rsignell-usgs/ndbc_sos_owslib.ipynb

## ndbc_sos_owslib.ipynb
{
 "metadata": {
  "name": "NDBC_SOS"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "# Wind Speed and Gust from NDBC SOS service\nGet CSV data from NDBC SOS service using OWSlib, then read CSV data and plot using Pandas"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from datetime import datetime\nimport cStringIO\nimport pandas as pd\nfrom owslib.sos import SensorObservationService",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# pick a buoy, any buoy\nsta_id='44066'  # texas tower\n#sta_id='44013'  # boston buoy",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# pick a start & stop time\nstart = '2013-06-12T00:00:00Z'\nstop = '2013-06-14T00:00:00Z'",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from IPython.core.display import HTML\nHTML('<iframe src=http://www.ndbc.noaa.gov/station_page.php?station=%s width=950 height=400></iframe>' % sta_id)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe src=http://www.ndbc.noaa.gov/station_page.php?station=44066 width=950 height=400></iframe>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": "<IPython.core.display.HTML at 0x3c1d0d0>"
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ndbc=SensorObservationService('http://sdf.ndbc.noaa.gov/sos/server.php?request=GetCapabilities&service=SOS')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "id=ndbc.identification\nid.title",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": "'National Data Buoy Center SOS'"
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "contents = ndbc.contents\nnetwork = contents['network-all']\nnetwork.description",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": "'All stations on the NDBC SOS server'"
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "id.title",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": "'National Data Buoy Center SOS'"
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "rfs = network.response_formats",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "print '\\n'.join(rfs)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "text/xml;subtype=\"om/1.0.0\"\ntext/csv\ntext/tab-separated-values\napplication/vnd.google-earth.kml+xml\ntext/xml;schema=\"ioos/0.6.1\"\napplication/ioos+xml;version=0.6.1\n"
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "station = contents['station-%s' % sta_id]    ",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "station.name",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": "'urn:ioos:station:wmo:44066'"
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "station.description",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": "'Texas Tower #4'"
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "getob = ndbc.get_operation_by_name('getobservation')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "getob.parameters",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": "{'observedProperty': {'values': ['air_temperature',\n   'air_pressure_at_sea_level',\n   'sea_water_electrical_conductivity',\n   'currents',\n   'sea_water_salinity',\n   'sea_floor_depth_below_sea_surface',\n   'sea_water_temperature',\n   'waves',\n   'winds']},\n 'responseFormat': {'values': ['text/xml;subtype=\"om/1.0.0\"',\n   'text/csv',\n   'text/tab-separated-values',\n   'application/vnd.google-earth.kml+xml',\n   'text/xml;schema=\"ioos/0.6.1\"',\n   'application/ioos+xml;version=0.6.1']}}"
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# issue the SOS get_obs request\nresponse = ndbc.get_observation(offerings=['urn:ioos:station:wmo:%s' % sta_id],\n                                 responseFormat='text/csv',\n                                 observedProperties=['winds'],\n                                 eventTime='%s/%s' % (start,stop))\n                                 ",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df2 = pd.read_csv(cStringIO.StringIO(response.strip()),index_col='date_time',parse_dates=True)  # skip the units row ",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df2.head()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<pre>\n&ltclass 'pandas.core.frame.DataFrame'&gt\nDatetimeIndex: 5 entries, 2013-06-12 00:50:00 to 2013-06-12 04:50:00\nData columns (total 9 columns):\nstation_id                      5  non-null values\nsensor_id                       5  non-null values\nlatitude (degree)               5  non-null values\nlongitude (degree)              5  non-null values\ndepth (m)                       5  non-null values\nwind_from_direction (degree)    5  non-null values\nwind_speed (m/s)                5  non-null values\nwind_speed_of_gust (m/s)        5  non-null values\nupward_air_velocity (m/s)       0  non-null values\ndtypes: float64(7), object(2)\n</pre>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": "                                     station_id  \\\ndate_time                                         \n2013-06-12 00:50:00  urn:ioos:station:wmo:44066   \n2013-06-12 01:50:00  urn:ioos:station:wmo:44066   \n2013-06-12 02:50:00  urn:ioos:station:wmo:44066   \n2013-06-12 03:50:00  urn:ioos:station:wmo:44066   \n2013-06-12 04:50:00  urn:ioos:station:wmo:44066   \n\n                                                  sensor_id  \\\ndate_time                                                     \n2013-06-12 00:50:00  urn:ioos:sensor:wmo:44066::anemometer1   \n2013-06-12 01:50:00  urn:ioos:sensor:wmo:44066::anemometer1   \n2013-06-12 02:50:00  urn:ioos:sensor:wmo:44066::anemometer1   \n2013-06-12 03:50:00  urn:ioos:sensor:wmo:44066::anemometer1   \n2013-06-12 04:50:00  urn:ioos:sensor:wmo:44066::anemometer1   \n\n                     latitude (degree)  longitude (degree)  depth (m)  \\\ndate_time                                                               \n2013-06-12 00:50:00              39.26              -72.18         -5   \n2013-06-12 01:50:00              39.26              -72.18         -5   \n2013-06-12 02:50:00              39.26              -72.18         -5   \n2013-06-12 03:50:00              39.26              -72.18         -5   \n2013-06-12 04:50:00              39.26              -72.18         -5   \n\n                     wind_from_direction (degree)  wind_speed (m/s)  \\\ndate_time                                                             \n2013-06-12 00:50:00                           240                10   \n2013-06-12 01:50:00                           240                 8   \n2013-06-12 02:50:00                           250                 8   \n2013-06-12 03:50:00                           260                 9   \n2013-06-12 04:50:00                           280                 8   \n\n                     wind_speed_of_gust (m/s)  upward_air_velocity (m/s)  \ndate_time                                                                 \n2013-06-12 00:50:00                        11                        NaN  \n2013-06-12 01:50:00                         9                        NaN  \n2013-06-12 02:50:00                        10                        NaN  \n2013-06-12 03:50:00                        10                        NaN  \n2013-06-12 04:50:00                        10                        NaN  "
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df2[['wind_speed_of_gust (m/s)','wind_speed (m/s)']].plot(figsize=(12,4),title=station.description,legend=True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": "<matplotlib.axes.AxesSubplot at 0x54266d0>"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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A4b9/cUxNTZGWloZXr14ByF3iUb58eRw6dAgHDx6EhYUFxo0blyuRf/r0KSpX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       "text": "<matplotlib.figure.Figure at 0x5426950>"
      }
     ],
     "prompt_number": 19
    }
   ],
   "metadata": {}
  }
 ]
}