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.
Currently pybind11 doesn't check when you define a new object (e.g. a
class, function, or exception) that overwrites an existing one. If the
thing being overwritten is a class, this leads to a segfault (because
pybind still thinks the type is defined, even though Python no longer
has the type). In other cases this is harmless (e.g. replacing a
function with an exception), but even in that case it's most likely a
bug.
This code doesn't prevent you from actively doing something harmful,
like deliberately overwriting a previous definition, but detects
overwriting with a run-time error if it occurs in the standard
class/function/exception/def registration interfaces.
All of the additions are in non-template code; the result is actually a
tiny decrease in .so size compared to master without the new test code
(977304 to 977272 bytes), and about 4K higher with the new tests.
With this there is no more need for manual user declarations like
`PYBIND11_DECLARE_HOLDER_TYPE(T, std::shared_ptr<T>)`. Existing ones
will still compile without error -- they will just be ignored silently.
Resolves#446.
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.
`auto var = l[0]` has a strange quirk: `var` is actually an accessor and
not an object, so any later assignment of `var = ...` would modify l[0]
instead of `var`. This is surprising compared to the non-auto assignment
`py::object var = l[0]; var = ...`.
By overloading `operator=` on lvalue/rvalue, the expected behavior is
restored even for `auto` variables.
This also adds the `hasattr` and `getattr` functions which are needed
with the new attribute behavior. The new functions behave exactly like
their Python counterparts.
Similarly `object` gets a `contains` method which calls `__contains__`,
i.e. it's the same as the `in` keyword in Python.
The custom exception handling added in PR #273 is robust, but is overly
complex for declaring the most common simple C++ -> Python exception
mapping that needs only to copy `what()`. This add a simpler
`py::register_exception<CppExp>(module, "PyExp");` function that greatly
simplifies the common basic case of translation of a simple CppException
into a simple PythonException, while not removing the more advanced
capabilities of defining custom exception handlers.
The current inheritance testing isn't sufficient to detect a cache
failure; the test added here breaks PR #390, which caches the
run-time-determined return type the first time a function is called,
then reuses that cached type even though the run-time type could be
different for a future call.
This adds a static local variable (in dead code unless actually needed)
in the overload code that is used for storage if the overload is for
some convert-by-value type (such as numeric values or std::string).
This has limitations (as written up in the advanced doc), but is better
than simply not being able to overload reference or pointer methods.
This clears the Python error at the error_already_set throw site, thus
allowing Python calls to be made in destructors which are triggered by
the exception. This is preferable to the alternative, which would be
guarding every Python API call with an error_scope.
This effectively flips the behavior of error_already_set. Previously,
it was assumed that the error stays in Python, so handling the exception
in C++ would require explicitly calling PyErr_Clear(), but nothing was
needed to propagate the error to Python. With this change, handling the
error in C++ does not require a PyErr_Clear() call, but propagating the
error to Python requires an explicit error_already_set::restore().
The change does not break old code which explicitly calls PyErr_Clear()
for cleanup, which should be the majority of user code. The need for an
explicit restore() call does break old code, but this should be mostly
confined to the library and not user code.
