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Sage as a candidate Computer Algebra System for the Physics Derivation Graph

Published 2015-12-25T20:05:00.001Z by Physics Derivation Graph

I was excited to use Sage's online notebook interface to see whether it could handle the variety of expressions and inference rules in the Physics Derivation Graph.

I found a few unexpected behaviors. There's good support for many of the inference rules. When I tried to apply Sage to more advanced uses, either I don't understand the math well enough, or support in Sage doesn't exist.

Expectation value doesn't render Latex

Expectation values are used in the variance relation identity. I was impressed by the ability of Sage to use Expectation value from SymPy.
x=var('x')
from sympy.stats import E # expectation value
expr1=E((x-E(x))^2)==E(x^2)-(E(x))^2
expr2=E(x^2-2*x*E(x)+E(x)^2)==E(x^2)-(E(x))^2
print(bool(expr1.lhs() == expr2.lhs()))
expr3=E(x^2)-2*E(x)*E(x)+E(x)^2==E(x^2)-(E(x))^2
print(bool(expr3.lhs() == expr2.lhs()))
True
True

However, I wasn't able to display the content in LaTeX:
print(latex(expr3))
0 == 0

This is unfortunate but not a blocker

Real Part of Expression isn't what I was expecting

Sage doesn't produce the output I expected from .real_part() 

forget()
x=var('x')
assume(x,'real')
this=cos(x) + I*sin(x)
this.real_part()
cos(real_part(x))*cosh(imag_part(x)) - cos(real_part(x))*sinh(imag_part(x))

What I was looking for was that this.real_part() would return cos(x); similarly this.imag_part() should return sin(x).

Symbolic Levi-Civita not supported in Sage?

A Levi-Civita operator can be defined
levicivita = SymmetricGroup(3)
levicivita([1,2,3]).sign()
1

However, I can't use symbols as arguments
forget()
h,j,k=var('h,j,k')
assume(h,'integer')
assume(j,'integer')
assume(k,'integer')
levicivita = SymmetricGroup(3)
levicivita([j,k,m]).sign()
Invalid permutation vector: [h,j, k]

Del (aka Nabla) lacks symbolic support in Sage?

Symbolic cross-products work as desired
h,j,k=var('h,j,k')
m,n,p=var('m,n,p')
E = vector(SR, [h,j,k])
F = vector(SR, [m,n,p])
print(E.cross_product(F))
(-k*n + j*p, k*m - h*p, -j*m + h*n)

 Other vector calculus operations appear to lack support for symbolic manipulation
https://en.wikipedia.org/wiki/Del#Gradient
https://en.wikipedia.org/wiki/Del#Divergence
https://en.wikipedia.org/wiki/Del#Curl

 I don't quite understand what's being done with the following:
forget()
V = VectorSpace(SR,3)
h,j,k=var('h,j,k')
m,n,p=var('m,n,p')
E = vector(SR, [h,j,k])
print(E.curl([1,2,3]))
print(E.div([1,2,3]))
(0, -1, 1)
1