'Synchronizing' Podcasts with a Portable Device

Do you also like listening to podcasts? I do and my usual use case is the following: At first I discover a new podcast on the web. Then I use a program like gpodder or Miro to download all the episodes, which end up in one plain directory of the hard drive. At last I want to 'synchronize' the episodes with a portable device.

A lot of the time the portable device will have less memory than the size of the downloaded files or it is not desirable to fill up the portable device with just one podcast. So 'sychronizing' should copy only some episodes at a time to the portable device and remember which episodes have been copied. After listening/watching an episode, it can be deleted on the portable device to free some space for new episodes. New episodes should be copied during the next synchronization. No more capabilities except for playback and deleting episodes are assumed on the side of the portable device. My use case is a bit similar to the use cases discussed here.

The following script implements this idea of synchronization. It does so by building a simple Sqlite-Database which contains the information if an episode has been copied to the portable device already.

Remarks on Presentations

Here are some simple suggestions that I find useful to improve slides for (scientific) presentations. Many suggestions are subjective and no exhaustiveness is claimed. Therefore feel free to differ (and comment).

The suggestions can be downloaded here as pdf and odp.

Very Simple Thermal Simulation Of Tiled Multi-Core System

Let's assume a tiled multi-core architecture, eg. a two dimensional grid of nxm compute tiles. There might be several tasks running on every tile, which results in an increase of temperature of the corresponding tiles. Idle tiles will otherwise cool down.

The following video shows the result of a very simple thermal simulation with a 2x2 grid and a single task. The toy "thermal management" migrates the task if the average temperature of a tile exceeds a threshold temperature and migrates the task to the tile with the minimum average temperature.

The video was created using this script (Python+Numpy+Matplotlib+ffmpeg). A toy model for temperature conduction is being used. Albeit the temperature model not being physically accurate, the simulation might still be useful to quickly evaluate more sophisticated thermal management strategies.

Ear training with Anki

This post was created in collaboration with Thomas Fischbach after a discussion whether it would be possible for a person with only relative hearing to gain perfect hearing by practicing identifying musical notes with a flash card program.

No conclusive answer to this question will be given within this post, but you may try for yourself using  the following script. It can be used to create an Anki deck with sounds. Anki is an excellent flash card program (similar to Mnemosyne). csound is a software synthesizer used for sound generation. Installation instructions are provided with the script.

Dark Frame Analysis

To improve noisy video, shot at low light conditions, it is useful to measure the distribution of the noise. Therefore I recorded several minutes of video in a setup, where no light would enter the camera (see Dark-frame subtraction). A script was used to simply sum up a big amount of recorded frames. This 'noise accumulation frame' was used for further analysis.

The camera that was being used, is a consumer grade Panasonic HC-V500. Some strange effects will be unveiled further down and you might be interesting in testing if your camera has these as well. The simple script that was used to create the plots can be downloaded here (requires Scipy, Numpy, Opencv and Matplotlib).

Unfortunately there is no 1:1 correspondence of the pixels that end up in the video file and the real pixels on the CMOS sensor. It is therefore unknown if effects seen further down, are a result of the sensor or the image processing in the camera, esp. video compression. It would be favorable to take still images at the highest possible resolution in an automated way, but at least my video camera does not have this feature.

The distribution of the blue-channel in the noise accumulation frame looks like this:

The other channels (red, green) are almost indistinguishable from this. The following plot shows the distribution of red+green+blue channel:

It can be seen that a big part of the noise is approximately normal distributed. However if you look at the noise accumulation frame directly, some structure is visible, which looks a bit like the electric field of a quadrupole:

Even though there is no direct correspondence of the pixels in the video file and the pixels on the CMOS sensors, "hot pixels" are still present (Why?). These can be seen easily by looking at details of the picture above. Keep in mind that mu is around 3.61 and sigma is around 0.47, so all values above 5 should be extremely unlikely. The plots below simply show the same as the plot above subdivided into 4 parts:

Octave vs Python

"Don't do loops in Octave." is a well known truth. But sometimes loops are just too handy  or cannot be avoided at all. I was curious whether there is a difference in execution time of loops in Python and Octave since both are interpreted languages.

tic;
for i=1:100
for j=1:100
for k=1:100
  vain1 = i^2+j^2+k^2;
  vain2 = i^2+j^2+k^2;
  ...
  vain10 = i^2+j^2+k^2;
endfor
endfor
endfor
t = toc;
disp(num2str(t));

The following two scripts do nothing, but executing 3 nested loops with 100 iterations each and doing some useless computation within the loops. One script is in Python and one is in Octave. The Octave script is also listed above. Octave version 3.6.4 and Python version 2.7.3 was used for the comparison. The results are devastating.

user@machine:~/scripts/python_vs_octave$ octave loops.m
...
39.48
...
user@machine:~/scripts/python_vs_octave$ python loops.py
3.10390710831

Even in this simple example, the Octave script takes more than 10 times as long as the equivalent Python script. The difference becomes even bigger, if the amount of computation inside the loops is increased. Since Python also comes with sophisticated matrix processing capabilities (NumPy, SciPy) and if severe performance degradation for more sophisticated numerical analysis is not acceptable, the proverb above can be simply shortened to "Don't do Octave."


PS.: Another popular data analysis software is R. On the same machine using R version 2.14.1, the execution time of the equivalent script was 18.35s, which is in between the execution time of the Python and Octave scripts.

Characterize CPU Cooling

Did you ever want to find out how good your PC's cooling system does under stress? Maybe you have just bought/built a PC and you want to find out if it keeps sufficiently cool.

The temperatures of the individual cores can be obtained easily with the lm-sensors package. To put a little stress on the CPU, cpuburn is very helpful.

Here is a little Python script that records the temperatures and frequencies of the CPU over time and another Octave script that visualizes the temperature data. The package cpufrequtils is required to obtain the CPU frequencies. For validation purposes, mpstat is used to record the CPU utilization. If you benchmark your CPU with a program that does not fully utilize the CPU during certain periods of time, this data might be helpful to correct for strange effects in the temperature curve.

Assuming a CPU with 2 cores and using burnP6 to create some stress , the script might be executed as follows:
$python rec_sensor_log 5000 sensors_log.csv & burnP6 & burnP6

The first parameter is the time between two temperature samples in milliseconds and the second is the name of the output file. Depending on the amount of available cores in the CPU, several instances of burnP6 (or equivalent) should be started. The script parses the output from lm-sensors in a not very sophisticated way. If you have more/less cores, you will probably have to make modifications to the script.

The visualization script (vis_log.m) will prompt for the input filename. Frequency scaling might occur at high temperatures. If this is detected, a vertical line is drawn into the plot.

Here is a sample plot that was created during a quick test on my laptop. Under stress the temperature rises up to approx. 70°C and the fan spins faster to keep the temperature at about this level. As soon as the stress is removed, the temperature quickly falls below 50°C.