Hi,
I am developping a package using the buffer interface, and with Python
2.7 - Numpy 1.5, the following annoying warning has been reported.
__main__:1: RuntimeWarning: Item size computed from the PEP 3118
buffer format string does not match the actual item size.
Beside warning it appears that
I just ran into the following:
>>> np.dtype(u"f4")
Traceback (most recent call last):
File "", line 1, in
TypeError: data type not understood
Is that the expected behaviour?
Thanks in advance,
Antony Lee
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Hi all,
I'd like to be able to speed up the following code.
def replace_dead(cube, dead):
# cube.shape == (320, 640, 1200)
# dead.shape == (320, 640)
# cube[i,j,:] are bad points to be replaced via interpolation if
dead[i,j] == True
bands = np.arange(0, cube.shape[0])
for line i
On 11/16/10 10:01 AM, Christopher Barker wrote:
OK -- I'll whip up a test similar to yours -- stay tuned!
Here's what I've done:
import numpy as np
from maproomlib.utility import file_scanner
def gen_file():
f = file('test.dat', 'w')
for i in range(1200):
f.write('1 ' * 2048)
On 11/16/10 8:57 AM, Darren Dale wrote:
> In my case, I am making an assumption about the integrity of the file.
That does make things easier, but less universal. I guess this is the
whole trade-off about "reusable code". It sure it a lot easier to write
code that does the one thing you need tha
On Tue, Nov 16, 2010 at 11:46 AM, Christopher Barker
wrote:
> On 11/16/10 7:31 AM, Darren Dale wrote:
>> On Tue, Nov 16, 2010 at 9:55 AM, Pauli Virtanen wrote:
>>> Tue, 16 Nov 2010 09:41:04 -0500, Darren Dale wrote:
>>> [clip]
That loop takes 0.33 seconds to execute, which is a good start. I
On 11/16/10 7:31 AM, Darren Dale wrote:
> On Tue, Nov 16, 2010 at 9:55 AM, Pauli Virtanen wrote:
>> Tue, 16 Nov 2010 09:41:04 -0500, Darren Dale wrote:
>> [clip]
>>> That loop takes 0.33 seconds to execute, which is a good start. I need
>>> some help converting this example to return an actual num
On Tue, Nov 16, 2010 at 9:55 AM, Pauli Virtanen wrote:
> Tue, 16 Nov 2010 09:41:04 -0500, Darren Dale wrote:
> [clip]
>> That loop takes 0.33 seconds to execute, which is a good start. I need
>> some help converting this example to return an actual numpy array. Could
>> anyone please offer a sugge
On Tue, Nov 16, 2010 at 8:13 AM, SoTaNeZ wrote:
> Hi all.
>
> I got this exception while executin numpy.linalg.solve(a,b) being:
>
> a = array([[ 1.e+000, -4.19430400e+006, 0.e+000,
> 0.e+000, 0.e+000, 0.e+000,
> 0
Hi all.
I got this exception while executin numpy.linalg.solve(a,b) being:
a = array([[ 1.e+000, -4.19430400e+006, 0.e+000,
0.e+000, 0.e+000, 0.e+000,
0.e+000, 0.e+000, 0.e+000],
@Scott
Thanks for the tip.
On Tue, Nov 16, 2010 at 3:42 PM, Scott Sinclair wrote:
> On 15 November 2010 14:15, srinivas zinka wrote:
> > Thank you for the reply.
> > I just downloaded the following zip file:
> > http://docs.scipy.org/doc/numpy-1.5.x/numpy-html.zip
> > When I try to search for
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