From b77baee1e01fd92756dc80eef34ccf83b586ca3b Mon Sep 17 00:00:00 2001
From: Gen Tolhurst <gen.tolhurst@bom.gov.au>
Date: Wed, 11 Dec 2024 12:12:46 +1100
Subject: [PATCH] example for using acs_regional_stats to calculate timeseries

---
 .../FAQ_example_timeseries_stats.ipynb        | 3673 ++++++++++++++---
 1 file changed, 3000 insertions(+), 673 deletions(-)

diff --git a/example_notebooks/FAQ_example_timeseries_stats.ipynb b/example_notebooks/FAQ_example_timeseries_stats.ipynb
index 0168583..cbe4b4e 100644
--- a/example_notebooks/FAQ_example_timeseries_stats.ipynb
+++ b/example_notebooks/FAQ_example_timeseries_stats.ipynb
@@ -11,6 +11,8 @@
     "1. calculate regional averages.\n",
     "2. then visualise time series for a selected region\n",
     "\n",
+    "I have tested this with a 14-core CPU and 63 GB of memory. Most will work with fewer resources, but the BARPA example is very large.\n",
+    "\n",
     "\n"
    ]
   },
@@ -63,11 +65,11 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "82328e62-9f62-426a-aa38-fec5409edaef",
+   "id": "c316a9c6-1a4a-4f99-829b-76a0152ea92e",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %load_ext memory_profiler\n"
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
@@ -92,52 +94,22 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "ec380109-886c-4ead-b282-16365b9ec236",
+   "cell_type": "markdown",
+   "id": "f7420132-2040-409e-85f4-0d2fe649d78a",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['/g/data/ia39/ncra/heat/data/TXx/raw/individual_models/GWL-timeseries/TXx_AGCD-05i_CNRM-ESM2-1_ssp370_r1i1p1f2_CSIRO_CCAM-v2203-SN_v1-r1_GWL12.nc',\n",
-       " '/g/data/ia39/ncra/heat/data/TXx/raw/individual_models/GWL-timeseries/TXx_AGCD-05i_ACCESS-ESM1-5_ssp370_r6i1p1f1_CSIRO_CCAM-v2203-SN_v1-r1_GWL12.nc',\n",
-       " '/g/data/ia39/ncra/heat/data/TXx/raw/individual_models/GWL-timeseries/TXx_AGCD-05i_EC-Earth3_ssp370_r1i1p1f1_BOM_BARPA-R_v1-r1_GWL12.nc',\n",
-       " '/g/data/ia39/ncra/heat/data/TXx/raw/individual_models/GWL-timeseries/TXx_AGCD-05i_ACCESS-ESM1-5_ssp370_r6i1p1f1_BOM_BARPA-R_v1-r1_GWL12.nc',\n",
-       " '/g/data/ia39/ncra/heat/data/TXx/raw/individual_models/GWL-timeseries/TXx_AGCD-05i_CMCC-ESM2_ssp370_r1i1p1f1_CSIRO_CCAM-v2203-SN_v1-r1_GWL12.nc',\n",
-       " '/g/data/ia39/ncra/heat/data/TXx/raw/individual_models/GWL-timeseries/TXx_AGCD-05i_EC-Earth3_ssp370_r1i1p1f1_CSIRO_CCAM-v2203-SN_v1-r1_GWL12.nc',\n",
-       " '/g/data/ia39/ncra/heat/data/TXx/raw/individual_models/GWL-timeseries/TXx_AGCD-05i_CESM2_ssp370_r11i1p1f1_BOM_BARPA-R_v1-r1_GWL12.nc',\n",
-       " '/g/data/ia39/ncra/heat/data/TXx/raw/individual_models/GWL-timeseries/TXx_AGCD-05i_ACCESS-CM2_ssp370_r4i1p1f1_CSIRO_CCAM-v2203-SN_v1-r1_GWL12.nc',\n",
-       " '/g/data/ia39/ncra/heat/data/TXx/raw/individual_models/GWL-timeseries/TXx_AGCD-05i_MPI-ESM1-2-HR_ssp370_r1i1p1f1_BOM_BARPA-R_v1-r1_GWL12.nc',\n",
-       " '/g/data/ia39/ncra/heat/data/TXx/raw/individual_models/GWL-timeseries/TXx_AGCD-05i_ACCESS-CM2_ssp370_r4i1p1f1_BOM_BARPA-R_v1-r1_GWL12.nc',\n",
-       " '/g/data/ia39/ncra/heat/data/TXx/raw/individual_models/GWL-timeseries/TXx_AGCD-05i_CMCC-ESM2_ssp370_r1i1p1f1_BOM_BARPA-R_v1-r1_GWL12.nc',\n",
-       " '/g/data/ia39/ncra/heat/data/TXx/raw/individual_models/GWL-timeseries/TXx_AGCD-05i_CESM2_ssp370_r11i1p1f1_CSIRO_CCAM-v2203-SN_v1-r1_GWL12.nc',\n",
-       " '/g/data/ia39/ncra/heat/data/TXx/raw/individual_models/GWL-timeseries/TXx_AGCD-05i_NorESM2-MM_ssp370_r1i1p1f1_BOM_BARPA-R_v1-r1_GWL12.nc']"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "# choose a list of files to open\n",
-    "# this list of files will be combined to form a dataset with model, time, lat, and lon dimensions\n",
-    "# we will calculate regional averages and then calculate timeseries for a selected region for each model\n",
+    "# Large amounts of daily data from a single file and a custom region \n",
     "\n",
+    "An example of a single large file. Daily data from 1980 to 2099 with 0.1 degree resolution.\n",
     "\n",
-    "var = \"TXx\"\n",
-    "gwl=\"GWL12\"\n",
-    "path = f\"/g/data/ia39/ncra/heat/data/{var}/raw/individual_models/GWL-timeseries\"\n",
-    "\n",
-    "filelist = glob(f\"{path}/{var}_*_{gwl}.nc\")\n",
-    "filelist"
+    "This data needs a mask suitable for it. The new mask does not need to be reused, so we only calculate it within the acs_regional_stats function using `mask = \"fractional\"`. You can define it outside the function if you wish.\n",
+    " "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "fc93a2e1-3db9-403c-a348-e1ccee72a242",
+   "execution_count": 6,
+   "id": "a29fba8e-731e-447a-bf0e-1f01f6810934",
    "metadata": {},
    "outputs": [
     {
@@ -506,160 +478,2401 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 1GB\n",
-       "Dimensions:  (model: 13, time: 45, lat: 691, lon: 886)\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 10GB\n",
+       "Dimensions:  (time: 43800, lat: 226, lon: 261)\n",
        "Coordinates:\n",
-       "  * lat      (lat) float64 6kB -44.5 -44.45 -44.4 -44.35 ... -10.1 -10.05 -10.0\n",
-       "  * lon      (lon) float64 7kB 112.0 112.0 112.1 112.2 ... 156.2 156.2 156.2\n",
-       "  * time     (time) datetime64[ns] 360B 2004-01-01 2005-01-01 ... 2048-01-01\n",
-       "Dimensions without coordinates: model\n",
+       "  * lat      (lat) float64 2kB -32.0 -31.9 -31.8 -31.7 ... -9.8 -9.7 -9.6 -9.5\n",
+       "  * lon      (lon) float64 2kB 132.0 132.1 132.2 132.3 ... 157.8 157.9 158.0\n",
+       "  * time     (time) datetime64[ns] 350kB 1980-01-01T15:00:00 ... 2099-12-31T1...\n",
        "Data variables:\n",
-       "    TXx      (model, time, lat, lon) float32 1GB nan nan nan ... 31.64 31.64\n",
-       "Attributes:\n",
-       "    pathway:                 ssp370\n",
-       "    GWL:                     GWL 1.2 degC\n",
-       "    bias_correction_method:  raw\n",
-       "    contact:                 Mitchell Black (mitchell.black@bom.gov.au)\n",
-       "    code:                    https://github.com/AusClimateService/hazards-heat</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-e1a9488d-28ed-4051-9f90-73f9d891f003' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e1a9488d-28ed-4051-9f90-73f9d891f003' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>model</span>: 13</li><li><span class='xr-has-index'>time</span>: 45</li><li><span class='xr-has-index'>lat</span>: 691</li><li><span class='xr-has-index'>lon</span>: 886</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b95a6149-3845-4467-a9fe-4db7fa5730a5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b95a6149-3845-4467-a9fe-4db7fa5730a5' class='xr-section-summary' >Coordinates: <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-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'>-44.5 -44.45 -44.4 ... -10.05 -10.0</div><input id='attrs-120f84ff-cc82-4728-918a-68062c86eb74' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-120f84ff-cc82-4728-918a-68062c86eb74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56c5b128-e4eb-43e4-b32f-586a60a3d241' class='xr-var-data-in' type='checkbox'><label for='data-56c5b128-e4eb-43e4-b32f-586a60a3d241' 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>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-44.5 , -44.45, -44.4 , ..., -10.1 , -10.05, -10.  ])</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'>112.0 112.0 112.1 ... 156.2 156.2</div><input id='attrs-f62740f3-93af-439f-8fdc-b6186aa99e31' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f62740f3-93af-439f-8fdc-b6186aa99e31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95341cbc-0e31-4667-b619-6a07c731a37c' class='xr-var-data-in' type='checkbox'><label for='data-95341cbc-0e31-4667-b619-6a07c731a37c' 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>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([112.  , 112.05, 112.1 , ..., 156.15, 156.2 , 156.25])</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'>2004-01-01 ... 2048-01-01</div><input id='attrs-152a1a5e-4f2b-460a-ba0e-63f2edf2073d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-152a1a5e-4f2b-460a-ba0e-63f2edf2073d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5749c280-1f28-4b81-9638-0d8704776034' class='xr-var-data-in' type='checkbox'><label for='data-5749c280-1f28-4b81-9638-0d8704776034' 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'><pre>array([&#x27;2004-01-01T00:00:00.000000000&#x27;, &#x27;2005-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2006-01-01T00:00:00.000000000&#x27;, &#x27;2007-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2008-01-01T00:00:00.000000000&#x27;, &#x27;2009-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2010-01-01T00:00:00.000000000&#x27;, &#x27;2011-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2012-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2014-01-01T00:00:00.000000000&#x27;, &#x27;2015-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2016-01-01T00:00:00.000000000&#x27;, &#x27;2017-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2019-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-01-01T00:00:00.000000000&#x27;, &#x27;2021-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2022-01-01T00:00:00.000000000&#x27;, &#x27;2023-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2024-01-01T00:00:00.000000000&#x27;, &#x27;2025-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2026-01-01T00:00:00.000000000&#x27;, &#x27;2027-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2028-01-01T00:00:00.000000000&#x27;, &#x27;2029-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2030-01-01T00:00:00.000000000&#x27;, &#x27;2031-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2032-01-01T00:00:00.000000000&#x27;, &#x27;2033-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2034-01-01T00:00:00.000000000&#x27;, &#x27;2035-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2036-01-01T00:00:00.000000000&#x27;, &#x27;2037-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2038-01-01T00:00:00.000000000&#x27;, &#x27;2039-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2040-01-01T00:00:00.000000000&#x27;, &#x27;2041-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2042-01-01T00:00:00.000000000&#x27;, &#x27;2043-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2044-01-01T00:00:00.000000000&#x27;, &#x27;2045-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2046-01-01T00:00:00.000000000&#x27;, &#x27;2047-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2048-01-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-43d98d41-5e5a-4f66-9e43-ce898d9dd6cf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-43d98d41-5e5a-4f66-9e43-ce898d9dd6cf' 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>TXx</span></div><div class='xr-var-dims'>(model, time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan ... 31.63 31.64 31.64</div><input id='attrs-de0df4a9-be52-4bd3-a9ad-cf824b69fca3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de0df4a9-be52-4bd3-a9ad-cf824b69fca3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63cf7ea7-d5e2-4c98-b525-db5831cbad33' class='xr-var-data-in' type='checkbox'><label for='data-63cf7ea7-d5e2-4c98-b525-db5831cbad33' 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>annual maximum daily maximum temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>cell_methods :</span></dt><dd>time: maximum (interval: 1Y)</dd></dl></div><div class='xr-var-data'><pre>array([[[[       nan,        nan,        nan, ...,        nan,\n",
-       "                 nan,        nan],\n",
-       "         [       nan,        nan,        nan, ...,        nan,\n",
-       "                 nan,        nan],\n",
-       "         [       nan,        nan,        nan, ...,        nan,\n",
-       "                 nan,        nan],\n",
-       "         ...,\n",
-       "         [       nan,        nan,        nan, ...,        nan,\n",
-       "                 nan,        nan],\n",
-       "         [       nan,        nan,        nan, ...,        nan,\n",
-       "                 nan,        nan],\n",
-       "         [       nan,        nan,        nan, ...,        nan,\n",
-       "                 nan,        nan]],\n",
+       "    tmaxscr  (time, lat, lon) float32 10GB 296.5 296.5 297.7 ... 303.9 303.8\n",
+       "Attributes: (12/31)\n",
+       "    Conventions:              CF-1.7\n",
+       "    title:                    Queensland future climate projections derived f...\n",
+       "    institution:              Queensland Department of Environment and Science\n",
+       "    references:               The Long Paddock\n",
+       "    comment:                  Data produced for the LongPaddock Queensland Fu...\n",
+       "    contact:                  longpaddock@qld.gov.au\n",
+       "    ...                       ...\n",
+       "    rlat0:                    -20.0\n",
+       "    schmidt:                  0.19\n",
+       "    date_created:             2020-01-20T18:14:26+10:00\n",
+       "    date_produced:            2016-04-15\n",
+       "    date_metadata_modified:   2020-01-20\n",
+       "    history:                  2016-04-15: Interpolated from the original cubi...</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-214119b6-c9f4-4a3b-bf23-670b342f7578' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-214119b6-c9f4-4a3b-bf23-670b342f7578' 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>: 43800</li><li><span class='xr-has-index'>lat</span>: 226</li><li><span class='xr-has-index'>lon</span>: 261</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d0237cc2-e478-4f17-a5cb-4efade3e8ccb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d0237cc2-e478-4f17-a5cb-4efade3e8ccb' class='xr-section-summary' >Coordinates: <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-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'>-32.0 -31.9 -31.8 ... -9.6 -9.5</div><input id='attrs-2315ef68-f5f5-42b9-9d8a-9d9adcf29719' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2315ef68-f5f5-42b9-9d8a-9d9adcf29719' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9cc8e134-4f4d-475d-b6a2-af5ac9aee027' class='xr-var-data-in' type='checkbox'><label for='data-9cc8e134-4f4d-475d-b6a2-af5ac9aee027' 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>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-32. , -31.9, -31.8, ...,  -9.7,  -9.6,  -9.5])</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'>132.0 132.1 132.2 ... 157.9 158.0</div><input id='attrs-cbc71407-6fc2-496a-bd1e-85ca160ceb5a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cbc71407-6fc2-496a-bd1e-85ca160ceb5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ac07c58-ab7b-46ac-ba4f-32d3e02f3797' class='xr-var-data-in' type='checkbox'><label for='data-0ac07c58-ab7b-46ac-ba4f-32d3e02f3797' 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>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([132. , 132.1, 132.2, ..., 157.8, 157.9, 158. ])</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'>1980-01-01T15:00:00 ... 2099-12-...</div><input id='attrs-da09eb1c-17fc-4331-a86f-a0564a4faae8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da09eb1c-17fc-4331-a86f-a0564a4faae8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92bd0595-eb77-4add-bf50-127efcfe84ce' class='xr-var-data-in' type='checkbox'><label for='data-92bd0595-eb77-4add-bf50-127efcfe84ce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-01-01T15:00:00.000000000&#x27;, &#x27;1980-01-02T15:00:00.000000000&#x27;,\n",
+       "       &#x27;1980-01-03T15:00:00.000000000&#x27;, ..., &#x27;2099-12-29T15:00:00.000000000&#x27;,\n",
+       "       &#x27;2099-12-30T15:00:00.000000000&#x27;, &#x27;2099-12-31T15:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dfe40650-8ebd-4c0c-9322-c8203b0e2882' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dfe40650-8ebd-4c0c-9322-c8203b0e2882' 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>tmaxscr</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'>296.5 296.5 297.7 ... 303.9 303.8</div><input id='attrs-d0e2bf05-0f7c-4417-8ed6-10ce78d5387a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d0e2bf05-0f7c-4417-8ed6-10ce78d5387a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23e72076-66b4-4d7a-aed0-5ffe4c76fa74' class='xr-var-data-in' type='checkbox'><label for='data-23e72076-66b4-4d7a-aed0-5ffe4c76fa74' 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>Maximum screen temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><pre>array([[[296.495  , 296.495  , 297.6995 , ..., 296.20163, 296.201  ,\n",
+       "         296.18832],\n",
+       "        [297.18292, 297.69257, 299.8666 , ..., 296.22473, 296.22125,\n",
+       "         296.20654],\n",
+       "        [300.7233 , 300.50687, 300.77   , ..., 296.21326, 296.2043 ,\n",
+       "         296.18796],\n",
+       "        ...,\n",
+       "        [301.83707, 301.83   , 301.83707, ..., 302.75876, 302.73044,\n",
+       "         302.71716],\n",
+       "        [301.866  , 301.8082 , 301.81705, ..., 302.76062, 302.73022,\n",
+       "         302.70657],\n",
+       "        [301.8507 , 301.7903 , 301.79645, ..., 302.78326, 302.73773,\n",
+       "         302.69806]],\n",
        "\n",
-       "        [[       nan,        nan,        nan, ...,        nan,\n",
-       "                 nan,        nan],\n",
-       "         [       nan,        nan,        nan, ...,        nan,\n",
-       "                 nan,        nan],\n",
-       "         [       nan,        nan,        nan, ...,        nan,\n",
-       "                 nan,        nan],\n",
+       "       [[294.63998, 294.63998, 298.13535, ..., 296.00012, 295.99203,\n",
+       "         295.97873],\n",
+       "        [296.09506, 298.4952 , 305.5681 , ..., 296.0175 , 296.0068 ,\n",
+       "         295.9937 ],\n",
+       "        [306.3986 , 307.68228, 309.61   , ..., 296.03198, 296.01883,\n",
+       "         296.00757],\n",
        "...\n",
-       "         [32.42604  , 32.418995 , 32.411953 , ..., 32.634235 ,\n",
-       "          32.63676  , 32.640102 ],\n",
-       "         [32.43136  , 32.42508  , 32.418804 , ..., 32.637756 ,\n",
-       "          32.64146  , 32.646183 ],\n",
-       "         [32.43643  , 32.430813 , 32.425194 , ..., 32.64127  ,\n",
-       "          32.64622  , 32.652397 ]],\n",
+       "        [303.34576, 303.43768, 303.51788, ..., 303.7633 , 303.76035,\n",
+       "         303.77393],\n",
+       "        [303.42108, 303.50974, 303.57114, ..., 303.7771 , 303.76892,\n",
+       "         303.7846 ],\n",
+       "        [303.46082, 303.52963, 303.56357, ..., 303.77234, 303.77457,\n",
+       "         303.78488]],\n",
        "\n",
-       "        [[13.805057 , 13.812392 , 13.81971  , ..., 17.704859 ,\n",
-       "          17.605011 , 17.51389  ],\n",
-       "         [13.847793 , 13.849306 , 13.850808 , ..., 17.635239 ,\n",
-       "          17.537561 , 17.460068 ],\n",
-       "         [13.872973 , 13.873719 , 13.8744545, ..., 17.606482 ,\n",
-       "          17.516771 , 17.468271 ],\n",
-       "         ...,\n",
-       "         [32.036495 , 32.033604 , 32.030712 , ..., 31.677088 ,\n",
-       "          31.683363 , 31.68917  ],\n",
-       "         [32.043385 , 32.03845  , 32.033512 , ..., 31.652983 ,\n",
-       "          31.659399 , 31.665565 ],\n",
-       "         [32.049248 , 32.042545 , 32.035843 , ..., 31.630966 ,\n",
-       "          31.637596 , 31.644121 ]]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8554f8ba-4a80-4e13-b8ae-8c3ddb8c91b2' class='xr-section-summary-in' type='checkbox'  ><label for='section-8554f8ba-4a80-4e13-b8ae-8c3ddb8c91b2' 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-edff7e69-2c57-4a16-8760-55439628540b' class='xr-index-data-in' type='checkbox'/><label for='index-edff7e69-2c57-4a16-8760-55439628540b' 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([ -44.5, -44.45,  -44.4, -44.35,  -44.3, -44.25,  -44.2, -44.15,  -44.1,\n",
-       "       -44.05,\n",
+       "       [[293.07   , 293.07   , 294.7339 , ..., 298.55847, 298.55313,\n",
+       "         298.545  ],\n",
+       "        [293.61127, 295.29037, 298.9984 , ..., 298.56287, 298.54913,\n",
+       "         298.5346 ],\n",
+       "        [299.2349 , 300.80988, 301.62296, ..., 298.56302, 298.54553,\n",
+       "         298.52798],\n",
+       "        ...,\n",
+       "        [304.00717, 303.909  , 303.80823, ..., 303.84888, 303.80252,\n",
+       "         303.79697],\n",
+       "        [303.93155, 303.91095, 303.88   , ..., 303.89355, 303.82388,\n",
+       "         303.761  ],\n",
+       "        [303.81918, 303.82623, 303.83588, ..., 303.95392, 303.8948 ,\n",
+       "         303.84088]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-216bbb42-11f1-446e-b042-65e30a02b58f' class='xr-section-summary-in' type='checkbox'  ><label for='section-216bbb42-11f1-446e-b042-65e30a02b58f' 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-7e153428-e949-46be-88b1-8c5402852d44' class='xr-index-data-in' type='checkbox'/><label for='index-7e153428-e949-46be-88b1-8c5402852d44' 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([              -32.0,               -31.9,               -31.8,\n",
+       "                     -31.7,               -31.6,               -31.5,\n",
+       "                     -31.4,               -31.3,               -31.2,\n",
+       "                     -31.1,\n",
        "       ...\n",
-       "       -10.45,  -10.4, -10.35,  -10.3, -10.25,  -10.2, -10.15,  -10.1, -10.05,\n",
-       "        -10.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=691))</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-79878cc6-730d-46f3-9a5c-da0eea41630c' class='xr-index-data-in' type='checkbox'/><label for='index-79878cc6-730d-46f3-9a5c-da0eea41630c' 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([ 112.0, 112.05,  112.1, 112.15,  112.2, 112.25,  112.3, 112.35,  112.4,\n",
-       "       112.45,\n",
+       "       -10.399999999999999, -10.299999999999997,               -10.2,\n",
+       "       -10.099999999999998,               -10.0,  -9.899999999999999,\n",
+       "        -9.799999999999997,                -9.7,  -9.599999999999998,\n",
+       "                      -9.5],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=226))</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-7faad7ed-7519-4739-9f7a-22e430751891' class='xr-index-data-in' type='checkbox'/><label for='index-7faad7ed-7519-4739-9f7a-22e430751891' 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([132.0, 132.1, 132.2, 132.3, 132.4, 132.5, 132.6, 132.7, 132.8, 132.9,\n",
        "       ...\n",
-       "        155.8, 155.85,  155.9, 155.95,  156.0, 156.05,  156.1, 156.15,  156.2,\n",
-       "       156.25],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=886))</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-2ab37ae4-2637-4b39-b1f5-c2174071c3df' class='xr-index-data-in' type='checkbox'/><label for='index-2ab37ae4-2637-4b39-b1f5-c2174071c3df' 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;2004-01-01&#x27;, &#x27;2005-01-01&#x27;, &#x27;2006-01-01&#x27;, &#x27;2007-01-01&#x27;,\n",
-       "               &#x27;2008-01-01&#x27;, &#x27;2009-01-01&#x27;, &#x27;2010-01-01&#x27;, &#x27;2011-01-01&#x27;,\n",
-       "               &#x27;2012-01-01&#x27;, &#x27;2013-01-01&#x27;, &#x27;2014-01-01&#x27;, &#x27;2015-01-01&#x27;,\n",
-       "               &#x27;2016-01-01&#x27;, &#x27;2017-01-01&#x27;, &#x27;2018-01-01&#x27;, &#x27;2019-01-01&#x27;,\n",
-       "               &#x27;2020-01-01&#x27;, &#x27;2021-01-01&#x27;, &#x27;2022-01-01&#x27;, &#x27;2023-01-01&#x27;,\n",
-       "               &#x27;2024-01-01&#x27;, &#x27;2025-01-01&#x27;, &#x27;2026-01-01&#x27;, &#x27;2027-01-01&#x27;,\n",
-       "               &#x27;2028-01-01&#x27;, &#x27;2029-01-01&#x27;, &#x27;2030-01-01&#x27;, &#x27;2031-01-01&#x27;,\n",
-       "               &#x27;2032-01-01&#x27;, &#x27;2033-01-01&#x27;, &#x27;2034-01-01&#x27;, &#x27;2035-01-01&#x27;,\n",
-       "               &#x27;2036-01-01&#x27;, &#x27;2037-01-01&#x27;, &#x27;2038-01-01&#x27;, &#x27;2039-01-01&#x27;,\n",
-       "               &#x27;2040-01-01&#x27;, &#x27;2041-01-01&#x27;, &#x27;2042-01-01&#x27;, &#x27;2043-01-01&#x27;,\n",
-       "               &#x27;2044-01-01&#x27;, &#x27;2045-01-01&#x27;, &#x27;2046-01-01&#x27;, &#x27;2047-01-01&#x27;,\n",
-       "               &#x27;2048-01-01&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;YS-JAN&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-13247b9d-1cd2-4031-a3dd-33ccde3c8957' class='xr-section-summary-in' type='checkbox'  checked><label for='section-13247b9d-1cd2-4031-a3dd-33ccde3c8957' 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>pathway :</span></dt><dd>ssp370</dd><dt><span>GWL :</span></dt><dd>GWL 1.2 degC</dd><dt><span>bias_correction_method :</span></dt><dd>raw</dd><dt><span>contact :</span></dt><dd>Mitchell Black (mitchell.black@bom.gov.au)</dd><dt><span>code :</span></dt><dd>https://github.com/AusClimateService/hazards-heat</dd></dl></div></li></ul></div></div>"
+       "       157.1, 157.2, 157.3, 157.4, 157.5, 157.6, 157.7, 157.8, 157.9, 158.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=261))</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-eacd21f2-6869-4d53-9fab-d0f14744ebe5' class='xr-index-data-in' type='checkbox'/><label for='index-eacd21f2-6869-4d53-9fab-d0f14744ebe5' 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;1980-01-01 15:00:00&#x27;, &#x27;1980-01-02 15:00:00&#x27;,\n",
+       "               &#x27;1980-01-03 15:00:00&#x27;, &#x27;1980-01-04 15:00:00&#x27;,\n",
+       "               &#x27;1980-01-05 15:00:00&#x27;, &#x27;1980-01-06 15:00:00&#x27;,\n",
+       "               &#x27;1980-01-07 15:00:00&#x27;, &#x27;1980-01-08 15:00:00&#x27;,\n",
+       "               &#x27;1980-01-09 15:00:00&#x27;, &#x27;1980-01-10 15:00:00&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2099-12-22 15:00:00&#x27;, &#x27;2099-12-23 15:00:00&#x27;,\n",
+       "               &#x27;2099-12-24 15:00:00&#x27;, &#x27;2099-12-25 15:00:00&#x27;,\n",
+       "               &#x27;2099-12-26 15:00:00&#x27;, &#x27;2099-12-27 15:00:00&#x27;,\n",
+       "               &#x27;2099-12-28 15:00:00&#x27;, &#x27;2099-12-29 15:00:00&#x27;,\n",
+       "               &#x27;2099-12-30 15:00:00&#x27;, &#x27;2099-12-31 15:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=43800, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-39db0355-6b22-49f9-9a5c-e635d52e63cf' class='xr-section-summary-in' type='checkbox'  ><label for='section-39db0355-6b22-49f9-9a5c-e635d52e63cf' class='xr-section-summary' >Attributes: <span>(31)</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-1.7</dd><dt><span>title :</span></dt><dd>Queensland future climate projections derived from CMIP5 data downscaled using CCAM on a 10km grid</dd><dt><span>institution :</span></dt><dd>Queensland Department of Environment and Science</dd><dt><span>references :</span></dt><dd>The Long Paddock</dd><dt><span>comment :</span></dt><dd>Data produced for the LongPaddock Queensland Future Climate Dashboard http://longpaddock.qld.gov.au</dd><dt><span>contact :</span></dt><dd>longpaddock@qld.gov.au</dd><dt><span>domain :</span></dt><dd>QLD-10</dd><dt><span>geospatial_lon_min :</span></dt><dd>132.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>158.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-32.0</dd><dt><span>geospatial_lat_max :</span></dt><dd>-9.5</dd><dt><span>experiment :</span></dt><dd>Climate change run using CCAM 0.1 deg stretched grid nudged by 1-D spectral filter using 6-hourly data from CCAM 0.5 deg global regular grid</dd><dt><span>driving_model_id :</span></dt><dd>ACCESS1-0Q</dd><dt><span>driving_experiment_id :</span></dt><dd>rcp45</dd><dt><span>driving_experiment :</span></dt><dd>ACCESS1-0Q; rcp45; r2i1p1</dd><dt><span>experiment_id :</span></dt><dd>rcp45</dd><dt><span>intermediate_model_id :</span></dt><dd>CSIRO-CCAM-r1312M</dd><dt><span>intermediate_experiment :</span></dt><dd>CSIRO-CCAM-r1312M 0.5 deg; rcp45; r1i1p1</dd><dt><span>source :</span></dt><dd>CSIRO Conformal-Cubic Atmospheric Model (CCAM) version r2671M</dd><dt><span>model_id :</span></dt><dd>CSIRO-CCAM-r2671M</dd><dt><span>parent_experiment_id :</span></dt><dd>historical</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>il :</span></dt><dd>192</dd><dt><span>kl :</span></dt><dd>27</dd><dt><span>rlong0 :</span></dt><dd>145.0</dd><dt><span>rlat0 :</span></dt><dd>-20.0</dd><dt><span>schmidt :</span></dt><dd>0.19</dd><dt><span>date_created :</span></dt><dd>2020-01-20T18:14:26+10:00</dd><dt><span>date_produced :</span></dt><dd>2016-04-15</dd><dt><span>date_metadata_modified :</span></dt><dd>2020-01-20</dd><dt><span>history :</span></dt><dd>2016-04-15: Interpolated from the original cubic conformal grid to regular lat/lon grid using pcc2hist</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.Dataset> Size: 1GB\n",
-       "Dimensions:  (model: 13, time: 45, lat: 691, lon: 886)\n",
+       "<xarray.Dataset> Size: 10GB\n",
+       "Dimensions:  (time: 43800, lat: 226, lon: 261)\n",
        "Coordinates:\n",
-       "  * lat      (lat) float64 6kB -44.5 -44.45 -44.4 -44.35 ... -10.1 -10.05 -10.0\n",
-       "  * lon      (lon) float64 7kB 112.0 112.0 112.1 112.2 ... 156.2 156.2 156.2\n",
-       "  * time     (time) datetime64[ns] 360B 2004-01-01 2005-01-01 ... 2048-01-01\n",
-       "Dimensions without coordinates: model\n",
+       "  * lat      (lat) float64 2kB -32.0 -31.9 -31.8 -31.7 ... -9.8 -9.7 -9.6 -9.5\n",
+       "  * lon      (lon) float64 2kB 132.0 132.1 132.2 132.3 ... 157.8 157.9 158.0\n",
+       "  * time     (time) datetime64[ns] 350kB 1980-01-01T15:00:00 ... 2099-12-31T1...\n",
        "Data variables:\n",
-       "    TXx      (model, time, lat, lon) float32 1GB nan nan nan ... 31.64 31.64\n",
-       "Attributes:\n",
-       "    pathway:                 ssp370\n",
-       "    GWL:                     GWL 1.2 degC\n",
-       "    bias_correction_method:  raw\n",
-       "    contact:                 Mitchell Black (mitchell.black@bom.gov.au)\n",
-       "    code:                    https://github.com/AusClimateService/hazards-heat"
+       "    tmaxscr  (time, lat, lon) float32 10GB 296.5 296.5 297.7 ... 303.9 303.8\n",
+       "Attributes: (12/31)\n",
+       "    Conventions:              CF-1.7\n",
+       "    title:                    Queensland future climate projections derived f...\n",
+       "    institution:              Queensland Department of Environment and Science\n",
+       "    references:               The Long Paddock\n",
+       "    comment:                  Data produced for the LongPaddock Queensland Fu...\n",
+       "    contact:                  longpaddock@qld.gov.au\n",
+       "    ...                       ...\n",
+       "    rlat0:                    -20.0\n",
+       "    schmidt:                  0.19\n",
+       "    date_created:             2020-01-20T18:14:26+10:00\n",
+       "    date_produced:            2016-04-15\n",
+       "    date_metadata_modified:   2020-01-20\n",
+       "    history:                  2016-04-15: Interpolated from the original cubi..."
