class THnSparse: public THnBase

    Efficient multidimensional histogram.

 Use a THnSparse instead of TH1 / TH2 / TH3 / array for histogramming when
 only a small fraction of bins is filled. A 10-dimensional histogram with 10
 bins per dimension has 10^10 bins; in a naive implementation this will not
 fit in memory. THnSparse only allocates memory for the bins that have
 non-zero bin content instead, drastically reducing both the memory usage
 and the access time.

 To construct a THnSparse object you must use one of its templated, derived
 classes:
 THnSparseD (typedef for THnSparseT<ArrayD>): bin content held by a Double_t,
 THnSparseF (typedef for THnSparseT<ArrayF>): bin content held by a Float_t,
 THnSparseL (typedef for THnSparseT<ArrayL>): bin content held by a Long_t,
 THnSparseI (typedef for THnSparseT<ArrayI>): bin content held by an Int_t,
 THnSparseS (typedef for THnSparseT<ArrayS>): bin content held by a Short_t,
 THnSparseC (typedef for THnSparseT<ArrayC>): bin content held by a Char_t,

 They take name and title, the number of dimensions, and for each dimension
 the number of bins, the minimal, and the maximal value on the dimension's
 axis. A TH2 h("h","h",10, 0., 10., 20, -5., 5.) would correspond to
   Int_t bins[2] = {10, 20};
   Double_t xmin[2] = {0., -5.};
   Double_t xmax[2] = {10., 5.};
   THnSparse hs("hs", "hs", 2, bins, min, max);

 * Filling
 A THnSparse is filled just like a regular histogram, using
 THnSparse::Fill(x, weight), where x is a n-dimensional Double_t value.
 To take errors into account, Sumw2() must be called before filling the
 histogram.
 Bins are allocated as needed; the status of the allocation can be observed
 by GetSparseFractionBins(), GetSparseFractionMem().

 * Fast Bin Content Access
 When iterating over a THnSparse one should only look at filled bins to save
 processing time. The number of filled bins is returned by
 THnSparse::GetNbins(); the bin content for each (linear) bin number can
 be retrieved by THnSparse::GetBinContent(linidx, (Int_t*)coord).
 After the call, coord will contain the bin coordinate of each axis for the bin
 with linear index linidx. A possible call would be
   cout << hs.GetBinContent(0, coord);
   cout <<" is the content of bin [x = " << coord[0] "
        << " | y = " << coord[1] << "]" << endl;

 * Efficiency
 TH1 and TH2 are generally faster than THnSparse for one and two dimensional
 distributions. THnSparse becomes competitive for a sparsely filled TH3
 with large numbers of bins per dimension. The tutorial hist/sparsehist.C
 shows the turning point. On a AMD64 with 8GB memory, THnSparse "wins"
 starting with a TH3 with 30 bins per dimension. Using a THnSparse for a
 one-dimensional histogram is only reasonable if it has a huge number of bins.

 * Projections
 The dimensionality of a THnSparse can be reduced by projecting it to
 1, 2, 3, or n dimensions, which can be represented by a TH1, TH2, TH3, or
 a THnSparse. See the Projection() members. To only project parts of the
 histogram, call
   THnSparse::GetAxis(12)->SetRange(from_bin, to_bin);

 * Internal Representation
 An entry for a filled bin consists of its n-dimensional coordinates and
 its bin content. The coordinates are compacted to use as few bits as
 possible; e.g. a histogram with 10 bins in x and 20 bins in y will only
 use 4 bits for the x representation and 5 bits for the y representation.
 This is handled by the internal class THnSparseCompactBinCoord.
 Bin data (content and coordinates) are allocated in chunks of size
 fChunkSize; this parameter can be set when constructing a THnSparse. Each
 chunk is represented by an object of class THnSparseArrayChunk.

 Translation from an n-dimensional bin coordinate to the linear index within
 the chunks is done by GetBin(). It creates a hash from the compacted bin
 coordinates (the hash of a bin coordinate is the compacted coordinate itself
 if it takes less than 8 bytes, the size of a Long64_t.
 This hash is used to lookup the linear index in the TExMap member fBins;
 the coordinates of the entry fBins points to is compared to the coordinates
 passed to GetBin(). If they do not match, these two coordinates have the same
 hash - which is extremely unlikely but (for the case where the compact bin
 coordinates are larger than 4 bytes) possible. In this case, fBinsContinued
 contains a chain of linear indexes with the same hash. Iterating through this
 chain and comparing each bin coordinates with the one passed to GetBin() will
 retrieve the matching bin.