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- 来自专栏数据科学（冷冻工厂）
HiChIP 数据分析: 鉴定 Loops
鉴定HiChIP Loops 由于 HiChIP 实验流程中包含免疫沉淀步骤，因此可以把 loops 定义为被靶向蛋白结合的区间（即 anchor regions）之间的相互作用。 此外，hichipper 还根据互作 anchors 的基因组位置及距离对 loops 进行筛选：仅保留 intrachromosomal loops；anchors 之间的最小距离设为 5 kb，以避免纳入自连接 loops，而最大距离设为 2 Mb，因为更远距离的相互作用生物学意义较小。 和 intrachromosomal loops。 最后，“Reads in Loops” 是指包含在环中的读段对，即唯一、染色体内、宽度在 5 kb 到 2 Mb 之间且锚点已映射的 PETs。
27110编辑于 2025-09-17 - 来自专栏ml
hdu 3853LOOPS (概率DP)
LOOPS Time Limit: 15000/5000 MS (Java/Others) Memory Limit: 125536/65536 K (Java/Others) Total Submission But because of the plot of the Boss Incubator, she is trapped in a labyrinth called LOOPS. ? The planform of the LOOPS is a rectangle of R*C grids. At the beginning Homura is in the top left corner of the LOOPS ((1, 1)), and the exit of the labyrinth portal, your task is help poor Homura calculate the EXPECT magic power she need to escape from the LOOPS
77190发布于 2018-03-26 - 来自专栏前端文章小tips
JS：事件循环机制（Event Loops）
** 事件循环，即 Event Loops。用于协调事件、用户交互、JavaScript 脚本、DOM 渲染、网络请求等等的执行顺序问题。 to=https%3A%2F%2Fhtml.spec.whatwg.org%2Fmultipage%2Fwebappapis.html%23event-loops) * [并发模型与事件循环 - JavaScript
2.9K10编辑于 2021-12-08 - 来自专栏生信修炼手册
chromatin loops:染色质环简介
进一步分析发现染色质环中有很大部分为promoter-enhancer loops， 这也解释了增强子对靶基因的调控机制，虽然增强子与靶基因线性距离很远，但是增强子与靶基因启动子位于一个染色质环上，空间距离近
3.9K31发布于 2019-12-19 - 来自专栏这里只有VxWorks
一次Dhrystone测试
Test (time for 1000000 number of loops): 58.000000 2. Test (time for 1000000 number of loops): 58.000000 3. Test (time for 1000000 number of loops): 58.000000 4. Test (time for 1000000 number of loops): 58.000000 5. Test (time for 1000000 number of loops): 58.000000 6.
1.9K10发布于 2021-09-15 - 来自专栏Postgresql源码分析
pg_hint_plan定制执行计划
------ Hash Right Join (cost=89.82..337.92 rows=17877 width=540) (actual time=0.053..0.059 rows=3 loops t1.id) -> Seq Scan on t3 (cost=0.00..32.60 rows=2260 width=8) (actual time=0.002..0.002 rows=3 loops =1) -> Hash (cost=1.03..1.03 rows=3 width=8) (actual time=0.005..0.005 rows=3 loops=1) =1) -> Hash (cost=1.03..1.03 rows=3 width=8) (actual time=0.005..0.005 rows=3 loops=1) =1) -> Hash (cost=1.03..1.03 rows=3 width=8) (actual time=0.006..0.006 rows=3 loops=1)
42820编辑于 2022-05-12 - 来自专栏AustinDatabases
PostgreSQL 查询语句开发写不好是必然，不是PG的锅
--- HashAggregate (cost=1194.15..1206.65 rows=1000 width=59) (actual time=20.612..21.072 rows=958 loops -> Hash Join (cost=715.56..1084.68 rows=14596 width=29) (actual time=5.115..16.883 rows=14596 loops Seq Scan on payment p (cost=0.00..253.96 rows=14596 width=10) (actual time=0.005..1.142 rows=14596 loops -> Hash (cost=70.81..70.81 rows=4581 width=6) (actual time=0.879..0.880 rows=4581 loops =1) -> Hash (cost=64.00..64.00 rows=1000 width=19) (actual time=0.318..0.318 rows=1000 loops
33210编辑于 2024-01-26 - 来自专栏bisal的个人杂货铺
Explain Analyze的作用和效果
**************** EXPLAIN: -> Table scan on y1 (cost= rows=1e+6) (actual time=0.0339..145 rows=1e+6 loops =) row in set (0.20 sec) 其中 (actual time=0.0339..145 rows=1e+6 loops=1) 这条就代表实际执行数据。 loops=1：循环次数。 第二个例子 第一次执行 对表 t1、t2 做内连，求满足条件的总记录数，连接 KEY 为 ID。执行计划表示先嵌套循环连接后，再做 COUNT 聚合计算。 嵌套循环内联部分：Nested loop inner join (cost=5839.68 rows=10169) (actual time=0.057..27.721 rows=10000 loops index lookup on a using PRIMARY (id=b.id) (cost=0.38 rows=) (actual time=0.001..0.002 rows= loops=)
31210编辑于 2025-08-18 - 来自专栏北京马哥教育
