docs: remove/deprecate pyproto
This commit is contained in:
Georg Brandl
David Hewitt
parent
24445df633
commit
a21cf677e3
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@ -144,20 +144,12 @@ For example, you can see `type({})` shows `dict` and `type(type({}))` shows `typ
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## 4. Protocol methods
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Python has some built-in special methods called dunder, such as `__iter__`.
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They are called [abstract objects layer](https://docs.python.org/3/c-api/abstract.html) in
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Python has some built-in special methods called dunder methods, such as `__iter__`.
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They are called "slots" in the [abstract objects layer](https://docs.python.org/3/c-api/abstract.html) in
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Python/C API.
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We provide a way to implement those protocols by using `#[pyproto]` and specific traits, such
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as `PyIterProtocol`.
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[`src/class`] defines these traits.
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Each protocol method has a corresponding FFI function.
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For example, `PyIterProtocol::__iter__` has
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`pub unsafe extern "C" fn iter<T>(slf: *mut PyObject) -> *mut PyObject`.
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When `#[pyproto]` finds that `T` implements `PyIterProtocol::__iter__`, it automatically
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sets `iter<T>` on the type object of `T`.
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Also, [`src/class/methods.rs`] has utilities for `#[pyfunction]` and [`src/class/impl_.rs`] has
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some internal tricks for making `#[pyproto]` flexible.
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We provide a way to implement those protocols similarly, by recognizing special
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names in `#[pymethods]`, with a few new ones for slots that can not be
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implemented in Python, such as GC support.
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## 5. Procedural macros to simplify usage for users.
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@ -1,5 +1,5 @@
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use criterion::{criterion_group, criterion_main, Criterion};
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use pyo3::{class::PyObjectProtocol, prelude::*, type_object::LazyStaticType};
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use pyo3::{prelude::*, type_object::LazyStaticType};
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/// This is a feature-rich class instance used to benchmark various parts of the pyclass lifecycle.
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#[pyclass]
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@ -19,10 +19,7 @@ impl MyClass {
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self.elements.push(new_element);
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self.elements.len()
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}
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}
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#[pyproto]
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impl PyObjectProtocol for MyClass {
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/// A basic __str__ implementation.
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fn __str__(&self) -> &'static str {
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"MyClass"
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@ -2,7 +2,7 @@
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PyO3 exposes a group of attributes powered by Rust's proc macro system for defining Python classes as Rust structs.
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The main attribute is `#[pyclass]`, which is placed upon a Rust `struct` or a fieldless `enum` (a.k.a. C-like enum) to generate a Python type for it. They will usually also have *one* `#[pymethods]`-annotated `impl` block for the struct, which is used to define Python methods and constants for the generated Python type. (If the [`multiple-pymethods`] feature is enabled each `#[pyclass]` is allowed to have multiple `#[pymethods]` blocks.) Finally, there may be multiple `#[pyproto]` trait implementations for the struct, which are used to define certain python magic methods such as `__str__`.
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The main attribute is `#[pyclass]`, which is placed upon a Rust `struct` or a fieldless `enum` (a.k.a. C-like enum) to generate a Python type for it. They will usually also have *one* `#[pymethods]`-annotated `impl` block for the struct, which is used to define Python methods and constants for the generated Python type. (If the [`multiple-pymethods`] feature is enabled each `#[pyclass]` is allowed to have multiple `#[pymethods]` blocks.) `#[pymethods]` may also have implementations for Python magic methods such as `__str__`.
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This chapter will discuss the functionality and configuration these attributes offer. Below is a list of links to the relevant section of this chapter for each:
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@ -16,7 +16,7 @@ This chapter will discuss the functionality and configuration these attributes o
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- [`#[classmethod]`](#class-methods)
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- [`#[classattr]`](#class-attributes)
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- [`#[args]`](#method-arguments)
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- [`#[pyproto]`](class/protocols.html)
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- [Magic methods and slots](class/protocols.html)
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## Defining a new class
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@ -920,9 +920,9 @@ enum BadSubclass{
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## Implementation details
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The `#[pyclass]` macros rely on a lot of conditional code generation: each `#[pyclass]` can optionally have a `#[pymethods]` block as well as several different possible `#[pyproto]` trait implementations.
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The `#[pyclass]` macros rely on a lot of conditional code generation: each `#[pyclass]` can optionally have a `#[pymethods]` block.
