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        "author": "Bradley M. Froehle",
        "author_email": "brad.froehle@gmail.com",
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            "Programming Language :: Python :: 2",
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        "description": "PyMUMPS: A parallel sparse direct solver\n========================================\n\nRequirements\n------------\n\n* [MUMPS](http://graal.ens-lyon.fr/MUMPS/)\n* [mpi4py](https://code.google.com/p/mpi4py/)\n\nGetting Started\n---------------\n\nInstall using `python setup.py install` or run from the local checkout.\n\nExamples\n--------\n\nCentralized input & output. The sparse matrix and right hand side are\ninput only on the rank 0 process. The system is solved using all\navailable processes and the result is available on the rank 0 process.\n\n```python\nfrom mumps import DMumpsContext\nctx = DMumpsContext()\nif ctx.myid == 0:\n    ctx.set_centralized_sparse(A)\n    x = b.copy()\n    ctx.set_rhs(x) # Modified in place\nctx.run(job=6) # Analysis + Factorization + Solve\nctx.destroy() # Cleanup\n```\n\nRe-use symbolic or numeric factorizations.\n\n```python\nfrom mumps import DMumpsContext\nctx = DMumpsContext()\nif ctx.myid == 0:\n    ctx.set_centralized_assembled_rows_cols(A.row+1, A.col+1) # 1-based\nctx.run(job=1) # Analysis\n\nif ctx.myid == 0:\n    ctx.set_centralized_assembled_values(A.data)\nctx.run(job=2) # Factorization\n\nif ctx.myid == 0:\n    x = b1.copy()\n    ctx.set_rhs(x)\nctx.run(job=3) # Solve\n\n# Reuse factorizations by running `job=3` with new right hand sides\n# or analyses by supplying new values and running `job=2` to repeat\n# the factorization process.\n```",
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        "license": "BSD",
        "maintainer": "Stephan Rave",
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