Thanks. I've looked briefly at a lot of different kinds of "spectra" - audio, solar, image fft, distributions, xps, even Raman that may evolve with time - and as you suggest you may not be interested so much in some abstract comparison as in extracting some model information. Comparing spectra may be with the intent of resolving a given one into component pieces- how much of each basis element is in the measured thing. Generally you have lines with some profile- gauss and lorentz would be well known - and then a continuum which could be anything with blackbody and I guess fluorescence as examples. Then you have instrument issues to resolve- baseline and maybe broadening could be factors for a library.
You could imagine developing a language around common things- consider maybe writing "R" packages that use GSL. CRAN's R may be a good open replacement for MATLAB. I played with python briefly and any language that enforces white space, and IIRC earlier distinguished space and tab lol, is a bit of a suspect ... I've also run into various language-vs-library issues and thinking about business issues. I've got one "program" to make downloading citation information less distracting from diverse sources targeted at academics or anyone doing internet research ( this could be companies writing white papers or technical reports for their own products compared to competitors, political hacks writing position or policy papers if the internet sites supply Bibtex for their works ). The code itself is almost the opposite of science- it is a collection of hacks tried in the order in which I discovered they may be useful to try to download citation information without bothering the user much. After looking at maybe 100's of hacks, some patterns emerged and in the conversion from an awful bash script to c/c++ it looked like you could come up with a mini-language based on "subroutine" or method calls. The dev version uses readline for interaction which appears to have some licensing issues but since I almost always just write for myself I don't usually notice stuff like that. btw, as their are likely academics here if you have your own horror or success stories getting citation information for your publication efforts please share as appropriate here or on the texhax list . Thanks. note new address Mike Marchywka 306 Charles Cox Drive Canton, GA 30115 2295 Collinworth Drive Marietta GA 30062. formerly 487 Salem Woods Drive Marietta GA 30067 404-788-1216 (C)<- leave message 989-348-4796 (P)<- emergency ________________________________________ From: Fritz Sonnichsen <[email protected]> Sent: Tuesday, March 16, 2021 9:53 AM To: Mike Marchywka Cc: [email protected] Subject: Re: Checking GSL for Spectroscopy Mark I am converting someone's MATLAB code so I am not sure what he is doing yet--but several years ago I did spectral analysis in MATLAB and probably very similar. This is for Raman and LIBS spectra. 1) "Usually" I apply a high pass filter to the spectrum. This gets rid of the noise I need control over this since as you would expect the signal and noise can get pretty close! Intuition comes into play here. 2) Next I baseline the spectra. This removes any constant bias. For LIBS I was usually able to further filter "spikes" and then take a mean of the remaining line, subtracting this from the overall spectrum. Raman can get a bit more difficult-I am, at least, subtracting the fluorescent line which can have a lot of features (e.g. spikes). At times, if you know this background you can subtract it first but you get all types of complications from normalization. Again--intuition comes into play. 3) The resulting spectrum needs to be compared to a database. For LIBS the latter is quite small--mostly atomic/elemental data such as NIST. I could generally do a discrete comparison of the spike locations using a peak-finder, align them with the known examples and get a pretty high hit rate. This was for qualitative data. Raman is, again, much more complex. The data I was using was constrained and simpler but the case in hand here is much more complex. We are doing mixed plastics at the moment. My colleague found the best matches by taking a stats correlation with 44000 entries and pulling out the values closest to "one". It works remarkably well. I don't think there is much above that cannot be written in C in a reasonable amount of time. But we are looking ahead and would like to draw on the collective experience of the science community. This type of analysis is quite common and there are enough new wheels out there that we don't want to re-invent old ones! Very important is that "intuition" part. I would think a lot of this issue has been better solved since I was doing this. There are a lot of adjustments that could be made-for example iterating trial baselines, rejecting noise at varied levels etc. Processors are faster now and the AI movement has brought in PCA and a lot of other techniques that begin to transcend my current state of knowledge (I work more on the physics end of things and would prefer to use routines from the communities if possible to save time). Thanks for your interest Mark! Fritz On Tue, Mar 16, 2021 at 9:25 AM Mike Marchywka <[email protected]<mailto:[email protected]>> wrote: Can you comment on how you compare spectra? Just for my own personal interest, not sure if will further the thread here however.. Not sure a "dot product" in the conventional sense would help much. You could imagine comparing peak positions and relative heights or a fit to a continuum for example. Peaks plus black body in some vector comparison? note new address Mike Marchywka 306 Charles Cox Drive Canton, GA 30115 2295 Collinworth Drive Marietta GA 30062. formerly 487 Salem Woods Drive Marietta GA 30067 404-788-1216 (C)<- leave message 989-348-4796 (P)<- emergency ________________________________________ From: Help-gsl <[email protected]<mailto:[email protected]>> on behalf of Fritz Sonnichsen <[email protected]<mailto:[email protected]>> Sent: Tuesday, March 16, 2021 9:15 AM To: [email protected]<mailto:[email protected]> Subject: Checking GSL for Spectroscopy I am preparing to convert MATLAB code to something more general. The new code will run on LInux and ARM processors. For a lot of reasons I am not going to use Python. We also want to keep this project "close" to scientists and do not want to turn it into a full time computer programming job. So the final word is that I am looking for something that can be called by (and hopefully is written) in C. Worse case I will just write the code myself but would prefer to start integrating our systems into something with a lot of pre-written and vetted routines. GSL looks like a good choice. Maybe R comes next. We have a mix of needs but I will point out a few: 1) Baselining a spectrum 2) Finding peaks in that spectrum 3) using Pearson correlation to compare the spectrum QUICKLY to about 50,000 recorded examples. We also have some uses with basic statistics and we do some image processing. So my question is--does GSL position itself in these areas? MATLAB (with packages) does them all. I am not sure how active GSL, if it is keeping up with AI, imaging and spectroscopy--or is it fading or giving way to popular languages for example. I was surprised that the 600+ page manual did not seem to show anything relating to the simple spectral analysis described above for example. Certainly I can search the web for others' code but at some point if I cannot attach to a well established product I will just write it myself. Any comments appreciated thanks Fritz
