notebooks/sat_farfield.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SAT Optics Simulation: Far-Field\n",
    "\n",
    "This notebook models the far-field beam of the Small Aperture Telescope.  We use the near-field beam simulated in the $\\verb|sat_holography|$ notebook and the fourier relationship between near- and far-fields to propagate this beam into the far-field. \n",
    "\n",
    "Grace E. Chesmore\n",
    "March 2022\n",
    "\n",
    "### Read in the near-field beam\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/10000 [00:00<?, ?it/s]/home/chesmore/.local/lib/python3.8/site-packages/sosat_optics/ot_geo.py:67: RuntimeWarning: invalid value encountered in sqrt\n",
      "  amp = (c * r ** 2) / (1 + np.sqrt(1 - ((1 + k) * c ** 2 * r ** 2)))\n",
      "/home/chesmore/.local/lib/python3.8/site-packages/sosat_optics/ot_geo.py:109: RuntimeWarning: invalid value encountered in sqrt\n",
      "  coeff_2 = (c * 2) / (1 + np.sqrt(1 - ((1 + k) * c ** 2 * r ** 2)))\n",
      "/home/chesmore/.local/lib/python3.8/site-packages/sosat_optics/ot_geo.py:111: RuntimeWarning: invalid value encountered in sqrt\n",
      "  np.sqrt(1 - ((1 + k) * c ** 2 * r ** 2))\n",
      "/home/chesmore/.local/lib/python3.8/site-packages/sosat_optics/ot_geo.py:112: RuntimeWarning: invalid value encountered in sqrt\n",
      "  * (1 + np.sqrt(1 - ((1 + k) * c ** 2 * r ** 2))) ** 2\n",
      "/home/chesmore/.local/lib/python3.8/site-packages/sosat_optics/ray_trace.py:31: RuntimeWarning: invalid value encountered in sqrt\n",
      "  s2 = (n1 / n2) * np.cross(N_surf, (np.cross(-N_surf, s1))) - N_surf * np.sqrt(\n",
      "100%|██████████| 10000/10000 [00:15<00:00, 660.61it/s]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.font_manager as font_manager\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import interpolate\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import sosat_optics\n",
    "from sosat_optics import ot_geo, opt_analyze,ray_trace\n",
    "\n",
    "# Load in telescope geometry\n",
    "tele_geo = ot_geo.SatGeo()\n",
    "tele_geo.n_scan = 100  # sample number in 1D of 2D grid\n",
    "tele_geo.y_source = (\n",
    "    ot_geo.y_lyot + 300\n",
    ")  # distance of holography source from SAT focal plane.\n",
    "# tele_geo.lambda_ = 0.002  # wavelength of source, here we choose 150GHz.\n",
    "tele_geo.lambda_ = 0.0023\n",
    "tele_geo.k = 2 * np.pi / tele_geo.lambda_  # update kappa\n",
    "fwhms = np.load('../feedhorn/fwhm_feedhorns.npy')\n",
    "tele_geo.th_fwhp_x = np.deg2rad(fwhms[np.where(fwhms[:,0]== int(opt_analyze.m_to_ghz(tele_geo.lambda_))),1])\n",
    "tele_geo.th_fwhp_y = np.deg2rad(fwhms[np.where(fwhms[:,0]== int(opt_analyze.m_to_ghz(tele_geo.lambda_))),2])\n",
    "\n",
    "rx = [0,0,0]  # receiver feed position in focal plane [mm]\n",
    "# x_sim, y_sim, a_sim, p_sim = ray_trace.rx_to_lyot_OLD(rx, tele_geo, 0, \"b\")\n",
    "sb = ray_trace.getNearField(tele_geo, rx,plot=False) # get near field\n",
    "sb2 = ray_trace.sim2d(sb) # get 2d near field"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Propagating Aperture-field into the Far-field\n",
    "\n",
    "Now that we have our near-fields $b(x,y)$ above the SAT window, we want to propagate the fields into the far-field $B(\\theta,\\phi)$ using the relation:\n",
    "\n",
    "$$ B(\\theta,\\phi) = \\int_{aperture} b(x,y)e^{i2\\pi(x\\theta + y\\phi)} dx dy$$\n",
