Regrid performance

regrid_performance.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import iris"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import iris.tests.stock as stock"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cube = stock.global_pp()\n",
      "\n",
      "print cube\n",
      "cube.coord('longitude').guess_bounds()\n",
      "cube.coord('latitude').guess_bounds()"
     ],
     "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: time\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import biggus\n",
      "import numpy as np\n",
      "\n",
      "nx, ny = 70, 70\n",
      "data = biggus.ConstantArray((nx, ny), 0)\n",
      "\n",
      "grid_cube = iris.cube.Cube(data)\n",
      "coord = cube.coord('longitude').points; lon = np.linspace(int(coord.min()), int(coord.max()), nx)\n",
      "coord = cube.coord('latitude').points; lat = np.linspace(int(coord.min()), int(coord.max()), ny)\n",
      "\n",
      "lon_coord, lat_coord = cube.coord('longitude').copy(lon), cube.coord('latitude').copy(lat)\n",
      "lon_coord.guess_bounds()\n",
      "lat_coord.guess_bounds()\n",
      "grid_cube.add_dim_coord(lon_coord, 1)\n",
      "grid_cube.add_dim_coord(lat_coord, 0)\n",
      "print grid_cube"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "unknown / (unknown)                 (latitude: 70; longitude: 70)\n",
        "     Dimension coordinates:\n",
        "          latitude                           x              -\n",
        "          longitude                          -              x\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%time\n",
      "regridded_cube = cube.regrid(grid_cube, iris.analysis.AreaWeighted())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 2.03 s, sys: 0 ns, total: 2.03 s\n",
        "Wall time: 2.03 s\n"
       ]
      }
     ],
     "prompt_number": 5
    }
   ],
   "metadata": {}
  }
 ]
}