ation, and finally read back the data.
The library also can use MPI to parallelize.
Best regards,
Michael
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Heya, I'm not a numbers guy, but I maintain servers for scientists and
researchers who are. Someone pointed out that our numpy installation on
a particular server was only using one core. I'm unaware of the who/how
the previous version of numpy/OpenBLAS were installed, so I installed
them fro
attribution to the original author(s).
Michael Droettboom, chair
Jacob Vanderplas
Phil Elson
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+1. This seems nicer than patching __init__.py itself, in that it is much
more transparent.
Good idea.
Michael
On Thu, Feb 11, 2016 at 7:19 PM Matthew Brett
wrote:
> Hi,
>
> Over at https://github.com/numpy/numpy/issues/5479 we're discussing
> Windows wheels.
>
> On t
to simplify the whole
CI setup process. We hope we can help each other rather than compete.
Best,
Michael
On Sat, Feb 6, 2016 at 5:53 PM Chris Barker wrote:
> On Sat, Feb 6, 2016 at 3:42 PM, Michael Sarahan
> wrote:
>
>> FWIW, we (Continuum) are working on a CI system that builds
ssing as a requirement of
scipy).
Best,
Michael
On Sat, Feb 6, 2016 at 5:22 PM Chris Barker wrote:
> On Fri, Feb 5, 2016 at 3:24 PM, Nathaniel Smith wrote:
>
>> On Fri, Feb 5, 2016 at 1:16 PM, Chris Barker
>> wrote:
>>
>
>
>> >> > If we set up a
walker.com/
It may be that your openblas has a dependency that it can't load for some
reason. Dependency walker works on .pyd files as well as .dll files.
Hth,
Michael
On Wed, Jan 27, 2016, 07:40 G Young wrote:
> I do have my site.cfg file pointing to my library which contains a .lib
>
this is helpful information.
Best,
Michael
On Wed, Jan 27, 2016, 03:39 G Young wrote:
> Hello all,
>
> I'm trying to update the documentation for building Numpy from source, and
> I've hit a brick wall in trying to build the library using OpenBLAS because
> I can'
ou do not need "source")
Hth,
Michael
On Mon, Jan 25, 2016, 17:21 G Young wrote:
> With regards to testing numpy, both Conda and Pip + Virtualenv work quite
> well. I have used both to install master and run unit tests, and both pass
> with flying colors. This chart here
&g
Continuum provides MKL free now - you just need to have a free anaconda.org
account to get the license: http://docs.continuum.io/mkl-optimizations/index
HTH,
Michael
On Wed, Dec 16, 2015 at 12:35 PM Edison Gustavo Muenz <
edisongust...@gmail.com> wrote:
> Sometime ago I saw th
Announcement: pyMIC v0.7
=
I'm happy to announce the release of pyMIC v0.7.
pyMIC is a Python module to offload computation in a Python program to the
Intel Xeon Phi coprocessor. It contains offloadable arrays and device
management functions. It supports invocation of
s
you. Try cd'ing to a different folder (importantly, one NOT containing a
numpy folder!) and run the test command from there.
HTH,
Michael
On Sun, Oct 18, 2015 at 6:46 PM Luke Zoltan Kelley
wrote:
> Thanks Yu,
>
> There was nothing in my PYTHONPATH at first, and adding my numpy d
-BEGIN PGP SIGNED MESSAGE-
Hash: SHA1
Hi,
I am sorry I sent the wrong pull request. Here is the correct one:
https://github.com/numpy/numpy/pull/6460
Best regards,
Michael
Am 12.10.2015 um 20:19 schrieb Michael Behrisch:
> Hi list, I encountered a problem in my code which depends
submitted a pull
request: https://github.com/behrisch/numpy/pull/1
Although it is mainly a workaround for the bug mentioned, I would be
happy if it could get accepted because I have only limited control of
the environment.
