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    <title>MaplePrimes - answers and comments on Question, Bug in plottools[transform] &amp; Where is transform/object</title>
    <link>http://www.mapleprimes.com/questions/144465-Bug-In-Plottoolstransform--Where</link>
    <language>en-us</language>
    <copyright>2026 Maplesoft, A Division of Waterloo Maple Inc.</copyright>
    <generator>Maplesoft Document System</generator>
    <lastBuildDate>Wed, 10 Jun 2026 12:31:17 GMT</lastBuildDate>
    <pubDate>Wed, 10 Jun 2026 12:31:17 GMT</pubDate>
    <itunes:subtitle />
    <itunes:summary />
    <description>The latest answers and comments added to the Question, Bug in plottools[transform] &amp; Where is transform/object</description>
    <image>
      <url>http://www.mapleprimes.com/images/mapleprimeswhite.jpg</url>
      <title>MaplePrimes - answers and comments on Question, Bug in plottools[transform] &amp; Where is transform/object</title>
      <link>http://www.mapleprimes.com/questions/144465-Bug-In-Plottoolstransform--Where</link>
    </image>
    <item>
      <title>works in M12</title>
      <link>http://www.mapleprimes.com/questions/144465-Bug-In-Plottoolstransform--Where?ref=Feed:MaplePrimes:Bug in plottools[transform] &amp; Where is transform/object:Comments#answer144468</link>
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" alt="" width="821" height="827"&gt;&lt;/p&gt;
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&lt;p style="margin: 0 0 0 0; padding-top: 0px; padding-bottom: 0px;" align="left"&gt;&lt;img style="vertical-align: -60;" src="/view.aspx?sf=144468/455810/f99767148c0a1e593e7ecc03764e8c01.gif" alt="simp := plot(sin(x), x = 0 .. Pi);" width="576" height="77" align="middle"&gt;&lt;br&gt;&lt;br&gt;&lt;/p&gt;
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&lt;tbody&gt;
&lt;tr valign="baseline"&gt;
&lt;td&gt;
&lt;p style="margin: 0 0 0 0; padding-top: 0px; padding-bottom: 0px;" align="center"&gt;&lt;a href="http://www.maplesoft.com/support/faqs/MapleNet/redirect.aspx?param=plot_java_14206"&gt;&lt;img style="border: none;" src="/view.aspx?sf=144468/455810/9309f49f71d582c30b2a82847d4945a5.gif" alt="" width="400" height="400" align="middle"&gt;&lt;/a&gt;&lt;/p&gt;
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&lt;td style="color: #000000; font-family: Times, serif; font-weight: bold; font-style: normal;" align="right"&gt;&amp;nbsp;&lt;/td&gt;
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&lt;p style="margin: 0 0 0 0; padding-top: 0px; padding-bottom: 0px;" align="left"&gt;&lt;img style="vertical-align: -6;" src="/view.aspx?sf=144468/455810/442add3af02aab953e63235b139b937c.gif" alt="interface(verboseproc)" width="147" height="23"&gt;&lt;/p&gt;
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&lt;tbody&gt;
&lt;tr valign="baseline"&gt;
&lt;td&gt;
&lt;p style="margin: 0 0 0 0; padding-top: 0px; padding-bottom: 0px;" align="center"&gt;&lt;img style="vertical-align: -6;" src="/view.aspx?sf=144468/455810/457a243b8f520972f1dc6384f56e45e0.gif" alt="1" width="13" height="23"&gt;&lt;/p&gt;
&lt;/td&gt;
&lt;td style="color: #000000; font-family: Times, serif; font-weight: bold; font-style: normal;" align="right"&gt;(1)&lt;/td&gt;
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&lt;p style="margin: 0 0 0 0; padding-top: 0px; padding-bottom: 0px;" align="left"&gt;&lt;img style="vertical-align: -6;" src="/view.aspx?sf=144468/455810/dbfd6c72d8420c4a001cb3a2c69cdc80.gif" alt="``" width="11" height="23"&gt;&lt;/p&gt;
&lt;/td&gt;
&lt;/tr&gt;
&lt;/tbody&gt;
&lt;/table&gt;
&lt;input type="hidden" name="sequence" value="1"&gt; &lt;input type="hidden" name="cmd" value="none"&gt;&lt;/form&gt;
&lt;p&gt;&amp;nbsp;&lt;/p&gt;
&lt;p&gt;&lt;a href="/view.aspx?sf=144468/455810/transform_test.mw"&gt;Download transform_test.mw&lt;/a&gt;&lt;/p&gt;
&lt;p&gt;It works here in M12&lt;/p&gt;</itunes:summary>
      <description>&lt;p&gt;&lt;br&gt; &lt;img 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" alt="" width="821" height="827"&gt;&lt;/p&gt;
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&lt;p style="margin: 0 0 0 0; padding-top: 0px; padding-bottom: 0px;" align="left"&gt;&lt;img style="vertical-align: -60;" src="/view.aspx?sf=144468/455810/f99767148c0a1e593e7ecc03764e8c01.gif" alt="simp := plot(sin(x), x = 0 .. Pi);" width="576" height="77" align="middle"&gt;&lt;br&gt;&lt;br&gt;&lt;/p&gt;
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&lt;tbody&gt;
&lt;tr valign="baseline"&gt;
&lt;td&gt;
&lt;p style="margin: 0 0 0 0; padding-top: 0px; padding-bottom: 0px;" align="center"&gt;&lt;a href="http://www.maplesoft.com/support/faqs/MapleNet/redirect.aspx?param=plot_java_14206"&gt;&lt;img style="border: none;" src="/view.aspx?sf=144468/455810/9309f49f71d582c30b2a82847d4945a5.gif" alt="" width="400" height="400" align="middle"&gt;&lt;/a&gt;&lt;/p&gt;
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&lt;td style="color: #000000; font-family: Times, serif; font-weight: bold; font-style: normal;" align="right"&gt;&amp;nbsp;&lt;/td&gt;
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&lt;p style="margin: 0 0 0 0; padding-top: 0px; padding-bottom: 0px;" align="left"&gt;&lt;img style="vertical-align: -6;" src="/view.aspx?sf=144468/455810/442add3af02aab953e63235b139b937c.gif" alt="interface(verboseproc)" width="147" height="23"&gt;&lt;/p&gt;
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&lt;tbody&gt;
&lt;tr valign="baseline"&gt;
&lt;td&gt;
&lt;p style="margin: 0 0 0 0; padding-top: 0px; padding-bottom: 0px;" align="center"&gt;&lt;img style="vertical-align: -6;" src="/view.aspx?sf=144468/455810/457a243b8f520972f1dc6384f56e45e0.gif" alt="1" width="13" height="23"&gt;&lt;/p&gt;
&lt;/td&gt;
&lt;td style="color: #000000; font-family: Times, serif; font-weight: bold; font-style: normal;" align="right"&gt;(1)&lt;/td&gt;
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&lt;/table&gt;
&lt;p style="margin: 0 0 0 0; padding-top: 0px; padding-bottom: 0px;" align="left"&gt;&lt;img style="vertical-align: -6;" src="/view.aspx?sf=144468/455810/dbfd6c72d8420c4a001cb3a2c69cdc80.gif" alt="``" width="11" height="23"&gt;&lt;/p&gt;
&lt;/td&gt;
&lt;/tr&gt;
&lt;/tbody&gt;
&lt;/table&gt;
&lt;input type="hidden" name="sequence" value="1"&gt; &lt;input type="hidden" name="cmd" value="none"&gt;&lt;/form&gt;
&lt;p&gt;&amp;nbsp;&lt;/p&gt;
&lt;p&gt;&lt;a href="/view.aspx?sf=144468/455810/transform_test.mw"&gt;Download transform_test.mw&lt;/a&gt;&lt;/p&gt;
&lt;p&gt;It works here in M12&lt;/p&gt;</description>
      <guid>144468</guid>
      <pubDate>Mon, 11 Mar 2013 18:19:09 Z</pubDate>
      <itunes:author>Christopher2222</itunes:author>
      <author>Christopher2222</author>
    </item>
    <item>
      <title>nonnumeric input</title>
      <link>http://www.mapleprimes.com/questions/144465-Bug-In-Plottoolstransform--Where?ref=Feed:MaplePrimes:Bug in plottools[transform] &amp; Where is transform/object:Comments#answer144469</link>
      <itunes:summary>&lt;p&gt;How about having `f` return unevaluated if its arguments are not of type numeric.&lt;/p&gt;
