Linear interpolation of histograms

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Revisjon per 17. jul. 2011 kl. 19:29 av Read@uio.no (diskusjon | bidrag)

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The root macro th1fmorph.C is an implementation of the method described in the article Linear Interpolation of Histograms. There is also a version for TH1D.

Bugfix 17.07.2011: Histograms with multiple empty bins can now be interpolated with resulting in bins with negative probability. A warning is printed if this should happen again. See the demonstration on youTube.

If the two histograms in the example in the article (see Fig.1) are hist60 and hist70, representing the invariant mass distributions of a hypothetical particle with mass 60 or 70 GeV, respectively, then the interpolated histogram hist65 may be created with:

TH1F *hist65 = (TH1F *)th1morph("hist65","Interpolation to 65 GeV",hist60,hist70,60,70,65);

Some examples, in the form of simple animations, serve to illustrate the successful interpolation between 2 Gaussian distributions (execute the root macro gausfilm.C) and between 2 exponential distributions (execute the root macro expfilm.C). The examples assume you have previously downloaded th1fmorph.C .

One should always test the interpolation by comparison of an interpolated histogram to a regular histogram (say you have parameter-ordered histograms A, B, C - compare the interpolation between A and C at the value of the parameter to which B corresponds to B itself and check that it makes sense).

To illustrate the limitations of the method, an amusing failure to interpolate between 2 Gaussian distributions on top of a large, exponential background is provided (execute the root macro bumpfilm.C).

The beginning of the macro documents the arguments in some more detail:

TH1F *th1fmorph(Char_t *chname="TH1F-interpolated", 
		Char_t *chtitle="Interpolated histogram",
		TH1F *hist1,TH1F *hist2,
		Double_t par1,Double_t par2,Double_t parinterp,
		Double_t morphedhistnorm=1,
		Int_t idebug=1)
{
  //--------------------------------------------------------------------------
  // Author           : Alex Read 
  // Version 0.1 of ROOT implementation, 05.05.2011
  // *
  // *      Perform a linear interpolation between two histograms as a function
  // *      of the characteristic parameter of the distribution.
  // *
  // *      The algorithm is described in Read, A. L., "Linear Interpolation
  // *      of Histograms", NIM A 425 (1999) 357-360.
  // *      
  // *      This ROOT-based CINT implementation is based on the FORTRAN77
  // *      implementation used by the DELPHI experiment at LEP (d_pvmorph.f).
  // *      The use of double precision allows pdf's to be accurately 
  // *      interpolated down to something like 10**-15.
  // *
  // *      The input histograms don't have to be identical, the binning is also
  // *      interpolated.
  // *
  // *      Extrapolation is allowed (a warning is given) but the extrapolation 
  // *      is not as well-defined as the interpolation and the results should 
  // *      be used with great care.
  // *
  // *      Data in the underflow and overflow bins are completely ignored. 
  // *      They are neither interpolated nor do they contribute to the 
  // *      normalization of the histograms.
  // *
  // * Input arguments:
  // * ================
  // * chname, chtitle : The ROOT name and title of the interpolated histogram.
  // *                   Defaults for the name and title are "THF1-interpolated"
  // *                   and "Interpolated histogram", respectively.
  // *
  // * hist1, hist2    : The two input histograms.
  // *
  // * par1,par2       : The values of the linear parameter that characterises
  // *                   the histograms (e.g. a particle mass).
  // *
  // * parinterp       : The value of the linear parameter we wish to 
  // *                   interpolate to. 
  // * 
  // * morphedhistnorm : The normalization of the interpolated histogram 
  // *                   (default is 1.0).  
  // * 
  // * idebug          : Default is zero, no internal information displayed. 
  // *                   Values between 1 and increase the verbosity of 
  // *                   informational output which may prove helpful to
  // *                   understand errors and pathalogical results.
  // * 
  // * The routine returns a pointer (TH1F *) to a new histogram which is
  // * the interpolated result.
  // *
  // *------------------------------------------------------------------------