Timeline for unevenly spaced data
Current License: CC BY-SA 3.0
19 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Oct 7, 2014 at 18:40 | comment | added | alancalvitti | @belisarius, sooner or later there might be. Maybe fractal in frequency plane like mathworld.wolfram.com/DevilsStaircase.html | |
| Oct 6, 2014 at 20:07 | answer | added | eldo | timeline score: 2 | |
| Oct 6, 2014 at 19:06 | history | tweeted | twitter.com/#!/StackMma/status/519201937645117442 | ||
| Oct 6, 2014 at 18:56 | answer | added | ybeltukov | timeline score: 4 | |
| Oct 6, 2014 at 17:33 | comment | added | Dr. belisarius | @alancalvitti And then there is the devil-z transform | |
| Oct 6, 2014 at 17:28 | comment | added | Igor Rivin | @alancalvitti No doubt, but not in Mathematica :( I am perfectly happy to find/prduce implementations (and again, your suggestions are very welcome), but since WRI is making a very big deal of its data handling capabilities, one might hope that there are tools already available. Of course, one might be wrong... | |
| Oct 6, 2014 at 17:25 | comment | added | alancalvitti |
@IgorRivin, at least some special types of generalization of Fourier analysis like chirp-z transforms (off the unit disc) can be made fast like FFT.
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| Oct 6, 2014 at 17:14 | comment | added | Igor Rivin | @Sektor thanks! Looking forward to words of wisdom! | |
| Oct 6, 2014 at 17:03 | comment | added | Sektor | @IgorRivin Thanks Igor ! Already downloaded it and will try a few things on the data :) | |
| Oct 6, 2014 at 16:54 | comment | added | Igor Rivin | @alancalvitti Thanks! That is very cool (though the concerns I expressed before [implementing this is work, and in Mathematica at least will probably be quite slow, when a lot of data is involved] still stand, alas). | |
| Oct 6, 2014 at 16:50 | comment | added | alancalvitti | There are generalizations of frequency methods for un-even data eg, en.wikipedia.org/wiki/Non-uniform_discrete_Fourier_transform | |
| Oct 6, 2014 at 16:39 | comment | added | Igor Rivin | @Sektor here you go (warning, large): dl.dropboxusercontent.com/u/5188175/valnorms.m | |
| Oct 6, 2014 at 16:37 | comment | added | Dr. belisarius | @IgorRivin Much better with an example, yes | |
| Oct 6, 2014 at 16:36 | comment | added | Igor Rivin | @Sektor Sure, watch this space for the Dropbox link. | |
| Oct 6, 2014 at 16:35 | comment | added | Sektor | @IgorRivin Can you supply a sample data set or should I experiment on one of my own ? | |
| Oct 6, 2014 at 16:24 | comment | added | Igor Rivin | ... is not clearly the right thing. In any case, there is the actual mathematical/scientific problem, and then there is the functionality provided by mathematica, which seems to not be quite what is needed (of course, I can write my own Gauss convolver, or whatever, but this will be actual work and run slowly, too boot :() | |
| Oct 6, 2014 at 16:23 | comment | added | Igor Rivin | @Belisarius Of course. In my case I am doing a computational experiment where I am computing spectral invariants of random (in a certain sense) matrices (so, I have a list of pairs of the form (eigenvalue, some_function_of_eigenvector) of my matrices, which, to make things more annoying, are not precisely the same size. If I do the experiment 1000 times, and compute the mean of my function, and plot it vs the ordinal number of eigenvector, all is well, and the curve is smooth. If I want to plot it against the corresponding eigenvalue, lots of noise. Moving average does sort of work, but... | |
| Oct 6, 2014 at 16:17 | comment | added | Dr. belisarius | But you need some assumption about the underlying relation and the noise. Otherwise your perceived "noise" could be your signal. | |
| Oct 6, 2014 at 16:11 | history | asked | Igor Rivin | CC BY-SA 3.0 |