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To support this flexibility the `#[pyclass]` macro expands to a blob of boilerplate code which sets up the structure for ["dtolnay specialization"](https://github.com/dtolnay/case-studies/blob/master/autoref-specialization/README.md). This implementation pattern enables the Rust compiler to use `#[pymethods]` and `#[pyproto]` implementations when they are present, and fall back to default (empty) definitions when they are not.
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To support this flexibility the `#[pyclass]` macro expands to a blob of boilerplate code which sets up the structure for ["dtolnay specialization"](https://github.com/dtolnay/case-studies/blob/master/autoref-specialization/README.md). This implementation pattern enables the Rust compiler to use `#[pymethods]` implementations when they are present, and fall back to default (empty) definitions when they are not.
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This simple technique works for the case when there is zero or one implementations. To support multiple `#[pymethods]` for a `#[pyclass]` (in the [`multiple-pymethods`] feature), a registry mechanism provided by the [`inventory`](https://github.com/dtolnay/inventory) crate is used instead. This collects `impl`s at library load time, but isn't supported on all platforms. See [inventory: how it works](https://github.com/dtolnay/inventory#how-it-works) for more details.
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@ -1,19 +1,15 @@
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# Class customizations
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# Magic methods and slots
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Python's object model defines several protocols for different object behavior, such as the sequence, mapping, and number protocols. You may be familiar with implementing these protocols in Python classes by "magic" methods, such as `__str__` or `__repr__`. Because of the double-underscores surrounding their name, these are also known as "dunder" methods.
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In the Python C-API which PyO3 is implemented upon, many of these magic methods have to be placed into special "slots" on the class type object, as covered in the previous section. There are two ways in which this can be done:
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- [Experimental for PyO3 0.15, may change slightly in PyO3 0.16] In `#[pymethods]`, if the name of the method is a recognised magic method, PyO3 will place it in the type object automatically.
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- [Stable, but expected to be deprecated in PyO3 0.16] In special traits combined with the `#[pyproto]` attribute.
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- [New in PyO3 0.15, recommended in PyO3 0.16] In `#[pymethods]`, if the name of the method is a recognised magic method, PyO3 will place it in the type object automatically.
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- [Deprecated in PyO3 0.16] In special traits combined with the `#[pyproto]` attribute.
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(There are also many magic methods which don't have a special slot, such as `__dir__`. These methods can be implemented as normal in `#[pymethods]`.)
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This chapter gives a brief overview of the available methods. An in depth example is given in the following sub-chapters.
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### Magic methods in `#[pymethods]`
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In PyO3 0.15, if a function name in `#[pymethods]` is a recognised magic method, it will be automatically placed into the correct slot in the Python type object. The function name is taken from the usual rules for naming `#[pymethods]`: the `#[pyo3(name = "...")]` attribute is used if present, otherwise the Rust function name is used.
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If a function name in `#[pymethods]` is a recognised magic method, it will be automatically placed into the correct slot in the Python type object. The function name is taken from the usual rules for naming `#[pymethods]`: the `#[pyo3(name = "...")]` attribute is used if present, otherwise the Rust function name is used.
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The magic methods handled by PyO3 are very similar to the standard Python ones on [this page](https://docs.python.org/3/reference/datamodel.html#special-method-names) - in particular they are the the subset which have slots as [defined here](https://docs.python.org/3/c-api/typeobj.html). Some of the slots do not have a magic method in Python, which leads to a few additional magic methods defined only in PyO3:
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- Magic methods for garbage collection
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@ -41,14 +37,16 @@ given signatures should be interpreted as follows:
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string object. This is indicated by `object (Python type)`.
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#### Basic object customization
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### Basic object customization
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- `__str__(<self>) -> object (str)`
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- `__repr__(<self>) -> object (str)`
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- `__hash__(<self>) -> isize`
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Objects that compare equal must have the same hash value.
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<details>
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<summary>Disabling Python's default hash</summary>
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By default, all `#[pyclass]` types have a default hash implementation from Python. Types which should not be hashable can override this by setting `__hash__` to `None`. This is the same mechanism as for a pure-Python class. This is done like so:
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```rust
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@ -66,33 +64,142 @@ given signatures should be interpreted as follows:
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</details>
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- `__richcmp__(<self>, object, pyo3::basic::CompareOp) -> object`
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Overloads Python comparison operations (`==`, `!=`, `<`, `<=`, `>`, and `>=`).
