One of the virtues of a programmer is laziness – if you find yourself doing something more than twice, you should automate it. If the thing you want to automate is some commands in your terminal, you’re probably tempted to reach for the most immediate tool at hand, a shell script. This week, Peter explained why you might instead want to use Python, and how to go about using it to replace shell scripts.

You can find the original slides for this talk here.


  • Advantages/disadvantages of Python
  • Running a parameter scan example
  • Command line arguments
  • Working with filesystem paths
  • Working with string formatting

Why use Python?

  • Nicer syntax:
    • Here’s how to get the length of an array/list plus one in bash:

      $(( ${#array[@]} + 1 ))
    • and in Python:

      len(array) + 1
  • Better data structures:
    • Associative arrays (dicts in Python) only in bash 4.0+
    • classes for encapsulating data and logic
  • Error handling much easier in Python
  • Easier to write portable Python vs portable shell scripts
    • e.g. <( ) process substitution is bash-only
  • Conditionals are more comprehensible in Python
    • e.g. [[ -z "$foo" ]] vs if not foo
  • Testing is much easier!

Why not use Python?

  • Not every machine has Python (and some only have Python 2)
    • Every *nix machine has some POSIX shell
    • Windows is a different matter…
  • Might need to install external modules for Python
    • Only a problem on machines with e.g. IP whitelist
  • Very simple things might be faster/easier using bash
    • e.g. find . -name "*.inp" | xargs grep "nx = 4"

Running a parameter scan for a simulation

Some different methods:

  • Edit input file by hand, save a new copy
    • Very easy to make a mistake!
  • Use sed and regular expressions to replace values in old file
    • Can take a long time to get that regular expression correct!
  • Use variable substitution in bash to echo a string into a file
    • Careful about escaping variables!
  • Use a template file/string and format it with Python

Typical things we might want to do in a shell script

Creating an input file for each set of parameters

  • Parse arguments passed on command line
  • Move about the file system
  • Create/remove/copy files and directories
  • Loop over multiple lists
  • Read a file
  • Replace text
  • Write text to a file
  • Run another program


Our program is going to (roughly) look like the following:

def create_directory():

def make_input_file():

def run_program():

for parameter in parameters:

Maybe we can reuse things?

Traditional Python scripts

def create_directory():
if __name__ == "__main__":
    # Actually do work

But why?

  • __name__ for a file/module is only __main__ when it is being run
  • This allows us to not only run the program, but also import it to reuse the functions in other programs

A word about functions

  • Wrapping logic up in functions is A Good Idea
  • Enables reuse of bits of code
  • Helps separate concerns
  • Allows documentation and testing of individual functions

Best practices

def make_input_file(nx, species, dryrun=False, filename=None):
    """Some documentation
    Write down any assumptions about input parameters

    Returns: name of new input file
    # Do stuff

Command line arguments

Not great in bash

while getopts ":n:" opt; do
  case ${opt} in
    n ) num_procs=$OPTARG ;;
    \? ) echo "Usage: scan [-n]" ;;
  • Quickly becomes very complicated
  • No support for long options
  • Handling of options which require arguments is a pain

Better in Python

  • Can use built-in argparse module
    • Lots of other external modules to do this!
  • Automatically handles -h/--help cases
  • Allows us to specify expected type and number of arguments to an option
  • Easy to specify both short and long forms
  • Arguments are stored in the parameter name by default

Basic usage

import argparse
parser = argparse.ArgumentParser(description="Run a parameter scan")
parser.add_argument("-n", "--numprocs", type=int, default=1,
                    help="Number of processors")
args = parser.parse_args()


  • Running scan --help then gives:
usage: scan [-h] [-n NUMPROCS]

Run a parameter scan

optional arguments:
  -h, --help            show this help message and exit
  -n NUMPROCS, --numprocs NUMPROCS
                        Number of processors

Lots of options

parser.add_argument("inputfile", nargs=1,
                    help="""Positional argument
                    requiring exactly one argument""")

parser.add_argument("-n", "--dry-run", action="store_true",
                    help="Set an optional flag to True")

parser.add_argument("--nx", nargs="+", dest="nx_list",
                    help="""Require at least one argument
                    if present, and store in a named variable")

Accessing the arguments

results = parser.parse_args()

if results.flag:
    # Do something
if results.nx_list is not None:
    for nx in results.nx_list:
        # Iterate over parameters

