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   "metadata": {},
   "source": [
    "# More efficent use of `xarray.open_mfdataset()`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32b10f7c-8b63-4d33-b4c7-aefca37cd6c8",
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   "source": [
    "A common workflow associated with data analysis using `xarray` is a \"chain\"-type workflow, where one operation leads to another, then to another, and so on, until the final result is produced. Each operation produces a `DataSet` or `DataArray` that is a step closer to the final result.\n",
    "\n",
    "Most workflows of this type have a 'correct' order of operations that will minimise the amount of computation done on the dataset. Running the workflow in the 'wrong' order won't necessarily fail, it may just take longer. Whilst this isn't an issue with smaller datasets, when analysing the datasets produced large models like AUS2200, running these operations out-of-order can often result in hangs or crashes.\n",
    "\n",
    "`xarray` often works like this internally as well. A single `xarray` operation can include a chain of smaller operations that produce the desired result. `xarray.open_mfdataset()` is one of these operations, internally consisting of a series of `xarray.open_dataset()` operations followed by an `xarray.concat()`. These operations can be parallelised over a dask cluster by passing `parallel=True` to `open_mfdataset()`.\n",
    "\n",
    "A standard wokflow using `open_mfdataset()` might look like the following:"
   ]
  },
  {
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   "id": "05a9d543-aa39-45f7-a73f-1772bf84bac7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import glob\n",
    "import dask\n",
    "import logging\n",
    "from dask.distributed import Client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b2502d48-2b9d-4b59-a775-46ddffd804a1",
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       "                        <strong>Workers:</strong> 4\n",
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       "                        <strong>Total memory:</strong> 0 B\n",
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       "\n",
       "    <details style=\"margin-left: 48px;\">\n",
       "        <summary style=\"margin-bottom: 20px;\">\n",
       "            <h3 style=\"display: inline;\">Workers</h3>\n",
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       "\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 0</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:37883\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
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       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/33671/status\" target=\"_blank\">/proxy/33671/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
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       "                        <td style=\"text-align: left;\">\n",
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       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /jobfs/88765053.gadi-pbs/dask-scratch-space/worker-bwdk_rwc\n",
       "                        </td>\n",
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       "\n",
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       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 1</h4>\n",
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       "                            <strong>Comm: </strong> tcp://127.0.0.1:46247\n",
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       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
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       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/33189/status\" target=\"_blank\">/proxy/33189/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 0 B\n",
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       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:39157\n",
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       "\n",
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       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 2</h4>\n",
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       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:43269\n",
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       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
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       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/39417/status\" target=\"_blank\">/proxy/39417/status</a>\n",
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       "                        <td style=\"text-align: left;\">\n",
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       "                            <strong>Nanny: </strong> tcp://127.0.0.1:36467\n",
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       "                            <strong>Local directory: </strong> /jobfs/88765053.gadi-pbs/dask-scratch-space/worker-suf370ot\n",
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       "\n",
       "                    \n",
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       "        \n",
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       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
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       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 3</h4>\n",
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       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:42635\n",
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       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
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       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/43755/status\" target=\"_blank\">/proxy/43755/status</a>\n",
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       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 0 B\n",
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       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:35937\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /jobfs/88765053.gadi-pbs/dask-scratch-space/worker-_yp9kdcz\n",
       "                        </td>\n",
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       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
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       "        \n",
       "\n",
       "    </details>\n",
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       "\n",
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       "    </div>\n",
       "</div>\n",
       "            </details>\n",
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       "\n",
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      "text/plain": [
       "<Client: 'tcp://127.0.0.1:35797' processes=4 threads=4>"
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     },
     "execution_count": 2,
     "metadata": {},
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    }
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    "client = Client(threads_per_worker=1,memory_limit=0,silence_logs=logging.ERROR)\n",
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   "cell_type": "code",
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       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
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       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
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       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
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       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
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       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
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       "\n",
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       "\n",
       ".xr-array-wrap {\n",
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       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
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       "\n",
       ".xr-preview {\n",
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       "\n",
       ".xr-array-preview,\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-dim-list:after {\n",
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       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
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       "\n",
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       "\n",
       ".xr-var-list,\n",
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       "\n",
       ".xr-var-item > div,\n",
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       "\n",
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       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
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       ".xr-var-dims {\n",
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       "\n",
       ".xr-var-preview {\n",
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       "\n",
       ".xr-index-preview {\n",
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       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
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       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
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       "  width: auto;\n",
       "  z-index: 1;\n",
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       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
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       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
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       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
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       "  grid-template-columns: 125px auto;\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
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       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (longitude: 1440, latitude: 721, level: 37, time: 278)\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 -180.0 -179.8 -179.5 ... 179.2 179.5 179.8\n",
       "  * latitude   (latitude) float32 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n",
       "  * level      (level) int32 1 2 3 5 7 10 20 30 ... 850 875 900 925 950 975 1000\n",
       "  * time       (time) datetime64[ns] 2000-01-01 2000-02-01 ... 2023-02-01\n",
       "Data variables:\n",
       "    w          (time, level, latitude, longitude) float32 dask.array&lt;chunksize=(1, 37, 721, 1440), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    Conventions:  CF-1.6\n",
       "    license:      Licence to use Copernicus Products: https://apps.ecmwf.int/...\n",
       "    summary:      ERA5 is the fifth generation ECMWF atmospheric reanalysis o...\n",
       "    title:        ERA5 pressure-levels monthly-averaged vertical_velocity 200...\n",
       "    history:      2020-11-06 00:34:01 UTC+1100 by era5_replication_tools-1.5....</pre><div class='xr-wrap' style='display:none'><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-504e8653-2b4c-4939-96a6-cd2b8e2ddc84' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-504e8653-2b4c-4939-96a6-cd2b8e2ddc84' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>longitude</span>: 1440</li><li><span class='xr-has-index'>latitude</span>: 721</li><li><span class='xr-has-index'>level</span>: 37</li><li><span class='xr-has-index'>time</span>: 278</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a5980809-47a7-4c44-b93d-a933faf1262d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a5980809-47a7-4c44-b93d-a933faf1262d' 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'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-180.0 -179.8 ... 179.5 179.8</div><input id='attrs-1c9b99f5-017c-46f7-8c03-0c2a9af80cab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c9b99f5-017c-46f7-8c03-0c2a9af80cab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55d81e1e-6e9a-45bf-847c-9e25743a58c0' class='xr-var-data-in' type='checkbox'><label for='data-55d81e1e-6e9a-45bf-847c-9e25743a58c0' 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>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([-180.  , -179.75, -179.5 , ...,  179.25,  179.5 ,  179.75],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>90.0 89.75 89.5 ... -89.75 -90.0</div><input id='attrs-fb735729-a020-4b3f-98e3-3bb1f7d60344' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fb735729-a020-4b3f-98e3-3bb1f7d60344' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32059a02-65e0-4a22-9e0d-a46a36be2733' class='xr-var-data-in' type='checkbox'><label for='data-32059a02-65e0-4a22-9e0d-a46a36be2733' 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>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 5 7 ... 900 925 950 975 1000</div><input id='attrs-96c9b089-ce7c-46d0-b3dc-bec3b58234c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-96c9b089-ce7c-46d0-b3dc-bec3b58234c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-428fb1ee-f5a6-445c-ab2b-ce067adc4a14' class='xr-var-data-in' type='checkbox'><label for='data-428fb1ee-f5a6-445c-ab2b-ce067adc4a14' 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>units :</span></dt><dd>millibars</dd><dt><span>long_name :</span></dt><dd>pressure_level</dd></dl></div><div class='xr-var-data'><pre>array([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  125,\n",
       "        150,  175,  200,  225,  250,  300,  350,  400,  450,  500,  550,  600,\n",
       "        650,  700,  750,  775,  800,  825,  850,  875,  900,  925,  950,  975,\n",
       "       1000], dtype=int32)</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'>2000-01-01 ... 2023-02-01</div><input id='attrs-680fc3e5-d9a4-4dd2-b97a-43f3c4af1113' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-680fc3e5-d9a4-4dd2-b97a-43f3c4af1113' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-114a6f0d-0795-444c-af48-db9ea0314060' class='xr-var-data-in' type='checkbox'><label for='data-114a6f0d-0795-444c-af48-db9ea0314060' 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>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2000-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-03-01T00:00:00.000000000&#x27;, ..., &#x27;2022-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-01-01T00:00:00.000000000&#x27;, &#x27;2023-02-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8d2f5a79-d730-4f5b-a885-6aa6925a061d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8d2f5a79-d730-4f5b-a885-6aa6925a061d' class='xr-section-summary' >Data variables: <span>(1)</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>w</span></div><div class='xr-var-dims'>(time, level, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 37, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-465956ee-18ba-4785-be61-9d4b5251bb09' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-465956ee-18ba-4785-be61-9d4b5251bb09' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a5bed42-72f0-4946-8079-0bfa5f9c0296' class='xr-var-data-in' type='checkbox'><label for='data-5a5bed42-72f0-4946-8079-0bfa5f9c0296' 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>units :</span></dt><dd>Pa s**-1</dd><dt><span>long_name :</span></dt><dd>Vertical velocity</dd><dt><span>standard_name :</span></dt><dd>lagrangian_tendency_of_air_pressure</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                <thead>\n",
       "                    <tr>\n",
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       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 39.78 GiB </td>\n",
       "                        <td> 146.54 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (278, 37, 721, 1440) </td>\n",
       "                        <td> (1, 37, 721, 1440) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 278 chunks in 557 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-ea3d1cc9-87f7-4cc5-be49-12421dabe989' class='xr-section-summary-in' type='checkbox'  ><label for='section-ea3d1cc9-87f7-4cc5-be49-12421dabe989' class='xr-section-summary' >Indexes: <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-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-529cea3f-cd1b-481b-81c9-30eca94896bc' class='xr-index-data-in' type='checkbox'/><label for='index-529cea3f-cd1b-481b-81c9-30eca94896bc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -180.0, -179.75,  -179.5, -179.25,  -179.0, -178.75,  -178.5, -178.25,\n",
       "        -178.0, -177.75,\n",
       "       ...\n",
       "         177.5,  177.75,   178.0,  178.25,   178.5,  178.75,   179.0,  179.25,\n",
       "         179.5,  179.75],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;longitude&#x27;, length=1440))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e526f9e5-ac58-4973-91f1-82d91e51e184' class='xr-index-data-in' type='checkbox'/><label for='index-e526f9e5-ac58-4973-91f1-82d91e51e184' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,\n",
       "        87.75,\n",
       "       ...\n",
       "       -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5, -89.75,\n",
       "        -90.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;latitude&#x27;, length=721))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-01a584fc-9d37-4549-9b6b-4273c994b45b' class='xr-index-data-in' type='checkbox'/><label for='index-01a584fc-9d37-4549-9b6b-4273c994b45b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  125,\n",
       "        150,  175,  200,  225,  250,  300,  350,  400,  450,  500,  550,  600,\n",
       "        650,  700,  750,  775,  800,  825,  850,  875,  900,  925,  950,  975,\n",
       "       1000],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-28f5fcd3-0fab-42f7-b1c1-0b277d53b0d8' class='xr-index-data-in' type='checkbox'/><label for='index-28f5fcd3-0fab-42f7-b1c1-0b277d53b0d8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2000-01-01&#x27;, &#x27;2000-02-01&#x27;, &#x27;2000-03-01&#x27;, &#x27;2000-04-01&#x27;,\n",
       "               &#x27;2000-05-01&#x27;, &#x27;2000-06-01&#x27;, &#x27;2000-07-01&#x27;, &#x27;2000-08-01&#x27;,\n",
       "               &#x27;2000-09-01&#x27;, &#x27;2000-10-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-05-01&#x27;, &#x27;2022-06-01&#x27;, &#x27;2022-07-01&#x27;, &#x27;2022-08-01&#x27;,\n",
       "               &#x27;2022-09-01&#x27;, &#x27;2022-10-01&#x27;, &#x27;2022-11-01&#x27;, &#x27;2022-12-01&#x27;,\n",
       "               &#x27;2023-01-01&#x27;, &#x27;2023-02-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=278, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-daf9e6a2-8cba-43ba-b24e-babe99ce31a9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-daf9e6a2-8cba-43ba-b24e-babe99ce31a9' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>license :</span></dt><dd>Licence to use Copernicus Products: https://apps.ecmwf.int/datasets/licences/copernicus/</dd><dt><span>summary :</span></dt><dd>ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate. This file is part of the ERA5 replica hosted at NCI Australia. For more information please see http://dx.doi.org/10.25914/5f48874388857</dd><dt><span>title :</span></dt><dd>ERA5 pressure-levels monthly-averaged vertical_velocity 20000101-20000131</dd><dt><span>history :</span></dt><dd>2020-11-06 00:34:01 UTC+1100 by era5_replication_tools-1.5.2: era5_patch_tool -c /g/data/zt77/admin/global.json sort_level /g/data/rt52/era5/pressure-levels/monthly-averaged/w/2000/w_era5_moda_pl_20000101-20000131.nc\n",
       "2020-08-30 17:20:11 UTC+1000 by era5_replication_tools-1.0.0: mv /g/data/rt52/admin/incoming/era5/pressure-levels/monthly-averaged/w/2000/.w_era5_moda_pl_20000101-20000131.tmp /g/data/rt52/era5/pressure-levels/monthly-averaged/w/2000/w_era5_moda_pl_20000101-20000131.nc\n",
       "2020-08-30 17:20:08 UTC+1000 by era5_replication_tools-1.0.0: nccopy -k4 -d5 -s /g/data/zt77/admin/incoming/era5/pressure-levels/monthly-averaged/w/2000/w_era5_moda_pl_20000101-20000131.nc /g/data/rt52/admin/incoming/era5/pressure-levels/monthly-averaged/w/2000/.w_era5_moda_pl_20000101-20000131.tmp\n",
       "2020-08-30 17:19:29 UTC+1000 by era5_replication_tools-1.0.0: curl --connect-timeout 20 --show-error --silent --max-time 36000 -o /g/data/zt77/admin/incoming/era5/pressure-levels/monthly-averaged/w/2000/w_era5_moda_pl_20000101-20000131.nc http://136.156.133.32/cache-compute-0009/cache/data1/adaptor.mars.internal-1598771944.531227-8273-1-e737a382-f57a-4be9-92ba-aa7225ec2821.nc\n",
       "2020-08-30 07:19:09 GMT by grib_to_netcdf-2.16.0: /opt/ecmwf/eccodes/bin/grib_to_netcdf -S param -o /cache/data1/adaptor.mars.internal-1598771944.531227-8273-1-e737a382-f57a-4be9-92ba-aa7225ec2821.nc /cache/tmp/e737a382-f57a-4be9-92ba-aa7225ec2821-adaptor.mars.internal-1598771944.533176-8273-1-tmp.grib</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (longitude: 1440, latitude: 721, level: 37, time: 278)\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 -180.0 -179.8 -179.5 ... 179.2 179.5 179.8\n",
       "  * latitude   (latitude) float32 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n",
       "  * level      (level) int32 1 2 3 5 7 10 20 30 ... 850 875 900 925 950 975 1000\n",
       "  * time       (time) datetime64[ns] 2000-01-01 2000-02-01 ... 2023-02-01\n",
       "Data variables:\n",
       "    w          (time, level, latitude, longitude) float32 dask.array<chunksize=(1, 37, 721, 1440), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Conventions:  CF-1.6\n",
       "    license:      Licence to use Copernicus Products: https://apps.ecmwf.int/...\n",
       "    summary:      ERA5 is the fifth generation ECMWF atmospheric reanalysis o...\n",
       "    title:        ERA5 pressure-levels monthly-averaged vertical_velocity 200...\n",
       "    history:      2020-11-06 00:34:01 UTC+1100 by era5_replication_tools-1.5...."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "era5_vert_winds_files=sorted(glob.glob('/g/data/rt52/era5/pressure-levels/monthly-averaged/w/20*/*.nc'))\n",
    "ds=xr.open_mfdataset(era5_vert_winds_files,parallel=True)\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "26b65dc6-3fba-463c-a63f-05b0af47224e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x14e042141b20>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Pick out the 850hPa pressure level\n",
    "ds_850=ds.sel(level=850)\n",
    "### Pick out the grid point corresponding to Melbourne\n",
    "ds_850_mel=ds_850.sel(latitude=-37.81, longitude=144.96, method='nearest')\n",
    "ds_850_mel['w'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75e45cf2-9c79-4a3f-be02-8c03199a5510",
   "metadata": {},
   "source": [
    "Now, this is a fairly contrived example, but it does highlight the idea behind the 'chain' workflow. The dataset files are opened, then concatenated, then a pressure level is picked out, followed by a single point. `mf_dataset` takes an argument, `preprocess`, that can change the order of operations within the workflow and arrive at the same result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a750b766-1557-4e6e-b4b9-80071d1b86f1",
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
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       "\n",
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       ".xr-var-data,\n",
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       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
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       "\n",
       ".xr-attrs,\n",
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       "\n",
       ".xr-attrs dt,\n",
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       "\n",
       ".xr-attrs dt {\n",
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       "\n",
       ".xr-attrs dt:hover span {\n",
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       "  background: var(--xr-background-color);\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-icon-database,\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (time: 278)\n",
       "Coordinates:\n",
       "    longitude  float32 145.0\n",
       "    latitude   float32 -37.75\n",
       "    level      int32 850\n",
       "  * time       (time) datetime64[ns] 2000-01-01 2000-02-01 ... 2023-02-01\n",
       "Data variables:\n",
       "    w          (time) float32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    Conventions:  CF-1.6\n",
       "    license:      Licence to use Copernicus Products: https://apps.ecmwf.int/...\n",
       "    summary:      ERA5 is the fifth generation ECMWF atmospheric reanalysis o...\n",
       "    title:        ERA5 pressure-levels monthly-averaged vertical_velocity 200...\n",
       "    history:      2020-11-06 00:34:01 UTC+1100 by era5_replication_tools-1.5....</pre><div class='xr-wrap' style='display:none'><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-cf61bae1-4b4a-4b64-a131-70e539cb5fb6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cf61bae1-4b4a-4b64-a131-70e539cb5fb6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 278</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-054cc1d7-940d-40a2-8ea1-2354264351e9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-054cc1d7-940d-40a2-8ea1-2354264351e9' 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>longitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>145.0</div><input id='attrs-b38117b9-384e-401f-bb16-f9b01dd7c76d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b38117b9-384e-401f-bb16-f9b01dd7c76d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4598f8e-e2e3-49d5-8cda-274fb67a6c07' class='xr-var-data-in' type='checkbox'><label for='data-d4598f8e-e2e3-49d5-8cda-274fb67a6c07' 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>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array(145., dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-37.75</div><input id='attrs-dd2d4989-2484-4353-a676-462d9bbe094e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd2d4989-2484-4353-a676-462d9bbe094e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66334ae8-0ee2-420f-9a75-50803ce49cd8' class='xr-var-data-in' type='checkbox'><label for='data-66334ae8-0ee2-420f-9a75-50803ce49cd8' 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>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array(-37.75, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>level</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>850</div><input id='attrs-a1f768ce-a966-499f-a0e8-d98e1e5913b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1f768ce-a966-499f-a0e8-d98e1e5913b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fc6490d-929f-4f18-ac56-44d82317b831' class='xr-var-data-in' type='checkbox'><label for='data-0fc6490d-929f-4f18-ac56-44d82317b831' 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>units :</span></dt><dd>millibars</dd><dt><span>long_name :</span></dt><dd>pressure_level</dd></dl></div><div class='xr-var-data'><pre>array(850, dtype=int32)</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'>2000-01-01 ... 2023-02-01</div><input id='attrs-b2c355a3-2fbe-4630-88cf-f8858df339d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2c355a3-2fbe-4630-88cf-f8858df339d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94d2f327-f9c9-4ca9-8ee4-84fa0e4b038a' class='xr-var-data-in' type='checkbox'><label for='data-94d2f327-f9c9-4ca9-8ee4-84fa0e4b038a' 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>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2000-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-03-01T00:00:00.000000000&#x27;, ..., &#x27;2022-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-01-01T00:00:00.000000000&#x27;, &#x27;2023-02-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-411be581-473a-4f58-bab3-bbd4c6785183' class='xr-section-summary-in' type='checkbox'  checked><label for='section-411be581-473a-4f58-bab3-bbd4c6785183' class='xr-section-summary' >Data variables: <span>(1)</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>w</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-ade2a53a-5759-4b6a-ba3d-6f9eb3846746' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ade2a53a-5759-4b6a-ba3d-6f9eb3846746' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e18919f-50de-456b-8bfa-9f0f3f20146f' class='xr-var-data-in' type='checkbox'><label for='data-5e18919f-50de-456b-8bfa-9f0f3f20146f' 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>units :</span></dt><dd>Pa s**-1</dd><dt><span>long_name :</span></dt><dd>Vertical velocity</dd><dt><span>standard_name :</span></dt><dd>lagrangian_tendency_of_air_pressure</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-484c16f2-0f6e-4377-a9bd-0fa07dd705c3' class='xr-section-summary-in' type='checkbox'  ><label for='section-484c16f2-0f6e-4377-a9bd-0fa07dd705c3' class='xr-section-summary' >Indexes: <span>(1)</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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c41e1fa6-f843-418e-af6d-963e4b70767b' class='xr-index-data-in' type='checkbox'/><label for='index-c41e1fa6-f843-418e-af6d-963e4b70767b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2000-01-01&#x27;, &#x27;2000-02-01&#x27;, &#x27;2000-03-01&#x27;, &#x27;2000-04-01&#x27;,\n",
       "               &#x27;2000-05-01&#x27;, &#x27;2000-06-01&#x27;, &#x27;2000-07-01&#x27;, &#x27;2000-08-01&#x27;,\n",
       "               &#x27;2000-09-01&#x27;, &#x27;2000-10-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-05-01&#x27;, &#x27;2022-06-01&#x27;, &#x27;2022-07-01&#x27;, &#x27;2022-08-01&#x27;,\n",
       "               &#x27;2022-09-01&#x27;, &#x27;2022-10-01&#x27;, &#x27;2022-11-01&#x27;, &#x27;2022-12-01&#x27;,\n",
       "               &#x27;2023-01-01&#x27;, &#x27;2023-02-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=278, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-15a37d94-ab78-4198-9d10-824509022f11' class='xr-section-summary-in' type='checkbox'  checked><label for='section-15a37d94-ab78-4198-9d10-824509022f11' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>license :</span></dt><dd>Licence to use Copernicus Products: https://apps.ecmwf.int/datasets/licences/copernicus/</dd><dt><span>summary :</span></dt><dd>ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate. This file is part of the ERA5 replica hosted at NCI Australia. For more information please see http://dx.doi.org/10.25914/5f48874388857</dd><dt><span>title :</span></dt><dd>ERA5 pressure-levels monthly-averaged vertical_velocity 20000101-20000131</dd><dt><span>history :</span></dt><dd>2020-11-06 00:34:01 UTC+1100 by era5_replication_tools-1.5.2: era5_patch_tool -c /g/data/zt77/admin/global.json sort_level /g/data/rt52/era5/pressure-levels/monthly-averaged/w/2000/w_era5_moda_pl_20000101-20000131.nc\n",
       "2020-08-30 17:20:11 UTC+1000 by era5_replication_tools-1.0.0: mv /g/data/rt52/admin/incoming/era5/pressure-levels/monthly-averaged/w/2000/.w_era5_moda_pl_20000101-20000131.tmp /g/data/rt52/era5/pressure-levels/monthly-averaged/w/2000/w_era5_moda_pl_20000101-20000131.nc\n",
       "2020-08-30 17:20:08 UTC+1000 by era5_replication_tools-1.0.0: nccopy -k4 -d5 -s /g/data/zt77/admin/incoming/era5/pressure-levels/monthly-averaged/w/2000/w_era5_moda_pl_20000101-20000131.nc /g/data/rt52/admin/incoming/era5/pressure-levels/monthly-averaged/w/2000/.w_era5_moda_pl_20000101-20000131.tmp\n",
       "2020-08-30 17:19:29 UTC+1000 by era5_replication_tools-1.0.0: curl --connect-timeout 20 --show-error --silent --max-time 36000 -o /g/data/zt77/admin/incoming/era5/pressure-levels/monthly-averaged/w/2000/w_era5_moda_pl_20000101-20000131.nc http://136.156.133.32/cache-compute-0009/cache/data1/adaptor.mars.internal-1598771944.531227-8273-1-e737a382-f57a-4be9-92ba-aa7225ec2821.nc\n",
       "2020-08-30 07:19:09 GMT by grib_to_netcdf-2.16.0: /opt/ecmwf/eccodes/bin/grib_to_netcdf -S param -o /cache/data1/adaptor.mars.internal-1598771944.531227-8273-1-e737a382-f57a-4be9-92ba-aa7225ec2821.nc /cache/tmp/e737a382-f57a-4be9-92ba-aa7225ec2821-adaptor.mars.internal-1598771944.533176-8273-1-tmp.grib</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (time: 278)\n",
       "Coordinates:\n",
       "    longitude  float32 145.0\n",
       "    latitude   float32 -37.75\n",
       "    level      int32 850\n",
       "  * time       (time) datetime64[ns] 2000-01-01 2000-02-01 ... 2023-02-01\n",
       "Data variables:\n",
       "    w          (time) float32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Conventions:  CF-1.6\n",
       "    license:      Licence to use Copernicus Products: https://apps.ecmwf.int/...\n",
       "    summary:      ERA5 is the fifth generation ECMWF atmospheric reanalysis o...\n",
       "    title:        ERA5 pressure-levels monthly-averaged vertical_velocity 200...\n",
       "    history:      2020-11-06 00:34:01 UTC+1100 by era5_replication_tools-1.5...."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def _preprocess(ds):\n",
    "    return ds.sel(level=850,latitude=-37.81, longitude=144.96, method='nearest')\n",
    "era5_vert_winds_files=glob.glob('/g/data/rt52/era5/pressure-levels/monthly-averaged/w/20*/*.nc')\n",
    "ds_850_mel=xr.open_mfdataset(era5_vert_winds_files,parallel=True,preprocess=_preprocess)\n",
    "ds_850_mel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "71bc0bd6-f800-4969-b823-a34284bb3dab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x14df929ccf40>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_850_mel['w'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd46c2bf-ced4-4b56-834d-cb0e5589def6",
   "metadata": {},
   "source": [
    "The `preprocess` argument to `mf_dataset` takes a function that takes a single `xarray` `DataSet` as its argument, and can return either a `DataSet` or `DataArray`. The function given as the `preprocess` argument will then operate on every dataset from each file passed to `open_mfdataset` before the concatenation occurs. In this example, it's more efficient to perform the `sel` after `open_mfdataset`, however that isn't always the case."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b06c95d8-81e4-4445-94c7-a3b2d9a371da",
   "metadata": {},
   "source": [
    "## AUS2200 model output data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efb7c020-a4c6-4e95-b2d4-45a4b028e3ed",
   "metadata": {},
   "source": [
    "The raw output from the AUS2200 model is directly converted to netCDF from the UM Fields File format, and as such, contains many different fields in the same file. Often, the same file can contain fields with different grid types and different time scales. As a 2.2km atmosphere model, the size of these datasets is also rather large. A single hourly output file weighs in at around 8GB of compressed netCDF, so constructing time-series data can involve reading over multiple TB of files. For this example, we'll plot surface temperature of a single point in a 2-week model run at 10 minute intervals from hourly files. This involves reading from 336 files, each containing 39 variables, with 2 different grid projections, 2 different time scales and a pseudo level coordinate that is only applied to 3 fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7497bd19-2969-4b5d-a939-19850bbe7f0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.55 s, sys: 199 ms, total: 1.75 s\n",
      "Wall time: 2.01 s\n"
     ]
    },
    {
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       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;fld_s00i024&#x27; (time: 6, lat: 2120, lon: 2600)&gt;\n",
       "[33072000 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * time                      (time) datetime64[ns] 2022-02-22T00:10:00 ... 2...\n",
       "  * lat                       (lat) float64 -48.79 -48.77 ... -6.852 -6.832\n",
       "  * lon                       (lon) float64 107.5 107.5 107.6 ... 159.0 159.0\n",
       "    theta_level_height        float64 ...\n",
       "    model_theta_level_number  int32 ...\n",
       "    sigma_theta               float64 ...\n",
       "    height                    float64 ...\n",
       "    height_0                  float64 ...\n",
       "Attributes:\n",
       "    standard_name:    surface_temperature\n",
       "    long_name:        SURFACE TEMPERATURE AFTER TIMESTEP\n",
       "    units:            K\n",
       "    um_stash_source:  m01s00i024\n",
       "    grid_mapping:     latitude_longitude</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'fld_s00i024'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 6</li><li><span class='xr-has-index'>lat</span>: 2120</li><li><span class='xr-has-index'>lon</span>: 2600</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-fcd3555c-4924-4654-96a4-7e3411dc5a24' class='xr-array-in' type='checkbox' checked><label for='section-fcd3555c-4924-4654-96a4-7e3411dc5a24' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[33072000 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-83d49313-3eb8-419f-9f23-5793f28734ab' class='xr-section-summary-in' type='checkbox'  checked><label for='section-83d49313-3eb8-419f-9f23-5793f28734ab' class='xr-section-summary' >Coordinates: <span>(8)</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'>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'>2022-02-22T00:10:00 ... 2022-02-...</div><input id='attrs-e5c0ccd7-79f8-493f-9841-063714907ec6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5c0ccd7-79f8-493f-9841-063714907ec6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8fcf6fd8-6a3e-43a5-9540-e00fec5c0fc0' class='xr-var-data-in' type='checkbox'><label for='data-8fcf6fd8-6a3e-43a5-9540-e00fec5c0fc0' 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>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-02-22T00:10:00.000000000&#x27;, &#x27;2022-02-22T00:19:59.999999744&#x27;,\n",
       "       &#x27;2022-02-22T00:30:00.000000000&#x27;, &#x27;2022-02-22T00:40:00.000000000&#x27;,\n",
       "       &#x27;2022-02-22T00:50:00.000000000&#x27;, &#x27;2022-02-22T01:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</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'>-48.79 -48.77 ... -6.852 -6.832</div><input id='attrs-c7336745-350e-457e-add1-766c7901a674' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7336745-350e-457e-add1-766c7901a674' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70ed8716-1b2e-4c4a-80e3-db862c899f9e' class='xr-var-data-in' type='checkbox'><label for='data-70ed8716-1b2e-4c4a-80e3-db862c899f9e' 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>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-48.787998, -48.7682  , -48.748398, ...,  -6.871399,  -6.851597,\n",
       "        -6.831799])</pre></div></li><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'>107.5 107.5 107.6 ... 159.0 159.0</div><input id='attrs-b6ab3adb-17de-4529-bf19-867923dfe8f5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b6ab3adb-17de-4529-bf19-867923dfe8f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b542b99-6ba3-469f-b0e9-f013e219833b' class='xr-var-data-in' type='checkbox'><label for='data-6b542b99-6ba3-469f-b0e9-f013e219833b' 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>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([107.519997, 107.539795, 107.559593, ..., 158.940598, 158.960388,\n",
