUZpL+1WoElHZTbLq8HdRIREZEBlF5GS0vASV4lQbldesmLiIjIQXqxzqxIv9DFRERERKRrCsgiWgJOWjCztwPb3P098f/fAWx29/dmWjEROYiZnUV0hbmzgQJwM/ACd78104pJ7jQasHFzjTB06g0oFuCBhw5ciroROpdfv2VeZYVhtNJAqRgcNE1jtm3zpfJVfqttE5N1HtrV/tzjmSgkSytXAtcC7zGzgOgyt2dnWyURacXdf2Rmnwf+FhgFPqaALJ1wHDwKyKUiFFucqDe+dP7zOqtTdtBodC9HqFW+ym/20K4GxULvriWkkCwHcff/NLOtZvYo4Ajgp+6+Net6iciM3g78CNgH/FnGdZEcq9VnDsjtik6WOnBBB6CnUzhUvspfaArJMpN/AS4EjiQaWRaR/nUosAwYAZYAu7OtjuRVrwJyIgk6E5PRV+CV8d4GHJWv8heSZuPLTK4DzgPOAm7IuC4iMrvLgb8CrgbemXFdREQGgkaSpSV3r5nZt4Dt7t67WfAi0lNm9jJgyt0/bmYF4Ptm9mR3/2bWdZP8qdUBwp6NJidzSCvj0Vzm5KvzXo0GqnyVv5A0kiwtxSfsPYborHkR6VPu/lF3f178e8Pdz1FAlk6VilFQrvfgEtTNJ1l1e4lgla/ye1n+fCgky0HMbD1wO/ANd/9d1vUREZHFUSwGPQnKM61C0Kugo/JV/mIEZXP3ufcSERGRgWZmW4CNC3yYCjCxwMeYj36oRz/UAYarHuvc/bD57qyQLCIiIovCzH7s7meqHv1RB9VjdppuISIiIiLSRCFZRERERKSJQrKIiIgsliuyrkCsH+rRD3UA1WNGmpMsIiIiItJEI8kiIiIiIk0UkkVERGRRmNk/mtmvzewXZnadma3MqB7PN7MNZhaa2aKvqGBm55nZb8zsdjN742IfP67DlWa22cxuzeL4cR3Wmtm3zOxX8fPx2qzq0opCsoiIiCyWrwGnuvtpwG+ByzKqx63ABcCNi33g+PLxHwSeDqwHXhRfxGuxXQWcl8Fx0+rAX7j7eqKr/L4qo79FSwrJIiIisijc/avuXo//+x/AmozqcZu7/yaLYwNnA7e7+53uXgOuAZ692JVw9xuBbYt93KY63O/ut8S/7wRuA1ZnWac0hWQRERHJwkXAl7OuRAZWA/ek/r+JPgqGWTGzY4FHAT/MuCr7FbOugIiIiAwOM/s6cGSLTW9298/F+7yZ6Kv2q7Osh/QHM1sGfBZ4nbtPZl2fhEKyiIiI9Iy7P3W27WZ2IfBHwFN8AdehnaseGboXWJv6/5r4tqFkZiNEAflqd7826/qkabqFiIiILAozOw94A3C+u+/Juj4Z+RFwkpkdZ2Yl4IXA5zOuUybMzICPALe5+7uzrk8zhWQRERFZLB8AlgNfM7OfmdmHs6iEmT3XzDYBjwW+ZGY3LNax4xMXXw3cQHSi2qfcfcNiHT9hZp8AfgA8zMw2mdkfL3YdgMcDLwWeHL8efmZmz8igHi3pinsiIiIiIk00kiwiIiIi0kQhWURERESkiUKyiIiIiEgThWQRERERkSYKySIiIiIiTRSSRURERESaKCSLiIiIiDRRSBYRERFZJGZ2lpn9wsyWmNmYmW0ws1OzrpccTBcTEREREVlEZva3wBJgFNjk7n+fcZWkBYVkERERkUVkZiXgR8A+4HHu3si4StKCpluIiIiILK5DgWXAcqIRZelDGkkWERERWURm9nngGuA44Ch3f3XGVZIWillXQERERGRYmNnLgCl3/7iZFYDvm9mT3f2bWddNptNIsoiIiIhIE81JFhERERFpopAsIiIiItJEIVlEREREpIlCsoiIiIhIE4VkEREREZEmCskiIiIiIk0UkkVEREREmigki4iIiIg0+f8R2pFeEyNuNQAAAABJRU5ErkJggg==\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(1, 3, tight_layout=True, figsize=(10, 5))\n",
    "\n",
    "for dim, ax in zip('xyz', axes):\n",
    "    sim_forward.plot(**{dim:0}, ax=ax)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e46de8d-e037-4aaa-bc13-c23facda4bd5",
   "metadata": {},
   "source": [
    "### Defining Objective Function\n",
    "\n",
    "Next, we'll define the objective function as the sum of the absolute value of the fields at the \"intensity\" monitor location.\n",
    "\n",
    "We write this function as a function of the `SimulationData` returned by the solver to make it simple to compute after the fact."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ecd00b21-088d-43c3-ad33-ed7cdebd7db6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:35:04.675064Z",
     "iopub.status.busy": "2022-07-21T20:35:04.674932Z",
     "iopub.status.idle": "2022-07-21T20:35:04.677538Z",
     "shell.execute_reply": "2022-07-21T20:35:04.677248Z"
    }
   },
   "outputs": [],
   "source": [
    "def compute_objective(sim_data):\n",
    "    \"\"\" Computes both the (complex-valued) electric fields at the measure point and the intensity (the objective function).\"\"\"\n",
    "\n",
    "    # get the measurement monitor fields and positions\n",
    "    measure_monitor = sim_data.simulation.get_monitor_by_name(measurement_monitor_name)\n",
    "    measure_amps = sim_data[measurement_monitor_name].amps.sel(direction='+')\n",
    "    \n",
    "    # sum their absolute values squared to give intensity\n",
    "    power = np.sum(np.abs(measure_amps)**2)\n",
    "\n",
    "    # return both the complex-valued raw fields and the intensity\n",
    "    return measure_amps, power"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc8cf727-46e2-4db4-9a56-0ed2bee0b9a8",
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    "### Running forward simulation\n",
    "\n",
    "Finally, we will run the forward simulation and evaluate the objective function and the fields at the measurement point."
   ]
  },
  {
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   "id": "9411639c-c481-44c8-be1e-77dd4a78fa65",
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     "shell.execute_reply": "2022-07-21T20:35:58.397293Z"
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       "\u001b[2;36m[16:35:51]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = success                                            \u001b]8;id=818350;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=292156;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#298\u001b\\\u001b[2m298\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></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\">[16:35:52] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> downloading file <span style=\"color: #008000; text-decoration-color: #008000\">\"output/monitor_data.hdf5\"</span> to              <a href=\"file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#574\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">574</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         <span style=\"color: #008000; text-decoration-color: #008000\">\"data/forward.hdf5\"</span>                                         <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[16:35:52]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m downloading file \u001b[32m\"output/monitor_data.hdf5\"\u001b[0m to              \u001b]8;id=46234;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=864090;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#574\u001b\\\u001b[2m574\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m         \u001b[32m\"data/forward.hdf5\"\u001b[0m                                         \u001b[2m             \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b39fe62b971e4ab78adf3acce7d6b7a0",
       "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[16:35:58] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> loading SimulationData from data/forward.hdf5               <a href=\"file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#398\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">398</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[16:35:58]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m loading SimulationData from data/forward.hdf5               \u001b]8;id=648536;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=543133;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#398\u001b\\\u001b[2m398\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data_forward = web.run(sim_forward, task_name='forward', path='data/forward.hdf5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "75d353eb-122e-42bd-98a5-d9e35755ccea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:35:58.722914Z",
     "iopub.status.busy": "2022-07-21T20:35:58.722810Z",
     "iopub.status.idle": "2022-07-21T20:35:58.726241Z",
     "shell.execute_reply": "2022-07-21T20:35:58.725943Z"
    }
   },
   "outputs": [],
   "source": [
    "measured_amps_forward, objective_fn = compute_objective(sim_data_forward)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "686512e6-dc3d-4e17-ae64-c8bee6e3dcfa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:35:58.727577Z",
     "iopub.status.busy": "2022-07-21T20:35:58.727411Z",
     "iopub.status.idle": "2022-07-21T20:35:58.730427Z",
     "shell.execute_reply": "2022-07-21T20:35:58.730136Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Tidy3dDataArray (f: 1, mode_index: 3)>\n",
      "array([[ 0.13917935+0.71563459j, -0.0029132 +0.00777597j,\n",
      "        -0.11693535+0.1247125j ]])\n",
      "Coordinates:\n",
      "    direction   <U1 '+'\n",
      "  * f           (f) float64 2.998e+14\n",
      "  * mode_index  (mode_index) int64 0 1 2\n",
      "Attributes:\n",
      "    units:      sqrt(W)\n",
      "    long_name:  mode amplitudes <xarray.DataArray ()>\n",
      "array(0.56079979)\n",
      "Coordinates:\n",
      "    direction  <U1 '+'\n"
     ]
    }
   ],
   "source": [
    "print(measured_amps_forward, objective_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "23de56fa-5e2e-435e-8be6-56f69a85a57f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:35:58.731779Z",
     "iopub.status.busy": "2022-07-21T20:35:58.731571Z",
     "iopub.status.idle": "2022-07-21T20:35:59.082774Z",
