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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# UPARSE analysis pipeline form amplicon sequences\n",
    "\n",
    "We will follow the Illumina paired end tutorial.\n",
    "\n",

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# emacs backups
*~
\#*#

# data files
*.log
*.tsv
*.dat

# image files

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# Unix shell

# run if zero exit
cd tmp/a/b/c && tar xvf ~/archive.tar  # untar if dir exists
# run if non-zero exit
cd tmp/a/b/c || mkdir -p tmp/a/b/c
cd tmp/a/b/c || mkdir -p tmp/a/b/c && tar xvf -C tmp/a/b/c ~/archive.tar

which
whereis

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"""
Mappings from file extensions to biopython types.

Copied from Erik Matsens seqmagick https://github.com/fhcrc/seqmagick/

"""
import argparse
import contextlib
import copy
import functools

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# from https://gist.github.com/stephenturner/5700920
# http://inundata.org/2011/09/29/customizing-your-rprofile/

## Returns names(df) in single column, numbered matrix format.
.env$n <- function(df) matrix(names(df)) 
 
# Shorten S3 methods so s(obj) instead of summary(obj)
s <- base::summary;
h <- utils::head;
n <- base::names;

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// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console

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#!/usr/bin/env python

import fileinput
import sys
from argparse import ArgumentParser

# https://docs.python.org/dev/library/argparse.html

parser = argparse.ArgumentParser(description = """docstring here""")
parser.add_argument('-i','--input', 

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# Following code will generate a random sample of size 4.
rand_smpl = [ mylist[i] for i in sorted(random.sample(xrange(len(mylist)), 4)) ]