On Sun, Jan 15, 2012 at 6:54 AM, David Cournapeau courn...@gmail.comwrote:
On Sat, Jan 14, 2012 at 11:53 PM, Nathan Faggian
nathan.fagg...@gmail.com wrote:
Hi,
I am finding it less than useful to have the negative index wrapping on
nd-arrays. Here is a short example:
import numpy as
On Sat, Jan 14, 2012 at 4:53 PM, Nathan Faggian nathan.fagg...@gmail.comwrote:
Hi,
I am finding it less than useful to have the negative index wrapping on
nd-arrays. Here is a short example:
import numpy as np
a = np.zeros((3, 3))
a[:,2] = 1000
print a[0,-1]
print a[0,-1]
print
On Mon, Jan 16, 2012 at 3:24 PM, Charles R Harris charlesr.har...@gmail.com
wrote:
On Sat, Jan 14, 2012 at 4:53 PM, Nathan Faggian
nathan.fagg...@gmail.comwrote:
Hi,
I am finding it less than useful to have the negative index wrapping on
nd-arrays. Here is a short example:
import
On Mon, Jan 16, 2012 at 3:30 PM, Benjamin Root ben.r...@ou.edu wrote:
On Mon, Jan 16, 2012 at 3:24 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Sat, Jan 14, 2012 at 4:53 PM, Nathan Faggian nathan.fagg...@gmail.com
wrote:
Hi,
I am finding it less than useful to have the
Hi,
I am sorry for the late reply.
Benjamin has hit the nail on the head. I guess I am seeing numpy
fancy indexing as equivalent to integer based coordinate sampling
and trying to compare numpy's fancy indexing to something like
map_coordinates in scipy.
I have never used np.ravel_multi_index()
On 15/01/12 00:53, Nathan Faggian wrote:
Hi,
I am finding it less than useful to have the negative index wrapping
on nd-arrays. Here is a short example:
import numpy as np
a = np.zeros((3, 3))
a[:,2] = 1000
print a[0,-1]
print a[0,-1]
print a[-1,-1]
In all cases 1000 is printed
On Sat, Jan 14, 2012 at 11:53 PM, Nathan Faggian
nathan.fagg...@gmail.com wrote:
Hi,
I am finding it less than useful to have the negative index wrapping on
nd-arrays. Here is a short example:
import numpy as np
a = np.zeros((3, 3))
a[:,2] = 1000
print a[0,-1]
print a[0,-1]
print
Hi,
I am finding it less than useful to have the negative index wrapping on
nd-arrays. Here is a short example:
import numpy as np
a = np.zeros((3, 3))
a[:,2] = 1000
print a[0,-1]
print a[0,-1]
print a[-1,-1]
In all cases 1000 is printed out.
What I am after is a way to say please don't wrap
