If the performance becomes an issue, maybe something like
global sin, cos, imported
def _import_stuff():
global sin, cos, imported
if not imported:
from sympy import sin, cos
imported = True
at the module level would be an alternative. Or if global scares you,
use some kind of namespace.
Aaron Meurer
Sent from my iPad
On Oct 3, 2012, at 1:38 PM, Joachim Durchholz <[email protected]> wrote:
> Am 03.10.2012 18:11, schrieb Ondřej Čertík:
>> I would point out, that the in my experience, it's quite slow to import
>> things inside a function,
>
> I can imagine. The import will run on every call to the function, and that's
> always slower than simply looking up the imported name in the dictionary of
> the class object.
>
> > which might or might not be a problem, depending
>> on the situation.
>
> It's probably proportional to the number of calls to the function.
>
> > I don't know at the moment, how C.something is doing
>> performance wise.
>
> It's a call to a Python function that does a dictionary lookup.
> I suspect it's slower than a direct call but faster than an import inside the
> function.
>
> That's a very vague estimate and probably not worth the bits needed to store
> it.
> For performance, there's another aspect: all the usually-used workarounds for
> circular dependency problems will get some attention by whoever is tuning
> Python for performance. Atypical stuff like C won't get this love, so even if
> it's fast now, it will fall back behind the more typical techniques as Python
> improves.
> So while I wouldn't disregard benchmarking results, looking at how Python is
> being used in the wild is as important.
>
> BTW I counted around 890 call sites to C.
> That's not too bad. Changing the calls to raises() wasn't much different.
>
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