{
 "metadata": {
  "name": "NECOFS_water_levels"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.display import HTML\n",
      "\n",
      "with open('creative_commons.txt', 'r') as f:\n",
      "    html = f.read()\n",
      "    \n",
      "with open('./styles/custom.css', 'r') as f:\n",
      "    styles = f.read()\n",
      "    \n",
      "HTML(styles)\n",
      "\n",
      "# name of file\n",
      "name = 'NECOFS_water_levels'\n",
      "\n",
      "html = \"\"\"\n",
      "<small>\n",
      "<p> This post was written as an IPython notebook. It is available for <a href=\"http://rsignell-usgs.github.io/blog/downloads/notebooks/%s.ipynb\">download</a> or as a static <a href=\"http://\n",
      "nbviewer.ipython.org/url/rsignell-usgs.github.com/blog/downloads/notebooks/%s.ipynb\">html</a>.</p>\n",
      "<p></p>\n",
      "%s \"\"\" % (name, name, html)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Extract NECOFS water levels using NetCDF4-Python and analyze/visualize with Pandas"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Demonstration using the NetCDF4-Python library to extract time-series of sea surface height from a triangular grid ocean model (FVCOM) via OPeNDAP, specifying the desired URL, time range, and lat/lon locations of interest.  \n",
      "\n",
      "NECOFS (Northeastern Coastal Ocean Forecast System) is run by groups at the University of Massachusetts Dartmouth and the Woods Hole Oceanographic Institution, led by Drs. C. Chen, R. C. Beardsley, G. Cowles and B. Rothschild. Funding is provided to run the model by the NOAA-led Integrated Ocean Observing System and the State of Massachusetts.\n",
      "\n",
      "NECOFS is a coupled numerical model that uses nested weather models, a coastal ocean circulation model, and a wave model. The ocean model is a volume-mesh model with horizontal resolution that is finer in complicated regions. It is layered (not depth-averaged) and includes the effects of tides, winds, and varying water densities caused by temperature and salinity changes.\n",
      "\n",
      "* Model description: http://fvcom.smast.umassd.edu/research_projects/NECOFS/model_system.html\n",
      "* THREDDS server with other forecast and archive products: http://www.smast.umassd.edu:8080/thredds/catalog.html"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plot forecast water levels from NECOFS model from list of lon,lat locations\n",
      "# (uses the nearest point, no interpolation)\n",
      "from pylab import *\n",
      "import netCDF4\n",
      "import datetime as dt\n",
      "import pandas as pd\n",
      "from StringIO import StringIO"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In the cells below, specify a NECOFS dataset to extract data from.   Massbay is most detailed for Massachusetts \n",
      "waters, GOM3 extends over entire Gulf of Maine (and more).  There are forecasts runs, \n",
      "archived forecasts runs, and hindcast simulations available.  Move the cell\n",
      "you are interested in to the bottom of the list, so it's executed last (thus\n",
      "overwriting the other URLs).  Poor man coding!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# FVCOM GOM3v11 NECOFS Forecast Archive (Apr 01,2010-Nov 10,2011)\n",
      "model='GOM3'\n",
      "url='http://www.smast.umassd.edu:8080/thredds/dodsC/fvcom/archives/necofs_gom3v11'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# FVCOM GOM3v13 NECOFS Forecast Archive (May 09,2013-Jul 31,2013)\n",
      "model='GOM3'\n",
      "url='http://www.smast.umassd.edu:8080/thredds/dodsC/fvcom/archives/necofs_gom3v13'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# FVCOM GOM3v12 NECOFS Forecast Archive (Nov 11,2011-May 08,2013)\n",
      "model='GOM3'\n",
      "url='http://www.smast.umassd.edu:8080/thredds/dodsC/fvcom/archives/necofs_gom3v12'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#NECOFS GOM3 grid forecast\n",
      "model='GOM3'\n",
      "url='http://www.smast.umassd.edu:8080/thredds/dodsC/FVCOM/NECOFS/Forecasts/NECOFS_GOM3_FORECAST.nc'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#NECOFS MassBay grid forecast\n",
      "model='Massbay'\n",
      "url='http://www.smast.umassd.edu:8080/thredds/dodsC/FVCOM/NECOFS/Forecasts/NECOFS_FVCOM_OCEAN_MASSBAY_FORECAST.nc'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Specify a list of stations where you want forecast data.\n",
      "    Just move the cell with the `x= ` you want so that it gets executed last."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dms2dd(d,m,s):\n",
      "    return d+(m+s/60.)/60.\n",
