Sum SymPy expression over NumPy array

So if I do this

import sympy as sp
import numpy as np
u = np.random.uniform(0, 1, 10)
w, k = sp.symbols('w k')
l = sum(1 - sp.log(k + w) + sp.exp(k + w) for k in u)


I get what I want (symbolic sum over u

as a function w

). However, it would be much more convenient to write

f = 1 - sp.log(k + w) + sp.exp(k + w)
l = sum(f for k in u)


But then I get

10*exp(k + w) - 10*log(k + w) + 10


What's happening? Is there a way to get the amount I want? (Sympy has several ways to sum over integers, but I haven't found one for arrays) (Version: Python 2.7.6, NumPy 1.8.1, SymPy


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2 answers

The problem is that f

there is no value for everyone k

. Try it:

sum([f.subs(dict(k=k)) for k in u])


and it will give you the correct result. Where is subs()

used to force an estimate f

for each value k




Making a function f that returns a final calculation is what needs to happen here to make it work the way you use it.

f = lambda k,w : 1 - sp.log(k + w) + sp.exp(k + w)

l = sum(f(k,w) for k in u)




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