iris_snippets.ipynb

{
 "metadata": {
  "kernelspec": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "display_name": "IOOS (Python 2)",
   "language": "python",
   "name": "ioos_python2"
  },
  "name": "",
  "signature": "sha256:26aa231a7f37ad796044ce8ceaff8c2a5fd3729b96309e865b3c10cfc1daca60"
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import iris\n",
      "from iris.unit import Unit\n",
      "from iris.cube import CubeList\n",
      "from iris.exceptions import CoordinateNotFoundError, CoordinateMultiDimError\n",
      "\n",
      "iris.FUTURE.netcdf_promote = True\n",
      "iris.FUTURE.cell_datetime_objects = True\n",
      "\n",
      "\n",
      "def time_coord(cube):\n",
      "    \"\"\"Return the variable attached to time axis and rename it to time.\"\"\"\n",
      "    try:\n",
      "        cube.coord(axis='T').rename('time')\n",
      "    except CoordinateNotFoundError:\n",
      "        pass\n",
      "    timevar = cube.coord('time')\n",
      "    return timevar\n",
      "\n",
      "\n",
      "def z_coord(cube):\n",
      "    \"\"\"Heuristic way to return the\n",
      "    dimensionless vertical coordinate.\"\"\"\n",
      "    try:\n",
      "        z = cube.coord(axis='Z')\n",
      "    except CoordinateNotFoundError:\n",
      "        z = cube.coords(axis='Z')\n",
      "        for coord in cube.coords(axis='Z'):\n",
      "            if coord.ndim == 1:\n",
      "                z = coord\n",
      "    return z\n",
      "\n",
      "\n",
      "def time_near(cube, datetime):\n",
      "    \"\"\"Return the nearest index to a `datetime`.\"\"\"\n",
      "    timevar = time_coord(cube)\n",
      "    try:\n",
      "        time = timevar.units.date2num(datetime)\n",
      "        idx = timevar.nearest_neighbour_index(time)\n",
      "    except IndexError:\n",
      "        idx = -1\n",
      "    return idx\n",
      "\n",
      "\n",
      "def time_slice(cube, start, stop=None):\n",
      "    \"\"\"TODO: Re-write to use `iris.FUTURE.cell_datetime_objects`.\"\"\"\n",
      "    istart = time_near(cube, start)\n",
      "    if stop:\n",
      "        istop = time_near(cube, stop)\n",
      "        if istart == istop:\n",
      "            raise ValueError('istart must be different from istop!'\n",
      "                             'Got istart {!r} and '\n",
      "                             ' istop {!r}'.format(istart, istop))\n",
      "        return cube[istart:istop, ...]\n",
      "    else:\n",
      "        return cube[istart, ...]\n",
      "\n",
      "\n",
      "def bbox_extract_2Dcoords(cube, bbox):\n",
      "    \"\"\"Extract a sub-set of a cube inside a lon, lat bounding box\n",
      "    bbox=[lon_min lon_max lat_min lat_max].\n",
      "    NOTE: This is a work around too subset an iris cube that has\n",
      "    2D lon, lat coords.\"\"\"\n",
      "    lons = cube.coord('longitude').points\n",
      "    lats = cube.coord('latitude').points\n",
      "\n",
      "    def minmax(v):\n",
      "        return np.min(v), np.max(v)\n",
      "\n",
      "    inregion = np.logical_and(np.logical_and(lons > bbox[0],\n",
      "                                             lons < bbox[2]),\n",
      "                              np.logical_and(lats > bbox[1],\n",
      "                                             lats < bbox[3]))\n",
      "    region_inds = np.where(inregion)\n",
      "    imin, imax = minmax(region_inds[0])\n",
      "    jmin, jmax = minmax(region_inds[1])\n",
      "    return cube[..., imin:imax+1, jmin:jmax+1]\n",
      "\n",
      "\n",
      "def intersection(cube, bbox):\n",
      "    \"\"\"Sub sets cube with 1D or 2D lon, lat coords.\n",
      "    Using `intersection` instead of `extract` we deal with 0-360\n",
      "    longitudes automagically.\"\"\"\n",
      "    try:\n",
      "        method = \"Using iris `cube.intersection`\"\n",