      "  "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dms2dd(41,33,15.7)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "41.55436111111111"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "-dms2dd(70,30,20.2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "-70.50561111111111"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = '''\n",
      "Station, Lat, Lon\n",
      "Falmouth Harbor,    41.541575, -70.608020\n",
      "Sage Lot Pond, 41.554361, -70.505611\n",
      "'''"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = '''\n",
      "Station, Lat, Lon\n",
      "Boston,             42.368186, -71.047984\n",
      "Carolyn Seep Spot,    39.8083, -69.5917\n",
      "Falmouth Harbor,  41.541575, -70.608020\n",
      "'''"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Enter desired (Station, Lat, Lon) values here:\n",
      "x = '''\n",
      "Station, Lat, Lon\n",
      "Boston,             42.368186, -71.047984\n",
      "Scituate Harbor,    42.199447, -70.720090\n",
      "Scituate Beach,     42.209973, -70.724523\n",
      "Falmouth Harbor,    41.541575, -70.608020\n",
      "Marion,             41.689008, -70.746576\n",
      "Marshfield,         42.108480, -70.648691\n",
      "Provincetown,       42.042745, -70.171180\n",
      "Sandwich,           41.767990, -70.466219\n",
      "Hampton Bay,        42.900103, -70.818510\n",
      "Gloucester,         42.610253, -70.660570\n",
      "'''"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create a Pandas DataFrame\n",
      "obs=pd.read_csv(StringIO(x.strip()), sep=\",\\s*\",index_col='Station')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obs"
     ],
     "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>Lat</th>\n",
        "      <th>Lon</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Station</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Boston</th>\n",
        "      <td> 42.368186</td>\n",
        "      <td>-71.047984</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Scituate Harbor</th>\n",
        "      <td> 42.199447</td>\n",
        "      <td>-70.720090</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Scituate Beach</th>\n",
        "      <td> 42.209973</td>\n",
        "      <td>-70.724523</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Falmouth Harbor</th>\n",
        "      <td> 41.541575</td>\n",
        "      <td>-70.608020</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Marion</th>\n",
        "      <td> 41.689008</td>\n",
        "      <td>-70.746576</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Marshfield</th>\n",
        "      <td> 42.108480</td>\n",
        "      <td>-70.648691</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Provincetown</th>\n",
        "      <td> 42.042745</td>\n",
        "      <td>-70.171180</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Sandwich</th>\n",
        "      <td> 41.767990</td>\n",
        "      <td>-70.466219</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Hampton Bay</th>\n",
        "      <td> 42.900103</td>\n",
        "      <td>-70.818510</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Gloucester</th>\n",
        "      <td> 42.610253</td>\n",
        "      <td>-70.660570</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "                       Lat        Lon\n",
        "Station                              \n",
        "Boston           42.368186 -71.047984\n",
        "Scituate Harbor  42.199447 -70.720090\n",
        "Scituate Beach   42.209973 -70.724523\n",
        "Falmouth Harbor  41.541575 -70.608020\n",
        "Marion           41.689008 -70.746576\n",
        "Marshfield       42.108480 -70.648691\n",
        "Provincetown     42.042745 -70.171180\n",
        "Sandwich         41.767990 -70.466219\n",
        "Hampton Bay      42.900103 -70.818510\n",
        "Gloucester       42.610253 -70.660570"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# find the indices of the points in (x,y) closest to the points in (xi,yi)\n",
      "def nearxy(x,y,xi,yi):\n",
      "    ind=ones(len(xi),dtype=int)\n",
      "    for i in arange(len(xi)):\n",
      "        dist=sqrt((x-xi[i])**2+(y-yi[i])**2)\n",
      "        ind[i]=dist.argmin()\n",
      "    return ind"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# open NECOFS remote OPeNDAP dataset \n",
      "nc=netCDF4.Dataset(url).variables"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# find closest NECOFS nodes to station locations\n",
      "obs['0-Based Index'] = nearxy(nc['lon'][:],nc['lat'][:],obs['Lon'],obs['Lat'])\n",
      "obs"
     ],
     "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>Lat</th>\n",
        "      <th>Lon</th>\n",
