diff --git a/Autograd23FabricationAwareInvdes.ipynb b/Autograd23FabricationAwareInvdes.ipynb
index ace1fb3d..a165eee1 100644
--- a/Autograd23FabricationAwareInvdes.ipynb
+++ b/Autograd23FabricationAwareInvdes.ipynb
@@ -169,8 +169,9 @@
     "\n",
     "Using the prediction model, we can simulate the expected experimental performance of device designs such as the wavelength division multiplexer (WDM) in the [Adjoint Optimization of a Wavelength Division Multiplexer](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd9WDM/). The WDM was optimized with a relatively large feature size and an erosion/dilation penalty to enhance robustness against fabrication variations. This optimization allows it to tolerate predicted fabrication variations quite well; however, some performance degradation is still observed. As illustrated in the figure below, the fabrication-predicted transmission spectra get distorted, insertion loss is increased, and further crosstalk is introduced. This performance degradation can be mitigated with FAID.\n",
     "\n",
-    "<img src=\"img/flux_ID_nopredict.png\" width=\"500\" alt=\"Performance without fabrication prediction\">\n",
-    "<img src=\"img/flux_ID_predict.png\" width=\"500\" alt=\"Performance with fabrication prediction\">"
+    "![\"Performance without fabrication prediction\"](img/flux_ID_nopredict.png)\n",
+    "\n",
+    "![\"Performance with fabrication prediction\"](img/flux_ID_predict.png)"
    ]
   },
   {
@@ -53738,7 +53739,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.0"
+   "version": "3.10.14"
   },
   "title": "Adjoint Optimization of a WDM in Tidy3D with PreFab for Fabrication Awareness | Flexcompute"
  },
diff --git a/CriticalPointAL.ipynb b/CriticalPointAL.ipynb
new file mode 100644
index 00000000..9941d0b3
--- /dev/null
+++ b/CriticalPointAL.ipynb
@@ -0,0 +1,2049 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Critical point in electromagnetic Anderson localization in three dimensions\n",
+    "\n",
+    "Anderson localization (AL) is a wave interference phenomenon that can suppress wave transport in disordered media. The recent work by Yamilov, Cao, and Skipetrov (https://arxiv.org/abs/2408.04853) demonstrates a clear transition between diffusive and localized regimes for light in 3D. Using Tidy3D, finite-difference time-domain (FDTD), simulations of electromagnetic wave propagation through randomly distributed perfect electric conducting (PEC) spheres, a critical frequency (mobility edge) where this transition occurs has been identified.\n",
+    "\n",
+    "In this notebook, we will qualitatively reproduce the results presented in Fig. 1a of the paper for spheres with a radius of 75 nm, exploring two different simulation domain sizes (2λ)³ and (3λ)³. This notebook is contributed by Dr. Alexey Yamilov"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import scipy.io as sio\n",
+    "\n",
+    "import tidy3d as td\n",
+    "from tidy3d import web\n",
+    "from tidy3d import SpatialDataArray\n",
+    "\n",
+    "td.config.logging_level = \"ERROR\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define study parameters\n",
+    "\n",
+    "We now define global parameters for the simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Free space central wavelength in um and center frequency\n",
+    "wavelength = 0.50\n",
+    "freq0 = td.C_0 / wavelength\n",
+    "\n",
+    "# Bandwidth of the excitation pulse in Hz, chosen so that Fourier transform can be computed for f0a frequencies\n",
+    "fwidth = freq0 / 7.0\n",
+    "\n",
+    "\n",
+    "# Space between PML and slab\n",
+    "space = 1 * wavelength\n",
+    "\n",
+    "# Sphere radius in um\n",
+    "sphere_r = 0.075\n",
+    "\n",
+    "# Define number of random realizations\n",
+    "nseeds = 30\n",
+    "\n",
+    "# Define random seed (to replicate disordered slab, if needed)\n",
+    "seeds = 0 + np.arange(nseeds)\n",
+    "\n",
+    "# Grids per wavelength\n",
+    "grids_pw = 20\n",
+    "dt0 = 0.99 * wavelength / (grids_pw * td.C_0 * np.sqrt(3))\n",
+    "\n",
+    "# Number of PML layers to use along z direction\n",
