Hi,
I've got some questions on the numpy.vectorize function.
Currently, i'm doing this kind of work :
[ code]
def calc_0d(x, y):
make complexe calculation using two scalars x and y
[ ... ]
return res1, res2, res 3
# vectorize the function
calc = vectorize(calc_0d)
res1,
On Wed, Jan 30, 2008 at 12:49:44AM -0800, LB wrote:
My problem is that the complexe calculations made in calc_0d use some
parameters, which are currently defined at the head of my python file.
This is not very nice and I can't define a module containing theses
two functions and call them with
* Robin [EMAIL PROTECTED] [2008-01-29 19:23:11 +]:
On Jan 29, 2008 7:16 PM, Lou Pecora [EMAIL PROTECTED] wrote:
Hmmm... Interesting. I am using Python 2.4.4. It
would be nice to have other Mac people with same/other
Python and numpy versions try the argsort bug code.
I don't see
Try use a closure.
On Jan 30, 12:49 am, LB [EMAIL PROTECTED] wrote:
Hi,
I've got some questions on the numpy.vectorize function.
Currently, i'm doing this kind of work :
[ code]
def calc_0d(x, y):
make complexe calculation using two scalars x and y
[ ... ]
return res1,
On a side note, given that I've seen quite a few posts about
vectorize() over the past several months...
I've written hundreds or thousands of functions that are intended
to work with numeric/numpy arrays and/or scalars and I've _never_
(not once!) found a need for the vectorize function.
On Jan 29, 2008, at Jan 29:9:25 PM, Andrew Straw wrote:
I'm pretty sure there's code floating around the pyglet mailing list.
I'd be happy to add it to
http://code.astraw.com/projects/motmot/wiki/pygarrayimage if it seems
reasonable. (pygarrayimage goes from numpy array to pyglet texture).
In the following piece of code:
import numpy as N
R = N.arange(9).reshape(3,3)
ax = [1,2]
R
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
R[ax,:][:,ax] = 100
R
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
Why R is not updated?
I was expecting:
R
array([[0, 1, 2],
A Wednesday 30 January 2008, Nadav Horesh escrigué:
In the following piece of code:
import numpy as N
R = N.arange(9).reshape(3,3)
ax = [1,2]
R
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
R[ax,:][:,ax] = 100
R
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
But:
R[ax,:] = 100
R
array([[ 0, 1, 2],
[100, 100, 100],
[100, 100, 100]])
R[:,ax] = 200
R
array([[ 0, 200, 200],
[100, 200, 200],
[100, 200, 200]])
Do I get an array view only if the array is contiguous?
Nadav.
On Wed, 2008-01-30 at 16:08 +0100,
Am Mittwoch, 30. Januar 2008 16:21:40 schrieb Nadav Horesh:
But:
R[ax,:] = 100
This is calling __setitem__, i.e. does not create either a view or a copy.
Non-contiguous views (e.g. using [::2]) are also possible AFAIK, but fancy
indexing is something different.
--
Ciao, / /
/--/
you simply need to change the definition of ax:
ax = slice(1,3)
and all works fine.
L.
On 1/30/08, Francesc Altet [EMAIL PROTECTED] wrote:
A Wednesday 30 January 2008, Nadav Horesh escrigué:
In the following piece of code:
import numpy as N
R = N.arange(9).reshape(3,3)
ax = [1,2]
On Jan 30, 2008 8:21 AM, Nadav Horesh [EMAIL PROTECTED] wrote:
But:
R[ax,:] = 100
R
array([[ 0, 1, 2],
[100, 100, 100],
[100, 100, 100]])
R[:,ax] = 200
R
array([[ 0, 200, 200],
[100, 200, 200],
[100, 200, 200]])
Do I get an array view only if
or you can maybe use numpy.ix_:
ax = [1,2]
R[numpy.ix_(ax,ax)] = 100
hth,
L.
On 1/30/08, lorenzo bolla [EMAIL PROTECTED] wrote:
you simply need to change the definition of ax:
ax = slice(1,3)
and all works fine.
L.
On 1/30/08, Francesc Altet [EMAIL PROTECTED] wrote:
A Wednesday 30
On Jan 30, 2008 10:10 AM, Charles R Harris [EMAIL PROTECTED]
wrote:
[SNIP]
IIRC, the way to do closures in Python is something like
In [5]: def factory(x) :
...: def f() :
...: print x
...: f.x = x
...: return f
...:
In [6]: f = factory(Hello
On Jan 30, 2008 10:10 AM, Charles R Harris [EMAIL PROTECTED]
wrote:
On Jan 30, 2008 2:22 AM, Gael Varoquaux [EMAIL PROTECTED]
wrote:
On Wed, Jan 30, 2008 at 12:49:44AM -0800, LB wrote:
My problem is that the complexe calculations made in calc_0d use some
parameters, which are
On Jan 30, 2008 10:18 AM, Timothy Hochberg [EMAIL PROTECTED] wrote:
On Jan 30, 2008 10:10 AM, Charles R Harris [EMAIL PROTECTED]
wrote:
[SNIP]
IIRC, the way to do closures in Python is something like
In [5]: def factory(x) :
...: def f() :
...: print x
Thank you very much,
that what I was looking for.
Charles made a good point about offsets, counts and strides --- I really should
go and reread the documentation.
Nadav.
-Original Message-
From: [EMAIL PROTECTED] on behalf of lorenzo bolla
Sent: Wed 30-Jan-08 17:31
To: Discussion of
Timothy Hochberg wrote:
On Jan 29, 2008 5:48 PM, Travis E. Oliphant [EMAIL PROTECTED]
mailto:[EMAIL PROTECTED] wrote:
Joris De Ridder wrote:
On 30 Jan 2008, at 00:32, Travis E. Oliphant wrote:
Matthew Brett wrote:
Hi,
median moved
Thank you Gael, I think this could work for my case.
It will be a bit tricky, since calc_0d is already a closure in which
I've defined a function : the parameters x and y are to main
parameters of an ODE.
So calc_0d define a function, integrate it sing scipy.integrate.odeint
and returns some
On 30/01/2008, Francesc Altet [EMAIL PROTECTED] wrote:
A Wednesday 30 January 2008, Nadav Horesh escrigué:
In the following piece of code:
import numpy as N
R = N.arange(9).reshape(3,3)
ax = [1,2]
R
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
R[ax,:][:,ax] =
On Jan 30, 2008 12:43 PM, Anne Archibald [EMAIL PROTECTED] wrote:
On 30/01/2008, Francesc Altet [EMAIL PROTECTED] wrote:
A Wednesday 30 January 2008, Nadav Horesh escrigué:
In the following piece of code:
import numpy as N
R = N.arange(9).reshape(3,3)
ax = [1,2]
R
On Jan 30, 2008 10:41 AM, Travis E. Oliphant [EMAIL PROTECTED] wrote:
Yes, we could start to do that (spit out a warning in 1.0.5). This
should definitely be done in 1.0.6
Perhaps we use axis=None to start with and then check for that and spit
out the warning (and change axis to 0). Thanks
Hi,
I would like to ask some details about the build process of numpy
when visual studio is used for C compilation, and g77 for blas/lapack.
As I understand it, the current situation consists in using libraries
built the Unix way (e.g. libblas.a, static library built with ar +
ranlib), taking
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