Automation and Make

Introduction

Overview

Teaching: 15 min
Exercises: 15 min
Questions

  • How can I make my results easier to reproduce?

Objectives

  • Explain what Make is for.

  • Explain why Make differs from shell scripts.

  • Name other popular build tools.

Let’s imagine that we’re interested in testing Zipf’s Law in some of our favorite books.

Zipf’s Law

The most frequently-occurring word occurs approximately twice as often as the second most frequent word. This is Zipf’s Law.

We’ve compiled our raw data, the books we want to analyze (check out e.g. head books/isles.txt) and have prepared several Python scripts that together make up our analysis pipeline.

Our directory has the Python scripts and data files we we will be working with:

|- books
|  |- abyss.txt
|  |- isles.txt
|  |- last.txt
|  |- LICENSE_TEXTS.md
|  |- sierra.txt
|- plotcount.py
|- wordcount.py
|- zipf_test.py

The first step is to count the frequency of each word in a book.

$ python wordcount.py books/isles.txt isles.dat

Let’s take a quick peek at the result.

$ head -5 isles.dat

This shows us the top 5 lines in the output file:

the 3822 6.7371760973
of 2460 4.33632998414
and 1723 3.03719372466
to 1479 2.60708619778
a 1308 2.30565838181

We can see that the file consists of one row per word. Each row shows the word itself, the number of occurrences of that word, and the number of occurrences as a percentage of the total number of words in the text file.

We can do the same thing for a different book:

$ python wordcount.py books/abyss.txt abyss.dat
$ head -5 abyss.dat

the 4044 6.35449402891
and 2807 4.41074795726
of 1907 2.99654305468
a 1594 2.50471401634
to 1515 2.38057825267

Let’s visualize the results. The script plotcount.py reads in a data file and plots the 10 most frequently occurring words as a text-based bar plot:

$ python plotcount.py isles.dat ascii

the   ########################################################################
of    ##############################################
and   ################################
to    ############################
a     #########################
in    ###################
is    #################
that  ############
by    ###########
it    ###########

plotcount.py can also show the plot graphically:

$ python plotcount.py isles.dat show

Close the window to exit the plot.

plotcount.py can also create the plot as an image file (e.g. a PNG file):

$ python plotcount.py isles.dat isles.png

Finally, let’s test Zipf’s law for these books:

$ python zipf_test.py abyss.dat isles.dat

Book	First	Second	Ratio
abyss	4044	2807	1.44
isles	3822	2460	1.55

So we’re not too far off from Zipf’s law.

Together these scripts implement a common workflow:

  1. Read a data file.
  2. Perform an analysis on this data file.
  3. Write the analysis results to a new file.
  4. Plot a graph of the analysis results.
  5. Save the graph as an image, so we can put it in a paper.
  6. Make a summary table of the analyses

Running wordcount.py and plotcount.py at the shell prompt, as we have been doing, is fine for one or two files. If, however, we had 5 or 10 or 20 text files, or if the number of steps in the pipeline were to expand, this could turn into a lot of work. Plus, no one wants to sit and wait for a command to finish, even just for 30 seconds.

The most common solution to the tedium of data processing is to write a shell script that runs the whole pipeline from start to finish.

Using your text editor of choice (e.g. nano), add the following to a new file named run_pipeline.sh.

# USAGE: bash run_pipeline.sh
# to produce plots for isles and abyss
# and the summary table for the Zipf's law tests

python wordcount.py books/isles.txt isles.dat
python wordcount.py books/abyss.txt abyss.dat

python plotcount.py isles.dat isles.png
python plotcount.py abyss.dat abyss.png

# Generate summary table
python zipf_test.py abyss.dat isles.dat > results.txt

Run the script and check that the output is the same as before:

$ bash run_pipeline.sh

This shell script solves several problems in computational reproducibility:

  1. It explicitly documents our pipeline, making communication with colleagues (and our future selves) more efficient.
  2. It allows us to type a single command, bash run_pipeline.sh, to reproduce the full analysis.
  3. It prevents us from repeating typos or mistakes. You might not get it right the first time, but once you fix something it’ll stay fixed.

Despite these benefits it has a few shortcomings.

Let’s adjust the width of the bars in our plot produced by plotcount.py.

Edit plotcount.py so that the bars are 0.8 units wide instead of 1 unit. (Hint: replace width = 1.0 with width = 0.8 in the definition of plot_word_counts.)

Now we want to recreate our figures. We could just bash run_pipeline.sh again. That would work, but it could also be a big pain if counting words takes more than a few seconds. The word counting routine hasn’t changed; we shouldn’t need to recreate those files.

Alternatively, we could manually rerun the plotting for each word-count file. (Experienced shell scripters can make this easier on themselves using a for-loop.)

for book in abyss isles; do
    python plotcount.py $book.dat $book.png
done

With this approach, however, we don’t get many of the benefits of having a shell script in the first place.

Another popular option is to comment out a subset of the lines in run_pipeline.sh:

# USAGE: bash run_pipeline.sh
# to produce plots for isles and abyss
# and the summary table for the Zipf's law tests

# These lines are commented out because they don't need to be rerun.
#python wordcount.py books/isles.txt isles.dat
#python wordcount.py books/abyss.txt abyss.dat

python plotcount.py isles.dat isles.png
python plotcount.py abyss.dat abyss.png

# This line is also commented out because it doesn't need to be rerun.
python zipf_test.py abyss.dat isles.dat > results.txt

Then, we would run our modified shell script using bash run_pipeline.sh.

But commenting out these lines, and subsequently uncommenting them, can be a hassle and source of errors in complicated pipelines.

What we really want is an executable description of our pipeline that allows software to do the tricky part for us: figuring out what steps need to be rerun.

Make was developed by Stuart Feldman in 1977 as a Bell Labs summer intern, and remains in widespread use today. Make can execute the commands needed to run our analysis and plot our results. Like shell scripts it allows us to execute complex sequences of commands via a single shell command. Unlike shell scripts it explicitly records the dependencies between files - what files are needed to create what other files - and so can determine when to recreate our data files or image files, if our text files change. Make can be used for any commands that follow the general pattern of processing files to create new files, for example:

There are now many build tools available, for example Apache ANT, doit, and nmake for Windows. There are also build tools that build scripts for use with these build tools and others e.g. GNU Autoconf and CMake. Which is best for you depends on your requirements, intended usage, and operating system. However, they all share the same fundamental concepts as Make.

Why Use Make if it is Almost 40 Years Old?

Today, researchers working with legacy codes in C or FORTRAN, which are very common in high-performance computing, will, very likely encounter Make.

Researchers are also finding Make of use in implementing reproducible research workflows, automating data analysis and visualisation (using Python or R) and combining tables and plots with text to produce reports and papers for publication.

Make’s fundamental concepts are common across build tools.

GNU Make is a free, fast, well-documented, and very popular Make implementation. From now on, we will focus on it, and when we say Make, we mean GNU Make.

Key Points