做到这二十条，Python程序性能轻松翻倍！
,best of3:569µsper loop 1000loops,best of3:256µsper loop 7. ,best of3:287µsper loop 100loops,best of3:214µsper loop 100loops,best of3:128µsper loop 100loops,best ,best of3:183ns per loop 100000loops,best of3:169ns per loop 100000loops,best of3:103ns per loop 三种情况中 使用**而不是pow %timeit-n10000c=pow(2,20) %timeit-n10000c=2**20 10000loops,best of3:284ns per loop 10000loops ,best of3:16.8ms per loop 100loops,best of3:2.02ms per loop 100loops,best of3:798µsper loop 可见json比cPickle
1.2K70发布于 2018-05-02 - 来自专栏CDA数据分析师
20个小招数教你如果快速完成Python 性能优化升级
, best of 3: 287 µs per loop 7100 loops, best of 3: 214 µs per loop 8100 loops, best of 3: 128 µs per , best of 3: 183 ns per loop 6100000 loops, best of 3: 169 ns per loop 7100000 loops, best of 3: 103 ns per loop10000 loops, best of 3: 16.9 ns per loop **就是快10倍以上！ , best of 3: 1.58 ms per loop 7100 loops, best of 3: 17 ms per loop 由c实现的包，速度快10倍以上！ , best of 3: 16.8 ms per loop 11100 loops, best of 3: 2.02 ms per loop 12100 loops, best of 3: 798 µs
61220发布于 2018-07-30 - 来自专栏北京马哥教育
做到这二十条，Python程序性能轻松翻倍！
,best of3:569µsper loop 1000loops,best of3:256µsper loop 7. ,best of3:287µsper loop 100loops,best of3:214µsper loop 100loops,best of3:128µsper loop 100loops,best ,best of3:183ns per loop 100000loops,best of3:169ns per loop 100000loops,best of3:103ns per loop 三种情况中 使用**而不是pow %timeit-n10000c=pow(2,20) %timeit-n10000c=2**20 10000loops,best of3:284ns per loop 10000loops ,best of3:16.8ms per loop 100loops,best of3:2.02ms per loop 100loops,best of3:798µsper loop 可见json比cPickle
62150发布于 2018-09-28 - 来自专栏数据科学与人工智能
【Python环境】Python性能优化的20条建议
, best of 3: 287 µs per loop100 loops, best of 3: 214 µs per loop100 loops, best of 3: 128 µs per loop100 (a) ...:100000 loops, best of 3: 11.8 µs per loop join对于累加的方式，有大约5倍的提升。 , best of 3: 183 ns per loop100000 loops, best of 3: 169 ns per loop100000 loops, best of 3: 103 ns per loop10000 loops, best of 3: 16.9 ns per loop **就是快10倍以上！ , best of 3: 16.8 ms per loop100 loops, best of 3: 2.02 ms per loop100 loops, best of 3: 798 µs per loop
943100发布于 2018-02-26 - 来自专栏AustinDatabases
PostgreSQL 同一种SQL为什么这样写会提升45%性能 --程序员和DBA思维方式不同决定
Subquery Scan on ranked_scores (cost=0.14..3.58 rows=1 width=16) (actual time=0.027..0.032 rows=3 loops =1) -> Limit (cost=3.57..3.57 rows=1 width=8) (actual time=0.063..0.067 rows=2 loops =1) -> Sort (cost=8.13..8.13 rows=1 width=254) (actual time=0.134..0.135 rows=4 loops=1) =1) -> GroupAggregate (cost=3.71..5.77 rows=1 width=36) (actual time=0.072..0.079 rows=2 loops=1 =1) -> Limit (cost=3.57..3.57 rows=1 width=8) (actual time=0.045..0.049 rows=2 loops
25110编辑于 2024-05-20 - 来自专栏爱可生开源社区
第 56 期：EXPLAIN ANALYZE 怎么用？
=1) 1 row in set (0.20 sec) 其中 (actual time=0.0339..145 rows=1e+6 loops=1) 这条就代表实际执行数据。 loops=1：循环次数。 第二个例子 第一次执行 对表 t1、t2 做内连，求满足条件的总记录数，连接 KEY 为 ID。执行计划表示先嵌套循环连接后，再做 COUNT 聚合计算。 嵌套循环内联部分：Nested loop inner join (cost=5839.68 rows=10169) (actual time=0.057..27.721 rows=10000 loops =1) -> Nested loop inner join (cost=5901.50 rows=508) (actual time=0.264..20.447 rows=118 loops= =1) -> Filter: (a.r1 = 10) (cost=0.38 rows=0) (actual time=0.002..0.002 rows=0 loops=10000)
32310编辑于 2025-07-16 - 来自专栏AustinDatabases
MySQL 的SQL引擎很差吗？ 由一个同学提出问题引出的实验
