Mon, 08 Aug 2011 11:27:14 -0400, Angus McMorland wrote:
I've just upgraded to the latest numpy from git along with upgrading
Ubuntu to natty. Now some of my code, which relies on ctypes-wrapping of
data structures from a messaging system, fails with the error message:
RuntimeWarning: Item
Hi,
I'm running numpy 1.6.1rc2 + python 2.7.1 64-bit from python.org on OSX 10.6.8.
I have a f2py'd fortran routine that inputs and outputs fortran real*8
scalars, and I normally call it like
tu,tv,E,El,IF,HF,HFI = LW.rotate2u(u,v,NN,ff,0)
I now want to call it over 2D arrays UT,VT,N,f
Using
On Wed, Aug 10, 2011 at 3:45 AM, George Nurser gnur...@gmail.com wrote:
Hi,
I'm running numpy 1.6.1rc2 + python 2.7.1 64-bit from python.org on OSX
10.6.8.
I have a f2py'd fortran routine that inputs and outputs fortran real*8
scalars, and I normally call it like
tu,tv,E,El,IF,HF,HFI =
On 10 August 2011 04:01, Pauli Virtanen p...@iki.fi wrote:
Mon, 08 Aug 2011 11:27:14 -0400, Angus McMorland wrote:
I've just upgraded to the latest numpy from git along with upgrading
Ubuntu to natty. Now some of my code, which relies on ctypes-wrapping of
data structures from a messaging
Works fine with the [...]s.
Thanks very much.
--George
On 10 August 2011 17:15, Mark Wiebe mwwi...@gmail.com wrote:
On Wed, Aug 10, 2011 at 3:45 AM, George Nurser gnur...@gmail.com wrote:
Hi,
I'm running numpy 1.6.1rc2 + python 2.7.1 64-bit from python.org on OSX
10.6.8.
I have a f2py'd
A coworker is trying to load a 1Gb text data file into a numpy array
using numpy.loadtxt, but he says it is using up all of his machine's 6Gb
of RAM. Is there a more efficient way to read such text data files?
-- Russell
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NumPy-Discussion mailing
Hi,
I think this one might be for Pauli.
I've run into an odd problem that seems to be an interaction of
numpydoc and autosummary and large classes.
In summary, large classes and numpydoc lead to large tables of class
methods, and there seems to be an error in the creation of the large
tables
On Wed, Aug 10, 2011 at 3:28 PM, Matthew Brett matthew.br...@gmail.com wrote:
Hi,
I think this one might be for Pauli.
I've run into an odd problem that seems to be an interaction of
numpydoc and autosummary and large classes.
In summary, large classes and numpydoc lead to large tables of
On 10 Aug 2011, at 19:22, Russell E. Owen wrote:
A coworker is trying to load a 1Gb text data file into a numpy array
using numpy.loadtxt, but he says it is using up all of his machine's 6Gb
of RAM. Is there a more efficient way to read such text data files?
The npyio routines (loadtxt as
There was also some work on a semi-mutable array type that allowed
appending along one axis, then 'freezing' to yield a normal numpy
array (unfortunately I'm not sure how to find it in the mailing list
archives). One could write such a setup by hand, using mmap() or
realloc(), but I'd be inclined
On Wed, Aug 10, 2011 at 04:01:37PM -0400, Anne Archibald wrote:
A 1 Gb text file is a miserable object anyway, so it might be desirable
to convert to (say) HDF5 and then throw away the text file.
+1
G
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NumPy-Discussion mailing list
On 10 Aug 2011, at 22:03, Gael Varoquaux wrote:
On Wed, Aug 10, 2011 at 04:01:37PM -0400, Anne Archibald wrote:
A 1 Gb text file is a miserable object anyway, so it might be desirable
to convert to (say) HDF5 and then throw away the text file.
+1
There might be concerns about ensuring data
On 10. aug. 2011, at 21.03, Gael Varoquaux wrote:
On Wed, Aug 10, 2011 at 04:01:37PM -0400, Anne Archibald wrote:
A 1 Gb text file is a miserable object anyway, so it might be desirable
to convert to (say) HDF5 and then throw away the text file.
+1
G
+1 and a very warm recommendation
Came across this today when trying to determine what was wrong with my code:
import numpy as np
matched_to = np.array([-1] * 5)
in_ellipse = np.array([False, True, True, True, False])
match = np.array([False, True, True])
matched_to[in_ellipse][match] = 3
I would expect matched_to to now be
Hi,
On Wed, Aug 10, 2011 at 12:38 PM, Skipper Seabold jsseab...@gmail.com wrote:
On Wed, Aug 10, 2011 at 3:28 PM, Matthew Brett matthew.br...@gmail.com
wrote:
Hi,
I think this one might be for Pauli.
I've run into an odd problem that seems to be an interaction of
numpydoc and autosummary
On Wed, Aug 10, 2011 at 6:17 PM, Matthew Brett matthew.br...@gmail.com wrote:
Hi,
On Wed, Aug 10, 2011 at 12:38 PM, Skipper Seabold jsseab...@gmail.com wrote:
On Wed, Aug 10, 2011 at 3:28 PM, Matthew Brett matthew.br...@gmail.com
wrote:
Hi,
I think this one might be for Pauli.
I've run
Hi,
On Wed, Aug 10, 2011 at 5:03 PM, josef.p...@gmail.com wrote:
On Wed, Aug 10, 2011 at 6:17 PM, Matthew Brett matthew.br...@gmail.com
wrote:
Hi,
On Wed, Aug 10, 2011 at 12:38 PM, Skipper Seabold jsseab...@gmail.com
wrote:
On Wed, Aug 10, 2011 at 3:28 PM, Matthew Brett
hi,
i am trying to invert matrices like this:
[[ 0.01643777 -0.13539939 0.11946689]
[ 0.12479926 0.01210898 -0.09217618]
[-0.13050087 0.07575163 0.01144993]]
in perl using Math::MatrixReal;
and in various online calculators i get
[ 2.472715991745 3.680743681735 -3.831392002314 ]
[
On 8/10/2011 8:50 PM, jp d wrote:
i am trying to invert matrices like this:
[[ 0.01643777 -0.13539939 0.11946689]
[ 0.12479926 0.01210898 -0.09217618]
[-0.13050087 0.07575163 0.01144993]]
in perl using Math::MatrixReal;
and in various online calculators i get
[ 2.472715991745
The matrix in singular, so you can not expect a stable inverse.
Nadav.
From: numpy-discussion-boun...@scipy.org [numpy-discussion-boun...@scipy.org]
On Behalf Of jp d [yo...@yahoo.com]
Sent: 11 August 2011 03:50
To: numpy-discussion@scipy.org
Subject:
The svs are
1.1695e-01, 1.1682e-01, 6.84719250e-10
so if you try
np.linalg.pinv(a,1e-5)
array([[ 0.41097834, 3.12024106, -3.26279309],
[-3.38526587, 0.30274957, 1.89394811],
[ 2.98692033, -2.30459609, 0.28627222]])
you at least get an answer that's not
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