pelson/mega_merge.ipynb

## mega_merge.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If I have a bunch of small tiles for a domain, but they don't necessarily cover the whole area. Is there a way of merging them together to make a single cube in Iris?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Short answer: no\n",
    "\n",
    "Long answer: see this notebook :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import iris\n",
    "import iris.cube\n",
    "import iris.coords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "  a.iris {\n",
       "      text-decoration: none !important;\n",
       "  }\n",
       "  table.iris {\n",
       "      white-space: pre;\n",
       "      border: 1px solid;\n",
       "      border-color: #9c9c9c;\n",
       "      font-family: monaco, monospace;\n",
       "  }\n",
       "  th.iris {\n",
       "      background: #303f3f;\n",
       "      color: #e0e0e0;\n",
       "      border-left: 1px solid;\n",
       "      border-color: #9c9c9c;\n",
       "      font-size: 1.05em;\n",
       "      min-width: 50px;\n",
       "      max-width: 125px;\n",
       "  }\n",
       "  tr.iris :first-child {\n",
       "      border-right: 1px solid #9c9c9c !important;\n",
       "  }\n",
       "  td.iris-title {\n",
       "      background: #d5dcdf;\n",
       "      border-top: 1px solid #9c9c9c;\n",
       "      font-weight: bold;\n",
       "  }\n",
       "  .iris-word-cell {\n",
       "      text-align: left !important;\n",
       "      white-space: pre;\n",
       "  }\n",
       "  .iris-subheading-cell {\n",
       "      padding-left: 2em !important;\n",
       "  }\n",
       "  .iris-inclusion-cell {\n",
       "      padding-right: 1em !important;\n",
       "  }\n",
       "  .iris-panel-body {\n",
       "      padding-top: 0px;\n",
       "  }\n",
       "  .iris-panel-title {\n",
       "      padding-left: 3em;\n",
       "  }\n",
       "  .iris-panel-title {\n",
       "      margin-top: 7px;\n",
       "  }\n",
       "</style>\n",
       "<table class=\"iris\" id=\"4631622208\">\n",
       "    <tr class=\"iris\">\n",
       "<th class=\"iris iris-word-cell\">Air Temperature (K)</th>\n",
       "<th class=\"iris iris-word-cell\">time</th>\n",
       "<th class=\"iris iris-word-cell\">latitude</th>\n",
       "<th class=\"iris iris-word-cell\">longitude</th>\n",
       "</tr>\n",
       "    <tr class=\"iris\">\n",
       "<td class=\"iris-word-cell iris-subheading-cell\">Shape</td>\n",
       "<td class=\"iris iris-inclusion-cell\">2</td>\n",
       "<td class=\"iris iris-inclusion-cell\">37</td>\n",
       "<td class=\"iris iris-inclusion-cell\">49</td>\n",
       "</td>\n",
       "    <tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Dimension coordinates</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\ttime</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tlatitude</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tlongitude</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Auxiliary coordinates</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tforecast_period</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Scalar coordinates</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tforecast_reference_time</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">1859-09-01 06:00:00</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\theight</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">1.5 m</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Attributes</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tConventions</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">CF-1.5</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tModel scenario</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">E1</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tSTASH</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">m01s03i236</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tsource</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">Data from Met Office Unified Model 6.05</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Cell methods</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tmean</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">time (6 hour)</td>\n",
       "</tr>\n",
       "</table>\n",
       "        "
      ],
      "text/plain": [
       "<iris 'Cube' of air_temperature / (K) (time: 2; latitude: 37; longitude: 49)>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fname = iris.sample_data_path('E1_north_america.nc')\n",
    "\n",
    "full_cube = iris.load_cube(fname)[0:2]\n",
    "full_cube"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup the problem so that we have a bunch of chunks, but not necessarily complete ones...\n",
    "chunks = [full_cube[:, 0:10, 0:20], full_cube[:, 2:10, 22:],\n",
    "          full_cube[:, 12:28, 0:10], full_cube[:, 10:30, 10:],\n",
    "          full_cube[:, 30:, 2:-3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import iris.plot as iplt\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at these individual chunks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/pelson/dev/scitools/iris/lib/iris/coords.py:1000: UserWarning: Coordinate 'longitude' is not bounded, guessing contiguous bounds.\n",
      "  'contiguous bounds.'.format(self.name()))\n",
      "/Users/pelson/dev/scitools/iris/lib/iris/coords.py:1000: UserWarning: Coordinate 'latitude' is not bounded, guessing contiguous bounds.\n",
      "  'contiguous bounds.'.format(self.name()))\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112de8400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "norm = plt.Normalize(vmin=full_cube.data.min(), vmax=full_cube.data.max())\n",
    "for cube in chunks:\n",
    "    iplt.pcolormesh(cube[0], norm=norm)\n",
    "plt.gca().coastlines()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The problem boils down to us wanting this data in the form of a single cube..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One approach is to regrid each smaller cube into a single big one, and join those together:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lon range:  225.0 315.0\n",
      "Lat range:  15.0 60.0\n"
     ]
    }
   ],
   "source": [
    "all_lons = sorted(set().union(*[cube.coord('longitude').points for cube in chunks]))\n",
