Thanks, Kalevi, the limit approach works well for the issue above.
But I find another interesting limit issue:
import sympy as sp
sp.init_printing()
sigma=sp.Symbol('sigma', positive=True)
mu=sp.Symbol('mu',domain=sp.S.Reals)
x=sp.Symbol('x')
C=sp.Symbol('C')
f=1/sp.sqrt(2*sp.pi*sigma**2)*sp.exp(-(x-mu)**2/(2*sigma**2))
g=sp.integrate(f,x)+C
t=sp.solve(g.subs(x,mu)-1/2,C)[0]
print("Without simplify everything looks ok:")
h=g.subs(C,t)
print(h)
print(sp.limit(h,x,sp.oo))
print(sp.limit(h,x,-sp.oo))
print("Without simplify the result looks strange:")
h=sp.simplify(g.subs(C,t))
print(h)
print(sp.limit(h,x,sp.oo))
print(sp.limit(h,x,-sp.oo))
The output I get is
rd@h370:~/Downloads$ python3 ~/Downloads/example_integrate_gauss.py
Without simplify everything looks ok:
erf(sqrt(2)*(-mu + x)/(2*sigma))/2 + 0.5
1.00000000000000
0
Without simplify the result looks strange:
-erf(sqrt(2)*(mu - x)/(2*sigma))/2 + 0.5
0
0
rd@h370:~/Downloads$
The simply output still looks ok for me, but the first limit output after
applying simplify seems to be wrong (?)
Rainer
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