On Thu, Feb 05, 2009 at 10:29:42PM -0600, Robert Kern wrote:
I have no opinion on the above, as I don't have this use case. However, as
you are talking about implementing something, I jump on the occasion to
suggest another gadget, slightly related: I would like named axis.
Suppose you
On Fri, Feb 06, 2009 at 12:20:37AM -0600, Robert Kern wrote:
On Fri, Feb 6, 2009 at 00:13, Christopher Barker chris.bar...@noaa.gov
wrote:
Travis Oliphant wrote:
What do people think
about adding a default dictionary to every instance of a NumPy array.
It sound kind of heavyweight to
Hi Travis
2009/2/6 Travis Oliphant oliph...@enthought.com:
Thus newarr = arr[['name', 'age']].copy() would be exactly the same
size as arr because elements are copied wholesale and each row is a
single element in the NumPy array.Some infrastructure would have to
be implemented at a
Hi Robert
2009/2/6 Robert Kern robert.k...@gmail.com:
This could be implemented but would require adding information to the
NumPy array.
More than that, though. Every function and method that takes an axis
or reduces an axis will need to be rewritten. For that reason, I'm -1
on the
On Fri, Feb 6, 2009 at 4:22 AM, Stéfan van der Walt ste...@sun.ac.zawrote:
Hi Robert
2009/2/6 Robert Kern robert.k...@gmail.com:
This could be implemented but would require adding information to the
NumPy array.
More than that, though. Every function and method that takes an axis
or
Hi,
+1 on the idea but how will this work with other numpy methods?
suppose *arr* is a structured array with dtype:
[('name', 'S25'),
('height', float),
('age', int),
('gender', 'S8')
]
Would you be able to first define a list of columns such as
cols=['height', 'age']
arr[cols]
This
Hi,
We would like to use the numpy.distutils machinery for numexpr and I'd
like to add different compiler flags depending on the compiler used.
Anbody knows a simple way to do this with numpy.distutils (ore plain
distutils)?
Thanks,
--
Francesc Alted
A Friday 06 February 2009, Francesc Alted escrigué:
Hi,
We would like to use the numpy.distutils machinery for numexpr and
I'd like to add different compiler flags depending on the compiler
used. Anbody knows a simple way to do this with numpy.distutils (ore
plain distutils)?
I've figured
I ended up solving my problem in SWIG, so I might as well post it
here. I just made my own 'array' and 'zeros' functions with floating
point precision as follows:
%pythoncode %{
from numpy import array as np_array
def array (n, type='float32'):
return(np_array(n, type))
from numpy
Darren Dale wrote:
I have a package (nearly ready to
submit to this list for request for comment) that uses a dict subclass
to describe the dimensionality of a quantity, like {m:1, s:-1}.
I'm looking forward to that -- you may have just saved me a bunch of coding!
-Chris
--
Christopher
On Fri, Feb 6, 2009 at 12:09 PM, Christopher Barker
chris.bar...@noaa.govwrote:
Darren Dale wrote:
I have a package (nearly ready to
submit to this list for request for comment) that uses a dict subclass
to describe the dimensionality of a quantity, like {m:1, s:-1}.
I'm looking forward
Hi,
I accidently stumbled upon this odd behavior by numpy.any. The following
code leaks memory -
for i in xrange(1000):
print N.any({'whatever': N.arange(1000)})
Ofcourse, I called any on a dict object by accident, but it should not
really leak memory.
I am running numpy
Hi Folks,
I wonder if there's a way to fill an existing array from an iterator
without creating a temporary array. That is, I'm looking for something
that has the effect of
target = np.array(xrange(9), dtype = float)
target[:] = np.fromiter(repeat(3.14159, 9), dtype=float)
without
Hi all,
Just curious. Is it possible to use xblas with numpy ?
http://www.netlib.org/xblas/
Nils
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On Fri, Feb 6, 2009 at 13:24, Suchindra Sandhu suchin...@gmail.com wrote:
Hi,
I accidently stumbled upon this odd behavior by numpy.any. The following
code leaks memory -
for i in xrange(1000):
print N.any({'whatever': N.arange(1000)})
Ofcourse, I called any on a dict
On Fri, Feb 6, 2009 at 09:31, Bruce Southey bsout...@gmail.com wrote:
Hi,
+1 on the idea but how will this work with other numpy methods?
suppose *arr* is a structured array with dtype:
[('name', 'S25'),
('height', float),
('age', int),
('gender', 'S8')
]
Would you be able to first
In numpy/random/mtrand/randomkit.c on line 159, the initial mersenne twister
key (populated from /dev/urandom) gets bit-wise and'ed with 0x. I'm
just curious as why this is done. A bit-wise and with all ones should just
give you your original quantity back, right? I don't think there
On Fri, Feb 6, 2009 at 15:24, Michael S. Gilbert
michael.s.gilb...@gmail.com wrote:
In numpy/random/mtrand/randomkit.c on line 159, the initial mersenne twister
key (populated from /dev/urandom) gets bit-wise and'ed with 0x. I'm
just curious as why this is done. A bit-wise and with
On Sun, Feb 1, 2009 at 7:39 PM, Darren Dale dsdal...@gmail.com wrote:
On Sun, Feb 1, 2009 at 7:33 PM, Pierre GM pgmdevl...@gmail.com wrote:
On Feb 1, 2009, at 6:32 PM, Darren Dale wrote:
Is there an analog to __array_wrap__ for preprocessing arrays on
their way *into* a ufunc? For
On Fri, Feb 6, 2009 at 03:22, Stéfan van der Walt ste...@sun.ac.za wrote:
Hi Robert
2009/2/6 Robert Kern robert.k...@gmail.com:
This could be implemented but would require adding information to the
NumPy array.
More than that, though. Every function and method that takes an axis
or reduces
On Fri, Feb 6, 2009 at 3:30 PM, Robert Kern robert.k...@gmail.com wrote:
On Fri, Feb 6, 2009 at 03:22, Stéfan van der Walt ste...@sun.ac.za
wrote:
Hi Robert
2009/2/6 Robert Kern robert.k...@gmail.com:
This could be implemented but would require adding information to the
NumPy array.
On Feb 6, 2009, at 4:25 PM, Darren Dale wrote:
I've been looking at how ma implements things like multiply() and
MaskedArray.__mul__. I'm surprised that MaskedArray.__mul__ actually
calls ma.multiply() rather than calling
super(MaskedArray,self).__mul__().
There's some under-the-hood
Ok, so isn't this a slight waste of memory then (a doubling on 64-bit
platforms)? Of course the tradeoff is whether you want to maintain two
codebases for 32- and 64-bit or just one. The advantages of a single codebase
probably outweight an increase in memory usage since we're only talking
On Fri, Feb 6, 2009 at 16:57, Michael S. Gilbert
michael.s.gilb...@gmail.com wrote:
Ok, so isn't this a slight waste of memory then (a doubling on 64-bit
platforms)? Of course the tradeoff is whether you want to maintain two
codebases for 32- and 64-bit or just one. The advantages of a
I'm not going to modify the upstream source and risk introducing bugs.
I agree, its not worth risking it to save 2k of memory.
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On Fri, Feb 6, 2009 at 5:18 PM, Pierre GM pgmdevl...@gmail.com wrote:
On Feb 6, 2009, at 4:25 PM, Darren Dale wrote:
I've been looking at how ma implements things like multiply() and
MaskedArray.__mul__. I'm surprised that MaskedArray.__mul__ actually
calls ma.multiply() rather than
I'm not going to modify the upstream source and risk introducing bugs.
BTW, there is a 64-bit version of the reference mersenne twister
implementation available [1].
Mike
[1] http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/VERSIONS/C-LANG/mt19937-64.c
On Fri, Feb 6, 2009 at 6:11 PM, Darren Dale dsdal...@gmail.com wrote:
On Fri, Feb 6, 2009 at 5:18 PM, Pierre GM pgmdevl...@gmail.com wrote:
On Feb 6, 2009, at 4:25 PM, Darren Dale wrote:
I've been looking at how ma implements things like multiply() and
MaskedArray.__mul__. I'm
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