AdvancedGaussianSources.ipynb

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    "# Advanced Gaussian sources\n",
    "\n",
    "Tidy3D natively offers sources like ModeSource, PlaneWave, GaussianBeam, etc. However, due to the python and open-source nature of Tidy3D, we can easily define arbitrary sources such as vortex beams. In this notebook, we demonstrate how to create these advanced sources in a way that is easily usable in multiple scenarios.\n",
    "<br><br>\n",
    "It can often be tedious to create easily usable customized simulation objects from scratch, as there are various details to consider. For instance, if one wants to create an easily-usable beam source from scratch, one must create functionality to automatically interpret the source plane into field coordinates, handle conversion for polar and azimuthal angles, handle different direction specifications, etc. This all becomes very complicated to build, so there is often a tradeoff between ease of construction and ease of use.\n",
    "<br><br>\n",
    "Fortunately, we can take advantage of the fact that Tidy3D's [Python frontend](https://github.com/flexcompute/tidy3d/tree/981c0ebb22ba4dd317818ebc3729edb1e0005f2f) is open source is open source to create complex Gaussian sources without needing to consider any of these extraneous functionalities. Namely, we use the Tidy3D [BeamProfile](https://github.com/flexcompute/tidy3d/blob/develop/tidy3d/components/beam.py) class to handle all extra usability considerations, so we only need to define the scalar field profile for the propagation direction. The BeamProfile object will then automatically create, interpolate, and rotate the field amplitudes according to the geometry of the source plane and specified angles.\n",
    "<br><br>\n",
    "This is just one way in which we can take advantage of the open source nature of Tidy3D, and these principles can be applied to any base object class that Tidy3D provides."
   ]
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    "# Standard python imports\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Tidy3D import\n",
    "import tidy3d as td\n",
    "from tidy3d import web"
   ]
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    "If we look at how Tidy3D's GaussianBeam uses the abstract BeamProfile class, we see that the BeamProfile class already handles polarization angles, propagation angles, sourcetimes, direction, and can interpret user-defined source plane geometry. Thus we need only define the extra parameters our advanced Gaussian sources need on top of these. With each source, we will demonstrate its use after defining it as an object."
   ]
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   "source": [
    "## Hermite-Gaussian source\n",
    "\n",
    "Here we show how to create a Hermite-Gaussian mode in Tidy3D using our [CustomFieldSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.CustomFieldSource.html) object. We then validate the field profile change along the propagation direction.<br><br>\n",
    "In cartesian coordinates, where $x$ is the propagation direction, the mode has field profile\n",
    "\n",
    "<center>$E_{l,m}(x,y,z)=E_0\\frac{\\omega_0}{\\omega(z)}H_l(\\frac{\\sqrt{2}x}{\\omega(z)}) H_m(\\frac{\\sqrt{2}y}{\\omega(z)})\\exp(-\\frac{x^2+y^2}{\\omega^2(z)})\\exp(-i\\frac{k(x^2+y^2)}{2R(z)})\\exp( i\\psi(x))\\exp( -ikz)$</center>\n",
    "\n",
    "where:\n",
    "<li>$\\omega_0$ is the beam waist</li>\n",
    "<li>$z_R=\\frac{\\pi n_\\epsilon\\omega_0^2}{\\lambda}$ is the Rayleigh length</li>\n",
    "<li>$\\omega(z)=\\omega_0\\sqrt{1+\\frac{z^2}{z_R^2}}$ is the beam radius</li>\n",
    "<li>$k=\\frac{2\\pi n}{\\lambda}$ is the wavenumber</li>\n",
    "<li>$R(z)=z(1+\\frac{z_R^2}{z^2})$ is the wavefront curvature</li>\n",
    "<li>$H_l,H_m$ are Hermite polynomials of orders $l$ and $m$</li>\n",
    "<li>$\\psi(z)=(l+m+1)\\arctan(\\frac{z}{z_R})$ is the phase function</li>"
   ]
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   "source": [
    "from scipy.special import hermite\n",
    "from tidy3d.components.beam import BeamProfile\n",
    "\n",
    "\n",
    "def HGM(\n",
    "    size,\n",
    "    m,\n",
    "    l,\n",
    "    source_time,\n",
    "    center=(0, 0, 0),\n",
    "    direction=\"+\",\n",
    "    pol_angle=0,\n",
    "    angle_phi=0,\n",
    "    angle_theta=0,\n",
    "    w0=1,\n",
    "    waist_distance=0,\n",
    "):\n",
    "    freqs = [source_time.freq0]\n",
    "\n",
    "    class HGM_obj(BeamProfile):\n",
    "        \"\"\"Component for constructing HGM beam data. The normal direction is implicitly\n",
    "        defined by the ``size`` parameter.\n",
    "        \"\"\"\n",
    "\n",
    "        def scalar_field(self, points, background_n):\n",
    "            \"\"\"Scalar field for HGM beam.\n",
    "            Scalar field corresponding to the analytic beam in coordinate system such that the\n",
    "            propagation direction is z and the ``E``-field is entirely ``x``-polarized. The field is\n",
    "            computed on an unstructured array ``points`` of shape ``(3, ...)``.\n",
    "            \"\"\"\n",
    "            x, y, z = points\n",
    "            z += waist_distance\n",
    "\n",
    "            zR = np.pi * background_n * w0**2 / (td.C_0 / np.array(self.freqs))\n",
    "            wz = w0 * np.sqrt(1 + z**2 / zR**2)\n",
    "            Rz = td.inf if np.any(z == 0) else z * (1 + zR**2 / z**2)\n",
    "            k = 2 * np.pi * np.array(self.freqs) / td.C_0 * background_n\n",
    "            psi_z = (l + m + 1) * np.arctan(z / zR)\n",
    "\n",
    "            E = source_time.amplitude * w0 / wz\n",
