Command-Line Programs

Learning Objectives {.objectives}

  • Use the values of command-line arguments in a program.
  • Handle flags and files separately in a command-line program.
  • Read data from standard input in a program so that it can be used in a pipeline.

The IPython Notebook and other interactive tools are great for prototyping code and exploring data, but sooner or later we will want to use our program in a pipeline or run it in a shell script to process thousands of data files. In order to do that, we need to make our programs work like other Unix command-line tools. For example, we may want a program that reads a dataset and prints the average inflammation per patient.

Switching to Shell Commands

In this lesson we are switching from typing commands in a Python interpreter to typing commands in a shell terminal window (such as bash). When you see a $ in front of a command that tells you to run that command in the shell rather than the Python interpreter.

This program does exactly what we want - it prints the average inflammation per patient for a given file.

$ python readings.py --mean data/inflammation-01.csv
5.45
5.425
6.1
...
6.4
7.05
5.9

We might also want to look at the minimum of the first four lines

$ head -4 inflammation-01.csv | python readings.py --min

or the maximum inflammations in several files one after another:

$ python readings.py --max inflammation-*.csv

Our scripts should do the following:

  1. If no filename is given on the command line, read data from standard input.
  2. If one or more filenames are given, read data from them and report statistics for each file separately.
  3. Use the --min, --mean, or --max flag to determine what statistic to print.

To make this work, we need to know how to handle command-line arguments in a program, and how to get at standard input. We'll tackle these questions in turn below.

Command-Line Arguments

Using the text editor of your choice, save the following in a text file called sys-version.py:

In [1]:
import sys
print('version is', sys.version)

version is 3.4.3 |Continuum Analytics, Inc.| (default, Jun  4 2015, 15:29:08) 
[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]

The first line imports a library called sys, which is short for "system". It defines values such as sys.version, which describes which version of Python we are running. We can run this script from the command line like this:

$ python sys-version.py
version is 2.7.5 |Anaconda 1.8.0 (x86_64)| (default, Oct 24 2013, 07:02:20)
[GCC 4.0.1 (Apple Inc. build 5493)]

Create another file called argv-list.py and save the following text to it.

In [3]:
import sys
print('sys.argv is', sys.argv)

sys.argv is ['/home/takluyver/anaconda/envs/ipy3-release/lib/python3.4/site-packages/IPython/kernel/__main__.py', '-f', '/home/takluyver/.ipython/profile_default/security/kernel-72b202d0-f67b-4b79-99e4-a857c00ab31e.json', '--profile-dir', '/home/takluyver/.ipython/profile_default']

The strange name argv stands for "argument values". Whenever Python runs a program, it takes all of the values given on the command line and puts them in the list sys.argv so that the program can determine what they were. If we run this program with no arguments:

$ python argv-list.py
sys.argv is ['argv-list.py']

the only thing in the list is the full path to our script, which is always sys.argv[0]. If we run it with a few arguments, however:

$ python argv-list.py first second third
sys.argv is ['argv-list.py', 'first', 'second', 'third']

then Python adds each of those arguments to that magic list.

With this in hand, let's build a version of readings.py that always prints the per-patient mean of a single data file. The first step is to write a function that outlines our implementation, and a placeholder for the function that does the actual work. By convention this function is usually called main, though we can call it whatever we want:

In [4]:
!cat code/readings-01.py

import sys
import numpy

def main():
    script = sys.argv[0]
    filename = sys.argv[1]
    data = numpy.loadtxt(filename, delimiter=',')
    for m in data.mean(axis=1):
        print m

This function gets the name of the script from sys.argv[0], because that's where it's always put, and the name of the file to process from sys.argv[1]. Here's a simple test:

$ python code/readings-01.py data/inflammation-01.csv

There is no output because we have defined a function, but haven't actually called it. Let's add a call to main:

In [5]:
!cat code/readings-02.py

import sys
import numpy

def main():
    script = sys.argv[0]
    filename = sys.argv[1]
    data = numpy.loadtxt(filename, delimiter=',')
    for m in data.mean(axis=1):
        print m

main()

and run that:

$ python code/readings-02.py data/inflammation-01.csv
5.45
5.425
6.1
5.9
5.55
6.225
5.975
6.65
6.625
6.525
6.775
5.8
6.225
5.75
5.225
6.3
6.55
5.7
5.85
6.55
5.775
5.825
6.175
6.1
5.8
6.425
6.05
6.025
6.175
6.55
6.175
6.35
6.725
6.125
7.075
5.725
5.925
6.15
6.075
5.75
5.975
5.725
6.3
5.9
6.75
5.925
7.225
6.15
5.95
6.275
5.7
6.1
6.825
5.975
6.725
5.7
6.25
6.4
7.05
5.9

The Right Way to Do It

If our programs can take complex parameters or multiple filenames, we shouldn't handle sys.argv directly. Instead, we should use Python's argparse library, which handles common cases in a systematic way, and also makes it easy for us to provide sensible error messages for our users.

Handling Multiple Files

The next step is to teach our program how to handle multiple files. Since 60 lines of output per file is a lot to page through, we'll start by using three smaller files, each of which has three days of data for two patients:

$ ls data/small-*.csv
data/small-01.csv  data/small-02.csv  data/small-03.csv
$ cat data/small-01.csv
0,0,1
0,1,2
$ python code/readings-02.py data/small-01.csv
0.333333333333
1.0

Using small data files as input also allows us to check our results more easily: here, for example, we can see that our program is calculating the mean correctly for each line, whereas we were really taking it on faith before. This is yet another rule of programming: test the simple things first.

We want our program to process each file separately, so we need a loop that executes once for each filename. If we specify the files on the command line, the filenames will be in sys.argv, but we need to be careful: sys.argv[0] will always be the name of our script, rather than the name of a file. We also need to handle an unknown number of filenames, since our program could be run for any number of files.

The solution to both problems is to loop over the contents of sys.argv[1:]. The '1' tells Python to start the slice at location 1, so the program's name isn't included; since we've left off the upper bound, the slice runs to the end of the list, and includes all the filenames. Here's our changed program readings-03.py:

In [7]:
!cat code/readings-03.py

import sys
import numpy

def main():
    script = sys.argv[0]
    for filename in sys.argv[1:]:
        data = numpy.loadtxt(filename, delimiter=',')
        for m in data.mean(axis=1):
            print m

main()

and here it is in action:

$ python code/readings-03.py data/small-01.csv data/small-02.csv
0.333333333333
1.0
13.6666666667
11.0

The Right Way to Do It

At this point, we have created three versions of our script called readings-01.py, readings-02.py, and readings-03.py. We wouldn't do this in real life: instead, we would have one file called readings.py that we committed to version control every time we got an enhancement working. For teaching, though, we need all the successive versions side by side.

Handling Command-Line Flags

The next step is to teach our program to pay attention to the --min, --mean, and --max flags. These always appear before the names of the files, so we could just do this:

In [8]:
!cat code/readings-04.py

import sys
import numpy

def main():
    script = sys.argv[0]
    action = sys.argv[1]
    filenames = sys.argv[2:]

    for f in filenames:
        data = numpy.loadtxt(f, delimiter=',')

        if action == '--min':
            values = data.min(axis=1)
        elif action == '--mean':
            values = data.mean(axis=1)
        elif action == '--max':
            values = data.max(axis=1)

        for m in values:
            print m

main()

This works:

$ python code/readings-04.py --max data/small-01.csv
1.0
2.0

but there are several things wrong with it:

  1. main is too large to read comfortably.

  2. If action isn't one of the three recognized flags, the program loads each file but does nothing with it (because none of the branches in the conditional match). Silent failures like this are always hard to debug.

