On 10/17/2011 03:22 AM, Charles R Harris wrote:
On Sun, Oct 16, 2011 at 6:13 PM, Nathaniel Smith n...@pobox.com
mailto:n...@pobox.com wrote:
The solution is just to call it 'longdouble', which clearly
communicates 'this does some quirky thing that depends on your C
compiler and
Beginner's question?
I have this dictionary dtypes of names and types:
dtypes
{'names': ['col1', 'col2', 'col3', 'col4', 'col5'], 'formats': [type
'numpy.float16', type 'numpy.float16', type 'numpy.float16', type
'numpy.float16', type 'numpy.float16']}
and this array y
y
array([[ 0, 1, 2,
13.10.2011 12:59, Alex van der Spek kirjoitti:
gives me a confusing result. I only asked to name the columns and change
their
types to half precision floats.
Structured arrays shouldn't be thought as an array with named columns,
as they are somewhat different.
What am I missing? How to do
On Mon, Oct 17, 2011 at 2:20 AM, Dag Sverre Seljebotn
d.s.seljeb...@astro.uio.no wrote:
On 10/17/2011 03:22 AM, Charles R Harris wrote:
On Sun, Oct 16, 2011 at 6:13 PM, Nathaniel Smith n...@pobox.com
mailto:n...@pobox.com wrote:
The solution is just to call it 'longdouble',
On Mon, Oct 17, 2011 at 6:17 AM, Pauli Virtanen p...@iki.fi wrote:
13.10.2011 12:59, Alex van der Spek kirjoitti:
gives me a confusing result. I only asked to name the columns and change
their
types to half precision floats.
Structured arrays shouldn't be thought as an array with named
17.10.2011 15:48, josef.p...@gmail.com kirjoitti:
On Mon, Oct 17, 2011 at 6:17 AM, Pauli Virtanen p...@iki.fi wrote:
[clip]
What am I missing? How to do this?
np.rec.fromarrays(arr.T, dtype=dt)
y.astype(float16).view(dt)
I think this will give surprises if the original array is not in
On Mon, Oct 17, 2011 at 10:18 AM, Pauli Virtanen p...@iki.fi wrote:
17.10.2011 15:48, josef.p...@gmail.com kirjoitti:
On Mon, Oct 17, 2011 at 6:17 AM, Pauli Virtanen p...@iki.fi wrote:
[clip]
What am I missing? How to do this?
np.rec.fromarrays(arr.T, dtype=dt)
y.astype(float16).view(dt)
On 10/14/11 5:04 AM, Neal Becker wrote:
suppose I have:
In [10]: u
Out[10]:
array([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]])
And I have a vector v:
v = np.array ((0,1,0,1,0))
I want to form an output vector which selects items from u where v is the
index
of the row of u to be
I recently put together a Cython example which uses the neighborhood
iterator. It was trickier than I thought it would be, so I thought to
share it with the community. The function takes a 1-dimensional array
and returns a 2-dimensional array of neighborhoods in the original
area. This is
Hi,
On Sun, Oct 16, 2011 at 6:22 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Sun, Oct 16, 2011 at 6:13 PM, Nathaniel Smith n...@pobox.com wrote:
On Sun, Oct 16, 2011 at 4:29 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Sun, Oct 16, 2011 at 4:16 PM, Nathaniel Smith
Hi,
On Mon, Oct 17, 2011 at 9:19 PM, T J tjhn...@gmail.com wrote:
I recently put together a Cython example which uses the neighborhood
iterator. It was trickier than I thought it would be, so I thought to
share it with the community. The function takes a 1-dimensional array
and returns a
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