gridded_data_tutorial_2.ipynb

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    "[0: NumPy and the ndarray](gridded_data_tutorial_0.ipynb) | [1: Introduction to xarray](gridded_data_tutorial_1.ipynb)  | **2: Daymet data access** | [3: Investigating SWE at Mt. Rainier with Daymet](gridded_data_tutorial_3.ipynb)"
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    "# Notebook 2: Daymet data access\n",
    "Waterhackweek 2020 | Steven Pestana (spestana@uw.edu)\n",
    "\n",
    "**By the end of this notebook you will be able to:**\n",
    "* Access DayMet with xarray, describe its data structure and components\n",
    "* Select subsets of DayMet in time and space\n",
    "* Plot DayMet maps and time series with xarray\n",
    "\n",
    "---"
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    "\n",
    "## What is Daymet?\n",
    "\n",
    "\n",
    "<div align=\"left\" style=\"display:inline-block;float:left;width:50%;\">\n",
    "\n",
    "Daymet is a dataset of gridded daily weather data at 1 km resolution for continental North America, Hawaii, and Puerto Rico and is produced from a combination of daily meteorlogical observations and computer models to interpolate/extrapolate those observations. ([Read more about how Daymet is produced here](https://daymet.ornl.gov/overview))\n",
    " \n",
    "    \n",
    "The data products produced by the Daymet algorithms are available at the [Oak Ridge National Lab DAAC](https://daymet.ornl.gov/) as NetCDF files in the Lambert Conformal Conic projection system.   \n",
    "    \n",
    "Also check out [this recent article about Daymet](https://earthdata.nasa.gov/learn/articles/daymet-and-daymet-2019-data) for more information.\n",
    "\n",
    "    \n",
    "### How can we access this data? \n",
    " * [ORNL DAAC web portal](https://daymet.ornl.gov/getdata)\n",
    " * [ulmo](https://ulmo.readthedocs.io/en/latest/api.html?highlight=daymet#module-ulmo.nasa.daymet) *(DAYMET single pixel timeseries)*\n",
    " * [daymetpy](bluegreen-labs.github.io/daymetpy) *([NetCDF Subset and Single Pixel Extraction web service](https://daymet.ornl.gov/web_services.html))*\n",
    " * [OPeNDAP](https://en.wikipedia.org/wiki/OPeNDAP) Application Programming Interface (API)\n",
    "\n",
    "</div>\n",
    "\n",
    "\n",
    "<div align=\"right\" style=\"display:inline-block;float:right;width:50%;\">\n",
    "\n",
    "<img src=\"https://daymet.ornl.gov/static/graphics/homepage_map.png\" width=300>\n",
    "\n",
    "</div>\n"
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   "source": [
    "We will be using the [**OPeNDAP**](https://en.wikipedia.org/wiki/OPeNDAP) (Open-source Project for a Network Data Access Protocol) endpoint and **xarray** to access the \"*Daily Surface Weather Data on a 1-km Grid for North America, Version 3*\" data product\n",
    "* [THREDDS Data Server catalog page for this product](https://thredds.daac.ornl.gov/thredds-daymet/catalog/daymet-v3-agg/catalog.html?dataset=daymet-v3-agg/na.ncml)\n",
    "  * OPeNDAP endpoint url: `https://thredds.daac.ornl.gov/thredds-daymet/dodsC/daymet-v3-agg/na.ncml` (xarray can directly read from this url)\n",
    "\n",
    "---"
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   "source": [
    "**To get started, import xarray and other libraries we'll be using in this notebook...**"
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   "source": [
    "import xarray as xr\n",
    "\n",
    "# for working with geospatial data\n",
    "import geopandas as gpd\n",
    "from pyproj import CRS\n",
    "from shapely import geometry\n",
    "\n",
    "# for plotting, import matplotlib.pyplot\n",
    "import matplotlib.pyplot as plt\n",
    "# tell jupyter to display plots \"inline\" in the notebook\n",
    "%matplotlib inline"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Use xarray to open the dataset at this OPeNDAP endpoint**\n"
   ]
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   "execution_count": 2,
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   "outputs": [],
   "source": [
    "ds = xr.open_dataset( \"https://thredds.daac.ornl.gov/thredds-daymet/dodsC/daymet-v3-agg/na.ncml\" )"
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   "source": [
    "**What did we just open with xarray? Look at the dataset's structure and metadata:**\n",
    "\n",
    "* *Dimensions* of **time**, **x**, and **y**\n",
    "* *Coordinates* of **time**, **x**, **y**, **lat**, and **lon**\n",
    "* *Data variables*\n",
    "* *Attributes*\n",
    "\n"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                  (nv: 2, time: 14600, x: 7814, y: 8075)\n",
       "Coordinates:\n",
       "  * x                        (x) float32 -4560250.0 -4559250.0 ... 3252750.0\n",
       "  * y                        (y) float32 4984000.0 4983000.0 ... -3090000.0\n",
       "  * time                     (time) datetime64[ns] 1980-01-01 ... 2019-12-31\n",
       "    lat                      (y, x) float32 ...\n",
       "    lon                      (y, x) float32 ...\n",
       "Dimensions without coordinates: nv\n",
       "Data variables:\n",
       "    lambert_conformal_conic  int16 -32767\n",
       "    yearday                  (time) int16 0 1 2 3 4 5 ... 360 361 362 363 364\n",
       "    time_bnds                (time, nv) datetime64[ns] 1980-01-01 ... 2020-01-01\n",
       "    dayl                     (time, y, x) float32 ...\n",
       "    prcp                     (time, y, x) float32 ...\n",
       "    srad                     (time, y, x) float32 ...\n",
       "    swe                      (time, y, x) float32 ...\n",
       "    tmax                     (time, y, x) float32 ...\n",
       "    tmin                     (time, y, x) float32 ...\n",
       "    vp                       (time, y, x) float32 ...\n",
       "Attributes:\n",
       "    start_year:        1980\n",
       "    source:            Daymet Software Version 3.0\n",
       "    Version_software:  Daymet Software Version 3.0\n",
       "    Version_data:      Daymet Data Version 3.0\n",
       "    Conventions:       CF-1.6\n",
       "    citation:          Please see http://daymet.ornl.gov/ for current Daymet ...\n",
       "    references:        Please see http://daymet.ornl.gov/ for current informa...\n",
       "    title:             Daymet: Daily Surface Weather Data on a 1-km Grid for ...\n",
       "    institution:       Oak Ridge National Laboratory Distributed Active Archi...\n",
