[SciPy-user] is there a difference between numpy and scipy array ?

[Stef Mientki]

Robert Kern wrote:
> Stef Mientki wrote:
>   
>> Forgive my ignorance, but reading more and more docs,
>> I now get he feeling there's a difference between Numpy en Scipy arrays,
>> is that so ?
>>     
>
> No. scipy is a package that *uses* numpy. That's all.
>
>   
I found it again:
from: SciPy Course Outline, by 
Author: 	Dave Kuhlman

At times you may need to convert an array from one type to another, for 
example from a numpy array to a scipy array or the reverse. The array 
protocol will help. In particular, the asarray() function can convert an 
array without copying. Examples:

In [8]: import numpy
In [9]: import scipy
In [10]: a1 = zeros((4,6))
In [11]: type(a1)
Out[11]: <type 'scipy.ndarray'>

In [12]: a2 = numpy.asarray(a1)
In [13]: type(a2)
Out[13]: <type 'numpy.ndarray'>

In [14]: a3 = numpy.zeros((3,5))
In [15]: type(a3)
Out[15]: <type 'numpy.ndarray'>

In [16]: a4 = scipy.asarray(a3)
In [17]: type(a4)
Out[17]: <type 'scipy.ndarray'>

So after all there is a difference,
but I still don't know what exactly the difference is ;-)

== now something weird happens here:
[Dbg]>>> aap=(11,12,11,23)
[Dbg]>>> type(aap)
<type 'tuple'>
[Dbg]>>> IOI=scipy.asarray(aap)          <== make it a scipy array
[Dbg]>>> print IOI
[11 12 11 23]
[Dbg]>>> type(IOI)
<type 'numpy.ndarray'>                   <== it has NOT become a scipy array ??

Maybe in general this (small ?) difference might not make a big deal,
but as I want to do real calculations, with mainly scipy functions,
I want to optimize the array type 
(probably i4, but maybe f8 is even faster depending on the kind of calculations)


cheers,
Stef Mientki


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