Advanced CLI in Galaxy

Overview

Questions:
  • How can I combine existing commands to do new things?

  • How can I perform the same actions on many different files?

  • How can I find files?

  • How can I find things in files?

Objectives:
  • Redirect a command’s output to a file.

  • Process a file instead of keyboard input using redirection.

  • Construct command pipelines with two or more stages.

  • Explain what usually happens if a program or pipeline isn’t given any input to process.

  • Explain Unix’s ‘small pieces, loosely joined’ philosophy.

  • Write a loop that applies one or more commands separately to each file in a set of files.

  • Trace the values taken on by a loop variable during execution of the loop.

  • Explain the difference between a variable’s name and its value.

  • Explain why spaces and some punctuation characters shouldn’t be used in file names.

  • Demonstrate how to see what commands have recently been executed.

  • Re-run recently executed commands without retyping them.

  • Use grep to select lines from text files that match simple patterns.

  • Use find to find files and directories whose names match simple patterns.

  • Use the output of one command as the command-line argument(s) to another command.

  • Explain what is meant by ‘text’ and ‘binary’ files, and why many common tools don’t handle the latter well.

Requirements:
Time estimation: 2 hours
Level: Intermediate Intermediate
Supporting Materials:
Last modification: Oct 19, 2021
License: Tutorial Content is licensed under Creative Commons Attribution 4.0 International License The GTN Framework is licensed under MIT

Best viewed in a Jupyter Notebook

This tutorial is best viewed in a Jupyter notebook! You can load this notebook in Jupyter on one of the UseGalaxy.* servers

Launching the notebook in Jupyter in Galaxy

  1. Instructions to Launch JupyterLab
  2. Open a Terminal in JupyterLab with File -> New -> Terminal
  3. Run wget https://training.galaxyproject.org/training-material/topics/data-science/tutorials/cli-advanced/tutorial.md.ipynb
  4. Select the notebook that appears in the list of files on the left.

Downloading the notebook

  1. Right click this link: tutorial.ipynb
  2. Save Link As..

This tutorial will walk you through the basics of how to use the Unix command line.

comment Comment

This tutorial is significantly based on the Carpentries “The Unix Shell” lesson, which is licensed CC-BY 4.0. Adaptations have been made to make this work better in a GTN/Galaxy environment.

Agenda

In this tutorial, we will cover:

  1. Pipes and Filtering
    1. Capturing output from commands
    2. Filtering output
    3. Solution
    4. Passing output to another command
    5. Combining multiple commands
    6. Tools designed to work together
    7. Nelle’s Pipeline: Checking Files
  2. Loops
    1. Nelle’s Pipeline: Processing Files
    2. Beginning and End
  3. Finding Things

Pipes and Filtering

Now that we know a few basic commands, we can finally look at the shell’s most powerful feature: the ease with which it lets us combine existing programs in new ways. We’ll start with the directory called shell-lesson-data/molecules that contains six files describing some simple organic molecules. The .pdb extension indicates that these files are in Protein Data Bank format, a simple text format that specifies the type and position of each atom in the molecule.

cd ~/Desktop/shell-lesson-data/
ls molecules

Let’s go into that directory with cd and run an example command wc cubane.pdb:

cd molecules
wc cubane

wc is the ‘word count’ command: it counts the number of lines, words, and characters in files (from left to right, in that order).

If we run the command wc *.pdb, the * in *.pdb matches zero or more characters, so the shell turns *.pdb into a list of all .pdb files in the current directory:

wc *.pdb

Note that wc *.pdb also shows the total number of all lines in the last line of the output.

If we run wc -l instead of just wc, the output shows only the number of lines per file:

wc -l *.pdb

The -m and -w options can also be used with the wc command, to show only the number of characters or the number of words in the files.

tip Why Isn’t It Doing Anything?

What happens if a command is supposed to process a file, but we don’t give it a filename? For example, what if we type:

$ wc -l

but don’t type *.pdb (or anything else) after the command? Since it doesn’t have any filenames, wc assumes it is supposed to process input given at the command prompt, so it just sits there and waits for us to give it some data interactively. From the outside, though, all we see is it sitting there: the command doesn’t appear to do anything.

If you make this kind of mistake, you can escape out of this state by holding down the control key (Ctrl) and typing the letter C once and letting go of the Ctrl key. Ctrl+C

Capturing output from commands

Which of these files contains the fewest lines? It’s an easy question to answer when there are only six files, but what if there were 6000? Our first step toward a solution is to run the command:

wc -l *.pdb > lengths.txt

The greater than symbol, >, tells the shell to redirect the command’s output to a file instead of printing it to the screen. (This is why there is no screen output: everything that wc would have printed has gone into the file lengths.txt instead.) The shell will create the file if it doesn’t exist. If the file exists, it will be silently overwritten, which may lead to data loss and thus requires some caution. ls lengths.txt confirms that the file exists:

ls lengths.txt

We can now send the content of lengths.txt to the screen using cat lengths.txt. The cat command gets its name from ‘concatenate’ i.e. join together, and it prints the contents of files one after another. There’s only one file in this case, so cat just shows us what it contains:

cat lengths.txt

tip Output Page by Page

We’ll continue to use cat in this lesson, for convenience and consistency, but it has the disadvantage that it always dumps the whole file onto your screen. More useful in practice is the command less, which you use with less lengths.txt. This displays a screenful of the file, and then stops. You can go forward one screenful by pressing the spacebar, or back one by pressing b. Press q to quit.

Filtering output

Next we’ll use the sort command to sort the contents of the lengths.txt file. But first we’ll use an exercise to learn a little about the sort command:

question What Does sort -n Do?

The file shell-lesson-data/numbers.txt contains the following lines:

10
2
19
22
6

If we run sort on this file, the output is:

10
19
2
22
6

If we run sort -n on the same file, we get this instead:

2
6
10
19
22

Explain why -n has this effect.

solution Solution

The -n option specifies a numerical rather than an alphanumerical sort.

We will also use the -n option to specify that the sort is numerical instead of alphanumerical. This does not change the file; instead, it sends the sorted result to the screen:

sort -n lengths.txt

We can put the sorted list of lines in another temporary file called sorted-lengths.txt by putting > sorted-lengths.txt after the command, just as we used > lengths.txt to put the output of wc into lengths.txt. Once we’ve done that, we can run another command called head to get the first few lines in sorted-lengths.txt:

sort -n lengths.txt > sorted-lengths.txt

Using -n 1 with head tells it that we only want the first line of the file; -n 20 would get the first 20, and so on. Since sorted-lengths.txt contains the lengths of our files ordered from least to greatest, the output of head must be the file with the fewest lines.

tip Redirecting to the same file

It’s a very bad idea to try redirecting the output of a command that operates on a file to the same file. For example:

$ sort -n lengths.txt > lengths.txt

Doing something like this may give you incorrect results and/or delete the contents of lengths.txt.

question What Does >> Mean?

We have seen the use of >, but there is a similar operator >> which works slightly differently. We’ll learn about the differences between these two operators by printing some strings. We can use the echo command to print strings e.g.

code-in Input: Bash

$ echo The echo command prints text

code-out Output

The echo command prints text

Now test the commands below to reveal the difference between the two operators:

code-in Input: Bash

$ echo hello > testfile01.txt

and:

code-in Input: Bash

$ echo hello >> testfile02.txt

Hint: Try executing each command twice in a row and then examining the output files.

Solution

In the first example with >, the string ‘hello’ is written to testfile01.txt, but the file gets overwritten each time we run the command.

We see from the second example that the >> operator also writes ‘hello’ to a file (in this casetestfile02.txt), but appends the string to the file if it already exists (i.e. when we run it for the second time).

# Explore the possible solutions here!

question Appending Data

We have already met the head command, which prints lines from the start of a file. tail is similar, but prints lines from the end of a file instead.

Consider the file shell-lesson-data/data/animals.txt. After these commands, select the answer that corresponds to the file animals-subset.txt:

$ head -n 3 animals.txt > animals-subset.txt
$ tail -n 2 animals.txt >> animals-subset.txt
  1. The first three lines of animals.txt
  2. The last two lines of animals.txt
  3. The first three lines and the last two lines of animals.txt
  4. The second and third lines of animals.txt

solution Solution

Option 3 is correct. For option 1 to be correct we would only run the head command. For option 2 to be correct we would only run the tail command. For option 4 to be correct we would have to pipe the output of head into tail -n 2 by doing head -n 3 animals.txt | tail -n 2 > animals-subset.txt

# Explore the possible solutions here!

Passing output to another command

In our example of finding the file with the fewest lines, we are using two intermediate files lengths.txt and sorted-lengths.txt to store output. This is a confusing way to work because even once you understand what wc, sort, and head do, those intermediate files make it hard to follow what’s going on. We can make it easier to understand by running sort and head together:

sort -n lengths.txt | head -n 1

The vertical bar, |, between the two commands is called a pipe. It tells the shell that we want to use the output of the command on the left as the input to the command on the right.

This has removed the need for the sorted-lengths.txt file.

Combining multiple commands

Nothing prevents us from chaining pipes consecutively. We can for example send the output of wc directly to sort, and then the resulting output to head. This removes the need for any intermediate files.

We’ll start by using a pipe to send the output of wc to sort:

wc -l *.pdb | sort -n

We can then send that output through another pipe, to head, so that the full pipeline becomes:

wc -l *.pdb | sort -n | head -n 1

This is exactly like a mathematician nesting functions like log(3x) and saying ‘the log of three times x’. In our case, the calculation is ‘head of sort of line count of *.pdb’.

The redirection and pipes used in the last few commands are illustrated below:

Redirects and Pipes of different commands.

wc -l *.pdb will direct the output to the shell. wc -l *.pdb > lengths will direct output to the file lengths. wc -l *.pdb | sort -n | head -n 1 will build a pipeline where the output of the wc command is the input to the sort command, the output of the sort command is the input to the head command and the output of the head command is directed to the shell

question Piping Commands Together

In our current directory, we want to find the 3 files which have the least number of lines. Which command listed below would work?

  1. wc -l * > sort -n > head -n 3
  2. wc -l * | sort -n | head -n 1-3
  3. wc -l * | head -n 3 | sort -n
  4. wc -l * | sort -n | head -n 3

solution Solution

Option 4 is the solution. The pipe character | is used to connect the output from one command to the input of another. > is used to redirect standard output to a file. Try it in the shell-lesson-data/molecules directory!

# Explore the possible solutions here!

Tools designed to work together

This idea of linking programs together is why Unix has been so successful. Instead of creating enormous programs that try to do many different things, Unix programmers focus on creating lots of simple tools that each do one job well, and that work well with each other. This programming model is called ‘pipes and filters’. We’ve already seen pipes; a filter is a program like wc or sort that transforms a stream of input into a stream of output. Almost all of the standard Unix tools can work this way: unless told to do otherwise, they read from standard input, do something with what they’ve read, and write to standard output.

The key is that any program that reads lines of text from standard input and writes lines of text to standard output can be combined with every other program that behaves this way as well. You can and should write your programs this way so that you and other people can put those programs into pipes to multiply their power.

question Pipe Reading Comprehension

A file called animals.txt (in the shell-lesson-data/data folder) contains the following data:

2012-11-05,deer
2012-11-05,rabbit
2012-11-05,raccoon
2012-11-06,rabbit
2012-11-06,deer
2012-11-06,fox
2012-11-07,rabbit
2012-11-07,bear

What text passes through each of the pipes and the final redirect in the pipeline below?

$ cat animals.txt | head -n 5 | tail -n 3 | sort -r > final.txt

Hint: build the pipeline up one command at a time to test your understanding

solution Solution

The head command extracts the first 5 lines from animals.txt. Then, the last 3 lines are extracted from the previous 5 by using the tail command. With the sort -r command those 3 lines are sorted in reverse order and finally, the output is redirected to a file final.txt. The content of this file can be checked by executing cat final.txt. The file should contain the following lines:

2012-11-06,rabbit
2012-11-06,deer
2012-11-05,raccoon
# Explore the possible solutions here!

question Pipe Construction

For the file animals.txt from the previous exercise, consider the following command:

$ cut -d , -f 2 animals.txt

The cut command is used to remove or ‘cut out’ certain sections of each line in the file, and cut expects the lines to be separated into columns by a Tab character. A character used in this way is a called a delimiter. In the example above we use the -d option to specify the comma as our delimiter character. We have also used the -f option to specify that we want to extract the second field (column). This gives the following output:

deer
rabbit
raccoon
rabbit
deer
fox
rabbit
bear

The uniq command filters out adjacent matching lines in a file. How could you extend this pipeline (using uniq and another command) to find out what animals the file contains (without any duplicates in their names)?

solution Solution

$ cut -d , -f 2 animals.txt | sort | uniq
# Explore the possible solutions here!

question Which Pipe?

