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# Extending
Each *attrs*-decorated class has a `__attrs_attrs__` class attribute.
It's a tuple of {class}`attrs.Attribute` carrying metadata about each attribute.
So it is fairly simple to build your own decorators on top of *attrs*:
```{doctest}
>>> from attr import define
>>> def print_attrs(cls):
... print(cls.__attrs_attrs__)
... return cls
>>> @print_attrs
... @define
... class C:
... a: int
(Attribute(name='a', default=NOTHING, validator=None, repr=True, eq=True, eq_key=None, order=True, order_key=None, hash=None, init=True, metadata=mappingproxy({}), type=<class 'int'>, converter=None, kw_only=False, inherited=False, on_setattr=None, alias='a'),)
```
:::{warning}
The {func}`attrs.define` / {func}`attr.s` decorator **must** be applied first because it puts `__attrs_attrs__` in place!
That means that is has to come *after* your decorator because:
```python
@a
@b
def f():
pass
```
```python
def original_f():
pass
f = a(b(original_f))
```
:::
## Wrapping the Decorator
A more elegant way can be to wrap *attrs* altogether and build a class [DSL](https://en.wikipedia.org/wiki/Domain-specific_language) on top of it.
An example for that is the package [*environ-config*](https://github.com/hynek/environ-config) that uses *attrs* under the hood to define environment-based configurations declaratively without exposing *attrs* APIs at all.
Another common use case is to overwrite *attrs*'s defaults.
### Mypy
Unfortunately, decorator wrapping currently [confuses](https://github.com/python/mypy/issues/5406) mypy's *attrs* plugin.
At the moment, the best workaround is to hold your nose, write a fake *Mypy* plugin, and mutate a bunch of global variables:
```python
from mypy.plugin import Plugin
from mypy.plugins.attrs import (
attr_attrib_makers,
attr_class_makers,
attr_dataclass_makers,
)
# These work just like `attr.dataclass`.
attr_dataclass_makers.add("my_module.method_looks_like_attr_dataclass")
# This works just like `attr.s`.
attr_class_makers.add("my_module.method_looks_like_attr_s")
# These are our `attr.ib` makers.
attr_attrib_makers.add("my_module.method_looks_like_attrib")
class MyPlugin(Plugin):
# Our plugin does nothing but it has to exist so this file gets loaded.
pass
def plugin(version):
return MyPlugin
```
Then tell *Mypy* about your plugin using your project's `mypy.ini`:
```ini
[mypy]
plugins=<path to file>
```
:::{warning}
Please note that it is currently *impossible* to let mypy know that you've changed defaults like *eq* or *order*.
You can only use this trick to tell *Mypy* that a class is actually an *attrs* class.
:::
### Pyright
Generic decorator wrapping is supported in [*Pyright*](https://github.com/microsoft/pyright) via `typing.dataclass_transform` / {pep}`689`.
For a custom wrapping of the form:
```
@typing.dataclass_transform(field_specifiers=(attr.attrib, attr.field))
def custom_define(f):
return attr.define(f)
```
## Types
*attrs* offers two ways of attaching type information to attributes:
- {pep}`526` annotations,
- and the *type* argument to {func}`attr.ib`.
This information is available to you:
```{doctest}
>>> from attr import attrib, define, field, fields
>>> @define
... class C:
... x: int = field()
... y = attrib(type=str)
>>> fields(C).x.type
<class 'int'>
>>> fields(C).y.type
<class 'str'>
```
Currently, *attrs* doesn't do anything with this information but it's very useful if you'd like to write your own validators or serializers!
(extending-metadata)=
## Metadata
If you're the author of a third-party library with *attrs* integration, you may want to take advantage of attribute metadata.
Here are some tips for effective use of metadata:
- Try making your metadata keys and values immutable.
This keeps the entire {class}`~attrs.Attribute` instances immutable too.
- To avoid metadata key collisions, consider exposing your metadata keys from your modules.:
```python
from mylib import MY_METADATA_KEY
@define
class C:
x = field(metadata={MY_METADATA_KEY: 1})
```
Metadata should be composable, so consider supporting this approach even if you decide implementing your metadata in one of the following ways.
- Expose `field` wrappers for your specific metadata.
This is a more graceful approach if your users don't require metadata from other libraries.
