I made a small change to the array creation function so that if you
explicitly specify dtype=object, then the logic for determining the
shape of the array is changed.
The new logic for constructing dtype=object arrays from Python sequences
is that the shape is determined by nested lists (and
Hello all
In some situations, I have to work with very large matrices. My Windows
machine has 3 GB RAM, so I would expect to be able to use most of my
process's address space for my matrix.
Unfortunately, with matrices much larger than 700 or 800 MB, one starts
running into heap fragmentation
For 1-d inputs I think r_ should act like vstack, and c_ should act
like column_stack.
Currently r_ and c_ both act like hstack for 1-d inputs.
Background:
I keep getting bitten by the fact that this doesn't work:
a = array([1,2,3])
b = array([[1,2,3],[2,3,4]])
c = array([4,5,6])
r_[b,c]
Thanks a lot Francescm, Bruce and Robert!
Your response was all the essentials I wanted to know.
As soon as I'll be back from my vacations time
I'll try numpys PRNG.
Sebastian
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Hi,
The latest svn version of numpy seems to be causing a segmentation fault
when converting an array containing strings to floats using .astype().
It only seems to happen when converting 'from' strings. Casting between
numerical types and 'to' strings is no problem.
:yves $ python -c import
Hi all,Is it expected that the following script would spend over half of the total execution time in defmatrix.py:103(__array_finalize__)?from numpy import matrixm=matrix( (1,1) )for i in range(100):
m[0,0] = 0.Python version is 2.5b1, NumPy 0.9.8.More importantly, is there a way to avoid
On Wednesday 19 July 2006 14:41,
[EMAIL PROTECTED] wrote:
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Yves Frederix wrote:
Hi,
The latest svn version of numpy seems to be causing a segmentation fault
when converting an array containing strings to floats using .astype().
It only seems to happen when converting 'from' strings. Casting between
numerical types and 'to' strings is no problem.
Kevin Jacobs [EMAIL PROTECTED] wrote:
Hi all,
Is it expected that the following script would spend over half of the
total execution time in defmatrix.py:103(__array_finalize__)?
from numpy import matrix
m=matrix( (1,1) )
for i in range(100):
m[0,0] = 0.
Python version is 2.5b1,
Kevin Jacobs [EMAIL PROTECTED] wrote:
On 7/19/06, *Travis Oliphant* [EMAIL PROTECTED]
mailto:[EMAIL PROTECTED] wrote:
Kevin Jacobs [EMAIL PROTECTED]
mailto:[EMAIL PROTECTED] wrote:
On 7/19/06, *Kevin Jacobs [EMAIL PROTECTED]
mailto:[EMAIL PROTECTED]
mailto: [EMAIL
On 7/19/06, Travis Oliphant [EMAIL PROTECTED] wrote:
Are you now using SVN NumPy, or are we still looking at NumPy 0.9.8 numbers?Sorry -- SVN updated as of about two hours ago.Thanks,-Kevin
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I am not sure where to look for this, sorry if it is RTFM or JPS
(just plain stupid):
Is there a way to set a default to print the entire array, rather than
an ellipses version of it? If not, why doesn't
pprint.pformat(numpy.random.normal(0,1,(100, 100)), width=1000) at
least give me something
Keith Goodman wrote:
Is there much speed to be gained by compiling atlas for a dual core system?
I imagine the answer is yes. However, Clint Whaley literally just today
announced the first release of a beta version of ATLAS whose
configuration utility actually supports Core Duo, although
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