{
 "metadata": {
  "name": "coord_categorisation"
 },
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 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Coordinate Categorisation\n",
      "\n",
      "### Introduction\n",
      "\n",
      "Coordinate categorisation allows data within a given coordinate on a cube to be categorised, notably in terms of adding a time category, for example days of week, month or year, or season membership. Such categorisation appears as an aux coord on the cube, having the same dimensions as the time coordinate.\n",
      "\n",
      "Let's take a look at an example. In this example we will take the A1B-scenario climate futures data for North America, which contains 240 yearly timesteps from 1860 to 2099, and categorise this time coord first by year and then by decade. We will use the former categorisation to extract data from the cube from after the year 1980 and the latter categorisation to plot the cube's data on a series of per-decade aggregations."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### The Example\n",
      "\n",
      "We will start by importing required libraries, checking versions of Iris and Cartopy and loading the A1B scenario data for North America."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "\n",
      "import cartopy\n",
      "import cartopy.crs as ccrs\n",
      "import iris\n",
      "import iris.coord_categorisation as coord_cat"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print('Iris version: {}'.format(iris.__version__))\n",
      "print('Cartopy version: {}'.format(cartopy.__version__))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Iris version: 1.5.1\n",
        "Cartopy version: 0.9.0\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a1b_cube = iris.load_cube(iris.sample_data_path('A1B_north_america.nc'))\n",
      "print a1b_cube"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "air_temperature / (K)               (time: 240; latitude: 37; longitude: 49)\n",
        "     Dimension coordinates:\n",
        "          time                           x              -              -\n",
        "          latitude                       -              x              -\n",
        "          longitude                      -              -              x\n",
        "     Auxiliary coordinates:\n",
        "          forecast_period                x              -              -\n",
        "     Scalar coordinates:\n",
        "          forecast_reference_time: 1859-09-01 06:00:00\n",
        "          height: 1.5 m\n",
        "     Attributes:\n",
        "          Conventions: CF-1.5\n",
        "          Model scenario: A1B\n",
        "          STASH: m01s03i236\n",
        "          source: Data from Met Office Unified Model 6.05\n",
        "     Cell methods:\n",
        "          mean: time (6 hour)\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we will categorise the time coord by year and use the resultant aux coord to extract a subset of the cube that has a year value of 1980 or greater."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "coord_cat.add_year(a1b_cube, 'time')\n",
      "after_1980 = a1b_cube.extract(iris.Constraint(year=lambda cell: cell>1979))\n",
      "print after_1980"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "air_temperature / (K)               (time: 120; latitude: 37; longitude: 49)\n",
        "     Dimension coordinates:\n",
        "          time                           x              -              -\n",
        "          latitude                       -              x              -\n",
        "          longitude                      -              -              x\n",
        "     Auxiliary coordinates:\n",
        "          forecast_period                x              -              -\n",
        "          year                           x              -              -\n",
        "     Scalar coordinates:\n",
        "          forecast_reference_time: 1859-09-01 06:00:00\n",
        "          height: 1.5 m\n",
        "     Attributes:\n",
        "          Conventions: CF-1.5\n",
        "          Model scenario: A1B\n",
        "          STASH: m01s03i236\n",
        "          source: Data from Met Office Unified Model 6.05\n",
        "     Cell methods:\n",
        "          mean: time (6 hour)\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Categorising a time coordinate by year is one of the functions built-in to `iris.coord_categorisation`, meaning that happily for us we do not have to write it ourselves. In the above block of code we do two things: add the categorised coordinate and then extract by year greater than 1980. Note that the categorised coordinate appears as an aux coord (`year`) on the cube printed above, and shares a dimension with the time coordinate, as would be expected.\n",
      "\n",
      "Now we will also add a 'decade' aux coord to our smaller cube `after_1980` above. Unlike categorising by year, this is not a built-in function, so we will not get this for free!\n",
      "\n",
      "Instead we will first write a function that will perform the categorisation. This function has the same generalised style as all coord categorisation functions in that it takes a coordinate and a specific point from that coordinate as its argument, and returns the point, categorised as required. This generalised style also means this, or any function like it, can be used with the general coord categorisation function `iris.coord_categorisation.add_categorised_coord`, as will be demonstrated below."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def decade_from_time(coord, point):\n",
      "    years = coord.units.num2date(point).year\n",
