IOOS Models-bottom temp.ipynb

{
 "metadata": {
  "name": "IOOS Models-bottom temp"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Using Iris to access data from US-IOOS models"
     ]
    },
    {
     "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": 1,
       "text": [
        "<IPython.core.display.HTML at 0x4020f90>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import datetime as dt\n",
      "import time\n",
      "\n",
      "import iris"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def time_near(cube,start):\n",
      "    #    coord_names = [coord.name() for coord in cube.coords()]\n",
      "    #    timevar = cube.coord(coord_names[0]))\n",
      "    timevar=cube.coord('time')\n",
      "    try:\n",
      "        itime = timevar.nearest_neighbour_index(timevar.units.date2num(start))\n",
      "    except:\n",
      "        itime = -1\n",
      "    return timevar.points[itime]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def var_lev_date(url=None,var=None,mytime=None,lev=0,subsample=1):\n",
      "    time0=time.time()\n",
      "    cubes = iris.load(url)\n",
      "    cube = cubes.extract(iris.Constraint(name=var.strip()))[0]\n",
      "    try:\n",
      "        cube.coord(axis='T').rename('time')\n",
      "    except:\n",
      "        pass\n",
      "    slice = cube.extract(iris.Constraint(time=time_near(cube,mytime)))\n",
      "    slice = slice[lev,::subsample,::subsample]  \n",
      "    print 'slice retrieved in %f seconds' % (time.time()-time0)\n",
      "    return slice"
     ],
     "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",
      "    lon = mod(lon,360.)-360.\n",
      "    pcolormesh(lon,lat,ma.masked_invalid(slice.data),vmin=5.,vmax=30.);\n",
      "    axis([-78., -68., 34., 42. ]);\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": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# use contraints to select nearest time\n",
      "#mytime=dt.datetime(2008,7,28,12)  #specified time...\n",
      "mytime=dt.datetime.utcnow()      # .... or now"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model='MARACOOS/ESPRESSO'\n",
      "url='http://tds.marine.rutgers.edu/thredds/dodsC/roms/espresso/2009_da/his'\n",
      "var='potential temperature'\n",
      "lev=0\n",
      "slice=var_lev_date(url=url,var=var, mytime=mytime, lev=lev)\n",
      "myplot(slice,model=model)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "slice retrieved in 36.458963 seconds\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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yMgAgOTkZPXr0sNTv0aOHsP/Pvn37qg1cWSUZefnll3Hy5EkcO3YMixYtwuDB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RnM3t3jWURsPMhGWkrFz0QCK1KTxIWZKQ6IANA10QcRa/GwWTQaKqZli+6bzNBz0di0W6gMIhDO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\nRAghhBCm+j82eddlZkUgDwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4859890>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model='USGS/COAWST'\n",
      "url='http://geoport.whoi.edu/thredds/dodsC/coawst_4/use/fmrc/coawst_4_use_best.ncd'\n",
      "var='potential temperature'\n",
      "lev=0\n",
      "slice=var_lev_date(url=url,var=var, mytime=mytime, lev=lev, subsample=1)\n",
      "myplot(slice,model=model)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "slice retrieved in 382.020732 seconds\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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HHkFhYSG6deuGQ4cO4eGHHwagTv/D+jj9/e9/x0MPPYRRo0bh0UcftZ2HkaXV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L8YBZRH7YNFY107Ei3jHY9fQkOj2743al9ZMFaoY0Uj1qxV7dWofWkB/noTsA43cmBWtAxO9ZWq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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4ec35d0>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model='MARACOOS/NYHOPS'\n",
      "url='http://colossus.dl.stevens-tech.edu/thredds/dodsC/fmrc/NYBight/NYHOPS_Forecast_Collection_for_the_New_York_Bight_best.ncd'\n",
      "var='Water Temperature'\n",
      "lev=-1\n",
      "slice=var_lev_date(url=url,var=var, mytime=mytime, lev=lev)\n",
      "myplot(slice,model=model)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ValueError",
       "evalue": "Bounds shape must be compatible with points shape.",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-10-1e3dff0de36e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mvar\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Water Temperature'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mlev\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mslice\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvar_lev_date\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvar\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvar\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmytime\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmytime\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlev\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlev\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[0mmyplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mslice\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m<ipython-input-4-d9023d1bf69c>\u001b[0m in \u001b[0;36mvar_lev_date\u001b[1;34m(url, var, mytime, lev, subsample)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mvar_lev_date\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvar\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmytime\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlev\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msubsample\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mtime0\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0mcubes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0miris\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m     \u001b[0mcube\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcubes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextract\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miris\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mConstraint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvar\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;32mtry\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/__init__.pyc\u001b[0m in \u001b[0;36mload\u001b[1;34m(uris, constraints, callback)\u001b[0m\n\u001b[0;32m    196\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    197\u001b[0m     \"\"\"\n\u001b[1;32m--> 198\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_load_collection\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0muris\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconstraints\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmerged\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcubes\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    199\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    200\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/__init__.pyc\u001b[0m in \u001b[0;36m_load_collection\u001b[1;34m(uris, constraints, callback)\u001b[0m\n\u001b[0;32m    166\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    167\u001b[0m         \u001b[0mcubes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_generate_cubes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0muris\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 168\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0miris\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcube\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_CubeFilterCollection\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_cubes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcubes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconstraints\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    169\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mEOFError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    170\u001b[0m         raise iris.exceptions.TranslationError(\n",
