Fixes issue of std of hist with negative content (#142)

* fix: handle negative variance in standard deviation calculation for Hist1D

* fix: add tests for handling negative content in Hist1D

* update the test

Author: mmikhasenko

src/hist1d.jl

@@ -157,7 +157,10 @@ by the bin counts.
 When the histogram is `Hist2D`, return tuple instead, e.g `(mean(project(h, :x)), mean(project(h, :y)))` etc.
 """
 Statistics.mean(h::Hist1D) = Statistics.mean(bincenters(h), Weights(bincounts(h)))
-Statistics.std(h::Hist1D) = Statistics.var(bincenters(h), Weights(bincounts(h)); corrected=false) |> sqrt
+Statistics.std(h::Hist1D) = begin
+    var = Statistics.var(bincenters(h), Weights(bincounts(h)); corrected=false)
+    var < 0 ? NaN : sqrt(var)
+end
 Statistics.median(h::Hist1D) = Statistics.median(bincenters(h), Weights(bincounts(h)))
 Statistics.quantile(h::Hist1D, p) = Statistics.quantile(bincenters(h), Weights(bincounts(h)), p)
 

test/runtests.jl

@@ -639,4 +639,6 @@ end
     @test FHist.effective_entries(h) ≈ 2.76923076923077
 end
 
+include("test-algebraic-content.jl")
+
 include("hdf5.jl")

test/test-algebraic-content.jl

@@ -0,0 +1,10 @@
+using Test
+using FHist
+using FHist.Statistics
+@testset "Negative content" begin
+    h0 = Hist1D([1, 3, 4, 5, 6, 7, 3]; binedges = range(0, 10, 5))
+    original_mean = mean(h0)
+    h0.bincounts .-= mean(h0.bincounts)  # This makes sum of weights ≈ 0
+    @test abs(sum(h0.bincounts)) < 1e-10  # Verify weights sum to approximately zero
+    @test isnan(std(h0))   # Std should be NaN with near-zero sum of weights
+end