93 lines
2.2 KiB
Julia
93 lines
2.2 KiB
Julia
using CSV, DataFrames, Statistics
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function read_csvs(csv_files)
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full_vec = []
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for file in csv_files
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vec = (CSV.File(file; header=false)|>Tables.matrix)[1, 1:end-1]
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push!(full_vec, vec)
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end
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return hcat(full_vec...)
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end
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input_dir = "per_phreeqc/"
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csv_files = readdir(input_dir, join=true)
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csv_files = filter(x -> occursin(r"worker_timings_\d+.csv", x), csv_files)
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mat = read_csvs(csv_files)
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# calculate the mean of each row
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mean_vec = vec(mean(mat; dims=2))
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mean_intervalls = collect(0:1000:5000)
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# calculate the mean of each intervall
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mean_cummulative = [mean(@view mean_vec[mean_intervalls[i]+1:mean_intervalls[i+1]])
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for i in 1:length(mean_intervalls)-1] .* 1e3
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using Plots
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p = plot(mean_vec .* 1e3,
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xlabel="Iteration",
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ylabel="Runimte [ms]",
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xguidefontsize=8,
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yguidefontsize=8,
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xtickfontsize=6,
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ytickfontsize=6,
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legend=false,
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formatter=:plain,
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yminorgrid=true,
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ylimits=(0, 16),
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yticks=collect(0:2:16),
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yminorticks=4,
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linecolor=:black,
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)
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display(p)
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savefig(p, "phreeeqc_per_iteration_barite.pdf")
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mean(mean_vec) * 1e3
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input_dir = "phreeqc_dolo/"
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csv_files = readdir(input_dir, join=true)
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csv_files = filter(x -> occursin(r"worker_timings_\d+.csv", x), csv_files)
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mat = read_csvs(csv_files)
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# calculate the mean of each row
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mean_vec = vec(mean(mat; dims=2))
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mean_intervalls = collect(0:1000:10000)
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# calculate the mean of each intervall
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mean_cummulative = [mean(@view mean_vec[mean_intervalls[i]+1:mean_intervalls[i+1]])
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for i in 1:length(mean_intervalls)-1] .* 1e3
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using Plots
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p = plot(mean_vec,
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xlabel="Iteration",
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ylabel="Runimte [ms]",
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xguidefontsize=8,
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yguidefontsize=8,
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xtickfontsize=6,
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ytickfontsize=6,
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legend=false,
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formatter=:plain,
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yminorgrid=true,
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# ylimits=(0, 16),
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# yticks=collect(0:2:16),
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yminorticks=4,
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linecolor=:black,
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)
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display(p)
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savefig(p, "phreeeqc_per_iteration_barite.pdf")
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mean(mean_vec) * 1e6
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maximum(mean_vec) * 1e6
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minimum(mean_vec) * 1e6
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