-12') and (o.requiredDate < DATE'2003-01-14')) (cost=0.71 rows=1) (actual time=0.060..0.062 rows=1 loops select #2)) and (d.orderNumber = (select #2))) (cost=0.65 rows=4) (actual time=0.009..0.011 rows=4 loops and (orders.requiredDate < DATE'2003-01-14')) (cost=0.46 rows=1) (actual time=0.028..0.032 rows=1 loops and (orders.requiredDate < DATE'2003-01-14')) (cost=0.46 rows=1) (actual time=0.028..0.032 rows=1 loops =1) -> Nested loop left join (cost=1.89 rows=9) (actual time=0.034..0.039 rows=4 loops=1)
24410编辑于 2024-05-22 - 来自专栏IT技术订阅
Postgresql_根据执行计划优化SQL
Seq Scan on db_jcxx.t_jcxxgl_tjaj (cost=0.00..9.76 rows= width=) (actual time=1.031..1.055 rows= loops =) -> Sort (cost=36328.67..36328.68 rows= width=) (actual time=88957.653..88957.672 rows= loops=) Nested Loop Semi Join (cost=17099.76..36328.66 rows= width=) (actual time=277.794..88932.662 rows= loops =) -> Nested Loop (cost=3223.92..3231.97 rows= width=) (actual time=127.285..127.496 rows= loops=) =) -> Nested Loop (cost=1.12..2547.17 rows= width=) (actual time=0.136..0.689 rows= loops=)
1.1K20编辑于 2022-06-23 - 来自专栏民工哥技术之路
进阶数据库系列（九）：PostgreSQL 执行计划
Seq Scan on db_test.t_test(cost=0.00..22.32 rows=1032 width=56) (actual time=0.060..1.167 rows=1032 loops loops=1，#循环的次数 Output，#输出的字段名 Buffers，#缓冲命中数 shared read，#代表数据来自disk(磁盘)而并非cache(缓存)，当再次执行sql，会发现变成shared Scan on db_test.t_ms_aj (cost=0.00..22.32 rows=1032 width=56) (actual time=0.060..1.167 rows=1032 loops =2) -> Sort (cost=73.98..76.56 rows=1032 width=52) (actual time=2.963..3.154 rows=338 loops -> Nested Loop (cost=743.92..3214.11 rows=1 width=0) (actual time=8.702..61097.110 rows=1461 loops=
1.5K50编辑于 2023-08-22 - 来自专栏AustinDatabases
PostgreSQL 哈希链接 和 哈希聚合
: 9.835 ms (15 rows) Limit (cost=511.60..511.62 rows=5 width=33) (actual time=9.752..9.755 rows=5 loops =1) -> Sort (cost=511.60..513.10 rows=599 width=33) (actual time=9.751..9.753 rows=5 loops=1) -> HashAggregate (cost=495.66..501.65 rows=599 width=33) (actual time=9.555..9.645 rows=599 loops > Seq Scan on rental (cost=0.00..310.44 rows=16044 width=18) (actual time=0.006..1.233 rows=16044 loops =1) -> Sort (cost=1847.14..1848.64 rows=599 width=33) (actual time=17.281..17.284 rows=5 loops=1
60710编辑于 2024-03-02 - 来自专栏机器学习与统计学
R Programming week2 Control Structures
For loops are mostcommonly used for iterating over the elements of an object (list, vector, etc.) for These following loops have the samebehavior: x<- c("a", "b", "c", "d") for(iin 1:4) { print(x[i]) } for loops can be nested. x<- matrix(1:6, 2, 3) for(iin seq_len(nrow(x))) { for(j in seq_len(ncol(x Nestingbeyond 2–3 levels is often very difficult to read/understand While While loops begin by testing can potentially result ininfinite loops if not written properly.
49420发布于 2019-04-10 - 来自专栏AustinDatabases
PostgreSQL 真实压测，分析PG18 17 16 15 14 之间在处理SQL和系统性能稳定性的差异
Limit (cost=201709.20..201709.21 rows=1 width=122) (actual time=136579.199..136579.684 rows=3.00 loops orders o (cost=5512.62..21455.72 rows=490863 width=26) (actual time=27.673..6695.270 rows=493110.00 loops idx_orders_date (cost=0.00..5389.90 rows=490863 width=0) (actual time=26.047..26.058 rows=493110.00 loops (cost=115506.50..116733.66 rows=490863 width=25) (actual time=69253.277..76183.559 rows=493110.00 loops 3 -> Aggregate (cost=0.07..0.08 rows=1 width=32) (actual time=10692.491..10692.558 rows=1.00 loops
67810编辑于 2025-11-14
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