&lt;pre&gt;restart:                                          

simp:=plot(sin(x), x=0..2*Pi):                    

f:=(xx,yy)-&amp;gt;`if`(yy::numeric and xx::numeric,     
                 `if`(yy &amp;gt; 0, [xx,4*yy], [xx,yy]),
                 'procname'(xx,yy)):              

trns:=plottools:-transform(f):                                               

trns(simp);    
&lt;/pre&gt;
&lt;!--break--&gt;
&lt;p&gt;acer&lt;/p&gt;</itunes:summary>
      <description>&lt;p&gt;How about having `f` return unevaluated if its arguments are not of type numeric.&lt;/p&gt;
&lt;pre&gt;restart:                                          

simp:=plot(sin(x), x=0..2*Pi):                    

f:=(xx,yy)-&amp;gt;`if`(yy::numeric and xx::numeric,     
                 `if`(yy &amp;gt; 0, [xx,4*yy], [xx,yy]),
                 'procname'(xx,yy)):              

trns:=plottools:-transform(f):                                               

trns(simp);    
&lt;/pre&gt;
&lt;!--break--&gt;
&lt;p&gt;acer&lt;/p&gt;</description>
      <guid>144469</guid>
      <pubDate>Mon, 11 Mar 2013 18:38:48 Z</pubDate>
      <itunes:author>acer</itunes:author>
      <author>acer</author>
    </item>
    <item>
      <title>List</title>
      <link>http://www.mapleprimes.com/questions/144465-Bug-In-Plottoolstransform--Where?ref=Feed:MaplePrimes:Bug in plottools[transform] &amp; Where is transform/object:Comments#answer144470</link>
      <itunes:summary>&lt;p&gt;The following works for your example:&lt;/p&gt;
&lt;p&gt;f:=(x,y)-&amp;gt;[x,`if`(y&amp;gt;0,4*y,y)];&lt;br&gt;&lt;br&gt;or this weirder version:&lt;/p&gt;
&lt;p&gt;f:=(x,y)-&amp;gt;[`if`(x&amp;lt;1,x^2,x*y),`if`(y&amp;gt;0,4*y,y)];&lt;/p&gt;</itunes:summary>
      <description>&lt;p&gt;The following works for your example:&lt;/p&gt;
&lt;p&gt;f:=(x,y)-&amp;gt;[x,`if`(y&amp;gt;0,4*y,y)];&lt;br&gt;&lt;br&gt;or this weirder version:&lt;/p&gt;
&lt;p&gt;f:=(x,y)-&amp;gt;[`if`(x&amp;lt;1,x^2,x*y),`if`(y&amp;gt;0,4*y,y)];&lt;/p&gt;</description>
      <guid>144470</guid>
      <pubDate>Mon, 11 Mar 2013 18:46:08 Z</pubDate>
      <itunes:author>Preben Alsholm</itunes:author>
      <author>Preben Alsholm</author>
    </item>
    <item>
      <title>How to use plottools:-transform</title>
      <link>http://www.mapleprimes.com/questions/144465-Bug-In-Plottoolstransform--Where?ref=Feed:MaplePrimes:Bug in plottools[transform] &amp; Where is transform/object:Comments#answer144474</link>
      <itunes:summary>&lt;p&gt;Excellent questions, TomM. There is no bug, but the answers to your questions are very poorly documented, if documented at all. It took me several hours on 17-Jan-2013 to figure out the answers to those same questions.&lt;/p&gt;
&lt;p&gt;TomM wrote:&lt;/p&gt;
&lt;blockquote&gt;
&lt;p&gt;On Maple 16.02, plattools[transform] only works for very simple procedures. It does not work for procedures&amp;nbsp; with the `if` function even with the simple arrow definition. or with any if-block in a more general proc definition.&lt;/p&gt;
&lt;/blockquote&gt;
&lt;p&gt;You can use procedures of arbitrary complexity, but it requires a little trick at the beginning of the procedure. Let's call the procedure which is passed to &lt;strong&gt;plottools:-transform &lt;/strong&gt;"the point transformer", and let's call the procedure that &lt;strong&gt;plottools:-transform &lt;/strong&gt;returns "the plot transformer".&amp;nbsp; When the plot transformer is called, the first call that it makes to the point transformer will be with symbolic (&lt;em&gt;i.e. &lt;/em&gt;non-numeric) arguments. It does this to check the number of dimensions returned (because &lt;strong&gt;plottools:-transform&lt;/strong&gt; can be used to transform 2D to 3D and vice versa). On this first call, the point transformer must return a list (or other object) with the correct number of elements.&lt;/p&gt;
