{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e5c619a9",
   "metadata": {},
   "source": [
    "# Usage Examples\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2891cd6",
   "metadata": {},
   "source": [
    "## Imports Used for All Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "1a7a0ea1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pypssfss import (analyze, atoutputs, extract_result, inch, Layer, \n",
    "                      mil, mm, plot_sheet, polyring, res2fresnel, ThetaPhi)\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc354eb5",
   "metadata": {},
   "source": [
    "## Class A Radome\n",
    "This example is from from Y. T. Lo and S. W. Lee, *Antenna Handbook*, pp. 31-17 through 31-18"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9374e1c0",
   "metadata": {},
   "source": [
    "### Specify the radome dielectric layers in `strata`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "0f967568",
   "metadata": {},
   "outputs": [],
   "source": [
    "skin = Layer(epsr=3.2, tandel=0.015, width=35*mil)\n",
    "strata = [Layer(),\n",
    "          skin,\n",
    "          Layer(epsr=1.1, tandel=0.005, width=0.25*inch),\n",
    "          skin,\n",
    "          Layer()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6c73116",
   "metadata": {},
   "source": [
    "### Specify desired frequencies and scan angles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "269e1044",
   "metadata": {},
   "outputs": [],
   "source": [
    "freqs = np.linspace(2, 16, 100)\n",
    "scan = ThetaPhi([0, 40, 60, 70, 80], 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4adb405f",
   "metadata": {},
   "source": [
    "### Analyze the radome performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "0926cd67",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = analyze(strata, freqs, scan, showprogress=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19e9db54",
   "metadata": {},
   "source": [
    "### Extract frequency, scan angle, and transmission coefficients from computed results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "81ba5569",
   "metadata": {},
   "outputs": [],
   "source": [
    "datamat = extract_result(results, atoutputs(\"fghz theta s21mag(te,te) s21mag(tm,tm)\"))\n",
    "f, thetas, s21magte, s21magtm = map(np.array, zip(*datamat))\n",
    "tepwr = s21magte ** 2\n",
    "tmpwr = s21magtm ** 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6834ac38",
   "metadata": {},
   "source": [
    "### Plot TE transmitted power versus frequency for each scan angle\n",
    "Compare to plot 31-14a on page 31-18 of Lo and Lee."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "7083d5fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for theta in np.unique(thetas):\n",
    "    ind = theta == thetas\n",
    "    plt.plot(f[ind], tepwr[ind], label = f'θ = {theta}')\n",
    "plt.legend()\n",
    "plt.xlabel('Frequency (GHz)')\n",
    "plt.ylabel('Fractional Transmitted Power')\n",
    "plt.title('Class A Radome Power Transmission for TE Pol')\n",
    "plt.ylim(0, 1); plt.yticks(np.arange(0, 1.1, 0.2))\n",
    "plt.xlim(2, 16); plt.xticks(np.arange(2, 17, 2)); plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18faf195",
   "metadata": {},
   "source": [
    "### Plot TM transmitted power versus frequency for each scan angle\n",
    "Compare to plot 31-14b on page 31-18 of Lo and Lee."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "73b1c111",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for theta in np.unique(thetas):\n",
    "    ind = theta == thetas\n",
    "    plt.plot(f[ind], tmpwr[ind], label = f'θ = {theta}')\n",
    "plt.legend()\n",
    "plt.xlabel('Frequency (GHz)')\n",
    "plt.ylabel('Fractional Transmitted Power')\n",
    "plt.title('Class A Radome Power Transmission for TM Pol')\n",
    "plt.ylim(0, 1); plt.yticks(np.arange(0, 1.1, 0.2))\n",
    "plt.xlim(2, 16); plt.xticks(np.arange(2, 17, 2)); plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf782120",
   "metadata": {},
   "source": [
    "## Double Square Loop FSS Exported to HFSS Fresnel Table\n",
    "This example is adapted from \"Antenna Radome SBR+ Using Fresnel Boundary\" beginning on the page labeled \"1-66\" of the 2024R2 HFSS Help PDF file."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b49ca7b5",
   "metadata": {},
   "source": [
    "### Create and plot the sheet triangulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "815ea7c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RWGSheet: style=polyring, class=J, 1136 nodes, 3032 edges, 1896 faces, Zs=0.0 + 0.0im Ω\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = np.sqrt(2) * np.array([1, 2.125])\n",
    "b = np.sqrt(2) * np.array([1.5, 3.125])\n",
    "sheet = polyring(a=a, b=b, units=mm, sides=4, orient=45, s1=[8,0], s2=[0,8], ntri=2000)\n",
    "print(sheet)\n",
    "plot_sheet(sheet, unitcell=True, linewidth=0.75)\n",
    "plt.title(\"Double Square Ring for Fresnel Table Example\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c39ef99e",
   "metadata": {},
   "source": [
    "### Complete the model, analyze it, export results to Fresnel table\n",
    "This demonstration analysis takes about 12 seconds on a core i7-14700 CPU.  For actual use, one would likely\n",
    "choose to analyze more finely in theta, say every 5 degrees, out to a larger maximum value, say 70 degrees: `steering = ThetaPhi(range(0, 71, 5), 0)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "c4306769",
   "metadata": {},
   "outputs": [],
   "source": [
