Find the Index OPeNDAP style

Find the Index OPeNDAP style.ipynb

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   "source": [
    "import numpy as np\n",
    "import datetime as dt\n",
    "\n",
    "from netCDF4 import Dataset, date2index, num2date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "# simple function to find the index associated with a value in an array\n",
    "# many assumptions in this function, like assuming there is only one index associated\n",
    "# with a given value, for example.\n",
    "def find_index(array, value):\n",
    "    norm = np.abs(array-value)\n",
    "    return np.where(norm == norm.min())[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# open the remote dataset via OPeNDAP\n",
    "dataset = Dataset('http://tds.hycom.org/thredds/dodsC/GLBu0.08/reanalysis/3hrly', 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# define your desired lat/lon and time range values here\n",
    "wanted_lat = 26.1\n",
    "wanted_lon = -86.4\n",
    "start_date = dt.datetime(2011, 1, 1)\n",
    "end_date = dt.datetime(2011,1, 31, 23, 59, 59)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "# get all of the lat, lon, and time data\n",
    "lat = dataset['lat'][:]\n",
    "lon = dataset['lon'][:]\n",
    "time_var = dataset['time']\n",
    "#time = dataset['time'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# find the index values coresponding to the desired lat/lon values\n",
    "lat_index = find_index(lat, wanted_lat)\n",
    "lon_index = find_index(lon, wanted_lon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 26.07999992]\n",
      "[-86.40002441]\n"
     ]
    }
   ],
   "source": [
    "# check lat and lon index to see if correct, or close\n",
    "print(lat[lat_index])\n",
    "print(lon[lon_index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# find the start and end index values that corespond to the \n",
    "# start_time and end_time datetime objects\n",
    "# see http://unidata.github.io/netcdf4-python/#netCDF4.date2index for info on this magic\n",
    "# start_time_index will be exact match or just after the start_date\n",
    "start_time_index = date2index(start_date, time_var, select='after')\n",
    "# end_time_index will be exact match or just before the end_date\n",
    "end_time_index = date2index(end_date, time_var, select='before')\n",
    "time_index_range = list(range(start_time_index,end_time_index + 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2011-01-01 00:00:00\n",
      "2011-01-31 21:00:00\n"
     ]
    }
   ],
   "source": [
    "# check start and end time index to see if correct, or close\n",
    "print(num2date(time_var[start_time_index], units=time_var.units, calendar=time_var.calendar))\n",
    "print(num2date(time_var[end_time_index], units=time_var.units, calendar=time_var.calendar))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# get water_temp data for the given lat/lon and time range\n",
    "data = dataset['water_temp'][time_index_range, 0, lat_index, lon_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(248, 1, 1)"
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     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# data array shape is times, single lat, single lon\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24.8798\n"
     ]
    }
   ],
   "source": [
    "# compute the mean of the time series at point lat/lon\n",
    "print(data.mean())"
   ]
  }
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