     "shell.execute_reply": "2022-07-21T20:35:59.082387Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">&lt;</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff; font-weight: bold\">Figure</span><span style=\"color: #000000; text-decoration-color: #000000\"> size 576x432 with </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span><span style=\"color: #000000; text-decoration-color: #000000\"> Axes</span><span style=\"font-weight: bold\">&gt;</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 576x432 with \u001b[0m\u001b[1;36m2\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, tight_layout=True, figsize=(8, 6))\n",
    "ax = sim_data_forward.plot_field('field_pattern', 'Ey', val='real', freq=freq0, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5784623a-c351-4d8b-a53b-1e38ee226515",
   "metadata": {},
   "source": [
    "## Adjoint Problem\n",
    "\n",
    "Now that we have the fields at the measurement position, we can define the adjoint source and simulation.\n",
    "\n",
    "### Adjoint source\n",
    "\n",
    "The adjoint source is defined by the derivative of the forward objective with respect to its fields.\n",
    "\n",
    "Since the objective here is given by the norm squared of each of the modal amplitudes, it's simple to show that the adjoint source is composed of the sum of each of the modal profiles, each weighted with the complex conjugate of the respecitve amplitude.\n",
    "\n",
    "Therefore, we will inject a point source for each component of the measured E with the correct amplitude and phase to take the complex conjugate into account."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "67384ca7-f3b7-4ad9-9e78-8112d63830bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:35:59.084352Z",
     "iopub.status.busy": "2022-07-21T20:35:59.084235Z",
     "iopub.status.idle": "2022-07-21T20:35:59.088167Z",
     "shell.execute_reply": "2022-07-21T20:35:59.087897Z"
    }
   },
   "outputs": [],
   "source": [
    "adjoint_sources = []\n",
    "\n",
    "for mode_index in range(num_modes):\n",
    "\n",
    "    amp_forward = complex(measured_amps_forward.sel(mode_index=mode_index).values)\n",
    "    \n",
    "    adjoint_sources.append(\n",
    "        td.ModeSource(\n",
    "            source_time=td.GaussianPulse(\n",
    "                freq0=freq0,\n",
    "                fwidth=freqw,\n",
    "                phase=float(+ np.pi / 2 - np.angle(amp_forward)),\n",
    "                amplitude=np.abs(amp_forward),\n",
    "            ),\n",
    "            center=measurement_monitor.center,\n",
    "            size=measurement_monitor.size,\n",
    "            direction=\"-\",\n",
    "            mode_index=mode_index,\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e209ac5-133e-4810-bbeb-8b1fdc550642",
   "metadata": {},
   "source": [
    "### Adjont simulation\n",
    "\n",
    "We then make an adjoint simulation, which is just a copy of the base simulation with the adjoint sources added."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e5940a0b-dad6-4835-8635-0e7d385b7a19",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:35:59.089579Z",
     "iopub.status.busy": "2022-07-21T20:35:59.089416Z",
     "iopub.status.idle": "2022-07-21T20:35:59.094955Z",
     "shell.execute_reply": "2022-07-21T20:35:59.094461Z"
    }
   },
   "outputs": [],
   "source": [
    "sim_adjoint = sim_base.copy(update={'sources': list(sim_base.sources) + adjoint_sources})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "16feb6a0-1ad2-416c-bd62-f778231cb4a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:35:59.096637Z",
     "iopub.status.busy": "2022-07-21T20:35:59.096265Z",
     "iopub.status.idle": "2022-07-21T20:35:59.271095Z",
     "shell.execute_reply": "2022-07-21T20:35:59.270639Z"
    }
   },
   "outputs": [
    {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(1, 3, tight_layout=True, figsize=(10, 5))\n",
    "\n",
    "for dim, ax in zip('xyz', axes):\n",
    "    sim_adjoint.plot(**{dim:0}, ax=ax)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "744a32d9-cdd6-4d3c-b2fe-776c6a1586c0",
   "metadata": {},
   "source": [
    "### Running adjoint simulation\n",
    "\n",
