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    <title>topic Script tool dependency - using a conditional? in Python Questions</title>
    <link>https://community.esri.com/t5/python-questions/script-tool-dependency-using-a-conditional/m-p/639371#M49838</link>
    <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Trying to make a script tool for a repetitive data conversion task I perform regularly. Have two "Processing Methods". The first, "Individual", takes each input file (from the Input Directory - folder) and produces an equivalent, converted output file. The second, shown below as "Combine" takes the same inputs, but combines them into one combined output file.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;"Individual" is the default, and I have got that working perfectly. If this "Individual" option is used, the input filename is extracted to form the output filename.&amp;nbsp;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;But if someone uses the combined option, then you have many input filenames which are likely (but not always) going to have some sequence. Therefore I want to give the user the opportunity to provide a filename for the single output file. I, however, need this option to be blank/uneditable when the "Individual" method is selected.&amp;nbsp;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;I have had a bit of a play around, and searched the web. Have also tried looking for existing tools that have this functionality so that I may be able to compare and replicate.&amp;nbsp;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;IMG 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" /&gt;&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
    <pubDate>Tue, 10 Nov 2020 13:07:30 GMT</pubDate>
    <dc:creator>CraigMacDonell</dc:creator>
    <dc:date>2020-11-10T13:07:30Z</dc:date>
    <item>
      <title>Script tool dependency - using a conditional?</title>
      <link>https://community.esri.com/t5/python-questions/script-tool-dependency-using-a-conditional/m-p/639371#M49838</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Trying to make a script tool for a repetitive data conversion task I perform regularly. Have two "Processing Methods". The first, "Individual", takes each input file (from the Input Directory - folder) and produces an equivalent, converted output file. The second, shown below as "Combine" takes the same inputs, but combines them into one combined output file.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;"Individual" is the default, and I have got that working perfectly. If this "Individual" option is used, the input filename is extracted to form the output filename.&amp;nbsp;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;But if someone uses the combined option, then you have many input filenames which are likely (but not always) going to have some sequence. Therefore I want to give the user the opportunity to provide a filename for the single output file. I, however, need this option to be blank/uneditable when the "Individual" method is selected.&amp;nbsp;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;I have had a bit of a play around, and searched the web. Have also tried looking for existing tools that have this functionality so that I may be able to compare and replicate.&amp;nbsp;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;IMG 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" 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      <pubDate>Tue, 10 Nov 2020 13:07:30 GMT</pubDate>
      <guid>https://community.esri.com/t5/python-questions/script-tool-dependency-using-a-conditional/m-p/639371#M49838</guid>
      <dc:creator>CraigMacDonell</dc:creator>
      <dc:date>2020-11-10T13:07:30Z</dc:date>
    </item>
    <item>
      <title>Re: Script tool dependency - using a conditional?</title>
      <link>https://community.esri.com/t5/python-questions/script-tool-dependency-using-a-conditional/m-p/1000986#M59027</link>
      <description>&lt;P&gt;I suggest the &lt;A href="https://pro.arcgis.com/en/pro-app/arcpy/geoprocessing_and_python/customizing-script-tool-behavior.htm" target="_self"&gt;ToolValidator&lt;/A&gt; for this.&amp;nbsp; Modifying the example given in the enable/disable a parameter section, the code would look something like:&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;LI-CODE lang="python"&gt;def updateParameters(self):
    # params[0] = Individual or Combine
    # params[4] = Output Filename
    if self.params[0].value == "Combine":
        self.params[4].enabled = True
    else:
        self.params[4].enabled = False&lt;/LI-CODE&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Fri, 13 Nov 2020 01:27:51 GMT</pubDate>
      <guid>https://community.esri.com/t5/python-questions/script-tool-dependency-using-a-conditional/m-p/1000986#M59027</guid>
      <dc:creator>RandyBurton</dc:creator>
      <dc:date>2020-11-13T01:27:51Z</dc:date>
    </item>
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