{
    "info": {
        "author": "Michael Delgado",
        "author_email": "delgado.michaelt@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6"
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        "description": "=================\nParameterize Jobs\n=================\n\n.. image:: https://img.shields.io/pypi/v/parameterize_jobs.svg\n        :target: https://pypi.python.org/pypi/parameterize_jobs\n\n.. image:: https://img.shields.io/travis/ClimateImpactLab/parameterize_jobs.svg\n        :target: https://travis-ci.org/ClimateImpactLab/parameterize_jobs\n\n.. image:: https://readthedocs.org/projects/parameterize-jobs/badge/?version=latest\n        :target: https://parameterize-jobs.readthedocs.io/en/latest/?badge=latest\n        :alt: Documentation Status\n\n.. image:: https://pyup.io/repos/github/ClimateImpactLab/parameterize_jobs/shield.svg\n     :target: https://pyup.io/repos/github/ClimateImpactLab/parameterize_jobs/\n     :alt: Updates\n\n``parameterize_jobs`` is a lightweight, pure-python toolkit for concisely and clearly creating large, parameterized, mapped job specifications.\n\n\n* Free software: MIT license\n* Documentation: https://parameterize-jobs.readthedocs.io\n\nFeatures\n--------\n\n* Expand a job's dimensionality by multiplying ``ComponentSet``, ``Constant``, or ``ParallelComponentSet`` objects\n* Extend the number of jobs by adding ``ComponentSet``, ``Constant``, or ``ParallelComponentSet`` objects\n* Jobs are provided to functions as dictionaries of parameters\n* The helper decorator ``@expand_kwargs`` turns these kwarg dictionaries into\n  named argument calls\n* Works seamlessly with many task running frameworks, including dask's `client.map` and profiling tools\n\nTODOs\n-----\n\nView and submit issues on the `issues page <https://github.com/ClimateImpactLab/parameterize_jobs/issues>`_.\n\nQuickstart\n----------\n\n``ComponentSet`` objects are the base objects, and can be defined with any number of named iterables:\n\n.. code-block:: python\n\n    >>> import parameterize_jobs as pjs\n\n    >>> a = pjs.ComponentSet(a=range(5))\n    >>> a\n    <ComponentSet {'a': 5}>\n\nThese objects have defined lengths (if the provided iterable has a defined length), and can be indexed and iterated over:\n\n.. code-block:: python\n\n    >>> len(a)\n    5\n\n    >>> a[0]\n    {'a': 0}\n\n    >>> list(a)\n    [{'a': 0},\n     {'a': 1},\n     {'a': 2},\n     {'a': 3},\n     {'a': 4}]\n\nAdding two ``ComponentSet`` objects extends the total job length\n\n.. code-block:: python\n\n    >>> a2 = pjs.ComponentSet(a=range(3))\n\n    >>> a+a2\n    <MultiComponentSet [{'a': 5}, {'a': 3}]>\n\n    >>> len(a+a2)\n    8\n\n    >>> list(a+a2)\n\n    [{'a': 0},\n     {'a': 1},\n     {'a': 2},\n     {'a': 3},\n     {'a': 4},\n     {'a': 0},\n     {'a': 1},\n     {'a': 2}]\n\nMultiplying two ``ComponentSet`` objects expands their dimensionality:\n\n.. code-block:: python\n\n    >>> b = pjs.ComponentSet(b=range(3))\n\n    >>> a*b\n    <ComponentSet {'a': 5, 'b': 3}>\n\n    >>> len(a*b)\n    15\n\n    >>> (a*b)[-1]\n    {'a': 4, 'b': 2}\n\n    >>> list(a*b)\n    [{'a': 0, 'b': 0},\n     {'a': 0, 'b': 1},\n     {'a': 0, 'b': 2},\n     {'a': 1, 'b': 0},\n     {'a': 1, 'b': 1},\n     {'a': 1, 'b': 2},\n     {'a': 2, 'b': 0},\n     {'a': 2, 'b': 1},\n     {'a': 2, 'b': 2},\n     {'a': 3, 'b': 0},\n     {'a': 3, 'b': 1},\n     {'a': 3, 'b': 2},\n     {'a': 4, 'b': 0},\n     {'a': 4, 'b': 1},\n     {'a': 4, 'b': 2}]\n\nThese parameterized job specifications can be used in mappable jobs. The helper decorator ``expand_kwargs`` modifies a function to accept a dictionary and expands them into keyword arguments:\n\n.. code-block:: python\n\n    >>> @pjs.expand_kwargs\n    ... def my_simple_func(a, b, c=1):\n    ...     