| bioawk -c fastx \ | |
| '{print $name "\t" length($seq) "\t" length($qual)}' <path/to/fastq.gz> | \ | |
| awk '$2!=$3{print}' |
virsh edit Windows10<video> section and change it to the following one: <video>
<model type='qxl' ram='131072' vram='131072' vgamem='32768' heads='1' primary='yes'/>
<address type='pci' domain='0x0000' bus='0x00' slot='0x02' function='0x0'/>
</video>
| #!/usr/bin/env bash | |
| # sra-paired.sh | |
| # Kamil Slowikowski | |
| # April 23, 2014 | |
| # | |
| # Check if an SRA file contains paired-end sequencing data. | |
| # | |
| # See documentation for the SRA Toolkit: | |
| # http://www.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?view=toolkit_doc&f=fastq-dump |
| \usepackage{fontspec} %加這個就可以設定字體 | |
| \usepackage{xeCJK} %讓中英文字體分開設置 | |
| \setCJKmainfont{微軟正黑體} %設定中文為系統上的字型，而英文不去更動，使用原TeX字型 | |
| \XeTeXlinebreaklocale "zh" %這兩行一定要加，中文才能自動換行 | |
| \XeTeXlinebreakskip = 0pt plus 1pt %這兩行一定要加，中文才能自動換行 | |
| \defaultCJKfontfeatures{AutoFakeBold=6,AutoFakeSlant=.4} %以後不用再設定粗斜 | |
| \newCJKfontfamily\Kai{標楷體} %定義指令\Kai則切換成標楷體 | |
| \newCJKfontfamily\Hei{微軟正黑體} %定義指令\Hei則切換成正黑體 | |
| \newCJKfontfamily\NewMing{新細明體} %定義指令\NewMing則切換成新細明體 |
| #!/usr/bin/env python3 | |
| import pandas as pd | |
| import requests | |
| import xmltodict | |
| import argparse | |
| from rich import print | |
| from rich.console import Console | |
| # https://github.com/Textualize/rich/issues/67 |
| # After Ubuntu 16.04, Systemd becomes the default. | |
| # It is simpler than https://gist.github.com/Doowon/38910829898a6624ce4ed554f082c4dd | |
| [Unit] | |
| Description=Jupyter Notebook | |
| [Service] | |
| Type=simple | |
| PIDFile=/run/jupyter.pid | |
| ExecStart=/home/phil/Enthought/Canopy_64bit/User/bin/jupyter-notebook --config=/home/phil/.jupyter/jupyter_notebook_config.py |
Python version of the MATLAB code in this Stack Overflow post: http://stackoverflow.com/a/18648210/97160
The example shows how to determine the best-fit plane/surface (1st or higher order polynomial) over a set of three-dimensional points.
Implemented in Python + NumPy + SciPy + matplotlib.
| # | |
| # This script calculates the depth of coverage and breadth of coverage for a given bam. | |
| # Outputs a dictionary containing the contig/chromosome names and the depth and breadth of coverage for each | |
| # and for the entire genome. | |
| # | |
| # If you optionally specify the name of the mitochondrial chromosome (e.g. mtDNA, chrM, chrMT) | |
| # The script will also generate breadth and depth of coverage for the nuclear genome AND the ratio | |
| # of mtDNA:nuclearDNA; which can act as a proxy in some cases for mitochondrial count within an individual. | |
| # | |
| # Author: Daniel E. Cook |
| def splitDataFrameList(df,target_column,separator): | |
| ''' df = dataframe to split, | |
| target_column = the column containing the values to split | |
| separator = the symbol used to perform the split | |
| returns: a dataframe with each entry for the target column separated, with each element moved into a new row. | |
| The values in the other columns are duplicated across the newly divided rows. | |
| ''' | |
| def splitListToRows(row,row_accumulator,target_column,separator): | |
| split_row = row[target_column].split(separator) |
