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We're current copy by creating an Eigen::Map into the input numpy
array, then assigning that to the basic eigen type, effectively having
Eigen do the copy.  That doesn't work for negative strides, though:
Eigen doesn't allow them.

This commit makes numpy do the copying instead by allocating the eigen
type, then having numpy copy from the input array into a numpy reference
into the eigen object's data.  This also saves a copy when type
conversion is required: numpy can do the conversion on-the-fly as part
of the copy.

Finally this commit also makes non-reference parameters respect the
convert flag, declining the load when called in a noconvert pass with a
convertible, but non-array input or an array with the wrong dtype.

This removes the convert-from-arithemtic-scalar constructor of
any_container as it can result in ambiguous calls, as in:

    py::array_t<float>({ 1, 2 })

which could be intepreted as either of:

    py::array_t<float>(py::array_t<float>(1, 2))
    py::array_t<float>(py::detail::any_container({ 1, 2 }))

Removing the convert-from-arithmetic constructor reduces the number of
implicit conversions, avoiding the ambiguity for array and array_t.
This also re-adds the array/array_t constructors taking a scalar
argument for backwards compatibility.

This adds support for constructing `buffer_info` and `array`s using
arbitrary containers or iterator pairs instead of requiring a vector.

This is primarily needed by PR #782 (which makes strides signed to
properly support negative strides, and will likely also make shape and
itemsize to avoid mixed integer issues), but also needs to preserve
backwards compatibility with 2.1 and earlier which accepts the strides
parameter as a vector of size_t's.

Rather than adding nearly duplicate constructors for each stride-taking
constructor, it seems nicer to simply allow any type of container (or
iterator pairs).  This works by replacing the existing vector arguments
with a new `detail::any_container` class that handles implicit
conversion of arbitrary containers into a vector of the desired type.
It can also be explicitly instantiated with a pair of iterators (e.g.
by passing {begin, end} instead of the container).

When attempting to get a raw array pointer we return nullptr if given a
nullptr, which triggers an error_already_set(), but we haven't set an
exception message, which results in "Unknown internal error".

Callers that want explicit allowing of a nullptr here already handle it
(by clearing the exception after the call).

The extends the previous unchecked support with the ability to
determine the dimensions at runtime.  This incurs a small performance
hit when used (versus the compile-time fixed alternative), but is still considerably
faster than the full checks on every call that happen with
`.at()`/`.mutable_at()`.

This adds bounds-unchecked access to arrays through a `a.unchecked<Type,
Dimensions>()` method.  (For `array_t<T>`, the `Type` template parameter
is omitted).  The mutable version (which requires the array have the
`writeable` flag) is available as `a.mutable_unchecked<...>()`.

Specifying the Dimensions as a template parameter allows storage of an
std::array; having the strides and sizes stored that way (as opposed to
storing a copy of the array's strides/shape pointers) allows the
compiler to make significant optimizations of the shape() method that it
can't make with a pointer; testing with nested loops of the form:

    for (size_t i0 = 0; i0 < r.shape(0); i0++)
        for (size_t i1 = 0; i1 < r.shape(1); i1++)
            ...
                r(i0, i1, ...) += 1;

over a 10 million element array gives around a 25% speedup (versus using
a pointer) for the 1D case, 33% for 2D, and runs more than twice as fast
with a 5D array.

* Add value_type member alias to py::array_t (resolve #632)

* Use numpy scalar name in py::array_t function signatures (e.g. float32/64 instead of just float)

This makes array_t respect overload resolution and noconvert by failing
to load when `convert = false` if the src isn't already an array of the
correct type.

* Make string conversion stricter

The string conversion logic added in PR #624 for all std::basic_strings
was derived from the old std::wstring logic, but that was underused and
turns out to have had a bug in accepting almost anything convertible to
unicode, while the previous std::string logic was much stricter.  This
restores the previous std::string logic by only allowing actual unicode
or string types.

Fixes #685.

* Added missing 'requires numpy' decorator

(I forgot that the change to a global decorator here is in the
not-yet-merged Eigen PR)

* `array_t(const object &)` now throws on error
* `array_t::ensure()` is intended for casters —- old constructor is
  deprecated
* `array` and `array_t` get default constructors (empty array)
* `array` gets a converting constructor
* `py::isinstance<array_T<T>>()` checks the type (but not flags)

There is only one special thing which must remain: `array_t` gets
its own `type_caster` specialization which uses `ensure` instead
of a simple check.

* Also added unsafe version without checks

The current integer caster was unnecessarily strict and rejected
various kinds of NumPy integer types when calling C++ functions
expecting normal integers. This relaxes the current behavior.

This patch adds an extra base handle parameter to most ``py::array`` and
``py::array_t<>`` constructors. If specified along with a pointer to
data, the base object will be registered within NumPy, which increases
the base's reference count. This feature is useful to create shallow
copies of C++ or Python arrays while ensuring that the owners of the
underlying can't be garbage collected while referenced by NumPy.

The commit also adds a simple test function involving a ``wrap()``
function that creates shallow copies of various N-D arrays.

- This actually works with no changes, I just wasn't 100% convinced and
  decided to write a test to see if it's true.