intake-demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Intake ESGF test\n",
    "\n",
    "Demonstration of sample intake catalogue for esgf datasets in hh5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import intake"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the catalogue\n",
    "\n",
    "The sample catalogue is at /g/data/hh5/public/apps/nci-intake-catalogue/catalogue.yaml. If you're using the hh5 conda environment, this can be accessed via Intake's default catalogue list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/json": "catalogue:\n  args:\n    path: /g/data/hh5/public/apps/nci-intake-catalogue/catalogue.yaml\n  description: ''\n  driver: intake.catalog.local.YAMLFileCatalog\n  metadata:\n    version: 1\n",
      "text/plain": [
       "catalogue:\n",
       "  args:\n",
       "    path: /g/data/hh5/public/apps/nci-intake-catalogue/catalogue.yaml\n",
       "  description: ''\n",
       "  driver: intake.catalog.local.YAMLFileCatalog\n",
       "  metadata:\n",
       "    version: 1\n"
      ]
     },
     "metadata": {
      "application/json": {
       "root": "catalogue"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "intake.cat.nci"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If not using hh5 you can load the catalogue manually"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/json": "catalogue:\n  args:\n    path: /g/data/hh5/public/apps/nci-intake-catalogue/catalogue.yaml\n  description: ''\n  driver: intake.catalog.local.YAMLFileCatalog\n  metadata:\n    version: 1\n",
      "text/plain": [
       "catalogue:\n",
       "  args:\n",
       "    path: /g/data/hh5/public/apps/nci-intake-catalogue/catalogue.yaml\n",
       "  description: ''\n",
       "  driver: intake.catalog.local.YAMLFileCatalog\n",
       "  metadata:\n",
       "    version: 1\n"
      ]
     },
     "metadata": {
      "application/json": {
       "root": "catalogue"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "intake.open_catalog('/g/data/hh5/public/apps/nci-intake-catalogue/catalogue.yaml')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspecting the catalogue\n",
    "\n",
    "From the catalogue, you can get a list of entries by converting it to a list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['era5', 'era5_land', 'ecmwf', 'esgf']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(intake.cat.nci)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The child catalogues are then accessed as attributes of the parent.\n",
    "\n",
    "You can also get a list of catalogue descriptions in Ipython using `.gui` - however this only shows datasets, not directories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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&lt;span class=&quot;l l-Scalar l-Scalar-Plain&quot;&gt;https://pcmdi.llnl.gov/mips/cmip5/&lt;/span&gt;\\n\\n&lt;span class=&quot;nt&quot;&gt;direct_access&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;:&lt;/span&gt; &lt;span class=&quot;l l-Scalar l-Scalar-Plain&quot;&gt;forbid&lt;/span&gt;\\n&lt;span class=&quot;nt&quot;&gt;user_parameters&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;:&lt;/span&gt; &lt;span class=&quot;p p-Indicator&quot;&gt;[]&lt;/span&gt;\\n&lt;span class=&quot;nt&quot;&gt;metadata&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;:&lt;/span&gt; \\n&lt;span class=&quot;nt&quot;&gt;args&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;:&lt;/span&gt; \\n  &lt;span class=&quot;nt&quot;&gt;esmcol_obj&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;:&lt;/span&gt; &lt;span class=&quot;p p-Indicator&quot;&gt;{{&lt;/span&gt;&lt;span class=&quot;nv&quot;&gt;CATALOG_DIR&lt;/span&gt;&lt;span class=&quot;p p-Indicator&quot;&gt;}}&lt;/span&gt;&lt;span class=&quot;l l-Scalar l-Scalar-Plain&quot;&gt;/cmip5/catalogue_latest.json&lt;/span&gt;\\n&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;\"},\"id\":\"1013\",\"type\":\"panel.models.markup.HTML\"},{\"attributes\":{\"reload\":false},\"id\":\"1016\",\"type\":\"panel.models.location.Location\"},{\"attributes\":{\"children\":[{\"id\":\"1007\"},{\"id\":\"1008\"}],\"margin\":[0,0,0,0],\"name\":\"Select Data