[ -z $var ] works unreasonably well

There is a subreddit /r/nononoyes for videos of things that look like they’ll go horribly wrong, but amazingly turn out ok.

[ -z $var ] would belong there.

It’s a bash statement that tries to check whether the variable is empty, but it’s missing quotes. Most of the time, when dealing with variables that can be empty, this is a disaster.

Consider its opposite, [ -n $var ], for checking whether the variable is non-empty. With the same quoting bug, it becomes completely unusable:

Input Expected [ -n $var ]
“” False True!
“foo” True True
“foo bar” True False!

These issues are due to a combination of word splitting and the fact that [ is not shell syntax but traditionally just an external binary with a funny name. See my previous post Why Bash is like that: Pseudo-syntax for more on that.

The evaluation of [ is defined in terms of the number of argument. The argument values have much less to do with it. Ignoring negation, here’s a simplified excerpt from POSIX test:

# Arguments Action Typical example
0 False [ ]
1 True if $1 is non-empty [ "$var" ]
2 Apply unary operator $1 to $2 [ -x "/bin/ls" ]
3 Apply binary operator $2 to $1 and $3 [ 1 -lt 2 ]

Now we can see why [ -n $var ] fails in two cases:

When the variable is empty and unquoted, it’s removed, and we pass 1 argument: the literal string “-n”. Since “-n” is not an empty string, it evaluates to true when it should be false.

When the variable contains foo bar and is unquoted, it’s split into two arguments, and so we pass 3: “-n”, “foo” and “bar”. Since “foo” is not a binary operator, it evaluates to false (with an error message) when it should be true.

Now let’s have a look at [ -z $var ]:

Input Expected [ -z $var ] Actual test
“” True: is empty True 1 arg: is “-z” non-empty
“foo” False: not empty False 2 args: apply -z to foo
“foo bar” False: not empty False (error) 3 args: apply “foo’ to -z and bar

It performs a completely wrong and unexpected action for both empty strings and multiple arguments. However, both cases fail in exactly the right way!

In other words, [ -z $var ] works way better than it has any possible business doing.

This is not to say you can skip quoting of course. For “foo bar”, [ -z $var ] in bash will return the correct exit code, but prints an ugly error in the process. For ” ” (a string with only spaces), it returns true when it should be false, because the argument is removed as if empty. Bash will also incorrectly pass var="foo -o x" because it ends up being a valid test through code injection.

The moral of the story? Same as always: quote, quote quote. Even when things appear to work.

ShellCheck is aware of this difference, and you can check the code used here online. [ -n $var ] gets an angry red message, while [ -z $var ] merely gets a generic green quoting warning.

Swearing in the Linux kernel: now interactive

Graphs showing a rise in "crap" and fall in "fuck" over time.

If you’ve followed discussions on Linux, you may at some point have bumped into a funny graph showing how many times frustrated Linux kernel developers have put four letter words into the source code.

Today, for the first time in 12 years, it’s gotten a major revamp!

You can now interactively plot any words of your choice with commit level granularity.


Did you find any interesting insights? Post a comment!

Technically correct: floating point calculations in bc

Whenever someone asks how to do floating point math in a shell script, the answer is typically bc:

$  echo "scale=9; 22/7" | bc

However, this is technically wrong: bc does not support floating point at all! What you see above is arbitrary precision FIXED point arithmetic.

The user’s intention is obviously to do math with fractional numbers, regardless of the low level implementation, so the above is a good and pragmatic answer. However, technically correct is the best kind of correct, so let’s stop being helpful and start pedantically splitting hairs instead!

Fixed vs floating point

There are many important things that every programmer should know about floating point, but in one sentence, the larger they get, the less precise they are.

In fixed point you have a certain number of digits, and a decimal point fixed in place like on a tax form: 001234.56. No matter how small or large the number, you can always write down increments of 0.01, whether it’s 000000.01 or 999999.99.

Floating point, meanwhile, is basically scientific notation. If you have 1.23e-4 (0.000123), you can increment by a millionth to get 1.24e-4. However, if you have 1.23e4 (12300), you can’t add less than 100 unless you reserve more space for more digits.

We can see this effect in practice in any language that supports floating point, such as Haskell:

> truncate (16777216 - 1 :: Float)
> truncate (16777216 + 1 :: Float)

Subtracting 1 gives us the decremented number, but adding 1 had no effect with floating point math! bc, with its arbitrary precision fixed points, would instead correctly give us 16777217! This is clearly unacceptable!

Floating point in bc

The problem with the bc solution is, in other words, that the math is too correct. Floating point math always introduces and accumulates rounding errors in ways that are hard to predict. Fixed point doesn’t, and therefore we need to find a way to artificially introduce the same type of inaccuracies! We can do this by rounding a number to a N significant bits, where N = 24 for float and 52 for double. Here is some bc code for that:


define trunc(x) {
  auto old, tmp
  old=scale; scale=0; tmp=x/1; scale=old
  return tmp
define fp(bits, x) {
  auto i
  if (x < 0) return -fp(bits, -x);
  if (x == 0) return 0;
  while (x < 1) { x*=2; i+=1; }
  while (x >= 2) { x/=2; i-=1; }
  return trunc(x * 2^bits + 0.5) / 2^(i)

define float(x) { return fp(24, x); }
define double(x) { return fp(52, x); }
define test(x) {
  print "Float:  ", float(x), "\n"
  print "Double: ", double(x), "\n"

With this file named fp, we can try it out:

$ bc -ql fp <<< "22/7"

$ bc -ql fp <<< "float(22/7)"

The first number is correct to 30 decimals. Yuck! However, with our floating point simulator applied, we get the desired floating point style errors after ~7 decimals!

Let's write a similar program for doing the same thing but with actual floating point, printing them out up to 30 decimals as well:

{-# LANGUAGE RankNTypes #-}
import Control.Monad
import Data.Number.CReal
import System.Environment

main = do
    input <- liftM head getArgs
    putStrLn . ("Float:  " ++) $ showNumber (read input :: Float)
    putStrLn . ("Double: " ++) $ showNumber (read input :: Double)
    showNumber :: forall a. Real a => a -> String
    showNumber = showCReal 30 . realToFrac

Here's a comparison of the two:

$ bc -ql fp <<< "x=test(1000000001.3)"
Float:  1000000000.000000000000000000000000000000
Double: 1000000001.299999952316284179687500000000

$ ./fptest 1000000001.3
Float:  1000000000.0
Double: 1000000001.2999999523162841796875

Due to differences in rounding and/or off-by-one bugs, they're not always identical like here, but the error bars are similar.

Now we can finally start doing floating point math in bc!

dd is not a disk writing tool

If you’ve ever used dd, you’ve probably used it to read or write disk images:

# Write myfile.iso to a USB drive
dd if=myfile.iso of=/dev/sdb bs=1M

Usage of dd in this context is so pervasive that it’s being hailed as the magic gatekeeper of raw devices. Want to read from a raw device? Use dd. Want to write to a raw device? Use dd.

This belief can make simple tasks complicated. How do you combine dd with gzip? How do you use pv if the source is raw device? How do you dd over ssh?

The fact of the matter is, dd is not a disk writing tool. Neither “d” is for “disk”, “drive” or “device”. It does not support “low level” reading or writing. It has no special dominion over any kind of device whatsoever.

dd just reads and writes file.

On UNIX, the adage goes, everything is a file. This includes raw disks. Since raw disks are files, and dd can copy files, dd be used to copy raw disks.

But do you know what else can read and write files? Everything:

# Write myfile.iso to a USB drive
cp myfile.iso /dev/sdb

# Rip a cdrom to a .iso file
cat /dev/cdrom > myfile.iso

# Create a gzipped image
gzip -9 < /dev/sdb > /tmp/myimage.gz

However, this does not mean that dd is useless! The reason why people started using it in the first place is that it does exactly what it’s told, no more and no less.

If an alias specifies -a, cp might try to create a new block device rather than a copy of the file data. If using gzip without redirection, it may try to be helpful and skip the file for not being regular. Neither of them will write out a reassuring status during or after a copy.

dd, meanwhile, has one job*: copy data from one place to another. It doesn’t care about files, safeguards or user convenience. It will not try to second guess your intent, based on trailing slashes or types of files. When this is no longer a convenience, like when combining it with other tools that already read and write files, one should not feel guilty for leaving dd out entirely.

This is not to say I think dd is overrated! Au contraire! It’s one of my favorite Unix tools!

dd is the swiss army knife of the open, read, write and seek syscalls. It’s unique in its ability to issue seeks and reads of specific lengths, which enables a whole world of shell scripts that have no business being shell scripts. Want to simulate a lseek+execve? Use dd! Want to open a file with O_SYNC? Use dd! Want to read groups of three byte pixels from a PPM file? Use dd!

It’s a flexible, unique and useful tool, and I love it. My only issue is that, far too often, this great tool is being relegated to and inappropriately hailed for its most generic and least interesting capability: simply copying a file from start to finish.

* dd actually has two jobs: Convert and Copy. A post on comp.unix.misc (incorrectly) claimed that the intended name “cc” was taken by the C compiler, so the letters were shifted in the same way we ended up with a Window system called X. A more likely explanation is given in that thread as pointed out by PaweĊ‚ and Bruce in the comments: the name, syntax and purpose is almost identical to the JCL “Dataset Definition” command found in 1960s IBM mainframes.

I’m not paranoid, you’re just foolish

Remember this dialog from when you installed your distro?

Fake dialog saying "In the event of physical theft, grant perpetrators access to" with options for "My browsing history, My email and social media, My photos and documents, and similar". All boxes are checked by default.

Most distros have a step like this. If you don’t immediately recognize it, you might have used a different installer with different wording. For example, the graphical Ubuntu installer calls it “Encrypt the new Ubuntu installation for security”, while the text installer even more opaquely calls it “Use entire disk and set up encrypted LVM”.