    "Let's run the adjoint simulation to get the adjoint fields at the box locations so we can compute the gradient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3a0c3306-0853-4ed9-be91-a8d82fb4f608",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:35:59.272791Z",
     "iopub.status.busy": "2022-07-21T20:35:59.272647Z",
     "iopub.status.idle": "2022-07-21T20:36:52.539364Z",
     "shell.execute_reply": "2022-07-21T20:36:52.538948Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         <span style=\"color: #008000; text-decoration-color: #008000\">'7b0734de-0d3e-4ace-885a-436a2531b3dc'</span>.                     <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[16:36:00]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Uploaded task \u001b[32m'adjoint'\u001b[0m with task_id                        \u001b]8;id=862024;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=348362;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#120\u001b\\\u001b[2m120\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m         \u001b[32m'7b0734de-0d3e-4ace-885a-436a2531b3dc'\u001b[0m.                     \u001b[2m             \u001b[0m\n"
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      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
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       "\n"
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       "</pre>\n"
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       "\u001b[2;36m[16:36:03]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = queued                                             \u001b]8;id=455390;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=835932;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#253\u001b\\\u001b[2m253\u001b[0m\u001b]8;;\u001b\\\n"
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       "</pre>\n"
      ],
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       "\u001b[2;36m[16:36:07]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Maximum flex unit cost: \u001b[1;36m0.20\u001b[0m                                \u001b]8;id=619583;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=578332;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#244\u001b\\\u001b[2m244\u001b[0m\u001b]8;;\u001b\\\n"
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       "</pre>\n"
      ],
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       "\u001b[2;36m[16:36:24]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m starting up solver                                          \u001b]8;id=277374;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=860231;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#269\u001b\\\u001b[2m269\u001b[0m\u001b]8;;\u001b\\\n"
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       "\u001b[2;36m[16:36:34]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m running solver                                              \u001b]8;id=980602;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=800317;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#275\u001b\\\u001b[2m275\u001b[0m\u001b]8;;\u001b\\\n"
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       "\u001b[2;36m[16:36:39]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m early shutoff detected, exiting.                            \u001b]8;id=209824;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=264333;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#286\u001b\\\u001b[2m286\u001b[0m\u001b]8;;\u001b\\\n"
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       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = postprocess                                        \u001b]8;id=95767;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=166420;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#292\u001b\\\u001b[2m292\u001b[0m\u001b]8;;\u001b\\\n"
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       "\u001b[2;36m[16:36:45]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m status = success                                            \u001b]8;id=46838;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=128441;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#298\u001b\\\u001b[2m298\u001b[0m\u001b]8;;\u001b\\\n"
