While neatening up my shop I got to thinking how nice it is to just measure something on a whim. Next thing you know I'm logging the temperature of my soldering iron. It's Currie controlled. But how fast and regular are it's cycles? I didn't know.
Fifteen minutes later I'm looking at the waveforms in a spreadsheet chart that just happened to have three lines, one for each of the DS18B20s that I happened to have in the setup. Each response was noticeably different even with them that close together. I also saw large variations that might be due to air movements. Hmm. Let's improve the apparatus.
Now this thing responds. I can blow gently on it and see a response within seconds. Gently fanning it with a file folder from a foot away works too. So then I leave the room to see what the air does while I'm gone.
This takes the power R to resolve. I download this open-source package and read a few tutorials to remember the basics. First grab the data, focusing on most recent.
Label the columns and make them look like variables.
Find the average of all the sensors and then compare the deviation from this for each thermometer pair.
Plot can draw all kinds of charts but this is the default when you give it a collection of columns (click to enlarge).
This shows the slightest of air movement variability in the e-w direction. Notice that only the east thermometer is swinging 10 degrees. There are also some outlier samples created as I returned to shut down the acquisition. Interesting.
This also gave me a chance to reacquaint myself with R. My spreadsheet was choking on data that R read and processed in a blink. FFTs and convolution filters were just as easy. Power worth mastering.