       "    end_year:          2019</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-27ade424-db04-47a7-bc49-5b9aff2c5270' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-27ade424-db04-47a7-bc49-5b9aff2c5270' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>nv</span>: 2</li><li><span class='xr-has-index'>time</span>: 14600</li><li><span class='xr-has-index'>x</span>: 7814</li><li><span class='xr-has-index'>y</span>: 8075</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-40b1cebf-7b07-4494-864d-38add9a47e11' class='xr-section-summary-in' type='checkbox'  checked><label for='section-40b1cebf-7b07-4494-864d-38add9a47e11' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-4560250.0 -4559250.0 ... 3252750.0</div><input id='attrs-4cc5df8d-ef81-49ac-8691-d240a487b61e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4cc5df8d-ef81-49ac-8691-d240a487b61e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45aa7764-f18f-4bc5-b7c1-91a0b1e339cb' class='xr-var-data-in' type='checkbox'><label for='data-45aa7764-f18f-4bc5-b7c1-91a0b1e339cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([-4560250., -4559250., -4558250., ...,  3250750.,  3251750.,  3252750.],\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'>4984000.0 4983000.0 ... -3090000.0</div><input id='attrs-bdc96708-c0a8-422e-82ec-007c9eced4ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bdc96708-c0a8-422e-82ec-007c9eced4ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79047d68-65bc-4437-a63e-26b1cb652994' class='xr-var-data-in' type='checkbox'><label for='data-79047d68-65bc-4437-a63e-26b1cb652994' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 4984000.,  4983000.,  4982000., ..., -3088000., -3089000., -3090000.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-01-01 ... 2019-12-31</div><input id='attrs-11844f9e-f1b9-4305-bccd-2b91cbe6b642' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11844f9e-f1b9-4305-bccd-2b91cbe6b642' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a687ab3-fea0-4b0e-8916-e78a3bde0484' class='xr-var-data-in' type='checkbox'><label for='data-9a687ab3-fea0-4b0e-8916-e78a3bde0484' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-01-01T00:00:00.000000000&#x27;, &#x27;1980-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-01-03T00:00:00.000000000&#x27;, ..., &#x27;2019-12-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-12-30T00:00:00.000000000&#x27;, &#x27;2019-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</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'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-952128b7-ae09-4f06-9c2e-b45a804983c6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-952128b7-ae09-4f06-9c2e-b45a804983c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef7a1ce6-b0fd-4979-a33b-07f85866eeb1' class='xr-var-data-in' type='checkbox'><label for='data-ef7a1ce6-b0fd-4979-a33b-07f85866eeb1' title='Show/Hide data 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></dl></div><div class='xr-var-data'><pre>[63098050 values with dtype=float32]</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'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-857ecc0d-1f5f-4456-9b9a-f910e7ed1202' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-857ecc0d-1f5f-4456-9b9a-f910e7ed1202' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3112fe57-dd7e-4d16-9a04-d620cf2440ae' class='xr-var-data-in' type='checkbox'><label for='data-3112fe57-dd7e-4d16-9a04-d620cf2440ae' title='Show/Hide data 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></dl></div><div class='xr-var-data'><pre>[63098050 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0fe70fe3-5199-4eda-8bfc-cc38e5a3be11' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0fe70fe3-5199-4eda-8bfc-cc38e5a3be11' class='xr-section-summary' >Data variables: <span>(10)</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>lambert_conformal_conic</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2b12c62b-ab45-4aac-9188-1ba0294688ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b12c62b-ab45-4aac-9188-1ba0294688ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47db7d6e-74a3-4a5a-b45f-d6067725d953' class='xr-var-data-in' type='checkbox'><label for='data-47db7d6e-74a3-4a5a-b45f-d6067725d953' title='Show/Hide data 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>longitude_of_central_meridian :</span></dt><dd>-100.0</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>42.5</dd><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>standard_parallel :</span></dt><dd>[25. 60.]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array(-32767, dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>yearday</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-63ebd37a-c51d-43e0-8cf7-522bbd93d721' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-63ebd37a-c51d-43e0-8cf7-522bbd93d721' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b19822a0-4256-401f-89e9-e4577247e4df' class='xr-var-data-in' type='checkbox'><label for='data-b19822a0-4256-401f-89e9-e4577247e4df' title='Show/Hide data 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>yearday</dd></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 362, 363, 364], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, nv)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-df6055ef-336c-4ef2-95bb-8fd765e1fd94' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-df6055ef-336c-4ef2-95bb-8fd765e1fd94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be3056dd-f521-4652-911b-a530d63cf537' class='xr-var-data-in' type='checkbox'><label for='data-be3056dd-f521-4652-911b-a530d63cf537' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;1980-01-01T00:00:00.000000000&#x27;, &#x27;1980-01-02T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1980-01-02T00:00:00.000000000&#x27;, &#x27;1980-01-03T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1980-01-03T00:00:00.000000000&#x27;, &#x27;1980-01-04T00:00:00.000000000&#x27;],\n",
       "       ...,\n",
       "       [&#x27;2019-12-29T00:00:00.000000000&#x27;, &#x27;2019-12-30T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2019-12-30T00:00:00.000000000&#x27;, &#x27;2019-12-31T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2019-12-31T00:00:00.000000000&#x27;, &#x27;2020-01-01T00:00:00.000000000&#x27;]],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dayl</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e4164ae5-cdb3-48bb-88e3-4a9360ca91d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4164ae5-cdb3-48bb-88e3-4a9360ca91d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85829d5e-7a9d-4941-b4db-36786b0ab399' class='xr-var-data-in' type='checkbox'><label for='data-85829d5e-7a9d-4941-b4db-36786b0ab399' title='Show/Hide data 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>daylength</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>grid_mapping :</span></dt><dd>lambert_conformal_conic</dd><dt><span>cell_methods :</span></dt><dd>area: mean</dd></dl></div><div class='xr-var-data'><pre>[921231530000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prcp</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-18ad2aba-8dc9-4503-b139-34e7d9a16722' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18ad2aba-8dc9-4503-b139-34e7d9a16722' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9d0439e-bcf0-4efa-bb46-bfabbc2dd79a' class='xr-var-data-in' type='checkbox'><label for='data-d9d0439e-bcf0-4efa-bb46-bfabbc2dd79a' title='Show/Hide data 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>daily total precipitation</dd><dt><span>units :</span></dt><dd>mm/day</dd><dt><span>grid_mapping :</span></dt><dd>lambert_conformal_conic</dd><dt><span>cell_methods :</span></dt><dd>area: mean time: sum</dd></dl></div><div class='xr-var-data'><pre>[921231530000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>srad</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-27077f45-bf52-44ea-ae04-f052affbb478' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-27077f45-bf52-44ea-ae04-f052affbb478' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02a649d6-abc3-422d-8f3e-7efdb2353ebd' class='xr-var-data-in' type='checkbox'><label