+    "npml = 2 * grids_pw\n",
+    "\n",
+    "# We use a uniform grid size to speed up the meshing process\n",
+    "dl = wavelength / grids_pw\n",
+    "grid_spec = td.GridSpec.uniform(dl=dl)\n",
+    "\n",
+    "# Frequencies to analyse\n",
+    "freqs = freq0 * np.linspace(0.7, 1.4, 2001)\n",
+    "\n",
+    "# Define boundary conditions: Periodic in x and y, and PML in z\n",
+    "\n",
+    "sys = \"PEC\"\n",
+    "periodic_bc = td.Boundary(plus=td.Periodic(), minus=td.Periodic())\n",
+    "pml = td.Boundary(plus=td.Absorber(num_layers=npml), minus=td.Absorber(num_layers=npml))\n",
+    "boundary_spec = td.BoundarySpec(x=periodic_bc, y=periodic_bc, z=pml)\n",
+    "\n",
+    "# Gaussian source offset; the source peak is at time t = offset/fwidth\n",
+    "offset = 10.0\n",
+    "\n",
+    "# Defining spheres material as Perfect Electric Conductor\n",
+    "medium = td.PEC\n",
+    "medium_out = td.Medium(permittivity=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The realized volume filling fraction depends on the overlap between spheres, smoothing and discretization, it is computed after simulation is done. Accounting for only sphere overlaps, $ff$ can be found as $ff_{appx}  = 1 - e^{(-ff0)}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Nominal volume filling fraction, actual filling fraction is lower due to overlap between spheres\n",
+    "ff0 = 0.95\n",
+    "\n",
+    "# Approximate filling fraction\n",
+    "ff_appx = 1 - np.exp(-ff0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we will define a function for generating the random structure as a function of the seed:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def generate_structure(seed, L):\n",
+    "    structures = []\n",
+    "\n",
+    "    # Compute number of spheres to place: volume * nominal_density = number of spheres\n",
+    "    num_spheres = int(\n",
+    "        (L + 2 * sphere_r)\n",
+    "        * (L + 2 * sphere_r)\n",
+    "        * (L + 2 * sphere_r)\n",
+    "        * ff0\n",
+    "        / (4 * np.pi / 3 * sphere_r**3)\n",
+    "    )\n",
+    "    np.random.seed(seed)\n",
+    "\n",
+    "    # Randomly position spheres\n",
+    "    for i in range(num_spheres):\n",
+    "        position_x = np.random.uniform(-L / 2 - sphere_r, L / 2 + sphere_r)\n",
+    "        position_y = np.random.uniform(-L / 2 - sphere_r, L / 2 + sphere_r)\n",
+    "        position_z = np.random.uniform(-L / 2 - sphere_r, L / 2 + sphere_r)\n",
+    "        radius_i = sphere_r\n",
+    "        name_i = f\"sphere_i={i}\"\n",
+    "        sphere_i = td.Sphere(\n",
+    "            center=[position_x, position_y, position_z], radius=radius_i\n",
+    "        )\n",
+    "        structure_i = td.Structure(geometry=sphere_i, medium=medium)\n",
+    "        structures.append(structure_i)\n",
+    "\n",
+    "    # Define effective medium around the slab\n",
+    "    box1 = td.Box(center=[0, 0, -L / 2 - space], size=[td.inf, td.inf, 2 * space])\n",
+    "    box2 = td.Box(center=[0, 0, L / 2 + space], size=[td.inf, td.inf, 2 * space])\n",
+    "    struct1 = td.Structure(geometry=box1, medium=medium_out)\n",
+    "    structures.append(struct1)\n",
+    "    struct2 = td.Structure(geometry=box2, medium=medium_out)\n",
+    "    structures.append(struct2)\n",
+    "\n",
+    "    return structures"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Defining a function for creating the simulation object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_sim(seed, L, run_time):\n",
+    "    # Defining simulation size\n",
+    "    Lz_tot = space + L + space\n",
+    "    sim_size = [L, L, Lz_tot]\n",
+    "\n",
+    "    structures = generate_structure(seed, L)\n",
+    "\n",
+    "    # Define incident plane wave\n",
+    "    gaussian = td.GaussianPulse(freq0=freq0, fwidth=fwidth, offset=offset, phase=0)\n",
+    "    source = td.PlaneWave(\n",
+    "        size=(td.inf, td.inf, 0.0),\n",
+    "        center=(0, 0, -Lz_tot / 2.0 + 0.1 * wavelength),\n",
+    "        source_time=gaussian,\n",
+    "        direction=\"+\",\n",
+    "        pol_angle=0,\n",
+    "    )\n",
