Metadata-Version: 2.1
Name: PyMUMPS
Version: 0.3.1
Summary: Python bindings for MUMPS, a parallel sparse direct solver
Home-page: http://github.com/sdrave/pymumps
Author: Bradley M. Froehle
Author-email: brad.froehle@gmail.com
Maintainer: Stephan Rave
Maintainer-email: stephan.rave@uni-muenster.de
License: BSD
Description: PyMUMPS: A parallel sparse direct solver
        ========================================
        
        Requirements
        ------------
        
        * [MUMPS](http://graal.ens-lyon.fr/MUMPS/)
        * [mpi4py](https://code.google.com/p/mpi4py/)
        
        Getting Started
        ---------------
        
        Install using `python setup.py install` or run from the local checkout.
        
        Examples
        --------
        
        Centralized input & output. The sparse matrix and right hand side are
        input only on the rank 0 process. The system is solved using all
        available processes and the result is available on the rank 0 process.
        
        ```python
        from mumps import DMumpsContext
        ctx = DMumpsContext()
        if ctx.myid == 0:
            ctx.set_centralized_sparse(A)
            x = b.copy()
            ctx.set_rhs(x) # Modified in place
        ctx.run(job=6) # Analysis + Factorization + Solve
        ctx.destroy() # Cleanup
        ```
        
        Re-use symbolic or numeric factorizations.
        
        ```python
        from mumps import DMumpsContext
        ctx = DMumpsContext()
        if ctx.myid == 0:
            ctx.set_centralized_assembled_rows_cols(A.row+1, A.col+1) # 1-based
        ctx.run(job=1) # Analysis
        
        if ctx.myid == 0:
            ctx.set_centralized_assembled_values(A.data)
        ctx.run(job=2) # Factorization
        
        if ctx.myid == 0:
            x = b1.copy()
            ctx.set_rhs(x)
        ctx.run(job=3) # Solve
        
        # Reuse factorizations by running `job=3` with new right hand sides
        # or analyses by supplying new values and running `job=2` to repeat
        # the factorization process.
        ```
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Mathematics
Description-Content-Type: text/markdown