for='data-02a649d6-abc3-422d-8f3e-7efdb2353ebd' title='Show/Hide data 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>daylight average incident shortwave radiation</dd><dt><span>units :</span></dt><dd>W/m2</dd><dt><span>grid_mapping :</span></dt><dd>lambert_conformal_conic</dd><dt><span>cell_methods :</span></dt><dd>area: mean time: mean</dd></dl></div><div class='xr-var-data'><pre>[921231530000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swe</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8fbd7d45-fb17-4c42-900f-ca0beaf76e58' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8fbd7d45-fb17-4c42-900f-ca0beaf76e58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f48d324-4098-4ff1-b4e3-5a84f35c04ab' class='xr-var-data-in' type='checkbox'><label for='data-6f48d324-4098-4ff1-b4e3-5a84f35c04ab' title='Show/Hide data 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>snow water equivalent</dd><dt><span>units :</span></dt><dd>kg/m2</dd><dt><span>grid_mapping :</span></dt><dd>lambert_conformal_conic</dd><dt><span>cell_methods :</span></dt><dd>area: mean time: mean</dd></dl></div><div class='xr-var-data'><pre>[921231530000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tmax</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-89e1effd-e79b-4dc7-8792-64724cdbf25d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89e1effd-e79b-4dc7-8792-64724cdbf25d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab87ec6f-34f9-4119-954e-4cc33823531c' class='xr-var-data-in' type='checkbox'><label for='data-ab87ec6f-34f9-4119-954e-4cc33823531c' title='Show/Hide data 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>daily maximum temperature</dd><dt><span>units :</span></dt><dd>degrees C</dd><dt><span>grid_mapping :</span></dt><dd>lambert_conformal_conic</dd><dt><span>cell_methods :</span></dt><dd>area: mean time: maximum</dd></dl></div><div class='xr-var-data'><pre>[921231530000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tmin</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-39b75562-d233-48bd-ac29-2cd9cc2f2fe0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39b75562-d233-48bd-ac29-2cd9cc2f2fe0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05ccffb5-5639-42a7-8b78-d7847cc96a0f' class='xr-var-data-in' type='checkbox'><label for='data-05ccffb5-5639-42a7-8b78-d7847cc96a0f' title='Show/Hide data 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>daily minimum temperature</dd><dt><span>units :</span></dt><dd>degrees C</dd><dt><span>grid_mapping :</span></dt><dd>lambert_conformal_conic</dd><dt><span>cell_methods :</span></dt><dd>area: mean time: minimum</dd></dl></div><div class='xr-var-data'><pre>[921231530000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vp</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0d995c46-53e7-42a8-ac59-810ccfac4998' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d995c46-53e7-42a8-ac59-810ccfac4998' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b4022d2-dde6-4786-bed2-f052675951b9' class='xr-var-data-in' type='checkbox'><label for='data-9b4022d2-dde6-4786-bed2-f052675951b9' title='Show/Hide data 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>daily average vapor pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>grid_mapping :</span></dt><dd>lambert_conformal_conic</dd><dt><span>cell_methods :</span></dt><dd>area: mean time: mean</dd></dl></div><div class='xr-var-data'><pre>[921231530000 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d0407ee5-56d9-43b4-9f63-4f1cd30496bc' class='xr-section-summary-in' type='checkbox'  ><label for='section-d0407ee5-56d9-43b4-9f63-4f1cd30496bc' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>start_year :</span></dt><dd>1980</dd><dt><span>source :</span></dt><dd>Daymet Software Version 3.0</dd><dt><span>Version_software :</span></dt><dd>Daymet Software Version 3.0</dd><dt><span>Version_data :</span></dt><dd>Daymet Data Version 3.0</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>citation :</span></dt><dd>Please see http://daymet.ornl.gov/ for current Daymet data citation information</dd><dt><span>references :</span></dt><dd>Please see http://daymet.ornl.gov/ for current information on Daymet references</dd><dt><span>title :</span></dt><dd>Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 3 (Continental North America)</dd><dt><span>institution :</span></dt><dd>Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC)</dd><dt><span>end_year :</span></dt><dd>2019</dd></dl></div></li></ul></div></div>"
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       "<xarray.Dataset>\n",
       "Dimensions:                  (nv: 2, time: 14600, x: 7814, y: 8075)\n",
       "Coordinates:\n",
       "  * x                        (x) float32 -4560250.0 -4559250.0 ... 3252750.0\n",
       "  * y                        (y) float32 4984000.0 4983000.0 ... -3090000.0\n",
       "  * time                     (time) datetime64[ns] 1980-01-01 ... 2019-12-31\n",
       "    lat                      (y, x) float32 ...\n",
       "    lon                      (y, x) float32 ...\n",
       "Dimensions without coordinates: nv\n",
       "Data variables:\n",
       "    lambert_conformal_conic  int16 ...\n",
       "    yearday                  (time) int16 ...\n",
       "    time_bnds                (time, nv) datetime64[ns] ...\n",
       "    dayl                     (time, y, x) float32 ...\n",
       "    prcp                     (time, y, x) float32 ...\n",
       "    srad                     (time, y, x) float32 ...\n",
       "    swe                      (time, y, x) float32 ...\n",
       "    tmax                     (time, y, x) float32 ...\n",
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       "Attributes:\n",
       "    start_year:        1980\n",
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       "    Version_software:  Daymet Software Version 3.0\n",
       "    Version_data:      Daymet Data Version 3.0\n",
       "    Conventions:       CF-1.6\n",
       "    citation:          Please see http://daymet.ornl.gov/ for current Daymet ...\n",
       "    references:        Please see http://daymet.ornl.gov/ for current informa...\n",
       "    title:             Daymet: Daily Surface Weather Data on a 1-km Grid for ...\n",
       "    institution:       Oak Ridge National Laboratory Distributed Active Archi...\n",
       "    end_year:          2019"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reference figure for an example of xarray data structure components:\n",
    "\n",
    "<img src=\"https://xarray.pydata.org/en/stable/_images/dataset-diagram.png\" width=700>"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "**Look at a single data variable and its metadata:**"
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  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;prcp&#x27; (time: 14600, y: 8075, x: 7814)&gt;\n",
       "[921231530000 values with dtype=float32]\n",
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       "    units:         mm/day\n",
       "    grid_mapping:  lambert_conformal_conic\n",
