Working through the mbsystem cookbook as an IPython Notebook

mbcookbook.ipynb

{
 "metadata": {
  "name": "mbcookbook"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Setup"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!wget http://www.ldeo.columbia.edu/res/pi/MB-System/mbcookbook.pdf"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 138
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!open mbcookbook.pdf"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Data and source code is available here: ftp://ftp.ldeo.columbia.edu/pub/MB-System"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!wget ftp://ftp.ldeo.columbia.edu/pub/MB-System/mbexamples.tgz"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 138
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!wget ftp://ftp.ldeo.columbia.edu/pub/MB-System/MB-SystemExamples.5.1.0.tar.gz"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!wget ftp://ftp.ldeo.columbia.edu/pub/MB-System/loihi.tgz"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# TODO: use python to find the current version number\n",
      "!wget ftp://ftp.ldeo.columbia.edu/pub/MB-System/mbsystem-5.3.2012.tar.gz"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ls -lh *gz"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "-rw-r--r--  1 schwehr  5000    26M Feb  2 10:21 MB-SystemExamples.5.1.0.tar.gz\r\n",
        "-rw-r--r--  1 schwehr  5000   375M Feb  2 10:25 loihi.tgz\r\n",
        "-rw-r--r--  1 schwehr  5000    25M Feb  2 10:21 mbexamples.tgz\r\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!tar tf mbexamples.tgz | wc -l"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "     356\r\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!tar tfvv mbexamples.tgz | grep -v CVS | head"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "drwxr-xr-x vschmidt/vschmidt 0 2007-03-09 13:23 mbexamples/\r\n",
        "drwxr-xr-x vschmidt/vschmidt 0 2005-10-31 13:17 mbexamples/mblist/\r\n",
        "-rwxr-xr-x vschmidt/vschmidt 982 2002-10-31 14:07 mbexamples/mblist/mblist.cmd\r\n",
        "drwxr-xr-x vschmidt/vschmidt   0 2005-10-31 13:17 mbexamples/data/\r\n",
        "-rw-r--r-- vschmidt/vschmidt 339293 2002-10-31 14:07 mbexamples/data/example_hs.mb24\r\n",
        "-rw-r--r-- vschmidt/vschmidt   1276 2002-10-31 14:07 mbexamples/data/.gmtdefaults\r\n",
        "-rw-r--r-- vschmidt/vschmidt   1449 2002-10-31 14:07 mbexamples/data/README\r\n",
        "-rw-r--r-- vschmidt/vschmidt  45240 2002-10-31 14:07 mbexamples/data/example_sb.mb16\r\n",
        "-rw-r--r-- vschmidt/vschmidt 508844 2002-10-31 14:07 mbexamples/data/example_sb2000.mb33\r\n",
        "-rw-r--r-- vschmidt/vschmidt  59000 2002-10-31 14:07 mbexamples/data/example_sb.mb11\r\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!tar xf mbexamples.tgz"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ls mbexamples"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "README             \u001b[34mdata\u001b[m\u001b[m/              \u001b[34mmbbath\u001b[m\u001b[m/            \u001b[34mmbinfo\u001b[m\u001b[m/            \u001b[34mmbm_plot\u001b[m\u001b[m/\r\n",
        "\u001b[34mcookbook_examples\u001b[m\u001b[m/ gridfilelist       \u001b[34mmbgrid\u001b[m\u001b[m/            \u001b[34mmblist\u001b[m\u001b[m/            \u001b[34mxbt\u001b[m\u001b[m/\r\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ls mbexamples/cookbook_examples/"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[31mexample_sb.mb16.cmd\u001b[m\u001b[m*     example_sb.mb16.jpg      example_sb_mb16.png      stats                    survey_filelist.124      survey_filelist.124.ps   survey_heading.dat\r\n",
        "example_sb.mb16.gif      example_sb.mb16.ps       \u001b[34mother_data_sets\u001b[m\u001b[m/         stats_percent            \u001b[31msurvey_filelist.124.cmd\u001b[m\u001b[m* survey_heading\r\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 3: Surveying Your Survey with MB-System"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "3.1 Managing Your Data with mbdatalist"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pwd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "u'/Users/schwehr/Desktop/mbcookbook'"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd ~/Desktop/mbcookbook/mbexamples/cookbook_examples/other_data_sets/ew0204survey"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/Users/schwehr/Desktop/mbcookbook/mbexamples/cookbook_examples/other_data_sets/ew0204survey"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ls | head"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "00020504090010.mb183\r\n",
        "00020504090010.mb183.fbt\r\n",
        "00020504090010.mb183.fnv\r\n",
        "00020504090010.mb183.inf\r\n",
        "00020504091010.mb183\r\n",
        "00020504091010.mb183.fbt\r\n",
        "00020504091010.mb183.fnv\r\n",
        "00020504091010.mb183.inf\r\n",
        "00020504092010.mb183\r\n",
        "00020504092010.mb183.fbt\r\n"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!ls *.mb183 > tmplist\n",
      "!wc -l tmplist"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "      64 tmplist\r\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!mbdatalist -I tmplist | head -4"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "00020504090010.mb183 183 1.000000\r\n",