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The `CompareOp` argument indicates the comparison operation being performed.
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<details>
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<summary>Return type</summary>
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The return type will normally be `PyResult<bool>`, but any Python object can be returned.
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If the `object` is not of the type specified in the signature, the generated code will
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automatically `return NotImplemented`.
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</details>
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- `__getattr__(<self>, object) -> object`
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- `__setattr__(<self>, object, object) -> ()`
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- `__setattr__(<self>, value: object) -> ()`
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- `__delattr__(<self>, object) -> ()`
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Overrides attribute access.
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- `__bool__(<self>) -> bool`
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Determines the "truthyness" of an object.
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- `__call__(<self>, ...) -> object` - here, any argument list can be defined
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as for normal `pymethods`
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#### Iterable objects
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### Iterable objects
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Iterators can be defined using these methods:
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- `__iter__(<self>) -> object`
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- `__next__(<self>) -> Option<object> or IterNextOutput` ([see details](#returning-a-value-from-iteration))
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#### Awaitable objects
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Returning `None` from `__next__` indicates that that there are no further items.
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Example:
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```rust
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use pyo3::prelude::*;
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#[pyclass]
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struct MyIterator {
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iter: Box<dyn Iterator<Item = PyObject> + Send>,
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}
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#[pymethods]
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impl MyIterator {
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fn __iter__(slf: PyRef<Self>) -> PyRef<Self> {
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slf
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}
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fn __next__(mut slf: PyRefMut<Self>) -> Option<PyObject> {
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slf.iter.next()
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}
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}
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```
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In many cases you'll have a distinction between the type being iterated over
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(i.e. the *iterable*) and the iterator it provides. In this case, the iterable
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only needs to implement `__iter__()` while the iterator must implement both
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`__iter__()` and `__next__()`. For example:
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```rust
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# use pyo3::prelude::*;
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#[pyclass]
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struct Iter {
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inner: std::vec::IntoIter<usize>,
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}
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#[pymethods]
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impl Iter {
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fn __iter__(slf: PyRef<Self>) -> PyRef<Self> {
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slf
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}
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fn __next__(mut slf: PyRefMut<Self>) -> Option<usize> {
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slf.inner.next()
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}
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}
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#[pyclass]
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struct Container {
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iter: Vec<usize>,
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}
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#[pymethods]
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impl Container {
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fn __iter__(slf: PyRef<Self>) -> PyResult<Py<Iter>> {
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let iter = Iter {
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inner: slf.iter.clone().into_iter(),
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};
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Py::new(slf.py(), iter)
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}
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}
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# Python::with_gil(|py| {
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# let container = Container { iter: vec![1, 2, 3, 4] };
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# let inst = pyo3::PyCell::new(py, container).unwrap();
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# pyo3::py_run!(py, inst, "assert list(inst) == [1, 2, 3, 4]");
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# pyo3::py_run!(py, inst, "assert list(iter(iter(inst))) == [1, 2, 3, 4]");
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# });
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```
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For more details on Python's iteration protocols, check out [the "Iterator Types" section of the library
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documentation](https://docs.python.org/library/stdtypes.html#iterator-types).
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#### Returning a value from iteration
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This guide has so far shown how to use `Option<T>` to implement yielding values
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during iteration. In Python a generator can also return a value. To express
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this in Rust, PyO3 provides the [`IterNextOutput`] enum to both `Yield` values
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and `Return` a final value - see its docs for further details and an example.
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### Awaitable objects
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- `__await__(<self>) -> object`
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- `__aiter__(<self>) -> object`
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- `__anext__(<self>) -> Option<object> or IterANextOutput`
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#### Mapping & Sequence types
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### Mapping & Sequence types
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- `__len__(<self>) -> usize`
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Implements the built-in function `len()` for the sequence.
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- `__contains__(<self>, object) -> bool`
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Implements membership test operators.
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Should return true if `item` is in `self`, false otherwise.
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For objects that don’t define `__contains__()`, the membership test simply
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traverses the sequence until it finds a match.
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<details>
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<summary>Disabling Python's default contains</summary>
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By default, all `#[pyclass]` types with an `__iter__` method support a default implementation of the `in` operator. Types which do not want this can override this by setting `__contains__` to `None`. This is the same mechanism as for a pure-Python class. This is done like so:
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By default, all `#[pyclass]` types with an `__iter__` method support a
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default implementation of the `in` operator. Types which do not want this
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can override this by setting `__contains__` to `None`. This is the same
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mechanism as for a pure-Python class. This is done like so:
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```rust
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# use pyo3::prelude::*;
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@ -107,38 +214,50 @@ given signatures should be interpreted as follows:
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}
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```
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</details>
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- `__getitem__(<self>, object) -> object`
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Implements retrieval of the `self[a]` element.
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*Note:* Negative integer indexes are not handled specially.
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- `__setitem__(<self>, object, object) -> ()`
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Implements assignment to the `self[a]` element.
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Should only be implemented if elements can be replaced.
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- `__delitem__(<self>, object) -> ()`
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#### Descriptors
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Implements deletion of the `self[a]` element.
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Should only be implemented if elements can be deleted.
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* `fn __concat__(&self, other: impl FromPyObject) -> PyResult<impl ToPyObject>`
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Concatenates two sequences.
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Used by the `+` operator, after trying the numeric addition via
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the `__add__` and `__radd__` methods.
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* `fn __repeat__(&self, count: isize) -> PyResult<impl ToPyObject>`
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Repeats the sequence `count` times.
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Used by the `*` operator, after trying the numeric multiplication via
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the `__mul__` and `__rmul__` methods.
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### Descriptors
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- `__get__(<self>, object, object) -> object`
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- `__set__(<self>, object, object) -> ()`
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- `__delete__(<self>, object) -> ()`
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#### Numeric types
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### Numeric types
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Binary arithmetic operations (`+`, `-`, `*`, `@`, `/`, `//`, `%`, `divmod()`,
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`pow()` and `**`, `<<`, `>>`, `&`, `^`, and `|`) and their reflected versions:
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(If the `object` is not of the type specified in the signature, the generated code
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will automatically `return NotImplemented`.)
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- `__pos__(<self>) -> object`
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- `__neg__(<self>) -> object`
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- `__abs__(<self>) -> object`
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- `__invert__(<self>) -> object`
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- `__index__(<self>) -> object (int)`
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- `__int__(<self>) -> object (int)`
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- `__float__(<self>) -> object (float)`
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- `__iadd__(<self>, object) -> ()`
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- `__isub__(<self>, object) -> ()`
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- `__imul__(<self>, object) -> ()`
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- `__imatmul__(<self>, object) -> ()`
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- `__itruediv__(<self>, object) -> ()`
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- `__ifloordiv__(<self>, object) -> ()`
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- `__imod__(<self>, object) -> ()`
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- `__ipow__(<self>, object, object) -> ()`
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- `__ilshift__(<self>, object) -> ()`
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- `__irshift__(<self>, object) -> ()`
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- `__iand__(<self>, object) -> ()`
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- `__ixor__(<self>, object) -> ()`
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- `__ior__(<self>, object) -> ()`
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- `__add__(<self>, object) -> object`
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- `__radd__(<self>, object) -> object`
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- `__sub__(<self>, object) -> object`
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@ -168,21 +287,91 @@ given signatures should be interpreted as follows:
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- `__pow__(<self>, object, object) -> object`
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- `__rpow__(<self>, object, object) -> object`
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#### Buffer objects
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In-place assignment operations (`+=`, `-=`, `*=`, `@=`, `/=`, `//=`, `%=`,
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`**=`, `<<=`, `>>=`, `&=`, `^=`, `|=`):
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- `__iadd__(<self>, object) -> ()`
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- `__isub__(<self>, object) -> ()`
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- `__imul__(<self>, object) -> ()`
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- `__imatmul__(<self>, object) -> ()`
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- `__itruediv__(<self>, object) -> ()`
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- `__ifloordiv__(<self>, object) -> ()`
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- `__imod__(<self>, object) -> ()`
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- `__ipow__(<self>, object, object) -> ()`
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- `__ilshift__(<self>, object) -> ()`
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- `__irshift__(<self>, object) -> ()`
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- `__iand__(<self>, object) -> ()`
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- `__ixor__(<self>, object) -> ()`
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- `__ior__(<self>, object) -> ()`
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Unary operations (`-`, `+`, `abs()` and `~`):
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- `__pos__(<self>) -> object`
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- `__neg__(<self>) -> object`
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- `__abs__(<self>) -> object`
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- `__invert__(<self>) -> object`
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Coercions:
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- `__index__(<self>) -> object (int)`
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- `__int__(<self>) -> object (int)`
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- `__float__(<self>) -> object (float)`
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### Buffer objects
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- `__getbuffer__(<self>, *mut ffi::Py_buffer, flags) -> ()`
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- `__releasebuffer__(<self>, *mut ffi::Py_buffer)` (no return value, not even `PyResult`)
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#### Garbage Collector Integration
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### Garbage Collector Integration
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|
||||
- `__traverse__(<self>, visit: pyo3::class::gc::PyVisit) -> Result<(), pyo3::class::gc::PyTraverseError>`
|
||||
If your type owns references to other Python objects, you will need to integrate
|
||||
with Python's garbage collector so that the GC is aware of those references. To
|
||||
do this, implement the two methods `__traverse__` and `__clear__`. These
|
||||
correspond to the slots `tp_traverse` and `tp_clear` in the Python C API.