The file animals.txt contains 8 lines of data formatted as follows:

2012-11-05,deer
2012-11-05,rabbit
2012-11-05,raccoon
2012-11-06,rabbit
...

The uniq command has a -c option which gives a count of the number of times a line occurs in its input. Assuming your current directory is shell-lesson-data/data/, what command would you use to produce a table that shows the total count of each type of animal in the file?

  1. sort animals.txt | uniq -c
  2. sort -t, -k2,2 animals.txt | uniq -c
  3. cut -d, -f 2 animals.txt | uniq -c
  4. cut -d, -f 2 animals.txt | sort | uniq -c
  5. cut -d, -f 2 animals.txt | sort | uniq -c | wc -l

solution Solution

Option 4. is the correct answer. If you have difficulty understanding why, try running the commands, or sub-sections of the pipelines (make sure you are in the shell-lesson-data/data directory).

# Explore the possible solutions here!

Nelle’s Pipeline: Checking Files

Nelle has run her samples through the assay machines and created 17 files in the north-pacific-gyre/2012-07-03 directory described earlier. As a quick check, starting from her home directory, Nelle types:

cd ~/Desktop/shell-lesson-data/north-pacific-gyre/2012-07-03
wc -l *.txt

The output is 18 lines that look like this:

300 NENE01729A.txt
300 NENE01729B.txt
300 NENE01736A.txt
300 NENE01751A.txt
300 NENE01751B.txt
300 NENE01812A.txt
... ...

Now she types this:

wc -l *.txt | sort -n | head -n 5

Whoops: one of the files is 60 lines shorter than the others. When she goes back and checks it, she sees that she did that assay at 8:00 on a Monday morning — someone was probably in using the machine on the weekend, and she forgot to reset it. Before re-running that sample, she checks to see if any files have too much data:

wc -l *.txt | sort -n | tail -n 5

Those numbers look good — but what’s that ‘Z’ doing there in the third-to-last line? All of her samples should be marked ‘A’ or ‘B’; by convention, her lab uses ‘Z’ to indicate samples with missing information. To find others like it, she does this:

ls *Z.txt

Sure enough, when she checks the log on her laptop, there’s no depth recorded for either of those samples. Since it’s too late to get the information any other way, she must exclude those two files from her analysis. She could delete them using rm, but there are actually some analyses she might do later where depth doesn’t matter, so instead, she’ll have to be careful later on to select files using the wildcard expressions NENE*A.txt NENE*B.txt.

question Removing Unneeded Files

Suppose you want to delete your processed data files, and only keep your raw files and processing script to save storage. The raw files end in .dat and the processed files end in .txt. Which of the following would remove all the processed data files, and only the processed data files?

  1. rm ?.txt
  2. rm *.txt
  3. rm * .txt
  4. rm *.*

solution Solution

  1. This would remove .txt files with one-character names
  2. This is correct answer
  3. The shell would expand * to match everything in the current directory, so the command would try to remove all matched files and an additional file called .txt
  4. The shell would expand *.* to match all files with any extension, so this command would delete all files

Loops

Loops are a programming construct which allow us to repeat a command or set of commands for each item in a list. As such they are key to productivity improvements through automation. Similar to wildcards and tab completion, using loops also reduces the amount of typing required (and hence reduces the number of typing mistakes).

Suppose we have several hundred genome data files named basilisk.dat, minotaur.dat, and unicorn.dat. For this example, we’ll use the creatures directory which only has three example files, but the principles can be applied to many many more files at once. First, go into the creatures directory.

# Change directories here!

The structure of these files is the same: the common name, classification, and updated date are presented on the first three lines, with DNA sequences on the following lines. Let’s look at the files:

head -n 5 basilisk.dat minotaur.dat unicorn.dat

We would like to print out the classification for each species, which is given on the second line of each file. For each file, we would need to execute the command head -n 2 and pipe this to tail -n 1. We’ll use a loop to solve this problem, but first let’s look at the general form of a loop:

for thing in list_of_things
do
    operation_using $thing    # Indentation within the loop is not required, but aids legibility
done

and we can apply this to our example like this:

for filename in basilisk.dat minotaur.dat unicorn.dat
do
    head -n 2 $filename | tail -n 1
done

tip Follow the Prompt

The shell prompt changes from $ to > and back again as we were typing in our loop. The second prompt, >, is different to remind us that we haven’t finished typing a complete command yet. A semicolon, ;, can be used to separate two commands written on a single line.

When the shell sees the keyword for, it knows to repeat a command (or group of commands) once for each item in a list. Each time the loop runs (called an iteration), an item in the list is assigned in sequence to the variable, and the commands inside the loop are executed, before moving on to the next item in the list. Inside the loop, we call for the variable’s value by putting $ in front of it. The $ tells the shell interpreter to treat the variable as a variable name and substitute its value in its place, rather than treat it as text or an external command.

In this example, the list is three filenames: basilisk.dat, minotaur.dat, and unicorn.dat. Each time the loop iterates, it will assign a file name to the variable filename and run the head command. The first time through the loop, $filename is basilisk.dat. The interpreter runs the command head on basilisk.dat and pipes the first two lines to the tail command, which then prints the second line of basilisk.dat. For the second iteration, $filename becomes minotaur.dat. This time, the shell runs head on minotaur.dat and pipes the first two lines to the tail command, which then prints the second line of minotaur.dat. For the third iteration, $filename becomes unicorn.dat, so the shell runs the head command on that file, and tail on the output of that. Since the list was only three items, the shell exits the for loop.

tip Same Symbols, Different Meanings

Here we see > being used as a shell prompt, whereas > is also used to redirect output. Similarly, $ is used as a shell prompt, but, as we saw earlier, it is also used to ask the shell to get the value of a variable.

If the shell prints > or $ then it expects you to type something, and the symbol is a prompt.

