OK, this is embarrassing.
I had in fact made the kernel only almost symmetric, it was slightly
offset to one side.
This caused it. Only a wetware bug.
Sorry for having wasted your time!
On 03/23/2017 03:38 AM, josef.p...@gmail.com wrote:
On Wed, Mar 22, 2017 at 10:06 PM, Thøger Emil Rivera-Thorsen
<thoger.e...@gmail.com <mailto: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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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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