Fast way to apply function elementwise to a numpy array

Question

I have a sets of numpy arrays which I create using

for longtuple in itertools.product([0,1], repeat = n + m -1 ):
    outputs = set(np.convolve(v, longtuple, 'valid').tostring() for v in itertools.product([0,1], repeat = m))
    if (len(outputs) == 2**m):
        print "Hooray!"

However I would actually like to take every element x of np.convolve(v, longtuple, 'valid') and apply x >> k & 1 (for values of k that I will specify) and then add that resulting array to the set instead. Is there an efficient way to do this?

My use of set and tostring() is simply to see if there are any duplicates. I am not sure it is correct however.

just be aware that for example -0. and 0. are different in this sense... — seberg
– seberg, Commented Mar 6, 2014 at 13:26

BrenBarn · Accepted Answer · 2014-03-05 20:18:26Z

1

You can just take the result of convolve and apply your expression to it:

set((np.convolve(v, longtuple, 'valid') >> k & 1).tostring() for v in itertools.product([0,1], repeat = m))

edited Mar 5, 2014 at 20:18

answered Mar 5, 2014 at 20:01

BrenBarn

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2 Comments

Simd Over a year ago

I am worried my use of tostring() is wrong. I am only using it so I can add the arrays to the set. But it doesn't make sense to do >> k & 1 after calling tostring() does it?

BrenBarn Over a year ago

Sorry, I messed it up. See my edited answer. You want to do >> k & 1 on the result of convolve, then apply tostring after that.

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