If you type > or $ yourself, it is an instruction from you that the shell should redirect output or get the value of a variable.

When using variables it is also possible to put the names into curly braces to clearly delimit the variable name: $filename is equivalent to ${filename}, but is different from ${file}name. You may find this notation in other people’s programs.

We have called the variable in this loop filename in order to make its purpose clearer to human readers. The shell itself doesn’t care what the variable is called; if we wrote this loop as:

for x in basilisk.dat minotaur.dat unicorn.dat
do
    head -n 2 $x | tail -n 1
done

or:

for temperature in basilisk.dat minotaur.dat unicorn.dat
do
    head -n 2 $temperature | tail -n 1
done

it would work exactly the same way.

Don’t do this.

Programs are only useful if people can understand them, so meaningless names (like x) or misleading names (like temperature) increase the odds that the program won’t do what its readers think it does.

question Variables in Loops

This exercise refers to the shell-lesson-data/molecules directory. ls gives the following output:

cubane.pdb  ethane.pdb  methane.pdb  octane.pdb  pentane.pdb  propane.pdb

What is the output of the following code?

for datafile in *.pdb
do
    ls *.pdb
done

Now, what is the output of the following code?

for datafile in *.pdb
do
   ls $datafile
done

Why do these two loops give different outputs?

solution Solution

The first code block gives the same output on each iteration through the loop. Bash expands the wildcard *.pdb within the loop body (as well as before the loop starts) to match all files ending in .pdb and then lists them using ls. The expanded loop would look like this:

$ for datafile in cubane.pdb  ethane.pdb  methane.pdb  octane.pdb  pentane.pdb  propane.pdb
> do
>     ls cubane.pdb  ethane.pdb  methane.pdb  octane.pdb  pentane.pdb  propane.pdb
> done
cubane.pdb  ethane.pdb  methane.pdb  octane.pdb  pentane.pdb  propane.pdb
cubane.pdb  ethane.pdb  methane.pdb  octane.pdb  pentane.pdb  propane.pdb
cubane.pdb  ethane.pdb  methane.pdb  octane.pdb  pentane.pdb  propane.pdb
cubane.pdb  ethane.pdb  methane.pdb  octane.pdb  pentane.pdb  propane.pdb
cubane.pdb  ethane.pdb  methane.pdb  octane.pdb  pentane.pdb  propane.pdb
cubane.pdb  ethane.pdb  methane.pdb  octane.pdb  pentane.pdb  propane.pdb

The second code block lists a different file on each loop iteration. The value of the datafile variable is evaluated using $datafile, and then listed using ls.

cubane.pdb
ethane.pdb
methane.pdb
octane.pdb
pentane.pdb
propane.pdb
# Explore the possible solutions here!

question Limiting Sets of Files

What would be the output of running the following loop in thei shell-lesson-data/molecules directory?

for filename in c*
do
    ls $filename
done
  1. No files are listed.
  2. All files are listed.
  3. Only cubane.pdb, octane.pdb and pentane.pdb are listed.
  4. Only cubane.pdb is listed.

solution Solution

4 is the correct answer. * matches zero or more characters, so any file name starting with the letter c, followed by zero or more other characters will be matched.

How would the output differ from using this command instead?

for filename in *c*
do
    ls $filename
done
  1. The same files would be listed.
  2. All the files are listed this time.
  3. No files are listed this time.
  4. The files cubane.pdb and octane.pdb will be listed.
  5. Only the file octane.pdb will be listed.

solution Solution

4 is the correct answer. * matches zero or more characters, so a file name with zero or more characters before a letter c and zero or more characters after the letter c will be matched.

# Explore the possible solutions here!

question Saving to a File in a Loop - Part One

In the shell-lesson-data/molecules directory, what is the effect of this loop?

for alkanes in *.pdb
do
    echo $alkanes
    cat $alkanes > alkanes.pdb
done
  1. Prints cubane.pdb, ethane.pdb, methane.pdb, octane.pdb, pentane.pdb and propane.pdb, and the text from propane.pdb will be saved to a file called alkanes.pdb.
  2. Prints cubane.pdb, ethane.pdb, and methane.pdb, and the text from all three files would be concatenated and saved to a file called alkanes.pdb.
  3. Prints cubane.pdb, ethane.pdb, methane.pdb, octane.pdb, and pentane.pdb, and the text from propane.pdb will be saved to a file called alkanes.pdb.
  4. None of the above.

solution Solution

1 is correct. The text from each file in turn gets written to the alkanes.pdb file. However, the file gets overwritten on each loop iteration, so the final content of alkanes.pdb is the text from the propane.pdb file.

# Explore the possible solutions here!

question Saving to a File in a Loop - Part Two

Also in the shell-lesson-data/molecules directory, what would be the output of the following loop?

for datafile in *.pdb
do
    cat $datafile >> all.pdb
done
  1. All of the text from cubane.pdb, ethane.pdb, methane.pdb, octane.pdb, and pentane.pdb would be concatenated and saved to a file called all.pdb.
  2. The text from ethane.pdb will be saved to a file called all.pdb.
  3. All of the text from cubane.pdb, ethane.pdb, methane.pdb, octane.pdb, pentane.pdb and propane.pdb would be concatenated and saved to a file called all.pdb.
  4. All of the text from cubane.pdb, ethane.pdb, methane.pdb, octane.pdb, pentane.pdb and propane.pdb would be printed to the screen and saved to a file called all.pdb.

solution Solution

3 is the correct answer. >> appends to a file, rather than overwriting it with the redirected output from a command. Given the output from the cat command has been redirected, nothing is printed to the screen.

