Today I got another complaint in a row of complaints of my Jabber contacts, arguing that they can’t send me messages although my account seems to be online in their buddy list. That happens when I put my notebook to sleep, this time I got informed by Micha.
Here are 3 steps to patch this problem, dealing with gajim-remote, PowerManagement-Utils and DBus.
This annoying events happens when I was online with my notebook and close the lid so the notebook goes sleeping. Unfortunately my Jabber client Gajim doesn’t notice that I’m going to disconnect and so the Jabber server isn’t informed about my absence. Due to connection instabilities the server waits some time of inactivity until it recognizes that there is really no more client before it tells all my friends I’m gone. During this time I appear online but messages are not able to reach my client, so they are lost in hell. That sucks, I know, and now I’ve reacted.
First of all I checked how to tell Gajim to disconnect via command line and found the tool
gajim-remote , it comes with Gajim itself. Here are some examples of using it:
Of course the manpage will give you more information.
Ok, so far, next task is to understand what is done when the lid is closed. The task to suspend or hilbernate is, at least in my case, done by
pm-utils (PowerManagement-Utils). It comes with some tools like
pm-hibernate and so on. To tell these tools to do something before respectively after suspending there is a directory in
/etc/pm/sleep.d . Here You can leave some script that look like those in
Here is a smart example now located in
/etc/pm/sleep.d/01users on my notebook, you can use it as skeleton:
Make it executable and give it a try. It checks for each logged-in user whether there is a
.awake in its
$HOME to execute it before suspending respectively after resuming.
Next step is telling Gajim to change its status. Unfortunately the
gajim-remote script is speaking to the running Gajim-instance via DBus. You may have heard about DBus, there are two main options of DBus buses: system- and session-bus. To speak to Gajim you use the session DBus and need the bus address. That is a problem, this address is acquired while your X-login, and you don’t know it from a remote session or if the system executes scripts while suspending. So if you just try to execute
gajim-remote change_status offline in your
.suspend you’ll get an error like
D-Bus is not present on this machine or python module is missing or
Failed to open connection to "session" message bus .
Your DBus session address within an X-session is set in your environment in
echo $DBUS_SESSION_BUS_ADDRESS ).
So what are your options to get this address for your
- You can export your
envto a file when you login (maybe automatically via
.xinitrc) to parse it
- All addresses are saved in
$HOME/.dbus/session-bus/, so try to find the right one..
- Get it from a process environment
The last possibility is of course the nicest one. So check if Gajim is running and extract the
/proc/GAJIM_PID/environ ! Here is how it can be done:
That’s it, great work! Save this file in
$HOME/.suspend and give it the right for execution. You can also write a similar script for
$HOME/.awake to reconnect to your Jabber server, but you eventually don’t want to reconnect each time you open the lid..
So the next time I close my laptops lid Gajim disconnects immediately! No annoyed friends anymore :P
This is about taking a screenshot of a JPanel to save the visible stuff as an image to disk.
Sometimes it may happen that you create a swing GUI with a panel to draw cool stuff. Here you can learn how to let the user take a screenshot of this graphic with a single click on a button.
First of all create such a JPanel and fill it with crazy graphics, then create a BufferedImage with the size of the panel and tell the panel to draw its content to this image instead of printing the content to the screen and, last but not least, save this image:
You see it’s very simple. Of course it’s also possible to create other types of image with ImageIO, like JPEG or GIF, but for more information take a look in the documentation. In a project that will be published you should think about using a JFileChooser instead of hard coding the name of the new image ;-)
Just dealt with an annoying topic: How to add a link to a Java swing frame. It’s not that hard to create some blue labels, but it’s a bit tricky to call a browser browsing a specific website…
As I mentioned the problem is to call the users web browser. Since Java SE 6 they’ve added a new class called Desktop. With it you may interact with the users specific desktop. The call for a browser is more than simple, just tell the desktop to browse to an URL:
Unfortunately there isn’t support for every OS, before you could use it you should check if it is supported instead of falling into runtime errors..
So far.. But what if this technique isn’t supported!? Yeah, thats crappy ;) You have to check which OS is being used, and decide what’s to do! I searched a little bit through the Internet and developed the following solutions:
Combining these solutions, one could create a browse function. Extending the
javax.swing.JLabel class, implementing
java.awt.event.MouseListener and adding some more features (such as blue text, overloading some functions…) I developed a new class Link, see attachment.
Of course it is also attached, so feel free to use it on your own ;)
Unfortunately I’m one of these guys that don’t have a Mac, shame on me! So I just could test these technique for Win and Linux. If you are a proud owner of a different OS please test it and let me know whether it works or not. If you have improvements please tell me also.
Someone informed me about a serious bug, so I spent the last few days with rebuilding the iso2l.
This tool was a project in 2007, the beginning of my programming experiences. Not bad I think, but nowadays a pharmacist told me that there is a serious bug. It works very well on small compounds, but the result is wrong for bigger molecules.. Publishing software known to fail is not that nice and may cause serious problems, so I took some time to look into our old code…
Let me explain the problem on a little example. Let’s denote the atom Carbon has two isotopes. The first one with a mass of 12 and an abundance of 0.9 and another one with a mass of 13 and an abundance of 0.1 (these numbers are only for demonstration). Let’s further assume a molecule consisting of 30 Carbon atoms. Since we can choose from two isotopes for each position in this molecule and each position is independent from the others there are many many possibilities to create this molecule from these two isotopes, exactly . Let’s for example say we are only using 15 isotopes of and 15 isotopes of , there still exists combinations of these elements. Each combination has an abundance of .
Unfortunately in the first version we created a tree to calculate the isotopic distribution (here it would be a binary tree with a depth of 10). If a branch has an abundance smaller than a threshold it’s cut to decrease the number of calculations and the number of numeric instabilities. If this thresh is here none of these combinations would give us a peak, but if you add them all together (they all have the same mass of ) there would be a peak with this mass and an abundance of above the threshold. Not only the threshold is killing peaks. This problem is only shifted if you decrease the thresh or remove it, because you’ll run in abundances that aren’t representable for your machine, especially in larger compounds (number of carbons > 100). So I had to improve this and rejected this tree-approach.
And while I’m touching the code, I translated the tool to English, increased the isotopic accuracy and added some more features. In figure 1 you can see the output of version 1 for . As I told this version loses many peaks. I contrast you can see the output of version 2 for the same compound in figure 2. Here the real isotopic cluster is visible.
Please try it out and tell me if you find further bugs or space for improvements or extensions!
The maps are very simple, there are just two types of fields: wall and floor. Walls aren’t accessible and block views. Bots can just move through floor-fields, but these fields are toxic, so they’ll decrease the health of a bot. This toxicity should be kept in mind while programming an AI, but shouldn’t play any role in this article. Here is an example how a map is presented to the bot (sample map provided by the organizers):
The number sign (#) identifies walls, spaces are floor fields. The task is to get the bot understanding the maps topology. It’s a big goal if you can split the map to areas or rooms to know whether you’re in the same room as an opponent and what’s the best possibility to change the room. I’ll explain my statements on a very simple example:
You’ll immediately see there are two rooms, one major one and a small lumber room at the bottom right corner of the map. But how should the bot see it!?
My first attempt was to build rooms based on horizontal and vertical histograms of wall-fields. Something like image processing.. This attempt was fast rejected, inefficient and not rely good working.
The second idea was to sample way-points to the map and building rooms based on visibility between these points. I’ve read that autonomous vacuum cleaners are working based on this technique ;-)
One night later I found a much better solution, it’s based on divisive clustering. Starting with the whole map repeat the following steps recursively:
- Is this part of the map intersected by a wall-field?
- YES: If this part is larger than a threshold split this part of the map in four parts with ideally same size and repeat this algorithm with each part, otherwise stop
- NO: We found a room! Label all its fields and try to connect it to the left, right, bottom and top if there are no walls intersecting
If this is done, we’ve found the main parts of each room. After wards I try to expand each room to neighbor fields that are unlabeled and doesn’t have neighbors with different labels. Fields that are connected to more than one room are doors.
To explain the idea of the algorithm I’ll present the procedure on my small example. At the beginning there are of course intersecting wall-fields, so it is split into four parts. Three of them aren’t intersected anymore, so they are labeled:
Since the rooms with label 1, 2 and 3 are connected and not intersected by a wall-field, they are merged together:
But the fourth part contains wall fields, so it is split into four more parts. Three of them are still intersected, but one gets labeled:
Splitting the remaining parts give to small rooms, so they are rejected by the size-threshold. So we try to expand the found rooms and end in the following situation:
D represents a door. Doors are saved separately to remember how rooms are connected and know the possibilities of fleeing out of a room if a predator comes in to catch you…
All this parsing is done in a separate thread, so the bot is able to do it’s work even if the maps are large and take a long time to parse… Of course here is a lot of space for improvements, but for this contest it is enough I think.
Unfortunately I cant recommend further readings, I don’t know any previous work like this.