2009/7/4 Ben Park benpar...@gmail.com:
import numpy as np
import numpy.ma as ma
# There is no effect on the following assignment of ma.masked.
a1 = ma.arange(10).reshape((2,5))
a1.ravel()[np.array([0,2,2])] = ma.masked
In some situations ravel has to return a copy of the data instead of a
Sun, 05 Jul 2009 20:27:02 -0700, d_l_goldsmith kirjoitti:
[clip]
c:\Python25\Lib\site-packages\numpypython
Just don't run Python inside Numpy's package directory. This is not Numpy-
specific: doing a thing like that just breaks relative imports.
Also, Robert's answer in the old thread applies
Thanks, Pauli; learn something new everyday! ;-)
DG
--- On Mon, 7/6/09, Pauli Virtanen p...@iki.fi wrote:
From: Pauli Virtanen p...@iki.fi
Subject: Re: [Numpy-discussion] The baffling behavior that just won't die
To: numpy-discussion@scipy.org
Date: Monday, July 6, 2009, 12:33 AM
Sun, 05
Pauli Virtanen wrote:
Sun, 05 Jul 2009 20:27:02 -0700, d_l_goldsmith kirjoitti:
[clip]
c:\Python25\Lib\site-packages\numpypython
Just don't run Python inside Numpy's package directory. This is not Numpy-
specific: doing a thing like that just breaks relative imports.
I noticed
--- On Mon, 7/6/09, David Cournapeau da...@ar.media.kyoto-u.ac.jp wrote:
avoid this. I can't understand why anyone would got into
site-packages ?
Why, to look at the source, of course - I did it all the time in Mac Unix,
too - must not have ever tried to run anything while in there I guess;
Le vendredi 03 juillet 2009 à 10:00 -0600, Charles R Harris a écrit :
What do you mean by erratic? Are the computed roots different from
known roots? The connection between polynomial coefficients and
polynomial values becomes somewhat vague when the polynomial degree
becomes large, it is
David Goldsmith wrote:
--- On Mon, 7/6/09, David Cournapeau da...@ar.media.kyoto-u.ac.jp wrote:
avoid this. I can't understand why anyone would got into
site-packages ?
Why, to look at the source, of course - I did it all the time in Mac Unix,
too - must not have ever tried to
2009/7/6 Ian Mallett geometr...@gmail.com:
randvecs = numpy.array([x,y,z]).reshape((size,size,3))
Try numpy.array([x, y, z]).T.reshape((size, size, 3))
Regards
Stéfan
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I'm trying to fledge out Cython's numpy.pxd, and have a question:
Is there documentation anywhere, or a simple-to-remember rule, on
whether the return value of a NumPy C API is incref-ed or not?
E.g. PyArray_BASE, PyArray_DESCR obviously does not incref; while I'm
assuming that PyArray_New*
On Mon, Jul 6, 2009 at 3:44 AM, Fabrice Silva si...@lma.cnrs-mrs.fr wrote:
Le vendredi 03 juillet 2009 à 10:00 -0600, Charles R Harris a écrit :
What do you mean by erratic? Are the computed roots different from
known roots? The connection between polynomial coefficients and
polynomial
On Mon, Jul 6, 2009 at 8:16 AM, Charles R Harris
charlesr.har...@gmail.comwrote:
On Mon, Jul 6, 2009 at 3:44 AM, Fabrice Silva si...@lma.cnrs-mrs.frwrote:
Le vendredi 03 juillet 2009 à 10:00 -0600, Charles R Harris a écrit :
What do you mean by erratic? Are the computed roots different
On Mon, Jul 6, 2009 at 7:30 AM, Dag Sverre Seljebotn
da...@student.matnat.uio.no wrote:
I'm trying to fledge out Cython's numpy.pxd, and have a question:
Is there documentation anywhere, or a simple-to-remember rule, on
whether the return value of a NumPy C API is incref-ed or not?
E.g.
Le lundi 06 juillet 2009 à 08:16 -0600, Charles R Harris a écrit :
Double precision breaks down at about degree 25 if things are well
scaled, so that is suspicious in itself. Also, the companion matrix
isn't Hermitean so that property of the roots isn't preserved by the
algorithm. If it were
On Mon, 06 Jul 2009 16:53:42 +0200
Fabrice Silva si...@lma.cnrs-mrs.fr wrote:
Le lundi 06 juillet 2009 à 08:16 -0600, Charles R Harris
a écrit :
Double precision breaks down at about degree 25 if
things are well
scaled, so that is suspicious in itself. Also, the
companion matrix
isn't
Le lundi 06 juillet 2009 à 17:13 +0200, Nils Wagner a écrit :
IIRC, the coefficients of your polynomial are complex.
So, you cannot guarantee that the roots are complex
conjugate pairs.
Correct! If the construction is done with X1 and X1* treated separately,
the coefficients appear to be
Hi -- We are subclassing from np.rec.recarray and are confused about how
some methods of np.rec.recarray relate to (differ from) analogous methods of
its parent, np.ndarray. Below are specific questions about the __eq__,
__getitem__ and view methods, we'd appreciate answers to our specific
Right.
DG
--- On Mon, 7/6/09, David Cournapeau da...@ar.media.kyoto-u.ac.jp wrote:
From: David Cournapeau da...@ar.media.kyoto-u.ac.jp
Subject: Re: [Numpy-discussion] The baffling behavior that just won't die
To: Discussion of Numerical Python numpy-discussion@scipy.org
Date: Monday, July
On Jul 6, 2009, at 1:12 PM, Elaine Angelino wrote:
Hi -- We are subclassing from np.rec.recarray and are confused about
how some methods of np.rec.recarray relate to (differ from)
analogous methods of its parent, np.ndarray. Below are specific
questions about the __eq__, __getitem__
Le lundi 06 juillet 2009 à 17:57 +0200, Fabrice Silva a écrit :
Le lundi 06 juillet 2009 à 17:13 +0200, Nils Wagner a écrit :
IIRC, the coefficients of your polynomial are complex.
So, you cannot guarantee that the roots are complex
conjugate pairs.
Correct! If the construction is done
Hello,
I run Ubuntu 9.04 which has python-numpy 1.2 installed through apt-get. I
would like to upgrade to 1.3 in order to be able to use the
scikits.timeseries package. However, I cannot seem to do it using
apt-get/aptitude/synaptic or at least not that I've discovered.
Currently:
python -c
On Fri, Jul 3, 2009 at 10:21 PM, Alan Jacksona...@ajackson.org wrote:
I've tried the same scheme using R and it seems to give the right
answers
quantile( rf(1000,10,10), .99)
99%
4.84548
quantile( rf(1000,11,10), .99)
99%
4.770002
quantile( rf(1000,11,11), .99)
Hi, I want to be able to do something like:
import numpy
x=numpy.array([1,4,4,6,7,3,4,2])
x.median()
rather than
numpy.median(x)
so that in a function, I can call
x.median()
and allow x to be a masked array or a numpy array.
Using the ma.median version by default is something of a workaround,
I ran into this same problem a few days ago. The issue is that Python
imports from /usr/python2.6/dist-packages before
/usr/local/python2.6/dist-packages causing your numpy 1.3 (assuming its
installed there) to be hidden by the snaptic numpy.
To solve the problem, I added this line to my
Great! Solved/
A question, however, any reason not to say replace the apt
dist-packages/numpy with the v1.3?
Chris Colbert wrote:
I ran into this same problem a few days ago. The issue is that Python
imports from /usr/python2.6/dist-packages before
/usr/local/python2.6/dist-packages
ask the package maintainers
I typically build numpy with threaded atlas support, so the repo version
isn't good for me anyway.
And, the single threaded atlas repo libs are busted anyway...
On Mon, Jul 6, 2009 at 7:29 PM, John [H2O] washa...@gmail.com wrote:
Great! Solved/
A question,
On Fri, Jul 3, 2009 at 10:21 PM, Alan Jacksona...@ajackson.org wrote:
I don't see any problem here. If you can replicate your results, we
would need more information about the versions.
Josef
'''
np.version.version
'1.3.0'
scipy.version.version
'0.8.0.dev5789'
'''
In [4]:
That didn't fix it. I messed around some more, but I couldn't get the
spherical coordinates working. I decided to rework my first method. By
raising the radius to the one third power, like for the other method,
basically the same thing is accomplished. It's working now, thanks. :D
vecs =
I'm using the Enthought Python Distribution. When I define a matrix and
transpose it, it appears that the result is no longer a matrix (see below).
This is both surprising and disappointing. Any suggestions will be
appreciated.
In [16]: A=matrix([[1,2,3],[4,5,6],[7,8,9]])
In [17]:
you actually have to call the method as transpose(). What you requested was
the actual method.
import numpy as np
a = np. matrix([[1,2,3],[4,5,6],[7,8,9]])
a
matrix([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
b = a.transpose()
b
matrix([[1, 4, 7],
[2, 5, 8],
[3, 6,
I should clarify, everything in python is an object. Even methods of
classes. The syntax to invoke a method is the method name followed by the
parenthese (). If you leave off the parentheses, python return the method
object. This can be useful if you want to pass the method to another
function or
and my grammar just sucks tonight...
On Tue, Jul 7, 2009 at 1:46 AM, Chris Colbert sccolb...@gmail.com wrote:
I should clarify, everything in python is an object. Even methods of
classes. The syntax to invoke a method is the method name followed by the
parenthese (). If you leave off the
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