        "00020504091010.mb183 183 1.000000\r\n",
        "00020504092010.mb183 183 1.000000\r\n",
        "00020504093010.mb183 183 1.000000\r\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!mbdatalist -F-1 -I tmplist | head -4"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "00020504090010.mb183 183 1.000000\r\n",
        "00020504091010.mb183 183 1.000000\r\n",
        "00020504092010.mb183 183 1.000000\r\n",
        "00020504093010.mb183 183 1.000000\r\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!mbdatalist -I tmplist > datalist-1\n",
      "!wc -l datalist-1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "      64 datalist-1\r\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# I am not sure why the instructions switched to filelist.124 when we already have tmplist\n",
      "!diff filelist.124 tmplist"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "65d64\r\n",
        "< filelist.124\r\n"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# mbdatalist \"-N\" option creats \"inf\", \"fbt\", and \"fnv\" files.  But we already have them\n",
      "!ls -l 00020504160010.mb183*"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "-rw-r--r--  1 schwehr  5000  659772 Oct 23  2002 00020504160010.mb183\r\n",
        "-rw-r--r--  1 schwehr  5000   72004 Oct 24  2002 00020504160010.mb183.fbt\r\n",
        "-rw-r--r--  1 schwehr  5000    5808 Oct 24  2002 00020504160010.mb183.fnv\r\n",
        "-rw-r--r--  1 schwehr  5000    2520 Oct 24  2002 00020504160010.mb183.inf\r\n"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Sadly, the unix file command knows nothing about them\n",
      "!file 00020504160010.mb183*"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "00020504160010.mb183:     data\r\n",
        "00020504160010.mb183.fbt: data\r\n",
        "00020504160010.mb183.fnv: ASCII text\r\n",
        "00020504160010.mb183.inf: ASCII text\r\n"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!mbdatalist -F-1 -I datalist-1 -R170.133/170.35/42.2/42.4 > survey-datalist\n",
      "!head -4 survey-datalist"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "00020504100010.mb183 183 1.000000\r\n",
        "00020504101010.mb183 183 1.000000\r\n",
        "00020504102010.mb183 183 1.000000\r\n",
        "00020504103010.mb183 183 1.000000\r\n"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We loose a whole bunch of files by re\n",
      "!wc -l survey-datalist"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "      42 survey-datalist\r\n"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "3.2. Plotting Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Anything else in this directory?\n",
      "!ls | egrep -v 'mb183(.fbt|.fnv|.inf)?$'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "datalist-1\r\n",
        "filelist.124\r\n",
        "survey-datalist\r\n",
        "tmplist\r\n"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Make a navigation plot\n",
      "!mbm_plot -I survey-datalist -N"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r\n",
        "Plot generation shellscript <survey-datalist.cmd> created.\r\n",
        "\r\n",
        "Instructions:\r\n",
        "  Execute <survey-datalist.cmd> to generate Postscript plot <survey-datalist.ps>.\r\n",
        "  Executing <survey-datalist.cmd> also invokes ghostview to view the plot on the screen.\r\n"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!ls -ltr | tail -3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "-rw-r--r--  1 schwehr  5000  234941 Feb  2 11:06 survey-datalist.png\r\n",
        "-rw-r--r--  1 schwehr  5000  597651 Feb  2 19:48 survey-datalist.ps\r\n",
        "-rwxrwxr-x  1 schwehr  5000    1797 Feb  2 19:49 survey-datalist.cmd\r\n"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Ouch... csh script\n",
      "!head -1 survey-datalist.cmd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "#! /bin/csh -f\r\n"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!./survey-datalist.cmd 2>&1 | tail -5"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Resetting GMT fonts...\r\n",
        "Running ghostview in background...\r\n",
        "[1] 87556\r\n",
        "All done!\r\n",
        "ghostview: Command not found.\r\n"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!ls -ltr | tail -3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "-rw-r--r--  1 schwehr  5000    1428 Feb  2 10:44 survey-datalist\r\n",
        "-rwxrwxr-x  1 schwehr  5000    1797 Feb  2 10:49 survey-datalist.cmd\r\n",
        "-rw-r--r--  1 schwehr  5000   37368 Feb  2 10:50 survey-datalist.ps\r\n"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# I do not have ghostscript setup on my machine, so make a PNG image\n",
      "# with ImageMagick/GraphicsMagick and open that\n",
      "!convert survey-datalist.ps survey-datalist.png"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "im = imread('survey-datalist.png')\n",
      "imshow(im, cmap=get_cmap('gray'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "<matplotlib.image.AxesImage at 0x10f515b10>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Make a color filled plot\n",