        "      <th>0-Based Index</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Station</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Boston</th>\n",
        "      <td> 42.368186</td>\n",
        "      <td>-71.047984</td>\n",
        "      <td> 90913</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Scituate Harbor</th>\n",
        "      <td> 42.199447</td>\n",
        "      <td>-70.720090</td>\n",
        "      <td> 37964</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Scituate Beach</th>\n",
        "      <td> 42.209973</td>\n",
        "      <td>-70.724523</td>\n",
        "      <td> 28474</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Falmouth Harbor</th>\n",
        "      <td> 41.541575</td>\n",
        "      <td>-70.608020</td>\n",
        "      <td> 47470</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Marion</th>\n",
        "      <td> 41.689008</td>\n",
        "      <td>-70.746576</td>\n",
        "      <td> 49654</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Marshfield</th>\n",
        "      <td> 42.108480</td>\n",
        "      <td>-70.648691</td>\n",
        "      <td> 24272</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Provincetown</th>\n",
        "      <td> 42.042745</td>\n",
        "      <td>-70.171180</td>\n",
        "      <td> 26595</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Sandwich</th>\n",
        "      <td> 41.767990</td>\n",
        "      <td>-70.466219</td>\n",
        "      <td> 38036</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Hampton Bay</th>\n",
        "      <td> 42.900103</td>\n",
        "      <td>-70.818510</td>\n",
        "      <td> 13022</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Gloucester</th>\n",
        "      <td> 42.610253</td>\n",
        "      <td>-70.660570</td>\n",
        "      <td> 22082</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "                       Lat        Lon  0-Based Index\n",
        "Station                                             \n",
        "Boston           42.368186 -71.047984          90913\n",
        "Scituate Harbor  42.199447 -70.720090          37964\n",
        "Scituate Beach   42.209973 -70.724523          28474\n",
        "Falmouth Harbor  41.541575 -70.608020          47470\n",
        "Marion           41.689008 -70.746576          49654\n",
        "Marshfield       42.108480 -70.648691          24272\n",
        "Provincetown     42.042745 -70.171180          26595\n",
        "Sandwich         41.767990 -70.466219          38036\n",
        "Hampton Bay      42.900103 -70.818510          13022\n",
        "Gloucester       42.610253 -70.660570          22082"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Specify the range of dates you want to extract, either all time steps, a specified number of days around the \n",
      "current date or for a specific period in the past (for archive datasets)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# get time values and convert to datetime objects\n",
      "time_var = nc['time']\n",
      "\n",
      "# all time steps\n",
      "istart = 0\n",
      "istop = len(time_var)-1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Use specific times (UTC) ...\n",
      "start = dt.datetime(2012,2,16,0,0,0)\n",
      "istart = netCDF4.date2index(start,time_var,select='nearest')\n",
      "stop = dt.datetime(2012,2,18,0,0,0)\n",
      "istop = netCDF4.date2index(stop,time_var,select='nearest')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Use times near \"NOW\" ...\n",
      "start = dt.datetime.utcnow() - dt.timedelta(days=1)\n",
      "istart = netCDF4.date2index(start,time_var,select='nearest')\n",
      "stop = dt.datetime.utcnow() + dt.timedelta(days=3)\n",
      "istop = netCDF4.date2index(stop,time_var,select='nearest')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 'istart,istop=',istart,istop"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "istart,istop= 85 144\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# get all time steps of water level from each station\n",
      "nsta=len(obs)\n",
      "ii=range(istart,istop)\n",
      "z=ones((len(ii),nsta))\n",
      "for i in range(nsta):\n",
      "    z[:,i] = nc['zeta'][ii,obs['0-Based Index'][i]]\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "jd = netCDF4.num2date(time_var[ii],time_var.units)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# make a DataFrame out of the interpolated time series at each location\n",
      "zvals=pd.DataFrame(z,index=jd,columns=obs.index)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# list out a few values\n",