# Explore the possible solutions here!

Let’s continue with our example in the shell-lesson-data/creatures directory. Here’s a slightly more complicated loop:

for filename in *.dat
do
    echo $filename
    head -n 100 $filename | tail -n 20
done

The shell starts by expanding *.dat to create the list of files it will process. The loop body then executes two commands for each of those files. The first command, echo, prints its command-line arguments to standard output. For example:

echo hello there

prints:

hello there

In this case, since the shell expands $filename to be the name of a file, echo $filename prints the name of the file. Note that we can’t write this as:

for filename in *.dat
do
    $filename
    head -n 100 $filename | tail -n 20
done

because then the first time through the loop, when $filename expanded to basilisk.dat, the shell would try to run basilisk.dat as a program. Finally, the head and tail combination selects lines 81-100 from whatever file is being processed (assuming the file has at least 100 lines).

tip Spaces in Names

Spaces are used to separate the elements of the list that we are going to loop over. If one of those elements contains a space character, we need to surround it with quotes, and do the same thing to our loop variable. Suppose our data files are named:

red dragon.dat
purple unicorn.dat

To loop over these files, we would need to add double quotes like so:

$ for filename in "red dragon.dat" "purple unicorn.dat"
> do
>     head -n 100 "$filename" | tail -n 20
> done

It is simpler to avoid using spaces (or other special characters) in filenames.

The files above don’t exist, so if we run the above code, the head command will be unable to find them, however the error message returned will show the name of the files it is expecting:

head: cannot open ‘red dragon.dat’ for reading: No such file or directory
head: cannot open ‘purple unicorn.dat’ for reading: No such file or directory

Try removing the quotes around $filename in the loop above to see the effect of the quote marks on spaces. Note that we get a result from the loop command for unicorn.dat when we run this code in the creatures directory:

head: cannot open ‘red’ for reading: No such file or directory
head: cannot open ‘dragon.dat’ for reading: No such file or directory
head: cannot open ‘purple’ for reading: No such file or directory
CGGTACCGAA
AAGGGTCGCG
CAAGTGTTCC
...

We would like to modify each of the files in shell-lesson-data/creatures, but also save a version of the original files, naming the copies original-basilisk.dat and original-unicorn.dat. We can’t use:

cp *.dat original-*.dat

because that would expand to:

cp basilisk.dat minotaur.dat unicorn.dat original-*.dat

This wouldn’t back up our files, instead we get an error.

This problem arises when cp receives more than two inputs. When this happens, it expects the last input to be a directory where it can copy all the files it was passed. Since there is no directory named original-*.dat in the creatures directory we get an error.

Instead, we can use a loop:

for filename in *.dat
do
    cp $filename original-$filename
done

This loop runs the cp command once for each filename. The first time, when $filename expands to basilisk.dat, the shell executes:

cp basilisk.dat original-basilisk.dat

The second time, the command is:

cp minotaur.dat original-minotaur.dat

The third and last time, the command is:

cp unicorn.dat original-unicorn.dat

Since the cp command does not normally produce any output, it’s hard to check that the loop is doing the correct thing. However, we learned earlier how to print strings using echo, and we can modify the loop to use echo to print our commands without actually executing them. As such we can check what commands would be run in the unmodified loop.

The following diagram shows what happens when the modified loop is executed, and demonstrates how the judicious use of echo is a good debugging technique.

The for loop 'for filename in *.dat; do echo cp $filename original-$filename; done' will successively assign the names of all '*.dat' files in your current directory to the variable '$filename' and then execute the command. With the files 'basilisk.dat', 'minotaur.dat' and 'unicorn.dat' in the current directory the loop will successively call the echo command three times and print three lines: 'cp basislisk.dat original-basilisk.dat', then 'cp minotaur.dat original-minotaur.dat' and finally 'cp unicorn.dat original-unicorn.dat'.

Nelle’s Pipeline: Processing Files

Nelle is now ready to process her data files using goostats.sh — a shell script written by her supervisor. This calculates some statistics from a protein sample file, and takes two arguments:

  1. an input file (containing the raw data)
  2. an output file (to store the calculated statistics)

Since she’s still learning how to use the shell, she decides to build up the required commands in stages. Her first step is to make sure that she can select the right input files — remember, these are ones whose names end in ‘A’ or ‘B’, rather than ‘Z’. Starting from her home directory, Nelle types:

cd ~/Desktop/shell-lesson-data/north-pacific-gyre/2012-07-03
for datafile in NENE*A.txt NENE*B.txt
do
    echo $datafile
done

Her next step is to decide what to call the files that the goostats.sh analysis program will create. Prefixing each input file’s name with ‘stats’ seems simple, so she modifies her loop to do that:

for datafile in NENE*A.txt NENE*B.txt
do
    echo $datafile stats-$datafile
done

She hasn’t actually run goostats.sh yet, but now she’s sure she can select the right files and generate the right output filenames.

tip Top Terminal Tip: Re-running previous commands

Typing in commands over and over again is becoming tedious, though, and Nelle is worried about making mistakes, so instead of re-entering her loop, she presses . In response, the shell redisplays the whole loop on one line (using semi-colons to separate the pieces):

for datafile in NENE*A.txt NENE*B.txt; do echo $datafile stats-$datafile; done

Using the left arrow key, Nelle backs up and changes the command echo to bash goostats.sh:

for datafile in NENE*A.txt NENE*B.txt; do bash goostats.sh $datafile stats-$datafile; done

When she presses Enter, the shell runs the modified command. However, nothing appears to happen — there is no output. After a moment, Nelle realizes that since her script doesn’t print anything to the screen any longer, she has no idea whether it is running, much less how quickly. She kills the running command by typing Ctrl+C, uses to repeat the command, and edits it to read:

for datafile in NENE*A.txt NENE*B.txt; do echo $datafile; bash goostats.sh $datafile stats-$datafile; done

Beginning and End

We can move to the beginning of a line in the shell by typing Ctrl+A and to the end using Ctrl+E.

When she runs her program now, it produces one line of output every five seconds or so:

1518 times 5 seconds, divided by 60, tells her that her script will take about two hours to run. As a final check, she opens another terminal window, goes into north-pacific-gyre/2012-07-03, and uses cat stats-NENE01729B.txt to examine one of the output files. It looks good, so she decides to get some coffee and catch up on her reading.

tip Those Who Know History Can Choose to Repeat It

Another way to repeat previous work is to use the history command to get a list of the last few hundred commands that have been executed, and then to use !123 (where ‘123’ is replaced by the command number) to repeat one of those commands. For example, if Nelle types this:

$ history | tail -n 5
  456  ls -l NENE0*.txt
  457  rm stats-NENE01729B.txt.txt
  458  bash goostats.sh NENE01729B.txt stats-NENE01729B.txt
  459  ls -l NENE0*.txt
  460  history

then she can re-run goostats.sh on NENE01729B.txt simply by typing !458. This number will be different for you, you should check your history before running it!

tip Other History Commands

There are a number of other shortcut commands for getting at the history.