    "\n",
    "where we integrate over the aperture, which is the window of the SAT."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 936x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb2_data = ray_trace.sim_data(sb2,80,2)\n",
    "\n",
    "plt.figure(figsize=(13, 5))\n",
    "plt.subplot(121)\n",
    "plt.pcolormesh(sb2_data.x_data, sb2_data.y_data, np.abs(sb2_data.a_data), shading='auto')\n",
    "plt.colorbar()\n",
    "plt.xlabel(\"x [cm]\")\n",
    "plt.ylabel(\"y [cm]\")\n",
    "plt.title(\"Near-field of SAT\")\n",
    "plt.axis(\"equal\")\n",
    "plt.subplot(122)\n",
    "plt.pcolormesh(sb2_data.x_data, sb2_data.y_data, sb2_data.p_data, shading='auto')\n",
    "plt.colorbar()\n",
    "plt.xlabel(\"x [cm]\")\n",
    "plt.ylabel(\"y [cm]\")\n",
    "plt.title(\"Near-field of SAT\")\n",
    "plt.axis(\"equal\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pix_num = 100\n",
    "x_new, y_new, beam_data = opt_analyze.zero_pad(sb2_data.x_data, sb2_data.y_data, sb2_data.a_data, pix_num)\n",
    "x_data, y_data, phase_data = opt_analyze.zero_pad(sb2_data.x_data,sb2_data.y_data, sb2_data.p_data, pix_num)\n",
    "beam_fft, phase_fft = opt_analyze.a2b(beam_data, np.rad2deg(phase_data))\n",
    "x_ang, y_ang = opt_analyze.coords_spat_to_ang(\n",
    "    x_data / 1e2, y_data / 1e2, opt_analyze.m_to_ghz(tele_geo.lambda_)\n",
    ")\n",
    "\n",
    "plt.figure(figsize=(6, 5))\n",
    "plt.title(\"SAT Far-field 90GHz\")\n",
    "plt.pcolormesh(\n",
    "    x_ang * 180 * 60 / np.pi,\n",
    "    y_ang * 180 * 60 / np.pi,\n",
    "    20 * np.log10(abs(beam_fft) / np.max(abs(beam_fft))),\n",
    "    vmin=-60,\n",
    "    shading=\"auto\",\n",
    ")\n",
    "plt.xlabel(\"[arcmin]\")\n",
    "plt.ylabel(\"[arcmin]\")\n",
    "plt.colorbar(label=\"dB\")\n",
    "plt.axis(\"equal\")\n",
    "plt.xlim(-80, 80)\n",
    "plt.ylim(-80, 80)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicted Beam Size\n",
    "Our simulated beam size determines the resolution of our telescope.  We can compare this to the beam size of an Airy disc with aperture $D$ at wavelength $\\lambda$:\n",
    "\n",
    "$$\\theta_{FWHM} = 1.025 \\frac{\\lambda}{D} $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated FWHM: 21.50 arcmin\n"
     ]
    }
   ],
   "source": [
    "fwhm_est = ray_trace.ff_fwhm_est(beam_fft, x_ang, y_ang)\n",
    "print(\"Estimated FWHM: {:.2f} arcmin\".format(fwhm_est))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now for multiple frequencies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  2%|▏         | 457/22500 [00:01<00:43, 508.56it/s]/home/chesmore/.local/lib/python3.8/site-packages/sosat_optics/ot_geo.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  coeff_3 = (c ** 3 * (k + 1) * r ** 2) / (\n",
      "/home/chesmore/.local/lib/python3.8/site-packages/sosat_optics/ray_trace.py:1410: RuntimeWarning: invalid value encountered in true_divide\n",
      "  N_hat = norm_temp / np.sqrt(sum(norm_temp ** 2))\n",
      "100%|██████████| 22500/22500 [00:33<00:00, 665.80it/s]\n",
      "  0%|          | 64/22500 [00:00<00:35, 639.79it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated FWHM: 29.15 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 22500/22500 [00:33<00:00, 673.85it/s]\n",
      "  1%|          | 132/22500 [00:00<00:34, 647.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated FWHM: 27.53 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 22500/22500 [00:32<00:00, 682.53it/s]\n",
      "  0%|          | 64/22500 [00:00<00:35, 638.71it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated FWHM: 26.08 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 22500/22500 [00:33<00:00, 671.87it/s]\n",
      "  0%|          | 56/22500 [00:00<00:40, 552.70it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated FWHM: 24.29 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 22500/22500 [00:38<00:00, 583.91it/s]\n",
      "  0%|          | 55/22500 [00:00<00:41, 545.57it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated FWHM: 23.37 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 22500/22500 [00:36<00:00, 622.15it/s]\n",
      "  0%|          | 56/22500 [00:00<00:40, 556.64it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated FWHM: 22.52 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 22500/22500 [00:37<00:00, 604.74it/s]\n",
      "  0%|          | 59/22500 [00:00<00:38, 582.19it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated FWHM: 21.54 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 22500/22500 [00:37<00:00, 606.66it/s]\n",
      "  0%|          | 56/22500 [00:00<00:40, 552.16it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated FWHM: 19.06 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 22500/22500 [00:37<00:00, 592.45it/s]\n",
      "  0%|          | 55/22500 [00:00<00:41, 544.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated FWHM: 18.35 arcmin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 22500/22500 [00:36<00:00, 610.37it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated FWHM: 17.70 arcmin\n"
     ]
    }
   ],
   "source": [
    "freq_arr = [85, 90, 95, 102, 106, 110, 115, 130, 135, 140]\n",
    "\n",
    "for ii,freq in enumerate(freq_arr):\n",
    "\n",
    "    tele_geo.n_scan = 150  # sample number in 1D of 2D grid\n",
    "    tele_geo.lambda_ = opt_analyze.ghz_to_m(freq)\n",
    "    tele_geo.k = 2 * np.pi / tele_geo.lambda_  # update kappa\n",
    "    tele_geo.th_fwhp_x = np.deg2rad(fwhms[np.where(fwhms[:,0]== int(freq)),1])*np.sqrt(2)\n",
    "    tele_geo.th_fwhp_y = np.deg2rad(fwhms[np.where(fwhms[:,0]== int(freq)),2])*np.sqrt(2)\n",
    "\n",
    "    rx = [0,0,0]  # receiver feed position in focal plane [mm]\n",
    "    \n",
    "    sb = ray_trace.getNearField(tele_geo, rx,plot=False) # get near field\n",
    "    sb2 = ray_trace.sim2d(sb) # get 2d near field\n",
    "    sb2_data = ray_trace.sim_data(sb2,200,.25)\n",
    "\n",
    "    pix_num = 100\n",
    "    x_new, y_new, beam_data = opt_analyze.zero_pad(sb2_data.x_data, sb2_data.y_data, sb2_data.a_data, pix_num)\n",
    "    x_data, y_data, phase_data = opt_analyze.zero_pad(sb2_data.x_data,sb2_data.y_data, sb2_data.p_data, pix_num)\n",
    "    beam_fft, phase_fft = opt_analyze.a2b(beam_data, np.rad2deg(phase_data))\n",
    "    x_ang, y_ang = opt_analyze.coords_spat_to_ang(\n",
    "        x_data / 1e2, y_data / 1e2, opt_analyze.m_to_ghz(tele_geo.lambda_)\n",
    "    )\n",
    "    fwhm_est = ray_trace.ff_fwhm_est(beam_fft, x_ang, y_ang)\n",
    "    print(\"Estimated FWHM: {:.2f} arcmin\".format(fwhm_est))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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