intro_xarray_cartopy.ipynb

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    "# Work with climate model data using `xarray`, `dask` and `cartopy`\n",
    "----\n",
    "\n",
    "Create by: @vichawans\n",
    "\n",
    "Adapt from: [Project Pythia](https://foundations.projectpythia.org/core/xarray/xarray-intro.html)\n",
    "\n",
    "\n"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "# Introduction to Xarray"
   ]
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   "metadata": {},
   "source": [
    "---"
   ]
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    "## Overview\n",
    "\n",
    "The examples in this tutorial focus on the fundamentals of working with gridded, labeled data using Xarray. Xarray works by introducing additional abstractions into otherwise ordinary data arrays. In this tutorial, we demonstrate the usefulness of these abstractions. The examples in this tutorial explain how the proper usage of Xarray abstractions generally leads to simpler, more robust code.\n",
    "\n",
    "The following topics will be covered in this tutorial:\n",
    "\n",
    "1. Open netCDF data using Xarray\n",
    "1. Basic subsetting and aggregation of a `Dataset`\n",
    "1. Brief introduction to plotting with Xarray"
   ]
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   "metadata": {},
   "source": [
    "---"
   ]
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   "source": [
    "## Imports"
   ]
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   "source": [
    "In earlier tutorials, we explained the abbreviation of commonly used scientific Python package names in import statements. Just as `numpy` is abbreviated `np`, and just as `pandas` is abbreviated `pd`, the name `xarray` is often abbreviated `xr` in import statements. In addition, we also import `pythia_datasets`, which provides sample data used in these examples."
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   "source": [
    "\n",
    "import xarray as xr\n",
    "import os\n",
    "\n",
    "# for plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs"
   ]
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   "source": [
    "## Introducing the `DataArray` and `Dataset`\n",
    "\n",
    "As stated in earlier tutorials, NumPy arrays contain many useful features, making NumPy an essential part of the scientific Python stack.  Xarray expands on these features, adding streamlined data manipulation capabilities. These capabilities are similar to those provided by Pandas, except that they are focused on gridded N-dimensional data instead of tabular data. \n",
    "\n",
    "Its interface is based largely on the netCDF data model (variables, attributes, and dimensions), but it goes beyond the traditional netCDF interfaces in order to provide additional useful functionality, similar to netCDF-java's [Common Data Model (CDM)](https://docs.unidata.ucar.edu/netcdf-java/current/userguide/common_data_model_overview.html). "
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    "### A `DataArray` object\n",
    "\n",
    "The `DataArray` in one of the most basic elements of Xarray; a `DataArray` object is similar to a numpy `ndarray` object. (For more information, see the documentation [here](http://xarray.pydata.org/en/stable/user-guide/data-structures.html#dataarray).) In addition to retaining most functionality from NumPy arrays, Xarray `DataArrays` provide two critical pieces of functionality:\n",
    "\n",
    "1. Coordinate names and values are stored with the data, making slicing and indexing much more powerful.\n",
    "2. Attributes, similar to those in netCDF files, can be stored in a container built into the `DataArray`.\n"
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    "### The `Dataset`: a container for `DataArray`s with shared coordinates\n",
    "\n",
    "Along with the `DataArray`, the other main object type in Xarray is the `Dataset`.  `Datasets` are containers similar to Python dictionaries; each `Dataset` can hold one or more `DataArrays`. In addition, the `DataArrays` contained in a `Dataset` can share coordinates, although this behavior is optional.  (For more information, see the [official documentation page](http://xarray.pydata.org/en/stable/user-guide/data-structures.html#dataset).)\n",
    "\n",
    "`Dataset` objects are most often created by loading data from a data file. "
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    "## Opening netCDF data\n",
    "\n",
    "Xarray has close ties to the netCDF data format; as such, netCDF was chosen as the premier data file format for Xarray. Hence, Xarray can easily open netCDF datasets, provided they conform to certain limitations (for example, 1-dimensional coordinates).\n",
    "\n",
    "### Access netCDF data with `xr.open_dataset`"
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   "source": [
    "The data file for this example, <code>NARR_19930313_0000.nc</code>, which is present in the Pythia dataset repository."
   ]
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   "source": [
    "This time only auto-download!"
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      "Downloading: 'https://github.com/ProjectPythia/pythia-datasets/raw/main/data/NARR_19930313_0000.nc' to E:\\PycharmProjects\\ai4er\\jupyter-workshop\\data\\NARR_19930313_0000.nc\n"
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      "Downloaded in E:\\PycharmProjects\\ai4er\\jupyter-workshop\\data\\NARR_19930313_0000.nc\n"
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   "source": [
    "import os\n",
    "from utils import guarantee_existence, downloader\n",
    "\n",
    "url = [\"https://github.com/ProjectPythia/pythia-datasets/raw/main/data/NARR_19930313_0000.nc\"]\n",
    "data_dir = guarantee_existence(os.path.abspath(\"./data\"))\n",
    "downloader(url, target_dir=data_dir)\n",
    "\n",
    "filepath = os.path.join(data_dir, \"NARR_19930313_0000.nc\")"
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   "source": [
    "Once we have a valid path to a data file that Xarray knows how to read, we can open the data file and load it into Xarray; this is done by passing the path to Xarray's `open_dataset` method, as shown below:"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                       (time1: 1, isobaric1: 29, y: 119, x: 268)\n",
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       "  * isobaric1                     (isobaric1) float32 100.0 125.0 ... 1e+03\n",
       "  * y                             (y) float32 -3.117e+03 -3.084e+03 ... 714.1\n",
       "  * x                             (x) float32 -3.324e+03 ... 5.343e+03\n",
       "Data variables:\n",
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       "    LambertConformal_Projection   int32 ...\n",
       "    lat                           (y, x) float64 ...\n",
       "    lon                           (y, x) float64 ...\n",
       "    Geopotential_height_isobaric  (time1, isobaric1, y, x) float32 ...\n",
       "    v-component_of_wind_isobaric  (time1, isobaric1, y, x) float32 ...\n",
       "    Temperature_isobaric          (time1, isobaric1, y, x) float32 ...\n",
       "Attributes:\n",
       "    Originating_or_generating_Center:     US National Weather Service, Nation...\n",
       "    Originating_or_generating_Subcenter:  North American Regional Reanalysis ...\n",
       "    GRIB_table_version:                   0,131\n",
       "    Generating_process_or_model:          North American Regional Reanalysis ...\n",
       "    Conventions:                          CF-1.6\n",
       "    history:                              Read using CDM IOSP GribCollection v3\n",
       "    featureType:                          GRID\n",
       "    History:                              Translated to CF-1.0 Conventions by...\n",
       "    geospatial_lat_min:                   10.753308882144761\n",
       "    geospatial_lat_max:                   46.8308828962289\n",
       "    geospatial_lon_min:                   -153.88242040519995\n",
       "    geospatial_lon_max:                   -42.666108129242815</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-b23485bd-f5f5-4d72-98f1-77a5c6a85c37' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b23485bd-f5f5-4d72-98f1-77a5c6a85c37' 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'>time1</span>: 1</li><li><span class='xr-has-index'>isobaric1</span>: 29</li><li><span class='xr-has-index'>y</span>: 119</li><li><span class='xr-has-index'>x</span>: 268</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2f168971-5fa7-44f8-b02d-6c5c8fbd4607' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2f168971-5fa7-44f8-b02d-6c5c8fbd4607' 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'>time1</span></div><div class='xr-var-dims'>(time1)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1993-03-13</div><input id='attrs-0a2b36ee-f875-4a31-8902-89db7f05afaf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a2b36ee-f875-4a31-8902-89db7f05afaf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-259e6f22-3a36-433d-bcd6-85ff017beba0' class='xr-var-data-in' type='checkbox'><label for='data-259e6f22-3a36-433d-bcd6-85ff017beba0' 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>GRIB forecast or observation time</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1993-03-13T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>isobaric1</span></div><div class='xr-var-dims'>(isobaric1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>100.0 125.0 150.0 ... 975.0 1e+03</div><input id='attrs-7b32e031-3a58-478d-bb1f-948901e6d92e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b32e031-3a58-478d-bb1f-948901e6d92e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7549c303-61e5-41e0-9012-5850737a0f69' class='xr-var-data-in' type='checkbox'><label for='data-7549c303-61e5-41e0-9012-5850737a0f69' 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>hPa</dd><dt><span>long_name :</span></dt><dd>Isobaric surface</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>Grib_level_type :</span></dt><dd>100</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Pressure</dd><dt><span>_CoordinateZisPositive :</span></dt><dd>down</dd></dl></div><div class='xr-var-data'><pre>array([ 100.,  125.,  150.,  175.,  200.,  225.,  250.,  275.,  300.,  350.,\n",
       "        400.,  450.,  500.,  550.,  600.,  650.,  700.,  725.,  750.,  775.,\n",
       "        800.,  825.,  850.,  875.,  900.,  925.,  950.,  975., 1000.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-3.117e+03 -3.084e+03 ... 714.1</div><input id='attrs-2fef602b-4191-4dc6-8c98-4658353524dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2fef602b-4191-4dc6-8c98-4658353524dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18d6ad83-ca77-458f-829a-43370835125d' class='xr-var-data-in' type='checkbox'><label for='data-18d6ad83-ca77-458f-829a-43370835125d' 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>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>km</dd><dt><span>_CoordinateAxisType :</span></dt><dd>GeoY</dd></dl></div><div class='xr-var-data'><pre>array([-3.116548e+03, -3.084085e+03, -3.051622e+03, -3.019159e+03,\n",
       "       -2.986696e+03, -2.954233e+03, -2.921770e+03, -2.889307e+03,\n",
       "       -2.856844e+03, -2.824381e+03, -2.791918e+03, -2.759455e+03,\n",
       "       -2.726992e+03, -2.694529e+03, -2.662066e+03, -2.629603e+03,\n",
       "       -2.597140e+03, -2.564677e+03, -2.532214e+03, -2.499751e+03,\n",
       "       -2.467288e+03, -2.434825e+03, -2.402362e+03, -2.369899e+03,\n",
       "       -2.337436e+03, -2.304973e+03, -2.272510e+03, -2.240047e+03,\n",
       "       -2.207584e+03, -2.175121e+03, -2.142658e+03, -2.110195e+03,\n",
       "       -2.077732e+03, -2.045269e+03, -2.012806e+03, -1.980343e+03,\n",
       "       -1.947880e+03, -1.915417e+03, -1.882954e+03, -1.850491e+03,\n",
       "       -1.818028e+03, -1.785565e+03, -1.753102e+03, -1.720639e+03,\n",
       "       -1.688176e+03, -1.655713e+03, -1.623250e+03, -1.590787e+03,\n",
       "       -1.558324e+03, -1.525861e+03, -1.493398e+03, -1.460935e+03,\n",
       "       -1.428472e+03, -1.396009e+03, -1.363546e+03, -1.331083e+03,\n",
       "       -1.298620e+03, -1.266157e+03, -1.233694e+03, -1.201231e+03,\n",
       "       -1.168768e+03, -1.136305e+03, -1.103842e+03, -1.071379e+03,\n",
       "       -1.038916e+03, -1.006453e+03, -9.739901e+02, -9.415271e+02,\n",
       "       -9.090641e+02, -8.766011e+02, -8.441381e+02, -8.116751e+02,\n",
       "       -7.792121e+02, -7.467491e+02, -7.142861e+02, -6.818231e+02,\n",
       "       -6.493601e+02, -6.168971e+02, -5.844341e+02, -5.519711e+02,\n",
       "       -5.195081e+02, -4.870451e+02, -4.545821e+02, -4.221191e+02,\n",
       "       -3.896561e+02, -3.571931e+02, -3.247301e+02, -2.922671e+02,\n",
       "       -2.598041e+02, -2.273411e+02, -1.948781e+02, -1.624151e+02,\n",
       "       -1.299521e+02, -9.748907e+01, -6.502608e+01, -3.256308e+01,\n",
       "       -1.000744e-01,  3.236293e+01,  6.482593e+01,  9.728893e+01,\n",
       "        1.297519e+02,  1.622149e+02,  1.946779e+02,  2.271409e+02,\n",
       "        2.596039e+02,  2.920669e+02,  3.245299e+02,  3.569930e+02,\n",
       "        3.894559e+02,  4.219189e+02,  4.543820e+02,  4.868449e+02,\n",
       "        5.193079e+02,  5.517709e+02,  5.842339e+02,  6.166970e+02,\n",
       "        6.491600e+02,  6.816229e+02,  7.140859e+02], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-3.324e+03 -3.292e+03 ... 5.343e+03</div><input id='attrs-54c92e37-ddbe-4272-b222-9b0d50e9e913' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-54c92e37-ddbe-4272-b222-9b0d50e9e913' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97cae5e5-228b-465a-8246-6d4f1db4ff2c' class='xr-var-data-in' type='checkbox'><label for='data-97cae5e5-228b-465a-8246-6d4f1db4ff2c' 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>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>km</dd><dt><span>_CoordinateAxisType :</span></dt><dd>GeoX</dd></dl></div><div class='xr-var-data'><pre>array([-3324.4707, -3292.0078, -3259.5447, ...,  5278.2246,  5310.6875,\n",
       "        5343.1504], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9603a4df-6197-4fcb-9285-2a547c41845f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9603a4df-6197-4fcb-9285-2a547c41845f' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>u-component_of_wind_isobaric</span></div><div class='xr-var-dims'>(time1, isobaric1, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8f8b781f-d050-4d28-93fa-c658507c0d35' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8f8b781f-d050-4d28-93fa-c658507c0d35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-209b3379-852b-44a8-a3e9-d7702d974483' class='xr-var-data-in' type='checkbox'><label for='data-209b3379-852b-44a8-a3e9-d7702d974483' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>u-component of wind @ Isobaric surface</dd><dt><span>units :</span></dt><dd>m s^-1</dd><dt><span>description :</span></dt><dd>u-component of wind</dd><dt><span>grid_mapping :</span></dt><dd>LambertConformal_Projection</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_7-15-131-33_L100</dd><dt><span>Grib1_Center :</span></dt><dd>7</dd><dt><span>Grib1_Subcenter :</span></dt><dd>15</dd><dt><span>Grib1_TableVersion :</span></dt><dd>131</dd><dt><span>Grib1_Parameter :</span></dt><dd>33</dd><dt><span>Grib1_Level_Type :</span></dt><dd>100</dd><dt><span>Grib1_Level_Desc :</span></dt><dd>Isobaric surface</dd></dl></div><div class='xr-var-data'><pre>[924868 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LambertConformal_Projection</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1d562760-9a97-4136-9454-57ebf8ec164d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d562760-9a97-4136-9454-57ebf8ec164d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1eb81daa-8b18-48a5-8b90-94cef8296c60' class='xr-var-data-in' type='checkbox'><label for='data-1eb81daa-8b18-48a5-8b90-94cef8296c60' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping_name :</span></dt><dd>lambert_conformal_conic</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>50.000003814697266</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>-107.00000762939453</dd><dt><span>standard_parallel :</span></dt><dd>50.000003814697266</dd><dt><span>earth_radius :</span></dt><dd>6367470.0</dd><dt><span>_CoordinateTransformType :</span></dt><dd>Projection</dd><dt><span>_CoordinateAxisTypes :</span></dt><dd>GeoX GeoY</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-53906467-1d49-4521-a6d0-b9da55eb342f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53906467-1d49-4521-a6d0-b9da55eb342f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-406315de-ef7c-4b19-b7f2-b52cd88a63de' class='xr-var-data-in' type='checkbox'><label for='data-406315de-ef7c-4b19-b7f2-b52cd88a63de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude coordinate</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lat</dd></dl></div><div class='xr-var-data'><pre>[31892 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9747c9bf-8319-447d-9113-19d5e6fd9b8a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9747c9bf-8319-447d-9113-19d5e6fd9b8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b05d9048-d95b-484e-8fb6-6e6b1800d75d' class='xr-var-data-in' type='checkbox'><label for='data-b05d9048-d95b-484e-8fb6-6e6b1800d75d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude coordinate</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lon</dd></dl></div><div class='xr-var-data'><pre>[31892 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Geopotential_height_isobaric</span></div><div class='xr-var-dims'>(time1, isobaric1, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7c72d29f-4d98-400e-93ef-db3bcfca8297' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c72d29f-4d98-400e-93ef-db3bcfca8297' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56056f51-b6ec-4e43-b40a-7b4b1fc6a690' class='xr-var-data-in' type='checkbox'><label for='data-56056f51-b6ec-4e43-b40a-7b4b1fc6a690' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Geopotential height @ Isobaric surface</dd><dt><span>units :</span></dt><dd>gpm</dd><dt><span>description :</span></dt><dd>Geopotential height</dd><dt><span>grid_mapping :</span></dt><dd>LambertConformal_Projection</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_7-15-131-7_L100</dd><dt><span>Grib1_Center :</span></dt><dd>7</dd><dt><span>Grib1_Subcenter :</span></dt><dd>15</dd><dt><span>Grib1_TableVersion :</span></dt><dd>131</dd><dt><span>Grib1_Parameter :</span></dt><dd>7</dd><dt><span>Grib1_Level_Type :</span></dt><dd>100</dd><dt><span>Grib1_Level_Desc :</span></dt><dd>Isobaric surface</dd></dl></div><div class='xr-var-data'><pre>[924868 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v-component_of_wind_isobaric</span></div><div class='xr-var-dims'>(time1, isobaric1, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b43b6503-7dc6-4a34-8c57-a0ce8fa48d27' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b43b6503-7dc6-4a34-8c57-a0ce8fa48d27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8040e17-4cfe-4b20-98b9-05cd89725e18' class='xr-var-data-in' type='checkbox'><label for='data-d8040e17-4cfe-4b20-98b9-05cd89725e18' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>v-component of wind @ Isobaric surface</dd><dt><span>units :</span></dt><dd>m s^-1</dd><dt><span>description :</span></dt><dd>v-component of wind</dd><dt><span>grid_mapping :</span></dt><dd>LambertConformal_Projection</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_7-15-131-34_L100</dd><dt><span>Grib1_Center :</span></dt><dd>7</dd><dt><span>Grib1_Subcenter :</span></dt><dd>15</dd><dt><span>Grib1_TableVersion :</span></dt><dd>131</dd><dt><span>Grib1_Parameter :</span></dt><dd>34</dd><dt><span>Grib1_Level_Type :</span></dt><dd>100</dd><dt><span>Grib1_Level_Desc :</span></dt><dd>Isobaric surface</dd></dl></div><div class='xr-var-data'><pre>[924868 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Temperature_isobaric</span></div><div class='xr-var-dims'>(time1, isobaric1, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-76544457-d9c3-4c82-a0dc-3995fb0d7e20' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-76544457-d9c3-4c82-a0dc-3995fb0d7e20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f7df604-59ce-4a13-a8ba-c8f0656d3d1f' class='xr-var-data-in' type='checkbox'><label for='data-4f7df604-59ce-4a13-a8ba-c8f0656d3d1f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Temperature @ Isobaric surface</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>description :</span></dt><dd>Temperature</dd><dt><span>grid_mapping :</span></dt><dd>LambertConformal_Projection</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_7-15-131-11_L100</dd><dt><span>Grib1_Center :</span></dt><dd>7</dd><dt><span>Grib1_Subcenter :</span></dt><dd>15</dd><dt><span>Grib1_TableVersion :</span></dt><dd>131</dd><dt><span>Grib1_Parameter :</span></dt><dd>11</dd><dt><span>Grib1_Level_Type :</span></dt><dd>100</dd><dt><span>Grib1_Level_Desc :</span></dt><dd>Isobaric surface</dd></dl></div><div class='xr-var-data'><pre>[924868 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1e9dce53-709a-4c28-a54e-5720fc429034' class='xr-section-summary-in' type='checkbox'  ><label for='section-1e9dce53-709a-4c28-a54e-5720fc429034' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1e205016-c304-463f-94a5-08a9d9009cb9' class='xr-index-data-in' type='checkbox'/><label for='index-1e205016-c304-463f-94a5-08a9d9009cb9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1993-03-13&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time1&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>isobaric1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5103bb57-6091-4fbb-b0fe-4645825ebc7b' class='xr-index-data-in' type='checkbox'/><label for='index-5103bb57-6091-4fbb-b0fe-4645825ebc7b' 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(Float64Index([ 100.0,  125.0,  150.0,  175.0,  200.0,  225.0,  250.0,  275.0,\n",
       "               300.0,  350.0,  400.0,  450.0,  500.0,  550.0,  600.0,  650.0,\n",
       "               700.0,  725.0,  750.0,  775.0,  800.0,  825.0,  850.0,  875.0,\n",
       "               900.0,  925.0,  950.0,  975.0, 1000.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;isobaric1&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0efb34ab-2345-424b-829b-5b223d5bb694' class='xr-index-data-in' type='checkbox'/><label for='index-0efb34ab-2345-424b-829b-5b223d5bb694' 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(Float64Index([-3116.548095703125, -3084.085205078125,   -3051.6220703125,\n",
       "                -3019.1591796875,   -2986.6962890625, -2954.233154296875,\n",
       "              -2921.770263671875,  -2889.30712890625,  -2856.84423828125,\n",
       "              -2824.381103515625,\n",
       "              ...\n",
       "                  421.9189453125,  454.3819580078125,  486.8449401855469,\n",
       "               519.3079223632812,  551.7709350585938,  584.2339477539062,\n",
       "               616.6969604492188,  649.1599731445312,  681.6229248046875,\n",
       "                     714.0859375],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=119))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-89f5902b-628c-40ef-80c4-4ce7a078fa8b' class='xr-index-data-in' type='checkbox'/><label for='index-89f5902b-628c-40ef-80c4-4ce7a078fa8b' 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(Float64Index([   -3324.470703125,      -3292.0078125, -3259.544677734375,\n",
       "              -3227.081787109375,  -3194.61865234375,  -3162.15576171875,\n",
       "              -3129.692626953125, -3097.229736328125,   -3064.7666015625,\n",
       "                -3032.3037109375,\n",
       "              ...\n",
       "                 5050.9833984375,   5083.44677734375,   5115.90966796875,\n",
       "                5148.37255859375,   5180.83544921875,     5213.298828125,\n",
       "                   5245.76171875,     5278.224609375,          5310.6875,\n",
       "                  5343.150390625],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=268))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5cf77fb3-dc81-4bec-b9f1-8979cc8baab4' class='xr-section-summary-in' type='checkbox'  ><label for='section-5cf77fb3-dc81-4bec-b9f1-8979cc8baab4' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Originating_or_generating_Center :</span></dt><dd>US National Weather Service, National Centres for Environmental Prediction (NCEP)</dd><dt><span>Originating_or_generating_Subcenter :</span></dt><dd>North American Regional Reanalysis Project</dd><dt><span>GRIB_table_version :</span></dt><dd>0,131</dd><dt><span>Generating_process_or_model :</span></dt><dd>North American Regional Reanalysis (NARR)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>history :</span></dt><dd>Read using CDM IOSP GribCollection v3</dd><dt><span>featureType :</span></dt><dd>GRID</dd><dt><span>History :</span></dt><dd>Translated to CF-1.0 Conventions by Netcdf-Java CDM (CFGridWriter2)\n",
       "Original Dataset = DatasetScan#narr-a_221_19930313_0000_000.grb; Translation Date = 2018-01-04T23:57:16.043Z</dd><dt><span>geospatial_lat_min :</span></dt><dd>10.753308882144761</dd><dt><span>geospatial_lat_max :</span></dt><dd>46.8308828962289</dd><dt><span>geospatial_lon_min :</span></dt><dd>-153.88242040519995</dd><dt><span>geospatial_lon_max :</span></dt><dd>-42.666108129242815</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                       (time1: 1, isobaric1: 29, y: 119, x: 268)\n",
       "Coordinates:\n",
       "  * time1                         (time1) datetime64[ns] 1993-03-13\n",
       "  * isobaric1                     (isobaric1) float32 100.0 125.0 ... 1e+03\n",
       "  * y                             (y) float32 -3.117e+03 -3.084e+03 ... 714.1\n",
       "  * x                             (x) float32 -3.324e+03 ... 5.343e+03\n",
       "Data variables:\n",
       "    u-component_of_wind_isobaric  (time1, isobaric1, y, x) float32 ...\n",
       "    LambertConformal_Projection   int32 ...\n",
       "    lat                           (y, x) float64 ...\n",
       "    lon                           (y, x) float64 ...\n",
       "    Geopotential_height_isobaric  (time1, isobaric1, y, x) float32 ...\n",
       "    v-component_of_wind_isobaric  (time1, isobaric1, y, x) float32 ...\n",
       "    Temperature_isobaric          (time1, isobaric1, y, x) float32 ...\n",
       "Attributes:\n",
       "    Originating_or_generating_Center:     US National Weather Service, Nation...\n",
       "    Originating_or_generating_Subcenter:  North American Regional Reanalysis ...\n",
       "    GRIB_table_version:                   0,131\n",
       "    Generating_process_or_model:          North American Regional Reanalysis ...\n",
       "    Conventions:                          CF-1.6\n",
       "    history:                              Read using CDM IOSP GribCollection v3\n",
       "    featureType:                          GRID\n",
       "    History:                              Translated to CF-1.0 Conventions by...\n",
       "    geospatial_lat_min:                   10.753308882144761\n",
       "    geospatial_lat_max:                   46.8308828962289\n",
       "    geospatial_lon_min:                   -153.88242040519995\n",
       "    geospatial_lon_max:                   -42.666108129242815"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = xr.open_dataset(filepath)\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subsetting the `Dataset`\n",
    "\n",
    "Xarray's `open_dataset()` method, shown in the previous example, returns a `Dataset` object, which must then be assigned to a variable; in this case, we call the variable `ds`. Once the netCDF dataset is loaded into an Xarray `Dataset`, we can pull individual `DataArrays` out of the `Dataset`, using the technique described earlier in this tutorial.  In this example, we retrieve isobaric pressure data, as shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;isobaric1&#x27; (isobaric1: 29)&gt;\n",