        "\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;36mfrom_cubes\u001b[1;34m(cubes, constraints)\u001b[0m\n\u001b[0;32m    133\u001b[0m         \u001b[0mpairs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0m_CubeFilter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconstraint\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mconstraint\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mconstraints\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    134\u001b[0m         \u001b[0mcollection\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_CubeFilterCollection\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpairs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 135\u001b[1;33m         \u001b[1;32mfor\u001b[0m \u001b[0mcube\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcubes\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    136\u001b[0m             \u001b[0mcollection\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_cube\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcube\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    137\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mcollection\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/__init__.pyc\u001b[0m in \u001b[0;36m_generate_cubes\u001b[1;34m(uris, callback)\u001b[0m\n\u001b[0;32m    157\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mscheme\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'http'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'https'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    158\u001b[0m             \u001b[0murls\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m':'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mgroups\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 159\u001b[1;33m             \u001b[1;32mfor\u001b[0m \u001b[0mcube\u001b[0m \u001b[1;32min\u001b[0m \u001b[0miris\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_http\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murls\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\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    160\u001b[0m                 \u001b[1;32myield\u001b[0m \u001b[0mcube\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    161\u001b[0m         \u001b[1;32melse\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/io/__init__.pyc\u001b[0m in \u001b[0;36mload_http\u001b[1;34m(urls, callback)\u001b[0m\n\u001b[0;32m    198\u001b[0m     \u001b[1;31m# Call each iris format handler with the appropriate filenames\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    199\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mhandling_format_spec\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfnames\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mhandler_map\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miteritems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 200\u001b[1;33m         \u001b[1;32mfor\u001b[0m \u001b[0mcube\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mhandling_format_spec\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfnames\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\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    201\u001b[0m             \u001b[1;32myield\u001b[0m \u001b[0mcube\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    202\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_cubes\u001b[1;34m(filenames, callback)\u001b[0m\n\u001b[0;32m    461\u001b[0m             \u001b[1;31m# formula term.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    462\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcf_var\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhas_formula_terms\u001b[0m\u001b[1;33m(\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[0mcube\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_load_cube\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcf_var\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    464\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    465\u001b[0m                 \u001b[1;31m# Process any associated formula terms and attach\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;36m_load_cube\u001b[1;34m(engine, cf, cf_var, filename)\u001b[0m\n\u001b[0;32m    366\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    367\u001b[0m     \u001b[1;31m# Run pyke inference engine with forward chaining rules.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 368\u001b[1;33m     \u001b[0mengine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mactivate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_PYKE_RULE_BASE\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    369\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    370\u001b[0m     \u001b[1;31m# Populate coordinate attributes with the untouched attributes from the\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/pyke/knowledge_engine.pyc\u001b[0m in \u001b[0;36mactivate\u001b[1;34m(self, *rb_names)\u001b[0m\n\u001b[0;32m    294\u001b[0m         \u001b[0madd\u001b[0m \u001b[0myour\u001b[0m \u001b[0mfacts\u001b[0m \u001b[0mbefore\u001b[0m \u001b[0mdoing\u001b[0m \u001b[0mthis\u001b[0m\u001b[0;31m!