> I’ve seen vectorisation taken to the extreme, with negative consequences in
> terms of both speed and readability, in both Python and MATLAB codebases, so
> I would suggest some discussion / wisdom about when not to vectorise.
I agree and there is actually a warning in the introduction abou
lysis to close squares.
Nicolas
>
>
>
>> 23 дек. 2016 г., в 12:14, Kiko написал(а):
>>
>>
>>
>> 2016-12-22 17:44 GMT+01:00 Nicolas P. Rougier :
>>
>> Dear all,
>>
>> I've just put online a (kind of) book on Numpy and more spec
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
Hi all,
I'm trying to understand why viewing an array as bytes before clearing makes
the whole operation faster.
I imagine there is some kind of special treatment for byte arrays but I've no
clue.
# Native float
Z_float = np.ones(100, float)
Z_int = np.ones(100, int)
%timeit Z_fl
Dear all,
I've just put online a (kind of) book on Numpy and more specifically about
vectorization methods. It's not yet finished, has not been reviewed and it's a
bit rough around the edges. But I think there are some material that can be
interesting. I'm specifically happy with the boids exa
Hi all,
Given an array V that is a view of a base B, I was wondering if it is possible
to find a (string) index such that `V = B[index]`
For example:
```
B = np.arange(8*8).reshape(8,8)
V = B[::2,::2]
index = find_view(B,V)
print(index)
"::2,::2"
print(np.allclose(V, eval("B[%s]" % index)))
T
Hi all,
It's my great pleasure to announce that "100 Numpy exercises" is now complete.
I've also made a notebook out of them such that you can now test them on binder.
https://github.com/rougier/numpy-100
If you spot errors or have better solutions to propose, PR welcome.
(I'm still fighting
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
___
NumPy-Disc
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.
>>
t; >>> s = pd.Series(range(len(B)), index=B)
> >>> s[A].values
> array([ 1., 2., 0., nan])
>
>
>
> On Wed, Dec 30, 2015 at 8:45 AM, Nicolas P. Rougier
> wrote:
>
> I’m scratching my head around a small problem but I can’t find a vectorized
&
I’m scratching my head around a small problem but I can’t find a vectorized
solution.
I have 2 arrays A and B and I would like to get the indices (relative to B) of
elements of A that are in B:
>>> A = np.array([2,0,1,4])
>>> B = np.array([1,2,0])
>>> print (some_function(A,B))
[1,2,0]
# A[0]
> 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
Typed list in numpy would be a nice addition indeed and your cython
implementation is nice (and small).
In my case I need to ensure a contiguous storage to allow easy upload onto the
GPU.
But my implementation is quite slow, especially when you add one item at a time:
>>> python benchmark.py
P
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
>
> This sounds like it
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
Hi all,
I've just updated the collection of numpy exercices (collected from this list
and stack overflow) that lives at:
https://github.com/rougier/numpy-100
http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html
Unfortunately, I also realized there are currently "only" 60 exercices
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
colas
> On 14 Dec 2014, at 09:03, Jerome Kieffer wrote:
>
> On Sat, 13 Dec 2014 16:53:06 +0100
> "Nicolas P. Rougier" wrote:
>
>>
>> Hi all,
>>
>> Does anyone has a simple 2D linear interpolation for resizing an image
>> (without
Hi all,
Does anyone has a simple 2D linear interpolation for resizing an image (without
scipy) ?
Ideally, something like ```def zoom(Z, ratio): ...``` where Z is a 2D scalar
array and ratio the scaling factor.
(I'm currently using ```scipy.ndimage.interpolation.zoom``` but I would like to
avo
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), (
Hi,
I've a small problem for which I cannot find a solution and I'm quite sure
there is an obvious one:
I've an array Z (any dtype) with some data.
I've a (sorted) array I (of integer, same size as Z) that tells me the index
of Z[i] (if necessary, the index can be stored in Z).
Now, I have
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:
>
>
rk to do what you want to do.
>
>
> On Sun, Jun 22, 2014 at 8:30 PM, Nicolas P. Rougier
> wrote:
>
> Thanks, I'll try your solution.
>
> Data (L) is not so big actually, it represents pixels on screen and (I)
> represents line position (for grid
ay. Note that the
> output isn't 100% identical; youd need to do a little tinkering to figure out
> the correct/desired rounding behavior.
>
>
> On Sun, Jun 22, 2014 at 5:16 PM, Nicolas P. Rougier
> wrote:
>
> Thanks for the answer.
> I was secretly hop
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-Disc
ity is fine, and everything is vectorized, so I doubt you will get
> huge gains.
>
> You could take a look at the functions in scipy.spatial, and see how they
> perform for your problem parameters.
>
>
> On Sun, Jun 22, 2014 at 10:22 AM, Nicolas P. Rougier
> wrote:
>
Hi,
I have an array L with regular spaced values between 0 and width.
I have a (sorted) array I with irregular spaced values between 0 and width.
I would like to find the closest value in I for any value in L.
Currently, I'm using the following script but I wonder if I missed an obvious
(and
How would you do that ?
5. Create a vector with values ranging from 10 to 99
9. Create a 5x5 matrix with values 1,2,3,4 just below the diagonal
On 29 May 2014, at 07:04, nicky van foreest wrote:
> Hi,
>
> Very helpful, these exercises.
>
> Pertaining to exercise 9. Is there a reason not t
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 Nicolas,
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