    "            E *= hermite(l)(np.real(np.sqrt(2) * x / wz)) * hermite(m)(np.real(np.sqrt(2) * y / wz))\n",
    "            E *= np.exp(-(x**2 + y**2) / wz**2) * np.exp(-1j * k * (x**2 + y**2) / (2 * Rz))\n",
    "            E *= np.exp(1j * psi_z) * np.exp(-1j * k * z)\n",
    "\n",
    "            return E\n",
    "\n",
    "    HGM_obj = HGM_obj(\n",
    "        center=center,\n",
    "        size=size,\n",
    "        freqs=freqs,\n",
    "        angle_theta=angle_theta,\n",
    "        angle_phi=angle_phi,\n",
    "        pol_angle=pol_angle,\n",
    "        direction=direction,\n",
    "    )\n",
    "    field_data = HGM_obj.field_data\n",
    "    field_dataset = td.FieldDataset(\n",
    "        Ex=field_data.Ex,\n",
    "        Ey=field_data.Ey,\n",
    "        Ez=field_data.Ez,\n",
    "        Hx=field_data.Hx,\n",
    "        Hy=field_data.Hy,\n",
    "        Hz=field_data.Hz,\n",
    "    )\n",
    "    HGM_source = td.CustomFieldSource(\n",
    "        center=center, size=size, source_time=source_time, field_dataset=field_dataset\n",
    "    )\n",
    "    return HGM_source"
   ]
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   "source": [
    "Create and run an example $m=2,l=1$ Hermite-Gaussian beam. To ensure that the BeamProfile object is working correctly, we specify extra angles and a non-unity beam waist:"
   ]
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   "id": "7d5d3f36-fb9e-489b-9d9a-e715d7ad131f",
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   "source": [
    "freq0 = td.C_0 / 1.55\n",
    "hgm = HGM(\n",
    "    size=(0, 4, 4),\n",
    "    m=2,\n",
    "    l=1,\n",
    "    w0=1.2,\n",
    "    pol_angle=np.pi / 24,\n",
    "    angle_phi=np.pi / 36,\n",
    "    source_time=td.GaussianPulse(freq0=freq0, fwidth=freq0 / 10),\n",
    ")"
   ]
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   "id": "47a6a1cd-10c3-4abd-8ca6-fb63a62f1197",
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   "source": [
    "sim_size = (5.1, 5, 5)\n",
    "sim_center = (sim_size[0] / 2 - 0.05, 0, 0)\n",
    "\n",
    "num_field_monitors = 61\n",
    "field_monitor_xs = np.linspace(\n",
    "    sim_center[0] - sim_size[0] / 2 + 0.1, sim_size[0] - 0.1, num_field_monitors\n",
    ")\n",
    "field_monitors = []\n",
    "for x in field_monitor_xs:\n",
    "    field_monitor = td.FieldMonitor(\n",
    "        center=(x, 0, 0), size=(0, sim_size[1], sim_size[2]), name=str(x), freqs=[freq0]\n",
    "    )\n",
    "    field_monitors.append(field_monitor)\n",
    "\n",
    "HGM_test_sim = td.Simulation(\n",
    "    center=sim_center,\n",
    "    size=sim_size,\n",
    "    structures=[],\n",
    "    sources=[hgm],\n",
    "    monitors=field_monitors,\n",
    "    run_time=1e-13,\n",
    ")"
   ]
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       "\u001b[2;36m14:41:04 EDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'HGM test'\u001b[0m with task_id                               \n",
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       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-0257e0fe-404a-45f1-a0ec-67aaca21b32b\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-0257e0fe-404</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-0257e0fe-404a-45f1-a0ec-67aaca21b32b\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">a-45f1-a0ec-67aaca21b32b'</span></a>.                                         \n",
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       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=866864;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0257e0fe-404a-45f1-a0ec-67aaca21b32b\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=987515;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0257e0fe-404a-45f1-a0ec-67aaca21b32b\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=866864;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0257e0fe-404a-45f1-a0ec-67aaca21b32b\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=502109;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0257e0fe-404a-45f1-a0ec-67aaca21b32b\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=866864;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0257e0fe-404a-45f1-a0ec-67aaca21b32b\u001b\\\u001b[32m-0257e0fe-404\u001b[0m\u001b]8;;\u001b\\\n",
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:41:08 EDT </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Minimum cost depends on task       \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
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       "\u001b[2;36m14:41:08 EDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Minimum cost depends on task       \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
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       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:41:11 EDT </span>loading simulation from simulation_data.hdf5                       \n",
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       "\u001b[2;36m14:41:11 EDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
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   "source": [
    "hgm_sim_data = web.run(simulation=HGM_test_sim, task_name=\"HGM test\")"
   ]
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    "Display field in propagation direction:"
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hgm_sim_data.plot_field(str(field_monitor_xs[0]), \"Ey\", f=freq0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a72fcc2f-fb94-4085-b1d5-4d7614c0098b",
   "metadata": {},
   "source": [