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       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         <span style=\"color: #008000; text-decoration-color: #008000\">\"data/adjoint.hdf5\"</span>                                         <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
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       "\u001b[2;36m[16:36:46]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m downloading file \u001b[32m\"output/monitor_data.hdf5\"\u001b[0m to              \u001b]8;id=397452;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=547795;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#574\u001b\\\u001b[2m574\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m         \u001b[32m\"data/adjoint.hdf5\"\u001b[0m                                         \u001b[2m             \u001b[0m\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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       "\u001b[2;36m[16:36:52]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m loading SimulationData from data/adjoint.hdf5               \u001b]8;id=31502;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=342539;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#398\u001b\\\u001b[2m398\u001b[0m\u001b]8;;\u001b\\\n"
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   "source": [
    "sim_data_adjoint = web.run(sim_adjoint, task_name='adjoint', path='data/adjoint.hdf5')"
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  {
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   "id": "70d9b136-1d1a-497f-9811-d9c698185180",
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     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">&lt;</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff; font-weight: bold\">Figure</span><span style=\"color: #000000; text-decoration-color: #000000\"> size 1008x360 with </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span><span style=\"color: #000000; text-decoration-color: #000000\"> Axes</span><span style=\"font-weight: bold\">&gt;</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 1008x360 with \u001b[0m\u001b[1;36m4\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(14, 5))\n",
    "\n",
    "ax1 = sim_data_forward.plot_field('field_pattern', 'Ey', val='real', freq=freq0, ax=ax1)\n",
    "ax2 = sim_data_adjoint.plot_field('field_pattern', 'Ey', val='real', freq=freq0, ax=ax2)\n",
    "\n",
    "ax1.set_title('forward')\n",
    "ax2.set_title('adjoint')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f5b0755-ed4e-4a80-9870-f36b968415a1",
   "metadata": {},
   "source": [
    "### Computing adjoint gradient\n",
    "\n",
    "Now that we have both the forward and adjoint fields at the box locations, we can compute the gradient by taking the product of the electric field components and summing them over the volume of each box.\n",
    "\n",
    "We'll write two functions to help out with this.  \n",
    "\n",
    "1. The first will grab the electric fields within the box volumes at the yee cell locations.  Note that the first and last cells are placed outside of the box volume to be able to interpolate to the box boundaries, so those can be excluded with the `slice(first, last)` operation.\n",
    "\n",
    "2. The second will unpack the electric fields for both forward and adjoint simulations and sum their products together.  The result will be a dictionary mapping the orginal structure names to the gradient of the measured intensity with respect to the structure permittivity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "acbbe519-6fed-48dd-b0c8-192d8849b2d1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:36:53.593173Z",
     "iopub.status.busy": "2022-07-21T20:36:53.592997Z",
     "iopub.status.idle": "2022-07-21T20:36:53.598009Z",
     "shell.execute_reply": "2022-07-21T20:36:53.597726Z"
    }
   },
   "outputs": [],
   "source": [
    "def unpack_grad_monitors(sim_data):\n",
    "    \"\"\"Grab the electric field within each of the structures' volumes and package as a dictionary.\"\"\"\n",
