* tests: refactor and cleanup
* refactor: more consistent
* tests: vendor six
* tests: more xfails, nicer system
* tests: simplify to info
* tests: suggestions from @YannickJadoul and @bstaletic
* tests: restore some pypy tests that now pass
* tests: rename info to env
* tests: strict False/True
* tests: drop explicit strict=True again
* tests: reduce minimum PyTest to 3.1
I found that the numpy array tests already contained an empty-shaped
array test, but none with data in it.
Following PEP 3118, scalars have an empty shape and ndim 0. This
works already and is now also documented/covered by a test.
This PR adds a new py::ellipsis() method which can be used in
conjunction with NumPy's generalized slicing support. For instance,
the following is now valid (where "a" is a NumPy array):
py::array b = a[py::make_tuple(0, py::ellipsis(), 0)];
- UPDATEIFCOPY is deprecated, replaced with similar (but not identical)
WRITEBACKIFCOPY; trying to access the flag causes a deprecation
warning under numpy 1.14, so just check the new flag there.
- Numpy `repr` formatting of floats changed in 1.14.0 to `[1., 2., 3.]`
instead of the pre-1.14 `[ 1., 2., 3.]`. Updated the tests to
check for equality with the `repr(...)` value rather than the
hard-coded (and now version-dependent) string representation.
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.
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).
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.
* 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 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.
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.
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.
* 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)
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.
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
* `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.
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.
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.