      "        cube = cube.intersection(longitude=(bbox[0], bbox[2]),\n",
      "                                 latitude=(bbox[1], bbox[3]))\n",
      "    except CoordinateMultiDimError:\n",
      "        method = \"Using iris `bbox_extract_2Dcoords`\"\n",
      "        cube = bbox_extract_2Dcoords(cube, bbox)\n",
      "    print(method)\n",
      "    return cube\n",
      "\n",
      "\n",
      "def get_cube(url, name_list=None, bbox=None, time=None, units=None):\n",
      "    cubes = iris.load_raw(url)\n",
      "    if name_list:\n",
      "        in_list = lambda cube: cube.standard_name in name_list\n",
      "        cubes = CubeList([cube for cube in cubes if in_list(cube)])\n",
      "        if not cubes:\n",
      "            raise ValueError('Cube does not contain {!r}'.format(name_list))\n",
      "        else:\n",
      "            cube = cubes.merge_cube()\n",
      "    if bbox:\n",
      "        cube = intersection(cube, bbox)\n",
      "    if time:\n",
      "        if isinstance(time, datetime):\n",
      "            start, stop = time, None\n",
      "        elif isinstance(time, tuple):\n",
      "            start, stop = time[0], time[1]\n",
      "        else:\n",
      "            raise ValueError('Time must be start or (start, stop).'\n",
      "                             '  Got {!r}'.format(time))\n",
      "        cube = time_slice(cube, start, stop)\n",
      "    if units:\n",
      "        if not cube.units == units:\n",
      "            cube.convert_units(units)\n",
      "    return cube"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import time\n",
      "import contextlib\n",
      "\n",
      "\n",
      "@contextlib.contextmanager\n",
      "def timeit(log=None):\n",
      "    t = time.time()\n",
      "    yield\n",
      "    elapsed = time.strftime(\"%H:%M:%S\", time.gmtime(time.time()-t))\n",
      "    if log:\n",
      "        log.info(elapsed)\n",
      "    else:\n",
      "        print(elapsed)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import numpy as np\n",
      "import numpy.ma as ma\n",
      "import iris.quickplot as qplt\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "\n",
      "def plot_surface(cube, model=''):\n",
      "    z = z_coord(cube)\n",
      "    positive = z.attributes.get('positive', None)\n",
      "    if positive == 'up':\n",
      "        idx = np.argmax(z.points)\n",
      "    else:\n",
      "        idx = np.argmin(z.points)\n",
      "\n",
      "    c = cube[idx, ...]\n",
      "    c.data = ma.masked_invalid(c.data)\n",
      "    t = time_coord(cube)\n",
      "    t = t.units.num2date(t.points)[0]\n",
      "    qplt.pcolormesh(c)\n",
      "    plt.title('{}: {}\\nVariable: {} level: {}'.format(model, t, c.name(), idx))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(iris.__version__)\n",
      "print(iris.__file__)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1.7.2-DEV\n",
        "/home/filipe/miniconda/envs/ioos/lib/python2.7/site-packages/iris/__init__.pyc\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from datetime import datetime, timedelta\n",
      "\n",
      "start = datetime.utcnow() - timedelta(days=7)\n",
      "stop = datetime.utcnow()\n",
      "\n",
      "name_list = ['sea_water_potential_temperature', 'sea_water_temperature']\n",
      "\n",
      "bbox = [-76.4751, 38.3890, -71.7432, 42.9397]\n",
      "\n",
      "units = Unit('Kelvin')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model = 'MARACOOS/ESPRESSO'\n",
      "url = 'http://tds.marine.rutgers.edu/thredds/dodsC/roms/espresso/2009_da/his'\n",
      "\n",
      "with timeit():\n",
      "    cube = get_cube(url, name_list=name_list, bbox=bbox,\n",
      "                    time=start, units=units)\n",