|
||||
`__traverse__` must call `visit.call()` for each reference to another Python
|
||||
object. `__clear__` must clear out any mutable references to other Python
|
||||
objects (thus breaking reference cycles). Immutable references do not have to be
|
||||
cleared, as every cycle must contain at least one mutable reference.
|
||||
|
||||
- `__traverse__(<self>, pyo3::class::gc::PyVisit) -> Result<(), pyo3::class::gc::PyTraverseError>`
|
||||
- `__clear__(<self>) -> ()`
|
||||
|
||||
Example:
|
||||
|
||||
```rust
|
||||
use pyo3::prelude::*;
|
||||
use pyo3::PyTraverseError;
|
||||
use pyo3::gc::PyVisit;
|
||||
|
||||
#[pyclass]
|
||||
struct ClassWithGCSupport {
|
||||
obj: Option<PyObject>,
|
||||
}
|
||||
|
||||
#[pymethods]
|
||||
impl ClassWithGCSupport {
|
||||
fn __traverse__(&self, visit: PyVisit) -> Result<(), PyTraverseError> {
|
||||
if let Some(obj) = &self.obj {
|
||||
visit.call(obj)?
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
|
||||
fn __clear__(&mut self) {
|
||||
// Clear reference, this decrements ref counter.
|
||||
self.obj = None;
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
[`IterNextOutput`]: {{#PYO3_DOCS_URL}}/pyo3/class/iter/enum.IterNextOutput.html
|
||||
|
||||
|
||||
### `#[pyproto]` traits
|
||||
|
||||
PyO3 can use the `#[pyproto]` attribute in combination with special traits to implement the magic methods which need slots. The special traits are listed below. See also the [documentation for the `pyo3::class` module]({{#PYO3_DOCS_URL}}/pyo3/class/index.html).
|
||||
|
||||
Before PyO3 0.15 this was the only supported solution for implementing magic methods. Due to complexity in the implementation and usage, these traits are expected to be deprecated in PyO3 0.16 in favour of the `#[pymethods]` solution.
|
||||
Due to complexity in the implementation and usage, these traits are deprecated in PyO3 0.16 in favour of the `#[pymethods]` solution.
|
||||
|
||||
All `#[pyproto]` methods can return `T` instead of `PyResult<T>` if the method implementation is infallible. In addition, if the return type is `()`, it can be omitted altogether.
|
||||
|
||||
|
|
@ -190,44 +379,15 @@ All `#[pyproto]` methods can return `T` instead of `PyResult<T>` if the method i
|
|||
|
||||
The [`PyObjectProtocol`] trait provides several basic customizations.
|
||||
|
||||
##### Attribute access
|
||||
|
||||
To customize object attribute access, define the following methods:
|
||||
|
||||
* `fn __str__(&self) -> PyResult<impl ToPyObject<ObjectType=PyString>>`
|
||||
* `fn __repr__(&self) -> PyResult<impl ToPyObject<ObjectType=PyString>>`
|
||||
* `fn __hash__(&self) -> PyResult<impl PrimInt>`
|
||||
* `fn __richcmp__(&self, other: impl FromPyObject, op: CompareOp) -> PyResult<impl ToPyObject>`
|
||||
* `fn __getattr__(&self, name: impl FromPyObject) -> PyResult<impl IntoPy<PyObject>>`
|
||||
* `fn __setattr__(&mut self, name: impl FromPyObject, value: impl FromPyObject) -> PyResult<()>`
|
||||
* `fn __delattr__(&mut self, name: impl FromPyObject) -> PyResult<()>`
|
||||
|
||||
Each method corresponds to Python's `self.attr`, `self.attr = value` and `del self.attr` code.
