On Fri, Jul 29, 2011 at 4:12 AM, Hans Meine me...@informatik.uni-hamburg.de
wrote:
/home/hmeine/new_numpy/lib64/python2.6/site-packages/vigra/arraytypes.pyc in
reshape(self, shape, order)
587
588 def reshape(self, shape, order='C'):
-- 589 res =
Am 29.07.2011 um 17:07 schrieb Mark Wiebe:
I dug a little bit into the relevant 1.5.x vs 1.6.x code, in the places I
would most suspect a change, but couldn't find anything obvious.
Thanks for having a look. This strengthens my suspicion that the behavior
change was not intentional.
Have a
Am 29.07.2011 um 20:23 schrieb Nathaniel Smith:
Even so, surely this behavior should be consistent between base class
ndarrays and subclasses? If returning 0d arrays is a good idea, then
we should do it everywhere. If it's a bad idea, then we shouldn't do
it at all...?
Very well put. That's
On Sun, Jul 31, 2011 at 12:50 AM, Hans Meine
me...@informatik.uni-hamburg.de wrote:
Am 29.07.2011 um 20:23 schrieb Nathaniel Smith:
Even so, surely this behavior should be consistent between base class
ndarrays and subclasses? If returning 0d arrays is a good idea, then
we should do it
Hi,
On Sun, Jul 31, 2011 at 7:36 PM, Charles R Harris charlesr.har...@gmail.com
wrote:
On Sun, Jul 31, 2011 at 12:50 AM, Hans Meine
me...@informatik.uni-hamburg.de wrote:
Am 29.07.2011 um 20:23 schrieb Nathaniel Smith:
Even so, surely this behavior should be consistent between base
Am Donnerstag, 28. Juli 2011, 17:42:38 schrieb Matthew Brett:
If I understand you correctly, the problem is that, for 1.5.1:
class Test(np.ndarray): pass
type(np.min(Test((1,
type 'numpy.float64'
and for 1.6.0 (and current trunk):
class Test(np.ndarray): pass
Am Freitag, 29. Juli 2011, 11:31:24 schrieb Hans Meine:
Am Donnerstag, 28. Juli 2011, 17:42:38 schrieb Matthew Brett:
Was there a particular case you ran into where this was a problem?
[...]
Basically, the problem arose because our ndarray subclass does not support
zero-rank-instances fully.
On Fri, Jul 29, 2011 at 4:12 AM, Hans Meine me...@informatik.uni-hamburg.de
wrote:
Am Freitag, 29. Juli 2011, 11:31:24 schrieb Hans Meine:
Am Donnerstag, 28. Juli 2011, 17:42:38 schrieb Matthew Brett:
Was there a particular case you ran into where this was a problem?
[...]
Basically,
On Thu, Jul 28, 2011 at 9:58 AM, Hans Meine me...@informatik.uni-hamburg.de
wrote:
Hi again!
Am Donnerstag, 21. Juli 2011, 16:56:21 schrieb Hans Meine:
import numpy
class Test(numpy.ndarray):
pass
a1 = numpy.ndarray((1,))
a2 = Test((1,))
assert type(a1.min()) ==
On Fri, Jul 29, 2011 at 10:07 AM, Mark Wiebe mwwi...@gmail.com wrote:
On Thu, Jul 28, 2011 at 9:58 AM, Hans Meine
me...@informatik.uni-hamburg.de wrote:
Hi again!
Am Donnerstag, 21. Juli 2011, 16:56:21 schrieb Hans Meine:
import numpy
class Test(numpy.ndarray):
pass
a1 =
On Jul 28, 2011 8:43 AM, Matthew Brett matthew.br...@gmail.com wrote:
So, 1.6.0 is returning a zero-dimensional scalar of the given type,
and 1.5.1 returns a python scalar.
Zero dimensional scalars are designed to behave in a similar way to
python scalars, so the change should be all but
Hi again!
Am Donnerstag, 21. Juli 2011, 16:56:21 schrieb Hans Meine:
import numpy
class Test(numpy.ndarray):
pass
a1 = numpy.ndarray((1,))
a2 = Test((1,))
assert type(a1.min()) == type(a2.min()), \
%s != %s % (type(a1.min()), type(a2.min()))
#
Hi,
On Thu, Jul 28, 2011 at 7:58 AM, Hans Meine
me...@informatik.uni-hamburg.de wrote:
Hi again!
Am Donnerstag, 21. Juli 2011, 16:56:21 schrieb Hans Meine:
import numpy
class Test(numpy.ndarray):
pass
a1 = numpy.ndarray((1,))
a2 = Test((1,))
assert type(a1.min()) ==
Hi,
I have the same problem as Martin DRUON, who wrote 10 days ago:
I have a problem with the ufunc return type of a numpy.ndarray derived
class. In fact, I subclass a numpy.ndarray using the tutorial :
http://docs.scipy.org/doc/numpy/user/basics.subclassing.html
But, for example, if I
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