I want an algorithm to iterate over list slices. Slices size is set outside the function and can differ.
In my mind it is something like:
for list_of_x_items in fatherList: foo(list_of_x_items)
Is there a way to properly define
list_of_x_items or some other way of doing this using python 2.5?
edit1: Clarification Both “partitioning” and “sliding window” terms sound applicable to my task, but I am no expert. So I will explain the problem a bit deeper and add to the question:
The fatherList is a multilevel numpy.array I am getting from a file. Function has to find averages of series (user provides the length of series) For averaging I am using the
mean() function. Now for question expansion:
edit2: How to modify the function you have provided to store the extra items and use them when the next fatherList is fed to the function?
for example if the list is lenght 10 and size of a chunk is 3, then the 10th member of the list is stored and appended to the beginning of the next list.
Answer to the last part of the question:
question update: How to modify the
function you have provided to store
the extra items and use them when the
next fatherList is fed to the
If you need to store state then you can use an object for that.
class Chunker(object): """Split `iterable` on evenly sized chunks. Leftovers are remembered and yielded at the next call. """ def __init__(self, chunksize): assert chunksize > 0 self.chunksize = chunksize self.chunk =  def __call__(self, iterable): """Yield items from `iterable` `self.chunksize` at the time.""" assert len(self.chunk) < self.chunksize for item in iterable: self.chunk.append(item) if len(self.chunk) == self.chunksize: # yield collected full chunk yield self.chunk self.chunk = 
chunker = Chunker(3) for s in "abcd", "efgh": for chunk in chunker(s): print ''.join(chunk) if chunker.chunk: # is there anything left? print ''.join(chunker.chunk)
abc def gh
If you want to divide a list into slices you can use this trick:
list_of_slices = zip(*(iter(the_list),) * slice_size)
zip(*(iter(range(10)),) * 3) [(0, 1, 2), (3, 4, 5), (6, 7, 8)]
If the number of items is not dividable by the slice size and you want to pad the list with None you can do this:
map(None, *(iter(range(10)),) * 3) [(0, 1, 2), (3, 4, 5), (6, 7, 8), (9, None, None)]
It is a dirty little trick
OK, I’ll explain how it works. It’ll be tricky to explain but I’ll try my best.
First a little background:
In Python you can multiply a list by a number like this:
[1, 2, 3] * 3 -> [1, 2, 3, 1, 2, 3, 1, 2, 3] ([1, 2, 3],) * 3 -> ([1, 2, 3], [1, 2, 3], [1, 2, 3])
And an iterator object can be consumed once like this:
iter([1, 2, 3]) l.next() 1 l.next() 2 l.next() 3l=
The zip function returns a list of tuples, where the i-th tuple contains the i-th element from each of the argument sequences or iterables. For example:
zip([1, 2, 3], [20, 30, 40]) -> [(1, 20), (2, 30), (3, 40)] zip(*[(1, 20), (2, 30), (3, 40)]) -> [[1, 2, 3], [20, 30, 40]]
The * in front of zip used to unpack arguments. You can find more details here.
zip(*[(1, 20), (2, 30), (3, 40)])
is actually equivalent to
zip((1, 20), (2, 30), (3, 40))
but works with a variable number of arguments
Now back to the trick:
list_of_slices = zip(*(iter(the_list),) * slice_size)
iter(the_list) -> convert the list into an iterator
(iter(the_list),) * N -> will generate an N reference to the_list iterator.
zip(*(iter(the_list),) * N) -> will feed those list of iterators into zip. Which in turn will group them into N sized tuples. But since all N items are in fact references to the same iterator
iter(the_list) the result will be repeated calls to
next() on the original iterator
I hope that explains it. I advice you to go with an easier to understand solution. I was only tempted to mention this trick because I like it.
If you want to be able to consume any iterable you can use these functions:
from itertools import chain, islice def ichunked(seq, chunksize): """Yields items from an iterator in iterable chunks.""" it = iter(seq) while True: yield chain([it.next()], islice(it, chunksize-1)) def chunked(seq, chunksize): """Yields items from an iterator in list chunks.""" for chunk in ichunked(seq, chunksize): yield list(chunk)
Use a generator:
big_list = [1,2,3,4,5,6,7,8,9] slice_length = 3 def sliceIterator(lst, sliceLen): for i in range(len(lst) - sliceLen + 1): yield lst[i:i + sliceLen] for slice in sliceIterator(big_list, slice_length): foo(slice)
sliceIterator implements a “sliding window” of width
sliceLen over the squence
lst, i.e. it produces overlapping slices: [1,2,3], [2,3,4], [3,4,5], … Not sure if that is the OP’s intention, though.
Do you mean something like:
def callonslices(size, fatherList, foo): for i in xrange(0, len(fatherList), size): foo(fatherList[i:i+size])
If this is roughly the functionality you want you might, if you desire, dress it up a bit in a generator:
def sliceup(size, fatherList): for i in xrange(0, len(fatherList), size): yield fatherList[i:i+size]
def callonslices(size, fatherList, foo): for sli in sliceup(size, fatherList): foo(sli)
I am not sure, but it seems you want to do what is called a moving average. numpy provides facilities for this (the convolve function).
>>> x = numpy.array(range(20)) >>> x array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]) >>> n = 2 # moving average window >>> numpy.convolve(numpy.ones(n)/n, x)[n-1:-n+1] array([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5, 18.5])
The nice thing is that it accomodates different weighting schemes nicely (just change
numpy.ones(n) / n to something else).
You can find a complete material here:
from itertools import chain, islice def ichunked(seq, chunksize): it = iter(seq) while True: try: yield chain([next(it)], islice(it, chunksize - 1)) except StopIteration: return
Your question could use some more detail, but how about:
def iterate_over_slices(the_list, slice_size): for start in range(0, len(the_list)-slice_size): slice = the_list[start:start+slice_size] foo(slice)