\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    295\u001b[0m         '''\n\u001b[1;32m--> 296\u001b[1;33m         \u001b[1;32mfor\u001b[0m \u001b[0mrb_name\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrb_names\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_rb\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrb_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mactivate\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    297\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    298\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mlookup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkb_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mentity_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpat_context\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpatterns\u001b[0m\u001b[1;33m)\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/pyke/rule_base.pyc\u001b[0m in \u001b[0;36mactivate\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    157\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mknowledge_bases\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mroot_name\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    158\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mregister_fc_rules\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcurrent_rb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 159\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_fc_rules\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcurrent_rb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    160\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    161\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mreset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\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/pyke/rule_base.pyc\u001b[0m in \u001b[0;36mrun_fc_rules\u001b[1;34m(self, stop_at_rb)\u001b[0m\n\u001b[0;32m    145\u001b[0m         \u001b[0mrb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    146\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[0mrb\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mstop_at_rb\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 147\u001b[1;33m             \u001b[1;32mfor\u001b[0m \u001b[0mfc_rule\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfc_rules\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mfc_rule\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\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    148\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mrb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparent\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    149\u001b[0m             \u001b[0mrb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparent\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/local/python27_epd/lib/python2.7/site-packages/pyke/fc_rule.pyc\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     88\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     89\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mran\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 90\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrule_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     91\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     92\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mnew_fact\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfact_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m)\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/_pyke_rules/compiled_krb/fc_rules_cf_fc.pyc\u001b[0m in \u001b[0;36mfc_build_auxiliary_coordinate_latitude\u001b[1;34m(rule, context, index)\u001b[0m\n\u001b[0;32m    257\u001b[0m             \u001b[0mcf_coord_var\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcf_var\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcf_group\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauxiliary_coordinates\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlookup_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'coordinate'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    258\u001b[0m             build_auxiliary_coordinate(engine, cf_coord_var,\n\u001b[1;32m--> 259\u001b[1;33m             coord_name=CF_VALUE_STD_NAME_LAT)\n\u001b[0m\u001b[0;32m    260\u001b[0m             \u001b[0mengine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrule_triggered\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrule\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    261\u001b[0m             \u001b[0mrule\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrule_base\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnum_fc_rules_triggered\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\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/_pyke_rules/compiled_krb/fc_rules_cf_fc.pyc\u001b[0m in \u001b[0;36mbuild_auxiliary_coordinate\u001b[1;34m(engine, cf_coord_var, coord_name, coord_system)\u001b[0m\n\u001b[0;32m   1337\u001b[0m                                  \u001b[0mbounds\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbounds_data\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1338\u001b[0m                                  \u001b[0mattributes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mattributes\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1339\u001b[1;33m                                  coord_system=coord_system)\n\u001b[0m\u001b[0;32m   1340\u001b[0m     \u001b[0mcube\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_aux_coord\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcoord\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_dims\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1341\u001b[0m     \u001b[0mengine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprovides\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'coordinates'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcoord\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcf_coord_var\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcf_name\u001b[0m\u001b[1;33m)\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/coords.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, points, standard_name, long_name, var_name, units, bounds, attributes, coord_system)\u001b[0m\n\u001b[0;32m    344\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    345\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpoints\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpoints\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 346\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbounds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbounds\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    347\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    348\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\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/coords.pyc\u001b[0m in \u001b[0;36mbounds\u001b[1;34m(self, bounds)\u001b[0m\n\u001b[0;32m   1384\u001b[0m             \u001b[1;31m# NB. Use _points to avoid triggering any lazy array.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1385\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_points\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mbounds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1386\u001b[1;33m                 raise ValueError(\"Bounds shape must be compatible with points \"\n\u001b[0m\u001b[0;32m   1387\u001b[0m                                  \"shape.\")\n\u001b[0;32m   1388\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_bounds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbounds\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mValueError\u001b[0m: Bounds shape must be compatible with points shape."
