Digital Earth Africa: DEA101-en Introduction to the Digital Earth Africa Sandbox

<div class="xblock xblock-public_view xblock-public_view-vertical" data-course-id="course-v1:digitalearthafrica+DEA101+2021" data-init="VerticalStudentView" data-runtime-class="LmsRuntime" data-runtime-version="1" data-block-type="vertical" data-usage-id="block-v1:digitalearthafrica+DEA101+2021+type@vertical+block@2e0efc2c1842471c9e2ab5848777c224" data-request-token="1b5e9822881611efa85e837f534d2eba" data-graded="False" data-has-score="False"> <div class="vert-mod"> <div class="vert vert-0" data-id="block-v1:digitalearthafrica+DEA101+2021+type@html+block@7c4c08e84e1642a2a33a3c33e938cbba"> <div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-course-id="course-v1:digitalearthafrica+DEA101+2021" data-init="XBlockToXModuleShim" data-runtime-class="LmsRuntime" data-runtime-version="1" data-block-type="html" data-usage-id="block-v1:digitalearthafrica+DEA101+2021+type@html+block@7c4c08e84e1642a2a33a3c33e938cbba" data-request-token="1b5e9822881611efa85e837f534d2eba" data-graded="False" data-has-score="False"> <script type="json/xblock-args" class="xblock-json-init-args"> {"xmodule-type": "HTMLModule"} </script> <p style="text-align: left;">This tutorial introduces xarray (pronounced <em style="box-sizing: border-box;">ex-array</em>), a Python library for working with labeled multi-dimensional arrays. It is widely used to handle Earth observation data, which often involves multiple dimensions — for instance, longitude, latitude, time, and channels/bands. It can also display metadata such as the dataset Coordinate Reference System (CRS).</p> <p style="text-align: left;">Accessing data held in an <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">xarray.DataArray</span></code> or <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">xarray.Dataset</span></code> is very similar to calling values from dictionaries.</p> <div style="box-sizing: border-box;"> <p style="box-sizing: border-box; line-height: 24px; margin: 0px 0px 24px; font-size: 16px; text-align: left;"><strong style="box-sizing: border-box;">Note:</strong> This tutorial is about using xarray. An introduction to remote sensing datasets and more information on Earth observation products used on the Digital Earth Africa platform can be found in <a class="reference external" href="/courses/course-v1:digitalearthafrica+DEA101+2021/jump_to_id/2de47e98b0e946e8a429cfb5e8557fa9" target="_blank" style="box-sizing: border-box; color: #2980b9; text-decoration-line: none; cursor: pointer;">Session 2: Digital Earth Africa products</a>.</p> </div> <p style="text-align: left;">Follow the instructions below to download the tutorial and open it in the Sandbox.</p> <section id="Download-the-tutorial-notebook" style="box-sizing: border-box; color: #000000;"> <h2 style="box-sizing: border-box; margin-top: 0px; font-weight: bold; font-size: 24px; color: #336699; text-align: left;">Download the tutorial notebook</h2> <p style="box-sizing: border-box; line-height: 24px; margin: 0px 0px 24px; font-size: 16px; text-align: left;"><a class="reference external" href="/assets/courseware/v1/2f0bce1e8a10f124f1ffd4860f3df870/asset-v1:digitalearthafrica+DEA101+2021+type@asset+block/05_download-xarray.ipynb" style="text-align: left; box-sizing: border-box; color: #3091d1; text-decoration-line: none; cursor: pointer; outline: 0px;" target="[object Object]">Download the Python basics 5 tutorial notebook</a></p> <p style="box-sizing: border-box; line-height: 24px; margin: 0px 0px 24px; font-size: 16px; text-align: left;"><a download="guinea_bissau.nc" href="/assets/courseware/v1/3bf8043c113807b904f270bedf71f0ed/asset-v1:digitalearthafrica+DEA101+2021+type@asset+block/guinea_bissau.nc" style="box-sizing: border-box; color: #2980b9; text-decoration-line: none; cursor: pointer;">Download the exercise NetCDF file (6 MB)</a></p> <p style="box-sizing: border-box; line-height: 24px; margin: 0px 0px 24px; font-size: 16px; text-align: left;">To view this notebook on the Sandbox, you will need to first download the notebook and the NetCDF file to your computer, then upload it to the Sandbox. Ensure you have followed the set-up prerequisities listed in <a href="/courses/course-v1:digitalearthafrica+DEA101+2021/jump_to_id/9c210d02e6fe484a8ce67c67a1e9c2fa" target="[object Object]">Python basics 1: Jupyter, </a>and then follow these instructions:</p> <ol class="arabic simple" style="box-sizing: border-box; margin-top: 0px; margin-bottom: 24px; padding-left: 0px; list-style-position: initial; list-style-image: initial; line-height: 24px; text-align: left;"> <li style="text-align: left; box-sizing: border-box; list-style: decimal; margin-left: 24px;"> <p style="text-align: left; box-sizing: border-box; line-height: 24px; margin: 0px; font-size: 16px;">Download the notebook by clicking the link above. Download the NetCDF file by clicking the second link above.  If necessary, rename the NetCDF file to<span style="font-size: 1em; text-rendering: optimizelegibility; margin: 0px; padding: 0px; border: 0px; outline: 0px; font-variant-numeric: inherit; font-variant-east-asian: inherit; font-stretch: inherit; line-height: 1.4em; font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; vertical-align: baseline; color: #313131;"> </span><span style="color: #313131; font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif;"> </span><code class="docutils literal notranslate" style="text-rendering: optimizelegibility; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; margin: 0px; padding: 2px 5px; border: 1px solid #e1e4e5; outline: 0px; font-variant-numeric: inherit; font-variant-east-asian: inherit; font-stretch: inherit; line-height: 1.4em; vertical-align: baseline; border-radius: 3px; background-image: initial; background-color: #ffffff; color: #e74c3c; box-sizing: border-box; white-space: nowrap; max-width: 100%; overflow-x: auto;"><span class="pre" style="text-rendering: optimizelegibility; margin: 0px; padding: 0px; border: 0px; outline: 0px; font-style: inherit; font-variant: inherit; font-weight: inherit; font-stretch: inherit; line-height: 1.4em; font-family: inherit; vertical-align: baseline; box-sizing: border-box;">guinea_bissau.nc</span></code><span style="text-rendering: optimizelegibility; margin: 0px; padding: 0px; border: 0px; outline: 0px; font-variant-numeric: inherit; font-variant-east-asian: inherit; font-stretch: inherit; line-height: 1.4em; font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; vertical-align: baseline; color: #000000;">.</span></p> </li> <li style="text-align: left; box-sizing: border-box; list-style: decimal; margin-left: 24px;"> <p style="text-align: left; box-sizing: border-box; line-height: 24px; margin: 0px; font-size: 16px;">On the Sandbox, open the <strong style="box-sizing: border-box;">Training</strong> folder.</p> </li> <li style="text-align: left; box-sizing: border-box; list-style: decimal; margin-left: 24px;"> <p style="text-align: left; box-sizing: border-box; line-height: 24px; margin: 0px; font-size: 16px;">Click the <strong style="box-sizing: border-box;">Upload Files</strong> button as shown below.</p> </li> </ol> <p style="box-sizing: border-box; line-height: 24px; margin: 0px 0px 24px; font-size: 16px; text-align: left;"><img alt="Upload button." class="no-scaled-link" src="https://learn.digitalearthafrica.org/asset-v1:digitalearthafrica+DEA101+2021+type@asset+block@05_solution_uploadbutton.png" style="box-sizing: border-box; border: 0px; vertical-align: middle; width: 400px;" /></p> <ol class="arabic simple" start="4" style="box-sizing: border-box; margin-top: 0px; margin-bottom: 24px; padding-left: 0px; list-style-position: initial; list-style-image: initial; line-height: 24px; text-align: left;"> <li style="text-align: left; box-sizing: border-box; list-style: decimal; margin-left: 24px;"> <p style="text-align: left; box-sizing: border-box; line-height: 24px; margin: 0px; font-size: 16px;">Select the downloaded notebook using the file browser. Click <strong style="box-sizing: border-box;">OK</strong>.</p> </li> <li style="text-align: left; box-sizing: border-box; list-style: decimal; margin-left: 24px;">Repeat to upload the NetCDF (.nc) file to the <strong>Training </strong>folder. It may take a while for the upload to complete. </li> <li style="text-align: left; box-sizing: border-box; list-style: decimal; margin-left: 24px;"> <p style="text-align: left; box-sizing: border-box; line-height: 24px; margin: 0px; font-size: 16px;">Both files will appear in the <strong>Training</strong> folder. Double-click the tutorial notebook to open it and begin the tutorial.</p> </li> </ol> <p style="box-sizing: border-box; line-height: 24px; margin: 0px 0px 24px; font-size: 16px; text-align: left;">You can now use the tutorial notebook as an interactive version of this webpage.</p> <div class="admonition note" style="box-sizing: border-box; -webkit-font-smoothing: antialiased; padding: 12px; line-height: 24px; margin-bottom: 24px; background: #e7f2fa;"> <p class="admonition-title" style="box-sizing: border-box; line-height: 1; margin: -12px -12px 12px; font-size: inherit; font-family: inherit; font-variant-numeric: normal; font-variant-east-asian: normal; font-weight: bold; font-stretch: normal; text-rendering: auto; -webkit-font-smoothing: antialiased; color: #ffffff; background: #6ab0de; padding: 6px 12px; text-align: left;">Note</p> <p style="box-sizing: border-box; line-height: 24px; margin: 0px; font-size: 16px; text-align: left;">The tutorial notebook should look like the text and code below. However, the tutorial notebook outputs are blank (i.e. no results showing after code cells). Follow the instructions in the notebook to run the cells in the tutorial notebook. Refer to this page to check your outputs look similar.</p> </div> </section> </div> </div> </div> <script type="text/javascript"> (function (require) { require(['/static/js/dateutil_factory.be68acdff619.js?raw'], function () { require(['js/dateutil_factory'], function (DateUtilFactory) { DateUtilFactory.transform('.localized-datetime'); }); }); }).call(this, require || RequireJS.require); </script> <script> function emit_event(message) { parent.postMessage(message, '*'); } </script> </div>

<div class="xblock xblock-public_view xblock-public_view-vertical" data-course-id="course-v1:digitalearthafrica+DEA101+2021" data-init="VerticalStudentView" data-runtime-class="LmsRuntime" data-runtime-version="1" data-block-type="vertical" data-usage-id="block-v1:digitalearthafrica+DEA101+2021+type@vertical+block@e2a5b225912d413d98cd094163614a11" data-request-token="1b5e9822881611efa85e837f534d2eba" data-graded="False" data-has-score="False"> <div class="vert-mod"> <div class="vert vert-0" data-id="block-v1:digitalearthafrica+DEA101+2021+type@html+block@f59d9f76fdaa43b08cdc5a952a4f14bb"> <div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-course-id="course-v1:digitalearthafrica+DEA101+2021" data-init="XBlockToXModuleShim" data-runtime-class="LmsRuntime" data-runtime-version="1" data-block-type="html" data-usage-id="block-v1:digitalearthafrica+DEA101+2021+type@html+block@f59d9f76fdaa43b08cdc5a952a4f14bb" data-request-token="1b5e9822881611efa85e837f534d2eba" data-graded="False" data-has-score="False"> <script type="json/xblock-args" class="xblock-json-init-args"> {"xmodule-type": "HTMLModule"} </script> <p style="text-align: left;">Xarray makes datasets easier to interpret when performing data analysis. To demonstrate this, we can start by opening a NetCDF file ( file suffix <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">.nc</span></code><span style="color: #000000;">) of the area of Guinea-Bissau we used in the previous </span>matplotlib lesson<span>.</span></p> <p style="text-align: left;">Please ensure you have renamed your downloaded  <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">.nc</span></code><span style="color: #e74c3c; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap;"> </span><span style="font-size: 1em;">file as </span> <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">guinea_bissau.nc</span></code><span style="color: #000000;">.</span></p> <p style="text-align: left;"></p> </div> </div> <div class="vert vert-1" data-id="block-v1:digitalearthafrica+DEA101+2021+type@html+block@0eca64078b084075addf8633be167a4c"> <div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-course-id="course-v1:digitalearthafrica+DEA101+2021" data-init="XBlockToXModuleShim" data-runtime-class="LmsRuntime" data-runtime-version="1" data-block-type="html" data-usage-id="block-v1:digitalearthafrica+DEA101+2021+type@html+block@0eca64078b084075addf8633be167a4c" data-request-token="1b5e9822881611efa85e837f534d2eba" data-graded="False" data-has-score="False"> <script type="json/xblock-args" class="xblock-json-init-args"> {"xmodule-type": "HTMLModule"} </script> <h3>Recap</h3> <p style="text-align: left;"><span><span>In the previous lesson, we plotted the </span><code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">.JPG</span></code><span style="font-family: Arial, sans-serif;"> version of the image using </span><code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">imread</span></code><span style="font-family: Arial, sans-serif;"> and </span><code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">imshow</span></code><span style="font-family: Arial, sans-serif;">.