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   "source": [
    "# Parameter scan\n",
    "\n",
    "<img src=\"img/splitter.png\" alt=\"diagram\" width=\"400\"/>\n",
    "\n",
    "Run this notebook in your browser using [Binder](https://mybinder.org/v2/gh/flexcompute-readthedocs/tidy3d-docs/readthedocs?labpath=docs%2Fsource%2Fnotebooks%2FParameterScan.ipynb).\n",
    "\n",
    "In this notebook, we will show an example of using tidy3d to evaluate device performance over a set of many design parameters.\n",
    "\n",
    "This example will also provide a walkthrough of Tidy3D's [Job](https://docs.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.web.container.Job.html) and [Batch](https://docs.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.web.container.Batch.html) features for managing both individual simulations and sets of simulations.\n",
    "\n",
    "For demonstration, we look at the splitting ratio of a directional coupler as we vary the coupling length between two waveguides. The sidewall of the waveguides is slanted, deviating from the vertical direction by `sidewall_angle`."
   ]
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   "source": [
    "# standard python imports\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import gdspy\n",
    "\n",
    "# tidy3D imports\n",
    "import tidy3d as td\n",
    "from tidy3d import web\n",
    "\n",
    "# set tidy3d to only print error information to reduce verbosity\n",
    "td.config.logging_level = 'error'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "First we set up some global parameters"
   ]
  },
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   "source": [
    "# wavelength / frequency\n",
    "lambda0 = 1.550                     # all length scales in microns\n",
    "freq0 = td.constants.C_0 / lambda0\n",
    "fwidth = freq0 / 10\n",
    "\n",
    "# Permittivity of waveguide and substrate\n",
    "wg_n = 3.48\n",
    "sub_n = 1.45\n",
    "mat_wg = td.Medium(permittivity=wg_n**2)\n",
    "mat_sub = td.Medium(permittivity=sub_n**2)\n",
    "\n",
    "# Waveguide dimensions\n",
    "\n",
    "# Waveguide height\n",
    "wg_height = 0.22\n",
    "# Waveguide width\n",
    "wg_width = 0.45\n",
    "# Waveguide separation in the beginning/end\n",
    "wg_spacing_in = 8\n",
    "# Angle of the sidewall deviating from the vertical ones, positive values for the base larger than the top\n",
    "sidewall_angle = np.pi/6\n",
    "# Total device length along propagation direction\n",
    "device_length = 100\n",
    "# Length of the bend region\n",
    "bend_length = 16\n",
    "# space between waveguide and PML\n",
    "pml_spacing = 1\n",
    "# resolution control: minimum number of grid cells per wavelength in each material\n",
    "grid_cells_per_wvl = 16"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define waveguide bends and coupler\n",
    "\n",
    "Here is where we define our directional coupler shape programmatically in terms of the geometric parameters"
   ]
  },
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   "source": [
    "def bend_pts(bend_length, width, npts=10):\n",
    "    \"\"\" Set of points describing a tanh bend from (0, 0) to (length, width)\"\"\"\n",
    "    x = np.linspace(0, bend_length, npts)\n",
    "    y = width*(1 + np.tanh(6*(x/bend_length - 0.5)))/2\n",
    "    return np.stack((x, y), axis=1)\n",
    "\n",
    "def arm_pts(length, width, coup_length, bend_length, npts_bend=30):\n",
    "    \"\"\" Set of points defining one arm of an integrated coupler \"\"\"\n",
    "    ### Make the right half of the coupler arm first\n",
    "    # Make bend and offset by coup_length/2\n",
    "    bend = bend_pts(bend_length, width, npts_bend)\n",
