time_series_extract_3d.ipynb

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   "cells": [
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Extract time series from 3D [time,lat,lon] dataset"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Extract a forecast time-series of wave height from Global NCEP WaveWatch 3\n",
      "# and plot the time-series using Pandas\n",
      "\n",
      "# This is just one of the many datasets in Unidata's THREDDS Catalog Collection:\n",
      "# http://thredds.ucar.edu/thredds/catalog.html\n",
      "# and this approach would work for any CF-Compliant NetCDF or OPeNDAP dataset"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pylab import *\n",
      "import netCDF4\n",
      "import pandas as pd\n",
      "import datetime as dt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
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    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# NCEP WaveWatch 3 wave model: New England Coastal Grid:\n",
      "url='http://thredds.ucar.edu/thredds/dodsC/grib/NCEP/WW3/Coastal_US_East_Coast/best'  # forecast\n",
      "url='http://geoport.whoi.edu/thredds/dodsC/fmrc/NCEP/ww3/cfsr/4m/best'   # 30 year hindcast"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# NetCDF4-Python can open OPeNDAP dataset just like a local NetCDF file\n",
      "nc = netCDF4.Dataset(url)\n",
      "nc.variables.keys()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "[u'lat',\n",
        " u'lon',\n",
        " u'time',\n",
        " u'time1',\n",
        " u'u-component_of_wind_surface',\n",
        " u'v-component_of_wind_surface',\n",
        " u'Significant_height_of_combined_wind_waves_and_swell_surface',\n",
        " u'Primary_wave_direction_degree_true_surface',\n",
        " u'Primary_wave_mean_period_surface']"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lat = nc.variables['lat'][:]\n",
      "lon = nc.variables['lon'][:]\n",
      "time_var = nc.variables['time']\n",
      "dtime = netCDF4.num2date(time_var[:],time_var.units)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# determine what longitude convention is being used\n",
      "print lon.min(),lon.max()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "-99.0 -60.0\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Specify desired station time series location\n",
      "# note we add 360 because of the lon convention in this dataset\n",
      "lati = 41.01; loni = -70.2  # Provincetown, MA"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Function to find index to nearest point\n",
      "def near(array,value):\n",
      "    idx=(abs(array-value)).argmin()\n",
      "    return idx"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Find nearest point to desired location (no interpolation)\n",
      "ix = near(lon, loni)\n",
      "iy = near(lat, lati)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Extract desired times.  Here we select 3 days before the present time through the end of the forecast\n",
      "start = dt.datetime.utcnow()- dt.timedelta(days=3)\n",
      "istart = netCDF4.date2index(start,time_var,select='nearest')\n",
      "istop = len(time_var)-1\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Extract desired times.  Here we select a specific time of interest\n",
      "start = dt.datetime(2000,1,1,0,0,0)\n",
      "istart = netCDF4.date2index(start,time_var,select='nearest')\n",
      "stop = dt.datetime(2000,1,7,0,0,0)\n",
      "istop = netCDF4.date2index(stop,time_var,select='nearest')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get all time records of variable [vname] at indices [iy,ix]\n",
      "vname = 'Significant_height_of_combined_wind_waves_and_swell_surface'\n",
      "var = nc.variables[vname]\n",
      "hs = var[istart:istop,iy,ix]\n",
      "tim = dtime[istart:istop]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create Pandas time series object\n",
      "ts = pd.Series(hs,index=tim)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Use Pandas time series plot method\n",
      "figure(figsize=(16,4))\n",
      "ts.plot(title='%s (Lon=%.2f, Lat=%.2f)' % (vname, lon[ix], lat[iy]))\n",
      "ylabel(var.units)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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M6tWrXe0vv/zSPPPMMw/0OkSSCvW0iiQgLy8vhg0bRkhICMuXL2fZsmWMGjXqnsdt2LAB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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2b69350>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vis = bearcart.Chart(ts)\n",
      "vis"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/home/local/python27_epd/lib/python2.7/site-packages/IPython/core/formatters.py:239: FormatterWarning: Exception in text/html formatter: 0.89999998 is not JSON serializable\n",
        "  FormatterWarning,\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "<bearcart.bearcart.Chart at 0x38aa750>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    }
   ],
   "metadata": {}
  }
 ]
}