Re: Derivative of experimental data
- To: mathgroup at smc.vnet.net
- Subject: [mg124720] Re: Derivative of experimental data
- From: Armand Tamzarian <mike.honeychurch at gmail.com>
- Date: Thu, 2 Feb 2012 04:56:16 -0500 (EST)
- Delivered-to: l-mathgroup@mail-archive0.wolfram.com
- References: <jgauad$eq8$1@smc.vnet.net>
On Feb 1, 7:49 pm, Gabriel Landi <gtla... at gmail.com> wrote: > Dear MathGroup users, > > I have a question which is very important for my current research, and > which involves not only Mathematica, but computer science in general. > > I have experimental data which is not very noisy, a small example of > which may be downloaded here. > > Basically I need to compute the derivative of this data. Here are my > options so far: > > Option 1: Finite differencing. Its terrible since the noise enhances > dramatically. > Option 2: Fitting some arbitrary function. The problem is that the > general functional form of the data changes from experiment to > experiment, so it is not possible to find a function which fits > adequately in all cases. > Option 3: Savitzky-Golay filters (self-implemented in Mathematica, based > on the discussion in Numerical Recipes, 3rd Ed.). It doesn't seem to > make much of a difference; probably because my data is not really that > noisy. > Option 4: Smoothing Splines filter. I am currently using Mr. Ludsteck > package HPFilter. So far it is by far the best outcome. However, it is > not free of some wild oscillations that are clearly non-analytical and > which are giving me quite the headache. > > Any suggestions are more than welcome. > I really appreciate any help I can get. > > Best regards, > > Gabriel Landi taking the numerical derivative of experimental data is a noisy process just as the reverse, integrating, is a smoothing process. that is the way it is. incidentally there is a Savitzky-Golay in the wolfram library but it seems no one ever goes there now to search for code. Mike