On Tue, Jan 9, 2018 at 12:53 PM, Tyler Reddy wrote:
> One common issue in computational geometry is the need to operate rapidly on
> arrays with "heterogeneous shapes."
>
> So, an array that has rows with different numbers of columns -- shape (1,3)
> for the first
On Tue, Jan 9, 2018 at 3:40 AM, Ilhan Polat wrote:
> I couldn't find an item to place this but I think ilaenv and also calling
> the function twice (one with lwork=-1 and reading the optimal block size and
> the call the function again properly with lwork=) in LAPACK needs
On Tue, 2018-01-09 at 12:27 +, martin.gfel...@swisscom.com wrote:
> Hi Derek
>
> I have a related question:
>
> Given:
>
> a = numpy.array([[0,1,2],[3,4]])
> assert a.ndim == 1
> b = numpy.array([[0,1,2],[3,4,5]])
> assert b.ndim == 2
>
> Is there an elegant way to
Hi Derek
I have a related question:
Given:
a = numpy.array([[0,1,2],[3,4]])
assert a.ndim == 1
b = numpy.array([[0,1,2],[3,4,5]])
assert b.ndim == 2
Is there an elegant way to force b to remain a 1-dim object array?
I have a use case where normally the
I couldn't find an item to place this but I think ilaenv and also calling
the function twice (one with lwork=-1 and reading the optimal block size
and the call the function again properly with lwork=) in LAPACK
needs to be gotten rid of.
That's a major annoyance during the wrapping of LAPACK
Hi all,
As mentioned earlier [1][2], there's work underway to revise and
update the BLAS standard -- e.g. we might get support for strided
arrays and lose xerbla! There's a draft at [3]. They're interested in
feedback from users, so I've written up a first draft of comments
about what we would