    "To see how the field evolves, run the following code to visualize along the propagation direction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "58e9385b-a077-4044-9cec-763236d28a51",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5aa71cfa5cc143d48434743beaf3346d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.05000000000000018, description='Ey', max=5.0, min=0.0500000000000001…"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import ipywidgets as widgets\n",
    "\n",
    "component = \"Ey\"\n",
    "\n",
    "field_slider = widgets.FloatSlider(\n",
    "    value=0,\n",
    "    min=field_monitor_xs[0],\n",
    "    max=field_monitor_xs[-1],\n",
    "    step=None,\n",
    "    description=component,\n",
    "    disabled=False,\n",
    "    continuous_update=True,\n",
    "    orientation=\"horizontal\",\n",
    "    readout=True,\n",
    "    readout_format=\".3f\",\n",
    ")\n",
    "\n",
    "\n",
    "def update_plot(x):\n",
    "    if x not in field_monitor_xs:\n",
    "        x = min(field_monitor_xs, key=lambda ll: abs(ll - x))\n",
    "    hgm_sim_data.plot_field(str(x), component, f=freq0)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "widgets.interactive(update_plot, x=field_slider)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7211088d-c600-47aa-9ee3-67abb636a2b0",
   "metadata": {},
   "source": [
    "## Laguerre-Gaussian source\n",
    "\n",
    "Here we show how to create an Orbital Angular Momentum (OAM) beam in Tidy3D using our [CustomFieldSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.CustomFieldSource.html) object. We then validate the field profile change along the propagation direction.<br><br>\n",
    "In cylindrical coordinates, where $z$ is the propagation direction, the OAM has field profile\n",
    "\n",
    "<center>$E_{l,p}(r,\\phi,z)=E_0\\sqrt{\\frac{2p!}{\\pi(p+|l|)!}} \\frac{1}{\\omega(z)} (\\frac{r\\sqrt{2}}{\\omega(z)})^{|l|} \\exp(-\\frac{r^2}{\\omega^2(z)}) L_p^{|l|}(\\frac{2r^2}{\\omega^2(z)}) \\exp(-ik\\frac{r^2}{2R(z)}) \\exp(-il\\phi) \\exp( i\\psi(z))$</center>\n",
    "\n",
    "where:\n",
    "<li>$\\omega_0$ is the beam waist</li>\n",
    "<li>$z_R=\\frac{\\pi n_\\epsilon\\omega_0^2}{\\lambda}$ is the Rayleigh length</li>\n",
    "<li>$\\omega(z)=\\omega_0\\sqrt{1+\\frac{z^2}{z_R^2}}$ is the beam radius</li>\n",
    "<li>$R(z)=z(1+\\frac{z_R^2}{z^2})$ is the wavefront curvature</li>\n",
    "<li>$k=\\frac{2\\pi n}{\\lambda}$ is the wavenumber</li>\n",
    "<li>$L_p^{|l|}$ is the generalized Laguerre polynomial</li>\n",
    "<li>$\\psi(z)=(|l| + 2p + 1)\\arctan(\\frac{z}{z_R})$ is the phase function</li>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ec84e602-efee-43d1-a39c-d58d12f5fa59",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.special import factorial, genlaguerre\n",
    "\n",
    "\n",
    "def OAM(\n",
    "    size,\n",
    "    p,\n",
    "    l,\n",
    "    source_time,\n",
    "    center=(0, 0, 0),\n",
    "    direction=\"+\",\n",
    "    pol_angle=0,\n",
    "    angle_phi=0,\n",
    "    angle_theta=0,\n",
    "    w0=1,\n",
    "    waist_distance=0,\n",
    "):\n",
    "    freqs = [source_time.freq0]\n",
    "\n",
    "    class OAM_obj(BeamProfile):\n",
    "        \"\"\"Component for constructing OAM beam data. The normal direction is implicitly\n",
    "        defined by the ``size`` parameter.\n",
    "        \"\"\"\n",
    "\n",
    "        def scalar_field(self, points, background_n):\n",
    "            \"\"\"Scalar field for OAM beam.\n",
    "            Scalar field corresponding to the analytic beam in coordinate system such that the\n",
    "            propagation direction is z and the ``E``-field is entirely ``x``-polarized. The field is\n",
    "            computed on an unstructured array ``points`` of shape ``(3, ...)``.\n",
    "            \"\"\"\n",
    "            x, y, z = points\n",
    "            z += waist_distance\n",
    "            r = np.sqrt(x**2 + y**2)\n",
    "            phi = np.arctan2(y, x)\n",
    "            absl = np.abs(l)\n",
    "\n",
    "            zR = np.pi * background_n * w0**2 / (td.C_0 / np.array(self.freqs))\n",
    "            wz = w0 * np.sqrt(1 + z**2 / zR**2)\n",
    "            Rz = td.inf if np.any(z == 0) else z * (1 + zR**2 / z**2)\n",
    "            k = 2 * np.pi * np.array(self.freqs) / td.C_0 * background_n\n",
    "            psi_z = (np.abs(l) + 2 * p + 1) * np.arctan(z / zR)\n",
    "\n",
    "            E = genlaguerre(p, absl)(2 * r**2 / wz**2) if p != 0 else 1\n",
    "            E *= (\n",
    "                source_time.amplitude\n",
    "                * np.sqrt(2 * factorial(p) / (np.pi * factorial(p + absl)))\n",
    "                / wz\n",
    "            )\n",
    "            E *= (r * np.sqrt(2) / wz) ** absl * np.exp(-(r**2) / wz**2)\n",
    "            E *= np.exp(-1j * k * r**2 / (2 * Rz)) * np.exp(-1j * l * phi) * np.exp(1j * psi_z)\n",
    "\n",
    "            return E\n",
    "\n",
    "    OAM_obj = OAM_obj(\n",
    "        center=center,\n",
    "        size=size,\n",
    "        freqs=freqs,\n",
    "        angle_theta=angle_theta,\n",
    "        angle_phi=angle_phi,\n",
    "        pol_angle=pol_angle,\n",
    "        direction=direction,\n",
    "    )\n",
    "    field_data = OAM_obj.field_data\n",
    "    field_dataset = td.FieldDataset(\n",
    "        Ex=field_data.Ex,\n",
    "        Ey=field_data.Ey,\n",
    "        Ez=field_data.Ez,\n",
    "        Hx=field_data.Hx,\n",
    "        Hy=field_data.Hy,\n",
    "        Hz=field_data.Hz,\n",
    "    )\n",
    "    OAM_source = td.CustomFieldSource(\n",
    "        center=center, size=size, source_time=source_time, field_dataset=field_dataset\n",