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model='Global RTOFS/NCEP'\n",
      "url='http://ecowatch.ncddc.noaa.gov/thredds/dodsC/hycom/hycom_reg1_agg/HYCOM_Region_1_Aggregation_best.ncd'\n",
      "var='sea_water_temperature'  \n",
      "lev=1\n",
      "subsample=1\n",
      "slice=var_lev_date(url=url,var=var, mytime=mytime, lev=lev, subsample=subsample)\n",
      "myplot(slice,model=model)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model='RUTGERS/NWA'\n",
      "url='http://oceanus.esm.rutgers.edu:8090/thredds/dodsC/ROMS/NWA/Run03/Output'\n",
      "var='time-averaged potential temperature'\n",
      "lev=0\n",
      "slice=var_lev_date(url=url,var=var, mytime=mytime, lev=lev, subsample=1)"
     ],
     "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/_pyke_rules/compiled_krb/fc_rules_cf_fc.py:1163: UserWarning: Ignoring netCDF variable 'TIC' invalid units 'millimole_carbon meter-3'\n",
        "  warnings.warn(msg.format(msg_name, msg_units))\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 'SiOH' invalid units 'millimole_silica meter-3'\n",
        "  warnings.warn(msg.format(msg_name, msg_units))\n"
       ]
      },
      {
       "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/_pyke_rules/compiled_krb/fc_rules_cf_fc.py:1163: UserWarning: Ignoring netCDF variable 'NO3' invalid units 'millimole_N03 meter-3'\n",
        "  warnings.warn(msg.format(msg_name, msg_units))\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 'NH4' invalid units 'millimole_NH4 meter-3'\n",
        "  warnings.warn(msg.format(msg_name, msg_units))\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 'PO4' invalid units 'millimole_PO4 meter-3'\n",
        "  warnings.warn(msg.format(msg_name, msg_units))\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "slice retrieved in 338.939230 seconds\n"
       ]
      },
      {
       "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/_pyke_rules/compiled_krb/fc_rules_cf_fc.py:1163: UserWarning: Ignoring netCDF variable 'oxyg' invalid units 'millimole_O2 meter-3'\n",
        "  warnings.warn(msg.format(msg_name, msg_units))\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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uLtzUGH+9GH99GHsCgKoOWq6VmZmJs2fPAgBOnz6N9evXo0ePHmjZsiUWLFiA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\nGf8FUJoOt8Yeu/wAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3662450>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "myplot(slice,model=model)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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kzwolDcw5FdUhS/HQ9xOXV6A0tHHXcsRqJ+S+tbFBOS5l8gD28+LKbPiIiYlx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\n8BqekmhVuiYAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fc7cb866910>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print cubes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nc = netCDF4.Dataset(url)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import netCDF4"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "url='[prefetch]http://colossus.dl.stevens-tech.edu/thredds/dodsC/fmrc/NYBight/NYHOPS_Forecast_Collection_for_the_New_York_Bight_best.ncd'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nc = netCDF4.Dataset(url)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print nc.variables['temp']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<type 'netCDF4.Variable'>\n",
        "float32 temp(u'time', u'sigma', u'ypos', u'xpos')\n",
        "    long_name: Water Temperature\n",
        "    units: Celsius\n",
        "    standard_name: sea_water_temperature\n",
        "    coordinates: lon lat sigma time \n",
        "unlimited dimensions = ()\n",
        "current size = (165954, 11, 22, 124)\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t = nc.variables['temp'][-1,0,:,:]"
     ],
     "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-5-7c4314f018a8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvariables\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'temp'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\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/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": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print nc.variables['xpos']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<type 'netCDF4.Variable'>\n",
        "float32 xpos(u'xpos',)\n",
        "    long_name: X-coordinate in Cartesian system\n",
        "    units: m\n",
        "    axis: X\n",
        "    _CoordinateAxisType: GeoX\n",
        "unlimited dimensions = ()\n",
        "current size = (124,)\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print nc.variables['xpos'][:]"
     ],
     "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-5-0ecdc13e1e89>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[0mnc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvariables\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'xpos'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\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/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": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "url='http://oceanus.esm.rutgers.edu:8090/thredds/dodsC/ROMS/NWA/Run03/Output'\n",
      "var='time-averaged potential temperature'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "temp = iris.load_cube(url,var)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "url='http://colossus.dl.stevens-tech.edu/thredds/dodsC/fmrc/NYBight/NYHOPS_Forecast_Collection_for_the_New_York_Bight_best.ncd'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cubes=iris.load(url)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ValueError",
       "evalue": "Bounds shape must be compatible with points shape.",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-12-1913e1e486e3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcubes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0miris\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\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/__init__.pyc\u001b[0m in \u001b[0;36mload\u001b[1;34m(uris, constraints, callback)\u001b[0m\n\u001b[0;32m    196\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    197\u001b[0m     \"\"\"\n\u001b[1;32m--> 198\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_load_collection\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0muris\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconstraints\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmerged\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcubes\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    199\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    200\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/__init__.pyc\u001b[0m in \u001b[0;36m_load_collection\u001b[1;34m(uris, constraints, callback)\u001b[0m\n\u001b[0;32m    166\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    167\u001b[0m         \u001b[0mcubes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_generate_cubes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0muris\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 168\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0miris\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcube\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_CubeFilterCollection\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_cubes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcubes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconstraints\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    169\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mEOFError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    170\u001b[0m         raise iris.exceptions.TranslationError(\n",
        "\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;36mfrom_cubes\u001b[1;34m(cubes, constraints)\u001b[0m\n\u001b[0;32m    133\u001b[0m         \u001b[0mpairs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0m_CubeFilter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconstraint\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mconstraint\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mconstraints\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    134\u001b[0m         \u001b[0mcollection\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_CubeFilterCollection\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpairs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 135\u001b[1;33m         \u001b[1;32mfor\u001b[0m \u001b[0mcube\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcubes\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    136\u001b[0m             \u001b[0mcollection\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_cube\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcube\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    137\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mcollection\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/__init__.pyc\u001b[0m in \u001b[0;36m_generate_cubes\u001b[1;34m(uris, callback)\u001b[0m\n\u001b[0;32m    157\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mscheme\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'http'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'https'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    158\u001b[0m             \u001b[0murls\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m':'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mgroups\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 159\u001b[1;33m             \u001b[1;32mfor\u001b[0m \u001b[0mcube\u001b[0m \u001b[1;32min\u001b[0m \u001b[0miris\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_http\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murls\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\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    160\u001b[0m                 \u001b[1;32myield\u001b[0m \u001b[0mcube\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    161\u001b[0m         \u001b[1;32melse\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/io/__init__.pyc\u001b[0m in \u001b[0;36mload_http\u001b[1;34m(urls, callback)\u001b[0m\n\u001b[0;32m    198\u001b[0m     \u001b[1;31m# Call each iris format handler with the appropriate filenames\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    199\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mhandling_format_spec\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfnames\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mhandler_map\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miteritems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 200\u001b[1;33m         \u001b[1;32mfor\u001b[0m \u001b[0mcube\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mhandling_format_spec\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfnames\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\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    201\u001b[0m             \u001b[1;32myield\u001b[0m \u001b[0mcube\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    202\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_cubes\u001b[1;34m(filenames, callback)\u001b[0m\n\u001b[0;32m    461\u001b[0m             \u001b[1;31m# formula term.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    462\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcf_var\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhas_formula_terms\u001b[0m\u001b[1;33m(\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[0mcube\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_load_cube\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcf_var\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    464\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    465\u001b[0m                 \u001b[1;31m# Process any associated formula terms and attach\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;36m_load_cube\u001b[1;34m(engine, cf, cf_var, filename)\u001b[0m\n\u001b[0;32m    366\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    367\u001b[0m     \u001b[1;31m# Run pyke inference engine with forward chaining rules.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 368\u001b[1;33m     \u001b[0mengine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mactivate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_PYKE_RULE_BASE\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    369\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    370\u001b[0m     \u001b[1;31m# Populate coordinate attributes with the untouched attributes from the\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/pyke/knowledge_engine.pyc\u001b[0m in \u001b[0;36mactivate\u001b[1;34m(self, *rb_names)\u001b[0m\n\u001b[0;32m    294\u001b[0m         \u001b[0madd\u001b[0m \u001b[0myour\u001b[0m \u001b[0mfacts\u001b[0m \u001b[0mbefore\u001b[0m \u001b[0mdoing\u001b[0m \u001b[0mthis\u001b[0m\u001b[0;31m!