&lt;p&gt;TomM wrote:&lt;/p&gt;
&lt;blockquote&gt;
&lt;p&gt;simp:= plot(sin(x), x= 0..Pi);&lt;br&gt;f:=(xx,yy)-&amp;gt;`if`(yy &amp;gt; 0, [xx,4*yy], [xx,yy]);&lt;br&gt;trns:=plottools:-transform(f);&lt;br&gt;trns(simp);&lt;/p&gt;
&lt;/blockquote&gt;
&lt;p&gt;So, for the above, your point transformer should be written:&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;f:=(xx,yy)-&amp;gt; `if`(not (xx::numeric and yy::numeric), [xx,yy], `if`(yy &amp;gt; 0, [xx,4*yy], [xx,yy]));&lt;/strong&gt;&lt;/p&gt;
&lt;p&gt;For an example of a much more complex point transformer, see my MaplePrimes post &lt;a href="http://www.mapleprimes.com/questions/142352-BubblePlot-Plotting-View#comment142370"&gt;BubblePlot plotting view: Modification so that the axes don't change&lt;/a&gt;.&lt;/p&gt;
&lt;p&gt;TomM wrote:&lt;/p&gt;
&lt;blockquote&gt;
&lt;p&gt;From examining op(trns), the error seems to occur in the procedure: `transform/object`. However, I cannot find a way to print out this procedure, even with verboseproc=2, with print or op. Where is this procedure, and how can I print it out?&lt;/p&gt;
&lt;/blockquote&gt;
&lt;p&gt;It is a procedure local to module &lt;strong&gt;plottools&lt;/strong&gt;. So, to print it out, do&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;kernelopts(opaquemodules= false);&amp;nbsp; #Get access to module locals&lt;/strong&gt;&lt;br&gt;&lt;strong&gt;interface(verboseproc= 2);&lt;/strong&gt;&lt;br&gt;&lt;strong&gt;eval(plottools:-`transform/object`);&lt;/strong&gt;&lt;/p&gt;
&lt;p&gt;You can also &lt;strong&gt;showstat&lt;/strong&gt; that procedure, or &lt;strong&gt;trace&lt;/strong&gt; it; which is how I figured out the answer to your first problem.&lt;/p&gt;
&lt;p&gt;TomM wrote:&lt;strong&gt;&lt;br&gt;&lt;/strong&gt;&lt;/p&gt;
&lt;blockquote&gt;
&lt;p&gt;On the other hand, with f:=(xx,yy)-&amp;gt; [xx, 3*max(0,yy)+yy], the transform works and produces the same plot as the non-working sequence above.&amp;nbsp;&lt;/p&gt;
&lt;/blockquote&gt;
&lt;p&gt;It works because the above &lt;strong&gt;f&lt;/strong&gt; will return a list of two elements even if it is passed symbolic arguments. But if it was inside an &lt;strong&gt;`if`&lt;/strong&gt;, then it would just return the unevaluated &lt;strong&gt;`if`&lt;/strong&gt;, which is not a list, and doesn't have two elements.&lt;/p&gt;</itunes:summary>
      <description>&lt;p&gt;Excellent questions, TomM. There is no bug, but the answers to your questions are very poorly documented, if documented at all. It took me several hours on 17-Jan-2013 to figure out the answers to those same questions.&lt;/p&gt;
&lt;p&gt;TomM wrote:&lt;/p&gt;
&lt;blockquote&gt;
&lt;p&gt;On Maple 16.02, plattools[transform] only works for very simple procedures. It does not work for procedures&amp;nbsp; with the `if` function even with the simple arrow definition. or with any if-block in a more general proc definition.&lt;/p&gt;
&lt;/blockquote&gt;