    "    )\n",
    "    return OAM_source"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21aaf9c2-bdc8-4fbc-a8fb-8f7150047470",
   "metadata": {},
   "source": [
    "We will demonstrate this functionality by defining a $p=0,l=1$ OAM source that propagates in the $x$ direction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2576fe20-464d-44b6-a5b6-e5c157ca090a",
   "metadata": {},
   "outputs": [],
   "source": [
    "freq0 = td.C_0 / 1.55\n",
    "oam = OAM(\n",
    "    size=(0, 4, 4),\n",
    "    p=0,\n",
    "    l=1,\n",
    "    # pol_angle=-np.pi/24,\n",
    "    # angle_theta=-np.pi/6,\n",
    "    source_time=td.GaussianPulse(freq0=freq0, fwidth=freq0 / 10),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71a6c80e-861d-405b-ac0a-966cc44552e3",
   "metadata": {},
   "source": [
    "Create and run an example Laguerre-Gaussian (OAM) beam:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "13851ef1-bb22-4fa1-a2d3-ae8162549f62",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_size = (5.1, 5, 5)\n",
    "sim_center = (sim_size[0] / 2 - 0.05, 0, 0)\n",
    "\n",
    "num_field_monitors = 61\n",
    "field_monitor_xs = np.linspace(\n",
    "    sim_center[0] - sim_size[0] / 2 + 0.1, sim_size[0] - 0.1, num_field_monitors\n",
    ")\n",
    "field_monitors = []\n",
    "for x in field_monitor_xs:\n",
    "    field_monitor = td.FieldMonitor(\n",
    "        center=(x, 0, 0), size=(0, sim_size[1], sim_size[2]), name=str(x), freqs=[freq0]\n",
    "    )\n",
    "    field_monitors.append(field_monitor)\n",
    "\n",
    "OAM_test_sim = td.Simulation(\n",
    "    center=sim_center,\n",
    "    size=sim_size,\n",
    "    structures=[],\n",
    "    sources=[oam],\n",
    "    monitors=field_monitors,\n",
    "    run_time=1e-13,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a3005466-e0e6-4cd2-9017-7ae39f509dd2",
   "metadata": {},
   "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\">14:41:12 EDT </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'OAM test'</span> with task_id                               \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-f79a7a43-6f5b-427d-8a06-2365e8f16512'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:41:12 EDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'OAM test'\u001b[0m with task_id                               \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-f79a7a43-6f5b-427d-8a06-2365e8f16512'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-f79a7a43-6f5b-427d-8a06-2365e8f16512\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-f79a7a43-6f5</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-f79a7a43-6f5b-427d-8a06-2365e8f16512\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">b-427d-8a06-2365e8f16512'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=374416;https://tidy3d.simulation.cloud/workbench?taskId=fdve-f79a7a43-6f5b-427d-8a06-2365e8f16512\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=158365;https://tidy3d.simulation.cloud/workbench?taskId=fdve-f79a7a43-6f5b-427d-8a06-2365e8f16512\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=374416;https://tidy3d.simulation.cloud/workbench?taskId=fdve-f79a7a43-6f5b-427d-8a06-2365e8f16512\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=88388;https://tidy3d.simulation.cloud/workbench?taskId=fdve-f79a7a43-6f5b-427d-8a06-2365e8f16512\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=374416;https://tidy3d.simulation.cloud/workbench?taskId=fdve-f79a7a43-6f5b-427d-8a06-2365e8f16512\u001b\\\u001b[32m-f79a7a43-6f5\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=374416;https://tidy3d.simulation.cloud/workbench?taskId=fdve-f79a7a43-6f5b-427d-8a06-2365e8f16512\u001b\\\u001b[32mb-427d-8a06-2365e8f16512'\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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/ee7143f0-3861-47de-ae8f-8f55df7232f1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=412876;https://tidy3d.simulation.cloud/folders/ee7143f0-3861-47de-ae8f-8f55df7232f1\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2aac3792b5c74c37b495e0023baebdc8",
       "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\">14:41:14 EDT </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Minimum cost depends on task       \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:41:14 EDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Minimum cost depends on task       \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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\">14:41:15 EDT </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:41:15 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7dc19c150df249eba4f90464b72fa888",
       "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\">14:41:18 EDT </span>loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:41:18 EDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "oam_sim_data = web.run(simulation=OAM_test_sim, task_name=\"OAM test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a704cc24-2d21-4638-ad86-bc5e87a562e0",
   "metadata": {},
   "source": [
    "Display field in propagation direction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2cdfe57e-9df8-443c-b1f9-834bbe5a95c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "oam_sim_data.plot_field(str(field_monitor_xs[30]), \"E\", f=freq0, val=\"abs^2\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b258c00a-4d0d-4af2-82c9-a6a010e4fa78",
   "metadata": {},
   "source": [