       "array([ 100.,  125.,  150.,  175.,  200.,  225.,  250.,  275.,  300.,  350.,\n",
       "        400.,  450.,  500.,  550.,  600.,  650.,  700.,  725.,  750.,  775.,\n",
       "        800.,  825.,  850.,  875.,  900.,  925.,  950.,  975., 1000.],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "  * isobaric1  (isobaric1) float32 100.0 125.0 150.0 175.0 ... 950.0 975.0 1e+03\n",
       "Attributes:\n",
       "    units:                   hPa\n",
       "    long_name:               Isobaric surface\n",
       "    positive:                down\n",
       "    Grib_level_type:         100\n",
       "    _CoordinateAxisType:     Pressure\n",
       "    _CoordinateZisPositive:  down</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'>'isobaric1'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>isobaric1</span>: 29</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d7c2f3dd-63f9-4f95-b5fa-56fd37789fd0' class='xr-array-in' type='checkbox' checked><label for='section-d7c2f3dd-63f9-4f95-b5fa-56fd37789fd0' 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>100.0 125.0 150.0 175.0 200.0 225.0 ... 900.0 925.0 950.0 975.0 1e+03</span></div><div class='xr-array-data'><pre>array([ 100.,  125.,  150.,  175.,  200.,  225.,  250.,  275.,  300.,  350.,\n",
       "        400.,  450.,  500.,  550.,  600.,  650.,  700.,  725.,  750.,  775.,\n",
       "        800.,  825.,  850.,  875.,  900.,  925.,  950.,  975., 1000.],\n",
       "      dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-857f5c05-451e-457f-9b9b-0c9ae06b71a9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-857f5c05-451e-457f-9b9b-0c9ae06b71a9' 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'>isobaric1</span></div><div class='xr-var-dims'>(isobaric1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>100.0 125.0 150.0 ... 975.0 1e+03</div><input id='attrs-46c2b3cb-e3a6-475d-9fe5-7952e6804b38' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-46c2b3cb-e3a6-475d-9fe5-7952e6804b38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac85f81f-a55a-460e-b6dd-0609bc091f47' class='xr-var-data-in' type='checkbox'><label for='data-ac85f81f-a55a-460e-b6dd-0609bc091f47' 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>hPa</dd><dt><span>long_name :</span></dt><dd>Isobaric surface</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>Grib_level_type :</span></dt><dd>100</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Pressure</dd><dt><span>_CoordinateZisPositive :</span></dt><dd>down</dd></dl></div><div class='xr-var-data'><pre>array([ 100.,  125.,  150.,  175.,  200.,  225.,  250.,  275.,  300.,  350.,\n",
       "        400.,  450.,  500.,  550.,  600.,  650.,  700.,  725.,  750.,  775.,\n",
       "        800.,  825.,  850.,  875.,  900.,  925.,  950.,  975., 1000.],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f74e2335-5bbe-4b4a-b7fb-1b04603fdb6d' class='xr-section-summary-in' type='checkbox'  ><label for='section-f74e2335-5bbe-4b4a-b7fb-1b04603fdb6d' 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>isobaric1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-41f5f25b-4bcf-415c-9c10-9bb88d329a41' class='xr-index-data-in' type='checkbox'/><label for='index-41f5f25b-4bcf-415c-9c10-9bb88d329a41' 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(Float64Index([ 100.0,  125.0,  150.0,  175.0,  200.0,  225.0,  250.0,  275.0,\n",
       "               300.0,  350.0,  400.0,  450.0,  500.0,  550.0,  600.0,  650.0,\n",
       "               700.0,  725.0,  750.0,  775.0,  800.0,  825.0,  850.0,  875.0,\n",
       "               900.0,  925.0,  950.0,  975.0, 1000.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;isobaric1&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a7ce6b60-4533-4c30-8ce8-a938d362d564' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a7ce6b60-4533-4c30-8ce8-a938d362d564' 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>units :</span></dt><dd>hPa</dd><dt><span>long_name :</span></dt><dd>Isobaric surface</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>Grib_level_type :</span></dt><dd>100</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Pressure</dd><dt><span>_CoordinateZisPositive :</span></dt><dd>down</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'isobaric1' (isobaric1: 29)>\n",
       "array([ 100.,  125.,  150.,  175.,  200.,  225.,  250.,  275.,  300.,  350.,\n",
       "        400.,  450.,  500.,  550.,  600.,  650.,  700.,  725.,  750.,  775.,\n",
       "        800.,  825.,  850.,  875.,  900.,  925.,  950.,  975., 1000.],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "  * isobaric1  (isobaric1) float32 100.0 125.0 150.0 175.0 ... 950.0 975.0 1e+03\n",
       "Attributes:\n",
       "    units:                   hPa\n",
       "    long_name:               Isobaric surface\n",
       "    positive:                down\n",
       "    Grib_level_type:         100\n",
       "    _CoordinateAxisType:     Pressure\n",
       "    _CoordinateZisPositive:  down"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.isobaric1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(As described earlier in this tutorial, we can also use dictionary syntax to select specific `DataArrays`; in this case, we would write `ds['isobaric1']`.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many of the subsetting operations usable on `DataArrays` can also be used on `Datasets`.  However, when used on `Datasets`, these operations are performed on every `DataArray` in the `Dataset`, as shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                       (time1: 1, y: 119, x: 268)\n",
       "Coordinates:\n",
       "  * time1                         (time1) datetime64[ns] 1993-03-13\n",
       "    isobaric1                     float32 1e+03\n",
       "  * y                             (y) float32 -3.117e+03 -3.084e+03 ... 714.1\n",
       "  * x                             (x) float32 -3.324e+03 ... 5.343e+03\n",
       "Data variables:\n",
       "    u-component_of_wind_isobaric  (time1, y, x) float32 ...\n",
       "    LambertConformal_Projection   int32 ...\n",
       "    lat                           (y, x) float64 ...\n",
       "    lon                           (y, x) float64 ...\n",
       "    Geopotential_height_isobaric  (time1, y, x) float32 ...\n",
       "    v-component_of_wind_isobaric  (time1, y, x) float32 ...\n",
       "    Temperature_isobaric          (time1, y, x) float32 ...\n",
       "Attributes:\n",
       "    Originating_or_generating_Center:     US National Weather Service, Nation...\n",
       "    Originating_or_generating_Subcenter:  North American Regional Reanalysis ...\n",
       "    GRIB_table_version:                   0,131\n",
       "    Generating_process_or_model:          North American Regional Reanalysis ...\n",
       "    Conventions:                          CF-1.6\n",
       "    history:                              Read using CDM IOSP GribCollection v3\n",
       "    featureType:                          GRID\n",
       "    History:                              Translated to CF-1.0 Conventions by...\n",
       "    geospatial_lat_min:                   10.753308882144761\n",
       "    geospatial_lat_max:                   46.8308828962289\n",
       "    geospatial_lon_min:                   -153.88242040519995\n",
       "    geospatial_lon_max:                   -42.666108129242815</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-5dbcb438-c304-4822-aa48-3fdab75f68e2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5dbcb438-c304-4822-aa48-3fdab75f68e2' 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'>time1</span>: 1</li><li><span class='xr-has-index'>y</span>: 119</li><li><span class='xr-has-index'>x</span>: 268</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-053ba887-43f7-45b3-9d9b-e897da19e404' class='xr-section-summary-in' type='checkbox'  checked><label for='section-053ba887-43f7-45b3-9d9b-e897da19e404' 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'>time1</span></div><div class='xr-var-dims'>(time1)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1993-03-13</div><input id='attrs-196c7d2a-275f-4103-8542-1dc5148c749d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-196c7d2a-275f-4103-8542-1dc5148c749d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69e4b1b0-a73e-4faf-b356-dc4034f49db0' class='xr-var-data-in' type='checkbox'><label for='data-69e4b1b0-a73e-4faf-b356-dc4034f49db0' 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>GRIB forecast or observation time</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1993-03-13T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>isobaric1</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1e+03</div><input id='attrs-fca25240-6474-46b1-bc1a-bf33531a2edc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fca25240-6474-46b1-bc1a-bf33531a2edc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1143aed7-3e9e-4f6a-9e76-27c75f67dc42' class='xr-var-data-in' type='checkbox'><label for='data-1143aed7-3e9e-4f6a-9e76-27c75f67dc42' 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>hPa</dd><dt><span>long_name :</span></dt><dd>Isobaric surface</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>Grib_level_type :</span></dt><dd>100</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Pressure</dd><dt><span>_CoordinateZisPositive :</span></dt><dd>down</dd></dl></div><div class='xr-var-data'><pre>array(1000., dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-3.117e+03 -3.084e+03 ... 714.1</div><input id='attrs-5607521b-caa9-47cf-ab13-4b5148935766' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5607521b-caa9-47cf-ab13-4b5148935766' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9e321fc-1ddd-40ad-a90b-1e5a7848b39e' class='xr-var-data-in' type='checkbox'><label for='data-e9e321fc-1ddd-40ad-a90b-1e5a7848b39e' 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>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>km</dd><dt><span>_CoordinateAxisType :</span></dt><dd>GeoY</dd></dl></div><div class='xr-var-data'><pre>array([-3.116548e+03, -3.084085e+03, -3.051622e+03, -3.019159e+03,\n",
       "       -2.986696e+03, -2.954233e+03, -2.921770e+03, -2.889307e+03,\n",
       "       -2.856844e+03, -2.824381e+03, -2.791918e+03, -2.759455e+03,\n",
       "       -2.726992e+03, -2.694529e+03, -2.662066e+03, -2.629603e+03,\n",
       "       -2.597140e+03, -2.564677e+03, -2.532214e+03, -2.499751e+03,\n",
       "       -2.467288e+03, -2.434825e+03, -2.402362e+03, -2.369899e+03,\n",
       "       -2.337436e+03, -2.304973e+03, -2.272510e+03, -2.240047e+03,\n",
       "       -2.207584e+03, -2.175121e+03, -2.142658e+03, -2.110195e+03,\n",
       "       -2.077732e+03, -2.045269e+03, -2.012806e+03, -1.980343e+03,\n",
       "       -1.947880e+03, -1.915417e+03, -1.882954e+03, -1.850491e+03,\n",
       "       -1.818028e+03, -1.785565e+03, -1.753102e+03, -1.720639e+03,\n",
       "       -1.688176e+03, -1.655713e+03, -1.623250e+03, -1.590787e+03,\n",
       "       -1.558324e+03, -1.525861e+03, -1.493398e+03, -1.460935e+03,\n",
       "       -1.428472e+03, -1.396009e+03, -1.363546e+03, -1.331083e+03,\n",
       "       -1.298620e+03, -1.266157e+03, -1.233694e+03, -1.201231e+03,\n",
       "       -1.168768e+03, -1.136305e+03, -1.103842e+03, -1.071379e+03,\n",
       "       -1.038916e+03, -1.006453e+03, -9.739901e+02, -9.415271e+02,\n",
       "       -9.090641e+02, -8.766011e+02, -8.441381e+02, -8.116751e+02,\n",
       "       -7.792121e+02, -7.467491e+02, -7.142861e+02, -6.818231e+02,\n",
       "       -6.493601e+02, -6.168971e+02, -5.844341e+02, -5.519711e+02,\n",
       "       -5.195081e+02, -4.870451e+02, -4.545821e+02, -4.221191e+02,\n",
       "       -3.896561e+02, -3.571931e+02, -3.247301e+02, -2.922671e+02,\n",
       "       -2.598041e+02, -2.273411e+02, -1.948781e+02, -1.624151e+02,\n",
       "       -1.299521e+02, -9.748907e+01, -6.502608e+01, -3.256308e+01,\n",
       "       -1.000744e-01,  3.236293e+01,  6.482593e+01,  9.728893e+01,\n",
       "        1.297519e+02,  1.622149e+02,  1.946779e+02,  2.271409e+02,\n",
       "        2.596039e+02,  2.920669e+02,  3.245299e+02,  3.569930e+02,\n",
       "        3.894559e+02,  4.219189e+02,  4.543820e+02,  4.868449e+02,\n",
       "        5.193079e+02,  5.517709e+02,  5.842339e+02,  6.166970e+02,\n",
       "        6.491600e+02,  6.816229e+02,  7.140859e+02], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-3.324e+03 -3.292e+03 ... 5.343e+03</div><input id='attrs-4c3c48f5-161f-4525-970a-301248967f71' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c3c48f5-161f-4525-970a-301248967f71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79ae1f80-5af8-4efd-8069-697c0eebf40c' class='xr-var-data-in' type='checkbox'><label for='data-79ae1f80-5af8-4efd-8069-697c0eebf40c' 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>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>km</dd><dt><span>_CoordinateAxisType :</span></dt><dd>GeoX</dd></dl></div><div class='xr-var-data'><pre>array([-3324.4707, -3292.0078, -3259.5447, ...,  5278.2246,  5310.6875,\n",
       "        5343.1504], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9271e8a5-4ad9-443e-a066-881f4f43804f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9271e8a5-4ad9-443e-a066-881f4f43804f' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>u-component_of_wind_isobaric</span></div><div class='xr-var-dims'>(time1, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e67da8d3-a015-4274-93c5-29f89586ee9e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e67da8d3-a015-4274-93c5-29f89586ee9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91b70095-59f3-4666-8169-02564f9de989' class='xr-var-data-in' type='checkbox'><label for='data-91b70095-59f3-4666-8169-02564f9de989' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>u-component of wind @ Isobaric surface</dd><dt><span>units :</span></dt><dd>m s^-1</dd><dt><span>description :</span></dt><dd>u-component of wind</dd><dt><span>grid_mapping :</span></dt><dd>LambertConformal_Projection</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_7-15-131-33_L100</dd><dt><span>Grib1_Center :</span></dt><dd>7</dd><dt><span>Grib1_Subcenter :</span></dt><dd>15</dd><dt><span>Grib1_TableVersion :</span></dt><dd>131</dd><dt><span>Grib1_Parameter :</span></dt><dd>33</dd><dt><span>Grib1_Level_Type :</span></dt><dd>100</dd><dt><span>Grib1_Level_Desc :</span></dt><dd>Isobaric surface</dd></dl></div><div class='xr-var-data'><pre>[31892 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LambertConformal_Projection</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-832dd0db-5ec7-487e-9bcf-0de7a147bfe2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-832dd0db-5ec7-487e-9bcf-0de7a147bfe2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-636e6aad-641e-4940-88c8-7d275e02a2da' class='xr-var-data-in' type='checkbox'><label for='data-636e6aad-641e-4940-88c8-7d275e02a2da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping_name :</span></dt><dd>lambert_conformal_conic</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>50.000003814697266</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>-107.00000762939453</dd><dt><span>standard_parallel :</span></dt><dd>50.000003814697266</dd><dt><span>earth_radius :</span></dt><dd>6367470.0</dd><dt><span>_CoordinateTransformType :</span></dt><dd>Projection</dd><dt><span>_CoordinateAxisTypes :</span></dt><dd>GeoX GeoY</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-79877fbe-44c3-4b60-8c5c-f4b722e74a38' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-79877fbe-44c3-4b60-8c5c-f4b722e74a38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47bcbe6d-2db8-43f8-b3dd-4599a7e3f473' class='xr-var-data-in' type='checkbox'><label for='data-47bcbe6d-2db8-43f8-b3dd-4599a7e3f473' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude coordinate</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lat</dd></dl></div><div class='xr-var-data'><pre>[31892 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-03bef424-6ae5-44ed-9f80-af082f2f3f57' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03bef424-6ae5-44ed-9f80-af082f2f3f57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc9ef35f-5c3f-444f-a038-4137ab4ee763' class='xr-var-data-in' type='checkbox'><label for='data-fc9ef35f-5c3f-444f-a038-4137ab4ee763' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude coordinate</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lon</dd></dl></div><div class='xr-var-data'><pre>[31892 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Geopotential_height_isobaric</span></div><div class='xr-var-dims'>(time1, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d935c36d-f342-4a0c-8022-7dde757e0ec4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d935c36d-f342-4a0c-8022-7dde757e0ec4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2432b35e-9130-422e-b3ec-076fa2c5a4c9' class='xr-var-data-in' type='checkbox'><label for='data-2432b35e-9130-422e-b3ec-076fa2c5a4c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Geopotential height @ Isobaric surface</dd><dt><span>units :</span></dt><dd>gpm</dd><dt><span>description :</span></dt><dd>Geopotential height</dd><dt><span>grid_mapping :</span></dt><dd>LambertConformal_Projection</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_7-15-131-7_L100</dd><dt><span>Grib1_Center :</span></dt><dd>7</dd><dt><span>Grib1_Subcenter :</span></dt><dd>15</dd><dt><span>Grib1_TableVersion :</span></dt><dd>131</dd><dt><span>Grib1_Parameter :</span></dt><dd>7</dd><dt><span>Grib1_Level_Type :</span></dt><dd>100</dd><dt><span>Grib1_Level_Desc :</span></dt><dd>Isobaric surface</dd></dl></div><div class='xr-var-data'><pre>[31892 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v-component_of_wind_isobaric</span></div><div class='xr-var-dims'>(time1, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-eec044c5-31f4-48d3-8fae-33ac9ce6fb8a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eec044c5-31f4-48d3-8fae-33ac9ce6fb8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29f3f465-0b39-4f3d-a171-8f542689ba53' class='xr-var-data-in' type='checkbox'><label for='data-29f3f465-0b39-4f3d-a171-8f542689ba53' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>v-component of wind @ Isobaric surface</dd><dt><span>units :</span></dt><dd>m s^-1</dd><dt><span>description :</span></dt><dd>v-component of wind</dd><dt><span>grid_mapping :</span></dt><dd>LambertConformal_Projection</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_7-15-131-34_L100</dd><dt><span>Grib1_Center :</span></dt><dd>7</dd><dt><span>Grib1_Subcenter :</span></dt><dd>15</dd><dt><span>Grib1_TableVersion :</span></dt><dd>131</dd><dt><span>Grib1_Parameter :</span></dt><dd>34</dd><dt><span>Grib1_Level_Type :</span></dt><dd>100</dd><dt><span>Grib1_Level_Desc :</span></dt><dd>Isobaric surface</dd></dl></div><div class='xr-var-data'><pre>[31892 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Temperature_isobaric</span></div><div class='xr-var-dims'>(time1, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d3fcf173-5c1b-4ec0-9a2e-b0627baf5935' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d3fcf173-5c1b-4ec0-9a2e-b0627baf5935' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f106921d-337d-47ce-b925-129f3ae826ca' class='xr-var-data-in' type='checkbox'><label for='data-f106921d-337d-47ce-b925-129f3ae826ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Temperature @ Isobaric surface</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>description :</span></dt><dd>Temperature</dd><dt><span>grid_mapping :</span></dt><dd>LambertConformal_Projection</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_7-15-131-11_L100</dd><dt><span>Grib1_Center :</span></dt><dd>7</dd><dt><span>Grib1_Subcenter :</span></dt><dd>15</dd><dt><span>Grib1_TableVersion :</span></dt><dd>131</dd><dt><span>Grib1_Parameter :</span></dt><dd>11</dd><dt><span>Grib1_Level_Type :</span></dt><dd>100</dd><dt><span>Grib1_Level_Desc :</span></dt><dd>Isobaric surface</dd></dl></div><div class='xr-var-data'><pre>[31892 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f3880461-b5d6-4a5f-9506-151a5d15f6a4' class='xr-section-summary-in' type='checkbox'  ><label for='section-f3880461-b5d6-4a5f-9506-151a5d15f6a4' 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>time1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4db9e9cc-4073-4635-8ecc-f241d93eab42' class='xr-index-data-in' type='checkbox'/><label for='index-4db9e9cc-4073-4635-8ecc-f241d93eab42' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1993-03-13&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time1&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-31258e70-74bb-4ff8-8716-a351254a98f3' class='xr-index-data-in' type='checkbox'/><label for='index-31258e70-74bb-4ff8-8716-a351254a98f3' 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(Float64Index([-3116.548095703125, -3084.085205078125,   -3051.6220703125,\n",
       "                -3019.1591796875,   -2986.6962890625, -2954.233154296875,\n",
       "              -2921.770263671875,  -2889.30712890625,  -2856.84423828125,\n",
       "              -2824.381103515625,\n",
       "              ...\n",
       "                  421.9189453125,  454.3819580078125,  486.8449401855469,\n",
       "               519.3079223632812,  551.7709350585938,  584.2339477539062,\n",
       "               616.6969604492188,  649.1599731445312,  681.6229248046875,\n",
       "                     714.0859375],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=119))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fb4038eb-6f4c-4252-bdae-a506124c6025' class='xr-index-data-in' type='checkbox'/><label for='index-fb4038eb-6f4c-4252-bdae-a506124c6025' 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(Float64Index([   -3324.470703125,      -3292.0078125, -3259.544677734375,\n",
       "              -3227.081787109375,  -3194.61865234375,  -3162.15576171875,\n",
       "              -3129.692626953125, -3097.229736328125,   -3064.7666015625,\n",
       "                -3032.3037109375,\n",
       "              ...\n",
       "                 5050.9833984375,   5083.44677734375,   5115.90966796875,\n",
       "                5148.37255859375,   5180.83544921875,     5213.298828125,\n",
       "                   5245.76171875,     5278.224609375,          5310.6875,\n",
       "                  5343.150390625],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=268))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6b89bb1c-ffe9-468d-a8a1-dfa6a1a5182f' class='xr-section-summary-in' type='checkbox'  ><label for='section-6b89bb1c-ffe9-468d-a8a1-dfa6a1a5182f' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Originating_or_generating_Center :</span></dt><dd>US National Weather Service, National Centres for Environmental Prediction (NCEP)</dd><dt><span>Originating_or_generating_Subcenter :</span></dt><dd>North American Regional Reanalysis Project</dd><dt><span>GRIB_table_version :</span></dt><dd>0,131</dd><dt><span>Generating_process_or_model :</span></dt><dd>North American Regional Reanalysis (NARR)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>history :</span></dt><dd>Read using CDM IOSP GribCollection v3</dd><dt><span>featureType :</span></dt><dd>GRID</dd><dt><span>History :</span></dt><dd>Translated to CF-1.0 Conventions by Netcdf-Java CDM (CFGridWriter2)\n",
       "Original Dataset = DatasetScan#narr-a_221_19930313_0000_000.grb; Translation Date = 2018-01-04T23:57:16.043Z</dd><dt><span>geospatial_lat_min :</span></dt><dd>10.753308882144761</dd><dt><span>geospatial_lat_max :</span></dt><dd>46.8308828962289</dd><dt><span>geospatial_lon_min :</span></dt><dd>-153.88242040519995</dd><dt><span>geospatial_lon_max :</span></dt><dd>-42.666108129242815</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                       (time1: 1, y: 119, x: 268)\n",
       "Coordinates:\n",
       "  * time1                         (time1) datetime64[ns] 1993-03-13\n",
       "    isobaric1                     float32 1e+03\n",
       "  * y                             (y) float32 -3.117e+03 -3.084e+03 ... 714.1\n",
       "  * x                             (x) float32 -3.324e+03 ... 5.343e+03\n",
       "Data variables:\n",
       "    u-component_of_wind_isobaric  (time1, y, x) float32 ...\n",
       "    LambertConformal_Projection   int32 ...\n",
       "    lat                           (y, x) float64 ...\n",
       "    lon                           (y, x) float64 ...\n",
       "    Geopotential_height_isobaric  (time1, y, x) float32 ...\n",
       "    v-component_of_wind_isobaric  (time1, y, x) float32 ...\n",
       "    Temperature_isobaric          (time1, y, x) float32 ...\n",
       "Attributes:\n",
       "    Originating_or_generating_Center:     US National Weather Service, Nation...\n",
       "    Originating_or_generating_Subcenter:  North American Regional Reanalysis ...\n",
       "    GRIB_table_version:                   0,131\n",
       "    Generating_process_or_model:          North American Regional Reanalysis ...\n",
       "    Conventions:                          CF-1.6\n",
       "    history:                              Read using CDM IOSP GribCollection v3\n",
       "    featureType:                          GRID\n",
       "    History:                              Translated to CF-1.0 Conventions by...\n",
       "    geospatial_lat_min:                   10.753308882144761\n",
       "    geospatial_lat_max:                   46.8308828962289\n",
       "    geospatial_lon_min:                   -153.88242040519995\n",
       "    geospatial_lon_max:                   -42.666108129242815"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_1000 = ds.sel(isobaric1=1000.0)\n",
    "ds_1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As shown above, the subsetting operation performed on the `Dataset` returned a new `Dataset`.  If only a single `DataArray` is needed from this new `Dataset`, it can be retrieved using the familiar dot notation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       ".xr-section-summary > span {\n",
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       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
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       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
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       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
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       "\n",
       ".xr-section-summary,\n",
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       "\n",
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       "\n",
       ".xr-attrs dt {\n",
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       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
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       "\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
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       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;Temperature_isobaric&#x27; (time1: 1, y: 119, x: 268)&gt;\n",
       "[31892 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * time1      (time1) datetime64[ns] 1993-03-13\n",
       "    isobaric1  float32 1e+03\n",
       "  * y          (y) float32 -3.117e+03 -3.084e+03 -3.052e+03 ... 681.6 714.1\n",
       "  * x          (x) float32 -3.324e+03 -3.292e+03 ... 5.311e+03 5.343e+03\n",
       "Attributes:\n",
       "    long_name:           Temperature @ Isobaric surface\n",
       "    units:               K\n",
       "    description:         Temperature\n",
       "    grid_mapping:        LambertConformal_Projection\n",
       "    Grib_Variable_Id:    VAR_7-15-131-11_L100\n",
       "    Grib1_Center:        7\n",
       "    Grib1_Subcenter:     15\n",
       "    Grib1_TableVersion:  131\n",
       "    Grib1_Parameter:     11\n",
       "    Grib1_Level_Type:    100\n",
