On 10/12/06, Stefan van der Walt <[EMAIL PROTECTED]> wrote:
> On Thu, Oct 12, 2006 at 08:58:21AM -0500, Greg Willden wrote:
> > On 10/11/06, Bill Baxter <[EMAIL PROTECTED]> wrote:
> I tried to explain the argument at
>
> http://www.scipy.org/NegativeSquareRoot
>
The proposed fix for those who want sqrt(-1) to return 1j is:
from numpy.lib import scimath as SM
SM.sqrt(-1)
But that creates a new namespace alias, different from numpy. So I'll
call numpy.array() to create a new array, but SM.sqrt() when I want a
square root.
Am I wrong to want some simple way to change the behavior of
numpy.sqrt itself?
Seems like you can get that effect via something like:
for n in numpy.lib.scimath.__all__:
numpy.__dict__[n] = numpy.lib.scimath.__dict__[n]
If that sort of function were available as "numpy.use_scimath()", then
folks who want numpy to be like scipy can achieve that with just one
line at the top of their files. The import under a different name
doesn't quite achieve the goal of making that behavior numpy's
"default".
I guess I'm thinking mostly of the educational uses of numpy, where
you may have users that haven't learned about much about numerical
computing yet. I can just imagine the instructor starting off by
saying "ok everyone we're going to learn numpy today! First everyone
type this: 'import numpy, from numpy.lib import scimath as SM' --
Don't worry about all the things there you don't understand."
Whereas "import numpy, numpy.use_scimath()" seems easier to explain
and much less intimidating as your first two lines of numpy to learn.
Or is that just a bad idea for some reason?
--bb
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