files/Iris_CFSR_wave_wind.ipynb

{
 "metadata": {
  "name": "Iris_CFSR_wave_wind"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Using Iris to access NCEP CFSR 30-year Wave Hindcast\n",
      "This demonstrates extracting data from a large aggregated archive via OPeNDAP, taking advantage of CF conventions to enable a plot without specification of lon,lat variables, and save extracted data to GRIB2"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.display import HTML\n",
      "HTML('<iframe src=http://scitools.org.uk/iris/ width=800 height=350></iframe>')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=http://scitools.org.uk/iris/ width=800 height=350></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "<IPython.core.display.HTML at 0x3a43650>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy\n",
      "import matplotlib.pyplot as plt\n",
      "import datetime as dt\n",
      "\n",
      "import iris\n",
      "import iris.plot as iplt\n",
      "import cartopy.crs as ccrs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def time_near(cube,start):\n",
      "    timevar=cube.coord('time')\n",
      "    itime = timevar.nearest_neighbour_index(timevar.units.date2num(start))\n",
      "    return timevar.points[itime]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def myplot(slice,model=None):\n",
      "    # make the plot\n",
      "    figure(figsize=(12,8))\n",
      "    lat=slice.coord(axis='Y').points\n",
      "    lon=slice.coord(axis='X').points\n",
      "    time=slice.coord('time')[0]\n",
      "    subplot(111,aspect=(1.0/cos(mean(lat)*pi/180.0)))\n",
      "    pcolormesh(lon,lat,masked_invalid(slice.data));\n",
      "    colorbar()\n",
      "    grid()\n",
      "    date_str=time.units.num2date(time.points)\n",
      "    plt.title('%s: %s: %s' % (model,slice.long_name,date_str));"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Extract some data using OPeNDAP"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "# DAP URL: 30 year East Coast wave hindcast (Wave Watch 3 driven by CFSR Winds) \n",
      "#cubes = iris.load('http://geoport.whoi.edu/thredds/dodsC/fmrc/NCEP/ww3/cfsr/4m/best'); # 4 arc minute resolution\n",
      "cubes = iris.load('http://geoport.whoi.edu/thredds/dodsC/fmrc/NCEP/ww3/cfsr/10m/best'); # 10 arc minute resolution"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/home/local/python27_epd/lib/python2.7/site-packages/Iris-1.5.0_dev-py2.7.egg/iris/fileformats/cf.py:514: UserWarning: Missing CF-netCDF grid mapping variable u'LatLon_Projection', referenced by netCDF variable u'u-component_of_wind_surface'\n",
        "  warnings.warn(message % (name, nc_var_name))\n",
        "/home/local/python27_epd/lib/python2.7/site-packages/Iris-1.5.0_dev-py2.7.egg/iris/fileformats/cf.py:514: UserWarning: Missing CF-netCDF grid mapping variable u'LatLon_Projection', referenced by netCDF variable u'v-component_of_wind_surface'\n",
        "  warnings.warn(message % (name, nc_var_name))\n",
        "/home/local/python27_epd/lib/python2.7/site-packages/Iris-1.5.0_dev-py2.7.egg/iris/fileformats/cf.py:514: UserWarning: Missing CF-netCDF grid mapping variable u'LatLon_Projection', referenced by netCDF variable u'Significant_height_of_combined_wind_waves_and_swell_surface'\n",
        "  warnings.warn(message % (name, nc_var_name))\n",
        "/home/local/python27_epd/lib/python2.7/site-packages/Iris-1.5.0_dev-py2.7.egg/iris/fileformats/cf.py:514: UserWarning: Missing CF-netCDF grid mapping variable u'LatLon_Projection', referenced by netCDF variable u'Primary_wave_direction_degree_true_surface'\n",
        "  warnings.warn(message % (name, nc_var_name))\n",
        "/home/local/python27_epd/lib/python2.7/site-packages/Iris-1.5.0_dev-py2.7.egg/iris/fileformats/cf.py:514: UserWarning: Missing CF-netCDF grid mapping variable u'LatLon_Projection', referenced by netCDF variable u'Primary_wave_mean_period_surface'\n",
        "  warnings.warn(message % (name, nc_var_name))\n",
        "/home/local/python27_epd/lib/python2.7/site-packages/Iris-1.5.0_dev-py2.7.egg/iris/fileformats/_pyke_rules/compiled_krb/fc_rules_cf_fc.py:1163: UserWarning: Ignoring netCDF variable 'Primary_wave_direction_degree_true_surface' invalid units 'deg'\n",
        "  warnings.warn(msg.format(msg_name, msg_units))\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print cubes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0: Significant height of combined wind waves and swell @ Ground or water surface / (m) (time: 90584; latitude: 331; longitude: 301)\n",
        "1: u-component of wind @ Ground or water surface / (m/s) (time: 90584; latitude: 331; longitude: 301)\n",
        "2: v-component of wind @ Ground or water surface / (m/s) (time: 90584; latitude: 331; longitude: 301)\n",
        "3: Primary wave direction (degree true) @ Ground or water surface / (unknown) (time: 90584; latitude: 331; longitude: 301)\n",
        "4: Primary wave mean period @ Ground or water surface / (s) (time: 90584; latitude: 331; longitude: 301)\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# use contraints to select geographic subset and nearest time\n",
      "mytime=dt.datetime(1991,10,31,12)  #specified time...   Nov 1, 1991 was the \"Perfect Storm\"\n",
      "#mytime=dt.datetime.utcnow()      # .... or now"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cubes2 = cubes.extract(iris.Constraint(time=time_near(cubes[0],mytime),\n",
      "    longitude=lambda cell: -71.5 < cell < -64.0,\n",
      "    latitude=lambda cell: 40.0 < cell < 46.0))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hsig=cubes2[0]\n",
      "u=cubes2[1]\n",