\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    295\u001b[0m         '''\n\u001b[1;32m--> 296\u001b[1;33m         \u001b[1;32mfor\u001b[0m \u001b[0mrb_name\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrb_names\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_rb\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrb_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mactivate\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    297\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    298\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mlookup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkb_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mentity_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpat_context\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpatterns\u001b[0m\u001b[1;33m)\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/pyke/rule_base.pyc\u001b[0m in \u001b[0;36mactivate\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    157\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mknowledge_bases\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mroot_name\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    158\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mregister_fc_rules\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcurrent_rb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 159\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_fc_rules\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcurrent_rb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    160\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    161\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mreset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\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/pyke/rule_base.pyc\u001b[0m in \u001b[0;36mrun_fc_rules\u001b[1;34m(self, stop_at_rb)\u001b[0m\n\u001b[0;32m    145\u001b[0m         \u001b[0mrb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    146\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[0mrb\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mstop_at_rb\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 147\u001b[1;33m             \u001b[1;32mfor\u001b[0m \u001b[0mfc_rule\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfc_rules\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mfc_rule\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\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    148\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mrb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparent\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    149\u001b[0m             \u001b[0mrb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparent\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/local/python27_epd/lib/python2.7/site-packages/pyke/fc_rule.pyc\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     88\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     89\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mran\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 90\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrule_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     91\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     92\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mnew_fact\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfact_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m)\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/_pyke_rules/compiled_krb/fc_rules_cf_fc.pyc\u001b[0m in \u001b[0;36mfc_build_auxiliary_coordinate_latitude\u001b[1;34m(rule, context, index)\u001b[0m\n\u001b[0;32m    257\u001b[0m             \u001b[0mcf_coord_var\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcf_var\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcf_group\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauxiliary_coordinates\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlookup_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'coordinate'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    258\u001b[0m             build_auxiliary_coordinate(engine, cf_coord_var,\n\u001b[1;32m--> 259\u001b[1;33m             coord_name=CF_VALUE_STD_NAME_LAT)\n\u001b[0m\u001b[0;32m    260\u001b[0m             \u001b[0mengine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrule_triggered\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrule\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    261\u001b[0m             \u001b[0mrule\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrule_base\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnum_fc_rules_triggered\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\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/_pyke_rules/compiled_krb/fc_rules_cf_fc.pyc\u001b[0m in \u001b[0;36mbuild_auxiliary_coordinate\u001b[1;34m(engine, cf_coord_var, coord_name, coord_system)\u001b[0m\n\u001b[0;32m   1337\u001b[0m                                  \u001b[0mbounds\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbounds_data\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1338\u001b[0m                                  \u001b[0mattributes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mattributes\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1339\u001b[1;33m                                  coord_system=coord_system)\n\u001b[0m\u001b[0;32m   1340\u001b[0m     \u001b[0mcube\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_aux_coord\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcoord\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_dims\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1341\u001b[0m     \u001b[0mengine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprovides\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'coordinates'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcoord\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcf_coord_var\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcf_name\u001b[0m\u001b[1;33m)\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/coords.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, points, standard_name, long_name, var_name, units, bounds, attributes, coord_system)\u001b[0m\n\u001b[0;32m    344\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    345\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpoints\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpoints\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 346\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbounds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbounds\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    347\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    348\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\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/coords.pyc\u001b[0m in \u001b[0;36mbounds\u001b[1;34m(self, bounds)\u001b[0m\n\u001b[0;32m   1384\u001b[0m             \u001b[1;31m# NB. Use _points to avoid triggering any lazy array.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1385\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_points\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mbounds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1386\u001b[1;33m                 raise ValueError(\"Bounds shape must be compatible with points \"\n\u001b[0m\u001b[0;32m   1387\u001b[0m                                  \"shape.\")\n\u001b[0;32m   1388\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_bounds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbounds\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mValueError\u001b[0m: Bounds shape must be compatible with points shape."
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}