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        "author": "Koos Zevenhoven",
        "author_email": "koos.zevenhoven@aalto.fi",
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        "classifiers": [
            "Development Status :: 5 - Production/Stable",
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            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3"
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        "description": "np -- create numpy arrays as ``np[1,3,5]``, and more\n====================================================\n\n``np``  = ``numpy`` + handy tools\n\nIt's easy: start by importing ``np`` (the alias for numpy):\n\n.. code-block:: python\n\n    import np\n\n\nCreate a 1-D array: \n  \n.. code-block:: python\n\n    np[1, 3, 5]\n\n\nCreate a 2-D matrix:\n\n.. code-block:: python\n\n    np.m[1, 2, 3: \n        :4, 5, 6:\n        :7, 8, 9]\n\n\nFor the numerical Python package ``numpy`` itself, see http://www.numpy.org/.\n\nThe idea of ``np`` is to provide a way of creating numpy arrays with a compact syntax and without an explicit function call. Making the module name ``np`` subscriptable, while still keeping it essentially an alias for numpy, does this in a clean way.\n\nAny feedback is very welcome: ``koos.zevenhoven@aalto.fi``.\n\nGetting Started\n===============\n\nRequirements\n------------\n\n* Works best with Python 3.5+ (Tested also with 3.4 and 2.7)\n* numpy (you should install this using your python package manager like ``conda`` or ``pip``)\n\nInstallation\n------------\n\n``np`` can be installed with ``pip`` or ``pip3``:\n\n.. code-block:: bash\n\n    $ pip install np\n\n\nor directly from the source code:\n\n.. code-block:: bash\n\n    $ git clone https://github.com/k7hoven/np.git\n    $ cd np\n    $ python setup.py install \n\nBasic Usage\n===========\n\nEven before the ``np`` tool, a popular style of using ``numpy`` has been to import it as ``np``:\n\n.. code-block:: python\n\n    >>> import numpy as np\n    >>> my_array = np.array([3, 4, 5])\n    >>> my_2d_array = np.array([[1, 2], [3, 4]])\n\nThe most important feature of ``np`` is to make the creation of arrays less verbose, while everything else works as before. The above code becomes:\n\n.. code-block:: python\n\n    >>> import np\n    >>> my_array = np[3, 4, 5]\n    >>> my_2d_array = np[[1, 2], [3, 4]]\n    >>> my_matrix = np.m[1, 2: 3, 4]\n    >>> my_matrix2 = np.m[1, 2, 3:\n    ...                  :4, 5, 6:\n    ...                  :7, 8, 9]\n    >>> my_row_vector = np.m[1, 2, 3]\n\n\nAs you can see from the above example, you can create numpy arrays by subscripting the ``np`` module. Since most people would have numpy imported as ``np`` anyway, this requires no additional names to clutter the namespace. Also, the syntax ``np[1,2,3]`` resembles the syntax for ``bytes`` literals, ``b\"asd\"``.\n\nThe above also shows how you can use ``np.m`` and colons to easily create matrices (NxM) or row vectors (1xM).\n\nThe `np` package also provides a convenient way of ensuring something is a numpy array, that is, a shortcut to ``numpy.asarray()``:\n\n.. code-block:: python\n\n    >>> import np\n    >>> mylist = [1, 3, 5]\n    >>> mylist + [7, 9, 11]\n    [1, 3, 5, 7, 9, 11]\n    >>> np(mylist) + [7, 9, 11]\n    array([8, 12, 16])\n\n\nAs an experimental feature, there are also shortcuts for giving the arrays a specific data type (numpy dtype):\n\n.. code-block:: python\n\n    >>> np[1, 2, 3]\n    array([1, 2, 3])\n    >>> np.f[1, 2, 3]\n    array([ 1.,  2.,  3.])\n    >>> np.f2[1, 2, 3]\n    array([ 1.,  2.,  3.], dtype=float16)\n    >>> np.u4[1, 2, 3]\n    array([1, 2, 3], dtype=uint32)\n    >>> np.c[1, 2, 3]\n    array([ 1.+0.j,  2.+0.j,  3.+0.j])\n\n\nChangelog\n=========\n\n1.0.0 (2017-09-20)\n------------------\n\n- Creating matrices is now even simpler::\n \n    np.m[1, 2: 3, 4] == np.array([[1, 2], [3, 4]])\n\n    np.m[1, 2:\n        :3, 4] == np.array([[1, 2], [3, 4]])\n\n    np.m[1, 2] == np.array([[1, 2]])\n\n    np.m[1, 2].T == np.array([[1],\n                              [2]])\n\n\n- ``np(...)`` corresponds to ``np.asarray(...)``\n- Many improvements to error handling\n- Some more cleanups to type shortcuts\n\n0.2.0 (2016-03-29)\n------------------\n\n- Quick types are now ``np.i``, ``np.f``, ``np.u``, ``np.c``, or with the \n  number of *bytes* per value appended: \n  ``np.i4`` -> int32, ``np.u2`` -> uint16, ``np.c16`` -> complex128, ...\n  (still somewhat experimental)\n- Removed the old np.i8 and np.ui8 which represented 8-bit types, which\n  was inconsistent with short numpy dtype names which correspond to numbers of\n  bytes. The rest of the bit-based shortcuts are deprecated and will be removed\n  later.\n- Handle Python versions >=3.5 better; now even previously imported plain numpy\n  module objects become the exact same object as np. \n- Tests for all np functionality\n- Ridiculously slow tests that runs the numpy test suite several times to\n  make sure that np does not affect numpy functionality.\n- Remove numpy from requirements and give a meaningful error instead if numpy\n  is missing (i.e. install it using your package manager like conda or pip)\n- Better reprs for subscriptable array creator objects and the np/numpy module.\n\n0.1.4 (2016-01-26)\n------------------\n\n- Bug fix\n\n0.1.2 (2015-06-17)\n------------------\n\n- Improved experimental dtype shortcuts: np.f[1,2], np.i32[1,2], etc.\n\n0.1.1 (2015-06-17)\n------------------\n\n- PyPI-friendly readme\n\n0.1.0 (2015-06-17)\n------------------\n\n- First distributable version\n- Easy arrays such as np[[1,2],[3,4]]\n- Shortcut for np.asanyarray(obj): np(obj)\n- Experimental dtype shortcuts: np.f64[[1,2],[3,4]]",
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