Hi,
I would like to know if it would be possible at all to put
blas/lapack/atlas code + various scripts to build them for windows in an
automated way somewhere in svn.scipy.org, a bit like what svn.python.org
does for external dependencies ? The rationale is that to build atlas,
you need to
Hi,
Well, as there were no replies to our second proposal for the date/time
dtype, I assume that everbody agrees with it ;-) At any rate, we would
like to proceed with the implementation phase very soon now.
However, it happens that Enthought is sponsoring this job and they
clearly stated
Sure. Make a directory called vendor/ next to trunk/.
On 2008-07-25, David Cournapeau [EMAIL PROTECTED] wrote:
Hi,
I would like to know if it would be possible at all to put
blas/lapack/atlas code + various scripts to build them for windows in an
automated way somewhere in svn.scipy.org,
Hi Stephane,
This is a good suggestion, I'm ccing the numpy list on this. Because I'm
wondering if it wouldn't be a better fit to do it directly at the
numpy.ma level.
I'm sure they already thought about this (and 'inf' values as well) and
if they don't do it , there's probably some good
[EMAIL PROTECTED] wrote:
Sure. Make a directory called vendor/ next to trunk/.
Great, thanks.
cheers,
David
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Hi all,
I found myself busy today trying to understand what went wrong in my FFT code.
I wrote a minimal example/testing code to check the FFT output against an
analytic result and also tried to reverse the transformation to get the
original function back. Most curiously, the results depend on
Charles Doutriaux wrote:
Hi Stephane,
This is a good suggestion, I'm ccing the numpy list on this. Because I'm
wondering if it wouldn't be a better fit to do it directly at the
numpy.ma level.
I'm sure they already thought about this (and 'inf' values as well) and
if they don't do it ,
Hi All,
I'm sending a copy of this reply here because i think we could get some
good answer.
Basically it was suggested to automarically mask NaN (and Inf ?) when
creating ma.
I'm sure you already thought of this on this list and was curious to
know why you decided not to do it.
Just so I
Hi Bruce,
Thx for the reply, we're aware of this, basically the question was why
not mask NaN automatically when creating a nump.ma array?
C.
Bruce Southey wrote:
Charles Doutriaux wrote:
Hi Stephane,
This is a good suggestion, I'm ccing the numpy list on this. Because I'm
wondering
Charles Doutriaux wrote:
Hi Bruce,
Thx for the reply, we're aware of this, basically the question was why
not mask NaN automatically when creating a nump.ma array?
C.
Bruce Southey wrote:
Charles Doutriaux wrote:
Hi Stephane,
This is a good suggestion, I'm ccing the
I mean not having to it myself.
data is a numpy array with NaN in it
masked_data = numpy.ma.array(data)
returns a masked array with a mask where NaN were in data
C.
Bruce Southey wrote:
Charles Doutriaux wrote:
Hi Bruce,
Thx for the reply, we're aware of this, basically the question was
Hi,
There is some unexpected behaviour (to me) when 0-dimensional
arrays are compared with values. For example:
numpy.array([0]).squeeze() == 0
True
numpy.array([None]).squeeze() == None
False
numpy.array(['a']).squeeze() == 'a'
array(True, dtype=bool)
Note that each test
Charles Doutriaux wrote:
I mean not having to it myself.
data is a numpy array with NaN in it
masked_data = numpy.ma.array(data)
returns a masked array with a mask where NaN were in data
Checking for nans is an expensive operation, so it makes sense to make
it optional rather than impose the
Oh, I guess this one's for me...
On Thursday 01 January 1970 04:21:03 Charles Doutriaux wrote:
Basically it was suggested to automarically mask NaN (and Inf ?) when
creating ma.
I'm sure you already thought of this on this list and was curious to
know why you decided not to do it.
Because
Hi Pierre,
Thanks for the answer, I'm ccing cdat's discussion list.
It makes sense, that's also the way we develop things here NEVER assume
what the user is going to do with the data BUT give the user the
necessary tools to do what you're assuming he/she wants to do (as simple
as possible)
Reminder, please test the Mac installer for rc2 so we have time to fix any
bugs before the release next week.
Also, I committed my build script to the trunk/tools/osxbuild. bdist_mpkg
0.4.3 is required.
Thank you,
Chris
On Thu, Jul 24, 2008 at 11:03 AM, Jarrod Millman [EMAIL PROTECTED]
wrote:
On Fri, Jul 25, 2008 at 2:48 PM, Christopher Burns [EMAIL PROTECTED] wrote:
Reminder, please test the Mac installer for rc2 so we have time to fix any
bugs before the release next week.
I just tried it; it installs with no problems and tests run with no failures.
Perhaps I do not understand something properly, if so could someone please
explain the behavior I notice with numpy.linalg.svd when acting on arrays.
It gives the incorrect answer, but works fine with matrices. My numpy is
1.1.0.
R = n.array([[3.6,.35],[.35,1.8]])
V,D,W = n.linalg.svd(R)
On Fri, Jul 25, 2008 at 12:32 PM, Frank Lagor [EMAIL PROTECTED] wrote:
Perhaps I do not understand something properly, if so could someone please
explain the behavior I notice with numpy.linalg.svd when acting on arrays.
It gives the incorrect answer, but works fine with matrices. My numpy is
On Fri, Jul 25, 2008 at 12:36 PM, Keith Goodman [EMAIL PROTECTED] wrote:
On Fri, Jul 25, 2008 at 12:32 PM, Frank Lagor [EMAIL PROTECTED] wrote:
Perhaps I do not understand something properly, if so could someone please
explain the behavior I notice with numpy.linalg.svd when acting on arrays.
On Fri, Jul 25, 2008 at 14:32, Frank Lagor [EMAIL PROTECTED] wrote:
Perhaps I do not understand something properly, if so could someone please
explain the behavior I notice with numpy.linalg.svd when acting on arrays.
It gives the incorrect answer, but works fine with matrices. My numpy is
On Fri, Jul 25, 2008 at 9:39 PM, Keith Goodman [EMAIL PROTECTED] wrote:
On Fri, Jul 25, 2008 at 12:36 PM, Keith Goodman [EMAIL PROTECTED]
wrote:
On Fri, Jul 25, 2008 at 12:32 PM, Frank Lagor [EMAIL PROTECTED]
wrote:
Perhaps I do not understand something properly, if so could someone
Thanks so much for your help on the '*' confusion. It makes sense now.
Thanks,
Frank
On Fri, Jul 25, 2008 at 3:57 PM, [EMAIL PROTECTED] wrote:
Send Numpy-discussion mailing list submissions to
numpy-discussion@scipy.org
To subscribe or unsubscribe via the World Wide Web, visit
On Jul 25, 2008, at 2:48 PM, Christopher Burns wrote:
Reminder, please test the Mac installer for rc2 so we have time to
fix any bugs before the release next week.
Dual G5, 10.5.4, Python 2.5.2 (r252:60911, Feb 22 2008, 07:57:53)
installed as expected, passed all tests:
Ran 1300 tests in
How does python (or numpy/scipy) do exponentiation? If I do x**p,
where p is some positive integer, will it compute x*x*...*x (p times),
or will it use logarithms?
-gideon
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On Fri, Jul 25, 2008 at 1:24 PM, Gideon Simpson [EMAIL PROTECTED] wrote:
How does python (or numpy/scipy) do exponentiation? If I do x**p,
where p is some positive integer, will it compute x*x*...*x (p times),
or will it use logarithms?
Here are some examples:
np.array([[1,2], [3,4]])**2
On Fri, Jul 25, 2008 at 1:32 PM, Keith Goodman [EMAIL PROTECTED] wrote:
On Fri, Jul 25, 2008 at 1:24 PM, Gideon Simpson [EMAIL PROTECTED] wrote:
How does python (or numpy/scipy) do exponentiation? If I do x**p,
where p is some positive integer, will it compute x*x*...*x (p times),
or will it
Fri, 25 Jul 2008 16:24:35 -0400, Gideon Simpson wrote:
How does python (or numpy/scipy) do exponentiation? If I do x**p, where
p is some positive integer, will it compute x*x*...*x (p times), or will
it use logarithms?
For floats it will call operating system's pow, which supposedly is
Francesc,
Could you clarify a couple of points ?
[datetime64]
If I understand properly, your datetime64 would be time units from the POSIX
epoch (1970/01/01 00:00:00), right ? So
+7d would be 1970/01/08 (7 days after the epoch)
-7W would be 1969/11/13 (7*7 days before the epoch)
With this
On Fri, Jul 25, 2008 at 2:13 PM, Robert Pyle [EMAIL PROTECTED] wrote:
On Jul 25, 2008, at 2:48 PM, Christopher Burns wrote:
Reminder, please test the Mac installer for rc2 so we have time to
fix any bugs before the release next week.
