Monday, September 22, 2014

Stylus behaviour on Microsoft Surface 3 tablets

Note: The purpose of this post is basically just so we have a link when this comes up in future bugreports.

Some stylus devices have two buttons on the stylus, plus the tip itself which acts as a button. In the kernel, these two are forwarded to userspace as BTN_STYLUS and BTN_STYLUS2. Userspace then usually maps those two into right and middle click, depending on your configuration. The pen itself used BTN_TOOL_PEN when it goes into proximity.

The default stylus that comes with the Wacom Intuos Pro [1] has an eraser on the other side of the pen. If you turn the pen around it goes out of proximity and comes back in as BTN_TOOL_RUBBER. [2] In the wacom X driver we handle this accordingly, through two different devices available via the X Input Extension. For example the GIMP assigns different tools assigned to each device. [3]

In the HID spec there are a couple of different fields (In Range, Tip Switch, Barrel Switch, Eraser and Invert) that matter here. Barrel Switch is the stylus button, In Range and Tip Switch are proximity and "touching the surface". Invert signals which side of the pen points down, and Eraser is triggered when the eraser touches the surface. In Wacom tablets, Invert is always on when the eraser touches because that's how the pens designed.

Microsoft, in its {in}finite wisdom has decided to make the lower button an "eraser" button on its Surface 3 Pen. So what happens now is that once you press the button, In Range goes to zero, then to one in the next event, together with Invert. Eraser comes on once you touch the surface but curiously that also causes Invert to go off. Anyway, that's a low-level detail that will get handled. What matters to users is that on the press of that button, the pen goes virtually out of proximity, comes back in as eraser and then hooray, you can now use it as an eraser tool without having actually moved it. Of course, since the button controls the mode it doesn't actually work as button, you're left with the second button on the stylus only.

Now, the important thing here is: that's the behaviour you get if you have one of these devices. We could work around this in software by detecting the mode button, flipping bits here and there and trying to emulate a stylus button based on the mode switches. But we won't. The overlords have decreed and it's too much effort to hack around the intended behaviour for little gain.

[1] If marketing decides to rename products so that need a statement "Bamboo pen tablets are now Intuos. Intuos5 is now Intuos Pro." then you've probably screwed up.
[2] Isn't it nice to see some proper queen's English for a change? For those of you on the other side of some ocean: eraser.
[3] GIMP, rubber tool, I'm not making this up, seriously

Friday, September 19, 2014

pointer acceleration in libinput - a userstudy

This post is a summary, the full writeup with data is here (PDF). The texts are quite similar, so if you plan to read the paper you can skip this post.

Following pointer acceleration in libinput - an analysis, we decided to run an actual userstudy to gather some data on how our acceleration behaves, and - more importantly - test if a modified acceleration method is better.

We developed two new pointer acceleration methods (plus the one already in libinput). As explained previously, the pointer acceleration method is a function mapping input speed of a device into cursor speed in pixels. The faster one moves the mouse, the further the cursor moves per "mickey" (a 1-device unit movement). In a simplest example, input deltas of 1 may result in a 1 pixel movement, input deltas of 10 may result in a 30 pixel movement.

The three pointer acceleration methods used in this study were nicknamed:

A shortening of "smooth and simple", this method is used in libinput 0.5 as well as in the X.Org stack since ~2008.
a modification of 'smooth' with roughly the same profile, but the maximum acceleration is applied at a higher speed. This method was developed by Hans de Goede and very promising in personal testing.
a linear acceleration method with a roughly similar speed-to-acceleration profile as the first two. This method was developed to test if a simple function could achieve similar results, as the more complex "smooth" and "stretched" methods.
The input data expected by all three methods is in units/ms. Touchpad devices are normalised to 400 dpi, other devices are left as-is. It is impossible to detect in software what resolution a generic mouse supports, so any acceleration method differs between devices. This is intended by the manufacturer, high-resolution devices are sold as "faster" for this reason.

The three pointer acceleration methods
As the graph shows, the base profile is roughly identical and the main difference is how quickly the maximum acceleration factor is reached.

Study description

Central component was a tool built on libinput that displays a full-screen white window, with a round green target. Participants were prompted by GTK dialog boxes on the steps to take next. Otherwise the study was unsupervised and self-guided.

The task required participants to click on a round target with a radius of 15, 30 and 45 pixels. Targets were grouped, each "set" consisted of 15 targets of the same size. On a successful click within the target, a new target appeared on one out of 12 possible locations, arranged in a grid of 4x3 with grid points 300 pixels apart. The location of the target was randomly selected but was never on the same location twice in a row.

Screenshot of the study tool with the first target (size 45) visible.

