I have class MyList that inherits from list. When I pass instance of list ie. [0, 3, 5, 1] to MyList, how to construct MyList to avoid copy and have self have no-copy reference to other content.
I have tried with:
other.__class__ = MyList : gives TypeError
and with
super(MyList, cls).__new__(other) : gives TypeError
and with
super(MyList, other) : gives TypeError
lastly with
self[:] = other[:] : gives id(self) != id(other)
Also simple MyList([0, 1, 3, 4]) would not solve problem when I do some operations in-place inside MyList.
class MyList(list):
def __new__(cls, other):
other.__class__ = MyList
return other
# add bunch of methods that work inplace on list
def merge(self,):
pass
def sort(self,):
pass
def find(self, x):
pass
def nextNonMember(self, x):
pass
Alternative way that I want to avoid is:
class MyNotSoFancyList(object):
def __init__(self, other):
self.list = other
I expect to have this behavior:
t = [0, 1, 3, 100, 20, 4]
o = MyList(t)
o.sort()
assert(t == o)
Question is probably not so trivial one for me when I dont know Python on "low" level. It seems its not possible. Thus I wanted to ask, maybe someone knows some trick xD.
EDIT
Until now there was one hint in message to be deleted. Need some time to digest it, so will keep it here:
#RobertGRZELKA I think I kinda got to a conclusion with myself that this simply can't be done. As when you create an object of the class, it instantiates a new list in memory and references it. So if want to reference another list, there is no point in the new object. Bottom line I believe you will have to have the reference as an attribute of the class, implement your methods, and then override the list methods you are going to use so that they work on the referenced list. Tell me when you read that and I will delete this answer – Tomerikoo 2 hours ago
Try this
class MyList(list):
def __init__(self,value):
self.extend(value)
I dont really understand why you would want it unless you want to add more methods to the list object. but that should give you a list
t = [0, 1, 3, 100, 20, 4]
o = MyList(t)
o.sort()
t.sort()
assert(t==o)
Related
I am creating a class that inherits from collections.UserList that has some functionality very similar to NumPy's ndarray (just for exercise purposes). I've run into a bit of a roadblock regarding recursive functions involving the modification of class attributes:
Let's take the flatten method, for example:
class Array(UserList):
def __init__(self, initlist):
self.data = initlist
def flatten(self):
# recursive function
...
Above, you can see that there is a singular parameter in the flatten method, being the required self parameter. Ideally, a recursive function should take a parameter which is passed recursively through the function. So, for example, it might take a lst parameter, making the signature:
Array.flatten(self, lst)
This solves the problem of having to set lst to self.data, which consequently will not work recursively, because self.data won't be changed. However, having that parameter in the function is going to be ugly in use and hinder the user experience of an end user who may be using the function.
So, this is the solution I've come up with:
def flatten(self):
self.data = self.__flatten(self.data)
def __flatten(self, lst):
...
return result
Another solution could be to nest __flatten in flatten, like so:
def flatten(self):
def __flatten(lst):
...
return result
self.data = __flatten(self.data)
However, I'm not sure if nesting would be the most readable as flatten is not the only recursive function in my class, so it could get messy pretty quickly.
Does anyone have any other suggestions? I'd love to know your thoughts, thank you!
A recursive method need not take any extra parameters that are logically unnecessary for the method to work from the caller's perspective; the self parameter is enough for recursion on a "child" element to work, because when you call the method on the child, the child is bound to self in the recursive call. Here is an example:
from itertools import chain
class MyArray:
def __init__(self, data):
self.data = [
MyArray(x) if isinstance(x, list) else x
for x in data]
def flatten(self):
return chain.from_iterable(
x.flatten() if isinstance(x, MyArray) else (x,)
for x in self.data)
Usage:
>>> a = MyArray([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
>>> list(a.flatten())
[1, 2, 3, 4, 5, 6, 7, 8]
Since UserList is an iterable, you can use a helper function to flatten nested iterables, which can deal likewise with lists and Array objects:
from collections import UserList
from collections.abc import Iterable
def flatten_iterable(iterable):
for item in iterable:
if isinstance(item, Iterable):
yield from flatten_iterable(item)
else:
yield item
class Array(UserList):
def __init__(self, initlist):
self.data = initlist
def flatten(self):
self.data = list(flatten_iterable(self.data))
a = Array([[1, 2], [3, 4]])
a.flatten(); print(a) # prints [1, 2, 3, 4]
b = Array([Array([1, 2]), Array([3, 4])])
b.flatten(); print(b) # prints [1, 2, 3, 4]
barrier of abstraction
Write array as a separate module. flatten can be generic like the example implementation here. This differs from a_guest's answer in that only lists are flattened, not all iterables. This is a choice you get to make as the module author -
# array.py
from collections import UserList
def flatten(t): # generic function
if isinstance(t, list):
for v in t:
yield from flatten(v)
else:
yield t
class array(UserList):
def flatten(self):
return list(flatten(self.data)) # specialization of generic function
why modules are important
Don't forget you are the module user too! You get to reap the benefits from both sides of the abstraction barrier created by the module -
As the author, you can easily expand, modify, and test your module without worrying about breaking other parts of your program
As the user, you can rely on the module's features without having to think about how the module is written or what the underlying data structures might be
# main.py
from array import array
t = array([1,[2,3],4,[5,[6,[7]]]]) # <- what is "array"?