  • Ctrl+R enters a history search mode ‘reverse-i-search’ and finds the most recent command in your history that matches the text you enter next. Press Ctrl+R one or more additional times to search for earlier matches. You can then use the left and right arrow keys to choose that line and edit it then hit Return to run the command.
  • !! retrieves the immediately preceding command (you may or may not find this more convenient than using )
  • !$ retrieves the last word of the last command. That’s useful more often than you might expect: after bash goostats.sh NENE01729B.txt stats-NENE01729B.txt, you can type less !$ to look at the file stats-NENE01729B.txt, which is quicker than doing and editing the command-line.

question Doing a Dry Run

A loop is a way to do many things at once — or to make many mistakes at once if it does the wrong thing. One way to check what a loop would do is to echo the commands it would run instead of actually running them.

Suppose we want to preview the commands the following loop will execute without actually running those commands:

cd ~/Desktop/shell-lesson-data/pdb/
for datafile in *.pdb
do
    cat $datafile >> all.pdb
done

What is the difference between the two loops below, and which one would we want to run?

code-in Version 1

for datafile in *.pdb
do
    echo cat $datafile >> all.pdb
done

code-in Version 2

for datafile in *.pdb
do
    echo "cat $datafile >> all.pdb"
done

tip Solution

The second version is the one we want to run. This prints to screen everything enclosed in the quote marks, expanding the loop variable name because we have prefixed it with a dollar sign.

The first version appends the output from the command echo cat $datafile to the file, all.pdb. This file will just contain the list; cat cubane.pdb, cat ethane.pdb, cat methane.pdb etc.

Try both versions for yourself to see the output! Be sure to open the all.pdb file to view its contents.

# Explore the possible solutions here!

question Nested Loops

Suppose we want to set up a directory structure to organize some experiments measuring reaction rate constants with different compounds and different temperatures. What would be the result of the following code:

for species in cubane ethane methane
do
    for temperature in 25 30 37 40
    do
        mkdir $species-$temperature
    done
done

solution Solution

We have a nested loop, i.e. contained within another loop, so for each species in the outer loop, the inner loop (the nested loop) iterates over the list of temperatures, and creates a new directory for each combination.

Try running the code for yourself to see which directories are created!

# Explore the possible solutions here!

Finding Things

In the same way that many of us now use ‘Google’ as a verb meaning ‘to find’, Unix programmers often use the word ‘grep’. ‘grep’ is a contraction of ‘global/regular expression/print’, a common sequence of operations in early Unix text editors. It is also the name of a very useful command-line program.

grep finds and prints lines in files that match a pattern. For our examples, we will use a file that contains three haiku taken from a 1998 competition in Salon magazine. For this set of examples, we’re going to be working in the writing subdirectory:

cd
cd Desktop/shell-lesson-data/writing
cat haiku.txt

tip Forever, or Five Years

We haven’t linked to the original haiku because they don’t appear to be on Salon’s site any longer. As Jeff Rothenberg said, ‘Digital information lasts forever — or five years, whichever comes first.’ Luckily, popular content often has backups.

Let’s find lines that contain the word ‘not’:

grep not haiku.txt

Here, not is the pattern we’re searching for. The grep command searches through the file, looking for matches to the pattern specified. To use it type grep, then the pattern we’re searching for and finally the name of the file (or files) we’re searching in.

The output is the three lines in the file that contain the letters ‘not’.

By default, grep searches for a pattern in a case-sensitive way. In addition, the search pattern we have selected does not have to form a complete word, as we will see in the next example.

Let’s search for the pattern: ‘The’.

grep The haiku.txt

This time, two lines that include the letters ‘The’ are outputted, one of which contained our search pattern within a larger word, ‘Thesis’.

To restrict matches to lines containing the word ‘The’ on its own, we can give grep with the -w option. This will limit matches to word boundaries.

Later in this lesson, we will also see how we can change the search behavior of grep with respect to its case sensitivity.

grep -w The haiku.txt

Note that a ‘word boundary’ includes the start and end of a line, so not just letters surrounded by spaces. Sometimes we don’t want to search for a single word, but a phrase. This is also easy to do with grep by putting the phrase in quotes.

grep -w "is not" haiku.txt

We’ve now seen that you don’t have to have quotes around single words, but it is useful to use quotes when searching for multiple words. It also helps to make it easier to distinguish between the search term or phrase and the file being searched. We will use quotes in the remaining examples.

Another useful option is -n, which numbers the lines that match:

grep -n "it" haiku.txt

Here, we can see that lines 5, 9, and 10 contain the letters ‘it’.

We can combine options (i.e. flags) as we do with other Unix commands. For example, let’s find the lines that contain the word ‘the’. We can combine the option -w to find the lines that contain the word ‘the’ and -n to number the lines that match:

grep -n -w "the" haiku.txt

Now we want to use the option -i to make our search case-insensitive:

grep -n -w -i "the" haiku.txt

Now, we want to use the option -v to invert our search, i.e., we want to output the lines that do not contain the word ‘the’.

grep -n -w -v "the" haiku.txt

If we use the -r (recursive) option, grep can search for a pattern recursively through a set of files in subdirectories.

Let’s search recursively for Yesterday in the shell-lesson-data/writing directory:

grep -r Yesterday .

grep has lots of other options. To find out what they are, we can type:

grep --help

question Using grep

Which command would result in the following output:

and the presence of absence:
  1. grep "of" haiku.txt
  2. grep -E "of" haiku.txt
  3. grep -w "of" haiku.txt
  4. grep -i "of" haiku.txt

solution Solution

The correct answer is 3, because the -w option looks only for whole-word matches. The other options will also match ‘of’ when part of another word.

# Explore the possible solutions here!

tip Wildcards

grep’s real power doesn’t come from its options, though; it comes from the fact that patterns can include wildcards. (The technical name for these is regular expressions, which is what the ‘re’ in ‘grep’ stands for.) Regular expressions are both complex and powerful; if you want to do complex searches, please look at the lesson on our website. As a taster, we can find lines that have an ‘o’ in the second position like this:

$ grep -E "^.o" haiku.txt
You bring fresh toner.
Today it is not working
Software is like that.

We use the -E option and put the pattern in quotes to prevent the shell from trying to interpret it. (If the pattern contained a *, for example, the shell would try to expand it before running grep.) The ^ in the pattern anchors the match to the start of the line. The . matches a single character (just like ? in the shell), while the o matches an actual ‘o’.

# Explore the possible solutions here!

question Tracking a Species

Leah has several hundred data files saved in one directory, each of which is formatted like this:

2013-11-05,deer,5
2013-11-05,rabbit,22
2013-11-05,raccoon,7
2013-11-06,rabbit,19
2013-11-06,deer,2

She wants to write a shell script that takes a species as the first command-line argument and a directory as the second argument. The script should return one file called species.txt containing a list of dates and the number of that species seen on each date. For example using the data shown above, rabbit.txt would contain:

2013-11-05,22
2013-11-06,19

Put these commands and pipes in the right order to achieve this:

cut -d : -f 2
>
|
grep -w $1 -r $2
|
$1.txt
cut -d , -f 1,3

Hint: use man grep to look for how to grep text recursively in a directory and man cut to select more than one field in a line.

An example of such a file is provided in shell-lesson-data/data/animal-counts/animals.txt

solution Solution

grep -w $1 -r $2 | cut -d : -f 2 | cut -d , -f 1,3 > $1.txt

Actually, you can swap the order of the two cut commands and it still works. At the command line, try changing the order of the cut commands, and have a look at the output from each step to see why this is the case.

You would call the script above like this:

$ bash count-species.sh bear .
# Explore the possible solutions here!

question Little Women

You and your friend, having just finished reading Little Women by Louisa May Alcott, are in an argument. Of the four sisters in the book, Jo, Meg, Beth, and Amy, your friend thinks that Jo was the most mentioned. You, however, are certain it was Amy. Luckily, you have a file LittleWomen.txt containing the full text of the novel (shell-lesson-data/writing/data/LittleWomen.txt). Using a for loop, how would you tabulate the number of times each of the four sisters is mentioned?

Hint: one solution might employ the commands grep and wc and a |, while another might utilize grep options. There is often more than one way to solve a programming task, so a particular solution is usually chosen based on a combination of yielding the correct result, elegance, readability, and speed.

solution Solutions

for sis in Jo Meg Beth Amy
do
	echo $sis:
	grep -ow $sis LittleWomen.txt | wc -l
done

Alternative, slightly inferior solution:

for sis in Jo Meg Beth Amy
do
	echo $sis:
	grep -ocw $sis LittleWomen.txt
done

This solution is inferior because grep -c only reports the number of lines matched. The total number of matches reported by this method will be lower if there is more than one match per line.

Perceptive observers may have noticed that character names sometimes appear in all-uppercase in chapter titles (e.g. ‘MEG GOES TO VANITY FAIR’). If you wanted to count these as well, you could add the -i option for case-insensitivity (though in this case, it doesn’t affect the answer to which sister is mentioned most frequently).

# Explore the possible solutions here!

While grep finds lines in files, the find command finds files themselves. Again, it has a lot of options; to show how the simplest ones work, we’ll use the directory tree shown below.

A file tree under the directory 'writing' contians several sub-directories and files such that 'writing' contains directories 'data', 'thesis', 'tools' and a file 'haiku.txt'; 'writing/data' contains the files 'Little Women.txt', 'one.txt' and 'two.txt'; 'writing/thesis' contains the file 'empty-draft.md'; 'writing/tools' contains the directory 'old' and the files 'format' and 'stats'; and 'writing/tools/old' contains a file 'oldtool'.

Nelle’s writing directory contains one file called haiku.txt and three subdirectories: thesis (which contains a sadly empty file, empty-draft.md); data (which contains three files LittleWomen.txt, one.txt and two.txt); and a tools directory that contains the programs format and stats, and a subdirectory called old, with a file oldtool.

For our first command, let’s run find . (remember to run this command from the shell-lesson-data/writing folder).

find .

As always, the . on its own means the current working directory, which is where we want our search to start. find’s output is the names of every file and directory under the current working directory. This can seem useless at first but find has many options to filter the output and in this lesson we will discover some of them.

The first option in our list is -type d that means ‘things that are directories’. Sure enough, find’s output is the names of the five directories in our little tree (including .):

find . -type d

Notice that the objects find finds are not listed in any particular order. If we change -type d to -type f, we get a listing of all the files instead:

find . -type f

Now let’s try matching by name:

find . -name *.txt

We expected it to find all the text files, but it only prints out ./haiku.txt. The problem is that the shell expands wildcard characters like * before commands run. Since *.txt in the current directory expands to haiku.txt, the command we actually ran was:

find . -name haiku.txt

find did what we asked; we just asked for the wrong thing.

To get what we want, let’s do what we did with grep: put *.txt in quotes to prevent the shell from expanding the * wildcard. This way, find actually gets the pattern *.txt, not the expanded filename haiku.txt:

find . -name "*.txt"

tip Listing vs. Finding

ls and find can be made to do similar things given the right options, but under normal circumstances, ls lists everything it can, while find searches for things with certain properties and shows them.

As we said earlier, the command line’s power lies in combining tools. We’ve seen how to do that with pipes; let’s look at another technique. As we just saw, find . -name "*.txt" gives us a list of all text files in or below the current directory. How can we combine that with wc -l to count the lines in all those files?

The simplest way is to put the find command inside $():

wc -l $(find . -name "*.txt")

When the shell executes this command, the first thing it does is run whatever is inside the $(). It then replaces the $() expression with that command’s output. Since the output of find is the four filenames ./data/one.txt, ./data/LittleWomen.txt, ./data/two.txt, and ./haiku.txt, the shell constructs the command:

wc -l ./data/one.txt ./data/LittleWomen.txt ./data/two.txt ./haiku.txt

which is what we wanted. This expansion is exactly what the shell does when it expands wildcards like * and ?, but lets us use any command we want as our own ‘wildcard’.

