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A tiny Python module for taking control of your library's public API.

Project description

metamodule - Useful tools and gee-whiz tricks for defining Python APIs

In Python, writing a metaclass lets you create new kinds of class objects whose behaviour you can control.

By analogy (and bit of abuse of English), writing a metamodule lets you create module objects with customized behaviour. is a single-file, permissively-licensed Python library that makes it easy and safe to use custom module subtypes as the public interface for your library. For example, ordinarily in Python it’s easy to issue a deprecation warning when someone calls a deprecated function (, but it’s very difficult to issue a deprecation warning when someone accesses a deprecated constant (mymodule.FOO). Another commonly-requested (though somewhat dangerous) feature is the ability to delay importing a submodule until the first time it’s accessed (mymodule.submodule.subfunction()). With metamodule, these are both easy to solve: we just give mymodule a custom __getattr__ method that does what we want. (And in fact, you don’t even need to write this __getattr__ – metamodule includes an implementation that provides both of the above features out-of-the-box.)

Example / documentation

In the source directory of this project, try starting a Python REPL and running:

>>> import examplepkg

examplepkg is a module object:

>>> import types
>>> isinstance(examplepkg, types.ModuleType)

But it’s not a regular module object; it’s a custom subclass:

>>> examplepkg
<FancyModule 'examplepkg' from 'examplepkg/'>

And this subclass has superpowers:

# Automatically loads the submodule on first access:
>>> examplepkg.submodule.subattr
... submodule loading ...
'look ma no import'

# Imports are cached so future usage is just as fast as regular access:
>>> examplepkg.submodule.subattr
'look ma no import'

# Accessing this attribute triggers a warning:
>>> examplepkg.a
__main__:1: FutureWarning: 'a' attribute will become 2 in next release

# But regular attributes continue to work fine, with no speed penalty:
>>> examplepkg.b

# reload() works fine (except on CPython 3.3, which is buggy)
>>> import imp
>>> imp.reload(examplepkg)
<FancyModule 'examplepkg' from 'examplepkg/__init__.pyc'>

# And functions defined in the package use the same globals dict
# as the package itself. (On py2 replace .__globals__ with .func_globals)
>>> examplepkg.__dict__ is examplepkg.f.__globals__

To accomplish this, all we had to do was put the following code at the top of examplepkg/

# WARNING: this should be placed at the *very top* of your module,
# *before* you import any code that might recursively re-import
# your package.
import metamodule
del metamodule

# Any strings in this set name submodules that will be lazily imported:
# NB: you probably shouldn't use this unless you have a real,
# specific need for it, since it can cause import errors and other
# side-effects to appear at weird and confusing places.

# Attributes that we want to warn users about:
__warn_on_access__["a"] = (
    # Attribute value
    # Warning issued when attribute is accessed
    FutureWarning("'a' attribute will become 2 in next release"))

You can also define your own ModuleType subclass and pass it as the second argument to metamodule.install. Your class can do anything you can regularly do with a Python class – define special methods like __getattribute__, use properties, have a custom __repr__, whatever you want. Note that your class instance’s __dict__ will be the module globals dict, so assigning to is equivalent to creating a global variable in your module named foo, and vice-versa.

The one thing to watch out for is that your class’s __init__ will not be called – instead, you should define a method __metamodule_init__ which will be called immediately after your metamodule class is installed.

Versions supported

Metamodule is currently tested against:

  • CPython 2.6, 2.7
  • CPython 3.2, 3.3, 3.4, and pre-releases of 3.5

I suspect it will work on pretty much every version of CPython that has a working ctypes, I just don’t have convenient access to older versions to test.

As far as I know we do not yet support PyPy, Jython, etc., but we will as soon they catch up with Python 3.5 and start allowing __class__ assignment on module objects.

How it works

Python has always allowed these kinds of tricks to some extent, via the mechanism of assigning a new object to sys.modules["mymodule"]; this object can then have whatever behaviour you like. This can work well, but the end result is that you have two different objects that both represent the same module: your original module object (which owns the globals() namespace where your module code executes), and your custom object. Depending on the relative order of the assignment to sys.modules and imports of submodules, you can end up with different pieces of code in the same program thinking that mymodule refers to one or the other of these objects. If they don’t share the same __dict__, then their namespaces can get out of sync; alternatively, if they do share the same __dict__, then this means that your custom object can’t subclass ModuleType (module objects don’t allow reassignment of their __dict__ attribute), which breaks reload(). All in all it’s a bit of a mess. It’s possible to write correct code using this method, if you are extremely careful – for example apipkg is a somewhat similar library uses this approach, but to keep things workable it requires that your library’s public interface be defined entirely by apipkg calls. There’s no easy way to take a legacy Python package and incrementally switch it over to using apipkg.

The key feature that metamodule provides is: it makes it easy to set up sys.modules["mymodule"] so that it is both (a) an instance of a class that you control, so you can have arbitrary properties etc., AND (b) a regular subclass of ModuleType with your’s globals() as its __dict__ attribute, so that you can continue using the usual Python approach to defining your API.

This combination makes it easy and safe to transition an existing library to using metamodule – just add a call to metamodule.install at the top of your, and nothing at all will change, except that you can now start taking advantage of your new superpowers at your leisure.

How do we do it? On CPython 3.5 and later, this is easy: metamodule uses __class__ assignment on module objects (a feature that was added to CPython explicitly to support this usage).

On CPython 3.4 and earlier, it uses ctypes hacks. These are ugly, but safe so long as no one goes back in time and changes the internal memory layout of module objects on old, already-released versions of Python. (Which is not going to happen.) Basically, we instantiate a new object of the specified ModuleType subclass, and then we use some arcane knowledge of how these objects are laid out in order to swap the guts of your original module and the new object. Then we assign the new object into sys.modules. This preserves the key invariant that at any given point there’s exactly one module that owns your globals dict, and it’s in sys.modules. It does, however, mean that things will go horribly wrong if you call metamodule.install after someone else has already imported your module. So unless you only want to support Python 3.5+, then make sure to call metamodule.install right at the top of your module definition file.

These two tricks together let us safely support all versions of CPython, and as alternative implementations like PyPy catch up with CPython 3.5 in supporting __class__ assignment, we’ll support those too.

Change history


  • When looking up __metamodule_init__, go straight to the class without checking the instance. This makes our behavior more consistent with regular __init__, and avoids accidentally triggering __getattr__. (Thanks to Antony Lee for the report + fix.)


  • First public release.

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