[yt-users] Clump tracking!

[Patrick Rieser]

As I am running a little bit out of time to finish this, I thought about 
doint this the simple and "brute force" way.
So this is the current plan: I am going to iterate through all snapshots 
we have, and write out all clump objects with pickle. Starting at 
snapshot 0 i am going to estimate the position of the center of mass of 
the clump in the new snapshot using it's bulk velocity and see if I can 
find the clump in the surrounding area (looping through all clumps in 
the following snapshot). If I find one or more, I will compare the mass 
and identify it as the same or not (if the mass doesn't match maybe i 
check if a second one vanished). This might not be a very elegant way, 
but I hope it works for our system (galaxy cluster). I am quite new to 
this stuff, so if I got any horrible mistakes here, please correct me.

> For instance, we include a Cython
> kD-tree that we use to provide a nearest-neighbor search when doing
> merger trees.

Thanks, I will take a look into that!


> and you didn't
> want to go full-on "lagrangian coherent structures"

I took a look at it and it seems really interesting. It's really a pity 
I don't have enough time at the moment.

best wishes,
Patrick



On 2012-10-05 18:07, Matthew Turk wrote:
> Hi Patrick,
>
> Thanks for writing, and welcome to yt-users.  :)
>
> On Thu, Oct 4, 2012 at 9:42 AM, Patrick Rieser
> <patrick.rieser at uibk.ac.at> wrote:
>> Heya everyone!
>>
>> I am trying to track clumps across multiple snapshots (from flash). Now my
>> questions is, has anybody done something like this and would be willing to
>> share his/her code?
> David Collins wrote some code that did this, a couple years ago.  I
> don't know the current status.
>
> If you wanted to write a new set of code to do this (and you didn't
> want to go full-on "lagrangian coherent structures") there are some
> things in yt that could help out.  For instance, we include a Cython
> kD-tree that we use to provide a nearest-neighbor search when doing
> merger trees.  This is used in a very simple way in the code in
> yt/analysis_modules/halo_merger_tree/enzofof_merger_tree.py , where
> halos are loaded into a variable called halo_kdtree.  This then gets
> searched with a ball query.  You could in principle load the clumps
> into the same kdtree structure, perform the search, and then apply
> selection criteria for clump tracking based on that.  (Of course this
> is just the first step in identifying clump motion -- but it would be
> a way to reduce from N^2 searching.)
>
> Let us know if you run into any tricks or have any successes -- this
> is a pretty cool idea, and I'd love to see where it leads you!
>
> -Matt
>
>> Best Wishes,
>> Patrick
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