&lt;p&gt;You can use procedures of arbitrary complexity, but it requires a little trick at the beginning of the procedure. Let's call the procedure which is passed to &lt;strong&gt;plottools:-transform &lt;/strong&gt;"the point transformer", and let's call the procedure that &lt;strong&gt;plottools:-transform &lt;/strong&gt;returns "the plot transformer".&amp;nbsp; When the plot transformer is called, the first call that it makes to the point transformer will be with symbolic (&lt;em&gt;i.e. &lt;/em&gt;non-numeric) arguments. It does this to check the number of dimensions returned (because &lt;strong&gt;plottools:-transform&lt;/strong&gt; can be used to transform 2D to 3D and vice versa). On this first call, the point transformer must return a list (or other object) with the correct number of elements.&lt;/p&gt;
&lt;p&gt;TomM wrote:&lt;/p&gt;
&lt;blockquote&gt;
&lt;p&gt;simp:= plot(sin(x), x= 0..Pi);&lt;br&gt;f:=(xx,yy)-&amp;gt;`if`(yy &amp;gt; 0, [xx,4*yy], [xx,yy]);&lt;br&gt;trns:=plottools:-transform(f);&lt;br&gt;trns(simp);&lt;/p&gt;
&lt;/blockquote&gt;
&lt;p&gt;So, for the above, your point transformer should be written:&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;f:=(xx,yy)-&amp;gt; `if`(not (xx::numeric and yy::numeric), [xx,yy], `if`(yy &amp;gt; 0, [xx,4*yy], [xx,yy]));&lt;/strong&gt;&lt;/p&gt;
&lt;p&gt;For an example of a much more complex point transformer, see my MaplePrimes post &lt;a href="http://www.mapleprimes.com/questions/142352-BubblePlot-Plotting-View#comment142370"&gt;BubblePlot plotting view: Modification so that the axes don't change&lt;/a&gt;.&lt;/p&gt;
&lt;p&gt;TomM wrote:&lt;/p&gt;
&lt;blockquote&gt;
&lt;p&gt;From examining op(trns), the error seems to occur in the procedure: `transform/object`. However, I cannot find a way to print out this procedure, even with verboseproc=2, with print or op. Where is this procedure, and how can I print it out?&lt;/p&gt;
&lt;/blockquote&gt;
&lt;p&gt;It is a procedure local to module &lt;strong&gt;plottools&lt;/strong&gt;. So, to print it out, do&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;kernelopts(opaquemodules= false);&amp;nbsp; #Get access to module locals&lt;/strong&gt;&lt;br&gt;&lt;strong&gt;interface(verboseproc= 2);&lt;/strong&gt;&lt;br&gt;&lt;strong&gt;eval(plottools:-`transform/object`);&lt;/strong&gt;&lt;/p&gt;
&lt;p&gt;You can also &lt;strong&gt;showstat&lt;/strong&gt; that procedure, or &lt;strong&gt;trace&lt;/strong&gt; it; which is how I figured out the answer to your first problem.&lt;/p&gt;
&lt;p&gt;TomM wrote:&lt;strong&gt;&lt;br&gt;&lt;/strong&gt;&lt;/p&gt;
&lt;blockquote&gt;
&lt;p&gt;On the other hand, with f:=(xx,yy)-&amp;gt; [xx, 3*max(0,yy)+yy], the transform works and produces the same plot as the non-working sequence above.&amp;nbsp;&lt;/p&gt;
&lt;/blockquote&gt;
&lt;p&gt;It works because the above &lt;strong&gt;f&lt;/strong&gt; will return a list of two elements even if it is passed symbolic arguments. But if it was inside an &lt;strong&gt;`if`&lt;/strong&gt;, then it would just return the unevaluated &lt;strong&gt;`if`&lt;/strong&gt;, which is not a list, and doesn't have two elements.&lt;/p&gt;</description>
      <guid>144474</guid>
      <pubDate>Mon, 11 Mar 2013 20:04:27 Z</pubDate>
      <itunes:author>Carl Love</itunes:author>
      <author>Carl Love</author>
    </item>
    <item>
      <title>One doesn't on M16.02 and Two Do But Why?</title>
      <link>http://www.mapleprimes.com/questions/144465-Bug-In-Plottoolstransform--Where?ref=Feed:MaplePrimes:Bug in plottools[transform] &amp; Where is transform/object:Comments#answer144477</link>
      <itunes:summary>&lt;p&gt;From Christopher, it would seem that the code worked in Maple 12, but a bug (or undocumented behavior) has been introduced by the time Maple reached Maple 16.02&lt;/p&gt;