    "To see how the field amplitude evolves, run the following code to visualize along the propagation direction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "23c71226-2795-4e70-9555-7c240f51966b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "04eb5d9bcc6245079b4869ab2f984419",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.05000000000000018, description='E', max=5.0, min=0.05000000000000018…"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import ipywidgets as widgets\n",
    "\n",
    "component = \"E\"\n",
    "\n",
    "field_slider = widgets.FloatSlider(\n",
    "    value=0,\n",
    "    min=field_monitor_xs[0],\n",
    "    max=field_monitor_xs[-1],\n",
    "    step=None,\n",
    "    description=component,\n",
    "    disabled=False,\n",
    "    continuous_update=True,\n",
    "    orientation=\"horizontal\",\n",
    "    readout=True,\n",
    "    readout_format=\".3f\",\n",
    ")\n",
    "\n",
    "\n",
    "def update_plot(x):\n",
    "    if x not in field_monitor_xs:\n",
    "        x = min(field_monitor_xs, key=lambda ll: abs(ll - x))\n",
    "    oam_sim_data.plot_field(str(x), component, f=freq0, val=\"abs^2\")\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "widgets.interactive(update_plot, x=field_slider)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96eb9edb-7266-466a-b858-ad24566ad4df",
   "metadata": {},
   "source": [
    "## Bessel beam source\n",
    "\n",
    "Here we show how to create an Bessel beam in Tidy3D using our [CustomFieldSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.CustomFieldSource.html) object. We then validate the field amplitude change along the propagation direction.<br><br>\n",
    "In cylindrical coordinates $(\\rho, \\phi, z)$, where $z$ is the propagation direction, the Bessel beam has field profile\n",
    "\n",
    "<center>$U_\\beta(r)=J_m(k_T\\rho)e^{im\\phi}e^{-i\\beta z}$</center>\n",
    "\n",
    "where:\n",
    "<li>$k=\\frac{2\\pi n}{\\lambda}$ is the wavenumber</li>\n",
    "<li>$J_m$ is the Bessel function of the first kind and $m^{th}$ order</li>\n",
    "<li>$k_T=\\sqrt{k^2-\\beta^2}$</li>\n",
    "<br>\n",
    "Source: Saleh & Teich, <i>Fundamentals of Photonics</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9793a27b-c702-412f-8afe-3015351acff3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.special import jv\n",
    "\n",
    "\n",
    "def Bessel(\n",
    "    size,\n",
    "    m,\n",
    "    beta,\n",
    "    source_time,\n",
    "    center=(0, 0, 0),\n",
    "    direction=\"+\",\n",
    "    pol_angle=0,\n",
    "    angle_phi=0,\n",
    "    angle_theta=0,\n",
    "    w0=1,\n",
    "    waist_distance=0,\n",
    "):\n",
    "    if not isinstance(m, int):\n",
    "        raise Exception(\"Error: m must be an integer\")\n",
    "    freqs = [source_time.freq0]\n",
    "\n",
    "    class Bessel_obj(BeamProfile):\n",
    "        \"\"\"Component for constructing Bessel beam data. The normal direction is implicitly\n",
    "        defined by the ``size`` parameter.\n",
    "        \"\"\"\n",
    "\n",
    "        def scalar_field(self, points, background_n):\n",
    "            \"\"\"Scalar field for Bessel beam.\n",
    "            Scalar field corresponding to the analytic beam in coordinate system such that the\n",
    "            propagation direction is z and the ``E``-field is entirely ``x``-polarized. The field is\n",
    "            computed on an unstructured array ``points`` of shape ``(3, ...)``.\n",
    "            \"\"\"\n",
    "            x, y, z = points\n",
    "            z += waist_distance\n",
    "            rho = np.sqrt(x**2 + y**2)\n",
    "            phi = np.arctan2(y, x)\n",
    "\n",
    "            k = 2 * np.pi * np.array(self.freqs) / td.C_0 * background_n\n",
    "            k_T = np.sqrt(k**2 - beta**2)\n",
    "\n",
    "            E = source_time.amplitude * jv(m, k_T * rho)\n",
    "            E *= np.exp(1j * m * phi) * np.exp(-1j * beta * z)\n",
    "\n",
    "            return E\n",
    "\n",
    "    Bessel_obj = Bessel_obj(\n",
    "        center=center,\n",
    "        size=size,\n",
    "        freqs=freqs,\n",
    "        angle_theta=angle_theta,\n",
    "        angle_phi=angle_phi,\n",
    "        pol_angle=pol_angle,\n",
    "        direction=direction,\n",
    "    )\n",
    "    field_data = Bessel_obj.field_data\n",
    "    field_dataset = td.FieldDataset(\n",
    "        Ex=field_data.Ex,\n",
    "        Ey=field_data.Ey,\n",
    "        Ez=field_data.Ez,\n",
    "        Hx=field_data.Hx,\n",
    "        Hy=field_data.Hy,\n",
    "        Hz=field_data.Hz,\n",
    "    )\n",
    "    Bessel_source = td.CustomFieldSource(\n",
    "        center=center, size=size, source_time=source_time, field_dataset=field_dataset\n",
    "    )\n",
    "    return Bessel_source"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "395121c5-498f-448c-9c18-a110a53275c6",
   "metadata": {},
   "source": [
    "Create and run example a $m=0,\\beta=0$ Bessel beam:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bb4a5088-43e1-4612-b966-01bc25955104",
   "metadata": {},
   "outputs": [],
   "source": [
    "freq0 = td.C_0 / 1.55\n",
    "bessel = Bessel(\n",
    "    size=(0, 4, 4), m=0, beta=0, source_time=td.GaussianPulse(freq0=freq0, fwidth=freq0 / 10)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6983edb3-82b6-4c88-be92-8533c5c99714",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_size = (5.1, 5, 5)\n",
    "sim_center = (sim_size[0] / 2 - 0.05, 0, 0)\n",
    "\n",
    "num_field_monitors = 61\n",
    "field_monitor_xs = np.linspace(\n",
    "    sim_center[0] - sim_size[0] / 2 + 0.1, sim_size[0] - 0.1, num_field_monitors\n",
    ")\n",
    "field_monitors = []\n",
    "for x in field_monitor_xs:\n",
    "    field_monitor = td.FieldMonitor(\n",