+    "\n",
+    "    # Transmitted flux through the slab\n",
+    "    flux_monitor = td.FluxMonitor(\n",
+    "        center=[0.0, 0.0, L / 2.0 + space / 2.0],\n",
+    "        size=[td.inf, td.inf, 0],\n",
+    "        freqs=freqs,\n",
+    "        name=\"flux_monitor\",\n",
+    "    )\n",
+    "\n",
+    "    # Records time-dependent transmitted flux through the slab\n",
+    "    flux_time_monitor = td.FluxTimeMonitor(\n",
+    "        center=[0.0, 0.0, L / 2.0 + space / 2.0],\n",
+    "        size=[td.inf, td.inf, 0],\n",
+    "        name=\"flux_time_monitor\",\n",
+    "    )\n",
+    "\n",
+    "    monitors = [flux_monitor, flux_time_monitor]  # ,eps_monitor]\n",
+    "\n",
+    "    # Define simulation object\n",
+    "    sim = td.Simulation(\n",
+    "        size=sim_size,\n",
+    "        grid_spec=grid_spec,\n",
+    "        structures=structures,\n",
+    "        sources=[source],\n",
+    "        monitors=monitors,\n",
+    "        run_time=run_time,\n",
+    "        boundary_spec=boundary_spec,\n",
+    "        shutoff=1e-15,\n",
+    "    )\n",
+    "\n",
+    "    return sim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a simulation batch for L = 2 wavelengths\n",
+    "sims2 = {}\n",
+    "for seed in seeds:\n",
+    "    sims2[\"%i\" % seed] = make_sim(seed, L=2 * wavelength, run_time=3e-12)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a simulation batch for L = 3 wavelengths\n",
+    "sims3 = {}\n",
+    "for seed in seeds:\n",
+    "    sims3[\"%i\" % seed] = make_sim(seed, L=3 * wavelength, run_time=6e-12)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "65f3c2436cd7471ca21343bb7a23165f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:23:55 CET </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span> tasks.                      \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m13:23:55 CET\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m30\u001b[0m tasks.                      \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:24:53 CET </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.750</span> for the whole batch.                \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m13:24:53 CET\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.750\u001b[0m for the whole batch.                \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after the\n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Batch has completed.                                               \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after the\n",
+       "\u001b[2;36m             \u001b[0mBatch has completed.                                               \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "27a65bfb94024373930206a990d436ca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:26:00 CET </span>Batch complete.                                                    \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m13:26:00 CET\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fcbfe84d9d2140f68215e86f36db8e7b",
+       "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"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
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+    {
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+     "output_type": "stream",
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+    {
+     "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": [
+    "# Running batch\n",
+    "batch3 = web.Batch(simulations=sims3, verbose=True)\n",
+    "batch_data3 = batch3.run()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we will load the batch data and calculate $\\langle \\log{(g)} \\rangle$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def analyse_g(batch_data, L):\n",
+    "    T_of_f = []\n",
+    "\n",
+    "    # Load frequency-resolved transmission for all realizations of disorder\n",
+    "    for seed in seeds:\n",
+    "        sim_data = batch_data[\"%i\" % seed]\n",
+    "        fT = sim_data[\"flux_monitor\"].flux\n",
+    "        T_of_f = np.hstack([T_of_f, fT.values])\n",
+    "\n",
