<p>I was asked to develop a data reduction pipeline for a telescope and am facing a problem. </p>

<p>I have the standard lamp spectra and need to identify the emission lines in order to find a wavelength solution to apply to the spectra taken.</p>

<p>I managed to find the signal peaks and calculate their centroids, I am unsure - however - of how to match the centroids I found with a list of the emission lines of the lamps.</p>

<p>I basically have to do what "Identify" in IRAF does, but in python. I know there have been some packages that do that (like specutils), but they are outdated and recall deprecated libraries.</p>

<p>Any tip on where to go or what to do to have a centroid-wavelength match would be greatly appreciated.</p>

CC BY-SA 4.0
2020-05-26T17:40:56.367Z
I’m voting to close this question because this is purely a software question.  Matching peaks of anything to datasets is a problem independent of astronomy
Carl Witthoft
CC BY-SA 4.0
2020-05-26T17:41:59.057Z
Start by looking up papers on how such line-matching is done.  Good chance there's tests for relative amplitudes as well as line centroids, for example.
Carl Witthoft
CC BY-SA 4.0
2020-05-26T20:59:34.523Z
StackOverflow is not very good in hard algorithmical things, but I think you could try https://cs.stackexchange.com or https://scicomp.stackexchange.com . The list of all sites is here: https://stackexchange.com/sites .
peterh
CC BY-SA 4.0
2020-05-27T00:44:14.323Z
This is a different and more complicated but related problem, looking for scale and rotation independent pattern matching of 2D points [How (the heck) does Astrometry.net work?](https://astronomy.stackexchange.com/q/33575/7982) whereas you have a pattern of 1D points and already have an approximate guess for scale and offset. I don't think this is off-topic here and I'm voting to leave it open, but I do agree that in this case you might get a better response to your algorithm question in a computer-based SE site.
uhoh
<data-analysis><python><raw-data>
Wavelength Calibration in Python - Line Identification algorithm
KurasaHakai
# Wavelength Calibration in Python - Line Identification algorithm

(Asked by: username_0 on 2020-05-25)

I was asked to develop a data reduction pipeline for a telescope and am facing a problem.

I have the standard lamp spectra and need to identify the emission lines in order to find a wavelength solution to apply to the spectra taken.

I managed to find the signal peaks and calculate their centroids, I am unsure - however - of how to match the centroids I found with a list of the emission lines of the lamps.

I basically have to do what "Identify" in IRAF does, but in python. I know there have been some packages that do that (like specutils), but they are outdated and recall deprecated libraries.

Any tip on where to go or what to do to have a centroid-wavelength match would be greatly appreciated.

**Tags:** data-analysis, python, raw-data

> **Comments:**
> **username_1**: I’m voting to close this question because this is purely a software question.  Matching peaks of anything to datasets is a problem independent of astronomy  
> **username_1**: Start by looking up papers on how such line-matching is done.  Good chance there's tests for relative amplitudes as well as line centroids, for example.  
> **username_2**: StackOverflow is not very good in hard algorithmical things, but I think you could try https://cs.stackexchange.com or https://scicomp.stackexchange.com . The list of all sites is here: https://stackexchange.com/sites .  
> **username_3**: This is a different and more complicated but related problem, looking for scale and rotation independent pattern matching of 2D points [How (the heck) does Astrometry.net work?](https://astronomy.stackexchange.com/q/33575/7982) whereas you have a pattern of 1D points and already have an approximate guess for scale and offset. I don't think this is off-topic here and I'm voting to leave it open, but I do agree that in this case you might get a better response to your algorithm question in a computer-based SE site.  

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