ffmpeg -i https://public.ynjn.jp/collection/3ccd186d07a9a99a24cd5c67f09588b2/streaming/d4t3hoqp9kodv2doh0oga1hxkn960wma.m3u8 -c copy kanemaru-saki.behind-the-scenes.mp4

$ ./devel/copyclips/showboxes.scm /tmp/kane.mp4
ftyp 32 bytes at pos 0
moov 1110034 bytes at pos 32
*mvhd 108 bytes at pos 40 0 over minimum
*trak 894707 bytes at pos 148
**tkhd 92 bytes at pos 156 0 over minimum
**edts 36 bytes at pos 248
***elst 28 bytes at pos 256 -4 over minimum
**mdia 894571 bytes at pos 284
***mdhd 32 bytes at pos 292 0 over minimum
***hdlr 45 bytes at pos 324 12 over minimum
***minf 894486 bytes at pos 369
****vmhd 20 bytes at pos 377 0 over minimum
****dinf 36 bytes at pos 397
*****dref 28 bytes at pos 405 0 over minimum
****stbl 894422 bytes at pos 433
*****stsd 194 bytes at pos 441 15 over minimum
*****stts 274352 bytes at pos 635 274320 over minimum
*****stss 2352 bytes at pos 274987 2336 over minimum
*****ctts 193640 bytes at pos 277339 193624 over minimum
*****stsc 179392 bytes at pos 470979 179376 over minimum
*****stsz 137192 bytes at pos 650371 137172 over minimum
*****stco 107292 bytes at pos 787563 107276 over minimum
*trak 215114 bytes at pos 894855
**tkhd 92 bytes at pos 894863 0 over minimum
**edts 36 bytes at pos 894955
***elst 28 bytes at pos 894963 -4 over minimum
**mdia 214978 bytes at pos 894991
***mdhd 32 bytes at pos 894999 0 over minimum
***hdlr 45 bytes at pos 895031 12 over minimum
***minf 214893 bytes at pos 895076
****smhd 16 bytes at pos 895084
****dinf 36 bytes at pos 895100
*****dref 28 bytes at pos 895108 0 over minimum
****stbl 214833 bytes at pos 895136
*****stsd 123 bytes at pos 895144 -56 over minimum
*****stts 24 bytes at pos 895267 -8 over minimum
*****stsc 40 bytes at pos 895291 24 over minimum
*****stsz 107296 bytes at pos 895331 107276 over minimum
*****stco 107288 bytes at pos 1002627 107272 over minimum
*****sgpd 26 bytes at pos 1109915
*****sbgp 28 bytes at pos 1109941
*udta 97 bytes at pos 1109969
**meta 89 bytes at pos 1109977 0 over minimum
free 8 bytes at pos 1110066
mdat 2882851964 bytes at pos 1110074
$ stat /tmp/kane.mp4
  Size: 2883962038      Blocks: 5632744    IO Block: 4096

-empty_hdlr_name true -use_editlist false -write_btrt false -write_tmcd false

 --ytdl-raw-options=downloader-args='ffmpeg_i1:-http_persistent 0' 

julia> db = SQLite.DB("sql/wn.db")
SQLite.DB("sql/wn.db")

julia> sql = WeatherNews.DB.sql_fn(db)
(::WeatherNews.DB.var"#sql#sql_fn##0"{SQLite.DB}) (generic function with 2 methods)

julia> q = """
       SELECT title,
              SUM(c) AS n
         FROM (SELECT seg.id,
                      seg.n AS title,
                      s.caster_id = ? AS c
                 FROM segment seg
                      LEFT JOIN schedule s ON s.segment_id = seg.id
                      JOIN caster c ON c.id = s.caster_id
                WHERE s.jst LIKE '2025%'
                ORDER BY seg.id) seg2
        GROUP BY title
        ORDER BY id;
       """;

julia> casters = ["ailin", "sayane", "matsu", "shirai", "takayama", "komaki2018", "tokita", "ogawa", "uozumi", "aohara2023", "okamoto2023", "tanabe2025", "matsumoto2025", "kawabata", "fukuyoshi"];

julia> count2025 = map(caster -> (caster => sql(q, [caster_id[caster]])), casters);

julia> tab2025 = reduce((res, pair) -> hcat(res, DataFrame(pair.first => pair.second[!, :n]); makeunique=true), count2025; init=count2025[1].second[!, :title])
8×16 DataFrame
 Row │ x1           ailin  sayane  matsu  shirai  takayama  komaki2018  tokita  ogawa  uozumi  aohara2023  okamoto2023  tanabe2025  matsumoto2025  kawabata  fukuyoshi 
     │ String       Int64  Int64   Int64  Int64   Int64     Int64       Int64   Int64  Int64   Int64       Int64        Int64       Int64          Int64     Int64     
─────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
   1 │ Morning          0       0      0       0         0           0       0     29      72          71            0          27             28         0         75
   2 │ Sunshine         0       1      1      15         0           0       0     36      66          73            3          34             38         9         29
   3 │ Coffee Time      2       5    100      21         0           0       9     23      30          33           22          30             33        37          4
   4 │ Afternoon       41      24      1      48         0          17      47     51      23          24           21          14              9        25          0
   5 │ Evening         55       0      0       7         0          79      90     21       1           5           79           0              1         1          0
   6 │ Moon            67       0      0       0         0         121      55     31       0           1           77           0              0         0          0
   7 │ _                4       0      0       0         0           0       1      0       0           0            1           0              0         0          0
   8 │ au PAY           5       3      3       5         0           7       8      9       8           8            7           3              3         0          0

julia> caster_table = sql("select * from caster order by id");

julia> caster_id = map(row -> (row.n => row.id), eachrow(caster_table)) |> Dict
Dict{String, Int64} with 32 entries:
  "fukuyoshi"     => 34
  "hiyama2018"    => 11
  "nagamine"      => 28
  "tokita"        => 3
  "kobayashi"     => 9
  "takayama"      => 1
  "matsu"         => 6
  "kawabata"      => 7
  "morita"        => 21
  "uchida"        => 24
  "ida"           => 22
  "matsumoto2025" => 35
  "mutou"         => 19
  "aohara2023"    => 13
  "maie"          => 14
  "sayane"        => 15
  "ktr"           => 26
  "ogawa"         => 5
  "ohshima"       => 10
  "kakuta"        => 29
  "komaki2018"    => 8
  "suzuki"        => 20
  "uozumi"        => 4
  "ailin"         => 2
  "okamoto2023"   => 12
  "hatayama"      => 18
  "negoro"        => 30
  "shirai"        => 16
  "fujioka"       => 25
  "tanabe2025"    => 36
  "unozawa"       => 23
  "hokawa"        => 17

SELECT DATE(jst, '+9 hours') AS datejst,
       SUM(view_count) AS daily_views
  FROM schedule
 WHERE segment_id IN (1, 2, 3, 4, 5, 6)
   AND view_count IS NOT NULL
 GROUP BY datejst
 ORDER BY jst;