Best regards,
Michael
-BEGIN PGP SIGNATURE-
Version: GnuPG v2.0.22 (GNU
l & load_library.
Version 0.1
----
Initial release.
Dr.-Ing. Michael Klemm
Senior Application Engineer
Software and Services Group
Developer Relations Division
Phone +49 89 9914 2340
Cell+49 174 2417583
Intel Deutschland GmbH
Registered Address: Am Campeon 10-12, 85
er
or vice versa. All I assume now is that a temporary matrix is C order, but
that I can control when constructing the matrix.
Thanks for helping on this one!
Kind regards,
-michael
Intel GmbH
Dornacher Strasse 1
85622 Feldkirchen/Muenchen, Deutschland
Sitz der Gesellschaft: Feldk
ing U,
sigma, and V matrixes with Fortran storage. Is there any way to force these
kind of algorithms to not change the storage order? That would make passing
the matrixes to the native dgemm operation much easier.
Cheers,
-michael
Dr.-Ing. Michael Klemm
Senior Application Engineer
Sof
than a
> GPU).
What type of code are you offloading?
Cheers,
-michael
Intel GmbH
Dornacher Strasse 1
85622 Feldkirchen/Muenchen, Deutschland
Sitz der Gesellschaft: Feldkirchen bei Muenchen
Geschaeftsfuehrer: Christian Lamprechter, Hannes Schwaderer, Douglas Lusk
Registergericht: Muenc
Announcement: pyMIC v0.5
=
I'm happy to announce the release of pyMIC v0.5.
pyMIC is a Python module to offload computation in a Python program to the
Intel Xeon Phi coprocessor. It contains offloadable arrays and device
management functions. It supports invocation of
Stefan van der Walt writes:
> On 2014-10-27 15:26:58, D. Michael McFarland wrote:
>> What I would like to ask about is the situation this illustrates, where
>> both NumPy and SciPy provide similar functionality (sometimes identical,
>> to judge by the documentation). Is t
wisdom on the topic.
Regards,
Michael
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internal product teams to see if we
are able to help here. That’s no guarantee, but I can try ☺.
Cheers,
-michael
Dr.-Ing. Michael Klemm
Senior Application Engineer
Software and Services Group
Developer Relations Division
Phone+49 89 9914 2340
Cell +49 174 2417583
From: numpy
material is still work in progress and needs
some polish here and
there. Still it could be useful for others and even a starting point for a
simple BLAS implementation.
Cheers,
Michael
[1]: http://www.cs.utexas.edu/users/flame/pubs/BLISTOMSrev2.pdf
-
On May 29, 2014, at 3:16 PM, Michael McNeil Forbes
wrote:
> On May 29, 2014, at 1:41 AM, Ralf Gommers wrote:
>> On Thu, May 29, 2014 at 5:35 AM, Michael McNeil Forbes
>> wrote:
>>> I just noticed that meshgrid() silently ignore extra arguments. It just
>>&g
On May 29, 2014, at 1:41 AM, Ralf Gommers wrote:
> On Thu, May 29, 2014 at 5:35 AM, Michael McNeil Forbes
> wrote:
>> I just noticed that meshgrid() silently ignore extra arguments. It just
>> burned me (I forgot that it is meshgrid(indexing='ij') and tried
>
guments. If this is not a design decision, I will open an issue and PR.
Michael.
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then
> benchmark against MKL, Accelerate and OpenBLAS. If I can get the
> performance better than 75% of their speed, without any assembly or dark
So what percentage on performance did you achieve so far?
Cheers,
Michael
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Hello,
I'm trying to build numpy from source to use AMD's ACML for matrix
multiplication (specifically the multi-threaded versions gfortran64_mp).
I'm able to successfully compile and use a working version of np.dot, but
my resulting installation doesn't pass numpy's test suite, instead, I get a
s
xrange should be more memory efficient than range:
http://stackoverflow.com/questions/135041/should-you-always-favor-xrange-over-range
Replacing arrays with lists is probably a bad idea for a lot of reasons.