    "    bend[:, 0] += coup_length / 2\n",
    "    # Add starting point as (0, 0)\n",
    "    right_half = np.concatenate(([[0, 0]], bend))\n",
    "    # Add an extra point to make sure waveguide is straight past the bend\n",
    "    right_half = np.concatenate((right_half, [[right_half[-1, 0] + 0.1, width]]))\n",
    "    # Add end point as (length/2, width)\n",
    "    right_half = np.concatenate((right_half, [[length/2, width]]))\n",
    "\n",
    "    # Make the left half by reflecting and omitting the (0, 0) point\n",
    "    left_half = np.copy(right_half)[1:, :]\n",
    "    left_half[:, 0] = -left_half[::-1, 0]\n",
    "    left_half[:, 1] = left_half[::-1, 1]\n",
    "    \n",
    "    return np.concatenate((left_half, right_half), axis=0)\n",
    "\n",
    "def make_coupler(\n",
    "    length, \n",
    "    wg_spacing_in,\n",
    "    wg_width,\n",
    "    wg_spacing_coup,\n",
    "    coup_length,\n",
    "    bend_length,\n",
    "    npts_bend=30):\n",
    "    \"\"\" Make an integrated coupler using the gdspy FlexPath object. \"\"\"\n",
    "\n",
    "    # Compute one arm of the coupler\n",
    "    arm_width = (wg_spacing_in - wg_width - wg_spacing_coup)/2\n",
    "    arm = arm_pts(length, arm_width, coup_length, bend_length, npts_bend)\n",
    "    # Reflect and offset bottom arm\n",
    "    coup_bot = np.copy(arm)\n",
    "    coup_bot[:, 1] = -coup_bot[::-1, 1] - wg_width/2 - wg_spacing_coup/2\n",
    "    # Offset top arm\n",
    "    coup_top = np.copy(arm)\n",
    "    coup_top[:, 1] += wg_width/2 + wg_spacing_coup/2\n",
    "    \n",
    "    # Create waveguides as GDS paths\n",
    "    path_bot = gdspy.FlexPath(coup_bot, wg_width, layer=1, datatype=0)\n",
    "    path_top = gdspy.FlexPath(coup_top, wg_width, layer=1, datatype=1)\n",
    "    \n",
    "    return [path_bot, path_top]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Simulation and Submit Job\n",
    "\n",
    "The following function creates a tidy3d simulation object for a set of design parameters.\n",
    "\n",
    "Note that the simulation has not been run yet, just created."
   ]
  },
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   "source": [
    "def make_sim(coup_length, wg_spacing_coup, domain_field=False):\n",
    "    \"\"\" Make a simulation with a given length of the coupling region and\n",
    "    distance between the waveguides in that region. If ``domain_field``\n",
    "    is True, a 2D in-plane field monitor will be added.\n",
    "    \"\"\"\n",
    "    \n",
    "    gdspy.current_library = gdspy.GdsLibrary()\n",
    "    lib = gdspy.GdsLibrary()\n",
    "\n",
    "    # Geometry must be placed in GDS cells to import into Tidy3D\n",
    "    coup_cell = lib.new_cell('Coupler')\n",
    "\n",
    "    substrate = gdspy.Rectangle(\n",
    "        (-device_length/2, -wg_spacing_in/2-10),\n",
    "        (device_length/2, wg_spacing_in/2+10),\n",
    "        layer=0)\n",
    "    coup_cell.add(substrate)\n",
    "\n",
    "    # Add the coupler to a gdspy cell\n",
    "    gds_coup = make_coupler(\n",
    "        device_length,\n",
    "        wg_spacing_in,\n",
    "        wg_width,\n",
    "        wg_spacing_coup,\n",
    "        coup_length,\n",
    "        bend_length)\n",
    "    coup_cell.add(gds_coup)\n",
    "\n",
    "    # Substrate\n",
    "    [oxide_geo] = td.PolySlab.from_gds(\n",
    "        gds_cell=coup_cell,\n",
    "        gds_layer=0,\n",
    "        gds_dtype=0,\n",
    "        slab_bounds=(-10, 0),\n",
    "        axis=2)\n",
    "    \n",
    "    oxide = td.Structure(\n",
    "        geometry=oxide_geo,\n",
    "        medium=mat_sub)\n",
    "        \n",
    "    # Waveguides (import all datatypes if gds_dtype not specified)\n",