      "v=cubes2[2]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print cubes2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0: Significant height of combined wind waves and swell @ Ground or water surface / (m) (latitude: 35; longitude: 44)\n",
        "1: u-component of wind @ Ground or water surface / (m/s) (latitude: 35; longitude: 44)\n",
        "2: v-component of wind @ Ground or water surface / (m/s) (latitude: 35; longitude: 44)\n",
        "3: Primary wave direction (degree true) @ Ground or water surface / (unknown) (latitude: 35; longitude: 44)\n",
        "4: Primary wave mean period @ Ground or water surface / (s) (latitude: 35; longitude: 44)\n"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print[coord.name() for coord in hsig.coords()]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['latitude', 'longitude', u'time']\n"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Plot using Iris.plot \n",
      "There is also Iris.quickplot, but I wanted to add my own title here and control the orientation of the colorbar, so I used the more flexible Iris.plot"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def myplot(slice,u=None,v=None,model=None):\n",
      "    # make the plot\n",
      "    figure(figsize=(12,8))\n",
      "    lat=slice.coord(axis='Y').points\n",
      "    lon=slice.coord(axis='X').points\n",
      "    time=slice.coord('time')[0]\n",
      "    subplot(111,aspect=(1.0/cos(mean(lat)*pi/180.0)))\n",
      "    pcolormesh(lon,lat,slice.data);\n",
      "    isub = 2\n",
      "    [lon2d,lat2d]=meshgrid(lon,lat)\n",
      "    Q = quiver(lon[::isub,::isub],lat[::isub,::isub],u[::isub,::isub],v[::isub,::isub],scale=20)\n",
      "    maxstr='%3.1f m/s' % maxvel\n",
      "    qk = quiverkey(Q,0.92,0.08,maxvel,maxstr,labelpos='W')\n",
      "    colorbar()\n",
      "    grid()\n",
      "    date=time.units.num2date(time.points)\n",
      "    date_str=date[0].strftime('%Y-%m-%d %H:%M:%S %Z')\n",
      "    plt.title('%s: %s: %s' % (model,slice.long_name,date_str));"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lat=hsig.coord(axis='Y').points\n",
      "lon=hsig.coord(axis='X').points\n",
      "\n",
      "hsig = ma.masked_invalid(hsig.data)\n",
      "u = ma.masked_invalid(u.data)\n",
      "v = ma.masked_invalid(v.data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "[lon2d,lat2d]=meshgrid(lon,lat)\n",
      "isub=2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Q = quiver(lon2d[::isub,::isub],lat2d[::isub,::isub],u[::isub,::isub],v[::isub,::isub],scale=20)\n",
      "maxstr='%3.1f m/s' % maxvel\n",
      "qk = quiverkey(Q,0.92,0.08,maxvel,maxstr,labelpos='W')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "NameError",
       "evalue": "name 'maxvel' is not defined",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-46-8ca14d94a330>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mQ\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mquiver\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlon2d\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0misub\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0misub\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlat2d\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0misub\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0misub\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mu\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0misub\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0misub\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0misub\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0misub\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mscale\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmaxstr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'%3.1f m/s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mmaxvel\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mqk\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mquiverkey\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mQ\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0.92\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0.08\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmaxvel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmaxstr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabelpos\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'W'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mNameError\u001b[0m: name 'maxvel' is not defined"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SSCQkFAopNDSUNBpNGZsnT57QpEmTSK1Wk6+vL33xxRckkUhIIBBQ48aNKSYmhjZt2kTN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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x6976450>"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Q = quiver(lon2d[::isub,::isub],lat2d[::isub,::isub],u[::isub,::isub],v[::isub,::isub],scale=20)\n",
      "maxstr='%3.1f m/s' % maxvel\n",
      "qk = quiverkey(Q,0.92,0.08,maxvel,maxstr,labelpos='W')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "TypeError",
       "evalue": "ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-39-8ca14d94a330>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mQ\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mquiver\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlon2d\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0misub\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0misub\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlat2d\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0misub\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0misub\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mu\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0misub\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0misub\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0misub\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0misub\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mscale\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mmaxstr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'%3.1f m/s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mmaxvel\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mqk\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mquiverkey\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mQ\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0.92\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0.08\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmaxvel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmaxstr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabelpos\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'W'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/local/python27_epd/lib/python2.7/site-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mquiver\u001b[1;34m(*args, **kw)\u001b[0m\n\u001b[0;32m   2875\u001b[0m         \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhold\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2876\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2877\u001b[1;33m         \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mquiver\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2878\u001b[0m         \u001b[0mdraw_if_interactive\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2879\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/local/python27_epd/lib/python2.7/site-packages/matplotlib/axes.pyc\u001b[0m in \u001b[0;36mquiver\u001b[1;34m(self, *args, **kw)\u001b[0m\n\u001b[0;32m   6625\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mquiver\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6626\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_hold\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcla\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 6627\u001b[1;33m         \u001b[0mq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmquiver\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mQuiver\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   6628\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_collection\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6629\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate_datalim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mXY\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/local/python27_epd/lib/python2.7/site-packages/matplotlib/quiver.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, ax, *args, **kw)\u001b[0m\n\u001b[0;32m    417\u001b[0m                                             **kw)\n\u001b[0;32m    418\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpolykw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkw\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 419\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_UVC\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mU\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mV\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mC\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    420\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_initialized\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    421\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/local/python27_epd/lib/python2.7/site-packages/matplotlib/quiver.pyc\u001b[0m in \u001b[0;36mset_UVC\u001b[1;34m(self, U, V, C)\u001b[0m\n\u001b[0;32m    461\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    462\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mset_UVC\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mU\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mV\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mC\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 463\u001b[1;33m         \u001b[0mU\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmasked_invalid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mU\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    464\u001b[0m         \u001b[0mV\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmasked_invalid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mV\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    465\u001b[0m         \u001b[0mmask\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmask_or\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mU\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mV\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshrink\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/local/python27_epd/lib/python2.7/site-packages/numpy/ma/core.pyc\u001b[0m in \u001b[0;36mmasked_invalid\u001b[1;34m(a, copy)\u001b[0m\n\u001b[0;32m   2239\u001b[0m         \u001b[0mcls\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2240\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2241\u001b[1;33m         \u001b[0mcondition\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m~\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2242\u001b[0m         \u001b[0mcls\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mMaskedArray\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2243\u001b[0m     \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mview\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mTypeError\u001b[0m: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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DmjJlyogn8SxYsWKFpk+f/tzzY+1myuLe1dWlgoKC6HFRUZHa29tj1nR2dqqoqCh6nJOT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\nAIP+D9BxfxEiEGoBAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x69a2d90>"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "myplot(cubes[0],u=cubes[1].data,v=cubes[2].data,model='foo')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "RuntimeError",