You'll lose nice vectorization of simple operations, and all of numpy's
other benefits.
polation
* control of baselines in stackplot
* many improvements to text and color handling
For a complete list of what's new, see
<http://matplotlib.org/users/whats_new.html#new-in-matplotlib-1-3>http://matplotlib.org/users/whats_new.html#new-in-matplotlib-1-3
Have fun, and enjo
Apologies: I didn't realize the link to the raw results only exists for
users with edit permissions. The public URL for the raw results is:
https://docs.google.com/spreadsheet/ccc?key=0AjrPjlTMRTwTdHpQS25pcTZIRWdqX0pNckNSU01sMHc&usp=sharing
Mike
On 07/18/2013 09:42 AM, Michael D
We have had 508 responses to the matplotlib user survey. Quite a nice
turnout!
You can view the results here:
https://docs.google.com/spreadsheet/viewanalytics?key=0AjrPjlTMRTwTdHpQS25pcTZIRWdqX0pNckNSU01sMHc&gridId=0#chart
and from there, you can access the complete raw results.
I will be d
ing lists.
Cheers,
Michael Droettboom, and the matplotlib team
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On 2013-04-19 01:02:59 +, Benjamin Root said:
>
>
>
> On Thu, Apr 18, 2013 at 7:31 PM, K.-Michael Aye
> wrote:
> I don't understand why sometimes a direct assignment of a new dtype is
> possible (but messes up the values), and why at other times a seemingl
k (most recent call last)
in ()
> 1 val.dtype='float64'
AttributeError: attribute 'dtype' of 'numpy.generic' objects is not writable
=== end of code
So why is there an error in the 2nd case, but no error in the first
case? Is there a logic to it?
Thanks,
Micha
I'm pleased to announce the release of matplotlib 1.2.1. This is a bug
release and improves stability and quality over the 1.2.0 release from
four months ago. All users on 1.2.0 are encouraged to upgrade.
Since github no longer provides download hosting, our tarballs and
binaries are back on
Austin TX *
Winners will be announced during the conference days
* Friday-Saturday, June 27 - 28: SciPy 2013 Sprints, Austin TX & remote
We look forward to exciting submissions that push the boundaries of
plotting, in this, our first attempt at this kind of competition.
The SciPy Pl
Austin TX *
Winners will be announced during the conference days
* Friday-Saturday, June 27 - 28: SciPy 2013 Sprints, Austin TX & remote
We look forward to exciting submissions that push the boundaries of
plotting, in this, our first attempt at this kind of competition.
The SciPy Pl
t of 3: 16.7 s per loop
%timeit accum_custom(accmap, a, func=np.sum)
>>> 1 loops, best of 3: 945 ms per loop
Best regards,
Michael
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If I may also point out another simple (but critical for astropy) bugfix
related to masked arrays:
https://github.com/numpy/numpy/pull/2747
Mike
On 11/14/2012 02:46 PM, Thomas Robitaille wrote:
I've recently opened a couple of pull requests that fix bugs with
MaskedArray - these are pretty st
As numpy.fromfile seems to require full file object functionalities
like seek, I can not use it with the sys.stdin pipe.
So how could I stream a binary pipe directly into numpy?
I can imagine storing the data in a string and use StringIO but the
files are 3.6 GB large, just the binary, and that w
Is it possible to use sys.stdin as input for numpy.fromfile?
I can't make it work. This simple example:
import sys
import numpy as np
# I know the stream format, so I skip parsing the header
keys = ['lot','lon','tb','qual']
dt = [(key,'f8') for key in keys]
# skip the header, it has 'end' in
> This looks interesting:
>
> http://code.google.com/p/blaze-lib/
So maybe you also want to have a look at
http://flens.sf.net
Just to promote my own baby in this context too ;-)
Cheers,
Michael___
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s also
includes a patch and regression test for ticket #1156 so it can be
closed out too after review.
http://projects.scipy.org/numpy/ticket/1156
http://projects.scipy.org/numpy/ticket/2100
Michael.
