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This fixes a bug introduced in b68959e8
when passing in a two-dimensional, but conformable, array as the value
for a compile-time Eigen vector (such as VectorXd or RowVectorXd).  The
commit switched to using numpy to copy into the eigen data, but this
broke the described case because numpy refuses to broadcast a (N,1)
into a (N).

This commit fixes it by squeezing the input array whenever the output
array is 1-dimensional, which will let the problematic case through.
(This shouldn't squeeze inappropriately as dimension compatibility is
already checked for conformability before getting to the copy code).

This udpates all the remaining tests to the new test suite code and
comment styles started in #898.  For the most part, the test coverage
here is unchanged, with a few minor exceptions as noted below.

- test_constants_and_functions: this adds more overload tests with
  overloads with different number of arguments for more comprehensive
  overload_cast testing.  The test style conversion broke the overload
  tests under MSVC 2015, prompting the additional tests while looking
  for a workaround.

- test_eigen: this dropped the unused functions `get_cm_corners` and
  `get_cm_corners_const`--these same tests were duplicates of the same
  things provided (and used) via ReturnTester methods.

- test_opaque_types: this test had a hidden dependence on ExampleMandA
  which is now fixed by using the global UserType which suffices for the
  relevant test.

- test_methods_and_attributes: this required some additions to UserType
  to make it usable as a replacement for the test's previous SimpleType:
  UserType gained a value mutator, and the `value` property is not
  mutable (it was previously readonly).  Some overload tests were also
  added to better test overload_cast (as described above).

- test_numpy_array: removed the untemplated mutate_data/mutate_data_t:
  the templated versions with an empty parameter pack expand to the same
  thing.

- test_stl: this was already mostly in the new style; this just tweaks
  things a bit, localizing a class, and adding some missing
  `// test_whatever` comments.

- test_virtual_functions: like `test_stl`, this was mostly in the new
  test style already, but needed some `// test_whatever` comments.
  This commit also moves the inherited virtual example code to the end
  of the file, after the main set of tests (since it is less important
  than the other tests, and rather length); it also got renamed to
  `test_inherited_virtuals` (from `test_inheriting_repeat`) because it
  tests both inherited virtual approaches, not just the repeat approach.

Put the caster's temporary array on life support to ensure correct
lifetime when it's being used as a subcaster.

Many of the Eigen type casters' name() methods weren't wrapping the type
description in a `type_descr` object, which thus wasn't adding the
"{...}" annotation used to identify an argument which broke the help
output by skipping eigen arguments.

The test code I had added even had some (unnoticed) broken output (with
the "arg0: " showing up in the return value).

This commit also adds test code to ensure that named eigen arguments
actually work properly, despite the invalid help output.  (The added
tests pass without the rest of this commit).

Fixes #754.

Fixes #738

The current check for conformability fails when given a 2D, 1xN or Nx1
input to a row-major or column-major, respectively, Eigen::Ref, leading
to a copy-required state in the type_caster, but this later failed
because the copy was also non-conformable because it had the same shape
and strides (because a 1xN or Nx1 is both F and C contiguous).

In such cases we can safely ignore the stride on the "1" dimension since
it'll never be used: only the "N" dimension stride needs to match the
Eigen::Ref stride, which both fixes the non-conformable copy problem,
but also avoids a copy entirely as long as the "N" dimension has a
compatible stride.

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.

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.

This adds automatic casting when assigning to python types like dict,
list, and attributes.  Instead of:

    dict["key"] = py::cast(val);
    m.attr("foo") = py::cast(true);
    list.append(py::cast(42));

you can now simply write:

    dict["key"] = val;
    m.attr("foo") = true;
    list.append(42);

Casts needing extra parameters (e.g. for a non-default rvp) still
require the py::cast() call. set::add() is also supported.

All usage is channeled through a SFINAE implementation which either just returns or casts. 

Combined non-converting handle and autocasting template methods via a
helper method that either just returns (handle) or casts (C++ type).

Adding or removing tests is a little bit cumbersome currently: the test
needs to be added to CMakeLists.txt, the init function needs to be
predeclared in pybind11_tests.cpp, then called in the plugin
initialization.  While this isn't a big deal for tests that are being
committed, it's more of a hassle when working on some new feature or
test code for which I temporarily only care about building and linking
the test being worked on rather than the entire test suite.

This commit changes tests to self-register their initialization by
having each test initialize a local object (which stores the
initialization function in a static variable).  This makes changing the
set of tests being build easy: one only needs to add or comment out
test names in tests/CMakeLists.txt.

A couple other minor changes that go along with this:

- test_eigen.cpp is now included in the test list, then removed if eigen
  isn't available.  This lets you disable the eigen tests by commenting
  it out, just like all the other tests, but keeps the build working
  without eigen eigen isn't available.  (Also, if it's commented out, we
  don't even bother looking for and reporting the building with/without
  eigen status message).

- pytest is now invoked with all the built test names (with .cpp changed
  to .py) so that it doesn't try to run tests that weren't built.

Use simple asserts and pytest's powerful introspection to make testing
simpler. This merges the old .py/.ref file pairs into simple .py files
where the expected values are right next to the code being tested.

This commit does not touch the C++ part of the code and replicates the
Python tests exactly like the old .ref-file-based approach.

Functions returning specialized Eigen matrices like Eigen::DiagonalMatrix and
Eigen::SelfAdjointView--which inherit from EigenBase but not
DenseBase--isn't currently allowed; such classes are explicitly copyable
into a Matrix (by definition), and so we can support functions that
return them by copying the value into a Matrix then casting that
resulting dense Matrix into a numpy.ndarray.  This commit does exactly
that.

Some Eigen objects, such as those returned by matrix.diagonal() and
matrix.block() have non-standard stride values because they are
basically just maps onto the underlying matrix without copying it (for
example, the primary diagonal of a 3x3 matrix is a vector-like object
with .src equal to the full matrix data, but with stride 4).  Returning
such an object from a pybind11 method breaks, however, because pybind11
assumes vectors have stride 1, and that matrices have strides equal to
the number of rows/columns or 1 (depending on whether the matrix is
stored column-major or row-major).

This commit fixes the issue by making pybind11 use Eigen's stride
methods when copying the data.

Eigen::Ref is a common way to pass eigen dense types without needing a
template, e.g. the single definition `void
func(Eigen::Ref<Eigen::MatrixXd> x)` can be called with any double
matrix-like object.

The current pybind11 eigen support fails with internal errors if
attempting to bind a function with an Eigen::Ref<...> argument because
Eigen::Ref<...> satisfies the "is_eigen_dense" requirement, but can't
compile if actually used: Eigen::Ref<...> itself is not default
constructible, and so the argument std::tuple containing an
Eigen::Ref<...> isn't constructible, which results in compilation
failure.

This commit adds support for Eigen::Ref<...> by giving it its own
type_caster implementation which consists of an internal type_caster of
the referenced type, load/cast methods that dispatch to the internal
type_caster, and a unique_ptr to an Eigen::Ref<> instance that gets
set during load().

There is, of course, no performance advantage for pybind11-using code of
using Eigen::Ref<...>--we are allocating a matrix of the derived type
when loading it--but this has the advantage of allowing pybind11 to bind
transparently to C++ methods taking Eigen::Refs.

Add and declare to Python functions

    double_mat_cm() --- compute 2* a column-major matrix
    double_mat_rm() --- compute 2* a row-major matrix

to 'eigen.cpp' tests / example.