       "       158.980194])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>theta_level_height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-514b914a-cf27-4585-a36e-52ebe8b658a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-514b914a-cf27-4585-a36e-52ebe8b658a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-348ef9bd-e2cc-41e3-98a0-b7694b310df9' class='xr-var-data-in' type='checkbox'><label for='data-348ef9bd-e2cc-41e3-98a0-b7694b310df9' 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>bounds :</span></dt><dd>theta_level_height_bnds</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>level_height</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>model_theta_level_number</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e544d677-8f4a-4102-8cfe-8cfeb99298c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e544d677-8f4a-4102-8cfe-8cfeb99298c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5c84b86-2143-4563-be1c-e34ea621afc7' class='xr-var-data-in' type='checkbox'><label for='data-a5c84b86-2143-4563-be1c-e34ea621afc7' 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>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>model_level_number</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma_theta</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-82b7bb04-8d91-40a3-96aa-01a6e12f6f77' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-82b7bb04-8d91-40a3-96aa-01a6e12f6f77' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ed6cbfb-bc1c-4dcd-b2ae-022fe803e4b7' class='xr-var-data-in' type='checkbox'><label for='data-6ed6cbfb-bc1c-4dcd-b2ae-022fe803e4b7' 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>bounds :</span></dt><dd>sigma_theta_bnds</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>sigma</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</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'>...</div><input id='attrs-9acd344d-f404-4799-92ed-af0a02a4a0aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9acd344d-f404-4799-92ed-af0a02a4a0aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f680597b-7fe9-4d76-89c8-82d4799e41e3' class='xr-var-data-in' type='checkbox'><label for='data-f680597b-7fe9-4d76-89c8-82d4799e41e3' 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>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height_0</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ca92e481-c199-4da6-8c64-cd87b1985a27' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca92e481-c199-4da6-8c64-cd87b1985a27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e7d87c2-627d-481d-bf05-1cf04d6aeb4c' class='xr-var-data-in' type='checkbox'><label for='data-4e7d87c2-627d-481d-bf05-1cf04d6aeb4c' 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>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ecfdc255-f9d8-459c-805f-644ad3f7cd37' class='xr-section-summary-in' type='checkbox'  ><label for='section-ecfdc255-f9d8-459c-805f-644ad3f7cd37' class='xr-section-summary' >Indexes: <span>(3)</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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-06fb507d-0a35-4039-a926-e41dc26ffac6' class='xr-index-data-in' type='checkbox'/><label for='index-06fb507d-0a35-4039-a926-e41dc26ffac6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([          &#x27;2022-02-22 00:10:00&#x27;,\n",
       "               &#x27;2022-02-22 00:19:59.999999744&#x27;,\n",
       "                         &#x27;2022-02-22 00:30:00&#x27;,\n",
       "                         &#x27;2022-02-22 00:40:00&#x27;,\n",
       "                         &#x27;2022-02-22 00:50:00&#x27;,\n",
       "                         &#x27;2022-02-22 01:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-84f7df73-5d87-46fc-bca1-f3386386a351' class='xr-index-data-in' type='checkbox'/><label for='index-84f7df73-5d87-46fc-bca1-f3386386a351' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -48.78799819946289,   -48.7681999206543,  -48.74839782714844,\n",
       "       -48.728599548339844, -48.708797454833984,  -48.68899917602539,\n",
       "        -48.66919708251953,  -48.64939880371094,  -48.62959671020508,\n",
       "       -48.609798431396484,\n",
       "       ...\n",
       "        -7.009998321533203,  -6.990200042724609,   -6.97039794921875,\n",
       "        -6.950599670410156,  -6.930797576904297,  -6.910999298095703,\n",
       "        -6.891197204589844,   -6.87139892578125,  -6.851596832275391,\n",
       "        -6.831798553466797],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=2120))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c397d7a6-4e9b-4d96-b4dc-5a38cd5ea4c9' class='xr-index-data-in' type='checkbox'/><label for='index-c397d7a6-4e9b-4d96-b4dc-5a38cd5ea4c9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 107.5199966430664,   107.539794921875,  107.5595932006836,\n",
       "       107.57939910888672, 107.59919738769531,  107.6189956665039,\n",
       "        107.6387939453125, 107.65859985351562, 107.67839813232422,\n",
       "       107.69819641113281,\n",
       "       ...\n",
       "         158.802001953125, 158.82179260253906,  158.8415985107422,\n",
       "       158.86138916015625, 158.88119506835938,  158.9010009765625,\n",
       "       158.92079162597656,  158.9405975341797, 158.96038818359375,\n",
       "       158.98019409179688],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=2600))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6fe4bff7-8e74-4852-ad53-2181cb21fe57' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6fe4bff7-8e74-4852-ad53-2181cb21fe57' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>surface_temperature</dd><dt><span>long_name :</span></dt><dd>SURFACE TEMPERATURE AFTER TIMESTEP</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>um_stash_source :</span></dt><dd>m01s00i024</dd><dt><span>grid_mapping :</span></dt><dd>latitude_longitude</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'fld_s00i024' (time: 6, lat: 2120, lon: 2600)>\n",
       "[33072000 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * time                      (time) datetime64[ns] 2022-02-22T00:10:00 ... 2...\n",
       "  * lat                       (lat) float64 -48.79 -48.77 ... -6.852 -6.832\n",
       "  * lon                       (lon) float64 107.5 107.5 107.6 ... 159.0 159.0\n",
       "    theta_level_height        float64 ...\n",
       "    model_theta_level_number  int32 ...\n",
       "    sigma_theta               float64 ...\n",
       "    height                    float64 ...\n",
       "    height_0                  float64 ...\n",
       "Attributes:\n",
       "    standard_name:    surface_temperature\n",
       "    long_name:        SURFACE TEMPERATURE AFTER TIMESTEP\n",
       "    units:            K\n",
       "    um_stash_source:  m01s00i024\n",
       "    grid_mapping:     latitude_longitude"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "ds=xr.open_dataset('/g/data/hh5/tmp/WACI-Hackathon-2023/AUS2200/flood22-barra-soil-24hspinup/atmos/umnsa_spec_20220222T0000_10min.nc')\n",
    "ds.fld_s00i024"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f53847c0-9529-41fd-9b05-aa53f2abecb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 41s, sys: 10.1 s, total: 1min 51s\n",
      "Wall time: 2min 17s\n"
     ]
    },
    {
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       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                   (lat: 2120, lon: 2600, pseudo_level: 5,\n",
       "                               lon_0: 2600, lat_0: 2121, time_0: 2304,\n",
       "                               time: 2304, bnds: 2)\n",
       "Coordinates:\n",
       "  * lat                       (lat) float64 -48.79 -48.77 ... -6.852 -6.832\n",
       "  * lon                       (lon) float64 107.5 107.5 107.6 ... 159.0 159.0\n",
       "  * pseudo_level              (pseudo_level) int32 1 2 3 4 5\n",
       "  * lon_0                     (lon_0) float64 107.5 107.5 107.5 ... 159.0 159.0\n",
       "  * lat_0                     (lat_0) float64 -48.8 -48.78 ... -6.842 -6.822\n",
       "  * time_0                    (time_0) datetime64[ns] 2022-02-19T12:05:00 ......\n",
       "  * time                      (time) datetime64[ns] 2022-02-19T12:10:00 ... 2...\n",
       "    theta_level_height        float64 5.0\n",
       "    model_theta_level_number  int32 1\n",
       "    sigma_theta               float64 0.9994\n",
       "    height                    float64 10.0\n",
       "    height_0                  float64 1.5\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables: (12/39)\n",
       "    fld_s00i010               (time, lat, lon) float32 dask.array&lt;chunksize=(6, 414, 520), meta=np.ndarray&gt;\n",
       "    latitude_longitude        (time) int32 -2147483647 ... -2147483647\n",
       "    lat_bnds                  (time, lat, bnds) float64 dask.array&lt;chunksize=(6, 414, 2), meta=np.ndarray&gt;\n",
       "    lon_bnds                  (time, lon, bnds) float64 dask.array&lt;chunksize=(6, 520, 2), meta=np.ndarray&gt;\n",
       "    theta_level_height_bnds   (time, bnds) float64 dask.array&lt;chunksize=(6, 2), meta=np.ndarray&gt;\n",
       "    sigma_theta_bnds          (time, bnds) float64 dask.array&lt;chunksize=(6, 2), meta=np.ndarray&gt;\n",
       "    ...                        ...\n",
       "    fld_s21i101               (time, lat, lon) float64 dask.array&lt;chunksize=(6, 414, 520), meta=np.ndarray&gt;\n",
       "    fld_s21i104               (time, time_0, lat, lon) float32 dask.array&lt;chunksize=(6, 2304, 414, 520), meta=np.ndarray&gt;\n",
       "    fld_s30i403               (time, lat, lon) float32 dask.array&lt;chunksize=(6, 414, 520), meta=np.ndarray&gt;\n",
       "    fld_s30i404               (time, lat, lon) float32 dask.array&lt;chunksize=(6, 414, 520), meta=np.ndarray&gt;\n",
       "    fld_s30i405               (time, lat, lon) float32 dask.array&lt;chunksize=(6, 414, 520), meta=np.ndarray&gt;\n",
       "    fld_s30i406               (time, lat, lon) float32 dask.array&lt;chunksize=(6, 414, 520), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    history:      File /scratch/ly62/dr4292/cylc-run/u-cs142-barra/share/cycl...\n",
       "    Conventions:  CF-1.6\n",
       "    source:       Data from Met Office Unified Model\n",
       "    um_version:   12.2</pre><div class='xr-wrap' style='display:none'><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-d262429c-9f21-4a6f-ab04-e7e253f865b8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d262429c-9f21-4a6f-ab04-e7e253f865b8' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 2120</li><li><span class='xr-has-index'>lon</span>: 2600</li><li><span class='xr-has-index'>pseudo_level</span>: 5</li><li><span class='xr-has-index'>lon_0</span>: 2600</li><li><span class='xr-has-index'>lat_0</span>: 2121</li><li><span class='xr-has-index'>time_0</span>: 2304</li><li><span class='xr-has-index'>time</span>: 2304</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-78f26f4a-9aed-4ec2-9d52-36896e3888dd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-78f26f4a-9aed-4ec2-9d52-36896e3888dd' class='xr-section-summary' >Coordinates: <span>(12)</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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-48.79 -48.77 ... -6.852 -6.832</div><input id='attrs-ef5501d6-b570-48eb-a58f-abab27d6e498' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef5501d6-b570-48eb-a58f-abab27d6e498' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e858a6d1-3e5a-492b-9f93-4da639d4aa9f' class='xr-var-data-in' type='checkbox'><label for='data-e858a6d1-3e5a-492b-9f93-4da639d4aa9f' 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>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-48.787998, -48.7682  , -48.748398, ...,  -6.871399,  -6.851597,\n",
       "        -6.831799])</pre></div></li><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'>107.5 107.5 107.6 ... 159.0 159.0</div><input id='attrs-19a60266-c3d9-4510-b7ee-e7b3abb307ca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-19a60266-c3d9-4510-b7ee-e7b3abb307ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a564a728-acdd-4181-8f1b-cccb1618d727' class='xr-var-data-in' type='checkbox'><label for='data-a564a728-acdd-4181-8f1b-cccb1618d727' 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>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([107.519997, 107.539795, 107.559593, ..., 158.940598, 158.960388,\n",
       "       158.980194])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pseudo_level</span></div><div class='xr-var-dims'>(pseudo_level)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4 5</div><input id='attrs-5721095b-c5f7-4260-9da5-ce1bb221e360' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5721095b-c5f7-4260-9da5-ce1bb221e360' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ffc91199-2d28-4c19-94e2-45f623e424d4' class='xr-var-data-in' type='checkbox'><label for='data-ffc91199-2d28-4c19-94e2-45f623e424d4' 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>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>pseudo_level</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4, 5], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon_0</span></div><div class='xr-var-dims'>(lon_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>107.5 107.5 107.5 ... 159.0 159.0</div><input id='attrs-d0577eb9-89e5-4a70-a1a1-b27ead492b29' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d0577eb9-89e5-4a70-a1a1-b27ead492b29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02feaf39-5f78-47b4-9682-48f02e774220' class='xr-var-data-in' type='checkbox'><label for='data-02feaf39-5f78-47b4-9682-48f02e774220' 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>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_0_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([107.510101, 107.5299  , 107.549698, ..., 158.930695, 158.9505  ,\n",
       "       158.970306])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat_0</span></div><div class='xr-var-dims'>(lat_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-48.8 -48.78 ... -6.842 -6.822</div><input id='attrs-4c347c9b-4363-4e41-8d9c-7176ed34e527' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c347c9b-4363-4e41-8d9c-7176ed34e527' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10ec6496-af57-40ba-b72b-ec540d7a3587' class='xr-var-data-in' type='checkbox'><label for='data-10ec6496-af57-40ba-b72b-ec540d7a3587' 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>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_0_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-48.797901, -48.778103, -48.758301, ...,  -6.8615  ,  -6.841702,\n",
       "        -6.821903])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_0</span></div><div class='xr-var-dims'>(time_0)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-02-19T12:05:00 ... 2022-03-...</div><input id='attrs-40a8a408-43a2-4903-ab16-46471761787b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40a8a408-43a2-4903-ab16-46471761787b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04bbb619-2a97-4ff5-847d-1a87dce5f456' class='xr-var-data-in' type='checkbox'><label for='data-04bbb619-2a97-4ff5-847d-1a87dce5f456' 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>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_0_bnds</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-02-19T12:05:00.000000000&#x27;, &#x27;2022-02-19T12:15:00.000000000&#x27;,\n",
       "       &#x27;2022-02-19T12:24:59.999999744&#x27;, ..., &#x27;2022-03-07T23:35:00.000000256&#x27;,\n",
       "       &#x27;2022-03-07T23:45:00.000000000&#x27;, &#x27;2022-03-07T23:55:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</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'>2022-02-19T12:10:00 ... 2022-03-08</div><input id='attrs-d98edd10-3adc-435a-a64e-391dd90a8823' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d98edd10-3adc-435a-a64e-391dd90a8823' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-613c5e3d-c615-445c-aabd-c81d37e5b66f' class='xr-var-data-in' type='checkbox'><label for='data-613c5e3d-c615-445c-aabd-c81d37e5b66f' 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>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-02-19T12:10:00.000000000&#x27;, &#x27;2022-02-19T12:19:59.999999744&#x27;,\n",
       "       &#x27;2022-02-19T12:30:00.000000000&#x27;, ..., &#x27;2022-03-07T23:40:00.000000256&#x27;,\n",
       "       &#x27;2022-03-07T23:50:00.000000000&#x27;, &#x27;2022-03-08T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>theta_level_height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.0</div><input id='attrs-94ab1dfd-84bb-434d-86ae-d62a170bcc98' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94ab1dfd-84bb-434d-86ae-d62a170bcc98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7ed0b6a-4df7-407b-b91d-b43ed99e765f' class='xr-var-data-in' type='checkbox'><label for='data-e7ed0b6a-4df7-407b-b91d-b43ed99e765f' 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>bounds :</span></dt><dd>theta_level_height_bnds</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>level_height</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>array(5.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>model_theta_level_number</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-04461ccb-dde7-42d8-a224-3816a82e92b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04461ccb-dde7-42d8-a224-3816a82e92b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-659f19d2-2923-480e-b472-dc4eda6516f7' class='xr-var-data-in' type='checkbox'><label for='data-659f19d2-2923-480e-b472-dc4eda6516f7' 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>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>model_level_number</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>array(1, dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma_theta</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9994</div><input id='attrs-9896c49f-9017-4a2b-96ca-9249b07a5f02' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9896c49f-9017-4a2b-96ca-9249b07a5f02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73b48023-ddcb-4119-90a1-1ed5276e7f8a' class='xr-var-data-in' type='checkbox'><label for='data-73b48023-ddcb-4119-90a1-1ed5276e7f8a' 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>bounds :</span></dt><dd>sigma_theta_bnds</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>sigma</dd></dl></div><div class='xr-var-data'><pre>array(0.99942381)</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'>10.0</div><input id='attrs-57738a9a-ba93-401d-bfb9-8ed7d565ea50' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57738a9a-ba93-401d-bfb9-8ed7d565ea50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f96e6f9-feb3-402f-8143-9b396ab4a1fa' class='xr-var-data-in' type='checkbox'><label for='data-8f96e6f9-feb3-402f-8143-9b396ab4a1fa' 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>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>array(10.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height_0</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.5</div><input id='attrs-54c0707b-e55a-4d2f-9d21-f911c2fe2da3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-54c0707b-e55a-4d2f-9d21-f911c2fe2da3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9cd6bdf5-81da-4ecb-b50b-cb63a3fb4cc0' class='xr-var-data-in' type='checkbox'><label for='data-9cd6bdf5-81da-4ecb-b50b-cb63a3fb4cc0' 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>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>array(1.5)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f70c8ecb-8c91-43e6-a4e5-6a4e75a4c3fd' class='xr-section-summary-in' type='checkbox'  ><label for='section-f70c8ecb-8c91-43e6-a4e5-6a4e75a4c3fd' class='xr-section-summary' >Data variables: <span>(39)</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>fld_s00i010</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=(6, 414, 520), meta=np.ndarray&gt;</div><input id='attrs-765fb915-2c08-4b82-8038-5bd3e6e9a770' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-765fb915-2c08-4b82-8038-5bd3e6e9a770' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-774ef660-da9f-49b0-b813-1c849a85aca4' class='xr-var-data-in' type='checkbox'><label for='data-774ef660-da9f-49b0-b813-1c849a85aca4' 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>specific_humidity</dd><dt><span>long_name :</span></dt><dd>SPECIFIC HUMIDITY AFTER TIMESTEP</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>um_stash_source :</span></dt><dd>m01s00i010</dd><dt><span>grid_mapping :</span></dt><dd>latitude_longitude</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 47.31 GiB </td>\n",
       "                        <td> 4.93 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2304, 2120, 2600) </td>\n",
       "                        <td> (6, 414, 520) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 11520 chunks in 769 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude_longitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>-2147483647 ... -2147483647</div><input id='attrs-f4171bee-6e0a-4fe3-9a1a-78a253f283e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4171bee-6e0a-4fe3-9a1a-78a253f283e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36fa1792-79a1-4a24-b128-03e27634ddec' class='xr-var-data-in' type='checkbox'><label for='data-36fa1792-79a1-4a24-b128-03e27634ddec' 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>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>earth_radius :</span></dt><dd>6371229.0</dd></dl></div><div class='xr-var-data'><pre>array([-2147483647, -2147483647, -2147483647, ..., -2147483647,\n",
       "       -2147483647, -2147483647], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(time, lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6, 414, 2), meta=np.ndarray&gt;</div><input id='attrs-dafecdcc-b110-4e7c-a53a-4c6e0ebb5567' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dafecdcc-b110-4e7c-a53a-4c6e0ebb5567' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a26198f-bade-4082-a928-7ec1adb66d0f' class='xr-var-data-in' type='checkbox'><label for='data-7a26198f-bade-4082-a928-7ec1adb66d0f' 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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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 74.53 MiB </td>\n",
       "                        <td> 38.81 kiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2304, 2120, 2) </td>\n",
       "                        <td> (6, 414, 2) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 2304 chunks in 1153 graph layers </td>\n",
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       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(time, lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6, 520, 2), meta=np.ndarray&gt;</div><input id='attrs-a9b92c57-e257-4f86-a4d6-bb2bd6bb4b0e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a9b92c57-e257-4f86-a4d6-bb2bd6bb4b0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d23d37d2-6fd4-4dc6-b565-7fa076f8b431' class='xr-var-data-in' type='checkbox'><label for='data-d23d37d2-6fd4-4dc6-b565-7fa076f8b431' 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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       "                        <th> Shape </th>\n",
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       "                    <tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>theta_level_height_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6, 2), meta=np.ndarray&gt;</div><input id='attrs-297b1f65-f9d7-4018-9b2c-ef8b794464b3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-297b1f65-f9d7-4018-9b2c-ef8b794464b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a36f785-98d9-462e-9aab-b30c0a439cd1' class='xr-var-data-in' type='checkbox'><label for='data-6a36f785-98d9-462e-9aab-b30c0a439cd1' 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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       "                        <th> Array </th>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 36.00 kiB </td>\n",
       "                        <td> 96 B </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2304, 2) </td>\n",
       "                        <td> (6, 2) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 384 chunks in 1153 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma_theta_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6, 2), meta=np.ndarray&gt;</div><input id='attrs-c26993ff-d1be-481e-b6ac-bcf6e6f336ac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c26993ff-d1be-481e-b6ac-bcf6e6f336ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d76a9972-49c8-41f8-9fe2-5ef97a9a7b97' class='xr-var-data-in' type='checkbox'><label for='data-d76a9972-49c8-41f8-9fe2-5ef97a9a7b97' 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>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fld_s00i024</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=(6, 414, 520), meta=np.ndarray&gt;</div><input id='attrs-62c46d64-d01f-4112-87d0-f101a522179c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-62c46d64-d01f-4112-87d0-f101a522179c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba4eb5c1-bace-41e4-afd1-5a04c437a782' class='xr-var-data-in' type='checkbox'><label for='data-ba4eb5c1-bace-41e4-afd1-5a04c437a782' 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>surface_temperature</dd><dt><span>long_name :</span></dt><dd>SURFACE TEMPERATURE AFTER TIMESTEP</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>um_stash_source :</span></dt><dd>m01s00i024</dd><dt><span>grid_mapping :</span></dt><dd>latitude_longitude</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 47.31 GiB </td>\n",
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       "                        <th> Shape </th>\n",
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       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fld_s03i209</span></div><div class='xr-var-dims'>(time, lat, lon_0)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6, 414, 2600), meta=np.ndarray&gt;</div><input id='attrs-027c2cc0-34e5-4f06-bb66-a40ef62ab1ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-027c2cc0-34e5-4f06-bb66-a40ef62ab1ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a174ec4-c650-476e-848f-b837d229dc3e' class='xr-var-data-in' type='checkbox'><label for='data-5a174ec4-c650-476e-848f-b837d229dc3e' 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>eastward_wind</dd><dt><span>long_name :</span></dt><dd>10 METRE WIND U-COMP</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>um_stash_source :</span></dt><dd>m01s03i209</dd><dt><span>grid_mapping :</span></dt><dd>latitude_longitude</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 47.31 GiB </td>\n",
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       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fld_s03i210</span></div><div class='xr-var-dims'>(time, lat_0, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6, 2121, 520), meta=np.ndarray&gt;</div><input id='attrs-c7f59b78-ae88-4dde-bba5-0836301c51c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7f59b78-ae88-4dde-bba5-0836301c51c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76951a73-e463-45d1-ba52-a3210305806a' class='xr-var-data-in' type='checkbox'><label for='data-76951a73-e463-45d1-ba52-a3210305806a' 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>northward_wind</dd><dt><span>long_name :</span></dt><dd>10 METRE WIND V-COMP</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>um_stash_source :</span></dt><dd>m01s03i210</dd><dt><span>grid_mapping :</span></dt><dd>latitude_longitude</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 47.33 GiB </td>\n",
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       "                        <th> Dask graph </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fld_s03i236</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=(6, 414, 520), meta=np.ndarray&gt;</div><input id='attrs-9842f309-538f-4d1b-ab00-d31cdf5f50b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9842f309-538f-4d1b-ab00-d31cdf5f50b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84299cea-bf85-455b-ba19-d1dbcb29b482' class='xr-var-data-in' type='checkbox'><label for='data-84299cea-bf85-455b-ba19-d1dbcb29b482' 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>air_temperature</dd><dt><span>long_name :</span></dt><dd>TEMPERATURE AT 1.5M</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>um_stash_source :</span></dt><dd>m01s03i236</dd><dt><span>grid_mapping :</span></dt><dd>latitude_longitude</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fld_s04i202</span></div><div class='xr-var-dims'>(time, time_0, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6, 2304, 414, 520), meta=np.ndarray&gt;</div><input id='attrs-3fde0434-baf2-426d-ad86-0ee5fce1999a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3fde0434-baf2-426d-ad86-0ee5fce1999a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-930b560c-8a72-4a04-92f9-c763af03eb93' class='xr-var-data-in' type='checkbox'><label for='data-930b560c-8a72-4a04-92f9-c763af03eb93' 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>stratiform_snowfall_amount</dd><dt><span>long_name :</span></dt><dd>LARGE SCALE SNOW AMOUNT     KG/M2/TS</dd><dt><span>units :</span></dt><dd>kg m-2</dd><dt><span>um_stash_source :</span></dt><dd>m01s04i202</dd><dt><span>cell_methods :</span></dt><dd>time_0: mean</dd><dt><span>grid_mapping :</span></dt><dd>latitude_longitude</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 106.45 TiB </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fld_s09i203</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=(6, 414, 520), meta=np.ndarray&gt;</div><input id='attrs-17ebafca-005b-41ae-8860-69cef1a0c7c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-17ebafca-005b-41ae-8860-69cef1a0c7c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-952e5c8e-00c3-45da-8bd6-11905a47c0ba' class='xr-var-data-in' type='checkbox'><label for='data-952e5c8e-00c3-45da-8bd6-11905a47c0ba' 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>low_type_cloud_area_fraction</dd><dt><span>long_name :</span></dt><dd>LOW CLOUD AMOUNT</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>um_stash_source :</span></dt><dd>m01s09i203</dd><dt><span>grid_mapping :</span></dt><dd>latitude_longitude</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fld_s09i204</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=(6, 414, 520), meta=np.ndarray&gt;</div><input id='attrs-c7a6a564-58f9-449c-85b9-9fdaaa3cf08d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7a6a564-58f9-449c-85b9-9fdaaa3cf08d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-830756a0-5a28-4507-aedb-2ef7b7397f5c' class='xr-var-data-in' type='checkbox'><label for='data-830756a0-5a28-4507-aedb-2ef7b7397f5c' 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>medium_type_cloud_area_fraction</dd><dt><span>long_name :</span></dt><dd>MEDIUM CLOUD AMOUNT</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>um_stash_source :</span></dt><dd>m01s09i204</dd><dt><span>grid_mapping :</span></dt><dd>latitude_longitude</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                        <td> 47.31 GiB </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fld_s09i205</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=(6, 414, 520), meta=np.ndarray&gt;</div><input id='attrs-365d6e6e-759b-4288-b882-32922c56ab19' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-365d6e6e-759b-4288-b882-32922c56ab19' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1015e6b9-b8c2-4efd-9ae3-e4684522557a' class='xr-var-data-in' type='checkbox'><label for='data-1015e6b9-b8c2-4efd-9ae3-e4684522557a' 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>high_type_cloud_area_fraction</dd><dt><span>long_name :</span></dt><dd>HIGH CLOUD AMOUNT</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>um_stash_source :</span></dt><dd>m01s09i205</dd><dt><span>grid_mapping :</span></dt><dd>latitude_longitude</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fld_s09i217</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=(6, 414, 520), meta=np.ndarray&gt;</div><input id='attrs-8c5db237-2e85-46be-9cd0-43392bcba874' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c5db237-2e85-46be-9cd0-43392bcba874' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99c57b7a-95c0-4338-9d71-4f2e7cf898df' class='xr-var-data-in' type='checkbox'><label for='data-99c57b7a-95c0-4338-9d71-4f2e7cf898df' 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>cloud_area_fraction</dd><dt><span>long_name :</span></dt><dd>cloud_area_fraction_assuming_maximum_random_overlap</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>um_stash_source :</span></dt><dd>m01s09i217</dd><dt><span>grid_mapping :</span></dt><dd>latitude_longitude</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                        <td> 47.31 GiB </td>\n",
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       "                    <tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fld_s16i222</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=(6, 414, 520), meta=np.ndarray&gt;</div><input id='attrs-398d6454-3b34-46dd-a59b-4f06515dd8e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-398d6454-3b34-46dd-a59b-4f06515dd8e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4a3c9e4-0e18-413e-bbb1-4f642841352e' class='xr-var-data-in' type='checkbox'><label for='data-a4a3c9e4-0e18-413e-bbb1-4f642841352e' 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>air_pressure_at_sea_level</dd><dt><span>long_name :</span></dt><dd>PRESSURE AT MEAN SEA LEVEL</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>um_stash_source :</span></dt><dd>m01s16i222</dd><dt><span>grid_mapping :</span></dt><dd>latitude_longitude</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> (6, 414, 520) </td>\n",
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       "                    <tr>\n",
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       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fld_s21i100</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=(6, 414, 520), meta=np.ndarray&gt;</div><input id='attrs-05699316-f16d-4592-9afc-3e81b84ee16a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-05699316-f16d-4592-9afc-3e81b84ee16a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-467e7216-55e8-4438-99ab-d4196f39f29c' class='xr-var-data-in' type='checkbox'><label for='data-467e7216-55e8-4438-99ab-d4196f39f29c' 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>lightning_flash_rate</dd><dt><span>units :</span></dt><dd>s-1</dd><dt><span>um_stash_source :</span></dt><dd>m01s21i100</dd><dt><span>grid_mapping :</span></dt><dd>latitude_longitude</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fld_s21i101</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6, 414, 520), meta=np.ndarray&gt;</div><input id='attrs-40b12310-aa16-4ef2-8b30-f2b737f0bd1a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40b12310-aa16-4ef2-8b30-f2b737f0bd1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9d1b004-0bf8-4276-9c9f-0ffa119903be' class='xr-var-data-in' type='checkbox'><label for='data-a9d1b004-0bf8-4276-9c9f-0ffa119903be' 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>flag_for_location_of_storms</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>um_stash_source :</span></dt><dd>m01s21i101</dd><dt><span>grid_mapping :</span></dt><dd>latitude_longitude</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 106.45 TiB </td>\n",
       "                        <td> 11.09 GiB </td>\n",
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       "                    <tr>\n",
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       "                    <tr>\n",
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       "        <td>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
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       "                    <tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fld_s30i404</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=(6, 414, 520), meta=np.ndarray&gt;</div><input id='attrs-d8e17a6f-7a73-4822-91b4-845d0c28becc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8e17a6f-7a73-4822-91b4-845d0c28becc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1640c85b-dbbc-4dc7-9b24-6269d0905ad2' class='xr-var-data-in' type='checkbox'><label for='data-1640c85b-dbbc-4dc7-9b24-6269d0905ad2' 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>atmosphere_mass_per_unit_area</dd><dt><span>long_name :</span></dt><dd>TOTAL COLUMN WET MASS  RHO GRID</dd><dt><span>units :</span></dt><dd>kg m-2</dd><dt><span>um_stash_source :</span></dt><dd>m01s30i404</dd><dt><span>grid_mapping :</span></dt><dd>latitude_longitude</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 47.31 GiB </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fld_s30i405</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=(6, 414, 520), meta=np.ndarray&gt;</div><input id='attrs-2759e364-11f5-4ebc-a734-bf1f1ddbc67b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2759e364-11f5-4ebc-a734-bf1f1ddbc67b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0b050a7-9d28-427a-aa1d-bcb8625f81ff' class='xr-var-data-in' type='checkbox'><label for='data-a0b050a7-9d28-427a-aa1d-bcb8625f81ff' 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>atmosphere_cloud_liquid_water_content</dd><dt><span>long_name :</span></dt><dd>TOTAL COLUMN QCL  RHO GRID</dd><dt><span>units :</span></dt><dd>kg m-2</dd><dt><span>um_stash_source :</span></dt><dd>m01s30i405</dd><dt><span>grid_mapping :</span></dt><dd>latitude_longitude</dd></dl></div><div class='xr-var-data'><table>\n",
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       "\n",
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       "  <text x=\"31.276018\" y=\"149.122172\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,31.276018,149.122172)\">2304</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fld_s30i406</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=(6, 414, 520), meta=np.ndarray&gt;</div><input id='attrs-c93f2854-7a7c-4cf7-a37c-89f88c5e4101' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c93f2854-7a7c-4cf7-a37c-89f88c5e4101' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f92f7891-e2ef-4a2b-8d9c-24feb2d29c76' class='xr-var-data-in' type='checkbox'><label for='data-f92f7891-e2ef-4a2b-8d9c-24feb2d29c76' 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>atmosphere_cloud_ice_content</dd><dt><span>long_name :</span></dt><dd>TOTAL COLUMN QCF  RHO GRID</dd><dt><span>units :</span></dt><dd>kg m-2</dd><dt><span>um_stash_source :</span></dt><dd>m01s30i406</dd><dt><span>grid_mapping :</span></dt><dd>latitude_longitude</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 47.31 GiB </td>\n",
       "                        <td> 4.93 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2304, 2120, 2600) </td>\n",
       "                        <td> (6, 414, 520) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 11520 chunks in 769 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-5a74fcc9-6478-4105-bcb4-34a29593a221' class='xr-section-summary-in' type='checkbox'  ><label for='section-5a74fcc9-6478-4105-bcb4-34a29593a221' class='xr-section-summary' >Indexes: <span>(7)</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-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f0267b4e-9e64-4176-876f-13aa3ede41b2' class='xr-index-data-in' type='checkbox'/><label for='index-f0267b4e-9e64-4176-876f-13aa3ede41b2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -48.78799819946289,   -48.7681999206543,  -48.74839782714844,\n",
