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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。

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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

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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

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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

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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.

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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)

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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

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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=)

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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

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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

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做到这二十条，Python程序性能轻松翻倍！

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【Python环境】Python性能优化的20条建议

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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

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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)

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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 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 loops...and (orders.requiredDate loops...=1) -> Nested loop left join (cost=1.89 rows=9) (actual time=0.034..0.039 rows=4 loops=1)

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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=)

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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=

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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

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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.

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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

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点击加载更多

HiChIP 数据分析: 鉴定 Loops

JS：事件循环机制（Event Loops）

hdu 3853LOOPS (概率DP)

chromatin loops:染色质环简介

一次Dhrystone测试

pg_hint_plan定制执行计划

PostgreSQL 查询语句开发写不好是必然，不是PG的锅

Explain Analyze的作用和效果

做到这二十条，Python程序性能轻松翻倍！

20个小招数教你如果快速完成Python 性能优化升级

做到这二十条，Python程序性能轻松翻倍！

【Python环境】Python性能优化的20条建议

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

第 56 期：EXPLAIN ANALYZE 怎么用？

MySQL 的SQL引擎很差吗？由一个同学提出问题引出的实验

Postgresql_根据执行计划优化SQL

进阶数据库系列（九）：PostgreSQL 执行计划

PostgreSQL 哈希链接和哈希聚合

R Programming week2 Control Structures

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

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