      "!mbm_plot -I survey-datalist -G1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r\n",
        "Plot generation shellscript <survey-datalist.cmd> created.\r\n",
        "\r\n",
        "Instructions:\r\n",
        "  Execute <survey-datalist.cmd> to generate Postscript plot <survey-datalist.ps>.\r\n",
        "  Executing <survey-datalist.cmd> also invokes ghostview to view the plot on the screen.\r\n"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!./survey-datalist.cmd 2>&1 | tail -5"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Resetting GMT fonts...\r\n",
        "Running ghostview in background...\r\n",
        "[1] 87581\r\n",
        "All done!\r\n",
        "ghostview: Command not found.\r\n"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!convert survey-datalist.ps survey-datalist.png"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "im = imread('survey-datalist.png')\n",
      "imshow(im)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "<matplotlib.image.AxesImage at 0x1122c2550>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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EUSHpQ/txNJ/6JMCBp8LaR9t38HyjavNcialKVsm99oKKonABvdUr3xAl4oY/Dl4s0TVH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\nB5CKJpE4gFQ0icQBpKJJJA7w/3wb0J1kPbbDAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Contours, color and navigation\n",
      "!mbm_plot -I survey-datalist -G1 -N -C"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r\n",
        "Plot generation shellscript <survey-datalist.cmd> created.\r\n",
        "\r\n",
        "Instructions:\r\n",
        "  Execute <survey-datalist.cmd> to generate Postscript plot <survey-datalist.ps>.\r\n",
        "  Executing <survey-datalist.cmd> also invokes ghostview to view the plot on the screen.\r\n"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!./survey-datalist.cmd 2>&1 | tail -5"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Resetting GMT fonts...\r\n",
        "Running ghostview in background...\r\n",
        "[1] 87515\r\n",
        "All done!\r\n",
        "ghostview: Command not found.\r\n"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!convert survey-datalist.ps survey-datalist.png\n",
      "im = imread('survey-datalist.png')\n",
      "imshow(im)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "<matplotlib.image.AxesImage at 0x1131032d0>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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A8qZOyHbLJyg8d9Mw+sSHcfs/vyY7vxhLYx2YGzhwIh3MDRgbahj/yNs898lm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\nGoOhAEzRGAwFYIrGYCgAUzQGQwH+H8+rzbiWpDtdAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Shaded relief plot of the bathymetry\n",
      "!mbm_plot -I survey-datalist -G2\n",
      "!./survey-datalist.cmd 2>&1 | tail -5\n",
      "!convert survey-datalist.ps survey-datalist.png\n",
      "im = imread('survey-datalist.png')\n",
      "imshow(im)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r\n",
        "Plot generation shellscript <survey-datalist.cmd> created.\r\n",
        "\r\n",
        "Instructions:\r\n",
        "  Execute <survey-datalist.cmd> to generate Postscript plot <survey-datalist.ps>.\r\n",
        "  Executing <survey-datalist.cmd> also invokes ghostview to view the plot on the screen.\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Resetting GMT fonts...\r\n",
        "Running ghostview in background...\r\n",
        "[1] 68348\r\n",
        "All done!\r\n",
        "ghostview: Command not found.\r\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "<matplotlib.image.AxesImage at 0x113a99090>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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9UbrW3onqWhjozD9iORLw/IubePbpKRLJNNHAJP1WDtdxmN9ez/HHHUtF02zw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FAAAg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\nLlFWVibC4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      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Multibeam backscatter plot\n",
      "!mbm_plot -I survey-datalist -G5\n",
      "!./survey-datalist.cmd 2>&1 | tail -5\n",
      "!convert survey-datalist.ps survey-datalist.png\n",
      "im = imread('survey-datalist.png')\n",
      "imshow(im)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r\n",
        "Plot generation shellscript <survey-datalist.cmd> created.\r\n",
        "\r\n",
        "Instructions:\r\n",
        "  Execute <survey-datalist.cmd> to generate Postscript plot <survey-datalist.ps>.\r\n",
        "  Executing <survey-datalist.cmd> also invokes ghostview to view the plot on the screen.\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Resetting GMT fonts...\r\n",
        "Running ghostview in background...\r\n",
        "[1] 68390\r\n",
        "All done!\r\n",
        "ghostview: Command not found.\r\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 55,
       "text": [
        "<matplotlib.image.AxesImage at 0x11445f410>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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NDZxOJ+l0mlQqhdvtplKpoNfrMRgMtLW1EYvFaG5uplCs/+7Cq9VqNZqbmxkc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\nJYAUaBIkLAGkQJMgYQnw/wFYOYQBa28QKgAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "3.3 Extracting Statistics"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pwd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 56,
       "text": [
        "u'/Users/schwehr/Desktop/mbcookbook/mbexamples/cookbook_examples/other_data_sets/ew0204survey'"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!mbinfo -I00020504090010.mb183"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r\n",
        "Swath Data File:      00020504090010.mb183\r\n",
        "MBIO Data Format ID:  183\r\n",
        "Format name:          MBF_HSDS2LAM\r\n",
        "Informal Description: L-DEO HSDS2 processing format\r\n",
        "Attributes:           STN Atlas multibeam sonars, \r\n",
        "                      Hydrosweep DS2, Hydrosweep MD, \r\n",
        "                      Fansweep 10, Fansweep 20, \r\n",
        "                      bathymetry, amplitude, and sidescan,\r\n",
        "                      up to 1440 beams and 4096 pixels,\r\n",
        "                      XDR binary, L-DEO.\r\n",
        "\r\n",
        "Data Totals:\r\n",
        "Number of Records:                          37\r\n",
        "Bathymetry Data (140 beams):\r\n",
        "  Number of Beams:             5180\r\n",
        "  Number of Good Beams:        5106     98.57%\r\n",
        "  Number of Zero Beams:          74      1.43%\r\n",
        "  Number of Flagged Beams:        0      0.00%\r\n",
        "Amplitude Data (140 beams):\r\n",
        "  Number of Beams:             5180\r\n",
        "  Number of Good Beams:        5106     98.57%\r\n",
        "  Number of Zero Beams:          74      1.43%\r\n",
        "  Number of Flagged Beams:        0      0.00%\r\n",
        "Sidescan Data (2180 pixels):\r\n",
        "  Number of Pixels:           73815\r\n",
        "  Number of Good Pixels:      73815     100.00%\r\n",
        "  Number of Zero Pixels:          0      0.00%\r\n",
        "  Number of Flagged Pixels:       0      0.00%\r\n",
        "\r\n",
        "Navigation Totals:\r\n",
        "Total Time:             0.1593 hours\r\n",
        "Total Track Length:     3.4493 km\r\n",
        "Average Speed:         21.6521 km/hr (11.7038 knots)\r\n",
        "\r\n",
        "Start of Data:\r\n",
        "Time:  05 04 2002 08:59:56.460000  JD124 (2002-05-04T08:59:56.460000)\r\n",
        "Lon:   169.879233333     Lat:    42.134316667     Depth:  5057.7613 meters\r\n",
        "Speed:  0.000"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0 km/hr ( 0.0000 knots)  Heading:  46.1206 degrees\r\n",
        "Sonar Depth:    5.8000 m  Sonar Altitude: 5051.9613 m\r\n",
        "\r\n",
        "End of Data:\r\n",
        "Time:  05 04 2002 09:09:29.968000  JD124 (2002-05-04T09:09:29.968000)\r\n",
        "Lon:   169.908366667     Lat:    42.156333333     Depth:  4950.8452 meters\r\n",
        "Speed: 21.0345 km/hr (11.3700 knots)  Heading:  43.9014 degrees\r\n",
        "Sonar Depth:    5.1000 m  Sonar Altitude: 4945.7452 m\r\n",
        "\r\n",
        "Limits:\r\n",
        "Minimum Longitude:     169.826833737   Maximum Longitude:     169.960536614\r\n",
        "Minimum Latitude:       42.096614600   Maximum Latitude:       42.197029161\r\n",
        "Minimum Sonar Depth:     4.6000   Maximum Sonar Depth:     6.7000\r\n",
        "Minimum Altitude:     4817.2718   Maximum Altitude:     5051.9613\r\n",
        "Minimum Depth:        4770.9467   Maximum Depth:        5347.7265\r\n",
        "Minimum Amplitude:       7.0000   Maximum Amplitude:     241.0000\r\n",
        "Minimum Sidescan:        0.0000   Maximum Sidescan:      255.0000\r\n"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!mbinfo -F-1 -Isurvey-datalist | wc -l"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "     506\r\n"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!mbinfo -F-1 -Isurvey-datalist | head -20"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r\n",
        "Swath Data File:      00020504100010.mb183\r\n",
        "MBIO Data Format ID:  183\r\n",
        "Format name:          MBF_HSDS2LAM\r\n",
        "Informal Description: L-DEO HSDS2 processing format\r\n",
        "Attributes:           STN Atlas multibeam sonars, \r\n",
        "                      Hydrosweep DS2, Hydrosweep MD, \r\n",
        "                      Fansweep 10, Fansweep 20, \r\n",
        "                      bathymetry, amplitude, and sidescan,\r\n",
        "                      up to 1440 beams and 4096 pixels,\r\n",
        "                      XDR binary, L-DEO.\r\n",
        "\r\n",
        "Swath Data File:      00020504101010.mb183\r\n",
        "MBIO Data Format ID:  183\r\n",
        "Format name:          MBF_HSDS2LAM\r\n",
        "Informal Description: L-DEO HSDS2 processing format\r\n",
        "Attributes:           STN Atlas multibeam sonars, \r\n",
        "                      Hydrosweep DS2, Hydrosweep MD, \r\n",
        "                      Fansweep 10, Fansweep 20, \r\n",
