Allows equivalent integral types and numpy dtypes

Allows use of vectors as python buffers, so for example they can be adopted without a copy by numpy.asarray
Allows faster conversion of buffers to vectors by copying instead of individually casting the elements

* 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 won't affect much, but makes the code consistent with the
non-copying branch.

Fixes #697

The `decltype(...)` in the template parameter that gives us SFINAE
matching for a lambda makes MSVC 2017 ICE; this works around if by
changing the test to an explicit not-a-function-or-pointer test, which
seems to work everywhere.

RTD updated their build environment which broke the 1.8.14.dev build of
doxygen that we were using. The update also breaks the conda-forge build
of 1.8.13 (but that version has other issues).

Luckily, the RTD update did bring their doxygen version up to 1.8.11
which is enough to parse the C++11 code we need (ref qualifiers) and it
also avoids the segfault found in 1.8.13.

Since we're using the native doxygen, conda isn't required anymore and
we can simplify the RTD configuration.

[skip ci]

Some versions of Python 2.7 reportedly (#713) have issues with
PyUnicode_Decode being passed the encoding string, so just skip it
entirely by calling the PyUnicode_DecodeUTF* function directly.  This
will also be slightly more efficient by avoiding having to check the
encoding string, and (for python 2) going through the unicode class's
decode (python 3 fast-tracks this for all utf-{8,16,32} encodings;
python 2 only fast-tracked for the exact string "utf-8", which we
weren't passing anyway (we had "utf8")).

This doesn't work for PyPy, however: its `PyUnicode_DecodeUTF{8,16,32}`
appear rather broken: the UTF8 one segfaults, while the 16/32 require
recasting into a non-const `char *` (and might segfault; I didn't get
far enough to find out).  Just avoid the whole thing by keeping the
encoding-passed-as-string version for PyPy, which seems to work
reliably.

The duration calculation was using %, but that's only supported on
duration objects when the arithmetic type supports %, and hence fails
for floats.  Fixed by subtracting off the calculated values instead.

This adds a linux docker clang 4.0 with libc++ and c++1z build.

* Add `pytest.ini` config file and set default options there instead of
  in `CMakeLists.txt` (command line arguments).

* Change all output capture from `capfd` (filedescriptors) to `capsys`
  (Python's `sys.stdout` and `sys.stderr`). This avoids capturing
  low-level C errors, e.g. from the debug build of Python.

* Set pytest minimum version to 3.0 to make it easier to use new
  features. Removed conditional use of `excinfo.match()`.

* Clean up some leftover function-level `@pytest.requires_numpy`.

Nightlies for pypy no longer run on Ubuntu 12.04; change the pypy build
distribution to the travis-ci trusty (i.e. 14.04) beta container.

The pypy build was also installing numpy and scipy for the *system*
python version, which was pointless; this also adds a guard to the
eigen/numpy/scipy install code with a !PYPY check.

When using pybind::options to disable function signatures, user-defined
docstrings only get appended if they exist, but newlines were getting
appended unconditionally, so the docstring could end up with blank lines
(depending on which overloads, in particular, provided docstrings).

This commit suppresses the empty lines by only adding newlines for
overloads when needed.

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.

Remove extraneous `enum_` python constructor

Added in 6fb48490

The second constructor can't be doing anything--the signatures are
exactly the same, and so the first is always going to be the one
invoked by the dispatcher.

Expose enum_ entries as new "__members__" attribute

Commit 11a337f1 added major and minor python version
checking to cast.h but does not use the macros defined
via the Python.h inclusion. This may be due to an
intention to use the variables defined by the cmake
module FindPythonInterpreter, but nothing in the
pybind11 repo does anything to convert the cmake
variables to preprocessor defines.

* The definition of `PySequence_Fast` is more restrictive on PyPy, so
  use the slow path instead.
* `PyDict_Next` has been fixed in PyPy -> remove workaround.

Slightly reduces binary size (range for loops over tuple/list benefit
a lot). The iterators are compatible with std algorithms.

The added type aliases are required by `std::iterator_traits`.
Python iterators satisfy the `InputIterator` concept in C++.