</span></span></p> <div class="nbinput docutils container" style="box-sizing: border-box; display: flex; align-items: flex-start; margin: 0px; width: 696.469px; padding-top: 5px; color: #000000; font-family: Arial, sans-serif; text-align: left;"> <div class="prompt highlight-none notranslate" style="box-sizing: border-box; margin: 0px; width: 4.5ex; padding-top: 5px; position: relative; user-select: none;"> <div class="highlight" style="box-sizing: border-box; background: none; border: none; overflow-x: auto; margin: 0px 0.3ex 0px 0px; padding: 0px; box-shadow: none; position: absolute; right: 0px;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: pre; line-height: 1.4; margin-top: 0px; margin-bottom: 0px; padding: 0px; overflow: hidden; border: none; box-shadow: none; background: none; color: #307fc1;"><span style="box-sizing: border-box; border: none; padding: 0px; margin: 0px; box-shadow: none; background: none;"></span>[1]: </pre> </div> </div> <div class="input_area highlight-ipython3 notranslate" style="box-sizing: border-box; margin: 0px; flex: 1 1 0%; overflow: auto; border: 1px solid #e0e0e0; border-radius: 2px;"> <div class="highlight" style="box-sizing: border-box; background: #f8f8f8; border: none; overflow: auto hidden; margin: 0px; padding: 0px; box-shadow: none;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: pre; line-height: 1.4; margin-top: 0px; margin-bottom: 0px; padding: 5px; overflow: auto; border: none; box-shadow: none;"><span style="box-sizing: border-box;"></span><span class="c1" style="box-sizing: border-box; color: #408080; font-style: italic;"># Review of Python basics lesson 3: matplotlib</span> <span class="o" style="box-sizing: border-box; color: #666666;">%</span><span class="k" style="box-sizing: border-box; color: #008000; font-weight: bold;">matplotlib</span> inline <span class="kn" style="box-sizing: border-box; color: #008000; font-weight: bold;">import</span> <span class="nn" style="box-sizing: border-box; color: #0000ff; font-weight: bold;">numpy</span> <span class="k" style="box-sizing: border-box; color: #008000; font-weight: bold;">as</span> <span class="nn" style="box-sizing: border-box; color: #0000ff; font-weight: bold;">np</span> <span class="kn" style="box-sizing: border-box; color: #008000; font-weight: bold;">from</span> <span class="nn" style="box-sizing: border-box; color: #0000ff; font-weight: bold;">matplotlib</span> <span class="kn" style="box-sizing: border-box; color: #008000; font-weight: bold;">import</span> <span class="n" style="box-sizing: border-box;">pyplot</span> <span class="k" style="box-sizing: border-box; color: #008000; font-weight: bold;">as</span> <span class="n" style="box-sizing: border-box;">plt</span> <span class="n" style="box-sizing: border-box;">im</span> <span class="o" style="box-sizing: border-box; color: #666666;">=</span> <span class="n" style="box-sizing: border-box;">np</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">copy</span><span class="p" style="box-sizing: border-box;">(</span><span class="n" style="box-sizing: border-box;">plt</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">imread</span><span class="p" style="box-sizing: border-box;">(</span><span class="s1" style="box-sizing: border-box; color: #ba2121;">'Guinea_Bissau.JPG'</span><span class="p" style="box-sizing: border-box;">))</span> <span class="n" style="box-sizing: border-box;">plt</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">imshow</span><span class="p" style="box-sizing: border-box;">(</span><span class="n" style="box-sizing: border-box;">im</span><span class="p" style="box-sizing: border-box;">)</span> </pre> </div> </div> </div> <div class="nboutput docutils container" style="box-sizing: border-box; display: flex; align-items: flex-start; margin: 0px; width: 696.469px; color: #000000; font-family: Arial, sans-serif; text-align: left;"> <div class="prompt highlight-none notranslate" style="box-sizing: border-box; margin: 0px; width: 4.5ex; padding-top: 5px; position: relative; user-select: none;"> <div class="highlight" style="box-sizing: border-box; background: none; border: none; overflow-x: auto; margin: 0px 0.3ex 0px 0px; padding: 0px; box-shadow: none; position: absolute; right: 0px;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: pre; line-height: normal; margin-top: 0px; margin-bottom: 0px; padding: 0px; overflow: hidden; border: none; box-shadow: none; background: none; color: #bf5b3d;"><span style="box-sizing: border-box; border: none; padding: 0px; margin: 0px; box-shadow: none; background: none;"></span>[1]: </pre> </div> </div> <div class="output_area docutils container" style="box-sizing: border-box; flex: 1 1 0%; overflow: auto;"> <div class="highlight" style="box-sizing: border-box; background: unset; border: none; overflow: auto hidden; margin: 0px; padding: 0px; box-shadow: none; color: unset;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: pre; line-height: normal; margin-top: 0px; margin-bottom: 0px; padding: 5px; overflow: auto; border: none; box-shadow: none; background: unset;">&lt;matplotlib.image.AxesImage at 0x7f6fc79feba8&gt; </pre> </div> </div> </div> <div class="nboutput nblast docutils container" style="box-sizing: border-box; display: flex; align-items: flex-start; margin: 0px 0px 19px; width: 696.469px; padding-bottom: 5px; color: #000000; font-family: Arial, sans-serif;"> <div class="output_area docutils container" style="box-sizing: border-box; flex: 1 1 0%; overflow: auto;"><img alt="../_images/python_basics_05_xarray_9_1.png" src="https://learn.digitalearthafrica.org/asset-v1:digitalearthafrica+DEA101+2021+type@asset+block@python_basics_05_xarray_9_1.png" style="box-sizing: border-box; border: 0px; vertical-align: middle; padding: 5px; margin: 0px; float: left;" /></div> </div> <p></p> </div> </div> <div class="vert vert-2" data-id="block-v1:digitalearthafrica+DEA101+2021+type@html+block@4495e7db62844d9890ec8fa6fd684998"> <div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-course-id="course-v1:digitalearthafrica+DEA101+2021" data-init="XBlockToXModuleShim" data-runtime-class="LmsRuntime" data-runtime-version="1" data-block-type="html" data-usage-id="block-v1:digitalearthafrica+DEA101+2021+type@html+block@4495e7db62844d9890ec8fa6fd684998" data-request-token="1b5e9822881611efa85e837f534d2eba" data-graded="False" data-has-score="False"> <script type="json/xblock-args" class="xblock-json-init-args"> {"xmodule-type": "HTMLModule"} </script> <h3 style="text-align: left;"><span>Load data with xarray</span></h3> <p style="text-align: left;"><span>Using xarray, we will now read in a </span><code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">.nc</span></code><span style="color: #000000;"> file of the area. </span><code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">.nc</span></code><span> NetCDF files are a filetype used for storing scientific array-oriented data. </span></p> <p style="text-align: left;"><span>Remembering that image data is an array, this makes both formats almost equivalent.</span></p> <p style="box-sizing: border-box; line-height: 24px; margin: 0px 0px 24px; font-size: 16px; text-align: left;">We could also load the <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">.JPG</span></code> as a dataset using xarray, but the NetCDF file contains more metadata so it will be easier to interpret.</p> <p style="text-align: left;">As usual, we will first import the xarray package.</p> <div class="nbinput nblast docutils container" style="color: #000000; box-sizing: border-box; display: flex; align-items: flex-start; margin: 0px 0px 19px; width: 696.469px; padding-top: 5px; padding-bottom: 5px; font-family: Arial, sans-serif;"> <div class="input_area highlight-ipython3 notranslate" style="box-sizing: border-box; margin: 0px; flex: 1 1 0%; overflow: auto; border: 1px solid #e0e0e0; border-radius: 2px; text-align: left;"> <div class="highlight" style="box-sizing: border-box; background: #f8f8f8; border: none; overflow: auto hidden; margin: 0px; padding: 0px; box-shadow: none;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: pre; line-height: 1.4; margin-top: 0px; margin-bottom: 0px; padding: 5px; overflow: auto; border: none; box-shadow: none;"><span style="box-sizing: border-box;"></span><span class="c1" style="box-sizing: border-box; color: #408080; font-style: italic;"># Import the xarray package as xr</span> <span class="kn" style="box-sizing: border-box; color: #008000; font-weight: bold;">import</span> <span class="nn" style="box-sizing: border-box; color: #0000ff; font-weight: bold;">xarray</span> <span class="k" style="box-sizing: border-box; color: #008000; font-weight: bold;">as</span> <span class="nn" style="box-sizing: border-box; color: #0000ff; font-weight: bold;">xr</span> </pre> </div> </div> </div> <div class="nbinput nblast docutils container" style="color: #000000; box-sizing: border-box; display: flex; align-items: flex-start; margin: -19px 0px 19px; width: 696.469px; padding-top: 5px; padding-bottom: 5px; font-family: Arial, sans-serif; text-align: left;"> <div class="input_area highlight-ipython3 notranslate" style="box-sizing: border-box; margin: 0px; flex: 1 1 0%; overflow: auto; border: 1px solid #e0e0e0; border-radius: 2px;"> <div class="highlight" style="box-sizing: border-box; background: #f8f8f8; border: none; overflow: auto hidden; margin: 0px; padding: 0px; box-shadow: none;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: pre; line-height: 1.4; margin-top: 0px; margin-bottom: 0px; padding: 5px; overflow: auto; border: none; box-shadow: none;"><span style="box-sizing: border-box;"></span><span class="c1" style="box-sizing: border-box; color: #408080; font-style: italic;"># Use xarray to open the data file</span> <span class="n" style="box-sizing: border-box;">guinea_bissau</span> <span class="o" style="box-sizing: border-box; color: #666666;">=</span> <span class="n" style="box-sizing: border-box;">xr</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">open_dataset</span><span class="p" style="box-sizing: border-box;">(</span><span class="s1" style="box-sizing: border-box; color: #ba2121;">'guinea_bissau.nc'</span><span class="p" style="box-sizing: border-box;">)</span> </pre> </div> </div> </div> <div class="nbinput docutils container" style="color: #000000; box-sizing: border-box; display: flex; align-items: flex-start; margin: -19px 0px 0px; width: 696.469px; padding-top: 5px; font-family: Arial, sans-serif; text-align: left;"> <div class="input_area highlight-ipython3 notranslate" style="box-sizing: border-box; margin: 0px; flex: 1 1 0%; overflow: auto; border: 1px solid #e0e0e0; border-radius: 2px;"> <div class="highlight" style="box-sizing: border-box; background: #f8f8f8; border: none; overflow: auto hidden; margin: 0px; padding: 0px; box-shadow: none;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: pre; line-height: 1.4; margin-top: 0px; margin-bottom: 0px; padding: 5px; overflow: auto; border: none; box-shadow: none;"><span style="box-sizing: border-box;"></span><span class="c1" style="box-sizing: border-box; color: #408080; font-style: italic;"># Inspect the xarray.DataSet by</span> <span class="c1" style="box-sizing: border-box; color: #408080; font-style: italic;"># typing its name</span> <span class="n" style="box-sizing: border-box;">guinea_bissau</span> </pre> </div> </div> </div> <p style="text-align: left;"><img 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" alt="" /></p> <p style="text-align: left;">Let’s inspect the data. This image (our dataset) has a height <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">y</span></code> of 501 pixels and a width <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">x</span></code> of 500 pixels. It has 3 <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">variables</span></code>, which correspond to the red, green, and blue bands needed to plot a colour image. Each of these bands has a value at each of the pixels, so we have a total of 751500 values (the result of 501 x 500 x 3). Each of the <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">x</span></code> and <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">y</span></code> values are associated with a longitude and latitude and their values are stored under the <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">Coordinates</span></code> section.</p> <p style="text-align: left;">The <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">xarray.DataSet</span></code> uses numpy arrays in the backend. What we see above is functionally similar to a bunch of numpy arrays holding the image band measurements and the longitude/latitude coordinates, combined with a dictionary of key-value pairs that defines the <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">Attributes</span></code> such as CRS and resolution (<code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">res</span></code>). That sounds more complicated, doesn’t it? Accessing the data through <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">xarray</span></code> avoids the complications of managing uncorrelated numpy arrays by themselves. </p> <p style="box-sizing: border-box; line-height: 24px; margin: 0px 0px 24px; font-size: 16px; font-family: Arial, sans-serif; color: #000000;"><img alt="Xarray dataset breakdown" class="no-scaled-link" src="https://learn.digitalearthafrica.org/asset-v1:digitalearthafrica+DEA101+2021+type@asset+block@dataset-diagram.png" style="box-sizing: border-box; border: 0px; vertical-align: middle; width: 600px;" /></p> <p style="text-align: left;"><em style="box-sizing: border-box;">A visualisation of an</em> <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">xarray.Dataset</span></code><em style="box-sizing: border-box;">. The coordinates are each a single dimension;</em> <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">latitude</span></code><em style="box-sizing: border-box;">,</em> <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">longitude</span></code> <em style="box-sizing: border-box;">and</em> <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">time</span></code><em style="box-sizing: border-box;">. Each</em> <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">variable</span></code> <em style="box-sizing: border-box;">(in this case</em> <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">temperature</span></code> <em style="box-sizing: border-box;">and</em> <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">precipitation</span></code><em style="box-sizing: border-box;">) holds one value at each of the three coordinate dimensions.</em></p> <p></p> </div> </div> </div> <script type="text/javascript"> (function (require) { require(['/static/js/dateutil_factory.be68acdff619.js?raw'], function () { require(['js/dateutil_factory'], function (DateUtilFactory) { DateUtilFactory.transform('.localized-datetime'); }); }); }).call(this, require || RequireJS.require); </script> <script> function emit_event(message) { parent.postMessage(message, '*'); } </script> </div>

<div class="xblock xblock-public_view xblock-public_view-vertical" data-course-id="course-v1:digitalearthafrica+DEA101+2021" data-init="VerticalStudentView" data-runtime-class="LmsRuntime" data-runtime-version="1" data-block-type="vertical" data-usage-id="block-v1:digitalearthafrica+DEA101+2021+type@vertical+block@c6d05947b45848e580e701b279d6755a" data-request-token="1b5e9822881611efa85e837f534d2eba" data-graded="False" data-has-score="False"> <div class="vert-mod"> <div class="vert vert-0" data-id="block-v1:digitalearthafrica+DEA101+2021+type@html+block@e2d14a60153d4897abe935914206dbf2"> <div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-course-id="course-v1:digitalearthafrica+DEA101+2021" data-init="XBlockToXModuleShim" data-runtime-class="LmsRuntime" data-runtime-version="1" data-block-type="html" data-usage-id="block-v1:digitalearthafrica+DEA101+2021+type@html+block@e2d14a60153d4897abe935914206dbf2" data-request-token="1b5e9822881611efa85e837f534d2eba" data-graded="False" data-has-score="False"> <script type="json/xblock-args" class="xblock-json-init-args"> {"xmodule-type": "HTMLModule"} </script> <h3 style="text-align: left;">xarray.Dataset and xarray.DataArray</h3> <p style="text-align: left;">An <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">xarray.Dataset</span></code> can be seen as a dictionary structure for packing up the data, dimensions and attributes all linked together.</p> <p style="text-align: left;">As in the NetCDF we loaded, Digital Earth Africa follows the convention of storing spectral bands as separate variables, with each one as 3-dimensional cubes containing the temporal dimension.</p> <p style="text-align: left;">To access a single variable we can use the same format as if it were a Python dictionary.</p> <div class="highlight-python notranslate" style="box-sizing: border-box; border: 1px solid #e1e4e5; overflow-x: auto; margin: 1px 0px 24px; color: #000000; font-family: Arial, sans-serif; text-align: left;"> <div class="highlight" style="box-sizing: border-box; background: #f8f8f8; border: none; overflow-x: auto; margin: 0px; padding: 0px;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: pre; line-height: 1.4; margin-top: 0px; margin-bottom: 0px; padding: 12px; overflow: auto;"><span style="box-sizing: border-box;"></span><span class="n" style="box-sizing: border-box;">var</span> <span class="o" style="box-sizing: border-box; color: #666666;">=</span> <span class="n" style="box-sizing: border-box;">dataset_name</span><span class="p" style="box-sizing: border-box;">[</span><span class="s1" style="box-sizing: border-box; color: #ba2121;">'variable_name'</span><span class="p" style="box-sizing: border-box;">]</span> </pre> </div> </div> <p style="text-align: left;">Alternatively, we can use the <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">.</span></code> notation.