        "                      bathymetry, amplitude, and sidescan,\r\n"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!mbinfo -F-1 -Isurvey-datalist | tail -20"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Start of Data:\r\n",
        "Time:  05 04 2002 09:59:44.485000  JD124 (2002-05-04T09:59:44.485000)\r\n",
        "Lon:   170.062583333     Lat:    42.272616667     Depth:  4508.8810 meters\r\n",
        "Speed:  0.0000 km/hr ( 0.0000 knots)  Heading:  44.6704 degrees\r\n",
        "Sonar Depth:    6.0000 m  Sonar Altitude: 4502.8810 m\r\n",
        "\r\n",
        "End of Data:\r\n",
        "Time:  05 04 2002 16:59:44.722000  JD124 (2002-05-04T16:59:44.722000)\r\n",
        "Lon:   170.271766667     Lat:    42.420866667     Depth:  1446.6716 meters\r\n",
        "Speed: 21.4741 km/hr (11.6076 knots)  Heading:  35.9473 degrees\r\n",
        "Sonar Depth:    5.4000 m  Sonar Altitude: 1441.2716 m\r\n",
        "\r\n",
        "Limits:\r\n",
        "Minimum Longitude:     170.003974345   Maximum Longitude:     170.361588384\r\n",
        "Minimum Latitude:       42.161915723   Maximum Latitude:       42.446878937\r\n",
        "Minimum Sonar Depth:     3.6000   Maximum Sonar Depth:     7.9000\r\n",
        "Minimum Altitude:      990.7620   Maximum Altitude:     4740.1799\r\n",
        "Minimum Depth:         973.2481   Maximum Depth:        6223.7055\r\n",
        "Minimum Amplitude:       0.0000   Maximum Amplitude:     250.0000\r\n",
        "Minimum Sidescan:        0.0000   Maximum Sidescan:      255.0000\r\n"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 4: Processing Multibeam Data with MB-System"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "4.2: Identifying the MB-System Data Format"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pwd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 73,
       "text": [
        "u'/Users/schwehr/Desktop/mbcookbook/mbexamples/cookbook_examples/other_data_sets/ew0204survey'"
       ]
      }
     ],
     "prompt_number": 73
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!ls | head -5"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "00020504090010.mb183\r\n",
        "00020504090010.mb183.fbt\r\n",
        "00020504090010.mb183.fnv\r\n",
        "00020504090010.mb183.inf\r\n",
        "00020504091010.mb183\r\n"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!man mbio | grep -A 10 183"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "           MBIO Data Format ID:  183\r\n",
        "           Format name:          MBF_HSDS2LAM\r\n",
        "           Informal Description: L-DEO HSDS2 processing format\r\n",
        "           Attributes:           STN Atlas multibeam sonars,\r\n",
        "                        Hydrosweep DS2, Hydrosweep MD,\r\n",
        "                        Fansweep 10, Fansweep 20,\r\n",
        "                        bathymetry, amplitude, and sidescan,\r\n",
        "                        up to 1440 beams and 4096 pixels,\r\n",
        "                        XDR binary, L-DEO.\r\n",
        "\r\n",
        "\r\n"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "4.3: Format Conversion"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "4.3.3 Converting Your Data - Sea Beam 2100"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!man mbio | grep -B 2 'SeaBeam 2100'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "           MBIO Data Format ID:  41\r\n",
        "           Format name:          MBF_SB2100RW\r\n",
        "           Informal Description: SeaBeam 2100 series vender format\r\n",
        "           Attributes:           SeaBeam 2100, bathymetry,\r\n",
        "--\r\n",
        "           MBIO Data Format ID:  42\r\n",
        "           Format name:          MBF_SB2100B1\r\n",
        "           Informal Description: SeaBeam 2100 series vender format\r\n",
        "           Attributes:           SeaBeam 2100, bathymetry,\r\n",
        "--\r\n",
        "           MBIO Data Format ID:  43\r\n",
        "           Format name:          MBF_SB2100B2\r\n",
        "           Informal Description: SeaBeam 2100 series vender format\r\n",
        "           Attributes:           SeaBeam 2100, bathymetry and\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "--\r\n",
        "       The  per-beam  parameters  in  the  SB2100 data format include angle-from-vertical and angle-forward. Angle-from-vertical is the same as theta except that it is\r\n",
        "       signed based on the acrosstrack direction (positive to starboard, negative to port). The angle-forward values are also defined slightly differently from phi, in\r\n",
        "       that  angle-forward  is signed differently on the port and starboard sides. The SeaBeam 2100 External Interface Specifications document includes both discussion\r\n"
       ]
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pwd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 85,
       "text": [
        "u'/Users/schwehr/Desktop/mbcookbook/mbexamples'"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# What mb### do we have for the files in the examples?\n",
      "!find . | egrep '\\.mb[0-9]+$' | xargs basename | cut -d\\. -f2 | sort -u"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "mb11\r\n",