+    "    normalized_L = L / wavelength\n",
+    "    normalized_freqs = freqs / freq0\n",
+    "\n",
+    "    # Calculate of the ensemble-averaged <log(g)> as a function of frequency\n",
+    "    Lp = normalized_L * wavelength\n",
+    "    ka = 2 * np.pi / wavelength * normalized_freqs.T\n",
+    "    T_of_f_ave = np.exp(\n",
+    "        np.transpose(np.mean(np.log(T_of_f.reshape(int(nseeds), -1)), axis=0))\n",
+    "    )\n",
+    "    logg = np.log(\n",
+    "        np.multiply(T_of_f_ave, ka.flatten() ** 2) * Lp**2 / (2 * np.pi) * 0.80\n",
+    "    )\n",
+    "\n",
+    "    return logg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logg2 = analyse_g(batch_data2, 2 * wavelength)\n",
+    "logg3 = analyse_g(batch_data3, 3 * wavelength)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plotting the data, we observe the curves crossing at the critical point (the mobility edge). To the left/right of the critical point conductance decreases/increases with an increase of the system size L. At the critical point, the conductance is invariant with the system size. This indicates a sharp Anderson transition in the L→∞ limit, see https://arxiv.org/abs/2408.04853 for details.\n",
+    "\n",
+    "Smoother data can be obtained by increasing `nseeds`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "normalized_freqs = freqs / freq0\n",
+    "normalized_L2 = 2 * wavelength / wavelength\n",
+    "normalized_L3 = 3 * wavelength / wavelength\n",
+    "\n",
+    "ax.plot(\n",
+    "    (2 * np.pi / wavelength * normalized_freqs.T * sphere_r),\n",
+    "    logg2,\n",
+    "    \"r.\",\n",
+    "    markersize=10,\n",
+    "    linewidth=1,\n",
+    "    label=f\"Size=({normalized_L2}λ)³, f={ff_appx*100:.0f}%, r={1000*sphere_r}nm\",\n",
+    ")\n",
+    "\n",
+    "ax.plot(\n",
+    "    (2 * np.pi / wavelength * normalized_freqs.T * sphere_r),\n",
+    "    logg3,\n",
+    "    \"b.\",\n",
+    "    markersize=10,\n",
+    "    linewidth=1,\n",
+    "    label=f\"Size=({normalized_L3}λ)³, f={ff_appx*100:.0f}%, r={1000*sphere_r}nm\",\n",
+    ")\n",
+    "\n",
+    "ax.set_xlabel(\"kr\", fontsize=12)\n",
+    "ax.set_ylabel(r\"$\\langle\\log(g)\\rangle$\", fontsize=12)\n",
+    "ax.legend(fontsize=12, loc=\"best\")\n",
+    "\n",
+    "ax.legend()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "applications": [
+   "Nanophotonics"
+  ],
+  "description": "This notebook demonstrates the critical point in electromagnetic Anderson localization.",
+  "feature_image": "./img/Anderson_localization_critical_point.png",
+  "features": [
+   "Parameter sweep"
+  ],
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "keywords": "Anderson Localization, nanophotonics, Tidy3D, FDTD",
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  },
+  "redirect_from": "/tidy3d/q7ras6JBww",
+  "title": "Critical Point in Electromagnetic Anderson Localization in Three Dimensions | Flexcompute"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/MetalOxideSunscreen.ipynb b/MetalOxideSunscreen.ipynb
index 10c7442d..94542c63 100644
--- a/MetalOxideSunscreen.ipynb
+++ b/MetalOxideSunscreen.ipynb
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63,13 +63,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "06a4af5ba3f5493eaba6ca397d395c37",
+       "model_id": "ec1af1ebdaae4389b4fefa88bec04988",
        "version_major": 2,
        "version_minor": 0
       },
@@ -79,44 +79,6 @@
      },
      "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\">12:17:24 -03 </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Unable to fit with weighted RMS error under               </span>\n",
-       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'tolerance_rms'</span><span style=\"color: #800000; text-decoration-color: #800000\"> of </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1e-05</span><span style=\"color: #800000; text-decoration-color: #800000\">                                           </span>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[2;36m12:17:24 -03\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Unable to fit with weighted RMS error under               \u001b[0m\n",
