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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Reading CTD data with PySeabird"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Author: Guilherme Castel\u00e3o"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "pySeabird is a package to parse/load CTD data files. It should be an easy task but the problem is that the format have been changing along the time. Work with multiple ships/cruises data requires first to understand each file, to normalize it into a common format for only than start your analysis. That can still be done with few general regular expression rules, but I would rather use strict rules. If I'm loading hundreds or thousands of profiles, I want to be sure that no mistake passed by. I rather ignore a file in doubt and warn it, than belive that it was loaded right and be part of my analysis.\n",
      "\n",
      "With that in mind, I wrote this package with the ability to load multiple rules, so new rules can be added without change the main engine.\n",
      "\n",
      "For more information, check the documentatio"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "\n",
      "from seabird.cnv import fCNV\n",
      "from gsw import z_from_p"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's first download an example file with some CTD data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!wget https://raw.githubusercontent.com/castelao/seabird/master/tests/test_data/dPIRX003.cnv.OK"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "--2015-02-20 18:49:22--  https://raw.githubusercontent.com/castelao/seabird/master/tests/test_data/dPIRX003.cnv.OK\r\n",
        "Resolving raw.githubusercontent.com... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "199.27.79.133\r\n",
        "Connecting to raw.githubusercontent.com|199.27.79.133|:443... connected.\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "HTTP request sent, awaiting response... 200 OK\r\n",
        "Length: 47291 (46K) [text/plain]\r\n",
        "Saving to: 'dPIRX003.cnv.OK'\r\n",
        "\r\n",
        "\r",
        "dPIRX003.cnv.OK       0%[                      ]       0  --.-KB/s             "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "dPIRX003.cnv.OK     100%[=====================>]  46.18K  --.-KB/s   in 0.08s  \r\n",
        "\r\n",
        "2015-02-20 18:49:22 (560 KB/s) - 'dPIRX003.cnv.OK' saved [47291/47291]\r\n",
        "\r\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "profile = fCNV('dPIRX003.cnv.OK')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Using rules from: cnv.yaml\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The profile dPIRX003.cnv.OK was loaded with the default rule cnv.yaml"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"Header: %s\" % profile.attributes.keys()\n",
      "print \"Data: %s\" % profile.keys()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Header: ['file_type', 'seasave', 'start_time', 'nquan', 'longitude', 'datetime', 'bad_flag', 'nvalues', 'latitude', 'filename', 'md5']\n",
        "Data: ['timeS', 'pressure', 'temperature', 'temperature2', 'conductivity', 'conductivity2', 'potemperature', 'potemperature2', 'salinity', 'salinity2', 'flag']\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We have latitude in the header, and pressure in the data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "z = z_from_p(profile['pressure'], profile.attributes['latitude'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from matplotlib import pyplot as plt\n",
      "\n",
      "plt.plot(profile['temperature'], z,'b')\n",
      "plt.plot(profile['temperature2'], z,'g')\n",
      "plt.xlabel('temperature')\n",
      "plt.ylabel('depth')\n",
      "plt.title(profile.attributes['filename'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<matplotlib.text.Text at 0x10076f290>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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YdFUpcYgcmMLMtvoCODVvJVwzSyS4Sa9vCcRXZGp5lF8ffQQnD5sAZ10KFbNg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\nRKTIlDxERKTIlDxERKTIlDx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       "text": [
        "<matplotlib.figure.Figure at 0x10516c610>"
       ]
      }
     ],
     "prompt_number": 6
    }
   ],
   "metadata": {}
  }
 ]
}