Timeline for The tail of scipy deconvolve
Current License: CC BY-SA 4.0
8 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jun 4, 2020 at 12:53 | comment | added | Aleksejs Fomins | Ok, this really helps. Thanks again | |
| Jun 4, 2020 at 12:52 | vote | accept | Aleksejs Fomins | ||
| Jun 4, 2020 at 12:52 | history | bounty ended | Aleksejs Fomins | ||
| Jun 4, 2020 at 6:23 | comment | added | 2vrk1504 | I will also like to add that longer your kernel is (more number of samples), more accurately you compute the inverse FT and hence, a lesser error between the original signal and the deconv output is achieved. | |
| Jun 4, 2020 at 6:21 | comment | added | 2vrk1504 | The main assumption is that the Fourier Transform of the kernel exists and that it is non-zero everywhere in the frequency domain, which I think is the case for most smooth causal functions. Essentially, we want to be able to get back the original signal, so in the filtering process, no frequency information must be lost. This will not be true if the FT of the kernel is zero for certain frequencies (eg: an ideal LPF). | |
| Jun 3, 2020 at 12:59 | comment | added | Aleksejs Fomins | Thanks for your work :). So you mentioned that the calculation of the inverse kernel has certain assumptions. Can you comment on what assumptions these are. In particular, can the stability of this procedure be guaranteed (or at least estimated) if the kernel is guaranteed to be finite and smooth? | |
| Jun 2, 2020 at 16:42 | review | Late answers | |||
| Jun 2, 2020 at 17:20 | |||||
| Jun 2, 2020 at 16:23 | history | answered | 2vrk1504 | CC BY-SA 4.0 |