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        "author": "Benjamin F. Maier",
        "author_email": "bfmaier@physik.hu-berlin.de",
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        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7"
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        "description": "binpacking\n==========\n\nThis package contains greedy algorithms to solve two typical bin packing\nproblems, (i) sorting items into a constant number of bins, (ii) sorting\nitems into a low number of bins of constant size. Here's a usage example\n\n.. code:: python\n\n    >>> import binpacking\n    >>>\n    >>> b = { 'a': 10, 'b': 10, 'c':11, 'd':1, 'e': 2,'f':7 }\n    >>> bins = binpacking.to_constant_bin_number(b,4) # 4 being the number of bins\n    >>> print(\"===== dict\\n\",b,\"\\n\",bins)\n    ===== dict\n     {'a': 10, 'b': 10, 'c': 11, 'd': 1, 'e': 2, 'f': 7}\n     [{'c': 11}, {'b': 10}, {'a': 10}, {'f': 7, 'e': 2, 'd': 1}]\n    >>>\n    >>> b = list(b.values())\n    >>> bins = binpacking.to_constant_volume(b,11) # 11 being the bin volume\n    >>> print(\"===== list\\n\",b,\"\\n\",bins)\n    ===== list\n     [10, 10, 11, 1, 2, 7]\n     [[11], [10], [10], [7, 2, 1]]\n\nConsider you have a list of items, each carrying a weight *w\\_i*.\nTypical questions are\n\n#. How can we distribute the items to a minimum number of bins *N* of\n   equal volume *V*?\n#. How can we distribute the items to exactly *N* bins where each\n   carries items that sum up to approximately equal weight?\n\nProblems like this can easily occur in modern computing. Assume you have\nto run computations where a lot of files of different sizes have to be\nloaded into the memory. However, you only have a machine with 8GB of\nRAM. How should you bind the files such that you have to run your\nprogram a minimum amount of times? This is equivalent to solving problem\n1.\n\nWhat about problem 2? Say you have to run a large number of\ncomputations. For each of the jobs you know the time it will probably\ntake to finish. However, you only have a CPU with 4 cores. How should\nyou distribute the jobs to the 4 cores such that they will all finish at\napproximately the same time?\n\nThe package provides the command line tool \"binpacking\" using which one\ncan easily bin pack csv-files containing a column that can be identified\nwith a weight. To see the usage enter\n\n.. code:: bash\n\n    $ binpacking -h\n    Usage: binpacking [options]\n\n    Options:\n      -h, --help            show this help message and exit\n      -f FILEPATH, --filepath=FILEPATH\n                            path to the csv-file to be bin-packed\n      -V V_MAX, --volume=V_MAX\n                            maximum volume per bin (constant volume algorithm will\n                            be used)\n      -N N_BIN, --n-bin=N_BIN\n                            number of bins (constant bin number algorithm will be\n                            used)\n      -c WEIGHT_COLUMN, --weight-column=WEIGHT_COLUMN\n                            integer (or string) giving the column number (or\n                        column name in header) where the weight is stored\n      -H, --has-header      parse this option if there is a header in the csv-file\n      -d DELIM, --delimiter=DELIM\n                            delimiter in the csv-file (use \"tab\" for tabs)\n      -q QUOTECHAR, --quotechar=QUOTECHAR\n                            quotecharacter in the csv-file\n      -l LOWER_BOUND, --lower-bound=LOWER_BOUND\n                            weights below this bound will not be considered\n      -u UPPER_BOUND, --upper-bound=UPPER_BOUND\n                            weights exceeding this bound will not be considered\n\nInstall\n-------\n\n.. code:: bash\n\n    pip install binpacking\n\nExamples\n--------\n\nIn the repository's directory\n\n.. code:: bash\n\n    cd examples/\n    binpacking -f hamlet_word_count.csv -V 2000 -H -c count -l 10 -u 1000\n    binpacking -f hamlet_word_count.csv -N 4 -H -c count \n\nor in Python\n\n.. code:: python\n\n    import binpacking\n\n    b = { 'a': 10, 'b': 10, 'c':11, 'd':1, 'e': 2,'f':7 }\n    bins = binpacking.to_constant_bin_number(b,4)\n    print(\"===== dict\\n\",b,\"\\n\",bins)\n\n    b = list(b.values())\n    bins = binpacking.to_constant_volume(b,11)\n    print(\"===== list\\n\",b,\"\\n\",bins)",
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        "summary": "Heuristic distribution of weighted items to bins (either a fixed number of bins or a fixed number of volume per bin). Data may be in form of list, dictionary, list of tuples or csv-file.",
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