On 11/02/2008, Matthew Brett [EMAIL PROTECTED] wrote:
I can also see that this could possibly be improved by using a for
loop to iterate over the output elements, so that there was no need to
duplicate the large input array, or perhaps a blocked iteration that
duplicated arrays of modest
Hi,
I can also see that this could possibly be improved by using a for
loop to iterate over the output elements, so that there was no need to
duplicate the large input array, or perhaps a blocked iteration that
duplicated arrays of modest size would be better. But how can a single
float per
Hi,
I am sorry if I have missed something obvious, but is there any way in
python of doing this:
import numpy as np
a = np.arange(10)
b = np.arange(10)+1
a.data = b.data # raises error, but I hope you see what I mean
?
Thanks a lot for any pointers.
Matthew
On Feb 10, 2008 5:15 PM, Matthew Brett [EMAIL PROTECTED] wrote:
Hi,
I am sorry if I have missed something obvious, but is there any way in
python of doing this:
import numpy as np
a = np.arange(10)
b = np.arange(10)+1
a.data = b.data # raises error, but I hope you see what I mean
?
Not
On Feb 10, 2008 6:48 PM, Matthew Brett [EMAIL PROTECTED] wrote:
import numpy as np
a = np.arange(10)
b = np.arange(10)+1
a.data = b.data # raises error, but I hope you see what I mean
?
Not really, no. Can you describe your use case in more detail?
Yes - I am just writing
import numpy as np
a = np.arange(10)
b = np.arange(10)+1
a.data = b.data # raises error, but I hope you see what I mean
?
Not really, no. Can you describe your use case in more detail?
Yes - I am just writing the new median implementation. To allow
future optimization, I would
Ah, I see. You definitely do not want to reassign the .data buffer in
this case. An out= parameter does not reassign the memory location
that the array object points to. It should use the allocated memory
that was already there. It shouldn't copy anything at all;
otherwise, median(x, out=out)
On Feb 10, 2008 7:17 PM, Matthew Brett [EMAIL PROTECTED] wrote:
Ah, I see. You definitely do not want to reassign the .data buffer in
this case. An out= parameter does not reassign the memory location
that the array object points to. It should use the allocated memory
that was already
Matthew Brett wrote:
import numpy as np
a = np.arange(10)
b = np.arange(10)+1
a.data = b.data # raises error, but I hope you see what I mean
?
Not really, no. Can you describe your use case in more detail?
Yes - I am just writing the new median implementation. To allow
future