This version pulls the processing of each file out of the loop into a function of its own. It also checks that action is one of the allowed flags before doing any processing, so that the program fails fast:

In [9]:
!cat code/readings-05.py

import sys
import numpy

def main():
    script = sys.argv[0]
    action = sys.argv[1]
    filenames = sys.argv[2:]
    assert action in ['--min', '--mean', '--max'], \
           'Action is not one of --min, --mean, or --max: ' + action
    for f in filenames:
        process(f, action)

def process(filename, action):
    data = numpy.loadtxt(filename, delimiter=',')

    if action == '--min':
        values = data.min(axis=1)
    elif action == '--mean':
        values = data.mean(axis=1)
    elif action == '--max':
        values = data.max(axis=1)

    for m in values:
        print m

main()

This is four lines longer than its predecessor, but broken into more digestible chunks of 8 and 12 lines.

Python has a module named argparse that helps handle complex command-line flags. We will not cover this module in this lesson but you can go to Tshepang Lekhonkhobe's Argparse tutorial that is part of Python's Official Documentation.

Handling Standard Input

The next thing our program has to do is read data from standard input if no filenames are given so that we can put it in a pipeline, redirect input to it, and so on. Let's experiment in another script called count-stdin.py:

In [10]:
!cat code/count-stdin.py

import sys

count = 0
for line in sys.stdin:
    count += 1

print count, 'lines in standard input'

This little program reads lines from a special "file" called sys.stdin, which is automatically connected to the program's standard input. We don't have to open it—Python and the operating system take care of that when the program starts up—but we can do almost anything with it that we could do to a regular file. Let's try running it as if it were a regular command-line program:

$ python code/count-stdin.py < data/small-01.csv
2 lines in standard input

A common mistake is to try to run something that reads from standard input like this:

$ python code/count_stdin.py data/small-01.csv

i.e., to forget the < character that redirect the file to standard input. In this case, there's nothing in standard input, so the program waits at the start of the loop for someone to type something on the keyboard. Since there's no way for us to do this, our program is stuck, and we have to halt it using the Interrupt option from the Kernel menu in the Notebook.

We now need to rewrite the program so that it loads data from sys.stdin if no filenames are provided. Luckily, numpy.loadtxt can handle either a filename or an open file as its first parameter, so we don't actually need to change process. That leaves main:

In [12]:
!cat code/readings-06.py

import sys
import numpy

def main():
    script = sys.argv[0]
    action = sys.argv[1]
    filenames = sys.argv[2:]
    assert action in ['--min', '--mean', '--max'], \
           'Action is not one of --min, --mean, or --max: ' + action
    if len(filenames) == 0:
        process(sys.stdin, action)
    else:
        for f in filenames:
            process(f, action)

def process(filename, action):
    data = numpy.loadtxt(filename, delimiter=',')

    if action == '--min':
        values = data.min(axis=1)
    elif action == '--mean':
        values = data.mean(axis=1)
    elif action == '--max':
        values = data.max(axis=1)

    for m in values:
        print m

main()

Let's try it out:

$ python code/readings-06.py --mean data/small-01.csv
0.333333333333
1.0

That's better. In fact, that's done: the program now does everything we set out to do.

Challenges

Arithmetic on the command line

Write a command-line program that does addition and subtraction:

$ python arith.py add 1 2
3
$ python arith.py subtract 3 4
-1

Finding particular files

Using the glob module introduced earlier, write a simple version of ls that shows files in the current directory with a particular suffix. A call to this script should look like this:

$ python my_ls.py py
left.py
right.py
zero.py

Changing flags

Rewrite readings.py so that it uses -n, -m, and -x instead of --min, --mean, and --max respectively. Is the code easier to read? Is the program easier to understand?

Adding a help message

Separately, modify readings.py so that if no parameters are given (i.e., no action is specified and no filenames are given), it prints a message explaining how it should be used.

Adding a default action

Separately, modify readings.py so that if no action is given it displays the means of the data.

A file-checker

Write a program called check.py that takes the names of one or more inflammation data files as arguments and checks that all the files have the same number of rows and columns. What is the best way to test your program?

Counting lines

Write a program called line-count.py that works like the Unix wc command:

  • If no filenames are given, it reports the number of lines in standard input.
  • If one or more filenames are given, it reports the number of lines in each, followed by the total number of lines.