It’s very common to use find and grep together. The first finds files that match a pattern; the second looks for lines inside those files that match another pattern. Here, for example, we can find PDB files that contain iron atoms by looking for the string ‘FE’ in all the .pdb files above the current directory:

grep "FE" $(find .. -name "*.pdb")

question Matching and Subtracting

The -v option to grep inverts pattern matching, so that only lines which do not match the pattern are printed. Given that, which of the following commands will find all files in /data whose names end in s.txt but whose names also do not contain the string net? (For example, animals.txt or amino-acids.txt but not planets.txt.) Once you have thought about your answer, you can test the commands in the shell-lesson-data directory.

  1. find data -name "*s.txt" | grep -v net
  2. find data -name *s.txt | grep -v net
  3. grep -v "net" $(find data -name "*s.txt")
  4. None of the above.

solution Solution

The correct answer is 1. Putting the match expression in quotes prevents the shell expanding it, so it gets passed to the find command.

Option 2 is incorrect because the shell expands *s.txt instead of passing the wildcard expression to find.

Option 3 is incorrect because it searches the contents of the files for lines which do not match ‘net’, rather than searching the file names.

# Explore the possible solutions here!

tip Binary Files

We have focused exclusively on finding patterns in text files. What if your data is stored as images, in databases, or in some other format?

A handful of tools extend grep to handle a few non text formats. But a more generalizable approach is to convert the data to text, or extract the text-like elements from the data. On the one hand, it makes simple things easy to do. On the other hand, complex things are usually impossible. For example, it’s easy enough to write a program that will extract X and Y dimensions from image files for grep to play with, but how would you write something to find values in a spreadsheet whose cells contained formulas?

A last option is to recognize that the shell and text processing have their limits, and to use another programming language. When the time comes to do this, don’t be too hard on the shell: many modern programming languages have borrowed a lot of ideas from it, and imitation is also the sincerest form of praise.

The Unix shell is older than most of the people who use it. It has survived so long because it is one of the most productive programming environments ever created — maybe even the most productive. Its syntax may be cryptic, but people who have mastered it can experiment with different commands interactively, then use what they have learned to automate their work. Graphical user interfaces may be easier to use at first, but once learned, the productivity in the shell is unbeatable. And as Alfred North Whitehead wrote in 1911, ‘Civilization advances by extending the number of important operations which we can perform without thinking about them.’

question find Pipeline Reading Comprehension

Write a short explanatory comment for the following shell script:

wc -l $(find . -name "*.dat") | sort -n

solution Solution

  1. Find all files with a .dat extension recursively from the current directory
  2. Count the number of lines each of these files contains
  3. Sort the output from step 2. numerically

Key points

  • wc counts lines, words, and characters in its inputs.

  • cat displays the contents of its inputs.

  • sort sorts its inputs.

  • head displays the first 10 lines of its input.

  • tail displays the last 10 lines of its input.

  • command > [file] redirects a command’s output to a file (overwriting any existing content).

  • command >> [file] appends a command’s output to a file.

  • [first] | [second] is a pipeline: the output of the first command is used as the input to the second.

  • The best way to use the shell is to use pipes to combine simple single-purpose programs (filters).

  • A for loop repeats commands once for every thing in a list.

  • Every for loop needs a variable to refer to the thing it is currently operating on.

  • Use $name to expand a variable (i.e., get its value). ${name} can also be used.

  • Do not use spaces, quotes, or wildcard characters such as ‘*’ or ‘?’ in filenames, as it complicates variable expansion.

  • Give files consistent names that are easy to match with wildcard patterns to make it easy to select them for looping.

  • Use the up-arrow key to scroll up through previous commands to edit and repeat them.

  • Use Ctrl+R to search through the previously entered commands.

  • Use history to display recent commands, and ![number] to repeat a command by number.

  • find finds files with specific properties that match patterns.

  • grep selects lines in files that match patterns.

  • --help is an option supported by many bash commands, and programs that can be run from within Bash, to display more information on how to use these commands or programs.

  • man [command] displays the manual page for a given command.

  • $([command]) inserts a command’s output in place.

Frequently Asked Questions

Have questions about this tutorial? Check out the FAQ page for the Foundations of Data Science topic to see if your question is listed there. If not, please ask your question on the GTN Gitter Channel or the Galaxy Help Forum

Feedback

Did you use this material as an instructor? Feel free to give us feedback on how it went.

Click here to load Google feedback frame

Citing this Tutorial

  1. The Carpentries, Helena Rasche, Bazante Sanders, 2021 Advanced CLI in Galaxy (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/data-science/tutorials/cli-advanced/tutorial.html Online; accessed TODAY
  2. Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012

details BibTeX

@misc{data-science-cli-advanced,
author = "The Carpentries and Helena Rasche and Bazante Sanders",
title = "Advanced CLI in Galaxy (Galaxy Training Materials)",
year = "2021",
month = "10",
day = "19"
url = "\url{https://training.galaxyproject.org/training-material/topics/data-science/tutorials/cli-advanced/tutorial.html}",
note = "[Online; accessed TODAY]"
}
@article{Batut_2018,
    doi = {10.1016/j.cels.2018.05.012},
    url = {https://doi.org/10.1016%2Fj.cels.2018.05.012},
    year = 2018,
    month = {jun},
    publisher = {Elsevier {BV}},
    volume = {6},
    number = {6},
    pages = {752--758.e1},
    author = {B{\'{e}}r{\'{e}}nice Batut and Saskia Hiltemann and Andrea Bagnacani and Dannon Baker and Vivek Bhardwaj and Clemens Blank and Anthony Bretaudeau and Loraine Brillet-Gu{\'{e}}guen and Martin {\v{C}}ech and John Chilton and Dave Clements and Olivia Doppelt-Azeroual and Anika Erxleben and Mallory Ann Freeberg and Simon Gladman and Youri Hoogstrate and Hans-Rudolf Hotz and Torsten Houwaart and Pratik Jagtap and Delphine Larivi{\`{e}}re and Gildas Le Corguill{\'{e}} and Thomas Manke and Fabien Mareuil and Fidel Ram{\'{\i}}rez and Devon Ryan and Florian Christoph Sigloch and Nicola Soranzo and Joachim Wolff and Pavankumar Videm and Markus Wolfien and Aisanjiang Wubuli and Dilmurat Yusuf and James Taylor and Rolf Backofen and Anton Nekrutenko and Björn Grüning},
    title = {Community-Driven Data Analysis Training for Biology},
    journal = {Cell Systems}
}
                

Congratulations on successfully completing this tutorial!