        "mb16\r\n",
        "mb183\r\n",
        "mb24\r\n",
        "mb32\r\n",
        "mb33\r\n"
       ]
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Of a slightly more (but not totally) python way of getting this list.  Not a particularly good style\n",
      "all_files = !find . | egrep '\\.mb[0-9]+$'\n",
      "import os\n",
      "ext_list = [os.path.splitext(filepath)[1].replace('.mb','') for filepath in all_files]\n",
      "sorted([int(mb_fmt_num) for mb_fmt_num in set(ext_list)])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 91,
       "text": [
        "[11, 16, 24, 32, 33, 183]"
       ]
      }
     ],
     "prompt_number": 91
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\"You will find that the data files [snip] have already been converted to MB-system Format ID 57\".  Really?  Oh, I have to unpack loihi.tgz."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd .."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/Users/schwehr/Desktop/mbcookbook\n"
       ]
      }
     ],
     "prompt_number": 92
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ls -lh loihi.tgz"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "-rw-r--r--  1 schwehr  5000   375M Feb  2 10:25 loihi.tgz\r\n"
       ]
      }
     ],
     "prompt_number": 94
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!tar xf loihi.tgz"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 95
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!du -hs loihi"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "661M\tloihi\r\n"
       ]
      }
     ],
     "prompt_number": 96
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd loihi"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/Users/schwehr/Desktop/mbcookbook/loihi"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 99
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# TODO: show a cleaner way to do this with just python\n",
      "all_files = !find . | egrep '\\.mb[0-9]+$'\n",
      "ext_list = [os.path.splitext(filepath)[1].replace('.mb','') for filepath in all_files]\n",
      "sorted([int(mb_fmt_num) for mb_fmt_num in set(ext_list)])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 100,
       "text": [
        "[16, 57, 121]"
       ]
      }
     ],
     "prompt_number": 100
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!man mbio | egrep -A 4 'ID:[ ]+(16|57)$'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "           MBIO Data Format ID:  16\r\n",
        "           Format name:          MBF_SBSIOSWB\r\n",
        "           Informal Description: SIO Swath-bathy SeaBeam\r\n",
        "           Attributes:           Sea Beam, bathymetry, 19 beams,\r\n",
        "                                 binary, centered, SIO.\r\n",
        "--\r\n",
        "           MBIO Data Format ID:  57\r\n",
        "           Format name:          MBF_EM300MBA\r\n",
        "           Informal Description: Simrad multibeam processing format\r\n",
        "           Attributes:           Old and new Simrad multibeams,\r\n",
        "                        EM12S, EM12D, EM121, EM120,\r\n"
       ]
      }
     ],
     "prompt_number": 107
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!find . -name \\*.mb16"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "./TUNE04WT/TUNE04WT.SWSB.91oct07p.mb16\r\n",
        "./TUNE04WT/TUNE04WT.SWSB.91oct08p.mb16\r\n",
        "./TUNE04WT/TUNE04WT.SWSB.91oct09p.mb16\r\n",
        "./TUNE04WT/TUNE04WT.SWSB.91oct10p.mb16\r\n",
        "./TUNE04WT/TUNE04WT.SWSB.91oct11p.mb16\r\n",
        "./TUNE04WT/TUNE04WT.SWSB.91oct12p.mb16\r\n",
        "./TUNE04WT/TUNE04WT.SWSB.91oct15p.mb16\r\n",
        "./TUNE04WT/TUNE04WT.SWSB.91oct16p.mb16\r\n",
        "./TUNE04WT/TUNE04WT.SWSB.91oct17p.mb16\r\n"
       ]
      }
     ],
     "prompt_number": 101
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!cp ./TUNE04WT/TUNE04WT.SWSB.91oct07p.mb16 test.mb16"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 102
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!mbinfo -Itest.mb16 | grep Minimum"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Minimum Longitude:    -157.806590278   Maximum Longitude:    -155.714683638\r\n",
        "Minimum Latitude:       18.867588784   Maximum Latitude:       21.210987915\r\n",
        "Minimum Sonar Depth:     0.0000   Maximum Sonar Depth:     0.0000\r\n",
        "Minimum Altitude:        0.0000   Maximum Altitude:     4805.0000\r\n",
        "Minimum Depth:          43.0000   Maximum Depth:        4886.0000\r\n"
       ]
      }
     ],
     "prompt_number": 108
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# This does not work, so I will just paste in the results I got from the command line\n",
      "!mbcopy -F16/57 -Itest.mb16 -Otest.mb57"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 109
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Ran this:\n",
      "\n",
      "    mbcopy -F16/57 -Itest.mb16 -Otest.mb57 -V\n",
      "\n",
      "Got this:\n",
      "\n",
      "    Program MBcopy\n",
      "    Version $Id: mbcopy.c 1934 2012-02-22 07:51:16Z caress $\n",
      "    MB-system Version 5.3.2012\n",
      "    \n",