    "    coupler1_geo, coupler2_geo = td.PolySlab.from_gds(\n",
    "        gds_cell=coup_cell,\n",
    "        gds_layer=1,\n",
    "        slab_bounds=(0, wg_height),\n",
    "        sidewall_angle=sidewall_angle,\n",
    "        axis=2)\n",
    "    \n",
    "    coupler1 = td.Structure(\n",
    "        geometry=coupler1_geo,\n",
    "        medium=mat_wg\n",
    "    )\n",
    "    \n",
    "    coupler2 = td.Structure(\n",
    "        geometry=coupler2_geo,\n",
    "        medium=mat_wg\n",
    "    )\n",
    "    \n",
    "    # Simulation size along propagation direction\n",
    "    sim_length = 2 + 2*bend_length + coup_length\n",
    "\n",
    "    # Spacing between waveguides and PML\n",
    "    sim_size = [\n",
    "        sim_length,\n",
    "        wg_spacing_in + wg_width + 2*pml_spacing,\n",
    "        wg_height + 2*pml_spacing]\n",
    "\n",
    "\n",
    "    # source\n",
    "    src_pos = -sim_length/2 + 0.5\n",
    "    msource = td.ModeSource(\n",
    "        center=[src_pos , wg_spacing_in / 2 , wg_height / 2],\n",
    "        size=[0, 3, 2],\n",
    "        source_time = td.GaussianPulse(\n",
    "            freq0=freq0,\n",
    "            fwidth=fwidth),\n",
    "        direction='+',\n",
    "        mode_spec=td.ModeSpec(),\n",
    "        mode_index=0,\n",
    "    )\n",
    "\n",
    "    mon_in = td.ModeMonitor(\n",
    "        center=[(src_pos + 0.5), wg_spacing_in / 2, wg_height / 2],\n",
    "        size=[0, 3, 2],\n",
    "        freqs=[freq0],\n",
    "        mode_spec=td.ModeSpec(),\n",
    "        name='in')\n",
    "    mon_ref_bot = td.ModeMonitor(\n",
    "        center=[(src_pos + 0.5), -wg_spacing_in / 2, wg_height / 2],\n",
    "        size=[0, 3, 2],\n",
    "        freqs=[freq0],\n",
    "        mode_spec=td.ModeSpec(),\n",
    "        name='refect_bottom')\n",
    "    mon_top = td.ModeMonitor(\n",
    "        center=[-(src_pos + 0.5), wg_spacing_in / 2, wg_height / 2],\n",
    "        size=[0, 3, 2],\n",
    "        freqs=[freq0],\n",
    "        mode_spec=td.ModeSpec(),\n",
    "        name='top')\n",
    "    mon_bot = td.ModeMonitor(\n",
    "        center=[-(src_pos + 0.5), -wg_spacing_in / 2, wg_height / 2],\n",
    "        size=[0, 3, 2],\n",
    "        freqs=[freq0],\n",
    "        mode_spec=td.ModeSpec(),\n",
    "        name='bottom')\n",
    "    monitors = [mon_in, mon_ref_bot, mon_top, mon_bot]\n",
    "    \n",
    "    if domain_field == True:\n",
    "        domain_monitor = td.FieldMonitor(\n",
    "            center = [0,0,wg_height/2],\n",
    "            size = [td.inf, td.inf, 0],\n",
    "            freqs = [freq0],\n",
    "            name='field'\n",
    "        )\n",
    "        monitors.append(domain_monitor)\n",
    "\n",
    "    # initialize the simulation\n",
    "    sim = td.Simulation(\n",
    "        size=sim_size,\n",
    "        grid_spec = td.GridSpec.auto(min_steps_per_wvl=grid_cells_per_wvl),\n",
    "        structures=[oxide, coupler1, coupler2],\n",
    "        sources=[msource],\n",
    "        monitors=monitors,\n",
    "        run_time=20/fwidth,\n",
    "        boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML())\n",
    "    )\n",
    "\n",
    "    return sim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspect Simulation\n",
    "\n",
    "Let's create and inspect a single simulation to make sure it was defined correctly before doing the full scan. The sidewalls of the waveguides deviate from the vertical direction by 30 degree. We also add an in-plane field monitor to have a look at the fields evolution in this one simulation. We will not use such a monitor in the batch to avoid storing unnecesarrily large amounts of data."