       "    cell_methods:  area: mean time: sum</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'prcp'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 14600</li><li><span class='xr-has-index'>y</span>: 8075</li><li><span class='xr-has-index'>x</span>: 7814</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6c2a9fd3-4eb7-4481-84de-20422cf1fd1b' class='xr-array-in' type='checkbox' checked><label for='section-6c2a9fd3-4eb7-4481-84de-20422cf1fd1b' 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>[921231530000 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-b9df6c95-4e22-4444-a238-2d79b5dc861c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b9df6c95-4e22-4444-a238-2d79b5dc861c' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-4560250.0 -4559250.0 ... 3252750.0</div><input id='attrs-06f49ad8-364a-42d4-82c8-db9a0538315e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06f49ad8-364a-42d4-82c8-db9a0538315e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-449d776e-784b-4a5a-b49a-449390cd55fb' class='xr-var-data-in' type='checkbox'><label for='data-449d776e-784b-4a5a-b49a-449390cd55fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([-4560250., -4559250., -4558250., ...,  3250750.,  3251750.,  3252750.],\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'>4984000.0 4983000.0 ... -3090000.0</div><input id='attrs-d4bed7ec-230c-40f8-8cfd-8412334ddee5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4bed7ec-230c-40f8-8cfd-8412334ddee5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f47bb64d-2a63-4f75-8fac-c07145c69101' class='xr-var-data-in' type='checkbox'><label for='data-f47bb64d-2a63-4f75-8fac-c07145c69101' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 4984000.,  4983000.,  4982000., ..., -3088000., -3089000., -3090000.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-01-01 ... 2019-12-31</div><input id='attrs-d47d368a-be25-4bd1-96ef-fb6c16e494da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d47d368a-be25-4bd1-96ef-fb6c16e494da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57dc8c79-8cbe-44da-b195-0964135d2239' class='xr-var-data-in' type='checkbox'><label for='data-57dc8c79-8cbe-44da-b195-0964135d2239' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-01-01T00:00:00.000000000&#x27;, &#x27;1980-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-01-03T00:00:00.000000000&#x27;, ..., &#x27;2019-12-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-12-30T00:00:00.000000000&#x27;, &#x27;2019-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</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'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e5e6fe55-4d66-4f75-bc37-432c44e21c23' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5e6fe55-4d66-4f75-bc37-432c44e21c23' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-630ed354-b29f-4671-a2ee-190a8ef0709d' class='xr-var-data-in' type='checkbox'><label for='data-630ed354-b29f-4671-a2ee-190a8ef0709d' title='Show/Hide data 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></dl></div><div class='xr-var-data'><pre>[63098050 values with dtype=float32]</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'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f453d6c3-424e-4c2d-9af0-c9494d9def6d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f453d6c3-424e-4c2d-9af0-c9494d9def6d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d87e5357-5aa6-4262-a8ca-875e4cc31f4c' class='xr-var-data-in' type='checkbox'><label for='data-d87e5357-5aa6-4262-a8ca-875e4cc31f4c' title='Show/Hide data 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></dl></div><div class='xr-var-data'><pre>[63098050 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f357e31b-86e8-47bc-968a-3bb47208b594' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f357e31b-86e8-47bc-968a-3bb47208b594' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>daily total precipitation</dd><dt><span>units :</span></dt><dd>mm/day</dd><dt><span>grid_mapping :</span></dt><dd>lambert_conformal_conic</dd><dt><span>cell_methods :</span></dt><dd>area: mean time: sum</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'prcp' (time: 14600, y: 8075, x: 7814)>\n",
       "[921231530000 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * x        (x) float32 -4560250.0 -4559250.0 ... 3251750.0 3252750.0\n",
       "  * y        (y) float32 4984000.0 4983000.0 4982000.0 ... -3089000.0 -3090000.0\n",
       "  * time     (time) datetime64[ns] 1980-01-01 1980-01-02 ... 2019-12-31\n",
       "    lat      (y, x) float32 ...\n",
       "    lon      (y, x) float32 ...\n",
       "Attributes:\n",
       "    long_name:     daily total precipitation\n",
       "    units:         mm/day\n",
       "    grid_mapping:  lambert_conformal_conic\n",
       "    cell_methods:  area: mean time: sum"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.prcp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What coordinate reference system does DayMet use?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'grid_mapping_name': 'lambert_conformal_conic',\n",
       " 'longitude_of_central_meridian': -100.0,\n",
       " 'latitude_of_projection_origin': 42.5,\n",
       " 'false_easting': 0.0,\n",
       " 'false_northing': 0.0,\n",
       " 'standard_parallel': array([25., 60.]),\n",
       " 'semi_major_axis': 6378137.0,\n",
       " 'inverse_flattening': 298.257223563,\n",
       " 'longitude_of_prime_meridian': 0.0}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.lambert_conformal_conic.attrs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This gives us all the information we need to define the coordinate ystem, and therefore transform other coordinates into or out of this coordinate space.\n",
    "\n",
    "(Note that the `semi_major_axis`, `inverse_flattening`, and `longitude_of_prime_meridian` correspond to the [WGS84 Ellipsoid](https://en.wikipedia.org/wiki/World_Geodetic_System#WGS84).)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We now know what the data structure is and what the metadata tells us!**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "**Now we are going to subset the DayMet data to a smaller area to work with.** This Dataset contains a lot of information, which will take a long time to load if we tried to look at everything at once.\n",
    "\n",
    "Let's start by selecting a region around Washington state."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember that we have these options for [indexing or slicing](https://xarray.pydata.org/en/stable/indexing.html) xarray DataArrays and Datasets:\n",
    "\n",
    "| Dimension lookup | Index lookup | DataArray syntax | Dataset syntax |\n",
    "| ---              | ---          | ---              | ---            |\n",
    "| positional       | by integer   | `da[:,0]`        | *n/a* |\n",
    "| positional       | by label     | `da.loc[:,'east_watershed']` | *n/a* |\n",
    "| by name          | by integer   | `da.isel(watershed=0)` | `ds.isel(location=0)` |\n",
    "| by name          | by label     | `da.sel(watershed='east_watershed')` | `ds.sel(location='stream_gage_1')` |\n",
    "\n",
    "\n",