return a * b * c\n\n    >>> list(map(my_simple_func, a*b))\n    [0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 0, 2, 4, 6, 8, 0, 3, 6, 9, 12]\n\nJobs do not have to be the combinatorial product of all components:\n\n.. code-block:: python\n\n    >>> ab1 = pjs.ComponentSet(a=[0, 1], b=[0, 1])\n    >>> ab2 = pjs.ComponentSet(a=[10, 11], b=[-1, 1])\n\n    >>> list(map(my_simple_func, ab1 + ab2))\n    [0, 0, 0, 1, -10, -11, 10, 11]\n\nA ``Constant`` object is simply a ``ComponentSet`` object defined with single values passed as keyword arguments rather than iterables passed as keyword arguments:\n\n.. code-block:: python\n\n    >>> c = pjs.Constant(c=5)\n\n    >>> list(map(my_simple_func, (ab1 + ab2) * c))\n    [0, 0, 0, 5, -50, -55, 50, 55]\n\nA ``ParallelComponentSet`` object is simply a ``MultiComponentSet`` object where each ``Component`` is a ``Constant`` object.\n\n.. code-block:: python\n\n    >>> pcs = pjs.ParallelComponentSet(a = [1, 2],\n                               b = [10, 20])\n\n    >>> list(map(my_simple_func, pcs))\n    [10, 40]\n\nArbitrarily complex combinations of ComponentSets can be created:\n\n.. code-block:: python\n\n    >>> c1 = pjs.Constant(c=1)\n    >>> c2 = pjs.Constant(c=2)\n\n    >>> list(map(my_simple_func, (ab1 + ab2) * c1 + (ab1 + ab2) * c2))\n    [0, 0, 0, 1, -10, -11, 10, 11, 0, 0, 0, 2, -20, -22, 20, 22]\n\nAnything can be inside a ``ComponentSet`` iterable, including data, functions, or other objects:\n\n.. code-block:: python\n\n    >>> transforms = (\n    ...     pjs.Constant(transform=lambda x: x, transform_name='linear')\n    ...     + pjs.Constant(transform=lambda x: x**2, transform_name='quadratic'))\n    ...\n\n    >>> fps = pjs.Constant(\n    ...     read_pattern='source/my-fun-data_{year}.csv',\n    ...     write_pattern='transformed/my-fun-data_{transform_name}_{year}.csv')\n\n    >>> years = pjs.ComponentSet(year=range(1980, 2018))\n\n    >>> @pjs.expand_kwargs\n    ... def process_data(read_pattern, write_pattern, transform, transform_name, year):\n    ...\n    ...     df = pd.read_csv(read_pattern.format(year=year))\n    ...\n    ...     transformed = transform(df)\n    ...\n    ...     transformed.to_csv(\n    ...         write_pattern.format(\n    ...             transform_name=transform_name,\n    ...             year=year))\n    ...\n\n    >>> _ = list(map(process_data, transforms * fps * years))\n\nThis works seamlessly with dask's `client.map <http://distributed.dask.org/en/latest/api.html#distributed.Client.map>`_ to provide intuitive job parameterization:\n\n.. code-block:: python\n\n    >>> import dask.distributed as dd\n    >>> client = dd.LocalClient()\n    >>> futures = client.map(my_simple_func, (ab1 + ab2) * c1 + (ab1 + ab2) * c2)\n    >>> dd.progress(futures)\n\n\n=======\nHistory\n=======\n\n0.1.0 (2018-11-30)\n------------------\n\n* First release on PyPI.\n\n\n",
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