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).
