mantao/Plots.jl
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fix histograms with NaN

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This commit is contained in:
Michael Krabbe Borregaard 2019-01-13 12:44:59 +01:00
parent 7750003033
commit d29a44efa7
1 changed files with 10 additions and 4 deletions
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14
src/recipes.jl
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@ -590,7 +590,7 @@ wand_edges(x...) = (@warn("Load the StatPlots package in order to use :wand bins
function _auto_binning_nbins(vs::NTuple{N,AbstractVector}, dim::Integer; mode::Symbol = :auto) where N function _auto_binning_nbins(vs::NTuple{N,AbstractVector}, dim::Integer; mode::Symbol = :auto) where N
_cl(x) = ceil(Int, NaNMath.max(x, one(x))) _cl(x) = ceil(Int, NaNMath.max(x, one(x)))
_iqr(v) = (q = quantile(v, 0.75) - quantile(v, 0.25); q > 0 ? q : oftype(q, 1)) _iqr(v) = (q = quantile(filter(!isnan,v), 0.75) - quantile(filter(!isnan,v), 0.25); q > 0 ? q : oftype(q, 1))
_span(v) = ignorenan_maximum(v) - ignorenan_minimum(v) _span(v) = ignorenan_maximum(v) - ignorenan_minimum(v)
n_samples = length(LinearIndices(first(vs))) n_samples = length(LinearIndices(first(vs)))
@ -622,7 +622,7 @@ function _auto_binning_nbins(vs::NTuple{N,AbstractVector}, dim::Integer; mode::S
end end
end end
_hist_edge(vs::NTuple{N,AbstractVector}, dim::Integer, binning::Integer) where {N} = StatsBase.histrange(vs[dim], binning, :left) _hist_edge(vs::NTuple{N,AbstractVector}, dim::Integer, binning::Integer) where {N} = StatsBase.histrange(filter(!isnan,vs[dim]), binning, :left)
_hist_edge(vs::NTuple{N,AbstractVector}, dim::Integer, binning::Symbol) where {N} = _hist_edge(vs, dim, _auto_binning_nbins(vs, dim, mode = binning)) _hist_edge(vs::NTuple{N,AbstractVector}, dim::Integer, binning::Symbol) where {N} = _hist_edge(vs, dim, _auto_binning_nbins(vs, dim, mode = binning))
_hist_edge(vs::NTuple{N,AbstractVector}, dim::Integer, binning::AbstractVector) where {N} = binning _hist_edge(vs::NTuple{N,AbstractVector}, dim::Integer, binning::AbstractVector) where {N} = binning
@ -635,11 +635,17 @@ _hist_edges(vs::NTuple{N,AbstractVector}, binning::Union{Integer, Symbol, Abstra
_hist_norm_mode(mode::Symbol) = mode _hist_norm_mode(mode::Symbol) = mode
_hist_norm_mode(mode::Bool) = mode ? :pdf : :none _hist_norm_mode(mode::Bool) = mode ? :pdf : :none
function _filternans(vs::NTuple{N,AbstractVector}) where N
_invertedindex(v, not) = [j for (i,j) in enumerate(v) if !(i ∈ not)]
nots = union(Set.(findall.(isnan, vs))...)
_invertedindex.(vs, Ref(nots))
end
function _make_hist(vs::NTuple{N,AbstractVector}, binning; normed = false, weights = nothing) where N function _make_hist(vs::NTuple{N,AbstractVector}, binning; normed = false, weights = nothing) where N
edges = _hist_edges(vs, binning) edges = _hist_edges(vs, binning)
h = float( weights == nothing ? h = float( weights == nothing ?
StatsBase.fit(StatsBase.Histogram, vs, edges, closed = :left) : StatsBase.fit(StatsBase.Histogram, _filternans(vs), edges, closed = :left) :
StatsBase.fit(StatsBase.Histogram, vs, StatsBase.Weights(weights), edges, closed = :left) StatsBase.fit(StatsBase.Histogram, _filternans(vs), StatsBase.Weights(weights), edges, closed = :left)
) )
normalize!(h, mode = _hist_norm_mode(normed)) normalize!(h, mode = _hist_norm_mode(normed))
end end