Hello everyone,
My name is Matthew Turk, and I'm an astronomer working on an analysis
and visualization package ( yt;
http://yt-project.org/ ) for the
output of simulation codes. My work has focused on enabling
comparison of astrophysical results independent of the simulation
platform that generates them; this includes things like IO, data
structures, units, and so on. Additionally, on top of this level of
base support for different codes, we have endeavored to build not just
an astrophysical analysis toolkit, but a generic volumetric analysis
toolkit that can be applied to astronomy.
yt has been deployed on platforms ranging from netbooks, laptops,
local clusters, etc to supercomputer facilities in the US-based NSF
and DOE systems as well as European supercomputer centers. In the
past, yt has primarily been distributed to these systems using an
install script; this builds a (virtualenv-based) isolated environment
of libraries that cover the majority of dependencies. We only
reluctantly took this route, as it provides maintenance overhead on
our part, but for the most part we have found it to increase the size
of our community and the ease of installation. However, I'd like to
move away from this in the long run, and to that end we've begun
efforts to provide PPAs, MacPorts, and easier pip-installability.
We've also worked with consultants at several supercomputer centers to
build yt modules, which has been quite successful for us.
As noted by many others in this thread, it seems to me that the best
way to address this in the long term is to build a coherent set of
packages and to standardize on that. For me, this is not just about
enabling people to use yt, but opening up to them the ability to use
yt in conjunction with other awesome python packages out there, and
reducing the overhead to this process. One issue I have been
particularly interested in is creating a statically linked python
library that included all dependencies -- such as matplotlib (which
was particularly difficult as it was C++), numpy, h5py, etc. In the
past we have done this for Crays running Compute Node Linux, but the
overhead to modifying the libraries linked in, upgrading, etc etc was
simply too high. We explored using the CMake-ified Python build
system provided by Kitware (which in principle should make statically
linking Python libraries much easier) but were unable to make this
work.
Looking forward to the hangout,
Matt
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