Np

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- 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

------------------

- 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.

------------------

- Bug fix

------------------

- Improved experimental dtype shortcuts: np.f[1,2], np.i32[1,2], etc.

------------------

- PyPI-friendly readme

------------------

- 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]]