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Python Expert: how to inherit built-in class and override every member function w.r.t. the base-class member function?

It is known that in Python, due to optimization concerns, we cannot add/modify member functions of a built-in class, e.g., adding an sed function to the built-in str class to perform re.sub(). Thus, the only way to achieve so is to inherit the class (or subclassing). i.e.,
class String(str):
def __init__(self, value='', **kwargs):
super().__init__()
def sed(self, src, tgt):
return String(re.sub(src, tgt, self))
The problem with this is that after sub-classing, member functions return base-class instance instead of the inherited class instance. For example, I would like to chain String edits String(' A b C d E [!] ').sed(...).lower().sed(...).strip().sed('\[.*\]', '').split() and so on. However, functions such as .lower() and .strip() returns an str instead of String, so cannot perform .sed(...) afterwards. And I do not want to keep casting to String after every function call.
So I did a manual over-ride of every base-class methods as follows:
class String(str):
for func in dir(str):
if not func.startswith('_'):
exec(f'{func}=lambda *args, **kwargs: [(String(i) if type(i)==str else i) for i in [str.{func}(*args, **kwargs)]][0]')
def __init__(self, value='', **kwargs):
super().__init__()
def sed(self, src, tgt):
return String(re.sub(src, tgt, self))
However, not every member function returns a simple str object, e.g., for functions such as .split(), they return a list of str; other functions like .isalpha() or .find() return boolean or integer. In general, I want to add more string-morphing functions and do not want to manually over-ride member functions of each return type in order to return inherited-class objects rather than base-class objects. So is there a more elegant way of doing this? Thanks!

Python's built-in classes are not designed to support that style of inheritance
easily. Also, the whole idea seems flawed to my eye. Even if you do figure out
a way to solve the problem as you've framed it, what's the advantage over good
old functions?
# Special String objects with new methods.
s = String('foo bar')
result = s.sed('...', '...')
# Regular str instances passed to ordinary functions.
s = 'foo bar'
result = sed(s, '...', '...')
That said, here's one way to try. I have not tested it
extensively, it might have a flaw, and I would never use it in real code.
The basic idea is to capture objects returned during low-level
attribute access, and if the object is callable return
a wrapped version of it that will perform the needed
data conversions.
import re
from functools import wraps
class String(str):
def __getattribute__(self, attr):
obj = object.__getattribute__(self, attr)
return wrapped(obj) if callable(obj) else obj
def __init__(self, value='', **kwargs):
super().__init__()
def sed(self, src, tgt):
return re.sub(src, tgt, self)
def wrapped(func):
#wraps(func)
def wrapper(*xs, **kws):
obj = func(*xs, **kws)
return convert(obj)
return wrapper
def convert(obj):
if isinstance(obj, str):
return String(obj)
elif isinstance(obj, list):
return [convert(x) for x in obj]
elif isinstance(obj, tuple):
return tuple(convert(x) for x in obj)
else:
return obj
Demo:
s = String('foo bar')
got = s.sed('foo', 'bzz').upper().split()
print(got)
print(type(got))
print(type(got[0]))
Output:
['BZZ', 'BAR']
<class 'list'>
<class '__main__.String'>

Python - extending properties like you'd extend a function

Question
How can you extend a python property?
A subclass can extend a super class's function by calling it in the overloaded version, and then operating on the result. Here's an example of what I mean when I say "extending a function":
# Extending a function (a tongue-in-cheek example)
class NormalMath(object):
def __init__(self, number):
self.number = number
def add_pi(self):
n = self.number
return n + 3.1415
class NewMath(object):
def add_pi(self):
# NewMath doesn't know how NormalMath added pi (and shouldn't need to).
# It just uses the result.
n = NormalMath.add_pi(self)
# In NewMath, fractions are considered too hard for our users.
# We therefore silently convert them to integers.
return int(n)
Is there an analogous operation to extending functions, but for functions that use the property decorator?
I want to do some additional calculations immediately after getting an expensive-to-compute attribute. I need to keep the attribute's access lazy. I don't want the user to have to invoke a special routine to make the calculations. basically, I don't want the user to ever know the calculations were made in the first place. However, the attribute must remain a property, since i've got legacy code I need to support.
Maybe this is a job for decorators? If I'm not mistaken, decorator is a function that wraps another function, and I'm looking to wrap a property with some more calculations, and then present it as a property again, which seems like a similar idea... but I can't quite figure it out.
My Specific Problem
I've got a base class LogFile with an expensive-to-construct attribute .dataframe. I've implemented it as a property (with the property decorator), so it won't actually parse the log file until I ask for the dataframe. So far, it works great. I can construct a bunch (100+) LogFile objects, and use cheaper methods to filter and select only the important ones to parse. And whenever I'm using the same LogFile over and over, i only have to parse it the first time I access the dataframe.
Now I need to write a LogFile subclass, SensorLog, that adds some extra columns to the base class's dataframe attribute, but I can't quite figure out the syntax to call the super class's dataframe construction routines (without knowing anything about their internal workings), then operate on the resulting dataframe, and then cache/return it.
# Base Class - rules for parsing/interacting with data.
class LogFile(object):
def __init__(self, file_name):
# file name to find the log file
self.file_name = file_name
# non-public variable to cache results of parse()
self._dataframe = None
def parse(self):
with open(self.file_name) as infile:
...
...
# Complex rules to interpret the file
...
...
self._dataframe = pandas.DataFrame(stuff)
#property
def dataframe(self):
"""
Returns the dataframe; parses file if necessary. This works great!
"""
if self._dataframe is None:
self.parse()
return self._dataframe
#dataframe.setter
def dataframe(self,value):
self._dataframe = value
# Sub class - adds more information to data, but does't parse
# must preserve established .dataframe interface
class SensorLog(LogFile):
def __init__(self, file_name):
# Call the super's constructor
LogFile.__init__(self, file_name)
# SensorLog doesn't actually know about (and doesn't rely on) the ._dataframe cache, so it overrides it just in case.
self._dataframe = None
# THIS IS THE PART I CAN'T FIGURE OUT
# Here's my best guess, but it doesn't quite work:
#property
def dataframe(self):
# use parent class's getter, invoking the hidden parse function and any other operations LogFile might do.
self._dataframe = LogFile.dataframe.getter()
# Add additional calculated columns
self._dataframe['extra_stuff'] = 'hello world!'
return self._dataframe
#dataframe.setter
def dataframe(self, value):
self._dataframe = value
Now, when these classes are used in an interactive session, the user should be able to interact with either in the same way.
>>> log = LogFile('data.csv')
>>> print log.dataframe
#### DataFrame with 10 columns goes here ####
>>> sensor = SensorLog('data.csv')
>>> print sensor.dataframe
#### DataFrame with 11 columns goes here ####
I have lots of existing code that takes a LogFile instance which provides a .dataframe attribute and dos something interesting (mostly plotting). I would LOVE to have SensorLog instances present the same interface so they can use the same code. Is it possible to extend the super-class's dataframe getter to take advantage of existing routines? How? Or am I better off doing this a different way?
Thanks for reading that huge wall of text. You are an internet super hero, dear reader. Got any ideas?

You should be calling the superclass properties, not bypassing them via self._dataframe. Here's a generic example:
class A(object):
def __init__(self):
self.__prop = None
#property
def prop(self):
return self.__prop
#prop.setter
def prop(self, value):
self.__prop = value
class B(A):
def __init__(self):
super(B, self).__init__()
#property
def prop(self):
value = A.prop.fget(self)
value['extra'] = 'stuff'
return value
#prop.setter
def prop(self, value):
A.prop.fset(self, value)
And using it:
b = B()
b.prop = dict((('a', 1), ('b', 2)))
print(b.prop)
Outputs:
{'a': 1, 'b': 2, 'extra': 'stuff'}
I would generally recommend placing side-effects in setters instead of getters, like this:
class A(object):
def __init__(self):
self.__prop = None
#property
def prop(self):
return self.__prop
#prop.setter
def prop(self, value):
self.__prop = value
class B(A):
def __init__(self):
super(B, self).__init__()
#property
def prop(self):
return A.prop.fget(self)
#prop.setter
def prop(self, value):
value['extra'] = 'stuff'
A.prop.fset(self, value)
Having costly operations within a getter is also generally to be avoided (such as your parse method).

If I understand correctly what you want to do is call the parent's method from the child instance. The usual way to do that is by using the super built-in.
I've taken your tongue-in-cheek example and modified it to use super in order to show you:
class NormalMath(object):
def __init__(self, number):
self.number = number
def add_pi(self):
n = self.number
return n + 3.1415
class NewMath(NormalMath):
def add_pi(self):
# this will call NormalMath's add_pi with
normal_maths_pi_plus_num = super(NewMath, self).add_pi()
return int(normal_maths_pi_plus_num)
In your Log example, instead of calling:
self._dataframe = LogFile.dataframe.getter()
you should call:
self._dataframe = super(SensorLog, self).dataframe
You can read more about super here
Edit: Even thought the example I gave you deals with methods, to do the same with #properties shouldn't be a problem.

You have some possibilities to consider:
1/ Inherit from logfile and override parse in your derived sensor class. It should be possible to modify your methods that work on dataframe to work regardless of the number of members that dataframe has - as you are using pandas a lot of it is done for you.
2/ Make sensor an instance of logfile then provide its own parse method.
3/ Generalise parse, and possibly some of your other methods, to use a list of data descriptors and possibly a dictionary of methods/rules either set in your class initialiser or set by a methods.
4/ Look at either making more use of the methods already in pandas, or possibly, extending pandas to provide the missing methods if you and others think that they would be accepted into pandas as useful extensions.
Personally I think that you would find the benefits of options 3 or 4 to be the most powerful.

The problem is that you're missing a self going into the parent class. If your parent is a singleton then a #staticmethod should work.
class X():
x=1
#staticmethod
def getx():
return X.x
class Y(X):
y=2
def getyx(self):
return X.getx()+self.y
wx = Y()
wx.getyx()
3

Subclassing method decorators in python

I am having trouble thinking of a way that's good python and consistent with oop principles as I've been taught to figure out how to create a family of related method decorators in python.
The mutually inconsistent goals seem to be that I want to be able to access both decorator attributes AND attributes of the instance on which the decorated method is bound. Here's what I mean:
from functools import wraps
class AbstractDecorator(object):
"""
This seems like the more natural way, but won't work
because the instance to which the wrapped function
is attached will never be in scope.
"""
def __new__(cls,f,*args,**kwargs):
return wraps(f)(object.__new__(cls,*args,**kwargs))
def __init__(decorator_self, f):
decorator_self.f = f
decorator_self.punctuation = "..."
def __call__(decorator_self, *args, **kwargs):
decorator_self.very_important_prep()
return decorator_self.f(decorator_self, *args, **kwargs)
class SillyDecorator(AbstractDecorator):
def very_important_prep(decorator_self):
print "My apartment was infested with koalas%s"%(decorator_self.punctuation)
class UsefulObject(object):
def __init__(useful_object_self, noun):
useful_object_self.noun = noun
#SillyDecorator
def red(useful_object_self):
print "red %s"%(useful_object_self.noun)
if __name__ == "__main__":
u = UsefulObject("balloons")
u.red()
which of course produces
My apartment was infested with koalas...
AttributeError: 'SillyDecorator' object has no attribute 'noun'
Note that of course there is always a way to get this to work. A factory with enough arguments, for example, will let me attach methods to some created instance of SillyDecorator, but I was kind of wondering whether there is a reasonable way to do this with inheritance.

#miku got the key idea of using the descriptor protocol. Here is a refinement that keeps the decorator object separate from the "useful object" -- it doesn't store the decorator info on the underlying object.
class AbstractDecorator(object):
"""
This seems like the more natural way, but won't work
because the instance to which the wrapped function
is attached will never be in scope.
"""
def __new__(cls,f,*args,**kwargs):
return wraps(f)(object.__new__(cls,*args,**kwargs))
def __init__(decorator_self, f):
decorator_self.f = f
decorator_self.punctuation = "..."
def __call__(decorator_self, obj_self, *args, **kwargs):
decorator_self.very_important_prep()
return decorator_self.f(obj_self, *args, **kwargs)
def __get__(decorator_self, obj_self, objtype):
return functools.partial(decorator_self.__call__, obj_self)
class SillyDecorator(AbstractDecorator):
def very_important_prep(decorator_self):
print "My apartment was infested with koalas%s"%(decorator_self.punctuation)
class UsefulObject(object):
def __init__(useful_object_self, noun):
useful_object_self.noun = noun
#SillyDecorator
def red(useful_object_self):
print "red %s"%(useful_object_self.noun)
>>> u = UsefulObject("balloons")
... u.red()
My apartment was infested with koalas...
red balloons
The descriptor protocol is the key here, since it is the thing that gives you access to both the decorated method and the object on which it is bound. Inside __get__, you can extract the useful object identity (obj_self) and pass it on to the __call__ method.
Note that it's important to use functools.partial (or some such mechanism) rather than simply storing obj_self as an attribute of decorator_self. Since the decorated method is on the class, only one instance of SillyDecorator exists. You can't use this SillyDecorator instance to store useful-object-instance-specific information --- that would lead to strange errors if you created multiple UsefulObjects and accessed their decorated methods without immediately calling them.
It's worth pointing out, though, that there may be an easier way. In your example, you're only storing a small amount of information in the decorator, and you don't need to change it later. If that's the case, it might be simpler to just use a decorator-maker function: a function that takes an argument (or arguments) and returns a decorator, whose behavior can then depend on those arguments. Here's an example:
def decoMaker(msg):
def deco(func):
#wraps(func)
def wrapper(*args, **kwargs):
print msg
return func(*args, **kwargs)
return wrapper
return deco
class UsefulObject(object):
def __init__(useful_object_self, noun):
useful_object_self.noun = noun
#decoMaker('koalas...')
def red(useful_object_self):
print "red %s"%(useful_object_self.noun)
>>> u = UsefulObject("balloons")
... u.red()
koalas...
red balloons
You can use the decoMaker ahead of time to make a decorator to reuse later, if you don't want to retype the message every time you make the decorator:
sillyDecorator = decoMaker("Some really long message about koalas that you don't want to type over and over")
class UsefulObject(object):
def __init__(useful_object_self, noun):
useful_object_self.noun = noun
#sillyDecorator
def red(useful_object_self):
print "red %s"%(useful_object_self.noun)
>>> u = UsefulObject("balloons")
... u.red()
Some really long message about koalas that you don't want to type over and over
red balloons
You can see that this is much less verbose than writing a whole class inheritance tree for different kinds of decoratorts. Unless you're writing super-complicated decorators that store all sorts of internal state (which is likely to get confusing anyway), this decorator-maker approach might be an easier way to go.

Adapted from http://metapython.blogspot.de/2010/11/python-instance-methods-how-are-they.html. Note that this variant sets attributes on the target instance, hence, without checks, it is possible to overwrite target instance attributes. The code below does not contain any checks for this case.
Also note that this example sets the punctuation attribute explicitly; a more general class could auto-discover it's attributes.
from types import MethodType
class AbstractDecorator(object):
"""Designed to work as function or method decorator """
def __init__(self, function):
self.func = function
self.punctuation = '...'
def __call__(self, *args, **kw):
self.setup()
return self.func(*args, **kw)
def __get__(self, instance, owner):
# TODO: protect against 'overwrites'
setattr(instance, 'punctuation', self.punctuation)
return MethodType(self, instance, owner)
class SillyDecorator(AbstractDecorator):
def setup(self):
print('[setup] silly init %s' % self.punctuation)
class UsefulObject(object):
def __init__(self, noun='cat'):
self.noun = noun
#SillyDecorator
def d(self):
print('Hello %s %s' % (self.noun, self.punctuation))
obj = UsefulObject()
obj.d()
# [setup] silly init ...
# Hello cat ...

Python extension methods

OK, in C# we have something like:
public static string Destroy(this string s) {
return "";
}
So basically, when you have a string you can do:
str = "This is my string to be destroyed";
newstr = str.Destroy()
# instead of
newstr = Destroy(str)
Now this is cool because in my opinion it's more readable. Does Python have something similar? I mean instead of writing like this:
x = SomeClass()
div = x.getMyDiv()
span = x.FirstChild(x.FirstChild(div)) # so instead of this
I'd like to write:
span = div.FirstChild().FirstChild() # which is more readable to me
Any suggestion?

You can just modify the class directly, sometimes known as monkey patching.
def MyMethod(self):
return self + self
MyClass.MyMethod = MyMethod
del(MyMethod)#clean up namespace
I'm not 100% sure you can do this on a special class like str, but it's fine for your user-defined classes.
Update
You confirm in a comment my suspicion that this is not possible for a builtin like str. In which case I believe there is no analogue to C# extension methods for such classes.
Finally, the convenience of these methods, in both C# and Python, comes with an associated risk. Using these techniques can make code more complex to understand and maintain.

You can do what you have asked like the following:
def extension_method(self):
#do stuff
class.extension_method = extension_method

I would use the Adapter pattern here. So, let's say we have a Person class and in one specific place we would like to add some health-related methods.
from dataclasses import dataclass
#dataclass
class Person:
name: str
height: float # in meters
mass: float # in kg
class PersonMedicalAdapter:
person: Person
def __init__(self, person: Person):
self.person = person
def __getattr__(self, item):
return getattr(self.person, item)
def get_body_mass_index(self) -> float:
return self.person.mass / self.person.height ** 2
if __name__ == '__main__':
person = Person('John', height=1.7, mass=76)
person_adapter = PersonMedicalAdapter(person)
print(person_adapter.name) # Call to Person object field
print(person_adapter.get_body_mass_index()) # Call to wrapper object method
I consider it to be an easy-to-read, yet flexible and pythonic solution.

You can change the built-in classes by monkey-patching with the help of forbidden fruit
But installing forbidden fruit requires a C compiler and unrestricted environment so it probably will not work or needs hard effort to run on Google App Engine, Heroku, etc.
I changed the behaviour of unicode class in Python 2.7 for a Turkish i,I uppercase/lowercase problem by this library.
# -*- coding: utf8 -*-
# Redesigned by #guneysus
import __builtin__
from forbiddenfruit import curse
lcase_table = tuple(u'abcçdefgğhıijklmnoöprsştuüvyz')
ucase_table = tuple(u'ABCÇDEFGĞHIİJKLMNOÖPRSŞTUÜVYZ')
def upper(data):
data = data.replace('i',u'İ')
data = data.replace(u'ı',u'I')
result = ''
for char in data:
try:
char_index = lcase_table.index(char)
ucase_char = ucase_table[char_index]
except:
ucase_char = char
result += ucase_char
return result
curse(__builtin__.unicode, 'upper', upper)
class unicode_tr(unicode):
"""For Backward compatibility"""
def __init__(self, arg):
super(unicode_tr, self).__init__(*args, **kwargs)
if __name__ == '__main__':
print u'istanbul'.upper()

You can achieve this nicely with the following context manager that adds the method to the class or object inside the context block and removes it afterwards:
class extension_method:
def __init__(self, obj, method):
method_name = method.__name__
setattr(obj, method_name, method)
self.obj = obj
self.method_name = method_name
def __enter__(self):
return self.obj
def __exit__(self, type, value, traceback):
# remove this if you want to keep the extension method after context exit
delattr(self.obj, self.method_name)
Usage is as follows:
class C:
pass
def get_class_name(self):
return self.__class__.__name__
with extension_method(C, get_class_name):
assert hasattr(C, 'get_class_name') # the method is added to C
c = C()
print(c.get_class_name()) # prints 'C'
assert not hasattr(C, 'get_class_name') # the method is gone from C

I'd like to think that extension methods in C# are pretty much the same as normal method call where you pass the instance then arguments and stuff.
instance.method(*args, **kwargs)
method(instance, *args, **kwargs) # pretty much the same as above, I don't see much benefit of it getting implemented in python.

After a week, I have a solution that is closest to what I was seeking for. The solution consists of using getattr and __getattr__. Here is an example for those who are interested.
class myClass:
def __init__(self): pass
def __getattr__(self, attr):
try:
methodToCall = getattr(myClass, attr)
return methodToCall(myClass(), self)
except:
pass
def firstChild(self, node):
# bla bla bla
def lastChild(self, node):
# bla bla bla
x = myClass()
div = x.getMYDiv()
y = div.firstChild.lastChild
I haven't test this example, I just gave it to give an idea for who might be interested. Hope that helps.

C# implemented extension methods because it lacks first class functions, Python has them and it is the preferred method for "wrapping" common functionality across disparate classes in Python.
There are good reasons to believe Python will never have extension methods, simply look at the available built-ins:
len(o) calls o.__len__
iter(o) calls o.__iter__
next(o) calls o.next
format(o, s) calls o.__format__(s)
Basically, Python likes functions.

Mapping obj.method({argument:value}) to obj.argument(value)

I don't know if this will make sense, but...
I'm trying to dynamically assign methods to an object.
#translate this
object.key(value)
#into this
object.method({key:value})
To be more specific in my example, I have an object (which I didn't write), lets call it motor, which has some generic methods set, status and a few others. Some take a dictionary as an argument and some take a list. To change the motor's speed, and see the result, I use:
motor.set({'move_at':10})
print motor.status('velocity')
The motor object, then formats this request into a JSON-RPC string, and sends it to an IO daemon. The python motor object doesn't care what the arguments are, it just handles JSON formatting and sockets. The strings move_at and velocity are just two of what might be hundreds of valid arguments.
What I'd like to do is the following instead:
motor.move_at(10)
print motor.velocity()
I'd like to do it in a generic way since I have so many different arguments I can pass. What I don't want to do is this:
# create a new function for every possible argument
def move_at(self,x)
return self.set({'move_at':x})
def velocity(self)
return self.status('velocity')
#and a hundred more...
I did some searching on this which suggested the solution lies with lambdas and meta programming, two subjects I haven't been able to get my head around.
UPDATE:
Based on the code from user470379 I've come up with the following...
# This is what I have now....
class Motor(object):
def set(self,a_dict):
print "Setting a value", a_dict
def status(self,a_list):
print "requesting the status of", a_list
return 10
# Now to extend it....
class MyMotor(Motor):
def __getattr__(self,name):
def special_fn(*value):
# What we return depends on how many arguments there are.
if len(value) == 0: return self.status((name))
if len(value) == 1: return self.set({name:value[0]})
return special_fn
def __setattr__(self,attr,value): # This is based on some other answers
self.set({attr:value})
x = MyMotor()
x.move_at = 20 # Uses __setattr__
x.move_at(10) # May remove this style from __getattr__ to simplify code.
print x.velocity()
output:
Setting a value {'move_at': 20}
Setting a value {'move_at': 10}
10
Thank you to everyone who helped!

What about creating your own __getattr__ for the class that returns a function created on the fly? IIRC, there's some tricky cases to watch out for between __getattr__ and __getattribute__ that I don't recall off the top of my head, I'm sure someone will post a comment to remind me:
def __getattr__(self, name):
def set_fn(self, value):
return self.set({name:value})
return set_fn
Then what should happen is that calling an attribute that doesn't exist (ie: move_at) will call the __getattr__ function and create a new function that will be returned (set_fn above). The name variable of that function will be bound to the name parameter passed into __getattr__ ("move_at" in this case). Then that new function will be called with the arguments you passed (10 in this case).
Edit
A more concise version using lambdas (untested):
def __getattr__(self, name):
return lambda value: self.set({name:value})

There are a lot of different potential answers to this, but many of them will probably involve subclassing the object and/or writing or overriding the __getattr__ function.
Essentially, the __getattr__ function is called whenever python can't find an attribute in the usual way.
Assuming you can subclass your object, here's a simple example of what you might do (it's a bit clumsy but it's a start):
class foo(object):
def __init__(self):
print "initting " + repr(self)
self.a = 5
def meth(self):
print self.a
class newfoo(foo):
def __init__(self):
super(newfoo, self).__init__()
def meth2(): # Or, use a lambda: ...
print "meth2: " + str(self.a) # but you don't have to
self.methdict = { "meth2":meth2 }
def __getattr__(self, name):
return self.methdict[name]
f = foo()
g = newfoo()
f.meth()
g.meth()
g.meth2()
Output:
initting <__main__.foo object at 0xb7701e4c>
initting <__main__.newfoo object at 0xb7701e8c>
5
5
meth2: 5

You seem to have certain "properties" of your object that can be set by
obj.set({"name": value})
and queried by
obj.status("name")
A common way to go in Python is to map this behaviour to what looks like simple attribute access. So we write
obj.name = value
to set the property, and we simply use
obj.name
to query it. This can easily be implemented using the __getattr__() and __setattr__() special methods:
class MyMotor(Motor):
def __init__(self, *args, **kw):
self._init_flag = True
Motor.__init__(self, *args, **kw)
self._init_flag = False
def __getattr__(self, name):
return self.status(name)
def __setattr__(self, name, value):
if self._init_flag or hasattr(self, name):
return Motor.__setattr__(self, name, value)
return self.set({name: value})
Note that this code disallows the dynamic creation of new "real" attributes of Motor instances after the initialisation. If this is needed, corresponding exceptions could be added to the __setattr__() implementation.

Instead of setting with function-call syntax, consider using assignment (with =). Similarly, just use attribute syntax to get a value, instead of function-call syntax. Then you can use __getattr__ and __setattr__:
class OtherType(object): # this is the one you didn't write
# dummy implementations for the example:
def set(self, D):
print "setting", D
def status(self, key):
return "<value of %s>" % key
class Blah(object):
def __init__(self, parent):
object.__setattr__(self, "_parent", parent)
def __getattr__(self, attr):
return self._parent.status(attr)
def __setattr__(self, attr, value):
self._parent.set({attr: value})
obj = Blah(OtherType())
obj.velocity = 42 # prints setting {'velocity': 42}
print obj.velocity # prints <value of velocity>