[Numpydiscussion] Convolve returning zero array
R.Jager at mapperlithography.com
R.Jager at mapperlithography.com
Tue Dec 16 01:34:03 CST 2003
OK, here is the example:
[code]
from numarray import *
import numarray.convolve as conv
lx=20
center=10
index=arange(lx,type='Float64')
data1=(abs(centerindex))<3
print 'The binary pattern:'
print data1
PSF=5
xwidth=18
xmid=xwidth/2
sigma=PSF/(sqrt(8*log(2)))
index=arange(xwidth,type='Float64')
ShortGauss = (1/(sqrt(2*pi)*sigma)) *
exp(0.5*((indexxmid)/sigma)*((indexxmid)/sigma))
print '\nThe short gauss:'
print ShortGauss
image=conv.convolve(data1,ShortGauss,mode=conv.SAME)
print '\nThe convolution of the binary pattern with the short gauss:'
print image
PSF=5
xwidth=22
xmid=xwidth/2
sigma=PSF/(sqrt(8*log(2)))
index=arange(xwidth,type='Float64')
LongGauss = (1/(sqrt(2*pi)*sigma)) *
exp(0.5*((indexxmid)/sigma)*((indexxmid)/sigma))
print '\nThe long gauss:'
print LongGauss
image=conv.convolve(data1,LongGauss,mode=conv.SAME)
print '\nThe convolution of the binary pattern with the long gauss:'
print image
[\code]
When I run this code, the first convolution gives an array of zeros, the
second convolution is good.
I also did a quick try with convolve from nd_image, but this function was
slower than the convolve from numarray with a large Gaussian distribution.
Remco Jager
MAPPER Lithography
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+>
  Nadav Horesh 
  <nadavh at visionsen
  se.com> 
  
  15/12/2003 14:13 
  
+>
>
 
 To: R.Jager at mapperlithography.com 
 cc: Peter Verveer <verveer at emblheidelberg.de>, numpydiscussion at lists.sourceforge.net 
 Subject: Re: [Numpydiscussion] Convolve returning zero array 
>
1. Can you post an example?
2. Whenever convolution runs slow because the arrays are large, use FFT
(with a proper padding)  it can be an order of magnitude (or more)
faster.
Nadav.
R.Jager at mapperlithography.com wrote:
> Hi Peter,
>
> At first I used numarray 0.6. This morning I have installed 0.8 but the
> results are the same. I will try the convolve functions in the nd_image
> package.
>
> Thanks,
>
> Remco Jager
>
> MAPPER Lithography
> Lorentzweg 1
> 2628 CJ Delft, The Netherlands
> tel.: +31 (0)15 2789439
> fax: +31 (0)152789473
> http://www.mapperlithography.com
>
> This email, attachments and (any part of) its content are (i) intended
for
> the named addressee(s) only and (ii) strictly confidential and
proprietary.
> All rights are reserved by MAPPER Lithography. Any unauthorized use,
> disclosure and/or copying are strictly prohibited, except with prior and
> express written permission by MAPPER Lithography. Should you have
received
> this email, attachments and its content by mistake, please bring this to
> our attention and destroy this email and attachments in full. Thank you.
>
>
> +>
>   Peter Verveer 
>   <verveer at emblhei
>   delberg.de> 
>   
>   15/12/2003 12:04 
>   
> +>
>
>
> 

>  To: R.Jager at mapperlithography.com,
numpydiscussion at lists.sourceforge.net

>  cc:

>  Subject: Re: [Numpydiscussion] Convolve returning zero array

>
>
>
>
>
>
> Hi Remco,
>
> Sounds like a bug. Which version of numarray do you use? Version 0.8 of
> numarray should have appeared on sourceforge now. If the convolve in that
> version does still not work, you could try out the convolution function
in
> the new nd_image package that is part of numarray 0.8. If that does not
> work,
> let me know since I am the author of that package, and will fix problems
> with
> it.
>
> Cheers, Peter
>
> On Monday 15 December 2003 11:24, R.Jager at mapperlithography.com wrote:
>
>>Hi list,
>>
>>I already posted this on the numarray forum on freshmeat, but Jay T
>
> Miller
>
>>advised me to post my problem to this list. OK, now for the problem: I
>
> try
>
>>to convolve a Gaussian distribution with a binary pattern. For small
>
> values
>
>>of the sigma of the Gaussian distribution the convolution returns an
>
> array
>
>>of zeros. For a large value the results are OK.
>>I did some more research and found out that the zero array is returned if
>>the length of the Gaussian is smaller than the length of the binary
>>pattern. In the function call the Gaussian is the kernel and the binary
>>pattern is the data. The convolution mode is 'SAME'. I have swapped the
>>data and kernel in the convolve function call, but this has no influence
>
> on
>
>>the result, as this is swapped again in convolve.py. A quick and dirty
>>workaround is to always make the Gaussian distribution longer than the
>>binary pattern, but for very large binary patterns this increases the
>>calculation time significantly. Does anyone have an idea how to solve
>
> this
>
>>properly?
>>
>>Met vriendelijke groeten,
>>
>>Remco Jager
>>
>>MAPPER Lithography
>>Lorentzweg 1
>>2628 CJ Delft, The Netherlands
>>tel.: +31 (0)15 2789439
>>fax: +31 (0)152789473
>>http://www.mapperlithography.com
>>
>>This email, attachments and (any part of) its content are (i) intended
>
> for
>
>>the named addressee(s) only and (ii) strictly confidential and
>
> proprietary.
>
>>All rights are reserved by MAPPER Lithography. Any unauthorized use,
>>disclosure and/or copying are strictly prohibited, except with prior and
>>express written permission by MAPPER Lithography. Should you have
>
> received
>
>>this email, attachments and its content by mistake, please bring this to
>>our attention and destroy this email and attachments in full. Thank you.
>>
>>
>>
>>
>>
>>
>>
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>
> 
> Dr. Peter J. Verveer
> Cell Biology and Cell Biophysics Programme
> European Molecular Biology Laboratory
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> Germany
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