    "dwidth = 3 * mm\n",
    "duroid = Layer(epsr=2.2, tandel=0.0009, width=dwidth)\n",
    "strata = [Layer(),\n",
    "          duroid,\n",
    "          sheet, \n",
    "          duroid, \n",
    "          Layer(width = -2 * dwidth)] # Note width of final layer = negative sum of all other layer widths!\n",
    "steering = ThetaPhi(range(0, 41, 10), 0)\n",
    "freqs = np.arange(10, 15, 2)\n",
    "results = analyze(strata, freqs, steering, resultfile=\"double_square_loop.res\", showprogress=False)\n",
    "res2fresnel(results, \"double_square_loop.rttbl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "b1230685",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# HFSS-compatible Fresnel reflection/transmission table created by PSSFSS\n",
      "# Created on 2025-05-12 at 19:09:53.751\n",
      "RTTable\n",
      "# <num_theta_step> = <number_of_points> - 1\n",
      "9\n",
      "# MultiFreq <freq_start_ghz> <freq_stop_ghz> <num_freq_steps>\n",
      "MultiFreq 10.0 14.0 2\n",
      "# Data section follows. Frequency loops within theta\n",
      "#<rte_rl> <rte_im> <rtm_rl> <rtm_im> <tte_rl> <tte_im> <ttm_rl> <ttm_im>\n",
      "-0.47241  0.75965  0.47241 -0.75965  0.10767 -0.43143  0.10767 -0.43143\n",
      "0.22824  0.96686 -0.22824 -0.96686 -0.02489 -0.08785 -0.02489 -0.08785\n",
      "0.73137  0.01686 -0.73137 -0.01686  0.66109  0.13865  0.66109  0.13865\n",
      "-0.48624  0.75348  0.47716 -0.75563  0.10558 -0.42739  0.10279 -0.43440\n",
      "0.20804  0.97191 -0.22065 -0.96885 -0.02343 -0.08246 -0.02541 -0.08498\n"
     ]
    }
   ],
   "source": [
    "# View first few lines of the Fresnel Table file:\n",
    "with open(\"double_square_loop.rttbl\", \"r\") as file:\n",
    "    for i, line in enumerate(file):\n",
    "        if i >= 14:\n",
    "            break\n",
    "        print(line.strip())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31e6d9ed",
   "metadata": {},
   "source": [
    "## Additional Usage Examples\n",
    "There are many additional examples in the [Usage Examples](https://simonp0420.github.io/PSSFSS.jl/stable/examples/)\n",
    "section of the PSSFSS documentation. Also, be sure to check out the\n",
    "[Element Gallery](https://simonp0420.github.io/PSSFSS.jl/stable/PSS_&_FSS_Element_Gallery/) that illustrates the\n",
    "range of FSS/PSS geometries supported by `pypssfss`.  Each entry in the gallery is a so-called \"demo card\".\n",
    "Clicking on a card opens a page showing the code used to create the element."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27314180",
   "metadata": {},
   "source": [
    "## Example of additional documentation from `doc` versus `help`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "94ab3f61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function res2fresnel in module pypssfss.pypssfss:\n",
      "\n",
      "res2fresnel(results: juliacall.VectorValue | str, tepfile: str) -> None\n",
      "    Wrapper function for the Julia PSSFSS res2fresnel function.  Creates an HFSS SBR+-compatible\n",
      "    Fresnel table from a PSSFSS result file, or from the vector of results returned by the\n",
      "    analyze function. For detailed documentation, type doc(res2fresnel) or see the Julia PSSFSS\n",
      "    version documentation at\n",
      "    https://simonp0420.github.io/PSSFSS.jl/stable/reference/#PSSFSS.Outputs.res2fresnel\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(res2fresnel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "f5114245",
   "metadata": {},
   "outputs": [
    {
     "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=\"background-color: #272822\">                                                                                                                   </span>\n",
       "<span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">res2fresnel(results::Vector{Result}, fresnelfile::AbstractString)</span><span style=\"background-color: #272822\">                                                 </span>\n",
       "<span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">res2fresnel(resultfile::AbstractString, fresnelfile::AbstractString)</span><span style=\"background-color: #272822\">                                              </span>\n",
       "<span style=\"background-color: #272822\">                                                                                                                   </span>\n",
       "\n",
       "Create an HFSS-compatible \"Fresnel table\" file from <span style=\"color: #008080; text-decoration-color: #008080; background-color: #000000; font-weight: bold\">results</span>, the vector of <span style=\"color: #008080; text-decoration-color: #008080; background-color: #000000; font-weight: bold\">Result</span> objects returned by  the <span style=\"color: #008080; text-decoration-color: #008080; background-color: #000000; font-weight: bold\">analyze</span> \n",
       "function.  If the first positional argument is an <span style=\"color: #008080; text-decoration-color: #008080; background-color: #000000; font-weight: bold\">AbstractString</span>, it is  assumed to be the name of a PSSFSS results\n",
       "file, from which the vector of results will be read.                                                               \n",
       "\n",
       "Since Fresnel tables contain data for only a single ϕ value, if the input <span style=\"color: #008080; text-decoration-color: #008080; background-color: #000000; font-weight: bold\">result</span> vector contains data for multiple \n",
