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    "info": {
        "author": "Jed Hollom",
        "author_email": "Jedhollom@gmail.com",
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        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2.7"
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        "description": "# Mpimap\n\nThis package is a wrapper for `mpi4py` to allow for the easy running of functions in parallel on a single computer or on multiple nodes of a HPC cluster.\n\nThe code used to implement the `mpimap` methods will also function when no `mpi` environment is used, or only a single processor is specified.\n\n## Setup\n\nOnce imported, create an instance of the `Mpimap` class:\n\n```\nmpi = mpimap.Mpimap()\n```\n\nTo have each `mpi` process print its information, use:\n\n```\nmpi.info()\n```\n\nAt this point, all `mpi` instance still continue to process all lines within the `script.py` being run or command sent to the interpreter. To put all worker nodes into a \"listening\" state where they only accept commands sent from the head process, use:\n\n```\nmpi.start()\n```\n\nFrom this point, command in the `script.py` running or command sent to the interpreter will only be processed by the head process. To determined the status of each worker process after it has been started, use:\n\n```\nmpi.status()\n```\n\nTo kill all worker nodes once finished, use:\n\n```\nmpi.stop()\n```\n\n## Functions\n\nTo run code on each of the worker nodes once they have being \"listening\" for jobs, include the code in a function with no arguments and use:\n\n```\nmpi.run(func)\n```\n\n`Mpimap` include a `map()` function which behaves as the `builtin` version included with python:\n\n```\noutput = mpi.map(func, args)\n```\n\nThis will send a copy of the function to all worker nodes, and then queue the args list, sending values to each node not currently running a job. The input order is maintained by the output.\n\n`Mpimap` also includes the function `gmap()`. This is a special instance of `map()` that is intended for running groups of jobs where an argument returning a \"failed\" state results in all jobs within that group being canceled:\n\n```\noutput = mpi.gmap(func, args, groupind=0, failstate=None)\n```\n\nFor this function, `args` is a list of lists. The argument `groupind` determines which entry in each list run by the function is used to determine that jobs group. The argument `failstate` is the value checked to determine if the job was a success or failure.\n\nAn addition static function is included called `gmatrix`. This can be used to generate a list of all possible combinations of two lists, include an input id number and append constants to all combinations:\n\n```\nx = [1, 2, 3]\ny = [10, 20]\nconstants = ('a', 'b')\nout = mpi.gmatrix(x, y, *constants)\n```\n\nThis example would return:\n\n```\n>>> out\n[('0', 1, 10, 'a', 'b'),\n('1', 1, 20, 'a', 'b'),\n('2', 2, 10, 'a', 'b'),\n('3', 2, 20, 'a', 'b'),\n('4', 3, 10, 'a', 'b'),\n('5', 3, 20, 'a', 'b')]\n```\n\n## Example\n\nTo test the provided functions and check the difference in processing time between `builtin.map()` and `mpimap.map()`, run the example python script:\n\n```\nmpirun -n <Number of processors you want to use> python example.py\n```\n\nThe full working example is given here:\n\n```\nimport mpimap\nimport time\n\ndef func_cheap(*args):\n\t\"\"\"Do nothing\"\"\"\n\treturn\n\n\ndef func_expensive(n):\n\t\"\"\"Basic factorising problem\"\"\"\n\tfactors = set([])\n\tfor i in xrange(n - 1):\n\t\ti = i + 1\n\t\t# Skip factors\n\t\tif i in factors:\n\t\t\tcontinue\n\t\t# Find factors\n\t\tif n % i == 0:\n\t\t\tfactors.add(i)\n\t\t\tfactors.add(n / i)\n\n\treturn sorted(factors)\n\n\n# Build mpi\nmpi = mpimap.Mpimap(sleep=0, debug=False)\nmpi.info()\nmpi.start()\n\n# Run function on all nodes\nmpi.run(func_cheap)\n\n# Set up function and arguments\nargs = range(5000, 10000)\n\n# Not in parallel\nt0 = time.time()\nres = map(func_expensive, args)\ndt = time.time() - t0\nprint '\\nNon Parallel: {}'.format(dt)\n\n# Parallel\nt0 = time.time()\nres = mpi.map(func_expensive, args)\ndt = time.time() - t0\nprint '\\nParallel: {}\\n'.format(dt)\n\nmpi.stop()\nmpi.exit()\n```\n\n",
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