How does Python help with Calculus?
kirby urner <[email protected]>
| Newsgroups | gmane.comp.python.education |
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| Message-ID | <CAPJgG3QwdBNs8vjukn02oSxqfg4DgrmgooCYm8rYHqGy_o7EAA@mail.gmail.com> |
The obvious answer is via Sage and SymPy, which will do differentiation and
integration symbolically to some degree.
http://www.scipy-lectures.org/advanced/sympy.html#differentiation
However even plain old core Python helps in that students get a sense of
functions as top-level citizens. I'm not saying Python is alone in
providing this.
If the C language could be written:
function func(function f1, function f2):{ }
with type function both eaten and returned, then we could use C for this
kind of thing also.
http://mathforum.org/kb/message.jspa?messageID=10168568 (more on this
general topic)
The repl below (you may skip modal window) shows my latest slimmed down
version of Compose, something I introduce to the O'Reilly course as well.
https://repl.it/HxMo/2
Function type objects don't ordinarily multiply but what if we want to
write h = f * g instead of h(x) = f(g(x)).
For one thing, why mention x at this point (the argument object) as we're
simply defining a function, not calling it with an input right?
The Compose class is just the ticket, swallowing and wrapping a function
with a __mul__ API. Now * is your compose operator. Or use __matmul__ for
@ symbol.
Note then, the use of Compose as a class decorator to the same end.
Feel free to recycle this animal in your own lesson plans. MIT license or
whatever.
Kirby
PS: one of the Pycon keynotes was about the affordability of nuke energy,
with the claim / calculation that it's less risky to workers than coal. I
didn't have time to go up to the podium after and listen in on the
conversation. We should have started a BOF. Simulating / modeling risk is
something I'm into through CERM Academy. We could start a thread on
Facebook.
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