mirror of
https://gitlab.cern.ch/wotsubo/PSBoardDataBase.git
synced 2025-06-08 05:55:42 +09:00
279 lines
8.2 KiB
Julia
279 lines
8.2 KiB
Julia
### A Pluto.jl notebook ###
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# v0.19.46
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using Markdown
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using InteractiveUtils
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# ╔═╡ 7c69d12c-80a5-11ef-2674-e155a3483342
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begin
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using Pkg
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Pkg.activate("..")
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Pkg.status()
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true || include("../src/PSBoardDataBase.jl")
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end
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# ╔═╡ effa7ed9-2ac4-4468-a474-e2bb662580fe
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begin
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using PSBoardDataBase
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using SQLite
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using DataFrames
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using DBInterface
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using Tables
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using CairoMakie
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using Statistics
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using PlutoUI
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end
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# ╔═╡ f25e7e08-8a73-4cac-ac7c-d310725c558d
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md"""
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# Detailed research on clock skew measurement
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- distribution of clock skews
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- consistency of multiple measurement on the same board
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"""
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# ╔═╡ 1a6322d4-9deb-4709-aa2e-df7d8be5b16f
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TableOfContents()
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# ╔═╡ 11537f91-e16b-45f0-9768-6df842371d36
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db = SQLite.DB("../psboard_qaqc.db")
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# ╔═╡ 268d3015-b8d3-48d9-b74a-062e258e0ec1
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SQLite.tables(db)
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# ╔═╡ 62105832-df1f-4834-8da6-c542e22207d1
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md"""
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## Single runs
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"""
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# ╔═╡ dea6f143-7916-4765-92f6-2bfb97a72835
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qaqc_single_results =
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DBInterface.execute(db, sql"select * from qaqc_single_run_results") |> DataFrame
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# ╔═╡ 41543c0c-d7c4-447b-a268-0d356c88d92c
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md"""
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## PS Board list
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"""
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# ╔═╡ 633ecdee-7e2f-4de8-833a-21cd0351c1f1
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DBInterface.execute(
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db,
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sql"""
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select * from ps_boards
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""",
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) |> DataFrame
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# ╔═╡ 87f1966e-3b07-4f9d-8fc4-7b7aa4319d50
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md"""
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## Run list
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"""
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# ╔═╡ f379d43c-9300-41f4-b0fc-3c9d749e3105
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qaqc_runs = DBInterface.execute(db, sql"select * from qaqc_runs") |> DataFrame
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# ╔═╡ 33e099bc-ac4b-4b5f-88e7-20f4463c98ef
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md"""
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## Positions
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"""
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# ╔═╡ 0e13f848-0efb-4775-9e3e-518b32588a79
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qaqc_positions = DBInterface.execute(db, sql"select * from qaqc_positions") |> DataFrame
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# ╔═╡ 181c3fe6-d087-42e2-b175-3fb84c42e3e8
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position_id_skew_map = select(qaqc_positions, [:id, :rising_ns]) |> Tables.rowtable |> Dict
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# ╔═╡ 322cb530-65a5-4973-86f8-01ccc2439cc4
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md"""
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# Clock Analysis
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main part
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"""
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# ╔═╡ c1caca5f-4cfd-4f22-82b4-7925002359e6
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clk_files =
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readdir("../test/input/slavelogs/main/", join = true) |>
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filter(endswith("_clk.txt")) |>
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filter(!contains("nagoya")) |>
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filter(!contains("630_190"))
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# ╔═╡ 3e5607fd-2a8a-4a1a-9e7b-3f23ef216fad
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"""
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Get `(psbid, runid)`.
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"""
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function parse_filename(filename::AbstractString)
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m = match(r"(?<psbid>\d+)_(?<runid>\d+)_clk\.txt", filename)
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parse(Int64, m[:psbid]), parse(Int64, m[:runid])
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end
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# ╔═╡ c1b9c0c3-00f8-4199-b07f-8888f1be625c
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parse_filename("190_23_clk.txt")
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# ╔═╡ d6d04013-e0e4-49d5-a450-07ae164bfaa3
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# Get skew and rise up time from clock measurement files
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# Use measurements recorded in qaqc_single_results
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df_rawskews =
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clk_files .|>
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(
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file -> begin
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skew_width = PSBoardDataBase.ClockParser.get_skew_and_riseup(file)
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psbid, runid = parse_filename(file)
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(psbid = psbid, runid = runid, skew = skew_width[1], width = skew_width[2])
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end
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) |>
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filter(
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x ->
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filter(
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[:psboard_id, :runid] =>
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((psbid, runid) -> (psbid == x.psbid && runid == x.runid)),
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qaqc_single_results,
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) |> !isempty,
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) |>
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DataFrame
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# ╔═╡ d7541b93-4c49-4dcd-bda0-91e447f44596
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# substract result of measurements of position dependency
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df_skews = let
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df = leftjoin(
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df_rawskews,
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@view(qaqc_single_results[!, [:psboard_id, :runid, :position]]),
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on = [:psbid => :psboard_id, :runid],
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)
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leftjoin!(df, @view(qaqc_positions[!, [:id, :rising_ns]]), on = [:position => :id])
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transform!(df, [:skew, :rising_ns] => ByRow((x, y) -> x - y) => :skew)
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select!(df, Not(:rising_ns))
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select!(df, Not(:position))
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end
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# ╔═╡ 420dce0e-4757-48d9-84ec-7ddfac2fdff6
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stephist(df_skews.width |> skipmissing |> collect)
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# ╔═╡ d082e07c-3b42-4362-bebf-63356979a49b
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gdf_skews_on_psbid = groupby(df_skews, :psbid)
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# ╔═╡ 25688d24-5aee-43d3-aff9-b9efa0556070
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combine(nrow, gdf_skews_on_psbid)
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# ╔═╡ 239a808c-0411-4542-ae68-6ae6af333bd2
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df_nrow_ordered = let
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df = combine(nrow, gdf_skews_on_psbid)
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sort!(df, :nrow, rev = true)
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end
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# ╔═╡ 8e57bde1-5f97-483d-906e-8ebfb65016d0
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@view(df_nrow_ordered[findall(>(1), df_nrow_ordered.nrow), :])
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# ╔═╡ 92c2ac3f-8034-4e9e-aadb-8bb166fbc948
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df_skew_stats = let
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df = combine(
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gdf_skews_on_psbid,
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sdf -> begin
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if nrow(sdf) == 1
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(; mean_skew = mean(sdf.skew), std_skew = missing, n = 1)
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else
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(; mean_skew = mean(sdf.skew), std_skew = std(sdf.skew), n = nrow(sdf))
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end
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end,
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)
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dropmissing!(df)
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df
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end
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# ╔═╡ 893253c3-f0b2-401f-b892-b23291bcf5c1
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fig_skew_stats = let
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fig, ax, sc = scatter(
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df_skew_stats.mean_skew,
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df_skew_stats.std_skew,
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marker = :x,
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color = (Makie.wong_colors()[1], 0.8),
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axis = (title = "skew mean vs std", xlabel = "mean", ylabel = "std"),
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)
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text!(
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ax,
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df_skew_stats.mean_skew,
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df_skew_stats.std_skew,
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text = string.(df_skew_stats.psbid),
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color = (:gray, 0.5),
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)
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fig
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end
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# ╔═╡ 6467dcaa-6bd6-45c7-8c08-b310a09b8b0b
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save("clock_skew_stats.svg", fig_skew_stats)
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# ╔═╡ 79e2f5d8-4609-4e9f-949e-6dc1f88c0b19
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df_skew_stats_abnormals =
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filter([:mean_skew, :std_skew] => ((m, s) -> m > -5 && s > 1), df_skew_stats)
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# ╔═╡ d607e10e-854f-4652-9a34-9e22a188e315
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let
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df = df_skew_stats_abnormals
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fig, ax, sc = scatter(
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df.mean_skew,
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df.std_skew,
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marker = :x,
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color = (Makie.wong_colors()[1], 0.8),
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axis = (title = "skew mean vs std", xlabel = "mean", ylabel = "std"),
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)
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text!(
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ax,
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df.mean_skew,
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df.std_skew,
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text = string.(df.psbid) .* "," .* string.(df.n),
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color = (:gray, 0.7),
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)
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fig
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end
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# ╔═╡ 2795fd06-2f59-4e5b-829d-a8e428646790
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md"""
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## 分散が異常に大きいやつ
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基本的に予想通り、分散は小さく複数回の測定で整合的な結果が得られているが、いくつか例外があった。
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はじめはpsbid 127(4回測定)が含まれていたが、これはデータベースの編集ミスであることがわかり、修正した結果、消えた。
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### psbid: 291
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- run: 83, 94
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- 83でcommunication error(SFP半抜け)
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### psbid: 460
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- run: 105, 132
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- psbid 444と同じく電源の抜き差しによってクロックの0と1000が繰り返されたパターン
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- 追試に送られてる
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### psbid: 545 (**問題の**)
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- run: 126, 132
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- どちらも測定結果自体には変なところはない
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- どちらも1回だけ立ち上がりがある
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- 立ち上がりもそれほど長くない
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- 126が電源が不安定なときだったかもしれないが、記録がない
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"""
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# ╔═╡ Cell order:
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# ╟─f25e7e08-8a73-4cac-ac7c-d310725c558d
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# ╠═7c69d12c-80a5-11ef-2674-e155a3483342
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# ╠═effa7ed9-2ac4-4468-a474-e2bb662580fe
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# ╠═1a6322d4-9deb-4709-aa2e-df7d8be5b16f
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# ╠═11537f91-e16b-45f0-9768-6df842371d36
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# ╠═268d3015-b8d3-48d9-b74a-062e258e0ec1
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# ╟─62105832-df1f-4834-8da6-c542e22207d1
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# ╠═dea6f143-7916-4765-92f6-2bfb97a72835
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# ╟─41543c0c-d7c4-447b-a268-0d356c88d92c
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# ╠═633ecdee-7e2f-4de8-833a-21cd0351c1f1
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# ╟─87f1966e-3b07-4f9d-8fc4-7b7aa4319d50
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# ╠═f379d43c-9300-41f4-b0fc-3c9d749e3105
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# ╟─33e099bc-ac4b-4b5f-88e7-20f4463c98ef
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# ╠═0e13f848-0efb-4775-9e3e-518b32588a79
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# ╠═181c3fe6-d087-42e2-b175-3fb84c42e3e8
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# ╟─322cb530-65a5-4973-86f8-01ccc2439cc4
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# ╠═c1caca5f-4cfd-4f22-82b4-7925002359e6
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# ╠═3e5607fd-2a8a-4a1a-9e7b-3f23ef216fad
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# ╠═c1b9c0c3-00f8-4199-b07f-8888f1be625c
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# ╠═d6d04013-e0e4-49d5-a450-07ae164bfaa3
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# ╠═d7541b93-4c49-4dcd-bda0-91e447f44596
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# ╠═420dce0e-4757-48d9-84ec-7ddfac2fdff6
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# ╠═d082e07c-3b42-4362-bebf-63356979a49b
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# ╠═25688d24-5aee-43d3-aff9-b9efa0556070
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# ╠═239a808c-0411-4542-ae68-6ae6af333bd2
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# ╠═8e57bde1-5f97-483d-906e-8ebfb65016d0
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# ╠═92c2ac3f-8034-4e9e-aadb-8bb166fbc948
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# ╠═893253c3-f0b2-401f-b892-b23291bcf5c1
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# ╠═6467dcaa-6bd6-45c7-8c08-b310a09b8b0b
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# ╠═79e2f5d8-4609-4e9f-949e-6dc1f88c0b19
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# ╠═d607e10e-854f-4652-9a34-9e22a188e315
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# ╠═2795fd06-2f59-4e5b-829d-a8e428646790
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