Visualizing NetCDF Soil Data in Python.ipynb

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    "# Visualizing NetCDF Files of SSURGO Soil Data in Python\n",
    "\n",
    "Connor Cozad, Norman Levine\n",
    "<br/>College of Charleston\n",
    "<br/>Spring 2023"
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    "One of the advantages of the NetCDF format is that it is widely accepted among scientific software applications. Many different software packages support the visualization and manipulation of NetCDF files.\n",
    "\n",
    "At this point in the process, we already have our NetCDF files containing soil data on hand. This notebook provides an example of how to open these files in Python and create a basic plot of some of the data.\n",
    "\n",
    "First, we will need Cartopy, Matplotlib, and Xarray imported."
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    "import cartopy.crs as ccrs       # for map plotting\n",
    "import matplotlib.pyplot as plt  # for basic plotting\n",
    "import xarray as xr              # for working with data from NetCDF"
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    "Use xarray to import our NetCDF file."
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       "    chkey        (y, x, z) object ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-798ce277-228e-4510-8f56-cea824bea992' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-798ce277-228e-4510-8f56-cea824bea992' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 201</li><li><span class='xr-has-index'>x</span>: 246</li><li><span class='xr-has-index'>z</span>: 204</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1ee7d933-93d3-4838-8afa-601c65fa3bae' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1ee7d933-93d3-4838-8afa-601c65fa3bae' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.198e+06 1.198e+06 ... 1.2e+06</div><input id='attrs-355c6529-386d-45ce-9b0e-514bc4dfce1f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-355c6529-386d-45ce-9b0e-514bc4dfce1f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-315e63c4-2c9a-4bdd-8575-d160b6b8c9f2' class='xr-var-data-in' type='checkbox'><label for='data-315e63c4-2c9a-4bdd-8575-d160b6b8c9f2' title='Show/Hide data 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><dt><span>grid_spacing :</span></dt><dd>10 m</dd><dt><span>_CoordinateAxisType :</span></dt><dd>GeoY</dd></dl></div><div class='xr-var-data'><pre>array([1197880., 1197890., 1197900., ..., 1199860., 1199870., 1199880.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.46e+06 1.46e+06 ... 1.462e+06</div><input id='attrs-5495e7f7-e6dc-478e-93c7-cff3760a46cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5495e7f7-e6dc-478e-93c7-cff3760a46cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d0a379e-0ca7-4bd5-8084-1ad80f72be4c' class='xr-var-data-in' type='checkbox'><label for='data-0d0a379e-0ca7-4bd5-8084-1ad80f72be4c' title='Show/Hide data 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><dt><span>grid_spacing :</span></dt><dd>10 m</dd><dt><span>_CoordinateAxisType :</span></dt><dd>GeoX</dd></dl></div><div class='xr-var-data'><pre>array([1459820., 1459830., 1459840., ..., 1462250., 1462260., 1462270.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z</span></div><div class='xr-var-dims'>(z)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 199 200 201 202 203</div><input id='attrs-06e9a123-1a42-40bd-a65d-d9e95d449d38' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06e9a123-1a42-40bd-a65d-d9e95d449d38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8c69b52-e63f-4491-b54c-6b07c6dde78d' class='xr-var-data-in' type='checkbox'><label for='data-a8c69b52-e63f-4491-b54c-6b07c6dde78d' title='Show/Hide data 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>cm</dd></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 201, 202, 203], dtype=int64)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-42a31234-e36b-4eb9-a208-e6f16f02854b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-42a31234-e36b-4eb9-a208-e6f16f02854b' class='xr-section-summary' >Data variables: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>pointid</span></div><div class='xr-var-dims'>(y, x, z)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0ece5fc4-b698-416d-990d-c54d5bad160b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ece5fc4-b698-416d-990d-c54d5bad160b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96748558-70bc-4c31-82fb-8681a1061f17' class='xr-var-data-in' type='checkbox'><label for='data-96748558-70bc-4c31-82fb-8681a1061f17' title='Show/Hide data 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 :</span></dt><dd>albers_conical_equal_area</dd></dl></div><div class='xr-var-data'><pre>[10086984 