autoprop is a library for automatically filling in classes with properties (e.g. obj.x) corresponding to each accessor method (e.g. obj.get_x(), obj.set_x()). The biggest reasons to use autoprop are:

• Less boilerplate than defining properties manually.
• Sophisticated support for cached properties.

Install autoprop using pip:

$ pip install autoprop

To use autoprop, import the autoprop module and use it directly as a class decorator:

>>> import autoprop
>>>
>>> @autoprop
... class Vector2D(object):
...
...     def __init__(self, x, y):
...         self._x = x
...         self._y = y
...
...     def get_x(self):
...         return self._x
...
...     def set_x(self, x):
...         self._x = x
...
...     def get_y(self):
...         return self._y
...
...     def set_y(self, y):
...         self._y = y
...
>>> v = Vector2D(1, 2)
>>> v.x, v.y
(1, 2)

The decorator searches your class for methods beginning with get_, set_, or del_ and uses them to create properties. The names of the properties are taken from whatever comes after the underscore. For example, the method get_x would be used to make a property called x. Any combination of getter, setter, and deleter methods is allowed for each property.

If you have properties that are expensive to calculate, it's easy to cache them:

>>> @autoprop.cache
... class Simulation(object):
...
...     def get_data(self):
...         print("expensive calculation...")
...         return 42
...
>>> s = Simulation()
>>> s.data
expensive calculation...
42
>>> s.data
42

It's also easy to cache some properties but not others:

>>> @autoprop
... class Simulation(object):
...
...     def get_cheap(self):
...         print("cheap calculation...")
...         return 16
...
...     @autoprop.cache
...     def get_expensive(self):
...         print("expensive calculation...")
...         return 42
...
>>> s = Simulation()
>>> s.cheap
cheap calculation...
16
>>> s.cheap
cheap calculation...
16
>>> s.expensive
expensive calculation...
42
>>> s.expensive
42

The @autoprop.cache() decorator accepts a policy keyword argument that determines how the cache will be managed. The following policies are supported:

• overwrite: This is the default policy. Values are cached by overwriting the property itself, such that future lookups will directly access the cached value with no overhead. This is exactly equivalent to using functools.cached_property. Unlike normal properties, there is no way to customize what happens when setting or deleting these properties. Setting the property will update its value, and deleting it will cause its value to be recalculated on the next access.

• manual: Cached values are never recalculated automatically, but can be recalculated and/or changed manually. There are two ways to do this:

  1. Specify provide_mutators=True to @autoprop.cache(). This will instruct autoprop to provide default setter and deleter implementations for the property, which will allow the cached value to be changed or dropped, respectively.
  2. Call autoprop.set_cached_attr() and/or autoprop.del_cached_attr(). These functions allow you to implement your own setter and deleter functions, which is often the entire purpose of using this policy.

  This policy has ≈10x more overhead than the overwrite policy, but allows you to control what happens when the attribute is set or deleted (like a regular property).

• automatic: Cached values are automatically recalculated if certain other attributes of the object change. In order to use this policy, you must specify watch=<list of attributes> to @autoprop.cache(). The watch argument must be iterable, and each item must either be the name of an attribute (e.g. a string) or a callable that will accept the object in question and return any value. The cached value will be recalculated whenever any of the "watched" values change. The cache can also be recalculated manually, in any of the ways described for the manual policy.

  This policy has ≈25x more overhead than the overwrite policy, but allows cached values to stay up to date when the attributes they depend on change.

• immutable: Properties are never recalculated, and are furthermore not allowed to have setter or deleter methods (an error will be raised if any such methods are found). As the name implies, this is for properties and classes that are intended to be immutable.

  Note that autoprop.immutable is an alias for @autoprop.cache(policy='immutable').

• dynamic: Properties are recalculated every time they are accessed. This is exactly equivalent what autoprop does when caching is disabled, which is exactly equivalent to using @property. Use this policy when you want to specify @autoprop.cache at the class-level, but also need to prevent a few properties from being cached.

  Note that @autoprop.dynamic is an alias for @autoprop.cache(policy='dynamic').

Besides having the right prefix, there are two other criteria that methods must meet in order to be made into properties. The first is that they must take the right number of arguments. Getters and deleters must not require any arguments (other than self). Setters must accept exactly one argument (other than self), which is the value to set. Default, variable, and keyword arguments are all ignored; all that matters is that the function can be called with the expected number of arguments.

Any methods that have the right name but the wrong arguments are silently ignored. This can be nice for getters that require, for example, an index. Even though such a getter can't be made into a property, autoprop allows it to follow the same naming conventions as any getters that can be:

>>> @autoprop
... class Vector2D(Vector2D):
...
...     def get_coord(self, i):
...         if i == 0: return self.x
...         if i == 1: return self.y
...
...     def set_coord(self, i, new_coord):
...         if i == 0: self.x = new_coord
...         if i == 1: self.y = new_coord
...
>>> v = Vector2D(1, 2)
>>> v.get_x()
1
>>> v.get_coord(0)
1

In this way, users of your class can always expect to find accessors named get_* and set_*, and properties corresponding to those accessors for basic attributes that don't need any extra information.

The second criterion is that the property must have a name which is not already in use. This guarantees that nothing you explicitly add to your class will be overwritten, and it gives you the ability to manually customize how certain properties are defined if you'd so like. This criterion does not apply to superclasses, so it is possible for properties to shadow attributes defined in parent classes.