Robert Kern schrieb:
My point is that there is no need to change rand() and randn() to the new
interface. The new interface is already there: random.random() and
random.standard_normal().
Ok thanks for the responses and sorry for not searching the archives
about this.
I tend to share
Alexander Belopolsky schrieb:
In my view it is more important that code is easy to read rather than
easy to write. Interactive users will disagree, but in programming you
write once and read/edit forever :).
The insight about this disagreement imho suggests a compromise (or call
it a dual
Alexandre Fayolle schrieb:
On Fri, Jun 16, 2006 at 10:43:42AM +0200, Sven Schreiber wrote:
Again, there is no defense for abbreviating linear_least_squares
because it is unlikely to appear in an expression and waste valuable
horisontal space.
not true imho; btw, I would suggest ols
identity seems to be a crippled version of eye without any value
added, apart from backwards-compatibility;
So from a user point of view, which one does numpy recommend?
And from a developer point of view (which doesn't really apply to me, of
course), should identity maybe become an alias for
Keith Goodman schrieb:
How do I make a NxN diagonal matrix with a Nx1 column vector x along
the diagonal?
help(n.diag)
Help on function diag in module numpy.lib.twodim_base:
diag(v, k=0)
returns the k-th diagonal if v is a array or returns a array
with v as the k-th diagonal if v is
Travis Oliphant schrieb:
You can use a masked array specifically, or use nan's for missing values
and just tell Python you want a floating-point array (because it finds
the None object it's guessing incorrectly you want an object array.
asarray(x, dtype=float)
array([[ 1.,
Pierre GM schrieb:
I was also a bit surprised at the following behavior:
a = numpy.asarray([1,1])
a
array([1, 1])
a[0]=numpy.nan
a
array([0, 1])
Seems to affect only the int_ arrays:
a = numpy.asarray([1,1], dtype=float_)
a
array([1., 1.])
a[0]=numpy.nan
a
array([nan, 1.
Travis Oliphant schrieb:
Bill Baxter wrote:
So in short my proposal is to:
-- make a.T a property of array that returns a.swapaxes(-2,-1),
-- make a.H a property of array that returns
a.conjugate().swapaxes(-2,-1)
and maybe
-- make a.M a property of array that returns numpy.asmatrix(a)
Tim Hochberg schrieb:
-) .I for inverse; actually, why not add that to arrays as well as
syntactic sugar?
Because it encourages people to do the wrong thing numerically speaking?
My understanding is that one almost never wants to compute the inverse
directly, at least not if
Travis Oliphant schrieb:
1) .T Have some kind of .T attribute
+0
(the discussion in the .T thread convinced me it's better to keep the
matrix playground as a separate subclass, and so it's not important for
me what happens with pure arrays)
2) .H returns .T.conj()
+0
3) .M
Ed Schofield schrieb:
Okay ... Ed rolls up his sleeves ... let's make this the thread ;)
I'd like to know why you, Sven, and anyone else on the list have gone
back to using arrays after trying matrices. What was inconvenient about
them? I'd like a nice juicy list. The whole purpose of
JJ schrieb:
-
Hello Ed:
Here are a couple of examples off the top of my head:
a = mat(arange(10))
a.shape = (5,2)
b = a.copy()
c = hstack((a,b)) # should return a matrix
type(c)
type 'numpy.ndarray'
This hstack bug has been fixed recently.
JJ schrieb:
Travis Oliphant oliphant at ee.byu.edu writes:
Svd returns matrices now. Except for the list of singular values
which is still an array. Do you want a 1xn matrix instead of an
array?
Although I'm a matrix supporter, I'm not sure here. Afaics the pro
argument is to have
Travis Oliphant schrieb:
Because of this. I've removed the global_namespace functions (fft,
ifft, rand, and randn) from numpy. They are *no longer* in the
top-level name-space. If you want them, setup a startup-file
appropriately.
Ok I'm glad that's settled; however, do I
Jon Peirce schrieb:
There used to be a function generalized_inverse in the numpy.linalg
module (certainly in 0.9.2).