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>grid_code</span></div><div class='xr-var-dims'>(y, x, z)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-42ceac8a-bb2c-47c5-a2d2-ee8963169c96' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42ceac8a-bb2c-47c5-a2d2-ee8963169c96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9efabb1-c342-4d2f-8add-76e2c963d589' class='xr-var-data-in' type='checkbox'><label for='data-b9efabb1-c342-4d2f-8add-76e2c963d589' title='Show/Hide data 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 :</span></dt><dd>albers_conical_equal_area</dd></dl></div><div class='xr-var-data'><pre>[10086984 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MUKEY</span></div><div class='xr-var-dims'>(y, x, z)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6ed9d32b-a433-4210-8409-d34130eac6c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ed9d32b-a433-4210-8409-d34130eac6c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8a93db3-2908-4d3d-b7fd-6cec9b43e3ad' class='xr-var-data-in' type='checkbox'><label for='data-c8a93db3-2908-4d3d-b7fd-6cec9b43e3ad' title='Show/Hide data 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 :</span></dt><dd>albers_conical_equal_area</dd></dl></div><div class='xr-var-data'><pre>[10086984 values with dtype=object]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>comppct_r</span></div><div class='xr-var-dims'>(y, x, z)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-af65b33f-0283-41a5-adf9-1ef46b8214fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af65b33f-0283-41a5-adf9-1ef46b8214fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08726cff-621f-4bec-a05e-787c743031b5' class='xr-var-data-in' type='checkbox'><label for='data-08726cff-621f-4bec-a05e-787c743031b5' title='Show/Hide data 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 :</span></dt><dd>albers_conical_equal_area</dd></dl></div><div class='xr-var-data'><pre>[10086984 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>compname</span></div><div class='xr-var-dims'>(y, x, z)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-31dc0673-efa8-4b65-8189-ca3ee347ccc6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31dc0673-efa8-4b65-8189-ca3ee347ccc6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77dfaf46-9660-405c-a133-28c9b744aa6d' class='xr-var-data-in' type='checkbox'><label for='data-77dfaf46-9660-405c-a133-28c9b744aa6d' title='Show/Hide data 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 :</span></dt><dd>albers_conical_equal_area</dd></dl></div><div class='xr-var-data'><pre>[10086984 values with dtype=object]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mukey</span></div><div class='xr-var-dims'>(y, x, z)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1c8145b4-8128-42f0-89e7-3ecfe3188a1b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c8145b4-8128-42f0-89e7-3ecfe3188a1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5576f3bd-7403-4220-b56b-467111892b8e' class='xr-var-data-in' type='checkbox'><label for='data-5576f3bd-7403-4220-b56b-467111892b8e' title='Show/Hide data 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 :</span></dt><dd>albers_conical_equal_area</dd></dl></div><div class='xr-var-data'><pre>[10086984 values with dtype=object]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cokey</span></div><div class='xr-var-dims'>(y, x, z)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fc88ff88-2619-4fdf-91bf-0982819039ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc88ff88-2619-4fdf-91bf-0982819039ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-337f6f0b-023c-4e45-b13e-0b88c38193c1' class='xr-var-data-in' type='checkbox'><label for='data-337f6f0b-023c-4e45-b13e-0b88c38193c1' title='Show/Hide data 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 :</span></dt><dd>albers_conical_equal_area</dd></dl></div><div class='xr-var-data'><pre>[10086984 values with dtype=object]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>desgnmaster</span></div><div class='xr-var-dims'>(y, x, z)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c3a58bbb-d092-4145-beae-835310145ad0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c3a58bbb-d092-4145-beae-835310145ad0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0def9482-437e-416c-a9c3-260646f3381e' class='xr-var-data-in' type='checkbox'><label for='data-0def9482-437e-416c-a9c3-260646f3381e' title='Show/Hide data 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 :</span></dt><dd>albers_conical_equal_area</dd></dl></div><div class='xr-var-data'><pre>[10086984 values with dtype=object]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sieveno4_l</span></div><div