svn-shortlog — Compact & Beautiful Subversion Changelog
At work we periodically have short developer meetings to discuss what has happened in the last month. To do this, we go through the bugs in our issue tracking system, and the subversion commits in our repository. Unfortunately, getting an overview of the subversion commits was rather cumbersome, and we could not find any efficient tool to do this. Hence, svn-shortlog was born.
This is an attempt to format the subversion log of a one-month period in the following way:
- Beautiful HTML output.
- Compact representation of lots of information
- Usable with a not-so color rich beamer.
- Fully automatic.
Usage
- Install Ruby (both 1.8 or 1.9 should work).
- Download svn-shortlog.rb.
- Open svn-shortlog.rb with your favourite text editor, and configure the config section according to your needs.
- Doubleclick svn-shortlog.rb
- Open the generated changelog_....html file with your favourite browser.
Sample Output
Here is a sample output of one month of boost commits into trunk, taken from the public repository. The output is quite information dense, a quick description is in the screenshot:
Issues
Ideas, suggestions, problems? Please post them as a comment here, at the bug tracker.
Credits
This tool is based on the idea from my colleague Christoph Heindl and inspired by Linus’ Kernel shortlog and Gmail.
Online Password Encrypter for Apache
Apache uses (among other hashes) SHA-1 keys for encryption in the .htpasswd. I administer a subversion server, and from time to time I have to add new external users to the system. This is usually rather cumbersome because there is no easy way to get to their encrypted password.
Thats why I have created The Online Password Encrypter. Here users can enter their desired username and password, and the encrypted key is automatically generated online, without transmitting anything to any server.
Here is an iframe of the file. Click here for full screen.
The Online Password Encrypter is just one single HTML page, it does not depend on any other files. So it is easy to download it, modify and send it around. Feel free do whatever you want with it.
Have fun,
Martin
Beautiful Font Hinting in Ubuntu 8.10 and 9.04
Even though I have an LCD monitor, I always have the subpixel hinting switched off because it is just painfully ugly to my eyes. Even when hinting is switched to maximum, the fonts are quite blurry (if you don’t believe me, type xmag and take a screenshot of your font. You can see red and blue linese everywhere). My eyes hurt when I see this.
Thanks to Johan Kivinemi I have just found out how to bring back the excellent legacy subpixel hinting engine. This has a much more crisp hinting, and uses subpixels only where it really is an improvement:
Just open these files in your home directory, and copy the content into them:
~/.fonts.conf
<?xml version='1.0'?>
<!DOCTYPE fontconfig SYSTEM 'fonts.dtd'>
<fontconfig>
<match target="font">
<edit name="antialias" mode="assign">
<bool>true</bool>
</edit>
<edit name="hinting" mode="assign">
<bool>true</bool>
</edit>
<edit name="hintstyle" mode="assign">
<const>hintfull</const>
</edit>
<edit name="lcdfilter" mode="assign">
<const>lcdlegacy</const>
</edit>
<edit name="rgba" mode="assign">
<const>rgb</const>
</edit>
</match>
</fontconfig>
~/.Xresources
Xft.antialias: true Xft.hinting: true Xft.hintstyle: hintfull Xft.lcdfilter: lcdlegacy Xft.rgba: rgb
This should work in Ubuntu 8.04, 8.10, and 9.04 too, and makes all fonts much more crisp. Of course, your mileage may vary.
UPDATE: Comparison Screenshots
As promised on reddit, I got back from an awesome snowboard trip so I am able to put up extensive comparison screenshots of the two subpixel hinting engines. Move your mouse over the following images to see the differences. Watch especially out for letters like “m” where the spacing between the lines is very small. You might have to wait a bit for the image to load.
I have used all of the most important fonts that I usually use, and just for fun I have added “Dijkstra”, which just looks cool.
Sans Fonts
Mouse to see the same fonts with the legacy hinter.
Mono Fonts
Mouse to see the same fonts with the legacy hinter.
Zoomed Comparison Screenhots
Here is an excerpt with 400% magnifications. Mouse over the pictures to see the legacy hinter.
Zoomed Sans
Zoomed Mono
Java 1.5 Collections Hierarchy Graph
Here is an inheritence graph of some of the more important Java collection classes of Java 1.5. Instantiateable classes are blue and rectangular, abstract classes are just rectangular, and interfaces are elliptic. Click on the image for a printable size:
Java 1.5 Collections Hierarchy
As a sidenote, have written this post almost a year ago and just found it in my drafts. I have completely forgotten about it! So here it is anyways. And now I remember why I did not post it: becauseit contains only a minor subset of the collections! Thanks Artur Biesiadowski for reminding me about that…
Approximation of sqrt(x) in Java
Yesterday I have played a bit with reinventing a fast approximation for sqrt() in Java. This might be handy with J2ME. Wikipedia has a nice article about Approximations that depend on IEEE representation. My version works, and on my Intel Dual Core with an average error of 1.57%, maximum error 4.02% it is 3.5 times faster than the original sqrt. In addition, it is very simple to improve the precision to 0.000161% average error and 0.000775% maximum error which is then 1.56 times faster than Math.sqrt().
Sourcecode
I use floating point tricks based on my pow() approximation. Basically I just took the pow() formula and for a^b I substitued b with 0.5, then simplified this as much as possible. As it turns out the result is very simple and short. This initial approximation can be easily made more precise with Newton’s method:
public static double sqrt(final double a) {
final long x = Double.doubleToLongBits(a) >> 32;
double y = Double.longBitsToDouble((x + 1072632448) << 31);
// repeat the following line for more precision
//y = (y + a / y) * 0.5;
return y;
}
Here is a comparison of the performance and accurancy versus the number of repetitions:
| Repetitions | Average error |
Maximum error |
Speedup |
|---|---|---|---|
| 0 | 1.57% | 4.02% | 3.53 |
| 1 | 0.000161% | 0.000775% | 1.56 |
| 2 | 2.51e-8% | 3.00e-7 | 0.838 |
With 2 repetitions, the trouble is not worth the effort, as the approximation is already slower than the original Math.sqrt() which is more precise.