    "What we'll do is find the Latitude and Longitude bounds approximately around Washington state, then use `.sel()` method with a `slice` in the **x** and **y** dimensions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**To start, we can define a bounding box in Longitude and Latitude coordinates** using [geopandas](https://geopandas.org/) and [shapely](https://shapely.readthedocs.io/en/latest/manual.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify the region we want to look at with DayMet (lon_min, lat_min, lon_max, lat_max)\n",
    "# and the coordinate system we are providing the bounding box coordinates in (epsg:4326)\n",
    "\n",
    "bounding_box = gpd.GeoSeries(geometry.box(-125.0, 45.5, -117.0, 49.0), \n",
    "                             crs='epsg:4326')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Next, we need to transform these coordinates from EPSG:4326 into the Lambert Conformal Conic coordinate system used by DayMet.** \n",
    "\n",
    "Here we will use [pyproj](https://pyproj4.github.io/pyproj/stable/) to read DayMet's coordinate reference system and convert it into a format we can use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the coordinate reference system used by DayMet\n",
    "daymet_crs = CRS.from_cf(ds.lambert_conformal_conic.attrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reproject our bounding box polygon into this new coordinate system (Lambert Conformal Conic)\n",
    "bounding_box = bounding_box.to_crs(daymet_crs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    POLYGON ((-1258946.566 446607.700, -1183671.05...\n",
       "dtype: geometry"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Take a look at our bounding box object\n",
    "bounding_box"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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    "# Now we can get our bounds out in Daymet's coordinate system using\n",
    "bounding_box.bounds\n",
    "\n",
    "# These can be accessed indiviudally as for example:\n",
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now use the bounds to slice the DayMet dataset in the x and y dimensions to select the Washington area.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "  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-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",
       "  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",
       "  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-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-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, 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",
       "  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:                  (nv: 2, time: 14600, x: 653, y: 504)\n",
       "Coordinates:\n",
       "  * x                        (x) float32 -1836250.0 -1835250.0 ... -1184250.0\n",
       "  * y                        (y) float32 950000.0 949000.0 ... 448000.0 447000.0\n",
       "  * time                     (time) datetime64[ns] 1980-01-01 ... 2019-12-31\n",
       "    lat                      (y, x) float32 ...\n",
       "    lon                      (y, x) float32 ...\n",
       "Dimensions without coordinates: nv\n",
       "Data variables:\n",
       "    lambert_conformal_conic  int16 -32767\n",
       "    yearday                  (time) int16 0 1 2 3 4 5 ... 360 361 362 363 364\n",
       "    time_bnds                (time, nv) datetime64[ns] 1980-01-01 ... 2020-01-01\n",
       "    dayl                     (time, y, x) float32 ...\n",
       "    prcp                     (time, y, x) float32 ...\n",
       "    srad                     (time, y, x) float32 ...\n",
       "    swe                      (time, y, x) float32 ...\n",
       "    tmax                     (time, y, x) float32 ...\n",
       "    tmin                     (time, y, x) float32 ...\n",
       "    vp                       (time, y, x) float32 ...\n",
       "Attributes:\n",
       "    start_year:        1980\n",
       "    source:            Daymet Software Version 3.0\n",
       "    Version_software:  Daymet Software Version 3.0\n",
       "    Version_data:      Daymet Data Version 3.0\n",
       "    Conventions:       CF-1.6\n",
       "    citation:          Please see http://daymet.ornl.gov/ for current Daymet ...\n",
       "    references:        Please see http://daymet.ornl.gov/ for current informa...\n",
       "    title:             Daymet: Daily Surface Weather Data on a 1-km Grid for ...\n",
       "    institution:       Oak Ridge National Laboratory Distributed Active Archi...\n",
       "    end_year:          2019</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-061ec7fd-6f89-4b50-9660-ea16a5d78693' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-061ec7fd-6f89-4b50-9660-ea16a5d78693' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>nv</span>: 2</li><li><span class='xr-has-index'>time</span>: 14600</li><li><span class='xr-has-index'>x</span>: 653</li><li><span class='xr-has-index'>y</span>: 504</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8fe573d6-f734-40e3-893d-e25fad6dc7ba' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8fe573d6-f734-40e3-893d-e25fad6dc7ba' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1836250.0 ... -1184250.0</div><input id='attrs-06a7619b-d419-4326-a241-4c0f8d79573a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06a7619b-d419-4326-a241-4c0f8d79573a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-efe06bab-55a2-4475-a6e5-7434e1efb7fc' class='xr-var-data-in' type='checkbox'><label for='data-efe06bab-55a2-4475-a6e5-7434e1efb7fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([-1836250., -1835250., -1834250., ..., -1186250., -1185250., -1184250.],\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'>950000.0 949000.0 ... 447000.0</div><input id='attrs-265368be-bff0-4540-96b8-0e35fca5ad53' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-265368be-bff0-4540-96b8-0e35fca5ad53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da6a5f59-ffcc-470f-90fc-57f780d9b9b3' class='xr-var-data-in' type='checkbox'><label for='data-da6a5f59-ffcc-470f-90fc-57f780d9b9b3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([950000., 949000., 948000., ..., 449000., 448000., 447000.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-01-01 ... 