Hi,
I added a simple enhancement patch to provide vectorize with simple
keyword argument support. (I added a new kwvectorize decorator, but
suspect this could/should easily be rolled into the existing vectorize.)
http://projects.scipy.org/numpy/ticket/2100
This just reorders any kwargs
as well.
http://mail.scipy.org/pipermail/numpy-discussion/2010-September/052642.html
Michael.
On Sat, Apr 7, 2012 at 12:18 AM, Michael McNeil Forbes
michael.for...@gmail.com
wrote:
Hi,
I added a simple enhancement patch to provide vectorize with simple
keyword argument support. (I added
On 9 Apr 2012, at 3:02 AM, Nathaniel Smith wrote:
functools was added in Python 2.5, and so far numpy is still trying to
maintain 2.4 compatibility.
Thanks: I forgot about that. I have attached a 2.4 compatible patch,
updated docs, and tests for review to ticket #2100. This also
includes
I just noticed that meshgrid() silently ignore extra arguments. It just burned
me (I forgot that it is meshgrid(indexing='ij') and tried
meshgrid(indices='ij') which subtly broke my code.)
Is this intentional? I don't see why `meshgrid` does not have explicit
arguments. If this is not a
On May 29, 2014, at 1:41 AM, Ralf Gommers ralf.gomm...@gmail.com wrote:
On Thu, May 29, 2014 at 5:35 AM, Michael McNeil Forbes
michael.forbes+pyt...@gmail.com wrote:
I just noticed that meshgrid() silently ignore extra arguments. It just
burned me (I forgot that it is meshgrid(indexing='ij
On May 29, 2014, at 3:16 PM, Michael McNeil Forbes
michael.forbes+pyt...@gmail.com wrote:
On May 29, 2014, at 1:41 AM, Ralf Gommers ralf.gomm...@gmail.com wrote:
On Thu, May 29, 2014 at 5:35 AM, Michael McNeil Forbes
michael.forbes+pyt...@gmail.com wrote:
I just noticed that meshgrid
What are the semantics of the take function?
I would have expected that the following have the same shape and size:
a = array([1,2,3])
inds = a.nonzero()
a[inds]
array([1, 2, 3])
a.take(inds)
array([[1, 2, 3]])
Is there a bug somewhere here or is this intentional?
Michael.
Hi,
I have a list of tuples that I am using as keys and I would like to
sort this along with some other arrays using argsort. How can I do
this? I would like to do something like:
# These are constructed using lists because they accumulate using
append()
data = [1.0, 3,0]
keys =
)
key_array[:] = keys[:]
inds = argsort(data)
data_array[:] = data[inds]
key_array[:] = keys[inds]
Thanks!
Michael.
On 20 Jun 2007, at 4:57 AM, Francesc Altet wrote:
El dc 20 de 06 del 2007 a les 01:38 -0700, en/na Michael McNeil Forbes
va escriure:
Hi,
I have a list of tuples that I am using
. The
problem can be solved by creating an empty array first, then copying.
Thanks,
Michael.
On 6/20/07, Michael McNeil Forbes [EMAIL PROTECTED] wrote:
Hi,
I have a list of tuples that I am using as keys and I would like to
sort this along with some other arrays using argsort. How can I do
Hi,
Is it possible or easy to add a warning and/or error when array
assignments are made that lose information? I just got caught with
the following type of code:
def f():
return numpy.array([1j,2.0])
x = numpy.empty((2,),dtype=float)
x[:] = f()
I am pre-allocating arrays for speed,
Why are numpy warnings printed rather than issued using the standard
warnings library? I know that the behaviour can be controlled by
seterr(), but it seem rather unpythonic not to use the warnings library.
Is there an explicit reason for this choice? (It seems like a pretty
trivial
On 13 Nov 2007, at 8:46 AM, Travis E. Oliphant wrote:
Michael McNeil Forbes wrote:
Why are numpy warnings printed rather than issued using the standard
warnings library? ... in util.py ...
The warn option explicitly allows you to use the warnings library.
There is already the print mode
On 15 Nov 2007, at 2:45 AM, David Cournapeau wrote:
Which fortran compiler are you using ?
GNU Fortran (GCC) 3.4.6 20060404
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On 13 Nov 2007, at 9:43 AM, Geoffrey Zhu wrote:
On Nov 13, 2007 2:37 AM, David Cournapeau [EMAIL PROTECTED]
u.ac.jp wrote:
Geoffrey Zhu wrote:
Pointer problems are usually random...
...
The original MSI version hangs on numpy.test() if I open IDLE and type
import numpy
numpy.test()
If I
I have also been having random problems with the latest numpy from
svn built on an Intel core 2 Duo Linux box running in 64 bit mode
under Red Hat 3.4.6-8 with the gcc 3.4.6 20060404 and ATLAS 3.8.0.
Could you try without atlas ? Also, how did you configure atlas when
building it ? It seems
On 15 Nov 2007, at 8:23 PM, David Cournapeau wrote:
Could you try without atlas ? Also, how did you configure atlas when
building it ? It seems that atlas is definitely part of the problem
(everybody having the problem does use atlas), and that it involves
Core
2 duo.
