Numpy optimize the python process by explicitly define the element type of array. Just like C++. Python let you work with automatic converting... but it slows down the process. Like having extra code to check the type of your element array. I suggest you check the numpy reference instead of python reference when using numpy. Sincerely Yours, pujo On 5/23/06, Ivan Vilata i Balaguer wrote:

En/na Pujo Aji ha escrit::

...

use 'f' to tell numpy that its array element is a float type: b = numpy.array([1,2,3,4],'f')

an alternative is to put dot after the number: b = numpy.array([1. ,2. ,3. ,4.])

This hopefully solve your problem.

You're right, but according to Python reference docs, having an integer base and a negative integer exponent should still return a floating point result, without the need of converting the base to floating point beforehand.

I wonder if the numpy/numarray behavior is based on some implicit policy which states that operating integers with integers should always return integers, for return type predictability, or something like that. Could someone please shed some light on this? Thanks!

En/na Pujo Aji ha escrit::

...

On 5/23/06, *Ivan Vilata i Balaguer* mailto:ivilata@carabos.com> wrote: [...] According to http://docs.python.org/ref/power.html:

For int and long int operands, the result has the same type as the operands (after coercion) unless the second argument is negative; in that case, all arguments are converted to float and a float result is delivered.

Then, shouldn't be ``b ** -1 == array([1.0, 0.5, 0.33333333, 0.25 ])`` (i.e. a floating point result)? Is this behaviour intentional? (I googled for previous messages on the topic but I didn't find any.)

::

Ivan Vilata i Balaguer >qo< http://www.carabos.com/ Cárabos Coop. V. V V Enjoy Data ""

En/na Pujo Aji ha escrit::

Numpy optimize the python process by explicitly define the element type of array. Just like C++.

Python let you work with automatic converting... but it slows down the process. Like having extra code to check the type of your element array.

I suggest you check the numpy reference instead of python reference then using numpy.

OK, I see that predictability of the type of the output result matters. ;) Besides that, I've been told that, according to the manual, power() (as every other ufunc) uses its ``types`` member to find out the type of the result depending only on the types of its arguments. It makes sense to avoid checking for particular values with possibly large arrays for efficiency, as you point out. I expected Python-like behaviour, but I understand this is not the most appropriate thing to do for a high-performace package (but then, I was not able to find out using the public docs). Thanks for your help, :: Ivan Vilata i Balaguer >qo< http://www.carabos.com/ Cárabos Coop. V. V V Enjoy Data ""