Hi,
I've just tested the latest numpy with my new configuration (Opteron
2220, 64bits with RH5.2, compiled with ICC 10.1.018) and I got this
failure.
==
FAIL: test_umath.TestLogAddExp2.test_logaddexp2_values
[...]
On Wed, May 27, 2009 at 7:51 AM, Matthieu Brucher
matthieu.bruc...@gmail.com wrote:
Hi,
I've just tested the latest numpy with my new configuration (Opteron
2220, 64bits with RH5.2, compiled with ICC 10.1.018) and I got this
failure.
Thanks Robert,
I thought it was something like that but couldn't figure it out.
C.
On May 26, 2009, at 4:50 PM, Robert Kern wrote:
2009/5/26 Charles سمير Doutriaux doutria...@llnl.gov:
Hi there,
One of our users just found a bug in numpy that has to do with
casting.
Consider the
Hi,
I'm using PIL for image processing, but lately I also try numpy for the
flexibility and superior speed it offers. The first thing I noticed is that for
an RGB image with height=1600 and width=1900 while
img=Image.open('something.tif')
img.size
(1900,1600)
then
arr=asarray(img)
arr.shape
2009/5/27 cp lubensch.proletariat@gmail.com:
img=Image.open('something.tif')
img.size
(1900,1600)
then
arr=asarray(img)
arr.shape
(1600,1900,3)
This means that the array-image has 1600 color channels, 1900 image pixel rows
and 3 image pixel columns. Why is that?
No, it means that
On Wed, May 27, 2009 at 5:12 PM, cp lubensch.proletariat@gmail.com wrote:
Hi,
I'm using PIL for image processing, but lately I also try numpy for the
flexibility and superior speed it offers. The first thing I noticed is that
for
an RGB image with height=1600 and width=1900 while
Now that numpy-1.3 has been released, I was hoping I could engage the numpy
developers and community concerning my suggestion to improve the ufunc
wrapping mechanism. Currently, ufuncs call, on the way out, the
__array_wrap__ method of the input array with the highest
__array_priority__.
There
Hi,
I've written a very simple benchmark on recarrays:
import numpy, time
Z = numpy.zeros((100,100), dtype=numpy.float64)
Z_fast = numpy.zeros((100,100), dtype=[('x',numpy.float64),
('y',numpy.int32)])
Z_slow = numpy.zeros((100,100), dtype=[('x',numpy.float64),
('y',numpy.bool)])
t =
Testing the PIL vs numpy in calculating the mean value of each color channel of
an image I timed the following.
impil = Image.open(10.tif)
imnum = asarray(impil)
#in PIL
for i in range(1,10):
stats = ImageStat.Stat(impil)
stats.mean
# for numpy
for i in range(1,10):
arr=asarray(img)
arr.shape
(1600,1900,3)
No, it means that you have 1600 rows, 1900 columns and 3 colour channels.
According to scipy documentation at
http://pages.physics.cornell.edu/~myers/teaching/ComputationalMethods/python/arrays.html
you are right.
In this case I import numpy where
On Wed, May 27, 2009 at 9:31 AM, Nicolas Rougier
nicolas.roug...@loria.frwrote:
Hi,
I've written a very simple benchmark on recarrays:
import numpy, time
Z = numpy.zeros((100,100), dtype=numpy.float64)
Z_fast = numpy.zeros((100,100), dtype=[('x',numpy.float64),
('y',numpy.int32)])
cp wrote:
arr=asarray(img)
arr.shape
(1600,1900,3)
No, it means that you have 1600 rows, 1900 columns and 3 colour channels.
According to scipy documentation at
http://pages.physics.cornell.edu/~myers/teaching/ComputationalMethods/python/arrays.html
you are right.
In this case I
No, I don't have permission to edit.
Nicolas
On 27 May, 2009, at 18:01 , Charles R Harris wrote:
On Wed, May 27, 2009 at 9:31 AM, Nicolas Rougier nicolas.roug...@loria.fr
wrote:
Hi,
I've written a very simple benchmark on recarrays:
import numpy, time
Z = numpy.zeros((100,100),
Hi again,
I have a problem with the nonzero() function for matrix.
