coord_categorisation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dc388fcd",
   "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",
   "id": "a08f98d6",
   "metadata": {},
   "source": [
    "### The Example\n",
    "\n",
    "We will start by importing required libraries, checking versions of Iris and loading the A1B scenario data for North America."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "79433111",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import iris\n",
    "import iris.coord_categorisation as coord_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4bd842d9",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris version: 3.10.0\n"
     ]
    }
   ],
   "source": [
    "print('Iris version: {}'.format(iris.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "433dc3be",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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",
      "    Cell methods:\n",
      "        0                           time: mean (interval: 6 hour)\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"
     ]
    }
   ],
   "source": [
    "a1b_cube = iris.load_cube(iris.sample_data_path('A1B_north_america.nc'))\n",
    "print (a1b_cube)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "664e31a7",
   "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",
   "execution_count": 4,
   "id": "3e795550",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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",
      "    Cell methods:\n",
      "        0                           time: mean (interval: 6 hour)\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"
     ]
    }
   ],
   "source": [
    "coord_cat.add_year(a1b_cube, 'time')\n",
    "after_1980 = a1b_cube.extract(iris.Constraint(year=lambda cell: cell>1979))\n",
    "print (after_1980)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f210ef69",
   "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",
   "execution_count": 5,
   "id": "2d164af6",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "def decade_from_time(coord, point):\n",
    "    years = coord.units.num2date(point).year\n",
    "    return int(years/10) * 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2ea2fc54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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",
      "    Cell methods:\n",
      "        0                           time: mean (interval: 6 hour)\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"
     ]
    }
   ],
   "source": [
    "coord_cat.add_categorised_coord(after_1980, 'decade', 'time', decade_from_time)\n",
    "print (after_1980)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3ff5768",
   "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",
   "execution_count": 7,
   "id": "22cce129",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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",
      "    Cell methods:\n",
      "        0                           time: mean (interval: 6 hour)\n",
      "        1                           decade: mean\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"
     ]
    }
   ],
   "source": [
    "decadal_means = after_1980.aggregated_by('decade', iris.analysis.MEAN)\n",
    "print (decadal_means)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5af8d9d5",
   "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",
   "execution_count": 8,
   "id": "7bbda7e8",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DimCoord :  time / (hours since 1970-01-01 00:00:00, 360_day calendar)\n",
      "    points: [\n",
      "        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]\n",
      "    bounds: [\n",
      "        [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",
      "        ...,\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]]\n",
      "    shape: (12,)  bounds(12, 2)\n",
      "    dtype: float64\n",
      "    standard_name: 'time'\n",
      "    var_name: 'time'\n"
     ]
    }
   ],
   "source": [
    "print (decadal_means.coord('time'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d4b2087",
   "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",
   "execution_count": 9,
   "id": "dce5968c",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import iris.quickplot as qplt\n",
    "import matplotlib.pyplot as plt\n",
    "qplt.plot(after_1980[:, 0, 0], label=\"yearly\")\n",
    "qplt.plot(decadal_means[:, 0, 0], drawstyle='steps-mid', label=\"decadal\")\n",
    "plt.legend()\n",
    "qplt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd06617d-b638-467f-a95f-48039fbcd5d1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}