       "evalue": "NetCDF: Malformed or inaccessible DAP DATADDS",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-44-eb1625a11aca>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmyplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcubes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mu\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcubes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcubes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'foo'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[1;32m/home/local/python27_epd/lib/python2.7/site-packages/Iris-1.5.0_dev-py2.7.egg/iris/cube.pyc\u001b[0m in \u001b[0;36mdata\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1138\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data_manager\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1139\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1140\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data_manager\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1141\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1142\u001b[0m             except (MemoryError,\n",
        "\u001b[1;32m/home/local/python27_epd/lib/python2.7/site-packages/Iris-1.5.0_dev-py2.7.egg/iris/fileformats/manager.pyc\u001b[0m in \u001b[0;36mload\u001b[1;34m(self, proxy_array)\u001b[0m\n\u001b[0;32m    266\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproxy\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndenumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mproxy_array\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    267\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mproxy\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# 0 can come from slicing masked proxy; np.array(masked_constant).\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 268\u001b[1;33m                 \u001b[0mpayload\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mproxy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_orig_data_shape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmdi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdeferred_slice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    269\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    270\u001b[0m                 \u001b[1;31m# Explicitly set the data fill value when no mdi value has been specified\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/local/python27_epd/lib/python2.7/site-packages/Iris-1.5.0_dev-py2.7.egg/iris/fileformats/netcdf.pyc\u001b[0m in \u001b[0;36mload\u001b[1;34m(self, data_shape, data_type, mdi, deferred_slice)\u001b[0m\n\u001b[0;32m    253\u001b[0m         \u001b[0mvariable\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvariables\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvariable_name\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    254\u001b[0m         \u001b[1;31m# Get the NetCDF variable data and slice.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 255\u001b[1;33m         \u001b[0mpayload\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvariable\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdeferred_slice\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    256\u001b[0m         \u001b[0mdataset\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    257\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/local/python27_epd/lib/python2.7/site-packages/netCDF4.so\u001b[0m in \u001b[0;36mnetCDF4.Variable.__getitem__ (netCDF4.c:29289)\u001b[1;34m()\u001b[0m\n",
        "\u001b[1;32m/home/local/python27_epd/lib/python2.7/site-packages/netCDF4.so\u001b[0m in \u001b[0;36mnetCDF4.Variable._get (netCDF4.c:34647)\u001b[1;34m()\u001b[0m\n",
        "\u001b[1;31mRuntimeError\u001b[0m: NetCDF: Malformed or inaccessible DAP DATADDS"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Save extracted Cube to GRIB2 file"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# first add a Geographic Coord system  (required by GRIB2 writer)\n",
      "\n",
      "# here we add a spherical earth with specified radius\n",
      "slice.coord(axis='X').coord_system=iris.coord_systems.GeogCS(654321)\n",
      "slice.coord(axis='Y').coord_system=iris.coord_systems.GeogCS(654321)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# add a forecast_period (required by GRIB2 writer)\n",
      "slice.add_aux_coord(iris.coords.DimCoord(0, standard_name='forecast_period', units='hours'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# add a vertical coordinate (required by GRIB2 writer)\n",
      "slice.add_aux_coord(iris.coords.DimCoord(0, \"height\", units=\"m\"))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# GRIB2 stores data in long values.  Do we have huge values that would create problems?\n",
      "slice.data.max()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "nan"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# ah, we have NaNs.  This causes problems for GRIB2 writer\n",
      "# So convert to masked-array instead.\n",
      "slice.data=ma.masked_invalid(slice.data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Finally... save slice as grib2\n",
      "iris.save(slice,'hsig.grib2')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/home/local/python27_epd/lib/python2.7/site-packages/Iris-1.4.0_dev-py2.7.egg/iris/fileformats/grib_save_rules.py:186: UserWarning: Not yet translating standard name into grib param codes.\n",
        "discipline, parameterCategory and parameterNumber have been zeroed.\n",
        "  warnings.warn(\"Not yet translating standard name into grib param codes.\\n\"\n"
       ]
      }
     ],
     "prompt_number": 17
    }
   ],
   "metadata": {}
  }
 ]
}