   ]
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    "# Length of the coupling region\n",
    "coup_length = 10\n",
    "\n",
    "# Waveguide separation in the coupling region\n",
    "wg_spacing_coup = 0.10\n",
    "\n",
    "sim = make_sim(coup_length, wg_spacing_coup, domain_field=True)"
   ]
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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize geometry\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 4))\n",
    "sim.plot(z=wg_height/2+0.01, ax=ax1);\n",
    "sim.plot(x=0.1, ax=ax2);\n",
    "ax2.set_xlim([-3, 3])\n",
    "plt.show()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create and Submit Job\n",
    "\n",
    "The [Job](https://docs.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.web.container.Job.html) object provides an interface for managing simulations.\n",
    "\n",
    "`job = Job(simulation)` will create a job and upload the simulation to our server to run.\n",
    "\n",
    "Then, one may call various methods of `job` to monitor progress, download results, and get information.\n",
    "\n",
    "For more information, refer to the API reference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:42:45.248403Z",
     "iopub.status.busy": "2022-07-21T20:42:45.248230Z",
     "iopub.status.idle": "2022-07-21T20:45:03.002768Z",
     "shell.execute_reply": "2022-07-21T20:45:03.002345Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2ab45c8520b94888aec9067da614e4ca",
       "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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "df933ff82de94861adae27bcc262f2ba",
       "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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9015652179ca4f1296ea47b9ccf2bf4a",
       "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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fab84defb7e743f5b1df1dead3b10b99",
       "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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "17f294069fc24f149e2684f0370c843a",
       "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"
    }
   ],
   "source": [
    "# create job, upload sim to server to begin running\n",
    "job = web.Job(simulation=sim, task_name='CouplerVerify')\n",
    "\n",
    "# download the results and load them into a simulation\n",
    "sim_data = job.run(path='data/sim_data.hdf5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Postprocessing\n",
    "\n",
    "The following function takes a completed simulation (with data loaded into it) and computes the quantities of interest.\n",
    "\n",
    "For this case, we measure both the total transmission in the right ports and also the ratio of power between the top and bottom ports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:45:03.942595Z",
     "iopub.status.busy": "2022-07-21T20:45:03.942471Z",
     "iopub.status.idle": "2022-07-21T20:45:03.945033Z",
     "shell.execute_reply": "2022-07-21T20:45:03.944797Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def measure_transmission(sim_data):\n",
    "    \"\"\" Constructs a \"row\" of the scattering matrix when sourced from top left port \"\"\"\n",
    "\n",
    "    input_amp = sim_data['in'].amps.sel(direction='+')\n",
    "    \n",
    "    amps = np.zeros(4, dtype=complex)\n",
    "    directions = ('-', '-', '+', '+')\n",
    "    for i, (monitor, direction) in enumerate(zip(sim_data.simulation.monitors[:4], directions)):\n",
    "        amp = sim_data[monitor.name].amps.sel(direction=direction)\n",
    "        amp_normalized = amp / input_amp\n",
    "        amps[i] = np.squeeze(amp_normalized.values)\n",
    "    \n",
    "    return amps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:45:03.946341Z",
     "iopub.status.busy": "2022-07-21T20:45:03.946144Z",
     "iopub.status.idle": "2022-07-21T20:45:03.953207Z",
     "shell.execute_reply": "2022-07-21T20:45:03.952957Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mode amplitudes in each port: \n",
      "\n",
      "\tmonitor     = \"in\"\n",
      "\tamplitude^2 = 0.00\n",
      "\tphase       = -1.16 (rad)\n",
      "\n",
      "\tmonitor     = \"refect_bottom\"\n",
      "\tamplitude^2 = 0.00\n",
      "\tphase       = 0.60 (rad)\n",
      "\n",
      "\tmonitor     = \"top\"\n",
      "\tamplitude^2 = 0.97\n",
