Calibration Examples

{
 "metadata": {
  "name": "Calibration Example"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": "Initialization"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import skrf as rf \nfrom pylab import * \n\n\n# create frequency, and media objects\nfreq = rf.Frequency(75,110,101, unit = 'ghz')\nwg = rf.media.RectangularWaveguide(freq, a=100*rf.mil, z0=50)\nfs = rf.media.Freespace(freq, ep_r=1, mu_r=1, z0 = 50 )",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 40
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": "Examples of use "
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "        \n        "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# create a network\nds90 = wg.delay_short(90,'deg', name = '90deg delay short')\n#plot its phase\nds90.plot_s_deg()\n# this will write a network to disk as a touchstone file.\n#ds90.write_touchstone()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PRgfjv746h1cOncJFrvAkIiInMaAReUhEcADW67WYpA3F4do2rPmwFs0dXOFJ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\n",
       "text": "<matplotlib.figure.Figure at 0xae644d0>"
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# create a match line in freespace 10deg long @ center frequency\nfs.line(10,'deg').plot_s_deg(1,0)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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J2e12/dN/7dCB05d149agNj1QrkfuLTO6rKzDvpQc+pQYPUos63PmSktLtWfP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\n0RRjmCMiIiKaYv8HRap4RsSgyUsAAAAASUVORK5CYII=\n",
       "text": "<matplotlib.figure.Figure at 0xae71810>"
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": "One-port Waveguide Calibration "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# delay short length @ wr10\nd_delay_short = 1077e-6 \nwg_cal = rf.Calibration(\n                         ideals = [\n                                   wg.short(),\n                                   wg.delay_short(d_delay_short),\n                                   wg.match(),\n                                   ],\n                         measured = [\n                                     rf.Network('path/to/short.s1p'),\n                                     rf.Network('path/to/delayShort.s1p'),\n                                     rf.Network('path/to/match.s1p'),\n                                     ]\n                         )\n# ignore the following errors     ",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "IOError",
       "evalue": "[Errno 2] No such file or directory: 'path/to/short.s1p'",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mIOError\u001b[0m                                   Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-43-ac02b80d6964>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      8\u001b[0m                                    ],\n\u001b[0;32m      9\u001b[0m                          measured = [\n\u001b[1;32m---> 10\u001b[1;33m                                      \u001b[0mrf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mNetwork\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'path/to/short.s1p'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m                                      \u001b[0mrf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mNetwork\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'path/to/delayShort.s1p'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m                                      \u001b[0mrf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mNetwork\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'path/to/match.s1p'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/alex/data/docs/code/path/skrf/network.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, file, name, comments, f_unit, **kwargs)\u001b[0m\n\u001b[0;32m    426\u001b[0m             \u001b[1;31m# open file in 'binary' mode because we are going to try and\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    427\u001b[0m             \u001b[1;31m# upickle it first\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 428\u001b[1;33m             \u001b[0mfid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_fid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'rb'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    429\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    430\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/alex/data/docs/code/path/skrf/util.pyc\u001b[0m in \u001b[0;36mget_fid\u001b[1;34m(file, *args, **kwargs)\u001b[0m\n\u001b[0;32m    151\u001b[0m     '''\n\u001b[0;32m    152\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbasestring\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 153\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    154\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    155\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mfile\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mIOError\u001b[0m: [Errno 2] No such file or directory: 'path/to/short.s1p'"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": "One-port Freespace Calibration "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# define you delays here\n#d = []\n\n\nfs_cal = rf.Calibration(\n                     ideals = [\n                               fs.delay_short(d[0]),\n                               fs.delay_short(d[1]),\n                               fs.delay_short(d[2]),\n                               fs.delay_short(d[3]),\n                               fs.match(),\n                               ],\n                     measured = [\n                                 rf.Network('path/to/delayShort0.s1p'),\n                                 rf.Network('path/to/delayShort1.s1p'),\n                                 rf.Network('path/to/delayShort2.s1p'),\n                                 rf.Network('path/to/delayShort3.s1p'),\n                                 rf.Network('path/to/match.s1p'),\n                                 ]\n                     )\n# ignore the following errors  ",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}