</p> <div class="highlight-python notranslate" style="box-sizing: border-box; border: 1px solid #e1e4e5; overflow-x: auto; margin: 1px 0px 24px; color: #000000; font-family: Arial, sans-serif; text-align: left;"> <div class="highlight" style="box-sizing: border-box; background: #f8f8f8; border: none; overflow-x: auto; margin: 0px; padding: 0px;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: pre; line-height: 1.4; margin-top: 0px; margin-bottom: 0px; padding: 12px; overflow: auto;"><span style="box-sizing: border-box;"></span><span class="n" style="box-sizing: border-box;">var</span> <span class="o" style="box-sizing: border-box; color: #666666;">=</span> <span class="n" style="box-sizing: border-box;">dataset_name</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">variable_name</span> </pre> </div> </div> <p style="text-align: left;">A single variable pulled from an <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">xarray.Dataset</span></code> is known as an <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">xarray.DataArray</span></code>. In the visualisation image on the previous page, the example <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">xarray.Dataset</span></code> is formed out of two <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">xarray.DataArray</span></code>s — one each for <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">temperature</span></code> and <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">precipitation</span></code>.</p> <p style="box-sizing: border-box; text-align: left;"><strong style="box-sizing: border-box;">Note:</strong> Variable names are often strings (enclosed in quotation marks). This is true of most Digital Earth Africa datasets, where variables such as satellite bands have names such as <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">'red'</span></code>, <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">'green'</span></code>, <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">'nir'</span></code>. Do not forget the quotation marks in first call method above, or the data will not load.</p> <p style="text-align: left;">The dataset we loaded only has 1 timestep. We will now pull just the <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">'red'</span></code> variable data from our <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">guinea_bissau</span></code> <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">xarray.Dataset</span></code>, and then plot it.</p> <div class="nbinput docutils container" style="color: #000000; box-sizing: border-box; display: flex; align-items: flex-start; margin: 0px; width: 696.469px; padding-top: 5px; font-family: Arial, sans-serif;"> <div class="input_area highlight-ipython3 notranslate" style="box-sizing: border-box; margin: 0px; flex: 1 1 0%; overflow: auto; border: 1px solid #e0e0e0; border-radius: 2px; text-align: left;"> <div class="highlight" style="box-sizing: border-box; background: #f8f8f8; border: none; overflow: auto hidden; margin: 0px; padding: 0px; box-shadow: none;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: pre; line-height: 1.4; margin-top: 0px; margin-bottom: 0px; padding: 5px; overflow: auto; border: none; box-shadow: none;"><span style="box-sizing: border-box;"></span><span class="n" style="box-sizing: border-box;">guinea_bissau</span><span class="p" style="box-sizing: border-box;">[</span><span class="s1" style="box-sizing: border-box; color: #ba2121;">'red'</span><span class="p" style="box-sizing: border-box;">]</span> <span class="c1" style="box-sizing: border-box; color: #408080; font-style: italic;"># This is equivalent to</span> <span class="c1" style="box-sizing: border-box; color: #408080; font-style: italic;"># guinea_bissau.red</span> <span class="c1" style="box-sizing: border-box; color: #408080; font-style: italic;"># Try it!</span> </pre> </div> </div> </div> <div class="nboutput nblast docutils container" style="color: #000000; box-sizing: border-box; display: flex; align-items: flex-start; margin: 0px 0px 19px; width: 696.469px; padding-bottom: 5px; font-family: Arial, sans-serif; text-align: left;"> <div class="output_area rendered_html docutils container" style="box-sizing: border-box; flex: 1 1 0%; overflow: auto; padding: 5px; margin: 0px;"> <div style="box-sizing: border-box;"></div> </div> </div> <div class="nbinput docutils container" style="color: #000000; box-sizing: border-box; display: flex; align-items: flex-start; margin: -19px 0px 0px; width: 696.469px; padding-top: 5px; font-family: Arial, sans-serif; text-align: left;"> <div class="input_area highlight-ipython3 notranslate" style="box-sizing: border-box; margin: 0px; flex: 1 1 0%; overflow: auto; border: 1px solid #e0e0e0; border-radius: 2px;"> <div class="highlight" style="box-sizing: border-box; background: #f8f8f8; border: none; overflow: auto hidden; margin: 0px; padding: 0px; box-shadow: none;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: pre; line-height: 1.4; margin-top: 0px; margin-bottom: 0px; padding: 5px; overflow: auto; border: none; box-shadow: none;"><span style="box-sizing: border-box;"></span><span class="c1" style="box-sizing: border-box; color: #408080; font-style: italic;"># Plot using imshow</span> <span class="n" style="box-sizing: border-box;">plt</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">imshow</span><span class="p" style="box-sizing: border-box;">(</span><span class="n" style="box-sizing: border-box;">guinea_bissau</span><span class="p" style="box-sizing: border-box;">[</span><span class="s1" style="box-sizing: border-box; color: #ba2121;">'red'</span><span class="p" style="box-sizing: border-box;">],</span> <span class="n" style="box-sizing: border-box;">cmap</span><span class="o" style="box-sizing: border-box; color: #666666;">=</span><span class="s1" style="box-sizing: border-box; color: #ba2121;">'Reds'</span><span class="p" style="box-sizing: border-box;">)</span> </pre> </div> </div> </div> <div class="nboutput docutils container" style="color: #000000; box-sizing: border-box; display: flex; align-items: flex-start; margin: 0px; width: 696.469px; font-family: Arial, sans-serif;"> <div class="output_area docutils container" style="box-sizing: border-box; flex: 1 1 0%; overflow: auto;"> <div class="highlight" style="box-sizing: border-box; background: unset; border: none; overflow: auto hidden; margin: 0px; padding: 0px; box-shadow: none; color: unset; text-align: left;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: pre; line-height: normal; margin-top: 0px; margin-bottom: 0px; padding: 5px; overflow: auto; border: none; box-shadow: none; background: unset;">&lt;matplotlib.image.AxesImage at 0x7f6fb0b77400&gt; </pre> </div> </div> </div> <div class="nboutput nblast docutils container" style="color: #000000; box-sizing: border-box; display: flex; align-items: flex-start; margin: 0px 0px 19px; width: 696.469px; padding-bottom: 5px; font-family: Arial, sans-serif;"> <div class="output_area docutils container" style="box-sizing: border-box; flex: 1 1 0%; overflow: auto;"><img alt="../_images/python_basics_05_xarray_22_1.png" src="https://learn.digitalearthafrica.org/asset-v1:digitalearthafrica+DEA101+2021+type@asset+block@python_basics_05_xarray_22_1.png" style="box-sizing: border-box; border: 0px; vertical-align: middle; padding: 5px; margin: 0px;" /></div> </div> <div style="box-sizing: border-box;"> <p style="box-sizing: border-box; line-height: 24px; margin: 0px 0px 24px; font-size: 16px; text-align: left;"><strong style="box-sizing: border-box;">Note:</strong> <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">cmap</span></code> stands for ‘colour map’. What happens if you remove this argument? <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">plt.imshow</span></code> will then plot all the <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">red</span></code> data values in the default colour scheme, which is a purple-to-yellow sequential colour map known as <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">viridis</span></code>. This is because matplotlib does not know the variable name <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">red</span></code> is associated with the colour red — it is just a name. You can find out more about matplotlib colourmaps through this <a class="reference external" href="https://matplotlib.org/tutorials/colors/colormaps.html" target="_blank" style="box-sizing: border-box; color: #3091d1; text-decoration-line: none; cursor: pointer; outline: 0px;">matplotlib tutorial</a>.</p> </div> <p style="text-align: left;">We stated above that <em style="box-sizing: border-box;">xarray uses numpy arrays in the backend</em>. You can access these numpy arrays by adding <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">.values</span></code> after a <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">DataArray</span></code> name. In the example below, <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">guinea_bissau.red</span></code> is the <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">DataArray</span></code> name.</p> <div class="nbinput docutils container" style="box-sizing: border-box; display: flex; align-items: flex-start; margin: 0px; width: 696.469px; padding-top: 5px; color: #000000; font-family: Arial, sans-serif;"> <div class="input_area highlight-ipython3 notranslate" style="box-sizing: border-box; margin: 0px; flex: 1 1 0%; overflow: auto; border: 1px solid #e0e0e0; border-radius: 2px; text-align: left;"> <div class="highlight" style="box-sizing: border-box; background: #f8f8f8; border: none; overflow: auto hidden; margin: 0px; padding: 0px; box-shadow: none;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: pre; line-height: 1.4; margin-top: 0px; margin-bottom: 0px; padding: 5px; overflow: auto; border: none; box-shadow: none;"><span style="box-sizing: border-box;"></span><span class="n" style="box-sizing: border-box;">guinea_bissau</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">red</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">values</span> </pre> </div> </div> </div> <div class="nboutput nblast docutils container" style="box-sizing: border-box; display: flex; align-items: flex-start; margin: 0px 0px 19px; width: 696.469px; padding-bottom: 5px; color: #000000; font-family: Arial, sans-serif;"> <div class="output_area docutils container" style="box-sizing: border-box; flex: 1 1 0%; overflow: auto;"> <div class="highlight" style="box-sizing: border-box; background: unset; border: none; overflow: auto hidden; margin: 0px; padding: 0px; box-shadow: none; color: unset; text-align: left;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: pre; line-height: normal; margin-top: 0px; margin-bottom: 0px; padding: 5px; overflow: auto; border: none; box-shadow: none; background: unset;">array([[1007., 1007., 1069., ..., 489., 476., 422.], [ 988., 991., 1048., ..., 456., 459., 444.], [ 976., 1023., 1062., ..., 453., 454., 448.], ..., [ 518., 518., 546., ..., 551., 577., 553.], [ 517., 527., 523., ..., 530., 572., 570.], [ 502., 490., 473., ..., 545., 576., 580.]]) </pre> </div> </div> </div> <p></p> </div> </div> <div class="vert vert-1" data-id="block-v1:digitalearthafrica+DEA101+2021+type@html+block@b7bc8b1717d34d8cb903d31699af2d54"> <div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-course-id="course-v1:digitalearthafrica+DEA101+2021" data-init="XBlockToXModuleShim" data-runtime-class="LmsRuntime" data-runtime-version="1" data-block-type="html" data-usage-id="block-v1:digitalearthafrica+DEA101+2021+type@html+block@b7bc8b1717d34d8cb903d31699af2d54" data-request-token="1b5e9822881611efa85e837f534d2eba" data-graded="False" data-has-score="False"> <script type="json/xblock-args" class="xblock-json-init-args"> {"xmodule-type": "HTMLModule"} </script> <h3 style="text-align: left;">Indexing data</h3> <p style="text-align: left;">It is possible to index <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">xarray.DataArrays</span></code> by position using the numpy <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">[:,:]</span></code> syntax we introduced in previous lessons, without converting to numpy arrays. However, we can also use the xarray syntaxes, which explictly label coordinates instead of relying on knowing the order of dimensions.</p> <p style="text-align: left;">Each method is demonstrated below.</p> <ul class="simple" style="box-sizing: border-box; margin-top: 0px; margin-bottom: 0px; padding-left: 0px; list-style-position: initial; list-style-type: none; text-align: left;"> <li style="box-sizing: border-box; list-style: none; text-align: left;"> <p style="text-align: left;"><code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">[:,:]</span></code>: numpy syntax — requires knowing order of dimensions and positional indexes</p> </li> <li style="box-sizing: border-box; list-style: none; text-align: left;"> <p style="text-align: left;"><code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">isel(coordinate_name</span> <span class="pre" style="box-sizing: border-box;">=</span> <span class="pre" style="box-sizing: border-box;">coordinate_index)</span></code>: index selection — xarray syntax for selecting data based on its positional index (similar to numpy)</p> </li> <li style="box-sizing: border-box; list-style: none; text-align: left;"> <p style="text-align: left;"><code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">sel(coordinate_name</span> <span class="pre" style="box-sizing: border-box;">=</span> <span class="pre" style="box-sizing: border-box;">coordinate_value)</span></code>: value selection — xarray syntax for selecting data based on its value in that dimension</p> </li> </ul> <p style="text-align: left;">For example, we can call out the value of <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">green</span></code> in the top leftmost pixel of the image dataset.</p> <div class="nbinput docutils container" style="box-sizing: border-box; display: flex; align-items: flex-start; margin: 0px; width: 696.469px; padding-top: 5px; color: #000000; font-family: Arial, sans-serif; text-align: left;"> <div class="input_area highlight-ipython3 notranslate" style="box-sizing: border-box; margin: 0px; flex: 1 1 0%; overflow: auto; border: 1px solid #e0e0e0; border-radius: 2px;"> <div class="highlight" style="box-sizing: border-box; background: #f8f8f8; border: none; overflow: auto hidden; margin: 0px; padding: 0px; box-shadow: none;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: pre; line-height: 1.4; margin-top: 0px; margin-bottom: 0px; padding: 5px; overflow: auto; border: none; box-shadow: none;"><span style="box-sizing: border-box;"></span><span class="c1" style="box-sizing: border-box; color: #408080; font-style: italic;"># Using numpy syntax</span> <span class="n" style="box-sizing: border-box;">guinea_bissau</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">green</span><span class="p" style="box-sizing: border-box;">[</span><span class="mi" style="box-sizing: border-box; color: #666666;">0</span><span class="p" style="box-sizing: border-box;">,</span><span class="mi" style="box-sizing: border-box; color: #666666;">0</span><span class="p" style="box-sizing: border-box;">]</span> </pre> </div> </div> </div> <p style="text-align: left;"><img 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" /></p> <p style="text-align: left;"><span>In this case our positional indexes for </span><code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">x</span></code><span> and </span><code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">y</span></code><span> are the same, but it is important to note </span><code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">y</span></code><span> is the first dimension, and </span><code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">x</span></code><span> is the second. This is not immediately obvious and can cause confusion. For this reason, it is recommended to use one of the xarray syntaxes shown below. They explicitly call on the dimension names.