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rate this with the vectorize
class, then this should be easy to patch 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
> > wrote:
> Hi,
>
>> I added a simple enhanceme
into the correct position (filling in
defaults as needed) and then class the "vectorize"d function.
If people think this is reasonable, I can improve the patch with more
comprehensive testing and error messages.
Michael.
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On 04/03/2012 12:48 PM, Chris Barker wrote:
It would be nice to have a clean C++ wrapper around ndarrays, but that
doesn't exist yet (is there a good reason for that?)
Check out:
http://code.google.com/p/numpy-boost/
Mike
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27; and then convert later to
the type of the original array?
As one can see in this case, the result would be much closer to the true value.
Michael
On 2012-01-24 19:01:40 +, Val Kalatsky said:
Just what Bruce said.
You can run the following to confirm:
np.mean(data - data.mean())
If
ersion with the same
results.
Am I really soo tired that I can't see what I am doing wrong here?
For completion, the data was read by a osgeo.gdal dataset method called
ReadAsArray()
My numpy.__version__ gives me 1.6.1 and my whole setup is based on
Enthought's EP
Hi Everyone,
First off, thanks for all your hard work on numpy, its a really great help!
I was wondering if there was a standard 'groupby' in numpy, that
similar to that in itertools.
I know its not hard to write with np.diff, but I have found myself
writing it on more than a couple of occasions, a
tly
understand how to keep the points where I did the evaluation of the
function.
Maybe it is also imaginable to do something with functional tricks as 'map'?
Thanks for any suggestions!
Best regards,
Michael
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The return type of PyArray_BYTES in the old API compatibility code seems
to have changed recently to (void *) which breaks matplotlib builds.
This pull request changes it back. Is this correct?
https://github.com/numpy/numpy/pull/121
Mike
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@scipy.org
[mailto:numpy-discussion-boun...@scipy.org] בשם Michael Katz
נשלח: Tuesday, June 21, 2011 10:06
אל: Discussion of Numerical Python
נושא: [Numpy-discussion] faster in1d() for monotonic case?
The following call is a bottleneck for me:
np.in1d( large_array.field_of_interest, values_o
The following call is a bottleneck for me:
np.in1d( large_array.field_of_interest, values_of_interest )
I'm not sure how in1d() is implemented, but this call seems to be slower than
O(n) and faster than O( n**2 ), so perhaps it sorts the values_of_interest and
does a binary search for each
fromfile(f, dtype=dt)
Now the data is read in and I can access it, but I have the 'junk' in
the array, which annoys me.
Is there a way to remove the junk data, or skip it with fromfile ?
Another issue is that when accessing one sensor, I do it this
l the available numpy function names.
From: Neil Crighton
To: numpy-discussion@scipy.org
Sent: Sun, May 29, 2011 10:03:25 AM
Subject: Re: [Numpy-discussion] finding elements that match any in a set
Michael Katz yahoo.com> writes:
> Yes, thanks, np.in1d is w
:18, Michael Katz wrote:
> Yes, thanks, np.in1d is what I needed. I didn't know how to find that.
>
> It still seems counterintuitive to me that
>
> indexes = np.where( records.integer_field in values )
>
> does not work whereas
>
> indexes = np.where( re
d get overridden the
same way.
From: Christopher Barker
To: Discussion of Numerical Python
Sent: Fri, May 27, 2011 5:48:37 PM
Subject: Re: [Numpy-discussion] finding elements that match any in a set
On 5/27/11 9:48 AM, Michael Katz wrote:
> I have a numpy array, rec
I have a numpy array, records, with named fields including a field named
"integer_field". I have an array (or list) of values of interest, and I want to
get the indexes where integer_field has any of those values.
Because I can do
indexes = np.where( records.integer_field > 5 )
I thought I
of temp arrays being created.
Michael
From: Robert Kern
To: Discussion of Numerical Python
Sent: Tue, May 3, 2011 5:14:06 PM
Subject: Re: [Numpy-discussion] general newbie question about applying an
arbitrary function to all elements in a numpy array
On Tue,
I have a basic question about applying functions to array elements. There is a
rambling introduction here and then I get to the ACTUAL QUESTION about 2/3 of
the way down.
RAMBLING INTRODUCTION
I was trying to do a simple mapping of array values to other values. So I had:
un
ions ``numpy.unique1d``, ``numpy.setmember1d``,
> ``numpy.intersect1d_nu`` and ``numpy.lib.ufunclike.log2`` were removed.
>
>
> ``numpy.ma``
>
>
> Several deprecated items were removed from the ``numpy.ma`` module::
>
>* ``numpy.ma.MaskedArray`` "raw_data" method
>* ``numpy.ma.
idate next weekend if
> these are solved. As far as I know no one is working on the doc
> issues right now. It would be great if someone could step up and do
> this.
>
> Thanks,
> Ralf
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>
Hello, I stumbled upon this group tonight (
http://mail.scipy.org/pipermail/numpy-discussion/2010-October/053420.html)
while searching Google for examples of Cellular Automata(CA) using Numpy.
The "Game of Life Strides" example looks great, but I don't fully comprehend
how this example is working:
Just wanted to make the developers aware of this bug that causes one of
our pyfits unit tests to fail:
http://projects.scipy.org/numpy/ticket/1733
Mike
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case.
As we already have the 'convenience' of both linspace and arange, which
in principle could be done by one function alone if we'd precalculate
all required information ourselves, why not go the full way, and take
all overhead away from the user?
) but that's what I want to avoid!)
Best regards and Happy New Year!
Michael
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I'm a hopeful Matlab refugee trying to understand the numpy way.
Perhaps someone can explain why some numpy functions require
shape specifications in different ways. For example, below I create
a random 2-by-3 array, and then a "ones" 2-by-3 array:
A = numpy.random.randn(2,3)
B = numpy.ones((2,3
Christian
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--
Michael Droettboom
Science Software Branch
Space Telescope Science Institute
Baltimore, Mar
http://mail.scipy.org/mailman/listinfo/numpy-discussion
--
Michael Droettboom
Science Software Branch
Space Telescope Science Institute
Baltimore, Maryland, USA
Changes in HEAD
Modified doc/sphinxext/docscrape.py
diff --git a/doc/sphinxext/docscrape.py b/doc/sphinxext/docscra
.scipy.org/doc/numpy/reference/generated/numpy.recarray.html
Is this a bug?
Mike
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Hi,
The following example demonstrates a rather unexpected result:
>>> import numpy
>>> x = numpy.array( complex( 1.0 , 1.0 ) , numpy.object )
>>> print x.real
(1+1j)
>>> print x.imag
0
Shouldn't real and imag return an error in such a situation?
Thanks,
Mike
___
, September 9, 2010, Robert Kern wrote:
> On Thu, Sep 9, 2010 at 15:59, Alexander Michael wrote:
>> Clever and concise (and expect that it works), but isn't this less
>> efficient? Sorting is O(n*log(n)), while the code I gave is O(n).
>> Using argsort has the potential
Clever and concise (and expect that it works), but isn't this less
efficient? Sorting is O(n*log(n)), while the code I gave is O(n).
Using argsort has the potential to use less memory, though.
On Tuesday, September 7, 2010, Zachary Pincus wrote:
>> indices = argsort(a1)
>> ranks = zeros_like(indi
Clever and concise (and expect that it works), but isn't this less
efficient? Sorting is O(n*log(n)), while the code I gave is O(n).
Using argsort has the potential to use less memory, though.
