# Problem solving with Unix commands

I am starting to realize that the Unix command-line toolbox can fix absolutely any problem related to text wrangling. Let me tell you about a problem I had, and how I used some Unix command-line utilities to solve it.

## The problem

I’m working on research for my master thesis. As with many statisticians, I am running a lot of simulations. I first simulate some data according to some numerical seed (to ensure reproducibility), and then use an algorithm to estimate something based on that data. For each simulation run, I create some files, typically like so:

dataset-directory/0001_data.csv
dataset-directory/0001_A.csv


Sometimes a run fails. This doesn’t really matter in this case: For any failed simulation, I can just do another one. For the 0001 data, I had a successful run with algorithm A. Therefore I want to use the 0001 data on algorithm B as well. But what I do need is to keep track of which runs have failed.

After running algorithm A on a lot of data, I end up with a large list of files like

dataset-directory/0001_data.csv
dataset-directory/0001_A.csv
dataset-directory/0002_data.csv
dataset-directory/0002_A.csv
dataset-directory/0003_data.csv
dataset-directory/0003_A.csv
dataset-directory/0004_data.csv
dataset-directory/0005_data.csv
dataset-directory/0005_A.csv
dataset-directory/0006_data.csv
dataset-directory/0006_A.csv
dataset-directory/0007_data.csv
dataset-directory/0007_A.csv
dataset-directory/0008_data.csv
dataset-directory/0009_data.csv
dataset-directory/0009_A.csv
...
dataset-directory/0499_A.csv


The astute observer will note that the file for algorithm A on data 0004 and 0008 are missing. How can I get a list of all the numbers for which A didn’t succeed?

I certainly could go over manually, but that would be error prone, and incredibly boring. It’s much better to write a program to do it!

## The solution

To be obtuse: Those that didn’t succeed are the numbers from 0001 to 0500 except those that succeeded. And one handy command to get a list of numbers is seq:

$> seq 10 1 2 3 4 5 6 7 8 9 10  (If only one number is given, it is implied that the sequence starts with 1. seq 2 10 would do what you think it would, as well.) Now, if we can get a list of all the successful runs, we should be able to get what we want by cross-checking the list of successful runs with a seq command which prints all possible numbers! Most command-line utilities do one pretty specific thing. For example, with cut you can get the characters on specific locations on each line $> cat text
Lorem ipsum
dolor sit amet
$> cat text | cut -c 2-5 orem olor  Notice here the use of the so-called pipe operator |. Like I said, most utilities do one specific thing, and it does that thing well. The neat thing is that these can be combined. By using these pipes, the output from the command to the left of the pipe is directed to the command to the right. Note that these commands treat the input as a stream of lines, which is often really handy. We can get a list of the successful file names by piping the list of files into a grep command, which is a command which can use regular expressions. Since all files start with an equal length of 4 digits, we can match these to the regular expression \d\d\d\d, matching 4 digits in a row, and add the file ending for the A algorithm to the regular expression. To get the list of files with one line for each file, we can simply do ls. (Although ls doesn’t give each file its own line when calling it separately, it turns out that piping the output from ls will.) $> ls dataset-directory | grep '\d\d\d\d_A.csv'
0009_A.csv
0001_A.csv
0002_A.csv
0005_A.csv
0007_A.csv
0003_A.csv
0006_A.csv
...


For some reason, these show up in a scrambled order after using grep. We can use sort to fix that. And we are only interested in the numbers, so we can use cut -c 1-4 to extract the number parts.

$> ls dataset-directory | grep '\d\d\d\d_A.csv' | sort | cut -c 1-4 0001 0002 0003 0005 0006 0007 0009 ... 0499  These numbers aren’t exactly the same as the numbers from the seq command, since these are zero-padded. Therefore we write a quick Python script to parse them as integers. # parse.py import sys for line in sys.stdin: i = int(line) print(i)  Now, piping into this script will give us the numbers that we want: $> ls dataset-directory | grep '\d\d\d\d_A.csv' | cut -c 1-4 | python3 parse.py
1
2
3
5
6
7
9
...
499


We’re getting there! Now we have to figure out how to cross-check these lists of numbers. Luckily, there exists a command called comm, which checks for common characters in two input streams. To get the input of a sequence of commands such as the one above, we can evaluate it and redirect it, which we do by wrapping it in <(...).

$> comm <(ls dataset-directory | grep '\d\d\d\d_A.csv' | cut -c 1-4 | python3 parse.py) <(seq 500) 1 2 3 4 5 6 7 8 9 10 ... 500  This output is a bit disorienting. If we read the manual of comm (by doing man comm), we see that comm “produces three text columns as output: lines only in file1; lines only in file2; and lines in both files.” To suppress column 1 – which is empty, since no numbers are only from the file list – call comm with the flag -1. And since we are not interested in the numbers which are in both streams, we suppress with the -3 flag as well. $> comm -1 -3 <(ls dataset-directory | grep '\d\d\d\d_A.csv' | cut -c 1-4 | python3 parse.py) <(seq 500)
4
8
...
500


And we’re done!

Update: This post generated some interesting discussion on Hacker News. There are many ways to solve this problem, and the way I did it is probably not the best. Be sure to check it out for tips on how to improve.