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Wed, 22 Mar 2017 19:40:15 -0700

On Wed, Mar 22, 2017 at 10:06 PM, Th?ger Emil Rivera-Thorsen <
thoger.e...@gmail.com> wrote:

> Dear list;
>
> I am honestly not certain whether this, or the SciPy list, is the
> appropriate place to post this; please let me know if I got it wrong.
>
> I am convolving a 1D data set containing a relatively narrow peak, with a
> relatively narrow Gaussian kernel, in order to emulate the effect of
> atmospheric seeing on astrophysical observations.
>
> I have a 1D data array 45 pixels long, and a Gaussian kernel, and run
> np.convolve(data, kernel, mode='same') on the two arrays, the resulting
> array's peak is shifted relative to the origin. I have attached a plot to
> illustrate.
>
> The original data is shown in blue. When I convolve it with a symmetric
> kernel (black), I get an offset resulting peak (magenta). If I flip the
> kernel -- even though it is perfectly symmetric -- the resulting curve is
> offset in the opposite direction (yellow). However, if I offset the kernel
> so it is centered exactly one pixel below the central value, the output
> array gets centered correct (red), even if I flip the (now no longer
> symmetric) kernel.
>
> This is using Numpy 1.11.3, python 2.7.13, on Anaconda 4.3.0 64-bit on
> Ubuntu 16.10
>
> Using astropy.convolution, reproduces the correct red curve, so I can use
> that for now, but it seems to me this is either a bug or, if it is indeed
> the intended behavior, a word of caution would be merited in the docstring.
>
> Cheers,
>
> Emil Rivera-Thorsen
>
>
>
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> NumPy-Discussion@scipy.org
> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>
>
Can you provide an example to replicate?

I haven't seen this behavior, it looks centered to me, at least for odd
window length..
AFAIR, I had to try a bit in the past for how to set the window and
location with even window length.

>>> np.__version__
'1.11.2'

>>> x = np.linspace(-1, 1, 21)
>>> w = stats.norm.pdf(np.linspace(-3, 3, 5))
>>> np.column_stack((x, np.convolve(x, w, mode='same')))[8:13]
array([[ -2.00000000e-01,  -1.33368234e-01],
       [ -1.00000000e-01,  -6.66841169e-02],
       [  0.00000000e+00,   1.51788304e-17],
       [  1.00000000e-01,   6.66841169e-02],
       [  2.00000000e-01,   1.33368234e-01]])


>>> x = np.abs(np.linspace(-1, 1, 21))
>>> w = stats.norm.pdf(np.linspace(-3, 3, 4))
>>> np.column_stack((x, np.convolve(x, w, mode='same')))[8:13]
array([[ 0.2       ,  0.12320129],
       [ 0.1       ,  0.07392077],
       [ 0.        ,  0.02552663],
       [ 0.1       ,  0.02552663],
       [ 0.2       ,  0.07392077]])


>>> w = stats.norm.pdf(np.linspace(-3, 3, 5))
>>> np.column_stack((x, np.convolve(x, w, mode='same')))[8:13]
array([[ 0.2       ,  0.13336823],
       [ 0.1       ,  0.06757049],
       [ 0.        ,  0.02767626],
       [ 0.1       ,  0.06757049],
       [ 0.2       ,  0.13336823]])

Josef
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