Skip to content

Instantly share code, notes, and snippets.

@avrilcoghlan
avrilcoghlan / run_cnvnator_on_assembly.pl
Last active December 12, 2015 07:39
Perl script to run CNVnator on a genome assembly
#!/usr/local/bin/perl
=head1 NAME
run_cnvnator_on_assembly.pl
=head1 SYNOPSIS
run_cnvnator_on_assembly.pl input_fasta input_bam output outputdir path_to_cnvnator windowsize
where input_fasta is the input fasta file,
@willurd
willurd / web-servers.md
Last active July 12, 2024 11:21
Big list of http static server one-liners

Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.

Discussion on reddit.

Python 2.x

$ python -m SimpleHTTPServer 8000
@magicznyleszek
magicznyleszek / jekyll-and-liquid.md
Last active January 12, 2024 03:46
Jekyll & Liquid Cheatsheet

Jekyll & Liquid Cheatsheet

A list of the most common functionalities in Jekyll (Liquid). You can use Jekyll with GitHub Pages, just make sure you are using the proper version.

Running

Running a local server for testing purposes:

@mattlewissf
mattlewissf / add-p.md
Last active July 13, 2024 01:41
Lightning Talk: Git add -p

git add -p is your friend

git add -p is basically "git add partial (or patch)"

Patch mode allows you to stage parts of a changed file, instead of the entire file. This allows you to make concise, well-crafted commits that make for an easier to read history. This feature can improve the quality of the commits. It also makes it easy to remove parts of the changes in a file that were only there for debugging purposes - prior to the commit without having to go back to the editor.

It allows you to see the changes (delta) to the code that you are trying to add, and lets you add them (or not) separately from each other using an interactive prompt. Here's how to use it:

from the command line, either use

  • git add -p
@bsweger
bsweger / useful_pandas_snippets.md
Last active April 19, 2024 18:04
Useful Pandas Snippets

Useful Pandas Snippets

A personal diary of DataFrame munging over the years.

Data Types and Conversion

Convert Series datatype to numeric (will error if column has non-numeric values)
(h/t @makmanalp)

@randyzwitch
randyzwitch / seaborn-stacked-bar.py
Created September 8, 2014 21:08
Python Seaborn Stacked Bar Chart
import pandas as pd
from matplotlib import pyplot as plt
import matplotlib as mpl
import seaborn as sns
%matplotlib inline
#Read in data & create total column
stacked_bar_data = pd.read_csv("C:\stacked_bar.csv")
stacked_bar_data["total"] = stacked_bar_data.Series1 + stacked_bar_data.Series2
@alimuldal
alimuldal / dunn.py
Last active October 5, 2023 06:04
Implementation of Dunn's multiple comparison test, following a Kruskal-Wallis 1-way ANOVA
import numpy as np
from scipy import stats
from itertools import combinations
from statsmodels.stats.multitest import multipletests
from statsmodels.stats.libqsturng import psturng
import warnings
def kw_dunn(groups, to_compare=None, alpha=0.05, method='bonf'):
"""
@rain1024
rain1024 / tut.md
Last active July 8, 2024 04:50
Install pdflatex ubuntu

PdfLatex is a tool that converts Latex sources into PDF. This is specifically very important for researchers, as they use it to publish their findings. It could be installed very easily using Linux terminal, though this seems an annoying task on Windows. Installation commands are given below.

  • Install the TexLive base
sudo apt-get install texlive-latex-base
  • Also install the recommended and extra fonts to avoid running into the error [1], when trying to use pdflatex on latex files with more fonts.
@wrobstory
wrobstory / Legend.ipynb
Last active October 13, 2017 10:20
Seaborn legend
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
@jonperron
jonperron / gist:733c3ead188f72f0a8a6f39e3d89295d
Last active November 30, 2023 17:29
Serve pandas dataframe as csv with Django
from django.http import HttpResponse
import pandas as pd
def foo():
results = pd.Dataframe()
response = HttpResponse(content_type='text/csv')
response['Content-Disposition'] = 'attachment; filename=filename.csv'
results.to_csv(path_or_buf=response,sep=';',float_format='%.2f',index=False,decimal=",")