> Still, it shouldn't segfault, and it's worth figuring out why it does.
> gdb has been mostly unenlightening for me since gdb won't let me
> navigate the traceback.
You could try faulthandler, it prints the (python) traceback after a crash:
http://pypi.python.org/pypi/faulthandler/
__
On Thu, Oct 6, 2011 at 17:25, Paul Ivanov wrote:
> Hi Andrea,
>
> On Tue, Oct 4, 2011 at 3:04 AM, Andrea Gavana wrote:
>> Hi All,
>> I was fiddling here and there with some code doing dynamic import of
>> stuff, and I noticed that this code:
>> import os
>> import sys
>> init_name = r"C:\Pyth
Hi Andrea,
On Tue, Oct 4, 2011 at 3:04 AM, Andrea Gavana wrote:
> Hi All,
> I was fiddling here and there with some code doing dynamic import of
> stuff, and I noticed that this code:
> import os
> import sys
> init_name = r"C:\Python27\Lib\site-packages\numpy\__init__.py"
> directory, module
I ended up fixing my problem by removing the 'buffering' flag and adding the
'copy' flag to each of the input arrays.
I think that nested_iters might be improved by an operand axes specification
for each layer of nesting like nditer uses, though I suppose that 3 layers
of nesting might be confusin
You can use the BLAS and LAPACK environment variables.
export BLAS=/path/to/libatlas.so
export LAPACK=/path/to/libatlas.so
python setup.py build
I've recently had problems with ATLAS solving equation systems
incorrectly for certain inputs with no adequate explanation.
Re-running the same simu
Hi again,
I have built the ATLAS dynamic shared libraries and now need to tell numpy to
build against them which are located in a different location to where it
expects them. Do you know how I can do that? The command I am using to build
numpy is:
python setup.py build --fcompiler=gnu95
but
On Thu, Oct 6, 2011 at 7:29 AM, Samuel John wrote:
> I just learned two things:
>
> 1. np.newaxis
> 2. Array dimension broadcasting rocks more than you think.
>
>
Yup. :)
>
> The x[:, np.newaxis] might not be the most intuitive solution but it's
> great and powerful.
> Intuitive would be to h
I just learned two things:
1. np.newaxis
2. Array dimension broadcasting rocks more than you think.
The x[:, np.newaxis] might not be the most intuitive solution but it's great
and powerful.
Intuitive would be to have x.T to transform [0,1,2,4] into [[0],[1],[2],[4]].
Thanks Warren :-)
Samuel
import numpy
# Say y is
y = numpy.array([1,2,3])
Y = numpy.vstack([y,y,y,y])
# Y is array([[1, 2, 3],
# [1, 2, 3],
# [1, 2, 3],
# [1, 2, 3]])
x = numpy.array([[0],[2],[4],[6]]) # a column-vector of your scalars x0, x1...
Y - x
Hope this is what you meant.
cheers,
Sa
On Thu, Oct 6, 2011 at 7:08 AM, Neal Becker wrote:
> Given a vector y, I want a matrix H whose rows are
>
> y - x0
> y - x1
> y - x2
> ...
>
>
> where x_i are scalars
>
> Suggestion?
>
>
In [15]: import numpy as np
In [16]: y = np.array([10.0, 20.0, 30.0])
In [17]: x = np.array([0, 1, 2, 4])
Given a vector y, I want a matrix H whose rows are
y - x0
y - x1
y - x2
...
where x_i are scalars
Suggestion?
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
Hi David,
Thanks for your reply. Nope, I didn't build the ATLAS libraries myself and am
trying to do that now. However, whenever I try to build the shared libraries
using the configure command:
[root@cn130 linux]# ../configure -Fa alg -fPIC --prefix=/gpfs/grace/atlas-3.8.4
it keeps building th
12 matches
Mail list logo
