小编Gep*_*ada的帖子

我的代码中有一个字符串列表;

A = ['a1', 'a2', 'a3' ...]
B = ['b1', 'b2', 'b3' ...]

我想用换行符分隔打印它们,如下所示:

>a1
b1
>a2
b2
>a3
b3

我试过了:

print '>' + A + '/n' + B

但/ n不被认为是换行符.

我需要重命名45个文件,我不想一个一个地去做.这些是文件名:

chr10.fasta         chr13_random.fasta  chr17.fasta         chr1.fasta          chr22_random.fasta  chr4_random.fasta  chr7_random.fasta  chrX.fasta
chr10_random.fasta  chr14.fasta         chr17_random.fasta  chr1_random.fasta   chr2.fasta          chr5.fasta         chr8.fasta         chrX_random.fasta
chr11.fasta         chr15.fasta         chr18.fasta         chr20.fasta         chr2_random.fasta   chr5_random.fasta  chr8_random.fasta  chrY.fasta
chr11_random.fasta  chr15_random.fasta  chr18_random.fasta  chr21.fasta         chr3.fasta          chr6.fasta         chr9.fasta         
chr12.fasta         chr16.fasta         chr19.fasta         chr21_random.fasta  chr3_random.fasta   chr6_random.fasta  chr9_random.fasta
chr13.fasta         chr16_random.fasta  chr19_random.fasta  chr22.fasta         chr4.fasta          chr7.fasta         chrM.fasta

我需要将扩展​​名".fasta"更改为".fa".我正在尝试编写一个bash脚本来执行此操作:

for i in $(ls chr*)

do

NEWNAME = `echo $i | sed 's/sta//g'`

mv $i $NEWNAME

done

但它不起作用.你能告诉我为什么,或者给出另一个快速解决方案吗？

谢谢!

我需要计算python脚本中大量数据的二项式置信区间.你知道python的任何函数或库可以做到这一点吗？

理想情况下,我希望在python上实现这样的功能http://statpages.org/confint.html.

谢谢你的时间.

我有一个13 GB的大文本文件,158,609,739行,我想随机选择155,000,000行.

我试图加扰文件,然后削减155000000第一行,但似乎我的ram内存(16GB)不足以做到这一点.我试过的管道是:

shuf file | head -n 155000000
sort -R file | head -n 155000000

现在,而不是选择行,我认为是更高效的内存从文件中删除3,609,739个随机行,以获得155000000行的最终文件.

我正在编写一个程序,我需要在python 中搜索strings 的字母list.比如我有一个list的string就像:

l = ['foo', 'biology', 'sequence']

我想要这样的东西:

l = ['ofo', 'lbyoogil', 'qceeenus']

最好的方法是什么？

谢谢你的帮助!

我知道使用timedelta函数你可以使用类似的东西将秒转换为h:m:s:

>> import datetime
>> str(datetime.timedelta(seconds=666)) 
'0:11:06'

但我需要将h:m:s转换为秒或分钟.

你知道一个可以做到这一点的功能吗？

我需要绘制一个带有不对称误差条的条形图...

matplotlib.pyplot.bar函数的文档说:

细节:xerr和yerr直接传递给errorbar(),因此它们也可以具有形状2xN,用于独立指定低位和高位错误.

但是,我不能给一个2xN阵列...

import numpy as np
import matplotlib.pyplot as plt

plt.bar(xrange(5), [2,5,3,4,7], yerr=[[1,4,2,3,6],[4,10,6,8,14]]) #DO NOT work!

并告诉我以下错误:

Traceback (most recent call last):
  File "bar_stacked.py", line 9, in <module>
    plt.bar(xrange(5), [2,5,3,4,7], yerr=[[1,4,2,3,6],[4,10,6,8,14]])
  File "/usr/lib/pymodules/python2.7/matplotlib/pyplot.py", line 1742, in bar
    ret = ax.bar(left, height, width, bottom, color, edgecolor, linewidth, yerr, xerr, ecolor, capsize, align, orientation, log, **kwargs)
  File "/usr/lib/pymodules/python2.7/matplotlib/axes.py", line 4253, in bar
    "incompatible sizes: bar() argument 'yerr' must be len(%s) or scalar" % nbars)
ValueError: incompatible sizes: bar() argument 'yerr' …

我正在尝试对已邀请我作为协作者的GitHub存储库进行更改。我可以创建新文件并对其进行修改，但是当我尝试对现有文件进行任何更改时，我看到以下消息：“您必须在分支上才能对该文件进行更改或提出更改建议”。

然后，只有在通过终端推送提交之后，我才能在GitHub页面上修改此文件。这是预期的行为吗？

谢谢您的时间，干杯

我开始编写代码,但一开始就失败了.

