Hi,
- in _get_over_density(), I am using the pf.h.sphere() method to cut out the particles inside the smoothing sphere, but I'm not sure I want to do this. I'm aware that as it is it won't work using the _partition_hierarchy_3d method. Can you give me a suggestion of the 'right' way to do this? I'd wonder if investing time in the KDtree scipy stuff Matt emailed about earlier is worth it, but we already have this domain decomposition plus oversampling machinery, and we definitely need the oversampling for this method. And domain decomposition makes data reading so much faster.
I thought about this some more, and it's obvious what is a better way to go. If pf.h.sphere() is a 'good' way to do this, for each task it should operate on pf, and skip the _partition_heirarchy_3d .data_source, but only choose a random center in its own sub-region. This will eliminate the over-sampling required in the interior sub-regions, but the edges still need padding. Does this make sense? Alternatively, if doing this domain decomposition and then randomly choosing centers granulates the randomness, which I frankly don't think is a problem, I could pre-build a list of random centers and each task would only use ones it owns. Thanks! _______________________________________________________ sskory@physics.ucsd.edu o__ Stephen Skory http://physics.ucsd.edu/~sskory/ _.>/ _Graduate Student ________________________________(_)_\(_)_______________