On Tuesday, September 7, 2010, Zachary Pincus wrote:
>> indices = argsort(a1)
>> ranks = zeros_like(indi
On Wed, Sep 8, 2010 at 5:35 PM, Michael Gilbert wrote:
> On Wed, 8 Sep 2010 15:44:02 -0400, Michael Gilbert wrote:
>> On Wed, Sep 8, 2010 at 12:23 PM, Charles R Harris wrote:
>> >
>> >
>> > On Wed, Sep 8, 2010 at 9:46 AM, Michael Gilbert
>> > wrote:
On Wed, 8 Sep 2010 15:44:02 -0400, Michael Gilbert wrote:
> On Wed, Sep 8, 2010 at 12:23 PM, Charles R Harris wrote:
> >
> >
> > On Wed, Sep 8, 2010 at 9:46 AM, Michael Gilbert
> > wrote:
> >>
> >> On Wed, 8 Sep 2010 09:43:56 -0600, Charles R Harris
On Wed, 8 Sep 2010 22:20:30 +0200, Sandro Tosi wrote:
> On Wed, Sep 8, 2010 at 22:10, Michael Gilbert
> wrote:
> > Here is an example:
> >
> > >>> 0.3/3.0 - 0.1
> > -1.3877787807814457e-17
> >
> > >>> mpmath.mpf(
On Wed, 8 Sep 2010 15:04:17 -0500, Robert Kern wrote:
> On Wed, Sep 8, 2010 at 14:44, Michael Gilbert
> wrote:
> > On Wed, Sep 8, 2010 at 12:23 PM, Charles R Harris wrote:
> >>
> >>
> >> On Wed, Sep 8, 2010 at 9:46 AM, Michael Gilbert
> >> wrot
On Wed, Sep 8, 2010 at 12:23 PM, Charles R Harris wrote:
>
>
> On Wed, Sep 8, 2010 at 9:46 AM, Michael Gilbert
> wrote:
>>
>> On Wed, 8 Sep 2010 09:43:56 -0600, Charles R Harris wrote:
>> > On Wed, Sep 8, 2010 at 9:26 AM, Michael Gilbert
>> > > >
On Wed, 8 Sep 2010 09:43:56 -0600, Charles R Harris wrote:
> On Wed, Sep 8, 2010 at 9:26 AM, Michael Gilbert > wrote:
>
> > Hi,
> >
> > Are there any plans to add support for decimal floating point
> > arithmetic, as defined in the 2008 revision of the IE
Hi,
Are there any plans to add support for decimal floating point
arithmetic, as defined in the 2008 revision of the IEEE 754 standard
[0], in numpy?
Thanks for any info.
Best wishes,
Mike
[0] http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4610935&tag=1
___
Calculating ranks by inverting the results of an argsort is
straightforward and fast for 1D arrays:
indices = argsort(a1)
ranks = zeros_like(indices)
ranks[indices] = arange(len(indices))
I was wondering if there was an equally pithy way to do this for
multiple data samples stored column-wise in
On Wed, 1 Sep 2010 21:15:22 + (UTC), Pauli Virtanen wrote:
> Wed, 01 Sep 2010 16:26:59 -0400, Michael Gilbert wrote:
> > I've been using numpy's float96 class lately, and I've run into some
> > strange precision errors.
> [clip]
> > >>> x
Hi,
I've been using numpy's float96 class lately, and I've run into some
strange precision errors. See example below:
>>> import numpy
>>> numpy.version.version
'1.5.0'
>>> sys.version
'3.1.2 (release31-maint, Jul 8 2010, 01:16:48) \n[GCC 4.4.4]'
>>> x = numpy.array( [0.01] , numpy.
On 08/17/2010 08:10 PM, Ralf Gommers wrote:
On Wed, Aug 18, 2010 at 3:08 AM, Charles R Harris
mailto:charlesr.har...@gmail.com>> wrote:
On Tue, Aug 17, 2010 at 11:27 AM, Michael Droettboom
mailto:md...@stsci.edu>> wrote:
I'm getting one unit test e
-Discussion@scipy.org
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htt
Dear numpy hackers,
I can't find the syntax for unpacking the 3 dimensions of a rgb array.
so i have a MxNx3 image array 'img' and would like to do:
red, green, blue = img[magical_slicing]
Which slicing magic do I need to apply?
