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    <title>topic Re: 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/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>
    <dc:creator>DanPatterson_Retired</dc:creator>
    <dc:date>2020-01-16T13:26:15Z</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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