FAQ
First a trap for new players, then a question to developers

Code accelerated by numpy can be slowed down by a large factor is you
neglect to import numpy.sum .

from timeit import Timer
frag = 'x=sum(linspace(0,1,1000))'
Timer(frag ,setup='from numpy import linspace').timeit(1000)
# 0.6 sec
Timer(frag, setup='from numpy import sum, linspace').timeit(1000) #
difference is I import numpy.sum
# 0.04 sec 15x faster!

This is obvious of course - but it is very easy to forget to import
numpy.sum and pay the price in execution.

Question:
Can I replace the builtin sum function globally for test purposes so
that my large set of codes uses the replacement?

The replacement would simply issue warnings.warn() if it detected an
ndarray argument, then call the original sum
I could then find the offending code and use the appropriate import to
get numpy.sum

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  • Albert Hopkins at Jul 2, 2011 at 2:54 am

    On Friday, July 1 at 19:17 (-0700), bdb112 said:

    Question:
    Can I replace the builtin sum function globally for test purposes so
    that my large set of codes uses the replacement?

    The replacement would simply issue warnings.warn() if it detected an
    ndarray argument, then call the original sum
    I could then find the offending code and use the appropriate import to
    get numpy.sum
    You shouldn't do this, but you could use the __builtins__ module

    e.g.
    __builtins__.sum = numpy.sum # bad
  • Chris Torek at Jul 2, 2011 at 3:13 am
    In article <f6dbf631-73a9-485f-8ada-bc7376ac686b at h25g2000prf.googlegroups.com>
    bdb112 wrote:
    First a trap for new players, then a question to developers

    Code accelerated by numpy can be slowed down by a large factor is you
    neglect to import numpy.sum .

    from timeit import Timer
    frag = 'x=sum(linspace(0,1,1000))'
    Timer(frag ,setup='from numpy import linspace').timeit(1000)
    # 0.6 sec
    Timer(frag, setup='from numpy import sum, linspace').timeit(1000) #
    difference is I import numpy.sum
    # 0.04 sec 15x faster!

    This is obvious of course - but it is very easy to forget to import
    numpy.sum and pay the price in execution.

    Question:
    Can I replace the builtin sum function globally for test purposes so
    that my large set of codes uses the replacement?
    The replacement would simply issue warnings.warn() if it detected an
    ndarray argument, then call the original sum
    I could then find the offending code and use the appropriate import to
    get numpy.sum

    Sure, just execute code along these lines before running any of
    the tests:

    import __builtin__
    import warnings

    _sys_sum = sum # grab it before we change __builtin__.sum

    def hacked_sum(sequence, start=0):
    if isinstance(sequence, whatever):
    warnings.warn('your warning here')
    return _sys_sum(sequence, start)

    __builtin__.sum = hacked_sum

    (You might want to grab a stack trace too, using the traceback
    module.) You said "without using import" but all you have to
    do is arrange for python to import this module before running
    any of your own code, e.g., with $PYTHONHOME and a modified
    site file.
    --
    In-Real-Life: Chris Torek, Wind River Systems
    Intel require I note that my opinions are not those of WRS or Intel
    Salt Lake City, UT, USA (40?39.22'N, 111?50.29'W) +1 801 277 2603
    email: gmail (figure it out) http://web.torek.net/torek/index.html

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