</span></p> <div class="nbinput docutils container" style="box-sizing: border-box; display: flex; align-items: flex-start; margin: 0px; width: 696.469px; padding-top: 5px; color: #000000; font-family: Arial, sans-serif;"> <div class="input_area highlight-ipython3 notranslate" style="box-sizing: border-box; margin: 0px; flex: 1 1 0%; overflow: auto; border: 1px solid #e0e0e0; border-radius: 2px;"> <div class="highlight" style="box-sizing: border-box; background: #f8f8f8; border: none; overflow: auto hidden; margin: 0px; padding: 0px; box-shadow: none;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: pre; line-height: 1.4; margin-top: 0px; margin-bottom: 0px; padding: 5px; overflow: auto; border: none; box-shadow: none;"><span style="box-sizing: border-box;"></span><span class="c1" style="box-sizing: border-box; color: #408080; font-style: italic;"># Using index selection, isel()</span> <span class="n" style="box-sizing: border-box;">guinea_bissau</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">green</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">isel</span><span class="p" style="box-sizing: border-box;">(</span><span class="n" style="box-sizing: border-box;">y</span><span class="o" style="box-sizing: border-box; color: #666666;">=</span><span class="mi" style="box-sizing: border-box; color: #666666;">0</span><span class="p" style="box-sizing: border-box;">,</span> <span class="n" style="box-sizing: border-box;">x</span><span class="o" style="box-sizing: border-box; color: #666666;">=</span><span class="mi" style="box-sizing: border-box; color: #666666;">0</span><span class="p" style="box-sizing: border-box;">)</span> </pre> </div> </div> </div> <div class="nboutput nblast docutils container" style="box-sizing: border-box; display: flex; align-items: flex-start; margin: 0px 0px 19px; width: 696.469px; padding-bottom: 5px; color: #000000; font-family: Arial, sans-serif;"> <div class="output_area rendered_html docutils container" style="box-sizing: border-box; flex: 1 1 0%; overflow: auto; padding: 5px; margin: 0px;"> <div style="box-sizing: border-box;"></div> </div> </div> <p style="text-align: left;"><img 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" alt="" /><span></span></p> <p style="text-align: left;">As before, the measurement for <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">green</span></code> in the top leftmost pixel is <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">730</span></code>. In this example, the <em style="box-sizing: border-box;">values</em> of <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">x</span></code> and <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">y</span></code> are latitude and longitude values, but their <em style="box-sizing: border-box;">positional indexes</em> are <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">0</span></code>.</p> <p style="text-align: left;">We can call out the value by using the index, as shown below.</p> <p style="text-align: left;"><strong style="box-sizing: border-box;">Note:</strong> The CRS we are using has units of metres. This means the <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">x</span></code> and <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">y</span></code> values are measured in metres.</p> <div style="box-sizing: border-box; text-align: left;"> <div class="nbinput docutils container" style="box-sizing: border-box; display: flex; align-items: flex-start; margin: 0px; width: 696.469px; padding-top: 5px; color: #000000; font-family: Arial, sans-serif;"> <div class="input_area highlight-ipython3 notranslate" style="box-sizing: border-box; margin: 0px; flex: 1 1 0%; overflow: auto; border: 1px solid #e0e0e0; border-radius: 2px;"> <div class="highlight" style="box-sizing: border-box; background: #f8f8f8; border: none; overflow: auto hidden; margin: 0px; padding: 0px; box-shadow: none;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: pre; line-height: 1.4; margin-top: 0px; margin-bottom: 0px; padding: 5px; overflow: auto; border: none; box-shadow: none;"><span style="box-sizing: border-box;"></span><span class="c1" style="box-sizing: border-box; color: #408080; font-style: italic;"># The value of x at the pixel located at index (0,0)</span> <span class="n" style="box-sizing: border-box;">guinea_bissau</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">green</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">x</span><span class="p" style="box-sizing: border-box;">[</span><span class="mi" style="box-sizing: border-box; color: #666666;">0</span><span class="p" style="box-sizing: border-box;">]</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">values</span> </pre> </div> </div> </div> <div class="nboutput nblast docutils container" style="box-sizing: border-box; display: flex; align-items: flex-start; margin: 0px 0px 19px; width: 696.469px; padding-bottom: 5px; color: #000000; font-family: Arial, sans-serif;"> <div class="output_area docutils container" style="box-sizing: border-box; flex: 1 1 0%; overflow: auto;"> <div class="highlight" style="box-sizing: border-box; background: unset; border: none; overflow: auto hidden; margin: 0px; padding: 0px; box-shadow: none; color: unset;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: pre; line-height: normal; margin-top: 0px; margin-bottom: 0px; padding: 5px; overflow: auto; border: none; box-shadow: none; background: unset;">array(388020.) </pre> </div> </div> </div> <div class="nbinput docutils container" style="box-sizing: border-box; display: flex; align-items: flex-start; margin: -19px 0px 0px; width: 696.469px; padding-top: 5px; color: #000000; font-family: Arial, sans-serif;"> <div class="input_area highlight-ipython3 notranslate" style="box-sizing: border-box; margin: 0px; flex: 1 1 0%; overflow: auto; border: 1px solid #e0e0e0; border-radius: 2px;"> <div class="highlight" style="box-sizing: border-box; background: #f8f8f8; border: none; overflow: auto hidden; margin: 0px; padding: 0px; box-shadow: none;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: pre; line-height: 1.4; margin-top: 0px; margin-bottom: 0px; padding: 5px; overflow: auto; border: none; box-shadow: none;"><span style="box-sizing: border-box;"></span><span class="c1" style="box-sizing: border-box; color: #408080; font-style: italic;"># The value of y at the pixel located at index (0,0)</span> <span class="n" style="box-sizing: border-box;">guinea_bissau</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">green</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">y</span><span class="p" style="box-sizing: border-box;">[</span><span class="mi" style="box-sizing: border-box; color: #666666;">0</span><span class="p" style="box-sizing: border-box;">]</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">values</span> </pre> </div> </div> </div> <div class="nboutput nblast docutils container" style="box-sizing: border-box; display: flex; align-items: flex-start; margin: 0px 0px 19px; width: 696.469px; padding-bottom: 5px; color: #000000; font-family: Arial, sans-serif;"> <div class="output_area docutils container" style="box-sizing: border-box; flex: 1 1 0%; overflow: auto;"> <div class="highlight" style="box-sizing: border-box; background: unset; border: none; overflow: auto hidden; margin: 0px; padding: 0px; box-shadow: none; color: unset;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: pre; line-height: normal; margin-top: 0px; margin-bottom: 0px; padding: 5px; overflow: auto; border: none; box-shadow: none; background: unset;">array(1338000.) </pre> </div> </div> </div> The interpretation of the cell above is: <div style="box-sizing: border-box; text-align: left;"> <p style="box-sizing: border-box; text-align: left;">Output the <strong style="box-sizing: border-box;">value</strong> of <strong style="box-sizing: border-box;">y</strong> in the <strong style="box-sizing: border-box;">1st position</strong> of the <strong style="box-sizing: border-box;">green</strong> variable from the dataset called <strong style="box-sizing: border-box;">guineau_bissau</strong>.</p> We see the first element (index of 0) in <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">x</span></code> has a value of 388020 metres, and the first value of <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">y</span></code> is 1338000 metres. <div style="box-sizing: border-box; text-align: left;"> <p style="box-sizing: border-box; text-align: left;">How about the second method, <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">sel()</span></code>? This method was not available in the numpy arrays we used in previous lessons, and is one of the strengths of xarray as you can use the value of the dimension without knowing its positional index.</p> <p style="box-sizing: border-box; text-align: left;">If we are given <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">(y,</span> <span class="pre" style="box-sizing: border-box;">x)</span> <span class="pre" style="box-sizing: border-box;">=</span> <span class="pre" style="box-sizing: border-box;">(1338000,</span> <span class="pre" style="box-sizing: border-box;">388020)</span></code>, we can find the <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">green</span></code> measurement at that point using <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">sel()</span></code>.</p> <div style="box-sizing: border-box; text-align: left;"> <div class="nbinput docutils container" style="box-sizing: border-box; display: flex; align-items: flex-start; margin: 0px; width: 696.469px; padding-top: 5px; color: #000000; font-family: Arial, sans-serif;"> <div class="input_area highlight-ipython3 notranslate" style="box-sizing: border-box; margin: 0px; flex: 1 1 0%; overflow: auto; border: 1px solid #e0e0e0; border-radius: 2px;"> <div class="highlight" style="box-sizing: border-box; background: #f8f8f8; border: none; overflow: auto hidden; margin: 0px; padding: 0px; box-shadow: none;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: pre; line-height: 1.4; margin-top: 0px; margin-bottom: 0px; padding: 5px; overflow: auto; border: none; box-shadow: none;"><span style="box-sizing: border-box;"></span><span class="n" style="box-sizing: border-box;">guinea_bissau</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">green</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">sel</span><span class="p" style="box-sizing: border-box;">(</span><span class="n" style="box-sizing: border-box;">y</span><span class="o" style="box-sizing: border-box; color: #666666;">=</span><span class="mi" style="box-sizing: border-box; color: #666666;">1338000</span><span class="p" style="box-sizing: border-box;">,</span> <span class="n" style="box-sizing: border-box;">x</span><span class="o" style="box-sizing: border-box; color: #666666;">=</span><span class="mi" style="box-sizing: border-box; color: #666666;">388020</span><span class="p" style="box-sizing: border-box;">)</span> </pre> </div> </div> </div> <p style="text-align: left;"><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAArwAAAF7CAYAAAAwv1jSAAAgAElEQVR4Ae2dz28cx5mw59/xH/H9CcECAnzai88L6MhNAsO2IHBzW+WgJDaykRxBSyE5JLaCLwlkUdbaWVNeJKHsxA4c23Jky4qiLD7CUAwffKkPb1W91VXVVd1dwyHZM3wIDGa6++368VRx+LDm7Z6F4QcCEIAABCAAAQhAAAIbTGCxwX2jaxCAAAQgAAEIQAACEDAIL5MAAhCAAAQgAAEIQGCjCSC8Gz28dA4CEIAABCAAAQhAoFl4D25smdv//H/s460b/y8heP/l8v74nNsvvxed8//Mn15w59z+5y3zp79Hh3gJAQhAAAIQgAAEIACBFRBYSnhz0TXGievv/2iMSG96/D3z+xf+rzmwjX3P/P6f/4+RuPRHzkd4UyZsQQACEIAABCAAAQisgsCKhLdrSl94u2PySo5PFV63Mvw9cz8tgi0IQAACEIAABCAAAQhMJnDMwisrvKWV3PIKL8I7eRwJhAAEIAABCEAAAhCoEDhG4XVpD2m6g7aqLLx6lGcIQAACEIAABCAAAQgsS+CYhNfJbnrBWtxkhDemYcwTs391y2xtFR5X982TNHjy1v2bV8z+F5PD08BPd117DlF/WuDxbT26dsYsFttm7/iqPFRN69beQ3WWkyEAAQhAAALHQOBYhNfevSG5O0Pes7Lwnt6UBi+8N/vZy/dvigTvtuc1W2FdXnil3is3d82VrS2z+2k+fvPeXjeBXLf2znv0aR0EIAABCEDAtH/xhEhoKS3BSq2/XZm7bZnP1f3j98JtzPR2ZrfDXRt0CBBeJeGe68IbVn8LMpyWkW0dRni/2PeiO9SurL45bd7ZXqsVXrNu7Z3TWNMWCEAAAhCAQIHAyld4C3VM2FUW3gknFkOe3L1iP35PViL9R/JX7nYJAW61NEobSD6ud3K3e9fJnqQXSHm27Jv7XcpBEE8vg3EaQjh23+xubZm4btvwqoSOiGXpPN+/Lg2iW81VHnqsa8dQmzu07ny3qhy/7iIqXKRNV/fNvl2V3rKvlf4Qe3ssGQupqcIwbkTt9YMdc+bpHfMoOr53fmEWC/94esfsyHaI2TPbizNm55qIssScMTsP3Mlu9bU7d/tOVKh/ORzzyOw8vTBnru3Z57gNoX2F9vZrYQ8EIAABCEAAAlMJLCW8ulJbWumdWrGLE9E9mi+eSKXJydJWEFBj0uPSmjxGZbATR4lSeeyk0e51AhyVb/yqqMb16yu1wVEZXcUNK64+viDAtr449aEXU5DqrM2u9Cwur9s3ochFJTzmIqRFgBOhzdj32mqMKe1TXI3PVnaD3BoT5DfsE+EVqU3zfp3IdvJrREwXIq9BVc14jBNeEd0gy4VyGrtEOAQgAAEIQAACAwSahXegrJkd6lYEe/Ln5TZZAe6JWCZ6vnfFFU4rgakY96S1J2xd+/rgynV3ce5cbb/tXyaVPUHM65/SZqmwJ7i+bYmw6j8CWW5xXqftQNp27VMqwX026XE9a4lnK5eRtNoivOBmwhuLrDEuJt1nvOCqGE+J8cJ7Pr2ELpfwJXrGKRCAAAQgAAEIVAhssPCqhLmUBZXDPgcnV/pxf7fy6MROV2j1PCu8mezpMfvsBTGUF0Q0k7iiDGpJbcKrZ8mzk3tN04gkfKi+aptLq7Hl1dYiF1tnJsFxY3VVXdNAIq6p4GbskjLaNtwKrApqd24qnE5cwwqshNm82lyUjXGrvH7/lBijKQ3dqrAUn9bftYtXEIAABCAAAQgcnsBGC29IU4hESpElYuiPp5LVIrxeUFXcrOT2pTWWwrQubZU+98/VI/Y5X3W1YplJbi64+XZ+67Nim7N/BrR//jn+ZyDuW2hrRXjH2evKshf2XttDDc0vDie8Xe5uyL2Nc3yt8I7EILzNY8YJEIAABCAAgcMS2GjhjcUqlrP+x/QOYyqhDcJbFLKCtFpRFYkbW7EsnBuPdCKSldi8TWPbtvysrPycqA0pK7+anv9jkbTTn5zLut+dl6cpITJu/WNRQxpfHk54Cyu8cf21Fd44BuFNaLABAQhAAAIQOA4Cmyu8VraiOyts5R/v5x+1+9XMIG1O/hJR1ovWQowbouLqpqYJhJQGifVl2vvZRu3pjXQmnsnx/Jhrd56yYSWx1+euziltHhTNiK/tmdwZI+Pi8ogzziUJ1tSG7HzXxl2ze7Vwh4uEScNGQw5vktLgLyxL9km1VnJ9isSUGIS3YbAIhQAEIAABCKyGwIYKby6FfluFystoLLNOECUtQAXNnRPHCPKiKGbyp2Jr83i1Tj9e9nxJCcj2p8OZt7876tqpbbQtcneIiMvz7ZH6gwjbfZ3wOhmNjscpDlJWgVHXCnnl/0HwQl/nErdVUxVSge2z9zXpPw2xuKeNWGorz5e125KaEC4kK+TwGr1ALV7l7V+k1rtLQ+9iN3J4lxo0ToIABCAAAQgcgsBGCq8TqEjuBJDKk664RlJoxfTmfX/LMT2vQXhVhKMc1/BRfBBoP0olkbT7tF6J88IblRcugovFNgy8l88Qv2vu9+qJyowlNZzjJFSF+n17L+O4TaGy8EJj5fvgJguvnD3KXqvwbS72WWOWew6S60XXbo8Ir9TkhLbL083v2jAeg/AuN2KcBQEIQAACEFiewEYK7/I4juHMfKX1GKpc3yqcyOer7KvvT1lCV18PJUIAAhCAAAQgcBIEEN5jpm5XRXWV+ZjrXrvq7D8HWUrEYTvh76SQ5OJOutjssBVzPgQgAAEIQAACJ0UA4T0m8tNyd4+pMXOvRtNPVpy7q93O0xLirw7WGJ4hAAEIQAACENgcAgjv5owlPYEABCAAAQhAAAIQKBBAeAtQ2AUBCEAAAhCAAAQgsDkEEN7NGUt6AgEIQAACEIAABCBQIIDwFqCwCwIQgAAEIAABCEBgcwggvJszlvQEAhCAAAQgAAEIQKBAAOEtQGEXBCAAAQhAAAIQgMDmEEB4N2cs6QkEIAABCEAAAhCAQIHARgnv/fv3DQ8YMAeYA8wB5gBzgDnAHGAOxHNgo4S3IPTsggAEIAABCEAAAhA45QQQ3lM+Aeg+BCAAAQhAAAIQ2HQCCO+mjzD9gwAEIAABCEAAAqecAMJ7yicA3YcABCAAAQhAAAKbTgDh3fQRpn8QgAAEIAABCEDglBNAeE/5BKD7EIAABCAAAQhAYNMJILybPsL0DwIQgAAEIAABCJxyAhstvAcHB+Yvf/mLef/99+1DXsu+0/7z6NoZszi/N2MMj8zO0wuzfWfGTaRpEIAABCAAAQisDYGNE969vT3z/e9/32xtbQ0+JEZiT93PnW2zWJwxOw9m3vN1aefMMdI8CEAAAhCAAASM2Sjh/c///M9ByS1JsJxzan4e7Jgzi9LK6Z7ZXizMovR4esc88oD2zucx26b/L0NWVnR+K2e7Er0o1dFaEvEQgAAEIAABCJxmAhslvCWhnbLvtEwAK6yTBdSlFQylPvTL87Ib0iV8GZPrzEfClXfmmip3fpxtCEAAAhCAAAQgME4A4d3aGqe0CRHV1d1K56akFNiYbgW2uCLbWm/WnGKZWQybEIAABCAAAQhAYIjARgnv7u5uc0qDnLPMjxOx6CP+sKrpSrPH7cpm/BF/mjtrV0jlPC+FLqWgE0hXUny+1BeXka+o+p748pKV0UxOh/s8vrprTD8m9CcU7mMWC5O0JRyf8ML2Je7zhHMIgQAEIAABCEAAAhGBjRLehw8fmgcPHphf//rX5nvf+5759re/3RNg2SfHJEZi5ZzmnzvbqcAVBLMT4k7W8hQAu23zZlVyc4mUbT3mWunO6faVVkA72e56VtrXHc1eDazudm1emEWSquDaHsQ2MNmzd1wYSo3Ias82ndRzx4YMC5sQgAAEIAABCEwmsFHC+9xzz5nf/OY3vVuPffXVV0Ye8Y/cnkxin3/++Xj3kq8z2TPGOBHtZNcWnK2y5vIqMaNimpVhTC6Eri25INq6EkGtddWdP0VQ0z52DNJ+TS+v3CLXvyDS5SD2QgACEIAABCAAgSqBjRLe+AK173znO+all14yv/jFL8xrr71mH/Ja9smxOLZKp3rApxJkdzWIpczJYLcSWypqioS6cqLUiWRF2JWaSHJPiAsxpcbovqYUgk5y5XQnunn6Qhqj1Ux/zoV++plEQgACEIAABCAAASGwUcL7k5/8JBHZWGprr+Wctp/SimVf6lYivKXUgpLQRpIq0hmLd+hb6bxwsHsxRcKjaHs7M11NLvY5alt3XsOrw57fUBWhEIAABCAAAQhsJoGNEl5JW3jvvfeMSGy+ihsLrxyTGInNUx3Gh7kvvLoKG4tmUf6ywkflsie8urLcXzl2ZZ0xZ2r3rbXiWLoHb9SoKTFRuK0zqc+3L1zA12elpyuzNA9Yj3bPNm5SKkZ3Dq8gAAEIQAACEIBATGCjhPfChQtWYuMOykVpH3/8sX3kF6iJ8Ir8Nv9YEY3SDM7v2Y/zVy68eieEkDpxxuzckS+P6Auv3ukhbkPer2HB9nJalUt/PLRlUfl6YpVyzyfIb9qaacLr6hzqU1oqWxCAAAQgAAEIQKBPYKOEV1dx5UK0PH83z+OVGI3vY1nDPVM++m9cwT1pClNWyU+6jdQPAQhAAAIQgMD8CWyU8MoKr0rs1Gc5Z/1/3KrqpJXQXprETHu/Lu2cKT6aBQEIQAACEIBAR2CjhFfycd98800zRXwlRmLX+sev2MoXVkySXd9Zu3JaSTWYBw+XyqAXw82jTbQCAhCAAAQgAIF1JbBRwpsPQpy/W8vjzc9hGwIQgAAEIAABCEBgswhstPBu1lDRGwhAAAIQgAAEIACBZQggvMtQ4xwIQAACEIAABCAAgbUhgPCuzVDRUAhAAAIQgAAEIACBZQggvMtQ4xwIQAACEIAABCAAgbUhgPCuzVDRUAhAAAIQgAAEIACBZQggvMtQ4xwIQAACEIAABCAAgbUhgPCuzVDRUAhAAAIQgAAEIACBZQggvMtQ4xwIQAACEIAABCAAgbUhgPCuzVDRUAhAAAIQgAAEIACBZQggvMtQ4xwIQAACEIAABCAAgbUhgPCuzVDRUAhAAAIQgAAEIACBZQggvMtQ4xwIQAACEIAABCAAgbUhgPCuzVDRUAhAAAIQgAAEIACBZQggvMtQ4xwIQAACEIAABCAAgbUhgPD2huqJ2b+6Zba2useVu096UUe+44t9c2Xritn/wtV0/+aW2bp5fzXVfrprtq7umxPo1WraTykQgAAEIAABCECggQDCG8OykrllUsG9b3ZFflclm3F9Q68z4R0KbT6G8DYj4wQIQAACEIAABNaXAMIbxs6t7Kay6w+W5FOkMawCdyuxobiB40/uXjFX7u47kd7aMrufurPsKq4v88rN3eoKr5y/dXM/WYnO2x2XZdupwp60a9eENeNkf9cm37LQ1qQse9D9Q6B9CP3nBQQgAAEIQAACEJgJgQ0W3n5qQieo+SquMaYktbVBsnIYSW7jthXWKF1BqrGCGtIMtO1dHXFKgzs/ktKs/rQsY4yX2SClsh3q0uOR/CYs8n8E8u0aJPZDAAIQgAAEIACBeRDYYOEVwD4dIazE+rxcXe2Mx8BKYSR98bHkdVn4OiEdO26MFdZYOH07g5BKfYl0eiH27Z50ftLmbBU2EV7X3qRu49to6yv3JymeDQhAAAIQgAAEIDBjAhsuvF7cEuHtVk2TcZksvJk8+kKshFpBHDteEN5Mbl2RUk7X1k6oC+eXhFmlNep7kNpEeF1749Xv8Fr/MbBs/D8LiagnBNmAAAQgAAEIQAACsySw8cLbW+VVicuHoyideZBsjwnt2PGCsBbrXl54NeVha0tXrLM2FYQ3yHCpy7rPtnNglVzjeIYABCAAAQhAAAIzInAKhDde5e1WTPtjMPTRfSyM5bhuBXbseEF4SxKdSXBX/tj5rv5UYOP2+5zdsFJbbm+fT7Rn8mp4dA4vIQABCEAAAhCAwAkROBXCq6uyo7cW8yuY6R0PnCz2L/KK5NkK4PTtfg5uLrFOQreWSmnIBVbLyi5yC8KrF61F7TdxGfFrN0tj+T6heUu1EIAABCAAAQhAYDKBUyK8IpS74Uschul4wY1yX1MB9mfHea2RmIayB46XhFfOsyLp6929W//iif752QpunHpgb3vmpTekc2gfO8nt0iBcykLS56y8RP5Lq9MBAi8gAAEIQAACEIDAyRM4NcJ78qhpAQQgAAEIQAACEIDASRBAeE+COnVCAAIQgAAEIAABCBwbAYT32FBTEQQgAAEIQAACEIDASRBAeE+COnVCAAIQgAAEIAABCBwbAYT32FBTEQQgAAEIQAACEIDASRBAeE+COnVCAAIQgAAEIAABCBwbAYT32FBTEQQgAAEIQAACEIDASRBAeE+COnVCAAIQgAAEIAABCBwbAYT32FBTEQQgAAEIQAACEIDASRBAeE+COnVCAAIQgAAEIAABCBwbAYT32FBTEQQgAAEIQAACEIDASRBAeE+COnVCAAIQgAAEIAABCBwbgY0S3oODA8MDBswB5gBzgDnAHGAOMAeYA/EcQHiRZP5JYA4wB5gDzAHmAHOAObDRcwDhXWqC3zDP/tMPzQd67mvPmsXiWXNDt3ne2F+aD/7jG2bx7RuufzLu8Tw49Lh/Zt546aw5+8q7Tfw+e+NF8+ofWv+Tf9e8+tIb5jPfZinjbLQd/1e88td/eNWcPXs2PF5847PB/r77ytmsbe+aV8P5r5p3D839wBzEbVqKw4fmzYsXzcXwuG72P8/H5KHZ/0UU84t983Cg7R++edFc339YYJPVNVJOPH4P96+PtLFrc73+Lqa1bCmzY/Sm+VD7//m+uR7YxTHx6yhezztwTN/8sNymA1tu6bxC/IdvRm27aC4WuKb8LpqLb35YGJ9C2b69w0xH+hL67MpP29Kfb+lx5TjAwo9BzrJXTrHP2ZwsxeRjXIrJ+hjPL17X5xVsprFBeJf4Bbvx7cWKRWfaYDGpT5jTez8038j+sZG58I3/+KDpj159HJcQ3vtvmBfPnm0W3r5EHhNbL5ZB0H37q9KrIppIqBPe6jlL/E7rmCzHxf+xj/6AO0mIJcTLbojJt1P+Khl94c3P89sFOdM+6bMrsxOefFvj5Llef9pOPScvK9+WOCe7Xf0HVjCj7Xjc7LGYX7leFehc0ly7VMIqdfTqu2i6cvpcXZ/iNvXHXXmUnseYDvcl7X/ON98OvMN8S8/vt8/392LMQOfBWJ8dh26u5ttSd76vjV2/vWP94fhJMfvLX/5i/vSnPw0+/va3vzX/zZRyf//735vbt2+bX/7yl+bnP/+5fZZt2S/Hx/qM8MZvehNfI7yn8c3kA/PDfyrIbUGCx37p6sc3XXhd/3JRra8uRyu5cxbeorhlYloSOLviFcuE/F514iEroZ1E+N+5yeXkv6O5cHR1pXWM1F98j5xQdmX10EpeScpK/Uzq9sLkV4U7UY05TVjV9GUW25G02XFJWR0YJ+35GObsx5iO9CXpt5Q9gbefR7329spybVUZlznXsZzW55Js5//MTImpvy/mPNmeM6tbt26Z999/v/r4n//5H/Nf//VfkwT1wYMH5tq1a2Zrayt8Ihh/Opi/ljiJ//zzz4vyi/BW3gDKE8pJz2KxMO7xDfPD9w7MQZLScMM8u1iYZ19zzyHWfgye7nv2tfwXNz2+2o/L87rYLo9xhYsVWz/eyZypiHASUynzwAuufjz/yhuFlIYsRmI15UFXP8P5XSqElUjdn6wA5+W9aN64f2BS6XSi+eofIuEM9ab7wkqt9tev2IY3Im2rHLfHXH1T2NvV1lfeNf1VV9eGWJy1/e9Kakbod15X3vf+yni/rtrYje+PJcr+we+twuZCoWIkK5IlqakIVlGcs/ZVYtJ2Tahfxzl+nlJ28Z8Cv4LY41LpZ6jTC6Kcl0ip77Oty/2zUBQtXcEu1RvqkLJcPZ0AZkwlNu97vh3+gamN6Uhfkvb4+nt1uP3pWEq5YyKelfehSysZ7G+hz/E8736vU3bFcajMia6MAu8SD/YV5e6kOIrwDtUtMvzxxx+bvb09+zwU+/zzz0fv510aXPceX94n55XKRXiX+GXprfAWhHex6OTI5n1aSc73ddsHB152NT/04MDYerKP0EuDyL6jf2O0Y1jJ1x06Vh8bL1/RyqWVrSCW0icf05PGsybIXiGlwcluJHuFtIFc7FQYXU6vSm1XhitT3lzyfd22E9qobdp+7aMVdMm51fLdm1XoS/y7aGNd2XlbD/z58XmhfYFVzrfAMqpDx6lf17Jzy/3B1xW2shR4wSytcNaEVwUqnOPLGJO3ilwURcSOQ9p+5VN8nlL2lJh4/G38BGErCW9UTr1/E8Z1Qhvayh9hOtKXwH4KSxujK9z6XErtiObgxPrTPrvzdZ6HNvbmb953t92aA92VP2H8onnAecfHa4rwfvbZZ+Z///d/rfQOpSKMie3Q8dKYI7xL/FJMEt5IXA/s6mD2cbjfp6u8TorzC9+cBK8uR/T4Jn1psq3vPr+yH49pPG+Sf3gmMi7IlopcWMEtrohm4tYT3v7qp3B3Qthd4JWLXVF4gzzq6mwss90+XeXNy7TjHbUvSOnZrh19SRZ+ro8qtP1y+33M+2frThj3zynNx35dE8czng+6ghitsK1OeF17bHl6kVeQ34G2TpGkpA+5oByy7IpMuX4URGyCbNrxq5SrY5vK2UAfkr5L3JR/JFqlbYTpSF+0T3m6gO6P++pex+kJhRxq6XM8LybVn/d5qvBGTP287Utyy/gQq+M+t+cx4f3ggw+MxOhD0htqffjmN7+51AqvnFcqE+HtvdGN/yJNEd5UUp24qtzagUiEty5Utq6aaC3R9tIkYN/YmI/841FNd6iXmwqmxmUyG4+vF8fwH63KaCSUdhwTydNyVU671dhc7NL2lOTQ7VO5tXUldZfOkfo7eXVS2rVB510uq2lbDppSGvSuEyUWts+ygq4rzjFf/zrnom1sevara/Ef9JUJr5eSXtkXC9IY9y8Wm2h/LElpH0fkLCojkaZof1q2F8i4nZ7TxXifnm+PndwKrxPxofoL/dG2V59HmE4SzkxSo7pS3tHvfojJ63fbIYVhtP5SnycKrx/rUNekfyhKfWBf+ns6Px5jwpu3X3J68326LXL84otxmlo5hSH8XTx71sbLeVpG/IzwhjeD6RPnyIQ35AZrjrB/RniLkzeeyEf7eorwSt729DlUEyu7X2VWUwI0J9Xuz6Q4kc7s9lp6XnjuZDOvP5XMkrxOE974jSd+Lau1udiGMYsl3fana6fE5G3VlXBdAZaYtP1+HOJy/e+5a0P0phlYu3P6dU0fU9sf/4c9FlLZvyrhteX00hdykSm02barL8V1SZpQpr53Ti5bhcl/xP7mh/5uEP12OYkeEk7fxxFJq/evwMj352hkV+obYTrSFzu/pI2Teed99Pz9JwK9OTlYv45dPlZThDetN/RjsL687WwHbvp7N9PnVQrvz372M/u3/+HDh+bu3bt2VfiVV14xV69eNZcvX7bPsi11ynGJE047OztFZ0B4l5g0Rya8iG1xkp78L/oU4Y3zscffnIuCpoKrElYQNl0xTdMeoouviuf025OLXdqe5YU3ltDeuNm2RekM+rsXtbknpEHW4/zhfvvS9vv+RuX22qKSLCsC0X2Acy6l86r7KrIr8Va8eqJakwVpf1mOepJiGUZC4SWiu9etl0a7vy+Q5XbV6ndt6sr2H5k3l93Nx7LAq9T129tjPyJNrcJ7dLJbY9qxcBfApWkIvf7KeC/NO5onfn7FY5m8TtJk/HmllfjqP3NurrgV3do8L8/xYp/1vYLnmf6N7OaxCu/f//736p0a5MI1OS5jPbTCK4smcucFkdtf/epXZn9/397uLJ4jcgs02S/HJU7v6BDH6GuEd4lfoNULr79ArXdR1GHuANBNQB1snpdlUk85sUyPKIe3KHF+RbcqvPmKr87vTDZzsUvr6gulrqrWUxqylWet119g5s4rrBIH8SyIsC8jb6u2JRbVtP1+nEeEV8bOlq3/YOj2QMpD9XdoQHbtOfZ4JnAVcXF1lGWgLIg1oYjne6m8ofNK8XF58etSbFZ2UUxL5/lyS7zCnIrqLpbbHW8R3qOVXWnTQH+lbyN96eZeqZyUd3meuPO6tIKOky27WL8rt5h24sejyNiOn64G+zISie54VNtTGm/2rY3wypySuzHUHjqfh4RX5TX+tHDKazlPy4+fEd4lfoGOQnhLd2koX8iWvUkt0f54AvB6Gs+hOzEMHavz9YIYyZWVL1nRVAGzwhat3uoKsMToeQXBtfIX3U2hJIi5RKbCuIzwap7w8Iqpa1skt76PsbzmzPK2lvqTtt+PaSK8hT4V2PXrmjA/vCjkaQxpP/wf/bDKW5MAra8kNZ0UxXU5UVO50PP7z05MOukuikp4P6nUH46n5Y+Xnfe/chGVln/Mwpu3Px0719epnEvnun0jTIvCmXLWsvP29sayNyf7/LWs8Fyof1qfXb+6Oy4U+mnHM169ntAenQs8F+UtjNvM+OgKr9wLV249JtsitfqQbdmv98odEl5Zvf3+97/fdOGaxH/yySdFZgjvMpPFX3Am99i1eZvJCl/p42+3L8nxTC5a0zc1Fxfu3cstyYqT9kR+0asXph1mFd5Lr35sX7gPrxPELudUxNCJcSeNbjuS4LBqmp6XcNOVYn+P3lQYC3KYrNT6+VoQRr3rQvgvXMU8/j3zkqsxyapxHOdf9yW03760/b59ifDKPnee1ivPuWj369LfzfqzkwK99VP2HARXzvd/5PXuCsmxvPyCNAQ2XjC0nMpHzcl4+3OdGGkbO/ntxw7Vn7fVbY+X3dD/IxZe29bAP+epfNyz/efCy2DysX/gH4mcjatxHWFaEE47LpUyR3nnbQ79LY9fb4U5Pz/qr3BIV2Yzhr3V3O6ftcBwrD1hvkxpEbcAACAASURBVFfay/H5/H2MxkKFV+aufKNa7aHvOUPCe+nSJfPXv/7V9lPSFl5//XUjeb35Q/bLcSlT4iW1QcuPnxHeaKBiMLzmTSadAxWxtSKc304Odim7VfPoC++q6ltGeFdVN+Wsep5QHnOKOXDcc0CFV+61K7cck215/OY3vwmvZb8cl7YNCa8uTvzgBz+wXyUsK8MitvlD9stXDUucnlPqN8KL8Bb/EypNllO/ryC3kt6S3oKON9ijnycI79EzZh7DmDnAHGifAyq8U9kNCS85vKb+MxUwce2TGGaOmc3X1btpSCpL70JD2B79XIlTE7rUjkPVG6dZlNIw+MeYf4yZA8wB5sDoHBDhldzbqQ9Z+a29d0surqQ1TBVfiZN4cniZqNVJVZts7EdemQPMAeYAc4A5wByYOgfkq4LltmNTH0NfLRzXqbcf++Uvf5nk8Mq2pDjI8Ti+9JqUBkR4dJKUJg77eANkDjAHmAPMAeYAc2Bd5gDCi/AivMwB5gBzgDnAHGAOMAc2eg4gvEzwjZ7g6/KfJ+1klYQ5wBxgDjAHmANHNwc2Snjrl7NxBAIQgAAEIAABCEDgtBJAeE/ryNNvCEAAAhCAAAQgcEoIILynZKDp5moIPLp2xizO77nC7mybxdM75tFqit7QUu6b3a0te1uZrav/bf776pa5cvfJyvv65O4Vs/tpVuynu65eX/9wvU/M/tUts3XzflbIEpt/f8dcfv2e+WrCqY/vXrbfCiTfDHT58i1z78v+SauK6Ze85B7pn22va/s7fy+Uk8XcujeFRqEcdkEAAhBYEQGEd0UgKeYUEHiwY84sto3XXdvhvfMLc+Yaylsb/fs3t8zW1X3jFNdJ5bB41koa2P/FvrmytZUKr5fdIME+pla3CLPcw/HQwvvlPXNLZHCC8H5175a5fPkd89h3zW2n0ruqmAF6bYesyEZtzLelNC+7QYQ9E6S3DTXREFhHApKDfO/evcHHsv36xz/+YT7//HPz5z//2bz77rv2WbZl/5QfhHcKJWIgYB6ZnacLcluQYGB1BE5GeMtibaU2yHfXRuNl+LDC6+TUr9iOCu9j887lyyaVwK/MvdfjfauKifp6qJeufZfvqqK7wuwKdNiX98HFWDajTA7VOE6GAARmQOC5554zr732WvXx4osvmu9+97vm4cOHk1r7u9/9zn5l8LPPPpt8Ymffr/XTw60tI8flq4UlvvaD8NbIVPbbj7SzVT7jZSh81F05l91rTMCK7Rmz8yDvQ0WE87BTt+1TBKI3pN1PyyJqpTiJy2DFQurjwsptlrZgV2ht/BWz/0VWTnFT23T/UCkNKruyqmkFcCm5Kwlu3ug8Jt+W+LJ0TkmNsLXZFdloFTc0wdUVVm51v13R9SvV1XM1mGcIQGCTCYiIDv2IDF+/ft185zvfGZXea9euDUpuLr26LeeVfhDeEpWhfVZ8Fmb7ThRU2hcd5uX6E7D/6FTydYeOrX/PD9eD4RVeleJdEzJnK6kIQXCNMS79IBJaL8QhxpYhZUb5w1vl3GFXlsT6tqwgh3dZ4c3TF0rkSzH5PredCquT3S59opd2EFdWk9ba/jitIcivk2PN9U1XsuPKeA0BCGwSgSnC+/HHH1vZHZNeFdhlnktMEd4SlcF9bkUvXs0tr/oOFsLBtSLQH/Ok+XLxWm/VP4k4tRuDwmvFNBJXTyk+p5iGkAtutu0kVi6Ui0Tax6Q5vE6InSifoPD6nFeRw6oYjsVEx+O8YIvUiuplk6/MVsW8Jra1/bZuJ9hOtiWtI5JrX3+1b6f2t4OOQ2DzCEwRXklpeOmll+wqrzzXfpYRXT2nVCbCW6Iysi8V3BEZGimLw+tAYM9sLwr5u9r0arqDBpze51hejV9FVensVlczPhUR7kTW3fUhrOgWhbcv0nl9tm1hRfcEhTd0v5wjGw7bF3mM345TKLz8quDmK8ChPBvnxTQRZp+HnN+JYbLwpqvLUl+1DaExvIAABDaBwJjwykVtssKrjwsXLlS7ffv27aVSGuS80g/CW6Iyus8JkE1rIJ1hlNb6B0wR3izNZf07vZIejApv6SKyWHi9zOp/7VZyM8HVi85UgHOxDR2Jy7WvoxXgGaQ02HZGq6Wh3fmLOCZ+HcXFq7fdqmtfZJOVWD2/Jra1/VEbqmIbxWg1PEMAAptHYEx48x7LRWy1H7mw7fHjx2Z3d9f86Ec/MnJBnP4tiJ9lvxyXOImvXRCH8NZIj+yX21FJWkO62jtyEofXlMAU4S1d0Lam3V1hs0eFN0470HojGU3P9wEjwmui87VI+xwJry03ulAufvNMUiGSAqZtxLI57YwoaooYxjH2dZQ+4IuKxTN+HdVUf1kTWzPhorVKe1zOcH/lt94IjkAAAutIYJXCq3d8kFXh+Ofrr782X375pZHn+Efi5KK4559/Pt4dXiO8AUXjC5+3ue3Ft/FswteKwEjaCjm81dFMhdWlDWhKgxPTfupBd04W72vR1AZd0c1XeI2/WC0cT86LV3XjZh9zSoOVyn5ebSKnU2Ji+Y26Y8vRNIcpMdG5piq8eTqFO8kKfrgtWVmKk37FdfEaAhDYKAIqvHIv3m9961thRfbcuXPhteyX4/IztMIbL0SIxEq+709/+tPklmc//vGP7X45HseXoCK8JSqT9rlVv8WCj7In4VrzoKE7MQwdW/NuH7r5nbxKUbnAesmMV3ntKmz3JRJuJTaS1CjFIYhzvuIb7uQQnefLDef0enbMwmv87ctGLu6yMjkYM57DK12tlaN5vj0ctR1WniNRL8h0T279OVy0VoPKfghsDgEVXunR3/72t+pDezwkvD/5yU8SiY2Fdui1nFf6QXhLVCbus2kNXJ0/kdaah1UvTHOrv3zbWnl8h4XXneOk1n/98Fa+4qtSHB/3txsLF5wZE8qIc4K95OobY77im7b4qIW3vPLpRLTLrS0J6HHGpEwqW15g9ZZjpTbrbc8GYyrFsxsCEFhfAiq8Dx48MHJBmmyL1OpDtmW/HJefIeH96quv7MVtIrBalr6f589yXOLkYjg5r/SD8JaoTNo38jH3pDIIWh8CFbG1Ipx+3fD69ImWQgACEIAABFZHQERUf+Trf2sPjRkSXrlPb/7NafldHnK5/e1vf2tvd6blx88Ib0yj5XV1xa+lEGLXikBBbmWVn9XdtRpFGgsBCEAAAkdEQIVXxHRnZ8e88sorvYfs1wvRhoQ3XsUt5e/qVxhLXq8cj+NL3UN4S1QG95G7O4hnww/afN3ze66XcrFa5dvXNhwD3YMABCAAAQj0CKjw9g5UdgwJ71gaQyy48Ws5r/SD8JaosA8CEIAABCAAAQhAoImA3kpMV1/HniVtYehHUhREimOhrb2WOImv/SC8NTLshwAEIAABCEAAAhCYTCDPsdVvVKs9a2rDlApqZU8tA+GdQpkYCEAAAhCAAAQgAIG1JYDwru3Q0XAIQAACEIAABCAAgSkEEN4plIiBAAQgAAEIQAACEFhbAhslvJLHwQMGzAHmAHOAOcAcYA4wB5gD8RxAeJFk/klgDjAHmAPMAeYAc4A5sNFzAOFlgm/0BI//u1vF6w/+4xtm8e0bjtlrz5rFP/3QfMAcGphD75pXz541Z+Xx0q/Nr186a15847OB+OVWJD5740Xz6h+yc//wqqvX1z9c72fmjZfOmrOvvLtk2x6a/V9cNBcv6uNN8+GEefFw/3p0zkXz5odZHw4OzJSYVcxtW8aHb46050PzZuij9vWiub7/cElu/f6urC8T+FMX/JkDp2cOILy8KfKHauoceO+H5huLZ82NKP7GtxfmG//xAQwjJvEfkHdfEdF9w3xmjzupHBbPJd58779hXjx7NhVeL7tBgn1MrW4RZivlSwrvh29eNBd/sW8eeg52++KI9Fq5vG72P/d99rKZSO+UmAr7eBwmvS7WFbVP6vl831y/mO1bVf2Uw/sIc2Dt58De3p75+c9/Pvj45JNPmvv5l7/8xfz+9783t2/fNr/85S9t+fIs27Jfjo+9zyG8/IKNTpKxSXQ6jn9gfvhPBbktSPDp4DFNTE9GeMtibaU2yHfUfi/DSwuvlcB8ddathCbymrzX+BXhNz9Mfv+sKId9U2KifiTlt+6fWJeV4hGRP1Q7WttNPO83zIE5zQF5H/3Zz35Wffz7v/+7/RIJEeOxdj948MBcu3bNxtv3Z/20sPIsX0gh8Z9//nmxbIS38c3ZfqS9WJhnX4t+yeSj7UVBhhrLHht8jkfMj5utFdtvmB++l7ehIsLH3b7Z1edTBKI3plf/UBZRK8VJXMY4FlIfF1Zus7QFm5Jg4180b9zPyiky0ja9e8iUhqyuogTHMVMEc0qMK9OtKGuKwcAKbHWFtiLomeDa9Iog5HF/eM37M3OAOXBgPykb4iAy/Prrr5t/+7d/s89Dsc8//3ySljZFeiVGziuVi/AW/wgO/+LKx9hd7uYN8+xi0eV1LlFeaWDYNzwGx83H/qNTydcdOnbc7ZxbfcMrvCrFr5p39femkooQBPfgwLj0g0hovRCHGFuGlBnlD58t5w67siTWt2XJlIace57ikB+328UUgmyleEJML33CnpOVo3xrwlvbn9TvBTzL4SV/d17vVcW5puPPc1GEYLa6OSzCOcRThHd/f9/89a9/tdI7tNI7VXBLcaU2ILxLvQE4yZXcTSu/WV5nCTT7VvcLdfws3SpuuFgtnzN2hT/N7T3+Ns6T76DwWjGNxNVzjc8ppiHkgpttO4mVC+UikfYxaQ6vE2InyqsR3vgCs3o6QzRWfiXYXexWWZkdivHH8rqqwl0T29r+RHj9BWtRrrLL6eWiNX7fozmdvz+yPSiAmzZ3xoRX8m5jQZU0hBqDb37zm0lsfN7QazmvVCbCu+Qvo6Y2SCpDkt6wZHmlwWHfXN5Eu39wimNSTXeYS/tPrh2xvB74VVSVzm51NWtfRYQ7kXV3fQgrukXh7Yt0Xp9tW1jRXY3wdvPDyeHQ6mdvZdbLa3zOWIwT7EJOrRVVv9+v+HZ3kNDUB/dsZXmS8Gbj5N/rqm3gvbD4R7ebI2WeHIfLOs+BMeHN+yY5vfk+3f7ggw/Miy/6C4qjtLch2ZV4OU/LiJ8R3qXflH0qQ+Vj7hgyr9f9DWyK8PKPT2mejwpv6SKyWHi9zOobnJXcTHAPsu1cbEO74nLt62gFeMUpDVLnoAh6uc1XZpNzJsS4+FRgO7EtiHBNbGv7kxXeyu/xlJil32crdVJe8Q96mOvwgc8JzYFVCq+kP9j30ocPzd27d82tW7fMK6+8Yq5evWouX75sn2Vb9svxhw/d7RF3dnaK44/wLjkpXCrDgovVluS3Xm/MU4S3dEEbf6xHhTdOO9C5FMloer7nmQluLrwH0fnJPIuE15ZbXTGIRXj5MUzkVfumz1MEc0LMYB1aV/xcK/Ng2kVrCU8tF+Et/nEtslJmPMNsQ+eACq/cYWHo9mRyXH5HhlZ4pSxJeRC5/dWvfmVzf//0pz8lc0e2JSdYjkucxGsb8t9BhHeZSefvyiCpDC61AdnJJ9ZmbZPDu+x4psLq0gY0pcGJaT/1oDsni/e/q5raUEtpOPAXq4XjyXk1mT1ESkNF+Kp5tNIeK56FC8tsWX5ldkpMpW4rwnGurb7PVYV3wh0hKu2p1qV18pz8gV72d4nzlv/nE3bHxy6WTbkbQ+2hYzIkvCqvUmbLo5YXjPA2vxnn8uO3SW3Y6Df1oTsxDB3TX+rT+tzJq7zh5gLrJTNe5bWrsN2XSLiV2EhSoxSHIM75im+4k0N0ni83nNP7vT+E8B54WYwF04poQWijesfyc2XOHCYmT5cYnYN5mwsy3WtPfk7Uv9H6iN3o90zG//gkc06sVXj/+Mc/2rswyLZIrT5kW25JJsel3UPCK6u33//+95tkV+JrX2yB8Da+6bpUhmxF1160xK3J5vRLt/K2VC9Mc//w8G1r5Tf3YeF15zip1f/g8xVfleL4uL/dWLjg7MCEMuKcYC+5ujKQr/imc+Qwwiv9yG/Zld9xoZwy4FISuhzc+II1bd+UGCeiXTnNsqvvg15gNQ+4VM7K6tI6eUZ8mQMbMwdUeOX9S8Sz9tD3tyHhvXTpkr19mcRK2oKsFpe+1EL2y3GJk9udSWqDlh8/I7z8ohUnRjxJeC1CUxFbK8Lckow5UhZ+uMCFOcAcOE1zQIVXLiL713/917A6+9xzz4XXsl+OC5ch4dXFih/84Ac2H1ju2Stimz/064wlTs8pMUd4EV6Ed+ocKMitrPizussftNKbK/uYF8wB5sBpmwMqvFP7PSS85PCa+s9UwMTxJrTsHLD5ut++4f5JkIsXyd3mH6ap/zARx1xhDjAHNnwOiPAO3Z0hPyYrv7W/x5IOIWkNU8VX4iSeHN4Nn2S1CcN+5J45wBxgDjAHmAPMgeOYA5JeUMqzre0b+mrhuL16+zH5pra4LNmWFIf8dmXxufqalAZEuPrflU4SnnmjZA4wB5gDzAHmAHNgnecAwovwIrzMAeYAc4A5wBxgDjAHNnoOILxM8I2e4Ov83yhtZzWFOcAcYA4wB5gDq5kDGyW89cvZOAIBCEAAAhCAAAQgcFoJILyndeTpNwQgAAEIQAACEDglBBDeWQ/0ntl+esc88m18dO2MWUTb403fM9uLhdm+Mx65kog722axWNjHmWva6pWUPJtC7Bic33Ptkf42jcdsujHjhjwx+1e3zJW7T5ra+OTuFbP7aXrK/Ztb9nY2cquara0rZv+L9Lgx982uPaZxpZj8nNr2V+be65ftN/zIt/xcvvyOeZyH/v2d6Phlc/n1e+arPMasqJwv75lbth2+TXd7renVzA4IQAACm0wA4Z3x6O6dXxxSqI5TeF1dmyq6dpo82DFnFtvG667dJWO00X2e8e9HaNoX++bK1lYivCLAW1u75r4Pctux0HrZvakRxvRjQg2jLx7fTQXWbsfS62X3nb9rUV5sE+nt71uunMfmncuXza17qtNu+zLSq/B5hgAEjoiA5Bvfu3dv8LFs1f/4xz/M559/bv785z+bd9991z7Ltuyf8oPwTqF0QjEI7wmBL1b7yOw8XZDbggQXT2fn0RHoCa+T2XSVOFs5/nQ3EWLXOBezFUnwpEb71dROZuUsJ5m6z4prLpz5eVaK85Xh9nK+unerv8JcLHtS7wiCAAQgMJmAfJHEa6+9Vn28+OKL5rvf/a55+PDhpDJ/97vfGfnK4GeffTb6xE4/leue5bjESXztB+GtkRnabyXHfXTvPsKPV/10VdU9D33Eb4XWpwDYuPDxuJMrPXexOGN2HhhTSmmolyEd0LYMdSY95j6y37FyZ+vXj+99WaFNoa3GmEEeaflru2X76MYh7UNFhNMgtiYTqIipFdTuzS2kL2T767JakuB+o2waRKvw9osxJpfZUswUKS6el+9MpRjhzfmwDQEIHBcBSSEb+hEZvn79uvnOd74zKr3Xrl0blFyXrtb9XdBtOa/0g/CWqAzt83IX58WmK7Gd6IYYf0780Xd6jlTozwuCaUwekwtvfrxfxpLCu8hXMittSz7edzFxH4cwrtuxnH3c/qFjcRyvpxAoCe+W2bq6bzSr1+XmdukKprfC268nT3HoR8ieaVJcPjfdm6c4pEf9ll11vWXufSnbLp1B0hCcsGo+cL7iWygpKUeOOwEmpaHAil0QgMCREpgivB9//LGV3THpVYFd5rnUSYS3RGVon70wq7TSpyf15VCOWCkKglgW0Vxg8+1UrKaUUY7Rlpae03a6iNI+letOcF1d3Xap9HXd51fco39Gkp7YORGv8idH2WgiUBLeOPfWmJ7gDglvtAKcpjj0G3WYHF4tLZZVTWfQY+lznq/rJFUueOtE1ZheDm9aSBDl/gVwvnx/4VpcZq8IdkAAAhBYEYEpwispDS+99JJd5ZXn2s8yoqvnlMpEeEtUBvc5sZOP9styV5HM6kfiXXk2XSBKFRgW3riRtTIqbYlPzV6nUi0H67Jn2xck0NVVZpJVsnabI32rju3adXQGDS4Jb7SaKy3MBTffLvbClVtNefBiPCbFxaKLO/NV1jTIiayu7soxL7x5nm9vtXasHGOMXfG9bDrhzuU6LYMtCEAAAqsiMCa8clGbrPDq48KFC9Wqb9++vVRKg5xX+kF4S1RG9+U5tvGtvyqSmUmRlUXN3/WSmwtuvp3L6HgZlbYM9C+vIwivtjV/RnhDDnNIYRngy6ExAkclvMYYK7XZarE0Z+Wy6/pYzKUVtZU7OlyOZVfia4LsZbUnwrVyKvGTcorHxobjEIAABIYJjAlvfrZcxFb7kQvbHj9+bHZ3d82PfvQjIxfE6Qpu/Cz75bjESXztgjiEt0Z68n6VX01zqEhmnArhc3pzQcoFN99OZHRSGZW2DPQtqcPG1Vd402JcXazwplTYaiVwzMJ7RLIrvS4Jb1l2bbS9j28/9aAssO3l1IS6dXyIhwAEIFAnsErh1Ts+yKpw/PP111+bL7/80shz/CNxclHc888/H+8OrxHegOIwL2KxdK8XYeXTlZuIZDHn0583NaVhUhlxu6b1L2mnPyUXb9+j7DZdrq7NFN4R6S+OxTTeROUEViC8lRSH3oVrq5Ld3kVjrk9WSqP77NYl1cVbQY7ifSnZPXVrK7vKsSzIuoLcpTloPM8QgAAEVkdAhVfuxfutb30rrMieO3cuvJb9clx+hlZ441VckVjJ9/3pT3+a3PLsxz/+sd0vx+P4Uo8Q3hKVoX3xSq2Ps5KYXZAm+bhhBTdfjfXbsRx26QndxU+5aCYyOqmM1QivXqAWS3zaZwGxycJbviWcTpNkXHQnz0sSWIHwyv0W7LesRbm/XoJDjm6+vWRr3WleMmNZzfJo3WpvnsaQV5qvwvpyoy+wmFROVrfeAaJ/YVteP9sQgAAEDkdAhVdK+dvf/lZ9aC1DwvuTn/wkkdhYaIdey3mlH4S3RGVsn5Xe+D68ms4gJ6r47div9dX71gb51bLzMs7v+Ts5RGV5qVV57onVaBmrEt6uX9qfRRB87ZD2ezO/UtjdazgaG+22v6gv/uclHOLFEgSWEN4guKXbl3X3aAz37o3jk68W9rHRLdCmd0DlVG8nFsutvyAt/qrf6HWaxpCVE0u0XtgWneu+xtjVmZTjc3bD8aSc6b0iEgIQgEALARXeBw8eGLkgTbZFavUh27JfjsvPkPB+9dVX9uI2EVgtqya6clzi5GI4Oa/0g/CWqBxq34aL36HYrPPJLq2hJ7b2n5JuVX6de0jbIQABCEAAAochoMIrZcjX/9YeWseQ8Mp9evNvTsvv8pDL7W9/+1t