GitLab

* Apply blacken-docs and fix language-hints

* Add blacken-docs pre-commit hook

* Add pycln pre-commit hook

* Enable a few builtin hooks

* Black no longer ignores pyi files

Co-authored-by: Jouke Witteveen <j.witteveen@cosine.nl>

* 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.

This is nice enough to be mentioned explicitly in the docs.

* WIP: module -> module_ without typedef

* refactor: allow py::module to work again

Why only for pointers? Because C casts are hard to grep for.

* 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.

* Enable py::ellipsis on Python 2

* Enable py::ellipsis tests on Python 2 and mention `Ellipsis` in the docs

* 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.

* Support raw string literals as input for py::eval
* Dedent only when needed

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.

* 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.