2019-12-31</div><input id='attrs-42b0697f-a6f4-4abd-84d3-37e208605001' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42b0697f-a6f4-4abd-84d3-37e208605001' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa8bcc15-abf0-45a7-a73a-e89b52581a49' class='xr-var-data-in' type='checkbox'><label for='data-aa8bcc15-abf0-45a7-a73a-e89b52581a49' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-01-01T00:00:00.000000000&#x27;, &#x27;1980-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-01-03T00:00:00.000000000&#x27;, ..., &#x27;2019-12-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-12-30T00:00:00.000000000&#x27;, &#x27;2019-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</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'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ba9e5bda-48ec-4661-b96e-b7198458a8fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba9e5bda-48ec-4661-b96e-b7198458a8fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d9eae2c-bdfe-4a8b-99bc-c189bacdbac6' class='xr-var-data-in' type='checkbox'><label for='data-0d9eae2c-bdfe-4a8b-99bc-c189bacdbac6' title='Show/Hide data 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></dl></div><div class='xr-var-data'><pre>[329112 values with dtype=float32]</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'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2d5142e5-ffa8-46bc-b723-d64d9801d406' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d5142e5-ffa8-46bc-b723-d64d9801d406' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b39435b6-8d2a-4c26-ba31-71a6fa3035bd' class='xr-var-data-in' type='checkbox'><label for='data-b39435b6-8d2a-4c26-ba31-71a6fa3035bd' title='Show/Hide data 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></dl></div><div class='xr-var-data'><pre>[329112 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a5ba1b0b-aa30-486b-a430-5159b586a22f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a5ba1b0b-aa30-486b-a430-5159b586a22f' class='xr-section-summary' >Data variables: <span>(10)</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>lambert_conformal_conic</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>-32767</div><input id='attrs-479d96f4-2893-4f07-bf9b-607d79106d21' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-479d96f4-2893-4f07-bf9b-607d79106d21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4c4d463-6570-4e2a-a8d2-26b3dd194dcf' class='xr-var-data-in' type='checkbox'><label for='data-c4c4d463-6570-4e2a-a8d2-26b3dd194dcf' title='Show/Hide data 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>longitude_of_central_meridian :</span></dt><dd>-100.0</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>42.5</dd><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>standard_parallel :</span></dt><dd>[25. 60.]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array(-32767, dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>yearday</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 360 361 362 363 364</div><input id='attrs-68958173-1782-4c48-8409-b2be50326cb7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68958173-1782-4c48-8409-b2be50326cb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9aac0f23-7a67-475a-92c7-eeae1537b3ed' class='xr-var-data-in' type='checkbox'><label for='data-9aac0f23-7a67-475a-92c7-eeae1537b3ed' title='Show/Hide data 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>yearday</dd></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 362, 363, 364], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, nv)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-01-01 ... 2020-01-01</div><input id='attrs-c59d0daf-ff10-4c98-8778-bc39f1dd3fc8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c59d0daf-ff10-4c98-8778-bc39f1dd3fc8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-669e8631-69f8-42fd-a9da-5f672db693c2' class='xr-var-data-in' type='checkbox'><label for='data-669e8631-69f8-42fd-a9da-5f672db693c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;1980-01-01T00:00:00.000000000&#x27;, &#x27;1980-01-02T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1980-01-02T00:00:00.000000000&#x27;, &#x27;1980-01-03T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1980-01-03T00:00:00.000000000&#x27;, &#x27;1980-01-04T00:00:00.000000000&#x27;],\n",
       "       ...,\n",
       "       [&#x27;2019-12-29T00:00:00.000000000&#x27;, &#x27;2019-12-30T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2019-12-30T00:00:00.000000000&#x27;, &#x27;2019-12-31T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2019-12-31T00:00:00.000000000&#x27;, &#x27;2020-01-01T00:00:00.000000000&#x27;]],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dayl</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b7b68387-c674-478a-8e65-f7c4c6bf06fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b7b68387-c674-478a-8e65-f7c4c6bf06fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10e324f8-4484-46ac-b85a-b93ae6e191c2' class='xr-var-data-in' type='checkbox'><label for='data-10e324f8-4484-46ac-b85a-b93ae6e191c2' title='Show/Hide data 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>daylength</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>grid_mapping :</span></dt><dd>lambert_conformal_conic</dd><dt><span>cell_methods :</span></dt><dd>area: mean</dd></dl></div><div class='xr-var-data'><pre>[4805035200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prcp</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-127e4591-89bd-45b7-9934-a79c4c0f1f26' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-127e4591-89bd-45b7-9934-a79c4c0f1f26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-172d960d-878a-4657-b447-67fceb861740' class='xr-var-data-in' type='checkbox'><label for='data-172d960d-878a-4657-b447-67fceb861740' title='Show/Hide data 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>daily total precipitation</dd><dt><span>units :</span></dt><dd>mm/day</dd><dt><span>grid_mapping :</span></dt><dd>lambert_conformal_conic</dd><dt><span>cell_methods :</span></dt><dd>area: mean time: sum</dd></dl></div><div class='xr-var-data'><pre>[4805035200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>srad</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-661a3565-1c59-46b5-9fe6-e7e9715b946f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-661a3565-1c59-46b5-9fe6-e7e9715b946f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c05481cc-1fad-4c93-9aed-a2baa6e82101' class='xr-var-data-in' type='checkbox'><label for='data-c05481cc-1fad-4c93-9aed-a2baa6e82101' title='Show/Hide data 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>daylight average incident shortwave radiation</dd><dt><span>units :</span></dt><dd>W/m2</dd><dt><span>grid_mapping :</span></dt><dd>lambert_conformal_conic</dd><dt><span>cell_methods :</span></dt><dd>area: mean time: mean</dd></dl></div><div class='xr-var-data'><pre>[4805035200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swe</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-83da9a71-499f-4a97-9664-b6fb7544f23b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83da9a71-499f-4a97-9664-b6fb7544f23b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9271010-edf2-4416-bad0-557d5f6d7830' class='xr-var-data-in' type='checkbox'><label for='data-c9271010-edf2-4416-bad0-557d5f6d7830' title='Show/Hide data 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>snow water equivalent</dd><dt><span>units :</span></dt><dd>kg/m2</dd><dt><span>grid_mapping :</span></dt><dd>lambert_conformal_conic</dd><dt><span>cell_methods :</span></dt><dd>area: mean time: mean</dd></dl></div><div class='xr-var-data'><pre>[4805035200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tmax</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-26a89525-b849-4269-9a24-8083a15011bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-26a89525-b849-4269-9a24-8083a15011bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9950350c-545e-444a-8b81-959f9097f433' class='xr-var-data-in' type='checkbox'><label for='data-9950350c-545e-444a-8b81-959f9097f433' title='Show/Hide data 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>daily maximum temperature</dd><dt><span>units :</span></dt><dd>degrees C</dd><dt><span>grid_mapping :</span></dt><dd>lambert_conformal_conic</dd><dt><span>cell_methods :</span></dt><dd>area: mean time: maximum</dd></dl></div><div class='xr-var-data'><pre>[4805035200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tmin</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e26c2abd-0f40-4b89-a8c2-79297011dab6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e26c2abd-0f40-4b89-a8c2-79297011dab6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aeb8577e-bf09-4d33-a1e0-a73722afc7d5' class='xr-var-data-in' type='checkbox'><label for='data-aeb8577e-bf09-4d33-a1e0-a73722afc7d5' title='Show/Hide data 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>daily minimum temperature</dd><dt><span>units :</span></dt><dd>degrees C</dd><dt><span>grid_mapping :</span></dt><dd>lambert_conformal_conic</dd><dt><span>cell_methods :</span></dt><dd>area: mean time: minimum</dd></dl></div><div class='xr-var-data'><pre>[4805035200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vp</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0ddb4efd-14ce-46c3-b931-a39409dff525' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ddb4efd-14ce-46c3-b931-a39409dff525' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d200a55-e600-46fb-a4c1-ca4aeaa8401f' class='xr-var-data-in' type='checkbox'><label for='data-2d200a55-e600-46fb-a4c1-ca4aeaa8401f' title='Show/Hide data 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>daily average vapor pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>grid_mapping :</span></dt><dd>lambert_conformal_conic</dd><dt><span>cell_methods :</span></dt><dd>area: mean time: mean</dd></dl></div><div class='xr-var-data'><pre>[4805035200 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8ab1e0f1-cdcd-4bf4-abdd-37596a96265f' class='xr-section-summary-in' type='checkbox'  ><label for='section-8ab1e0f1-cdcd-4bf4-abdd-37596a96265f' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>start_year :</span></dt><dd>1980</dd><dt><span>source :</span></dt><dd>Daymet Software Version 3.0</dd><dt><span>Version_software :</span></dt><dd>Daymet Software Version 3.0</dd><dt><span>Version_data :</span></dt><dd>Daymet Data Version 3.0</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>citation :</span></dt><dd>Please see http://daymet.ornl.gov/ for current Daymet data citation information</dd><dt><span>references :</span></dt><dd>Please see http://daymet.ornl.gov/ for current information on Daymet references</dd><dt><span>title :</span></dt><dd>Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 3 (Continental North America)</dd><dt><span>institution :</span></dt><dd>Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC)</dd><dt><span>end_year :</span></dt><dd>2019</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                  (nv: 2, time: 14600, x: 653, y: 504)\n",
       "Coordinates:\n",
       "  * x                        (x) float32 -1836250.0 -1835250.0 ... -1184250.0\n",
       "  * y                        (y) float32 950000.0 949000.0 ... 448000.0 447000.0\n",
       "  * time                     (time) datetime64[ns] 1980-01-01 ... 2019-12-31\n",
       "    lat                      (y, x) float32 ...\n",
       "    lon                      (y, x) float32 ...\n",
       "Dimensions without coordinates: nv\n",
       "Data variables:\n",
       "    lambert_conformal_conic  int16 -32767\n",
       "    yearday                  (time) int16 0 1 2 3 4 5 ... 360 361 362 363 364\n",
       "    time_bnds                (time, nv) datetime64[ns] 1980-01-01 ... 2020-01-01\n",
       "    dayl                     (time, y, x) float32 ...\n",
       "    prcp                     (time, y, x) float32 ...\n",
       "    srad                     (time, y, x) float32 ...\n",
       "    swe                      (time, y, x) float32 ...\n",
       "    tmax                     (time, y, x) float32 ...\n",
       "    tmin                     (time, y, x) float32 ...\n",
       "    vp                       (time, y, x) float32 ...\n",
       "Attributes:\n",
       "    start_year:        1980\n",
       "    source:            Daymet Software Version 3.0\n",
       "    Version_software:  Daymet Software Version 3.0\n",
       "    Version_data:      Daymet Data Version 3.0\n",
       "    Conventions:       CF-1.6\n",
       "    citation:          Please see http://daymet.ornl.gov/ for current Daymet ...\n",
       "    references:        Please see http://daymet.ornl.gov/ for current informa...\n",
       "    title:             Daymet: Daily Surface Weather Data on a 1-km Grid for ...\n",
       "    institution:       Oak Ridge National Laboratory Distributed Active Archi...\n",
       "    end_year:          2019"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select this region from our DayMet Dataset\n",
    "washington = ds.sel(x = slice(bounding_box.bounds.minx[0], bounding_box.bounds.maxx[0]), # Here we are slicing the x axis with slice(minx, maxx)\n",
    "                   y = slice(bounding_box.bounds.maxy[0], bounding_box.bounds.miny[0])) # Note that we are slicing the *inverted* y axis with slice(maxy,miny)\n",
    "\n",
    "# Look at what we selected from DayMet\n",
    "washington"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What does this look like now?** Plot tmax for the first day using `.isel()` on the DataArray:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fa508419b38>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "washington.tmax.isel(time=0).plot(x='lon', y='lat')\n",
    "\n",
    "# Note that this is equivalent to:\n",
    "# washington.tmax[0].plot(x='lon', y='lat')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "**We can also try selecting in time and space dimensions.** \n",
    "\n",
    "Here let's look at snow water equivalent (SWE) for a single water year (October 2016 - September 2017) for a point near Mt. Rainier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a point with Longitude and Latitude (EPSG:4326)\n",
    "my_point_lon_lat = gpd.GeoSeries(geometry.Point(-121.6, 46.9),\n",
    "                                 crs='epsg:4326')\n",
    "\n",
    "# Convert to the DayMet coordinate reference system as we did previously with our bounding box\n",
    "my_point = my_point_lon_lat.to_crs(daymet_crs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**xarray allows us to stack multiple methods in a single expression.** \n",
    "\n",
    "Here we have two `.sel()` methods applied to the `ds.swe` DataArray. The first selects a time slice, the second selects x and y coordinates for our point.\n",
    "\n",
    "Using `method='nearest'` means that it'll find the x and y coordinates nearest to what we provide with `my_point.x` and `my_point.y`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select from DayMet SWE a time slice and an x and y point using the 'nearest' method\n",
    "my_point_swe = washington.swe.sel(time=slice('10-2016','9-2017')).sel(x=my_point.x[0], y=my_point.y[0], method='nearest')\n",
    "\n",
    "# we separate our time and space selections because we need to use method='nearest' with x and y,\n",
    "# and that method does not work with slice objects like we used in the time dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       ".xr-wrap {\n",