       "ϕ values, only the value with minimum magnitude will be used.                                                      \n",
       "\n",
       "Fresnel tables may be formatted to contain only reflection coefficients (for a so-called \"opaque\" structure), or   \n",
       "they  may contain both reflection and transmission coefficients (a \"non-opaque\" structure). An opaque structure is \n",
       "one for which the s21 partition of the generalized scattering matrix is identically zero  for all frequencies and  \n",
       "scan angles.  The correct format to be written will be selected automatically by <span style=\"color: #008080; text-decoration-color: #008080; background-color: #000000; font-weight: bold\">res2fresnel</span>.                      \n",
       "\n",
       "\n",
       "                                   <span style=\"font-weight: bold; text-decoration: underline\">Requirements for Fresnel Table Compatibility</span>                                    \n",
       "\n",
       "The data in <span style=\"color: #008080; text-decoration-color: #008080; background-color: #000000; font-weight: bold\">results</span> must satisfy the following requirements:                                                       \n",
       "\n",
       "<span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 1 </span>Incidence angles rather than incremental phasings must be used.                                                 \n",
       "<span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 2 </span>θ angles must begin at 0 and be uniformly spaced up to the maximum θ value present.                             \n",
       "<span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 3 </span>The increment in θ values must divide evenly into 90.                                                           \n",
       "<span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 4 </span>If multiple frequencies are present, then they must have a uniform spacing.                                     \n",
       "\n",
       "A Fresnel table must contain θ values equally spaced between 0 and 90, inclusive.   If the <span style=\"color: #008080; text-decoration-color: #008080; background-color: #000000; font-weight: bold\">results</span> vector provided \n",
       "as input does not contain θ values all the way to 90, then the scattering matrix values  corresponding to the      \n",
       "maximum provided θ value will be copied into the remaining angular \"slots\" as necessary to provide  a complete     \n",
       "Fresnel table.                                                                                                     \n",
       "\n",
       "There are some limitations on the type of unit cell geometry that should be used for creating Fresnel tables.      \n",
       "First, a Fresnel  table contains data for only a single ϕ value.  This means that the geometry being analyzed must \n",
       "be such that the scattering matrix of the structure is essentially independent of ϕ.  As a counterexample, a strip \n",
       "grid is not a suitable structure, since its scattering properties are strongly dependent on ϕ.  Second, a Fresnel  \n",
       "table records only co-polarized (TE → TE and TM → TM) transmission and reflection coefficients.  This means that   \n",
       "the structure being analyzed must not  generate cross-polarized (TE → TM or TM → TE) transmission or reflection    \n",
       "coefficients of significant amplitude.                                                                             \n",
       "\n",
       "Fresnel tables consider only incidence from a single \"front\" region. When creating the Fresnel table, the front    \n",
       "region is taken  to be Region 1 of the PSSFSS model (i.e. the first layer present in the PSSFSS <span style=\"color: #008080; text-decoration-color: #008080; background-color: #000000; font-weight: bold\">strata</span> vector).    \n",
       "\n",
       "                                 <span style=\"font-weight: bold\">Additional Requirements for Non-Opaque Structures</span>                                 \n",
       "\n",
       "When used in an HFSS SBR+ model, the scattering properties read from the Fresnel table are applied to a            \n",
       "zero-thickness surface, so that the transmitted ray is launched from the same \"hit\" point of the surface that was  \n",
       "encountered by the incident  ray. Because of this, the phase reference plane for both input and output ports of the\n",
       "PSSFSS model should be located  at this front surface (i.e. the first interface plane in the <span style=\"color: #008080; text-decoration-color: #008080; background-color: #000000; font-weight: bold\">strata</span> vector).  This \n",
       "is accomplished by specifying zero  width for the first <span style=\"color: #008080; text-decoration-color: #008080; background-color: #000000; font-weight: bold\">Layer</span> object (i.e. using <span style=\"color: #008080; text-decoration-color: #008080; background-color: #000000; font-weight: bold\">Layer()</span> for the first layer), and \n",
       "then specifying the final layer's width to be the negative of the sum of all the other layer widths in the <span style=\"color: #008080; text-decoration-color: #008080; background-color: #000000; font-weight: bold\">strata</span>  \n",
       "vector. The negative width value shifts the output port reference plane to coincide with that of the input port.   \n",