print(t.flatten())
[1, 2, 3, 4, 5, 6, 7]
As the user, we don't have to answer "what is array?" anymore than you have to answer "what is dict?" or "what is iter?" We use these features without having to understand their implementation details. Their internals may change over time, but if the interface stays the same, our programs will continue to work without requiring change.
reusability
Good programs are reusable in many ways. See python's built-in functions for proof of this, or see the the guiding principles of the Unix philosophy -
Write programs that do one thing and do it well.
Write programs to work together.
If you wanted to use flatten in other areas of our program, we can reuse it easily -
# otherscript.py
from array import flatten
result = flatten(something)
Typically, all methods of a class have at least one argument which is called self in order to be able to reference the actual object this method is called on.
If you don't need self in your function, but you still want to include it in a class, you can use #staticmethod and just include a normal function like this:
class Array(UserList):
def __init__(self, initlist):
self.data = initlist
#staticmethod
def flatten():
# recursive function
...
Basically, #staticmethod allows you to make any function a method that can be called on a class or an instance of a class (object). So you can do this:
arr = Array()
arr.flatten()
as well as this:
Array.flatten()
Here is some further reference from Pyhon docs: https://docs.python.org/3/library/functions.html#staticmethod
I'm working through the Building Skills in Object Oriented Design in python and am on the wheel section for roulette. We've created a "Bin" class as an extended class from frozenset which will represent each of the positions on the roulette wheel. We then create a tuple of 38 empty "Bins", and now have to create class methods to be able to add odds/outcomes to the Bins.
My problem is that I've not been able to create a method to modify the Bin in position without the result not reverting to the frozenset class.
My desired output is to have:
class Bin(frozenset):
def add(self, other):
....do union of Bin class....
one = Bin(1, 2, 3)
two = Bin(4, 5)
one.add(two)
print(one)
>>> Bin(1, 2, 3, 4, 5)
Stuff I've tried
Extending the frozenset class with no methods defined/overridden
class Bin(frozenset):
pass
one = Bin([1,2,3])
two = Bin([4,5,6])
print(one|two)
print(type(one|two))
Which returns
frozenset({1, 2, 3, 4, 5, 6})
<class 'frozenset'>
I would have expected that by extending the class and using one of the extended methods that the output would remain as the "Bin" class.
I've also tried overriding the __ ror__ & union methods with the same result. I've tried to create a method which to brute force return the desired output. This however does not allow me to change the tuple of Bins as it doesn't operate in place
class Bin(frozenset):
def add(self, other):
self = Bin(self|other)
return self
one = Bin([1,2,3])
two = Bin([4,5,6])
one.add(two)
print(one)
Which returns
Bin({1, 2, 3})
Any insight into where in falling down in my thinking would and/or recommendations of stuff to read for further insight would be great.
frozenset.__or__ (which is called by the default implementation of Bin.__or__ when 'triggered' by one | two) has no idea that frozenset was subclassed by Bin, and that it should return a Bin instance.
You should implement Bin.__or__ and force it to return a Bin instance:
class Bin(frozenset):
def __or__(self, other):
# be wary of infinite recursion if using | incorrectly here,
# better to use the underlying __or__
return Bin(super().__or__(other))
one = Bin([1, 2, 3])
two = Bin([4, 5, 6])
print(one | two)
print(type(one | two))
Outputs
Bin({1, 2, 3, 4, 5, 6})
<class '__main__.Bin'>
You need to do something like this (to avoid infinite recursion):
class Bin(frozenset):
def __or__(self, other):
return Bin(frozenset(self) | other)
If you make a class as such in Python:
import numpy as np
class Foo:
def __init__(self, data):
self.data = data
self.data_copy = self.copy(self.data)
def copy(self, data):
a = []
for e in data:
a.append(e)
return a
def change(self, val):
for i in range(0, len(self.data_copy)):
self.data_copy[i] += val
And then create an instance of the class such as:
a = Foo([np.matrix([1,2,3]), np.matrix([5,6,7])])
Now if we call the a.change(np.matrix([5,5,2])) function, which should only modify the self.data_copy list, the self.data list also updates with the changes. It appears, even after making a new list, the Numpy matrices in the two lists remain linked.