      "    plot_surface(cube, model)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/home/filipe/miniconda/envs/ioos/lib/python2.7/site-packages/iris/fileformats/cf.py:1038: UserWarning: Ignoring formula terms variable u'h' referenced by data variable u'v' via variable u's_rho': Dimensions (u'eta_rho', u'xi_rho') do not span (u'ocean_time', u's_rho', u'eta_v', u'xi_v')\n",
        "  warnings.warn(msg)\n",
        "/home/filipe/miniconda/envs/ioos/lib/python2.7/site-packages/iris/fileformats/cf.py:1038: UserWarning: Ignoring formula terms variable u'zeta' referenced by data variable u'v' via variable u's_rho': Dimensions (u'ocean_time', u'eta_rho', u'xi_rho') do not span (u'ocean_time', u's_rho', u'eta_v', u'xi_v')\n",
        "  warnings.warn(msg)\n",
        "/home/filipe/miniconda/envs/ioos/lib/python2.7/site-packages/iris/fileformats/cf.py:1038: UserWarning: Ignoring formula terms variable u'h' referenced by data variable u'u' via variable u's_rho': Dimensions (u'eta_rho', u'xi_rho') do not span (u'ocean_time', u's_rho', u'eta_u', u'xi_u')\n",
        "  warnings.warn(msg)\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Using iris `bbox_extract_2Dcoords`\n",
        "00:00:11\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/home/filipe/miniconda/envs/ioos/lib/python2.7/site-packages/iris/fileformats/cf.py:1038: UserWarning: Ignoring formula terms variable u'zeta' referenced by data variable u'u' via variable u's_rho': Dimensions (u'ocean_time', u'eta_rho', u'xi_rho') do not span (u'ocean_time', u's_rho', u'eta_u', u'xi_u')\n",
        "  warnings.warn(msg)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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PAEfWXrF9Is/utgTbBoWJYz3kf297lVobPqev2NYHd7c3OvdbbbWca0DX1j1z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\natzWTWvRPQ8VM3042S5ZJ3Dm1Op9Nu9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xhtxxHKfP8YbccRynz/GG3HEcp8/xhtxxHKfP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\n5/8HcNNEmtCXbe4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f0bebd5ae50>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model = 'USGS/COAWST'\n",
      "url = 'http://geoport.whoi.edu/thredds/dodsC/coawst_4/use/fmrc/'\n",
      "url += 'coawst_4_use_best.ncd'\n",
      "\n",
      "with timeit():\n",
      "    cube = get_cube(url, name_list=name_list, bbox=bbox,\n",
      "                    time=start, units=units)\n",
      "    plot_surface(cube, model)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/home/filipe/miniconda/envs/ioos/lib/python2.7/site-packages/iris/fileformats/cf.py:1038: UserWarning: Ignoring formula terms variable u'h' referenced by data variable u'v' via variable u's_rho': Dimensions (u'eta_rho', u'xi_rho') do not span (u'time', u's_rho', u'eta_v', u'xi_v')\n",
        "  warnings.warn(msg)\n",
        "/home/filipe/miniconda/envs/ioos/lib/python2.7/site-packages/iris/fileformats/cf.py:1038: UserWarning: Ignoring formula terms variable u'zeta' referenced by data variable u'v' via variable u's_rho': Dimensions (u'time', u'eta_rho', u'xi_rho') do not span (u'time', u's_rho', u'eta_v', u'xi_v')\n",
        "  warnings.warn(msg)\n",
        "/home/filipe/miniconda/envs/ioos/lib/python2.7/site-packages/iris/fileformats/cf.py:1038: UserWarning: Ignoring formula terms variable u'h' referenced by data variable u'u' via variable u's_rho': Dimensions (u'eta_rho', u'xi_rho') do not span (u'time', u's_rho', u'eta_u', u'xi_u')\n",
        "  warnings.warn(msg)\n",
        "/home/filipe/miniconda/envs/ioos/lib/python2.7/site-packages/iris/fileformats/cf.py:1038: UserWarning: Ignoring formula terms variable u'zeta' referenced by data variable u'u' via variable u's_rho': Dimensions (u'time', u'eta_rho', u'xi_rho') do not span (u'time', u's_rho', u'eta_u', u'xi_u')\n",
        "  warnings.warn(msg)\n",
        "/home/filipe/miniconda/envs/ioos/lib/python2.7/site-packages/iris/fileformats/_pyke_rules/compiled_krb/fc_rules_cf_fc.py:1291: UserWarning: Gracefully filling 'time' dimension coordinate masked points\n",