      "\tphase       = 0.32 (rad)\n",
      "\n",
      "\tmonitor     = \"bottom\"\n",
      "\tamplitude^2 = 0.02\n",
      "\tphase       = 1.89 (rad)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# monitor and test out the measure_transmission function the results of the single run\n",
    "amps_arms = measure_transmission(sim_data)\n",
    "print('mode amplitudes in each port: \\n')\n",
    "for amp, monitor in zip(amps_arms, sim_data.simulation.monitors[:-1]):\n",
    "    print(f'\\tmonitor     = \"{monitor.name}\"')\n",
    "    print(f'\\tamplitude^2 = {abs(amp)**2:.2f}')\n",
    "    print(f'\\tphase       = {(np.angle(amp)):.2f} (rad)\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:45:03.954460Z",
     "iopub.status.busy": "2022-07-21T20:45:03.954295Z",
     "iopub.status.idle": "2022-07-21T20:45:05.016049Z",
     "shell.execute_reply": "2022-07-21T20:45:05.015746Z"
    }
   },
   "outputs": [
    {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(16, 3))\n",
    "# sim_data['field'].Ey.real.interp(z=0).plot()\n",
    "sim_data.plot_field('field', 'Ey', z=wg_height/2, freq=freq0, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1D Parameter Scan\n",
    "\n",
    "Now we will scan through the coupling length parameter to see the effect on splitting ratio.\n",
    "\n",
    "To do this, we will create a list of simulations corresponding to each parameter combination.\n",
    "\n",
    "We will use this list to create a [Batch](https://docs.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.web.container.Batch.html) object, which has similar functionality to [Job](https://docs.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.web.container.Job.html) but allows one to manage a *set* of jobs.\n",
    "\n",
    "First, we create arrays to store the input and output values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:45:05.017734Z",
     "iopub.status.busy": "2022-07-21T20:45:05.017583Z",
     "iopub.status.idle": "2022-07-21T20:45:05.020052Z",
     "shell.execute_reply": "2022-07-21T20:45:05.019722Z"
    }
   },
   "outputs": [],
   "source": [
    "# create variables to store parameters, simulation information, results\n",
    "Nl = 11\n",
    "\n",
    "ls = np.linspace(5, 12, Nl)\n",
    "split_ratios = np.zeros(Nl)\n",
    "efficiencies = np.zeros(Nl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Batch\n",
    "\n",
    "We now create our list of simulations and use them to initialize a [Batch](https://docs.simulation.cloud/projects/tidy3d/en/latest/_autosummary/tidy3d.web.container.Batch.html).\n",
    "\n",
    "For more information, refer to the API reference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:45:05.021531Z",
     "iopub.status.busy": "2022-07-21T20:45:05.021308Z",
     "iopub.status.idle": "2022-07-21T20:45:32.667925Z",
     "shell.execute_reply": "2022-07-21T20:45:32.666934Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "13d2c21b69904e78af6e8dde840ca69b",
       "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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "39b43beb229941158b82e4d2ea700245",
       "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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "413587a6e3674593bfa010a9b57cb5f2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    "# submit all jobs\n",
    "sims = {f'l={l:.2f}' : make_sim(l, wg_spacing_coup) for l in ls}\n",
    "batch = web.Batch(simulations=sims)"
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    "### Monitor Batch\n",
    "\n",
    "Here we can perform real-time monitoring of how many of the jobs in the batch have completed."
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       "\u001b[2;36m[16:45:39]\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch.                                         \u001b]8;id=229664;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/container.py\u001b\\\u001b[2mcontainer.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=454184;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/container.py#384\u001b\\\u001b[2m384\u001b[0m\u001b]8;;\u001b\\\n"
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    "### Load and visualize Reults\n",
    "\n",
    "Finally, we can compute the output quantities and load them into the arrays we created initally.\n",
    "\n",
    "Then we may plot the results."