      "    return (years/10) * 10"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "coord_cat.add_categorised_coord(after_1980, 'decade', 'time', decade_from_time)\n",
      "print after_1980"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "air_temperature / (K)               (time: 120; latitude: 37; longitude: 49)\n",
        "     Dimension coordinates:\n",
        "          time                           x              -              -\n",
        "          latitude                       -              x              -\n",
        "          longitude                      -              -              x\n",
        "     Auxiliary coordinates:\n",
        "          decade                         x              -              -\n",
        "          forecast_period                x              -              -\n",
        "          year                           x              -              -\n",
        "     Scalar coordinates:\n",
        "          forecast_reference_time: 1859-09-01 06:00:00\n",
        "          height: 1.5 m\n",
        "     Attributes:\n",
        "          Conventions: CF-1.5\n",
        "          Model scenario: A1B\n",
        "          STASH: m01s03i236\n",
        "          source: Data from Met Office Unified Model 6.05\n",
        "     Cell methods:\n",
        "          mean: time (6 hour)\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Performing this coord categorisation has added a further aux coord to our cube, called `decade`. This contains year values rounded to the decade they sit within, such that 1995 is rounded to 1990 or 2058 is rounded to 2050. This means there are 10 data values for each unique year value within our `decade` coord, so we can aggregate these 10 values together to get one mean value for each decade within our cube.\n",
      "\n",
      "Being able to do things like this is one of the real benefits of coord categorisation.\n",
      "\n",
      "Let's go ahead and aggregate our cube by decade to get decadal mean values. Then we can plot these decadal means."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "decadal_means = after_1980.aggregated_by('decade', iris.analysis.MEAN)\n",
      "print decadal_means"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "air_temperature / (K)               (time: 12; latitude: 37; longitude: 49)\n",
        "     Dimension coordinates:\n",
        "          time                           x             -              -\n",
        "          latitude                       -             x              -\n",
        "          longitude                      -             -              x\n",
        "     Auxiliary coordinates:\n",
        "          decade                         x             -              -\n",
        "          forecast_period                x             -              -\n",
        "          year                           x             -              -\n",
        "     Scalar coordinates:\n",
        "          forecast_reference_time: 1859-09-01 06:00:00\n",
        "          height: 1.5 m\n",
        "     Attributes:\n",
        "          Conventions: CF-1.5\n",
        "          Model scenario: A1B\n",
        "          STASH: m01s03i236\n",
        "          history: Mean of air_temperature aggregated over decade\n",
        "          source: Data from Met Office Unified Model 6.05\n",
        "     Cell methods:\n",
        "          mean: time (6 hour)\n",
        "          mean: decade\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As makes sense, our decadal means cube has only 12 timesteps; one for each decade in the 120-year time range of our cube of years after 1980. \n",
      "\n",
      "Note that the values within the time coordinate are now the mean time values across the range covered by each point:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print decadal_means.coord('time')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DimCoord([1984-12-01 00:00:00, 1994-12-01 00:00:00, 2004-12-01 00:00:00,\n",
        "       2014-12-01 00:00:00, 2024-12-01 00:00:00, 2034-12-01 00:00:00,\n",
        "       2044-12-01 00:00:00, 2054-12-01 00:00:00, 2064-12-01 00:00:00,\n",
        "       2074-12-01 00:00:00, 2084-12-01 00:00:00, 2094-12-01 00:00:00], bounds=[[1979-12-01 00:00:00, 1989-12-01 00:00:00],\n",
        "       [1989-12-01 00:00:00, 1999-12-01 00:00:00],\n",
        "       [1999-12-01 00:00:00, 2009-12-01 00:00:00],\n",
        "       [2009-12-01 00:00:00, 2019-12-01 00:00:00],\n",
        "       [2019-12-01 00:00:00, 2029-12-01 00:00:00],\n",
        "       [2029-12-01 00:00:00, 2039-12-01 00:00:00],\n",
        "       [2039-12-01 00:00:00, 2049-12-01 00:00:00],\n",
        "       [2049-12-01 00:00:00, 2059-12-01 00:00:00],\n",
        "       [2059-12-01 00:00:00, 2069-12-01 00:00:00],\n",
        "       [2069-12-01 00:00:00, 2079-12-01 00:00:00],\n",
        "       [2079-12-01 00:00:00, 2089-12-01 00:00:00],\n",
        "       [2089-12-01 00:00:00, 2099-12-01 00:00:00]], standard_name=u'time', calendar=u'360_day', var_name='time')\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To inspect the results visually, we can overlay the original time series information with the decadal means.\n",
      "\n",
      "For simplicity we're just going to plot the values for an arbitrary geographic location:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import iris.quickplot as qplt\n",
      "qplt.plot(after_1980[:, 0, 0])\n",
      "qplt.plot(decadal_means[:, 0, 0], drawstyle='steps-mid')\n",
      "qplt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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",
       "text": [
        "<matplotlib.figure.Figure at 0x526b550>"
       ]
      }
     ],
     "prompt_number": 9
    }
   ],
   "metadata": {}
  }
 ]
}