Learn “Make” by Examples¶

Table of contents¶

Setting up
Makefile no. 1 (The Basics)
Makefile no. 2 (all and clean)
Makefile no. 3 (Phony Targets)
Makefile no. 4 (Automatic Variables and Pattern Rules)
Makefile no. 5 (Wildcards and Path Substitution)
Debugging Makefiles

Makefiles¶

One of the things that might discourahe off from using Make is that existing Makefiles can seem daunting and it may seem difficult to tailor to your own needs.
In this hands-on tutorial we will iteratively construct a Makefile for a real data analysis project. The idea is to explain different features of Make by iterating through several versions of a Makefile for this project. Hopefully the experience that you gain from this tutorial allows you to create Makefiles for your own projects.

We will create a Makefile for a data analysis pipeline. The task is as follows:

Task: Given some datasets, create a summary report (in pdf) that contains the histograms of these datasets.

(Of course this data task is very simple to focus on how to use Make.)

Throughout the tutorial code blocks that start with a dollar sign ($) are intended to be typed in the terminal.

Setting up¶

We have created a basic repository for this task, that already contains everything that we need (except the Makefile of course!). To start, clone the base repository using git:

$ git clone https://github.com/alan-turing-institute/IntroToMake

This basic repository contains all the code that we’ll need in this tutorial, and should have this content:

.
├── data/
│   ├── input_file_1.csv
│   └── input_file_2.csv
├── LICENSE
├── output/
├── README.md
├── report/
│   └── report.tex
└── scripts/
    └── generate_histogram.py

data: directory with two datasets that we’re going to analyse
report: the input directory for the report
scripts: directory for the analysis script
output: output directory for the figures and the report

You’ll notice that there are two datasets in the data directory (input_file_1.csv and input_file_2.csv) and that there is already a basic Python script in scripts and a basic report LaTeX file in report.

If you want to follow along, ensure that you have the matplotlib and numpy packages installed:

$ pip install matplotlib numpy

You will also need a working version of pdflatex and, of course, make. For installation instructions for Make, see the installation instructions below.

Makefile no. 1 (The Basics)¶

Let’s create our first Makefile. In the terminal, move into the IntroToMake repository that you just cloned:

$ cd IntroToMake

Using your favourite editor, create a file called Makefile with the following contents:

# Makefile for analysis report

output/figure_1.png: data/input_file_1.csv scripts/generate_histogram.py
	python scripts/generate_histogram.py -i data/input_file_1.csv -o output/figure_1.png

output/figure_2.png: data/input_file_2.csv scripts/generate_histogram.py
	python scripts/generate_histogram.py -i data/input_file_2.csv -o output/figure_2.png

output/report.pdf: report/report.tex output/figure_1.png output/figure_2.png
	cd report/ && pdflatex report.tex && mv report.pdf ../output/report.pdf

The indentation in each of the recipes are tabs, Makefiles do not accept indentation with spaces.

You should now be able to type:

$ make output/report.pdf

If everything worked correctly, the two figures will be created and pdf report will be built.

Let’s go through the Makefile in a bit more detail. We have three rules, two for the figures and one for the report. Let’s look at the rule for output/figure_1.png first. This rule has the target output/figure_1.png that has two prerequisites: data/input_file_1.csv and scripts/generate_histogram.py. By giving the output file these prerequisites it will be updated if either of these files changes. This is one of the reasons why Make was created: to update output files when source files change.

You’ll notice that the recipe line calls Python with the script name and uses command line flags (-i and -o) to mark the input and output of the script. This isn’t a requirement for using Make, but it makes it easy to see which file is an input to the script and which is an output.

The rule for the PDF report is very similar, but it has three prerequisites (the LaTeX source and both figures). Notice that the recipe changes the working directory before calling LaTeX and also moves the generated PDF to the output directory. We’re doing this to keep the LaTeX intermediate files in the report directory. However, it’s important to distinguish the above rule from the following:

# don't do this
output/report.pdf: report/report.tex output/figure_1.png output/figure_2.png
	cd report/
	pdflatex report.tex
	mv report.pdf ../output/report.pdf

This rule places the three commands on separate lines. However, Make executes each line independently in a separate subshell, so changing the working directory in the first line has no effect on the second, and a failure in the second line won’t stop the third line from being executed. Therefore, we combine the three commands in a single recipe above.

This is what the dependency tree looks like for this Makefile:

DAG for Makefile no. 1 The dependency graph for our first Makefile, created using makefile2graph. Notice the similarity to the figure at the top!

Makefile no. 2 (all and clean)¶

In our first Makefile we have the basic rules in place. We could stick with this if we wanted to, but there are a few improvements we can make:

We now have to explicitly call make output/report.pdf if we want to make the report.
We have no way to clean up and start fresh.

