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I ended up simply writing a function

def LinfNorm(vec):
    norms = [abs(x) for x in vec]
    supnorm = max_symbolic(norms)
    return supnorm

and I just have that called in my other functions with a list of the variables as input. e.g.

x,y,s = var('x,y,s')
assume(x!=0)
assume(y!=0)

fsquare = [cos(s)/(LinfNorm([cos(s),sin(s)])), sin(s)/(LinfNorm([cos(s),sin(s)]))]
g1plot = parametric_plot(fsquare, (0,2*pi), aspect_ratio = 1, ymin=-1,ymax=1, color = 'black')
gplots = [parametric_plot((t*(x/(LinfNorm([x,y]))-x)+x, t*(y/(LinfNorm([x,y]))-y)+y,0),(x,-1,1), (y,-1,1)) for t in sxrange(0,1.1,.1)]

gstaticplots = [parametric_plot((t*cos(2*pi*s)/(LinfNorm([cos(2*pi*s),sin(2*pi*s)])), t*sin(2*pi*s)/(LinfNorm([cos(2*pi*s),sin(2*pi*s)]))), (t,0,1)) for s in sxrange(0,1.025,.025)]

animation = [g1plot + sum(gstaticplots) + parametric_plot((t*cos(s)/(LinfNorm([cos(s),sin(s)])), t*sin(s)/(LinfNorm([cos(s),sin(s)]))), (s,0,2*pi), color='green') for t in sxrange(0,1.025,.025)]

cc = animate(animation)
cc.show(delay=10,iterations=3)