      "    Comments:\n",
      "    Swath data modified by program mbprocess\n",
      "    Segmentation fault: 11"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!rm test.mb16 test.mb57"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 110
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!find . -name \\*.mb57"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "./MBARI1998EM300/mbari_1998_53_msn.mb57\r\n",
        "./MBARI1998EM300/mbari_1998_54_msn.mb57\r\n",
        "./MBARI1998EM300/mbari_1998_55_msn.mb57\r\n",
        "./MBARI1998EM300/mbari_1998_56_msn.mb57\r\n",
        "./MBARI1998EM300/mbari_1998_57_msn.mb57\r\n",
        "./MBARI1998EM300/mbari_1998_58_msn.mb57\r\n",
        "./MBARI1998EM300/mbari_1998_59_msn.mb57\r\n",
        "./MBARI1998EM300/mbari_1998_60_msn.mb57\r\n",
        "./MBARI1998EM300/mbari_1998_61_msn.mb57\r\n"
       ]
      }
     ],
     "prompt_number": 111
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "4.4 Survey and Organize the Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ls -lh"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "total 24\r\n",
        "drwxr-xr-x  156 schwehr  5000   5.2K Jun  4  2004 \u001b[34mHUGO97\u001b[m\u001b[m/\r\n",
        "drwxr-xr-x   13 schwehr  5000   442B Jun  4  2004 \u001b[34mMBARI1998EM300\u001b[m\u001b[m/\r\n",
        "-rw-r--r--    1 schwehr  5000   1.8K Jul  3  2004 README\r\n",
        "drwxr-xr-x   58 schwehr  5000   1.9K Jun  4  2004 \u001b[34mSumnerLoihi1997\u001b[m\u001b[m/\r\n",
        "drwxr-xr-x   68 schwehr  5000   2.3K Jun  4  2004 \u001b[34mTUNE04WT\u001b[m\u001b[m/\r\n",
        "-rw-r--r--    1 schwehr  5000   135B Nov 25  2002 datalist.mb-1\r\n",
        "-rw-r--r--    1 schwehr  5000    25B Nov 18  2002 datalistp.mb-1\r\n"
       ]
      }
     ],
     "prompt_number": 112
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cat datalist.mb-1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "HUGO97/datalist.mb-1 -1 100.0\r\n",
        "MBARI1998EM300/datalist.mb-1 -1 1.0\r\n",
        "SumnerLoihi1997/datalist.mb-1 -1 0.1\r\n",
        "TUNE04WT/datalist.mb-1 -1 0.001\r\n"
       ]
      }
     ],
     "prompt_number": 113
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cat datalistp.mb-1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "$PROCESSED\r\n",
        "datalist.mb-1\r\n"
       ]
      }
     ],
     "prompt_number": 114
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cat MBARI1998EM300/datalist.mb-1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "mbari_1998_53_msn.mb57 57\r\n",
        "mbari_1998_54_msn.mb57 57\r\n",
        "mbari_1998_55_msn.mb57 57\r\n",
        "mbari_1998_56_msn.mb57 57\r\n",
        "mbari_1998_57_msn.mb57 57\r\n",
        "mbari_1998_58_msn.mb57 57\r\n",
        "mbari_1998_59_msn.mb57 57\r\n",
        "mbari_1998_60_msn.mb57 57\r\n",
        "mbari_1998_61_msn.mb57 57\r\n"
       ]
      }
     ],
     "prompt_number": 115
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd MBARI1998EM300"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/Users/schwehr/Desktop/mbcookbook/loihi/MBARI1998EM300"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 116
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ls"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "README                  mbari_1998_53_msn.mb57  mbari_1998_55_msn.mb57  mbari_1998_57_msn.mb57  mbari_1998_59_msn.mb57  mbari_1998_61_msn.mb57\r\n",
        "datalist.mb-1           mbari_1998_54_msn.mb57  mbari_1998_56_msn.mb57  mbari_1998_58_msn.mb57  mbari_1998_60_msn.mb57\r\n"
       ]
      }
     ],
     "prompt_number": 117
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ls *.mb57 > list"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 118
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cat list"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "mbari_1998_53_msn.mb57\r\n",
        "mbari_1998_54_msn.mb57\r\n",
        "mbari_1998_55_msn.mb57\r\n",
        "mbari_1998_56_msn.mb57\r\n",
        "mbari_1998_57_msn.mb57\r\n",
        "mbari_1998_58_msn.mb57\r\n",
        "mbari_1998_59_msn.mb57\r\n",
        "mbari_1998_60_msn.mb57\r\n",
        "mbari_1998_61_msn.mb57\r\n"
       ]
      }
     ],
     "prompt_number": 119
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!mbdatalist -Ilist"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "mbari_1998_53_msn.mb57 57 1.000000\r\n",
        "mbari_1998_54_msn.mb57 57 1.000000\r\n",
        "mbari_1998_55_msn.mb57 57 1.000000\r\n",
        "mbari_1998_56_msn.mb57 57 1.000000\r\n",
        "mbari_1998_57_msn.mb57 57 1.000000\r\n",
        "mbari_1998_58_msn.mb57 57 1.000000\r\n",
        "mbari_1998_59_msn.mb57 57 1.000000\r\n",
        "mbari_1998_60_msn.mb57 57 1.000000\r\n",
        "mbari_1998_61_msn.mb57 57 1.000000\r\n"
       ]
      }
     ],
     "prompt_number": 121
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!rm list"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "rm: list: No such file or directory\r\n"
       ]
      }
     ],
     "prompt_number": 127
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "4.4.1.2 The Benefit"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd .."
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pwd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 128,
       "text": [
        "u'/Users/schwehr/Desktop/mbcookbook/loihi'"
       ]
      }
     ],
     "prompt_number": 128
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# This command will take a minute or 2 to run.\n",
      "!mbinfo -Idatalist.mb-1 | grep -A 40 'Data Totals:'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Data Totals:\r\n",
        "Number of Records:                      191764\r\n",
        "Bathymetry Data (135 beams):\r\n",
        "  Number of Beams:         12119082\r\n",
        "  Number of Good Beams:     9469099     78.13%\r\n",
        "  Number of Zero Beams:     1590395     13.12%\r\n",
        "  Number of Flagged Beams:  1059588      8.74%\r\n",
        "Amplitude Data (135 beams):\r\n",
        "  Number of Beams:          7285751\r\n",
        "  Number of Good Beams:     6054808     83.10%\r\n",
        "  Number of Zero Beams:      608084      8.35%\r\n",
        "  Number of Flagged Beams:   622859      8.55%\r\n",
        "Sidescan Data (1024 pixels):\r\n",
        "  Number of Pixels:        10155008\r\n",
        "  Number of Good Pixels:   10153984     99.99%\r\n",
        "  Number of Zero Pixels:          0      0.00%\r\n",
        "  Number of Flagged Pixels:    1024      0.01%\r\n",
        "\r\n",
        "Navigation Totals:\r\n",
        "Total Time:         -49797.0534 hours\r\n",
        "Total Track Length: 36092.8118 km\r\n",
        "Average Speed:          0.0000 km/hr ( 0.0000 knots)\r\n",
        "\r\n",
        "Start of Data:\r\n",
        "Time:  06 21 1997 19:01:07.393000  JD172 (1997-06-21T19:01:07.393000)\r\n",
        "Lon:  -155.221418300     Lat:    18.882305500     Depth:   301.0800 meters\r\n",
        "Speed:  1.9631 km/hr ( 1.0611 knots)  Heading: 229.1400 degrees\r\n",
        "Sonar Depth:    0.4300 m  Sonar Altitude:  300.6500 m\r\n",
        "\r\n",
        "End of Data:\r\n",
        "Time:  10 16 1991 21:57:55.000000  JD289 (1991-10-16T21:57:55.000000)\r\n",
        "Lon:  -157.878166667     Lat:    21.285280000     Depth:    33.0000 meters\r\n",
        "Speed: 20.5175 km/hr (11.0906 knots)  Heading:  31.2014 degrees\r\n",
        "Sonar Depth:    0.0000 m  Sonar Altitude:   33.0000 m\r\n",
        "\r\n",
        "Limits:\r\n",
        "Minimum Longitude:    -157.880116086   Maximum Longitude:    -153.382906577\r\n",
        "Minimum Latitude:       18.568354360   Maximum Latitude:       21.285303394\r\n",
        "Minimum Sonar Depth:    -2.6500   Maximum Sonar Depth:  1530.4800\r\n",
        "Minimum Altitude:    -1498.0800   Maximum Altitude:     5483.1300\r\n",
        "Minimum Depth:           0.0600   Maximum Depth:        5605.0000\r\n"
       ]
      }
     ],
     "prompt_number": 131
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!mbdatalist -I datalist.mb-1 -R155.5/155/18.7/19.2 "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "MBARI1998EM300/mbari_1998_53_msn.mb57 57 1.000000\r\n",
        "MBARI1998EM300/mbari_1998_54_msn.mb57 57 1.000000\r\n",
        "MBARI1998EM300/mbari_1998_55_msn.mb57 57 1.000000\r\n",
        "MBARI1998EM300/mbari_1998_56_msn.mb57 57 1.000000\r\n",
        "MBARI1998EM300/mbari_1998_57_msn.mb57 57 1.000000\r\n",
        "MBARI1998EM300/mbari_1998_58_msn.mb57 57 1.000000\r\n",
        "MBARI1998EM300/mbari_1998_59_msn.mb57 57 1.000000\r\n",
        "MBARI1998EM300/mbari_1998_60_msn.mb57 57 1.000000\r\n",
        "MBARI1998EM300/mbari_1998_61_msn.mb57 57 1.000000\r\n"
       ]
      }
     ],
     "prompt_number": 137
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# The above list does not match the cookbook.\n",
      "!mbdatalist -I datalist.mb-1 | wc -l"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "      31\r\n"
       ]
      }
     ],
     "prompt_number": 138
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!wc -l datalist.mb-1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "       4 datalist.mb-1\r\n"
       ]
      }
     ],
     "prompt_number": 135
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}

Hello, this should be run in jupyter?

Yes. This should be run in jupyter on an instance with mbsystem installed.

Would be better to convert this to the bash mode for jupyter so that all the bangs (!) aren't necessary.

Thanks. I ran these lines on the terminal and went fairly well, without the bangs. I tried to follow the cookbook instructions with the examples files but I got an error creating the ancillary data files, so I couldn't get forward.