Hello,
In Python (and hence Sage) you have very useful list comprehension
sage: my_list = [1, 2, 3, 4]
sage: [2^i for i in my_list]
[2, 4, 8, 16]
If you use vectors, you can alternatively do
sage: v = vector([1, 2, 3, 4])
sage: v.apply_map(lambda x: 2^x)
(2, 4, 8, 16)
Vincent
vdelecroix's answer is perfectly fine. I just wanted to add that if you insist on being able to use 2**v then maybe you will like numpy. See here for an example.
Update: It is possible to use 2.^v from within sage if v is a numpy array (see link above).
2.^x
works.
I guess I don't understand the relationship between sage and numpy or scipy or really any of the packages. Why is numpy not the default. What are the implications of doing the import? Are numpy vectors going to interact with the plotting correctly? Etc.
Ugh, I didn't even notice the difference between "through sage" and "with python". .^ works in one and errors in the other, while ^ is the reverse. I don't understand. What document gives a map for how all these things interact?
You are right, that works. I'll update the answer, thanks!
Numpy is a Python package intended for scientific computing and you have the option of using it through Sagemath. Doing the import will load the array function from numpy (in this case). I can't think of any errors you can run into if you try to plot from numpy arrays.
Asked: 2015-10-08 03:00:40 +0200
Seen: 478 times
Last updated: Oct 08 '15
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