class='xr-var-dims'>(y, x, z)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-aa514631-1397-428a-8a58-9d8ca73cb08e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa514631-1397-428a-8a58-9d8ca73cb08e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d75d5ae-dd50-4b88-b2f8-b08d63521b48' class='xr-var-data-in' type='checkbox'><label for='data-3d75d5ae-dd50-4b88-b2f8-b08d63521b48' title='Show/Hide data 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 :</span></dt><dd>albers_conical_equal_area</dd></dl></div><div class='xr-var-data'><pre>[10086984 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sieveno4_r</span></div><div class='xr-var-dims'>(y, x, z)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6a060a29-56b3-4d2d-ae1c-84147b102760' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a060a29-56b3-4d2d-ae1c-84147b102760' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05661e01-df43-4340-ad6a-0d670e468c4a' class='xr-var-data-in' type='checkbox'><label for='data-05661e01-df43-4340-ad6a-0d670e468c4a' title='Show/Hide data 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 :</span></dt><dd>albers_conical_equal_area</dd></dl></div><div class='xr-var-data'><pre>[10086984 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sieveno4_h</span></div><div class='xr-var-dims'>(y, x, z)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-73a244b0-0769-49ad-a1da-cea0f4bebd09' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-73a244b0-0769-49ad-a1da-cea0f4bebd09' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ccc6376-a5bd-44d2-8a89-d85a5ae23634' class='xr-var-data-in' type='checkbox'><label for='data-0ccc6376-a5bd-44d2-8a89-d85a5ae23634' title='Show/Hide data 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 :</span></dt><dd>albers_conical_equal_area</dd></dl></div><div class='xr-var-data'><pre>[10086984 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chkey</span></div><div class='xr-var-dims'>(y, x, z)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-84f7d5d8-c914-460f-836f-a3af3dd2c6af' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84f7d5d8-c914-460f-836f-a3af3dd2c6af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0666282f-5083-4129-907a-f3367f1fa727' class='xr-var-data-in' type='checkbox'><label for='data-0666282f-5083-4129-907a-f3367f1fa727' title='Show/Hide data 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 :</span></dt><dd>albers_conical_equal_area</dd></dl></div><div class='xr-var-data'><pre>[10086984 values with dtype=object]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-76a79290-7acc-412b-b4c8-1fa4526bbf8b' class='xr-section-summary-in' type='checkbox'  ><label for='section-76a79290-7acc-412b-b4c8-1fa4526bbf8b' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-248f0d7b-0422-4a85-90a5-8724f9721977' class='xr-index-data-in' type='checkbox'/><label for='index-248f0d7b-0422-4a85-90a5-8724f9721977' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([         1197880.0,          1197890.0,  1197900.000000001,\n",
       "               1197910.000000001,  1197920.000000001, 1197930.0000000019,\n",
       "              1197940.0000000019, 1197950.0000000028,  1197959.999999999,\n",
       "               1197969.999999999,\n",
       "              ...\n",
       "              1199790.0000000019, 1199800.0000000019,  1199809.999999999,\n",
       "               1199819.999999999,  1199829.999999999,          1199840.0,\n",
       "                       1199850.0,          1199860.0,  1199870.000000001,\n",
       "               1199880.000000001],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=201))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-02b17a79-f70b-4240-a972-5b117866dcaf' class='xr-index-data-in' type='checkbox'/><label for='index-02b17a79-f70b-4240-a972-5b117866dcaf' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([1459820.0000000037, 1459830.0000000037, 1459840.0000000037,\n",
       "              1459850.0000000037,          1459860.0,          1459870.0,\n",
       "                       1459880.0,          1459890.0,          1459900.0,\n",
       "                       1459910.0,\n",
       "              ...\n",
       "              1462180.0000000037, 1462190.0000000037, 1462200.0000000037,\n",
       "              1462210.0000000037, 1462220.0000000037, 1462230.0000000037,\n",
       "              1462240.0000000037, 1462250.0000000037, 1462260.0000000037,\n",
