04-MEEP_fields.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MEEP simulations\n",
    "\n",
    "We will look again at a waveguide, but in the time-domain. This will introduce concepts important when doing time-domain simulations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using MPI version 3.1, 1 processes\n"
     ]
    }
   ],
   "source": [
    "import meep as mp\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import Video"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simple geometry as before :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "resolution = 50 # pixels/μm\n",
    "cell_size = mp.Vector3(14,14)\n",
    "\n",
    "w = 0.5 # width of waveguide\n",
    "\n",
    "geometry = [mp.Block(center=mp.Vector3(),\n",
    "                     size=mp.Vector3(mp.inf,w,mp.inf),\n",
    "                     material=mp.Medium(epsilon=12))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An important difference from MPB is that we would like to avoid periodic boundary conditions. To do so, we can introduce [perfectly-matched layers](https://en.wikipedia.org/wiki/Perfectly_matched_layer) around the simulation region, see also https://meep.readthedocs.io/en/latest/Perfectly_Matched_Layer/.\n",
    "\n",
    "!['PML'](images/PML.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pml_layers = [mp.PML(thickness=2)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the time-domain, we now need to define a way to excite the electromagnetic fields in the system. The most reliable way to do so is with en eigensource (see https://meep.readthedocs.io/en/latest/Python_Tutorials/Eigenmode_Source/). The EigenModeSource object has a location and direction, and during the initialization of the simulation will solve for the requested mode profile with MPB in order to only provide an input for this mode.\n",
    "\n",
    "By sending a time-limited input, multiple frequency components can be represented in the same simulation. Here, we use a Gaussian source (Gaussian time pulse) :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "fsrc = 0.15 # frequency of eigenmode or constant-amplitude source\n",
    "kx = 0.4    # initial guess for wavevector in x-direction of eigenmode\n",
    "bnum = 1    # band number of eigenmode\n",
    "\n",
    "kpoint = mp.Vector3(kx)\n",
    "\n",
    "sources = [mp.EigenModeSource(src=mp.GaussianSource(fsrc,fwidth=0.2*fsrc),\n",
    "                              center=mp.Vector3(),\n",
    "                              size=mp.Vector3(y=3*w),\n",
    "                              direction=mp.NO_DIRECTION,\n",
    "                              eig_kpoint=kpoint,\n",
    "                              eig_band=bnum,\n",
    "                              #eig_parity=mp.EVEN_Y+mp.ODD_Z,\n",
    "                              eig_match_freq=True)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Defining the simulation object is similar to ModeSolver :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim = mp.Simulation(cell_size=cell_size,\n",
    "                    resolution=resolution,\n",
    "                    boundary_layers=pml_layers,\n",
    "                    sources=sources,\n",
    "                    geometry=geometry,\n",
    "                    symmetries=[mp.Mirror(mp.Y)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Initializing structure...\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00280809 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 14 x 14 x 0 with resolution 50\n",
      "     block, center = (0,0,0)\n",
      "          size (1e+20,0.5,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (12,12,12)\n",
      "time for set_epsilon = 0.322351 s\n",
      "-----------\n",
      "MPB solved for omega_1(0.4,0,0) = 0.165058 after 17 iters\n",
      "MPB solved for omega_1(0.355637,0,0) = 0.149677 after 7 iters\n",
      "MPB solved for omega_1(0.356547,0,0) = 0.15 after 5 iters\n",
      "MPB solved for omega_1(0.356547,0,0) = 0.15 after 2 iters\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(dpi=100)\n",
    "sim.plot2D(ax=f.gca())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `run` method will run the simulation until the prescribed condition is met. See https://meep.readthedocs.io/en/latest/Python_User_Interface/#run-and-step-functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "on time step 5189 (time=51.89), 0.395668 s/step\n",
      "Meep progress: 14.130000610351559/151.88999938964844 = 9.3% done in 4.0s, 39.0s to go\n",
      "on time step 6603 (time=66.03), 0.00283052 s/step\n",
      "Meep progress: 28.15000061035157/151.88999938964844 = 18.5% done in 8.0s, 35.2s to go\n",
      "on time step 8005 (time=80.05), 0.00285594 s/step\n",
      "Meep progress: 42.34000061035157/151.88999938964844 = 27.9% done in 12.0s, 31.1s to go\n",
      "on time step 9425 (time=94.25), 0.00281769 s/step\n",
      "Meep progress: 56.60000061035157/151.88999938964844 = 37.3% done in 16.0s, 27.0s to go\n",
      "on time step 10851 (time=108.51), 0.00280629 s/step\n",
      "Meep progress: 70.39000061035156/151.88999938964844 = 46.3% done in 20.0s, 23.2s to go\n",
      "on time step 12230 (time=122.3), 0.00290218 s/step\n",
      "Meep progress: 84.14000061035156/151.88999938964844 = 55.4% done in 24.0s, 19.3s to go\n",
      "on time step 13606 (time=136.06), 0.00290873 s/step\n",
      "Meep progress: 97.82000061035157/151.88999938964844 = 64.4% done in 28.0s, 15.5s to go\n",
      "on time step 14974 (time=149.74), 0.00292401 s/step\n",
      "run 0 finished at t = 151.89000000000001 (15189 timesteps)\n"
     ]
    }
   ],
   "source": [
    "sim.run(until=100) # Run for some time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simple built-in plotting tools, see https://meep.readthedocs.io/en/latest/Python_User_Interface/#data-visualization :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim.plot2D(output_plane=mp.Volume(center=mp.Vector3(), size=mp.Vector3(10,10)),\n",
    "           fields=mp.Ez,\n",
    "           field_parameters={'alpha':0.9})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are also options for video : https://meep.readthedocs.io/en/latest/Python_User_Interface/#Animate2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "on time step 10000 (time=100), 0.0271263 s/step\n",
      "Meep progress: 8.570000000000007/383.3333435058594 = 2.2% done in 4.0s, 175.0s to go\n",
      "on time step 11454 (time=114.54), 0.0027522 s/step\n",
      "Meep progress: 21.120000000000005/383.3333435058594 = 5.5% done in 8.0s, 137.3s to go\n",
      "on time step 12608 (time=126.08), 0.00346756 s/step\n",
      "Meep progress: 32.59/383.3333435058594 = 8.5% done in 12.0s, 129.2s to go\n",
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      "Normalizing field data...\n"
     ]
    }
   ],
   "source": [
    "f = plt.figure(dpi=100)\n",
    "animate = mp.Animate2D(sim,mp.Ez,f=f,normalize=True)\n",
    "sim.run(mp.at_every(1,animate),until_after_sources=50)\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating MP4...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<video src=\"media/oblique-source-normal.mp4\" controls  >\n",
       "      Your browser does not support the <code>video</code> element.\n",
       "    </video>"
      ],
      "text/plain": [
       "<IPython.core.display.Video object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = 'media/oblique-source-normal.mp4'\n",
    "animate.to_mp4(10,filename)\n",
    "Video(filename)"
   ]
  }
 ],
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