Each participant was tested for two acceleration methods, each acceleration method had 6 sets of 15 targets (2 sets per target size, order randomised). The two acceleration methods were randomly selected on startup, throughout the study they were simply referred to as "first" and "second" acceleration method with no further detail provided. Acceleration changed after 6 sets (participants were informed about it), and on completion of all 12 sets participants had to fill out a questionnaire and upload the data.

Statistical concepts

A short foray into statistics to help explain the numbers below. This isn't a full statistics course, I'm just aiming to explain the various definitions used below.

The mean of a dataset is what many people call the average: all values added up divided by the number of values. As a statistical tool, the mean is easy to calculate but is greatly affected by outliers. For skewed datasets the median is be more helpful: the middle value of the data array (array[len/2]). The closer the mean and the median are together, the more symmetrical the distribution is.

The standard deviation (SD) describes how far the data points spread from the median. The smaller the SD, the closer together are the data points. The SD is also used to estimate causality vs randomly induced sampling errors. Generally, if the difference between two items is more than 2 standard deviations, there's a 95% confidence that this is a true effect, not just randomness (95% certainty is a widely accepted standard in this domain). That 95% directly maps to the p-value you may have seen in other studies. A p-value of less than 0.05 equals a less than 5% chance of random factors causing the data differences. That translates into "statistically significant".

The ANOVA method is a standard statistical tool for studies like ours. (we're using one-way ANOVA only here, Wikipedia has an example here). If multiple sets of samples differ in only a single factor (e.g. pointer acceleration method), we start with the so-called Null-Hypothesis of "the factor has no influence, all results are the same on average". Our goal is to reject that hypothesis so we can say that the factor did actually change things. If we cannot reject the Null-Hypothesis, either our factor didn't change anything or the results are caused by random influences. The tools for ANOVA compare the mean value within each sets to the mean value differences across the sets and spit out a p-value. As above, a p-value less than 0.05 means greater than 95% confidence that the Null-Hypothesis can be rejected, i.e. we can say our factor did cause those differences.

One peculiarity of ANOVA is that the sample sets have to be the same size. This affects our samples, more see below.

Study participants

An email was sent to three Red Hat-internal lists with a link to the study description. One list was a specific developer list, the other two list were generic lists. As Red Hat employees, participants are expected to be familiar with Linux-based operating systems and the majority is more technical than the average user. The data collected does not make it possible to identify who took part in the study beyond the information provided in the questionnaire.

44 participants submitted results, 7 left-handed, 37 right-handed (no ambidextrous option was provded in the questionnaire). Gender distribution was 38 male, 6 female. Mean age was 33.3 years (SD 6.7) and participants had an mean 21.2 years of experience with mouse-like input devices (SD 4.9) and used those devices an average 58.1 hours per week (SD 20.0).

As all participants are familiar with Linux systems and thus exposed to the smooth acceleration method on their workstations, we expect a bias towards the smooth acceleration method.

Study data

Data was manually checked and verified, three result files were discarded for bugs or as extreme outliers, leaving us with 41 data files. The distribution of methods in these sets was: 27 for smooth, 25 for stretched and 30 for linear.

The base measurement was the so-called "Index of Difficulty" (ID), the number obtained by distance-to-target/width-of-target. This index gives an indication on how difficult it is to hit the target; a large target very close is easier to hit than a small target that is some distance away.

Illustration of the Index of Difficulty for a target.

In hindsight, the study was not ideally suited for evaluation based on ID. The targets were aligned on a grid and the ID based on the pointer position was very variable. As is visible in the graph below, there are few clear dividing lines to categorise the targets based on their ID. For the evaluation the targets were grouped into specific ID groups: ID < 4.2, ID < 8.4, ID < 12.9, ID < 16.9 < ID < 25 and ID > 25. The numbers were selected simply because there are clear gaps between the ID clusters. This division results in uneven group sizes, (I ran the same calculations with different group numbers, it does not have any real impact on the results.)

ID for each target with the divider lines shown
The top ID was 36.44, corresponding to a 15px radius target 1093 pixels away, the lowest ID was 1.45, corresponding to a 45px radius target 130 pixels away.

Number of targets per ID group
As said above, ANOVA requires equal-sized sample sets. ANOVA was performed separately between the methods (i.e. smooth vs stretched, then smooth vs linear, then stretched vs linear). Before each analysis, the two data arrays were cut to be of equal length. For example, comparing smooth and stretched in the ID max group shortened the smooth dataset to 150 elements. The order of targets was randomised.

Study Results

The following factors were analysed:

  • Time to click on target
  • Movement efficiency
  • Overshoot

Time to click on target

Time to click on a target was measured as the time between displaying the target and clicking on it. This does not take reaction time into account, but there is no reliable way of measuring reaction time in this setup.