-       "\u001b[2;36m             \u001b[0m\u001b[32m'tolerance_rms'\u001b[0m\u001b[31m of \u001b[0m\u001b[1;36m1e-05\u001b[0m\u001b[31m                                           \u001b[0m\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -140,20 +102,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# plotting for comparison\n",
     "fig, ax = plt.subplots()\n",
@@ -182,7 +133,7 @@
   },
   {
    "cell_type": "code",
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@@ -236,7 +187,7 @@
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+   "execution_count": null,
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@@ -292,117 +243,9 @@
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null}], &quot;grid_spec&quot;: {&quot;attrs&quot;: {}, &quot;grid_x&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;AutoGrid&quot;, &quot;min_steps_per_wvl&quot;: 10.0, &quot;max_scale&quot;: 1.4, &quot;dl_min&quot;: 0.0, &quot;mesher&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;GradedMesher&quot;}}, &quot;grid_y&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;AutoGrid&quot;, &quot;min_steps_per_wvl&quot;: 10.0, &quot;max_scale&quot;: 1.4, &quot;dl_min&quot;: 0.0, &quot;mesher&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;GradedMesher&quot;}}, &quot;grid_z&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;AutoGrid&quot;, &quot;min_steps_per_wvl&quot;: 10.0, &quot;max_scale&quot;: 1.4, &quot;dl_min&quot;: 0.0, &quot;mesher&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;GradedMesher&quot;}}, &quot;wavelength&quot;: null, &quot;override_structures&quot;: [{&quot;attrs&quot;: {}, &quot;geometry&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;Box&quot;, &quot;center&quot;: [0.0, 0.0, 0.0], &quot;size&quot;: [1.2, 1.2, 0.3]}, &quot;name&quot;: null, &quot;type&quot;: &quot;MeshOverrideStructure&quot;, &quot;dl&quot;: [0.004166666666666667, 0.004166666666666667, 0.004166666666666667], &quot;enforce&quot;: false}], &quot;snapping_points&quot;: [], &quot;type&quot;: &quot;GridSpec&quot;}, &quot;version&quot;: &quot;2.7.1&quot;, &quot;lumped_elements&quot;: [], &quot;subpixel&quot;: {&quot;attrs&quot;: {}, &quot;dielectric&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;PolarizedAveraging&quot;}, &quot;metal&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;Staircasing&quot;}, &quot;pec&quot;: {&quot;attrs&quot;: {}, &quot;type&quot;: &quot;PECConformal&quot;, &quot;timestep_reduction&quot;: 0.3}, &quot;type&quot;: &quot;SubpixelSpec&quot;}, &quot;courant&quot;: 0.99, &quot;normalize_index&quot;: 0, &quot;shutoff&quot;: 1e-05, &quot;run_time&quot;: 5e-12}\" />\n",
-       "    <script>\n",
-       "        \n",
-       "        /**\n",
-       "        * Simulation Viewer Injector\n",
-       "        *\n",
-       "        * Monitors the document for elements being added in the form:\n",
-       "        *\n",
-       "        *    <div class=\"simulation-viewer\" data-width=\"800\" data-height=\"800\" data-simulation=\"{...}\" />\n",
-       "        *\n",
-       "        * This script will then inject an iframe to the viewer application, and pass it the simulation data\n",
-       "        * via the postMessage API on request. The script may be safely included multiple times, with only the\n",
-       "        * configuration of the first started script (e.g. viewer URL) applying.\n",
-       "        *\n",
-       "        */\n",
-       "        (function() {\n",
-       "            const TARGET_CLASS = \"simulation-viewer\";\n",
-       "            const ACTIVE_CLASS = \"simulation-viewer-active\";\n",
-       "            const VIEWER_URL = \"https://feature-simulation-viewer.d3a9gfg7glllfq.amplifyapp.com/simulation-viewer\";\n",
-       "\n",
-       "            class SimulationViewerInjector {\n",
-       "                constructor() {\n",
-       "                    for (var node of document.getElementsByClassName(TARGET_CLASS)) {\n",
-       "                        this.injectViewer(node);\n",
-       "                    }\n",
-       "\n",
