class THnSparse: public TNamed

 Destruct a THnSparse

 Add "v" to the content of bin with coordinates "coord"

Create a new chunk of bin content

 Create a new THnSparse object that is of the same type as *this,
 but with dimensions and bins given by axes.

 Get the bin index for the n dimensional tuple x,
 allocate one if it doesn't exist yet and "allocate" is true.

 Get the bin index for the n dimensional tuple addressed by "name",
 allocate one if it doesn't exist yet and "allocate" is true.

 Get the bin index for the n dimensional coordinates coord,
 allocate one if it doesn't exist yet and "allocate" is true.

 Get content of bin with coordinates "coord"

 Return the content of the filled bin number "idx".
 If coord is non-null, it will contain the bin's coordinates for each axis
 that correspond to the bin.

 Get error of bin with coordinates "coord" as


 If errors are not enabled (via Sumw2() or CalculateErrors())
 return sqrt(contents).

 Get error of bin addressed by linidx as


 If errors are not enabled (via Sumw2() or CalculateErrors())
 return sqrt(contents).

 Return the index for fCurrentBinIndex.
 If it doesn't exist then return -1, or allocate a new bin if allocate is set

 Return THnSparseCompactBinCoord object.

 Generate an n-dimensional random tuple based on the histogrammed
 distribution. If subBinRandom, the returned tuple will be additionally
 randomly distributed within the randomized bin, using a flat
 distribution.

 Return the amount of filled bins over all bins

 Return the amount of used memory over memory that would be used by a
 non-sparse n-dimensional histogram. The value is approximate.

 Check whether bin coord is in range, as defined by TAxis::SetRange().
 Currently, TAxis::SetRange() does not allow to select all but over- and
 underflow bins (it instead resets the axis to "no range selected").
 Instead, simply call
    TAxis* axis12 = hsparse.GetAxis(12);
    axis12->SetRange(1, axis12->GetNbins());
    axis12->SetBit(TAxis::kAxisRange);
 to deselect the under- and overflow bins in the 12th dimension.

 Project all bins into a 1-dimensional histogram,
 keeping only axis "xDim".
 If "option" contains "E" errors will be calculated.

 Project all bins into a 2-dimensional histogram,
 keeping only axes "xDim" and "yDim".
 If "option" contains "E" errors will be calculated.

 Project all bins into a 3-dimensional histogram,
 keeping only axes "xDim", "yDim", and "zDim".
 If "option" contains "E" errors will be calculated.

 Project all bins into a ndim-dimensional histogram,
 keeping only axes "dim".
 If "option" contains "E" errors will be calculated.

 Scale contents and errors of this histogram by c:
 this = this * c

 Add contents of h scaled by c to this histogram:
 this = this + c * h
 Note that if h has Sumw2 set, Sumw2 is automatically called for this
 if not already set.

 Multiply this histogram by histogram h
 this = this * h
 Note that if h has Sumw2 set, Sumw2 is automatically called for this
 if not already set.

 Divide this histogram by h
 this = this/(h)
 Note that if h has Sumw2 set, Sumw2 is automatically called for
 this if not already set.
 The resulting errors are calculated assuming uncorrelated content.

 Replace contents of this histogram by multiplication of h1 by h2
 this = (c1*h1)/(c2*h2)
 Note that if h1 or h2 have Sumw2 set, Sumw2 is automatically called for
 this if not already set.
 The resulting errors are calculated assuming uncorrelated content.
 However, if option ="B" is specified, Binomial errors are computed.
 In this case c1 and c2 do not make real sense and they are ignored.

 Consistency check on (some of) the parameters of two histograms (for operations).

 Set the axis # of bins and bin limits on dimension idim

 Set content of bin with coordinates "coord" to "v"

 Set error of bin with coordinates "coord" to "e", enable errors if needed

 Enable calculation of errors

 Combine the content of "group" neighboring bins into
 a new bin and return the resulting THnSparse.
 For group=2 and a 3 dimensional histogram, all "blocks"
 of 2*2*2 bins will be put into a bin.

 Combine the content of "group" neighboring bins for each dimension
 into a new bin and return the resulting THnSparse.
 For group={2,1,1} and a 3 dimensional histogram, pairs of x-bins
 will be grouped.

 Clear the histogram

 Calculate the integral of the histogram

 Increment the bin content of "bin" by "w",
 return the bin index.

 Calculate errors (or not if "calc" == kFALSE)