emiliom/NDBC_SOS_owslib_AlaskaNDBCBuoy.ipynb

## NDBC_SOS_owslib_AlaskaNDBCBuoy.ipynb
{
 "metadata": {
  "name": "NDBC_SOS_owslib_AlaskaNDBCBuoy"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "# Wind Speed and Gust from NDBC buoy via NDBC SOS service\nGet IOOS CSV data from NDBC SOS service using OWSlib, then read CSV data and plot using Pandas.\nTested using an NDBC buoy in Alaska: _Station 46083 (LLNR 1082) - Fairweather Ground 105NM West of Juno, AK_. [Adapted from Rich Signell's gist](https://gist.github.com/rsignell-usgs/6024011) and my own previous OWSLib tests. 2013/10/30"
    },
    {
     "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='46083' # Station 46083 (LLNR 1082) - Fairweather Ground 105NM West of Juno, AK\n#sta_id='44066'  # texas tower",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# pick a start & stop time\nstart = '2013-10-25T00:00:00Z'\nstop = '2013-10-30T00:00:00Z'",
     "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=46083 width=950 height=400></iframe>",
       "output_type": "pyout",
       "prompt_number": 4,
       "text": "<IPython.core.display.HTML at 0x4a3c610>"
      }
     ],
     "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": [
      {
       "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": [
      {
       "output_type": "pyout",
       "prompt_number": 7,
       "text": "'All stations on the NDBC SOS server'"
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "rfs = network.response_formats\nprint '\\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": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "station = contents['station-%s' % sta_id]\nprint station.name, '\\n', station.description",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "urn:ioos:station:wmo:46083 \nFairweather Grounds 92NM Southeast of Yakutat, AK\n"
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "getob = ndbc.get_operation_by_name('getobservation')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "getob.parameters",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 11,
       "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": 11
    },
    {
     "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": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df2 = pd.read_csv(cStringIO.StringIO(response.strip()),index_col='date_time',parse_dates=True)\ndf2",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<pre>\n&lt;class 'pandas.core.frame.DataFrame'&gt;\nDatetimeIndex: 120 entries, 2013-10-25 00:50:00 to 2013-10-29 23:50:00\nData columns (total 9 columns):\nstation_id                      120  non-null values\nsensor_id                       120  non-null values\nlatitude (degree)               120  non-null values\nlongitude (degree)              120  non-null values\ndepth (m)                       120  non-null values\nwind_from_direction (degree)    120  non-null values\nwind_speed (m/s)                120  non-null values\nwind_speed_of_gust (m/s)        120  non-null values\nupward_air_velocity (m/s)       0  non-null values\ndtypes: float64(7), object(2)\n</pre>",
       "output_type": "pyout",
       "prompt_number": 15,
       "text": "<class 'pandas.core.frame.DataFrame'>\nDatetimeIndex: 120 entries, 2013-10-25 00:50:00 to 2013-10-29 23:50:00\nData columns (total 9 columns):\nstation_id                      120  non-null values\nsensor_id                       120  non-null values\nlatitude (degree)               120  non-null values\nlongitude (degree)              120  non-null values\ndepth (m)                       120  non-null values\nwind_from_direction (degree)    120  non-null values\nwind_speed (m/s)                120  non-null values\nwind_speed_of_gust (m/s)        120  non-null values\nupward_air_velocity (m/s)       0  non-null values\ndtypes: float64(7), object(2)"
