On 19 Dec 2008, at 17:10 , Sturla Molden wrote:
I am wondering if not scipy.signal.lfilter ought to be a part of the
core NumPy. Note that it is similar to the filter function found in
Matlab, and it makes a complement to numpy.convolve.
May I suggest that it is renamed or aliased to
On 1 Dec 2008, at 21:47 , Stéfan van der Walt wrote:
Hi Pierre
2008/12/1 Pierre GM [EMAIL PROTECTED]:
* `genloadtxt` is the base function that makes all the work. It
outputs 2 arrays, one for the data (missing values being substituted
by the appropriate default) and one for the mask. It
On 24 Nov 2008, at 19:45 , Francesc Alted wrote:
standards in computer science. For example, where Python writes:
asin, acos, atan, asinh, acosh, atanh
NumPy choose:
arcsin, arccos, arctan, arcsinh, arccosh, arctanh
So, IMHO, I think it would be better to rename the inverse
Thanks for the pointers. I'll produce some code to show what I have in
mind, and then come back to the list.
Cheers,
Joris
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Hi,
I'm interested in developing some general-use Python/Numpy code for
linear model fitting and comparison. The fitting is easy enough with
Numpy, but the automated comparison of the submodels to identify which
model describes best the data, requires some work. Before I embark on
this, I
On 20 Aug 2008, at 22:18 , Dag Sverre Seljebotn wrote:
Cython just had a release, and amongst the new features are efficient
NumPy array indexing for integers, real floats and Python objects.
You can get it at http://cython.org
For those new to Cython, I've written a tutorial specifically
Hi Dag,
General feedback is welcome; in particular, I need more opinions about
what syntax people would like. We seem unable to find something that
we
really like; this is the current best candidate (cdef is the way you
declare types on variables in Cython):
cdef int i = 4, j = 6
cdef
On 28 May 2008, at 16:30, Keith Goodman wrote:
Does anyone else get this seg fault?
def fn():
x = np.random.rand(5,2)
x.cumsum(None, out=x)
return x
:
fn()
*** glibc detected *** /usr/bin/python: double free or corruption
(out): 0x08212dc8 ***
I'm running 1.0.4 from
On 12 May 2008, at 04:59, David Cournapeau wrote:
Also, time-based releases are by definition predictable, and
as such, it is easier to plan upgrades for users
As long as it does not imply that users have to upgrade every 3
months, because for some users this is impossible and/or
On 24 Apr 2008, at 19:26, Rich Shepard wrote:
norm = 1 / (scale * sqrt(2 * pi))
y = norm * exp(-power((x - loc), 2) / (2 * scale**2))
Can do. So, scale would equate to width and loc to center, yes?
Scale is half the width between the inflection points, mind the factor
of 2.
J.
They are attached to the wiki page. Click on Attachments in the menu
on the left.
Joris
On 23 Apr 2008, at 17:19, Tommy Grav wrote:
On Apr 22, 2008, at 9:56 PM, Joris De Ridder wrote:
On http://www.scipy.org/JorisDeRidder I've just put an example how I
passed multidimensional Numpy
On 23 Apr 2008, at 17:50, Tommy Grav wrote:
On Apr 23, 2008, at 11:26 AM, Joris De Ridder wrote:
They are attached to the wiki page. Click on Attachments in the
menu
on the left.
Joris
Thanks. Didn't know that wiki's had that :)
I tried you example on a Mac OS X 10.5.2 (I am
On 17 Apr 2008, at 01:09, Stéfan van der Walt wrote:
Split infinitive -- I'd get in trouble for that.
Please use the latest patch (attached), which fixes a bug with
assignment.
I experimented with returning an (N,) array when converting using
vector.A, but I'm not convinced that that is
On 10 Apr 2008, at 05:21, Travis E. Oliphant wrote:
Right now it looks like there is a mix of attitudes, about the
financial
functions. They are a small enough addition, that I don't think it
matters terribly much what we do with them. So, it seems to me that
keeping them in numpy.lib
On 10 Apr 2008, at 23:23, Travis E. Oliphant wrote:
Cool. I started scipy.misc.info a long time ago to try and do
this. I
didn't advertise it well enough ;-)
Yep, I also started to write my own docsearch tool but neglected to
advertise it.
On 10 Apr 2008, at 23:58, Gael Varoquaux
On 04 Apr 2008, at 16:11, Travis E. Oliphant wrote:
snip
There are only two reasons that I can think of right now to keep
them in
NumPy instead of moving them to SciPy.
1) These are basic functions and a scipy toolkit would contain
much more.
Isn't this something you want to avoid?
On 26 Mar 2008, at 15:36, lorenzo bolla wrote:
numpy.tri
In [31]: T = numpy.tri(m)
In [32]: z.T * T + z * T.T
Out[32]:
array([[ 0., 1., 2., 3., 4.],
[ 1., 12., 7., 8., 9.],
[ 2., 7., 24., 13., 14.],
[ 3., 8., 13., 36., 19.],
[
I cannot confirm the problem on my intel macbook pro using the same
Python and Numpy versions. Although any(numpy.array(large_none)) takes
a significantly longer time than any(numpy.array(large_zero)), the
former does not segfault on my machine.
J.