Mean time to click on target

As is visible, an increasing ID increases the time-to-click. On a quick glance, we can see that the smooth method is slower than the other two in most ID groups, with linear and stretched being fairly close together. However, the differences are only statistically significant in the following cases:

  • ID 8.4: linear is faster than smooth and stretched
  • ID 12.9: linear and stretched are faster than smooth
  • ID 25: linear and stretched are faster than smooth
In all other combinations, there is no statistically significant difference between the three methods, but overall a slight advantage for the two methods stretched and linear.

Efficiency of movement

The most efficient path from the cursor position to the target is a straight line. However, most movements do not follow that straight line for a number of reasons. One of these reasons is basic anatomy - it is really hard to move a mouse in a straight line due to the rotary action of our wrists. Other reasons may be deficiencies in the pointer acceleration method. To measure the efficiency, we calculated the distance to the target (i.e. the straight line) and compared that to all the deltas added up to the total movement. Note that the distance is to the center of the target, whereas the actual movement may be to any point in the target. So for short distances and large targets, there is a chance that a movement may be less than the distance to the target.

Straight distance to target vs. movement path shows the efficiency of movement.

The efficiency was calculated as movement-path/distance, then normalised to a percent value. A value of 10 thus means the movement path was 10% longer than the straight line to the target centre).

Extra distance covered
Stretched seems to perform better than smooth and linear in all but one ID group and smooth performing worse than linear in all but ID group 4.2. Looking at the actual values however shows that the large standard deviation prevents statistical significance. The differences are only statistically significant in the following cases:
  • ID 4.2: stretched is more efficient than smooth and linear
In all other combinations, there is no statistically significant difference between the three methods.


Somewhat similar to the efficiency of movement, the overshoot is the distance the pointer has moved past the target. It was calculated by drawing a line perpendicular to the direct path from the pointer position to the target's far side. If the pointer moves past this line, the user has overshot the target. The maximum distance between the line and the pointer shows how much the user has overshot the target.

Illustration of pointer overshooting the target.
The red line shows the amount the pointer has overshot the target.
Overshoot was calculated in pixels, as % of the distance and as % of the actual path taken. Unsurprisingly, the graphs look rather the same so I'll only put one up here.

Overshoot in pixels by ID group
As the ID increases, the amount of overshooting increases too. Again the three pointer acceleration methods are largely the same, though linear seems to be slightly less affected by overshoot than smooth and stretched. The differences are only statistically significant in the following cases:
  • ID 4.2: if measured as percentage of distance, stretched has less overshoot than linear.
  • ID 8.4: if measured as percentage of movement path, linear has less overshoot than smooth.
  • ID 16.8: if measured as percentage of distance, stretched and linear have less overshoot than smooth.
  • ID 16.8: if measured as percentage of distance, linear has less overshoot than smooth.
  • ID 16.8: if measured in pixels, linear has less overshoot than smooth.
In all other combinations, there is no statistically significant difference between the three methods.


In summary, there is not a lot of difference between the three methods, though smooth has no significant advantage in any of the measurements. The race between stretched and linear is mostly undecided.

Questionnaire results

The above data was objectively measured. Equally important is the subjective feel of each acceleration method. At the end of the study, the following 14 questions were asked of each participant, with answer ranges in a 5-point Likert scale, ranging from "Strongly Disagree" to "Strongly Agree".

  1. The first acceleration method felt natural
  2. The first acceleration method allowed for precise pointer control
  3. The first acceleration method allowed for fast pointer movement
  4. The first acceleration method made it easy to hit the targets
  5. I would prefer the first acceleration method to be faster
  6. I would prefer the first acceleration method to be slower
  7. The second acceleration method felt natural
  8. The second acceleration method allowed for precise pointer control
  9. The second acceleration method allowed for fast pointer movement
  10. The second acceleration method made it easy to hit the targets
  11. I would prefer the second acceleration method to be faster
  12. I would prefer the second acceleration method to be slower
  13. The two acceleration methods felt different
  14. The first acceleration method was preferable over the second
The figure below shows that comparatively few "strongly agree" and "strongly disagree" answers were given, hinting that the differences between the methods were small.

Distribution of answers in the questionnaire
Looking at statistical significance, the questionnaire didn't really provide anything of value. Not even the question "The two acceleration methods felt different" provided any answers, and the question "The first acceleration method was preferable over the second" was likewise inconclusive. So the summary of the questionnaire is pretty much: on the whole none of the methods stood out as better or worse.

Likert frequencies for the question of which method is preferable


Subjective data was inconclusive, but the objective data goes slightly in favour of linear and stretched over the current smooth method. We didn't have enough sample sets to analyse separately for each device type, so from a maintainer's point of view the vote goes to linear. It allows replacing a rather complicated pointer acceleration method with 3 lines of code.

