Hi Everyone,
<xsl:when test="count(not(b/c)) >= 1">
This statement looks correct, but it isn't working for me. The
statement filters the sets of <b/> nodes where the first node does not
contain <c/>. But if the first <b/> node in the set contains a <c/>,
then the set is not filtered. It looks like the count() function is
only looking at the first <b/> node.
I tried the following statement instead:
<xsl:for-each select="attributeList/attribute">
<xsl:variable name="counter" select="count(not[notChangeable])"/>
</xsl:for-each>
<xsl:choose>
<xsl:when test="$counter >= 1">
...
But the processor replied that $counter was not set.
I tried test="not(b/c)" earlier, with the same results as the first
statement in this email: The statement appears to filter on the first
<b/> node in the set only.
Thanks,
Mark
On Thu, Jun 4, 2009 at 2:37 PM, Michael Dykman<mdykman(_at_)gmail(_dot_)com>
wrote:
On Thu, Jun 4, 2009 at 2:27 PM, Mark Peters
<markpeters(_dot_)work(_at_)gmail(_dot_)com> wrote:
Sorry, my XSL examples weren't quite right. I'm trying to determine
whether no instance of <c/> exists for each <b/> set.
So, in my XSL document, assume I'm processing each <a/> node.
1,
<xsl:when test="not(b/c)">
2.
<xsl:when test="count(not(b/c)) >= 1">
Thanks,
Mark
On Thu, Jun 4, 2009 at 2:20 PM, Mark Peters
<markpeters(_dot_)work(_at_)gmail(_dot_)com> wrote:
Hi Everyone,
I have the following XML document:
<a>
<b/>
<b/>
<b/>
</a>
<a>
<b/>
<b/>
<b>
<c/>
</b>
</a>
<a>
<b/>
<b/>
<b/>
</a>
For each <b/> set, I want to apply a transformation only if no single
instance of <c/> exists. I know how to check whether a node does
exist, but not how to check whether no single instance of the node
exists.
I've tried the following conditional statements without success:
1,
<xsl:when test="not(a/b/c)">
2.
<xsl:when test="count(not(a/b/c)) >= 1">
What conditional statement should I use?
Thanks in advance for any help.
Are you looking for something along the line of
<xsl:when test="not(count(b/c) >= 1)">
??
--
- michael dykman
- mdykman(_at_)gmail(_dot_)com
- All models are wrong. Some models are useful.
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