       "    Grib1_Level_Desc:    Isobaric surface</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'>'Temperature_isobaric'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time1</span>: 1</li><li><span class='xr-has-index'>y</span>: 119</li><li><span class='xr-has-index'>x</span>: 268</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-45674dba-a27c-425a-bae6-66d1b2b48e28' class='xr-array-in' type='checkbox' checked><label for='section-45674dba-a27c-425a-bae6-66d1b2b48e28' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[31892 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-260e4ab3-1d99-4087-b609-dfd6809b4d73' class='xr-section-summary-in' type='checkbox'  checked><label for='section-260e4ab3-1d99-4087-b609-dfd6809b4d73' 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'>time1</span></div><div class='xr-var-dims'>(time1)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1993-03-13</div><input id='attrs-5b6d5560-5bb3-465e-a28a-183b05e7856f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b6d5560-5bb3-465e-a28a-183b05e7856f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c83629a9-5789-4eab-b9c0-ed25c0925e3d' class='xr-var-data-in' type='checkbox'><label for='data-c83629a9-5789-4eab-b9c0-ed25c0925e3d' 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>GRIB forecast or observation time</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1993-03-13T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>isobaric1</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1e+03</div><input id='attrs-06fe9d75-9b4d-48d1-b218-f4d39b007a9e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06fe9d75-9b4d-48d1-b218-f4d39b007a9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce6f973f-86dc-4e21-a0ee-f7756dec4103' class='xr-var-data-in' type='checkbox'><label for='data-ce6f973f-86dc-4e21-a0ee-f7756dec4103' 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>hPa</dd><dt><span>long_name :</span></dt><dd>Isobaric surface</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>Grib_level_type :</span></dt><dd>100</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Pressure</dd><dt><span>_CoordinateZisPositive :</span></dt><dd>down</dd></dl></div><div class='xr-var-data'><pre>array(1000., dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-3.117e+03 -3.084e+03 ... 714.1</div><input id='attrs-d83a618a-a529-4f23-9b76-a575cdf03ecd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d83a618a-a529-4f23-9b76-a575cdf03ecd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd7e8a97-241a-4738-a8a3-27bd87e7cf42' class='xr-var-data-in' type='checkbox'><label for='data-fd7e8a97-241a-4738-a8a3-27bd87e7cf42' 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>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>km</dd><dt><span>_CoordinateAxisType :</span></dt><dd>GeoY</dd></dl></div><div class='xr-var-data'><pre>array([-3.116548e+03, -3.084085e+03, -3.051622e+03, -3.019159e+03,\n",
       "       -2.986696e+03, -2.954233e+03, -2.921770e+03, -2.889307e+03,\n",
       "       -2.856844e+03, -2.824381e+03, -2.791918e+03, -2.759455e+03,\n",
       "       -2.726992e+03, -2.694529e+03, -2.662066e+03, -2.629603e+03,\n",
       "       -2.597140e+03, -2.564677e+03, -2.532214e+03, -2.499751e+03,\n",
       "       -2.467288e+03, -2.434825e+03, -2.402362e+03, -2.369899e+03,\n",
       "       -2.337436e+03, -2.304973e+03, -2.272510e+03, -2.240047e+03,\n",
       "       -2.207584e+03, -2.175121e+03, -2.142658e+03, -2.110195e+03,\n",
       "       -2.077732e+03, -2.045269e+03, -2.012806e+03, -1.980343e+03,\n",
       "       -1.947880e+03, -1.915417e+03, -1.882954e+03, -1.850491e+03,\n",
       "       -1.818028e+03, -1.785565e+03, -1.753102e+03, -1.720639e+03,\n",
       "       -1.688176e+03, -1.655713e+03, -1.623250e+03, -1.590787e+03,\n",
       "       -1.558324e+03, -1.525861e+03, -1.493398e+03, -1.460935e+03,\n",
       "       -1.428472e+03, -1.396009e+03, -1.363546e+03, -1.331083e+03,\n",
       "       -1.298620e+03, -1.266157e+03, -1.233694e+03, -1.201231e+03,\n",
       "       -1.168768e+03, -1.136305e+03, -1.103842e+03, -1.071379e+03,\n",
       "       -1.038916e+03, -1.006453e+03, -9.739901e+02, -9.415271e+02,\n",
       "       -9.090641e+02, -8.766011e+02, -8.441381e+02, -8.116751e+02,\n",
       "       -7.792121e+02, -7.467491e+02, -7.142861e+02, -6.818231e+02,\n",
       "       -6.493601e+02, -6.168971e+02, -5.844341e+02, -5.519711e+02,\n",
       "       -5.195081e+02, -4.870451e+02, -4.545821e+02, -4.221191e+02,\n",
       "       -3.896561e+02, -3.571931e+02, -3.247301e+02, -2.922671e+02,\n",
       "       -2.598041e+02, -2.273411e+02, -1.948781e+02, -1.624151e+02,\n",
       "       -1.299521e+02, -9.748907e+01, -6.502608e+01, -3.256308e+01,\n",
       "       -1.000744e-01,  3.236293e+01,  6.482593e+01,  9.728893e+01,\n",
       "        1.297519e+02,  1.622149e+02,  1.946779e+02,  2.271409e+02,\n",
       "        2.596039e+02,  2.920669e+02,  3.245299e+02,  3.569930e+02,\n",
       "        3.894559e+02,  4.219189e+02,  4.543820e+02,  4.868449e+02,\n",
       "        5.193079e+02,  5.517709e+02,  5.842339e+02,  6.166970e+02,\n",
       "        6.491600e+02,  6.816229e+02,  7.140859e+02], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-3.324e+03 -3.292e+03 ... 5.343e+03</div><input id='attrs-f8784b3a-c640-4f55-86ef-60ef2e00c41c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8784b3a-c640-4f55-86ef-60ef2e00c41c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27e78224-4790-4ca2-92f6-a6fbc923483c' class='xr-var-data-in' type='checkbox'><label for='data-27e78224-4790-4ca2-92f6-a6fbc923483c' 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>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>km</dd><dt><span>_CoordinateAxisType :</span></dt><dd>GeoX</dd></dl></div><div class='xr-var-data'><pre>array([-3324.4707, -3292.0078, -3259.5447, ...,  5278.2246,  5310.6875,\n",
       "        5343.1504], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a5f5fcb8-7c7b-4b41-8123-1844869cc075' class='xr-section-summary-in' type='checkbox'  ><label for='section-a5f5fcb8-7c7b-4b41-8123-1844869cc075' 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>time1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4893ba20-6c5d-4ad3-8926-b25ed1a09c9b' class='xr-index-data-in' type='checkbox'/><label for='index-4893ba20-6c5d-4ad3-8926-b25ed1a09c9b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1993-03-13&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time1&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-dc4fcce9-10dd-4869-94ac-d607311e4b1a' class='xr-index-data-in' type='checkbox'/><label for='index-dc4fcce9-10dd-4869-94ac-d607311e4b1a' 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(Float64Index([-3116.548095703125, -3084.085205078125,   -3051.6220703125,\n",
       "                -3019.1591796875,   -2986.6962890625, -2954.233154296875,\n",
       "              -2921.770263671875,  -2889.30712890625,  -2856.84423828125,\n",
       "              -2824.381103515625,\n",
       "              ...\n",
       "                  421.9189453125,  454.3819580078125,  486.8449401855469,\n",
       "               519.3079223632812,  551.7709350585938,  584.2339477539062,\n",
       "               616.6969604492188,  649.1599731445312,  681.6229248046875,\n",
       "                     714.0859375],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=119))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5106338a-e741-4d99-8f34-55a7b620e90f' class='xr-index-data-in' type='checkbox'/><label for='index-5106338a-e741-4d99-8f34-55a7b620e90f' 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(Float64Index([   -3324.470703125,      -3292.0078125, -3259.544677734375,\n",
       "              -3227.081787109375,  -3194.61865234375,  -3162.15576171875,\n",
       "              -3129.692626953125, -3097.229736328125,   -3064.7666015625,\n",
       "                -3032.3037109375,\n",
       "              ...\n",
       "                 5050.9833984375,   5083.44677734375,   5115.90966796875,\n",
       "                5148.37255859375,   5180.83544921875,     5213.298828125,\n",
       "                   5245.76171875,     5278.224609375,          5310.6875,\n",
       "                  5343.150390625],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=268))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-efcf7444-b71f-4281-b406-a9ab8080d7d0' class='xr-section-summary-in' type='checkbox'  ><label for='section-efcf7444-b71f-4281-b406-a9ab8080d7d0' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Temperature @ Isobaric surface</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>description :</span></dt><dd>Temperature</dd><dt><span>grid_mapping :</span></dt><dd>LambertConformal_Projection</dd><dt><span>Grib_Variable_Id :</span></dt><dd>VAR_7-15-131-11_L100</dd><dt><span>Grib1_Center :</span></dt><dd>7</dd><dt><span>Grib1_Subcenter :</span></dt><dd>15</dd><dt><span>Grib1_TableVersion :</span></dt><dd>131</dd><dt><span>Grib1_Parameter :</span></dt><dd>11</dd><dt><span>Grib1_Level_Type :</span></dt><dd>100</dd><dt><span>Grib1_Level_Desc :</span></dt><dd>Isobaric surface</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'Temperature_isobaric' (time1: 1, y: 119, x: 268)>\n",
       "[31892 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * time1      (time1) datetime64[ns] 1993-03-13\n",
       "    isobaric1  float32 1e+03\n",
       "  * y          (y) float32 -3.117e+03 -3.084e+03 -3.052e+03 ... 681.6 714.1\n",
       "  * x          (x) float32 -3.324e+03 -3.292e+03 ... 5.311e+03 5.343e+03\n",
       "Attributes:\n",
       "    long_name:           Temperature @ Isobaric surface\n",
       "    units:               K\n",
       "    description:         Temperature\n",
       "    grid_mapping:        LambertConformal_Projection\n",
       "    Grib_Variable_Id:    VAR_7-15-131-11_L100\n",
       "    Grib1_Center:        7\n",
       "    Grib1_Subcenter:     15\n",
       "    Grib1_TableVersion:  131\n",
       "    Grib1_Parameter:     11\n",
       "    Grib1_Level_Type:    100\n",
       "    Grib1_Level_Desc:    Isobaric surface"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_1000.Temperature_isobaric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aggregation operations\n",
    "\n",
    "As covered earlier in this tutorial, you can use named dimensions in an Xarray `Dataset` to manually slice and index data.  However, these dimension names also serve an additional purpose: you can use them to specify dimensions to aggregate on.  There are many different aggregation operations available; in this example, we focus on `std` (standard deviation)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;u-component_of_wind_isobaric&#x27; (time1: 1, isobaric1: 29)&gt;\n",
       "array([[ 8.673963 , 10.212325 , 11.556413 , 12.254429 , 13.372146 ,\n",
       "        15.472462 , 16.091969 , 15.846294 , 15.195834 , 13.936979 ,\n",
       "        12.93888  , 12.060708 , 10.972139 ,  9.722328 ,  8.853286 ,\n",
       "         8.257241 ,  7.679721 ,  7.4516497,  7.2352104,  7.039894 ,\n",
       "         6.883371 ,  6.7821493,  6.7088237,  6.6865997,  6.7247376,\n",
       "         6.745023 ,  6.6859775,  6.5107226,  5.972262 ]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time1      (time1) datetime64[ns] 1993-03-13\n",
       "  * isobaric1  (isobaric1) float32 100.0 125.0 150.0 175.0 ... 950.0 975.0 1e+03</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'>'u-component_of_wind_isobaric'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time1</span>: 1</li><li><span class='xr-has-index'>isobaric1</span>: 29</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-aae50328-f9a3-40da-8226-097899cc8ef2' class='xr-array-in' type='checkbox' checked><label for='section-aae50328-f9a3-40da-8226-097899cc8ef2' 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>8.674 10.21 11.56 12.25 13.37 15.47 ... 6.725 6.745 6.686 6.511 5.972</span></div><div class='xr-array-data'><pre>array([[ 8.673963 , 10.212325 , 11.556413 , 12.254429 , 13.372146 ,\n",
       "        15.472462 , 16.091969 , 15.846294 , 15.195834 , 13.936979 ,\n",
       "        12.93888  , 12.060708 , 10.972139 ,  9.722328 ,  8.853286 ,\n",
       "         8.257241 ,  7.679721 ,  7.4516497,  7.2352104,  7.039894 ,\n",
       "         6.883371 ,  6.7821493,  6.7088237,  6.6865997,  6.7247376,\n",
       "         6.745023 ,  6.6859775,  6.5107226,  5.972262 ]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-f95189ee-804f-41b5-8884-ba95d22c6f7e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f95189ee-804f-41b5-8884-ba95d22c6f7e' 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'>time1</span></div><div class='xr-var-dims'>(time1)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1993-03-13</div><input id='attrs-e8d8b0f0-ac52-4e75-b734-cb8ba7c08a05' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8d8b0f0-ac52-4e75-b734-cb8ba7c08a05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e69b8174-665b-4161-9c80-d4b153adee1b' class='xr-var-data-in' type='checkbox'><label for='data-e69b8174-665b-4161-9c80-d4b153adee1b' 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>GRIB forecast or observation time</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1993-03-13T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>isobaric1</span></div><div class='xr-var-dims'>(isobaric1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>100.0 125.0 150.0 ... 975.0 1e+03</div><input id='attrs-e60ca3e7-b1ab-4b0f-a990-e4c15f329cfa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e60ca3e7-b1ab-4b0f-a990-e4c15f329cfa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85ab2913-8f63-4d16-945c-5501c08133d5' class='xr-var-data-in' type='checkbox'><label for='data-85ab2913-8f63-4d16-945c-5501c08133d5' 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>hPa</dd><dt><span>long_name :</span></dt><dd>Isobaric surface</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>Grib_level_type :</span></dt><dd>100</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Pressure</dd><dt><span>_CoordinateZisPositive :</span></dt><dd>down</dd></dl></div><div class='xr-var-data'><pre>array([ 100.,  125.,  150.,  175.,  200.,  225.,  250.,  275.,  300.,  350.,\n",
       "        400.,  450.,  500.,  550.,  600.,  650.,  700.,  725.,  750.,  775.,\n",
       "        800.,  825.,  850.,  875.,  900.,  925.,  950.,  975., 1000.],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-12124f44-76f8-400e-b833-78b4b0994b74' class='xr-section-summary-in' type='checkbox'  ><label for='section-12124f44-76f8-400e-b833-78b4b0994b74' 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>time1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b685c611-620a-433c-abcf-215cf6e4f602' class='xr-index-data-in' type='checkbox'/><label for='index-b685c611-620a-433c-abcf-215cf6e4f602' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1993-03-13&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time1&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>isobaric1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-23a2724f-0853-40ff-96b8-7baa94c0d9be' class='xr-index-data-in' type='checkbox'/><label for='index-23a2724f-0853-40ff-96b8-7baa94c0d9be' 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(Float64Index([ 100.0,  125.0,  150.0,  175.0,  200.0,  225.0,  250.0,  275.0,\n",
       "               300.0,  350.0,  400.0,  450.0,  500.0,  550.0,  600.0,  650.0,\n",
       "               700.0,  725.0,  750.0,  775.0,  800.0,  825.0,  850.0,  875.0,\n",
       "               900.0,  925.0,  950.0,  975.0, 1000.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;isobaric1&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-342056ec-8e50-4d6c-b8d3-40f4703dc0b3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-342056ec-8e50-4d6c-b8d3-40f4703dc0b3' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'u-component_of_wind_isobaric' (time1: 1, isobaric1: 29)>\n",
       "array([[ 8.673963 , 10.212325 , 11.556413 , 12.254429 , 13.372146 ,\n",
       "        15.472462 , 16.091969 , 15.846294 , 15.195834 , 13.936979 ,\n",
       "        12.93888  , 12.060708 , 10.972139 ,  9.722328 ,  8.853286 ,\n",
       "         8.257241 ,  7.679721 ,  7.4516497,  7.2352104,  7.039894 ,\n",
       "         6.883371 ,  6.7821493,  6.7088237,  6.6865997,  6.7247376,\n",
       "         6.745023 ,  6.6859775,  6.5107226,  5.972262 ]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time1      (time1) datetime64[ns] 1993-03-13\n",
       "  * isobaric1  (isobaric1) float32 100.0 125.0 150.0 175.0 ... 950.0 975.0 1e+03"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u_winds = ds['u-component_of_wind_isobaric']\n",
    "u_winds.std(dim=['x', 'y'])"
   ]
  },
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   "source": [
    "<div class=\"admonition alert alert-info\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Info</p>\n",
    "    Recall from previous tutorials that aggregations in NumPy operate over axes specified by numeric values. However, with Xarray objects, aggregation dimensions are instead specified through a list passed to the <code>dim</code> keyword argument.\n",
    "</div>"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this set of examples, we will be using the sample dataset defined above.  The calculations performed in these examples compute the mean temperature profile, defined as temperature as a function of pressure, over Colorado.  For the purposes of these examples, the bounds of Colorado are defined as follows:\n",
    " * x: -182km to 424km\n",
    " * y: -1450km to -990km\n",
    "    \n",
    "This dataset uses a Lambert Conformal projection; therefore, the data values shown above are projected to specific latitude and longitude values.  In this example, these latitude and longitude values are 37°N to 41°N and 102°W to 109°W. Using the original data values and the `mean` aggregation function as shown below yields the following mean temperature profile data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       ".xr-var-preview {\n",
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       "\n",
       ".xr-index-preview {\n",
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       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
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       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  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",
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       "  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",
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       "}\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",
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       "\n",
       ".xr-attrs dd {\n",
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       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;Temperature_isobaric&#x27; (time1: 1, isobaric1: 29)&gt;\n",
       "array([[215.078  , 215.76935, 217.243  , 217.82663, 215.83487, 216.10933,\n",
       "        219.99902, 224.66118, 228.80576, 234.88701, 238.78503, 242.66309,\n",
       "        246.44807, 249.26636, 250.84995, 253.37354, 257.0429 , 259.08398,\n",
       "        260.97955, 262.98364, 264.82138, 266.5198 , 268.22467, 269.7471 ,\n",
       "        271.18216, 272.66815, 274.13037, 275.54718, 276.97675]],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "  * time1      (time1) datetime64[ns] 1993-03-13\n",
       "  * isobaric1  (isobaric1) float32 100.0 125.0 150.0 175.0 ... 950.0 975.0 1e+03</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'>'Temperature_isobaric'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time1</span>: 1</li><li><span class='xr-has-index'>isobaric1</span>: 29</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-95c3264e-a117-4cf5-8f0b-d52076048c20' class='xr-array-in' type='checkbox' checked><label for='section-95c3264e-a117-4cf5-8f0b-d52076048c20' 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>215.1 215.8 217.2 217.8 215.8 216.1 ... 271.2 272.7 274.1 275.5 277.0</span></div><div class='xr-array-data'><pre>array([[215.078  , 215.76935, 217.243  , 217.82663, 215.83487, 216.10933,\n",
       "        219.99902, 224.66118, 228.80576, 234.88701, 238.78503, 242.66309,\n",
       "        246.44807, 249.26636, 250.84995, 253.37354, 257.0429 , 259.08398,\n",
       "        260.97955, 262.98364, 264.82138, 266.5198 , 268.22467, 269.7471 ,\n",
       "        271.18216, 272.66815, 274.13037, 275.54718, 276.97675]],\n",
       "      dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-6fc4c996-3811-4290-9d95-73691db2e3ec' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6fc4c996-3811-4290-9d95-73691db2e3ec' 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'>time1</span></div><div class='xr-var-dims'>(time1)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1993-03-13</div><input id='attrs-3b00ea3d-53b4-47f8-89db-c701b3d8c64d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b00ea3d-53b4-47f8-89db-c701b3d8c64d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79afad9d-737c-4655-adaa-fcae6a34b376' class='xr-var-data-in' type='checkbox'><label for='data-79afad9d-737c-4655-adaa-fcae6a34b376' 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>GRIB forecast or observation time</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1993-03-13T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>isobaric1</span></div><div class='xr-var-dims'>(isobaric1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>100.0 125.0 150.0 ... 975.0 1e+03</div><input id='attrs-b53b8b3b-82c7-4d2f-84f3-d5dbfc40eec9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b53b8b3b-82c7-4d2f-84f3-d5dbfc40eec9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32c0bd5c-12d6-4e16-bbf3-24ecfbd8cf70' class='xr-var-data-in' type='checkbox'><label for='data-32c0bd5c-12d6-4e16-bbf3-24ecfbd8cf70' 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>hPa</dd><dt><span>long_name :</span></dt><dd>Isobaric surface</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>Grib_level_type :</span></dt><dd>100</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Pressure</dd><dt><span>_CoordinateZisPositive :</span></dt><dd>down</dd></dl></div><div class='xr-var-data'><pre>array([ 100.,  125.,  150.,  175.,  200.,  225.,  250.,  275.,  300.,  350.,\n",
       "        400.,  450.,  500.,  550.,  600.,  650.,  700.,  725.,  750.,  775.,\n",
       "        800.,  825.,  850.,  875.,  900.,  925.,  950.,  975., 1000.],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e0ec0761-8a79-4757-aa51-85de9c3b09c8' class='xr-section-summary-in' type='checkbox'  ><label for='section-e0ec0761-8a79-4757-aa51-85de9c3b09c8' 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>time1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-af97ede9-43dd-4ad7-bc41-44193fe89ea3' class='xr-index-data-in' type='checkbox'/><label for='index-af97ede9-43dd-4ad7-bc41-44193fe89ea3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1993-03-13&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time1&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>isobaric1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8d281786-000d-441b-a15e-414396b3ef55' class='xr-index-data-in' type='checkbox'/><label for='index-8d281786-000d-441b-a15e-414396b3ef55' 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(Float64Index([ 100.0,  125.0,  150.0,  175.0,  200.0,  225.0,  250.0,  275.0,\n",
       "               300.0,  350.0,  400.0,  450.0,  500.0,  550.0,  600.0,  650.0,\n",
       "               700.0,  725.0,  750.0,  775.0,  800.0,  825.0,  850.0,  875.0,\n",
       "               900.0,  925.0,  950.0,  975.0, 1000.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;isobaric1&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e7469f18-d75d-49d1-b02c-6ec2c9e64966' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e7469f18-d75d-49d1-b02c-6ec2c9e64966' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'Temperature_isobaric' (time1: 1, isobaric1: 29)>\n",
       "array([[215.078  , 215.76935, 217.243  , 217.82663, 215.83487, 216.10933,\n",
       "        219.99902, 224.66118, 228.80576, 234.88701, 238.78503, 242.66309,\n",
       "        246.44807, 249.26636, 250.84995, 253.37354, 257.0429 , 259.08398,\n",
       "        260.97955, 262.98364, 264.82138, 266.5198 , 268.22467, 269.7471 ,\n",
       "        271.18216, 272.66815, 274.13037, 275.54718, 276.97675]],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "  * time1      (time1) datetime64[ns] 1993-03-13\n",
       "  * isobaric1  (isobaric1) float32 100.0 125.0 150.0 175.0 ... 950.0 975.0 1e+03"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temps = ds.Temperature_isobaric\n",
    "co_temps = temps.sel(x=slice(-182, 424), y=slice(-1450, -990))\n",
    "prof = co_temps.mean(dim=['x', 'y'])\n",
    "prof"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting with Xarray\n",
    "\n",
    "As demonstrated earlier in this tutorial, there are many benefits to storing data as Xarray `DataArrays` and `Datasets`. In this section, we will cover another major benefit: Xarray greatly simplifies plotting of data stored as `DataArrays` and `Datasets`. One advantage of this is that many common plot elements, such as axis labels, are automatically generated and optimized for the data being plotted.  The next set of examples demonstrates this and provides a general overview of plotting with Xarray.\n",
    "\n",
    "### Simple visualization with `.plot()`\n",
    "\n",
    "Similarly to [Pandas](../pandas/pandas), Xarray includes a built-in plotting interface, which makes use of [Matplotlib](../matplotlib) behind the scenes. In order to use this interface, you can call the `.plot()` method, which is included in every `DataArray`.\n",
    "\n",
    "In this example, we show how to create a basic plot from a `DataArray`. In this case, we are using the `prof` `DataArray` defined above, which contains a Colorado mean temperature profile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7efee28ba150>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prof.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the figure shown above, Xarray has generated a line plot, which uses the mean temperature profile and the `'isobaric'` coordinate variable as axes. In addition, the axis labels and unit information have been read automatically from the `DataArray`'s metadata."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Customizing the plot\n",
    "\n",
    "As mentioned above, the `.plot()` method of Xarray `DataArrays` uses Matplotlib behind the scenes. Therefore, knowledge of Matplotlib can help you more easily customize plots generated by Xarray.\n",
    "\n",
    "In this example, we need to customize the air temperature profile plot created above. There are two changes that need to be made:\n",
    "- swap the axes, so that the Y (vertical) axis corresponds to isobaric levels\n",
    "- invert the Y axis to match the model of air pressure decreasing at higher altitudes\n",
    "\n",
    "We can make these changes by adding certain keyword arguments when calling `.plot()`, as shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7efee2917790>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prof.plot(y=\"isobaric1\", yincrease=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting 2-D data\n",
    "\n",
    "In the previous example, we used `.plot()` to generate a plot from 1-D data, and the result was a line plot. In this section, we illustrate plotting of 2-D data.\n",
    "\n",
    "In this example, we illustrate basic plotting of a 2-D array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7efee206e390>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temps.sel(isobaric1=1000).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The figure above is generated by Matplotlib's `pcolormesh` method, which was automatically called by Xarray's `plot` method.  This occurred because Xarray recognized that the `DataArray` object calling the `plot` method contained two distinct coordinate variables.\n",
    "\n",
    "The plot generated by the above example is a map of air temperatures over North America, on the 1000 hPa isobaric surface. If a different map projection or added geographic features are needed on this plot, the plot can easily be modified using `cartopy`."
