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Merge pull request #1073 from mkborregaard/fix-2dhistogram-bins

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fix 2dhistogram bins
This commit is contained in:
Michael Krabbe Borregaard 2017-09-06 10:15:03 +02:00 committed by GitHub
commit b69c37f1bb
1 changed files with 9 additions and 7 deletions
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16
src/recipes.jl
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@ -505,8 +505,10 @@ function _auto_binning_nbins{N}(vs::NTuple{N,AbstractVector}, dim::Integer; mode
_span(v) = ignorenan_maximum(v) - ignorenan_minimum(v)
n_samples = length(linearindices(first(vs)))
# Estimator for number of samples in one row/column of bins along each axis:
n = max(1, n_samples^(1/N))
# The nd estimator is the key to most automatic binning methods, and is modified for twodimensional histograms to include correlation
nd = n_samples^(1/(2+N))
nd = N == 2 ? nd / (1-cor(first(vs), last(vs))^2)^(3//8) : nd # the >2-dimensional case does not have a nice solution to correlations
v = vs[dim]
@ -515,15 +517,15 @@ function _auto_binning_nbins{N}(vs::NTuple{N,AbstractVector}, dim::Integer; mode
end
if mode == :sqrt # Square-root choice
_cl(sqrt(n))
_cl(sqrt(n_samples))
elseif mode == :sturges # Sturges' formula
_cl(log2(n)) + 1
_cl(log2(n_samples) + 1)
elseif mode == :rice # Rice Rule
_cl(2 * n^(1/3))
_cl(2 * nd)
elseif mode == :scott # Scott's normal reference rule
_cl(_span(v) / (3.5 * std(v) / n^(1/3)))
_cl(_span(v) / (3.5 * std(v) / nd))
elseif mode == :fd # Freedman–Diaconis rule
_cl(_span(v) / (2 * _iqr(v) / n^(1/3)))
_cl(_span(v) / (2 * _iqr(v) / nd))
elseif mode == :wand
wand_edges(v) # this makes this function not type stable, but the type instability does not propagate
else