roduction about readability
vs speed with an example showing a clever optimization (by Jaime Fernández del
Río) that is hardly readable for the non-experts (including myself).
Nicolas
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.
> On 30 Dec 2016, at 20:36, Alex Rogozhnikov wrote:
>
> Hi Nicolas,
> that's a very nice work!
>
>> Comments/questions/fixes/ideas are of course welcome.
>
> Boids example brought my attention too, some comments on it:
> - I find using complex numbers here
Yes, clearing is not the proper word but the "trick" works only work for 0
(I'll get the same result in both cases).
Nicolas
> On 27 Dec 2016, at 20:52, Chris Barker wrote:
>
> On Mon, Dec 26, 2016 at 1:34 AM, Nicolas P. Rougier
> wrote:
>
> I'm try
Thanks for the explanation Sebastian, makes sense.
Nicolas
> On 26 Dec 2016, at 11:48, Sebastian Berg wrote:
>
> On Mo, 2016-12-26 at 10:34 +0100, Nicolas P. Rougier wrote:
>> Hi all,
>>
>>
>> I'm trying to understand why viewing an array as by
)
%timeit Z_float[...] = 0
1000 loops, best of 3: 361 µs per loop
%timeit Z_int[...] = 0
1000 loops, best of 3: 366 µs per loop
%timeit Z_float.view(np.byte)[...] = 0
1000 loops, best of 3: 267 µs per loop
%timeit Z_int.view(np.byte)[...] = 0
1000 loops, best of 3: 266 µs per loop
happy with the boids example that show a nice
combination of numpy and matplotlib strengths.
Book is online at: http://www.labri.fr/perso/nrougier/from-python-to-numpy/
Sources are available at: https://github.com/rougier/from-python-to-numpy
Comments/questions/fixes/ideas are of cours
;B[%s]" % index)))
True
```
I wrote a solution at
https://gist.github.com/rougier/b8c2256434b3a4a4271260cd4cc6cbc7
(not very thoroughly tested) but maybe there are better ways to do that (like a
magic numpy call ?)
(no use-case at all, only for
(I'm still fighting to fix exercise #54 that does not work as expected).
Nicolas
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
https://mail.scipy.org/mailman/listinfo/numpy-discussion
Hi all,
I've just added some exercises to the collection at
https://github.com/rougier/numpy-100
(and in the process, I've discovered np.argpartition... nice!)
If you have some ideas/comments/corrections... Still 20 to go...
Nicolas
___
In the end, I’ve only the list comprehension to work as expected
A = [0,0,1,3]
B = np.arange(8)
np.random.shuffle(B)
I = [list(B).index(item) for item in A if item in B]
But Mark's and Sebastian's methods do not seem to work...
> On 30 Dec 2015, at 19:51, Nicolas P. R
gt;> numpy.where(numpy.in1d(b, a))
> (array([1, 2, 5, 7], dtype=int64),)
> It would be interesting to see the benchmarks.
>
>
> On Wed, Dec 30, 2015 at 10:17 AM, Nicolas P. Rougier
> wrote:
>
> Yes, it is the expected result. Thanks.
> Maybe the set(a) & set(b) can
ices #indices of b where the elements of a in b occur
> array([1, 2, 5, 7], dtype=int64)
>
> -Mark
>
>
> On Wed, Dec 30, 2015 at 6:45 AM, Nicolas P. Rougier
> wrote:
>
> I’m scratching my head around a small problem but I can’t find a vectorized
> solution.
>
Thanks, I will make some benchmark and post results.
> On 30 Dec 2015, at 17:47, Sebastian Berg wrote:
>
> On Mi, 2015-12-30 at 17:12 +0100, Nicolas P. Rougier wrote:
>> Thanks for the quick answers. I think I will go with the .index and
>> list comprehension.
>>
Thanks for the quick answers. I think I will go with the .index and list
comprehension.
But if someone finds with a vectorised solution for the numpy 100 exercises...
Nicolas
> On 30 Dec 2015, at 16:31, Benjamin Root wrote:
>
> Maybe use searchsorted()? I will note that I have nee
nction(A,B))
[1,2,0]
# A[0] == 2 is in B and 2 == B[1] -> 1
# A[1] == 0 is in B and 0 == B[2] -> 2
# A[2] == 1 is in B and 1 == B[0] -> 0
Any idea ? I tried numpy.in1d with no luck.