        "  warnings.warn(msg.format(str(cf_coord_var.cf_name)))\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Using iris `bbox_extract_2Dcoords`\n",
        "00:00:44\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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sespfj7MSu96LHtMFso14u3yKs+oGcz/GRwadx3DwG/NlrzVSs7NJtxncTYDT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\nuNV8356Z1EZ23oOqVk6Kf2F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ddcT0m+rSt2vip/cwNxFLVQLtLv2mUqd9RuXeXTz/5qZx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\nM/ACYCnwB+DMEMJdlQc6juM4I8aQdeohhM0i8jb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       "text": [
        "<matplotlib.figure.Figure at 0x7f0bc3a64750>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model = 'HYCOM'\n",
      "url = 'http://ecowatch.ncddc.noaa.gov/thredds/dodsC/hycom/hycom_reg1_agg/'\n",
      "url += 'HYCOM_Region_1_Aggregation_best.ncd'\n",
      "\n",
      "with timeit():\n",
      "    cube = get_cube(url, name_list=name_list, bbox=bbox,\n",
      "                    time=start, units=units)\n",
      "    plot_surface(cube, model)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/home/filipe/miniconda/envs/ioos/lib/python2.7/site-packages/iris/fileformats/_pyke_rules/compiled_krb/fc_rules_cf_fc.py:1196: UserWarning: Ignoring netCDF variable 'salinity' invalid units 'psu'\n",
        "  warnings.warn(msg.format(msg_name, msg_units))\n",
        "/home/filipe/miniconda/envs/ioos/lib/python2.7/site-packages/iris/coords.py:775: UserWarning: Coordinate u'longitude' is not bounded, guessing contiguous bounds.\n",
        "  'contiguous bounds.'.format(self.name()))\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Using iris `cube.intersection`\n",
        "00:00:08\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/home/filipe/miniconda/envs/ioos/lib/python2.7/site-packages/iris/coords.py:775: UserWarning: Coordinate u'latitude' is not bounded, guessing contiguous bounds.\n",
        "  'contiguous bounds.'.format(self.name()))\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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DXQpXjQ/Cv4Kyh4qflbvoHTJjwp9LHfyAk8fZFu8OudfJPmC/7/0hBhwZaNH3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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f0bc34a7e90>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model = 'NYHOP'\n",
      "url = 'http://colossus.dl.stevens-tech.edu/thredds/dodsC/fmrc/NYBight/'\n",
      "url += 'NYHOPS_Forecast_Collection_for_the_New_York_Bight_best.ncd'\n",
      "\n",
      "with timeit():\n",
      "    cube = get_cube(url, name_list=name_list, bbox=bbox,\n",
      "                    time=start, units=units)\n",
      "    plot_surface(cube, model)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Using iris `bbox_extract_2Dcoords`\n",
        "00:01:23\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/home/filipe/miniconda/envs/ioos/lib/python2.7/site-packages/iris/coords.py:775: UserWarning: Coordinate u'Y-coordinate in Cartesian system' is not bounded, guessing contiguous bounds.\n",
        "  'contiguous bounds.'.format(self.name()))\n",
        "/home/filipe/miniconda/envs/ioos/lib/python2.7/site-packages/iris/coords.py:775: UserWarning: Coordinate u'X-coordinate in Cartesian system' is not bounded, guessing contiguous bounds.\n",
        "  'contiguous bounds.'.format(self.name()))\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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rJ8ot3xKe+nWwaaVFuDBncHsO+JCk9fLnoc/95SLzpnoN4uYvuaVYxo30+ZjR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\nklRicZAklVgcJEklFgdJUslXii4O9mp3HdAAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f0bc2f4d3d0>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model = 'RUTGERS/NWA'\n",
      "url = 'http://oceanus.esm.rutgers.edu:8090/thredds/dodsC/ROMS/NWA/Run03/Output'\n",
      "\n",
      "with timeit():\n",
