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    <title>topic Checking for ANYNODATA in a DEM always results in True in Python Questions</title>
    <link>https://community.esri.com/t5/python-questions/checking-for-anynodata-in-a-dem-always-results-in/m-p/213910#M16488</link>
    <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;I'm working on a script that would fill (&lt;EM&gt;NoData&lt;/EM&gt;) holes in a DEM with a raster calculation. The calculation fills the holes with the average of the surrounding cells and this proces should be repeated to get an accurate result. It works quite well, just the while loop never ends as&amp;nbsp;&lt;EM&gt;arcpy.GetRasterProperties_management&lt;/EM&gt; keeps finding &lt;EM&gt;ANYNODATA&lt;/EM&gt;. Within the DEM itself there is no &lt;EM&gt;NoData&lt;/EM&gt;, but on the outsidesthere is (outside of the extent).&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;The DEM I'm working with is cut (Extract by Mask) to a polygon which I've also set my extent to.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;STRONG&gt;Could anyone point me into a direction how I can check for NoData and stop the While loop as soon as the DEM is filled?&lt;/STRONG&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;My script looks like this:&lt;/P&gt;&lt;PRE class="lia-code-sample line-numbers language-none"&gt;&lt;CODE&gt;&lt;SPAN class="keyword token"&gt;import&lt;/SPAN&gt; arcpy

&lt;SPAN class="comment token"&gt;# Environment settings&lt;/SPAN&gt;
arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;env&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;overwriteOutput &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; &lt;SPAN class="token boolean"&gt;True&lt;/SPAN&gt;
arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;env&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;extent &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;"D:\..."&lt;/SPAN&gt;
arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;env&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;parallelProcessingFactor &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;"8"&lt;/SPAN&gt;
arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;env&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;processorType &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;"CPU"&lt;/SPAN&gt;
DEM &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;Raster&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;&lt;SPAN class="string token"&gt;"D:\..."&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;

&lt;SPAN class="comment token"&gt;#	Check for NoData in the raster and start filling as long as there is NoData&lt;/SPAN&gt;
NoDataCheck &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;GetRasterProperties_management&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;in_raster&lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt;DEM&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt; property_type&lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt;&lt;SPAN class="string token"&gt;"ANYNODATA"&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;
NoDataRes &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; int&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;NoDataCheck&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;getOutput&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;&lt;SPAN class="number token"&gt;0&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;
count &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; &lt;SPAN class="number token"&gt;0&lt;/SPAN&gt;
&lt;SPAN class="keyword token"&gt;print&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;&lt;SPAN class="string token"&gt;"Voor loop: "&lt;/SPAN&gt; &lt;SPAN class="operator token"&gt;+&lt;/SPAN&gt; str&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;NoDataRes&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;

&lt;SPAN class="keyword token"&gt;while&lt;/SPAN&gt; NoDataCheck&lt;SPAN class="punctuation token"&gt;:&lt;/SPAN&gt;
	&lt;SPAN class="comment token"&gt;# RasterCalc filling the holes&lt;/SPAN&gt;
	expression&lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt;&lt;SPAN class="string token"&gt;' Con(IsNull(DEM), FocalStatistics(DEM, NbrRectangle(5,5, "CELL"), "MEAN"), DEM)'&lt;/SPAN&gt;
	arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;gp&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;RasterCalculator_sa&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;expression&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt; DEM&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;
	count &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; count &lt;SPAN class="operator token"&gt;+&lt;/SPAN&gt; &lt;SPAN class="number token"&gt;1&lt;/SPAN&gt;
	NoDataCheck &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;GetRasterProperties_management&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;in_raster&lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt;DEM&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt; property_type&lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt;&lt;SPAN class="string token"&gt;"ANYNODATA"&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;
	NoDataRes &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; int&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;NoDataCheck&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;getOutput&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;&lt;SPAN class="number token"&gt;0&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;&lt;SPAN class="line-numbers-rows"&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;/SPAN&gt;&lt;/CODE&gt;&lt;/PRE&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
    <pubDate>Sat, 11 Dec 2021 10:29:43 GMT</pubDate>
    <dc:creator>GISTeam4</dc:creator>
    <dc:date>2021-12-11T10:29:43Z</dc:date>
    <item>
      <title>Checking for ANYNODATA in a DEM always results in True</title>
      <link>https://community.esri.com/t5/python-questions/checking-for-anynodata-in-a-dem-always-results-in/m-p/213910#M16488</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;I'm working on a script that would fill (&lt;EM&gt;NoData&lt;/EM&gt;) holes in a DEM with a raster calculation. The calculation fills the holes with the average of the surrounding cells and this proces should be repeated to get an accurate result. It works quite well, just the while loop never ends as&amp;nbsp;&lt;EM&gt;arcpy.GetRasterProperties_management&lt;/EM&gt; keeps finding &lt;EM&gt;ANYNODATA&lt;/EM&gt;. Within the DEM itself there is no &lt;EM&gt;NoData&lt;/EM&gt;, but on the outsidesthere is (outside of the extent).&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;The DEM I'm working with is cut (Extract by Mask) to a polygon which I've also set my extent to.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;STRONG&gt;Could anyone point me into a direction how I can check for NoData and stop the While loop as soon as the DEM is filled?&lt;/STRONG&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;My script looks like this:&lt;/P&gt;&lt;PRE class="lia-code-sample line-numbers language-none"&gt;&lt;CODE&gt;&lt;SPAN class="keyword token"&gt;import&lt;/SPAN&gt; arcpy