Dual G5, 10.5.4, Python 2.5.2 (r252:60911, Feb 22
Hi
In a recent thread there was an error in how a matrix is reconstructed from
its SVD decomposition. I apologize if this is just an old and settled issue
and I am just adding noise, but I got bitten by numpy's unfamiliar output
myself a long time ago and I see others get confused as well. So
Robert,
numpy/core/tests/test_ma.py is an old file from a previous install. You
need to remove the numpy directory and reinstall.
Unfortunately the installer does not cleanup old installs.
Chris
On Fri, Jul 25, 2008 at 1:13 PM, Robert Pyle [EMAIL PROTECTED] wrote:
MacBook Pro, Intel Core 2
On Fri, Jul 25, 2008 at 5:19 PM, Christopher Burns [EMAIL PROTECTED]
wrote:
Robert,
numpy/core/tests/test_ma.py is an old file from a previous install. You
need to remove the numpy directory and reinstall.
Whew!
Chuck
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Numpy-discussion
2008/7/25 Thomas J. Duck [EMAIL PROTECTED]:
Hi,
There is some unexpected behaviour (to me) when 0-dimensional
arrays are compared with values. For example:
numpy.array([0]).squeeze() == 0
True
numpy.array([None]).squeeze() == None
False
numpy.array(['a']).squeeze() == 'a'
For this goal, we are proposing a decoupling of the date/time use cases
in two different groups:
1. A pure ``datetime`` dtype (absolute or relative) that would be useful
for timestamping purposes in general (i.e. registering dates without a
need that they be evenly spaced in time).
I
Jarrod Millman wrote:
Hello,
The 1.1.1rc2 is now available:
http://svn.scipy.org/svn/numpy/tags/1.1.1rc2
The source tarball is here:
http://cirl.berkeley.edu/numpy/numpy-1.1.1rc2.tar.gz
Here is the universal Mac binary:
http://cirl.berkeley.edu/numpy/numpy-1.1.1rc2-py2.5-macosx10.5.dmg
On Fri, Jul 25, 2008 at 8:22 PM, Matt Knox [EMAIL PROTECTED] wrote:
The automatic string parsing has been mentioned before, but it is a feature
I am personally very fond of. I use it all the time, and I suspect a lot of
people would like it very much if they used it. It's not suited for high
On Fri, Jul 25, 2008 at 7:18 PM, David Cournapeau
[EMAIL PROTECTED] wrote:
Jarrod Millman wrote:
Hello,
The 1.1.1rc2 is now available:
http://svn.scipy.org/svn/numpy/tags/1.1.1rc2
The source tarball is here:
http://cirl.berkeley.edu/numpy/numpy-1.1.1rc2.tar.gz
Here is the
On Fri, Jul 25, 2008 at 19:19, Stéfan van der Walt [EMAIL PROTECTED] wrote:
2008/7/25 Thomas J. Duck [EMAIL PROTECTED]:
Hi,
There is some unexpected behaviour (to me) when 0-dimensional
arrays are compared with values. For example:
numpy.array([0]).squeeze() == 0
True
On Jul 25, 2008, at 5:22 PM, Charles R Harris wrote:
On Fri, Jul 25, 2008 at 2:13 PM, Robert Pyle
[EMAIL PROTECTED] wrote:
MacBook Pro, Intel Core 2 Duo, 10.5.4, Python 2.5.2 (r252:60911, Feb
22 2008, 07:57:53)
installed as expected, failed one test:
FAIL: check_testUfuncRegression
On Jul 25, 2008, at 7:19 PM, Christopher Burns wrote:
Robert,
numpy/core/tests/test_ma.py is an old file from a previous install.
You need to remove the numpy directory and reinstall.
Unfortunately the installer does not cleanup old installs.
Okay, all is well after all. 1300 tests,
Excellent! Thanks for testing Bob.
On Fri, Jul 25, 2008 at 9:39 PM, Robert Pyle [EMAIL PROTECTED] wrote:
Okay, all is well after all. 1300 tests, no errors.
Bob
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phone: 510.643.4014
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