Let’s remedy this by adding two new targets: all and clean. In your editor, change the Makefile contents to add the all and clean rules as follows:

# Makefile for analysis report

all: output/report.pdf

output/figure_1.png: data/input_file_1.csv scripts/generate_histogram.py
	python scripts/generate_histogram.py -i data/input_file_1.csv -o output/figure_1.png

output/figure_2.png: data/input_file_2.csv scripts/generate_histogram.py
	python scripts/generate_histogram.py -i data/input_file_2.csv -o output/figure_2.png

output/report.pdf: report/report.tex output/figure_1.png output/figure_2.png
	cd report/ && pdflatex report.tex && mv report.pdf ../output/report.pdf

clean:
	rm -f output/report.pdf
	rm -f output/figure_*.png

Note that we’ve added the all target to the top of the file. We do this because Make executes the first target when no explicit target is given. So you can now type make on the command line and it would do the same as make all. Also, note that we’ve only added the report as the prerequisite of all because that’s our desired output and the other rules help to build that output. If you have multiple outputs, you could add these as further prerequisites to the all target. Calling the main target all is a convention of Makefiles that many people follow.

The clean rule is typically at the bottom, but that’s more style than requirement. Note that we use the -f flag to rm to make sure it doesn’t complain when there is no file to remove.

You can try out the new Makefile by running:

$ make clean
$ make

Make should remove the output and intermediate files after the first command, and generate them again after the second.

Makefile no. 3 (Phony Targets)¶

Typically, all and clean are defined as so-called Phony Targets. These are targets that don’t actually create an output file. Such targets will always be run if they come up in a dependency, but will no longer be run if a directory/file is ever created that is called all or clean. We therefore add a line at the top of the Makefile to define these two as phony targets:

# Makefile for analysis report

.PHONY: all clean

all: output/report.pdf

output/figure_1.png: data/input_file_1.csv scripts/generate_histogram.py
	python scripts/generate_histogram.py -i data/input_file_1.csv -o output/figure_1.png

output/figure_2.png: data/input_file_2.csv scripts/generate_histogram.py
	python scripts/generate_histogram.py -i data/input_file_2.csv -o output/figure_2.png

output/report.pdf: report/report.tex output/figure_1.png output/figure_2.png
	cd report/ && pdflatex report.tex && mv report.pdf ../output/report.pdf

clean:
	rm -f output/report.pdf
	rm -f output/figure_*.pdf

Phony targets are also useful when you want to use Make recursively. In that case you would specify the subdirectories as phony targets. You can read more about phony targets in the documentation, but for now it’s enough to know that all and clean are typically declared as phony.

Sidenote: another target that’s typically phony is test, in case you have a directory of tests called test and want to have a target to run them that’s also called test.

Makefile no. 4 (Automatic Variables and Pattern Rules)¶

There’s nothing wrong with the Makefile we have now, but it’s somewhat verbose because we’ve declared all the targets explicitly using separate rules. We can simplify this by using Automatic Variables and Pattern Rules.

Automatic Variables. With automatic variables we can use the names of the prerequisites and targets in the recipe. Here’s how we would do that for the figure rules:

# Makefile for analysis report

.PHONY: all clean

all: output/report.pdf

output/figure_1.png: data/input_file_1.csv scripts/generate_histogram.py
	python scripts/generate_histogram.py -i $< -o $@

output/figure_2.png: data/input_file_2.csv scripts/generate_histogram.py
	python scripts/generate_histogram.py -i $< -o $@

output/report.pdf: report/report.tex output/figure_1.png output/figure_2.png
	cd report/ && pdflatex report.tex && mv report.pdf ../output/report.pdf

clean:
	rm -f output/report.pdf
	rm -f output/figure_*.pdf

We’ve replaced the input and output filenames in the recipes respectively by $<, which denotes the first prerequisite and $@ which denotes the target. You can remember $< because it’s like an arrow that points to the beginning (first prerequisite), and you can remember $@ (dollar at) as the target you’re aiming at.

There are more automatic variables that you could use, see the documentation.

Pattern Rules. Notice that the recipes for the figures have become identical! Because we don’t like to repeat ourselves, we can combine the two rules into a single rule by using pattern rules. Pattern rules allow you to use the % symbol as a wildcard and combine the two rules into one:

# Makefile for analysis report

.PHONY: all clean

all: output/report.pdf

output/figure_%.png: data/input_file_%.csv scripts/generate_histogram.py
	python scripts/generate_histogram.py -i $< -o $@

output/report.pdf: report/report.tex output/figure_1.png output/figure_2.png
	cd report/ && pdflatex report.tex && mv report.pdf ../output/report.pdf

clean:
	rm -f output/report.pdf
	rm -f output/figure_*.pdf

The % symbol is now a wildcard that (in our case) takes the value 1 or 2 based on the input files in the data directory. You can check that everything still works by running make clean followed by make.