Before this, `py::iterator` didn't do any error handling, so code like:
```c++
for (auto item : py::int_(1)) {
    // ...
}
```
would just silently skip the loop. The above now throws `TypeError` as
expected. This is a breaking behavior change, but any code which relied
on the silent skip was probably broken anyway.

Also, errors returned by `PyIter_Next()` are now properly handled.

Fixes test failure on Fedora 25.

Fixes some numpy tests failures on ppc64 in big-endian mode due to
little-endian assumptions.

Fixes #694.

test_eigen.py and test_numpy_*.py have the same
@pytest.requires_eigen_and_numpy or @pytest.requires_numpy on every
single test; this changes them to use pytest's global `pytestmark = ...`
instead to disable the entire module when numpy and/or eigen aren't
available.

This commit largely rewrites the Eigen dense matrix support to avoid
copying in many cases: Eigen arguments can now reference numpy data, and
numpy objects can now reference Eigen data (given compatible types).

Eigen::Ref<...> arguments now also make use of the new `convert`
argument use (added in PR #634) to avoid conversion, allowing
`py::arg().noconvert()` to be used when binding a function to prohibit
copying when invoking the function.  Respecting `convert` also means
Eigen overloads that avoid copying will be preferred during overload
resolution to ones that require copying.

This commit also rewrites the Eigen documentation and test suite to
explain and test the new capabilities.

Eigen::Ref objects, when returned, are almost always returned as
rvalues; what's important is the data they reference, not the outer
shell, and so we want to be able to use `::copy`,
`::reference_internal`, etc. to refer to the data the Eigen::Ref
references (in the following commits), rather than the Eigen::Ref
instance itself.

This moves the policy override into a struct so that code that wants to
avoid it (or wants to provide some other Return-type-conditional
override) can create a specialization of
return_value_policy_override<Return> in order to override the override.

This lets an Eigen::Ref-returning function be bound with `rvp::copy`,
for example, to specify that the data should be copied into a new numpy
array rather than referenced, or `rvp::reference_internal` to indicate
that it should be referenced, but a keep-alive used (actually, we used
the array's `base` rather than a py::keep_alive in such a case, but it
accomplishes the same thing).

Numpy raises ValueError when attempting to modify an array, while
py::array is raising a RuntimeError.  This changes the exception to a
std::domain_error, which gets mapped to the expected ValueError in
python.

numpy arrays aren't currently properly setting base: by setting `->base`
directly, the base doesn't follow what numpy expects and documents (that
is, following chained array bases to the root array).

This fixes the behaviour by using numpy's PyArray_SetBaseObject to set
the base instead, and then updates the tests to reflect the fixed
behaviour.

A few of pybind's numpy constants are using the numpy-deprecated names
(without "ARRAY_" in them); updated our names to be consistent with
current numpy code.

`is_template_base_of<T>` fails when `T` is `const` (because its
implementation relies on being able to convert a `T*` to a `Base<U>*`,
which won't work when `T` is const).

(This also agrees with std::is_base_of, which ignores cv qualification.)

Currently when we do a conversion between a numpy array and an Eigen
Vector, we allow the conversion only if the Eigen type is a
compile-time vector (i.e. at least one dimension is fixed at 1 at
compile time), or if the type is dynamic on *both* dimensions.

This means we can run into cases where MatrixXd allow things that
conforming, compile-time sizes does not: for example,
`Matrix<double,4,Dynamic>` is currently not allowed, even when assigning
from a 4-element vector, but it *is* allowed for a
`Matrix<double,Dynamic,Dynamic>`.

This commit also reverts the current behaviour of using the matrix's
storage order to determine the structure when the Matrix is fully
dynamic (i.e. in both dimensions).  Currently we assign to an eigen row
if the storage order is row-major, and column otherwise: this seems
wrong (the storage order has nothing to do with the shape!).  While
numpy doesn't distinguish between a row/column vector, Eigen does, but
it makes more sense to consistently choose one than to produce
something with a different shape based on the intended storage layout.

With the previous commit, output can be very confusing because you only
see positional arguments in the "invoked with" line, but you can have a
failure from kwargs as well (in particular, when a value is invalidly
specified via both via positional and kwargs).  This commits adds
kwargs to the output, and updates the associated tests to match.