Random Reduce Data manipulation

Hi guys,

I've spent some time with grasshopper now but still struggle slightly with data trees. I have stumbled upon an issue with the random reduce component.

In my definition I need to flatten the data tree to perform a random reduce, however by doing this I lose the information associated with the data tree (eg branches.) Is there a way to retain the original data tree structure when using this component? I plan to use this to divide surfaces and colour the segments randomly.

Thanks in advance

Attachments:

gh help.gh, 9 KB
GH HELP.PNG, 48 KB
gh2.PNG, 253 KB

Replies to This Discussion

Permalink Reply by Danny Boyes on May 28, 2014 at 12:59am

You could perform the random reduce on the tree paths and then use a split tree component to get the results you want.

You can get a flat list of paths with the tree statistics component.

Permalink Reply by Scott Walker on May 28, 2014 at 1:16am

Thanks for replying Danny,

I'm a bit unsure of what you mean exactly. Are you suggesting to split the tree and perform the random reduce on each branch separately? That might work in this scenario but what if I had 20 or 30 branches. Is there a simpler way other than pure repetition?

Please correct me if I may have misunderstood

Permalink Reply by Danny Boyes on May 28, 2014 at 2:32am

Something like this:

Attachments:

ghhelp_db.gh, 11 KB

Permalink Reply by Scott Walker on May 28, 2014 at 2:52am

That's perfect!
Thanks heaps for taking the time to help. Clearly I have a lot to learn when it comes to data trees.

Permalink Reply by Danny Boyes on May 28, 2014 at 3:56am

no problem!

Just be aware that this particular case reduced the panels evenly over the two sets, but a different Seed might work entirely another way. Is this what you wanted?

Permalink Reply by Scott Walker on May 28, 2014 at 8:37am

Yes that exactly right. I wanted the two trees to be randomly reduced together. In theory I might have one panel that is completely subtracted while the other remains in tact.

Thanks again

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