    "all_lats = sorted(set().union(*[cube.coord('latitude').points for cube in chunks]))\n",
    "\n",
    "print('Lon range: ', min(all_lons), max(all_lons))\n",
    "print('Lat range: ', min(all_lats), max(all_lats))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "  a.iris {\n",
       "      text-decoration: none !important;\n",
       "  }\n",
       "  table.iris {\n",
       "      white-space: pre;\n",
       "      border: 1px solid;\n",
       "      border-color: #9c9c9c;\n",
       "      font-family: monaco, monospace;\n",
       "  }\n",
       "  th.iris {\n",
       "      background: #303f3f;\n",
       "      color: #e0e0e0;\n",
       "      border-left: 1px solid;\n",
       "      border-color: #9c9c9c;\n",
       "      font-size: 1.05em;\n",
       "      min-width: 50px;\n",
       "      max-width: 125px;\n",
       "  }\n",
       "  tr.iris :first-child {\n",
       "      border-right: 1px solid #9c9c9c !important;\n",
       "  }\n",
       "  td.iris-title {\n",
       "      background: #d5dcdf;\n",
       "      border-top: 1px solid #9c9c9c;\n",
       "      font-weight: bold;\n",
       "  }\n",
       "  .iris-word-cell {\n",
       "      text-align: left !important;\n",
       "      white-space: pre;\n",
       "  }\n",
       "  .iris-subheading-cell {\n",
       "      padding-left: 2em !important;\n",
       "  }\n",
       "  .iris-inclusion-cell {\n",
       "      padding-right: 1em !important;\n",
       "  }\n",
       "  .iris-panel-body {\n",
       "      padding-top: 0px;\n",
       "  }\n",
       "  .iris-panel-title {\n",
       "      padding-left: 3em;\n",
       "  }\n",
       "  .iris-panel-title {\n",
       "      margin-top: 7px;\n",
       "  }\n",
       "</style>\n",
       "<table class=\"iris\" id=\"4734804824\">\n",
       "    <tr class=\"iris\">\n",
       "<th class=\"iris iris-word-cell\">Air Temperature (K)</th>\n",
       "<th class=\"iris iris-word-cell\">time</th>\n",
       "<th class=\"iris iris-word-cell\">latitude</th>\n",
       "<th class=\"iris iris-word-cell\">longitude</th>\n",
       "</tr>\n",
       "    <tr class=\"iris\">\n",
       "<td class=\"iris-word-cell iris-subheading-cell\">Shape</td>\n",
       "<td class=\"iris iris-inclusion-cell\">2</td>\n",
       "<td class=\"iris iris-inclusion-cell\">37</td>\n",
       "<td class=\"iris iris-inclusion-cell\">49</td>\n",
       "</td>\n",
       "    <tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Dimension coordinates</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\ttime</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tlatitude</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tlongitude</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Auxiliary coordinates</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tforecast_period</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Scalar coordinates</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tforecast_reference_time</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">1859-09-01 06:00:00</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\theight</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">1.5 m</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Attributes</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tConventions</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">CF-1.5</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tModel scenario</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">E1</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tSTASH</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">m01s03i236</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tsource</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">Data from Met Office Unified Model 6.05</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Cell methods</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tmean</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">time (6 hour)</td>\n",
       "</tr>\n",
       "</table>\n",
       "        "
      ],
      "text/plain": [
       "<iris 'Cube' of air_temperature / (K) (time: 2; latitude: 37; longitude: 49)>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import iris.analysis\n",
    "\n",
    "sample_points = [('longitude', all_lons), ('latitude', all_lats)]\n",
    "scheme = iris.analysis.Nearest(extrapolation_mode='mask')\n",
    "resampled = [cube.interpolate(sample_points, scheme) for cube in chunks]\n",
    "\n",
    "resampled[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/pelson/dev/scitools/iris/lib/iris/coords.py:1000: UserWarning: Coordinate 'longitude' is not bounded, guessing contiguous bounds.\n",
      "  'contiguous bounds.'.format(self.name()))\n",
      "/Users/pelson/dev/scitools/iris/lib/iris/coords.py:1000: UserWarning: Coordinate 'latitude' is not bounded, guessing contiguous bounds.\n",
      "  'contiguous bounds.'.format(self.name()))\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a3ac438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iplt.pcolormesh(resampled[0][0])\n",
    "plt.gca().coastlines()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "mega_cube = resampled[0].copy()\n",
    "for cube in resampled:\n",
    "    # Keep bolting on the non-masked parts of the resampled cubes\n",
    "    # until we have processed them all.\n",
    "    mega_cube.data = np.ma.where(\n",
    "        cube.data.mask, mega_cube.data, cube.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/pelson/dev/scitools/iris/lib/iris/coords.py:1000: UserWarning: Coordinate 'longitude' is not bounded, guessing contiguous bounds.\n",
      "  'contiguous bounds.'.format(self.name()))\n",
      "/Users/pelson/dev/scitools/iris/lib/iris/coords.py:1000: UserWarning: Coordinate 'latitude' is not bounded, guessing contiguous bounds.\n",
      "  'contiguous bounds.'.format(self.name()))\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107a5b0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iplt.pcolormesh(mega_cube[0])\n",
    "plt.gca().coastlines()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Environment (conda_iris-dev-py3)",
   "language": "python",
   "name": "conda_iris-dev-py3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}