      "    cube = get_cube(url, name_list=name_list, bbox=bbox,\n",
      "                    time=start, units=units)\n",
      "    plot_surface(cube, model)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/home/filipe/miniconda/envs/ioos/lib/python2.7/site-packages/iris/fileformats/_pyke_rules/compiled_krb/fc_rules_cf_fc.py:1196: UserWarning: Ignoring netCDF variable 'TIC' invalid units 'millimole_carbon meter-3'\n",
        "  warnings.warn(msg.format(msg_name, msg_units))\n",
        "/home/filipe/miniconda/envs/ioos/lib/python2.7/site-packages/iris/fileformats/_pyke_rules/compiled_krb/fc_rules_cf_fc.py:1196: UserWarning: Ignoring netCDF variable 'SiOH' invalid units 'millimole_silica meter-3'\n",
        "  warnings.warn(msg.format(msg_name, msg_units))\n",
        "/home/filipe/miniconda/envs/ioos/lib/python2.7/site-packages/iris/fileformats/_pyke_rules/compiled_krb/fc_rules_cf_fc.py:1196: UserWarning: Ignoring netCDF variable 'NO3' invalid units 'millimole_N03 meter-3'\n",
        "  warnings.warn(msg.format(msg_name, msg_units))\n",
        "/home/filipe/miniconda/envs/ioos/lib/python2.7/site-packages/iris/fileformats/_pyke_rules/compiled_krb/fc_rules_cf_fc.py:1196: UserWarning: Ignoring netCDF variable 'NH4' invalid units 'millimole_NH4 meter-3'\n",
        "  warnings.warn(msg.format(msg_name, msg_units))\n",
        "/home/filipe/miniconda/envs/ioos/lib/python2.7/site-packages/iris/fileformats/_pyke_rules/compiled_krb/fc_rules_cf_fc.py:1196: UserWarning: Ignoring netCDF variable 'PO4' invalid units 'millimole_PO4 meter-3'\n",
        "  warnings.warn(msg.format(msg_name, msg_units))\n",
        "/home/filipe/miniconda/envs/ioos/lib/python2.7/site-packages/iris/fileformats/_pyke_rules/compiled_krb/fc_rules_cf_fc.py:1196: UserWarning: Ignoring netCDF variable 'oxyg' invalid units 'millimole_O2 meter-3'\n",
        "  warnings.warn(msg.format(msg_name, msg_units))\n"
       ]
      },
      {
       "ename": "ValueError",
       "evalue": "Cube does not contain ['sea_water_potential_temperature', 'sea_water_temperature']",
       "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-ee0aebe85ed7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mtimeit\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[0;32m      5\u001b[0m     cube = get_cube(url, name_list=name_list, bbox=bbox,\n\u001b[1;32m----> 6\u001b[1;33m                     time=start, units=units)\n\u001b[0m\u001b[0;32m      7\u001b[0m     \u001b[0mplot_surface\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcube\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-1-47814451a39b>\u001b[0m in \u001b[0;36mget_cube\u001b[1;34m(url, name_list, bbox, time, units)\u001b[0m\n\u001b[0;32m     98\u001b[0m         \u001b[0mcubes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCubeList\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcube\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcube\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcubes\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0min_list\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcube\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[0;32m     99\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcubes\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 100\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Cube does not contain {!r}'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname_list\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    101\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    102\u001b[0m             \u001b[0mcube\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcubes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmerge_cube\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mValueError\u001b[0m: Cube does not contain ['sea_water_potential_temperature', 'sea_water_temperature']"
       ]
      }
     ],
     "prompt_number": 10
    }
   ],
   "metadata": {}
  }
 ]
}