In numpy0.9.8 it seems to have been moved to the numpy.linalg.old
subpackage. Does that mean it's being dropped? Did it have to move? Now
i have to add code to my package
Curzio Basso schrieb:
Well try it out and see for yourself ;-)
good point :-)
But for sums it doesn't make a difference, right... Note that it's
called nan*sum* and not nanwhatever.
sure, I was still thinking about the first post which was referring to
the average...
qrz
Right,
Bill Baxter schrieb:
Finally, I noticed that the atleast_nd methods return arrays
regardless of input type. At a minimum, atleast_1d and atleast_2d on
matrices should return matrices. I'm not sure about atleast_3d, since
matrices can't be 3d. (But my opinon is that the matrix type should
Thanks for helping out on matrix stuff, Bill!
Bill Baxter schrieb:
On 7/22/06, Sven Schreiber [EMAIL PROTECTED] wrote:
Note the array slicing works correct, but the equivalent thing with the
matrix does not.
Looks like it happened in rev 2698 of defmatrix.py, matrix.__getitem__
method
Travis Oliphant schrieb:
Sven Schreiber wrote:
The change was trying to fix up some cases but did break this one. The
problem is that figuring out whether or not to transpose the result is a
bit tricky. I've obviously still got it wrong.
Ok, this is obviously one of the places were
Hi,
there was a thread about this before, diag() is currently only
partly useful if you work with numpy-matrices, because the 1d-2d
direction doesn't work, as there are no 1d-numpy-matrices. This is
unfortunate because a numpy-matrix with shape (n,1) or (1,m) should be
naturally treated as a
Robert Kern schrieb:
Sven Schreiber wrote:
Hi,
there was a thread about this before, diag() is currently only
partly useful if you work with numpy-matrices, because the 1d-2d
direction doesn't work, as there are no 1d-numpy-matrices. This is
unfortunate because a numpy-matrix with shape (n
Robert Kern schrieb:
Sven Schreiber wrote:
That would be fine with me. However, I'd like to point out that after
some bug-squashing currently all numpy functions deal with
numpy-matrices correctly, afaik. The current behavior of numpy.diag
could be viewed as a violation of that principle
Here's my attempt at summarizing the diag-discussion.
The symptom is that currently transforming something like the vector
a b c
into the diagonal matrix
a 0 0
0 b 0
0 0 c
is not directly possible if you're working with numpy-matrices (i.e. the
vector is of type matrix and has shape (n,1) or
Hi,
notice the (confusing, imho) different defaults for the axis of the
following related functions:
nansum(a, axis=-1)
Sum the array over the given axis, treating NaNs as 0.
sum(x, axis=None, dtype=None)
Sum the array over the given axis. The optional dtype argument
is the data
Hi,
Satya Upadhya schrieb:
from Numeric import *
Well this list is about the numpy package, but anyway...
the power function is giving a resultant matrix in which each element of
matrix B is raised to the power of 0 so as to make it 1. But, taken as a
whole i.e. matrix B to the power of 0
Jordan Dawe schrieb:
I just tried to compile numpy-1.0b3 under windows using mingw. I got
this error:
...
Any ideas?
No, except that I ran into the same problem... Hooray, I'm not alone ;-)
-sven
-
Using Tomcat but
[EMAIL PROTECTED] schrieb:
Since no one has downloaded 1.0b3 yet, if someone wants to put up the
windows version for python2.3 i would be more than happy to be the first
person to download it :)
I'm sorry, this is *not* for python 2.3, but I posted a build of current
svn for python 2.4 under
Hi,
I experienced this strange bug which caused a totally unrelated variable
to be overwritten (no exception or error was raised, so it took me while
to rule out any errors of my own).
The context where this is in is a method of a class (Vecm.getSW()), and
the instance of Vecm is created within a
Hi,
is this normal behavior?:
import numpy as n; print n.mat(0.075).round(2); print
n.mat(0.575).round(2)
[[ 0.08]]
[[ 0.57]]
Again, yesterday's svn on windows.
cheers,
Sven
-
Using Tomcat but need to do more? Need to
in Matlab doesn't have an equivalent in Numpy... like
eigs()).
--bb
On 8/26/06, *Sven Schreiber* [EMAIL PROTECTED]
mailto:[EMAIL PROTECTED] wrote:
Hi,
I experienced this strange bug which caused a totally unrelated variable
to be overwritten (no exception or error was raised
Charles R Harris schrieb:
+1. I too suspect that what you have here is a reference/copy problem.
The only thing that is local to the class is the reference (pointer),
the data is global.
Chuck
Ok, so you guys were right, turns out that my problem was caused by the
fact that a local
Mathew Yeates schrieb:
My head is about to explode.
I have an M by N array of floats. Associated with the columns are
character labels
['a','b','b','c','d','e','e','e'] note: already sorted so duplicates
are contiguous
I want to replace the 2 'b' columns with the sum of the 2 columns.
Hi,
never mind that the following syntax is wrong, but is it supposed to
yield that SystemError instead of something more informative?
(This is with b5 on win32 and python 2.4.3)
b.reshape(3,3,axis = 1)
Traceback (most recent call last):
File interactive input, line 1, in ?
SystemError: NULL
Eric Emsellem schrieb:
Hi again
after some hours of debugging I finally (I think) found the problem:
numpy.sum([[0,1,2],[2,3,4]])
24
numpy.sum([[0,1,2],[2,3,4]],axis=0)
array([2, 4, 6])
numpy.sum([[0,1,2],[2,3,4]],axis=1)
array([3, 9])
Isn't the first line supposed to act as
Christopher Barker schrieb:
Sven Schreiber wrote:
on my 1.0b5 I also see this docstring which indeed seems obsolete.
I get this docs string from :
import numpy as N
N.__version__
'1.0b5'
a = N.arange(10)
help( a.sum)
sum(...)
a.sum(axis=None, dtype=None) - Sum
Travis Oliphant schrieb:
If not, shouldn't
numpy.sqrt(-1) raise a ValueError instead of returning silently nan?
This is user adjustable. You change the error mode to raise on
'invalid' instead of pass silently which is now the default.
-Travis
Could you please explain how
David Cournapeau schrieb:
Hi there,
I've just managed to nail down a bug which took me nearly two whole
days to find: this is coming from an unexpected (at least from me)
behaviour of numpy.
You have all my sympathy, I tripped over something similar not too long
ago, so welcome to the
David Cournapeau schrieb:
Sven Schreiber wrote:
Yes it's intended; as far as I understand the python/numpy syntax, +
is an operator, and that triggers assignment by copy (even if you do
something trivial as bar = +foo, you get a copy, if I'm not mistaken),
So basically, whenever you have
Andrew Straw schrieb:
The matplotlib .deb on my website is working fine for me with the latest
numpy .deb there. If there are any recent patches or anything you are
missing, please let me know -- it's not really a big deal to update
them, although it might take a couple of days for me to find
Wow! The list is so quiet despite the fact that the numpy 1.0 release is
officially announced on the website, and the download is on sourceforge.
Well ok, it was expected and the download counters are all at zero, but
still.
I want to thank everybody who made this possible very much! I'm not a
jeremito schrieb:
argsort() will do the trick. Thanks once again.
Jeremy
I was a bit confused by your question, maybe you can clarify what you
did in the end.
IIRC, if the eigenvalues returned by numpy are real numbers (due to the
type of the underlying matrix and algorithm), then they
izak marais schrieb:
Hi
Sorry if this is an obvious question, but what is the easiest way to
multiply matrices in numpy? Suppose I want to do A=B*C*D. The ' * '
operator apparently does element wise multiplication, as does the
'multiply' ufunc. All I could find was the numeric function
Johannes Loehnert schrieb:
Hi,
in extension to the previous answers, I'd like to say that it is strongly
preferable to use dot(A,dot(B,C)) or dot(dot(A,B),C) instead of A*B*C.
The reason is that with dot(), you can control of which operation is
performed
first, which can *massively*
Gael Varoquaux schrieb:
Hi all,
I didn't get any answers to this email. Is it because the proposed
addition to numpy is not of any interest to anybody apart from me ?
Maybe the way I introduced this is wrong. Please tell me what is wrong
with this proposition.
Well you didn't
Pierre GM schrieb:
On Sunday 12 November 2006 17:08, A. M. Archibald wrote:
On 12/11/06, Keith Goodman [EMAIL PROTECTED] wrote:
Is anybody interested in making x.max() and nanmax() behave the same
for matrices, except for the NaN part? That is, make
numpy.matlib.nanmax return a matrix instead
Charles R Harris schrieb:
In [1]: from scipy import linalg
In [2]: help(linalg.eig)
from scipy import linalg
help(linalg.eig)
Help on function eig in module scipy.linalg.decomp:
I expect scipy.linalg and numpy.linalg are different modules containing
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