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# open the list of files into one xarray dataset\n",
-    "def _preprocess(ds, var, gwl):\n",
-    "    # mask where precipitation data is unreliable\n",
-    "    return ds.convert_calendar(\"standard\")\n",
-    "\n",
-    "ds = xr.open_mfdataset(filelist, \n",
-    "                       preprocess= lambda ds: _preprocess(ds, var, gwl),\n",
-    "                       use_cftime=True,\n",
-    "                       combine=\"nested\",\n",
-    "                       concat_dim=\"model\",\n",
-    "                       combine_attrs= \"drop_conflicts\").load()\n",
+    "# this is a very large dataset which cannot be computed without \"chunks\". The kernel will die.\n",
+    "file = \"/g/data/rr3/downscaling/CMIP5_Downscaled_CCAM_QLD10/RCP45/daily/MaximumTemperature/tmaxscr.daily.ccam10_ACCESS1-0Q_rcp45.nc\"\n",
+    "ds = xr.open_dataset(file).convert_calendar(\"standard\")\n",
     "ds"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "f9c99f43-fc65-4eec-a3fa-97d1b0f62ea6",
+   "execution_count": 7,
+   "id": "a6e4a3be-c29f-4d36-a882-90f8205e7784",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 9.3 s, sys: 2.78 s, total: 12.1 s\n",
-      "Wall time: 12.2 s\n"
+      "CPU times: user 13.1 s, sys: 351 ms, total: 13.4 s\n",
+      "Wall time: 13.5 s\n"
      ]
-    },
-    {
-     "data": {
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# # define the mask for the diffrerent grid\n",
+    "# # can just use internal mask calculation in acs_regional_stats\n",
+    "regions= get_regions([\"ncra_regions\", \"australia\"])\n",
+    "\n",
+    "# mask = regions.mask_3D_frac_approx(ds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6499b1f1-fbcd-437e-9255-d621c8e88d53",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "!warning very slow! Calculating fractional mask every time is very slow.         \n",
+      "Please consider calculating `mask = regions.mask_3D_frac_approx(ds)` before function.\n",
+      "CPU times: user 42.1 s, sys: 16.8 s, total: 58.9 s\n",
+      "Wall time: 59 s\n"
+     ]
+    },
+    {
+     "data": {
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+       "}\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; Size: 2MB\n",
+       "Dimensions:       (time: 43800, region: 6)\n",
+       "Coordinates:\n",
+       "  * time          (time) datetime64[ns] 350kB 1980-01-01T15:00:00 ... 2099-12...\n",
+       "  * region        (region) int64 48B 2 4 6 7 8 9\n",
+       "    abbrevs       (region) &lt;U8 192B &#x27;NSW&#x27; &#x27;SA&#x27; &#x27;NT&#x27; &#x27;QLDNorth&#x27; &#x27;QLDSouth&#x27; &#x27;AUS&#x27;\n",
+       "    names         (region) &lt;U48 1kB &#x27;New South Wales and Australian Capital T...\n",
+       "Data variables:\n",
+       "    tmaxscr_mean  (time, region) float64 2MB 307.2 313.2 310.1 ... 315.9 314.2\n",
+       "Attributes:\n",
+       "    long_name:     Maximum screen temperature\n",
+       "    units:         K\n",
+       "    cell_methods:  time: mean</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-538fa09f-cc38-4e8e-8f38-a3849113b031' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-538fa09f-cc38-4e8e-8f38-a3849113b031' 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>: 43800</li><li><span class='xr-has-index'>region</span>: 6</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d450e53f-cfc9-4886-9108-d8e5e73649bf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d450e53f-cfc9-4886-9108-d8e5e73649bf' 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'>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'>1980-01-01T15:00:00 ... 2099-12-...</div><input id='attrs-466c1494-1acd-43ef-befa-ca16d1721fc5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-466c1494-1acd-43ef-befa-ca16d1721fc5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a24d528d-5494-4e28-b429-5aad79dad6ed' class='xr-var-data-in' type='checkbox'><label for='data-a24d528d-5494-4e28-b429-5aad79dad6ed' 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'><pre>array([&#x27;1980-01-01T15:00:00.000000000&#x27;, &#x27;1980-01-02T15:00:00.000000000&#x27;,\n",
+       "       &#x27;1980-01-03T15:00:00.000000000&#x27;, ..., &#x27;2099-12-29T15:00:00.000000000&#x27;,\n",
+       "       &#x27;2099-12-30T15:00:00.000000000&#x27;, &#x27;2099-12-31T15:00:00.000000000&#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'>region</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 4 6 7 8 9</div><input id='attrs-8c152194-538f-4f77-a886-b9fb7f4afd0e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8c152194-538f-4f77-a886-b9fb7f4afd0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc59b402-4a7d-484a-9282-bf8432d14ae7' class='xr-var-data-in' type='checkbox'><label for='data-bc59b402-4a7d-484a-9282-bf8432d14ae7' 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'><pre>array([2, 4, 6, 7, 8, 9])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abbrevs</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;NSW&#x27; &#x27;SA&#x27; ... &#x27;QLDSouth&#x27; &#x27;AUS&#x27;</div><input id='attrs-bf6f5a3f-77e8-4b9a-8b52-b6317760dc67' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bf6f5a3f-77e8-4b9a-8b52-b6317760dc67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb488179-4e83-4395-b9b7-2966befbb37d' class='xr-var-data-in' type='checkbox'><label for='data-bb488179-4e83-4395-b9b7-2966befbb37d' 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'><pre>array([&#x27;NSW&#x27;, &#x27;SA&#x27;, &#x27;NT&#x27;, &#x27;QLDNorth&#x27;, &#x27;QLDSouth&#x27;, &#x27;AUS&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>names</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>&lt;U48</div><div class='xr-var-preview xr-preview'>&#x27;New South Wales and Australian ...</div><input id='attrs-e9302a4a-96f1-4614-ae68-a703b60eceef' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e9302a4a-96f1-4614-ae68-a703b60eceef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8edd1a0-1886-4c7b-99b1-de879117ed7b' class='xr-var-data-in' type='checkbox'><label for='data-a8edd1a0-1886-4c7b-99b1-de879117ed7b' 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'><pre>array([&#x27;New South Wales and Australian Capital Territory&#x27;,\n",
+       "       &#x27;South Australia&#x27;, &#x27;Northern Territory&#x27;, &#x27;Queensland North&#x27;,\n",
+       "       &#x27;Queensland South&#x27;, &#x27;Australia&#x27;], dtype=&#x27;&lt;U48&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5e5ffced-6291-4c7e-98d8-e23ec2ecac8f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5e5ffced-6291-4c7e-98d8-e23ec2ecac8f' 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>tmaxscr_mean</span></div><div class='xr-var-dims'>(time, region)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>307.2 313.2 310.1 ... 315.9 314.2</div><input id='attrs-07b9177d-3efc-4e06-a2cc-8a7fcc9b1a7a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-07b9177d-3efc-4e06-a2cc-8a7fcc9b1a7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75cdc352-bf38-41c3-9792-616721b21af8' class='xr-var-data-in' type='checkbox'><label for='data-75cdc352-bf38-41c3-9792-616721b21af8' 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>Maximum screen temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><pre>array([[307.17308292, 313.18566073, 310.071908  , 304.82963394,\n",
+       "        308.29339746, 308.6596933 ],\n",
+       "       [307.18999672, 314.67849254, 310.77307459, 303.75367741,\n",
+       "        308.84670484, 308.99228385],\n",
+       "       [307.44474678, 315.06130648, 309.16306089, 304.5587817 ,\n",
+       "        309.32418275, 308.98109565],\n",
+       "       ...,\n",
+       "       [312.41811842, 319.04223078, 313.42749711, 309.515258  ,\n",
+       "        314.37465153, 313.63399966],\n",
+       "       [312.51893937, 317.28676442, 314.88036988, 310.20431457,\n",
+       "        314.95961976, 314.02220443],\n",
+       "       [310.7462113 , 316.81061168, 315.65826141, 310.3931671 ,\n",
+       "        315.944916  , 314.23597608]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cd6bfe13-549d-457e-8314-b991aa23aa86' class='xr-section-summary-in' type='checkbox'  ><label for='section-cd6bfe13-549d-457e-8314-b991aa23aa86' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3c856221-8a26-4bc8-92e8-5167b720fe18' class='xr-index-data-in' type='checkbox'/><label for='index-3c856221-8a26-4bc8-92e8-5167b720fe18' 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;1980-01-01 15:00:00&#x27;, &#x27;1980-01-02 15:00:00&#x27;,\n",
+       "               &#x27;1980-01-03 15:00:00&#x27;, &#x27;1980-01-04 15:00:00&#x27;,\n",
+       "               &#x27;1980-01-05 15:00:00&#x27;, &#x27;1980-01-06 15:00:00&#x27;,\n",
+       "               &#x27;1980-01-07 15:00:00&#x27;, &#x27;1980-01-08 15:00:00&#x27;,\n",
+       "               &#x27;1980-01-09 15:00:00&#x27;, &#x27;1980-01-10 15:00:00&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2099-12-22 15:00:00&#x27;, &#x27;2099-12-23 15:00:00&#x27;,\n",
+       "               &#x27;2099-12-24 15:00:00&#x27;, &#x27;2099-12-25 15:00:00&#x27;,\n",
+       "               &#x27;2099-12-26 15:00:00&#x27;, &#x27;2099-12-27 15:00:00&#x27;,\n",
+       "               &#x27;2099-12-28 15:00:00&#x27;, &#x27;2099-12-29 15:00:00&#x27;,\n",
+       "               &#x27;2099-12-30 15:00:00&#x27;, &#x27;2099-12-31 15:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=43800, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>region</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e634c6c0-994f-4aa0-a195-1b262adc2148' class='xr-index-data-in' type='checkbox'/><label for='index-e634c6c0-994f-4aa0-a195-1b262adc2148' 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([2, 4, 6, 7, 8, 9], dtype=&#x27;int64&#x27;, name=&#x27;region&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6c613997-680c-4dcc-9ad9-ba4f9e2d3f45' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6c613997-680c-4dcc-9ad9-ba4f9e2d3f45' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Maximum screen temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 2MB\n",
+       "Dimensions:       (time: 43800, region: 6)\n",
+       "Coordinates:\n",
+       "  * time          (time) datetime64[ns] 350kB 1980-01-01T15:00:00 ... 2099-12...\n",
+       "  * region        (region) int64 48B 2 4 6 7 8 9\n",
+       "    abbrevs       (region) <U8 192B 'NSW' 'SA' 'NT' 'QLDNorth' 'QLDSouth' 'AUS'\n",
+       "    names         (region) <U48 1kB 'New South Wales and Australian Capital T...\n",
+       "Data variables:\n",
+       "    tmaxscr_mean  (time, region) float64 2MB 307.2 313.2 310.1 ... 315.9 314.2\n",
+       "Attributes:\n",
+       "    long_name:     Maximum screen temperature\n",
+       "    units:         K\n",
+       "    cell_methods:  time: mean"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "var=\"tmaxscr\"\n",
+    "\n",
+    "da_summary = acs_regional_stats(ds=ds,\n",
+    "                                var=var,\n",
+    "                                regions = regions,\n",
+    "                                mask = \"fractional\", \n",
+    "                                dims = (\"lat\", \"lon\",), \n",
+    "                                how = [\"mean\"],\n",
+    "                                chunks=365,\n",
+    "                                outfile=\"out.csv\")\n",
+    "da_summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "f69b286b-89e2-496a-a603-af94ad476833",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1553f5d86620>,\n",
+       " <matplotlib.lines.Line2D at 0x1553f5d86a40>,\n",
+       " <matplotlib.lines.Line2D at 0x1553f5d87340>,\n",
+       " <matplotlib.lines.Line2D at 0x1553f5d86770>,\n",
+       " <matplotlib.lines.Line2D at 0x1553f5d86d70>,\n",
+       " <matplotlib.lines.Line2D at 0x1553f5d86ef0>]"
+      ]
+     },
+     "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": [
+    "da_summary[f\"{var}_mean\"].plot.line(x=\"time\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1e0631fb-55cf-47d1-b58c-e738b7b094e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='time', ylabel='tmaxscr_mean'>"
+      ]
+     },
+     "execution_count": 10,
+     "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": [
+    "# or, use seaborn to plot the range of values from multiple models\n",
+    "da= da_summary[f\"{var}_mean\"].sel(region=9).to_dataframe()\n",
+    "sns.lineplot(data=da, x=\"time\", y= f\"{var}_mean\",  errorbar=(\"pi\", 100) )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db408164-54ee-4608-b549-a23eb41f00c0",
+   "metadata": {},
+   "source": [
+    "# example for GWLs\n",
+    "use open_mfdataset to preprocess files to enable concatenation. \n",
+    "\n",
+    "apply acs_regional_stats on concatenated data with multiple models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "ec380109-886c-4ead-b282-16365b9ec236",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['/g/data/ia39/ncra/heat/data/TXx/raw/individual_models/GWL-timeseries/TXx_AGCD-05i_CNRM-ESM2-1_ssp370_r1i1p1f2_CSIRO_CCAM-v2203-SN_v1-r1_GWL12.nc',\n",
+       " '/g/data/ia39/ncra/heat/data/TXx/raw/individual_models/GWL-timeseries/TXx_AGCD-05i_ACCESS-ESM1-5_ssp370_r6i1p1f1_CSIRO_CCAM-v2203-SN_v1-r1_GWL12.nc',\n",
+       " '/g/data/ia39/ncra/heat/data/TXx/raw/individual_models/GWL-timeseries/TXx_AGCD-05i_EC-Earth3_ssp370_r1i1p1f1_BOM_BARPA-R_v1-r1_GWL12.nc',\n",
+       " '/g/data/ia39/ncra/heat/data/TXx/raw/individual_models/GWL-timeseries/TXx_AGCD-05i_ACCESS-ESM1-5_ssp370_r6i1p1f1_BOM_BARPA-R_v1-r1_GWL12.nc',\n",
+       " '/g/data/ia39/ncra/heat/data/TXx/raw/individual_models/GWL-timeseries/TXx_AGCD-05i_CMCC-ESM2_ssp370_r1i1p1f1_CSIRO_CCAM-v2203-SN_v1-r1_GWL12.nc',\n",
+       " '/g/data/ia39/ncra/heat/data/TXx/raw/individual_models/GWL-timeseries/TXx_AGCD-05i_EC-Earth3_ssp370_r1i1p1f1_CSIRO_CCAM-v2203-SN_v1-r1_GWL12.nc',\n",
+       " '/g/data/ia39/ncra/heat/data/TXx/raw/individual_models/GWL-timeseries/TXx_AGCD-05i_CESM2_ssp370_r11i1p1f1_BOM_BARPA-R_v1-r1_GWL12.nc',\n",
+       " '/g/data/ia39/ncra/heat/data/TXx/raw/individual_models/GWL-timeseries/TXx_AGCD-05i_ACCESS-CM2_ssp370_r4i1p1f1_CSIRO_CCAM-v2203-SN_v1-r1_GWL12.nc',\n",
+       " '/g/data/ia39/ncra/heat/data/TXx/raw/individual_models/GWL-timeseries/TXx_AGCD-05i_MPI-ESM1-2-HR_ssp370_r1i1p1f1_BOM_BARPA-R_v1-r1_GWL12.nc',\n",
+       " '/g/data/ia39/ncra/heat/data/TXx/raw/individual_models/GWL-timeseries/TXx_AGCD-05i_ACCESS-CM2_ssp370_r4i1p1f1_BOM_BARPA-R_v1-r1_GWL12.nc',\n",
+       " '/g/data/ia39/ncra/heat/data/TXx/raw/individual_models/GWL-timeseries/TXx_AGCD-05i_CMCC-ESM2_ssp370_r1i1p1f1_BOM_BARPA-R_v1-r1_GWL12.nc',\n",
+       " '/g/data/ia39/ncra/heat/data/TXx/raw/individual_models/GWL-timeseries/TXx_AGCD-05i_CESM2_ssp370_r11i1p1f1_CSIRO_CCAM-v2203-SN_v1-r1_GWL12.nc',\n",
+       " '/g/data/ia39/ncra/heat/data/TXx/raw/individual_models/GWL-timeseries/TXx_AGCD-05i_NorESM2-MM_ssp370_r1i1p1f1_BOM_BARPA-R_v1-r1_GWL12.nc']"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# choose a list of files to open\n",
+    "# this list of files will be combined to form a dataset with model, time, lat, and lon dimensions\n",
+    "# we will calculate regional averages and then calculate timeseries for a selected region for each model\n",
+    "\n",
+    "var = \"TXx\"\n",
+    "gwl=\"GWL12\"\n",
+    "path = f\"/g/data/ia39/ncra/heat/data/{var}/raw/individual_models/GWL-timeseries\"\n",
+    "\n",
+    "filelist = glob(f\"{path}/{var}_*_{gwl}.nc\")\n",
+    "filelist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "fc93a2e1-3db9-403c-a348-e1ccee72a242",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1.81 s, sys: 1.87 s, total: 3.68 s\n",
+      "Wall time: 1.95 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
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+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\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.Dataset&gt; Size: 1GB\n",
+       "Dimensions:  (model: 13, time: 45, lat: 691, lon: 886)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float64 6kB -44.5 -44.45 -44.4 -44.35 ... -10.1 -10.05 -10.0\n",
+       "  * lon      (lon) float64 7kB 112.0 112.0 112.1 112.2 ... 156.2 156.2 156.2\n",
+       "  * time     (time) datetime64[ns] 360B 2004-01-01 2005-01-01 ... 2048-01-01\n",
+       "Dimensions without coordinates: model\n",
+       "Data variables:\n",
+       "    TXx      (model, time, lat, lon) float32 1GB nan nan nan ... 31.64 31.64\n",
+       "Attributes:\n",
+       "    pathway:                 ssp370\n",
+       "    GWL:                     GWL 1.2 degC\n",
+       "    bias_correction_method:  raw\n",
+       "    contact:                 Mitchell Black (mitchell.black@bom.gov.au)\n",
+       "    code:                    https://github.com/AusClimateService/hazards-heat</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-4c4cb3a9-6e5f-4ae7-a42f-aa87875919f0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4c4cb3a9-6e5f-4ae7-a42f-aa87875919f0' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>model</span>: 13</li><li><span class='xr-has-index'>time</span>: 45</li><li><span class='xr-has-index'>lat</span>: 691</li><li><span class='xr-has-index'>lon</span>: 886</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b7b5d829-3c0a-4188-9533-0ce4d9a5ca57' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b7b5d829-3c0a-4188-9533-0ce4d9a5ca57' class='xr-section-summary' >Coordinates: <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-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'>-44.5 -44.45 -44.4 ... -10.05 -10.0</div><input id='attrs-658400b6-71d3-49ef-962d-6f59fcb8e244' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-658400b6-71d3-49ef-962d-6f59fcb8e244' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee61b924-d156-4cf7-8f87-4090b31643f7' class='xr-var-data-in' type='checkbox'><label for='data-ee61b924-d156-4cf7-8f87-4090b31643f7' 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>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-44.5 , -44.45, -44.4 , ..., -10.1 , -10.05, -10.  ])</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'>112.0 112.0 112.1 ... 156.2 156.2</div><input id='attrs-2178c4e3-2877-4be4-aee0-58f67ae6538f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2178c4e3-2877-4be4-aee0-58f67ae6538f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2c23b46-17dd-43ea-85a3-f63818c8c70e' class='xr-var-data-in' type='checkbox'><label for='data-e2c23b46-17dd-43ea-85a3-f63818c8c70e' 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>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([112.  , 112.05, 112.1 , ..., 156.15, 156.2 , 156.25])</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'>2004-01-01 ... 2048-01-01</div><input id='attrs-91d4cfbd-b822-43be-9bad-f030f7aaa3b5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-91d4cfbd-b822-43be-9bad-f030f7aaa3b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6e6c7ac-7da6-4904-8f9c-383c70ce1b23' class='xr-var-data-in' type='checkbox'><label for='data-f6e6c7ac-7da6-4904-8f9c-383c70ce1b23' 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'><pre>array([&#x27;2004-01-01T00:00:00.000000000&#x27;, &#x27;2005-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2006-01-01T00:00:00.000000000&#x27;, &#x27;2007-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2008-01-01T00:00:00.000000000&#x27;, &#x27;2009-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-01-01T00:00:00.000000000&#x27;, &#x27;2011-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-01-01T00:00:00.000000000&#x27;, &#x27;2015-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-01-01T00:00:00.000000000&#x27;, &#x27;2017-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2019-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-01T00:00:00.000000000&#x27;, &#x27;2021-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2022-01-01T00:00:00.000000000&#x27;, &#x27;2023-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-01T00:00:00.000000000&#x27;, &#x27;2025-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2026-01-01T00:00:00.000000000&#x27;, &#x27;2027-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2028-01-01T00:00:00.000000000&#x27;, &#x27;2029-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2030-01-01T00:00:00.000000000&#x27;, &#x27;2031-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2032-01-01T00:00:00.000000000&#x27;, &#x27;2033-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2034-01-01T00:00:00.000000000&#x27;, &#x27;2035-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2036-01-01T00:00:00.000000000&#x27;, &#x27;2037-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2038-01-01T00:00:00.000000000&#x27;, &#x27;2039-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2040-01-01T00:00:00.000000000&#x27;, &#x27;2041-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2042-01-01T00:00:00.000000000&#x27;, &#x27;2043-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2044-01-01T00:00:00.000000000&#x27;, &#x27;2045-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2046-01-01T00:00:00.000000000&#x27;, &#x27;2047-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2048-01-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7d182eea-0fe0-4e05-9a24-73acc986a2de' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7d182eea-0fe0-4e05-9a24-73acc986a2de' 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>TXx</span></div><div class='xr-var-dims'>(model, time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan ... 31.63 31.64 31.64</div><input id='attrs-6f9c64c1-9bc7-4145-9686-498b2175d74d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f9c64c1-9bc7-4145-9686-498b2175d74d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad494691-9c69-409c-b680-e3387d11dea5' class='xr-var-data-in' type='checkbox'><label for='data-ad494691-9c69-409c-b680-e3387d11dea5' 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>annual maximum daily maximum temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>cell_methods :</span></dt><dd>time: maximum (interval: 1Y)</dd></dl></div><div class='xr-var-data'><pre>array([[[[       nan,        nan,        nan, ...,        nan,\n",
+       "                 nan,        nan],\n",
+       "         [       nan,        nan,        nan, ...,        nan,\n",
+       "                 nan,        nan],\n",
+       "         [       nan,        nan,        nan, ...,        nan,\n",
+       "                 nan,        nan],\n",
+       "         ...,\n",
+       "         [       nan,        nan,        nan, ...,        nan,\n",
+       "                 nan,        nan],\n",
+       "         [       nan,        nan,        nan, ...,        nan,\n",
+       "                 nan,        nan],\n",
+       "         [       nan,        nan,        nan, ...,        nan,\n",
+       "                 nan,        nan]],\n",
+       "\n",
+       "        [[       nan,        nan,        nan, ...,        nan,\n",
+       "                 nan,        nan],\n",
+       "         [       nan,        nan,        nan, ...,        nan,\n",
+       "                 nan,        nan],\n",
+       "         [       nan,        nan,        nan, ...,        nan,\n",
+       "                 nan,        nan],\n",
+       "...\n",
+       "         [32.42604  , 32.418995 , 32.411953 , ..., 32.634235 ,\n",
+       "          32.63676  , 32.640102 ],\n",
+       "         [32.43136  , 32.42508  , 32.418804 , ..., 32.637756 ,\n",
+       "          32.64146  , 32.646183 ],\n",
+       "         [32.43643  , 32.430813 , 32.425194 , ..., 32.64127  ,\n",
+       "          32.64622  , 32.652397 ]],\n",
+       "\n",
+       "        [[13.805057 , 13.812392 , 13.81971  , ..., 17.704859 ,\n",
+       "          17.605011 , 17.51389  ],\n",
+       "         [13.847793 , 13.849306 , 13.850808 , ..., 17.635239 ,\n",
+       "          17.537561 , 17.460068 ],\n",
+       "         [13.872973 , 13.873719 , 13.8744545, ..., 17.606482 ,\n",
+       "          17.516771 , 17.468271 ],\n",
+       "         ...,\n",
+       "         [32.036495 , 32.033604 , 32.030712 , ..., 31.677088 ,\n",
+       "          31.683363 , 31.68917  ],\n",
+       "         [32.043385 , 32.03845  , 32.033512 , ..., 31.652983 ,\n",
+       "          31.659399 , 31.665565 ],\n",
+       "         [32.049248 , 32.042545 , 32.035843 , ..., 31.630966 ,\n",
+       "          31.637596 , 31.644121 ]]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c53007fc-3f33-4b57-aff2-b634f7e07bef' class='xr-section-summary-in' type='checkbox'  ><label for='section-c53007fc-3f33-4b57-aff2-b634f7e07bef' 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-33a6b6f7-e259-48a7-b6c1-bd2a37b65eba' class='xr-index-data-in' type='checkbox'/><label for='index-33a6b6f7-e259-48a7-b6c1-bd2a37b65eba' 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([ -44.5, -44.45,  -44.4, -44.35,  -44.3, -44.25,  -44.2, -44.15,  -44.1,\n",
+       "       -44.05,\n",
+       "       ...\n",
+       "       -10.45,  -10.4, -10.35,  -10.3, -10.25,  -10.2, -10.15,  -10.1, -10.05,\n",
+       "        -10.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=691))</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-76ef2267-4869-4934-a935-9a12ab68672c' class='xr-index-data-in' type='checkbox'/><label for='index-76ef2267-4869-4934-a935-9a12ab68672c' 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([ 112.0, 112.05,  112.1, 112.15,  112.2, 112.25,  112.3, 112.35,  112.4,\n",
+       "       112.45,\n",
+       "       ...\n",
+       "        155.8, 155.85,  155.9, 155.95,  156.0, 156.05,  156.1, 156.15,  156.2,\n",
+       "       156.25],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=886))</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-1555510f-5b74-4ed0-b98d-df1efe063874' class='xr-index-data-in' type='checkbox'/><label for='index-1555510f-5b74-4ed0-b98d-df1efe063874' 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;2004-01-01&#x27;, &#x27;2005-01-01&#x27;, &#x27;2006-01-01&#x27;, &#x27;2007-01-01&#x27;,\n",
+       "               &#x27;2008-01-01&#x27;, &#x27;2009-01-01&#x27;, &#x27;2010-01-01&#x27;, &#x27;2011-01-01&#x27;,\n",
+       "               &#x27;2012-01-01&#x27;, &#x27;2013-01-01&#x27;, &#x27;2014-01-01&#x27;, &#x27;2015-01-01&#x27;,\n",
+       "               &#x27;2016-01-01&#x27;, &#x27;2017-01-01&#x27;, &#x27;2018-01-01&#x27;, &#x27;2019-01-01&#x27;,\n",
+       "               &#x27;2020-01-01&#x27;, &#x27;2021-01-01&#x27;, &#x27;2022-01-01&#x27;, &#x27;2023-01-01&#x27;,\n",
+       "               &#x27;2024-01-01&#x27;, &#x27;2025-01-01&#x27;, &#x27;2026-01-01&#x27;, &#x27;2027-01-01&#x27;,\n",
+       "               &#x27;2028-01-01&#x27;, &#x27;2029-01-01&#x27;, &#x27;2030-01-01&#x27;, &#x27;2031-01-01&#x27;,\n",
+       "               &#x27;2032-01-01&#x27;, &#x27;2033-01-01&#x27;, &#x27;2034-01-01&#x27;, &#x27;2035-01-01&#x27;,\n",
+       "               &#x27;2036-01-01&#x27;, &#x27;2037-01-01&#x27;, &#x27;2038-01-01&#x27;, &#x27;2039-01-01&#x27;,\n",
+       "               &#x27;2040-01-01&#x27;, &#x27;2041-01-01&#x27;, &#x27;2042-01-01&#x27;, &#x27;2043-01-01&#x27;,\n",
+       "               &#x27;2044-01-01&#x27;, &#x27;2045-01-01&#x27;, &#x27;2046-01-01&#x27;, &#x27;2047-01-01&#x27;,\n",
+       "               &#x27;2048-01-01&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;YS-JAN&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d1930c0d-a98d-4ebd-b213-c2cc9adada31' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d1930c0d-a98d-4ebd-b213-c2cc9adada31' 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>pathway :</span></dt><dd>ssp370</dd><dt><span>GWL :</span></dt><dd>GWL 1.2 degC</dd><dt><span>bias_correction_method :</span></dt><dd>raw</dd><dt><span>contact :</span></dt><dd>Mitchell Black (mitchell.black@bom.gov.au)</dd><dt><span>code :</span></dt><dd>https://github.com/AusClimateService/hazards-heat</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 1GB\n",
+       "Dimensions:  (model: 13, time: 45, lat: 691, lon: 886)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float64 6kB -44.5 -44.45 -44.4 -44.35 ... -10.1 -10.05 -10.0\n",
+       "  * lon      (lon) float64 7kB 112.0 112.0 112.1 112.2 ... 156.2 156.2 156.2\n",
+       "  * time     (time) datetime64[ns] 360B 2004-01-01 2005-01-01 ... 2048-01-01\n",
+       "Dimensions without coordinates: model\n",
+       "Data variables:\n",
+       "    TXx      (model, time, lat, lon) float32 1GB nan nan nan ... 31.64 31.64\n",
+       "Attributes:\n",
+       "    pathway:                 ssp370\n",
+       "    GWL:                     GWL 1.2 degC\n",
+       "    bias_correction_method:  raw\n",
+       "    contact:                 Mitchell Black (mitchell.black@bom.gov.au)\n",
+       "    code:                    https://github.com/AusClimateService/hazards-heat"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# open the list of files into one xarray dataset\n",
+    "def _preprocess(ds):\n",
+    "    # mask where precipitation data is unreliable\n",
+    "    return ds.convert_calendar(\"standard\")\n",
+    "\n",
+    "ds = xr.open_mfdataset(filelist, \n",
+    "                       preprocess= lambda ds: _preprocess(ds),\n",
+    "                       use_cftime=True,\n",
+    "                       combine=\"nested\",\n",
+    "                       concat_dim=\"model\",\n",
+    "                       combine_attrs= \"drop_conflicts\").load()\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f9c99f43-fc65-4eec-a3fa-97d1b0f62ea6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 7.38 s, sys: 3.07 s, total: 10.5 s\n",
+      "Wall time: 10.4 s\n"
+     ]
+    },
+    {
+     "data": {
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+       "}\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.Dataset&gt; Size: 49kB\n",
+       "Dimensions:   (time: 45, region: 10, model: 13)\n",
+       "Coordinates:\n",
+       "  * time      (time) datetime64[ns] 360B 2004-01-01 2005-01-01 ... 2048-01-01\n",
+       "    abbrevs   (region) &lt;U8 320B &#x27;WANorth&#x27; &#x27;WASouth&#x27; &#x27;NSW&#x27; ... &#x27;QLDSouth&#x27; &#x27;AUS&#x27;\n",
+       "    names     (region) &lt;U48 2kB &#x27;Western Australia North&#x27; ... &#x27;Australia&#x27;\n",
+       "  * region    (region) int64 80B 0 1 2 3 4 5 6 7 8 9\n",
+       "Dimensions without coordinates: model\n",
+       "Data variables:\n",
+       "    TXx_mean  (model, time, region) float64 47kB nan nan nan ... 45.11 44.03\n",
+       "Attributes:\n",
+       "    long_name:      annual maximum daily maximum temperature\n",
+       "    standard_name:  air_temperature\n",
+       "    units:          degC\n",
+       "    regrid_method:  bilinear\n",
+       "    cell_methods:   time: maximum (interval: 1Y)</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-5507efe7-1583-4eaf-8e8b-3e8a59ca1fa9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5507efe7-1583-4eaf-8e8b-3e8a59ca1fa9' 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>: 45</li><li><span class='xr-has-index'>region</span>: 10</li><li><span>model</span>: 13</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8c5baacf-8c6e-4aff-941a-6cf829c37451' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8c5baacf-8c6e-4aff-941a-6cf829c37451' 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'>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'>2004-01-01 ... 2048-01-01</div><input id='attrs-0fb857cc-ef98-49ff-b998-54994e7dd01d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0fb857cc-ef98-49ff-b998-54994e7dd01d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5e33e30-5780-4f70-b372-3227a83289d2' class='xr-var-data-in' type='checkbox'><label for='data-f5e33e30-5780-4f70-b372-3227a83289d2' 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'><pre>array([&#x27;2004-01-01T00:00:00.000000000&#x27;, &#x27;2005-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2006-01-01T00:00:00.000000000&#x27;, &#x27;2007-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2008-01-01T00:00:00.000000000&#x27;, &#x27;2009-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-01-01T00:00:00.000000000&#x27;, &#x27;2011-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-01-01T00:00:00.000000000&#x27;, &#x27;2015-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-01-01T00:00:00.000000000&#x27;, &#x27;2017-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2019-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-01T00:00:00.000000000&#x27;, &#x27;2021-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2022-01-01T00:00:00.000000000&#x27;, &#x27;2023-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-01T00:00:00.000000000&#x27;, &#x27;2025-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2026-01-01T00:00:00.000000000&#x27;, &#x27;2027-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2028-01-01T00:00:00.000000000&#x27;, &#x27;2029-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2030-01-01T00:00:00.000000000&#x27;, &#x27;2031-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2032-01-01T00:00:00.000000000&#x27;, &#x27;2033-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2034-01-01T00:00:00.000000000&#x27;, &#x27;2035-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2036-01-01T00:00:00.000000000&#x27;, &#x27;2037-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2038-01-01T00:00:00.000000000&#x27;, &#x27;2039-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2040-01-01T00:00:00.000000000&#x27;, &#x27;2041-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2042-01-01T00:00:00.000000000&#x27;, &#x27;2043-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2044-01-01T00:00:00.000000000&#x27;, &#x27;2045-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2046-01-01T00:00:00.000000000&#x27;, &#x27;2047-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2048-01-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abbrevs</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;WANorth&#x27; &#x27;WASouth&#x27; ... &#x27;AUS&#x27;</div><input id='attrs-6b11480d-1525-4774-9af5-d34b2fbe6a69' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6b11480d-1525-4774-9af5-d34b2fbe6a69' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f50fcd28-9564-4dfa-a054-fcd1843a8a8a' class='xr-var-data-in' type='checkbox'><label for='data-f50fcd28-9564-4dfa-a054-fcd1843a8a8a' 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'><pre>array([&#x27;WANorth&#x27;, &#x27;WASouth&#x27;, &#x27;NSW&#x27;, &#x27;VIC&#x27;, &#x27;SA&#x27;, &#x27;TAS&#x27;, &#x27;NT&#x27;, &#x27;QLDNorth&#x27;,\n",
+       "       &#x27;QLDSouth&#x27;, &#x27;AUS&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>names</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>&lt;U48</div><div class='xr-var-preview xr-preview'>&#x27;Western Australia North&#x27; ... &#x27;A...</div><input id='attrs-7ee21f97-bb28-45dc-ad0a-5da17d79f9f3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7ee21f97-bb28-45dc-ad0a-5da17d79f9f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7cd4de6b-8469-460f-b33a-9789ae85440f' class='xr-var-data-in' type='checkbox'><label for='data-7cd4de6b-8469-460f-b33a-9789ae85440f' 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'><pre>array([&#x27;Western Australia North&#x27;, &#x27;Western Australia South&#x27;,\n",
+       "       &#x27;New South Wales and Australian Capital Territory&#x27;, &#x27;Victoria&#x27;,\n",
+       "       &#x27;South Australia&#x27;, &#x27;Tasmania&#x27;, &#x27;Northern Territory&#x27;,\n",
+       "       &#x27;Queensland North&#x27;, &#x27;Queensland South&#x27;, &#x27;Australia&#x27;], dtype=&#x27;&lt;U48&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>region</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9</div><input id='attrs-64497cbc-8408-46fd-b87d-7e575790536c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-64497cbc-8408-46fd-b87d-7e575790536c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9150b511-b54a-4131-bc81-6ec504623fd3' class='xr-var-data-in' type='checkbox'><label for='data-9150b511-b54a-4131-bc81-6ec504623fd3' 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'><pre>array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-04c4184d-2329-45c6-8b57-f7e79a11110b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-04c4184d-2329-45c6-8b57-f7e79a11110b' 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>TXx_mean</span></div><div class='xr-var-dims'>(model, time, region)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 43.19 45.11 44.03</div><input id='attrs-c05ce866-d75f-4be1-af56-1dd23570a5fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c05ce866-d75f-4be1-af56-1dd23570a5fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51bed0ae-baac-4625-8637-37911a0fa25a' class='xr-var-data-in' type='checkbox'><label for='data-51bed0ae-baac-4625-8637-37911a0fa25a' 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>annual maximum daily maximum temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>cell_methods :</span></dt><dd>time: maximum (interval: 1Y)</dd></dl></div><div class='xr-var-data'><pre>array([[[        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        ...,\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan]],\n",
+       "\n",
+       "       [[        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "...\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan]],\n",
+       "\n",
+       "       [[        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        ...,\n",
+       "        [46.58202996, 45.07097501, 42.51886784, ..., 42.35056172,\n",
+       "         43.06443082, 44.26006765],\n",
+       "        [46.08975087, 44.13992066, 42.5203711 , ..., 43.27065759,\n",
+       "         45.20931421, 44.47848891],\n",
+       "        [45.88626555, 43.5185472 , 42.76288277, ..., 43.18956864,\n",
+       "         45.10550326, 44.02799513]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aec3c68b-a9e7-4933-94ed-dca78509b1bd' class='xr-section-summary-in' type='checkbox'  ><label for='section-aec3c68b-a9e7-4933-94ed-dca78509b1bd' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9568f61e-c866-4480-aaba-61165722e458' class='xr-index-data-in' type='checkbox'/><label for='index-9568f61e-c866-4480-aaba-61165722e458' 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;2004-01-01&#x27;, &#x27;2005-01-01&#x27;, &#x27;2006-01-01&#x27;, &#x27;2007-01-01&#x27;,\n",
+       "               &#x27;2008-01-01&#x27;, &#x27;2009-01-01&#x27;, &#x27;2010-01-01&#x27;, &#x27;2011-01-01&#x27;,\n",
+       "               &#x27;2012-01-01&#x27;, &#x27;2013-01-01&#x27;, &#x27;2014-01-01&#x27;, &#x27;2015-01-01&#x27;,\n",
+       "               &#x27;2016-01-01&#x27;, &#x27;2017-01-01&#x27;, &#x27;2018-01-01&#x27;, &#x27;2019-01-01&#x27;,\n",
+       "               &#x27;2020-01-01&#x27;, &#x27;2021-01-01&#x27;, &#x27;2022-01-01&#x27;, &#x27;2023-01-01&#x27;,\n",
+       "               &#x27;2024-01-01&#x27;, &#x27;2025-01-01&#x27;, &#x27;2026-01-01&#x27;, &#x27;2027-01-01&#x27;,\n",
+       "               &#x27;2028-01-01&#x27;, &#x27;2029-01-01&#x27;, &#x27;2030-01-01&#x27;, &#x27;2031-01-01&#x27;,\n",
+       "               &#x27;2032-01-01&#x27;, &#x27;2033-01-01&#x27;, &#x27;2034-01-01&#x27;, &#x27;2035-01-01&#x27;,\n",
+       "               &#x27;2036-01-01&#x27;, &#x27;2037-01-01&#x27;, &#x27;2038-01-01&#x27;, &#x27;2039-01-01&#x27;,\n",
+       "               &#x27;2040-01-01&#x27;, &#x27;2041-01-01&#x27;, &#x27;2042-01-01&#x27;, &#x27;2043-01-01&#x27;,\n",
+       "               &#x27;2044-01-01&#x27;, &#x27;2045-01-01&#x27;, &#x27;2046-01-01&#x27;, &#x27;2047-01-01&#x27;,\n",
+       "               &#x27;2048-01-01&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;YS-JAN&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>region</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-17e76cd9-d6af-46b7-bce6-c950d814ee4e' class='xr-index-data-in' type='checkbox'/><label for='index-17e76cd9-d6af-46b7-bce6-c950d814ee4e' 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([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=&#x27;int64&#x27;, name=&#x27;region&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-09dd8ed8-a6bb-4b52-9a70-dd9ce349e62e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-09dd8ed8-a6bb-4b52-9a70-dd9ce349e62e' 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>long_name :</span></dt><dd>annual maximum daily maximum temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>cell_methods :</span></dt><dd>time: maximum (interval: 1Y)</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 49kB\n",
+       "Dimensions:   (time: 45, region: 10, model: 13)\n",
+       "Coordinates:\n",
+       "  * time      (time) datetime64[ns] 360B 2004-01-01 2005-01-01 ... 2048-01-01\n",
+       "    abbrevs   (region) <U8 320B 'WANorth' 'WASouth' 'NSW' ... 'QLDSouth' 'AUS'\n",
+       "    names     (region) <U48 2kB 'Western Australia North' ... 'Australia'\n",
+       "  * region    (region) int64 80B 0 1 2 3 4 5 6 7 8 9\n",
+       "Dimensions without coordinates: model\n",
+       "Data variables:\n",
+       "    TXx_mean  (model, time, region) float64 47kB nan nan nan ... 45.11 44.03\n",
+       "Attributes:\n",
+       "    long_name:      annual maximum daily maximum temperature\n",
+       "    standard_name:  air_temperature\n",
+       "    units:          degC\n",
+       "    regrid_method:  bilinear\n",
+       "    cell_methods:   time: maximum (interval: 1Y)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# use acs_regional_stats to calculate the regional mean for each model and month\n",
+    "da_summary =  acs_regional_stats(ds=ds, var=var, mask = mask, dims = (\"lat\", \"lon\",), how = [\"mean\"],)\n",
+    "da_summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "9c70eab6-af30-4754-8e6c-85753e42912b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 6.76 s, sys: 2.96 s, total: 9.72 s\n",
+      "Wall time: 9.73 s\n"
+     ]
+    },
+    {
+     "data": {
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 49kB\n",
+       "Dimensions:   (model: 13, time: 45, region: 10)\n",
+       "Coordinates:\n",
+       "  * time      (time) datetime64[ns] 360B 2004-01-01 2005-01-01 ... 2048-01-01\n",
+       "    abbrevs   (region) &lt;U8 320B &#x27;WANorth&#x27; &#x27;WASouth&#x27; &#x27;NSW&#x27; ... &#x27;QLDSouth&#x27; &#x27;AUS&#x27;\n",
+       "    names     (region) &lt;U48 2kB &#x27;Western Australia North&#x27; ... &#x27;Australia&#x27;\n",
+       "  * region    (region) int64 80B 0 1 2 3 4 5 6 7 8 9\n",
+       "Dimensions without coordinates: model\n",
+       "Data variables:\n",
+       "    TXx_mean  (model, time, region) float64 47kB nan nan nan ... 45.11 44.03\n",
+       "Attributes:\n",
+       "    long_name:      annual maximum daily maximum temperature\n",
+       "    standard_name:  air_temperature\n",
+       "    units:          degC\n",
+       "    regrid_method:  bilinear\n",
+       "    cell_methods:   time: maximum (interval: 1Y)</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-05c7b0ce-b6dc-4685-b9a0-5606a7e010e4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-05c7b0ce-b6dc-4685-b9a0-5606a7e010e4' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>model</span>: 13</li><li><span class='xr-has-index'>time</span>: 45</li><li><span class='xr-has-index'>region</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-632696b5-9056-4cb5-9e02-fdbf57ab450c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-632696b5-9056-4cb5-9e02-fdbf57ab450c' 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'>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'>2004-01-01 ... 2048-01-01</div><input id='attrs-344b862d-890c-42eb-b1e1-0584937dd0af' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-344b862d-890c-42eb-b1e1-0584937dd0af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74fb47c9-2fe8-4621-a6ab-f741bddf32a2' class='xr-var-data-in' type='checkbox'><label for='data-74fb47c9-2fe8-4621-a6ab-f741bddf32a2' 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'><pre>array([&#x27;2004-01-01T00:00:00.000000000&#x27;, &#x27;2005-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2006-01-01T00:00:00.000000000&#x27;, &#x27;2007-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2008-01-01T00:00:00.000000000&#x27;, &#x27;2009-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-01-01T00:00:00.000000000&#x27;, &#x27;2011-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2012-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-01-01T00:00:00.000000000&#x27;, &#x27;2015-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-01-01T00:00:00.000000000&#x27;, &#x27;2017-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2019-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-01T00:00:00.000000000&#x27;, &#x27;2021-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2022-01-01T00:00:00.000000000&#x27;, &#x27;2023-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-01T00:00:00.000000000&#x27;, &#x27;2025-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2026-01-01T00:00:00.000000000&#x27;, &#x27;2027-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2028-01-01T00:00:00.000000000&#x27;, &#x27;2029-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2030-01-01T00:00:00.000000000&#x27;, &#x27;2031-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2032-01-01T00:00:00.000000000&#x27;, &#x27;2033-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2034-01-01T00:00:00.000000000&#x27;, &#x27;2035-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2036-01-01T00:00:00.000000000&#x27;, &#x27;2037-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2038-01-01T00:00:00.000000000&#x27;, &#x27;2039-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2040-01-01T00:00:00.000000000&#x27;, &#x27;2041-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2042-01-01T00:00:00.000000000&#x27;, &#x27;2043-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2044-01-01T00:00:00.000000000&#x27;, &#x27;2045-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2046-01-01T00:00:00.000000000&#x27;, &#x27;2047-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2048-01-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abbrevs</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;WANorth&#x27; &#x27;WASouth&#x27; ... &#x27;AUS&#x27;</div><input id='attrs-dfc16e4a-9a3f-475f-bb4d-e92d58b28929' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dfc16e4a-9a3f-475f-bb4d-e92d58b28929' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f5980f8-2e77-46ff-bba4-dbdff13b1226' class='xr-var-data-in' type='checkbox'><label for='data-1f5980f8-2e77-46ff-bba4-dbdff13b1226' 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'><pre>array([&#x27;WANorth&#x27;, &#x27;WASouth&#x27;, &#x27;NSW&#x27;, &#x27;VIC&#x27;, &#x27;SA&#x27;, &#x27;TAS&#x27;, &#x27;NT&#x27;, &#x27;QLDNorth&#x27;,\n",
+       "       &#x27;QLDSouth&#x27;, &#x27;AUS&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>names</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>&lt;U48</div><div class='xr-var-preview xr-preview'>&#x27;Western Australia North&#x27; ... &#x27;A...</div><input id='attrs-6aa56f3c-7b9c-4894-afc3-24ec67243303' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6aa56f3c-7b9c-4894-afc3-24ec67243303' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8321991-e0a6-4978-a65d-c94bf8594274' class='xr-var-data-in' type='checkbox'><label for='data-f8321991-e0a6-4978-a65d-c94bf8594274' 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'><pre>array([&#x27;Western Australia North&#x27;, &#x27;Western Australia South&#x27;,\n",
+       "       &#x27;New South Wales and Australian Capital Territory&#x27;, &#x27;Victoria&#x27;,\n",
+       "       &#x27;South Australia&#x27;, &#x27;Tasmania&#x27;, &#x27;Northern Territory&#x27;,\n",
+       "       &#x27;Queensland North&#x27;, &#x27;Queensland South&#x27;, &#x27;Australia&#x27;], dtype=&#x27;&lt;U48&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>region</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9</div><input id='attrs-82946055-efac-48e9-996a-fc497c86c5e2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-82946055-efac-48e9-996a-fc497c86c5e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7079c49-f39e-4157-b36a-58e0ec132a7e' class='xr-var-data-in' type='checkbox'><label for='data-a7079c49-f39e-4157-b36a-58e0ec132a7e' 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'><pre>array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f59fea91-8783-4de3-86ba-192348c5a234' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f59fea91-8783-4de3-86ba-192348c5a234' 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>TXx_mean</span></div><div class='xr-var-dims'>(model, time, region)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 43.19 45.11 44.03</div><input id='attrs-11071cc3-ffd1-4dbf-8001-14ebe4f73ffb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11071cc3-ffd1-4dbf-8001-14ebe4f73ffb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47cbad00-3a27-4ea8-a62b-99b2a6fe1624' class='xr-var-data-in' type='checkbox'><label for='data-47cbad00-3a27-4ea8-a62b-99b2a6fe1624' 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>annual maximum daily maximum temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>cell_methods :</span></dt><dd>time: maximum (interval: 1Y)</dd></dl></div><div class='xr-var-data'><pre>array([[[        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        ...,\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan]],\n",
+       "\n",
+       "       [[        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "...\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan]],\n",
+       "\n",
+       "       [[        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        ...,\n",
+       "        [46.58202996, 45.07097501, 42.51886784, ..., 42.35056172,\n",
+       "         43.06443082, 44.26006765],\n",
+       "        [46.08975087, 44.13992066, 42.5203711 , ..., 43.27065759,\n",
+       "         45.20931421, 44.47848891],\n",
+       "        [45.88626555, 43.5185472 , 42.76288277, ..., 43.18956864,\n",
+       "         45.10550326, 44.02799513]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a00c552f-d971-41af-9957-e87a6cb98c77' class='xr-section-summary-in' type='checkbox'  ><label for='section-a00c552f-d971-41af-9957-e87a6cb98c77' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a8fbb910-b3af-4286-a0d1-35142132daf0' class='xr-index-data-in' type='checkbox'/><label for='index-a8fbb910-b3af-4286-a0d1-35142132daf0' 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;2004-01-01&#x27;, &#x27;2005-01-01&#x27;, &#x27;2006-01-01&#x27;, &#x27;2007-01-01&#x27;,\n",
+       "               &#x27;2008-01-01&#x27;, &#x27;2009-01-01&#x27;, &#x27;2010-01-01&#x27;, &#x27;2011-01-01&#x27;,\n",
+       "               &#x27;2012-01-01&#x27;, &#x27;2013-01-01&#x27;, &#x27;2014-01-01&#x27;, &#x27;2015-01-01&#x27;,\n",
+       "               &#x27;2016-01-01&#x27;, &#x27;2017-01-01&#x27;, &#x27;2018-01-01&#x27;, &#x27;2019-01-01&#x27;,\n",
+       "               &#x27;2020-01-01&#x27;, &#x27;2021-01-01&#x27;, &#x27;2022-01-01&#x27;, &#x27;2023-01-01&#x27;,\n",
+       "               &#x27;2024-01-01&#x27;, &#x27;2025-01-01&#x27;, &#x27;2026-01-01&#x27;, &#x27;2027-01-01&#x27;,\n",
+       "               &#x27;2028-01-01&#x27;, &#x27;2029-01-01&#x27;, &#x27;2030-01-01&#x27;, &#x27;2031-01-01&#x27;,\n",
+       "               &#x27;2032-01-01&#x27;, &#x27;2033-01-01&#x27;, &#x27;2034-01-01&#x27;, &#x27;2035-01-01&#x27;,\n",
+       "               &#x27;2036-01-01&#x27;, &#x27;2037-01-01&#x27;, &#x27;2038-01-01&#x27;, &#x27;2039-01-01&#x27;,\n",
+       "               &#x27;2040-01-01&#x27;, &#x27;2041-01-01&#x27;, &#x27;2042-01-01&#x27;, &#x27;2043-01-01&#x27;,\n",
+       "               &#x27;2044-01-01&#x27;, &#x27;2045-01-01&#x27;, &#x27;2046-01-01&#x27;, &#x27;2047-01-01&#x27;,\n",
+       "               &#x27;2048-01-01&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;YS-JAN&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>region</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-222b29db-eb5c-4c5e-ba27-7c8ab74d30a2' class='xr-index-data-in' type='checkbox'/><label for='index-222b29db-eb5c-4c5e-ba27-7c8ab74d30a2' 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([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=&#x27;int64&#x27;, name=&#x27;region&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ebd07361-2d90-44d9-8d6d-ce500e10b118' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ebd07361-2d90-44d9-8d6d-ce500e10b118' 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>long_name :</span></dt><dd>annual maximum daily maximum temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>cell_methods :</span></dt><dd>time: maximum (interval: 1Y)</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 49kB\n",
+       "Dimensions:   (model: 13, time: 45, region: 10)\n",
+       "Coordinates:\n",
+       "  * time      (time) datetime64[ns] 360B 2004-01-01 2005-01-01 ... 2048-01-01\n",
+       "    abbrevs   (region) <U8 320B 'WANorth' 'WASouth' 'NSW' ... 'QLDSouth' 'AUS'\n",
+       "    names     (region) <U48 2kB 'Western Australia North' ... 'Australia'\n",
+       "  * region    (region) int64 80B 0 1 2 3 4 5 6 7 8 9\n",
+       "Dimensions without coordinates: model\n",
+       "Data variables:\n",
+       "    TXx_mean  (model, time, region) float64 47kB nan nan nan ... 45.11 44.03\n",
+       "Attributes:\n",
+       "    long_name:      annual maximum daily maximum temperature\n",
+       "    standard_name:  air_temperature\n",
+       "    units:          degC\n",
+       "    regrid_method:  bilinear\n",
+       "    cell_methods:   time: maximum (interval: 1Y)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# use acs_regiona_stats to calculate the regional mean for each model and month\n",
+    "da_summary =  acs_regional_stats(ds=ds, var=var, mask = mask, dims = (\"lat\", \"lon\",), how = [\"mean\"], chunks=4,)\n",
+    "da_summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "20eee231-ccb4-4cf1-aa04-b9936849b34e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# select the region you like and plot the regional average for each month of each model\n",
+    "da_summary[f\"{var}_mean\"].sel(region=9).plot.line(x=\"time\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "9eca8294-125f-4f1e-8fe5-11e7bf5fea2a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='time', ylabel='TXx_mean'>"
+      ]
+     },
+     "execution_count": 16,
+     "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": [
+    "# or, use seaborn to plot the range of values from multiple models\n",
+    "da= da_summary[f\"{var}_mean\"].sel(region=9).to_dataframe()\n",
+    "sns.lineplot(data=da, x=\"time\", y= f\"{var}_mean\",  errorbar=(\"pi\", 100) )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f2d0dd5-d42f-4456-8621-405fcbba938b",
+   "metadata": {},
+   "source": [
+    "# Smaller regions eg LGAs\n",
+    "A larger number of regions is more memory intensive. Here is an example of many LGA regions, instead of the ~10 NCRA regions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "cac0a342-d68a-4b03-8dc8-21d5fccd7b3f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<regionmask.Regions 'aus_local_gov'>\n",
+       "overlap:  True\n",
+       "\n",
+       "Regions:\n",
+       "  0 10050                      Albury\n",
+       "  1 10180                    Armidale\n",
+       "  2 10250                     Ballina\n",
+       "  3 10300                   Balranald\n",
+       "  4 10470                    Bathurst\n",
+       " ..   ...                         ...\n",
+       "542 74660                 West Arnhem\n",
+       "543 74680                   West Daly\n",
+       "544 79399           Unincorporated NT\n",
+       "545 89399          Unincorporated ACT\n",
+       "546 99399 Unincorp. Other Territories\n",
+       "\n",
+       "[547 regions]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file = '/g/data/py18/BARPA/output/CMIP6/DD/AUS-15/BOM/ACCESS-CM2/ssp370/r4i1p1f1/BARPA-R/v1-r1/day/tas/latest/tas_AUS-15_ACCESS-CM2_ssp370_r4i1p1f1_BOM_BARPA-R_v1-r1_day_201501-201512.nc'\n",
+    "ds = xr.open_dataset(file)\n",
+    "\n",
+    "regions = get_regions([\"aus_local_gov\"])\n",
+    "regions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12c2f4fd-5226-46d0-a106-7e449d3a390a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "1b08fe81-0954-421c-ba50-7146bf8102ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 15.4 s, sys: 1.75 s, total: 17.2 s\n",
+      "Wall time: 17.2 s\n"
+     ]
+    },
+    {
+     "data": {
       "text/html": [
        "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
        "<defs>\n",
@@ -1024,144 +3237,209 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 49kB\n",
-       "Dimensions:   (time: 45, region: 10, model: 13)\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 24kB\n",
+       "Dimensions:   (time: 365, region: 7)\n",
        "Coordinates:\n",
-       "  * time      (time) datetime64[ns] 360B 2004-01-01 2005-01-01 ... 2048-01-01\n",
-       "    abbrevs   (region) &lt;U8 320B &#x27;WANorth&#x27; &#x27;WASouth&#x27; &#x27;NSW&#x27; ... &#x27;QLDSouth&#x27; &#x27;AUS&#x27;\n",
-       "    names     (region) &lt;U48 2kB &#x27;Western Australia North&#x27; ... &#x27;Australia&#x27;\n",
-       "  * region    (region) int64 80B 0 1 2 3 4 5 6 7 8 9\n",
-       "Dimensions without coordinates: model\n",
+       "  * time      (time) datetime64[ns] 3kB 2015-01-01T12:00:00 ... 2015-12-31T12...\n",
+       "    height    float64 8B 1.5\n",
+       "  * region    (region) int64 56B 105 172 216 287 459 511 531\n",
+       "    abbrevs   (region) &lt;U5 140B &#x27;17200&#x27; &#x27;24600&#x27; &#x27;31000&#x27; ... &#x27;62810&#x27; &#x27;71000&#x27;\n",
+       "    names     (region) &lt;U9 252B &#x27;Sydney&#x27; &#x27;Melbourne&#x27; ... &#x27;Hobart&#x27; &#x27;Darwin&#x27;\n",
        "Data variables:\n",
-       "    TXx_mean  (model, time, region) float64 47kB nan nan nan ... 45.11 44.03\n",
+       "    tas_mean  (time, region) float64 20kB 303.6 298.9 297.7 ... 295.1 303.8\n",
        "Attributes:\n",
-       "    long_name:      annual maximum daily maximum temperature\n",
+       "    long_name:      Near-Surface Air Temperature\n",
        "    standard_name:  air_temperature\n",
-       "    units:          degC\n",
-       "    regrid_method:  bilinear\n",
-       "    cell_methods:   time: maximum (interval: 1Y)</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-3f2f98e8-a12a-41d8-8598-98a2655a9f2b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3f2f98e8-a12a-41d8-8598-98a2655a9f2b' 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>: 45</li><li><span class='xr-has-index'>region</span>: 10</li><li><span>model</span>: 13</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-395f9497-dd0b-4044-813f-237a796082ee' class='xr-section-summary-in' type='checkbox'  checked><label for='section-395f9497-dd0b-4044-813f-237a796082ee' 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'>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'>2004-01-01 ... 2048-01-01</div><input id='attrs-7ccfb1d6-ee73-4dd9-ba8f-2efaa2f387f7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7ccfb1d6-ee73-4dd9-ba8f-2efaa2f387f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72a697cf-5beb-4fd8-b314-befd35d6994d' class='xr-var-data-in' type='checkbox'><label for='data-72a697cf-5beb-4fd8-b314-befd35d6994d' 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'><pre>array([&#x27;2004-01-01T00:00:00.000000000&#x27;, &#x27;2005-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2006-01-01T00:00:00.000000000&#x27;, &#x27;2007-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2008-01-01T00:00:00.000000000&#x27;, &#x27;2009-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2010-01-01T00:00:00.000000000&#x27;, &#x27;2011-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2012-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2014-01-01T00:00:00.000000000&#x27;, &#x27;2015-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2016-01-01T00:00:00.000000000&#x27;, &#x27;2017-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2019-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-01-01T00:00:00.000000000&#x27;, &#x27;2021-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2022-01-01T00:00:00.000000000&#x27;, &#x27;2023-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2024-01-01T00:00:00.000000000&#x27;, &#x27;2025-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2026-01-01T00:00:00.000000000&#x27;, &#x27;2027-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2028-01-01T00:00:00.000000000&#x27;, &#x27;2029-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2030-01-01T00:00:00.000000000&#x27;, &#x27;2031-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2032-01-01T00:00:00.000000000&#x27;, &#x27;2033-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2034-01-01T00:00:00.000000000&#x27;, &#x27;2035-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2036-01-01T00:00:00.000000000&#x27;, &#x27;2037-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2038-01-01T00:00:00.000000000&#x27;, &#x27;2039-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2040-01-01T00:00:00.000000000&#x27;, &#x27;2041-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2042-01-01T00:00:00.000000000&#x27;, &#x27;2043-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2044-01-01T00:00:00.000000000&#x27;, &#x27;2045-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2046-01-01T00:00:00.000000000&#x27;, &#x27;2047-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2048-01-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abbrevs</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;WANorth&#x27; &#x27;WASouth&#x27; ... &#x27;AUS&#x27;</div><input id='attrs-88aab716-7048-4a0a-8d23-9aa7b161bda0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-88aab716-7048-4a0a-8d23-9aa7b161bda0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f2f9f8d-1927-4dbe-b207-47ac262c4a2a' class='xr-var-data-in' type='checkbox'><label for='data-3f2f9f8d-1927-4dbe-b207-47ac262c4a2a' 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'><pre>array([&#x27;WANorth&#x27;, &#x27;WASouth&#x27;, &#x27;NSW&#x27;, &#x27;VIC&#x27;, &#x27;SA&#x27;, &#x27;TAS&#x27;, &#x27;NT&#x27;, &#x27;QLDNorth&#x27;,\n",
-       "       &#x27;QLDSouth&#x27;, &#x27;AUS&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>names</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>&lt;U48</div><div class='xr-var-preview xr-preview'>&#x27;Western Australia North&#x27; ... &#x27;A...</div><input id='attrs-b6be4d05-10b7-4666-b0c0-585b3ffade95' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b6be4d05-10b7-4666-b0c0-585b3ffade95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd661fae-68af-463c-b3f6-d74165e00c28' class='xr-var-data-in' type='checkbox'><label for='data-cd661fae-68af-463c-b3f6-d74165e00c28' 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'><pre>array([&#x27;Western Australia North&#x27;, &#x27;Western Australia South&#x27;,\n",
-       "       &#x27;New South Wales and Australian Capital Territory&#x27;, &#x27;Victoria&#x27;,\n",
-       "       &#x27;South Australia&#x27;, &#x27;Tasmania&#x27;, &#x27;Northern Territory&#x27;,\n",
-       "       &#x27;Queensland North&#x27;, &#x27;Queensland South&#x27;, &#x27;Australia&#x27;], dtype=&#x27;&lt;U48&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>region</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9</div><input id='attrs-4e8588d5-3c97-45fa-a7a5-28468f3579c5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4e8588d5-3c97-45fa-a7a5-28468f3579c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca22e1c7-5b50-45bf-9fc5-0a8c9053c982' class='xr-var-data-in' type='checkbox'><label for='data-ca22e1c7-5b50-45bf-9fc5-0a8c9053c982' 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'><pre>array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1295ec0b-5bc8-4382-b6ce-9def19d14b69' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1295ec0b-5bc8-4382-b6ce-9def19d14b69' 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>TXx_mean</span></div><div class='xr-var-dims'>(model, time, region)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 43.19 45.11 44.03</div><input id='attrs-c23dcd94-f01f-4906-84b3-2f2d2d297913' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c23dcd94-f01f-4906-84b3-2f2d2d297913' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebd8f7cd-4cee-46aa-9b0c-d2555f89e3ba' class='xr-var-data-in' type='checkbox'><label for='data-ebd8f7cd-4cee-46aa-9b0c-d2555f89e3ba' 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>annual maximum daily maximum temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>cell_methods :</span></dt><dd>time: maximum (interval: 1Y)</dd></dl></div><div class='xr-var-data'><pre>array([[[        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        ...,\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan]],\n",
-       "\n",
-       "       [[        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "...\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan]],\n",
-       "\n",
-       "       [[        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        ...,\n",
-       "        [46.58202996, 45.07097501, 42.51886784, ..., 42.35056172,\n",
-       "         43.06443082, 44.26006765],\n",
-       "        [46.08975087, 44.13992066, 42.5203711 , ..., 43.27065759,\n",
-       "         45.20931421, 44.47848891],\n",
-       "        [45.88626555, 43.5185472 , 42.76288277, ..., 43.18956864,\n",
-       "         45.10550326, 44.02799513]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c694d75e-8ae0-4398-8b9d-170c74452b54' class='xr-section-summary-in' type='checkbox'  ><label for='section-c694d75e-8ae0-4398-8b9d-170c74452b54' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f4a0f9e6-88ba-4760-bce0-e9d91c63a42b' class='xr-index-data-in' type='checkbox'/><label for='index-f4a0f9e6-88ba-4760-bce0-e9d91c63a42b' 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;2004-01-01&#x27;, &#x27;2005-01-01&#x27;, &#x27;2006-01-01&#x27;, &#x27;2007-01-01&#x27;,\n",
-       "               &#x27;2008-01-01&#x27;, &#x27;2009-01-01&#x27;, &#x27;2010-01-01&#x27;, &#x27;2011-01-01&#x27;,\n",
-       "               &#x27;2012-01-01&#x27;, &#x27;2013-01-01&#x27;, &#x27;2014-01-01&#x27;, &#x27;2015-01-01&#x27;,\n",
-       "               &#x27;2016-01-01&#x27;, &#x27;2017-01-01&#x27;, &#x27;2018-01-01&#x27;, &#x27;2019-01-01&#x27;,\n",
-       "               &#x27;2020-01-01&#x27;, &#x27;2021-01-01&#x27;, &#x27;2022-01-01&#x27;, &#x27;2023-01-01&#x27;,\n",
-       "               &#x27;2024-01-01&#x27;, &#x27;2025-01-01&#x27;, &#x27;2026-01-01&#x27;, &#x27;2027-01-01&#x27;,\n",
-       "               &#x27;2028-01-01&#x27;, &#x27;2029-01-01&#x27;, &#x27;2030-01-01&#x27;, &#x27;2031-01-01&#x27;,\n",
-       "               &#x27;2032-01-01&#x27;, &#x27;2033-01-01&#x27;, &#x27;2034-01-01&#x27;, &#x27;2035-01-01&#x27;,\n",
-       "               &#x27;2036-01-01&#x27;, &#x27;2037-01-01&#x27;, &#x27;2038-01-01&#x27;, &#x27;2039-01-01&#x27;,\n",
-       "               &#x27;2040-01-01&#x27;, &#x27;2041-01-01&#x27;, &#x27;2042-01-01&#x27;, &#x27;2043-01-01&#x27;,\n",
-       "               &#x27;2044-01-01&#x27;, &#x27;2045-01-01&#x27;, &#x27;2046-01-01&#x27;, &#x27;2047-01-01&#x27;,\n",
-       "               &#x27;2048-01-01&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;YS-JAN&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>region</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-51cda2eb-5a55-48b2-8f35-fc81f820870a' class='xr-index-data-in' type='checkbox'/><label for='index-51cda2eb-5a55-48b2-8f35-fc81f820870a' 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([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=&#x27;int64&#x27;, name=&#x27;region&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fba45916-76cd-4ba4-b3b6-00105cd8651f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fba45916-76cd-4ba4-b3b6-00105cd8651f' 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>long_name :</span></dt><dd>annual maximum daily maximum temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>cell_methods :</span></dt><dd>time: maximum (interval: 1Y)</dd></dl></div></li></ul></div></div>"
+       "    units:          K\n",
+       "    cell_methods:   time: point (interval: 1H) time: mean (interval: 1D)\n",
+       "    grid_mapping:   crs</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-71316a90-aed1-460e-99b7-87d880069778' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-71316a90-aed1-460e-99b7-87d880069778' 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>: 365</li><li><span class='xr-has-index'>region</span>: 7</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-66d077e5-5f6a-4006-a6c2-aae6b85aac49' class='xr-section-summary-in' type='checkbox'  checked><label for='section-66d077e5-5f6a-4006-a6c2-aae6b85aac49' class='xr-section-summary' >Coordinates: <span>(5)</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'>2015-01-01T12:00:00 ... 2015-12-...</div><input id='attrs-24ea5044-aac7-42fc-8d0d-eb6c271d5393' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-24ea5044-aac7-42fc-8d0d-eb6c271d5393' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ba4a584-0fdb-4125-bd67-57c1d16ad082' class='xr-var-data-in' type='checkbox'><label for='data-1ba4a584-0fdb-4125-bd67-57c1d16ad082' 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'><pre>array([&#x27;2015-01-01T12:00:00.000000000&#x27;, &#x27;2015-01-02T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-01-03T12:00:00.000000000&#x27;, ..., &#x27;2015-12-29T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-12-30T12:00:00.000000000&#x27;, &#x27;2015-12-31T12:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.5</div><input id='attrs-a415b811-ad86-4cfb-b472-8e25cf0f45a7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a415b811-ad86-4cfb-b472-8e25cf0f45a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9923727-a2d7-452c-8f03-4f4e7102be52' class='xr-var-data-in' type='checkbox'><label for='data-a9923727-a2d7-452c-8f03-4f4e7102be52' 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'><pre>array(1.5)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>region</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>105 172 216 287 459 511 531</div><input id='attrs-5eebde73-df1b-4031-b471-a3fa92f5a0de' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5eebde73-df1b-4031-b471-a3fa92f5a0de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42211fd7-f709-4d06-b379-758891126e81' class='xr-var-data-in' type='checkbox'><label for='data-42211fd7-f709-4d06-b379-758891126e81' 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'><pre>array([105, 172, 216, 287, 459, 511, 531])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abbrevs</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;17200&#x27; &#x27;24600&#x27; ... &#x27;62810&#x27; &#x27;71000&#x27;</div><input id='attrs-ed9de268-6b9a-4fb5-8ea5-76ee46444cb8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ed9de268-6b9a-4fb5-8ea5-76ee46444cb8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44bd05bf-b0dd-443a-a528-0de3e1501744' class='xr-var-data-in' type='checkbox'><label for='data-44bd05bf-b0dd-443a-a528-0de3e1501744' 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'><pre>array([&#x27;17200&#x27;, &#x27;24600&#x27;, &#x27;31000&#x27;, &#x27;40070&#x27;, &#x27;57080&#x27;, &#x27;62810&#x27;, &#x27;71000&#x27;],\n",
+       "      dtype=&#x27;&lt;U5&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>names</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>&lt;U9</div><div class='xr-var-preview xr-preview'>&#x27;Sydney&#x27; &#x27;Melbourne&#x27; ... &#x27;Darwin&#x27;</div><input id='attrs-ac394164-7e31-4148-b226-4ae95ce43840' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac394164-7e31-4148-b226-4ae95ce43840' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d42014b-f379-4ca6-bd58-03ef4709d010' class='xr-var-data-in' type='checkbox'><label for='data-4d42014b-f379-4ca6-bd58-03ef4709d010' 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'><pre>array([&#x27;Sydney&#x27;, &#x27;Melbourne&#x27;, &#x27;Brisbane&#x27;, &#x27;Adelaide&#x27;, &#x27;Perth&#x27;, &#x27;Hobart&#x27;,\n",
+       "       &#x27;Darwin&#x27;], dtype=&#x27;&lt;U9&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fe724373-5de2-42fc-8052-ca7ad9efe17c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fe724373-5de2-42fc-8052-ca7ad9efe17c' 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_mean</span></div><div class='xr-var-dims'>(time, region)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>303.6 298.9 297.7 ... 295.1 303.8</div><input id='attrs-64c87917-5c11-45ec-ab76-327f17d161ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64c87917-5c11-45ec-ab76-327f17d161ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e502723-ee9f-4406-ae33-d4c27813bba3' class='xr-var-data-in' type='checkbox'><label for='data-7e502723-ee9f-4406-ae33-d4c27813bba3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: point (interval: 1H) time: mean (interval: 1D)</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd></dl></div><div class='xr-var-data'><pre>array([[303.62606603, 298.90228271, 297.67453964, ..., 295.98632812,\n",
+       "        290.62532552, 302.10986121],\n",
+       "       [299.81812237, 292.96035767, 298.59043539, ..., 294.73046875,\n",
+       "        287.7218967 , 301.9091209 ],\n",
+       "       [298.9881081 , 305.26364136, 299.80412386, ..., 296.98803711,\n",
+       "        295.94270833, 302.55385754],\n",
+       "       ...,\n",
+       "       [291.83450627, 294.13345337, 294.85775863, ..., 294.97119141,\n",
+       "        287.17358398, 303.62594965],\n",
+       "       [293.31097971, 300.98919678, 295.21259796, ..., 292.96264648,\n",
+       "        289.24970161, 304.07631492],\n",
+       "       [295.63364882, 304.24740601, 296.42826313, ..., 293.9609375 ,\n",
+       "        295.08170573, 303.80798929]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f94708f1-dadd-4514-b1fc-b954ab856cc7' class='xr-section-summary-in' type='checkbox'  ><label for='section-f94708f1-dadd-4514-b1fc-b954ab856cc7' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-34a8432f-4659-4f13-badb-493234c373c5' class='xr-index-data-in' type='checkbox'/><label for='index-34a8432f-4659-4f13-badb-493234c373c5' 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;2015-01-01 12:00:00&#x27;, &#x27;2015-01-02 12:00:00&#x27;,\n",
+       "               &#x27;2015-01-03 12:00:00&#x27;, &#x27;2015-01-04 12:00:00&#x27;,\n",
+       "               &#x27;2015-01-05 12:00:00&#x27;, &#x27;2015-01-06 12:00:00&#x27;,\n",
+       "               &#x27;2015-01-07 12:00:00&#x27;, &#x27;2015-01-08 12:00:00&#x27;,\n",
+       "               &#x27;2015-01-09 12:00:00&#x27;, &#x27;2015-01-10 12:00:00&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2015-12-22 12:00:00&#x27;, &#x27;2015-12-23 12:00:00&#x27;,\n",
+       "               &#x27;2015-12-24 12:00:00&#x27;, &#x27;2015-12-25 12:00:00&#x27;,\n",
+       "               &#x27;2015-12-26 12:00:00&#x27;, &#x27;2015-12-27 12:00:00&#x27;,\n",
+       "               &#x27;2015-12-28 12:00:00&#x27;, &#x27;2015-12-29 12:00:00&#x27;,\n",
+       "               &#x27;2015-12-30 12:00:00&#x27;, &#x27;2015-12-31 12:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=365, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>region</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-50412d05-1046-499d-b475-d9571a6802b9' class='xr-index-data-in' type='checkbox'/><label for='index-50412d05-1046-499d-b475-d9571a6802b9' 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([105, 172, 216, 287, 459, 511, 531], dtype=&#x27;int64&#x27;, name=&#x27;region&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5f6baf27-5141-438b-9c89-175f5c4b0c3d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5f6baf27-5141-438b-9c89-175f5c4b0c3d' 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>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: point (interval: 1H) time: mean (interval: 1D)</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.Dataset> Size: 49kB\n",
-       "Dimensions:   (time: 45, region: 10, model: 13)\n",
+       "<xarray.Dataset> Size: 24kB\n",
+       "Dimensions:   (time: 365, region: 7)\n",
        "Coordinates:\n",
-       "  * time      (time) datetime64[ns] 360B 2004-01-01 2005-01-01 ... 2048-01-01\n",
-       "    abbrevs   (region) <U8 320B 'WANorth' 'WASouth' 'NSW' ... 'QLDSouth' 'AUS'\n",
-       "    names     (region) <U48 2kB 'Western Australia North' ... 'Australia'\n",
-       "  * region    (region) int64 80B 0 1 2 3 4 5 6 7 8 9\n",
-       "Dimensions without coordinates: model\n",
+       "  * time      (time) datetime64[ns] 3kB 2015-01-01T12:00:00 ... 2015-12-31T12...\n",
+       "    height    float64 8B 1.5\n",
+       "  * region    (region) int64 56B 105 172 216 287 459 511 531\n",
+       "    abbrevs   (region) <U5 140B '17200' '24600' '31000' ... '62810' '71000'\n",
+       "    names     (region) <U9 252B 'Sydney' 'Melbourne' ... 'Hobart' 'Darwin'\n",
        "Data variables:\n",
-       "    TXx_mean  (model, time, region) float64 47kB nan nan nan ... 45.11 44.03\n",
-       "Attributes:\n",
-       "    long_name:      annual maximum daily maximum temperature\n",
-       "    standard_name:  air_temperature\n",
-       "    units:          degC\n",
-       "    regrid_method:  bilinear\n",
-       "    cell_methods:   time: maximum (interval: 1Y)"
+       "    tas_mean  (time, region) float64 20kB 303.6 298.9 297.7 ... 295.1 303.8\n",
+       "Attributes:\n",
+       "    long_name:      Near-Surface Air Temperature\n",
+       "    standard_name:  air_temperature\n",
+       "    units:          K\n",
+       "    cell_methods:   time: point (interval: 1H) time: mean (interval: 1D)\n",
+       "    grid_mapping:   crs"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "%%time\n",
-    "# use acs_regiona_stats to calculate the regional mean for each model and month\n",
-    "da_summary =  acs_regional_stats(ds=ds, var=var, mask = mask, dims = (\"lat\", \"lon\",), how = [\"mean\"],)\n",
+    "# select only the LGAs you are interested in\n",
+    "var=\"tas\"\n",
+    "\n",
+    "city_list = [\"Melbourne\", \"Sydney\", \"Darwin\", \"Perth\", \"Hobart\", \"Adelaide\", \"Brisbane\"]\n",
+    "\n",
+    "# only calculate the mask for the selected LGAs\n",
+    "mask = regions[regions.map_keys(city_list)].mask_3D_frac_approx(ds)\n",
+    "\n",
+    "da_summary = acs_regional_stats(ds=ds,\n",
+    "                                var=var,\n",
+    "                                mask = mask.where(mask.names.isin(city_list), drop=True), \n",
+    "                                dims = (\"lat\", \"lon\",), \n",
+    "                                how = [\"mean\"],\n",
+    "                                outfile=\"out.csv\",\n",
+    "                               chunks=1)\n",
     "da_summary"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "9c70eab6-af30-4754-8e6c-85753e42912b",
+   "execution_count": 19,
+   "id": "b73311d3-32b5-4e7b-95f4-4058b99de5b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1553f61a2b30>"
+      ]
+     },
+     "execution_count": 19,
+     "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": [
+    "da_summary[f\"{var}_mean\"].plot(x=\"time\", hue=\"region\")\n",
+    "plt.legend(da_summary.names.values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50bfab27-8263-40fd-b181-84359fcefc6b",
+   "metadata": {},
+   "source": [
+    "# Managing memory for all (or many) LGAs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "3f9556e6-ecee-4a52-9ded-113101961694",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<regionmask.Regions 'aus_local_gov'>\n",
+       "overlap:  True\n",
+       "\n",
+       "Regions:\n",
+       "  0 10050                      Albury\n",
+       "  1 10180                    Armidale\n",
+       "  2 10250                     Ballina\n",
+       "  3 10300                   Balranald\n",
+       "  4 10470                    Bathurst\n",
+       " ..   ...                         ...\n",
+       "542 74660                 West Arnhem\n",
+       "543 74680                   West Daly\n",
+       "544 79399           Unincorporated NT\n",
+       "545 89399          Unincorporated ACT\n",
+       "546 99399 Unincorp. Other Territories\n",
+       "\n",
+       "[547 regions]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file = '/g/data/py18/BARPA/output/CMIP6/DD/AUS-15/BOM/ACCESS-CM2/ssp370/r4i1p1f1/BARPA-R/v1-r1/day/tas/latest/tas_AUS-15_ACCESS-CM2_ssp370_r4i1p1f1_BOM_BARPA-R_v1-r1_day_201501-201512.nc'\n",
+    "ds = xr.open_dataset(file)\n",
+    "\n",
+    "regions = get_regions([\"aus_local_gov\"])\n",
+    "regions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "ed2492a4-de15-4f0f-8092-d5beb3e0fa30",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 5.01 s, sys: 2.3 s, total: 7.31 s\n",
-      "Wall time: 7.31 s\n"
+      "CPU times: user 1min 42s, sys: 2min 30s, total: 4min 12s\n",
+      "Wall time: 4min 13s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# building this mask is very memory intensive ~50GB and a few minutes\n",
+    "mask = regions.mask_3D_frac_approx(ds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "95ffb7b3-870e-4021-ae85-dda71f462d5c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 8min 38s, sys: 4min 20s, total: 12min 59s\n",
+      "Wall time: 12min 59s\n"
      ]
     },
     {
@@ -1530,201 +3808,322 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 49kB\n",
-       "Dimensions:   (model: 13, time: 45, region: 10)\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2MB\n",
+       "Dimensions:   (time: 365, region: 545)\n",
        "Coordinates:\n",
-       "  * time      (time) datetime64[ns] 360B 2004-01-01 2005-01-01 ... 2048-01-01\n",
-       "    abbrevs   (region) &lt;U8 320B &#x27;WANorth&#x27; &#x27;WASouth&#x27; &#x27;NSW&#x27; ... &#x27;QLDSouth&#x27; &#x27;AUS&#x27;\n",
-       "    names     (region) &lt;U48 2kB &#x27;Western Australia North&#x27; ... &#x27;Australia&#x27;\n",
-       "  * region    (region) int64 80B 0 1 2 3 4 5 6 7 8 9\n",
-       "Dimensions without coordinates: model\n",
+       "  * time      (time) datetime64[ns] 3kB 2015-01-01T12:00:00 ... 2015-12-31T12...\n",
+       "    height    float64 8B 1.5\n",
+       "  * region    (region) int64 4kB 0 1 2 3 4 5 6 7 ... 540 541 542 543 544 545 546\n",
+       "    abbrevs   (region) &lt;U5 11kB &#x27;10050&#x27; &#x27;10180&#x27; &#x27;10250&#x27; ... &#x27;89399&#x27; &#x27;99399&#x27;\n",
+       "    names     (region) &lt;U37 81kB &#x27;Albury&#x27; ... &#x27;Unincorp. Other Territories&#x27;\n",
        "Data variables:\n",
-       "    TXx_mean  (model, time, region) float64 47kB nan nan nan ... 45.11 44.03\n",
+       "    tas_mean  (time, region) float64 2MB 303.1 297.2 298.4 ... 302.8 291.7 294.8\n",
        "Attributes:\n",
-       "    long_name:      annual maximum daily maximum temperature\n",
+       "    long_name:      Near-Surface Air Temperature\n",
        "    standard_name:  air_temperature\n",
-       "    units:          degC\n",
-       "    regrid_method:  bilinear\n",
-       "    cell_methods:   time: maximum (interval: 1Y)</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-63837cbb-337b-457d-9544-32549e59e731' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-63837cbb-337b-457d-9544-32549e59e731' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>model</span>: 13</li><li><span class='xr-has-index'>time</span>: 45</li><li><span class='xr-has-index'>region</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-eed46c45-b542-4333-a6b0-c948bc3b06f3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eed46c45-b542-4333-a6b0-c948bc3b06f3' 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'>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'>2004-01-01 ... 2048-01-01</div><input id='attrs-c4868c49-23f3-4bfa-9228-5c1b02015cd7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c4868c49-23f3-4bfa-9228-5c1b02015cd7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e391827b-9f98-42c5-8b1a-92bce33aac47' class='xr-var-data-in' type='checkbox'><label for='data-e391827b-9f98-42c5-8b1a-92bce33aac47' 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'><pre>array([&#x27;2004-01-01T00:00:00.000000000&#x27;, &#x27;2005-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2006-01-01T00:00:00.000000000&#x27;, &#x27;2007-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2008-01-01T00:00:00.000000000&#x27;, &#x27;2009-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2010-01-01T00:00:00.000000000&#x27;, &#x27;2011-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2012-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2014-01-01T00:00:00.000000000&#x27;, &#x27;2015-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2016-01-01T00:00:00.000000000&#x27;, &#x27;2017-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2019-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-01-01T00:00:00.000000000&#x27;, &#x27;2021-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2022-01-01T00:00:00.000000000&#x27;, &#x27;2023-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2024-01-01T00:00:00.000000000&#x27;, &#x27;2025-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2026-01-01T00:00:00.000000000&#x27;, &#x27;2027-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2028-01-01T00:00:00.000000000&#x27;, &#x27;2029-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2030-01-01T00:00:00.000000000&#x27;, &#x27;2031-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2032-01-01T00:00:00.000000000&#x27;, &#x27;2033-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2034-01-01T00:00:00.000000000&#x27;, &#x27;2035-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2036-01-01T00:00:00.000000000&#x27;, &#x27;2037-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2038-01-01T00:00:00.000000000&#x27;, &#x27;2039-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2040-01-01T00:00:00.000000000&#x27;, &#x27;2041-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2042-01-01T00:00:00.000000000&#x27;, &#x27;2043-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2044-01-01T00:00:00.000000000&#x27;, &#x27;2045-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2046-01-01T00:00:00.000000000&#x27;, &#x27;2047-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2048-01-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abbrevs</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;WANorth&#x27; &#x27;WASouth&#x27; ... &#x27;AUS&#x27;</div><input id='attrs-b5d74e39-7013-4ea9-9408-8947736270c2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b5d74e39-7013-4ea9-9408-8947736270c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd558582-1799-4f18-8d8e-d113e007c078' class='xr-var-data-in' type='checkbox'><label for='data-bd558582-1799-4f18-8d8e-d113e007c078' 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'><pre>array([&#x27;WANorth&#x27;, &#x27;WASouth&#x27;, &#x27;NSW&#x27;, &#x27;VIC&#x27;, &#x27;SA&#x27;, &#x27;TAS&#x27;, &#x27;NT&#x27;, &#x27;QLDNorth&#x27;,\n",
-       "       &#x27;QLDSouth&#x27;, &#x27;AUS&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>names</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>&lt;U48</div><div class='xr-var-preview xr-preview'>&#x27;Western Australia North&#x27; ... &#x27;A...</div><input id='attrs-573fff64-140d-4a52-864c-86ca4dc8bfca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-573fff64-140d-4a52-864c-86ca4dc8bfca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc24ec7a-5cc5-4acf-adb9-572c935b6ac1' class='xr-var-data-in' type='checkbox'><label for='data-bc24ec7a-5cc5-4acf-adb9-572c935b6ac1' 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'><pre>array([&#x27;Western Australia North&#x27;, &#x27;Western Australia South&#x27;,\n",
-       "       &#x27;New South Wales and Australian Capital Territory&#x27;, &#x27;Victoria&#x27;,\n",
-       "       &#x27;South Australia&#x27;, &#x27;Tasmania&#x27;, &#x27;Northern Territory&#x27;,\n",
-       "       &#x27;Queensland North&#x27;, &#x27;Queensland South&#x27;, &#x27;Australia&#x27;], dtype=&#x27;&lt;U48&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>region</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9</div><input id='attrs-b233ff5a-b4a1-40d6-8dd3-7cad81fd8e07' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b233ff5a-b4a1-40d6-8dd3-7cad81fd8e07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9dbad1bb-8b22-40f0-8c4f-23f352f73c36' class='xr-var-data-in' type='checkbox'><label for='data-9dbad1bb-8b22-40f0-8c4f-23f352f73c36' 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'><pre>array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a40bf623-f40b-44a8-b898-4493b2161bfd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a40bf623-f40b-44a8-b898-4493b2161bfd' 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>TXx_mean</span></div><div class='xr-var-dims'>(model, time, region)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 43.19 45.11 44.03</div><input id='attrs-0c2aa4f1-98b6-40a3-98fe-20d212c30c1d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0c2aa4f1-98b6-40a3-98fe-20d212c30c1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28c8d087-a8c6-4d59-b0c8-0506f05dbd7a' class='xr-var-data-in' type='checkbox'><label for='data-28c8d087-a8c6-4d59-b0c8-0506f05dbd7a' 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>annual maximum daily maximum temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>cell_methods :</span></dt><dd>time: maximum (interval: 1Y)</dd></dl></div><div class='xr-var-data'><pre>array([[[        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        ...,\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan]],\n",
-       "\n",
-       "       [[        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
+       "    units:          K\n",
+       "    cell_methods:   time: point (interval: 1H) time: mean (interval: 1D)\n",
+       "    grid_mapping:   crs</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-791ea934-62e9-42a5-8150-0cd3911e2ab0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-791ea934-62e9-42a5-8150-0cd3911e2ab0' 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>: 365</li><li><span class='xr-has-index'>region</span>: 545</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5d1c700a-b769-444f-8868-ed6238dec6b9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5d1c700a-b769-444f-8868-ed6238dec6b9' class='xr-section-summary' >Coordinates: <span>(5)</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'>2015-01-01T12:00:00 ... 2015-12-...</div><input id='attrs-c619f6f8-453f-4710-b06f-afc7d2484d1f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c619f6f8-453f-4710-b06f-afc7d2484d1f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c71d349-5fb7-4479-8494-b39ac50b3aa9' class='xr-var-data-in' type='checkbox'><label for='data-2c71d349-5fb7-4479-8494-b39ac50b3aa9' 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'><pre>array([&#x27;2015-01-01T12:00:00.000000000&#x27;, &#x27;2015-01-02T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-01-03T12:00:00.000000000&#x27;, ..., &#x27;2015-12-29T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-12-30T12:00:00.000000000&#x27;, &#x27;2015-12-31T12:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.5</div><input id='attrs-6e0f2512-8eeb-4c33-b90d-5bc3228bc889' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6e0f2512-8eeb-4c33-b90d-5bc3228bc889' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d7d0547-ec53-4b4a-a425-24486aa6bac7' class='xr-var-data-in' type='checkbox'><label for='data-9d7d0547-ec53-4b4a-a425-24486aa6bac7' 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'><pre>array(1.5)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>region</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 542 543 544 545 546</div><input id='attrs-5e2e42b0-d272-406e-8598-e1ad63167841' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5e2e42b0-d272-406e-8598-e1ad63167841' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6f84f40-9b73-42f2-9b16-d2f04d773e64' class='xr-var-data-in' type='checkbox'><label for='data-b6f84f40-9b73-42f2-9b16-d2f04d773e64' 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'><pre>array([  0,   1,   2, ..., 544, 545, 546])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abbrevs</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;10050&#x27; &#x27;10180&#x27; ... &#x27;89399&#x27; &#x27;99399&#x27;</div><input id='attrs-6c64c467-1fc9-4192-91a8-65340e329d36' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6c64c467-1fc9-4192-91a8-65340e329d36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f3f067e-11f9-477d-8bc1-3c461a9752c0' class='xr-var-data-in' type='checkbox'><label for='data-5f3f067e-11f9-477d-8bc1-3c461a9752c0' 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'><pre>array([&#x27;10050&#x27;, &#x27;10180&#x27;, &#x27;10250&#x27;, &#x27;10300&#x27;, &#x27;10470&#x27;, &#x27;10500&#x27;, &#x27;10550&#x27;,\n",
+       "       &#x27;10600&#x27;, &#x27;10650&#x27;, &#x27;10750&#x27;, &#x27;10800&#x27;, &#x27;10850&#x27;, &#x27;10900&#x27;, &#x27;10950&#x27;,\n",
+       "       &#x27;11150&#x27;, &#x27;11200&#x27;, &#x27;11250&#x27;, &#x27;11300&#x27;, &#x27;11350&#x27;, &#x27;11400&#x27;, &#x27;11450&#x27;,\n",
+       "       &#x27;11500&#x27;, &#x27;11520&#x27;, &#x27;11570&#x27;, &#x27;11600&#x27;, &#x27;11650&#x27;, &#x27;11700&#x27;, &#x27;11720&#x27;,\n",
+       "       &#x27;11730&#x27;, &#x27;11750&#x27;, &#x27;11800&#x27;, &#x27;12000&#x27;, &#x27;12150&#x27;, &#x27;12160&#x27;, &#x27;12350&#x27;,\n",
+       "       &#x27;12380&#x27;, &#x27;12390&#x27;, &#x27;12700&#x27;, &#x27;12730&#x27;, &#x27;12750&#x27;, &#x27;12850&#x27;, &#x27;12870&#x27;,\n",
+       "       &#x27;12900&#x27;, &#x27;12930&#x27;, &#x27;12950&#x27;, &#x27;13010&#x27;, &#x27;13310&#x27;, &#x27;13340&#x27;, &#x27;13450&#x27;,\n",
+       "       &#x27;13550&#x27;, &#x27;13660&#x27;, &#x27;13800&#x27;, &#x27;13850&#x27;, &#x27;13910&#x27;, &#x27;14000&#x27;, &#x27;14100&#x27;,\n",
+       "       &#x27;14170&#x27;, &#x27;14220&#x27;, &#x27;14300&#x27;, &#x27;14350&#x27;, &#x27;14400&#x27;, &#x27;14500&#x27;, &#x27;14550&#x27;,\n",
+       "       &#x27;14600&#x27;, &#x27;14650&#x27;, &#x27;14700&#x27;, &#x27;14750&#x27;, &#x27;14850&#x27;, &#x27;14870&#x27;, &#x27;14900&#x27;,\n",
+       "       &#x27;14920&#x27;, &#x27;14950&#x27;, &#x27;15050&#x27;, &#x27;15240&#x27;, &#x27;15270&#x27;, &#x27;15300&#x27;, &#x27;15350&#x27;,\n",
+       "       &#x27;15520&#x27;, &#x27;15560&#x27;, &#x27;15650&#x27;, &#x27;15700&#x27;, &#x27;15750&#x27;, &#x27;15800&#x27;, &#x27;15850&#x27;,\n",
+       "       &#x27;15900&#x27;, &#x27;15950&#x27;, &#x27;15990&#x27;, &#x27;16100&#x27;, &#x27;16150&#x27;, &#x27;16200&#x27;, &#x27;16260&#x27;,\n",
+       "       &#x27;16350&#x27;, &#x27;16380&#x27;, &#x27;16400&#x27;, &#x27;16490&#x27;, &#x27;16550&#x27;, &#x27;16610&#x27;, &#x27;16700&#x27;,\n",
+       "       &#x27;16900&#x27;, &#x27;16950&#x27;, &#x27;17000&#x27;, &#x27;17040&#x27;, &#x27;17080&#x27;, &#x27;17100&#x27;, &#x27;17150&#x27;,\n",
+       "       &#x27;17200&#x27;, &#x27;17310&#x27;, &#x27;17350&#x27;, &#x27;17400&#x27;, &#x27;17420&#x27;, &#x27;17550&#x27;, &#x27;17620&#x27;,\n",
+       "       &#x27;17640&#x27;, &#x27;17650&#x27;, &#x27;17750&#x27;, &#x27;17850&#x27;, &#x27;17900&#x27;, &#x27;17950&#x27;, &#x27;18020&#x27;,\n",
+       "       &#x27;18050&#x27;, &#x27;18100&#x27;, &#x27;18200&#x27;, &#x27;18250&#x27;, &#x27;18350&#x27;, &#x27;18400&#x27;, &#x27;18450&#x27;,\n",
+       "       &#x27;18500&#x27;, &#x27;18710&#x27;, &#x27;19399&#x27;, &#x27;20110&#x27;, &#x27;20260&#x27;, &#x27;20570&#x27;, &#x27;20660&#x27;,\n",
+       "       &#x27;20740&#x27;, &#x27;20830&#x27;, &#x27;20910&#x27;, &#x27;21010&#x27;, &#x27;21110&#x27;, &#x27;21180&#x27;, &#x27;21270&#x27;,\n",
        "...\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan]],\n",
-       "\n",
-       "       [[        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        ...,\n",
-       "        [46.58202996, 45.07097501, 42.51886784, ..., 42.35056172,\n",
-       "         43.06443082, 44.26006765],\n",
-       "        [46.08975087, 44.13992066, 42.5203711 , ..., 43.27065759,\n",
-       "         45.20931421, 44.47848891],\n",
-       "        [45.88626555, 43.5185472 , 42.76288277, ..., 43.18956864,\n",
-       "         45.10550326, 44.02799513]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-58d69d01-eb93-415c-a864-8eed892d3425' class='xr-section-summary-in' type='checkbox'  ><label for='section-58d69d01-eb93-415c-a864-8eed892d3425' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d2b0a7ec-d0c4-4235-8ae9-b0aa36af5a24' class='xr-index-data-in' type='checkbox'/><label for='index-d2b0a7ec-d0c4-4235-8ae9-b0aa36af5a24' 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;2004-01-01&#x27;, &#x27;2005-01-01&#x27;, &#x27;2006-01-01&#x27;, &#x27;2007-01-01&#x27;,\n",
-       "               &#x27;2008-01-01&#x27;, &#x27;2009-01-01&#x27;, &#x27;2010-01-01&#x27;, &#x27;2011-01-01&#x27;,\n",
-       "               &#x27;2012-01-01&#x27;, &#x27;2013-01-01&#x27;, &#x27;2014-01-01&#x27;, &#x27;2015-01-01&#x27;,\n",
-       "               &#x27;2016-01-01&#x27;, &#x27;2017-01-01&#x27;, &#x27;2018-01-01&#x27;, &#x27;2019-01-01&#x27;,\n",
-       "               &#x27;2020-01-01&#x27;, &#x27;2021-01-01&#x27;, &#x27;2022-01-01&#x27;, &#x27;2023-01-01&#x27;,\n",
-       "               &#x27;2024-01-01&#x27;, &#x27;2025-01-01&#x27;, &#x27;2026-01-01&#x27;, &#x27;2027-01-01&#x27;,\n",
-       "               &#x27;2028-01-01&#x27;, &#x27;2029-01-01&#x27;, &#x27;2030-01-01&#x27;, &#x27;2031-01-01&#x27;,\n",
-       "               &#x27;2032-01-01&#x27;, &#x27;2033-01-01&#x27;, &#x27;2034-01-01&#x27;, &#x27;2035-01-01&#x27;,\n",
-       "               &#x27;2036-01-01&#x27;, &#x27;2037-01-01&#x27;, &#x27;2038-01-01&#x27;, &#x27;2039-01-01&#x27;,\n",
-       "               &#x27;2040-01-01&#x27;, &#x27;2041-01-01&#x27;, &#x27;2042-01-01&#x27;, &#x27;2043-01-01&#x27;,\n",
-       "               &#x27;2044-01-01&#x27;, &#x27;2045-01-01&#x27;, &#x27;2046-01-01&#x27;, &#x27;2047-01-01&#x27;,\n",
-       "               &#x27;2048-01-01&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;YS-JAN&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>region</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-be9e51ce-fe94-4074-9c8b-9789598e2166' class='xr-index-data-in' type='checkbox'/><label for='index-be9e51ce-fe94-4074-9c8b-9789598e2166' 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([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=&#x27;int64&#x27;, name=&#x27;region&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-43ef9fe4-1c32-4a35-84e5-17c2236378a0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-43ef9fe4-1c32-4a35-84e5-17c2236378a0' 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>long_name :</span></dt><dd>annual maximum daily maximum temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>regrid_method :</span></dt><dd>bilinear</dd><dt><span>cell_methods :</span></dt><dd>time: maximum (interval: 1Y)</dd></dl></div></li></ul></div></div>"
+       "       &#x27;53290&#x27;, &#x27;53360&#x27;, &#x27;53430&#x27;, &#x27;53570&#x27;, &#x27;53640&#x27;, &#x27;53710&#x27;, &#x27;53780&#x27;,\n",
+       "       &#x27;53800&#x27;, &#x27;53920&#x27;, &#x27;53990&#x27;, &#x27;54060&#x27;, &#x27;54130&#x27;, &#x27;54170&#x27;, &#x27;54200&#x27;,\n",
+       "       &#x27;54280&#x27;, &#x27;54310&#x27;, &#x27;54340&#x27;, &#x27;54410&#x27;, &#x27;54480&#x27;, &#x27;54550&#x27;, &#x27;54620&#x27;,\n",
+       "       &#x27;54690&#x27;, &#x27;54760&#x27;, &#x27;54830&#x27;, &#x27;54900&#x27;, &#x27;54970&#x27;, &#x27;55040&#x27;, &#x27;55110&#x27;,\n",
+       "       &#x27;55180&#x27;, &#x27;55250&#x27;, &#x27;55320&#x27;, &#x27;55390&#x27;, &#x27;55460&#x27;, &#x27;55530&#x27;, &#x27;55600&#x27;,\n",
+       "       &#x27;55670&#x27;, &#x27;55740&#x27;, &#x27;55810&#x27;, &#x27;55880&#x27;, &#x27;55950&#x27;, &#x27;56090&#x27;, &#x27;56160&#x27;,\n",
+       "       &#x27;56230&#x27;, &#x27;56300&#x27;, &#x27;56370&#x27;, &#x27;56460&#x27;, &#x27;56580&#x27;, &#x27;56620&#x27;, &#x27;56730&#x27;,\n",
+       "       &#x27;56790&#x27;, &#x27;56860&#x27;, &#x27;57000&#x27;, &#x27;57080&#x27;, &#x27;57140&#x27;, &#x27;57210&#x27;, &#x27;57280&#x27;,\n",
+       "       &#x27;57350&#x27;, &#x27;57420&#x27;, &#x27;57490&#x27;, &#x27;57630&#x27;, &#x27;57700&#x27;, &#x27;57770&#x27;, &#x27;57840&#x27;,\n",
+       "       &#x27;57910&#x27;, &#x27;57980&#x27;, &#x27;58050&#x27;, &#x27;58190&#x27;, &#x27;58260&#x27;, &#x27;58330&#x27;, &#x27;58400&#x27;,\n",
+       "       &#x27;58470&#x27;, &#x27;58510&#x27;, &#x27;58540&#x27;, &#x27;58570&#x27;, &#x27;58610&#x27;, &#x27;58680&#x27;, &#x27;58760&#x27;,\n",
+       "       &#x27;58820&#x27;, &#x27;58890&#x27;, &#x27;59030&#x27;, &#x27;59100&#x27;, &#x27;59170&#x27;, &#x27;59250&#x27;, &#x27;59310&#x27;,\n",
+       "       &#x27;59320&#x27;, &#x27;59330&#x27;, &#x27;59340&#x27;, &#x27;59350&#x27;, &#x27;59360&#x27;, &#x27;59370&#x27;, &#x27;60210&#x27;,\n",
+       "       &#x27;60410&#x27;, &#x27;60610&#x27;, &#x27;60810&#x27;, &#x27;61010&#x27;, &#x27;61210&#x27;, &#x27;61410&#x27;, &#x27;61510&#x27;,\n",
+       "       &#x27;61610&#x27;, &#x27;61810&#x27;, &#x27;62010&#x27;, &#x27;62210&#x27;, &#x27;62410&#x27;, &#x27;62610&#x27;, &#x27;62810&#x27;,\n",
+       "       &#x27;63010&#x27;, &#x27;63210&#x27;, &#x27;63410&#x27;, &#x27;63610&#x27;, &#x27;63810&#x27;, &#x27;64010&#x27;, &#x27;64210&#x27;,\n",
+       "       &#x27;64610&#x27;, &#x27;64810&#x27;, &#x27;65010&#x27;, &#x27;65210&#x27;, &#x27;65410&#x27;, &#x27;65610&#x27;, &#x27;65810&#x27;,\n",
+       "       &#x27;70200&#x27;, &#x27;70420&#x27;, &#x27;70540&#x27;, &#x27;70620&#x27;, &#x27;70700&#x27;, &#x27;71000&#x27;, &#x27;71300&#x27;,\n",
+       "       &#x27;72200&#x27;, &#x27;72300&#x27;, &#x27;72330&#x27;, &#x27;72800&#x27;, &#x27;73600&#x27;, &#x27;74050&#x27;, &#x27;74550&#x27;,\n",
+       "       &#x27;74560&#x27;, &#x27;74660&#x27;, &#x27;74680&#x27;, &#x27;79399&#x27;, &#x27;89399&#x27;, &#x27;99399&#x27;], dtype=&#x27;&lt;U5&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>names</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>&lt;U37</div><div class='xr-var-preview xr-preview'>&#x27;Albury&#x27; ... &#x27;Unincorp. Other Te...</div><input id='attrs-bf2f6065-0ed7-4eb1-a163-fded96257a58' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bf2f6065-0ed7-4eb1-a163-fded96257a58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47893dba-dcf7-4309-8cbe-e1968b910065' class='xr-var-data-in' type='checkbox'><label for='data-47893dba-dcf7-4309-8cbe-e1968b910065' 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'><pre>array([&#x27;Albury&#x27;, &#x27;Armidale&#x27;, &#x27;Ballina&#x27;, &#x27;Balranald&#x27;, &#x27;Bathurst&#x27;,\n",
+       "       &#x27;Bayside (NSW)&#x27;, &#x27;Bega Valley&#x27;, &#x27;Bellingen&#x27;, &#x27;Berrigan&#x27;,\n",
+       "       &#x27;Blacktown&#x27;, &#x27;Bland&#x27;, &#x27;Blayney&#x27;, &#x27;Blue Mountains&#x27;, &#x27;Bogan&#x27;,\n",
+       "       &#x27;Bourke&#x27;, &#x27;Brewarrina&#x27;, &#x27;Broken Hill&#x27;, &#x27;Burwood&#x27;, &#x27;Byron&#x27;,\n",
+       "       &#x27;Cabonne&#x27;, &#x27;Camden&#x27;, &#x27;Campbelltown (NSW)&#x27;, &#x27;Canada Bay&#x27;,\n",
+       "       &#x27;Canterbury-Bankstown&#x27;, &#x27;Carrathool&#x27;, &#x27;Central Coast (NSW)&#x27;,\n",
+       "       &#x27;Central Darling&#x27;, &#x27;Cessnock&#x27;, &#x27;Clarence Valley&#x27;, &#x27;Cobar&#x27;,\n",
+       "       &#x27;Coffs Harbour&#x27;, &#x27;Coolamon&#x27;, &#x27;Coonamble&#x27;, &#x27;Cootamundra-Gundagai&#x27;,\n",
+       "       &#x27;Cowra&#x27;, &#x27;Cumberland&#x27;, &#x27;Dubbo&#x27;, &#x27;Dungog&#x27;, &#x27;Edward River&#x27;,\n",
+       "       &#x27;Eurobodalla&#x27;, &#x27;Fairfield&#x27;, &#x27;Federation&#x27;, &#x27;Forbes&#x27;,\n",
+       "       &#x27;Georges River&#x27;, &#x27;Gilgandra&#x27;, &#x27;Glen Innes Severn&#x27;,\n",
+       "       &#x27;Goulburn Mulwaree&#x27;, &#x27;Greater Hume&#x27;, &#x27;Griffith&#x27;, &#x27;Gunnedah&#x27;,\n",
+       "       &#x27;Gwydir&#x27;, &#x27;Hawkesbury&#x27;, &#x27;Hay&#x27;, &#x27;Hilltops&#x27;, &#x27;Hornsby&#x27;,\n",
+       "       &#x27;Hunters Hill&#x27;, &#x27;Inner West&#x27;, &#x27;Inverell&#x27;, &#x27;Junee&#x27;, &#x27;Kempsey&#x27;,\n",
+       "       &#x27;Kiama&#x27;, &#x27;Ku-ring-gai&#x27;, &#x27;Kyogle&#x27;, &#x27;Lachlan&#x27;, &#x27;Lake Macquarie&#x27;,\n",
+       "       &#x27;Lane Cove&#x27;, &#x27;Leeton&#x27;, &#x27;Lismore&#x27;, &#x27;Lithgow&#x27;, &#x27;Liverpool&#x27;,\n",
+       "       &#x27;Liverpool Plains&#x27;, &#x27;Lockhart&#x27;, &#x27;Maitland&#x27;, &#x27;Mid-Coast&#x27;,\n",
+       "       &#x27;Mid-Western&#x27;, &#x27;Moree Plains&#x27;, &#x27;Mosman&#x27;, &#x27;Murray River&#x27;,\n",
+       "       &#x27;Murrumbidgee&#x27;, &#x27;Muswellbrook&#x27;, &#x27;Nambucca Valley&#x27;, &#x27;Narrabri&#x27;,\n",
+       "       &#x27;Narrandera&#x27;, &#x27;Narromine&#x27;, &#x27;Newcastle&#x27;, &#x27;North Sydney&#x27;,\n",
+       "...\n",
+       "       &#x27;Rockingham&#x27;, &#x27;Sandstone&#x27;, &#x27;Serpentine-Jarrahdale&#x27;, &#x27;Shark Bay&#x27;,\n",
+       "       &#x27;South Perth&#x27;, &#x27;Stirling&#x27;, &#x27;Subiaco&#x27;, &#x27;Swan&#x27;, &#x27;Tammin&#x27;,\n",
+       "       &#x27;Three Springs&#x27;, &#x27;Toodyay&#x27;, &#x27;Trayning&#x27;, &#x27;Upper Gascoyne&#x27;,\n",
+       "       &#x27;Victoria Park&#x27;, &#x27;Victoria Plains&#x27;, &#x27;Vincent&#x27;, &#x27;Wagin&#x27;,\n",
+       "       &#x27;Wandering&#x27;, &#x27;Wanneroo&#x27;, &#x27;Waroona&#x27;, &#x27;West Arthur&#x27;, &#x27;Westonia&#x27;,\n",
+       "       &#x27;Wickepin&#x27;, &#x27;Williams&#x27;, &#x27;Wiluna&#x27;, &#x27;Wongan-Ballidu&#x27;, &#x27;Woodanilling&#x27;,\n",
+       "       &#x27;Wyalkatchem&#x27;, &#x27;Wyndham-East Kimberley&#x27;, &#x27;Yalgoo&#x27;, &#x27;Yilgarn&#x27;,\n",
+       "       &#x27;York&#x27;, &quot;Break O&#x27;Day&quot;, &#x27;Brighton&#x27;, &#x27;Burnie&#x27;,\n",
+       "       &#x27;Central Coast (Tas.)&#x27;, &#x27;Central Highlands (Tas.)&#x27;,\n",
+       "       &#x27;Circular Head&#x27;, &#x27;Clarence&#x27;, &#x27;Derwent Valley&#x27;, &#x27;Devonport&#x27;,\n",
+       "       &#x27;Dorset&#x27;, &#x27;Flinders (Tas.)&#x27;, &#x27;George Town&#x27;, &#x27;Glamorgan-Spring Bay&#x27;,\n",
+       "       &#x27;Glenorchy&#x27;, &#x27;Hobart&#x27;, &#x27;Huon Valley&#x27;, &#x27;Kentish&#x27;, &#x27;King Island&#x27;,\n",
+       "       &#x27;Kingborough&#x27;, &#x27;Latrobe (Tas.)&#x27;, &#x27;Launceston&#x27;, &#x27;Meander Valley&#x27;,\n",
+       "       &#x27;Northern Midlands&#x27;, &#x27;Sorell&#x27;, &#x27;Southern Midlands&#x27;, &#x27;Tasman&#x27;,\n",
+       "       &#x27;Waratah-Wynyard&#x27;, &#x27;West Coast&#x27;, &#x27;West Tamar&#x27;, &#x27;Alice Springs&#x27;,\n",
+       "       &#x27;Barkly&#x27;, &#x27;Belyuen&#x27;, &#x27;Central Desert&#x27;, &#x27;Coomalie&#x27;, &#x27;Darwin&#x27;,\n",
+       "       &#x27;East Arnhem&#x27;, &#x27;Katherine&#x27;, &#x27;Litchfield&#x27;, &#x27;MacDonnell&#x27;,\n",
+       "       &#x27;Palmerston&#x27;, &#x27;Roper Gulf&#x27;, &#x27;Tiwi Islands&#x27;, &#x27;Victoria Daly&#x27;,\n",
+       "       &#x27;Wagait&#x27;, &#x27;West Arnhem&#x27;, &#x27;West Daly&#x27;, &#x27;Unincorporated NT&#x27;,\n",
+       "       &#x27;Unincorporated ACT&#x27;, &#x27;Unincorp. Other Territories&#x27;], dtype=&#x27;&lt;U37&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d69a26e6-6a22-4d60-902e-d4aff6db3f11' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d69a26e6-6a22-4d60-902e-d4aff6db3f11' 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_mean</span></div><div class='xr-var-dims'>(time, region)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>303.1 297.2 298.4 ... 291.7 294.8</div><input id='attrs-f813984c-02dc-40f2-967f-07a142a1ac8c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f813984c-02dc-40f2-967f-07a142a1ac8c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc068a75-d4e9-4e98-93c1-84c1265433ed' class='xr-var-data-in' type='checkbox'><label for='data-cc068a75-d4e9-4e98-93c1-84c1265433ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: point (interval: 1H) time: mean (interval: 1D)</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd></dl></div><div class='xr-var-data'><pre>array([[303.06374795, 297.23376463, 298.42299769, ..., 301.65508899,\n",
+       "        299.11614204, 296.30974085],\n",
+       "       [302.78928183, 298.54175059, 300.24185627, ..., 301.36101319,\n",
+       "        296.74984028, 295.90574982],\n",
+       "       [303.08368689, 298.67292911, 300.19160016, ..., 302.4548882 ,\n",
+       "        297.8965796 , 296.62589859],\n",
+       "       ...,\n",
+       "       [291.99723754, 285.98999268, 293.88448028, ..., 302.9580381 ,\n",
+       "        285.83992256, 293.60259794],\n",
+       "       [294.05764453, 288.31439858, 293.59143612, ..., 302.88994758,\n",
+       "        288.7319057 , 293.87447115],\n",
+       "       [296.60948578, 289.87005354, 294.45204951, ..., 302.82060494,\n",
+       "        291.6514326 , 294.8187207 ]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b14065ab-790a-4ee1-88f8-0aa8b4396cbc' class='xr-section-summary-in' type='checkbox'  ><label for='section-b14065ab-790a-4ee1-88f8-0aa8b4396cbc' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7a46ccca-50bd-4642-ab79-168660540c86' class='xr-index-data-in' type='checkbox'/><label for='index-7a46ccca-50bd-4642-ab79-168660540c86' 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;2015-01-01 12:00:00&#x27;, &#x27;2015-01-02 12:00:00&#x27;,\n",
+       "               &#x27;2015-01-03 12:00:00&#x27;, &#x27;2015-01-04 12:00:00&#x27;,\n",
+       "               &#x27;2015-01-05 12:00:00&#x27;, &#x27;2015-01-06 12:00:00&#x27;,\n",
+       "               &#x27;2015-01-07 12:00:00&#x27;, &#x27;2015-01-08 12:00:00&#x27;,\n",
+       "               &#x27;2015-01-09 12:00:00&#x27;, &#x27;2015-01-10 12:00:00&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2015-12-22 12:00:00&#x27;, &#x27;2015-12-23 12:00:00&#x27;,\n",
+       "               &#x27;2015-12-24 12:00:00&#x27;, &#x27;2015-12-25 12:00:00&#x27;,\n",
+       "               &#x27;2015-12-26 12:00:00&#x27;, &#x27;2015-12-27 12:00:00&#x27;,\n",
+       "               &#x27;2015-12-28 12:00:00&#x27;, &#x27;2015-12-29 12:00:00&#x27;,\n",
+       "               &#x27;2015-12-30 12:00:00&#x27;, &#x27;2015-12-31 12:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=365, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>region</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-18ce9252-bdd7-43d0-9980-97bd9de9282b' class='xr-index-data-in' type='checkbox'/><label for='index-18ce9252-bdd7-43d0-9980-97bd9de9282b' 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([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "       ...\n",
+       "       537, 538, 539, 540, 541, 542, 543, 544, 545, 546],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;region&#x27;, length=545))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-86145394-263f-4d4f-8286-c0f1993ab81f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-86145394-263f-4d4f-8286-c0f1993ab81f' 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>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: point (interval: 1H) time: mean (interval: 1D)</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.Dataset> Size: 49kB\n",
-       "Dimensions:   (model: 13, time: 45, region: 10)\n",
+       "<xarray.Dataset> Size: 2MB\n",
+       "Dimensions:   (time: 365, region: 545)\n",
        "Coordinates:\n",
-       "  * time      (time) datetime64[ns] 360B 2004-01-01 2005-01-01 ... 2048-01-01\n",
-       "    abbrevs   (region) <U8 320B 'WANorth' 'WASouth' 'NSW' ... 'QLDSouth' 'AUS'\n",
-       "    names     (region) <U48 2kB 'Western Australia North' ... 'Australia'\n",
-       "  * region    (region) int64 80B 0 1 2 3 4 5 6 7 8 9\n",
-       "Dimensions without coordinates: model\n",
+       "  * time      (time) datetime64[ns] 3kB 2015-01-01T12:00:00 ... 2015-12-31T12...\n",
+       "    height    float64 8B 1.5\n",
+       "  * region    (region) int64 4kB 0 1 2 3 4 5 6 7 ... 540 541 542 543 544 545 546\n",
+       "    abbrevs   (region) <U5 11kB '10050' '10180' '10250' ... '89399' '99399'\n",
+       "    names     (region) <U37 81kB 'Albury' ... 'Unincorp. Other Territories'\n",
        "Data variables:\n",
-       "    TXx_mean  (model, time, region) float64 47kB nan nan nan ... 45.11 44.03\n",
+       "    tas_mean  (time, region) float64 2MB 303.1 297.2 298.4 ... 302.8 291.7 294.8\n",
        "Attributes:\n",
-       "    long_name:      annual maximum daily maximum temperature\n",
+       "    long_name:      Near-Surface Air Temperature\n",
        "    standard_name:  air_temperature\n",
-       "    units:          degC\n",
-       "    regrid_method:  bilinear\n",
-       "    cell_methods:   time: maximum (interval: 1Y)"
+       "    units:          K\n",
+       "    cell_methods:   time: point (interval: 1H) time: mean (interval: 1D)\n",
+       "    grid_mapping:   crs"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "%%time\n",
-    "# use acs_regiona_stats to calculate the regional mean for each model and month\n",
-    "da_summary =  acs_regional_stats(ds=ds, var=var, mask = mask, dims = (\"lat\", \"lon\",), how = [\"mean\"], chunks=4,)\n",
+    "# select only the LGAs you are interested in\n",
+    "var=\"tas\"\n",
+    "\n",
+    "da_summary = acs_regional_stats(ds=ds,\n",
+    "                                var=var,\n",
+    "                                mask = mask, \n",
+    "                                dims = (\"lat\", \"lon\",), \n",
+    "                                how = [\"mean\"],\n",
+    "                                outfile=\"out.csv\",\n",
+    "                               chunks=1)\n",
     "da_summary"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "20eee231-ccb4-4cf1-aa04-b9936849b34e",
+   "execution_count": null,
+   "id": "47c3cf06-3739-47d9-9d00-83763fc2bcd0",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# select the region you like and plot the regional average for each month of each model\n",
-    "da_summary[f\"{var}_mean\"].sel(region=9).plot.line(x=\"time\");"
-   ]
+   "outputs": [],
+   "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "9eca8294-125f-4f1e-8fe5-11e7bf5fea2a",
+   "execution_count": null,
+   "id": "41c06e83-2df7-4e29-bb0b-d5a1dde32eca",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f805a3d-ee2d-40aa-a003-5b6755992048",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='time', ylabel='TXx_mean'>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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vn/HxdF2Hrp/orIhGo+W9YEIIdMvOu9hYZmzaYYDjGRbHsdEUfIoIQRDgkUSkTAu6xfNqD89Xpsi5lJMZVRaRMGyYeQRUls1xPJICIBQ8yTqXkFfGYEyHYXG0+/ObRZQhi+6+MdN2MM2KM0LmnJqfCN18883YuHEjLrvssgnv55zjwQcfxKpVq3D55Zejs7MTr3vd6/CrX/1qxsfbtGkTwuFw9m3x4sUVvHpC5h6bO7Ds/GYIAW6dUNKwZu0cG07oGEuZCHncUxNVZtBNXvY6Id2yi+2cz8qsD5mpYDpl2OgZSWLbkVEcG02h1VfaaVCGXxGRMuy81mpMJjIBlg2YdTjkkpBaqWkgdN9992Hbtm3YtGnTlI8NDAwgHo/ji1/8It761rfioYcewlVXXYWrr74ajz322LSPeccddyASiWTfenp6KvlXIGTOMW0OizvIMw4aVzA9fdBgcwfHRlPwSCx70uTWFzlIlblzLK5ZkPO9+GnIIoOZowiccwejCQN7+mLYcmgEr/ZGoRkc80PegoqaZyIIAtr8Ctr9asF/lgluoTfNEiLkhJodjvb09OCWW27BQw89lLPmh3P3F/XKK6/Exz72MQDAGWecgaeffhrf+ta3cPHFF+d8XFVVoaqFP0EQQvJjcQc255BYfk8fishg2G4g5J8mHzOc0DGSMDAvMPF3VxSYOzSwxVvydQPu7J24bkGeYUda/oRscKdbNkYSBvoiGobjBmzHQdgjoyUsz7hdvlglpQodUI0QIePULBDaunUrBgYGsGHDhuz7bNvG448/jjvvvBOJRAKSJGHNmjUT/typp56KJ598stqXSwhJs20HNkfeNUJCtnMs9813/BLSyakejyxiLGXCcZyyBBTuslVe5I6xiSQmIJIyYQ7GcTyiIaZZUCWGNr9SUiF2pTFBKGlXGiHNpmaB0KWXXoodO3ZMeN9NN92E1atX4/bbb4eqqjjnnHOwe/fuCZ+zZ88enHTSSdW8VELIOCbnsDkvaDIzg4DkNDvHxpLuEtLWHEtI1XR9kWZyeAsYHDidTMeYv8DJzrmosojesRS4AwRVCd1hT0kF2NUiiTRUkZDxahYIBYNBrF27dsL7/H4/2tvbs+//h3/4B7znPe/BRRddhEsuuQS///3v8Zvf/AabN2+uwRUTQgDAsgs/nVGk6dvNj0dS4NMsIVUkhtEkR9KwyhII6RaHZTtlObEJeST4FLGuT39ycWcJ0YkQIRl1/Rt81VVX4Vvf+ha+9KUvYd26dfj2t7+NX/ziF7jwwgtrfWmEzFmmzVHoaB8lPdHYmlSbEtVMDER1tHhzt4FnBjKWq2DaTc+VZy6RIAgNFwQB7nRpw5r6vSBkrqqrSRK5Tno+8IEP4AMf+ED1L4YQkpNlcxSaAXJPhAxoFkdgXPDQH9GgWTbaA9M3OEiCgGjKBFqLveITUoYFVt+v/ypOYgKShg3TdlCmRjZCGtrcfkYghBRMswqrDwIAWRRg2BMXlSYNC8cjGsLTnAZlqLKIqGaBF7jBPpeEbpepY6xxySKDyR3qHCMkjQIhQkhBdJPnPUwxQxAEOM7EQGgwpiNpWPDPUvujSgyaaZecHuPcQVK3GzKdVU4iE8A5zRIiJGNuPyMQQgriOO7W9UJPhABAZCy7c0y3bBwbTSGgzj5nR5UYdIuXHAjpFodh22VpnW90DkCBECFp9IxASJMYSxroj2oV3SxucwcWd4oKhFSRIZJyO8eG4gaimomgZ/YyRUEQACf/xa3TMSwOs0wdY83AtGgDPSEABUKENI0jI0ls74lg/2C8Yh1BFndg2/lvnh9Plhg0iyNl2Dg2moRXlvKeuyOJAiIpo+CvOZ5u2bCd4oK4ZiMJNEuIkAwKhAhpAknDwmjCgCIx7BuI47W+2Iy7vYpl2rzoYCKzqLQ3ksJowkDYm/8SUo8kIpayZ13cOpNyts43Oik9zoAQQoEQIU0hkjKRMm20+mR0Bj04OpLEK8ci2VRUuVi2A9vOf73GeJlFpaMJA7IoFvQYqsygWVZJdUJJw4Io0FMekBmqaFc0jUpIo6BnBUKawGBMhyyy7JC/7hYvRpMGdhyLYCCqle3rWNwBh1P0KgkHAsaSJlp8+Z8GAenFrem0WjE4dxBJmVQonSaJAizOqYWeEFAgREhZJXQL+wdiVZ3amzQsjCYNBMZtdmeCgPkhLyyLY0dvBIeHE2WZw2Px0v5ePkWEJBY+kVkQBEAovmA6qpmIpqwJ/0ZzmSwyWLZbPE7IXEeBECFl1BfRsHcgjiMjyap9zUjKRMqw4ZWnzuNpD6jwSRJeOx7Fnv4YjGk2wOfLsh2UUmrc6lPQ7p9+ivRMFFHEWJEF08NxA7bDqWMsTWQCTNuBWeLPAyHNgJ4VCCkT3bJxPKpBYgwHhxMYiJUvJTWT8WmxXAIeCe0BFYeGk9jZG0FCL75bSLdsCCWFQsVTJYa4ZhV82mbaHANRHX6lsHRcM3NTmzRUkRCAAiFCymY0YSKuWZgXVCFCwIGBRElBRz5ypcVyUSUR80Me9EU17DgWwWiiuJMVzSxumGI5eGQRmlX4hOmxpImYblJabBLHEahGiBBQIERIWTiOg+ORFFSJgQkC2vwKIppR0Zk+gJsW08zcabHJRCZgQdiLuGZhT3+sqFZ03SpuhlA5yCKDaTsFB0LDcR2CUFynWzNjggDdpECIEAqECCmDSMrEcMJAyOOmXwRBwLyAB8cjKfRUsF5oMKZDYtOnxSYTBAGtPgUJw0LSKOy0ynEcGFaNBxI6QLKAUzbNtDEY1xFQKS02mSTSUEVCAAqECCmLwZgO23YmtGfLIkPYo+DAcAKDMb3sXzPftNhkiuSerCQL7MAybQc2L3zzfDmpEkMklf/NeyxpIqHbsy52nYvcWUJ0IkQIBUKElEgzbfRHtexp0Hh+VYIIAfsH4gWfwMymkLTYZAKAuFbY9ZSyZ6xcVElETLfyLvIdiGmQmZD3idlcIqVnM1Vz1AMh9YgCIUJKNJIwENct+NXcAUmmXmjfQHnrhQpNi43nkUSMJo2CJgubnMPiTs1qhAB3wrSeZ8F0QrcwkjAQzBGgEvdEiGYJEUKBECEl4dxB71gKHkmcNiCpRL1QsWmxDFVmSBhWev9WfizbAa/xiVBmEGA+gxVHkwY0k8NLabGcMsXn1DlG5joKhAgpwVjKxFjSRGiWBaKZeqGDZaoXKiUtBrit6IbFC2rvt2wOB6h5mkmAMGvBNOcO+qMaPLRSY1oiE2BzTrOEyJxHzxKElGAwpoE7Tl4Ti/2qBKFM9UKlpMUAt3Xa5iioYNrkDuphe7siMozNskw2plsYS5mUFpuNIFAgROY8CoQIKVLKsNEf1XMWSU+n3a9gLOXWCxUzxwcoPS2W4QYU+Q9WtG0HqNFU6fE8soiEbs24LmQ0ocO0HFqymgfTqn1wS0gt0bMEIUUaTuhImTZ8BdSgCIKAzqAHvWMp9Iwkivq6pabFMjwyQzSV/8oK3bIh1kH3lSoxaBaftmDasjn6ojq1zOdBEmiWECEUCJGacxynoO6lemBzB8cj2oxF0tORRYYWr4L9QwkMRAvfR1ZqWiwjs7IikWd6LFXD9RrjSSKDPUPBdCRlIqbRpvl8SCJDyqDUGJnbKBAiNdcb0fBqbxS6VdiAv1oaSxpukbSnuJutX5UgCQx7+mOIJGeudxmvXGkxIN2BZeXXgQW46zXqIRACAMaEaQu9RxIGuONAok3zs3KHKtoN90KEkHKiZwpSc0MxHQcG43jteAxagXukaqU/pgEo7Wbb5legmRy7+6N5ByPlSotlMIEhps0eiHHuwKzhnrHJFNGdgzSZbtnoj+kI0mlQXiTRLZamFnoyl1EgRGrKsDhimok2v4rjYynsOh4t+wTmckvoFgYLLJKezrygipGEgT39sby6d8qVFstQZZbXYEWTc9hOjfeMjeORGVKmPeUUMZI0kdBM+CkQyossMlichiqSuY0CIVJTCd2CZnIEVAnzw170RzXs7I0iXsB8m2obSRjpIunSb7ZMENAV9OB4RMP+gTj4DJ1k5UyLZXhlESlj9knNlu3Ath1IrD6eMlRJhG5OTesNxnSIjIHVQVF3IxCZAMt2T/sImavq41mNzFkJw4LtuLUnIhPQHfZiOGHg1WMRRPNI2VSbZXMcG0uVJQjKkESGdr+Cw8MJ9IxOP3m63Gkx4EQH1mzzhCy79nvGxhOZANtxJgRwKcPGUEJHsMi6rbnIDRgdmiVE5jQKhEhNjSWNCacMTBDQHfIgqpnYeSyKsRx1ILU0mjQRTZllSYuN55FFhLwK9g7E0T9NJ1m502JAZkq0g6Q+SyBUZ6kxAGAQJiyOHU0a0IzyBopzgeMIVCNE5jQKhEjNmDZHJGVNuXEJ6XRRwrDwSm8UI4n6CYb6oxoEQahIQBBQJSiMYXdfbEoAWIm0WMZ0hcfjWdypg1GKE6mSO2E6M35hIKpBKWKcwVzHBAG6SYEQmbsoECI1k9Td2pRcr+AzgwcNk+OVY5Gy7OcqVVy3MBTXEa7g2oZWvwLD4nitLzahaLwSabEMjywiNsukZjO9Z6yeqDJDyrChWxwxvXKBYrOTRBqqSOY2CoRIzSQMyy3AnaEFfV5QBecOXu2NoC9S+PDBchqO624wUuGJxZ1BFZGUgT19sWxwUom0WIZHYtAMe8YWftPi9bBmbILxBdORpAnd4vBQWqxg7iwhOhEicxcFQqRmIikzrxRTe0AFEwTsPB7BsbFUTYa/mTbH8TEN/jIWSU/HTQ160RfVsH8whniFTzskkcF2HCRmGFug2/UzQyhDZAI4HMR1C8cjKfhkOg0qhiQy6BbPe9UKIc2GAiFSEzZ3MJYw8071tPoUKKKIXb1R7B+MV/1JezRhIJIyqrbNXGQCOgIqjoykcGgoXrG0WAYThBkHK+pm/UyVHo8JAobjOqIpCwHqFiuKxARYNs0SInMXBUKkJhKGhZRlFZRmCntlBD0S9g3EsfN4/tOYS2XZHD2jSUgiq2owoEoiwh4ZR0e1iqXFMryyiNGkOe0cI82063JlhUdiSJkctsMh1+H1NQJZZLBshzrHyJxFL6FITSR1G6ZV+M3Lp0iQRYbesRSSho1VXUG0+ZUKXaVrMK5jKKajM+ip6NfJxa9KEJlQ8Zu8KjEkdAsp054yldmyuTtDqA67sVRJxGBcQ9hb2Z+BZiYyATbnNEuIzFn0EorURDRlggnF/fjJIsOCsBcJ3cL2o2M4OpqsWN2QYXEcHk7CI0s1OxHxyGLFT6JUiUG37Jx1QhZ3YPH6TI15FRFhr0JDFEslCBQIkTmLAiFSdZw7GEkaJXVfZdrrJcawszeKvf3xGdu/i9UXSSGSNNDiq05tUK24aTcByRyrTSzuwOYOJLH+AiHATZnSSo3SmRbVCJG5iQIhUnVJ04Zm2PDIpf/4hb0yWn0KDgwlsLM3UtYdZSnDxpHRFPzq3LjRqhLDaHJqwbRlc3COujwRIuUhCTRLiMxdFAiRqkvqFjSLQ5XK0wXlkUXMD3nQH9Wx4+hY2YYv9o6lENdMhOZI2sUji4jr1pSN7qbtwOZ8TgSDc5Uksln3zRHSrCgQIlUX1UyU+3DBXdjqgW5y7Dg2hsPDiRk3uc8mppk4OppE2KvMmZUNHtkdUDh575jNHWCO/BvMVRIToBm8pN8ZQhpV3QRCmzZtgiAIuPXWW7Pve//73w9BECa8nXfeebW7SFIyx3EwmjQrMgFYEAS0B1R4JQm7+2I4MpIoqojacRwcHU1Cs+w5tbJBZAIshyNpTj4R4qi7sdKkrCRRgMk5TE4F02TuqYtn+S1btuCuu+7C+vXrp3zsrW99K7773e9m/1tRqE22kaVMGwndquiE5oBHgiAA+wYSkCURC1u8Bf35saSJ4xENbT61QldYv2SBIZIyJvybmTYHq5/XTKQCZJEhbljQy5iyJqRR1PzZLR6P48Ybb8Tdd9+N1tbWKR9XVRXz58/PvrW1tdXgKkm5JHQbumlDlSr7o+dXJXhkEXv6ohiI5b+jjHMHPaNJcI45ubdKlUVEEpabDkvTTBus5s8UpJIyQxU1k+qEyNxT86e3m2++GRs3bsRll12W8+ObN29GZ2cnVq1ahQ9/+MMYGBiY8fF0XUc0Gp3wRupHXDMBCFWpu3Hbqhl298UwmjDy+jPDCQP9UR2tvrl58uiVRWiWhfGb7zXLhkSR0BzgQKflq2QOqumz23333Ydt27Zh06ZNOT/+tre9DT/60Y/wpz/9CV/+8pexZcsWvOlNb4KuT98VtGnTJoTD4ezb4sWLK3X5pAgjSaOqJy1tfgWGybG7LzbjLi3AbRM/MpIAEwClwidW9UqRGHTLya4vcRwHuulQ6/wcIDE26+8IIc2oZs/2PT09uOWWW3DvvffC48m9uuA973kPNm7ciLVr1+Id73gHfve732HPnj148MEHp33cO+64A5FIJPvW09NTqb8CKZBm2ojrVlnmBxViXlBFVDOxuy82436ywbiOwZiOdv/cqw0ajwnIzmOyuAPbcepu8zwpP1USEdWsik1pJ6Re1SwQ2rp1KwYGBrBhwwZIkgRJkvDYY4/ha1/7GiRJgm1PvWF1d3fjpJNOwt69e6d9XFVVEQqFJryR+pDQLegmr3rtjSAI6Ap5MBTXsac/NmVODgDolo3Dw0l4ZWnOn36okoiRdCrRsh3Ydn2u1yDlpUoMumlDo/QYmWNq1jV26aWXYseOHRPed9NNN2H16tW4/fbbIYpTb5bDw8Po6elBd3d3tS6TlFFCt+HAqclgPiYImB/y4ngkBVkUsKorOGF3WH9Ew1jSQHe4sA6zZuSR3eF6mmnD4hyWQ6mxuUAWGQzLLZguZf0NIY2mZoFQMBjE2rVrJ7zP7/ejvb0da9euRTwex2c+8xlcc8016O7uxqFDh/DJT34SHR0duOqqq2p01aQUo0kDao4At1pE5u4nOzKShMQYVnYGwJiQXaURmCOrNGajSiIiKRNJw4YAwLYpNTYXiEwAhwMtx4kpIc2sLuYI5SKKInbs2IEf/OAHGBsbQ3d3Ny655BL89Kc/RTAYrPXlkQLplo2oVplBioWQRYZ2v4qDQ3EokoCT2v04NpZEXDOxgE6DALg3RAduKlNN13PNlenacx0TMGWyOCHNrq4Coc2bN2f/v9frxR/+8IfaXQwpq4RuQzM4gsHab3H3yCLCXgX7BhKwHeDYaAotc2iVRj4kJiCSMtDGVJopPYfIjCFKnWNkjpmbPcKk6hK6BY76qTXJDFw8OJSAZtnwz6FVGvnwyiKiKXfSMEVCc4cqi0gYdnqtCiFzAwVCdWam9u5GNpo0INfZUL6wV0ZAkTAvkHt8w1zmkUWkLBvRlAlWJ8ErqTxVYjAsmyZMkzmlvu5Mc5xm2nitL4pIsrmOpk2bI6pZ8Nbhygq/KkEW6ddgMllksG0Oi3MqlJ5DZJHBtDi10JM5he4AdSSmWRiOG4jpzRUIJXQLmmFXfZAiKY0gCDBtB+Icqp1KGhZ++eJRRFPN9TtYGIFOhMicQnemOhJJGYjrbjDUTBKGDcvhE+b2kPqnSiIShlU3dV3V8P1nDuM7Tx3Cj54/UutLqRmJCU33YoyQmdCdqU5w7mAobrhFqprZVK/IIkkDskA/ao3GI4lIaBYkcW4EQpbN8cTeQQDAyz1jtb2YGlLThfK0aoPMFXR3qhNxw0LCsNDmV5Ay7Oyup0Zn2RxjydrPDyKF8yoi2v1qXdZ2VcJLPWOIae7v3bGxFEYTzXUymy9FZNAt2+0YJGQOoECoTsQ0C5blwCOLcBwg1iQ1CgnDRsqikf2NKuSV58x8pcf2DE7471d6IzW6ktpSJAYzvWqDkLmAAqE6MZLQsykIryxiKGE0xdF00rBg2Q51ZpG6ppk2nj04DAA4pcudXP9Kb7SWl1QzIhPAHYc6x8icQXenOqCZNsaSJvyKO9TPq7i1GYkmmCkUTZm0v4vUvecPjkAzObpCKq46cyEA4JVjc/NEKCNpNEd6npDZUCBUB+K6hZRxIn3kkUXoto241thPRJw7GE2ac6bGhDSuTFrs4lWdWLswDAA4MpJEpElS1IVSRIZoqrGffwjJFwVCdSCWMuE4mHBywgSGsWRjF2smDAspoz4HKRKSEdNMbDsyCgC4eNU8hL0ylrT5AACvNnid0Ku9EQzF9YL/XGZ0gkWrNsgcQIFQjXHuYDCuTykm9skiRhJGQz8RJQ0bhs2hSPRjRurX0/uHYXEHyzr82QAocyrUyOmxF4+M4hP378CX/rC74D+rZFZtUOcYmQNo02SNJQwLCd1C0DNxK7tPETGccAcstviUGl1dbpppw+IOHMcBd5D9X+444I4DOAB3gNGEQfVBpO6dSIvNy75v7YIQfrvjeEMXTP9mey8AYE9/DKbNC2pYkEV3qrhm2gjQQmLS5OgnvMZimgXDdqBKE0+EJJHB4hwxrb4CIc208VLPGFKGBQ7ASb9g5I6TXVIuwIEDAYCDsKd+rp2QyYbievbU5w0nd2Tfv3aBeyJ0aCiBuGYh4Gmsp8r+qIYXDrnpPps7ODKSxIp5gbz/vCAI4E7zLoEmZLzG+u1uQqMJA9I0pyaq5KbHFqeP6+vBaNJAJGWgw++BILh1TZn/JaTRPLF3EA6A0xaE0Bn0ZN/f6lewsMWLY2MpvHo8gtcta6/dRRbhD6/2YfzwjYODiYICIQCQmdA0g10JmQkVb9SQYXGMpkz4lNzxqE8REUnVz7oNx3EwENUhMxGKxCCLDCITKAgiDStXWiyj2nVCv3+lD//4yx0YLqK4eTzT5nh4Zz8AYEHYDe4ODicKfhxFYohpZlPMMyNkJhQI1VBMM92uqmmmLntkEZppZ8f+11pMtzCS0BFssDQBIbn0jCaxfzABkQk4f0XHlI+vXRACALxyrPJ1QjZ38MNnD2H7sUjJC1+f3j+MsZSJNr+CazYsAgAcGIwX/DiKxKCZnFZtkKZHgVANxTQT3HGm3e7NBAEO3KGE9WA0YUC3OO0NI03h8fRp0JmLWxD2ylM+njkROjAUR6LCKaJXeyOIpl/w/Om1AfRFtKIf63evHAcAvPW0+Ti5002HHRxKFHyyo0oiDItDpwnTpMlRIFQjjuNgMG7AK818uuKVRQwndHBe2+Npy+Y4HtGmTeMR0kgcx5kxLQYAHQEV80MecAfYdbyyp0LP7B/O/n+bO/jZCz1FPc6hoQRe7Y2CCcBb1nRhUasPEhOQMGwMxApLuYlMgM05UnWSmifV9bMtPfjbn7yImFYfL8QriQKhGkkYNhLa9GmxDJ8iIqG7m+lrKZIyEdMsBKmVljSBvQNxHI9oUCQ2YyH02oXp9FgFBytyx8HTB9xA6NqzFwMAHnmtv6hTod+92gcAOG95O9oDKmSRZWcjHRgqvE5IEASkaNXGnHP/tqO47Rfb8euXe/GDZw7X+nIqrqi72iOPPIJHHnkEAwMD4Hzisel3vvOdslxYs4trFnTbRruszvh5qiRi2NIRzzFrqJqG4jocx4FEy1NJE8icBp23rG3GFyNrF4Txx10DFa0T2tMfw0jCgFcWcd05i7FvIIZtR8bw0xeO4JZLV+X9OEnDwqOvDQAArljXnX3/sg4/DgwlcHAwjtcvL6z7TRFZNmXXbAZjOr771EF84MJl6AjM/Dw8lzyxdxC3/e/27H//8JnD+MuLljf14uyC/2af/exn8Za3vAWPPPIIhoaGMDo6OuGN5GckqUMU8vvnFxnDaA3XbWimjcGYjqBau0CMkHKxuYMn9s6cFsvI1AntHYhVbKZOJi12ztI2yCLD9ecuAeDWCh2PpPJ+nMf2DCJl2ljY4sX69HUDwPJ5fgDFnQgpEkNCt2DXODVfCV9+aDe+uXk/vv7I3lpfSt145VgEH/3hVljcwdvXd6MjoKAvquEP6ZPGZlXwidC3vvUtfO9738N73/veSlzPnGDaHKMJE75Z0mIZPkXEaMIseDpsuYwlTSQMC90hb9W/Nql/u/ti2HJ4BO85e3FDvGp85VgEo0kTAVXCmUtaZ/zcrpAH84IqBmM6XuuLzvr5hXIcB0+nA6HzV7inNavnh3DWklZsOzKKn27pwa2XzX4q5DgOfrvDLZK+Yt18CONGWizrOFEwXShFYohrFjTThr+J0uKO42DzbjcY3nKo+i/gDw8nEE1ZWLcoPPsnV0nPSBI3fW8LEoaN81e048vXno5vPLofX3tkL7771CG8ff2CWl9ixRT8rGUYBs4///xKXMucEdcsJHU778JjnyIhZdRmG73jOOiPapAYm/DkSgjgBvWbfrcLP93Sg9+/0hivGh9LnwZdsLIjr8At20ZfgXUbh4YT6ItqUESGDSedCLJuSJ8KPbp7AL1js58K7eqL4dBwEorE8KbVXRM+tqzdPREaiOkFD0hURAbdbr6C6T39cfRF3Rqs1/qiFR8cybmDF4+M4ku/fw1v+c/HcPG/b8Y77nwSv0sHr7U2kjDwF995HoMxHavnB/Gt926AKon489ctgSwK2Hp4FNuPjtX6Mium4EDoQx/6EH784x9X4lrmjJhmzdg2P5nIBNiOU5Mpr/H07KBQDeuTSP36465+DCfctO3mPQM1vprZmTbH0/uGAMyeFsuo5GDFp9KnQWed1DJhLMUp84PYcFIruAP8NI8Ossxp0MWr5k3ZDRbwSOgMujUwhZ4KCYIAx3HqZqhruWzefeJnlTvA9p6xsn8NzbTxp9f6ccf92/G6TY/gqm8+jW9u3o89/SdmOn3mN6/WvCsrZdj40Pe34MBQAgtbvPj+B87NPt93hjzYmK43+95Th2p4lZVV8Fmnpmm466678Mc//hHr16+HLE+8QX7lK18p28U1I7dtXoNa4EZ2VWQYjld/3UZmdlBHgGYHkYksm+N/tx7N/vee/jiOjaawsLV+U6gvHB5FwrDR7ldwWvqkZzaZvWN7+mPQLXvKXsBSZOqDXr986kDHG85dgq2HR7F59wDec/ZiLGjJ/e86ljTwVDq4u2Jtd87PWdbhx0BMx8GhONYtLCwdIzFW8TlK1ZYplldEBsPm2Hp4FOevnPo9KFQkZeLhnf34485+PL53EMlxdWUBVcLFp8zDm0/twvkr2nHt/zyDQ8NJ/OfDe/FP71hT8tcuhmVz/M1PXsS2I2MIe2V8/wPnoCvkmfA5N12wDL96qRe/2d6LT1yxesIqmmZR8InQ9u3bccYZZ4AxhldeeQUvvvhi9u2ll16qwCU2l6ThToouNN/uVURENKOqr8xs7qCXZgeRaWzeM4iBmI4Wr5wtzq33U6HMDfCiVfPyXg3THfagza/A4g5298XKdi1HR5M4MpKEyAScu6xtysdXdQVxduZUaMv0p0IP7+qHxR2s6gpgZWfufWLLO9IF04PF1QlVqnNMt2x89Y978PT+oYo8fi4J3cKWQyMAgOvPdccVbDtSep2QaXO87auP4+9//jJ+/2ofkoaN+SEP/vy8Jfj+B87F1k9fhm/ccBb+7MyF6Ax58C9XrgUAfO/pg3i1guMZpuM4Dv7p16/ij7v6oUgM3/6Ls7GyMzjl805f3IIzl7TAtB38+LnSpp7Xq4LvcI8++mglrmPOiOsWNJOjzVdYDOqVRYwlDcQ0q2qTnSMpE7GUhY5A5TbIJw0Lv9l+HOsWhrGmO79X6KT2bO7g5+mUzVVnLkSbX8H2YxFs3j2IG85dUpf1ZEnDwpaD7g0w37QY4KaH1i4I4/G9g3jlWATrF7WU5Xoyp0GnLwpPSWdlXH/uErxweBSb9wzgPedMPRWyuZOtzXrbNKdBALBsXvEF06rEoJl22U/DAODbTxzEV/+4F10hFc984lKwPMsFSvHM/mGYtoMlbT5cfdYifP+Zw9h2ZAycOyV9/e1Hx9Ab0eBXRHzwwmV485r5WLswNO3vwkWr5mHj+m48uP04PvWrV/CLj55flb9/xp1/2ocfP3cEggB87bozcM7SqcF4xk0XLMOLR17Evc8ewV+9cUXZfw5qrf5bPJrMWNKAKAhTfjkcx8E9Tx7Apt/twliOVnkh/Wciqeq10Q/FdDio7Oyg/916FPc+exi3/2I7PvfgThwZSVbsa5HyeWLvIHojGoIeCW9b243zlrfDIzP0RbWynpqU07MHhmHYHItavdkTknydGKxYvoLpzBDFXHvOMsafCt23Zeqr8W1HRjEQ0xFQJbzh5OkfJ/P3PTKShGkXtjJDERl0k0MzyrtqYyCm4ZuP7gMA9Ed17KjSctvMqeUbT5mHNQtC8MgMkZSJA0OF72Mb7+l97vfzolXz8HdvOQXrFoVnfUHwT29fg4Aq4cUjY7hvhlO/cvvZCz348sN7AACfecdpeOsMQTQAvG3tfHSFVAzF9Ww9WjMp6g63ZcsW3Hbbbbjuuutw9dVXT3gj07NsjuG4kbNt/pFdA/jVS714ev8wbv/FdvRHp06V9coihuJGVdZtaKaNgZhW8dlBL40rUnzu4Aj+5ifb8LVH9mKwwHUApHq44+Bn6dqgK89YCK8iwiOL2WF9j6bTT/UmmxY7eV7BJ1aZgundfbGCA4lcBqIa9g3EIQB4XY602HiZDrLH9gzi2OjEDrLMTemyUztnfJXeGVThV0RY3MHR0cJebEgig+1waFZ50/L/+fAeJMbV0PxxV39ZHz+X8W3zF6+aB1lkWL+wBQCw7fBYSY/9zIGJYxDy0RXy4O/e7I5H+Lffv4aheOWf9x7dPYA77t8BAPjoxSvwF+cvnfXPyCLDe887CQDw3acOFby3rt4VHAjdd999uOCCC7Bz50788pe/hGma2LlzJ/70pz8hHK6fmQj1KK5bSBr2lEBoOK7j208eAOAeQ/dGNNz2i+04PDzxGNunSEga1Vm3MZY0Eddt+NTKHYEmdAv701ux//XKtXj98nZwx615+Oi9W/Hdpw7WvKOCTPXM/mH0jCThV0W8fdwE4zee0gnAPS0qR7BQTqNJIxt0F5IWy1jU4kWLT4Zhc+zpL/3EK3PTXLMghBbfzKnnk7uCOGdp+lTohROnQn1RDVsPu7UtM6XFAPdEeVn6VKiY9BhQ3lUbr/VFs3VPN7zODfQe3ln5QOjgUAJHR1NQRIbz0oH7WemxBZl/y2Lolp39868vIBACgPe9/iSs6Q4hkjKx6bevFX0N+RhNGPjbH78Imzu46syFuO3yU/L+s9efuwSKxLD9aATbjoxV7iJroOBA6Atf+AL+8z//Ew888AAURcF//dd/YdeuXbj22muxZMmSSlxj3dJMG9ECbtQxzYI9aU2F4zi489F9SBg2VnUF8M0bz8LiNh9GEgY+cf8OvDZu2aMiMZh25dvoM7ODZFHIu6C0GK/2RsAdtxj1jMUt+OQVp+Lf37Uepy0IwbA57n/xGD78wxfwi21HoZf51SgpjuM42Xbud6xfMKHo//RFLWj1yYhpFl4sQ/FpOT21bwjcAU7uDEzbfTUTQRBw2oLytdGfGKKYX6fSDee6r8Yf3zOYPdH5wyt9cACcsbglr7/T8nSdUFEF02VcteE4Dj7/4C5wxx3+eNvlp0BkAl7ri6GnwqnxzKngOctasz+7mflNpRRMv3hkDLrFMS+oYsW83AXr05FEhs9ftRaCAPxi21E8d2B49j9UpO89fQgx3cLq+UH82zXrC6pJag+ouPJ0d6jid586WKlLrImCA6H9+/dj48aNAABVVZFIJCAIAj72sY/hrrvuKvsF1rOhuI4XD4/itePRvE4uhuI61En1No/uHsQLh0chMQG3XLoKnUEP/u3qdTilK4i4buFT//fKhFcqkiBgNFHZOqG4bmE0YVR8dtD2o+4NZXzx6er5IWy6ah3++e1rsLTdh4Ru43tPH8JHfrgVD+3sQyRlYjRhYCiuoz+q4XgkhaOjSRweTuDgUBz7BuLY0x+ryUoSy+b41mP7q3LEXyvPHxrBwaEEvLKId54+cdKsyITsacuju+snPZYybPz65V4Abv1GsdaVabDiaMLIbrPPd/fXys4AXresLdtBZtocD+10i6TH7xWbSWawYlETpkWGhGaXZdXG5j2DeGLvEBSR4fa3rkaLT8E5S91g5KEKnwplAqHxp4JnLmkB4C7ijSSLO4HOFL6ft7y9qEaBM5e0ZlerfOpXr8Cwyn+imtAtfO/pQwCAmy9ZCaXAES4A8P4LlgIAfvdKX0HrX+pdwV1jbW1tiMXco+GFCxfilVdewbp16zA2NoZkcm4VujoOENdtaCMJ9Ec1LGz1YkGLN2e7ecpwT4/GL3gcSRi4+wk3JXb9uUuyW6KDHhmf+7O12PS7Xdh2ZAz/+uBOfOyyVbh41Tz4FAkjycqu2xhLmtDyWAhbqu3pV9brJ801EQQBZy9tw5lLWvHYngHc+9wRDMZ0fP1P+/B17Mvrsf2KiLveezZC3uoNgtxyaAQPpms2Qh4J586w1bwROY6TLejcuK475xLgN57SiV+91IvnD44goRc+JqIS7n7yAI5HNHQEFFx2atfsf2AamTqhXcejsGxedBPBsweH4QBY1RXAvGD+v2PXnbMEzx0cweN7B9EV9iCquR2d587Q7TPesnE7xxzHKeiGndk5VuqqDcvm+PyDuwC4N9WT0sHZm9fMx7MHRvDwzj588MJlRT/+TDTTzgYsmTQuAHQEVCxt9+HQcBIv9oxO+Fi+TsyDKv53/vbLV+MPr/Rh70Ac9zx5EH/1xhVFP1YuP3n+CCIpE0vbfXkHz5OdtiCMc5e14fmDI7j32cP4h8tXl/Uaa6Xg3+Q3vOENePjhhwEA1157LW655RZ8+MMfxvXXX49LL7207BdYr4bjOj7+85cxktCxIOyDKonYNxDH1sOjODycmDLvJ6aZ0EwOb7r13XEc/Pdj+xDXLayY58fVZy6c8PkeWcSnNq7BRSd3wOYOvvzQbjy4vRdeRURKr9y6DZs7OB5JwSdV9gYWTZnZV6bT7dsRmYA3re7Ct27cgA9euAwt6aCGCYDEBCgSg1cWEVAlhL0yWn0yOgKK+6Rt2Nk6jGoZnzf/8sN7phS2NrptR8awbyAORWL4s0k/rxnLO/xY3OaDYfOqzoaZzlP7hvDwzn4IAP7uzadM26aej8VtPgQ9EnSLY99A8R1GT88wRHEmk0+FAODy0+bnPaF+SZsPIhMQ1y0MFliUq0ru4MFS55jdt6UH+wbiaPXJuPmSldn3vzkdoG45NJqza7Ycnjs4At3i6A57cPKkeUtnpXfIbSuiTihl2Hixx/1zhRRKTxb2yfjkFacCAL72yN6Ci9pnolt29kX3Ry5ekffPTC4fSJ8K/fi5I00zcbzgZ4U777wTmuZ2NN1xxx2QZRlPPvkkrr76anz6058u+wXWq8/8ZieePziCnb1RfOYdp+GU+UH4FBExzcKu4zEcG01hSbsPnUEPFMltz2QCsq/Cntg7hGcPjGRTYrleXcoiw8ffcgqCHhkP7jiObz1+AJGUiYtXzUNct9DqL/98n0jKRDRloa0Cjz3eK+kBYovbfGidpVhUkRj+7IyFuPL0BXCAWeuWfv5CD37w7GE8uW8Il582v1yXPCPHcbI1Bq0+GaNJE5//3S78x7vWN8VASsdx8NN0+/bbTpuP8DQnbYIg4JJV8/CDZw9j8+5BvHlNdf79cxmM6bgz3Z79rg2LCp6oPBlLzxN65sAwdvRGsLqIuVcxzcy2iRdz07z+XPdUCHBfKLylgH9fWWRY3OrFoeEkDg4lCpoQLAgCHDjQSkjZRDUT/5lu2b71slUTfoaWtPtwSlcQu/tjeHT3AK46c1HRX2c6j43rFpt8GnbWSa24/8Vj2FpEndDWw6MwbQfdYQ9Oai9t8v/VZy3ET1/owfMHR/CZX+/Et//i7JIeL+OX246hP6qjK6Ti6rNyv4jJ12WndmFhixfHxlL49Uu9uPacxWW5xloq+ESora0NCxa4tQGMMdx222349a9/ja985StobS3vZuZ69s/vWIPV8906nn/81Q5sOTQCQRAQ8spYEPbA5g5eORbBtiOj6B1LYThhwCu7N8SxpIFvPb4fAHDt2Yuz3Ry5MEHARy5anm2h/cmWHvx861EMxKa215fDcFyH7TgV3yKerQ8q4OYkCPkVb1+QHpW//egYIqnqdJ0dj2gYiOmQmIB/u2Y92vwKekaS+Noje5ui1XTHsQh29cUgiwKuPmvmm1Sm/mLHsUjNxiDY3MF//nEP4rqFlZ2BbP1FqbLzhI4VVyf0/MER2NzB0nZfUUXbK+YFcN5yNxV23vL2gl+wLO8ovmCaCQzJEho1vvnofgwnDCyf5892io335jXuqVClusceS88PytU1mDkReunIWMF1UM8ccE8+X19kfdB4giDgc3+2FhIT8Mdd/WX5t7C5g/953D0N+tCFy0sehiiJDO97fbqV/unmaKUv6m63f/9+fOpTn8L111+PgQH3h+v3v/89Xn311bJeXD3rCKj4r+vOwGkLQtAtjs89uDNbvCgIAlp8CuaHvNBNju1HxzCWNLNt8996/ABimoWl7T68a8Psr3wEQcD15y7BRy9aDgHAI68N4Eu/341YmW/yumWjP6YjWIW6jmx90DRpsVIsaPFixTw/uHMid19pmS6pU7tD6A57ccdbV0NiAp7aP4z7XzxWlWuopEwq5i1r5s968+0MeXDaghAcAI/vrU3R9C9fPIYdxyLwyAz/8JZTyhbYZ/aO7ToeLapwOJOuLaWW5P+9cSXevWER/vINywv+s5k6oaImTIus6BcWPSNJfOdJt9PoH684Nef3IxMIPbZ7sOxdoj0jSewfTEBkAi7IMXjylPlB+BURCcMueCBotlC6hLTYeKu6gvhQ+nv7mV+/imSJYwt+98pxHBxKIOyVcX2OALQY7zlnMTwyw67j0ewJZSMr+Nnhsccew7p16/Dcc8/h/vvvRzzu5sq3b9+Of/7nfy77BdYznyLh5ktW4k2rO8Ed4Ot/2of7thzJRsgiE9DmdwOiVp8MWWR4at8Qnto3BCYAt1y6qqAn6I3rF+Dv3+K2mj53cAS/2FbeG+xY0qxKgetowkDPSBICTtxYyu3Cle6rvif3VedG/GJ6Rk2mA2V1dwh/eZH7ZPaDZw7VXTt5IXYej2L7sQgkJuCaWU6DMi5JF5yO3/JdLXv7Y7j3ucMAgL98w/KiTl6mc1K7H35VRMq0szOw8pUy7Gz6NN+2+VxafQre9/qlRaWvl5cwS0iRGFLpVRuF+tIfdsOwOS5Y2Y43rc5djLxuYRidQdWt7yvzC5hMt9iGJa05u2FFJuDMJYW30cd1Cy8fLT7VOZ2/vXRlNv30tUfyaxDJxXEcfPNRN/vwF+cvLalGbrwWn5I9GW6GVvqCA6FPfOIT+NznPoeHH34YinLiF/GSSy7BM888U9aLq3eC4Bbu3vIm9xUaAPzouSP45ub9E14tikyAT5EQSZn41mPuD+W7NyyedkHiTC5aNQ/vWO+mJsv9ars/qkHKM/1UikyNxNIOf8W6ui5Mp8d2HItUrPgyw7R5NtV35uIT6eG3njYfbz61C9wB/v0Pu3NOCy+XXcej+JufbMO3nzhQ9r9vpjbo0tWdeXc5XbCiAxITsvUo1ZIybPzHQ7thcwfnr2gvqUssF5EJOK27uHlCLxweKVstSbEyafi+qFbwRnlVYtAtDs0srE5o6+FR/OblXggC8I9XrJk2fcSYgMsqlB7Lts2fMv34hLPSL2IKKZjecshNdS5u82JRa/m+pz5FwmfeeRoA4NtPHMBrfcWlYh/bM4idx6PwyiJuymOCdCHen368h3f2V3z+U6UVHAjt2LEDV1111ZT3z5s3D8PD1e3SqbUWn4KQxy2Mfd/rl+KjF6+AAOD3r/Zh0+92Tamov/uJAxhLmVjS5sN7SigwW5p+Et1xLFK24Ypx3cJIwsjZEl1u07XNl9P8sAcrOwNueqzC3WO7+2JImTbCXhnL552o9xIEAR+9eAVO7gwgplv4wu92VWQwpLun7iAODSfxfy/34sM/fAE/eOZQWToL9/THsO3IGJgAvGtD/j+zAY+UXeJYzVOhbz95AL3pVvm/vmRlRZa/ntg7VlggNH4FQ62W0gY9MjoCbjB7aLiwAFUSGWzuQC+gU8hxHHzuwZ0AgHdvWIQ1C2YuMM+kx/64q79sq4QMi+PpfW4dz0xTxc8qYrDis2Vom5/Om9d04bJTu2BxBx/94dai5sd9c7P7wvv6c5eUvblmVVcQF67sAHeAHz57uKyPXW0FB0ItLS04fnzq0rUXX3wRCxcWX42+adMmCIKAW2+9NefHP/KRj0AQBHz1q18t+muUW0CVcHJXEBwO4pqFjeu68Ym3rYYsuqmrT//fK4imc+rPHhjGY3sG0ymxk4uuWTBtjuXz/GCC2xWzu8hXCpMNx3Vopj1hzlGlbD86BqAy9UHjvSF9KvTk3sq2cWeeOM9Y3DLlNE2RGO5426kIe2UcGEzgG4/uK3tx4a6+GHb3xyAxASvnBaCZHD/fehQf+sEW3LflSEk1BpnaoDee0on54fy7jADgkvSr78f2DJZlEN9snto3hIcyrfKXrapYUJ9J5+7szb9OyLA4XjhUelqsHDLpsWIKpuGgoBOhB7Yfx4tHxuBTRHz8LbOvczh/RTv8iljWJawvHB5BwrDREVCwZoZOv8xp7qHhZN47v7I1X2VMi4236ep1WNjidvp95IdbC3ohtfXwCJ4/OAJZFPDhiyozmylzKnTf86U9z9RawXfjG264Abfffjv6+vogCAI453jqqafw93//93jf+95X1EVs2bIFd911F9avX5/z47/61a/w3HPPZbvV6sm8oIqV8wKIagZ0y8b5Kzrwr1euhV8V8VpfDLffvx0HBuP45mY3z3vVmYuwqitY9NcbSRhY2uHHKfPdx3j0tUFYJe51MiyOY6MpBCq8YBVwp2sfj2hgArIrCyol0z32Sm+kotO4s/VBi1tyfnxeUMXtl58CJrgTlx/YXt7tzfdvcxegvml1J75y7en45BWn4qQ2HxKGjR89dwQf+sELuH/b0YJnfhwYjOP5QyNgAnBtAadBGWcvbYNfFTGcMAo+PSnUUPxEq/w1Zy3CunHTystt+bwAvLJbWJvvqcpLPaNIme7NuJiUeDmVUjAtiwxRLb/fJc208W+/d3dnffTiFegKzR5Iq5KYnf5drgnt2WW7q+bNuFIi7JOz84XySY9FUmY2PVroTKh8zQuq+O5N5yCoSnj+0Aju+MWOvF9IZWqDrjpzIbrD5auTG+9NqztxUrsPUc3C/WWuWa2mggOhz3/+81iyZAkWLlyIeDyONWvW4KKLLsL555+PT33qUwVfQDwex4033oi77747Z/v9sWPH8Nd//df40Y9+BFme/Uat6zqi0eiEt0pb1OrDknYfhuI6bO7gtAVhfOma09ERUHF0NIVbf/oSRpMmFrV6s23wxUgZNhgDFrf68Lr01OKXj45hrMTusZGEgahmIeipQrdYupZmxbxAxYuyu0IerOpy02NPVyg9FkmZ2J8erpcptsxl3aIW3HSB+6rsnqcO4tUyBQZHR5N4Pt218WdnLoQgCHj98nZ87foz8Q9vOQULW7yIaRa+m15T8uD23ikLUbnjYCxp4MBgHC8cGsFDO/tw35Yj+Ho6sLhw5TwsbC38iVQWWbZovZLpMZs7+M+H063y8wI5W7PLSWRCNsWTb53Q0+NSKJWuwZtN9kRoqPChkIrEEEvZeaWtvvf0IRwdTWF+yIMPF9DhVu42+vHzg2aTHayYx1LR5w+OgDvuv2ehp6WFWNUVxDf//CyITMD9Lx7D1/80e/H0a31RPPLaAATBHaBYKYwJeN/rl4IJwKEq1gKWW8F3IlmW8aMf/Qj/8i//ghdffBGcc5x55pk4+eSTi7qAm2++GRs3bsRll12Gz33ucxM+xjnHe9/7XvzDP/wDTjvttLweb9OmTfjsZz9b1LUUizEBy+cFkDI4BmIa5oc8WNLmw7+/az0+8+tXcTjdIXXLm04uar9LxkhSx9J2P1r9Cs5f0Y7vPX0Iu/tiGIhp2bx/oXh6krQisqo8QVcrLZZx4coO7OmP48m9g9hY5Fj5mbzcMwYHbt3WbF08V56+APsG4nhszyC++LvX8NX3nIH2Ir9vGf/3Ui8cAOcubcPiccWaTBBw0ap5uGBlBx7dPYCfPH8EAzEd33r8AH7x4jEs7/BjJGFgNGlgNGlOm+IRAFx7dvHD7S45ZR7+8Gofnto3jI9ebJc8wySXX710DNuPRaBKDH9fxlb5maxdEMbWw6P4w85++BQRZyxunbaQ3LJ5tsX49TVOiwEnZgkdGUkWvCpElRgShgXNsmccFDoU1/GN9A37Hy4/paCU+5tWd05Ywrq4rfgi5P6ohtf6YhAE4A0nzx4IbTipFT99oSevE6Fyt83P5A0nz8O/XrkWn/zlDnzl4T04qd2HK8+YvhTlv9O1QW9bO7/gJbCFes85i/GWNV0lfZ9qreiX5CtWrMCKFaVFmvfddx+2bduGLVu25Pz4v/3bv0GSJPzt3/5t3o95xx134O/+7u+y/x2NRrF4ceUnX6qSiJO7AkiZNkYSBtoDKjoCKr54zXr8+LnDWNkZLGoSbUZcs+BVRCxK/7C9blk7BAADMR17++NY2u4vaoLxWMrEcNyo+CTpjB3ZQumWqny9C1Z04DtPHcKrvVGMJoyyFwxmRuufsXj606AMQRDw15esxOHhBA4NJ7Hpd6/h365ZX/S4+7GkgUdec181XzXNyguRCbjs1C5cvGoeHt7Zj5++0IPBmD5l0KEAIOyV0eZX0OpX0JZ+W9Mdyu6DKsap3SF0BlUMxHQ8f3Akr5tRIfYNxLOFmn950fKiTq6KseGkFnz/GXc+zdfSN/yFLV6csbgFpy9uwfqF4eyJ5yu9UcR1C2GvPGONSrV0hlT4FBFJw8axsVRB319FYhhNup1jMw2E/8lzRxDTLaxdGJr2Z3M6mSWszx4YwUM7+0vaPZY5DVq/qCWv57hMwfTLR8dm3edYjplQhbjhdUtwcCiOu584iH/4+XYsbPHi7Bx75o4MJ/Gb9JLh//fGlVM+Xm4BVSpbW36tFHz1juPgf//3f/Hoo49iYGAAnE88Zr///vvzepyenh7ccssteOihh+DxTD1W3Lp1K/7rv/4L27ZtK6jDQlVVqGpll4VOJ+iRcXJXINvNlfkB+cuLSgsYHcdBRDOwqiuY/YEL+9wOpf2DCbxyLIoNJ7UWFQj1R1PgVZgkDbgtuwMxHSITcGqVbgidIU92dP/T+4ewcX356szctRpjAE603s7GI4v4xyvW4NafvYjd/TH8+uVjRa8TeHDHcZi2g1VdAZw2SzeOLDJcsa4bl57aiSf3DkG3eDbYafMraPHKRS8RnQkT3I30P996FJt3D5YtELK5g0d3D+CHzxzOtsq/ucyt8jNZ1hHAV959Op47OIKXesawdyCGY2MpHBtL4cEdx8EE4OTOIM5Y0pJNGZy3rK2kHU/lwgQByzr8eLU3igNDiYICISYIcABYfOa6xMyMpbevXzBjXc50Lju1qyxLWDP1QW/MIy0GuGmusFdGJGViZ28Up09T9zeaMLDruFt2cV6VAiEA+MTbTsXh4SQe2tmPv/zhVvzy/50/5fv3P4/vB3eAN5zckV0UTGZW8DPfLbfcgve+9704ePAgAoEAwuHwhLd8bd26FQMDA9iwYQMkSYIkSXjsscfwta99DZIkYfPmzRgYGMCSJUuyHz98+DA+/vGPY+nSpYVedtV0Bj1YOS+ASMqAUcJenvEiKRNBjzxlMNzZJ7mvBg4MxnE8ohXcbprQLQxE9Wn3RpVbJi22qjNQle60jMxMoSf2lbd77MhIEiMJA4rIZm0LHm9+2IMPpuuFfvTcEQwUMV9IM+3spvurzlyU94sFVRJx6alduGJdN85b3o5VXUF0BNSKBEEZmeGKW4+MlrzyxHEcPLN/CH/zk234r0f2YiRpoDvsqVir/ExO7griz887Cf/x7tPxow+dh09ecSo2ruvGwhYvuAPs7o/hp1t66iotlrGslM6xPPSOuT/TC4scZpnZoVbKElbL5nhi7+zzg8ZjTMi+qNk6Q3rsuYPuadDJnYG8Z2uVg8gEfPW6M7BuYRgjCQMf+N4WRJInfqcGYhp+vtVtnqjGaVCzKPgI4d5778X999+PK664oqQvfOmll2LHjh0T3nfTTTdh9erVuP3229Hd3Y3LL798wscvv/xyvPe978VNN91U0teutMWtPiQMC0eGk5gf8pb0KtDmDpKmhdMWhOGRJwYPr1vehp++0IM9/TFEkibGUmZBKa6huI6UaaPNr+K1vija/WpFf6l3ZPaLVbCjJ5fzV7bjnqcOYmdvFMNxveS6nIxMt9jahaGCa18uO7ULj7w2gFd7o/jW4/vx6Y3TD5rL5U+vDSCmWegKqVU7mi/W4jYfVs4LYN9gHE/uGyq6VuvlnjH84NlD2NPvnjYEVAnv3rAIG9d3V6T2qBABVcLrl7dnvxcDMQ0v94zhpZ4Ith8bQ1fQU7W6uHycmDBdeMF0Po6NpQCg6Kne5VjC+vLRMUQ1NyV5egHPOWctacWjuwex7cgoPoDcp1GZ4vdyTpPOl0+RcM9fnI0/+8ZT2D+YwF/9aCu+d9O5UCSGe548CMPiOHNJS3YnHZldwYFQOBzG8uWF77iZLBgMYu3atRPe5/f70d7enn1/e/vEHzJZljF//nyccsrs8yhqiTEBK+YFkDJsDMS0kloXx5IG2vxqztbT81e6/z49oylEUiaGYnregZBhcfSOuS3z+wfjuO1/t6PVp+AbN5yFQAW6xxzHyQ5SXFflG0Jn0IPV84N4rS+Gp/cP4x2nlyc9llmbcWYe9UGTCYKAm9+4En9734vYcmgUzxwYznu+jM0d/Oolt1X1z85YWBfpltm88ZR52DcYx6OvDRQcCO3pj+EHzxzKrjJQJYYrz1iIq85cWLe1CZ1BD968Zj7eXMB2+Gpallm+OpSA4zhlPU2zuYO+aGknQoDbPba7P4aHd/YXFQhl6oPecHJHQb8jGzKDFWc4EcoUSldqftBsOkMe3PP+c/Cu/34aT+8fxqd+tQP/eMUa/OhZdwr8/3tj9U9IG1nB5+Gf+cxn8NnPfhapVKoS19M03OLpIHyqhJEiZ9iYNodhcyxp8+Ws4Zkf8mJRujj02GgK/TEt71kx41vmH9szCAfASNLAd56uzN6YY2MpjCQMSEzA6vnFz1EqViY99mSZ0mOGxbMbyM/Msz5ossVtvuzurrseP5D3QLLnDg7jeERDQJXKvkKiUi46eR6Y4KaL9vTHkDQs8FnmofSMJPGF3+7Cx3/+Ml4+6u46e/v6btz9vrPx3vNOqtsgqBEsafOBCUBMszBc5hlbAzENNncgMaGkE+bLSlzCujlTH3RK7t1m0zl9cQuYAPRGNByPTL3PDcZ07B2IQxCQHWNSC6d2h3DnDWeBCcDPXjiK6+9+FnHdwqquAC6dZp8bya3gZ5J3v/vd+MlPfoLOzk4sXbp0ymyfbdu2FX0xmzdvnvHjhw4dKvqxayHkkbGqM4AdvZGilpmOJAx0hTyYN0Mq58zFLTg6msK+wRhWzQ9gJGHMehydaZmXGYMAdyJvxsM7+3HxyfOmLRIsVqZb7NTuwtNI5XDByg58+8mD2Hm8POmxV3sjMGy34HhJCW2j7z57ER7fO4jjEQ33Pns4r8L6X6a32V+xrntKurRetfoVnLG4BduOjOHjP385+36PzOCVRXhlER5FzP5/B+6JG3fcjrZLTunE9a9bgvl5DOUjs1MkhsWtPhweSeLAYKLo8Ru59KbTYvPDnpJOK9enl7AOxHQ8s3+4oIBmKK5nZ5ZdlGPb/Ez8qoTV80PYeTyKbYfHsHH9xOfTZ9PdYqvnh8rehVqoS1Z34jPvPA3/9H+vYme6ePuv3riiqAL1uazgE6H3v//92Lp1K/78z/8c11xzDa688soJb2SizpAHKzr8iGkmhtMDF/OhmTYguKcGM/1Qn7PMzQO/0huFKonoi6ZmLZrOtMy3+GTsHYhjIKbDI7Ns182dj+4reArxbDJpjXU16mLoCKjZTrWn9pd+KjR+mnQpR9CqJOKv0gPPHth+HHv7YzN+/s7jUbzWF8uejjSSa85ahDafgvE/zprJMZo00RvRcGAwgVd7o3jh8Ci2HnaDoPOWt+Hr15+Jj715FQVBZZadMF3gzrHZHCuxUDqjlCWsmbU6a7pD6Czi52bDDHvHxu+Mqwfve/1S3HTBUgDA4jZvdik3yV/BJ0IPPvgg/vCHP+DCCy+sxPU0pSVtfqiyiJ6RJPqiGnyyiLBPnnGA4UjCwOI2L1p9M3d0vS4dCB0cTEAUBIwkTEQ1Ey0zDPkY3zL/RPoJ49ylbfjQG5bhxZ4x9EU1/Oi5w/jghaXXggFufVBmAm8tC0YvXNmBXcejeHLvEN55evF78YAT9UFnzTBNOl9nLmnFxavm4bE9g/jG5n348rvPmPaV9C9fPLFOo3WmQS51aP2iFnz/A+fCcRwYtjuLJmXYSJkWkobt/rdpI2VY0EyOU+YHS1pHQ2a2vMOPzbsHcXCwvAXTmROhUgMhwK0T+vFzR/DHXf341yvX5n3Skc+2+ZmcdVILfvjs4ZydY89UcNFqsT61cQ3WLwpj3cKWinaANquC/8UWL16MUKj2Q8EaCWMCusNenLmkFesXhaHIDMcjKYwmjZx1EgndgkdmWNTmm/W0YUmbH11BFQ6AfYNx2JzPuDBwfMu84zjZ05ELV3bAp0i4+RL3dOLXL/did9/MpxP5OjKSRCRlQpVYTW9sF6xwh1Du6otNGShYiJGEgUPD7rTwcqUQP3jhMvhVEfsHE3hge2/Ozzk2msJzB9LrNGaYKlvvBEGAKokIe2XMD3uwrCOA0xaEseGkVly4sgNvXjMf7zh9AQVBFbZ8XMF0OR0bLa1jbLzXL2+Hr8AlrJw7eHxP/ms1ctmwxH2B+WpvZMLpeF9Ew8GhBJgAnFtHXVkiE3DVmYtqvseuURUcCH35y1/Gbbfd1nD1OvVAFhkWtHhx1pJWrF0YhiK6AVEkZWYX6TmOg7GUgYWtXoTy2J6tSmzC3qOAIqMvqk9bXJhpmfcpEvb0xzEY0+GVxexE1bNPasMbT5kH7gBf+9PeKXupipFJi53aHarK4MbptAfU7L9VKemxl9LTpFd0Bso2g6nVp+D9rz8xWyhXoPZ/Lx87sU6jgcfZk/qwNN1CfzyilXVzeG+JrfPjeWQxG8zku4T1ld4IhhMGAqqUTXEVanGbFx0BBabtTNgn98wB93lj7cJwXs/PpDEUfFf68z//czz66KNYsWIFgsEg2traJryR2SkSw6JWH848qQVrukMQBPfJKJoyEdUsBDwyFrbkd6NjTMh2Lb3SG0HAIyGuWRhNTB1cZ1gcx8e07Jb5J/e5r5rOWdo2oYD5QxcuR9gr48hIEv+bHs5Vih3HxgDUNi2Wke0e21t8IJSZJj3dtvliveW0Lpw6P4iUaeOuJ/ZP+NhY0sAju9zFpYWuLCAkl7BXRkfATa8eGk6W7XFPzBAqT01XIUtYHcfJft4FK9uLfuElCEI27T0+PVaPaTFSuoJrhL761a9W4DLmJlUSsaTdj86QB/1RDT0jScR1G2u6gwVNXn798g4Ae7FvIA7d5JBFAf1RDV0hdUJqbSRhIKKZ6Ap64DgOntzn/lJfuHLiL3XYK+MjFy3Hl/6wGz97oQfnr2gvet+UzZ1sm3m19ovN5PwVHbjr8QPY3R/DQFQruJCSOw5eyhRKl6E+aDwmCLj5kpW45acv4dkDI3j2wHB2fP9vdxyHYXOc3Dn7Og1C8rWsw4+huIGDg/Gy7UErZ40QMPsS1pRh45kDQ3j0tUE8unsAR9OpuYuKTItlbDipFQ/t7J9QMP10jecHkcooOBD6i7/4i7w+74tf/CI++tGPoqWlpdAvMed4ZBEntfvRGfRgNGkUPHvjpHYf2v0KhhMGdvVFsaY7hOGEnp2qCkxsmXefVKIYip9IizmOA9N2oEjuK6gLV3bgsT2DeO7gCL7+p31FLwc9OJRAXLfglcW6yF+3+RWctiCEV3qjeGr/UMGD2g4MJhBJmfDKYkXmIZ3U7sdVZyzE/247iv95fD/WLwqDCUJ2ncbVZ+W/ToOQ2SzrCGDLodGy1QnFNPdUGwC6yxQItfgUnH1SK547eGIJ65HhJB7dPYBHdw/gmf3D0MetM1IkhotOnlfy4NRMucDWw2NwHAdHR1M4OpqCxASck2PZKWlcFZtI9oUvfAHXXnstBUIF8CoivErhTx6qLGLV/CCe2T+MV45FcNaSVgwnDIzET+wRm7xlPjM7KJMWi6RMjCQMLGnzQWQCBEHAX128AjuORbC7P4YHtvfiyiIKdDNpsdMWhOpmAvKFJ8/DK71RPLmv8EAos21+3cJwxeqd3nPOYjyxbxD9UR0/fu4IFrZ6EW2QdRqksZxYtVGeQOh4xG2dD3vlsg68fPOaLjx3cATfefIgfvzcYeyftCNtQdiDS1Z34pJTOnH+yvaiFlBP5v6OCxiK6zg6msq2za9fFC54JhypbxWrXHVmmRpLyscjs2y65NVeNw0VUCX0RfRssfNAVMu2zPPxabH0sLGUaSHoEScUR7cHVNx0vlvA+8NnD6MvUvhy0O1Ha982P9n5y9vBBGBPfxz9BS48fanAbfPF8MgiPpqeLfSb7b247/keAMCVpzfGOg3SODLLVw8PJ/OecTaTUneMTSezhPXYWAr7BxMQmYDXLWvDJ962Gn+49SI89Yk34fNXrcNla7rKEgQB7u/hmgXu89bWw6N4ltJiTYsGDjQBVRJxejrQ2NMfg27ZCKgSopqB0YSBhG6hP6plT4f29MeyabENS9y0GHfcx5ncJfaW07qwbmEYusVx56N7Cwpwbe5kA7N1dVAflNHqV7A2/QT3VAErN1KGnZ3eWu76oMnOPqkNF67sAHfc1SeNtE6DNI75YQ+8sgjD5tkgphQn6oPKO/xySbsPn9p4Kq47ZzG+ccNZ2PbpN+OnH3k9PnrxCpwyP1ixdPGGcQXTJwYpFjapmtQ/CoSaxCldQbR4ZVjcwe6+GEQmQGQM/TFtQss8cKJj6txlbVAkBs3k8EgMisRgWBMDISYI+OtLVkIRGV4+Gsm7hRUA9g3EkTLdoCzzyrNeXJDuHnuigEDold4ILO6gK6SiO1z5KccffsNy+NJF81es6y6ogJ6QfDBByLbRHyjDYMVyF0qP96E3LMcXr1mPjeu7yza2YjZnndQCwG1WOB7RoIis6JZ8Ur8oEGoSfo+Mk7vcYuTM3IuQR8Zw3EDv6ImWeT5piCIAJA0LQa+EFp8M05564rOgxYsbX7cEAHDPkwfzXiK7PV0ftHZh/dQHZbx+hZse2zcQz3tw5LZx2+arUbDc5ldw++Wrcflp83E1tcyTCilnnVBver1GuVNjtZIJejKLac9Y0tIw+/1I/igQahKecVObM+koryJCM21ENDNbuLinL4ahuOF2i6WPfXWLo8PvgVeRwJF7gOKVZyzEynkBJAwbX/3jHhwcSsyaJsvWB9VRWiyj1adk01u3/eJlfOepg0gZM+9XezEzP6iC9UGTnXVSK/76kpVUnEkqJnNaW47OsXJOla4H3WEvFow7/aVmheZUsUDoDW94A7ze5vhlaAQeWcRp6Tkgr/XFsrU+rT4FYa+cPZHJpIJel06L2dwBEwQEPBJUiQEQcgY4IhPwt5euhMgEvNgzhr+970V86Acv4H8e249tR0an1BaZNs/W09RTofR4t1x6Ms5f0Q7uuBvd/9+Pt2U3S082ENVwbCwFJrg7swhpFuNPhEptcqlUsXQtnTkuFUaF0s2p4EDonnvuyfl+y7Jwxx13ZP/7t7/9Lbq7G2s7diNzZxH5EPJIMGyOPekt5j5Fyp4GccfB0+m0WKZGJmXY8KoiAqobCMmiAGua7pFlHQH809vX4JylrVBEhoGYjgd2HMc///pV3Pjt57Dpd7vwyK5+RFIm9vTHYFgcYa+MJXW6DqLVp+COt52Kf3r7GnQGVQzFdXz+t7vwrw/sxMCkbrLMtvlTuoJlbQsmpNaWtPvABCCSMjGWnDqRPl82d9AXLc/m+XqSKZhWJVbV02BSPQU/o3/84x/Hb3/7W9x9993ZlRqvvfYabrjhBkQiEWzatKnsF0lmJzIBAY+MVV1BvHB4FK/2RnHagoknMbtzpMWShoX5YU92kKIiugXT083IOWtJK85a0grNtLH96BiePziCLYdGMZI08PT+YTy9fxgCkN2Mvm5huO4HAJ6ztA3rFobxsxd6cP+Lx/D8oRG8fHQM15+7BFeevgCSyLLb5ivdLUZItamSiK6QB8cjGo6OpdCanjVWqIGYBps7kJhQ8FDYevbmNV348kO78fb1CyasIiLNo+AToRdffBH9/f1Yt24dHn74YXzjG9/AWWedhbVr1+Kll16qwCWSfIU8UnZ68ys5NjU/mUmLLW/LBj6W42Sf+BSJQU230s7GI4s4d1k7/vpNJ+O7N52Dr7z7dFx3zmIsn+eHA7flGwBOb5A0kkcW8b7XL8V/vecMnLYgBN3i+N7Th3DLT1/CjmMRvHR0DEB164MIqZbMCU6mxqcYmY6x+WFP3TVHlGJxmw8v//NbsOnqdbW+FFIhBZ8ILVu2DI8//jg+9rGP4a1vfStEUcQPfvADXHfddZW4PlIAryJhVbpzbFdfFJbNIaVPdrjjZGfmZLrF3JMftz4oI6BKiBZ4PM4EASd3BXFyVxA3vu4kDMV1bDk0grGkiUtP7SzHX61qTmr3Y9NV6/Cn1wbwnacO4shIEp/85Q4AgF8VcXJn+ddqEFJrC1u8eOHwKI6OFr989ViTdYyNJ1VoijypD0V9dx944AH85Cc/wfnnn4+Wlhbcfffd6O3tLfe1kQJ5ZHerfVCVoJl8whj61/piGE4Y8Ckizlx8Ii3mUyQExk1i9SsSTD77idBMOgIq3ra2G9efu6RiaygqSRAEXHpqF/77xg14y5oTQwzPWNTSVK90CclY1OrW8ZUyVLGSM4QIqaSC71If+chHcO211+K2227D448/ju3bt0NVVaxbtw4/+9nPKnGNJE8eWYQqM5wyP9NGfyI9ljkNygxRBICUaaMjoICNu7mrMkO17/U2d8oy3r/cQl4Zf/Omk/Fv16zH29d14/0XLKv1JRFSEQtb3eDlaBlSYwvKPFWakEorOBB66qmn8Nxzz+Hv//7vIQgC5s+fj9/+9rf4l3/5F3zgAx+oxDWSPKkSg0cSs/OEdqTrhNzdYm4g9IZ0WsxxHDiOg9CkCa2qxMAEVtXAZDCuoz9W+B6zalnTHcJHLl6B+SF6gifNaVE6EBqIaVOmy+ertwlb58ncUHAgtHXrVpx++ulT3n/zzTdj69atZbkoUhxBEBDyylgxz50LsvN4FDZ3sOt4FCOZtFi66yll2vAoIoLq5EBIhCwKU+YCVRLnHJRwIqR2Wrwy/IoI7gDHI8WdCmVOkyg1RhpN3oHQzp07AQCqOn1b5JYtW0q/IlKSgCphQdgLnyIiadg4NJzIpsVet6wtW7OTMmyEPPKU/VWqxKCIYtGvCgtl2hxSOlVX6jA3QkhxBEHI1gkVmx6jGiHSqPIOhDZs2ID/+I//yHmz6u/vxzvf+U781V/9VVkvjhTOI4tgzE3nAMCOoxE8td+dlnzhynnZz9Msjo7A1KCWMQE+deoW+krRTBseSYQoCnVZJ0TIXJEJYI4WUTAd00xENQsA0E2BEGkweQdC9957L770pS/hoosuwv79+ye8f82aNYhEIjRHqA54ZAZZYjg1HQj938u949JiLQCQHXo23YTkgCrlNUuoHDSTw6swSGz6idaEkMrL1AkdK6KF/njErfELe2WavE4aTt6B0DXXXINXXnkFHR0dOP300/Ef//EfuPLKK/GRj3wE//RP/4TNmzdjxYoVlbxWkgePLEKRRJycHqw4FNcBAOcta8+mxZKGBa8iTpgfNJ5XEVGtkMSwOdp8KmTGYOXYfE8IqY5SOseacccYmTsKCt07Ozvxy1/+EjfeeCNuu+02+P1+PPvss1i3jiZu1gtZZPApDAtbvfDIDJrpnuxkdosBQNKwsaDVM+2MH1ViEODW7FRyPQZ3HDABCHokyBKbdfs7IaRyxs8SKvR3/0R9EHVWksZTUNfY6OgobrjhBvzqV7/CJz7xCXR2duI973kPFUnXmZBHBufA6vluesw/Li0GADbn2V1guahypnOssic0usmhygx+VYJXphMhQmqpO+wBE9wXSqMFTpen1nnSyPIOhB544AGsWbMG+/fvx9atW/GFL3wB27dvx8UXX4wLL7wQn/rUp2BZViWvleTJp0jgcLJbky9Y2ZE9/dEtG4rEZszju1voWcULpjXThk9xt977FAlWiROtCSHFk0WGrvSsrELrhHqbeL0GaX55B0Lvete78Dd/8zd45plnsHr1agCA3+/Hf//3f+OBBx7AD3/4Q5x99tkVu1CSP48sQgBwxbr5uP2tq/GhC5dnP5Y03ODDr0wfCMli/stXS6HbNlp9MgRBgCKxqtUlEUJyK7ZzjGqESCPLOxB6/vnn8clPfhKMTf0jb37zm7Fjxw5s2LChrBdHiuORGRSJgTvugtXxs4LctRrqhLUauQRUCWaFZwlxx0HQ4w50VBpwJxkhzabYWUJUI0QaWd53n69+9auIxWLTfjwUCuGee+4py0WR0ngkEaokQp8UyHDHgQBMWauRS0CVYDmVC4RMm0MWWTZIk0UGBgGchioSUjOLiugcs7mDvnT7/MIWX0Wui5BKyjsQ+v73v49UqviFfKR6GBMQ9EydDq2ZNjyyiOA0bfPjqRIDKrj4ImW4gxQzKTpZYpBE0FBFQmooO0toLP8aoYGYBis9m2xecPrNA4TUq7wDIVp/0FhCHmVKsXPSsBH2yvDI4jR/6gRVEiEKlZv2rFnutYjpFJ0sCpCqUKBNCJlepkZoIKpDt/IbZ5FJi80Pe7K/z4Q0koIKMyo5U4aUl0dmUw50dMtGm3/6tvnxVJlBloSK7RwzbY6w70SKTmYMkkAt9ITUUmYytAPgeLoTbDbHqGOMNLiCBiquWrVq1mBoZGSkpAsi5aHKIkQmwLI5JJG5/8tYXmkxwC1eVkS3c8yL2U+QCmFzB0wQ4BvXucaYAFUREEvRiRAhtSIIAha2eLG7P4ajYyks7fDP+mdo2SppdAUFQp/97GcRDocrdS2kjDwygyoxGOlAKGXa8Coi/HnuAWJMgF8VMRw3yn5tuuXWKvmUiQGWV5YwGi9skBshpLwWtbqBUL6zhE4MU6SOMdKYCgqErrvuOnR2dlbqWkgZqZIIjywiZdjwKW590MJW77RrNXIJqlK2G6ScNJPDr4pTapV8igiLatEIqalCd47RVGnS6PK+K1J9UOMJeaRsjc9sazVy8SgiKhGXTFerVEiQRgipjOwsoTyHKlKNEGl01DXWxPyqBNtxTqzVyLM+KEOVRAhC+b/33EHOFR+yyFCRyIsQkrdF6YDm2Ggqr999qhEijS7vOyOnPVANJ5N6Suo2/KoEv1JY0bO7c8xdvqpI5TkRNCwOVRImTLvOUEQGkTHY3KE2XEJqZH56+WrKtDGSMNAemH42UFy3EEm5dX3dYaoRIo2pbnIRmzZtgiAIuPXWW7Pv+8xnPoPVq1fD7/ejtbUVl112GZ577rnaXWSD8cgiFFFAVDMxL6AWnN5UJAZZKu/OsZRpwyNJEzrGMmRJgMQEWr5KSA3JIsP89PLV2dJjmdOgkEfKrsshpNHURSC0ZcsW3HXXXVi/fv2E969atQp33nknduzYgSeffBJLly7FW97yFgwODtboShuLR2JQJRGSyBDMY63GZLLI4JFYWXeO6aaNsF/KeeIjiwySKNAsIUJqLFMndGyWgunMstWFrbRagzSumgdC8XgcN954I+6++260trZO+NgNN9yAyy67DMuXL8dpp52Gr3zlK4hGo9i+fXuNrraxSCKDX5XgU8ScNTn5CHqksp4IWQ5H2Ju7aFti7nRpi9ZsEFJTJzrHZm6hp2WrpBnUPBC6+eabsXHjRlx22WUzfp5hGLjrrrsQDodx+umnT/t5uq4jGo1OeJvLQh4p77UaufgUqWxrNmzugIHBN821CIIAryzCojUbhNTUiZ1j+aXGqGOMNLLijgnK5L777sO2bduwZcuWaT/ngQcewHXXXYdkMonu7m48/PDD6OjomPbzN23ahM9+9rOVuNyG1BX2oD1YfCCj5ljVUSzdsuFRWM5C6QyfImIgRidChNRSpgNstllCvdQ6T5pAzU6Eenp6cMstt+Dee++FxzP9seoll1yCl156CU8//TTe+ta34tprr8XAwMC0n3/HHXcgEolk33p6eipx+Q3Dp0gIlVDEqEonVnWUKmXYOQcpjueRRXCHToQIqaVMjdBgTIdmTr989RidCJEmULNAaOvWrRgYGMCGDRsgSRIkScJjjz2Gr33ta5AkCbbt/vL5/X6sXLkS5513Hu655x5IkoR77rln2sdVVRWhUGjCGymeKjEoIitLnZBuc7ROUx+UIYsMQrmOoAghRQl7ZQQzy1cj058KUY0QaQY1S41deuml2LFjx4T33XTTTVi9ejVuv/12iGLuUwPHcaDrejUukSAdCEkMZomdXJnBbP5ZhjrKogBKjBFSe4tavdjVF8PR0RSWdQSmfNzmTnYFD50IkUZWs0AoGAxi7dq1E97n9/vR3t6OtWvXIpFI4POf/zze+c53oru7G8PDw/jmN7+Jo0eP4t3vfneNrnruEQQBAVXEQLS05auGzaGIQs75QePJEsum4iRauUFIzSwcFwjlMhDTYKWHn3YG6USINK6aFkvPRBRFvPbaa/j+97+PoaEhtLe345xzzsETTzyB0047rdaXN6cEVAnHeGnLVzWTw6tI03aMZSgig8wEWNyBVFyjGyGkDLKzhKbpHMukxeaHPDQJnjS0ugqENm/enP3/Ho8H999/f+0uhmSpRbbej6eZNhYGvGCzPGFmZwnZDkCDagmpmROdY7lnCWWWrdKOMdLoKPdAZqVKIgQAvISFqLbjIJTHdGtJTNck0ZoNQmpq/CyhXMtXT8wQorQYaWwUCJFZZTrHzCI7x2zuQBSEvJe+emVGazYIqbFMykszOYYTU2sEsx1jrXQiRBobBUJkVqrEIEsMplVccKKZNjzyzIMUx/MpEi1eJaTGpHHLV3PtHKOp0qRZUCBEZiWJDF6l+FlCmmkjoMpQ86x+ViSGErJwhJAyWTTDzrFjNFWaNAkKhEhegqpcdCCkWxwtvvzr8hWRQRAoEiKk1rKBUI7OsRPDFCkQIo2NAiGSF68iFlUs7RZZOvCr+beAySIDIOQs0CSEVM90O8cSuoVIygQAdIepWJo0NgqESF5USUQx+Srd4lAlEb4864MAQBIFSKI7S4gQUjvTzRLqj7ppsZBHQrCEXYaE1AMKhEheVJlBkgrvHNMtDo8iwlvALCJZZJBY8V1qhJDyyJwITV6+Sqs1SDOhQIjkpdgW+pRpo9UnzzpIcTx3ujS10NeDpGHV+hJIDYW8MkLp/YC9406F+qI0TJE0DwqESF4UMRMIFRaccMcp+OicMQGqUlpqLK5bVGNUIt2y0RfVKBia4xam02Pj64T6Iu7iazoRIs2AAiGSF0EQ4FdFGFb+J0KWzdODFAvf5OKVi58lpJk2BmIaUuOO8knhNJMj4JGgF/A9J81n/ITpjEyNEAVCpBlQIETyFvTIBa2+0DL1QQUUSmf4FBF2kSdCuuXewAs9vSIT6ZaNkEeCSYHQnLYox86xbGqMpkqTJkCBEMlbZudYvjTTRlCVoEiF/5jJIoOD4idZ+xUJFhVbl8TiHB5Jou69OS7XLKFMsfRC2jNGmgAFQiRvqsQgQMhrnpDjONAsG60+paivlZklVAzbceCRGN3AS+A4DuAAIhOK/TaQOmDaHENxvaQXBQtb0i30oylwxwHnDgZiVCNEmgcFQiRvqswgS8KsdUKmzdEbSaHVq6DFX9yMEVkUIApCwemxzIJXSWSwqVi6aKbtQJEYWnwyRCbQ6VqDiqZMKBJDf0zDaNIoqoGgK6RCYgJ0i2M4biCime7vGRPQGaQTIdL4KBAieVMlcdYW+mjKxEBMw6JWH9YvDiNU5LA1d5aQUHDBtG7ZUGWGgFp4gTY5QbdsKJKIFp8MVSp+zxypLZNzdIVUrFvUApEJ6B1LTZgHlA9JZJifnh59dDSJkfQm+sx2ekIaHQVCJG8iE+BTpJwnQjZ30B/VYHGO0xaEcGp3CL4iusUyZJG506ULLHjWTA6/KsKvSkVNwiYuw+IIqBK8shv8FtItSOoHdwC/KmFhixdnLWnF0g4/IpqJgahW0Gnr+M6xkbgbCNEMIdIsKBAiBQl4xCndWCnDnTfT6pdx+uIWLG7zl/xKURbd9FahdT66ZaPVq0ARGUTGiu48m+t0myPslSAIAoIeiU6EGpDNHTAI8Ehu16ZXEXHK/CDOXNyCFr+M/lgquy9sNpk6oaOjKYwk3UBoARVKkyZBgRApiFeRwOHeFB3HwUjCQFQzsWKeH+sWtqClyOLoyQRBgFcWC65NceC+ApZEoajUGjnBmz7RC3pk+ndsQIbFocjChPEVgiCgPaBi/aIWnDo/BAcOeiNJ6NbM6bIJJ0KJTCBEJ0KkOVAhBSlIpnPMtDkG4xqCqozV3WF0BlUIQnnrBXyKiMFY/ic6ps0hi+4Tfya1RidChTNtDpEJ8KT3w6lS8R18pHYM2114rOYYXyGLDEva/WgLqDgynMCxMQ0Ss9ARUHM+1vhZQtxx/z8FQqRZUCBECqJKDKIooD+mYWGLFyvmBdx6nArwyIUNVdRMGx7JXfAqMgEiK3wlCHFPEjwSyy7KVSUx28FHxbGNQ7dstPk9M75ACagSTu0OoT2gYtfxKFKGnXMAamZw4lDcAEs/HtUIkWZBqTFSEI8sosUrY013CKctCFcsCAIASSxsho1mcoS8EiSRFZ1aI+5kbp8qZgdhqjKDIrFZ0yekvlic57XnTxAEzAuoUEQ2bQo06JER9rqPRTOESLOhQIgURBYZzljcipPaSy+IzudrFcKwOcLeEzVKXoWKpYth2PaEsQeq5AZCdLrWaASocn6/Q4wJUGYZQrpo0joNKpYmzYICIVKwaqVHZJHlPcyPOw6Y4NYVZXhkERa10Bcs03KdIQgCAgUu3CW1Zdockihk05v58MhsxnEV41NhQY+U12kTIY2AAiFStxSRQWZCXi30hsWhSmzC7KJCT5TIicnck2+gIY8y4yBNUl90yy2U9hQQCHllacYT1PEnQl0hOg0izYPuFKRuFTJLKGW6RZ6ecakACoQK506UZlNuoKrMIAh0utYoDIvDp7CCfgcUic24RzAzSwhwp0oT0izoTkHqliS6tSn5pMZ0k6PVp0zokCl2X9lcplscXmVqy7VHEsEEqrlqFIbFC15vo0gMM03AGH8iND+cu82ekEZEgRCpa/lukedwEPBM7GDL7Cujm3f+dIsj7JGntFznu3CX1AfuOAWvuJFmqf3rCnmyn0OpMdJMKBAidc2nSLPuG7NsDlEQ4JOnBkKiSNOlC2E7HH7P1BuoKjEookirNhoAdxwIAgqqDwLcE1g2Q3OCyAR0pwumM0tYCWkGFAiRujZb3QIAaBaHR2ZTBsHJogCJhirmjTvubqpcnUbuzjHqHGsEmcaBQjrGgPyaE9511iKctiCE81e0l3qZhNQNmixN6ppbtzBzIKObNlp8SnYAYEZmqGK+iyXnutluoAFVwjGuVfmqSKF0i0OZZrXGTCRRgDhLc8KbVndidXeQWudJU6ETIVLX3K4XAc4Mp0K6xdHiyx3Tq3J+xdYk3XItT38D9cgSbRxrAIbFEfSIYAXO+6KaOjJXUSBE6prbQj/9cb3jOIAA+KZZ9eFTaKhivnTTRsgjTXsD9cgMjLrw6p5pc4Q8yuyfmAOtpSFzEQVCpK65r1Knn3hr2ByKKEzbIeOeKNGNOx8md2ZMeaiSCFkUaLBinXOACfO0CuGR8+vSJKSZUCBE6ppbwDn9MkjN5PBI0rR1LZIooKDNrXOUk+40yrV5PCPbOUYF03XLtDlkSYBnhu/jTGabLk1IM6JAiNQ1xgQosjDtiZBu2gj7pGn3nykigygIs3aelYvNHSQNqypfq5wMm0MVZ+40YkyA30Mt9PXMsNzvo0cqLhCSRIFeN5A5hwIhUve8sghzmhMhk3OEvNOnc9zFrZh1FlG5jCQMDMX1hntVrZs852qNyYKqRKmxOmZYHB5ZnNJBmS9aS0PmIvqpJ3XPK4s5Awt34/z09UGA+wrX3VdW+Zu3ZXNYnCPQgMGCbnEEPNOfrGV4FYkqrgpkcwdHR5NV+ZnQbY7wDC8MZkNrachcRIEQqXuKLMLJcfvVTBuqzOCboR5CZgySMH2xdTmNJk20BxQokgi9wepoTJujxTt7p5EqMTBUL9XYDDTThiwyRKswz8p2+LQdlPnInKBSIETmEgqESN2Tpyl41k0OnyLNODiOMQGqMvO03HLInAYtbvXBrzZgQbEAeJTZnw5o51jhDJsj6JUqHhxnJoN7ikyLAdU9QSWkXlAgROpepuB58qtU3bbR6pu6IHQyryxV/Ik9cxrUHlAR8kjT1jTVI9PmkEUhr91UqiRCpc6xgpjpAmZVYtBMu2Jfx7DcOq+ZOv9mU80TVELqBQVCpO5NN/GWcwcBdfZ6CJ+Su8aoXMafBolMgEeWgAZKHbmF0mJeu6lEJsCnig1XA1VLlsPR5lfR6lcQ1yvXUeh2jIlFd4wBJ05QKTVG5pK6CYQ2bdoEQRBw6623AgBM08Ttt9+OdevWwe/3Y8GCBXjf+96H3t7e2l4oqbpcW+RNm0MU83v1K4ksZ41RuYw/DQLcoXQiYw1zM9EtGwFFyrtjKNhgJ161J8AjM8wLqtCtCp4I2Rz+IlZrTOaRpu/SJKQZ1UUgtGXLFtx1111Yv3599n3JZBLbtm3Dpz/9aWzbtg33338/9uzZg3e+8501vFJSC7m2yOum2yY8U6H0+D9fqeEok0+DgMabwGzYHGFv/gW2XkVEg8R4NWdzB6IgQJVEhL0yvLKIlFGZYMgosWMsY7ouTUKaVc23z8fjcdx44424++678bnPfS77/nA4jIcffnjC537961/HueeeiyNHjmDJkiXVvlRSI5kt8mNJI/s+zbLR5lfyOsWQ2YlOJzZLPVGhRpMmOgJq9jQISE9gTneO5VN3U3sOvDOMIJhMlUQIQEX+PZuNYbmTnlXZndHU4lMwFNdLquOZnlOWnzdZYjQigcwpNT8Ruvnmm7Fx40Zcdtlls35uJBKBIAhoaWmZ9nN0XUc0Gp3wRhqfV5m4A8mwbbT68lssKUsMklj+luDMadCiVu+E+TuMCfA3SB2NZXMwYeaJ0pO5qzZYQ/z9as3dhSdmOxs7Qyoszt1lwWVk2Rwim30gZj4UkUGgUIjMITUNhO677z5s27YNmzZtmvVzNU3DJz7xCdxwww0IhULTft6mTZsQDoezb4sXLy7nJZMaGb8DyXEcwJl5L9Z4sihAZOW/cec6DcoIeaSGWEVh2Nw9rcijdT4jM7mYOsdmZ9ocflXMdja2eBV4ZQmpMnePGTaHKrGil62OJ4kMgFD2YI2QelWzQKinpwe33HIL7r33Xng8nhk/1zRNXHfddeCc45vf/OaMn3vHHXcgEolk33p6esp52aRGZOnEDiTd4lDzrA8C3NSYPMMG+2JMdxqU0SidY7rJ4ZVFqAV0GonMneZNgdDsDJsjOG7AoVcR0e5XENPK2z2WScMW8n2cjiQK6eaE+v/5JaQcalYjtHXrVgwMDGDDhg3Z99m2jccffxx33nkndF2HKIowTRPXXnstDh48iD/96U8zngYBgKqqUNWpr9BJYxtfC5R50s83nZNpCY6nynfjnuk0CJjYOTbb2opa0i2O7paZX4jkEvCIGIhRIJSPyfVXHUEVR8dSZa2xMiyOrlB5nveUceMqGqLEjZAS1SwQuvTSS7Fjx44J77vpppuwevVq3H777ROCoL179+LRRx9Fe3t7ja6W1Jo8bqiiZtpYFPAW1CbskUSM8vKsOJjtNAiY2Dkmsvq9m3DHgb+IlQw+2jk2q8yk58mTz8NeGT5FRNKwEShhHcZ4FudFfR9zkZjbpWnZDlB6Exohda9mgVAwGMTatWsnvM/v96O9vR1r166FZVl417vehW3btuGBBx6Abdvo6+sDALS1tUFR8iuUJc1BERmkdGBhOw6CBbYJl7MleLbTIKAxOsds7p5IFFIonaFKDALceq3ZJnvXi6RhpX+OqlMRML5jbDyPLKI9oKB3VCtLIJT5HhTzfcxFEhlkUYBu0okfmRtq3jU2naNHj+LXv/41jh49ijPOOAPd3d3Zt6effrrWl0eqTE7vQDIsDlEQ4CvwSd9d3Fq6fE6DgMboHDMsDkUWimrl9sjuiVcjFIQDbtA3HNcxVoXFpxmmzaGILGfdTkdABXecsiyvNWx3hUc5A26PLFKNEJkzaj5HaLzNmzdn///SpUupa4FkSSKDzASMJU34VLHgm7dcpjqdfE6DMkIeCccjWlm+biXolg2PJM64tHY6mRMv03JQpoxMRcU0E61+FVHNmP2Ty8SwOFp8Ss6AucWrwKdKSOgWgp7S8k+ZHWPlDIS8FAiROaRuT4QImcybrqvwKWLBT/qyeGKoYrHyPQ3K8MhSheZZl4duuZOIi0ltSSKDV2ENcyKUMCwEVBFwULUXWKbtIODJ/XOqSAydAbUsu8cMy60PKmdRvkcWwZ3G+N4SUioKhEjD8CkiBAF5D1IcT5YYxBKHKhZyGgS4nWNMqN8FlhbnCHiKP84JeeSGaKHXTBuqxNARVKFKYtWCNw4+48TutoD7c1zqz4duc4RK+D7mIlVwLQ0h9YYCIdIwVMk9CSqmOybTCVNszU6hp0FAfe8cc5ziC6UzvIoEjvr7u00W0yy0+VV0Bj1QJRFaFYqA3VOnqR1j44W9MgKqVPqpkFPYipR8yCJriDlYhJQDBUKkYSgSQ8AjFVXcq4juUMViX32PpSZumM/H+M6xeqOn60pK2Xnl3uTrewKx4zgwuY3OkAqRCQj7JOhlnuqci8UdyOLMgZAsMnSGVCSM4gMhd05VYStS8iGzE3OwCGl2FAiRhqGIDAFFLLhjDHC7uGRJKHq6tG7Z6Ap5CqrDqOfOMd3i7glbCZOIPbIIRRRglnFid7kldBt+RcqmU0NeGVYVAjfDmr5jbLw2vwoGAVaRPyO6ZUOR2JQW/VLJkgCJCbB4/f3sElJuFAiRhhH2ylg1P1j0HBifIsIs4ond5g5EgcFXRPqhXneO6ZaNoEcsaCjlZKrEIFex5qYYMd1EZ9CTLa73KRKYgLK0rc/ESM+PUmbpyAt5JAS9xafH3K/Diur8m4nE3Lld5VxLQ0i9okCINAyW3nFVrGKHKuqWDVVmee82G69eO8cszhH2ljaUVBYZPHW8fNWyOQQIaA+c+Ht6ZRGKyCo+LNCwOYJ5FDBLIkNXUEXSLD4QCnqK6/ybSWZRMbXQk7mAAiEyZ8gSK2qoombyolr2gfrsHHMcB3BQlrkzYa9ctydCMc1CyCuhZVyXoUd266I0q7J1QjZ38g7aW/0KRKG4Qn6TO2Vb0zFeZlJ1Pf3cElIpFAiROUMpMqWmW3ZRLftAfXaOmbaTHsBX+q+/VxErnmYqVtK00B2eWNclCAJafUrlC9gF5F23E/TICBWRHnM7/1BSwftMVJkVXbtESCOhQIjMGbJ4Yj9WIRyg6Hk79dg55hbYimXpNPLIIuA4ddc5ppnu1OxW/9QA1q9KFQ3eLJtDZMKshdIZIhPQFfIgaRR2SmXabmdapXbZ+RQRdp19XwmpBAqEyJwhiYJbAFrAcb9pc8iiUFR9EFCfnWOGxRFQpbIsH3ULpuuvliSqmWj1KznTRj5FhFjBdKWR3TGW/79vi1eBLAkF1Vu5qzVEeMpcKJ0hiQxOWTb0EVLfKBAic4YsMkiMFdQJkzLck4VSirTrrXOsnJOIVcktPq6ngmnuODBtB10hT84iYq8iQpUZ9ArVCWVSj4UEQkGPhLBHLig9plvuaIByBLS5yDRdmswRFAiROcMNhAqbjaJZNsJeuaQ9TvXWOSagfJOIM8s+6ykQSugWAh4Jrf7cy0xVSYRfrdyEaffETSyok4ul02OpArrHDJsj5K3cxluZlb6fj5BGQIEQmTNEJkCRCjsRsriDkLe07eD11DlmcwdMKO8k4mCdnXjFdAtd6b1i02nxKpU7EeK8qI3yYZ8MVWLQ8px87aByhdKAm0oudT8fIY2AAiEyp3gVMe96Fp7uyilmt9l49dQ5lpmJVM5JxD5FqpubpWlziIIw6yoUvypVrP5FAPIulB4vqEpoC6gYTRroi6YQ16xpT2PcIZ+l7YqbTTGpZEIaUeXOVQmpQ15ZzDs1ppnu+oJiC6UzxneOVarDJ1+6xd0amTIW2Lp/p/q4WcY0C2GfjPAsp3heRYQksnQxfPn+LWzuQJhl2ep0BEHAmu4QFrZ4MRzXMRg30BdJQWQMQY8Er3wi3WZYHLJUuY4xYHIqubY/t4RUEgVCZE6ZbeXBeLrJ4VelkoOGTOfYcNwo6XHKQbc4ukJqWScRqzKDJJU/qChGyrSwfJ5/1poun+zuWdPN8l6zaZcWoMgiQ0dARUdAxVLLRiRpYjCmYzhhYCxlQGIMQdUdYqlK5Q1oJ8ukkgtt6yek0VAgROYUd6hifqcXum1jgS9351GhQh4JxyNayY9TKu5wBIqoX5mJJ905VutAKGXY8Mi5ZwdNJokMAVXCSMJAoIxPg4bFoYqz7xjLhyqJ6AyJ6Ax5kDJsjKUMDER1jCYNxHULyzv8ZV+tMZlHZoimilv/QUijoECIzCluq7EAx3FmvYk4jlNU0Wsu9dA55jhu2qbcc2cynWMpw0aRA7jLIqaZmBdS81450epT0BdNlfUaTJu7KzNK6DLMxauI8CpedIe9SOgWxlImfFVIs3pozQaZAygQInOKLAoQ00MV3TkpuZk2hyiysnXljO8cK/dNMl+6xbNBS7m1eGWMJcyyP26+uOPA5BzzQ568/4xXEQEHeQXF+TJsXpHdX+P5VankAv58qVL9rlAhpFyoa4zMKdkC0Fk6YTTThlcSy/aqux46xzJpm0oEQkGPDO7U7u8W1ywEPfKEBauz8SkiVEksa+t/pVvaq00WGSqcfSOk5igQInOKLDLIjM163K+ZHEFv+ab21sPOMd3iCHjFipxI+VQxWzBdC3HdQldILag2xyuLUOXyDVZ0U4+oaAFztcmiUCf9gIRUTvP8xhKSh0wnjDlLC71hc7R4y1fwUg87x0ybI+ypTBGPX5Hgldw6oWoz00tO2/0zzw6ajDEBYa8EPc8BhrNfh5tuVWs8IqGcJJFBZAJtoSdNjQIhMud45JmHxGUGKZY6P2iyWu8cc+D+3StBZAJa/HLeU5HLKZoy0ZLH7KBcQl4ZVplqYAybQ65wS3u1KdlZQnQuRJpX8/zGEpKn2SYh6yaHKrOSFq3mUsvOsex8mwrWr4S9Cswa1AlpFsf8sAesiJSfT5HABJSlINi0ODwSq/kspXKSRAGSyCgQIk2teX5jCcmTLLIZb3yaZcOnSGU/PfHIDAJqs3PMLZRm8BSx+iFffkWEJMxef1VOKcOGV2ZoLbJv3yu7M5D0MtQJGTZH0NNcjbgSEyCx+tiTR0ilUCBE5hxFYhCEGU6ELButPrnsw+pUSYQi1aZzLLNaoxyD/qaTCR5TVUyPxXQTLT6l6HZyj+yOSNDKsIDV4k7ZTxFrTUjvM6MaIdLMKBAic44sMjiOO1QxF84dBNTyTl8Gats5Zli8qBqaQigSQ9BTvTohx3Fg2RydocKKpMcTBAGtPqVM3xOn5rvkKsEjU2qMNDcKhMicI4kCJCl3Aahpc0hS+QYpjlfLzjHuVOe0os2vQC/D6Uo+Uqa7UqPUAC/gkUquETJtDklkUCtUjF5LXlmCTUMVSRNrvt9aQmaR6YTJVfegmxweSSx7x1hGLTrH3C44N8VRaT5FBBOEqkwjjusWWnxKyQGeVxYhCqXVwZg2hyqxpuoYy5BmmMBOSDNovt9aQmbhTpfO3UKfMm0EPVLFOn9q0TlmWBxKCRvRC+FXJagyq3h6zHEcmCWmxTK8iljyNRsWhyKy9FLf5tJMXXCE5EI/4WTOmWmoomnzojuQ8lGLzjHd4lDl6sy3USUGvyKVbVrzdFKmDa9SeloMcIvY/WpptVuZjrFKb4OvBVkUIFbplI+QWqBAiMxJXnlqm7e7fLOyu6Jq0TmmWzZCHqmoOTuFEgQB7X6lLF1YM4nrFlrLkBbLaPGWVttk2ZUpsK8H7nRpzLqfj5BGRYEQmZM8sjjliV23ePZEo1JUiUERq9s5ZtocQU/1btL+9Cyd6brySuU4DizO0RnMf9P8bPyqBKeErVqC4DRloTTgngi5QxWphZ40p+b8zSVkFqokTjnqz6RbKrWGAkh3jnmq2zkmAFVt6/YrElSRVawovFzdYuN5FRGSWNzSWJs7YEJzFkoDgMwYJGHmtTSENLLm/M0lZBbuUMWJ79NNtz6o0nUeQbV6nWPuag1W0eBuMq8swqOUb6v7ZHHNQptfKWsK0yeL8EhiUROmTZu7y1YrOLW7lhgToMg0XZo0LwqEyJwkpetlxqdvHDgIVGFFglepXueYbnKokliV1vkMxgS0+ZSKTJh2HAeWwzEvUL60GODWwQRUqajaJrdjrLmWrU7mlcWczQWENIPm/c0lZAayxCCKJ17lWjYHY0JVhg6qUvU6x3TLhl+RIFW5BTrolcArcONMmTa8slSRKdmtPgWGXXggZNocPlWsSjF6rXhkkU6ESNOiQIjMSYrIIAknpktrJodHrtwgxfE8cvU6xwybI+St/v4rnyJBkoqruZlJJdJiGV5FBJzCi7ybcdnqZIrESiglJ6S+USBE5iSJpTth0gWgmmUjqFZukOJ4mc4xo0qdY7VYBOpXRHglsayDFR3HgckdzAuWPkQxF58iQpXEguu3HKeyIxfqgSwyCBQKkSZFgRCZkySRQZFOtAQbtl3RQYrjZTrHKl0w7XYzCVUtlM6QRIawVy5rnVDSsOEr0xDFXLyyCFUurMg7M3uqWQulM2ZbVExII6NAiMxZquRu1Xaf3AX41OrdzIKqVPHUmG7ZUCRWs43oYZ9c1tkzcd1Cu1+p2N+HMQFhrwS9gODNsN3VGs1cKA3MvKiYkEZXN7+9mzZtgiAIuPXWW7Pvu//++3H55Zejo6MDgiDgpZdeqtn1kebjU9yhiplBitWoD8rwViFdZVgcHrl2N2m/IoFh6gTvYrhDFB10VCgtlhHyygV1R5mWA7lJl62Ol6mpo4Jp0ozq4rd3y5YtuOuuu7B+/foJ708kErjgggvwxS9+sUZXRpqZKong4NDSA/qq2WJejc4x3eIIeeSa7b/yqSI8SnkWsCYNG361cmmxDJ8iQWT579UybA6vwqrelVdtk2vqCGkmNW91iMfjuPHGG3H33Xfjc5/73ISPvfe97wUAHDp0KO/H03Uduq5n/zsajZblOknzcQtABWgWR1fIU9WAwSOLUGQBUc2sWG2S7VRnLtJ0VElEUJUxljTgV0u7jrhuYUGLp+JpPq8sQpEYdJPnVQBt2BxBtbwzjeqRJDJITKhagT8h1VTzlzE333wzNm7ciMsuu6wsj7dp0yaEw+Hs2+LFi8vyuKT5yKIb+HDHQbDKLeZeRcTKziAcOOiLpsp+MpQpaq1VfVBGm1+BXsRsnvG448B2HLQHKpsWAwCPzOCVxbwHK3LHafqOsQyvQrOESHOqaSB03333Ydu2bdi0aVPZHvOOO+5AJBLJvvX09JTtsUlzkSUGxgSIggCfXP2Tk4UtXpyxqBXtfhV90RSShlW2xzZsDlVkVU335eIWoJfWbVTpbrHxBEFAq09BVDMxkjCQNKyZ02SO0/QdYxnudGkKhEjzqdm5eU9PD2655RY89NBD8HjKd7SsqipUtfKvHEnjkxmDzASITKjZq/qwT8bahWEcGZFwaDiJlGGjzV/6vjPD4jXtGMvwKxI8EoOWZ6opl7huYWFr5dNiGd0tXsiigOGEgbhuIZIy4cCBKorwyO6byNyBmJLEmnbr/GQeWQTnDqq2H4aQKqlZILR161YMDAxgw4YN2ffZto3HH38cd955J3RdhyjOjVdapDZkUcjumFJq2PWjSAwr5gUQ8so4MJhAb0TDvIBa0jXpFkebX4FY47UPHpnBp0pI6XZRgRB3HNico8NfvRc3AVVCQA1gaQegmTYSuoWEbmM0aSCqmYjFLHA4EOCmi5q9YyxDEgUKgkhTqlkgdOmll2LHjh0T3nfTTTdh9erVuP322ykIIhUnpee/VGuQ4kwEQUBn0IOgKuPgUBw9I0n4VbnodJBhc4RrsFpjMkFwF7DuT8SL+vNJw0ZAlRCqQlosl8wJUHsAWNLug27ZSOhucDSaNMCE5t06P5ksMneMdo26EAmplJo9UwaDQaxdu3bC+/x+P9rb27PvHxkZwZEjR9Db2wsA2L17NwBg/vz5mD9/fnUvmDSlrpAHIU9tbrK5eBURq+eHEPLK2D8YR180hXkBT1EnO9WYVZSPoEcqejlDtdNis1Eldw1Hm1/B4jZfrS+nqmTGIDJGW+hJ06nrM91f//rXOPPMM7Fx40YAwHXXXYczzzwT3/rWt2p8ZaRZLG7zIeyrn0AIcCccL2r14YzFregIuIXUhczisWwOkQl1Ezz4VAmKWHjrNXcc8Cqnxcj0JFGAVONUKyGVUB8vGdM2b9484b/f//734/3vf39NroWQWgt73UJqvyLhwFAcXUFPXoP7MpOya7FjLBefLMKrSEiZdkF1T0ndhr+GaTEykSwyt06ofM2NhNSF+nimJITkJIsMy+b50R32YiCm59WGbljcXSBaJ7UrjAlo8coFT5hOGBY6AmrdnGzNdbIoQGR0yyDNh36qCalzssiwojOAoEfCSMKY9fN1m1dl5k4hQl4ZdgGzhDLdYm2B2heyE5cgCHVzykhIOdFPNSENIKBKWNkVgA0HCX3m3AR3HPhKXGlRbv70Di/Lnr1OyHEcDMcNBDzFd82RyqDTOdKMKBAipEF0Bj1Y3u7HWMqAOU1AwZ30fJs6u2H5VHeprWbOHAhppo3eSAo+VcTJXYG6Se8Rl1cWaZQQaTr19bKREDKjJe1+JAwbR0dT6A57wCbNdDkxUbq+XuPIIkPIK2EwauRcBMsdByMJAzZ3sKwjgJPafXT6UIdkiUGiOiHSZCgQIqSBiEzAinkBxHULQ3EdncGJ62ncjjERnjo8SWnxKugd06a8P2m4wwnb/AqWdQTQESh9xQipDHclTa2vgpDyoh9pQhqMVxGxqiuI/9/e/cdEXf9xAH9+7rg7fhx8AAWuExTjizhDU1fDNRNsq3CaNmWZMV394awlOsu5/Iuy5Y+tabbW7w1ba2kraVbOzVJpBZoBGtrIUJRUlFR+nKCA3Ov7R91nnpx62t3x+dzn+dhuw8+9ffN57sVbX3x+3MdqUdB1pd/vvb5rXiTGWmHR4ee9JDhioADaE8wHvII2z1Vc7ruGnDQnJmQmIy3RwSZIx2xWC+8co6jDI0JEBpSaYEdOmhO/n+2Ew2bRrqXp9w5AjdPnnVbx9n8eV9F7bQAiQMeVfgxz2nHv8AQMc/JDE40gxqrAxkaIogwbISKDGpEch8u9/Th5oQf3qHH/PoZD0e3T0GNtVjhjrTjbfhUJDity053ISo0f0gfe0p2xWS3gYyAp2rARIjIoi0XBvWlOdPcO4MLlXqQm2GGz6ufRGoEMT3BABLh3uBMpCfo8ckU3Z7PyYmmKPvyJJjIwR4wVuRmJcNgs+NvTC3uMVXe3zl/P9ww1NkHGZLX8c8TxxrsViYyMjRCRwalxNvwv3QmLoiDeboFNx7f1WCzKv6fwyKhy0pxIjWcjS9GDp8aIooArKRbdadf4dHAKOz2feiW6G2yEiKKAovzz+UJERHRn2AgRRQl+/g4R0Z3T78UERERERGHGRoiIiIhMi40QERERmRYbISIiIjItNkJERERkWmyEiIiIyLTYCBEREZFpsREiIiIi02IjRERERKbFRoiIiIhMi40QERERmRYbISIiIjItNkJERERkWlH/9HkRAQB0dXUN8Z4QERFRsHz/b/v+Hw+XqG+EPB4PACArK2uI94SIiIjulMfjgaqqYZtfkXC3WkPM6/Xi7NmzSExMhKIoQ7IPXV1dyMrKwl9//YWkpKQh2YdQYRb9iqY8zKJPzKJf0ZTHl6WlpQWKosDtdsNiCd+VPFF/RMhisSAzM3OodwMAkJSUZPgfUB9m0a9oysMs+sQs+hVNeVRVjUgWXixNREREpsVGiIiIiEyLjVAEOBwOlJeXw+FwDPWu/GfMol/RlIdZ9IlZ9Cua8kQ6S9RfLE1ERER0MzwiRERERKbFRoiIiIhMi40QERERmRYbISIiIjItNkJBWLduHR588EEkJiYiPT0dTz75JP744w+/MSKCV199FW63G3FxcSgqKsLRo0f9xvT29qKsrAzDhw9HQkICZs+ejdOnT/uNyc7OhqIofq9XXnlFd1k+/PBDFBUVISkpCYqioKOjY9D3am9vx8KFC6GqKlRVxcKFCwOOM0oeI9Tm0qVLKCsrQ15eHuLj4zFy5EgsW7YMnZ2dfvOEuzaRzGKEugDAkiVLkJOTg7i4OKSlpWHOnDlobGz0G2OkNRNMHqPU5vqxM2bMgKIo+Prrr/3eM8KaCTaLUepSVFQ0aD+ffvppvzEhqYvQbT3++ONSUVEhR44ckUOHDsnMmTNl5MiRcvnyZW3M+vXrJTExUb766itpaGiQ+fPnyz333CNdXV3amOeff15GjBghu3fvlrq6Opk+fbrcf//9cu3aNW3MqFGjZM2aNdLa2qq9PB6P7rJs2rRJ1q1bJ+vWrRMA0t7ePuh7FRcXS35+vlRXV0t1dbXk5+fLrFmzQpYl0nmMUJuGhgaZO3eu7NixQ5qamuSHH36Q3NxcmTdvnt/3CndtIpnFCHUREfnggw+kqqpKmpubpba2Vp544gnJysryW/9GWjPB5DFKbXw2btwoM2bMEABSWVnp954R1kywWYxSl8LCQlm8eLHffnZ0dPh9r1DUhY3QXWhraxMAUlVVJSIiXq9XXC6XrF+/Xhtz9epVUVVV3n//fRER6ejoEJvNJlu3btXGnDlzRiwWi+zatUvbNmrUKNm0aVNkgsjdZbne3r17AzYOv//+uwCQ/fv3a9tqamoEgDQ2NoYnjIQvj4jxauPzxRdfiN1ul/7+fhEZmtqEK4uIcety+PBhASBNTU0iYtw143NjHhFj1ebQoUOSmZkpra2tg5oHo62ZW2URMU5dCgsLZfny5TedN1R14amxu+A7NJ+amgoAaG5uxrlz5/DYY49pYxwOBwoLC1FdXQ0AqK2tRX9/v98Yt9uN/Px8bYzPhg0bMGzYMEycOBFvvPEG+vr6dJUlGDU1NVBVFQUFBdq2KVOmQFXVO5rnToUrj48Ra9PZ2YmkpCTExPzzaMGhqE24svgYrS7d3d2oqKjA6NGjkZWVBcDYayZQHh8j1KanpwcLFizAO++8A5fLNWheI62Z22XxMUJdAOCzzz7D8OHDcd9992HlypXweDzae6GqS9Q/dDXURAQvvfQSpk6divz8fADAuXPnAAAZGRl+YzMyMnDq1CltjN1uR0pKyqAxvr8PAMuXL8fkyZORkpKCX375BatXr0ZzczM+/vhj3WQJxrlz55Cenj5oe3p6ul/eUApnHsCYtbl48SJef/11LFmyRNsW6dqEMwtgrLq8++67WLVqFbq7uzF27Fjs3r0bdrtdm8doa+ZWeQDj1GbFihV46KGHMGfOnIBzG2nN3C4LYJy6lJaWYvTo0XC5XDhy5AhWr16Nw4cPY/fu3do8oagLG6E7tHTpUvz222/46aefBr2nKIrfn0Vk0LYb3ThmxYoV2tcTJkxASkoKSkpKtO49lEKd5XZz3O08wQp3HqPVpqurCzNnzsS4ceNQXl5+yzluNc9/Fe4sRqpLaWkpHn30UbS2tuLNN9/EU089hZ9//hmxsbEB57jZPKES7jxGqM2OHTuwZ88e1NfX33J+I6yZYLMYoS4AsHjxYu3r/Px85Obm4oEHHkBdXR0mT54ccI5A89wOT43dgbKyMuzYsQN79+5FZmamtt13+PHGDrStrU3reF0uF/r6+tDe3n7TMYFMmTIFANDU1BSSDD7/JUswXC4Xzp8/P2j733//fUfzBCvceQLRc208Hg+Ki4vhdDpRWVkJm83mN0+kahPuLIHouS6qqiI3NxfTpk3Dl19+icbGRlRWVmrzGG3N3CpPIHqszZ49e3D8+HEkJycjJiZGO+06b948FBUVafMYYc0EkyUQPdYlkMmTJ8Nms+HPP//U5glJXYK+msjEvF6vvPjii+J2u+XYsWMB33e5XLJhwwZtW29vb8CLpbdt26aNOXv27KCLpW/0zTffCAA5deqUbrJc73YXSx84cEDbtn///pBfXBipPIHotTadnZ0yZcoUKSwslO7u7kHzRKI2kcoSiF7rcqPe3l6Ji4uTiooKETHumrlZnkD0WJvW1lZpaGjwewGQzZs3y4kTJ0TEOGsmmCyB6LEugfjy+C66DlVd2AgF4YUXXhBVVWXfvn1+t/H19PRoY9avXy+qqsr27duloaFBFixYEPD2+czMTPn++++lrq5OHnnkEb/b56urq2Xjxo1SX18vJ06ckG3btonb7ZbZs2frLktra6vU19fLRx99JADkxx9/lPr6erl48aI2pri4WCZMmCA1NTVSU1Mj48ePD/mtwJHKY5TadHV1SUFBgYwfP16ampr85rnxNu1w1iZSWYxSl+PHj8vatWvl119/lVOnTkl1dbXMmTNHUlNT5fz589o8RlkzweQxSm0CwU1un9f7mgkmi1Hq0tTUJK+99pocPHhQmpub5bvvvpOxY8fKpEmTQv5vGRuhIAAI+Lr+Nx+v1yvl5eXicrnE4XDItGnTpKGhwW+eK1euyNKlSyU1NVXi4uJk1qxZ0tLSor1fW1srBQUFoqqqxMbGSl5enpSXlwf9m3Aks5SXl992nosXL0ppaakkJiZKYmKilJaWBnWkRY95jFIb3xGtQK/m5mZtXLhrE6ksRqnLmTNnZMaMGZKeni42m00yMzPlmWeeGfRbq1HWTDB5jFKbm817YyNkhDUTTBaj1KWlpUWmTZsmqampYrfbJScnR5YtW+b3y7ZIaOqi/LvTRERERKbDi6WJiIjItNgIERERkWmxESIiIiLTYiNEREREpsVGiIiIiEyLjRARERGZFhshIiIiMi02QkRERGRabISISJf27dsHRVHQ0dEx1LtCRFGMnyxNRLpQVFSEiRMn4q233gIA9PX14dKlS8jIyICiKEO7c0QUtWKGegeIiAKx2+1wuVxDvRtEFOV4aoyIhtyzzz6LqqoqbN68GYqiQFEUbNmyxe/U2JYtW5CcnIxvv/0WeXl5iI+PR0lJCbq7u/HJJ58gOzsbKSkpKCsrw8DAgDZ3X18fVq1ahREjRiAhIQEFBQXYt2/f0AQlIt3hESEiGnKbN2/GsWPHkJ+fjzVr1gAAjh49OmhcT08P3n77bWzduhUejwdz587F3LlzkZycjJ07d+LEiROYN28epk6divnz5wMAnnvuOZw8eRJbt26F2+1GZWUliouL0dDQgNzc3IjmJCL9YSNERENOVVXY7XbEx8drp8MaGxsHjevv78d7772HnJwcAEBJSQk+/fRTnD9/Hk6nE+PGjcP06dOxd+9ezJ8/H8ePH8fnn3+O06dPw+12AwBWrlyJXbt2oaKiAmvXro1cSCLSJTZCRGQY8fHxWhMEABkZGcjOzobT6fTb1tbWBgCoq6uDiGDMmDF+8/T29mLYsGGR2Wki0jU2QkRkGDabze/PiqIE3Ob1egEAXq8XVqsVtbW1sFqtfuOub56IyLzYCBGRLtjtdr+LnENh0qRJGBgYQFtbGx5++OGQzk1E0YF3jRGRLmRnZ+PAgQM4efIkLly4oB3V+S/GjBmD0tJSLFq0CNu3b0dzczMOHjyIDRs2YOfOnSHYayIyOjZCRKQLK1euhNVqxbhx45CWloaWlpaQzFtRUYFFixbh5ZdfRl5eHmbPno0DBw4gKysrJPMTkbHxk6WJiIjItHhEiIiIiEyLjRARERGZFhshIiIiMi02QkRERGRabISIiIjItNgIERERkWmxESIiIiLTYiNEREREpsVGiIiIiEyLjRARERGZFhshIiIiMq3/A6xgGtYfxHqiAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
-    "# or, use seaborn to plot the range of values from multiple models\n",
-    "da= da_summary[f\"{var}_mean\"].sel(region=9).to_dataframe()\n",
-    "sns.lineplot(data=da, x=\"time\", y= f\"{var}_mean\",  errorbar=(\"pi\", 100) )"
+    "# From original BARPA data \n",
+    "Daily data from 2015 to 2100.\n",
+    "\n",
+    "~15km resolution\n",
+    "\n",
+    "apply acs_regional_stats inside open_mfdataset as part of the preprocessor. This is very effective at managing memory. \n",
+    "\n",
+    "It is quite slow because there is a lot of data!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "6584dd80-897b-4f70-911e-777376b81119",
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 28,
+   "id": "8e3b6f6b-3432-4668-b322-5936d3d9088f",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['/g/data/py18/BARPA/output/CMIP6/DD/AUS-15/BOM/ACCESS-CM2/ssp370/r4i1p1f1/BARPA-R/v1-r1/day/tas/latest/tas_AUS-15_ACCESS-CM2_ssp370_r4i1p1f1_BOM_BARPA-R_v1-r1_day_201501-201512.nc',\n",
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+       " '/g/data/py18/BARPA/output/CMIP6/DD/AUS-15/BOM/ACCESS-CM2/ssp370/r4i1p1f1/BARPA-R/v1-r1/day/tas/latest/tas_AUS-15_ACCESS-CM2_ssp370_r4i1p1f1_BOM_BARPA-R_v1-r1_day_208201-208212.nc',\n",
+       " '/g/data/py18/BARPA/output/CMIP6/DD/AUS-15/BOM/ACCESS-CM2/ssp370/r4i1p1f1/BARPA-R/v1-r1/day/tas/latest/tas_AUS-15_ACCESS-CM2_ssp370_r4i1p1f1_BOM_BARPA-R_v1-r1_day_208301-208312.nc',\n",
+       " '/g/data/py18/BARPA/output/CMIP6/DD/AUS-15/BOM/ACCESS-CM2/ssp370/r4i1p1f1/BARPA-R/v1-r1/day/tas/latest/tas_AUS-15_ACCESS-CM2_ssp370_r4i1p1f1_BOM_BARPA-R_v1-r1_day_208401-208412.nc',\n",
+       " '/g/data/py18/BARPA/output/CMIP6/DD/AUS-15/BOM/ACCESS-CM2/ssp370/r4i1p1f1/BARPA-R/v1-r1/day/tas/latest/tas_AUS-15_ACCESS-CM2_ssp370_r4i1p1f1_BOM_BARPA-R_v1-r1_day_208501-208512.nc',\n",
+       " '/g/data/py18/BARPA/output/CMIP6/DD/AUS-15/BOM/ACCESS-CM2/ssp370/r4i1p1f1/BARPA-R/v1-r1/day/tas/latest/tas_AUS-15_ACCESS-CM2_ssp370_r4i1p1f1_BOM_BARPA-R_v1-r1_day_208601-208612.nc',\n",
+       " '/g/data/py18/BARPA/output/CMIP6/DD/AUS-15/BOM/ACCESS-CM2/ssp370/r4i1p1f1/BARPA-R/v1-r1/day/tas/latest/tas_AUS-15_ACCESS-CM2_ssp370_r4i1p1f1_BOM_BARPA-R_v1-r1_day_208701-208712.nc',\n",
+       " '/g/data/py18/BARPA/output/CMIP6/DD/AUS-15/BOM/ACCESS-CM2/ssp370/r4i1p1f1/BARPA-R/v1-r1/day/tas/latest/tas_AUS-15_ACCESS-CM2_ssp370_r4i1p1f1_BOM_BARPA-R_v1-r1_day_208801-208812.nc',\n",
+       " '/g/data/py18/BARPA/output/CMIP6/DD/AUS-15/BOM/ACCESS-CM2/ssp370/r4i1p1f1/BARPA-R/v1-r1/day/tas/latest/tas_AUS-15_ACCESS-CM2_ssp370_r4i1p1f1_BOM_BARPA-R_v1-r1_day_208901-208912.nc',\n",
+       " '/g/data/py18/BARPA/output/CMIP6/DD/AUS-15/BOM/ACCESS-CM2/ssp370/r4i1p1f1/BARPA-R/v1-r1/day/tas/latest/tas_AUS-15_ACCESS-CM2_ssp370_r4i1p1f1_BOM_BARPA-R_v1-r1_day_209001-209012.nc',\n",
+       " '/g/data/py18/BARPA/output/CMIP6/DD/AUS-15/BOM/ACCESS-CM2/ssp370/r4i1p1f1/BARPA-R/v1-r1/day/tas/latest/tas_AUS-15_ACCESS-CM2_ssp370_r4i1p1f1_BOM_BARPA-R_v1-r1_day_209101-209112.nc',\n",
+       " '/g/data/py18/BARPA/output/CMIP6/DD/AUS-15/BOM/ACCESS-CM2/ssp370/r4i1p1f1/BARPA-R/v1-r1/day/tas/latest/tas_AUS-15_ACCESS-CM2_ssp370_r4i1p1f1_BOM_BARPA-R_v1-r1_day_209201-209212.nc',\n",
+       " '/g/data/py18/BARPA/output/CMIP6/DD/AUS-15/BOM/ACCESS-CM2/ssp370/r4i1p1f1/BARPA-R/v1-r1/day/tas/latest/tas_AUS-15_ACCESS-CM2_ssp370_r4i1p1f1_BOM_BARPA-R_v1-r1_day_209301-209312.nc',\n",
+       " '/g/data/py18/BARPA/output/CMIP6/DD/AUS-15/BOM/ACCESS-CM2/ssp370/r4i1p1f1/BARPA-R/v1-r1/day/tas/latest/tas_AUS-15_ACCESS-CM2_ssp370_r4i1p1f1_BOM_BARPA-R_v1-r1_day_209401-209412.nc',\n",
+       " '/g/data/py18/BARPA/output/CMIP6/DD/AUS-15/BOM/ACCESS-CM2/ssp370/r4i1p1f1/BARPA-R/v1-r1/day/tas/latest/tas_AUS-15_ACCESS-CM2_ssp370_r4i1p1f1_BOM_BARPA-R_v1-r1_day_209501-209512.nc',\n",
+       " '/g/data/py18/BARPA/output/CMIP6/DD/AUS-15/BOM/ACCESS-CM2/ssp370/r4i1p1f1/BARPA-R/v1-r1/day/tas/latest/tas_AUS-15_ACCESS-CM2_ssp370_r4i1p1f1_BOM_BARPA-R_v1-r1_day_209601-209612.nc',\n",
+       " '/g/data/py18/BARPA/output/CMIP6/DD/AUS-15/BOM/ACCESS-CM2/ssp370/r4i1p1f1/BARPA-R/v1-r1/day/tas/latest/tas_AUS-15_ACCESS-CM2_ssp370_r4i1p1f1_BOM_BARPA-R_v1-r1_day_209701-209712.nc',\n",
+       " '/g/data/py18/BARPA/output/CMIP6/DD/AUS-15/BOM/ACCESS-CM2/ssp370/r4i1p1f1/BARPA-R/v1-r1/day/tas/latest/tas_AUS-15_ACCESS-CM2_ssp370_r4i1p1f1_BOM_BARPA-R_v1-r1_day_209801-209812.nc',\n",
+       " '/g/data/py18/BARPA/output/CMIP6/DD/AUS-15/BOM/ACCESS-CM2/ssp370/r4i1p1f1/BARPA-R/v1-r1/day/tas/latest/tas_AUS-15_ACCESS-CM2_ssp370_r4i1p1f1_BOM_BARPA-R_v1-r1_day_209901-209912.nc',\n",
+       " '/g/data/py18/BARPA/output/CMIP6/DD/AUS-15/BOM/ACCESS-CM2/ssp370/r4i1p1f1/BARPA-R/v1-r1/day/tas/latest/tas_AUS-15_ACCESS-CM2_ssp370_r4i1p1f1_BOM_BARPA-R_v1-r1_day_210001-210012.nc']"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# for large amounts of daily data and a custom region (new mask needed)"
+    "# choose a list of files to open\n",
+    "# this list of files will be combined to form a dataset with model, time, lat, and lon dimensions\n",
+    "# we will calculate regional averages and then calculate timeseries for a selected region for each model\n",
+    "\n",
+    "\n",
+    "path = f\"/g/data/py18/BARPA/output/CMIP6/DD/AUS-15/BOM/ACCESS-CM2/ssp370/r4i1p1f1/BARPA-R/v1-r1/day/tas/latest\"\n",
+    "\n",
+    "filelist = sorted(glob(f\"{path}/*.nc\"))\n",
+    "filelist"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "a29fba8e-731e-447a-bf0e-1f01f6810934",
+   "execution_count": 29,
+   "id": "60f8bf95-640d-457e-9eb5-4545792e9786",
    "metadata": {},
    "outputs": [
     {
@@ -2093,172 +4492,124 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 10GB\n",
-       "Dimensions:  (time: 43800, lat: 226, lon: 261)\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 989MB\n",
+       "Dimensions:    (time: 365, lat: 436, lon: 777, bnds: 2)\n",
        "Coordinates:\n",
-       "  * lat      (lat) float64 2kB -32.0 -31.9 -31.8 -31.7 ... -9.8 -9.7 -9.6 -9.5\n",
-       "  * lon      (lon) float64 2kB 132.0 132.1 132.2 132.3 ... 157.8 157.9 158.0\n",
-       "  * time     (time) datetime64[ns] 350kB 1980-01-01T15:00:00 ... 2099-12-31T1...\n",
+       "  * time       (time) datetime64[ns] 3kB 2015-01-01T12:00:00 ... 2015-12-31T1...\n",
+       "  * lat        (lat) float64 3kB -53.58 -53.42 -53.27 ... 13.32 13.48 13.63\n",
+       "  * lon        (lon) float64 6kB 88.04 88.19 88.34 88.5 ... 207.6 207.8 207.9\n",
+       "    height     float64 8B ...\n",
+       "Dimensions without coordinates: bnds\n",
        "Data variables:\n",
-       "    tmaxscr  (time, lat, lon) float32 10GB 296.5 296.5 297.7 ... 303.9 303.8\n",
-       "Attributes: (12/31)\n",
-       "    Conventions:              CF-1.7\n",
-       "    title:                    Queensland future climate projections derived f...\n",
-       "    institution:              Queensland Department of Environment and Science\n",
-       "    references:               The Long Paddock\n",
-       "    comment:                  Data produced for the LongPaddock Queensland Fu...\n",
-       "    contact:                  longpaddock@qld.gov.au\n",
-       "    ...                       ...\n",
-       "    rlat0:                    -20.0\n",
-       "    schmidt:                  0.19\n",
-       "    date_created:             2020-01-20T18:14:26+10:00\n",
-       "    date_produced:            2016-04-15\n",
-       "    date_metadata_modified:   2020-01-20\n",
-       "    history:                  2016-04-15: Interpolated from the original cubi...</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-d7215460-9552-44f0-ae90-31f180b99688' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d7215460-9552-44f0-ae90-31f180b99688' 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>: 43800</li><li><span class='xr-has-index'>lat</span>: 226</li><li><span class='xr-has-index'>lon</span>: 261</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f1b13841-8850-4c32-ab42-82d3e94e5d14' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f1b13841-8850-4c32-ab42-82d3e94e5d14' class='xr-section-summary' >Coordinates: <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-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'>-32.0 -31.9 -31.8 ... -9.6 -9.5</div><input id='attrs-58a717ea-a9ca-4332-84b5-d06da2525a83' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58a717ea-a9ca-4332-84b5-d06da2525a83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd974ffc-8072-4ccf-a5ee-ea6996dce9bf' class='xr-var-data-in' type='checkbox'><label for='data-cd974ffc-8072-4ccf-a5ee-ea6996dce9bf' 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>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-32. , -31.9, -31.8, ...,  -9.7,  -9.6,  -9.5])</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'>132.0 132.1 132.2 ... 157.9 158.0</div><input id='attrs-65aa5010-35e4-4678-9a76-0a89d591b1b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-65aa5010-35e4-4678-9a76-0a89d591b1b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-148c8f5d-bd3a-48da-ab3e-18c6d36d92d1' class='xr-var-data-in' type='checkbox'><label for='data-148c8f5d-bd3a-48da-ab3e-18c6d36d92d1' 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>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([132. , 132.1, 132.2, ..., 157.8, 157.9, 158. ])</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'>1980-01-01T15:00:00 ... 2099-12-...</div><input id='attrs-46ea6d9f-852f-4b59-9ebc-bd9e1b84c73c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-46ea6d9f-852f-4b59-9ebc-bd9e1b84c73c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3eabae12-f304-4dc1-bad7-26fbfb5961b5' class='xr-var-data-in' type='checkbox'><label for='data-3eabae12-f304-4dc1-bad7-26fbfb5961b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-01-01T15:00:00.000000000&#x27;, &#x27;1980-01-02T15:00:00.000000000&#x27;,\n",
-       "       &#x27;1980-01-03T15:00:00.000000000&#x27;, ..., &#x27;2099-12-29T15:00:00.000000000&#x27;,\n",
-       "       &#x27;2099-12-30T15:00:00.000000000&#x27;, &#x27;2099-12-31T15:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ebbf8259-79b2-49c8-9b17-50d92c56db9a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ebbf8259-79b2-49c8-9b17-50d92c56db9a' 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>tmaxscr</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'>296.5 296.5 297.7 ... 303.9 303.8</div><input id='attrs-5da6470c-6a8b-44db-a679-d64e53b592d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5da6470c-6a8b-44db-a679-d64e53b592d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b4eca53-dd64-4035-9910-2c4ae5ed0b90' class='xr-var-data-in' type='checkbox'><label for='data-5b4eca53-dd64-4035-9910-2c4ae5ed0b90' 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>Maximum screen temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><pre>array([[[296.495  , 296.495  , 297.6995 , ..., 296.20163, 296.201  ,\n",
-       "         296.18832],\n",
-       "        [297.18292, 297.69257, 299.8666 , ..., 296.22473, 296.22125,\n",
-       "         296.20654],\n",
-       "        [300.7233 , 300.50687, 300.77   , ..., 296.21326, 296.2043 ,\n",
-       "         296.18796],\n",
-       "        ...,\n",
-       "        [301.83707, 301.83   , 301.83707, ..., 302.75876, 302.73044,\n",
-       "         302.71716],\n",
-       "        [301.866  , 301.8082 , 301.81705, ..., 302.76062, 302.73022,\n",
-       "         302.70657],\n",
-       "        [301.8507 , 301.7903 , 301.79645, ..., 302.78326, 302.73773,\n",
-       "         302.69806]],\n",
-       "\n",
-       "       [[294.63998, 294.63998, 298.13535, ..., 296.00012, 295.99203,\n",
-       "         295.97873],\n",
-       "        [296.09506, 298.4952 , 305.5681 , ..., 296.0175 , 296.0068 ,\n",
-       "         295.9937 ],\n",
-       "        [306.3986 , 307.68228, 309.61   , ..., 296.03198, 296.01883,\n",
-       "         296.00757],\n",
-       "...\n",
-       "        [303.34576, 303.43768, 303.51788, ..., 303.7633 , 303.76035,\n",
-       "         303.77393],\n",
-       "        [303.42108, 303.50974, 303.57114, ..., 303.7771 , 303.76892,\n",
-       "         303.7846 ],\n",
-       "        [303.46082, 303.52963, 303.56357, ..., 303.77234, 303.77457,\n",
-       "         303.78488]],\n",
-       "\n",
-       "       [[293.07   , 293.07   , 294.7339 , ..., 298.55847, 298.55313,\n",
-       "         298.545  ],\n",
-       "        [293.61127, 295.29037, 298.9984 , ..., 298.56287, 298.54913,\n",
-       "         298.5346 ],\n",
-       "        [299.2349 , 300.80988, 301.62296, ..., 298.56302, 298.54553,\n",
-       "         298.52798],\n",
-       "        ...,\n",
-       "        [304.00717, 303.909  , 303.80823, ..., 303.84888, 303.80252,\n",
-       "         303.79697],\n",
-       "        [303.93155, 303.91095, 303.88   , ..., 303.89355, 303.82388,\n",
-       "         303.761  ],\n",
-       "        [303.81918, 303.82623, 303.83588, ..., 303.95392, 303.8948 ,\n",
-       "         303.84088]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e7ff8049-44b8-4d5d-ae2a-f3c0a188b3d0' class='xr-section-summary-in' type='checkbox'  ><label for='section-e7ff8049-44b8-4d5d-ae2a-f3c0a188b3d0' 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-7c9a2c06-6054-4e4b-81cd-16deb5af62ad' class='xr-index-data-in' type='checkbox'/><label for='index-7c9a2c06-6054-4e4b-81cd-16deb5af62ad' 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([              -32.0,               -31.9,               -31.8,\n",
-       "                     -31.7,               -31.6,               -31.5,\n",
-       "                     -31.4,               -31.3,               -31.2,\n",
-       "                     -31.1,\n",
+       "    tas        (time, lat, lon) float64 989MB ...\n",
+       "    time_bnds  (time, bnds) datetime64[ns] 6kB ...\n",
+       "    crs        int32 4B ...\n",
+       "Attributes: (12/61)\n",
+       "    axiom_version:             0.1.0\n",
+       "    axiom_schema:              cordex-1D.json\n",
+       "    axiom_schemas_version:     0.1.0\n",
+       "    productive_version:        dcb907f\n",
+       "    Conventions:               CF-1.11, ACDD-1.3\n",
+       "    title:                     Bureau of Meteorology Atmospheric Regional Pro...\n",
+       "    ...                        ...\n",
+       "    date_metadata_modified:    2024-08-02T08:00:56Z\n",
+       "    geospatial_lat_min:        -53.5755\n",
+       "    geospatial_lat_max:        13.632\n",
+       "    geospatial_lon_min:        88.0355\n",
+       "    geospatial_lon_max:        207.9275\n",
+       "    history:                   Sun Jun 30 10:15:58 2024: /g/data/access/ngm/m...</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-a66c4b0d-ecaa-4635-b187-05699193ab14' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a66c4b0d-ecaa-4635-b187-05699193ab14' 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>: 365</li><li><span class='xr-has-index'>lat</span>: 436</li><li><span class='xr-has-index'>lon</span>: 777</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d0267c45-e3ed-49ac-9962-8b201166ce19' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d0267c45-e3ed-49ac-9962-8b201166ce19' 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'>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'>2015-01-01T12:00:00 ... 2015-12-...</div><input id='attrs-97d629ba-5f3c-47f9-9d67-97f607519203' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97d629ba-5f3c-47f9-9d67-97f607519203' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7d8ea57-9866-41fb-b3e9-7e2c4a5aa708' class='xr-var-data-in' type='checkbox'><label for='data-a7d8ea57-9866-41fb-b3e9-7e2c4a5aa708' 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>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2015-01-01T12:00:00.000000000&#x27;, &#x27;2015-01-02T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-01-03T12:00:00.000000000&#x27;, ..., &#x27;2015-12-29T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-12-30T12:00:00.000000000&#x27;, &#x27;2015-12-31T12:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-53.58 -53.42 ... 13.48 13.63</div><input id='attrs-c09b6208-30e3-49b7-a3f7-5cfaa207e494' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c09b6208-30e3-49b7-a3f7-5cfaa207e494' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-648d6e26-38a3-4a9f-9a1b-531f28760cb7' class='xr-var-data-in' type='checkbox'><label for='data-648d6e26-38a3-4a9f-9a1b-531f28760cb7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-53.5755, -53.421 , -53.2665, ...,  13.323 ,  13.4775,  13.632 ])</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'>88.04 88.19 88.34 ... 207.8 207.9</div><input id='attrs-1126709f-b582-49a6-9708-3561b7fe1ca9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1126709f-b582-49a6-9708-3561b7fe1ca9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a405c38-77e3-411a-b5e8-aef46740cec7' class='xr-var-data-in' type='checkbox'><label for='data-6a405c38-77e3-411a-b5e8-aef46740cec7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([ 88.0355,  88.19  ,  88.3445, ..., 207.6185, 207.773 , 207.9275])</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-e3e33c94-65f0-4d9d-9cff-b84d5b14f070' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e3e33c94-65f0-4d9d-9cff-b84d5b14f070' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76daec70-fef5-4af0-bb65-e98fd48b0cfc' class='xr-var-data-in' type='checkbox'><label for='data-76daec70-fef5-4af0-bb65-e98fd48b0cfc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</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-d425485c-e703-416b-be80-30c27b00e9ea' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d425485c-e703-416b-be80-30c27b00e9ea' class='xr-section-summary' >Data variables: <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-var-name'><span>tas</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'>...</div><input id='attrs-2c5a8eaf-e359-4790-9182-4f0dc7c31f8c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c5a8eaf-e359-4790-9182-4f0dc7c31f8c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d6a6cf5-f708-4650-bdaf-418ba967299f' class='xr-var-data-in' type='checkbox'><label for='data-0d6a6cf5-f708-4650-bdaf-418ba967299f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: point (interval: 1H) time: mean (interval: 1D)</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd></dl></div><div class='xr-var-data'><pre>[123651780 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7be033a4-8510-400d-bb15-d10907302e12' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7be033a4-8510-400d-bb15-d10907302e12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a58f3a9-9cc7-40da-9b3c-f7ba92790aa6' class='xr-var-data-in' type='checkbox'><label for='data-9a58f3a9-9cc7-40da-9b3c-f7ba92790aa6' 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'><pre>[730 values with dtype=datetime64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>crs</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-66bafa78-ca19-4262-abb5-eaec0306bf26' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66bafa78-ca19-4262-abb5-eaec0306bf26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d640a14f-4953-40dc-8589-64b2268f2f5e' class='xr-var-data-in' type='checkbox'><label for='data-d640a14f-4953-40dc-8589-64b2268f2f5e' 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>earth_radius :</span></dt><dd>6371229.0</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cabba9dd-7f6c-4dc2-88ca-f85f5b0842f9' class='xr-section-summary-in' type='checkbox'  ><label for='section-cabba9dd-7f6c-4dc2-88ca-f85f5b0842f9' 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-09196a4b-0bac-4395-90bc-e3c4ea5a9c9b' class='xr-index-data-in' type='checkbox'/><label for='index-09196a4b-0bac-4395-90bc-e3c4ea5a9c9b' 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;2015-01-01 12:00:00&#x27;, &#x27;2015-01-02 12:00:00&#x27;,\n",
+       "               &#x27;2015-01-03 12:00:00&#x27;, &#x27;2015-01-04 12:00:00&#x27;,\n",
+       "               &#x27;2015-01-05 12:00:00&#x27;, &#x27;2015-01-06 12:00:00&#x27;,\n",
+       "               &#x27;2015-01-07 12:00:00&#x27;, &#x27;2015-01-08 12:00:00&#x27;,\n",
+       "               &#x27;2015-01-09 12:00:00&#x27;, &#x27;2015-01-10 12:00:00&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2015-12-22 12:00:00&#x27;, &#x27;2015-12-23 12:00:00&#x27;,\n",
+       "               &#x27;2015-12-24 12:00:00&#x27;, &#x27;2015-12-25 12:00:00&#x27;,\n",
+       "               &#x27;2015-12-26 12:00:00&#x27;, &#x27;2015-12-27 12:00:00&#x27;,\n",
+       "               &#x27;2015-12-28 12:00:00&#x27;, &#x27;2015-12-29 12:00:00&#x27;,\n",
+       "               &#x27;2015-12-30 12:00:00&#x27;, &#x27;2015-12-31 12:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=365, 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-6ad82505-1d58-4030-afdf-797d83e800aa' class='xr-index-data-in' type='checkbox'/><label for='index-6ad82505-1d58-4030-afdf-797d83e800aa' 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([-53.5755,  -53.421, -53.2665,  -53.112, -52.9575,  -52.803, -52.6485,\n",
+       "        -52.494, -52.3395,  -52.185,\n",
        "       ...\n",
-       "       -10.399999999999999, -10.299999999999997,               -10.2,\n",
-       "       -10.099999999999998,               -10.0,  -9.899999999999999,\n",
-       "        -9.799999999999997,                -9.7,  -9.599999999999998,\n",
-       "                      -9.5],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=226))</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-9f09d529-f1a8-40cb-b000-5b5e3eac5244' class='xr-index-data-in' type='checkbox'/><label for='index-9f09d529-f1a8-40cb-b000-5b5e3eac5244' 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([132.0, 132.1, 132.2, 132.3, 132.4, 132.5, 132.6, 132.7, 132.8, 132.9,\n",
+       "        12.2415,   12.396,  12.5505,   12.705,  12.8595,   13.014,  13.1685,\n",
+       "         13.323,  13.4775,   13.632],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=436))</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-1a4a2dd2-1ff8-4699-8d38-539252ce57c5' class='xr-index-data-in' type='checkbox'/><label for='index-1a4a2dd2-1ff8-4699-8d38-539252ce57c5' 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([ 88.0355,    88.19,  88.3445,   88.499,  88.6535,   88.808,  88.9625,\n",
+       "         89.117,  89.2715,   89.426,\n",
        "       ...\n",
-       "       157.1, 157.2, 157.3, 157.4, 157.5, 157.6, 157.7, 157.8, 157.9, 158.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=261))</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-84fd243a-d564-42c8-a10c-bd81baea9759' class='xr-index-data-in' type='checkbox'/><label for='index-84fd243a-d564-42c8-a10c-bd81baea9759' 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;1980-01-01 15:00:00&#x27;, &#x27;1980-01-02 15:00:00&#x27;,\n",
-       "               &#x27;1980-01-03 15:00:00&#x27;, &#x27;1980-01-04 15:00:00&#x27;,\n",
-       "               &#x27;1980-01-05 15:00:00&#x27;, &#x27;1980-01-06 15:00:00&#x27;,\n",
-       "               &#x27;1980-01-07 15:00:00&#x27;, &#x27;1980-01-08 15:00:00&#x27;,\n",
-       "               &#x27;1980-01-09 15:00:00&#x27;, &#x27;1980-01-10 15:00:00&#x27;,\n",
-       "               ...\n",
-       "               &#x27;2099-12-22 15:00:00&#x27;, &#x27;2099-12-23 15:00:00&#x27;,\n",
-       "               &#x27;2099-12-24 15:00:00&#x27;, &#x27;2099-12-25 15:00:00&#x27;,\n",
-       "               &#x27;2099-12-26 15:00:00&#x27;, &#x27;2099-12-27 15:00:00&#x27;,\n",
-       "               &#x27;2099-12-28 15:00:00&#x27;, &#x27;2099-12-29 15:00:00&#x27;,\n",
-       "               &#x27;2099-12-30 15:00:00&#x27;, &#x27;2099-12-31 15:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=43800, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3291b36a-673a-4542-859a-8e259fa02bb9' class='xr-section-summary-in' type='checkbox'  ><label for='section-3291b36a-673a-4542-859a-8e259fa02bb9' class='xr-section-summary' >Attributes: <span>(31)</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-1.7</dd><dt><span>title :</span></dt><dd>Queensland future climate projections derived from CMIP5 data downscaled using CCAM on a 10km grid</dd><dt><span>institution :</span></dt><dd>Queensland Department of Environment and Science</dd><dt><span>references :</span></dt><dd>The Long Paddock</dd><dt><span>comment :</span></dt><dd>Data produced for the LongPaddock Queensland Future Climate Dashboard http://longpaddock.qld.gov.au</dd><dt><span>contact :</span></dt><dd>longpaddock@qld.gov.au</dd><dt><span>domain :</span></dt><dd>QLD-10</dd><dt><span>geospatial_lon_min :</span></dt><dd>132.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>158.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-32.0</dd><dt><span>geospatial_lat_max :</span></dt><dd>-9.5</dd><dt><span>experiment :</span></dt><dd>Climate change run using CCAM 0.1 deg stretched grid nudged by 1-D spectral filter using 6-hourly data from CCAM 0.5 deg global regular grid</dd><dt><span>driving_model_id :</span></dt><dd>ACCESS1-0Q</dd><dt><span>driving_experiment_id :</span></dt><dd>rcp45</dd><dt><span>driving_experiment :</span></dt><dd>ACCESS1-0Q; rcp45; r2i1p1</dd><dt><span>experiment_id :</span></dt><dd>rcp45</dd><dt><span>intermediate_model_id :</span></dt><dd>CSIRO-CCAM-r1312M</dd><dt><span>intermediate_experiment :</span></dt><dd>CSIRO-CCAM-r1312M 0.5 deg; rcp45; r1i1p1</dd><dt><span>source :</span></dt><dd>CSIRO Conformal-Cubic Atmospheric Model (CCAM) version r2671M</dd><dt><span>model_id :</span></dt><dd>CSIRO-CCAM-r2671M</dd><dt><span>parent_experiment_id :</span></dt><dd>historical</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>il :</span></dt><dd>192</dd><dt><span>kl :</span></dt><dd>27</dd><dt><span>rlong0 :</span></dt><dd>145.0</dd><dt><span>rlat0 :</span></dt><dd>-20.0</dd><dt><span>schmidt :</span></dt><dd>0.19</dd><dt><span>date_created :</span></dt><dd>2020-01-20T18:14:26+10:00</dd><dt><span>date_produced :</span></dt><dd>2016-04-15</dd><dt><span>date_metadata_modified :</span></dt><dd>2020-01-20</dd><dt><span>history :</span></dt><dd>2016-04-15: Interpolated from the original cubic conformal grid to regular lat/lon grid using pcc2hist</dd></dl></div></li></ul></div></div>"
+       "        206.537, 206.6915,  206.846, 207.0005,  207.155, 207.3095,  207.464,\n",
+       "       207.6185,  207.773, 207.9275],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=777))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-259205f5-acc3-4d75-a14b-f9cc6f1e300b' class='xr-section-summary-in' type='checkbox'  ><label for='section-259205f5-acc3-4d75-a14b-f9cc6f1e300b' class='xr-section-summary' >Attributes: <span>(61)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>axiom_version :</span></dt><dd>0.1.0</dd><dt><span>axiom_schema :</span></dt><dd>cordex-1D.json</dd><dt><span>axiom_schemas_version :</span></dt><dd>0.1.0</dd><dt><span>productive_version :</span></dt><dd>dcb907f</dd><dt><span>Conventions :</span></dt><dd>CF-1.11, ACDD-1.3</dd><dt><span>title :</span></dt><dd>Bureau of Meteorology Atmospheric Regional Projections for Australia (BARPA)</dd><dt><span>activity_id :</span></dt><dd>DD</dd><dt><span>source :</span></dt><dd>Met Office Unified Model version 11.9 and Joint UK Land Environment Simulator version 6.0, nudged-atmosphere configuration</dd><dt><span>source_type :</span></dt><dd>ARCM</dd><dt><span>project :</span></dt><dd>BARPA</dd><dt><span>program :</span></dt><dd>Australian Climate Service (ACS)</dd><dt><span>summary :</span></dt><dd>The Bureau of Meteorology Atmospheric Regional Projections for Australia (BARPA) delivers high-resolution dynamical downscaling of CMIP experiments over CORDEX-Australasia and Australian domains.</dd><dt><span>naming_authority :</span></dt><dd>Bureau of Meteorology</dd><dt><span>publisher_type :</span></dt><dd>institution</dd><dt><span>publisher_institution :</span></dt><dd>Bureau of Meteorology</dd><dt><span>publisher_name :</span></dt><dd>Bureau of Meteorology</dd><dt><span>publisher_url :</span></dt><dd>http://www.bom.gov.au</dd><dt><span>creator_type :</span></dt><dd>institution</dd><dt><span>creator_institution :</span></dt><dd>Bureau of Meteorology</dd><dt><span>processing_level :</span></dt><dd>Level 1: Post-processing of Level 0b data with Controlled Vocabulary (CV) &amp; Data Reference System (DRS) applied</dd><dt><span>version_realisation :</span></dt><dd>v1-r1</dd><dt><span>contact :</span></dt><dd>Bureau of Meteorology (data_governance_office@bom.gov.au)</dd><dt><span>institution_id :</span></dt><dd>BOM</dd><dt><span>source_id :</span></dt><dd>BARPA-R</dd><dt><span>institution :</span></dt><dd>Bureau of Meteorology</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>grid :</span></dt><dd>Unrotated latitude-longitude with 0.1545 degree grid spacing</dd><dt><span>variable_id :</span></dt><dd>tas</dd><dt><span>variable_version :</span></dt><dd>v20231001</dd><dt><span>driving_variant_label :</span></dt><dd>r4i1p1f1</dd><dt><span>driving_experiment_id :</span></dt><dd>ssp370</dd><dt><span>driving_institution_id :</span></dt><dd>CSIRO-ARCCSS</dd><dt><span>driving_source_id :</span></dt><dd>ACCESS-CM2</dd><dt><span>project_id :</span></dt><dd>BARPA</dd><dt><span>domain :</span></dt><dd>Australasia</dd><dt><span>domain_id :</span></dt><dd>AUS-15</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>time_coverage_start :</span></dt><dd>20150101T1200Z</dd><dt><span>time_coverage_end :</span></dt><dd>20151231T1200Z</dd><dt><span>time_coverage_duration :</span></dt><dd>PT364D0H0S</dd><dt><span>time_coverage_resolution :</span></dt><dd>PT1440M0S</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table v79</dd><dt><span>keywords :</span></dt><dd>Earth Science, Atmosphere, Models, Services; Continent, Australia/New Zealand; Australia; 1 km - &lt; 10 km or approximately .01 degree - &lt; .09 degree; 10 km - &lt; 50 km or approximately .09 degree - &lt; .5 degree; 1 minute - &lt; 1 hour; Hourly - &lt; Daily; Daily - &lt; Weekly; Monthly - &lt; Annual; Government Agencies-Non-US, Australia, AU/BOM; Other; CLIMATE MODELS; netCDF-4 classic</dd><dt><span>keywords_vocabulary :</span></dt><dd>GCMD Science Keywords, Version 17.1</dd><dt><span>keywords_reference :</span></dt><dd>Global Change Master Directory (GCMD). 2023. GCMD Keywords, Version 17.1, Greenbelt, MD: Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC) National Aeronautics and Space Administration (NASA). URL (GCMD Keyword Forum Page): https://forum.earthdata.nasa.gov/app.php/tag/GCMD+Keywords</dd><dt><span>frequency :</span></dt><dd>day</dd><dt><span>tracking_id :</span></dt><dd>0d8042d2-6482-402e-943b-b69361e0a8b6</dd><dt><span>creation_date :</span></dt><dd>2022-12-20T05:58:48Z</dd><dt><span>references :</span></dt><dd>https://doi.org/10.25914/z1x6-dq28</dd><dt><span>license :</span></dt><dd>https://doi.org/10.25914/z1x6-dq28</dd><dt><span>acknowledgement :</span></dt><dd>The production of BARPA was supported with funding from the Australian Climate Service.</dd><dt><span>driving_experiment :</span></dt><dd>gap-filling scenario reaching 7.0 based on SSP3</dd><dt><span>date_modified :</span></dt><dd>2024-08-02T08:00:56Z</dd><dt><span>date_metadata_modified :</span></dt><dd>2024-08-02T08:00:56Z</dd><dt><span>geospatial_lat_min :</span></dt><dd>-53.5755</dd><dt><span>geospatial_lat_max :</span></dt><dd>13.632</dd><dt><span>geospatial_lon_min :</span></dt><dd>88.0355</dd><dt><span>geospatial_lon_max :</span></dt><dd>207.9275</dd><dt><span>history :</span></dt><dd>Sun Jun 30 10:15:58 2024: /g/data/access/ngm/miniconda3/envs/analysis3-21.10/bin/python /home/565/cst565/postproc_master/postproc_master/utils/fix_latlon.py --input-directory /g/data/py18/BARPA/output/CMIP6/DD/AUS-15/BOM/ACCESS-CM2/ssp370/r4i1p1f1/BARPA-R/v1-r1 --output-directory /g/data/py18/admin/staging/BARPA/output/CMIP6/DD/AUS-15/BOM/ACCESS-CM2/ssp370/r4i1p1f1/BARPA-R/v1-r1 --time-resolution day --variable tas --time-code * --reference-values -53.5755 13.6320 88.0355 207.9275 0.1545 --round-reference 4 --diff-threshold 1e-4 --log-level DEBUG\n",
+       "Sun Oct 29 11:11:34 2023: /g/data/access/ngm/miniconda3/envs/analysis3-21.10/bin/python /g/data/tp28/dev/chs548/git/productive/utils/fix_cordex.py -i /g/data/ia39/australian-climate-service/release/CORDEX-CMIP6/output/AUS-15/BOM/CSIRO-ARCCSS-ACCESS-CM2/ssp370/r4i1p1f1/BOM-BARPA-R/v1/day/tas/tas_AUS-15_CSIRO-ARCCSS-ACCESS-CM2_ssp370_r4i1p1f1_BOM-BARPA-R_v1_day_201501-201512.nc /g/data/ia39/australian-climate-service/release/CORDEX-CMIP6/output/AUS-15/BOM/CSIRO-ARCCSS-ACCESS-CM2/ssp370/r4i1p1f1/BOM-BARPA-R/v1/day/tas/tas_AUS-15_CSIRO-ARCCSS-ACCESS-CM2_ssp370_r4i1p1f1_BOM-BARPA-R_v1_day_201601-201612.nc /g/data/ia39/australian-climate-service/release/CORDEX-CMIP6/output/AUS-15/BOM/CSIRO-ARCCSS-ACCESS-CM2/ssp370/r4i1p1f1/BOM-BARPA-R/v1/day/tas/tas_AUS-15_CSIRO-ARCCSS-ACCESS-CM2_ssp370_r4i1p1f1_BOM-BARPA-R_v1_day_201701-201712.nc 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-ot /g/data/py18/admin/staging/BARPA/output/{mip_era}/{activity_id}/{domain_id}/{institution_id}/{driving_source_id}/{driving_experiment_id}/{driving_variant_label}/{source_id}/{file_source_configuration_id}/{frequency}/{variable_id}/{variable_version}/{variable_id}_{domain_id}_{driving_source_id}_{driving_experiment_id}_{driving_variant_label}_{institution_id}_{source_id}_{version_realisation}_{frequency}_{StartTime}-{EndTime}.nc --compression 1 --chunking ideal --chunk-size 1048576 --packing --additional-placeholder {&quot;variable_version&quot;:&quot;v20231001&quot;, &quot;activity_id&quot;:&quot;rcm&quot;, &quot;mip_era&quot;:&quot;CMIP6&quot;, &quot;project_id&quot;:&quot;CORDEX&quot;} --log-level INFO --nworkers 8 --nthreads 1\n",
+       "Tue Dec 20 16:57:45 2022: /g/data/access/ngm/miniconda3/envs/analysis3-21.10.1/bin/python /home/565/cst565/cylc-run/pp_BARPA-R_CM2_ssp370/run1/bin/master_postproc.py --datestart 20150101T0000Z --dateend 20151231T2359Z --config_files /home/565/cst565/cylc-run/pp_BARPA-R_CM2_ssp370/run1/share/config_files/config_file_BARPA-R.json /home/565/cst565/cylc-run/pp_BARPA-R_CM2_ssp370/run1/share/config_files/config_file_ssp370.json /home/565/cst565/cylc-run/pp_BARPA-R_CM2_ssp370/run1/share/config_files/config_file_ACCESS-CM2.json /home/565/cst565/cylc-run/pp_BARPA-R_CM2_ssp370/run1/share/config_files/config_file_tas_1Dm.json /home/565/cst565/cylc-run/pp_BARPA-R_CM2_ssp370/run1/share/config_files/config_file_publisher_info.json /home/565/cst565/cylc-run/pp_BARPA-R_CM2_ssp370/run1/share/config_files/config_file_misc.json /home/565/cst565/cylc-run/pp_BARPA-R_CM2_ssp370/run1/share/config_files/config_file_license.json --kwargs {} --cleanup True --rechunk-time False --n_workers 8 --n_threads 1\n",
+       "Tue May 31 00:53:27 2022: /g/data3/hh5/public/apps/miniconda3/envs/analysis3-22.01/bin/python /home/548/eh6215/cylc-run/cg282_ACCESS-CM2_ssp370_2014_sciB/bin/convert2nc.py --ifiles_template /home/548/eh6215/cylc-run/cg282_ACCESS-CM2_ssp370_2014_sciB/share/cycle/20141201T0000Z/um/barpara_slv15m_{:%Y%m%d_%H} --current_cycle 20141201T0000Z --cycle_freq P1M --stream_freq 6H --stashcodes --n_threads 1 --n_workers 9 --suite_name u-cg282 --suite_version_number 227458 --suite_url https://code.metoffice.gov.uk/svn/roses-u/c/g/2/8/2/trunk --ofile_template /home/548/eh6215/cylc-run/cg282_ACCESS-CM2_ssp370_2014_sciB/share/cycle/20141201T0000Z/nc/SLV15M/{bom_name}-CMIP6-ACCESS-CM2-ssp370-r4i1p1f1-barpa_r-v1-{date1:%Y%m%d%H%M}-{date2:%Y%m%d%H%M}.nc --ofile_freq 1D --realization r4i1p1f1 --engine netcdf4 --output_format netcdf --version latest --rcm_model barpa_r</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.Dataset> Size: 10GB\n",
-       "Dimensions:  (time: 43800, lat: 226, lon: 261)\n",
+       "<xarray.Dataset> Size: 989MB\n",
+       "Dimensions:    (time: 365, lat: 436, lon: 777, bnds: 2)\n",
        "Coordinates:\n",
-       "  * lat      (lat) float64 2kB -32.0 -31.9 -31.8 -31.7 ... -9.8 -9.7 -9.6 -9.5\n",
-       "  * lon      (lon) float64 2kB 132.0 132.1 132.2 132.3 ... 157.8 157.9 158.0\n",
-       "  * time     (time) datetime64[ns] 350kB 1980-01-01T15:00:00 ... 2099-12-31T1...\n",
+       "  * time       (time) datetime64[ns] 3kB 2015-01-01T12:00:00 ... 2015-12-31T1...\n",
+       "  * lat        (lat) float64 3kB -53.58 -53.42 -53.27 ... 13.32 13.48 13.63\n",
+       "  * lon        (lon) float64 6kB 88.04 88.19 88.34 88.5 ... 207.6 207.8 207.9\n",
+       "    height     float64 8B ...\n",
+       "Dimensions without coordinates: bnds\n",
        "Data variables:\n",
-       "    tmaxscr  (time, lat, lon) float32 10GB 296.5 296.5 297.7 ... 303.9 303.8\n",
-       "Attributes: (12/31)\n",
-       "    Conventions:              CF-1.7\n",
-       "    title:                    Queensland future climate projections derived f...\n",
-       "    institution:              Queensland Department of Environment and Science\n",
-       "    references:               The Long Paddock\n",
-       "    comment:                  Data produced for the LongPaddock Queensland Fu...\n",
-       "    contact:                  longpaddock@qld.gov.au\n",
-       "    ...                       ...\n",
-       "    rlat0:                    -20.0\n",
-       "    schmidt:                  0.19\n",
-       "    date_created:             2020-01-20T18:14:26+10:00\n",
-       "    date_produced:            2016-04-15\n",
-       "    date_metadata_modified:   2020-01-20\n",
-       "    history:                  2016-04-15: Interpolated from the original cubi..."
+       "    tas        (time, lat, lon) float64 989MB ...\n",
+       "    time_bnds  (time, bnds) datetime64[ns] 6kB ...\n",
+       "    crs        int32 4B ...\n",
+       "Attributes: (12/61)\n",
+       "    axiom_version:             0.1.0\n",
+       "    axiom_schema:              cordex-1D.json\n",
+       "    axiom_schemas_version:     0.1.0\n",
+       "    productive_version:        dcb907f\n",
+       "    Conventions:               CF-1.11, ACDD-1.3\n",
+       "    title:                     Bureau of Meteorology Atmospheric Regional Pro...\n",
+       "    ...                        ...\n",
+       "    date_metadata_modified:    2024-08-02T08:00:56Z\n",
+       "    geospatial_lat_min:        -53.5755\n",
+       "    geospatial_lat_max:        13.632\n",
+       "    geospatial_lon_min:        88.0355\n",
+       "    geospatial_lon_max:        207.9275\n",
+       "    history:                   Sun Jun 30 10:15:58 2024: /g/data/access/ngm/m..."
       ]
      },
-     "execution_count": 13,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# this is a very large dataset which cannot be computed without \"chunks\". The kernel will die.\n",
-    "file = \"/g/data/rr3/downscaling/CMIP5_Downscaled_CCAM_QLD10/RCP45/daily/MaximumTemperature/tmaxscr.daily.ccam10_ACCESS1-0Q_rcp45.nc\"\n",
-    "ds = xr.open_dataset(file).convert_calendar(\"standard\")\n",
-    "ds"
+    "xr.open_dataset(filelist[0])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "a6e4a3be-c29f-4d36-a882-90f8205e7784",
+   "execution_count": 30,
+   "id": "5497df99-51b4-4182-be90-fa2dfb408fb9",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 13.2 s, sys: 470 ms, total: 13.6 s\n",
-      "Wall time: 14 s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "%%time\n",
-    "# # define the mask for the diffrerent grid\n",
-    "# # can just use internal mask calculation in acs_regional_stats\n",
-    "regions= get_regions([\"ncra_regions\", \"australia\"])\n",
+    "# these files are on a different lat lon grid so we need to make a custom mask for this data\n",
+    "ds= xr.open_dataset(filelist[0])\n",
     "\n",
-    "# mask = regions.mask_3D_frac_approx(ds)"
+    "regions = get_regions([\"ncra_regions\", \"australia\"])\n",
+    "mask = regions.mask_3D_frac_approx(ds)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "6499b1f1-fbcd-437e-9255-d621c8e88d53",
-   "metadata": {
-    "scrolled": true
-   },
+   "execution_count": 31,
+   "id": "3c92b06f-7985-47a0-a2f6-d80e86268bbf",
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "!warning very slow! Calculating fractional mask every time is very slow.         \n",
-      "Please consider calculating `mask = regions.mask_3D_frac_approx(ds)` before function.\n",
-      "CPU times: user 26.6 s, sys: 14.1 s, total: 40.7 s\n",
-      "Wall time: 40.8 s\n"
+      "CPU times: user 11min 49s, sys: 3min 12s, total: 15min 2s\n",
+      "Wall time: 14min 51s\n"
      ]
     },
     {
@@ -2627,109 +4978,109 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2MB\n",
-       "Dimensions:       (time: 43800, region: 6)\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 3MB\n",
+       "Dimensions:   (time: 31411, region: 10)\n",
        "Coordinates:\n",
-       "  * time          (time) datetime64[ns] 350kB 1980-01-01T15:00:00 ... 2099-12...\n",
-       "  * region        (region) int64 48B 2 4 6 7 8 9\n",
-       "    abbrevs       (region) &lt;U8 192B &#x27;NSW&#x27; &#x27;SA&#x27; &#x27;NT&#x27; &#x27;QLDNorth&#x27; &#x27;QLDSouth&#x27; &#x27;AUS&#x27;\n",
-       "    names         (region) &lt;U48 1kB &#x27;New South Wales and Australian Capital T...\n",
+       "  * time      (time) object 251kB 2015-01-01 12:00:00 ... 2100-12-31 12:00:00\n",
+       "    height    float64 8B 1.5\n",
+       "  * region    (region) int64 80B 0 1 2 3 4 5 6 7 8 9\n",
+       "    abbrevs   (region) &lt;U8 320B &#x27;WANorth&#x27; &#x27;WASouth&#x27; &#x27;NSW&#x27; ... &#x27;QLDSouth&#x27; &#x27;AUS&#x27;\n",
+       "    names     (region) &lt;U48 2kB &#x27;Western Australia North&#x27; ... &#x27;Australia&#x27;\n",
        "Data variables:\n",
-       "    tmaxscr_mean  (time, region) float64 2MB 307.2 313.2 310.1 ... 315.9 314.2\n",
+       "    tas_mean  (time, region) float64 3MB 304.8 302.6 305.4 ... 308.0 309.0 305.7\n",
        "Attributes:\n",
-       "    long_name:     Maximum screen temperature\n",
-       "    units:         K\n",
-       "    cell_methods:  time: mean</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-adecea45-62f7-40d0-a8b2-84ef8c865fa0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-adecea45-62f7-40d0-a8b2-84ef8c865fa0' 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>: 43800</li><li><span class='xr-has-index'>region</span>: 6</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a8710455-76e6-448c-915c-45f574d369a4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a8710455-76e6-448c-915c-45f574d369a4' 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'>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'>1980-01-01T15:00:00 ... 2099-12-...</div><input id='attrs-864ae732-236e-4bf7-8609-276abb48c26c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-864ae732-236e-4bf7-8609-276abb48c26c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dee5a058-5c8c-4a0a-aab7-e683a65a1900' class='xr-var-data-in' type='checkbox'><label for='data-dee5a058-5c8c-4a0a-aab7-e683a65a1900' 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'><pre>array([&#x27;1980-01-01T15:00:00.000000000&#x27;, &#x27;1980-01-02T15:00:00.000000000&#x27;,\n",
-       "       &#x27;1980-01-03T15:00:00.000000000&#x27;, ..., &#x27;2099-12-29T15:00:00.000000000&#x27;,\n",
-       "       &#x27;2099-12-30T15:00:00.000000000&#x27;, &#x27;2099-12-31T15:00:00.000000000&#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'>region</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 4 6 7 8 9</div><input id='attrs-6e1c50f4-53e7-40f9-b5c0-fd4760fc4c2d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6e1c50f4-53e7-40f9-b5c0-fd4760fc4c2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a166214-1d91-4eca-8fec-c79e307f9462' class='xr-var-data-in' type='checkbox'><label for='data-9a166214-1d91-4eca-8fec-c79e307f9462' 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'><pre>array([2, 4, 6, 7, 8, 9])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abbrevs</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;NSW&#x27; &#x27;SA&#x27; ... &#x27;QLDSouth&#x27; &#x27;AUS&#x27;</div><input id='attrs-df78d3c6-0f14-44e4-a49d-63e9503924e6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-df78d3c6-0f14-44e4-a49d-63e9503924e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdb1afc3-95bd-4f98-8829-9754c4941fca' class='xr-var-data-in' type='checkbox'><label for='data-fdb1afc3-95bd-4f98-8829-9754c4941fca' 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'><pre>array([&#x27;NSW&#x27;, &#x27;SA&#x27;, &#x27;NT&#x27;, &#x27;QLDNorth&#x27;, &#x27;QLDSouth&#x27;, &#x27;AUS&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>names</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>&lt;U48</div><div class='xr-var-preview xr-preview'>&#x27;New South Wales and Australian ...</div><input id='attrs-d20e544e-bdda-4057-afc0-95c48698d19c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d20e544e-bdda-4057-afc0-95c48698d19c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02b5eec2-efc9-4ad9-978c-8386d984e150' class='xr-var-data-in' type='checkbox'><label for='data-02b5eec2-efc9-4ad9-978c-8386d984e150' 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'><pre>array([&#x27;New South Wales and Australian Capital Territory&#x27;,\n",
-       "       &#x27;South Australia&#x27;, &#x27;Northern Territory&#x27;, &#x27;Queensland North&#x27;,\n",
-       "       &#x27;Queensland South&#x27;, &#x27;Australia&#x27;], dtype=&#x27;&lt;U48&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3699f376-2e78-4603-8561-c8f3936c76a5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3699f376-2e78-4603-8561-c8f3936c76a5' 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>tmaxscr_mean</span></div><div class='xr-var-dims'>(time, region)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>307.2 313.2 310.1 ... 315.9 314.2</div><input id='attrs-8acc67e0-0fa2-4fb2-b835-ae9dd959ac73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8acc67e0-0fa2-4fb2-b835-ae9dd959ac73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6a61619-c3ef-4582-8a2a-269b88e4d6bf' class='xr-var-data-in' type='checkbox'><label for='data-e6a61619-c3ef-4582-8a2a-269b88e4d6bf' 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>Maximum screen temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><pre>array([[307.17308292, 313.18566073, 310.071908  , 304.82963394,\n",
-       "        308.29339746, 308.6596933 ],\n",
-       "       [307.18999672, 314.67849254, 310.77307459, 303.75367741,\n",
-       "        308.84670484, 308.99228385],\n",
-       "       [307.44474678, 315.06130648, 309.16306089, 304.5587817 ,\n",
-       "        309.32418275, 308.98109565],\n",
+       "    long_name:      Near-Surface Air Temperature\n",
+       "    standard_name:  air_temperature\n",
+       "    units:          K\n",
+       "    cell_methods:   time: point (interval: 1H) time: mean (interval: 1D)\n",
+       "    grid_mapping:   crs</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-c0273e1b-4a98-43e1-8173-02fe444a1cc5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c0273e1b-4a98-43e1-8173-02fe444a1cc5' 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>: 31411</li><li><span class='xr-has-index'>region</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1972be91-f2d9-4715-94fd-c1612bac2426' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1972be91-f2d9-4715-94fd-c1612bac2426' class='xr-section-summary' >Coordinates: <span>(5)</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'>object</div><div class='xr-var-preview xr-preview'>2015-01-01 12:00:00 ... 2100-12-...</div><input id='attrs-8273d0da-5093-415b-99bc-85991f47d261' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8273d0da-5093-415b-99bc-85991f47d261' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-539b1bc4-c109-430b-a6f1-557812675de5' class='xr-var-data-in' type='checkbox'><label for='data-539b1bc4-c109-430b-a6f1-557812675de5' 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'><pre>array([cftime.DatetimeProlepticGregorian(2015, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(2015, 1, 2, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(2015, 1, 3, 12, 0, 0, 0, has_year_zero=True),\n",
        "       ...,\n",
-       "       [312.41811842, 319.04223078, 313.42749711, 309.515258  ,\n",
-       "        314.37465153, 313.63399966],\n",
-       "       [312.51893937, 317.28676442, 314.88036988, 310.20431457,\n",
-       "        314.95961976, 314.02220443],\n",
-       "       [310.7462113 , 316.81061168, 315.65826141, 310.3931671 ,\n",
-       "        315.944916  , 314.23597608]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9933f1d9-e301-4ce7-9357-58339f0f04e0' class='xr-section-summary-in' type='checkbox'  ><label for='section-9933f1d9-e301-4ce7-9357-58339f0f04e0' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5a5e6912-eca2-427e-a885-db157e7721e2' class='xr-index-data-in' type='checkbox'/><label for='index-5a5e6912-eca2-427e-a885-db157e7721e2' 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;1980-01-01 15:00:00&#x27;, &#x27;1980-01-02 15:00:00&#x27;,\n",
-       "               &#x27;1980-01-03 15:00:00&#x27;, &#x27;1980-01-04 15:00:00&#x27;,\n",
-       "               &#x27;1980-01-05 15:00:00&#x27;, &#x27;1980-01-06 15:00:00&#x27;,\n",
-       "               &#x27;1980-01-07 15:00:00&#x27;, &#x27;1980-01-08 15:00:00&#x27;,\n",
-       "               &#x27;1980-01-09 15:00:00&#x27;, &#x27;1980-01-10 15:00:00&#x27;,\n",
-       "               ...\n",
-       "               &#x27;2099-12-22 15:00:00&#x27;, &#x27;2099-12-23 15:00:00&#x27;,\n",
-       "               &#x27;2099-12-24 15:00:00&#x27;, &#x27;2099-12-25 15:00:00&#x27;,\n",
-       "               &#x27;2099-12-26 15:00:00&#x27;, &#x27;2099-12-27 15:00:00&#x27;,\n",
-       "               &#x27;2099-12-28 15:00:00&#x27;, &#x27;2099-12-29 15:00:00&#x27;,\n",
-       "               &#x27;2099-12-30 15:00:00&#x27;, &#x27;2099-12-31 15:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=43800, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>region</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4e38d249-d1f0-4f0b-a61b-c2a5ed937a92' class='xr-index-data-in' type='checkbox'/><label for='index-4e38d249-d1f0-4f0b-a61b-c2a5ed937a92' 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([2, 4, 6, 7, 8, 9], dtype=&#x27;int64&#x27;, name=&#x27;region&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-464dbfa6-e79b-43c1-8f95-65b73115b070' class='xr-section-summary-in' type='checkbox'  checked><label for='section-464dbfa6-e79b-43c1-8f95-65b73115b070' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Maximum screen temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div></li></ul></div></div>"
+       "       cftime.DatetimeProlepticGregorian(2100, 12, 29, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(2100, 12, 30, 12, 0, 0, 0, has_year_zero=True),\n",
+       "       cftime.DatetimeProlepticGregorian(2100, 12, 31, 12, 0, 0, 0, has_year_zero=True)],\n",
+       "      dtype=object)</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'>1.5</div><input id='attrs-1f176881-cb21-4f6c-ba7b-bd2076ec47d6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1f176881-cb21-4f6c-ba7b-bd2076ec47d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a091971f-b731-4403-8199-2bb12b4b6122' class='xr-var-data-in' type='checkbox'><label for='data-a091971f-b731-4403-8199-2bb12b4b6122' 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'><pre>array(1.5)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>region</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9</div><input id='attrs-96814daa-b52d-4ce5-a41e-60405b87046e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-96814daa-b52d-4ce5-a41e-60405b87046e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51e7ccfb-4e63-4d4d-b829-cd119e4fbf3f' class='xr-var-data-in' type='checkbox'><label for='data-51e7ccfb-4e63-4d4d-b829-cd119e4fbf3f' 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'><pre>array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abbrevs</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;WANorth&#x27; &#x27;WASouth&#x27; ... &#x27;AUS&#x27;</div><input id='attrs-0bc8e0fe-84b4-4bff-ae5b-1240ec201440' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0bc8e0fe-84b4-4bff-ae5b-1240ec201440' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe682712-aa93-4bd9-a0cf-d340ee3f6f75' class='xr-var-data-in' type='checkbox'><label for='data-fe682712-aa93-4bd9-a0cf-d340ee3f6f75' 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'><pre>array([&#x27;WANorth&#x27;, &#x27;WASouth&#x27;, &#x27;NSW&#x27;, &#x27;VIC&#x27;, &#x27;SA&#x27;, &#x27;TAS&#x27;, &#x27;NT&#x27;, &#x27;QLDNorth&#x27;,\n",
+       "       &#x27;QLDSouth&#x27;, &#x27;AUS&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>names</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>&lt;U48</div><div class='xr-var-preview xr-preview'>&#x27;Western Australia North&#x27; ... &#x27;A...</div><input id='attrs-9dc450b6-3476-4da3-a5b4-774b3dae4fa9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9dc450b6-3476-4da3-a5b4-774b3dae4fa9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3bab8baf-982b-4bfe-a7b7-0c5e80f78520' class='xr-var-data-in' type='checkbox'><label for='data-3bab8baf-982b-4bfe-a7b7-0c5e80f78520' 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'><pre>array([&#x27;Western Australia North&#x27;, &#x27;Western Australia South&#x27;,\n",
+       "       &#x27;New South Wales and Australian Capital Territory&#x27;, &#x27;Victoria&#x27;,\n",
+       "       &#x27;South Australia&#x27;, &#x27;Tasmania&#x27;, &#x27;Northern Territory&#x27;,\n",
+       "       &#x27;Queensland North&#x27;, &#x27;Queensland South&#x27;, &#x27;Australia&#x27;], dtype=&#x27;&lt;U48&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b6563b6e-0b3d-43c5-a45f-45ff545f2fd2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b6563b6e-0b3d-43c5-a45f-45ff545f2fd2' 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_mean</span></div><div class='xr-var-dims'>(time, region)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>304.8 302.6 305.4 ... 309.0 305.7</div><input id='attrs-1a3bfb2c-d0ef-4abe-a99c-8a45f47c1943' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a3bfb2c-d0ef-4abe-a99c-8a45f47c1943' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba5339f1-bae0-4d2d-9916-61a3dbd19c4a' class='xr-var-data-in' type='checkbox'><label for='data-ba5339f1-bae0-4d2d-9916-61a3dbd19c4a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: point (interval: 1H) time: mean (interval: 1D)</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd></dl></div><div class='xr-var-data'><pre>array([[304.80948792, 302.61846092, 305.39045282, ..., 300.96084859,\n",
+       "        303.11890624, 303.6816118 ],\n",
+       "       [304.08548991, 303.23931882, 305.32404467, ..., 301.0598836 ,\n",
+       "        303.25881691, 303.71681522],\n",
+       "       [303.78032566, 301.65532745, 306.00503307, ..., 301.40956284,\n",
+       "        303.80644538, 303.95331954],\n",
+       "       ...,\n",
+       "       [311.52253919, 309.22864474, 301.66113788, ..., 307.04369216,\n",
+       "        306.71892622, 308.21184098],\n",
+       "       [311.78475448, 305.4219888 , 305.43202198, ..., 307.43756365,\n",
+       "        308.38989531, 308.03748643],\n",
+       "       [310.67510247, 303.57851544, 302.00986151, ..., 307.99297781,\n",
+       "        309.02316145, 305.73953463]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9f5364a2-ae57-4c2d-b5a6-4e8aa0fdbd38' class='xr-section-summary-in' type='checkbox'  ><label for='section-9f5364a2-ae57-4c2d-b5a6-4e8aa0fdbd38' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4213a938-12cd-465a-b42f-52775fa718ca' class='xr-index-data-in' type='checkbox'/><label for='index-4213a938-12cd-465a-b42f-52775fa718ca' 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(CFTimeIndex([2015-01-01 12:00:00, 2015-01-02 12:00:00, 2015-01-03 12:00:00,\n",
+       "             2015-01-04 12:00:00, 2015-01-05 12:00:00, 2015-01-06 12:00:00,\n",
+       "             2015-01-07 12:00:00, 2015-01-08 12:00:00, 2015-01-09 12:00:00,\n",
+       "             2015-01-10 12:00:00,\n",
+       "             ...\n",
+       "             2100-12-22 12:00:00, 2100-12-23 12:00:00, 2100-12-24 12:00:00,\n",
+       "             2100-12-25 12:00:00, 2100-12-26 12:00:00, 2100-12-27 12:00:00,\n",
+       "             2100-12-28 12:00:00, 2100-12-29 12:00:00, 2100-12-30 12:00:00,\n",
+       "             2100-12-31 12:00:00],\n",
+       "            dtype=&#x27;object&#x27;,\n",
+       "            length=31411,\n",
+       "            calendar=&#x27;proleptic_gregorian&#x27;,\n",
+       "            freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>region</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c59cd99a-7f66-47f6-841c-b9f647ecfce6' class='xr-index-data-in' type='checkbox'/><label for='index-c59cd99a-7f66-47f6-841c-b9f647ecfce6' 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([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=&#x27;int64&#x27;, name=&#x27;region&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0af318b4-8f33-410c-a04e-355572e6a780' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0af318b4-8f33-410c-a04e-355572e6a780' 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>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: point (interval: 1H) time: mean (interval: 1D)</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.Dataset> Size: 2MB\n",
-       "Dimensions:       (time: 43800, region: 6)\n",
+       "<xarray.Dataset> Size: 3MB\n",
+       "Dimensions:   (time: 31411, region: 10)\n",
        "Coordinates:\n",
-       "  * time          (time) datetime64[ns] 350kB 1980-01-01T15:00:00 ... 2099-12...\n",
-       "  * region        (region) int64 48B 2 4 6 7 8 9\n",
-       "    abbrevs       (region) <U8 192B 'NSW' 'SA' 'NT' 'QLDNorth' 'QLDSouth' 'AUS'\n",
-       "    names         (region) <U48 1kB 'New South Wales and Australian Capital T...\n",
+       "  * time      (time) object 251kB 2015-01-01 12:00:00 ... 2100-12-31 12:00:00\n",
+       "    height    float64 8B 1.5\n",
+       "  * region    (region) int64 80B 0 1 2 3 4 5 6 7 8 9\n",
+       "    abbrevs   (region) <U8 320B 'WANorth' 'WASouth' 'NSW' ... 'QLDSouth' 'AUS'\n",
+       "    names     (region) <U48 2kB 'Western Australia North' ... 'Australia'\n",
        "Data variables:\n",
-       "    tmaxscr_mean  (time, region) float64 2MB 307.2 313.2 310.1 ... 315.9 314.2\n",
+       "    tas_mean  (time, region) float64 3MB 304.8 302.6 305.4 ... 308.0 309.0 305.7\n",
        "Attributes:\n",
-       "    long_name:     Maximum screen temperature\n",
-       "    units:         K\n",
-       "    cell_methods:  time: mean"
+       "    long_name:      Near-Surface Air Temperature\n",
+       "    standard_name:  air_temperature\n",
+       "    units:          K\n",
+       "    cell_methods:   time: point (interval: 1H) time: mean (interval: 1D)\n",
+       "    grid_mapping:   crs"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "%%time\n",
+    "# open the list of files into one xarray dataset\n",
+    "def _preprocess(ds):\n",
+    "    # open file, drop redundant variables, define chunks sizes (make sure lat lon are not split or regional averages)\n",
+    "    data = ds.drop_vars([\"height\", \"time_bnds\", \"crs\"]).chunk({\"lat\":-1, \"lon\":-1})\n",
+    "    # Calculate region mean per file\n",
+    "    da_summary = acs_regional_stats(ds=data, var=\"tas\", mask =mask, dims = (\"lat\", \"lon\",), how = [\"mean\"],)\n",
+    "    return da_summary\n",
     "\n",
-    "var=\"tmaxscr\"\n",
+    "# preprocess does the area averaging and open_mfdataset cleverly concatenates the data\n",
+    "ds = xr.open_mfdataset(filelist, \n",
+    "                       preprocess= lambda ds: _preprocess(ds),\n",
+    "                       use_cftime=True,).load()\n",
     "\n",
-    "da_summary = acs_regional_stats(ds=ds,\n",
-    "                                var=var,\n",
-    "                                regions = regions,\n",
-    "                                mask = \"fractional\", \n",
-    "                                dims = (\"lat\", \"lon\",), \n",
-    "                                how = [\"mean\"],\n",
-    "                                chunks=365,\n",
-    "                                outfile=\"out.csv\")\n",
-    "da_summary"
+    "ds"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "f69b286b-89e2-496a-a603-af94ad476833",
+   "execution_count": 32,
+   "id": "9587ee7b-94dd-480f-9a0d-c4389add10ec",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x153d0f5b2da0>,\n",
-       " <matplotlib.lines.Line2D at 0x153d0f509c00>,\n",
-       " <matplotlib.lines.Line2D at 0x153d10a29d50>,\n",
-       " <matplotlib.lines.Line2D at 0x153d10a29c90>,\n",
-       " <matplotlib.lines.Line2D at 0x153d0f5b2f50>,\n",
-       " <matplotlib.lines.Line2D at 0x153d0f5b2d70>]"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2739,46 +5090,22 @@
     }
    ],
    "source": [
-    "da_summary[f\"{var}_mean\"].plot.line(x=\"time\")"
+    "var=\"tas\"\n",
+    "ds[f\"{var}_mean\"].sel(region=9).plot.line(x=\"time\");"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "1e0631fb-55cf-47d1-b58c-e738b7b094e6",
+   "execution_count": null,
+   "id": "f1dcb802-801a-4ee8-8e6b-2a4b9c80d29e",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='time', ylabel='tmaxscr_mean'>"
-      ]
-     },
-     "execution_count": 19,
-     "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": [
-    "# or, use seaborn to plot the range of values from multiple models\n",
-    "da= da_summary[f\"{var}_mean\"].sel(region=9).to_dataframe()\n",
-    "sns.lineplot(data=da, x=\"time\", y= f\"{var}_mean\",  errorbar=(\"pi\", 100) )"
-   ]
+   "outputs": [],
+   "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f1dcb802-801a-4ee8-8e6b-2a4b9c80d29e",
+   "id": "2e99ed92-9230-4923-a3f9-c2157e2ded58",
    "metadata": {},
    "outputs": [],
    "source": []