Source\"},\"id\":\"1006\",\"type\":\"Column\"},{\"attributes\":{\"disabled\":true,\"icon\":null,\"label\":\"\\u2699\",\"margin\":[5,10,5,10],\"min_width\":50,\"width\":50},\"id\":\"1011\",\"type\":\"Toggle\"},{\"attributes\":{\"margin\":[5,10,5,10],\"min_width\":200,\"options\":[\"cmip5\",\"cmip5_all\",\"cmip6\",\"cmip6_all\",\"cordex\",\"cordex_all\"],\"size\":9,\"value\":[\"cmip5\"],\"width_policy\":\"min\"},\"id\":\"1008\",\"type\":\"MultiSelect\"},{\"attributes\":{\"client_comm_id\":\"cc4ee93dadad4609bbc17d2e9b2e4843\",\"comm_id\":\"c5e239de7bca405f8c8bd9bfef534392\",\"plot_id\":\"1001\"},\"id\":\"1015\",\"type\":\"panel.models.comm_manager.CommManager\"},{\"attributes\":{\"children\":[{\"id\":\"1003\"},{\"id\":\"1004\"}],\"margin\":[0,0,0,0],\"name\":\"Row00029\"},\"id\":\"1002\",\"type\":\"Row\"},{\"attributes\":{\"css_classes\":[\"markdown\"],\"margin\":[5,5,5,5],\"max_height\":40,\"name\":\"Markdown00018\",\"text\":\"&lt;h4&gt;Sources&lt;/h4&gt;\"},\"id\":\"1007\",\"type\":\"panel.models.markup.HTML\"},{\"attributes\":{\"children\":[{\"id\":\"1005\"},{\"id\":\"1012\"}],\"margin\":[0,0,0,0],\"name\":\"Row00028\"},\"id\":\"1004\",\"type\":\"Row\"},{\"attributes\":{\"height\":35,\"margin\":[5,5,5,5],\"name\":\"PNG00025\",\"text\":\"&lt;img src=&quot;data:image/png;base64,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&quot; 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       "    var render_items = [{\"docid\":\"7ea3f427-a06b-4144-8133-6bec47987c50\",\"root_ids\":[\"1001\"],\"roots\":{\"1001\":\"a36ceb31-c935-4b0d-9780-e7a36eae6efd\"}}];\n",
       "    root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
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      ],
      "text/plain": [
       "Column\n",
       "    [0] Row\n",
       "        [0] PNG(str)\n",
       "        [1] Row\n",
       "            [0] Column\n",
       "                [0] Column(name='Select Data Source')\n",
       "                    [0] Markdown(str, max_height=40)\n",
       "                    [1] MultiSelect(min_width=200, options=OrderedDict([('cmip5', ...]), size=9, value=[name: cmip5\n",
       "container: xa...], width_policy='min')\n",
       "                [1] Row(name='Controls')\n",
       "                    [0] Toggle(name='📊', width=50)\n",
       "                    [1] Toggle(disabled=True, name='⚙', width=50)\n",
       "            [1] Column(height=240, name='Description', scroll=True, sizing_mode='stretch_width', width_policy='max')\n",
       "                [0] Markdown(str)\n",
       "    [1] Column(name='Plot', width_policy='max')"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "1001"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intake.cat.nci.esgf.gui"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting the cordex catalogue, we get a list of the available filters and how many options there are"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p><strong>cordex catalog with 11462 dataset(s) from 636562 asset(s)</strong>:</p> <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>project</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>domain</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>institute</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>driving_model</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>experiment</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ensemble</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rcm_name</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rcm_version</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_frequency</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variable</th>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_range</th>\n",
       "      <td>1137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>path</th>\n",
       "      <td>636562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>version</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cordex = intake.cat.nci.esgf.cordex\n",
    "cordex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's a little bit of metadata available, e.g. the dataset description:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CORDEX (Latest Versions)\n",
      "\n",
      "Datasets on Gadi, both publised and replicated. Only the latest available file versions are in the listing, see catalogue 'cordex_all' for only the latest versions\n",
      "\n",
      "Catalogue columns match those used by ESGF search (esgf.nci.org.au)\n",
      "\n",
      "Project: al33, rr3\n",
      "Maintained By: NCI\n",
      "Contact: help@nci.org.au\n",
      "References:\n",
      "    - https://cordex.org/\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(cordex.description)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Searching\n",
