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License: GNU General Public License v3.0
Large Atomic Numeriment Visualization
License: GNU General Public License v3.0
The LAMMPS dump file format looks like this:
ITEM: TIMESTEP
0
ITEM: NUMBER OF ATOMS
500
ITEM: BOX BOUNDS pp pp pp
0.0000000000000000e+00 8.3979809569125372e+00
0.0000000000000000e+00 8.3979809569125372e+00
0.0000000000000000e+00 8.3979809569125372e+00
ITEM: ATOMS id type xs ys zs
... (atom positions
ITEM: TIMESTEP
1
...
For large systems, if atoms are already colored and divided into chunks, we could read the positions by jumping around in the file. If we have some sort of header (another file, i.e. json is fine), we could know exact positions in the file and number of particles and lod levels etc. Then we could navigate a big system.
If the server and client disagrees due to timezones or other reasons, it should still work.
When moving the camera around, the chunks are being sorted so that they are usually almost sorted. According to https://www.toptal.com/developers/sorting-algorithms/, we should probably use insertion sort which is faster on random data and nearly sorted data.
This could be used to make the atoms flash when it happens.
The State class should be very general. It must have:
Each particle has
The number of columns should be specified in the JSON file describing the importer.
If the server has a lot of memory and you want to watch time evolution, it can be nice to load all timesteps.
Here we denote the simulation as #1, the post processing caching as #2 and the visualization client as #3.
#1 produces data. #1 and #2 communicates either through MPI (another world than COMM_WORLD) or through files. Whenever a new timestep is available, #2 reads that and places atoms in chunks.
Once it has done that, it writes to state.json with new date. The file it writes should be moved to another location after writing so that we don't get problems with writing to a file that we read from.
The client (a Python script) periodically reads the state.json. Once we see an update, we fetch the new file. Once the file is copied over, the state.json is written to client disk so we can update.
According to this post, parsing using Spirit X3 is much faster than sscanf or similar. Code here: http://boost-spirit.com/home/2015/05/16/spirit-3-0-0/
If a compute measures a 3d scalar field, marching cubes could use this as input to produce iso scalar surfaces. This could be density, stress, whatever.
Some client settings might take a lot of time on the server (resizing chunks and LOD levels), so some settings should need an apply mouse click.
These are:
Now there is only a single level of chunks. For the 270 million atom system, an ideal chunk size was ~100 ร , but then you get more particles than you want.
Ideally we should have at least three levels of chunk sizes with octrees so a large portion can quickly be sorted out as not relevant, and high resolution chunk sizes (small).
Modifiers can modify any property of the particle. For instance slice, type coloring, property coloring etc.
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