       "As an example:                                                                                                     \n",
       "\n",
       "<span style=\"background-color: #272822\">                                                                                                                   </span>\n",
       "<span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">strata </span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> [Layer(), Layer(width</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #ae81ff; text-decoration-color: #ae81ff; background-color: #272822\">2</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">mm, ϵᵣ</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #ae81ff; text-decoration-color: #ae81ff; background-color: #272822\">2.2</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">) Layer(width</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #ae81ff; text-decoration-color: #ae81ff; background-color: #272822\">3.3</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">mm, ϵᵣ</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #ae81ff; text-decoration-color: #ae81ff; background-color: #272822\">3.0</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">), Layer(width</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #ae81ff; text-decoration-color: #ae81ff; background-color: #272822\">2</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">mm, ϵᵣ</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=</span><span style=\"color: #ae81ff; text-decoration-color: #ae81ff; background-color: #272822\">2.2</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">), </span><span style=\"background-color: #272822\">                </span>\n",
       "<span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">Layer(width</span><span style=\"color: #ff4689; text-decoration-color: #ff4689; background-color: #272822\">=-</span><span style=\"color: #ae81ff; text-decoration-color: #ae81ff; background-color: #272822\">7.3</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">mm)]</span><span style=\"background-color: #272822\">                                                                                              </span>\n",
       "<span style=\"background-color: #272822\">                                                                                                                   </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[48;2;39;40;34m                                                                                                                   \u001b[0m\n",
       "\u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mres2fresnel(results::Vector{Result}, fresnelfile::AbstractString)\u001b[0m\u001b[48;2;39;40;34m                                                \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\n",
       "\u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mres2fresnel(resultfile::AbstractString, fresnelfile::AbstractString)\u001b[0m\u001b[48;2;39;40;34m                                             \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\n",
       "\u001b[48;2;39;40;34m                                                                                                                   \u001b[0m\n",
       "\n",
       "Create an HFSS-compatible \"Fresnel table\" file from \u001b[1;36;40mresults\u001b[0m, the vector of \u001b[1;36;40mResult\u001b[0m objects returned by  the \u001b[1;36;40manalyze\u001b[0m \n",
       "function.  If the first positional argument is an \u001b[1;36;40mAbstractString\u001b[0m, it is  assumed to be the name of a PSSFSS results\n",
       "file, from which the vector of results will be read.                                                               \n",
       "\n",
       "Since Fresnel tables contain data for only a single ϕ value, if the input \u001b[1;36;40mresult\u001b[0m vector contains data for multiple \n",
       "ϕ values, only the value with minimum magnitude will be used.                                                      \n",
       "\n",
       "Fresnel tables may be formatted to contain only reflection coefficients (for a so-called \"opaque\" structure), or   \n",
       "they  may contain both reflection and transmission coefficients (a \"non-opaque\" structure). An opaque structure is \n",
       "one for which the s21 partition of the generalized scattering matrix is identically zero  for all frequencies and  \n",
       "scan angles.  The correct format to be written will be selected automatically by \u001b[1;36;40mres2fresnel\u001b[0m.                      \n",
       "\n",
       "\n",
       "                                   \u001b[1;4mRequirements for Fresnel Table Compatibility\u001b[0m                                    \n",
       "\n",
       "The data in \u001b[1;36;40mresults\u001b[0m must satisfy the following requirements:                                                       \n",
       "\n",
       "\u001b[1;33m 1 \u001b[0mIncidence angles rather than incremental phasings must be used.                                                 \n",
       "\u001b[1;33m 2 \u001b[0mθ angles must begin at 0 and be uniformly spaced up to the maximum θ value present.                             \n",
       "\u001b[1;33m 3 \u001b[0mThe increment in θ values must divide evenly into 90.                                                           \n",
       "\u001b[1;33m 4 \u001b[0mIf multiple frequencies are present, then they must have a uniform spacing.                                     \n",
       "\n",
