So looking at a lot of raw data is pretty tough, but matlab handles it with ease.
So the above image shows a sample amiga track from a brand new disk, just recently formatted, within the amiga. It is basically as perfect of a sample as one could expect. A few things to notice about it:
- nice tight groupings
- Lots of space between ranges
- No real data points fall in the middle
- 3 separate ranges all within a reasonable definable tolerance
now let’s look at a bad track. Now this track was written on my amiga 15-20 years ago, and to tell you how bad this disk was getting —- it literally self-destructed a day or two after I sampled this data. I’m not sure if I posted about this, but at least one track was completely scraped off the disk —- it could have been lots of wear/tear with the read head constantly touching one particular track, but in any event, the disk was in bad shape.
Now the two images aren’t exactly the same size, so don’t inadvertently read into that. But now let’s notice what’s so wrong with this picture:
- much fatter groupings.
- a bunch of data in no-mans land. Compare especially the space between 4us and 5us.
- Almost the anti-thesis of the first picture!
You can see where reading the second disk poses some real problems, and I think that some PLL just isn’t going to deal well with this!
Now that this disk is gone, I really can’t work with it. I’ve got to find other samples that aren’t as bad that I can work with to further refine my hardware/software.
If anyone is interested, I took a capture using my Saleae Logic analyzer and then processed it with a small C program that spits out basically .csv. Then I imported the .csv into matlab, and turned off lines connecting the data points, and turned on a dot to represent a data point. And just for clarification, if you haven’t been following the blog, the y index represents time between low-going edges. The x index is basically the number of edges, so the zero-ith first delta t is on the left, and the rightmost dot would be the last delta t sampled.
I’m very happy to have the ability to visualize this stuff!