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Tweak plotting to collect outliers into a single bin; drop UnicodePlots dependency #13
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2a7af86
Add `simpleunicodehistogram`
ericphanson 03ccac4
tweak `simpleunicodehistogram` for outliers
ericphanson 9d70bbf
rename things, add configuration, fix tests, update readme
ericphanson e1cca72
use Julia 1.0 compatible syntax
ericphanson 83f0367
fix omission of minimum
ericphanson e05bf53
bump version
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,17 +1,15 @@ | ||
| name = "BenchmarkHistograms" | ||
| uuid = "a80a1652-aad8-438d-b80b-ecb1a674e33b" | ||
| authors = ["Eric Hanson <[email protected]> and contributors"] | ||
| version = "0.1.1" | ||
| version = "0.2.0" | ||
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| [deps] | ||
| BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf" | ||
| Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7" | ||
| Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" | ||
| UnicodePlots = "b8865327-cd53-5732-bb35-84acbb429228" | ||
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| [compat] | ||
| BenchmarkTools = "0.7, 1.0" | ||
| UnicodePlots = "1.3" | ||
| julia = "1" | ||
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| [extras] | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,68 @@ | ||
| # Modified from https://github.com/JuliaCI/BenchmarkTools.jl/pull/180#issuecomment-711128281 by @brenhinkeller | ||
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| const BLOCKS = [" ","▏","▎","▍","▌","▋","▊","▉","█","█"] | ||
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| function simple_unicode_histogram(io::IO, x::AbstractArray; | ||
| nbins::Integer=ceil(Int, log2(length(x))+1), | ||
| plot_width::Integer=30, show_counts::Bool=true, | ||
| outlier_quantile = 0.999, | ||
| xlabel="", ylabel="") | ||
| # Find bounds. Our naive attempt is to use equal width | ||
| # bins from the minimum to the maximum. | ||
| l, M = extrema(x) | ||
| # our lower bounds are exclusive, so we want to be sure to get the min | ||
| l = prevfloat(l) | ||
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| # Now, we check: if we don't have some big outliers, we'd expect | ||
| # the 99.9 percentile, `Q`, to be within a few bins of the maximum. | ||
| # Here, we choose 2. If it is not, then we decide that indeed | ||
| # there are outliers. We will instead divide the range from | ||
| # the minimum to `Q` equally with `nbins-1` bins, and then reserve | ||
| # the last bin to hold everything greater than `Q`. | ||
| Q = quantile(x, outlier_quantile) | ||
| initial_dx = (M - l) / nbins | ||
| truncate = M - Q > 2*initial_dx | ||
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| # our "upper bound" | ||
| u = truncate ? Q : M | ||
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| # Fill histogram | ||
| hist_counts = fill(0, nbins) | ||
| dx = truncate ? (u - l) / (nbins - 1) : initial_dx | ||
| for xi in x | ||
| index = ceil(Int, (xi - l) / dx) | ||
| if 1 <= index <= nbins | ||
| hist_counts[index] += 1 | ||
| else | ||
| hist_counts[end] += 1 | ||
| end | ||
| end | ||
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| if truncate | ||
| bin_edges = [range(l;stop=u,length=nbins); M] | ||
| else | ||
| bin_edges = range(l;stop=u,length=nbins+1) | ||
| end | ||
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| # Print the histogram | ||
| d = ceil(Int, -log10(u-l))+1 | ||
| scale = plot_width/maximum(hist_counts) | ||
| lower_labels = string.(round.(bin_edges[1:end-1], digits=d+ceil(Int,log10(nbins)-1))) | ||
| upper_labels = string.(round.(bin_edges[2:end], digits=d+ceil(Int,log10(nbins)-1))) | ||
| longest_lower = maximum(length.(lower_labels)) | ||
| longest_upper = maximum(length.(upper_labels)) | ||
| !isempty(ylabel) && println(io, ylabel, "\n") | ||
| for i=1:nbins | ||
| nblocks = hist_counts[i] * scale | ||
| block_string = repeat("█", floor(Int, nblocks)) * BLOCKS[ceil(Int,(nblocks - floor(nblocks))*8)+1] | ||
| print(io, " (", lower_labels[i], " "^(longest_lower - length(lower_labels[i]))) | ||
| print(io, " - ", upper_labels[i], " "^(longest_upper - length(upper_labels[i])), "] ") | ||
| printstyled(io, block_string; color=:green) | ||
| if show_counts | ||
| print(io, hist_counts[i]) | ||
| end | ||
| println(io) | ||
| end | ||
| isempty(xlabel) || println(io, "\n", " "^max(plot_width ÷2 + 6 - length(xlabel)÷2, 0), xlabel) | ||
| return nothing | ||
| end |
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the outlier change is especially nice here, where on my m1 macbook, I had such severe outliers that @MasonProtter had to generate the plot in order for it to look Gaussian (#6). With the new outlier bin, I can plot it myself 😄