Numeric question

[Cameron Hooper]

Chris Barker wrote:

> 
> 
> Emile van Sebille wrote:
> 
>> OK.  It's x.  So let's build a contiguous x and try it again:
> 
>> >>> x = array(inputs[0][:])
> 
> or
> 
> x = inputs[0][:].copy()
> 
> The copy method isn't in the version of the docs I have, but it's very
> handy and quite clear as to what it is doing.
> 
> or
> 
> x = inputs[0,:].copy()
> 
> Numpy arrays are N-dimensional, you might as well index them that way.
> 
> come to think of it, why not:
> 
> inputs[0].copy() ?
> 
> 
> -Chris
> 

Thanks for the replies. So my understanding now is that the concatenate 
function really combines two references to two separate locations in memory 
into a single object. The two locations are not contiguous. Data is not 
'moved around' so to speak. So x = concatenate(y,z) can be viewed as 
returning a reference to two non-contiguous references.  But x = 
array(concatenate(y,z)) (or using copy()) actually copies data.

Cameron