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df2.head()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>station_id</th>\n      <th>sensor_id</th>\n      <th>latitude (degree)</th>\n      <th>longitude (degree)</th>\n      <th>depth (m)</th>\n      <th>wind_from_direction (degree)</th>\n      <th>wind_speed (m/s)</th>\n      <th>wind_speed_of_gust (m/s)</th>\n      <th>upward_air_velocity (m/s)</th>\n    </tr>\n    <tr>\n      <th>date_time</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2013-10-25 00:50:00</th>\n      <td> urn:ioos:station:wmo:46083</td>\n      <td> urn:ioos:sensor:wmo:46083::anemometer1</td>\n      <td> 58.24</td>\n      <td>-137.99</td>\n      <td>-5</td>\n      <td> 150</td>\n      <td> 5</td>\n      <td> 6</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2013-10-25 01:50:00</th>\n      <td> urn:ioos:station:wmo:46083</td>\n      <td> urn:ioos:sensor:wmo:46083::anemometer1</td>\n      <td> 58.24</td>\n      <td>-137.99</td>\n      <td>-5</td>\n      <td> 150</td>\n      <td> 4</td>\n      <td> 5</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2013-10-25 02:50:00</th>\n      <td> urn:ioos:station:wmo:46083</td>\n      <td> urn:ioos:sensor:wmo:46083::anemometer1</td>\n      <td> 58.24</td>\n      <td>-137.99</td>\n      <td>-5</td>\n      <td> 160</td>\n      <td> 4</td>\n      <td> 5</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2013-10-25 03:50:00</th>\n      <td> urn:ioos:station:wmo:46083</td>\n      <td> urn:ioos:sensor:wmo:46083::anemometer1</td>\n      <td> 58.24</td>\n      <td>-137.99</td>\n      <td>-5</td>\n      <td> 130</td>\n      <td> 7</td>\n      <td> 8</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2013-10-25 04:50:00</th>\n      <td> urn:ioos:station:wmo:46083</td>\n      <td> urn:ioos:sensor:wmo:46083::anemometer1</td>\n      <td> 58.24</td>\n      <td>-137.99</td>\n      <td>-5</td>\n      <td> 150</td>\n      <td> 7</td>\n      <td> 8</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 14,
       "text": "                                     station_id  \\\ndate_time                                         \n2013-10-25 00:50:00  urn:ioos:station:wmo:46083   \n2013-10-25 01:50:00  urn:ioos:station:wmo:46083   \n2013-10-25 02:50:00  urn:ioos:station:wmo:46083   \n2013-10-25 03:50:00  urn:ioos:station:wmo:46083   \n2013-10-25 04:50:00  urn:ioos:station:wmo:46083   \n\n                                                  sensor_id  \\\ndate_time                                                     \n2013-10-25 00:50:00  urn:ioos:sensor:wmo:46083::anemometer1   \n2013-10-25 01:50:00  urn:ioos:sensor:wmo:46083::anemometer1   \n2013-10-25 02:50:00  urn:ioos:sensor:wmo:46083::anemometer1   \n2013-10-25 03:50:00  urn:ioos:sensor:wmo:46083::anemometer1   \n2013-10-25 04:50:00  urn:ioos:sensor:wmo:46083::anemometer1   \n\n                     latitude (degree)  longitude (degree)  depth (m)  \\\ndate_time                                                               \n2013-10-25 00:50:00              58.24             -137.99         -5   \n2013-10-25 01:50:00              58.24             -137.99         -5   \n2013-10-25 02:50:00              58.24             -137.99         -5   \n2013-10-25 03:50:00              58.24             -137.99         -5   \n2013-10-25 04:50:00              58.24             -137.99         -5   \n\n                     wind_from_direction (degree)  wind_speed (m/s)  \\\ndate_time                                                             \n2013-10-25 00:50:00                           150                 5   \n2013-10-25 01:50:00                           150                 4   \n2013-10-25 02:50:00                           160                 4   \n2013-10-25 03:50:00                           130                 7   \n2013-10-25 04:50:00                           150                 7   \n\n                     wind_speed_of_gust (m/s)  upward_air_velocity (m/s)  \ndate_time                                                                 \n2013-10-25 00:50:00                         6                        NaN  \n2013-10-25 01:50:00                         5                        NaN  \n2013-10-25 02:50:00                         5                        NaN  \n2013-10-25 03:50:00                         8                        NaN  \n2013-10-25 04:50:00                         8                        NaN  "
      }
     ],
     "prompt_number": 14
    },
    {
     "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": [
      {
       "output_type": "pyout",
       "prompt_number": 16,
       "text": "<matplotlib.axes.AxesSubplot at 0x59ce790>"
      },
      {
       "output_type": "display_data",
       "png": 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QVVVFYWEhIiMjUVxcDEtLS3z00UdsXW1tbWRnZ8ssX3OExv9VQHXBheqjgqao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\nTaZNmyY7d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      }
     ],
     "prompt_number": 16
    }
   ],
   "metadata": {}
  }
 ]
}