Thanks for your hel
> negligible:
>
> import time
> def f(x):
>time.sleep(0.5)
>return 2*x
>
> df = deco.persistent_locals(f)
>
> %timeit f(1)
> 10 loops, best of 3: 501 ms per loop
> %timeit df(1)
> 10 loops, best of 3: 502 ms per loop
>
> Conclusion
>
&g
Well, I just found that I can build shared libs in ATLAS 3.9.23, but
not 3.9.24 or 25.
This begs the question: has anyone actually successfully compiled
ATLAS v3.9.25 shared libs on RHEL/CentOS 5.x?
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Warm regards,
Michael Green
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PIC -c zsytf2.f -o zsytf2.o
gfortran -O2 -fPIC -c zsytrf.f -o zsytrf.o
...
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Michael Green
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#x27;ve been getting with numpy.
Please help.
--
Warm regards,
Michael Green
On Thu, Jun 17, 2010 at 1:32 AM, Robert Kern wrote:
> On Wed, Jun 16, 2010 at 17:29, Michael Green wrote:
>> Bright minds,
>>
>> I'm trying to build Numpy v1.4.1 from source on CentOS 5.2 x64
/temp.linux-x86_64-2.6/numpy/linalg/lapack_litemodule.o
build/temp.linux-x86_64-2.6/numpy/linalg/python_xerbla.o
-L/usr/local/lib -Lbuild/temp.linux-x86_64-2.6 -llapack -lptf77blas
-lptcblas -latlas -lgfortran -o
build/lib.linux-x86_64-2.6/numpy/linalg/lapack_lite.so" failed with
exit s
1 == a2
array([ True, True], dtype=bool) # Looks good
>> a2 == a1
False # Should I have expected this?
--
Michael Droettboom
Science Software Branch
Space Telescope Science Institut
et around it from your code is to cast the chararray
pyfits returns to a regular ndarray. The cast does not perform a copy,
so should be very efficient:
In [6]: from numpy import char
In [7]: import numpy as np
In [8]: c = char.array(['a ', 'b '])
In [9]: c
Out[9]:
chararray(
On Thu, 8 Apr 2010 09:06:16 +0200, ioannis syntychakis wrote:
> thanks for all your answers.
> Now i can get al the values above the 150, but i would also like to have
> their positions in de matrix.
>
> excample:
>
> [[1. 4. 5. 6. 7. 1
> 2. 5. 7. 8. 9. 3
> 3. 5. 7. 1. 3. 7]]
>
> so, if i no
On Wed, 7 Apr 2010 16:40:24 +0200, ioannis syntychakis wrote:
> Hallo Everybody,
>
> I am new in this mail list and python. But I am working at something and I
> need your help.
>
> I have a very big matrix. What I want is to search in that matrix for values
> above the (for example:) 150. If the
Hi,
I am applying Monte Carlo for a problem involving mixed deterministic
and random values. In order to avoid a lot of special handling and
corner cases, I am using using numpy arrays full of a single value to
represent the deterministic quantities.
Anyway, I found that the standard deviation t
building numpy on various flavors
> of
> CentOS/RHEL5.x.
>
We (STScI) routinely build Numpy on RHEL5.x 64-bit systems for our internal
use. We need more detail about what you're doing and what errors you're seeing
to diagnose the problem.
Mike
--
Michael Droettboom
Science Soft
e the "array too large" exception before
trying to dereference the NULL array pointer (ret) in PyArray_FromFile
(see attached patch). But my question is: is this an appropriate fix
for 1.4 (it seems pretty straightforward), or should I only make this to
the trunk?
Mike
--
Mic
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