On Wed, Apr 16, 2008 at 01:53:30PM +0200, Friedrich Hagedorn wrote:
> > Many physicists use Mathematica to do numerical work, like you seem to be
> > wanting to do. It is not its core job, but it does it. Same thing with
> > sympy. We are just all suggesting it is not the best tool avalaible.
> Ok. But here we can use the advantage of Python: To use sympy for
> symbolic calculation and then numpy for the numeric evaluation and
> further numeric calculation (eg root-finding).
+1.
> In [35]: f = Lambda(x, term)
> In [36]: f(x)
> Out[36]:
> 2
> x + x
> In [37]: f(2)
> Out[37]: 6
> And not: f(2).evalf()! This eval should be done with python math or
> mpmath to be fast as possible.
> In [45]: f([0, 1, 2, 3])
> Out[45]: [0, 2, 6, 12]
> And not: map(f, [0,1,2,3])! This eval should be done in numpy with a dict
> for operationnames (eg. numpy.arccos <-> sympy.acos).
I don't agree here. You are putting too much magic, I think it should be:
In [45]: f(array([0, 1, 2, 3])
Out[45]: array([0, 2, 6, 12])
Arrays and list are not the same things and we to keep list semantic, for
example we want:
>>> f = 2*x
>>> F = lambdify(f, [x])
>>> F([0, 1, 2, 3])
[0, 1, 2, 3, 0, 1, 2, 3]
[...]
> Which variant, A or B, would you prefere? I like B because the object you
> want to eval is the term. And the only information sympy have to know
> is the order of the symbols. And further you dont need to create a
> new variable name ("term" -> "f(x)").
A. (aka lambdify) is almost implemented, so let's stick with A.
my 2 cents (I am not a sympy developer so this is all sweet talking.
Gaël
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