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Re: Derivative of experimental data

  • To: mathgroup at
  • Subject: [mg124744] Re: Derivative of experimental data
  • From: Ingolf Dahl <ingolf.dahl at>
  • Date: Sat, 4 Feb 2012 06:26:05 -0500 (EST)
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  • References: <>

Yet another method:
Download my "Obtuse" interpolation package from and 
Use the Interpolation functions with the options 
Method -> "ObtuseAngle", InterpolationOrder -> 2, NeighborLevel -> 3 
Method -> "ObtuseAngle", InterpolationOrder -> 2, SmoothenDistance -> 0.2

See examples below "options" on the help page

Best regards

Ingolf Dahl

> -----Original Message-----
> From: Gabriel Landi [mailto:gtlandi at]
> Sent: den 1 februari 2012 09:48
> To: mathgroup at
> Subject: Derivative of experimental data
> 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
> 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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