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       "\n"
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     "text": [
      "(4, 11)\n",
      "[[ 1.06298468e-02-1.99378491e-02j  8.02398073e-03-1.97936523e-02j\n",
      "   1.05311830e-02-2.36576224e-02j  1.75104295e-02-1.46165821e-02j\n",
      "   1.08674508e-02-2.60412575e-02j  7.64328670e-03-1.01263247e-02j\n",
      "   2.25508322e-02-2.26424108e-02j  4.82513902e-05-2.27543933e-02j\n",
      "   1.78356978e-02-1.25398567e-02j  1.10857269e-02-2.68290851e-02j\n",
      "   1.07438535e-02-1.89892085e-02j]\n",
      " [ 7.40022116e-03+2.92804849e-03j -2.51813672e-03-8.29557603e-03j\n",
      "  -1.39052153e-03+1.50436878e-02j  6.67990710e-03-6.80094674e-03j\n",
      "  -7.42048531e-03+2.40167816e-03j  8.64911550e-03+4.86721420e-03j\n",
      "  -3.60153428e-03+9.65963621e-04j  9.55525519e-04+2.05731684e-04j\n",
      "   1.24902833e-03+3.44426000e-03j  4.51199305e-03-3.69917633e-04j\n",
      "  -9.33921481e-03+4.29490460e-03j]\n",
      " [-3.39899554e-01+4.80541719e-01j  4.22731377e-01+5.60334341e-01j\n",
      "   7.52872694e-01-2.69140715e-01j -3.62240472e-02-8.78632156e-01j\n",
      "  -9.08017834e-01-2.40409773e-01j -5.21665507e-01+8.27099562e-01j\n",
      "   6.37223805e-01+7.63310539e-01j  9.14056174e-01-3.73521075e-01j\n",
      "  -7.83964182e-02-9.54763137e-01j -8.85064723e-01-1.95501988e-01j\n",
      "  -4.13928123e-01+7.24090356e-01j]\n",
      " [ 6.51852572e-01+4.64235097e-01j  5.61908258e-01-4.21509576e-01j\n",
      "  -1.96814548e-01-5.54929432e-01j -4.61899126e-01+1.81052550e-02j\n",
      "  -8.38539945e-02+3.14487797e-01j  1.52686887e-01+9.65595591e-02j\n",
      "   2.37933359e-02-1.99214643e-02j  4.50370313e-02+1.10915613e-01j\n",
      "   2.66534200e-01-2.18302635e-02j  8.78224531e-02-3.99001175e-01j\n",
      "  -4.69165809e-01-2.67331951e-01j]]\n"
     ]
    }
   ],
   "source": [
    "amps_batch = []\n",
    "for task_name, sim_data in batch_results.items():\n",
    "    amps_arms_i = np.array(measure_transmission(sim_data))\n",
    "    amps_batch.append(amps_arms_i)\n",
    "amps_batch = np.stack(amps_batch, axis=1)\n",
    "print(amps_batch.shape) # (4, num_freqs)\n",
    "print(amps_batch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:51:08.471027Z",
     "iopub.status.busy": "2022-07-21T20:51:08.470935Z",
     "iopub.status.idle": "2022-07-21T20:51:08.472757Z",
     "shell.execute_reply": "2022-07-21T20:51:08.472522Z"
    }
   },
   "outputs": [],
   "source": [
    "powers = abs(amps_batch)**2\n",
    "power_top = powers[2]\n",
    "power_bot = powers[3]\n",
    "power_out = power_top + power_bot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:51:08.474091Z",
     "iopub.status.busy": "2022-07-21T20:51:08.473990Z",
     "iopub.status.idle": "2022-07-21T20:51:08.544422Z",
     "shell.execute_reply": "2022-07-21T20:51:08.544080Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 432x288 with \u001b[0m\u001b[1;36m1\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m\n"
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     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ls, 100*power_top, label='% in top port')\n",
    "plt.plot(ls, 100*power_bot, label='% in bottom port')\n",
    "plt.plot(ls, 100*power_out, label='% power transmitted to output ports')\n",
    "plt.plot(ls, 100*np.ones_like(ls), '--', label='unity (100%)')\n",
    "plt.xlabel('coupling length (um)')\n",
    "plt.ylabel('splitting ratio (%)')\n",
    "plt.ylim(0, 110)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final Remarks\n",
    "\n",
    "Batches provide some other convenient functionality for managing large numbers of jobs.\n",
    "\n",
    "For example, one can save the batch information to file and load the batch at a later time, if needing to disconnect from the service while the jobs are running."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T20:51:08.546119Z",
     "iopub.status.busy": "2022-07-21T20:51:08.545977Z",
     "iopub.status.idle": "2022-07-21T20:51:09.438779Z",
     "shell.execute_reply": "2022-07-21T20:51:09.438374Z"
    }
   },
   "outputs": [],
   "source": [
    "# save batch metadata\n",
    "batch.to_file('data/batch_data.json')\n",
    "\n",
    "# load batch metadata into a new batch\n",
    "loaded_batch = web.Batch.from_file('data/batch_data.json')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more reference, refer to our documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">↑</span> <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">simulation.json</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">45.9/45.9 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">?</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100.0%\u001b[0m • \u001b[32m45.9/45.9 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m0:00:00\u001b[0m\n"
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