Hello all
Consider the following example:
In [43]: x = N.zeros((3,2))
In [44]: x.flags
Out[44]:
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : True
WRITEABLE : True
ALIGNED : True
UPDATEIFCOPY : False
In [45]: x[:,[1,0]].flags
Out[45]:
C_CONTIGUOUS : False
F_CONTIGUOUS :
On 23/05/07, Albert Strasheim [EMAIL PROTECTED] wrote:
Consider the following example:
First a comment: almost nobody needs to care how the data is stored
internally. Try to avoid looking at the flags unless you're
interfacing with a C library. The nice feature of numpy is that it
hides all
Hello all
On Wed, 23 May 2007, Anne Archibald wrote:
On 23/05/07, Albert Strasheim [EMAIL PROTECTED] wrote:
Consider the following example:
First a comment: almost nobody needs to care how the data is stored
internally. Try to avoid looking at the flags unless you're
interfacing with a
On Wed, May 23, 2007 at 09:49:08AM -0400, Anne Archibald wrote:
On 23/05/07, Albert Strasheim [EMAIL PROTECTED] wrote:
Is it correct that the F_CONTIGUOUS flag is set in the case of the fancy
indexed x? I'm running NumPy 1.0.3.dev3792 here.
Numpy arrays are always stored in contiguous
On 23/05/07, Albert Strasheim [EMAIL PROTECTED] wrote:
If you are correct that this is in fact a fresh new array, I really
don't understand where the values of these flags. To recap:
In [19]: x = N.zeros((3,2))
In [20]: x.flags
Out[20]:
C_CONTIGUOUS : True
F_CONTIGUOUS : False
On 5/23/07, Albert Strasheim [EMAIL PROTECTED] wrote:
Hello all
On Wed, 23 May 2007, Anne Archibald wrote:
On 23/05/07, Albert Strasheim [EMAIL PROTECTED] wrote:
Consider the following example:
First a comment: almost nobody needs to care how the data is stored
internally. Try to avoid
Albert Strasheim wrote:
Hello all
Consider the following example:
In [43]: x = N.zeros((3,2))
In [44]: x.flags
Out[44]:
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : True
WRITEABLE : True
ALIGNED : True
UPDATEIFCOPY : False
In [45]: x[:,[1,0]].flags
Out[45]:
