Russell E. Owen wrote:
> But I still don't understand why one shouldn't just use dtype=float or
> numpy.float. Does that result in an array with a different type of float
> than numpy.float_ (float64)? Or does it just somehow speed up numpy
> because it doesn't have to convert the python type i
In article <[EMAIL PROTECTED]>,
Robert Kern <[EMAIL PROTECTED]> wrote:
> Christopher Barker wrote:
>
> > I can only help with one:
> >> - Even after reading the book I'm not really clear on why one would use
> >> numpy.float_ instead of numpy.float or float
> >
> > They float and numpy.float a
Christopher Barker wrote:
> I can only help with one:
>> - Even after reading the book I'm not really clear on why one would use
>> numpy.float_ instead of numpy.float or float
>
> They float and numpy.float are the same, and numpy.float_ is the same as
> numpy.float64:
>
> >>> import numpy
>
Russell E. Owen wrote:
> So I finally bit the bullet and converted most of my code from Numeric
> and numarray to numpy.
good for you!
I can only help with one:
> - Even after reading the book I'm not really clear on why one would use
> numpy.float_ instead of numpy.float or float
They float a
Russell E. Owen wrote:
> So I finally bit the bullet and converted most of my code from Numeric
> and numarray to numpy. (I haven't yet tried to convert one package that
> makes heavy use of nd_image and has C extensions).
>
> But it left me with a few questions:
>
> - What exception does numpy
So I finally bit the bullet and converted most of my code from Numeric
and numarray to numpy. (I haven't yet tried to convert one package that
makes heavy use of nd_image and has C extensions).
But it left me with a few questions:
- What exception does numpy throw if it runs out of memory? (I c
