[Numpy-discussion] array slicing questions
Tue Jul 31 08:23:34 CDT 2012
On Tue, Jul 31, 2012 at 10:23 AM, Vlastimil Brom
> 2012/7/30 eat <email@example.com>:
> > Hi,
> > A partial answer to your questions:
> > On Mon, Jul 30, 2012 at 10:33 PM, Vlastimil Brom <
> > wrote:
> >> Hi all,
> >> I'd like to ask for some hints or advice regarding the usage of
> >> numpy.array and especially slicing.
> >> I only recently tried numpy and was impressed by the speedup in some
> >> parts of the code, hence I suspect, that I might miss some other
> >> oportunities in this area.
> >> I currently use the following code for a simple visualisation of the
> >> search matches within the text, the arrays are generally much larger
> >> than the sample - the texts size is generally hundreds of kilobytes up
> >> to a few MB - with an index position for each character.
> >> First there is a list of spans(obtained form the regex match objects),
> >> the respective character indices in between these slices should be set
> >> to 1:
> >> >>> import numpy
> >> >>> characters_matches = numpy.zeros(10)
> >> >>> matches_spans = numpy.array([[2,4], [5,9]])
> >> >>> for start, stop in matches_spans:
> >> ... characters_matches[start:stop] = 1
> >> ...
> >> >>> characters_matches
> >> array([ 0., 0., 1., 1., 0., 1., 1., 1., 1., 0.])
> >> Is there maybe a way tu achieve this in a numpy-only way - without the
> >> python loop?
> >> (I got the impression, the powerful slicing capabilities could make it
> >> possible, bud haven't found this kind of solution.)
> >> In the next piece of code all the character positions are evaluated
> >> with their "neighbourhood" and a kind of running proportions of the
> >> matched text parts are computed (the checks_distance could be
> >> generally up to the order of the half the text length, usually less :
> >> >>>
> >> >>> check_distance = 1
> >> >>> floating_checks_proportions = 
> >> >>> for i in numpy.arange(len(characters_matches)):
> >> ... lo = i - check_distance
> >> ... if lo < 0:
> >> ... lo = None
> >> ... hi = i + check_distance + 1
> >> ... checked_sublist = characters_matches[lo:hi]
> >> ... proportion = (checked_sublist.sum() / (check_distance * 2 +
> >> ... floating_checks_proportions.append(proportion)
> >> ...
> >> >>> floating_checks_proportions
> >> [0.0, 0.33333333333333331, 0.66666666666666663, 0.66666666666666663,
> >> 0.66666666666666663, 0.66666666666666663, 1.0, 1.0,
> >> 0.66666666666666663, 0.33333333333333331]
> >> >>>
> > Define a function for proportions:
> > from numpy import r_
> > from numpy.lib.stride_tricks import as_strided as ast
> > def proportions(matches, distance= 1):
> > cd, cd2p1, s= distance, 2* distance+ 1, matches.strides
> > # pad
> > m= r_[[0.]* cd, matches, [0.]* cd]
> > # create a suitable view
> > m= ast(m, shape= (m.shape, cd2p1), strides= (s, s))
> > # average
> > return m[:-2* cd].sum(1)/ cd2p1
> > and use it like:
> > In : matches
> > Out: array([ 0., 0., 1., 1., 0., 1., 1., 1., 1., 0.])
> > In : proportions(matches).round(2)
> > Out: array([ 0. , 0.33, 0.67, 0.67, 0.67, 0.67, 1. , 1. ,
> > 0.33])
> > In : proportions(matches, 5).round(2)
> > Out: array([ 0.27, 0.36, 0.45, 0.55, 0.55, 0.55, 0.55, 0.55,
> > 0.36])
> >> I'd like to ask about the possible better approaches, as it doesn't
> >> look very elegant to me, and I obviously don't know the implications
> >> or possible drawbacks of numpy arrays in some scenarios.
> >> the pattern
> >> for i in range(len(...)): is usually considered inadequate in python,
> >> but what should be used in this case as the indices are primarily
> >> needed?
> >> is something to be gained or lost using (x)range or np.arange as the
> >> python loop is (probably?) inevitable anyway?
> > Here np.arange(.) will create a new array and potentially wasting memory
> > it's not otherwise used. IMO nothing wrong looping with xrange(.) (if you
> > really need to loop ;).
> >> Is there some mor elegant way to check for the "underflowing" lower
> >> bound "lo" to replace with None?
> >> Is it significant, which container is used to collect the results of
> >> the computation in the python loop - i.e. python list or a numpy
> >> array?
> >> (Could possibly matplotlib cooperate better with either container?)
> >> And of course, are there maybe other things, which should be made
> >> better/differently?
> >> (using Numpy 1.6.2, python 2.7.3, win XP)
> > My 2 cents,
> > -eat
> >> Thanks in advance for any hints or suggestions,
> >> regards,
> >> Vlastimil Brom
> >> _______________________________________________
> >> NumPy-Discussion mailing list
> >> NumPy-Discussion@scipy.org
> >> http://mail.scipy.org/mailman/listinfo/numpy-discussion
> thank you very much for your suggestions!
> do I understand it correctly, that I have to special-case the function
> for distance = 0 (which should return the matches themselves without
> However, more importantly, I am getting a ValueError for some larger,
> (but not completely unreasonable) "distance"
> >>> proportions(matches, distance= 8190)
> Traceback (most recent call last):
> File "<input>", line 1, in <module>
> File "<input>", line 11, in proportions
> File "C:\Python27\lib\site-packages\numpy\lib\stride_tricks.py",
> line 28, in as_strided
> return np.asarray(DummyArray(interface, base=x))
> File "C:\Python27\lib\site-packages\numpy\core\numeric.py", line
> 235, in asarray
> return array(a, dtype, copy=False, order=order)
> ValueError: array is too big.
> the distance= 8189 was the largest which worked in this snippet,
> however, it might be data-dependent, as I got this error as well e.g.
> for distance=4529 for a 20k text.
> Is this implementation-limited, or could it be solved in some
> alternative way which wouldn't have such limits (up to the order of,
> say, millions)?
Apparently ast(.) does not return a view of the original matches rather a
copy of size (n* (2* distance+ 1)), thus you may run out of memory.
Surely it can be solved up to millions of matches, but perhaps much slower
> Thanks again
> NumPy-Discussion mailing list
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