On 24 Mar 2008, at 14:05, Martin
On 24 Mar 2008, at 18:27, Martin Manns wrote:
I cannot confirm the problem on my intel macbook pro using the same
Python and Numpy versions. Although any(numpy.array(large_none))
takes
a significantly longer time than any(numpy.array(large_zero)), the
former does not segfault on my
On 21 Mar 2008, at 12:29, Sebastian Haase wrote:
... and what does the p stand for in
N.intp
type 'numpy.int32'
It stands for pointer. An intp is an integer large enough to contain
a pointer address.
J.
Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm
numpy.diff
See http://www.scipy.org/Numpy_Example_List
J.
On 22 Mar 2008, at 03:43, Chris Withers wrote:
Hi All,
Say I have an array like:
measurements = array([100,109,115,117])
What do I do to it to get:
array([9, 6, 2])
Is the following really the best way?
result = []
for i
On 21 Mar 2008, at 18:22, Joe Harrington wrote:
What you have brought up is really a documentation problem: how do I
find the name of the routine I want?
One way of dealing with this, could be the implementation of a doc()
function in numpy that helps you to find what you want. A (still
array, it is implemented in terms of a Numpy array (http://matt.eifelle.com/item/5) Matthieu2008/3/19, Joris De Ridder [EMAIL PROTECTED]>: Hi, I'm passing (possibly non-contiguous) numpy arrays (data + shape + strides + ndim) with ctypes to my C++ function (with external "C" to make ctypes happy).
Hi,
I'm passing (possibly non-contiguous) numpy arrays (data + shape +
strides + ndim) with ctypes to my C++ function (with external C to
make ctypes happy). Has anyone made a C++ class derived from a ctypes-
numpy-array with an overloaded [] operator to allow easy indexing
(e.g.
I am new to the world of Python and numpy
Welcome.
I have successfully imported the data into lists and then created a
single array from the lists.
I think putting each quantity in a 1D array is more practical in this
case.
I can get the rainfall total over the entire period using:
On 07 Mar 2008, at 10:02, Fernando Perez wrote:
Chris B gave what I think is a good reply to this, but feel free to
ask if you have further questions. I think it's important that we
reach some consensus on why this a good idea on technical grounds
without anyone feeling like the decision is
On 06 Mar 2008, at 19:15, Fernando Perez wrote:
http://www.cython.org/
is an evolved version of Pyrex (which is used by numpy and scipy) with
lots of improvements. We'd like to position Cython as the preferred
way of writing most, if not all, new extension code written for numpy
and scipy,
On 12 Feb 2008, at 12:31, Matthew Brett wrote:
def median(a, axis=0, out=None)
(same signature as max, min etc)
I would be slightly in favour of this option.
Using the same signature would be convenient in code like
def myfunc(myarray, somefunc):
# do stuff
...
x =
This feature request has been repeatedly asked before (e.g. 6 months
ago). The relevant ticket (#558, although this only asks for axis
support) mentions a milestone 1.1. I would like to ask if it could be
moved somewhat higher on the priority list.
I provided some code for axis support
On 30 Jan 2008, at 00:32, Travis E. Oliphant wrote:
Matthew Brett wrote:
Hi,
median moved mediandim0
implementation of medianwithaxis or similar, with same call
signature as mean.
But - for the median function change - do we agree that this should
be
changed? I think it is a
On 14 Sep 2007, at 23:51, Robert Kern wrote:
You can hide some of the surprises, but not all of them.
I guess it's impossible to make a bullet-proof fix. When arange()
gets a 'stop' value of 0.60009, it cannot possibly know
whether this stop value is supposed to be 0.6, or
Might using
min(ceil((stop-start)/step), ceil((stop-start)/step-r))
with r = finfo(double).resolution instead of ceil((stop-start)/step)
perhaps be useful?
Joris
On 14 Sep 2007, at 11:37, Ed Schofield wrote:
Hi everyone,
This was reported yesterday as a bug in Debian's numpy package:
On 14 Sep 2007, at 15:54, Lou Pecora wrote:
I thought this is what the linspace function was
written for in numpy. Why not use that?
AFAIK, linspace() is written to generate N evenly spaced numbers
between start and stop inclusive. Similar but not quite the same as
arange().
It works
the question is how to reduce user astonishment.
IMHO this is exactly the point. There seems to be two questions here:
1) do we want to reduce user astonishment, and 2) if yes, how could
we do this? Not everyone seems to be convinced of the first question,
replying that in many cases
A related question, just out of curiosity: is there a technical
reason why Numpy has been coded in C rather than C++?
Joris
On 05 Sep 2007, at 02:24, David Goldsmith wrote:
Anyone have a well-tested SWIG-based C++ STL valarray = numpy.array
typemap to share? Thanks!
DG
--
Hi,
Perhaps a stupid question, but I don't seem to find any info about it
on the web.
I would like to take up a (simple) Numpy Trac ticket, and fix it in
the Numpy trunk. How can I assign the ticket to myself? After logging
in, I don't see any obvious way of doing this. Secondly, committing
Hi,
I'm confused by the output of apply_along_axis() in the following very simple
example:
In [93]: a = arange(12.).reshape(2,2,3)
In [95]: a
Out[95]:
array([[[ 0., 1., 2.],
[ 3., 4., 5.]],
[[ 6., 7., 8.],
[ 9., 10., 11.]]])
In [96]: def myfunc(b):
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