    "    \n",
    "    def select_volume_data(scalar_field_data, box):\n",
    "        \"\"\"select the fields within the volume of a box, excluding boundaries.\"\"\"\n",
    "        scalar_field_f0 = scalar_field_data.isel(f=0)\n",
    "\n",
    "        # grab the coordinates of the data\n",
    "        xs = scalar_field_f0.coords['x']\n",
    "        ys = scalar_field_f0.coords['y']\n",
    "        zs = scalar_field_f0.coords['z']\n",
    "        \n",
    "        # get the bounds of the box\n",
    "        (xmin, ymin, zmin), (xmax, ymax, zmax) = box.bounds\n",
    "\n",
    "        # compute the indices where the coordinates are inside the box\n",
    "        in_x = np.where(np.logical_and(xs >= xmin, xs <= xmax))\n",
    "        in_y = np.where(np.logical_and(ys >= ymin, ys <= ymax))\n",
    "        in_z = np.where(np.logical_and(zs >= zmin, zs <= zmax))\n",
    "        \n",
    "        # select the coordinates at these indices\n",
    "        x_sel = xs[in_x]\n",
    "        y_sel = ys[in_y]\n",
    "        z_sel = zs[in_z]\n",
    "    \n",
    "        # select the scalar field data only at the points inside the box\n",
    "        return scalar_field_f0.sel(x=x_sel, y=y_sel, z=z_sel)\n",
    "\n",
    "    \n",
    "    def unpack_box(field_data, box):\n",
    "        \"\"\"Unpack an individual FieldData for a given box.\"\"\"\n",
    "    \n",
    "        # get the electric field components\n",
    "        Ex = field_data.Ex\n",
    "        Ey = field_data.Ey\n",
    "        Ez = field_data.Ez\n",
    "\n",
    "        # select their volume data and stack together along first axis\n",
    "        fields_in_volume = [select_volume_data(field, box) for field in (Ex, Ey, Ez)]\n",
    "        return fields_in_volume\n",
    "        \n",
    "    # unpack field data in each box\n",
    "    return {box.name: unpack_box(sim_data[box.name], box.geometry) for box in boxes_quad}\n",
    "\n",
    "\n",
    "def calc_gradient_adjoint_yee(sim_data_forward, sim_data_adjoint, **kwargs):\n",
    "    \"\"\"Compute the gradient from both the forward SimulationData and the adjoint SimulationData.\"\"\"\n",
    "\n",
    "    # grab the electric fields from forward and adjoint at each of the box locations\n",
    "    E_dict_forward = unpack_grad_monitors(sim_data_forward)\n",
    "    E_dict_adjoint = unpack_grad_monitors(sim_data_adjoint)\n",
    "\n",
    "    def compute_derivate(E_forward, E_adjoint):\n",
    "        \"\"\"Compute adjoint derivative given the forward and adjoint fields within a box.\"\"\"\n",
    "        dV = dl ** 3\n",
    "        field_sums = [np.sum(dV * Efor * Eadj) for Efor, Eadj in zip(E_forward, E_adjoint)]\n",
    "        return sum(field_sums)\n",
    "\n",
    "    # compute gradient for each box\n",
    "    return {quad: compute_derivate(E_dict_forward[quad], E_dict_adjoint[quad]) for quad in quadrants}\n"
   ]
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    "Now we can call this function on our forwrd and adjoint simulation data."
   ]
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    "grad_adj_dict = calc_gradient_adjoint_yee(sim_data_forward, sim_data_adjoint)"
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    "## Numerical Gradient\n",
    "\n",
    "As a sanity check, we can compare our adjoint-computed gradient against one computed using numerical derivatives.\n",
    "\n",
    "Recall that the derivative of a function `f(x)` can be approximated using numerical derivatives using `df/dx ~ [f(x+d) - f(x-d)] / 2d` and a step size of `d`.\n",
    "\n",
    "Therefore, we can approximate the gradient by running two forward simulations for each box, where we manually shift the permittivity by a small `d` value and compute the change in objective function value.\n",
    "\n",
    "We note that compared to adjoint, this is extremely inneficient as it requires `O(N)` simulations to compute a gradient of length `N`, wheras adjoint only requires a single additional simulaton and is therefore `O(1)`.\n",
    "\n",
    "So this approach works best for checking on small problems, such as this one, where `N=4`."