-       "                    // Monitor for newly added nodes to the DOM\n",
-       "                    this.observer = new MutationObserver(this.onMutations.bind(this));\n",
-       "                    this.observer.observe(document.body, {childList: true, subtree: true});\n",
-       "                }\n",
-       "\n",
-       "                onMutations(mutations) {\n",
-       "                    for (var mutation of mutations) {\n",
-       "                        if (mutation.type === 'childList') {\n",
-       "                            /**\n",
-       "                            * Have found that adding the element does not reliably trigger the mutation observer.\n",
-       "                            * It may be the case that setting content with innerHTML does not trigger.\n",
-       "                            *\n",
-       "                            * It seems to be sufficient to re-scan the document for un-activated viewers\n",
-       "                            * whenever an event occurs, as Jupyter triggers multiple events on cell evaluation.\n",
-       "                            */\n",
-       "                            var viewers = document.getElementsByClassName(TARGET_CLASS);\n",
-       "                            for (var node of viewers) {\n",
-       "                                this.injectViewer(node);\n",
-       "                            }\n",
-       "                        }\n",
-       "                    }\n",
-       "                }\n",
-       "\n",
-       "                injectViewer(node) {\n",
-       "                    // (re-)check that this is a valid simulation container and has not already been injected\n",
-       "                    if (node.classList.contains(TARGET_CLASS) && !node.classList.contains(ACTIVE_CLASS)) {\n",
-       "                        // Mark node as injected, to prevent re-runs\n",
-       "                        node.classList.add(ACTIVE_CLASS);\n",
-       "\n",
-       "                        var uuid;\n",
-       "                        if (window.crypto && window.crypto.randomUUID) {\n",
-       "                            uuid = window.crypto.randomUUID();\n",
-       "                        } else {\n",
-       "                            uuid = \"\" + Math.random();\n",
-       "                        }\n",
-       "\n",
-       "                        var frame = document.createElement(\"iframe\");\n",
-       "                        frame.width = node.dataset.width || 800;\n",
-       "                        frame.height = node.dataset.height || 800;\n",
-       "                        frame.src = VIEWER_URL + \"?uuid=\" + uuid;\n",
-       "\n",
-       "                        var postMessageToViewer;\n",
-       "                        postMessageToViewer = event => {{\n",
-       "                            if(event.data.type === 'viewer' && event.data.uuid===uuid){{\n",
-       "                                var simulation = JSON.parse(node.dataset.simulation);\n",
-       "                                frame.contentWindow.postMessage({ type: 'jupyter', uuid, value: simulation}, '*');\n",
-       "\n",
-       "                                // Run once only\n",
-       "                                window.removeEventListener('message', postMessageToViewer);\n",
-       "                            }}\n",
-       "                        }};\n",
-       "                        window.addEventListener(\n",
-       "                            'message',\n",
-       "                            postMessageToViewer,\n",
-       "                            false\n",
-       "                        );\n",
-       "\n",
-       "                        node.appendChild(frame);\n",
-       "                    }\n",
-       "                }\n",
-       "            }\n",
-       "\n",
-       "            if (!window.simulationViewerInjector) {\n",
-       "                window.simulationViewerInjector = new SimulationViewerInjector();\n",
-       "            }\n",
-       "        })();\n",
-       "    \n",
-       "    </script>\n",
