Hey guys,
We are sitting at a sprint, trying to use sympy to implement a clean way
of generation of numpy vectors.
What we want is to specify formulas for these vectors, and at the end of
the day, we sample these formulas on a given set of values. It is
important for us to seprate the specification from the sampling. All the
rest (incuding the fact the sympy is able to compute closed form
formulas or not) does not matters, as long as we can get numerical
values.
Where this fails is when we try to do some convolutions: I can't figure
out a way of using lambdify with none evaluated integrals. I don't see
why sympy shouldn't be abe to cal scipy.integrate to get numerical values
for these integrals. We don't actually need to use lambdify: we are going
to evaluate these functions on vector of length < 1000.
What is the right strategy here? (I guess my e-mail is unclear, if I need
to ask the question like this).
A side question: subs is not working the way I thought it would on
'Integral' objects:
In [1]: f = Lambda(x, exp(-x**2))
In [2]: conv = Integral(f(x-y)*f(y), (y, -oo, oo))
In [3]: conv
Out[3]:
∞
⌠
⎮ 2 2
⎮ - y - (x - y)
⎮ ℯ dy
⌡
-∞
In [4]: conv.subs({x:0})
Out[4]:
∞
⌠
⎮ 2 2
⎮ - y - (x - y)
⎮ ℯ dy
⌡
-∞
I would like 'Out[4]' to have x substituted :). I can see why it is hard
(sympy is probably not tracking that the integration variable does not
depend on x), but I'd still like it :).
Cheers,
Gaël
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