ndbc_group_test.ipynb

{
 "metadata": {
  "name": "ndbc_group_test"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Reading NDBC's heirarchical NetCDF4 buoy data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "NDBC decided in 2011 to go with NetCDF4 files using groups to store buoy data. \n",
      "Can we read them using standard tools?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import urllib\n",
      "import netCDF4\n",
      "from IPython.core.display import HTML\n",
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If you visit http://www.nodc.noaa.gov/BUOY/ and select the \"41013\" buoy, then select a one of the links from the \"Buoy Data (netCDF)\" table, you will find yourself on a THREDDS dataset page like the one below"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "HTML('<iframe src=http://data.nodc.noaa.gov/thredds/catalog/ndbc/cmanwx/2013/08/catalog.html?dataset=ndbc/cmanwx/2013/08/NDBC_41013_201308_D2_v00.nc width=900 height=500></iframe>')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=http://data.nodc.noaa.gov/thredds/catalog/ndbc/cmanwx/2013/08/catalog.html?dataset=ndbc/cmanwx/2013/08/NDBC_41013_201308_D2_v00.nc width=900 height=500></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "<IPython.core.display.HTML at 0x37b6610>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# try to open DAP URL\n",
      "dap_url='http://data.nodc.noaa.gov/thredds/dodsC/ndbc/cmanwx/2013/08/NDBC_41013_201308_D2_v00.nc'\n",
      "nc = netCDF4.Dataset(dap_url)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "RuntimeError",
       "evalue": "NetCDF: String match to name in use",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-4-0b3628dfc3e2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# try to open DAP URL\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mdap_url\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'http://data.nodc.noaa.gov/thredds/dodsC/ndbc/cmanwx/2013/08/NDBC_41013_201308_D2_v00.nc'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mnc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnetCDF4\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdap_url\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[1;32m/home/local/python27_epd/lib/python2.7/site-packages/netCDF4.so\u001b[0m in \u001b[0;36mnetCDF4.Dataset.__init__ (netCDF4.c:15904)\u001b[1;34m()\u001b[0m\n",
        "\u001b[1;31mRuntimeError\u001b[0m: NetCDF: String match to name in use"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# since DAP didn't work, download the entire NetCDF file\n",
      "urllib.urlretrieve('http://data.nodc.noaa.gov/thredds/fileServer/ndbc/cmanwx/2013/08/NDBC_41013_201308_D2_v00.nc',\n",
      "                                                                                    'NDBC_41013_201308_D2_v00.nc')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "('NDBC_41013_201308_D2_v00.nc', <httplib.HTTPMessage instance at 0x37daef0>)"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# open the local netcdf file\n",
      "nc = netCDF4.Dataset('NDBC_41013_201308_D2_v00.nc')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# see if there are variables at the top level\n",
      "nc.variables.keys()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "[u'time', u'time10', u'timem', u'time_wpm_20', u'wave_wpm', u'wave_wpm_bnds']"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# convert time to Python datetime objects\n",
      "time_var = nc.variables['time']\n",
      "print time_var\n",
      "jd = netCDF4.num2date(time_var[:],time_var.units)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<type 'netCDF4.Variable'>\n",
        "int32 time(u'time',)\n",
        "    long_name: time\n",
        "    standard_name: time\n",
        "    units: seconds since 1970-01-01 00:00:00 UTC\n",
        "unlimited dimensions = ()\n",
        "current size = (744,)\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# see if there are groups\n",
      "nc.groups.keys()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "[u'payload_1']"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# only one group. Let's take a look:\n",
      "payload1= nc.groups['payload_1']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# are there variables in payload_1?\n",
      "payload1.variables.keys()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "[]"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# are there more groups in payload_1?\n",
      "payload1.groups.keys()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "[u'anemometer_1',\n",
        " u'anemometer_2',\n",
        " u'barometer_1',\n",
        " u'barometer_2',\n",
        " u'air_temperature_sensor_1',\n",
        " u'air_temperature_sensor_2',\n",
        " u'humidity_sensor_1',\n",
        " u'ocean_temperature_sensor_1',\n",
        " u'gps_1',\n",
        " u'wave_sensor_1']"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# let's pick 'anemometer_2' from 'payload_1'\n",
      "payload1_anemometer2 = payload1.groups['anemometer_2']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# any groups in group 2?\n",
      "payload1_anemometer2.groups.keys()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "[]"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# any variables in group 2?\n",
      "payload1_anemometer2.variables.keys()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "[u'wind_speed',\n",
        " u'wind_speed_qc',\n",
        " u'wind_speed_detail_qc',\n",
        " u'wind_speed_release',\n",
        " u'wind_direction',\n",
        " u'wind_direction_qc',\n",
        " u'wind_direction_detail_qc',\n",
        " u'wind_direction_release',\n",
        " u'wind_gust',\n",
        " u'wind_gust_qc',\n",
        " u'wind_gust_detail_qc',\n",
        " u'wind_gust_release',\n",
        " u'continuous_wind_speed',\n",
        " u'continuous_wind_speed_qc',\n",
        " u'continuous_wind_speed_detail_qc',\n",
        " u'continuous_wind_speed_release',\n",
        " u'continuous_wind_direction',\n",
        " u'continuous_wind_direction_qc',\n",
        " u'continuous_wind_direction_detail_qc',\n",
        " u'continuous_wind_direction_release',\n",
        " u'hourly_max_gust',\n",
        " u'hourly_max_gust_qc',\n",
        " u'hourly_max_gust_detail_qc',\n",
        " u'hourly_max_gust_release',\n",
        " u'direction_of_hourly_max_gust',\n",
        " u'direction_of_hourly_max_gust_qc',\n",
        " u'direction_of_hourly_max_gust_detail_qc',\n",
        " u'direction_of_hourly_max_gust_release']"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# open the wind speed variable from /payload_1/anemometer_2\n",
      "var = payload1_anemometer2.variables['wind_speed']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print var"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<type 'netCDF4.Variable'>\n",
        "float32 wind_speed(u'time',)\n",
        "    long_name: wind_speed\n",
        "    standard_name: wind_speed\n",
        "    units: m/s\n",
        "path = /payload_1/anemometer_2\n",
        "unlimited dimensions = ()\n",
        "current size = (744,)\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create Pandas time series object, using the time from group 0, and data from group 12\n",
      "ts = pd.Series(var,index=jd)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Make a Plot\n",
      "ts.plot(title=var.long_name,figsize=(12,4))\n",
      "ylabel(var.units)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "<matplotlib.text.Text at 0x3604550>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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C6SjVMmuILBfV1cCkSRRZTk4GTj8dKCgAvvvO++MOJnwtcBkAvpVBZSX138GK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\nip07qf6mpHi//TVrrLj22uC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ooxg9ejRuu+020w9Oy24hIsn5+SQ+Ah1Jdh7tBFok65m4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\nIBAIBAKBQCCw4P8Dj1sbn8AYFg4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x35ee3d0>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    }
   ],
   "metadata": {}
  }
 ]
}