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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'>

How can I return self and another variable in a python class method while method chaining?

I understand what I am asking here is probably not the best code design, but the reason for me asking is strictly academic. I am trying to understand how to make this concept work.
Typically, I will return self from a class method so that the following methods can be chained together. My understanding is by returning self, I am simply returning an instance of the class, for the following methods to work on.
But in this case, I am trying to figure out how to return both self and another value from the method. The idea is if I do not want to chain, or I do not call any class attributes, I want to retrieve the data from the method being called.
Consider this example:
class Test(object):
def __init__(self):
self.hold = None
def methoda(self):
self.hold = 'lol'
return self, 'lol'
def newmethod(self):
self.hold = self.hold * 2
return self, 2
t = Test()
t.methoda().newmethod()
print(t.hold)
In this case, I will get an AttributeError: 'tuple' object has no attribute 'newmethod' which is to be expected because the methoda method is returning a tuple which does not have any methods or attributes called newmethod.
My question is not about unpacking multiple returns, but more about how can I continue to chain methods when the preceding methods are returning multiple values. I also understand that I can control the methods return with an argument to it, but that is not what I am trying to do.
As mentioned previously, I do realize this is probably a bad question, and I am happy to delete the post if the question doesnt make any sense.

Following the suggestion by #JohnColeman, you can return a special tuple with attribute lookup delegated to your object if it is not a normal tuple attribute. That way it acts like a normal tuple except when you are chaining methods.
You can implement this as follows:
class ChainResult(tuple):
def __new__(cls, *args):
return super(ChainResult, cls).__new__(cls, args)
def __getattribute__(self, name):
try:
return getattr(super(), name)
except AttributeError:
return getattr(super().__getitem__(0), name)
class Test(object):
def __init__(self):
self.hold = None
def methoda(self):
self.hold = 'lol'
return ChainResult(self, 'lol')
def newmethod(self):
self.hold = self.hold * 2
return ChainResult(self, 2)
Testing:
>>> t = Test()
>>> t.methoda().newmethod()
>>> print(t.hold)
lollol
The returned result does indeed act as a tuple:
>>> t, res = t.methoda().newmethod()
>>> print(res)
2
>>> print(isinstance(t.methoda().newmethod(), tuple))
True
You could imagine all sorts of semantics with this, such as forwarding the returned values to the next method in the chain using closure:
class ChainResult(tuple):
def __new__(cls, *args):
return super(ChainResult, cls).__new__(cls, args)
def __getattribute__(self, name):
try:
return getattr(super(), name)
except AttributeError:
attr = getattr(super().__getitem__(0), name)
if callable(attr):
chain_results = super().__getitem__(slice(1, None))
return lambda *args, **kw: attr(*(chain_results+args), **kw)
else:
return attr
For example,
class Test:
...
def methodb(self, *args):
print(*args)
would produce
>>> t = Test()
>>> t.methoda().methodb('catz')
lol catz
It would be nice if you could make ChainResults invisible. You can almost do it by initializing the tuple base class with the normal results and saving your object in a separate attribute used only for chaining. Then use a class decorator that wraps every method with ChainResults(self, self.method(*args, **kw)). It will work okay for methods that return a tuple but a single value return will act like a length 1 tuple, so you will need something like obj.method()[0] or result, = obj.method() to work with it. I played a bit with delegating to tuple for a multiple return or to the value itself for a single return; maybe it could be made to work but it introduces so many ambiguities that I doubt it could work well.

Monkeypatch with instance method

I'm trying to monkeypatch how pandas Panel's slicing (__getitem__). This is straightforward to do with a basic function, foo.
from pandas import Panel
Panel.__getitem__ = ORIGINAL_getitem
def newgetitem(panel, *args, **kwargs):
""" Append a string to return of panel.__getitem__"""
out = super(Panel, panel).__getitem__(*args, **kwargs)
return out+'custom stuff added'
Panel.__getitem__ = newgetitem
WhereORIGINAL_getitem is storing the original Panel method. I'm trying to extend to the case where foo() is not a function, but an instance method of an object, Foo. For example:
class Foo:
name = 'some name'
def newgetitem(self, panel, *args, **kwargs):
""" Append a string to return of panel.__getitem__,
but take attributes from self, like self.name
"""
out = super(Panel, panel).__getitem__(*args, **kwargs)
return out+'custom stuff added including name' + self.name
Foo.foo() must access the attribute self.name. Therefore, the monkeypatched function would need a reference to the Foo instance somehow, in addition to the Panel. How can I monkepatch panel with Foo.foo() and make self.name accessible?
The switching between the monkey patched function happens in another method, Foo.set_backend()
class Foo:
name = 'some name'
def foo(self):
return 'bar, called by %s' % self.name
def set_backend(self, backend):
""" Swap between new or original slicing."""
if backend != 'pandas':
Panel.__getitem__ = newgetitem
else:
Panel.__getitem__ = ORIGINAL_getitem
What I really need is for newgetitem to maintain a reference to self.
Solution Attempts
So far I've tried taking making newgetitem() a pure function, and using partial functions to pass a reference to self in. This doesn't work. Something like:
import functools
def newgetitem(foo_instance, panel, *args, **kwargs):
....
class Foo:
...
def set_backend(self, backend):
""" Swap between new or original slicing."""
if backend != 'pandas':
partialfcn = functools.partial(newgetitem, self)
Panel.__getitem__ = partialfcn
else:
Panel.__getitem__ = ORIGINAL_getitem
But this doesn't work. A reference to self is passed, but no access from the calling Panel possible. That is:
panel['50']
Passes a reference to Foo, not to Panel.
Yes, I know this is bad practice, but it's just a workaround for the time-being.

You can use patch from mock framework to handle your case. Even it is designed for testing, its primary work is monkey patching in defined contex.
Your set_backend() method could be:
def set_backend(self, backend):
if backend != 'pandas' and self._patched_get_item is None:
self._patched_get_item = patch("pandas.Panel.__getitem__", autospec=True, side_effect=self._getitem)
self._patched_get_item.start()
elif backend == 'pandas' and self._patched_get_item is not None:
self._patched_get_item.stop()
self._patched_get_item = None
That will work either when self._getitem is a method or a reference to a function.

One way to do this is to create a closure (a function with reference to names other than locals or globals). A simple closure:
def g(x):
def f():
"""f has no global or local reference to x, but can refer to the locals of the
context it was created in (also known as nonlocals)."""
return x
return f
func = g(1)
assert func() == 1
I don't have pandas on my system, but it works much the same with a dict.
class MyDict(dict):
pass
d = MyDict(a=1, b=2)
assert d['a'] == 1
class Foo:
name = 'name'
def create_getitem(fooself, cls):
def getitem(self, *args, **kwargs):
out = super(cls, self).__getitem__(*args, **kwargs)
return out, 'custom', fooself.name
# Above references fooself, a name that is not defined locally in the
# function, but as part of the scope the function was created in.
return getitem
MyDict.__getitem__ = Foo().create_getitem(MyDict)
assert d['a'] == (1, 'custom', Foo.name)
print(d['a'])

The basics of monkey patching are straightforward but it can quickly become tricky and subtle, especially if you're aiming at finding a solution that would work for both Python 2 and Python 3.
Furthermore, quickly hacked solutions are usually not very readable/maintenable, unless you manage to wrap the monkey patching logic nicely.
That's why I invite you to have a look at a library that I wrote especially for this purpose. It is named Gorilla and you can find it on GitHub.
In short, it provides a cool set of features, it has a wide range of unit tests, and it comes with a fancy doc that should cover everything you need to get started. Make sure to also check the FAQ!

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>

How to implement property() with dynamic name (in python)

I am programming a simulations for single neurons. Therefore I have to handle a lot of Parameters. Now the Idea is that I have two classes, one for a SingleParameter and a Collection of parameters. I use property() to access the parameter value easy and to make the code more readable. This works perfect for a sinlge parameter but I don't know how to implement it for the collection as I want to name the property in Collection after the SingleParameter. Here an example:
class SingleParameter(object):
def __init__(self, name, default_value=0, unit='not specified'):
self.name = name
self.default_value = default_value
self.unit = unit
self.set(default_value)
def get(self):
return self._v
def set(self, value):
self._v = value
v = property(fget=get, fset=set, doc='value of parameter')
par1 = SingleParameter(name='par1', default_value=10, unit='mV')
par2 = SingleParameter(name='par2', default_value=20, unit='mA')
# par1 and par2 I can access perfectly via 'p1.v = ...'
# or get its value with 'p1.v'
class Collection(object):
def __init__(self):
self.dict = {}
def __getitem__(self, name):
return self.dict[name] # get the whole object
# to get the value instead:
# return self.dict[name].v
def add(self, parameter):
self.dict[parameter.name] = parameter
# now comes the part that I don't know how to implement with property():
# It shoule be something like
# self.__dict__[parameter.name] = property(...) ?
col = Collection()
col.add(par1)
col.add(par2)
col['par1'] # gives the whole object
# Now here is what I would like to get:
# col.par1 -> should result like col['par1'].v
# col.par1 = 5 -> should result like col['par1'].v = 5
Other questions that I put to understand property():
Why do managed attributes just work for class attributes and not for instance attributes in python?
How can I assign a new class attribute via __dict__ in python?

Look at built-in functions getattr and setattr. You'll probably be a lot happier.

Using the same get/set functions for both classes forces you into an ugly hack with the argument list. Very sketchy, this is how I would do it:
In class SingleParameter, define get and set as usual:
def get(self):
return self._s
def set(self, value):
self._s = value
In class Collection, you cannot know the information until you create the property, so you define the metaset/metaget function and particularize them only later with a lambda function:
def metaget(self, par):
return par.s
def metaset(self, value, par):
par.s = value
def add(self, par):
self[par.name] = par
setattr(Collection, par.name,
property(
fget=lambda x : Collection.metaget(x, par),
fset=lambda x, y : Collection.metaset(x,y, par))

Properties are meant to dynamically evaluate attributes or to make them read-only. What you need is customizing attribute access. __getattr__ and __setattr__ do that really fine, and there's also __getattribute__ if __getattr__ is not enough.
See Python docs on customizing attribute access for details.

Have you looked at the traits package? It seems that you are reinventing the wheel here with your parameter classes. Traits also have additional features that might be useful for your type of application (incidently I know a person that happily uses traits in neural simulations).

Now I implemented a solution with set-/getattr:
class Collection(object):
...
def __setattr__(self, name, value):
if 'dict' in self.__dict__:
if name in self.dict:
self[name].v = value
else:
self.__dict__[name] = value
def __getattr__(self, name):
return self[name].v
There is one thing I quite don't like that much: The attributes are not in the __dict__. And if I have them there as well I would have a copy of the value - which can be dangerous...

Finally I succeded to implement the classes with property(). Thanks a lot for the advice. It took me quite a bit to work it out - but I can promise you that this exercise helps you to understand better pythons OOP.
I implemented it also with __getattr__ and __setattr__ but still don't know the advantages and disadvantages to the property-solution. But this seems to be worth another question. The property-solutions seems to be quit clean.
So here is the code:
class SingleParameter(object):
def __init__(self, name, default_value=0, unit='not specified'):
self.name = name
self.default_value = default_value
self.unit = unit
self.set(default_value)
def get(*args):
self = args[0]
print "get(): "
print args
return self._v
def set(*args):
print "set(): "
print args
self = args[0]
value = args[-1]
self._v = value
v = property(fget=get, fset=set, doc='value of parameter')
class Collection(dict):
# inheriting from dict saves the methods: __getitem__ and __init__
def add(self, par):
self[par.name] = par
# Now here comes the tricky part.
# (Note: this property call the get() and set() methods with one
# more argument than the property of SingleParameter)
setattr(Collection, par.name,
property(fget=par.get, fset=par.set))
# Applying the classes:
par1 = SingleParameter(name='par1', default_value=10, unit='mV')
par2 = SingleParameter(name='par2', default_value=20, unit='mA')
col = Collection()
col.add(par1)
col.add(par2)
# Setting parameter values:
par1.v = 13
col.par1 = 14
# Getting parameter values:
par1.v
col.par1
# checking identity:
par1.v is col.par1
# to access the whole object:
col['par1']
As I am new I am not sure how to move on:
how to treat follow up questions (like this itself):
get() is seems to be called twice - why?
oop-design: property vs. "__getattr__ & __setattr__" - when should I use what?
is it rude to check the own answer to the own question as accepted?
is it recommended to rename the title in order to put correlated questions or questions elaborated with the same example into the same context?
Other questions that I put to understand property():
Why do managed attributes just work for class attributes and not for instance attributes in python?
How can I assign a new class attribute via __dict__ in python?

I have a class that does something similar, but I did the following in the collection object:
setattr(self, par.name, par.v)