7uzMtP35GeGMaK3mN8K4E4xwLKcitpJ30JHiObadNEIAABCAAgSMmoMIrYrqzs2NeeeWV3kP264VoQ8Ibr+aW8nf1K4wlr1eOx/GlbiK8JSqH2jdH4dU7ScRpGP3XiNv4wNu0Er0gUVJKoosMx88mAgIQgAAEILC5BFR4p/ZwSHjH0hhiwY1fy3mlH4S3RIV9EIAABCAAAQhAAAJNBPRWYrr6OvYsaQtDP5KiIFIcC23ttcRJfO0H4a2RYT8EIAABCEAAAhCAwGQCeY6tfqNa7VlTG6ZUUCt7ahkI7xTKxEAAAhCAAAQgAAEIrC0BhHdth46GQwACEIAABCAAAQhMIYDwTqFEDAQgAAEIQAACEIDA2hJAeNd26Gg4BCAAAQhAAAIQgMAUAgjvFErEQAACEIAABCAAAQisLQGEd22HjoZDAAIQgAAEIAABCEwhgPBOoUQMBCAAAQhAAAIQgMDaEkB413boaDgEIAABCEAAAhCAwBQCCO8USsRAAAIQgAAEIAABCKwtAYR3bYeOhkMAAhCAAAQgAAEITCGA8JYofbprv7f5yt0nhaP3ze7WFbP/hR7Kt3V/9/zk7hWzdXXf2NK+2DdXkvO7uGmvxuubVg5REIAABCAAAQhA4HQQQHh74/zE7F/dMrs3RXp3zf3e8Vw48+3eCekOhDflwRYEIAABCEAAAhA4YgIIbw7YCqmIrojsltn9NA5w+7a2tuwK8O6n+bYxRs6/um/2b7qYrZv3TWmFd/fmFVuGLetmpNUFIb4vZdmYQn22eel+F9u1257v25xKvJP78kp2dz6vIAABCEAAAhCAwDoT2GDhdTKncpo/1yTPyqkX0E404yEWuRxIabDCumXi8vvCu9WlOBjfTpXeQeGVdpTqj8U8ldikbmNS+Y67xWsIQAACEIAABCCwoQQ2WHhlxLKVT13lVLnsDaqLD6u6BfksC2ckwIVzEuksHDc2Z9inTxSOp+KdCm8s6KE7tgxXXlJ3COAFBCAAAQhAAAIQOD0ENlx4/Yqmiq59juQ0H2cRT724zB5LV0tdeCqcPQEuCGsinZGMhurjc+LXPmBIeNN0BZ9GYfup+cex9A/0PTSGFxCAAAQgAAEIQGCzCGy88PZWeauru0MpECqPMvgzFN5qn+LJGvcv7k8cw2sIQAACEIAABCCweQROgfDGq7wDK5yFlVU73HZ/nCO7CuHN2nHYlIZkVXpskroV35C2MRbOcQhAAAIQgAAEILDmBE6F8IZV3oGV0CTtIBvUoZSC3opvQZyTsr1Ad3dScALaXeSWbRfjY2HO4rML05K6pV+xXGf9ZBMCEIAABCAAAQhsIoFTIryyyrsbfVlEPpR9aUwirCR2kql5syqpyfYk4d01+/JlFJpbnIu4rc/n417dN/clNopJ6pOGqhRrecmKb5zKIGV2/TD+DhHaj6TPbEAAAhCAAAQgAIENIXBqhHdDxotuQAACEIAABCAAAQg0EkB4G4ERDgEIQAACEIAABCCwXgQQ3vUaL1oLAQhAAAIQgAAEINBIAOFtBEY4BCAAAQhAAAIQgMB6EUB412u8aC0EIAABCEAAAhCAQCMBhLcRGOEQgAAEIAABCEAAAutFAOFdr/GitRCAAAQgAAEI6SghDgAAH0tJREFUQAACjQQQ3kZghEMAAhCAAAQgAAEIrBcBhHe9xovWQgACEIAABCAAAQg0EkB4G4ERDgEIQAACEIAABCCwXgQQ3vUaL1oLAQhAAAIQgAAEINBIAOFtBEY4BCAAAQhAAAIQgMB6EUB4G8frX179xPA4XQwapwjhEIAABCAAAQjMjADC2zggyO7pkl0Zb34gAAEIQAACEFhvAghv4/ghvAhv45QhHAIQgAAEIACBEyaA8DYOwKMnXxsep4tB4xQhHAIQgAAEIACBmRFAeBsHBNk9XbIr480PBCAAAQhAAALrTQDhbRw/hBfhbZwyRx7+6NoZs1gs7GP7zp7ZXizM9p3VVrt3/ozZebDaMikNAhCAAAQgcFwEEN5G0ggvwts4ZY42/MGOOZMI7hEI751ts1ggvEc7kJQOAQhAAAJHSQDhbaSL8CK8jVPmaMMR3qPlS+kQgAAEILARBBDexmFEeBHexikzIfyR2Xl6YbavudVaSU/oUhLciq2mLCye3jGPtES78upSGexxe6ywwuulOJRxfk9L6J7zGF9PnC4h55+5prW7NocyJaUiLte2bdvsZW3s+qVVZ/1bbJu0ddnxuP9aBM8QgAAEIACBEQII7wig/DDCi/Dmc+Lw2yqPedqAl71IJPfOi+BGUji2wuuP90Q1FkcvpbGM2no0xh6P2+bbG7XL5PWo6GoZxpha27u2GeMEW/s3of+Hh08JEIAABCBwCgggvI2DjPAivI1TZkJ4QSBNLn9ajJPAIIkjwpuIqxaRnWNjYnnVOH3OhdeeHwuwBGZ9yM+RkKxeK7eREGt1+pzKr+7N+q+7eYYABCAAAQgMEEB4B+CUDiG8CG9pXhxun5PFILG2sEwgowoSQc0k0hgnhG61tiaHcX21mKjCkrzqYV9/SG1Qcbbn6EqtD07aWu+fi64fT/qv7eAZAhCAAAQgMEAA4R2AUzp05ML70TvmmXM3zFMX3jHvhy+5+Mi8cO5tcytsnz7pPHLuA2xL82C1+2IB1ZK98PnbjQWh1G0Vy0Qi5dy+8PbO9WU4wY7jte7suSe8WdtsWzJBnSi8qeTH9WZ1aL/1Wfsfn8JrCEAAAhCAQIUAwlsBU9t9/OIlsnvDPIXwntg33NXmwur2O7lL5S8TyFplE4Q3LTcvaIkV3p4AS5lZeycKb3KhW9K0rLzkGBsQgAAEIACBNgIIbxuvAekqiOlbb5unzt0wz1x/7M/rYi5dv22PyfGnXv6oKzdZ4dV4EV73eOEtWd19bC5d6PbJMbe/svKblFmJGVjhPH7Jn1cbG6fIEuEl4fUXefVyXLPYQeGtSWO8qluLibqRCW4x99a3IwjsqPDW+tfVW8w/9mI9LPFdGbyCAAQgAAEICAGEt3Ee1OVP5TRKPagKbyqribAmcqpldvEitrde7rZVhJ86d9tc+qgiikmZlRiEt/unI2PROEWWCM8kNpTgxDRIZOlCtkHh7S4UiwWxJ5JWTuNboWUXzGXCa3rxXpol3UAFfYLw6kVscdtc2XpB3IT+B1a8gAAEIAABCNQJILx1NsUjqxJeXZFVeQ2rwD05VentRNqdMyC4mbDV24z8TmFTnAgr3VkTXqnES5/mrsa3JJPDY8IbxYRcXpXSuA+6QlusJxJanzvr7qDQ3QNYpNWKtLZvivDa+vP+qexq4/Lj2YVwGsYzBCAAAQhAYIAAwjsAp3SoLkh9MX1UXeHt5LUXM0F49ZxudTcqD9mtrtTWx25Y/EvzgH0QgAAEIAABCKwPAYS3cazq0tQX3vd9nm5YvX3Sj1F5DTFThNdLrZav4qurxvU2Dosd55X5NE4RwiEAAQhAAAIQmBkBhLdxQOpSqDKrqQbdhWVBZlcsvKEtfiU5ufiNld6VrfQ2ThHCIQABCEAAAhCYGQGEt3FAgmT2hLITXF1x1efVCK+7UO2Z6/d6d2jQeqorvL1V4/JKZr1vpzu+cYoQDgEIQAACEIDAzAggvI0DMiiFKpZ639yV5PB+beLUBSfPfbnupLogp9qu5MssCnE9iSdGxpsfCEAAAhCAAATWmwDC2zh+g8KLMK4sjWBOnBunCOEQgAAEIAABCMyMAMLbOCBzEjHacjwr0I1ThHAIQAACEIAABGZGAOFtHBAk83gkc06cG6cI4RCAAAQgAAEIzIwAwts4IHMSMdpyPPLdOEUIhwAEIAABCEBgZgQQ3sYBQTKPRzLnxLlxihAOAQhAAAIQgMDMCCC8jQMyJxGjLccj341ThHAIQAACEIAABGZGAOFtHBAk83gkc06cG6cI4RCAAAQgAAEIzIwAwts4IHMSMdpyPPLdOEUIhwAEIAABCEBgZgQQ3sYBQTKPRzLnxLlxihAOAQhAAAIQgMDMCCC8MxsQmgMBCEAAAhCAAAQgsFoCCO9qeVIaBCAAAQhAAAIQgMDMCCC8MxsQmgMBCEAAAhCAAAQgsFoCCO9qeVIaBCAAAQhAAAIQgMDMCCC8MxsQmgMBCEAAAhCAAAQgsFoCCO9qeVIaBCAAAQhAAAIQgMDMCCC8MxsQmgMBCEAAAhCAAAQgsFoCCO9qeVIaBCAAAQhAAAIQgMDMCCC8MxsQmgMBCEAAAhCAAAQgsFoCCO9qeVIaBCAAAQhAAAIQgMDMCCC8MxsQmgMBCEAAAhCAAAQgsFoCCO9qeVIaBFZMYM9sLxZmoY/ze/3yH+yYM3p8sTBnrj1aKubRtTNdPYszZudBvxj2QAACEIAABNaRAMLbOGr/8uonhsfpYtA4RVYY7mU3kty98wuziLaNl91Och+Znacz6Z0Q42Q3klx7TrS9wl5RFAQgAAEIQOC4CSC8jcSR3dMluzLeJ/XjJHTbpGu6ToK377hWWQF+escka7qZrI7HuDI7aXZl2/rzsk8KBvVCAAIQgAAEDkEA4W2Eh/AivI1TZunw3mquLSlewY1fx9XEUjwhJhPkUNKdbbNY5MIdjvICAhCAAAQgsDYEEN7GoXr05GvD43QxaJwiKwsfEl6X1jAss27FdkLMoPCS1rCyAaUgCEAAAhA4MQIIbyN6ZPd0ya6M90n9DKU0aB5vPV2hy+MdjxlIaeDitZMafuqFAAQgAIEVEkB4G2EivAhv45RZPtyuvKYXqVl5lTsy6IVrNu2gk1tj3Iqu3NUh5OROiHHlRqu5WjfCu/z4cSYEIAABCMyGAMLbOBQIL8LbOGUOFx7E092abPuOF1oVXindC627dZlIa2HFdkJMkGkRarlYzZ4TSfDhesLZEIAABCAAgRMjgPA2okd4Ed7GKTMa7lIXonvtDt4ZoSCzeQ1ekvVODvlhuz0hppxSUSyNnRCAAAQgAIFZE0B4G4cH4UV4G6fM0uFF4cxWXUv5ufl54zEliS6sJC/dE06EAAQgAAEInCwBhLeRP8KL8DZOmeXD/SpsyMU1TkxD/q6U7FMVwmpu75xpMS6dobsFmZNm0hmWHzzOhAAEIACBORFAeBtHA+FFeBunzOHCvcDqVwt38hsVm+TnLkyQ3yhExVjLKcUkObzcfzemx2sIQAACEFhzAghv4wAivAhv45QhHAIQgAAEIACBEyaA8DYOwEkI7/vXb5unzt0wL7zVIJtvvW2eevkjviRjBV8U0jhFCIcABCAAAQhAYGYEEN7GAVkL4RXZPXcD4V2B7Mp48wMBCEAAAhCAwHoTQHgbx68uvB+ZF0Qyz71tLvkV2Zp03npZ4vRx21z6KFu5/egd84wev/COudVb4X1sLl3Q893zM9cfu9VclV09/9zb5pYVP21fd97girGWwypx4wwhHAIQgAAEIACBuRFAeBtHZFx4O6FUqY3FMpXdLraL6YtpXk65DC/OKqqJ8PYF2ZWpMpwJtwiyloPwNs4QwiEAAQhAAAIQmBsBhLdxRKYIr8qrimlYfQ0rt5FoqlheeMe8/+Rro/m6T/ntR086WdVy8zZoPeG4llmVVS2zsLq8ojSAvI3rvN04RQiHAAQgAAEIQGBmBBDexgGpi5uuzPZlVoVXZVa3XVkqn+68nrxGEhyE1kuplpevAA+tzmr57hyEtz6e3ap34xQhHAIQgAAEIACBmRFAeBsHpC5I48KrInpY4VVpVQHOt7We7i4NKtUquPl2J3f1/p3emMYpQjgEIAABCEAAAjMjgPA2DkhdCCcI71IpDVqu3pZMt3UlWeVVjxfyb7XekCahZagAn16ZrY9nx6RxihAOAQhAAAIQgMDMCCC8jQNSFySVSBXRTjzjFV1djdU0BH3W1dpHT7Sc7oK2NGbseFdvSFt4O7rrQ7iYTcofEN7RPOBOCOtMNiOmcYoQDgEIQAACEIDAzAggvI0DUpc7FdFh4ZXzU+ktSKeuyFo57W5zFqRYZVSOy6qtxoeL1LpVX5XaJN9X4nwZsYwnfdM6QpmbIa9JHydeoNc4RQiHAAQgAAEIQGBmBBDexgFZRpg4Z71luXGKEA4BCEAAAhCAwMwIILyNA4K8rre8LjN+jVOEcAhAAAIQgAAEZkYA4W0ckGWEiXPWW5IbpwjhEIAABCAAAQjMjADC2zggyOt6y+sy49c4RQiHAAQgAAEIQGBmBBDexgFZRpg4Z70luXGKEA4BCEAAAhCAwMwIILyNA4K8rre8LjN+jVOEcAhAAAIQgAAEZkYA4W0ckGWEiXPWW5IbpwjhEIAABCAAAQjMjADC2zggyOt6y+sy49c4RQiHAAQgAAEIQGBmBBDexgFZRpg4Z70luXGKEA4BCEAAAhCAwMwIILwzGxCaAwEIQAACEIAABCCwWgII72p5UhoEIAABCEAAAhCAwMwIILwzGxCaAwEIQAACEIAABCCwWgII72p5UhoEIAABCEAAAhCAwMwIILwzGxCaAwEIQAACEIAABCCwWgII72p5UhoEIAABCEAAAhCAwMwIILwzGxCaAwEIQAACEIAABCCwWgII72p5UhoEIAABCEAAAhCAwMwIILwzGxCaAwEIQAACEIAABCCwWgII72p5UhoEIAABCEAAAhCAwMwIILwzGxCaAwEIQAACEIAABCCwWgII72p5UhoEliKwd35hFov648y1R77cR2bn6Xrc9p1+9aWyS3HuzFL5Z8zOg365pT1pXaXz+uWX2vLo2pmERz9mz2wnvEp1ZTFP7xilWGo7+yAAAQhAYHMJILyNY/svr35ieJwuBo1TZKlwK4qThMwL4/m9fj13tq0kdnLoY/NyH+yYM4uF6STaF+XPz/erfOb78wa4uG2jLXPbsYgW2tNrszG983oxXmQjBr1zTM6pUHfeAbYhAAEIQGBjCSC8jUOL7J4u2ZXxPo6flQivMcaWoyJoxTYWzq4nuZwaL8GdLHex8qoXnx6Wmu2KayrFTjLDvkp70r67cvJ2JP2yAtyJtWtKJrg2Jut7pf5eV9gBAQhAAAIbRwDhbRxShBfhbZwyk8JT6Rs6JRO7LDQppyR9WbxuWqHNV4L1oDyLLJ7fMY9CakNZTONTyhKcRshWUndRZvvnlPb0pRjhLXFiHwQgAIHTSADhbRz1R0++NjxOF4PGKbJUeCKqgyUMCW9+zH/0X0pfSOpw54WV2OTY8hvjq8JSdtrmIL9+xVnzmvMV336r8hXmtNxQz5DU9wtlDwQgAAEIbAgBhLdxIJHd0yW7Mt7H8WOFN7kIK74wLV6pzEWua50TzIVJ5bCTXpXHxSJPB8hlsStzqVd2lda1f1Sifay2OXCIxTSLKbXJ9T3m5KJCecJWUz1KBbAPAhCAAAQ2mgDC2zi8CC/C2zhlJoU3r/AW5bgvfEnlkYha+Q0CWBHePH50pTiprbd6mx/VvOFYRJ2g5kLuc5NjCY4L8+1M5NqvEMf7amXHRfEaAhCAAAQ2kwDC2ziuCC/C2zhlJoU3C2+Q1UnF94Kc/KkgT0lpqEhxr+Rsh5VRrSc6pikLWT9qHKrpESXZ1Yv3eoK8ZB+iZvMSAhCAAATWkwDC2zhuCC/C2zhlJoXXRK9/cj2loR87sMcLZ5JK0BPE+PwlZbEkvBXZldpCDm9cte7PUzEqsiunWp6ZTIc83t7+rDI2IQABCEBg4wggvI1DivAivI1TZlL4UQjvYJlWOqOV10yA+40eEd7K+b2V2QHZtXXm7fIN6fVlQHbllF68LWfKSravkCcIQAACENgoAghv43AivAhv45SZFF4WtNKpDSu8A3Jp68tXOr1Exjm1tgW6P19hzZpny4xjfP1dHq2T5l75pXKi1WYnzdHFeL1yswJksxDTa1/hNHZBAAIQgMBmEkB4G8cV4UV4G6fMpHAnY/GdGbLXQQAbhNfW7OOzi9w0laHfuKnxTl7zcvJ+xMdVXLu7RcR9TC9US2OjlWhdvc36E8oMnKRnXrBDbFpHv+/sgQAEIACBTSWA8DaOLMKL8DZOGcIhAAEIQAACEDhhAghv4wAgvAhv45QhHAIQgAAEIACBEyaA8DYOAMKL8DZOGcIhAAEIQAACEDhhAghv4wAMC+9jc+nCDfPUOf+48I55P3wV8UfmBbv/bfPCy/74yx/Zryl+//rt7hyJ8fvLdWkdt82lj06ffJaZHC2HxilCOAQgAAEIQAACMyOA8DYOSF24VEQj4RV5DdKrwtsdf+Gtr82jt95OZdfL8jPXH1sZ7ten9SC8fTZHI76NU4RwCEAAAhCAAARmRgDhbRyQqmSpuAbBzcW0E14rurry68+rC+7RSFy1H9ounsM/HI1ThHAIQAACEIAABGZGAOFtHJCaKGpaQl1cVXjfNrcSmdT92cpvEoP01rgfx/7GKUI4BCAAAQhAAAIzI4DwNg5ITbCWF14vsx+9Y57x6Qw2B3gwjxcBro3DUexvnCKEQwACEIAABCAwMwIIb+OAVIVKUxrOdSu4t/zFaS6FQVdyu+PlsqbGIb1lfqvn0jhFCIcABCAAAQhAYGYEEN7GAalLlubsdqkJdqU25PSWRVZXhsOdHXSVt7rCq/Vw0Vp9LFYrvY1ThHAIQAACEIAABGZGAOFtHJBhyVIZVemNV3PLwivl9aQ3SHJJ3LQOhHd4LErsltvXOEUIhwAEIAABCEBgZgQQ3sYBOS7Jop7l5PQouDVOEcIhAAEIQAACEJgZAYS3cUCOQqgocz5yWxqLxilCOAQgAAEIQAACMyOA8DYOSEmI2DdvYT3s+DROEcIhAAEIQAACEJgZAYS3cUAOK0+cv35y3DhFCIcABCAAAQhAYGYEEN7GAUFY109YDztmjVOEcAhAAAIQgAAEZkYA4W0ckMPKE+evnzA3ThHCIQABCEAAAhCYGQGEt3FAENb1E9bDjlnjFCEcAhCAAAQgAIGZEUB4GwfksPLE+esnzI1ThHAIQAACEIAABGZGAOFtHBCEdf2E9bBj1jhFCIcABCAAAQhAYGYEEN6ZDQjNgQAEIAABCEAAAhBYLQGEd7U8KQ0CEIAABCAAAQhAYGYEEN6ZDQjNgQAEIAABCEAAAhBYLQGEd7U8KQ0CEIAABCAAAQhAYGYEEN6ZDQjNgQAEIAABCEAAAhBYLQGEd7U8KQ0CEIAABCAAAQhAYGYEEN6ZDQjNgQAEIAABCEAAAhBYLQGEd7U8KQ0CEIAABCAAAQhAYGYEEN6ZDQjNgQAEIAABCEAAAhBYLQGEd7U8KQ0CEIAABCAAAQhAYGYEEN6ZDQjNgQAEIAABCEAAAhBYLQGEd7U8KQ0CMyewZ7af3jGPfCsfXTtjFtH2eOP3zPZiYbbvjEeuJOLOtlksFvZx5pq2eiUlUwgEIAABCJwiAghv42D/y6ufGB6ni0HjFJl1+N75RaPg5t05TuF1dSG6+RiwDQEIQAACrQQQ3kZiyO7pkl0Z7036QXg3aTTpCwQgAAEITCWA8E4l5eMQXoS3ccpMC3+wY874j+7dR/jbZi+cqauq7nnoI34rtHE5IV3hkdl52qUGuPPPmJ0HxpRSGuplSIO0LaFxoy9sHed3uvrPa8/S/iSpFYM8RqskAAIQgAAEIJAQQHgTHOMbj558bXicLgbjs+KQEV7u4rzYdCW2E8MQ48+JP+5Pz5E2+fOCYBqTx+TCmx/vl7Gk8C4WJm5rv1zftkVf9NPzDsma0yEAAQhA4FQSQHgbhx3ZPV2yK+N95D/2wiy34lquqy+uEmdlNQhiWURzgc23U+GdUkY5ptxutzdtZ32fSnAnuK6ubnuoFo5BAAIQgAAE6gQQ3jqb4hGEF+EtToxD7XRiJ6kGZbmrSKZd5S2JcleeTV8IaQ3jK7xdN2plVNrSndh7lUq1HPbpFdHKs55khTzsd3WVmegZPEMAAhCAAATGCSC844ySCIQX4U0mxMo28hzb+NZfFcnMhNfKoubvesnNV3Tz7VxGx8uotGWAQ15HEF5ta/6M8A7Q5BAEIAABCCxDAOFtpIbwIryNU2aJcJVfXb2tSGacClHIA5aKc8HNtxMZnVRGpS0DvUzqsHH1Fd60GFcXK7wpFbYgAAEIQKCdAMLbyAzhRXgbp8yS4bFYuteLsPLpikxE0spvfMGXxPjzpqY0TCojbte0riXt9Kfk4u17ZO/k0Amuq6vbnlYfURCAAAQgAIGcAMKbExnZRngR3pEp0n44Xqn1Z1tJzC5Ik3zc/C4N+XYsh116QifCuWgmMupXeIfLWI3wBhmPJD7ts4BAeNsnE2dAAAIQgECJAMJbojKwD+FFeAemx/KHrPT275PrClTx27Ff6+vuoxvJr9aal3F+z9/JQVMjjDFealWeE+GVckbLWJXwSmWuLO3PIgi+dkj7zVcKKxGeIQABCEBgOQIIbyM3hBfhbZwyKwhH/FYAkSIgAAEIQOAUE0B4Gwf/KIT3/eu3zVPnbphnrj+ufKnFY3Ppwg3z1Lm3za11/+KLj94xz5y7YZ668I55f0360jhFjiAc4T0CqBQJAQhAAAKniADC2zjYRyG842VukPCuieTGY9I4RY4gfI7Cq3eSiNMw+q/jfOAjAEOREIAABCAAgUkEEN5JmLqgWIT6rz8yL8jqpa5gvvV2tnKrx982L7zs417+yJRWeHWfW/n9qHGFVwX5trl03bWhW0HWY4UVY99e237bj3hFuWv7Jb8ibeNe/ihalZ4Qk6/wRtuXlElhtTvm8cJb2oe4fVmqhfYlaV8WM1G+u9HnFQQgAAEIQAAC60gA4W0ctb7kqkSphHmRFWm7kKcqqBB2MS+89XVfeFXWVJ7D84DgJfLWb4uT2NvmGZsa0dX/lAqhimeoy8fo8Sf9tqsYd6kYE2K0Hk1p0O283nO3zaWPPNseD+3HAA89J7Rfx6n9uXGKEA4BCEAAAhCAwMwIILyNA1IVXhWskGfbSWdJCEV0tSxdvXRxhfOCFA4IXkV4te5bunqqoqnt1e3k/K/NI60zHO9kNrS9V8aEmLxc3Q6C2/Xf1dNta18eab2BdcdSma7yuXGKEA4BCEAAAhCAwMwIILyNA1ITqVRavYB5MQuiFlZJU3FNz1VpjGNU+uJ9Q5Kn8d0qaVpHSWi1PK3fr/Dmwhu2JV7r0Xb5c4diVHA1Jt9+0q14O+HV9mgdpXq17Ufz3DhFCIcABCAAAQhAYGYEEN7GAdlY4c3FM98uynpFeJOV1ywmLzffRngbZyThEIAABCAAAQiMEUB4xwhlx2vC2/+YXUUvvt1YabWyW9GspjQ0f4SvdU9f4e2tAGuduhIbhPeGOZKUhlBPx4OUhmzysQkBCEAAAhCAwFIEEN5GbFXhDR/vdxeE1S9aiz+e7wQvpD6obPYu5ErPG29Lu/DqhWjhOYioynrXP40J7Y6kWI/pc4jJV3Tz7d4K79em+2dC6+aitcZpSzgEIAABCEDgVBNAeBuHvy6Zkj8aSaHcHWCpHF6Xh6orrlYYX35n+duS+TsdaHlV8cyEXVZX3YVuKs3at7fNUd2WTL+IQtsaVpIjCX7KXtymK9gD/wDoPw3cpaFxhhMOAQhAAAIQ2DwCCG/jmFaFV1cqw90GCiu3+Z0Q1mq7E976t71NiWm/sCzcYSLI69HUUxvbxilCOAQgAAEIQAACMyOA8DYOSE2KktXdJBVBV0jbRa9e10mUNUUyp8S0t11XfDU9IjwHAW4vs4Vt4xQhHAIQgAAEIACBmRFAeBsHZFCUwiqv5ppGF3itbDVXP87v6ggCaEX7qAR7isxOiVlOTsMqr/4zEXKLlytvcByzsWqcIoRDAAIQgAAEIDAzAghv44C0iBKxRy+jx8G4cYoQDgEIQAACEIDAzAggvI0DchyCRR3zEuXGKUI4BCAAAQhAAAIzI4DwNg4IMjovGT2O8WicIoRDAAIQgAAEIDAzAghv44Ach2BRx7ykunGKEA4BCEAAAhCAwMwIILyNA4KMzktGj2M8GqcI4RCAAAQgAAEIzIwAwts4IMchWNQxL6lunCKEQwACEIAABCAwMwII78wGhOZAAAIQgAAEIAABCKyWAMK7Wp6UBgEIQAACEIAABCAwMwII78wGhOZAAAIQgAAEIAABCKyWAMK7Wp6UBgEIQAACEIAABCAwMwII78wGhOZAAAIQgAAEIAABCKyWAMK7Wp6UBgEIQAACEIAABCAwMwII78wGhOZAAAIQgAAEIAABCKyWAMK7Wp6UBgEIQAACEIAABCAwMwII78wGhOZAAAIQgAAEIAABCKyWAMK7Wp6UBgEIQAACEIAABCAwMwII78wGhOZAAAIQgAAEIAABCKyWAMK7Wp6UBgEIQAACEIAABCAwMwII78wGhOZAAAIQgAAEIAABCKyWwP8H+HbBCj6Px0EAAAAASUVORK5CYII=" alt="" /></p> </div> </div> </div> </div> <p style="text-align: left;">Finally, we can select ranges of <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">DataArrays</span></code> with the xarray syntaxes using <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">slice</span></code>. The three cells below all extract the same extent from the <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">guinea_bissau.green</span></code> <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">DataArray</span></code>.</p> <p style="text-align: left;">The xarray syntaxes for ranges using <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">slice</span></code> are:</p> <div class="highlight-python notranslate" style="box-sizing: border-box; border: 1px solid #e1e4e5; overflow-x: auto; margin: 1px 0px 24px; color: #000000; font-family: Arial, sans-serif;"> <div class="highlight" style="box-sizing: border-box; background: #f8f8f8; border: none; overflow-x: auto; margin: 0px; padding: 0px; text-align: left;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: pre; line-height: 1.4; margin-top: 0px; margin-bottom: 0px; padding: 12px; overflow: auto;"><span style="box-sizing: border-box;"></span><span class="n" style="box-sizing: border-box;">dataarray_name</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">isel</span><span class="p" style="box-sizing: border-box;">(</span><span class="n" style="box-sizing: border-box;">dimension_name</span> <span class="o" style="box-sizing: border-box; color: #666666;">=</span> <span class="nb" style="box-sizing: border-box; color: #008000;">slice</span><span class="p" style="box-sizing: border-box;">(</span><span class="n" style="box-sizing: border-box;">index_start</span><span class="p" style="box-sizing: border-box;">,</span> <span class="n" style="box-sizing: border-box;">index_end</span><span class="p" style="box-sizing: border-box;">))</span> <span class="n" style="box-sizing: border-box;">dataarray_name</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">sel</span><span class="p" style="box-sizing: border-box;">(</span><span class="n" style="box-sizing: border-box;">dimension_name</span> <span class="o" style="box-sizing: border-box; color: #666666;">=</span> <span class="nb" style="box-sizing: border-box; color: #008000;">slice</span><span class="p" style="box-sizing: border-box;">(</span><span class="n" style="box-sizing: border-box;">value_start</span><span class="p" style="box-sizing: border-box;">,</span> <span class="n" style="box-sizing: border-box;">value_end</span><span class="p" style="box-sizing: border-box;">))</span> </pre> </div> </div> <p style="text-align: left;">As before, it is easier to call upon the dimensions by name rather than using the implicit numpy square bracket syntax, although it still works.