[SciPy-User] Using scipy.signal.fftconvolve() and scipy.signal.convolve()

[josef.pktd at gmail.com]

On Tue, Jan 18, 2011 at 9:57 PM,  <josef.pktd at gmail.com> wrote:
> On Tue, Jan 18, 2011 at 9:21 PM, Chris Rodgers
> <chris.rodgers at berkeley.edu> wrote:
>> While the output from the convolution statements looks qualitatively
>> correct to me, I always have difficulty interpreting them because I
>> don't know the "lags" associated with each value. By lag I mean the
>> amount by which one signal was delayed before calculating the dot
>> product of the two signals. In Matlab for example, the conv function
>> returns both the values and a separate array called "lags" of the same
>> size, which is very helpful.
>>
>> The scipy documentation is not very clear on this point. Does anyone
>> know a good resource documenting the lags in each of the 3 modes of
>> operation ('same', 'valid', 'full')? I tried convolving dummy
>> sequences ([0,1,0,0] etc) but I couldn't figure it out.
>
> valid and full have a clear definition
> for valid, signal convolve cuts on both sides symmetrically, I think.
typo
for "same", signal convolve ...

> I wrote some helper functions, so I don't have to think. And I found
> the comparison
> with ndimage useful, because it has an option for this.
>
> integer is start, decimal is end, if I did it correctly:
>
>>>> from scipy.signal import convolve
>>>> convolve(np.arange(1,10), [0.1, 0, 1], mode='valid')
> array([ 1.3,  2.4,  3.5,  4.6,  5.7,  6.8,  7.9])
>>>> convolve(np.arange(1,10), [0.1, 0, 1], mode='full')
> array([ 0.1,  0.2,  1.3,  2.4,  3.5,  4.6,  5.7,  6.8,  7.9,  8. ,  9. ])
>>>> convolve(np.arange(1,10), [0.1, 0, 1], mode='same')
> array([ 0.2,  1.3,  2.4,  3.5,  4.6,  5.7,  6.8,  7.9,  8. ])
>
> not so clear with even length window
>
>>>> convolve(np.arange(1,10), [0.1, 0, 0, 1], mode='full')
> array([ 0.1,  0.2,  0.3,  1.4,  2.5,  3.6,  4.7,  5.8,  6.9,  7. ,  8. ,
>        9. ])
>>>> convolve(np.arange(1,10), [0.1, 0, 0, 1], mode='same')
> array([ 0.2,  0.3,  1.4,  2.5,  3.6,  4.7,  5.8,  6.9,  7. ])
>
>>>> convolve(np.arange(1,10), [0.1, 0, 0,0, 1], mode='full')
> array([ 0.1,  0.2,  0.3,  0.4,  1.5,  2.6,  3.7,  4.8,  5.9,  6. ,  7. ,
>        8. ,  9. ])
>>>> convolve(np.arange(1,10), [0.1, 0, 0,0, 1], mode='same')
> array([ 0.3,  0.4,  1.5,  2.6,  3.7,  4.8,  5.9,  6. ,  7. ])
>
> I think I usually avoided same
>
> Josef
>
>>
>> Thanks!!
>> Chris
>>
>>
>>
>> On Tue, Jan 18, 2011 at 1:19 PM, David Baddeley
>> <david_baddeley at yahoo.com.au> wrote:
>>> What you're getting form fftconvolve looks about right - with ordinary convolve
>>> I suspect your problem might be that you're using 8 bit ints and it's
>>> overflowing & thus giving you the random noise pattern. Ffts cast their inputs
>>> to double first. I'd suggest casting your image to float - ie:
>>> a = a.astype('f')
>>> before doing the standard convolutions.
>>>
>>> cheers,
>>> David
>>>
>>>
>>> ----- Original Message ----
>>> From: Ábel Dániel <abli at freemail.hu>
>>> To: scipy-user <scipy-user at scipy.org>
>>> Sent: Wed, 19 January, 2011 8:58:28 AM
>>> Subject: [SciPy-User] Using scipy.signal.fftconvolve() and
>>> scipy.signal.convolve()
>>>
>>> [apologies if this might get duplicated, it appears my first
>>> submission didn't show up on the mailling list]
>>>
>>> Hi!
>>>
>>> I would like to ask some help with the use of scipy.signal.convolve
>>> and scipy.signal.fftconvolve. (On a greyscale 2d image.)
>>>
>>> Based on the documentation of fftconvolve (which is simply 'See
>>> convolve.'), I am assuming that they should give (mostly) the same
>>> result. (I.e. the result won't be exactly identical since they are
>>> using different methods, but they shouldn't be too different.)
>>>
>>> However, I am getting drastically different results: using convolve
>>> results in basically random noise, while fftconvolve gives a very
>>> sharp peak.
>>>
>>> I uploaded a short program with the input and the results I am getting
>>> to http://hal.elte.hu/~abeld/scipy_signal_issue/
>>>
>>> Am I doing something wrong? Should there be such a difference in the
>>> output of these functions? What is causing the difference?
>>>
>>> (I am using Ubuntu Lucid, version of python-scipy package is
>>> 0.7.0-2ubuntu0.1)
>>>
>>> Thanks in advance,
>>> Daniel Abel
>>> abli at freemail.hu
>>>
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>>>
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