      "zvals.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>Station</th>\n",
        "      <th>Boston</th>\n",
        "      <th>Scituate Harbor</th>\n",
        "      <th>Scituate Beach</th>\n",
        "      <th>Falmouth Harbor</th>\n",
        "      <th>Marion</th>\n",
        "      <th>Marshfield</th>\n",
        "      <th>Provincetown</th>\n",
        "      <th>Sandwich</th>\n",
        "      <th>Hampton Bay</th>\n",
        "      <th>Gloucester</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2014-02-03 13:01:53</th>\n",
        "      <td>-1.548685</td>\n",
        "      <td> 0.486000</td>\n",
        "      <td>-0.640000</td>\n",
        "      <td> 0.050240</td>\n",
        "      <td> 0.341351</td>\n",
        "      <td>-1.300522</td>\n",
        "      <td>-1.359574</td>\n",
        "      <td>-1.372528</td>\n",
        "      <td>-0.289000</td>\n",
        "      <td>-1.287029</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-03 13:58:08</th>\n",
        "      <td>-0.804319</td>\n",
        "      <td> 0.486000</td>\n",
        "      <td>-0.640000</td>\n",
        "      <td> 0.311570</td>\n",
        "      <td> 0.902277</td>\n",
        "      <td>-0.764226</td>\n",
        "      <td>-0.890149</td>\n",
        "      <td>-0.852898</td>\n",
        "      <td>-0.289000</td>\n",
        "      <td>-0.710075</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-03 15:00:00</th>\n",
        "      <td>-0.128420</td>\n",
        "      <td> 0.486000</td>\n",
        "      <td>-0.043429</td>\n",
        "      <td> 0.533869</td>\n",
        "      <td> 1.233727</td>\n",
        "      <td>-0.036886</td>\n",
        "      <td>-0.082767</td>\n",
        "      <td>-0.008128</td>\n",
        "      <td>-0.060291</td>\n",
        "      <td>-0.025076</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-03 16:01:53</th>\n",
        "      <td> 0.659285</td>\n",
        "      <td> 0.748702</td>\n",
        "      <td> 0.791304</td>\n",
        "      <td> 0.548589</td>\n",
        "      <td> 1.119893</td>\n",
        "      <td> 0.758620</td>\n",
        "      <td> 0.665184</td>\n",
        "      <td> 0.720584</td>\n",
        "      <td> 0.792306</td>\n",
        "      <td> 0.827408</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-03 16:58:08</th>\n",
        "      <td> 1.568771</td>\n",
        "      <td> 1.455214</td>\n",
        "      <td> 1.462268</td>\n",
        "      <td> 0.476323</td>\n",
        "      <td> 0.730958</td>\n",
        "      <td> 1.449316</td>\n",
        "      <td> 1.387045</td>\n",
        "      <td> 1.440940</td>\n",
        "      <td> 1.415007</td>\n",
        "      <td> 1.476997</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "Station                Boston  Scituate Harbor  Scituate Beach  \\\n",
        "2014-02-03 13:01:53 -1.548685         0.486000       -0.640000   \n",
        "2014-02-03 13:58:08 -0.804319         0.486000       -0.640000   \n",
        "2014-02-03 15:00:00 -0.128420         0.486000       -0.043429   \n",
        "2014-02-03 16:01:53  0.659285         0.748702        0.791304   \n",
        "2014-02-03 16:58:08  1.568771         1.455214        1.462268   \n",
        "\n",
        "Station              Falmouth Harbor    Marion  Marshfield  Provincetown  \\\n",
        "2014-02-03 13:01:53         0.050240  0.341351   -1.300522     -1.359574   \n",
        "2014-02-03 13:58:08         0.311570  0.902277   -0.764226     -0.890149   \n",
        "2014-02-03 15:00:00         0.533869  1.233727   -0.036886     -0.082767   \n",
        "2014-02-03 16:01:53         0.548589  1.119893    0.758620      0.665184   \n",
        "2014-02-03 16:58:08         0.476323  0.730958    1.449316      1.387045   \n",
        "\n",
        "Station              Sandwich  Hampton Bay  Gloucester  \n",
        "2014-02-03 13:01:53 -1.372528    -0.289000   -1.287029  \n",
        "2014-02-03 13:58:08 -0.852898    -0.289000   -0.710075  \n",
        "2014-02-03 15:00:00 -0.008128    -0.060291   -0.025076  \n",
        "2014-02-03 16:01:53  0.720584     0.792306    0.827408  \n",
        "2014-02-03 16:58:08  1.440940     1.415007    1.476997  "
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# plotting at DataFrame is easy!\n",
      "ax=zvals.plot(figsize=(16,4),grid=True,title=('NECOFS Forecast Water Level from %s Forecast' % model),legend=False);\n",
      "\n",
      "# but of course we aren't exactly satisfied with the default plot, so...\n",
      "# Specify exactly the way we want the dates labeled\n",
      "xax = ax.get_xaxis()\n",
      "xax.set_major_locator(DayLocator())\n",