|
||||
|
||||
##### String Conversions
|
||||
|
||||
* `fn __repr__(&self) -> PyResult<impl ToPyObject<ObjectType=PyString>>`
|
||||
* `fn __str__(&self) -> PyResult<impl ToPyObject<ObjectType=PyString>>`
|
||||
|
||||
Possible return types for `__str__` and `__repr__` are `PyResult<String>` or `PyResult<PyString>`.
|
||||
|
||||
##### Comparison operators
|
||||
|
||||
* `fn __richcmp__(&self, other: impl FromPyObject, op: CompareOp) -> PyResult<impl ToPyObject>`
|
||||
|
||||
Overloads Python comparison operations (`==`, `!=`, `<`, `<=`, `>`, and `>=`).
|
||||
The `op` argument indicates the comparison operation being performed.
|
||||
The return type will normally be `PyResult<bool>`, but any Python object can be returned.
|
||||
If `other` is not of the type specified in the signature, the generated code will
|
||||
automatically `return NotImplemented`.
|
||||
|
||||
* `fn __hash__(&self) -> PyResult<impl PrimInt>`
|
||||
|
||||
Objects that compare equal must have the same hash value.
|
||||
The return type must be `PyResult<T>` where `T` is one of Rust's primitive integer types.
|
||||
|
||||
##### Other methods
|
||||
|
||||
* `fn __bool__(&self) -> PyResult<bool>`
|
||||
|
||||
Determines the "truthyness" of the object.
|
||||
|
||||
#### Emulating numeric types
|
||||
|
||||
The [`PyNumberProtocol`] trait can be implemented to emulate [numeric types](https://docs.python.org/3/reference/datamodel.html#emulating-numeric-types).
|
||||
|
|
@ -247,13 +407,7 @@ The [`PyNumberProtocol`] trait can be implemented to emulate [numeric types](htt
|
|||
* `fn __or__(lhs: impl FromPyObject, rhs: impl FromPyObject) -> PyResult<impl ToPyObject>`
|
||||
* `fn __xor__(lhs: impl FromPyObject, rhs: impl FromPyObject) -> PyResult<impl ToPyObject>`
|
||||
|
||||
These methods are called to implement the binary arithmetic operations
|
||||
(`+`, `-`, `*`, `@`, `/`, `//`, `%`, `divmod()`, `pow()` and `**`, `<<`, `>>`, `&`, `^`, and `|`).
|
||||
|
||||
If `rhs` is not of the type specified in the signature, the generated code
|
||||
will automatically `return NotImplemented`. This is not the case for `lhs`
|
||||
which must match signature or else raise a TypeError.
|
||||
|
||||
These methods are called to implement the binary arithmetic operations.
|
||||
|
||||
The reflected operations are also available:
|
||||
|
||||
|
|
@ -275,9 +429,6 @@ The reflected operations are also available:
|
|||
The code generated for these methods expect that all arguments match the
|
||||
signature, or raise a TypeError.
|
||||
|
||||
This trait also has support the augmented arithmetic assignments (`+=`, `-=`,
|
||||
`*=`, `@=`, `/=`, `//=`, `%=`, `**=`, `<<=`, `>>=`, `&=`, `^=`, `|=`):
|
||||
|
||||
* `fn __iadd__(&'p mut self, other: impl FromPyObject) -> PyResult<()>`
|
||||
* `fn __isub__(&'p mut self, other: impl FromPyObject) -> PyResult<()>`
|
||||
* `fn __imul__(&'p mut self, other: impl FromPyObject) -> PyResult<()>`
|
||||
|
|
@ -294,7 +445,7 @@ This trait also has support the augmented arithmetic assignments (`+=`, `-=`,
|
|||
* `fn __ixor__(&'p mut self, other: impl FromPyObject) -> PyResult<()>`
|
||||
|
||||
|
||||
The following methods implement the unary arithmetic operations (`-`, `+`, `abs()` and `~`):
|
||||
The following methods implement the unary arithmetic operations:
|
||||
|
||||
* `fn __neg__(&'p self) -> PyResult<impl ToPyObject>`
|
||||
* `fn __pos__(&'p self) -> PyResult<impl ToPyObject>`
|
||||
|
|
@ -305,9 +456,6 @@ Support for coercions:
|
|||
|
||||
* `fn __int__(&'p self) -> PyResult<impl ToPyObject>`
|
||||
* `fn __float__(&'p self) -> PyResult<impl ToPyObject>`
|
||||
|
||||
Other:
|
||||
|
||||
* `fn __index__(&'p self) -> PyResult<impl ToPyObject>`
|
||||
|
||||
#### Emulating sequential containers (such as lists or tuples)
|
||||
|
|
@ -406,139 +554,26 @@ For a mapping, the keys may be Python objects of arbitrary type.