&lt;SPAN class="comment token"&gt;# Environment settings&lt;/SPAN&gt;
arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;env&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;overwriteOutput &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; &lt;SPAN class="token boolean"&gt;True&lt;/SPAN&gt;
arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;env&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;extent &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;"D:\..."&lt;/SPAN&gt;
arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;env&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;parallelProcessingFactor &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;"8"&lt;/SPAN&gt;
arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;env&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;processorType &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;"CPU"&lt;/SPAN&gt;
DEM &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;Raster&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;&lt;SPAN class="string token"&gt;"D:\..."&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;

&lt;SPAN class="comment token"&gt;#	Check for NoData in the raster and start filling as long as there is NoData&lt;/SPAN&gt;
NoDataCheck &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;GetRasterProperties_management&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;in_raster&lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt;DEM&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt; property_type&lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt;&lt;SPAN class="string token"&gt;"ANYNODATA"&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;
NoDataRes &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; int&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;NoDataCheck&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;getOutput&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;&lt;SPAN class="number token"&gt;0&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;
count &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; &lt;SPAN class="number token"&gt;0&lt;/SPAN&gt;
&lt;SPAN class="keyword token"&gt;print&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;&lt;SPAN class="string token"&gt;"Voor loop: "&lt;/SPAN&gt; &lt;SPAN class="operator token"&gt;+&lt;/SPAN&gt; str&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;NoDataRes&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;

&lt;SPAN class="keyword token"&gt;while&lt;/SPAN&gt; NoDataCheck&lt;SPAN class="punctuation token"&gt;:&lt;/SPAN&gt;
	&lt;SPAN class="comment token"&gt;# RasterCalc filling the holes&lt;/SPAN&gt;
	expression&lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt;&lt;SPAN class="string token"&gt;' Con(IsNull(DEM), FocalStatistics(DEM, NbrRectangle(5,5, "CELL"), "MEAN"), DEM)'&lt;/SPAN&gt;
	arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;gp&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;RasterCalculator_sa&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;expression&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt; DEM&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;
	count &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; count &lt;SPAN class="operator token"&gt;+&lt;/SPAN&gt; &lt;SPAN class="number token"&gt;1&lt;/SPAN&gt;
	NoDataCheck &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;GetRasterProperties_management&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;in_raster&lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt;DEM&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt; property_type&lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt;&lt;SPAN class="string token"&gt;"ANYNODATA"&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;
	NoDataRes &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; int&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;NoDataCheck&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;getOutput&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;&lt;SPAN class="number token"&gt;0&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;&lt;SPAN class="line-numbers-rows"&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;/SPAN&gt;&lt;/CODE&gt;&lt;/PRE&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Sat, 11 Dec 2021 10:29:43 GMT</pubDate>
      <guid>https://community.esri.com/t5/python-questions/checking-for-anynodata-in-a-dem-always-results-in/m-p/213910#M16488</guid>
      <dc:creator>GISTeam4</dc:creator>
      <dc:date>2021-12-11T10:29:43Z</dc:date>
    </item>
    <item>
      <title>Re: Checking for ANYNODATA in a DEM always results in True</title>
      <link>https://community.esri.com/t5/python-questions/checking-for-anynodata-in-a-dem-always-results-in/m-p/213911#M16489</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;You also need a nodata check within your while loop to check if all was removed during the loop.&amp;nbsp;&amp;nbsp;&lt;/P&gt;&lt;P&gt;It will remain True, until it is checked again.&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Thu, 16 Jan 2020 13:26:15 GMT</pubDate>
      <guid>https://community.esri.com/t5/python-questions/checking-for-anynodata-in-a-dem-always-results-in/m-p/213911#M16489</guid>
      <dc:creator>DanPatterson_Retired</dc:creator>
      <dc:date>2020-01-16T13:26:15Z</dc:date>
    </item>
    <item>
      <title>Re: Checking for ANYNODATA in a DEM always results in True</title>
      <link>https://community.esri.com/t5/python-questions/checking-for-anynodata-in-a-dem-always-results-in/m-p/213912#M16490</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;@Dan: You're right, but it's not the cause of the problem. I copied my code falsely and I already had that part in there (sorry for that). The correct script is in the main post now.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;STRONG&gt;This is what's going on:&lt;/STRONG&gt;&lt;/P&gt;&lt;P&gt;After some trial and error I've noticed the raster always result in a squared extend. This means a shape more complex than just 4 outer corners will contain NoData outside of the actual extent.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;To make this visual, in the image below my extent is set to the black outlined polygon. The raster is masked to this shape. My script starts filling holes, but it will continue untill all the red (NoData) has been filled. So it always tries to work with a square extent.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;IMG alt="" class="jive-emoji image-1 jive-image j-img-centered" 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" 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      <pubDate>Fri, 17 Jan 2020 07:21:18 GMT</pubDate>
      <guid>https://community.esri.com/t5/python-questions/checking-for-anynodata-in-a-dem-always-results-in/m-p/213912#M16490</guid>
      <dc:creator>GISTeam4</dc:creator>
      <dc:date>2020-01-17T07:21:18Z</dc:date>
    </item>
    <item>
      <title>Re: Checking for ANYNODATA in a DEM always results in True</title>
      <link>https://community.esri.com/t5/python-questions/checking-for-anynodata-in-a-dem-always-results-in/m-p/213913#M16491</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;&lt;A href="https://community.esri.com/migrated-users/31507"&gt;GIS Team&lt;/A&gt;‌ glad you posted the correct code now&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Fri, 17 Jan 2020 13:13:54 GMT</pubDate>
      <guid>https://community.esri.com/t5/python-questions/checking-for-anynodata-in-a-dem-always-results-in/m-p/213913#M16491</guid>
      <dc:creator>DanPatterson_Retired</dc:creator>
      <dc:date>2020-01-17T13:13:54Z</dc:date>
    </item>
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