Derivative of experimental data
- To: mathgroup at smc.vnet.net
- Subject: [mg124696] Derivative of experimental data
- From: Gabriel Landi <gtlandi at gmail.com>
- Date: Wed, 1 Feb 2012 03:48:04 -0500 (EST)
- Delivered-to: l-mathgroup@mail-archive0.wolfram.com
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
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