python/examples/bend-flux.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Initializing structure...\n",
      "field decay(t = 50.050000000000004): 4.825189380557793e-09 / 4.825189380557793e-09 = 1.0\n",
      "field decay(t = 100.05000000000001): 0.028801809879425856 / 0.028801809879425856 = 1.0\n",
      "field decay(t = 150.1): 0.026893465093385728 / 0.028801809879425856 = 0.9337421921042772\n",
      "field decay(t = 200.15): 2.315839733669588e-13 / 0.028801809879425856 = 8.040604890333206e-12\n",
      "run 0 finished at t = 200.15 (4003 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "field decay(t = 50.050000000000004): 1.6976522694449004e-10 / 1.6976522694449004e-10 = 1.0\n",
      "field decay(t = 100.05000000000001): 4.691071063910547e-07 / 4.691071063910547e-07 = 1.0\n",
      "field decay(t = 150.1): 2.992872733686027e-07 / 4.691071063910547e-07 = 0.6379934758846145\n",
      "field decay(t = 200.15): 0.003927813565272278 / 0.003927813565272278 = 1.0\n",
      "field decay(t = 250.20000000000002): 0.00015009081939073963 / 0.003927813565272278 = 0.03821230740627967\n",
      "field decay(t = 300.2): 8.806226395700877e-11 / 0.003927813565272278 = 2.24201740977755e-08\n",
      "run 0 finished at t = 300.2 (6004 timesteps)\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\n",
    "# transmission around a 90-degree waveguide bend in 2d\n",
    "from __future__ import division\n",
    "\n",
    "import meep as mp\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "resolution = 10 # pixels/um\n",
    "\n",
    "sx = 16  # size of cell in X direction\n",
    "sy = 32  # size of cell in Y direction\n",
    "cell = mp.Vector3(sx,sy,0)\n",
    "\n",
    "dpml = 1.0\n",
    "pml_layers = [mp.PML(dpml)]\n",
    "\n",
    "pad = 4  # padding distance between waveguide and cell edge\n",
    "w = 1    # width of waveguide\n",
    "\n",
    "wvg_xcen =  0.5*(sx-w-2*pad)  # x center of vert. wvg\n",
    "wvg_ycen = -0.5*(sy-w-2*pad)  # y center of horiz. wvg\n",
    "\n",
    "geometry = [mp.Block(size=mp.Vector3(mp.inf,w,mp.inf),\n",
    "                     center=mp.Vector3(0,wvg_ycen,0),\n",
    "                     material=mp.Medium(epsilon=12))]\n",
    "\n",
    "fcen = 0.15  # pulse center frequency\n",
    "df = 0.1     # pulse width (in frequency)\n",
    "sources = [mp.Source(mp.GaussianSource(fcen,fwidth=df),\n",
    "                     component=mp.Ez,\n",
    "                     center=mp.Vector3(-0.5*sx+dpml,wvg_ycen,0),\n",
    "                     size=mp.Vector3(0,w,0))]\n",
    "\n",
    "sim = mp.Simulation(cell_size=cell,\n",
    "                    boundary_layers=pml_layers,\n",
    "                    geometry=geometry,\n",
    "                    sources=sources,\n",
    "                    resolution=resolution)\n",
    "\n",
    "nfreq = 100  # number of frequencies at which to compute flux\n",
    "\n",
    "# reflected flux\n",
    "refl_fr = mp.FluxRegion(center=mp.Vector3(-0.5*sx+dpml+0.5,wvg_ycen,0),size=mp.Vector3(0,2*w,0))\n",
    "refl = sim.add_flux(fcen,df,nfreq,refl_fr)\n",
    "\n",
    "# transmitted flux\n",
    "tran_fr = mp.FluxRegion(center=mp.Vector3(0.5*sx-dpml,wvg_ycen,0),size=mp.Vector3(0,2*w,0))\n",
    "tran = sim.add_flux(fcen,df,nfreq,tran_fr)\n",
    "\n",
    "pt = mp.Vector3(0.5*sx-dpml-0.5,wvg_ycen)\n",
    "\n",
    "sim.run(until_after_sources=mp.stop_when_fields_decayed(50,mp.Ez,pt,1e-3))\n",
    "\n",
    "# for normalization run, save flux fields data for reflection plane\n",
    "straight_refl_data = sim.get_flux_data(refl)\n",
    "\n",
    "# save incident power for transmission plane\n",
    "straight_tran_flux = mp.get_fluxes(tran)\n",
    "\n",
    "sim.reset_meep()\n",
    "\n",
    "geometry = [mp.Block(mp.Vector3(sx-pad,w,mp.inf),center=mp.Vector3(-0.5*pad,wvg_ycen),material=mp.Medium(epsilon=12)),\n",
    "            mp.Block(mp.Vector3(w,sy-pad,mp.inf),center=mp.Vector3(wvg_xcen,0.5*pad),material=mp.Medium(epsilon=12))]\n",
    "\n",
    "sim = mp.Simulation(cell_size=cell,\n",
    "                    boundary_layers=pml_layers,\n",
    "                    geometry=geometry,\n",
    "                    sources=sources,\n",
    "                    resolution=resolution)\n",
    "\n",
    "# reflected flux\n",
    "refl = sim.add_flux(fcen, df, nfreq, refl_fr)\n",
    "\n",
    "tran_fr = mp.FluxRegion(center=mp.Vector3(wvg_xcen,0.5*sy-dpml-0.5,0),size=mp.Vector3(2*w,0,0))\n",
    "tran = sim.add_flux(fcen,df,nfreq,tran_fr)\n",
    "\n",
    "# for normal run, load negated fields to subtract incident from refl. fields\n",
    "sim.load_minus_flux_data(refl,straight_refl_data)\n",
    "\n",
    "pt = mp.Vector3(wvg_xcen,0.5*sy-dpml-0.5)\n",
    "\n",
    "sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, pt, 1e-3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f0ed6347710>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bend_refl_flux = mp.get_fluxes(refl)\n",
    "bend_tran_flux = mp.get_fluxes(tran)\n",
    "\n",
    "flux_freqs = mp.get_flux_freqs(refl)\n",
    "\n",
    "wl = []\n",
    "Rs = []\n",
    "Ts = []\n",
    "for i in range(nfreq):\n",
    "    wl = np.append(wl, 1/flux_freqs[i])\n",
    "    Rs = np.append(Rs,-bend_refl_flux[i]/straight_tran_flux[i])\n",
    "    Ts = np.append(Ts,bend_tran_flux[i]/straight_tran_flux[i])\n",
    "\n",
    "plt.figure(dpi=200)\n",
    "plt.plot(wl,Rs,'bo-',label='reflectance')\n",
    "plt.plot(wl,Ts,'ro-',label='transmittance')\n",
    "plt.plot(wl,1-Rs-Ts,'go-',label='loss')\n",
    "plt.axis([5.0, 10.0, 0, 1])\n",
    "plt.xlabel(\"wavelength (μm)\")\n",
    "plt.legend(loc=\"upper right\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}