In the source code below, almost all that's special about subclasses is their respective data, which are initialized from globals.
Both the constructor and self representer (a VPython draw, not a __repr__ in this case) are inherited from a Tetrahedron superclass. http://www.4dsolutions.net/ocn/python/quantamods.py How might this be useful in a math learning context? There's a quickie conversion going on, twixt two coordinate systems, with one vector's .xyz attribute an argument to the next vector's initializer. That'd be one thing to focus on (object translation). Caveats: I'm using arguments provided by a collaborator. Although I've got a lot of faith in 'em, I'm just taking 'em as given (assumed true). The output is visual (VPython) and to my eye the results were quite believable. However, in pure Chakovians, with (1,0,0,0)(0,1,0,0)(0,0,1,0) and (0,0,0,1) as my four vertices, I'd be sorely tempted to anchor my A modules (+/-) as at least two of those 24. On the other hand, I'm also quite focused on the Coupler as 8 MITEs meeting at the origin (0,0,0,0) with As and Bs permuting accordingly (lots of ways to go). Sorry about all the jargon, for those not trained in slogging through this namespace. I call it "gnu math" and teach it with Python. Kirby
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