e.g.

    In [1]: multiply.reduce((1<<30, 1<<30, 1<<30))
    Out[1]: 0

instead of

    In [2]: (1<<30) * (1<<30) * (1<<30)
    Out[2]: 1237940039285380274899124224

    In [3]: 1<<90
    Out[3]: 1237940039285380274899124224

also multiply.reduce returns int64, instead of python int:

    In [4]: type( multiply.reduce([1,2,3]) )
    Out[4]: numpy.int64

which also leads to overflow-related problems if we further compute with
this value and other integers and results exceeds int64 - it becomes
float:

    In [5]: idx0_stop = 18446744073709551615

    In [6]: stride0   = numpy.int64(1)

    In [7]: byte0_stop = idx0_stop * stride0

    In [8]: byte0_stop
    Out[8]: 1.8446744073709552e+19

and then it becomes a real problem for BigArray.__getitem__()

    wendelin.core/bigarray/__init__.py:326: RuntimeWarning: overflow encountered in long_scalars
      page0_min  = min(byte0_start, byte0_stop+byte0_stride) // pagesize # TODO -> fileh.pagesize

and then

    >           vma0 = self._fileh.mmap(page0_min, page0_max-page0_min+1)
    E           TypeError: integer argument expected, got float

~~~~

So just avoid multiple.reduce() and do our own mul() properly the same
way sum() is builtin into python, and we avoid overflow-related
problems.

This shows how to first generate such arrays (in steps, as every
transaction change should fit in memory), and then gather data from
whole array using C/Fortran/etc code.

It shows how to compute mean via NumPy's ndarray.mean()

It also shows that e.g. ndarray.var() wants to create temporaries in
size of original ndarray and that would fail, because it does not fit
into RAM.

ndarray.var() should not need to create such temporaries in principle -
all it has to do is to first compute mean, and then compute

    sum (Xi - <X>)^2

in a loop.

<X> is scalar, Xi - is just access to original array.

~~~~

So this also show NumPy can be incrementally improved to avoid creating
such temporaries, and then it will work.

Like C bzero / memset & memcopy - but work on python buffers. We
leverage NumPy for doing actual work, and this way NumPy becomes a
depenency.

Having NumPy as a dependency is ok - we'll for sure need it later as we
are trying to build out-of-core ndarrays.