       "                       1462270.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=246))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>z</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-76acba2f-98a7-4bca-94ca-3357b5fad8e5' class='xr-index-data-in' type='checkbox'/><label for='index-76acba2f-98a7-4bca-94ca-3357b5fad8e5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            194, 195, 196, 197, 198, 199, 200, 201, 202, 203],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;z&#x27;, length=204))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-007e828e-6259-4fbf-9b8a-2421959eb1a6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-007e828e-6259-4fbf-9b8a-2421959eb1a6' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (y: 201, x: 246, z: 204)\n",
       "Coordinates:\n",
       "  * y            (y) float64 1.198e+06 1.198e+06 1.198e+06 ... 1.2e+06 1.2e+06\n",
       "  * x            (x) float64 1.46e+06 1.46e+06 1.46e+06 ... 1.462e+06 1.462e+06\n",
       "  * z            (z) int64 0 1 2 3 4 5 6 7 8 ... 196 197 198 199 200 201 202 203\n",
       "Data variables:\n",
       "    pointid      (y, x, z) float64 ...\n",
       "    grid_code    (y, x, z) float64 ...\n",
       "    MUKEY        (y, x, z) object ...\n",
       "    comppct_r    (y, x, z) float64 ...\n",
       "    compname     (y, x, z) object ...\n",
       "    mukey        (y, x, z) object ...\n",
       "    cokey        (y, x, z) object ...\n",
       "    desgnmaster  (y, x, z) object ...\n",
       "    sieveno4_l   (y, x, z) float64 ...\n",
       "    sieveno4_r   (y, x, z) float64 ...\n",
       "    sieveno4_h   (y, x, z) float64 ...\n",
       "    chkey        (y, x, z) object ..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = xr.open_dataset('ravenel246x201meters.nc')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d71ffb7-5bc2-442a-a2e0-ec19ef23d7bb",
   "metadata": {},
   "source": [
    "Now, we need to select what data we want to plot. As an example, let's plot the `sieveno4_l` variable, which shows the low-end estimate (hence the `l` at the end) for the percentage of soil (by weight) that passes through a number 4 sieve. This gives us an indication of the fineness/coarseness of the soil. Notice that we used `.sel` to select the depth of the soil we wanted to display, which is specified by `z=0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b193129e-116a-419a-99cd-fffc1b16f222",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_subset = data['sieveno4_r'].sel(z=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbc2cdec-4991-4b14-bc69-a800ebf99172",
   "metadata": {},
   "source": [
    "The following code chunk shows a quick and easy way to display some data. Unfortunatley, the data is shown backwards here. The next code chunk demonstrates how to fix this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1c4192dd-4e00-4ca9-831f-6ae3d62a1fb3",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.imshow(data.sieveno4_l.sel(z=0))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26a29516-43a1-461b-8807-14fd77d3c224",
   "metadata": {},
   "source": [
    "To get an appropriate plot that references the x- and y-coordinates built in to our NetCDF file, we use the `.plot` on our `data_subset` object. Notice that the data is now mirrored both ways, and displays correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9fff948f-9224-44ba-9fbb-1c11dfba9c59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes()\n",
    "data_subset.plot.imshow(ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a51ba744-0211-43f9-aae8-9f072e6b6236",
   "metadata": {},
   "source": [
    "Now, we can make our map a bit more useful by making it larger, and giving it a better title and axis descriptions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4f0b8edb-603b-4ba0-9ca2-de7987b352c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "ax = plt.axes()\n",
    "data_subset.plot.imshow(ax=ax, cmap='Oranges')\n",
    "plt.title('No. 4 Sieve Soil Fraction')\n",
    "plt.xlabel('x coordinate (m)')\n",
    "plt.ylabel('y coordinate (m)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c2d330c-8209-40e8-810c-1953fecc9232",
   "metadata": {},
   "source": [
    "Finally, it's also possible to plot this data on an actual map in Python as well. The plots above were simply colored grids, they were not plotted on maps per se. The example below specifies a projection used by Cartopy to plot the data. This example is simply a proof of concept, allowing us to see the data as the tiny box of various colors in the middle, and a line that marks where the coast is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8eac8a2d-505e-41f2-809e-66dd7a8bab46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "albers = ccrs.AlbersEqualArea(central_longitude=-96, central_latitude=23, standard_parallels=(29.5, 45.5))\n",
    "ax = plt.axes(projection=albers)\n",
    "data_subset.plot.imshow(ax=ax, transform=albers)\n",
    "ax.coastlines()\n",
    "ax.set_extent([-80.5, -80, 32.5, 33])\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}