David
It seems to
My expectation was that array would iterate over a set. This is
incorrect:
array(set([1,2,3]))
array(set([1, 2, 3]), dtype=object)
Is this the intended behaviour? A trivial work-around that does what
I need is
array(list(set([1,2,3])))
array([1, 2, 3])
but I was wondering if this was
On 16 Nov 2007, at 1:46 AM, Michael McNeil Forbes wrote:
On 15 Nov 2007, at 8:23 PM, David Cournapeau wrote:
Could you try without atlas ? Also, how did you configure atlas when
building it ? It seems that atlas is definitely part of the problem
(everybody having the problem does use atlas
On 23 Jun 2008, at 12:37 PM, Alan McIntyre wrote:
Ugh. That just seems like a lot of unreadable ugliness to me. If
this comment magic is the only way to make that stuff execute
properly
under doctest, I think I'd rather just skip it in favor of clean,
uncluttered, non-doctestable code
On 23 Jun 2008, at 1:28 PM, Anne Archibald wrote:
2008/6/23 Michael McNeil Forbes [EMAIL PROTECTED]:
Thus, one can argue that all examples should also be doctests. This
generally makes things a little more ugly, but much less ambiguous.
This is a bit awkward. How do you give an example
On 2 Jul 2008, at 3:59 PM, Robert Kern wrote:
On Wed, Jul 2, 2008 at 17:43, Nathan Jensen
[EMAIL PROTECTED] wrote:
Hi,
I was wondering if there was any way to speed up the global import of
numpy modules. For a simple import numpy, it takes ~250 ms. In
comparison, importing Numeric is
On 15 Jul 2008, at 6:33 AM, Bruce Southey wrote:
Hi,
Following Travis's suggestion below, I would like to suggest that the
following definitions be depreciated or removed in this forthcoming
release:
numpy.Inf
numpy.Infinity
numpy.infty
numpy.PINF
numpy.NAN
numpy.NaN
...
While this
On Sep 5, 2008, at 8:52 AM, Keith Goodman wrote:
Here's another difference:
a = np.random.randn(10)
timeit np.sum(a[np.where(a0)])
100 loops, best of 3: 3.44 ms per loop
timeit a[a 0].sum()
100 loops, best of 3: 2.21 ms per loop
Here is an even faster method (but much more ugly!):
On Sep 5, 2008, at 8:52 AM, Keith Goodman wrote:
Here's another difference:
a = np.random.randn(10)
timeit np.sum(a[np.where(a0)])
100 loops, best of 3: 3.44 ms per loop
timeit a[a 0].sum()
100 loops, best of 3: 2.21 ms per loop
Here is an even faster method (but much more ugly!):
The hotshot profiler used to do this, but I don't think it is really
supported anymore... I have not used it in a while, but agree that a
line-by-line profiler can be very nice.
Michael.
On Sep 15, 2008, at 6:27 AM, Robin wrote:
Hi,
I am using the prun feature of Ipython which is very
On 10 Mar 2009, at 10:33 AM, Michael S. Gilbert wrote:
On Tue, 10 Mar 2009 17:21:23 +0100, Mark Bakker wrote:
Hello,
I want to convert an array to a string.
I like array2string, but it puts these annoying square brackets
around
the array, like
[[1 2 3],
[3 4 5]]
Anyway we can
np.array([0,1,2,3])[1:-1]
array([1, 2])
but
np.array([0,1,2,3])[np.s_[1:-1]]
array([1, 2, 3])
np.array([0,1,2,3])[np.index_exp[1:-1]]
array([1, 2, 3])
Possible fix:
class IndexExpression(object):
...
def __len__(self):
return 0
(Presently this returns sys.maxint)
Does
Hi,
Is there a way of performing vectorized ?axpy (daxpy) operations
without making copies or dropping into C?
i.e: I want to do
big = (1,5000)
A = np.ones(big,dtype=float)
B = np.ones(big,dtype=float)
a = 1.5
B += a*A
without making any copies?
(I know I could go
A *= a
B += A
A /= a
Thanks Pauli,
On 23 Jun 2009, at 12:46 PM, Pauli Virtanen wrote:
from scipy.lib.blas import get_blas_funcs
axpy, = get_blas_funcs(['axpy'], [A, B])
res = axpy(A.ravel(), B.ravel(), A.size, a)
res.base is B
...
Works provided A and B are initially in C-order so that ravel()
doesn't create
There is also numpy.s_:
inds = np.s_[...,2,:]
z[inds]
(Though there are some problems with negative indices: see for example
http://www.mail-archive.com/numpy-discussion@scipy.org/msg18245.html)
On 8 Aug 2009, at 10:02 PM, T J wrote:
On Sat, Aug 8, 2009 at 8:54 PM, Neil
Submitted as ticket 1196
http://projects.scipy.org/numpy/ticket/1196
On 5 Jun 2009, at 4:12 PM, Robert Kern wrote:
On Fri, Jun 5, 2009 at 16:14, Michael McNeil Forbes
mfor...@physics.ubc.ca wrote:
np.array([0,1,2,3])[1:-1]
array([1, 2])
but
np.array([0,1,2,3])[np.s_[1:-1]]
array([1
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