Nicolas
___
NumPy-Discussion mailing list
NumPy-Discuss
> On 28 Dec 2015, at 19:58, Chris Barker wrote:
>
> On Wed, Dec 23, 2015 at 4:01 AM, Nicolas P. Rougier
> wrote:
> But my implementation is quite slow, especially when you add one item at a
> time:
>
> >>> python benchmark.py
> Python list, append 10
= ArrayList( np.arange(10), [3,3,4])
> >>> print(L)
> [[0 1 2], [3 4 5], [6 7 8 9]]
> >>> print(L.data)
> [0 1 2 3 4 5 6 7 8 9]
>
>
> does an ArrayList act like a numpy array in other ways:
>
> L * 5
>
> L* some_array
>
> in which case, how does it
Yes, you can append/insert/remove items.
It works pretty much like a python list in fact (but with a single data type
for all elements).
Nicolas
> On 22 Dec 2015, at 20:19, Chris Barker wrote:
>
> sorry for being so lazy as to not go look at the project pages, but
>
> T
I've coded a typed dynamic list based on numpy array (needed for the glumpy
project).
Code is available from https://github.com/rougier/numpy-list
A Numpy array list is a strongly typed list whose type can be anything that can
be interpreted as a numpy data type.
>>> L = ArrayList( [[0], [1,2
t;only" 60 exercices...
So, if you remember a nice question that has been answered on this list (or
elsewhere)...
Or you can also make a PR on github.
Nicolas
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman
Extended deadline: 15th May 2015
EuroScipy 2015, the annual conference on Python in science will take place in
Cambridge, UK on 26-30 August 2015. The conference features two days of
tutorials followed by two days of scientific talk
-
Submission deadline in 3 days !!!
-
EuroScipy 2015, the annual conference on Python in science will take place in
Cambridge, UK on 26-30 August 2015. The conference features two days of
tutorials followed by two days of scientific t
[Apology for cross-posting]
Dear all,
EuroScipy 2015, the annual conference on Python in science will take place in
Cambridge, UK on 26-30 August 2015. The conference features two days of
tutorials followed by two days of scientific talks & posters and an extra day
dedicated to developer spri
Dear all,
EuroScipy 2015, the annual conference on Python in science will take place in
Cambridge, UK on 26-30 August 2015. The conference features two days of
tutorials followed by two days of scientific talks & posters and an extra day
dedicated to developer sprints. It is the major event in
Thanks Jérôme, I will look into your code. Having other filter might be useful
for my case.
While looking for code, I've also found this (pure python) implementation:
http://stackoverflow.com/questions/12729228/simple-efficient-bilinear-interpolation-of-images-in-numpy-and-python
Ni
ke to
avoid the scipy dependency)
Thanks.
Nicolas
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
Hello,
I've found a strange behavior or I'm missing something obvious (or np.unique is
not supposed to work with structured arrays).
I'm trying to extract unique values from a simple structured array but it does
not seem to work as expected.
Here is a minimal script showing the problem:
impor
Nicolas
On 07 Aug 2014, at 14:04, Gregor Thalhammer wrote:
>
> Am 07.08.2014 um 13:59 schrieb Gregor Thalhammer
> :
>
>>
>> Am 07.08.2014 um 13:16 schrieb Nicolas P. Rougier :
>>
>>>
>>> Hi,
>>>
>>> I've a small prob
Nice ! Thanks Stéfan.
I will add it to the numpy 100 problems.
Nicolas
On 07 Aug 2014, at 13:31, Stéfan van der Walt wrote:
> Hi Nicolas
>
> On Thu, Aug 7, 2014 at 1:16 PM, Nicolas P. Rougier
> wrote:
>> Here is a small example:
>>
>> Z = [(0,0), (1,1), (
t; Wrong (15 not in I) but ideally, I would like this to be converted to
[(0,0), (0,0)]
Any idea ?
Nicolas
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
vector graphics module in there would make it even better. Would
> be nice if those projects could be merged.
>
I'm part of vispy actually, those are side experiments for this project.
Nicolas
>
> On Sun, Jun 22, 2014 at 9:51 PM, Nicolas P. Rougier
> wrote:
>
>
is able to draw any amiunt of grids/ticks (as in
matplotlib). Some old example are available from here:
https://github.com/rougier/gl-agg
I tested your solution and it is faster by only a tiny amount but the way you
wrote it might open the door for other improvements. Thanks.
Nicolas
On 22 Ju
Thanks, I'll try your solution.
Data (L) is not so big actually, it represents pixels on screen and (I)
represents line position (for grids). I need to compute this quantity everytime
the user zoom in or out.
Nicolas
On 22 Jun 2014, at 19:05, Eelco Hoogendoorn wrote:
> Well
Hi all,
Michiaki Ariga has started conversion of 100 numpy exercises in Julia.
I know this might be a bit off-topic but I thought it was interesting enough.
Github repository is at https://github.com/chezou/julia-100-exercises
Nicolas
___
NumPy
Thanks for the answer.
I was secretly hoping for some kind of hardly-known numpy function that would
make things faster auto-magically...
Nicolas
On 22 Jun 2014, at 10:30, Eelco Hoogendoorn wrote:
> Perhaps you could simplify some statements, but at least the algorithmic
> complex
[0,width]
I = np.sort(np.random.randint(0,width,10))
# n regular spaced values in [0,width]
L = np.linspace(0, width, n)
print I[find_closest(I,L)]
Nicolas
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/
:
>
> http://stackoverflow.com/questions/16970982/find-unique-rows-in-numpy-array/16973510#16973510
>
> It may not work properly on floats, but I think it is a very cool use of
> dtypes. Then again I'm obviously biased...
>
> I remained astonished when I discovere
Hi Valentin,
Thanks for reminded me about this great tool.
I intended to use it after I get all 100 exercises but it really helps track
errors quickly.
I will now use it to keep a notebook up to date with each commit .
Nicolas
On 28 May 2014, at 23:46, Valentin Haenel wrote:
> Hi Nico
Thanks, you just inaugurated the master section.
Nicolas
On 27 May 2014, at 21:48, Jaime Fernández del Río wrote:
> On Tue, May 27, 2014 at 12:27 PM, Nicolas Rougier
> wrote:
> Any other tricky stride_trick tricks ? I promised to put them in the master
> section.
>
>
On 27 May 2014, at 21:09, Chris Barker wrote:
> On Mon, May 26, 2014 at 9:57 PM, Nicolas Rougier
> wrote:
>
> I've updated the numpy exercices collection and made it available on github
> at:
>
> https://github.com/rougier/numpy-100
>
>
> very usefu
. The master and archmaster sections still need to be populated...
Once finished, I'll make an ipython notebook as well.
Nicolas
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
Seems to be related to the masked values:
print r2010[:3,:3]
[[-- -- --]
[-- -- --]
[-- -- --]]
print abs(r2010)[:3,:3]
[[-- -- --]
[-- -- --]
[-- -- --]]
print r2010[ r2010[:3,:3] <0 ]
[-- -- -- -- -- -- -- -- --]
print r2010[ abs(r2010)[:3,:3] < 0]
[]
Nicolas
On 13 Mar 2014,
I never noticed this kind of cast before (1.8.0), it's just a bit surprising.
It was convenient to write translations (for a bunch of points) such as:
Z = np.ones((n,2),dtype=np.float32) + (300,300)
but I can live with Z += 300,300
Nicolas
On 03 Mar 2014, at 23:02, Benjamin Root
; print Z.dtype
float64
Is this the expected behavior ?
What it the difference between the two lines ?
Nicolas
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
Hi,
I've tried to resize a record array that was first empty (on purpose, I need it)
and I got the following error (while it's working for regular array).
Traceback (most recent call last):
File "test_resize.py", line 10, in
print np.resize(V,2)
File
"/usr/locaL/Cellar/python/2.7.6/Fr
core numpy
(np.list ?) ?
Nicolas
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
((10,4), np.float32)
Z.strides
(16,4)
Z = np.zeros(10, (np.float32,4))
Z.strides
(16,4)
Nicolas
On Aug 31, 2013, at 7:51 AM, Stéfan van der Walt wrote:
> Hi Nicolas
>
> On Fri, 30 Aug 2013 17:26:51 +0200, Nicolas Rougier wrote:
>>>>> Z = np.zeros(10, [('a
Hi,
I'm a bit lost with the following example (numpy 1.7.1, osx 10.8):
>>> Z = np.zeros(10, [('a', np.float32, 3), ('b', np.float32, 4)])
>>> Z['a'].dtype
dtype('float32')
>>> Z.dtype['a']
dtype(('
(10,2)
B = np.repeat( A[::2], 2, axis=0 )
Nicolas
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
.int64(1))
[[ 0.40274243 -0.33922682]]
Nicolas
On May 24, 2013, at 2:02 PM, Emanuele Olivetti wrote:
> import numpy as np
> print np.random.multivariate_normal(mean=np.zeros(2), cov=np.eye(2), size=1)
> print np.random.multivariate_normal(mean=n
> Can you file a bug in the bug tracker so this won't get lost?
