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
Related
This feels like a fairly simple concept I'm trying to do.
Just as an example:
Say I have a list [1, 2, 3, 4]
That changes to [2, 3, 4, 1]
I need to be able to identify the change so that I can represent and update the data in JSON without updating the entire list.
Bit of background - This is for use in MIDI, the actual lists can be quite a bit longer than this, and the JSON can be nested with varying complexity. There also may be more than a single change occurring at once. It's not going to be possible to update the entire JSON or nested lists due to time complexity. I am doing it this way currently but in order to expand I need to be able to identify when a specific change occurs and have some way of representing this. It needs to be doable in Python 2 WITHOUT any external packages as it's being used in a Python installation that's embedded within a DAW (Ableton Live).
Does anyone know of anything that may help with this problem? Any help or reading material would be greatly appreciated.
EDIT:
I've tried looping over both lists and comparing the values but this detects it as a change in all values which is no faster than just resending the whole list, potentially much slower as I've got two nested for loops first THEN still send the entire list out over MIDI.
how about this, make a class that track its changes, for example
#from collections.abc import MutableSequence #this for python 3.3+
from collections import MutableSequence
class TrackingList(MutableSequence):
"""list that track its changes"""
def __init__(self,iterable=()):
self.data = list(iterable)
self.changes =[]
def __len__(self):
return len(self.data)
def __getitem__(self,index):
return self.data[index]
def __setitem__(self,index,value):
self.data[index]=value
self.changes.append(("set",index,value))
def __delitem__(self,index):
del self.data[index]
self.changes.append(("del",index))
def insert(self,index,value):
self.data.insert(index,value)
self.changes.append(("insert",index,value))
def __str__(self):
return str(self.data)
example use
>>> tl=TrackingList([1,2,3,4])
>>> print(tl)
[1, 2, 3, 4]
>>> tl.changes
[]
>>> tl[0],tl[-1] = tl[-1],tl[0]
>>> print(tl)
[4, 2, 3, 1]
>>> tl.changes
[('set', 0, 4), ('set', -1, 1)]
>>> tl.append(32)
>>> tl.changes
[('set', 0, 4), ('set', -1, 1), ('insert', 4, 32)]
>>> print(tl)
[4, 2, 3, 1, 32]
>>>
the collections.abc make it easy to make container classes and you get for free a bunch of method, in the case MutableSequence those are: append, reverse, extend, pop, remove, __iadd__, __contains__, __iter__, __reversed__, index, and count
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)
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)
I have a class that handles a Numpy matrix and some additional infos.
import numpy as np
class MyClass:
def __init__(self, v):
self.values = v
plop = MyClass(np.matrix([[1, 2], [3, 4]]))
The matrix being named values, to access it, I write:
plop.values[1, 1] # Returns 4
Is it possible to access it directly? I mean, doing:
plop[1, 1] # Should returns 4 too
I saw this post but it seams that this solution allows only one level of [].
Thanks!
Just add this method to you class
def __getitem__(self, indices):
return self.values[indices]
Also, given the opportunity, it would be useful to see how __getitem__ and slice objects work
you access it directly I think.
plop = np.matrix([[1, 2], [3, 4]])
plot[1, 1]
I have been thinking about a class which could be useful for list transformations.
Here is my current implementation:
class ListTransform(object):
"""Specs: stores original list + transformations.
Transformations are stored in a list.
Every transformation is a func call, with
one parameter, transformations are done in place.
"""
def __init__(self, _list):
self.orig_list = _list
self.reset()
def addtransform(self,t):
self.transforms.append(t)
def reset(self, ts = []):
self.transforms = ts
def getresult(self):
li = self.orig_list[:] # start from a copy from the original
# call all the in-place transform functions in order
for transform in self.transforms:
transform(li)
return li
def pick_transform(pickindexes):
"""Only includes elements with specific indexes
"""
def pt(li):
newli = []
for idx in pickindexes:
newli.append(li[idx])
del li[:] # clear all the elements
li.extend(newli)
return pt
def map_transform(fn_for_every_element):
"""Creates a transformation, which will call a specific
function for every element in a list
"""
def mt(li):
newli = map(fn_for_every_element, li)
del li[:] # clear
li.extend(newli)
return mt
# example:
# the object which stores the original list and the transformations
li = ListTransform([0,10,20,30,40,50,60,70,80,90])
# transformations
li.addtransform(map_transform(lambda x: x + (x/10)))
li.addtransform(pick_transform([5,6,7]))
# getting result, prints 55, 66, 77
print li.getresult()
This works well, however, the feeling of implementing something in a substandard manner bothers me.
What Python features would you use in this implementation, I haven't used? How would you improve the overall design/ideas behind this class? How would you improve the code?
Also, since reinventing the wheel feels awkward: what are the standard tools replacing this class?
Thanks.
Having a general scope and not a particular use case in mind, I would look at this in a more "functional" way:
Don't make the tranformations in place -- rather return new lists. This is how standard functions in functional programming work (and also map(), filter() and reduce() in Python).
Concentrate on the transformations rather than on the data. In particular, I would not create a class like your ListTransform at all, but rather only have some kind of transformation objects that can be chained.
To code this having functional programming in mind, the transforms would simply be functions, just like in your design. All you would need in addition is some kind of composition for the transforms:
def compose(f, g):
return lambda lst: f(g(lst))
(For the sake of simplicity the given implementation has only two parameters instead of an arbitrary number.) Your example would now be very simple:
from functools import partial
map_transform = partial(map, lambda x: x + (x/10))
pick_transform = lambda lst: [lst[i] for i in (5,6,7)]
transform = compose(pick_transform, map_transform)
print transform([0,10,20,30,40,50,60,70,80,90])
# [55, 66, 77]
An alternative would be to implement the transforms as classes instead of functions.
Do not use an empty list as default argument. Use None and test for it:
def some_method(self, arg=None):
if arg is None:
arg = []
do_your_thing_with(arg)
I's a well known Python's beginner pitfall.
You could extend the list class itself, and apply the transforms lazily as the elements are needed. Here is a short implementation - it does not allow for index manipulation on the transforms, but you can apply any mapping transform in a stack.
class ListTransform(list):
def __init__(self, *args):
list.__init__(self, *args)
self.transforms = []
def __getitem__(self, index):
return reduce(lambda item, t: t(item), self.transforms, list.__getitem__(self, index))
def __iter__(self):
for index in xrange(len(self)):
yield self[index]
def __repr__(self):
return "'[%s]'" % ", ".join(repr(item) for item in self)
__str__ = lambda s: repr(s).strip("'")
And you are ready to go:
>>> a = ListTransform( range(10))
>>> a
'[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]'
>>> a.transforms.append(lambda x: 2 * x)>>> a
'[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]'
>>> a.transforms.append(lambda x: x + 5)
>>> a
'[5, 7, 9, 11, 13, 15, 17, 19, 21, 23]'
>>> a.append(0)
>>> a
'[5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 5]'
Ok - I may have overreached with the "reduce" call in the getitem method - but that is the fun part. :-)
Feel free to rewrite it in more lines for readability:
def __getitem__(self, index):
item = list.__getitem__(self, index)
for t in self.transforms:
item = t(item)
return item
If you like the idea, you could include a "filter" member to create filtering functions for the items, and check for the number of parameters on the transforms to allow them to work with indexes, and even reach other list items.