Somehow, some people have gotten it into their heads that not granting the new owner access to all your data after they steal your computer is a sign of paranoia. It’s 2015, and there actually exists people who have information based jobs and spend half their lives online, who not only think disk encryption is unnecessary but that it’s a sign you’re doing something illegal.

I have no idea what kind of poorly written crime dramas or irrational prime ministers they get this ridiculous notion from.

An office desk covered in broken glass after a break-in.
The last time my laptop was stolen from a locked office building.

Here’s a photo from 2012, when my company laptop was taken from a locked office with an alarm system.

Was this the inevitable FBI raid I was expecting and encrypted my drive to thwart?

Or was it a junkie stealing an office computer from a company and user who, thanks to encryption, didn’t have to worry about online accounts, design documents, or the source code for their unreleased product?

Hundreds of thousands of computers are lost or stolen every year. I’m not paranoid for using disk encryption, you’re just foolish if you don’t.

Parameterized Color Cell Compression

I came across a quaint and adorable paper from SIGGRAPH’86: Two bit/pixel Full Color Encoding. It describes Color Cell Compression, an early ancestor of Adaptive Scalable Texture Compression which is all the rage these days.

Like ASTC, it offers a fixed 2 bits/pixel encoding for color images. However, the first of many d’awwws in this paper comes as early as the second line of the abstract, when it suggests that a fixed rate is useful not for the random access we covet for rendering today, but simply for doing local image updates!

The algorithm can compress a 640×480 image in just 11 seconds on a 3MHz VAX 11/750, and decompress it basically in real time. This means that it may allow full color video, unlike these impractical, newfangled transform based algorithms people are researching.

CCC actually works astonishingly well. Here’s our politically correct Lenna substitute:


The left half of the image is 24bpp, while the right is is 2bpp. Really the only way to tell is in the eyes, and I’m sure there’s an interesting, evolutionary explanation for that.

If we zoom in, we can get a sense of what’s going on:


The image is divided into 4×4 cells, and each cell is composed of only two different colors. In other words, the image is composed of 4×4 bitmaps with a two color palette, each chosen from an image-wide 8bit palette. A 4×4 bitmap would take up 16 bits, and two 8bit palette indices would take up 16 bits, for a total of 32 bits per 16 pixels — or 2 bits per pixel.

The pixels in each cell are divided into two groups based on luminosity, and each group gets its own color based on the average color in the group. One of the reasons this works really well, the author says, is because video is always filmed so that a change in chromaticity has an associated change in luminosity — otherwise on-screen features would be invisible to the folks at home who still have black&white TVs!

We now know enough to implement this lovely algorithm: find an 8bit palette covering the image, then for each cell of 4×4 pixels, divide the pixels into two groups based on whether their luminosity is over or under the cell average. Find the average color of each part, and find its closest match in the palette.

However, let’s experiment! Why limit ourselves to 4×4 cells with 2 colors each from a palette of 256? What would happen if we used 8×8 cells with 3 colors each from a palette of 512? That also comes out to around 2 bpp.

Parameterizing palette and cell size is easy, but how do we group pixels into k colors based on luminosity? Simple: instead of using the mean, we use k-means!

Here’s a colorful parrot in original truecolor on the left, CCC with 4×4 cells in the middle, and 32×32 cells (1.01 bpp) on the right. Popartsy!


Here’s what we get if we only allow eight colors per horizontal line. The color averaging effect is really pronounced:


And here’s 3 colors per 90×90 cell:

The best part about this paper is the discussion of applications. For 24fps video interlaced at 320×480, they say, you would need a transfer rate of 470 kb/s. Current microcomputers have a transfer rate of 625 kb/s, so this is well within the realm of possibility. Today’s standard 30 megabyte hard drives could therefore store around 60 seconds of animation!

Apart from the obvious benefits of digital video like no copy degradation and multiple resolutions, you can save space when panning a scene by simply transmitting the edge in the direction of the pan!

You can even use CCC for electronic shopping. Since the images are so small and decoding so simple, you can make cheap terminals in great quantities, transmit images from a central location and provide accompanying audio commentary via cable!

In addition to one-to-many applications, you can have one-to-one electronic, image based communication. In just one minute on a 9600bps phone connection, a graphic arts shop can transmit a 640×480 image to clients for approval and comment.

You can even do many-to-many teleconferencing! Imagine the ability to show the speaker’s face, or a drawing they wish to present to the group on simple consumer hardware!

Truly, the future is amazing.

Here’s the JuicyPixel based Haskell implementation I used. It doesn’t actually encode the image, it just simulates the degradation. Ironically, this version is slower than the authors’ original, even though the hardware is five or six orders of magnitude faster!

Apart from the parameterization, I added two other improvements: Firstly, instead of the naive RGB based average suggestion in the paper, it uses the YCrCb average. Second, instead of choosing the palette from the original image, it chooses it from the averages. This doesn’t matter much for colorful photograph, but gives better results for images lacking gradients.