Done.
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
;out=a' or out=b' since nothing in the 'dot' documentation
warned me about such problem.
Nicolas
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
yone confirm this behavior ? (tested using numpy 1.7.1)
Nicolas
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
Thanks, I filed a new issue on the bug tracker.
Nicolas
On May 22, 2013, at 8:15 PM, eat wrote:
> Hi,
>
> FWIW, apparently bug related to dtype of np.eye(.)
>
>
> On Wed, May 22, 2013 at 8:07 PM, Nicolas Rougier
> wrote:
>
>
> Hi all,
>
> I g
e(4, dtype=np.float32)
print U[0]
# output: ([[1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0],
[0.0, 0.0, 0.0, 1.0]],)
The first output is obviously wrong. Can anyone confirm ?
(using numpy 1.7.1 on osx 10.8.3)
Nicolas
___
NumPy-Discussi
Shame on me ! How did I forget this one...
Thanks, just added it.
Nicolas
>
> Hi Nicolas, that looks good. You're linking to some other tutorials at the
> bottom, maybe you can add http://scipy-lectures.github.io/ (has both an intro
> and an advanced numpy tuto
ectly
the formated entry. Here is an example:
#. Find indices of non-zero elements from [1,2,0,0,4,0]
.. code:: python
# Author: Somebody
print np.nonzero([1,2,0,0,4,0])
If you can provide the (assumed) level of the answer, that would be even better.
very problem and ended up coding a "group class"
which is a "split" structured array (each field is stored as a single array)
offering the same interface as a regular structured array.
http://www.loria.fr/~rougier/coding/software/numpy_group.py
Nicolas
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
>
> bincount takes a weights argument which should do exactly what you are
> looking for.
Fantastic ! Thanks !
Nicolas
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
y-with-repeated-indices
but it is only for a fixed increment from what I've understood.
Nicolas
#
import numpy as np
n,p = 5,100
nodes = np.zeros( n, [('value', 'f4', 1)] )
links = np.zeros( p, [('source', 'i4', 1
I did not know that. Thanks for the clear explanation.
Nicolas
On Feb 12, 2013, at 19:25 , Jaime Fernández del Río wrote:
> On Tue, Feb 12, 2013 at 9:53 AM, Nicolas Rougier
> wrote:
> Did I do something wrong or is it expected behavior ?
>
> Try:
>
> print (Z.view(
# True
print (Z.view('f4'))[:50].base is Z # False
print Z[:50].view('f4').base is Z # False
Did I do something wrong or is it expected behavior ?
Nicolas
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
I ended coding the dtype reduction, it's not foolproof but it might be useful
for others as well.
Nicolas
import numpy as np
def dtype_reduce(dtype, level=0, depth=0):
"""
Try to reduce dtype up to a given level when it is possible
dtype = [ ('ve
7; parameter won't be useful in your case so you have to set it to 'src'.
Nicolas
On Dec 30, 2012, at 12:21 , deb wrote:
> Thanks Zach for your interest
>
> I was thinking about ndimage.generic_filter when I wrote about generic filter.
> For generic_filter I used trivial fun
Yep, I'm trying to construct dtype2 programmaticaly and was hoping for some
function giving me a "canonical" expression of the dtype. I've started playing
with fields but it's just a bit harder than I though (lot of different cases
and recursion).
Thanks for the ans
, 'f4')]),
('color', [('r', 'f4'),
('g', 'f4'),
('b', 'f4'),
('a', 'f4')]) ] )
dtype2 = np.dtype( [ (
Yep exactly.
I just want to make sure that we talked enough on the principle first
(ie. goals and technical approach), and that indeed the code is good
enough to look at.
I get it from your answer that it is, so I went ahead
https://github.com/numpy/numpy/pull/2730
Thanks
-nicolas
On Mon, Nov
I've pushed my code to a branch here
https://github.com/leschef/numpy/tree/faster_dot
with the commit
https://github.com/leschef/numpy/commit/ea037770e03f23aca1a06274a1a8e8bf0e0e2ee4
Let me know if that's enough to create a pull request.
Thanks,
-nicolas
On Sat, Nov 10, 2012 at 4:39
n
a.dot(a.T), which is awesome.
-nicolas
Nath: same trend if arrays are different.