这是我的代码:

import csv
reader = csv.reader(open("QstartRefseqhg19.head"), dialect='excel-tab' )

for row in reader:
    C = row[1].split(",")[1:]
    C1 = [float(i) for i in C]

    print C1 

并且错误日志说:

Traceback (most recent call last):
  File "/home/geparada/workspace/SJtag/src/TagGen.py", line 8, in <module>
    C1 = [float(i) for i in C]
ValueError: empty string for float()

我也试过了

import csv
reader = csv.reader(open("QstartRefseqhg19.head"), dialect='excel-tab' )

for row in reader:
    C = row[1].split(",")[1:]       
    C1 = map(float, C)        

    print C1

我的输入文件如下所示:

NM_032291   0,227,291,316,388,445,500,676,688,700,725,777,863,956,1031,1532,1660,1787,1847,1959,2115,2248,2451,2516,2681,   tttctctcagcatcttcttggtagcctgcctgtaggtgaagaagcaccagcagcatccatggcctgtcttttggcttaacacttatctcctttggctttgacagcggacggaatagacctcagcagcggcgtggtgaggacttagctgggacctggaatcgtatcctcctgtgttttttcagactccttggaaattaaggaatgcaattctgccaccatgatggaaggattgaaaaaacgtacaaggaaggcctttggaatacggaagaaagaaaaggacactgattctacaggttcaccagatagagatggaattcagcccagcccacacgaaccaccctacaatagcaaagcagagtgtgcgcgtgaaggaggaaaaaaagtttcgaagaaaagcaatggggcaccaaatggattttatgcggaaattgattgggaaagatataactcacctgagctggatgaagaaggctacagcatcagacccgaggaacccggctctaccaaaggaaagcacttttattcttcaagtgaatcggaagaagaagaagaatcacataagaaatttaatatcaagattaaaccattgcaatctaaagacattcttaagaatgctgcaactgtagatgaattgaaggcatcaataggcaacatcgcactttccccatcaccagtgaggaaaagtccgaggcgcagcccgggtgcaattaaaaggaacttatccagtgaagaagtggcaagacccaggcgttccacaccaactccagaacttataagcaaaaagcctccagatgacactacggcccttgctcctctctttggcccaccactagaatcagcttttgatgaacagaagacagaagttcttttagatcagcctgagatatggggttcaggccaaccaattaatccaagcatggagtcgccaaagttaacaaggccttttcccactggaacacctccaccactgcctccaaaaaatgtaccagctaccccaccccgaacaggatcccccttaacaattggaccaggaaatgaccagtcagccacagaggtcaaaattgaaaaactaccatccatcaatgacttggacagcatttttgggccagtattgtcccccaagtctgttgctgttaatgctgaagaaaagtgggtccatttttctgatacatccccggaacatgttactccggagttgactccaagggaaaaagtggtgtccccaccagctacaccagacaacccagctgactccccagctccaggccctctcggccccccaggtcccacaggccccccagggcctcctgggcctcctcgcaatgtactatcgccgctcaatttagaagaagtccagaagaaagtcgctgagcagaccttcattaaagatgattacttagaaacaatctcatctcctaaagattttgggttgggacaaagagcaactccacctcccccaccaccacccacctacaggactgtggtttcgtcccccggacctggctcgggccctggtccggggaccaccagtggtgcatcatcccctgctcgaccagccactcctttggttccttgcagaagtaccactccacctccacctcctccccggcctccatcccggccaaagctacctccaggaaaacctggagttggagatgtgtccagaccttttagccctcccattcattcttccagccctcctccaatagcacccttagcgcgggctgaaagcacttcttcaatatcgtcaaccaattccttgagcgcagccaccactcccacagttgagaatgaacagccttccctcgtttggtttgacagaggaaagttttatttgacttttgaaggttcttccaggggacccagccccctaaccatgggagctcaggacactctccctgttgcagcagcatttacagaaacagtcaatgcctatttcaaaggagcagacccaagcaaatgtatcgttaagattaccggagaaatggtgttgtcatttcctgctggcatcaccagacactttgccaacaacccgtccccagctgctctgacttttcgggtgataaatttcagcaggttagaacacgtcctgccaaacccccaacttctctgctgtgataatacacaaaatgatgccaataccaaggaattctgggtaaacatgccaaatttgatgactcacctaaagaaagtgtctgaacaaaaaccccaggctacatattataacgttgacatgctcaaatatcaggtgtctgcccagggcattcagtccacacctctgaacctggcagtgaattggcgatgtgagccttcaagcactgacctgcgcatagattacaaatataatacagatgcaatgacgactgctgtggccctcaacaatgtgcagttcctggtccccatcgacggaggagtcaccaagctccaggcagtgctcccaccagcagtctggaatgctgaacaacagagaatattgtggaagattcctgatatctctcagaagtcagaaaatggaggggtgggttctttgttggcaagatttcagttatctgaaggcccaagcaaaccttctccattggttgtgcagttcacaagtgaaggaagcaccctttctggctgtgacattgaacttgttggagcagggtatcgattttcactcatcaagaaaaggtttgctgcaggaaaatacttggcagataactaatgaaatcttatgcaaggatttggaggattcatataatggagaactgatgtatgagaaacagattttaattttggtttgatgaaaacaaaccaatatctgcacttgggatatatcaggtggaaagtcaatgactttcatctgtgatttccctcacacactaccatgatgaccagtcctacagtatttacttctaggtgtaatattgttaatggttttaaaatgtaattattgtatttgtaaattgtactctcattccagtaaggcagttagacacttgagttttagcattttaccattcctgaaatggatgtaatttaaactgtggtatgtaaatttaatagtagtattgttgaatggcacaatgcttacagaggtagattgcattttgtcaatatataaaatttaaatataatattgatagctgtcataaagggggtgccacatattaaagaaacttaagtggaaccagaagaaaaagaaacaaacttacttttcttcaatgcttagtatgttttactctagtgctaaataaaaactctatcttcaaatgtttagtgggttaaattgagaaactatttcagaaaaaaattctaaggttacagcatattcaaagaaaagcattagttaccactttttaaaaagcttttttttcaaactgcaaatttcataaaaatgcaaactgtgtaaacagggcctcttatttttataacttgtgtaaaaagggaaagcaattcatatttaaagtttaagtatattaaattataatcaagagtaaagaagatgttgaagtcttaactacttgcccctctctacagtttcgcaaatgtggggattgctgaataatcagtcagactaaaaccaaaattgtgattttaagatttcaagactttccgtagttgaactggttaagaatttttgcttagttactctgaatagatgatcttactcatccagtatgggggaatgatacctcacgtcttcctctttacccacaggaatcaaaacgctgagactgagaattttagggaaaaaaaagtccactgtttagatccagaaggagagttttaatcattgtttatatcatttgagaatgaaaaaataagcttcataaatgaaattctattcacattactgtgtaataaatttccttttggatgattaggattcattgtataaaactgtaaatctttgccattcttggagaagcaaaaggagagttatcaaaaatgtatgtcgtttcatcgttgcaaggtataataaaaactgtaattattcaatctggccctgccatatgaacatttagaaagacaaacttcttcgggagtctcagttgtaaaaccttccctcattaatatctgaaaatgttagtcttcctttaagtcatagaacttatttaaacataaaccaatttctattacaggttatgctattaaatagctgtaattattaagttattatttttataattagttgttaaatttcattttacacccactcaaatttaacaaagaatctttagcccctttaaattttagaattaaattaaatttttaaagttttacttctaaaatgagattgtgactggcaattgtttatagtgaaactttttaaattaatctttgtactcctctatcagtgcttgctaccaagagaatgtccaaaatgatttgttttaccatgggaaaattcttactattcaacaaactctcagttggccccctacagcagtctggtgttgaagtttctttgaacgaactaaatatactcattttatgtaaaggtatccaatttgattttgaaaccaaaatagaaaatgcaaaattctaaattccatgaaacatggaatttatgacaccaaaatcaatggagagtaagcagcagcaaactgagaattatccagcatatgaatataacaatgtgtttttaagtaatcaattcatttaaaaaattgaatattaatacaaagcatattaaaaacatgtaaatatta
NM_001080397    0,397,490,715,1443,1597,1774,1980,  atgatccccgcagccagcagcaccccgccgggagatgccctcttccccagcgtggccccacaggacttctggaggtcccaggtcacgggctactcggggtccgtgacacgacacctcagtcaccgggccaacaacttcaaacgacaccccaagaggaggaagtgcattcgtccctccccacccccgccccccaacaccccgtgcccgcttgagctggtggacttcggggacctgcacccccagaggtccttccgggagctgcttttcaacggctgcattctctttggcatcgagttcagctacgccatggagacggcgtacgtgaccccggtgctcctgcagatgggcctgcccgaccagctctacagcctggtgtggttcatcagccccatcctcggattcctactgcagcctctgttgggtgcttggagtgaccggtgtacctcaaggtttggaaggagacgccctttcattcttgtcctggctataggggcactgctgggcctctcgctcttgctgaatggccgggacattggcatcgccctggctgacgtgaccgggaaccacaagtggggcctgctgctgaccgtgtgcggtgtggtgctgatggactttagcgccgactcggcggacaaccccagccacgcctacatgatggacgtgtgcagccccgcagaccaggaccgaggcctgaacatccacgccctcctggcaggtctcggaggaggctttggatacgtggtcggcggaatccactgggataaaacgggcttcgggagggccctggggggacagctccgagtcatttacctcttcactgcggtcaccctgagcgtcaccaccgtcctgaccctggtcagcatccctgagaggccgctgcggccgccgagtgagaagcgggcagccatgaagagccccagcctcccgctgcccccgtccccacccgtcctgccagaggaaggccctggcgacagcctcccgtcgcacacggccaccaacttctccagccccatctcgccgcccagccccctcacgcccaagtacggcagcttcatcagcagggacagctccctgacgggcatcagcgagttcgcctcatcctttggcacggccaacatagacagcgtcctcattgactgcttcacgggcggccacgacagctacctggccatccctggcagcgtccccaggccgcccatcagcgtcagcttcccccgggcccccgacggcttctaccgccaggaccgtggacttctggagggcagagagggtgccctgacctccggctgtgacggggacattctgagggtgggctccttggacacctctaagccgaggtcatcagggattctgaagagacctcagaccttggccatcccggacgcagccggaggagggggtcccgaaaccagcaggagaaggaatgtgaccttcagtcagcaggtggccaatatcctgctcaacggcgtgaagtatgagagcgagctgacgggctccagcgagcgcgcggagcagcctctgtccgtggggcgcctctgctccaccatctgcaacatgcccaaggcgctacgcaccctctgcgtcaaccacttcctggggtggctctcattcgaggggatgttgctcttctacacagacttcatgggcgaggtggtgtttcagggggaccccaaggccccgcacacatcagaggcgtatcagaagtacaacagcggcgtgaccatgggctgctggggcatgtgtatctacgccttcagtgctgccttctactcagctatcctggagaagctggaggagttcctcagcgtccgcaccctctacttcatcgcctatctcgccttcggcctggggaccgggcttgccaccctctccaggaacctctacgtggtcctgtcgctctgcataacctacgggattttattttccaccctgtgcaccttgccttactcgctgctctgcgattactatcagagtaagaagtttgcagggtccagtgcggacggcacccggcggggcatgggcgtggacatctctctgctgagctgccagtacttcctggctcagattctggtctccctggtcctggggcccctgacctcggccgtgggcagtgccaacggggtgatgtacttctccagcctcgtgtccttcctgggctgcctgtactcctccctgtttgtcatttatgaaattcctcccagcgacgctgcagacgaggagcaccggcccctcctgctgaacgtctgacatcgcggagcctcgactccggacacgcgcctgcacctgggggtctggagcaggccgaccagtgaggaccaaagggccttgttggacagggggactggctgcctactggaatgtaaatatgtgataaaataataaatgacagcggcaaagccta
NM_001145277    0,182,283,388,470,579,757,  gaaacctggtcagagagtcgcaccgcttccgtccgtcggacagaggaacggtggaagtcgccggaagttcggtgggctccaggcgtcgcgatggaggagagcgggtacgagtcggtgctctgtgtcaagcctgacgtccacgtctaccgcatccctccgcgggctaccaaccgtggctacagggctgcggagtggcagctggaccagccatcatggagtggccggctgaggatcactgcaaagggacagatggcctacatcaagctggaggacaggacgtcaggggagctctttgctcaggccccggtggatcagtttcctggcacagctgtggagagtgtgacggattccagcaggtacttcgtgatccgcatcgaagatggaaatgggcgacgggcgtttattggaattggcttcggggaccgaggtgatgcctttgacttcaatgttgcattgcaggaccatttcaagtgggtgaaacagcagtgtgaatttgcaaaacaagcccagaacccagaccaaggccctaaactggacctgggcttcaaggagggccagaccatcaagctcaacatcgcaaacatgaagaagaaggaaggagcagctgggaatccccgagtccggcctgccagcacaggagggctgagcctgcttccccctcccccaggggggaaaacctccaccctgatccctccccctggggagcagttggctgtggggggatccctcgtccagccagcagttgctcccagttcagatcaacttccagccagacccagccaggcacaggctgggtccagttctgacctgagcacggtttttcctcatgtgacttctgggaaggcgctccctcatctgggccaaaggaaggaggacgaagccctcctcagctggcctgtgtttggggcatgaatctctcctctcctccttgtctggctctgttgacaaaccgggcatgtttggcagtaaattggcaccgtgtcacactgtttcctgggattcaagtatgcaaccagaacacaggagaagaaaagctccaggatccctgtccccatctgtcctcttgatgtgagagagactctgagacttcttccatcgcaatgacctgtattaaacacaagccccccaagcaaaagaagaggttgagtttgctgccaggattcagatcagcccttcccagggtctgcaggtgtcacatgatcacagttcagcgggaggctttccgtacccacactggctgtagccacttcagtccatctgccctccagaggaggggtttcttcctgatttttagcaggtttagaggctgcagcttgagctacaatcaggagggaaattggaaggattagcagcttttaaaaatgtttaaatattttgctttgctaatgtgctgatccgcactaactcatctttgcaaaaggaactgctccctcggcgtgccccagctggggcctctgaagggattcctcactgtgggcagctgccctgagcttcaggcagcagtgtttatctctggccagttgtctggtttccatgtattctaggccaggtaggcaacacagagccaaggcgggtgctggaagccagacggaacagtgttggggcaggaaggtggatgctgttgtcatggagctgtgggagttggcactctgtctgctggtggccctctcggctcacatgttcacagtgcagctcctggcagacttgggttttctctttggtggtttctaaagtgccttatctgcaaacaacttcttttctccttcaggaactgtgaatggctagaagaaggagctcagtaaactagaagtccagggttgcttggtttactggtttataagaaatctgaaagcacctctgacattccttttattaactcacctctcagttgaaagatttcttctttgaaaggtcaagaccgtgaactgaaaaaagtgttggcctttttgcgggaccagatttttaagataaaataaatatttttacttctgtcattgtatgtgaaaaaaaaaaaaa …

