mantao/Plots.jl
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move filtering to makebins

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This commit is contained in:
Michael Krabbe Borregaard 2019-01-14 10:21:35 +01:00
parent 979f9495f1
commit 667ed161c0
1 changed files with 7 additions and 6 deletions
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13
src/recipes.jl
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@ -589,9 +589,9 @@ Plots.@deps stepbins path
wand_edges(x...) = (@warn("Load the StatPlots package in order to use :wand bins. Defaulting to :auto", once = true); :auto)
function _auto_binning_nbins(vs::NTuple{N,AbstractVector}, dim::Integer; mode::Symbol = :auto) where N
_cl(x) = ceil(Int, NaNMath.max(x, one(x)))
_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)
_cl(x) = ceil(Int, max(x, one(x)))
_iqr(v) = (q = quantile(v, 0.75) - quantile(v, 0.25); q > 0 ? q : oftype(q, 1))
_span(v) = maximum(v) - minimum(v)
n_samples = length(LinearIndices(first(vs)))
@ -622,7 +622,7 @@ function _auto_binning_nbins(vs::NTuple{N,AbstractVector}, dim::Integer; mode::S
end
end
_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::Integer) where {N} = StatsBase.histrange(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::AbstractVector) where {N} = binning
@ -643,10 +643,11 @@ function _filternans(vs::NTuple{N,AbstractVector}) where N
end
function _make_hist(vs::NTuple{N,AbstractVector}, binning; normed = false, weights = nothing) where N
vs = _filternans(vs)
edges = _hist_edges(vs, binning)
h = float( weights == nothing ?
StatsBase.fit(StatsBase.Histogram, _filternans(vs), edges, closed = :left) :
StatsBase.fit(StatsBase.Histogram, _filternans(vs), StatsBase.Weights(weights), edges, closed = :left)
StatsBase.fit(StatsBase.Histogram, vs, edges, closed = :left) :
StatsBase.fit(StatsBase.Histogram, vs, StatsBase.Weights(weights), edges, closed = :left)
)
normalize!(h, mode = _hist_norm_mode(normed))
end