On Fri, Nov 9, 2012 at 3:05 PM, Nathaniel Smith wrote:
> In this case it's even possible the blas is being smart enough to notice
> that you've passed it the same pointer twice with the transpose switch o
ht'] - right)**2).sum())
Out[28]: 0.015331409
#
# CCl
#
While the MSE are small, I'm wondering whether:
- It's a bug: it should be exactly the same
- It's a feature: BLAS is taking shortcuts when you have A.A'. The
difference is not significant. Quick: PR that asap!
I don
It's a pain to test because I cannot do the test in a single python session.
I'm going to try to integrate most of your suggestions, I cannot
guarantee I'll have time to do them all though.
-nicolas
On Fri, Nov 9, 2012 at 8:56 AM, Nathaniel Smith wrote:
> On Fri, Nov 9, 2012 at
gt; blas version accept the same stuff, so if this isn't in the current version,
> there will be probably some adjustment later on that side. What blas do you
> use? I think ATLAS was one that was causing problem.
>
>
> When we did this in Theano, it was more complicated then this di
wrote:
> On Fri, 2012-11-09 at 00:24 +0100, Sebastian Berg wrote:
>> Hey,
>>
>> On Thu, 2012-11-08 at 14:44 -0800, Nicolas SCHEFFER wrote:
>> > Well, hinted by what Fabien said, I looked at the C level dot function.
>> > Quite verbose!
>> >
>> &
ht be too easy to be true.
On Thu, Nov 8, 2012 at 12:06 PM, Nicolas SCHEFFER
wrote:
> I've made the necessary changes to get the proper order for the output array.
> Also, a pass of pep8 and some tests (fixmes are in failing tests)
> http://pastebin.com/M8TfbURi
>
> -n
>
I've made the necessary changes to get the proper order for the output array.
Also, a pass of pep8 and some tests (fixmes are in failing tests)
http://pastebin.com/M8TfbURi
-n
On Thu, Nov 8, 2012 at 11:38 AM, Nicolas SCHEFFER
wrote:
> Thanks for all the responses folks. This is indee
hand scipy is always compiled with
>>> >> lapack. Thus this makes more sens in scipy.
>>> >
>>> > Well, numpy.dot already contains multiple fallback cases for when it is
>>> > compiled with BLAS and not. So I'm +1 on just making this a
Sorry, I'm away from the lab and did not have a chance to test is yet.
I will do next week.
Nicolas
On Oct 11, 2012, at 15:48 , Nathaniel Smith wrote:
> On Thu, Oct 11, 2012 at 10:50 AM, Nicolas Rougier
> wrote:
>> I missed the original post but I personally find this ad
ed result. This also work with sparse
array for example when the kernel is very small. I suspect the PR will be quite
efficient compared to what I did.
Nicolas
On Oct 10, 2012, at 18:55 , Cera, Tim wrote:
> On Wed, Oct 10, 2012 at 1:58 AM, Travis E. Oliphant
> wrote:
> I'm not
Hi,
I'm trying to write a code for doing a 2D integral. It works well when
I'm doing it with normal "for" loops, but requires two nested loops,
and is much too slow for my application. I would like to know if it is
possible to do it faster, for example with fancy indexing and the use
of numpy.cums
],
[-1.0*s, c]])
# Fudge some data to work with
data = numpy.random.uniform(-1.0, 1.0, (M,N,2))
numpy.dot(data,rotation)
Nicolas
On Feb 24, 2012, at 13:11 , Bob Dowling wrote:
> import math
> import numpy
> import numpy.random
>
> # Rotation angle
> theta = math.pi/6.0
>
Hello list,
please ignore my previous email. I just found
http://www.scipy.org/ParallelProgramming
Thanks,
nick
On Fri, Nov 18, 2011 at 15:27, Nicolas Bock wrote:
> Hello list,
>
> is it possible to get numpy to do a matrix product in parallel? I presume
> that the multiply in
Hello list,
is it possible to get numpy to do a matrix product in parallel? I presume
that the multiply in the back-end is really done by dgemm() or equivalent,
so is it possible to use a parallel dgemm() there?
Thanks,
nick
___
NumPy-Discussion mailin
Thanks.
I just uploaded it to pypi.
Nicolas
On Sep 16, 2011, at 22:21 , Samuel John wrote:
> Hi Nicolas,
>
> that looks great.
> Could you make this available such that `pip install glumpy` would work?
>
> cheers,
> Samuel
>
>
Hi folks,
I am pleased to announce a new release of glumpy, a small python library for
the (very) fast vizualization of numpy arrays, (mainly two dimensional) that
has been designed with efficiency in mind. If you want to draw nice figures for
inclusion in a scientific article, you’d better us
Hi all,
I have a 3D orthogonal and non-uniform grid representing a scalar field. I'm
using matplotlib.image.NonUniformImage() to plot it similarly to imshow().