```{doctest}
>>> from attrs import fields, NOTHING
>>> MY_TYPE_METADATA = '__my_type_metadata'
>>>
>>> def typed(
... cls, default=NOTHING, validator=None, repr=True,
... eq=True, order=None, hash=None, init=True, metadata=None,
... converter=None
... ):
... metadata = metadata or {}
... metadata[MY_TYPE_METADATA] = cls
... return field(
... default=default, validator=validator, repr=repr,
... eq=eq, order=order, hash=hash, init=init,
... metadata=metadata, converter=converter
... )
>>>
>>> @define
... class C:
... x: int = typed(int, default=1, init=False)
>>> fields(C).x.metadata[MY_TYPE_METADATA]
<class 'int'>
```
(transform-fields)=
## Automatic Field Transformation and Modification
*attrs* allows you to automatically modify or transform the class' fields while the class is being created.
You do this by passing a *field_transformer* hook to {func}`~attrs.define` (and friends).
Its main purpose is to automatically add converters to attributes based on their type to aid the development of API clients and other typed data loaders.
This hook must have the following signature:
```{eval-rst}
.. function:: your_hook(cls: type, fields: list[attrs.Attribute]) -> list[attrs.Attribute]
:noindex:
```
- *cls* is your class right *before* it is being converted into an attrs class.
This means it does not yet have the `__attrs_attrs__` attribute.
- *fields* is a list of all `attrs.Attribute` instances that will later be set to `__attrs_attrs__`.
You can modify these attributes any way you want:
You can add converters, change types, and even remove attributes completely or create new ones!
For example, let's assume that you really don't like floats:
```{doctest}
>>> def drop_floats(cls, fields):
... return [f for f in fields if f.type not in {float, 'float'}]
...
>>> @frozen(field_transformer=drop_floats)
... class Data:
... a: int
... b: float
... c: str
...
>>> Data(42, "spam")
Data(a=42, c='spam')
```
A more realistic example would be to automatically convert data that you, e.g., load from JSON:
```{doctest}
>>> from datetime import datetime
>>>
>>> def auto_convert(cls, fields):
... results = []
... for field in fields:
... if field.converter is not None:
... results.append(field)
... continue
... if field.type in {datetime, 'datetime'}:
... converter = (lambda d: datetime.fromisoformat(d) if isinstance(d, str) else d)
... else:
... converter = None
... results.append(field.evolve(converter=converter))
... return results
...
>>> @frozen(field_transformer=auto_convert)
... class Data:
... a: int
... b: str
... c: datetime
...
>>> from_json = {"a": 3, "b": "spam", "c": "2020-05-04T13:37:00"}
>>> Data(**from_json) # ****
Data(a=3, b='spam', c=datetime.datetime(2020, 5, 4, 13, 37))
```
Or, perhaps you would prefer to generate dataclass-compatible `__init__` signatures via a default field *alias*.
Note, *field_transformer* operates on {class}`attrs.Attribute` instances before the default private-attribute handling is applied so explicit user-provided aliases can be detected.
```{doctest}
>>> def dataclass_names(cls, fields):
... return [
... field.evolve(alias=field.name)
... if not field.alias
... else field
... for field in fields
... ]
...
>>> @frozen(field_transformer=dataclass_names)
... class Data:
... public: int
... _private: str
... explicit: str = field(alias="aliased_name")
...
>>> Data(public=42, _private="spam", aliased_name="yes")
Data(public=42, _private='spam', explicit='yes')
```
## Customize Value Serialization in `asdict()`
*attrs* allows you to serialize instances of *attrs* classes to dicts using the {func}`attrs.asdict` function.
However, the result can not always be serialized since most data types will remain as they are:
```{doctest}
>>> import json
>>> import datetime
>>> from attrs import asdict
>>>
>>> @frozen
... class Data:
... dt: datetime.datetime
...
>>> data = asdict(Data(datetime.datetime(2020, 5, 4, 13, 37)))
>>> data
{'dt': datetime.datetime(2020, 5, 4, 13, 37)}
>>> json.dumps(data)
Traceback (most recent call last):
...
TypeError: Object of type datetime is not JSON serializable
```
To help you with this, {func}`~attrs.asdict` allows you to pass a *value_serializer* hook.
It has the signature
```{eval-rst}
.. function:: your_hook(inst: type, field: attrs.Attribute, value: typing.Any) -> typing.Any
:noindex:
```
```{doctest}
>>> from attr import asdict
>>> def serialize(inst, field, value):
... if isinstance(value, datetime.datetime):
... return value.isoformat()
... return value
...
>>> data = asdict(
... Data(datetime.datetime(2020, 5, 4, 13, 37)),
... value_serializer=serialize,
... )
>>> data
{'dt': '2020-05-04T13:37:00'}
>>> json.dumps(data)
'{"dt": "2020-05-04T13:37:00"}'
```