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First and foremost it ruins your second recording if you have scheduled to record two programs back-to-back. It is possible to write an AppleScript that reacts to the RecordingDone signal in EyeTV, but my previous foray into that area ended up with a simple script that said something like on RecordingDone(recordingID)Д®nd RecordingDoneThis works to some extent but it does have some serious flaws.

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One feature that I _really_ miss in EyeTV is the ability to put my Mac to sleep after a recording.

My experiments show that this value does indeed model CPU strength much better than clock speed or BogoMIPS - but it is far from perfect! Nbench returns two scores, an integer and a floating point score, so I chose to use the average of these two scores as peer strength. I have used the nbench benchmarking suite - primarily because it is available as source code, so that I can compile and run it on any platform that I would like to test.

Because of that I in Scavenger chose to use a real CPU benchmark for the strength value, thinking that a CPU benchmark would be written specifically to exercise all the various parts of a CPU and therefore would be more apt at describing its relative strength. I expected that these values were pretty useless at estimating peer strength, and my experiments up until now seem to confirm that suspicion.

In related systems, such as Spectra and Chroma, the CPUs clock speed and its Linux BogoMIPS rating has been used, respectively. This is all fairly straight forward, but the tricky part is finding that magical 'strength' value for each peer. That is luckily easily done - fetch the source here.

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Note: The SQLite version included in Mac OS X 10.6 does not allow extensions, so you will have to build it yourself.

Before executing your select statements be sure to load the extension in SQLite by issuing the following command (within SQLite): select load_extension('/full/path/to/libsqlitefunctions.dylib') ДЄnd you're done! Now you can issue select statements with stdev as an aggregate function.

Copy the resulting library to some location where you want to store it.

Linux$ gcc -fPIC -lm -shared extension-functions.c -o libsqlitefunctions.so

Compile it: Mac$ gcc -fno-common -dynamiclib extension-functions.c -o libsqlitefunctions.dylib.

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Fetch the source code for the extension here.

Luckily, this is possible in SQLite through a simple extension. Working with my data in a relational database I found that there were only one piece of functionality that I was missing, and that was the possibility to select out both an average _and_ the standard deviation of a data set. 'When are you coming to the stdev part!', you say? Calm down, I'm getting there. All other questions about the data, and how the different test results relate to each other, can thus be formulated in simple SQL instead of through increasingly complex Python scripts. So instead of writing a lot of data handling scripts, this time I have written only one - a script that imports the log data into an SQLite database. This small relational-database-in-a-file is excruciatingly easy to use from almost any programming language you could think of.

I have done this a thousand times before. Normally I would be logging such data into flat files, collecting some giga bytes of log files that I would then need to write a handful of Python, Perl, or Ruby scripts to make any sense of. At the moment I am collecting data - hoards of data - my test suite is a veritable cornucopia of boring data.

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