Hi Alex, On Thu, Feb 13, 2014 at 4:07 PM, Alex Bogert <bogart.alex@gmail.com> wrote:

Hi,

Matt asked me to write yt-dev about yt-3.0 kdtree decomposition tutorials. I'm looking for a robust data flow to do volume rendering of AMR data on the GPU.

From our email chain : " But the grids can just be accessed by getting pf.h.grids. To get blocks from the tree in 3.0 we have a .tiles attribute on the data source which will do the full decomposition into non overlapping tiles. If you write to yt-dev this will also help me remember to document this." -mjturk

Alex

The two interfaces are as follows; note that this is in yt-3.0; as you might have seen there are some tricky bits with the unit refactor for this right now. The first is for getting spatially contiguous blocks from a data source; this will work for both octree structures and patch structures, but for octree structures will be slow. You can do: for block, mask in some_source.blocks: the block object will be something that is similar to an AMRGridPatch object, in that it has LeftEdge, RightEdge, ActiveDimensions, get_global_startindex() and so on, and it respects dictionary-like access. So for instance: v1, v2 = 0.0, 0.0 for block, mask in some_source.blocks: v1 += block["CellVolume"].sum() v2 += block["CellVolume"][mask].sum() print v1, v2 The mask describes which cells are covered by finer cells. The other interface, tiles, provides a slicing interface to non-overlapping objects. For instance: v1, v2 = 0.0, 0.0 for grid, node, (sl, dims, gi) in tiles.slice_traverse(): v1 += grid["CellVolume"].sum() v2 += grid["CellVolume"][sl].sum() I hope that helps, Matt

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