On Mon, Aug 22, 2011 at 2:21 PM, Ralf Gommers
wrote:
>
>
> You can use this file standalone without installing scikits.image:
> https://github.com/stefanv/scikits.image/blob/master/scikits/image/color/colorconv.py
>
> Ralf
>
>
>
> ___
> NumPy-Discussion
On Mon, Aug 22, 2011 at 5:39 AM, He Shiming wrote:
> > On Sat, Aug 20, 2011 at 4:17 PM, David Warde-Farley
> > wrote:
> >
> > Thanks, I'll check it out.
> >
> > --
> > Best regards,
> > He Shiming
> >
>
> Hi again. Project scikits.image appeared to be difficult to install
> under ubuntu. It comp
> On Sat, Aug 20, 2011 at 4:17 PM, David Warde-Farley
> wrote:
>
> Thanks, I'll check it out.
>
> --
> Best regards,
> He Shiming
>
Hi again. Project scikits.image appeared to be difficult to install
under ubuntu. It complains about something related to OpenCV, and I
didn't see any option to comp
On Sunday, August 21, 2011, Torgil Svensson
wrote:
> Since the result is one-dimensional after using boolean indexing you
> can always do:
>
> a[b][:, np.newaxis]
> array([[2],
> [3],
> [4]])
>
> a[b][np.newaxis, :]
> array([[2, 3, 4]])
>
> //Torgil
Correct, which I already noted
Since the result is one-dimensional after using boolean indexing you
can always do:
a[b][:, np.newaxis]
array([[2],
[3],
[4]])
a[b][np.newaxis, :]
array([[2, 3, 4]])
//Torgil
On Sat, Aug 20, 2011 at 10:17 PM, Benjamin Root wrote:
> On Sat, Aug 20, 2011 at 2:47 AM, Olivier Ver
On Sat, 20 Aug 2011 16:18:55 -0700, Chris Withers wrote:
> I've got a tree of nested dicts that at their leaves end in numpy arrays
> of identical sizes.
>
> What's the easiest way to persist these to disk so that I can pick up
> with them where I left off?
Depends on your requirements.
You can
Hi
My bad. Very sorry about that, guys.
There's a patch for this here:
https://github.com/walshb/numpy/tree/fix_np_lookfor_segv
And I submitted a pull request. I'll add something to the tests too when I
have a little more time.
Cheers
Ben
> --
>
> Message: 3
> D
Hi!
On 21. aug. 2011, at 00.18, Chris Withers wrote:
> Hi All,
>
> I've got a tree of nested dicts that at their leaves end in numpy arrays
> of identical sizes.
>
> What's the easiest way to persist these to disk so that I can pick up
> with them where I left off?
Probably giving them names