      "xax.set_major_formatter(DateFormatter('%b %d'))\n",
      "\n",
      "xax.set_minor_locator(HourLocator(byhour=range(0,24,3)))\n",
      "xax.set_minor_formatter(DateFormatter('%H'))# read units from dataset for ylabel\n",
      "xax.set_tick_params(which='major', pad=15)\n",
      "xax.grid(b=True,which='minor')\n",
      "setp(gca().get_xticklabels(), rotation=0, horizontalalignment='center')\n",
      "ylabel(nc['zeta'].units)\n",
      "# plot the legend outside the axis frame\n",
      "box = ax.get_position()\n",
      "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
      "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5));\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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wZctqdO7sgXbtSmBvD5w8Ke2UDMMwDMMwjLQ1iT6YCQkJiIqKgpOTU7X3k5KS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\nEXXt2pW2bt1KAoGAEhISyMrKqs73+tmzZ1cvsRU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70v6YqUiuQGi/UPA0eXB86Fg9uAQA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\nEE50FYPvvRLqoiewyLf4oHIkqUkSaC5ZhTGhHPQdvwti8VxcuHCNdU1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zjC5cqH0z/+tb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fD6xdC2hp1b5vZbxOnEkyssikqioah+yvvFZIKm+DzZuBPn2ADz8EQkKAo0dFU2C6ugKTJwP7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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x30135d0>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# make a new DataFrame of maximum water levels at all stations\n",
      "b=pd.DataFrame(zvals.idxmax(),columns=['time of max water level (UTC)'])\n",
      "# create heading for new column containing max water level\n",
      "zmax_heading='zmax (%s)' % nc['zeta'].units\n",
      "# Add new column to DataFrame\n",
      "b[zmax_heading]=zvals.max()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b"
     ],
     "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>time of max water level (UTC)</th>\n",
        "      <th>zmax (meters)</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Station</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Boston</th>\n",
        "      <td>2014-02-04 07:01:53</td>\n",
        "      <td> 2.118573</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Scituate Harbor</th>\n",
        "      <td>2014-02-04 07:01:53</td>\n",
        "      <td> 2.013487</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Scituate Beach</th>\n",
        "      <td>2014-02-04 07:01:53</td>\n",
        "      <td> 2.015532</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Falmouth Harbor</th>\n",
        "      <td>2014-02-04 04:58:08</td>\n",
        "      <td> 0.595833</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Marion</th>\n",
        "      <td>2014-02-04 04:01:53</td>\n",
        "      <td> 1.235376</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Marshfield</th>\n",
        "      <td>2014-02-04 07:01:53</td>\n",
        "      <td> 2.022758</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Provincetown</th>\n",
        "      <td>2014-02-04 07:01:53</td>\n",
        "      <td> 2.066864</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Sandwich</th>\n",
        "      <td>2014-02-04 07:01:53</td>\n",
        "      <td> 2.114579</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Hampton Bay</th>\n",
        "      <td>2014-02-04 07:01:53</td>\n",
        "      <td> 1.962028</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Gloucester</th>\n",
        "      <td>2014-02-04 07:01:53</td>\n",
        "      <td> 1.957828</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
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       "text": [
        "                time of max water level (UTC)  zmax (meters)\n",
        "Station                                                     \n",
        "Boston                    2014-02-04 07:01:53       2.118573\n",
        "Scituate Harbor           2014-02-04 07:01:53       2.013487\n",
        "Scituate Beach            2014-02-04 07:01:53       2.015532\n",
        "Falmouth Harbor           2014-02-04 04:58:08       0.595833\n",
        "Marion                    2014-02-04 04:01:53       1.235376\n",
        "Marshfield                2014-02-04 07:01:53       2.022758\n",
        "Provincetown              2014-02-04 07:01:53       2.066864\n",
        "Sandwich                  2014-02-04 07:01:53       2.114579\n",
        "Hampton Bay               2014-02-04 07:01:53       1.962028\n",
        "Gloucester                2014-02-04 07:01:53       1.957828"
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