    "\n",
    "Searching a ESM catalogue returns a new catalogue with only the matching values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p><strong>cordex catalog with 6 dataset(s) from 66 asset(s)</strong>:</p> <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>project</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>domain</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>institute</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>driving_model</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>experiment</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ensemble</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rcm_name</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rcm_version</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_frequency</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variable</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_range</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>path</th>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>version</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unsw_day_tas_aus44i = cordex.search(institute='UNSW', time_frequency='day', variable='tas', domain='AUS-44i')\n",
    "unsw_day_tas_aus44i"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can get a list of available values for the filters with `.unique()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rcm_name': {'count': 3, 'values': ['WRF360J', 'WRF360K', 'WRF360L']}}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unsw_day_tas_aus44i.unique('rcm_name')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A list of all the matching datasets is available as `.keys()` - I've set up the sample catalogue so the keys this returns match the dataset IDs from ESGF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['cordex.output.AUS-44i.UNSW.CSIRO-BOM-ACCESS1-0.historical.r1i1p1.WRF360J.v1.day.tas.v20180614',\n",
      " 'cordex.output.AUS-44i.UNSW.CSIRO-BOM-ACCESS1-0.historical.r1i1p1.WRF360K.v1.day.tas.v20180614',\n",
      " 'cordex.output.AUS-44i.UNSW.CSIRO-BOM-ACCESS1-0.historical.r1i1p1.WRF360L.v1.day.tas.v20180614',\n",
      " 'cordex.output.AUS-44i.UNSW.CSIRO-BOM-ACCESS1-3.historical.r1i1p1.WRF360J.v1.day.tas.v20180614',\n",
      " 'cordex.output.AUS-44i.UNSW.CSIRO-BOM-ACCESS1-3.historical.r1i1p1.WRF360K.v1.day.tas.v20180614',\n",
      " 'cordex.output.AUS-44i.UNSW.CSIRO-BOM-ACCESS1-3.historical.r1i1p1.WRF360L.v1.day.tas.v20180614']\n"
     ]
    }
   ],
   "source": [
    "from pprint import pp\n",
    "pp(list(unsw_day_tas_aus44i.keys()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting the datasets\n",
    "\n",
    "There are a few ways to get to a dataset from here - you can index the catalogue as a dictionary and call `.to_dask()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (bnds: 2, lat: 133, lon: 238, time: 20089)\n",
       "Coordinates:\n",
       "  * lon        (lon) float64 88.75 89.25 89.75 90.25 ... -153.8 -153.2 -152.8\n",
       "  * lat        (lat) float64 -53.25 -52.75 -52.25 -51.75 ... 11.75 12.25 12.75\n",
       "  * time       (time) datetime64[ns] 1951-01-01T12:00:00 ... 2005-12-31T12:00:00\n",
       "    height     float64 2.0\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (time, bnds) datetime64[ns] dask.array&lt;chunksize=(1826, 2), meta=np.ndarray&gt;\n",
       "    tas        (time, lat, lon) float32 dask.array&lt;chunksize=(1826, 133, 238), meta=np.ndarray&gt;\n",
       "Attributes: (12/29)\n",
       "    postprocessing_version:          1.0\n",
       "    intake_esm_varname:              [&#x27;tas&#x27;]\n",
       "    experiment:                      ACCESS1.0 historical run\n",
       "    CORDEX_domain:                   AUS-44i\n",
       "    model_id:                        UNSW-WRF360L\n",
       "    driving_model_id:                CSIRO-BOM-ACCESS1-0\n",
       "    ...                              ...\n",
       "    experiment_id:                   historical\n",
       "    product:                         output\n",
       "    wrf_schemes_ra_sw_physics:       CAM; Collins et al. (2004, NCAR)\n",
       "    Conventions:                     CF-1.4\n",
       "    wrf_schemes_sf_surface_physics:  Noah; Chen and Dudhia (2001, MWR)\n",
