{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Reading CTD data with PySeabird" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Author: Guilherme Castelão" ] }, { "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", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "from seabird.cnv import fCNV\n", "from gsw import z_from_p" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's first download an example file with some CTD data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2016-09-04 21:50:24-- https://raw.githubusercontent.com/castelao/seabird/master/tests/data/CTD/dPIRX003.cnv\n", "Resolving raw.githubusercontent.com... 151.101.24.133\n", "Connecting to raw.githubusercontent.com|151.101.24.133|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 47291 (46K) [text/plain]\n", "Saving to: ‘dPIRX003.cnv’\n", "\n", "dPIRX003.cnv 100%[===================>] 46.18K --.-KB/s in 0.05s \n", "\n", "2016-09-04 21:50:25 (849 KB/s) - ‘dPIRX003.cnv’ saved [47291/47291]\n", "\n" ] } ], "source": [ "!wget https://raw.githubusercontent.com/castelao/seabird/master/sampledata/CTD/dPIRX003.cnv" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "DEBUG:root:Openning file: dPIRX003.cnv\n" ] } ], "source": [ "profile = fCNV('dPIRX003.cnv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The profile dPIRX003.cnv.OK was loaded with the default rule cnv.yaml" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Header: ['instrument_type', u'sbe_model', u'file_type', u'seasave', u'start_time', u'nquan', 'LONGITUDE', 'datetime', u'bad_flag', u'nvalues', 'LATITUDE', 'filename', 'md5']\n", "Data: [u'timeS', u'PRES', u'TEMP', u'TEMP2', u'CNDC', u'CNDC2', u'potemperature', u'potemperature2', u'PSAL', u'PSAL2', 'flag']\n" ] } ], "source": [ "print(\"Header: %s\" % profile.attributes.keys())\n", "print(\"Data: %s\" % profile.keys())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have latitude in the header, and pressure in the data." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "z = z_from_p(profile['PRES'], profile.attributes['LATITUDE'])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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xIQ/0vQIzC3RpIlKCgouI1HoX3DuBr5ucTwt3Lp9e/hpHH5YU6JJEZC8UXESk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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib import pyplot as plt\n", "\n", "plt.plot(profile['TEMP'], z,'b')\n", "plt.plot(profile['TEMP2'], z,'g')\n", "plt.xlabel('temperature')\n", "plt.ylabel('depth')\n", "plt.title(profile.attributes['filename'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 2 }