{
 "metadata": {
  "name": "",
  "signature": "sha256:d1cfad559bf3d606a7fb16c3b19e94514234ded28688e77af5e133844c2edbad"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Using OWSLib to access a SOS 2.0 service\n",
      "\n",
      "Uses a 52North SOS service hosting IOOS data (http://ioossos.axiomalaska.com)\n",
      "\n",
      "## Service setup and metadata"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "from owslib.sos import SensorObservationService\n",
      "from owslib.swe.observation.sos200 import SOSGetObservationResponse\n",
      "#service = SensorObservationService('http://geoviqua.dev.52north.org/SOS-Q/sos/kvp',version='2.0.0')\n",
      "\n",
      "service = SensorObservationService('http://ioossos.axiomalaska.com/52n-sos-ioos-stable/sos/kvp',version='2.0.0')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "service.contents"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "{'urn:ioos:network:test:all': <owslib.swe.observation.sos200.SosObservationOffering at 0x1119d6f50>,\n",
        " 'urn:ioos:station:test:0': <owslib.swe.observation.sos200.SosObservationOffering at 0x1119db590>,\n",
        " 'urn:ioos:station:test:1': <owslib.swe.observation.sos200.SosObservationOffering at 0x1119db710>,\n",
        " 'urn:ioos:station:test:2': <owslib.swe.observation.sos200.SosObservationOffering at 0x1119db810>,\n",
        " 'urn:ioos:station:test:3': <owslib.swe.observation.sos200.SosObservationOffering at 0x1119db910>,\n",
        " 'urn:ioos:station:test:4': <owslib.swe.observation.sos200.SosObservationOffering at 0x1119dba10>,\n",
        " 'urn:ioos:station:test:5': <owslib.swe.observation.sos200.SosObservationOffering at 0x1119dbb10>,\n",
        " 'urn:ioos:station:test:6': <owslib.swe.observation.sos200.SosObservationOffering at 0x1119dbc10>,\n",
        " 'urn:ioos:station:test:7': <owslib.swe.observation.sos200.SosObservationOffering at 0x1119dbd10>,\n",
        " 'urn:ioos:station:test:8': <owslib.swe.observation.sos200.SosObservationOffering at 0x1119dbe10>,\n",
        " 'urn:ioos:station:test:9': <owslib.swe.observation.sos200.SosObservationOffering at 0x1119dbf10>}"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "response = service.get_observation(responseFormat='http://www.opengis.net/waterml/2.0', offerings=['urn:ioos:station:test:8'], observedProperties=['http://mmisw.org/ont/cf/parameter/sea_water_temperature'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## WaterML2.0 observation parser, plotting with pandas\n",
      "\n",
      "### Parsing response, showing result as a WaterML2.0 MeasurementTimeseriesObservation "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from owslib.etree import etree\n",
      "import pandas as pd\n",
      "from pandas import Series\n",
      "\n",
      "et = etree.fromstring(response)\n",
      "parsed_response = SOSGetObservationResponse(et)\n",
      "\n",
      "parsed_response.observations[0]\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<owslib.swe.observation.waterml2.MeasurementTimeseriesObservation at 0x11274ded0>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Plot the first two results"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for o in parsed_response.observations[0:2]:\n",
      "    series = o.get_result()\n",
      "    dates = [p.datetime for p in series]\n",
      "    values = [p.value for p in series]  \n",
      "    \n",
      "    series = pd.Series(values, index=dates)\n",
      "    series.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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HCb7UDXFFYxZso8L5nPE/l/wP76S/w4lzJ5zuM3v9bO4ZeA+hwer+8fPnw1VX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\nAjQAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x112768910>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Using OM_Observation result type"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "response = service.get_observation(responseFormat='http://www.opengis.net/om/2.0', offerings=['urn:ioos:station:test:8'], observedProperties=['http://mmisw.org/ont/cf/parameter/sea_water_temperature'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "et = etree.fromstring(response)\n",
      "parsed_response = SOSGetObservationResponse(et)\n",
      "parsed_response.observations[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<owslib.swe.observation.om.OM_MeasurementObservation at 0x1126c2550>"
       ]
      }
     ],
     "prompt_number": 13
    }
   ],
   "metadata": {}
  }
 ]
}