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Derivative of experimental data

  • To: mathgroup at smc.vnet.net
  • Subject: [mg124733] Derivative of experimental data
  • From: Robert McHugh <bob_mchugh_2000 at yahoo.com>
  • Date: Fri, 3 Feb 2012 02:09:10 -0500 (EST)
  • Delivered-to: l-mathgroup@mail-archive0.wolfram.com
  • Reply-to: Robert McHugh <bob_mchugh_2000 at yahoo.com>

Gabriel,   I have had good results for a similar problem using the methods described in the link below.  

http://www.holoborodko.com/pavel/numerical-methods/numerical-derivative/smooth-low-noise-differentiators/

The algorithm described above can be implemented very efficiently with ListConvolve 


For the system I analyzed, the system has a 24 hour period and measurements are recorded every minute.  While the general trend of the data was clear to a human observer, the data was noisy and the analysis required estimates of the derivative of the data.  In this case I used N = 51 (i.e. 25 data points on either side of the time of interest).  

Best Regards,

Bob



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