&lt;p&gt;On the other hand, Acer and Preben have functions which work. What general cases does transform work for? The Help page is quiet on this. &amp;nbsp;The answer seems to lie in the procedure `transform/object`. Acer and Preben, did you have access to the code for that procedure, or did you discover your fixes by quessing and trial and error?&lt;/p&gt;
&lt;p&gt;Does anyone know how to see the code for `transform/object`?&lt;br&gt;&lt;br&gt;PS: &amp;nbsp;I really meant 2*Pi in the plot, but that is not really essential to the "bug".&amp;nbsp;&lt;/p&gt;</itunes:summary>
      <description>&lt;p&gt;From Christopher, it would seem that the code worked in Maple 12, but a bug (or undocumented behavior) has been introduced by the time Maple reached Maple 16.02&lt;/p&gt;
&lt;p&gt;On the other hand, Acer and Preben have functions which work. What general cases does transform work for? The Help page is quiet on this. &amp;nbsp;The answer seems to lie in the procedure `transform/object`. Acer and Preben, did you have access to the code for that procedure, or did you discover your fixes by quessing and trial and error?&lt;/p&gt;
&lt;p&gt;Does anyone know how to see the code for `transform/object`?&lt;br&gt;&lt;br&gt;PS: &amp;nbsp;I really meant 2*Pi in the plot, but that is not really essential to the "bug".&amp;nbsp;&lt;/p&gt;</description>
      <guid>144477</guid>
      <pubDate>Mon, 11 Mar 2013 20:15:08 Z</pubDate>
      <itunes:author>TomM</itunes:author>
      <author>TomM</author>
    </item>
    <item>
      <title>A somewhat similar version fails</title>
      <link>http://www.mapleprimes.com/questions/144465-Bug-In-Plottoolstransform--Where?ref=Feed:MaplePrimes:Bug in plottools[transform] &amp; Where is transform/object:Comments#comment144471</link>
      <itunes:summary>&lt;p&gt;I tried this before I saw yours, but my version fails:&lt;/p&gt;
&lt;p&gt;f:=proc(x,y) if not type([x,y],list(numeric)) then return 'procname(_passed)' end if;&lt;br&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; `if`(y&amp;gt;0,[x,4*y],[x,y])&lt;br&gt;end proc;&lt;br&gt;&lt;br&gt;&lt;strong&gt;Edited&lt;/strong&gt;: In the initial version of this comment I forgot 'return' . But the edited is the one intended and it doesn't work.&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Edited once again:&lt;/strong&gt; The version works if&amp;nbsp; _passed is replaced by x,y. Probably because _passed will be arguments passed to another procedure.&lt;/p&gt;</itunes:summary>
      <description>&lt;p&gt;I tried this before I saw yours, but my version fails:&lt;/p&gt;
&lt;p&gt;f:=proc(x,y) if not type([x,y],list(numeric)) then return 'procname(_passed)' end if;&lt;br&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; `if`(y&amp;gt;0,[x,4*y],[x,y])&lt;br&gt;end proc;&lt;br&gt;&lt;br&gt;&lt;strong&gt;Edited&lt;/strong&gt;: In the initial version of this comment I forgot 'return' . But the edited is the one intended and it doesn't work.&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Edited once again:&lt;/strong&gt; The version works if&amp;nbsp; _passed is replaced by x,y. Probably because _passed will be arguments passed to another procedure.&lt;/p&gt;</description>
      <guid>144471</guid>
      <pubDate>Mon, 11 Mar 2013 19:11:09 Z</pubDate>
      <itunes:author>Preben Alsholm</itunes:author>
      <author>Preben Alsholm</author>
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