I am still quite busy due to the fact I changed job this week, but I decided to share some of my Python material to manipulate low-level flux reversal data captured with e.g. KryoFlux.
In fact, I had written a raw stream converter for KryoFlux, that I will include in CBM Flux Studio, and I had to somehow test that it was working correctly. As I haven’t coded a g64 converter yet, and probably won’t have time to do so for a while still, I decided to put together a Jupyter Notebook to do the conversion and provide me with some feedback relatively quickly and effortlessly.
I also decided to show how to decode what KryoFlux refers to as a Duplicator Info (DI) sector, usually found on track 36 of original Commodore 64 disks. Specifically, I put together a Notebook to quickly align to an FM sector and decode its contents. In fact, DI sectors are often written using FM encoding.
From the above screenshot you can easily spot the strings “508-040A” and “C64 WONDERMAT NORMAL” within the DI block of California Games.
Could “C64 Wondermat Normal” be the original name of what we now refer to as “Vorpal later”? It’s possible that Wondermat refers to the disk mastering script, but I’d be looking at feedback from somebody who was involved with its development back in the day, before settling the case.
BTW, I am aware of Chuck Summerville’s interview contending that California Games used the Vorpal Loader. However, part of what he recalls is inaccurate, so I guess it would be better to search for sources who actually worked at the loader system itself, not just at games using the loader.
To Scott Nelson, the coder of Vorpal Loader: if you are out there, I think that insights and anecdotes on the loader design process would be a great resource to document a historical piece of superb software from an era when means to share engineering techniques and collaboration tools in general were scarce.
It would also be interesting to know whether the background of his father Harry might have encouraged Scott’s interest in computers and coding (source).
Anyway, in the spirit of sharing information and methods, both Jupyter Notebooks are available in the relevant repo on GitHub. Bear in mind these are just for experimenting and quick prototyping.
If you click on either Notebook, GitHub will render a non-interactive version of it. If you are interested in running these Notebooks interactively, perhaps replacing the embedded raw data block with one you decoded from a different title, you can click on either badge included in the README.md file at the root of the repo, where it reads “launch binder”.
Stay tuned for more!