diff --git a/conda-env/dev.yml b/conda-env/dev.yml
index eca2f9cf..5c7f1e6e 100644
--- a/conda-env/dev.yml
+++ b/conda-env/dev.yml
@@ -32,6 +32,7 @@ dependencies:
     - pandoc
     - ipython # Required for nbsphinx syntax highlighting
     - gsw-xarray # Required for vertical regridding example
+    - pooch # Required for xarray tutorial data
     # Quality Assurance
     # ==================
     - types-python-dateutil
diff --git a/docs/api.rst b/docs/api.rst
index 1ced17a3..2c7a87d3 100644
--- a/docs/api.rst
+++ b/docs/api.rst
@@ -33,6 +33,7 @@ Below is a list of top-level API functions that are available in ``xcdat``.
     create_grid
     create_uniform_grid
     create_zonal_grid
+    tutorial.open_dataset
 
 Accessors
 ---------
diff --git a/docs/examples/temporal-average-yearly.gif b/docs/examples/temporal-average-yearly.gif
deleted file mode 100644
index f3ee3424..00000000
Binary files a/docs/examples/temporal-average-yearly.gif and /dev/null differ
diff --git a/docs/examples/temporal-average.ipynb b/docs/examples/temporal-average.ipynb
index ad73d0b6..4fbafcc3 100644
--- a/docs/examples/temporal-average.ipynb
+++ b/docs/examples/temporal-average.ipynb
@@ -8,7 +8,7 @@
     "\n",
     "Author: [Tom Vo](https://github.com/tomvothecoder/) & [Jiwoo Lee](https://github.com/lee1043/)\n",
     "\n",
-    "Updated: 04/01/24 [xcdat v0.6.1]\n",
+    "Updated: 10/03/24 [xcdat v0.7.2]\n",
     "\n",
     "Related APIs:\n",
     "\n",
@@ -22,11 +22,11 @@
    "source": [
     "## Overview\n",
     "\n",
-    "Suppose we have netCDF4 files for air temperature data (`tas`) with monthly, daily, and 3hr frequencies.\n",
+    "Suppose air temperature data (`air`) with monthly, daily, and 3hr frequencies.\n",
     "\n",
     "We want to calculate averages using these files with the time dimension removed (a single time snapshot), and averages by time group (yearly, seasonal, and daily).\n",
     "\n",
-    "The data used in this example can be found through the [Earth System Grid Federation (ESGF) search portal](https://aims2.llnl.gov/metagrid/search).\n"
+    "The data used in this example can be found in the [xarray-data repository](https://github.com/pydata/xarray-data).\n"
    ]
   },
   {
@@ -40,16 +40,16 @@
     "First, create the conda environment:\n",
     "\n",
     "```bash\n",
-    "conda create -n xcdat_notebook_0.7.0 -c conda-forge xcdat=0.7.0 xesmf matplotlib ipython ipykernel cartopy nc-time-axis gsw-xarray jupyter\n",
+    "conda create -n xcdat_notebook_0.7.2 -c conda-forge xcdat=0.7.2 xesmf matplotlib ipython ipykernel cartopy nc-time-axis gsw-xarray jupyter pooch\n",
     "```\n",
     "\n",
-    "Then install the kernel from the `xcdat_notebook_0.7.0` environment using `ipykernel` and name the kernel with the display name (e.g., `xcdat_notebook_0.7.0`):\n",
+    "Then install the kernel from the `xcdat_notebook_0.7.2` environment using `ipykernel` and name the kernel with the display name (e.g., `xcdat_notebook_0.7.2`):\n",
     "\n",
     "```bash\n",
-    "python -m ipykernel install --user --name xcdat_notebook_0.7.0 --display-name xcdat_notebook_0.7.0\n",
+    "python -m ipykernel install --user --name xcdat_notebook_0.7.2 --display-name xcdat_notebook_0.7.2\n",
     "```\n",
     "\n",
-    "Then to select the kernel `xcdat_notebook_0.7.0` in Jupyter to use this kernel.\n"
+    "Then to select the kernel `xcdat_notebook_0.7.2` in Jupyter to use this kernel.\n"
    ]
   },
   {
@@ -67,7 +67,7 @@
     "\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
-    "import xcdat\n"
+    "import xcdat as xc\n"
    ]
   },
   {
@@ -94,11 +94,7 @@
    "source": [
     "### Open the `Dataset`\n",
     "\n",
-    "In this example, we will be calculating the time weighted averages with the time dimension removed (single snapshot) for monthly `tas` data.\n",
-    "\n",
-    "We are using xarray's OPeNDAP support to read a netCDF4 dataset file directly from its source. The data is not loaded over the network until we perform operations on it (e.g., temperature unit adjustment).\n",
-    "\n",
-    "_More information on the xarray's OPeNDAP support can be found [here](https://docs.xarray.dev/en/stable/user-guide/io.html#opendap)._\n"
+    "In this example, we will be calculating the time weighted averages with the time dimension removed (single snapshot) for 6-hourly air temperature data (`\"air\"`)\n"
    ]
   },
   {
@@ -140,6 +136,7 @@
        "}\n",
        "\n",
        "html[theme=dark],\n",
+       "html[data-theme=dark],\n",
        "body[data-theme=dark],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -190,7 +187,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -198,7 +195,8 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
@@ -210,6 +208,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -472,178 +474,194 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 221MB\n",
-       "Dimensions:    (time: 1980, bnds: 2, lat: 145, lon: 192)\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 31MB\n",
+       "Dimensions:    (lat: 25, time: 2920, lon: 53, bnds: 2)\n",
        "Coordinates:\n",
-       "  * lat        (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
-       "  * lon        (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 354.4 356.2 358.1\n",
-       "    height     float64 8B 2.0\n",
-       "  * time       (time) object 16kB 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
+       "  * lat        (lat) float32 100B 75.0 72.5 70.0 67.5 ... 22.5 20.0 17.5 15.0\n",
+       "  * lon        (lon) float32 212B 200.0 202.5 205.0 207.5 ... 325.0 327.5 330.0\n",
+       "  * time       (time) object 23kB 2013-01-01 00:00:00 ... 2014-12-31 18:00:00\n",
        "Dimensions without coordinates: bnds\n",
        "Data variables:\n",
-       "    time_bnds  (time, bnds) object 32kB ...\n",
-       "    lat_bnds   (lat, bnds) float64 2kB ...\n",
-       "    lon_bnds   (lon, bnds) float64 3kB ...\n",
-       "    tas        (time, lat, lon) float32 220MB -27.19 -27.19 ... -25.29 -25.29\n",
-       "Attributes: (12/48)\n",
-       "    Conventions:                     CF-1.7 CMIP-6.2\n",
-       "    activity_id:                     CMIP\n",
-       "    branch_method:                   standard\n",
-       "    branch_time_in_child:            0.0\n",
-       "    branch_time_in_parent:           87658.0\n",
-       "    creation_date:                   2020-06-05T04:06:11Z\n",
-       "    ...                              ...\n",
-       "    variant_label:                   r10i1p1f1\n",
-       "    version:                         v20200605\n",
-       "    license:                         CMIP6 model data produced by CSIRO is li...\n",
-       "    cmor_version:                    3.4.0\n",
-       "    tracking_id:                     hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f2...\n",
-       "    DODS_EXTRA.Unlimited_Dimension:  time</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-3c05f936-ca87-4abf-8d6b-d99c848f8967' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3c05f936-ca87-4abf-8d6b-d99c848f8967' 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>: 1980</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 145</li><li><span class='xr-has-index'>lon</span>: 192</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0a5b41cc-c55f-46fe-8cb0-b79f75ce9143' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0a5b41cc-c55f-46fe-8cb0-b79f75ce9143' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -88.75 -87.5 ... 88.75 90.0</div><input id='attrs-7d3e51a8-b132-43da-a77a-683e8a0fc76a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d3e51a8-b132-43da-a77a-683e8a0fc76a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a5acf52-2d27-4192-a334-e8c910644106' class='xr-var-data-in' type='checkbox'><label for='data-5a5acf52-2d27-4192-a334-e8c910644106' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</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><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-90.  , -88.75, -87.5 , -86.25, -85.  , -83.75, -82.5 , -81.25, -80.  ,\n",
-       "       -78.75, -77.5 , -76.25, -75.  , -73.75, -72.5 , -71.25, -70.  , -68.75,\n",
-       "       -67.5 , -66.25, -65.  , -63.75, -62.5 , -61.25, -60.  , -58.75, -57.5 ,\n",
-       "       -56.25, -55.  , -53.75, -52.5 , -51.25, -50.  , -48.75, -47.5 , -46.25,\n",
-       "       -45.  , -43.75, -42.5 , -41.25, -40.  , -38.75, -37.5 , -36.25, -35.  ,\n",
-       "       -33.75, -32.5 , -31.25, -30.  , -28.75, -27.5 , -26.25, -25.  , -23.75,\n",
-       "       -22.5 , -21.25, -20.  , -18.75, -17.5 , -16.25, -15.  , -13.75, -12.5 ,\n",
-       "       -11.25, -10.  ,  -8.75,  -7.5 ,  -6.25,  -5.  ,  -3.75,  -2.5 ,  -1.25,\n",
-       "         0.  ,   1.25,   2.5 ,   3.75,   5.  ,   6.25,   7.5 ,   8.75,  10.  ,\n",
-       "        11.25,  12.5 ,  13.75,  15.  ,  16.25,  17.5 ,  18.75,  20.  ,  21.25,\n",
-       "        22.5 ,  23.75,  25.  ,  26.25,  27.5 ,  28.75,  30.  ,  31.25,  32.5 ,\n",
-       "        33.75,  35.  ,  36.25,  37.5 ,  38.75,  40.  ,  41.25,  42.5 ,  43.75,\n",
-       "        45.  ,  46.25,  47.5 ,  48.75,  50.  ,  51.25,  52.5 ,  53.75,  55.  ,\n",
-       "        56.25,  57.5 ,  58.75,  60.  ,  61.25,  62.5 ,  63.75,  65.  ,  66.25,\n",
-       "        67.5 ,  68.75,  70.  ,  71.25,  72.5 ,  73.75,  75.  ,  76.25,  77.5 ,\n",
-       "        78.75,  80.  ,  81.25,  82.5 ,  83.75,  85.  ,  86.25,  87.5 ,  88.75,\n",
-       "        90.  ])</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'>0.0 1.875 3.75 ... 356.2 358.1</div><input id='attrs-4991656d-24e9-462b-8973-804ec84141a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4991656d-24e9-462b-8973-804ec84141a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5d51085-a5f5-4405-bdb9-b8e75fa22e97' class='xr-var-data-in' type='checkbox'><label for='data-a5d51085-a5f5-4405-bdb9-b8e75fa22e97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</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><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.   ,   1.875,   3.75 ,   5.625,   7.5  ,   9.375,  11.25 ,  13.125,\n",
-       "        15.   ,  16.875,  18.75 ,  20.625,  22.5  ,  24.375,  26.25 ,  28.125,\n",
-       "        30.   ,  31.875,  33.75 ,  35.625,  37.5  ,  39.375,  41.25 ,  43.125,\n",
-       "        45.   ,  46.875,  48.75 ,  50.625,  52.5  ,  54.375,  56.25 ,  58.125,\n",
-       "        60.   ,  61.875,  63.75 ,  65.625,  67.5  ,  69.375,  71.25 ,  73.125,\n",
-       "        75.   ,  76.875,  78.75 ,  80.625,  82.5  ,  84.375,  86.25 ,  88.125,\n",
-       "        90.   ,  91.875,  93.75 ,  95.625,  97.5  ,  99.375, 101.25 , 103.125,\n",
-       "       105.   , 106.875, 108.75 , 110.625, 112.5  , 114.375, 116.25 , 118.125,\n",
-       "       120.   , 121.875, 123.75 , 125.625, 127.5  , 129.375, 131.25 , 133.125,\n",
-       "       135.   , 136.875, 138.75 , 140.625, 142.5  , 144.375, 146.25 , 148.125,\n",
-       "       150.   , 151.875, 153.75 , 155.625, 157.5  , 159.375, 161.25 , 163.125,\n",
-       "       165.   , 166.875, 168.75 , 170.625, 172.5  , 174.375, 176.25 , 178.125,\n",
-       "       180.   , 181.875, 183.75 , 185.625, 187.5  , 189.375, 191.25 , 193.125,\n",
-       "       195.   , 196.875, 198.75 , 200.625, 202.5  , 204.375, 206.25 , 208.125,\n",
-       "       210.   , 211.875, 213.75 , 215.625, 217.5  , 219.375, 221.25 , 223.125,\n",
-       "       225.   , 226.875, 228.75 , 230.625, 232.5  , 234.375, 236.25 , 238.125,\n",
-       "       240.   , 241.875, 243.75 , 245.625, 247.5  , 249.375, 251.25 , 253.125,\n",
-       "       255.   , 256.875, 258.75 , 260.625, 262.5  , 264.375, 266.25 , 268.125,\n",
-       "       270.   , 271.875, 273.75 , 275.625, 277.5  , 279.375, 281.25 , 283.125,\n",
-       "       285.   , 286.875, 288.75 , 290.625, 292.5  , 294.375, 296.25 , 298.125,\n",
-       "       300.   , 301.875, 303.75 , 305.625, 307.5  , 309.375, 311.25 , 313.125,\n",
-       "       315.   , 316.875, 318.75 , 320.625, 322.5  , 324.375, 326.25 , 328.125,\n",
-       "       330.   , 331.875, 333.75 , 335.625, 337.5  , 339.375, 341.25 , 343.125,\n",
-       "       345.   , 346.875, 348.75 , 350.625, 352.5  , 354.375, 356.25 , 358.125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-93d69da6-96ff-4189-9c7c-3118092445f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93d69da6-96ff-4189-9c7c-3118092445f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e71bd7a9-5251-4a56-9a97-729d9132941f' class='xr-var-data-in' type='checkbox'><label for='data-e71bd7a9-5251-4a56-9a97-729d9132941f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>array(2.)</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'>object</div><div class='xr-var-preview xr-preview'>1850-01-16 12:00:00 ... 2014-12-...</div><input id='attrs-c7e125f5-bb70-470d-8380-c9e670da6691' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7e125f5-bb70-470d-8380-c9e670da6691' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-efb23d2d-7a90-48df-a84d-7dec10277103' class='xr-var-data-in' type='checkbox'><label for='data-efb23d2d-7a90-48df-a84d-7dec10277103' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeProlepticGregorian(1850, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1850, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1850, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "    air        (time, lat, lon) float64 31MB -31.95 -30.65 ... 23.04 22.54\n",
+       "    lon_bnds   (lon, bnds) float32 424B 198.8 201.2 201.2 ... 328.8 328.8 331.2\n",
+       "    lat_bnds   (lat, bnds) float32 200B 76.25 73.75 73.75 ... 16.25 16.25 13.75\n",
+       "    time_bnds  (time, bnds) object 47kB 2013-01-01 00:00:00 ... 2015-01-01 00...\n",
+       "Attributes:\n",
+       "    Conventions:  COARDS\n",
+       "    title:        4x daily NMC reanalysis (1948)\n",
+       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
+       "    platform:     Model\n",
+       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...</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-b9085a12-8536-4de7-8b99-420b0394b05d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b9085a12-8536-4de7-8b99-420b0394b05d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 25</li><li><span class='xr-has-index'>time</span>: 2920</li><li><span class='xr-has-index'>lon</span>: 53</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c9e10d7f-ba5f-4872-b975-4bb6f05abf82' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c9e10d7f-ba5f-4872-b975-4bb6f05abf82' 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'>float32</div><div class='xr-var-preview xr-preview'>75.0 72.5 70.0 ... 20.0 17.5 15.0</div><input id='attrs-fc0c65db-9822-4c60-a5ab-05d58ef1d8c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc0c65db-9822-4c60-a5ab-05d58ef1d8c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-565b91ed-ae5d-4db7-979d-c0527545848b' class='xr-var-data-in' type='checkbox'><label for='data-565b91ed-ae5d-4db7-979d-c0527545848b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd></dl></div><div class='xr-var-data'><pre>array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n",
+       "       45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n",
+       "       15. ], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>200.0 202.5 205.0 ... 327.5 330.0</div><input id='attrs-6facb519-1ec1-4f76-ac68-3cd5093f2dbe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6facb519-1ec1-4f76-ac68-3cd5093f2dbe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-357e9ce2-718a-4658-bdf6-b74a17e10c5e' class='xr-var-data-in' type='checkbox'><label for='data-357e9ce2-718a-4658-bdf6-b74a17e10c5e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd></dl></div><div class='xr-var-data'><pre>array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
+       "       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
+       "       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
+       "       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
+       "       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
+       "       325. , 327.5, 330. ], dtype=float32)</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'>object</div><div class='xr-var-preview xr-preview'>2013-01-01 00:00:00 ... 2014-12-...</div><input id='attrs-eedd2f66-43db-4929-b2cb-5dc83d8b0580' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eedd2f66-43db-4929-b2cb-5dc83d8b0580' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76cf3b4f-2853-421c-a07b-38cda8a336dd' class='xr-var-data-in' type='checkbox'><label for='data-76cf3b4f-2853-421c-a07b-38cda8a336dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeGregorian(2013, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2013, 1, 1, 6, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2013, 1, 1, 12, 0, 0, 0, has_year_zero=False),\n",
        "       ...,\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f33dde60-2d48-4538-91ec-6f24ae571a2c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f33dde60-2d48-4538-91ec-6f24ae571a2c' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c7fedac4-5df1-460a-af8f-3a3e9d5d05cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7fedac4-5df1-460a-af8f-3a3e9d5d05cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eeea8a8d-0e99-4c8c-b50d-57047e0361d0' class='xr-var-data-in' type='checkbox'><label for='data-eeea8a8d-0e99-4c8c-b50d-57047e0361d0' 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>_ChunkSizes :</span></dt><dd>[1 2]</dd></dl></div><div class='xr-var-data'><pre>[3960 values with dtype=object]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3baf878a-d103-4392-8ced-ad703a7ef53b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3baf878a-d103-4392-8ced-ad703a7ef53b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67eab732-58f5-43b4-817d-0c4e95efd081' class='xr-var-data-in' type='checkbox'><label for='data-67eab732-58f5-43b4-817d-0c4e95efd081' 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>_ChunkSizes :</span></dt><dd>[145   2]</dd></dl></div><div class='xr-var-data'><pre>[290 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1536fe8f-207c-4634-8893-ae49891e9e89' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1536fe8f-207c-4634-8893-ae49891e9e89' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48a99f94-2abf-491a-82de-c1953eccd6d3' class='xr-var-data-in' type='checkbox'><label for='data-48a99f94-2abf-491a-82de-c1953eccd6d3' 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>_ChunkSizes :</span></dt><dd>[192   2]</dd></dl></div><div class='xr-var-data'><pre>[384 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-27.19 -27.19 ... -25.29 -25.29</div><input id='attrs-fb49267e-84d0-4455-bd96-1217188086a9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fb49267e-84d0-4455-bd96-1217188086a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8873175-3ca4-437c-9aa5-625f33be242b' class='xr-var-data-in' type='checkbox'><label for='data-a8873175-3ca4-437c-9aa5-625f33be242b' 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([[[-27.186966 , -27.186966 , -27.186966 , ..., -27.186966 ,\n",
-       "         -27.186966 , -27.186966 ],\n",
-       "        [-25.952972 , -25.990189 , -26.04216  , ..., -25.809921 ,\n",
-       "         -25.856598 , -25.905975 ],\n",
-       "        [-24.976852 , -25.09407  , -25.20755  , ..., -24.622055 ,\n",
-       "         -24.738693 , -24.856827 ],\n",
+       "       cftime.DatetimeGregorian(2014, 12, 31, 6, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 12, 31, 12, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 12, 31, 18, 0, 0, 0, has_year_zero=False)],\n",
+       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e4af14c0-844a-4ee1-8e9c-2afbf3b14030' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e4af14c0-844a-4ee1-8e9c-2afbf3b14030' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>air</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'>-31.95 -30.65 ... 23.04 22.54</div><input id='attrs-1b863114-ddcb-4161-ad6a-88e10cfa897a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1b863114-ddcb-4161-ad6a-88e10cfa897a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-845e3e46-3805-4085-9517-a261c7a46c5c' class='xr-var-data-in' type='checkbox'><label for='data-845e3e46-3805-4085-9517-a261c7a46c5c' 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([[[-31.95, -30.65, -29.65, ..., -40.35, -37.65, -34.55],\n",
+       "        [-29.35, -28.65, -28.45, ..., -40.35, -37.85, -33.85],\n",
+       "        [-23.15, -23.35, -24.26, ..., -39.95, -36.76, -31.45],\n",
+       "        ...,\n",
+       "        [ 23.45,  23.05,  23.25, ...,  22.25,  21.95,  21.55],\n",
+       "        [ 22.75,  23.05,  23.64, ...,  22.75,  22.75,  22.05],\n",
+       "        [ 23.14,  23.64,  23.95, ...,  23.75,  23.64,  23.45]],\n",
+       "\n",
+       "       [[-31.05, -30.45, -30.05, ..., -41.15, -39.55, -37.35],\n",
+       "        [-29.55, -29.05, -28.95, ..., -42.15, -40.65, -37.45],\n",
+       "        [-19.95, -20.26, -21.05, ..., -42.35, -39.76, -34.65],\n",
+       "        ...,\n",
+       "        [ 23.25,  22.75,  23.05, ...,  22.25,  21.95,  21.64],\n",
+       "        [ 23.05,  23.55,  23.64, ...,  22.45,  22.35,  21.95],\n",
+       "        [ 23.14,  24.05,  24.25, ...,  23.25,  23.25,  23.45]],\n",
+       "\n",
+       "       [[-30.85, -30.95, -30.85, ..., -38.85, -37.05, -34.45],\n",
+       "        [-28.55, -28.76, -29.15, ..., -42.85, -41.15, -37.45],\n",
+       "        [-16.95, -17.65, -18.95, ..., -41.95, -39.95, -34.95],\n",
        "        ...,\n",
-       "        [-35.20546  , -35.180023 , -35.165436 , ..., -35.312057 ,\n",
-       "         -35.273193 , -35.239227 ],\n",
-       "        [-35.80052  , -35.79355  , -35.789337 , ..., -35.815628 ,\n",
-       "         -35.811234 , -35.81041  ],\n",
-       "        [-36.399933 , -36.399933 , -36.399933 , ..., -36.399933 ,\n",
-       "         -36.399933 , -36.399933 ]],\n",
-       "\n",
-       "       [[-38.816116 , -38.816116 , -38.816116 , ..., -38.816116 ,\n",
-       "         -38.816116 , -38.816116 ],\n",
-       "        [-36.574646 , -36.62578  , -36.67726  , ..., -36.420868 ,\n",
-       "         -36.47139  , -36.521835 ],\n",
-       "        [-35.614334 , -35.73802  , -35.86261  , ..., -35.23851  ,\n",
-       "         -35.363724 , -35.48958  ],\n",
        "...\n",
-       "        [-12.968658 , -12.7821045, -12.606537 , ..., -13.613556 ,\n",
-       "         -13.394165 , -13.176605 ],\n",
-       "        [-14.207672 , -14.1249695, -14.035736 , ..., -14.471008 ,\n",
-       "         -14.386902 , -14.306213 ],\n",
-       "        [-16.90924  , -16.90924  , -16.90924  , ..., -16.90924  ,\n",
-       "         -16.90924  , -16.90924  ]],\n",
-       "\n",
-       "       [[-28.990494 , -28.990494 , -28.990494 , ..., -28.990494 ,\n",
-       "         -28.990494 , -28.990494 ],\n",
-       "        [-28.192917 , -28.224579 , -28.261307 , ..., -28.095932 ,\n",
-       "         -28.125992 , -28.15802  ],\n",
-       "        [-27.607407 , -27.705643 , -27.805115 , ..., -27.311615 ,\n",
-       "         -27.410828 , -27.508362 ],\n",
        "        ...,\n",
-       "        [-24.256271 , -24.140594 , -24.037537 , ..., -24.61853  ,\n",
-       "         -24.488495 , -24.36644  ],\n",
-       "        [-24.629013 , -24.613388 , -24.549866 , ..., -24.752045 ,\n",
-       "         -24.721603 , -24.666412 ],\n",
-       "        [-25.28923  , -25.28923  , -25.28923  , ..., -25.28923  ,\n",
-       "         -25.28923  , -25.28923  ]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8b0d331c-7002-471d-b485-4708fcf2acbf' class='xr-section-summary-in' type='checkbox'  ><label for='section-8b0d331c-7002-471d-b485-4708fcf2acbf' 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-77bdb3b8-8860-43d8-91bf-ae0eca79a37d' class='xr-index-data-in' type='checkbox'/><label for='index-77bdb3b8-8860-43d8-91bf-ae0eca79a37d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -90.0, -88.75,  -87.5, -86.25,  -85.0, -83.75,  -82.5, -81.25,  -80.0,\n",
-       "       -78.75,\n",
-       "       ...\n",
-       "        78.75,   80.0,  81.25,   82.5,  83.75,   85.0,  86.25,   87.5,  88.75,\n",
-       "         90.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=145))</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-87435ba7-b699-4630-8a76-83b72b15458f' class='xr-index-data-in' type='checkbox'/><label for='index-87435ba7-b699-4630-8a76-83b72b15458f' 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([    0.0,   1.875,    3.75,   5.625,     7.5,   9.375,   11.25,  13.125,\n",
-       "          15.0,  16.875,\n",
-       "       ...\n",
-       "        341.25, 343.125,   345.0, 346.875,  348.75, 350.625,   352.5, 354.375,\n",
-       "        356.25, 358.125],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=192))</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-891ef422-eb80-4724-9996-02870fbd2df6' class='xr-index-data-in' type='checkbox'/><label for='index-891ef422-eb80-4724-9996-02870fbd2df6' 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([1850-01-16 12:00:00, 1850-02-15 00:00:00, 1850-03-16 12:00:00,\n",
-       "             1850-04-16 00:00:00, 1850-05-16 12:00:00, 1850-06-16 00:00:00,\n",
-       "             1850-07-16 12:00:00, 1850-08-16 12:00:00, 1850-09-16 00:00:00,\n",
-       "             1850-10-16 12:00:00,\n",
+       "        [ 21.64,  22.14,  24.34, ...,  22.34,  22.24,  21.54],\n",
+       "        [ 23.64,  24.74,  25.14, ...,  22.34,  22.34,  21.64],\n",
+       "        [ 25.04,  26.04,  25.64, ...,  22.94,  22.64,  22.64]],\n",
+       "\n",
+       "       [[-27.36, -28.36, -29.66, ..., -29.86, -29.16, -28.36],\n",
+       "        [-23.26, -23.86, -24.66, ..., -31.86, -30.66, -28.86],\n",
+       "        [-10.76, -11.36, -11.86, ..., -32.66, -30.06, -26.26],\n",
+       "        ...,\n",
+       "        [ 20.54,  20.74,  22.24, ...,  21.94,  21.54,  21.14],\n",
+       "        [ 23.14,  24.04,  24.44, ...,  22.14,  21.94,  21.24],\n",
+       "        [ 24.64,  25.24,  25.34, ...,  22.54,  22.34,  22.04]],\n",
+       "\n",
+       "       [[-28.06, -28.86, -29.86, ..., -31.46, -31.66, -31.36],\n",
+       "        [-23.26, -23.86, -24.76, ..., -33.56, -32.86, -31.46],\n",
+       "        [-10.16, -10.96, -11.76, ..., -33.26, -30.56, -26.86],\n",
+       "        ...,\n",
+       "        [ 20.64,  20.54,  21.94, ...,  22.14,  21.94,  21.54],\n",
+       "        [ 22.94,  23.74,  24.04, ...,  22.54,  22.54,  22.04],\n",
+       "        [ 24.54,  24.94,  24.94, ...,  23.34,  23.04,  22.54]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>198.8 201.2 201.2 ... 328.8 331.2</div><input id='attrs-803ae8ba-8733-4a4b-b23a-f3592c8a9e8b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-803ae8ba-8733-4a4b-b23a-f3592c8a9e8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e22a041-e149-4c89-8e55-c713c082099e' class='xr-var-data-in' type='checkbox'><label for='data-1e22a041-e149-4c89-8e55-c713c082099e' 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>xcdat_bounds :</span></dt><dd>True</dd></dl></div><div class='xr-var-data'><pre>array([[198.75, 201.25],\n",
+       "       [201.25, 203.75],\n",
+       "       [203.75, 206.25],\n",
+       "       [206.25, 208.75],\n",
+       "       [208.75, 211.25],\n",
+       "       [211.25, 213.75],\n",
+       "       [213.75, 216.25],\n",
+       "       [216.25, 218.75],\n",
+       "       [218.75, 221.25],\n",
+       "       [221.25, 223.75],\n",
+       "       [223.75, 226.25],\n",
+       "       [226.25, 228.75],\n",
+       "       [228.75, 231.25],\n",
+       "       [231.25, 233.75],\n",
+       "       [233.75, 236.25],\n",
+       "       [236.25, 238.75],\n",
+       "       [238.75, 241.25],\n",
+       "       [241.25, 243.75],\n",
+       "       [243.75, 246.25],\n",
+       "       [246.25, 248.75],\n",
+       "...\n",
+       "       [281.25, 283.75],\n",
+       "       [283.75, 286.25],\n",
+       "       [286.25, 288.75],\n",
+       "       [288.75, 291.25],\n",
+       "       [291.25, 293.75],\n",
+       "       [293.75, 296.25],\n",
+       "       [296.25, 298.75],\n",
+       "       [298.75, 301.25],\n",
+       "       [301.25, 303.75],\n",
+       "       [303.75, 306.25],\n",
+       "       [306.25, 308.75],\n",
+       "       [308.75, 311.25],\n",
+       "       [311.25, 313.75],\n",
+       "       [313.75, 316.25],\n",
+       "       [316.25, 318.75],\n",
+       "       [318.75, 321.25],\n",
+       "       [321.25, 323.75],\n",
+       "       [323.75, 326.25],\n",
+       "       [326.25, 328.75],\n",
+       "       [328.75, 331.25]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>76.25 73.75 73.75 ... 16.25 13.75</div><input id='attrs-9f9c95d1-d842-418c-96ad-4ba06a38cfa6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f9c95d1-d842-418c-96ad-4ba06a38cfa6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ccab7c5e-3dd8-4fc4-b6c3-99c857d378c4' class='xr-var-data-in' type='checkbox'><label for='data-ccab7c5e-3dd8-4fc4-b6c3-99c857d378c4' 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>xcdat_bounds :</span></dt><dd>True</dd></dl></div><div class='xr-var-data'><pre>array([[76.25, 73.75],\n",
+       "       [73.75, 71.25],\n",
+       "       [71.25, 68.75],\n",
+       "       [68.75, 66.25],\n",
+       "       [66.25, 63.75],\n",
+       "       [63.75, 61.25],\n",
+       "       [61.25, 58.75],\n",
+       "       [58.75, 56.25],\n",
+       "       [56.25, 53.75],\n",
+       "       [53.75, 51.25],\n",
+       "       [51.25, 48.75],\n",
+       "       [48.75, 46.25],\n",
+       "       [46.25, 43.75],\n",
+       "       [43.75, 41.25],\n",
+       "       [41.25, 38.75],\n",
+       "       [38.75, 36.25],\n",
+       "       [36.25, 33.75],\n",
+       "       [33.75, 31.25],\n",
+       "       [31.25, 28.75],\n",
+       "       [28.75, 26.25],\n",
+       "       [26.25, 23.75],\n",
+       "       [23.75, 21.25],\n",
+       "       [21.25, 18.75],\n",
+       "       [18.75, 16.25],\n",
+       "       [16.25, 13.75]], dtype=float32)</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'>object</div><div class='xr-var-preview xr-preview'>2013-01-01 00:00:00 ... 2015-01-...</div><input id='attrs-77636d8c-5b5c-40ea-9093-33f4f6188715' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-77636d8c-5b5c-40ea-9093-33f4f6188715' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1d16bb0-0d15-4c14-bfe7-d6f536f50a36' class='xr-var-data-in' type='checkbox'><label for='data-b1d16bb0-0d15-4c14-bfe7-d6f536f50a36' 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>xcdat_bounds :</span></dt><dd>True</dd></dl></div><div class='xr-var-data'><pre>array([[cftime.DatetimeGregorian(2013, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "        cftime.DatetimeGregorian(2013, 1, 1, 6, 0, 0, 0, has_year_zero=False)],\n",
+       "       [cftime.DatetimeGregorian(2013, 1, 1, 6, 0, 0, 0, has_year_zero=False),\n",
+       "        cftime.DatetimeGregorian(2013, 1, 1, 12, 0, 0, 0, has_year_zero=False)],\n",
+       "       [cftime.DatetimeGregorian(2013, 1, 1, 12, 0, 0, 0, has_year_zero=False),\n",
+       "        cftime.DatetimeGregorian(2013, 1, 1, 18, 0, 0, 0, has_year_zero=False)],\n",
+       "       ...,\n",
+       "       [cftime.DatetimeGregorian(2014, 12, 31, 6, 0, 0, 0, has_year_zero=False),\n",
+       "        cftime.DatetimeGregorian(2014, 12, 31, 12, 0, 0, 0, has_year_zero=False)],\n",
+       "       [cftime.DatetimeGregorian(2014, 12, 31, 12, 0, 0, 0, has_year_zero=False),\n",
+       "        cftime.DatetimeGregorian(2014, 12, 31, 18, 0, 0, 0, has_year_zero=False)],\n",
+       "       [cftime.DatetimeGregorian(2014, 12, 31, 18, 0, 0, 0, has_year_zero=False),\n",
+       "        cftime.DatetimeGregorian(2015, 1, 1, 0, 0, 0, 0, has_year_zero=False)]],\n",
+       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0ecc182a-45e4-49c0-92fd-a26ccb860457' class='xr-section-summary-in' type='checkbox'  ><label for='section-0ecc182a-45e4-49c0-92fd-a26ccb860457' 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-be9e1836-9efb-40d8-980f-1cb04417867f' class='xr-index-data-in' type='checkbox'/><label for='index-be9e1836-9efb-40d8-980f-1cb04417867f' 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([75.0, 72.5, 70.0, 67.5, 65.0, 62.5, 60.0, 57.5, 55.0, 52.5, 50.0, 47.5,\n",
+       "       45.0, 42.5, 40.0, 37.5, 35.0, 32.5, 30.0, 27.5, 25.0, 22.5, 20.0, 17.5,\n",
+       "       15.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;))</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-404eb739-f2be-48b2-bf8c-83b7730bf60b' class='xr-index-data-in' type='checkbox'/><label for='index-404eb739-f2be-48b2-bf8c-83b7730bf60b' 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([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0, 222.5,\n",
+       "       225.0, 227.5, 230.0, 232.5, 235.0, 237.5, 240.0, 242.5, 245.0, 247.5,\n",
+       "       250.0, 252.5, 255.0, 257.5, 260.0, 262.5, 265.0, 267.5, 270.0, 272.5,\n",
+       "       275.0, 277.5, 280.0, 282.5, 285.0, 287.5, 290.0, 292.5, 295.0, 297.5,\n",
+       "       300.0, 302.5, 305.0, 307.5, 310.0, 312.5, 315.0, 317.5, 320.0, 322.5,\n",
+       "       325.0, 327.5, 330.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-307579b9-716a-42f5-b421-1d35d259b45b' class='xr-index-data-in' type='checkbox'/><label for='index-307579b9-716a-42f5-b421-1d35d259b45b' 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([2013-01-01 00:00:00, 2013-01-01 06:00:00, 2013-01-01 12:00:00,\n",
+       "             2013-01-01 18:00:00, 2013-01-02 00:00:00, 2013-01-02 06:00:00,\n",
+       "             2013-01-02 12:00:00, 2013-01-02 18:00:00, 2013-01-03 00:00:00,\n",
+       "             2013-01-03 06:00:00,\n",
        "             ...\n",
-       "             2014-03-16 12:00:00, 2014-04-16 00:00:00, 2014-05-16 12:00:00,\n",
-       "             2014-06-16 00:00:00, 2014-07-16 12:00:00, 2014-08-16 12:00:00,\n",
-       "             2014-09-16 00:00:00, 2014-10-16 12:00:00, 2014-11-16 00:00:00,\n",
-       "             2014-12-16 12:00:00],\n",
-       "            dtype=&#x27;object&#x27;,\n",
-       "            length=1980,\n",
-       "            calendar=&#x27;proleptic_gregorian&#x27;,\n",
-       "            freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-db6bbef0-5211-4db6-8fb4-0dea51e8c338' class='xr-section-summary-in' type='checkbox'  ><label for='section-db6bbef0-5211-4db6-8fb4-0dea51e8c338' class='xr-section-summary' >Attributes: <span>(48)</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 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>0.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>87658.0</dd><dt><span>creation_date :</span></dt><dd>2020-06-05T04:06:11Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.30</dd><dt><span>experiment :</span></dt><dd>all-forcing simulation of the recent past</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.CSIRO.ACCESS-ESM1-5.historical.none.r10i1p1f1</dd><dt><span>grid :</span></dt><dd>native atmosphere N96 grid (145x192 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>history :</span></dt><dd>2020-06-05T04:06:11Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia</dd><dt><span>institution_id :</span></dt><dd>CSIRO</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>nominal_resolution :</span></dt><dd>250 km</dd><dt><span>notes :</span></dt><dd>Exp: ESM-historical; Local ID: HI-14; Variable: tas ([&#x27;fld_s03i236&#x27;])</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>ACCESS-ESM1-5</dd><dt><span>parent_time_units :</span></dt><dd>days since 0101-1-1</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>10</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>run_variant :</span></dt><dd>forcing: GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2, N2O, CH4, CFC11, CFC12, CFC113, HCFC22, HFC125, HFC134a)</dd><dt><span>source :</span></dt><dd>ACCESS-ESM1.5 (2019): \n",
-       "aerosol: CLASSIC (v1.0)\n",
-       "atmos: HadGAM2 (r1.1, N96; 192 x 145 longitude/latitude; 38 levels; top level 39255 m)\n",
-       "atmosChem: none\n",
-       "land: CABLE2.4\n",
-       "landIce: none\n",
-       "ocean: ACCESS-OM2 (MOM5, tripolar primarily 1deg; 360 x 300 longitude/latitude; 50 levels; top grid cell 0-10 m)\n",
-       "ocnBgchem: WOMBAT (same grid as ocean)\n",
-       "seaIce: CICE4.1 (same grid as ocean)</dd><dt><span>source_id :</span></dt><dd>ACCESS-ESM1-5</dd><dt><span>source_type :</span></dt><dd>AOGCM</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>Amon</dd><dt><span>table_info :</span></dt><dd>Creation Date:(30 April 2019) MD5:5bd755e94c2173cb3050a0f3480f60c4</dd><dt><span>title :</span></dt><dd>ACCESS-ESM1-5 output prepared for CMIP6</dd><dt><span>variable_id :</span></dt><dd>tas</dd><dt><span>variant_label :</span></dt><dd>r10i1p1f1</dd><dt><span>version :</span></dt><dd>v20200605</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by CSIRO is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment.  Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>cmor_version :</span></dt><dd>3.4.0</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f29eb467cd1</dd><dt><span>DODS_EXTRA.Unlimited_Dimension :</span></dt><dd>time</dd></dl></div></li></ul></div></div>"
+       "             2014-12-29 12:00:00, 2014-12-29 18:00:00, 2014-12-30 00:00:00,\n",
+       "             2014-12-30 06:00:00, 2014-12-30 12:00:00, 2014-12-30 18:00:00,\n",
+       "             2014-12-31 00:00:00, 2014-12-31 06:00:00, 2014-12-31 12:00:00,\n",
+       "             2014-12-31 18:00:00],\n",
+       "            dtype=&#x27;object&#x27;, length=2920, calendar=&#x27;standard&#x27;, freq=&#x27;6h&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-22fcd2d5-727c-4d8a-91f9-bccefc3f95d1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-22fcd2d5-727c-4d8a-91f9-bccefc3f95d1' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>COARDS</dd><dt><span>title :</span></dt><dd>4x daily NMC reanalysis (1948)</dd><dt><span>description :</span></dt><dd>Data is from NMC initialized reanalysis\n",
+       "(4x/day).  These are the 0.9950 sigma level values.</dd><dt><span>platform :</span></dt><dd>Model</dd><dt><span>references :</span></dt><dd>http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.Dataset> Size: 221MB\n",
-       "Dimensions:    (time: 1980, bnds: 2, lat: 145, lon: 192)\n",
+       "<xarray.Dataset> Size: 31MB\n",
+       "Dimensions:    (lat: 25, time: 2920, lon: 53, bnds: 2)\n",
        "Coordinates:\n",
-       "  * lat        (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
-       "  * lon        (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 354.4 356.2 358.1\n",
-       "    height     float64 8B 2.0\n",
-       "  * time       (time) object 16kB 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
+       "  * lat        (lat) float32 100B 75.0 72.5 70.0 67.5 ... 22.5 20.0 17.5 15.0\n",
+       "  * lon        (lon) float32 212B 200.0 202.5 205.0 207.5 ... 325.0 327.5 330.0\n",
+       "  * time       (time) object 23kB 2013-01-01 00:00:00 ... 2014-12-31 18:00:00\n",
        "Dimensions without coordinates: bnds\n",
        "Data variables:\n",
-       "    time_bnds  (time, bnds) object 32kB ...\n",
-       "    lat_bnds   (lat, bnds) float64 2kB ...\n",
-       "    lon_bnds   (lon, bnds) float64 3kB ...\n",
-       "    tas        (time, lat, lon) float32 220MB -27.19 -27.19 ... -25.29 -25.29\n",
-       "Attributes: (12/48)\n",
-       "    Conventions:                     CF-1.7 CMIP-6.2\n",
-       "    activity_id:                     CMIP\n",
-       "    branch_method:                   standard\n",
-       "    branch_time_in_child:            0.0\n",
-       "    branch_time_in_parent:           87658.0\n",
-       "    creation_date:                   2020-06-05T04:06:11Z\n",
-       "    ...                              ...\n",
-       "    variant_label:                   r10i1p1f1\n",
-       "    version:                         v20200605\n",
-       "    license:                         CMIP6 model data produced by CSIRO is li...\n",
-       "    cmor_version:                    3.4.0\n",
-       "    tracking_id:                     hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f2...\n",
-       "    DODS_EXTRA.Unlimited_Dimension:  time"
+       "    air        (time, lat, lon) float64 31MB -31.95 -30.65 ... 23.04 22.54\n",
+       "    lon_bnds   (lon, bnds) float32 424B 198.8 201.2 201.2 ... 328.8 328.8 331.2\n",
+       "    lat_bnds   (lat, bnds) float32 200B 76.25 73.75 73.75 ... 16.25 16.25 13.75\n",
+       "    time_bnds  (time, bnds) object 47kB 2013-01-01 00:00:00 ... 2015-01-01 00...\n",
+       "Attributes:\n",
+       "    Conventions:  COARDS\n",
+       "    title:        4x daily NMC reanalysis (1948)\n",
+       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
+       "    platform:     Model\n",
+       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..."
       ]
      },
      "execution_count": 2,
@@ -652,11 +670,10 @@
     }
    ],
    "source": [
-    "filepath = \"https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r10i1p1f1/Amon/tas/gn/v20200605/tas_Amon_ACCESS-ESM1-5_historical_r10i1p1f1_gn_185001-201412.nc\"\n",
-    "ds = xcdat.open_dataset(filepath)\n",
+    "ds = xc.tutorial.open_dataset(\"air_temperature\", use_cftime=True)\n",
     "\n",
     "# Unit adjust (-273.15, K to C)\n",
-    "ds[\"tas\"] = ds.tas - 273.15\n",
+    "ds[\"air\"] = ds.air - 273.15\n",
     "\n",
     "ds"
    ]
@@ -667,7 +684,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ds_avg = ds.temporal.average(\"tas\", weighted=True)"
+    "ds_avg = ds.temporal.average(\"air\", weighted=True)"
    ]
   },
   {
@@ -709,6 +726,7 @@
        "}\n",
        "\n",
        "html[theme=dark],\n",
+       "html[data-theme=dark],\n",
        "body[data-theme=dark],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -759,7 +777,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -767,7 +785,8 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
@@ -779,6 +798,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -1041,114 +1064,79 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tas&#x27; (lat: 145, lon: 192)&gt; Size: 223kB\n",
-       "array([[-48.01481628, -48.01481628, -48.01481628, ..., -48.01481628,\n",
-       "        -48.01481628, -48.01481628],\n",
-       "       [-44.94085363, -44.97948214, -45.01815398, ..., -44.82408252,\n",
-       "        -44.86273067, -44.9009281 ],\n",
-       "       [-44.11875274, -44.23060624, -44.33960158, ..., -43.76766492,\n",
-       "        -43.88593717, -44.00303006],\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;air&#x27; (lat: 25, lon: 53)&gt; Size: 11kB\n",
+       "array([[-12.77355822, -12.96694863, -13.26337329, ..., -22.33409932,\n",
+       "        -21.21188356, -19.71195205],\n",
+       "       [-10.41560616, -10.35602397, -10.40066096, ..., -23.39409589,\n",
+       "        -21.56424315, -18.79073973],\n",
+       "       [ -8.3812363 ,  -8.82269178,  -9.08830479, ..., -22.54210959,\n",
+       "        -19.56648973, -15.43440068],\n",
        "       ...,\n",
-       "       [-18.21076615, -18.17513373, -18.13957458, ..., -18.32720478,\n",
-       "        -18.28428828, -18.2486193 ],\n",
-       "       [-18.50778243, -18.49301854, -18.47902819, ..., -18.55410851,\n",
-       "        -18.5406963 , -18.52413098],\n",
-       "       [-19.07366375, -19.07366375, -19.07366375, ..., -19.07366375,\n",
-       "        -19.07366375, -19.07366375]])\n",
+       "       [ 24.49986301,  23.80333219,  23.47931507, ...,  23.66092466,\n",
+       "         23.13796233,  22.66645548],\n",
+       "       [ 24.97920205,  24.78700685,  24.32039384, ...,  23.70954795,\n",
+       "         23.6270274 ,  23.29383562],\n",
+       "       [ 25.21615068,  25.23573973,  24.96414384, ...,  24.18820548,\n",
+       "         24.13144521,  24.15510274]])\n",
        "Coordinates:\n",
-       "  * lat      (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
-       "  * lon      (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
-       "    height   float64 8B 2.0\n",
+       "  * lat      (lat) float32 100B 75.0 72.5 70.0 67.5 65.0 ... 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 212B 200.0 202.5 205.0 207.5 ... 325.0 327.5 330.0\n",
        "Attributes:\n",
        "    operation:  temporal_avg\n",
        "    mode:       average\n",
-       "    freq:       month\n",
-       "    weighted:   True</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 145</li><li><span class='xr-has-index'>lon</span>: 192</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-3e5ffbba-4920-40ce-b0ae-9470cb14f557' class='xr-array-in' type='checkbox' checked><label for='section-3e5ffbba-4920-40ce-b0ae-9470cb14f557' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-48.01 -48.01 -48.01 -48.01 -48.01 ... -19.07 -19.07 -19.07 -19.07</span></div><div class='xr-array-data'><pre>array([[-48.01481628, -48.01481628, -48.01481628, ..., -48.01481628,\n",
-       "        -48.01481628, -48.01481628],\n",
-       "       [-44.94085363, -44.97948214, -45.01815398, ..., -44.82408252,\n",
-       "        -44.86273067, -44.9009281 ],\n",
-       "       [-44.11875274, -44.23060624, -44.33960158, ..., -43.76766492,\n",
-       "        -43.88593717, -44.00303006],\n",
+       "    freq:       hour\n",
+       "    weighted:   True</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'air'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 25</li><li><span class='xr-has-index'>lon</span>: 53</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-97300a09-0c45-4dbf-8bba-692d49674f16' class='xr-array-in' type='checkbox' checked><label for='section-97300a09-0c45-4dbf-8bba-692d49674f16' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-12.77 -12.97 -13.26 -13.67 -14.13 ... 24.74 24.67 24.19 24.13 24.16</span></div><div class='xr-array-data'><pre>array([[-12.77355822, -12.96694863, -13.26337329, ..., -22.33409932,\n",
+       "        -21.21188356, -19.71195205],\n",
+       "       [-10.41560616, -10.35602397, -10.40066096, ..., -23.39409589,\n",
+       "        -21.56424315, -18.79073973],\n",
+       "       [ -8.3812363 ,  -8.82269178,  -9.08830479, ..., -22.54210959,\n",
+       "        -19.56648973, -15.43440068],\n",
        "       ...,\n",
-       "       [-18.21076615, -18.17513373, -18.13957458, ..., -18.32720478,\n",
-       "        -18.28428828, -18.2486193 ],\n",
-       "       [-18.50778243, -18.49301854, -18.47902819, ..., -18.55410851,\n",
-       "        -18.5406963 , -18.52413098],\n",
-       "       [-19.07366375, -19.07366375, -19.07366375, ..., -19.07366375,\n",
-       "        -19.07366375, -19.07366375]])</pre></div></div></li><li class='xr-section-item'><input id='section-c70c3ab1-f9d1-4e47-8c10-0f1c08a20e7f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c70c3ab1-f9d1-4e47-8c10-0f1c08a20e7f' 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'>-90.0 -88.75 -87.5 ... 88.75 90.0</div><input id='attrs-352b7338-fb3e-42e3-a12f-36b6b949165d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-352b7338-fb3e-42e3-a12f-36b6b949165d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1361be04-0953-446b-b62b-a08b8e18744b' class='xr-var-data-in' type='checkbox'><label for='data-1361be04-0953-446b-b62b-a08b8e18744b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</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><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-90.  , -88.75, -87.5 , -86.25, -85.  , -83.75, -82.5 , -81.25, -80.  ,\n",
-       "       -78.75, -77.5 , -76.25, -75.  , -73.75, -72.5 , -71.25, -70.  , -68.75,\n",
-       "       -67.5 , -66.25, -65.  , -63.75, -62.5 , -61.25, -60.  , -58.75, -57.5 ,\n",
-       "       -56.25, -55.  , -53.75, -52.5 , -51.25, -50.  , -48.75, -47.5 , -46.25,\n",
-       "       -45.  , -43.75, -42.5 , -41.25, -40.  , -38.75, -37.5 , -36.25, -35.  ,\n",
-       "       -33.75, -32.5 , -31.25, -30.  , -28.75, -27.5 , -26.25, -25.  , -23.75,\n",
-       "       -22.5 , -21.25, -20.  , -18.75, -17.5 , -16.25, -15.  , -13.75, -12.5 ,\n",
-       "       -11.25, -10.  ,  -8.75,  -7.5 ,  -6.25,  -5.  ,  -3.75,  -2.5 ,  -1.25,\n",
-       "         0.  ,   1.25,   2.5 ,   3.75,   5.  ,   6.25,   7.5 ,   8.75,  10.  ,\n",
-       "        11.25,  12.5 ,  13.75,  15.  ,  16.25,  17.5 ,  18.75,  20.  ,  21.25,\n",
-       "        22.5 ,  23.75,  25.  ,  26.25,  27.5 ,  28.75,  30.  ,  31.25,  32.5 ,\n",
-       "        33.75,  35.  ,  36.25,  37.5 ,  38.75,  40.  ,  41.25,  42.5 ,  43.75,\n",
-       "        45.  ,  46.25,  47.5 ,  48.75,  50.  ,  51.25,  52.5 ,  53.75,  55.  ,\n",
-       "        56.25,  57.5 ,  58.75,  60.  ,  61.25,  62.5 ,  63.75,  65.  ,  66.25,\n",
-       "        67.5 ,  68.75,  70.  ,  71.25,  72.5 ,  73.75,  75.  ,  76.25,  77.5 ,\n",
-       "        78.75,  80.  ,  81.25,  82.5 ,  83.75,  85.  ,  86.25,  87.5 ,  88.75,\n",
-       "        90.  ])</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'>0.0 1.875 3.75 ... 356.2 358.1</div><input id='attrs-f0733fd6-5aa5-4b68-a3ae-7e345b89f3b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f0733fd6-5aa5-4b68-a3ae-7e345b89f3b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b08471e9-4344-4a94-ba31-6b0f12f07931' class='xr-var-data-in' type='checkbox'><label for='data-b08471e9-4344-4a94-ba31-6b0f12f07931' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</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><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.   ,   1.875,   3.75 ,   5.625,   7.5  ,   9.375,  11.25 ,  13.125,\n",
-       "        15.   ,  16.875,  18.75 ,  20.625,  22.5  ,  24.375,  26.25 ,  28.125,\n",
-       "        30.   ,  31.875,  33.75 ,  35.625,  37.5  ,  39.375,  41.25 ,  43.125,\n",
-       "        45.   ,  46.875,  48.75 ,  50.625,  52.5  ,  54.375,  56.25 ,  58.125,\n",
-       "        60.   ,  61.875,  63.75 ,  65.625,  67.5  ,  69.375,  71.25 ,  73.125,\n",
-       "        75.   ,  76.875,  78.75 ,  80.625,  82.5  ,  84.375,  86.25 ,  88.125,\n",
-       "        90.   ,  91.875,  93.75 ,  95.625,  97.5  ,  99.375, 101.25 , 103.125,\n",
-       "       105.   , 106.875, 108.75 , 110.625, 112.5  , 114.375, 116.25 , 118.125,\n",
-       "       120.   , 121.875, 123.75 , 125.625, 127.5  , 129.375, 131.25 , 133.125,\n",
-       "       135.   , 136.875, 138.75 , 140.625, 142.5  , 144.375, 146.25 , 148.125,\n",
-       "       150.   , 151.875, 153.75 , 155.625, 157.5  , 159.375, 161.25 , 163.125,\n",
-       "       165.   , 166.875, 168.75 , 170.625, 172.5  , 174.375, 176.25 , 178.125,\n",
-       "       180.   , 181.875, 183.75 , 185.625, 187.5  , 189.375, 191.25 , 193.125,\n",
-       "       195.   , 196.875, 198.75 , 200.625, 202.5  , 204.375, 206.25 , 208.125,\n",
-       "       210.   , 211.875, 213.75 , 215.625, 217.5  , 219.375, 221.25 , 223.125,\n",
-       "       225.   , 226.875, 228.75 , 230.625, 232.5  , 234.375, 236.25 , 238.125,\n",
-       "       240.   , 241.875, 243.75 , 245.625, 247.5  , 249.375, 251.25 , 253.125,\n",
-       "       255.   , 256.875, 258.75 , 260.625, 262.5  , 264.375, 266.25 , 268.125,\n",
-       "       270.   , 271.875, 273.75 , 275.625, 277.5  , 279.375, 281.25 , 283.125,\n",
-       "       285.   , 286.875, 288.75 , 290.625, 292.5  , 294.375, 296.25 , 298.125,\n",
-       "       300.   , 301.875, 303.75 , 305.625, 307.5  , 309.375, 311.25 , 313.125,\n",
-       "       315.   , 316.875, 318.75 , 320.625, 322.5  , 324.375, 326.25 , 328.125,\n",
-       "       330.   , 331.875, 333.75 , 335.625, 337.5  , 339.375, 341.25 , 343.125,\n",
-       "       345.   , 346.875, 348.75 , 350.625, 352.5  , 354.375, 356.25 , 358.125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-62808eca-14bc-4115-9159-ce1fc3ed2b1e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-62808eca-14bc-4115-9159-ce1fc3ed2b1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eee82a8a-c544-468e-a6f2-906854e2326a' class='xr-var-data-in' type='checkbox'><label for='data-eee82a8a-c544-468e-a6f2-906854e2326a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>array(2.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-72391c93-a1c7-4d5a-9a27-f4c52ecdf617' class='xr-section-summary-in' type='checkbox'  ><label for='section-72391c93-a1c7-4d5a-9a27-f4c52ecdf617' 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>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-baa316fb-9a54-4655-9c4d-d4a98765b22d' class='xr-index-data-in' type='checkbox'/><label for='index-baa316fb-9a54-4655-9c4d-d4a98765b22d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -90.0, -88.75,  -87.5, -86.25,  -85.0, -83.75,  -82.5, -81.25,  -80.0,\n",
-       "       -78.75,\n",
-       "       ...\n",
-       "        78.75,   80.0,  81.25,   82.5,  83.75,   85.0,  86.25,   87.5,  88.75,\n",
-       "         90.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=145))</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-76dbbc5b-1871-43fb-82ec-941c4588ddc0' class='xr-index-data-in' type='checkbox'/><label for='index-76dbbc5b-1871-43fb-82ec-941c4588ddc0' 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([    0.0,   1.875,    3.75,   5.625,     7.5,   9.375,   11.25,  13.125,\n",
-       "          15.0,  16.875,\n",
-       "       ...\n",
-       "        341.25, 343.125,   345.0, 346.875,  348.75, 350.625,   352.5, 354.375,\n",
-       "        356.25, 358.125],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=192))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cbb04b44-cf90-4bc0-9644-84f34a7ab327' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cbb04b44-cf90-4bc0-9644-84f34a7ab327' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>operation :</span></dt><dd>temporal_avg</dd><dt><span>mode :</span></dt><dd>average</dd><dt><span>freq :</span></dt><dd>month</dd><dt><span>weighted :</span></dt><dd>True</dd></dl></div></li></ul></div></div>"
+       "       [ 24.49986301,  23.80333219,  23.47931507, ...,  23.66092466,\n",
+       "         23.13796233,  22.66645548],\n",
+       "       [ 24.97920205,  24.78700685,  24.32039384, ...,  23.70954795,\n",
+       "         23.6270274 ,  23.29383562],\n",
+       "       [ 25.21615068,  25.23573973,  24.96414384, ...,  24.18820548,\n",
+       "         24.13144521,  24.15510274]])</pre></div></div></li><li class='xr-section-item'><input id='section-0192c224-77c9-4b02-b7b0-118225d4f3ef' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0192c224-77c9-4b02-b7b0-118225d4f3ef' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>75.0 72.5 70.0 ... 20.0 17.5 15.0</div><input id='attrs-e675d020-fb9a-4ebd-a4f9-ab4b6e22d0de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e675d020-fb9a-4ebd-a4f9-ab4b6e22d0de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c45dcc6-0ccc-4dfb-b88c-09cfa76d5d61' class='xr-var-data-in' type='checkbox'><label for='data-9c45dcc6-0ccc-4dfb-b88c-09cfa76d5d61' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd></dl></div><div class='xr-var-data'><pre>array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n",
+       "       45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n",
+       "       15. ], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>200.0 202.5 205.0 ... 327.5 330.0</div><input id='attrs-27db86d0-7bc0-4cbf-8402-dfb34fea061a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-27db86d0-7bc0-4cbf-8402-dfb34fea061a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-350ab485-7628-412e-8f26-1d618f9b169e' class='xr-var-data-in' type='checkbox'><label for='data-350ab485-7628-412e-8f26-1d618f9b169e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd></dl></div><div class='xr-var-data'><pre>array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
+       "       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
+       "       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
+       "       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
+       "       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
+       "       325. , 327.5, 330. ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dc3e0f46-d234-4767-bf4d-e1392ae07525' class='xr-section-summary-in' type='checkbox'  ><label for='section-dc3e0f46-d234-4767-bf4d-e1392ae07525' 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>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e1930caa-8cea-4767-b6d8-5eb01e37deff' class='xr-index-data-in' type='checkbox'/><label for='index-e1930caa-8cea-4767-b6d8-5eb01e37deff' 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([75.0, 72.5, 70.0, 67.5, 65.0, 62.5, 60.0, 57.5, 55.0, 52.5, 50.0, 47.5,\n",
+       "       45.0, 42.5, 40.0, 37.5, 35.0, 32.5, 30.0, 27.5, 25.0, 22.5, 20.0, 17.5,\n",
+       "       15.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;))</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-af1be6ef-b7f9-4b55-9b05-083a1eb971fd' class='xr-index-data-in' type='checkbox'/><label for='index-af1be6ef-b7f9-4b55-9b05-083a1eb971fd' 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([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0, 222.5,\n",
+       "       225.0, 227.5, 230.0, 232.5, 235.0, 237.5, 240.0, 242.5, 245.0, 247.5,\n",
+       "       250.0, 252.5, 255.0, 257.5, 260.0, 262.5, 265.0, 267.5, 270.0, 272.5,\n",
+       "       275.0, 277.5, 280.0, 282.5, 285.0, 287.5, 290.0, 292.5, 295.0, 297.5,\n",
+       "       300.0, 302.5, 305.0, 307.5, 310.0, 312.5, 315.0, 317.5, 320.0, 322.5,\n",
+       "       325.0, 327.5, 330.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-301f55e3-88ea-4b94-87ca-bdf761bb7793' class='xr-section-summary-in' type='checkbox'  checked><label for='section-301f55e3-88ea-4b94-87ca-bdf761bb7793' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>operation :</span></dt><dd>temporal_avg</dd><dt><span>mode :</span></dt><dd>average</dd><dt><span>freq :</span></dt><dd>hour</dd><dt><span>weighted :</span></dt><dd>True</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'tas' (lat: 145, lon: 192)> Size: 223kB\n",
-       "array([[-48.01481628, -48.01481628, -48.01481628, ..., -48.01481628,\n",
-       "        -48.01481628, -48.01481628],\n",
-       "       [-44.94085363, -44.97948214, -45.01815398, ..., -44.82408252,\n",
-       "        -44.86273067, -44.9009281 ],\n",
-       "       [-44.11875274, -44.23060624, -44.33960158, ..., -43.76766492,\n",
-       "        -43.88593717, -44.00303006],\n",
+       "<xarray.DataArray 'air' (lat: 25, lon: 53)> Size: 11kB\n",
+       "array([[-12.77355822, -12.96694863, -13.26337329, ..., -22.33409932,\n",
+       "        -21.21188356, -19.71195205],\n",
+       "       [-10.41560616, -10.35602397, -10.40066096, ..., -23.39409589,\n",
+       "        -21.56424315, -18.79073973],\n",
+       "       [ -8.3812363 ,  -8.82269178,  -9.08830479, ..., -22.54210959,\n",
+       "        -19.56648973, -15.43440068],\n",
        "       ...,\n",
-       "       [-18.21076615, -18.17513373, -18.13957458, ..., -18.32720478,\n",
-       "        -18.28428828, -18.2486193 ],\n",
-       "       [-18.50778243, -18.49301854, -18.47902819, ..., -18.55410851,\n",
-       "        -18.5406963 , -18.52413098],\n",
-       "       [-19.07366375, -19.07366375, -19.07366375, ..., -19.07366375,\n",
-       "        -19.07366375, -19.07366375]])\n",
+       "       [ 24.49986301,  23.80333219,  23.47931507, ...,  23.66092466,\n",
+       "         23.13796233,  22.66645548],\n",
+       "       [ 24.97920205,  24.78700685,  24.32039384, ...,  23.70954795,\n",
+       "         23.6270274 ,  23.29383562],\n",
+       "       [ 25.21615068,  25.23573973,  24.96414384, ...,  24.18820548,\n",
+       "         24.13144521,  24.15510274]])\n",
        "Coordinates:\n",
-       "  * lat      (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
-       "  * lon      (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
-       "    height   float64 8B 2.0\n",
+       "  * lat      (lat) float32 100B 75.0 72.5 70.0 67.5 65.0 ... 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 212B 200.0 202.5 205.0 207.5 ... 325.0 327.5 330.0\n",
        "Attributes:\n",
        "    operation:  temporal_avg\n",
        "    mode:       average\n",
-       "    freq:       month\n",
+       "    freq:       hour\n",
        "    weighted:   True"
       ]
      },
@@ -1158,7 +1146,7 @@
     }
    ],
    "source": [
-    "ds_avg.tas"
+    "ds_avg.air"
    ]
   },
   {
@@ -1169,7 +1157,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x157ef6990>"
+       "<matplotlib.collections.QuadMesh at 0x163e40aa0>"
       ]
      },
      "execution_count": 5,
@@ -1178,7 +1166,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -1188,7 +1176,7 @@
     }
    ],
    "source": [
-    "ds_avg.tas.plot(label=\"weighted\")"
+    "ds_avg.air.plot(label=\"weighted\")"
    ]
   },
   {
@@ -1226,7 +1214,7 @@
    "source": [
     "### Open the `Dataset`\n",
     "\n",
-    "In this example, we will be calculating the weighted grouped time averages for `tas` data.\n",
+    "In this example, we will be calculating the weighted grouped time averages for `air` data.\n",
     "\n",
     "We are using xarray's OPeNDAP support to read a netCDF4 dataset file directly from its source. The data is not loaded over the network until we perform operations on it (e.g., temperature unit adjustment).\n",
     "\n",
@@ -1272,6 +1260,7 @@
        "}\n",
        "\n",
        "html[theme=dark],\n",
+       "html[data-theme=dark],\n",
        "body[data-theme=dark],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -1322,7 +1311,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -1330,7 +1319,8 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
@@ -1342,6 +1332,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -1604,178 +1598,194 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 221MB\n",
-       "Dimensions:    (time: 1980, bnds: 2, lat: 145, lon: 192)\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 31MB\n",
+       "Dimensions:    (lat: 25, time: 2920, lon: 53, bnds: 2)\n",
        "Coordinates:\n",
-       "  * lat        (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
-       "  * lon        (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 354.4 356.2 358.1\n",
-       "    height     float64 8B 2.0\n",
-       "  * time       (time) object 16kB 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
+       "  * lat        (lat) float32 100B 75.0 72.5 70.0 67.5 ... 22.5 20.0 17.5 15.0\n",
+       "  * lon        (lon) float32 212B 200.0 202.5 205.0 207.5 ... 325.0 327.5 330.0\n",
+       "  * time       (time) object 23kB 2013-01-01 00:00:00 ... 2014-12-31 18:00:00\n",
        "Dimensions without coordinates: bnds\n",
        "Data variables:\n",
-       "    time_bnds  (time, bnds) object 32kB ...\n",
-       "    lat_bnds   (lat, bnds) float64 2kB ...\n",
-       "    lon_bnds   (lon, bnds) float64 3kB ...\n",
-       "    tas        (time, lat, lon) float32 220MB -27.19 -27.19 ... -25.29 -25.29\n",
-       "Attributes: (12/48)\n",
-       "    Conventions:                     CF-1.7 CMIP-6.2\n",
-       "    activity_id:                     CMIP\n",
-       "    branch_method:                   standard\n",
-       "    branch_time_in_child:            0.0\n",
-       "    branch_time_in_parent:           87658.0\n",
-       "    creation_date:                   2020-06-05T04:06:11Z\n",
-       "    ...                              ...\n",
-       "    variant_label:                   r10i1p1f1\n",
-       "    version:                         v20200605\n",
-       "    license:                         CMIP6 model data produced by CSIRO is li...\n",
-       "    cmor_version:                    3.4.0\n",
-       "    tracking_id:                     hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f2...\n",
-       "    DODS_EXTRA.Unlimited_Dimension:  time</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-b0623419-1362-4ae6-b7f7-2b5bb9168c98' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b0623419-1362-4ae6-b7f7-2b5bb9168c98' 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>: 1980</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 145</li><li><span class='xr-has-index'>lon</span>: 192</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9127b4ce-85be-42e5-b59b-0e71e2227cec' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9127b4ce-85be-42e5-b59b-0e71e2227cec' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -88.75 -87.5 ... 88.75 90.0</div><input id='attrs-aac4708c-39d3-4faf-a943-c4b6a878a5c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aac4708c-39d3-4faf-a943-c4b6a878a5c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3cebb249-cb76-498c-bdd6-7e55a00ec665' class='xr-var-data-in' type='checkbox'><label for='data-3cebb249-cb76-498c-bdd6-7e55a00ec665' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</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><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-90.  , -88.75, -87.5 , -86.25, -85.  , -83.75, -82.5 , -81.25, -80.  ,\n",
-       "       -78.75, -77.5 , -76.25, -75.  , -73.75, -72.5 , -71.25, -70.  , -68.75,\n",
-       "       -67.5 , -66.25, -65.  , -63.75, -62.5 , -61.25, -60.  , -58.75, -57.5 ,\n",
-       "       -56.25, -55.  , -53.75, -52.5 , -51.25, -50.  , -48.75, -47.5 , -46.25,\n",
-       "       -45.  , -43.75, -42.5 , -41.25, -40.  , -38.75, -37.5 , -36.25, -35.  ,\n",
-       "       -33.75, -32.5 , -31.25, -30.  , -28.75, -27.5 , -26.25, -25.  , -23.75,\n",
-       "       -22.5 , -21.25, -20.  , -18.75, -17.5 , -16.25, -15.  , -13.75, -12.5 ,\n",
-       "       -11.25, -10.  ,  -8.75,  -7.5 ,  -6.25,  -5.  ,  -3.75,  -2.5 ,  -1.25,\n",
-       "         0.  ,   1.25,   2.5 ,   3.75,   5.  ,   6.25,   7.5 ,   8.75,  10.  ,\n",
-       "        11.25,  12.5 ,  13.75,  15.  ,  16.25,  17.5 ,  18.75,  20.  ,  21.25,\n",
-       "        22.5 ,  23.75,  25.  ,  26.25,  27.5 ,  28.75,  30.  ,  31.25,  32.5 ,\n",
-       "        33.75,  35.  ,  36.25,  37.5 ,  38.75,  40.  ,  41.25,  42.5 ,  43.75,\n",
-       "        45.  ,  46.25,  47.5 ,  48.75,  50.  ,  51.25,  52.5 ,  53.75,  55.  ,\n",
-       "        56.25,  57.5 ,  58.75,  60.  ,  61.25,  62.5 ,  63.75,  65.  ,  66.25,\n",
-       "        67.5 ,  68.75,  70.  ,  71.25,  72.5 ,  73.75,  75.  ,  76.25,  77.5 ,\n",
-       "        78.75,  80.  ,  81.25,  82.5 ,  83.75,  85.  ,  86.25,  87.5 ,  88.75,\n",
-       "        90.  ])</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'>0.0 1.875 3.75 ... 356.2 358.1</div><input id='attrs-9de057d5-c3d2-4d4b-9093-a294fc7fb1ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9de057d5-c3d2-4d4b-9093-a294fc7fb1ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5faa323b-ea95-4f98-88cc-a0a12ef2e9a3' class='xr-var-data-in' type='checkbox'><label for='data-5faa323b-ea95-4f98-88cc-a0a12ef2e9a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</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><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.   ,   1.875,   3.75 ,   5.625,   7.5  ,   9.375,  11.25 ,  13.125,\n",
-       "        15.   ,  16.875,  18.75 ,  20.625,  22.5  ,  24.375,  26.25 ,  28.125,\n",
-       "        30.   ,  31.875,  33.75 ,  35.625,  37.5  ,  39.375,  41.25 ,  43.125,\n",
-       "        45.   ,  46.875,  48.75 ,  50.625,  52.5  ,  54.375,  56.25 ,  58.125,\n",
-       "        60.   ,  61.875,  63.75 ,  65.625,  67.5  ,  69.375,  71.25 ,  73.125,\n",
-       "        75.   ,  76.875,  78.75 ,  80.625,  82.5  ,  84.375,  86.25 ,  88.125,\n",
-       "        90.   ,  91.875,  93.75 ,  95.625,  97.5  ,  99.375, 101.25 , 103.125,\n",
-       "       105.   , 106.875, 108.75 , 110.625, 112.5  , 114.375, 116.25 , 118.125,\n",
-       "       120.   , 121.875, 123.75 , 125.625, 127.5  , 129.375, 131.25 , 133.125,\n",
-       "       135.   , 136.875, 138.75 , 140.625, 142.5  , 144.375, 146.25 , 148.125,\n",
-       "       150.   , 151.875, 153.75 , 155.625, 157.5  , 159.375, 161.25 , 163.125,\n",
-       "       165.   , 166.875, 168.75 , 170.625, 172.5  , 174.375, 176.25 , 178.125,\n",
-       "       180.   , 181.875, 183.75 , 185.625, 187.5  , 189.375, 191.25 , 193.125,\n",
-       "       195.   , 196.875, 198.75 , 200.625, 202.5  , 204.375, 206.25 , 208.125,\n",
-       "       210.   , 211.875, 213.75 , 215.625, 217.5  , 219.375, 221.25 , 223.125,\n",
-       "       225.   , 226.875, 228.75 , 230.625, 232.5  , 234.375, 236.25 , 238.125,\n",
-       "       240.   , 241.875, 243.75 , 245.625, 247.5  , 249.375, 251.25 , 253.125,\n",
-       "       255.   , 256.875, 258.75 , 260.625, 262.5  , 264.375, 266.25 , 268.125,\n",
-       "       270.   , 271.875, 273.75 , 275.625, 277.5  , 279.375, 281.25 , 283.125,\n",
-       "       285.   , 286.875, 288.75 , 290.625, 292.5  , 294.375, 296.25 , 298.125,\n",
-       "       300.   , 301.875, 303.75 , 305.625, 307.5  , 309.375, 311.25 , 313.125,\n",
-       "       315.   , 316.875, 318.75 , 320.625, 322.5  , 324.375, 326.25 , 328.125,\n",
-       "       330.   , 331.875, 333.75 , 335.625, 337.5  , 339.375, 341.25 , 343.125,\n",
-       "       345.   , 346.875, 348.75 , 350.625, 352.5  , 354.375, 356.25 , 358.125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-c5c790ff-1e4d-4c7c-95af-5b2c48482e9a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c5c790ff-1e4d-4c7c-95af-5b2c48482e9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-758562b2-df4b-4b88-9f45-bad93720712b' class='xr-var-data-in' type='checkbox'><label for='data-758562b2-df4b-4b88-9f45-bad93720712b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>array(2.)</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'>object</div><div class='xr-var-preview xr-preview'>1850-01-16 12:00:00 ... 2014-12-...</div><input id='attrs-e3c56119-1402-4546-9ab1-415e3603e5a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e3c56119-1402-4546-9ab1-415e3603e5a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-201b13c8-a539-436a-984d-f8e18725467a' class='xr-var-data-in' type='checkbox'><label for='data-201b13c8-a539-436a-984d-f8e18725467a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeProlepticGregorian(1850, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1850, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1850, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
+       "    air        (time, lat, lon) float64 31MB -31.95 -30.65 ... 23.04 22.54\n",
+       "    lon_bnds   (lon, bnds) float32 424B 198.8 201.2 201.2 ... 328.8 328.8 331.2\n",
+       "    lat_bnds   (lat, bnds) float32 200B 76.25 73.75 73.75 ... 16.25 16.25 13.75\n",
+       "    time_bnds  (time, bnds) object 47kB 2013-01-01 00:00:00 ... 2015-01-01 00...\n",
+       "Attributes:\n",
+       "    Conventions:  COARDS\n",
+       "    title:        4x daily NMC reanalysis (1948)\n",
+       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
+       "    platform:     Model\n",
+       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...</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-ada6b702-1a93-4e01-9a16-40d3dd2a5cdb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ada6b702-1a93-4e01-9a16-40d3dd2a5cdb' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 25</li><li><span class='xr-has-index'>time</span>: 2920</li><li><span class='xr-has-index'>lon</span>: 53</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a429102a-8bad-4102-8e89-00df79ae2f3e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a429102a-8bad-4102-8e89-00df79ae2f3e' 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'>float32</div><div class='xr-var-preview xr-preview'>75.0 72.5 70.0 ... 20.0 17.5 15.0</div><input id='attrs-ca250b4b-b140-47c7-9fa7-fec57978510a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca250b4b-b140-47c7-9fa7-fec57978510a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4af1a55-4b3c-4812-9108-7b68d3de5ce6' class='xr-var-data-in' type='checkbox'><label for='data-f4af1a55-4b3c-4812-9108-7b68d3de5ce6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd></dl></div><div class='xr-var-data'><pre>array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n",
+       "       45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n",
+       "       15. ], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>200.0 202.5 205.0 ... 327.5 330.0</div><input id='attrs-f7c06510-6369-43ec-991e-771a46bf34c5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7c06510-6369-43ec-991e-771a46bf34c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7c17ecd-0f4a-4e2a-8d77-02b16dbfffa6' class='xr-var-data-in' type='checkbox'><label for='data-a7c17ecd-0f4a-4e2a-8d77-02b16dbfffa6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd></dl></div><div class='xr-var-data'><pre>array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
+       "       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
+       "       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
+       "       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
+       "       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
+       "       325. , 327.5, 330. ], dtype=float32)</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'>object</div><div class='xr-var-preview xr-preview'>2013-01-01 00:00:00 ... 2014-12-...</div><input id='attrs-07f74edd-6454-4c70-9083-1214e9ff5a21' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-07f74edd-6454-4c70-9083-1214e9ff5a21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6fd8dfd8-35d5-4f39-a82d-eef9c6276a38' class='xr-var-data-in' type='checkbox'><label for='data-6fd8dfd8-35d5-4f39-a82d-eef9c6276a38' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeGregorian(2013, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2013, 1, 1, 6, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2013, 1, 1, 12, 0, 0, 0, has_year_zero=False),\n",
        "       ...,\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-477d4cfe-c0ef-455d-8d7b-6557190f6512' class='xr-section-summary-in' type='checkbox'  checked><label for='section-477d4cfe-c0ef-455d-8d7b-6557190f6512' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-42a90b43-1dd8-4ecc-a2e9-ffb6b1e28de8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42a90b43-1dd8-4ecc-a2e9-ffb6b1e28de8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d5dcba7-1626-44a0-8000-e62ffdaa3514' class='xr-var-data-in' type='checkbox'><label for='data-6d5dcba7-1626-44a0-8000-e62ffdaa3514' 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>_ChunkSizes :</span></dt><dd>[1 2]</dd></dl></div><div class='xr-var-data'><pre>[3960 values with dtype=object]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ad1d2ca4-952f-46d8-87e2-f3e168f34dda' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad1d2ca4-952f-46d8-87e2-f3e168f34dda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8e47316-8728-458b-a389-efcb47e394a1' class='xr-var-data-in' type='checkbox'><label for='data-a8e47316-8728-458b-a389-efcb47e394a1' 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>_ChunkSizes :</span></dt><dd>[145   2]</dd></dl></div><div class='xr-var-data'><pre>[290 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c34ac53c-49e8-4cc1-9b6e-709a0430a378' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c34ac53c-49e8-4cc1-9b6e-709a0430a378' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cdf6aa46-f373-42b5-8ce5-e2addf6ee64d' class='xr-var-data-in' type='checkbox'><label for='data-cdf6aa46-f373-42b5-8ce5-e2addf6ee64d' 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>_ChunkSizes :</span></dt><dd>[192   2]</dd></dl></div><div class='xr-var-data'><pre>[384 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-27.19 -27.19 ... -25.29 -25.29</div><input id='attrs-43ab34cc-ebed-48b1-a9ec-1786a8e4884c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-43ab34cc-ebed-48b1-a9ec-1786a8e4884c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97bb0abb-f836-4568-b2d1-f8664cb3e595' class='xr-var-data-in' type='checkbox'><label for='data-97bb0abb-f836-4568-b2d1-f8664cb3e595' 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([[[-27.186966 , -27.186966 , -27.186966 , ..., -27.186966 ,\n",
-       "         -27.186966 , -27.186966 ],\n",
-       "        [-25.952972 , -25.990189 , -26.04216  , ..., -25.809921 ,\n",
-       "         -25.856598 , -25.905975 ],\n",
-       "        [-24.976852 , -25.09407  , -25.20755  , ..., -24.622055 ,\n",
-       "         -24.738693 , -24.856827 ],\n",
+       "       cftime.DatetimeGregorian(2014, 12, 31, 6, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 12, 31, 12, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 12, 31, 18, 0, 0, 0, has_year_zero=False)],\n",
+       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e3acbaf9-70f6-4bf8-8319-45103af64188' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e3acbaf9-70f6-4bf8-8319-45103af64188' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>air</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'>-31.95 -30.65 ... 23.04 22.54</div><input id='attrs-a36baa73-f8a3-42c0-9434-0fbcaab2afb4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a36baa73-f8a3-42c0-9434-0fbcaab2afb4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8799861f-fda7-4995-8855-25db5e9058b1' class='xr-var-data-in' type='checkbox'><label for='data-8799861f-fda7-4995-8855-25db5e9058b1' 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([[[-31.95, -30.65, -29.65, ..., -40.35, -37.65, -34.55],\n",
+       "        [-29.35, -28.65, -28.45, ..., -40.35, -37.85, -33.85],\n",
+       "        [-23.15, -23.35, -24.26, ..., -39.95, -36.76, -31.45],\n",
+       "        ...,\n",
+       "        [ 23.45,  23.05,  23.25, ...,  22.25,  21.95,  21.55],\n",
+       "        [ 22.75,  23.05,  23.64, ...,  22.75,  22.75,  22.05],\n",
+       "        [ 23.14,  23.64,  23.95, ...,  23.75,  23.64,  23.45]],\n",
+       "\n",
+       "       [[-31.05, -30.45, -30.05, ..., -41.15, -39.55, -37.35],\n",
+       "        [-29.55, -29.05, -28.95, ..., -42.15, -40.65, -37.45],\n",
+       "        [-19.95, -20.26, -21.05, ..., -42.35, -39.76, -34.65],\n",
+       "        ...,\n",
+       "        [ 23.25,  22.75,  23.05, ...,  22.25,  21.95,  21.64],\n",
+       "        [ 23.05,  23.55,  23.64, ...,  22.45,  22.35,  21.95],\n",
+       "        [ 23.14,  24.05,  24.25, ...,  23.25,  23.25,  23.45]],\n",
+       "\n",
+       "       [[-30.85, -30.95, -30.85, ..., -38.85, -37.05, -34.45],\n",
+       "        [-28.55, -28.76, -29.15, ..., -42.85, -41.15, -37.45],\n",
+       "        [-16.95, -17.65, -18.95, ..., -41.95, -39.95, -34.95],\n",
        "        ...,\n",
-       "        [-35.20546  , -35.180023 , -35.165436 , ..., -35.312057 ,\n",
-       "         -35.273193 , -35.239227 ],\n",
-       "        [-35.80052  , -35.79355  , -35.789337 , ..., -35.815628 ,\n",
-       "         -35.811234 , -35.81041  ],\n",
-       "        [-36.399933 , -36.399933 , -36.399933 , ..., -36.399933 ,\n",
-       "         -36.399933 , -36.399933 ]],\n",
-       "\n",
-       "       [[-38.816116 , -38.816116 , -38.816116 , ..., -38.816116 ,\n",
-       "         -38.816116 , -38.816116 ],\n",
-       "        [-36.574646 , -36.62578  , -36.67726  , ..., -36.420868 ,\n",
-       "         -36.47139  , -36.521835 ],\n",
-       "        [-35.614334 , -35.73802  , -35.86261  , ..., -35.23851  ,\n",
-       "         -35.363724 , -35.48958  ],\n",
        "...\n",
-       "        [-12.968658 , -12.7821045, -12.606537 , ..., -13.613556 ,\n",
-       "         -13.394165 , -13.176605 ],\n",
-       "        [-14.207672 , -14.1249695, -14.035736 , ..., -14.471008 ,\n",
-       "         -14.386902 , -14.306213 ],\n",
-       "        [-16.90924  , -16.90924  , -16.90924  , ..., -16.90924  ,\n",
-       "         -16.90924  , -16.90924  ]],\n",
-       "\n",
-       "       [[-28.990494 , -28.990494 , -28.990494 , ..., -28.990494 ,\n",
-       "         -28.990494 , -28.990494 ],\n",
-       "        [-28.192917 , -28.224579 , -28.261307 , ..., -28.095932 ,\n",
-       "         -28.125992 , -28.15802  ],\n",
-       "        [-27.607407 , -27.705643 , -27.805115 , ..., -27.311615 ,\n",
-       "         -27.410828 , -27.508362 ],\n",
        "        ...,\n",
-       "        [-24.256271 , -24.140594 , -24.037537 , ..., -24.61853  ,\n",
-       "         -24.488495 , -24.36644  ],\n",
-       "        [-24.629013 , -24.613388 , -24.549866 , ..., -24.752045 ,\n",
-       "         -24.721603 , -24.666412 ],\n",
-       "        [-25.28923  , -25.28923  , -25.28923  , ..., -25.28923  ,\n",
-       "         -25.28923  , -25.28923  ]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fe9089e6-8ce4-4307-9e01-e54f9f3678df' class='xr-section-summary-in' type='checkbox'  ><label for='section-fe9089e6-8ce4-4307-9e01-e54f9f3678df' 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-617a0bdf-b7f0-4698-b8eb-eeaa20b8f4d1' class='xr-index-data-in' type='checkbox'/><label for='index-617a0bdf-b7f0-4698-b8eb-eeaa20b8f4d1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -90.0, -88.75,  -87.5, -86.25,  -85.0, -83.75,  -82.5, -81.25,  -80.0,\n",
-       "       -78.75,\n",
-       "       ...\n",
-       "        78.75,   80.0,  81.25,   82.5,  83.75,   85.0,  86.25,   87.5,  88.75,\n",
-       "         90.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=145))</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-0c36c3db-e36f-4aa6-b846-36d9887cc156' class='xr-index-data-in' type='checkbox'/><label for='index-0c36c3db-e36f-4aa6-b846-36d9887cc156' 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([    0.0,   1.875,    3.75,   5.625,     7.5,   9.375,   11.25,  13.125,\n",
-       "          15.0,  16.875,\n",
-       "       ...\n",
-       "        341.25, 343.125,   345.0, 346.875,  348.75, 350.625,   352.5, 354.375,\n",
-       "        356.25, 358.125],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=192))</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-243d501c-b2f8-4b8d-96f8-caf01a52b2f0' class='xr-index-data-in' type='checkbox'/><label for='index-243d501c-b2f8-4b8d-96f8-caf01a52b2f0' 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([1850-01-16 12:00:00, 1850-02-15 00:00:00, 1850-03-16 12:00:00,\n",
-       "             1850-04-16 00:00:00, 1850-05-16 12:00:00, 1850-06-16 00:00:00,\n",
-       "             1850-07-16 12:00:00, 1850-08-16 12:00:00, 1850-09-16 00:00:00,\n",
-       "             1850-10-16 12:00:00,\n",
+       "        [ 21.64,  22.14,  24.34, ...,  22.34,  22.24,  21.54],\n",
+       "        [ 23.64,  24.74,  25.14, ...,  22.34,  22.34,  21.64],\n",
+       "        [ 25.04,  26.04,  25.64, ...,  22.94,  22.64,  22.64]],\n",
+       "\n",
+       "       [[-27.36, -28.36, -29.66, ..., -29.86, -29.16, -28.36],\n",
+       "        [-23.26, -23.86, -24.66, ..., -31.86, -30.66, -28.86],\n",
+       "        [-10.76, -11.36, -11.86, ..., -32.66, -30.06, -26.26],\n",
+       "        ...,\n",
+       "        [ 20.54,  20.74,  22.24, ...,  21.94,  21.54,  21.14],\n",
+       "        [ 23.14,  24.04,  24.44, ...,  22.14,  21.94,  21.24],\n",
+       "        [ 24.64,  25.24,  25.34, ...,  22.54,  22.34,  22.04]],\n",
+       "\n",
+       "       [[-28.06, -28.86, -29.86, ..., -31.46, -31.66, -31.36],\n",
+       "        [-23.26, -23.86, -24.76, ..., -33.56, -32.86, -31.46],\n",
+       "        [-10.16, -10.96, -11.76, ..., -33.26, -30.56, -26.86],\n",
+       "        ...,\n",
+       "        [ 20.64,  20.54,  21.94, ...,  22.14,  21.94,  21.54],\n",
+       "        [ 22.94,  23.74,  24.04, ...,  22.54,  22.54,  22.04],\n",
+       "        [ 24.54,  24.94,  24.94, ...,  23.34,  23.04,  22.54]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>198.8 201.2 201.2 ... 328.8 331.2</div><input id='attrs-9671dcbd-67b5-441b-bc73-457dc4e2b2bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9671dcbd-67b5-441b-bc73-457dc4e2b2bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0714e5c-9ce2-4372-b2c4-fee2a89ee878' class='xr-var-data-in' type='checkbox'><label for='data-d0714e5c-9ce2-4372-b2c4-fee2a89ee878' 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>xcdat_bounds :</span></dt><dd>True</dd></dl></div><div class='xr-var-data'><pre>array([[198.75, 201.25],\n",
+       "       [201.25, 203.75],\n",
+       "       [203.75, 206.25],\n",
+       "       [206.25, 208.75],\n",
+       "       [208.75, 211.25],\n",
+       "       [211.25, 213.75],\n",
+       "       [213.75, 216.25],\n",
+       "       [216.25, 218.75],\n",
+       "       [218.75, 221.25],\n",
+       "       [221.25, 223.75],\n",
+       "       [223.75, 226.25],\n",
+       "       [226.25, 228.75],\n",
+       "       [228.75, 231.25],\n",
+       "       [231.25, 233.75],\n",
+       "       [233.75, 236.25],\n",
+       "       [236.25, 238.75],\n",
+       "       [238.75, 241.25],\n",
+       "       [241.25, 243.75],\n",
+       "       [243.75, 246.25],\n",
+       "       [246.25, 248.75],\n",
+       "...\n",
+       "       [281.25, 283.75],\n",
+       "       [283.75, 286.25],\n",
+       "       [286.25, 288.75],\n",
+       "       [288.75, 291.25],\n",
+       "       [291.25, 293.75],\n",
+       "       [293.75, 296.25],\n",
+       "       [296.25, 298.75],\n",
+       "       [298.75, 301.25],\n",
+       "       [301.25, 303.75],\n",
+       "       [303.75, 306.25],\n",
+       "       [306.25, 308.75],\n",
+       "       [308.75, 311.25],\n",
+       "       [311.25, 313.75],\n",
+       "       [313.75, 316.25],\n",
+       "       [316.25, 318.75],\n",
+       "       [318.75, 321.25],\n",
+       "       [321.25, 323.75],\n",
+       "       [323.75, 326.25],\n",
+       "       [326.25, 328.75],\n",
+       "       [328.75, 331.25]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>76.25 73.75 73.75 ... 16.25 13.75</div><input id='attrs-de8d2f7f-518b-44fd-8c82-f4d57fd9b8e2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de8d2f7f-518b-44fd-8c82-f4d57fd9b8e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe711149-3c55-436b-b546-78ffdcb424d1' class='xr-var-data-in' type='checkbox'><label for='data-fe711149-3c55-436b-b546-78ffdcb424d1' 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>xcdat_bounds :</span></dt><dd>True</dd></dl></div><div class='xr-var-data'><pre>array([[76.25, 73.75],\n",
+       "       [73.75, 71.25],\n",
+       "       [71.25, 68.75],\n",
+       "       [68.75, 66.25],\n",
+       "       [66.25, 63.75],\n",
+       "       [63.75, 61.25],\n",
+       "       [61.25, 58.75],\n",
+       "       [58.75, 56.25],\n",
+       "       [56.25, 53.75],\n",
+       "       [53.75, 51.25],\n",
+       "       [51.25, 48.75],\n",
+       "       [48.75, 46.25],\n",
+       "       [46.25, 43.75],\n",
+       "       [43.75, 41.25],\n",
+       "       [41.25, 38.75],\n",
+       "       [38.75, 36.25],\n",
+       "       [36.25, 33.75],\n",
+       "       [33.75, 31.25],\n",
+       "       [31.25, 28.75],\n",
+       "       [28.75, 26.25],\n",
+       "       [26.25, 23.75],\n",
+       "       [23.75, 21.25],\n",
+       "       [21.25, 18.75],\n",
+       "       [18.75, 16.25],\n",
+       "       [16.25, 13.75]], dtype=float32)</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'>object</div><div class='xr-var-preview xr-preview'>2013-01-01 00:00:00 ... 2015-01-...</div><input id='attrs-50c1275b-3b95-4367-a4bb-ff5448f20b18' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-50c1275b-3b95-4367-a4bb-ff5448f20b18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b24969ed-f6ae-4762-a1fc-54c03994c968' class='xr-var-data-in' type='checkbox'><label for='data-b24969ed-f6ae-4762-a1fc-54c03994c968' 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>xcdat_bounds :</span></dt><dd>True</dd></dl></div><div class='xr-var-data'><pre>array([[cftime.DatetimeGregorian(2013, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "        cftime.DatetimeGregorian(2013, 1, 1, 6, 0, 0, 0, has_year_zero=False)],\n",
+       "       [cftime.DatetimeGregorian(2013, 1, 1, 6, 0, 0, 0, has_year_zero=False),\n",
+       "        cftime.DatetimeGregorian(2013, 1, 1, 12, 0, 0, 0, has_year_zero=False)],\n",
+       "       [cftime.DatetimeGregorian(2013, 1, 1, 12, 0, 0, 0, has_year_zero=False),\n",
+       "        cftime.DatetimeGregorian(2013, 1, 1, 18, 0, 0, 0, has_year_zero=False)],\n",
+       "       ...,\n",
+       "       [cftime.DatetimeGregorian(2014, 12, 31, 6, 0, 0, 0, has_year_zero=False),\n",
+       "        cftime.DatetimeGregorian(2014, 12, 31, 12, 0, 0, 0, has_year_zero=False)],\n",
+       "       [cftime.DatetimeGregorian(2014, 12, 31, 12, 0, 0, 0, has_year_zero=False),\n",
+       "        cftime.DatetimeGregorian(2014, 12, 31, 18, 0, 0, 0, has_year_zero=False)],\n",
+       "       [cftime.DatetimeGregorian(2014, 12, 31, 18, 0, 0, 0, has_year_zero=False),\n",
+       "        cftime.DatetimeGregorian(2015, 1, 1, 0, 0, 0, 0, has_year_zero=False)]],\n",
+       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-36f7eb5d-3756-49e2-a989-b685c0473b46' class='xr-section-summary-in' type='checkbox'  ><label for='section-36f7eb5d-3756-49e2-a989-b685c0473b46' 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-ec742bd4-f6c4-48c1-aa31-489dc933e3c5' class='xr-index-data-in' type='checkbox'/><label for='index-ec742bd4-f6c4-48c1-aa31-489dc933e3c5' 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([75.0, 72.5, 70.0, 67.5, 65.0, 62.5, 60.0, 57.5, 55.0, 52.5, 50.0, 47.5,\n",
+       "       45.0, 42.5, 40.0, 37.5, 35.0, 32.5, 30.0, 27.5, 25.0, 22.5, 20.0, 17.5,\n",
+       "       15.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;))</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-779f6d29-50b6-4a46-80f7-8959c37ab55d' class='xr-index-data-in' type='checkbox'/><label for='index-779f6d29-50b6-4a46-80f7-8959c37ab55d' 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([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0, 222.5,\n",
+       "       225.0, 227.5, 230.0, 232.5, 235.0, 237.5, 240.0, 242.5, 245.0, 247.5,\n",
+       "       250.0, 252.5, 255.0, 257.5, 260.0, 262.5, 265.0, 267.5, 270.0, 272.5,\n",
+       "       275.0, 277.5, 280.0, 282.5, 285.0, 287.5, 290.0, 292.5, 295.0, 297.5,\n",
+       "       300.0, 302.5, 305.0, 307.5, 310.0, 312.5, 315.0, 317.5, 320.0, 322.5,\n",
+       "       325.0, 327.5, 330.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-54aa43b6-1e8f-4230-9a88-ef49ccc084e9' class='xr-index-data-in' type='checkbox'/><label for='index-54aa43b6-1e8f-4230-9a88-ef49ccc084e9' 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([2013-01-01 00:00:00, 2013-01-01 06:00:00, 2013-01-01 12:00:00,\n",
+       "             2013-01-01 18:00:00, 2013-01-02 00:00:00, 2013-01-02 06:00:00,\n",
+       "             2013-01-02 12:00:00, 2013-01-02 18:00:00, 2013-01-03 00:00:00,\n",
+       "             2013-01-03 06:00:00,\n",
        "             ...\n",
-       "             2014-03-16 12:00:00, 2014-04-16 00:00:00, 2014-05-16 12:00:00,\n",
-       "             2014-06-16 00:00:00, 2014-07-16 12:00:00, 2014-08-16 12:00:00,\n",
-       "             2014-09-16 00:00:00, 2014-10-16 12:00:00, 2014-11-16 00:00:00,\n",
-       "             2014-12-16 12:00:00],\n",
-       "            dtype=&#x27;object&#x27;,\n",
-       "            length=1980,\n",
-       "            calendar=&#x27;proleptic_gregorian&#x27;,\n",
-       "            freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-66621b41-3f73-4ba9-855c-eb5a19727930' class='xr-section-summary-in' type='checkbox'  ><label for='section-66621b41-3f73-4ba9-855c-eb5a19727930' class='xr-section-summary' >Attributes: <span>(48)</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 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>0.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>87658.0</dd><dt><span>creation_date :</span></dt><dd>2020-06-05T04:06:11Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.30</dd><dt><span>experiment :</span></dt><dd>all-forcing simulation of the recent past</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.CSIRO.ACCESS-ESM1-5.historical.none.r10i1p1f1</dd><dt><span>grid :</span></dt><dd>native atmosphere N96 grid (145x192 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>history :</span></dt><dd>2020-06-05T04:06:11Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia</dd><dt><span>institution_id :</span></dt><dd>CSIRO</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>nominal_resolution :</span></dt><dd>250 km</dd><dt><span>notes :</span></dt><dd>Exp: ESM-historical; Local ID: HI-14; Variable: tas ([&#x27;fld_s03i236&#x27;])</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>ACCESS-ESM1-5</dd><dt><span>parent_time_units :</span></dt><dd>days since 0101-1-1</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>10</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>run_variant :</span></dt><dd>forcing: GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2, N2O, CH4, CFC11, CFC12, CFC113, HCFC22, HFC125, HFC134a)</dd><dt><span>source :</span></dt><dd>ACCESS-ESM1.5 (2019): \n",
-       "aerosol: CLASSIC (v1.0)\n",
-       "atmos: HadGAM2 (r1.1, N96; 192 x 145 longitude/latitude; 38 levels; top level 39255 m)\n",
-       "atmosChem: none\n",
-       "land: CABLE2.4\n",
-       "landIce: none\n",
-       "ocean: ACCESS-OM2 (MOM5, tripolar primarily 1deg; 360 x 300 longitude/latitude; 50 levels; top grid cell 0-10 m)\n",
-       "ocnBgchem: WOMBAT (same grid as ocean)\n",
-       "seaIce: CICE4.1 (same grid as ocean)</dd><dt><span>source_id :</span></dt><dd>ACCESS-ESM1-5</dd><dt><span>source_type :</span></dt><dd>AOGCM</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>Amon</dd><dt><span>table_info :</span></dt><dd>Creation Date:(30 April 2019) MD5:5bd755e94c2173cb3050a0f3480f60c4</dd><dt><span>title :</span></dt><dd>ACCESS-ESM1-5 output prepared for CMIP6</dd><dt><span>variable_id :</span></dt><dd>tas</dd><dt><span>variant_label :</span></dt><dd>r10i1p1f1</dd><dt><span>version :</span></dt><dd>v20200605</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by CSIRO is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment.  Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>cmor_version :</span></dt><dd>3.4.0</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f29eb467cd1</dd><dt><span>DODS_EXTRA.Unlimited_Dimension :</span></dt><dd>time</dd></dl></div></li></ul></div></div>"
+       "             2014-12-29 12:00:00, 2014-12-29 18:00:00, 2014-12-30 00:00:00,\n",
+       "             2014-12-30 06:00:00, 2014-12-30 12:00:00, 2014-12-30 18:00:00,\n",
+       "             2014-12-31 00:00:00, 2014-12-31 06:00:00, 2014-12-31 12:00:00,\n",
+       "             2014-12-31 18:00:00],\n",
+       "            dtype=&#x27;object&#x27;, length=2920, calendar=&#x27;standard&#x27;, freq=&#x27;6h&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-47254f43-d391-4514-8a43-c766093bd80d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-47254f43-d391-4514-8a43-c766093bd80d' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>COARDS</dd><dt><span>title :</span></dt><dd>4x daily NMC reanalysis (1948)</dd><dt><span>description :</span></dt><dd>Data is from NMC initialized reanalysis\n",
+       "(4x/day).  These are the 0.9950 sigma level values.</dd><dt><span>platform :</span></dt><dd>Model</dd><dt><span>references :</span></dt><dd>http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.Dataset> Size: 221MB\n",
-       "Dimensions:    (time: 1980, bnds: 2, lat: 145, lon: 192)\n",
+       "<xarray.Dataset> Size: 31MB\n",
+       "Dimensions:    (lat: 25, time: 2920, lon: 53, bnds: 2)\n",
        "Coordinates:\n",
-       "  * lat        (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
-       "  * lon        (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 354.4 356.2 358.1\n",
-       "    height     float64 8B 2.0\n",
-       "  * time       (time) object 16kB 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
+       "  * lat        (lat) float32 100B 75.0 72.5 70.0 67.5 ... 22.5 20.0 17.5 15.0\n",
+       "  * lon        (lon) float32 212B 200.0 202.5 205.0 207.5 ... 325.0 327.5 330.0\n",
+       "  * time       (time) object 23kB 2013-01-01 00:00:00 ... 2014-12-31 18:00:00\n",
        "Dimensions without coordinates: bnds\n",
        "Data variables:\n",
-       "    time_bnds  (time, bnds) object 32kB ...\n",
-       "    lat_bnds   (lat, bnds) float64 2kB ...\n",
-       "    lon_bnds   (lon, bnds) float64 3kB ...\n",
-       "    tas        (time, lat, lon) float32 220MB -27.19 -27.19 ... -25.29 -25.29\n",
-       "Attributes: (12/48)\n",
-       "    Conventions:                     CF-1.7 CMIP-6.2\n",
-       "    activity_id:                     CMIP\n",
-       "    branch_method:                   standard\n",
-       "    branch_time_in_child:            0.0\n",
-       "    branch_time_in_parent:           87658.0\n",
-       "    creation_date:                   2020-06-05T04:06:11Z\n",
-       "    ...                              ...\n",
-       "    variant_label:                   r10i1p1f1\n",
-       "    version:                         v20200605\n",
-       "    license:                         CMIP6 model data produced by CSIRO is li...\n",
-       "    cmor_version:                    3.4.0\n",
-       "    tracking_id:                     hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f2...\n",
-       "    DODS_EXTRA.Unlimited_Dimension:  time"
+       "    air        (time, lat, lon) float64 31MB -31.95 -30.65 ... 23.04 22.54\n",
+       "    lon_bnds   (lon, bnds) float32 424B 198.8 201.2 201.2 ... 328.8 328.8 331.2\n",
+       "    lat_bnds   (lat, bnds) float32 200B 76.25 73.75 73.75 ... 16.25 16.25 13.75\n",
+       "    time_bnds  (time, bnds) object 47kB 2013-01-01 00:00:00 ... 2015-01-01 00...\n",
+       "Attributes:\n",
+       "    Conventions:  COARDS\n",
+       "    title:        4x daily NMC reanalysis (1948)\n",
+       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
+       "    platform:     Model\n",
+       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..."
       ]
      },
      "execution_count": 6,
@@ -1784,11 +1794,10 @@
     }
    ],
    "source": [
-    "filepath = \"https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r10i1p1f1/Amon/tas/gn/v20200605/tas_Amon_ACCESS-ESM1-5_historical_r10i1p1f1_gn_185001-201412.nc\"\n",
-    "ds = xcdat.open_dataset(filepath)\n",
+    "ds = xc.tutorial.open_dataset(\"air_temperature\", use_cftime=True)\n",
     "\n",
     "# Unit adjust (-273.15, K to C)\n",
-    "ds[\"tas\"] = ds.tas - 273.15\n",
+    "ds[\"air\"] = ds.air - 273.15\n",
     "\n",
     "ds"
    ]
@@ -1808,7 +1817,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ds_yearly = ds.temporal.group_average(\"tas\", freq=\"year\", weighted=True)"
+    "ds_yearly = ds.temporal.group_average(\"air\", freq=\"year\", weighted=True)"
    ]
   },
   {
@@ -1850,6 +1859,7 @@
        "}\n",
        "\n",
        "html[theme=dark],\n",
+       "html[data-theme=dark],\n",
        "body[data-theme=dark],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -1900,7 +1910,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -1908,7 +1918,8 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
@@ -1920,6 +1931,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -2182,373 +2197,122 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tas&#x27; (time: 165, lat: 145, lon: 192)&gt; Size: 37MB\n",
-       "array([[[-48.75573349, -48.75573349, -48.75573349, ..., -48.75573349,\n",
-       "         -48.75573349, -48.75573349],\n",
-       "        [-45.65206528, -45.69302368, -45.73506165, ..., -45.52127838,\n",
-       "         -45.56386566, -45.60668945],\n",
-       "        [-44.77523422, -44.90583801, -45.03297043, ..., -44.37118149,\n",
-       "         -44.50630951, -44.64050293],\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;air&#x27; (time: 2, lat: 25, lon: 53)&gt; Size: 21kB\n",
+       "array([[[-13.70783562, -13.86459589, -14.15039041, ..., -22.78481507,\n",
+       "         -21.60773288, -20.08236986],\n",
+       "        [-11.13442466, -11.05367808, -11.11855479, ..., -23.82136986,\n",
+       "         -22.01928082, -19.28924658],\n",
+       "        [ -8.83209589,  -9.28934247,  -9.61319863, ..., -23.25331507,\n",
+       "         -20.34797945, -16.27200685],\n",
        "        ...,\n",
-       "        [-20.50597572, -20.48132133, -20.45456505, ..., -20.58895874,\n",
-       "         -20.55752182, -20.53087234],\n",
-       "        [-20.79759216, -20.78425217, -20.77545547, ..., -20.83267975,\n",
-       "         -20.82335663, -20.80768394],\n",
-       "        [-21.20114899, -21.20114899, -21.20114899, ..., -21.20114899,\n",
-       "         -21.20114899, -21.20114899]],\n",
-       "\n",
-       "       [[-48.95254898, -48.95254898, -48.95254898, ..., -48.95254898,\n",
-       "         -48.95254898, -48.95254898],\n",
-       "        [-45.83190918, -45.8649025 , -45.89875031, ..., -45.7321701 ,\n",
-       "         -45.76544189, -45.79859543],\n",
-       "        [-44.93536758, -45.03795624, -45.13800812, ..., -44.61143112,\n",
-       "         -44.71986008, -44.82937241],\n",
-       "...\n",
-       "        [-14.91627121, -14.89926147, -14.88381004, ..., -14.99542999,\n",
-       "         -14.96513653, -14.93853188],\n",
-       "        [-15.40592194, -15.39668083, -15.38595486, ..., -15.43246269,\n",
-       "         -15.42605591, -15.41356754],\n",
-       "        [-15.94499969, -15.94499969, -15.94499969, ..., -15.94499969,\n",
-       "         -15.94499969, -15.94499969]],\n",
-       "\n",
-       "       [[-47.59732056, -47.59732056, -47.59732056, ..., -47.59732056,\n",
-       "         -47.59732056, -47.59732056],\n",
-       "        [-44.72136688, -44.76342773, -44.80350494, ..., -44.59239197,\n",
-       "         -44.63444519, -44.67822647],\n",
-       "        [-43.85031891, -43.96956253, -44.08713913, ..., -43.47090149,\n",
-       "         -43.59676361, -43.72407913],\n",
+       "        [ 24.33323973,  23.56957534,  23.15760274, ...,  23.84621918,\n",
+       "          23.34989041,  22.90093836],\n",
+       "        [ 24.71765753,  24.47973288,  23.99550685, ...,  23.96486301,\n",
+       "          23.88226712,  23.57558219],\n",
+       "        [ 24.94203425,  24.93717808,  24.67607534, ...,  24.47040411,\n",
+       "          24.40113699,  24.42145205]],\n",
+       "\n",
+       "       [[-11.83928082, -12.06930137, -12.37635616, ..., -21.88338356,\n",
+       "         -20.81603425, -19.34153425],\n",
+       "        [ -9.69678767,  -9.65836986,  -9.68276712, ..., -22.96682192,\n",
+       "         -21.10920548, -18.29223288],\n",
+       "        [ -7.93037671,  -8.3560411 ,  -8.56341096, ..., -21.83090411,\n",
+       "         -18.785     , -14.59679452],\n",
        "        ...,\n",
-       "        [-14.52023029, -14.47407913, -14.43230724, ..., -14.67551422,\n",
-       "         -14.62093163, -14.56736755],\n",
-       "        [-14.91123581, -14.89230919, -14.86901569, ..., -14.9820118 ,\n",
-       "         -14.96266842, -14.93872261],\n",
-       "        [-15.6184063 , -15.6184063 , -15.6184063 , ..., -15.6184063 ,\n",
-       "         -15.6184063 , -15.6184063 ]]])\n",
+       "        [ 24.6664863 ,  24.03708904,  23.8010274 , ...,  23.47563014,\n",
+       "          22.92603425,  22.4319726 ],\n",
+       "        [ 25.24074658,  25.09428082,  24.64528082, ...,  23.45423288,\n",
+       "          23.37178767,  23.01208904],\n",
+       "        [ 25.49026712,  25.53430137,  25.25221233, ...,  23.90600685,\n",
+       "          23.86175342,  23.88875342]]])\n",
        "Coordinates:\n",
-       "  * lat      (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
-       "  * lon      (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
-       "    height   float64 8B 2.0\n",
-       "  * time     (time) object 1kB 1850-01-01 00:00:00 ... 2014-01-01 00:00:00\n",
+       "  * lat      (lat) float32 100B 75.0 72.5 70.0 67.5 65.0 ... 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 212B 200.0 202.5 205.0 207.5 ... 325.0 327.5 330.0\n",
+       "  * time     (time) object 16B 2013-01-01 00:00:00 2014-01-01 00:00:00\n",
        "Attributes:\n",
        "    operation:  temporal_avg\n",
        "    mode:       group_average\n",
        "    freq:       year\n",
-       "    weighted:   True</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 165</li><li><span class='xr-has-index'>lat</span>: 145</li><li><span class='xr-has-index'>lon</span>: 192</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-67308fa7-a39b-4c72-9f66-73882099b027' class='xr-array-in' type='checkbox' checked><label for='section-67308fa7-a39b-4c72-9f66-73882099b027' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-48.76 -48.76 -48.76 -48.76 -48.76 ... -15.62 -15.62 -15.62 -15.62</span></div><div class='xr-array-data'><pre>array([[[-48.75573349, -48.75573349, -48.75573349, ..., -48.75573349,\n",
-       "         -48.75573349, -48.75573349],\n",
-       "        [-45.65206528, -45.69302368, -45.73506165, ..., -45.52127838,\n",
-       "         -45.56386566, -45.60668945],\n",
-       "        [-44.77523422, -44.90583801, -45.03297043, ..., -44.37118149,\n",
-       "         -44.50630951, -44.64050293],\n",
+       "    weighted:   True</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'air'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2</li><li><span class='xr-has-index'>lat</span>: 25</li><li><span class='xr-has-index'>lon</span>: 53</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b28fc934-1efe-4e04-87d4-9f02f45a8ae0' class='xr-array-in' type='checkbox' checked><label for='section-b28fc934-1efe-4e04-87d4-9f02f45a8ae0' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-13.71 -13.86 -14.15 -14.58 -15.1 ... 24.46 24.39 23.91 23.86 23.89</span></div><div class='xr-array-data'><pre>array([[[-13.70783562, -13.86459589, -14.15039041, ..., -22.78481507,\n",
+       "         -21.60773288, -20.08236986],\n",
+       "        [-11.13442466, -11.05367808, -11.11855479, ..., -23.82136986,\n",
+       "         -22.01928082, -19.28924658],\n",
+       "        [ -8.83209589,  -9.28934247,  -9.61319863, ..., -23.25331507,\n",
+       "         -20.34797945, -16.27200685],\n",
        "        ...,\n",
-       "        [-20.50597572, -20.48132133, -20.45456505, ..., -20.58895874,\n",
-       "         -20.55752182, -20.53087234],\n",
-       "        [-20.79759216, -20.78425217, -20.77545547, ..., -20.83267975,\n",
-       "         -20.82335663, -20.80768394],\n",
-       "        [-21.20114899, -21.20114899, -21.20114899, ..., -21.20114899,\n",
-       "         -21.20114899, -21.20114899]],\n",
-       "\n",
-       "       [[-48.95254898, -48.95254898, -48.95254898, ..., -48.95254898,\n",
-       "         -48.95254898, -48.95254898],\n",
-       "        [-45.83190918, -45.8649025 , -45.89875031, ..., -45.7321701 ,\n",
-       "         -45.76544189, -45.79859543],\n",
-       "        [-44.93536758, -45.03795624, -45.13800812, ..., -44.61143112,\n",
-       "         -44.71986008, -44.82937241],\n",
-       "...\n",
-       "        [-14.91627121, -14.89926147, -14.88381004, ..., -14.99542999,\n",
-       "         -14.96513653, -14.93853188],\n",
-       "        [-15.40592194, -15.39668083, -15.38595486, ..., -15.43246269,\n",
-       "         -15.42605591, -15.41356754],\n",
-       "        [-15.94499969, -15.94499969, -15.94499969, ..., -15.94499969,\n",
-       "         -15.94499969, -15.94499969]],\n",
-       "\n",
-       "       [[-47.59732056, -47.59732056, -47.59732056, ..., -47.59732056,\n",
-       "         -47.59732056, -47.59732056],\n",
-       "        [-44.72136688, -44.76342773, -44.80350494, ..., -44.59239197,\n",
-       "         -44.63444519, -44.67822647],\n",
-       "        [-43.85031891, -43.96956253, -44.08713913, ..., -43.47090149,\n",
-       "         -43.59676361, -43.72407913],\n",
+       "        [ 24.33323973,  23.56957534,  23.15760274, ...,  23.84621918,\n",
+       "          23.34989041,  22.90093836],\n",
+       "        [ 24.71765753,  24.47973288,  23.99550685, ...,  23.96486301,\n",
+       "          23.88226712,  23.57558219],\n",
+       "        [ 24.94203425,  24.93717808,  24.67607534, ...,  24.47040411,\n",
+       "          24.40113699,  24.42145205]],\n",
+       "\n",
+       "       [[-11.83928082, -12.06930137, -12.37635616, ..., -21.88338356,\n",
+       "         -20.81603425, -19.34153425],\n",
+       "        [ -9.69678767,  -9.65836986,  -9.68276712, ..., -22.96682192,\n",
+       "         -21.10920548, -18.29223288],\n",
+       "        [ -7.93037671,  -8.3560411 ,  -8.56341096, ..., -21.83090411,\n",
+       "         -18.785     , -14.59679452],\n",
        "        ...,\n",
-       "        [-14.52023029, -14.47407913, -14.43230724, ..., -14.67551422,\n",
-       "         -14.62093163, -14.56736755],\n",
-       "        [-14.91123581, -14.89230919, -14.86901569, ..., -14.9820118 ,\n",
-       "         -14.96266842, -14.93872261],\n",
-       "        [-15.6184063 , -15.6184063 , -15.6184063 , ..., -15.6184063 ,\n",
-       "         -15.6184063 , -15.6184063 ]]])</pre></div></div></li><li class='xr-section-item'><input id='section-307c0814-8064-43b9-afdd-589a7546326d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-307c0814-8064-43b9-afdd-589a7546326d' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -88.75 -87.5 ... 88.75 90.0</div><input id='attrs-d9d01e86-6e87-4fc8-9f59-4433efa781dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9d01e86-6e87-4fc8-9f59-4433efa781dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f526631-4071-4bf4-bfe9-f5a4a35b92b8' class='xr-var-data-in' type='checkbox'><label for='data-7f526631-4071-4bf4-bfe9-f5a4a35b92b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</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><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-90.  , -88.75, -87.5 , -86.25, -85.  , -83.75, -82.5 , -81.25, -80.  ,\n",
-       "       -78.75, -77.5 , -76.25, -75.  , -73.75, -72.5 , -71.25, -70.  , -68.75,\n",
-       "       -67.5 , -66.25, -65.  , -63.75, -62.5 , -61.25, -60.  , -58.75, -57.5 ,\n",
-       "       -56.25, -55.  , -53.75, -52.5 , -51.25, -50.  , -48.75, -47.5 , -46.25,\n",
-       "       -45.  , -43.75, -42.5 , -41.25, -40.  , -38.75, -37.5 , -36.25, -35.  ,\n",
-       "       -33.75, -32.5 , -31.25, -30.  , -28.75, -27.5 , -26.25, -25.  , -23.75,\n",
-       "       -22.5 , -21.25, -20.  , -18.75, -17.5 , -16.25, -15.  , -13.75, -12.5 ,\n",
-       "       -11.25, -10.  ,  -8.75,  -7.5 ,  -6.25,  -5.  ,  -3.75,  -2.5 ,  -1.25,\n",
-       "         0.  ,   1.25,   2.5 ,   3.75,   5.  ,   6.25,   7.5 ,   8.75,  10.  ,\n",
-       "        11.25,  12.5 ,  13.75,  15.  ,  16.25,  17.5 ,  18.75,  20.  ,  21.25,\n",
-       "        22.5 ,  23.75,  25.  ,  26.25,  27.5 ,  28.75,  30.  ,  31.25,  32.5 ,\n",
-       "        33.75,  35.  ,  36.25,  37.5 ,  38.75,  40.  ,  41.25,  42.5 ,  43.75,\n",
-       "        45.  ,  46.25,  47.5 ,  48.75,  50.  ,  51.25,  52.5 ,  53.75,  55.  ,\n",
-       "        56.25,  57.5 ,  58.75,  60.  ,  61.25,  62.5 ,  63.75,  65.  ,  66.25,\n",
-       "        67.5 ,  68.75,  70.  ,  71.25,  72.5 ,  73.75,  75.  ,  76.25,  77.5 ,\n",
-       "        78.75,  80.  ,  81.25,  82.5 ,  83.75,  85.  ,  86.25,  87.5 ,  88.75,\n",
-       "        90.  ])</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'>0.0 1.875 3.75 ... 356.2 358.1</div><input id='attrs-acbdfad3-ba82-4a26-9daf-d8911a1544a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-acbdfad3-ba82-4a26-9daf-d8911a1544a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6a1fc14-94fe-40ab-87c0-386d36f46eff' class='xr-var-data-in' type='checkbox'><label for='data-e6a1fc14-94fe-40ab-87c0-386d36f46eff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</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><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.   ,   1.875,   3.75 ,   5.625,   7.5  ,   9.375,  11.25 ,  13.125,\n",
-       "        15.   ,  16.875,  18.75 ,  20.625,  22.5  ,  24.375,  26.25 ,  28.125,\n",
-       "        30.   ,  31.875,  33.75 ,  35.625,  37.5  ,  39.375,  41.25 ,  43.125,\n",
-       "        45.   ,  46.875,  48.75 ,  50.625,  52.5  ,  54.375,  56.25 ,  58.125,\n",
-       "        60.   ,  61.875,  63.75 ,  65.625,  67.5  ,  69.375,  71.25 ,  73.125,\n",
-       "        75.   ,  76.875,  78.75 ,  80.625,  82.5  ,  84.375,  86.25 ,  88.125,\n",
-       "        90.   ,  91.875,  93.75 ,  95.625,  97.5  ,  99.375, 101.25 , 103.125,\n",
-       "       105.   , 106.875, 108.75 , 110.625, 112.5  , 114.375, 116.25 , 118.125,\n",
-       "       120.   , 121.875, 123.75 , 125.625, 127.5  , 129.375, 131.25 , 133.125,\n",
-       "       135.   , 136.875, 138.75 , 140.625, 142.5  , 144.375, 146.25 , 148.125,\n",
-       "       150.   , 151.875, 153.75 , 155.625, 157.5  , 159.375, 161.25 , 163.125,\n",
-       "       165.   , 166.875, 168.75 , 170.625, 172.5  , 174.375, 176.25 , 178.125,\n",
-       "       180.   , 181.875, 183.75 , 185.625, 187.5  , 189.375, 191.25 , 193.125,\n",
-       "       195.   , 196.875, 198.75 , 200.625, 202.5  , 204.375, 206.25 , 208.125,\n",
-       "       210.   , 211.875, 213.75 , 215.625, 217.5  , 219.375, 221.25 , 223.125,\n",
-       "       225.   , 226.875, 228.75 , 230.625, 232.5  , 234.375, 236.25 , 238.125,\n",
-       "       240.   , 241.875, 243.75 , 245.625, 247.5  , 249.375, 251.25 , 253.125,\n",
-       "       255.   , 256.875, 258.75 , 260.625, 262.5  , 264.375, 266.25 , 268.125,\n",
-       "       270.   , 271.875, 273.75 , 275.625, 277.5  , 279.375, 281.25 , 283.125,\n",
-       "       285.   , 286.875, 288.75 , 290.625, 292.5  , 294.375, 296.25 , 298.125,\n",
-       "       300.   , 301.875, 303.75 , 305.625, 307.5  , 309.375, 311.25 , 313.125,\n",
-       "       315.   , 316.875, 318.75 , 320.625, 322.5  , 324.375, 326.25 , 328.125,\n",
-       "       330.   , 331.875, 333.75 , 335.625, 337.5  , 339.375, 341.25 , 343.125,\n",
-       "       345.   , 346.875, 348.75 , 350.625, 352.5  , 354.375, 356.25 , 358.125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-3e6df02b-76ba-4c4d-8bcc-8eb5f0bd3743' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e6df02b-76ba-4c4d-8bcc-8eb5f0bd3743' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54f2b6cf-0b14-42f4-8822-e9b575e05f7c' class='xr-var-data-in' type='checkbox'><label for='data-54f2b6cf-0b14-42f4-8822-e9b575e05f7c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>array(2.)</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'>object</div><div class='xr-var-preview xr-preview'>1850-01-01 00:00:00 ... 2014-01-...</div><input id='attrs-77da6fb0-80b4-494c-b8a6-9cc118797961' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-77da6fb0-80b4-494c-b8a6-9cc118797961' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c77fb89b-2c2d-4146-8ab1-28d09aee40c7' class='xr-var-data-in' type='checkbox'><label for='data-c77fb89b-2c2d-4146-8ab1-28d09aee40c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeProlepticGregorian(1850, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1851, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1852, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1853, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1854, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1855, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1856, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1857, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1858, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1859, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1860, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1861, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1862, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1863, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1864, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1865, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1866, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1867, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1868, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1869, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1870, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1871, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1872, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1873, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1874, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1875, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1876, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1877, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1878, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1879, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1880, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1881, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1882, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1883, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1884, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1885, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1886, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1887, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1888, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1889, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1890, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1891, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1892, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1893, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1894, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1895, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1896, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1897, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1898, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1899, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1900, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1901, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1902, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1903, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1904, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1905, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1906, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1907, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1908, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1909, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1910, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1911, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1912, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1913, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1914, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1915, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1916, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1917, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1918, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1919, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1920, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1921, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1922, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1923, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1924, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1925, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1926, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1927, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1928, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1929, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1930, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1931, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1932, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1933, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1934, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1935, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1936, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1937, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1938, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1939, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1940, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1941, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1942, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1943, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1944, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1945, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1946, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1947, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1948, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1949, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1950, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1951, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1952, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1953, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1954, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1955, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1956, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1957, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1958, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1959, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1960, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1961, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1962, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1963, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1964, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1965, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1966, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1967, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1968, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1969, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1970, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1971, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1972, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1973, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1974, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1975, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1976, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1977, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1978, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1979, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1980, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1981, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1982, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1983, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1984, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1985, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1986, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1987, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1988, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1989, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1990, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1991, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1992, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1993, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1994, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1995, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1996, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1997, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1998, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1999, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2000, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2001, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2002, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2003, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2004, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2005, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2006, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2007, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2008, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2009, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2010, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2011, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2012, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2013, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 1, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-44ad1151-cd63-4871-9fe6-2365af2dac49' class='xr-section-summary-in' type='checkbox'  ><label for='section-44ad1151-cd63-4871-9fe6-2365af2dac49' 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-f9c275a8-9ff5-4dcb-b3d6-7703eaa37074' class='xr-index-data-in' type='checkbox'/><label for='index-f9c275a8-9ff5-4dcb-b3d6-7703eaa37074' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -90.0, -88.75,  -87.5, -86.25,  -85.0, -83.75,  -82.5, -81.25,  -80.0,\n",
-       "       -78.75,\n",
-       "       ...\n",
-       "        78.75,   80.0,  81.25,   82.5,  83.75,   85.0,  86.25,   87.5,  88.75,\n",
-       "         90.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=145))</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-85a0237a-2aac-4250-a0af-ee0eb05fb8ec' class='xr-index-data-in' type='checkbox'/><label for='index-85a0237a-2aac-4250-a0af-ee0eb05fb8ec' 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([    0.0,   1.875,    3.75,   5.625,     7.5,   9.375,   11.25,  13.125,\n",
-       "          15.0,  16.875,\n",
-       "       ...\n",
-       "        341.25, 343.125,   345.0, 346.875,  348.75, 350.625,   352.5, 354.375,\n",
-       "        356.25, 358.125],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=192))</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-8a6c353c-f69f-4ede-8fd1-b458a62952e0' class='xr-index-data-in' type='checkbox'/><label for='index-8a6c353c-f69f-4ede-8fd1-b458a62952e0' 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([1850-01-01 00:00:00, 1851-01-01 00:00:00, 1852-01-01 00:00:00,\n",
-       "             1853-01-01 00:00:00, 1854-01-01 00:00:00, 1855-01-01 00:00:00,\n",
-       "             1856-01-01 00:00:00, 1857-01-01 00:00:00, 1858-01-01 00:00:00,\n",
-       "             1859-01-01 00:00:00,\n",
-       "             ...\n",
-       "             2005-01-01 00:00:00, 2006-01-01 00:00:00, 2007-01-01 00:00:00,\n",
-       "             2008-01-01 00:00:00, 2009-01-01 00:00:00, 2010-01-01 00:00:00,\n",
-       "             2011-01-01 00:00:00, 2012-01-01 00:00:00, 2013-01-01 00:00:00,\n",
-       "             2014-01-01 00:00:00],\n",
-       "            dtype=&#x27;object&#x27;,\n",
-       "            length=165,\n",
-       "            calendar=&#x27;proleptic_gregorian&#x27;,\n",
-       "            freq=&#x27;YS-JAN&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-83599b4f-15a1-45ab-8a4f-804dfdf8e2cf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-83599b4f-15a1-45ab-8a4f-804dfdf8e2cf' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>operation :</span></dt><dd>temporal_avg</dd><dt><span>mode :</span></dt><dd>group_average</dd><dt><span>freq :</span></dt><dd>year</dd><dt><span>weighted :</span></dt><dd>True</dd></dl></div></li></ul></div></div>"
+       "        [ 24.6664863 ,  24.03708904,  23.8010274 , ...,  23.47563014,\n",
+       "          22.92603425,  22.4319726 ],\n",
+       "        [ 25.24074658,  25.09428082,  24.64528082, ...,  23.45423288,\n",
+       "          23.37178767,  23.01208904],\n",
+       "        [ 25.49026712,  25.53430137,  25.25221233, ...,  23.90600685,\n",
+       "          23.86175342,  23.88875342]]])</pre></div></div></li><li class='xr-section-item'><input id='section-86fb69dd-8da3-44db-bf78-a7585be2d9e9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-86fb69dd-8da3-44db-bf78-a7585be2d9e9' 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'>float32</div><div class='xr-var-preview xr-preview'>75.0 72.5 70.0 ... 20.0 17.5 15.0</div><input id='attrs-9adc6589-ba6d-4c35-bca3-e3778d490433' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9adc6589-ba6d-4c35-bca3-e3778d490433' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66446f65-e60c-4c13-9e02-093d7a703343' class='xr-var-data-in' type='checkbox'><label for='data-66446f65-e60c-4c13-9e02-093d7a703343' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd></dl></div><div class='xr-var-data'><pre>array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n",
+       "       45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n",
+       "       15. ], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>200.0 202.5 205.0 ... 327.5 330.0</div><input id='attrs-70dc0b78-6d29-4831-a321-f520e939fd4d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70dc0b78-6d29-4831-a321-f520e939fd4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4119898b-b598-4129-89a4-3e242a36c16d' class='xr-var-data-in' type='checkbox'><label for='data-4119898b-b598-4129-89a4-3e242a36c16d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd></dl></div><div class='xr-var-data'><pre>array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
+       "       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
+       "       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
+       "       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
+       "       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
+       "       325. , 327.5, 330. ], dtype=float32)</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'>object</div><div class='xr-var-preview xr-preview'>2013-01-01 00:00:00 2014-01-01 0...</div><input id='attrs-6b6a1d9e-951f-49d4-96cc-714f23cb82f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b6a1d9e-951f-49d4-96cc-714f23cb82f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88fd3cb8-1337-4b59-ba86-a07a8a0e58a9' class='xr-var-data-in' type='checkbox'><label for='data-88fd3cb8-1337-4b59-ba86-a07a8a0e58a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeGregorian(2013, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 1, 1, 0, 0, 0, 0, has_year_zero=False)],\n",
+       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cad425cc-bddf-49af-a762-96a8ce283b6f' class='xr-section-summary-in' type='checkbox'  ><label for='section-cad425cc-bddf-49af-a762-96a8ce283b6f' 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-0325af8d-016a-4e40-9f37-ca231973ceae' class='xr-index-data-in' type='checkbox'/><label for='index-0325af8d-016a-4e40-9f37-ca231973ceae' 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([75.0, 72.5, 70.0, 67.5, 65.0, 62.5, 60.0, 57.5, 55.0, 52.5, 50.0, 47.5,\n",
+       "       45.0, 42.5, 40.0, 37.5, 35.0, 32.5, 30.0, 27.5, 25.0, 22.5, 20.0, 17.5,\n",
+       "       15.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;))</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-5f24f1f0-87e1-4583-b771-d426ac72a702' class='xr-index-data-in' type='checkbox'/><label for='index-5f24f1f0-87e1-4583-b771-d426ac72a702' 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([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0, 222.5,\n",
+       "       225.0, 227.5, 230.0, 232.5, 235.0, 237.5, 240.0, 242.5, 245.0, 247.5,\n",
+       "       250.0, 252.5, 255.0, 257.5, 260.0, 262.5, 265.0, 267.5, 270.0, 272.5,\n",
+       "       275.0, 277.5, 280.0, 282.5, 285.0, 287.5, 290.0, 292.5, 295.0, 297.5,\n",
+       "       300.0, 302.5, 305.0, 307.5, 310.0, 312.5, 315.0, 317.5, 320.0, 322.5,\n",
+       "       325.0, 327.5, 330.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-484648f1-1598-44a3-9ec0-24bfc729c76d' class='xr-index-data-in' type='checkbox'/><label for='index-484648f1-1598-44a3-9ec0-24bfc729c76d' 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([2013-01-01 00:00:00, 2014-01-01 00:00:00],\n",
+       "            dtype=&#x27;object&#x27;, length=2, calendar=&#x27;standard&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5c45d031-c885-4672-9d3d-6eef5adfec5c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5c45d031-c885-4672-9d3d-6eef5adfec5c' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>operation :</span></dt><dd>temporal_avg</dd><dt><span>mode :</span></dt><dd>group_average</dd><dt><span>freq :</span></dt><dd>year</dd><dt><span>weighted :</span></dt><dd>True</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'tas' (time: 165, lat: 145, lon: 192)> Size: 37MB\n",
-       "array([[[-48.75573349, -48.75573349, -48.75573349, ..., -48.75573349,\n",
-       "         -48.75573349, -48.75573349],\n",
-       "        [-45.65206528, -45.69302368, -45.73506165, ..., -45.52127838,\n",
-       "         -45.56386566, -45.60668945],\n",
-       "        [-44.77523422, -44.90583801, -45.03297043, ..., -44.37118149,\n",
-       "         -44.50630951, -44.64050293],\n",
+       "<xarray.DataArray 'air' (time: 2, lat: 25, lon: 53)> Size: 21kB\n",
+       "array([[[-13.70783562, -13.86459589, -14.15039041, ..., -22.78481507,\n",
+       "         -21.60773288, -20.08236986],\n",
+       "        [-11.13442466, -11.05367808, -11.11855479, ..., -23.82136986,\n",
+       "         -22.01928082, -19.28924658],\n",
+       "        [ -8.83209589,  -9.28934247,  -9.61319863, ..., -23.25331507,\n",
+       "         -20.34797945, -16.27200685],\n",
        "        ...,\n",
-       "        [-20.50597572, -20.48132133, -20.45456505, ..., -20.58895874,\n",
-       "         -20.55752182, -20.53087234],\n",
-       "        [-20.79759216, -20.78425217, -20.77545547, ..., -20.83267975,\n",
-       "         -20.82335663, -20.80768394],\n",
-       "        [-21.20114899, -21.20114899, -21.20114899, ..., -21.20114899,\n",
-       "         -21.20114899, -21.20114899]],\n",
-       "\n",
-       "       [[-48.95254898, -48.95254898, -48.95254898, ..., -48.95254898,\n",
-       "         -48.95254898, -48.95254898],\n",
-       "        [-45.83190918, -45.8649025 , -45.89875031, ..., -45.7321701 ,\n",
-       "         -45.76544189, -45.79859543],\n",
-       "        [-44.93536758, -45.03795624, -45.13800812, ..., -44.61143112,\n",
-       "         -44.71986008, -44.82937241],\n",
-       "...\n",
-       "        [-14.91627121, -14.89926147, -14.88381004, ..., -14.99542999,\n",
-       "         -14.96513653, -14.93853188],\n",
-       "        [-15.40592194, -15.39668083, -15.38595486, ..., -15.43246269,\n",
-       "         -15.42605591, -15.41356754],\n",
-       "        [-15.94499969, -15.94499969, -15.94499969, ..., -15.94499969,\n",
-       "         -15.94499969, -15.94499969]],\n",
-       "\n",
-       "       [[-47.59732056, -47.59732056, -47.59732056, ..., -47.59732056,\n",
-       "         -47.59732056, -47.59732056],\n",
-       "        [-44.72136688, -44.76342773, -44.80350494, ..., -44.59239197,\n",
-       "         -44.63444519, -44.67822647],\n",
-       "        [-43.85031891, -43.96956253, -44.08713913, ..., -43.47090149,\n",
-       "         -43.59676361, -43.72407913],\n",
+       "        [ 24.33323973,  23.56957534,  23.15760274, ...,  23.84621918,\n",
+       "          23.34989041,  22.90093836],\n",
+       "        [ 24.71765753,  24.47973288,  23.99550685, ...,  23.96486301,\n",
+       "          23.88226712,  23.57558219],\n",
+       "        [ 24.94203425,  24.93717808,  24.67607534, ...,  24.47040411,\n",
+       "          24.40113699,  24.42145205]],\n",
+       "\n",
+       "       [[-11.83928082, -12.06930137, -12.37635616, ..., -21.88338356,\n",
+       "         -20.81603425, -19.34153425],\n",
+       "        [ -9.69678767,  -9.65836986,  -9.68276712, ..., -22.96682192,\n",
+       "         -21.10920548, -18.29223288],\n",
+       "        [ -7.93037671,  -8.3560411 ,  -8.56341096, ..., -21.83090411,\n",
+       "         -18.785     , -14.59679452],\n",
        "        ...,\n",
-       "        [-14.52023029, -14.47407913, -14.43230724, ..., -14.67551422,\n",
-       "         -14.62093163, -14.56736755],\n",
-       "        [-14.91123581, -14.89230919, -14.86901569, ..., -14.9820118 ,\n",
-       "         -14.96266842, -14.93872261],\n",
-       "        [-15.6184063 , -15.6184063 , -15.6184063 , ..., -15.6184063 ,\n",
-       "         -15.6184063 , -15.6184063 ]]])\n",
+       "        [ 24.6664863 ,  24.03708904,  23.8010274 , ...,  23.47563014,\n",
+       "          22.92603425,  22.4319726 ],\n",
+       "        [ 25.24074658,  25.09428082,  24.64528082, ...,  23.45423288,\n",
+       "          23.37178767,  23.01208904],\n",
+       "        [ 25.49026712,  25.53430137,  25.25221233, ...,  23.90600685,\n",
+       "          23.86175342,  23.88875342]]])\n",
        "Coordinates:\n",
-       "  * lat      (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
-       "  * lon      (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
-       "    height   float64 8B 2.0\n",
-       "  * time     (time) object 1kB 1850-01-01 00:00:00 ... 2014-01-01 00:00:00\n",
+       "  * lat      (lat) float32 100B 75.0 72.5 70.0 67.5 65.0 ... 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 212B 200.0 202.5 205.0 207.5 ... 325.0 327.5 330.0\n",
+       "  * time     (time) object 16B 2013-01-01 00:00:00 2014-01-01 00:00:00\n",
        "Attributes:\n",
        "    operation:  temporal_avg\n",
        "    mode:       group_average\n",
@@ -2562,24 +2326,7 @@
     }
    ],
    "source": [
-    "ds_yearly.tas"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![tas yearly averages](../examples/temporal-average-yearly.gif)\n",
-    "\n",
-    "_This GIF was created using [xmovie](https://github.com/jbusecke/xmovie)._\n",
-    "\n",
-    "Sample `xmovie` code:\n",
-    "\n",
-    "```python\n",
-    "import xmovie\n",
-    "mov = xmovie.Movie(ds_yearly_avg.tas)\n",
-    "mov.save(\"temporal-average-yearly.gif\")\n",
-    "```\n"
+    "ds_yearly.air"
    ]
   },
   {
@@ -2597,7 +2344,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ds_season = ds.temporal.group_average(\"tas\", freq=\"season\", weighted=True)"
+    "ds_season = ds.temporal.group_average(\"air\", freq=\"season\", weighted=True)"
    ]
   },
   {
@@ -2639,6 +2386,7 @@
        "}\n",
        "\n",
        "html[theme=dark],\n",
+       "html[data-theme=dark],\n",
        "body[data-theme=dark],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -2689,7 +2437,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -2697,7 +2445,8 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
@@ -2709,6 +2458,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -2971,217 +2724,175 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tas&#x27; (time: 661, lat: 145, lon: 192)&gt; Size: 147MB\n",
-       "array([[[-32.70588303, -32.70588303, -32.70588303, ..., -32.70588303,\n",
-       "         -32.70588303, -32.70588303],\n",
-       "        [-30.99376678, -31.03758621, -31.08932686, ..., -30.84562302,\n",
-       "         -30.89412689, -30.94400978],\n",
-       "        [-30.0251503 , -30.14543724, -30.26419067, ..., -29.66037178,\n",
-       "         -29.78108025, -29.90287781],\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;air&#x27; (time: 9, lat: 25, lon: 53)&gt; Size: 95kB\n",
+       "array([[[-30.45474576, -30.36682203, -30.50084746, ..., -31.07915254,\n",
+       "         -30.06944915, -28.82122881],\n",
+       "        [-28.51614407, -28.47813559, -28.70733051, ..., -32.36279661,\n",
+       "         -30.97228814, -28.43313559],\n",
+       "        [-25.16995763, -25.59728814, -26.29101695, ..., -30.48627119,\n",
+       "         -27.90881356, -23.83478814],\n",
        "        ...,\n",
-       "        [-37.72314072, -37.68549347, -37.65416718, ..., -37.82619858,\n",
-       "         -37.79034424, -37.75682831],\n",
-       "        [-38.27464676, -38.26372528, -38.25014496, ..., -38.29218292,\n",
-       "         -38.29063797, -38.28456116],\n",
-       "        [-38.74358749, -38.74358749, -38.74358749, ..., -38.74358749,\n",
-       "         -38.74358749, -38.74358749]],\n",
-       "\n",
-       "       [[-54.29086304, -54.29086304, -54.29086304, ..., -54.29086304,\n",
-       "         -54.29086304, -54.29086304],\n",
-       "        [-51.11771393, -51.17523575, -51.23055267, ..., -50.93516541,\n",
-       "         -50.99657059, -51.05614471],\n",
-       "        [-50.31804657, -50.48666382, -50.64956665, ..., -49.79003143,\n",
-       "         -49.97007751, -50.14521027],\n",
+       "        [ 22.49648305,  21.70326271,  21.33059322, ...,  21.98830508,\n",
+       "          21.48665254,  20.91936441],\n",
+       "        [ 23.11813559,  22.87080508,  22.33012712, ...,  22.24177966,\n",
+       "          22.01559322,  21.52033898],\n",
+       "        [ 23.63673729,  23.56084746,  23.25      , ...,  22.92516949,\n",
+       "          22.73563559,  22.64847458]],\n",
+       "\n",
+       "       [[-15.6326087 , -15.84429348, -16.19054348, ..., -24.78972826,\n",
+       "         -23.77891304, -22.51769022],\n",
+       "        [-14.49692935, -14.45345109, -14.51820652, ..., -25.3298913 ,\n",
+       "         -23.72633152, -21.39619565],\n",
+       "        [-14.0205163 , -14.63826087, -15.10815217, ..., -25.11967391,\n",
+       "         -22.38184783, -18.55915761],\n",
        "...\n",
-       "        [-12.34277439, -12.2246685 , -12.10663223, ..., -12.74492168,\n",
-       "         -12.60908794, -12.47839165],\n",
-       "        [-13.12640381, -13.0661087 , -13.00387573, ..., -13.306077  ,\n",
-       "         -13.25871468, -13.19972038],\n",
-       "        [-14.28846931, -14.28846931, -14.28846931, ..., -14.28846931,\n",
-       "         -14.28846931, -14.28846931]],\n",
-       "\n",
-       "       [[-28.99049377, -28.99049377, -28.99049377, ..., -28.99049377,\n",
-       "         -28.99049377, -28.99049377],\n",
-       "        [-28.19291687, -28.22457886, -28.26130676, ..., -28.09593201,\n",
-       "         -28.12599182, -28.15802002],\n",
-       "        [-27.60740662, -27.7056427 , -27.80511475, ..., -27.31161499,\n",
-       "         -27.41082764, -27.50836182],\n",
+       "        [ 26.01706044,  25.27870879,  24.99217033, ...,  25.69024725,\n",
+       "          25.31126374,  24.87184066],\n",
+       "        [ 26.52486264,  26.35104396,  25.80881868, ...,  25.68881868,\n",
+       "          25.7889011 ,  25.55848901],\n",
+       "        [ 26.51387363,  26.54862637,  26.21848901, ...,  26.18178571,\n",
+       "          26.18318681,  26.29879121]],\n",
+       "\n",
+       "       [[-26.34846774, -26.2608871 , -26.3808871 , ..., -33.07903226,\n",
+       "         -32.06798387, -30.86830645],\n",
+       "        [-25.42      , -24.84927419, -24.40548387, ..., -34.53137097,\n",
+       "         -32.82782258, -30.17967742],\n",
+       "        [-23.18104839, -23.56475806, -23.57475806, ..., -35.44693548,\n",
+       "         -31.91258065, -26.92330645],\n",
        "        ...,\n",
-       "        [-24.25627136, -24.14059448, -24.03753662, ..., -24.61853027,\n",
-       "         -24.48849487, -24.36643982],\n",
-       "        [-24.62901306, -24.61338806, -24.54986572, ..., -24.75204468,\n",
-       "         -24.72160339, -24.66641235],\n",
-       "        [-25.28923035, -25.28923035, -25.28923035, ..., -25.28923035,\n",
-       "         -25.28923035, -25.28923035]]])\n",
+       "        [ 23.29919355,  22.54145161,  22.6083871 , ...,  23.37830645,\n",
+       "          23.0675    ,  22.66298387],\n",
+       "        [ 24.2958871 ,  24.28612903,  24.03177419, ...,  23.80258065,\n",
+       "          23.90830645,  23.57903226],\n",
+       "        [ 24.89733871,  25.07612903,  24.90967742, ...,  24.54758065,\n",
+       "          24.57322581,  24.56040323]]])\n",
        "Coordinates:\n",
-       "  * lat      (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
-       "  * lon      (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
-       "    height   float64 8B 2.0\n",
-       "  * time     (time) object 5kB 1850-01-01 00:00:00 ... 2015-01-01 00:00:00\n",
+       "  * lat      (lat) float32 100B 75.0 72.5 70.0 67.5 65.0 ... 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 212B 200.0 202.5 205.0 207.5 ... 325.0 327.5 330.0\n",
+       "  * time     (time) object 72B 2013-01-01 00:00:00 ... 2015-01-01 00:00:00\n",
        "Attributes:\n",
        "    operation:            temporal_avg\n",
        "    mode:                 group_average\n",
        "    freq:                 season\n",
        "    weighted:             True\n",
        "    dec_mode:             DJF\n",
-       "    drop_incomplete_djf:  False</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 661</li><li><span class='xr-has-index'>lat</span>: 145</li><li><span class='xr-has-index'>lon</span>: 192</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-10accefc-0322-45ff-a16c-e3b0cb5b65a9' class='xr-array-in' type='checkbox' checked><label for='section-10accefc-0322-45ff-a16c-e3b0cb5b65a9' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-32.71 -32.71 -32.71 -32.71 -32.71 ... -25.29 -25.29 -25.29 -25.29</span></div><div class='xr-array-data'><pre>array([[[-32.70588303, -32.70588303, -32.70588303, ..., -32.70588303,\n",
-       "         -32.70588303, -32.70588303],\n",
-       "        [-30.99376678, -31.03758621, -31.08932686, ..., -30.84562302,\n",
-       "         -30.89412689, -30.94400978],\n",
-       "        [-30.0251503 , -30.14543724, -30.26419067, ..., -29.66037178,\n",
-       "         -29.78108025, -29.90287781],\n",
+       "    drop_incomplete_djf:  False</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'air'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 9</li><li><span class='xr-has-index'>lat</span>: 25</li><li><span class='xr-has-index'>lon</span>: 53</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2b208214-f296-4060-84b8-b02c6bf01dc3' class='xr-array-in' type='checkbox' checked><label for='section-2b208214-f296-4060-84b8-b02c6bf01dc3' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-30.45 -30.37 -30.5 -30.89 -31.48 ... 24.98 24.97 24.55 24.57 24.56</span></div><div class='xr-array-data'><pre>array([[[-30.45474576, -30.36682203, -30.50084746, ..., -31.07915254,\n",
+       "         -30.06944915, -28.82122881],\n",
+       "        [-28.51614407, -28.47813559, -28.70733051, ..., -32.36279661,\n",
+       "         -30.97228814, -28.43313559],\n",
+       "        [-25.16995763, -25.59728814, -26.29101695, ..., -30.48627119,\n",
+       "         -27.90881356, -23.83478814],\n",
        "        ...,\n",
-       "        [-37.72314072, -37.68549347, -37.65416718, ..., -37.82619858,\n",
-       "         -37.79034424, -37.75682831],\n",
-       "        [-38.27464676, -38.26372528, -38.25014496, ..., -38.29218292,\n",
-       "         -38.29063797, -38.28456116],\n",
-       "        [-38.74358749, -38.74358749, -38.74358749, ..., -38.74358749,\n",
-       "         -38.74358749, -38.74358749]],\n",
-       "\n",
-       "       [[-54.29086304, -54.29086304, -54.29086304, ..., -54.29086304,\n",
-       "         -54.29086304, -54.29086304],\n",
-       "        [-51.11771393, -51.17523575, -51.23055267, ..., -50.93516541,\n",
-       "         -50.99657059, -51.05614471],\n",
-       "        [-50.31804657, -50.48666382, -50.64956665, ..., -49.79003143,\n",
-       "         -49.97007751, -50.14521027],\n",
+       "        [ 22.49648305,  21.70326271,  21.33059322, ...,  21.98830508,\n",
+       "          21.48665254,  20.91936441],\n",
+       "        [ 23.11813559,  22.87080508,  22.33012712, ...,  22.24177966,\n",
+       "          22.01559322,  21.52033898],\n",
+       "        [ 23.63673729,  23.56084746,  23.25      , ...,  22.92516949,\n",
+       "          22.73563559,  22.64847458]],\n",
+       "\n",
+       "       [[-15.6326087 , -15.84429348, -16.19054348, ..., -24.78972826,\n",
+       "         -23.77891304, -22.51769022],\n",
+       "        [-14.49692935, -14.45345109, -14.51820652, ..., -25.3298913 ,\n",
+       "         -23.72633152, -21.39619565],\n",
+       "        [-14.0205163 , -14.63826087, -15.10815217, ..., -25.11967391,\n",
+       "         -22.38184783, -18.55915761],\n",
        "...\n",
-       "        [-12.34277439, -12.2246685 , -12.10663223, ..., -12.74492168,\n",
-       "         -12.60908794, -12.47839165],\n",
-       "        [-13.12640381, -13.0661087 , -13.00387573, ..., -13.306077  ,\n",
-       "         -13.25871468, -13.19972038],\n",
-       "        [-14.28846931, -14.28846931, -14.28846931, ..., -14.28846931,\n",
-       "         -14.28846931, -14.28846931]],\n",
-       "\n",
-       "       [[-28.99049377, -28.99049377, -28.99049377, ..., -28.99049377,\n",
-       "         -28.99049377, -28.99049377],\n",
-       "        [-28.19291687, -28.22457886, -28.26130676, ..., -28.09593201,\n",
-       "         -28.12599182, -28.15802002],\n",
-       "        [-27.60740662, -27.7056427 , -27.80511475, ..., -27.31161499,\n",
-       "         -27.41082764, -27.50836182],\n",
+       "        [ 26.01706044,  25.27870879,  24.99217033, ...,  25.69024725,\n",
+       "          25.31126374,  24.87184066],\n",
+       "        [ 26.52486264,  26.35104396,  25.80881868, ...,  25.68881868,\n",
+       "          25.7889011 ,  25.55848901],\n",
+       "        [ 26.51387363,  26.54862637,  26.21848901, ...,  26.18178571,\n",
+       "          26.18318681,  26.29879121]],\n",
+       "\n",
+       "       [[-26.34846774, -26.2608871 , -26.3808871 , ..., -33.07903226,\n",
+       "         -32.06798387, -30.86830645],\n",
+       "        [-25.42      , -24.84927419, -24.40548387, ..., -34.53137097,\n",
+       "         -32.82782258, -30.17967742],\n",
+       "        [-23.18104839, -23.56475806, -23.57475806, ..., -35.44693548,\n",
+       "         -31.91258065, -26.92330645],\n",
        "        ...,\n",
-       "        [-24.25627136, -24.14059448, -24.03753662, ..., -24.61853027,\n",
-       "         -24.48849487, -24.36643982],\n",
-       "        [-24.62901306, -24.61338806, -24.54986572, ..., -24.75204468,\n",
-       "         -24.72160339, -24.66641235],\n",
-       "        [-25.28923035, -25.28923035, -25.28923035, ..., -25.28923035,\n",
-       "         -25.28923035, -25.28923035]]])</pre></div></div></li><li class='xr-section-item'><input id='section-a9ffb522-8d34-4ee8-bbc7-a7ca7b5f728c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a9ffb522-8d34-4ee8-bbc7-a7ca7b5f728c' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -88.75 -87.5 ... 88.75 90.0</div><input id='attrs-6c79cca8-0ee4-47b4-b464-0b6bc1d015a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c79cca8-0ee4-47b4-b464-0b6bc1d015a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac0efdbf-af21-4cd5-ad28-24ea869f2751' class='xr-var-data-in' type='checkbox'><label for='data-ac0efdbf-af21-4cd5-ad28-24ea869f2751' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</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><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-90.  , -88.75, -87.5 , -86.25, -85.  , -83.75, -82.5 , -81.25, -80.  ,\n",
-       "       -78.75, -77.5 , -76.25, -75.  , -73.75, -72.5 , -71.25, -70.  , -68.75,\n",
-       "       -67.5 , -66.25, -65.  , -63.75, -62.5 , -61.25, -60.  , -58.75, -57.5 ,\n",
-       "       -56.25, -55.  , -53.75, -52.5 , -51.25, -50.  , -48.75, -47.5 , -46.25,\n",
-       "       -45.  , -43.75, -42.5 , -41.25, -40.  , -38.75, -37.5 , -36.25, -35.  ,\n",
-       "       -33.75, -32.5 , -31.25, -30.  , -28.75, -27.5 , -26.25, -25.  , -23.75,\n",
-       "       -22.5 , -21.25, -20.  , -18.75, -17.5 , -16.25, -15.  , -13.75, -12.5 ,\n",
-       "       -11.25, -10.  ,  -8.75,  -7.5 ,  -6.25,  -5.  ,  -3.75,  -2.5 ,  -1.25,\n",
-       "         0.  ,   1.25,   2.5 ,   3.75,   5.  ,   6.25,   7.5 ,   8.75,  10.  ,\n",
-       "        11.25,  12.5 ,  13.75,  15.  ,  16.25,  17.5 ,  18.75,  20.  ,  21.25,\n",
-       "        22.5 ,  23.75,  25.  ,  26.25,  27.5 ,  28.75,  30.  ,  31.25,  32.5 ,\n",
-       "        33.75,  35.  ,  36.25,  37.5 ,  38.75,  40.  ,  41.25,  42.5 ,  43.75,\n",
-       "        45.  ,  46.25,  47.5 ,  48.75,  50.  ,  51.25,  52.5 ,  53.75,  55.  ,\n",
-       "        56.25,  57.5 ,  58.75,  60.  ,  61.25,  62.5 ,  63.75,  65.  ,  66.25,\n",
-       "        67.5 ,  68.75,  70.  ,  71.25,  72.5 ,  73.75,  75.  ,  76.25,  77.5 ,\n",
-       "        78.75,  80.  ,  81.25,  82.5 ,  83.75,  85.  ,  86.25,  87.5 ,  88.75,\n",
-       "        90.  ])</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'>0.0 1.875 3.75 ... 356.2 358.1</div><input id='attrs-36aceb10-32b1-4590-96d4-bfa0c46dfb55' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36aceb10-32b1-4590-96d4-bfa0c46dfb55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a41726b-5ab3-4308-9b94-8b79f3cc6830' class='xr-var-data-in' type='checkbox'><label for='data-5a41726b-5ab3-4308-9b94-8b79f3cc6830' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</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><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.   ,   1.875,   3.75 ,   5.625,   7.5  ,   9.375,  11.25 ,  13.125,\n",
-       "        15.   ,  16.875,  18.75 ,  20.625,  22.5  ,  24.375,  26.25 ,  28.125,\n",
-       "        30.   ,  31.875,  33.75 ,  35.625,  37.5  ,  39.375,  41.25 ,  43.125,\n",
-       "        45.   ,  46.875,  48.75 ,  50.625,  52.5  ,  54.375,  56.25 ,  58.125,\n",
-       "        60.   ,  61.875,  63.75 ,  65.625,  67.5  ,  69.375,  71.25 ,  73.125,\n",
-       "        75.   ,  76.875,  78.75 ,  80.625,  82.5  ,  84.375,  86.25 ,  88.125,\n",
-       "        90.   ,  91.875,  93.75 ,  95.625,  97.5  ,  99.375, 101.25 , 103.125,\n",
-       "       105.   , 106.875, 108.75 , 110.625, 112.5  , 114.375, 116.25 , 118.125,\n",
-       "       120.   , 121.875, 123.75 , 125.625, 127.5  , 129.375, 131.25 , 133.125,\n",
-       "       135.   , 136.875, 138.75 , 140.625, 142.5  , 144.375, 146.25 , 148.125,\n",
-       "       150.   , 151.875, 153.75 , 155.625, 157.5  , 159.375, 161.25 , 163.125,\n",
-       "       165.   , 166.875, 168.75 , 170.625, 172.5  , 174.375, 176.25 , 178.125,\n",
-       "       180.   , 181.875, 183.75 , 185.625, 187.5  , 189.375, 191.25 , 193.125,\n",
-       "       195.   , 196.875, 198.75 , 200.625, 202.5  , 204.375, 206.25 , 208.125,\n",
-       "       210.   , 211.875, 213.75 , 215.625, 217.5  , 219.375, 221.25 , 223.125,\n",
-       "       225.   , 226.875, 228.75 , 230.625, 232.5  , 234.375, 236.25 , 238.125,\n",
-       "       240.   , 241.875, 243.75 , 245.625, 247.5  , 249.375, 251.25 , 253.125,\n",
-       "       255.   , 256.875, 258.75 , 260.625, 262.5  , 264.375, 266.25 , 268.125,\n",
-       "       270.   , 271.875, 273.75 , 275.625, 277.5  , 279.375, 281.25 , 283.125,\n",
-       "       285.   , 286.875, 288.75 , 290.625, 292.5  , 294.375, 296.25 , 298.125,\n",
-       "       300.   , 301.875, 303.75 , 305.625, 307.5  , 309.375, 311.25 , 313.125,\n",
-       "       315.   , 316.875, 318.75 , 320.625, 322.5  , 324.375, 326.25 , 328.125,\n",
-       "       330.   , 331.875, 333.75 , 335.625, 337.5  , 339.375, 341.25 , 343.125,\n",
-       "       345.   , 346.875, 348.75 , 350.625, 352.5  , 354.375, 356.25 , 358.125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-faee9ce4-0942-420b-9b4f-2bf0bf11d3b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-faee9ce4-0942-420b-9b4f-2bf0bf11d3b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ccf6e25d-1425-4202-bd97-73c0c97c3a42' class='xr-var-data-in' type='checkbox'><label for='data-ccf6e25d-1425-4202-bd97-73c0c97c3a42' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>array(2.)</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'>object</div><div class='xr-var-preview xr-preview'>1850-01-01 00:00:00 ... 2015-01-...</div><input id='attrs-8b935383-03dc-4740-8a45-d7c763d7d39c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b935383-03dc-4740-8a45-d7c763d7d39c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cde76706-6e6c-4bb2-b308-965b3f774b2e' class='xr-var-data-in' type='checkbox'><label for='data-cde76706-6e6c-4bb2-b308-965b3f774b2e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeProlepticGregorian(1850, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1850, 4, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1850, 7, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       ...,\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 7, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 10, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2015, 1, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7200d624-17f1-405d-840a-4ddd8a34c2b5' class='xr-section-summary-in' type='checkbox'  ><label for='section-7200d624-17f1-405d-840a-4ddd8a34c2b5' 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-bc318f46-1b7e-4d14-b822-9f7850a71ff1' class='xr-index-data-in' type='checkbox'/><label for='index-bc318f46-1b7e-4d14-b822-9f7850a71ff1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -90.0, -88.75,  -87.5, -86.25,  -85.0, -83.75,  -82.5, -81.25,  -80.0,\n",
-       "       -78.75,\n",
-       "       ...\n",
-       "        78.75,   80.0,  81.25,   82.5,  83.75,   85.0,  86.25,   87.5,  88.75,\n",
-       "         90.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=145))</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-858958e5-7618-4c3d-8f64-9a34ff2422af' class='xr-index-data-in' type='checkbox'/><label for='index-858958e5-7618-4c3d-8f64-9a34ff2422af' 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([    0.0,   1.875,    3.75,   5.625,     7.5,   9.375,   11.25,  13.125,\n",
-       "          15.0,  16.875,\n",
-       "       ...\n",
-       "        341.25, 343.125,   345.0, 346.875,  348.75, 350.625,   352.5, 354.375,\n",
-       "        356.25, 358.125],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=192))</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-7ac49bd8-5b4f-4292-9b41-a8c75aa9bef6' class='xr-index-data-in' type='checkbox'/><label for='index-7ac49bd8-5b4f-4292-9b41-a8c75aa9bef6' 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([1850-01-01 00:00:00, 1850-04-01 00:00:00, 1850-07-01 00:00:00,\n",
-       "             1850-10-01 00:00:00, 1851-01-01 00:00:00, 1851-04-01 00:00:00,\n",
-       "             1851-07-01 00:00:00, 1851-10-01 00:00:00, 1852-01-01 00:00:00,\n",
-       "             1852-04-01 00:00:00,\n",
-       "             ...\n",
-       "             2012-10-01 00:00:00, 2013-01-01 00:00:00, 2013-04-01 00:00:00,\n",
-       "             2013-07-01 00:00:00, 2013-10-01 00:00:00, 2014-01-01 00:00:00,\n",
-       "             2014-04-01 00:00:00, 2014-07-01 00:00:00, 2014-10-01 00:00:00,\n",
-       "             2015-01-01 00:00:00],\n",
-       "            dtype=&#x27;object&#x27;,\n",
-       "            length=661,\n",
-       "            calendar=&#x27;proleptic_gregorian&#x27;,\n",
-       "            freq=&#x27;QS-OCT&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-81e3dac5-8cf0-4231-a0dc-69b1eac2a266' class='xr-section-summary-in' type='checkbox'  checked><label for='section-81e3dac5-8cf0-4231-a0dc-69b1eac2a266' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>operation :</span></dt><dd>temporal_avg</dd><dt><span>mode :</span></dt><dd>group_average</dd><dt><span>freq :</span></dt><dd>season</dd><dt><span>weighted :</span></dt><dd>True</dd><dt><span>dec_mode :</span></dt><dd>DJF</dd><dt><span>drop_incomplete_djf :</span></dt><dd>False</dd></dl></div></li></ul></div></div>"
+       "        [ 23.29919355,  22.54145161,  22.6083871 , ...,  23.37830645,\n",
+       "          23.0675    ,  22.66298387],\n",
+       "        [ 24.2958871 ,  24.28612903,  24.03177419, ...,  23.80258065,\n",
+       "          23.90830645,  23.57903226],\n",
+       "        [ 24.89733871,  25.07612903,  24.90967742, ...,  24.54758065,\n",
+       "          24.57322581,  24.56040323]]])</pre></div></div></li><li class='xr-section-item'><input id='section-26a157e5-ded5-495e-aa34-7f18ad5c1c49' class='xr-section-summary-in' type='checkbox'  checked><label for='section-26a157e5-ded5-495e-aa34-7f18ad5c1c49' 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'>float32</div><div class='xr-var-preview xr-preview'>75.0 72.5 70.0 ... 20.0 17.5 15.0</div><input id='attrs-3d8f90ae-d7f4-41da-a563-23ba9f447b1e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d8f90ae-d7f4-41da-a563-23ba9f447b1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc4fe08d-d00b-497a-9b61-c14be85f8229' class='xr-var-data-in' type='checkbox'><label for='data-cc4fe08d-d00b-497a-9b61-c14be85f8229' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd></dl></div><div class='xr-var-data'><pre>array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n",
+       "       45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n",
+       "       15. ], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>200.0 202.5 205.0 ... 327.5 330.0</div><input id='attrs-2cdda3cf-e165-40a4-87a9-90aa4a3d5f7b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2cdda3cf-e165-40a4-87a9-90aa4a3d5f7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc09e9e7-87d6-471a-abc6-7e4d9e41247a' class='xr-var-data-in' type='checkbox'><label for='data-fc09e9e7-87d6-471a-abc6-7e4d9e41247a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd></dl></div><div class='xr-var-data'><pre>array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
+       "       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
+       "       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
+       "       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
+       "       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
+       "       325. , 327.5, 330. ], dtype=float32)</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'>object</div><div class='xr-var-preview xr-preview'>2013-01-01 00:00:00 ... 2015-01-...</div><input id='attrs-12425dbb-ac3c-49b5-978c-355328e60451' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12425dbb-ac3c-49b5-978c-355328e60451' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ccd4b24f-2c23-47e5-8f83-ba44d90de406' class='xr-var-data-in' type='checkbox'><label for='data-ccd4b24f-2c23-47e5-8f83-ba44d90de406' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeGregorian(2013, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2013, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2013, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2013, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2015, 1, 1, 0, 0, 0, 0, has_year_zero=False)],\n",
+       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e4d1c398-a917-4853-ba7d-ac7d02a92c2e' class='xr-section-summary-in' type='checkbox'  ><label for='section-e4d1c398-a917-4853-ba7d-ac7d02a92c2e' 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-0e4bb5c9-7274-4c0f-9e38-96e9bf5955e5' class='xr-index-data-in' type='checkbox'/><label for='index-0e4bb5c9-7274-4c0f-9e38-96e9bf5955e5' 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([75.0, 72.5, 70.0, 67.5, 65.0, 62.5, 60.0, 57.5, 55.0, 52.5, 50.0, 47.5,\n",
+       "       45.0, 42.5, 40.0, 37.5, 35.0, 32.5, 30.0, 27.5, 25.0, 22.5, 20.0, 17.5,\n",
+       "       15.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;))</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-1ec637cb-32f0-4c41-be90-d4c9a368b564' class='xr-index-data-in' type='checkbox'/><label for='index-1ec637cb-32f0-4c41-be90-d4c9a368b564' 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([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0, 222.5,\n",
+       "       225.0, 227.5, 230.0, 232.5, 235.0, 237.5, 240.0, 242.5, 245.0, 247.5,\n",
+       "       250.0, 252.5, 255.0, 257.5, 260.0, 262.5, 265.0, 267.5, 270.0, 272.5,\n",
+       "       275.0, 277.5, 280.0, 282.5, 285.0, 287.5, 290.0, 292.5, 295.0, 297.5,\n",
+       "       300.0, 302.5, 305.0, 307.5, 310.0, 312.5, 315.0, 317.5, 320.0, 322.5,\n",
+       "       325.0, 327.5, 330.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a099483f-bda7-4fec-9259-255a15091276' class='xr-index-data-in' type='checkbox'/><label for='index-a099483f-bda7-4fec-9259-255a15091276' 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([2013-01-01 00:00:00, 2013-04-01 00:00:00, 2013-07-01 00:00:00,\n",
+       "             2013-10-01 00:00:00, 2014-01-01 00:00:00, 2014-04-01 00:00:00,\n",
+       "             2014-07-01 00:00:00, 2014-10-01 00:00:00, 2015-01-01 00:00:00],\n",
+       "            dtype=&#x27;object&#x27;, length=9, calendar=&#x27;standard&#x27;, freq=&#x27;QS-OCT&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-204f4310-d2a9-4315-bd3e-8a10e7c82c7a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-204f4310-d2a9-4315-bd3e-8a10e7c82c7a' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>operation :</span></dt><dd>temporal_avg</dd><dt><span>mode :</span></dt><dd>group_average</dd><dt><span>freq :</span></dt><dd>season</dd><dt><span>weighted :</span></dt><dd>True</dd><dt><span>dec_mode :</span></dt><dd>DJF</dd><dt><span>drop_incomplete_djf :</span></dt><dd>False</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'tas' (time: 661, lat: 145, lon: 192)> Size: 147MB\n",
-       "array([[[-32.70588303, -32.70588303, -32.70588303, ..., -32.70588303,\n",
-       "         -32.70588303, -32.70588303],\n",
-       "        [-30.99376678, -31.03758621, -31.08932686, ..., -30.84562302,\n",
-       "         -30.89412689, -30.94400978],\n",
-       "        [-30.0251503 , -30.14543724, -30.26419067, ..., -29.66037178,\n",
-       "         -29.78108025, -29.90287781],\n",
+       "<xarray.DataArray 'air' (time: 9, lat: 25, lon: 53)> Size: 95kB\n",
+       "array([[[-30.45474576, -30.36682203, -30.50084746, ..., -31.07915254,\n",
+       "         -30.06944915, -28.82122881],\n",
+       "        [-28.51614407, -28.47813559, -28.70733051, ..., -32.36279661,\n",
+       "         -30.97228814, -28.43313559],\n",
+       "        [-25.16995763, -25.59728814, -26.29101695, ..., -30.48627119,\n",
+       "         -27.90881356, -23.83478814],\n",
        "        ...,\n",
-       "        [-37.72314072, -37.68549347, -37.65416718, ..., -37.82619858,\n",
-       "         -37.79034424, -37.75682831],\n",
-       "        [-38.27464676, -38.26372528, -38.25014496, ..., -38.29218292,\n",
-       "         -38.29063797, -38.28456116],\n",
-       "        [-38.74358749, -38.74358749, -38.74358749, ..., -38.74358749,\n",
-       "         -38.74358749, -38.74358749]],\n",
-       "\n",
-       "       [[-54.29086304, -54.29086304, -54.29086304, ..., -54.29086304,\n",
-       "         -54.29086304, -54.29086304],\n",
-       "        [-51.11771393, -51.17523575, -51.23055267, ..., -50.93516541,\n",
-       "         -50.99657059, -51.05614471],\n",
-       "        [-50.31804657, -50.48666382, -50.64956665, ..., -49.79003143,\n",
-       "         -49.97007751, -50.14521027],\n",
+       "        [ 22.49648305,  21.70326271,  21.33059322, ...,  21.98830508,\n",
+       "          21.48665254,  20.91936441],\n",
+       "        [ 23.11813559,  22.87080508,  22.33012712, ...,  22.24177966,\n",
+       "          22.01559322,  21.52033898],\n",
+       "        [ 23.63673729,  23.56084746,  23.25      , ...,  22.92516949,\n",
+       "          22.73563559,  22.64847458]],\n",
+       "\n",
+       "       [[-15.6326087 , -15.84429348, -16.19054348, ..., -24.78972826,\n",
+       "         -23.77891304, -22.51769022],\n",
+       "        [-14.49692935, -14.45345109, -14.51820652, ..., -25.3298913 ,\n",
+       "         -23.72633152, -21.39619565],\n",
+       "        [-14.0205163 , -14.63826087, -15.10815217, ..., -25.11967391,\n",
+       "         -22.38184783, -18.55915761],\n",
        "...\n",
-       "        [-12.34277439, -12.2246685 , -12.10663223, ..., -12.74492168,\n",
-       "         -12.60908794, -12.47839165],\n",
-       "        [-13.12640381, -13.0661087 , -13.00387573, ..., -13.306077  ,\n",
-       "         -13.25871468, -13.19972038],\n",
-       "        [-14.28846931, -14.28846931, -14.28846931, ..., -14.28846931,\n",
-       "         -14.28846931, -14.28846931]],\n",
-       "\n",
-       "       [[-28.99049377, -28.99049377, -28.99049377, ..., -28.99049377,\n",
-       "         -28.99049377, -28.99049377],\n",
-       "        [-28.19291687, -28.22457886, -28.26130676, ..., -28.09593201,\n",
-       "         -28.12599182, -28.15802002],\n",
-       "        [-27.60740662, -27.7056427 , -27.80511475, ..., -27.31161499,\n",
-       "         -27.41082764, -27.50836182],\n",
+       "        [ 26.01706044,  25.27870879,  24.99217033, ...,  25.69024725,\n",
+       "          25.31126374,  24.87184066],\n",
+       "        [ 26.52486264,  26.35104396,  25.80881868, ...,  25.68881868,\n",
+       "          25.7889011 ,  25.55848901],\n",
+       "        [ 26.51387363,  26.54862637,  26.21848901, ...,  26.18178571,\n",
+       "          26.18318681,  26.29879121]],\n",
+       "\n",
+       "       [[-26.34846774, -26.2608871 , -26.3808871 , ..., -33.07903226,\n",
+       "         -32.06798387, -30.86830645],\n",
+       "        [-25.42      , -24.84927419, -24.40548387, ..., -34.53137097,\n",
+       "         -32.82782258, -30.17967742],\n",
+       "        [-23.18104839, -23.56475806, -23.57475806, ..., -35.44693548,\n",
+       "         -31.91258065, -26.92330645],\n",
        "        ...,\n",
-       "        [-24.25627136, -24.14059448, -24.03753662, ..., -24.61853027,\n",
-       "         -24.48849487, -24.36643982],\n",
-       "        [-24.62901306, -24.61338806, -24.54986572, ..., -24.75204468,\n",
-       "         -24.72160339, -24.66641235],\n",
-       "        [-25.28923035, -25.28923035, -25.28923035, ..., -25.28923035,\n",
-       "         -25.28923035, -25.28923035]]])\n",
+       "        [ 23.29919355,  22.54145161,  22.6083871 , ...,  23.37830645,\n",
+       "          23.0675    ,  22.66298387],\n",
+       "        [ 24.2958871 ,  24.28612903,  24.03177419, ...,  23.80258065,\n",
+       "          23.90830645,  23.57903226],\n",
+       "        [ 24.89733871,  25.07612903,  24.90967742, ...,  24.54758065,\n",
+       "          24.57322581,  24.56040323]]])\n",
        "Coordinates:\n",
-       "  * lat      (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
-       "  * lon      (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
-       "    height   float64 8B 2.0\n",
-       "  * time     (time) object 5kB 1850-01-01 00:00:00 ... 2015-01-01 00:00:00\n",
+       "  * lat      (lat) float32 100B 75.0 72.5 70.0 67.5 65.0 ... 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 212B 200.0 202.5 205.0 207.5 ... 325.0 327.5 330.0\n",
+       "  * time     (time) object 72B 2013-01-01 00:00:00 ... 2015-01-01 00:00:00\n",
        "Attributes:\n",
        "    operation:            temporal_avg\n",
        "    mode:                 group_average\n",
@@ -3197,7 +2908,7 @@
     }
    ],
    "source": [
-    "ds_season.tas"
+    "ds_season.air"
    ]
   },
   {
@@ -3253,6 +2964,7 @@
        "}\n",
        "\n",
        "html[theme=dark],\n",
+       "html[data-theme=dark],\n",
        "body[data-theme=dark],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -3303,7 +3015,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -3311,7 +3023,8 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
@@ -3323,6 +3036,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -3585,70 +3302,63 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;time&#x27; (time: 661)&gt; Size: 5kB\n",
-       "array([cftime.DatetimeProlepticGregorian(1850, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1850, 4, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1850, 7, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       ...,\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 7, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 10, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2015, 1, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;time&#x27; (time: 9)&gt; Size: 72B\n",
+       "array([cftime.DatetimeGregorian(2013, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2013, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2013, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2013, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2015, 1, 1, 0, 0, 0, 0, has_year_zero=False)],\n",
        "      dtype=object)\n",
        "Coordinates:\n",
-       "    height   float64 8B 2.0\n",
-       "  * time     (time) object 5kB 1850-01-01 00:00:00 ... 2015-01-01 00:00:00\n",
+       "  * time     (time) object 72B 2013-01-01 00:00:00 ... 2015-01-01 00:00:00\n",
        "Attributes:\n",
-       "    bounds:         time_bnds\n",
-       "    axis:           T\n",
-       "    long_name:      time\n",
        "    standard_name:  time\n",
-       "    _ChunkSizes:    1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'time'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 661</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2e85201e-4915-4bf6-b69b-96e8294e4d2c' class='xr-array-in' type='checkbox' checked><label for='section-2e85201e-4915-4bf6-b69b-96e8294e4d2c' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>1850-01-01 00:00:00 1850-04-01 00:00:00 ... 2015-01-01 00:00:00</span></div><div class='xr-array-data'><pre>array([cftime.DatetimeProlepticGregorian(1850, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1850, 4, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1850, 7, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       ...,\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 7, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 10, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2015, 1, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></div></li><li class='xr-section-item'><input id='section-919a3813-174f-4c30-a270-3c061f72d4f9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-919a3813-174f-4c30-a270-3c061f72d4f9' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-e81f99b9-6fd9-4d1e-9cf8-d9ddda39c096' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e81f99b9-6fd9-4d1e-9cf8-d9ddda39c096' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-934bda2f-a218-4f99-b056-60f846718874' class='xr-var-data-in' type='checkbox'><label for='data-934bda2f-a218-4f99-b056-60f846718874' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>array(2.)</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'>object</div><div class='xr-var-preview xr-preview'>1850-01-01 00:00:00 ... 2015-01-...</div><input id='attrs-f8c4e3f2-4518-4a3d-8cbb-cd90f4a0974d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8c4e3f2-4518-4a3d-8cbb-cd90f4a0974d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a6d514a-0df5-41bf-af04-7d4aed5ed379' class='xr-var-data-in' type='checkbox'><label for='data-5a6d514a-0df5-41bf-af04-7d4aed5ed379' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeProlepticGregorian(1850, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1850, 4, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1850, 7, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       ...,\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 7, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 10, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2015, 1, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d985cdfc-fc47-49cd-86f2-cfea82dfcb76' class='xr-section-summary-in' type='checkbox'  ><label for='section-d985cdfc-fc47-49cd-86f2-cfea82dfcb76' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0ab80f26-3ab8-478e-81b8-dae27858beca' class='xr-index-data-in' type='checkbox'/><label for='index-0ab80f26-3ab8-478e-81b8-dae27858beca' 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([1850-01-01 00:00:00, 1850-04-01 00:00:00, 1850-07-01 00:00:00,\n",
-       "             1850-10-01 00:00:00, 1851-01-01 00:00:00, 1851-04-01 00:00:00,\n",
-       "             1851-07-01 00:00:00, 1851-10-01 00:00:00, 1852-01-01 00:00:00,\n",
-       "             1852-04-01 00:00:00,\n",
-       "             ...\n",
-       "             2012-10-01 00:00:00, 2013-01-01 00:00:00, 2013-04-01 00:00:00,\n",
-       "             2013-07-01 00:00:00, 2013-10-01 00:00:00, 2014-01-01 00:00:00,\n",
-       "             2014-04-01 00:00:00, 2014-07-01 00:00:00, 2014-10-01 00:00:00,\n",
-       "             2015-01-01 00:00:00],\n",
-       "            dtype=&#x27;object&#x27;,\n",
-       "            length=661,\n",
-       "            calendar=&#x27;proleptic_gregorian&#x27;,\n",
-       "            freq=&#x27;QS-OCT&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9f1bb52c-4689-4b9b-b72f-008f8a0b6560' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9f1bb52c-4689-4b9b-b72f-008f8a0b6560' 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>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div></li></ul></div></div>"
+       "    long_name:      Time\n",
+       "    bounds:         time_bnds</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'time'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 9</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-60103ed1-a36c-4db2-8754-1ea483511b89' class='xr-array-in' type='checkbox' checked><label for='section-60103ed1-a36c-4db2-8754-1ea483511b89' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>2013-01-01 00:00:00 2013-04-01 00:00:00 ... 2015-01-01 00:00:00</span></div><div class='xr-array-data'><pre>array([cftime.DatetimeGregorian(2013, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2013, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2013, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2013, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2015, 1, 1, 0, 0, 0, 0, has_year_zero=False)],\n",
+       "      dtype=object)</pre></div></div></li><li class='xr-section-item'><input id='section-f491df92-e212-40b2-9ad3-193dcefbc0ef' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f491df92-e212-40b2-9ad3-193dcefbc0ef' class='xr-section-summary' >Coordinates: <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 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'>2013-01-01 00:00:00 ... 2015-01-...</div><input id='attrs-2b393463-fcda-4fec-b7d0-09a4893c6502' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b393463-fcda-4fec-b7d0-09a4893c6502' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2f1bb2d-de0e-4de8-a715-b3ea2b6363b7' class='xr-var-data-in' type='checkbox'><label for='data-a2f1bb2d-de0e-4de8-a715-b3ea2b6363b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeGregorian(2013, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2013, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2013, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2013, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2015, 1, 1, 0, 0, 0, 0, has_year_zero=False)],\n",
+       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-460305e9-f1b4-4e06-950c-3273be23751f' class='xr-section-summary-in' type='checkbox'  ><label for='section-460305e9-f1b4-4e06-950c-3273be23751f' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c2775747-54e1-4b80-aa39-d1865475e0ad' class='xr-index-data-in' type='checkbox'/><label for='index-c2775747-54e1-4b80-aa39-d1865475e0ad' 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([2013-01-01 00:00:00, 2013-04-01 00:00:00, 2013-07-01 00:00:00,\n",
+       "             2013-10-01 00:00:00, 2014-01-01 00:00:00, 2014-04-01 00:00:00,\n",
+       "             2014-07-01 00:00:00, 2014-10-01 00:00:00, 2015-01-01 00:00:00],\n",
+       "            dtype=&#x27;object&#x27;, length=9, calendar=&#x27;standard&#x27;, freq=&#x27;QS-OCT&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5edf2277-410a-4d9d-be8d-772a55999fc0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5edf2277-410a-4d9d-be8d-772a55999fc0' 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>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></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'time' (time: 661)> Size: 5kB\n",
-       "array([cftime.DatetimeProlepticGregorian(1850, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1850, 4, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(1850, 7, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       ...,\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 7, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 10, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2015, 1, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
+       "<xarray.DataArray 'time' (time: 9)> Size: 72B\n",
+       "array([cftime.DatetimeGregorian(2013, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2013, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2013, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2013, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2015, 1, 1, 0, 0, 0, 0, has_year_zero=False)],\n",
        "      dtype=object)\n",
        "Coordinates:\n",
-       "    height   float64 8B 2.0\n",
-       "  * time     (time) object 5kB 1850-01-01 00:00:00 ... 2015-01-01 00:00:00\n",
+       "  * time     (time) object 72B 2013-01-01 00:00:00 ... 2015-01-01 00:00:00\n",
        "Attributes:\n",
-       "    bounds:         time_bnds\n",
-       "    axis:           T\n",
-       "    long_name:      time\n",
        "    standard_name:  time\n",
-       "    _ChunkSizes:    1"
+       "    long_name:      Time\n",
+       "    bounds:         time_bnds"
       ]
      },
      "execution_count": 11,
@@ -3660,88 +3370,10 @@
     "ds_season.time"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Visualize averages derived from monthly data on a specific point\n"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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C6N/guOOOy9v2qU99ilit1rz7VSGKjYmQ4kH31Pvd5ZdfTgCQu+66K2/7cccdl7dAVO73thAej4cAIHfffXfRfWZzP5bPj1/96lfT3mfqPa7c6+D27dsJAPLkk08WHSODwZgZJi9nMJYQLpcL//rXv7Bt2zZ873vfw2WXXYb9+/fjpptuwrHHHqtIpJ977jlkMhl89KMfRSaTUX70ej3OPvtsvPjii8p7RiIRfPWrX8WKFSugVquhVqthNpsRjUaxZ88eZb9TTjkFf/vb3/C1r30NL774IuLxeN7Y9u3bh5GREXzkIx/Jk7qbzWa85z3vwZYtW6ZJDd/1rnfl/X/dunVIJBJ5Ms3XXnsN73rXu+ByuaBSqaDRaPDRj34UgiCUJeGeysTEBD75yU+io6MDarUaGo0GXV1dAJD3+5Y7xhdeeAEA8KEPfShvv6uuuqqs8SxbtgyXXHIJ7rnnHkVq/fDDD8Pr9eIzn/mMst/TTz+Nc889F62trXl/03e84x0AgM2bNyv7btq0CW9729tgs9mUY/bNb34TXq93mgR23bp1WLVq1byNsxjljEmtVuPDH/4wnnjiCUXCLwgCfvvb3+Kyyy6Dy+VSjgXHcfjwhz+cdyyam5uxfv36vPMbABwOB84777xpYzp8+DCuuuoqNDc3K2M6++yzAWTPhX379iny1Fw6Oztx5pln5m17+umnYbfbcemll+aN67jjjkNzc7MyruOOOw5arRaf+MQn8Otf/3qaFLQYxc6197///dNKTmZzvgD0PNdoNDOO4dprr0U8Hs8zbnzggQeg0+mUc/7gwYPYu3evMs7cz3/nO9+J0dFRReL8wgsv4Pzzz0dTU5PyfiqVCh/4wAfKOialKPR337RpE44++uhp9d8bNmwAIQSbNm3K2/7Od74z73p21FFHAcA0czl5+8DAwIzj4jgO73znO5X/q9VqrFixAi0tLTj++OOV7U6nE42NjXmlPps2bcL555+Pjo6OaeOPxWLTSnkKXb8ATCsfKsXFF18MlUo16/f4/Oc/jx07duCll14CQMtwfvvb3+Lqq6+G2Wwu+/PL5ZJLLsn7f6m/Ve7Yy/3eFsLpdGL58uW48847cdddd+G1116bVh4zm/uxzHve854Zf99yr4MrVqyAw+HAV7/6Vdx3333YvXv3jO/NYDCmw4JuBmMJctJJJ+GrX/0qfv/732NkZARf/OIX0dfXp5ipjY+PAwBOPvlkaDSavJ/HHnssr375qquuws9+9jN87GMfw3PPPYetW7di27ZtaGhoyAusf/KTn+CrX/0qnnzySZx77rlwOp24/PLLceDAAQC03hygjsdTaW1thSiK8Pv9edvlIEpGp9MBgPK5AwMDeMtb3oLh4WH8+Mc/VhYcfv7zn+ftVy6iKOLCCy/EE088gRtvvBH/+Mc/sHXrVqU+sdD7zTRGr9cLtVo9bb/m5uayx/X5z38eBw4cwMaNGwHQOvbTTz89r4Z5fHwcf/nLX6b9PeXaSPlvunXrVlx44YUAaB3ySy+9hG3btuHmm28u+DsW+nsdyTgLMZsxXXvttUgkEnj00UcB0Anr6OgorrnmmrxjQQhBU1PTtOOxZcuWafX5hX7HSCSCt7zlLfjPf/6Db3/723jxxRexbds2PPHEE3ljks/r3KBQZuq28fFxBAIBaLXaaeMaGxtTxrV8+XL8/e9/R2NjI66//nosX74cy5cvx49//OOSx1Eey9Rzq9D5V+75UuoYFeKYY47BySefjAceeAAAXRT53e9+h8suuwxOp1P5bAD48pe/PO3zP/3pT+d9vtfrLfhdmc33pxiFfiev11v0GiU/n4v8O8lotdqS2xOJxIzjMhqN0Ov1014/9T3l7bnvOdvxz3T9Koe5vsdll12G7u5u5Xr94IMPIhqN4vrrry/7s2fDbP5Wuce03O9tIWSPg4suugh33HEHTjjhBDQ0NOBzn/scwuGw8v5AefdjgJ4fVqt1xt+33OugzWbD5s2bcdxxx+HrX/86jjnmGLS2tuKWW26ZszcKg7EUYe7lDMYSR6PR4JZbbsGPfvQj7Ny5EwDgdrsBAH/4wx+ULG4hgsEgnn76adxyyy342te+pmxPJpPw+Xx5+5pMJtx222247bbbMD4+rmS9L730Uuzdu1eZmI2Ojk77nJGREfA8D4fDMavf7cknn0Q0GsUTTzyR93tMNagpl507d+L111/Hgw8+iKuvvlrZfvDgwTm9H0AnpJlMBl6vN29yOjY2VvZ7nHfeeVi7di1+9rOfwWw249VXX8Xvfve7vH3cbjfWrVuH73znOwXfQ550P/roo9BoNHj66afzJvZPPvlkwdfNprduOeMsxGzGJGchH3jgAVx33XV44IEH0NraqgTtAD0WHMfhX//6lxIA5DJ1W6HfcdOmTRgZGcGLL76oZLcBTOvxLP9N5YlzLlP/xm63Gy6XC88+++y0fQHktbJ6y1vegre85S0QBAHbt2/HT3/6U3zhC19AU1MTPvjBDxZ8vTyWsbExtLW1Kdvl82/qWMo5X2Rmcx5cc801+PSnP409e/bg8OHD0xZF5OvPTTfdhHe/+90F32P16tXK71TouzKb708xCv1OLper6DUKyI69Wqml8fM8j+uvvx5f//rX8cMf/hD33HMPzj//fOVvXy3M5ntbiK6uLtx///0AgP379+Pxxx/HrbfeilQqhfvuu6/s+7FMud/F2VwHjz32WDz66KMghOCNN97Agw8+iG9961swGAx5934Gg1EcFnQzGEuI0dHRglkOWQorT6QvuugiqNVqHDp0qKRMjeM4EEKm3bD/7//+D4IgFH1dU1MTNmzYgNdffx133303YrEYVq9ejba2Njz88MP48pe/rEwcotEo/vjHPyqO5rNBfo/c8RFC8L//+7+zep9S7wcAv/jFL+b0fgBw7rnn4o477sBDDz2Ez33uc8r2hx9+eFbv87nPfQ6f/OQnEQwG0dTUhPe97315z19yySX461//iuXLl5dcvOA4Dmq1Ok8OGo/H8dvf/nZW45nrOOdjTNdccw0+9alP4d///jf+8pe/4IYbbsh77SWXXILvfe97GB4enib7Lpdyz4XVq1ejubkZjz/+OG644QZl+8DAAF5++eW84PWSSy7Bo48+CkEQcOqpp5Y1DpVKhVNPPRVr1qzBQw89hFdffbVo0H3OOecAAB566CGceOKJyvbHH398moN9uefLXLjyyitxww034MEHH8Thw4fR1taWtyiyevVqrFy5Eq+//jq++93vlnyvc889F0899RTGx8cV5YAgCHny9fnk/PPPx+23345XX301T6Hxm9/8BhzH4dxzz12Qz50vzj//fPzpT3/CyMhI3rn3m9/8BkajcU7ttHQ63axVQ+XysY99DLfeeis+9KEPYd++ffj+979f8TFNZS7f22KsWrUK3/jGN/DHP/4Rr776KoDy78ezZS7XQY7jsH79evzoRz/Cgw8+qIyRwWDMDAu6GYwlxEUXXYT29nZceumlWLNmDURRxI4dO/DDH/4QZrMZn//85wHQVlDf+ta3cPPNN+Pw4cN4+9vfDofDgfHxcWzdulXJWlutVrz1rW/FnXfeCbfbje7ubmzevBn3338/7HZ73mefeuqpuOSSS7Bu3To4HA7s2bMHv/3tb/OC6TvuuAMf+tCHcMkll+C6665DMpnEnXfeiUAgoLTEmQ0XXHABtFotrrzyStx4441IJBK49957p8nUy2XNmjVYvnw5vva1r4EQAqfTib/85S+KXHouXHjhhXjrW9+KG2+8EdFoFCeddBJeeumlWQe5H/7wh3HTTTfhn//8J77xjW8o0kiZb33rW9i4cSPOOOMMfO5zn8Pq1auRSCTQ19eHv/71r7jvvvvQ3t6Oiy++GHfddReuuuoqfOITn4DX68UPfvCDgpmQuTDTOAsx2zHJQd2VV16JZDKptIGSOfPMM/GJT3wC11xzDbZv3463vvWtMJlMGB0dxb///W8ce+yx+NSnPlVyTGeccQYcDgc++clP4pZbboFGo8FDDz2E119/PW8/nudx22234brrrsN73/teXHvttQgEArjtttvQ0tKSV+/7wQ9+EA899BDe+c534vOf/zxOOeUUaDQaDA0N4YUXXsBll12GK664Avfddx82bdqEiy++GJ2dnUgkEvjVr34FAHjb295WdMxHHXUUPvzhD+Puu++GRqPB2972NuzcuRM/+MEPpslRyz1f5oLdbscVV1yBBx98EIFAAF/+8pentSz8xS9+gXe84x246KKLsGHDBrS1tcHn82HPnj149dVX8fvf/x4A8I1vfANPPfUUzjvvPHzzm9+E0WjEz3/+84Jt2+aDL37xi/jNb36Diy++GN/61rfQ1dWFZ555Bvfccw8+9alPleVvUEluueUWpV7/m9/8JpxOJx566CE888wzuOOOO2Cz2Wb9nsceeyyeeOIJ3HvvvTjxxBPB8zxOOumkeRmv3W7HRz/6Udx7773o6urCpZdeWvExTaXc720h3njjDXzmM5/B+973PqxcuRJarRabNm3CG2+8oWSQy70fz5Zyr4NPP/007rnnHlx++eVYtmwZCCF44oknEAgEcMEFFxzRsWMwlhSVcnBjMBiLz2OPPUauuuoqsnLlSmI2m4lGoyGdnZ3kIx/5SMHWH08++SQ599xzidVqJTqdjnR1dZH3vve95O9//7uyz9DQEHnPe95DHA4HsVgs5O1vfzvZuXPnNPfqr33ta+Skk04iDoeD6HQ6smzZMvLFL35xWquoJ598kpx66qlEr9cTk8lEzj//fPLSSy/l7SM73ea2JCMk60Kb61j8l7/8haxfv57o9XrS1tZGvvKVryiO4bktWcp1L9+9eze54IILiMViIQ6Hg7zvfe8jAwMD0xxxZzPGQCBArr32WmK324nRaCQXXHAB2bt3b1nu5bls2LCBqNVqMjQ0VPD5yclJ8rnPfY709PQQjUZDnE4nOfHEE8nNN99MIpGIst+vfvUrsnr1auXvdPvtt5P7779/2ri7urrIxRdfXPCzpv79ZzPOQpQ7JpmrrrqKACBnnnlmyfc89dRTiclkIgaDgSxfvpx89KMfzWuxdPbZZ5Njjjmm4OtffvllcvrppxOj0UgaGhrIxz72MfLqq69Oc+wmhJBf/vKXZMWKFUSr1ZJVq1aRX/3qV+Syyy4jxx9/fN5+6XSa/OAHP1DOWbPZTNasWUOuu+46cuDAAUIIdR2+4oorSFdXF9HpdMTlcpGzzz6bPPXUUzMex2QySb70pS+RxsZGotfryWmnnUZeeeWVgn+vcs4X2aH6zjvvnPZZhdzLZZ5//nmli8L+/fsLjvX1118n73//+0ljYyPRaDSkubmZnHfeeeS+++7L2++ll14ip512GtHpdKS5uZl85StfIb/85S+P2L282N+9v7+fXHXVVcTlchGNRkNWr15N7rzzzjyX8mLHRXYF//3vf1/WOKZy9dVXE5PJNG17sfEW+o6++eab5NJLLyU2m41otVqyfv36aX+jYuMs9Df1+Xzkve99L7Hb7YTjOMWFvNS5Uex6WYgXX3yRACDf+973Cj5fiGJjKvTZxY59sWt4ob9BOd/bQoyPj5MNGzaQNWvWEJPJRMxmM1m3bh350Y9+lOf4Tkh59+Ni54f8XKF73EzXwb1795Irr7ySLF++nBgMBmKz2cgpp5xCHnzwwaK/F4PBmA5HiGQjy2AwGIyaJZVKobu7G2eddRYef/zxSg+nKLUyzoUmEAhg1apVuPzyy/HLX/6y0sNhMKqWL33pS7j33nsxODg4zZSNwWAwagUmL2cwGIwaZnJyEvv27cMDDzyA8fHxqjW1qZVxLgRjY2P4zne+g3PPPRculwv9/f340Y9+hHA4rJR0MBiMfLZs2YL9+/fjnnvuwXXXXccCbgaDUdOwoJvBYDBqmGeeeQbXXHMNWlpacM8998zYfqtS1Mo4FwKdToe+vj58+tOfhs/nUwyr7rvvPqUFF4PByEf2+7jkkkvw7W9/u9LDYTAYjCOCycsZDAaDwWAwGAwGg8FYIPiZd2EwGAwGg8FgMBgMBoMxF1jQzWAwGAwGg8FgMBgMxgLBgm4Gg8FgMBgMBoPBYDAWiLo3UhNFESMjI7BYLOA4rtLDYTAYDAaDwWAwGAxGHUAIQTgcRmtrK3i+eD677oPukZERdHR0VHoYDAaDwWAwGAwGg8GoQwYHB9He3l70+boPui0WCwB6IKxWa4VHw2AwGAwGg8FgMBiMeiAUCqGjo0OJOYtR90G3LCm3Wq0s6GYwGAwGg8FgMBgMxrwyUxkzM1JjMBgMBoPBYDAYDAZjgWBBN4PBYDAYDAaDwWAwGAsEC7oZDAaDwWAwGAwGg8FYIFjQzWAwGAwGg8FgMBgMxgLBgm4Gg8FgMBgMBoPBYDAWCBZ0MxgMBoPBYDBmRTCWRjCWrvQwGAwGoyZgQTeDwWAwGAwGo2x80RR+959+PLx1AKmMWOnhMBgMRtXDgm4Gg8FgMBgMRlmIIsFzu8aQyohIpAVMRpKVHhKDwWBUPSzoZjAYDAaDwWCUxdY+H8aCCeX/46FEib0ZDAaDAbCgm8FgMBgMBoNRBuOhBP5z2AcAaLLqAQATLOhmMBiMGWFBN4PBYDAYDAajJGlBxHO7xiASglVNFpy+3AUAmAgzeTmDwWDMhLrSA2AwGAwGg8FgVDcvHfTAG0nBpFPhvDWNEAkBQE3VkhkBOrWqwiNkMBiM6oVluhkMBoPBYDAYRRn0xfDaQAAAcMHRzTBoVTDp1LDo1SAEmGTZbgaDwSgJC7oZDAaDwWAwGAVJpAU8t2sMALCu3YYet0l5Tq7rHg+xoJvBYDBKUdGg+95778W6detgtVphtVpx+umn429/+5vyPCEEt956K1pbW2EwGHDOOedg165dFRwxg8FgMBgMxtLhxX2TCCcysBs1eMvKhrznmJkag8FglEdFg+729nZ873vfw/bt27F9+3acd955uOyyy5TA+o477sBdd92Fn/3sZ9i2bRuam5txwQUXIBwOV3LYDAaDwWAwGHXPwYkw9oyGwHHARcc0Q6vOnzY2WnQAWNswBoPBmImKBt2XXnop3vnOd2LVqlVYtWoVvvOd78BsNmPLli0ghODuu+/GzTffjHe/+91Yu3Ytfv3rXyMWi+Hhhx+u5LAZDAaDwWAw6ppEWsDf90wAAE7udqLVbpi2j5zp9sfSSKSFRR0fg8Fg1BJVU9MtCAIeffRRRKNRnH766ejt7cXY2BguvPBCZR+dToezzz4bL7/8ctH3SSaTCIVCeT8MBoPBYDAYjPIZ8scQTwmwGTQ4bZmr4D4GrQpWgwYAM1NjMBiMUlQ86H7zzTdhNpuh0+nwyU9+En/6059w9NFHY2yMmnY0NTXl7d/U1KQ8V4jbb78dNptN+eno6FjQ8TMYDAaDwWDUG4P+OACg222EiueK7tdkZRJzBoPBmImKB92rV6/Gjh07sGXLFnzqU5/C1Vdfjd27dyvPc1z+hZ4QMm1bLjfddBOCwaDyMzg4uGBjZzAYDAaDwahHhqWgu91hLLkfczBnMBiMmVFXegBarRYrVqwAAJx00knYtm0bfvzjH+OrX/0qAGBsbAwtLS3K/hMTE9Oy37nodDrodLqFHTSDwWAwGAxGnZJIC/BEaBDdVqCWO5cmixx0s0w3g8FgFKPime6pEEKQTCbR09OD5uZmbNy4UXkulUph8+bNOOOMMyo4QgaDwWAwGIw6hBBAFDAciIMQwGnSwqQrnZ9plOTlwTgzU2MwGIxiVDTT/fWvfx3veMc70NHRgXA4jEcffRQvvvginn32WXAchy984Qv47ne/i5UrV2LlypX47ne/C6PRiKuuuqqSw2YwGAwGg8GoPw7+HRjdgXHbxQDUM2a5AUCvUcFu1CAQS2MilESnq7QcncFgMJYiFQ26x8fH8ZGPfASjo6Ow2WxYt24dnn32WVxwwQUAgBtvvBHxeByf/vSn4ff7ceqpp+L555+HxWKp5LAZDAZjSSKIBKmMCINWVemhMBiM+YYQYHwXIGSQGngV0J+CdufMQTdA67oDsTTGwwkWdJeg3xvFgfEIzlrphl7DrqMMxlKiokH3/fffX/J5juNw66234tZbb12cATEYDAajIKJI8Mf/DmE0mMCFxzThqBZrpYdU8xycCOOlg16cucKFFY1sMZlRYeJ+IB1HRhSh8u4DWk8qK9MNAI0WHfaNhVlddwmCsTSefmMUqYwIngfOW1Pcn4jBYNQfVVfTzWAwGIzqY8dQAMOBOERC8NyuMeweCVV6SDXP64NB+KIpPP3GKHYOBys9nLomlsrgjaEA6yVditAIACCcyEAtxNHOTcKi15T1UuZgXhpRJHh2Fw24AeDNoRD80VSFR1UbTIQS+POOYYwG45UeCoNxRLCgm8FgMBglCSfSeOWQFwCdXBMCPL97DLtGWKA4VwghGJOygoQAG3ePY3ufr8Kjqj88kSSe3zWG+//Vi3/smcCzO0crPaTqRQq6Q4kMAGAFym+52mChZmqheBrxFDNTm8q2Ph9GAglo1Tza7AaIhOClQ55KD6sm2NLrw+HJKJ58bQQ+tlBRMQKxFJ58bRgvHWTn7VxhQTdjzuwYDODXL/exzAGDUee8uG8SqYyIVrseHzy5A+s7bEqgyDK0c8MXTSGVEaFRcTixywEA+NcBD/51YBKEkAqPrrYhhKDXE8UTrw7ht6/0Y9dICBmRHlOvdNwZBQgNAQD6tasAAK3pAUAsL4DWa1RwGGlWnEnM8xkPJbDlMF1QO3d1I847qhEcBxwYj7Ds7QykBRED3igA2sbuydeGEUtlKjyqpcfesRAe+s8Aej1RbOvzsWvoHGFBN2NOEEKwvc8HXzSFTXvH2SSRwahTDk1GcHAiAp7jcN6aJvA8h3NXN+K4DjsIAf6+hwXec2E0SAOTRqseb13VgLesdAMAtvf58fc9ExBFdk2dLYQQ7BwO4rdb+vHka8Po98bAccDKJjM+cHIHjFoVCAHLlhUikwIikxBEgt2ao5HmDbBpMoC/r+y3yErMWdAtkxZEPLtzDCIhWNlkxlEtFrjNOhwteWL8a7+HzZ9KMOCLIS0QmHVq2AwaBONp/HnHCNICC/oWg7QgYuPucfztzTEl0CYEmAiz7/hcYEE3Y054IimEJQnaSCCBvWPhCo+IwWDMN6mMiBf3jAKE4IQuuyIh5TgO56xuwHGddiXj/eYQC7xnw5gUdLfYaKByUrcTFxzdBI4Ddg4H8dedo8iwieWs2DsWxsbd4/BGUtCqeZzQ5cA1Z/TgknWtaLUblPPXE2HqrGlExgAiIijqEOfNSDhWQK9WARN7yn6LRjnoZuo3hX8f8MAXTcGsU+P8NU3gOA4AcPpyF9Q8h+FAHIc90QqPsno5PEmPzYpGM644vg16jQpjwQT+tnOMLUwuMJ5IEo9sHcDO4SA4Djh1mRPLGkwAsvcvxuxgQTdjTvRJch+Nit5A/n3Ag2SG1XExGPXEa2++gaMP/BKrkm/i1B5X3nMcx+GcVQ04vtMOgGa83xgKLP4gaxS5nlsOugFgbZsNFx/bAhXP4cB4BH/eMcJkfLNg/zhd/D2m1YqPvaUHZ69qgM2YNQJzm2nQzUqiChAcBgBM8G6A46BtWUu3e/YDQnly3iYrPb4TLNMNAOj1RLFjMAAAuPCYprxWixa9BidIZSX/PuBhAWQBRJHg8GQEALCswQSHSYt3HdcKNc/h0EQE/zwwWeER1ieEELw5FMQj/xmAN5KCSafCe05oxxnL3WiVuhmMse/4nGBBN2NO9Eqrj2escMNu1CCSzGBrLzMBYjDqhYlwAhP7twEgOE3bC22BuwXHcTh7VYMyefzHngkM+mKLO9AaJJURlWxrsy2/JdPKJgsuP64NWjWPAV8MWw57KzHEmiOVETHgpefe8Z0O6NTTeyArQTfLdE8nRIPu4Qz9LrvblgM6M5BJAv7est6iwaIDx1H382hyadfdxlMCNu4egy4TxiXxJ9EV+u+0fU7scsCgVcEXTWEX6wYxjbFQArGUAK2aR7uD9n5vs+lxmXMATeHdeG0ggFcH/BUeZX2REUT8becY/r5nHBmRoNttxIdP60KHkx7/ZknNwjLdc4MF3YxZk0gLGJHMP5Y3mHH2qgYAwGsDAdYCg8GoAwgh2LR7HNb4IFwmLVzaDBAcKLgvx3F460o3VjaZAWRVMIzijIcSIASw6NUw69TTnu90GXHu6kYAwJCfGS2Vw4AvhoxIYDNo4DZrC+7jttDtnkiS1dHmQggQGoFACPqloLvdaQQajqLPT+wu6210ahWcJnqMJ5awmoAQgr/vGUc0KWCZ2Idl5gwwsAVIRvL202tUOKXHCQB45bCHqVqmcEjKcve4TVDxVFWJyDg6A1txNvcqtJkI/rl/EgcnIiXehTEjoqioWf590IN9Y2HwHIe3rHTj8uPaYNRm71FNVr2ysBZZ4gtrc4EF3YxZ0++NgRDAbdbCZtBgWYMZPW4TBJFg837mvMuoHob8MQz5Y6z0YZa8ORxE0DMCA4mjy0VXuDGxt+j+HMeh28VqvcpFluY150jLp9Imyfg8kSSr7S6DQzkyVLludipOoxY8xyGZFhFmE8YsiQCQiiKSEhFWu2DRU9MqNEpBt+cAIKTLeqtGCzNT2z0awsGJCFQ8hzMcIag4jrrAj+6Ytu+6NhtsBg2iSQGvsaxtHnI99/IGc3ajZz8AoNWuxynmSRACPLtzFFsOezESiENgMv3Zs+sJ4JWfon94FK8NBAAAl6xvwUndzmnXUq2ah0tSDLF7/exhQTdj1vRKph/dbpOy7exVDVDxHHo9UeV5BqOS9Hmi+P32Ifx++xDueeEQHnypF399cxTb+3zo90ZZL9kiRJMZ/PugB/bEEDocBuiMFvqEZx9dES+C7Fw8EU6y+sQZmGqiVgirQQ29RgVBJPAyBVFJRJEo9528CfoU1CoeThOt8fYs4UzsNKT+3JOwQ+TVaLMb6GTb2grobTTg9h4q660apbrupRx0v9pPg+fTu8ywpiayT4y8Nq0Fm1rF44wV1C9je7+ftcOS8EVT8EVTUPFcduEXoAtAADhwONE4hh63CWmB4JVDXjy2bRD3bT6EP+8Yxn/7/ZgIJ1gSaCZSUcBzAOlEDG9s3wwAOK7TXvI6yiTmc4cF3YxZIYpEkY/KmS0AcJi0iqHS5v2TLDPDqDivS6ZeWjW9zPljaewbC+NfBzx44tVh3Lf5EJ5+Y6SCI6xO/nXAg2RaRCfG6M2183RAowdSsaIScwBwmbTQqDikMiL8MRYkFoMQokxWptZz58JxnGJMtZQDmBnZ9zcEtj6CRDIFnYZXjH6KIdd1eyLsHFWQgu5RkUqd2xzSMeQ4oHENfTxZnou5svgWWpqLGvGUoJxba40+gIiAwQFoTVRePjldMbS6yYImqx6pjIj/HGbeOAAUA7V2hwF6jeTPEA8AkQl6XnIc+NAILlllxPlHNWJlkxl6jQqpjIjDk1H8c/8kHtoygF/88zD2jLJ6+aL4ekFAcGgyAot/L9wmNc5a4S75EiXoZvelWcOCbsasGA8nEE8JBSc3p/Q4YdapEYil8aokUWEwKkEokVYyX1ee0onrzl6Gd5/QhrNWurGqyQK75Gh8YDzC2gflQAi9+fJiGseYQzTb5VoBuFfTHUpIzHmeU6Sl7GZcnHAihUgiBZ7j0Ci1sCoGyyjMQNwPjOxAeHg37PFB9Lhyaj+L4GZtw6YTGoZICAbkem5HTmZRruv2HqS9vGegwUzN1CLJpVnzORygHgxOkxaGsLRI6VoBtJ1AHw9tn/YaTqqfBYA3hoLMGwe55SI5GVfvQfqvrR2wtgEA1L6DWNduxyXrWvHJs5fhQ6d24q2r3Ohxm6BV84inBGzrYwsZRfH3YjyUhD+WhkGM4J0dKWhUpUNDuSxqPJRgqrZZwoJuxqyQA5ku5/TJjU6twlnSjWNrrxfhRHk1YAzGfLNrOARC6Cq506SFUatGl8uEk7uduHhdC645s0fpN7mf9ZhXCMUzSGVEOFIjMGs4wGCnWZoGKeieSWJuW9pZrhlJRpDcfDfWTD4Ht0U74+SG9T2eAR911fZH03DHDuVP0IvQYGZBdx5CGgiPI5LMIKhphEmngiOnzRoszfQaIGSyQU8JtGoeLslMbSkqNEakoLvNpgd8h+lGZw/QchzAq6iqIDRdYdXhNKLbbYRIyJJ35I4mMxiVFhrl+zQApZ4b7lVZv4EcBQbHcWi06nFilxOXH9+Ga8/sAccB3khqSS4AzQghiI4dQL83ipjGhU6nEa7QzIoWl0kLrZpHKiPCx1Rts4IF3YxZ0eehLVm63caCz69ptqDVrkdaIPj3Ac9iDq3m8UVT+POOYTz9xghe2DuBLYe9eHMoiIMTEYwG4wjG06w+qQxEkWDXSBAAcGy7reh+a5qtAIC9Y2F2XCU8URqItGEcPMcBzmVUyufoLktiLsuhWaa7CL5DiEXCsCcG0aUNzri7fDx9kRTSrGRnOr7DiKcFxNMCnIl+dNmnO8FPRc50+6LsmAIAwmMAEeHPaJFUmdFmN+abJ+VKzMt0MVcWi5bgdUDOdHca4kAiBPBqwN5J26/JgWKBbDcAnNhJ5f17x8JL+tzs9URBCPUHsOqlBaB0HAgM0seuFXQhmONof/lE4WupQatS1FesleV0MqFxHBoaRwZqpFZcQDPYngP0Pl8CnufQYGFmanOBBd2MsomE/Gje9xu0Bf+bV8+dC8dxOHd1IziO3jjkGxBjZl4+5MHhySgOjEewYzCAVw558fc94/jL6yN4dOsgfvXvXry4b7LSw6xuRBF9E36EExkYtCqsKJH5WtZA5WfBeJoFiRKyuVRrhvbshXMZ/ZdXlSUxl+XQk+Ekc5EthL9Pybh0xvfPuLtZp4ZJp4JICCZZtjsfUQAC/fBHUxB4DRw6DvrAzGZfJq0KBq0KhNDAe8kjZV3HiBPguGw9dy6NR9N/fYeB9MzXyqVa153KiMrv3CqO0o32DkAlBY5tJ9F/J/dOax8GAB1OA6wGDVIZcUm3wZKl5XlmXr7DtD7e5AaMTkBnoTJzAJjcV/S9OqX+0gMs6J7Gzp2vI5oSkDC14S0nHgfO0kyvq2UsrrXYlu7C2pHAgm5G2YwffBWGdABrkm/ChOLBdKNVj7WtNMO4ndXSlEU0mcGhCSrdP325C6f2OLG2zYZlDSY02/SwGuhNe+dwkLlul2LnH5H450+gTwdwdIsV6hLyXY2Kx3JJuraXScwBUHMpXToIG6I00LZ3Zp+Us10lJOY2g0Zx3Gby3SkQAtGXDbrdsYMzBjDUTI1NbgoSHAIyKUwmVRg1HwuHSVvWZJHjOMVMjS1kAAgNQSQEg4Jcz10g6DY1AEYXnZB7D8z4lrkGgEtJRTQWTEAkBBa9GuaolJWVFy4BwNoC2NrocRx5bdrrOY7DMa1UgbVrZGmaf6UyIga8NEAuLC1fmd3WIJv8FV8I7nDS83nQF1tS5+JMDHhjGOuj18vVR6+DWacGmtfTJ0dfn/H18gL7KMt0zwoWdDPKJjRC67kcBhUw/N+S+57YRW/gvZ4ogjFW2z0Tu0ZCEAlBq12P05a5cMYKNy44ugmXHdeGK0/pxLVndqPBokNGJNjNnDgLk4oiOb4PoXAEbaEdWNtWXFous1qSmO8fCzNDEADeaBL2xBAMWhXNIqhzjL7sXVmJeaC/4OuZ43YJopOIRULIQI2MzgGDSgTGd834smzfYxYg5uHvRVoQMYAmeEwraB2yr5e2wJkBt5nWHC/5hSFCgNAIoskMAupGGLQqpR47D47LSqMnZq75dJt14DkOsZSA6BJaJB4K0GCxw6YGAlIZTm7QDWSz3QXahwHA0a1WcBwNEpfi3GnAF0NGJLAaNIr/AoRMtj7elRt0zywxb7UboOI5hBMZBJbg8SxEIi3g+TcHYUmOodmqQ/uyY+gTTUfTxfbIBC07KYFspuZlpU+zggXdjLIQMhkkPXSi7TBq6Q2jhJOpw6RFl8sIQoA3h2euXVzKEEKUY1QsUOQ4Duuk+uSdw0G2YlsIXy8mwkkQAMvEPjhVM5c2dDqNMGhViKWEJS8/ywgi/NE07IlBGLWq6ZPFXIl5CTlfE3PcLowkLQ/pmiG0HA8OHDDyKg18StDMZHyF8R2GP5ZCQN8Bi7MJOkcblZ+WyHrJsLZhEskQkIwglBQR1TZk+3MXQpGY99L62hJoVDysBlpfv5ScuEcC9DvapfIBYgbQW6lCIJeG1bS+OxUtuIBh1WsUSbTsTbKUyLqWm7LnYqCfzjd1Zto7XqYMiblGxStS6KV+j5c5OBEBHxqCUU3Q2dqSPUc1BmpSBwCjb5R8D7NODbNODZEQdm+aBSzoZpTF+PBhECEFTmOAye6mssixN0u+Zl27HQCwcyTI+naXoN8bQyiehk7DY1WTpeh+q5st0Kp5+KIpDPlZrfxURO8hSS7KodmiBQa3zvgaFc9htXTMl7rE3BdLgYhpuNKjtLe5o2f6To05cr4iEvMm5rhdGH8/IokMgvo2GNrXAyo1EPVQmXQJ5LZi/lgKyczSyRqWJBkBwuPwx9II6NupDLVJytaMzywxb8hpG7akFzCD1Lthglgh8urC9dwyJhdgbpAWNoovusnIbRmXSnZREAlGJQ8bpZ5bNqLMhVcBrVL7sOHChmrHSOV5u0dDS0qBJYpE6ZCzolCrMNeK6cdTlpiXUGDIixiDfhZ0A4A3moI9MQSHUQuVa8o52rKO/juxiyoMisBxnNKthAXd5cOCbkZZTA7Qm6y+cRm49lPoxqFtJdsHLXObYNGrEU8J2D++dE1BikIIsP95jL72V4AQHNViLdlCSKdWKQHiTqYeyIcQ+If2I5kRMek6AU6TFhjdUZbUdHUzPaaHJiNLWiblCadgTY7BrCbgtGbA3Dh9J3s3XQ1Px4tKzGV5uTeSRCqzdI9nHpLpVySZRlDfhkaXPZs5HN1R8qUmnRoWvRqELD1jqqL4eyESgpGMFRmVgU7QG9ZIUtMhIB4o+XKnSQuOA+JLTP48jdAICCEYFqhrdsF67lzkc7YMNYHdSGXqgfjSyHSPhxLIiAQGrQqWWBFpuUzrcdTVPDSqLHzksrzBBL1GhXAis6SysyPBOOIpAToNj1a7dC4SQh21gWwWNhdZYh4aKSox75CDbl98aS+ySXjCSdgSQ5Kibcriur2bKjTSiWwdfRFkBcFYkN2XyoUF3YyyiIxTV1hH+yqg+Vha2xn3l+zbyfOcku1+YyiwCKOsMULDSA1sAzfwH1iTozi2jBpkuQXWgYkIM1TLJTKOSZ8PAqeBbc254K0tdJW2SGuWXFoko7pURlRW2Zci3mgStvhQVlpeSGbK89me3UUm3ha9BmYdDRInl3rNrExoBJl0EmFBi5jGRU1oWo+nz03snbFFi+IGHWYZBQCA7zCC8TS8unZY9GqaudZbs8Z/M9Qda1Q8XZjDEjdTCw0jmhLgVzdAp+HhNulK7y9nFf39My5o2iXzT/8SyXTL/bk7jWlwMR/A8dQHoxBaU7ZGvkC2W63isaaFLgYvJUO1w5P0nFrmNkHFS/ef8BiQDFMH+ELHswyJebNVD62aRyItLO3vu0Qo6IUx7YdBq6HtQHPheTrHB4Cx0hLzrJkaU16WCwu6GTMSDEfBhUbAAWjqXA2otUDLcfTJodIS3rVtVqh4DqPBBJOgTGVyHyZCCRAAazO7lDrDUjRZ9Wiy6iGIBLtHWbZbJjq2H4EYzSIe2+EEOk+nTwz/F8iUvslyHIc1zUxi7olQEzWjVl08QwPkBN3FXcwb5X7drK6b4u+TpOWtcJi01KjO0gJYmmjt5/jOki/POpizCSPt9dULf5TWc+fVfsqZ2ImZDeqydd1L9JgKGSAyjmgyg7CuCc1WPXi+SD23jNEJWJrLqp13SJnuYGxpZLrl9qg9/DjdYG2lyYlitEuGahN7aVA5BdnF/NDk0lhgJ4Tk1HPnSMvlbKtzGS3JKURDaZM/nucUFcdSUg4UIpbKQBOkSgyDs40q16YiB93+vpKqoUarDhwHhBMZRJPFpeiMLCzoZszI8MABcESEzuKE3iIZLrSdSFdyA4NKn89CGLVqrGqiF9DXBwOLMNoagRCIk/swIa26LlNNUqlZGcgZ8TeHmKGazOjh3SAAdI3LaAarYTU1B8kkC7ZmmYosMe/zRJFI1/8EpxBBvw/GtA8GnXr66ncuZUjMm5U+vSzoBgAE+hFO0nruZps0yeG47OLlyGslDdWYI3wO4TGQdAyeBBDRNWKZO2eC3rBact+dpD8lUILupZr5iowBooCQqEVSZVFc8mdEcTEvHXTn1nTX+32KEKIE3S2CNB8qtXAJ0MULWztdwChwj2q06NFo1UEQCfaM1X+22xdNIRBLQ8Vz6HIZs0/ILepyW4VNpWFV2RLzpR50eyO0nluv5qFuWF54J4MDcHTRe1KJBWGdOtvtgLUOKw8WdDNmxD9EVxotzTmSU72VthcAZjSskiXm+8bCSzagmUZkAkHfJOICh7BlGZxmLTDwSlkvlQ3V/LE0M1QDIKbiCI71AgA6V66lGzkO6DyVPh7cWtIQBKATcLeFTnAOTiw9/4FEWoA6SANog6sd0BqL71yGxFx23B5jQSJ13Q0OI6IE3TnBTdMxVDYZ8xVdwACyme5ALM2uob7DiCYFTKpboNFo8uuQNYZssDNDtnvJtw2TFssnORfAcYq53IzIEvPgYMEMrYxVrwHPcciIBOE6z4J5Iikk0yJ0KgJrosygG8hmu0deK3iPWisZqu0aCdX9wsVhqbSr02mETq2iG+N+unjG8dRErRhlSMxlM7WRQHxJG/t6wnHY5LaghcxSZZolQ7XRN2ZYEGZmarOBBd2MkqQFEclJ2h/R1bE6/0nZUG1yX9HVRYDWzMo9ppdiC4yCePZhPJRAQN8B0+pzoeI4KqOKemd8qVbNK3Jo1o4NGO7di1RGgKB3YFl7e/aJprX0ZpyKAuOlnfYBLGmJ+WQ4CXtiEDo1D427xORGRp54F5GYsyAxh+AgCBHgFYxIqq2K+QwA2ge9SVooKqHI0GtUSuZwyU9ufIfhj6YQ1Lejy2WEeqr5ZK6LeYnJolsKMn3R9NKchIeGQQjBqEhN1BrLDboNdiqdJqSkiznPc9lsd7S+67oVabkmCF5I0UVLS/PML3Svku5RMWByujR6dbMFap6DJ5xUVHH1iqw4yVtE80ieQbb2wjLoXGaQmLtMWph0KqQFsqSzspHJIajFJAwGY377tak0rKb3p0Sw5IJwi6TcYqVk5cGCbkZJhie8MCS90Kl52FunTMYtTZIERSxpWMVxHNYrhmpMEg0AidE9CMTS8Bm7sWb5MiqdIqTsbLdsqHZwIoJYqr6zCDMxfJhmtGxtq/Mn4LwK6JCy3QP/Kem0D0Bp1zbkjyGcqO9J4lS8kQRsieHC/bkLYe8qKTFnQWIO/l4k0iK86maoeW66d4NsqDa5n7bCKgKr6wZ11A2NwBdLIWDowPLc2k8Z1wqqHkgEgdB0Z2gZi04NnYaHSAh8S6TuOI/gMOJpAQFNI7RqXvm+loVSO1+6PZv8nv46P76yiVoXP0Y3OHoKG1FOhVcBbVL7sKHt0xaJ9BoVVjTSc7zeExayGsKizzkPy5GWy8wgMec4Dh0O1jos5aGmyBp3Dz3/iqHS5HTYKG6o1mST/FtCCTa3LwMWdDNKMjawHwCB0dEMTlegh7Sc7R7dUdKwanWzBToNj0AsjX7v0r3gAQBiPnjGhiCCg7FlNVxmXdb4a3xXSdWATKNFj2abZKi2hNxNpxKOp5CcoKvhHSuOmb5Dy3oaHMb9Mxr/2AwatNkNtJPbEmtxF54YgFpMQm80U4OvmShDYs6CRImc/txNVn3WlVfG0iRlDsWSbrGsrhtAoB+JVBpeYkFaY0WP2zR9H5Ume26W6NnNcVxOXXd9B4XTSISAZBiRlIiItgENFl3WjK4cGuX2bMMl71fZtmH1u4hJCMGwVObVnJmFtFym5TjaPiw8VnCRSO7ZvXcsXNctLSMJGnSb9ZJZWjpOPYOA8oLuMiTm2dZhS3MOSgih5mgAjM1lHFO5Z/fkPrrgWQC3SQeNikMqI8IXXWLX0TnAgm5GScIjNKCxTc1yy7iWS4ZVqZKrYVo1j6NbqBvn60u8fZg4QQ3UQvo2HNPVRDfa2rKqgRlq5GUUQ7Xhpase2HmwF9pMBBajHvbmAqYgam22bm7glZJyUyBrqLZviUnMM5P0e05Xv8u8LZQpMV/Sdd2pKBCZUPpzN+VKy3ORs90jO4qeo7LR1ZIOun2HqeGSvh2tdj30miKZGqWf9J6SChe5jnnJ1XVL9dwBzgaR15QvLZfJDXBKGKrJbcMCdZzpDsbTiCQz0JM4bIKPbpza+7gUWmPWH6eAYrDDaYDVoEEyLdat3wghBBEp023WSUG39xCdD5nc1NirHGaQmMtB91gwiWRm6ZU9hSJRGGKj4DnA0lKg5/lULC30+IuZoqoWnufQyO71ZcOCbkZRArEU1KEBcABcHUW+oBwHdJxMHw9tKznBkSXmvZ4ognW88j0T3v43kcyIiFqXY0WuPFLOdo/umLEHKkDl0Fo1VQ8M+paeoVoiLWD4ML0RNLYvpwF2IdpOpNmvyATgO1zyPVc1WcBzHMZDiSWzaktXv+lxMTaXcSOWyZOY9017Ws7MLmkHcz+V3k/CjozKkF/PnUvjUdn6uSLn6JJvz0IIRO8hjIUSCOrbcZS0iFsQRzcNZlIxwN9bdLcGKdO95Hr3hoYAAOOcGwDKN1HLRXYxL1CLLCO3DQvUca9uuZ57mWqCerNYmmgf7tnQJi0MT+6jKoQcOI5T2ofVa8/ueFqAINLFRiXolluFuWdxT2pYnZWYF2h1ZTNoYDdqIOaoE5YSwdGD4EDAG51QmcpYyMjtsDH6etHd5G4lrK57ZljQzSjK0NgY9JkgzAYNtM7u4js2raWT70QQ8BQ3VnGYtOhyGUEIbXe1JEmE4B3pBcChedna/BpkRzdgbaEupkPbZnwrrZrHUS1L11DtjaEgTJF+GLUqNHQdVXxHjSGbSex/ueR7GrQqpV3J3iXQpgUAQqEQ9PFJ8BxgbZ3FBCdPYj79e99o0StBYmQpBokA4O+DQAiGQRUtzcWCbpUm6xZbxFBNp1bRdnhA3ZsqFSTmRcDnRVzgkDK3K6qUgvCqnKxXcYn5ku3VHRoBAcGwYqJWZruwXNxygDNKy3cKYJNquoPxNESxPtVYcvDWCak/92yk5TKWJsDeUbR92NGtVnAclUUH63ABQ5aWm3QqWn4jZLKLj+VIy2V0ZsDWQR8Xk5g7lm7rsPg4VbRxs1FiNB1Nr6fhMZq4KEAL61ZSNizoZhTFO0hNLIyuDkBT4qas0mTNQAa3lpTwyu3Ddo4El6RjbGSEGqiFdY04qrst/0mOAzrPoI+HXy1aQ5PLsW12ANRQbSllv9KCiNf7JmBNjqLVZgDnLNJvUqbjFHrjCA5l68SKsKYlKzFfCrL90OgBAAQwN0JlKJE9LESexDxfrqdV80oPzyUrifb3IZrMIKBrhVmnhkXO4hSi9Tj6r/fgtGyXjBwcLcmMgu8wxkIJhHUtOKbDDc1U1/KpyJLdyX2AUDhQcZq04DgglhKWzvVTFIDwOJJpEX6VG2qeUxZzZoXOTNUuQFE5r1WvhprnIIgEoTo1pxwOxAFC0DiXeu5c5Gz36I5p7cOseo3S8mrXaP0tsIcVablkohbop99ZXZkeI7ko96TCZQ+dLtlMbellugXJRE3XWEaHEhmtKduurUgJqVw25Qmn6tp3YD6oaNB9++234+STT4bFYkFjYyMuv/xy7NuXvzq1YcMGcByX93PaaadVaMRLB1EkiE/QL2jReu5cWk+gZiChkZKOscvcJlj0asRTwpIzqwKAsUOvgwBQN64uPNFxr6Q1NJlkyRZCMg0WHVpseoiEYPfo0sjMAsCe0RBU4WEYVAQul4ses1LoLEDzsfTxDA7xy9xmqHkOgVga/jrMKkwlPkYX13jXHCaLM7iYK2ZqSzFIjPuBRBCRlIiQrgVNNn1psyqTG7B30kXLIlI+RbIfXnrHMzSyH8F4GkFDB9Z12GZ+gbUN0Nvo5N17sOAuWjWv1B0vmWx3ZBwQMwiLGiTUNrgtuunmfuXSKAU4RYJujstpG1aH19JoMoNALA1z2gObOk1LnKxtM7+wEO5VgN5KSyIKqDOOliTme0frbzE4N9MNAPBIruWuleW5wOcyg8RcbknmCSeXVueXuB/piA8EHEzNs7zXt6yn/47vnLa4DtBOECadCiIhS1OFNQsqGnRv3rwZ119/PbZs2YKNGzcik8ngwgsvRDSaX8/69re/HaOjo8rPX//61wqNeOkwHorDEB2CmufgaC3HOdKc7Y86+J+iu/E8h7WSAdiBiaVlVoVUDNFxWl/YvGJ94X04DuiUFpWGthbN0OQitw/bPRKqu5txIUSRYHufH/bEEFpsevCu5eXdmDtOpft5DwHh8aK7adU82qQbc69n5tr6moYQCF66uKZtnIWMT4bns5mFAoZKS9pMTXKJHYcLIq8pXs+di5ztHt1R0pxufKm1ZxHSmJSUV46O1bDqy2hvxXHZbPf4rqK7uZeamVqQLor7+AaA45S69jnhXg1wPJWdRr0Fd7FJdd312DZMrufu5Mag5nlaIlaqDVMpeJ4mLwBgeHr7sOUNZmjVPILxNEbqbBEzqrQLU9PfezatwqYyg8TcqFUrHgZLyQtH8PYikRYQ1jXBZZulos3RQ49rOp5dEMmB4zg0s37dZVHRoPvZZ5/Fhg0bcMwxx2D9+vV44IEHMDAwgP/+9795++l0OjQ3Nys/TqezQiNeOgyPDEErxGAx6cHbO8p7UbtkqOY5AMR8RXeT+04OeGNIZZaOFCU6sgexZBoxrQvd7SVWwxuPphmaVKykI7zMikYzVDwHXzQF7xIw/zowEUEwnkZDepjKbcuV8xmd2QBxhmx3t9SKqK/eg+7IBFKxMAROA2tT99zeQ67r9uyftgou1zCPh5JLK0gElHruPoGqMGSvgJK4V1PlQDJSMDvbYNGB5zhEk8KSqpNPTPbBG4oipTLh6OWzqEdslBaCfYfphLEAbsVMrf6vnQAUJdoYJ9VzW48g6NYas07dRQzVHHKmuw7NU7P13FJ/7rlKy2Va1kvtw8ZpKVQOGhWvzJ321pmqLU9eHh6l1z+1Nlu+MFtkBUaRc1J2MV9Kdd3Rsf0QCRA1dcKqL1HmVAiezyoFi7S1ZGZq5VFVNd3BIK1VmRpUv/jii2hsbMSqVavw8Y9/HBMThYv5GfNHYJhO+IwN3YCqzC+ouYHedAgBhv9bdDeXSQubQYOMSDDgq/OgJgdP35sAAK5hFYzaEseUVwGdp9LHg/8pKOfJRafOmn8dqHPJPiEE2/p80GYi6DHEoVLxtNVaucgO8ZN7Sy4M9bho0D0ciNf1wlDGcxDxlICQvgVuaxlBYSHsXXTyXUBi7jZT6WoiLSytjgWEAP5+BONpeLVtsOjV5WUUVepsb9QC5SUaFQ+nWa6TXyKZWQADB3dCIABx9qDNMYvz1NxAf0ShqLHSkjNTk03UBOpePCcTtVwaSkvM7QbZwbz+FjWGA3GoxCQaiJTld8xiQagQWmNWMTg8vX2Y3HZ1/3ikrjxxlB7dOnU2k+pcVv7ccyp5Jn+BaU93LrWgWxSQnKAqS7W7p3SZUzFko0/f4YKeI3LQPRpcOuqBuVA1QTchBDfccAPOOussrF27Vtn+jne8Aw899BA2bdqEH/7wh9i2bRvOO+88JJOFb5DJZBKhUCjvhzE7khkBaQ91jnSWIy3PRW4fNvp60cwCx3FY1kCDmkOTSyToziQRlWpnHV3Hzrx/8zpqYJEIlnTflVnZSM2/Dta5ZL/fG8NkOAl3aohKba2tNDNYLpYm2luekJJlEHYjbS0iiKSub8yxsQMgAGLmzmyrltnC83SSA0yTmKt4TpHyLaUgEZEJIB2HN04Q0TZgeYO5/ImO3KLF31vQFVqe3CyVVmyiSOAZoAFd27JjZj9hlLPdRa6j8vnpi6aUtkV1SzIMJIJICQQezgWe4+A2z8FELRf3KrpQHPUAkclpT9drTXciLcATScKWGIZVpwKMLsBgP/I3bpfbh+2n9/8c2uwGWPRqJNIC+rz1M3eK5MrL5VZhrjlIy2VmkJi32Q3gOQ6heLou3eCnERpBPB5DhtfD6Gyf23sYnZLDPqG13VNostEF9nAis2Tarc6Fqgm6P/OZz+CNN97AI488krf9Ax/4AC6++GKsXbsWl156Kf72t79h//79eOaZZwq+z+233w6bzab8dHSUKY1mKAz5orAkRqDX8DC3zMLlEKArveYGWotcoq/fcqk/da8nWretRHJJTxxAOJZEQm1DZ3vnzC9QabJy/YEtJR3hAWBZgwkqnoMnkqrrC972fhqErDV4qXvxXOR8cs382Jt0EloAjuPQLWW7++tocpNHJomEZwAAoHavmNvqt4ws5/NMdzGXzb+WVF23vw+EEAyIbhBOpVzvysLopJJdQoCRHdOeXmrHs294GFzcB7VKhZ6Vx8z+DeR+0oGBgt93q14NrZqHIJK6vnYCoOZSAEIqOwReC6dZm9+2ci5ockp8CixsyEF3KJ6pq0WN0WAChABt4ii0c70XFcLcKBkqTm8fxvOc0ipv92h9LLATQrJBN4nQxRuOp4vjR0IJiblWzSseG/W8qK7g70UslUFQ3wa3dRZJiqnI2e7RN6bNSXVqFTqc9L0PTda34vJIqIqg+7Of/SyeeuopvPDCC2hvL70K09LSgq6uLhw4ML2YHwBuuukmBINB5WdwsHR7IMZ0xob7oBJTsJjNgLl5di/mOKD9FPp4aHtRaXSr3QC9RoV4SsDoEpg8evrfhEiApGMFnOUa17SdAKh19CYkr/4WQa9RKZKpA+P1cTOeymgwjkFfDCqIWK7x0I1zmejYOwFbOz03S/RDl+u6ez3R+qxHDgwgnkohobbC6mw4sveydUoS88Q0iXmu+deSwd+HSDKDSVUzdJqsMV/ZyH3lx94osIixtOrkD+2jWRV7czfUujmUQBjs9PtOSEEJNJdjJlb3EnOpntvL0e/7EZmo5SIvbEzunTYZN+vU0Kg4iITUVYnJsJ+2CmtX+nMfobQ8FznbPbJjmpnqUZLEvM8TRTxVuvSsFkhmRKWEyxSWenPbO2anYCvEDBJzua570L8Egm5fL+IpAQF9u9LGc040rKEJobgfCE6PrVY0yIpLFnQXo6JBNyEEn/nMZ/DEE09g06ZN6OmZ+aLl9XoxODiIlpbCvft0Oh2sVmveD2N2hEZoPbe5aRmVjs6WxqOpNDoZLlrnpeI59LjpRe9wva+KCRmEh6nEyda5tvyMoloHtJ1IHw+8MmO2WzZZOVCnF7ztfTTLvd4egx4ZelOe7aKQjFzbPfxq0TKIdocBakkuVZcGdb7DiKUEBPQdSl3rnCkhMZeDxMlwckmoWiAKQHAAvlgKQX0belym2bdkcq2gEslUbNqCm8ukVerkQ/H6NlObCCeQmjgIDkDbsqPm/kYzuJi7LXQiWvdBd1A2UXMBOEITtVxcK6gBWMxHW5LlQNuG1V9d90ggDkMmAKcqTn93exkKtnJxraRmqun4NPWA26xDg0UHQSR10QFGznLrNSpo/LSTxhFJy2VmkJjL/boHfLH6vi+l48gEh5HIiDTTfST3erWWzu+Bgia/yxpM4DhqphZO1M8C23xS0aD7+uuvx+9+9zs8/PDDsFgsGBsbw9jYGOJxOgmORCL48pe/jFdeeQV9fX148cUXcemll8LtduOKK66o5NDrllAiDS7QDw6Ao22OFz6VOhssDm0tGiwukySXh+o0SJQRfb0IRajzbnvHLDOz7SfR4xkaVVoQFWN5gxk8x2EynKyryQ1A6y1lydJxJskAzdkzt0UhgErX5DKI4VcL7qJR8cpqeN25mBMCeA/R1W9DO1zzkfEqIjF3GrXQqnmkMiJ8dXZeFiQ0DAgZTCbUiGmcynVuVvCqbG/UKRJTtYpXJk7jdd6v+/V+H2zJYThNWhibV839jRrWUMlqeKyggeKSMFMTBfr7AxjM2AEAjZZ5CrrVuqwcuITE3F9H9bOBeAr2+CBMWingVpXRxq5ceJ4q3QCqGJwyh5Kz3XvqwMVcNlGzadLZ7OlcWoUVooTEvMWqh07DI54S6ru3tL8P8VQGcY0dGpMNBu0cW9rJyEafk3uATP5xM+nUimz/8FLxa5olFQ267733XgSDQZxzzjloaWlRfh577DEAgEqlwptvvonLLrsMq1atwtVXX41Vq1bhlVdegcViqeTQ65aByRAsyTGYdWroGo6gpqb1eBoshsdpLV0BulxGqHgO/li6rmvp/AM7kRYIIpZls3PeBahiQDZWmqHNlUGbrampt2z39j4fCAGWN5phi0k35iNxiuW4bLZ7aFvRfui5EvO6Iu5HJuZHQgBCutYjk5zJ5ErMcxaI+BwztSXRTsTfh3hawCjfDJWKL69VWCFa1tPz1N8/rQdys63+j2c8JWC4/yBUYhpNbsfcVS0AvY7K8t8C2W456J6oZ8l+ZAIQM0jzOngEuhDUMF9BN5CVmE9Ml5g7pEx3MF4f9/m0ICKaFGBPDEGnmcd67lxa1tM5VGRimpR3dbMFHAeMBBI1v8AuZ7qb0kP0vDE3zI8hHVBSYs7znFKSV0+mdNNQpOXzoGgDAGsbNQ0UMgWVrLJ/CavrLkzF5eWFfjZs2AAAMBgMeO655zAxMYFUKoX+/n48+OCDzBxtAZkcPgyeCDDb7PSLNVe0RqBJcukuUjerU6vQLtU61q3EXBQRGqQr/+b2o8HPVmYKAB2n0CyNv1+RBxZDdjGvp9Zh4UQae8eojO7kVq2SrTniGrqGo+jNPR0vavrXLQVMI4EEkpnar59TkKTlIV0LzCYj9JojXP0G8iXmU+R8rTb6PR8OLIF2Iv4++KNUWt7uMMz92OptVLYLAKP52e5We3ZxrV6lkTtHgjDHBmHSqWBtWTl3VYuMLIuc2D0tKJT7n8dSgtIzuO6Q6rmDmgaA42A3aqBTz8P3Xsa1gmZ7E0HFsE3GZpAy3dH6yHSHExnwYgb29BgtHVmIoFtjAJqkTj5D+e3DzDq1EjDuqXFDNTnobkhIyRn3EShapqIzZ2X/k3unPd3lrHOzVEIAfy+icj33fATdHJfNdhfo2S0H3YO+OBLpOpozzRNVYaTGqA4IIQiP0XpuS8sK+uU6EjokQzXPgWmZGhn5C1q3UpTgAIKhEDK8Hi1dc7yZ6G1As3TznSHbvbyR1tSMhxJ1Y1rT741BEAlabHq0CFLAbW4EdEeoduF5oKN0P3S7UQuHUQOREAzWk8upFHQH9e1H3jIolyISc3lxbdhf50F3JgmERuGPpRDSt85NWp6LYqj2Zp4aY3mDGXqNCqF4Gr11OGEURYLXBwOwJwbRbNWDO1InY4BKVlVS3bG8cCehUfFKXfd4vaoHpKDbw0v13Efan3sqKk1WFjxFzuuQlDSBOrknheJpWJMjMKgIOL2NdhxYCNokQzXP9PZhssR871ioptUZkUQGHMnAnhiiG+ajnjuXBnkhuEDQLfkKjQYT9RkgxrxAIoRoGgjrWuZH0QbQxSCOp0mgqCfvKYdJC5dZC5GQ+lYQzBEWdDMUJsJJGMKDUPEc7LNtFVYIozN7Ey6S7e6R+nWPBOOIpeovwxAe2oV4WkDQ1IVO1xFMwDtOo4sgngMFe6HKGLVqtEsS9nrp2S2vhLvMOsAnuZvOV2ZB6YceKmqylJWY10nQLWSAQD9iyQwC+vb5kZzJFJGYt9j14DkOwXgaoXo2WAkMIp3JYFIwIqm2YJl0fZszjh5Ab6XHM2fSqFHxWNtGJ92vDwaO7DOqkMOeCOLRMOyCl56fR1JKIqPWZSf0E9O/63L/89G6Dbpp9nmMzLOJWi4NhSXmdinTHU6kkRHE+f/cRSaUSMOeGIROLUnLjzRBUQxzA+DoosdyivfI8gYztGoegVi6ps/ZSDIDW2IEel6gC+mWIygjKUSexNyf95RVr4HLrAUhddo6zNcLAJjgGyDy6vkrJ9GZsx4OBbLdKxS/JhZ0T4UF3QyFwUk/TKlJWPVqqFzz1P5C7jU9/iZ14p2CVa9Bo1UHQuqwbpYQBAbo5E7bfNSRSXhNrqzsaoZs90rZxbxOJOay0YpZqwL89CYyb0G3Sp1VZBTph94jBd199dI6LDgICBmEiB4xjROu+cx08zw1rQLygkSdWqVM8od8dZzt9vfBH0shoGtDo1UHq/4IzZV4PuvpMMVQbV27HRxHlSD15okx5I/DnhiCy6wDb2mik7z5oEnq8z2xBxDzgz/ZZb8u+58nI7SmleMwmLYBmEcTtVycy6jDcTIMBIeUzUatClo1D0LqI9sdimdgjw9Bp1EtjLQ8FznbPbojT+2iVfOKUnDvWO0aqoWTGTjjfbTXuXvl/C9g5EnMp7uYd7lkiXkdBt3+XqQEERPqVnAc4JyvTDeQ7dk9tnOaSnC5NAft80brYpFtPmFBN0PBM3QQHAjMjkYqaZ4P7J2ApYlm16ZMGmWWuetUYh4aQSjgg8Bp0Ni15sjfr+sM+u/EnmkrtrmsaDSD42jGph6yilFJAWEXfXThRqWhfXfni9bjaRYs5i3YD73NboBGxdG+y/Xgbuw7DAKCEb4F4Lj5zXQDWTmfZ39hiXk913X7e+GPpRHUtynXtSOmZV1WyheZUDbbDBplQej1ocD8fFaV4IumYE8MwqSd56DGuQzQ6GkQGsw3+GyWXHcnQon6q5OXstxpvROeJJ32zauJmoxKnV0czjFZ4jhOMVML1IGDeSzkgT4ThE6jBuxdC/thrhVS+7DENDXWUS20xGrfWARCjZ6zkXgajng/tGo+62Ex38gLwQWMv2Tfln5vnSyqy0iKtrhURmYzaKBRzWPI51pOVYKpKOA9lPdUo0UHi16NVEasTwXBEcCCbgYA6saZnKDSXVvrPNbUcFy2bnb4v/RCMIXlDVkzi3QdrYolx/YgnMggYOhET+M8LGJYmumkkYjAwH+K7mbSqRWjpYN14GIelluKyDVfjm7aUmm+yO2H3v/ytGy3Oqd1WF2shvsOI5kR4dG2QcVnJ8PzRhGJuVz2MOSvg2NYiGQEQmQSgXgaIV0rljceobRcRmfJlulMWbg8rsMOANg9Eqoroz9fJAlbfBiG+c4k8qrsBHw8v7WV3NouLRB460w5kGeiBsCiV8OoVS/MZ8mGdZN789QEctuwWnfbBgDipYorlb2dLuIsJDyfvT8N57cP63AYYdapkUgLNakUTAsi1NFRaIQ4NDrDwi1gNEgS8/DYtISFvKgeTmTq63sfGgKEDMJEh5jGOf+L67wq6zU0RWLOcVyOi3ntnZcLCQu6GQCowZE5PgSdmoe5aZ7lUg1r6MQxFS1YS9cgrYqlhToyqyIE/v6dIABE9yrFvfWI6ZLaXI29SSV8RZAl5gfrQGIelWq6zdF+uuFIXcsLIfdDD48V7Ife7aqT1mGJIBD1IJYSEdC1wWHSUvfd+aSIxLzVrgfH0UxXuA4UGNPw9yEYTyOsdsFotqBhPic5sqHa+E4gk50YdjqNcJq0SGVE7K1xF2OZRFqAGB6HRoxDbzDMr6oFyA8KcxaBeZ5TJObj9SYxlzLdHs4NYIGy3DKObhqIpqJ5aoJs0F37331VgAbd2oYFysxOpWWd1D5sMq8FK89zWN1Ms921KDGPJDJwxPuh4jmo3cvp77gQaE1FJeZqFY82SYVVVy7mkv+NR9MKcNz8lpHJyBJz7yGqHsoha5Jcvx025gILuhkAgMHxSRjTftgMGnCO7vl9c15FgxoAGNw6LZOYuypWNxLzqAch3wREToWGznmQlsvYOugkVMwUNacDqMQcoAZ1kRpugZMRRMRSAlRiEoaY3CpsAWroZuiHLpupjQZq3OVUMlYJaBogqPRoWIgbMZANunMk5jq1SnFMHqpHF/NAv9IqbFmDCdx81iY6ugGDgwbcE9kMLcdxWNdOVTSvDwXqQh7pj6Vgj1OTKo2rZ35VLQC9huos1GleNmaUkM3U6qr/uSgCYRp0j2CBnMtz4VXZ1oE5cl67gV5r/DWe6U6n09BHad9sQ/M8O20XQ2PItmAdzm8ftkaSmB+ejNbcvSmSU8/NNcxjq7BClJCYy3XdffVilgoo9/pRnhrTzXumGwBMbsDWRtWX4zvznmqT2mXGUgJG620R8whgQTcDABAYPgAAMLnaqDR0vmk5jtbiRj1ZM6wcZJffw55IXUwcMxN7EYinEdS3o6f5CPqdT4XjgE4p2z38Ku0xXQCLXoNWux6E1LbEPJqkkwhnahRqnlBHfINjYT4stx96gT6zchuMmq5RkoKMCXUrAMxP385C2DroQsY0iXmdtg4jBKKvF/5YCkFdG5bPVz23DMdls91TJOZHt1qhVfPwRlJ1sZjhjaRgSwxK0vIFULXwPNAou2znK6+abfT7UFdmatEJmtFX6zCcpPf2BXEuz0VuHTiZbR3oMNFMd623soxM9EElpkE0RujsLYv3wXLiwnOAmuJJNFr0cFt0EERSc+apseAkDOkANBoV4JyHtoClKCExl5Vsw4E4Upk6KHFMRoDIBAgIBrCAQTeQzXaPvpGXUFPxnOI5cqiG56DzDQu6GYgkMxB9feAAONoWaOVWowda1tPHg9MztG12A7RqHtGkgPFQ7ZtVBQd2QhAJErblaJrvCY5rOW0lIqRpnXwRVjTSFfAD47UrO41IJmpNmVFw4BbWKVZvy7obF8p217rEXBSVAHiIW+AbMc8X7I8qB911V9cd9yMa9CElckiZWxS54rzSfCzNIobHaPsbCZ1apRgq7aiD9mH+UBiW5DgM822ilov8PfccpBlvebOU6fZEkvUx+QaUem7B3AxflAa8CyovBwB7t+TrEFeuOXKmO5zI1PSxTUwcBACkrF3g+EWcQpvcVPFCCDCS3z5slaRsG6yx66o4SY1LBWvnwtfGl5CYO4waWA0aCCKpD6NP6TuX0DUgRnRQ85zStm/eaTyKlgXEvMq1RmaF5GtycKI+kmnzAQu6GRjwxmBLDsOkU0PXsIBBTftJdKXRd3har2m1ileCmsOTNb4qFvMhNDkMAg72jqPnV2YKSNluycl8aHtejWcuK5vojXg4EFfqomuNSCIDEAJ3WrqYL3R7lk6pH/rkfqrKyEFeta1Zl9PQMJBJQlDpMJShkmT3QsnLgYIS81a7ARwH+GPpmi57mIa/F75YCmFtE7oa7PNfJw/QIEY+plOy3evb7QCAQ5ORmu9YkJzsBQcCrcW9cKoWcxNgdNEynZyOBRa9BmadGoQAE+E6yXYHZRO1JoiEwKBVwaJboNpZmQK+DgatSmmbGYjXrsRcmKROzWQ+esfPFjnbPbIj777fmLNYVFN4qMKSLJRr+VSKSMw5jlNczPvqoa5bUrT5dG0AAIdJC34h7kkANaJtkJRDo/mGap1OE9Q8h2A8DU+kdr/z8wkLuhkYGRuBLhOB1aijstCFwuDIthMZ2jrtaVlifqjGg27i2Q9/LIWwvgXdLe6F+ZCGNfR4puPA6OsFd7HqNWi2UYl5rR7TSDIDfSYIM4nQLN9Cnp8AzSbITtEDW/Keas1RY0yGa2xyAyhlHQFDOwh4mHVqWI60j3QpCkjM9RqVkmWrK4m5vx/+qNQqrGGepeW5yBLziV30uEq4zDp0OI0gBHhzKLhwn78ICFL7GW3jAk7EOQ5okgzVpriYN9nqzExNKpWZlEzUGi26+V8ILkRDAYm5ZKYWrFUztWQEQngMAAfevcALwIVwLgcMdqrOyCmNkK+pvmiqdjrApGLgpMwo716k2vgSEnOlX3etKtlkCFHut5NSGdmCKdpkWiSJ+cTuvMUgrZpHp7SYUatz0PmGBd0MxKVWYSZ3B6BewMwXQOtmAdpvcorbYY/bBJ7j4ImkavemDCA8tAvJjIigqUdpNTXv8DzQKbViG/xPXj/kXBQX8xqtqYkkM7AnBml/SdsinJ9AtmZ+fBd1+5ZQ8Zzy96xJifmUeu4Fr+ssKjGvs9ZhoojYxGHE0wIixnZ0uxfoOw9QE0WTm9boTunZe1wHVS+8ORxEplYm3lNIpjPQhfoAAOaFNqmSXcz9fdRpW6LFJpup1eDC2lRSUSW4GBHtABbYRC0XWwegM0uGdXTBT3Yw99fq/d3fi2RaRFTrgtk8D21AZ0tu+7ChbPswk1YFg1YFQmjgXRN4DyKdERDTuGC0zaPvTSm0pmxbsikS8w6nATzHwR9L1/T8E5EJ+r1XaTAqGScuqKINoN91g4OWPObc6wHktA6rzTnofMOC7iUOIQTqIG3FpG9cYCMLgE4ara00SJxSl6TXqJRayEOeGv2CJsMIjfUBAMztx9BgcaFoOpZOapJh2kKsAHKA461RaU80mYE9Pgitml94abmMtVWqnROp234OPbLLaa1J0FJRuroPYJA0AcjWry4oBbJdbXa5rrtOMt2RcQSCIQi8BvamTujU8+y2nUueodqrecY1y9xmWPRqxFMC9teYoZJMwDsOXSYCtVoNfcMCy3eNTsDaQr/nE9mJouJgXg+Zbrn23+TGWJxmtxd8sU0mV2IuOe7bjXTyX7O9un2HkcyICOg7YDMssES/GM3rsqa0ATp34zhOaVFYMyos7wGkMgJ8xu6FL3fIRV4IniIx16lVaLHT737N3d9zkY2K7V2YjNF77oIZpspwXDbbPaVnN+3kAUyEkjVvojgfsKB7iRNPZWCKjYADYGhcpKBGznYPv0pXxnLokqQoNduyxbMf4WQGEW0jWpsaF/azVGqgIzfbPT27JfcHjyQzNZn9isbisCZHoVUtYtAN0NpuABjdkZcF65KymKPBRG2ZAfn7aIBmbsRInE5wmhcj6JYl5pmkInlrd9C6bl80VbNeA3n4+xBMpBHStaKnwbLwn9e0ln73ox4gOKRs5nkO66Ta7teHAgs/jgUgOkrrq8liqVoaJUO1HKluo1UHjgNC8XTtn58hen6IlhZ4pGBsXvvHz4TsEu89AAiZ2u7VLYqArxfJjICAoQPWhSzNKYVGT00VAZrtlnBLEvPJWqjrFtIQvYeRFgj8hi6Y9YscdM/gYl7TQbekKsnYu+CXjBMXpEf3VJrW0uMaGARiPmWzUatWFtpr3q9pHmBB9xIn4h2FRoxDrdFCZW9fnA91r6ZO0en4tN5+ThO9ONSMRGoKZHIfoskMfMae+XctL0TLcfQmHPMBnn3TntZreJolBhBK1OAEMjgEngjQmGxUVrtYOLppFkzI5E1srHoNTLoak/EBirQ8ZetWpJ2LkunOy3bRzIJeo1JqzOrCKTbQj3hKQFDftjjHVKPPSqNHd+Q9tbbNChXPYSyYqMmFy5RkUqVyL4LqCqCtrTiOmo1JE3CdWqXch2q+rluq5w5qGpERCbRqXgl8FwVrG6C30jpP3yE45Ex3LRqpRcYgpGJIEDUi2gZYF8oNuhxkibn3oOLtIC+meGoh0+3vRyqVREJlRkLnpu0BF4tciflEvhRaNlMb8schiDVolppJAUHaQz6ga4dICHQafnGUBHprNjEyJdu9vFGWmNfwYsY8wYLuJU5ikk7GM5Z2alS1GPA80H4yfTy4LU8i6cyRn9WcQ3Q6jqSnD2mBIGDsXnjzCoBmg9okR9P+l/OOJUBlZ1ZpFTlUY9IeQgi0Un2n2rWMTo4Xi7x+6P/NayvkMkmTm1rIKAD0nJBWvyc1dGHNatDQlkyLgSzny3Exr5vWYUIGGV8/9XDQtynB2oKjGKrtBVLZY2jUqrGqiWbb3xyuMUM1IQPil0udFsnNWGfJmYBn5aby4kktLlwoiKISdI+K1AW+yapfHBM1GY7Lk5jLyqtoUkAyU9iHpGrxHUYyLSCoa4NGo4FOXcHps0ly9idEadPkttBrz2QkWf1zJ89+pDIi/IYumPSaxT0ngZw+8vlBd4NFB6NWhVRGxEgtLggHB+k9Vm/FRIZm7d2mRTJOBLI9u8fezFNeLndLnXT88dox+lsgWNC9xMl4pfoPR9fifnDLOhowxryA5FYLUDm0iueQFgjCtSbt8xxALJFGTOOE0dG4sPXcubSfRGu8IhNKRjMXeUW+1uppEmkRltgAAEC3WJPwXNyraFuhTDKvRZMs1aqZoDsyrhirjBAngEWSlssUkZgDdVDXHRpCIplCSmUEb3IpLZEWHEsLYGmiLa+mqIVWSa0Cx4I1dmyDA0gkkkipTLC6WhbvcxUX86zEvC7quqOTtHxLrUVfgmbw2heif/xMKBLzg9BzAozSYl/NSczlem5DB6yGCgSKU7FJykQps+k0asFzHJJpsbrnToRQEzWBBt2LWs8t414FcPw0iTnHcVkXc28NLghLi+twLsOwtGDYbFvEe717JaAxUJNkubYcgNWghkGrgkgI/LXq5zBPsKB7KSMKSmZB5VzknpNqHZVGA3ntw3ieyzqc1pJ8FwA8+7PScssi1s1pDNnMV//L056Wg+5a698bCXlhSAegVvFQV6I9C8dla7sHt1KpObLtN2rGnE5eiHF0YzwiS8sX8fwsIDFvs2cN/mKpKp4gzoS/D7FUBiF9K5zmRZzc5BmqvZancHEr7YPSNeXjkPEckkyq2uFazOunezVVeUU9dOES2YnqWChR/VnDYkgZUGJpwVCATsArEnRbWmibKyEDeA9mJea1FHSn40BoJMdErYLSchm71D4zQINutYqHU1oQrmoztdAwkIoiQdQI6VsWt55bRmsC7J308RSJeVct9+uWA11Hj6IiW9TvPK+itd1AXitbjuNqvnR0vmBB91ImPIp0KokMr4fB2br4n99+El1t9PcrzsoAlJtyTX05MynA14toKgOfoQuNi5lJBKg5Ha+ixkqBgbynZMOXULy2gpvUBFVAZMwtdGGhEjQdQ2uVUlHFbEkJuqNVPLHJRVn97lHksotSe5yLLOeTJOYGrUoJDmu6X7e/D3FJcupaLGm5TOPRklrIp7gYA4BFp4ZOw0MkBL4ayirExw6AAEhYu2DULuJEXKMHXFIN+Xj2O67madawpoLDXCRpeUjbiGhSgJrnFlfhIsNx2RIT3yHYlLZhtXNuwt8PEIKIyoaU2qyUbFUUmxR0h0cVQ9oGWYVVzUG35wAAIGDoBOFUMFci0w3kSMzzXcy7XEZwHF24iFSzYmAqiSBdOOQ4hAytCMTS4DlO6Qi0aMgu5t6DeSa08v3RVyvJigWCBd1LGX8fkhkRIX0LrIZFnjAC1ExNvvANbVM2y0F3Td2UfYdBxDS8ghExjWtxM4kArU2UHU0HtuQ9ZatRebkweRAAkLEtsgojF16VrVOSyiDkFdtoUqj+LG0mqThcx8xdCCcy4LhFbBskY22fLjGv9dZh6QQQHlNM1BatnltGrctmFXLKHziOUxaGPOEauYYmgkgEJwBwULsq8H1XXMz3AIRAxXPKd6RmJeZSpntEpL16W+wGqBer5Gkqjm76b2CwNjPdklrIq8t6YlQcg4O2DBUFZYGloRYczKWg26OjJY0VyXQDORLz8Wlu23Iv+/5aynbLi+uWFgyF6cNGq25hW1gWwtwIWJrpeTm+W9nskO6P3lpKpi0ALOhewhB/n2IMYqnUha9dah82sUcxq3KY6A3NF62hm7JnH1IZEWPaLvA8vzgmalPpOJVmFbyH6I1Ewir1E60pebkogEjBGRwVDLoBQC69CPQDogitmlcWMqpeYu7vp32IjU6Mp2mQ6zBqF/9GXEBiXvNmaoEBgBAEYEVKbV78oBvIluhM7qd1dBKKk3E1T75z8fUinhIQ1jXCbrUu/ue7llPVQCKoLFI11XJddyqmBBJ9KRsAoKMS0nIZaxsNcBJBOFV0ka1menUTogTdE+o2AKhcu7BcOC6b7ZbOWXe1O5jHfNTHh1dhQkPVlRWp6QbyJeaT+Z1fZBfzmqrr9mfrueV7aofDWJmxyNnu0R1K6ZOLycsBsKB76SKkkfYPQSBAUN9auaDbKtV7iYJSmyRPXmumpluqVYsmBfiM3XCatYtnopaL0Zk1rRnI1nbLE4R4qoYcY0MjyKQSyPA6qO0VKH3IxdJKJ+TpBDUlQw2Zqcn13M5llZOWy0yRmMuyN08khXiqRs7LXPx9EAjBmKoJwCL1Qp2KpQmwttKFlZw2LbVXAnGYyvT17ZVZvFBpaG03AEzQ7Ixc1z1eiw7m4VEAADE4MBCmk952Z4Um4ABVZZgbAQCODC0lC9SK8irqAZJhgFdjDFQ1IC9kVxwl6KZzJznTHYinkcpUoZ+DlOWGvRPBNF34rVimGygqMW+XgtWaaRkoioqCDM4eDErqsYp4OABUOcSr6XdHuhbJ1/VALF2b7djmCRZ0L1WCg0il0kipTFCbXJWTnQHZli2BPgBZeXkkmamNIDHQD2RSCIk6RLRNaFxME6CpyG2uJvcprpx6jUpxVa6Zum7fYaQFEUF9OyyVKH3Iheez56i0mlwTZmo5GRo4l2EiLAfdFTo/re1UDplJAr5eGLVquKVAdThQQxkFGX8fEpJSSK9RLW6v2VwUQ7UdSlZBbh9UE/JyacIYT2WoiZqpQuen7GI+sQcQBaX+eSKcrClDOgCKtDysa0IsJUCjqlA9dy6S8Zc1SRcu4ykBiXQN3N+la2jG2o5Ihs6TqiLTDWTN1IJDgCjCqFXDpFNRg/BqXHDz7AcAiM4ViCbp375iNd0AXWgrIDGX1ZaheKY2AsTIGE0KqHUIahoQitN67lZ7hYJujT7r4zBGu2uYdWpo1fySdzBnQfdSxd+HZIbWIlqNFb6ByPVeco9WjQomXQ21FZGkSaOaToDjKpdJBGg2wd4pteXItmKrOYm53J5F3w5TJW/KMrLEXVpNrolMYsxH5bK8GsTWXvlMN89ns4lSf1Q52z1Ya3XdiRAQ8yKWFhHUtcBl0lauhVDjUVlptGRIKQeukWSm+n0HQsMQ0wlERA0i2gbFgXnRsXcDWiN1qvb3wSb1shdEAk81L64VIkiD7lEi1XPbDFDxlW5xRaW8mvCwUhNd1S7bMlLQHTXR8es0/OK1BpwJUwMNcIQ0DbyA6vVzSEWVxaCYdRlEQsBxgGkxTROnojVm2+XmSMzNOjU0Kg4iIQjVgiJD6VDShUG/3CpMB20le8m3nwysuRhYdg4A6jXCJOYs6F66+PuRzIgI6lsrv2or19VEJhS3Q3utOJiLIuDZDwKCXo7We1U00w1knXh92T6JNWWmlooC4TGkBRp0V3QlXEau6w4OAUI6R16eqt6WQvKN2NaOcIZHLCWA5zhFglgRciXmQkaR8dWcg7nkFh5UuSCo9JWRRMuoNEpAI8tMtWpeab1Y1WoMAPD3Ip4WENC1Q6dVw6StUEDD89QRHgDGd4HjuNrs1y2KQJgaaw1kpHruSkrLZeS+0lEPWo00y1n18t1MSvlOBQxSpr7S86Vccuu6A/kS88lIlR1b70GaDLA0IcrR89GkVYOv9GKQnJHNkZhzHAebbPhXC3MmX26rMFlaXuHvvLWF1nars/dG+T5Z9fekBWTWQffg4CCGhoaU/2/duhVf+MIX8Mtf/nJeB8ZYQNJxIDKOZFpEUNdWeSdOnRkwueljqd2V01gjdd3BQSAdR4rTYYJvpN1RKh10y1nZQJ/SWzrbNqw2biAiIQjyTqTVpuoIug0O2jpMFIDAABxGLVQ8h1RGRChRpZnEXGm5NLl1VcpvQMbWkZWY+/vQZpfrupO1ITWVkRQPk+pmAKhcdlZG6dmbbRcoZ7yq2skYoPXcKQEBfTvcJl3lFANANuj27AeEdNZMrZbqumNeIJMCUWlwKEYn3hWr7cxFa1Tu8+08lfJW/WJGcJBe8/U2BIgFQJU4l+cypa67ajPdcj23ayXC0j2zovXcMsUk5rXS2i6dUNzrSaX6c5eJnKyo+mTaAjLr2ddVV12FF154AQAwNjaGCy64AFu3bsXXv/51fOtb35r3ATIWAMl1N6yyIq02VcfKrSLfpRkkub1A1feZlWqUfPouEE4Fl1lX2aAGoBJzrYkG3CG6QCZnuqs2QMzF34tURkTA0A41z0GvqQJBDsfllEH0QcVzyk3ZW41BjZAGglIA5lyGsSAdY8XrOjku62I+uQcmnRpOkxaE1FDrMEKUoHuUp0H3ovfonkruxFuu6652J2OAumyHx2imu1ImarlYW6mxp5AGPAeyZmrVHhzmIl3zw9oGxNMEGlWFS55ykVRtzeIEAGA8VMXnJpC3cBlKygvYVRAo5iIrCKTvfm7bsKpRYQnprLu2e5XS/7oqFtTzJOZ7lc12yUsmWO0ljoEBpUNJECaEExmo+ArWc5fAKZU9+aq5LG+BmfVsdufOnTjlFNrm6fHHH8fatWvx8ssv4+GHH8aDDz443+NjLARSYDupbgFQJU6c8kVPmswqDubVfMEjRAm6R7V0MlFxaTlAAxvnMvpYmjRYa0VeLpl/pYRsPXdFM1+5KAtDU8zUqnHVNjhIF110FsDkVoKGqph8y3I+zwFAFGqvdVjMByQjEDkVhgVaM1vxYNHSTGXm6QR1jAXQYMmWQFQt/l7ado23I602VV4xwHHZbPfEbmWRyhdN1Y4SQ8p6jUtO2632KqjnlpEWh+xpaqYWiqer23MgN+iWTEirLtNtaQZUauW7n6fCqhbjVH8fvR/pbYC5EZFqynQDOQvBOUF3rWS6lXrurLS82aqvfPKnALnzerEWDOoWgFn/VdLpNHQ6Otn8+9//jne9610AgDVr1mB0dHR+R8dYGPx9ICAYl7I0VZHptnXQCU/cT3t5yvU00VT1fjnDY9RQSaXBIKHtUKoiqAGyNchy0C3d3ELxdPWsfhciMg6kYkgSNcK65uq5KQPZhaHIJJCMwKU4mFfhqm3OZJEAGK+0c3ku1naaXcgkgeCgYqY2EqiRbKK0MBg1NCPDqaBV85XP2PCqbMZLkpi7c87Pqr2GSufpmIq2Bay4YgAAmo6h//oOw8ClFJVQzWS7paB7MOMAUAW1nblI56gmPgmXnp6TE9Wa7Y776QIbxwOOLmXBuirmS7nwKnpNBYDgAFQ8p8h4q6auW0pOwL0S4DhEkvRYVqxH91Tcq6ZJzOWgu+rNfJX+3DnScmf1ZbkBOg/VqDgIIqmNWvkFYNZB9zHHHIP77rsP//rXv7Bx40a8/e1vBwCMjIzA5XLN+wAZ84zkupsRAY+a9petWI/uXDR6wEIz7/D3w6JXQ81zyIhEqf+pOjzU7ZI4l2E8SrMgVZHpBmhWluOkADGsrM6nMiKS1di/U0aahIcNrSCcqvLBTC5aE+2LDAD+vjwztapDNlZxLkMglkYyLULNc8pCQUXhecC1gj72HFTquifCidpoEShNcgI6apzorKRzeS6KxJwG3TaDBhoVvYZW5QSHEMW/YYij1/6KKwYAWndsbqS1vJ79isS8Juq603Eg6gEBwaGkbKJWRRNwvZXK9wlBt4a2tKzaum7FiLINUOuUzh9VoQycij3fTE3xc6iGum5RpCZqgHLdr6qabkCSmHfTxxPUUE1uXRtKVHFf6ZgPiAcAXgVi78Sgj2a6O6ppoS0HjuNyJOZVcG5WgFmf8d///vdxxRVX4M4778TVV1+N9evXAwCeeuopRXbOqGIk1924zg1B1MOsU1e2R3cuji66Su/vA9+yDnajBp5ICr5YCrZKtzWbCiFKi4mYdTmio0J1mKjJaI2AuYlm43290LSsg0mnQjQpIBhPV0/Lk6lIE52AvgOIVEfNFyEEIhEhEhEZayvE4BCEyT1QtzciLgQwGCIYDmtBIEAkIgQiQBAFCETIvo5k6HPSdoEIIIRAzauhU+mgUWmg43XZx6rsYzU3S4l9IkglxhwPOLox7qWT2gaLrnpkpq4VwOgbgPcALCvOh92oQSCWxkgggR63qdKjK44oKpnkCdlErRoCRSB/4k0IbdFi1mEsmIAnkqyeccpI3SoSogpBbWN1KAZk3Cvp+HyH0WTtxL6xcPUGh7lIWe4ob0U4rYFGTeAwqZASUsq1iCB7PSOEQAT9t9B25TERlf2mvs9Mz8uP5ecIiUKMDWE480/0xVsQH9AiyNvLe58C4yrFXBfDOHDA6A66aK3NILPzt9jumwQAPNPnLirdrcTiGwcOiPuA8D4g3g/wMQwGYzgYjWCiV4felK3w68oc60zKOIIZAtJ4APC+BvBqYPTfwPjL2DLuRTwlQDtsx3Zf6bndTO9/xOOTiY0AoT3AngEgehCEEOyJeZERCX6xww5jidZmM33Gyc0nY617bXnjmA1yltvahkCSQyRJ67nlhcJqxGnSYjyUYEF3uZxzzjnweDwIhUJwOBzK9k984hMwGme3unL77bfjiSeewN69e2EwGHDGGWfg+9//PlavXq3sQwjBbbfdhl/+8pfw+/049dRT8fOf/xzHHHPMbIfOAJR67pChHYhXSZZbxt4F9L9CFwYIgcOkpUF3NFV9E/GYl64y8iqMadsBeOEyVdgZeirOZTTo9vcCLetg1WsQTQoIxdPVI4OXIIRATMchBgaQEQUM8k4kxSig0sIT57LBq5hRAtvc/08NbHP/X9brxOxzclCcu59C3E9vzLHDIJkJ7I/7IYgEj+yxlbwpHwk8x0On0kGr0kKr0tLHfM7jqdu9B6ET4tBa26HlCEYCtA1fVf3NHT10EhYPAFEP2uwGBGJpDPvj1fddzyUyRmXxah3GRTuAaHVIogHA0kqPaSpKz1OjE2456A4nsarJUukR5iMtsAX1rSBxdWV7nU/FtQLoewnwHUZLywUAaKabSIsZuRBCkBbTSAkppMQUUkIKaSGdfZzzXFpITw8sywxmCwWcUwNb0XcYYrAPPs6OPRk/7EYNfrXTWokjWJxMCEj5EBeS8KWTiAR52AOOmV+3mBCRftdFAVDrEIv5kRSjUKs4xIUI4lUnyCGAmAaSSSAyCpFTIyGE4YlFEUhWOEsb6AOEJGCwAukwCCEIJYMQCZAU1QilqiQBoDUBRAASASA6AWhN4FVJpDMCfHE1wM/9Op8RF0it6cuVltMsd4utOuu5ZbIO5lVaVrLAzGmWqFKp8gJuAOju7p71+2zevBnXX389Tj75ZGQyGdx888248MILsXv3bphMdOJ1xx134K677sKDDz6IVatW4dvf/jYuuOAC7Nu3DxZLlU0iqp0c112/VjZRq6IMsq2dThqTESDmy9Z1V6ORhZTlhqMb41F6U2uspqAGoHXd/S/TCzMhsBk0GA0mCpqpEUKQETNIi2nlJyNmkBby/y8HpHJgmxug5mZxczO+hbK/U18nEhEk6gECuwGNAbsSzyKUyCA9asbOcJWoBwBAZwXHq6AS0uBFATa9CZGEABUxwaE3QsWpoOJU4DkeKp7+q+bU9P+cStmm4lTgOA4ZMUMn5EIKSSGpTM6TQhJpIa1MquOZOOKZMt29J3YDUS+gTgE7H8Cu4SAiSYKUz4WxvdbSAXuh7SottPw8B0RqLVW2eA8B3oNocxyNXSMhDAeq3ExNun7C0QVfhE6kqiaDrFJT9+3AAF24NDrhVmo7q3CCIwXdHm0bEF/c40gIgUCEaUGx/P90JoWUEEIqEUN8/FkMJRNIxdN4ePcO6DQku5/02qohHgAIQRD0XlTo/s6BA8dx4DkePMeDw/THyvPgi+/Lcfn7gy/8eOr72CPgImEQ8PDxayFyGpzY0AazTqvso4yx1PtI+x0JRTOUwWHAMw5oDcAxH8GAL46EbxxOkxbvXtl2RJ8JzJydnfH1hcadAq1Jdh2HhGMVHpJUje/s7oJWrSr92hmY6TiXfH7Hw+DMOmDVRYB7JRIpAb5xOrb3r+4uS2k54+fPcG8q5zzhOA7grPS6ZF0DdJ0OpzCOQ5MRnNbgxPqO0gtDpT7DqFkAubcoKMpVOHow2C+3CqtOabmM0qubZbrL5w9/+AMef/xxDAwMIJXKP3Cvvvpq2e/z7LPP5v3/gQceQGNjI/773//irW99KwghuPvuu3HzzTfj3e9+NwDg17/+NZqamvDwww/juuuum8vwly5xP5AMA7wKXlUjgGh1mYKoNLR+yt8P+PvgMK0EUKW1H1I9N9yrMDFOJ7SLUc9dTnCsbMskkE5NIh1PIb33D9gfITgYCyA2oMWhhCG7n/SaihOnNX4wOJCKEvCcCiatHga1Pi9oVYLaKUHs1IB36n5Fn5Nfx/P571HsdbybBl9NZ2CjoQM7h4M41e3EGcvd83o45AxaUkgqQbjyuNj2TBLJ1KtI8RqkTA1IEYJoKgORACp1Ev6Ef05j4cBBo9JAyxcJyqcE6IW2T5PJu1ZIQfcBtK85CQBtIZQWxOpdqZeCbtHWDf84vS65TFW0KGTvkILuQaD1+GzbsGrzHcgkgSBtbTWmogvArjKcy0UiZoNkOfDNySoXyjTLGeapwfVM0mSQGJCYAEb/g5TKhUAyjb0eI1pshWukOY5TFq7kf+XvjLxNo9IUDWDlABNAwe0zBsLgwAXuBadpwqOps+HmXfjAuk602IwAh7z3qSiEACO7gWQEg6QJ/YIbLk0rVrjNlR1XLmN7ALURaFwLmFsw5g/ApMqg3WpGs6m50qMrTMNaIB4BUgnA0YEWcxrhRAYauNFqrlBdf8wHpFOA1gq0nQKodZgUkzCrojBoVWi3tlZmXMVoOwUIjgKBIWC1C502YNSnAQQr3Ib5vb8fMaFhIJMCNAYQcxOGJKl5NfbnzkVWhvki1CSZr5aSt0Vi1kH3T37yE9x88824+uqr8ec//xnXXHMNDh06hG3btuH6668/osEEg0EAgNPpBAD09vZibGwMF154obKPTqfD2WefjZdffpkF3bMlp/4jKM3Bqs4UxN5Fg+5AHxwdtISg6lo2xAN0RZnjQFwrMHGQtj/Jle/KAVNegCykp2+Ttk/bViKYnh0pIOkFxv8LP9+IUCYKVVQDR6K45FDNq6HhNdkflQZqXg01p4aKV2Uzt1MCXp7joeYLZ3WVjG+BwJbneaihAr/tfqh4J/hj34efvc5BJMC1x/Yo7sFVg7OHBl++XrjsywEsTFDDcZwSuFpQpqonMAiMHAA0BuCkz2MsHEds4hBUagEfPqaVBhzilMy6/LjIdlm6Km+LpCNz/n2UWnVeA50oQhfthzY+BI2zBUESQzKhwksDEXQ6bAWDeRVfQSmikFYCxbChFRkxDDXPVVeJzpR+3bLHRCieRiItVMTLgRCCDKHXMiWz7NmPVCqAlNaE10JD8CZjGIzbEB3ilYB5anAt/zvfaHgNDZClf5UAmTdBm94OLWeAvfU07ByKoV1rwYU97fmBtfQ6Wb1SMaIegAARUYuIqhlGjRrtdmv1TWo5jp6nE3vQwXvQL7gxEUpgRWMVBd053R8A+v0BqrBHdy72TmBgi3KNcpt1CCcy8ISTilnlouM5kB2bml6LqqpH91RcK6hiKB4AwqOwGanitipb1+ZIy32xNKJJAWqeQ0sV13MD1P0/1yS56vyaFphZn/X33HMPfvnLX+LKK6/Er3/9a9x4441YtmwZvvnNb8Ln8815IIQQ3HDDDTjrrLOwdi01HBgbGwMANDU15e3b1NSE/v7+gu+TTCaRTGaldKFQaM5jWizSYhrP9j47Td5V6PFU6RXH5T8u9R788BZwKT84zTIc8h5EIJ1GMJ1Cb9Cf/76571HovQrI00rJwmaFoxvo/ScQGIBjNZ0gRpPCgk0Y5bpdJfglRbLGOUFzemIXMrFBpI0OhA9vxKu+UYjIYOOwA+JQZo7B8eyRg2IlSFZppm/jTdAMvwaN2g5Py1uRifjgNBhw6bJuJZjODbDVfIX6Yke9tBZVpUXM3AGR0IlDVd6YZZfTQD/cLXR8VdM2TJksUvd6TzgNNa9Du82ARlPjnN5SlsCXG6QX2i4bKSUyCSQyOYZUvAAkg8DgvxAkJngSKTzXuxcdwcISOcV4jtcUzKZr+OwNXJb7ydeuqY/l/8v7ytcu5bUc8p5DaBhcwgNOa8JgYBS+tAdOkw4HgyS7T+5nTH391LFM3WfKWHLHl7tPqeegNYITU+Cik0BwAJzBDo0mgUgyg/6AF61Slnbqe007FtLj3Hrl3CB4Wr2y/FjKKufukxJT0+W03oNAdBSEb8Gu0BaIBDgctmMkUd41nuf4goFvNmCm18PcfeRMc+42eZGwIOk44BsFCMGy1qMxPhFAJsGj09JdPYaEuYSGAQAe3gWIPNrshuoLuGWkoLtJnASwRmlpWBUkw9REj+OUa33WubyKAwRrGx1zzAckI2iw6NDriWIyXMF70xTXcgBKj+6qWqyUUWsB10rqYD6xGw73mQCqtMRRTqLl9OdusRuqxxi5CDzPwW7SwhNOwhtNsqB7JgYGBnDGGWcAAAwGA8LhMADgIx/5CE477TT87Gc/m9NAPvOZz+CNN97Av//972nPFTIuKRYY3H777bjtttvmNIZKIYgCBsODC/shhABjWwAhAxJx4A3fAQgiwdZJO94MLmz2o+iiQMFgnQMfOwxOEMDt/jX6Uhmk0iIe3bMDDqNuuvwudwEAfJ6MjoDMGEzPKTgeex1IhQCLEz5fP6JCGEatCtH09K8TB25aUFwwk8ypiwTNmiMLjt3rAO8IkOEQaFiFNw+PQS1waLe0V15mmIvSnqUDkQz9Gxu1quqc3JqbaCY5HYebeAEAwXi6OmTRUzI0E0p/7rmvfqt5NdS8es51abmB29S69RRvRWr4v0jyZhibjsFrSQ80hEOzyZy3v5zhnPN3dj7w9QKxIYBvwvDQCxiIxxBRafH3/irzFkkO08DhzV8Blibsj4fgj6UR222qqKutXKKg4TXQphLQqo1IW1fDGtNCp9LipJaOPFVDnjRbCqzl4FrNL8KEXWOggUxwCO7UEAxaG+IpAeOhBForlTksRZAG3SOCA+CqXGYqOe07hElwRMBYMFlyXreoyBlEcxM11wIQitNrTlWV401FowdMDXTBIDgIt5n27vZUakE4HVey7nAtVzaHpR7dVbmgDgCNR0tB917YO84GQFucVcX9XSYVoya5ADVR20fVZx3V/J3PwSUF3b5oCssaKj2axWXWZ31zczO8Xi+6urrQ1dWFLVu2YP369ejt7Z2zOcRnP/tZPPXUU/jnP/+J9vb2vM8CaMa7paVF2T4xMTEt+y1z00034YYbblD+HwqF0NHRMadxLRZqXo3zO89XnEpBkO9amuNQCqBwG46cx8p75DidkrgfIm+EqFYh5VgD4/gECE/QaXOD57g8B9Vc99Q8J1UU31YKQggECCjbv0OtBVI+IHAYAmdFREijP5hETFy42kl5Qjhj8CtkoBk7AI3BBM2KS7HLA6SDUaxqcuD8lS3T959tu6f5xuAAjE4g5oMlPgKO45ERCaIpobpuejnBYqTaenhORc6ATOyBMToAo7YNsZQAXzRVWYfwVDR7I3b0AADGgnTCVdFgK0cmb8YUCanaBngHAYHDqp6z4Z8cgRocLlu+Im/BRSSiEoQXrWeXFtfk65F8TVQeyxcgkjUTkvfJ/X92tymv9/QBGguIazX8YTOs6jg6rWa0mU3T9i/1+Xn7TPns3P0L7pPzuOjrTW6QVExRjlj0egTjIpIZQMWppv2uMxkrqThVXuA7NSieGhxP3ZYr3+bkTNzECMCrcLDjvRgNetFk1eOt7Z0lx1ERXMuB4BA432F0OM7E/vEwBnyx6gy6Q8MghKAvbQe0VW6oZGoANHqYSByWhA8hrgGheJVITacsXALZTHfVlTpNxd5Jg+7AIBra5NKnZGVqZ32HqQu8yU3nIRLK/b2a5h+5OJdRKXwyDENsBDoNj2RaRDCeVjwyKo6/jybSTG4QrRlD/gkAQLuzir/zOSxlM7VZn/XnnXce/vKXv+CEE07A//t//w9f/OIX8Yc//AHbt29XzM7KhRCCz372s/jTn/6EF198ET09PXnP9/T0oLm5GRs3bsTxxx8PAEilUti8eTO+//3vF3xPnU4Hna5KvhhloubVWO1cPfOOR8LAFsDUB7hWYKLlHRgYGIBJp8L7Vi+f8aUzIU/8igbmxQL5YtuMXSB9L0HUt8NhPQV7R0M42mbB+g7btH1zW60A+QsSAIpmkqfKsssOjkdeA3SNgLUFaD0VByeGYddEcXRDA5pNVdb2RMa5DIj5oAr0waxbhXAig1A8XT03PSENBGnvYziXIRqg/ViqZnyFkIJu+PvgMi9DzBeDJ5KsbNAtTxYtTYDOjIwgKlmOJkuV1nmZGwG9FUiE4EyPwqBVFcwm8hwPvVoPvbpCv0c6Dgy8CZgcwDEfRuR1P4ypBC5d3oIVjVWW6fYeAt54nE50j/0Y9o6F8Lc3x9Bq1eMD64sHtoWCew7c/NfRy5lEWzu8kiF/1TjAT8W5HDi8GfD3oqP7HOwfBwZ8MZy2zFXpkeWTTgAxL138UzVAq+YXxdhzzkh13bznADp4L3ahAePhROWDblHMynaloDuVERFP0XtSVUqic7F1AEPbgeAA7Cto7WxaIAjG03As9nesgLQcyKnprtZjqVIDDauB0TfATe6F3XAUxtMJBGKpKgq6s+eoN5pCLCVAo+LQXG0ddIqgmKmxoHtmbr75ZrS10ZYJn/zkJ+F0OvHvf/8bl156Kd7xjnfM6r2uv/56PPzww/jzn/8Mi8Wi1HDbbDYYDAZwHIcvfOEL+O53v4uVK1di5cqV+O53vwuj0YirrrpqtkNf2vjl1gLd2fqkeZJK5dYwqjAPE7RWDTD8BpCMwtfUjTGPD3pixipHFThdTu6n/7pXAQDGQ0cu311wHD30Ruw7DJv+aBp0J9JoRZVka4KDgJABdBbA5EZ4nEq2qzrodkoLhKFRNDQSDKIKHKIn9tB/XdT13xtNQRAJDFpV9RkmynAcHe/wf8F5D6LNvh4HJyIY8serK5vo78/LLPii1DzRWU3O5TJybWfcDyRCeQ7mpSS8eTXeC5kUy8kk+iRHz3KcyyuCuRHQmYFkBN0qLwAOY8EEUhkRWnWVSE0BIDwCEAKfaERaY8QyRxXXc8vYOgDPAbRxk9iFNRgLJirfSz48Shcw1Dr6PUI2y63XqCpiRDgrbJJSNOoBLyTgMuswHkpgMpJc3KBbFOjiHwC4V+Y9JQfdFl0VqwYajwJG3wAm9sBhXYvxEBCoFjM1Qgr25261G6qzHK8Azpygu2rKShaJWd81VqxYgUAgoPz//e9/P37yk5/gQx/6ENasWTOr97r33nsRDAZxzjnnoKWlRfl57LHHlH1uvPFGfOELX8CnP/1pnHTSSRgeHsbzzz/PenTPBlHIZhIdXQjK9UnVKpUyNQBaIyBk0CDSAKwqHMzTiWyfXvdqRJIZRJMCOA6KS3BVYu8EeBWQCMKligIAgtVyAwGmmX9FkzSrYKrmoFtvo7J9IqJFpNKuipqppaLZG3ETdf0fC8oLQrrqvqm5pUyI9yDa7XTxqur6dQeyi5bhZAapjAie46pTbqrR02ARAIKDcBi1UPEcUhlRqU2tGEIGCPTRx1KWBqjiTDfH0Ww3AEtsAFaDBoJIMBKIV3hgUwiNAABGCe38UtX13DJSXbcrMwEQoixgVxSv5Lbt7AF4Oj1WnMurdeEyF51Zui8RIDiszEs8i22mFhyibQE1BsCSnywJV3v5GADYuxXflkaRJgOrxsE85pVa/6oBWwcGfbXRnzsXu1ELnqP3pHCywvekRWbWQXex+t1IJAK9fnbZPkJIwZ8NGzYo+3Ach1tvvRWjo6NIJBLYvHmz4m7OKJPQMJ3saI2AqWHeM93zDsfR1mEAHGk6mQjE0hDFuXkGzBveg9kaJZNLmSS4TNrqMdgohFqrrIA3pOjxDCWq6ELny5fzRardaEVGcrZ1Seeot5KZ7ok99Ny0ttBJF3JUGNUqLZexdQIqDZCMoEND20aOBBKV/77nIi+2Obrhk/7ODpOmejMLdklGHhiEiueUoNYTrbDLvqJqMUM0uOGPyr3OqzToBhQTKM53GJ1SzeSAr8oWhUIjIIRgQLADqJEJuLkJUGlgUWdgTPswEU7O2Rdo3piiZAOy98qqnS9NRWkbOAC3pCCZXOwFYUVavlxZvACAZEZAKkNLAU26KlYN8DzNdgNojNGMfdU4mMtJCnsHCK9WMt01sdAmoeI5OEz0++SrtEJwkSl7Viubk3Ech2/+/+2deZhcZZm373Nqr+rael+TTjoLhISEHUGHZFQQ3AFFESXq4IwLM4DK6KgsOuIyn4wonxtiQMcRnfkGRxEVlEWQLUDCEsKSvdP7XtW1V53z/fHWOVXVXd3pTnqrqve+rrq6U3Wq+lTlnDrv8zy/5/dcdx1ud+5LPZPJ8OSTT7Jp06Y530HJHGAsGAPLQVHMzO2S7k8KLof+3XgindgsraQyOqF4ioB7ERdng6+In9kLcn9IXMjqlnpQAyKgHTlIMHkYaDOPgUUnPibmy+aNZxlPlEBPNwjZftez+OKHAaF8iCUzuOyLsJjo2yV+1p+QuysbdNcv5dYHED101Sth4BWqYwdx2FpJpDQGFrtH3iA+Jsy/sn2oQ91ikbNkq7MgEhmd20WQi5jZOxBOMBhO0FG3iPOQjQVjcAWhRJq0pmNVlaUd0ATbhVIoOszyhjgvssSCbl2HUBeRZIZhtR6HTaVuqfSeTodqAV8L7vR+grFeutI1jERTi3deRYZEFVG1mOoGyK90L+FjNJ9AG/Q8J8zUVp4JsPBjw8ygu1BabqjY7FYVh3UJB90ggu6uZwlE9qPo65eOvNwoUgRXMDCeIJ7KYLeqS+NaOQuqPXaGxpMMRZK013oWe3cWjBmX53bs2MGOHTvQdZ0XXnjB/PeOHTt4+eWX2bhxI3fcccc87qrkqMnr54a8zO1Svohk91UJ9VDtFNWkRTVdSCdzC8Y6YXqXG8dUAgucrJu1N9aFoqcZWypBt/GZ+pqFnAuWvnu5QWAZKCq2+Cg1NnEsDC1GJTE6LOSlSi47H46nzB7zpkV0Lp8x2b4/dXgPLdlebiODv+gYSUtvE9ic5vfQ0g66c72dJCPUebOV7sWuKkwwAAIIeuxLu//Y6jA/zzZNqFoGwgmiySWiFooOQypOJA1RezUNXufS/jzzCbShKAqt6jCQa4lZFIykemC5aNHIklMGLvHrkYFR6Q73UusSS/xwPE08lVmYvx8dFjfVkvM+ybKkZ3RPxN8GDi8uNU0gdpjxbFvRohIfy12Pajry+rmdS1d1NQXVFWqmNuMj/8EHHwTgwx/+MLfccgs+n2/edkoyh6STZr8XQSHZNjO3S/mLzxkQfbPxMZqVQfqoXty+7qHXhCzSFRCyOEqokgiix9PuwZkO4Y33EVZbF2eMyEQmSMtTGc1cHCz5SrfNKeTcY10so48hljM4nlx4aWf/S+JnsF309AF7+sXczuaAc2n3xhtUd4hK8vgAy5pT7AO6RmOcsnwJTASYkLTMSaKXcLLN7hZtMJFBMWd6sWf2AsRGYXzAVLUMd4vvz9qlaqKWT3UHjBzEFT5IrbeZwXCCwyOxxTf+AtE+BoStteiKhcBiO4DPhmyAWK9l+7rDcdaxSGvLway0vG5Nwd2hpe6BMxGnX5iSJsI4Y734XDZCsRQD4QRtCzFSajDbFx9YJhJWeSz5Gd35KArUH4e1czvNyf2MuNsZjSWpX0xlY9czoo0suBw8tXS+Js79tlJoJ5mAcf0cXuyWpwVm1o2o27ZtkwF3KTHWKU5SVwBcQeKpXE/Nkr6IKIqZJKhL9wAwHFnE6qwh3204ARSl0EStFKR8igLVK7BbVKoTh9F0ffENLLRMrvKVrcRHsvtksyg4lpI78FRkA7GGjDBbWXAzNV3POzbXmXe/1ieC7tVLISiYCXZ3rpqIWEh0jcQWv8dT1wv6uXVdX/rmXwZZXwxGD5kO5iPRJKnMIlVrjMW4vw3sbvNcWZIO8BMxxh6NHmK5XwQMh4aWiMQ8m1QfttYBJTBLOh9fM6gW/JYEjnSI/sUyU4uHINSTm6aQx5L3wJmIopgmdYx2mkmtBUu4TTEqDEpgRvdE6sU1tTHViaqlFteANp2E7p3i99bT0DTdrHQvK5H53Pnkz+pe9Ov8AlICq1rJMWEENdkFmHEBcdstS9v8C/KMqkTQPbJYMpR8Z+hsz6xR5a722JfW6JjpqF6Joig0pLNmaostMQ91iwuJzSmku+TGiXgcM5ybvtgYx2hSjOxZcDO1cK+Q8lmsptdAOJ6iK+uuvLp+Eft3Z0t2sVsdO4TNohBPZcwAd9HIyrOxWMHXQjSZIZ4SybbgUq8omgvvQ7jtFtx2i5g2s1ifqVFJzB6nhtR9yY4Ly8ddLRLXWoaV1kFgCfV1hw4DMKDWApRWpdtiA28TVQ4rvkQv/aEEmcUwUDQSQr4WUy0EwvjLmNFdEu7lBqaZWmfOwXwhrk2pmHAuB9OAMJ+ScC7Px9sEriBui04wdnBxHcz7XhCO8K4g1KyiNyRGFzptlqU9PWcKgm4bigKJlEY0uUCtD0uAEokWJEfNxH7uUpJKZR14q1LDWDPxxZOXG87Q3kbw1Ii7siZqiyo1mi1BMZIrqI1iS0fMBMyikWeqZDicGkF3yWTCfS1gseFRk3hSQwxGFtiB16hy16w2pXz50nJvqVRnINfXPdZJS5U4HroWu6/bqHL728BiNQNWv8uGdaknLY2Fd2QAJR03q90LbqoEInGRNXWjdjWapptJ1NpSqHTnjQ5rSHehKgpjsUWufIFYhEdEEqBHE1ML/K4SSGLkE2jDaVOpSfeS1vTF8cWYYJJqYASJTptl6Rt/5WNMLwh1EXSK/V4QH5fhfbkJL67JrUHdY+L7vLYU1IGQlZgfL47P6L7FczDXdTj8tPi99VRQFHNUWFu1qzQKFBOwWlQC2Tikkvq6l/iqQXJMJCMwLmYIm/3cpSSVcnjBU4vLquJLdBNN5rLOC4op382NquscEV94jaVgUmVgd0NVAw6rSiB+ePHN1AwZWrafG0pQfqZaILAcl81CINFFIrWAcyc1LdfPnXdslpy03MBdDe6abDVRfG8tuplanrQcKB1pOUya2VtrVrwWIagZ2iP2w9sArgCjsRRpTcduVUungpit3NlG99OUNc80rgOLRrgHdJ2UrYqwLq5FJSUvB/C3oaDQhEgeGAntBSMZhdFsQmhSP3cJzejOx10jjEkzaYKaMKlbkIBxGmn5WCzF0HgSRSkxOXT9Opw2C8H4IULh8cXZh+F9QtFmdUDjiUBOaVNSn+UEqrPJl0VXtC0gMuguZ0YPiZ9VdWAXlvwldxEJtmNRFRq1PoCFr3abztCK6QwdTabpzsp3V9aV2KiD6pU4bBb88S5T9bAohHpEQki1FMjQzEp3qcjPAILtqIpCs270dS/QMTp6QCTWbC7TJbZkpeUGtWKx1pIWEsWu0eji9XtpGRjLfodmg27D9GVJm6jlY1S7Rw/m9XYuwgLHkO9mK4m5fm576VRpAstAtUI8xAq3OMcWXWI+JvwPIs5GQMw+Lpl2JwN/KygK1WoUe3p84R3Mh/aI6mxV3aTqbMnN6DZQFNMjw58U16XxRHp+pftaBobETOtiQffBoQggpmk4bSWkGqiqw+5vQNG1XIvMQtP5lPjZtBGsdlIZjZ7seVKKJmoGNaaDeeWYqZXYt7NkVpjzudvNu0ruIpLtRTeMqhZchmJUufOcofcNRNB1qPc5SudzNKheYVa6Q4vpBt+9Q/ysW2smhKCwp7tkyAa8dZkBFD29cGZq5mzu40Xygpy0vCXgKi1puUG2rzsY78Si6EQSmcWbjzpyIOs54DInFhgJlZKodENOZjrWaRo+Do4vcAtEOpnzxJjYz10qnyOI/uOsYqxdFdejzuFFTAqB2T8bspWgiZqB1QFV9XgcFryJXvrCCxx0m14Dayc9VHIzuvPJnvvOSBd2qyrGuc+num3ssGh3sLlE29UEDmSNB9trSqxQAThbhJKsauxVEukFVluOD4hrkaJAyykAdI/GyGg6Xqe1tDwcJmCaqS32KMsFRAbd5cyEfm4owYtIYBkoCn49jD09vrCVbl3Pk++eYN69d0AENh11JVhJ9LVgd7qwanHSxii5hSYVh/5swNh8UsFDhnu5t5SCbncNOKrwWHV8id6FqSRmUjCQ7UOsn+xavqqhBI9NEIs1mwtLJsFK+wiAWblfcA5vFz8b14sFDzmlTUmYf0HOTC3cR7VDR1EglswQWcg2nZH9oKVFFdEjgkOjb7e21AyAsn3dNfFO7FaVaDLDwGKNYYuHzMT6oD1b1Sy1fm4D/zI8WTO1wXCS9EI57BdJCOUzVgrjVaciq3JRxg7jy/Z1j85n0G1KyztMjxaDjKabPcjttaUXdNubTsBmUfDFuxgdG13YP25ch2rXCDNHcgqbtmp36SiFilCJs7pl0F2uxEYhNgKKmlt4kd/TXSIXEZsTvI247BZ8ie6FdY8M90xyhk6mNXNUTEkG3aoFR62ozNrGDizc4iaf3hfEzHNPbU7+msUwrimpSreiQHAFbrsVf7xrYYyABl8TgbfTb8oIS15aDmKxlm03WIlICi1KX/d4v1iMKwq0nApkg9WECFaD7hIJbpx+cdM1rJEec5EzuJBmakZyqHa1mbww/n5JmKjlUyP8J9RQF21esXzqHF6kpFDv80IWHWhjSBf+DSVZ6QYItOGwqtRk+tB0feFaIMyEUACq6ic9PFpqRYp8qhqEOiOdoF4NAfNspmYG3asnPdQ9GiOZ1nDbLdSXWqINhDeGtwkFnXjXSwv3d5ORnKKt9TTzbuM7p5T7uSF3HY0mM0STizzCdoGQQXe50r9b/PQ1m67G8VSGREoEWSUlPc0aVfnjXQs7Nsz4sqtdY36Gh4YjpDUdv8tm9kiWGvb6VVgU8MUOm0HugqHrOWl5y8nmIlw8pJtBTUn1dAME23HbxTE6PJ5Em++xN/kKjOxnWPLScoPsoq0x3Qm6vjiVbqO6ULfWrC4MZ6vcXqe1tPpm80aH1VYtsJmalsktxrOJy1RGM4OZklEMGLiCQtmia3TYhPFX52L0dWsa9Dwnfm/aZJpklazU1N+KgkKdEsKaidO7UPO6iySEDIYjSQbDCWHnUoqBoqqaSe26zAAwj0F3dFjcVIvZbpXPwWyhYnlN6VZmM7XHAZDu3bVwf7R7h0gK+ZrM5Ho8laE/24LRVuJBtzDSrCwH8xJaOUhmjKZB97Pi96aN5t1GldtlLzGzleBynDYVf7yb0UhyYeZ45jtD1+ek5Xv6hRlIR31VyV48lOoVOGwWvIk+QuPhhf3jowchOiQy8HmO2yCynZouJLAee+kF3Q6bii89BKmYGaDNC8lozrAmr+2h5KXlBtUrQLXg08fxZMYIxVIL67SfGC9aXRgupbnS+Rh93aOdZg/1ggXdo4dEn6fdbfZ5jkSS6Lq4DrntJWSoZJCtdrdqQonRle2vXFBG9gt5uc0JdceZ50fJBt12D3hq8Tisoq97IYLugoTQ5H7uF7vGAFhR6yndJGY2UAumhRHtvH2PGkaJ/jazQJHPgayJ2vIS7Oc2sDaKNi5t9LA49+abTBq6suv41tPMpNDhkSi6Lq5DJTPlZRpqKkxiXkKRl2TGDO3JXpBdpuM25KS7JWf+5W/DbrPh0qPYU6MLswAf2S+Cmzxn6Iyms38wG3SXmmt5Pq4girsaBZ1E//6F/dvGRaRh/aSLs9HP7bZbsKglltBwVKFU1eOxW/DHD8/v2JuBl7Nz4xuERJ8ykZYbWB0QWI5VVVmh9ACY0wIWhO5nxYLc12wuWiHXh1xdapJoo4Uj3EONS1zyF2xEi7EYr1lt9nnmm6iVZOIy29ftix7CbVNJpjV6xhZYjWGohRo3kEY1DShLVl4O4G/L9nX30L8QQffowUkJIYN0RuOlHhFYbWjxz/++zBdZlYs/LkbLjc1XMthMXkyWlo8n0gxkFQPLa0q3MusN1BByNJJIpsU1eL4Z2C3k5Y4qqDvOvNvs5y5h1/J8TDM1GXRLSpauZ8TPpo2iopil5MaFGVhsKP7WbLW7a2EyYqYz9DrTGbp7NEY8lcFlt9Dsd83/PswjelBUa1KD+xbujybCuUV4y8mTHg6XonN5PsF23A7R190/nw685rGZV+UuF2m5QXZ02DJdjERasL7uTCoX0LSdXvBQSTpug5BEO6pAy1CrDwEwGk3Nv+u2ruecoetylUTTRK2qxJIXBv42cU1KRljlFufdgvZ1x0M5pUvTJsZiKXRdSDVdpTSKaSKBNqqyQfdQJEkyPc9+I/lj7CYYf+0diBBLZvA6rSXptm3ibQbVgos4zvRY9liZ4/M+FTNd9PPHfxocyBYqGnxO3KWmYMsj4LYx5O4gns7kVJDzha7nWpxaTjHXoJD7ril1abmBaaZWIQ7mMuguNyKDufECE5yhS25cWD7BbF93otvsX5s30sncYjFPvrsn61q+staDWmqV2AlYsmZqysg+8QW/EPQ8Jyq0/paipjVGpbtkJVPBFVRlzdTmrVITGxULnLy58QB7ykVabpDt667TBrFmYgtX6e7bJRQuTn+B5DSZ1sx9aA6UWMJNUcxqtzfejaooJNOaWR2dN8I9ItFmsZmjHyE3HqbkZPoGFqs5EWSFKmS7C9rXnWeghqfWVH75XbbSVA4Y+FuxW1SC2ghqJjm/LRD5CaEiruUvZKXlJzT7S/tab7GCrxmHVcWX7COV0YnO9eSC4X3iePTUTppzDoX93KVMwGVnyL2SVAbSo12ih32+GOuEcJ/4/2vaZN4djqcYjiRRFGgNlth1aAqM64CUl0tKE6PKXbPKNAAyKLlxYfkE23HZLPjiPfPvvDuUdYZ2BYXEFGHytTdbTewodfku4Kxbga6oZKKjwuV+vtE06N4pfm+eXOUGGI+XeNDtb8PtsuPIjBMa6Z+fSqKRYQ8sA6cPKDNpuYHTB94GfE4LwdghhiNJMykzb+RXF1pPLah+HR6JktF0fC4bwVLsm832dVtCh/FnlU4jkXlu0zGCmpoOsXg07s4GUzWlWukGcX0FGlNCidEzFie1EJMgJhioQc5hu2T7uQ2yTvtum0pVon9+F+GhbuHdYLUXJIRAeA50DkdRFDihxTd/+7BQ+FtRFYV6rR+Yh75u07V81aSHNE035dAlrRhAKEmcbi9jzhbiKS1nVjwfdD4lfjZsEO0Pxt3ZKneDz4mzlFUteRiV7vFEmnhqgWegLwIy6C4nUnExjgnEonECJTcuLB9vEy6XmC89PnR4fv+WId9tWGeaVwyEE4TjaWwWpeTHNAD4PB7CjkYS6YzIVM83Q3tE1cvmKuhPyme81CvdVjuummVYFHCPd879eDtdzzs2y1hablCzGquqsiw7Omzeq93D+4RSyGqHxhMLHjKqNe2l6r5rmKmFugi6xWJtZL4VQ/ny3SzxVMb0Fik5mX4+1aI9xxXvw2dNouk6Awsxhm2CgRpQUOkueQJt2fGgPfMbdA9mXcurCxNCAC925wzUSlIVOBHTwVwE3aNzeV3StFyrQ5GguzcUJ57K4LRZaPQ55+7vLhJ+t41Bdwex1DxKzGMjuUTGhHW8kcAohzWogcNqMb+7FuQ7dJGRQXc50feiqNB6aidlbwFCsay8vBQvzqoFT33W0Gz4wPz1eyUjYj4vFLqWZ6Xly2s82Cylf9r4XDZGnW2kMjrpwb3z/wdNN/0TJy1yDMZLvacbUGtWmvO657yve7xfBIWqtUD6bEjLV5eLtNwga8rTovWg6GkOz3fQbVS5GzeKoCaPknffddeIhFcmTSNC2TKvDvuRoeyxajGNxyAnIfQ6raVdqXH6oKoeBeiwiHFMPWMLYP6VZ6BmfI+OZYOogKuEkxgG/jZcNgve+Qy6dT2XEKordC1PZzR2dQsDtfWlbKCWj78VFAWvPo49PT63le6xTmFGZ3NNMqOD3Pfmsmp3acv0swTddobd7cTSiO+38YG5/yOHnxHHaPVK0ygVhNqys8xM1AzqsiP55tULZ4lQ+tGDRKDrOWn5hPnHAIl0xpRueEux0g046zuwW1R8sW4G5qvfq3931hm6ETw15t17BwzX8vIIbJw2C9EqkQFPDO4X4ynmi+iwSGQU8RnIx5APl+rxCUCwHY/Dij/RRf9cOxr3vSh+1nSYQWG+tHxVuUjLDaoawOHFbwd/vJuu+TRTGx/IHaOtpxQ8NBpNMhpNoSoKbdUl2kenKKaTcV1G9CHPqzeGIS0PLC9IYJR8P3c+WdMoY3RY73wH3RMM1AyM/8fyqHQvw2W3UJUcYDg8T33ykcHsTGmrqVgw2DcoDNSqHFZWlGqCbSJWB1Q14LRZ8CZ65zboNqXlHZPM6KB8+rkNAm4bGdXBsCObYJjranc6Ab3Z9pEJVe6RaIrxRBqrqtAUKH3VQD712aC7EirdJby6lRQwsl9cSKz2SfOPIVfldtktOKwlWmEILMfjsOCN9dI3GqFlPgyNTPlu7jMci6YYDCdQFYUVtWVyIQZsvkZSFhfJRAJP6LBpDjTnGNWZ6pVFjVYMSt69HKCqEZfbgyU0xGj/QVjbMDevq+X1kOUdm2UrLQcRKNauxhcdIzh0kAPjy0yp4pxjVLlr10w6Ro2FY3PAWbrfnQD+ZTDwKoFUL9DO8Hz2dJsmVYUjhAazzuU1pTZ2rRjVHXDwcWqTXcAp8z82bIKBGoieWcMg1V/qPd0AriAujw9VD6GNdZNMd2C3znFtyDg2g+2Txla+cDhroNbiK4vKrEmgDUfPQXzRXtPb52jQNI1kMi9ZN3QYVDf4VkK8MOkUS6YJjUdwqdDktRCPl34V02fTcakZ+pwdxNUo9O+HptMnFbmOmp7nQbOCpw7czQWf6cH+EC41Q1PARSaVJLMAk3MXihqXgkvNMBqOLtnjxGazYbEc+/W/hFe3kgKM+ceNJ066kEB+P3cJX5ir6nF5vFiiw4T6D0J77ZGfMxuiw8JgZaIzdFZa3hJ04bKX8KJ7An63nVFnK4l0t+hnnY+gO5MSi0WYtsqdymgkUqJloGR7ugFUFVfDSugdIjW4H10/bW56gMcOiZ54q6OgOlO20nKDmlXYu56lOX2Y/ZpOz1h87hNfifFcsm3CmDDISSTbSz3hlu3rrkr0ga4RjqdIZbS5b5dJhMX3KEwOusOGiVoZVLp9LWB14NXieOP9hJVGwvHU/CS/ihiogUhUZjQdi6rgLeXvTQNFwVazHNu+LnyJHkaiSRrmuhfY6OeecGyORpMcMgzUmstEWm7gb8tWunvYGzs6hUsymWT//v1oWra1T9PAvhbswJgCof0F26cyGpsCGVRVob+r8xjfwNIgo+mcFEyjKLXs5wxx5769BSO9jhpdhyTgO0PI9Q8cKHhYS2Y4KajhsGns37+/6EuUKpouPleUFPv27VuyvimBQIDGxsZj2r8y+JaWFBgvTOEMbWQ3S1q6qyg461fCwDDx/v3AKUd8yqwwFt7BFWKubZa92aC7o67EF90T8LlsHHS2EU91iqC742/n/o/07xYGf05fQW/nRAzncptFwTHXlY0FpqpxNerz23FHOhmLpQi45yDAMGdzH2/2csaSGbrHylRabhBYDhYbNbZxPMlBukZq5j7o7t4BWlpMKpjQl5jOaOaM8JKXSHrqwOrAlo4T1EcY0WsYjabMfro5w+iX9TWDw2veres6Q9k+3bpSdi43UFWoXomlfzfL6eVFGukLxecn6C5ioAZ5E0mc1vKpzGb7un2JXobG5zjojo2KUUxZFU0+L3aJXu72Gk95SPXz8bfitKm4UyMkopFZJ9t0XaenpweLxUJbWxuqqorRikmvGAlYRMEWjidJpHVcNhWPozw+T03XTa+BakscNZMAm7tgvXjUpBKQGANFBXe1+JlF13WGo0l0XRTO5lz9sQQYjiTQluj703WdaDRKf78wI2xqajrq1yrhCExi0vVs1nhhRUEfcj7mjO4Sv5j4mzpg19OoYwdIpDNzJ/fU9Vx/TsM68+5oMm26Jq8sk35uA7/LxpizhcSoLnpaE+GCRfKcYEjLm08q2vNlkO9cvlSznDPFUrMCt91KJtHHwGiIgPsYFRmZNAy8LH6vzx2bh0ei6DrUVtnLT1puYLFC9Qq8o2GCsYN0jbbN7etn0jmTv9bTJskEe8biJNMaHoel9ANFVQV/G8rQHloYYIQaRqPJeQi6s/LdCSZV0WSGWDKDokCwlJ3L86npgP7dtGk9vMgmesbirKqf4+9QKGqgBjkn6jlJ7C0V/G247Raqwn0Mj8eBORzbZSSE/K1gzyXvMprOrqxredkYqOVj92D11mNVR7N93WupncX3WTqdJhqN0tzcjNudTT5qEcAmEur2wsSIruuEU2BTweu2YS/ltpwJRNIqmq5jc9iwJTRQNXA4jl1irkXAbhMBvKMwwZvKaFhTCgrg9ThKfo1UDJemkkxrWO1WnPalF5q6XKKdtb+/n/r6+qOWmi+tdIJk9uTLd1smjwkzCJfyuLA8nHUdOKxijmf/yPjcvXC4R8jLLYXO0PsGIug61PscZZf99jmtpC0uRizV4o7hOZYshXuFzFS1TBrBNBGj/aGk+7kNXEEc3moUdMZ65mAc29AeSCfF4sYY/URufEhbGY0PKUrNanxOG8H4IfpCibmdh9z3oqjYOH1FR9nlu5aXxUIna6ZWnx0fNOcO0ak4jBwUv+eNCoOciVrAZSuLCRCAaPVQFKr1YWzpyPw4mE9hoAZlNi7MwFOHw+XBoqeIDHfP7WubXgOFCaF9A+NEDQO1Um8jmYqsxNyX6Jm1mVomI0x47fZsckfTIJP97ijSzpjWdDQdFCifcz2LJasoSSt2EWhrmdxncbRkkuIaryAq5xMwpvXYrWp5XIeKYDM+14y+yHsyNUbCKZU6+ob68jobKpG+XWKh4wpMcuI00HXdXPCUeqUbVxBHVQBF1xjtncP50oZ8t3aNMKPLkpOWl1eVG6A6W23qUZvQdV1IGOcSozpTu2Za+ZWu6+w4NApAk79E3aEnYK8TUvp43xyMY+vPk5bnXXA7Kybo7sBhs1CtDWNJhufOJVrXcwZqLacWVWIcMOdzl8lCPJu0qU73g67P/azu4b3C7MtTKySSeZgmaqWuGMjH7gFvI16njUD8MP2hOBltjheNRQzUDEaz/bllYaJmoKo4asRxmh46MHevm4yIEVcwSVr+QlfWQK3ZZwZVZUegDYdVxZvoPepZ3WbAl0mAjihSqJMT5YkyDhKt2eMjo+lgzVb408d4TUpmnfqtrqL94WbQXWYJjHys2fc2p0n1OWYujuXy/R+sBPLHhDWfPKV8d99ghOFIEptFoclf4qMGFAVbrUguxOYioIGsM7QhLc85QyfTGoeyi+6VZdbPDaI6YreqDNlbiKUyotKtz9GCMRXPjbhqKe4zYLC7J8xAOIHDpnLK8qndzUuJqqZVAGgj+0VC42hJxXJVrrxjMxRPMZIdY9UaLI9ExZTYPSj+VrxOK8HYQbPH+pgZ2S/GB1ls0LRx0sPhuJhaoChizmxZUNUAFhtuNYk7NczIUS6+p2QK13Ios3Fh+VR34LSp1Kc6SWV0huZynOUUBmoGRvBUVpVuwF2/AgAldJj0XC3Ch/aI65u3QRQpsoxFUxwcKlMDtXz8rThtFjzJQcLjkWN7rXT2GLcUT6Al09nK+BLrzZ0LzEq3pufGIabjM1o73XDDDWzatKnwTi2TC9rtbjZv3sxVV11lPqzruhmI2mbxeX7pS1/iYx/72Iy3X2yseZ/rMa2ZjpKLL76Ym2++ed7/TvmdEZXE2GEY7xfZxqbi8l1d13lszyAAJy0L4l6CvRKzxdsgqoipoTmqzI7sF5lGm6vAwfvQcIS0puNz2Uq/n7MIiqJQW2Vn3FHPeMYiArxw79y8eN+Lol/WUwv+qftw0xmNx/aK4/O09uqycYcPNK1CURRs8WFCYyNH/0IDr4iLclUdVNWbdxvJoAafo7THWM2U2tV4nbZsX/ccBd2d2Sp306aCWdIGxqiwRp+zbI5LVAv4W3FlZ/YOR5Jzt8DJpHMJognScsAMRmfTS1oS1HSgoNCi96HombmVmE9hoAbi2m7IhANlFnQ7a5djVRW88V5G5qoFYsBICBUemy9me7mX17jLSzEwEacfqyeAgk5y5BjcxHU9F3RbJ39vappOKisRLnVT1GLkB926xSEMz/Ll9lkUReHXv/71kV8wFRWfqcUubhMfzmjogKrkAtMj0dfXxy233MK//Mu/mPdt3boVRVFQFAWr1cqyZcv4+Mc/zsjI5PVJLBYjGAxSXV1NLJa73v7gBz/A6/WSTqfN+8bHx7HZbLzhDW8oeI1HHnkERVF49dVXi+7jDTfcULA/jQ31vOv8N/Gj732XSKz4d+jHPvYxLBYLd911l3mf8RpT3bZu3Wpue+6552KxWHjiiScmvfZ1113HV7/6VUKhUNG/PVeU3xlRSXQ9LX7WnyACxiK80hdmcDxZVlXEYKuooqjj/cSjc9DXbTpDryuQ9uwdENngjroy6ecsQp3Xga5YGLZl3RiH50Cyr+t5BmonT2sw8tzhUcLxNF6nlU1tgWP/20sEq9MDvkYARruKX3RmhHlsnlBw9+ERERCWTQX2SNSsxue04k900z8yduwS3vEBcawrCrQWn4JgBN3Ly0VabpDt7fQnukmmNaLJzNy87sgB4THi8IK30N0137m8plxM1Ay8TWB347NpeBO9cxt0T2GgBhBLZUimNRSl/CrdircJh8OBVYszNtRz7C+YTojjEyb1c7/SGwZgfTlXubPYa5YDoI8cOvoXySRFu4OiCpXQBJLZqqxFVbBMY55aqhgS74yRXDAStqmjSAbrWk5abi9+Lc+Xls90HXr77bfzute9jvb29oL73/KWt9DT08OBAwf48Y9/zG9/+1s+8YlPTHr+//t//4/169ezbt06/ud//se8f8uWLYyPj/P000+b9z3yyCM0Njayfft2otGoef9DDz1Ec3Mza9ZMTsAanHDCCfT09HDo0CEefPBB3vnui/jOzf+HN7z+bMLhcMG20WiUX/7yl3z2s5/l9ttvN+/v6ekxb9/+9rfx+XwF991yyy0AHDp0iMcff5xPfepTBc83OPHEE2lvb+fnP//5lPs7F5TfGVEpxEO5zG1L8UVjRtN5fO8QAKcur8ZpK49qjbMqgOKuAXSGu/Yc24ulkzlJZMPEwEZ8iZZNP2cRjKpTjyW7SJ6Lvu7RQznZbuP6KTeLpzI8tV9kWc9cWVN2hiu2GtEGET3aNoj4mPgsocBRX9f1yjFRM3BX4/LXYVN0PJFO+sPHGNgYvdy1q4uOu9E0nYPDxnzuMvuMA8tQFYW6jOjrnjMztcG8SuKExWEoniaZ1rCoSnk5bYN4r9UdeB1WArFOesfmSIkxjYEa5EzUqhxWsx+ybFAtqAExvi/aPwfXpOF9YiygK1jQF59Ma+bn2Boss/O8CI7adgDUcBfa0SYuzSp3ccduo597KVS5N2/ezJVXXslVV11FMBikoaGBH/3oR0QiET784Q/j9Xrp6Ojg97//fcHzHn74YU4//XQcDgdNTU187nOfM6u7qqpw0dvO4wvXXsNnr/0s1c0raFy9kRu+/K+mxNwIdt/97nejKMqk4PdnP/sZ7e3t+ANB3rf1Y4QjsaKqgS9/+cucfspJAAUO8KeccgrXXXfdlO/7rrvu4h3veMek+x0OB42NjbS2tnLuuedyySWXcN99903a7vbbb+eyyy7jsssuKwhQ165dS3NzMw899JB530MPPcQ73/lOOjo6eOyxxwru37Jly5T7CIgKd2Mjzc3NbNiwgU9+6lPcfe99vLRrF9/4xjcKtv2v//ov1q1bx+c//3n++te/ciA7x7yxsdG8+f1+FEWZdB/Atm3beNvb3sbHP/5xfvnLXxKJTG6xeMc73sEvfvGLaff5WFn8s0JydPTszJmreBuKbvJSd4jRaAq33VJWVUQQY5kAxo+1r3voNVGdcQXFXNksoXiKUCyFokBToMT74KfBGBd0UM8eQ2Ndoh/7WDBGMDWsL+psarD9wDDxVIbaKjvrmuZwLMwSwdUg+rrTQ/uOrle+f7f4GWgDZ64KMxxJEklksKpl4NEwUxQFpXY1PldWYn4sfd3JSE5B0Hp60U16Q3ESKQ2nzUKDt8w+Y28TqFZ8liTO9NhRmyoVoGniuxSm6OcWC/Wgx16eRlU1HVQ5rQTjhxiJpojNhXpgGgM1KN9+bgNbtajKJoePoSprYI6xK0wIjWaNBN12S/m0kEyDp64dBXDH+xiPH+V1Pp1A13WS2EimtYJbIpUhkkiZPcgTH5+L22zbYe68805qa2t56qmnuPLKK/n4xz/Oe97zHs466yyeffZZzjvvPD74wQ+aVdquri4uuOACTjvtNJ577jm+//3vc/vtt/Ov//qv5mtaFIVf/eLn2J1uHn/8Cb75lS/x5W/czP1/uBeA7dtFUnfbtm309PSY/wbYu3cvv/71r7nnt7/lnv/6Dx7+6xN8/ZbvF01gXL51K6+8vJsdzzxtJjGef/55duzYUSCbzmdkZIQXX3yRU0+depoRwL59+/jDH/6AzVb4/bF3714ef/xx3vve9/Le976Xxx57jH37cgrIzZs38+CDD5r/fvDBB9m8eTPnnHOOeX8ymeTxxx8/YtA9EauqsnrNWt547nkFFXbIJQL8fj8XXHAB27Ztm/Hr6rrOtm3buOyyyzjuuONYs2YNv/rVryZtd/rpp/PUU0+RSMyhL8cEFrXB9y9/+Qv/9m//xjPPPENPTw93330373rXu8zHt27dyp133lnwnDPOOKOoHr+i0DLQvVP8PkWVO5XReGKfqHKfvqK67Awt3A0rCXU+TWLgGOXQxuK7YV3Bl56xqK/3Osu6Z7bG40BRYETzkHD6cSTHYPTgpBm7MyYxnlNgNJ805WaheIqdWcfy16+uQy3DhXh10wpGFCuJSAh9vB9liuTYlBhGdBMUGEaVuzngKr8K13TUrsbrfJjgSCddIxFOba8+8nOK0b1DVL18TWJebxFyo8Lc5XdsWqzga8Y5MIYv0cNwdMWxv2aoK+uL4SwYa2cwmDVRqy03ablBsB2rxUo1wzjSYXpD8WMbPXUEAzUo03FheXjqVzC8C/TRQyJpebQtXlpGmKjBpH7u4WzQXTZz44+A6qnB4vSgxyOMD3TiWz619LcomTRoaVI6/N9HDyNmXOU9rOnEUyLh5HFYJj0+F3xyyyrs1pm/7saNG/niF78IwOc//3m+/vWvU1tbyxVXXAGIXt7vf//7PP/885x55pl873vfo62tjVtvvRVFUTjuuOPo7u7mn//5n7nuuutQVVWY7p2wns987gtUOaysXX45t/7wJ/z5z/fz5vPfSl1dHQCBQIDGxsaC/dE0jTvuuAOvyw7La/ng+y7mzw89wleL7Ht9YzOb3/hmfvWfP+MtW14PiED+nHPOYeXK4tOKDh48iK7rNDc3T3rsnnvuoaqqikwmQzybdJloHvaTn/yE888/n2BQKMDe8pa38JOf/MRMOmzevJmrr76adDpNLBZjx44d/M3f/A2ZTIbvfOc7ADzxxBPEYrHZB90W8f/asWoND/35T+b9r732Gk888YQZiF922WX84z/+I9dffz3qDFoY/vSnPxGNRjnvvPPM599+++18+MMfLtiupaWFRCJBb28vy5cvn9W+z5RFXbFFIhE2btzIrbfeOuU2Rg+Ccbv33nsXcA+XKAMvi2qNo6qoYQ3A84dHGU+IXtkNLeXXq+Rv6gAUUqEBSISPuH1RkpHcbOqGQhm0EXS3lLkztN2qmiY8o85sAHIs87p7nhPVGX/LlAoMgMf2DJHWdFqDLtprylPWV+NzE3Y2kcroRPtnqcgYHxA31TLJQKkze2wuK9PPbUp8rXirvKLPs+/A0ckjM+ncxIfW06dcyOf6ucv0Mw604bJZ8MV7zGrfMTH4ivhZs6royBuj0l1W48LysbnA30KV00ogdoieY5WYT2OgZmBUustOrp/F17AcXVHJRENkoqNH/0IjB0QbmaMKfC0FDxmtFdVl+hlOQlHQs8am8YEDs3++YRZmsVEsoM5ouX7u+Qi4j4YTT8yZDFssFmpqatiwYYN5X0ODWKf09/cDsHv3bl73utcV9E+fffbZjI+Pc/jw4UmvG0tm0K1Omhrr6e/rE+ufaWhvb8fr9Yr1J9DU3Gr+7Ykk0hqXXf5h7v7vXxGPx0mlUvz85z/nIx/5yJSvbxifOZ2TFVpbtmxh586dPPnkk1x55ZWcd955XHnllebjmUyGO++8k8suu8y877LLLuPOO+80Z7Vv2bKFSCTC9u3beeSRR1izZg319fWcc845bN++nUgkwkMPPcSyZcumTAxMhWEUp+t6wed/++23c95551FbKxQ/F1xwAZFIhD/96U9FX2cit99+O5dccglWq6gzv//97+fJJ5/klVdeKdjO5RLr/fze9LlmUSvd559/Pueff/602xg9CJI8zDFhJxVd4CTSGbYfyPXKlmM1rK46wMv2GkgOEhvYh6t18sifI9K/W3xB+pomzZQ1HJJbAuUddAPUeZ2MRFP0W5tpYJfofzuayoKmibYHEAZqU9AfjvNyr3CIfMPqurI1qbNZVPTgCoh1Eu5+DU/HWTN/slHlrl5ZYJKoabppotZWAT2IBagqnqY1WDr78YT2Mxg5jfrZSr/7d4mKrNM3pZojmkzTFxJVgLIzUTPImqn5Ej3sO9YRV7oOg4a0vHgSeNAwUSu3cWH5VHfgdbxGINJ57LPkpzFQMwiVeaXb63YRd9bjivUy3rcP/8riqr4jYkjLa1ZPuqaNRMRnWCmVbgA1uIxM38ukj0a2n0mABWx2F5/cUjPp4eFIgrSm43XY5k2ub7PMbr0wUT6tKErBfcb6Q8smDCYGfMZ9+dsCOB12FAUyuk5St6IoqniNdGJKY2NzfzJpsZ0CitVu/u2JfzOZ1jj3/LficDi4++67cTgcJBIJLrrooilf3whMR0ZGzIq7gcfjYdUq0fb2ne98hy1btnDjjTfyla98BYA//vGPdHV1cckllxQ8L5PJcN9993H++eezatUqWltbefDBBxkZGeGcc84BRG/1ihUr+Otf/8qDDz7I3/7t3065j1OhKApWVeG1V1+hvX2F+bd/+tOf0tvbawbNxv23334755577rSvOTw8zK9//WtSqRTf//73C57/k5/8pKB3fHh4GGDS5zaXLPlo7KGHHqK+vp41a9ZwxRVXTJkRqhhCPaLvVrVMKTt79uAosWSGoNtWlr2yAA6rBS0g5B+h7qM0U5vCGTqaTJsZ8LKfgUyur7uLOnFcxccgdhRjrob3ZqszrimrMwB/3TOIrsPaRi+NZd6T7KwXmd7E0AFxoZ0Jup43N77w2OwPJ0ikNBw2lXpvmVYNp0GtW4PXYaX6aPq6dR06nxK/t5xSNGEJQr6v6+K8qHKU/ojFovhbcTps2DMREuMjx+YGHxmA2CioVghOlqpnNN0c+1TrKeNjNtvX7Y930zcSPvpRbPFQTg49xTUeYDQmPtNAmY65UhRF9LMD0YGjVF9pWi4hVDc5IWTIy6srKOg2euX1scPi85kpug6aSFIoNid2q1pwU9VsQGtR8Tqtkx6fq9t8J+nXrVvHY489VnD+PvbYY3i9XlpackoJRVFwZc2JY6lM7nqSdTG32WxmdXgSqayJl8Ux5XUomR0VZrdZ+dCHPsS2bdvYtm0b73vf+3C7p064d3R04PP5eOmll474Xq+//nr+z//5P3R3dwOiIvy+972PnTt3Ftw+8IEPFBiqbdmyhYceeoiHHnqIzZs3m/efc845/PGPf+SJJ56YtbTcYP+eV3nwT/fx9myr8b333ks4HGbHjh0F+/Rf//Vf/PrXv2ZoaGja1/v5z39Oa2srzz33XMHzv/3tb3PnnXcWjD978cUXaW1tNRMX88GSDrrPP/98fv7zn/PAAw/wrW99i+3bt/O3f/u30za5JxIJQqFQwa2sMKrcdWuFXGoCsWSGZw+JgOmsVbXl14+Yh7NOLPBi/Xtnb1QVHYZQtxh7UX98wUPGYr7W6ygbx/fpqM1Wnwaieq7H9WhGh3VlDdSaTpyyOnNoKMqBwSgWVeGsjsmZ8nIjWNdCyuIiFouLvteZMNYpFt5Wu5Dr5mG6lgfLsNd4JlSvpMrtwJkeY6BvlqOERvbnXPWnCWYODIrPuJynFmCxYQ80Y1EVqmLdxyYxNyqJ1SvEMTuB0WiSjKZjt6r4XGWaxADw1OGqCmAjjSPSffSu8L3Pi+vZFAZqIAyqIgmxoC/XSjfkAsTE4MGje4FQl5DxWh0QKOzR1DSd0UqTlwOe6kYyqo1kIg6RWRSxtDToiGu7Ovk8NkZb2SxqSV+bPvGJT9DZ2cmVV17Jyy+/zP/+7/9y/fXXc80110zqHzaC7kRaQzfCqUwSNI329nb+/Oc/09vbO3kWtjFezD71NcYcFWa1cMUVV/DAAw/w+9//flppOYCqqrzpTW/i0UcfPeJ73bx5MyeccAI33XQTAwMD/Pa3v+Xyyy9n/fr1BbfLL7+c3/zmNwwMDAAi6H700UfZuXOnWekGEXTfdtttxOPxGQXd6XSa3t5euru7eeGFF/jud7/LBee9iRM2nMgn/+kaQCQC3vrWt7Jx48aCfbrooouoq6vjP/7jP6b9G7fffjsXX3zxpPf0kY98hNHRUX73u9+Z2z7yyCNHrJwfK0s66L7kkkt461vfyvr163n729/O73//e1599dWCD2kiX/va1/D7/eatra1tAfd4nklGc47GLcWdCbcfGCaZ1qj3OVhdPzkoLye8DSvQFZV4eGT2lVmjyh1sn5S8OJyVlrdWgLQccpXu4UiKdKBd3Dnbvu7ocG7u8RQBja7rPLJHfGlvaPWXbS9iPvU+J2POFiKJ9MzHsfVlM9R1x02ag1pxo8ImYnXgqc8m23pfnl01sTPrINu0MTdbdQK6rnMwz0StnFECy0Rfd6KHkWNxMDdHhU12LQcK5nOXaysJAIqCWrsKj0O4mB/VvO4ZGKhBzkTNabOUdWLYVbscUEiNDx2dd4spLZ/sNRCOp0lrOlZVwess42TQBPxuJ2F7ozA8G+2c+ROzVW4sxdUqS2lU2LHQ0tLCvffey1NPPcXGjRv5h3/4Bz760Y+aZmz5WC1qbm63jlj/6Dqk43zrW9/i/vvvp62tjZNOyjOV1TWxjcUKlqnXQPmf5+rVqznrrLNYu3YtZ5xxxhHfw8c+9jHuuuuuorL1iVxzzTXcdtttfO9738Pj8fDGN75x0jZbtmzB6/Xys5/9zPx3LBZj1apVZk88iKA7HA7T0dExo9hr165dNDU1sWzZMjZv3syvfvUrrr32c/zvH/6M0+Wmt7eX3/3ud0Xl9IqicOGFFxaduW3wzDPP8NxzzxV9vtfr5dxzzzWfH4/Hufvuu02DvfmipL5pmpqaWL58Oa+99tqU23z+85/nmmuuMf8dCoXKJ/DueU5kG72NBeOtDMLxFM91jgJwVkdteS9wgPqgjxft9TgS/cJxe0Jf9pQUyHfXTXq4UkzUDKocVlx2C7FkhhFHK3UAoweEHHqKivUkjF7u4Iop/x9e7g3TH0pgt6qcseIonadLjNoqByFnC7WRPcQH9uFcuXn6J2gZGMgm1uoLj81URqMnmxBaVqlBN+BrXYf68gs4x/YxGk3NrB8zMphLCk0x8QFgIJwgmsxgt6o0l3vSLbAMp03FF+9h5Ggr3bFRCPeJz7WmeNA9WO4mavlUd+B1PkZw/BC9ozHWz9bEdAYGagBjZS4tNwj6vRywVRNLjsLY4UmqtGnR9cLZ8RMwpOUBj72kK7OzxeeyEnI2EYh3khw+gL3ttCM/SdNy7VFF5klr2f5jWFpBd/48aQNjvnM+E5O355xzDk899dSMXtdlt5CMaWz7z19Ra0+JCS7pOG9/+9t5+9vfXvC8G66/nhs+8wlxnbd5QFG46qqruOqqqwpeO53RzGSl3aKi6zp9fX38/d///ZHfNHDuuefS0tLCL3/5S97//vcDcMcddxTd9tJLL+XSSy8FhNy8GFartUDG3d7eXjTh3draOuNE+A033MANN9ww6X5N1xkIJ9B0qKtvIJWaOiFsuKUbbN26tWCU2imnnDLt/vzmN78xf7/99ts544wzOPPMM2e0/0fL0jk7ZsDQ0BCdnZ00NTVNuY3D4cDn8xXcygJNy80/bjmlqMnVU/uHSWs6LYHydYTOp67KQdjZQjKjEeufhRw63CMqsxYr1BaaKcVTGXORWPaL7iyKolCbXRD3ZXxC8pRJQ+jwEZ6ZJZOGnufF7y3FDdTSGY2/7hkE4LT2atz2ksr3HTV2q4qanSkfHTyck5VNxdBeMSfdUTVJDtkzGiet6VQ5rATLfLE9Hdb6NVQ5rPgSfXQPDs/sSYezVe7a1dMm5w4M5ZQEZTlPOh9/K067FUc6zNjoDD/HiRj9sv42sBe/5gyNV4CJmkGwHY/LjiMdZmiob/bPn4GBGpT/uDCDGo+dkLORWCqDNjJL4698r4HqyS7KwxFxna8kaTkIP5xUlehNTg4emllrXrgH0EU7nmXyMWcE3BZVKf/vzQk4rCqqoqDpkCB7LGUSIrCeSDou7lfVac3WkpmcVH9wcICbb76Zrq6uSSOupkJRFH70ox8V9CuXCmrWTA3EunGhsNlsfPe73533v7OoQff4+LjZ1A6wf/9+du7cyaFDhxgfH+czn/kMjz/+OAcOHOChhx7i7W9/O7W1tbz73e9ezN1eHIb25EyqimR7o8k0u7pF//pZq2rKvsoNIqBRqtsBiPbNoq/bkJbXrpnUg9g9GkPXIei2la+JUhEMiflAJCl6M2Hmfd0Du0Uw6fRBdUfRTXZ2jhKOixF2Jy0LzMEelw7BYC0xW4BoIgUjR+hN7DfM/Y4XF+Y8OkdyAWElnN9T4vTjDDYDOqOdLx95+2QEerNu8K2nT7mZruvs6R8HqIikJVYHNr9IYKeHDhzda0xTSTQwxoWVtYmagdVutj8w+BqJ9BRGSsWYoYEa5I0LK/Og2+e0EXE2o+kQHzwwuycPZMcBTeE1MGw6l5f3Z1gMW6AZTbGQiIVFAeJIjGYTHhZ70YJPvhS60q5N+YZq0bQikhI6IsDOR9fNMWHY3NNOh8n/PBsaGvj617/Oj370I3N29kzYuHEjH/zgB2f1XpYKxrzu1LEYfM6Sj33sY6xdW3yiyVyyqEH3008/zUknnWT2O1xzzTWcdNJJXHfddVgsFl544QXe+c53smbNGi6//HLWrFnD448/LmbcVRqGgVrTxqKZxucPj5HRdBr9TloraJRQVf0yNMVKNBIWme0joWl50vL1kx42R4VV0GcIQjUAQl5rVgVmGnQb1ZmmTZMCRRDmfk8dEBf2szpqsZXhCLvpqPc5GHO2MJ5Ii5mxU5FOwGB20V3k2Mz1c1eGAmM6PM1Cepvom0HQ3b0j15ZjGAUW22wsTl8ojlVVWFXmfhgGzrp2ALSRWfR2GiSjwvQPpuznTmU0RrNV2YqodAPOhjU4rCr+WCf9oVmMY5uBgZqBUen2lXnQraoK1uplAMRHe4+sFMrnCAkhw1G/kpzLDfweJ+P2euIpDcZmoCDID7onIEZbieTSUpKWLyTGeLRURiOtZpOLE4PuTErcFEUE3VOQL9W3W4W0fGBgwJSAVwLW7DpyISvdC8WilvI2b948rd7+j3/84wLuzRImMigW64oiZnNPIKPpvHB4DIBNbYGF3bdFpsHvocfRSCTRJz6jqvrpnzCyXywWbS5hojYBs5+7QqTlBrVecTEdHE+gB5aLbPX4gDCvcUyT5Ar3iRF2iioSQkV4Yv8QiZRGndfB8U2VlzCr9zp4ztlKdGT39GZqA6+I4NBdA1UNBQ/FUxlzdnQl93MbBJev48Az92EfO0goGsPnnuJ8zaRzCcu206etLjx7UJgxHtfkq5j2B3d2AoQz2kUsmZndbN2h10SQWFUPrkDRTYYjSXRdLErd8zS3d8lR3YHXaSUe6aF3ODQz08MZGqgZmJXuCmgz8fsDxGwBYsmk6OueIsFTQGwExvvFdWnCBAgDc1xYhcnLQSRruhxNJFLDwkytyLrSJDoM8VGwU1QxkMroaHo2lqywhLqBRVVwWFUSaY2YbsMLWRfz/FFi2Sq31TnlmDCAVDbgzpdZVxrGLPZUZuEq3QtFZZ4hpYaxaKxZVXRx81p/mPFEGo/DwpqGygpqGvKqiPp0VUQDczb3uklffMm0Rl+2MlEpJmoGNR4HFlUhkdIIaY5c0HckF3PDZ2CKEXYjkSTPd4qE0N+srqs46RkI6f64s4l4Wic1Pjy1075xbDacMCk4PDySa3vwOst/oX0kHIEW7B4/Fj3FQOerU2/Yv0sk2Rze6Y2poin2DghpeSW1P9hrluOwqrhSo4yMzXIChDn/eGpJnmmiVu7O5fm4q3H6alB0jVDP1KavBczQQA1Ekj0cF72a5d7TDaISHXY0EktmchXXI2Ecm4HiXgOxZEa8HlTEFI2J+F02ws5G4mktp1aZCqPlQbWKJMYEzCq3pfKk5fkYScVYGrSJEnMtnft9mjFhUNlSfQOj0q3pOtoCSswXAhl0L3VSceh9QfzeWnxM2M5DowCc2BqoOBOL2ioHYVcrqYxOYvBAcfMKg3QyJzlrOGHSw71jcTRdx+u0VsRiJh+LqpgyuwKJ+XSV2XQiFyhOkSn/695BNF1nRa2HZZXQJ1sEh9WCr6qKsKNBjA4rlshIhIUDPxR11Df6uSv1M5yEomBrELLRsc7dxbfR9ZyBWuup01YXdnSOoOvQXus2TQUrApsLJasOivTNYkxgOpk7jqfp5+4ZFQvNivpMFQVPk0hEpPpfm5mb7wwN1EBMKdF0MeqqEnxHaqrshBxNxFKZIweIBkY/9xTHplHl9jqt2CtQEu132QjbG4indZHsiY1OvbGRwCgymxtyQaLdWiFKlimwWVQsqoIOJMl+3xntEMmoCMKt9qLtoQa6rud9npV3XBqoqoIlm3BIz2DsWSlRuf+rpULfi6IPxFM7yc0YoGcsRs9YHIuqsGG240nKAJtFxRVsIq06iEajWZfNKRh6TXyWrmDRkWuHR0Vg01phVW4DY2E8OJ7IM1PbP7VBXW/+sbls0sNdozFe6xtHUeD1q6fvUSx36r1H6Ovu3y0+Z3+LOD4n0Gn0c1eY18B01LSJ5ESi9xX0YhfmkQOiRcJim7L1AYR03zChPKlt5kY15YIaFOduYvAIJn/5jOwX1RtXADx1RTeJpzK80idmK3fUVUaPvIGv5ThUBVzhg4SONAN9FgZqUCgtr4RKWLXHISrdqQx6uFckfKYjMQ6hLvH7FFL0Su7nBlHd11Qbw2o1mq4L2X4xUvHcY0WCxbSmkc5WIiu1n9tAURTcWUO1cMZCStPRjT7ulLh+Y5u+yp3RdPH/QWUH3ZBnplZmEvPK/l9d6uh6TlrecnLRfkSjyr2mwYunArLexWjwuwg5mxlPZKZ3hzblu+uKfpaHs/3clWREl4/pYB5OgK9FZGVTMQj3Tt5Y13PS8ubJx6au6zzyqjC2W9/sr6xKVxEMM7VIMiMq2hODxL6su3YRBcZ4Is3QeBJFYWb9oRVCw/K1KBYbJMIM9BSpgBlV7qaN045n2dUdIpnWqKmys7wClQSO2nYAtCM56+djmlStnrJP3vhca6vsFWf+Z61ejsvpxJ6J0N93hOrsLAzUoHJM1Az8Lhspm4+o4iGZSucC6qkwvAa8jeAsXogYrvCg22O3YFUVxuyNwrRrKgXB8D7QNZEILiYtT+VGW1XSrPOpcGY/Vw2VaMZCLJUhEx0Rx6NqBev06yCzym0RY8gqGWvWH0BWuiULx8h+YWJhtUPDhkkPjyfSvNpXeX2IE2n0ORlzNAvp7ugUC8dkJCeHLOIMnc5o9I0JKWSlmagZ1HvzKt2qJaesKOZiPtYpDP4s1qKB4mv94/SMxbFbVc7sqJnP3S4J6r3CLTaUtojqwXheIiMyJAzpFBXqJo8DNKrc9V4nTltlS/jysdjs2OtEG0T//hcKH4wMipnnigItp0z5Gpqms+OQ6GU+eVmwIiqHE3EbDubjAzNzh9YyOclpbfF+bk3Tea5zFIBNbRX4uVpsWGvaAQh3vzL1drM0UANMN/hK6UW2qApBj+hBjiVnIDGfgdfASLSyg25FUfC7bYQdjcTTGWGmVowhozd+ssoSCvuPJcL8rNpjx+uwkrE40DSdWDxBIq2hHWFMGMjPMx+bKivdkoWmK1tJbDyxqGvk84dH0XSdloCLBp9zgXdu6VBgpjZ2WMh5JtK/W2RsfU3grp70cG8oTlrT8TgsFeEIWwyjGj0aTYn5stP1dRvHZsN6Yf6TRzqj8ehrg4AIZCqh7/BI1HkdoKj0qw0kM1qhxNyoclevLGr6I0eFTY0/KzEf75kQ2BhV7ppVRc93gz0D44TjaVx2C2sbK8uE0iAQDBKzBUik0jMbHTZ6SPg52N1CEVOE/UMRxmIpnDYLx1XgxALA7OtO9k1jpjYLAzUDo9JdSb4j1R7R1x1NTRMggjguje/WabwGjEp3sEISF8Xwu2yEHI0kUhpEh3IzpA00LZdwL9I+pum6uJYhg8R8FEXB7bDi93rNam1S0xlKqMSS6Sk9HjRdJ5WR/dwGxmeXL7kvB+T/7FIlNpLr8ypSqUlntNyYsAqucgPUVDlI2QNEFTeJRLJ4f5LpWj65Kgv5o8LclVeVyeKyW/A6RYA8OJ7M9XWPdYnqrEFiPCcvbT550us83zXGWCyFx2HhlOWV1yNbDKfNQoPPyZizhdFongmVrufNjZ9soKbrulnplqPCJtO0cj0oCnqoh/CYmAVPMir8BkCMCZsGY0zYia3+ih1343VYibqa0HSI9M/ATM2oJNasBrX4Z2a0Pa1v8VXs5xpoE0G0PtZFPDpefKNZGKgZjGWrtIEKDLpjyQyEusUowGIM7RVKDHfNlFL9dEYzExeVWukGEXRnLE5C1uw1euK6KXRYXPdtrkkjLAFzlrRFVSrOwHcmWCwWnE4XLrsF3epCQyEUTzMcSRJLpicFkvmfp7VCvzPzURVxA0iXUbVb/s8uVbqeFQvy6pVFKzWv9IWJJjN4ndaKM6mZiEVVqPM5GXM2E0kWMaqKDosLtaJC/WT5LgjTL6i8UWETMfq6B8MJ0cflrhYKgXzZfu/zYmHjawZv4cU4mkzz5D4R/JzVUSsztnl01HkYc7aIKkuoSxgChbqEc6zFJoKYCfSHE4TjaexWleYKbXuYDneVH4tfVFt792UD7e4dwuTL2wj+timfm29CubE1sAB7uzRRFMWsZMUGjxB063peP3fxSuLgeIJDw1EURUzUqFT8wTrUqlrQNTr3vjR5g1kaqIFIwlVipbvG4yBu9RPW7OLcnsowddBwLZ96lvdoLIWug8OmVs7s+CIYx8+wVUwvmKQgMJNrHUWTa3K01Qxw+rG4/Hh9QvGnAGlNJxRPMxhOEIqlzOp2UkrLC1AUpSz7uuX/7lIkkxKBDRStcuu6zs5sv9zGtsobE1aMBp+DkKOZ8XiRvm6jyh1sLzpLWtN0eiq8n9vAkJgPhMV8XYJ5LuYgJGdGdaalsMqdzmj89rlu4qkMtV4H65p8C7HLJUNHfRVxq5+BlIN0OiV6E/uyi/HaNUVbSPb0iwrZ8hp3xVYMj0RVi0ikhQ6/JCpghvlk2+nT9tA9e3AUgLWNlWtCaWCrFj2b6dEeIdGdinCvGG9nsYnv0yIYvdwddVUVFRgWw98iqt1DxcbazdJADSCSzJDK6ChK5RipQbYirSj0qXXo6MX7ujNpUemG6fu5DRM1dwXNji+CcW72q9mge2zCDHTjs6xZNem5YrRVdj73Eg8S//u//5sNGzbgcrmoqanhTW96E5GIkNJv27aN448/HqfTyXHHHcf3vve9guf+8z//M2vWrMHtdrNy5Uq+9KUvkUrl2hefe+45tmzZgtfrxefzccopp/D000+bj/+/u3/NCSedjtPl4oS1q/jpj26lymE1R4udcNwqrr/xX/nAh7bSXF/NKSes5qfbbl+Qz6UUMPq6ZaVbMr/07RKyHlcg11ebR/dYnP5QAquqsL658saEFaPR5xLS3VhKLAwNOXSBfLe4tLw/nCCZ1nDaLNRWVa7cDPIczMezC2/j+BveJz7L4b3ZHkRXgemXruvc91If3aNxHDaVt25okm6mE6jx2Al47AzbW0S1anjfEY/NvQMi6F5VX9lqlumoXyGMEVOD+0l37RC9iQ7vtD2yY7EUr/WLcVaVbEJp4A1Uk7B6iSXTU48PglwlsaajqBw6nsqwuyc7fk1+rjStEOd1qn8PsUSeJPooDNQA0ZoCeJ22ikq2B902FAWGrQ3CWKlYX/fIAVGwcHjB2zTla5n93BUsLYecEV8P2UTGeH8u4RYdFn3eippLvOeRSmvo6SSKlsRGWqi2Fuo2i/7enp4e3v/+9/ORj3yE3bt389BDD3HhhRei6zq33XYbX/jCF/jqV7/K7t27uemmm/jSl77EnXfeaT7f6/Vyxx138NJLL3HLLbdw22238e///u/m4x/4wAdobW1l+/btPPPMM3zuc5/DZhPJjGeeeYb3vve9vO997+OFF17ghhtu4LrrruO/fvEfYi3gtqGg8INbb2HDxpP401+eYOtHP8Y/fuqTvPzyy0fzX1p2GJVuQw1QDlR2en8pkj8mrPnkorIeo1/uuCYfrgqWR+Wzss7Dn+xehrUqookU7tFDULdGyNCiw2KBOIUcsis7n7s54KzozDdAXbbSPTSeQNN01MAy4WQeHxM+A1P0ID6+b4hXesOoisLbT2yu6F65qVAUhY66Kg4MtDAc2U9N904hlbS7iy5shiNJhsaTWFSF9prp53tWMjV1TbzmCqLHRhjb9UdqnBahEFKn/m58rnMUXRcj2Oq9lWtCaRB023nN0UQ8tU9UEWs6im9oupYX/y59sWuMVEanzuuoeNUQgL9pJS6Xi1gsxoEDezl+bbYCexQGagAv94hEUaUlh60WlYDLRjjRRCy5C3vosEhc5K+P8tseprmOV7pzuYHPaUVRIKK7SNn92JMhkXCr6ci1PQSWiWM0Ey94biIZx/PkLVhVBWWhJ2q84dNFVWHF6OnpIZ1Oc+GFF7J8uVDzbNggJgF95Stf4Vvf+hYXXnghACtWrOCll17ihz/8IZdffjkAX/ziF83Xam9v59Of/jS//OUvufbaawE4dOgQn/3sZznuOHEOr16da2u4+eabeeMb38iXvvQlANasWcNLL73Ev/3bv7F161YcVguqAhdccAGf+tQnSaQyfO5zn+O279/KQw89ZL5mJWM1Kt2ajq7rZbE+l5XupcbYYZFxtFih6cRJD4fiKVNyuqktsMA7t3Rx2iy013oYczYLEzBDYm5Iy6eQ70L+fG65SPS7bNgsCqmMLlQDVjv4W8WD3c/m3EybTzKfs7snZPZxv/H4ejlLehpW1Vcx5mxlNJpCM1z269cVTa4Z53lbtUuOCpsGRVVxN4kFylg4Jr47mzdNuX0ineGFLmFCebKsxgJ5RlWpjHAnL0Z0WIxiUy1QPTko17Rc29OmtkBZLJCOGdWCt0ksxPsP7MrdfxQGaoPjCV7sFsftqe1TO/KXK9VVDiK2aiKaRVQ8I/25BzUtN95qmn5ugOGI+N6tZOdyEIkMY7LIuKtZ3GnI9o2gewppeTw7n9tqWdrn+MaNG3njG9/Ihg0beM973sNtt93GyMgIAwMDdHZ28tGPfpSqqirz9q//+q/s3bvXfP5///d/8/rXv57Gxkaqqqr40pe+xKFDue/Ha665hr/7u7/jTW96E1//+tcLnrt7927OPvvsgv05++yzee2118hkMnn7eCJVDis1VQ6qnDYaGxvp7+9HIvyajCMsrZWHxFxWupcaXdl+kPoThIR3As91ijFhbdVuUwosEaxt8PLEgRaGRl+hbeQAiqblyXcnz+YGcQExTdQCMlhUVYXaKgc9Y3EGwglRDaheCSMH4fDTk8z9ukZj3P9SHwCntgdZ3yLbHaajye/E4fYwZq0hFE8QcNmnlJYbQfequsocuTQbapafQHTf44xEU+iNJ6IU+e40eLErRDKtUe2xs6JWKggAAm4xPiiV0UmNdmPLpETfdj5GJdGofk1g32Bu/NpxFTp+rRj1y9fRv+95UgOvEUmk8ejRWRuoATz62iC6LhJ3lagiqHbb2auojFjraWZQSMy9jeLB0GExtcDmLDreykDXdVnpzsPnshGOpxmzN1DNy+IzTcVz8v3ayUF3KqOhKVaiZ/wTHq8DWODAe+L30nSbWizcf//9PPbYY9x3331897vf5Qtf+AK//e1vAbjttts444wzJj0H4IknnuB973sfN954I+eddx5+v5+77rqLb33rW+a2N9xwA5deeim/+93v+P3vf8/111/PXXfdxbvf/e6ildli48IMObqBoihoZWQcdiwoikLAbS8rh3xZ6V5KxEMwkF3YFDFQG4kk2ZGVlssKzWRW1HqIuVuIpzUiw71i/nEympXvthd9zuB4kkRKw25VqZdJDCBnpjY4PsFMzbhgZKvco9Ekv32um4yms6q+itevmpkZUCWjKAora6sYc7YIQx9XsGj/YSieoi8UR1FE64RkehqXrSJjqyKuKQz4N0y5XTyVYfsBoco4eVlQVmOzOKwWbJ5qkhYP8WTWVX8iA9M7QxvXpg0tfjnyJg9vy1qqHFY8iUH2dvUflYFa53CU/YMRVEWp2O9ZI0juK2b8ZaybalZN21YynkiTTGuoilLxJn+QGzs3ZMlOIQn3iOSarolj0zV55Gc8pYGi4HC6UKwOoYZbyNssv7MVReHss8/mxhtvZMeOHdjtdv7617/S0tLCvn37WLVqVcFtxQqx3vnrX//K8uXL+cIXvsCpp57K6tWrOXjw4KTXX7NmDVdffTX33XcfF154Idu2bQNg3bp1PProowXbPvbYY6xZs8YM7CVHxm5VyybgBlnpXlr07BRfdoG2SaOYdF3ngZf7yWg6K2o9skJTBLtVZXljLdGeGgbHI1TtfUA8UHf8lBfig0PCxbLJ75TGX1lMMzXDwbyqHuweYVDl9EHNKuKpDP+7s5tYMkODz8l5JzTKAGaGdNRX8fuD6zgYGqK9Y0vRz21vtsrd7HdVvLP2TLBarYTXvZ8D/WOo4w7qJ4+VBeDpAyPEkhmqPXbWNUt3/XyCVQ5CjibiyV68o52FicpEWIxdhKL93P3hOIdHYqiKwomtUu1SgMOLr66V8cMH6N2/ixPd2QBxFmPC/vLaACDmyVeqAVhNto+9W88mHUY7c4lgs597atdygJGstDzgriwjuqkwEg99SZeY7JIYh4N/FQ8W8XXQdZ141rXcaVv6ibUnn3ySP//5z5x77rnU19fz5JNPMjAwwPHHH88NN9zAP/7jP+Lz+Tj//PNJJBI8/fTTjIyMcM0117Bq1SoOHTrEXXfdxWmnncbvfvc77r77bvO1Y7EYn/3sZ7n44otZsWIFhw8fZvv27Vx00UUAfPrTn+a0007jK1/5CpdccgmPP/44t9566ySHdEllsfTPmkpBy0D3TvF7kSr3a/3jHBqOYlUVNq+tkwHOFKxp9DLqbGFoPImeFAZpU8l3NU3nucOiR066Q+eYFHQrSm6h3Xwy4ymN3zzXzXAkiddp5R2bmuU87lnQFnShO/3sqL6AXntxKaQhLe+Qx+WMaWtqJG4LsH8wUvTxUDzFjkMjALx+da1cdE8g6LYRdjQW7+s2DNR8zcIdegLPdYrv0dUNVXidsoI4kdrlYtKD4/CjJMZHZmWgtrsnTH8ogd2qcsbKyuvlNjB6sAeVaqIZIBUTDtvjfcLo02KF6smGlPkMR6VzeT7LswadewYiRN3Zvu7YqPhZM1nRkkpr6DqoCthLQM3i8/n4y1/+wgUXXMCaNWv44he/yLe+9S3OP/98/u7v/o4f//jH3HHHHWzYsIFzzjmHO+64w6x0v/Od7+Tqq6/mU5/6FJs2beKxxx4zTdFAyNCHhob40Ic+xJo1a3jve9/L+eefz4033gjAySefzK9+9Svuuusu1q9fz3XXXceXv/xltm7duhgfhWSJIEsoS4WBl7OjbqomVRIS6QwPvyIy3aetqDZHPUgms7zazRNVrSTDzxOKp/FX14uFYhH2DIwTiqVw2S0cL2dKmxgVhfFEmlgyIxzyO7ag13SwK17HXx4/YEry37Gp2TRjkcwMq0VlRa2HV3rD7O2P0OQv7M+MJTOmz4BMBs0cQ/3TF4oznkhPOi4f2zNEWtNpCbpYKZVCkwh67LzmbCIeyoiqdiadM/maxrU8lszwcnZMmDT3LI6naS1e5/2E4xGGI26aVpw5IwO1VEbjsb2DAJzWXo3bXrnftXarSp3XwUA4wV/7HRznGKFx6CC2TDbJVr3yiP2++TO6JdDod9JW7aZzOMruqA+z3GNzga9l0vaJjAaqBYfVUhKFn+OPP54//OEPUz5+6aWXcumll075+De/+U2++c1vFtx31VVXAWC32/nFL34x7d+/6KKLzMp3MQ4cODDpvp07d077mpLSZumnqioFc0zYSZOk0E/sG2Y8kSbgtnHq8sk9NpIcVotKQ9tqdBSGxhPQsK5oD5Cu6zxzUFS9NrYGsJVA1nahcFgtBNxi8WJUu0NplV93urh/dz+JlEaDz8klp7XJcUtHSUedCKaNOdz57B0YR9eh3ueQfYezwOOw0ugXx+P+gcJqd38ozsu9IjD8m9VSKVSMWo+DmDXASNqGrqVEfydkjZWyvYxFgu7X+sOkNZ16n4Mmv/w+KIq3maBfJHaHIskZS8t3do4SjqfxOq1y7jlwwYYmWoMuRm2NHB6N8dBTz9K95zkxZ3qKMXb5DJkzuuX3qsFp7WJNuTPkzc1DrumYNFFD13WSafG4nKYhkRwdMtJYCoR7YaxLBNsTLsYD4YQ5l3vL2nppUDMD1jTXMOTpoC+ukmkobqrUNRqjdyyOVVXY2CZ7ECdiSszH4zx/eJSfPX6QA4OiveENq2t532ltpuGaZPa017qxqArDkSTD2YWggRGIG4G5ZOYYFex9g7lkhq7rPJJ1fl7b6DUDc0khTQEnVovKoKWeWDKTGx80vFe0P3lqwVMz6XmHhkUbz6q6KpnMmApVpbrtOBSgS68lZDnyNSeWzPDUfmH6d1ZHrUwMI8zULj6lldM2bcRpU3GF93Hw0EGe7wpz2FJc0ZaPWemW8nKTZdVu6n0OQmqQbiNXWWRUWFrT0QFVUbAt8VFhEslSRX6LLwU89bD+Qmh/g5CXZxHmaX1ous7qhirapSRyRrQGXfQ0v5knGi/jULT4xdWoch/f5Ktoyd5UGAH1Y3uG+PPufpJpjeaAkw+cuZxT26ul6dwx4rBaaKsWsvL8ancineHQUDaIkdLyWbMi6/TeORw1qzYHhqIcGo5iURXO7qhM5+eZYLOotARdhBxNjMVSubFBpknV5B5PTdPpHBatEG3VcuTidLg6Xo9S087BwJm81hc+4vZP7B8imdao8zo4vkmOYDNQFIX2Fas5sa2alUFhiNaj1PNfO4f43fM9JNKZos9LpDOMJ9KAnNGdj6IonNZeDYrCE5ZTSTWdXFQ1kMoI0zqnTZXJNYnkKJFB91JAVaFuLSx/XcHdu7pDdI/GsVtVzllTt0g7V3qoqsLqBi8oCq/0Tl7cDEeS7MvKT0+Wcv2iGJXutKZjswjzvvec0iYrBHOIUck2TNMADg5FSWs6QbeNGvlZz5q6Kgdep5VURufwSAxN03k06/y8qS2A3y1lpdOxvMZNyNEogu6xTkgnYWiveLDIQnxgPEE8lcFuVWn0SQXBtFTV4zzlMiKOel7pndxWks9IJMnzWXM62Q5RBKsdi6+JZr+Lk9oCVC9fj6LAq31h0/9mIoZzucdhkfLoCayqqyLgttHtWMEL7tMntTgmUhnSmpSWSyTHigy6lyixZIZH9wgDlTNXVktH2FmytkFUBvYOjOf6lLIYDsYr6zwyiJyC1qCL2io77bVuLjtzOSctC8rq9hzTUVeFokDvWJxwXCwI813L5UJ79iiKYhqq7R8c56WeEIPjSZw2C6evqFzn55myrNpD1FbNcEJFSyeh80nIpIRjeZF58p1ZaXlr0CW/H2bAqnpxzveF4oxGk0W30XWdR/cMouliPOiyGqkgKEqgDRAKjdNPPYOLTm5FUUSxotgEA6ONR1a5J6OqCqcuF9+Pzx4cIaPpBY8fGomCDhZFwSrPc4nkqJFB9xLlr3sGiSUz1FbZ2dQmq7GzpcnvxOeykUxrHMi7AEeTaV7qFoZKp8gq95Q4rBY++Lp23n1Sq3TLnyc8DqtpPLVvIEI6o5mLRSktP3pWZhUE+wYiPL53CIDTV1TLCs0MqK2y43HaGLU3Eo6nofOJ7AOrixpSGv3cUlo+MzwOK21B8Vm91j+52h1PZfj9i73s6R9HUcRoO8kUVK8UPwPLwOmjrdrNScvENf3Pu/uIpwpl5iNR2c89Hcc3ealyWAnH06bppIGxhrJLablEckzIoHsJ0jMW48VuIS3bcly9nCd7FCiKYla7X8nrn3uuc4y0ptPgc9IScE31dIlkQch3Me8ciZFMa1Q5rFKqewy0Bl3YLArheJrxRBq/y8bGVmmWOBMURWFZtScnMc+IHthi0vJ0RqM7O9pumQy6Z8wa47o0ofWpdyzOfz55iFd6w6iKwjlr6qRZ5XQE2+GkD8AJ7zLvOqujRsybj6f5y6uFMnOz0i2D7qJYLarpkP/0gRF0XVS7o8k0PaNxABzSzE8iOSbkGbQEea5zFF0XJl+tQbmYOVrWNIqAZv9AhEQ6Qyqj8dzhUUBUuWXGVrLYGEF353CMXdlEW0e9Rx6bx4DNohZUXs9eVSunPswC0dfdxGgsK3+2OkQ1cQI9Y3FSGR2PwyL9B2bBqvoqVEVhIJxgJJJE13WePjDML7d3MhZL4XPZeM+prWbVVjINgWVgzxnM2iwqbz6hsajM3Kx0S+XWlGxo9eOwqQxHkuzN+t681jeOputYVEV+j0okx4g8g5Yg565r5Jy1dbxBSsuOiboqB9UeO2lNZ29/hJd7wsSSGXwuG6ulfFeyBAh67NRU2dF0ndf6hNx0VZ10Kj5WVteLz7DJ72RNgzzXZ8OyajcRey2hpCr8MGpWTTJWglw/d1vQLZNEs8Blt7CsRqisdh4e5dc7u3jkNdHDvabBywfOWEazVGEdNS0Bl5mw+NNLQmauaTqjUeGbUV0lg+6pcFgtbGwNALD9wDC6rpuKDDmyTiI5duRZtARRVYWTlwXxOOQoq2NBURTWNhoS8xDPHBQzT09aFpCmP5Ilw6q8edxOm4WWoFxwHyvHN3l5+8Ym3rmpRQaEs8TjsFLnczHqahMS84YTim4n+7mPHiMptPPQKAcGo9gsCm9e18AFGxql98AcYMjMxxNpHn51gLFYikx2EodXrqumZVNbAKuq0DsWZ3dPmK7RGIoCVjmbe1Zs3bqVd73rXYu9G5Ilhgy6JWWN0dd9YDDKSDSFw6ZyQrNvkfdKIsnRkae6WFHrkR4Oc4CiKKyq9+KyywDmaFhe42Zf9d/wQvNFUNMx6fFEOkNfKAEg3bWPglX1VeZ5Xut18P7Tl7G+xS8TRHNEvsz8pe4Qz2YnlgQ9dvkZHwGPw8oJLWKN9OfdfQA0+Jyo8nOTSI4ZGXRLypqgx069L2dGs6HFj8MqF+KSpUO914HPJUYCrpZSaMkSYHm1h4xqZ0/EYxoq5XN4JIam6wTcNnxynOWscdosvGV9I2d11PC+09qokYZpc06+zPz5w8IvQ/Zzz4xTllWjKgrp7OgwYwyj5MhkMhk0TTvyhpKKRAbdkrLHqHarisKmtsDi7oxEMgFFUXjbiU286fgGVsrFjWQJ0BxwYrMojCfSDEUmz5M2+rmla/nRs6bByxkra2Sv7DxiyMwNpHP5zPC7baYXhqooLK8uzevST3/6U2pqakgkEgX3X3TRRXzoQx8C4Le//S2nnHIKTqeTlStXcuONN5JOp81tb775ZjZs2IDH46GtrY1PfOITjI/nxv3dcccdBAIB7rnnHtatW4fD4eDgwYOz3g9JZSC/7SVlzwnNflqCLl7XUYNXVmUkS5AGn5MNrVJeKlkaWC2q6S1wcCg66fFO2c8tKQHyZeYAQVnpnjGnr6jGYVNZ1+zDOaFNR9d1UpnUotyKKW+m4j3veQ+ZTIbf/OY35n2Dg4Pcc889fPjDH+aPf/wjl112Gf/4j//ISy+9xA9/+EPuuOMOvvrVr5rbq6rKd77zHV588UXuvPNOHnjgAa699tqCvxONRvna177Gj3/8Y3bt2kV9ff2s9kNSOSyqo8Rf/vIX/u3f/o1nnnmGnp4e7r777gLjAV3XufHGG/nRj37EyMgIZ5xxBv/3//5fTjihuLGLRFIMl93Ce09tW+zdkEgkkpJhWbWHA4NRDg1HOGV5bnxVJJFmcDyJogjncolkKdMScLF5bT0HBiNSJj0LaqocfPycDhRFIR6PFzyW1tLc9sJti7JfV2y4AptlZsUTl8vFpZdeyrZt23jPe94DwM9//nNaW1vZvHkz55xzDp/73Oe4/PLLAVi5ciVf+cpXuPbaa7n++usBuOqqq8zXW7FiBV/5ylf4+Mc/zve+9z3z/lQqxfe+9z02btx4VPshqRwWtdIdiUTYuHEjt956a9HHv/nNb3LzzTdz6623sn37dhobG3nzm99MOBxe4D2VSCQSiaRyWJ41SDs8HCOdyfUodo6IKned1yGN6iQlwaa2AO86qQW7VYo7Z0M5KK+uuOIK7rvvPrq6ugDYtm0bW7duRVEUnnnmGb785S9TVVVl3q644gp6enqIRsX33IMPPsib3/xmWlpa8Hq9fOhDH2JoaIhIJDcD3m63c+KJJx71fkgqh0WtdJ9//vmcf/75RR/TdZ1vf/vbfOELX+DCCy8E4M4776ShoYH//M//5O///u8XclclEolEIqkYajx2qhxWxhNpukfjpkv5oaHcfG6JRFJ5WFUrV2y4YtH+9mw46aST2LhxIz/96U8577zzeOGFF/jtb38LgKZp3HjjjWaMkY/T6eTgwYNccMEF/MM//ANf+cpXqK6u5tFHH+WjH/0oqVTK3Nblch0xeJ5uPySVw5IdWLh//356e3s599xzzfscDgfnnHMOjz32mAy6JRKJRCKZJxRFYVmNm5e6QxwcjrCsxo2u6+Z8bmmiJpFUJoqizFjivRT4u7/7O/793/+drq4u3vSmN9HWJtoNTz75ZF555RVWrVpV9HlPP/006XSab33rW6iqUEn86le/mvP9kFQOS1Zr09vbC0BDQ0PB/Q0NDeZjxUgkEoRCoYKbRCKRSCSS2WFIzA0ztbFYinA8jUVVaA64FnPXJBKJZEZ84AMfoKuri9tuu42PfOQj5v3XXXcdP/3pT7nhhhvYtWsXu3fv5pe//CVf/OIXAejo6CCdTvPd736Xffv28bOf/Ywf/OAHc74fksphyQbdBhMlG7quTyvj+NrXvobf7zdvMpMkkUgkEsnsMarZA+EEkUTarHI3+p2yP1YikZQEPp+Piy66iKqqqgKz5vPOO4977rmH+++/n9NOO40zzzyTm2++meXLlwOwadMmbr75Zr7xjW+wfv16fv7zn/O1r31tzvdDUjksWXl5Y2MjICreTU1N5v39/f2Tqt/5fP7zn+eaa64x/x0KhWTgLZFIJBLJLHHbrdT7HPSHEhwajtI5HAOktFwikZQWPT09fOADH8DhcBTcf95553HeeedN+byrr76aq6++uuC+D37wg+bvW7duZevWrZOed8cdd8xqPySVwZJNVa9YsYLGxkbuv/9+875kMsnDDz/MWWedNeXzHA4HPp+v4CaRSCQSiWT2LK8WY5YODkVM53IZdEskklJgeHiYu+66iwceeIBPfvKTFb8fksVlUSvd4+Pj7Nmzx/z3/v372blzJ9XV1SxbtoyrrrqKm266idWrV7N69Wpuuukm3G43l1566SLutUQikUgklcHyGjfbDwzzat84GU3HblVp8DkXe7ckEonkiJx88smMjIzwjW98g7Vr11b8fkgWl0UNup9++mm2bNli/tuQhV9++eXccccdXHvttcRiMT7xiU8wMjLCGWecwX333YfX612sXZZIJBKJpGJo8juxWRRSGR2A1qALiypny0okkqXPgQMHFnsXgKWzH5LFZVGD7s2bN6Pr+pSPK4rCDTfcwA033LBwOyWRSCQSiQQAq0WlNehm/2AEgFY5n1sikUgkklmzZHu6JRKJRCKRLD7LanKBtuznlkgkEolk9sigWyKRSCQSyZSsrPWgKgp+l43aKvti745EIlkEplOmSiTlzlwc/0t2ZJhEIpFIJJLFJ+C28/7T23DYLCiK7OeWSCoJi8UCiAlCLpdrkfdGIlkcolExvcNmsx31a8igWyKRSCQSybTUS8dyiaQisVqtuN1uBgYGsNlsqKoUyUoqB13XiUaj9Pf3EwgEzCTU0SCDbolEIpFIJBKJRDIJRVFoampi//79HDx4cLF3RyJZFAKBAI2Njcf0GjLolkgkEolEIpFIJEWx2+2sXr2aZDK52LsikSw4NpvtmCrcBjLolkgkEolEIpFIJFOiqipOp2wzkUiOFtmYIZFIJBKJRCKRSCQSntk3ZwAADE1JREFUyTwhg26JRCKRSCQSiUQikUjmCRl0SyQSiUQikUgkEolEMk+UfU+3Mcw8FAot8p5IJBKJRCKRSCQSiaRcMGJMI+acirIPusPhMABtbW2LvCcSiUQikUgkEolEIik3wuEwfr9/yscV/UhheYmjaRrd3d14vV4URVns3ZmSUChEW1sbnZ2d+Hy+xd4diUQyz8hzXiKpLOQ5L5FUDvJ8rxx0XSccDtPc3IyqTt25XfaVblVVaW1tXezdmDE+n0+enBJJBSHPeYmkspDnvERSOcjzvTKYrsJtII3UJBKJRCKRSCQSiUQimSdk0C2RSCQSiUQikUgkEsk8IYPuJYLD4eD666/H4XAs9q5IJJIFQJ7zEkllIc95iaRykOe7ZCJlb6QmkUgkEolEIpFIJBLJYiEr3RKJRCKRSCQSiUQikcwTMuiWSCQSiUQikUgkEolknpBBt0QikUgkEolEIpFIJPOEDLrniK997WucdtppeL1e6uvrede73sUrr7xSsI2u69xwww00NzfjcrnYvHkzu3btKtgmkUhw5ZVXUltbi8fj4R3veAeHDx8u2Ka9vR1FUQpun/vc5+b9PUokkhwLec6/+uqrvPOd76S2thafz8fZZ5/Ngw8+OO/vUSKR5Jirc/5HP/oRmzdvxufzoSgKo6Ojk/7WV7/6Vc466yzcbjeBQGAe35VEIpmKhTznDRKJBJs2bUJRFHbu3DkP70qyWMige454+OGH+eQnP8kTTzzB/fffTzqd5txzzyUSiZjbfPOb3+Tmm2/m1ltvZfv27TQ2NvLmN7+ZcDhsbnPVVVdx9913c9ddd/Hoo48yPj7O2972NjKZTMHf+/KXv0xPT495++IXv7hg71UikSzsOf/Wt76VdDrNAw88wDPPPMOmTZt429veRm9v74K+Z4mkkpmrcz4ajfKWt7yFf/mXf5nybyWTSd7znvfw8Y9/fF7fk0QimZqFPOcNrr32Wpqbm+fl/UgWGV0yL/T39+uA/vDDD+u6ruuapumNjY3617/+dXObeDyu+/1+/Qc/+IGu67o+Ojqq22w2/a677jK36erq0lVV1f/whz+Y9y1fvlz/93//94V5IxKJZEbM1zk/MDCgA/pf/vIXc5tQKKQD+p/+9KeFeGsSiaQIR3PO5/Pggw/qgD4yMjLl39i2bZvu9/vnetclEslRMN/n/L333qsfd9xx+q5du3RA37Fjx3y8DckiISvd88TY2BgA1dXVAOzfv5/e3l7OPfdccxuHw8E555zDY489BsAzzzxDKpUq2Ka5uZn169eb2xh84xvfoKamhk2bNvHVr36VZDI5329JIpFMw3yd8zU1NRx//PH89Kc/JRKJkE6n+eEPf0hDQwOnnHLKQr09iUQygaM55yUSSekyn+d8X18fV1xxBT/72c9wu91zt9OSJYN1sXegHNF1nWuuuYbXv/71rF+/HsCUgTY0NBRs29DQwMGDB81t7HY7wWBw0jb5MtJ/+qd/4uSTTyYYDPLUU0/x+c9/nv379/PjH/94Pt+WRCKZgvk85xVF4f777+ed73wnXq8XVVVpaGjgD3/4g+z1lEgWiaM95yUSSWkyn+e8ruts3bqVf/iHf+DUU0/lwIEDc7bfkqWDDLrngU996lM8//zzPProo5MeUxSl4N+6rk+6byITt7n66qvN30888USCwSAXX3yxWf2WSCQLy3ye87qu84lPfIL6+noeeeQRXC4XP/7xj3nb297G9u3baWpqmrs3IpFIZsRcn/MSiWRpM5/n/He/+11CoRCf//znj3k/JUsXKS+fY6688kp+85vf8OCDD9La2mre39jYCDDJ+Ki/v9/MkDU2NpJMJhkZGZlym2KceeaZAOzZs2dO3oNEIpk5833OP/DAA9xzzz3cddddnH322Zx88sl873vfw+Vyceedd87nW5NIJEU4lnNeIpGUHvN9zj/wwAM88cQTOBwOrFYrq1atAuDUU0/l8ssvn4N3IFkKyKB7jtB1nU996lP8z//8Dw888AArVqwoeHzFihU0NjZy//33m/clk0kefvhhzjrrLABOOeUUbDZbwTY9PT28+OKL5jbF2LFjB4CseEkkC8hCnfPRaBQAVS38ulZVFU3T5uW9SSSSyczFOS+RSEqHhTrnv/Od7/Dcc8+xc+dOdu7cyb333gvAL3/5S7761a/OzZuRLDpSXj5HfPKTn+Q///M/+d///V+8Xq+Z9fL7/bhcLhRF4aqrruKmm25i9erVrF69mptuugm3282ll15qbvvRj36UT3/609TU1FBdXc1nPvMZNmzYwJve9CYAHn/8cZ544gm2bNmC3+9n+/btXH311bzjHe9g2bJli/b+JZJKY6HO+de97nUEg0Euv/xyrrvuOlwuF7fddhv79+/nrW9966K9f4mk0piLcx5EVay3t9dUp73wwgt4vV6WLVtmGjQdOnSI4eFhDh06RCaTMef1rlq1iqqqqoV94xJJhbJQ5/zE9btxjnd0dBRU1iUlzsIbppcnQNHbtm3bzG00TdOvv/56vbGxUXc4HPrf/M3f6C+88ELB68RiMf1Tn/qUXl1drbtcLv1tb3ubfujQIfPxZ555Rj/jjDN0v9+vO51Ofe3atfr111+vRyKRhXqrEolEX7hzXtd1ffv27fq5556rV1dX616vVz/zzDP1e++9dyHepkQiyTJX5/z1119/xNe5/PLLi27z4IMPLsyblUgkC3rO57N//345MqwMUXRd1+cvpJdIJBKJRCKRSCQSiaRykT3dEolEIpFIJBKJRCKRzBMy6JZIJBKJRCKRSCQSiWSekEG3RCKRSCQSiUQikUgk84QMuiUSiUQikUgkEolEIpknZNAtkUgkEolEIpFIJBLJPCGDbolEIpFIJBKJRCKRSOYJGXRLJBKJRCKRSCQSiUQyT8igWyKRSCQSiUQikUgkknlCBt0SiUQikVQADz30EIqiMDo6uti7IpFIJBJJRaHouq4v9k5IJBKJRCKZWzZv3symTZv49re/DUAymWR4eJiGhgYURVncnZNIJBKJpIKwLvYOSCQSiUQimX/sdjuNjY2LvRsSiUQikVQcUl4ukUgkEkmZsXXrVh5++GFuueUWFEVBURTuuOOOAnn5HXfcQSAQ4J577mHt2rW43W4uvvhiIpEId955J+3t7QSDQa688koymYz52slkkmuvvZaWlhY8Hg9nnHEGDz300OK8UYlEIpFISgBZ6ZZIJBKJpMy45ZZbePXVV1m/fj1f/vKXAdi1a9ek7aLRKN/5zne46667CIfDXHjhhVx44YUEAgHuvfde9u3bx0UXXcTrX/96LrnkEgA+/OEPc+DAAe666y6am5u5++67ectb3sILL7zA6tWrF/R9SiQSiURSCsigWyKRSCSSMsPv92O323G73aak/OWXX560XSqV4vvf/z4dHR0AXHzxxfzsZz+jr6+Pqqoq1q1bx5YtW3jwwQe55JJL2Lt3L7/4xS84fPgwzc3NAHzmM5/hD3/4A9u2beOmm25auDcpkUgkEkmJIINuiUQikUgqFLfbbQbcAA0NDbS3t1NVVVVwX39/PwDPPvssuq6zZs2agtdJJBLU1NQszE5LJBKJRFJiyKBbIpFIJJIKxWazFfxbUZSi92maBoCmaVgsFp555hksFkvBdvmBukQikUgkkhwy6JZIJBKJpAyx2+0FBmhzwUknnUQmk6G/v583vOENc/raEolEIpGUK9K9XCKRSCSSMqS9vZ0nn3ySAwcOMDg4aFarj4U1a9bwgQ98gA996EP8z//8D/v372f79u184xvf4N57752DvZZIJBKJpPyQQbdEIpFIJGXIZz7zGSwWC+vWraOuro5Dhw7Nyetu27aND33oQ3z6059m7dq1vOMd7+DJJ5+kra1tTl5fIpFIJJJyQ9F1XV/snZBIJBKJRCKRSCQSiaQckZVuiUQikUgkEolEIpFI5gkZdEskEolEIpFIJBKJRDJPyKBbIpFIJBKJRCKRSCSSeUIG3RKJRCKRSCQSiUQikcwTMuiWSCQSiUQikUgkEolknpBBt0QikUgkEolEIpFIJPOEDLolEolEIpFIJBKJRCKZJ2TQLZFIJBKJRCKRSCQSyTwhg26JRCKRSCQSiUQikUjmCRl0SyQSiUQikUgkEolEMk/IoFsikUgkEolEIpFIJJJ5QgbdEolEIpFIJBKJRCKRzBP/H+mwRUYYTKaJAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 1000x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# plot time series of temporal averages for a specific grid point: seasonal and yearly averages derived from monthly time series\n",
-    "lat_point = 30\n",
-    "lon_point = 30\n",
-    "\n",
-    "start_year = \"2005-01-01\"\n",
-    "end_year = \"2014-12-31\"\n",
-    "\n",
-    "plt.figure(figsize=(10, 3))\n",
-    "ax = plt.subplot()\n",
-    "\n",
-    "ds.tas.sel(lat=lat_point, lon=lon_point, time=slice(start_year, end_year)).plot(\n",
-    "    ax=ax, label=\"monthly (RAW DATA)\", alpha=0.5\n",
-    ")\n",
-    "ds_season.tas.sel(lat=lat_point, lon=lon_point, time=slice(start_year, end_year)).plot(\n",
-    "    ax=ax, label=\"season\", alpha=0.5\n",
-    ")\n",
-    "ds_yearly.tas.sel(lat=lat_point, lon=lon_point, time=slice(start_year, end_year)).plot(\n",
-    "    ax=ax, label=\"yearly\", alpha=0.5\n",
-    ")\n",
-    "\n",
-    "plt.title(\"Seasonal and yearly averages derived from monthly time series\")\n",
-    "\n",
-    "plt.legend()\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Monthly Averages\n",
-    "\n",
-    "**Group time coordinates by year and month**\n",
-    "\n",
-    "For this example, we will be loading a subset of daily time series data for `tas` using OPeNDAP.\n",
-    "\n",
-    "---\n",
-    "\n",
-    "**NOTE:**\n",
-    "\n",
-    "For OPeNDAP servers, the default file size request limit is 500MB in the TDS server configuration. Opening up a dataset over OPeNDAP also introduces an overhead compared to direct file access.\n",
-    "\n",
-    "**The workaround is to use Dask to request the data in manageable chunks, which overcomes file size limitations and can improve performance.**\n",
-    "\n",
-    "We have a few ways to chunk our request:\n",
-    "\n",
-    "1. Specify `chunks` with `\"auto\"` to let Dask determine the chunksize.\n",
-    "2. Specify a specify the file size to chunk on (e.g., `\"100MB\"`) or number of chunks as an integer (`100` for 100 chunks).\n",
-    "\n",
-    "Visit this page to learn more about chunking and performance: https://docs.xarray.dev/en/stable/user-guide/dask.html#chunking-and-performance\n",
-    "\n",
-    "---\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -3777,6 +3409,7 @@
        "}\n",
        "\n",
        "html[theme=dark],\n",
+       "html[data-theme=dark],\n",
        "body[data-theme=dark],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -3827,7 +3460,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -3835,7 +3468,8 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
@@ -3847,6 +3481,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -4109,384 +3747,101 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2GB\n",
-       "Dimensions:    (lat: 145, bnds: 2, lon: 192, time: 14608)\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;lat&#x27; (lat: 25)&gt; Size: 100B\n",
+       "array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n",
+       "       45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n",
+       "       15. ], dtype=float32)\n",
        "Coordinates:\n",
-       "  * lat        (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
-       "  * lon        (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 354.4 356.2 358.1\n",
-       "    height     float64 8B ...\n",
-       "  * time       (time) object 117kB 2010-01-01 03:00:00 ... 2015-01-01 00:00:00\n",
-       "Dimensions without coordinates: bnds\n",
-       "Data variables:\n",
-       "    lat_bnds   (lat, bnds) float64 2kB dask.array&lt;chunksize=(145, 2), meta=np.ndarray&gt;\n",
-       "    lon_bnds   (lon, bnds) float64 3kB dask.array&lt;chunksize=(192, 2), meta=np.ndarray&gt;\n",
-       "    tas        (time, lat, lon) float32 2GB dask.array&lt;chunksize=(1205, 145, 192), meta=np.ndarray&gt;\n",
-       "    time_bnds  (time, bnds) object 234kB 2010-01-01 03:00:00 ... 2015-01-01 0...\n",
-       "Attributes: (12/48)\n",
-       "    Conventions:                     CF-1.7 CMIP-6.2\n",
-       "    activity_id:                     CMIP\n",
-       "    branch_method:                   standard\n",
-       "    branch_time_in_child:            0.0\n",
-       "    branch_time_in_parent:           87658.0\n",
-       "    creation_date:                   2020-06-05T04:54:56Z\n",
-       "    ...                              ...\n",
-       "    variant_label:                   r10i1p1f1\n",
-       "    version:                         v20200605\n",
-       "    license:                         CMIP6 model data produced by CSIRO is li...\n",
-       "    cmor_version:                    3.4.0\n",
-       "    tracking_id:                     hdl:21.14100/b79e6a05-c482-46cf-b3b8-83b...\n",
-       "    DODS_EXTRA.Unlimited_Dimension:  time</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-bdfa4917-4114-43b7-bc6a-742e9e5ad131' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bdfa4917-4114-43b7-bc6a-742e9e5ad131' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 145</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lon</span>: 192</li><li><span class='xr-has-index'>time</span>: 14608</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-61b42fc3-f1af-42f0-bd79-35ab064e02f5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-61b42fc3-f1af-42f0-bd79-35ab064e02f5' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -88.75 -87.5 ... 88.75 90.0</div><input id='attrs-f337de09-f4f1-4dc6-8452-7d9de6ce96a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f337de09-f4f1-4dc6-8452-7d9de6ce96a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37b1e8dd-12e1-4528-8903-3a1b677a8d69' class='xr-var-data-in' type='checkbox'><label for='data-37b1e8dd-12e1-4528-8903-3a1b677a8d69' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</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><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-90.  , -88.75, -87.5 , -86.25, -85.  , -83.75, -82.5 , -81.25, -80.  ,\n",
-       "       -78.75, -77.5 , -76.25, -75.  , -73.75, -72.5 , -71.25, -70.  , -68.75,\n",
-       "       -67.5 , -66.25, -65.  , -63.75, -62.5 , -61.25, -60.  , -58.75, -57.5 ,\n",
-       "       -56.25, -55.  , -53.75, -52.5 , -51.25, -50.  , -48.75, -47.5 , -46.25,\n",
-       "       -45.  , -43.75, -42.5 , -41.25, -40.  , -38.75, -37.5 , -36.25, -35.  ,\n",
-       "       -33.75, -32.5 , -31.25, -30.  , -28.75, -27.5 , -26.25, -25.  , -23.75,\n",
-       "       -22.5 , -21.25, -20.  , -18.75, -17.5 , -16.25, -15.  , -13.75, -12.5 ,\n",
-       "       -11.25, -10.  ,  -8.75,  -7.5 ,  -6.25,  -5.  ,  -3.75,  -2.5 ,  -1.25,\n",
-       "         0.  ,   1.25,   2.5 ,   3.75,   5.  ,   6.25,   7.5 ,   8.75,  10.  ,\n",
-       "        11.25,  12.5 ,  13.75,  15.  ,  16.25,  17.5 ,  18.75,  20.  ,  21.25,\n",
-       "        22.5 ,  23.75,  25.  ,  26.25,  27.5 ,  28.75,  30.  ,  31.25,  32.5 ,\n",
-       "        33.75,  35.  ,  36.25,  37.5 ,  38.75,  40.  ,  41.25,  42.5 ,  43.75,\n",
-       "        45.  ,  46.25,  47.5 ,  48.75,  50.  ,  51.25,  52.5 ,  53.75,  55.  ,\n",
-       "        56.25,  57.5 ,  58.75,  60.  ,  61.25,  62.5 ,  63.75,  65.  ,  66.25,\n",
-       "        67.5 ,  68.75,  70.  ,  71.25,  72.5 ,  73.75,  75.  ,  76.25,  77.5 ,\n",
-       "        78.75,  80.  ,  81.25,  82.5 ,  83.75,  85.  ,  86.25,  87.5 ,  88.75,\n",
-       "        90.  ])</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'>0.0 1.875 3.75 ... 356.2 358.1</div><input id='attrs-7228b4cd-a995-4919-b65c-73ce6199e7bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7228b4cd-a995-4919-b65c-73ce6199e7bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f05f146d-2988-4ef4-bbc5-0cd2d5e76d2a' class='xr-var-data-in' type='checkbox'><label for='data-f05f146d-2988-4ef4-bbc5-0cd2d5e76d2a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</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><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.   ,   1.875,   3.75 ,   5.625,   7.5  ,   9.375,  11.25 ,  13.125,\n",
-       "        15.   ,  16.875,  18.75 ,  20.625,  22.5  ,  24.375,  26.25 ,  28.125,\n",
-       "        30.   ,  31.875,  33.75 ,  35.625,  37.5  ,  39.375,  41.25 ,  43.125,\n",
-       "        45.   ,  46.875,  48.75 ,  50.625,  52.5  ,  54.375,  56.25 ,  58.125,\n",
-       "        60.   ,  61.875,  63.75 ,  65.625,  67.5  ,  69.375,  71.25 ,  73.125,\n",
-       "        75.   ,  76.875,  78.75 ,  80.625,  82.5  ,  84.375,  86.25 ,  88.125,\n",
-       "        90.   ,  91.875,  93.75 ,  95.625,  97.5  ,  99.375, 101.25 , 103.125,\n",
-       "       105.   , 106.875, 108.75 , 110.625, 112.5  , 114.375, 116.25 , 118.125,\n",
-       "       120.   , 121.875, 123.75 , 125.625, 127.5  , 129.375, 131.25 , 133.125,\n",
-       "       135.   , 136.875, 138.75 , 140.625, 142.5  , 144.375, 146.25 , 148.125,\n",
-       "       150.   , 151.875, 153.75 , 155.625, 157.5  , 159.375, 161.25 , 163.125,\n",
-       "       165.   , 166.875, 168.75 , 170.625, 172.5  , 174.375, 176.25 , 178.125,\n",
-       "       180.   , 181.875, 183.75 , 185.625, 187.5  , 189.375, 191.25 , 193.125,\n",
-       "       195.   , 196.875, 198.75 , 200.625, 202.5  , 204.375, 206.25 , 208.125,\n",
-       "       210.   , 211.875, 213.75 , 215.625, 217.5  , 219.375, 221.25 , 223.125,\n",
-       "       225.   , 226.875, 228.75 , 230.625, 232.5  , 234.375, 236.25 , 238.125,\n",
-       "       240.   , 241.875, 243.75 , 245.625, 247.5  , 249.375, 251.25 , 253.125,\n",
-       "       255.   , 256.875, 258.75 , 260.625, 262.5  , 264.375, 266.25 , 268.125,\n",
-       "       270.   , 271.875, 273.75 , 275.625, 277.5  , 279.375, 281.25 , 283.125,\n",
-       "       285.   , 286.875, 288.75 , 290.625, 292.5  , 294.375, 296.25 , 298.125,\n",
-       "       300.   , 301.875, 303.75 , 305.625, 307.5  , 309.375, 311.25 , 313.125,\n",
-       "       315.   , 316.875, 318.75 , 320.625, 322.5  , 324.375, 326.25 , 328.125,\n",
-       "       330.   , 331.875, 333.75 , 335.625, 337.5  , 339.375, 341.25 , 343.125,\n",
-       "       345.   , 346.875, 348.75 , 350.625, 352.5  , 354.375, 356.25 , 358.125])</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-53d182a8-fa12-4516-ab39-5c8d9298c30c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53d182a8-fa12-4516-ab39-5c8d9298c30c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b23f156-9faf-45f7-a6a9-408b727d08a5' class='xr-var-data-in' type='checkbox'><label for='data-8b23f156-9faf-45f7-a6a9-408b727d08a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span 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'>2010-01-01 03:00:00 ... 2015-01-...</div><input id='attrs-461bfc7a-6ea5-498e-8281-d4c9081ab5c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-461bfc7a-6ea5-498e-8281-d4c9081ab5c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74719e7d-0c17-48f7-9ea1-2773f8e504d8' class='xr-var-data-in' type='checkbox'><label for='data-74719e7d-0c17-48f7-9ea1-2773f8e504d8' 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>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeProlepticGregorian(2010, 1, 1, 3, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2010, 1, 1, 6, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2010, 1, 1, 9, 0, 0, 0, has_year_zero=True),\n",
-       "       ...,\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 12, 31, 18, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 12, 31, 21, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2015, 1, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f69b89ea-b51f-4a24-a2cc-200e1ea1b9a8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f69b89ea-b51f-4a24-a2cc-200e1ea1b9a8' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(145, 2), meta=np.ndarray&gt;</div><input id='attrs-825ac0e4-ed64-490a-a509-1e6574726f11' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-825ac0e4-ed64-490a-a509-1e6574726f11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-979e3e0a-4091-4024-ac92-7c3fda009196' class='xr-var-data-in' type='checkbox'><label for='data-979e3e0a-4091-4024-ac92-7c3fda009196' 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>_ChunkSizes :</span></dt><dd>[145   2]</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 2.27 kiB </td>\n",
-       "                        <td> 2.27 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (145, 2) </td>\n",
-       "                        <td> (145, 2) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"77\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"27\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"27\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"27\" y1=\"0\" x2=\"27\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 27.018056951081515,0.0 27.018056951081515,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"13.509028\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
-       "  <text x=\"47.018057\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,47.018057,60.000000)\">145</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(192, 2), meta=np.ndarray&gt;</div><input id='attrs-8c60a0f1-6b00-4836-9855-d2e775f3e4fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c60a0f1-6b00-4836-9855-d2e775f3e4fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e096b07f-8b07-4231-aa82-567abfb16745' class='xr-var-data-in' type='checkbox'><label for='data-e096b07f-8b07-4231-aa82-567abfb16745' 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>_ChunkSizes :</span></dt><dd>[192   2]</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 3.00 kiB </td>\n",
-       "                        <td> 3.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (192, 2) </td>\n",
-       "                        <td> (192, 2) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 25.60913412115106,0.0 25.60913412115106,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"12.804567\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
-       "  <text x=\"45.609134\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.609134,60.000000)\">192</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1205, 145, 192), meta=np.ndarray&gt;</div><input id='attrs-36106d49-70a5-4679-80f9-e51edfc1c880' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36106d49-70a5-4679-80f9-e51edfc1c880' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c51d50e1-1372-4a9e-b8c4-df901c288bd2' class='xr-var-data-in' type='checkbox'><label for='data-c51d50e1-1372-4a9e-b8c4-df901c288bd2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.52 GiB </td>\n",
-       "                        <td> 127.97 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (14608, 145, 192) </td>\n",
-       "                        <td> (1205, 145, 192) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 13 chunks in 3 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"157\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"10\" y1=\"25\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"31\" />\n",
-       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"37\" />\n",
-       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"42\" />\n",
-       "  <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"48\" />\n",
-       "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"54\" />\n",
-       "  <line x1=\"44\" y1=\"34\" x2=\"44\" y2=\"60\" />\n",
-       "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"56\" y1=\"46\" x2=\"56\" y2=\"71\" />\n",
-       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
-       "  <line x1=\"68\" y1=\"58\" x2=\"68\" y2=\"83\" />\n",
-       "  <line x1=\"74\" y1=\"64\" x2=\"74\" y2=\"89\" />\n",
-       "  <line x1=\"79\" y1=\"69\" x2=\"79\" y2=\"95\" />\n",
-       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,96.00085180870013 10.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"36\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"15\" y1=\"5\" x2=\"42\" y2=\"5\" />\n",
-       "  <line x1=\"21\" y1=\"11\" x2=\"48\" y2=\"11\" />\n",
-       "  <line x1=\"27\" y1=\"17\" x2=\"54\" y2=\"17\" />\n",
-       "  <line x1=\"33\" y1=\"23\" x2=\"60\" y2=\"23\" />\n",
-       "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
-       "  <line x1=\"44\" y1=\"34\" x2=\"71\" y2=\"34\" />\n",
-       "  <line x1=\"50\" y1=\"40\" x2=\"77\" y2=\"40\" />\n",
-       "  <line x1=\"56\" y1=\"46\" x2=\"83\" y2=\"46\" />\n",
-       "  <line x1=\"62\" y1=\"52\" x2=\"89\" y2=\"52\" />\n",
-       "  <line x1=\"68\" y1=\"58\" x2=\"94\" y2=\"58\" />\n",
-       "  <line x1=\"74\" y1=\"64\" x2=\"100\" y2=\"64\" />\n",
-       "  <line x1=\"79\" y1=\"69\" x2=\"106\" y2=\"69\" />\n",
-       "  <line x1=\"80\" y1=\"70\" x2=\"107\" y2=\"70\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"36\" y1=\"0\" x2=\"107\" y2=\"70\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"10.0,0.0 36.76889881419403,0.0 107.35713410831168,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"80\" y1=\"70\" x2=\"107\" y2=\"70\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"80\" y1=\"96\" x2=\"107\" y2=\"96\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"107\" y1=\"70\" x2=\"107\" y2=\"96\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"80.58823529411765,70.58823529411765 107.35713410831168,70.58823529411765 107.35713410831168,96.00085180870013 80.58823529411765,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"93.972685\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >192</text>\n",
-       "  <text x=\"127.357134\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,127.357134,83.294544)\">145</text>\n",
-       "  <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">14608</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></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'>object</div><div class='xr-var-preview xr-preview'>2010-01-01 03:00:00 ... 2015-01-...</div><input id='attrs-6942012d-01a1-4491-809e-0e8614c4daf8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6942012d-01a1-4491-809e-0e8614c4daf8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f836f812-e163-4a68-9413-305420060ef8' class='xr-var-data-in' type='checkbox'><label for='data-f836f812-e163-4a68-9413-305420060ef8' 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>xcdat_bounds :</span></dt><dd>True</dd></dl></div><div class='xr-var-data'><pre>array([[cftime.DatetimeProlepticGregorian(2010, 1, 1, 3, 0, 0, 0, has_year_zero=True),\n",
-       "        cftime.DatetimeProlepticGregorian(2010, 1, 1, 6, 0, 0, 0, has_year_zero=True)],\n",
-       "       [cftime.DatetimeProlepticGregorian(2010, 1, 1, 6, 0, 0, 0, has_year_zero=True),\n",
-       "        cftime.DatetimeProlepticGregorian(2010, 1, 1, 9, 0, 0, 0, has_year_zero=True)],\n",
-       "       [cftime.DatetimeProlepticGregorian(2010, 1, 1, 9, 0, 0, 0, has_year_zero=True),\n",
-       "        cftime.DatetimeProlepticGregorian(2010, 1, 1, 12, 0, 0, 0, has_year_zero=True)],\n",
-       "       ...,\n",
-       "       [cftime.DatetimeProlepticGregorian(2014, 12, 31, 18, 0, 0, 0, has_year_zero=True),\n",
-       "        cftime.DatetimeProlepticGregorian(2014, 12, 31, 21, 0, 0, 0, has_year_zero=True)],\n",
-       "       [cftime.DatetimeProlepticGregorian(2014, 12, 31, 21, 0, 0, 0, has_year_zero=True),\n",
-       "        cftime.DatetimeProlepticGregorian(2015, 1, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
-       "       [cftime.DatetimeProlepticGregorian(2015, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "        cftime.DatetimeProlepticGregorian(2015, 1, 1, 3, 0, 0, 0, has_year_zero=True)]],\n",
-       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8b1b2c03-d3ee-4d88-9fec-fdfe9974cc4c' class='xr-section-summary-in' type='checkbox'  ><label for='section-8b1b2c03-d3ee-4d88-9fec-fdfe9974cc4c' 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-19d8f8b2-8678-4642-a676-2d030b1fac15' class='xr-index-data-in' type='checkbox'/><label for='index-19d8f8b2-8678-4642-a676-2d030b1fac15' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -90.0, -88.75,  -87.5, -86.25,  -85.0, -83.75,  -82.5, -81.25,  -80.0,\n",
-       "       -78.75,\n",
-       "       ...\n",
-       "        78.75,   80.0,  81.25,   82.5,  83.75,   85.0,  86.25,   87.5,  88.75,\n",
-       "         90.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=145))</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-359ebd15-4a33-4039-96b5-8a058aa97dc4' class='xr-index-data-in' type='checkbox'/><label for='index-359ebd15-4a33-4039-96b5-8a058aa97dc4' 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([    0.0,   1.875,    3.75,   5.625,     7.5,   9.375,   11.25,  13.125,\n",
-       "          15.0,  16.875,\n",
-       "       ...\n",
-       "        341.25, 343.125,   345.0, 346.875,  348.75, 350.625,   352.5, 354.375,\n",
-       "        356.25, 358.125],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=192))</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-3fdc1b66-5bcd-4ee5-882f-79fc6a3a5fb5' class='xr-index-data-in' type='checkbox'/><label for='index-3fdc1b66-5bcd-4ee5-882f-79fc6a3a5fb5' 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([2010-01-01 03:00:00, 2010-01-01 06:00:00, 2010-01-01 09:00:00,\n",
-       "             2010-01-01 12:00:00, 2010-01-01 15:00:00, 2010-01-01 18:00:00,\n",
-       "             2010-01-01 21:00:00, 2010-01-02 00:00:00, 2010-01-02 03:00:00,\n",
-       "             2010-01-02 06:00:00,\n",
-       "             ...\n",
-       "             2014-12-30 21:00:00, 2014-12-31 00:00:00, 2014-12-31 03:00:00,\n",
-       "             2014-12-31 06:00:00, 2014-12-31 09:00:00, 2014-12-31 12:00:00,\n",
-       "             2014-12-31 15:00:00, 2014-12-31 18:00:00, 2014-12-31 21:00:00,\n",
-       "             2015-01-01 00:00:00],\n",
-       "            dtype=&#x27;object&#x27;,\n",
-       "            length=14608,\n",
-       "            calendar=&#x27;proleptic_gregorian&#x27;,\n",
-       "            freq=&#x27;3h&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f629a43d-8869-42df-adb6-1edd4c1e9723' class='xr-section-summary-in' type='checkbox'  ><label for='section-f629a43d-8869-42df-adb6-1edd4c1e9723' class='xr-section-summary' >Attributes: <span>(48)</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 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>0.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>87658.0</dd><dt><span>creation_date :</span></dt><dd>2020-06-05T04:54:56Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.30</dd><dt><span>experiment :</span></dt><dd>all-forcing simulation of the recent past</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>3hrPt</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.CSIRO.ACCESS-ESM1-5.historical.none.r10i1p1f1</dd><dt><span>grid :</span></dt><dd>native atmosphere N96 grid (145x192 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>history :</span></dt><dd>2020-06-05T04:54:56Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia</dd><dt><span>institution_id :</span></dt><dd>CSIRO</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>nominal_resolution :</span></dt><dd>250 km</dd><dt><span>notes :</span></dt><dd>Exp: ESM-historical; Local ID: HI-14; Variable: tas ([&#x27;fld_s03i236&#x27;])</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>ACCESS-ESM1-5</dd><dt><span>parent_time_units :</span></dt><dd>days since 0101-1-1</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>10</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>run_variant :</span></dt><dd>forcing: GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2, N2O, CH4, CFC11, CFC12, CFC113, HCFC22, HFC125, HFC134a)</dd><dt><span>source :</span></dt><dd>ACCESS-ESM1.5 (2019): \n",
-       "aerosol: CLASSIC (v1.0)\n",
-       "atmos: HadGAM2 (r1.1, N96; 192 x 145 longitude/latitude; 38 levels; top level 39255 m)\n",
-       "atmosChem: none\n",
-       "land: CABLE2.4\n",
-       "landIce: none\n",
-       "ocean: ACCESS-OM2 (MOM5, tripolar primarily 1deg; 360 x 300 longitude/latitude; 50 levels; top grid cell 0-10 m)\n",
-       "ocnBgchem: WOMBAT (same grid as ocean)\n",
-       "seaIce: CICE4.1 (same grid as ocean)</dd><dt><span>source_id :</span></dt><dd>ACCESS-ESM1-5</dd><dt><span>source_type :</span></dt><dd>AOGCM</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>3hr</dd><dt><span>table_info :</span></dt><dd>Creation Date:(30 April 2019) MD5:5bd755e94c2173cb3050a0f3480f60c4</dd><dt><span>title :</span></dt><dd>ACCESS-ESM1-5 output prepared for CMIP6</dd><dt><span>variable_id :</span></dt><dd>tas</dd><dt><span>variant_label :</span></dt><dd>r10i1p1f1</dd><dt><span>version :</span></dt><dd>v20200605</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by CSIRO is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment.  Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>cmor_version :</span></dt><dd>3.4.0</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/b79e6a05-c482-46cf-b3b8-83b9a7d0cfdd</dd><dt><span>DODS_EXTRA.Unlimited_Dimension :</span></dt><dd>time</dd></dl></div></li></ul></div></div>"
+       "  * lat      (lat) float32 100B 75.0 72.5 70.0 67.5 65.0 ... 22.5 20.0 17.5 15.0\n",
+       "Attributes:\n",
+       "    standard_name:  latitude\n",
+       "    long_name:      Latitude\n",
+       "    units:          degrees_north\n",
+       "    axis:           Y\n",
+       "    bounds:         lat_bnds</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'lat'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 25</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-685387be-90d5-4d6a-9da5-a10943667d71' class='xr-array-in' type='checkbox' checked><label for='section-685387be-90d5-4d6a-9da5-a10943667d71' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>75.0 72.5 70.0 67.5 65.0 62.5 60.0 ... 27.5 25.0 22.5 20.0 17.5 15.0</span></div><div class='xr-array-data'><pre>array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n",
+       "       45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n",
+       "       15. ], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-5eb54038-4390-43cd-863f-30dbff4a46dc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5eb54038-4390-43cd-863f-30dbff4a46dc' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>75.0 72.5 70.0 ... 20.0 17.5 15.0</div><input id='attrs-83d975db-f521-4144-84d5-df738cb318d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83d975db-f521-4144-84d5-df738cb318d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2bf7d876-9867-4a8a-8580-cba8bba1e7a2' class='xr-var-data-in' type='checkbox'><label for='data-2bf7d876-9867-4a8a-8580-cba8bba1e7a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd></dl></div><div class='xr-var-data'><pre>array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n",
+       "       45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n",
+       "       15. ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d592d36c-f4f7-4eb4-8269-a87e488d6f11' class='xr-section-summary-in' type='checkbox'  ><label for='section-d592d36c-f4f7-4eb4-8269-a87e488d6f11' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0cfdc232-baf1-433b-b033-bb0007aa655f' class='xr-index-data-in' type='checkbox'/><label for='index-0cfdc232-baf1-433b-b033-bb0007aa655f' 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([75.0, 72.5, 70.0, 67.5, 65.0, 62.5, 60.0, 57.5, 55.0, 52.5, 50.0, 47.5,\n",
+       "       45.0, 42.5, 40.0, 37.5, 35.0, 32.5, 30.0, 27.5, 25.0, 22.5, 20.0, 17.5,\n",
+       "       15.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fe23c982-8a9c-4467-9434-4632b844567a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fe23c982-8a9c-4467-9434-4632b844567a' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.Dataset> Size: 2GB\n",
-       "Dimensions:    (lat: 145, bnds: 2, lon: 192, time: 14608)\n",
+       "<xarray.DataArray 'lat' (lat: 25)> Size: 100B\n",
+       "array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n",
+       "       45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n",
+       "       15. ], dtype=float32)\n",
        "Coordinates:\n",
-       "  * lat        (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
-       "  * lon        (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 354.4 356.2 358.1\n",
-       "    height     float64 8B ...\n",
-       "  * time       (time) object 117kB 2010-01-01 03:00:00 ... 2015-01-01 00:00:00\n",
-       "Dimensions without coordinates: bnds\n",
-       "Data variables:\n",
-       "    lat_bnds   (lat, bnds) float64 2kB dask.array<chunksize=(145, 2), meta=np.ndarray>\n",
-       "    lon_bnds   (lon, bnds) float64 3kB dask.array<chunksize=(192, 2), meta=np.ndarray>\n",
-       "    tas        (time, lat, lon) float32 2GB dask.array<chunksize=(1205, 145, 192), meta=np.ndarray>\n",
-       "    time_bnds  (time, bnds) object 234kB 2010-01-01 03:00:00 ... 2015-01-01 0...\n",
-       "Attributes: (12/48)\n",
-       "    Conventions:                     CF-1.7 CMIP-6.2\n",
-       "    activity_id:                     CMIP\n",
-       "    branch_method:                   standard\n",
-       "    branch_time_in_child:            0.0\n",
-       "    branch_time_in_parent:           87658.0\n",
-       "    creation_date:                   2020-06-05T04:54:56Z\n",
-       "    ...                              ...\n",
-       "    variant_label:                   r10i1p1f1\n",
-       "    version:                         v20200605\n",
-       "    license:                         CMIP6 model data produced by CSIRO is li...\n",
-       "    cmor_version:                    3.4.0\n",
-       "    tracking_id:                     hdl:21.14100/b79e6a05-c482-46cf-b3b8-83b...\n",
-       "    DODS_EXTRA.Unlimited_Dimension:  time"
+       "  * lat      (lat) float32 100B 75.0 72.5 70.0 67.5 65.0 ... 22.5 20.0 17.5 15.0\n",
+       "Attributes:\n",
+       "    standard_name:  latitude\n",
+       "    long_name:      Latitude\n",
+       "    units:          degrees_north\n",
+       "    axis:           Y\n",
+       "    bounds:         lat_bnds"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# The size of this file is approximately 1.45 GB, so we will be chunking our\n",
-    "# request using Dask to avoid hitting the OPeNDAP file size request limit for\n",
-    "# this ESGF node.\n",
-    "ds2 = xcdat.open_dataset(\n",
-    "    \"https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r10i1p1f1/3hr/tas/gn/v20200605/tas_3hr_ACCESS-ESM1-5_historical_r10i1p1f1_gn_201001010300-201501010000.nc\",\n",
-    "    chunks={\"time\": \"auto\"},\n",
-    "    add_bounds=[\"T\"],\n",
-    ")\n",
+    "ds.air.lat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualize averages derived from monthly data on a specific point\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 3))\n",
+    "ax = plt.subplot()\n",
     "\n",
-    "# Unit adjust (-273.15, K to C)\n",
-    "ds2[\"tas\"] = ds2.tas - 273.15\n",
+    "subset_kwargs = {\"lat\": 30, \"lon\": 200, \"time\": slice(\"2013-01-01\", \"2014-01-01\")}\n",
+    "\n",
+    "ds_air_sub = ds.air.sel(**subset_kwargs).load()\n",
+    "ds_air_sub.plot(ax=ax, label=\"monthly (RAW DATA)\", alpha=0.5)\n",
+    "\n",
+    "ds_season_sub = ds_season.air.sel(**subset_kwargs).load()\n",
+    "ds_season_sub.plot(ax=ax, label=\"season\", alpha=0.5)\n",
+    "\n",
+    "ds_yearly_sub = ds_yearly.air.sel(**subset_kwargs).load()\n",
+    "ds_yearly_sub.plot(ax=ax, label=\"yearly\", alpha=0.5)\n",
+    "\n",
+    "plt.title(\"Seasonal and yearly averages derived from monthly time series\")\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Monthly Averages\n",
     "\n",
-    "ds2"
+    "**Group time coordinates by year and month**\n"
    ]
   },
   {
@@ -4495,7 +3850,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ds2_monthly_avg = ds2.temporal.group_average(\"tas\", freq=\"month\", weighted=True)"
+    "ds_monthly_avg = ds.temporal.group_average(\"air\", freq=\"month\", weighted=True)"
    ]
   },
   {
@@ -4537,6 +3892,7 @@
        "}\n",
        "\n",
        "html[theme=dark],\n",
+       "html[data-theme=dark],\n",
        "body[data-theme=dark],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -4587,7 +3943,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -4595,7 +3951,8 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
@@ -4607,6 +3964,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -4869,275 +4230,193 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tas&#x27; (time: 61, lat: 145, lon: 192)&gt; Size: 14MB\n",
-       "dask.array&lt;truediv, shape=(61, 145, 192), dtype=float64, chunksize=(1, 145, 192), chunktype=numpy.ndarray&gt;\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;air&#x27; (time: 24, lat: 25, lon: 53)&gt; Size: 254kB\n",
+       "array([[[-28.68322581, -28.48645161, -28.47975806, ..., -30.65854839,\n",
+       "         -29.74362903, -28.47419355],\n",
+       "        [-26.07677419, -26.1275    , -26.4225    , ..., -32.56790323,\n",
+       "         -31.10516129, -28.44282258],\n",
+       "        [-22.77056452, -23.31516129, -24.0425    , ..., -31.16564516,\n",
+       "         -28.38290323, -24.14491935],\n",
+       "        ...,\n",
+       "        [ 22.68814516,  22.00096774,  21.77314516, ...,  22.2183871 ,\n",
+       "          21.73451613,  21.1183871 ],\n",
+       "        [ 23.31951613,  23.16701613,  22.69822581, ...,  22.43774194,\n",
+       "          22.19072581,  21.71556452],\n",
+       "        [ 23.90346774,  23.89201613,  23.58532258, ...,  23.15459677,\n",
+       "          22.94741935,  22.8891129 ]],\n",
+       "\n",
+       "       [[-32.41607143, -32.44866071, -32.73848214, ..., -31.54482143,\n",
+       "         -30.43017857, -29.20544643],\n",
+       "        [-31.216875  , -31.080625  , -31.23696429, ..., -32.13571429,\n",
+       "         -30.82517857, -28.42241071],\n",
+       "        [-27.82642857, -28.12392857, -28.78044643, ..., -29.73410714,\n",
+       "         -27.38392857, -23.49142857],\n",
+       "...\n",
+       "        [ 24.89908333,  24.20008333,  24.072     , ...,  24.86183333,\n",
+       "          24.51025   ,  23.99566667],\n",
+       "        [ 25.815     ,  25.66191667,  25.12158333, ...,  24.95408333,\n",
+       "          25.07108333,  24.73558333],\n",
+       "        [ 26.02341667,  26.06766667,  25.74575   , ...,  25.56633333,\n",
+       "          25.59183333,  25.63025   ]],\n",
+       "\n",
+       "       [[-26.34846774, -26.2608871 , -26.3808871 , ..., -33.07903226,\n",
+       "         -32.06798387, -30.86830645],\n",
+       "        [-25.42      , -24.84927419, -24.40548387, ..., -34.53137097,\n",
+       "         -32.82782258, -30.17967742],\n",
+       "        [-23.18104839, -23.56475806, -23.57475806, ..., -35.44693548,\n",
+       "         -31.91258065, -26.92330645],\n",
+       "        ...,\n",
+       "        [ 23.29919355,  22.54145161,  22.6083871 , ...,  23.37830645,\n",
+       "          23.0675    ,  22.66298387],\n",
+       "        [ 24.2958871 ,  24.28612903,  24.03177419, ...,  23.80258065,\n",
+       "          23.90830645,  23.57903226],\n",
+       "        [ 24.89733871,  25.07612903,  24.90967742, ...,  24.54758065,\n",
+       "          24.57322581,  24.56040323]]])\n",
        "Coordinates:\n",
-       "  * lat      (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
-       "  * lon      (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
-       "    height   float64 8B ...\n",
-       "  * time     (time) object 488B 2010-01-01 00:00:00 ... 2015-01-01 00:00:00\n",
+       "  * lat      (lat) float32 100B 75.0 72.5 70.0 67.5 65.0 ... 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 212B 200.0 202.5 205.0 207.5 ... 325.0 327.5 330.0\n",
+       "  * time     (time) object 192B 2013-01-01 00:00:00 ... 2014-12-01 00:00:00\n",
        "Attributes:\n",
        "    operation:  temporal_avg\n",
        "    mode:       group_average\n",
        "    freq:       month\n",
-       "    weighted:   True</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 61</li><li><span class='xr-has-index'>lat</span>: 145</li><li><span class='xr-has-index'>lon</span>: 192</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f4dba92c-65e3-42f6-834c-8638ff3d802e' class='xr-array-in' type='checkbox' checked><label for='section-f4dba92c-65e3-42f6-834c-8638ff3d802e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 145, 192), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 12.96 MiB </td>\n",
-       "                        <td> 217.50 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (61, 145, 192) </td>\n",
-       "                        <td> (1, 145, 192) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 61 chunks in 641 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
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-       "  <text x=\"12.862745\" y=\"123.487745\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,12.862745,123.487745)\">61</text>\n",
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-       "</table></div></div></li><li class='xr-section-item'><input id='section-4a57feb5-26c9-4347-991e-26dfaa4f9c41' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4a57feb5-26c9-4347-991e-26dfaa4f9c41' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -88.75 -87.5 ... 88.75 90.0</div><input id='attrs-3b15297e-3383-48d5-900e-721d56e550c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b15297e-3383-48d5-900e-721d56e550c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78822c5d-5137-40d3-a35c-ad2c3d7d998d' class='xr-var-data-in' type='checkbox'><label for='data-78822c5d-5137-40d3-a35c-ad2c3d7d998d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</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><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-90.  , -88.75, -87.5 , -86.25, -85.  , -83.75, -82.5 , -81.25, -80.  ,\n",
-       "       -78.75, -77.5 , -76.25, -75.  , -73.75, -72.5 , -71.25, -70.  , -68.75,\n",
-       "       -67.5 , -66.25, -65.  , -63.75, -62.5 , -61.25, -60.  , -58.75, -57.5 ,\n",
-       "       -56.25, -55.  , -53.75, -52.5 , -51.25, -50.  , -48.75, -47.5 , -46.25,\n",
-       "       -45.  , -43.75, -42.5 , -41.25, -40.  , -38.75, -37.5 , -36.25, -35.  ,\n",
-       "       -33.75, -32.5 , -31.25, -30.  , -28.75, -27.5 , -26.25, -25.  , -23.75,\n",
-       "       -22.5 , -21.25, -20.  , -18.75, -17.5 , -16.25, -15.  , -13.75, -12.5 ,\n",
-       "       -11.25, -10.  ,  -8.75,  -7.5 ,  -6.25,  -5.  ,  -3.75,  -2.5 ,  -1.25,\n",
-       "         0.  ,   1.25,   2.5 ,   3.75,   5.  ,   6.25,   7.5 ,   8.75,  10.  ,\n",
-       "        11.25,  12.5 ,  13.75,  15.  ,  16.25,  17.5 ,  18.75,  20.  ,  21.25,\n",
-       "        22.5 ,  23.75,  25.  ,  26.25,  27.5 ,  28.75,  30.  ,  31.25,  32.5 ,\n",
-       "        33.75,  35.  ,  36.25,  37.5 ,  38.75,  40.  ,  41.25,  42.5 ,  43.75,\n",
-       "        45.  ,  46.25,  47.5 ,  48.75,  50.  ,  51.25,  52.5 ,  53.75,  55.  ,\n",
-       "        56.25,  57.5 ,  58.75,  60.  ,  61.25,  62.5 ,  63.75,  65.  ,  66.25,\n",
-       "        67.5 ,  68.75,  70.  ,  71.25,  72.5 ,  73.75,  75.  ,  76.25,  77.5 ,\n",
-       "        78.75,  80.  ,  81.25,  82.5 ,  83.75,  85.  ,  86.25,  87.5 ,  88.75,\n",
-       "        90.  ])</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'>0.0 1.875 3.75 ... 356.2 358.1</div><input id='attrs-d4f250ee-06d6-4773-8311-916e1037b787' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4f250ee-06d6-4773-8311-916e1037b787' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9661358c-1d28-4293-ae08-6a76d836c124' class='xr-var-data-in' type='checkbox'><label for='data-9661358c-1d28-4293-ae08-6a76d836c124' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</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><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.   ,   1.875,   3.75 ,   5.625,   7.5  ,   9.375,  11.25 ,  13.125,\n",
-       "        15.   ,  16.875,  18.75 ,  20.625,  22.5  ,  24.375,  26.25 ,  28.125,\n",
-       "        30.   ,  31.875,  33.75 ,  35.625,  37.5  ,  39.375,  41.25 ,  43.125,\n",
-       "        45.   ,  46.875,  48.75 ,  50.625,  52.5  ,  54.375,  56.25 ,  58.125,\n",
-       "        60.   ,  61.875,  63.75 ,  65.625,  67.5  ,  69.375,  71.25 ,  73.125,\n",
-       "        75.   ,  76.875,  78.75 ,  80.625,  82.5  ,  84.375,  86.25 ,  88.125,\n",
-       "        90.   ,  91.875,  93.75 ,  95.625,  97.5  ,  99.375, 101.25 , 103.125,\n",
-       "       105.   , 106.875, 108.75 , 110.625, 112.5  , 114.375, 116.25 , 118.125,\n",
-       "       120.   , 121.875, 123.75 , 125.625, 127.5  , 129.375, 131.25 , 133.125,\n",
-       "       135.   , 136.875, 138.75 , 140.625, 142.5  , 144.375, 146.25 , 148.125,\n",
-       "       150.   , 151.875, 153.75 , 155.625, 157.5  , 159.375, 161.25 , 163.125,\n",
-       "       165.   , 166.875, 168.75 , 170.625, 172.5  , 174.375, 176.25 , 178.125,\n",
-       "       180.   , 181.875, 183.75 , 185.625, 187.5  , 189.375, 191.25 , 193.125,\n",
-       "       195.   , 196.875, 198.75 , 200.625, 202.5  , 204.375, 206.25 , 208.125,\n",
-       "       210.   , 211.875, 213.75 , 215.625, 217.5  , 219.375, 221.25 , 223.125,\n",
-       "       225.   , 226.875, 228.75 , 230.625, 232.5  , 234.375, 236.25 , 238.125,\n",
-       "       240.   , 241.875, 243.75 , 245.625, 247.5  , 249.375, 251.25 , 253.125,\n",
-       "       255.   , 256.875, 258.75 , 260.625, 262.5  , 264.375, 266.25 , 268.125,\n",
-       "       270.   , 271.875, 273.75 , 275.625, 277.5  , 279.375, 281.25 , 283.125,\n",
-       "       285.   , 286.875, 288.75 , 290.625, 292.5  , 294.375, 296.25 , 298.125,\n",
-       "       300.   , 301.875, 303.75 , 305.625, 307.5  , 309.375, 311.25 , 313.125,\n",
-       "       315.   , 316.875, 318.75 , 320.625, 322.5  , 324.375, 326.25 , 328.125,\n",
-       "       330.   , 331.875, 333.75 , 335.625, 337.5  , 339.375, 341.25 , 343.125,\n",
-       "       345.   , 346.875, 348.75 , 350.625, 352.5  , 354.375, 356.25 , 358.125])</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-76cd5c9f-3fe0-4d51-9586-343ffc08dd2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-76cd5c9f-3fe0-4d51-9586-343ffc08dd2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5cefe20-09c5-4e0c-9f95-97efb2dcbfe6' class='xr-var-data-in' type='checkbox'><label for='data-b5cefe20-09c5-4e0c-9f95-97efb2dcbfe6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span 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'>2010-01-01 00:00:00 ... 2015-01-...</div><input id='attrs-59f9013e-77ee-46c9-b82c-e1426dafb051' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-59f9013e-77ee-46c9-b82c-e1426dafb051' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a616374d-72e7-446c-b81f-f7aaacfb7d30' class='xr-var-data-in' type='checkbox'><label for='data-a616374d-72e7-446c-b81f-f7aaacfb7d30' 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>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeProlepticGregorian(2010, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2010, 2, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2010, 3, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2010, 4, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2010, 5, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2010, 6, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2010, 7, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2010, 8, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2010, 9, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2010, 10, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2010, 11, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2010, 12, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2011, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2011, 2, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2011, 3, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2011, 4, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2011, 5, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2011, 6, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2011, 7, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2011, 8, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2011, 9, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2011, 10, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2011, 11, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2011, 12, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2012, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2012, 2, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2012, 3, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2012, 4, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2012, 5, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2012, 6, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2012, 7, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2012, 8, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2012, 9, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2012, 10, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2012, 11, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2012, 12, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2013, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2013, 2, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2013, 3, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2013, 4, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2013, 5, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2013, 6, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2013, 7, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2013, 8, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2013, 9, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2013, 10, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2013, 11, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2013, 12, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 2, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 3, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 4, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 5, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 6, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 7, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 8, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 9, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 10, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 11, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 12, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2015, 1, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7ddf8d25-82b6-48c8-88d7-ebef2a9ba80c' class='xr-section-summary-in' type='checkbox'  ><label for='section-7ddf8d25-82b6-48c8-88d7-ebef2a9ba80c' 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-23fb81a4-292b-4159-aa6e-d224060976cf' class='xr-index-data-in' type='checkbox'/><label for='index-23fb81a4-292b-4159-aa6e-d224060976cf' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -90.0, -88.75,  -87.5, -86.25,  -85.0, -83.75,  -82.5, -81.25,  -80.0,\n",
-       "       -78.75,\n",
-       "       ...\n",
-       "        78.75,   80.0,  81.25,   82.5,  83.75,   85.0,  86.25,   87.5,  88.75,\n",
-       "         90.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=145))</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-c67d0c15-26ae-40af-baf3-8d29d4eefae4' class='xr-index-data-in' type='checkbox'/><label for='index-c67d0c15-26ae-40af-baf3-8d29d4eefae4' 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([    0.0,   1.875,    3.75,   5.625,     7.5,   9.375,   11.25,  13.125,\n",
-       "          15.0,  16.875,\n",
-       "       ...\n",
-       "        341.25, 343.125,   345.0, 346.875,  348.75, 350.625,   352.5, 354.375,\n",
-       "        356.25, 358.125],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=192))</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-c1b4a1ac-5b56-4efe-bf07-e395853b3f75' class='xr-index-data-in' type='checkbox'/><label for='index-c1b4a1ac-5b56-4efe-bf07-e395853b3f75' 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([2010-01-01 00:00:00, 2010-02-01 00:00:00, 2010-03-01 00:00:00,\n",
-       "             2010-04-01 00:00:00, 2010-05-01 00:00:00, 2010-06-01 00:00:00,\n",
-       "             2010-07-01 00:00:00, 2010-08-01 00:00:00, 2010-09-01 00:00:00,\n",
-       "             2010-10-01 00:00:00, 2010-11-01 00:00:00, 2010-12-01 00:00:00,\n",
-       "             2011-01-01 00:00:00, 2011-02-01 00:00:00, 2011-03-01 00:00:00,\n",
-       "             2011-04-01 00:00:00, 2011-05-01 00:00:00, 2011-06-01 00:00:00,\n",
-       "             2011-07-01 00:00:00, 2011-08-01 00:00:00, 2011-09-01 00:00:00,\n",
-       "             2011-10-01 00:00:00, 2011-11-01 00:00:00, 2011-12-01 00:00:00,\n",
-       "             2012-01-01 00:00:00, 2012-02-01 00:00:00, 2012-03-01 00:00:00,\n",
-       "             2012-04-01 00:00:00, 2012-05-01 00:00:00, 2012-06-01 00:00:00,\n",
-       "             2012-07-01 00:00:00, 2012-08-01 00:00:00, 2012-09-01 00:00:00,\n",
-       "             2012-10-01 00:00:00, 2012-11-01 00:00:00, 2012-12-01 00:00:00,\n",
-       "             2013-01-01 00:00:00, 2013-02-01 00:00:00, 2013-03-01 00:00:00,\n",
+       "    weighted:   True</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'air'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 24</li><li><span class='xr-has-index'>lat</span>: 25</li><li><span class='xr-has-index'>lon</span>: 53</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-18b73513-d680-4eba-acad-ad1aefadbab7' class='xr-array-in' type='checkbox' checked><label for='section-18b73513-d680-4eba-acad-ad1aefadbab7' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-28.68 -28.49 -28.48 -28.67 -28.99 ... 24.98 24.97 24.55 24.57 24.56</span></div><div class='xr-array-data'><pre>array([[[-28.68322581, -28.48645161, -28.47975806, ..., -30.65854839,\n",
+       "         -29.74362903, -28.47419355],\n",
+       "        [-26.07677419, -26.1275    , -26.4225    , ..., -32.56790323,\n",
+       "         -31.10516129, -28.44282258],\n",
+       "        [-22.77056452, -23.31516129, -24.0425    , ..., -31.16564516,\n",
+       "         -28.38290323, -24.14491935],\n",
+       "        ...,\n",
+       "        [ 22.68814516,  22.00096774,  21.77314516, ...,  22.2183871 ,\n",
+       "          21.73451613,  21.1183871 ],\n",
+       "        [ 23.31951613,  23.16701613,  22.69822581, ...,  22.43774194,\n",
+       "          22.19072581,  21.71556452],\n",
+       "        [ 23.90346774,  23.89201613,  23.58532258, ...,  23.15459677,\n",
+       "          22.94741935,  22.8891129 ]],\n",
+       "\n",
+       "       [[-32.41607143, -32.44866071, -32.73848214, ..., -31.54482143,\n",
+       "         -30.43017857, -29.20544643],\n",
+       "        [-31.216875  , -31.080625  , -31.23696429, ..., -32.13571429,\n",
+       "         -30.82517857, -28.42241071],\n",
+       "        [-27.82642857, -28.12392857, -28.78044643, ..., -29.73410714,\n",
+       "         -27.38392857, -23.49142857],\n",
+       "...\n",
+       "        [ 24.89908333,  24.20008333,  24.072     , ...,  24.86183333,\n",
+       "          24.51025   ,  23.99566667],\n",
+       "        [ 25.815     ,  25.66191667,  25.12158333, ...,  24.95408333,\n",
+       "          25.07108333,  24.73558333],\n",
+       "        [ 26.02341667,  26.06766667,  25.74575   , ...,  25.56633333,\n",
+       "          25.59183333,  25.63025   ]],\n",
+       "\n",
+       "       [[-26.34846774, -26.2608871 , -26.3808871 , ..., -33.07903226,\n",
+       "         -32.06798387, -30.86830645],\n",
+       "        [-25.42      , -24.84927419, -24.40548387, ..., -34.53137097,\n",
+       "         -32.82782258, -30.17967742],\n",
+       "        [-23.18104839, -23.56475806, -23.57475806, ..., -35.44693548,\n",
+       "         -31.91258065, -26.92330645],\n",
+       "        ...,\n",
+       "        [ 23.29919355,  22.54145161,  22.6083871 , ...,  23.37830645,\n",
+       "          23.0675    ,  22.66298387],\n",
+       "        [ 24.2958871 ,  24.28612903,  24.03177419, ...,  23.80258065,\n",
+       "          23.90830645,  23.57903226],\n",
+       "        [ 24.89733871,  25.07612903,  24.90967742, ...,  24.54758065,\n",
+       "          24.57322581,  24.56040323]]])</pre></div></div></li><li class='xr-section-item'><input id='section-77cd4be8-ed95-4921-a8bc-b4f8222f07c7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-77cd4be8-ed95-4921-a8bc-b4f8222f07c7' 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'>float32</div><div class='xr-var-preview xr-preview'>75.0 72.5 70.0 ... 20.0 17.5 15.0</div><input id='attrs-2257ed85-1488-4949-a31f-b4875a7643da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2257ed85-1488-4949-a31f-b4875a7643da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da3ee1fd-728a-4664-b3d3-375f08337fe0' class='xr-var-data-in' type='checkbox'><label for='data-da3ee1fd-728a-4664-b3d3-375f08337fe0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd></dl></div><div class='xr-var-data'><pre>array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n",
+       "       45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n",
+       "       15. ], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>200.0 202.5 205.0 ... 327.5 330.0</div><input id='attrs-a5ad0638-4a5f-45f6-8579-940715bb6ebc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a5ad0638-4a5f-45f6-8579-940715bb6ebc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ea81552-387a-4bbd-8187-6563a37e6cee' class='xr-var-data-in' type='checkbox'><label for='data-4ea81552-387a-4bbd-8187-6563a37e6cee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd></dl></div><div class='xr-var-data'><pre>array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
+       "       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
+       "       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
+       "       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
+       "       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
+       "       325. , 327.5, 330. ], dtype=float32)</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'>object</div><div class='xr-var-preview xr-preview'>2013-01-01 00:00:00 ... 2014-12-...</div><input id='attrs-66759d62-3f7b-4cb2-a523-fdd6dff39298' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66759d62-3f7b-4cb2-a523-fdd6dff39298' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-860e05a7-dc70-49f3-8925-627ca5a6b9bf' class='xr-var-data-in' type='checkbox'><label for='data-860e05a7-dc70-49f3-8925-627ca5a6b9bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeGregorian(2013, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2013, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2013, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2013, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2013, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2013, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2013, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2013, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2013, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2013, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2013, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2013, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 12, 1, 0, 0, 0, 0, has_year_zero=False)],\n",
+       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d96a58b0-fc9d-4983-b6ef-27ca2db4a85e' class='xr-section-summary-in' type='checkbox'  ><label for='section-d96a58b0-fc9d-4983-b6ef-27ca2db4a85e' 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-b21ee24e-1e2b-4cd2-bb0c-1d58de94d0fb' class='xr-index-data-in' type='checkbox'/><label for='index-b21ee24e-1e2b-4cd2-bb0c-1d58de94d0fb' 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([75.0, 72.5, 70.0, 67.5, 65.0, 62.5, 60.0, 57.5, 55.0, 52.5, 50.0, 47.5,\n",
+       "       45.0, 42.5, 40.0, 37.5, 35.0, 32.5, 30.0, 27.5, 25.0, 22.5, 20.0, 17.5,\n",
+       "       15.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;))</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-48470378-59fd-480f-88dd-008ccb6a6b42' class='xr-index-data-in' type='checkbox'/><label for='index-48470378-59fd-480f-88dd-008ccb6a6b42' 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([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0, 222.5,\n",
+       "       225.0, 227.5, 230.0, 232.5, 235.0, 237.5, 240.0, 242.5, 245.0, 247.5,\n",
+       "       250.0, 252.5, 255.0, 257.5, 260.0, 262.5, 265.0, 267.5, 270.0, 272.5,\n",
+       "       275.0, 277.5, 280.0, 282.5, 285.0, 287.5, 290.0, 292.5, 295.0, 297.5,\n",
+       "       300.0, 302.5, 305.0, 307.5, 310.0, 312.5, 315.0, 317.5, 320.0, 322.5,\n",
+       "       325.0, 327.5, 330.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2174d8c0-f35f-494d-b1e7-8da9d93de04a' class='xr-index-data-in' type='checkbox'/><label for='index-2174d8c0-f35f-494d-b1e7-8da9d93de04a' 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([2013-01-01 00:00:00, 2013-02-01 00:00:00, 2013-03-01 00:00:00,\n",
        "             2013-04-01 00:00:00, 2013-05-01 00:00:00, 2013-06-01 00:00:00,\n",
        "             2013-07-01 00:00:00, 2013-08-01 00:00:00, 2013-09-01 00:00:00,\n",
        "             2013-10-01 00:00:00, 2013-11-01 00:00:00, 2013-12-01 00:00:00,\n",
        "             2014-01-01 00:00:00, 2014-02-01 00:00:00, 2014-03-01 00:00:00,\n",
        "             2014-04-01 00:00:00, 2014-05-01 00:00:00, 2014-06-01 00:00:00,\n",
        "             2014-07-01 00:00:00, 2014-08-01 00:00:00, 2014-09-01 00:00:00,\n",
-       "             2014-10-01 00:00:00, 2014-11-01 00:00:00, 2014-12-01 00:00:00,\n",
-       "             2015-01-01 00:00:00],\n",
-       "            dtype=&#x27;object&#x27;,\n",
-       "            length=61,\n",
-       "            calendar=&#x27;proleptic_gregorian&#x27;,\n",
-       "            freq=&#x27;MS&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a143c565-98ee-4ddb-bb50-e8d336234dbd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a143c565-98ee-4ddb-bb50-e8d336234dbd' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>operation :</span></dt><dd>temporal_avg</dd><dt><span>mode :</span></dt><dd>group_average</dd><dt><span>freq :</span></dt><dd>month</dd><dt><span>weighted :</span></dt><dd>True</dd></dl></div></li></ul></div></div>"
+       "             2014-10-01 00:00:00, 2014-11-01 00:00:00, 2014-12-01 00:00:00],\n",
+       "            dtype=&#x27;object&#x27;, length=24, calendar=&#x27;standard&#x27;, freq=&#x27;MS&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b65a7db7-0c12-42fa-9400-327934c5df0b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b65a7db7-0c12-42fa-9400-327934c5df0b' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>operation :</span></dt><dd>temporal_avg</dd><dt><span>mode :</span></dt><dd>group_average</dd><dt><span>freq :</span></dt><dd>month</dd><dt><span>weighted :</span></dt><dd>True</dd></dl></div></li></ul></div></div>"
       ],
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-       "<xarray.DataArray 'tas' (time: 61, lat: 145, lon: 192)> Size: 14MB\n",
-       "dask.array<truediv, shape=(61, 145, 192), dtype=float64, chunksize=(1, 145, 192), chunktype=numpy.ndarray>\n",
+       "<xarray.DataArray 'air' (time: 24, lat: 25, lon: 53)> Size: 254kB\n",
+       "array([[[-28.68322581, -28.48645161, -28.47975806, ..., -30.65854839,\n",
+       "         -29.74362903, -28.47419355],\n",
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+       "        ...,\n",
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+       "         -27.38392857, -23.49142857],\n",
+       "...\n",
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+       "          25.59183333,  25.63025   ]],\n",
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+       "        [ 24.89733871,  25.07612903,  24.90967742, ...,  24.54758065,\n",
+       "          24.57322581,  24.56040323]]])\n",
        "Coordinates:\n",
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-       "  * lon      (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
-       "    height   float64 8B ...\n",
-       "  * time     (time) object 488B 2010-01-01 00:00:00 ... 2015-01-01 00:00:00\n",
+       "  * lat      (lat) float32 100B 75.0 72.5 70.0 67.5 65.0 ... 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 212B 200.0 202.5 205.0 207.5 ... 325.0 327.5 330.0\n",
+       "  * time     (time) object 192B 2013-01-01 00:00:00 ... 2014-12-01 00:00:00\n",
        "Attributes:\n",
        "    operation:  temporal_avg\n",
        "    mode:       group_average\n",
@@ -5151,7 +4430,7 @@
     }
    ],
    "source": [
-    "ds2_monthly_avg.tas"
+    "ds_monthly_avg.air"
    ]
   },
   {
@@ -5160,9 +4439,7 @@
    "source": [
     "### Daily Averages\n",
     "\n",
-    "**Group time coordinates by year, month, and day**\n",
-    "\n",
-    "For this example, we will be opening a subset of 3hr time series data for `tas` using OPeNDAP.\n"
+    "**Group time coordinates by year, month, and day**\n"
    ]
   },
   {
@@ -5171,17 +4448,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# The size of this file is approximately 1.17 GB, so we will be chunking our\n",
-    "# request using Dask to avoid hitting the OPeNDAP file size request limit for\n",
-    "# this ESGF node.\n",
-    "ds3 = xcdat.open_dataset(\n",
-    "    \"https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r10i1p1f1/3hr/tas/gn/v20200605/tas_3hr_ACCESS-ESM1-5_historical_r10i1p1f1_gn_201001010300-201501010000.nc\",\n",
-    "    chunks={\"time\": \"auto\"},\n",
-    "    add_bounds=[\"T\"],\n",
-    ")\n",
-    "\n",
-    "# Unit adjust (-273.15, K to C)\n",
-    "ds3[\"tas\"] = ds3.tas - 273.15"
+    "ds_day_avg = ds.temporal.group_average(\"air\", freq=\"day\", weighted=True)"
    ]
   },
   {
@@ -5223,6 +4490,7 @@
        "}\n",
        "\n",
        "html[theme=dark],\n",
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        "body[data-theme=dark],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
@@ -5273,7 +4541,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
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@@ -5281,7 +4549,8 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
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@@ -5293,6 +4562,10 @@
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+       ".xr-section-item input:focus + label {\n",
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@@ -5555,192 +4828,182 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tas&#x27; (time: 14608, lat: 145, lon: 192)&gt; Size: 2GB\n",
-       "dask.array&lt;sub, shape=(14608, 145, 192), dtype=float32, chunksize=(1205, 145, 192), chunktype=numpy.ndarray&gt;\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;air&#x27; (time: 730, lat: 25, lon: 53)&gt; Size: 8MB\n",
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+       "         -35.475 ],\n",
+       "        [-28.575 , -28.5775, -28.875 , ..., -41.9025, -40.325 ,\n",
+       "         -36.85  ],\n",
+       "        [-19.15  , -19.9275, -21.3275, ..., -41.675 , -39.455 ,\n",
+       "         -34.525 ],\n",
+       "        ...,\n",
+       "        [ 23.15  ,  22.825 ,  22.85  , ...,  22.7475,  22.17  ,\n",
+       "          21.795 ],\n",
+       "        [ 23.175 ,  23.575 ,  23.5925, ...,  23.0225,  22.85  ,\n",
+       "          22.3975],\n",
+       "        [ 23.47  ,  23.845 ,  23.95  , ...,  23.8725,  23.8975,\n",
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+       "\n",
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+       "         -30.0775],\n",
+       "        [-25.3275, -25.95  , -26.9275, ..., -37.225 , -36.5525,\n",
+       "         -34.55  ],\n",
+       "        [-19.6275, -21.0775, -22.8525, ..., -35.4525, -34.2775,\n",
+       "         -31.25  ],\n",
+       "...\n",
+       "        [ 23.215 ,  22.265 ,  22.015 , ...,  23.74  ,  23.195 ,\n",
+       "          22.195 ],\n",
+       "        [ 24.3675,  24.515 ,  23.895 , ...,  23.415 ,  22.995 ,\n",
+       "          22.27  ],\n",
+       "        [ 25.4175,  25.5925,  25.1925, ...,  23.6425,  23.19  ,\n",
+       "          22.72  ]],\n",
+       "\n",
+       "       [[-28.935 , -29.535 , -30.385 , ..., -29.41  , -28.96  ,\n",
+       "         -28.46  ],\n",
+       "        [-23.835 , -24.06  , -24.56  , ..., -32.585 , -31.635 ,\n",
+       "         -30.035 ],\n",
+       "        [-10.21  , -10.785 , -11.435 , ..., -33.685 , -31.035 ,\n",
+       "         -27.135 ],\n",
+       "        ...,\n",
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+       "          21.64  ],\n",
+       "        [ 24.84  ,  25.59  ,  25.54  , ...,  23.065 ,  22.715 ,\n",
+       "          22.39  ]]])\n",
        "Coordinates:\n",
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-       "    height   float64 8B ...\n",
-       "  * time     (time) object 117kB 2010-01-01 03:00:00 ... 2015-01-01 00:00:00</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 14608</li><li><span class='xr-has-index'>lat</span>: 145</li><li><span class='xr-has-index'>lon</span>: 192</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-32734076-2f7d-49e1-ab50-e1a2c9ada802' class='xr-array-in' type='checkbox' checked><label for='section-32734076-2f7d-49e1-ab50-e1a2c9ada802' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1205, 145, 192), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-d9f134e9-d487-4d11-b506-dba454dda01b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d9f134e9-d487-4d11-b506-dba454dda01b' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -88.75 -87.5 ... 88.75 90.0</div><input id='attrs-e7a21441-2576-4fcf-8e40-02019f020cb9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7a21441-2576-4fcf-8e40-02019f020cb9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a6ec67e-3178-4448-a662-1afe3bc99a80' class='xr-var-data-in' type='checkbox'><label for='data-0a6ec67e-3178-4448-a662-1afe3bc99a80' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</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><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-90.  , -88.75, -87.5 , -86.25, -85.  , -83.75, -82.5 , -81.25, -80.  ,\n",
-       "       -78.75, -77.5 , -76.25, -75.  , -73.75, -72.5 , -71.25, -70.  , -68.75,\n",
-       "       -67.5 , -66.25, -65.  , -63.75, -62.5 , -61.25, -60.  , -58.75, -57.5 ,\n",
-       "       -56.25, -55.  , -53.75, -52.5 , -51.25, -50.  , -48.75, -47.5 , -46.25,\n",
-       "       -45.  , -43.75, -42.5 , -41.25, -40.  , -38.75, -37.5 , -36.25, -35.  ,\n",
-       "       -33.75, -32.5 , -31.25, -30.  , -28.75, -27.5 , -26.25, -25.  , -23.75,\n",
-       "       -22.5 , -21.25, -20.  , -18.75, -17.5 , -16.25, -15.  , -13.75, -12.5 ,\n",
-       "       -11.25, -10.  ,  -8.75,  -7.5 ,  -6.25,  -5.  ,  -3.75,  -2.5 ,  -1.25,\n",
-       "         0.  ,   1.25,   2.5 ,   3.75,   5.  ,   6.25,   7.5 ,   8.75,  10.  ,\n",
-       "        11.25,  12.5 ,  13.75,  15.  ,  16.25,  17.5 ,  18.75,  20.  ,  21.25,\n",
-       "        22.5 ,  23.75,  25.  ,  26.25,  27.5 ,  28.75,  30.  ,  31.25,  32.5 ,\n",
-       "        33.75,  35.  ,  36.25,  37.5 ,  38.75,  40.  ,  41.25,  42.5 ,  43.75,\n",
-       "        45.  ,  46.25,  47.5 ,  48.75,  50.  ,  51.25,  52.5 ,  53.75,  55.  ,\n",
-       "        56.25,  57.5 ,  58.75,  60.  ,  61.25,  62.5 ,  63.75,  65.  ,  66.25,\n",
-       "        67.5 ,  68.75,  70.  ,  71.25,  72.5 ,  73.75,  75.  ,  76.25,  77.5 ,\n",
-       "        78.75,  80.  ,  81.25,  82.5 ,  83.75,  85.  ,  86.25,  87.5 ,  88.75,\n",
-       "        90.  ])</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'>0.0 1.875 3.75 ... 356.2 358.1</div><input id='attrs-73119a11-a2b1-45ff-af13-969d5c93f7f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-73119a11-a2b1-45ff-af13-969d5c93f7f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c0a886f-a50a-4766-b1a5-218b5bb2eb92' class='xr-var-data-in' type='checkbox'><label for='data-5c0a886f-a50a-4766-b1a5-218b5bb2eb92' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</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><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.   ,   1.875,   3.75 ,   5.625,   7.5  ,   9.375,  11.25 ,  13.125,\n",
-       "        15.   ,  16.875,  18.75 ,  20.625,  22.5  ,  24.375,  26.25 ,  28.125,\n",
-       "        30.   ,  31.875,  33.75 ,  35.625,  37.5  ,  39.375,  41.25 ,  43.125,\n",
-       "        45.   ,  46.875,  48.75 ,  50.625,  52.5  ,  54.375,  56.25 ,  58.125,\n",
-       "        60.   ,  61.875,  63.75 ,  65.625,  67.5  ,  69.375,  71.25 ,  73.125,\n",
-       "        75.   ,  76.875,  78.75 ,  80.625,  82.5  ,  84.375,  86.25 ,  88.125,\n",
-       "        90.   ,  91.875,  93.75 ,  95.625,  97.5  ,  99.375, 101.25 , 103.125,\n",
-       "       105.   , 106.875, 108.75 , 110.625, 112.5  , 114.375, 116.25 , 118.125,\n",
-       "       120.   , 121.875, 123.75 , 125.625, 127.5  , 129.375, 131.25 , 133.125,\n",
-       "       135.   , 136.875, 138.75 , 140.625, 142.5  , 144.375, 146.25 , 148.125,\n",
-       "       150.   , 151.875, 153.75 , 155.625, 157.5  , 159.375, 161.25 , 163.125,\n",
-       "       165.   , 166.875, 168.75 , 170.625, 172.5  , 174.375, 176.25 , 178.125,\n",
-       "       180.   , 181.875, 183.75 , 185.625, 187.5  , 189.375, 191.25 , 193.125,\n",
-       "       195.   , 196.875, 198.75 , 200.625, 202.5  , 204.375, 206.25 , 208.125,\n",
-       "       210.   , 211.875, 213.75 , 215.625, 217.5  , 219.375, 221.25 , 223.125,\n",
-       "       225.   , 226.875, 228.75 , 230.625, 232.5  , 234.375, 236.25 , 238.125,\n",
-       "       240.   , 241.875, 243.75 , 245.625, 247.5  , 249.375, 251.25 , 253.125,\n",
-       "       255.   , 256.875, 258.75 , 260.625, 262.5  , 264.375, 266.25 , 268.125,\n",
-       "       270.   , 271.875, 273.75 , 275.625, 277.5  , 279.375, 281.25 , 283.125,\n",
-       "       285.   , 286.875, 288.75 , 290.625, 292.5  , 294.375, 296.25 , 298.125,\n",
-       "       300.   , 301.875, 303.75 , 305.625, 307.5  , 309.375, 311.25 , 313.125,\n",
-       "       315.   , 316.875, 318.75 , 320.625, 322.5  , 324.375, 326.25 , 328.125,\n",
-       "       330.   , 331.875, 333.75 , 335.625, 337.5  , 339.375, 341.25 , 343.125,\n",
-       "       345.   , 346.875, 348.75 , 350.625, 352.5  , 354.375, 356.25 , 358.125])</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-1406f461-ede6-42c3-85e0-db98af548761' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1406f461-ede6-42c3-85e0-db98af548761' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30250d20-381f-465c-9a0b-fd09796392ac' class='xr-var-data-in' type='checkbox'><label for='data-30250d20-381f-465c-9a0b-fd09796392ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span 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'>2010-01-01 03:00:00 ... 2015-01-...</div><input id='attrs-5250ed80-1335-436c-9bda-71a7cc7c0e2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5250ed80-1335-436c-9bda-71a7cc7c0e2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6d80735-0e37-46da-b74f-dd9964cb9d93' class='xr-var-data-in' type='checkbox'><label for='data-b6d80735-0e37-46da-b74f-dd9964cb9d93' 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>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeProlepticGregorian(2010, 1, 1, 3, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2010, 1, 1, 6, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2010, 1, 1, 9, 0, 0, 0, has_year_zero=True),\n",
+       "  * lat      (lat) float32 100B 75.0 72.5 70.0 67.5 65.0 ... 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 212B 200.0 202.5 205.0 207.5 ... 325.0 327.5 330.0\n",
+       "  * time     (time) object 6kB 2013-01-01 00:00:00 ... 2014-12-31 00:00:00\n",
+       "Attributes:\n",
+       "    operation:  temporal_avg\n",
+       "    mode:       group_average\n",
+       "    freq:       day\n",
+       "    weighted:   True</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'air'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 730</li><li><span class='xr-has-index'>lat</span>: 25</li><li><span class='xr-has-index'>lon</span>: 53</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-508a3e24-9176-4928-b3ef-1151b5010b88' class='xr-array-in' type='checkbox' checked><label for='section-508a3e24-9176-4928-b3ef-1151b5010b88' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-31.28 -30.85 -30.47 -30.15 -29.85 ... 24.02 23.89 23.07 22.72 22.39</span></div><div class='xr-array-data'><pre>array([[[-31.2775, -30.85  , -30.475 , ..., -39.7775, -37.975 ,\n",
+       "         -35.475 ],\n",
+       "        [-28.575 , -28.5775, -28.875 , ..., -41.9025, -40.325 ,\n",
+       "         -36.85  ],\n",
+       "        [-19.15  , -19.9275, -21.3275, ..., -41.675 , -39.455 ,\n",
+       "         -34.525 ],\n",
+       "        ...,\n",
+       "        [ 23.15  ,  22.825 ,  22.85  , ...,  22.7475,  22.17  ,\n",
+       "          21.795 ],\n",
+       "        [ 23.175 ,  23.575 ,  23.5925, ...,  23.0225,  22.85  ,\n",
+       "          22.3975],\n",
+       "        [ 23.47  ,  23.845 ,  23.95  , ...,  23.8725,  23.8975,\n",
+       "          23.8225]],\n",
+       "\n",
+       "       [[-29.55  , -29.65  , -29.85  , ..., -34.1775, -32.3525,\n",
+       "         -30.0775],\n",
+       "        [-25.3275, -25.95  , -26.9275, ..., -37.225 , -36.5525,\n",
+       "         -34.55  ],\n",
+       "        [-19.6275, -21.0775, -22.8525, ..., -35.4525, -34.2775,\n",
+       "         -31.25  ],\n",
+       "...\n",
+       "        [ 23.215 ,  22.265 ,  22.015 , ...,  23.74  ,  23.195 ,\n",
+       "          22.195 ],\n",
+       "        [ 24.3675,  24.515 ,  23.895 , ...,  23.415 ,  22.995 ,\n",
+       "          22.27  ],\n",
+       "        [ 25.4175,  25.5925,  25.1925, ...,  23.6425,  23.19  ,\n",
+       "          22.72  ]],\n",
+       "\n",
+       "       [[-28.935 , -29.535 , -30.385 , ..., -29.41  , -28.96  ,\n",
+       "         -28.46  ],\n",
+       "        [-23.835 , -24.06  , -24.56  , ..., -32.585 , -31.635 ,\n",
+       "         -30.035 ],\n",
+       "        [-10.21  , -10.785 , -11.435 , ..., -33.685 , -31.035 ,\n",
+       "         -27.135 ],\n",
+       "        ...,\n",
+       "        [ 21.69  ,  21.99  ,  23.49  , ...,  22.265 ,  22.015 ,\n",
+       "          21.415 ],\n",
+       "        [ 23.39  ,  24.44  ,  24.94  , ...,  22.415 ,  22.315 ,\n",
+       "          21.64  ],\n",
+       "        [ 24.84  ,  25.59  ,  25.54  , ...,  23.065 ,  22.715 ,\n",
+       "          22.39  ]]])</pre></div></div></li><li class='xr-section-item'><input id='section-ec69eff7-4f03-4991-b57f-a7da4bfcd00c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ec69eff7-4f03-4991-b57f-a7da4bfcd00c' 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'>float32</div><div class='xr-var-preview xr-preview'>75.0 72.5 70.0 ... 20.0 17.5 15.0</div><input id='attrs-e983023e-4a98-4348-a3ca-b620b59c0332' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e983023e-4a98-4348-a3ca-b620b59c0332' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bef326af-2dd1-4ca8-bef0-76a5660dac37' class='xr-var-data-in' type='checkbox'><label for='data-bef326af-2dd1-4ca8-bef0-76a5660dac37' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd></dl></div><div class='xr-var-data'><pre>array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n",
+       "       45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n",
+       "       15. ], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>200.0 202.5 205.0 ... 327.5 330.0</div><input id='attrs-95dc9b29-70c1-4a8a-ae51-69ee1ae3908b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95dc9b29-70c1-4a8a-ae51-69ee1ae3908b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4edbff8f-37ec-46cd-810e-3f8be4752207' class='xr-var-data-in' type='checkbox'><label for='data-4edbff8f-37ec-46cd-810e-3f8be4752207' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd></dl></div><div class='xr-var-data'><pre>array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
+       "       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
+       "       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
+       "       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
+       "       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
+       "       325. , 327.5, 330. ], dtype=float32)</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'>object</div><div class='xr-var-preview xr-preview'>2013-01-01 00:00:00 ... 2014-12-...</div><input id='attrs-bd13efca-2413-497d-96ac-c35ed1dd2663' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd13efca-2413-497d-96ac-c35ed1dd2663' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04af684c-1f35-4317-bc7a-6c043ef09d04' class='xr-var-data-in' type='checkbox'><label for='data-04af684c-1f35-4317-bc7a-6c043ef09d04' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeGregorian(2013, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2013, 1, 2, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2013, 1, 3, 0, 0, 0, 0, has_year_zero=False),\n",
        "       ...,\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 12, 31, 18, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 12, 31, 21, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2015, 1, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-239434a8-973b-4652-aec7-b15adb206c7b' class='xr-section-summary-in' type='checkbox'  ><label for='section-239434a8-973b-4652-aec7-b15adb206c7b' 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-6b1e9a39-e468-4a21-9dbf-c47c52e417c0' class='xr-index-data-in' type='checkbox'/><label for='index-6b1e9a39-e468-4a21-9dbf-c47c52e417c0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -90.0, -88.75,  -87.5, -86.25,  -85.0, -83.75,  -82.5, -81.25,  -80.0,\n",
-       "       -78.75,\n",
-       "       ...\n",
-       "        78.75,   80.0,  81.25,   82.5,  83.75,   85.0,  86.25,   87.5,  88.75,\n",
-       "         90.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=145))</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-c2acd9b9-5ad8-460a-af7a-00562cbbe547' class='xr-index-data-in' type='checkbox'/><label for='index-c2acd9b9-5ad8-460a-af7a-00562cbbe547' 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([    0.0,   1.875,    3.75,   5.625,     7.5,   9.375,   11.25,  13.125,\n",
-       "          15.0,  16.875,\n",
-       "       ...\n",
-       "        341.25, 343.125,   345.0, 346.875,  348.75, 350.625,   352.5, 354.375,\n",
-       "        356.25, 358.125],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=192))</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-ceef8ece-84e6-4144-968b-f488c406bce5' class='xr-index-data-in' type='checkbox'/><label for='index-ceef8ece-84e6-4144-968b-f488c406bce5' 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([2010-01-01 03:00:00, 2010-01-01 06:00:00, 2010-01-01 09:00:00,\n",
-       "             2010-01-01 12:00:00, 2010-01-01 15:00:00, 2010-01-01 18:00:00,\n",
-       "             2010-01-01 21:00:00, 2010-01-02 00:00:00, 2010-01-02 03:00:00,\n",
-       "             2010-01-02 06:00:00,\n",
+       "       cftime.DatetimeGregorian(2014, 12, 29, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 12, 30, 0, 0, 0, 0, has_year_zero=False),\n",
+       "       cftime.DatetimeGregorian(2014, 12, 31, 0, 0, 0, 0, has_year_zero=False)],\n",
+       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b4471ae6-e97e-418e-9498-6193bebd5ab9' class='xr-section-summary-in' type='checkbox'  ><label for='section-b4471ae6-e97e-418e-9498-6193bebd5ab9' 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-94937013-ecb7-4391-aee3-d917549102e0' class='xr-index-data-in' type='checkbox'/><label for='index-94937013-ecb7-4391-aee3-d917549102e0' 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([75.0, 72.5, 70.0, 67.5, 65.0, 62.5, 60.0, 57.5, 55.0, 52.5, 50.0, 47.5,\n",
+       "       45.0, 42.5, 40.0, 37.5, 35.0, 32.5, 30.0, 27.5, 25.0, 22.5, 20.0, 17.5,\n",
+       "       15.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;))</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-a667b0b0-e51a-4bbc-b003-51c5ad212bd0' class='xr-index-data-in' type='checkbox'/><label for='index-a667b0b0-e51a-4bbc-b003-51c5ad212bd0' 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([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0, 222.5,\n",
+       "       225.0, 227.5, 230.0, 232.5, 235.0, 237.5, 240.0, 242.5, 245.0, 247.5,\n",
+       "       250.0, 252.5, 255.0, 257.5, 260.0, 262.5, 265.0, 267.5, 270.0, 272.5,\n",
+       "       275.0, 277.5, 280.0, 282.5, 285.0, 287.5, 290.0, 292.5, 295.0, 297.5,\n",
+       "       300.0, 302.5, 305.0, 307.5, 310.0, 312.5, 315.0, 317.5, 320.0, 322.5,\n",
+       "       325.0, 327.5, 330.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7a3be10b-e7c7-442d-8879-154b4df159f9' class='xr-index-data-in' type='checkbox'/><label for='index-7a3be10b-e7c7-442d-8879-154b4df159f9' 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([2013-01-01 00:00:00, 2013-01-02 00:00:00, 2013-01-03 00:00:00,\n",
+       "             2013-01-04 00:00:00, 2013-01-05 00:00:00, 2013-01-06 00:00:00,\n",
+       "             2013-01-07 00:00:00, 2013-01-08 00:00:00, 2013-01-09 00:00:00,\n",
+       "             2013-01-10 00:00:00,\n",
        "             ...\n",
-       "             2014-12-30 21:00:00, 2014-12-31 00:00:00, 2014-12-31 03:00:00,\n",
-       "             2014-12-31 06:00:00, 2014-12-31 09:00:00, 2014-12-31 12:00:00,\n",
-       "             2014-12-31 15:00:00, 2014-12-31 18:00:00, 2014-12-31 21:00:00,\n",
-       "             2015-01-01 00:00:00],\n",
-       "            dtype=&#x27;object&#x27;,\n",
-       "            length=14608,\n",
-       "            calendar=&#x27;proleptic_gregorian&#x27;,\n",
-       "            freq=&#x27;3h&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f7df7628-6571-426b-aef5-b57b824c0345' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f7df7628-6571-426b-aef5-b57b824c0345' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "             2014-12-22 00:00:00, 2014-12-23 00:00:00, 2014-12-24 00:00:00,\n",
+       "             2014-12-25 00:00:00, 2014-12-26 00:00:00, 2014-12-27 00:00:00,\n",
+       "             2014-12-28 00:00:00, 2014-12-29 00:00:00, 2014-12-30 00:00:00,\n",
+       "             2014-12-31 00:00:00],\n",
+       "            dtype=&#x27;object&#x27;, length=730, calendar=&#x27;standard&#x27;, freq=&#x27;D&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1b706d4d-994e-4d86-bfc5-9767f6922f3c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1b706d4d-994e-4d86-bfc5-9767f6922f3c' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>operation :</span></dt><dd>temporal_avg</dd><dt><span>mode :</span></dt><dd>group_average</dd><dt><span>freq :</span></dt><dd>day</dd><dt><span>weighted :</span></dt><dd>True</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'tas' (time: 14608, lat: 145, lon: 192)> Size: 2GB\n",
-       "dask.array<sub, shape=(14608, 145, 192), dtype=float32, chunksize=(1205, 145, 192), chunktype=numpy.ndarray>\n",
+       "<xarray.DataArray 'air' (time: 730, lat: 25, lon: 53)> Size: 8MB\n",
+       "array([[[-31.2775, -30.85  , -30.475 , ..., -39.7775, -37.975 ,\n",
+       "         -35.475 ],\n",
+       "        [-28.575 , -28.5775, -28.875 , ..., -41.9025, -40.325 ,\n",
+       "         -36.85  ],\n",
+       "        [-19.15  , -19.9275, -21.3275, ..., -41.675 , -39.455 ,\n",
+       "         -34.525 ],\n",
+       "        ...,\n",
+       "        [ 23.15  ,  22.825 ,  22.85  , ...,  22.7475,  22.17  ,\n",
+       "          21.795 ],\n",
+       "        [ 23.175 ,  23.575 ,  23.5925, ...,  23.0225,  22.85  ,\n",
+       "          22.3975],\n",
+       "        [ 23.47  ,  23.845 ,  23.95  , ...,  23.8725,  23.8975,\n",
+       "          23.8225]],\n",
+       "\n",
+       "       [[-29.55  , -29.65  , -29.85  , ..., -34.1775, -32.3525,\n",
+       "         -30.0775],\n",
+       "        [-25.3275, -25.95  , -26.9275, ..., -37.225 , -36.5525,\n",
+       "         -34.55  ],\n",
+       "        [-19.6275, -21.0775, -22.8525, ..., -35.4525, -34.2775,\n",
+       "         -31.25  ],\n",
+       "...\n",
+       "        [ 23.215 ,  22.265 ,  22.015 , ...,  23.74  ,  23.195 ,\n",
+       "          22.195 ],\n",
+       "        [ 24.3675,  24.515 ,  23.895 , ...,  23.415 ,  22.995 ,\n",
+       "          22.27  ],\n",
+       "        [ 25.4175,  25.5925,  25.1925, ...,  23.6425,  23.19  ,\n",
+       "          22.72  ]],\n",
+       "\n",
+       "       [[-28.935 , -29.535 , -30.385 , ..., -29.41  , -28.96  ,\n",
+       "         -28.46  ],\n",
+       "        [-23.835 , -24.06  , -24.56  , ..., -32.585 , -31.635 ,\n",
+       "         -30.035 ],\n",
+       "        [-10.21  , -10.785 , -11.435 , ..., -33.685 , -31.035 ,\n",
+       "         -27.135 ],\n",
+       "        ...,\n",
+       "        [ 21.69  ,  21.99  ,  23.49  , ...,  22.265 ,  22.015 ,\n",
+       "          21.415 ],\n",
+       "        [ 23.39  ,  24.44  ,  24.94  , ...,  22.415 ,  22.315 ,\n",
+       "          21.64  ],\n",
+       "        [ 24.84  ,  25.59  ,  25.54  , ...,  23.065 ,  22.715 ,\n",
+       "          22.39  ]]])\n",
        "Coordinates:\n",
-       "  * lat      (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
-       "  * lon      (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
-       "    height   float64 8B ...\n",
-       "  * time     (time) object 117kB 2010-01-01 03:00:00 ... 2015-01-01 00:00:00"
+       "  * lat      (lat) float32 100B 75.0 72.5 70.0 67.5 65.0 ... 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 212B 200.0 202.5 205.0 207.5 ... 325.0 327.5 330.0\n",
+       "  * time     (time) object 6kB 2013-01-01 00:00:00 ... 2014-12-31 00:00:00\n",
+       "Attributes:\n",
+       "    operation:  temporal_avg\n",
+       "    mode:       group_average\n",
+       "    freq:       day\n",
+       "    weighted:   True"
       ]
      },
      "execution_count": 17,
@@ -5749,623 +5012,24 @@
     }
    ],
    "source": [
-    "ds3.tas"
+    "ds_day_avg.air"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 18,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "ds3_day_avg = ds3.temporal.group_average(\"tas\", freq=\"day\", weighted=True)"
+    "### Visualize averages derived from 6-hourly data on a specific point\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
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-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tas&#x27; (time: 1827, lat: 145, lon: 192)&gt; Size: 407MB\n",
-       "dask.array&lt;truediv, shape=(1827, 145, 192), dtype=float64, chunksize=(1, 145, 192), chunktype=numpy.ndarray&gt;\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
-       "  * lon      (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
-       "    height   float64 8B ...\n",
-       "  * time     (time) object 15kB 2010-01-01 00:00:00 ... 2015-01-01 00:00:00\n",
-       "Attributes:\n",
-       "    operation:  temporal_avg\n",
-       "    mode:       group_average\n",
-       "    freq:       day\n",
-       "    weighted:   True</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1827</li><li><span class='xr-has-index'>lat</span>: 145</li><li><span class='xr-has-index'>lon</span>: 192</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-fae8b988-6558-4be9-a4b9-28d5774a692c' class='xr-array-in' type='checkbox' checked><label for='section-fae8b988-6558-4be9-a4b9-28d5774a692c' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 145, 192), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 388.06 MiB </td>\n",
-       "                        <td> 217.50 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1827, 145, 192) </td>\n",
-       "                        <td> (1, 145, 192) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1827 chunks in 18293 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"169\" height=\"157\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "\n",
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-       "  <text x=\"100.023323\" y=\"127.868331\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >192</text>\n",
-       "  <text x=\"139.458411\" y=\"89.228283\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,139.458411,89.228283)\">145</text>\n",
-       "  <text x=\"35.294118\" y=\"92.574214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,92.574214)\">1827</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-263ba1ce-9073-4544-a2b7-8ea6e2bfe3bd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-263ba1ce-9073-4544-a2b7-8ea6e2bfe3bd' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -88.75 -87.5 ... 88.75 90.0</div><input id='attrs-27499397-42ed-4363-a320-1b36e93d6e58' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-27499397-42ed-4363-a320-1b36e93d6e58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c98d121e-655d-4f0b-b49d-7c9b4f6b433b' class='xr-var-data-in' type='checkbox'><label for='data-c98d121e-655d-4f0b-b49d-7c9b4f6b433b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</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><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-90.  , -88.75, -87.5 , -86.25, -85.  , -83.75, -82.5 , -81.25, -80.  ,\n",
-       "       -78.75, -77.5 , -76.25, -75.  , -73.75, -72.5 , -71.25, -70.  , -68.75,\n",
-       "       -67.5 , -66.25, -65.  , -63.75, -62.5 , -61.25, -60.  , -58.75, -57.5 ,\n",
-       "       -56.25, -55.  , -53.75, -52.5 , -51.25, -50.  , -48.75, -47.5 , -46.25,\n",
-       "       -45.  , -43.75, -42.5 , -41.25, -40.  , -38.75, -37.5 , -36.25, -35.  ,\n",
-       "       -33.75, -32.5 , -31.25, -30.  , -28.75, -27.5 , -26.25, -25.  , -23.75,\n",
-       "       -22.5 , -21.25, -20.  , -18.75, -17.5 , -16.25, -15.  , -13.75, -12.5 ,\n",
-       "       -11.25, -10.  ,  -8.75,  -7.5 ,  -6.25,  -5.  ,  -3.75,  -2.5 ,  -1.25,\n",
-       "         0.  ,   1.25,   2.5 ,   3.75,   5.  ,   6.25,   7.5 ,   8.75,  10.  ,\n",
-       "        11.25,  12.5 ,  13.75,  15.  ,  16.25,  17.5 ,  18.75,  20.  ,  21.25,\n",
-       "        22.5 ,  23.75,  25.  ,  26.25,  27.5 ,  28.75,  30.  ,  31.25,  32.5 ,\n",
-       "        33.75,  35.  ,  36.25,  37.5 ,  38.75,  40.  ,  41.25,  42.5 ,  43.75,\n",
-       "        45.  ,  46.25,  47.5 ,  48.75,  50.  ,  51.25,  52.5 ,  53.75,  55.  ,\n",
-       "        56.25,  57.5 ,  58.75,  60.  ,  61.25,  62.5 ,  63.75,  65.  ,  66.25,\n",
-       "        67.5 ,  68.75,  70.  ,  71.25,  72.5 ,  73.75,  75.  ,  76.25,  77.5 ,\n",
-       "        78.75,  80.  ,  81.25,  82.5 ,  83.75,  85.  ,  86.25,  87.5 ,  88.75,\n",
-       "        90.  ])</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'>0.0 1.875 3.75 ... 356.2 358.1</div><input id='attrs-4dd42519-900f-4f07-93e6-912f32a3ee2d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4dd42519-900f-4f07-93e6-912f32a3ee2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79dcc932-55c4-4ad0-b888-40fbc36e7e49' class='xr-var-data-in' type='checkbox'><label for='data-79dcc932-55c4-4ad0-b888-40fbc36e7e49' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</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><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.   ,   1.875,   3.75 ,   5.625,   7.5  ,   9.375,  11.25 ,  13.125,\n",
-       "        15.   ,  16.875,  18.75 ,  20.625,  22.5  ,  24.375,  26.25 ,  28.125,\n",
-       "        30.   ,  31.875,  33.75 ,  35.625,  37.5  ,  39.375,  41.25 ,  43.125,\n",
-       "        45.   ,  46.875,  48.75 ,  50.625,  52.5  ,  54.375,  56.25 ,  58.125,\n",
-       "        60.   ,  61.875,  63.75 ,  65.625,  67.5  ,  69.375,  71.25 ,  73.125,\n",
-       "        75.   ,  76.875,  78.75 ,  80.625,  82.5  ,  84.375,  86.25 ,  88.125,\n",
-       "        90.   ,  91.875,  93.75 ,  95.625,  97.5  ,  99.375, 101.25 , 103.125,\n",
-       "       105.   , 106.875, 108.75 , 110.625, 112.5  , 114.375, 116.25 , 118.125,\n",
-       "       120.   , 121.875, 123.75 , 125.625, 127.5  , 129.375, 131.25 , 133.125,\n",
-       "       135.   , 136.875, 138.75 , 140.625, 142.5  , 144.375, 146.25 , 148.125,\n",
-       "       150.   , 151.875, 153.75 , 155.625, 157.5  , 159.375, 161.25 , 163.125,\n",
-       "       165.   , 166.875, 168.75 , 170.625, 172.5  , 174.375, 176.25 , 178.125,\n",
-       "       180.   , 181.875, 183.75 , 185.625, 187.5  , 189.375, 191.25 , 193.125,\n",
-       "       195.   , 196.875, 198.75 , 200.625, 202.5  , 204.375, 206.25 , 208.125,\n",
-       "       210.   , 211.875, 213.75 , 215.625, 217.5  , 219.375, 221.25 , 223.125,\n",
-       "       225.   , 226.875, 228.75 , 230.625, 232.5  , 234.375, 236.25 , 238.125,\n",
-       "       240.   , 241.875, 243.75 , 245.625, 247.5  , 249.375, 251.25 , 253.125,\n",
-       "       255.   , 256.875, 258.75 , 260.625, 262.5  , 264.375, 266.25 , 268.125,\n",
-       "       270.   , 271.875, 273.75 , 275.625, 277.5  , 279.375, 281.25 , 283.125,\n",
-       "       285.   , 286.875, 288.75 , 290.625, 292.5  , 294.375, 296.25 , 298.125,\n",
-       "       300.   , 301.875, 303.75 , 305.625, 307.5  , 309.375, 311.25 , 313.125,\n",
-       "       315.   , 316.875, 318.75 , 320.625, 322.5  , 324.375, 326.25 , 328.125,\n",
-       "       330.   , 331.875, 333.75 , 335.625, 337.5  , 339.375, 341.25 , 343.125,\n",
-       "       345.   , 346.875, 348.75 , 350.625, 352.5  , 354.375, 356.25 , 358.125])</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-e8b9b597-5c2c-49b9-86fd-574914ce51cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8b9b597-5c2c-49b9-86fd-574914ce51cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d029aae5-ccac-4007-a1ca-c0c93467d82b' class='xr-var-data-in' type='checkbox'><label for='data-d029aae5-ccac-4007-a1ca-c0c93467d82b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span 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'>2010-01-01 00:00:00 ... 2015-01-...</div><input id='attrs-a06baf98-1ec6-442f-9dd8-f00d48fad24d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a06baf98-1ec6-442f-9dd8-f00d48fad24d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9e3557d-edcc-46d7-a3ce-27eec14551a4' class='xr-var-data-in' type='checkbox'><label for='data-a9e3557d-edcc-46d7-a3ce-27eec14551a4' 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>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeProlepticGregorian(2010, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2010, 1, 2, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2010, 1, 3, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       ...,\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 12, 30, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2014, 12, 31, 0, 0, 0, 0, has_year_zero=True),\n",
-       "       cftime.DatetimeProlepticGregorian(2015, 1, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
-       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f9648fd7-5f88-4b9c-bf7c-0187847d5fb4' class='xr-section-summary-in' type='checkbox'  ><label for='section-f9648fd7-5f88-4b9c-bf7c-0187847d5fb4' 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-3f4c5948-6595-4e64-92dc-9d7f2e4bec7d' class='xr-index-data-in' type='checkbox'/><label for='index-3f4c5948-6595-4e64-92dc-9d7f2e4bec7d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -90.0, -88.75,  -87.5, -86.25,  -85.0, -83.75,  -82.5, -81.25,  -80.0,\n",
-       "       -78.75,\n",
-       "       ...\n",
-       "        78.75,   80.0,  81.25,   82.5,  83.75,   85.0,  86.25,   87.5,  88.75,\n",
-       "         90.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=145))</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-fc11ddbf-aa25-4394-ba07-0dbc7f72ee6c' class='xr-index-data-in' type='checkbox'/><label for='index-fc11ddbf-aa25-4394-ba07-0dbc7f72ee6c' 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([    0.0,   1.875,    3.75,   5.625,     7.5,   9.375,   11.25,  13.125,\n",
-       "          15.0,  16.875,\n",
-       "       ...\n",
-       "        341.25, 343.125,   345.0, 346.875,  348.75, 350.625,   352.5, 354.375,\n",
-       "        356.25, 358.125],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=192))</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-4175c887-5635-496e-b650-0c72634cfad9' class='xr-index-data-in' type='checkbox'/><label for='index-4175c887-5635-496e-b650-0c72634cfad9' 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([2010-01-01 00:00:00, 2010-01-02 00:00:00, 2010-01-03 00:00:00,\n",
-       "             2010-01-04 00:00:00, 2010-01-05 00:00:00, 2010-01-06 00:00:00,\n",
-       "             2010-01-07 00:00:00, 2010-01-08 00:00:00, 2010-01-09 00:00:00,\n",
-       "             2010-01-10 00:00:00,\n",
-       "             ...\n",
-       "             2014-12-23 00:00:00, 2014-12-24 00:00:00, 2014-12-25 00:00:00,\n",
-       "             2014-12-26 00:00:00, 2014-12-27 00:00:00, 2014-12-28 00:00:00,\n",
-       "             2014-12-29 00:00:00, 2014-12-30 00:00:00, 2014-12-31 00:00:00,\n",
-       "             2015-01-01 00:00:00],\n",
-       "            dtype=&#x27;object&#x27;,\n",
-       "            length=1827,\n",
-       "            calendar=&#x27;proleptic_gregorian&#x27;,\n",
-       "            freq=&#x27;D&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e827f76a-a633-4369-a80d-c0980212f53e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e827f76a-a633-4369-a80d-c0980212f53e' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>operation :</span></dt><dd>temporal_avg</dd><dt><span>mode :</span></dt><dd>group_average</dd><dt><span>freq :</span></dt><dd>day</dd><dt><span>weighted :</span></dt><dd>True</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'tas' (time: 1827, lat: 145, lon: 192)> Size: 407MB\n",
-       "dask.array<truediv, shape=(1827, 145, 192), dtype=float64, chunksize=(1, 145, 192), chunktype=numpy.ndarray>\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float64 1kB -90.0 -88.75 -87.5 -86.25 ... 87.5 88.75 90.0\n",
-       "  * lon      (lon) float64 2kB 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
-       "    height   float64 8B ...\n",
-       "  * time     (time) object 15kB 2010-01-01 00:00:00 ... 2015-01-01 00:00:00\n",
-       "Attributes:\n",
-       "    operation:  temporal_avg\n",
-       "    mode:       group_average\n",
-       "    freq:       day\n",
-       "    weighted:   True"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds3_day_avg.tas"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Visualize averages derived from 3-hourly data on a specific point\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
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L8vJyvWzdunUCEDk5OcLr9erlb731lgDEO++8o5f19n6JR2/u594+G6K/b//+/WMWoDvv63xNZs+eLfLz87sscF5//fXCZrPpv9Npp53W5b0rkUgOPtIMXiKRfO+IvUyAPR4Pt956KwMGDMBkMmEymXA6nXi9XjZv3rzf/f/mN79h3bp1fPHFF4Bmzvzcc89xySWX7DM4VG1tLVdffTUFBQWYTCbMZjOFhYUAccfys5/9LGZ75MiR+P1+Per9kiVLALjwwgtj6l1wwQX7dU6nnXZazPbQoUOBrn6oQ4cOpbGxUTeF//TTTwG6mFKfe+65OBwOFi9eHFM+evRo+vTpo2/bbDYGDRrUxSWhJ7Kzs5kwYUJM2ciRI/fZx5w5c8jMzIwxr3z88cfJyMjgF7/4xT6P++STTzJ27FhsNpv+2y1evDjmdxsxYgTjxo1j/vz5etnmzZtZvXo1l112mV723nvvMXz4cEaPHk04HNY/s2fPjuuKMXPmTFJSUrqM6dNPP+WEE04gKSkJo9GI2WzmjjvuoKGhQf+PLFu2DIDzzjsvpu0555yDyRQbSua9995jxowZ5Obmxozr5JNPjulrwoQJNDc3M2fOHN5+++0ubhzdsWTJElwuV5f/9d7/1x07drBlyxb9f915LKeccgpVVVVs3bo1ps3ZZ5/dqzFceumlrFixIqb9/PnzGT9+vJ5p4KOPPiIcDnPxxRfHHNtmszFt2jT999m6dSuVlZVccMEFMWbNhYWFTJ48uVfj6Ym9f3ev18uXX37JOeecE/OsMRqNXHTRRZSXl3e5Lvtzb+/rHoq6mvj9ft5//33OPfdcZs2axSuvvMLYsWO555579LpRP+XoZ283lVNPPRWj0ahvjxw5EkAfQ/TaXnTRRRgMHdNHp9PJ2WefzapVq+K6I/WG7u6nvZk+fTqjRo2KeWY8+eSTKIrClVdeeUDH7onRo0eTl5enb0d/q+nTp5OQkNClPHqtDuR+6Uxv7ufePhui/OxnP8NsNvd4vn6/n8WLF3PWWWeRkJDQZdx+v193jZgwYQLr16/n2muv5aOPPorryiKRSL47UliXSCTfK16vl4aGBnJzc/WyCy64gH/84x9cccUVfPTRR6xevZo1a9aQkZGBz+fb72OcccYZFBUV6RO4BQsW4PV6ue6663psp6oqs2bN4o033uD3v/89ixcvZvXq1fpkJN5Y0tLSYratVmtM3YaGBkwmU5d62dnZ+3VOqampMdsWi6XHcr/fH3P8vYN4KYpCdnY2DQ0NPZ5P9Jz253c40D6sVitXXXUVL774Is3NzdTV1fHKK69wxRVX6Ne1Ox599FGuueYaJk6cyOuvv86qVatYs2YNJ510UpfjXnbZZaxcuZItW7YAmiBotVqZM2eOXqempoYNGzZgNptjPi6XCyFEl8lyvGjmq1evZtasWQD897//5YsvvmDNmjXcfvvtQOx/BOgScDHe/6ampoZ33323y7iisRii47rooot4+umnKS0t5eyzzyYzM5OJEyeyaNGiHq9jQ0ND3MCPe/9fa2pqALjlllu6jOXaa6+NGUtP1ygeF154IVarVfeb3bRpE2vWrOHSSy/tcvzx48d3Of7ChQv1Y0evbbz7bX/vwXjsfU5NTU0IIeKea/SZt/c9tz/3dvS+7o7o/2XIkCH6IiNo9/vs2bMpLy/XF4n69+8fc906C/Kd+4oS79kG8X/X3NxcVFWlqampx/F2x/5kB/j1r3/N4sWL2bp1K6FQiP/+97+cc845B+X33ZsDfQ4fyP3Smd7cz719NkTpzTVuaGggHA7z+OOPd+n3lFNOien3tttu4+GHH2bVqlWcfPLJpKWlcfzxx8f13ZdIJAeOjAYvkUi+V/73v/8RiUSYPn06AC0tLbz33nvceeed/OEPf9DrBQIBGhsbD+gYBoOB6667jj/+8Y888sgj/Otf/+L4449n8ODBPbbbuHEj69evZ8GCBVxyySV6+Y4dOw5oHKBNeMPhMA0NDTGT3+4Cdh1sosevq6uLEdiFEFRXV+tBlg4XrrnmGh588EGefvpp/H4/4XCYq6++ep/tnn/+eaZPn84TTzwRU+52u7vUnTNnDjfffDMLFizg/vvv57nnnuPMM8+M0eSlp6djt9tjgut1Jj09PWY7Xk7ml19+GbPZzHvvvYfNZtPL33rrrZh60f9FTU1NjNYu+r/Z+7gjR47k/vvvjzuuzotgl156KZdeeiler5fly5dz5513ctppp7Ft27YYQW7vsaxevbpL+d7/1+j533bbbfz85z+P29fe91tv81anpKRwxhln8Oyzz3Lfffcxf/58bDZbzGJK9PivvfZat+cSPZ944++ubH/Z+5xSUlIwGAxUVVV1qVtZWQl0/e8cTPr37x+j4e1M1KIpqgV/9913CQQC+v7O/53eEL223Z2rwWDQ7ymbzRZzrCjdCaj7k+P8ggsu4NZbb+Wf//wnxxxzDNXV1ftcmP2hOZD7ZW/2dT/vz7MBeneNU1JSdKuQ7q5p3759AW1x8eabb+bmm2+mubmZTz75hD/+8Y/Mnj2bsrKybv+XEolk/5DCukQi+d7Ys2cPt9xyC0lJSVx11VWANmEQQnTRnD711FNEIpEDPtYVV1zBXXfdxYUXXsjWrVv5y1/+ss820cnL3mPpHLl+f5kxYwYPPfQQL7zwAr/+9a/18hdffPGA+9wfjj/+eB566CGef/55brrpJr389ddfx+v1cvzxx+93n3tr2A4mOTk5nHvuufzrX/8iGAxy+umnx5jld4eiKF1+tw0bNrBy5UoKCgpiylNSUjjzzDN59tlnmTRpEtXV1TEm8KCZJv/5z38mLS1Nn4zuL4qiYDKZYkyJfT4fzz33XEy9qVOnAlpk87Fjx+rlr732WpcI76eddhrvv/8+/fv375WZMIDD4eDkk08mGAxy5plnUlxc3K2AO2PGDF555RXeeeedGFP4vf+vgwcPZuDAgaxfv54///nPvRrH/nDppZfyyiuv8P777/P8889z1llnkZycrO+fPXs2JpOJnTt39mheP3jwYHJycnjppZe4+eab9Xu8tLSUFStW7LeAui8cDgcTJ07kjTfe4OGHH9bTjamqyvPPP09+fj6DBg06qMfsjMlk4owzzuC1116jpKREj8AuhODDDz+kf//+uuA4YsSI73SswYMHk5eXx4svvsgtt9yiX1uv18vrr7+uR4gHLZJ6bW0tNTU1uuVGMBjko48++k5jAG0h4Morr+Qf//gHK1asYPTo0UyZMmWf7b7P59jeHMz7pbv7+UCeDfsiISGBGTNmsHbtWkaOHKlbDOyL5ORkzjnnHCoqKrjxxhspKSlh2LBhB2VMEslPHSmsSySSg8LGjRt137ba2lo+++wz5s+fj9Fo5M0339S1vImJiUydOpW//vWvpKenU1RUxLJly5g3b17M5Hx/SU5O5uKLL+aJJ56gsLCQ008/fZ9thgwZQv/+/fnDH/6AEILU1FTefffdfZoO98SsWbOYOnUqv//97/F6vRx99NF88cUXXQS274sTTzyR2bNnc+utt9La2sqUKVPYsGEDd955J2PGjImbxmxfRP2G//Of/+ByubDZbPTt2zeu+fuB8Jvf/IaJEycCxPiW98Rpp53Gvffey5133sm0adPYunUr99xzD3379u0i8IJmCr9w4UKuv/568vPzOeGEE2L233jjjbz++utMnTqVm266iZEjR6KqKnv27OHjjz/mt7/9rT7G7jj11FN59NFHueCCC7jyyitpaGjg4Ycf7rKocNRRRzFnzhweeeQRjEYjM2fOpLi4mEceeYSkpKQYf+B77rmHRYsWMXnyZH79618zePBg/H4/JSUlvP/++zz55JPk5+fzq1/9CrvdzpQpU8jJyaG6upoHHniApKSkHq0pLr74Yv7v//6Piy++mPvvv5+BAwfy/vvvxxWq/v3vf3PyyScze/Zs5s6dS15eHo2NjWzevJlvvvmm27RZvWHWrFnk5+dz7bXXUl1dHWMCD5rwd88993D77beza9cuTjrpJFJSUqipqWH16tU4HA7uvvtuDAYD9957L1dccQVnnXUWv/rVr2hubuauu+76XsykAR544AFOPPFEZsyYwS233ILFYuFf//oXGzdu5KWXXtovrfGBcO+99/LBBx9w0kkncdddd5GYmMhTTz3F+vXre50mrDcYDAYeeughLrzwQk477TSuuuoqAoEAf/3rX2lububBBx/U6/7iF7/gjjvu4Pzzz+d3v/sdfr+fv//9799pQbYz1157LQ899BBff/01Tz31VK/a9O/fH7vdzgsvvMDQoUNxOp3k5uYe9AWcKN/lfunN/dzbZ8P+8re//Y1jjz2W4447jmuuuYaioiLcbjc7duzg3Xff1eOinH766QwfPpyjjz6ajIwMSktLeeyxxygsLGTgwIEHdtEkEklXDl1sO4lEciQQjXAc/VgsFpGZmSmmTZsm/vznP+tpjzpTXl4uzj77bJGSkiJcLpc46aSTxMaNG0VhYWFMJOzeRoOPsnTpUgGIBx98sNfj37RpkzjxxBOFy+USKSkp4txzz9XTIXWOQhw9bufUcp3Pv3N06+bmZnHZZZeJ5ORkkZCQIE488USxZcuW/YoG/+qrr8Y9zt7RyuONy+fziVtvvVUUFhYKs9kscnJyxDXXXCOamppi2hYWFopTTz21yximTZsmpk2bFlP22GOPib59+wqj0RgTPXjatGniqKOO6tLH3tGg40Ud7kxRUVGPGQP2JhAIiFtuuUXk5eUJm80mxo4dK956661uo1BHIhFRUFAgAHH77bfH7dPj8Yg//elPYvDgwcJisejpi2666SZRXV2t1wPEddddF7ePp59+WgwePFhYrVbRr18/8cADD4h58+Z1+Y/4/X5x8803i8zMTGGz2cQxxxwjVq5cKZKSksRNN90U02ddXZ349a9/Lfr27SvMZrNITU0V48aNE7fffrvweDxCCCGeeeYZMWPGDJGVlSUsFovIzc0V5513np6ariei96PT6RQul0ucffbZYsWKFXF/r/Xr14vzzjtPZGZmCrPZLLKzs8XMmTPFk08+qdfpKUNEvPslyh//+EcBiIKCgm4jZb/11ltixowZIjExUVitVlFYWCjOOecc8cknn8TUe+qpp8TAgQOFxWIRgwYNEk8//XS3/4296SkafHe/+2effSZmzpwpHA6HsNvt4phjjhHvvvtu3HPvzT3c0zji8e2334pTTz1VuFwu/f+09/G7I3pv/vWvf+2yL94z66233hITJ04UNptNOBwOcfzxx4svvviiS9v3339fjB49WtjtdtGvXz/xj3/8o9to8N1d156emdOnTxepqakx6dL2xUsvvSSGDBkizGZzTN/dRYOP93yMN97urmFv7pd49PZ+7s2zoafft7vn8u7du8Vll10m8vLyhNlsFhkZGWLy5Mnivvvu0+s88sgjYvLkySI9PV1YLBbRp08fcfnll4uSkpIez00ikewfihB7hWmWSCSSHym//e1veeKJJygrKztoWl/J98+GDRv0CM/R4Es/RVasWMGUKVN44YUX9jt7gETyU6K2tpbCwkJuuOEGHnrooUM9HIlEIvnekGbwEonkR8+qVavYtm0b//rXv7jqqqukoP4jYefOnZSWlvLHP/6RnJycLunmjmQWLVrEypUrGTduHHa7nfXr1/Pggw8ycODAbgNSSSQ/dcrLy9m1axd//etfMRgM/OY3vznUQ5JIJJLvFSmsSySSHz3RwEannXYa991336EejqSX3HvvvTz33HMMHTqUV1999ScVPTgxMZGPP/6Yxx57DLfbTXp6OieffDIPPPBATCR5iUTSwVNPPcU999xDUVERL7zwQkw2BYlEIjkSkWbwEolEIpFIJBKJRCKRHGYY9l1FIpFIJBKJRCKRSCQSyQ+JFNYlEolEIpFIJBKJRCI5zJDCukQikUgkEolEIpFIJIcZMsBcHFRVpbKyEpfLhaIoh3o4EolEIpFIJBKJRCI5AhBC4Ha7yc3NxWDoWXcuhfU4VFZWUlBQcKiHIZFIJBKJRCKRSCSSI5CysjLy8/N7rCOF9Ti4XC5Au4CJiYmHeDQSiUQikUgkEolEIjkSaG1tpaCgQJc5e0IK63GImr4nJiZKYV0ikUgkEolEIpFIJAeV3rhbywBzEolEIpFIJBKJRCKRHGZIYV0ikUgkEolEIpFIJJLDDCmsSyQSiUQikUgkEolEcpghfda/A5FIhFAodKiHIZH84JjNZoxG46EehkQikUgkEolEcsRySIX1Bx54gDfeeIMtW7Zgt9uZPHkyf/nLXxg8eLBepzvH+4ceeojf/e53cfctWLCASy+9tEu5z+fDZrN953ELIaiurqa5ufk79yWR/FhJTk4mOzu7V8ExJBKJRCKRSCQSyf5xSIX1ZcuWcd111zF+/HjC4TC33347s2bNYtOmTTgcDgCqqqpi2nzwwQdcfvnlnH322T32nZiYyNatW2PKDoagDuiCemZmJgkJCVJYkfykEELQ1tZGbW0tADk5OYd4RBKJRCKRSCTg9ocIhlXSnNZDPRSJ5KBwSIX1Dz/8MGZ7/vz5ZGZm8vXXXzN16lQAsrOzY+q8/fbbzJgxg379+vXYt6IoXdoeDCKRiC6op6WlHfT+JZIfA3a7HYDa2loyMzOlSbxEIpFIJJJDihCCpz7bDcDV0/pjt8i5ieTHz2EVYK6lpQWA1NTUuPtramr43//+x+WXX77PvjweD4WFheTn53Paaaexdu3agzLGqI96QkLCQelPIvmxEr0HZNwGiUQikUgkh5pGb1D/7gmED+FIJJKDx2EjrAshuPnmmzn22GMZPnx43DrPPPMMLpeLn//85z32NWTIEBYsWMA777zDSy+9hM1mY8qUKWzfvj1u/UAgQGtra8xnX0jTd8lPHXkPHB58saOeDzdWIYQ41EORSCQSieSQ0RaM6N8F8p0oOTI4bKLBX3/99WzYsIHPP/+82zpPP/00F1544T59z4855hiOOeYYfXvKlCmMHTuWxx9/nL///e9d6j/wwAPcfffdBz54iUQiOUSs3t0IwNg+KWQmHpy4HBKJRCKR/JjZWetFQaGi2cfIvCQMBqlgkPw4OSw06zfccAPvvPMOS5YsIT8/P26dzz77jK1bt3LFFVfsd/8Gg4Hx48d3q1m/7bbbaGlp0T9lZWX7fYwjGUVReOuttw7JsZcuXYqiKN858n5DQwOZmZmUlJQclHEd6Xz77bfk5+fj9XoP9VAkPdBZmx75gTTrQgiavEFUVWotJBKJRNIVfyiC9xCYoaud3oOrdjXw/KpSlmyppbhy3xazEsnhyiEV1oUQXH/99bzxxht8+umn9O3bt9u68+bNY9y4cYwaNeqAjrNu3bpuo1ZbrVYSExNjPkciTzzxBCNHjtTPcdKkSXzwwQeHelg/CA888ACnn346RUVFAJSUlKAoiv5JSkrimGOO4d13343b/s9//jNGo5EHH3wwpjwnJ4e//OUvMWW33noriqKwePHimPLjjz+eCy64oNsxdh6Pw+Fg4MCBzJ07l6+//jpu/fLyciwWC0OGDNHL7rrrrph+4n2iCxYrVqzAaDRy0kkndel7xIgRTJgwgf/7v//rdrySQ0+kk8Cs8MNoDTZWtLJgRQmf76j/QY4nkUgkkh8PQgj+vWwX8z7fTSii/rDHVgVZ7mKcgZqY8ppW/w86DonkYHJIhfXrrruO559/nhdffBGXy0V1dTXV1dX4fL6Yeq2trbz66qvdatUvvvhibrvtNn377rvv5qOPPmLXrl2sW7eOyy+/nHXr1nH11Vd/r+dzuJOfn8+DDz7IV199xVdffcXMmTM544wzKC4uPtRDi8vBClzm8/mYN29e3P/PJ598QlVVFV9++SUTJkzg7LPPZuPGjV3qzZ8/n9///vc8/fTTMeXTp09nyZIlMWVLly6loKAgpjwYDLJy5UpmzJjR41jnz59PVVUVxcXF/POf/8Tj8TBx4kSeffbZLnUXLFjAeeedR1tbG1988QUAt9xyC1VVVfonPz+fe+65J6asoKAA0NxKbrjhBj7//HP27NnTpf9LL72UJ554gkgk0mWf5PCgszZ9f0MI+EMR3llfyeaq/dM4RIX0r0ub9u+AEolEIjniaWoLoQpBRBV4/D+sdt3QWkbfpi8YXvM2dHo/SjswyY+ZQyqsP/HEE7S0tDB9+nRycnL0z8KFC2PqvfzyywghmDNnTtx+9uzZE5OPvbm5mSuvvJKhQ4cya9YsKioqWL58ORMmTPhez+dw5/TTT+eUU05h0KBBDBo0iPvvvx+n08mqVav22ba+vp6zzjqLhIQEBg4cyDvvvBOzf9myZUyYMAGr1UpOTg5/+MMfCIc7HtJFRUU89thjMW1Gjx7NXXfdpW8risKTTz7JGWecgcPh4L777oup7/V6SUxM5LXXXospf/fdd3E4HLjd7rhj/+CDDzCZTEyaNKnLvrS0NLKzsxkyZAj3338/oVCoi/C9bNkyfD4f99xzD16vl+XLl+v7ZsyYwRdffKGfq9vtZu3atfzhD39g6dKler0vv/wSn8+3T2E9OTmZ7OxsioqKmDVrFq+99hoXXngh119/PU1NHcKREIL58+dz0UUXccEFFzBv3jwAnE4n2dnZ+sdoNOJyubqUeb1eXnnlFa655hpOO+00FixY0GUss2fPpqGhgWXLlvU4ZsmhQ/0OSovNVa3srPXw4cbq/WqXkmA+8INKJBKJ5IimxdehaAl9l5fUgRBq07/aw836dxmAVfJj5pCbwcf7zJ07N6belVdeSVtbG0lJSXH7Wbp0aYyw8X//93+UlpYSCASora3lo48+iiuoHczzCIbVQ/I50AdQJBLh5Zdfxuv19ura3H333Zx33nls2LCBU045hQsvvJDGRi2wVUVFBaeccgrjx49n/fr1PPHEE8ybN6+LsN0b7rzzTs444wy+/fZbLrvssph9DoeD888/n/nz58eUz58/n3POOQeXyxW3z+XLl3P00Uf3eNxQKMR///tfAMzmWGFk3rx5zJkzB7PZzJw5c3TBGDRh3ePxsGbNGkCLrTBo0CDOOecc1qxZQ1ub9uKIxmMYMGBAL65CLDfddBNut5tFixbpZUuWLKGtrY0TTjiBiy66iFdeeaXbxYp4LFy4kMGDBzN48GB++ctfMn/+/C7/JYvFwqhRo/jss8/2e8ySH4bOmvX9fRSYjR2Pf3+o99YTBpkFQCKRSCTdEOkkoAfDP7CwrnYoiZL85fp3KapLfswcNtHgf8yEIoJ/LtlxSI593YwBWEy9nzx/++23TJo0Cb/fj9Pp5M0332TYsGH7bDd37lzdsuHPf/4zjz/+OKtXr+akk07iX//6FwUFBfzjH/9AURSGDBlCZWUlt956K3fccQcGQ+/XhC644IIYIX337t0x+6+44gomT55MZWUlubm51NfX895778UIsntTUlJCbm5u3H2TJ0/GYDDg8/lQVZWioiLOO+88fX9rayuvv/46K1asAOCXv/wlU6ZM4fHHHycxMZGBAweSl5fH0qVLmTRpEkuXLmXatGlkZmbSr18/vvjiC0488USWLl26T616d0R90jsHx5s3bx7nn38+RqORo446igEDBrBw4cJeB2CcN28ev/zlLwE46aST8Hg8LF68mBNOOCGmXl5engzKdxjT2Wdd3U9pvbPQHdmfYHFSVpdIJBJJN4T9bYysep2GhH4Ew/HnXt/fwQP61yR/BdWuEcD+L2bvjRBCpquVHDIOi2jwkh+OwYMHs27dOlatWsU111zDJZdcwqZNmwBNCHc6nfqnsx/zyJEj9e8OhwOXy0VtbS0AmzdvZtKkSTEPsilTpuDxeCgv71jZ7A370oBPmDCBo446Svfhfu655+jTpw9Tp07tto3P5+s23d/ChQtZu3Yt77zzDgMGDOCpp54iNTVV3//iiy/Sr18/PbDh6NGj6devHy+//LJeZ/r06brJ+9KlS5k+fToA06ZNY+nSpQQCAVatWsXMmTP3ef7xiGq8o9e3ubmZN954Qxe2QVtE2Nufvju2bt3K6tWrOf/88wEwmUz84he/iNvebrfr1gGSww/1OwjrneuHZWR3iUQikRwELLXrSQg1UNCyhmD4B455EyOsV2IOf/eMNrvqPDy5bBe76jzfuS+J5ECQmvWDgNmocN2M/TdvPljH3h8sFotuin300UezZs0a/va3v/Hvf/+bq6++Okar3FkbvbdpuKIoqO2mTvFWHPcWMA0GQxcz63gB5BwOxz7P4YorruAf//gHf/jDH5g/fz6XXnppjyue6enpMf7enSkoKGDgwIEMHDgQp9PJ2WefzaZNm8jMzAS0IGzFxcWYTB23iqqqzJs3jyuvvBLQTOF/85vf0NDQwNq1a/WFg2nTpvH4448za9asXvmrd8fmzZsB9GwJL774In6/n4kTJ+p1hBCoqsqmTZv2aSkxb948wuEweXl5Me3NZjNNTU2kpKTo5Y2NjfTv3/+Axi35/uksZO+Pa2CrP8QnxRX0afmaZlsBqlrU67aKUEn0V+C2ZgNannejQWFcYco+WkokEonkSEcNB/XvG7aXMCRn/7M4HTCRDmHdIMIUNa9kZ+o0Nle1cuzAdJzW3os922vclDW1sb6sBYC311Vy04mDDvqQJZJ9ITXrBwFFUbCYDIfk813NcoQQBALawy01NZUBAwbon84Cak8MGzaMFStWxAjjK1aswOVy6QJhRkZGTBDA1tbWLibuveWXv/wle/bs4e9//zvFxcVccsklPdYfM2aMbj3QE9OmTWP48OHcf//9gOYy8NVXX7F06VLWrVunf5YvX86aNWv0qPEzZszA6/Xy6KOPMnDgQLKysvT+vvrqK/73v//Rt29fCgsLD+h8H3vsMRITE3UT9Xnz5vHb3/42Zkzr169nxowZ+9Suh8Nhnn32WR555JEu7QsLC3nhhRdi6m/cuJExY8Yc0Lgl3z+dteP7o1lfsaOe/Ja15LauZ1jte/ulWU9rKWZY7f/Ia11HeVMbX+yoZ/m2OsI/cIoeiUQikRx+KIFm/XugsYyWtoOT2adXtGvWm+x9AIW0tl2Mq3wBa7iVTzbV9Ny2E6GIynsbqnRBXSI5lEhh/SfEH//4Rz777DNKSkr49ttvuf3221m6dCkXXnjhd+r32muvpaysjBtuuIEtW7bw9ttvc+edd3LzzTfr/uozZ87kueee47PPPmPjxo1ccsklGI3GAzpeSkoKP//5z/nd737HrFmzyM/P77H+7NmzKS4u7la73pnf/va3/Pvf/6aiooJ58+YxYcIEpk6dyvDhw/XPsccey6RJk/RAc/369aNPnz48/vjjTJs2Te8rNzeXwsJCnnzyyV5r1Zubm6murqa0tJRFixZxzjnn8OKLL/LEE0+QnJzMunXr+Oabb7jiiitixjR8+HDmzJnDs88+22PKu/fee4+mpiYuv/zyLu3POeecmOB5JSUlVFRUdPFjl/ywhCLdB5KM57Pem7y23kCEFF+Hm8v+mCom+CoBSPRX8OpXHW4uoYg0pZdIJJKfOoqvY67lCtTw9Be78QZ+oBRuYS2feostn12pxwFgVIM4g3U0eIM9tYzhvQ2VPe4XQuxXYFaJ5LsghfWfEDU1NVx00UUMHjyY448/ni+//JIPP/yQE0888Tv1m5eXx/vvv8/q1asZNWoUV199NZdffjl/+tOf9Dq33XYbU6dO5bTTTuOUU07hzDPP/E7m1ZdffjnBYLBLxPh4jBgxgqOPPppXXnlln3VPO+00ioqKuP/++3n++ec5++yz49Y7++yzef755wkGtYf/jBkzcLvdur96lGnTpuF2u3strF966aXk5OQwZMgQrrnmGpxOJ6tXr+aCCy4ANK36sGHD9KBznTnzzDNpbGzk3Xff7bb/efPmccIJJ8TNrHD22WfriwEAL730ErNmzTpgiwDJd6fJG+SfS3bwyebauPtjhXXYUt3KP5fsYFNlz7nTE8wGrJGOOm9+uQ1fsHcTD2tAywLhCDXERO35wVP0SCQSieTwQggqKzsEXVdAe3f9Z/kuWv3fv4ZdiWhzsohiptY5hEZ7EQCmSADDfhii1laW4QjEf+8CvPFNBf9etouyRhnTR/L9owiZfLALra2tJCUl0dLSQmJiYsw+v9/P7t276du3b7dByyTfPy+88AK/+c1vqKysxGKx7LP++++/zy233MLGjRv3Kzr9T5VAIMDAgQN56aWXmDJlStw68l74/vlkUw3fVmhmePF85UobvLzxTQUAp47M4X8bOlxN+qY7OKZfGtlJXX+bJV9/i23dM/p2cdbPmDR2FEflxk+PqaNG2LTw/9HSpk2I1uWch9+cDMDFkwpJc1r36/wOFi2+EEu21DKuMIWC1IRDMgaJRCL5qbKj1s3GilZmDXSwfmHntL0Ka/IvJmKwMr4olWMHpve6z8+216EKmDYoo9dtyj95grLSnWxNn0VTQhH9GpaR6d1KWdJ4fLkTuWRy0T77UCMRvlxwKwDf5F5A0OTU9x3TL41J/dP4v0XbABhXmMLU/RifRBKlJ1lzb6TUIvlR0dbWRnFxMQ888ABXXXVVrwR1gFNOOYWrrrqKioqK73mERwalpaXcfvvt3Qrqkh8GYzcBJIUQbKxoobbVT5p3B7ZQM62+WK3F7novL63eE7e9o60qZtsWaqGm1a9vhyIqa/c0UdHsi6nnaarRBXUAR7C+U5tDt+77cXE1u+u9vPb1/mWfkEgkEsl35931Veyu9/LFt9sBCJhc+E2JgMAVqAZg7Z6mXsc28YcifFXSxDelTftnbt4eYC7RpQUrDhu0BWSjGqC3IZ62l3W8HxMDse/KVbsaYs7BEKfTJm+Qb8tbUFWhv6sb98MEXyLZGymsS35UPPTQQ4wePZqsrCxuu+22/Wr7m9/8hoKCgu9pZEcWgwYN4qqrrjrUw/jJY+xmdrGtxsOiTTUUb/iagQ2fMrrqFT7bXo8iwpgivrhtOmOJxKazsYVbWV/WQltQ8yvcXNXK0q11vPpVWUy91cXbYrYHNnxKpkfLVtAbX/nvC7f/B/KHlEgkEkm31NZowq3PlIQxVXOhS/RrZWFVsGpXY6/6CYQ63ieRfQRA9QbCeh2lPRJ9osPJpVOKmDmiDwAmNbDPBWVPIEyDJ8Bn3+7Uy/YW1gGCnYX1OFLUghUlfLJZs4pbtauRRZtqeGZFSY/Hlkh6Qgrrkh8Vd911F6FQiMWLF+N0OvfdQCL5EWPqxsmutEETtlP8sZrzgQ1LGFv5IgmdNN7xMLQL9FGtgy2s+a97A5oGI6oFEIKY4HYNNZovYsTQYdFS2LwKhHpIhXXpyyWRSCSHHktIy0UeNieSnKvFJeos8JY09C7vub9T0NNID966jd4g/1m+i9ejVlXtmnVhspKcYCEl0QVownpPXr/batz8d/ku3llfiTXi1sujCw2d6byQ0NPL59Mttaza1dB9BYmkl0hhXSKRSA5TjJ2E9c6md4Gw9t2kduSUNUb8pLbtxiAiFLR81WO/SkgT1j2WTABsYc0vPipwx6aE62iXEGkGoCJxNGVJ47TjqiESQk371H5IJBKJ5MdHZbOP9zZU9ipAXNRqy2BPZPDgowBwBusxtr+r4r0lmrxB/rt8F09/vpuWdneuzqbvkR404purtIXmimYfe+rcKKrWXhjb46eY7BgNCiY1QJrTwnsbKlm5s6sA/dl2bYG7uS2ENdwhrNvCLYyteIEkf4eLVfT9Cz0vJEgkBwsprEskEslhSmdhfVNVqx7lPRgV1iMdfuadteyJ/ioMavcTKyWsRbB1W7MAzWcdoeIPRVBVEZNbNtwpyrutfSLmNyUiCqfQassFwBms5Zs9+06NeLDZVuNmc1VrjxoTiUQikRw4C9eUsb3Gw8fFWp7ycERl1a4GtlR3zToSFdbN9kSszhQSkjMBQZK/PV5QnGf1uxsq8QTCtPhCvP+tpslu65SdJNzDQnBtY6suXL/99W69e2FqF9bNNvqkJmBSA5TUt7G9xsOqXQ1dLME6G7FZw54u5zSk7kMS/Zpl2do9TfRp/pKB9Z8QiXT1p09t201q2y5soWZyWjdgjmjv297660ske2M61AOQSCQSSXw6T1EWt6dvs5gMuubbrHb4p2d4d+jfjSJEqm839Y6uEeQBDO2a9ZTc/ph830IwgCPUSCCcy7ZabeJjCzUTMDljNOZWVZt0BI0OThuaxZKSLBL9lbgCNexs9lPR7CMv2d7zOQlBnTtAqsOCyXjg68URVejR783dBOKTSCQSycGhuT24aElDm66d7p/hxNzpOa4L6wlaZpFgUj9oriXFt4fGhH5xNesNno7ga9Ut2gJ05zgkPVlt2ba/yxh/ORuyzyKimBFCEFHMGAxGrYLJjkGJtUKL9mk2dj63jsVtW7sZ/O6UY/FaUslrXUeKbw8Z3m202nLZVV7F2Nb1AITbaoHsjo5CfgbVLwIgYHRijXjo07Ka4szTWL07lckDeh8NXyKJIjXrEolEcpgSb5Ly7vpKypt8IESMBiBqpue2ahOHFF/8SPAAhrAmrGdnpOFpr+8KVBOOCNz+MDmtGxhd9QqFzV92aDWEwBzWJmJBo4NEuxm3VTOjdwa1hQR3L8wkiytbeeHLPXzUrqU5UDpfm0MZiV4ikUh+CijtAU+DnczAO38HsLYL68Km+YobMgYAkOwrA6HGU6x3JRImYfOr9Gn+UtvsrpGqkuwvAwS57g2YRIiyJh8Rgxl9+dZsQ1GUuMJ6lL1zpVvbY7i0WVLxWLOpdQwBwBHUFihSfSUdlUOxAV3D3o4AetaI9n5WhEqqr5SyJpmTXXJgSGFdIpFIDlPUbjQKSb5yxlcswCC6RkEvTxwDQLK/DEXESXmjRlDag/Bgtuum8K5ANSFVxRVq0ILGAdnuYiLRyVjQi9UIoJCdkYbZaMBj0draQ80Y1SBqL6z8Vu/WJjPbatz7qNkzqjR9l0gkkh+czs/eYFhlR7s1lkENYVTbteRmLQDwoIFDiBgsmFUfzmBd71yWWvagNu4it3U9RjUY12d92bY6Xlq2Xt/W3kHaey1isHREaW/XrBtEGKXT+7LzAkD0XWQNuxlZ9aq+CB4waufQZknTjhFuQhERUtt2620NwViT+Uhb/Gj3pog/xte9J4orW3jqs13UdkqnKvlpI4V1iUQiOUzpTqNQ1LwCYxyfdFUx0mLLI2ywYVRDuAJxtNchX/uESUEx2agzaNpxV6CacFgloWFTTPUl67ZpwX4CblQBQaOdYwdpQvpxR/UhZNDM3m2hFsQ+4rJHVKEHEOoJIQSfba9jR62n2zrdCeu96V8ikUgk+4eCZj21aFMN9mAjBjVEKKLy7voqclvXMaF8PgARxYxi1nzGXQk2+gwYDmjWXk1tId2cvjvqW9y0tWcmcQWqCYSDhCIdz3VVFXxT2kSgtU4vcwTrsYe0WCthg5VwVMA3WYkmWDdFOrTrn22r1xcOLMFmCptWMLD+ExJCWuwVVTESNGq52gNGJxGDBUWouALVJLbnjQdQQh3aciEEG7aXxpxLsy0f0FzWult878yW6lY+Lq7B7Q+zZGvtPutLfhpIYf0nzvTp07nxxht7VXfp0qUoikJzczMACxYsIDk5+Xsbm0TykyfQyujKl8lv7ojubg27sYea41YPGRNAMdBs1yYInSPYdlTyIYQ2oTEYjBT1HQAoWCJtqEEvBGM13r663ZrmIeBGFYKQMUEPfDe6IJmktPYgdeGWGL+/eEQj93YZUkSNEbJ31nn5qqSJd9dXdttXd36M76yr6HEMEolEIjkwFm+uJdFfyajq1xjY8CkN3iAGNURey1q9jvaO6BAvQslaCreoa9ZrX5fT4gvhDfpp8rXgCdfRFNpDbXAbFYH1/GP1e6xQKlii7GGD5y1e3j6fp759io9LPqbeV091u8Y5IRSrxc7wbgMgolg6tNiKAiYbEOu3vq3GTVW7f3xyw1py3BtxBjXhv82cxp7kCbqQj6LgNacCmrVZ52gyPm9HMNbKFj+V1VoclYDRSZVrOLVOzYTeHPHtM8WoLxjhg287FgJkghVJFBlgTtJrJk+eTFVVFUlJSYd6KBLJEYkQAiHA0C4MOxqKCYRbyW/9hirXcCIGCznuDQAETC7cliwSA5VYItHAbwkAeM1ppLMDa8SDPxTB1jmSTsjbLnTbMBoUjhuSy/r1CYT9XkTAAyHN59BnTsYeaibRX6lNfBRNsx4wOjEqnVLK2VKAEuyhZlbvbiQv2U5RukPfr6oCRdH8HduCES0asBIbEG7hmjLq3AEunNiHzEQbgU45doUQKIqCqgr9umj9xr+G9Z6etTYSiURypNP5uXswqfcEyPZo1lcpvlKWrNtGur8So+hYbPUZ7Zgjbqo8YbwhL1sMPmqUBgLhGna7Hfi8gs8/8GE2RXDZzASaduMINdBg70fQ6MAfriCgaL7gxlAjgv4IBDuad7C9aTub9ljIsgzFFtS04BHFjFGEOmKnWDPJd1g6Bt0urCeEGhlc/zE1zqFUJY5CFQJVFViCzXrVRnsR2zJmdTnvNksaiYHqWH91wO9poc4dIMOlWRLY2v3dy5KPpt4xCGe7dVvnYLDdEVJVTBE/Axo+xaz6aUk+f59tJD8NpLAu6TUWi4Xs7Ox9V5RIJPuNKlTeWb+HulaViycXYjUZwd+s789xbyTZX65PSMqSxlHvGETfxs/Jap88hYwJHF2UQkmxZppujvhp9Yf2EtZ9qAJCBhtmo4LFZCA1NY3aSi8EPdoHaEjoT37L17iCNXy+vZ5hBc1EVEHQ6MBq7tCaBC3JANjbc7V/tr1OF9aFELzyVRnBiMqFEwtJ8uxgfMVb7EmaQI1rmC6I17k1jceWajeZiTaspo7+n/9yD4WpCXxb0cKcCX1IbZ+E7e0ioIgwQpGvNIlE8tPGH4rw3MpScpPtnDoy56D1qxJGGAIEgnuoxIOfMO6WD2kLVVOtNBAggp8wbZEKLNWwPaAtHjd6gzSZglgiXoKBnfgtWkT0SAiCoRAD/fVYMVLgKaci9XhylQjZqooNEzbVTEbeuRRk21hbu5ZtjdtpDVdh8KxmS8BNmGT8SSfSt3mlPs76hAGcUZTSMXCT9j7MdW/AFm6lsPlLql1H8b8NVSgKHBvQFqgrE0dRljQu5pz7ZTjon+EkufUoylYUxxwjvW0H5kgbu+o8ZLisKHQEpwuYEgEIGe2YDAqRSIAEi5GeEBHB0Lr3cQS1nO+7aisJRfrw6lfl9ElN4NiBMpL8TxU5s/kJ4fV6ueaaa3jjjTdwuVzccsstMfuff/55HnvsMbZu3YrD4WDmzJk89thjZGZqPq1Lly5lxowZNDU1dTF/LykpoV+/fqxevZqjjz5aL3/88cd5+OGHKSkpOegrvBLJj5Gt1W521Xk4fmgWFpMBIQTbm7fzefnnLNlZgVEx4/k6G5/fgqt6FSkEsGPG0boCEybCBjvlSUdTnzAQgLChQ4MQMjo4bmAGOzZrmgRzxNc1UnqojYiqEjbZsLQLxYpFE653lteQp2gTlxZbHvktX2MNezCoYSpq6tqPkYDN1DHpCFmSMYNumu8LxebHjZoaVjT5sDdtxagG6dv0OX5TImF1QEzatehIV+7qMG+sdweobxfmF2+uITnBQlailZykjhRx+c1fkedex8asM/Fa5IRGIpH8dNle48ETCLOtxs2p7L+wXu+rZ2fzTtxBN96Ql03e3YRUP2ZfBJsSwqlu0Z1oVX+zFui00/QubLDhMJhJtCTiMDtINBoxWVrI8+7BSzY1CVMwK3bMBjtJgUZGiCa9bW7IRIJIJpmOeCWrN+2hb9bRnFh4ImPSx9Ow/UmCgXqaFMEapZpmdhCwWBgUDOC1ZtOvT7622B3FrL0P9eB3QIqvjEalLwBNjQ0YgFrH4C4LvmeMztO+ZI+lZNVbeh8NCf1Ib9uBJdKGvV0IVyNhrO0ZU/xGFw6rkbTEFAYKJ5ur3ERCWts1JY1Utfg5YWgmCZaO46n+Fl1QB7CEPWyuaqWm1U9Nq18K6z9hpLB+MBBCWyI8FBjNXUxKu+N3v/sdS5Ys4c033yQ7O5s//vGPfP3114wePRqAYDDIvffey+DBg6mtreWmm25i7ty5vP/++/vsu6ioiBNOOIH58+fHCOvz589n7ty5UlCXSNp5/1vNpy0YUZkxzMWysmWUucu0IG5ARITYUleJ3x8kV62kslNkkbDBRmPCQBRlDxZfAxYlgaBahUordky4jQoRNYI1QYtia1J9NHmDeu7zUESlvKoOf0glbO0srGv1beFW6tu0SVKbOZWwwYpJDWALN6ME2v3NbYkx5uhBSwpmNJ912s349fF2Wiho9YdwdLJdz/YUE45Mi8l1C5oWJiqc7015k4/yJh8bK2DqoHQQKqCQ3/oNAH0bP2dj9pn7+AUkEonkyCXUm7Qce9EWamN783a2Nm6l3lcfs8/fnnfcjAF72INDmMGUTGI4gF0o2IQJgymRsrQTKfDsoi5xPMcVDWRcoebnXdHs45NdVgZ43bSFwGfqsNBMDsTGGEn2lcNe3t32cAvzvyjhqmn9cGBihtdNWBSyQ/GxwZaAhyArLEF2R6qwJgxllBqbJUUxtwdBDXfETMn0bKbRXoRRhDC0C+ChdjeyuBhNbEs7gaF1H+C2ZtFm0c7NHGkj0P7u1t6RAlUxETImcN1x/UAI3IvMWh9BL0IIPt+uXd8+qQmMLkjWD6HulQbOEvHiD+3/byk58pDC+sEgEoLPHjk0xz7ut2Cy7LOax+Nh3rx5PPvss5x44okAPPPMM+Tn5+t1LrvsMv17v379+Pvf/86ECRPweDw4nc59HuOKK67g6quv5tFHH8VqtbJ+/XrWrVvHG2+8cQAnJpEcuQihsqriG2oMVYTVMEbFSIZpCCNdGYRUP0HhxWIsJVuU0mowsS15NCHVS0AEtGmM6iGgakK1X62n2aCZxreE1+LZ0IYlFT7bU45dNVP6zUdcYhmKy+Ji/R4/gR1lFAAhgx2rUZOUjXbt/k4INRAIq6iKiZGFGYQbUzG1VWEPNVPfoh1DtcQ+C8LWRAQKBhHGGvEgSO7Y12nSKAQxOWmt4VaCERWbMHSqI/D4u6aji8fyrbWMqHkrJj2dWZV5bCUSyU8TIQSBsBqzSBp1Ndq7js1sJKJGKG0tZWvTVkpaS/To6AbFQFFSEVkJWSSYEqivrMGk2Mh0JjGk6QuMAsrtY3EGa0luD2Jabx2A1TqAWquWV91s7HiuOy0mPJYMAOyhRgxqGNVgIslXRkHL14Dm1lXQ8jUJoQa9XZs5jYRQA7aQJmS/sqaM8wo9mNQAYVManpxzKSSCI7iTWsNWdpuSQFTRWvs6WdVTGJ4+HLvJDlZXl2uV7C9nQMMSKpK0VKcRg4WIoee5dIs9n/XZZxMyJhAxaOKTQUQIBjTrMdHuQhY0JoCiaNddUVAsCUAjhGIt3SJ7LaqIcKywbo14CEc66uwdt0Vvt9dvLDnyOKTC+gMPPMAbb7zBli1bsNvtTJ48mb/85S8MHjxYrzN37lyeeeaZmHYTJ05k1apVPfb9+uuv8//+3/9j586d9O/fn/vvv5+zzjrrezmPHwM7d+4kGAwyadIkvSw1NTXmWq9du5a77rqLdevW0djYiNr+INmzZw/Dhg3b5zHOPPNMrr/+et58803OP/98nn76aWbMmEFRUdFBPx+J5MeKGiilqu1zaoxGCtR0cp25TM+fzrbNuwkEm3Bbs7GTRGGolByRRqO1L8KhLbCpIkJI+NsF9zZCqheL2E2Krw4fIRqxIhAEUGlS/DThpy5QzGcV2kr+VyWNJAe3slHx4lWtmCo+JNWeRFuwBi8ejMEy/ITB6MRqMmJMyUJtq8IRakDxtyIAV0pWzPmkuRJotmTgDNaS4iulJSFZ39d50hgRAhEjrHsIhyMEI52EdWLN6PW6oRZcwRrN9L99UuIM1sWYDEJsah6JRCL5KfHhxmq2VMdm8wirIsbV6NvyFt4t3sqA/Ba8lOMPd+TyzkzIZEjqEAYkD8DWHpRNVQUTmj7AFmqmMvECHME6/IDbmkXEYNGFdbel+3hGDquWBi1ksGNWfSSEtOf20LoPABAo1DsGkeorjXmmt9qyNWG9PR5KU1uI3bt3te/LAcWAEQNXTJjB2+sG0hgqoSa4hWAkwJrqNaytXcuwtGH0McSabzXb8kn2V5DetoMWm2bmHjA66A2+do06aAK+UQ0SbmsF8iCkXcuwwRrTxmBtX+AOtbGmpHME+1gBWwRj86pbwl6+3N2oB2UNRlRse51LKKLywqpSshJtnDzi4MUnkBxeHFJhfdmyZVx33XWMHz+ecDjM7bffzqxZs9i0aRMOR8eNc9JJJzF//nx922LpefVr5cqV/OIXv+Dee+/lrLPO4s033+S8887j888/Z+LEiQf/RIxmTcN9KDCae1VNdJOTOIrX62XWrFnMmjWL559/noyMDPbs2cPs2bMJBnsXXdlisXDRRRcxf/58fv7zn/Piiy/y2GOP9aqtRHKkE1JDfF3zNY21/8SsQKJ9INMKzmZY6jDq6moJf/MiwxSFb3LmgKKQ5dkMaH50UQyKEaviwGpwENVvJ4oUhgktxdl6xxlcfNRoPCEPy9ZvIaD6wdSHBCWV0qYGTIoFi+olpKg0KSoVnj1UehVa2ippNVSDqAYDhBUXKVVvMinNgLFCJdm3ByFUIgYzfXNjhfVR+cm87hiAM1hLuncH1a7hlNR7KUp3EFJVEv2VKEJlyRb4WbBDWDeKENsr6khLSY69ThEVhCDTu5U2cwoeaxajq19FESoCAw2OARjVQNy0dEYRAiHYUN7MyPzkLvslEonkSEFVBUu31ZKXnMDgbFcXQR20BVOzUTNz39a0jX99vRxfpIUtW2FS/zQcZgcDUwYyOGUwafa0Lu237d6tP2vTfSVYAk34AY8lg6DRQWF7vVZbrKDYOZiayWhgVJ9kPPUZpPj24ArUkNamCd3NtgLKko4mYHLRYsvThXWBgtuSRTbFMebrpWV7SEFz00qymxmRn8SATBcGxUi6pT9p5r64EhtJspdT76tnQ90G1jTXYlVqGCBScGGhyjUSa8SDPdSsLzYEjQ4S7WYMCvtMQRolMTkFb2MNOyqqGTtsgG45FtlLWFesmnm9EvKyeneHsN5lXh6OFdatEQ/JvlIGNnzKrpTjCEb6xgaLBUobvDS1hWhqC0lh/QjmkArrH374Ycz2/PnzyczM5Ouvv2bq1Kl6udVq3a8o5I899hgnnngit912GwC33XYby5Yt47HHHuOll146OIPvjKL0yhT9UDJgwADMZjOrVq2iT58+ADQ1NbFt2zamTZvGli1bqK+v58EHH6SgoACAr776qqcu43LFFVcwfPhw/vWvfxEKhfj5z39+UM9DIvkxUu4uZ1n5MloCLagK5AonY0Ufjko7CoBlyxaTjwAhyGjbjkkNYBBhPJZMmm0FPfYdUToW7EImBwnmBBLMCWSbszAFWvCbBtJUm00ikGVu4qhINV5FEBh2HgOyLbhDbmqVRLbu2YGPMH7ChBQjbZFWNod8uK1l5AdaKSQRnykZhy32tWExGTh6wmTqPlqJM1iLNdTCm2vhnHH5bNy6nWG17wHwdd4vqWtsimm7YccevNYOAV4IgSoERc0ryHYXEzLa+TbrLBShWfkkBSoJGe0MqftIC2oUB0vEy+LNtVJYl0gkRzQ76jysL2thfVkL2Uk2vTzRX0Gf5jWUJo5hW5NgS8MW1tfsINVhxhdpwaAYSTLlcmq/GRS4CjAohm6PsWHtl+hvoOr1tAbC+MzJRIw2fAarvpjsMyXHtMtNtsdszxySxcvFOaT49tCneTUKgojBzM60abqveI1jCLmt6wFQEPjNWp+2kBYPxRZu0c3khSODy47tq/dvNiqEIgJFMZBm6cNZgyZR7i7nm9pv2OFppkxxU6a4yRYOQvjIMmmpSZM6CeuT+6cxNCeR/1u0bZ/XPtFuJs2kCetm1c+nW2oZa9Asu/bWrBvbXccM7WbupogPoRiI7JVIPWp5piSkItoasYQ9DKn7CIA+Latx+2eQaItV0HWW97fVuBmU1dXkX/Lj57DyWW9p0UxdUlNTY8qXLl1KZmYmycnJTJs2jfvvv1+PUB6PlStXctNNN8WUzZ49u1stbyAQIBDoMJ9sbW2NW+/HjNPp5PLLL+d3v/sdaWlpZGVlcfvtt2MwaA/pPn36YLFYePzxx7n66qvZuHEj9957734fZ+jQoRxzzDHceuutXHbZZdjt9n03kkiOUPxhPysqV7ClcQsADrOD8Wo2OThpir5lVZVM71a9TaZnC1HzuKrEEXEDSP7ymEIURUvRs2ZLGLRUroQNHRO2owpz2LqthQQCtEX8oCg4A7WYMGCwZNMnsYij0tvT2yQNYsumYpraQqgI9pj74cw8htbwWhpYy0ZDPbtEM1nGVI4ydh2Pyeai1ZZHkr+cVF8JVeZRvPZVGcNqF+l1kvwVuibBa0nHEazHGnHTJlIwCFXzGVTB2LKHbLeWIscc8VHQ0rFoaA176Nf4ebeCOmhR6YOmfcfYkEgkksONiCpw+0MkJ8RXAAkhaGoLkWQ3E+gUfOyVNWUA2AP15Na+zm7FzY7WLTSXjmZ7rYe2QITGljT62MaRbC7ApFgpTCyMe4zOpLWV6N9d7TnDPRZt/j1lYAaKcrYeMC3KqIKkmCjnUapdR5Hh3U5CSNMu70o5LiaoW8CcRJs5Vd/vb09/ZlZ9FDWtINvTkTotYI2VEy47ti//XqZp60MRFUVRKEgsoCCxgB0JQ3h/WzHVeKlWvFQFv6RJCTIeL5mqABSCJifGdn/w3GQblc1++qZ3bxrfP8OBqUGb35oiAZrbQig2TdjuIqzbtH4sES9Da98nyV9OyGinNv0yhEjV/c2jZvBBWxp9rT7Kmzoi4ocMCdS0+vVgsfH434Yq8qbacVgPK9FOchDofjntB0YIwc0338yxxx7L8OHD9fKTTz6ZF154gU8//ZRHHnmENWvWMHPmzBjhem+qq6vJyoo11czKyqK6ujpu/QceeICkpCT9E9UsH2n89a9/ZerUqfzsZz/jhBNO4Nhjj2XcOC2nZEZGBgsWLODVV19l2LBhPPjggzz88MMHdJzLL7+cYDAYE7BOIvkpIYRgR9MOXt7ysi6oD08fzpwhc8ghGqk9gKoKWmtLsES8hA1WIooZW7gVW7gFgUKzLT9u/xkuK+lOK/kpCZgdyexIm8HmjJNjBHtz+wRBCbYyqvp1Rla/jiugPQM9lgxMhk6Pf4sDV/uKvQEFkymVosQBzBl+KaPsg7AJE21KmPVU8tGeNyltLY0x4TMZFJrsmsVOim8PAAmhRhIDHc/cZJ82mYwYzBTkaedlC7sZXvMOoysXYo60oQqB0R0bHTjD26HlSPKX6/6L3WEPN+m/gUQikfyYeHtdBfO/KGFnnSfu/u21Hp5ZUcJHxdV0foQ3+dzUBDZR3fgUyw3l7FZaCIsgNmMCLgYyzHEygx0nkG7uhyvcFquS7YZAW0tMwLcordYcxhamMKFvKlZTVzEi1WHtUgZw7oS+bEs/gWZbPjtTp9HgGNClzpaMk2i2FbA9bSYRg0UL1gYxgjpAQLHFbHdeHLDvtVCQmVLEeJHDdNGHPiIRoVioVwJ8aahiuVJOOW4CBjuG9vfn6aNymT44g5OGd2/RO2VAOkZLu7CuBjAZlU4+67ELLcZ2n/VkX7muyTdHfNRX7GRDeaf3WbsZfNCa0uW6Kqi0+vZtnh8v5ovkx89hs/xy/fXXs2HDBj7//POY8l/84hf69+HDh3P00UdTWFjI//73vx5NrPeOjNhTtMTbbruNm2++Wd9ubW09IgV2p9PJc889x3PPPaeX/e53v9O/z5kzhzlz5sS06TzhnT59esz23LlzmTt3bpfjVFVVMXz4cMaPH38QRy+R/DhwB90sL19OaWspACm2FKbnTyfHmQOqioIWSM2kBvjb4u2MiWzAiua7FzQ5dDNAtzW7i+9bPKYNyuCpmoFdyg1WTVhPa9uNJaLlfs0Ia4Kvx5KhTS6iGC0xCvyQwY7RoGAwGElLmczM5gC7RQurjE5agk38b9f/yHHkMCl3EtmObCwmA032PhQ1rSAxUEW/hmVdtNvJfk1YDxtsmB3JWIwKtlCz7qOY7d6IEFkY/E36GJ3BurjnXJIymbqEAeS51+vXK0pCUNPKRFQRe44SiURymFPaoGW0WF/WTP+MrhZCq3ZpwvPWajebq5ppCVfQGCrB4FmNI1hLADAKhWwcZIh8JvU9n9cbtZgmCcEGBjYsxh5qZnfKFP5vkfZ8PCo3kVlHaYJpeVMbizbV0OoLM9DaQArogeGiNCT057T8JABdwO1Mdwul2Yk2/OZktmSe0u35B01OtmSeDMCJw7LY2DqSwubYgNIBozMmSnqUM8fksXZPE9MHZ8SUG9qDsrmwMFpk0uo8Da//G0zBPbREAnyj1NCkbiKzNY/C9NEkWMyM6ZPS7RgHZ7swGw04nZrJuVENEAIIxzeDV+zJGBW6LDQn+ivZXuthVDR9W7uZvGq0Y4jEXldzxEdrLzKlqHKR+ojksBDWb7jhBt555x2WL18ek0osHjk5ORQWFrJ9+/Zu62RnZ3fRotfW1nbRtkexWq1YrfueFEt6xuPxsHnzZh5//PEDMqGXSH7MqEKluL6YVVWrCKkhDIqBcVnjGJM5BlN7mhciQRRFU2pEI5eH6nZiBVpseQwYNhrxuSZ8tthyAW0ypApBn9QE9jS2MbFfrPmfyxY/yKSxXVh3ddJuKwjCBitN9sJY63pFIWJPB/YQMthpsvfRBd1m5wBSWcZAUnA7TmVElpHNjcVUeat4Y/sb9E3qy6DEMQRMifjMmh9gZ7P+NnMKCaEmTGqniYwtGUVRcAZr9XppbbtpiwhKy8pwAtWu4eS3fINJ9VOWNJ6+TR0LuXWOgUQMVgLGzpNZbRkk2mdEiAN+we2q81DrDjCxb6pMiSORSA4b6t0B2tRGGkMlNIZKiYgQBhEhN1hLqrBRIFyYE8aQ27aLkGpGVTueX4XNX2IPNQOaW1KNS4uZUlzZyvFDszAaFN74pkL3pW6uqyIF8FgzdIupOsdAVIMJS7vm19gplVj0HdWd33ScrGM6owuSWVfWrG9fNa0fCRYTyjEnULqskhRfGaXJE0n1lVCROIo4sjp90x1xTdeNez3DTxjSh3EFI9j99k6WN1axyQJeJcLXtSuo8G9kZMZIhqcPx2qMLxdEtd42u3YskxogJcECjVEz+FitP7ZkDAaFSCRWkE4KVOLpfFHaNevCZCPsGgS1a9mTPIE+zV9iVn34Al2F9b1FcymrH5kcUmFdCMENN9zAm2++ydKlS+nbt+8+2zQ0NFBWVkZOTvdRDydNmsSiRYti/NY//vhjJk+efFDGLYnP9ddfz0svvcSZZ54pTeAlPxnagmHcoSY+r1hOTZvm05ftyGZ6wXRSbbGCNZGA/jI1q35MER/Ods1y4cBhOByJfJN+Amltu6h2ahOp44dmMijLhcVkIBhW9UlSZ47KTaS4spUBmR3Ca9T0bm9qnENRDeYuL3Vvv5PZ2baFekd/hGLSNSYhcyKlyRMxR3wELdkclz+IMVmjWFO9hq2NW9ndspst9TvZ408hxdGPwc3fxPTbkNAfe8vXKO3TipDBhsGehNGgkBDsCDhnC7ewvWwXw8LN2njMaazPOQeBAoqBHPe32MItWnAjg5UEi5FTxg1g8ydfANF0PGUkhJoxqKEuwXv2h7fXaZqo7EQbRekOhBC4A+EuwX0kEonkh8AT9FBcs54d7uW4CWOO+Mhs207EkoPTNoxj1T44sbAzdRr1tnxy23ZhVv0Ew2EsYc2kPiHYYdLuCtboKcFAe4+5bOaY52ZUsPeZU6hzDCbVt5uSZC39r6U9j3qGq0Og/fnYvPZUcfE9bLtb+LxuxgDMRoX15c0IoWnUo2btRqOBremzsETaCJqcVCWO1PrqIqZ2j2Gv4ZgMBjAYMaQMpF+zQmvqqRhpxGmpxhf28WXVl3rat1EZo3CYYxcACtPat002ClMTaPQFMCigtAvbEYMlJl0e9uT296k25lrHYDK9W3EEGwiITi697e1Vo43AgFNY2zKIkCGBo3xf4w6ECYVio8VDV036d3nvSQ5fDqmwft111/Hiiy/y9ttv43K5dG14UlISdrsdj8fDXXfdxdlnn01OTg4lJSX88Y9/JD09PSZn+sUXX0xeXh4PPPAAAL/5zW+YOnUqf/nLXzjjjDN4++23+eSTT7qY2EsOLgsWLGDBggWHehgSyQ9Gi8/P/Yv/R5O6jTF9krAYLUzMnsjw9OFxJyYiHOg0xRDkuL8FBD5zMk5HCmaTQmNCPxoT+gHapOWo3ES9r3iCOsD0wZkUpjkoSu8I1mNyJMfU8VgysES8VLu0mCB7C+uqI4s6Z0dhWNVUFxFVUJU4Si9XFAWXxcXMPjMZnTmaL6u+ZGvDTuqDu1iCgc3pozml/hss7SFRvJZ0vJZ03aQ9bLSh2JPimk8mBqoxqiFAwW9ORCgdaWq2p80k172B0mQt/aYqwO5M1Pe3WVJJCDVgibThCNYTUQfFvVY9sbe7lKddk7FqVyOrdjUwdVAG4wq7N4+USCSSg0VIDVHSUsKWxi2Uu8vZtXElSaFWzJYcMpUURkQySPOZiQQrMGOhIaE/dc7BIFSilkaL1u5kVPWr7c/VDswRH9aIm0B7EDdPINzFSsvevnDqMyXTmNCXxoS+5KXYOW5gOqZ2gTzdaeXnY/NwWE0oihIrpPaS6HvtsmP7UtvqjzH/Nxm0xdq93ar2R4NsNhgQKLqAH7Ua8ww8k28CFagGMxmkcdaAaXjVCtbWrqXR38i62nVsqNvAkNQhjM4YzWVT+lLnCdA/o11YN9sxGBRMaoBgROjCdthg5ZLJRR0DMFmJmOwQ1tzR3NZskgKVWMNubIEGQHvfR83ohdGKyWLVfxuj1QYBDyLg7XJuwlPPqKpXKEsaT2NCXymsH6EcUmH9iSeeADRf6M7Mnz+fuXPnYjQa+fbbb3n22Wdpbm4mJyeHGTNmsHDhQlyuDjObPXv26FHNASZPnszLL7/Mn/70J/7f//t/9O/fn4ULF34/OdYlEslPkkpPJQuLP6QqoAVE65vUF5c6nM+Lg+QeHSTN2dWETg0HY7bzWtcBUOcYjE1VNVO6TgzNSeyVGbbFZGBwdqzpoZI+iLKk8eS617UH7Dm+fUf8/vb2745GGnbZun9NpNpSObnvyYzJrKa59QN2NpVTGd7Nh6YahoZt9CUJ1Z5Gc7CPLqyHDDYM1vhmkokBTaMdMDkRSuxxvdYMtluP17cFAou9o5+QwY7HkkmqrwRnsA41jplkT6za1cC6smbmjO+jl0UXFKJ+osu31UlhXSKRfG8IIfCqDWxtraSxuJlgRHtnVDe5yQwGKRCZ5PrtmAkC2uKsIaKZX7ut7VmSFAMhgw2z6iMxUBUjqAeMTkJGO85gHc5ArS4QBsPaA9NkUAi3C3z2kGb55GtPoQYwpiCZnKTYiOS6prkXnD4qh69Lm2j0hvDvFQwt0WbuYr2Un5KAy2bC3Qt/7e4wGBS2ZsxicN3H7E49lvHtCw1mkxHV0HE8p8VCfsJgBqUMorS1lG9qv6HaW82mhk1sbthMv+R+jMkcg6K0LxyYbBgUzQy+TVV1zXpBVmqXhY+wNRn8mrAdMLkIGh1Yw26UUJtep6q+PTiq2aZbLgCYrQ7Agwh2FdYdpZ9gDzUzqH4Rq/pcSSief4DkR88hN4PvCbvdzkcffbTPfpYuXdql7JxzzuGcc8450KFJJBIJoKVHW7atjqHZifRJSyAQCbCqchXFDcW0BP2YDTbyrWM5qWgmj32ixdJ4dmUpcycXkeKIFb7VUNcsFj5zMlWu4WSGVTL2EvCNPTn57QtFoSJpDBWJo/XtzvTPjJ1g7a3pjvodTh6QTnGlls5y6qD0uIfKdmTzp2mXcN/Hy6kMbCCgGNhsaGC3aMFpbmD62HHULvkaAFUxY7LacIcN7H12iYEqoGvO3ngIAYqlQ9sSMtrwWtJJ9ZXgCNazrdbN+KLUHnqIZeVOTSD/YmdHGiLpri6RSH4IvCEvWxq3sNn7GX7VTbIwkxRJxGl2MiR1CGt3lzFSdA24GU2DCR0p1QBCRi0oXKK/Mqa+35yMz5yEM1jHwIZPUVCpdwzijW8qSLAYiURCpPjKcVuzsEQ0QbKzsG41GfkuDMh0MSDTxcI1e6hs3nfkcpvZyC+PKeSJpTu/03Gb7YWsLrgUoZj09+re71dne8ozRVEoSiqiKKmIKk8V39R+Q2lrKTubd7KzeSfD04dzXN5xKCYbBkXTrEciQhfWMe3lsw64cRBdWvabEgm1+7VX1zdpFl2AlRA+oMFvwGbuENYtdidQgwi2xVh/RVRBSU0T0WUBRUSkZv0I5bAIMCeRSCSHK6t3N7KpspVNla2cMd7EZxWf4Q1pK9xFzsE4fH0wKVYqmn0x7RZvqeWccbEBM9Vwh7CuKkbazGnsSj0WoRjpl+7AYFCYfVQ2HxXHTzN5QMSROC+b0rfLpKuz+eKFE/tgt2j7nVYTN54wsL2r7qVXk9FAkimXRGM2o9MdfL7tA3xKmEr3aj5oKCMXD9k4MIogJoOBkMmBpV1rFDVRjGqAfOZkUhLMNLV1n6pmTEEymCxEFDNGESJssBG2aIsdCaFGPt9eT5rDQt90x34FieusmYhnqi+RSCQHk9LWUhaVLqLF5yMYacQZ8WEIjeDkoukUJRWgKApVAS3waLXrKLLdWhqzkMHOjtTpjKp+jbDBiteSpvfpdCWiNjaSFIhNhdlmTmbi+EmULtmMIlQGNCzFFnZTnjSOtmCEvNYNFLR8padMCxoTYrKS9GRptT/szzO5s5Y5qvk/EAunqLVW9MjmvZzZTXF87XOcOZzqPJV6Xz3ratexvWk7G+s3kpWQxWB7Jkq7Zh01hBDau0OYulrVteDCBQjFgMOVxIikLCo2l2BS/dR5AmTaOhSY4wfmYjd3vJ+jedpN4bb2mADaGawvbyYQQRfWE0JNhCJ5+31dJIc/UliXSCQ/CSKqQAjR5YUciqjdBsQBcPvDhFQfZYFv+LBEE8iTrElML5hOTZVK+db3qXaN4NWvYts1ertq0UW7GXyzLZ8te+VF75OqTY4O1mRob7ISbdS0aiv/UUG8M7bOk4O9NA77M7FSFAMDBp9LQ4WBLWZBi9JIW8TNogQzBcF6DAk5TDcopKSk4a3WzP7aLGm6dgjAb0piUJaLL3c3xj1GhsvKMf20iWlObj61FbvxmZL062kPN4NQeXtdJb8YX0Busj1uP/EIhDsL671uJpFIJPuFEIL1detZWbkSgaCszsgJ3lYKwwZKbBls2mMmb5jAbBAk+TWhu8lWSLMtn/6Ny9mVchw+Syobss9GVYwkOeycMiIHh9WEeWse3zSWYA3H5mv3m5Iwphbyde4vyXF/S17rWvJbviYh2IBQjLomPqpVr3fEpgV1WA/O+ynTZaWiybfvimhm7FGmDc4gJcGyX8/0vYkGZevs+rX3wvrepNvTOaHwBJKsSaypXsNnFZ+R0+8MbXwijCnkRQhNGGevPOsAfpOmVw8YnRiMRgyWaCR5P3XuAJkmFVUVqIoRs9kSM0+x2FztdX1EVEH0Vd3gCWILu/V6zkANqjhqfy+H5EeAFNYlEskRjxCC51eVEoqoXDqlry6MbqxoYdGmGmYdlcVRuUlx21X6trHJu5KICKEo6YzNHMu4rHE0t0XYte5V8rzbMUf8em7YKN5AhMpmX8ykQg11RIvtLKinOS26QJyfYmdy/7S4Pu/fhTPH5LKjVpu4xQtUZzN1L6z3ltNH5dLgCVCQ7uL11Ck4gVFWwYDsOoorPWy2ZEJgDYvLAkzOdeFtNyDobMoJmmY9OcHSra9iTpJNn8AFBp/JxvAuAuYkEAJVMWEQYWzhVvzmZFr9IXLp/cSus2ZdGhRKJJLvg7AaZln5MrY2amkuh6UNI3FnA/3DuwBIbdvNtpr+bKtxMyJNxRHxIBQDbms2qsHE13kX6X21tWvUzx+dp7teqbb4cUH8pkTMRoWw0UZZ8njMkTYyvVtJ9ZV0qRs0JlCeOBaDojBtcAYmg9JtkNP9ZVL/NIwGpds0b91hUBQKUhP2XbEXdBbWE+29y/QxLmsce1r3UNNWw6eVXzClXU9vCLSwp7GNsMEed3G7xZqH35RInWMgBkUhJy2FMjStvD+kQjhARGipVaOa82mDM2j0BEm3prATLYNMKKLqC+tGNYhJ7YgQ7wrWSDP4I5SDc9dJJBLJYYo3EOaj4moavUHc/jCtvg7T6kWbtFRrHxfXdGnX5G/ize1vsrx8ORERwmFM5dxB5zIxZyImg4k1uxtxBbR2ehocIMtdzID6T1FEmA83xpqzi6jZt3EvX/ZOL1hFUZjYLy0mDduBMnVQOg6rkTNG55JgMTEyP5mR+clx61rN3/11MCDTycR+aTGTFQNmpuRN4ijHqWRYBqAoCpXeMt5u3sB6Yw1eQnjNaTH9+MzJDMpycurIHH3hYMaQDn/MzhMSg92Fx5qlbSiK7l+ZENK08rb99LEMdtKsy4mPRCI52LSF2nhn5ztsbdyKgsKxeccyLX8a+Z6Neh1nsFb/XrVnBwBeczqqwcTEfvFjcXSOkWKwOWNiguxKncqe5Am02PIxdxK49yRPJGTsuphZ4xzGtvQTUA1mTEaF0QXJDM/ruqB9oFhNRo4bmEFWYlf/7niML0olM9G638J9lKmDMgBNKC9qD4hn6mQG39v1aYNi4ITCEzAbzFR6KykWmmbb0xzNdmLtkk4NIGRysC73fCqSxmFQFIzWBLISrZgjfu2dE/KhtgvrUa362D4pnDAsC8WiuciZI342V3Vo0q2h1phj2EPNRGSi9SMSqVmXHBLuuusu3nrrLdatW9dtnenTpzN69Ggee+yxH2xckiOD6hY/TW1BhuYksnhLLTtrO0wBoy/Slm78oSNqhCWlX/JF+RoURcWomMixjiDDPIBQ0EFUSauqKontEc6NapCEUCMBo4O+TVre7yZ7H1pNsSaEIhpgbm9h/Xt6v44rTGVcYe+CrFk7TeA6m8QfDKwmA2aDnQLbODItgxiYUs9ubzUVuClTPEA1uYSxYiJiMHPM4AJMRgM5SXZ+fXzHNVyyRZvAdr5enYVrgDZzKo5gvR7JeH8vbSgcpl/DMryWdFShLQJ0jpAskUgkB0pdWx0f7P4AT8iDxWhhduFsChILKK2qw9rJpNka9mANuwmYXCQGtEXfVlsOyQlmJvdP58td8V2EdCwO/dkXNtiodQzWrbksRgODslxsq3ETNtpYn30OiYEqBtV/AmiWTrtTj9W7Ohxidxw7MJ1jiR/gtDeMK0zp4ufeWbO+P+eYZE1iSt4UlpYtZW24nqG4sIU1wTlsiG8RF73e2rEAcwJGg4JJ9ROMqBD2o6oQNlm7pr+zJGjm76qPL3bUM6Gv9k63hFvobHemiEjMwr/kyEFq1iXfO4qi8NZbbx3qYUh+Qry0eg8fbqymqsVHvTvWdzzYbub8/JelXdpVe6t5ZesrPPPNYnbUttLSmsSQhNkUGHIwIHh5dZle1+yvxyA6XpWuQDUZbTv0bXuoOcbXDrQ86wARQ6zJ3cEy6/suKIrCxZMKuWBin4MirEf94oe154k/bqA20bIaXJzUdxbn9DudTBJQFUFFpJJFhkq20ojb6MLQQwwB0HzW9ePsNdY2szYhS2gX1uNpOXocd2sJmd6t9G36Qk//1vl33FcWE4lEIonHzuadvLnjTTwhD4mWJGbmnk6+K5+IKli85ltAM1OPRnU/quZtRlW9QqZnCwBuSxbnd0otCT3EOEnpS5s5jVZrNjvSpsW4XVlNBk4clqVvh412Gu1F3Y7bdIQG7/gu5zU0dSh9k/oSUYx8o9TgaI8pEDDG1/x3vt4GgwJmO0ZFwaxGNett7Wbwti6B7zBr8wNTJNbH3xLUFggC7TnoDTIa/BGL1KxLJJIjliZvqEv+8KgmNsbcWYRYXr6c4vpitte6MRus5FnHkB+xM7j2HawRDzXOoexOPU5vY22riuk3MVCFPdSsbztCDTTtNRnQ86wbrVw4sQ/FVa04LCZGFRw888LvwsH0kz9nXD4l9V7G9NGE587mjoqikOnIYgr5lKsePjClEzDsYCuNFBtKOMa9mVHqRIyGWEH8l8cUsqfRy+iCZL2sf4aTqYMyKEix4wmEaavy4F7zZYdmfT/nLuZgs/49omqphazCT4p7B/WO/nyyuTZm4iWRSCQ9IYTgq5qvWFO9BoACVwFK2yje/qaFyf1NjCpIxhHSYnZ4Lel4LRk4g7VakLdIRx5utzVbXwT9+dg8PIEwW6vd8XOQW51syDk77ngURcFi2ktQVQxUuYaT497InqTxMbu+r6Cnh5rvkhpVURSm5U9j16b/0aA0sCe8leFk4LFmEC9OfWdff4fFBGarrln3hlVEyIeqRs3g99asa2b75og/ptgc0jT1PlMy1rBHE9blYvIRidSs/4SYPn06N9xwAzfeeCMpKSlkZWXxn//8B6/Xy6WXXorL5aJ///588MEHeptly5YxYcIErFYrOTk5/OEPfyAcDsf0+etf/5rf//73pKamkp2dzV133aXvLyoqAuCss87Scle2b0d57rnnKCoqIikpifPPPx+320087rnnHkaMGNGlfNy4cdxxxx0HflEkRzS+UIQGTzCmbG+z6ZZwBZu9H7CxfiMCQUpDG6f67KSZ8sls2441opnQJ/s1rXpUs2rxaSbZLTYtVUpa2y7dTxrAEazvalrXrlkXBjOZiTZmDM5kQt/U75y79nAk3Wnl6KJUfUKUn2LXfNrbTfhw5YCikGTKYpDjJCY5h+EQZtoMJjY0rOKlLS+xrWlbjCY7w2VlXGFqzCTLYFAYV5hCZqKNfhlOhg/sB4A93AJC3W9NuLlTwB4R1DQZA+s+oW/T5xQ1rWBjRcsBXQ+JRPLTI6SG+Lj0Y11QH5kxklP7ncr2au0581VpE2FV6AE2vZZ0Kl0j2ZD9c4qzfsbW9Fk02ftQkTiGsLFjwbMwzRE3KGp35MWJnn7ayJyYbV/BVNbmnk+LvSCmfPZR2b0+zo8Jq8nIqIIkhuclHVCU+wRzAlOStHnpLqWFWtrwWjK6rX/KiBzyUuxMHZQOJhtGg4JRDRGOhFCDPgQQNtq6Zqdp16ybVX/MO82gavOJoFET5g0ioluDSY4spLB+EBBCEIqEDslnfyeizzzzDOnp6axevZobbriBa665hnPPPZfJkyfzzTffMHv2bC666CLa2tqoqKjglFNOYfz48axfv54nnniCefPmcd9993Xp0+Fw8OWXX/LQQw9xzz33sGjRIgDWrNFeUPPnz6eqqkrfBti5cydvvfUW7733Hu+99x7Lli3jwQcfjDvuyy67jE2bNsW037BhA2vXrmXu3Ln7dQ0kP06+LW9he038xZzOVLd0CFvLt9VhUEMUNq1gZNXrZLk36WbwwUgL5Z5P2Nn2OUHVR7PHxPi04zmhzUOur5RkXxkJoQa9L2vYg1ENEIq0p31pN0Grcwyiyd5hmliZOAoAS8SL0xBrgh9N3RYvD+uRjqIonD4ql8kD2v0OTVY2FF7C+uxzURSFmUefxXjTODIc07Cb7LQGW/mk9BNe3fYqZe6ynjvvjC0JVTGiCBVr2L3f8QA6+41uLdcWZBIDWjqjdO+OmLoVzT5W726UpvESiaQLnqCHN7e/yc7mnRgUA4NcE3FEjqLV16HwCEcEkUgnYd2cDopCmyUdtzWbpoQitmacRFny+LjB1aL+y8NyE7sdR5rTwsyhmml9/06BSwdmubh0ShEA/TIcTBuSTcAU209esj0mcN2RxswhWd/JUionZwJFQls0WafU0mJyAfE19oOzXZx3dAEumxlMNpR2c3cR9BEJaBYUYUM8YT260CIwdZqDRBf/o4s4ykHUrPuCETZWtHRRbkgODUembcsPTFgN899v/3tIjv2rEb/CbOxdygmAUaNG8ac//QmA2267jQcffJD09HR+9atfAXDHHXfwxBNPsGHDBt59910KCgr4xz/+gaIoDBkyhMrKSm699VbuuOMODO0PmpEjR3LnnXcCMHDgQP7xj3+wePFiTjzxRDIytFXG5ORksrNjV2dVVWXBggW4XNoL6KKLLmLx4sXcf//9Xcadn5/P7NmzmT9/PuPHayZa8+fPZ9q0afTr129/LpnkR0it288nm7XI6zed2HM02JdW74nZLmxeRZZnMwAu9zo2Naayvn43dTUfYVHbSLLmYXNNx9p2FGs2ehjT3s4ZrNP9nqMkBBsJRlQsJgPmYCthtPypJcmTSAxUEzA6KEsaR2pbCbZwCwkBTdPuD0WwGA0d0eANvb9nj2RUkx01pAX6s2QPYVOOhSTg+D5pBE27WVe3jnpfPe/ufJep+VMZnj58350qCn5TMgmhBuzhZkSnEHO+YCRujvnO2EMdmnOPx40/FNEtJCJ75c99ZY22iOC0mnqcLEskkp8W1d5qPtz9IW3hNmwmGycVncTClR6gjpUbd1Dg3UqVayRhow1fm1sPUOa1pPPr4wfy98Xbu/R58vCuGu78lASuntYfWw/ZPCxGA+lOK9dM7x8TSBQgOcHCNdP7YzEaqPMEurSVz7WeUVMHMEykUU8bHiVEfXAds5L77ruhwYBitgFulJCPSFAT1iNGW1fzfINRM49XA5gjPsKqigUDRLTfK2TQhHmDCBM6SD7r766vpKLZx846D6eNzP1OLgOS747UrP/EGDlypP7daDSSlpYWY16elaWtMNbW1rJ582YmTZoUk4ZpypQpeDweysvL4/YJkJOTQ21tLfuiqKhIF9R70+5Xv/oVL730En6/n1AoxAsvvMBll122z+NIfvx01pbvTwAVa9iNy7uRXTTzmVLOZ6KYDQ1r+GbDYqyRNjJFAqf6DIxQXRgUU4xWNa1tJyY1gEChxZYPaH7owbAKagRTu79YwOgiYE7im5zz2Zh1FkIx6UHOTMEmWnwhnli6kzfXVnSYwRuPXE3F/tD59d85qJ0QRo7OPpoLh17IkNQhACwvX876uvW96jeavs0eatbNAteXNfPksp2s3dPUfUMhsIU7hHVTxE+rL6iPM6LEX2Rp8Had5Eokkp8mWxu38vaOt2kLt5FmS+OcQeeQ68zV9/dvXE5e6zoGNWjR1z/6QrMY9JmTCccT1trZO2BpFLvFGDe3d5SoptZmjl/PZjZ26fvCiX04aXg2R0lhvUcUg5mWhAGMFVn4zGl41QrM9up9NwTdvF2E2lDbhXVNgO9KNL2eWfV1ZCYJawvdYZOdvGQ7CoJIJPIdzqaDimbNBWxXnZfXvynfR23J943UrB8ETAYTvxrxq0N27P3BbI6dbCqKElMWfZCrquYXs/eDPWru2bk8Xp9qLxxn9rfd6aefjtVq5c0338RqtRIIBDj77PgBVCRHFrrZFxCKqF0Cj0VRVYEt1Iw5WEklXpq9ayinhJBJW5U2CoU0SzYZoZ3kiVTa7P1I9u3B0vINjfa+WCIdKd6img6/OQmPJYMkfzkJwQbCERUCXoQQqIqRkFF74UY6+RMGTZoPmc/dxMfF2ot7T2MbQukIMCeJpfMENeqqYDfZmVEwA5vJxrradXxR8QVCCEZnju6xr87CelSz/ml72relW+v0oHc6kRCpvlJMkbaYCP8mNYDX3SG8q52etxvKm/Xv0gpeIpGoQuXLqi9ZW7sWgL5JfTmhzwmx1o9CJTGgBSdN9FdiDbeS6NfcbFqtuV36PBh0CVjWDZ2fY+lOK5m9zIH+U8agwM7U6WRYc0gwhcjLqubzys/JceaQZO05poCh3bxdCftR2+OjYOoaWwDaTd1Dmt96uH0+JNo16yP75mLaGZ27xwk2+B2paPLtu5Lke0UK6wcBRVH2yxT9x8KwYcN4/fXXY4T2FStW4HK5yMvL63U/ZrP5oKz2mUwmLrnkEubPn4/VauX8888nIeHQp7yS/LB0l+/aF/axpXYznpq/0yQ8iOj8RIGIYxRjWnaRg5OElGNRVW0ytSllCkn+ShJCjSQGKmM061HazKm4MvKgdS3OYC0vrCrljCJtRTtgcoGioCixE51owBdzpI2d0RedEIiQ9l0cgc+LAyHczeJcqJOfnKIoTMqZhEkx8VXNV6yoXEFERBiXNa7bfn0mbZJkD7ews85LrXsfmu+SzxhS/1EX/3aT6ifsbdIN6U1qRz+LN9eS7Csl0V+FWjCr5/73orypje01HiYPSDsigwtKJD81gpEgi0oXUdqqpQQdmzWWidkTOylAtKeIMxhrPZjbuh5Xex71FluHsD6pfxpflTQybVAmy7bVMql/2n6PaULfVNaVNXPcwO6DnnUmw2UlJcFMgtXUrRZfEouiKKgGEzWuo5iW66LNsooqbxWf7vmUMwacgUHp3oBZsbTPX0NtqLRH/bfEF9ZH98tj56ZqTBG/pjAAlGgMHHOCnplPRA6+sC459EhhXdIt1157LY899hg33HAD119/PVu3buXOO+/k5ptv1v3Ve0NRURGLFy9mypQpWK1WUlLiJbboHVdccQVDhw4F4IsvvjjgfiSHH5XNPuxmY5dgNtUtfho6+dJFX1QAgUiA3S272d60nXJPOQFPE414QIEUYSVXuLAkjKIiaTaFrU9jEGHc9eU40HKdB0wu6hyDyPJsIsuzWTdzDpicWMOalr3NnMaggv6ESs0khJrI9mxkZbGJfoEwAZuLs8fm8963lQRCnVLBtWvWLZ3S7tjCLVQ3uVEVI6pFmhYCtAVjF/GMBoWIKshLiZ2wKIrChJwJKIrCmuo1fFn1JapQOTrr6LhmnZ0168W1ni776z0B0julqQu563VBXaCgtIvnJjVAyNugj9MU8eurMqaInyF1HwHQUNkHhmj+pGWNbSTazSTZu1+QefUrzazQZFR6PZGWSCSHJy2BFj7Y/QGN/kaMipEZfWYwKGVQTJ2wKkjz7mBgw6eAZtZsjvj0eCoQq1k/pl8a49uzaQzLTTwgn+EpA9I5pl9ar9saDQoXTyqiB4t6yV50vlY2s4lj+szk1W2vUuWtYm3t2h4XlRVrexT3kBdBu6ufOb4CKjMtlXKTAXPE1xFErl2zbrDYUdqtDdWDJKxbw61kuTdRkTg6xmpQcmiQPuuSbsnLy+P9999n9erVjBo1iquvvprLL79cD1DXWx555BEWLVpEQUEBY8aM2XeDHhg4cCCTJ09m8ODBTJw48Tv1JTl8qPcEWLimjAUrSmLKvYEwL63ew5byevo0rcIRrMcbDLC1cSvv73qf+Rvn8+meTylzlyGEIEU1MFRNY5xlCjmpF+NyTKQmZRoAIYP2wmms1gSloNHBz8fmUevQJlXJ/jLdX7ks6Wg2Z5xMnWMQtY5BKBYXe1InAdCnebWew9tvSsRiMnRJi3P86P6AFhE+ijNYS6s/jNeSgTso7aZB0+RARx7fy47tyznj8umTGn/CMj57PMfkHAOgCe3VX8aNxO43JQOaZty0V25agOdWlsa021qmaba2ps/iy4IrKE/SJlhm1U/xzo5I9AoCo9C0GdnujXq5t6kOXzBCWWMbr31d3iXIYXe0+EK9qieRSA5PKjwVvL79dRr9jTjMDs4ccGYXQR0gEg7Rv3G5vl2WdDReS7q+3WLLI2y0xQh/USH7uwT32t+2BoPSo/+7JJbO6VnNRoUkaxLH5h0LwOrq1dS11XXb1mjRIvObA0265YXR0o1gbE7AoCiaz3pEgBqBdpN3g8mK0u6iJSIH553Sp3kNue4N5Ll7FydG8v0iNes/IZYuXdqlrKSkpEtZ50nstGnTWL169X71+dZbb8Vsn3766Zx++ukxZXfddVdMPnaAG2+8kRtvvLHHvoUQ1NTUcNVVV3U7JsmPj51xtJ+gCesABY2fobatp827mJe2jSfB0rHOmGpLpX9yfwqc/fjyvTfIpJ5ySx71joHUOwbq9cJGG9aIR0/HFjQ6SbZbmDx6OI2LPsKs+khsN0cMGp202nL1fLNGg0K1YygZxjWYIz5SfSWAZgZvMxs4YWgWX5jrafaFmNg3FZvQ/N0tES+KCFPUtIr0Ni3tl9uSidsvTdUATh2Rw9elTYwr1KxtnFYTzn3kux2bNRaDYmBF5Qq+qfkGVVWZlBsbCPO8Y/qy9S3NOsIebsZt7BpFWYgOrUh9YxNWIGS0gaLoCzsmNYAiYrX/pogfImFyPB3Cui3UTLMvSHGl9rv7gr1z+1G6SfEjkUgOf4rri1lesRwhBBkJGZxcdDJOizNu3XBLhR4Po9Y5hPqEgUQUMwMbPiVgdLIjbQYAF0zsE7e95PCk8xPc0h7Ib3DKYEpaS9jVvItFpYs4d/C5mONkgFFs2n/FGmgiYhdEDGaM3bnIWRIwKGiadVVAOKBbgxnMVmjXrIuD4bMuhB5HIeqiES+GleSHQwrrkh8NtbW1PPfcc1RUVHDppZce6uFIDiK+UIdwo6oCg0EhrIbZ4y5ht+9LGvyLiBg0M/PESIQkawoDkwfSP7k/aXbNl++j4mqc0Vy1nTQWUaKmXI6gJqwHjA4MBhien0ztUaPY+e0qvW5oLzN1RYGwquXATY6U6SbyflMiNrMRm9nIrKM6BMKAv10joobI8mwmy7NJ3+exZh7gVTrySE6wcPzQ/c9xOzpzNEbFyGcVn7Gubh0REeHYvGP1yUROkp2a9Bx81duxh5pxW7sK6xEhMKCwrqxZ175HU+CEDZrG3xTxd3lJmlU/3tqdGNWgXmYPN+PxhwmE9y82x4EqzLbXuGn2hRhflHpgHUgkkgMmokb4ovILNtZrC3YDkgcwo8+MuAJZlLrSbQA0JPRnV+pU/buqmPBYMggZEzhnXD6ZLmly/GMiVrOuCeuKojAtfxo13hqaA82srFzJ1PypXdoa283graEmKpogbEjoPhig2dGuWfdrZvCRYHuQWxMmoxHFGNWsf/f4UMJbj1nV4us4g3UY1HDM4rbkh0cK65IfDVlZWaSnp/Of//znO/m9Sw4/ounYVBFhya5idjbvpCVSgSpCNIVacaCSIEzk4mJ0wZmMzesTs8rbFgyzuaKRCSEtr/newvqFE/vQ/HU6DbvKdVP3oNGhv2gzi4ZTsWU1/pCKUAwkOJPwtXW89IwGBVUIvJZ0kv1Rs2iFVltOl7y1AFZbgrZKrobIbd0Qs89jySQnSU7IvisjMkZgMBhYXracb+u/RRUqU/On6v+LsFV7RthCzYAW7C/RX0mTvQjVYCKiClQRYdmmCiaI9hQ4xqiw3kmzTmwQPJPq56s1K7EArdZsEgPVJISaaPIG91vzcKCaivc2aNGk85Lt5CbHD0gkkUgOPv6wn49LP6bcrblTTcyZyNjMsfu8l7dvKyYRaLXmdBQqCk0JRfpmQTfuP5LDl87x4yyd5gLRTCbv7XqPjfUbKUosok9irNWE0app1hWh4gtBuDsTeACLFkTOHG7XrEdCqEKLvWMwKLrP+sEwgw81lnScn1BxBOuIiCEYpCXYIUMK65IfDfF8UyU/flShUt9UjKh+m50WG+vWd5gRjko2MdFbRz81n2SsKBhwmVO7TIyCYRVnsB4FQdhgI2h0cPqoHKwmIxFVkJloIy03g4ZdndqYnB3+fOmDqE4aTdjXSpOtDwXpLhr2NHeMUYWsRBveto5FAI8lg4jB2u0krX9eDiVle3S/9YjBQqs1m6DJyemjvp8UPT81jko7CqNiZMmeJRQ3FBMREaYXTMegGAhaUlDQtN5Z7k0UNq/EICJUuUZQmjIJIbRFIrOqadVVxagHGNQ166ofY7vpajTooCXiw9CsuTRUJo5maHg5FU1t1DU1orQHFuwtByKrd34OegLSnUIi+aFo8jfx/u73aQm0YDaYOb7wePol9dtnu0AwoEeB7xzx/aJJhTy3svR7G6/k+6fzIzyqWY/SJ7EPI9JH8G39t3y651N+MeQX2DulZjPZYl0mwgYb9Z4gcTHZMSgKJjWg+ayHAwghiCgWTAYFpd18/mCYwe/cFrUEVABBYqAaVc6/DylSWJdIJD84qlCp8laxo2kHu1p2sXPPMiLhZpLCgGM4NvtwUkwFnOheTVuoc3R4QTDY9WUWigiSfZrGu8WWC4rCgExXTJ2oyVmUQCfNOgYDe5In0pagadPPKkyhMDWBt9dpflv+cIRTRmSz1RUmJ/w5Fc0+Wm05uq91PBJcHTlW3dYsirPO0Lcd+/DLlvSeIalDUFD4dM+nbGncgipUZvaZSdiWghlI9pWR4usI+Jbq201p8jFEhCCiCkwRzdwvZLCDonDZlL5sKTHiajXT4uuI5t9mTsMa9uAM1OA0BPEKM4OGjsRe8i00tSE8dSgpHf+xqDtHTxgOQFp/a12F/j3STRpDiURycCltLWVR6SKCkSAui4uT+55Mur2ru1U8gm2tGEQEVTHiN3W8F9KdVhLtZlploMkfLZ2f4fHirRyTewzlnnKa/E0sK1vG7KLZ+gK/wepsF4c1AkZH97prkxVFAaMaIBJRIRLs0KwrSicz+O8urFdWVpIAtNpySPRXYg81IWX1Q4ucMUokkh+MiBphTc0atjZuxRvSNM7C34LV7yZTJJIrnNh9Vr5NGas1aG3t0ocabGNXnZU1JY3MGpZNSFV5YdUeRrYLZE32bgL0mO0YFPSgLEGjIyZSbud3kctmxmUzM21wBlur3QzNTsRuMTJxaF+qt2ur5y3WPI7JjB9MCKCz61k0urjk+2Fw6mCM/5+9P4+S67zOe+Hfe4aae57RQKMxEiABEBwAUpxFSqQmU5I12PGgKJ+/xHEsJ75ed91Ey8laTnJvtG7+uHauc69vcr9Y8iRbgyVRpiiK8zwCJAACIMZuNHqeu7q7pjO93x9nqHOqqhuNgSQI1rMWF7urTp06ha7zvvvZ+9nPFipPnn+SU/OncKSDEtuEDsEYttHGvfQsveOazpnz/PydcVRFBL15ludpkIgp3LZ9PYPHFLLuU16Q7SZ/MsY0RcuhpLdwa28L1nQ7MAr5eZTWTcE1WY4kdkGyfvGf9dxMOYFQJ+t11PHeQkrJ4enDvDr2KhJJT7qHh/ofYj4n+KvDQ+zpbeLGDc3B8YeGF3j2xBSNSZ27t7WzvasBu+B6nJhqqkpO8/CN6/jFsQnuuIQ56nVcXUjH1arHdEXnE32f4O9P/z0D2QFOzJ1gZ5s7fhgtjlDVoM88F2vnk9ev4OGixV1SjsR2zHJlXY2hqSJwg+cKVNZ1b+RsTm+lsTiG5pTqlfUPGB/o6LZvfetb7Nu3j4aGBjo7O/nCF77AyZMng+dN0+Rf/+t/ze7du0mn06xbt46vfe1rjI2NrXre73znOwghqv4rFqtH+FwqHMe58EF11HEN42LvASklzw8/z2OnXuHo+BRLBcm65BY2Tqe43+5nfWI/bSJD2soGo9FyBbeKPp/cGJxnanaeRw6NMbZQ5Kl3J3n86AQxy3d5FywkNtSWF4fmlzpCpaQ1RMhSLeJ0c18L/2h/H8mYtwkLweKOrzDQeg/Z5Hq01dhWxw4Axht2k02sX9O/UR2Xjq0tW3lo40MoQuHMwhkOLh4krzUghcLptvsZbt4fzDFuKQ4zMl9gZHqepOl6GPjmcrqigBpDauX+QVNNYilusifl+SJYsQYakxpa0jUjdIxcpCqyluDmQoV103Y4cG6OuVxtaWQ9fqqjjvcOlmPxzPAzvDL2ChLJztadPLzlYVJ6ijcGZ5lZKvHMianIa571fl8smLx02jU8tUtLABhqKhhT6aOjIc5v3L6RzR0rJ37ruHqR0NWgup6O1a5/dqQ62N+9H4CXRl8iW3L3HITA1spxyfoNG1f2LVBjCMWlbNIsgO0SaFvoqMoVrKzbFq26q/Qo6G4rmSoN6nnhDxYfaGX9+eef53d/93fZt28flmXxh3/4hzz44IMcP36cdDpNPp/nrbfe4t/9u3/HjTfeyPz8PL//+7/Pww8/zIEDB1Y9d2NjY4T4AyQSl2/qFIvFUBSFsbExOjo6iMVi9XEGdXykIKUrRZ+enkZRFGKx2IVfBByYPMCBiaOcm83Tl9iH5vQxuKRw8+zLKAgmGm5AkTbNxWF2TP+c8YY9CLsEwJm2+7hh8qekzHnOT86AR36Lpk3JdGgqutLg5Vg7lprk+p7G6gvQy71iM6mtOIoeuXfdfrMLO6mK5o1MZdzPvOoM296bebtHoaRFr6VuIvTeYXPzZj4lPsXj5x5nvDDEI41dbI3fiq27f4OF5Aaai8M0FsfIlCZpLQzhaypMNcmDN3QF0nUr3gyUzQgfunkrJ58/EDBkO9aIEIJY0v17qoUZ2s78kDZlK7OpLVhriG5qeS/oannO8atnZzk4NM9LZ2b4/U9sD2bx+rDrbL2OOt4T5M08j597nIncBALBHb13cEPr7iBBpi6NccPk8wy23AlE56q35c7Qu3iYs52fBDYhPbJuqSk+vbuH7785TB3XBlRF8Nv3bkYIVm172tu5l6HFIcZz4zx9/mm+sPULKEJBiHI1Xm1YZTKKEKDGgSKOUYSYge3J4GOqEpB1x7l0N3gpJWZhCSHcgsa+nZuYf+XFemX9KsAHStYff/zxyO/f/va36ezs5ODBg9xzzz00NTXx5JNPRo750z/9U/bv38/58+fp61t5HqUQgu7u6nE9lwtFUdi0aRPj4+MXrPDXUce1jFQqRV9fH4pyYYHOu7Pv8ubEmziOZEP8Ztp0Vy6cNOaI2XkcobEU72IutYnmojsarX/+FQAcoWGLWNmh2y4rZIQQJHSFxpJ7L2YTvQB8/Loa49FClfXxht1VTzckdBbyF+4dTOjlzXW1yrqmKpR0tz9xZ08Drek4Jcvm1o31cVvvJfqb+vnMps9wYuJ7zDvTnDIPsUm7E0WoFL3ESdqcQfd61X2YajLyt7UTrYBr/mQqSfSmrkjl3Iq7f9tEMkNMFbTmBrBzsI0hZvu2YNm1lSdhwh3ud8zmTf785UG2dWX43B5XATAy716jHyd9941y7z3UZfB11PFeYKYww2MDj7FsLhNTYzy48UE2NGzgL145h2E7/NZdm+leOopdmqR7+TiWfTuaP7ZLWmybfQaArsV3gFtxim7LVzzdQG9zkk/t6qY1vbYkdx1XP8L7xkpQhMIDGx/g+ye/z0Rugrcm3+LW7luJyQK+biqRvEAiX3O/M9IsIa0ijuMZzKkKjj828DJk8E+9O8XQwEluKFqYWgOpVAMLQqA6ZlWiuI73F1dVz3o261YxWltXDmaz2SxCCJqbm1c91/LyMhs3bsS2bfbu3ct//I//kZtuuumKXGcsFqOvrw/LsrCvwEzDDxteH5hlernEp3f1rF5ZrOOahaqqaJq2JlXJ8OIwz408B8ANbXs5s1weXdNUcivii/FupNCYS/azXj1AzC735RpqmpiuYnpjtXoXD4EQzKa2oCoCBWgsjnvnWUdjUo+MUAmQ7mAqs5OCkqEQq15jHtjRyU8Pj61qGgcQ18vnXu37H35OVRT2b6qT9PcLfY197Gq6jxdzT5K1xjlbeJEtybswvYRPJVEHMJVEhDzbifL3wFRTqA0drgzRq1zYMZf4i1iKTEKPStWlsyKRtqVEt3JeL335Pd4Zdfe/05PLwWPhaoaUkumlUuRc9WpHHXVcWQwsDPDU+aewHIumeBOf2fQZWhItGJbDvJfMzRZMUqVploC0MUPetGn0yHp77mz5ZI5F3rBwSu49Lb1pETtrKb/quObRGGvk7t67efr807w5+SYbGjagSSsg67UM6iLwWrOODE2xd4eDxK2s66rA8CrrXIYM/uholjZveo2hponHE9iORBUGbwzO8eANV74AuhoMy+G7rw8xnzd58IYubljXdOEXXaO4asi6lJI/+IM/4K677mLXrl01jykWi/ybf/Nv+LVf+zUaG1de7Hbs2MF3vvMddu/ezeLiIv/lv/wX7rzzTg4fPsy2bduqji+VSpRK5SBosYapVSWEEOi6jq7ra/h01xbeGnVv5vFlq8pxu446wpgpzPD4uceRUrKtZRvXZW5l6egTFLUmluOddC6fANyKeE9TgvEsvL3uV9k28wythXOA2+f32d09qMkeRo4NkDLn2DbzDIu9PczlFERxgT57GSkUluJdfLx/BbItBEPt92DatQlOSzrGP76j/4KfKa6FK+srqwrCVfd6Suv9R0diHVuS9zBQeJEla5KzhRfYqUcTtiU1Q9x2A2kBqBGyXk6uGGoSXYtRirWgWG4fquNV1tGSpHSVudB5E9biihJ16TjcMvY3ALyh/BNu2dhCJq5FkkDBsaGfa8nqXzo9w9aODC1rrNKdm8kxtlDgY1va6u1bdVyzsB3Jq2dn2diWWnPbkZSSA5MHeHPiTQDWN6znwY0PkvAIUjj5ZpXyLM1PA66HxVuDM9y3001Cd+ZOBMfF7Dyj8wUaPLLuxOt96R91bG/ZzrnFc5xdOMtT55/ilhseIv/2Y5xvvo2NidUpmYG7zmvSoOhNELBFDF1RsHyyLi+vZ903lzPUFPGU1+IlTY6NLrzvZH1geon45NukY508cYyPNFn/QA3mwvjGN77BkSNH+Nu//duaz5umya/+6q/iOA7/9//9f696rttvv53f+I3f4MYbb+Tuu+/m+9//Ptu3b+dP//RPax7/rW99i6ampuC/DRs2XPbn+SigXtSpYzUsGUv8bOBnmI7Jusw6Pr7h4yjLY2yaf5md04+xdfYZUuY8ppJkJrWVvX3NfOP+rQhFJx8rO+P6ru2ZTDgxJGnND2JY4X71Tta3N7FnffN7+rlSsRBZV1cmPGEyVK+Avv+4YV0TDVonW5L3oAqdJWua1r5BNK38tyjqzUxmduIIldlkf8TwrbKyriiCglpOEi9JjwToyapexbQxs+L6KG0jctxjR1xVSCw0o9eXHIZnqhtWbVn93781UvuNauDHb4/y+uAcJyaW1vyaOur4sOHIyAJvnpvjhwfXdm+YjsmTQ08GRH13+24+t/lzJLQEUkpGFwqcnS4rXkZHyrPRhXQ4OTCIlJKSaZI2ZoLn4vYSi8VyZR09Oj60jo8ehBDcu/5e0nqabCnL8aTF2+t+lbGGPTSnVi/+OarnleMYmIbXDqjFUK6gwVzMKlfWE/FyoktzVpj//h4itXCK/vlX2D35k/f9va82XBWV9d/7vd/jpz/9KS+88ALr11e7JpumyVe/+lUGBwd55plnVq2q14KiKOzbt4/Tp0/XfP6b3/wmf/AHfxD8vri4WCfsddRxGSjZJX428DNyZo7WRCuf3vRpNEVDFrLBMe7sa8GJjocwtTRSuiZvjpQU9ObgOENNoasKLc3RinlbfoDJzE56lt4BYCGxgfgq5PlKQVcV/tH+vuDntaC4AtGq473D1s4Mn9+7jrb0Jk7O9PDi+C/IWnO8pExxC+3oqBhqksGWuzjXcgdSqBHSLZPNobO5j+cdDT/cNoQXWHkjAcNIm7MryuDDwVTSyjK64Eryw60bRcsmFdMihD9M1jfNvUjamOVEx0MsFcvGiWvFWrwZ6qjj/YSfmLoSio+L+X4vlZZ4fOhxpvPTCCG4Z/093NB2Q/D8wEyOnx6K+hMtzYwQplVpY5aCafPEG0fpkuXWyJiVY7loguHJ4GP1ynodkNAS3N93P/9w9h8YL55m06a72dm+ntQKbvI+bMWrrDsGk3NuLCV0V/mhaOWedSnlJd1HrflBepaPAl7cpWuu27w0UeX7T9b13Pj7/p5XKz7QyrqUkm984xv86Ec/4plnnmHTpk1Vx/hE/fTp0zz11FO0tV38LEopJYcOHaKnp6fm8/F4nMbGxsh/dVwYdRVlHbVgORaPDz7OXHGOtJ7ms5s/S1yNc3Q0yyvHBiPHLiTWk4u7ZnDh6nNei1Y1NVUE5io+GksTdC8fI2kuYCsxJhquv6DhVlgC/6v7Lz0h192UoLtp7dMl7Pqoxw8EmzsyNKV09vdt5mu7v0xcjTMrTF4VYxjYoKdoTMWQniNvWAavafHg55zuSuJnU5sB14wu+C7pyUivO0DKWI2sl4P5pDlX8xj/teF7wvAM61THoGv5XTLGFNtnn74kiZOsKz3quIpgO5K/em2Inx5+f017D44M8QeP/zfenRohoSV4eMvDEaI+PJevIuoA2SnXzd3x1o2MMc1/e36AxRmXXORi7YArHz4yOM7o1CwASqxeWa/DxYaGDezp2IMQgknrIF1NFw6ofbKuOgaG4bbuxuIeWVfc76KQ9qWNWbNNts88iZDuPmOoaYQQrO9oBq5MZf3lMzP84MDw2o1RrSs3bvvDjg+UrP/u7/4uf/3Xf813v/tdGhoamJiYYGJigkLBrTRYlsWXv/xlDhw4wN/8zd9g23ZwjGGUvzhf+9rX+OY3vxn8/u///b/nF7/4BQMDAxw6dIjf+q3f4tChQ/zzf/7P3/fP+EHi+Ngi745fuP/+YlAP8upYDVJKnht+jtHlUXRF57ObP0tDzJWvP3l8krgVld9OZXaEXuv+/3N7epCplkjPt6aIwFwFYDnWAUg2zr8GwFjDbmwlvqZxWT56mi6+InmxuPe6DhK6yp1b29/z96pjdXSkOvj81s+jigQLosSrYgwzpvOVW8tqrjA51lTB4Z6vcLL9wSChtJDs40THQ7zT9cXy91OrJutJK7ti64N0ylW/lDlf8739r3H4FHnDJflhmW1jcYyG0kTw+7mZHAfOzV1wnb7QbZI3LF45M0O2XoGv433A2EKB2WWDgenc+/aek7lJ/vvb36NoFRiZUfnSti/Rm3Gnifjf/6oWE+++Shlukm0mtRWAlsIQSEnadEn5cqwzMETNGNNYtg0IqJP1OkK4ved2WhOtFKwCzw0/d8F1O0zWLY+sdzW7ag1FdSvrirQvaUqIY0RNVw3V/a52t7p94jEun6y/MTjHyHy0pWRVWO9/Nf9qxQcqg/+zP/szAO67777I49/+9rf5+te/zsjICD/96U8B2Lt3b+SYZ599Nnjd+fPnI+OjFhYW+Gf/7J8xMTFBU1MTN910Ey+88AL79+9/zz7L1YaiafOLY24Qt6UjU9sd+xIQXUvqpfUPE0qWzbmZPP3tqYhJ2pXE6xOvc2r+FEIIPtX/KdqTLkk1vapgmKwvxzqZT5bHL7Z5s8u3dTWwtXM7Qz9KML5QYCne5crN27Yy0ngzS/EuUuY8GWMagUQKhcnM9cDVN8rq5r4WbtrQXDfzukrQnmznS937+fHQ02RFiQPmcT6l/FLwfHgET0xVKOgtFPRo+8VCciMAt2xodh9QFIQeB8oBiOqYK1fWQ6N1/KAfomurH7SFz7BUdIlzxpiOnC/mufc6juTHb7v+DRtaU3Q1rqz8WCmRkC2YTC8VOTScZXguz4mJJb5+Rz9np5fpakrQmNCDaxnPFtnakVl1tnAddVwshufyxDWFzlW+v5eLidwEjw48ihAWGbWNLal7afIMI2eWS3z39fOR+zdpzrMhe4Cm4ggltSFIso013khbYZCYnaOxNE7acMl6LtZG2nDHQ2ZKU4A7bWKucHn9xHVcW9AUjU9s/AQ/PPVDzi2e4925d7m+7foVj7eUGALQZAnHcsm64inAFK9nXZH2JXnk+JV6gJLWEPgGqTH3PhRWyXWGvwLrvbWCyW8lpFW+pt7m9764cjXjAyXrF8oi9ff3r6mS+9xzz0V+/+M//mP++I//+HIu7UMPIzTj90qaW9WNsj68ePL4JKcnl9ne1cBn99RuCbkcHJs5xluTbwHw8Q0fZ0NjWWb+0hm3Ghi3XbL+budnyCbciub9OzpJx7VIpVsIweh1X+fkwBDL8S7X1E0IRppvBaCgt7Bxwa2qzyX7sbwqxoUq663pGHM546Ik7JeLOlG/utCe7uRjspdXGWOGEo+cfYQv3vIQth2PzD4O+xHcv6Oz6jz7+8tu8SKWAmaD31VprFi9Dves604hcN8Nk4Nar10uua/LGFORx1Xp9iguFMpVcDtkUFcw7apeyJWc6v/8pWibSrZgcnZ6mUePjKMqgn9+7xZimsJfvjqEYTl8ZncP13XXJ4LUceXgm8L9T5/cfkXOV9m/O748zs8Gf4ZhG7TEu2iL3Y4qyh3of/Vq2DzOZuP8a3QtH/dGLULKcRNsjtAoak2oXTtg/AgduVOB6iWnt1HSMmSMKbpy7wJQiLVQqnuX1FGB9mQ7t/fczitjr/Dy6Mv0ZnqDxFElEokUJfxksFt1VnV3zxKqhiBaWb+Y3nXbI8aGmuLtnl8N+lzVmBtbqY6BaTuoyuUXeiRr5BF2max/1LnHVeMGX8eVhQztCVfyS75SkFfH1Q9/fvOpySvvBH0ue44XRl4AYF/3Pna07og8f2p0js7lEyTNBQCKajnAX9ecZGtntfGOmmomm3QJfeWGY2gZj+wLJjLlHkPbXj0Y+sLeXm7Z2MIv3bhuzZ+tjmsLWiJNAzHukOuIaY0slBZ4efJxuirio7DTf3tDnEqEK8pCd11zLcU9TkgHZwVX3nBlHVy1iePIChm8+3NDYZTtM0+iWzkM06a5cJ7WvEuofZmtIk1MWwbqlTAePTLOf3t+gBMTiziOREgLpHNR7UyTi27AZDuS//7CWYqmHZjdTSzWewrreG9wWS130kH1yEzYp2RseYxHBx7FsA16M73safp4hKhXonP5BN3LxxBI5pL9gTQYoKA309uS4vaP3QtAR+4UmlPEUuLkY62UNHeP021XXjyT2lJP3NZREzd23Mi6zDpMx+SpoadwZO045oa+DgBUp1xZD8i6oiGEm2CypeTQ8AL/vxcHmV0u1TxXJaySu5bbSixiSCW0uDvWVBoX1Wa4GtZ8a4dk8FeZaPJ9R52sX6Owa/Q/XiqGZnP86K0RsnkzcpPV952rEyXL5idvj3JsLHvhg68AJnOTPDH0BBLJjtYd3NJ5C08en+Sl0+Xe2vWF42yee8H7TWBoZXJea740wI3rm2lviHPHltqmkqfaH+Bwz5dZSpRVAhdSVzWldO7Z3kEmflUMwqjjA4CWcL97GWJsjH+chlgD2VKWn5z5CYtG2eMjHFg3XmD+rfCqD36ADuCYKwRJVpSsK9KmaEVNgXyyvm38p7TmB9m08ArTR59hx/TjAEgEi3H3e686FgXTrqjMuz+fmXITdK+encW2DG4d/Wt2T/6ElfwOVaeE4kT71NPxciXFtGWk3zATf2/aaer4aKFWLHE5LU3d537CvpHvELOWgyTWyNIIjw48iumYrG9Yzw2N9zGRLd+LUsrye0oHxbGCHvTxht2c6niQ8YZdwfF5vZU7trYhmvtYincFj09mdiKFxlT6usg1zSY3XTCZXMdHE0IIHuh7gJgaYzI/ycHJgzWPiydSNCd11+zNSwb7MngUDSEEirRwHMmzJ6ZYLlk8d3K65rkqYXn7lSM02jIx7rvOTQwIPYkQrsHc+95mGKqsyxUSGB8V1Mn6NYpsDUnkpeJHb40yNJvnF8cnPvJSlA8D3hpaYHAmxxPHJms+r13BHtNsKctjg49hORYbGjZw7/p7WShYHB3N8ua5uWBedFshLK+Vgfs2QHwFP4VkTOU3b9/IbZvLZP2Gde6khlv7W7CVOAW9hS/dvD54/LZNrTXPVUcdPtR4uTrmiBa+sPULNMYaWTQWeeTMI2RLbpKrzZPEx3WFhsTq82/xKuslNYMjXGLvWLXJemVlXUgbKaOVRP9H/05tzQ+yPusGcHOpTZxp+3iksm5YTmSdr1ymFwsW9uI4qmOQNmZwarB12zLZO/59bhn9a7qX3glOIs0iO6Z+TlvuDBCtVNZRx3uFtVTxlksWb5+fp2TZkcdjS65be1v+LJYtGV4ajuxTn970af7hsLs/+hV4y5GBYmTz3IvsG/0OzQX3PHlvGsRSvDt4j4Le4vbvCsFI0y0ASKEESi+9oZ1zLXcA7hQJW02wr74/1bECGmIN3LP+HgAOTB5gIjdRfZAaR1GEp9Zw7w/Vn5KjaCgCFJxLivl9sh5PJPjax/q5qc/zalFjKEKgOqXL4hKXpJQJVdalY69y4LWPennpGsVPPKMhICBMl4vlohWp/lxNzvC2I3H3zWu73L8Wg4+iWb2o5Y3QbOfYlamGFawCjw48SsEq0J5s56H+h1AVFTtERkzHIa6oCMcOupQKejNClAlFbI2zygE+sbOLff2tCAEHzrkmP+m4GjzenLoAqarjIw814ZJ1S4kjhUpDrIEvbP0Cj5x9JKiwf37L52lONPP/vXtTpHd9JfiVdVNNYgsdRVqwRrLuGwI50q1s2yK2YlJ0IbGeU+2fBCDljX1TpYkTrgpSu/XJoXzfyxouu8byQiDZ7Z9/lZLawHyqn+TEAZqLwzQXh5lNb+XZE+We+Y+6NLGO9w6G7UQMH2vh7w+OMJczmFws8qldZYWV//1XpMO5xSFem3waW9r0Nfbxqf5PoSkaQlpsmnuZztxJBlrvxrQ3Y1oS3crRmTsJlM0bfcWMP5IN3N5efy/Oxns503YflpLA1Nz15R/f0c9/fcZiKdZFUXd7bK7vqY8FrmNlbG/ZztDiEKfnT/P0+af56vavoquhmEZLoAjX68SHL4NH0RCIQAYfvERdY8+6ZzAn1OiIXLTEFamsR7nDGl4gJYRVXvKjTdbrlfVrFOsWD7Ft5imQzhXtM1+tevNBwbId/sdLA3z/wPAHfSnvKd46P8+fPnOa87P5i36tP/YJuCIuu/4s9WwpS0bP8NnNnyXmLfJh8xDLlhw6P4e17MoJZ1JbOdN6X6S6fzEJFkURtKRjiNAkgkxCKz9+jSdr6rh8aA2dSKGQi3UEj2ViGb6w9Qu0JFrImTkeOfsIc8U5GhL6BQkDgNO+k+VYJzPpbdiKG1ytWFm3ozJzRVpIQORn2Dfyl2ydezYU2ES/z341HcDxem0Vx8Jy5KqtT0KAFXrMrjG/1ixGvSw2LrzuVv1D16vbeRTHJGa5UvhaffJ11HGxqBVL/I8XB7Eu8P2ay7lJp7MVI9/8OGXOmeGp87/Aljb9jf0BUQfoW3w7IOXNhWFeODVNybbpzJ2qep+i5pJsKVTON9/GXLKf2dQmVH+/EYKZ9HYWvOkmX7+jH11V+NLN68nFO7CVGO0N8fr+VMcFcXfv3WT0DNlSlpfHXo4+qcUjY0IdoaH6k7AUFSG85G/otlmre7tvMCe0SrIe9yrrl0fWI9xhLQZzVjE6IWUFD5iPCupk/RpF7+Ih2vIDpI1ZTNvhBweGIz3Ea8XYQjmDJ4lW06+WqsrEYpFcyWZs4cNjdlQ0bb77+nkODs1f+GAPz5+cRkp44ngNedQF4EwcY8/4D0mYC5ettJBS8vT5pxnPjRNTY3x282dJ62VpcXhRNm2HV46edTcQoXKm7T5y8U4e2NlFe0OcPetru55eCI1Jjd6WJFs6M+/ZGLo6rk3oqWYOrvt1TnQ8GHk8raf5/JbP05Zocwn7mUeYLcyucJYoROM6jnZ/gaV4dzAL940zk/zw4EhVgOPL+XRVoCrCde+1JZNHngYk7bkz7j0qZVUvry3KgZTtkQ5VWti2jNzXtSrrYRmhU6pO+Dkll/Dk9VZMJUnCyrJOTmKHyHpDaZI9E3/PzWPfJW4tXlbwZtoO339zmD977ix/8/oQudJHOxj7qGJ4Lh84wFdiNre2OcuR+8RTqUyQ45h1FMO22Ny0mYf6HwqIOkCrU7634/Yy744vYTuS9vzpyLmlUDDUVPD7WOONnOp4ECk0NKV2CN3itdD0tZVft1K7Vx11hJHQEtzfdz8CwfHZ4wxmQy2EWiJibGoLvZwA8mXw0opW1tdI1h1PbbViZV0al1X4q7UnHRlZ4PGj47VjUrPA6Hx5n5IrGa18RFBfPa5F2FbQh6U7eU5NLjMyX+DNc3MXeGE1Hj14ht7swaCScny8bMBU71+/dLw1NM/kYpEXTq3N/GM8W7jwQasgdvIfSJlzbJl7/rIdPV8Ze4WzC2dRhMKn+j9FWzJqABeeoTk0mydpLQBQ1JpAKPzSjevY2dPIb96+kQd2dnEpEELw1Vs38HDd1b2Oi4SqCCw1iRRaxDwNIKWneHjrw7Qn2ylYBR45+wgzhQsnOcPVDulV1jVpMDyX59xstOonPWmf8P5TpMXoQoG4Va5sSwk41eTVCZENR+ik4yqKNLFltLJeq0UpXJmQZnVi0/Eq60WtMZD7pkQRUSyv+Q2lCRKW+3tzYfiy1pLjY4uMLhQomjZTi6WLSlzWce3AJ+qqY9CbfZtMqZyMviR/Fdtk2F7kgJhASofe9EY+ufGTqIrK4eEFTnvTUNKinAjw7z3byAcTS3wYahrFG1d1++boXudz9Zv6moPHNnekqYU6Wa9jrVjfsJ4bO24E4Lnh58ibHmlVNURIFu8oWrlyHhjM2RHyq66QUKqE3xolKyvrQc+6gX0ZfiVOjULf0+9O8e74EqemakwoMvMRw2BRl8HXcc3BLGejdLtIqUYP81rRN/EUG7IH2T7zFMtFi9cHyoT/Ysl60bSZXlrbGImLQVhadqX6899rXGyQ+3dvlCX+ykVI6fyg3fDkhAlz8bL+jY5MH+Hw9GEA7u+7n/UN66uOCX+2Z05MkfKCn4LuGpbU+8rruFrw5Vs2VD2W1JI8vOVhOlOdFK0ij5x5hKm826fd3+5WyrZ1RUcNhuMhLea2mageKa+sPgvbX4/d+1jxDObCZN2REsxCFem2Q2OmbKG5QZS0sB1n1TntAnBC5N8oVSf/bMNNKphqClN1P4NiFlDM8nU1FcdC53QuK3hbKtYr6XW4UJ0SN0w+wobsm+yY/gWq440LXGOMEW6LOjt/ilflOI6QdCut7O/8OKqiMrNc4pkTUzx6ZBwA3SknrDSnhGoXEctuYi48og0kn9ndze/ct4WPbWnjn96zOXgm5hHwe7d38Dv3beF37tuyYgL5YrxZ6qhjf89+2hJtFKwCzw4/W94L9HIboy10gnxWeHRbhKyv8Q19BZVSQdY9eb24zJbaWtNOfNRUVRkV6q8ayeuPEuqrx7UIs0DKMxGL2fnLcu9tKrpGdRljquoGW+m+Xcl47i9eOcdfvzbE5BWezRumrh+WOfAXQ7grcTEvtR2JlDJIkuhO8ZKrYQMLA7w0+hIAt/fczvaW7TWPsyyb7qV3aPd6/9KGGwAV9Gb29bfSnqmeWV1HHe8nfv22Pr5wUy+t6VjN5xNagl/a8kt0p7sp2SV+evanTOQm+PSuHh66oZtPXh9VhIT7AtWY+/1WpBv8VC5JvsGccI2kUaSFwImYBjm2VdWzBwT98OD2KwpAcUxsh0ifoiNl1Tos7XLStpDPVRtRejL4ZKaBjd1uZV2YeUSpPKotZZalw6rXK3+pCJteAhf0BriaDE3ruLJoz50lZbrKCs0p0b10FGDFEYOV8PfE0/OneWLoSWwpWS8b2EU/vlH8Qj40s9l2UK0oGUhYS4icm5QLG8kpjk1CV4PvZyau8Sv7NvClm9cHLVhCiOCYlfrSm1O115o66qgFTdF4YOMDqEJlaHGI47PHAXeUmg/HS9gCrgwet7IeXpfX6pMQVNYrZfCK+x6KtLEvQ4oeqaw70f2pljWFY0QVaXWDuTquPZj5IMjTnAKnJmtITK4AasVOB4fm+b+ePVNzxrdvcnZuJlf13JXCh0WafznT0y6G6P+3Fwb4uzeHQ2RCXlJCYyI3wS/OPcHh4QVKuV5u6rxp5YPnB+mff5Wts8+RNOdp9ca2zSc2sL2iIllHHR8EOhsTbGqvLVf1EVfjfG7z5+hJ92DYBo8OPMp8aYrr1zVW+SSE70nNm3vrtyJVmumE3eCFF1xRiErApW24ZL3impxwZV3RURSBKk0sJ1r1cJzo+iwEEOpZ15xSFVl3fFKuZ9jR546osgtZ7MoKhwfdyV8Wga585Wpr4nMnp/j2y+dqTrqo48OPmO0ZFiouEelZOgZcXPL95NxJnhp6CunY9NHIXtmJLk0sR5I3LP7h8HhwrGkWkV7w7xPzhL0YkPW83spi3HWXn85sJ65HQ+V1zclIP/pq+NyeHnZ0N3Brf8uaP0sddQC0J9u5rec2AF4ee5mF4gJCK5N1W9HLxRtVC5K/YeNP/+lsweR7b7o+Sd978zwnJyp4gb2CG7yiui1b0q5JqteKsKLTdqLTS6waSYD5xeXI7/We9TquPZiFgLTG7MvrdV4tgKpFjF84NY1pS544NhmpnISDurWOklgrwtz1w3I/h01CpJQcG8syuMYkxoW4+qHhheBnw3KYyBYpWeV/mIuVwS8UF3hs8DFmcwV0pwuZu27lbK2UiKGyg+nW2WcR0mEp3k0u3olWlwLW8SFCTI3xuc2fozfTGxD20eXRquPCZD2ddoN4XwZftUza0cq6kDYiNxk9xiqCWayWwSthGbzuVdYtHIeq0W1VSqgQWVcdg+mlEs+fmmZg2g2KpFfJkHqSeCJNXFNIG7MUDBtb6EgRvXd1u1Alt5/LGbx5bi6YV70aKleQ1Zalt88vkC2YEc+UOj788Oejx2w3ITSdcdVamlNEcawLtlkkzXk2z73AfOFdnjn/DBLJzqbN3CQ7UBBoTgnLdjg7NsOe8R+wYeFNAN48NeKaMgqNvNeelTAXOTVwFoBcrI1T7Z/kTNvHGWm8ZU0TIVbCtq4GPr27Z00jIOuooxI3dtzI+ob1WI7FU+efwtFDJqNCL+89aizoWTfNsimo//xLp2cYW3B9ksYWijz2znjkfbA95Yla0aaouNN2FOyapHqtcCRodpG4mcWWMqrKqnGbzy1VxMMf8Tnr9dXjWoSZDwI13Y5WRS6qEiLlqn1WF6piPxOax2tdguHFpWCla1ouWZybyV01UspwcD+zbPDEsUl+8vbomkYhzS4bQZBTifnlIk2FERQnOh7KCs9YttfmsAuQN/M8OvAoRatIg95Of/J2hFjl77c8yfx42b3Ul8CPN+wCrnyipo463mvoqs5nNn+GDQ0bMB2Tnw38jGOzx8qmP0STms0NrnpEXUkGLz2yjm8wZ0MxSkKlZYBVqIphwj3rjqIHPeuW4+BIScqYQbdySAm5UnmNEELghCvrssRr77zLzOHHefStc0gpyS+71yDiGdDdGdIJK4vlSAwtjaNHlQgxO1+13v7FK+d46fQMrw9e2EW/MuG3lkDwctqH6rj6cOCcqyjx45SC1oz00jiaU7xgZf3G8R9QXH6DiZmfIJFc33Y993Xeip8KUqWJaUuasidImfP0Lr4N0uHd85MULQdTTVDyxrIlrCyaN/0hr7diqQlm0ttwFI3kZZD1Ouq4HAghuH/D/cTUGFP5KY5bZVPiiMGcGgv2IdOojvEKF1AlBSM6a8jg/X3qcophtiO5aexvuWn8e1DKBT5KQHXmFoLkgd/SW+9Zr+OagpQSu5QLHLnj9hJNxZEgYvT3Ptup7mmsghmtnPTPvcyOqcfoWXQNxi5UoB2dL1f1i4ZF3/xrdC+9c3EfaA0IX8dKm/ufvzTIj98erZrH+kEhHHMuh8w1nn53ssbR0FwYYvPs8+iWe/2PH609vs0cP8rO6cfYPfljlNDiZsmQTNdcrvXS6nPZJo8NPsaisUhjrJF97fejitXN4eZnxqr6WE01yXxyIwD6e5ioqaOO9wq6ovPpTZ+mr7EPy7F4fvh5vnPsO3z/5Pd5dexVJvJjOJ6sVglk8LUN5qTl3pe2EgdPtljpzi6tElilqjX2ut7W8jXpca+X0CUkSn6GPRM/4qbx7+FIyV++ei56ztB6oDoGm8//iHWLh9m48BqGZTM+7RKVJTsOsVSkEmioaWQVWc+tmBwdz17Yl0RxTFrzA6i2e+xaxsCpdbJ+TWFm2ZXe+pV1Q01hKa6BluYUV/9OmEWGyHJImUJ1SuxouZ5719+LkFZw32hOCdN2ImqytDmH7ikOLSURzFBvKo56I0Y1iloT/+TOfr54Uy9fvKm3XhWv4wNFJpbhvvX3AXC0NMoc7vfXEVo5llRU9z/ACu0n/jdfv0ChRASV9QqyLhQUIRDy8irrsrAQJLCVwkxkalDNseteD70/EaLuBn+RsG2b559/nvn5+piVqxE/OTTKz94aCL77cWuZnVOP0Zd9A3DJrGE5/PlLg/zkULWcM4LSUiQY614+RnNxhI0Lr6Pa1RJNgIw5S7rkVtRT8fKYIXtxgnVLR+iff3VN1eOLQWT2+wqbu7/pD8/X7r98vxFx6wwFoO+O1/YX6Fs4QGfuJLeM/Q0xa5mBFZIOcn4IgKS5wKb5l0JvWK60a8aFPQwc6fDk0JNM5aeIq3E+u/mzqJRdSJ86XjupUFx0pwUsxcsGXDOprUjhbiL1ynodH1Zoisan+j/F/u79dKQ6AJgpzPD21Nv89OxPeWf5EQYKL3HaGCePiSrdYKMywBFeZd1R40HPujSj7UrSLIJZqCrLK1qc37p7E3s3NPPFff0kYyqKtJlazBPPDrjHSAvHkxnqVo723GlXaWNHe9YV7zqaCiMUC8Xg93lLBz0VGZtVUjPoiajfhG4XkCust2u5yxsXjrN95in2jf4VCXNhRbO68PquXo7ZRx1XHTYqM2ydfYakZy5nqiks1U12aU5pVfXe4bOPc1hxq4zrlR72tn3MVWvYVnDfqI6B5TjIkEliQ2kCzXObN5UEBa0ZcJNP4I4YjekqzakY/e1p+i/gbVFHHe8HtrZsdU19VY23xRQmTlQGD+C1SVlmyExxrWpSe4U568H8dgfncmTwXmzqX9Nq00vc6/FGnHqfT9Zl8BcHVVV56KGHWFhYeA8up47LgeNIzs3kUazqPvV1i4dJGnM4UvK9N897svDViWt+aX7FACptzlbfYI7D/tmfsHvyJ6h2kc6Gsuu3LJUJormChPtSEV6LLlSc+aArM4bl8OiRMd5dZV59VRJEyogLc2fuZM1zlyybQ6fKEvSO3Ckai6MIaUfMOfTQiKjhuTyPHBolWyiTeSklL42+xLnFc6hC5TObPkNLoiUiW3pntGwgODSb46eHx1guWTiFBfccLZsoaQ2AYDKzE3BddC9pbm4ddVwl0BSNW7tv5Svbv8LXb/g6D/Q9wPaW7SS1JLY0WTBHeSN7nKeUIV61jjBSfJux3DBmuC3FI822Gg8MgSor67ZZJLu0RFXLrhqjMaHz8R2dtDZkSHjGVwMTC5w5Xx6r5khozQ9yy9jfsHX2WTqWjkdGt2lOOZjTZIl83l2PHKEhtBjoyYi0v6A3oyYaIpeiSCswJarEWuTqmuUTKEnfwhvRSksI4aCunuy7ttB25oe0584gvPJCtLJeWrGyfmT6CE+eeQKALbKZXU4rZ33PF8cMVdYNTMtBhhLUjaXxYPKCqSYp6M2Rcxf05jV5LtRRx/uNu9ffTSbWSE6YHBMz1WTd6ze3zfK67MeTK5H2vGHxyKFRRmfdPaBqzro3vx0k1mXE7s7cufI12VbEeLXShBUIkgf+PiTkR/ue1C58SDV2797NwMAAmzZtutLXU8dlIOcZuukrmMr1LB/FsG4hPvo6G6TJcPN+bEeuWK04OjBc83GAlDlXTSpDAWnSWkBTukJPlReP5cKVHd0WXoQuJKU8N5vjHjqu6PtfDA4MzXF6MipDr5Tu//zoBPfv6Cyb2lRU3VJG7X7Qd4YXgvE3S/FuGkoT9M+/xrGuz0WWwrAM/ocHRwCw7Em+dIs7M/3Q9CGOzhxFIPjExk/Qk3FdcWsFMFJKfvSWq9BQheBGj6zr6VZOxz+PbZQoesHQZ/f0rHmMSB11XO1I6Smua72O61qvQ0rJ2MjrLFrj9DeP8875dyjIPFPGKV6ZGGHEeJV1mXVsaNhA2lxEIpFKvNyzXpFgnZjNcm52lObKNw1XPRSVYFa7YwaVQXCdc7uWj5dfZhUq3ODLa7DqmIxNud4Sppp09wMtQUsmyfSyGzDl9TbaWvJMVoixFLN2wnctt7kIJQ9idm7FsUDhhHG9Z/3aQnjrkwg29bRjzblJft2uLYM/NHWIV8ZeIZ/PsVW2sFO2YjhFZr3vqrSNCA1wTAMRKhY0FUbIlFxlmKUkaMykKKkZ4p4jfSV5r6OOqwVxNc79PXfylwMHOS8WcchGkqr+2DU33naTXv6yWmt5tWyHwZkcA9M52v1KdhVZV4P3cCq8kNYMKTFny5V1aZkR6XvtyrpP1suVdSnlRzaGvCSy/r/9b/8b//P//D/zH//jf+SWW24hnY7KhBobG6/IxdVxccgbNm25MzSWxms+H7eWKS4tsCHrOqKON+zGsBySsdrmKWYhWyW9sJUYqmOQNmpU1m0z2Hzjdi7yvGOVyfrs4soVfT8BcDE3ZMRU8gKSn9ll1wW5o+GDmfW9WKg2ybAdSXvuNE3FEQZb7ubkxBJ5w+bLHnmWFWOdfEJedZ5SzpP3Cc603stN498jZc4GvbM+/B7RMPy++dPzp3l17FUA7ui9gy3NW4JjDMtBtYter6379wl7ACwVzWAElUw0Y5caKMjyeJt6318d1yqEEPzB/fswLIcmY4J1h08zisYbsc0ktCVsaTO8NMzw0jC5+bcpiHk6HJ1+lrFlCUw3kjLVJLpdYGphiUazRtI1TNaFCH5XpUncKifhHMeJ3vfSjsgIE2Z0tObJM2fZhCt3VxXXpl6oofCgoZNkpnrdUa3a/hdrIdVhsq5Kk9IKiVZjhbapj3Lgdq0gvF+bagpVUTBWqay/PfV2sD9tdlrZLBXXBNfJk/fYiGNF91hpl6BUVrKp0kT1iImpJvncnnUcONdcJuta0xX+lHXUceWwvX0T20ULJ5nnpHWagp2nGW9f8CrrllUtg6/Vb16yHFfRJJ2gDUqpZTDnrbOOfYmVdavE3FzZGE86Jo5t0ZI/x2Kip3bc7t2j/l6iSBsp15YIvhZxSWT9U5/6FAAPP/xwZLP0N0/7Uv+gdVwWnOUpts0+U/24cPsadTtHafZc8LjqGKuS9aSdp1LkOJvcRGfuJGljtkZlvSxtiVtL0Z6UsOGFvXJ27rF3JphcLPIbt28kpq2N3EUq6zVueqsi2FvIGx8YWa9VKbAdydbZZwEoaY2MNN3C8Fw5oWHn3SC5qDWRsLIkrMWIeZyPBmeBLFDUGihpjUgEAknCijpNixrZUUXA6PIoT59/GoA9HXu4sePG4PkTE4tMDRxm38zTnG/ex1jjXgAmF0PEX9oMj7t+BSLZTNwWFAx3Lbh/R+cH9m9eRx3vB5Ix1V1LnTg6Kr0yQV9iH/t7Wti5XuX84nmGl4Y5zdsUhMU5OcuEnCUns/QVEjThkFb6WGdLNKcU9NWGISrH6ni/q9IKZlWDGwwpIUMe4UTJuqiQHTYXXYVNUWsMEq5hst/S3Iwar3YYVq1Lr6yH3X0Vx1pRBh993P05b1j81atDbO9q4OM7OtfwZnVcjbDUBODuIZYSQ1EEluIbNJYi39KDkwd5ffx1APZ170M5MYtBlkxcYz5vBr4PjhX9nipWAWG498ahnq/Sl32T1vyg954JdFVQ0JvL90C9sl7HVQwllmKP0s6EnWcem1fGXuCXt/+Sy8WCynqYrLv/DxRKUgIShBKM9FVlaMxylcGcQHjGdc4qsfuqsIpREznLQB9+ietmnmIp3o3s/1rVS6QTHiVXcs0fpURZkyPKtYdLIuvPPvvslb6OOq4EKsb/jDXeSFNxlPNN+9k5/RgxO8/BQ4fwQxvNMSjZNlDb4bs5ZlNpIzaX6qczd5KkNV+dZXOsINBLWIvRHhmjHNQ59sojGE5NunK1/+vZM/zTezbz5PEJepqS3L65bcXXhN+mFhk2LzCr9f1EreymHUomNBVHGWm6BYCTE0tc192AU3CrYEvxTlTHQHcKgSFPGLGia+6W11tBCEw1RczOEbeihnKiBtEv2Iv8fPANHOmwuXkzd667M3hudrnEz9+Z4PaZpwDoW3gjIOvhoHxhbpaNuLNrRTxNolQC3MX9xg3NF/iXqaOOawQ+gQ7IrqA10UpropW9nXsZGxrizZljjMZ6KRhL5BybGSvLuGJi64KUtYBuxtjoGGjYJLxt2hY6WqU6xXsvzS5EyL20LQQhci7tVasizYXzAJS0hpC6qfx8TFUjPeu+wooV1vI1VbxDgZ8qzRX9UcLJVv+QQ8ML5A2bQ8MLHwhZz5UsfnZknN3rm9jZU1cSXip8Yg6gO0VQhEfg3d/9GOLNiTd5c8JVBO7v3s+t3bfylvMoUDYdVDxCXlkMUEpZpG0DgpLWwPmmfQFZtxWdhoROwZu1Dm5l/TO7e96DT1tHHVcAegJNVbjZ6uKcojOyfJ5Xx1/lYz0fC8i6bZZCLp9eZd2Lg3dO/5yEtcDh7q9gWO7IT9UjxhKBplbTwoCsW5c2Pk1apahCyjHRp04AruFjoUaRLXCn15PAMgKnpmn8RwWXRNbvvffeK30ddVwBVJoUDTfdwvnm2wLZs+aUaCmeD55XpbEikR3PFjg/NU8caMqkyC67ZHsxvg5b0VEdE6W0AJT70sMy+IS1iBm6AWW4t3GN8xK/9+YwiwWTczP5CFmfWS4xOJPjpg3NaKoSqfDXUtNUyig/yBveTWpKktaCK7cTSqRFIGXO4mt9HntnnOu6G5iacse0lbRGCrEW9GKBJqearIuCS9aLuivjM9UkMTtHwopKXnHMiITUdAq8u/Qc2zI63eluHuh7IBJs/+yd8WAOLrhSWR9NM2+zc+oop9seIGYueteZQRGCO7e08/dvjbCrty4rrOMjBC0JuAZscTOLI1siT+tS0kmaxuT1dOaXOefolJwphuUy2VQDRmmWeWeSRbnIMcWiScbpJEWL0sp6Kgi3F5ztnP559HHHjqhvFOyIwVwl/Ep7UWvkzq3t7mu8NSCvt6CrAi1RbncrqWlSjhEZBxc934URVvgo0lqxZ910Vl/fPwi8eHqG0YUCowuFOlm/DITVHrpdwA5V1jWnhOM4vDH+BgcmDwBwe8/t3Nx1MwCKX3nTEoABnhdLZfVPKcwwsVjEVJNIoVLUmxluuoWWwnm2XLcHVRFughs3WZVOJbmuO2qmWEcdVw20JKoQNBBjk3YdghKHpg5RsAp0Ky6lcywjqMH5y6djFFAcxx3lDCTNeUqWje3IQALvKDpqLRNPj8A7l+jIXioVK9piTaRygXXdv4+9/VR4lfWPKtZM1o8cOcKuXbtQFIUjR46seuyePXsu+8LquHhIyyfrghMdDyGFV5FR4jhCQ5FWxHxOc4wqibiPv3tjmD2GSyLDBnSOomOJOCpmtRNwRAa/SDF8dxrl3uZalV0fGxbeIG3McLLjIYpmbRn8X73qGlWYtsMdW9qji0CNm9m0HXQrhxQqlpq46IBvsWiiK8qK7QIXA91YYNfkz8gYU0ynt3O27T6cUvnfRnVMrpv5BQMtd2NqbnB89Ox5mnBJcl5vobE4RtKYqzq38IIVQ00H/08zQ6Kisq5KK+j9saXJ2cKLqHqepngfn970aXQlqrRYKlq05QeC3+1QNSQz8jxNxSKb518kG18HuAF/CuhrS/FP79lM+gr8u9VRx4cGeoL5ZB8thfOsX3wLR/ZHnvaVRY43oqrJsUiIRnpkA8nOTzG98AjvkiInCyxiMa9YZOU8lsiTGf47ZuU2NjRuYEPDhuqZuB6qZPDS8aqLq0PGG9nW5RKV0g1fYXHmUQZa72GHpkTIupJoBHM+YloXxpqM4MLJBGmvOCUkWllf2+ItpWRm2aAt7UqrrzRKV3iiybWKC/4dQsR6oPUeNoiQDN4ucmz+LXLiNAB3rLuDvZ173YMdO/juyWQLLCyieuo9WaH2KM5Pkqa8LwKMNt3CaNMtfL7NTSQvxzoZbLmTfGxlBV8ddVwVUMsO8D1qLzdu2Mgr4y9wcu4kZ4tj7MTBMIohsi6hmOWGob9kWZTvgYS1SMlysB1JU9GdJGIp8ZreQsKLCaV1aTJ4p6KQ6FgGMhRmVg9AkkE7i9T95Ldz1SRrPwismazv3buXiYkJOjs72bt3L0KImqYA9Z71DxBez9ZUejsLyb7y40JgqKmq3mV3BunK335/TrBMtMBimVA6igZ2DdLthAzmrFykkhOZI7yKo2Tv4iEAmgvDlOJbqO6SLGNk3utRu0Bl3SouccvY31DSGni751dXOWM1CobN/3jRlcz9T5/cflGvrYXW+cNguH3dHblTTKWvQ5jRyltL4Ty96iHOtd6JlOWe86LWyMc3pzl/4BiixsgkaXjjaJQEcV3BVN1FrrKyrjjuHGbpOAwWXiFvz9OWyPC5zZ8j6WUxw7BsGWRjAVRZfu/JrLsIt+YHkV49Lae3k/Y2k0z8ksQ7ddTxocZI4820FM7TkTuDWVqG8AQKf13U4gghUB0TC0/mnsjQraZBtpJxbIpYDGsai84Eo0JgOyYD2QEGvJnqicUTdGHTSjJIEIBLWBSiZLhWZX02tYW2/Nng93hDiKy0bOJ418MAxFQFPZFmMrMTIR0a4zoslWfGgzs6NHi/tbjBV1RApVV7tQ+rv9YarB0cmufF0zPs7m3iE9d3XfgFdbwneOv8PC+cWuXv4H0nj3d+lsX4OvoVgaUkkEjOOkOMLAjWt6S4s/fOiIcKdnk8m5NoAYaCZLVjR79HacOddBBWhPnoaXL3u3985yb+4hX3S1uvqddxVUOIoEXJUNPsbr+epniaJ4aeYMLOMi+m2D/3GjtzY5xpuw9HZmDyODgWSWchOE3cXqZkOlDK0rfgekFMZK6nU6km64rqFlysVVpYV0NYPQqwlC8gG1dOwj7y9ghdHucQMbct5qNeWV+zPfPg4CAdHR3BzwMDAwwODlb9NzAwcIEzlfGtb32Lffv20dDQQGdnJ1/4whc4eTI6Q1pKyR/90R+xbt06kskk9913H8eOHbvguf/+7/+e66+/nng8zvXXX8+Pf/zjNV/XhxZe9srv+QrDUFNVj6nSrGnq41cN/J7L+XV3M5/s42T7g4A7ixcIRisEcOzIyBQtNC6FkAx+JelkOBLTnFIkw/fsySn+6rUhzHCVxdutZUVl/fjYIn/+0iAzyyVKls3zz7ume3FrCVUatWc6roCZ5dpzhC8VosLhecPiwUB1UFIz5GKuBLWx5GY6S6Vi4PJc1Jtoa3Qzo8IxI8Fx+NzbNnSiK0pQSahM0ijSxHYkL4y8wKI1gSJUbmz+OE3x2nJ1x3FoKJXdC/wZzdJxIn8jv/q+FK8Hx3V8tJGLd1LUGgGJWqpoWfHWP6kmgqBL4lY1tFgCRRCYxSXQ6NR6uUV2c4/YwwO9D7Ovex896R4Egmx+klfEKKeZ50TbJylpHiFxzIgMXkg7qDguxsv9uGbD+uilaeV9IqyoimkKuioYbL2bgbZ7kcK770PB2/Onym6/a+pZr0jaysr9xEPY52OtwdprA+54y3dGsxc48r1BrmTxnZcHefNctQLqo4SXTtf+O0wtFfnzlwaZW3LjgoLWDEKgCIEpYhwXswzJCaSEu3vvjhJ1ANtASrfHVkm6bQjLSwvueFEvCeQnj/1ktaGmuWVjC5va03Q3Jbhtc2uglmtN11ao1FHH1Yjhvi8w2HInS4keFEXQ39TPw1seJqYmWBAlXmYYrTjAzqnHkI4DilZVXI1bSxi2TWr2OKo0WYp3M96wh5hWvXarngzevsSedSek0lUEYJsRdVLlqn5+OrReaEl3xCn2R7qyvmayvnHjxmAD3rhxIxs3biSXy/Huu+9y+PDh4L8LSeTDeP755/nd3/1dXnvtNZ588kksy+LBBx8klytXcf/zf/7P/B//x//Bf/2v/5U333yT7u5uPvnJT7K0tLTieV999VV+5Vd+hd/8zd/k8OHD/OZv/iZf/epXef3119d8bR9KWL6rqisj2xHqu7K06qyy6pQi5NeHEThEusGTE2/hZMenmE/1A2AHZL184w5MLzM8k40yZ88pWErJ5Ew5aBEr9b2EHlekHQkWD51fYGbJ7VX34Tu/hwM4R8Ivjk2QLZg8cWyS0xNLtOdOB89rjnFRN/wVHxPhJS3GGtxWkaQxz8CIS4SX45282/FpwB3PptpFjp8+A0gMNeWOtvFmYCrSIm/aNc+NlkJRBKbiVg1885BAXigtDkwe4NjscYQQbEreQXO8fcVLTprzEfMq1TFBSmzLrJn2WI51fET9Ouuooww/QXp2ZCIYjQgEcnRHi0fuE1uJo8bcOecxzyPCVnQyaY9AKzHaUx3s697HF7d9ka/v+jr9bbtwBByMS84UXyTv35G2EXF8V6QdKN6yid7g8Y6GOPu3l38PL3hh2bKuKgghuKmvGU0RbO5sdj9LaM0+NLxQ6zQronIqRZisO47k8PACM8slLFuSMBcivhkXQpUR3/uMN87NMZ83een0zAd6HathdrnEoeGFqqTvlcLoQiGyN8+GEt/PnZhmMV8MvqOOcEmzIqCpZ4qzYgFFWuxuuZ3dHburT26bSCSOoiPibpyj2wUGZ3LYnlTX927xYWhpepoSfOGmXv7R/j7u2LLynldHHVczCpkNTDbcEHmsO93Nfc23k5IaOWHykhjFMMeYHT7FfHa+atRy3FqiZDrMnXM522RmBwhRUwavaK5m/VLd4B2PmyjCVWkp0qIUil8rEwlKyJzVVaC5rVwf5cr6JWlUBwYG+OIXv8g777wTkcP7ZH6tMvjHH3888vu3v/1tOjs7OXjwIPfccw9SSv7kT/6EP/zDP+SXf/mXAfiLv/gLurq6+O53v8tv//Zv1zzvn/zJn/DJT36Sb37zmwB885vf5Pnnn+dP/uRP+Nu//dtL+chXJSzbYXShQG9z0jVa8yqrHS2N/PK9W5nPGZyYcJMa9+3ZzJFXzwCwGO+msTThyi9rbNSOdOWNQnpEXotDSFLpCL8ZxptVajs8cmiMjuVxtoRP55H5oakshWKp6vFKyJC0W2DXvDYt3D9fo7IemdtqOyTM+chccrXG7NbVEK4QzSyXaM9c5vgxb9HKxTtgCXSnEMwmN9UklpoMRrQ1GJOcOL3MJggq7qpWHtX07IkpfulGt08cKRGW+/dX9CSb2lOcn42qKSwlgeaUGJUzzEy+iQA2aTvpdhKr9pg2llyDu6V4Nw2lCUCiSgPbKlYtnhKBrSbobKyPaavjow3Dk93G7Bwvnprm07t73MUqkMFHFVCWEkPV48jQvWiLGJrv8KvoaCGJYlJLcvvuf8GZ+R8xxhSONcnLDLCfDJ1mtEotcIJeeVNNMpPaSkthCKN5C0pLI5z6IYvx7shrVBEm6+7P913XyZ1b21l+d5BxyjJ425EIaZM2ZjCVBFJeWExc2UYVlsUfG1vkmRNuu9DHtzSwZ+LvKWkNOPIb3uepHhkbhq4qFCrN+N5HvFcE+EriLz3vF8F7M63j+28OR35/7OgEv3n7Rvc9BRFPBSncyt/RhdcoiREa4hqbC61sSG6qfXLbwJFu64jjJcV0u4CqlN3gi1ojSXMheImhpj7wJE4ddVwJrBStpfUW7pLreZ1xsqLEK4zRWzzJi0fVcCMW4Mrgx8bH6C3NIRHMJ9zW2VpkXfVl8JdaWTe92F7RiKmuoZ1fFITqGF6V7j1sKxpCURH4ZP2S3v6awCWtXP/qX/0rNm3axOTkJKlUiqNHj/LCCy9w66238txzz13yxWSzrvShtdV15hwcHGRiYoIHH3wwOCYej3PvvffyyiuvrHieV199NfIagIceemjF15RKJRYXFyP/fRjw7MlpfvTWKK96kj+8vhCpJtBVhc7GBJ+8vouv7ttALOTwuBTvRhWgSaO2A6+MztgVWtRwzFH8yrp7jC+lV2T0RvadI0uFChXECjL4cM+i5s2ALz/p/hwJyryfIz3r4fNJScxaJgzNMS45O/dXrw6RK13aYuXDl6oX1QYsxQ3WGwy3sm4qbtDhB82NpYmg3y6new7NfmXdsTg7HfpsVtGVOwHEUty1tYObt22gKVn+25lqgmnyHJVjSAm72m7kU9OvcuPED1ecl+xen0vWs4neoAKiOQaOUXIDJiUWfJbleBfrmhNc11Xv/Kvjow2/sh6z8wzPe/eXdJDeWia1RGQ9s5Q4Qk9GKto+gQeXmFRNboun0RrvZUf6IZJqE0Xh8JoyzrGlQ5FBN4q0A7IuUTjTdh8H1v8marIRseE2TrY/yOn2T0TOrYX2jLhWfmNdVcDfA7y1PFcscuP4D9g1+Qh7Jn9UU9LuONI1DvIiLr+y7q8phFp7JhbLhkTCWEaRNglrsaYqqlYAp9dyNL6CuNAWcsUVWVcItZIIk4vFGkde+DyrJb1rKfZmlkJTT2IaIkTWbRSGSwc4t3QCoajcpm9gI41BcrsKtuGquxQdJe62e+lOASnB9hJVbhtKGYaaXtP3oqOhnmiu4+rGSsUVW2gk0LhD9tIhk1jC4V3zLWas4apj49YS1pTbdryY6MX22me1GveI4o0ItS/VYM7jJraeRlFcn5bwWhReSQzbCea+20JHKJrbIoNd0yfto4JLIuuvvvoq/+E//Ac6OjpQFAVVVbnrrrv41re+xb/8l//yki5ESskf/MEfcNddd7Fr1y4AJiZcktDVFe2B7erqCp6rhYmJiYt6zbe+9S2ampqC/zZs2HBJn+H9xlGvD+ytoQUAhLex+e6JALt6m9zKe7K8cZlqkrZMHNWpPbotPHfRFjqKUnbzjmlKIIP3gy2/p7CSrPuy9piM9n0Lx6p50zlhsm4XcQpZNs8+T6Y0wU1jf8f2mSf5ydujwTF+TGuu4BbsSKo2+wuZ6lUismxJyXi2sNKha4I/Rs9S4hQ1l9D6hLy1tZXbNrey7PV8p41ZMj5Z9yrreH8LRdqR6hJmwSPOOpquE9MU9mzuJREKsueE5ICYAGzaYn3sTW0JnlNKKyeoEqb7PcvpbfS0NSNw/x2lVURKiSViHO16mNnUFs623sO+/ta19azWUcc1DMOb5hCzc+V71Q61jlTI4C0ljhpPo4aq57YSI51Ol5+vNP/xTpBQG7ku9Uk2CHfm+ED+NC+LEXK4a7QIyeClooJQkEIjpiogBPOpfkw1FSGZcU3lrm3t3LCukY1t6ejb+rN4vTXeXp4LvDFUx0QY0eSf40j+5vUh/uSp0/zZ82cZnc8Fr/XbdWyjxJ+/PMhj74xH9gfh7UVCOjVHB1k1Es5aDZOk9xPiKmwEOjeT48+eP8uJiehaf7Frte1I/vr1If7fFwci7R1hLBdDj9fY6zVVBJV1SyicLx1gxhhACIX7++5ns5ecrmyVCOBYONL1z+npcIs7ul2kZDmhynqFDF5Ns66p2kDVx6/d1scN6xp5YGfdc6WOqxsr3bK259quo3CbXEevzKDYJY7bpznNfMSvSZF24EW0FOsMHo/VqqwHMvhLnLPuVdZtLYUiRNWoznDcblhOIIO3lViosm7XK+sXC9u2yWRciV97eztjY64Z1saNG6sM4taKb3zjGxw5cqSmTL1yM6kle7uc13zzm98km80G/w0PV2ehrjaEgxk/E3xu0iV2Uq3ODIvem5nMXM+7HZ+hu7XJy24ZNTPtEtACGYoeyeJ1NMS5YYNHHJ3VK+t+1UWY0cBNUPumC1djdKfI1tln6cydZNfkT4nby7TmB+lcPsHm2edJmAuBJN6wHDcgkJJw3OZIWZbfeNCc0kVl54Rwnc73jv0de8e/z8T82vsmq2Bb5X8zNUGpIvNvaynu2NLOr9+7m6SuErcWSZpur39A1r0MZ9W/tZHDkRJLCUnaY5kgo1HA4i05iCkc2mWSnQ37OXT0aPByZ4UZx0Aw7s9Uk7Q2ufe96hg4ZjGorBf1Zk63P0BRb/7AA+U66vig8aWb1wcGjzG77LOBY3ncRSAUHRG6VywljqqpkWSrLWLE+25iKrODqcyOiDQdohUWRahcp/Szz+kmJgXzosQLYphRlryedXfNiMfKapuYFr1XK5fGff2tPHhDd5U0UlX9VihvDF3FaJ5KE9Flw2Jm2V3fDcvh54fPB+uwb4iqSpOlosXJiQolVjhArKHKqrV06cKmJX8uSDrXAT85NOr+278TLVrUcu4/PrbIk8cna8YHy0WL2WWDgmEzvVTbgLXg9aP2LbzOraN/RbzC5FRT3IDdQXJYTDNrDiKE4Paue7mu9bpgn5Mr9MhKq4TErSQq8TTtmRiaU8KwLByv+udUGKbu37lx1TF+XY0JHryhuz7BpI6rHrf2uxOEdvZEY8jA/BlAaNwsu9gmG1GlybvKLEfFDA4ySJD6LY5+Yhlqy+AVf876pZL1oLKeQRFu/BqOOSNWV3ZZBu8IDaGqXtuM85GurF/SqrRr1y6OHDnC5s2bue222/jP//k/E4vF+O///b+zefPmiz7f7/3e7/HTn/6UF154gfXry+603d2uHHhiYoKenrKD7dTUVFXlPIzu7u6qKvpqr4nH48TjHy7pkxCCe7Z38MKpaZpTOotFM3CDd2qQdVSdwda7ALCVCbc6Ks2apDlSWVdiqAqsa04wtlDk1o0tyDNeoGabmLbDoZEFINqDBuV5p5VjGxRvBINaUX2IVNadIo2l8apr2zz3IiDpzJ3kMF/hlbMpxmYXuWn87yhqTTjyn5TfX4Znz/vnLV2cwZyZY/vMk8HvcwszwCVm3s184GBri1iVTM/RvQUz3oiiQMKrdtuKXp4Rq5TJele4L9x0zXxMJVkmy4qCozdgkuN1MUZetLBFS3KL0U0uZ7A8dQ6fFqwUFCElMcdNUJhqCkVzHaw1p4TtOXzaStRJty1Td9at46ONvrYUt123kfmZarKeNywcoaIoClKUVUummkJTBEU9DbhJOluJEc+0MdB6DwCqujJZB1dS3kOGtsT1vCHHmRNFDopJeqTkY5Yry4/reqA7jFeQdW2NM8kVzQ0d/DVeVqzxVKwnlaTPKPlrvcD0WmjC7vWRo0Pn8pMA4aDNrrGgr5t/A2XmTeaTfTjODVd81voFJ4pcfYV1BCK47jDJrlXD+MUxN37qb0uxraKlKaxkqJS7j8znGZrNM583SJgLrFs8DEBr/hzjjXuC4xRFIByDt8Ukw6KIEIL+xO3saPXGo3r7nFhhQoAvdXeEjhJLoakqYJBbWgxGt4lYClvRXW8eJU5S+3DFeHXUsRI6GxL8zn1bqtZvv7IObktiY3GMm6wkTbRwVJnhuK4wpqfYbzbSYhTQHDc+DuJLasvgy0qqyyPrUk+hGAK1orIeXk1Nxyn3rAsdRdHqlXUusbL+b//tvw2yIv/r//q/MjQ0xN13381jjz3G//l//p9rPo+Ukm984xv86Ec/4plnnmHTpqiZyKZNm+ju7ubJJ8tkyTAMnn/+ee64444Vz/uxj30s8hqAJ554YtXXfBgxXRwma41Rsko4lhmQZT/4WQlSjQeEq1b/tpREZPBCCD6/13VQ3dyRQahlg7kjIwscOr8AVFd7A6fgivm5/pzvKoSCMt1eqY+u/LrW/DleH5ijNPYOcWspMgvcPVIizWoZ/MXc75WvL+aXVzhyDbCK3oimBJ/e04OaDs9XF9i659gfz0QqaCW1oRxRqTrrW5Io0iIZUynZJeaKcwxnzzHoZDkl5jky+yo/H/w5Pzz1Q35sn+MXYpBFYaArKe7VN6KjYlklzyzO+5wryQ2tYmA0aKhJtHjKnQ0tDWx/VGCIrN/a30K6Xpmoow5kwjeYy5ed2R2bmWUDR6hML5eQSvleyest7gSMWNkY0lJiJPUyoa+srFeGVX5lJSUFd8hetssWhIRR5nim9C6LlEiEKuuVgdnmjqjcfSVonncGjoXtyKrKeqWJaNB6JD2Jo2O6/cVCo7fdrYCG948ICQyvTd55RWGWrbPPkDAXavZOt2Xd8a4thfOUrJVVQ+8VrkKuHqmg//VrQ8HPq0n2a7XJWY6kqTBMpjQR8ZUpGDY/ODDCG4NzFE8+w97x7wfPVSocHGkzUDrIqFgGobIpcQcteh/NKe97pUaTQZVwbD9RrKOqKqm0m1CYzy4gvcp6LB4PSIihpteciKqjjg8DErpapRZOxsux/3KsA3clkmymmZvkOuZTWzirx3hdGcMMGXCGybpea866Em17ulgElXXN7VlXnMrKeij56sggcWsruuePIhDUK+sXjYceeij4efPmzRw/fpy5uTlaWlouqv/pd3/3d/nud7/LI488QkNDQ1ANb2pqIplMIoTg93//9/lP/+k/sW3bNrZt28Z/+k//iVQqxa/92q8F5/na175Gb28v3/rWtwDXAO+ee+7hf//f/3c+//nP88gjj/DUU0/x0ksvXcrHvWpxcuEQZ/NDzE++wbLowhCztMsk1gW+z44SQ+CZPNQk62E3xhiqECR0le4mL2j0NlJhm5EMvVopzZZ+1aWCrGPXlC7KkHmF6tSW14WR9FzeM6Wp8mcLbe5uz7p7nrZ0jNmcgSYvrrJeGSwYxcsg62bB7fHW4vQ2JzmolCsWU+ntCN0L0rU4jhojT4EiFpOqyZRxCtMp8OSYzoQzzBAFpse/yyjuIpufOsesMUZeL9G+9C5Z6VYRcoqKIyApNTbre2lUhpjOLSIL2ej89ZUq694MeFuJIYWGHndr8ZpjYBtugG6LGF++ZT0j8wVu29R66f8+ddRxLUHPAAIhnaCCQchUbWaphAhV1ot6s0vG/aQd7n2XiJWPqazo1qqsAyh2ERXBDtlOO0kOylkWnQIvijl2yFGkbEcIQcl0F+Iv37Ke4fk8N/e1sBb4c3cVaWM5TpWCqTL5ZzuSpDHHjpnHUR2LEx0PInGTC62NaXIQ7DlA1FzUribrnQM/htwMDaUJbGdv1fWF5aAlyw7maV8JDM/lOTezejvU1ejZoSiiphtf5aWGEyVxvTpwtwpL7Jx+HFvRsexdweMLBXefj1nL9C4eirwmrC6xHZtDsy8wZ43SJQV72EjrxuvpbkyU552r0YkzlZBW2YBKFQIt4e6DdikfqMQaUylEPAPmgmcuV2/PquPaxvUb2jnwqvtzSW3AUJPBGNBOtYstqXsZKLzMtDB4WUxzm1xHEo1SiKzXUiEpqm8Caq2pDbkSfs+6jKVRvZ718FIUcYOfP8+WuefdtxM6SkgG/1GurF+xEpjv4H4x+LM/+zMA7rvvvsjj3/72t/n6178OwP/yv/wvFAoF/sW/+BfMz89z22238cQTT9DQUCY658+fRwllg+644w7+7u/+jn/7b/8t/+7f/Tu2bNnC9773PW677baL/2BXKaSUdGhJGh2JY5tM58aZFvOcUhYpTH0fcXobvZle1mXW0Z3uRg/JY2w15sngDcxaveNEK+tVzpNquaoSN+a5fvLnjDfsjri7QnmeMHalDL52ZT0spdSdlSrrrstyzM6TMl0X/IxRJuvhXklHyiCIdGIZupQc0xfrBl9B1u1insWiSWNCX+EFK0OaeWwpyQmVhdIsw7KAEDmKWBxLaLQuPEPxZIxlc5kheRZDcQOcZQwWigobWpOczmrknTw5DCzvs8bUGOMLFh0yRV5dx/Wte9ne2UFKT3F+qRtn9gXiqJzTWlAUt7VA5KaiF7ciWXeTE76ztRZLeAZzpaCyLtUYG1pTbGhN1T5HHXV8BKFpGqaaQLcL6P5UCqvcOiIBLURiSmoGiSvf9WGLGAmtTDStikqnCPGPzsY4cs4j61YRVREUibFOCNJOindklgmR51juKK0NDklrD1s73cTAxd6/qqYj8Mi6LcFcXQZvmQbXT/3MHVUJpMwF11NEUVE8eXJ4Asl4NrT+O2EZvPuzarr/nnFruWZl3ab8b2Zcgcr6q2dnmc8bfHpXN48fXdnc1kd4x7yU4Pa9wEoO0pWPF0Pzj2tV2WR+FnBb5ZzCIuDGfgXDRnFMUp7PCsB4w256lt4hbrt/r0VjkRdHXmQsP4QK7JPdZJRWtnU3RE0MfbK+UmXdl9UqmhvIe47wy4sLnJt1k9CpRIzeni4mT49gaOma8t466riWoMfidGRiTC8bGGqKktYQkHVDTdOgdbEt9XHGzZ+wKEZ5iRH2swFbWb1FxFfT+uPT1nor5UoWjx+dYNuCe086ehoh8GTw5ePCMXl84IngZ1toKIHBnHXh9qNrGB+oXnUtkgYhBH/0R3/EH/3RH614TK1xcV/+8pf58pe/fBlXd3VDCMEnspNsWlxkrP3TbGiXHDh5jAlFkkcykZtgIjfBwcmDKEKhK9XFeEmSUTsxRTsINzgyZLWsxamorFft196NO5ddpjN5DKM0TmNpHFOtcFpdSQbv9axXItw3XWWgFsJ0eju9i4dImfP0z78cmaMePocMucFbsUbidu6ie9ZlhexHd4rM54wLknXLsTgxd4L54jw5K0fezLM0fZJTYoA8Mwye+SHH8vOcbGhFomDbI1CKM1PwqmpqHEXmSaAitU6EvoHPbN9KRolhjp5nYqnETPOn+I3du9BVnZ8d/++0SoXzsT3c3Lmf/nY3eFloWKSEO7JQUXWEJ1lX89OR613ROMRzdfYNSYTu96wbgXmfrPcC1lFHFWKa4lb07AK6FzD5yUNbuPdhRIIsBA0JjYVYmbRYSgxVEaiKwHZk1VipcMx033WdWLQzdtTt9ZX4yiiLpC24VfYy6EjeVVTSDXOktQMsmi2k4z1cLISqeUZBLll3qirr0fVkfORcQNTB9SSxHYkjNFRv/VBClfV4boyt2QMMNt9Rs7IeXsJr7SV2qLI+ni3S2bh6a9iF8Jo3HnXP+qY1jWULH3Mxwe17BduRERIeRuXnCSc3au7T+fJ+e+zsILu29BHTFMzsGPtGvhO0fMymNjOf7KNn6R0sa54J6yDffTeLIx0UCbu06+niKEtCrU4k+MWFFQwC/b50FLdNT427+6Y/uQSgrbkJabj+Msuxjpou13XUcU1BjQU3tKXEMdVyAjantwGQUlvYnvoES7kzLAuTF5QpkvYMGa1yEnsZwpPBK9jYjnTbtdaAF0/PcH4uT9dyzk2f6n6LZ9QMWobHuBnlBHbMzrlu8PXK+gdL1uu4PChehTsmdbbEW7FlF8tKB3OdX2DPBofR5VFGl0fJmTnGc+OMl2aBY1i5NHPmeRRU0vlRbKcT1RsJljcsSqYTMpirrqwrIUfyiclJfE2F7xruCNXtn/cDNrtcTVIdo0oC40PaF5a+A8wl++lePo7qGHQvHYs+GZbBOzKoZDnxRihMoDnGRWXnKo3XNKe46ug3KSVn58Z4c/oF5ovzkefsUhZHuAFqSk+RUkHXkuhKAl0k2dXeyb2bN5DW07x6WtAmzyAQnEzcy3yyn7t6t4PjsKD9HIslciKJ7v8tfMd2JRFZSJ1Ec/CzoqoILwjSClGyvuKIHCOHpghMNcWv39YH8zPB6DbHmxkv1bqhXB11VCKmKVief4jmr4FG2edBQMC2bUXnl2/uRVcVV7qL/3gMRcBv37sZy5Yk9KicO7w2x1QFx18P/CSlEkdXChRMiWNZbKGZtvZPMBc/R7aU5Sdnf8K+rn3c3HUzirgIMqNorkkYngy+orIuKtbN02fP0B/6XbcLLlnXNFTdveZwG1XH8kkai2O058/gNIZcvf1jQiSy1npsh+x4nnl3ghs3NK/9s1WeK3R+y5YkYypLxZWTybVev9bg9r3C64OzVY8pjokUSlXPetiwr9ZOJwvlyrlaXODNc3PcubWd+NibZW8GoKC3sCjgkJhi2MkxU4qxUbayPtPLTSdfhGVX3eUIrSphIJXVK+vSN5jz9h7Vq6z7VX1LiROL6djrbuXQRIai1lSXwddx7UONBSufoaVZinXRmh9kMbGO4eZ9wWEy1s2dcj1vMM4UktHC8/QnbqctXntstRK0Pa3gN7UCciULZNkwTsbK40HDZ5GETCvjLYA72Wo+uRGhuQk5gVNzOsVHBXWy/iGGCMabGDjeeDRLidMQa2RnWw8723YipSRbyjKaG6U7OcS70+doSksmJwpkFQNr+ufMHX2LnnQPHYkennrHJKW0sClwY4xVBRp6zK+EWCSt6rnjthJDsQtIv2rvEWZTSXpk3a5901krEMYQSloD+Vi05WIhsYHmojduLyyZBLB9GXyTpyYo1Y5AVkBlhUizizVllwCOdPh/33iGV0ZfZ1N7kv7WVra3biejZ0jraXTlIKcHCszpN/HLu/4xS5OnI6/f0tjKxkZ3RJslGoMgqqSVg3cUBeH3D4X+vRKyHJyH5X4yXm4XSTh5RMwNbvTCLGFx6EpGPhjLSCkx1CRxXXVnQwvhmhP6lXX18qpWddRxLUJTlMCdN0h+eq0jthKnIaEFNKmkNgYjo5R4WAbvBipxTaWWb2MlWS95a4NwiiAlthJD80iKKt1raIl38MD2fbww8gKn5k/xxsQbjC6P8omNnyCtp6vfpBYULZiXazkyUDAFqEj+pY2ZyO+6U8BypNtm5ZnVhd3g/TYo3S4grbJiy/c1iVTWa6zHTsgLYLWWqrWg0vF8JTl5GGECfFFtV+8Rzk4tozolupeOM5XejhQKeyb+HkfoyP7fjhwbvtya1x6qrCesLKMLbgwQ3hdzGByWowyXTrNOuBLYJrWNL279Aj3Jdk4deJ1ZfEf3aqMsccGe9XJlHUDzzBzTXmucqSZRFQVVcb0goLbLdR11XFMQgpm+hxgcm6WkNTLesItsYh15vTXSM+UoOnFUPibX8RpLpNoSFMy3uGVbc83T+j3rrgx+7euZHVLpAq4vk1LDPySkYPVHLw437XPHlXrSXiHti1LFXmuok/UPMYRWnkU6MuFWSk01RSJEroUQNCeaaU40c0PbDcjtkvnSPG8PDXI8P8EEKpZjMbw0zLGpAU7mFlGFzrI5TY4FipSAaLAS88bcqdKMmpR5sJQ4ul1AOB4p96osppokYWW9EQy1ZPCrV9ZPt93PfHIjUmhMpa+jZ+kdluLdnOj8NDeP/g0xO+eRTncxcGSosp5oQuDJty/mhq8k606pKngDyJayPHP+GV4acWeX53NtfPVjXyGllwPv0sgxRtGZVZM1Z9uG3Wr37djI8Otvu6/TGviVfaGMp1I9gzajmliAo6fobCiTZ0URLMc6yRhT5DKbEHIu8tqSliFuLcMKI3KOD41jS7CUpJu00bxKoTTKTvn1ynoddVRBUwSWJ3f3DTNtwyM2is7nblzHpLELsgcYarmNW7z7X8TKCTbrAr2E4XVU1wTphHt/FkqueskSMXSPpPhTHYSiElNjPND3AOsb1vPiyIuMLo/yvZPf44G+B9jYuPHCH84bp6NIVxbp+40EHmah5J+Usoqsa34SVWgoWjwYJRo875N1pxAEbwDZvJcc9s3lhVI7eRpat7UVJ4uU8cSxCQqmzcM3rqsijmE3ectxqgLGnx1xfUA+uyfUTiAlXUvHWIp3kzf6qxQR7zcKps32madoKo6SMSZxhF7uZa0Yuxf+TtVsVQwpxpJmFkNTMCyHc+fP42BwWswzwhITZHBI0k4TO5wMU7Gb6Mn0gJGPVtWEWr0f+qPbVhgV5cvgpRYl63FrCSiPQQzPTK/Vf19HHdcaCq07mVzy7lGhkI+1r3ishsIt9DLXcQNTxlkOz72Gppvs794fWQeFWvYouZj4WUoZeJFIobiS9hrxogi3m3qx6WK8GylUhKJ4731xiYJrDXWy/iGGEhpvMjQ2TReuicSdG1d29BVC0JpoZXt8HSkJheYH2XNdP2PLYxyZGOCkOIklDeacWQrKAgvWIeYGi+zK9dPb0Etvphddd2+2hJmNyN4CqHEwQ1k4j4Sv62ihND654g0vL1BZN7QMjreJF3tvY2CqmZnUVqDs/ittk/bcWdZn3+Zkx4MIr+Ij402BfPvYWJY7trStafZuuLKejqksOdHKupSSd+fe5eXRlzEdE1XorE/cxJaGbRGinjcsRibcgNXW3EkH+ze18sZgWVIYvp7Ojg6GcVUKW3raWddcri75My/D/fSKp3B46KZNESWEIgTHun4JzSnRmWlFyXtE3z9XuhOyyyvKDYfGp2jGNZhTBKAlyjJ4P7lS71mvo44qNCb1gGxrHhENRpxpCdozcU6uv5+3i9dheiNtoGyWBQTr3UpI6mrgdJ7QVOJpd53w1yhb0YMWpwB+pUIIdrTuoCvVxZNDTzJTmOFnAz/jxo4bub3n9urXRc6hIYRAcdzEqwiN5hFmLiDLYwsFlgvFYHJHXm8jZc4G1W5H0VB0V+ofJuv+6E7dLpT7k4F80VPz+P8+qDXnrIfnc4d75WvBcSTHxtyk81zOoC0TXc/CPdy1xsCdmnQJ4gNmZ0DKU7NH2DT/MgC50o2By3nRtDk9ucy2rsz7SuBNy6GpOAq44+zCqPRlCe9vVfu0lFCIVtalqvA/XjmMbZ1gTCwive0nHdtEV3wX95LCXBhn3vJ6UT1H6eA9hFolxReav5/XTiIHPga+ciUeVYQYSgpVETSndFpSOqYtScc/2IRJHXW8H6gc77kSRhtvonfxbYZa7uLTvXcyXGjlzYk3OTh5kJyZ497195b3AOH3jbvJ2ZH5PLPLBjesawyUWz6Kps2pySW2dTZgO2Gz6hjD8wVuUGvsaSHvLMfbO4LJJqq71whp11zrPyqok/UPMRS1XGEtOz6mqoKNmvAJlm1QKqXpSVyH1tbP1PgOCk6WtsJPKEqDrIhjOQbnFs9xbvGc+1LDpCjGaZdJ2kiiat1k5FIgpZZaWSZvSxkYzDmaO6NbkVbtjL23MY813si6xcNVT4erTF/ct43/5/mQrMc3FHIsts4+B8DmuRcg5l1T3JPBS5NCyeD4+CK7ekO9kCvA/0yJZJqkaqKVyj3reTPPc8PPBf8uPekedqQ/RlxJo2vRwOAnb42ybuQ8ccDU3crZHVvaeHd8Meh/DLs+60092EInm+jFqphzF0gE/YDFNoMeUa0iaFGEQAoVU03R3ZhAs6LfDSfdDtkBhGPW7K2M+b3watKVf+pJ12DOLiJNL0NaJ+t11FEFVRHcc30vZ14/QqmYx3EkltezLnT3nhFCwdTce9YPsmLJcttLWM5dC4oi+K27NrmVB0WApnmTdV1IoQbJPR9Cif7ekmjhl7f9Mq+Ovco7M+9wePowY7kxHtz4IE3xFdZIz/THlyb6ZN3RU6hmLqiIfu/NYdKlaXYjMZUkBb2JlDkbVLttobkyeCHc+esewpX1YrFUDlRsi5JlB2vmSpX18Lka1RUIn3/K0F5US+Ju2A7NhfPE7By5UjvL08Psmn+J4eZ9ZBPrg+PCW5o1XW5xyhnlROjPj45zbibPmeklvnhT+bXvNVKl6RWfc+yKKS6ryeCN5aBfHMCwZvnFwOMsGWfo8uTuHTTTE9vJROpefue+LThHhjh8aBzdXPJOakXfQ2hVlXXVa41YqT0rkMH7VbpktEBhqkk0RSCE4Dc/1o/tyCpSUUcd1yLWKiAZbrqViYYbMNUUcU1lX/c+UnqKF4Zf4MTcCfJWnoc2PuT6IilqQJillPzgwAjg+rLs7GkMziml5CdvjzKeLXJyYgmJDJKllhKnZDnl+DWEcMLQH8vox/RCcZU3CvYFTcmzeXfdb0pd/LSmqx11sv4hht/rh20Gc0wNdW09h1J1g8WR6QUOvzkcPC6EQkptYYtsJiNNGpOf4KaN14E+z8jSCBO5CUxRYFzkGBfeDG61SL8QtNiSbtLBuV1DCBB2ecYieNm5Vdzg83oLJ9sfpHfxEIq0IqYxPipJpR/Uho2NYlYOR3evhHhDkL1XHYOxhcKayLpfIZLxBhR7Ht0uYtmSgewAzw8/T8EqoAiF23tuZ0/HHv7LuTMAgfTUR3Zukk32MlIo5JPd3r+14Ff39/EPh8fobIhzXXdZ/ioSjRzs/Q0cobGxIhit6ucz8+6YOqGg6dH+8ZhWXrl7mhKoi2UJkqGmaGtupjDu/k2WSxZNyfIi5zgycLF2F3TFk8ELdKeI9Ii/qJP1OuqoiVjcrXSrjsHB8/P0FfKRx8Pw17SYrnG+eT8xO0dBa77ge0SMsxQdxXOOB8/sU40S/kryDqApGnevv5vehl6ePf8s0/lpvn/y+9yz/h6ua72u+k1DMnjHcQJDO6mngelIr7EfrBlaGlvokcccoaF4o0SDCSDSQfPaBnS7wGIuj/+vIG2Tn7w9Sp/nbL4iWQ+Z1Qlz9Zno4dfXIuuOI9kx/TgAh461c/30z1Adg20zT3Ng/T8un8fb0xaLJtlsFj/lEh63589nv9Cc9iuN5tzgis9VVq8jlfVKIUFhAcuRTGsag/Y4k2RZNDLodoFumWa9tpXh7l9lwrO5SugqBc8wUfXbEZyoSVXNnnWtutUrcs0eWQ/2wmQLhNJUbs+6t9970xTqqOOjgLV4agAgROAW78eJN7TdQEpL8eTQk5xfPM8jZx/hM5s+Qyq03huhNtDlUjSZ9syJqWDs5sh8gc7GeNAqW9Qa3dGktSrrYbLu7R1+TK+GxsbV6EANYNoOf/6yu879ywe2XXP3fJ2sf4ihaOUKa7iyviZ4hFpbYTSK7+xuqyk6Up2sb+nnps6bsB2b6YVzvHHmNGcKWeYosiRUxpQcw8oSx+QMvbSxG8cd6+M4QcXcraxT0w3+5++Mo56eoAFwhM58qp/5VD+b5l6qSda1SrKulFsCgo/ozWW0lBhxTfOcYwvu+La1/SsF57P1DIozj3QKHJh6AbJjALQl2nhg4wO0J6N9QWpFetOXIC7Fu9FjZUKdiWv8o/19Nd/bl8BWuh0HPT8BWS/gOG7lSqsIzMNkvSmpo8fKZD0Xa+eWzmZOn1ZQpMVS0YyQdcM0gqC6pGbcgEov/w2nZty/i1PvWa+jjppQY2Wyfuj8Al1eu0oi6SYuw7e2H2TFNIWxxr2X9oaKW6X0Qx+XDFdW1leu1m9u2kzndZ08OfQk47lxnj7/NCPLI9zTe08wecJ/H59gOY4d9KxL38k+JHX2ZZCWEqezOQ05N+gD10BP0WMIAXF7mcbiGHm9XCVVpB2RsQvHYmy+gL9iSmqQdceO9DoLs7jqrPPI+lrjkPBYy5Q5F3weP6Hgw7+OqcVSsH+GH/8gkTZWrqxbVrSy7kgZlNcrK+ujc0M8tjzEOQ10zULY0Cla2aE0skMqTMfWIf3EufdvGSSHvO9E5aSAWm7wmr+nrCCD9xMMwd6japTUdDDP3VSSaPUe9To+gliNom7vagjadsIIJ3w3NW3i4S0P87OBnzGVn+LHZ37M5xq2BtXtR94eC46t5MNHRrKR3wWCeIisKwKEVh0vhttNA4Wut47YQkER4oLmdq8PlFtKLcdZvY3rQ4j6avYhhqqWK+t6QNbXWln3jI+kSaVjjpA2ca9SX9IaIhkqVVHpblzPLrWNj8lePiU3sSN5F7uSm2mRcWwhOeOM85wyzKQzg2mVyTp6ynMQrnaDPzGxFBhR+NUXKBN0W9Hpak4T0xQ+sbOrqt88mKsbSj4IrzfenTUscDQvi2jn1+wq6S8ilpZiWhZ4QZzn/OK7CAR7O/fype1fqiLqUF1Zby66sqFsvLemudxqqAz2yjJ4b1Ez8p4JXKLK8TY8Wzauq2h6OeEhmtaTSSfRFZesTy+VeOLYBNNLnnt/fgFwg6kgUaLFQ66i7nXNlK6tRbGOOq4UfKWL5hgIQSCD9yvr4TGSQWU9dM/uCKlt1gTPpd2HIzQUZe1kHSATy/D5rZ9nX/c+d3Tk3El+cOoHzBRCJnFK2cnesYyArIuYS9aFYwaJCJ/UCi3Ox7b10JgoX48jNNR0G7aWQnVMrp96lJ3TP49cj28aBm4gp4T6G6VQqgM424is76qdX9UUaWoxZEBXy6vOLD/vJ46hYkoHZVd6IaJ98ldDn2XczK74nF1Jni2DvePfY9vs08G/43R+mp8P/py/PvMoY2IZR9HZlF7Pfc4GdqvbWWe4AflyrDM4j/89LI969dycnWiy3qF6zrrwfHFW8rHxyboIeToU9bJSzlBTqHX39zo+glhprfv07m7WNdee3FMZr3anu/nlbb9MQ6yBbCnLj0efZ4ESQjpV1fQLIais6418elcPQqu+Bt/8FMdBenIeW7h7WSYZCwpEtldaz+ZN/viJk/z1a0PB9bx5rrw2VymCrgHUyfqHGH5lPW4veUZvAlOtllfWgn/DdC6fYN/Id+hYPhk8F7OWAYkjVEwlWS2rUfSgSqGiEIv1c2PTDu6S67nV6SahZigKk6P2GR4d+DGz1rJ3Yl8Gb9Uky4pvwhQKLrWES7AtJU5fa4rfuXcLu9dXy9eD/hZjOXhM9XrjLSWOqggcL5D0WwbWBMfCxuG4NcrTzhg5YZIkxue3fp471t2BptQWp1RKcFKGO1JmMdHDQv7CI+rCqKqsa1EVgV1yP7OlVpP1cFIjqauRyno+2eP9Ld2/yXMnpzk2tsj3D7htEXbeDfBKWoZmzyAJIbBDo9psoWOLemW9jjpqQfNl8N7YNOnJxRVPXRNeB/1bNdxbe+91HRf3hopaQdZV1JAXhkSgrKHiqAiFfd37eHjrw6T1NAulBX546oe8M/2OWzEXSrl0auQDoqom3OSCtM2ARPvqLUdNQIUEUioaaizF6b5fYTJzPRJR7RwfqmBLxwz2CffzKNVz1m0jkgTR7ULNCR4+HjlUrhTJGmxdmmXi3ZE7FfwcBJj+23qfV0gZSSh80JX1xVze29NhIdRjHyTCKwixtjBIwlqkLT/AbHGaxwYe4wenfsBgdhDhmPTKDPdlbmN/w14aiZOwsjQYkwBkE73BeYKtR/FnNHtTYCoN5kKJn+AavNimWCqt6m8TrtKlm8v3it+zXkcdHzWEv/a/c9+W4OeYqtRUlP5SjQkYUPYyaU+2k7cNXhKjzDnzkWMutLQ5UgbJ1pLawIbWFMSqOYrw18tQi8y+zR389r2b0fWEN2ddBr3tL77xBvtG/wJn8jhHhheqznc1JEivNOpk/UMM34TFz1wZapJfvW0NY3cAfEd3K4sqTbbMPR849iZs7+bSGkCIKokaigIhaWVX9zrUWAKBYB0ZPpa5h+tFO5oQjC+P8IR1jkNiCkOl7CgZ3qz9QM8LwlS9vAEnUy7BtpSEW5WvsQGva06wscM1uVBK4QqCLFfWFYGt+2Q9z1qHrS+UsrwkRjljjiIVjQ2ygbsb72ZdZl3VsWG1QKUEz/9sljdf+WLQkYmSYb9SIRwTKSW24fWV15D+xUMyeF0V6GY5UbGkt4OqowiBGurx9N2PLY+sG2omMjrO1sqLrammPtLjNOqoYzWoHilXHcP1lfCqtGqt6kJoof0nd/bz67f3kYpdZKeaEjXrcoRWVmDhGc6ttacR6M308tXrvsrGxo040uHF0Rd5/NzjFOxiQMIwctjSTe7Gk25yVTgWRa+vPEhUqPFIohdAjyfcNT2WZrD1LhaSG1gN0raic3txques22ZkedecYjWhX+n8tQ4LzZAPJw50uxB5QbCPVcrjQ4mCNfeTXkF874V3AImtxFiKdwGuAs9P7FeauDm2xTxFXhdjPDP6COcWzyEQbG/Zzuca93KL7KYh0YGeagagPX8WIR0MNU1RKyfSRVBZ98m6V1F3rMju64jqyrrvx6NIi5H5Cjd/20J6pni+Pw7A/us3Bz+bauqa61mto461oKOhfE+EJ06kYlrN9S0cI1Yiraf5/NbP05vuxsLhoBxgzjwXPF+ZiNStHAlzIfhdOg4Jy40jzVgzAEKvJuuBwZxTTvLGYnH3+j1PEyiPbGwdeCTwDckb7mt3NFs0FN0xmtdiTFrvWf8QQ/GcfxOexM1UU5G5oquiRrDYu/g2Z9rud+du4/YpQ+1REL4U21STtDc3kl5OM+s/p6W4QWkjQyOF1EZOyVcZEsvMTD3BTjlPp2yK3Ex+IKV4kvNUIsmCr5xv3YB9PsZCYj0NFZfxj/b3cWJikds3t3H+tTjLgFJaiByTLZhYqXgFWV/mQpBScnj6MI8vvM6iKBFT49yV2Yu+NIEo1O7/CxtvVMqK/EqLI1Q+v7eXteDXb+/j2Ngit29qizwuAlmhhe1I7KJv9JeoClDaM3Hu2tZOJu6Nv4iXPQ0s1MDVOWzI5MMpuN+rWKY5QhrCAZKhpkjH6jL4OuqoBd98UXMMHFmurPskvjFZw2wHaE5dolpF0SIJTamoiJCPhRTKRbfhJLUkn9n0Gd6ZeYdXxl5hMDvIVH6KPlEkAWDmcByXDCbicSwvIVvwgiif4EotDqoeSf7qnnLKXy/zemvVaLEIHCvoGQe3IlNVubaNCBnUHMNtx1qDD2atIC8sgw9DIN1EgEd6/QS06knCfYRN0lQl4qX0viBmLABQ0JoCmfpSvCtI8jtWee2fyE3w7MxLnFHcti1dOlzXch03d91MS6KF8eG/Jgs4WpJtfd28dbLcppBN9BL+45Z71n3i7VXWbTNSLZeo5c4qD2GyPrNccityPuxSsNcmk+XAPx53TVBLloOpJOqV9To+ktjUnmZXb1PgP3THljZyhkVXY5zRhbKx5T3b25ldNljfsroaN67G+eyGTzBx6HnOYHCu8DqmLNKpXxckKP/m9SGmFkvsnfoHEtYiQ823M964B2EXg/ZWK+aqrhS9hq+WE66su/tUoDBTFE+RZSBNr6UqpGoqeEnh3uGf0Tw/xVvr/lF1AvcaQJ2sf5ih6CiCYOMrqZmoM/AqEDVMwZJeRiyQrWjezVWrGuD1ihW0ZpoUQVO63L8nFLdam5IqNzbfwnrlZV4Qs5SEw1E5g8Y8dy0Osrl9F0IIppaKIGVQ3dVicfDisURjO6/3fg2EQk/FDdjdlKC7KeF9Hi/oLUYDJSjPXDVj5cr6aom3RWORZ84/w9jyGI5jb/LtOQAAYzlJREFU0SVT7Gm4k+6WVmZHHiU78i5LxQdpSEQDbXN5lvbcaWZSW6NqBCkDMuwILZL5XA2dDQk6r6tRgQuRdUeCY3jOyjUylgD7+lvLv6zfx0TmLDPprW6Qq+rBOL1K2B5Zd+KN0cdDrRaGmuZTu3rW9HnqqOMjBy8pqkiLTU0actyrNntkfUd3A28MztK+lnGba0FFz7qoMJir1R+8Fggh2NOxh550D08MPUG2lOV5Z5h+UnQYS9jSnaOraDE0zwNjaM4NDANy7ZP10Hl1L3noG3Lm9VZWgyKtiApIkU41WbeilW3NKWGusYmx5rZgFSuOEa5iQZrodqFM1v2kc0TdFZWZp6wFOmYPMtJ085qu50rAr2wV9Sby6Q0cF58jp7ey03O4t22LidwEb068yfDSMHOFSRQJvTSwq+uz3LPx+uBc0nPWl3qaeKaZhKZQ9JRY2URUbeZPX/G/fyLoWTcj+69dQwafSbn/popjVc+jtw0My8EWOplEKI5p2YiUnou0UC5KQVJHHdcKhBB88vqu4PfbNrfVPO6WjauvtWFoWpx9dOPIJSaA0eJhTKeA5Xwcx5HkpodJKnqQANy48Bq09IPXT26o6fKYxVg1WS/L4N3xbI7QIi2dvtky3mSp5nSchZz7c8lbf1RzCXAnGH3QrUfvBepk/cMMj2j5O5+pJtdM1sPVUR8JKwtSErcryHqtDLVHGIt6E22KIBkaRWTiVm+EY+NYBi0kuU1sw+y+l0PDB1kQBi+OPUFWDnBn7508cmjBI4u+I1EMcG/yxoQeGJqtJmUMAtIaZN1UXRO1gpfZi1k5anWNSyk5NX+KF0dfxLANdEVnX3ILGTkGWgqreRMADaVJnjh8ji/dti3yevH6f2PrbBaJQMpbyk+ESimafvnzH8OGPY6UOCW3si61NfgV6EnOtd4JQMyRQcJHqVHukUU3yJOxCrKuJfHDp1i6IUiY1FFHHRVQXa+N83N54vayV7kVqJ7xXEJX+a27Nq/q4HtR8GbS+pCqHiHrUijVbU0XgY5UB1/Z/hVeHHmRSV7kpDKHNf0iu80SltJEPBYjazsoihUYVQY961rClTSGLyBWXVlf9eNJOzJDXdQaA2q7ZNBUkuhOAdUxgvFpC3mD4bkCN6xrDPa1pqROtuCes2YS14iS9XysDUVaJM0FYk6eAu41+/kAYUTdlp1QZb07d4qW/BkMLQ3sB2CpaDIwneOGdY1XfBb4UtEMlHcFrYnfvGMTf/6S+5xUVGYpcCj3GpOnT7jXLgSbY910yiJpYsSVij3FcPcaR0tB3Lten6zHe7l/RyfPnJgCyr2zQnXJuCq9flTHjiRFZA0ZvKrFaEnpTBetar8By6Bo2jiKFlWmJFs41vtllqx6WFtHHbVwyepwb876DU4HQ4kbGS0eZso4xYtjkr7YreyefCQYtebDyc8RV903LGoNBIXyGmRdymhl3RFqtKVT9Q0n3b1EDanFbMdxP5jXzqPWmDZ1LaDes/5hhlfB9uFoybX3adWYja06JppTLMvgPbJeS07mV3cLWrM7x7Sl3CtftPBc3x1ss4RpOzhKjC3NO/i0sontsgVFwujyKD84+QPOLr9JrOSa/NiKTjxWvrYb1pWJor1KdcRXCtQyEjLVJJmEFjGYq3S0LFgFfjH0C54+/zSGbdCd7uar132VrXq7WyFQVNRUKwW9GYEkN3Gm6n3mcu5C0lCajC4WjhkELl/Zt2nFz7BWlGWFrlGf4/Why1ryolVg2g6o2oqVdZ+sk4ga+jmhyroSv0i36jrq+ChBUbzkIxj5BbciqGjEQ9VCVantxXFp71dRWVejbvCOUC+7lzemxnhg4wPs0/vRpMJkYZKfm+cYFyXaGzN0NSZQHCtIrgY93Go84iIPICoSjGFHb1kjheFW1svkV2ElGbz0CLHrF2KZ7vr2w4MjPPXuJG+dLxslhZONtQzmKivrQ823B/OJIyPavEjYDyiDc4bIelL11GNO2TjtZ0fGeebEFC+cdturlksWuZJV21jtIvHCyWkynmFfUWsKJg0sWVMcYJCXlVFmrWmEEFzfdj2/tuPX2JfcTBo/OC6rFJZLFsLbaxzdJ+vu36igN2NqaXRVIeW1RfX50nXPxFT4ZL1CBo+UNUxsNfe+kBamXfn3LZEr2dgiRldjNI7Jx9owKlz666ijDheXvKIE7VxFuvWt9CdvQxEKJ2ZP8/+8/peYWFUxpGOWsPKen5aWCe5xUctgzi8W2SaOI6v3KW8P9dfS8JJvOW4CEE8aL/x2m2sM9RTkhxlq1EyoFgFf8aV6tBpqCx1VmiSsbCCDL6ouEasV3DmJZgCW4p1uBizVxDvdv4ypJGii5G3ONoZRIG/Y2HEdRVHRFZUdso1dPZ9lVj/HwMIA08YZKD5PIxbpxK30taTobUnRnolHqjBVm3YIfvKgVvXdVJJk4lrQs67beUbnchwfW+T6dY0MLQ7x7PlnyVt5hBDs69rHzV03owiFWX/+o6KjKoLlWCdJc4G4tcRC3ij3lkoZJAocRYsGI45LqiWCWOzyK+t+D6pfqZAlN2CsZdyxGqR0P5finQspy42GUgYqBZGIEnIn7HcQqwdGddSxKjwV08z0NA2GjaUmr5zsvRIVBnNC0SKj2qQ3s/ZKoD/WSVzmOCVhRNgc5By9C2/Ro5ioEixvPSyPbvMr66GTeGuW398uQ9WZgt5KypwlDEXaQQ8kuL2LTmUS1zMhMtQ0jcostiM5MTJFX0cDS0V3PT82tsitXntQmOzXivGk5a6vk5mdjDfspqg3Yyy7lejoPHX3OmTlKLQIWXfTAapjUrIcErrKeNZNBhweztLZkODJ466z+tbODL90Y7WR6cVgcfgIreYsjtDIxrqZyI9yKv8My9Y0PSKHLgXdoodf3/nrNHoKqhkrZOjmVaveGJzj1VMT3DPrKdf0NGgxFD0BmGTjrg+Lqgh+dV8fJyeX2ONPbfESNIo3SrVydJsqrWq1h6qjeklks2IOPJaBLSW2qpPQotW8azBGr6OOK4ZLXvnjjVhqEmHmaM+dpVmJoyXuZrD4MnlziJfFIrfLdSRClFKVBro3eclQ0wGPUGtU1qM9654MPuK94sXNXvLQCX0S25GRcZ2q5+V0raFO1j/MUPRIReZiyJoSCweLglysncbSOClzwXNLJ8hQ1yLry/0PcmR5M/lYe/B8LubOG7cc063WYpPLeeZnSoyiJT05i0FGibOv/1OMLo9yePCH6OYhjio2OXWCzxVHeXD7rqr3XO0G9KWetY4x1SQxTUHG0kjcERC6XeD502NM229zbPYY4I6qeKDvATpT5VmxwSKiuIuH76Cr23mmlkplsm7kAtMbR2iRYETaJtJ7/EoEysJzx1Sk66q7kF1wn7jIyjrgtlIoAjeVYCP9JcEqUii6C2OqoTnyEhki66JeWa+jjtXhJVH99iJbiV30RIg1o6KyLhUNGZbBX2LPei0YUpAhxvZsIzGZ5aiic3LxLAeMARKkaM0e44bJI0Efo6zoWZeIYKJJeB8bbLmLruV3OdN2H3sn/z6ylirSjlTWAWyrQhXkyeAtJYajxMApoTolCiWLdYuHaS6cZ7z3oeDwcBWmFtkTnsGcoWYo6s0AmN74yvA89eA6K9zVCfWsK9LCxh1TulSs7sf2JeQAZ6YubIR6IewovE0ROJ7p57j5Osqgw7I1hyIUtsR72VpoZErdSkYLreMhQz3HUwm8fGaGuFOgaDk4Qg1GpslkK2SXgpFtS0WT67ob2L8p1M6guGNgFG+UKo5ZVeGrnjhTjm0qR8u5gblraFg5S/1arKjVUceVwq7eJg6PZNnaeZFFFiEoJLrRi2fZMvc8AOmmW9Ay97O8fJJFYfAiI9wu19HgqXI0xyBei6zHa8WplTJ4LcI7fDXp8eEZtu+2sGX5Oct21xTHW1UUaV2T60BdBv9hhqJFN7kafegrQQ0Re0uJU/CCkIbSBCCRQsH0+tVqusFrcfIeOa+c7W2hBJl0XxJoKzrrW5PBNdpeH2BvppdPp6/nJrsVTSSYVxVeHP8Fjw48ynzRlSr6C8veDc0rfh6hrVyx3rnRNUBTFDWQLxrGCMeWngiI+p6OPXx5+5ejRB2QfmVdqLRmYlje62N2IRgZ4Z5wOeiLVJwKt1sv2LhSZN2VrrvmO45jY3kGc8vyEnrHFS1YBHw3fgCMPEXLwVZ0OhrT0deEvjsiXq+s11HHavCTWz5ptZTYezfCS4uRb70OcNVSZrwZJVRZN9TkRbvBr4TpZXf9SzoFdskO9qX20dfYD8AEWU6MfpsjxltMkHPl5Zo7us3ftCwlgerJsu/ZVp6RPdlwPUd6vkQ+1laVKFakFamsAziVfht2yUuO6rS3uNVdxyxx4Lkf07fwOo2lcZRs2XE+osiuJRT1TI38ueQAtuquibpddlf2zyOdKFm3Q+RdeM+pjlHVigUQW2WM0kVDSqzSHG+LSV4WoyxbM2iKxk2de7g+/Vm+1LeXFDpC2lFFWqiy7tjhUXXlEaGK10+61PcJBlvuYj7ptsFt7qixH1RV1u1gf1yOdTKT2hKY0QXwKusAlhk1DJRWyR3JKvSasUkdddRRGwld5f9zZz/3bu+48MEVyCe7Ir+vzx7k9qmneMBsIiN1CsLiZTHCnJccVh0jWDMMNRXsebXc4IWvjnI8HyahRv23Qq2fT787iRMi67ZlBAla/5g1+ol+qFCvrH+YoUZ71tHXTtYUPRR4KBq7t/YzdehdGr3ecUNJBUFVrX7KcBDl/9yQ0FgqWvQ0pxELnkTR8iWQcRoTOsK7Rjtk2tO7fByLJkjdRSGWxHaGOb94nuGlYfa07+GTN9zCAzs7V505LJRV5OWeVFsARSXBgHOek8XncZItpPX13N93Pxsaas/39YMroWo0JnQe3r+NkZcPsmgVyBuhYKu0HGTzNBkdHeTLIB2hXpa5UwDFd3C3kaU8tm0Dgps2r82VvbspwUS2SHtD3K16KGVZfRD2mq6jpqmkqgLI8L+1kqiT9TrqWA3++LZMyZU3F/XmK7MOrICFjZ/hLfsmQNCkpSBUWS9qTVds/rRvKOST57ZkD5/e8gk2H3uD5wpjTNp5ZoXNrCiQkTpt5giW6AxomaXGiXmkr7spwT+7ZzNvnZ/nwLlyP7nU0xAatalIC6Wisl45J9yXwduK7sm0QRo5lNEDwSGqNBnPFuhpSl6wso7pkld/hjzAL+3fxttPvB60jEGoqmtHe9YJy/a9n1Xp9qUDNBWGids5pjI70FWFAtVmnxcL27E5OvU2jzsD5IWNVDTaY5v5jeu/QFJNUbRskgNjnMYl0ZbjEPPStiLUoy/N8mfxJf9WaERoc+d6Jifc78Fv37u59h7tjeuLjm6D4aZbGfVc8au+kkIE31unorLuxxS215oWee4alL/WUceVxKVOSSjEOwm7F0kEKXMe0LlTrucNxomhIPUesIdRHYOYVa6sZ/zKulZjjQjNWZdSIit61qWn5FGlyWLBjCpXrVIFWa9hOnoNoE7WP8yokDwqFyGD10JkXaKipFzZmm8u55vzrITwjeT3lnzllg0cHcuyt1NlfMglk45ZQgCxuDdiLSDrXvZ+aYJEfpwloTDTsIdeLc3e9fvIq8cZWhzi8PRhTs6f5Lbu29jZthOlciBr8HlWnkuseIvDsrnAK2IIQ8wjnEY64/38ynVfIlFj5nwAj6z7UtJkqoGErqKX8kGfJQClxaBaoDpGJAB0vGDSUa5UZd13gzeRJbeibyoJkv//9u47yq7yuhv/93nOOffcfqd3zah3IYmmQhU2LQ4G29i8Ma8B49gvv18cJ8trOQmshJJkmVTbAccpb2LsNyFAbMcm7y8kxg4G0YsQSJZAqKKRRlM07c7MLac9vz9OueeWmbkzulO1P2uxlubOLWcG6Zyzn72fvdXy/jn/6gXN2HdqGJucPYX+UXAeJ1g3eLAoWLeCuaZ/coA6wRMyETdYd7cXpQO1MzpWSuIMppMFHhjTwKpymfWMEq9YVt9i+ecbIYcALqNGCmKraITGFRwxu/EhhjHKdPSN7ME/HzmOWqsfcYTszKjvOhJR5aKeI4FYDYxMfrBeVAZfFKzbN3wmk71gfaT7GGIid76WLB0n+1NOsO77GUr8nMzJLrfUVWNJXR0a4yoU1Q5o49keBIxRaHLUO+dbBccj/MGmmcusuz1OVvY/D8VKYyjYhrAIIj70Dvqia7wGr1N1evQ0Xjz1IgZGz0AXFqpEEJHwtYjIdYgo9nU9HJC9/gZcmHn9YPzBuuVbeAg4Ja06DyHo/B1a2RDFVWvq0RBTx19M5zIYmFeeKqzctjDvKaWq95zrklWw+GE6ZfomCxQF6xNNjCGETF8qmMus72v6FHQphItO/zMAQIWEHcLur9EdqAcynZCF5p0zNCniVcGUXCt27z0tA6awR4zKJcrgJctJhPkXQI2stzUGWLxl8BSsL2SSkv8XfwqZdclXYmIxDhGqzvu+JpUfrLt/ToQVXLayDtBSzhq9gKWnIQEQbhmLG6w72QoMn4JuWhgKtkF3Fgh2LmuHKi/DyeRJvNz1MgYzg3jh1As40H8Al7VehtZoa9Hx1CciOOH9PBIYBJjTHVJiDPv79uO/Tz+HYWQRFxzr5LWoiV82caAO5Fb83MZHAXuWvWKmMVhQBu/eJ0iWBr1gz7r925AqU4Lq7FmXLR1CH4NhWdClUPE82nHEgs7/J+/9SgfrlhAlxwGaajXer78BOg9ha5mfScj5ygrX5X092Xiyc6UUbEti3J9Zj1es8/yGtmqcOXzC+1rIQWfEj32+kBHEelGLVajGSZHEQSWKlJnFafMs0sxEHQS2WqMAcnOAjYImoiLeCvTlStbt0W0FZeaFe9adYNPkAXCn83B15mTeU7jQvexL3sJqiWCPuWXhStDbiy1EGEm1GfHsGdSPHcLpxEW5rHxBsG7lZdbdYF23+6sIAcWyj1e2sliS7YSUfBuylcWJmssghCh7YWdUG8UrXa/gyJA9qSTIZWxBA+pYLfbIdcUv4Hb/AiZMrxkghMgL1k/2DmNl0v66KnMKgN1UFrp97WOM4cL2/HuHUp/DnPGgQuQy5f5RT6V+RC9YL8is+7fWlZpUQwipPEsK4P36G8CFgVTAPmcfqd2Flf2/QG90LerGjkCXQsjKMdRHVaSySTDYo0o1Kexdd0qdz9x7dbsZs4DF5fx+FFIusy4skTe+E0bG6wsFuNtDKVgn84lTDp37svxg3T/DUDAJVrDaa74G2HtMJuLfK5Y3DxHI20vPdDub5GaXJCcTa2TtGwDLGWeUCeUKbFSnw2t7vB2t0VYc6D+AN7rfwNn0WTx95Gksr1qOHc07kFBzr5GVAAISh2Za0KSItz80DQO/7PsZ5OQALGGiSqrFlVoCZ1kV9HIu9G4ZvFv6HYhAkRhkK4tMNreXbmhoAFnD7X6sQfOXVjrBulmpzLoctKsERRZCc8rVldD09zuWCtbd9+XBomC9dySDoVA7AECt5B5LQhYhq2pp3tcpZZLg5hz5F1KX1oXzusFn5ETF9vlWR8M44/vacsawuVVI3LDP/Qo4VqAK9U2fwNLmETz3/qs4ycZwUgygv+tHyCgbsLVhKxrCDbhkaTV+edoeGbmsLoJkcBtGus4gpdSgY+RtmMKALPIzrUZhA7LsKIRwsjkB+zwc0oe8bzcngujy7Wu0Soz79HMz6/4xc4wx9MXWOsH64fxg3b1mwMnUG/49624ZvJ4bOeQ1RtIRskahIZfFNi1R1BOmkGmZ2Hd2H97qfgu6pYOBYUPdBlwabsP+d09ijNs3umubCjL1zvWIw1c2amQhRO73wYWOx18/CW7pSDjB+mBoKbZMpUEVV7w966Zp5cYv+TLrJW/gvWC9dGbd4oEZrVAhhOQwwLvvc52NrEJSbYYmhXEycQkEkxDPdIEzIGTY25k0KQQwDmmiW0UnKSZM/5z14tFt3DJyjSodkqXBNPIz64uxDH5O77R3796Nm266CS0tLWCM4Sc/+Une9xljJf/78z//83Hf83vf+17J12QymXFfs2Dx/EwtnyxL7JO3HwQcTJLyZtxOJbNedDPBJe8iKtxyd6exnDsyztDTMEwLp3u6IYBxS/4kLuGC+gtw+7rbsbFuIxgYjg0dw5PvP4nXzrwG3R2LwxW4awaaFEE8KOM0RvACO4mzmS5ITMKWuh3YKF+AEGTIllZe4Ozbsw4AUEKQnbJ6PZ0rz3z14Ie5YxZa3t5HrwyeyZXZqyoHwcAgWxqSw8PQTAGdh4oXTcrEnZsiyResm1oKlrD3JxYG5IrvcxbjiAxCKiralPelzmd264h/ce2atY1esAkAWTlasQZzvLCpp7MNyw2yUND4TZEkrKlZgxvlpdhhtaCGJ8AAHB06ih9+8EP85MhPMKR34cu7VuArH1mFm7e0gMtBfFB/HXqja8G5vbXKv08cAPSCzLrIJiHgBOsFW8Mal6ywA1TLgGFZgGlgxYdPYtXZn9uvLTydWWaum3vBaNTh8DIIxhE0klD1Ya85nXACcq/pqT8L5JTic2HYvUZ835MsA9xZ3Ha3TEx2eu0c6cRTh57Cq12vImtokK1q3LLik7iy7UoEAWc7QAB37lyKGzbm/z30Mt7CzP3cRjrvl+BeE6oyneDCRFaOYf2qFVjdOIUSfW/xXsCyTK/HgD+zXvJlzlY9UZhZd/bRux3p/dxmtN7YOELIjNLkKMA4DCkEkwfsXiGcedlyt6nzRPfbTLjBurOQVzC1hPky69zK37MuCw3CyD3GhUkN5iptbGwMmzdvxuc//3l86lOfKvr+mTNn8r7+z//8T3zhC18o+Vy/eDyOQ4cO5T0WDC7CvbXOQoRLmnDpKl/eDENn9m5arvIyEJMF6/7SwWjhXmnGc83pDDezbv9jc5v0aNkMXj7aD/3EKUQAZKWJV+pDcghXtl2J9bXr8UrXKzg1cgpv97yNQwOHsK15G9YE6ryM0Zik4q1AGu9rPTCZgtZQHT6z5iac7uc4IHUCsFfjyrpn9easOz8jY2CBCIAhMH3Me5qbCXHfO69pkbvnjsmVyQTIqp1ZN3XsOXIKLbBHCU27cZRzE+rOQwYAM2svRJTKrPtXLSdq+kcIAfgUzsuV4D8PKBIDyyS9rwWTK1YGz8L55fxu13svWC86Lvv3wDlHPcLYKi1DvO1GaPJxfDD0AbpGu9A12oXqYDW21G/BqupV3sKCO0mDCQthfSDvfU3dHwwL9J49C8C+hsmqP9BjsBLtYKNdkIRun6MzQ1CzA6h23rOoG7yRyWVxCirXhKRgJNCAeLYbVZnTsMQq94Cc7wcBQ8/rDs98wblpaLnrC+wAnrnVCM5IuJLd6QGMaCN4uetlHBs6BsC+Por0KgwN1uNAEGheD8Cw93caPABZYsXXHueaxvzBup7JH5VnGYCw0JJ8FwDQH1qGpeoEzVxL4bnrnjB1X8PVia8d7g2621DOZRluZr14+s11GxqxviWOjpppjDElhIyr3FtXk+VPO3Hv7f3XpYMNH0Pb8B6YXEV1+kPACezHS2y58YNkaWBWfqWNZGmwzFxTZ9qzPgNuvPFG3HjjjeN+v6kpfyX46aefxq5du7B8+fIJ35cxVvTaxcp/31W4V3HC13F7tnpEO4veyBqsAuzMupMId4P1mzaX7jCe0XNLV0Wl0Ix5pZfefj/nH1swaF9EjWwKBzqHsMXpFpmVyyurqwvV4ablN+F48jhe7XoVw9lhPHfyOfxSiaGeZTAMHS+LDxCpakQqPYZY9Epc3/5xVAercEoMw2TuDMgs0vokXXeFAPPmrPv21wXs342bBYFlQfV1LJat/My6MJ1VQ16hf25y0FloEAjqdsmowUPT3r8nglUA4L0XAJgZ+/+LJYeLFgH82fRVU53XSch5RuIM79X/Ctb2/SeO11wx45/nv8mROAMT+ZnnSjWYY7HcNdZiMizmBHFSccbzZNWlaHG3RnmvkVAdrMXG1uXY1rwN+/r24eDAQQxmBvGLzl/g9TOvg2sdMEQduK87MBOWPZZOCiFoJGH49oif6evHid4RuPskFTUX6I0G6tCoRsFgZ2hMC3YzOtjZGOaOFvNzbgItJiMg5WeCLQEMB9sQz3YjkTmVC+rdpqRyEMiOeME7kNuzDgD7T/Tiyo5cUCkJHZIbrJt2hrvwntOwDLzb9y729OyBYRlgjGFT3SZc0nQJvvPch2AM2H96GB9d3whhpO0RZzxQeuuDN1LN8m5uU6lR9I7kfmeS0NEwdghRrQ8mV3Amtgkrp3qdceasA3aZq5c9m+R6yN3MuVE4um38zLoqS1hRanwcIeSclHt/afL8XlpuZt1/DkoGW3Ew2Iq24T1OsG7fI1u+c4NUMrNuj+70l8HLlgbhK4OXhLEoKz4XTFqsp6cH//Ef/4Hvf//7kz53dHQUHR0dME0TW7ZswR/90R9h69ats3CUs89/4yVPMYNzsOFjiGhnkVRboBkW0nKV9z1NjuCKVXVY2VC63K2j1h7pVR9VS2eL3Quxe2F1yuAjkQgY7NFtwtC9DMJkmXU/xhiWJ5ajPdaO/Wf3463ut9CbGcAB0YkMt5BiS7Ak2oRA3TWISLVQnXJEVeEwnP17stDQnAjh1aP9EBDYuaJEAx7L9DIbzDf+iDuzxd3MupXsguRb7bPnPOZuytzMupAq1IxNkp3AX0PQsANsTSoOqssWsjNkjaPvoWXkXRyrvgJmyKkUKDET038irFSWjpDFSmIMw6E2vL7k18tPT1SIzDmw4hro7x5CZ9XF3vFUghTPdQcWYLkscMB/zWB4fcndEExCi/uIL1vu/jkaiGJn605c1HQR3ut/D+/2vYsxfQynhvega1RHrbwUGRhwQ/1UoMYbGefvBv/s3iNYDd8+STVXBp8MtiAQUL0yeNMSzqgg5+cpuAm039xpeMTkou0DlhAYDrZiyfBbSGS7ck2N3My6HLL3rbvBumV55Z7252m57zmfn0wOgyPX9d6fIfow+SFeOv0ShrP2Ob850owr2q5AXajEtQu5cvHCTJeHcTAGZ5HC/pwPTuZXM3JhoH7sMADgdPxC6HIEqjLFShHGIJgEwICwDF8Z/CTBujuxpqAbvHCCd1FiUYgQMjMKKywBoC6mIh6UcawvV1lqcDXvvjDjbHH1P3bFqjq8ePgshDsu0jLtc6+THCssg3erPyVLBysog5esLCwjlyCjzPoc+/73v49YLIZPfvKTEz5v7dq1+N73vodNmzYhmUzir/7qr3DZZZfh3XffxapVq0q+JpvNIutrFpZMJks+bz5y/0ILxqFMMXBqrEmga8j+R6AZVt64Nn/3xlKCioQvXrF83NU2VrAfjTkX3oBqB3+ypUE17b2HJle8UUNTIXMZWxu2Yk31Grx26iX0s90AgAa5A59a/Rn88C37xsOtOGivCeMl7v6j12BA4LVj/QCAC9uri7upW0Yus+FvhqPavyfJqRoYOvUeAGAgtBQ16RMAAO67wfDmALMplg9OwJJUAJq3bUGXgtPOrFshu7OnW8q/qv85WHH71pqpxcH65iVV+MX7vVhaR6WGhEzGO4/OQTMszgDEm7Gn9X96nz/O9Mupv7dvP7gkckEvgrlg3eABJ1DLSTVdDGP4DZyKX4g1BecsVVKxpWELNtVtwpGhI3g69TI6xWn06kfwjNyDeh1YIaqRVWoR1XrtF/nLzLP2NcWtDFN856+k2oxIOIQM83WDN3VvkUESWnHRuTNmzGTFzUGFAMYCdqAsWZrXhd7bq+/+ftzzf8FigCR0uxTeoVjpvCafsmlnxoezw3il6xUcHz4OAIgoEexo2YFVVasm3FYlfPPISz7NKU+3GzY5x6S7/QDs9niSpSGs2dfIQae5VNG2tzLYgXkWqXSm/GDduUEvLINPp90+OBSsEzJbCpOBd1++DDHVXnDVTAv//V4vDnWPoKOhClKnrwxetkf9rvH1ubh4aQ3Wt8SBzrN49/k3wWFvxfESW0wqXQYvNDBLzztPS5ZuB+vO19y3+LiYLJhg/bvf/S5uv/32Sfeeb9++Hdu3b/e+vuyyy3DhhRfi0UcfxSOPPFLyNQ8//DAeeuihih7vbPGyFJCmHKx/Ymsb/voX9piXsCohqTYhpVQjK8cgmDxp8DdR93FWkEVmbvM7WUVjXMVYWkPAmemelaLndCMbVsK4Zum1iLz6X+i3NHSrW1ETLp45H1Qk3LptJfpeUNGZzmIom8tylCyb8W4CGbjv5+FesD4GIQR+ue9tKAAGwktRlTll7z307f92Gw75S+nPlR2sj3jd+3Uemn5mvWDvKQCYWafXgFL8e9zclkBTPIjaKN0sETKZSmWyy8V83Ti8YM7f26RCxzPu9UGNe380efE5YqztSrw1uhZgbNySfInbzeiuaalDevgAerT3kZJHcDrzAU6zUQhU4wIASyHAheWNOAta9jXFDda5rxnmiNqESDgAjTFIwoDmdGPPZda14gZzpp3FscaZ5CGYZGfdhZFr5Oc2mHMmn3jnf1PPe3/J0mH6mqepRn6SQDJHsafnTbw/uA+mMMEYw+a6zbi46WIEJglUM7oJrudG2JW8NjjVbxyWd1whaxSjAFJKFcL6ICJav53hZzLSst20bTrB+ogmEADw4qFutAftG/LJGsy5C/ysILN+uGsAMoCBLFV1ETJbAgXbbOPBXC8KVZbw0XWNWFEfxdLaEIa7/BNIYrh9Wzsa4vmxWzggI+vcVzNh2duNnIU8UTjpSsp1g5csPa8JpiyyMIz8ytZJBnwsSAsiWH/xxRdx6NAhPPXUU1N+Leccl1xyCQ4fPjzuc+6991589atf9b5OJpNYsmTJtI51tuUy69KUy+ADMsetF7XhzHAGy+siEEzGvqZbve+fS/Owwv3ZXudgWYUicUiWhqDT1Xe8TvBTwhjiUGGB42igDuFA6RuBeDSCpGx/fudAynvcGCdYF84YCX+VgVsGr5hpZNOjUNJ9AIChYDtqEjEMDQ2CGf7MulMWySuYWS+4CTak0LSb1/FwddFjPUN2lt3dn+/HGENTYhE2bCRkBsz2VpHJFu0mGwVWLs4ZBsLLUJM6jqwUzd0/+YJ1o0TFlMx9DUgnORSJc8TlJsTlJrTW7sCpob/GWbMHPdzAW6ITH7IsZKMTumkiIMtQLfuc7laJsUQbUkoNRtRGmDyAQMAZe2k5Jeb+MnhhAIW5dStXBj/e79XgAQRMA9Az9k2kc8MpB8IwYM8Jt5zZwHkZIaHD1HPXCfd6KCDQixQOpP8bYz2NCCoSWqOtuKLtCtQEixdWS/nfu49hc+oMFAAmU0ovijh71v1l8JJmL3aklWqE9UFvm9pYoBZgHKsbY6gKT32R1s2iM2F4Dfcmy6xLbhl8weg2Sbjl/ZW7nhJCJhbyxQNXr6kvut8MyBxrnPGQed8LJooCdZe7vZQ5C65eM87CxJZbBi90cFFYBq/B8jUZpTL4OfSP//iPuOiii7B58+Ypv1YIgXfeeQebNm0a9zmqqkJVp16GPR/kuuXyKTWYcy2pCWOJv3Oq7x9ZLDj9vx6cF2bWnd+vHARjDLLIenMYs1IMKxqiSKZ1bFlSNe3PPLTiLnT1J73AdUt7FQZGNbQkfNlh2d5jL1uafWPl/LylM+v2nnU7A+DLTDllnoqZRnqo1/kZojCkIOqr43aw7huXJLzVwspm1v3/tzWpOANerqIRTAAGU3Y2JRGlUndCzsW0K16maVldBG3VoXEX1JRpjngsZdsNt+OFn/9f9EVWw905zYITB+v+38dkC4z+ILM21IDuxv8XjUY/sqIXPNOFJNMwqO3HP7/3T9jauAWS6fbwiCKiSpADQexrtheg4yHFnvntlH6bVkEZvKUVN5izDK/B3HiHavIAYKbsc76wvM7GcjAKA3ZZZtawEDLzG/3Z84HzM+tj0HCQ96OHjUE3ZYSV5fhox5VYUbVi0t+VZGZQlzqKvshqGFCQHB1DrXN8Jf8KOiNWuTBziwhZO7vv718DAGNKHRrjQVy7vhHTURUNITOURCLAvMWMyUe3OX9/fddSmIY3Esqs4OI3IWRily6twanBFNY1x7G1vTjB48d9vTnEBCOlmbNgx53mnsIdwykVJvuczLowwC275F0wDi4sKGYGllHYM4qC9YoaHR3FkSNHvK+PHz+Od955BzU1NWhvt/dHJZNJ/OAHP8Bf/uVflnyPO+64A62trXj44YcBAA899BC2b9+OVatWIZlM4pFHHsE777yDv/7rv575H2gOMF9mvdI3heNlp8viy6ybTIHslpHLdpApWTpqUicA2I1/VkUC+PjmluL3mQJdCiOr5H4Hu9Y0FD/J10ldErrXHd4oVTdj5W4q8ubZq3aTPMVKQx+1R/5klAQa4ipY1r459e9Zh28WfKVYkgr3/44mhSEmyVJMZLztE1k5ikuWlpfJIYSUNtvFuhJn+PTF41eGVTLTX1ddhZNV2wAgtx87b896Llh3v+3P7E/Wmd7/fUXiMKUgILWiDa1Yxs9gxNqPN1kAY3oKr3a9ilPaG1jOAME5biq4npiWBUh24Gp3gxd5fUnGK4O3q6smyqy7jdCydtm88zhT7HGaXBjI6CZCTpbe+9mE4e1ZN2DhmNWJI2wIjAlwwbCUNeETK25DY8yuEsjoJjoHUlhWFylZRdcx/AYaRt9HXeooDjR+3F6Qhj3irHQTWMk7DksIGIaJwX67SiytVOU9NVjdgk9say/585ejoz6OQ0M9UJkG3bnWlptZZ5ZTmcCZL3BnlFknZBaFAhJu39ZR1nP9W7EmutwwZ+GYwYKA8LYMFfY58U9+kJ0JUhk5jqg5DMVKFZfBU2a9st566y3s2rXL+9otRb/zzjvxve99DwDw5JNPQgiBX/u1Xyv5HidPnszblzY0NIQvfelL6O7uRiKRwNatW7F7925ceumlM/eDzKFs3Ubg7OvoTFyMlgrP81Xl6QfrzJdFNrkC1f0XK6lehiJgjsFiMoaCSybc/16usv55ctnLcEuW5u2p9JfBCyEwmNJR7ZRI2s0ufNmgQBScAQEzBWPUnumbkRP4zMVLMPyaG6xncu/njvKp1Og2AELK3QRn5TgaxykzKoeqSHi//nq0Db8NSehe07rBUAdiQbohIuRczHZmfa6451DmK4Mvxb/XfbJfjT/GLOxGLPMAVqIaXNmGTW3rcHBgPzqFhqMshX7tddT3hLHeWgGTjYBbETQlooCkgIGBu2XwZq40XRK5LLvHaQpnMnncvf7ugi8zsvYCrxAAGISsQnaDdcMERH4ZvGxpMHQNZzCKX7KzSDP7OtHCItiIOgyzJsi+gPaZ/WfwYX8KW9qrSi5Eu4vfsWw3gvoQFKdh6Lhd07nsdIO3S1DfONwFptk3vW1tbYiPyUhm7GMyo+c2CldW7GMwsykcPWuX2k+WWXeDdcnSYFgCAc7srQZwsuqV6pRICKko/6lyogXZXBm822DOzazn33dKkgwBBgYByRmZnJETSFjDUMy010wTALhVYgTnIjCnwfrVV19dPCqlwJe+9CV86UtfGvf7zz//fN7X3/zmN/HNb36zEoe3IGSWX4e9yWXIyvFpdwMfz3TK6l3+UWd5DW4kOe8f4lBoCSwuT3m/fUnl/ANl9k0UMALZykKDvf/cXwa/58NBvHj4LHY1ZRBCcWYdATuzYRoazBG7DJ6Fa+ybSafkx98URziljkyq3D83KZALzjNyDNdtmF55IgCEFAlDoQ4MhTqwdPBlL1gfCC2tyCIKIeezoCLhmrUNeO793rk+lBnlXn8kXxbEHa+W/7zcOWUqmfWAnP9cN9iTILC6ei021a9H7S9/iX3aUQzJHF2p4+j68DgQN9A3aqJJacXu7hoEzEEMw4Bpar7gerzMutO3hMvjlqG7C77MzHp74C0mAVxxMusmDFPYwbrv/TPWEH7a04lf8m4AQFjI2CDq0KY0QjJSSDnd4F0f9ts3qb88NVwyWE8p1Yhn7QkoW878a+73NF6wziRnzrp9o3z8dBeWA9ClEOLRuPfzZuUYRLj0eLhyMWehmhlZ6KawR/1NEqzLzjVOtnQYloUAOIRzo15qewUhZH5wT5UGD064fYc798SSU93j7VkvqLqpiwVxnAcgW1lwL7Meg5W1z1/vf3gG7h0xFwb0RRitL4g962R8six5oxFKzUGcipu3tODpd7q8r6fbsAxA3liVwhJC7hu10xe2x+lVYqGhKCsy7rHZF/qo1oflAy+iN7oGupkrmXzxsJ0t33u8Fzu8Gbv+mY9BSJIMGBqsoU4AgB6oAmCXPgIAN7MwTAs/3HMK1cf7EEdlG8xBzu1RV6PVqItO/+bFvyjjZokAYDRQYhsBIWTKNi+pWrTB+mcuWYJXj/bjytV2QJfXjFMUB+uS73wz2SXGv72+8Prmzuh1g03GGJpFEFHRip1rPwE9kcXZ9Fmc5WcRDhgY0s5iKNuLdLYLZ7mGZN+TkAwLA+hBHCpSxllkzfwxYe7oNmuCbWaG2+zTyNjBPZw97pLizXTffbgP8dGTqLMEDFj4gA1inz6C1nQMkmBYgSqsFNWQwaEFaxEaTUO2MpMmM/xkK1PycX8VVh6ev180bNk3wVkpilhA9RaoB0IdkM9xggBzFui5MyXFKqPKjDlbFrjQ0ZPMYlmdDEOzG95RsE7I/KU3bgHOvIgPq7Zhopwfk+3zjGTpdmWWWXpyUlCREAyFYYxlEUMKAoDBQ8hChgTda84J2IF/ZhG2g6dgfYHzB7nnGqwvr48iHJCQ0szJnzwJEckFeiYP5B2ne91PK1UYDNl7YCrRobjc+xoh2QH18gF7Lnt4cACmdY33/YgqYSxrQjHtGwOdB/NvKhkDAhEgq6Hr7BAAwAxW2d9zFimYpWFgTMOZ4QxqvQ6XlfvndjJpwt09ZAQS5/Re/kWZM7FNiGk96I2snpO50ISQmTNZJns6WqtCuPWiNt9n2NlZxUxjOJh7/II2+zzlXxycbIuAYeZO6kEl/wYuEgwAY3YZt9f918nk14Yasbptqf2QsDCUHbID99RZnDy+F0kxCGEZOJtN4hQbAdgIRo29OH0sjQMjjagL1aEuVIf60S6MmhqsgDxupVl+Zt03QUSS7WDTMtCbzMIcHUMWIzjI+u2Sd1GDFqUGFwoNEeQWSQ0lDsZyQXS5FGfrlcWkvAZPKXOc+wKnwRyD3Q0+6ATrmhSBLOf2uQ+F2jHcO1r+gZTgButuaX5Z+81lFZawb+T7RrJYVheBmXWDdZpGQsh8pS3/KN7taUJarkLdROd4p+G0ZGmwLAEtZZ9nRs3i88PSxhocOTYIro3CBGBJCnQehmQNI7+sVsAS5x7DzDcUrC9w/vLxSgS8lWrMYMaavT8bLJC3kDCy9AYMjL2NY9WXewFhZTLrZZLzV+UF43l71t3mGIqVgSXsG8/Cm8o0fI2TwNCjuXPkfZl15z25cIP1ymXWOxprgH77z/o5BusAsLw+gmN9YzCkIA42/Oo5vx8hpLSZCJjLVeG2JuN8BsP+xk+gKnMKfZGVuHJ1PTa1JrwtNf6gm03Sfs+/cBz09VBZ1xzHNYFWvPHifnD4upk7ex79Ey4446gJ1qAmWIPV1auxLvoS2gd7cCT+UVyW+AC7PxxEEhpOC/v9h7PDGM4O4+jQUYj+YzhtHUPW0GH01KI904j6UD1qQ7XebHc3y2toGfQOjzgTROztXm4ZfNocxkHtTTDeAwCICAVr2HJcFd2IUxjI+5kNNe6Upxslq8X8f32GUzpMIVAdkqA4mfW9Lb+GJUNvomHskP0rKTHr3v7FSN6igBBA0BnTpkkRyIqMwYZtOHN2EMNqK5bXF4/wnAp3C5jkdJvXpTDWNsXwfvfI+C+SAmitCqFzWMNoVsfAmAZj1H6+wVW0VU9/AgohZOZIXEJasTvGT9jQ1G346UzGONplV7WmWPG/bbdq1d2fzpQgNCmEoDFc/FzLKHpsoaNgfYFTKphZBwDNqEz5iIjmysoZLNTHcsGt3rABH/TU5j0/dA4z3b3PLDNaZyL/H7LJlLwMDrN0JDKnETBHIYSAzkNFzYWCkTjSI/Zew4ySgOmUZLpdKyVL9/bBe1mOCo5uu3BZA94+aP85U4E59ee05YEQUraG+NyV8EoVHNs2Hs4YNDmK3uhaAHbDTn/vC3/QPdnisD+b7T90VeHgkuQ0SHPmhFsm4Jxr2Xj7tAFnvjhDCAEsVarRK+xrUb+0HE0dn0JjtYazmbPoS/Whe7ALEEAGBnrSnejLnPLe5qiegjCiGDOT4BiB1XcGe9MnsWFUg6XEADkIExYOiU68P/ZfiFj9qBUMK0U1VqIKo4jAMrWiwzOVhD0LvmCesMv9lQkh8N2XjwMAvrS9AfZyNYPOgxgL1AFOsG6MG6y7c9YtWJYFRdg3wYYUhCIxDDVcig9NOzi+anX9+L/PcjjXPtVwmsvJYdTFVGCiYF1WococXJjoT6bx/VeG0TZ0Em2wg/XCbv+EkPnBH5+P15gTgDOdyV7QtNJDAOzKILPUNhcnEeaeE5kSgi6NM1q4xHl1oaNgfYHzr1oFKhCsV2riAQvkVuLD+iBqI76mQ75/vO019kW7ZZyZwFNR7p71bM1aiBNH0B3dgOaR/U632dwixZKBVxAfPAAAsFQZRonM+tqOZuzt/gAAMOQr9cxl1jNFmXUhVS6zHuC5480ymoVOyHz32W3tePvDQexceW7Nus5FpZuQllJYOVB4VvYH4Poki8MbWxPoHcliRX00rzGdxJgXbHJh2dct3xg2f2a9iOyWrWu5hkawszsBScWSeCOWxO3Rd+nhFN46lsL76gZsb7sUfek+nE2fxUBmACsaVHQODGMY/Xh7rAdZM4VUphf9rBMBaGgeO4KT1ocYYBlwS0M9q8FVQkKAx6CIjF36aRRngAzVbu7GTdObFyyEQG3qKDoGX8Xh2o/gpcPV2LY8N1azf2DQ/n3yIJbWR5GUlgGDLzu//3EWiX3bsoRlIIQMNLjbvljevUD4HBfTubN4opp2sC4CEWxZUoXBMQ3L66OlXyQFvPubMwNJQApCdhYUeCBYtC2CEDI/5PUtmSSz7l0uUs45TAqV3ILJCypimRKGzktX11BmncxrlSiD/8i6Bvz3e724as25raRzbmesJaEjUtWQP/rM9+et7VXjX6ynaOeKOvxk72lsap24LDzbsAVvtNVBsnQ7WBc6TDNXblk/8h7cNkN2Zj1YFKyrwdxixEB4Oba2VwHwNZizNJiWhWi2GwFnnx6r4J51VrMcuhTCSKARlaiFKNXI6LI5DCoIWWwa40HcuKl58ifOoErOWB9P4bnSKkgPM8aQCCkYTutommSRVpE4rt9gjw3rHcnkPQ4uAYyBCRNH+0YxPDiIassCwCBNsDDKvL4iBiwz1wCvVCZbmDpkcCTkWmyq3+Q9blomBrOD6Ev14fUDr6Cv/yTOQkAIA4MsiywGMTS0F1ySEBUKNiqb0QENYbyNtBSEambtUXEFGSCTKWABe/GVi9ziQ0ozsersfwMA1vX9J94INuPCjirvdekxu7zckIO4ZUsrGGvD//nPEWQsafzeI/4O95YBVWShwc6sa4aVtzXsXCbDAMXXPlMOQ5E4rtswwUg4LnkLCrLQYSII2WlQJ2Tas07IfOVfsJ0ws865ncQyskC63+53wsMlF5Xde+vc1yE7sPdxYw5YtGedzDNRNfe/sBJZkwvaqrCyIXrOK+kSZ/hl0y1oTu6D0nxZ3vf8hylXsCxzWV0E/+uq5QhNsuLOOSCYDH/fHUMvXTYzppnQpVBRtsi/cscTbV6ZIJPdOesarPQQNvb8X98HV/Cfm6zi7ZbPQoAjVuExFV+6cjkYw6S/R0IIKVR4GSp1drpz51LopjWl7Kj/WhELyoDpjB6DiRcO9UE1ktiqWzCZAj5RcOkG8pYO4Q/WLa1o0dL9fuEkD4lLXhM6rdZE6vB7yIgojijrUD2WQpecQCjagfpwEnVjSZwQKriwx47pUhjcGrY7IOv53fINKYhAQAWD3dVYCHspdiyd9p7DhQHZzOCIr+lbygnWLTniBeADSuPElXKMex0DhGVCMtyGqiE0xFQc8pWon/M2qYKxpaZS5h54yd1WZl+fZadbv5Bovzoh85U/QJ+siag9rSKL7HAvLAFoUhif8jUsdRUG61IghKyvDH40UA/FTEMydXsk5yJDwfoCF1QkfG5HB2TOKrbv+FwDdcBeWUsr1ThWexVWqvmZ87wxbhXeQlnOsbuBt2AyBONgwoLQcyN7LMYBX75a58Vl8Gi7GFnpFXTFNyMaVHK/e2dMDreywOhZ5N2qVrAM3j5++0a3UlsXXBGVTguELEZZY+YzDoXXIavECUriDNIUe3j4s7uJkAKkcqPHgNxI0MJRoUXH540RKwjWhV60sOB9n41/TpQUO3CULR0RFkArYgjJHVjefC2QNDDU9yZUcxSSsx1K5/bir2TpeZl9wL5RDanubGIBy8kQWcOn855XmzqG/34viIh2FkyYyI7ZgbUl525eJ70uMAbBZQAahKmDO8H6suY61EbVvMz6uWJKQVNXpbytW+7it+SMAMxl1ml0GyHzFfPd109WzeWOljx49ARisBczSyWKWMG/eR4I5o1w7I5tRGtyL2AiNwJuEZmF3rBkptVFVVSFJ2ioMwf4BCtr/u9VMrNerrwGR85cccvIlViaBWNhSnWDR7gGe1s/i57YhrwqQ38ZPLL5XSoruWfdr1Id/Akhi9uEJYkVdMPGXHlzpbrfx4IKrlhVh0uW1qC1KgRw7jSYsxdW3YDO4tLEP6dbBm8WZtb1ogDX+740frCuqEHn9ZoXkJtMhswZRNDekqUaI17vEl0KerOFrYKKLp2HoDqZdfvzne8Pfpj3vET2NGQzg03d/4aNPU/DTNkd5U1fEFxWpZ27YGLqYM41sL7G3gtvWpWbVcwi+dvqrHIz605/AcW0qxK8YF2hzDoh85VUbhk8csF6SB8C4CxYBoqDde5b8DN5AAFZRsrpOA8A/eHlaKyyE4OdZ4fxrZ9/kFcdtNBRCo3MCP99QuHNmurrDDwHsXreicDgAchWBpYvsy4KytXtm6vxTzj+XgFuBoFbBljabphhcgXHqy9DlTQz2YBSmStCCHHdsrUVv3i/F9eub5yVz1vXHMdwWsfhnhFsWVJVsfe9eGmuqRqkgFMubgfUbjBsMXnSpkb28zVv1BvglMEXBqhesD7BHnhngZZBoCX5DgC76olzBviCdXd/pc7DABgAC70Dg/BfFXQphEBA8S6M7mIBS9pd6AdCS1GTPoGgnkRd6oj3OjFij4SDL1i/ZWsrfnawB9esbRj32IVTMcC1EWcLAAML2MdZycw6j+X/vbPkqZXBrz77c3QmLskF6xLtWSdkvvLf449lJy5JF86/cfffti6FSzbLZr4FOoOrUCSOjFKF9xp+BRkpBsEkxCIhJPvcLU3FW7IWMgrWyYzwz38vzEpHg7m/dpPN2Z0JYV+w7s6gzWbGvMe4pcNfLComKav0j8xzS3WEAHjKnhl5Kn4RzkZWQ9UqW4K6qTWB/aeHsWN57eRPLuO9jvWNoZVm1xKy6Cyri2DZ5ctm9TO3L6/F9gqcm8YlqWCMefuZ/cH6hNkc56ZPMtJ5mXUGARSM9Rxvz7qf5SuRd284TecYeLAKAKCaI7CcbUu6HEJatxcFZDOT9166FIbEmPN59uxhABBO5jzYvBb8+AmoRhINo+/nfhXOtUb4prAsqQnj7sn+n7ud8TNDELBvgrmTbTcrGawHwjC46v1+/OX6E77O14BvyfBbcLeVMcqsEzJv+e///Y1BS+GKmtckWZPCJRdbuW/PusFVbxzosG8akwjZi7lhfQD9WFwjiSlYJzMiqOT+sRY27fHvR5mLlS//54dDYUADhpJjGM0aiKqy3cm94DUTHaY/WOdc8jpS8rFeAEBWtktz9AqWFQLANWsbsLW9CjWRc98Csbw+ijt3LkU8SKcEQsgC4O49dwJsfwn6hJl1J9utGKN5wToAwMgvSxeW8/0JmoOGAjIGCx6TrSwkzsBCbmZ9DO5VRJNjMLkMydK9qgCXPTYNEE7ZvalpdqfkrN1MLhvvQELisAwdYX0g90LnOMvdC547UDvo5ZkBWJaALuUmn9REAugfrcy8YsYYdCmUy56x8raEWTXLgaNHoUlhBJxSeAAwpfm17Y8Qkq8qrGAopaN6kvvTaCSKId/X2jiz0/OD9SAiJbLvRsSu4Ilq9uLlYsqs0551MiP8ZSyamR+kMsZw7fpGbF9ei9ro7DeK8d/IhcP2zUo2m8b/3n0MnWeTJWc0TpRj8Hfk54x52XpLs28uslIMAKBNMlN4qjhnqI2qFVs9rIkE8lZECSFk3nLL4J1AtdwGc1DjAICAPlIUrDMjm/e1MNxgffzgsr0mjFOJi5AMtniPpZQacM4gBaNO5l1ANez9k5pSBbMgWHWz87oUhsQZspa9oPzSB2eA9CAsIWDwILgahQjEx//Zyt0L7nKnl2QGYTpjSt2M1a41DbigLYHPbmuf2nuOQ/fdhIsyK+r09suxt+V/YF/TrXmPp026ThEyn33ywjZsbE3ghonGMwIIqPlVMvq4wXp+GXxALj6HWBH7syJaHyDEpJ3oFxJKo5EZ4Q8gS5XTbZxkFvpsEQWjYX55shelJsxXh4tv1j6yrgGHukdwUUeuyQVj9h51mEDviH3jl5XtYD0g0w0GIYRUhKwCDFMug2dOsK4awxCWna3JzectyLS7C7cTNJhjjCHTthOnRrJQzBTqxo6gN7oGEmOQJQkppQZRrdc5NglWIGYv6PoyxWdimxDLdmM42AKJMwxngTDsaoFTXV3o7E8hrTYiFJBhhaqB1FDpgwlMMVh3boCH+3tgZU0Y4ZC33zSiyvjIusr1OBgILUU80wUAEBMuf+dIXEJWtv9/nY2sRN2YvU+/kiX6hJDKS4SUsnqkSIFcxtzkAei89BYXruQy9AZXocrFTejMcD0E45CtLFQjWbHmpvMBBetkxiyvj+BY3xhWN8bm+lCKrGiI4mjvKJY11uLQ6Vx25nj3IOqc+4Ck2oSu+BZcsaquZPb6grYqXNBWlfcYZyxvnITFZO/rXWvGb/RDCCFkCiQFDAySZQBC5IJ1Lk/YuFQE7eCPmxo6B+2TvS6FIBk6YPoy65Zp/+d81kTcBI4uhXEmfoH9Es4gSwxjgVovWM/ICQQUqSiz3hXbBLPqEue9mJdp55aBV/Z/gCXOa2tVGUaoGsBxAEBKqUVY7/feh6lTC9bdHiuWE/zrPJjXHKqSuqPrwYSF4WALImXG2v7r7vHqy8GFiaTaXPFxpYSQuSEFcvfLY0oNOupKn8OkQH5mPajY00D85wLGZYwptYhqfYhqfVhEsToF62Tm3HRBC0ayhj0Td5656YJmZHQLoVNdkDmHJOzsjCx0GJaAJoVxsPHjAICWqvKb2cic5d2IZeUo3DPGVN6HEELIBCTVObXagXq5mXWuBO1ztNAxnNYhkFtgZf4966bujcUUk+yxDpbI8kicQZE4UkquyV5GjiMgc1gFZfWBgIK0mXudxd0Z8gaCxrDz2gTiQRlKIoIzdnN4DIbavWDd4CqkCSoASils1KbzENQSM44rgnFvIaPcnfX+KlaTB/BB3bUAgPlRl0cIOVfMNyUprdTg6nGSWszXD8rNrK9tiuG9M7nxbJwBo4EGL1inMnhCysA5m5eBOmCv2IcCknfDJzullG5JpckD2LW2AUGFTynI5pzB5P6TT/UEzyaEEDItkuIthG7q+bE3p9dk8oTlj4xzZOUowrrdFs7istdnhFm+YN3S8UHPKACG7hG9xDvl1EYDODmQynvM7upuZ9ZdGSWBsyNZ1LD8pkuKoiLtjJGTGIPpZtaFiZBuB+tpJYGGWBDxletx5sCLMJnibbEC7OoARZrizamSPwJNl0IIzsJ2rclmL3vPG+dmmxLrhCwOQs6dg1JKzYQBtskDkEw7WGcMuGZtY16wzhjQF12DoVAbRgMNVAZPyKIhB8B8+x69YJ0FsLIhmtc8rlymL2vSH16OoCLh2vVUAk8IIRXDGCymADC8QB0ABA9M2A2eM0CTfME6ZK8aipn5mXXADv4nq6fcvrwWp4fS6E3myug5B2TGkVJys+HdDL7l6y6vSWE7m50x3B8rVwYvDKimfTOaleOIh2Sw8CocbrgeI3ItItpZMNjBq86DCE1U/1/qd1GQWdekyKw0GS33IxbT6CVCSDF3oRQAUoGJg3WDBxAwx2BwFS2JEDhn+PUrluHf3+3C5rYqAAxjgToAdQAmPW0vKNTxipzfJNXe9+iM0HHL4U0emPbYB/+N42CwAx/f0oKVDfNv3z4hhCxkVom95GyS/eUMzBunCQAWl3I3jP5u8L7u8pMJKhJu39aBL1253HuMMwZFYnkl7yOBRsSCMta01nmPZeR43j5xief2rEtC9/qpCCk3+aM/2AFNjsKQgt7ChC6FIU/xosUKMuvjjU2qtHIzXuP9OIXjYAkhC1Nj3J9Zr57wHNYT3YDhYCsa29d4571YUMHt2zqwsTVRFJyXW8GzEFBmnZzfnAY7kqXZox6cGyOTK9MuoekPL0cs240RtREWl6d8A0UIIWRyghfP8GWT7NtmDHnl47KV9aqhmL8bvJNZt3j5e7iDvv3eii99/E7zZxDShzASbMavrKrHqtEa9H9gfy8jJ/L2iecF65aGiGwhrQMf29pR9Hk6D0JigAl7v/lUs+Kymh+ca9IUu8lPU7l7SRUaJUrIohaqaUU4HMZZPQSTqxP+m++JrUdPbD3WKaVntxfes1MZPCGLhaxCCIGo3ofN3T/AsNoKADBYYNolND3RdchKUQwH2wCUf2NCCCGkfKLU/PPJMusMOBteifahNwDYwbLp7CFn/m7wprslqvy+KxJn+J/bOyCEQEDmXgY4o1Qho1R5z2GyCpkzGJZAVo4j4cusc8YQDqnAKKCYaZiW/XhADRZ+HAwedLLtAroUgjzFPes1iRi6vK8YdGnmmqDeffkyfPclu4t9bVSd5Nk2/+LHFavq8OLhszNybISQOSKrOLn68zjanwFQ3v3yeEF44aOLKVinZUtyfpNDcEe2hvQhNI0eAOCWwU/vH7pgEgbDS719iZRZJ4SQyiuVWZfk0lkXF2cMmhzF2y2fRW9kDToTF/ky67k96+mUvVfckEK4bkP588brYyoanNJOxhg+sq4BNZHcMXEGQFa960taSSCQF6wDl6ywP0+xMrCEgGAcXM4tGrhBrMFVL5uuS6EpX2ti0VyFgSaFsKG1akqvn4pESMGnL27D5iUJbF9eM/kLHLdsbcWW9ipsWZI7NloAJ2TxELIKwcqvYBo3WC94eIotPOa1Of1Rdu/ejZtuugktLS1gjOEnP/lJ3vfvuusuMMby/tu+ffuk7/ujH/0I69evh6qqWL9+PX784x/P0E9AFrxIHc4kLkBvdG1eBuVcgvVCdGNBCCGVZ0olAvNJ96zbNDmKY7VXYSjU4Z37tUwa/9++LnQOpJAcHgJgl5evbYpP+xgvaKvCNl9wKnEGSIq3Hzsj5793RJURDtkZbsVMwRLCHlnkux7dsrUF4YCE6zY2oboq4R1nYKqd3OUgGmJ2ltvgKjpqZ7YMvq06jGvWNkItMepuPMvqIti1pgGyxHHDxiZEVRk3bmyewaMkhMymqS4yjndPzbB4y+DnNFgfGxvD5s2b8e1vf3vc59xwww04c+aM998zzzwz4Xu++uqruO222/C5z30O7777Lj73uc/hM5/5DF5//fVKHz5ZDBjDh1XbcazmSpxKXOg9bDJl2g3m6qL5N5CL6YRBCCHzRokyeD5JsF7qfOw2mOsbGsHhnlH8cM8pJJP2yLRQNHbOC67+QNv9fLdBUlaOo73G3jsekDmCiuT9DIqVhiWcHiq+Y2hOhPC/rlqBDS0JiFAdAIaUUo3QVGekS7ntXoJJZXdpnyvrmuP44pXL0ZQo3hJACFmYpnp+XVJTuhFm4al9Md16z+me9RtvvBE33njjhM9RVRVNTU1lv+e3vvUtXHvttbj33nsBAPfeey9eeOEFfOtb38ITTzxxTsdLFifLqYPvja5Fx5C9qMNhTXtszG2XtKN7OIMfvX0KAGXWCSFkJlhSoCjjwCcrgy9xPnYz65KvDP7gh91oAGDK594h3f+ZnDPA1L0ckMkDaIoH8bkdHYgE7Fsy5lQMKGbaO77xLiOp1R/Hu5njyCqJvD3eZXEqFu3PkGlUGiFk1lWFyusLcvfly9A3ksWK+tIVQFbBlIjFlCib5+uowPPPP4+GhgasXr0aX/ziF9Hb2zvh81999VVcd911eY9df/31eOWVV2byMMkCZjjBuslVHK++DGmlCgPRVdN+v4DM0V4bxmUr63DZyrqp30ARQgiZXIksOp+kG3wpbmbdHeEJ2FltALAqEKz7M+sSY0DDeqTlGLpiFwCw96DXRVWEAva1wt2fzoXpHd94i75KMIJ0oMZ7n6lybwItJi+qm1tCyMJwYUc1NrTE8fEtLRM+LxFSsLIhOu6iouWL1bctq1lU997zuhv8jTfeiE9/+tPo6OjA8ePH8Qd/8Ae45pprsGfPHqhq6W6i3d3daGzMbwbT2NiI7u7ucT8nm80im811gU0mk5X5AciC0xPbgJ7YBihT7KpbyqXLym+iQwghZGosrhZlHGRmTfl9vGDdN7otYKYA2A3mzpW/SztnAAJhHGy/HUMp+/MKA/HCWfF2Zr30Nam1KoTGeBARVUI8OPVbOvfGV5dC0976RQgh06VIHNdtKL+Cejz+zPqOFbXn/H7zybwO1m+77Tbvzxs3bsTFF1+Mjo4O/Md//Ac++clPjvu6wlUXIcSE5V0PP/wwHnrooXM/YLJoUDkgIYTMb8IX1NqjMgUQnXrzsaaaONANSCJXBi+b9ighjZ/7/ui8bu9ORDzRFYZJCuyBbDaLjx+sBxUJn93WPu1jS7ZdhfTYa+hMXIKtdN0jhCxQli+1vtju4ed9Gbxfc3MzOjo6cPjw4XGf09TUVJRF7+3tLcq2+917770YHh72/uvs7KzYMZOFSTOmnp0hhBAyi3gu33C4dhfea/gYpCmUwbfXhPHrVyzDkvoqAHZmPagPAUJ4ZfC6dO5l8KqUK8d0g+4JbyYlJa85ksECM5b1zjReiHebPwNNji6qhkyEkPOLJSZ/zkK1oIL1/v5+dHZ2orl5/JXzHTt24Gc/+1neY88++yx27tw57mtUVUU8Hs/7jxBCCCHzF0duUdXg9ta4qQS1nAOxoALha0q35cy/ojr9obdfXK9AZl2Rcwfl/mnCwFgO5mXeLS7PWKPScCC3uEHNUAkhZP6Z0zL40dFRHDlyxPv6+PHjeOedd1BTU4Oamho8+OCD+NSnPoXm5macOHEC9913H+rq6vCJT3zCe80dd9yB1tZWPPzwwwCA3/qt38KVV16JP/3TP8XNN9+Mp59+Gj//+c/x0ksvzfrPRxYexgCxiFfnCCFkseDCVwHFpp57cOfyMpZ/K9Q8sg+AvVfcKjEebqoCvplopnOBmTAsDkTzLkYWV2esrDOq5n52ajBHCFmoVtRH0JQIoqXq3PuMzDdzmll/6623sHXrVmzduhUA8NWvfhVbt27F/fffD0mSsH//ftx8881YvXo17rzzTqxevRqvvvoqYrGY9x4nT57EmTNnvK937tyJJ598Eo899hguuOACfO9738NTTz2Fbdu2zfrPRxaej66zt0tc1FE9x0dCCCFkIlqwruixckoh3bnmF7QlAACc598KMWe3uCEFK7J4K/uC9ZjbBG6iwFhWIXyLBKICCwbjqY3as9Y5Y3mBOyGELCSyxPFrl7bjqtX1c30oFTenZ+arr74aYoIr4U9/+tNJ3+P5558veuzWW2/Frbfeei6HRs5TG1riaEoEUROeeFYvIYSQuXXcrIdUew1SSm7yRtYwJ33dLVtbkUzrqI44XeALyr/D+gAAQJPCRbN7p+v/uXoFDEtAle396xPmsBmDIUfADbvhnZBm7noUUWV8/rJl3p8JIYTMLwtqzzohM8G9UWuI26WGdVHV69hLCCFkftItgf7ISm/OOAAE5cln60qceYE6YI9W64muy33fGeGm81DFtkUFFSkvcz1ZxblQcxWEMxmsA/b84kRo5rL3hBBCpo+CdXLe+x+XLsG65jh+9YKWuT4UQgghZfKXvN92yRKsa45h+zTm68qc4XjNFThUd13e45ocwUy1MGET59aRQq6xnZAokCaEkPMV1TyR815DLIgbNjbN9WEQQgiZAtMXrbdUhabdWMhtrKYVjGnLStEJt+qdi8ky6yMiBDe3ziV1Ro6BEELI/EeZdUIIIYQsOJUKpOtjdjBsSPnBviZHK7ZnvdBkG63kcML7s6RSsE4IIecrCtYJIYQQsuCY1uTPKUcsqGB9Sxw6zw/Ws1IUujkzwfpke8QvWd3u/VlWFt8oIkIIIeWhMnhCCCGELDiVzHpXhwOwuAyLSeDC7iiflaIwZyhYv2pNPUwhsKk1UfL7gUjucSVAmXVCCDlfUbBOCCGEkPOau/+diVy6XpfCqFg7+ALhgDxhU1NZjXp/pmCdEELOXxSsE0IIIeS85u5/Z/7+75N1gZtBcqQKg6F2CHBEAsHJX0AIIWRRomCdEEIIIQvOkpowOgdSaEqcezBbEy09y7y1em72iwdkCYfqbwAAXCxReyFCCDlfUbBOCCGEkAXnVzY14WBXEuua4+f8XmsaY0hrJjqyMRzqGQFgz1//2Kbmc37v6VCkXFbfmKF984QQQuY/Wq4lhBBCyIITDsi4eGkNIuq55x0YY9jaXo3QhhsBAMerL6vYe0/3eFx6pdreE0IIWXAos04IIYQQAoC1XYQ9rQHoUhjL527LOgCgMR5ETzKDtU3nXjlACCFkYaJgnRBCCCEEdk85XQoDmLFG8GX71EWtSKYN1MeoGzwhhJyvKFgnhBBCCAEg+crPBeY2WldlCfUxaU6PgRBCyNyiPeuEEEIIIQD4HI5rI4QQQgpRsE4IIYQQAoBzX7BOTdgJIYTMMQrWCSGEEEIcqmLfGtVGaa84IYSQuUV71gkhhBBCHL92STsGUxqW1kbm+lAIIYSc5yhYJ4QQQghxVEcCqI4E5vowCCGEECqDJ4QQQgghhBBC5hsK1gkhhBBCCCGEkHmGgnVCCCGEEEIIIWSeoWCdEEIIIYQQQgiZZyhYJ4QQQgghhBBC5hkK1gkhhBBCCCGEkHmGRreVIIQAACSTyTk+EkIIIYQQQgghi4UbY7ox50QoWC9hZGQEALBkyZI5PhJCCCGEEEIIIYvNyMgIEonEhM9hopyQ/jxjWRa6uroQi8XAGMv7XjKZxJIlS9DZ2Yl4PD5HR0gIWWzo3EIImQl0biGEzCQ6x0ydEAIjIyNoaWkB5xPvSqfMegmcc7S1tU34nHg8Tn8hCSEVR+cWQshMoHMLIWQm0TlmaibLqLuowRwhhBBCCCGEEDLPULBOCCGEEEIIIYTMMxSsT5GqqnjggQegqupcHwohZBGhcwshZCbQuYUQMpPoHDOzqMEcIYQQQgghhBAyz1BmnRBCCCGEEEIImWcoWCeEEEIIIYQQQuYZCtYJIYQQQgghhJB5ZlEE6w8//DAuueQSxGIxNDQ04JZbbsGhQ4fyniOEwIMPPoiWlhaEQiFcffXVOHDgQN5z/v7v/x5XX3014vE4GGMYGhrK+/6JEyfwhS98AcuWLUMoFMKKFSvwwAMPQNO0CY8vk8ngrrvuwqZNmyDLMm655Zai5/zbv/0brr32WtTX1yMej2PHjh346U9/Oq3fByGkMmbr3AIAH//4x9He3o5gMIjm5mZ87nOfQ1dX16THuH//flx11VUIhUJobW3FH/7hH6KwFcnjjz+OzZs3IxwOo7m5GZ///OfR398/9V8IIaQiZvPc4spms9iyZQsYY3jnnXcmPcZyzi2ul19+GbIsY8uWLZO+LyFk5s33cwzFRuVbFMH6Cy+8gN/4jd/Aa6+9hp/97GcwDAPXXXcdxsbGvOf82Z/9Gb7xjW/g29/+Nt588000NTXh2muvxcjIiPecVCqFG264Affdd1/Jz3n//fdhWRb+7u/+DgcOHMA3v/lN/O3f/u24z3eZpolQKISvfOUr+OhHP1ryObt378a1116LZ555Bnv27MGuXbtw0003Ye/evdP4jRBCKmG2zi0AsGvXLvzrv/4rDh06hB/96Ec4evQobr311gmPL5lM4tprr0VLSwvefPNNPProo/iLv/gLfOMb3/Ce89JLL+GOO+7AF77wBRw4cAA/+MEP8Oabb+LXf/3Xz+E3Qwg5F7N5bnH9zu/8DlpaWso6vnLOLa7h4WHccccd+MhHPlLWexNCZt58P8dQbDQFYhHq7e0VAMQLL7wghBDCsizR1NQk/uRP/sR7TiaTEYlEQvzt3/5t0et/8YtfCABicHBw0s/6sz/7M7Fs2bKyj+3OO+8UN998c1nPXb9+vXjooYfKfm9CyMyazXPL008/LRhjQtO0cZ/zne98RyQSCZHJZLzHHn74YdHS0iIsyxJCCPHnf/7nYvny5Xmve+SRR0RbW9ukx0AImR0zfW555plnxNq1a8WBAwcEALF3794Jj6ecc4vrtttuE7//+78vHnjgAbF58+byfmBCyKyab+cYP4qNJrYoMuuFhoeHAQA1NTUAgOPHj6O7uxvXXXed9xxVVXHVVVfhlVdeOefPcj+nkizLwsjIyIy8NyFkembr3DIwMIDHH38cO3fuhKIo4z7v1VdfxVVXXZU32/T6669HV1cXTpw4AQDYuXMnTp06hWeeeQZCCPT09OCHP/whPvaxj037+AghlTWT55aenh588YtfxD/90z8hHA6X9Zpyzi0A8Nhjj+Ho0aN44IEHpnRMhJDZNd/OMdNxvsZGiy5YF0Lgq1/9Ki6//HJs3LgRANDd3Q0AaGxszHtuY2Oj973pOHr0KB599FHcc8890z/gcfzlX/4lxsbG8JnPfKbi700ImbrZOLf87u/+LiKRCGpra3Hy5Ek8/fTTEz6/u7u75Gf7j23nzp14/PHHcdtttyEQCKCpqQlVVVV49NFHp3x8hJDKm8lzixACd911F+655x5cfPHFZb+unHPL4cOH8Xu/93t4/PHHIcty2e9NCJld8/EcMx3na2y06IL1L3/5y9i3bx+eeOKJou8xxvK+FkIUPVaurq4u3HDDDfj0pz+dt/dzw4YNiEajiEajuPHGG6f13k888QQefPBBPPXUU2hoaJjWexBCKms2zi1f+9rXsHfvXjz77LOQJAl33HGH19BpvHNLqc/2P37w4EF85Stfwf333489e/bgv/7rv3D8+PEZWWQkhEzdTJ5bHn30USSTSdx7773jPmc65xbTNPHZz34WDz30EFavXl328RBCZt98PcdMxfkcGy2qpdDf/M3fxL//+79j9+7daGtr8x5vamoCYK8iNTc3e4/39vYWrSiVo6urC7t27cKOHTvw93//93nfe+aZZ6DrOgAgFApN+b2feuopfOELX8APfvCDcRsuEEJm12ydW+rq6lBXV4fVq1dj3bp1WLJkCV577TXs2LGj5LmlqampaAW8t7cXQG61/OGHH8Zll12Gr33tawCACy64AJFIBFdccQX++I//OO+4CSGza6bPLc899xxee+21vHJ2ALj44otx++234/vf//60zi0jIyN46623sHfvXnz5y18GYJeoCiEgyzKeffZZXHPNNWUfJyFkZszXc8xUnO+x0aII1oUQ+M3f/E38+Mc/xvPPP49ly5blfX/ZsmVoamrCz372M2zduhUAoGkaXnjhBfzpn/7plD7r9OnT2LVrFy666CI89thj4Dy/OKGjo2PaP8cTTzyBu+++G0888QTtJyVkHpjNc0upzwbsUShA6XPLjh07cN9990HTNAQCAQDAs88+i5aWFixduhSA3cm1sERVkqS8zyCEzK7ZOrc88sgj+OM//mPv666uLlx//fV46qmnsG3bNgDTO7cIIbB///6813znO9/Bc889hx/+8IdFPw8hZHbN93NMuSg2WiTB+m/8xm/gX/7lX/D0008jFot5q8GJRAKhUAiMMfz2b/82vv71r2PVqlVYtWoVvv71ryMcDuOzn/2s9z7d3d3o7u7GkSNHANgzRmOxGNrb21FTU4Ouri5cffXVaG9vx1/8xV+gr6/Pe627QjWegwcPQtM0DAwMYGRkxJs/6M4kfeKJJ3DHHXfgr/7qr7B9+3bvZwiFQkgkEpX6VRFCpmC2zi1vvPEG3njjDVx++eWorq7GsWPHcP/992PFihXYsWPHuMfnlqHedddduO+++3D48GF8/etfx/333++Vsd1000344he/iL/5m7/B9ddfjzNnzuC3f/u3cemll5Y9YoUQUlmzdW5pb2/P+9xoNAoAWLFiRV6WrdBk5xbGmLf31dXQ0IBgMFj0OCFk9s33cwxAsVHZZrP1/EwBUPK/xx57zHuOZVnigQceEE1NTUJVVXHllVeK/fv3573PAw88MOH7PPbYY+N+1mQ6OjomfN1VV11V8vt33nlnJX5FhJBpmK1zy759+8SuXbtETU2NUFVVLF26VNxzzz3i1KlTkx7jvn37xBVXXCFUVRVNTU3iwQcfLBqt9Mgjj4j169eLUCgkmpubxe23317WexNCZsZsnVsKHT9+vOyxSuWcWwqPhUa3ETI/LIRzDMVG5WFCUB0kIYQQQgghhBAynyy6bvCEEEIIIYQQQshCR8E6IYQQQgghhBAyz1CwTgghhBBCCCGEzDMUrBNCCCGEEEIIIfMMBeuEEEIIIYQQQsg8Q8E6IYQQQgghhBAyz1CwTgghhBBCCCGEzDMUrBNCCCGEEEIIIfMMBeuEEEIImdSDDz6ILVu2zPVhEEIIIecNJoQQc30QhBBCCJk7jLEJv3/nnXfi29/+NrLZLGpra2fpqAghhJDzGwXrhBBCyHmuu7vb+/NTTz2F+++/H4cOHfIeC4VCSCQSc3FohBBCyHmLyuAJIYSQ81xTU5P3XyKRAGOs6LHCMvi77roLt9xyC77+9a+jsbERVVVVeOihh2AYBr72ta+hpqYGbW1t+O53v5v3WadPn8Ztt92G6upq1NbW4uabb8aJEydm9wcmhBBCFgAK1gkhhBAyLc899xy6urqwe/dufOMb38CDDz6IX/3VX0V1dTVef/113HPPPbjnnnvQ2dkJAEilUti1axei0Sh2796Nl156CdFoFDfccAM0TZvjn4YQQgiZXyhYJ4QQQsi01NTU4JFHHsGaNWtw9913Y82aNUilUrjvvvuwatUq3HvvvQgEAnj55ZcBAE8++SQ45/iHf/gHbNq0CevWrcNjjz2GkydP4vnnn5/bH4YQQgiZZ+S5PgBCCCGELEwbNmwA57l1/8bGRmzcuNH7WpIk1NbWore3FwCwZ88eHDlyBLFYLO99MpkMjh49OjsHTQghhCwQFKwTQgghZFoURcn7mjFW8jHLsgAAlmXhoosuwuOPP170XvX19TN3oIQQQsgCRME6IYQQQmbFhRdeiKeeegoNDQ2Ix+NzfTiEEELIvEZ71gkhhBAyK26//XbU1dXh5ptvxosvvojjx4/jhRdewG/91m/h1KlTc314hBBCyLxCwTohhBBCZkU4HMbu3bvR3t6OT37yk1i3bh3uvvtupNNpyrQTQgghBZgQQsz1QRBCCCGEEEIIISSHMuuEEEIIIYQQQsg8Q8E6IYQQQgghhBAyz1CwTgghhBBCCCGEzDMUrBNCCCGEEEIIIfMMBeuEEEIIIYQQQsg8Q8E6IYQQQgghhBAyz1CwTgghhBBCCCGEzDMUrBNCCCGEEEIIIfMMBeuEEEIIIYQQQsg8Q8E6IYQQQgghhBAyz1CwTgghhBBCCCGEzDMUrBNCCCGEEEIIIfPM/w/G4f0AiyvcEgAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 1000x300 with 1 Axes>"
       ]
@@ -6375,27 +5039,16 @@
     }
    ],
    "source": [
-    "# plot time series of temporal averages for a specific grid point: daily and monthly averages derived from 3-hourly time series\n",
-    "lat_point = 30\n",
-    "lon_point = 30\n",
-    "\n",
-    "start_year = \"2010-01-01\"\n",
-    "end_year = \"2014-12-31\"\n",
-    "\n",
     "plt.figure(figsize=(10, 3))\n",
     "ax = plt.subplot()\n",
     "\n",
-    "ds2.tas.sel(lat=lat_point, lon=lon_point, time=slice(start_year, end_year)).plot(\n",
-    "    ax=ax, label=\"3-hourly (RAW DATA)\", alpha=0.5\n",
-    ")\n",
-    "ds3_day_avg.tas.sel(\n",
-    "    lat=lat_point, lon=lon_point, time=slice(start_year, end_year)\n",
-    ").plot(ax=ax, label=\"daily\", alpha=0.5)\n",
-    "ds2_monthly_avg.tas.sel(\n",
-    "    lat=lat_point, lon=lon_point, time=slice(start_year, end_year)\n",
-    ").plot(ax=ax, label=\"monthly\", alpha=0.5)\n",
+    "subset_kwargs = {\"lat\": 30, \"lon\": 200}\n",
     "\n",
-    "plt.title(\"Daily and monthly averages derived from 3-hourly time series\")\n",
+    "ds.air.sel(**subset_kwargs).plot(ax=ax, label=\"3-hourly (RAW DATA)\", alpha=0.5)\n",
+    "ds_day_avg.air.sel(**subset_kwargs).plot(ax=ax, label=\"daily\", alpha=0.5)\n",
+    "ds_monthly_avg.air.sel(**subset_kwargs).plot(ax=ax, label=\"monthly\", alpha=0.5)\n",
+    "\n",
+    "plt.title(\"Daily and monthly averages derived from 6-hourly time series\")\n",
     "plt.legend()\n",
     "plt.tight_layout()"
    ]
@@ -6403,11 +5056,8 @@
  ],
  "metadata": {
   "anaconda-cloud": {},
-  "interpreter": {
-   "hash": "937205ea97caa5d37934716e0a0c6b9e4ffcdaf6e0f20ca1ff82361fb532cef2"
-  },
   "kernelspec": {
-   "display_name": "Python 3.9.12 ('xcdat_dev')",
+   "display_name": "xcdat_notebook_0.7.2",
    "language": "python",
    "name": "python3"
   },
@@ -6421,7 +5071,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.12.7"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/xcdat/__init__.py b/xcdat/__init__.py
index cae99097..2275fa9d 100644
--- a/xcdat/__init__.py
+++ b/xcdat/__init__.py
@@ -1,5 +1,6 @@
 """Top-level package for xcdat."""
 
+from xcdat import tutorial  # noqa: F401
 from xcdat.axis import (  # noqa: F401
     center_times,
     get_dim_coords,
diff --git a/xcdat/tutorial.py b/xcdat/tutorial.py
new file mode 100644
index 00000000..387b0a2d
--- /dev/null
+++ b/xcdat/tutorial.py
@@ -0,0 +1,60 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import xarray as xr
+from xarray.tutorial import open_dataset as xr_open_dataset
+
+import xcdat.bounds  # noqa: F401
+
+if TYPE_CHECKING:
+    import os
+
+    from xarray.backends.api import T_Engine
+
+
+def open_dataset(
+    name: str,
+    cache: bool = True,
+    cache_dir: None | str | os.PathLike = None,
+    *,
+    engine: T_Engine = None,
+    **kws,
+) -> xr.Dataset:
+    """Open a dataset from the online repository (requires internet).
+
+    This function is a wrapper around ``xarray.tutorial.open_dataset`` that
+    adds missing bounds to the dataset. If a local copy of the dataset file is
+    found then always use that to avoid network traffic.
+
+    Available datasets:
+
+    * ``"air_temperature"``: NCEP reanalysis subset
+    * ``"air_temperature_gradient"``: NCEP reanalysis subset with approximate x,y gradients
+    * ``"basin_mask"``: Dataset with ocean basins marked using integers
+    * ``"ASE_ice_velocity"``: MEaSUREs InSAR-Based Ice Velocity of the Amundsen Sea Embayment, Antarctica, Version 1
+    * ``"rasm"``: Output of the Regional Arctic System Model (RASM)
+    * ``"ROMS_example"``: Regional Ocean Model System (ROMS) output
+    * ``"tiny"``: small synthetic dataset with a 1D data variable
+    * ``"era5-2mt-2019-03-uk.grib"``: ERA5 temperature data over the UK
+    * ``"eraint_uvz"``: data from ERA-Interim reanalysis, monthly averages of upper level data
+    * ``"ersstv5"``: NOAA's Extended Reconstructed Sea Surface Temperature monthly averages
+
+    Parameters
+    ----------
+    name : str
+        Name of the file containing the dataset.
+        e.g. 'air_temperature'
+    cache_dir : path-like, optional
+        The directory in which to search for and write cached data.
+    cache : bool, optional
+        If True, then cache data locally for use on subsequent calls
+    **kws : dict, optional
+        Passed to xarray.open_dataset
+    """
+    ds = xr_open_dataset(
+        name=name, cache=cache, cache_dir=cache_dir, engine=engine, **kws
+    )
+    ds = ds.bounds.add_missing_bounds(axes=["X", "Y", "T"])
+
+    return ds