-       "    "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "source = td.PlaneWave(\n",
     "    center=(0, 0, source_z),\n",
@@ -446,8 +289,15 @@
     "Simulations = {\n",
     "    \"proportion 10%\": sim.updated_copy(structures=get_particles(0.1)),\n",
     "    \"proportion 20%\": sim.updated_copy(structures=get_particles(0.2)),\n",
-    "}\n",
-    "\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "# visualizing\n",
     "Simulations[\"proportion 10%\"].plot_3d()"
    ]
@@ -456,180 +306,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will run both simulations in parallel using the [`web.Batch`](https://docs.flexcompute.com/projects/tidy3d/en/v1.5.0/_autosummary/tidy3d.web.container.Batch.html) function."
+    "\n",
+    "We will run both simulations in parallel using the [web.Batch](https://docs.flexcompute.com/projects/tidy3d/en/latest/_autosummary/tidy3d.web.container.Batch.html) function.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "de8d077236c046f3bbf767f8a5937409",
-       "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\">12:17:39 -03 </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span> tasks.                       \n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[2;36m12:17:39 -03\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m2\u001b[0m tasks.                       \n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:17:42 -03 </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">14.937</span> for the whole batch.               \n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[2;36m12:17:42 -03\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m14.937\u001b[0m for the whole batch.               \n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after the\n",
-       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Batch has completed.                                               \n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after the\n",
-       "\u001b[2;36m             \u001b[0mBatch has completed.                                               \n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e22a546b1bb84ca28d15204cefb0d3b3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:18:44 -03 </span>Batch complete.                                                    \n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[2;36m12:18:44 -03\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
-      ],
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "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": "febbbb69bd95461bb4ce95a7c71ff01c",
-       "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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "batch = web.Batch(simulations=Simulations, verbose=True)\n",
     "results = batch.run(path_dir=\"batch_simulations\")"
@@ -646,164 +331,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Transmitted 10%</th>\n",
-       "      <th>Reflected 10%</th>\n",
-       "      <th>Absorbed 10%</th>\n",
-       "      <th>Transmitted 20%</th>\n",
-       "      <th>Reflected 20%</th>\n",
-       "      <th>Absorbed 20%</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0.800000</th>\n",
-       "      <td>0.969677</td>\n",
-       "      <td>0.046212</td>\n",
-       "      <td>-0.015889</td>\n",
-       "      <td>0.921638</td>\n",
-       "      <td>0.017291</td>\n",
-       "      <td>0.061071</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.797894</th>\n",
-       "      <td>0.969730</td>\n",
-       "      <td>0.045390</td>\n",
-       "      <td>-0.015120</td>\n",
-       "      <td>0.922198</td>\n",
-       "      <td>0.016671</td>\n",
-       "      <td>0.061131</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.795800</th>\n",
-       "      <td>0.969793</td>\n",
-       "      <td>0.044534</td>\n",
-       "      <td>-0.014327</td>\n",
-       "      <td>0.922825</td>\n",
-       "      <td>0.016071</td>\n",
-       "      <td>0.061104</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.793716</th>\n",
-       "      <td>0.969888</td>\n",
-       "      <td>0.043687</td>\n",
-       "      <td>-0.013575</td>\n",