       "  display: block;\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-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",
       "  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",
       "  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-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-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, 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",
       "  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;swe&#x27; (time: 364)&gt;\n",
       "array([0., 0., 0., ..., 0., 0., 0.], dtype=float32)\n",
       "Coordinates:\n",
       "    x        float32 -1554250.0\n",
       "    y        float32 669000.0\n",
       "  * time     (time) datetime64[ns] 2016-10-01 2016-10-02 ... 2017-09-30\n",
       "    lat      float32 46.902435\n",
       "    lon      float32 -121.59386\n",
       "Attributes:\n",
       "    long_name:     snow water equivalent\n",
       "    units:         kg/m2\n",
       "    grid_mapping:  lambert_conformal_conic\n",
       "    cell_methods:  area: mean time: mean</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'swe'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 364</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-325af8a0-89b5-453c-99bb-2cd07ba46c21' class='xr-array-in' type='checkbox' checked><label for='section-325af8a0-89b5-453c-99bb-2cd07ba46c21' 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>0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0</span></div><div class='xr-array-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-02c00e77-1759-469c-9c47-1cad2a290c6c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-02c00e77-1759-469c-9c47-1cad2a290c6c' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1554250.0</div><input id='attrs-6e07f883-941e-4e6b-81f1-6c17ec0c226a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e07f883-941e-4e6b-81f1-6c17ec0c226a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91c6f9f5-be91-4845-b389-07cb8df69088' class='xr-var-data-in' type='checkbox'><label for='data-91c6f9f5-be91-4845-b389-07cb8df69088' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd></dl></div><div class='xr-var-data'><pre>array(-1554250., dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>669000.0</div><input id='attrs-87ec0b5c-955d-410d-aec9-0156d7cdf9f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87ec0b5c-955d-410d-aec9-0156d7cdf9f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c02e8006-0fb4-4f19-aaa6-aedcba804c06' class='xr-var-data-in' type='checkbox'><label for='data-c02e8006-0fb4-4f19-aaa6-aedcba804c06' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd></dl></div><div class='xr-var-data'><pre>array(669000., dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2016-10-01 ... 2017-09-30</div><input id='attrs-755b9d0f-0faf-446f-9c29-c8d0c978b454' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-755b9d0f-0faf-446f-9c29-c8d0c978b454' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80572d15-ee89-4fb4-aa9c-a684d7f8a7ee' class='xr-var-data-in' type='checkbox'><label for='data-80572d15-ee89-4fb4-aa9c-a684d7f8a7ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2016-10-01T00:00:00.000000000&#x27;, &#x27;2016-10-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-10-03T00:00:00.000000000&#x27;, ..., &#x27;2017-09-28T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-09-29T00:00:00.000000000&#x27;, &#x27;2017-09-30T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7ee8c36b-32ed-4038-93a3-6f8115a6a500' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7ee8c36b-32ed-4038-93a3-6f8115a6a500' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ef5059c-01a8-4f53-9b96-c1bef8343fe3' class='xr-var-data-in' type='checkbox'><label for='data-8ef5059c-01a8-4f53-9b96-c1bef8343fe3' title='Show/Hide data 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></dl></div><div class='xr-var-data'><pre>array(46.902435, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b8a50b84-f2fb-47bf-b33d-c1ffa12495f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8a50b84-f2fb-47bf-b33d-c1ffa12495f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6609f6b-750e-48be-94c3-4da0c7751fe1' class='xr-var-data-in' type='checkbox'><label for='data-e6609f6b-750e-48be-94c3-4da0c7751fe1' title='Show/Hide data 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></dl></div><div class='xr-var-data'><pre>array(-121.59386, dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a03b936f-e55e-45e6-990e-bb4249b68f4d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a03b936f-e55e-45e6-990e-bb4249b68f4d' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>snow water equivalent</dd><dt><span>units :</span></dt><dd>kg/m2</dd><dt><span>grid_mapping :</span></dt><dd>lambert_conformal_conic</dd><dt><span>cell_methods :</span></dt><dd>area: mean time: mean</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'swe' (time: 364)>\n",
       "array([0., 0., 0., ..., 0., 0., 0.], dtype=float32)\n",
       "Coordinates:\n",
       "    x        float32 -1554250.0\n",
       "    y        float32 669000.0\n",
       "  * time     (time) datetime64[ns] 2016-10-01 2016-10-02 ... 2017-09-30\n",
       "    lat      float32 ...\n",
       "    lon      float32 ...\n",
       "Attributes:\n",
       "    long_name:     snow water equivalent\n",
       "    units:         kg/m2\n",
       "    grid_mapping:  lambert_conformal_conic\n",
       "    cell_methods:  area: mean time: mean"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at the result\n",
    "my_point_swe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot the timseries of SWE at this point**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa50833aef0>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot SWE at this point\n",
    "my_point_swe.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Use the `.resample()` method to resample from our many daily SWE data into monthly mean SWE:**\n",
    "\n",
    "(If you are familiar with resampling in pandas, this is essentially the same thing!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa5082716d8>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the monthly aggregated data ('1M' means 1 Month, we could also resample to '2M' or something like '2W' for two weeks)\n",
    "my_point_swe.resample(time='1M').mean().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot a map of SWE on one day, and a marker where our point for the timeseries is:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(46.5, 47.2)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a map showing where our point is\n",
    "fig, ax = plt.subplots(figsize=(8,5))\n",
    "\n",
    "# Plot a map of SWE for April 1st 2017\n",
    "washington.swe.sel(time='4-1-2017').plot(x='lon', y='lat', cmap='Blues', ax=ax)\n",
    "\n",
    "# Plot our point\n",
    "my_point_lon_lat.plot(ax=ax, color='r', marker='*', markersize=50)\n",
    "\n",
    "# set axes limits\n",
    "ax.set_xlim([-122.2,-121.2])\n",
    "ax.set_ylim([46.5,47.2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:whw]",
   "language": "python",
   "name": "conda-env-whw-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}