gust_at_44066_ndbc_dap.ipynb

{
 "metadata": {
  "name": "gust_at_44066_ndbc_dap"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import netCDF4\n",
      "import pandas as pd\n",
      "import datetime as dt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "url='http://dods.ndbc.noaa.gov/thredds/dodsC/data/pwind/44066/44066p9999.nc'\n",
      "nc = netCDF4.Dataset(url).variables\n",
      "print nc.keys()\n",
      "print nc['gust_spd']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[u'time', u'latitude', u'longitude', u'gust_dir', u'gust_spd', u'gust_time']\n",
        "<type 'netCDF4.Variable'>\n",
        "float32 gust_spd(u'time', u'latitude', u'longitude')\n",
        "    long_name: Gust Speed\n",
        "    short_name: gsp\n",
        "    standard_name: gust_speed\n",
        "    units: meters/second\n",
        "    _FillValue: 99.0\n",
        "unlimited dimensions = (u'time',)\n",
        "current size = (16050, 1, 1)\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "time_var = nc['time']\n",
      "jd = netCDF4.num2date(time_var[:],time_var.units)\n",
      "start = dt.datetime(2013,6,13,10,0,0)\n",
      "stop = dt.datetime(2013,6,14,0,0,0)\n",
      "istart = netCDF4.date2index(start,time_var,select='nearest')\n",
      "istop = netCDF4.date2index(stop,time_var,select='nearest')\n",
      "gust = nc['gust_spd'][istart:istop,:,:].flatten()\n",
      "jd = jd[istart:istop]\n",
      "\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.Series(gust[:],index=jd)\n",
      "df.plot(figsize=(12,4),style='ro')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x30d9ad0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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jsHPnTgD2+jPl2vMD+jswv3naO2jPD+jvwPxmXbhwAUeOHMHWrVuxfPlynDhx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\nRERERFRJHKqJiIiIiCrp/wF+yPpKf1FVlQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x30a9310>"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gust"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "masked_array(data = [-- -- -- -- -- 5.0 -- -- -- -- -- 7.0 -- -- -- -- -- 6.0 -- -- -- -- --\n",
        " 8.0 -- -- -- -- -- 9.0 -- -- -- -- -- 10.0 -- -- -- -- -- 27.0 -- -- -- --\n",
        " -- 52.0 -- -- -- -- -- 6.0 -- -- -- -- -- 9.0 -- -- -- -- -- 7.0 -- -- --\n",
        " -- -- 7.0],\n",
        "             mask = [ True  True  True  True  True False  True  True  True  True  True False\n",
        "  True  True  True  True  True False  True  True  True  True  True False\n",
        "  True  True  True  True  True False  True  True  True  True  True False\n",
        "  True  True  True  True  True False  True  True  True  True  True False\n",
        "  True  True  True  True  True False  True  True  True  True  True False\n",
        "  True  True  True  True  True False  True  True  True  True  True False],\n",
        "       fill_value = 99.0)\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}