-       "      <td>0.923352</td>\n",
-       "      <td>0.015560</td>\n",
-       "      <td>0.061088</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.791643</th>\n",
-       "      <td>0.969990</td>\n",
-       "      <td>0.042879</td>\n",
-       "      <td>-0.012869</td>\n",
-       "      <td>0.923519</td>\n",
-       "      <td>0.015133</td>\n",
-       "      <td>0.061348</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.789581</th>\n",
-       "      <td>0.970064</td>\n",
-       "      <td>0.042090</td>\n",
-       "      <td>-0.012154</td>\n",
-       "      <td>0.923602</td>\n",
-       "      <td>0.014807</td>\n",
-       "      <td>0.061592</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.787530</th>\n",
-       "      <td>0.970105</td>\n",
-       "      <td>0.041276</td>\n",
-       "      <td>-0.011380</td>\n",
-       "      <td>0.923368</td>\n",
-       "      <td>0.014583</td>\n",
-       "      <td>0.062049</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.785490</th>\n",
-       "      <td>0.970146</td>\n",
-       "      <td>0.040430</td>\n",
-       "      <td>-0.010576</td>\n",
-       "      <td>0.922945</td>\n",
-       "      <td>0.014432</td>\n",
-       "      <td>0.062623</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.783460</th>\n",
-       "      <td>0.970217</td>\n",
-       "      <td>0.039589</td>\n",
-       "      <td>-0.009806</td>\n",
-       "      <td>0.922452</td>\n",
-       "      <td>0.014377</td>\n",
-       "      <td>0.063172</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0.781440</th>\n",
-       "      <td>0.970310</td>\n",
-       "      <td>0.038785</td>\n",
-       "      <td>-0.009095</td>\n",
-       "      <td>0.921654</td>\n",
-       "      <td>0.014391</td>\n",
-       "      <td>0.063955</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          Transmitted 10%  Reflected 10%  Absorbed 10%  Transmitted 20%  \\\n",
-       "0.800000         0.969677       0.046212     -0.015889         0.921638   \n",
-       "0.797894         0.969730       0.045390     -0.015120         0.922198   \n",
-       "0.795800         0.969793       0.044534     -0.014327         0.922825   \n",
-       "0.793716         0.969888       0.043687     -0.013575         0.923352   \n",
-       "0.791643         0.969990       0.042879     -0.012869         0.923519   \n",
-       "0.789581         0.970064       0.042090     -0.012154         0.923602   \n",
-       "0.787530         0.970105       0.041276     -0.011380         0.923368   \n",
-       "0.785490         0.970146       0.040430     -0.010576         0.922945   \n",
-       "0.783460         0.970217       0.039589     -0.009806         0.922452   \n",
-       "0.781440         0.970310       0.038785     -0.009095         0.921654   \n",
-       "\n",
-       "          Reflected 20%  Absorbed 20%  \n",
-       "0.800000       0.017291      0.061071  \n",
-       "0.797894       0.016671      0.061131  \n",
-       "0.795800       0.016071      0.061104  \n",
-       "0.793716       0.015560      0.061088  \n",
-       "0.791643       0.015133      0.061348  \n",
-       "0.789581       0.014807      0.061592  \n",
-       "0.787530       0.014583      0.062049  \n",
-       "0.785490       0.014432      0.062623  \n",
-       "0.783460       0.014377      0.063172  \n",
-       "0.781440       0.014391      0.063955  "
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "\n",
@@ -846,38 +376,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Proportion of scattered light at 10%: 0.05\n",
-      "Proportion of scattered light at 20%: 0.09\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots()\n",
     "ax = df[\"Transmitted 10%\"].plot()\n",
@@ -902,6 +403,27 @@
     "    \"Proportion of scattered light at 20%s: %.2f\" % (\"%\", df[\"Reflected 20%\"].mean())\n",
     ")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -930,7 +452,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.10.14"
   },
   "title": "Study of the UV blocking mechanism of metal oxide-based sunscreens using Tidy3D | Flexcompute"
  },