What I'd like to do is plot the values of the scalar field across a specific
line (say, from point A to B).
Any suggestion?
Thanks!
___
Have a look at glumpy: http://code.google.com/p/glumpy/
It's quite simple and very fast for images (it's based on OpenGL/shaders).
Nicolas
On Jun 28, 2011, at 6:38 AM, Nadav Horesh wrote:
> I have an application which generates and displays RGB images as rate of
> several f
Or just with a dot:
===
In [17]: np.tensordot(weights, matrices, (0,0))
Out[17]:
array([[ 5., 5., 5.],
[ 5., 5., 5.]])
In [18]: np.dot(matrices.T,weights).T
Out[18]:
array([[ 5., 5., 5.],
[ 5., 5., 5.]])
==
make matrices.T C_CONTIGUOUS for maximum speed.
-n
On Mon, Mar
ng to avoid creating a directory
> of symbolic links to every necessary library.
>
>
>
>
> Jonathan Tu
> ___
> NumPy-Discussion mailing list
> NumPy-Discussion@scipy.org
> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>
--
Nicolas Pinto
http://web.mit.edu/pinto
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
Thalhammer
wrote:
>
> Am 29.1.2011 um 22:01 schrieb Nicolas SCHEFFER:
>
>> Hi all,
>>
>> First email to the list for me, I just want to say how grateful I am
>> to have python+numpy+ipython etc... for my day to day needs. Great
>> combination of software.
Thanks for the prompt reply!
I quickly tried that and it actually helps compared to the full
vectorized version.
Depending on the dimensions, the chunk size has to be tuned (typically
100 or so)
But I don't get any improvement w/r to the simple for loop (i can
almost match the time though).
My gue
Hi all,
First email to the list for me, I just want to say how grateful I am
to have python+numpy+ipython etc... for my day to day needs. Great
combination of software.
Anyway, I've been having this bottleneck in one my algorithms that has
been bugging me for quite a while.
The objective is to sp
Chrome might have some feature where local javascript can't be executed...
just an idea
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
On Wed, Oct 6, 2010 at 5:26 AM, Chris Withers wrote:
> Hi All,
>
> Given an array such as:
>
> array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>
> How can I find the index of a particular number in the array?
>
> (ie: if it was a list, I'd do [1,2,3,4].index(3))
>
> cheers,
>
> Chris
> _
Maybe glumpy may be of some help:
http://code.google.com/p/glumpy/
Nicolas
On Fri, 2010-09-17 at 09:03 +0200, Massimo Di Stefano wrote:
> Hi,
>
>
> have yo already tryied Spyderlib :
>
>
> http://code.google.com/p/spyderlib/
>
>
> a matlab-like environment
2010/7/2 Charles R Harris
>
>
> On Fri, Jul 2, 2010 at 12:15 PM, Nicolas Bigaouette > wrote:
>
>> Hi all,
>>
>> I don't really know where to ask, so here it is.
>>
>> I was able to vectorize the normalization calculation in quantum
>> m
rmals[indices[i]] += N[i]
I would like to get rid of the last 'for' loop but did not find the proper way
to do it. Anyone got an idea ?
Nicolas
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
Hi Stefan,
It's seems that can be very useful indeed. I will give a try. Thank you.
Nicolas
2010/6/3 Stéfan van der Walt
> Hi Nicolas
>
> On 3 June 2010 02:03, Nicolas Gruel wrote:
> > Another question, it's seems that the rst file for each function in numpy
>
he rst file for each function in numpy
are autogenerated. Can you give me any information how to do it, please? I
didn't find it on numpy website but I probably miss the good link...
Thank you for any help.
Nicolas
here the two files:
foo.rst
y.
Thanks again.
Nicolas
On Mar 15, 2010, at 22:32 , Friedrich Romstedt wrote:
> Ok, so I send yet another version. Maybe Bruce is right, but I didn't
> care, because we have fret enough. Now it not only computes
> something, but also displays something :-(
>
> Ni
think) but unfortunately, it
does not seem to do anything at the moment, I need to investigate further where
is the problem.
Nicolas
New version:
'''
Channel flow past a cylindrical obstacle, using a LB method
Copyright (C) 2006 Jonas Latt
Address: Rue General Dufour 24, 1211 Gen
1 - 100 of 157 matches
Mail list logo