|
|||
The same exceptions should be raised for improper key values as
|
||||
for the `__getitem__()` method.
|
||||
|
||||
#### Garbage Collector Integration
|
||||
|
||||
If your type owns references to other Python objects, you will need to
|
||||
integrate with Python's garbage collector so that the GC is aware of
|
||||
those references.
|
||||
To do this, implement the [`PyGCProtocol`] trait for your struct.
|
||||
It includes two methods `__traverse__` and `__clear__`.
|
||||
These correspond to the slots `tp_traverse` and `tp_clear` in the Python C API.
|
||||
`__traverse__` must call `visit.call()` for each reference to another Python object.
|
||||
`__clear__` must clear out any mutable references to other Python objects
|
||||
(thus breaking reference cycles). Immutable references do not have to be cleared,
|
||||
as every cycle must contain at least one mutable reference.
|
||||
Example:
|
||||
```rust
|
||||
use pyo3::prelude::*;
|
||||
use pyo3::PyTraverseError;
|
||||
use pyo3::gc::{PyGCProtocol, PyVisit};
|
||||
|
||||
#[pyclass]
|
||||
struct ClassWithGCSupport {
|
||||
obj: Option<PyObject>,
|
||||
}
|
||||
|
||||
#[pyproto]
|
||||
impl PyGCProtocol for ClassWithGCSupport {
|
||||
fn __traverse__(&self, visit: PyVisit) -> Result<(), PyTraverseError> {
|
||||
if let Some(obj) = &self.obj {
|
||||
visit.call(obj)?
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
|
||||
fn __clear__(&mut self) {
|
||||
// Clear reference, this decrements ref counter.
|
||||
self.obj = None;
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
#### Iterator Types
|
||||
|
||||
Iterators can be defined using the
|
||||
[`PyIterProtocol`]({{#PYO3_DOCS_URL}}/pyo3/class/iter/trait.PyIterProtocol.html) trait.
|
||||
Iterators can be defined using the [`PyIterProtocol`] trait.
|
||||
It includes two methods `__iter__` and `__next__`:
|
||||
* `fn __iter__(slf: PyRefMut<Self>) -> PyResult<impl IntoPy<PyObject>>`
|
||||
* `fn __next__(slf: PyRefMut<Self>) -> PyResult<Option<impl IntoPy<PyObject>>>`
|
||||
|
||||
Returning `None` from `__next__` indicates that that there are no further items.
|
||||
These two methods can be take either `PyRef<Self>` or `PyRefMut<Self>` as their
|
||||
first argument, so that mutable borrow can be avoided if needed.
|
||||
|
||||
Example:
|
||||
For details, look at the `#[pymethods]` regarding iterator methods.
