Very basic 9-point smoothing using rolling_window()

smooth9.ipynb

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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import iris\n",
      "import iris.quickplot as qplt\n",
      "from iris.util import rolling_window\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cube = iris.load_cube(iris.sample_data_path('air_temp.pp'))\n",
      "print cube"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "air_temperature / (K)               (latitude: 73; longitude: 96)\n",
        "     Dimension coordinates:\n",
        "          latitude                           x              -\n",
        "          longitude                          -              x\n",
        "     Scalar coordinates:\n",
        "          forecast_period: 6477 hours, bound=(-28083.0, 6477.0) hours\n",
        "          forecast_reference_time: 1998-03-01 03:00:00\n",
        "          pressure: 1000.0 hPa\n",
        "          time: 1998-12-01 00:00:00, bound=(1994-12-01 00:00:00, 1998-12-01 00:00:00)\n",
        "     Attributes:\n",
        "          STASH: m01s16i203\n",
        "          source: Data from Met Office Unified Model\n",
        "     Cell methods:\n",
        "          mean within years: time\n",
        "          mean over years: time\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ndims = cube.ndim\n",
      "xcoord = cube.coord(axis='X')\n",
      "ycoord = cube.coord(axis='Y')\n",
      "xdim, = cube.coord_dims(xcoord)\n",
      "ydim, = cube.coord_dims(ycoord)\n",
      "cyclic = xcoord.circular\n",
      "\n",
      "yu_slicer = [slice(None) if i != ydim else slice(0, 1) for i in range(ndims)]\n",
      "yl_slicer = [slice(None) if i != ydim else slice(-1, None) for i in range(ndims)]\n",
      "    \n",
      "# For x-dimension we allow a cyclic axis, so the padding is just the adjacent row\n",
      "# if cyclice=False, but is the row from the opposite end if cyclic=True:\n",
      "if cyclic:\n",
      "    xu_slicer = [slice(None) if i != xdim else slice(-1, None) for i in range(ndims)]\n",
      "    xl_slicer = [slice(None) if i != xdim else slice(0, 1) for i in range(ndims)]\n",
      "else:\n",
      "    xu_slicer = [slice(None) if i != xdim else slice(0, 1) for i in range(ndims)]\n",
      "    xl_slicer = [slice(None) if i != xdim else slice(-1, None) for i in range(ndims)]\n",
      "    \n",
      "# Pad the data:\n",
      "padded = cube.data\n",
      "padded = np.concatenate((padded[yu_slicer], padded, padded[yl_slicer]), axis=ydim)\n",
      "padded = np.concatenate((padded[xu_slicer], padded, padded[xl_slicer]), axis=xdim)\n",
      "print('Cube had dimensions: {}, padded data has dimensions: {}'.format(cube.shape, padded.shape))\n",
      "\n",
      "# Apply rolling window twice, once for each of the x and y dimensions:\n",
      "if xdim > ydim:\n",
      "    xdim += 1\n",
      "else:\n",
      "    ydim += 1\n",
      "rw = rolling_window(padded, window=3, axis=ydim)\n",
      "print('After 1 application rolling-window data has dimensions: {}'.format(rw.shape))\n",
      "rw = rolling_window(rw, window=3, axis=xdim)\n",
      "print('After 2 applications rolling-window data has dimensions: {}'.format(rw.shape))\n",
      "\n",
      "# Average over the two new dimensions to get the 9-point smoothing of the original data:\n",
      "new_data = rw.mean(axis=(1, 3))\n",
      "\n",
      "# Create a new cube and copy the data in:\n",
      "new_cube = cube.copy()\n",
      "new_cube.data = new_data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Cube had dimensions: (73, 96), padded data has dimensions: (75, 98)\n",
        "After 1 application rolling-window data has dimensions: (73, 3, 98)\n",
        "After 2 applications rolling-window data has dimensions: (73, 3, 96, 3)\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plot the original cube:\n",
      "qplt.pcolormesh(cube)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/network/home/aopp/dawson/miniconda/envs/iris-dev/lib/python2.7/site-packages/Iris-1.8.0-py2.7.egg/iris/coords.py:779: UserWarning: Coordinate 'longitude' is not bounded, guessing contiguous bounds.\n",
        "  'contiguous bounds.'.format(self.name()))\n",
        "/network/home/aopp/dawson/miniconda/envs/iris-dev/lib/python2.7/site-packages/Iris-1.8.0-py2.7.egg/iris/coords.py:779: UserWarning: Coordinate 'latitude' is not bounded, guessing contiguous bounds.\n",
        "  'contiguous bounds.'.format(self.name()))\n",
        "/network/home/aopp/dawson/miniconda/envs/iris-dev/lib/python2.7/site-packages/Cartopy-0.12.x-py2.7-linux-x86_64.egg/cartopy/mpl/geoaxes.py:1267: RuntimeWarning: invalid value encountered in greater\n",
        "  to_mask = ((np.abs(dx_horizontal) > np.pi / 2) |\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<matplotlib.collections.QuadMesh at 0x7f7f7d32a110>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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GrQe+D3ysaAAD23dHM5WuN9mMPR4rkqeTrxIrwTHjcmZ/e/lJ1KSjeq0K9/oAPAVsz8vX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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f7f7d30cf90>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plot the new cube:\n",
      "qplt.pcolormesh(new_cube)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<matplotlib.collections.QuadMesh at 0x7f7f7d0a9f90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ez52nFZqoW2bQ+oiux7Gf76V6WzBK11v0OzuGwEs7zlu7dPCV6Azeb2YU6s+e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\nX7tdPyldIYTokHYf4Z8x2/x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       "text": [
        "<matplotlib.figure.Figure at 0x7f7f7d24b0d0>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}