ArcMAP_DAP.ipynb

{
 "metadata": {
  "name": "ArcMAP_DAP"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# In ArcMap, with the multidimensional supplemental tools installed, go to: \n",
      "# Geoprocessing=>Python  \n",
      "# and type these commands into the python command window"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import netCDF4\n",
      "url='http://www.smast.umassd.edu:8080/thredds/dodsC/fvcom/hindcasts/30yr_gom3/mean'\n",
      "nc = netCDF4.Dataset(url)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nc.variables.keys()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "[u'lat',\n",
        " u'latc',\n",
        " u'lon',\n",
        " u'lonc',\n",
        " u'z0b',\n",
        " u'nprocs',\n",
        " u'partition',\n",
        " u'x',\n",
        " u'y',\n",
        " u'xc',\n",
        " u'yc',\n",
        " u'siglay',\n",
        " u'siglev',\n",
        " u'h',\n",
        " u'nv',\n",
        " u'nbe',\n",
        " u'ntsn',\n",
        " u'nbsn',\n",
        " u'ntve',\n",
        " u'nbve',\n",
        " u'a1u',\n",
        " u'a2u',\n",
        " u'aw0',\n",
        " u'awx',\n",
        " u'awy',\n",
        " u'art2',\n",
        " u'art1',\n",
        " u'iint',\n",
        " u'time',\n",
        " u'Itime',\n",
        " u'Itime2',\n",
        " u'Times',\n",
        " u'zeta',\n",
        " u'file_date',\n",
        " u'u',\n",
        " u'v',\n",
        " u'omega',\n",
        " u'ww',\n",
        " u'ua',\n",
        " u'va',\n",
        " u'temp',\n",
        " u'salinity',\n",
        " u'km',\n",
        " u'kh',\n",
        " u'kq',\n",
        " u'q2',\n",
        " u'q2l',\n",
        " u'l',\n",
        " u'short_wave',\n",
        " u'net_heat_flux',\n",
        " u'uwind_stress',\n",
        " u'vwind_stress',\n",
        " u'fvcom_mesh']"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "temp = nc.variables['temp']\n",
      "print temp"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<type 'netCDF4.Variable'>\n",
        "float32 temp(u'time', u'siglay', u'node')\n",
        "    long_name: temperature\n",
        "    standard_name: sea_water_temperature\n",
        "    units: degrees_C\n",
        "    coordinates: time siglay lat lon\n",
        "    type: data\n",
        "    mesh: fvcom_mesh\n",
        "    location: node\n",
        "unlimited dimensions = ()\n",
        "current size = (396, 45, 48451)\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "timevar = nc.variables['time']\n",
      "print timevar"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<type 'netCDF4.Variable'>\n",
        "float32 time(u'time',)\n",
        "    long_name: time\n",
        "    units: days since 1858-11-17 00:00:00\n",
        "    format: modified julian day (MJD)\n",
        "    time_zone: UTC\n",
        "unlimited dimensions = ()\n",
        "current size = (396,)\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# get first (0) and last (-1) time values and convert to datestamps\n",
      "start = netCDF4.num2date(timevar[0],timevar.units)\n",
      "stop = netCDF4.num2date(timevar[-1],timevar.units)\n",
      "\n",
      "print('start: %s' % start.strftime('%Y-%b-%d %H:%M:%S'))\n",
      "print(' stop: %s' % stop.strftime('%Y-%b-%d %H:%M:%S'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "start: 1978-Jan-16 12:00:00\n",
        " stop: 2010-Dec-16 10:01:53\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "siglay = nc.variables['siglay']  # fraction of total depth\n",
      "h=nc.variables['h']    # water depth\n",
      "print siglay\n",
      "print h"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<type 'netCDF4.Variable'>\n",
        "float32 siglay(u'siglay', u'node')\n",
        "    long_name: Sigma Layers\n",
        "    standard_name: ocean_sigma_coordinate\n",
        "    positive: up\n",
        "    valid_min: -1.0\n",
        "    valid_max: 0.0\n",
        "    formula_terms: sigma: siglay eta: zeta depth: h\n",
        "unlimited dimensions = ()\n",
        "current size = (45, 48451)\n",
        "\n",
        "<type 'netCDF4.Variable'>\n",
        "float32 h(u'node',)\n",
        "    long_name: Bathymetry\n",
        "    standard_name: sea_floor_depth_below_geoid\n",
        "    units: m\n",
        "    coordinates: lat lon\n",
        "    type: data\n",
        "    mesh: fvcom_mesh\n",
        "    location: node\n",
        "unlimited dimensions = ()\n",
        "current size = (48451,)\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# z(meters) of layer 20 at node 2000\n",
      "node = 2000\n",
      "lev = 20\n",
      "z = siglay[lev,node]*h[node]\n",
      "print z"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "-851.425\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# plot sigma values at specified node\n",
      "import matplotlib.pyplot as plt\n",
      "plt.plot(siglay[:,node],'k-o')\n",
      "plt.grid()\n",
      "plt.xlabel('sigma level')\n",
      "plt.ylabel('fraction of total depth')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ly5cNz69cuYIGDRpU6kUnTpyIX375Be3atcPZs2fx4osvAgDOnTtndI/H22+/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\nhN06jD179lTqzwcFBWHmzJm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       "text": [
        "<matplotlib.figure.Figure at 0x33851d0>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    }
   ],
   "metadata": {}
  }
 ]
}