Metadata-Version: 1.1
Name: np
Version: 1.0.2
Summary: np = numpy++: numpy with added convenience functionality
Home-page: https://github.com/k7hoven/np
Author: Koos Zevenhoven
Author-email: koos.zevenhoven@aalto.fi
License: BSD
Description: np -- create numpy arrays as ``np[1,3,5]``, and more
        ====================================================
        
        ``np``  = ``numpy`` + handy tools
        
        It's easy: start by importing ``np`` (the alias for numpy):
        
        .. code-block:: python
        
            import np
        
        
        Create a 1-D array: 
          
        .. code-block:: python
        
            np[1, 3, 5]
        
        
        Create a 2-D matrix:
        
        .. code-block:: python
        
            np.m[1, 2, 3: 
                :4, 5, 6:
                :7, 8, 9]
        
        
        For the numerical Python package ``numpy`` itself, see http://www.numpy.org/.
        
        The 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.
        
        Any feedback is very welcome: ``koos.zevenhoven@aalto.fi``.
        
        Getting Started
        ===============
        
        Requirements
        ------------
        
        * Works best with Python 3.5+ (Tested also with 3.4 and 2.7)
        * numpy (you should install this using your python package manager like ``conda`` or ``pip``)
        
        Installation
        ------------
        
        ``np`` can be installed with ``pip`` or ``pip3``:
        
        .. code-block:: bash
        
            $ pip install np
        
        
        or directly from the source code:
        
        .. code-block:: bash
        
            $ git clone https://github.com/k7hoven/np.git
            $ cd np
            $ python setup.py install 
        
        Basic Usage
        ===========
        
        Even before the ``np`` tool, a popular style of using ``numpy`` has been to import it as ``np``:
        
        .. code-block:: python
        
            >>> import numpy as np
            >>> my_array = np.array([3, 4, 5])
            >>> my_2d_array = np.array([[1, 2], [3, 4]])
        
        The most important feature of ``np`` is to make the creation of arrays less verbose, while everything else works as before. The above code becomes:
        
        .. code-block:: python
        
            >>> import np
            >>> my_array = np[3, 4, 5]
            >>> my_2d_array = np[[1, 2], [3, 4]]
            >>> my_matrix = np.m[1, 2: 3, 4]
            >>> my_matrix2 = np.m[1, 2, 3:
            ...                  :4, 5, 6:
            ...                  :7, 8, 9]
            >>> my_row_vector = np.m[1, 2, 3]
        
        
        As 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"``.
        
        The above also shows how you can use ``np.m`` and colons to easily create matrices (NxM) or row vectors (1xM).
        
        The `np` package also provides a convenient way of ensuring something is a numpy array, that is, a shortcut to ``numpy.asarray()``:
        
        .. code-block:: python
        
            >>> import np
            >>> mylist = [1, 3, 5]
            >>> mylist + [7, 9, 11]
            [1, 3, 5, 7, 9, 11]
            >>> np(mylist) + [7, 9, 11]
            array([8, 12, 16])
        
        
        As an experimental feature, there are also shortcuts for giving the arrays a specific data type (numpy dtype):
        
        .. code-block:: python
        
            >>> np[1, 2, 3]
            array([1, 2, 3])
            >>> np.f[1, 2, 3]
            array([ 1.,  2.,  3.])
            >>> np.f2[1, 2, 3]
            array([ 1.,  2.,  3.], dtype=float16)
            >>> np.u4[1, 2, 3]
            array([1, 2, 3], dtype=uint32)
            >>> np.c[1, 2, 3]
            array([ 1.+0.j,  2.+0.j,  3.+0.j])
        
        
        Changelog
        =========
        
        1.0.0 (2017-09-20)
        ------------------
        
        - Creating matrices is now even simpler::
         
            np.m[1, 2: 3, 4] == np.array([[1, 2], [3, 4]])
        
            np.m[1, 2:
                :3, 4] == np.array([[1, 2], [3, 4]])
        
            np.m[1, 2] == np.array([[1, 2]])
        
            np.m[1, 2].T == np.array([[1],
                                      [2]])
        
        
        - ``np(...)`` corresponds to ``np.asarray(...)``
        - Many improvements to error handling
        - Some more cleanups to type shortcuts
        
        0.2.0 (2016-03-29)
        ------------------
        
        - Quick types are now ``np.i``, ``np.f``, ``np.u``, ``np.c``, or with the 
          number of *bytes* per value appended: 
          ``np.i4`` -> int32, ``np.u2`` -> uint16, ``np.c16`` -> complex128, ...
          (still somewhat experimental)
        - Removed the old np.i8 and np.ui8 which represented 8-bit types, which
          was inconsistent with short numpy dtype names which correspond to numbers of
          bytes. The rest of the bit-based shortcuts are deprecated and will be removed
          later.
        - Handle Python versions >=3.5 better; now even previously imported plain numpy
          module objects become the exact same object as np. 
        - Tests for all np functionality
        - Ridiculously slow tests that runs the numpy test suite several times to
          make sure that np does not affect numpy functionality.
        - Remove numpy from requirements and give a meaningful error instead if numpy
          is missing (i.e. install it using your package manager like conda or pip)
        - Better reprs for subscriptable array creator objects and the np/numpy module.
        
        0.1.4 (2016-01-26)
        ------------------
        
        - Bug fix
        
        0.1.2 (2015-06-17)
        ------------------
        
        - Improved experimental dtype shortcuts: np.f[1,2], np.i32[1,2], etc.
        
        0.1.1 (2015-06-17)
        ------------------
        
        - PyPI-friendly readme
        
        0.1.0 (2015-06-17)
        ------------------
        
        - First distributable version
        - Easy arrays such as np[[1,2],[3,4]]
        - Shortcut for np.asanyarray(obj): np(obj)
        - Experimental dtype shortcuts: np.f64[[1,2],[3,4]]
         
        
        
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3