    "        center=(x, 0, 0), size=(0, sim_size[1], sim_size[2]), name=str(x), freqs=[freq0]\n",
    "    )\n",
    "    field_monitors.append(field_monitor)\n",
    "\n",
    "Bessel_test_sim = td.Simulation(\n",
    "    center=sim_center,\n",
    "    size=sim_size,\n",
    "    structures=[],\n",
    "    sources=[bessel],\n",
    "    monitors=field_monitors,\n",
    "    run_time=1e-13,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "df5e6e57-a468-426a-91a8-af296ae98d02",
   "metadata": {},
   "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\">14:41:19 EDT </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'Bessel test'</span> with task_id                            \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-dec995a3-fc94-481b-932a-6efb49ed604f'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:41:19 EDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'Bessel test'\u001b[0m with task_id                            \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-dec995a3-fc94-481b-932a-6efb49ed604f'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-dec995a3-fc94-481b-932a-6efb49ed604f\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-dec995a3-fc9</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-dec995a3-fc94-481b-932a-6efb49ed604f\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">4-481b-932a-6efb49ed604f'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=842902;https://tidy3d.simulation.cloud/workbench?taskId=fdve-dec995a3-fc94-481b-932a-6efb49ed604f\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=765941;https://tidy3d.simulation.cloud/workbench?taskId=fdve-dec995a3-fc94-481b-932a-6efb49ed604f\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=842902;https://tidy3d.simulation.cloud/workbench?taskId=fdve-dec995a3-fc94-481b-932a-6efb49ed604f\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=520308;https://tidy3d.simulation.cloud/workbench?taskId=fdve-dec995a3-fc94-481b-932a-6efb49ed604f\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=842902;https://tidy3d.simulation.cloud/workbench?taskId=fdve-dec995a3-fc94-481b-932a-6efb49ed604f\u001b\\\u001b[32m-dec995a3-fc9\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=842902;https://tidy3d.simulation.cloud/workbench?taskId=fdve-dec995a3-fc94-481b-932a-6efb49ed604f\u001b\\\u001b[32m4-481b-932a-6efb49ed604f'\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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/ee7143f0-3861-47de-ae8f-8f55df7232f1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=742645;https://tidy3d.simulation.cloud/folders/ee7143f0-3861-47de-ae8f-8f55df7232f1\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "642d60cfcbe34ab6bdd3084afb049b92",
       "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\">14:41:23 EDT </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Minimum cost depends on task       \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:41:23 EDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Minimum cost depends on task       \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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\">14:41:24 EDT </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:41:24 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "afa37351e5664945a5679ab88c675567",
       "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\">14:41:30 EDT </span>loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:41:30 EDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bessel_sim_data = web.run(simulation=Bessel_test_sim, task_name=\"Bessel test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3867781d-4279-4422-915e-cfbe9a9eeb6a",
   "metadata": {},
   "source": [
    "Display field amplitude in propagation direction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "448c33dd-c500-4dba-84b4-c7801afd6d16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bessel_sim_data.plot_field(str(field_monitor_xs[0]), \"E\", val=\"abs\", f=freq0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97f1878a-46a9-4966-b22a-44880dcf7671",
   "metadata": {},
   "source": [
    "To see how the field amplitude evolves, run the following code to visualize along the propagation direction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8e2f90a0-cf5d-42c5-a053-4bfc08c6c3d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fd617a9742b84d5aacdcd9216effe02b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.05000000000000018, description='E', max=5.0, min=0.05000000000000018…"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import ipywidgets as widgets\n",
    "\n",
    "component = \"E\"\n",
    "\n",
    "field_slider = widgets.FloatSlider(\n",
    "    value=0,\n",
    "    min=field_monitor_xs[0],\n",
    "    max=field_monitor_xs[-1],\n",
    "    step=None,\n",
    "    description=component,\n",
    "    disabled=False,\n",
    "    continuous_update=True,\n",
    "    orientation=\"horizontal\",\n",
    "    readout=True,\n",
    "    readout_format=\".3f\",\n",
    ")\n",
    "\n",
    "\n",
    "def update_plot(x):\n",
    "    if x not in field_monitor_xs:\n",
    "        x = min(field_monitor_xs, key=lambda ll: abs(ll - x))\n",
    "    bessel_sim_data.plot_field(str(x), component, val=\"abs\", f=freq0)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "widgets.interactive(update_plot, x=field_slider)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83f68037-14d4-44d9-8878-0471febe3428",
   "metadata": {},
   "source": [
    "## Hyper-geometric Gaussian source\n",
    "\n",
    "Here we show how to create a Hyper-geometric Gaussian (HGG) beam in Tidy3D using our [CustomFieldSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.CustomFieldSource.html) object. We then validate the field profile change along the propagation direction.<br><br>\n",
    "In cylindrical coordinates, where $\\rho=\\frac{r}{\\omega_0}$ is the normalized radial coordinate and $Z=\\frac{z}{z_R}$ is the normalized longitude direction (the direction of propagation), the HGG has field profile\n",
    "\n",
    "<center>$E_{p,m}(\\rho,\\phi,Z)=\\sqrt{\\frac{2^{p+|m|+1}}{\\pi\\Gamma(p+|m|+1)}}\\frac{\\Gamma(\\frac{p}{2}+|m|+1)}{\\Gamma(|m|+1)}i^{|m|+1}Z^{\\frac{p}{2}}( Z+i)^{-(\\frac{p}{2}+|m|+1)}\\rho^{|m|}\\exp(-\\frac{i\\rho^2}{Z+i}) e^{im\\phi} {_1}F_1(-\\frac{p}{2},|m|+1;\\frac{\\rho^2}{Z(Z+i)})$</center>\n",
    "\n",
    "where:\n",
    "<li>$\\omega_0$ is the beam waist</li>\n",
    "<li>$z_R=\\frac{\\pi n_\\epsilon\\omega_0^2}{\\lambda}$ is the Rayleigh length</li>\n",
    "<li>$m$ is the rotational index</li>\n",
    "<li>$p\\geq-|m|$</li>\n",
    "<li>$\\Gamma(x)$ is the gamma function</li>\n",
    "<li>${_1}F_1(a,b;x)$ is a confluent hypergeometric function</li>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b5dec480-68fa-44a2-8665-ddd252ceacd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.special import gamma, hyp1f1\n",
    "\n",
    "\n",
    "def HGG(\n",
    "    size,\n",
    "    p,\n",
    "    m,\n",
    "    source_time,\n",
    "    center=(0, 0, 0),\n",
    "    direction=\"+\",\n",
    "    pol_angle=0,\n",
    "    angle_phi=0,\n",
    "    angle_theta=0,\n",
    "    w0=1,\n",
    "    waist_distance=0,\n",
    "):\n",
    "    if p < -np.abs(m):\n",
    "        raise Exception(\"Error: p must be greater than or equal to -|m|\")\n",
    "    freqs = [source_time.freq0]\n",
    "\n",
    "    class HGG_obj(BeamProfile):\n",
    "        \"\"\"Component for constructing HGG beam data. The normal direction is implicitly\n",
    "        defined by the ``size`` parameter.\n",
    "        \"\"\"\n",
    "\n",
    "        def scalar_field(self, points, background_n):\n",
    "            \"\"\"Scalar field for HGG beam.\n",
    "            Scalar field corresponding to the analytic beam in coordinate system such that the\n",
    "            propagation direction is z and the ``E``-field is entirely ``x``-polarized. The field is\n",
    "            computed on an unstructured array ``points`` of shape ``(3, ...)``.\n",
    "            \"\"\"\n",
    "            x, y, z = points\n",
    "            z += waist_distance\n",
    "\n",
    "            r = np.sqrt(x**2 + y**2) / w0\n",
    "            phi = np.arctan2(y, x)\n",
    "            abs_m = np.abs(m)\n",
    "\n",
    "            E = source_time.amplitude * np.sqrt(\n",
    "                2 ** (p + abs_m + 1) / (np.pi * gamma(p + abs_m + 1))\n",
    "            )\n",
    "\n",
    "            Z = z / (np.pi * background_n * w0**2 / (td.C_0 / np.array(self.freqs)))\n",
    "            if np.all(Z) == 0:\n",
    "                return E * r ** (p + abs_m) * np.exp(-(r**2) + 1j * m * phi)\n",
    "\n",
    "            E *= gamma(p / 2 + abs_m + 1) / gamma(abs_m + 1) * 1j ** (abs_m + 1) * Z ** (p / 2)\n",
    "            E *= (Z + 1j) ** (-(p / 2 + abs_m + 1)) * r**abs_m * np.exp(-(1j * r**2) / (Z + 1j))\n",
    "            E *= np.exp(1j * m * phi) * hyp1f1(-p / 2, abs_m + 1, r**2 / (Z * (Z + 1j)))\n",
    "\n",
    "            return E\n",
    "\n",
    "    HGG_obj = HGG_obj(\n",
    "        center=center,\n",
    "        size=size,\n",
    "        freqs=freqs,\n",
    "        angle_theta=angle_theta,\n",
    "        angle_phi=angle_phi,\n",
    "        pol_angle=pol_angle,\n",
    "        direction=direction,\n",
    "    )\n",
    "    field_data = HGG_obj.field_data\n",
    "    field_dataset = td.FieldDataset(\n",
    "        Ex=field_data.Ex,\n",
    "        Ey=field_data.Ey,\n",
    "        Ez=field_data.Ez,\n",
    "        Hx=field_data.Hx,\n",
    "        Hy=field_data.Hy,\n",
    "        Hz=field_data.Hz,\n",
    "    )\n",
    "    HGG_source = td.CustomFieldSource(\n",
    "        center=center, size=size, source_time=source_time, field_dataset=field_dataset\n",
    "    )\n",
    "    return HGG_source"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3eed53a4-04cd-49a0-bcc4-069f78e740a2",
   "metadata": {},
   "source": [
    "Create and run an example $p=1,m=2$ HGG beam:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dde4141e-ea33-4f36-949d-311ce5fbf606",
   "metadata": {},
   "outputs": [],
   "source": [
    "freq0 = td.C_0 / 1.55\n",
    "hgg = HGG(size=(0, 4, 4), p=1, m=2, source_time=td.GaussianPulse(freq0=freq0, fwidth=freq0 / 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8b307c51-4007-40aa-bac5-4f0ac7cf7d6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_size = (5.1, 5, 5)\n",
    "sim_center = (sim_size[0] / 2 - 0.05, 0, 0)\n",
    "\n",
    "num_field_monitors = 61\n",
    "field_monitor_xs = np.linspace(\n",
    "    sim_center[0] - sim_size[0] / 2 + 0.1, sim_size[0] - 0.1, num_field_monitors\n",
    ")\n",
    "field_monitors = []\n",
    "for x in field_monitor_xs:\n",
    "    field_monitor = td.FieldMonitor(\n",
    "        center=(x, 0, 0), size=(0, sim_size[1], sim_size[2]), name=str(x), freqs=[freq0]\n",
    "    )\n",
    "    field_monitors.append(field_monitor)\n",
    "\n",
    "HGG_test_sim = td.Simulation(\n",
    "    center=sim_center,\n",
    "    size=sim_size,\n",
    "    structures=[],\n",
    "    sources=[hgg],\n",
    "    monitors=field_monitors,\n",
    "    run_time=1e-13,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b611fea9-b5b1-4849-ba74-9c59c9c44395",
   "metadata": {},
