@ Aron Ahmadia:
> Conversion between ndarray > and PETSc Vecs is practically free, so I would keep them as numpy arrays > for as long as possible (this is the strategy in pyclaw). Thanks for the hint. I was wondering whether I should start with a working prototype based on numpy, then do parallelization with PETSc on demand - or start with PETSc wherever matrices and vectors are needed. > Also, I'm excited by the new opportunities for parallel programming and > development offered by the IPython parallel programming extensions ( > http://ipython.org/ipython-doc/dev/parallel/index.html), but have not had a > chance to try them out. If you get a chance to use them for development, > please let us know if you find them to be useful. You're not the first person to mention iPython, so I'll definitely have a look and let you know in case I have used it successfully. @ Lisandro Dalcin: > For matrices, it depends on the matrix type. For dense matrices, > currently petsc4py does not support getting the array. For sparse (aka > AIJ) matrices, you can use mat.getValuesCSR(), however this will > involve copies. For the other way, you can use > PETSc.Mat().createAIJ(size=(nrows,ncols), csr=(ai,aj,aa)). That's basically what I wanted to know. ps. Thanks for providing petsc4py. Being able to develop in Python is currently saving me a lot of time and pain (compared to the alternative, C++) -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://lists.mcs.anl.gov/pipermail/petsc-users/attachments/20120508/5f9008bd/attachment.htm>
