Constructor using Dict{Symbol, Key} from UnROOT's hist

[sgnoohc]

Below is an example of how one might turn a UnROOT's hist object into a Hist1D.

function make_fhist1d(th1::Dict{Symbol, Any})
    xmin = th1[:fXaxis_fXmin]
    xmax = th1[:fXaxis_fXmax]
    nbins = th1[:fXaxis_fNbins]
    bins = range(xmin, xmax, length=nbins+1)
    h = Hist1D(Float64; bins=bins)
    h.hist.weights .= th1[:fN][2:end-1]
    h.sumw2 .= th1[:fSumw2][2:end-1]
    return h
end

I am not sure what would be the best way or best place to incorporate stuff like this....

[sgnoohc]

I don't know how to get the bin edges directly rather than computing from xmin xmax and nbins.
I couldn't find the key in th1 from UnROOT.
(but must be there somewhere..?)

[Moelf]

I think we could use a method that returns ( edges counts and sumw2 ) in UnROOT.jl, since it's easy to construct histogram from those in either StatsBase or FHist and it doesn't bloat the dependency.

the individuals bin edges are indeed not stored when they are uniform, your observation is correct I think

[aminnj]

I agree with @Moelf. About the irregular binning, this is how to get it:

julia> using UnROOT, FHist

julia> f = ROOTFile("/Users/namin/.julia/dev/UnROOT/test/samples/histograms1d2d.root")

julia> th = f["myTH1D_nonuniform"];

julia> th[:fXaxis_fXmin], th[:fXaxis_fXmax], th[:fXaxis_fNbins], th[:fXaxis_fXbins]
(-2.0, 2.0, 2, [-2.0, 1.0, 2.0])

julia> th = f["myTH1D"];

julia> th[:fXaxis_fXmin], th[:fXaxis_fXmax], th[:fXaxis_fNbins], th[:fXaxis_fXbins]
(-2.0, 2.0, 2, Any[])

That is, :fXaxis_fXbins is filled if the histogram has irregular binning, otherwise min/max/nbins is sufficient to construct the edges with range(min,max,length=nbins+1)

[aminnj]

Another thing to be careful about if we want a method that returns edges, counts, sumw2 in UnROOT.jl: what do we do with overflow bins? If we return the full arrays as-is, then the user still has to go through and do counts[2:end-1] and sumw2[2:end-1]

[aminnj]

based on @sgnoohc's example, but only half-tested:

function make_fhist1d(th1::Dict{Symbol, Any}; overflow=false)
    xmin = th1[:fXaxis_fXmin]
    xmax = th1[:fXaxis_fXmax]
    nbins = th1[:fXaxis_fNbins]
    bins = isempty(th1[:fXaxis_fXbins]) ? range(xmin, xmax, length=nbins+1) : th1[:fXaxis_fXbins];
    h = Hist1D(Float64; bins=bins, overflow=overflow) # so if we push more later, overflow is respected
    counts = th1[:fN]
    sumw2 = th1[:fSumw2]
    if overflow
        counts[2] += counts[1]
        counts[end-1] += counts[end]
        sumw2[2] += sumw2[1]
        sumw2[end-1] += sumw2[end]
    end
    h.hist.weights .= counts[2:end-1]
    h.sumw2 .= sumw2[2:end-1]
    return h
end

and then 2D is to be done...