On 26 December 2011 19:56, wrote:
> I don't think I ever ran into an empty matrix in matlab, and wouldn't
> know how it behaves.
I think they behave like Octave matrices. I'm not sure about all cases
because I don't have access to Matlab, but I think Matlab handles it
about as sanely as Octave:
Hi all,
Two questions:
- Are dtypes supposed to be comparable (i.e. implement '==', '!=')?
- Are dtypes supposed to be hashable?
PyCUDA and PyOpenCL assume both in a few places, but at least
hashability doesn't seem to be true. (If so, __hash__ should be
implemented to throw an error. If not, w
2011/12/26 Jordi Gutiérrez Hermoso :
> On 26 December 2011 14:56, Ralf Gommers wrote:
>>
>>
>> On Mon, Dec 26, 2011 at 8:50 PM, wrote:
>>> I have a hard time thinking through empty 2-dim arrays, and don't know
>>> what rules should apply.
>>> However, in my code I might want to catch these cases
On 26 December 2011 14:56, Ralf Gommers wrote:
>
>
> On Mon, Dec 26, 2011 at 8:50 PM, wrote:
>> I have a hard time thinking through empty 2-dim arrays, and don't know
>> what rules should apply.
>> However, in my code I might want to catch these cases rather early
>> than late and then having to
tiles = numpy.zeros((header["width"], header["height"]), dtype =
[("0","http://f.web.de/?mc=021192
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On 26.12.2011, at 7:37PM, Fabian Dill wrote:
> I have a problem with a structured numpy array.
> I create is like this:
> tiles = numpy.zeros((header["width"], header["height"],3), dtype =
> numpy.uint8)
> and later on, assignments such as this:
> tiles[x, y,0] = 3
>
> Now uint8 is not sufficie
On Mon, Dec 26, 2011 at 8:50 PM, wrote:
> On Mon, Dec 26, 2011 at 1:51 PM, Ralf Gommers
> wrote:
> >
> >
> > 2011/12/25 Jordi Gutiérrez Hermoso
> >>
> >> I have been instructed to bring this issue to the mailing list:
> >>
> >> http://projects.scipy.org/numpy/ticket/1994
> >>
> > The issue is
On Mon, Dec 26, 2011 at 1:51 PM, Ralf Gommers
wrote:
>
>
> 2011/12/25 Jordi Gutiérrez Hermoso
>>
>> I have been instructed to bring this issue to the mailing list:
>>
>> http://projects.scipy.org/numpy/ticket/1994
>>
> The issue is this corner case:
>
idx = []
x = np.array([])
x[
2011/12/25 Jordi Gutiérrez Hermoso
> I have been instructed to bring this issue to the mailing list:
>
> http://projects.scipy.org/numpy/ticket/1994
>
> The issue is this corner case:
>>> idx = []
>>> x = np.array([])
>>> x[idx] #works
array([], dtype=float64)
>>> x[:, idx] #works
array([],
Hello!
I have a problem with a structured numpy array.
I create is like this:
tiles = numpy.zeros((header["width"], header["height"],3), dtype = numpy.uint8)
and later on, assignments such as this:
tiles[x, y,0] = 3
Now uint8 is not sufficient anymore, but only for the first of the 3 values.
uin
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