Thursday, July 10, 2014

pointer acceleration in libinput - an analysis

Following Christian's Wayland in Fedora Update post, and after Hans fixed the touchpad acceleration, I've been playing with pointer acceleration in libinput a bit. The main focus was not yet on changing it but rather on figuring out what we actually do and where the room for improvement is. There's a tool in my (rather messy) github wip/ptraccel-work branch to re-generate the graphs below.

This was triggered by a simple plan: I want a configuration interface in libinput that provides a sliding scale from -1 to 1 to adjust a device's virtual speed from slowest to fastest, with 0 being the default for that device. A user should not have to worry about the accel mechanism itself, which may be different for any given device, all they need to know is that the setting -0.5 means "halfway between default and 'holy cow this moves like molasses!'". The utopia is of course that for any given acceleration setting, every device feels equally fast (or slow). In order to do that, I needed the right knobs to tweak.

The code we currently have in libinput is pretty much 1:1 what's used in the X server. The X server sports a lot more configuration options, but what we have in libinput 0.4.0 is essentially what the default acceleration settings are in X. Armed with the knowledge that any #define is a potential knob for configuration I went to investigate. There are two defines that are labelled as adjustible parameters:

  • DEFAULT_THRESHOLD, set to 0.4
But what do they mean, exactly? And what exactly does a value of 0.4 represent?
[side-note: threshold was 4 until I took the constant multiplier out, it's now 0.4 upstream and all the graphs represent that.]

Pointer acceleration is nothing more than mapping some input data to some potentially faster output data. How much faster depends on how fast the device moves, and to get there one usually needs a couple of steps. The trick of course is to make it predictable, so that despite the acceleration, your brain thinks that the visible cursor is an extension of your hand at all speeds.

Let's look at a high-level outline of our pointer acceleration code:

  • calculate the velocity of the current movement
  • use that velocity to calculate the acceleration factor
  • apply accel to dx/dy
  • smoothen out the dx/dy to avoid abrupt changes between two events

Calculating pointer speed

We don't just use dx/dy as values, rather, we use the pointer velocity. There's a simple reason for that: dx/dy depends on the device's poll rate (or interrupt frequency). A device that polls twice as often sends half the dx/dy values in each event for the same physical speed.

Calculating the velocity is easy: divide dx/dy by the delta time. We use a set of "trackers" that store previous dx/dy values with their timestamp. As long as we get movement in the same cardinal direction, we take those into account. So if we have 5 events in direction NE, the speed is averaged over those 5 events, smoothing out abrupt speed changes.

The acceleration function

The speed we just calculated is passed to the acceleration function to calculate an acceleration factor.

Figure 1: Mapping of velocity in unit/ms to acceleration factor (unitless). X axes here are labelled in units/ms and mm/s.
This function is the only place where DEFAULT_THRESHOLD/DEFAULT_ACCELERATION are used, but they mostly just stretch the graph. The shape stays the same.

The output of this function is a unit-less acceleration factor that is applied to dx/dy. A factor of 1 means leaving dx/dy untouched, 0.5 is half-speed, 2 is double-speed.

Let's look at the graph for the accel factor output (red): for very slow speeds we have an acceleration factor < 1.0, i.e. we're slowing things down. There is a distinct plateau up to the threshold of 0.4, after that it shoots up to roughly a factor of 1.6 where it flattens out a bit until we hit the max acceleration factor

Now we can also put units to the two defaults: Threshold is clearly in units/ms, and the acceleration factor is simply a maximum. Whether those are mentally easy to map is a different question.

We don't use the output of the function as-is, rather we smooth it out using the Simpson's rule. The second (green) curve shows the accel factor after the smoothing took effect. This is a contrived example, the tool to generate this data simply increased the velocity, hence this particular line. For more random data, see Figure 2.

Figure 2: Mapping of velocity in unit/ms to acceleration factor (unitless) for a random data set. X axes here are labelled in units/ms and mm/s.
For the data set, I recorded the velocity from libinput while using Firefox a bit.

The smoothing takes history into account, so the data points we get depend on the usage. In this data set (and others I tested) we see that the majority of the points still lie on or close to the pure function, apparently the delta doesn't matter that much. Nonetheless, there are a few points that suggest that the smoothing does take effect in some cases.

It's important to note that this is already the second smoothing to take effect - remember that the velocity (may) average over multiple events and thus smoothens the input data. However, the two smoothing effects somewhat complement each other: velocity smoothing only happens when the pointer moves consistently without much change, the Simpson's smoothing effect is most pronounced when the pointer moves erratically.

Ok, now we have the basic function, let's look at the effect.

Pointer speed mappings

Figure 3: Mapping raw unaccelerated dx to accelerated dx, in mm/s assuming a constant pysical device resolution of 400 dpi that sends events at 125Hz. dx range mapped is 0..127
The graph was produced by sending 30 events with the same constant speed, then dividing by the number of events to reduce any effects tracker feeding has at the initial couple of events.

The two lines show the actual output speed in mm/s and the gain in mm/s, i.e. (output speed - input speed). We can see that the little nook where the threshold kicks in and after the acceleration is linear. Look at Figure 1 again: the linear acceleration is caused by the acceleration factor maxing out quickly.

Most of this graph is theoretical only though. On your average mouse you don't usually get a delta greater than 10 or 15 and this graph covers the theoretical range to 127. So you'd only ever be seeing the effect of up to ~120 mm/s. So a more realistic view of the graph is:

Figure 4: Mapping raw unaccelerated dx to accelerated dx, see Figure 3 for details. Zoomed in to a max of 120 mm/s (15 dx/event).
Same data as Figure 3, but zoomed to the realistic range. We go from a linear speed increase (no acceleration) to a quick bump once the threshold is hit and from then on to a linear speed increase once the maximum acceleration is hit.

And to verify, the ratio of output speed : input speed:

Figure 5: Mapping of the unit-less gain of raw unaccelerated dx to accelerated dx, i.e. the ratio of accelerated:unaccelerated.

Looks pretty much exactly like the pure acceleration function, which is to be expected. What's important here though is that this is the effective speed, not some mathematical abstraction. And it shows one limitation: we go from 0 to full acceleration within really small window.

Again, this is the full theoretical range, the more realistic range is:

Figure 6: Mapping of the unit-less gain of raw unaccelerated dx to accelerated dx, i.e. the ratio of accelerated:unaccelerated. Zoomed in to a max of 120 mm/s (15 dx/event).
Same data as Figure 5, just zoomed in to a maximum of 120 mm/s. If we assume that 15 dx/event is roughly the maximum you can reach with a mouse you'll see that we've reached maximum acceleration at a third of the maximum speed and the window where we have adaptive acceleration is tiny.

Tweaking threshold/accel doesn't do that much. Below are the two graphs representing the default (threshold=0.4, accel=2), a doubled threshold (threshold=0.8, accel=2) and a doubled acceleration (threshold=0.4, accel=4).

Figure 6: Mapping raw unaccelerated dx to accelerated dx, see Figure 3 for details. Zoomed in to a max of 120 mm/s (15 dx/event). Graphs represent thresholds:accel settings of 0.4:2, 0.8:2, 0.4:4.
Figure 7: Mapping of the unit-less gain of raw unaccelerated dx to accelerated dx, see Figure 5 for details. Zoomed in to a max of 120 t0.4 a4 (15 dx/event). Graphs represent thresholds:accel settings of 0.4:2, 0.8:2, 0.4:4.
Doubling either setting just moves the adaptive window around, it doesn't change that much in the grand scheme of things.

Now, of course these were all fairly simple examples with constant speed, etc. Let's look at a diagram of what is essentially random movement, me clicking around in Firefox for a bit:

Figure 8: Mapping raw unaccelerated dx to accelerated dx on a fixed random data set.
And the zoomed-in version of this:
Figure 9: Mapping raw unaccelerated dx to accelerated dx on a fixed random data set, zoomed in to events 450-550 of that set.
This is more-or-less random movement reflecting some real-world usage. What I find interesting is that it's very hard to see any areas where smoothing takes visible effect. the accelerated curve largely looks like a stretched input curve. tbh I'm not sure what I should've expected here and how to read that, pointer acceleration data in real-world usage is notoriously hard to visualize.


So in summary: I think there is room for improvement. We have no acceleration up to the threshold, then we accelerate within too small a window. Acceleration stops adjusting to the speed soon. This makes us lose precision and small speed changes are punished quickly.

Increasing the threshold or the acceleration factor doesn't do that much. Any increase in acceleration makes the mouse faster but the adaptive window stays small. Any increase in threshold makes the acceleration kick in later, but the adaptive window stays small.

We've already merged a number of fixes into libinput, but some more work is needed. I think that to get a good pointer acceleration we need to get a larger adaptive window [Citation needed]. We're currently working on that (and figuring out how to evaluate whatever changes we come up with).

A word on units

The biggest issue I was struggling with when trying to understand the code was that of units. The code didn't document used units anywhere but it turns out that everything was either in device units ("mickeys"), device units/ms or (in the case of the acceleration factors) was unitless.

Device units are unfortunately a pretty useless base entity, only slightly more precise than using the length of a piece of string. A device unit depends on the device resolution and of course that differs between devices. An average USB mouse tends to have 400 dpi (15.75 units/mm) but it's common to have 800 dpi, 1000 dpi and gaming mice go up to 8200dpi. A touchpad can have resolutions of 1092 dpi (43 u/mm), 3277 dpi (129 u/mm), etc. and may even have different resolutions for x and y.

This explains why until commit e874d09b4 the touchpad felt slower than a "normal" mouse. We scaled to a magic constant of 10 units/mm, before hitting the pointer acceleration code. Now, as said above the mouse would likely have a resolution of 15.75 units/mm, making it roughly 50% faster. The acceleration would kick in earlier on the mouse, giving the touchpad and the mouse not only different speeds but a different feel altogether.

Unfortunately, there is not much we can do about mice feeling different depending on the resolution. To my knowledge there is no way to query the resolution on a device. But for absolute devices that need pointer acceleration (i.e. touchpads) we can normalize to a fake resolution of 400 dpi and base the acceleration code on that. This provides the same feel on the mouse and the touchpad, as much as that is possible anyway.

Wednesday, May 21, 2014

configure fails with "No package 'foo' found" - and how to fix it

A common error when building from source is something like the error below:

configure: error: Package requirements (foo) were not met:

No package 'foo' found

Consider adjusting the PKG_CONFIG_PATH environment variable if you
installed software in a non-standard prefix.
Seeing that can be quite discouraging, but luckily, in many cases it's not too difficult to fix. As usual, there are many ways to get to a successful result, I'll describe what I consider the simplest.

What does it mean?

pkg-config is a tool that provides compiler flags, library dependencies and a couple of other things to correctly link to external libraries. For more details on it see Dan Nicholson's guide. If a build system requires a package foo, pkg-config searches for a file foo.pc in the following directories: /usr/lib/pkgconfig, /usr/lib64/pkgconfig, /usr/share/pkgconfig, /usr/local/lib/pkgconfig, /usr/local/share/pkgconfig. The error message simply means pkg-config couldn't find the file and you need to install the matching package from your distribution or from source.

What package provides the foo.pc file?

In many cases the package is the development version of the package name. Try foo-devel (Fedora, RHEL, SuSE, ...) or foo-dev (Debian, Ubuntu, ...). yum provides a great shortcut to install any pkg-config dependency:

$> yum install "pkgconfig(foo)"
will automatically search and install the right package, including its dependencies.
apt-get requires a bit more effort:
$> apt-get install apt-file
$> apt-file update
$> apt-file search --package-only foo.pc
$> apt-get install foo-dev
For those running Arch and pacman, the sequence is:
$> pacman -S pkgfile
$> pkgfile -u
$> pkgfile foo.pc
$> pacman -S extra/foo
zypper is the same as yum:
$> zypper in 'pkgconfig(foo)'
Once that's done you can re-run configure and see if all dependencies have been met. If more packages are missing, follow the same process for the next file.

Any users of other distributions - let me know how to do this on yours and I'll update the post

Where does the dependency come from?

In most projects using autotools the dependency is specified in the file and looks roughly like one of these:

PKG_CHECK_MODULES(DEPENDENCIES, foo [bar >= 1.4] banana)
The first argument is simple a name that is used in the build system, you can ingore it. After the comma is the list of space-separated dependencies. In this case this means we need foo.pc, bar.pc and banana.pc, and more specifically we need a bar.pc that is equal or newer to version 1.4 of the package. To install all three follow the above steps and you're good.

My version is wrong!

It's not uncommon to see the following error after installing the right package:

configure: error: Package requirements (foo >= 1.9) were not met:

Requested 'foo >= 1.9' but version of foo is 1.8

Consider adjusting the PKG_CONFIG_PATH environment variable if you
installed software in a non-standard prefix.
Now you're stuck and you have a problem. What this means is that the package version your distribution provides is not new enough to build your software. This is where the simple solutions and and it all gets a bit more complicated - with more potential errors. Unless you are willing to go into the deep end, I recommend moving on and accepting that you can't have the newest bits on an older distribution. Because now you have to build the dependencies from source and that may then require to build their dependencies from source and before you know you've built 30 packages. If you're willing read on, otherwise - sorry, you won't be able to run your software today.

Manually installing dependencies

Now you're in the deep end, so be aware that you may see more complicated errors in the process. First of all you need to figure out where to get the source from. I'll now use cairo as example instead of foo so you see actual data. On rpm-based distributions like Fedora run:

$> yum info cairo-devel    
Loaded plugins: auto-update-debuginfo, langpacks
Skipping unreadable repository '///etc/yum.repos.d/SpiderOak-stable.repo'
Installed Packages
Name        : cairo-devel
Arch        : x86_64
Version     : 1.13.1
Release     : 0.1.git337ab1f.fc20
Size        : 2.4 M
Repo        : installed
From repo   : fedora
Summary     : Development files for cairo
URL         :
License     : LGPLv2 or MPLv1.1
Description : Cairo is a 2D graphics library designed to provide high-quality
            : display and print output.
            : This package contains libraries, header files and developer
            : documentation needed for developing software which uses the cairo
            : graphics library.
The important field here is the URL line - got to that and you'll find the source tarballs. That should be true for most projects but you may need to google for the package name and hope. Search for the tarball with the right version number and download it. On Debian and related distributions, cairo is provided by the libcairo2-dev package. Run apt-cache show on that package:
$> apt-cache show libcairo2-dev
Package: libcairo2-dev
Source: cairo
Version: 1.12.2-3
Installed-Size: 2766
Maintainer: Dave Beckett 
Architecture: amd64
Provides: libcairo-dev
Depends: libcairo2 (= 1.12.2-3), libcairo-gobject2 (= 1.12.2-3),[...]
Suggests: libcairo2-doc
Description-en: Development files for the Cairo 2D graphics library
 Cairo is a multi-platform library providing anti-aliased
 vector-based rendering for multiple target backends.
 This package contains the development libraries, header files needed by
 programs that want to compile with Cairo.
Description-md5: 07fe86d11452aa2efc887db335b46f58
Tag: devel::library, role::devel-lib, uitoolkit::gtk
Section: libdevel
Priority: optional
Filename: pool/main/c/cairo/libcairo2-dev_1.12.2-3_amd64.deb
Size: 1160286
MD5sum: e29852ae8e8e5510b00b13dbc201ce66
SHA1: 2ed3534d02c01b8d10b13748c3a02820d10962cf
SHA256: a6099cfbcc6bd891e347dd9abc57b7f137e0fd619deaff39606fd58f0cc60d27
In this case it's the Homepage line that matters, but the process of downloading tarballs is the same as above. For Arch users, the interesting line is URL as well:
$> pacman -Si cairo | grep URL
Repository      : extra
Name            : cairo
Version         : 1.12.16-1
Description     : Cairo vector graphics library
Architecture    : x86_64
URL             :
Licenses        : LGPL MPL
zypper (Tizen, SailfishOS, Meego and others) doesn't have an interface for this, but you can run rpm on the package that you installed.
$> rpm -qi cairo-devel
Name        : cairo-devel
URL         :
This command would obviously work on other rpm-based distributions too (Fedora, RHEL, ...). Unlike yum, it does require the package to be installed but by the time you get here you've already installed it anyway :)

Now to the complicated bit: In most cases, you shouldn't install the new version over the system version because you may break other things. You're better off installing the dependency into a custom folder ("prefix") and point pkg-config to it. So let's say you downloaded the cairo tarball, now you need to run:

$> mkdir $HOME/dependencies/
$> tar xf cairo-someversion.tar.xz
$> cd cairo-someversion
$> autoreconf -ivf
$> ./configure --prefix=$HOME/dependencies
$> make && make install
$> export PKG_CONFIG_PATH=$HOME/dependencies/lib/pkgconfig:$HOME/dependencies/share/pkgconfig
# now go back to original project and run configure again
So you create a directory called dependencies, install cairo there. This will install cairo.pc as $HOME/dependencies/lib/cairo.pc. Now all you need to do is tell pkg-config that you want it to look there as well - so you set PKG_CONFIG_PATH. If you re-run configure in the original project, pkg-config will find the new version and configure should succeed. If you have multiple packages that all require a newer version, install them into the same path and you only need to set PKG_CONFIG_PATH once. Remember you need to set PKG_CONFIG_PATH in the same shell as you are running configure from.

If you keep seeing the version error the most common problem is that PKG_CONFIG_PATH isn't set in your shell, or doesn't point to the new cairo.pc file. A simple way to check is:

$> pkg-config --modversion cairo
Is the version number the one you installed or the system one? If it is the system one, you have a typo in PKG_CONFIG_PATH, just re-set it. If it still doesn't work do this:
$> cat $HOME/dependencies/lib/pkgconfig/cairo.pc

Name: cairo
Description: Multi-platform 2D graphics library
Version: 1.13.1

Requires.private:   gobject-2.0 glib-2.0 >= 2.14 [...]
Libs: -L${libdir} -lcairo
Libs.private:            -lz -lz    -lGL        
Cflags: -I${includedir}/cairo
If the Version field matches what pkg-config returns, then you're set. If not, keep adjusting PKG_CONFIG_PATH until it works. There is a rare case where the Version field in the installed library doesn't match what the tarball said. That's a defective tarball and you should report this to the project, but don't worry, this hardly ever happens. In almost all cases, the cause is simply PKG_CONFIG_PATH not being set correctly. Keep trying :)

Let's assume you've managed to build the dependencies and want to run the newly built project. The only problem is: because you built against a newer library than the one on your system, you need to point it to use the new libraries.