   "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\">14:41:31 EDT </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'HGG test'</span> with task_id                               \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-eb2cc343-02f5-4d18-9805-552770e2edaf'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:41:31 EDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'HGG test'\u001b[0m with task_id                               \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-eb2cc343-02f5-4d18-9805-552770e2edaf'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-eb2cc343-02f5-4d18-9805-552770e2edaf\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-eb2cc343-02f</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-eb2cc343-02f5-4d18-9805-552770e2edaf\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">5-4d18-9805-552770e2edaf'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=967449;https://tidy3d.simulation.cloud/workbench?taskId=fdve-eb2cc343-02f5-4d18-9805-552770e2edaf\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=736471;https://tidy3d.simulation.cloud/workbench?taskId=fdve-eb2cc343-02f5-4d18-9805-552770e2edaf\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=967449;https://tidy3d.simulation.cloud/workbench?taskId=fdve-eb2cc343-02f5-4d18-9805-552770e2edaf\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=190332;https://tidy3d.simulation.cloud/workbench?taskId=fdve-eb2cc343-02f5-4d18-9805-552770e2edaf\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=967449;https://tidy3d.simulation.cloud/workbench?taskId=fdve-eb2cc343-02f5-4d18-9805-552770e2edaf\u001b\\\u001b[32m-eb2cc343-02f\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=967449;https://tidy3d.simulation.cloud/workbench?taskId=fdve-eb2cc343-02f5-4d18-9805-552770e2edaf\u001b\\\u001b[32m5-4d18-9805-552770e2edaf'\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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/ee7143f0-3861-47de-ae8f-8f55df7232f1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=775715;https://tidy3d.simulation.cloud/folders/ee7143f0-3861-47de-ae8f-8f55df7232f1\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c8604216122746b091b127d4a6dc2399",
       "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\">14:41:34 EDT </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Minimum cost depends on task       \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:41:34 EDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Minimum cost depends on task       \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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\">14:41:35 EDT </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:41:35 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5bc6f47466b64b88bc9e82f665038a7d",
       "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\">14:41:37 EDT </span>loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:41:37 EDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hgg_sim_data = web.run(simulation=HGG_test_sim, task_name=\"HGG test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40b05fb4-59ac-447b-8b47-d1999861c568",
   "metadata": {},
   "source": [
    "Display field in propagation direction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e2065e4d-2484-4e3a-a8de-74170d8e7dd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hgg_sim_data.plot_field(str(field_monitor_xs[30]), \"Ey\", f=freq0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a4c8ee2-9719-4a2e-a531-05399e166601",
   "metadata": {},
   "source": [
    "To see how the field evolves, run the following code to visualize along the propagation direction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "496022b9-fc6b-4560-a34f-40697f7d49f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "25a91d5ed3164e9bbc0c5fbeb6af084b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.05000000000000018, description='Ey', max=5.0, min=0.0500000000000001…"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import ipywidgets as widgets\n",
    "\n",
    "component = \"Ey\"\n",
    "\n",
    "field_slider = widgets.FloatSlider(\n",
    "    value=0,\n",
    "    min=field_monitor_xs[0],\n",
    "    max=field_monitor_xs[-1],\n",
    "    step=None,\n",
    "    description=component,\n",
    "    disabled=False,\n",
    "    continuous_update=True,\n",
    "    orientation=\"horizontal\",\n",
    "    readout=True,\n",
    "    readout_format=\".3f\",\n",
    ")\n",
    "\n",
    "\n",
    "def update_plot(x):\n",
    "    if x not in field_monitor_xs:\n",
    "        x = min(field_monitor_xs, key=lambda ll: abs(ll - x))\n",
    "    hgg_sim_data.plot_field(str(x), component, f=freq0)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "widgets.interactive(update_plot, x=field_slider)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2af520b7-6503-4ebc-be44-611bc8081b78",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "description": "This notebook creates advanced gaussian beams that can be used by the interested user. In order to make these custom sources easy to use in multiple settings, we use Tidy3D's open-source BeamProfile object to build these sources. This also demonstrates how to take advantage of Tidy3D's open-source nature to save much complication.",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "gaussian, source, field, open-source, angular, hyper-geometric, bessel, hermite",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  },
  "title": "Creating advanced Gaussian sources using Tidy3D's open-source BeamProfile object"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}