masih

#!/bin/bash
 
# Script for installing Fish Shell on systems without root access.
# Fish Shell will be installed in $HOME/local/bin.
# It's assumed that wget and a C/C++ compiler are installed.
 
# exit on error
set -e
 
FISH_SHELL_VERSION=2.1.1

cmaureir

Source: https://confluence.rcs.griffith.edu.au/display/v20zCluster/Sun+Grid+Engine+SGE+state+letter+symbol+codes+meanings

Category	State	SGE Letter Code
Pending	pending	qw
Pending	pending, user hold	qw
Pending	pending, system hold	hqw
Pending	pending, user and system hold	hqw
Pending	pending, user hold, re-queue	hRwq
Pending	pending, system hold, re-queue	hRwq

bsweger

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)

stephenturner

## RNA-seq analysis with DESeq2
## Stephen Turner, @genetics_blog

# RNA-seq data from GSE52202
# http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse52202. All patients with
# ALS, 4 with C9 expansion ("exp"), 4 controls without expansion ("ctl")

# Import & pre-process ----------------------------------------------------

# Import data from featureCounts

obenshaindw

#!/usr/bin/bash
#
# make_gz.sh
#

# Call this script with a list of s3 locations with VCF files to parse
# aws --profile NDAR s3 ls s3:/S3_URL/ | awk '{print $4}' | xargs -n1 -P4 sh make_gz.sh
# xargs -n1 -P4 accepts one argument and runs 4 parallel processes
#

jfpuget

john-science

Reading & Writing GZIP Files in Python

I have been testing various ways to read and write text files with GZIP in Python. There were a lot of uninteresting results, but there were two I thought were worth sharing.

Writing GZIP files

If you have a big list of strings to write to a file, you might be tempted to do:

f = gzip.open(out_path, 'wb')

for line in lines:

JimGrange

#------------------------------------------------------------------------------
rm(list = ls())

set.seed(50)

# function for generating random draws from multivariate distribution
# n = number of draws; p = number of variables
# u = mean of each variable; s = SD of each variable
# corMat = correlation matrix
mvrnorm <- function(n, p, u, s, corMat) {

rasmusab

library(tidyverse)
library(purrr)
library(rstan)

### Defining the data ###
#########################
bumblebees <- c(1, 0,  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 
                0, 0, 1, 0, 0, 0, 0, 0, 0)
toddler_steps <- c(26, 16, 37, 101, 12, 122, 90, 55, 56, 39, 55, 15, 45, 8)

johnmyleswhite

library("ggplot2")
library("dplyr")

# Population size
n <- 2500

# Sessions per user if assigned to test
sessions_test <- as.integer(exp(rnorm(n, 0.5, 1)))

# Sessions per user if assigned to control