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   "source": [
    "# step size\n",
    "delta = 1e-3\n",
    "\n",
    "sims_batch_numerical = {}\n",
    "\n",
    "for quad_name in quadrants:\n",
    "    \n",
    "    def perturb_sim(quad_name, sign):\n",
    "        perturbed_structures = []\n",
    "        for structure in sim_forward.structures:\n",
    "            if structure.name == quad_name:\n",
    "                new_medium = structure.medium.copy(update={'permittivity': structure.medium.permittivity + sign * delta})\n",
    "                structure = structure.copy(update={'medium': new_medium})\n",
    "            perturbed_structures.append(structure)\n",
    "        return sim_forward.copy(update={'structures': perturbed_structures})\n",
    "    \n",
    "    sims_batch_numerical[quad_name + '_plus'] = perturb_sim(quad_name, +1)\n",
    "    sims_batch_numerical[quad_name + '_minus'] = perturb_sim(quad_name, -1)\n",
    "    \n",
    "# run a batch of each of these 8 calculations at once\n",
    "batch_data = web.Batch(simulations=sims_batch_numerical).run(path_dir='data')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40c6d37b-d0fd-4eb0-b7ad-b95ce8972e3e",
   "metadata": {},
   "source": [
    "### Computing numerical derivatve\n",
    "\n",
    "Next, we use the numerical derivative formula to compute the derivative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "33f8f31d-4654-4638-8151-ec1e15e64e9d",
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     "iopub.status.idle": "2022-07-21T20:40:02.873192Z",
     "shell.execute_reply": "2022-07-21T20:40:02.872844Z"
    }
   },
   "outputs": [
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   "source": [
    "# dict to store the objective functions {name: [f(x-d), f(x+d)]} for each simulation in batch\n",
    "obj_dict = {name:[None, None] for name in quadrants}\n",
    "\n",
    "for task_name, sim_data_delta in batch_data.items():\n",
    "\n",
    "    # compute the objective function f(x)\n",
    "    _, objective_fn_delta = compute_objective(sim_data_delta)\n",
    "    \n",
    "    # grab the original monitor name and also the direction of perturbation\n",
    "    monitor_name, pm = task_name.split('_')\n",
    "    index = 0 if pm == 'minus' else 1\n",
    "    \n",
    "    # add this objective function to the dict\n",
    "    obj_dict[monitor_name][index] = objective_fn_delta\n",
    "\n",
    "# process the objective function dict to compute the numerical derivative\n",
    "grad_num_dict = {}\n",
    "for monitor_name in quadrants:\n",
    "    \n",
    "    # strip out [f(x-d), f(x+d)]\n",
    "    objective_fn_minus, objective_fn_plus = obj_dict[monitor_name]\n",
    "    \n",
    "    # compute [f(x+d) - f(x-d)] / 2d\n",
    "    grad_num = (objective_fn_plus - objective_fn_minus) / 2 / delta\n",
    "    grad_num_dict[monitor_name] = grad_num\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfb7d0e9-c9d3-413e-870b-5b05c9454675",
   "metadata": {},
   "source": [
    "### Normalize and compare\n",
    "\n",
    "Finally, we can normalize the gradients (since the more important quantity is the direction) and compare them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "46ef378e-cb82-4e67-acfb-d83d192d8cea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:40:03.175859Z",
     "iopub.status.busy": "2022-07-21T20:40:03.175672Z",
     "iopub.status.idle": "2022-07-21T20:40:03.178981Z",
     "shell.execute_reply": "2022-07-21T20:40:03.178721Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adjoint gradient:    [ 0.73967238 -0.19840035  0.24629446 -0.59402114]\n",
      "Numerical gradient:  [ 0.74023678 -0.19943572  0.24918193 -0.59176285]\n",
      "RMS error 0.39 %\n"
     ]
    }
   ],
   "source": [
    "def normalize(grad_dict):\n",
    "    \"\"\"Normalize the gradient dictionary and return a normalized array.\"\"\"\n",
    "\n",
    "    # convert to array\n",
    "    grad_arr = np.array(list(grad_dict.values()))\n",
    "    \n",
    "    # take real part, if not already real\n",
    "    grad_arr = np.real(grad_arr)\n",
    "\n",
    "    # normalize\n",
    "    return grad_arr / np.linalg.norm(grad_arr)\n",
    "\n",
    "# normalize both adjoint and numerical gradients\n",
    "g_adj_arr = normalize(grad_adj_dict)\n",
    "g_num_arr = normalize(grad_num_dict)\n",
    "\n",
    "# print results\n",
    "print(\"Adjoint gradient:   \", g_adj_arr)\n",
    "print(\"Numerical gradient: \", g_num_arr)\n",
    "print(f\"RMS error {(np.linalg.norm(g_adj_arr - g_num_arr) / np.linalg.norm(g_num_arr)*100):.2f} %\")"
   ]
  },
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   ]
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