       "A Fresnel table must contain θ values equally spaced between 0 and 90, inclusive.   If the \u001b[1;36;40mresults\u001b[0m vector provided \n",
       "as input does not contain θ values all the way to 90, then the scattering matrix values  corresponding to the      \n",
       "maximum provided θ value will be copied into the remaining angular \"slots\" as necessary to provide  a complete     \n",
       "Fresnel table.                                                                                                     \n",
       "\n",
       "There are some limitations on the type of unit cell geometry that should be used for creating Fresnel tables.      \n",
       "First, a Fresnel  table contains data for only a single ϕ value.  This means that the geometry being analyzed must \n",
       "be such that the scattering matrix of the structure is essentially independent of ϕ.  As a counterexample, a strip \n",
       "grid is not a suitable structure, since its scattering properties are strongly dependent on ϕ.  Second, a Fresnel  \n",
       "table records only co-polarized (TE → TE and TM → TM) transmission and reflection coefficients.  This means that   \n",
       "the structure being analyzed must not  generate cross-polarized (TE → TM or TM → TE) transmission or reflection    \n",
       "coefficients of significant amplitude.                                                                             \n",
       "\n",
       "Fresnel tables consider only incidence from a single \"front\" region. When creating the Fresnel table, the front    \n",
       "region is taken  to be Region 1 of the PSSFSS model (i.e. the first layer present in the PSSFSS \u001b[1;36;40mstrata\u001b[0m vector).    \n",
       "\n",
       "                                 \u001b[1mAdditional Requirements for Non-Opaque Structures\u001b[0m                                 \n",
       "\n",
       "When used in an HFSS SBR+ model, the scattering properties read from the Fresnel table are applied to a            \n",
       "zero-thickness surface, so that the transmitted ray is launched from the same \"hit\" point of the surface that was  \n",
       "encountered by the incident  ray. Because of this, the phase reference plane for both input and output ports of the\n",
       "PSSFSS model should be located  at this front surface (i.e. the first interface plane in the \u001b[1;36;40mstrata\u001b[0m vector).  This \n",
       "is accomplished by specifying zero  width for the first \u001b[1;36;40mLayer\u001b[0m object (i.e. using \u001b[1;36;40mLayer()\u001b[0m for the first layer), and \n",
       "then specifying the final layer's width to be the negative of the sum of all the other layer widths in the \u001b[1;36;40mstrata\u001b[0m  \n",
       "vector. The negative width value shifts the output port reference plane to coincide with that of the input port.   \n",
       "As an example:                                                                                                     \n",
       "\n",
       "\u001b[48;2;39;40;34m                                                                                                                   \u001b[0m\n",
       "\u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mstrata\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m[\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mLayer\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mLayer\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwidth\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m2\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmm\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mϵᵣ\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m2.2\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mLayer\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwidth\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m3.3\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmm\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mϵᵣ\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m3.0\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mLayer\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwidth\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m2\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmm\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mϵᵣ\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m2.2\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m,\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m               \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\n",
       "\u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mLayer\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m(\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mwidth\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m=\u001b[0m\u001b[38;2;255;70;137;48;2;39;40;34m-\u001b[0m\u001b[38;2;174;129;255;48;2;39;40;34m7.3\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mmm\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m)\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m]\u001b[0m\u001b[48;2;39;40;34m                                                                                             \u001b[0m\u001b[48;2;39;40;34m \u001b[0m\n",
       "\u001b[48;2;39;40;34m                                                                                                                   \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pypssfss import doc\n",
    "doc(res2fresnel)"
   ]
  }
 ],
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