The following test program:
import numpy, scipy.sparse
Z = numpy.zeros((10,10))
Z[0,0] = Z[1,1] = 1
i = Z.nonzero()
print i
Zc = scipy.sparse.coo_matrix((Z[i],i))
Z = numpy.matrix(Z)
i = Z.nonzero()
print i
Zc =
On Wed, May 27, 2009 at 3:26 PM, Nicolas Rougier
nicolas.roug...@loria.fr wrote:
Hi again,
I have a problem with the nonzero() function for matrix.
The following test program:
import numpy, scipy.sparse
Z = numpy.zeros((10,10))
i = Z.nonzero()
print i
Zc =
On May 27, 2009, at 5:53 PM, Fernando Perez wrote:
Howdy,
I'm wondering if the code below illustrates a bug in loadtxt, or just
a 'live with it' limitation.
Have you tried np.lib.io.genfromtxt ?
dt = dtype(dict(names=['name','x','y','block'],
Hi Pierre,
On Wed, May 27, 2009 at 3:01 PM, Pierre GM pgmdevl...@gmail.com wrote:
Have you tried np.lib.io.genfromtxt ?
I didn't know about it, but it has the same problem as loadtxt:
In [5]: rdata.block[0,1] # incorrect
Out[5]: array([1, 1, 1])
In [6]: alt_data.block[0,1] # same thing,
On Wed, May 27, 2009 at 10:33, cp lubensch.proletariat@gmail.com wrote:
Testing the PIL vs numpy in calculating the mean value of each color channel
of
an image I timed the following.
impil = Image.open(10.tif)
imnum = asarray(impil)
#in PIL
for i in range(1,10):
stats =
Hi Fernando
2009/5/27 Fernando Perez fperez@gmail.com:
I'm wondering if the code below illustrates a bug in loadtxt, or just
a 'live with it' limitation.
I'm not sure whether this is a bug or not.
By specifying the dtype
dt = dtype(dict(names=['name','x','y','block'],
On May 27, 2009, at 6:15 PM, Fernando Perez wrote:
Hi Pierre,
On Wed, May 27, 2009 at 3:01 PM, Pierre GM pgmdevl...@gmail.com
wrote:
Have you tried np.lib.io.genfromtxt ?
I didn't know about it, but it has the same problem as loadtxt:
Oh yes indeed. Yet another case of
Hi Stefan,
2009/5/27 Stéfan van der Walt ste...@sun.ac.za:
Hi Fernando
2009/5/27 Fernando Perez fperez@gmail.com:
I'm wondering if the code below illustrates a bug in loadtxt, or just
a 'live with it' limitation.
I'm not sure whether this is a bug or not.
By specifying the dtype
dt
Hi Pierre,
On Wed, May 27, 2009 at 4:03 PM, Pierre GM pgmdevl...@gmail.com wrote:
Oh yes indeed. Yet another case of I-opened-my-mouth-too-soon'...
OK, so there's a trick. Kinda:
* Define a specific converter:
Thanks, that's an alternative, though I think I prefer my two-pass
hack, though I
On May 27, 2009, at 7:10 PM, Fernando Perez wrote:
Hi Pierre,
On Wed, May 27, 2009 at 4:03 PM, Pierre GM pgmdevl...@gmail.com
wrote:
Oh yes indeed. Yet another case of I-opened-my-mouth-too-soon'...
OK, so there's a trick. Kinda:
* Define a specific converter:
Thanks, that's an
Hi Fernando
2009/5/28 Fernando Perez fperez@gmail.com:
Well, since dtypes allow for nesting full arrays in this fashion,
where I can say that the 'block' field can have (2,3) shape, it seems
like it would be nice to be able to express this nesting into loading
of plain text files as well.
2009/5/27 Stéfan van der Walt ste...@sun.ac.za:
Hi Fernando
2009/5/28 Fernando Perez fperez@gmail.com:
Well, since dtypes allow for nesting full arrays in this fashion,
where I can say that the 'block' field can have (2,3) shape, it seems
like it would be nice to be able to express this
On Wed, May 27, 2009 at 1:21 PM, Nicolas Rougier
nicolas.roug...@loria.frwrote:
No, I don't have permission to edit.
Nicolas
You should ask for it then. Email stephan at ste...@sun.ac.za. The docs
are here http://docs.scipy.org/numpy/Front%20Page/.
Chuck
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