update(examples): skew_stats (change bin width, add skew time dependency analysis)

examples/Manifest.toml

@@ -2,7 +2,7 @@
 
 julia_version = "1.11.2"
 manifest_format = "2.0"
-project_hash = "5459dda24740eb0fbddbd283ef8d6654eb96910e"
+project_hash = "9bfd3227044b739391f5fee603ddc4de538f1c94"
 
 [[deps.ANSIColoredPrinters]]
 git-tree-sha1 = "574baf8110975760d391c710b6341da1afa48d8c"
@@ -20,6 +20,12 @@ weakdeps = ["ChainRulesCore", "Test"]
     AbstractFFTsChainRulesCoreExt = "ChainRulesCore"
     AbstractFFTsTestExt = "Test"
 
+[[deps.AbstractPlutoDingetjes]]
+deps = ["Pkg"]
+git-tree-sha1 = "6e1d2a35f2f90a4bc7c2ed98079b2ba09c35b83a"
+uuid = "6e696c72-6542-2067-7265-42206c756150"
+version = "1.3.2"
+
 [[deps.AbstractTrees]]
 git-tree-sha1 = "2d9c9a55f9c93e8887ad391fbae72f8ef55e1177"
 uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
@@ -541,6 +547,18 @@ git-tree-sha1 = "b1c2585431c382e3fe5805874bda6aea90a95de9"
 uuid = "34004b35-14d8-5ef3-9330-4cdb6864b03a"
 version = "0.3.25"
 
+[[deps.Hyperscript]]
+deps = ["Test"]
+git-tree-sha1 = "179267cfa5e712760cd43dcae385d7ea90cc25a4"
+uuid = "47d2ed2b-36de-50cf-bf87-49c2cf4b8b91"
+version = "0.0.5"
+
+[[deps.HypertextLiteral]]
+deps = ["Tricks"]
+git-tree-sha1 = "7134810b1afce04bbc1045ca1985fbe81ce17653"
+uuid = "ac1192a8-f4b3-4bfe-ba22-af5b92cd3ab2"
+version = "0.9.5"
+
 [[deps.IOCapture]]
 deps = ["Logging", "Random"]
 git-tree-sha1 = "b6d6bfdd7ce25b0f9b2f6b3dd56b2673a66c8770"
@@ -868,6 +886,11 @@ version = "0.3.28"
 uuid = "56ddb016-857b-54e1-b83d-db4d58db5568"
 version = "1.11.0"
 
+[[deps.MIMEs]]
+git-tree-sha1 = "65f28ad4b594aebe22157d6fac869786a255b7eb"
+uuid = "6c6e2e6c-3030-632d-7369-2d6c69616d65"
+version = "0.1.4"
+
 [[deps.MKL_jll]]
 deps = ["Artifacts", "IntelOpenMP_jll", "JLLWrappers", "LazyArtifacts", "Libdl", "oneTBB_jll"]
 git-tree-sha1 = "f046ccd0c6db2832a9f639e2c669c6fe867e5f4f"
@@ -1058,10 +1081,10 @@ uuid = "f57f5aa1-a3ce-4bc8-8ab9-96f992907883"
 version = "0.4.3"
 
 [[deps.PSBoardDataBase]]
-deps = ["AutoHashEquals", "CSV", "DBInterface", "DataFrames", "Dates", "Documenter", "Downloads", "FHist", "Printf", "SQLite", "StaticArrays", "Tables"]
+deps = ["AutoHashEquals", "CSV", "DBInterface", "DataFrames", "Dates", "Documenter", "Downloads", "Printf", "SQLite", "StaticArrays", "Tables"]
 path = ".."
 uuid = "779f6a9c-59fa-41f1-8ed1-e9a91eccb2f5"
-version = "0.3.1"
+version = "0.4.0"
 weakdeps = ["InteractiveUtils"]
 
     [deps.PSBoardDataBase.extensions]
@@ -1118,6 +1141,12 @@ git-tree-sha1 = "3ca9a356cd2e113c420f2c13bea19f8d3fb1cb18"
 uuid = "995b91a9-d308-5afd-9ec6-746e21dbc043"
 version = "1.4.3"
 
+[[deps.PlutoUI]]
+deps = ["AbstractPlutoDingetjes", "Base64", "ColorTypes", "Dates", "FixedPointNumbers", "Hyperscript", "HypertextLiteral", "IOCapture", "InteractiveUtils", "JSON", "Logging", "MIMEs", "Markdown", "Random", "Reexport", "URIs", "UUIDs"]
+git-tree-sha1 = "eba4810d5e6a01f612b948c9fa94f905b49087b0"
+uuid = "7f904dfe-b85e-4ff6-b463-dae2292396a8"
+version = "0.7.60"
+
 [[deps.PolygonOps]]
 git-tree-sha1 = "77b3d3605fc1cd0b42d95eba87dfcd2bf67d5ff6"
 uuid = "647866c9-e3ac-4575-94e7-e3d426903924"
@@ -1495,11 +1524,21 @@ git-tree-sha1 = "0c45878dcfdcfa8480052b6ab162cdd138781742"
 uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa"
 version = "0.11.3"
 
+[[deps.Tricks]]
+git-tree-sha1 = "7822b97e99a1672bfb1b49b668a6d46d58d8cbcb"
+uuid = "410a4b4d-49e4-4fbc-ab6d-cb71b17b3775"
+version = "0.1.9"
+
 [[deps.TriplotBase]]
 git-tree-sha1 = "4d4ed7f294cda19382ff7de4c137d24d16adc89b"
 uuid = "981d1d27-644d-49a2-9326-4793e63143c3"
 version = "0.1.0"
 
+[[deps.URIs]]
+git-tree-sha1 = "67db6cc7b3821e19ebe75791a9dd19c9b1188f2b"
+uuid = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
+version = "1.5.1"
+
 [[deps.UUIDs]]
 deps = ["Random", "SHA"]
 uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"

examples/Project.toml

@@ -1,7 +1,15 @@
 [deps]
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0"
+DBInterface = "a10d1c49-ce27-4219-8d33-6db1a4562965"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
 FHist = "68837c9b-b678-4cd5-9925-8a54edc8f695"
 PSBoardDataBase = "779f6a9c-59fa-41f1-8ed1-e9a91eccb2f5"
+PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8"
+Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+SQLite = "0aa819cd-b072-5ff4-a722-6bc24af294d9"
+Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
+Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"

examples/skew_stats.jl

@@ -17,11 +17,13 @@ begin
     using PSBoardDataBase
     using SQLite
     using DataFrames
+    using FHist
     using DBInterface
     using Tables
     using CairoMakie
     using Statistics
     using PlutoUI
+    using Dates
     using Random
     using Printf
 end
@@ -70,7 +72,11 @@ md"""
 """
 
 # ╔═╡ f379d43c-9300-41f4-b0fc-3c9d749e3105
-qaqc_runs = DBInterface.execute(db, sql"select * from qaqc_runs") |> DataFrame
+qaqc_runs = let df
+    df = DBInterface.execute(db, sql"select * from qaqc_runs") |> DataFrame
+    transform!(df, :run_datetime => ByRow(passmissing(s -> DateTime(s))) => :run_datetime)
+    df
+end
 
 # ╔═╡ 33e099bc-ac4b-4b5f-88e7-20f4463c98ef
 md"""
@@ -180,7 +186,7 @@ let
         title = "skews of all measurements (n_meas = $(length(skews)), n_psb = $(npsbid))",
         xlabel = "skew / ns",
     )
-    stephist!(ax, skews, bins = range(minimum(skews), maximum(skews), step = 1 / 57))
+    stephist!(ax, skews, bins = range(minimum(skews), maximum(skews), step = 1 / 56))
     fig
 end
 
@@ -192,7 +198,7 @@ md"""
 # ╔═╡ 420dce0e-4757-48d9-84ec-7ddfac2fdff6
 let
     skew_widths = df_skews.width |> skipmissing |> collect
-    bins = range(0, maximum(skew_widths) + 1 / 57, step = 1 / 57)# .- 0.01
+    bins = range(0, maximum(skew_widths) + 1 / 56, step = 1 / 56)# .- 0.01
     hist(
         skew_widths,
         bins = bins,
@@ -211,13 +217,61 @@ let
 end
 
 # ╔═╡ 99902640-fee3-4502-9c7e-cb08834bad0b
-maximum(skipmissing(df_skews.width)) / (1 / 57)
+maximum(skipmissing(df_skews.width)) / (1 / 56)
 
 # ╔═╡ c79c6684-1b03-41b5-aa90-ef8c7a8eb69c
 md"""
-この結果を元に、クロック試験のしきい値は$(round(9 * 1 / 57; digits = 2))ns以上に設定
+この結果を元に、クロック試験のしきい値は$(round(9 * 1 / 56; digits = 2))ns以上に設定
 """
 
+# ╔═╡ a1056aba-e484-4594-908b-4709896d0da0
+let
+    fig = Figure()
+    ax = Axis(
+        fig[1, 1],
+        limits = ((-0.5, 1), nothing),
+        title = "turn on curves for PSBID 1166",
+        xlabel = "delay / ns (position dependency is removed)",
+        ylabel = "counts",
+    )
+
+    clk_files_1166 = filter(clk_files) do filename
+        psbid, runid = parse_filename(filename)
+        psbid == 1166
+    end
+    for clk_file in clk_files_1166
+        psbid, runid = parse_filename(clk_file)
+        single_runs = filter(
+            [:psboard_id, :runid] => (
+                (ref_psbid, ref_runid) -> begin
+                    psbid == ref_psbid && runid == ref_runid
+                end
+            ),
+            qaqc_single_results,
+        )
+        offset_pos = let
+            qaqc_positions.rising_ns[single_runs.position[1]]
+        end
+
+        points =
+            eachline(clk_file) .|>
+            PSBoardDataBase.ClockParser._parse_line .|>
+            (x -> (x[1] - offset_pos, x[2]))
+
+        @info "" single_runs points
+
+        stds = map(points) do ((time, count))
+            sqrt(count * (1000 - count) / 1000)
+        end
+
+        scatterlines!(ax, points, label = "psbid $(psbid), runid $(runid)")
+        errorbars!(ax, points, stds)
+    end
+    axislegend(ax, position = :rb)
+
+    fig
+end
+
 # ╔═╡ 875bec26-e576-4f48-ba14-464bce503d75
 filter(:width => (x -> ismissing(x) || x < 0.06), df_skews)
 
@@ -241,8 +295,12 @@ combine(filter(groupby(qaqc_single_results, [:psboard_id, :position])) do sdf
 end) do sdf
     sdf = dropmissing(sdf, [:lvds_tx_skew])
     # @info "" select(sdf, [:psboard_id, :position, :lvds_tx_skew])
-    (skew_mean = mean(sdf.lvds_tx_skew), skew_std = std(sdf.lvds_tx_skew), nrow = nrow(sdf))
+    (
-end　|> (df -> begin
+        skew_mean = mean(sdf.lvds_tx_skew),
+        skew_std = std(sdf.lvds_tx_skew),
+        nrow = nrow(sdf),
+    )
+end |> (df -> begin
     filter(:skew_std => >(0.5), df)
 end)
 
@@ -418,7 +476,8 @@ md"""
 """
 
 # ╔═╡ 38d472ca-6347-4096-828d-fd1256130a59
-df_skews_selected = combine(
+df_skews_selected = let df
+    df = combine(
         gdf_skews_on_psbid,
         sdf -> begin
             all(ismissing, sdf.skew) && @info "" sdf
@@ -428,6 +487,7 @@ df_skews_selected = combine(
                     skew = sdf.skew |> first,
                     width = sdf.width |> first,
                     riseup = sdf.riseup |> first,
+                    runid = sdf.runid |> first,
                 )
             else
                 if sdf.psbid[1] == 291
@@ -437,6 +497,7 @@ df_skews_selected = combine(
                         skew = df.skew |> first,
                         width = sdf.width |> first,
                         riseup = sdf.riseup |> first,
+                        runid = sdf.runid |> first,
                     )
                 elseif sdf.psbid[1] == 460
                     df = filter(:runid => ==(132), sdf)
@@ -445,6 +506,7 @@ df_skews_selected = combine(
                         skew = df.skew |> first,
                         width = sdf.width |> first,
                         riseup = sdf.riseup |> first,
+                        runid = sdf.runid |> first,
                     )
                 elseif sdf.psbid[1] == 545
                     df = filter(:runid => ==(132), sdf)
@@ -453,22 +515,31 @@ df_skews_selected = combine(
                         skew = df.skew |> first,
                         width = sdf.width |> first,
                         riseup = sdf.riseup |> first,
+                        runid = sdf.runid |> first,
                     )
                 else
                     # assume that runid is chronological
                     i = argmax(sdf.runid .|> (id -> ismissing(id) ? -1 : id))
-                (skew = sdf.skew[i], width = sdf.width[i], riseup = sdf.riseup[i])
+                    (
+                        skew = sdf.skew[i],
+                        width = sdf.width[i],
+                        riseup = sdf.riseup[i],
+                        runid = sdf.runid[1],
+                    )
                 end
             end
         end,
     )
+    leftjoin!(df, select(qaqc_runs, [:id, :campaign_id]), on = :runid => :id)
+    df
+end
 
 # ╔═╡ 310710da-ebb2-4f54-b238-38d493a6a533
 let
     skews = df_skews_selected.skew |> skipmissing |> collect
     fig = Figure()
     ax = Axis(fig[1, 1], title = "skews (n = $(length(skews)))", xlabel = "skew / ns")
-    stephist!(ax, skews, bins = range(minimum(skews), maximum(skews), step = 1 / 57))
+    stephist!(ax, skews, bins = range(minimum(skews), maximum(skews), step = 1 / 56))
     fig
 end
 
@@ -530,7 +601,7 @@ md"""
 # ╔═╡ 939aba6b-b03c-42b6-83b3-9cea5f4eb858
 let
     skew_widths = df_skews_selected.width |> skipmissing |> collect
-    bins = range(0, maximum(skew_widths), step = 1 / 57)# .- 0.01
+    bins = range(0, maximum(skew_widths), step = 1 / 56)# .- 0.01
     hist(
         skew_widths,
         bins = bins,
@@ -551,6 +622,221 @@ end
 # ╔═╡ aa9b78bc-8d66-4df2-bd06-1cbf21190488
 filter(:width => (x -> ismissing(x) || x < 0.06), df_skews_selected)
 
+# ╔═╡ 76f44129-34c6-451d-af3f-4593dd1dda5c
+md"""
+## skewの時間依存性
+以下のrun
+- 382: ファームウェア最新
+"""
+
+# ╔═╡ 446a1b05-b24a-46d8-94a1-bd449245075d
+df_extra_measurements = let
+    df_configs = DataFrame(
+        position_name = ["B-$(i)-$(j)" for j in 1:9 for i in 0:1],
+        psbid = [
+            001142,
+            000973,
+            000990,
+            000992,
+            001113,
+            001030,
+            001121,
+            001141,
+            001050,
+            001053,
+            001110,
+            001248,
+            001242,
+            001276,
+            000872,
+            000861,
+            000525,
+            000862,
+        ],
+        position = [1, 10, 2, 11, 3, 12, 4, 13, 5, 14, 6, 15, 7, 16, 8, 17, 9, 18],
+    )
+    transform!(
+        df_configs,
+        [:psbid, :position] =>
+            ByRow(
+                (psbid, position) -> begin
+                    runid = 382
+                    clkfile = "../test/input/slavelogs/main/$(psbid)_$(runid)_clk.txt"
+                    skew, width =
+                        PSBoardDataBase.ClockParser.get_skew_and_riseup(clkfile)
+                    offset = qaqc_positions.rising_ns[findfirst(
+                        ==(position),
+                        qaqc_positions.id,
+                    )]
+                    [skew - offset, width]
+                end,
+            ) => [:skew, :width],
+    )
+end
+
+# ╔═╡ 148e42fc-d168-4e07-b4f8-f9c3f8c18efc
+df_compare = let
+    df_old_measurements =
+        filter(:psbid => in(df_extra_measurements.psbid), df_skews_selected)
+    df_combined = leftjoin!(
+        df_old_measurements,
+        df_extra_measurements,
+        on = :psbid,
+        makeunique = true,
+    )
+    rename!(
+        df_combined,
+        :skew => :skew_old,
+        :width => :width_old,
+        :runid => :runid_old,
+        :campaign_id => :campaign_id_old,
+        :skew_1 => :skew_new,
+        :width_1 => :width_new,
+    )
+    select!(
+        df_combined,
+        [
+            :psbid,
+            :runid_old,
+            :campaign_id_old,
+            :skew_old,
+            :width_old,
+            :skew_new,
+            :width_new,
+        ],
+    )
+    dropmissing!(df_combined)
+end
+
+# ╔═╡ 3266d1fd-b8f0-4eb0-9a8e-2050bc9a626f
+let
+    fig = Figure(size = (600, 500))
+    grd1 = fig[1, 1] = GridLayout()
+    Label(grd1[0, 1:2], "skew firmware dependency", tellwidth = false)
+    ax1 = Axis(
+        grd1[1, 1],
+        limits = ((-0.15, 1.15), nothing),
+        xticks = (0:1, ["old", "new(382)"]),
+        ylabel = "skew / ns",
+    )
+    ax2 = Axis(
+        grd1[1:2, 2],
+        limits = ((2.9, 6.7), nothing),
+        ylabel = "Δskew / ns",
+        # xgridvisible = false,
+        # xticksvisible = false,
+        # xticklabelsvisible = false,
+        xticks = (3:6, string.(3:6)),
+        xlabel = "campaign",
+        yminorticksvisible = true,
+        yminorgridvisible = true,
+    )
+    colsize!(grd1, 2, Relative(0.4))
+    for row in eachrow(df_compare)
+        scatterlines!(
+            ax1,
+            0:1,
+            [row.skew_old, row.skew_new],
+            color = Makie.wong_colors()[row.campaign_id_old],
+        )
+        scatter!(
+            ax2,
+            row.campaign_id_old,
+            row.skew_new - row.skew_old,
+            marker = :x,
+            color = Makie.wong_colors()[row.campaign_id_old],
+            alpha = 0.7,
+        )
+    end
+    text!(
+        ax1,
+        fill(1, nrow(df_compare)),
+        df_compare.skew_new,
+        text = string.(df_compare.psbid),
+        color = (:black, 0.5),
+        align = (:left, :center),
+    )
+    text!(
+        ax2,
+        df_compare.campaign_id_old,
+        df_compare.skew_new .- df_compare.skew_old,
+        text = string.(df_compare.psbid),
+        color = (:black, 0.5),
+        align = (:left, :center),
+        # fontsize = 17,
+    )
+    campaigns = df_compare.campaign_id_old |> unique |> sort!
+    Legend(
+        grd1[2, 1],
+        [
+            [LineElement(color = color), MarkerElement(color = color, marker = :circle)] for
+            color in Makie.wong_colors()[campaigns]
+        ],
+        string.(campaigns),
+        "campaign id for old measurements",
+        orientation = :horizontal,
+    )
+
+    fig
+end
+
+# ╔═╡ 660f2bd1-d4bc-45a8-9cf6-4e875aa9f7a2
+let
+    df = filter(:campaign_id => ==(6), df_skews_selected)
+    leftjoin!(df, select(qaqc_runs, [:id, :run_datetime]), on = :runid => :id)
+    # from UTC to JST(+9h)
+    transform!(df, :run_datetime => ByRow(dt -> dt + Hour(9)) => :run_datetime)
+
+    @assert all(!ismissing, df.run_datetime)
+    dropmissing!(df, :run_datetime)
+
+    transform!(df, :run_datetime => ByRow(Date) => :run_date)
+
+    fig = Figure()
+    gdf = groupby(df, :run_date)
+    for (i, sdf) in enumerate(gdf)
+        unit_converter = Makie.DateTimeConversion(Time)
+        ax = Axis(
+            fig[i, 1],
+            title = string(keys(gdf)[i]),
+            dim1_conversion = unit_converter,
+            limits = (
+                (
+                    Makie.convert_dim_value(unit_converter, Time(9)),
+                    Makie.convert_dim_value(unit_converter, Time(20)),
+                ),
+                (-2, 1),
+            ),
+        )
+        scatter!(ax, sdf.run_datetime .|> Time, sdf.skew, markersize = 7, alpha = 0.7)
+        mean_runs = Union{Float64, Missing}[]
+        std_runs = Union{Float64, Missing}[]
+        datetime_runs = Time[]
+        for sdf_samerun in groupby(sdf, :runid)
+            push!(mean_runs, mean(sdf_samerun.skew |> skipmissing))
+            push!(std_runs, std(sdf_samerun.skew |> skipmissing))
+            push!(datetime_runs, sdf_samerun.run_datetime |> first)
+        end
+        @info "" mean_runs std_runs datetime_runs
+        scatterlines!(
+            ax,
+            datetime_runs,
+            mean_runs,
+            marker = :hline,
+            markersize = 10,
+            color = Makie.wong_colors()[2],
+        )
+        errorbars!(
+            ax,
+            datetime_runs,
+            mean_runs,
+            std_runs,
+            color = Makie.wong_colors()[2],
+        )
+    end
+    fig
+end
+
 # ╔═╡ 86437ee6-ccea-43fa-bd93-d86fe055f28d
 md"""
 # プロットまとめ
@@ -569,6 +855,12 @@ df_skews_selected_valids = let
     df
 end
 
+# ╔═╡ 2dde2b39-8f8c-473b-8fed-393a9e3286d8
+sort(df_skews_selected_valids, :skew)
+
+# ╔═╡ 2a579bc2-79f8-4773-8588-a413acb8a6d6
+sort(df_skews_selected_valids, :skew, rev = true)
+
 # ╔═╡ cf658de8-a4c5-413e-b5e3-56b77a80336f
 sort(df_skews_selected, :width)
 
@@ -582,12 +874,17 @@ begin
     @assert all(==(1), df_skews_selected_valids.riseup)
 end
 
+# ╔═╡ 92d701aa-ab90-4c91-977d-2ce92823d130
+md"""
+## skew分布
+"""
+
 # ╔═╡ 4a4ef945-b312-44ed-ab62-ce01fc33f926
 let
     bins = range(
-        minimum(df_skews_selected_valids.skew) - 5 / 57,
+        minimum(df_skews_selected_valids.skew) - 5 / 56,
-        maximum(df_skews_selected_valids.skew) + 5 / 57,
+        maximum(df_skews_selected_valids.skew) + 5 / 56,
-        step = 2 / 57,
+        step = 2 / 56,
     )
     fig = Figure()
     ax = Axis(
@@ -615,15 +912,92 @@ let
     fig
 end
 
+# ╔═╡ 82063c4b-c0cf-4524-83fe-5207bb8363d8
+md"""
+### skew分布のcampaign依存性
+"""
+
+# ╔═╡ dff359b1-4827-40c6-86e4-0915974ef27d
+let
+    bins = range(
+        minimum(df_skews_selected_valids.skew) - 5 / 56,
+        maximum(df_skews_selected_valids.skew) + 5 / 56,
+        step = 8 / 56,
+    )
+    fig = Figure()
+    ax = Axis(
+        fig[1, 1],
+        title = "skews",
+        limits = (nothing, (nothing, nothing)),
+        xlabel = "skew / ns",
+    )
+    hists = Hist1D[]
+    for gdf in groupby(df_skews_selected_valids, :campaign_id)
+        push!(hists, Hist1D(gdf.skew, binedges = bins))
+    end
+    sh1 = stackedhist!(ax, hists, error_color = Pattern('/'))
+    labels = ["campaign $i" for i in 1:6]
+    elements =
+        [PolyElement(polycolor = sh1.attributes.color[][i]) for i in 1:length(labels)]
+    Legend(
+        fig[1, 1],
+        elements,
+        labels,
+        ["""
+         n = $(nrow(df_skews_selected_valids))
+         μ = $(@sprintf "%.2g" mean(df_skews_selected_valids.skew))
+         σ = $(@sprintf "%.2g" std(df_skews_selected_valids.skew))
+         """],
+        tellwidth = false,
+        tellheight = false,
+        halign = :left,
+        valign = :top,
+        margin = (10, 10, 10, 10),
+    )
+    fig
+end
+
+# ╔═╡ 7920b03b-1d1a-4b51-bfc0-86d1361f2ff1
+let
+    fig = Figure()
+    ax = Axis(
+        fig[1, 1],
+        xlabel = "psbid",
+        ylabel = "skew / ns",
+        title = "clock skew time dependency",
+    )
+    scatter!(ax, df_skews_selected_valids.psbid, df_skews_selected_valids.skew)
+    fig
+end
+
+# ╔═╡ e640424b-7f7b-4cca-a634-92749ceee170
+let
+    fig = Figure()
+    ax = Axis(
+        fig[1, 1],
+        xlabel = "runid",
+        ylabel = "skew / ns",
+        title = "clock skew time dependency",
+    )
+    @info "" qaqc_runs
+    scatter!(ax, df_skews_selected_valids.runid, df_skews_selected_valids.skew)
+    fig
+end
+
 # ╔═╡ 46b2a3cd-d2e6-4277-8b65-9c61f25f69e8
-3 / 57
+3 / 56
+
+# ╔═╡ 55bad662-cfdd-45c8-81bf-4e65e5c8434e
+md"""
+## 立ち上がり時間分布
+"""
 
 # ╔═╡ 13bb4978-b98d-44a3-a4b6-4241cadc609b
 let
     bins = range(
-        minimum(df_skews_selected_valids.width) - 1 / 57,
+        minimum(df_skews_selected_valids.width) - 1 / 56,
-        maximum(df_skews_selected_valids.width) + 2 / 57,
+        maximum(df_skews_selected_valids.width) + 2 / 56,
-        step = 1 / 57,
+        step = 1 / 56,
     )
     fig = Figure()
     ax = Axis(
@@ -632,7 +1006,7 @@ let
         xlabel = "time / ns",
         xticks = (
             bins,
-            string.(round.(bins, digits = 3)),# .* "," .* string.(round.(Int64, bins * 57)),
+            string.(round.(bins, digits = 3)),# .* "," .* string.(round.(Int64, bins * 56)),
         ),
         xticklabelrotation = π / 3,
     )
@@ -779,6 +1153,7 @@ end
 # ╠═420dce0e-4757-48d9-84ec-7ddfac2fdff6
 # ╠═99902640-fee3-4502-9c7e-cb08834bad0b
 # ╠═c79c6684-1b03-41b5-aa90-ef8c7a8eb69c
+# ╠═a1056aba-e484-4594-908b-4709896d0da0
 # ╠═875bec26-e576-4f48-ba14-464bce503d75
 # ╟─ec774495-c0be-47a4-9d2c-b48159c07013
 # ╠═d082e07c-3b42-4362-bebf-63356979a49b
@@ -812,14 +1187,27 @@ end
 # ╠═07342d98-3889-4a19-8419-6d113fffb42e
 # ╠═939aba6b-b03c-42b6-83b3-9cea5f4eb858
 # ╠═aa9b78bc-8d66-4df2-bd06-1cbf21190488
+# ╠═76f44129-34c6-451d-af3f-4593dd1dda5c
+# ╠═446a1b05-b24a-46d8-94a1-bd449245075d
+# ╠═148e42fc-d168-4e07-b4f8-f9c3f8c18efc
+# ╠═3266d1fd-b8f0-4eb0-9a8e-2050bc9a626f
+# ╠═660f2bd1-d4bc-45a8-9cf6-4e875aa9f7a2
 # ╠═86437ee6-ccea-43fa-bd93-d86fe055f28d
 # ╠═2a24c277-6679-49f3-b387-6937b7661a25
 # ╠═13b4112f-96ab-41a1-8ef1-d940771f6ece
+# ╠═2dde2b39-8f8c-473b-8fed-393a9e3286d8
+# ╠═2a579bc2-79f8-4773-8588-a413acb8a6d6
 # ╠═cf658de8-a4c5-413e-b5e3-56b77a80336f
 # ╠═7efd380b-a976-4a86-8dff-8afd551f03fb
 # ╠═6a856a55-2611-41d0-a2c7-3645c066fc3c
+# ╟─92d701aa-ab90-4c91-977d-2ce92823d130
 # ╠═4a4ef945-b312-44ed-ab62-ce01fc33f926
+# ╟─82063c4b-c0cf-4524-83fe-5207bb8363d8
+# ╠═dff359b1-4827-40c6-86e4-0915974ef27d
+# ╠═7920b03b-1d1a-4b51-bfc0-86d1361f2ff1
+# ╠═e640424b-7f7b-4cca-a634-92749ceee170
 # ╠═46b2a3cd-d2e6-4277-8b65-9c61f25f69e8
+# ╟─55bad662-cfdd-45c8-81bf-4e65e5c8434e
 # ╠═13bb4978-b98d-44a3-a4b6-4241cadc609b
 # ╠═6c3c7669-d594-425d-bf05-9aa217c7656e
 # ╠═e9b3f541-d87d-4424-8b82-be1b7b3273d8