This is a nice feature in some respects, but does not work had I passed in an ordinary Python list of numbers. Is this a bug, or just a side-effect of how Numpy matrices are copied? And if so, what's the best way to replicate the behaviour with ordinary Python lists?
When you make your "copy", you're just making a new list that contains the same objects as the old list. That is, when you iterate through data you're iterating through references to the objects in it, and when you append e you're appending a reference rather than a new or copied object. Thus any changes to those objects will be visible in any other list that references them. It seems like what you want is copies of the actual matrices. To do this, in your copy method, instead of appending e append something like numpy.array(e, copy=True). This will create true copies of the matrices and not just new references to the old ones.
More generally, Python objects are effectively always passed by reference. This doesn't matter for immutable objects (strings, integers, tuples, etc), but for lists, dictionaries, or user defined classes that can mutate, you will need to make explicit copies. Often the built in copy module, or simply constructing a new object directly from the old, is what you want to do.
Edit: I now see what you mean. I had slightly misunderstood your original question. You're referring to += mutating the matrix objects rather than truly being = self + other. This is simply a fact of how += works for most Python collection types. += is in fact a separate method, distinct from assigning the result of adding. You will still see this behavior with normal Python lists.
a = [1, 2, 3]
b = a
b += [4]
>>> print(a)
[1, 2, 3, 4]
You can see that the += is mutating the original object rather than creating a new one and setting b to reference it. However if you do:
b = b + [4]
>>> print(a)
[1, 2, 3]
>>> print(b)
[1, 2, 3, 4]
This will have the desired behavior. The + operator for collections (lists, numpy arrays) does indeed return a new object, however += usually just mutates the old one.
I would like to extend the list functionality, so we can used as below. How can add this methods to the list object?
# list([1,2,3,4,5]).even() should return [2,4]
You can't monkey patch list because it is defined in C extension modules and is therefore inmutable in this sense. You can subclass list:
class mylist(list):
def even(self):
return [x for x in self if x % 2 == 0]
>>> mylist([1,2,3,4,5]).even()
[2, 4]
I am befuddled over what I think is a very simple and straight forward subclass of a list in Python.
Suppose I want all the functionality of list. I want to add several methods to the default set of a list.
The following is an example:
class Mylist(list):
def cm1(self):
self[0]=10
def cm2(self):
for i,v in enumerate(self):
self[i]=self[i]+10
def cm3(self):
self=[]
def cm4(self):
self=self[::-1]
def cm5(self):
self=[1,2,3,4,5]
ml=Mylist([1,2,3,4])
ml.append(5)
print "ml, an instance of Mylist: ",ml
ml.cm1()
print "cm1() works: ",ml
ml.cm2()
print "cm2() works: ",ml
ml.cm3()
print "cm3() does NOT work as expected: ",ml
ml.cm4()
print "cm4() does NOT work as expected: ",ml
ml.cm5()
print "cm5() does NOT work as expected: ",ml
The output:
Mylist: [1, 2, 3, 4, 5]
cm1() works: [10, 2, 3, 4, 5]
cm2() works: [20, 12, 13, 14, 15]
cm3() does NOT work as expected: [20, 12, 13, 14, 15]
cm4() does NOT work as expected: [20, 12, 13, 14, 15]
cm5() does NOT work as expected: [20, 12, 13, 14, 15]
So it seems that a scalar assignment works as I expect and understand. List or slices do not work as I understand. By 'does not work,' I mean that the code in the method does not change the instance of ml as the first two methods do.
What do I need to do so that cm3() cm4() and cm5() work?
The problem here is that in cm3, cm4, and cm5, you are not modifying the object! You are creating a new one in the scope of the member function, and then assigning it to self. The outer scope doesn't respect this. In cm1 and cm2, you are modifying the same object, so the object stays the same.
Try using the id function to debug this:
def cm4(self):
self=self[::-1]
print 'DEBUG', id(self)
...
m1.cm4()
print 'DEBUG', id(self)
You'll see that the id is different.
So, you might ask, well how do I do this? You are lucky that with lists you can assign into a splice. This might not be as easy with other data structures. What this does is keeps the same list, but replaces the items. To do this, do:
self[:] = ...
So, for example:
self[:] = self[::-1]
Your misunderstanding is that there is something special about the word self. In those methods it is just a name in scope like any other name in python, so when you reassign it, you are just rebinding the name self to some other object - not mutating the parent object. In fact that argument doesn't even need to be named self, that is only a convention that python programmers use.
Here is reimplementation of your members to mutate properly:
def cm3(self):
self[:] = []
def cm4(self):
self.reverse()
def cm5(self):
self[:] = [1,2,3,4,5]
use self[:] instead of self, self = ... will rebind self variable to other object only, that will not change the list object.
def cm3(self):
self[:]=[]
def cm4(self):
self[:]=self[::-1]
def cm5(self):
self[:]=[1,2,3,4,5]