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Colour map\n",
    "To have a nice colour map I suggest having a look at [`cmocean`](https://matplotlib.org/cmocean/)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data analysis with `xarray`\n",
    "\n",
    "Now let us try and plot data from the CMIP6 archive.\n",
    "\n",
    "Download historical surface air temperature simulated by UKESM1 model from the archive and load.\n",
    "\n",
    "[Archive](https://esgf-node.llnl.gov/search/cmip6/)\n",
    "\n",
    "Select the following in the archive\n",
    "- Source ID: UKESM1-0-LL (This is the climate model we want)\n",
    "- Experiment ID: historical (This is the simulation we want to look at)\n",
    "- Variant Label: r1i1p1f2 (This is the initial model spin-up)\n",
    "- Frequency: mon (Monthly frequency)\n",
    "- Variable: tas (tas = surface air temperature)\n",
    "\n",
    "Then, click on [List files] then [HTTP Download] to download the files into data folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/vichawan/anaconda3/envs/cliaer-env/lib/python3.11/site-packages/xarray/conventions.py:431: SerializationWarning: variable 'tas' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  new_vars[k] = decode_cf_variable(\n"
     ]
    }
   ],
   "source": [
    "datapath = os.path.abspath('./data/tas_Amon_CESM2-WACCM_historical_r1i1p1f1_gn_185001-201412.nc')\n",
    "\n",
    "da = xr.open_dataset(datapath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (time: 1980, lat: 192, lon: 288, nbnd: 2)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
       "  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
       "  * time       (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00\n",
       "Dimensions without coordinates: nbnd\n",
       "Data variables:\n",
       "    tas        (time, lat, lon) float32 ...\n",
       "    time_bnds  (time, nbnd) object ...\n",
       "    lat_bnds   (lat, nbnd) float32 ...\n",
       "    lon_bnds   (lon, nbnd) float32 ...\n",
       "Attributes: (12/45)\n",
       "    Conventions:            CF-1.7 CMIP-6.2\n",
       "    activity_id:            CMIP\n",
       "    case_id:                4\n",
       "    cesm_casename:          b.e21.BWHIST.f09_g17.CMIP6-historical-WACCM.001\n",
       "    contact:                cesm_cmip6@ucar.edu\n",
       "    creation_date:          2019-01-30T22:37:15Z\n",
       "    ...                     ...\n",
       "    variable_id:            tas\n",
       "    variant_info:           CMIP6 CESM2 hindcast (1850-2014) with high-top at...\n",
       "    variant_label:          r1i1p1f1\n",
       "    branch_time_in_parent:  20075.0\n",
       "    branch_time_in_child:   674885.0\n",
       "    branch_method:          standard</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-09a08196-799a-49fd-a2c4-442f4761c47c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-09a08196-799a-49fd-a2c4-442f4761c47c' 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 class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li><li><span>nbnd</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-352c4c38-9287-4673-8ce8-a1ec530f40b8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-352c4c38-9287-4673-8ce8-a1ec530f40b8' 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 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-6aa240bb-ec94-411d-9d21-d7aa80466b9a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6aa240bb-ec94-411d-9d21-d7aa80466b9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71b542eb-9dff-411a-b913-6f03153394d6' class='xr-var-data-in' type='checkbox'><label for='data-71b542eb-9dff-411a-b913-6f03153394d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>title :</span></dt><dd>Latitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  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 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-b2ba5c27-3dbf-439d-bb76-1c7add7bc989' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2ba5c27-3dbf-439d-bb76-1c7add7bc989' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fbfd447a-6ca3-4aa3-8200-31229fcfa9c4' class='xr-var-data-in' type='checkbox'><label for='data-fbfd447a-6ca3-4aa3-8200-31229fcfa9c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>title :</span></dt><dd>Longitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>360.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-15 12:00:00 ... 2014-12-...</div><input id='attrs-9669c322-63ac-42f5-9d93-7125ea945ad3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9669c322-63ac-42f5-9d93-7125ea945ad3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9f38d2e-37ea-42f8-a4ac-edebffb025ba' class='xr-var-data-in' type='checkbox'><label for='data-a9f38d2e-37ea-42f8-a4ac-edebffb025ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>title :</span></dt><dd>time</dd><dt><span>type :</span></dt><dd>double</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 2, 14, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       ...,\n",
       "       cftime.DatetimeNoLeap(2014, 10, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 11, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 15, 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-46d05169-ff16-49fe-8a29-8798d3d7164a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-46d05169-ff16-49fe-8a29-8798d3d7164a' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5f282ae1-2be3-4e7b-b76b-617f38462a37' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f282ae1-2be3-4e7b-b76b-617f38462a37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc29cb15-3697-4fcf-bdd7-48683763e701' class='xr-var-data-in' type='checkbox'><label for='data-bc29cb15-3697-4fcf-bdd7-48683763e701' 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>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>comment :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>description :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>id :</span></dt><dd>tas</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>mipTable :</span></dt><dd>Amon</dd><dt><span>out_name :</span></dt><dd>tas</dd><dt><span>prov :</span></dt><dd>Amon ((isd.003))</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>time :</span></dt><dd>time</dd><dt><span>time_label :</span></dt><dd>time-mean</dd><dt><span>time_title :</span></dt><dd>Temporal mean</dd><dt><span>title :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>type :</span></dt><dd>real</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>variable_id :</span></dt><dd>tas</dd></dl></div><div class='xr-var-data'><pre>[109486080 values with 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, nbnd)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dcd37362-efcd-4e0e-9dbd-8dadba85fb57' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dcd37362-efcd-4e0e-9dbd-8dadba85fb57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08ccd579-329a-4471-b6cc-5bcdd6824d30' class='xr-var-data-in' type='checkbox'><label for='data-08ccd579-329a-4471-b6cc-5bcdd6824d30' 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>[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, nbnd)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b6159647-6fbf-4e97-b186-d7cb915e0d33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b6159647-6fbf-4e97-b186-d7cb915e0d33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb497727-34e7-4a15-9786-2eef8bc67aaa' class='xr-var-data-in' type='checkbox'><label for='data-eb497727-34e7-4a15-9786-2eef8bc67aaa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[384 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, nbnd)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4ba0c92e-4b22-4c96-8163-603ead5c644b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ba0c92e-4b22-4c96-8163-603ead5c644b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec7f598b-f200-4e14-9d57-49a6ac3c2cc3' class='xr-var-data-in' type='checkbox'><label for='data-ec7f598b-f200-4e14-9d57-49a6ac3c2cc3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[576 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d076784e-f4a7-4a12-a504-da22c2385404' class='xr-section-summary-in' type='checkbox'  ><label for='section-d076784e-f4a7-4a12-a504-da22c2385404' 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-e1636bb6-e22b-4939-ab6e-b773fa407660' class='xr-index-data-in' type='checkbox'/><label for='index-e1636bb6-e22b-4939-ab6e-b773fa407660' 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(Float64Index([             -90.0, -89.05759162303664,  -88.1151832460733,\n",
       "              -87.17277486910994,  -86.2303664921466, -85.28795811518324,\n",
       "               -84.3455497382199, -83.40314136125654, -82.46073298429319,\n",
       "              -81.51832460732984,\n",
       "              ...\n",
       "               81.51832460732984,   82.4607329842932,  83.40314136125653,\n",
       "               84.34554973821989,  85.28795811518324,   86.2303664921466,\n",
       "               87.17277486910996,  88.11518324607329,  89.05759162303664,\n",
       "                            90.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</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-d555ad14-85cf-4daa-9595-07099e7eb8ba' class='xr-index-data-in' type='checkbox'/><label for='index-d555ad14-85cf-4daa-9595-07099e7eb8ba' 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(Float64Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,\n",
       "                10.0,  11.25,\n",
       "              ...\n",
       "               347.5, 348.75,  350.0, 351.25,  352.5, 353.75,  355.0, 356.25,\n",
       "               357.5, 358.75],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</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-07a390a5-8c91-4909-993f-b421fdca2b93' class='xr-index-data-in' type='checkbox'/><label for='index-07a390a5-8c91-4909-993f-b421fdca2b93' 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([1850-01-15 12:00:00, 1850-02-14 00:00:00, 1850-03-15 12:00:00,\n",
       "             1850-04-15 00:00:00, 1850-05-15 12:00:00, 1850-06-15 00:00:00,\n",
       "             1850-07-15 12:00:00, 1850-08-15 12:00:00, 1850-09-15 00:00:00,\n",
       "             1850-10-15 12:00:00,\n",
       "             ...\n",
       "             2014-03-15 12:00:00, 2014-04-15 00:00:00, 2014-05-15 12:00:00,\n",
       "             2014-06-15 00:00:00, 2014-07-15 12:00:00, 2014-08-15 12:00:00,\n",
       "             2014-09-15 00:00:00, 2014-10-15 12:00:00, 2014-11-15 00:00:00,\n",
       "             2014-12-15 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=1980, calendar=&#x27;noleap&#x27;, freq=&#x27;None&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dad9d93d-6356-4815-8447-2081acac18a3' class='xr-section-summary-in' type='checkbox'  ><label for='section-dad9d93d-6356-4815-8447-2081acac18a3' class='xr-section-summary' >Attributes: <span>(45)</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>case_id :</span></dt><dd>4</dd><dt><span>cesm_casename :</span></dt><dd>b.e21.BWHIST.f09_g17.CMIP6-historical-WACCM.001</dd><dt><span>contact :</span></dt><dd>cesm_cmip6@ucar.edu</dd><dt><span>creation_date :</span></dt><dd>2019-01-30T22:37:15Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.29</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.NCAR.CESM2-WACCM.historical.none.r1i1p1f1</dd><dt><span>grid :</span></dt><dd>native 0.9x1.25 finite volume grid (192x288 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, 1850 Table Mesa Drive, Boulder, CO 80305, USA</dd><dt><span>institution_id :</span></dt><dd>NCAR</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by &lt;The National Center for Atmospheric Research&gt; 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>mip_era :</span></dt><dd>CMIP6</dd><dt><span>model_doi_url :</span></dt><dd>https://doi.org/10.5065/D67H1H0V</dd><dt><span>nominal_resolution :</span></dt><dd>100 km</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>CESM2-WACCM</dd><dt><span>parent_time_units :</span></dt><dd>days since 0001-01-01 00:00:00</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>1</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>source :</span></dt><dd>CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-6 mb); ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m); sea_ice: CICE5.1 (same grid as ocean); land: CLM5 0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-6 mb); aerosol: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-6 mb); atmosChem: WACCM (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-6 mb; landIce: CISM2.1; ocnBgchem: MARBL (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m)</dd><dt><span>source_id :</span></dt><dd>CESM2-WACCM</dd><dt><span>source_type :</span></dt><dd>AOGCM BGC CHEM AER</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>tracking_id :</span></dt><dd>hdl:21.14100/55a8af4c-3729-4051-9e53-f22295c2ec1c</dd><dt><span>variable_id :</span></dt><dd>tas</dd><dt><span>variant_info :</span></dt><dd>CMIP6 CESM2 hindcast (1850-2014) with high-top atmosphere (WACCM6) with interactive chemistry (TSMLT1), interactive land (CLM5), coupled ocean (POP2) with biogeochemistry (MARBL), interactive sea ice (CICE5.1), and non-evolving land ice (CISM2.1)</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>branch_time_in_parent :</span></dt><dd>20075.0</dd><dt><span>branch_time_in_child :</span></dt><dd>674885.0</dd><dt><span>branch_method :</span></dt><dd>standard</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (time: 1980, lat: 192, lon: 288, nbnd: 2)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
       "  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
       "  * time       (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00\n",
       "Dimensions without coordinates: nbnd\n",
       "Data variables:\n",
       "    tas        (time, lat, lon) float32 ...\n",
       "    time_bnds  (time, nbnd) object ...\n",
       "    lat_bnds   (lat, nbnd) float32 ...\n",
       "    lon_bnds   (lon, nbnd) float32 ...\n",
       "Attributes: (12/45)\n",
       "    Conventions:            CF-1.7 CMIP-6.2\n",
       "    activity_id:            CMIP\n",
       "    case_id:                4\n",
       "    cesm_casename:          b.e21.BWHIST.f09_g17.CMIP6-historical-WACCM.001\n",
       "    contact:                cesm_cmip6@ucar.edu\n",
       "    creation_date:          2019-01-30T22:37:15Z\n",
       "    ...                     ...\n",
       "    variable_id:            tas\n",
       "    variant_info:           CMIP6 CESM2 hindcast (1850-2014) with high-top at...\n",
       "    variant_label:          r1i1p1f1\n",
       "    branch_time_in_parent:  20075.0\n",
       "    branch_time_in_child:   674885.0\n",
       "    branch_method:          standard"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's select the variable and calculate the global mean "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "tas = da['tas']\n",
    "\n",
    "tas_monthly_mean = tas.mean(dim=['lat', 'lon'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7efee1f0d590>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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g3Sr1s80IOxK+M/767MhMf3bPleSzQ9QLQuENkNDmYpmc2efIuWIUl1UJfSTUngzGnqHaZIavfLsPv7qqna7vWOW7IcKMX8nwV1YDAPJLKvDkrT1N6FGb/JLQykDucjpQXa3AKemovC+vyOd+npyg5aUjNnnpF4zMNGPZPYWTZocIWtjFKdgr6urloQ+34O631pmeodfutyZ/cWkIB1oyw5CXVmLk7DU4c7HMZ7+qt18/HyGZt/ZQcBTeFoCEmVbKhAWl2j4wsvwjey8+WH9E2O5SRRWu+8cKTL/svKyXjYdFWcG9z0NLQyNjmhI6KJs4T9k9Q5GwQwQtMurW+kB1tYKvduVhw6HzOHjW95ucDJVV1Rj1+vfImL9V3DhI0RIkRIvhkXPFPvebGXouE1ZsxiK+dGcuHvpwi20FO7UyVpuJVbLOwTNFSH32f/jNOxv87isn7yLeWHkAT36+W/hSkb07DyfyS7Fo2wmcLSrDc1/swT4NfzQZeIdlBRWjc6UoEaCM/48Iu1/ISNghTOGn3EIs3HTUsgc6aH12JNqaOXGwbD5yAbtOFODLnbmW9G8WlyqqUFFVjdMXL+G+zI347iePf4KWIOEQLIeiK2r0seGZvOSEHf+X8Snzt+KrXXn494qfcSK/FK9+ux/ninxrsmQQmfUC8puzSLXz8ebjAIAfDpzzu69yJseXKN8Xe83+/NkuzP3+EG7612ocPFOEzO8PoaxSzg9qZc5prN53hnunWK2LjGZH1YdQs6O7q6CHfHYIU7j51TUAgEaRYbi1T2tT+mR/hkGr2TGcrdS8Sd7Oa/P+usOAw4F7rmnvs11peRVSn/0fWjWJQkqbeKzIOYMVOWfc+zWFHVFVZol6PH5rdkwwYxWUVuDON3/ALb1b4w/Duujq62R+Ke56cx1O5Jdi27ELePe+q3SPwx8C8VzpuaJLd+biyPliPJR+hbDtypzTKC6rMrUOU1S4RydQeKnCZ74vVhu2+0QBgBqty/WzVtV8v7RS930vLqvEhMxNAIBrOiV47a8y6KDMImpKoecmMXPmTAwcOBCxsbFITEzE6NGjkZOjrhpbVFSEjIwMJCcnIzo6Gj169MCcOXNUbcrKyjB16lQ0b94cjRo1wqhRo3D8+PFAngpxmR9PmheFwmqJKoUJq4IfdhIyM9GqXZmBC0oq8NSSH/HU57uFppY9uYUor6rGkXMlyCvwLtrpcDhw8EyRV2FV0WUSOmMyb6Yyl4mnUZJxOtWSi+auPYR9p4rwyrf7dPdVVlmNE/mlAID1B/3XVNQiuh4VgTBj6bikU+ZvxT+yc7Dt6AWf7RRFwYTMTZgyfyvOFJqpAfNQWOr7Oa9gfuM8AX7zEZFvDvDad/vxixeX4wiT7qCI8/tSJcxUmWuFh/B8TyAUmmrGashJBVetWoUpU6Zg/fr1WLZsGSorKzF8+HAUF3vs8NOmTUN2djY++OAD/PTTT5g2bRqmTp2KJUuWuNs8/PDDWLx4MRYsWIC1a9eiqKgIt956K6qqKDdLqBCsmh2jZiwzo3XsujaXGJW8SBhlT5e3iBaXVeL6Wasw+t/fq4QhcbQV/7ibD5/H37P3qswGMkIh14wlpdnhtzVS9oT9Trg/WeskqftcZX5/CCNfXYOzJprSRGZKlvPF5T73s+PNLzUvyot9tkX5vthnm/e46DGFzlq2DyfyS/HW6gPcMfAwKpSIpg4z36MatGYnOzsbEyZMQK9evZCamorMzEwcPXoUW7Z4EiutW7cOv/3tb5Geno4OHTpg8uTJSE1NxebNmwEABQUFeOeddzBr1iwMGzYM/fr1wwcffIBdu3bh22+/tevUCBNgfxuhEI3FOgM6UGMCqlV1+4Pdk4gsvAUjv8SzkLGLmmhp0BL0fvnmOsxZeQBvrz7o3qZ1mfbmFeKzLceF/mYOB7DzeD6ydohzwzg1ZlYjPm2swMaWUNFi+d5TGPPG9zh4RhDOLOin7gL67Bd7sCe3EG+uPKDxDd9sOXIeEzI34gAzLhmZX9TWl4+RP1FF7DMmun2sUMITbGQi+iol/HAMv/AEcO6we54KKgflgoKaiT8hwWOfTEtLQ1ZWFk6cOAFFUbBixQrs27cPN910EwBgy5YtqKiowPDhw93fad26NVJSUvDDDz9wj1NWVobCwkLVH2EOZrobhlo0Frt4LN2Vi6eW/IhbX1vrf782XRujkU487QZrIlL1JXigRKf+E5PcT0vQGPGvNXjkkx2qhG5aGodRr3+P33+0DVuO+Dap1P1+7WJrxORYViGn2fm/dzdj69F8TP94B1bvO6MqVCmDVqSO0cdt7Jx1WJlzBpPf2+zepijA/e9vxt+zxRmnRVo+tVDi+XwyvxRXPv8t/qHjGNx+2agnKDiRX6r5m2M1OzxNoNGoQ5GzuFFTdiBNSw3ajMWiKAqmT5+OtLQ0pKSkuLfPnj0bPXv2RHJyMiIiIjBixAi88cYbSEtLAwDk5eUhIiICTZs2VfXXsmVL5OXlcY81c+ZMxMfHu//atm1r3YkRphCs0VgysG+XP570X6NTizoE1Z7Jix1DaXkV/p69V+V/w87lPM2OVlFDofAsOF1WsBJdml2Mlo03MbPP4IHTvrUmDgewZPsJvLXqAApKKnDti9/h8cW7DAkKRs1YO4/n4965GzFy9hruftGzUqEx2JgI/wryHr9Q6v686fB5fPPjKczR0BaxQoXWs/D9z2fx6Cc71BpB5nn617f7cL64HG8Y1EhVMVrlb/ecwi9eXK6Z5kFsxqopDXGhjkmOd27sNn8zHWsRSG2L3ZqdoInGysjIwM6dO7F2rfpNd/bs2Vi/fj2ysrLQvn17rF69Gg899BBatWqFYcOGafanKIrmm8CMGTMwffp09/8LCwtJ4DEJrTeUnLyL2JtXiFGprSX8VYLfQVlGuGAXSzO1MYpK2LE2URuLKtyV+fzqd/vx5qoDmLPyAA6/eEvNfmaMlyo4mh1mzKoFTnAyojdaVlAQXXLh27NkeG9tCYyfTxfhVGEZ5m84ivt+0UH4vbqwZqwIHWasWtjTKSmvREyE3HSv9YxG+ynssL2WlAscflXCg/pZ2H2iAK3iozD+vzW5dNiyDexvotjPshesi9mbl82iX+/mv0izl4xnxjpyrgQ3vrIaLqcDB14YiapqBS6no+bcfDxTVpmxAhncYPcMHhTCztSpU5GVlYXVq1cjOTnZvb20tBSPP/44Fi9ejFtuqZk0+/Tpg+3bt+Of//wnhg0bhqSkJJSXl+PChQsq7c7p06cxaNAg7vEiIyMRGRlp7UkFkA0Hz+HNVQfw7KgUtGsWY/dwuNz0r5oU9HFR4RjaPdFn2483H8Ps7/ZjylBPqCnrs6MoCrJ2nESPVnHo2jLWmgFbQJVK2DGvX3aeq1YUOC3O37tm/xl8u+cUfn11e/c2VrOz60S+13cqhE6exjQ7osm6QiXs6F8weGYsVhgSTdzswsxWQDeytrACW4RBB+VThWXo2Fw93YuGwoZRs8JDNBN6XVlVjbnfH8Kgzs2R0iZe32CYA4teYthrzsoOO4/nY9Tr36sEikNnPIEt7G/C3xpf7NwT6XKq8u74gmey2ptXk2CwqlrBDz+fxf/N24Tnbk/xalcXq6qTB1QAacg+O4qiICMjA4sWLcLy5cvRsWNH1f6KigpUVFTAWcfbz+Vyofry3R8wYADCw8OxbNky9/7c3Fzs3r1bU9gJNe5+ez1W5JzB1AXb7B6KkK2C8FEA+OOnO3H8QilmLNrl3sZOit/9dBp/WLDdXcPHTqSisTR8CvyFnegCYe27552NmLfuCP67xuP8W1pehZe+2YstR85zNTfs/RNFJLGCoDDPjsiMVaXfjKW6P5w7K5O4jb2/Ycz8pWdRqq5WVFokVjgMcxkTZPUu0CxVGprIcEa7NG/dEbzw1V4p3zP2Goi0aWqnfs+5r9l/1mtc5VV8wdbfDNRa526E2CiPwDnh3U24VFGNP366kyvUs/dMFKBRL8xYNks7tmp2pkyZgvnz52PJkiWIjY11+9jEx8cjOjoacXFxGDJkCB577DFER0ejffv2WLVqFd577z28/PLL7rYTJ07EI488gmbNmiEhIQGPPvooevfu7dPMFYqczC8VN7IZUeimFuzkpUdgChQykwWr/WA/l1dWY8uRC+jfvgkiw+RNBEazp/rL0fOePCBvrz6IBZuO4d8rDqiSsNUiytmiVcFZmEFZcL7llXztBA8ZvwhRdA/b1qXlfM2hulrByNlrEMloT1jh0KjOjrvQCB4VrRpKrONt3bxIepCJMqqo5vvA8DRCKuGA2V/CaHa2Hb2AqmoFV3bwTtKnhUrYkRA2eefWNCYCFy9Veo2XJ9SrEwWKjqV7WLbRoH12apMDpqenq7ZnZmZiwoQJAIAFCxZgxowZGD9+PM6fP4/27dvj+eefxwMPPOBu/8orryAsLAx33XUXSktLccMNN+Ddd9+Fy+WfbZkwn9I6wk55ZTUuVVahpKwKz2T9iHsHted+T6nzHav5+fRFHDxTjOG9kkzrUy2UeLY/+8WP+HDDUYztn4xZd6VK92tXUkH2qGzUE0+zIxR2mM+lFVW4/d/fo1/bJkLNjsypC8021dWY98NhNIkJ5wpZ6mzMgr40QpBF3ztZUOo2ddRSxrmeshh5RFTnq6Ftq/Dztyh6LtjrqNY08bSHnm2scHaBSWtwxxs1Ebo7nxmOuKhwXWNUCzv6NTs8rZXvsijq9iph0yLNTiCxe4S2Cjt6VPlJSUnIzMz02SYqKgqvvfYaXnvtNbOGRvhg94kCtGsWw50sRG/idRfC62etxPELpUhNjseO4wXI/pHv+Mf+UsoDkNl12Ms1JrKP778WV3XU/xboCy0z1ocbjgIAPtt63Jiww1wOGcHnRH4p/vTpTgzp2gKTBneSPq76nuj3veDBXpvsXXnYcSwfO47lo3uSb58sM+sAHTtfig/W19yLQZ2bee2XCbXX0kgY0S75qqf0/rrDaNZY7H/IO6zoymlpdthfuNZvUVEUVFQpQodqrUX6k83H8K9v9+O523tx2/KS6JVrCEa8ZITni8p1CzvssyvjIM7TPskED8iY++x64ZGBCoESphGIIJzvfz6LW19bi5Gv8sNZRdTV7NSGoe447jsUm33LDIRmpxYzkv7VouUD4Xe/Bn12Xv12H9b+fBYvfP2ToeOy90T4hi4xWZcwz4g4GsvnbhWiuZZ1ZOVF8LBfl9HssIgcTXnXSV1SzXM9cvIu4qklP+KhD8XV7o34S2iaFplbovVb/MOC7ejz7Dc4fdG7NAiL1gvAY5/uxIn8Ujx8OaKtblvedWKfQbZtCede1vWBOXOxTOVIzsKaLMMkSoZwf+Nat4HTrep5Fdy+eiDr2K7ZIWEnhAhEyHHW9prssWyuDBnq5pfQC/tjFgk71dUK3l932JRcNqIfqK9FpK5jpFXCDjsEmben2jdeoxMl+z0Znxx+A8/HckaTIa6NZZ5mhz3YRc7Cp9LsCO5fuUZ+H39DiNnrcSK/RLNdXbiaHaGTtecze76sAKql2cnacRKXKqrx6RbfNQrZ8z18rgR3vPE9vt6V695WzISmq1I3cITJCo3oMR7llQqmzN+K6R9vx5FzxRj4/LcY/fr33LaqKDyJ3wrvXmrdf95zLqOtqQ9mLLuHGBSh50T94ZIPlTogFriOnCvBS9/sxboD5zB/0jW6j8v+UERmrNrsxADceV6MIqt6rc3vlL07Fw98sBWP3dTNHUKvVaXYb5hrLjOhaGlb3lt3GGeLyjH9xq4+v6/Hj+rAmSIs2HgULeOifPbFXpsygeOmagwm+uywiyW/4CP//l2qqELWjpNI79rCvU0rMki0KMkU3ywus7b2HztSreR+ohcPmVD5vyzZjW1H8/Ego6mqm1ahFpFmR3Svj18owdKdNUJV7bkdPFvMbcseV+ZXK9TSMQifc8GxzCzYaRk2j5GEHUIKo9FU7Pf/vaImk+mXO3MFrT3ImLEOnuFPWkYQKiSY/UVllbjpldVIu6I5vtpdc24vfZPjFnbUSelMG6Iqn4eMcMZbeBVFwV8uC4pj+7dB+2aN3Pt+Pn0RrZtEc/vSuicjX12jq/glu6DImCnZRaKgtAJZ20/g5t6tuG3Za5OTdxFvrPwZDw/zCHTsYilKdsdeun9+k4P/rj2E5Kaea1OukcxQJKgLNTvM+YrGyGJknalWFCzdmYuFm4/hiZE93NuPXSjFY5/swKTBnYT36qfci/jNfzfgiVt6cPezZULOFfnW+oqSclZIJB5l74NWgdE3Vv6MVTlnMLpfG939svCciq3S1vhT+ytQ2D1CEnYaAAfOFOHhBdsx9for/I4uKhVEhshY0iol3mLZOUI0CcRH+36sFUXB9I93oHFkGP462ndCLxlfhyXbT+BEfikWbj6GxpHeY1C5XujuVQzbV2lFFbLWHcbQbolom+A7wSTPr4T1b2AXj42HzuOut9ahcwuP8MMKD1qLuN4q31pmSplFesainfhqVx4+0TCdsH3d+eYPKLxUqUpjIHL01jJHfbOnxqmeNe2yghO7ELHbV+07gy93nMTTo3rh+IUSbDx0Hj1bxfk4wxomvbcZCTER6CZw3laN3ZDPDjDlclkE1lVl9nf7AQCLtp1ATLjviNfPttbci9osx74Q5RCqrKrGi1/vRe828UIfMNFzwwZKXKqTebnWTPeP7Bzufr1wXXa0NDucGUGlURKZP+uBZsfuIZKw0wCYvnA7dp0owOT3t2DvX0dg/6kipLSJ03T+LCitQHw0P1LB31BTFhkfI7VzqO9fTSwTZXGpogqHzxWja2Ks+y3y6PkSLN52AgDwl9t6+gwnrTthlVdWqyIy2KH48s2orKpW7bfKv+rVb/djwaZjiHD9hH3P3+yzLeuk+dTnu7H/9EW8cndf9zbWBPHZZQHiAKM1Y8/Q33Ie7PXSKyAB6mv+1a4aoWOnhrM7ey8LL+c6OXa+lNnvW/OmegarFbz7/SHknCriOh2rw4b5Wqvfzt0IAEhoHIG3VtUkaLxzgCeDPI8Dp4uw7Wg+AOBPI7r7bKsaO+d89JdtAfIKvB2Nq6oVXNSZsE9LeyLDFztOYkXOGQDA3Vf6Lu8jEu5YrZi6zIT3b/McM3aZp5w3T2n67IgclAXUB5+dBp1UsCFQUl6J+zI34fruibh/SGdbxpBf6nG2vOedDdh0+AL+MbYP7hromTCeyfoRZ4rKcH23RDzyyQ78cUQ3PJR+hVdfTj9d2tVCi8wX9ZuA2DE+v/QnvL/+CH6X1hFP3toTQF1HRvFhn1+6B+0SYtCzdRzGzlmHx27qxm/LHy5eWbYPb646gCcZVb6ZbzlsV2t/rsksqyc8n307fn/9EQA12alrYSfmIs6i5tThqKoXlamnUr/vBTtGl9Phc9KXcQ7mvUnXjQZ65os9mn1pRTLxTC3HmOSMuwTRf+w9K+WYsWoT1nl/rxqPfLwDKW3EmiMeMgKojN9RXTOwL1gh1t/CwKy/E6vl4ZVbYaP0pHzEOG21nk+e2KlofOZRL8xYpNkJbT7aeAwbDp3HhkPnNYWdn3ILoShAz9bGJqJaHHDg2PkS5BZcUuWGYR+yTYdr1PYfbjzqFnZ2nyjAuz8cBgC3094/snP4wo6fKgmtRUBEtXLZcfZimep7peVVOHK+GN1axqKiSkG1oqjOt3YR/+/aQ25hRyY6ZuvRC1i25xQAuHO+vPRNjqpNUVklSupM1Gy/r15W+8/8eq97m9ZRzxeXY+nOkxiV2gbxMXzt2qWKKkSFu7gTnL9vgwWMYMyqxnlv8DJVmQV1DjWfCxmzgUjYEXWlynDNcy5ltp2+WOazLy2nZJ6Pi1rI8j1GFlGBy9ioMLfw88nm4/hs63F8xkSpy/ySZXz1pDRzzGfRgs1GZomS7ImuIxspqRJmOG3rpsvQC+/Z1XZQ5iSxlPHvsVuS0IHdQyRhx2JEdVnKKqtw8+WcNXv/OgJRAhu4LxwO4Lp/rAAAZD98Hbon1QhPPPUh+0N64IMtwr7zCi6haaNwzQygWpwuvKSZ9EzmbaRaUfBsVs2bNOsIOmbOD/gptxBv3zMAMxbtQmlFFf5yWajRQsYkVlDiWfy12vb/6zKUV1bjDzd04R6jFtbUo9XX7+Ztwtaj+ViRcwZzJwz02v/++iN46vPdeGN8fyzdmYv1B8/hwXRjGkOelqGMmdjZRZj3HMvMXa3ionCSYwqphX0U2H5FQhS7N8zpgC9jiUxCP95h2XtWWMrPycL7vsqxVrAAilT97NdFizD7S9Uq67Dt6AWcuVgm9OWTEXaM5sESPU9smQpRW2FOJeZ8ygQ+Yi6jAQBcvy+5FzzdbQOXeswwdotjJOxYjOjZLmHUqYWXKoTCTnllNVxOh1Do2HW8wC3saPUz+b3N6N++KfJLfE/ch84WY+g/V6JLYmO0Ezi9svzw81n8+r8bMKxHS/c2mYWMhV1sWSGptkzB/I1H3bZ1UY0wGWdndvHRals7ue84ns8cw7ttRbXYfLb1sj/G8r2nufuf+nw3AKgSyYlMH1rw3o61tBsijYQIo1p20Rsre51dAq2jaAjC2lgSIeRamh2eU76M0M/6WYmqebOXjpdYDw5P+YTljwxBpxaNNfvilQDRwqj/iOjFg53zRPOqqC+1oMho1i5/Zu8T6zgtc2a8IViWZ8dutYkOKINyiCN6U1PNz4JnoayyCoNeXI5Rr4srDIseq715F/G/Pafw4td7hc6y3/1UY8bZf7pIFSrKhensnbWHAADfXv4+oE4GJjMnsuYVFydqo0I1OfEf66935WL6x9tVb6miNyItR9NatH7AokgM2Z/96cJLmosI60jMjud/P+Zh1OtrceBMEfd7PL8Hdi1mj8fzwzCzVIMWMpod0XOspwin7/3MuIRCmOczK6Dwc6/ovzYVqgKXvrXGbK9cYYfhZL7vTMe+SlbURUrYYc2Xgt8i+7v2d3Fn643xggzKNeYTKZ8dzjbNSyNwUBaaYOuBz47dkGbHYtQPrOJlmxXVkmL5KfcizhaV4WxRGbcv9YH5Y+ARHx2u6dgIAEnxnoRwMm+hvFBSrWRrIlhhhzcpqioIa/TxICetvnABFDiXak26on5l3nJqw75vS23N3a81hsnv15gn/7BgG76cep33GAULryiJm1XCDntthFFeEs+5ovB/g+5jmajZUVdI52/39OuzK01EAozat823YFRQWoF/ZO/FmP78aDA9P/v//ZiH5XtPS9VZkzl1tlSDKCpUWLtMIwFh7ddYYSic1ShJjJjrsyPjoFwPtDUy2H06JOxYDHt/K6sVhAtySfiCNTtUVSs+81KwP0rRQza4awvMv1yMkgebM0bkfMiOKIwTusUuKDILIOuMyVswWGFHtGidZZKXVSkK/vblHrRpGo3rujTH3ryLuLWPR6hgh8iLONK6Hv6q2dn1+NXv9gGoCb3l9sU6uHL2ayVr4y36WiUtuIu0hJ+A1Mu+6nsSwqhANbBm/xk8+fluTEzrKOxLtF+mrUizs+nQeeZ7PrtVITJjqXyuBG2fX7oHJwsu4Y2VB/QPoA61wrVMscwqiflAJez4mZyxUmVS9rT9encuZn69F8/c5ilA6nDoN5+xiPy+jHy/PkOh5yEOu5yUVVa7c7q8t+4wLl6qxPir27n3ix4F9i23slpBmA/3HpkfSpkoUaBD+83m6LkStIznOyDzTF7qBVT/GKvqnHtdWKFDNNGxw3ruiz3IqiNEaDkk8vwtjDpjis6dndhPF4oifzyfeXNpRVU1zhWVYevRfAzt1kLTzFczLr6Aw1tc/M3WrAX71MgsDiIt0Meba3IFvbxsH5wO79+I0GQmMGmyqHx2WJMW5zqy/iMy5yvS7OjRdNXiy3lcFpFJjEWrHAQPp0rYEV1/38et0DCnT/94BwBPMkVA/ZzLCTv6NcE8FI4vUX2GNDshDvuWU15ZDVyWC2pT8qdd0dy9v+5k+uXOkxjQvimSm8a4t9Wydv9ZvLX6AJ67nZ8BWObBKq3Qn3aeZc3+M7jnnY0Y3rMld7+oQrAofFTVVvBWLaPZYUdVV9ABgC92eraJJs1yCSGLRWQOZEP8RRMku583KZZXVuP2f3+P4xdK8bfRKejRKg4t4zQi5DQda3maHfPOV/N7+q1YUrlXwpxOL02dzPmIhR22rf5nU+Z3K6pT52/R0UCgTjkgauv5LHrJkDEzioR2o5eJ76AsHo+7bTX/c33F7qeNhJ0AUvujYn9cbMI/dqH6cMMR/GXJj3A6gIMza4pZshPl797bDAC493IWVkC9iKvNWL4fM603xK935WLRthMqXxG2q1n/qzGv/G/PqbpfBQBhxJiMVoR9I+ZqWAT7ZWCz6rLXkZdjiL0nMucjE3kiijISCRKV1Yq7lMG7PxzGz6drHJY7Nm/k1Val2dEwxbiPa5nPDvvZPOGAxeV0AHUee5lFTUazwwqKYu2RseskGgMPdixNY8JxQRCVaQUyQge7X/QCYNRnh4epkU5MV2FOh3v+qJDwn6uv2O2DRMJOAKl9nrXeUNjtq/edUX2nbttazjDJzYzalrWEnVqH3py8i9z9+07xt9ci0uxckhF2BDlQ2PMV2fNFafJzCzzCDtuVKNpHJouwaAJlDyUSGtVJ+Lz3s9fjFGOuEIW7svINz2wgJRwYDKMVa3aMTaC8S2qmZkfL5CXjBO3vGESnwwqwkWEuADYIOxL3Wm2uFZy76AWAfZ6FGiX9ApkI9nxrXp6Uy+Pxz+G6PmD3GVDoucWofiiXbzd709XVkT17eIuLzGKqaHzmIXJ0PH7Bk86e/c1pCUnf/3wWZ4vKhIv02SLfvigsImdZVhDhvSWp2gqOVVjKz+kjmm9Evk8s4myxnmsrylrNPha8XtlniXVq5zn0ste2rLIK6w+ew6WKKg0HZRnNju6mUm+0Zq4BMoKGTEkE0bOr6rfSPGFH/H3PZ9lkob6xRounioYTHEJ0L1UOyr670kx4aQSt55m3VVSrrd5h8zmQZieQcG52eRW/7gr7Y9xw8By2HctHUlwUdCPhVCfKxKryPxB0lr07D69+tx+xkWEY3a+Nz7Yix1sWGWHHXzOWelLVv/BKaQYkFlYZzY5MX6xQ5x4Xc+le+XYfdp+oSdoYFe79XiRlxpLy72HHEzhhR2aMIgdZlirVS4z3sxkT4XK/OMRFhyGvUF+//r7ts75EoorjMhh14hX77OjXsIimgHId2cw9femfS0VofV/Gv6e+YvfpkLBjMao3F/c2z0ZWs1NQWoFn3t2EUX1bqybTye9vQUFpBa5I1M5wWrdftWbHvEVaNMnkXDZtXSyrFB5XphKyli8JD3Gkhv7rIfNWLrf4624qTOQoMxmzUWu8pHTsOdQKOgA/g66/kSla2GXblxFAeT5Mmm0lfHakhCg/V0PWpOZvzTujGHUU9jePVYWgRIT6uPoFIxlE82N9KO4pg92mOBJ2LEYldCg1ppv4aE+BR3bxeW35fny39zS+23sa/do1cW+vTahX61iqhWoykDIxWDPBiprK9FUhYwoQvNbJRO2wi5rYp8Ca6yh66VYf17/7Y6bfiuhYutqaeM1ZeM+ATFdGhRLRcWWc3P03Y3m+b48Rq64Zy3dbkW+aVlseajNW4LSHLGa+PNUH7D4dEnYshr3B8zccwezlP+P3TMFIdnJTJbszMJGpJgOD3xMhk+1VxgYvPK7gTZrNRC3KtyLjb6Eeg3m/VtE1Z+uJ+eugzOIUFDWUE2Csmb1k3qStqsMkQuYZqhQI6uxiK5VXyETNjpnSjlF/JplyHv6+SLFzhJSQ5bupFDIO2aGA3bmCyEE5gMxe/nPNv9/td29ThUyrkrh5Pxii7MvqaAX+Z+73JNb+QIbGsgjLBjCIJlujOSuE9ZAk+hKde0yEC4qiQFEUobAjs+CzXfG+FQwZXgMxBr9NQBLPo1buIs9+flvxGPzzTau2SLMjJexYFY0l6Iudd+U0SoFbsIMhD5KZkGYnxBFJswUlbM0nVtjxnjAiXE5UVGk7E2v77PjGKqHD38KLMn3JRGPJ+FuoEAxX5sesp8L22Dk/IDLMJfSnkIkWEaUnkJtgPW15GYmNInM+RjU0/o5VzmfHd1uZxHrqMZin2RGlY5BBxhSnPnf9Zh1/NX4qPyrhPKW/XzMhM5a5kLBjMaIbvJfJYcNOPrwJMiLM6bPGTZXBN5BAJIfjIeUQKhCy2KlaVCRQzhSnv62Z/i615R0AoGerOJ9t1eHxvvsVRRjLOavrbiqFmb4ZVmGVI7FVJThEmKnZMerLFUifHfZlUk7I8n1cMwk1YcduyIxlM+pyA57PfDOW79tVpTEBC4UOqYKO5pkYZBQsMm+xQsc/gwu6MHrComuTX+I7ak0uw6sgqzXzMDSNCffR0rpFQF3Y1L83eKuQMSHJlIgIpGbHKoz+VmVyKvmr8auSEJxUprYAXnM/rZRBh90ZlEnYsRjR7dUKbeapyUWaZu1JRjSJWPSWaqJwIMy3wrYVRmJ49sss6KLhmhmKzQodBaW+M9uq8iCJEioKniE2wWTjKN+KX6vmfZnraNf8aabwze61623ezMhzo07uom+phEKRGVi0X0Izxx7X1NIRwuMGpzBrFLvPhsxYViOhIhUVXpQ1C3244Qj2nyoy1Zxk5kJkqrDD7BYtRNUaAqaoXxFmmrFUZUQEfclEqcjUSRNp3gIRjRWsZiy5CEYJM4lN2hqHiYYso8kmZbQxYtOT7+Ma1YoFUpMYSMEqENh9OiTs2Iy62KLnM89BWWbRA4AnFu/WNQaZCVZOMBK9fenuSizASCwYMrlz1BXFfWOm71O5hE+BVPiuYIhsokHhxG7R5CVzze0yY8kcVkYjaNeCYKZmR0aQMOyzIzuoOhgNUgjk/Qm5aCwKPQ9txAuk57NMwUAe7HdE9a5YZH7AMtFY4uPq70s0OSka15GHjNlHJkW9zC2T0ezIaFj8jVJRaXYkNBJmIpNU0C7hwKqFKBScUs0052r16+91MqzZCagZK2CHCgh2nw8JOxYjfqvjCzh8M5b+p0WqyKaJ2ho2J4wonFUqqaCJ0S8ywoHcG7yxN1oeWgVi+X2xWi3RcX33damCFXZ892XV3MUrnqtFKAgHLHa9zJspvFkVCar6np/Ou0Yfm4CasUJOs2MvJOzYjMoEwZqxOL9mmWc/X+DUqu7XPDOWQ+Mzty8TzWcq3ydTzT4SJhUTF16jZixh4kMJAUZshrRes2Omv1l9wC5Vv5nXUaYno89QqDnv8gg1Qd7u0yFhx2JkQmdFjpkyD3+JhBlLZr4RtWUT4MmEkoqQ8tmROK6MM7Nw4TVx8Vdrdny3lVHvC58h1sRn4v2TwWiETijgr8bC+HHNu5CByIAdYredS6g923bfNRJ2LMbUcGWJmUHGZ0cqk7GgrZN5okSChJnRS6rrKOhWLtpHMDCtMfhJucGU+/4KZOxecVtrJi+1w27Izfg+sett3lTNTgBOIdS0HjxCTmtJmp3QxkzTh8zCy0bViPs1z5zEanb0lETQfVwT8+xYlTHazAnYstxHNgmChD5sE3ZMDTwwrStNQi3hHg/y2TEXW4WdmTNnYuDAgYiNjUViYiJGjx6NnJwcVRuHw8H9e+mll9xtDhw4gDvuuAMtWrRAXFwc7rrrLpw6dSrQp2MIqxZTy8xYIs2OhLBjpupcrsaO/n7tEnbMvCcsMqYpobAagMnY7gky0NjmoGzqsxuA58JuNQEhjd33zFZhZ9WqVZgyZQrWr1+PZcuWobKyEsOHD0dxcbG7TW5urupv7ty5cDgcGDt2LACguLgYw4cPh8PhwPLly/H999+jvLwct912G6rtMoAzCN+OmdlNXPBRwoxVoV/YkUE0BPYMZKKi/EXltyKKSDKYB0TY1sTHzSq/B6E/EyNeBIMaPQiG0CAwU1MSCCG4IZixQg27FVW2JhXMzs5W/T8zMxOJiYnYsmULBg8eDABISkpStVmyZAmGDh2KTp06AQC+//57HD58GNu2bUNcXJy7n4SEBCxfvhzDhg0LwJloI3RQZn60osReMg/LJYuEHaFmgPksdio2YUDuvljzi3nmM6sSLoqQ6crMDNgN2V+mIWOXVtIo9GTWP+yeT4LKZ6egoAAAkJCQwN1/6tQpLF26FBMnTnRvKysrg8PhQGRkpHtbVFQUnE4n1q5dy+2nrKwMhYWFqj+7YLUBMjl5RNiV9Eyr/IXVyPjsyPgn2GXGksFccyDTL60oDQa78uwYxa6yGoRx7L5jQSPsKIqC6dOnIy0tDSkpKdw28+bNQ2xsLMaMGePeds0116BRo0b405/+hJKSEhQXF+Oxxx5DdXU1cnNzuf3MnDkT8fHx7r+2bdtack4AdGSAtcZcYVVFZJGphp00eSUvrEKmPpSUJkRiDHa9uJhqDgR7He2enohAYabwEAjzJz2a9RCKxqohIyMDO3fuxEcffaTZZu7cuRg/fjyioqLc21q0aIFPPvkEX3zxBRo3boz4+HgUFBSgf//+cLlc3H5mzJiBgoIC99+xY8dMP59aRPfXzOrJqrYmRleo+jUxh42ZsEcyMxlefZhUzX0r538mQhszf6uB0LqQIF7/sPueBUUh0KlTpyIrKwurV69GcnIyt82aNWuQk5ODhQsXeu0bPnw4Dhw4gLNnzyIsLAxNmjRBUlISOnbsyO0rMjJSZfayEzOL27FY9XYlU5m4MpDxoRLml1CbJ00VdkzriahP1Lc8O/Sc1j/sDniwVdhRFAVTp07F4sWLsXLlSk3hBADeeecdDBgwAKmpqZptmjdvDgBYvnw5Tp8+jVGjRpk+ZllEWgb2jcpMW7dVWhWZPC0VFmmXRMeVEchCATNvtd1OhIQ92JVB2Sj0mNY/7A6OtlXYmTJlCubPn48lS5YgNjYWeXl5AID4+HhER0e72xUWFuKTTz7BrFmzuP1kZmaiR48eaNGiBdatW4c//OEPmDZtGrp16xaQ8/CFTEZbM3+/djnwsecbUJ8dVei5eWas+oC5OVJM64qoR5j5chSIl4lQe2FpCNh9z2wVdubMmQMASE9PV23PzMzEhAkT3P9fsGABFEXBuHHjuP3k5ORgxowZOH/+PDp06IAnnngC06ZNs2rYpmKVUBJIf5lgGINM5t9QgyJTiGAiEG/wpIGsf9j9kulQ6KlBYWGh27G5NlePWfz1yz14Z+0hzf2RYU6UXS762CQmHPkl+quVBzvJTaNx/EJpQI4VEeZ0F8+MiXBJZZCu7zgdpJEhgocwp8PyF51AHIMwn4MvjITTKUgoJ4ne9TtoorFCFSkzVoj9disD6bOjUT2+IUBzPhFMBOL319B+46GCnaYsEnYsRiaDcqgp2QLps1OlclAO2GEJgqhDIH5/9Buvn9hpyiJhx2ZCTL5REUhhh8ocEARBBDd2auRI2LEYmXsbaku0XTZ1eusjCIIIPkizQwAIPS1PIH12WOz2+icIgiC8sTPXDgk7QUSoOd2VBzKDMkEQBBHUkINyCCNX6DO0hB2CIAiCqIXMWCGMzK0l6wtBEAQRqpCDMgGAoogIgiCI0IU0OyGMjPxCjrUEQRBEqELCTggjSirIQrIOQRAEEaqQGYsgCIIgiJCGNDshDLnhEARBEARpdkIaknUIgiAIArAz9RoJOxZDmh2CIAiCIDMWQRAEQRAhDpmxQhpS7RAEQRAEaXZCGDJjEQRBEATVxiIIgiAIIsSpJs1O6EKaHYIgCIIgM1ZII5NBmSAIgiBCFTJjEQRBEAQR0lRTnp3QhcxYBEEQBEGanZCGZB2CIAiCIAdlgiAIgiBCHHJQDmHIjEUQBEEQZMYKaSgaiyAIgiDIjBXakKxDEARBEKTZIQiCIAgitCGfnRCGFDsEQRAEQVXPQxqFPJQJgiAIAlWUVJAgCIIgiFCGHJRDGNLrEARBEAQ5KIc0ZMUiCIIgGjqJsZGICrdP5Aiz7cgEQRAEQYQ8dw5Ixkt3pto6BtLsWAwpdgiCIAjCXmwVdmbOnImBAwciNjYWiYmJGD16NHJyclRtHA4H9++ll15yt8nLy8M999yDpKQkNGrUCP3798enn34a6NPhQtFYBEEQREPG4bB7BDYLO6tWrcKUKVOwfv16LFu2DJWVlRg+fDiKi4vdbXJzc1V/c+fOhcPhwNixY91t7rnnHuTk5CArKwu7du3CmDFjcPfdd2Pbtm12nBZBEARBEJdxwH5px1afnezsbNX/MzMzkZiYiC1btmDw4MEAgKSkJFWbJUuWYOjQoejUqZN727p16zBnzhxcddVVAIAnn3wSr7zyCrZu3Yp+/fp5HbesrAxlZWXu/xcWFpp2TnUhvQ5BEARB2Iu0ZufYsWM4fvy4+/8bN27Eww8/jLffftvvwRQUFAAAEhISuPtPnTqFpUuXYuLEiartaWlpWLhwIc6fP4/q6mosWLAAZWVlSE9P5/Yzc+ZMxMfHu//atm3r99g1IWmHIAiCaMDUSzPWr3/9a6xYsQJAja/MjTfeiI0bN+Lxxx/Hc889Z3ggiqJg+vTpSEtLQ0pKCrfNvHnzEBsbizFjxqi2L1y4EJWVlWjWrBkiIyNx//33Y/HixejcuTO3nxkzZqCgoMD9d+zYMcPjFkFVzwmCIIiGTL0Udnbv3u02F3388cdISUnBDz/8gPnz5+Pdd981PJCMjAzs3LkTH330kWabuXPnYvz48YiKilJtf/LJJ3HhwgV8++232Lx5M6ZPn44777wTu3bt4vYTGRmJuLg41R9BEARBEKGJtM9ORUUFIiMjAQDffvstRo0aBQDo3r07cnNzDQ1i6tSpyMrKwurVq5GcnMxts2bNGuTk5GDhwoWq7QcOHMDrr7+O3bt3o1evXgCA1NRUrFmzBv/+97/x5ptvGhqTWVAwFkEQBNGwsV+1I63Z6dWrF958802sWbMGy5Ytw4gRIwAAJ0+eRLNmzaT6UhQFGRkZWLRoEZYvX46OHTtqtn3nnXcwYMAApKaqExOVlJQAAJxO9am4XC5UV9tYdewyJOwQBEEQDZl6acb6+9//jrfeegvp6ekYN26cW/jIyspym7f0MmXKFHzwwQeYP38+YmNjkZeXh7y8PJSWlqraFRYW4pNPPsHvfvc7rz66d++OK664Avfffz82btyIAwcOYNasWVi2bBlGjx4te3oEQRAEQYQY0mas9PR0nD17FoWFhWjatKl7++TJkxETEyPV15w5c9x9smRmZmLChAnu/y9YsACKomDcuHFefYSHh+Orr77Cn//8Z9x2220oKirCFVdcgXnz5mHkyJFS47ECclAmCIIgGjJBoNgxlmfH5XKpBB0A6NChg3Q/erMLT548GZMnT9bc36VLF3z22WfSxw8EZMYiCIIgGjLBYMYyJOx8+umn+Pjjj3H06FGUl5er9m3dutWUgREEQRAEQZiBtM/O7Nmzcd999yExMRHbtm3DVVddhWbNmuHgwYO4+eabrRhjvYYUOwRBEERDJhjKRUgLO2+88QbefvttvP7664iIiMAf//hHLFu2DL///e/dGZAJD2TGIgiCIBoywWDGkhZ2jh49ikGDBgEAoqOjcfHiRQA1xTh9JQQkCIIgCIKwA2lhJykpCefOnQMAtG/fHuvXrwcAHDp0SLfDccOCrglBEATRcAkCxY68sHP99dfjiy++AABMnDgR06ZNw4033oi7774bd9xxh+kDrO+Q/EcQBEE0ZBxBYMeSjsZ64okn0KZNGwDAAw88gISEBKxduxa33XYbOShzIFmHIAiCIOxFWti54oorkJubi8TERADAXXfdhbvuugvnzp1DYmIiqqqqTB8kQRAEQRCEUaTNWFp+OUVFRV7VyAn9iRMJgiAIor7glLBMOeuTGWv69OkAamxvf/nLX1SlIaqqqrBhwwb07dvX9AHWd0jUIQiCIEINl9OB6qqaFc7h8O2fKiMYWYVuYWfbtm0AajQVu3btQkREhHtfREQEUlNT8eijj5o/QoIgCIIggooabY3i/lzlQ9pxBoG0o1vYWbFiBQDgvvvuw6uvvoq4uDjLBhVKkBWLIAiCCDVkTFNBYMWSd1DOzMy0YhwhC8k6BEEQRKjhYrQ11YK3+mDw2ZF2UCYIgiAIomHDyi8iC4b9og4JO5ZD0VgEQRBEqOGS8MMhzQ5BEARBEPUOl4QAEwT+ySTsEARBEAQhh0wJiGAoF0HCjsWQFYsgCIIINepbUkESdixGoXgsgiAIop4hkk9kBBgyYzUASLNDEARB1DdEwoyUg3IQSDsk7BAEQRAEoULkgCxjmQoCKxYJO1ZDmh2CIAiivhEZ7ls8kMqgHASZdkjYsRjy2SEIgiCCARkNS1S4y+d+1owl9u/Rf1yrIGGHIAiCIBoAYRJSR7RA2GEFHJGWh6KxGgBkxiIIgiCCARmhQyTssD49VdW+F7ogkHVI2LEaknUIgiCIYEBGsxMV4VvYkQs9t1/aIWGHIAiCIBoAMiHg0SIHZanaWLqbWgYJO1ZDqh2CIAgiCJDR7IS7RNFY+o9LeXYaABSNRRAEQQQDMokARfWsWNNUl8TGAIAYDdOX/aIOCTsEQRAEUW+RUZpIZT0WhZMzDZ66tSceHd4VX/3+Om7bYCgEGmb3AEIdisYiCIIgrMLldKC6St9CI8qKzCJqyQpDcdHhyLi+i4+29gs7pNmxGJJ1CIIgCKuQ0ZrI+M7ImLFkBCO7IGHHYhRS7RAEQRAWIaOtkXFQFjVlj2tmhXSrIGGHIAiCIOop1kVFCTQ7jPQgqn0VBLIOCTtWQ3odgiAIwiqkTFNmHpc0O/qZOXMmBg4ciNjYWCQmJmL06NHIyclRtXE4HNy/l156CQBw+PBhzTaffPKJHaelgqxYBEEQhFVIVR+XauvffqNtrcJWYWfVqlWYMmUK1q9fj2XLlqGyshLDhw9HcXGxu01ubq7qb+7cuXA4HBg7diwAoG3btl5tnn32WTRq1Ag333yzXadGEARBEJYjlTtHol8Zi1d90OzYGnqenZ2t+n9mZiYSExOxZcsWDB48GACQlJSkarNkyRIMHToUnTp1AgC4XC6vNosXL8bdd9+Nxo0bc49bVlaGsrIy9/8LCwv9PhctSLFDEARBWIWZQonD4bFGSGmMyGdHjoKCAgBAQkICd/+pU6ewdOlSTJw4UbOPLVu2YPv27T7bzJw5E/Hx8e6/tm3b+jdwX5AdiyAIGxFVrybqN2Ym7GN7MtNMFQyanaARdhRFwfTp05GWloaUlBRum3nz5iE2NhZjxozR7Oedd95Bjx49MGjQIM02M2bMQEFBgfvv2LFjfo+fIAgiGAmCdYawECnNjoQhSyp/j0ROHrsImgzKGRkZ2LlzJ9auXavZZu7cuRg/fjyioqK4+0tLSzF//nw89dRTPo8VGRmJyMhIv8arF9LrEARhJ/YvM4SVyOTZEeFg7FhSQepCzY7xMZlFUAg7U6dORVZWFlavXo3k5GRumzVr1iAnJwcLFy7U7OfTTz9FSUkJ7r33XquGKg1ZsQiCsJNgqEtEWIeZEVZOB1Cls19WSyQaQTA8g7aasRRFQUZGBhYtWoTly5ejY8eOmm3feecdDBgwAKmpqT7bjBo1Ci1atLBiuARBEPUO+5cZwkrYaKyrOvL9XfUiI8CovkeaHd9MmTIF8+fPx5IlSxAbG4u8vDwAQHx8PKKjo93tCgsL8cknn2DWrFmaff38889YvXo1vvrqK8vHLYNChiyCIOwkCBYawjpYQcLvW832JdWZd2OnA6hWavuy/yG0VbMzZ84cFBQUID09Ha1atXL/1TVVLViwAIqiYNy4cZp9zZ07F23atMHw4cOtHrYUZMYiCMJO7F9mCCthMyiLHIGlTF4SY+B1G+byiBfBoNmx3YzF+5swYYKq3eTJk1FSUoL4+HjNvl544QUcO3YMTmfQBJgBIGGHIAh7CYa3asI6ZMo2CH1r2M+CzljzGa9luIQQFgiCSzIgCIIgTCUI1hnCQmSqj8vsF7VlK6jzBCOVMBQEzyAJOxZDih2CIOwkGN6qCetgb6+/+W7UDsp+anZUZiz7n0ESdixGITsWQRA2Yv8yE1hkakWFAjKCRJhLf1kH0WUUCTMuMmMRBEEQViJjjgg1zEyyJ4NdMhbrpiryswkX+LSy3xYJKKzgxBOiwslBmSAIgrAS9UIVBCtNALErRsUu7QUr3ImECrFmR/85sJobXr+iYwUaEnYshqxYBEEEmmBzDg0kdgkdtplqWAdlQVM2HJzbFfO5WrB4sUJWGEfCDLYCtCTsEARBhBgyb/uhhl1mrGAQKlmB6/2JV6F9sxh8NOka97Yw0cPA7Ba9p7PnywrXz9+RgjZNovH8Hb31DDlgBEVtrFCGMigTBBFo1JEyQbAKBxCnhHTHZvn1l0A6RquyEzPbWQHkui4tsOqxoarviYQddq/IKsHuZ/sdf3V7jL+6PQpKK/gd2wRpdiyGzFgEUf8Jhrd2GVQlBOrZ2GsxKjvICB1mmp5kkvuZeSwWoYOyyIzFfF/0oh4T4TFT8fxzgk2jSMKOxZCsQxD1n2AInZUh2MJ+jWA087PofK26NjL5bvxFS7ASCRjNGkfoP4jG4vX4yO64pXcrDO2e6N7G89lRjTEIVDtkxiIIghDgdABVdg9CgoaWa4ZFdOoupwNVPBuQn7CmHJfDgSoLX3WNylIt46Lw0i/7oHFkGB78cKvPfrVGP3lwZwDAypzT7m28ax5sMjYJOxZDSQUJov5T85Zaf37LgTSpBBtCzY5FztsuRrsREeZEeVW14b4cDt8uEFqnqEejdOeVbbX7ZT6L1i42WounhQu2Z5DMWBZTf6ZHgiC0qG+akrAGHHouulfhLmvMK+w1jwr3b2k1WvbBX/OZ2meH3e7d1g9ZzhZI2CEIghBQUl6fjFiAy8VqLxqWtCNKKhgR5nGsNVez4+ksMsy/HDOiYbH3VMZ4IGNp0Iq2qqVKEMYWbM8dCTtWQ6odggh66pniRoiqEraN47ADUZ6dCIsEQVbIipTQ7ESE+Xbu5aHlWyM6HXE4Ob8BT1smSjoYZLIOCTtWQ7IOQdiDjAATbG+h/iKTaybUEJ07K1yYedvZiKQoCc1OBCccXDQu1W5G6BA9x6L1qF1CDLctT4Bk2/JwBpnATcIOQRAhCS8cVgujwkGwykgqzY6fg6xvcpNQs8MIO2YKhWxXIp+dcEERTZHfkYxvjR4+e3AQburVEq+N6+/plxGieONJaROPV3/VF589eC1/jMaGYhkUjWUxFI1FEPbgkogXN1piwOlwoCoIf+Nm1sZyOhxCk4UVGB226HxVwo6J0iorXIt8dlxOByqqlMvf8x3JJCK34JL7c6NI30u61m0c0L4p3rrnSs3vadXUur1vG83vsKfgr8BtBqTZsZjgmwYJomEgrAPEYPQF3/4pnI+ZJoT6ZuITjZc1G5l5ZqIq4CxqraN3W5lLfl2X5gh3OXB1xwSh0C5TvohtaeQZCAYBh4U0OwRB1EvCnA5U+ogIcQkWHBaj5oxgzb+j1uz4uejYtGZZdVUt89lx6b/m6izO3vtlhIuWcVHY/MSNaBwVhoNnivD++iO4sWdLbluZOmCsVULmxSFYIWHHYoJQw00QIYFTUMWxbq4ZX79Fw9qLIF0DWOHN3yEG6SmqkCnoyYaem6l9iFSZx3y3FeVBEn2fFUSqFQXxMeEAgC4tY7H72ZvQKMK/0PeaY3g+17c8UzzIjGUxVPWcIKxBpLJnJ2iZtrymWl8P1iUgTKU58NdBOVjP0gMr6AiLYQq0KkaJCmfz9/h2OlYLo759doRjrLPENI4M074GBvPs+CvsBMMjRMKOxZBmhyCsQTQBs34RIjOVyMdF69vBMInzUEdjyX+frWgdrOeohWi4YRbl2VELO9772W1hIjOWhBlSZomRaxtaZiwSdgiCqJeIhB0ZzQ7blcwCaJfW4+qOCQCgaa6QiLrnwr6k1QfNDovo3FVCsEXCDk/kUj2PAmHGofGZh9GsyDJt+7dvqv+LHDq3aOzX982AfHYshjQ7BGENYs0Of3ER9cXzBdIKvw6kGMD6Hf366nYY3LUFbu3TCkNeWunV1kwfC7tkHavSdpgZls8SxcmErDouczCRpkRtxvLtBG/VGhPJCG9/ua0nWsVHYVRqa6k+Nj85DKXlVUhoFGH28KQhYYcgiHqJsLq1xKIm8pHQWnCsDq9l5a4wJjdL05gInzlOXCZqL+qXXkdc3NNMfyYWtWbHG/ZYbN4anibKKaHakZF1WjeJ1t322k7NUFFZjW5JsYiLCscjw7tJHKmG5o0jpb9jFSTsEARRLxG9HWuZDXiwCw7P5KXpoGyxJBDucqKssqa8NCtwiY7LRt37HXlez8xYwmtjkWZHpL1waphVecKZQ7XfN3o0O+/931X4/sBZ3HVlsrjxZRpFuvDmPQN0tw92SNixGMqgTNQl3OV5QyeMIzRjSTiiurzMBmrsisaKYIQddioRaS/MzaDs3/eDDVVmXxP77doyFg+md0azRhFYf/C81372OsoI3yL0RPwO7toCg7u20NXf4yO7Y+Oh8xjZu5X+QdQDSNixGFrSiLqEOZ2oqNJZx4DQRLQgqE05gr4E0UtawoXVWo/wMCdQVvO5srqaOa7v7zkFmgMR7AJql4Oy0blTZQFyWJRfidsX8KcR3QEA6w+e89ovpWkUCN/sKZn9Pj15cGdMHtzZ3E6DAIrGIogAE2pvynYhirAKE0S8sDgEb91a98xqOYA9B1UuGdH3XL6FNxnqmRVLBW/oIsHW8LGEEX/GhB1ut8yzQNYDfZCwYzF2PIf1dXKqr+OWpb6F8gYrotw5RpMK8s1YGpodn71ah8zCKm7r37FkkBH0DR9VcO7stlon71bxUTLdcmHPjTfvs/ckXFDKRGRqY0uhyJSAaMiQGcti7Mig7HI4UFkPpX3fAZahA8k65iB0UDb8Js1ZIFVtPQuMHo2RPz9FrYVMyglXcAxeWD1r+jLzcWWP5XI6UGXBSi0KZGKv3a19WmFQ52ZQANz55rrLY+Rfd1GFe5noQH/NWDHhLuSjAgBl6dcLaXZCEKNFDXkEcmGub1EfRmkI59ki1vqQU5nFReTfIwo9V71pS0TKiDRKIrRMFDLHlfHvcR+XWUDNNfV4PouEVaO/E6fqXnH6ZT5XKwqu7JCgCslmw8K1+uXBHot311QRf0Jhh+3Ye38Uk0yyHr7X2gIJOxZjx4NoZmpvq+zbPHhJ20KRBiDrIClObBbwlzAJU4CUYMRpq1VY04ggIYPWL0JompKYA6w+B/WxPH2J5ilW0HMIBBitY/D3ez5XXfb5ZocSrjEumX55qDWNvpde0TWPZnL6NIxZ039I2LEYsx5EGfnF37dJreOGc36gZi7cWrJOqAkHZkeABCOBuGfiEhD6fXZEYcFa3xYLHT53C9F6AZBZWEXPm7/7ZVCZAyUeXtG91PKB4d0ftq/a66uV8E/9PV7/zGeBvo09XxmtFu/6swkMG8g7ot/YKuzMnDkTAwcORGxsLBITEzF69Gjk5OSo2jgcDu7fSy+9pGq3bt06XH/99WjUqBGaNGmC9PR0lJaWBvJ0uJj1IMqkfzfTjKWeBOxZWUPNodfMs9GamO0mEHdMJGhIZVAWVj3n77fejMWOQf9xtTRRPIyWlogQlEfgIRORpP6e5zPvkrL1roQ+O8znWmGH7VNLcOJq/CRuilw0FtMt83lQ52YAgAmDOjCtjS0yE9M6AgAeHd7V0PfrG7bOlKtWrcKUKVOwfv16LFu2DJWVlRg+fDiKi4vdbXJzc1V/c+fOhcPhwNixY91t1q1bhxEjRmD48OHYuHEjNm3ahIyMDDj9fa0KImQWfDPr4hidnMzETE1VMGCmz46Wyr0hIK4vxH4WCEaCN2n1AmpMiDICq9mR8cNhZWCx2cf3fq1p9FcD2wIAUpPjfXfA9sUcS8bcLkrCGaYhrIoE19rLq3qp0zhhvrDDHw/P18pobSyWzPsG4n/TBuPWPp6Ef0ZfqJ+8pQdWPJqOKUOvMNZBPcPWaKzs7GzV/zMzM5GYmIgtW7Zg8ODBAICkpCRVmyVLlmDo0KHo1KmTe9u0adPw+9//Hn/+85/d27p06WLhyGUwR7Uj9xZkkRmLo0UIhAo1xGQdU01PNZqdIExQGICbJhIkRBEtWm25ZiwNdYGZJiBedJKq+rgqTEj/cYXmFZ6Dso5szY+P7IEB7Zviui4t0P+vy3weg3cso/OUgxO3yWqdRXMlK4jUXm89L3W8rVHhLlRUVQIALpX7/h2ypyvOoOzZzz4TkWEudG0Zq2prdA52OBzo2LyRsS/XQ4JK9VFQUAAASEhI4O4/deoUli5diokTJ7q3nT59Ghs2bEBiYiIGDRqEli1bYsiQIVi7dq3mccrKylBYWKj6swrTzFgSE4O/i6nKsVPCzmwVokmxvglDZo5XlK9DBjNvr9GuZK6NVOVoTlutaC2hGYvdLhijUVNNLUY1OzKBBbzjstOW1vejwl24vW8bqYrWqvpQIqFEd69qc25EmMefhSeosfJk46iwy+3YMfKPwbsOES4n7h/cCZ1bNMK1l01MAH/scqHnns+i8HwKPddH0Ag7iqJg+vTpSEtLQ0pKCrfNvHnzEBsbizFjxri3HTx4EADwzDPPYNKkScjOzkb//v1xww03YP/+/dx+Zs6cifj4ePdf27ZtzT8hk5Hxl/E7AoR9m5RQvVqFaGKoZ7KOqZo3LZW7EcyNupE5ruczzwleC9FzoY6w4uzX0H6IzBVy56a/Mc+8yf4WZXLnGK347jmuNeUirKo4zvYbwQg+vENUKwr+eWcq/jSiOzq3aOw1Fq0XSy1N4oyRPfDdI+loEuNb6JMRdtgxVAqEHUoqqI+gEXYyMjKwc+dOfPTRR5pt5s6di/HjxyMqyhPWWn25Xsz999+P++67D/369cMrr7yCbt26Ye7cudx+ZsyYgYKCAvffsWPHzD0ZBrOeQxlHVDNlEpXfg03Cjr+hplZh9HKYOVozncbt0pAZdYIXCsECswErV6lrQXH6Mlgby9+MwdUKf1wyxxWZsfwNqZZBRrMjA/vcRIbxnZVrUQD8ckAyHkz31H9y6MiBI1MglodM2D17PtUizQ4JO7oIigzKU6dORVZWFlavXo3kZH4J+jVr1iAnJwcLFy5UbW/VqsZRq2fPnqrtPXr0wNGjR7l9RUZGIjLS+qRngHl1S2S0KmYu/uwPP2jNWAEaR114mWf1YKqDsonRWDXjMud5lTlD9nLIPGP+5s7RfIMXLGoyhTVltBeigo8yyQz9NmNJ1OGSQcZJVxPO11gNp8gPhTcn6/HZ8Xf6i2AEmMFdW+DjzcfRo1Ucissqvdqy5yPS7JAZSx+2anYURUFGRgYWLVqE5cuXo2PHjppt33nnHQwYMACpqamq7R06dEDr1q29Qtb37duH9u3bWzJuO5B5CzLzTUyV78E2Ycf3/mDQSMhg5njNTSBpWldSAh3bVkZ4k1nwufuZE9Yy3fK2iaJ91N/zvZ+F15eiqdkRHNdfM5Zgv1HUflL6+33oshbmpl4tufvZnvq3b4K/jU7B/N9dzX0OeT4weiL3ZJ5p3jsQe+5NoiOw57mb8OXUNO73pTS2JOvowlbNzpQpUzB//nwsWbIEsbGxyMvLAwDEx8cjOtqTvruwsBCffPIJZs2a5dWHw+HAY489hqeffhqpqano27cv5s2bh7179+LTTz8N2LloYdZzKLMIWBWNZVcIuFiz49FIWFVvh3tcg5fDVF8FEzU7duUzchkVdiQioUQOo+qoJ8FxNY7BbSsj9HG2VWv67JhnxhI5RptrxvJ8Fs0n7D0Z0rUF/jiiOwCg25Nf+/xeZZWC31xT86LL99nhjCsAmp3wOr5EMRHay6+MLx7JOvqwVdiZM2cOACA9PV21PTMzExMmTHD/f8GCBVAUBePGjeP28/DDD+PSpUuYNm0azp8/j9TUVCxbtgydO3fmtg8kInurXuTU+6YcEoB6orQrz46ss2ugArGNLgLBG41lXl8yPalyr0j57Ojvl/eqLWPG0kKsYdHdFfe4ajOpMW2NEZ83PaHnRlDlMzLRNM9eJfZlhy9Aej8LesLC+c8Fvy1v1peZw2V+12a5SoQ6tpuxeH+soAMAkydPRklJCeLjtZNX/fnPf8axY8dQXFyMH374AWlpfPVgoDHrMbQrz47acdMme5FostWhgjaCVT5KZvZq5hhlehIvoDJ9GdPsyPjsiI4r85wb9Z0RwjVjscf1/XWVY66Mr5BIk2WqZsfTmcyzK5MksUqlleJdVO9Nomg8X9v14tLI8sxv62kxZWjNS7s6a7KH+Ohwv8bVUAgKB+WQxiRpR069b84xAe3Q10Ai4xdh5hhdTofQOdAIokkzwuVEeW2FQgEyZiyHw3fkhsxz43I4UOmjM5mXTaP3T6QZUAszPDxbtcxF3H41jsFDLj+W/r54TaMjXCirrL7cl/5+A1kbS0azwz6vakGB/70x/dpgy9ELuKE736+nFp5mR09mZ38vg0pbI+iLne9H9m6FCYM6onljdWj7q7/qi0+3HMfDwxpGuQd/IWHHYsyq5G2XZsfoGPT0pde3RnRUPW9lRghzOlDmY7/xzKW+90eG6Rd2ZNTdIgGlbh4QX/dHJAjKXBqjb/taUVOeBZIZD2dA7Db2XIVD0L9mGc431LxxBM4WlWNM/zZYtPWE137egh8V5gJQAUAsGIW7HO4SDCKBzMzpRPWMSfSrp0L6y3f3haIomnXMahH57GgJYf5qUWV8rthjVVUraBHrHT18e982uL1vG7/G1JAImjw7oYppeXZMFHaMTl7m1tySaStz7gYGo4FVmizR+YRLFFiUcWSUC9X23ZdMSn6ZcfmbcVim2jdLrUYEEF9TmafCaFLBhfdfi3uuaY8HhnTm7tfS7LiPK0hA6G/SQaMYLReh97moq2njfUvos6MxLpnCp7znX5TskIXV2FqhXW6IkLBjMWb5jsk4bop+SDKqdXb4ZkZjyYUm69/vb3i82lnW98/D6K01c3ERaXa0SiKIjuuveUbm2rA9+SvUy1T7ZrlQXO7+XFtCQAuR0KEaj0FBvV1CDP46OkVVB4ntiv18ffdEAMD/Xa5i7d3WewxspupAlmMR5T7SwqhWReR8zWsn46AsI9Szc7jMumBWkEtDh4QdizEr4ZPIZ0fG78Hom5qZReRlBCcJq4LfAplMSncWMzVVMn2JtBAy6fnZtqJq6iKhkp2fxRFLYhMCD16/Ru//eUbY4SfZ44dim5t92Pe90iru+Z97r8SGx2/AtZ0SNNp643Lpfy54C7PRKECZ/D8sIk2UzCwrci2QeQZljsv6HbHP073X1oTJ/+KKZl7fAYAmMeSAbAYk7FiMWUK5yMQgVeHZ4F23y4wlF1ni3xhVb3iihYz5bFeNKpHGj1W9yyTZE5nShM+CRgFL7nF1mBD0tlXlzmG28xY4dsvI3kmIDndhWI9EyQzJov2+G9xwWSsD1BGiBMeqGyrdMi5K89nlDSFMQugor/T2H4tkim3K4NI4BxFGf196zVgsMnKcjE8mKyCy68L//aIjFj00CO/8dqCq/Zu/6Y9nR/XCFYnqKueEMUjYsRqTol9Eb+hOgTCkaitjxhJkljWKdX4A/vbl+SwXGaT/GGbWUxJp/CJVwo7vvtjzFfUrU7FarFGSEMgEQr1RgbxJTAS2/eVG/OfeK4VtHarP/r1YaL3E8C6DSFvDbmPng56t4rzaylzzskpP5qpaR9kh3Vpw28o8Y6Jrp4oEdfk+dy30OiizaD37vN+tlolJlEFZVfPM6UD/dk0RFc5Ua3cAI1Ja4bca4eaEPBSNZSFFZZU+o2oc0K8G9Z7klTr7mX5NDIfVGoO/yGhg2AXD6eBMVpKaAV+TnTqbr34ThZlmOZlrI/JlYJ0iZSotR4iEHcH5qidz+Mz0KOOP5mRuIO861c2KfEvvVth/+iKu6dQMP54sVLVlTQnViuJebERmEjN9drRMU7zfsJbPDq8v9tJMTOuEyHAXhnRtgVtfWwtAzpeLdd5e9OAgZO046c5QzBuDL22HTLJDFnE0ltYe7++J/Gy0nkfeVhnFPStEkRdO4CFhx0L+9uUen/sdDoduTzWRzbrmTc07xwb/uLoOeRnGHGGXGYuZZsKcvsOyhYKe04HqyyG3vMsvExmkCnk3UQskZ8by3RlrxhIKO8x+UeSJOBqLaWui8C0yz9S9dv8e3x+KouDv2TnejRlUWXdFvx/2s4Qww+2L2S2nkeVod5n9CY08OVliIl2YMvQKVdtwCZ+dgR0SsPN4Prq2jEXbhBivvliEwRGm+OxIvFjwBFfBlKtdG0u+L5YwDc0OD0qKbD4k7FiITHIymb54/UolETMotFhVEFBqDBwtgXrxEXyf0YrxNGtOwXXWwqpq3SJE2id1PR79x5XRavHQMn+KNHMi4qLDUFpR8wCUcKpFq3Lr1N5nh0OorVE5VAsGJPKtYZF58ZBxduY1ZbfFRYVj8UODEOZ0cs0yYRLPRWSYE8sfSdclnDgE+uqAR2NxtokEDRlhSquvtgkxXtvYc6ASD4GHfHYsRBxBJfFGK5gk2MnLzIyo6kVL99eEGH074zkqqiqz++nzofbZEeRbUbU1b4E003FTpdmREnb8mxpUPjuCZ1fmsWIdYy+UVLg/P3lLDzgdwCt39eV+jx9h5flcrZZ2BJgn2KpNV/r74jWtKwz1a9cUvZP5JXbUPoC+jwvU3EM9v1l/zXq+ju8LrahX3uGqBHKGliDC+41rySx/HtEdY/snY/7vrnZvY+foan05QwkTIc2OhQjf0A2qcXndykRXGJ1wTM2gLBNhJWNakniT5vs+SWg3NL7Hw+lwuGv2+Gv6YBGN0agZSyTsiDU7fPMn75rLUK0oaBkXiVOFZRjYoSlW7TsDoKZu0G8HdVD7RUg41xs1K1j1W+Mfy7cphz2WMCWBhBlL5hTkEldKvOwJ2mreHwM+O1p7eX5sWs9NfEw4Zt2Vqtqm5aBMBAbS7FiISPUqIzuIasrI5FOReWmXiaqRQc5nxwNvcVc5Z0v0xfX5MDoZS5gshRErUj47AmHHYGFNkRAlk5vFqFOqVr+rHhuKjY/fgKT4aKZfh08BTXRYVfFIQVuROUmrrZH9LKpimIK+hFFg7AtEAH/XZrxoyfTQv30TAHXDvkWCLX9/ZLj3RZWrAceYsQRtTbwlxGVI2LEQkerV6GLK+5YqSZiEA2xMhP58GWb+AGXMWKpoK54qWaJfUaZUte+TYFgCs4K6X2Nv0mKHT/1mLNEkzwrM/pZMUFcRB/PZ9zerBGOsjZpKjIuqEwLOGwPbQKDZUTko6xdchaHnEs+jCJHJS0azY9RRWITofK9hEh+KjhsT4UJMhAsJjSJUKRRE1eFZXrijNx5K74yv/zBY2Ja3v1+7Jgh3OfC30SlSmh0egzp7kgYKtUuk+DEdMmNZiDgMWsKUI/LZMZirJDLMiZJy7bhgVQhxAB2U2UKFbEveJG40kkaUpVbsb8F8ljiwqBxBXcHWy5/X4ZkMRdpDmTdiGeds0fmezL/E7UskQIqKw7LPo5SGhbNNHXruu62qLwmBWPziwY5H/3FF+0UKPRkfPxm0ulr1WDrWHTiHsQOS8belP+k6rsvpwKYnhsHp0OcvxKNJTAT+OKL75ePV3OerO/IzFdfC3odfdG6Oj++/FuEuJ9bsP+PVVo+ws+mJYTiZX4rUtk24x9AYhbBfQg4SdizEzGgslZlKYMaSyjgsetNmVgGrQs/ZxbuWMKcTFVVVl/f71lqpK1b7pzUxanIRXRs2C23TmAgfLeuOwfvisP4/MrlzRKhMG34+u0VMpJSeitK1iBYPrTIU/BpI+oXgKkFbzbIvfr7QyPyiZF5iRBo/9XwiMQgBWnNP+2aN0L5ZI3VbUWcKVIn2dH9Pg1WPDcWqfWdw55XJPttVq4RgxW0ejeBkjdaTIb9FbKRX1XKhBpMcmE2HzFgWYqaDsihLsNax/PUZqJbQmsig0iLwNFUu/vnyJnyVZkfiuPxM1J7PwrIBEm/4LNEC06GMT4+MmUQkBLPX3NRnV5UU0vuLnVo0dn8W+RjJqPfLKjwrBtdRlfncpgnj/yPoV2VOErS1ys+NW8yS+SwSckX5mYwio/USPY9m6zbaJsTgN9e0F5a60EpJEClR9VyESEiiSufmQ8KOhZhZkDNMIByoPP0Fmg61vdj3GColtCYyiBZsmdBnLdMGD7UZy3u/aiwSfYkm7q4tG2PCoA54+raeOswk+g8sukx1NWi+20oIUZxtWoKKS0NQ/+zBa3FVxwQ8PKwLZtzcHanJ8cL0+CptjeBKsuZZrdP5YOLVuPvKtph6vXaivJrjej5LmbEkrrkI9rngLYUyWkl1zhfdQxBi1DeNh5Zfi1GTll7qZtau5cH0zgCAX1/dztRj8KBoLfMhM5aFmFnd2ikwU7GTl0jToY6w8n1cq/LsiCZmrRo6vCHIJYTzrb1QZej12VPdfsVtnhnVCwDw5Oe7fLYT+dnIOPxq7eYl91MJ1AayPGuVhdB6dge0T8DH918LAOjVOh73D+nsDiXXonljjzlAdM1rkw8CGvdSAdK6NEdal+aqzUKNg8Q6ZK52lx2D9yBkBA0ZYUem6Kfq2eRmkNSPnm+md2uBlTln8NtB7Q0fpy7skNkXx84tGmPvX0cgKtyF+RuO+nUM0TUnzY75kLBjIaIFQy4SwwPPxs5OSGw5Ba7/gcH8I1Y5MvK6DdeIFuFdMxkHZaegLymnV1ZwklggxRE8vo8rY8bSTn3v8PYFklgAuQKqxrFEyfBU/Wpsnz/parz23c/46+gUQQ8eShnNDs9XSOsUxZo33UPQEdllTLOj0ZkbkR8OqzkVaRHkIjb5WjxuW0FfeoTKt+4ZgJ9yL6JPG37yRCOofXbU+3g+REbQ9AGrPS4JO6ZDwo6FmKnZEWYJZjZdUr3ROlB3Wmd/aHKOmyaasSQyQpvpcM1eKN71V4eT69eaCEsiSCAqNCkXNcX/7HR4K2FYYUWYB4RzbbTGoq5nJTfe2sdvUOfmGNRZnwam9vr3a9eE268Q3guCRlN/f+NS2l1WsyPYL5PcTyTsyCzwqgzkfs4XWgIBe4zIMBf6MlFOZsBeO61r06t1HH48WYjBXfnV30WIHJAryUPZdMhnx0LE9Vxk3uo8n7mTCPObZIUd3iFkwmxVBR1NfFpEKnfWWZbd6+9bt5aAwj2WlDDq+cwLj2enzE4tGnnt1xoDD3XFamOaA5GwYsRlQEvYUTuF++7DDC3Q8kfS8cTIHvjDsC5MW45mR8snRKatxPkY+b5WX7zhyFw7LbM3DxnNjkzkHQvvOmgNy8yoUJYnRvZA88aRePKWHu5tWvc9876BePKWHnj17r5Sx7hzQDLaJcTg5t5JPtuJ7gkhD2l2LES4ENV50/b1fMsswpeYKBTeEBSD2hqrIkv4Ziynz/3a/YoWf99t1QkKfR9LfU/YSR5eahP2mv9qYDucLy7HwA4JuHfuRt8H4aAKMRZGtGg49PLMUKqHxfdkK/KzYpHyMVJ9z+FT66DVVYfmjTBpcCddbfWitfj4m1RQBnVf3uMRmXtZ1Jod38dt38y7qKUWot+XFtzbHGBhZ9LgTvjddR1V107r2iTGRuF313Xi7/TBS3emQlEU4f0hYcd8SLNjITJvpjLqff4i7flxlFUKQm5Znx2JJ8CqOj+8fjUzvDKf/3JrT0SFO1V+HOJwV89n0bnLODuL1Pfs1BUd4cIjw7tpqsDVsXK+tU8y2jZRFJGMZof3vGqasQwmKzQz6EbKisVprCnsSDxvPNjrLIrQkdEiyWjQtATKt+4ZgClDO+OmXr61EOox6r/XIrTMWGaWt6hL3efaiqgoPS+YomKlhDwk7FiITMFHsTOfb+FAUWqKITaNCccEJjJBYPGScpD0F61Fjzcp6ilU+H9pHbH7mZvQn/HNiBDkwhBfR2OzjCpVv5/2Pofmfy5vcgDXdmqGri0bo2vLWJ99yRRIFNXDEgxLn4Oyn8KBegz+mUm0+/VG60Vb1K/Mgi968kRmLK22PNjdWoLcTb2S8NhN3eW0v8yj72/RXq1zdEk8p/5il4Klinx2TIeEHQuRigwSTNzsIq4VWfLMqF7Y/OSNaBkXxfTrjUxmWTNRCTgCQa9Psie6QkOxA6BGsGCvnUy1bnP9KTyfuUKDqZOmA/MnXY3sPwxWnS9XsJU4royQxjVjaSxC6mvj+xhe2aMlx6DZVpBUUNSvVpZuo9Fw3OOK9jMNZAQjUV9mmkzY69w+wbf5SzTnaY3KSs1OXeyKiqok1Y7pkLBjITJmENFMx2bv9PXC5HI66pgCfE/yZpqmRKijctg9nv989fvr8MIdvXF73zbubWzpARFizQ7zWaT1koi0YQVQkYOyDFojcDgccDodwoWXNQUkNPJdpiJM5cdhwGdH43qx10OUIVniJ2F6HiRPv96NqxQFf7ihC9olxGDSdR119yvUXjCfY6PCBePywL7Q1BIZ5kSbJtFIaBSBVk2892v1Zqaphj3faTd2RZsm0arrJYPWuGQcn/3FruR+lFTQfMhB2UJkigSK2rLCDrfytyqzrAdulIPBEhD+ykVapiv2dHq2jkPP1nHYcuS8e9vZonJmDN6DYMM0xYup/rdy8eny+/LXV0F0nVWCL3O6oglyWI+WKLpUiZZxUdjMXF93X6yZRDRGidBz9nwiw0WaHf73RP2K4NfO0t9vdbWCaTd2xbQbuyJrx0ndx5V5mYiPFgg7DgdWPzYU5VVV3LYOhwMrH0uHoujRoHk+m6m8YM+3eeNIrP3TUMMpKzTNWAF4QUtNjseO4wX45QDfdbSsokMz3xGbhDyk2bEQ8WLq4H7mwRahE2lrRG/Hap+dwKGVW4d/nTzbHhjSGREuJyZolBKoYJIoCjU7zKFEIfwysItHGMeco+ULdFWHBJ/98obICgwiPw7WWT0yzIn/TRuM9ydexT2WjJDGG1dvjcRu7BhFwigkfhMy3N63NRyOmoy7tYgSu7FUab1M+Jkgkr1nbF4gHg4A7ZrF4IpEbT+tcJdT+BsA1PfPTFONOiLMt3ZU5vqzWBWNxfLJA4Ow5o9DcXUn3xXSzeazBwdh/NXt8AQT/k6YA2l2rETizVRGs2M0GaEbRbDfItSV2T3b+cn9PJ+7J8Vi5zPDERXuwo0vr/JqW8HYt6XMJEIzls+uNEN9uZo3jT5e+3U//CM7B/dc2x6j//297wNeJooRfEUC9YWSCs8YFN/3W6aEAFvF/fVf98P2o/nIuP4KfL7dW+vBap9kfKrMXNSaN47ET8+NQGSYEx1nfKV7DLWo800xfinNYrD2Z+2+RPeHXfCv6dQM3VvFojNTGNUqWCFNVIFbBrk8SaIXPP6zwnuZMJuIMCfaCnyOrGBA+6YY0L5pwI/bECBhx0JkfA5Ek6LW23wt6hBW5hg8lbyqBIRgkCailfmXW7ahzv9rs7jyzkflzCcSUATRWDKo75/nMzf0XGM9aRkXhVl3pepqWwtbNV10//JLPCZAzZw7l1GFnvvuFvmMENWjVRxu7dNas606Uk3/NRe3lbt/dTMBa19n3/2y1+nhYV2hALi1Tyv8+j8bvHuSfMbu6KdtNuma5DvyTgaVZsdUYYd/DNEYWP5775V4OutHvKKRsC+QDspE6EDCjoVIRWpoNG3TJBon8kvRp00T5nve7TQjSzjbgsJBmR0D5wVOpvq4DIWXPIt0kxjfPhIya4DIZ0dGZa+KuuF8LUog+LKw2bRF5xPmdCA2KgwXL1ViUOdmWO2jKGfrJlHIK7xUM17f3aoWfKFmRzUe321FPi5GEf0k2Gi72KgwvHBHb822wpcJ5p40iuRPx0um/ALf/JiHqdd34e43AjssM6OczYimG9azJYb1bKn5vUCYsYjQg4QdC5F5s9FatFY8mo7K6mpVVmS+aUqrjox+LYPVaGlzuGUbNGQdnkbimk4JuKpDArq3ihVGbrEaCV7NH5k8O1r3j+9Arrtbze8N65GIb386rcrcKpr4VRo/wbGcTgc2PH4DCksruQ7MALDooUFYvPUEhvdqiXveqcn+LBO1NqhzMyzfe1owkhpE5Vau6ZSACYM6oHOiMbOPzAsCi0siE6eel4lnbuuJA2eKMbAD33yR2rYJUk2u/8TeM6uy9QrlPIOHvaVPa2w9mo/kptHGOiAaJCTsWIhY2OH7sLBEhDkRASfKKpiIJD/HoC4Eyu9t9rh+eH35ftxzbQc89fnuy8f1741Kq/ggTy0tc6wwlxMfP3AtAOCRj3f4bDusRyL2nCxEWpfmOMdEedUiVy6Cf/94AohRPwz2Xr11z5XIK7yENk34k/zb9wzAy8v24enbemHcf9YDUEfaiBYXl8OBmIgwxESEYWi3RDRrFIE+yfFYkePR8PRv1xT92zXF7hMF7m1alykuKgyFlypxQ4+WWHm5j1v6tEJSfJRKU8kik4HX4XDgmVG9fJ+UDyINJn8M18rufZna8wbEmiwFwIRfdDQ0Dn+5umMCNhw6j5G9W2Hu94dM6VNV004kBDPXsVaD3am5OAppwqAO6Ng8Bn3bkm8LoR8SdixEpiKyaGkXZaH1UUHIawu7ALJjaBITjvySCjSNCceo1NYYldoaiqJgb24huiXF4vDZEsEofcMuXlWCaqRazr/+Wt0aRYbh+z9fD4fDgd/N2+RfZwyswy57nkum/ALz1h3Gn0Z09/sYLqfDS9BhF4wrOyQg++HBdb6lMJ98SztsUsBGkWHY8PgNcDkdXIdePRqL5Y+mY8/JQqS2beIWmAH49O9hkcnobIR4DTOm6NRUGkrm4f1yahreXn0Qdw9si/H/rfHfCQ/z3ZldNZAcDuCjSdegtKIKH6w/4nd/T93aE2+tOoBnR6Vg7JwfdH2neaMIjEptDZfTgUdv6oZ5PxzGvde2F37P5XTg+u7aZi6C4GFr6PnMmTMxcOBAxMbGIjExEaNHj0ZOTo6qjcPh4P699NJL7jbp6ele+3/1q18F+nSkkXKWFezWMldwu9VwYH7lrr7o1KKRqtaUw+HA83f0xr3XdvA9AB1o1V7yP9+NftQRSf6Zm9hhswn7erWOc39ObdsEL9/Vl5sETg/CGlUan2vRo9mpjWAb3EVdqyvM5dR8O48IEwugzRtHYnDXFiofIy2/FHdfdY5vJVo+W+zvsjYy5uqOnhQBYRpZq1PaxGP2uH5o29QTxcNqj3iKKtuEHdQkpWwUGWZKcu+JaR2x4fEbcAWjwRQ5Piuo0SC/cndftGkSjcdH9kBy08BHQBENA1s1O6tWrcKUKVMwcOBAVFZW4oknnsDw4cOxZ88eNGpUo87Mzc1Vfefrr7/GxIkTMXbsWNX2SZMm4bnnnnP/PzrafnuuULMjUIer+/J85qe+1/LZ8d7GTkJsX0O7J2Jo90TfA/GDMA3NDm+MZjhOu5wOr8VExkwlgv36XVe2RYfmjTCsR0t0bdkY0eEu3JaqT4NRFxk/G1EovdaCw7bd8PgNyC24hJ6MkCaC9XcSmRwjw1yY/7urUVGtIE6QJZhF5LNjlD+N6I7Z3+3HzDv6cPez1+btewZg8bYTuKOfJ6O3aFzs91kzVqOIMFys41NWaVMNJCsurcPhgIORT0WCOmUJJgKJrcJOdna26v+ZmZlITEzEli1bMHhwjTo+KUldcXfJkiUYOnQoOnXqpNoeExPj1dZuRIspm0dCHLlgzJQjqglkVWBDt5axyDl1ER2axeDwOW/zV5Uq/F3goGxwjC6HA1V1xAWtTNP+EhXuUpmqnrilpyn9Css2CJLw6amq3bRRBJoKSknUJSbCM3XoiTQbdEVzqf4B66JuHkzvjEnXddTUHLFHbdY4UuUQDtTxSxEci/2Nx0S6vIUdm2ogiSL+jKL2vxNodkjWIQJIUGVQLiiocXpMSOBnlT116hSWLl2KiRMneu378MMP0bx5c/Tq1QuPPvooLl68qHmcsrIyFBYWqv6sQJg0S2LSFOWvkEl9r95vzYLy4tjeeHxkdyyYfC13f5WqZIVA2FE5Aut/q+YFzYg0O+zC3axRpM9jaX3PX8qZjNDi6ta+97PCkpmLSzSj2WEjBf2FvSdWmrH86VtdfNX3s6vS7HBMeJW2+exY87uXqcxuV0VxomESNMKOoiiYPn060tLSkJKSwm0zb948xMbGYsyYMart48ePx0cffYSVK1fiqaeewmeffebVhmXmzJmIj493/7Vt29bUc6lFnBVZfyZcYYVgLWGHs01lxrJIs9MkJgKTB3dGUrzHV4UdoyixoRmlAkTlILQ0IW+M749b+rTCg+mdvPazsIsXG9LuL6yzsxDmFLhaIA2TWM9W+k1WPFg/nFIml4+/sPfEKjOWcAyCH0VirEcI5rrEMRc6nNHsPDq8GwDgTqbekl1mLJVvlInXWV2rjd+m1sl+ZO/g0sQToU3QRGNlZGRg586dWLt2rWabuXPnYvz48YiKUjt7Tpo0yf05JSUFXbp0wZVXXomtW7eif//+Xv3MmDED06dPd/+/sLDQEoFHNGlGaJSAcDi8hRctTUctbHOVqUaQZ0dmmpMzn3nDaj/YiZBnrjDFjCXhW8EysncrjOzdCgWlvgWYMJcT91zTHsv3nsagzubV0JERdthngbe2aGl2Zo/rhwc/2IL7DIY9s89VWxPznbD3pHljOdNaoGgSE4HPHhyEyDAntwI3q7VkHZRH9m6F9TNuQGJsJD7ZchyAfWasFKaOWWIcI7xx5h4Z1MVk+R19M20wjp4rkfIRIwh/CQphZ+rUqcjKysLq1auRnMxPl75mzRrk5ORg4cKFwv769++P8PBw7N+/nyvsREZGIjJSv4nCKKIXJq3Q2t5t4rHzeIGugn4ixI7P9rw9Q+A7oxWWLxotu1+UyViUcDE+OhyvjesHl9OBhz7cymmr4K+jU/CcophqFmDNWCLYU+T55KiisZhzb90kGksy0gyNr5Z1M65H0aVKNGtszW/pyVt6orC0Er++up0l/Wuh51b6ql/Emqbqhp6zms66bQPBNw8Pxo5j+bi1Tyv3trTL/lTNGkXgfIl37ikZXDrMWI0jw0jQIQKOrWYsRVGQkZGBRYsWYfny5ejYUfst85133sGAAQOQmpqq2aaWH3/8ERUVFWjVqpWwrZUM7toCnVo0wiiNqBy2kjnruDjnNwPwywHJWDLlF9zv8X12xFE3PMwUdkTaGK1iinxBwf9x3d63JoImpY1nYlVdJh2HuC21NUb29v0cmSXojLuqRrs4bVhX3d8RlVRQoKD75ZpKevPb1EVLQ9YqPhpdWppXr6kuLWIj8dHkawxHtRklzYAzNQur2RElFQy0sNMtKRZ3DWyremabNY7EpieGYeVj6X6btKxyfCYIf7FVszNlyhTMnz8fS5YsQWxsLPLy8gAA8fHxqtDxwsJCfPLJJ5g1a5ZXHwcOHMCHH36IkSNHonnz5tizZw8eeeQR9OvXD7/4BV9YCBRR4S58N30IHA4HsnZ4V4NmNTsXL3mEnTZNovHPO1PrtPVMms0l3qSFvi8Sc5tUtXVBx2GC/CMODXWOjFzx55u7o3/7prjuiubo99dlAIyb8ALBC3f0xp9v7oEyCR+Y+Jhw3HVlMqoVcDUs1QrwxdQ05JdUoEWsMQ2M0wGY55UT/FzfPRHv/d9V6GqCICcSdqps8tmpS+2zEe5yoqLK+N1mhahGkd7lWAjCLmwVdubMmQOgJikgS2ZmJiZMmOD+/4IFC6AoCsaNG+fVR0REBL777ju8+uqrKCoqQtu2bXHLLbfg6aefhstl/4/N11t/BDMR3j+4E95afRBj+rfhtnU5HVj+yBBUVCncjKfsIl6rYm/eOFIooMgs+DJaIFGWZ1bQ44aeqz4bE0uiwl1eWjVRqQyZ2lhmv7k6HA7ER4fjvOTb/j9+6UPbqdQsYEYFHaD2+gf+Nd0uC6vD4cDgri3EDTXo2rIx0ru1QGJspPA3I5N3KBBEhDlRUu6faPuPsX1wvqQc7ZuJSz8QRKCwVdjRu7BMnjwZkydP5u5r27YtVq1aZeawAsYVTAHD21Jb4zfXtEdrjbpHANDpcnZS0SLQqUVjLH9kCMJdTtzzzgafbeUEGE9bXsI+Eez9Zosp8vPs8Mfl7wJopmbHzHBzljATyySYkbgtkEKHyBG/PuBwOPDufVcBAL7dc4rb5j/3Xom3Vh3Ai2P4iQ3tQqSJ0sNdA62JbiUIfwgKB+WGxmcPXovDZ0vQqYXnzcfhANom6EuVLop0AjyCkbAvg2ascJe3sCPz/h+u5YHM2SQVBSZoG4iEiv4SLlFVW4QZwo5Vyf1E2KXZMROtc7ixZ0vc2DP46jtFWFyigyDsgp5sGxjQPgFjBySrsyL7+RarnVTQd78yoa+sBmZM/5qoub5tm7i3xUV7VPIiM5Yqe7TgWGaYtNxjUNUF8x3CL9OXmZip2TFjiIGM2FNnhK7/2BbtaBAzIkAJIhihJ9tGRFmRtTC6SNcKJndd6Qnvv1Sp3z7PHvYvt/bEq7/qi3fvG4h/3d0XAzs0xeMje/gcIxvp0bpJNG5OScKYfm0QGe7tWxWINYKrIZOQDqyq7WNmkjczhmjXcm1Vlt+AUs9OwepK8wRhF2TGshF1GQTrj/fufQOxat8Z3NQrCR9vrklqViaR6p9dfKLCXe7Q7tH92mB0vzZYmXPa05bzffatsWlMOJ66taZ+1L1zN+o+rtBMJVzczXPasUqzw55vi9hInLlYZs2BdGKfz079JyrM/iAJGUizQ4QqJOzYiFFnTKN5dprERLgFlFrKJDQ7wsguUcZolxOv/qovvvkxD/+X5smpJAo9l1kALwiSoqkdlH0nHRRhZdXmd357Jc4Xl+Pazs0w8+u9mHSd79IVVkI+O8a5umMCRvRKUgUjBDNP39YLd765DlOvv8LuoRCEqZCwYyNGSyLc2qc1Mr8/jHYJMTh6vqaieITGG6TIJFImUZpAXL+L+cxpGhHmxO1923gJXNyUggZXujY+otm8j+G9TUZ+qbJQ2Lmhh8d59d+/9s4CHkjsMieFghnL6XTgzXsG2D0M3QzskIC9fx2BKI5pmSDqM6SztBGjzosD2jfFikfT8c3Dg93bmjXi1xFyaUT2RF5WV8tEhPibVDBSQqWvFazV30eafgCYdmNX/N8vOuLLqfxSCKIEyjER+scYJPngfGKGvBBIxU6iH/mACHMgQYcIRUizYyNaEUd66NhcnbDr6o4J3HZamp2NTwzD+eJyHDxThPfWeScp5CF60xZpqrT8AXj9avkzPXZTNzRvHImbevErJjeJicBfbuupOUZ1kVTv/XrD/+v2FWzMnXAlnly8G7Pu6ut3X4HUsDRrHImPJl2DaAmhkyAIQgQJOwGGFT7U9aGM9ff5lF9g+d7TuH9IZ+5+LX+L+OhwxEeHo11CDEb0SkLnRHG2U7EZy7fwpiWQ8c1Y/GPERIRhylDj/gRqzY7nII+P7I7/rjmEv41O0d1XgMsaSXF995b4YYY5eVwC7bJzrYkV5AmCIAASdgIOK3xEqjQdxlaUvm2bqHLd1EXks+OS8CkQOyjzt3/3yBCsO3AOv9LIrMr7HrvJzFwl6jw7ns+TB3fGpOs6SWkxrHRQDibqW64YgiCIupCwE2BY4UOVWM+i9cRp4mu5jIMy+5/OLRqjs8+Mzr6lnQQNfyQjqDQ7dc5H1lwTzJodMyFhhyCI+g45KAcYtto3W4fGsmy8Jgo7UokP/cySUs5EiclUeRch8tnR4slbahImPj6yu3tbQ9HskKxDEER9h4SdAKOl2am0KLTHzBwpQs2H4YzQ3ttax0fj2k7NcHNKEhpF+q+A7J4UCwCqqvIyV+Z313XClieHYfJgj29UQxF2xl3VDgAwsIPvSDiCIIhghcxYAYbV5rBF9yoqrVk4zahiXItIbmLXfqlq6rxjOR34aPI1uvsQ8dmDg7D/dBFSk+M9x5WUA5vV0TBVNxA71gNDOqNf2yZI9eEbVovDYZ2WkiAIwiik2QkQL/2yDxIaRWDObzwJ4tg6NOVV+jMZy2CGVqQWkQBTUeXRTsmknQ+EmaRRZBj6tm1iavHVBiLrwOV0YNAVzU19lgiCIAIJzV4B4s4r2+KXdSudM5/LLdLsPD6yO3LyCnHvtR387ku02LEV1GUKCvordBjFXwtfQzFjEQRB1HdI2AkgPJ+XhEYROF9cjl5t4iw5Zqv4aPxv2hBT+hqV2hpf7crVzJfD+h2Fa2Ru5mGXA6y/yfJI2PHmkRu74p//2+f28yEIgggGSNixmXUzrkdZZTXiosLtHoqQiDAn5k4YqLm/gtHsyIS819don/pQLiLQTBl6BW7qlYROPlMNEARBBBYSdmwmMswlVTMqmLEqoswq/BWySLPjjcPhQJeWsXYPgyAIQgU5KBOmwWp2ZLDLZ8dfSNghCIKoH5CwQ5hGpUFh57GbuiE2Kgy/v6GLySPyjb+ZgRtKNBZBEER9h8xYhGkYNWN1aN4I2/8y3NQEiHowerRfX90O8zccxcPDAiucEQRBEMYgYYcwDX8SGAZa0AGM++z89fYU/PbaDujakpxwCYIg6gMk7BCmcUe/Nvh82wkM7trC7qHowmjoucvpQLckcsIlCIKoL5CwQ5hGVLgLC++/1u5h6KZ+ukUTBEEQspCDMtFg8TepIEEQBFE/IGGHaLCQrEMQBNEwIGGHaLCQrEMQBNEwIGGHaLCQZocgCKJhQMIO0WCJcIVGmQ6CIAjCNyTsEA2WCb+oyZVDyQEJgiBCGwo9Jxos8dHh+N+0IXYPgyAIgrAY0uwQBEEQBBHSkLBDEARBEERIQ8IOQRAEQRAhja3CzsyZMzFw4EDExsYiMTERo0ePRk5OjqqNw+Hg/r300kte/SmKgptvvhkOhwOff/55gM6CIAiCIIhgxlZhZ9WqVZgyZQrWr1+PZcuWobKyEsOHD0dxcbG7TW5urupv7ty5cDgcGDt2rFd///rXv6gEAEEQBEEQKmyNxsrOzlb9PzMzE4mJidiyZQsGDx4MAEhKSlK1WbJkCYYOHYpOnTqptu/YsQMvv/wyNm3ahFatWlk7cIIgCIIg6g1BFXpeUFAAAEhISODuP3XqFJYuXYp58+aptpeUlGDcuHF4/fXXvYQjHmVlZSgrK3P/v7Cw0I9REwRBEAQRzASNg7KiKJg+fTrS0tKQkpLCbTNv3jzExsZizJgxqu3Tpk3DoEGDcPvtt+s61syZMxEfH+/+a9u2rd/jJwiCIAgiOAkazU5GRgZ27tyJtWvXaraZO3cuxo8fj6ioKPe2rKwsLF++HNu2bdN9rBkzZmD69Onu/xcWFpLAQxAEQRAhSlAIO1OnTkVWVhZWr16N5ORkbps1a9YgJycHCxcuVG1fvnw5Dhw4gCZNmqi2jx07Ftdddx1Wrlzp1VdkZCQiIyPNGj5BEARBEEGMQ1EUxa6DK4qCqVOnYvHixVi5ciW6dNGuUTRhwgTs3r0bmzdvVm3Py8vD2bNnVdt69+6NV199Fbfddhs6duwoHEdhYSHi4+NRUFCAuLg4YydDEARBEERA0bt+26rZmTJlCubPn48lS5YgNjYWeXl5AID4+HhER0e72xUWFuKTTz7BrFmzvPpISkriOiW3a9dOl6BDEARBEERoY6uD8pw5c1BQUID09HS0atXK/VfXVLVgwQIoioJx48bZNFKCIAiCIOortpqxggUyYxEEQRBE/aNemLGChVp5j/LtEARBEET9oXbdFultSNgBcPHiRQCg8HOCIAiCqIdcvHgR8fHxmvvJjAWguroaJ0+eRGxsrKm1tWrz9xw7dozMYwQXekYIEfSMEL5o6M+Hoii4ePEiWrduDadT2w2ZNDsAnE6nZn4fM4iLi2uQDyGhH3pGCBH0jBC+aMjPhy+NTi1BUy6CIAiCIAjCCkjYIQiCIAgipCFhx0IiIyPx9NNPU2kKQhN6RggR9IwQvqDnQx/koEwQBEEQREhDmh2CIAiCIEIaEnYIgiAIgghpSNghCIIgCCKkIWGHIAiCIIiQhoQdAatXr8Ztt92G1q1bw+Fw4PPPP1ftLyoqQkZGBpKTkxEdHY0ePXpgzpw5qjbp6elwOByqv1/96leqNhcuXMA999yD+Ph4xMfH45577kF+fr7FZ0eYgegZOXXqFCZMmIDWrVsjJiYGI0aMwP79+1VtysrKMHXqVDRv3hyNGjXCqFGjcPz4cVUbekbqL2Y8IzSPhC4zZ87EwIEDERsbi8TERIwePRo5OTmqNoqi4JlnnkHr1q0RHR2N9PR0/Pjjj6o2NI9oQ8KOgOLiYqSmpuL111/n7p82bRqys7PxwQcf4KeffsK0adMwdepULFmyRNVu0qRJyM3Ndf+99dZbqv2//vWvsX37dmRnZyM7Oxvbt2/HPffcY9l5Eebh6xlRFAWjR4/GwYMHsWTJEmzbtg3t27fHsGHDUFxc7G738MMPY/HixViwYAHWrl2LoqIi3HrrraiqqnK3oWek/mLGMwLQPBKqrFq1ClOmTMH69euxbNkyVFZWYvjw4ar7/49//AMvv/wyXn/9dWzatAlJSUm48cYb3bUdAZpHfKIQugGgLF68WLWtV69eynPPPafa1r9/f+XJJ590/3/IkCHKH/7wB81+9+zZowBQ1q9f7962bt06BYCyd+9eU8ZOBIa6z0hOTo4CQNm9e7d7W2VlpZKQkKD85z//URRFUfLz85Xw8HBlwYIF7jYnTpxQnE6nkp2drSgKPSOhhJFnRFFoHmlInD59WgGgrFq1SlEURamurlaSkpKUF1980d3m0qVLSnx8vPLmm28qikLziAjS7PhJWloasrKycOLECSiKghUrVmDfvn246aabVO0+/PBDNG/eHL169cKjjz6qksbXrVuH+Ph4XH311e5t11xzDeLj4/HDDz8E7FwI8ykrKwMAREVFube5XC5ERERg7dq1AIAtW7agoqICw4cPd7dp3bo1UlJS3PefnpHQRc8zUgvNIw2DgoICAEBCQgIA4NChQ8jLy1PNEZGRkRgyZIj73tI84hsqBOons2fPxqRJk5CcnIywsDA4nU7897//RVpamrvN+PHj0bFjRyQlJWH37t2YMWMGduzYgWXLlgEA8vLykJiY6NV3YmIi8vLyAnYuhPl0794d7du3x4wZM/DWW2+hUaNGePnll5GXl4fc3FwANfc/IiICTZs2VX23ZcuW7vtPz0jooucZAWgeaSgoioLp06cjLS0NKSkpAOC+fy1btlS1bdmyJY4cOeJuQ/OINiTs+Mns2bOxfv16ZGVloX379li9ejUeeughtGrVCsOGDQNQY2evJSUlBV26dMGVV16JrVu3on///gAAh8Ph1beiKNztRP0hPDwcn332GSZOnIiEhAS4XC4MGzYMN998s/C7de8/PSOhid5nhOaRhkFGRgZ27tzppdUDvO+vnntL80gNZMbyg9LSUjz++ON4+eWXcdttt6FPnz7IyMjA3XffjX/+85+a3+vfvz/Cw8Pd0RZJSUk4deqUV7szZ854SfJE/WPAgAHYvn078vPzkZubi+zsbJw7dw4dO3YEUHP/y8vLceHCBdX3Tp8+7b7/9IyENqJnhAfNI6HH1KlTkZWVhRUrViA5Odm9PSkpCQC8tC915wiaR7QhYccPKioqUFFRAadTfRldLheqq6s1v/fjjz+ioqICrVq1AgBce+21KCgowMaNG91tNmzYgIKCAgwaNMiawRMBJz4+Hi1atMD+/fuxefNm3H777QBqFrrw8HC3OQIAcnNzsXv3bvf9p2ekYaD1jPCgeSR0UBQFGRkZWLRoEZYvX+4l5NaaL9k5ory8HKtWrXLfW5pHBNjlGV1fuHjxorJt2zZl27ZtCgDl5ZdfVrZt26YcOXJEUZSaCIlevXopK1asUA4ePKhkZmYqUVFRyhtvvKEoiqL8/PPPyrPPPqts2rRJOXTokLJ06VKle/fuSr9+/ZTKykr3cUaMGKH06dNHWbdunbJu3Tqld+/eyq233mrLORNyiJ6Rjz/+WFmxYoVy4MAB5fPPP1fat2+vjBkzRtXHAw88oCQnJyvffvutsnXrVuX6669XUlNT6RkJEfx9RmgeCW0efPBBJT4+Xlm5cqWSm5vr/ispKXG3efHFF5X4+Hhl0aJFyq5du5Rx48YprVq1UgoLC91taB7RhoQdAStWrFAAeP399re/VRRFUXJzc5UJEyYorVu3VqKiopRu3bops2bNUqqrqxVFUZSjR48qgwcPVhISEpSIiAilc+fOyu9//3vl3LlzquOcO3dOGT9+vBIbG6vExsYq48ePVy5cuBDgsyWMIHpGXn31VSU5OVkJDw9X2rVrpzz55JNKWVmZqo/S0lIlIyNDSUhIUKKjo5Vbb71VOXr0qKoNPSP1F3+fEZpHQhveswFAyczMdLeprq5Wnn76aSUpKUmJjIxUBg8erOzatUvVD80j2jgURVECp0ciCIIgCIIILOSzQxAEQRBESEPCDkEQBEEQIQ0JOwRBEARBhDQk7BAEQRAEEdKQsEMQBEEQREhDwg5BEARBECENCTsEQRAEQYQ0JOwQBEEQBBHSkLBDEES9ZeXKlXA4HMjPz7d7KARBBDGUQZkgiHpDeno6+vbti3/9618Aaoohnj9/Hi1btoTD4bB3cARBBC1hdg+AIAjCKBEREUhKSrJ7GARBBDlkxiIIol4wYcIErFq1Cq+++iocDgccDgfeffddlRnr3XffRZMmTfDll1+iW7duiImJwS9/+UsUFxdj3rx56NChA5o2bYqpU6eiqqrK3Xd5eTn++Mc/ok2bNmjUqBGuvvpqrFy50p4TJQjCdEizQxBEveDVV1/Fvn37kJKSgueeew4A8OOPP3q1KykpwezZs7FgwQJcvHgRY8aMwZgxY9CkSRN89dVXOHjwIMaOHYu0tDTcfffdAID77rsPhw8fxoIFC9C6dWssXrwYI0aMwK5du9ClS5eAnidBEOZDwg5BEPWC+Ph4REREICYmxm262rt3r1e7iooKzJkzB507dwYA/PKXv8T777+PU6dOoXHjxujZsyeGDh2KFStW4O6778aBAwfw0Ucf4fjx42jdujUA4NFHH0V2djYyMzPxwgsvBO4kCYKwBBJ2CIIIKWJiYtyCDgC0bNkSHTp0QOPGjVXbTp8+DQDYunUrFEVB165dVf2UlZWhWbNmgRk0QRCWQsIOQRAhRXh4uOr/DoeDu626uhoAUF1dDZfLhS1btsDlcqnasQISQRD1FxJ2CIKoN0RERKgci82gX79+qKqqwunTp3HdddeZ2jdBEMEBRWMRBFFv6NChAzZs2IDDhw/j7Nmzbu2MP3Tt2hXjx4/Hvffei0WLFuHQoUPYtGkT/v73v+Orr74yYdQEQdgNCTsEQdQbHn30UbhcLvTs2RMtWrTA0aNHTek3MzMT9957Lx555BF069YNo0aNwoYNG9C2bVtT+icIwl4ogzJBEARBECENaXYIgiAIgghpSNghCIIgCCKkIWGHIAiCIIiQhoQdgiAIgiBCGhJ2CIIgCIIIaUjYIQiCIAgipCFhhyAIgiCIkIaEHYIgCIIgQhoSdgiCIAiCCGlI2CEIgiAIIqQhYYcgCIIgiJDm/wGhI0ZDVlWJPQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tas_monthly_mean.plot() \n",
    "# may give an error and may have to install additional package\n",
    "# do this using the command \n",
    "# conda install -c conda-forge nc-time-axis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It may be more helpful to plot annual mean to reduce the data frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "tas_annual_mean = tas.groupby('time.year').mean(dim=['lat', 'lon', 'time'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7efee20cf050>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tas_annual_mean.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(adv) Optional: Plot observation with modelled data\n",
    "\n",
    "We have observation for global air temperature as well. Why not try and plot both the modelled and observation togerther?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting with `cartopy` and `matplotlib`\n",
    "\n",
    "Other than time series, it is also helpful to visualize data onto maps.\n",
    "\n",
    "Let's plot decadal mean surface air temperature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tas&#x27; (lat: 192, lon: 288)&gt;\n",
       "array([[226.07002, 226.07002, 226.07002, ..., 226.07002, 226.07002,\n",
       "        226.07002],\n",
       "       [226.45174, 226.42159, 226.27483, ..., 226.45795, 226.45937,\n",
       "        226.45647],\n",
       "       [226.97272, 226.94608, 226.94028, ..., 227.16414, 227.12578,\n",
       "        227.0682 ],\n",
       "       ...,\n",
       "       [259.81912, 259.83267, 259.84497, ..., 259.78278, 259.7944 ,\n",
       "        259.8058 ],\n",
       "       [259.70807, 259.71326, 259.7186 , ..., 259.6905 , 259.69687,\n",
       "        259.70273],\n",
       "       [259.6382 , 259.6387 , 259.63904, ..., 259.6366 , 259.6372 ,\n",
       "        259.63776]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0\n",
       "  * lon      (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8</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>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-39e7cf2a-024c-4155-a0ae-056c5097a76f' class='xr-array-in' type='checkbox' checked><label for='section-39e7cf2a-024c-4155-a0ae-056c5097a76f' 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>226.1 226.1 226.1 226.1 226.1 226.1 ... 259.6 259.6 259.6 259.6 259.6</span></div><div class='xr-array-data'><pre>array([[226.07002, 226.07002, 226.07002, ..., 226.07002, 226.07002,\n",
       "        226.07002],\n",
       "       [226.45174, 226.42159, 226.27483, ..., 226.45795, 226.45937,\n",
       "        226.45647],\n",
       "       [226.97272, 226.94608, 226.94028, ..., 227.16414, 227.12578,\n",
       "        227.0682 ],\n",
       "       ...,\n",
       "       [259.81912, 259.83267, 259.84497, ..., 259.78278, 259.7944 ,\n",
       "        259.8058 ],\n",
       "       [259.70807, 259.71326, 259.7186 , ..., 259.6905 , 259.69687,\n",
       "        259.70273],\n",
       "       [259.6382 , 259.6387 , 259.63904, ..., 259.6366 , 259.6372 ,\n",
       "        259.63776]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-e07403ff-c256-4184-87fd-a7c68c735e33' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e07403ff-c256-4184-87fd-a7c68c735e33' 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'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-2d7a0bff-aa17-404a-b4c3-498f76c760c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d7a0bff-aa17-404a-b4c3-498f76c760c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd79ab5c-e770-4f51-a2f8-102a433f8634' class='xr-var-data-in' type='checkbox'><label for='data-bd79ab5c-e770-4f51-a2f8-102a433f8634' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>title :</span></dt><dd>Latitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  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 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-ed2acf34-6831-4b71-819b-7d4d77757068' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed2acf34-6831-4b71-819b-7d4d77757068' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26f8c084-7343-4aad-bd4a-bc8814850c9a' class='xr-var-data-in' type='checkbox'><label for='data-26f8c084-7343-4aad-bd4a-bc8814850c9a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>title :</span></dt><dd>Longitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>360.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2f527d1e-cc05-4b98-b0a9-add5b5a5e7d7' class='xr-section-summary-in' type='checkbox'  ><label for='section-2f527d1e-cc05-4b98-b0a9-add5b5a5e7d7' 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-dfec1846-112d-4dc6-adcd-3e297bd6199b' class='xr-index-data-in' type='checkbox'/><label for='index-dfec1846-112d-4dc6-adcd-3e297bd6199b' 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(Float64Index([             -90.0, -89.05759162303664,  -88.1151832460733,\n",
       "              -87.17277486910994,  -86.2303664921466, -85.28795811518324,\n",
       "               -84.3455497382199, -83.40314136125654, -82.46073298429319,\n",
       "              -81.51832460732984,\n",
       "              ...\n",
       "               81.51832460732984,   82.4607329842932,  83.40314136125653,\n",
       "               84.34554973821989,  85.28795811518324,   86.2303664921466,\n",
       "               87.17277486910996,  88.11518324607329,  89.05759162303664,\n",
       "                            90.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</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-a1b8603b-29a7-4cd3-8eec-d174e23871a5' class='xr-index-data-in' type='checkbox'/><label for='index-a1b8603b-29a7-4cd3-8eec-d174e23871a5' 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(Float64Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,\n",
       "                10.0,  11.25,\n",
       "              ...\n",
       "               347.5, 348.75,  350.0, 351.25,  352.5, 353.75,  355.0, 356.25,\n",
       "               357.5, 358.75],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b6711a7b-2447-4dbc-81bc-b3ccfccd6aca' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b6711a7b-2447-4dbc-81bc-b3ccfccd6aca' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'tas' (lat: 192, lon: 288)>\n",
       "array([[226.07002, 226.07002, 226.07002, ..., 226.07002, 226.07002,\n",
       "        226.07002],\n",
       "       [226.45174, 226.42159, 226.27483, ..., 226.45795, 226.45937,\n",
       "        226.45647],\n",
       "       [226.97272, 226.94608, 226.94028, ..., 227.16414, 227.12578,\n",
       "        227.0682 ],\n",
       "       ...,\n",
       "       [259.81912, 259.83267, 259.84497, ..., 259.78278, 259.7944 ,\n",
       "        259.8058 ],\n",
       "       [259.70807, 259.71326, 259.7186 , ..., 259.6905 , 259.69687,\n",
       "        259.70273],\n",
       "       [259.6382 , 259.6387 , 259.63904, ..., 259.6366 , 259.6372 ,\n",
       "        259.63776]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0\n",
       "  * lon      (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tas_2005 = da['tas'].sel(time=slice('2005-01-01', '2015-01-01')).mean(dim='time')\n",
    "\n",
    "tas_2005"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7efee0636750>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tas_2005.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot this on a map projection and add coastlines using `cartopy`.\n",
    "\n",
    "And I show it with a cmocean colour map, look [here](https://matplotlib.org/cmocean/) if you want more options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cmocean\n",
    "\n",
    "fig = plt.figure(figsize=(4,3))\n",
    "ax = fig.add_subplot(projection=ccrs.PlateCarree())\n",
    "\n",
    "tas_2005.plot(ax=ax, cbar_kwargs={'orientation': 'horizontal', 'label':'T [K]'}, cmap=cmocean.cm.thermal)\n",
    "\n",
    "ax.coastlines(color=\"black\", alpha=0.5, lw=0.5)\n",
    "ax.set_title('Surface air temperature, 2005-2014')\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "Cartopy uses `projection` to display the map, you can find it all [here](https://scitools.org.uk/cartopy/docs/v0.15/crs/projections.html)."
   ],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "cliaer-env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}