$> export LD_LIBRARY_PATH=$HOME/dependencies/lib
and now you can, in the same shell, run your project.

Good luck!

Monday, May 19, 2014

Introducing tellme, a text-to-speech notifier

I've been hacking on a little tool the last couple of days and I think it's ready for others to look at it and provide suggestions to improve it. Or possibly even tell me that it already exists, in which case I'll save a lot of time. "tellme" is a simple tool that uses text-to-speech to let me know when a command finished. This is useful for commands that run for a couple of minutes - you can go off read something and the computer tells you when it's done instead of you polling every couple of seconds to check. A simple example:

tellme sudo yum update
runs yum update, and eventually says in a beautiful totally-not-computer-sounding voice "finished yum update successfully".

That was the first incarnation which was a shell script, I've started putting a few more features in (now in Python) and it now supports per-command configuration and a couple of other semi-smart things. For example:

whot@yabbi:~/xorg/xserver/Xi> tellme make
eventually says "finished xserver make successfully". With the default make configuration, it runs up the tree to search for a .git directory and then uses that as basename for the voice output. Which is useful when you rebuild all drivers simultaneously and the box tells you which ones finished and whether there was an error.

I put it up on github: It's still quite rough, but workable. Have a play with it and feel free to send me suggestions.

Wednesday, April 30, 2014

T440 touchpads, the story continues

I've just updated the post about X.Org synaptics support for the Lenovo T440, T540, X240, Helix, Yoga, X1 Carbon. For those following my blog, here is a rough diff of the updates:

  • All touchpads in this series need a kernel quirk to fix the min/max ranges. It's like a happy meal toy, make sure you collect them all.
  • A new kernel evdev input prop INPUT_PROP_TOPBUTTONPAD is available in 3.15. It marks the devices that require top software buttons. It will be backported to stable.
  • A new option was added HasSecondarySoftButtons was added to the synaptics driver. It is automatically set if INPUT_PROP_TOPBUTTONPAD is set and if set, the driver parses the SecondarySoftButtonAreas option and honours the values in it.
  • If you have the kernel min/max fixes and the new property, don't bother with DMI matching. Provide a xorg.conf.d snippet that unconditionally merges the SecondarySoftButtonAreas and rely on the driver for parsing it when appropriate

Thursday, March 27, 2014

Viewing the Xorg.log with journalctl

Those running Fedora Rawhide or GNOME 3.12 may have noticed that there is no Xorg.log file anymore. This is intentional, gdm now starts the X server so that it writes the log to the systemd journal. Update 29 Mar 2014: The X server itself has no capabilities for logging to the jornal yet, but no changes to the X server were needed anyway. gdm merely starts the server with a /dev/null logfile and redirects stdin/stderr to the journal.

Thus, to get the log file use journalctl, not vim, cat, less, notepad or whatever your $PAGER was before.

This leaves us with the following commands.

journalctl -e /usr/bin/Xorg 
Which would conveniently show something like this:
Mar 25 10:48:41 yabbi Xorg[5438]: (II) UnloadModule: "wacom"
Mar 25 10:48:41 yabbi Xorg[5438]: (II) evdev: Lenovo Optical USB Mouse: Close
Mar 25 10:48:41 yabbi Xorg[5438]: (II) UnloadModule: "evdev"
Mar 25 10:48:41 yabbi Xorg[5438]: (II) evdev: Integrated Camera: Close
Mar 25 10:48:41 yabbi Xorg[5438]: (II) UnloadModule: "evdev"
Mar 25 10:48:41 yabbi Xorg[5438]: (II) evdev: Sleep Button: Close
Mar 25 10:48:41 yabbi Xorg[5438]: (II) UnloadModule: "evdev"
Mar 25 10:48:41 yabbi Xorg[5438]: (II) evdev: Video Bus: Close
Mar 25 10:48:41 yabbi Xorg[5438]: (II) UnloadModule: "evdev"
Mar 25 10:48:41 yabbi Xorg[5438]: (II) evdev: Power Button: Close
Mar 25 10:48:41 yabbi Xorg[5438]: (II) UnloadModule: "evdev"
Mar 25 10:48:41 yabbi Xorg[5438]: (EE) Server terminated successfully (0). Closing log file.
The -e toggle jumps to the end and only shows 1000 lines, but that's usually enough. journalctl has a bunch more options described in the journalctl man page. Note the PID in square brackets though. You can easily limit the output to just that PID, which makes it ideal to attach to the log to a bug report.
journalctl /usr/bin/Xorg _PID=5438
Previously the server kept only a single backup log file around, so if you restarted twice after a crash, the log was gone. With the journal it's now easy to extract the log file from that crash five restarts ago. It's almost like the future is already here.