Further reading

The pathlib module

  • os and os.path modules more suited to lower-level operations
  • pathlib makes manipulating paths much easier


import pathlib
simpath = pathlib.Path().cwd()  # Current working directory
# PosixPath('/data/user/simulation')
# PosixPath('/data/user')
# [PosixPath('/data/user/simulation/template.inp'),
#  PosixPath('/data/user/simulation/C_nx4.inp')]

Building paths

# Known in advance
run001 = simpath / 'run001'
# /data/user/simulation/run001

# Not known in advance
subdirs = ['nx', nx_value]
nx_path = simpath.joinpath(*subdirs)
# /data/user/simulation/nx/4

Making and removing directories

# Create a directory
# Create a directory and its parents, don't throw if it already exists
nx_path.mkdir(parents=True, exist_ok=True)
# Delete a file (`rm`)
for temp_file in simpath.glob('*~'):
# Delete an empty directory (`rm -r`)

Copying and renaming files

  • pathlib doesn’t provide a copy function
  • Instead, we can use shutil module
  • Also, we only need str here if we’re not using Python 3.6
    import shutil
    restart_file = pathlib.Path("/data/user/old_simulation/restart")
    destination = pathlib.Path("/data/user/simulation/")
    shutil.copy(str(restart_file), str(destination))
  • Just renaming or moving a file can be done with pathlib:
    old_file = pathlib.Path("output.dat")
    backup = old_file.with_suffix(".bak")

Formatting text (“string interpolation”)

  • Python now has three different ways of formatting strings:
    • C printf style: print('%s' % "hello, world!")
    • format string method: print("{}".format("hello, world!"))
    • “f-strings” (only in 3.6):
        hello = "hello, world!"
  • The format method is the most powerful and widely supported

Further reading

Template files

# Dictionary with all our parameters in
parameters = {
    'nx': 4,
    'species': 'C',
# How we want new input files to be called
filename = "{species}_nx{nx}.inp"
# The "**" operator unpacks a dictionary into "key=value" pairs
new_inputfile = pathlib.Path(filename.format(**parameters))
# Read in template file and then write our formatted one
template_file = pathlib.Path('template.inp')
template = template_file.read_text()

Template files


Turns this…:

# template.inp
nx = {nx}
name = {species}

…into this:

# C_nx4.inp
nx = 4
name = 'C'

Other methods

  • The configparser deals very well with “INI” style files like the above
  • Allows treatment of such files very much like dictionaries

Running other programs

The subprocess module

import subprocess
output =['mpirun', '-n', str(num_procs), 'runsim'])
  • Arguments passed as a list of strings
  • Avoids problems with shell quoting, etc.

Running other programs

Capturing output

  • Sending the output into a pipe allows us to capture the output for later parsing
output =['mpirun', '-n', str(num_procs), 'runsim'],
# output.stdout is `bytes`, so we need to decode it into text

Further reading

Looping over multiple sets of parameters

  • For the Cartesian product of lists, we can use itertools.product:
import itertools
nx_list = [4, 8]
species_list = ['C', 'N']
for nx, species in itertools.product(nx_list, species_list):
    print(nx, species)
# 4 C
# 4 N
# 8 C
# 8 N
  • Lots of other methods for iterating over or combining sets of lists

Other useful things


  • Very useful to keep track of when you ran something
    • Could be done directly in file name or directory structure
  • Use the datetime module:
import datetime
# 2018-01-25 09:48:58.141256
print("{:%a %b %d %H:%M}".format(today))
# Thu Jan 25 09:48

Keeping a log

  • Also useful to keep track of what you ran as well as when
  • Lots of options for this:
    • Plain text file
    • Excel spreadsheet
    • Pandas database

Comma-separated values (CSV)

  • Simplest, actually useful file format:
Heading 1, Heading 2, Heading 3
value 1, value 2, value 3
value 1, value 2, value 3
value 1, value 2, value 3

Working with CSV files

Use the csv module

import csv

def write_heading():
    with open("simulation_log.csv", "w") as f:
        writer = csv.writer(f)
        writer.writerow(("Date", "nx", "species"))

def log_simulation(nx, species):
    with open("simulation_log.csv", "a") as f:
        writer = csv.writer(f)
        writer.writerow(("{}".format(today), nx, species))

Remote connections

paramiko + scp

  • Need two third-party modules, paramiko and scp, for transferring files:
from paramiko import SSHClient
from scp import SCPClient

ssh = SSHClient()

with SCPClient(ssh.get_transport()) as scp:
    scp.put('test.txt', 'test2.txt')

Further reading