我想运行这些sed行:

sed 's/\/1\/1/\/1/g' file -i && sed 's/\/2\/2/\/2/g' file -i

在这样的文件上:

chr9_paternal   126628489       126629719       616L7AAXX_HWUSI-EAS627_0005:1:1:1157:5733/1/1   0       +       126628489       126629719       255,0,0 2
       19,57   0,1173
chr20_paternal  34093622        34093697        616L7AAXX_HWUSI-EAS627_0005:1:1:1158:4527/1/1   0       +       34093622        34093697        255,0,0 1
       75      0
chr17_paternal  44627748        44633513        616L7AAXX_HWUSI-EAS627_0005:1:1:1158:5412/2/2   0       +       44627748        44633513        255,0,0 2
       36,40   0,5725
chr1_paternal   224204536       224204611       616L7AAXX_HWUSI-EAS627_0005:1:1:1158:2312/1   0       +       224204536       224204611       255,0,0 1
       75      0
chr7_paternal   132309510       132309585       616L7AAXX_HWUSI-EAS627_0005:1:1:1158:1687/2/2   0       +       132309510       132309585       255,0,0 1
       75      0
chr20_paternal  45708069        45708144        616L7AAXX_HWUSI-EAS627_0005:1:1:1158:13916/1  63      +       45708069        45708144        255,0,0 …