angular_loss_and_resolution/angular_loss.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# This is the file to calculate coupling loss \n",
    "# In the idea scenario we assume 2 lens are facing exactly centre to centre\n",
    "# In the misaligned scenario we assume a small angular misalignment is presented at the 1st lens\n",
    "\n",
    "# 3 types of losses are considered: \n",
    "# - lateral offset: the deviation of centre ray of incident beam onto RX fibre \n",
    "# - mode size mismatch (however for a 2-lens system w1 should be the same as w0, can be verified by simulation)\n",
    "# - clipping loss (if significant, also assume the beam core is at centre of lens)\n",
    "\n",
    "\n",
    "\n",
    "import sys\n",
    "sys.path.append('..') \n",
    "\n",
    "import importlib\n",
    "import modified_matrix_tracer\n",
    "importlib.reload(modified_matrix_tracer)\n",
    "\n",
    "from modified_matrix_tracer import GaussianBeam, FreeSpaceIterator, ThinLens, Misalignment\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Circle\n",
    "from cycler import cycler\n",
    "import numpy as np\n",
    "import operator\n",
    "\n",
    "#sample\n",
    "Nsamples = 1000\n",
    "scale = 1e3 #plot in mm\n",
    "\n",
    "\n",
    "\n",
    "ray_tracing_PlotON = 1\n",
    "\n",
    "MFD_mode_solver = []\n",
    "\n",
    "\n",
    "\n",
    "#fibre\n",
    "SMF28_fibre_para = {\n",
    "\"core_diameter\": 8.2e-6,\n",
    "\"core_beam_x0\":0,\n",
    "\"core_beam_theta\":0,\n",
    "\"NA\": 0.14\n",
    "}\n",
    "\n",
    "#lens\n",
    "TC18APC1550_para = {\n",
    "\"focal\" : 18.36e-3,\n",
    "\"clear_aperture\" : 10e-3,\n",
    "\"outer_diameter\" : 14.9e-3,\n",
    "    \"length\": 33.5e-3,\n",
    "#\"wavelength_range\": np.linspace(1465.0e-9, 1650.0e-9, num=38)\n",
    " \"wavelength_range\": [1465.0e-9, 1550.0e-9, 1648.0e-9],\n",
    " \"MFD_mode_solver\":[9.94746832156340e-6, 10.3988077139997e-6, 10.9710948505490e-6]\n",
    "}\n",
    "\n",
    "\n",
    "#10 colour keys\n",
    "color_cycle = plt.rcParams['axes.prop_cycle'].by_key()['color'] \n",
    "\n",
    "\n",
    "# We assume that the 2 lenses are ideally facing each other, no matter what actuator angle is\n",
    "# However, we add small lateral/angular movements to the TX lens to simulate misalignment\n",
    "\n",
    "#In this scenario we only consider angular misalignment standlone\n",
    "lateral_misalignment = 0\n",
    "\n",
    "#must use radians for this matrix_tracer \n",
    "angular_misalignment_deg = np.arange(0.0, 0.014, 0.0001)\n",
    "angular_misalignment = [i*(np.pi/180) for i in angular_misalignment_deg]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# assign colour by X in plots\n",
    "def assign_colour_cycle_by_xcvr(xx, color_cycle):\n",
    "    \n",
    "    if xx  == 0:\n",
    "        assigned_colour = color_cycle[xx]\n",
    "    else:\n",
    "        reminder = xx % len(color_cycle) \n",
    "        assigned_colour = color_cycle[reminder]\n",
    "    return assigned_colour\n",
    "    \n",
    "    \n",
    "    \n",
    "def plot_arbitrary_lens(radius, angle, centre_coord):\n",
    "    x0 = centre_coord[0]\n",
    "    y0 = centre_coord[1]\n",
    "    plt.plot([x0 - radius*np.sin(angle), x0 + radius*np.sin(angle)], \\\n",
    "             [y0 + radius*np.cos(angle), y0 - radius*np.cos(angle)],'r-')\n",
    "    \n",
    "\n",
    "      \n",
    "#Src: https://www.sciencedirect.com/science/article/pii/B9780444521903000355 \n",
    "#w0 is radius\n",
    "def lateral_loss_dB(d, w0):\n",
    "    \n",
    "    L_lat = -10*np.log10( np.exp( -((d/w0)**2) ))\n",
    "    \n",
    "    return L_lat\n",
    "    \n",
    "#Src: https://www.sciencedirect.com/science/article/pii/B9780444521903000355       \n",
    "def mode_loss_dB(w1, w0):\n",
    "    \n",
    "    L_mode = -10*np.log10(  4/(((w0/w1) + (w1/w0))**2) )\n",
    "    \n",
    "    return L_mode\n",
    "\n",
    "\n",
    "def beam_clipping_loss_dB(aperture_radius, beam_radius):\n",
    "    \n",
    "    L_clipping =  -10*np.log10((1 - np.exp(-2 * (aperture_radius/beam_radius)**2))**2)\n",
    "    \n",
    "    return L_clipping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The 5 step propgation between 2 lenses:\n",
    "\n",
    "#ideal\n",
    "def ideal_p2p_propagation(beam, lens, z, focal_length, Nsamples):\n",
    "    \n",
    "    #1. From TX fibre to 1st lens, propagate a free space distance of focal length\n",
    "    FreeSpaceIterator(beam, distance=focal_length, samples= Nsamples)\n",
    "    #2. Pass through 1 st lens\n",
    "    beam.propagate(lens)\n",
    "    #3. Propagate a free space distance z\n",
    "    FreeSpaceIterator(beam, distance=z, samples=Nsamples)\n",
    "    \n",
    "    #beam_diameter_on_2nd_lens = beam.beam_radius_upper()[-1] - beam.beam_radius_lower()[-1]\n",
    "    \n",
    "    #4. Pass through 2nd lens\n",
    "    beam.propagate(lens)\n",
    "    #5. Refocus to exit fibre end, propagate a free space distance of focal length\n",
    "    FreeSpaceIterator(beam, distance=focal_length, samples= Nsamples)\n",
    "    \n",
    "    return beam\n",
    "\n",
    "\n",
    "\n",
    "#angle misalignment at 1st lens\n",
    "def angular_misalignment_p2p_propagation(beam, lens, misalignment, z, focal_length, Nsamples):\n",
    "    \n",
    "    FreeSpaceIterator(beam, distance=focal_length, samples= Nsamples)\n",
    "    beam.propagate(lens)\n",
    "    \n",
    "    #add misalignment after 1st lens\n",
    "    beam.propagate(misalignment)\n",
    "    \n",
    "    FreeSpaceIterator(beam, distance=z, samples=Nsamples)\n",
    "    beam_diameter_on_2nd_lens = beam.beam_radius_upper()[-1] - beam.beam_radius_lower()[-1]\n",
    "    \n",
    "    beam.propagate(lens)\n",
    "    FreeSpaceIterator(beam, distance=focal_length, samples= Nsamples)\n",
    "    \n",
    "    return beam, beam_diameter_on_2nd_lens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***------wavelength: 1465.0nm, MFD: 9.9474683215634um ****------\n",
      "***------wavelength: 1550.0nm, MFD: 10.3988077139997um ****------\n",
      "---------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***------wavelength: 1648.0nm, MFD: 10.971094850549001um ****------\n"
     ]
    }
   ],
   "source": [
    "L_tot_all_wavelength = {}\n",
    "\n",
    "#plot for every wavelength\n",
    "for wavelength, MFD in zip(TC18APC1550_para[\"wavelength_range\"], TC18APC1550_para[\"MFD_mode_solver\"]):\n",
    "    \n",
    "    #print wavwlwngth and MFD (approximated by mode solver)\n",
    "    print(\"***------wavelength: {}nm, MFD: {}um ****------\".format(wavelength*1e9, MFD*1e6))\n",
    "    \n",
    "    # lateral deviation of Gaussian beam at the exit fibre end. Consider centre core, upper/lower radius\n",
    "    exit_deviation = {}\n",
    "    exit_deviation[\"centre\"] = []\n",
    "    exit_deviation[\"upper\"] = []\n",
    "    exit_deviation[\"lower\"] = []\n",
    "\n",
    "    # mode mismatch (increased beam diameter incident at exit fibre end)\n",
    "    exit_MFD = []\n",
    "    \n",
    "\n",
    "    beam_diameter_on_2nd_lens_vec = []\n",
    "        \n",
    "    #for each angle\n",
    "    for mis_angle in angular_misalignment:\n",
    "\n",
    "        #initiation\n",
    "        misalignment = Misalignment(lateral_misalignment, mis_angle)\n",
    "        \n",
    "    \n",
    "        #Triplet collimator TC18X-1550, focal = 18.36 mm\n",
    "        TC18APC1550 = ThinLens(TC18APC1550_para[\"focal\"])\n",
    "        #calculate zmax at this wavelength\n",
    "        #maximum working distance to maintain collimation\n",
    "        zmax = TC18APC1550.lens_maximum_working_distance(TC18APC1550_para[\"focal\"], wavelength, MFD) #meters\n",
    "    \n",
    "    \n",
    "        cases = {\"ideal\", \"misaligned\"}   \n",
    "    \n",
    "        positions = {}\n",
    "        positions[\"centre\"] = {}\n",
    "        positions[\"upper\"]  = {}\n",
    "        positions[\"lower\"]  = {}    \n",
    "    \n",
    "    \n",
    "        for scenario in cases:\n",
    "            \n",
    "            #centre core that pass through lens centre does not have lateral deviation  \n",
    "            beam = GaussianBeam(SMF28_fibre_para[\"core_beam_x0\"], SMF28_fibre_para[\"core_beam_theta\"], MFD, wavelength)\n",
    "            \n",
    "            #ideal scenario\n",
    "            if scenario == \"ideal\":\n",
    "                \n",
    "                #propagate the ideal process of p2p link\n",
    "                beam = ideal_p2p_propagation(beam, TC18APC1550, zmax, TC18APC1550_para[\"focal\"], Nsamples)\n",
    "            \n",
    "            \n",
    "                #store the last positions from optic axis for ideal scenario\n",
    "                positions[\"centre\"][\"ideal\"] = beam.centre_ray.vector[-1][0, 0]\n",
    "                positions[\"upper\"][\"ideal\"]  = beam.beam_radius_upper()[-1]\n",
    "                positions[\"lower\"][\"ideal\"]  = beam.beam_radius_lower()[-1]\n",
    "            \n",
    "            \n",
    "            #add misalignment  \n",
    "            #the misalignment is added immediately after the 1st lens\n",
    "            else: \n",
    "                \n",
    "                beam, beam_diameter_on_2nd_lens = angular_misalignment_p2p_propagation(beam, TC18APC1550, misalignment, zmax, TC18APC1550_para[\"focal\"], Nsamples)\n",
    "            \n",
    "                positions[\"centre\"][\"misaligned\"] = beam.centre_ray.vector[-1][0, 0]\n",
    "                positions[\"upper\"][\"misaligned\"]  = beam.beam_radius_upper()[-1]\n",
    "                positions[\"lower\"][\"misaligned\"]  = beam.beam_radius_lower()[-1]  \n",
    "                \n",
    "                beam_diameter_on_2nd_lens_vec.append(beam_diameter_on_2nd_lens) \n",
    "                \n",
    "                #print(scenario)\n",
    "                if (mis_angle == max(angular_misalignment) and ray_tracing_PlotON == 1 and wavelength == 1550.0e-9):\n",
    "                    \n",
    "                    # Plotting\n",
    "                    plt.figure()\n",
    "                    plt.plot(scale*np.array(beam.centre_ray.z_distance), scale *\n",
    "                            np.array(beam.beam_radius_upper()), color='black', linestyle='--')\n",
    "                    plt.plot(scale*np.array(beam.centre_ray.z_distance), scale *\n",
    "                            np.array(beam.beam_radius_lower()), color='black', linestyle='--')\n",
    "                    plt.plot(scale*np.array(beam.centre_ray.z_distance),\n",
    "                            [scale*i[0, 0] for i in beam.centre_ray.vector], color='black')\n",
    "\n",
    "\n",
    "                    zTx = TC18APC1550_para[\"focal\"]\n",
    "                    zRx = TC18APC1550_para[\"focal\"]+ zmax\n",
    "                    aperture_y = np.array([1, -1])*TC18APC1550_para[\"clear_aperture\"]/2\n",
    "                    aperture_x = np.array([1, 1])\n",
    "                    plt.plot(aperture_x*zTx*scale, aperture_y*scale, color='red')\n",
    "                    plt.plot(aperture_x*zRx*scale, aperture_y*scale, color='blue')\n",
    "                    plt.xlabel('(mm)')\n",
    "                    plt.ylabel('(mm)')\n",
    "                    plt.title('Beam propagation model for triplet collimator TC18X-1550, focal = 18.36 mm, angular misalignmet = {} deg, wavelength = {}'.format((mis_angle*180/np.pi), (wavelength*1e9)))\n",
    "                    print('---------------------------')\n",
    "                    plt.show() \n",
    "                \n",
    "                \n",
    "                \n",
    "                \n",
    "                \n",
    "                \n",
    "          \n",
    "\n",
    "            \n",
    "            \n",
    "        #exit beam diameter for this angle\n",
    "        misaligned_MFD = positions[\"upper\"][\"misaligned\"] - positions[\"lower\"][\"misaligned\"]\n",
    "        exit_MFD.append(misaligned_MFD)   \n",
    "            \n",
    "    \n",
    "        #exit deviation for this angle\n",
    "        for ray in positions.keys():\n",
    "\n",
    "            deviation = abs(positions[ray][\"misaligned\"] - positions[ray][\"ideal\"])\n",
    "            exit_deviation[ray].append(deviation)\n",
    "    \n",
    "    \n",
    "\n",
    "        \n",
    "        \n",
    "    #calculate loss against angle for this wavelength \n",
    "    #only consider the centre ray \n",
    "    \n",
    "    L_lat = []\n",
    "    for d in exit_deviation[\"centre\"]:\n",
    "        L_lat.append(lateral_loss_dB(d, MFD/2))\n",
    "\n",
    "\n",
    "    L_mode = []\n",
    "    for w1 in exit_MFD:\n",
    "        L_mode.append(mode_loss_dB(w1/2, MFD/2))\n",
    "        \n",
    "        \n",
    "    \n",
    "    L_clipping = []\n",
    "    for beam_diameter in beam_diameter_on_2nd_lens_vec:\n",
    "        L_clipping.append(beam_clipping_loss_dB(TC18APC1550_para[\"clear_aperture\"]/2, beam_diameter/2))\n",
    "    \n",
    "    \n",
    "\n",
    "    L_tot = [sum(x) for x in zip(L_lat, L_mode, L_clipping)]\n",
    "    \n",
    "    L_tot_all_wavelength[wavelength] = L_tot\n",
    "    \n",
    "    \n",
    "\n",
    " \n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": [
    "plt.figure()\n",
    "for wavelength in L_tot_all_wavelength.keys():\n",
    "    plt.plot([x*1e3 for x in angular_misalignment_deg], L_tot_all_wavelength[wavelength], label=\"{} nm\".format(wavelength*1e9) )\n",
    "\n",
    "plt.xlabel('actuator angle *1e-3 (deg)')\n",
    "plt.ylabel('Total loss (dB)')\n",
    "plt.title('Total coupling loss due to angular misalignment stand-alone, at zmax distance')\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.grid()\n",
    "plt.show()\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": []
  },
  {
   "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": {
  "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}