* chore: drop Python 3.5 support
* chore: more fstrings with flynt's help
* ci: drop Python 3.5
* chore: bump dependency versions
* docs: touch up py::args
* tests: remove deprecation warning
* Ban smartquotes
* Very minor tweaks (by-product of reviewing PR #3719).
Co-authored-by: Aaron Gokaslan <skylion.aaron@gmail.com>
Co-authored-by: Ralf W. Grosse-Kunstleve <rwgk@google.com>
* `#error BYE_BYE_GOLDEN_SNAKE`
* Removing everything related to 2.7 from ci.yml
* Commenting-out Centos7
* Removing `PYTHON: 27` from .appveyor.yml
* "PY2" removal, mainly from tests. C++ code is not touched.
* Systematic removal of `u` prefix from `u"..."` and `u'...'` literals. Collateral cleanup of a couple minor other things.
* Cleaning up around case-insensitive hits for `[^a-z]py.*2` in tests/.
* Removing obsolete Python 2 mention in compiling.rst
* Proper `#error` for Python 2.
* Using PY_VERSION_HEX to guard `#error "PYTHON 2 IS NO LONGER SUPPORTED.`
* chore: bump pre-commit
* style: run pre-commit for pyupgrade 3+
* tests: use sys.version_info, not PY
* chore: more Python 2 removal
* Uncommenting Centos7 block (PR #3691 showed that it is working again).
* Update pre-commit hooks
* Fix pre-commit hook
* refactor: remove Python 2 from CMake
* refactor: remove Python 2 from setup code
* refactor: simplify, better static typing
* feat: fail with nice messages
* refactor: drop Python 2 C++ code
* docs: cleanup for Python 3
* revert: intree
revert: intree
* docs: minor touchup to py2 statement
Co-authored-by: Henry Schreiner <henryschreineriii@gmail.com>
Co-authored-by: Aaron Gokaslan <skylion.aaron@gmail.com>
* Adding iostream.h thread-safety documentation.
* Restoring `TestThread` code with added `std::lock_guard<std::mutex>`.
* Updating new comments to reflect new information.
* Fixing up `git rebase -X theirs` accidents.
* Add py::object casting example to embedding docs
* Move implicit cast example to object.rst
* Move to bottom and improve implicit casting text
* Fix xref
* Improve wording as per @bstaletic's suggestion
The main change is to treat error_already_set as a separate category
of exception that arises in different circumstances and needs to be
handled differently. The asymmetry between Python and C++ exceptions
is further emphasized.
* Fix undefined memoryview format
* Add missing <algorithm> header
* Add workaround for py27 array compatibility
* Workaround py27 memoryview behavior
* Fix memoryview constructor from buffer_info
* Workaround PyMemoryView_FromMemory availability in py27
* Fix up memoryview tests
* Update memoryview test from buffer to check signedness
* Use static factory method to create memoryview
* Remove ndim arg from memoryview::frombuffer and add tests
* Allow ndim=0 memoryview and documentation fixup
* Use void* to align to frombuffer method signature
* Add const variants of frombuffer and frommemory
* Add memory view section in doc
* Fix docs
* Add test for null buffer
* Workaround py27 nullptr behavior in test
* Rename frombuffer to from_buffer
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)];
This also matches the Eigen example for the row-major case.
This also enhances one of the tests to trigger a failure (and fixes it in the PR). (This isn't really a flaw in pybind itself, but rather fixes wrong code in the test code and docs).
* Expand documentation to include explicit example of py::module::import
where one would expect it.
* Describe how to use unbound and bound methods to class Python classes.
[skip ci]
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".
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 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.
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.