An advantage of this is that if you now want to add another dataset, say input_file_3, then you would only need to add that to the rule for the report!

Makefile no. 5 (Wildcards and Path Substitution)¶

When Makefiles get more complex, you may want to use more advanced features such as building outputs for all the files in an input directory. While pattern rules will get you a long way, Make also has features for wildcards and string or path manipulation for when pattern rules are insufficient.

While previously our input files were numbered, we’ll now switch to a scenario where they have more meaningful names. Let’s switch over to the big_data branch:

$ git stash                     # stash the state of your working directory
$ git checkout big_data         # checkout the big_data branch

The directory structure now looks like this:

├── data/
│   ├── action.csv
│   ├── ...
│   ├── input_file_1.csv
│   ├── input_file_2.csv
│   ├── ...
│   └── western.csv
├── LICENSE
├── output/
├── README.md
├── report/
│   └── report.tex
└── scripts/
    └── generate_histogram.py

As you can see, the data directory now contains additional input files that are named more meaningfully (the data are IMBD movie ratings by genre). Also, the report.tex file has been updated to work with the expected figures.

We’ll adapt our Makefile to create a figure in the output directory called histogram_{genre}.png for each {genre}.csv file, while excluding the input_file_{N}.csv files.

Sidenote: if we were to remove the input_file_{N}.csv files, pattern rules would be sufficient here. This highlights that sometimes your directory structure and Makefile should be developed hand in hand.

Before changing the Makefile, run

$ make clean

to remove the output files.

We’ll show the full Makefile first, and then describe the different lines in more detail. The complete file is:

# Makefile for analysis report
#

ALL_CSV = $(wildcard data/*.csv)
INPUT_CSV = $(wildcard data/input_file_*.csv)
DATA = $(filter-out $(INPUT_CSV),$(ALL_CSV))
FIGURES = $(patsubst data/%.csv,output/figure_%.png,$(DATA))

.PHONY: all clean

all: output/report.pdf

$(FIGURES): output/figure_%.png: data/%.csv scripts/generate_histogram.py
	python scripts/generate_histogram.py -i $< -o $@

output/report.pdf: report/report.tex $(FIGURES)
	cd report/ && pdflatex report.tex && mv report.pdf ../$@

clean:
	rm -f output/report.pdf
	rm -f $(FIGURES)

First, we use the wildcard function to create a variable that lists all the CSV files in the data directory and one that lists only the old input_file_{N}.csv files:

ALL_CSV = $(wildcard data/*.csv)
INPUT_CSV = $(wildcard data/input_file_*.csv)

A code convention for Makefiles is to use all capitals for variable names and define them at the top of the file.

Next, we create a variable to list only the data files that we’re interested in by filtering out the INPUT_CSV from ALL_CSV:

DATA = $(filter-out $(INPUT_CSV),$(ALL_CSV))

This line uses the filter-out function to remove items in the INPUT_CSV variable from the ALL_CSV variable. Note that we use both the $( ... ) syntax for functions and variables. Finally, we’ll use the DATA variable to create a FIGURES variable with the desired output:

FIGURES = $(patsubst data/%.csv,output/figure_%.png,$(DATA))

Here we’ve used the patsubst function to transform the input in the DATA variable (that follows the data/{genre}.csv pattern) to the desired output filenames (using the output/figure_{genre}.png pattern). Notice that the % character marks the part of the filename that will be the same in both the input and output.

Now we use these variables for the figure generation rule as follows:

$(FIGURES): output/figure_%.png: data/%.csv scripts/generate_histogram.py
	python scripts/generate_histogram.py -i $< -o $@

This rule again applies a pattern: it takes a list of targets ($(FIGURES)) that all follow a given pattern (output/figure_%.png) and based on that creates a prerequisite (data/%.csv). Such a pattern rule is slightly different from the one we saw before because it uses two : symbols. It is called a static pattern rule.

Of course we have to update the report.pdf rule as well:

output/report.pdf: report/report.tex $(FIGURES)
	cd report/ && pdflatex report.tex && mv report.pdf ../$@

and the clean rule:

clean:
	rm -f output/report.pdf
	rm -f $(FIGURES)

If you run this Makefile, it will need to build 28 figures. You may want to use the -j flag to make to build these targets in parallel!

$ make -j 4

The ability to build targets in parallel is quite useful when your project has many dependencies!

The resulting PDF file should now look like this:

Report with all genres A compressed view of the report with histograms for all genres.

The Turing Way