|
||||
|
||||
```rust
|
||||
use pyo3::prelude::*;
|
||||
use pyo3::PyIterProtocol;
|
||||
#### Garbage Collector Integration
|
||||
|
||||
#[pyclass]
|
||||
struct MyIterator {
|
||||
iter: Box<dyn Iterator<Item = PyObject> + Send>,
|
||||
}
|
||||
|
||||
#[pyproto]
|
||||
impl PyIterProtocol for MyIterator {
|
||||
fn __iter__(slf: PyRef<Self>) -> PyRef<Self> {
|
||||
slf
|
||||
}
|
||||
fn __next__(mut slf: PyRefMut<Self>) -> Option<PyObject> {
|
||||
slf.iter.next()
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
In many cases you'll have a distinction between the type being iterated over (i.e. the *iterable*) and the iterator it
|
||||
provides. In this case, you should implement `PyIterProtocol` for both the iterable and the iterator, but the iterable
|
||||
only needs to support `__iter__()` while the iterator must support both `__iter__()` and `__next__()`. The default
|
||||
implementations in `PyIterProtocol` will ensure that the objects behave correctly in Python. For example:
|
||||
|
||||
```rust
|
||||
# use pyo3::prelude::*;
|
||||
# use pyo3::PyIterProtocol;
|
||||
|
||||
#[pyclass]
|
||||
struct Iter {
|
||||
inner: std::vec::IntoIter<usize>,
|
||||
}
|
||||
|
||||
#[pyproto]
|
||||
impl PyIterProtocol for Iter {
|
||||
fn __iter__(slf: PyRef<Self>) -> PyRef<Self> {
|
||||
slf
|
||||
}
|
||||
|
||||
fn __next__(mut slf: PyRefMut<Self>) -> Option<usize> {
|
||||
slf.inner.next()
|
||||
}
|
||||
}
|
||||
|
||||
#[pyclass]
|
||||
struct Container {
|
||||
iter: Vec<usize>,
|
||||
}
|
||||
|
||||
#[pyproto]
|
||||
impl PyIterProtocol for Container {
|
||||
fn __iter__(slf: PyRef<Self>) -> PyResult<Py<Iter>> {
|
||||
let iter = Iter {
|
||||
inner: slf.iter.clone().into_iter(),
|
||||
};
|
||||
Py::new(slf.py(), iter)
|
||||
}
|
||||
}
|
||||
|
||||
# Python::with_gil(|py| {
|
||||
# let container = Container { iter: vec![1, 2, 3, 4] };
|
||||
# let inst = pyo3::PyCell::new(py, container).unwrap();
|
||||
# pyo3::py_run!(py, inst, "assert list(inst) == [1, 2, 3, 4]");
|
||||
# pyo3::py_run!(py, inst, "assert list(iter(iter(inst))) == [1, 2, 3, 4]");
|
||||
# });
|
||||
```
|
||||
|
||||
For more details on Python's iteration protocols, check out [the "Iterator Types" section of the library
|
||||
documentation](https://docs.python.org/3/library/stdtypes.html#iterator-types).
|
||||
|
||||
##### Returning a value from iteration
|
||||
|
||||
This guide has so far shown how to use `Option<T>` to implement yielding values during iteration.
|
||||
In Python a generator can also return a value. To express this in Rust, PyO3 provides the
|
||||
[`IterNextOutput`]({{#PYO3_DOCS_URL}}/pyo3/class/iter/enum.IterNextOutput.html) enum to
|
||||
both `Yield` values and `Return` a final value - see its docs for further details and an example.
|
||||
Implement the [`PyGCProtocol`] trait for your struct.
|
||||
For details, look at the `#[pymethods]` regarding GC methods.
|
||||
|
||||
[`PyGCProtocol`]: {{#PYO3_DOCS_URL}}/pyo3/class/gc/trait.PyGCProtocol.html
|
||||
[`PyMappingProtocol`]: {{#PYO3_DOCS_URL}}/pyo3/class/mapping/trait.PyMappingProtocol.html
|
||||
[`PyNumberProtocol`]: {{#PYO3_DOCS_URL}}/pyo3/class/number/trait.PyNumberProtocol.html
|
||||
[`PyObjectProtocol`]: {{#PYO3_DOCS_URL}}/pyo3/class/basic/trait.PyObjectProtocol.html
|
||||
[`PySequenceProtocol`]: {{#PYO3_DOCS_URL}}/pyo3/class/sequence/trait.PySequenceProtocol.html
|
||||
[`PyIterProtocol`]: {{#PYO3_DOCS_URL}}/pyo3/class/iter/trait.PyIterProtocol.html
|
||||
|
|
|
|||
|
|
@ -165,7 +165,7 @@ quickly testing your Python extensions.
|
|||
|
||||
```rust
|
||||
use pyo3::prelude::*;
|
||||
use pyo3::{PyCell, PyObjectProtocol, py_run};
|
||||
use pyo3::{PyCell, py_run};
|
||||
|
||||
# fn main() {
|
||||
#[pyclass]
|
||||
|
|
@ -179,10 +179,7 @@ impl UserData {
|
|||
fn as_tuple(&self) -> (u32, String) {
|
||||
(self.id, self.name.clone())
|
||||
}
|
||||
}
|
||||
|
||||
#[pyproto]
|
||||
impl PyObjectProtocol for UserData {
|
||||
fn __repr__(&self) -> PyResult<String> {
|
||||
Ok(format!("User {}(id: {})", self.name, self.id))
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue