mirror of
https://gitlab.cern.ch/wotsubo/PSBoardDataBase.git
synced 2025-06-08 14:05:40 +09:00
530 lines
16 KiB
Julia
530 lines
16 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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using Random
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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(depth = 4)
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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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# ╔═╡ dd669f14-989b-45ee-87d8-5d9cf282fafd
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md"""
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## Dispatch
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"""
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# ╔═╡ 1e9c3944-0cd4-40da-9014-a9153d4e95ed
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qaqc_dispatch = DBInterface.execute(db, sql"select * from qaqc_dispatch") |> DataFrame
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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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# ╔═╡ 0e680044-e4e1-4f39-a5c5-afa5c53fc7a7
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clk_files |> filter(contains("168")) .|> PSBoardDataBase.ClockParser.get_skew_and_riseup
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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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# ╔═╡ e7faa647-79cd-4247-a3f2-9868c7b9d4ca
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md"""
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## skewの分布
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``skew = rising\_time_{psbid, position} - rising\_time_{position}``
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"""
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# ╔═╡ 3a412a98-4a2d-4bfb-8053-b0f480fae921
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let
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skews = skipmissing(df_skews.skew) |> collect
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npsbid = df_skews.psbid |> unique |> length
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fig = Figure()
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ax = Axis(
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fig[1, 1],
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title = "skews of all measurements (n_meas = $(length(skews)), n_psb = $(npsbid))",
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xlabel = "skew / ns",
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)
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stephist!(ax, skews, bins = range(minimum(skews), maximum(skews), step = 1 / 57))
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fig
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end
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# ╔═╡ b38bbed4-8721-4e92-a546-f7926cc07dd3
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md"""
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## 立ち上がり時間の分布
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"""
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# ╔═╡ 420dce0e-4757-48d9-84ec-7ddfac2fdff6
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let
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skew_widths = df_skews.width |> skipmissing |> collect
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hist(
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skew_widths,
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bins = range(minimum(skew_widths), maximum(skew_widths), step = 1 / 57),
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bar_labels = :y,
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label_size = 14,
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label_formatter = x -> "$(round(Int, x))",
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axis = (
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title = "clock rise span distribution",
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xlabel = "rise up span / ns",
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ylabel = "counts",
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limits = ((0, 0.18), (0, nothing)),
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),
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)
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end
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# ╔═╡ 99902640-fee3-4502-9c7e-cb08834bad0b
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maximum(skipmissing(df_skews.width)) / (1 / 57)
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# ╔═╡ c79c6684-1b03-41b5-aa90-ef8c7a8eb69c
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md"""
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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
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"""
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# ╔═╡ ec774495-c0be-47a4-9d2c-b48159c07013
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md"""
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## 各PSBoardごとの統計
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"""
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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 = let
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df = filter([:mean_skew, :std_skew] => ((m, s) -> m > -5 && s > 1), df_skew_stats)
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sort!(df, :psbid)
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df
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end
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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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!!! todo
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他の例外も確認する。
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`df_skew_stats_abnormals`を確認
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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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# ╔═╡ 26976b6c-3954-4a41-a99b-c1aaebdc645d
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md"""
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### skewの分布
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"""
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# ╔═╡ 38d472ca-6347-4096-828d-fd1256130a59
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df_skews_selected = combine(
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gdf_skews_on_psbid,
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sdf -> begin
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all(ismissing, sdf.skew) && @info "" sdf
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if nrow(sdf) == 1
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@assert sdf.skew |> first |> !ismissing
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(skew = sdf.skew |> first, width = sdf.width |> first)
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else
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if sdf.psbid[1] == 291
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df = filter(:runid => ==(94), sdf)
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@assert nrow(df) == 1
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(skew = df.skew |> first, width = sdf.width |> first)
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elseif sdf.psbid[1] == 460
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df = filter(:runid => ==(132), sdf)
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@assert nrow(df) == 1
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(skew = df.skew |> first, width = sdf.width |> first)
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elseif sdf.psbid[1] == 545
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@error "temp"
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df = filter(:runid => ==(132), sdf)
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@assert nrow(df) == 1
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(skew = df.skew |> first, width = sdf.width |> first)
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else
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# assume that runid is chronological
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i = argmax(sdf.runid .|> (id -> ismissing(id) ? -1 : id))
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(skew = sdf.skew[i], width = sdf.width[i])
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end
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end
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end,
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)
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# ╔═╡ 310710da-ebb2-4f54-b238-38d493a6a533
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let
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skews = df_skews_selected.skew |> skipmissing |> collect
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fig = Figure()
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ax = Axis(fig[1, 1], title = "skews (n = $(length(skews)))", xlabel = "skew / ns")
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stephist!(ax, skews, bins = range(minimum(skews), maximum(skews), step = 1 / 57))
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fig
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end
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# ╔═╡ e9964184-1a2a-4ab9-bc22-2705a25393ec
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filter(:skew => (x -> !ismissing(x) && x < -3), df_skews_selected)
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# ╔═╡ f2d0ea9b-ede7-496a-8d1f-9f748f9d1666
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transform(
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filter(:skew => ismissing, df_skews_selected),
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:psbid => ByRow(psbid -> begin
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psbid in qaqc_dispatch.psb_id
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end) => :is_dispatched,
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)
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# ╔═╡ f5bc8b1a-3fee-48ed-9910-367a1cda632e
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md"""
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- psbid: 255
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- Si not locked
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"""
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# ╔═╡ 9f3f780d-da6e-44b4-a002-c2f088681e9c
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df_skews_selected_sorted = let
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df = sort(df_skews_selected, :skew)
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dropmissing!(df, :skew)
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df
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end
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# ╔═╡ 6c3c7669-d594-425d-bf05-9aa217c7656e
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md"""
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## turn on curves
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- lines on
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- the minimum and maximum skews
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- the minimum and maximum widths
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"""
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# ╔═╡ e9b3f541-d87d-4424-8b82-be1b7b3273d8
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let
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psbid_width_min, psbid_width_max, psbid_width_typical = let
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df_normal = filter(:width => >(0.06), df_skews_selected_sorted)
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sort!(df_normal, :width)
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df_typical = filter(:width => (x -> 0.08 < x < 0.11), df_normal)
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rng = Random.Xoshiro(123)
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id_rand_typical = rand(rng, 1:nrow(df_typical))
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first(df_normal).psbid, last(df_normal).psbid, df_typical[id_rand_typical, :psbid]
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end
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psbid_with_lines = [
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psbid_width_max,
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psbid_width_min,
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psbid_width_typical,
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]
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fig = Figure()
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ax = Axis(
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fig[1, 1],
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title = "clock turn on curves",
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subtitle = "aligned on the last count = 0 points",
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xlabel = "ns",
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ylabel = "counts",
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limits = ((-0.05, 0.15), (0, 1000)),
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)
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for file in clk_files
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psbid, runid = parse_filename(file)
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if psbid == 255
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@info "Si not locked: skipped" psbid
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continue
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end
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single_runs = filter(
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[:psboard_id, :runid] => (
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(ref_psbid, ref_runid) -> begin
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psbid == ref_psbid && runid == ref_runid
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end
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),
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qaqc_single_results,
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)
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if nrow(single_runs) != 1
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if nrow(single_runs) != 0
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@info "skipped" psbid runid nrow(single_runs)
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end
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continue
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end
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# offset = qaqc_positions.rising_ns[single_runs.position[1]]
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offset_pos = let
|
|
# df = filter(:psbid => ==(psbid), df_skews_selected_sorted)
|
|
# @assert nrow(df) == 1 "nrow: $(nrow(df)), psbid: $(psbid)"
|
|
# df.skew[1] +
|
|
qaqc_positions.rising_ns[single_runs.position[1]]
|
|
end
|
|
if psbid in psbid_with_lines
|
|
points =
|
|
eachline(file) .|>
|
|
PSBoardDataBase.ClockParser._parse_line .|>
|
|
(x -> (x[1] - offset_pos, x[2]))
|
|
id_first_over0 = findfirst(points) do ((time, count))
|
|
count > 0
|
|
end
|
|
@info "" points[id_first_over0] points[id_first_over0 - 1]
|
|
points = map(points) do ((time, count))
|
|
time - points[id_first_over0][1], count
|
|
end
|
|
stds = map(points) do ((time, count))
|
|
sqrt(count * (1000 - count) / 1000)
|
|
end
|
|
label, color = if psbid == psbid_width_max
|
|
"lomgest ($psbid)", (:red, 0.8)
|
|
elseif psbid == psbid_width_min
|
|
"shortest ($psbid)", (:blue, 0.8)
|
|
elseif psbid == psbid_width_typical
|
|
"typical ($psbid)", (:green, 0.8)
|
|
end
|
|
scatterlines!(ax, points, label = label, color = color)
|
|
@info "" first.(points) stds
|
|
errorbars!(ax, points, stds, whiskerwidth = 10, color = color)
|
|
end
|
|
end
|
|
axislegend(ax, position = :rb)
|
|
fig
|
|
end
|
|
|
|
# ╔═╡ Cell order:
|
|
# ╟─f25e7e08-8a73-4cac-ac7c-d310725c558d
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|
# ╠═7c69d12c-80a5-11ef-2674-e155a3483342
|
|
# ╠═effa7ed9-2ac4-4468-a474-e2bb662580fe
|
|
# ╠═1a6322d4-9deb-4709-aa2e-df7d8be5b16f
|
|
# ╠═11537f91-e16b-45f0-9768-6df842371d36
|
|
# ╠═268d3015-b8d3-48d9-b74a-062e258e0ec1
|
|
# ╟─62105832-df1f-4834-8da6-c542e22207d1
|
|
# ╠═dea6f143-7916-4765-92f6-2bfb97a72835
|
|
# ╟─41543c0c-d7c4-447b-a268-0d356c88d92c
|
|
# ╠═633ecdee-7e2f-4de8-833a-21cd0351c1f1
|
|
# ╟─87f1966e-3b07-4f9d-8fc4-7b7aa4319d50
|
|
# ╠═f379d43c-9300-41f4-b0fc-3c9d749e3105
|
|
# ╟─33e099bc-ac4b-4b5f-88e7-20f4463c98ef
|
|
# ╠═0e13f848-0efb-4775-9e3e-518b32588a79
|
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# ╠═181c3fe6-d087-42e2-b175-3fb84c42e3e8
|
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# ╟─dd669f14-989b-45ee-87d8-5d9cf282fafd
|
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# ╠═1e9c3944-0cd4-40da-9014-a9153d4e95ed
|
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# ╟─322cb530-65a5-4973-86f8-01ccc2439cc4
|
|
# ╠═c1caca5f-4cfd-4f22-82b4-7925002359e6
|
|
# ╠═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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# ╠═0e680044-e4e1-4f39-a5c5-afa5c53fc7a7
|
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# ╠═d7541b93-4c49-4dcd-bda0-91e447f44596
|
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# ╟─e7faa647-79cd-4247-a3f2-9868c7b9d4ca
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# ╠═3a412a98-4a2d-4bfb-8053-b0f480fae921
|
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# ╟─b38bbed4-8721-4e92-a546-f7926cc07dd3
|
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# ╠═420dce0e-4757-48d9-84ec-7ddfac2fdff6
|
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# ╠═99902640-fee3-4502-9c7e-cb08834bad0b
|
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# ╠═c79c6684-1b03-41b5-aa90-ef8c7a8eb69c
|
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# ╟─ec774495-c0be-47a4-9d2c-b48159c07013
|
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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
|
|
# ╠═d607e10e-854f-4652-9a34-9e22a188e315
|
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# ╠═2795fd06-2f59-4e5b-829d-a8e428646790
|
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# ╟─26976b6c-3954-4a41-a99b-c1aaebdc645d
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# ╠═38d472ca-6347-4096-828d-fd1256130a59
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# ╠═310710da-ebb2-4f54-b238-38d493a6a533
|
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# ╠═e9964184-1a2a-4ab9-bc22-2705a25393ec
|
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# ╠═f2d0ea9b-ede7-496a-8d1f-9f748f9d1666
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# ╠═f5bc8b1a-3fee-48ed-9910-367a1cda632e
|
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# ╠═9f3f780d-da6e-44b4-a002-c2f088681e9c
|
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# ╠═6c3c7669-d594-425d-bf05-9aa217c7656e
|
|
# ╠═e9b3f541-d87d-4424-8b82-be1b7b3273d8
|