On 08-Sep-14 4:40 PM, Joseph Martinot-Lagarde wrote:
> Le 08/09/2014 15:29, Stefan Otte a écrit :
>> Hey,
>>
>> quite often I work with block matrices. Matlab offers the convenient notation
>>
>> [ a b; c d ]
This would appear to be a desirable way to go.
Numpy has something similar for str
Le 08/09/2014 15:29, Stefan Otte a écrit :
> Hey,
>
> quite often I work with block matrices. Matlab offers the convenient notation
>
> [ a b; c d ]
>
> to stack matrices. The numpy equivalent is kinda clumsy:
>
> vstack([hstack([a,b]), hstack([c,d])])
>
> I wrote the little function `stack` t
Le 08/09/2014 16:41, Sturla Molden a écrit :
> Stefan Otte wrote:
>
>> stack([[a, b], [c, d]])
>>
>> In my case `stack` replaced `hstack` and `vstack` almost completely.
>>
>> If you're interested in including it in numpy I created a pull request
>> [1]. I'm looking forward to getting some fe
On Mon, Sep 8, 2014 at 6:05 PM, Pierre-Andre Noel
wrote:
> > I think we could add new generators to NumPy though,
> > perhaps with a keyword to control the algorithm (defaulting to the
> current
> > Mersenne Twister).
>
> Why not do something like the C++11 ? In , a "generator"
> is the engine
On 08.09.2014 19:05, Pierre-Andre Noel wrote:
> > I think we could add new generators to NumPy though,
> > perhaps with a keyword to control the algorithm (defaulting to the
> current
> > Mersenne Twister).
>
...
>
> Here is how I propose to adapt this scheme to numpy. First, there would
> b
Blaze aims to do something like that; to make the notion of an array and how it
stores it's data far more flexible. But if it isn't a single strided ND array,
it isn't numpy. This concept lies at its very heart; and for good reasons I
would add.
-Original Message-
From: "Benjamin Root"
On Mon, Sep 8, 2014 at 10:00 AM, Benjamin Root wrote:
> Btw, on a somewhat related note, whoever can implement ndarray to be able
> to use views from other ndarrays stitched together would get a fruit basket
> from me come the holidays and possibly naming rights for the next kid...
>
Ben, you sh
> I think we could add new generators to NumPy though,
> perhaps with a keyword to control the algorithm (defaulting to the
current
> Mersenne Twister).
Why not do something like the C++11 ? In , a "generator"
is the engine producing randomness, and a "distribution" decides what is
the type
Btw, on a somewhat related note, whoever can implement ndarray to be able
to use views from other ndarrays stitched together would get a fruit basket
from me come the holidays and possibly naming rights for the next kid...
Cheers!
Ben Root
On Mon, Sep 8, 2014 at 12:55 PM, Benjamin Root wrote:
>
A use case would be "image stitching" or even data tiling. I have had to
implement something like this at work (so, I can't share it, unfortunately)
and it even goes so far as to allow the caller to specify how much the
tiles can overlap and such. The specification is ungodly hideous and I
doubt I
Sturla: im not sure if the intention is always unambiguous, for such more
flexible arrangements.
Also, I doubt such situations arise often in practice; if the arrays arnt a
grid, they are probably a nested grid, and the code would most naturally
concatenate them with nested calls to a stacking fun
On Mon, Sep 8, 2014 at 12:10 PM, Jaime Fernández del Río <
jaime.f...@gmail.com> wrote:
> On Mon, Sep 8, 2014 at 7:41 AM, Sturla Molden
> wrote:
>
>> Stefan Otte wrote:
>>
>> > stack([[a, b], [c, d]])
>> >
>> > In my case `stack` replaced `hstack` and `vstack` almost completely.
>> >
>> > If
Hi all,
Any input to this? Last time it generated a fair bit of discussion, which
I’ll summarise here.
It’s currently possible to calculate a weighted average using np.average,
but the corresponding functionality does not exist for (co)variance or
corrcoeff calculations. In this case it’s less
On 8 Sep 2014 10:42, "Sturla Molden" wrote:
>
> Stefan Otte wrote:
>
> > stack([[a, b], [c, d]])
> >
> > In my case `stack` replaced `hstack` and `vstack` almost completely.
> >
> > If you're interested in including it in numpy I created a pull request
> > [1]. I'm looking forward to getting
On Mon, Sep 8, 2014 at 7:41 AM, Sturla Molden
wrote:
> Stefan Otte wrote:
>
> > stack([[a, b], [c, d]])
> >
> > In my case `stack` replaced `hstack` and `vstack` almost completely.
> >
> > If you're interested in including it in numpy I created a pull request
> > [1]. I'm looking forward to
On Mon, Sep 8, 2014 at 10:41 AM, Sturla Molden
wrote:
> Stefan Otte wrote:
>
> > stack([[a, b], [c, d]])
> >
> > In my case `stack` replaced `hstack` and `vstack` almost completely.
> >
> > If you're interested in including it in numpy I created a pull request
> > [1]. I'm looking forward to
Stefan Otte wrote:
> stack([[a, b], [c, d]])
>
> In my case `stack` replaced `hstack` and `vstack` almost completely.
>
> If you're interested in including it in numpy I created a pull request
> [1]. I'm looking forward to getting some feedback!
As far as I can see, it uses hstack and vsta
On Sun, Sep 7, 2014 at 4:23 PM, Sturla Molden
wrote:
> Benjamin Root wrote:
> > In addition to issues with reproducibility, think of all of the unit
> tests
> > that would break!
>
> That is a reproducibility problem :)
>
> ___
> NumPy-Discussion maili
Hey,
quite often I work with block matrices. Matlab offers the convenient notation
[ a b; c d ]
to stack matrices. The numpy equivalent is kinda clumsy:
vstack([hstack([a,b]), hstack([c,d])])
I wrote the little function `stack` that does exactly that:
stack([[a, b], [c, d]])
In my ca
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