Partio - A library for particle IO and manipulation
This is the initial source code release of partio a tool we used for particle reading/writing. It started out as an abstraction for the commonalities in particle models (i.e. accessing many attributes associated with an index or entity).
# Install Location ~ adjust accordingly
prefix=$HOME/local
git clone https://github.com/wdas/partio.git
cd partio
make -j prefix=$prefix install
CMake is used to build the project, but we provide a top-level Makefile for convenience that takes care of all the steps.
See the Makefile for the user-tweakable variables and corresponding cmake options.
The typical usage for an installation into /usr/local
with a temporary staging directory of /tmp/stage is:
make DESTDIR=/tmp/stage prefix=/usr/local install
src/
lib/ Library code (public API in root)
lib/core Core library (KDtree traversal, data representations)
lib/io Input/Output (Different file formats)
py/ SWIG based python bindings
doc/ Doxygen documentation and (the start of) a manual
tests/ Start of regression tests (I need more)
tools/ Useful tools
partconvert <input format> <output format>
partinfo <particle file>
partview <particle file>
The goal of the library is to abstract the particle interface from the data representation. That is why Partio represents particles using three classes that inherit and provide more functionality
ParticlesInfo - Information about # of particles and attributes ParticlesData - Read only access to all particle data ParticlesDataMutable - Read/write access to all particle data
The functions used to get particle access are these:
readHeaders()
returns ParticlesInfo
reads only the minimum data necessary to get number of particles and
attributes
readCached()
returns ParticlesData
For multiple users in different threads using the same particle file
ParticlesData
create() and read()
returns ParticlesDataMutable
allows read/write access
Behind the scenes you could implement these classes however you like. Headers only representation is called core/ParticleHeader.{h,cpp}. Simple non-interleaved attributes is core/ParticleSimple.{h,cpp}.
All particles have the same data attributes. They have the model that they are of three basic types with a count of how many scalar values they have.
VECTOR[3]
FLOAT[d]
INT[d]
VECTOR[3] and FLOAT[3] have the same data representations.
VECTOR[4] is invalid however FLOAT[4] is valid as is FLOAT[1...infinity]
This seems to encompass the most common file formats for particles
There are multiple ways to access data in the API. Here are some tips
-
Use SIMD functions when possible prefer dataAsFloat(),data(arrayOfIndices) as opposed to data(int singleIndex) which accesses multiple pieces of data at once
-
Cache ParticleAttributes for quick access instead of calling attributeInfo() over a loop of particles
-
Use iterators to do linear operations over all particles They are much more optimized than both data() and the dataAsFloat or
Behind the scenes there are SimpleParticles, ParticleHeaders, and SimpleParticlesInterleaved. In the future I would like to write a disk-based cached back end that can dynamically only load the data that is necessary. create(), read() and readCached could be augmented to create different structures in these cases.
New readers and writers can be added in the io/ directory. You simply need to implement the interface ParticlesInfo, ParticlesData and ParticlesDataMutable (or as many as you need). Editing the io/readers.h to add prototypes and io/ParticleIO.cpp to add file extension bindings should be easy.
To the partio for python and publish it to we have to build it using docker and upload it to PyPi.
# build the docker
docker build -t partio:latest .
# run the build
docker run --rm -v $(pwd):/io partio:latest
# use twine to upload to pypi
twine upload dist/*- Andrew Selle, Walt Disney Animation Studios
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent