This reimplements the std::reference_wrapper<T> caster to be a shell
around the underlying T caster (rather than assuming T is a generic
type), which lets it work for things like `std::reference_wrapper<int>`
or anything else custom type caster with a lvalue cast operator.
This also makes it properly fail when None is provided, just as an
ordinary lvalue reference argument would similarly fail.
This also adds a static assert to test that T has an appropriate type
caster. It triggers for casters like `std::pair`, which have
return-by-value cast operators. (In theory this could be supported by
storing a local temporary for such types, but that's beyond the scope
of this PR).
This also replaces `automatic` or `take_ownership` return value policies
with `automatic_reference` as taking ownership of a reference inside a
reference_wrapper is not valid.
The new version of pytest now reports Python warnings by default. This
commit filters out some third-party extension warnings which are not
useful for pybind11 tests.
This commit also adds `doc()` to `object_api` as a shortcut for the
`attr("__doc__")` accessor.
The module macro changes from:
```c++
PYBIND11_PLUGIN(example) {
pybind11::module m("example", "pybind11 example plugin");
m.def("add", [](int a, int b) { return a + b; });
return m.ptr();
}
```
to:
```c++
PYBIND11_MODULE(example, m) {
m.doc() = "pybind11 example plugin";
m.def("add", [](int a, int b) { return a + b; });
}
```
Using the old macro results in a deprecation warning. The warning
actually points to the `pybind11_init` function (since attributes
don't bind to macros), but the message should be quite clear:
"PYBIND11_PLUGIN is deprecated, use PYBIND11_MODULE".
* Added template constructors to buffer_info that can deduce the item size, format string, and number of dimensions from the pointer type and the shape container
* Implemented actual buffer_info constructor as private delegate constructor taking rvalue reference as a workaround for the evaluation order move problem on GCC 4.8
At this point, there is only a single test for interpreter basics.
Apart from embedding itself, having a C++ test framework will also
benefit the C++-side features by allowing them to be tested directly.
All targets provided by pybind11:
* pybind11::module - the existing target for creating extension modules
* pybind11::embed - new target for embedding the interpreter
* pybind11::pybind11 - common "base" target (headers only)
Now that #851 has removed all multiple uses of a caster, it can just use
the default-constructed value with needing a reset. This fixes two
issues:
1. With std::experimental::optional (at least under GCC 5.4), the `= {}`
would construct an instance of the optional type and then move-assign
it, which fails if the value type isn't move-assignable.
2. With older versions of Boost, the `= {}` could fail because it is
ambiguous, allowing construction of either `boost::none` or the value
type.
MSVC by default uses the local codepage, which fails when it sees the
utf-8 in test_python_types.cpp. This adds the /utf-8 flag to the test
suite compilation to force it to interpret source code as utf-8.
Fixes#869
This extends py::vectorize to automatically pass through
non-vectorizable arguments. This removes the need for the documented
"explicitly exclude an argument" workaround.
Vectorization now applies to arithmetic, std::complex, and POD types,
passed as plain value or by const lvalue reference (previously only
pass-by-value types were supported). Non-const lvalue references and
any other types are passed through as-is.
Functions with rvalue reference arguments (whether vectorizable or not)
are explicitly prohibited: an rvalue reference is inherently not
something that can be passed multiple times and is thus unsuitable to
being in a vectorized function.
The vectorize returned value is also now more sensitive to inputs:
previously it would return by value when all inputs are of size 1; this
is now amended to having all inputs of size 1 *and* 0 dimensions. Thus
if you pass in, for example, [[1]], you get back a 1x1, 2D array, while
previously you got back just the resulting single value.
Vectorization of member function specializations is now also supported
via `py::vectorize(&Class::method)`; this required passthrough support
for the initial object pointer on the wrapping function pointer.
This attribute lets you disable (or explicitly enable) passing None to
an argument that otherwise would allow it by accepting
a value by raw pointer or shared_ptr.
This commit allows type_casters to allow their local values to be moved
away, rather than copied, when the type caster instance itself is an rvalue.
This only applies (automatically) to type casters using
PYBIND11_TYPE_CASTER; the generic type type casters don't own their own
pointer, and various value casters (e.g. std::string, std::pair,
arithmetic types) already cast to an rvalue (i.e. they return by value).
This updates various calling code to attempt to get a movable value
whenever the value is itself coming from a type caster about to be
destroyed: for example, when constructing an std::pair or various stl.h
containers. For types that don't support value moving, the cast_op
falls back to an lvalue cast.
There wasn't an obvious place to add the tests, so I added them to
test_copy_move_policies, but also renamed it to drop the _policies as it
now tests more than just policies.
Using a dynamic_cast instead of a static_cast is needed to safely cast
from a base to a derived type. The previous static_pointer_cast isn't
safe, however, when downcasting (and fails to compile when downcasting
with virtual inheritance).
Switching this to always use a dynamic_pointer_cast shouldn't incur any
additional overhead when a static_pointer_cast is safe (i.e. when
upcasting, or self-casting): compilers don't need RTTI checks in those
cases.
The Python method for /= was set as `__idiv__`, which should be
`__itruediv__` under Python 3.
This wasn't totally broken in that without it defined, Python constructs
a new object by calling __truediv__. The operator tests, however,
didn't actually test the /= operator: when I added it, I saw an extra
construction, leading to the problem. This commit also includes tests
for the previously untested *= operator, and adds some element-wise
vector multiplication and division operators.
Currently, `py::int_(1).cast<variant<double, int>>()` fills the `double`
slot of the variant. This commit switches the loader to a 2-pass scheme
in order to correctly fill the `int` slot.
Many of our `is_none()` checks in type caster loading return true, but
this should really be considered a deferral so that, for example, an
overload with a `py::none` argument would win over one that takes
`py::none` as a null option.
This keeps None-accepting for the `!convert` pass only for std::optional
and void casters. (The `char` caster already deferred None; this just
extends that behaviour to other casters).
This exposed a few underlying issues:
1. is_pod_struct was too strict to allow this. I've relaxed it to
require only trivially copyable and standard layout, rather than POD
(which additionally requires a trivial constructor, which std::complex
violates).
2. format_descriptor<std::complex<T>>::format() returned numpy format
strings instead of PEP3118 format strings, but register_dtype
feeds format codes of its fields to _dtype_from_pep3118. I've changed it
to return PEP3118 format codes. format_descriptor is a public type, so
this may be considered an incompatible change.
3. register_structured_dtype tried to be smart about whether to mark
fields as unaligned (with ^). However, it's examining the C++ alignment,
rather than what numpy (or possibly PEP3118) thinks the alignment should
be. For complex values those are different. I've made it mark all fields
as ^ unconditionally, which should always be safe even if they are
aligned, because we explicitly mark the padding.
Resolves#800.
Both C++ arrays and std::array are supported, including mixtures like
std::array<int, 2>[4]. In a multi-dimensional array of char, the last
dimension is used to construct a numpy string type.
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.
`EigenConformable::stride_compatible` returns false if the strides are
negative. In this case, do not use `EigenConformable::stride`, as it
is {0,0}. We cannot write negative strides in this element, as Eigen
will throw an assertion if we do.
The `type_caster` specialization for regular, dense Eigen matrices now
does a second `array_t::ensure` to copy data in case of negative strides.
I'm not sure that this is the best way to implement this.
I have added "TODO" tags linking these changes to Eigen bug #747, which,
when fixed, will allow Eigen to accept negative strides.
If a bound std::function is invoked with a bound method, the implicit
bound self is lost because we use `detail::get_function` to unbox the
function. This commit amends the code to use py::function and only
unboxes in the special is-really-a-c-function case. This makes bound
methods stay bound rather than unbinding them by forcing extraction of
the c function.
Enumerations on Python 2.7 were not always implicitly converted to
integers (depending on the target size). This patch adds a __long__
conversion function (only enabled on 2.7) which fixes this issue.
The attached test case fails without this patch.
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.
Python 3's `PyInstanceMethod_Type` hides itself via its `tp_descr_get`,
which prevents aliasing methods via `cls.attr("m2") = cls.attr("m1")`:
instead the `tp_descr_get` returns a plain function, when called on a
class, or a `PyMethod`, when called on an instance. Override that
behaviour for pybind11 types with a special bypass for
`PyInstanceMethod_Types`.
The Unicode support added in 2.1 (PR #624) inadvertently broke accepting
`bytes` as std::string/char* arguments. This restores it with a
separate path that does a plain conversion (i.e. completely bypassing
all the encoding/decoding code), but only for single-byte string types.
This commits adds base class pointers of offset base classes (i.e. due
to multiple inheritance) to `registered_instances` so that if such a
pointer is returned we properly recognize it as an existing instance.
Without this, returning a base class pointer will cast to the existing
instance if the pointer happens to coincide with the instance pointer,
but constructs a new instance (quite possibly with a segfault, if
ownership is applied) for unequal base class pointers due to multiple
inheritance.
When we are returned a base class pointer (either directly or via
shared_from_this()) we detect its runtime type (using `typeid`), then
end up essentially reinterpret_casting the pointer to the derived type.
This is invalid when the base class pointer was a non-first base, and we
end up with an invalid pointer. We could dynamic_cast to the
most-derived type, but if *that* type isn't pybind11-registered, the
resulting pointer given to the base `cast` implementation isn't necessarily valid
to be reinterpret_cast'ed back to the backup type.
This commit removes the "backup" type argument from the many-argument
`cast(...)` and instead does the derived-or-pointer type decision and
type lookup in type_caster_base, where the dynamic_cast has to be to
correctly get the derived pointer, but also has to do the type lookup to
ensure that we don't pass the wrong (derived) pointer when the backup
type (i.e. the type caster intrinsic type) pointer is needed.
Since the lookup is needed before calling the base cast(), this also
changes the input type to a detail::type_info rather than doing a
(second) lookup in cast().
We currently fail at runtime when trying to call a method that is
overloaded with both static and non-static methods. This is something
python won't allow: the object is either a function or an instance, and
can't be both.
Adding numpy to the pypy test exposed a segfault caused by the buffer
tests in test_stl_binders.py: the first such test was explicitly skipped
on pypy, but the second (test_vector_buffer_numpy) which also seems to
cause an occasional segfault was just marked as requiring numpy.
Explicitly skip it on pypy as well (until a workaround, fix, or pypy fix
are found).
Don't try to define these in the issues submodule, because that fails
if testing without issues compiled in (e.g. using
cmake -DPYBIND11_TEST_OVERRIDE=test_methods_and_attributes.cpp).
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).
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#775.
Assignments of the form `Type.static_prop = value` should be translated to
`Type.static_prop.__set__(value)` except when `isinstance(value, static_prop)`.
When make_tuple fails (for example, when print() is called with a
non-convertible argument, as in #778) the error message a less helpful
than it could be:
make_tuple(): unable to convert arguments of types 'std::tuple<type1, type2>' to Python object
There is no actual std::tuple involved (only a parameter pack and a
Python tuple), but it also doesn't immediately reveal which type caused
the problem.
This commit changes the debugging mode output to show just the
problematic type:
make_tuple(): unable to convert argument of type 'type2' to Python object
This commit adds `error_already_set::matches()` convenience method to
check if the exception trapped by `error_already_set` matches a given
Python exception type. This will address #700 by providing a less
verbose way to check exceptions.
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.
This extends the trivial handling to support trivial handling for
Fortran-order arrays (i.e. column major): if inputs aren't all
C-contiguous, but *are* all F-contiguous, the resulting array will be
F-contiguous and we can do trivial processing.
For anything else (e.g. C-contiguous, or inputs requiring non-trivial
processing), the result is in (numpy-default) C-contiguous layout.
The only part of the vectorize code that actually needs c-contiguous is
the "trivial" broadcast; for non-trivial arguments, the code already
uses strides properly (and so handles C-style, F-style, neither, slices,
etc.)
This commit rewrites `broadcast` to additionally check for C-contiguous
storage, then takes off the `c_style` flag for the arguments, which
will keep the functionality more or less the same, except for no longer
requiring an array copy for non-c-contiguous input arrays.
Additionally, if we're given a singleton slice (e.g. a[0::4, 0::4] for a
4x4 or smaller array), we no longer fail triviality because the trivial
code path never actually uses the strides on a singleton.
Instead of a segfault. Fixes#751.
This covers the case of loading a custom holder from a default-holder
instance. Attempting to load one custom holder from a different custom
holder (i.e. not `std::unique_ptr`) yields undefined behavior, just as
#588 established for inheritance.
We can't support this for classes from imported modules (which is the
primary purpose of a ctor argument base class) because we *have* to
have both parent and derived to properly extract a multiple-inheritance
base class pointer from a derived class pointer.
We could support this for actual `class_<Base, ...> instances, but since
in that case the `Base` is already present in the code, it seems more
consistent to simply always require MI to go via template options.
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.
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)
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.
* 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`.
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.
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.
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.
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.
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.
* Make tests buildable independently
This makes "tests" buildable as a separate project that uses
find_package(pybind11 CONFIG) when invoked independently.
This also moves the WERROR option into tests/CMakeLists.txt, as that's
the only place it is used.
* Use Eigen 3.3.1's cmake target, if available
This changes the eigen finding code to attempt to use Eigen's
system-installed Eigen3Config first. In Eigen 3.3.1, it exports a cmake
Eigen3::Eigen target to get dependencies from (rather than setting the
include path directly).
If it fails, we fall back to the trying to load allowing modules (i.e.
allowing our tools/FindEigen3.cmake). If we either fallback, or the
eigen version is older than 3.3.1 (or , we still set the include
directory manually; otherwise, for CONFIG + new Eigen, we get it via
the target.
This is also needed to allow 'tests' to be built independently, when
the find_package(Eigen3) is going to find via the system-installed
Eigen3Config.cmake.
* Add a install-then-build test, using clang on linux
This tests that `make install` to the actual system, followed by a build
of the tests (without the main pybind11 repository available) works as
expected.
To also expand the testing variety a bit, it also builds using
clang-3.9 instead of gcc.
* Don't try loading Eigen3Config in cmake < 3.0
It could FATAL_ERROR as the newer cmake includes a cmake 3.0 required
line.
If doing an independent, out-of-tree "tests" build, the regular
find_package(Eigen3) is likely to fail with the same error, but I think
we can just let that be: if you want a recent Eigen with proper cmake
loading support *and* want to do an independent tests build, you'll
need at least cmake 3.0.
* 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)
Now that only one shared metaclass is ever allocated, it's extremely
cheap to enable it for all pybind11 types.
* Deprecate the default py::metaclass() since it's not needed anymore.
* Allow users to specify a custom metaclass via py::metaclass(handle).
In order to fully satisfy Python's inheritance type layout requirements,
all types should have a common 'solid' base. A solid base is one which
has the same instance size as the derived type (not counting the space
required for the optional `dict_ptr` and `weakrefs_ptr`). Thus, `object`
does not qualify as a solid base for pybind11 types and this can lead to
issues with multiple inheritance.
To get around this, new base types are created: one per unique instance
size. There is going to be very few of these bases. They ensure Python's
MRO checks will pass when multiple bases are involved.
Instead of creating a new unique metaclass for each type, the builtin
`property` type is subclassed to support static properties. The new
setter/getters always pass types instead of instances in their `self`
argument. A metaclass is still required to support this behavior, but
it doesn't store any data anymore, so a new one doesn't need to be
created for each class. There is now only one common metaclass which
is shared by all pybind11 types.
* Fixed compilation error when defining function accepting some forms of std::function.
The compilation error happens only when the functional.h header is
present, and the build is done in debug mode, with NDEBUG being
undefined. In addition, the std::function must accept an abstract
base class by reference.
The compilation error occurred in cast.h, when trying to construct a
std::tuple<AbstractBase>, rather than a std::tuple<AbstractBase&>.
This was caused by functional.h using std::move rather than
std::forward, changing the signature of the function being used.
This commit contains the fix, along with a test that exhibits the
issue when compiled in debug mode without the fix applied.
* Moved new std::function tests into test_callbacks, added callback_with_movable test.
* Propagate unicode conversion failure
If returning a std::string with invalid utf-8 data, we currently fail
with an uninformative TypeError instead of propagating the
UnicodeDecodeError that Python sets on failure.
* Add support for u16/u32strings and literals
This adds support for wchar{16,32}_t character literals and the
associated std::u{16,32}string types. It also folds the
character/string conversion into a single type_caster template, since
the type casters for string and wstring were mostly the same anyway.
* Added too-long and too-big character conversion errors
With this commit, when casting to a single character, as opposed to a
C-style string, we make sure the input wasn't a multi-character string
or a single character with codepoint too large for the character type.
This also changes the character cast op to CharT instead of CharT& (we
need to be able to return a temporary decoded char value, but also
because there's little gained by bothering with an lvalue return here).
Finally it changes the char caster to 'has-a-string-caster' instead of
'is-a-string-caster' because, with the cast_op change above, there's
nothing at all gained from inheritance. This also lets us remove the
`success` from the string caster (which was only there for the char
caster) into the char caster itself. (I also renamed it to 'none' and
inverted its value to better reflect its purpose). The None -> nullptr
loading also now takes place only under a `convert = true` load pass.
Although it's unlikely that a function taking a char also has overloads
that can take a None, it seems marginally more correct to treat it as a
conversion.
This commit simplifies the size assumptions about character sizes with
static_asserts to back them up.
Clang on linux currently fails to run cmake:
$ CC=clang CXX=clang++ cmake ..
...
-- Configuring done
CMake Error at tools/pybind11Tools.cmake:135 (target_compile_options):
Error evaluating generator expression:
$<:-flto>
Expression did not evaluate to a known generator expression
Call Stack (most recent call first):
tests/CMakeLists.txt:68 (pybind11_add_module)
But investigating this led to various other -flto detection problems;
this commit thus overhauls LTO flag detection:
- -flto needs to be passed to the linker as well
- Also compile with -fno-fat-lto-objects under GCC
- Pass the equivalent flags to MSVC
- Enable LTO flags for via generator expressions (for non-debug builds
only), so that multi-config builds (like on Windows) still work
properly. This seems reasonable, however, even on single-config
builds (and simplifies the cmake code a bit).
- clang's lto linker plugins don't accept '-Os', so replace it with
'-O3' when doing a MINSIZEREL build
- Enable trying ThinLTO by default for test suite (only affects clang)
- Match Clang$ rather than ^Clang$ because, for cmake with 3.0+
policies in effect, the compiler ID will be AppleClang on macOS.
Use PROJECT_SOURCE_DIR instead of CMAKE_SOURCE_DIR as the base of the
path to libsize.py. This fixes an error if pybind11 is being built
directly within another project.
* Fix debugging output for nameless py::arg annotations
This fixes a couple bugs with nameless py::arg() (introduced in #634)
annotations:
- the argument name was being used in debug mode without checking that
it exists (which would result in the std::string construction throwing
an exception for being invoked with a nullptr)
- the error output says "keyword arguments", but py::arg_v() can now
also be used for positional argument defaults.
- the debugging output "in function named 'blah'" was overly verbose:
changed it to just "in function 'blah'".
* Fix missing space in debug test string
* Moved tests from issues to methods_and_attributes
This changes the function dispatching code for overloaded functions into
a two-pass procedure where we first try all overloads with
`convert=false` for all arguments. If no function calls succeeds in the
first pass, we then try a second pass where we allow arguments to have
`convert=true` (unless, of course, the argument was explicitly specified
with `py::arg().noconvert()`).
For non-overloaded methods, the two-pass procedure is skipped (we just
make the overload-allowed call). The second pass is also skipped if it
would result in the same thing (i.e. where all arguments are
`.noconvert()` arguments).
This adds support for controlling the `convert` flag of arguments
through the py::arg annotation. This then allows arguments to be
flagged as non-converting, which the type_caster is able to use to
request different behaviour.
Currently, AFAICS `convert` is only used for type converters of regular
pybind11-registered types; all of the other core type_casters ignore it.
We can, however, repurpose it to control internal conversion of
converters like Eigen and `array`: most usefully to give callers a way
to disable the conversion that would otherwise occur when a
`Eigen::Ref<const Eigen::Matrix>` argument is passed a numpy array that
requires conversion (either because it has an incompatible stride or the
wrong dtype).
Specifying a noconvert looks like one of these:
m.def("f1", &f, "a"_a.noconvert() = "default"); // Named, default, noconvert
m.def("f2", &f, "a"_a.noconvert()); // Named, no default, no converting
m.def("f3", &f, py::arg().noconvert()); // Unnamed, no default, no converting
(The last part--being able to declare a py::arg without a name--is new:
previous py::arg() only accepted named keyword arguments).
Such an non-convert argument is then passed `convert = false` by the
type caster when loading the argument. Whether this has an effect is up
to the type caster itself, but as mentioned above, this would be
extremely helpful for the Eigen support to give a nicer way to specify
a "no-copy" mode than the custom wrapper in the current PR, and
moreover isn't an Eigen-specific hack.
Issue #633 suggests people might be tempted to copy the test scripts
self-binding code, but that's a bad idea for pretty much anything other
than a test suite with self-contained test code.
This commit adds a comment as such with a reference to the
documentation that tells people how to do it instead.
* Minor doc syntax fix
The numpy documentation had a bad :file: reference (was using double
backticks instead of single backticks).
* Changed long-outdated "example" -> "tests" wording
The ConstructorStats internal docs still had "from example import", and
the main testing cpp file still used "example" in the module
description.
This commit rewrites the function dispatcher code to support mixing
regular arguments with py::args/py::kwargs arguments. It also
simplifies the argument loader noticeably as it no longer has to worry
about args/kwargs: all of that is now sorted out in the dispatcher,
which now simply appends a tuple/dict if the function takes
py::args/py::kwargs, then passes all the arguments in a vector.
When the argument loader hit a py::args or py::kwargs, it doesn't do
anything special: it just calls the appropriate type_caster just like it
does for any other argument (thus removing the previous special cases
for args/kwargs).
Switching to passing arguments in a single std::vector instead of a pair
of tuples also makes things simpler, both in the dispatch and the
argument_loader: since this argument list is strictly pybind-internal
(i.e. it never goes to Python) we have no particular reason to use a
Python tuple here.
Some (intentional) restrictions:
- you may not bind a function that has args/kwargs somewhere other than
the end (this somewhat matches Python, and keeps the dispatch code a
little cleaner by being able to not worry about where to inject the
args/kwargs in the argument list).
- If you specify an argument both positionally and via a keyword
argument, you get a TypeError alerting you to this (as you do in
Python).
* Abstract away some holder functionality (resolve#585)
Custom holder types which don't have `.get()` can select the correct
function to call by specializing `holder_traits`.
* Add support for move-only holders (fix#605)
* Clarify PYBIND11_NUMPY_DTYPE documentation
The current documentation and example reads as though
PYBIND11_NUMPY_DTYPE is a declarative macro along the same lines as
PYBIND11_DECLARE_HOLDER_TYPE, but it isn't. The changes the
documentation and docs example to make it clear that you need to "call"
the macro.
* Add satisfies_{all,any,none}_of<T, Preds>
`satisfies_all_of<T, Pred1, Pred2, Pred3>` is a nice legibility-enhanced
shortcut for `is_all<Pred1<T>, Pred2<T>, Pred3<T>>`.
* Give better error message for non-POD dtype attempts
If you try to use a non-POD data type, you get difficult-to-interpret
compilation errors (about ::name() not being a member of an internal
pybind11 struct, among others), for which isn't at all obvious what the
problem is.
This adds a static_assert for such cases.
It also changes the base case from an empty struct to the is_pod_struct
case by no longer using `enable_if<is_pod_struct>` but instead using a
static_assert: thus specializations avoid the base class, POD types
work, and non-POD types (and unimplemented POD types like std::array)
get a more informative static_assert failure.
* Prefix macros with PYBIND11_
numpy.h uses unprefixed macros, which seems undesirable. This prefixes
them with PYBIND11_ to match all the other macros in numpy.h (and
elsewhere).
* Add long double support
This adds long double and std::complex<long double> support for numpy
arrays.
This allows some simplification of the code used to generate format
descriptors; the new code uses fewer macros, instead putting the code as
different templated options; the template conditions end up simpler with
this because we are now supporting all basic C++ arithmetic types (and
so can use is_arithmetic instead of is_integral + multiple
different specializations).
In addition to testing that it is indeed working in the test script, it
also adds various offset and size calculations there, which
fixes the test failures under x86 compilations.
On a debian jessie machine, running 'python --version --noconftest' caused
pytest to try and run the test suite with the not-yet-compiled extension
module, thus failing the test. This commit chages the pytest detection
so that it only attempts to run an import statement.
* Fixed a regression that was introduced in the PyPy patch: use ht_qualname_meta instead of ht_qualname to fix PyHeapTypeObject->ht_qualname field.
* Added a qualname/repr test that works in both Python 3.3+ and previous versions
Add a BUILD_INTERFACE and a pybind11::pybind11 alias for the interface
library to match the installed target.
Add new cmake tests for add_subdirectory and consolidates the
.cpp and .py files needed for the cmake build tests:
Before:
tests
|-- test_installed_module
| |-- CMakeLists.txt
| |-- main.cpp
| \-- test.py
\-- test_installed_target
|-- CMakeLists.txt
|-- main.cpp
\-- test.py
After:
tests
\-- test_cmake_build
|-- installed_module/CMakeLists.txt
|-- installed_target/CMakeLists.txt
|-- subdirectory_module/CMakeLists.txt
|-- subdirectory_target/CMakeLists.txt
|-- main.cpp
\-- test.py
This commit includes modifications that are needed to get pybind11 to work with PyPy. The full test suite compiles and runs except for a last few functions that are commented out (due to problems in PyPy that were reported on the PyPy bugtracker).
Two somewhat intrusive changes were needed to make it possible: two new tags ``py::buffer_protocol()`` and ``py::metaclass()`` must now be specified to the ``class_`` constructor if the class uses the buffer protocol and/or requires a metaclass (e.g. for static properties).
Note that this is only for the PyPy version based on Python 2.7 for now. When the PyPy 3.x has caught up in terms of cpyext compliance, a PyPy 3.x patch will follow.
This replaces the current `all_of_t<Pred, Ts...>` with `all_of<Ts...>`,
with previous use of `all_of_t<Pred, Ts...>` becoming
`all_of<Pred<Ts>...>` (and similarly for `any_of_t`). It also adds a
`none_of<Ts...>`, a shortcut for `negation<any_of<Ts...>>`.
This allows `all_of` and `any_of` to be used a bit more flexible, e.g.
in cases where several predicates need to be tested for the same type
instead of the same predicate for multiple types.
This commit replaces the implementation with a more efficient version
for non-MSVC. For MSVC, this changes the workaround to use the
built-in, recursive std::conjunction/std::disjunction instead.
This also removes the `count_t` since `any_of_t` and `all_of_t` were the
only things using it.
This commit also rearranges some of the future std imports to use actual
`std` implementations for C++14/17 features when under the appropriate
compiler mode, as we were already doing for a few things (like
index_sequence). Most of these aren't saving much (the implementation
for enable_if_t, for example, is trivial), but I think it makes the
intention of the code instantly clear. It also enables MSVC's native
std::index_sequence support.
When compiling in C++17 mode the noexcept specifier is part of the
function type. This causes a failure in pybind11 because, by omitting
a noexcept specifier when deducing function return and argument types,
we are implicitly making `noexcept(false)` part of the type.
This means that functions with `noexcept` fail to match the function
templates in cpp_function (and other places), and we get compilation
failure (we end up trying to fit it into the lambda function version,
which fails since a function pointer has no `operator()`).
We can, however, deduce the true/false `B` in noexcept(B), so we don't
need to add a whole other set of overloads, but need to deduce the extra
argument when under C++17. That will *not* work under pre-C++17,
however.
This commit adds two macros to fix the problem: under C++17 (with the
appropriate feature macro set) they provide an extra `bool NoExceptions`
template argument and provide the `noexcept(NoExceptions)` deduced
specifier. Under pre-C++17 they expand to nothing.
This is needed to compile pybind11 with gcc7 under -std=c++17.
gcc 7 has both std::experimental::optional and std::optional, but this
breaks the test compilation as we are trying to use the same `opt_int`
type alias for both.
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).
* Added ternary support with descr args
Current the `_<bool>(a, b)` ternary support only works for `char[]` `a`
and `b`; this commit allows it to work for `descr` `a` and `b` arguments
as well.
* Add support for std::valarray to stl.h
This abstracts the std::array into a `array_caster` which can then be
used with either std::array or std::valarray, the main difference being
that std::valarray is resizable. (It also lets the array_caster be
potentially used for other std::array-like interfaces, much as the
list_caster and map_caster currently provide).
* Small stl.h cleanups
- Remove redundant `type` typedefs
- make internal list_caster methods private
stl casters were using a value cast to (Value) or (Key), but that isn't
always appropriate. This changes it to use the appropriate value
converter's cast_op_type.
A flake8 configuration is included in setup.cfg and the checks are
executed automatically on Travis:
* Ensures a consistent PEP8 code style
* Does basic linting to prevent possible bugs
Fixes#509.
The move policy was already set for rvalues in PR #473, but this only
applied to directly cast user-defined types. The problem is that STL
containers cast values indirectly and the rvalue information is lost.
Therefore the move policy was not set correctly. This commit fixes it.
This also makes an additional adjustment to remove the `copy` policy
exception: rvalues now always use the `move` policy. This is also safe
for copy-only rvalues because the `move` policy has an internal fallback
to copying.
Following commit 90d278, the object code generated by the python
bindings of nanogui (github.com/wjakob/nanogui) went up by a whopping
12%. It turns out that that project has quite a few enums where we don't
really care about arithmetic operators.
This commit thus partially reverts the effects of #503 by introducing
an additional attribute py::arithmetic() that must be specified if the
arithmetic operators are desired.
* `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.
* Deprecate the `py::object::str()` member function since `py::str(obj)`
is now equivalent and preferred
* Make `py::repr()` a free function
* Make sure obj.cast<T>() works as expected when T is a Python type
`obj.cast<T>()` should be the same as `T(obj)`, i.e. it should convert
the given object to a different Python type. However, `obj.cast<T>()`
usually calls `type_caster::load()` which only checks the type without
doing any actual conversion. That causes a very unexpected `cast_error`.
This commit makes it so that `obj.cast<T>()` and `T(obj)` are the same
when T is a Python type.
* Simplify pytypes converting constructor implementation
It's not necessary to maintain a full set of converting constructors
and assignment operators + const& and &&. A single converting const&
constructor will work and there is no impact on binary size. On the
other hand, the conversion functions can be significantly simplified.
Allows checking the Python types before creating an object instead of
after. For example:
```c++
auto l = list(ptr, true);
if (l.check())
// ...
```
The above is replaced with:
```c++
if (isinstance<list>(ptr)) {
auto l = reinterpret_borrow(ptr);
// ...
}
```
This deprecates `py::object::check()`. `py::isinstance()` covers the
same use case, but it can also check for user-defined types:
```c++
class Pet { ... };
py::class_<Pet>(...);
m.def("is_pet", [](py::object obj) {
return py::isinstance<Pet>(obj); // works as expected
});
```
This commit includes the following changes:
* Don't provide make_copy_constructor for non-copyable container
make_copy_constructor currently fails for various stl containers (e.g.
std::vector, std::unordered_map, std::deque, etc.) when the container's
value type (e.g. the "T" or the std::pair<K,T> for a map) is
non-copyable. This adds an override that, for types that look like
containers, also requires that the value_type be copyable.
* stl_bind.h: make bind_{vector,map} work for non-copy-constructible types
Most stl_bind modifiers require copying, so if the type isn't copy
constructible, we provide a read-only interface instead.
In practice, this means that if the type is non-copyable, it will be,
for all intents and purposes, read-only from the Python side (but
currently it simply fails to compile with such a container).
It is still possible for the caller to provide an interface manually
(by defining methods on the returned class_ object), but this isn't
something stl_bind can handle because the C++ code to construct values
is going to be highly dependent on the container value_type.
* stl_bind: copy only for arithmetic value types
For non-primitive types, we may well be copying some complex type, when
returning by reference is more appropriate. This commit returns by
internal reference for all but basic arithmetic types.
* Return by reference whenever possible
Only if we definitely can't--i.e. std::vector<bool>--because v[i]
returns something that isn't a T& do we copy; for everything else, we
return by reference.
For the map case, we can always return by reference (at least for the
default stl map/unordered_map).
When working on some particular feature, it's nice to be able to disable
all the tests except for the one I'm working on; this is currently
possible by editing tests/CMakeLists.txt, and commenting out the tests
you don't want.
This commit goes a step further by letting you give a list of tests you
do want when invoking cmake, e.g.:
cmake -DPYBIND11_TEST_OVERRIDE="test_issues.cpp;test_pickling.cpp" ..
changes the build to build just those two tests (and changes the `pytest`
target to invoke just the two associated tests).
This persists in the build directory until you disable it again by
running cmake with `-DPYBIND11_TEST_OVERRIDE=`. It also adds a message
after the pytest output to remind you that it is in effect:
Note: not all tests run: -DPYBIND11_TEST_OVERRIDE is in effect
If we need to initialize a holder around an unowned instance, and the
holder type is non-copyable (i.e. a unique_ptr), we currently construct
the holder type around the value pointer, but then never actually
destruct the holder: the holder destructor is called only for the
instance that actually has `inst->owned = true` set.
This seems no pointer, however, in creating such a holder around an
unowned instance: we never actually intend to use anything that the
unique_ptr gives us: and, in fact, do not want the unique_ptr (because
if it ever actually got destroyed, it would cause destruction of the
wrapped pointer, despite the fact that that wrapped pointer isn't
owned).
This commit changes the logic to only create a unique_ptr holder if we
actually own the instance, and to destruct via the constructed holder
whenever we have a constructed holder--which will now only be the case
for owned-unique-holder or shared-holder types.
Other changes include:
* Added test for non-movable holder constructor/destructor counts
The three alive assertions now pass, before #478 they fail with counts
of 2/2/1 respectively, because of the unique_ptr that we don't want and
don't destroy (because we don't *want* its destructor to run).
* Return cstats reference; fix ConstructStats doc
Small cleanup to the #478 test code, and fix to the ConstructStats
documentation (the static method definition should use `reference` not
`reference_internal`).
* Rename inst->constructed to inst->holder_constructed
This makes it clearer exactly what it's referring to.
* Add debugging info about so size to build output
This adds a small python script to tools that captures before-and-after
.so sizes between builds and outputs this in the build output via a
string such as:
------ pybind11_tests.cpython-35m-x86_64-linux-gnu.so file size: 924696 (decrease of 73680 bytes = 7.38%)
------ pybind11_tests.cpython-35m-x86_64-linux-gnu.so file size: 998376 (increase of 73680 bytes = 7.97%)
------ pybind11_tests.cpython-35m-x86_64-linux-gnu.so file size: 998376 (no change)
Or, if there was no .so during the build, just the .so size by itself:
------ pybind11_tests.cpython-35m-x86_64-linux-gnu.so file size: 998376
This allows you to, for example, build, checkout a different branch,
rebuild, and easily see exactly the change in the pybind11_tests.so
size.
It also allows looking at the travis and appveyor build logs to get an
idea of .so/.dll sizes across different build systems.
* Minor libsize.py script changes
- Use RAII open
- Remove unused libsize=-1
- Report change as [+-]xyz bytes = [+-]a.bc%