       "       -48.728599548339844, -48.708797454833984,  -48.68899917602539,\n",
       "        -48.66919708251953,  -48.64939880371094,  -48.62959671020508,\n",
       "       -48.609798431396484,\n",
       "       ...\n",
       "        -7.009998321533203,  -6.990200042724609,   -6.97039794921875,\n",
       "        -6.950599670410156,  -6.930797576904297,  -6.910999298095703,\n",
       "        -6.891197204589844,   -6.87139892578125,  -6.851596832275391,\n",
       "        -6.831798553466797],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=2120))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a0c0ed6c-5771-4e07-9bc1-c135759fba95' class='xr-index-data-in' type='checkbox'/><label for='index-a0c0ed6c-5771-4e07-9bc1-c135759fba95' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 107.5199966430664,   107.539794921875,  107.5595932006836,\n",
       "       107.57939910888672, 107.59919738769531,  107.6189956665039,\n",
       "        107.6387939453125, 107.65859985351562, 107.67839813232422,\n",
       "       107.69819641113281,\n",
       "       ...\n",
       "         158.802001953125, 158.82179260253906,  158.8415985107422,\n",
       "       158.86138916015625, 158.88119506835938,  158.9010009765625,\n",
       "       158.92079162597656,  158.9405975341797, 158.96038818359375,\n",
       "       158.98019409179688],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=2600))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>pseudo_level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-01f0ece5-3d13-440f-83b2-aee36b39065e' class='xr-index-data-in' type='checkbox'/><label for='index-01f0ece5-3d13-440f-83b2-aee36b39065e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4, 5], dtype=&#x27;int32&#x27;, name=&#x27;pseudo_level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-85f2567f-c2cb-45a5-8f7b-0d9a229c7149' class='xr-index-data-in' type='checkbox'/><label for='index-85f2567f-c2cb-45a5-8f7b-0d9a229c7149' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([107.51010131835938, 107.52989959716797, 107.54969787597656,\n",
       "       107.56950378417969, 107.58930206298828, 107.60910034179688,\n",
       "       107.62889862060547,  107.6487045288086, 107.66850280761719,\n",
       "       107.68830108642578,\n",
       "       ...\n",
       "       158.79209899902344, 158.81190490722656, 158.83169555664062,\n",
       "       158.85150146484375, 158.87130737304688, 158.89109802246094,\n",
       "       158.91090393066406, 158.93069458007812, 158.95050048828125,\n",
       "       158.97030639648438],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon_0&#x27;, length=2600))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c6282404-9b22-4b68-bcc1-8b0995425540' class='xr-index-data-in' type='checkbox'/><label for='index-c6282404-9b22-4b68-bcc1-8b0995425540' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -48.79790115356445,  -48.77810287475586,     -48.75830078125,\n",
       "       -48.738502502441406,  -48.71870040893555,  -48.69890213012695,\n",
       "       -48.679100036621094,   -48.6593017578125,  -48.63949966430664,\n",
       "        -48.61970138549805,\n",
       "       ...\n",
       "        -7.000102996826172, -6.9803009033203125,  -6.960502624511719,\n",
       "        -6.940700531005859,  -6.920902252197266,  -6.901100158691406,\n",
       "       -6.8813018798828125,  -6.861499786376953,  -6.841701507568359,\n",
       "        -6.821903228759766],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat_0&#x27;, length=2121))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7ca6d32c-8e9c-4058-bc6f-591e3721870f' class='xr-index-data-in' type='checkbox'/><label for='index-7ca6d32c-8e9c-4058-bc6f-591e3721870f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([          &#x27;2022-02-19 12:05:00&#x27;,\n",
       "                         &#x27;2022-02-19 12:15:00&#x27;,\n",
       "               &#x27;2022-02-19 12:24:59.999999744&#x27;,\n",
       "               &#x27;2022-02-19 12:35:00.000000256&#x27;,\n",
       "                         &#x27;2022-02-19 12:45:00&#x27;,\n",
       "               &#x27;2022-02-19 12:54:59.999999744&#x27;,\n",
       "               &#x27;2022-02-19 13:05:00.000000256&#x27;,\n",
       "                         &#x27;2022-02-19 13:15:00&#x27;,\n",
       "                         &#x27;2022-02-19 13:25:00&#x27;,\n",
       "                         &#x27;2022-02-19 13:35:00&#x27;,\n",
       "               ...\n",
       "                         &#x27;2022-03-07 22:25:00&#x27;,\n",
       "                         &#x27;2022-03-07 22:35:00&#x27;,\n",
       "                         &#x27;2022-03-07 22:45:00&#x27;,\n",
       "               &#x27;2022-03-07 22:54:59.999999744&#x27;,\n",
       "               &#x27;2022-03-07 23:05:00.000000256&#x27;,\n",
       "                         &#x27;2022-03-07 23:15:00&#x27;,\n",
       "               &#x27;2022-03-07 23:24:59.999999744&#x27;,\n",
       "               &#x27;2022-03-07 23:35:00.000000256&#x27;,\n",
       "                         &#x27;2022-03-07 23:45:00&#x27;,\n",
       "                         &#x27;2022-03-07 23:55:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_0&#x27;, length=2304, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bafa1027-6738-47ca-b48c-baf6c18ecc5c' class='xr-index-data-in' type='checkbox'/><label for='index-bafa1027-6738-47ca-b48c-baf6c18ecc5c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([          &#x27;2022-02-19 12:10:00&#x27;,\n",
       "               &#x27;2022-02-19 12:19:59.999999744&#x27;,\n",
       "                         &#x27;2022-02-19 12:30:00&#x27;,\n",
       "                         &#x27;2022-02-19 12:40:00&#x27;,\n",
       "                         &#x27;2022-02-19 12:50:00&#x27;,\n",
       "                         &#x27;2022-02-19 13:00:00&#x27;,\n",
       "               &#x27;2022-02-19 13:10:00.000000256&#x27;,\n",
       "                         &#x27;2022-02-19 13:20:00&#x27;,\n",
       "                         &#x27;2022-02-19 13:30:00&#x27;,\n",
       "                         &#x27;2022-02-19 13:40:00&#x27;,\n",
       "               ...\n",
       "                         &#x27;2022-03-07 22:30:00&#x27;,\n",
       "                         &#x27;2022-03-07 22:40:00&#x27;,\n",
       "               &#x27;2022-03-07 22:49:59.999999744&#x27;,\n",
       "                         &#x27;2022-03-07 23:00:00&#x27;,\n",
       "                         &#x27;2022-03-07 23:10:00&#x27;,\n",
       "                         &#x27;2022-03-07 23:20:00&#x27;,\n",
       "                         &#x27;2022-03-07 23:30:00&#x27;,\n",
       "               &#x27;2022-03-07 23:40:00.000000256&#x27;,\n",
       "                         &#x27;2022-03-07 23:50:00&#x27;,\n",
       "                         &#x27;2022-03-08 00:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=2304, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-647009bc-e0c8-48b0-87a2-ec742890a904' class='xr-section-summary-in' type='checkbox'  checked><label for='section-647009bc-e0c8-48b0-87a2-ec742890a904' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>history :</span></dt><dd>File /scratch/ly62/dr4292/cylc-run/u-cs142-barra/share/cycle/20220219T1200Z/aus2200/d0198/RA3/um/umnsa_spec_20220219T1200 converted with um2netcdf_iris.py v2.1 at 2023-03-31 21:36:37</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>source :</span></dt><dd>Data from Met Office Unified Model</dd><dt><span>um_version :</span></dt><dd>12.2</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                   (lat: 2120, lon: 2600, pseudo_level: 5,\n",
       "                               lon_0: 2600, lat_0: 2121, time_0: 2304,\n",
       "                               time: 2304, bnds: 2)\n",
       "Coordinates:\n",
       "  * lat                       (lat) float64 -48.79 -48.77 ... -6.852 -6.832\n",
       "  * lon                       (lon) float64 107.5 107.5 107.6 ... 159.0 159.0\n",
       "  * pseudo_level              (pseudo_level) int32 1 2 3 4 5\n",
       "  * lon_0                     (lon_0) float64 107.5 107.5 107.5 ... 159.0 159.0\n",
       "  * lat_0                     (lat_0) float64 -48.8 -48.78 ... -6.842 -6.822\n",
       "  * time_0                    (time_0) datetime64[ns] 2022-02-19T12:05:00 ......\n",
       "  * time                      (time) datetime64[ns] 2022-02-19T12:10:00 ... 2...\n",
       "    theta_level_height        float64 5.0\n",
       "    model_theta_level_number  int32 1\n",
       "    sigma_theta               float64 0.9994\n",
       "    height                    float64 10.0\n",
       "    height_0                  float64 1.5\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables: (12/39)\n",
       "    fld_s00i010               (time, lat, lon) float32 dask.array<chunksize=(6, 414, 520), meta=np.ndarray>\n",
       "    latitude_longitude        (time) int32 -2147483647 ... -2147483647\n",
       "    lat_bnds                  (time, lat, bnds) float64 dask.array<chunksize=(6, 414, 2), meta=np.ndarray>\n",
       "    lon_bnds                  (time, lon, bnds) float64 dask.array<chunksize=(6, 520, 2), meta=np.ndarray>\n",
       "    theta_level_height_bnds   (time, bnds) float64 dask.array<chunksize=(6, 2), meta=np.ndarray>\n",
       "    sigma_theta_bnds          (time, bnds) float64 dask.array<chunksize=(6, 2), meta=np.ndarray>\n",
       "    ...                        ...\n",
       "    fld_s21i101               (time, lat, lon) float64 dask.array<chunksize=(6, 414, 520), meta=np.ndarray>\n",
       "    fld_s21i104               (time, time_0, lat, lon) float32 dask.array<chunksize=(6, 2304, 414, 520), meta=np.ndarray>\n",
       "    fld_s30i403               (time, lat, lon) float32 dask.array<chunksize=(6, 414, 520), meta=np.ndarray>\n",
       "    fld_s30i404               (time, lat, lon) float32 dask.array<chunksize=(6, 414, 520), meta=np.ndarray>\n",
       "    fld_s30i405               (time, lat, lon) float32 dask.array<chunksize=(6, 414, 520), meta=np.ndarray>\n",
       "    fld_s30i406               (time, lat, lon) float32 dask.array<chunksize=(6, 414, 520), meta=np.ndarray>\n",
       "Attributes:\n",
       "    history:      File /scratch/ly62/dr4292/cylc-run/u-cs142-barra/share/cycl...\n",
       "    Conventions:  CF-1.6\n",
       "    source:       Data from Met Office Unified Model\n",
       "    um_version:   12.2"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "aus2200_file_list=sorted(glob.glob('/g/data/hh5/tmp/WACI-Hackathon-2023/AUS2200/flood22-barra/atmos/umnsa_spec_*.nc'))\n",
    "### Need this to prevent many warning messages\n",
    "with dask.config.set(**{'array.slicing.split_large_chunks':False}):\n",
    "    ds=xr.open_mfdataset(aus2200_file_list,chunks={'time': 6, 'time_0':6, 'lat':414, 'lon':520},parallel=True,combine=\"nested\",concat_dim=[\"time\"])\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a2d81554-b5c2-4cd1-a66b-6a1e453af052",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.29 s, sys: 428 ms, total: 3.71 s\n",
      "Wall time: 20.4 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x14df7404e640>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "da_surface_temp_melb=ds['fld_s00i024'].sel(lat=-37.81, lon=144.96, method='nearest')\n",
    "da_surface_temp_melb.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d2c6395-8748-43b1-8d41-ef844f6ab1c2",
   "metadata": {},
   "source": [
    "Note that it takes quite a bit of careful tuning and options passed to `open_mfdataset` to get a sensible `DataSet` out at all. Just running `open_mfdataset` with none of these settings takes twice as long, results in several thousand lines of warning messages about large chunks, and fails to handle the two overlapping time dimensions in the files correctly, resulting in every variable being defined over both time dimensions. That's all before the surface temperature plot is created. The timings in this notebook are based on a 'medium' sized job on the `normal` queue. When I ran the workflow with the unoptimised `open_mfdataset` call on an 'xxxlarge' sized job in the `normalsr` queue, it crashed before creating the average temperature plot due to exceeding the node's memory limit of 496GB.\n",
    "\n",
    "I spent about half a day coming up with the tuning settings used in `open_mfdataset`. These settings are not generic either, a different dataset will required different parameters to be opened correctly. In fact, different variables within the AUS2200 dataset will require different settings. \n",
    "\n",
    "All of this can be avoided by writing a one line function and using `preprocess` to pick out the surface temperature dataset from the beginning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f12e6a51-da7b-4945-b1e4-c7699666eefd",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Free up memory\n",
    "del(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7760b9e9-ed29-45f8-81ee-ac2c5025b631",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 59.9 s, sys: 4.24 s, total: 1min 4s\n",
      "Wall time: 1min 8s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
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       "  padding-right: 10px;\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                   (time: 2304, lat: 2120, lon: 2600)\n",
       "Coordinates:\n",
       "  * time                      (time) datetime64[ns] 2022-02-19T12:10:00 ... 2...\n",
       "  * lat                       (lat) float64 -48.79 -48.77 ... -6.852 -6.832\n",
       "  * lon                       (lon) float64 107.5 107.5 107.6 ... 159.0 159.0\n",
       "    theta_level_height        float64 5.0\n",
       "    model_theta_level_number  int32 1\n",
       "    sigma_theta               float64 0.9994\n",
       "    height                    float64 10.0\n",
       "    height_0                  float64 1.5\n",
       "Data variables:\n",
       "    fld_s00i024               (time, lat, lon) float32 dask.array&lt;chunksize=(6, 2120, 2600), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><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-47d3235d-3b87-4e30-a9b5-3282af1ada82' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-47d3235d-3b87-4e30-a9b5-3282af1ada82' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2304</li><li><span class='xr-has-index'>lat</span>: 2120</li><li><span class='xr-has-index'>lon</span>: 2600</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-70bb569b-c9a5-4ecf-87ac-b9d69dda3716' class='xr-section-summary-in' type='checkbox'  checked><label for='section-70bb569b-c9a5-4ecf-87ac-b9d69dda3716' class='xr-section-summary' >Coordinates: <span>(8)</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'>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'>2022-02-19T12:10:00 ... 2022-03-08</div><input id='attrs-ca6a981f-a5d3-48e0-bfe7-bcf3aac3180a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca6a981f-a5d3-48e0-bfe7-bcf3aac3180a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0cf9904a-f8e1-4487-8813-62117220db76' class='xr-var-data-in' type='checkbox'><label for='data-0cf9904a-f8e1-4487-8813-62117220db76' 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>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-02-19T12:10:00.000000000&#x27;, &#x27;2022-02-19T12:19:59.999999744&#x27;,\n",
       "       &#x27;2022-02-19T12:30:00.000000000&#x27;, ..., &#x27;2022-03-07T23:40:00.000000256&#x27;,\n",
       "       &#x27;2022-03-07T23:50:00.000000000&#x27;, &#x27;2022-03-08T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</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'>-48.79 -48.77 ... -6.852 -6.832</div><input id='attrs-5e27ecb4-9ec8-4e75-8aec-5831524a09bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e27ecb4-9ec8-4e75-8aec-5831524a09bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76e05ff1-a98a-451e-9fb2-35173ec7ef11' class='xr-var-data-in' type='checkbox'><label for='data-76e05ff1-a98a-451e-9fb2-35173ec7ef11' 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>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-48.787998, -48.7682  , -48.748398, ...,  -6.871399,  -6.851597,\n",
       "        -6.831799])</pre></div></li><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'>107.5 107.5 107.6 ... 159.0 159.0</div><input id='attrs-ec36476a-c86a-4ef4-bf92-46658370ecb3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec36476a-c86a-4ef4-bf92-46658370ecb3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c19490fa-5365-4c50-af62-6b22f3d94fac' class='xr-var-data-in' type='checkbox'><label for='data-c19490fa-5365-4c50-af62-6b22f3d94fac' 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>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([107.519997, 107.539795, 107.559593, ..., 158.940598, 158.960388,\n",
       "       158.980194])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>theta_level_height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.0</div><input id='attrs-9d12e914-1fe0-4864-b6c3-c923fdb35530' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d12e914-1fe0-4864-b6c3-c923fdb35530' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4cf22ff7-fae7-4b33-ade2-233666aed4c8' class='xr-var-data-in' type='checkbox'><label for='data-4cf22ff7-fae7-4b33-ade2-233666aed4c8' 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>bounds :</span></dt><dd>theta_level_height_bnds</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>level_height</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>array(5.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>model_theta_level_number</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-d0cdc56e-116a-45a5-b396-233335f29ee3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d0cdc56e-116a-45a5-b396-233335f29ee3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d7313b2-0c36-45e1-9d6c-783976592a41' class='xr-var-data-in' type='checkbox'><label for='data-3d7313b2-0c36-45e1-9d6c-783976592a41' 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>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>model_level_number</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>array(1, dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma_theta</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9994</div><input id='attrs-802b784b-d446-4afa-82d1-a57e14e95857' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-802b784b-d446-4afa-82d1-a57e14e95857' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4dd8164a-7445-4665-9c31-181b4fb9293f' class='xr-var-data-in' type='checkbox'><label for='data-4dd8164a-7445-4665-9c31-181b4fb9293f' 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>bounds :</span></dt><dd>sigma_theta_bnds</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>sigma</dd></dl></div><div class='xr-var-data'><pre>array(0.99942381)</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'>10.0</div><input id='attrs-24ee602d-8727-4a84-99ea-a9df1e05343c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24ee602d-8727-4a84-99ea-a9df1e05343c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8c3e765-bf7e-4d1a-93cd-68c3da742aec' class='xr-var-data-in' type='checkbox'><label for='data-b8c3e765-bf7e-4d1a-93cd-68c3da742aec' 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>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>array(10.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height_0</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.5</div><input id='attrs-3783f92d-fd96-4795-98c4-6d4cd8024038' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3783f92d-fd96-4795-98c4-6d4cd8024038' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b9d2b29-5210-48e9-bff7-ee0af34de51e' class='xr-var-data-in' type='checkbox'><label for='data-9b9d2b29-5210-48e9-bff7-ee0af34de51e' 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>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>array(1.5)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ad7095a8-986c-4a95-b524-4bd67b62e532' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ad7095a8-986c-4a95-b524-4bd67b62e532' class='xr-section-summary' >Data variables: <span>(1)</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>fld_s00i024</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=(6, 2120, 2600), meta=np.ndarray&gt;</div><input id='attrs-ad84f321-2ba9-4eed-8e48-42711dc4ad86' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad84f321-2ba9-4eed-8e48-42711dc4ad86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f725700b-008d-4b11-a004-63250b23148c' class='xr-var-data-in' type='checkbox'><label for='data-f725700b-008d-4b11-a004-63250b23148c' 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>surface_temperature</dd><dt><span>long_name :</span></dt><dd>SURFACE TEMPERATURE AFTER TIMESTEP</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>um_stash_source :</span></dt><dd>m01s00i024</dd><dt><span>grid_mapping :</span></dt><dd>latitude_longitude</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 47.31 GiB </td>\n",
       "                        <td> 126.16 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2304, 2120, 2600) </td>\n",
       "                        <td> (6, 2120, 2600) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 384 chunks in 769 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "               &#x27;2022-02-19 12:19:59.999999744&#x27;,\n",
       "                         &#x27;2022-02-19 12:30:00&#x27;,\n",
       "                         &#x27;2022-02-19 12:40:00&#x27;,\n",
       "                         &#x27;2022-02-19 12:50:00&#x27;,\n",
       "                         &#x27;2022-02-19 13:00:00&#x27;,\n",
       "               &#x27;2022-02-19 13:10:00.000000256&#x27;,\n",
       "                         &#x27;2022-02-19 13:20:00&#x27;,\n",
       "                         &#x27;2022-02-19 13:30:00&#x27;,\n",
       "                         &#x27;2022-02-19 13:40:00&#x27;,\n",
       "               ...\n",
       "                         &#x27;2022-03-07 22:30:00&#x27;,\n",
       "                         &#x27;2022-03-07 22:40:00&#x27;,\n",
       "               &#x27;2022-03-07 22:49:59.999999744&#x27;,\n",
       "                         &#x27;2022-03-07 23:00:00&#x27;,\n",
       "                         &#x27;2022-03-07 23:10:00&#x27;,\n",
       "                         &#x27;2022-03-07 23:20:00&#x27;,\n",
       "                         &#x27;2022-03-07 23:30:00&#x27;,\n",
       "               &#x27;2022-03-07 23:40:00.000000256&#x27;,\n",
       "                         &#x27;2022-03-07 23:50:00&#x27;,\n",
       "                         &#x27;2022-03-08 00:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=2304, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5ac30254-db07-45a7-ae33-361bead69a57' class='xr-index-data-in' type='checkbox'/><label for='index-5ac30254-db07-45a7-ae33-361bead69a57' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -48.78799819946289,   -48.7681999206543,  -48.74839782714844,\n",
       "       -48.728599548339844, -48.708797454833984,  -48.68899917602539,\n",
       "        -48.66919708251953,  -48.64939880371094,  -48.62959671020508,\n",
       "       -48.609798431396484,\n",
       "       ...\n",
       "        -7.009998321533203,  -6.990200042724609,   -6.97039794921875,\n",
       "        -6.950599670410156,  -6.930797576904297,  -6.910999298095703,\n",
       "        -6.891197204589844,   -6.87139892578125,  -6.851596832275391,\n",
       "        -6.831798553466797],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=2120))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cc8a595d-b26b-4e14-ae63-6ba8a4395a7f' class='xr-index-data-in' type='checkbox'/><label for='index-cc8a595d-b26b-4e14-ae63-6ba8a4395a7f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 107.5199966430664,   107.539794921875,  107.5595932006836,\n",
       "       107.57939910888672, 107.59919738769531,  107.6189956665039,\n",
       "        107.6387939453125, 107.65859985351562, 107.67839813232422,\n",
       "       107.69819641113281,\n",
       "       ...\n",
       "         158.802001953125, 158.82179260253906,  158.8415985107422,\n",
       "       158.86138916015625, 158.88119506835938,  158.9010009765625,\n",
       "       158.92079162597656,  158.9405975341797, 158.96038818359375,\n",
       "       158.98019409179688],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=2600))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dbf53cba-b051-420d-959e-ced2cd2888aa' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dbf53cba-b051-420d-959e-ced2cd2888aa' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                   (time: 2304, lat: 2120, lon: 2600)\n",
       "Coordinates:\n",
       "  * time                      (time) datetime64[ns] 2022-02-19T12:10:00 ... 2...\n",
       "  * lat                       (lat) float64 -48.79 -48.77 ... -6.852 -6.832\n",
       "  * lon                       (lon) float64 107.5 107.5 107.6 ... 159.0 159.0\n",
       "    theta_level_height        float64 5.0\n",
       "    model_theta_level_number  int32 1\n",
       "    sigma_theta               float64 0.9994\n",
       "    height                    float64 10.0\n",
       "    height_0                  float64 1.5\n",
       "Data variables:\n",
       "    fld_s00i024               (time, lat, lon) float32 dask.array<chunksize=(6, 2120, 2600), meta=np.ndarray>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "def _preprocess(ds):\n",
    "    return ds['fld_s00i024']\n",
    "ds_preprocess=xr.open_mfdataset(aus2200_file_list,preprocess=_preprocess,parallel=True)\n",
    "ds_preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5efa1cc1-8693-4123-b0cf-62644d8d6c71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.94 s, sys: 382 ms, total: 3.32 s\n",
      "Wall time: 18.8 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x14df74176c10>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "ds_preprocess['fld_s00i024'].sel(lat=-37.81, lon=144.96, method='nearest').plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f93e8f0-1d03-47fb-a8c1-205ca1fe2e17",
   "metadata": {},
   "source": [
    "## Other uses of `preprocess`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7495664-21e7-4693-8b50-d1043cd7bce0",
   "metadata": {},
   "source": [
    "`preprocess` can also be used to perform operations on datasets with inconsistencies, such as axes or units. Take for example the following hypothetical scenario. An experiment has been run and single variable files have been created. The experiment was recently extended, and single variable files are not yet available, but a time-series needs to be produced over the full extended timeframe. The single variable dataset looks as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3879f284-4aa6-4019-9837-51fb148d88b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
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       "\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:             (time: 1008, lat: 2120, lon: 2600, bnds: 2)\n",
       "Coordinates:\n",
       "    height_0            float64 1.5\n",
       "  * lat                 (lat) float64 -48.79 -48.77 -48.75 ... -6.852 -6.832\n",
       "  * lon                 (lon) float64 107.5 107.5 107.6 ... 158.9 159.0 159.0\n",
       "  * time                (time) datetime64[ns] 2022-02-22T00:10:00 ... 2022-03-01\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    tas                 (time, lat, lon) float32 dask.array&lt;chunksize=(144, 2120, 2600), meta=np.ndarray&gt;\n",
       "    lat_bnds            (time, lat, bnds) float64 dask.array&lt;chunksize=(144, 2120, 2), meta=np.ndarray&gt;\n",
       "    latitude_longitude  (time) int32 -2147483647 -2147483647 ... -2147483647\n",
       "    lon_bnds            (time, lon, bnds) float64 dask.array&lt;chunksize=(144, 2600, 2), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    Conventions:  CF-1.6\n",
       "    source:       Data from Met Office Unified Model\n",
       "    um_version:   12.2\n",
       "    NCO:          netCDF Operators version 5.0.5 (Homepage = http://nco.sf.ne...</pre><div class='xr-wrap' style='display:none'><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-45ad7cbc-6687-4375-8bfc-c5a67dde9b9d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-45ad7cbc-6687-4375-8bfc-c5a67dde9b9d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1008</li><li><span class='xr-has-index'>lat</span>: 2120</li><li><span class='xr-has-index'>lon</span>: 2600</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-42d072e0-d4d0-4d05-93f6-f459b7095e75' class='xr-section-summary-in' type='checkbox'  checked><label for='section-42d072e0-d4d0-4d05-93f6-f459b7095e75' 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>height_0</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.5</div><input id='attrs-e66b2de0-fec2-41d3-a9c9-5dfbe4ea9fda' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e66b2de0-fec2-41d3-a9c9-5dfbe4ea9fda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-289f1eef-0b99-4b7a-8b00-06079e207420' class='xr-var-data-in' type='checkbox'><label for='data-289f1eef-0b99-4b7a-8b00-06079e207420' 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>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>array(1.5)</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'>-48.79 -48.77 ... -6.852 -6.832</div><input id='attrs-c885398f-4c59-4d4c-a6bb-0bac59907ca9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c885398f-4c59-4d4c-a6bb-0bac59907ca9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-581b54a3-2a0a-436f-ab7e-08efebda571d' class='xr-var-data-in' type='checkbox'><label for='data-581b54a3-2a0a-436f-ab7e-08efebda571d' 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>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-48.787998, -48.7682  , -48.748398, ...,  -6.871399,  -6.851597,\n",
       "        -6.831799])</pre></div></li><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'>107.5 107.5 107.6 ... 159.0 159.0</div><input id='attrs-72835760-866c-4f17-9c84-8c2a693284d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-72835760-866c-4f17-9c84-8c2a693284d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14be5e5a-0d2d-4936-b401-b0aa8092bd91' class='xr-var-data-in' type='checkbox'><label for='data-14be5e5a-0d2d-4936-b401-b0aa8092bd91' 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>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([107.519997, 107.539795, 107.559593, ..., 158.940598, 158.960388,\n",
       "       158.980194])</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'>2022-02-22T00:10:00 ... 2022-03-01</div><input id='attrs-e934c34b-b188-4e1c-9321-94cc65a11384' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e934c34b-b188-4e1c-9321-94cc65a11384' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e501785b-615a-48fa-bc93-30f45f2cf04f' class='xr-var-data-in' type='checkbox'><label for='data-e501785b-615a-48fa-bc93-30f45f2cf04f' 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>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-02-22T00:10:00.000000000&#x27;, &#x27;2022-02-22T00:19:59.999999744&#x27;,\n",
       "       &#x27;2022-02-22T00:30:00.000000000&#x27;, ..., &#x27;2022-02-28T23:40:00.000000256&#x27;,\n",
       "       &#x27;2022-02-28T23:50:00.000000000&#x27;, &#x27;2022-03-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0bff0476-7646-4ce1-b1b9-4edd906cb7d9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0bff0476-7646-4ce1-b1b9-4edd906cb7d9' class='xr-section-summary' >Data variables: <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>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=(144, 2120, 2600), meta=np.ndarray&gt;</div><input id='attrs-afdb4684-278a-48c5-b2b5-b01bb1ac9e7e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-afdb4684-278a-48c5-b2b5-b01bb1ac9e7e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11bcbfe0-6bc8-4041-a19d-26e8152ef0ff' class='xr-var-data-in' type='checkbox'><label for='data-11bcbfe0-6bc8-4041-a19d-26e8152ef0ff' 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>air_temperature</dd><dt><span>long_name :</span></dt><dd>TEMPERATURE AT 1.5M</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>um_stash_source :</span></dt><dd>m01s03i236</dd><dt><span>grid_mapping :</span></dt><dd>latitude_longitude</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.70 GiB </td>\n",
       "                        <td> 2.96 GiB </td>\n",
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       "                        <th> Shape </th>\n",
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       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 7 chunks in 15 graph layers </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude_longitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>-2147483647 ... -2147483647</div><input id='attrs-42a04cf5-9c54-487e-936b-a15f5a900fe8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42a04cf5-9c54-487e-936b-a15f5a900fe8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-361b0cec-f3a4-4b42-9f9f-7c6a4ea83b7e' class='xr-var-data-in' type='checkbox'><label for='data-361b0cec-f3a4-4b42-9f9f-7c6a4ea83b7e' 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>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>earth_radius :</span></dt><dd>6371229.0</dd></dl></div><div class='xr-var-data'><pre>array([-2147483647, -2147483647, -2147483647, ..., -2147483647,\n",
       "       -2147483647, -2147483647], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(time, lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(144, 2600, 2), meta=np.ndarray&gt;</div><input id='attrs-b162e36e-6c00-4bae-97da-74720608618a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b162e36e-6c00-4bae-97da-74720608618a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55203d35-3de7-48c2-825e-aae8d7b68695' class='xr-var-data-in' type='checkbox'><label for='data-55203d35-3de7-48c2-825e-aae8d7b68695' 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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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-3d23e42b-df59-40a1-9fa4-abb63ba59f1a' class='xr-section-summary-in' type='checkbox'  ><label for='section-3d23e42b-df59-40a1-9fa4-abb63ba59f1a' class='xr-section-summary' >Indexes: <span>(3)</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-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-02443fc9-35b9-4f42-8a2c-be3c78a437a4' class='xr-index-data-in' type='checkbox'/><label for='index-02443fc9-35b9-4f42-8a2c-be3c78a437a4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -48.78799819946289,   -48.7681999206543,  -48.74839782714844,\n",
       "       -48.728599548339844, -48.708797454833984,  -48.68899917602539,\n",
       "        -48.66919708251953,  -48.64939880371094,  -48.62959671020508,\n",
       "       -48.609798431396484,\n",
       "       ...\n",
       "        -7.009998321533203,  -6.990200042724609,   -6.97039794921875,\n",
       "        -6.950599670410156,  -6.930797576904297,  -6.910999298095703,\n",
       "        -6.891197204589844,   -6.87139892578125,  -6.851596832275391,\n",
       "        -6.831798553466797],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=2120))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6ea45ef1-6dc9-44cc-bf23-a706360b24e5' class='xr-index-data-in' type='checkbox'/><label for='index-6ea45ef1-6dc9-44cc-bf23-a706360b24e5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 107.5199966430664,   107.539794921875,  107.5595932006836,\n",
       "       107.57939910888672, 107.59919738769531,  107.6189956665039,\n",
       "        107.6387939453125, 107.65859985351562, 107.67839813232422,\n",
       "       107.69819641113281,\n",
       "       ...\n",
       "         158.802001953125, 158.82179260253906,  158.8415985107422,\n",
       "       158.86138916015625, 158.88119506835938,  158.9010009765625,\n",
       "       158.92079162597656,  158.9405975341797, 158.96038818359375,\n",
       "       158.98019409179688],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=2600))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6c491d4c-5076-4394-a563-8695631cc6bc' class='xr-index-data-in' type='checkbox'/><label for='index-6c491d4c-5076-4394-a563-8695631cc6bc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([          &#x27;2022-02-22 00:10:00&#x27;,\n",
       "               &#x27;2022-02-22 00:19:59.999999744&#x27;,\n",
       "                         &#x27;2022-02-22 00:30:00&#x27;,\n",
       "                         &#x27;2022-02-22 00:40:00&#x27;,\n",
       "                         &#x27;2022-02-22 00:50:00&#x27;,\n",
       "                         &#x27;2022-02-22 01:00:00&#x27;,\n",
       "               &#x27;2022-02-22 01:10:00.000000256&#x27;,\n",
       "                         &#x27;2022-02-22 01:20:00&#x27;,\n",
       "                         &#x27;2022-02-22 01:30:00&#x27;,\n",
       "                         &#x27;2022-02-22 01:40:00&#x27;,\n",
       "               ...\n",
       "                         &#x27;2022-02-28 22:30:00&#x27;,\n",
       "                         &#x27;2022-02-28 22:40:00&#x27;,\n",
       "               &#x27;2022-02-28 22:49:59.999999744&#x27;,\n",
       "                         &#x27;2022-02-28 23:00:00&#x27;,\n",
       "                         &#x27;2022-02-28 23:10:00&#x27;,\n",
       "                         &#x27;2022-02-28 23:20:00&#x27;,\n",
       "                         &#x27;2022-02-28 23:30:00&#x27;,\n",
       "               &#x27;2022-02-28 23:40:00.000000256&#x27;,\n",
       "                         &#x27;2022-02-28 23:50:00&#x27;,\n",
       "                         &#x27;2022-03-01 00:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1008, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8d551371-015e-4697-a512-42968b0f2a56' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8d551371-015e-4697-a512-42968b0f2a56' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>source :</span></dt><dd>Data from Met Office Unified Model</dd><dt><span>um_version :</span></dt><dd>12.2</dd><dt><span>NCO :</span></dt><dd>netCDF Operators version 5.0.5 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:             (time: 1008, lat: 2120, lon: 2600, bnds: 2)\n",
       "Coordinates:\n",
       "    height_0            float64 1.5\n",
       "  * lat                 (lat) float64 -48.79 -48.77 -48.75 ... -6.852 -6.832\n",
       "  * lon                 (lon) float64 107.5 107.5 107.6 ... 158.9 159.0 159.0\n",
       "  * time                (time) datetime64[ns] 2022-02-22T00:10:00 ... 2022-03-01\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    tas                 (time, lat, lon) float32 dask.array<chunksize=(144, 2120, 2600), meta=np.ndarray>\n",
       "    lat_bnds            (time, lat, bnds) float64 dask.array<chunksize=(144, 2120, 2), meta=np.ndarray>\n",
       "    latitude_longitude  (time) int32 -2147483647 -2147483647 ... -2147483647\n",
       "    lon_bnds            (time, lon, bnds) float64 dask.array<chunksize=(144, 2600, 2), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Conventions:  CF-1.6\n",
       "    source:       Data from Met Office Unified Model\n",
       "    um_version:   12.2\n",
       "    NCO:          netCDF Operators version 5.0.5 (Homepage = http://nco.sf.ne..."
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "postprocessed_file_list=sorted(glob.glob(\"/g/data/hh5/tmp/WACI-Hackathon-2023/AUS2200/flood22-barra/tas_10min_202202*.nc\"))\n",
    "ds_single_var=xr.open_mfdataset(postprocessed_file_list,parallel=True)\n",
    "ds_single_var"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac38ed74-1811-490e-8859-1efea0309ab5",
   "metadata": {},
   "source": [
    "And the raw dataset looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "986b034b-570d-46b7-871d-ebe9f03a646b",
   "metadata": {},
   "outputs": [
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       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                   (time: 1008, lat: 2120, lon: 2600)\n",
       "Coordinates:\n",
       "  * time                      (time) datetime64[ns] 2022-03-01T00:10:00 ... 2...\n",
       "  * lat                       (lat) float64 -48.79 -48.77 ... -6.852 -6.832\n",
       "  * lon                       (lon) float64 107.5 107.5 107.6 ... 159.0 159.0\n",
       "    theta_level_height        float64 5.0\n",
       "    model_theta_level_number  int32 1\n",
       "    sigma_theta               float64 0.9994\n",
       "    height                    float64 10.0\n",
       "    height_0                  float64 1.5\n",
       "Data variables:\n",
       "    fld_s00i024               (time, lat, lon) float32 dask.array&lt;chunksize=(6, 2120, 2600), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><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-7bc100b3-c9de-4669-8c56-df8593990d5f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7bc100b3-c9de-4669-8c56-df8593990d5f' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1008</li><li><span class='xr-has-index'>lat</span>: 2120</li><li><span class='xr-has-index'>lon</span>: 2600</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2e0f0cd4-bd47-4e6b-bce5-1701dcb9f817' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2e0f0cd4-bd47-4e6b-bce5-1701dcb9f817' class='xr-section-summary' >Coordinates: <span>(8)</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'>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'>2022-03-01T00:10:00 ... 2022-03-08</div><input id='attrs-2497ef8c-f3dc-4d99-9ac8-d8a76626db7b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2497ef8c-f3dc-4d99-9ac8-d8a76626db7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5925f3b9-ce85-4015-9157-7d9ebbb68097' class='xr-var-data-in' type='checkbox'><label for='data-5925f3b9-ce85-4015-9157-7d9ebbb68097' 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>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-03-01T00:10:00.000000000&#x27;, &#x27;2022-03-01T00:19:59.999999744&#x27;,\n",
       "       &#x27;2022-03-01T00:30:00.000000000&#x27;, ..., &#x27;2022-03-07T23:40:00.000000256&#x27;,\n",
       "       &#x27;2022-03-07T23:50:00.000000000&#x27;, &#x27;2022-03-08T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</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'>-48.79 -48.77 ... -6.852 -6.832</div><input id='attrs-62dc760a-a4a8-4a0e-90ae-2930ceeb6f57' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-62dc760a-a4a8-4a0e-90ae-2930ceeb6f57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4cfc4f7e-389d-4b86-b0ca-cb96dff7ca9f' class='xr-var-data-in' type='checkbox'><label for='data-4cfc4f7e-389d-4b86-b0ca-cb96dff7ca9f' 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>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-48.787998, -48.7682  , -48.748398, ...,  -6.871399,  -6.851597,\n",
       "        -6.831799])</pre></div></li><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'>107.5 107.5 107.6 ... 159.0 159.0</div><input id='attrs-b8ebb244-b16d-447c-b7d2-57fdb6740980' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8ebb244-b16d-447c-b7d2-57fdb6740980' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b018b59-0c82-44f0-b453-f25d489d8ba5' class='xr-var-data-in' type='checkbox'><label for='data-8b018b59-0c82-44f0-b453-f25d489d8ba5' 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>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([107.519997, 107.539795, 107.559593, ..., 158.940598, 158.960388,\n",
       "       158.980194])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>theta_level_height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.0</div><input id='attrs-b5c865ef-e7b1-4188-a03c-0dc584769c5e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5c865ef-e7b1-4188-a03c-0dc584769c5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c4f4bba-1eaa-407b-b74e-5b71f65c1cf0' class='xr-var-data-in' type='checkbox'><label for='data-5c4f4bba-1eaa-407b-b74e-5b71f65c1cf0' 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>bounds :</span></dt><dd>theta_level_height_bnds</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>level_height</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>array(5.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>model_theta_level_number</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-53a48a1a-128a-4191-a642-8dc72e892345' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53a48a1a-128a-4191-a642-8dc72e892345' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56cdbf40-bafd-4508-93cc-1f3fd0d07617' class='xr-var-data-in' type='checkbox'><label for='data-56cdbf40-bafd-4508-93cc-1f3fd0d07617' 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>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>model_level_number</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>array(1, dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma_theta</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9994</div><input id='attrs-1c553dbe-ad62-4138-85ef-142b12c5f5bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c553dbe-ad62-4138-85ef-142b12c5f5bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea2449ea-e77c-460e-bcf7-6adba94dfa9b' class='xr-var-data-in' type='checkbox'><label for='data-ea2449ea-e77c-460e-bcf7-6adba94dfa9b' 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>bounds :</span></dt><dd>sigma_theta_bnds</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>sigma</dd></dl></div><div class='xr-var-data'><pre>array(0.99942381)</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'>10.0</div><input id='attrs-76ae98bd-ca1d-4ddd-846f-19fb224c89a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-76ae98bd-ca1d-4ddd-846f-19fb224c89a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9dc66af6-3506-41bc-b7c4-76bc4ba0e363' class='xr-var-data-in' type='checkbox'><label for='data-9dc66af6-3506-41bc-b7c4-76bc4ba0e363' 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>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>array(10.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height_0</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.5</div><input id='attrs-c0fb807c-45e4-4734-b53e-551815ad9619' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0fb807c-45e4-4734-b53e-551815ad9619' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d85ba6c-621c-4b0e-a889-cf111191921c' class='xr-var-data-in' type='checkbox'><label for='data-6d85ba6c-621c-4b0e-a889-cf111191921c' 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>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>array(1.5)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b57ac82e-c4db-47c9-9307-c5a235554957' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b57ac82e-c4db-47c9-9307-c5a235554957' class='xr-section-summary' >Data variables: <span>(1)</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>fld_s00i024</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=(6, 2120, 2600), meta=np.ndarray&gt;</div><input id='attrs-de8b1ff1-7eb2-4bf8-826c-77b92756994f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de8b1ff1-7eb2-4bf8-826c-77b92756994f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-222da409-66ff-4b29-9df2-afeefe1c12d0' class='xr-var-data-in' type='checkbox'><label for='data-222da409-66ff-4b29-9df2-afeefe1c12d0' 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>surface_temperature</dd><dt><span>long_name :</span></dt><dd>SURFACE TEMPERATURE AFTER TIMESTEP</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>um_stash_source :</span></dt><dd>m01s00i024</dd><dt><span>grid_mapping :</span></dt><dd>latitude_longitude</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.70 GiB </td>\n",
       "                        <td> 126.16 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1008, 2120, 2600) </td>\n",
       "                        <td> (6, 2120, 2600) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 168 chunks in 337 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-5abad538-2008-41ab-bd27-e54dad9e98f8' class='xr-section-summary-in' type='checkbox'  ><label for='section-5abad538-2008-41ab-bd27-e54dad9e98f8' class='xr-section-summary' >Indexes: <span>(3)</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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-42bb1979-cc0f-4209-bfa8-906210e4cfaf' class='xr-index-data-in' type='checkbox'/><label for='index-42bb1979-cc0f-4209-bfa8-906210e4cfaf' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([          &#x27;2022-03-01 00:10:00&#x27;,\n",
       "               &#x27;2022-03-01 00:19:59.999999744&#x27;,\n",
       "                         &#x27;2022-03-01 00:30:00&#x27;,\n",
       "                         &#x27;2022-03-01 00:40:00&#x27;,\n",
       "                         &#x27;2022-03-01 00:50:00&#x27;,\n",
       "                         &#x27;2022-03-01 01:00:00&#x27;,\n",
       "               &#x27;2022-03-01 01:10:00.000000256&#x27;,\n",
       "                         &#x27;2022-03-01 01:20:00&#x27;,\n",
       "                         &#x27;2022-03-01 01:30:00&#x27;,\n",
       "                         &#x27;2022-03-01 01:40:00&#x27;,\n",
       "               ...\n",
       "                         &#x27;2022-03-07 22:30:00&#x27;,\n",
       "                         &#x27;2022-03-07 22:40:00&#x27;,\n",
       "               &#x27;2022-03-07 22:49:59.999999744&#x27;,\n",
       "                         &#x27;2022-03-07 23:00:00&#x27;,\n",
       "                         &#x27;2022-03-07 23:10:00&#x27;,\n",
       "                         &#x27;2022-03-07 23:20:00&#x27;,\n",
       "                         &#x27;2022-03-07 23:30:00&#x27;,\n",
       "               &#x27;2022-03-07 23:40:00.000000256&#x27;,\n",
       "                         &#x27;2022-03-07 23:50:00&#x27;,\n",
       "                         &#x27;2022-03-08 00:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1008, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d8e64846-a344-4338-b0a5-2159eac94256' class='xr-index-data-in' type='checkbox'/><label for='index-d8e64846-a344-4338-b0a5-2159eac94256' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -48.78799819946289,   -48.7681999206543,  -48.74839782714844,\n",
       "       -48.728599548339844, -48.708797454833984,  -48.68899917602539,\n",
       "        -48.66919708251953,  -48.64939880371094,  -48.62959671020508,\n",
       "       -48.609798431396484,\n",
       "       ...\n",
       "        -7.009998321533203,  -6.990200042724609,   -6.97039794921875,\n",
       "        -6.950599670410156,  -6.930797576904297,  -6.910999298095703,\n",
       "        -6.891197204589844,   -6.87139892578125,  -6.851596832275391,\n",
       "        -6.831798553466797],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=2120))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7d9a48ac-45b1-42e6-802e-ce531832ec49' class='xr-index-data-in' type='checkbox'/><label for='index-7d9a48ac-45b1-42e6-802e-ce531832ec49' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 107.5199966430664,   107.539794921875,  107.5595932006836,\n",
       "       107.57939910888672, 107.59919738769531,  107.6189956665039,\n",
       "        107.6387939453125, 107.65859985351562, 107.67839813232422,\n",
       "       107.69819641113281,\n",
       "       ...\n",
       "         158.802001953125, 158.82179260253906,  158.8415985107422,\n",
       "       158.86138916015625, 158.88119506835938,  158.9010009765625,\n",
       "       158.92079162597656,  158.9405975341797, 158.96038818359375,\n",
       "       158.98019409179688],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=2600))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a09d42f8-3339-4b81-8091-3450ade0f829' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a09d42f8-3339-4b81-8091-3450ade0f829' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                   (time: 1008, lat: 2120, lon: 2600)\n",
       "Coordinates:\n",
       "  * time                      (time) datetime64[ns] 2022-03-01T00:10:00 ... 2...\n",
       "  * lat                       (lat) float64 -48.79 -48.77 ... -6.852 -6.832\n",
       "  * lon                       (lon) float64 107.5 107.5 107.6 ... 159.0 159.0\n",
       "    theta_level_height        float64 5.0\n",
       "    model_theta_level_number  int32 1\n",
       "    sigma_theta               float64 0.9994\n",
       "    height                    float64 10.0\n",
       "    height_0                  float64 1.5\n",
       "Data variables:\n",
       "    fld_s00i024               (time, lat, lon) float32 dask.array<chunksize=(6, 2120, 2600), meta=np.ndarray>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def _preprocess(ds):\n",
    "    return ds['fld_s00i024']\n",
    "raw_file_list=sorted(glob.glob(\"/g/data/hh5/tmp/WACI-Hackathon-2023/AUS2200/flood22-barra/atmos/umnsa_spec_202203*.nc\"))\n",
    "ds_raw=xr.open_mfdataset(raw_file_list,parallel=True,preprocess=_preprocess)\n",
    "ds_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "be22b550-f1bc-47f7-a106-7fc05fee66b5",
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:   (lat: 2120, lon: 2600, time: 2016)\n",
       "Coordinates:\n",
       "    height_0  float64 1.5\n",
       "  * lat       (lat) float64 -48.79 -48.77 -48.75 -48.73 ... -6.871 -6.852 -6.832\n",
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       "  * time      (time) datetime64[ns] 2022-02-22T00:10:00 ... 2022-03-08\n",
       "Data variables:\n",
       "    tas       (time, lat, lon) float32 dask.array&lt;chunksize=(6, 2120, 2600), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><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-4cd4459d-b10c-48bd-887e-bd15b9889956' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4cd4459d-b10c-48bd-887e-bd15b9889956' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 2120</li><li><span class='xr-has-index'>lon</span>: 2600</li><li><span class='xr-has-index'>time</span>: 2016</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-52e49fe9-5a77-4a20-9e56-bc64a77daf9c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-52e49fe9-5a77-4a20-9e56-bc64a77daf9c' 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>height_0</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.5</div><input id='attrs-b4a5073a-9a35-48bb-b34f-2caa7766679a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b4a5073a-9a35-48bb-b34f-2caa7766679a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3805276-c38b-4e9d-84a3-90a95f984f5d' class='xr-var-data-in' type='checkbox'><label for='data-e3805276-c38b-4e9d-84a3-90a95f984f5d' 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>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>array(1.5)</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'>-48.79 -48.77 ... -6.852 -6.832</div><input id='attrs-a8b62251-5d8b-4cfe-baea-2dafa15dee29' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8b62251-5d8b-4cfe-baea-2dafa15dee29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9effeedc-c744-4173-bfdd-ad009e29aff2' class='xr-var-data-in' type='checkbox'><label for='data-9effeedc-c744-4173-bfdd-ad009e29aff2' 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>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-48.787998, -48.7682  , -48.748398, ...,  -6.871399,  -6.851597,\n",
       "        -6.831799])</pre></div></li><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'>107.5 107.5 107.6 ... 159.0 159.0</div><input id='attrs-243318a8-698f-4267-a4ed-192be1329f60' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-243318a8-698f-4267-a4ed-192be1329f60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63d386f2-5f98-414a-8ed9-04bdbe85bbda' class='xr-var-data-in' type='checkbox'><label for='data-63d386f2-5f98-414a-8ed9-04bdbe85bbda' 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>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([107.519997, 107.539795, 107.559593, ..., 158.940598, 158.960388,\n",
       "       158.980194])</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'>2022-02-22T00:10:00 ... 2022-03-08</div><input id='attrs-f80c3d28-c31c-4f2f-94c4-b41dee413d66' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f80c3d28-c31c-4f2f-94c4-b41dee413d66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ea34fd0-c988-44d5-84a2-fc3692b83763' class='xr-var-data-in' type='checkbox'><label for='data-2ea34fd0-c988-44d5-84a2-fc3692b83763' 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>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-02-22T00:10:00.000000000&#x27;, &#x27;2022-02-22T00:19:59.999999744&#x27;,\n",
       "       &#x27;2022-02-22T00:30:00.000000000&#x27;, ..., &#x27;2022-03-07T23:40:00.000000256&#x27;,\n",