</p> <div class="nbinput docutils container" style="box-sizing: border-box; display: flex; align-items: flex-start; margin: 0px; width: 696.469px; padding-top: 5px; color: #000000; font-family: Arial, sans-serif;"> <div class="input_area highlight-ipython3 notranslate" style="box-sizing: border-box; margin: 0px; flex: 1 1 0%; overflow: auto; border: 1px solid #e0e0e0; border-radius: 2px;"> <div class="highlight" style="box-sizing: border-box; background: #f8f8f8; border: none; overflow: auto hidden; margin: 0px; padding: 0px; box-shadow: none;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: pre; line-height: 1.4; margin-top: 0px; margin-bottom: 0px; padding: 5px; overflow: auto; border: none; box-shadow: none;"><span style="box-sizing: border-box;"></span><span class="c1" style="box-sizing: border-box; color: #408080; font-style: italic;"># Numpy syntax - dimensions are not named</span> <span class="c1" style="box-sizing: border-box; color: #408080; font-style: italic;"># Valid but not recommended</span> <span class="n" style="box-sizing: border-box;">guinea_bissau</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">green</span><span class="p" style="box-sizing: border-box;">[:</span><span class="mi" style="box-sizing: border-box; color: #666666;">250</span><span class="p" style="box-sizing: border-box;">,</span> <span class="p" style="box-sizing: border-box;">:</span><span class="mi" style="box-sizing: border-box; color: #666666;">250</span><span class="p" style="box-sizing: border-box;">]</span> </pre> </div> </div> </div> <p style="text-align: left;"><img 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" alt="" /></p> <div class="nbinput docutils container" style="box-sizing: border-box; display: flex; align-items: flex-start; margin: -19px 0px 0px; width: 696.469px; padding-top: 5px; color: #000000; font-family: Arial, sans-serif;"> <div class="input_area highlight-ipython3 notranslate" style="box-sizing: border-box; margin: 0px; flex: 1 1 0%; overflow: auto; border: 1px solid #e0e0e0; border-radius: 2px;"> <div class="highlight" style="box-sizing: border-box; background: #f8f8f8; border: none; overflow: auto hidden; margin: 0px; padding: 0px; box-shadow: none;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: pre; line-height: 1.4; margin-top: 0px; margin-bottom: 0px; padding: 5px; overflow: auto; border: none; box-shadow: none;"><span style="box-sizing: border-box;"></span><span class="c1" style="box-sizing: border-box; color: #408080; font-style: italic;"># Index selection slice</span> <span class="n" style="box-sizing: border-box;">guinea_bissau</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">green</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">isel</span><span class="p" style="box-sizing: border-box;">(</span><span class="n" style="box-sizing: border-box;">x</span><span class="o" style="box-sizing: border-box; color: #666666;">=</span><span class="nb" style="box-sizing: border-box; color: #008000;">slice</span><span class="p" style="box-sizing: border-box;">(</span><span class="mi" style="box-sizing: border-box; color: #666666;">0</span><span class="p" style="box-sizing: border-box;">,</span><span class="mi" style="box-sizing: border-box; color: #666666;">250</span><span class="p" style="box-sizing: border-box;">),</span> <span class="n" style="box-sizing: border-box;">y</span><span class="o" style="box-sizing: border-box; color: #666666;">=</span><span class="nb" style="box-sizing: border-box; color: #008000;">slice</span><span class="p" style="box-sizing: border-box;">(</span><span class="mi" style="box-sizing: border-box; color: #666666;">0</span><span class="p" style="box-sizing: border-box;">,</span><span class="mi" style="box-sizing: border-box; color: #666666;">250</span><span class="p" style="box-sizing: border-box;">))</span> </pre> </div> </div> </div> <p style="text-align: left;"><img src="data:image/png;base64,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" alt="" /></p> <div class="nbinput docutils container" style="box-sizing: border-box; display: flex; align-items: flex-start; margin: -19px 0px 0px; width: 696.469px; padding-top: 5px; color: #000000; font-family: Arial, sans-serif;"> <div class="input_area highlight-ipython3 notranslate" style="box-sizing: border-box; margin: 0px; flex: 1 1 0%; overflow: auto; border: 1px solid #e0e0e0; border-radius: 2px;"> <div class="highlight" style="box-sizing: border-box; background: #f8f8f8; border: none; overflow: auto hidden; margin: 0px; padding: 0px; box-shadow: none;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: pre; line-height: 1.4; margin-top: 0px; margin-bottom: 0px; padding: 5px; overflow: auto; border: none; box-shadow: none;"><span style="box-sizing: border-box;"></span><span class="c1" style="box-sizing: border-box; color: #408080; font-style: italic;"># Value selection slice</span> <span class="n" style="box-sizing: border-box;">guinea_bissau</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">green</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">sel</span><span class="p" style="box-sizing: border-box;">(</span><span class="n" style="box-sizing: border-box;">x</span><span class="o" style="box-sizing: border-box; color: #666666;">=</span><span class="nb" style="box-sizing: border-box; color: #008000;">slice</span><span class="p" style="box-sizing: border-box;">(</span><span class="mi" style="box-sizing: border-box; color: #666666;">388020</span><span class="p" style="box-sizing: border-box;">,</span><span class="mi" style="box-sizing: border-box; color: #666666;">395500</span><span class="p" style="box-sizing: border-box;">),</span> <span class="n" style="box-sizing: border-box;">y</span><span class="o" style="box-sizing: border-box; color: #666666;">=</span><span class="nb" style="box-sizing: border-box; color: #008000;">slice</span><span class="p" style="box-sizing: border-box;">(</span><span class="mi" style="box-sizing: border-box; color: #666666;">1338000</span><span class="p" style="box-sizing: border-box;">,</span><span class="mi" style="box-sizing: border-box; color: #666666;">1330530</span><span class="p" style="box-sizing: border-box;">))</span> </pre> </div> </div> </div> <p style="text-align: left;"><img 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" alt="" /></p> <p></p> </div> </div> <div class="vert vert-2" data-id="block-v1:digitalearthafrica+DEA101+2021+type@html+block@2cb0d2341eb541e286a57beff8bd2957"> <div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-course-id="course-v1:digitalearthafrica+DEA101+2021" data-init="XBlockToXModuleShim" data-runtime-class="LmsRuntime" data-runtime-version="1" data-block-type="html" data-usage-id="block-v1:digitalearthafrica+DEA101+2021+type@html+block@2cb0d2341eb541e286a57beff8bd2957" data-request-token="1b5e9822881611efa85e837f534d2eba" data-graded="False" data-has-score="False"> <script type="json/xblock-args" class="xblock-json-init-args"> {"xmodule-type": "HTMLModule"} </script> <h3 style="text-align: left;">Plotting data</h3> <p style="text-align: left;">We can plot the selected area using matplotlib <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">plt.imshow</span></code>. We can also use the xarray <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">.plot()</span></code> function. Plotting is a good way to check the selection is showing the top left section of the image as expected.</p> <p style="text-align: left;">The syntax of <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">plt.imshow()</span></code> is:</p> <div class="highlight-python notranslate" style="box-sizing: border-box; border: 1px solid #e1e4e5; overflow-x: auto; margin: 1px 0px 24px; color: #000000; font-family: Arial, sans-serif;"> <div class="highlight" style="box-sizing: border-box; background: #f8f8f8; border: none; overflow-x: auto; margin: 0px; padding: 0px; text-align: left;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: pre; line-height: 1.4; margin-top: 0px; margin-bottom: 0px; padding: 12px; overflow: auto;"><span style="box-sizing: border-box;"></span><span class="n" style="box-sizing: border-box;">plt</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">imshow</span><span class="p" style="box-sizing: border-box;">(</span><span class="n" style="box-sizing: border-box;">data</span><span class="p" style="box-sizing: border-box;">)</span> </pre> </div> </div> <p style="text-align: left;">Therefore we copy the code into the brackets of <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">plt.imshow()</span></code>.</p> <div class="nbinput docutils container" style="box-sizing: border-box; display: flex; align-items: flex-start; margin: 0px; width: 696.469px; padding-top: 5px; color: #000000; font-family: Arial, sans-serif;"> <div class="input_area highlight-ipython3 notranslate" style="box-sizing: border-box; margin: 0px; flex: 1 1 0%; overflow: auto; border: 1px solid #e0e0e0; border-radius: 2px; text-align: left;"> <div class="highlight" style="box-sizing: border-box; background: #f8f8f8; border: none; overflow: auto hidden; margin: 0px; padding: 0px; box-shadow: none;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: pre; line-height: 1.4; margin-top: 0px; margin-bottom: 0px; padding: 5px; overflow: auto; border: none; box-shadow: none;"><span style="box-sizing: border-box;"></span><span class="n" style="box-sizing: border-box;">plt</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">imshow</span><span class="p" style="box-sizing: border-box;">(</span><span class="n" style="box-sizing: border-box;">guinea_bissau</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">green</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">sel</span><span class="p" style="box-sizing: border-box;">(</span><span class="n" style="box-sizing: border-box;">x</span><span class="o" style="box-sizing: border-box; color: #666666;">=</span><span class="nb" style="box-sizing: border-box; color: #008000;">slice</span><span class="p" style="box-sizing: border-box;">(</span><span class="mi" style="box-sizing: border-box; color: #666666;">388020</span><span class="p" style="box-sizing: border-box;">,</span><span class="mi" style="box-sizing: border-box; color: #666666;">395500</span><span class="p" style="box-sizing: border-box;">),</span> <span class="n" style="box-sizing: border-box;">y</span><span class="o" style="box-sizing: border-box; color: #666666;">=</span><span class="nb" style="box-sizing: border-box; color: #008000;">slice</span><span class="p" style="box-sizing: border-box;">(</span><span class="mi" style="box-sizing: border-box; color: #666666;">1338000</span><span class="p" style="box-sizing: border-box;">,</span><span class="mi" style="box-sizing: border-box; color: #666666;">1330530</span><span class="p" style="box-sizing: border-box;">)))</span> </pre> </div> </div> </div> <div class="nboutput docutils container" style="box-sizing: border-box; display: flex; align-items: flex-start; margin: 0px; width: 696.469px; color: #000000; font-family: Arial, sans-serif; text-align: left;"> <div class="output_area docutils container" style="box-sizing: border-box; flex: 1 1 0%; overflow: auto;"> <div class="highlight" style="box-sizing: border-box; background: unset; border: none; overflow: auto hidden; margin: 0px; padding: 0px; box-shadow: none; color: unset;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: pre; line-height: normal; margin-top: 0px; margin-bottom: 0px; padding: 5px; overflow: auto; border: none; box-shadow: none; background: unset;">&lt;matplotlib.image.AxesImage at 0x7f6fb0af2470&gt; </pre> </div> </div> </div> <div class="nboutput nblast docutils container" style="box-sizing: border-box; display: flex; align-items: flex-start; margin: 0px 0px 19px; width: 696.469px; padding-bottom: 5px; color: #000000; font-family: Arial, sans-serif;"> <div class="output_area docutils container" style="box-sizing: border-box; flex: 1 1 0%; overflow: auto;"><img alt="../_images/python_basics_05_xarray_43_1.png" src="https://learn.digitalearthafrica.org/asset-v1:digitalearthafrica+DEA101+2021+type@asset+block@python_basics_05_xarray_43_1.png" style="box-sizing: border-box; border: 0px; vertical-align: middle; padding: 5px; margin: 0px; float: left;" /></div> </div> <div style="box-sizing: border-box;"> <p style="box-sizing: border-box; line-height: 24px; margin: 0px 0px 24px; font-size: 16px; text-align: left;"><strong style="box-sizing: border-box;">Note:</strong> We did not specify a colourmap <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">cmap</span></code> so you can see it takes on the default colour scheme. Plotting true-colour (RGB) images is first introduced in Session 2: Loading data in the Sandbox.</p> </div> <p style="text-align: left;">The syntax of the xarray plot function is:</p> <div class="highlight-python notranslate" style="box-sizing: border-box; border: 1px solid #e1e4e5; overflow-x: auto; margin: 1px 0px 24px; color: #000000; font-family: Arial, sans-serif;"> <div class="highlight" style="box-sizing: border-box; background: #f8f8f8; border: none; overflow-x: auto; margin: 0px; padding: 0px; text-align: left;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: pre; line-height: 1.4; margin-top: 0px; margin-bottom: 0px; padding: 12px; overflow: auto;"><span style="box-sizing: border-box;"></span><span class="n" style="box-sizing: border-box;">data</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">plot</span><span class="p" style="box-sizing: border-box;">()</span> </pre> </div> </div> <p style="text-align: left;">Therefore we copy the code before we type <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">.plot()</span></code>.</p> <div class="nbinput docutils container" style="box-sizing: border-box; display: flex; align-items: flex-start; margin: 0px; width: 696.469px; padding-top: 5px; color: #000000; font-family: Arial, sans-serif;"> <div class="input_area highlight-ipython3 notranslate" style="box-sizing: border-box; margin: 0px; flex: 1 1 0%; overflow: auto; border: 1px solid #e0e0e0; border-radius: 2px; text-align: left;"> <div class="highlight" style="box-sizing: border-box; background: #f8f8f8; border: none; overflow: auto hidden; margin: 0px; padding: 0px; box-shadow: none;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: pre; line-height: 1.4; margin-top: 0px; margin-bottom: 0px; padding: 5px; overflow: auto; border: none; box-shadow: none;"><span style="box-sizing: border-box;"></span><span class="n" style="box-sizing: border-box;">guinea_bissau</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">green</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">sel</span><span class="p" style="box-sizing: border-box;">(</span><span class="n" style="box-sizing: border-box;">x</span><span class="o" style="box-sizing: border-box; color: #666666;">=</span><span class="nb" style="box-sizing: border-box; color: #008000;">slice</span><span class="p" style="box-sizing: border-box;">(</span><span class="mi" style="box-sizing: border-box; color: #666666;">388020</span><span class="p" style="box-sizing: border-box;">,</span><span class="mi" style="box-sizing: border-box; color: #666666;">395500</span><span class="p" style="box-sizing: border-box;">),</span> <span class="n" style="box-sizing: border-box;">y</span><span class="o" style="box-sizing: border-box; color: #666666;">=</span><span class="nb" style="box-sizing: border-box; color: #008000;">slice</span><span class="p" style="box-sizing: border-box;">(</span><span class="mi" style="box-sizing: border-box; color: #666666;">1338000</span><span class="p" style="box-sizing: border-box;">,</span><span class="mi" style="box-sizing: border-box; color: #666666;">1330530</span><span class="p" style="box-sizing: border-box;">))</span><span class="o" style="box-sizing: border-box; color: #666666;">.</span><span class="n" style="box-sizing: border-box;">plot</span><span class="p" style="box-sizing: border-box;">()</span> </pre> </div> </div> </div> <div class="nboutput docutils container" style="box-sizing: border-box; display: flex; align-items: flex-start; margin: 0px; width: 696.469px; color: #000000; font-family: Arial, sans-serif; text-align: left;"> <div class="output_area docutils container" style="box-sizing: border-box; flex: 1 1 0%; overflow: auto;"> <div class="highlight" style="box-sizing: border-box; background: unset; border: none; overflow: auto hidden; margin: 0px; padding: 0px; box-shadow: none; color: unset;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: pre; line-height: normal; margin-top: 0px; margin-bottom: 0px; padding: 5px; overflow: auto; border: none; box-shadow: none; background: unset;">&lt;matplotlib.collections.QuadMesh at 0x7f6fb0a0cba8&gt; </pre> </div> </div> </div> <div class="nboutput nblast docutils container" style="box-sizing: border-box; display: flex; align-items: flex-start; margin: 0px 0px 19px; width: 696.469px; padding-bottom: 5px; color: #000000; font-family: Arial, sans-serif;"> <div class="output_area docutils container" style="box-sizing: border-box; flex: 1 1 0%; overflow: auto;"><img alt="../_images/python_basics_05_xarray_47_1.png" src="https://learn.digitalearthafrica.org/asset-v1:digitalearthafrica+DEA101+2021+type@asset+block@python_basics_05_xarray_47_1.png" style="box-sizing: border-box; border: 0px; vertical-align: middle; padding: 5px; margin: 0px; float: left;" /></div> </div> <p style="text-align: left;">The advantage of using xarray’s <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">.plot()</span></code> is that it automatically shows the <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">x</span></code> and <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">y</span></code> axes using coordinate values, not their positional index.</p> </div> </div> </div> <script type="text/javascript"> (function (require) { require(['/static/js/dateutil_factory.be68acdff619.js?raw'], function () { require(['js/dateutil_factory'], function (DateUtilFactory) { DateUtilFactory.transform('.localized-datetime'); }); }); }).call(this, require || RequireJS.require); </script> <script> function emit_event(message) { parent.postMessage(message, '*'); } </script> </div>

<div class="xblock xblock-public_view xblock-public_view-vertical" data-course-id="course-v1:digitalearthafrica+DEA101+2021" data-init="VerticalStudentView" data-runtime-class="LmsRuntime" data-runtime-version="1" data-block-type="vertical" data-usage-id="block-v1:digitalearthafrica+DEA101+2021+type@vertical+block@509f359b6bbe49a7a90ee3931287544a" data-request-token="1b5e9822881611efa85e837f534d2eba" data-graded="False" data-has-score="False"> <div class="vert-mod"> <div class="vert vert-0" data-id="block-v1:digitalearthafrica+DEA101+2021+type@html+block@4c0dfe3c8b5245c8864b05f921d9d34f"> <div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-course-id="course-v1:digitalearthafrica+DEA101+2021" data-init="XBlockToXModuleShim" data-runtime-class="LmsRuntime" data-runtime-version="1" data-block-type="html" data-usage-id="block-v1:digitalearthafrica+DEA101+2021+type@html+block@4c0dfe3c8b5245c8864b05f921d9d34f" data-request-token="1b5e9822881611efa85e837f534d2eba" data-graded="False" data-has-score="False"> <script type="json/xblock-args" class="xblock-json-init-args"> {"xmodule-type": "HTMLModule"} </script> <section id="5.1-Can-you-access-to-the-crs-value-in-the-attributes-of-the-guinea_bissau-xarray.Dataset?" style="box-sizing: border-box; color: #000000;"> <h3 style="box-sizing: border-box; margin-top: 0px; font-weight: bold; font-size: 20px; color: #336699; text-align: left;">5.1 Can you access to the <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 15px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">crs</span></code> value in the attributes of the <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 15px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">guinea_bissau</span></code> <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 15px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">xarray.Dataset</span></code>?<a class="headerlink" href="https://training.digitalearthafrica.org/en/latest/python_basics/05_xarray.html#5.1-Can-you-access-to-the-crs-value-in-the-attributes-of-the-guinea_bissau-xarray.Dataset?" title="Permalink to this headline" style="box-sizing: border-box; color: #2980b9; text-decoration-line: none; cursor: pointer; display: inline-block; font-variant-numeric: normal; font-variant-east-asian: normal; font-weight: normal; font-stretch: normal; line-height: 1; font-family: inherit; font-size: 14px; text-rendering: auto; -webkit-font-smoothing: antialiased; visibility: hidden;"></a></h3> <p style="box-sizing: border-box; text-align: left;"><strong style="box-sizing: border-box;">Hint:</strong> You can call upon <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">attributes</span></code> in the same way you would select a <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">variable</span></code> or <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">coordinate</span></code>.</p> <div style="box-sizing: border-box; text-align: left;"> <div class="nbinput nblast docutils container" style="box-sizing: border-box; display: flex; align-items: flex-start; margin: 0px 0px 19px; width: 696.469px; padding-top: 5px; padding-bottom: 5px; text-align: left;"> <div class="prompt highlight-none notranslate" style="box-sizing: border-box; margin: 0px; width: 4.5ex; padding-top: 5px; position: relative; user-select: none;"> <div class="highlight" style="box-sizing: border-box; background: none; border: none; overflow-x: auto; margin: 0px 0.3ex 0px 0px; padding: 0px; box-shadow: none; position: absolute; right: 0px;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 16px; white-space: pre; line-height: 1.4; margin-top: 0px; margin-bottom: 0px; padding: 0px; overflow: hidden; border: none; box-shadow: none; background: none; color: #307fc1;"><span style="box-sizing: border-box; border: none; padding: 0px; margin: 0px; box-shadow: none; background: none;"></span>[ ]: </pre> </div> </div> <div class="input_area highlight-ipython3 notranslate" style="box-sizing: border-box; margin: 0px; flex: 1 1 0%; overflow: auto; border: 1px solid #e0e0e0; border-radius: 2px;"> <div class="highlight" style="box-sizing: border-box; background: #f8f8f8; border: none; overflow: auto hidden; margin: 0px; padding: 0px; box-shadow: none;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 16px; white-space: pre; line-height: 1.4; margin-top: 0px; margin-bottom: 0px; padding: 5px; overflow: auto; border: none; box-shadow: none;"><span style="box-sizing: border-box;"></span><span class="c1" style="box-sizing: border-box; color: #408080; font-style: italic;"># Replace the ? with the attribute name</span> guinea_bissau.<span class="o" style="box-sizing: border-box; color: #666666;">?</span> </pre> </div> </div> </div> </div> </section> <h3 style="box-sizing: border-box; margin-top: 0px; font-weight: bold; font-size: 20px; color: #336699; text-align: left;">5.2 Select the region of the <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 15px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">blue</span></code> variable delimited by these coordinates:<a class="headerlink" href="https://training.digitalearthafrica.org/en/latest/python_basics/05_xarray.html#5.2-Select-the-region-of-the-blue-variable-delimited-by-these-coordinates:" title="Permalink to this headline" style="box-sizing: border-box; color: #2980b9; text-decoration-line: none; cursor: pointer; display: inline-block; font-variant-numeric: normal; font-variant-east-asian: normal; font-weight: normal; font-stretch: normal; line-height: 1; font-family: inherit; font-size: 14px; text-rendering: auto; -webkit-font-smoothing: antialiased; visibility: hidden;"></a></h3> <p style="text-align: left;"><span style="color: #3c3c3c;">latitude of range [1335000, 1329030]</span></p> <p style="text-align: left;"><span style="color: #3c3c3c;">longitude of range [389520, 395490]</span></p> <div style="box-sizing: border-box; text-align: left;"> <p style="box-sizing: border-box; line-height: 24px; margin: 0px 0px 24px; font-size: 16px; text-align: left;"><strong style="box-sizing: border-box;">Hint:</strong> Do we want to use <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">sel()</span></code> or <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">isel()</span></code>? Which coordinate is <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">x</span></code> and which is <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 12px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">y</span></code>?</p> </div> <section id="5.3-Plot-the-selected-region-using-imshow,-then-plot-the-region-using-.plot()." style="box-sizing: border-box; color: #000000; font-family: Arial, sans-serif;"> <h3 style="box-sizing: border-box; margin-top: 0px; font-weight: bold; font-size: 20px; color: #336699;">5.3 Plot the selected region using <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 15px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">imshow</span></code>, then plot the region using <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 15px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">.plot()</span></code>.<a class="headerlink" href="https://training.digitalearthafrica.org/en/latest/python_basics/05_xarray.html#5.3-Plot-the-selected-region-using-imshow,-then-plot-the-region-using-.plot()." title="Permalink to this headline" style="box-sizing: border-box; color: #2980b9; text-decoration-line: none; cursor: pointer; display: inline-block; font-variant-numeric: normal; font-variant-east-asian: normal; font-weight: normal; font-stretch: normal; line-height: 1; font-family: inherit; font-size: 14px; text-rendering: auto; -webkit-font-smoothing: antialiased; visibility: hidden;"></a></h3> <div class="nbinput nblast docutils container" style="box-sizing: border-box; display: flex; align-items: flex-start; margin: 0px 0px 19px; width: 696.469px; padding-top: 5px; padding-bottom: 5px;"> <div class="prompt highlight-none notranslate" style="box-sizing: border-box; margin: 0px; width: 4.5ex; padding-top: 5px; position: relative; user-select: none;"> <div class="highlight" style="box-sizing: border-box; background: none; border: none; overflow-x: auto; margin: 0px 0.3ex 0px 0px; padding: 0px; box-shadow: none; position: absolute; right: 0px;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 16px; white-space: pre; line-height: 1.4; margin-top: 0px; margin-bottom: 0px; padding: 0px; overflow: hidden; border: none; box-shadow: none; background: none; color: #307fc1;"><span style="box-sizing: border-box; border: none; padding: 0px; margin: 0px; box-shadow: none; background: none;"></span>[ ]: </pre> </div> </div> <div class="input_area highlight-ipython3 notranslate" style="box-sizing: border-box; margin: 0px; flex: 1 1 0%; overflow: auto; border: 1px solid #e0e0e0; border-radius: 2px;"> <div class="highlight" style="box-sizing: border-box; background: #f8f8f8; border: none; overflow: auto hidden; margin: 0px; padding: 0px; box-shadow: none;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 16px; white-space: pre; line-height: 1.4; margin-top: 0px; margin-bottom: 0px; padding: 5px; overflow: auto; border: none; box-shadow: none;"><span style="box-sizing: border-box;"></span><span class="c1" style="box-sizing: border-box; color: #408080; font-style: italic;"># Plot using plt.imshow</span> </pre> </div> </div> </div> <div class="nbinput nblast docutils container" style="box-sizing: border-box; display: flex; align-items: flex-start; margin: -19px 0px 19px; width: 696.469px; padding-top: 5px; padding-bottom: 5px;"> <div class="prompt highlight-none notranslate" style="box-sizing: border-box; margin: 0px; width: 4.5ex; padding-top: 5px; position: relative; user-select: none;"> <div class="highlight" style="box-sizing: border-box; background: none; border: none; overflow-x: auto; margin: 0px 0.3ex 0px 0px; padding: 0px; box-shadow: none; position: absolute; right: 0px;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 16px; white-space: pre; line-height: 1.4; margin-top: 0px; margin-bottom: 0px; padding: 0px; overflow: hidden; border: none; box-shadow: none; background: none; color: #307fc1;"><span style="box-sizing: border-box; border: none; padding: 0px; margin: 0px; box-shadow: none; background: none;"></span>[ ]: </pre> </div> </div> <div class="input_area highlight-ipython3 notranslate" style="box-sizing: border-box; margin: 0px; flex: 1 1 0%; overflow: auto; border: 1px solid #e0e0e0; border-radius: 2px;"> <div class="highlight" style="box-sizing: border-box; background: #f8f8f8; border: none; overflow: auto hidden; margin: 0px; padding: 0px; box-shadow: none;"> <pre style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 16px; white-space: pre; line-height: 1.4; margin-top: 0px; margin-bottom: 0px; padding: 5px; overflow: auto; border: none; box-shadow: none;"><span style="box-sizing: border-box;"></span><span class="c1" style="box-sizing: border-box; color: #408080; font-style: italic;"># Plot using .plot()</span> </pre> </div> </div> </div> </section> <section id="Can-you-change-the-colour-map-to-'Blues'?" style="box-sizing: border-box; color: #000000; font-family: Arial, sans-serif;"> <h3 style="box-sizing: border-box; margin-top: 0px; font-weight: bold; font-size: 20px; color: #336699; text-align: left;">5.4 Can you change the colour map to <code class="docutils literal notranslate" style="box-sizing: border-box; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', Courier, monospace; font-size: 15px; white-space: nowrap; max-width: 100%; background-image: initial; background-color: #ffffff; border: 1px solid #e1e4e5; padding: 2px 5px; color: #e74c3c; overflow-x: auto;"><span class="pre" style="box-sizing: border-box;">'Blues'</span></code>?</h3> </section> </div> </div> </div> <script type="text/javascript"> (function (require) { require(['/static/js/dateutil_factory.be68acdff619.js?raw'], function () { require(['js/dateutil_factory'], function (DateUtilFactory) { DateUtilFactory.transform('.localized-datetime'); }); }); }).call(this, require || RequireJS.require); </script> <script> function emit_event(message) { parent.postMessage(message, '*'); } </script> </div>

<div class="xblock xblock-public_view xblock-public_view-vertical" data-course-id="course-v1:digitalearthafrica+DEA101+2021" data-init="VerticalStudentView" data-runtime-class="LmsRuntime" data-runtime-version="1" data-block-type="vertical" data-usage-id="block-v1:digitalearthafrica+DEA101+2021+type@vertical+block@30499597041d4ab18a5258dd6849b549" data-request-token="1b5e9822881611efa85e837f534d2eba" data-graded="False" data-has-score="False"> <div class="vert-mod"> <div class="vert vert-0" data-id="block-v1:digitalearthafrica+DEA101+2021+type@html+block@f661a5f7b2ea4912a88edc0f12379fcb"> <div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-course-id="course-v1:digitalearthafrica+DEA101+2021" data-init="XBlockToXModuleShim" data-runtime-class="LmsRuntime" data-runtime-version="1" data-block-type="html" data-usage-id="block-v1:digitalearthafrica+DEA101+2021+type@html+block@f661a5f7b2ea4912a88edc0f12379fcb" data-request-token="1b5e9822881611efa85e837f534d2eba" data-graded="False" data-has-score="False"> <script type="json/xblock-args" class="xblock-json-init-args"> {"xmodule-type": "HTMLModule"} </script> <p style="text-align: left;">Xarray is an important tool when dealing with Earth observation data in Python. Its flexible and intuitive interface allows multiple methods of selecting data and performing analysis.</p> </div> </div> </div> <script type="text/javascript"> (function (require) { require(['/static/js/dateutil_factory.be68acdff619.js?raw'], function () { require(['js/dateutil_factory'], function (DateUtilFactory) { DateUtilFactory.transform('.localized-datetime'); }); }); }).call(this, require || RequireJS.require); </script> <script> function emit_event(message) { parent.postMessage(message, '*'); } </script> </div>