       "    intake_esm_dataset_key:          cordex.output.AUS-44i.UNSW.CSIRO-BOM-ACC...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3d80eeaa-8621-4048-8bef-3bd7bb790402' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3d80eeaa-8621-4048-8bef-3bd7bb790402' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 133</li><li><span class='xr-has-index'>lon</span>: 238</li><li><span class='xr-has-index'>time</span>: 20089</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d05c6c8b-8467-4923-bad8-78018c756319' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d05c6c8b-8467-4923-bad8-78018c756319' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>88.75 89.25 89.75 ... -153.2 -152.8</div><input id='attrs-0cad62ea-e727-496f-9c33-9af614903e5a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0cad62ea-e727-496f-9c33-9af614903e5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c27a4b71-1365-4cf3-9ad9-c1fe57f5303b' class='xr-var-data-in' type='checkbox'><label for='data-c27a4b71-1365-4cf3-9ad9-c1fe57f5303b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([  88.75,   89.25,   89.75, ..., -153.75, -153.25, -152.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-53.25 -52.75 ... 12.25 12.75</div><input id='attrs-603c7f1e-2ce7-4b89-9d97-070e9eaf7cf4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-603c7f1e-2ce7-4b89-9d97-070e9eaf7cf4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59817cde-3fde-4657-a7ce-527141406a83' class='xr-var-data-in' type='checkbox'><label for='data-59817cde-3fde-4657-a7ce-527141406a83' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-53.25, -52.75, -52.25, -51.75, -51.25, -50.75, -50.25, -49.75, -49.25,\n",
       "       -48.75, -48.25, -47.75, -47.25, -46.75, -46.25, -45.75, -45.25, -44.75,\n",
       "       -44.25, -43.75, -43.25, -42.75, -42.25, -41.75, -41.25, -40.75, -40.25,\n",
       "       -39.75, -39.25, -38.75, -38.25, -37.75, -37.25, -36.75, -36.25, -35.75,\n",
       "       -35.25, -34.75, -34.25, -33.75, -33.25, -32.75, -32.25, -31.75, -31.25,\n",
       "       -30.75, -30.25, -29.75, -29.25, -28.75, -28.25, -27.75, -27.25, -26.75,\n",
       "       -26.25, -25.75, -25.25, -24.75, -24.25, -23.75, -23.25, -22.75, -22.25,\n",
       "       -21.75, -21.25, -20.75, -20.25, -19.75, -19.25, -18.75, -18.25, -17.75,\n",
       "       -17.25, -16.75, -16.25, -15.75, -15.25, -14.75, -14.25, -13.75, -13.25,\n",
       "       -12.75, -12.25, -11.75, -11.25, -10.75, -10.25,  -9.75,  -9.25,  -8.75,\n",
       "        -8.25,  -7.75,  -7.25,  -6.75,  -6.25,  -5.75,  -5.25,  -4.75,  -4.25,\n",
       "        -3.75,  -3.25,  -2.75,  -2.25,  -1.75,  -1.25,  -0.75,  -0.25,   0.25,\n",
       "         0.75,   1.25,   1.75,   2.25,   2.75,   3.25,   3.75,   4.25,   4.75,\n",
       "         5.25,   5.75,   6.25,   6.75,   7.25,   7.75,   8.25,   8.75,   9.25,\n",
       "         9.75,  10.25,  10.75,  11.25,  11.75,  12.25,  12.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1951-01-01T12:00:00 ... 2005-12-...</div><input id='attrs-5aae6cb3-40aa-49c4-91c0-d76372899416' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5aae6cb3-40aa-49c4-91c0-d76372899416' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcf6a86e-22d9-478e-a35c-c8eea266cf94' class='xr-var-data-in' type='checkbox'><label for='data-bcf6a86e-22d9-478e-a35c-c8eea266cf94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1951-01-01T12:00:00.000000000&#x27;, &#x27;1951-01-02T12:00:00.000000000&#x27;,\n",
       "       &#x27;1951-01-03T12:00:00.000000000&#x27;, ..., &#x27;2005-12-29T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-12-30T12:00:00.000000000&#x27;, &#x27;2005-12-31T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-13532f8b-7355-40b6-9d01-4eee12bd823b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13532f8b-7355-40b6-9d01-4eee12bd823b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94e5add6-7962-4ed3-83f0-23b6b4e9c974' class='xr-var-data-in' type='checkbox'><label for='data-94e5add6-7962-4ed3-83f0-23b6b4e9c974' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array(2.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c8079205-26dd-40dc-8a6b-d5c707ce5752' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c8079205-26dd-40dc-8a6b-d5c707ce5752' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1826, 2), meta=np.ndarray&gt;</div><input id='attrs-c7d2de4b-6685-454e-9d87-bd5e571ea184' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c7d2de4b-6685-454e-9d87-bd5e571ea184' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-659ce485-db47-4b23-b5db-486842e86190' class='xr-var-data-in' type='checkbox'><label for='data-659ce485-db47-4b23-b5db-486842e86190' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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       "    <tr><th> Type </th><td> datetime64[ns] </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1826, 133, 238), meta=np.ndarray&gt;</div><input id='attrs-65dd26ec-a2ff-4287-9bd3-be2c244d14bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-65dd26ec-a2ff-4287-9bd3-be2c244d14bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85c0afea-731a-4d61-928e-91473a436ca2' class='xr-var-data-in' type='checkbox'><label for='data-85c0afea-731a-4d61-928e-91473a436ca2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
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      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (bnds: 2, lat: 133, lon: 238, time: 20089)\n",
       "Coordinates:\n",
       "  * lon        (lon) float64 88.75 89.25 89.75 90.25 ... -153.8 -153.2 -152.8\n",
       "  * lat        (lat) float64 -53.25 -52.75 -52.25 -51.75 ... 11.75 12.25 12.75\n",
       "  * time       (time) datetime64[ns] 1951-01-01T12:00:00 ... 2005-12-31T12:00:00\n",
       "    height     float64 2.0\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (time, bnds) datetime64[ns] dask.array<chunksize=(1826, 2), meta=np.ndarray>\n",
       "    tas        (time, lat, lon) float32 dask.array<chunksize=(1826, 133, 238), meta=np.ndarray>\n",
       "Attributes: (12/29)\n",
       "    postprocessing_version:          1.0\n",
       "    intake_esm_varname:              ['tas']\n",
       "    experiment:                      ACCESS1.0 historical run\n",
       "    CORDEX_domain:                   AUS-44i\n",
       "    model_id:                        UNSW-WRF360L\n",
       "    driving_model_id:                CSIRO-BOM-ACCESS1-0\n",
       "    ...                              ...\n",
       "    experiment_id:                   historical\n",
       "    product:                         output\n",
       "    wrf_schemes_ra_sw_physics:       CAM; Collins et al. (2004, NCAR)\n",
       "    Conventions:                     CF-1.4\n",
       "    wrf_schemes_sf_surface_physics:  Noah; Chen and Dudhia (2001, MWR)\n",
       "    intake_esm_dataset_key:          cordex.output.AUS-44i.UNSW.CSIRO-BOM-ACC..."
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unsw_day_tas_aus44i['cordex.output.AUS-44i.UNSW.CSIRO-BOM-ACCESS1-0.historical.r1i1p1.WRF360L.v1.day.tas.v20180614'].to_dask()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or you can call to_dataset_dict(), which converts a catalogue into a dictionary of datasets. This lets you give options like chunk sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'project.product.domain.institute.driving_model.experiment.ensemble.rcm_name.rcm_version.time_frequency.variable.version'\n"
     ]
    },
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (bnds: 2, lat: 133, lon: 238, time: 20089)\n",
       "Coordinates:\n",
       "  * lon        (lon) float64 88.75 89.25 89.75 90.25 ... -153.8 -153.2 -152.8\n",
       "  * lat        (lat) float64 -53.25 -52.75 -52.25 -51.75 ... 11.75 12.25 12.75\n",
       "  * time       (time) datetime64[ns] 1951-01-01T12:00:00 ... 2005-12-31T12:00:00\n",
       "    height     float64 2.0\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (time, bnds) datetime64[ns] dask.array&lt;chunksize=(1826, 2), meta=np.ndarray&gt;\n",
       "    tas        (time, lat, lon) float32 dask.array&lt;chunksize=(1826, 133, 119), meta=np.ndarray&gt;\n",
       "Attributes: (12/29)\n",
       "    postprocessing_version:          1.0\n",
       "    intake_esm_varname:              [&#x27;tas&#x27;]\n",
       "    experiment:                      ACCESS1.0 historical run\n",
       "    CORDEX_domain:                   AUS-44i\n",
       "    model_id:                        UNSW-WRF360L\n",
       "    driving_model_id:                CSIRO-BOM-ACCESS1-0\n",
       "    ...                              ...\n",
       "    experiment_id:                   historical\n",
       "    product:                         output\n",
       "    wrf_schemes_ra_sw_physics:       CAM; Collins et al. (2004, NCAR)\n",
       "    Conventions:                     CF-1.4\n",
       "    wrf_schemes_sf_surface_physics:  Noah; Chen and Dudhia (2001, MWR)\n",
       "    intake_esm_dataset_key:          cordex.output.AUS-44i.UNSW.CSIRO-BOM-ACC...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-859bd66b-3f39-4806-8fd2-0a5264c6324b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-859bd66b-3f39-4806-8fd2-0a5264c6324b' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 133</li><li><span class='xr-has-index'>lon</span>: 238</li><li><span class='xr-has-index'>time</span>: 20089</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-eee8413b-482d-4a50-b958-0919963ffdfe' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eee8413b-482d-4a50-b958-0919963ffdfe' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>88.75 89.25 89.75 ... -153.2 -152.8</div><input id='attrs-2453f309-4fab-4515-af12-13cd1a43d92a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2453f309-4fab-4515-af12-13cd1a43d92a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e56503ca-6c00-41e5-af24-6a48e862a63d' class='xr-var-data-in' type='checkbox'><label for='data-e56503ca-6c00-41e5-af24-6a48e862a63d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([  88.75,   89.25,   89.75, ..., -153.75, -153.25, -152.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-53.25 -52.75 ... 12.25 12.75</div><input id='attrs-e0e5ea3e-008c-4c5c-b85d-e468e465f9a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0e5ea3e-008c-4c5c-b85d-e468e465f9a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa2c6b0f-9152-475b-9423-b6344839c215' class='xr-var-data-in' type='checkbox'><label for='data-fa2c6b0f-9152-475b-9423-b6344839c215' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-53.25, -52.75, -52.25, -51.75, -51.25, -50.75, -50.25, -49.75, -49.25,\n",
       "       -48.75, -48.25, -47.75, -47.25, -46.75, -46.25, -45.75, -45.25, -44.75,\n",
       "       -44.25, -43.75, -43.25, -42.75, -42.25, -41.75, -41.25, -40.75, -40.25,\n",
       "       -39.75, -39.25, -38.75, -38.25, -37.75, -37.25, -36.75, -36.25, -35.75,\n",
       "       -35.25, -34.75, -34.25, -33.75, -33.25, -32.75, -32.25, -31.75, -31.25,\n",
       "       -30.75, -30.25, -29.75, -29.25, -28.75, -28.25, -27.75, -27.25, -26.75,\n",
       "       -26.25, -25.75, -25.25, -24.75, -24.25, -23.75, -23.25, -22.75, -22.25,\n",
       "       -21.75, -21.25, -20.75, -20.25, -19.75, -19.25, -18.75, -18.25, -17.75,\n",
       "       -17.25, -16.75, -16.25, -15.75, -15.25, -14.75, -14.25, -13.75, -13.25,\n",
       "       -12.75, -12.25, -11.75, -11.25, -10.75, -10.25,  -9.75,  -9.25,  -8.75,\n",
       "        -8.25,  -7.75,  -7.25,  -6.75,  -6.25,  -5.75,  -5.25,  -4.75,  -4.25,\n",
       "        -3.75,  -3.25,  -2.75,  -2.25,  -1.75,  -1.25,  -0.75,  -0.25,   0.25,\n",
       "         0.75,   1.25,   1.75,   2.25,   2.75,   3.25,   3.75,   4.25,   4.75,\n",
       "         5.25,   5.75,   6.25,   6.75,   7.25,   7.75,   8.25,   8.75,   9.25,\n",
       "         9.75,  10.25,  10.75,  11.25,  11.75,  12.25,  12.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1951-01-01T12:00:00 ... 2005-12-...</div><input id='attrs-602b8667-302f-4103-8187-c833ab7f646c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-602b8667-302f-4103-8187-c833ab7f646c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36f31f34-4d03-4427-b751-0298ecb13162' class='xr-var-data-in' type='checkbox'><label for='data-36f31f34-4d03-4427-b751-0298ecb13162' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1951-01-01T12:00:00.000000000&#x27;, &#x27;1951-01-02T12:00:00.000000000&#x27;,\n",
       "       &#x27;1951-01-03T12:00:00.000000000&#x27;, ..., &#x27;2005-12-29T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-12-30T12:00:00.000000000&#x27;, &#x27;2005-12-31T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-a59dc582-4508-4815-960c-2f86a146e07c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a59dc582-4508-4815-960c-2f86a146e07c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ade23e8-e9fb-464e-9ffc-a27a85bc966e' class='xr-var-data-in' type='checkbox'><label for='data-5ade23e8-e9fb-464e-9ffc-a27a85bc966e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array(2.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9e1f429a-067e-4cbc-b9f0-f4c998fc0014' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9e1f429a-067e-4cbc-b9f0-f4c998fc0014' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1826, 2), meta=np.ndarray&gt;</div><input id='attrs-276b81cd-01ca-4bff-8d57-c40b7ab12f3b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-276b81cd-01ca-4bff-8d57-c40b7ab12f3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2fbaa361-cb6d-4d56-8b8e-e2106c98292f' class='xr-var-data-in' type='checkbox'><label for='data-2fbaa361-cb6d-4d56-8b8e-e2106c98292f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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       "    <tr><th> Shape </th><td> (20089, 2) </td> <td> (1827, 2) </td></tr>\n",
       "    <tr><th> Count </th><td> 33 Tasks </td><td> 11 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> datetime64[ns] </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1826, 133, 119), meta=np.ndarray&gt;</div><input id='attrs-8f73f64c-d920-42fc-b598-2326ba60ff5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8f73f64c-d920-42fc-b598-2326ba60ff5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-142ea39e-7e55-4a61-a62c-577948002b55' class='xr-var-data-in' type='checkbox'><label for='data-142ea39e-7e55-4a61-a62c-577948002b55' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
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      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (bnds: 2, lat: 133, lon: 238, time: 20089)\n",
       "Coordinates:\n",
       "  * lon        (lon) float64 88.75 89.25 89.75 90.25 ... -153.8 -153.2 -152.8\n",
       "  * lat        (lat) float64 -53.25 -52.75 -52.25 -51.75 ... 11.75 12.25 12.75\n",
       "  * time       (time) datetime64[ns] 1951-01-01T12:00:00 ... 2005-12-31T12:00:00\n",
       "    height     float64 2.0\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (time, bnds) datetime64[ns] dask.array<chunksize=(1826, 2), meta=np.ndarray>\n",
       "    tas        (time, lat, lon) float32 dask.array<chunksize=(1826, 133, 119), meta=np.ndarray>\n",
       "Attributes: (12/29)\n",
       "    postprocessing_version:          1.0\n",
       "    intake_esm_varname:              ['tas']\n",
       "    experiment:                      ACCESS1.0 historical run\n",
       "    CORDEX_domain:                   AUS-44i\n",
       "    model_id:                        UNSW-WRF360L\n",
       "    driving_model_id:                CSIRO-BOM-ACCESS1-0\n",
       "    ...                              ...\n",
       "    experiment_id:                   historical\n",
       "    product:                         output\n",
       "    wrf_schemes_ra_sw_physics:       CAM; Collins et al. (2004, NCAR)\n",
       "    Conventions:                     CF-1.4\n",
       "    wrf_schemes_sf_surface_physics:  Noah; Chen and Dudhia (2001, MWR)\n",
       "    intake_esm_dataset_key:          cordex.output.AUS-44i.UNSW.CSIRO-BOM-ACC..."
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unsw_day_tas_aus44i.to_dataset_dict(cdf_kwargs={'chunks': {'lon': 119}})['cordex.output.AUS-44i.UNSW.CSIRO-BOM-ACCESS1-0.historical.r1i1p1.WRF360L.v1.day.tas.v20180614']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Advanced searching\n",
    "\n",
    "You can get a list of all datasets that have both variables 'tasmax' and 'tasmin' with `require_all_on`. Columns named here must have valid matches for all permutations of the other filter - so each 'rcm_name' returned must have matches for both variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p><strong>cordex catalog with 12 dataset(s) from 132 asset(s)</strong>:</p> <div>\n",
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       "      <th>unique</th>\n",
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       "      <th>project</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
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       "      <th>ensemble</th>\n",
       "      <td>1</td>\n",
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       "      <th>rcm_name</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rcm_version</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_frequency</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variable</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_range</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>path</th>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>version</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cordex.search(institute='UNSW', time_frequency='day', variable=['tasmax', 'tasmin'], domain='AUS-44i', require_all_on=['rcm_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:analysis3]",
   "language": "python",
   "name": "conda-env-analysis3-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}

Just wanted to say thanks for this demo notebook Scott, just needed to use it and it contained exactly what I wanted to know! :)