       "       &#x27;2022-03-07T23:50:00.000000000&#x27;, &#x27;2022-03-08T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0ab016c6-03f1-4b57-ae47-bd33cf039fff' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0ab016c6-03f1-4b57-ae47-bd33cf039fff' class='xr-section-summary' >Data variables: <span>(1)</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>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=(6, 2120, 2600), meta=np.ndarray&gt;</div><input id='attrs-e0dc7051-a988-4510-bca2-b2e842252c2e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0dc7051-a988-4510-bca2-b2e842252c2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03ff1971-97ef-4f68-a652-0bce06ab1a65' class='xr-var-data-in' type='checkbox'><label for='data-03ff1971-97ef-4f68-a652-0bce06ab1a65' 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>air_temperature</dd><dt><span>long_name :</span></dt><dd>TEMPERATURE AT 1.5M</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>um_stash_source :</span></dt><dd>m01s03i236</dd><dt><span>grid_mapping :</span></dt><dd>latitude_longitude</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 41.40 GiB </td>\n",
       "                        <td> 126.16 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2016, 2120, 2600) </td>\n",
       "                        <td> (6, 2120, 2600) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 336 chunks in 351 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                </tbody>\n",
       "            </table>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-0c16f472-f957-41c4-b9d3-e0227fe4972b' class='xr-section-summary-in' type='checkbox'  ><label for='section-0c16f472-f957-41c4-b9d3-e0227fe4972b' class='xr-section-summary' >Indexes: <span>(3)</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-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1f2e4cff-f522-4fea-a208-f3cc85d73d9b' class='xr-index-data-in' type='checkbox'/><label for='index-1f2e4cff-f522-4fea-a208-f3cc85d73d9b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -48.78799819946289,   -48.7681999206543,  -48.74839782714844,\n",
       "       -48.728599548339844, -48.708797454833984,  -48.68899917602539,\n",
       "        -48.66919708251953,  -48.64939880371094,  -48.62959671020508,\n",
       "       -48.609798431396484,\n",
       "       ...\n",
       "        -7.009998321533203,  -6.990200042724609,   -6.97039794921875,\n",
       "        -6.950599670410156,  -6.930797576904297,  -6.910999298095703,\n",
       "        -6.891197204589844,   -6.87139892578125,  -6.851596832275391,\n",
       "        -6.831798553466797],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=2120))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-58dcd36b-98e9-4fe9-9996-fdb02f1803b7' class='xr-index-data-in' type='checkbox'/><label for='index-58dcd36b-98e9-4fe9-9996-fdb02f1803b7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 107.5199966430664,   107.539794921875,  107.5595932006836,\n",
       "       107.57939910888672, 107.59919738769531,  107.6189956665039,\n",
       "        107.6387939453125, 107.65859985351562, 107.67839813232422,\n",
       "       107.69819641113281,\n",
       "       ...\n",
       "         158.802001953125, 158.82179260253906,  158.8415985107422,\n",
       "       158.86138916015625, 158.88119506835938,  158.9010009765625,\n",
       "       158.92079162597656,  158.9405975341797, 158.96038818359375,\n",
       "       158.98019409179688],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=2600))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2038bb91-f5fd-4c8e-b751-a3bac1f6cd9b' class='xr-index-data-in' type='checkbox'/><label for='index-2038bb91-f5fd-4c8e-b751-a3bac1f6cd9b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([          &#x27;2022-02-22 00:10:00&#x27;,\n",
       "               &#x27;2022-02-22 00:19:59.999999744&#x27;,\n",
       "                         &#x27;2022-02-22 00:30:00&#x27;,\n",
       "                         &#x27;2022-02-22 00:40:00&#x27;,\n",
       "                         &#x27;2022-02-22 00:50:00&#x27;,\n",
       "                         &#x27;2022-02-22 01:00:00&#x27;,\n",
       "               &#x27;2022-02-22 01:10:00.000000256&#x27;,\n",
       "                         &#x27;2022-02-22 01:20:00&#x27;,\n",
       "                         &#x27;2022-02-22 01:30:00&#x27;,\n",
       "                         &#x27;2022-02-22 01:40:00&#x27;,\n",
       "               ...\n",
       "                         &#x27;2022-03-07 22:30:00&#x27;,\n",
       "                         &#x27;2022-03-07 22:40:00&#x27;,\n",
       "               &#x27;2022-03-07 22:49:59.999999744&#x27;,\n",
       "                         &#x27;2022-03-07 23:00:00&#x27;,\n",
       "                         &#x27;2022-03-07 23:10:00&#x27;,\n",
       "                         &#x27;2022-03-07 23:20:00&#x27;,\n",
       "                         &#x27;2022-03-07 23:30:00&#x27;,\n",
       "               &#x27;2022-03-07 23:40:00.000000256&#x27;,\n",
       "                         &#x27;2022-03-07 23:50:00&#x27;,\n",
       "                         &#x27;2022-03-08 00:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=2016, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-08b945e2-9d1a-4b26-8664-ff62b7b5e917' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-08b945e2-9d1a-4b26-8664-ff62b7b5e917' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:   (lat: 2120, lon: 2600, time: 2016)\n",
       "Coordinates:\n",
       "    height_0  float64 1.5\n",
       "  * lat       (lat) float64 -48.79 -48.77 -48.75 -48.73 ... -6.871 -6.852 -6.832\n",
       "  * lon       (lon) float64 107.5 107.5 107.6 107.6 ... 158.9 158.9 159.0 159.0\n",
       "  * time      (time) datetime64[ns] 2022-02-22T00:10:00 ... 2022-03-08\n",
       "Data variables:\n",
       "    tas       (time, lat, lon) float32 dask.array<chunksize=(6, 2120, 2600), meta=np.ndarray>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def _combined_preprocess(ds):\n",
    "    if \"fld_s00i024\" in ds:\n",
    "        da=ds['fld_s00i024']\n",
    "        da=da.drop(['theta_level_height','sigma_theta',\"model_theta_level_number\",\"height\"])\n",
    "        return da.rename(\"tas\")\n",
    "    return ds[\"tas\"]\n",
    "ds_combined=xr.open_mfdataset(postprocessed_file_list+raw_file_list,parallel=True,preprocess=_combined_preprocess,chunks={'time': 6})\n",
    "ds_combined"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46d95671-2292-4d8b-bd93-af113fdd3ae6",
   "metadata": {},
   "source": [
    "In this example, we've used `preprocess` to essentially post-process the raw data on the fly, renaming the UM field (`fld_s00i024`) to match the name of the post-processed field (`tas`), and dropping the coordinates not related to the surface temperature field. We now have a single contiguous dataset from two very different sources."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c0e5b9fa-9295-4e39-8ccb-2d6fe999607b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x14df7f1f9310>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wCgoKfG6zkHuwbDZ2zbLj7rzzTtx5550hx/XMM8+gqKgI//3vf/H3v/8dCQkJOO200/D4449jwIABAZ8bjHBzH/sM/N8X4DbYV6xY4XOfxWJBamqqrDFIGQdBxk5Mkp2dDY7j8MMPPwScbENNwFlZWXA4HJ0MicrKSk3GGo7hw4cDALZu3YpZs2b5PLZ161bh8VD4L2bB8F/kogUzxLqr+DkS3n77bVx11VV45JFHfO6vqamJKHX+4MGDkrQwgFvLEUoQ7+/5C+fhGzduHHQ6HX7//feAj7/yyisAEFZzJGbz5s249NJLOxlm48ePh8vlws6dO32MnUOHDmHGjBngeR6rVq1CUVGR5HMFgi2Wga5z//uys7ORlZUVVOSekpLi85pVVVXo1auX8LjD4Qiqj/H31GZnZwMAPvjgA5SWlgYdPztu4cKFOO+88wIewzx8SUlJePDBB/Hggw+iqqoKX375Je69917MmTNH9WxE9hlUVFR0eqy8vFwYNyOeM8+0hoydGGT27NlYvHgxjh492smFHI7p06fjsccew3//+1+flO6lS5dGNCaz2axol9GrVy9MmDABb7/9Nu68805hMVi/fj12794tqe5EdwpjBYJlvHT3VGAlcBzXybj+4osvcPToUfTv31+4T+5OVM0w1ogRIyS9DmP16tVwuVw+42dYrVa8/fbbmDBhgiRDnFFYWIiNGzfC6XT6GDxMiCo2Zg4fPowZM2bA6XRi1apVIY0AqQwaNAgFBQVYsmSJTyjr0KFDWLt2rc+1MXv2bCxduhROpxMTJ04M+prTpk0D4M6kGjNmjHD/Bx984BNmCsVpp50Gg8GAffv2hQzvDBo0CAMGDMCWLVs6GdahyMvLw7x587BlyxY89dRTqqd6T548GYmJiXj77bdx4YUXCveXlZXhu+++wwUXXCDpdZTOn4QXMnZikKlTp+IPf/gD5s+fj40bN2LatGlISkpCRUUF1qxZgxEjRgStTXP66adj6tSpuOOOO9DU1ISxY8di3bp1woKs1PswYsQIrFq1Cp999hkKCgqQkpISdhFi/P3vf8fMmTNx4YUX4oYbbkB1dTXuvfdeDB8+HPPnz5d0brU4dOiQsMju27cPgHvyBtzVkVm449ChQ+jXrx/mzp0r7PTfffddfPTRRzjzzDNRWlqKhoYGvP/++1i6dCnmzZuHUaNGqTZONWFZOq+99lon7UE4Zs+ejddffx2DBw/GyJEjsWnTJjz++OOdPBH9+vVDYmIi3nnnHQwZMgTJyckoLCwMaoCaTCbhs9aKzz//HC+//DLOOusslJaWwm63Y+PGjXjqqafQv3//gJ6bjz/+GHV1dSG9OgsWLMAbb7yBffv2CYbKn/70J9xyyy2YM2cOrrvuOlgsFnz77bd44okncMoppwi/jerqapx44omoqKjAK6+8gurqap9K4kVFRYq8PDqdDg899BCuueYanHvuubj22mvR0NCARYsWdQptXXLJJXjnnXcwa9Ys3HrrrZgwYQKMRiPKysqwcuVKnH322Tj33HMxbNgwXHrppXjiiSeg1+tx0kknYfv27XjiiSeQlpYmaS7p3bs3/u///g9//vOfsX//fpx++unIyMhAVVUVfv75Z8FLAwAvvfQSzjjjDJx22mmYN28eevXqhbq6OuzcuRO//PIL3n//fQDAxIkTMXv2bIwcORIZGRnYuXMn3nrrLaFCu5qkp6fj/vvvx3333YerrroKl156KWpra/Hggw8iISEBDzzwgKTXGTFiBD766CO8+OKLGDt2LHQ6nezf/5tvvomrr74ar776Kq666ioAgecpAIIhv3fvXlnn6NZ0qTyakAXLfjp27JjP/cGydF599VV+4sSJfFJSEp+YmMj369ePv+qqq3wydvyzsXie5+vq6vj58+fz6enpvMVi4WfOnMmvX7+eB8A//fTTisazefNmfurUqbzFYuEB8NOnT5f13r/++mt+0qRJfEJCAp+ZmclfddVVfFVVlazXUAP23gL9ibNLWJaE+L5169bxJ598Mp+fn88bjUbeYrHw48eP51944QUhU0TKuYNlYw0bNqzT/aWlpfyZZ57Z6X4A/I033hj0HOJsrGeffZYHwC9fvjzk+AJlY9XX1/MLFizgc3NzeYvFwh9//PH8Dz/8wE+fPr3Tb2DJkiX84MGDeaPRqCg7Tm127tzJX3DBBXxpaSmfkJDAJyQk8IMHD+bvuusuvra2NuBzZs6cySclJYXM1mJZN/7f44cffsgff/zxfHZ2Np+UlMQPGzaMf+ihh/iWlhbhGJYFFOwv3GcWLBuL8Z///IcfMGAAbzKZ+IEDB/KvvvpqwDnCbrfz//jHP/hRo0bxCQkJfHJyMj948GD+uuuu4/fs2SMc19HRwd9+++18bm4un5CQwE+aNIlft24dn5aW5pONFy4L8OOPP+ZPPPFEPjU1lTebzXxpaSl/wQUX8N98843PcVu2bOEvuugiPjc3lzcajXx+fj5/0kkn8f/617+EY+69915+3LhxfEZGBm82m/m+ffvyf/rTn/iampqQn12gz/H999/3uT9YRuJ//vMffuTIkbzJZOLT0tL4s88+m9++fbvPMXPnzuWTkpICnq+uro6/4IIL+PT0dJ7jOCELlJ3v8ccf7/Qc/98D+4zFYws0T/G8e94IlP3Vk+F4XoUiFETM8+677+Lyyy/Hjz/+iClTpnT1cOIS5mHZu3cvSktLwxa1kwPP83A6nXjzzTexYMECbNiwQdg5XnTRRThw4IDksBHRfVm1ahVOPPFEfPPNN5g+fbqqvyGprF27FlOnTsU777yDyy67LOrnJ+ITCmMRnViyZAmOHj2KESNGQKfTYf369Xj88ccxbdo0MnS6AczFfOzYsU4CR6V88sknAdOkeY8A9u2331blPET34JRTTgEAH6NWC1asWIF169Zh7NixSExMxJYtW7B48WIMGDAgqJCYILSAPDtEJz7//HMsWrQIe/fuRWtrKwoKCnDOOefgb3/7m6K0yGCEEyn61zuJd2pra3HgwAHh9nHHHafazryhocEnPj906FDqyRODNDc3+7Qz0Pp7/umnn3DHHXdgx44daG5uRnZ2Nk477TQ8+uijAdOxuxrm4QyFXq+nrKkeCBk7RJcRbsKYO3euT5l3giAILWFhvlAoEeoTXQ8ZO0SXsXHjxpCPZ2dnq97dmyAIIhj+nq9A9OnTJ2hRRKL7QsYOQRAEQRAxDQkiCIIgCIKIaSgbC4DL5UJ5eTlSUlJIeEYQBEEQPQSe59Hc3IzCwsKQCS1k7MDdo6S4uLirh0EQBEEQhAKOHDkSsno4GTvwNq47cuSIqqnVBEEQBEFoR1NTE4qLi4V1PBhk7MCbAp2amkrGDkEQBEH0MMJJUEigTBAEQRBETEPGDkEQBEEQMQ0ZOwRBEARBxDRk7BAEQRAEEdOQsUMQBEEQRExDxg5BEARBEDENGTsEQRAEQcQ0ZOwQBEEQBBHTdKmx8+KLL2LkyJFCMb/Jkyfjyy+/FB7/6KOPcNpppyE7Oxscx2Hz5s2dXsNqteLmm29GdnY2kpKScNZZZ6GsrCyK74IgCIIgiO5Mlxo7RUVFWLx4MTZu3IiNGzfipJNOwtlnn43t27cDAFpbWzF16lQsXrw46GvcdtttWLZsGZYuXYo1a9agpaUFs2fPhtPpjNbbIAiCIAiiG8PxPM939SDEZGZm4vHHH8eCBQuE+w4ePIg+ffrg119/xXHHHSfc39jYiJycHLz11lu4+OKLAXibev7vf//DaaedJumcTU1NSEtLQ2NjI7WLIAiCIIgegtT1u9todpxOJ5YuXYrW1lZMnjxZ0nM2bdoEu92OU089VbivsLAQw4cPx9q1a4M+z2q1oqmpyeePIAiCIIjYpMuNna1btyI5ORlmsxnXX389li1bhqFDh0p6bmVlJUwmEzIyMnzuz8vLQ2VlZdDnPfroo0hLSxP+iouLI3oPBEEQPYnaFise/mIHftxb09VDIYio0OXGzqBBg7B582asX78ef/zjHzF37lzs2LEjotfkeT5kB9SFCxeisbFR+Dty5EhE5yMIguhJ/H35Lrz8wwHcvORXOF3dSslAEJrQ5caOyWRC//79MW7cODz66KMYNWoUnn76aUnPzc/Ph81mQ319vc/91dXVyMvLC/o8s9ksZICxP4IgiHjht7JGAEBdqw2H69q6eDQEoT1dbuz4w/M8rFarpGPHjh0Lo9GIFStWCPdVVFRg27ZtmDJlilZDJAiC6LE4XTwO1LQKt4/Wt3fhaAgiOhi68uT33XcfzjjjDBQXF6O5uRlLly7FqlWrsHz5cgBAXV0dDh8+jPLycgDA7t27Abg9Ovn5+UhLS8OCBQtwxx13ICsrC5mZmbjzzjsxYsQInHLKKV32vgiCILorR+raYHW4hNtl9eTZIWKfLjV2qqqqcOWVV6KiogJpaWkYOXIkli9fjpkzZwIAPv30U8yfP184/pJLLgEAPPDAA1i0aBEA4Mknn4TBYMBFF12E9vZ2nHzyyXj99deh1+uj/n4IgiC6O3uqW3xul5Fnh4gDul2dna6A6uwQBBEvPL9yLx7/ardw+9IJxXj0vJFdOCKCUE6Pq7NDEARBaA/z5GQnmwAA9a32rhwOQUQFMnYIgiDiiJoWdwLIwLwUAEBdm60rh0MQUYGMHYIgiDjiWLOvsVPfSsYOEfuQsUMQBBFH+Ht26tsojEXEPmTsEARBxAk8z4s8O8kAgPo2GyhPhYh1yNghCIKIE1qsDqHGzoBct2fH6eLR1OHoymERhOaQsUMQBBEnMK9OstmANIsRFpO7HhnpdohYh4wdgiCIOIEZOyztPMPiST+njCwixiFjhyAIIk6oaXEbNTkpZgBAWqIRANDYTiJlIrYhY4cgCCJOONbcAQDITnYbO6mJ7o5BZOwQsQ4ZOwRBEHFCY7tbiJyR5A5fMc8OCZSJWIeMHYIgiDiBeXBSE9xGjmDskGeHiHHI2CEIgogTmjo8xo4nfMWMHjJ2iFiHjB2CIIg4oSmIZ4c0O0SsQ8YOQRBEnOD17Bh9/iVjh4h1yNghCIKIE5o8AuXUBHcYyytQJmOHiG3I2CEIgogT/D07FMYi4gUydgiCIOIEf80O1dkh4gUydgiCIOIAl4tHs9UdxkpL9E89pzo7RGxDxg5BEEQc0GJzgOfd/0/xaHZSRZodl4vvqqERhOaQsUMQBBEHsBCWSa9DgtHd7ZyFs3gegteHIGIRMnYIgiDigA67EwCQaNIL9yUY9TAb3MsAFRYkYhkydgiCIOKADrsLAJBg9J32qdYOEQ+QsUMQBBEHMM8OC2ExqNYOEQ+QsUMQBBEHMM9OYjBjhzw7RAxDxg5BEEQc0O7x7Jj9jB1WTZnCWEQsQ8YOQRBEHCCEsQy+0z7V2iHiATJ2CIIg4oBA2VgACZSJ+ICMHYIgiDigw+HJxjIE1uyQsUPEMmTsEARBxAEdNpaNFSSMRdlYRAxDxg5BEEQcECz1nFVRJs8OEcuQsUMQBBEHdDiCGDsUxiLiADJ2CIIg4gBvBWWqs0PEH2TsEARBxAHt9sCandREVmeHUs+J2IWMHYIgiDiA2kUQ8QwZOwRBEHGANUi7CKbZsTlcgkFEELEGGTsEQRBxQLAwVrLJAB3n/j+JlIlYhYwdgiCIOCBYGEun4+Di3f9/bPnuaA+LIKICGTsEQRBxADN2zH4VlMV89GtZtIZDEFGFjB2CIIg4gKWe+/fGAoA/TOsLABhTkhHVMRFEtCBjhyAIIg4I1vUcAKYPzAEANFNGFhGjkLFDEAQRBwTT7ABASoK71k5zB9XaIWITMnYIgiDiANb1PFAYi6WjU+o5EauQsUMQBBEHeMNYnY0dJlq2egwigog1yNghCIKIcXieD1pnBwDMnvu0NHZ4nsdtS3/FP76i9HYi+pCxQxAEEePYnC7wnlo65gCaHZPevRQ4XTwcTm0Mns1HGvDx5nI8t3KvJq9PEKEgY4cgCCLGYWnnQOd2EYDXswO4DSMtaLF6xc9OVsWQIKIEGTsEQRAxjtUTwtJxgFHPdXqceXbcx2pj7DicXgPH6iAhNBFdyNghCIKIcdpFaecc19nYMeh10HsaZGnl2RG/bodGBhVBBIOMHYIgiBiHGReBauwwzJ5ig1p5dtptXm8OeXaIaEPGDkEQRIzD0s4D6XUYgrGjkSEi1uyQZ4eINmTsEARBxDhCE9AAaecMk0Hb9HNfY4c8O0R0IWOHIAgixmkPUVCQoXVhwZpmq/B/Kl5IRBsydgiCIGIcr2Yn+JSvdRhrR0WTaDzk2SGiCxk7BEEQMQ4zYAL1xWJoHcaqbbEJ/ydjh4g2ZOwQBEHEOKH6YjGYZ8emkbHTbhdnY1EYi4guZOwQBEHEOCztO3TqubaaHbE3hzw7RLQhY4cgCCLG6XBIqLPDmoFqZIiIDRytavkQRDDI2CEIgohxOkJ0PGewlhFaVVAW19ahooJEtCFjhyAIIsaRVEHZ85gWXheni6d2EUSXQsYOQRBEjCPFs2PWMBvLX6NDmh0i2pCxQxAEEeNIaRdh0jAby9+4oWwsItqQsUMQBBHjdNilZGNpV1SwnTw7RBdDxg5BEESMwzQy5i5KPffX6JBnh4g2ZOwQBEHEON7eWOEbgUYjjEWeHSLakLFDEAQR4wianRDtIrQMY3UydsizQ0QZMnbilK1ljbhv2VbUtFjDH0wQRI9GKCoooV2EFiEmf82OVoULCSIYhq4eANE1zHluDQCgucOBZy8d3cWjIQhCSzoktYvQMozl+5rk2SGiDXl24py91S1dPQSCIDSmwyGlzo6WAmXS7BBdS5caOy+++CJGjhyJ1NRUpKamYvLkyfjyyy+Fx3mex6JFi1BYWIjExETMmDED27dv93mNGTNmgOM4n79LLrkk2m+lx2IOIVgkCCI2kJR6boxe6jllYxHRpktXuqKiIixevBgbN27Exo0bcdJJJ+Hss88WDJrHHnsM//znP/Hcc89hw4YNyM/Px8yZM9Hc3OzzOtdeey0qKiqEv5deeqkr3k6PhIwdgoh9pLSLEHpjaWCIMI1OaoLB5zZBRIsuXenmzJmDWbNmYeDAgRg4cCAefvhhJCcnY/369eB5Hk899RT+/Oc/47zzzsPw4cPxxhtvoK2tDe+++67P61gsFuTn5wt/aWlpXfSOeh6h6m4QBKE+24424urXN2BnRVPUztkupV2EUXuBcrrFBIDCWET0kSRQHjNmjKwX5TgOn376KXr16iX5OU6nE++//z5aW1sxefJkHDhwAJWVlTj11FOFY8xmM6ZPn461a9fiuuuuE+5/55138PbbbyMvLw9nnHEGHnjgAaSkpAQ9l9VqhdXqzUJqaorepNPd0HNdPQKCiC+ue2sTjja0Y8uRBmy6f6bm53O5eMFbE6pdhKDZ0aBJJ/MspVuMOFxHYSwi+kgydjZv3ow77rgDycnJYY/leR6LFy/2MSZCsXXrVkyePBkdHR1ITk7GsmXLMHToUKxduxYAkJeX53N8Xl4eDh06JNy+/PLL0adPH+Tn52Pbtm1YuHAhtmzZghUrVgQ956OPPooHH3xQ0vhiHR1H1g5BRIvXfjyAow3tAIDaVltUzik2LEKGsVg2llM7z05aohEAeXaI6CM59fyuu+5Cbm6upGOfeOIJyQMYNGgQNm/ejIaGBnz44YeYO3cuVq9eLTzO+S3GPM/73HfttdcK/x8+fDgGDBiAcePG4ZdffgnqkVq4cCFuv/124XZTUxOKi4sljzmWIFuHIKLHg5/tiPo5xYaFpN5YGhgiHZ3CWF6D6s11B7H/WCv+OnsodDqakAhtkGTsHDhwADk5OZJfdMeOHSgsLJR0rMlkQv/+/QEA48aNw4YNG/D000/jnnvuAQBUVlaioKBAOL66urqTt0fMmDFjYDQasWfPnqDGjtlshtlslvp2YhyaXAgilmFeFaOegz6EMRGN1PN0j2fH6nCC53ms/v0Y/vqJOyElJ8WMG0/sr/q5CQKQKFAuLS3tlAEViO+++w4AUFxcDL1emfCV53lYrVYhNCUOR9lsNqxevRpTpkwJ+vzt27fDbrf7GEiELy4XL/yfNlIEEdtISTsHtO6N5dXsAICLBx77aje+/71GOObxr3ZTRXdCMySHsWbPno2vv/4aCQkJAR9fuXIlzjrrLLS0SC9Sd9999+GMM85AcXExmpubsXTpUqxatQrLly8Hx3G47bbb8Mgjj2DAgAEYMGAAHnnkEVgsFlx22WUAgH379uGdd97BrFmzkJ2djR07duCOO+7A6NGjMXXqVMnjiDfEMXkKYxFEbCMl7RzQtl1Eh59mBwBeXLUPKWbfJej9jWX444x+qp+fICQbO3V1dbjwwgvx8ccfd/LarF69GnPmzME111wj6+RVVVW48sorUVFRgbS0NIwcORLLly/HzJnuDIW7774b7e3tuOGGG1BfX4+JEyfi66+/FjKtTCYTvv32Wzz99NNoaWlBcXExzjzzTDzwwAOKPUvxgF1s7FAYiyBiGinVkwFRuwinCy4Xr6p+hoXSUhON0Os4OD3e5Warw+c4F893ei5BqIFkY+frr7/GCSecgLlz5+Ltt98W7v/+++8xe/ZszJ8/H0899ZSsk7/yyishH+c4DosWLcKiRYsCPl5cXOwjZiaksflIg/B/8uwQRHTgu2ghF/pihWgCCnjDWIDb4EnQqbdhFLquG/VISzSiLkgmWld9RkTsI7moYGFhIb7++mt89913uOWWWwAAa9aswZlnnokrr7wSzz77rGaDJNTjQE0rrnzlZ+G200WTC0FEg0DhoWhcf8yzk2gKF8byPq52KKvd7q3zYwxR3GvN3pqgjxFEJMjqet6vXz8sX74cM2bMQFNTE5YtW4bLLrsML7zwglbjI1Rm29FGn9sOMnYIIioEMiDsThf0KnpQAiFodsJ4dox6DhwH8Dzrj2UMebwcrCKRdJLJAMArRNZxbsEyAKzfX6faOQlCjGTPTlNTE5qamtC7d2+88847WLp0Kc444ww89thjwmPxXIm4p3C4rs3ntl2DAmIEQXQmUJZTNDYbLIRkDqPZ4ThOs/5YTLOTaNIhL9U3ycViMqBXeqJwm7zNhBZINnbS09ORkZGBjIwMzJ49GzabDe+99x4yMzORkZEhPE50XxxOF/674YjffTSxEEQ0YN3EDSLhryMKm412ianngHYZWYLBZdDj/tlDfR4z6jlkJpmE2/Vt0aksTcQXksNYK1eu1HIcRBT4ble14Nn5w7S++Pf3++FwkWeHIKKB0J/KpEeL1QGe16Y1gz8d9vB9sRhmox7ocKjeH6vd5tUN9ctJxiPnjsB9y7YCcAujHz53OM567kcAQFO7HdnJVPSVUBfJxs706dO1HAcRBVg8fPbIAowrzcC/QZodgogWzLAxG3Sw2nWwOV1R8ax2SOh4zhDCWCobYR0O31o/ealeY8ao12FkUTpyUsw41mxFm436ZhHqIymMJVeLI6XaMhF9fitrAACcPCQXRs+kRmEsgogOdof7WjPqdTB4MpKicf1Z5YSxjOr3x3KKuq4neMJkSaJigizlnRUYJGOH0AJJxk5GRgaqq6slv2ivXr2wf/9+xYMitIHFwvNTE4XJlgTKBBEdbE73Im4y6ITNRjTCWO2iGjfh0KI/FtMqAd709wyLV6Nj1Lk/C4vZ/VibzbfQIEGogaQwFs/z+M9//oPk5GRJL2q32yMaFKENrVb3pJNsNqDFU7l0VyV54QgiGjADwqTXCbVmoqGZY+c1GySEsTTojyXucM7S30uzLMJ9rR7jxmJ0L0fHmqk/FqE+koydkpISvPzyy5JfND8/H0ajejUaCHVo9Rg4SWY9Gtq9GQ/f7qzCyUOCd5InCCJymAFh1Otg8HgzKho78J8fDuCyiSUYU6JNNitL5dbrwhs7WmRjMc+SyaATWlCIQ2rMu9w3Jwk/H6zD1qONuHBcsWrnJwhAorFz8OBBjYdBaA3P88IOKtls8En1fPyr3ThpcC446h1BEJph9+hzTAYd9J5Ff+GHW1HZ1IEPNpXh4OIzNTkvy0GQ0urKa+yop5sRBNJBPEssktc/1x05aGijyAChPpLr7BA9m3a7U5j0kswGDCtMw0NnDwPgDmX5FxskCEJdmGfHZNAJPekqmzo0P6/Lc+FLaexp1iCMJU47DwTrh8U6oje2k7FDqA8ZO3EC0+hwHGDxTDpXTu4t7KaO1rd32dgIIh5gAmWzQQddFL2orJO4lFNqKVAOlg3mJGOHiAJk7MQJTJycZDL4hKtogiGI6CDW7EgJKakF8+jqJVg7WoSx2m2hixoyz1OqZy5q6qC5iFAfMnbiBLE4WUw6GTsEERVsTLOj10VVH8c8O1K8SdpkY7HeXMHCWO5/2carieYiQgPI2IkTWgRjx1eTziaYBppgCEJTAml2ooG8MJZ22ViJQSo4s1pDqaKNF9PxEIRaSG4XcfjwYUnHlZSUKB4MoR3Ms5PsZ+ykkmeHIKKC2NiJrmbH/a+UczLvi5rGDqslxAop+sPOxeYmu5OH3cnDZKDsUEI9JBs7ffr0Ef7PCzsFzuc+juPgdFKp7+6I4Nkx+X7l6RYydggiGrB6MtHX7LA6O12TjcXqJoYL3bFCi4DbQDJR4IFQEcnGDsdxKCoqwrx58zBnzhwYDJKfSnQDBIFykDAWGTsEoQ3tNideXLUXW482AnAbFByi6NlhqecSTskagaopUGYBKf/zj++dgQ0H6zGlXxYACIUWAW9NIoJQC8kWS1lZGd544w28/vrr+Ne//oUrrrgCCxYswJAhQ7QcH6ES3jCWr0iQeXrarNSPhiC04PmVe/Hcyr3C7a7T7EgJY7FGoCp6dtj5/e7/1xVj8dEvR3HumF4AAIPIGnJQzz5CZST7CfPz83HPPfdg586d+OCDD1BfX4+JEydi0qRJePnll+GKQo8XQjksnZN5chis0Bd1GiYIbdhR0eRz26jnukSzIy2Mpb5mB0E0Q1nJZlw7rS+yk83ux3Wc4P1hLS4IQi0UBUWPP/54vPLKK9izZw8sFguuv/56NDQ0qDw0Qk1YCXZ/Y4cVGGQZEwRBqIu/kWE26CGhTZVqyApjaZCNJScbzOAJo9nJ2CFURtElt3btWlxzzTUYOHAgWlpa8PzzzyM9PV3loRFqwjQ5aRaTz/0WFsYizw5BaIKhk7ETZc2OnDCWBkUFmdki5fzss6IwFqE2kjU7FRUVePPNN/Haa6+hvr4el19+OdauXYthw4ZpOT5CJVgdnaCeHTJ2CCIquNtFRO98slLPNQhjBdPsBIIZOyRQ1g6Xi8d9y7YiJ8WMO04d1NXDiRqSjZ3S0lIUFhZi7ty5OOuss2A0GuF0OvHbb7/5HDdy5EjVB0lEDvPspPsZO6z2hY12UgShCf42htmo75IKykHK3PigRQVlVh9Qyltm8xFpdrTjx301WLrhCADgD9P6IiXBGOYZsYFkY8fhcODw4cN46KGH8Le//Q0AOlW5pDo73ZfGNhsAIM3ib+yQ25ggtKR3VpLPbVMX1dmR5tlRX7PDyzi/XvDs0HykFUfqvE2fT33ye/xw94mCViqWkWzsHDhwQMtxEBrTGCSMxSYXB+2kCEIT/L0kZmOUe2NJLOoHaK3ZCX8s8+zQfKQdLpGToqKxA4fq2tAvJ7kLRxQdZIWxiJ4Jz/NCBeWUBN+vXJhcKEZOEJrgHyKOtmfHycvPxtImjCVBoOzxNDuplIlmdPhl3lY1dsSFsROR76q+vh4bNmxAWVmZWuMhNMDqcAmCP//eWGxycdDkQhCa4G84JJkNUfXssDCSvgcIlPUkUNac2labz+26NluQI2MLycbOfffdh7a2NgCA3W7HH/7wB2RnZ2PixIkoLS3Feeedh46ODs0GSiinRVQd2b83FoWxCEJb/I2dlARDl2RjyaugrGIYS0Y2GDPIXDQfacb7G4/43FbTi9edkWzs/P3vf0dLSwsA4PHHH8fHH3+M999/H2VlZfjkk0/w888/4/HHH9dsoIRyWjq8Hc91frOs0VPdjOcpA4IgtMDqF8ZKNhuiWmfHqaA3lprZmXKKCrLNl5OnuUgrWMVqhqrVsrsxko0dcebV+++/j8WLF+O8885DYWEhZs+ejX/+859YsmSJJoMkIqPF6jV2/DGIOg1TBgRBqI//zjk5wRDVCsq8nK7nRm82ln+2baTIycYiT7N2tNp8+yCq6cXrzsi65Jgb9MiRI5gwYYLPYxMmTMChQ4fUGxmhGs3Ms5MQwNgRzbrk2SEI9fE3drKSzF3SG0tOUUGeV083o0SzQ2Es7Wi1+ho38eLZkZyNBQAvv/wykpOTYTabUV9f7/NYY2MjzGZzkGcSXYlUzw5lZBGE+oiNnTmjCqHXcQH1MzzPayJcZpsYKS/NUs8BdyjLZIjcBSUnG4sZZLTx0g4ma5g2MAff/36MjB1/SkpK8PLLLwMATCYTfvnlF5xwwgnC4ytXrsSgQfFTeron0WJ119gJaOyIXNt2ysgiCNVh4eGnLzkOZ40qBBDYy+F08T6bD7XPL8VwMYmKy1ntzoBzhly8AunwxwqeHdLsaILN4RL0WNlJ7j6JatZU6s5I/iUfPHgw5OOTJk3C9OnTIx0PoQHMbcn6YInhOA56HQeni6fdFEFoAFtcUhK8KeeB5DMOFw9D50s0YtjO3SzB2NHpOJj0OticLtV2/DykC6T1gmdHlVMTfrSKMnMzPcZOvGRjRW62e5g0aZJaL0WoDGvyGcjYAdzeHaeLJ4EyQWgAW0xMeu/1F0g/o9VmI9D5Q2EyuI0dtRZBIYwlQbXDJISUjaUNTNJgNuhg8Xjt4iWMpVpOQEVFBQ4fPqzWyxEq0saMnSAuaaqiTBDaIRgbIs9KIP2KVgs8C1OwTKtwqN0fi5eRes4SJkigrA0sEyvZbPB+z3YydmRx0kknoU+fPmq9HKEibZ4fuMUYeGdH6Z4EoR3WgMZO5+OcGm02vJ4ducaOOloOOUUNWR0wCqlrAwtjJYmMnf/6FRmMVVQLY7355ptChWWie9EWJoxlpJYRBKEZTLMjNjaCaXY0Pb/EzCq1+2PxcgTKnmPI2NGGFo9+M9lsQFm9t/u508VLqsPUk1HN2Bk/frxaL0WoDDN2Ek2Bv27mOqYwFkGoT6AwVrQ0Oy4XL9TLkSJQdh+nbn8sWQJlqqCsKa2iMiTizW2rzYHUBGNXDSsqRLGOJ9FVsDBWkpnCWAQRbWwBsqECGjsaLPDitg9SPTveKsoqh7GkCJSpzo6mtFi9a4E4H0WcpRWrqGbsbNmyBXqJan8iugienSCaHSGMRdlYBKE6zOAwijUzUdLsiL0zZol57UJ/LLWydHj5nh2qs6MNrKBgktmA3lkW4X4ydmSidi8VQh28qedBwlieyU2t8vAEQbgR168KF8bSQjMn9s4YJRYsFPfHUgM5AmU9CZQ1RRzGmjult3B/izX2CwtK1uycd955IR9vbGzUpNQ5ETltdk82VpAwlkFHAmWC0AKxd8TX2Ol8rBYLvFgvJHV+FjQ7KqUkM82OrK7nZOxoQovN69lJMOrRNzsJ+2ta46LGmmRj57PPPsPMmTORl5cX8HGnM/Ytw55KG6ugHCSMZdCTZocgtMDH2NF3gWZHRvVkBhunVaUFUI5mh1VQpjCWNrT69Uk0qh2y7MZINnaGDBmC888/HwsWLAj4+ObNm/H555+rNjBCPdrChbEoG4sgNMHqDBxGCrTsa3H9yWkVwRDCWHZ1NrDMbpGi2fHW2VHl1IQfraLUcwAwGtyfty0OPnDJV8DYsWPxyy+/BH3cbDajpKRElUER6iIUFQwSxiKBctfwyeajePn7/VQtNoZhOjj/MFLACspahrEkFhQEuraCsrc3Fs1FWsA8O2wtYJ4dO3l2vPzrX/8KGaoaMmQIDhw4oMqgCHVpt4cuKkip59GnuqkDty7dDADITjHh3NFFXTsgQhOEMJKfsRGtooJsx24OEsIOhOpFBT3/Bgrd+UOeHW1hXv4kj5dfyLyLgw9csrlvNpthsVjCH0h0K2wOl7C7tBjD9Mai3VRU2HSoDhMe+Va4/f7Gsi4cDaElgQoKAkGysTRYcJjIWJ5nR92igi6vaCcsbJhUVFAbWG8stvFlv8t4EChTUcEYh6WdA0BiiK7nAKWeR4snV+zxub12Xy2ONrQHOZroyQQzdgI5OS7+93rVy3fYnPKagALq98bae6xF8rHUCFRb/MuQeMNYsf95k7ET47C0c6OeC1pBVe+ZYCjdMzrsrmoGAPTJTkJuihkAsPlwQxeOiNAKZmx0NnYCuzmO1Klr9CrT7Lg3RR0qpZ6v2n0MAPD5loqwxwoVlMmzowmtfvpNCmMRMQNT3werngyQQDmatNkcONZsBQB8fMNUnDgoFwCwq7KpK4dFaIQ1iLERLDOprF7dZsqBOq6Hg7WVabepW1VXiveSfUzk2dGGdr+m0EaVxejdGTJ2Ypxw1ZMBqqAcTQ7VuhezdIsRaRYjBuanAAD2VEl39RM9BzmaHQBo6lDXwFCSes7miq6oqqujooKawja/TKCcnuhu/lnXau2yMUULyVeAXq9HdXW1lmMhNKDNT5AWCANNMFHjUG0rAKA0KwkAUJSRCACoaCTNTiwiR7MDABsO1kXl/KFgnp02lT07UmCp55QZqj4uFy9k5jL9ZkF6AgCgorGjy8YVLSRfAdT3qmci7OxChLEEgTJlY2kO8+yUZrozG3ulu42dow2xP9nEIwGbgCK4Z+eVNeqW7/CGsaSnnrNdf6tNXc/OPFEvpmBQI1DtaBcViWSb36wkEwCgsc3eJWOKJhTGinGkuLFZGIsqKGvPQY+xwzoOF6S5d1Y1LVbVsl+I7kOwdg3BPDtJITywap4/FEy82qZSJ+w0T6jkiknhi85SbyztYOJkjgMSPMZvSoL7u2lWOXzaHZFcVBAAvvrqK6SlpYU85qyzzopoQIS6SHFjG6ioYNTwD2NlJplg0utgc7pQ02ITPD1EbBAsGyqYZycvNUGb88sJY3k8O20qeXYcQbxbgaDeWNoh7pHItFGsbUSzSoZtd0aWsTN37tyQj3McRw1BuxlCnY2Qnh3KxooWLIzVO9vt2eE4DskJBtS12tASB7ureIOFsTppdoIc39CubjiBeQvlpJ4zzU6rSpodlvggxdghgbJ2tFi9Hc8ZKQkeY6eDwlg+VFZWwuVyBf0jQ6f7wSqohjJ2vBWUaYLREqvDiXKPELkkM0m4n+2uWuJgdxVvBM3GCpJ7rvZvQAhjySgqyLKx2lTIxuJ5PqhuKRB6ahehGf4dzwGvsRMPc4/kKyBYESyiexNsZylG6I1Fmh1NqWjoAM+7ax5lJ5uE+5PI2IlZhOtPH1yzc+OJ/TCkINV9vMOlWk8q8fn9e3OFgv0ebc7IxyLeQEnxLlEYSzuYp07s2Uk2uzU7LR2OmE9CUjUba/PmzZGMhdAAKRVUjYJmh7ZTWnKsxV3LIifF7LN5SPFMPq1k7MQcUursHFecgc9umircVvN3IHh2ZTQCFZepiDT9XGwsGQ1yGoHG9sLbFbC6SSxMCXg9Ow4Xr1rF7O6KZGNn7ty5SEzsLJ5sbGzECy+8gDFjxmDs2LGqDo6IHG82VojUcyoqGBVqPcaO2KsDeCcf0uzEHkHr7Ij+b9BzMOh1QpVzNT18wTxLoTDqdcJ4I00/FzeYlDIGqvmlHYHCWBaTXqjm3aiyXqy7IfkKeO2115CSkiLc/u6773DFFVegoKAAzz77LGbNmoWNGzdqMkhCOVLKxXvDWLFt2Xc1x1psAIDsZLPP/cme9E8KY8UeUjw7zAhIFsSiKho7CrKxAG8KfKTp58zY4jjvPBMK8uxoR2sAgTLHcSjKcCdLvLHuYFcMK2rIysYqKyvD66+/jldffRWtra246KKLYLfb8eGHH2Lo0KFajZGIACEbI6RAmSaYaFDj6YmVneJn7JjV39ET3YNgmhnxum8QpQEfa7aqlgUFSLv+A2ExGVDfZlfBs+PNxJKi+9RTI1DNCJSNBQAdnmKDsR5Gl3wFzJo1C0OHDsWOHTvw7LPPory8HM8++6yWYyNUQEpRMYOn67ndxeNgTavw4yfUpbrZXSU5x8+zI1SsjfHJJh4J3i7Cu/CzZoxCVp6anh2PsSEnjAWIWkZE+Ju0y+y6TmUwtCNQGAsArppcCgCqCuO7I5I9O19//TVuueUW/PGPf8SAAQO0HBOhIlLc2Myz89mWcny2pRzTB+bgjasnRGV88URZvTvtnPXDYiTHUfpnvCGlN5ZR52vsqFngza4wjGVRqWWEXUg7l5bNayL9oGa0+DUBZbDfRqwbO5KvgB9++AHNzc0YN24cJk6ciOeeew7Hjh3TcmyECkjT7Pg+tvr3Y+Td0YBjnjCWf5VcqrMTu1gl9MZiWUpJGmTl2WXUuBHDfpMRZ2PJPL+w8JJnR3W8YSzfZBVmYFpj/DOXfAVMnjwZL7/8MioqKnDddddh6dKl6NWrF1wuF1asWIHm5mYtx0koxCYpG6vzrotCKurT5Ml2YL2CGMmUeh6zBBcoe//PwshCgTcVw1h2oc6WvDppLP28NcLCgnKqJ4uPs8f4wtsVBAtjsSaxVko998ViseDqq6/GmjVrsHXrVtxxxx1YvHgxcnNzqS9WN0ROGEsMeRnUp8mziPkbO2xHHw/N+OKNYHWuuADZWEkaCNVtMo0NhlpeJrmNSNlcFOshla4gmEDZHCfetIi6ng8aNAiPPfYYysrKsGTJEtnPf/HFFzFy5EikpqYiNTUVkydPxpdffik8zvM8Fi1ahMLCQiQmJmLGjBnYvn27z2tYrVbcfPPNyM7ORlJSEs466yyUlZVF8rZiCpaNEaqCqn8YC6CFV20cTpcw2aT6GTspGqQcE90DKZod5lkVqtl2gzAWM7wi1Q/JPb+JPDuawUKSnT07TLMT29KFiIwdhl6vxznnnINPP/1U1vOKioqwePFibNy4ERs3bsRJJ52Es88+WzBoHnvsMfzzn//Ec889hw0bNiA/Px8zZ870CZnddtttWLZsGZYuXYo1a9agpaUFs2fPpj5dHpgbOaRnJ0D9CwqpqIvYkGHGDYMZP83W2C7qFY+wRTuUZ4Ndm1qGseQaO8z72BRhoTlBsyMxjMY+CxIoq0+rUEGZBMpRZ86cOZg1axYGDhyIgQMH4uGHH0ZycjLWr18Pnufx1FNP4c9//jPOO+88DB8+HG+88Qba2trw7rvvAnBXb37llVfwxBNP4JRTTsHo0aPx9ttvY+vWrfjmm2+CntdqtaKpqcnnL1aREsYyBJgI20mgrCqsOmmSSd9p4Un1FBVsbCNjJ9YI1ptOrI9g2TGskF+LinV25KZ+M5ixE2lVXXZ+uZqdWF94u4LWYAJlCmNFF6fTiaVLl6K1tRWTJ0/GgQMHUFlZiVNPPVU4xmw2Y/r06Vi7di0AYNOmTbDb7T7HFBYWYvjw4cIxgXj00UeRlpYm/BUXF2v3xroYq4Ry8YYAnh3KxlKXpg73ouEfwgK8C0uz1QEXFXaMKbyaHd8FpkMUMkgwsgrK3qaMqp2faXZkCpQFAzxSY4cEyt0GwdjxSz1nEgcSKGvM1q1bkZycDLPZjOuvvx7Lli3D0KFDUVlZCQDIy8vzOT4vL094rLKyEiaTCRkZGUGPCcTChQvR2Ngo/B05ckTld9V9kObZ6TwRtkVYX4PwpTFIJhYApCa6Jx+eV7fGCtH1BLv+OkTXFxMra1GCINIwVuTGjjzPEssai3UvQ7RxuXi02QOHsczG+PDsyGoXoQWDBg3C5s2b0dDQgA8//BBz587F6tWrhcf9S4zzPB+27Hi4Y8xmM8xmc9DHYwkmOgs12RkCCJQpjKUuTe0ecXJCZ2PHbNAjwahDh92FpnZ7QIOI6JkEq3MVqAu5FiUI5BobDLU1O1KLGgqeHQpjqUq73QnWgaNznR337VgPHUq+Am644Qa0tLQIt9966y2f2w0NDZg1a5bsAZhMJvTv3x/jxo3Do48+ilGjRuHpp59Gfn4+AHTy0FRXVwvenvz8fNhsNtTX1wc9Jt6RIlAO5NlpJ8+OqrAdcqAwFqBe2IDoXgTrOn7h2CIAwOB8b3NlLRqBKvXspKrs2ZFcQZkEyprADGiOAxKNQTQ7ZOy4eemll9DW1ibcvvHGG1FdXS3ctlqt+OqrryIeEM/zsFqt6NOnD/Lz87FixQrhMZvNhtWrV2PKlCkAgLFjx8JoNPocU1FRgW3btgnHxDvSemORZkdrvMZOYGeqsJPuIGMnlggWxhqQl4LVd83A+9dPFu5TO4zF87xIMyNPs9PlAmWnCzw1A1UN1vYjyWToFPWIF2NHchjL/4enxg/xvvvuwxlnnIHi4mI0Nzdj6dKlWLVqFZYvXw6O43DbbbfhkUcewYABAzBgwAA88sgjsFgsuOyyywAAaWlpWLBgAe644w5kZWUhMzMTd955J0aMGIFTTjkl4vHFAlLcyIEmIgpjqUtti6fjeXLg8GmqSmEDonsRarNRmpXkc1scxpISrg+H2DtilNkbK83i/j1aHS502J1ICBB2k0Iwz1YwxHOR3cnLrvxMBCZYJhbg/W1aydjRjqqqKlx55ZWoqKhAWloaRo4cieXLl2PmzJkAgLvvvhvt7e244YYbUF9fj4kTJ+Lrr79GSorX9fvkk0/CYDDgoosuQnt7O04++WS8/vrr0OuVXZyxhk3CzkofwLNDAmV1qfEYO/4dzxlejQQJlGMJOb2hWBjL4eLRYXch0RTZHCbOaJKr2Uk2GaDjABfvNsAVGzsyPTsmH2PHJbuBKRGYYJlYgG/qucPpCliKJBboUmPnlVdeCfk4x3FYtGgRFi1aFPSYhIQEPPvss3j22WdVHl1soLRdBIWx1KXOU0MnI8kU8PFUz0LX1Zodl4vHhoN1GFqYipQAYupYg+d5vLLmAAblp+CEATmqvrbTxcPpCq+ZYySZ9IKB0Wy1q2rsyNXs6HQcUhONaGizo7Hdjly/5rVSYR5iqe9FPBdR+rl6tNoCt4oAgEyLCSkJBjR3OPB7VQuGFqZGe3hRQZax89e//hUWiwWAWz/z8MMPIy0tDQB89DxE94DneUlu5IDZWOTZUZVQqefi+7tas/PexiO496OtGFmUhk9vOr5LxxINVu0+hr99sRMAcHDxmaq+tlgDIcXY4TgOyWYDmjocaO5wIDcl7FNCn99z7eu4wN7bcKSJjB2leKv2SjN2DHqdYPDFeip0NAn1Peh0HPJTE9Dc0YKGNlu0hxY1JBs706ZNw+7du4XbU6ZMwf79+zsdQ3QfxDH7UJMthbG0J1jHc0Z30ex89OtRAMBvZY1dOo5osfWodu/Tx9iR6FlJSTAKxk6kyC3o548aImW2abIECJ8Ew6jXwepwUUaWioQKYwEQwpQdMdwfS/IvcNWqVRoOg9AC8c4oVDZWoMmQup6rS1hjp5uknidFGDrpaWj5edt8wkjSPCveprCRj0tpqwiGGsYOC5/4pzuHwuQxdmI9OyiaCNlYAcJYgPf76YjhKsqqKpE2bNig5ssRESKeLEIWFQwwEVMHbvXgeV5G6nnXfu6WIJNhrOLUsD2HOBNSamZVioq1doQaNwpFvukWt76srlV5aKPNJi+MBXjHS5od9QiVjQV4qyjHslZT9lXQ0tKC9vZ2n/s2b96MOXPmYNKkSaoNjIgcZuzodVzImL0lwG5ejZ0l4abN5oTDs6gGD2N1D4Gy2LMTD3VOxMaO2u9XSDuX4VlJUbE/lk1mQT9/spIiN3ZqPc/NTJJesZ55osizox6CQDlcGIs8O0BZWRmmTp0qNM+8/fbb0dbWhquuugrjx4+H2WzGmjVrtBwrIRObRDe22MXMwl3k2VEPZsAY9VxQd3530eyItRXx0KfLKTJw1BbESsmE9Id5dtQQqkeq2clUwdipaXaXXMhKDpyFGAjWtJQ8O+rBPDvBPLdeYyd2PTuSfdb33nsvWlpa8PTTT+PDDz/E008/jdWrV2PUqFH4/fff0adPHy3HSShAal8asYs9NdGIY81WMnZUhE0gCUZ90HBGd9HsiGlotQfs5RVLiL05VocLZoN6mqVIjB01w1hKNTvM2KmNwNhhRpucfm9G8uyoTpsnGys5SBgrwfMbJYEygJUrV+K9997D1KlTccEFF6CwsBAXXngh7r33Xi3HR0SA3IJegDtNFXAbSpFUTiW8CP3JQnwP3SX1XOzdaGi3oQSWLhyN9jhEGT9qL642p3vhkGPsJJvdvwNVjB0F178YNcJYrCqvnHmEXSeUjaUeglCcwljhqaysRL9+/QC4G3AmJibi7LPP1mxgROSwhStUJhZjQp9MAMCVk0qF+8i7ow5SmjGyMFaH3QVrF+6urKLJrjt5mbRCXCJfbWPHqiAbSs1sLEGzo7DlQqRhLJ7nJYfSxQidzymMpRqC0RlkLUjwCJStMRzGkmXyi1sw6HQ6JCQoq6pJRAc5bvSXrxyHf10xBtdO6ytMuPGw2EUDu4RFJ8VsAItwdWXLCB/PTlvsf//iHnCqe3YUhLFSVQ1jRabZYTob1tdNLj6lL4zSxyBuX0Cogz2MpIE0OyJ4nsfJJ58Mg8H9lPb2dsyZMwcmk6/w7JdfflF3hIRi5Oyq0ixGnD68AIC7f1NzhwPHmq3on5us6RjjAWHRCVCpmqHTcUjxVM9t6rAjJ0V69oqaiHd2DXFg7Iond60EynKMDSEbSwVxuBSPYihYBlVThwN2p0v264i9ZlK8ywyWPUaeHfUItxbEQxhLsrHzwAMP+NymEFb3R4pHIRA5KWbsr2lFdXOHFsOKO6QuOmkWd/XcrvSoiReors4MiwYdWnp2JCYIiFG1qGCEAuX0RKPQuqG+1Sa7P5Y4JKokjEUCZfWwhfHymUmg7MXf2CG6P0o0AwCQ7enMXR+BMJHwIlU74c58au9SI0O8wMRynxyGOIyltlZKqLMjy9hRT6Ds9Swp0+zodBwyLCbUttpQq8DYEWsGpRZVBMQCZTJ21CJcSJU1ao3lnoix2cudAKBsZwkAGUnuCXdLWSNmP/sDnvl2j+pjiyccErUT3SH9XLzgx4NmS+y2t2ql2VEgUFajkrYtwjAWEJlImYVE5c4/Xs0OZWOpRTjvMpt7ujobVEske3b69OkT0DpPS0vDoEGDcOedd2LcuHGqDo6IDCWaAQDI9JSJX+ZpCrntaBNuPqm/rN0Z4UWYaEJodgBv35poN2HdU9WMG975BWeOLIg/gbJNuzBWOFFoIJLN6vfGUtouAois1g6rGi53/hGysSiMpRrhPDveoqaxm4Er2di57bbbAt7f0NCADRs2YPLkyfj6669x4oknqjU2IkKcLmXGTkZS52qnTe0OpFliu8CcVrBFL1APMjGsb01rlCsXv7BqH/ZUt+Cpb/YIiy0QfwLlr3dU4bjidKEnVKRYFWVjGYXn2hwu2V4RMVLqO4WDZWTVKcjIEn73IVrVBELQ7FAYSzXC6bfUaPra3ZFs7Nx6660hH3/ooYewaNEiMna6EWyu0Mn0yGQGMHZabGTsKIV5agL1IBPDPDut1uh5dniex88H6oTb4iygeBMov/vTYTicLjx2wShVXtumQCCcnCBq19FhR1ay8qy8SHtjAUCWJyNLkWdHYeq7ibWLIM+OaoTzMrKSB7F8zaum2bnggguwfft2tV6OUAHW90fuziojwM422t6GWELoSxOkeimDNeFss0Xvs953rBVHG9oDPhbrYSye59Hht6C+t7FMtddXUmdHr+OE30Gk6eeRpp4DEEog1Cjw7Dhc3kbEcqCigupjDSNWZ56dZqvDpzluLEEC5RjG6VQ22eSndc66UKPuR7zCPDVJQZrwMZgx1BolY4fneTz7XXDxeSy7tAF3mCfQxK5W93Mlxg6gXkaWmsZOdZMCY8fj2QkXvvVH6HpOAmXVCOvZEfUuU0Mv1h1Rzdj54IMPMHz4cLVejlABNlfoZBo7vdITO90X6wufljDjJVgTPgbT7LRFKYz13w1H8Mnmcug44NIJJcL9BR5jt93ujOmKqsFqipTVB/Z0yUW5saNO53NBsxOB7ifHE0Y7psizE76YZiCYoJo8O+oRLjPQqNcJYfZYFSlL1uw888wzAe9vbGzEhg0b8OWXX+Krr75SbWBE5LhcysJYSWYD/nHhKPy4twbHmq1Ys7cGK3ZU4cRBuVoMM+aRGsZij0fDi9ZidQjZdldMKsUl40uw5OfDAIDLJ5bgiRW/g+fdMfxYbQbbESTrbf3+WhRnRt4AVagzI9OzkqxSy4hI6+wAQG6qcs+OVGG+P1RUUF2cLh7MgRnKy5eWaESbzRmzG1vJxs6TTz4Z8P7U1FQMHjwYa9aswcSJE1UbGBE5bGclV6AMABeMLcIFY4vwyeajWLO3Bl9vr8Ij545Qe4hxARMoJ4Xx7CRHKfXc4XTh9Ke+FzwYY0szMKQgBeePKcLRhjZcNrEU/1lzAA1tdjS222UXk+spBCuNv7uyWZXXV+rZYfqJSIs6qq3Zcbl4WV5ibxhL3vlZEUYydtRB/DmG+i2mJhhR0dgRs7V2JBs7Bw4c0HIchAa4FAqUxZw42O3NqWmxot3mFCptEtJhguPEsJ4ddYSp4dhf0+oTqinKSATHcXjiIm8WUnqiEQ1t9phOPw8WxqpqVtb40h+lda5YGLGiMbJ2LWoYO6yausPFo77NJis7jAmUjTLnHxOlnquK+HMM59kBYleyIPkq6Nu3L2pra7UcC6EyTHwpV7MjJsVsEBbhyibqlaUEIfU8TDiIhbG0Ltl+uLbN53ZRRueQjTDxxXBGVrDPuUYlY8eqsIJ5YZpbM1ceJEtOKmrU2THqdUIpCrm6HbtCgTLrkK52+454RezZCRXSTE10zz9xb+wcPHgQTif9+HoSLIwVwVwHjuOEyS4eeiVpQbvEOjvMa9Zm186zs3J3Na55c6PPfdkBdutpnvIDMe3ZCSK+VpJmHQilYawCT4JApJ4dNersAECuwows5tkxyBQom0izoyrigoKhquB7qyjH5jVPqecxjFegHNnX3B16NvVkmGcnXAjQIjTj026Sv/aNjZ3uC1SaQC3dSHeGNQEdnJ+C+2cPxVMXHwdAWeZRIJT0xgKAwnR3GCtY/SOpqNEuAvDqdo7J9HgpTj03MM8OGTtqYJdo9Mb6PC9ZswMAO3bsQGVlZchjRo4cGdGACPVgRQWVCJTFsIVPjeaE8QhbVMNlYyUambGj3efskFgwLD3Ku7wWqwPPfrsHZx1XiGGFaVE5J1tMLSY9FhzfB7UeI6ehzQ670xWR1gVQ7tlhpR8qGjrA87zinnRqaHYA5ennDoWbLbPBfR2QsaMOUnu0eed5MnZw8sknByy4xXGccFFSqKv74FIhjAXEfixXa5hAOVwYyyKEsZwRLXJqkO5pDRKNMNb28kY8v3Iv/re1Ei99vx8HF5+p+TkBr6aNZQtlWEzQ6zg4XTxqW2wBi2vKgS0yZpnGTr6ozlFDmz1grzpp549cswMAOQrTzx0Kw2gmysZSFZtDWlacV6Acm5taWcbOTz/9hJycHK3GQqiMV7MT2WSXFuOxXDEddqfqdWWkhrHY4zzv3tWqPY5AGpVnLh0d8NhoZWZYHU6c+cwan/uiZegJ14fnXDodh6wkE6qbrahpsUZs7NgUCpTNBj2yk82oabGivLFdsbGj9Pz+KPXs2BWmnpOxoy7hmoAyYl2zI8vYKSkpQW4uFZbrKTjV8uwkxPZFwHj0y514afV+fHHL8aqGUqQKlMVhrjab+kaXeGf+26JThe81EMzYqdc4G2vLkcZO9zV1OITza4nX8+k1rLKTzahutqqi2/FqduR/j4XpCW5jp6FD8W9RrTAWq7NULTMbU2nqudlA2VhqInwPYTxslHoug2PHjqn5ckSEsDo7qnl2YjSWy3hp9X4AwONf7VbtNW0Ol+BBCKfZ0es4YVfbrkGbhqpm92JVkmkJaegAQFay25tQ16qOWDcYvxyu73SfWtlQ4XAEMnZYET0V0s+VanYAddLPpQpTwxG5Z0dhGIvq7KgCC2OFM3pjvfO55Ktw+vTpMJk6u1N5nsf//vc/nHfeeSgqKlJ1cERk+LvplZIa4xa/P2q6z8UdzMN5dgBtRcrMs5OXGr4wXIYn9by+Vdvv/PcA1YprW6KTARbYs+N+3zUqjMEagbFT4MnIKm9UbuwoLWroD2sZcUymZsepMIxOqefq4m3bEUazY4ntTa3kX+HKlSuRnp4u3N6/fz/+8pe/oKSkBJdffjksFguWLl2qxRgJhXhTT9XJxiJjRz5Mr2PUc5IWHUGkrEFhwYZ29wKebgmvAclKci9wtRp7dnZXdTZ2ouXZcfKdjR3mxVBjDDaJWolAsIys8gbltXYi8SyJYannzVaHrIKXSgXKCUJRQTJ21MCr2ZGeeh4oEamnI+sq6OjowNtvv40ZM2Zg6NCh2LJlCyoqKvDDDz/g7bffxrnnnqvVOAkFsMmCpXIqxStQjk2Vvj9qdlsWxMkS9TeJGho7zFgNF8ICgEyPh6PD7vLxTqmJ08VjT3ULAOCWkwegb04SAAgp4FoTyPPJCizKrSkTiIjCWEL6eeSenUizsVLMBkFHI8cItCtMPWcaJ/LsqAMLJ4bbbLF53u7kg/aN68lI/hXecMMNKCwsxPPPP48LL7wQR48exWeffQaO46CLUBNCaAMT+MlNffUn3lLP1dxResXJ0nIBvIUF1Td2mLEqRfybZNILi2Rdq/phpfc3HkG/+/4Hm8OFFLMBt508AJP7ZgEAfivrLFrWgoBhrBQWxlJToKwgjOXJBItEs6NWNhbHcd7u583SPU2Rpp6TZ0cdpArVLSa90EcxFud6yVfBv//9b/zxj3/E119/jRtvvBFZWVlajotQAa9nhwTKctBCsyNFrwOINDsaCJQFz05ieMNL3CZEC2Pnrg9+E/4/tncGdDpO8Gas3RedHnwBBcpahLEUXH8sjFXZ1CEYDXKJRDPkT44Cj5fi3lie8TpdvKD7IZTj1eyE/h44jvOmn8fgXC/5KnjzzTfx888/o6CgABdffDE+//xzOBzxEdboqVg9rkhzhCnM4tRzVxxMPmruKNvs0mrsMFhndC3CWGwCk5rWzYydWg2MHTFFGe6F/bIJJQDcbRLqNT4nENizw/QpkQqUxQu1EmMjO9kMo56DiweqFYbU1ApjAUBuiif9XMZYFPfGEn1eFMqKHKl1doDY1mdK/hVedtllWLFiBbZt24bBgwfjxhtvREFBAVwuF3bs2KHlGAmFqBfGcl8ALt4tUowlnC6+kxhPTc2O1Bo7DIuG2VhNMjQ7gCj9XOXsKP/Pl3Vdz0gyCU0nD9W1dXqe2gQSKDPPTn2bTbFHBfBdpJUYOzodJxQ1VBLK4nle8CxFev0DyvpjOQStiLIwFkC1dtRAqmYH8KafN2pcX6srkH0V9O7dGw8++CAOHjyIt956C+effz6uuOIKFBUV4ZZbbtFijIRC1ApjJRj1gsVfJbOwWHemoc2Gmf9cjYv/vd7H4FGzvoe3erI8zY4mnp12eZ4dIf1c5Wag/os3C18B3kVV7XMGwhlAoJxhMUHHuatYRxK+8zF2FHpWClitHQXdzx0uHuwnrUYYS0nnc6UV3A06Dsz+JM9O5AiaHQm/AwpjBYDjOJx++ul47733UF5ejjvvvBPff/+9mmMjIkStbCxAHcFkd2PV7mPYX9OKnw/U+Sxsak6wzENjkRhKTDK7jaIWDTxorJFraheHsQ7Ver02s0cW4NShecLtZPb+o9B0VjB2RJ4HvY5DZpKyInpixAaz0qJ+3vRz+ddcpJ4lfwTPjozPRKlAmeM4EimriGDsSKhkHcs11VRJo8rMzMQJJ5xARQW7GWzCMxsj/5qZsVOhYJfZXREbOAdFC7AWqecWszRjJ8XjRm7WYLGXI1AGgKwkbcJYhz0hqpMH5+K5y8b4tMVg718LY8+fYEU31SgsKBYnK+3zVegpLKgk/VwNz5IYZuzIycbypp7Lf/9szGTsRI6cMFYslxmRdRWsWLECd911F+677z7s3+8urb9r1y6cc845mDBhAgmWuxlqaXYAb6ihrF57LUW0EGfcVIqMODY5qEGbTM1OSoI2bmSe52WHsVitHbU9O8zYKcmydHosmp6dQAJlQCRSjqDWjrDRiMDQYGGsowoKCzJjS8fJb8QZCCZQlqfZkVa5NxAsqYLCWJHjDWOFNzpJoAzgjTfewGmnnYbXXnsNixcvxqRJk/D2229jwoQJyMjIwJYtW7B8+XItx0rIRM0wVv/cZADA7sqWiF+ruyC+oCsiKMsfCm/quTRvilaenTabU/BkSBUoZ2qk2TlU2woAKM0MYOyw9x9Nz47O37OjQhhLhbRvNcJYaoSwAN8sNakZmUoFyoCoZQT1x4oYOQ1hUzXabHUHJF8JTz75JB555BHU1NRg6dKlqKmpwZNPPolff/0Vr732GoYPH67lOAkFCKnnKkx4g/NTAQC7q5oifq3uQpPIoKjUKDwnt4Ky19hRd7Jhk5dBx0n2MmlVZ+eoZ/FmWVhiks3uyTYqnp0A2ViAKIylgmcnEmOD9cdSYoirudEB3Jl5HOfWOdVJNH6VVlAGRJ3PNag3FW8oCWPFtWdn3759uPjiiwEAF1xwAfR6Pf75z3+iX79+mg2OUA7P894wlgqancH5KQCAI3Xtqi/EXYX4fVRolGUmN/Wc7azU9ux49TpGyRoSlnqudvsGFgrJS03o9JhXs6P9b8wZxLOTL2RBRVK92P29R2LssNBxfZtddkVttT07Rr1O8PRJDWV9//sxAPKLCgLU+VxNvA1hpQiUY7davuQrobW1FUlJ7t41Op0OCQkJKC4u1mxgRGQ4XDyYt1mN3V1GkgmFHpFytMr5a02T6IL29+yoVTxRrmaHTTZqGzvMOyNVrwNAyEpq6nCoJtp2uXhB+MtCI2KYsfPexjLNXemBUs8BoE+22+O0dl+t4vdtVaGgX2qCESkeDZNcwyuSJqTB8IqUwxs74n5qSlqfMM8OaXYihxV3lCdQjj1jR5qQwMNXX32FtLQ0AIDL5cK3336Lbdu2+Rxz1llnqTc6QjHiLAY1wlgAMLFvFpb9ehRr99Vgav9sVV6zKxEbFP66iA6HU7LOJhRM7yI13TtF8OyoO9lc9+YmAMDReumLZlqiEZyn5kx9m00QqUZCfZtNMDKY50gM648FACt3VePs43pFfM5gBPPsTOmXjWSzAQ1tduyqaMaIojTZr61Wq4aC9AQ0V7WgvKEd/XKSJT/PplKNLTE5KWbsqmyW5NkRGymtCowdSj1XD7tDTlFBMnYAAHPnzvW5fd111/nc5jgOTifFWLsD4li3WhPemJJ0LPv1KHZXNqvyel2N2NjxT6lvt6lj7LAdeS9R4bxQiAXKPM8rTlv2hwl+zxxZIPk5eh2HDIsJda021LWqY+wwr06GxRhw8h2Ql4Lj+2djzd4aVTqPhyKYQDnBqMeg/BRsOlSPsvo2ZcaOXZ5WKxiF6Yn43WPsyEHtMBYgL/1cnNGopAoy80aTZydy7DLqHXn7IMZeZrXkK8HlcoX9I0On+yDeWaq1YJZkucOYh6NQyj8ahAqTqFXBuL7VfY6s5M4hm0Awz47DxaPDrs5E3yrKbFo4a7Cs5wqFBVWqtcMMmEAhLEa/HPfvTGvdQLDUc8Db+HLZr0cVvXa7zJ5owRCqKMtMP1dDM+SPnPRzcfhPSUjYRGEs1bDJyMZixk6L1RFRu5TuiHpXAtGtUKtVhJhiT8PGI3XtnfpJ9TSaOuyCQcP6wYhRo+s4z/OCdiFJ4qKXZNILpfLVCmWxxcli0sv2zuSnqltM8liL+3VCGTtpGqW8+xPMswMAfT0G19ajyvRpHXZ1sqF6pSurXK5mE1CGHM2O2Ni5YlKp7HN5iwrSBjpSHDKysVJEc6EWhU27EslXwg033ICWFm+NlbfeesvndkNDA2bNmqXu6AjFeAsKqpN6CgC9PMZOu92pejpytDlY4671kp1sxuCC1E6PKxFV+mN1uASRuNQdPsdxQmE9tVzJzIMltb6OmEhqvQRC8OyE8HRlWNzjrNe4GaGQeh7A83ntCX0BuI08Jb8F9pyECDMhWUbWRzI9TGpphsTkymgGyoytDIsR6ZbO2qxwsAxS0uxEjpwwlkGvE+afWMvIknwlvPTSS2hr84YvbrzxRlRXVwu3rVYrvvrqK3VHRyhGzRo7DLNBL7QQqJLRELA7csBj7PTNTkJxgHovrSp0HReHwuTof9QWKbMdWkoAD1Y42GIrR9gcCilhrHSPsdOgsWcnmECZjYF545TUuelwqKPZ6esRJTtdvCyDUwtjR05laTmhk0BQUUH1kPtdCJ3P49XY8Q9b9PQwRldjdThV7cHU+fXVN3YAINcT1qiS2COnrL5NWFQYe6tbutwzxIyd3tkWIdVYTKs1cs8O08qYDbqAC2owmFGilmeHGU3KjB1PGEWlCtPSjB1WzDBKqecBvhuO44R2GUo8TB2CZycyY2dYodfryLyRUuj6MJb00EkghGwslXRr8YycCspA7HY+J81OF9Bmc2D6Y6tw6pPf+9SjUBMWxlJzZwcAeakeV7YEz857G47g+L+vxP99tl2476HPd+CUf67GCX//TtVxyYV13u6dnYTjijM6Pa5GUTum+5FaY4eRqrJnp0nw7CgIY2Wo7NlpCW/s9BK8SdoK4UMZOwCQnug2dpSk4XZ4jI1IBcpGvQ5T+rnT8aVuMABtsrFYGKvF6gg7b9mdkZ1fyMYiz07EyNHsALHb+ZyMnS5g48F6VDZ14EBNK346UKfJOYQwVoQ7S3/yPALXKgkVh+/+8DcAwDs/HQYAbDpUh1fWHADgrr3Rld5Blj5bkJaAUcWdU4vVaFfgLSgoz6Oidn+sSMJYguHRoI4onXl2skNodtg5mzocmnY/DyVQBrzhtBoFFaQFzY4KxgYzMqplhI5tERobgUg2G4Qih+GMX7uMqr2BoGws9ZCj2QFit/O5rNnvr3/9KywWt8vfZrPh4YcfFooMivU8RGg+21Iu/H93ZTNOHJSr+jm0C2O5J145u0yHiwfP87j7g9987q9rtUlOyVabmmZ3GC072RzQ49GiQhirzcqagMozONXuj8VeR2phQzH5aQngOPfvSY3vS0oYy2LSw2TQweZwob7VJggm1SaUQBnwhqDu+uA3XDhOXrX4Do9XLyFCzw7gbashJXzE0KKoIMdxKMmyYHt5Ew7VtmFAXkrw80eq2RGKClI2VqTYZHp2YrU/luRZZNq0adi9e7dwe8qUKdi/f3+nY4jQVDZ24INfyoTbuyq0aazpzcbSSLMjU6Bc3WzFvmOt0Os4GHQcrA4XfjncgJlD81Qdn1RqW93jz/K0RCjNsgihLUCdMJbcVhEMZpR0B8+O2aBHTrIZ1c1WHG1oj8jYsTtdgv4lVDYWx3HItJhQ2dSBhjY7ijMVnzIk4Tw7k/pmYcWOKgDu/mBy3jsLYyWokA2Z72nTIieUqIVmB3BfJ9vLm3CwNrR+KFLNDrWLUA/Zmp0Y7XwuefZbtWqVhsOIHz7dchTiaMAujaoRq931mJEnQ6TIMBt0+OVQPQCgNNMCF8/jYG0blm+r7BJjx+niBYF0dopbl/HG/An403ub0dLhwJ7qFnXCWPbuEcZimhMlqeeAW7dT3WxFeUM7RhalKx4HCwexysyhSLcY3cZOu3ZC9lBFBQFg/pTe+NsXO8DzwDc7q3Dx+BLJr83CWJFqdgCgJNPtTZdTzFOLMJZ7LO76Q0fCjCVyzQ4ZO2rhEL4LeWGsWPPsyPolNjc3Y8WKFfjf//6HmpoarcYU06zbVwsAWHB8HwDAvmMtmlzQrFy9Gh3PxTDPzpYjDZIro6YlGrH5SAMA4PgB2ThntLvf0aEwu0OtqG+zwcUDHAehk3Pv7CQsu2Eqzh9bBKCrw1jq7qwaPJMW06DIhaWfl0UoUmaeifzUBOjCZKex0FWrhpqdcAJlnY7DHTMHAgC+21Ud8JhgCGEsFa4/sbEjVTelhUAZAIozPYVFw2l2ImxESr2x1IN52Qw6qQLlOE89/+233zB48GCcdtppmD17Nvr3749vvvlGy7HFJGx3NmNQDtISjbA7eezUIJSllWanKMPb42nx8l1BjxMXYktLNGLfMbdhMyA3GWeOcPdn2lHRpFp3cTkwD0OGxQSD32TMFllVw1gyNSdqe3YaPaEjll0klyKhsGBkVZSPeLKr2IIZimSVP4NAhDN2AGCoJ/Vb7ntn2Upq9Fcr9hg7LVaH5DR4b9d1dT27rCZVOM+OLUKBspmMHdWQq5+K1c7nklfCe++9FyUlJVizZg02btyI6dOn46abbtJybDGHy8ULu+PSzCSMKk4HAGwrV1aSPhRahbGyk824brq7uuxrPx4ImoK6+nfvTthi0uNAjbvadp/sZPTNSUaCUYc2mxMrdlapOj4psD5P2QG6bjNDQ40sIGHBk5kRp3ZRQRYKyojQs3O0IbIkhLI6928/UBFHf5Ki4Nmxu9zXiCGEsZOTLL0flBileq1AJBj1QtsOqd5Q7Tw77u+urD50dl6kmh21r4F4Rm4YK1Y7n0v+JW7cuBHPPvsspkyZgjFjxuDVV1/Fnj17fFpGEKE51mKF1eGCXsehID0Bg/Pd2Qx7qtT/DLUSKAPAvacPRkmmBXYnj4e/2BnwmOvf/kX4v83JCx6tPjlJ0Os4HOcx9F5YuVf18YWDeXaYOFlMkokZOyqEsRTqNlgFU7VSPxs83oA0hcZOL5U8O5sOu3Vb/XKTwx6bYlbP6AwGK88QqvCfUDW4xSrLC6mmsQPI1+1opdkpTHdn57XbnUIH+0AIoliF52ch164uPhoLyDU82TyhZufzXZVN+Gl/rVDyoyuQ/EusqalBSYlXoJeVlQWLxYJjx45pMrBYhE1UBWkJMOp1GOCZ9HdrIFL21tlR39jhOA6nD88HAEGLE4ojdW2wO3kkGHUo8OxQ7zptEABgS1kjftpfq/oYQ8Em6ewA6c8sfNKiwo6SLXhJZnkLntBpvDXylhw8z4s0O8rCWIUq9cdiFYDHlHQu4uhPklk9ozMYHRI2BFke75/D5f0cpSAYukZ10uZLsjzGTq1EY0ejoqJmg164ho+EKPoYqWaHCdgbNO6PFg8ww9c/ZB8MLQTKT674HRf/ez2+3h59Tz5D8i+R4zg0NzejqakJTU1NaGxs7HRfU5M2adSxAotzs13aII9nZ3dVs+oF9tgPXO0wFmOWR3cjZTJiu/PeWUmCMHVsaSbmjCoEAHwrU/wZKV7PTufFP1lFj4JS3QYruFfbYotY09RhdwkhjXQFdXYAr2enttUWUYPUmhDhQ3/U1E75U93UgZW7qyUV3jTqdYLxKSeU1W5TJk4PRqlnzjgk1bPDwtgqp54DQJFnLFvLgoffbTIL2fkjGDvtdmpNFCFyiwqKw1hqffZahVXlIKs31sCBA5GRkYGMjAxkZmaipaUFo0ePRkZGBtLT05GREX7HFs8wzw7TLAzMS4FRz6Gu1YYjdeqU42do0QhUTKbQvyiwm5kZdGL6ZCf53D5liLuY4tp90c3s+/53tzdSLLZmqLmr8e7u5S14Ym9CpONgeh2jnlO88KYmGgTjQ2mPrA67UzAgpdSrEYydCF3pThePJ1f8jh/3en9jJz+xGvNf24CjHk9VuGuE1QSSauzwPC8qO6BSGCure4SxAO/89cCn24XsUn867JGdn4WxnC5e1XBKvOF08UKpE6leNvbZO1w8mlUKI3s3311n7Ejecq5cuVLLccQFbKJiE1eCUY+RRenYdKgePx2oFe5XAy01O4BXyNtud8Lp4jtltLQF8AD09jN2JvV19/zZXt6EDrsz4qaJUth2tBHby90eyOG9OreJYLv4DrsLbTZHRNk07QrDWGaDHqkJBjR1OFDTYkVGAA+UVAS9TqIJXJBKweHgOA690hOxu6oZh+va0C8nvObGH+ZNM+l1giYpFCycKCd0FIi31h3E09/uAQAcXHwmAHSawMMaOylm7K5qlizQtjpcwgKjRp0dQKTZkRzG0s7YEafTf7L5KCZ7eneJEap2K6ztlGDUI9GoR7vdiYY2m7AJIeQhbjYtVbOTYNQjxWxAs9WBY81Wxd+hGK0331KQPJNPnz5dy3HEBSwbRexRGF3sNna2lzfhQhXPJWRjaWRAiCfxDrtT0FgwAmVp9fYz5nJTzEhJMKC5w4F1+2s1aZvhz55qrz5qYp/OpXnFrQrqWm0RGTutns8gUcFrZKeY0dThwLEWa8iy/OGob3N7dpTW2GEMyk/B7qpmbD/aqOh7YhlwWcnSjK6BeW6DatvRyDIVpYRIwxnZY0rSsWZvDT7ZXC6psKDY0Fcj9RwASrPcG4XKpg5JGwOtKigDwLmjewn97vYH6cT+2o8HAaDTvCCHDIsR7Y1O1LfZUdrZniIkIG6kapARUsxJNaP5mAPVTVZFm5tg4+gRYSwictjulqWRAsCQAncdjx0q19rRqs4OQ/y6rLs3w+XiA3p2/LuLc5w3K+vBT7d3Ol4LDte6Dc6LxxUHXHRZqwIAqG+NzKsgeHYU7O6ZbidUxosUWJgxMwLvEAChTMLmI8qMD1YgUeoOvW+2e4KtabFFVHRzZ0V48X9yGE/TJRPcBs7afbWSworM0DcbdCFr+Mghw2IUQntlErrBWzX07IzrnYknLx4FANhXHTqTNJLkCyaoZwY7IR+76NoxSiwqCHibzx5T0AQ3EF7Pjvbe+2BIfvd6vV7SHxGcurbOC8/gAveufWdFk6pCPK3DWBzHCVoUf9Gqv/EDuL1ZTJAt5nZPhdqDtW2obNQ+LZF5dkKFDFnYqC7CSbY1gpYBTCdSI7O+iz+hagrJ4ThPZ/gtZQ2Kni83FTst0SgYCkP/ulxxyqqUjuUpYbwPhemJgpF2TMI41E47BzxNOJlIWUIoS+ud9GnD3NmYta021Pp9xmJR/VWTSxWfIyPJ/Zk3kLGjGNb/zaDjwlYtF5OT4mk+26TOnNyjPDs8z6OkpAT3338/Pvroo6B/cnj00Ucxfvx4pKSkIDc3F+ecc45Ps1EAqKqqwrx581BYWAiLxYLTTz8de/bs8TlmxowZ4DjO5++SSy6RNRatcThdwq5QrMHon5sMg45Dc4dDEEyqQTQsabaId/gZN60BQljDCzvrYwBgdEkG+uW4XfS7q7TpEyZm/f46AMDIosDjAYBMzyRbH2GNj3Zh0ZPvys9RaWdVG6KmkBwG57s9kMearYpE08zbITWsodNxQldyh4vHpoP1ss8pFSlhtRwZPeHaIvjeQ1GaJcPY0Tj7xWIyCMaX/3XbIepUzipQKyFdJQ9rPOOtZC3vdyB4diLcbDGE9kU9wdj56aefcPrpp+Ppp5/Ggw8+iCNHjmDatGk4++yzff7ksHr1atx4441Yv349VqxYAYfDgVNPPRWtre44MM/zOOecc7B//3588skn+PXXX1FaWopTTjlFOIZx7bXXoqKiQvh76aWXZI1Faxrb7YJoUZwCbDbo0d9Tb0eKy10qXs2Odj8u5tnxT9NuC1AbZUQI42JgHiuuqK2x43C6hNo1bPEOREaYTDOpCAu8Es8OW1xldpf3p1alMFaS2YC8VPeYDgbRaYSi1Srf2yF2dNYq+C72HfOGWIJpV8719GkLB/OMSZn82wStlrobDTmFBbXU7DAGBSmKyr5rjous6zur+E2eHeXYhRo78sKpuQoaPodCy7CqVCSfefz48XjxxRdRUVGB22+/HcuWLUNRUREuueQSrFixQtHJly9fjnnz5mHYsGEYNWoUXnvtNRw+fBibNm0CAOzZswfr16/Hiy++iPHjx2PQoEF44YUX0NLSgiVLlvi8lsViQX5+vvCXlhZ8cbVarT61gaJRH4jFndMSjZ2KOzHdzi4VdTtah7EA7wLqH1MP5NkZFmKHN9Tz/n86UKfi6DpT3+Y2ODkudOuEYO9LLpGEsfI8uq5IK46qFcYCvKUDDigwdryGn3RvB9NzAdLCUf6sFImTc4LsVEP9LsUwt74UY6ddgzAWIC/9PBqpvqVC6wjf8YjbpMgJnfgjbDrI2FEMq54s1+jNTWXGjjphLKHAqsreTjnIvhISEhJwxRVX4Ntvv8W2bdtQXV2N008/HXV1kS9UjY1u8WNmpjtLxmq1Cudk6PV6mEwmrFmzxue577zzDrKzszFs2DDceeedaG4O7iV49NFHkZaWJvwVFxdHPPZw1HlcsYF22EOYbqdSTWNH+8mO1YOpafadjAKJk/uHaBFwytA8AMDq348F7bWlBmzBzAzQAFQM+44i8ew4nN5ifkrCGbmqeXY87znCMBbgNXaCZeCEQknrjKcvOU74vxJjR2yUMT3chEd8mxdnS6j5AwB5nu9Diq5MaX2lcJRmuj9/Kf2xolHEjWWVlvl1QFfaANef/DT3vH80TId1Ijh2mU1AGUp7wgXC5eIFHafa3k45KLoSysrK8Le//Q0zZ87E7t27cddddyE1VXlsFnBPRrfffjuOP/54DB8+HAAwePBglJaWYuHChaivr4fNZsPixYtRWVmJiooK4bmXX345lixZglWrVuH+++/Hhx9+iPPOOy/ouRYuXIjGxkbh78iRIxGNXQps4QzkUWCeHVXDWFHQ7LCFwl9XEqh5Y25KQqf7GIPzU1CYlgCbwyWp/YRSxOnPoVDDs9Mm0jEp2eGrtbNi4Z9w71kKkXl2WM0h6QtgaVYS/u/sYQA6G9RSEI+TaWb9cwCkptUGW9gDoZVnp3e217PjEKUUB0KrRsBiijK8TUHFRBK+FdPbk24vtWo00RlvjzKZYaxU9cJY4oSVrvTsSD6zzWbDsmXL8Morr+CHH37AGWecgaeeegqzZs2CTkZKWzBuuukm/Pbbbz4eG6PRiA8//BALFixAZmYm9Ho9TjnlFJxxxhk+z7322muF/w8fPhwDBgzAuHHj8Msvv2DMmDGdzmU2m2E2R77TlUN9gEwsBjN2Dta2RlzIjhGNMBZbQP3LxrOFrW92kuAFCLXD5DgOQwvTUN7Ygb3VLZjSL1uT8VZ5MgtyAvTEEsPc57URpH2zBU/HKfsO8jzGYX2bHVaHU/GipVbqOeDuWA9A6GAvB2YAyzUAvCn48ifdtaLqvq4AmY43zOiH4b2kbdLYwh6qHxRDaZuQcBSmJcJs0MHqcKGsvr1TkU6Gw+mC02PdaXn99xIMQN/PxKvPiuz9M41SWV17wMKlRHiEJqAy12jmWW6IcP4BvOsBx/kWpIw2kn+NBQUFSElJwdy5c/HCCy8gN9ddWMy/67kSD8/NN9+MTz/9FN9//z2Kiop8Hhs7diw2b96MxsZG2Gw25OTkYOLEiRg3blzQ1xszZgyMRiP27NkT0NjpCrzF3TovOtnJZuSkmHGs2Ypdlc2SGiWGIxo7O+atWb69ElvLGgURMlvYijIteG3+eKE+SCjYzrlCw/Rz5v4vyQy8SDBU8eyIYtRKKhenW4ww6XWwOV041mwVFls58DyPZk+pfTWqoAqenWOt4Hle1vtSGrNXauxsPOgbVnfx6FTa4ZoT+kp+D8WehXdnRRMa2+0h6wVFotUKhU7HoU92EnZVNuNATWtQY0dcSE7LMBYzdurb7GixOoTr3Jt5F9n7L0xPhFHPweZ0obKpQ+jRRkhHaRgrLTHy+YchCPaNesVV3NVA8idQX1+Pw4cP46GHHsKgQYOEHlnsT0lvLJ7ncdNNN+Gjjz7Cd999hz59+gQ9Ni0tDTk5OdizZw82btwYMvNr+/btsNvtKCgokDUeLWE9flKCFDAbnO+tt6MG0cjGOtWjtQGAnw54d9EsNTk90YjSrCRJvZDUyj4Kxe+erBGW6h4MbzaW8pRXZvApXfA4jpOV7hyIDrt3hx+ucJ4USjIt0HHuxVxuLF/wdshcAJmwWm5xxfX7fXs28TzvYwQAkNS2gjEgNxm90hPh4oGvt1eGPDaSYpLh6Ov57YozzfxhIWxAW89OaoJRqMwt9u6wTvVKKoeL0es4oQ+XkgxAQnkYSzz/RKrb0aoUg1y6tDfWjTfeiHfffReffPIJUlJSUFnpnkTS0tKQmOi24t9//33k5OSgpKQEW7duxa233opzzjkHp556KgBg3759eOeddzBr1ixkZ2djx44duOOOOzB69GhMnTpV9TErhe2wgxUw65eTjB/21EjufROKaLmxizMtuGR8MZZuOILyBq9HRkm6szfVUTvPzi6PAJyFDYMh9uzI9WAw2lVoBJmbasbRhnbFBmCzp2M4x6mz8JoMOhRnWnCotg17q1uQmxpch+WP0kJ72Z7fRYvVIat/GjMQzxiejy+3VcLF80JzSkYokbo/Oh2HWSPy8fIPB/DNzipcOC54UoNXjK3+5M4qS4cSiTOjTsfJe49KKMm0oKGtEYdq24RyDpWeZrE5EsXfoRiYl4L9Na3YWdGEqf21CW/HMkIYS8HvICfFM/9EbOwoC2GrTZf2xnrxxRcBuIsCinnttdcwb948ABBS3auqqlBQUICrrroK999/v3CsyWTCt99+i6effhotLS0oLi7GmWeeiQceeKBbVXRmtWhSgoQTBAGkCoUFrQ7xzk7bz4Cl7oq1DEwbIyfdmS2cahWx8qfN5hCEjoEqOYthlVudnq7jgUKP4c8X+W4mUgOQeROTzcpCaYE4rjgdh2rb8OO+GkyRsfh4NTvyPo8Us0HoVXas2SqEk8LB9FZsd+rivTo2pZwwIAcv/3AA+46F9jK027Wb3MWhxGBEszR/SaYFv5U14ohIRMyKoxZnRh52GlaY6g6TR9gjLV4RPDsKdLVq1drRoqK4EiR/Au+99x5sNq8r+eDBg3A6vZNHW1sbHnvsMVkn53k+4B8zdADglltuwZEjR2Cz2XDo0CE89NBDMJm8i09xcTFWr16N2tpaWK1W7N27F08//bSQvt5dYJ6dYOEEFo9WI81SbOxoXcSJLT7iyY5llQ2U0cBS7Yqd/vxe1QKed2tAwqUbmw16QZOhRBgLAG0KBblihFo7Cj07goEdYQqwGFb7Zm+Ynkj+KNXscByHvp4FXk6mHvvemHfBxfOCcFYpLIR0qLY1ZDaUlpM7G8P+ECJxm2dejkYBt76ebDZx2LChLXiZDbmMKXVLI37cW+vThoKQhtIwFuDNyDoWYcsIJQVFtUDy1XDppZeioaFBuD1y5EgcOnRIuN3c3IyFCxeqOrhYosUTUggm1mViPzVaRrAdrFHPaZ7BUCyq6soEoBUeN3YwAWUgskShIy0mtd2eENbgMF4dBvNKKd3VKKkr4w8zdpSKtsMZ2Epgi9v+MN4Nf7y1V+R/HicMcHuQvt5RJfk5LJTKwmA8D3ywyVti4pW5wRMcglGYlogEow52J48jITYlanz3wWCff1WTtVPlckaHXfsaW4yzRhUCAL7ZWS00/ZTb9DUU43tnIsVsQE2LFT/uq4n49eKNiMJYrNZOhC1rmKdTTtkJLZDVGyvUbSI04RYepnY/1mzt1GtKLtF0Y/dKTwTHuSf4ulYbOuxOYWeXF6K2jj8sVOTioaj3UjhYP6G+YcTJjEjFeUoqBvsjePsalOm4BJ2YCplYDOZlOVTbJssojSRuf9Yod0uHr7ZVSi70WOvn2Wm1OfD8yn0A3Ib1yUPygj43GO5sKGbsBfesaFVnB3AbEMwQDxbKEqonRyHNt39uMqYPzAEA/LDnGADv9atGBqDJoMOZI92JJt//fizi14s3lGZjAaJaOxEmjWhVZFMuXZf0HmeECylkWIzCjyHS9OtoVE9mJBj1glFzuK5NMA7MBh1SE6Uv9CaDTshU06I8PKt8W5AmTUfA2gPIzQJiqBHKkFPILhDsNycl9V8q+WkJ4Dj3glrTKn0SZK5sJcbfiKI0jOiVBpvThbs/+C2skeVwulDvMbhzRJ4dxg0n9pc9BkZvCS0bvKm22uxkmXcnWEYW2+xo2RdLDGuqu2av2/PCGnemhWjJIodJfbMAABs0bAYbq3iNHQVhLLU0O+za7ymeHSIyWsJ4djiO84ayItTtRKOgoBhW/OtIfTsqPfHdvNQE2aJYNdo0BKPcE1orTJfmbWIeAeWeHeVhGwbz9lU0doStmBuIFk84Qc0wllGvEyZBKa0TAN9y8UqNvysnlQIAvtlZhc+3VoQ8lhnLOq6zbmRq/ywsOD54iYtweDVq4cNYWmkUWOmEYN4lb1+s6OykWV2wVbuP4ZPNR4VrJjdM8U6pjOvtfv1tRxsFrxkhDaVdzwFvHbVIM2S1DOvKQdYn8NVXX+HTTz/Fp59+CpfLhW+//Va4/dVXX2k1xpigOUw2FhB52ILhrbETnR9XkSfr4khdmxAuUlIAjC1MkVQuDgZbmPMlpktnp0jvch0INaro5qaYYdRzcLp4wYiUQ7hyB0ph3jFxuYFQ+JSLVziW88cW4bRh7tDTijDaHfb7yUwyder2HKlImRk7r/54QAiV+eNtjaGVscM8O4HDWFZ79ATKADBjUI4Qavrwl6OwOV3guPCVyqXSKz0RBWkJcLh4/HqEvDtycLgiSz0H3N5tZwQ6SnFj2K5E1swzd+5cn9vXXXedz+2urI7YnbE6nIKFHSqkoJpnJ4oCRcDr2Tlc2yaIEwfkSes5JCbTok63cX94nhdCg1LDWJHuatSIU+t0HHqlJ+JgbRvK6ttlVzH1ljtQ29hJwOYjXiF6OFo9kx2nsHUG4C4wt+D4vvhqexXW7asJWf9I6IGWZIbO75jLJpQoOj9joKih7b+/34+Fs4Z0Osb73WsVxgpdWDAaHc/FcByHOSML8MVvFYKuxt3aQp3FjeM4jC3NwOe/VeCXQ/WatZPpKnaUNyEzySQ0PlUTu0N5GCs72QSOc5fgqG+zSW6a649ajWEjRfLV4HK5wv6JU9EJLyyEBYQxdtLVqbXDUk+jH8Zqwz5PSnKoLufB0CqM5e4v5b7o89KkXbCFnomnXOF30a7S7j5Ys0UpNAuaHfUEyoDXYJQaxhLrdSLZEI0sSoNJr0NNi03wIAaCdXrP8kzWYuRkCAZiXG9vSYuNh+ox4/GVOOu5NYI2AgDaNS6ixjw7B2paA+qXBM1OlK5/oPP1LjURQCrDCt26oD0ySx50d47UtWHWMz9g0qPf4tU1B1QpKismEoGyQa8TsmQjESm3dpOigpI/gauvvhrNzep15Y4nWkQ1V0KlgguC1BB6AClEMxsL8E0/Z4sQ61gsh8xkbcJYzAORnWyS/JmISwEoyTxkF3ikVXTZbyKUIDYY4XRiSmHF4n4rk1boraldnVTkBKNe6L+26VDwcEaN0N2+s2cnI0LRrF7H4bV544UxHKxtw29ljULfNZ7nhY73Wk3uRRkWmPTuhqCBSlVEW7MDuDvUiylQ2Ush1BeSWfKgu/Puz4eF///f5zuw6LPtqr6+LYLUc8Bb/sK/2asctGyfIgfJn8Abb7yB9vbIa8DEI80d0rJiWGXf3442RFTtNRp9scQwz055QzsOeiZ9qZVuxWgVxpKbiQVAcCl32L2ZPXIQXLcRxqn7hcm8CUWzJ6SotmZncj93dsz2conGDhuHCkYXE6u+tvZA0BAj09JkJZk6GTtqZAixlFwxRz36JavDJWR+aSXI1Os49M52X1+BfhdMsxMtzy7QeTG9MYKMt0Cw62BvdUtE+pHuxour9vnc/m5Xtaqv74jAswN4W+tsK1fes1HL9ilyUFxnh5COVO3EoLwUZCeb0WF3hdy5hqMjypNdTrIZZoMOLt5dxIrjIhQoqxzGKmfiZBm7TbNBL2STKNFQCWnfES7wLDywt0q+saOVZocZt00dDkk1kYS6KyoUmTuhv7umy7ajTZjw8Le45o0NmPzot1i527tIMFF5drIJ/o7U9MTIq/rmBRC5V3g8LMyw4zhtGx8KPbICeDqirdlhPHHhKGQnm/HedZM7eXoipU92ElLMBrTbneh33/98Krb3VIIZbWo2PY0k9RwARvRye1K3yKhe7o+35lgP8ewAJEBWijecEHqy5zhOqBYbLuMkFK1R7jKr03E+npxe6YmK9AJZrMO1yi0jmO5Grmu9MN0bypJLi0qZUMzYOVATukVBILSooAy4f1csli/FEGxqd49DjYq6zLPD+GZnNSoaO7DoU6/7n31fhemJPnNWWqJRFR1LZoBeaeycrKBmWqJR0+rl/XKDi5SFMHaUPLuM88cWYeNfTsGEPuq36tHrOEzzFC8EgP/8sF/1c0SbYNl8YsM9UiINY7HrbePBOh9dmhx6ZOr5wIEDkZmZGfKP6AzrPi1l4ZvtSeH8/LcKxd40NfoyyaVEZOwMDdNVPBhMjLv3WAvW769VfHH58/OBOgDAABm9ugBxKQD5xg5rfBlpIa1e6e4WBTanS7ZuR4uiggxvwcPwY2LeDjUq6iYY9Xj0vBGd7hcLKA/UeEOpYnvj/DFFEZ8fcBv3T1w4CqkJBgzv5f6tM51QvccrmaGgeawcQnl2WBg7WkUFo8UVnlpLgDZC5e3ljXh+5d6IG8ZKpanD63m9bKI3S1CJPi8YEYex8lORlmhEq82puBmrtxRD14axZJ39wQcfRFpamlZjiVlaJGp2AOD4AdlINOpxrNmKXZXNQsxUDl6BZPR+XD7GTqEyY6ck0wKLSY82mxOX/Hs9/jijH+45fXDEY2Pi0dGeJpZSYSJlJRlZaoWxdDoO/XKSsb28CXurW4TquXLGoHYYC3AbplvKGiVliTUJYSx1xnHphBIMKUjFOc//KNxnd7rgcvGoa7OhorEDHOfWG4h9K0MK5Bm7oTh/bBHOH1uEt9YdxLaj21HnyQBj+q50laoHB6NfbnAtl7ddRNfupNVmcr8s/HnWEDz8v51Yu68Wiz7djisnlwp6nki5/u1NOFLXjqMN7Xjk3M4GtdqwrNPUBCMeOXcE+mQl4eH/7RQMZjWINIyl03GY2CcTX++owrp9tUIBSTl4K4r3oDo7l1xyCXJzc7UaS8zSLGPhMxv0mNg3E6t2H8M3O6qUGTuCVyF6Py7xQsLSROWSYNTjppP647HluwEAH24qi9jYabc5hV13oUwdEUs/l6vZ4XleVa9K/1y3sbOnugWnDpM+Bi16YzHktLJQs1cS47jidDxy7gjct2wrAHfxtJpWK3Z4hJR9spKQbDYIEy0QWGsTKRmsga2nRUJDW5Q8O57spOpmK5o77D7fsVBUMMY8OwBw+aQSPPy/nQCA19cexDc7q/D9XSdCp0LIkFXFfvenw1Exdq57ayMAr+eYiecbVOwNGEkjUMbkfln4ekcV1u+vVSQ873HtIkivoxw5nh0AmDXCHcr6dEu5ovNFW7MDADOH5iMv1YzizEQhW0cJC47vI8Tm69tsitokiPm9yl0uISvJJDvtuJcnrFYusXgeo8PuAtMeqmHsDPDs4vdUSS/90G53CgJILcJYvWSFsdTT7Ii5ZHwx/nKmt6hfWX278H2zTQIn8u1oUbRNqA3lMXKi5dlJTTAKFW79Q1ldJVCOBhaTQWiECri/8wv+tVb180itIaWEsvo2fLm1olOWZ7rn+qiJsMu4GFuEYSzAm3258WC9UBxXKtEoxSAVysaKAnLDCacNy4eOc8elqxS0CRDU71H07GQmmbDyzhn4+rbpES2uZoMer88bj0SjHnYnH3H8mu2aSrMssg121kdLrmeHabTcGTmRfweD890L984K6cYOE8qa9DpNJhk5np0mFbOxxOh0HK45oS9OHOQ2jr/4rQK/e7LWBnr0WQlGHYYVpqJvdpLQsV1NMgXPjtvYOVznNjzUrjMTCPZ+9tf4hrK6oqhgNPH3HP9yuCGihA4AnXpubVOoT5HCGU//gD++84tw+67TBgHwXue7KppV0ytGGsYCgIG5KchMMqHd7sSWsgZZz7U5XcKmq8cIlF0uF4WwFNLSIc/YSUs0oo9nIvtdxm6ewSrWRtOzw86nxg9ap+NEJfEjS8Os9hiLrP2DHIrS3Z6d2labkM4vBfb5J0dYMZgxxKOB2nesRbJ4kukB0i1GTbyy3srObUE3Quwz84axtPk9XuppAfHKmgP4YFMZAGBQvtsbxnEcPrlxKlbcPh0GDcI64tpQDqdLEMMPVxjKlYOg26n2vUasMezZAYB7Th+Mi8YV4T9XjRPue+3HAxG9pn8PvEiK6IWjWVRRHwCmDXAb60UZidBx7pCsWrodNQxfnY7DpL7u5KP1+2plPbdN1Iuuq3tjxebV0M1QUrafFcBT0oiyrZuU544EJjrcUx1Z1e79nsycQIXgwpGaaBBqQ8jJyGLGrVox6sK0BKQmGOBw8dgjsd4OK8zo3/VbLUoyLTDoODR1OIS+Y+K6ISt2VGHYA1/h3Z8OCxWsczXQzADAqcPyce7oXj73DRRl3hn0Os3SwLOSzTDoOLh44LPfygXj/LiSdE3OJybYNeJd4Hru9R+KoYWpeOyCUThlaB7uPt3tFWmNsBu6f6j6SIT9CYMRaGNg8XjgdToOmUne5ptqoFY17cl93aGsdftlGjuiprRabDbkQMZOFFBStp/F45UZO90jRhoJrJr075XKjZ3GNjveXHcIAIQCgXLgOE4QNcvJyFIrE0s8DqZB2VkhrZJpncYp0AlGvWBQnPnMDzj7+R8x5K/L8fflu9Buc+LaNzfC6eJx37KtqPKkhZcqqKotFRYKAIDB+SmCZ1Rr9Drvb+RP/90i3C+nWrdShniukV1+10gsa3b8GdkrHQDQEaGx4x+O1apoYUOAauxJIg880yOx/m6RwjQ2kf4WmG5n06F6Wan5QrJMN1iLYv9q6AYImh0ZO/30CJT53aWuQSQMDjKRy2HN3hrh/zkKjB1AWfp5i0o1dsR4jR1pnwdzg2vl2QGAiR7Xdn2bHVuONMDmcOHFVfsw5K/LOx2bmmDQVLRbmJ6Ixy4YiRmDcvDwuSOimlDh7zUSF7/TEvabOFTbJrQGAUTtIqJcVLArSDS532O7jDBzIJiujM3RWnl2NgfQvIhD/1kq9wdkhkmk+q1+OcnISTHD6nDhl0MNkp/X1gXJMsGI/auhG6Bkp8/SdJsUGDutXVBUUG2YZ2ffsRbZGQAM8QSYoDBeLFRRljH5tSowbsMxVKZnh2V6ZCRpZ2DcevIAXD21jyQjpn9usuYGyEXjivH6/AkYWyq/Fkgk+LdGUeKNVUJGkkkQQouNYObZicXUc3/YdS1HUxeIDo9RMCDPHRo8UufWou0/1oLPtpQH7C6vhPmvbeh0n3iezhLCWN3Ls8NxHI7v767u/+mWo5Kf1106ngNk7EQFqY1AxbDMlSY/MZsUBM9ON7CmldIrPREpZgPsTr5TtolUxBNGTrJCz45QRVl6VlyzBnWOBM9OZZOkzMj6KNR7SbeY8Nc5Q/HTfSfj/esn45MbpwY9tneUwkpdweMXjsSZnnIRAHBccfQKr7LeRWIvprddRNcvMFrDCtVF6tnp8HxmA3Ldm6wWqwPby5tw1nM/4uYlv+LF1ftCPT0ixGnhgmdHJYEyM3bUyMy7cKy7AvnX26skZ2e3dyNJBRk7UaDFk4osy9jxeIHE7mkp8DzvtaajmHquNhzHCd6d3QpDWRWi0JPS2j+s59deGV3HWxUI0sMxIC8Zeh2HhjY7KiWUI9BasyPGbNBjfO9MDO/lXeQzk0w+2YeXTSgJ9NSYoCAtEc9fPgar7pyBW07qj7tPi7zqt1ROGuzOkF2+rUK4TwhdxIFnh4WAIvbseJ6fZjEKLUaue2uT4JV/a92hiGt+8TwvvDarneVPtmdTVqdaGEs9Y2dMaQYMOg61rTahuXI4KIwVR9idLmHXIKdsv+DZkRnG6rC7wIzu7vADi4SSLE9RPxleFTFMdPjwucMVh1BYi4ntRxs71eIIhreIpHrGZoJRL9RVkRLK0jobKxB6HSdUnb5+el+8ePlYAMDU/lk4Tmarjp5I7+wk3H7qIKGqcjSYMcht7Ow71ios2F7NXs/d7EglwZNlZHfyIY2RcJ4I9tklGHTo7enYLs7ArGzqwPd7jikep93pwsHaNqHY6BnD8wF0DoGyBrvqC5Qj/y0kGPXCBvQ3iV3Qu1NmMBk7GsN2+YA8waqg2ZEZxmoVlcfv6l4kkcL0CJUyKxgzmLHDasIooSgjEXmpZjhcvOSCWmpnYzHkiJTrWplmJ3oLLwAs/cNkPHreCFxzfF8cPyAbBxefiXeumdTlaaexSl6qGZlJJjhdPPZ6mmOq1YS2JyAW93YE0fbZHC6c+uT3+OPbm4K+DtvImI16XH18H+H+/NQEoY7T+xvLFI/z8a9248R/rBJu33zyANw/eyj+ceEon+OyktVNPVfTswMAI4vSAQDLt1dKOr67dDwHyNjRHKbXSTDqZJXsZk0T5Xp22EWbaNRrVlskWuR70ncrFJRu53leKAzGqv0qgeM4jOvtzjradKhe0nO0WmyYsbNDgmfH26NJ27YF/pRkWXDphBJVehUR4eE4Dr09HtDDHlFtLGj2pCIW3gbzvG48WIc91S34clvwBbqpw1vl+4pJpbjpxP4Y3isV718/GXOnuLutr9hRpaiiPQD8+/v9wv8zLEYY9TosOL5Pp/A60+yoIVDmeV51sTqrZ/XdzmpJVZ6702+RjB2NaVGo3/B6duQZO61d0CpCK/I9ReikaFT8qW21odXmBMd1dhXLZbwnu2fDwTpJxyspNSCFwQXSNEw8z0dVs0N0LSUeXdnvVc2wOV1weGIlPVmzJxWO4wQPdjDdjnjT5wySVSVkL3o2B3eeNgif33wCijMtGJyfigm9M+Fw8Xhn/aGIx8zaQgSCJVIca7ZG3KLJJjJG1CpDMLY0A+kWI5qtDqHpbiiEjufk2Yl95PbFYjDNTofdJauIU1e1itACbxhLvrHDXPpFGYmK084ZYs9OsMlSjBZ1dgBgiGeSPFDTGlKQ2W53Cu7raGp2iK5hkqe67crdx7pVef5owRbSYBlZ4hBqoGM2HaoT2nwE2xzMndIbgLvTemOAwoByCNU3Lc+zwbM6XJ0ahcrFKgrrqeXZ0es4oZjogZrwrXxard1HP0bGjsbI7XjOSDEbwDS1/r1UQtGdBGGRwrpUH2uxym6Mt8+TPdU/J3DWgxwG56cgyaRHc4dDUmaY15unrrGTl2pGusXoo88IBPPqmAzaNAEluhcnDs4FxwFbjjRgj+d3Ye4G5fmjhZB+HiSMJY6o/uHNjXj0fzt9Hn/3pyPC/0cWBS4bcPrwfAzMS0ZThwOj/u9rfLJZeq0Zf0IVODUZdEJGVoVCrSLDpoGxAwB9PALug7XhjZ12ysaKH5oVLnw6HSc8R45ux2tJd/2PK1IyLSaY9DrwPFAts1AbMwb6qWDsGPQ6jJERylJq4IaD4zihweRPB4KPo94jTs60mKJaSZjoGvJSEzC+1O19/GxLOYDYuP6lwrzgLAPRH7E3du2+Wrz0/X5hQwJ4N4h/mNYXKQmB5QZ6HYe/nTNCuP362oOyxijedFw4rjjksYXp7k1ehcIsVIYgTtbrVNXQsZpZByV4dlhvrO6w6SJjR2OU9MViKMnIiiXPjk7HIS/Ns8uR0a4BANZ5uvOyiqiRMrGPezH5OYSRwRCLHdXm5CHuVOOvQmRDsElfy/YMRPfi+AHu6rbf7qwCEBvXv1RYWChYIoPd2Tn0LO7ezcTAwbw6jAl9MvHkxe7sqeomeZsvl0d/883t09E/SI0dBtMqVigUQzPULCgopk+2WyN2oDZ8/7C2blTNn4wdjWEFBZWIVZXU2omFJqBiij1p41Liw4zq5g7sqmwGxwEzh+arMo4Jfdy6iJ8O1IUVDja1uy/wNA2MnZlD8wC4PUzVQSbDrqixQ3QtLKuHFXvrDtkv0SI/rLHTOQS+T1QklHmNc1OCa2kY4zwetGMys6WYc0mKUDfSkhsMrYwd5tk5cKwl7Fzo7R7Q9RsvMnY0JjLPjieMJSMji3l2YmWyY2K436ukV1H+xZMiPigvRbUFf2RRGkwGHWparCENL5vDJYggU4O4xCOhKMOCMSXp4HngU0/Iwh+268xW2CKD6HmMKkr32eBoYWh3VwpSQxsHDldnY6euzYb6VhsWfvQbDnk8FLkSmgWzDajNIS9xhPXW0ksIKwslNyIOY3lqB6lt7GQlgePcEYe6MG0tmhUm6GgBGTsao1SzA4g9O9LDWEI2VjdQv6sBM3Z2V0lv17DDU3QvnFtaDglGvVAFOFQoS2yYql1UkHHeGHePmudW7hWMWzFsh1uQHn6nSsQGJoMOk/t6a7Zo2QC2u1GQHroeV6AwVn2rDa+vPYglP3vFyYUSSlSI53E5iSNOjwdEJ2HFFTQ7CrJQxWjl2Ukw6lHoMcj2h/G4s3ZHWs2FciBjR2PU0ezEr2dnUL47vr2rQloDTADYW+02dpihpBZSdDuNnpBjSoJBs6KOF48vRq/0RDS02fH9751L2Fc2uXe4LPZPxAcneHQ7QHyFMMOVqGgMIAOoa7X7hKISjDpJRoFelDgi1djheV5o4aOT4tmJoL6YGJtD3YKCYoS2EWWNIY9jn1EqGTuxTyQF5lgVZTnNQFu7UaqfGgwtSIPZoEN1sxW7JYayfvd4gQaobuy4d84/7qsJangxfZUWISyGUa8Teut8vaOq0+Ns0g9Vz4OIPaYNzBH+3z9X3d9+dyacZue7ndWd7mtos/nUqpJjEKTIbNIszgaTEsYq8HhNyhvaIyosyLKx1CooKGZcb3d26s8HaoMew/O84qK6WkDGjsZE0ieJLZiBdibB6E7qdzVINOmFHevX2zsv7P7YHC4hJXKgSplYjHG9M2Ax6VHVZMXWo4F3NCxzTmvNBGsAGSgVnhk7LPZPxAd9spMwvncGCtIScNaowq4eTtRgnpAWqyOgAcI2jWLq2mw+dXnkeIGZZ6dFomdHXIdUSgp4floCDDoOVodLcnfxQFg19OwwL/eGg/VBDbJ2u1Mw9EizEwdEokYX+qQ0S28KJ3h2YkSzAwCnDmVejPDN5w7UtMLh4pFiNqgexkkw6jHds3v+JoBHBfAapoEmWDUZ4dEjHalrF/pgAe5dZJUnu4Q8O/EFx3F477rJ+P7uE0MWros1kswGIUwSKJTVbu8sUK5vtQmZqwDw9wtGSj5fipA4ItXYEXl2JBg7JoNO8EpvD7KpkgITKKut2QGAEb3SYTboUNdq88lsE8PWPh3XPTbfZOxoTCTVdIU+KTLSHNu7UeM1tTh5SC50HLDtaBOOhqm388Met4ZlSEGqJgX1WOp3oPAR4A1jae3ZSUs0otTTAFLsZWpstwu7qaw40m0QbjiOk9VwOFZg4uKZT36Pj37x7U7O5sR5U3rjtXnjAQAN7XahYe8Ll4+RVXyUFR5UEsaSKuMbXuhuDbMtAmPHK1BW39AwGXQYXZIOAPj5QOAGyYI42WzoFsVN4++qiDLM1anEjZeb6jZ2qpuluzJbY6ioICMr2SzUt/jvhiM+jzV12LF2bw0+/82dhv3F1goAwOxRBZqM5aTBuTDqOeyqbMbOAN3HG6Og2WGM6OX27ohFgmyCSTLp46ZdAEGIPVm3v7fF5zGmzRldki4UX+R5r8ZHbpNK1iy0NkzaNcPJi40daYs+89wGC5dLocNj7CRo4NkBgAm9WSgrcMIG83xpUVxVCbGz/e+mRObZcYchqpvcHXClWMdtMdQuQsxF44vx88E6vLH2IE4ZkovFX+5CZVMH9h/zpj4eqWvHr4cbAHg9MGqTbjHhlCF5+HJbJd7fWIa/zhnq87iW1ZP9GdErDZ//VuGz+2sWjOvuMcEQRDS4b9YQnPH0D8LtFqtDmHNZ3asEox5GvQ7pFiMa2uyCl1huw9RiT5f5I3XhKwgDAC+KoknN0BxZlA4A2FLWKHnu98cqet9a4C60ujdodmp3m4to66chLhcfkUCZ7VasDpdQryccsejZAYBzR/dCUUYiGtvtOOu5H7F2X62PoQMAf1++C4C7vk6BhuLcizy9bZb9WtapsFi0wliA17Mj3v01daO6FgQRLQb5CYxrRL30WBiLNQzN8Su2KTdzlRk7hyUaO2LPjpRsLAAYUpACk96tiTlSp6ySMhMoJ2iQjQUAY0rTYdBxONrQjkMBmoJ6M1O7x1xExo6GtIoKvinx7CSa9ELKutReLG0xlnrO0Os4XHN8H+F2r/RELJozFF//aRqeuvg4AO6L+rRheXjmktGajuWEAdnISzWjvs2Ob3b4prWyApDRuMCHeYydsvp21Htc6s0RhE0Joqei03G4b9Zg4Xa7KK2chbFYuIrJAxhyw1glMj07Ys2OVAeN2aDHEI9uZ3NZg6zxMTo09uxYTAaM94SyvgmQ3s82Xt3Fs0MzooYwr45Rzyku2Z2TakbzMQeqmzrCNpADIIjuYs2zAwBzp/RGdooZTe0OnDeml3ARD8xLwclDcmHU6zS7sMUY9DqcN6YIL67ah483H8WZI736oIZ2t9GRFoUmnEykfKi2DdvKG3HCgJxu5zomiGjxh2n98MbaQzja0O5TQ4cZPsyzk+g3RyTJzFxlxk5ZfTucLj5saIplY+k4yApHHVeUhi1HGrD5cIOiUgJaGzuAO3lk3f5afLuzCgtEm1FAVFBQ48xUqZBnR0OE6skRqNH7ZLmbrknpDeV08YLrMtY0O4B7opg9shCXTSzpdAGnJBijYugw2OSzevcxnwrXtS2sCWd0Un8HeAzgg57+Pi0dyhvPEkRPh3lpxJ4dtgFk84P/RkBuiYr81ATodRwcLl7omB4K5tmRW1H9OE+20+YjgbOdwtHhSblXuzeWGKaN/OlAHRrbfLPTolFgVQ5k7GhIcwR6HcaQArcrc2dFeGNH3CcpFj073YnB+Snon5sMm9OFFaJihyxDIzs5OmnfRZ6u8Efr3XF9CmMR8QzTp1g9C/2RujYhK4gZQgtnDcaYknSYDDp8fONU2RtRnY5Dkue1WiRoKdftc1cZDtSjKxTHFburFG8rbwrYuT0cTE+o5SawNCsJA3KT4XTxWPW7X0i/gzQ7cUOLCu3th3t0Gb8cDm/dM72OXqc8bEZIw+1lcoevPvOkvbtcvNAFOFodx3t56ov8a/U+VDd3dKsuwwQRbViIioVw/v39fuExNifmpiTgoxum4ve/nSE095UL8w5JqaL86Je7FJ2jd5YFaYlG2Bwu7JKw2fUnGp4dADh5iNu746/bYTrT7lLgklZEDYmkLxZjUt9McBywp7oF1WEaw4n1Ot2hiFOsM3ukO5S1Zk8N6lttPgX9MizR8eyUeAoLAsB/fz4i1NkhzQ4RjzAvBgtjib3q6SpmSAotIyR4dpjH/Zzj5OluOI7DKI8xpiSUFQ3NDgDMHOpuXbNqd7WPB4o1Ms3rJg2JydjRkEg6njPSLSYM86jyVwfocC2mLQarJ3dn+ucmY0hBKhwuHl9srRAqXaclGjUp0R6I6QNzhN43e6pbBJe9kuw/gujpJAieHfei6/AsvpdNLFG1yCYTNQczdpwuHn9ethW97/1CmJcfmDNM9nmY5+nXIw2ynysUFdTY2DmuOANZSSY0dziwQVRzx9uQuHv06CNjR0OaIygoKIb1hnp/Y1nI42I5E6u7cv6YXgCAZ7/bgx3l7orKxZnRu7gTjHr8/YIRAIC6Vhtpdoi4JsEvjMWMHrXDyslhwlgrdlTinZ8O+9ynpNDoSI+Mgc0tcmBFBbUOY+l1HE4a7PburNjp1i/anS5h85ffTXr0kbGjIWp4dgB3ETu9jsPPB+uwuzJ47LYtBpuAdneumFSK0iwLqpqsuO2/mwEAfbLV7bYejvREd8isvs0mhDq7S5ycIKIJa43QLhg7LJSj7lKXEiaM9eW2zk2L5WZjARBq7ew71iL0upJKtDw7AHAK6xm4vQo8z6O62Qqed5dd6S49+sjY0ZAWqzppwPlpCZjpEYF9+Etw706sFhTsziQY9XjwLF/3dB+RjiYapHtq+jS02YV+P6wxIkHEEyzjink1Nh1ya10SVG6GGUqzY3U48V2AIntKKExLQEqCAXYnH7S7eDCsGhl6gZg2IAeJRj2ONrRj29EmIYSVm5IAnQIjTwvI2NGQSPpi+cMK163aHfwiYhWbkyiMFVVmDMrFeaN7CbdZdkK0SPeIoSsa24VGpN3FdUwQ0UQIYzlcONZsxf4adxsDo8qhHOatZ7VkxKzdV4tmqwO5KnhXOY7DkHy3d2dXpbxQVrQEyoDbyDxxcA4A4MttFSK9TveZh8jY0ZAzRxRi4RmDMaV/dsSvdcKAbOg44PeqFpQ3BO6V0sY0OyROjToPnzsCf5zRD/939jAhgyJasC7MrCp9kknfbQp5EUQ0YckZjW12HBT1a5rYJ1PV87BF/KXv94P3VEhusTqwfn8tlni0OqcOU2fT0zfHXVj2UK209hQMVmA2WmVITh/u3pB/ua0SFY3uNSqvGxk7tCpqyPEDsnH8gMgNHcC9ez+uOB2/HG7A6t+P4dIJJZ2OaRWyscizE20STXrcc/rg8AdqQEqCERwHsH6DGd0kRk4Q0YYZBr9XNwv94rKSTBjo1yg0UsQbmoe/2ImrJvfGla/+5GOQnHNcL2wvb8Kvhxsi8nDIbTzKiKZnBwBOGpwLk0GHAzWt+G6XOwJRnBHdkH4oyLPTg5gxyK14/+K3ioCPtwkdz8mGjSf0Os4n24TSzol4ZUiB26jZXdmM+ja3sTOiKE3184zvnSkIjv+z5gCmPb7Sz9ApxNjSDDx76WicN7oXXr5qnOJzyW08ymCZaGrrlYKRbDbguKJ0AO5QHgAMzItuskYoyNjpQZw7uhc4DliztyagWK3VygTK5NmJN8SC5Fjsi0YQUuidlQSTQYc2mxO7PJmrWtUde33+eJ/b6RYjvrtjOrY8cCqeumQ0OI5DUYYF/7z4OKESvhJKFHh2eJ5HhyN6AmXG6NJ0n9tqe9QigYydHkRxpgUnebw7b6071Olxr2eHjJ14o1e6101Oxg4Rrxj0OsGb8OvhBgDeDC21OWFADp64cBQAoG92Ej64fjL65iQjTcVKzYDX2Klqsvp0cw+F3ckLYW1zFBskz/LodgCA49w9BLsLZOz0MK6cXAoA+HBTmVBEkME8OxTGiD8KRVVKu0vjPYLoCgbmuhfY7eWNALTd/J0/tghbHjgV394xHf1ztVnY0y1GoXxJWb007w7z6gDREygDbi3T1VP7AABuPrG/qlWrI6X7jISQxLQBOeidZUGz1YGvtvsWrmqivkhxS79cb2y8uxTxIoiuICvZ/ftnXca18uww0hKNmvYi5DhOtkiZeYA4LrrGDgDcP3sIvr/rRPxp5sConjccZOz0MHQ6Dqd46rj8Vtbo85hQ14d29nHHpL5Zwv+j1ZeLILoj/mUXYqFXoKDbkZh+bhV1PI92U2iO41CSZel2zahpVuyBsF38IVEdCQDUFymO6ZOdJPy/JCspxJEEEdukWXyNnTElGV00EvUoyWKencA11vyxOqKbdt4ToFWxB9LLk3lz1K+4IOvFlWKmMFY88vr88fjlcAMuC1CDiSDiBXGX7T7ZSZjaPyvE0T2D4gz3e5Iexopu2nlPgIydHkgvzw//aH07eJ4X3IUsjEWenfhkxqBcoRYTQcQrY0rSYdBxcLh4XDy+uNuFU5RQLLPWDtPsmKOYdt7doVWxB8Iyb1ptTjS225FuMcHp4kmzQxBE3JOVbMbHN07FL4frA1aa74kwzc6hulY4XXzYDurk2ekMmX09kESTXsi4YaEs1gQUIM8OQRDxzfBeabhqcm8Yu1HqcySUZiUhyaRHh92Fb3ZWhT2+3ePZ0ToTrScRG7+EOMRidv+Ir31jIwCvONmk18FM1jxBEETMoNdxsDnd3prr3toU9ngqMNsZMnZ6KAM8BazKGzuwane1IE6mEBZBEETsMTg/Vfj/ok+3hzy23Uatg/whY6eHsmjOMOH/T3z9O1qsrKAgGTsEQRCxxivzvM1EX197sFPpETFtNhbGovWA0aXGzqOPPorx48cjJSUFubm5OOecc7B7926fY6qqqjBv3jwUFhbCYrHg9NNPx549e3yOsVqtuPnmm5GdnY2kpCScddZZKCsri+ZbiTolWRacOdLdh2Tr0UZ8sOkoAGoVQRAEEYvkpiT43F6/vzbosUyzY6E6OwJdauysXr0aN954I9avX48VK1bA4XDg1FNPRWur22LleR7nnHMO9u/fj08++QS//vorSktLccoppwjHAMBtt92GZcuWYenSpVizZg1aWlowe/ZsOJ3Smqb1VP5xwSjh/0t+PgwAyLBQqwCCIIhY5IXLxwj/D1Vzh2l2SKDspUvdAMuXL/e5/dprryE3NxebNm3CtGnTsGfPHqxfvx7btm3DsGHusM0LL7yA3NxcLFmyBNdccw0aGxvxyiuv4K233sIpp5wCAHj77bdRXFyMb775BqeddlrU31e0SDTpMalvJtbvrxPuy6C+SARBEDHJrBEFuPeMwVj85S6UN3QEPa6NNDud6FaancZGd6+nzMxMAO7wFAAkJHjdd3q9HiaTCWvWrAEAbNq0CXa7HaeeeqpwTGFhIYYPH461a9cGPI/VakVTU5PPX0/lzasn+tzOtFD1ZIIgiFilMEgFfTEkUO5MtzF2eJ7H7bffjuOPPx7Dhw8HAAwePBilpaVYuHAh6uvrYbPZsHjxYlRWVqKiogIAUFlZCZPJhIwM3/4neXl5qKys7HQewK0VSktLE/6Ki4u1fXMa4t/0MZ3CWARBEDFLkaeC/v5jLXC5+IDHkEC5M93G2Lnpppvw22+/YcmSJcJ9RqMRH374IX7//XdkZmbCYrFg1apVOOOMM6DXh7ZYxW0U/Fm4cCEaGxuFvyNHjqj6XqLN/53tzczKpDAWQRBEzDKsMBUWkx41LTZsOlwf8BgKY3WmWxg7N998Mz799FOsXLkSRUVFPo+NHTsWmzdvRkNDAyoqKrB8+XLU1taiT58+AID8/HzYbDbU1/t+6dXV1cjLywt4PrPZjNTUVJ+/nszYUq9XKy/V3IUjIQiCILTEbNDj9OH5AIAL/7UOe6qaOx3Tbqeigv50qbHD8zxuuukmfPTRR/juu+8EAyYQaWlpyMnJwZ49e7Bx40acffbZANzGkNFoxIoVK4RjKyoqsG3bNkyZMkXz99AdGFqQij/PGoI7Tx2IEwdTI0iCIIhY5s+zhgj///CXo50eF8JYlHou0KUBvRtvvBHvvvsuPvnkE6SkpAgam7S0NCQmuuOS77//PnJyclBSUoKtW7fi1ltvxTnnnCMIktPS0rBgwQLccccdyMrKQmZmJu68806MGDFCyM6KdTiOw7XT+nb1MAiCIIgokJVsxt2nD8Jjy3dj6YbDWHB8H+SkeL36XoEyaXYYXerZefHFF9HY2IgZM2agoKBA+Pvvf/8rHFNRUYErr7wSgwcPxi233IIrr7zSR9cDAE8++STOOeccXHTRRZg6dSosFgs+++yzsLoegiAIguiJXHtCXwwtSEVDmx23LPkVDk/vLEAsUKY1kMHxPB9Yzh1HNDU1IS0tDY2NjT1ev0MQBEHEB9vLG3HmM+4yLK/NH48TB7llDOP+9g1qWqz48tYTMKQgttc0qet3txAoEwRBEAQhj2GFaTjDI1Zes6dGuL/V6hYoJ1EYS4CMHYIgCILooZx9XCEA4JPNR9Fhd8LmcAm9sdISqcgsg4wdgiAIguihnDwkDwVpCahpseGzLeVobLcDADgOSEkgzw6DjB2CIAiC6KEY9TpcNbk3AOCVNQfQ2G4DAKSYDdDpAhfWjUfI2CEIgiCIHsxlE0pgMemxq7IZX/zmKeFCfRJ9IGOHIAiCIHowaRYjLh7v7vH45De/u+8jvY4PZOwQBEEQRA9nwfF9oBeFrVITyNgRQ8YOQRAEQfRwijIsOH1YvnA7K5n6JIohY4cgCIIgYoA5owqE/xemJXThSLofZOwQBEEQRAwwfWAuMpNMAIDRJeldO5huBiXhEwRBEEQMkGjS479/mIQ91S04TRTSIsjYIQiCIIiYYUBeCgbkpXT1MLodFMYiCIIgCCKmIWOHIAiCIIiYhowdgiAIgiBiGjJ2CIIgCIKIacjYIQiCIAgipiFjhyAIgiCImIaMHYIgCIIgYhoydgiCIAiCiGnI2CEIgiAIIqYhY4cgCIIgiJiGjB2CIAiCIGIaMnYIgiAIgohpyNghCIIgCCKmoa7nAHieBwA0NTV18UgIgiAIgpAKW7fZOh4MMnYANDc3AwCKi4u7eCQEQRAEQcilubkZaWlpQR/n+HDmUBzgcrlQXl6OlJQUcBzX1cNRjaamJhQXF+PIkSNITU3t6uF0CfH+GcT7+wfoM4j39w/QZxDL75/neTQ3N6OwsBA6XXBlDnl2AOh0OhQVFXX1MDQjNTU15n7gcon3zyDe3z9An0G8v3+APoNYff+hPDoMEigTBEEQBBHTkLFDEARBEERMQ8ZODGM2m/HAAw/AbDZ39VC6jHj/DOL9/QP0GcT7+wfoM4j39w+QQJkgCIIgiBiHPDsEQRAEQcQ0ZOwQBEEQBBHTkLFDEARBEERMQ8YOQRAEQRAxDRk7BEEQBEHENGTsEARBEAQR05CxQ/RYfvnlF6GJa7yyYcMGPPXUU0LnX4KIN2geoHlACmTs9EAqKipwyy234J577sEzzzzT1cOJOuXl5Tj11FNx4oknYvPmzV09nC6hvLwcs2bNwsSJE/HMM88gNTUV8VQyq6qqCl988UVcvWd/aB6geSDe5wE5kLHTw1i0aBEGDBiAQ4cOobq6GrfddhseeughAIiLH/ndd9+N0tJSWCwW7Ny5EyeccEJXDynq3HnnnSguLkZycjL+/e9/w2q1Yvfu3eA4rquHFhWee+45FBYWYs6cOdi+fXtXD6dLoHmA5oF4nwdkwxM9Arvdzi9evJifPn06/+WXXwr333///Xzfvn27cGTRwWaz8TfddBPPcRy/dOlS4f6qqqouHFV0aWpq4hMSEvgRI0bwa9as4Xme57/55hu+pKSEX7VqVRePTntcLhf/xRdf8CeffDL/j3/8gx8zZgx/wQUX8E6ns6uHFjVoHqB5IN7nAaUYutrYIsLD8zwMBgMmT56MCRMmYPr06cJjdrsd119/Pdrb25GYmNiFo9QOnudhNBpxwgknYOvWraipqcGuXbuwcOFC1NTUQKfT4fLLL8e8efNgMpm6eria4HK5kJKSglWrVmHixInC/RMnTkR1dTVqamqE43S62HTYchyHvLw8XHnllTj//PMxfvx4zJjx/+3dfVBU5R4H8O9hAWUBFb2LaKWQAV7UAMm0NKU3u9ILN7XoXhxEJpnUQicZp5lqGrXs2qukSeNUejMUuy/ObYwCrzftoo6RdqU0ogTfUmS0WOJFXna/9w/aExuoCMsue57f5y85e3bn+X5neXjcc/acRBQWFmLGjBmeHp5b+Pr6YtKkSUrOAwBkHpB5oPs8vdoSnaurq2N5eTmtVmunj1utViYnJ1PTNI4fP56RkZH829/+xvr6ejePtHd0lr+lpYWLFi1iWFgYhwwZwsWLFzMnJ4eZmZns168fX331VTY0NHhw1K51ufeA3W6n3W6n1WrllClT+MQTT3hghL3LarVy//79PH369CX3efjhhxkfH8/a2lo3jsx9rtSB0eeBzvKrNg9c7j2gwjzgKrLY6YNWrFjBiIgIxsXFMSIiggUFBU6PNzc385133mFSUhKLi4tZWlrKhQsXMiYmhh999JGHRu06neVvbW0lSe7bt49z587lhx9+6PScrKwsxsbG8quvvvLEkF3uSu+B9qZOncoFCxaQbJv8jGDVqlUcMGAAx44dywEDBnDNmjX6ZN/a2qofujp27BgDAgL4xhtveHK4veJyHdhsNsPPA53lP3HiBEnys88+U2Ie6KyDH374gST1OdHBiPOAK8lipw85fvw4H3jgAY4ZM4YfffQRd+3axblz53LYsGGsqqpy2rez/7mFhIRwy5Yt7hquy10u/9mzZ/X9SktLefHiRZLU/+hVVVVR0zQeOHDAI2N3lat5Dzgmu+zsbMbExHhiuL2ioKCAv//977l9+3ZWVFTwhRde4JgxY5iRkaHv034yf+aZZzh06FCeOnWKZNvvRl1dndvH7Upd6YA05jxAXjr/vHnz9H3+97//GXYeILv+HjDqPOBqstjpQ/Lz8zl16lR+8803TtsHDBig/w/GMcn/duVeUlLCESNGOJ206G26kv+3HD1s3bqVoaGhPHz4cK+Pszd1p4P169dzzJgxLC8vd8cQe11WVhbj4+Odtq1du5bR0dHcsGEDSef/1dbV1XHkyJHMysrie++9xylTpvCDDz5w65hdrasdGHEeIC+f/6233iJJpxPTjTYPkFf/e2C0ecDV5ATlPoAkNE3D9OnTYTKZMHr0aP2xc+fOYdiwYejfvz8A6F8r1DRNf155eTmWL1+OCRMmYMqUKR7J0BNXk7+z55WVlWHjxo1ITk7GjTfe6M6hu0x3OnA8Z+TIkTh58iTMZrO7h+1ydrsdLS0tiI6ORlNTE/r16wcAmDVrFr7++mu8+eab+NOf/oSgoCD9JMzAwECkp6djxYoV8PPzw9KlS/HQQw95OEn3XU0HAAwzDzhcKX9ubi7mzJmDwMBAAMaaBxyu5j1gs9lgMpkMNQ/0Bjlduw9wLFxCQkIwe/ZsAG1vdgD48ccfcf78eYSHhzs9p7GxEatXr8b8+fORkJCA4OBgvPvuu/oE6E26k7+hoQErV67EvHnzkJCQAIvFgldffdXdQ3eZ7nTgWPhGRkaisbERx48fd+eQXY4kfHx8MGLECOzduxdnz57VHxs2bBjuvfde+Pn5YevWrQAAHx8f1NfX4/HHH8eKFSuQkZGBc+fOYdWqVZ6K0GNd7SA/Px8AUF9fb5h5ALj690B9fb2h5gGge78HgHHmgd4ii50+7rPPPkNERAQiIyOdLhYWEBCAIUOG4OLFi9i9eze2bNmCAQMGeHCkveNS+c1mMwYPHoy6ujrs2bMH77//PoKDgz040t5zqQ4cBg8ejO+//x6TJ0/2wOhcx7G4W7JkCaxWK/Ly8pweT0xMhI+PDy5cuKBvO3/+PIKDg/Hf//4Xb7/9NgYNGuTOIbtcVztwfMU4MDAQFovFMPNAd/L/7ne/M9Q80J3fA8A480CvcfdxM1WdOXOG+/btY2VlZYfHWlpa9H87jsE6jkGnpqbyySef1B8vLS3Vj0d70xn3vZHf2y4m58oOvPHbJqdOnWJeXh737dvHn376ieSvGdvnJ8lXXnmFwcHBLCkpcdoeFxfHhQsXumW8vcFVHTi+ddP++d6gN/J72zwgvweeIYsdN1i8eDGHDBnCCRMm0Gw2880332RNTY3TPna7ncuWLeP7779Pm81Gu93Ompoajh49moWFhTxz5gwfeughaprGHTt2eChJ96ien1S7A7vdzqysLAYHB3PatGkMDAzkvHnzeObMmQ77LVu2jJs3byZJJiQk8M4779S/Rn3w4EHGxsayqKjI7Rl6SvUOVM9PSgeeJoudXnTixAnef//9vOWWW7h3715WVlYyOzubY8eOdXqjbtq0iYMHD2ZcXBxLS0v17SUlJQwLC+Njjz3GgIAATp8+Xb/OhDdQPT8pHVRWVjIxMZGTJ0/m/v37efHiRW7YsIFjx47lv//9b32/9vkPHTpEkjx69CgffPBB+vv7c/r06TSbzXzkkUe87oJ5qnegen5SOugLZLHjYu0/Ut6xYwdTUlL4xRdfOO0TFhbGbdu2kSR//vlnrly5krm5uR0uErV27Vpqmsabb77Za1bxqucnpYP2+b/77juuWbOGZWVl+rbq6mrGxsbqi7bGxkYuX77cKb/jNaxWK4uKirhu3Tr9PkDeQPUOVM9PSgd9jSx2XKihoUG/yBXZdmx27969+s+Oq54mJCQwLy/PaXt7jjd4bW0tN27c2LuDdiHV85PSwW/zNzY26uclkG0XfbvrrrsYExPDjIwM/utf//LAKHuX6h2onp+UDvoiWey4yFNPPcXx48fzrrvuYk5OTofzMRx/zE6cOMGgoKArXvTKm046JCU/KR38Nv9v7+lVXl7O/v37c8aMGdy0aRNnzpzJqKgovvDCCyS970TTzqjeger5Semgr5LFTg81NTVx9uzZjImJYX5+PtPS0hgTE8N7773Xab/2V/gcN25cp29ob/vjRkp+Ujroan6y7RL/Dq2trVy6dClvvfVWr79xo+odqJ6flA76Olns9NDRo0cZGRnpdD5FcXExAwIC+NJLL3W4vUN2djYzMzP1ff/zn/9c8jYA3kD1/KR00NX8nUlOTmZSUhKbm5u9cqHnoHoHqucnpYO+ThY7PXTw4EFqmsYLFy6Q/PUP2osvvsiQkBCn+5S0trYyPj6e27ZtY0VFBe+44w76+/vrJ6p6I9Xzk9LB1eRvb//+/Zw6darX37SSlA5Uz09KB32dLHZ66Msvv+SYMWO4du1akr++wZubmxkREcGlS5fq+x4+fJjBwcGcMWMGfX19mZKSwtraWo+M21VUz09KB13Nb7PZeOTIEe7evZuPPfYYg4ODuWTJEjY3N3ts7K6iegeq5yelg75ObhdxBezk8vztjRw5EpGRkSguLsbZs2ehaRpaW1vh5+eHxx9/HFu3btUv//3999+jrq4OTU1NKCkpQX5+fp+/tLnq+QHpwFX5fXx8UFpaipdffhkVFRUoLi7G66+/Dj8/Pzcl6T7VO1A9PyAdeDtZ7FyG1WqFzWbTf3b8wQKA1tZWAEBISAjuv/9+lJWV4YMPPgAA+Pq23Ux+4MCBCAkJwYkTJwAAt956K3bt2oVdu3YhLi7OTSm6T/X8gHTg6vzJyclYt24dCgsLvebO1Kp3oHp+QDowAlnsdKKlpQWLFi1CUlISkpKSsHLlSn1F7nhj+/r64uLFi8jPz0dGRgbi4uKwbds2fPrpp/rrnD59GhaLBREREQCAsLAw3H777R7JdDVUzw9IB72VPyAgoMPd2/sq1TtQPT8gHRiKBw+h9UlFRUW84YYbOG3aNG7fvp0ZGRmMjo7m008/7bRfTk4OBw8ezOTkZJJt52KkpqbS39+fCxYsYGZmJoODg5mbm0vSe75SrHp+UjpQPT8pHaien5QOjEYWO+1YrVY++uijXLRokX6yWFNTE5977jnec889+r1I1q9fz4iICObl5TldK8Vut3PVqlWcP38+k5KSnK6c6w1Uz09KB6rnJ6UD1fOT0oERaeQVzrpSyE8//YQPP/wQsbGxiIuLA0lomoannnoK+/fvx549ewC0HaNtampCYGCg/lzHvt5M9fyAdKB6fkA6UD0/IB0Yka+nB+BJGzZsgKZpiIqKwrRp0xASEoK0tDT9jWq322EymVBbW4vrr78eQNsb2dfXVz/xzMEb39yq5wekA9XzA9KB6vkB6UAJbvwUqc/YsmULQ0NDecsttzAuLo4Wi4XPP/88STrdddpxbHXixIl8++23nbZ5M9Xzk9KB6vlJ6UD1/KR0oBLlFjt5eXmMjY3lW2+9RZL84YcfuHbtWgYGBnZ6cbeKigpaLBaWlZXp244dO0bS+ZfBW6ien5QOVM9PSgeq5yelA9Uo89Vz/nJqUktLCyZOnIi0tDQAwPDhwxEfH49rrrkG33zzTYfnFRYW4rrrrkN0dDS+/PJLTJw4EZMmTUJraytMJpNbM/SE6vkB6UD1/IB0oHp+QDpQleEXO4cOHUJNTY1+HPWPf/wj1q9fj4CAAH2foKAg1NbWIjIyUt/m+IU4evQorr/+ejz55JO46aabMG7cOJw6darDcdq+SvX8gHSgen5AOlA9PyAdKM8THye5w9///ndee+21HDVqFEeMGMFnn32WVVVV+uPtvyb42muvcfLkySTbvl7Yfp+RI0dS0zQmJibyyJEj7gvQQ6rnJ6UD1fOT0oHq+UnpQLQx5GKnpKSEo0eP5po1a3j48GGuX7+eFouFCxYs0O9Ia7PZ2NLSQpJ88MEHuWjRog6vU1NTwxdffJGFhYVuHX9PqZ6flA5Uz09KB6rnJ6UD8StDLXYcZ8fn5uby2muvpdVq1R9bt24dJ02axJUrV+rbbDYb7XY7R40axR07dpAkv/32Wz7yyCM8efKkewfvAqrnJ6UD1fOT0oHq+UnpQHRkqHN2HMdiKysrERUV5XQsNT09HQkJCfj4449x5MgRAICPjw9KSkpgNpsxfvx4LFmyBDfeeCMuXLgAi8XikQw9oXp+QDpQPT8gHaieH5AOREdevdjZuXMnsrKykJOTg88//1zfPnnyZOzbtw9VVVUAAJvNhsDAQCQnJ0PTNBQVFen7FhQU4Ouvv0Z0dDR27tyJvXv3oqioCP3793d7nqulen5AOlA9PyAdqJ4fkA5EF3j6o6XuOHPmDO+77z6GhoYyNTWV48aN48CBA3ngwAGSZGNjI0ePHs3MzEySzieg3XbbbVy4cKH+8/PPP0+LxcJ//OMf7g3RA6rnJ6UD1fOT0oHq+UnpQHSd1y126uvrOXfuXKakpLCiokLfPmHCBKanp5Nsu8DTe++9Rx8fnw43YEtNTWViYqL+c3V1tXsG7iKq5yelA9Xzk9KB6vlJ6UBcHa87jGU2m9GvXz+kp6cjIiICra2tAID77rtPvxCUyWTCww8/jOTkZDz66KPYs2cPSKKqqgrfffcd5syZo7+etx2PVT0/IB2onh+QDlTPD0gH4ip5cKHVbc3Nzfq/HWfdz5kzh/Pnz3fa1tjYyMTERIaGhnL69OkcPnw4J02a5PVn16uen5QOVM9PSgeq5yelA9F1GvnL5SG93NSpU5GRkYH09HSQ1O9Se+7cOZSWlqKkpATh4eH485//7Omh9grV8wPSger5AelA9fyAdCAuwWPLLBc6duwYhw4dyi+++ELf1v7ql0anen5SOlA9PykdqJ6flA7EpXndOTvt8ZcPpYqLixEUFISEhAQAwPLly7F48WJUV1d7cni9TvX8gHSgen5AOlA9PyAdiCvz6juYOS4c9fnnn2PWrFnYuXMnMjMz0dDQgM2bNyM0NNTDI+xdqucHpAPV8wPSger5AelAdIEHP1VyicbGRt5www3UNI39+vXjX/7yF08Pya1Uz09KB6rnJ6UD1fOT0oG4PEOcoHz33XcjMjISr732mpJXu1Q9PyAdqJ4fkA5Uzw9IB+LSDLHYsdlsMJlMnh6Gx6ieH5AOVM8PSAeq5wekA3FphljsCCGEEEJcild/G0sIIYQQ4kpksSOEEEIIQ5PFjhBCCCEMTRY7QgghhDA0WewIIYQQwtBksSOEEEIIQ5PFjhDCa+3evRuapqGmpsbTQxFC9GFynR0hhNdITExEXFwc1qxZAwBobm7Gjz/+iKFDh+r3RxJCiN/y6huBCiHU5u/vj7CwME8PQwjRx8lhLCGEV0hPT8eePXuQk5MDTdOgaRo2bdrkdBhr06ZNGDRoEHbs2IHo6GiYzWbMnj0b9fX1+Otf/4rw8HCEhITgiSeegM1m01+7ubkZy5YtwzXXXIPAwEBMnDgRu3fv9kxQIYTLySc7QgivkJOTg/LycowdOxYrVqwAABw5cqTDfg0NDXjjjTeQn5+Pn3/+GTNnzsTMmTMxaNAgFBQUoKKiArNmzcKUKVOQkpICAJg3bx6OHz+O/Px8DB8+HNu3b8cf/vAHfPXVV4iMjHRrTiGE68liRwjhFQYOHAh/f3+YzWb90FVZWVmH/VpaWpCbm4tRo0YBAGbPno3Nmzfj3LlzCAoKQkxMDG6//XZ8+umnSElJwbFjx7B161acPn0aw4cPBwBkZ2fjk08+wcaNG7Fq1Sr3hRRC9ApZ7AghDMVsNusLHQAYOnQowsPDERQU5LSturoaAHDo0CGQRFRUlNPrNDU1YciQIe4ZtBCiV8liRwhhKH5+fk4/a5rW6Ta73Q4AsNvtMJlMOHjwIEwmk9N+7RdIQgjvJYsdIYTX8Pf3dzqx2BXi4+Nhs9lQXV2N2267zaWvLYToG+TbWEIIrxEeHo4DBw7g+PHjOH/+vP7pTE9ERUUhNTUVaWlp+Oc//4nKykqUlJRg9erVKCgocMGohRCeJosdIYTXyM7OhslkQkxMDCwWC06ePOmS1924cSPS0tKwdOlSREdH44EHHsCBAwdw3XXXueT1hRCeJVdQFkIIIYShySc7QgghhDA0WewIIYQQwtBksSOEEEIIQ5PFjhBCCCEMTRY7QgghhDA0WewIIYQQwtBksSOEEEIIQ5PFjhBCCCEMTRY7QgghhDA0WewIIYQQwtBksSOEEEIIQ/s/Urhg6P5LiZsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_combined[\"tas\"].sel(lat=-37.81, lon=144.96, method='nearest').plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26f8111c-86d3-4cfa-9847-6b6d917493d5",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f92f1eca-5235-4d67-9112-447939c65caf",
   "metadata": {},
   "source": [
    "The `preprocess` argument to `open_mfdataset` provides good opportunities to optimise access to large datasets. Rather than spending time tuning chunk sizes and dask options, it may be possible to change the order of operations with the `preprocess` argument to just have the variable you need concatenated. We have also demonstrated how `preprocess` can be used to pull together inconsistent datasets in order to allow `open_mfdataset` to work at all.\n",
    "\n",
    "Like any `xarray` tuning settings, there is no silver bullet that will always improve performance. For smaller datasets, `preprocess` may actually increase runtime due to the overhead of having to run what ends up being very small operations multiple times. `preprocess` is another tool, like chunking, that can be used to improve data analysis workflows."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:analysis3-23.04] *",
   "language": "python",
   "name": "conda-env-analysis3-23.04-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": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}